xszheng2020/the_stack_dedup_python_hits_7 · Datasets at Hugging Face

20ddea3bbd375681758756b26a3e266d63f0568f

81,693

py

Python

pyboto3/dataexchange.py

gehad-shaat/pyboto3

4a0c2851a8bc04fb1c71c36086f7bb257e48181d

[ "MIT" ]

91

2016-12-31T11:38:37.000Z

2021-09-16T19:33:23.000Z

pyboto3/dataexchange.py

gehad-shaat/pyboto3

4a0c2851a8bc04fb1c71c36086f7bb257e48181d

[ "MIT" ]

7

2017-01-02T18:54:23.000Z

2020-08-11T13:54:02.000Z

pyboto3/dataexchange.py

gehad-shaat/pyboto3

4a0c2851a8bc04fb1c71c36086f7bb257e48181d

[ "MIT" ]

26

2016-12-31T13:11:00.000Z

2022-03-03T21:01:12.000Z

''' The MIT License (MIT) Copyright (c) 2016 WavyCloud 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. ''' def can_paginate(operation_name=None): """ Check if an operation can be paginated. :type operation_name: string :param operation_name: The operation name. This is the same name\nas the method name on the client. For example, if the\nmethod name is create_foo, and you\'d normally invoke the\noperation as client.create_foo(**kwargs), if the\ncreate_foo operation can be paginated, you can use the\ncall client.get_paginator('create_foo'). """ pass def cancel_job(JobId=None): """ This operation cancels a job. Jobs can be cancelled only when they are in the WAITING state. See also: AWS API Documentation Exceptions :example: response = client.cancel_job( JobId='string' ) :type JobId: string :param JobId: [REQUIRED]\nThe unique identifier for a job.\n """ pass def create_data_set(AssetType=None, Description=None, Name=None, Tags=None): """ This operation creates a data set. See also: AWS API Documentation Exceptions :example: response = client.create_data_set( AssetType='S3_SNAPSHOT', Description='string', Name='string', Tags={ 'string': 'string' } ) :type AssetType: string :param AssetType: [REQUIRED]\nThe type of file your data is stored in. Currently, the supported asset type is S3_SNAPSHOT.\n :type Description: string :param Description: [REQUIRED]\nA description for the data set. This value can be up to 16,348 characters long.\n :type Name: string :param Name: [REQUIRED]\nThe name of the data set.\n :type Tags: dict :param Tags: A data set tag is an optional label that you can assign to a data set when you create it. Each tag consists of a key and an optional value, both of which you define. When you use tagging, you can also use tag-based access control in IAM policies to control access to these data sets and revisions.\n\n(string) --\n(string) --\n\n\n\n :rtype: dict ReturnsResponse Syntax { 'Arn': 'string', 'AssetType': 'S3_SNAPSHOT', 'CreatedAt': datetime(2015, 1, 1), 'Description': 'string', 'Id': 'string', 'Name': 'string', 'Origin': 'OWNED'|'ENTITLED', 'OriginDetails': { 'ProductId': 'string' }, 'SourceId': 'string', 'Tags': { 'string': 'string' }, 'UpdatedAt': datetime(2015, 1, 1) } Response Structure (dict) -- 201 response Arn (string) -- The ARN for the data set. AssetType (string) -- The type of file your data is stored in. Currently, the supported asset type is S3_SNAPSHOT. CreatedAt (datetime) -- The date and time that the data set was created, in ISO 8601 format. Description (string) -- The description for the data set. Id (string) -- The unique identifier for the data set. Name (string) -- The name of the data set. Origin (string) -- A property that defines the data set as OWNED by the account (for providers) or ENTITLED to the account (for subscribers). OriginDetails (dict) -- If the origin of this data set is ENTITLED, includes the details for the product on AWS Marketplace. ProductId (string) -- SourceId (string) -- The data set ID of the owned data set corresponding to the entitled data set being viewed. This parameter is returned when a data set owner is viewing the entitled copy of its owned data set. Tags (dict) -- The tags for the data set. (string) -- (string) -- UpdatedAt (datetime) -- The date and time that the data set was last updated, in ISO 8601 format. Exceptions DataExchange.Client.exceptions.ThrottlingException DataExchange.Client.exceptions.ValidationException DataExchange.Client.exceptions.InternalServerException DataExchange.Client.exceptions.ServiceLimitExceededException DataExchange.Client.exceptions.AccessDeniedException :return: { 'Arn': 'string', 'AssetType': 'S3_SNAPSHOT', 'CreatedAt': datetime(2015, 1, 1), 'Description': 'string', 'Id': 'string', 'Name': 'string', 'Origin': 'OWNED'|'ENTITLED', 'OriginDetails': { 'ProductId': 'string' }, 'SourceId': 'string', 'Tags': { 'string': 'string' }, 'UpdatedAt': datetime(2015, 1, 1) } :returns: ProductId (string) -- """ pass def create_job(Details=None, Type=None): """ This operation creates a job. See also: AWS API Documentation Exceptions :example: response = client.create_job( Details={ 'ExportAssetToSignedUrl': { 'AssetId': 'string', 'DataSetId': 'string', 'RevisionId': 'string' }, 'ExportAssetsToS3': { 'AssetDestinations': [ { 'AssetId': 'string', 'Bucket': 'string', 'Key': 'string' }, ], 'DataSetId': 'string', 'Encryption': { 'KmsKeyArn': 'string', 'Type': 'aws:kms'|'AES256' }, 'RevisionId': 'string' }, 'ImportAssetFromSignedUrl': { 'AssetName': 'string', 'DataSetId': 'string', 'Md5Hash': 'string', 'RevisionId': 'string' }, 'ImportAssetsFromS3': { 'AssetSources': [ { 'Bucket': 'string', 'Key': 'string' }, ], 'DataSetId': 'string', 'RevisionId': 'string' } }, Type='IMPORT_ASSETS_FROM_S3'|'IMPORT_ASSET_FROM_SIGNED_URL'|'EXPORT_ASSETS_TO_S3'|'EXPORT_ASSET_TO_SIGNED_URL' ) :type Details: dict :param Details: [REQUIRED]\nThe details for the CreateJob request.\n\nExportAssetToSignedUrl (dict) --Details about the export to signed URL request.\n\nAssetId (string) -- [REQUIRED]The unique identifier for the asset that is exported to a signed URL.\n\nDataSetId (string) -- [REQUIRED]The unique identifier for the data set associated with this export job.\n\nRevisionId (string) -- [REQUIRED]The unique identifier for the revision associated with this export request.\n\n\n\nExportAssetsToS3 (dict) --Details about the export to Amazon S3 request.\n\nAssetDestinations (list) -- [REQUIRED]The destination for the asset.\n\n(dict) --The destination for the asset.\n\nAssetId (string) -- [REQUIRED]The unique identifier for the asset.\n\nBucket (string) -- [REQUIRED]The S3 bucket that is the destination for the asset.\n\nKey (string) --The name of the object in Amazon S3 for the asset.\n\n\n\n\n\nDataSetId (string) -- [REQUIRED]The unique identifier for the data set associated with this export job.\n\nEncryption (dict) --Encryption configuration for the export job.\n\nKmsKeyArn (string) -- [REQUIRED]The Amazon Resource Name (ARN) of the the AWS KMS key you want to use to encrypt the Amazon S3 objects. This parameter is required if you choose aws:kms as an encryption type.\n\nType (string) -- [REQUIRED]The type of server side encryption used for encrypting the objects in Amazon S3.\n\n\n\nRevisionId (string) -- [REQUIRED]The unique identifier for the revision associated with this export request.\n\n\n\nImportAssetFromSignedUrl (dict) --Details about the import from signed URL request.\n\nAssetName (string) -- [REQUIRED]The name of the asset. When importing from Amazon S3, the S3 object key is used as the asset name.\n\nDataSetId (string) -- [REQUIRED]The unique identifier for the data set associated with this import job.\n\nMd5Hash (string) -- [REQUIRED]The Base64-encoded Md5 hash for the asset, used to ensure the integrity of the file at that location.\n\nRevisionId (string) -- [REQUIRED]The unique identifier for the revision associated with this import request.\n\n\n\nImportAssetsFromS3 (dict) --Details about the import from Amazon S3 request.\n\nAssetSources (list) -- [REQUIRED]Is a list of S3 bucket and object key pairs.\n\n(dict) --The source of the assets.\n\nBucket (string) -- [REQUIRED]The S3 bucket that\'s part of the source of the asset.\n\nKey (string) -- [REQUIRED]The name of the object in Amazon S3 for the asset.\n\n\n\n\n\nDataSetId (string) -- [REQUIRED]The unique identifier for the data set associated with this import job.\n\nRevisionId (string) -- [REQUIRED]The unique identifier for the revision associated with this import request.\n\n\n\n\n :type Type: string :param Type: [REQUIRED]\nThe type of job to be created.\n :rtype: dict ReturnsResponse Syntax { 'Arn': 'string', 'CreatedAt': datetime(2015, 1, 1), 'Details': { 'ExportAssetToSignedUrl': { 'AssetId': 'string', 'DataSetId': 'string', 'RevisionId': 'string', 'SignedUrl': 'string', 'SignedUrlExpiresAt': datetime(2015, 1, 1) }, 'ExportAssetsToS3': { 'AssetDestinations': [ { 'AssetId': 'string', 'Bucket': 'string', 'Key': 'string' }, ], 'DataSetId': 'string', 'Encryption': { 'KmsKeyArn': 'string', 'Type': 'aws:kms'|'AES256' }, 'RevisionId': 'string' }, 'ImportAssetFromSignedUrl': { 'AssetName': 'string', 'DataSetId': 'string', 'Md5Hash': 'string', 'RevisionId': 'string', 'SignedUrl': 'string', 'SignedUrlExpiresAt': datetime(2015, 1, 1) }, 'ImportAssetsFromS3': { 'AssetSources': [ { 'Bucket': 'string', 'Key': 'string' }, ], 'DataSetId': 'string', 'RevisionId': 'string' } }, 'Errors': [ { 'Code': 'ACCESS_DENIED_EXCEPTION'|'INTERNAL_SERVER_EXCEPTION'|'MALWARE_DETECTED'|'RESOURCE_NOT_FOUND_EXCEPTION'|'SERVICE_QUOTA_EXCEEDED_EXCEPTION'|'VALIDATION_EXCEPTION'|'MALWARE_SCAN_ENCRYPTED_FILE', 'Details': { 'ImportAssetFromSignedUrlJobErrorDetails': { 'AssetName': 'string' }, 'ImportAssetsFromS3JobErrorDetails': [ { 'Bucket': 'string', 'Key': 'string' }, ] }, 'LimitName': 'Assets per revision'|'Asset size in GB', 'LimitValue': 123.0, 'Message': 'string', 'ResourceId': 'string', 'ResourceType': 'REVISION'|'ASSET' }, ], 'Id': 'string', 'State': 'WAITING'|'IN_PROGRESS'|'ERROR'|'COMPLETED'|'CANCELLED'|'TIMED_OUT', 'Type': 'IMPORT_ASSETS_FROM_S3'|'IMPORT_ASSET_FROM_SIGNED_URL'|'EXPORT_ASSETS_TO_S3'|'EXPORT_ASSET_TO_SIGNED_URL', 'UpdatedAt': datetime(2015, 1, 1) } Response Structure (dict) -- 201 response Arn (string) -- The ARN for the job. CreatedAt (datetime) -- The date and time that the job was created, in ISO 8601 format. Details (dict) -- Details about the job. ExportAssetToSignedUrl (dict) -- Details for the export to signed URL response. AssetId (string) -- The unique identifier for the asset associated with this export job. DataSetId (string) -- The unique identifier for the data set associated with this export job. RevisionId (string) -- The unique identifier for the revision associated with this export response. SignedUrl (string) -- The signed URL for the export request. SignedUrlExpiresAt (datetime) -- The date and time that the signed URL expires, in ISO 8601 format. ExportAssetsToS3 (dict) -- Details for the export to Amazon S3 response. AssetDestinations (list) -- The destination in Amazon S3 where the asset is exported. (dict) -- The destination for the asset. AssetId (string) -- The unique identifier for the asset. Bucket (string) -- The S3 bucket that is the destination for the asset. Key (string) -- The name of the object in Amazon S3 for the asset. DataSetId (string) -- The unique identifier for the data set associated with this export job. Encryption (dict) -- Encryption configuration of the export job. KmsKeyArn (string) -- The Amazon Resource Name (ARN) of the the AWS KMS key you want to use to encrypt the Amazon S3 objects. This parameter is required if you choose aws:kms as an encryption type. Type (string) -- The type of server side encryption used for encrypting the objects in Amazon S3. RevisionId (string) -- The unique identifier for the revision associated with this export response. ImportAssetFromSignedUrl (dict) -- Details for the import from signed URL response. AssetName (string) -- The name for the asset associated with this import response. DataSetId (string) -- The unique identifier for the data set associated with this import job. Md5Hash (string) -- The Base64-encoded Md5 hash for the asset, used to ensure the integrity of the file at that location. RevisionId (string) -- The unique identifier for the revision associated with this import response. SignedUrl (string) -- The signed URL. SignedUrlExpiresAt (datetime) -- The time and date at which the signed URL expires, in ISO 8601 format. ImportAssetsFromS3 (dict) -- Details for the import from Amazon S3 response. AssetSources (list) -- Is a list of Amazon S3 bucket and object key pairs. (dict) -- The source of the assets. Bucket (string) -- The S3 bucket that\'s part of the source of the asset. Key (string) -- The name of the object in Amazon S3 for the asset. DataSetId (string) -- The unique identifier for the data set associated with this import job. RevisionId (string) -- The unique identifier for the revision associated with this import response. Errors (list) -- The errors associated with jobs. (dict) -- An error that occurred with the job request. Code (string) -- The code for the job error. Details (dict) -- ImportAssetFromSignedUrlJobErrorDetails (dict) -- AssetName (string) -- The name of the asset. When importing from Amazon S3, the S3 object key is used as the asset name. When exporting to Amazon S3, the asset name is used as default target S3 object key. ImportAssetsFromS3JobErrorDetails (list) -- The list of sources for the assets. (dict) -- The source of the assets. Bucket (string) -- The S3 bucket that\'s part of the source of the asset. Key (string) -- The name of the object in Amazon S3 for the asset. LimitName (string) -- The name of the limit that was reached. LimitValue (float) -- The value of the exceeded limit. Message (string) -- The message related to the job error. ResourceId (string) -- The unique identifier for the resource related to the error. ResourceType (string) -- The type of resource related to the error. Id (string) -- The unique identifier for the job. State (string) -- The state of the job. Type (string) -- The job type. UpdatedAt (datetime) -- The date and time that the job was last updated, in ISO 8601 format. Exceptions DataExchange.Client.exceptions.ResourceNotFoundException DataExchange.Client.exceptions.ThrottlingException DataExchange.Client.exceptions.ValidationException DataExchange.Client.exceptions.InternalServerException DataExchange.Client.exceptions.AccessDeniedException :return: { 'Arn': 'string', 'CreatedAt': datetime(2015, 1, 1), 'Details': { 'ExportAssetToSignedUrl': { 'AssetId': 'string', 'DataSetId': 'string', 'RevisionId': 'string', 'SignedUrl': 'string', 'SignedUrlExpiresAt': datetime(2015, 1, 1) }, 'ExportAssetsToS3': { 'AssetDestinations': [ { 'AssetId': 'string', 'Bucket': 'string', 'Key': 'string' }, ], 'DataSetId': 'string', 'Encryption': { 'KmsKeyArn': 'string', 'Type': 'aws:kms'|'AES256' }, 'RevisionId': 'string' }, 'ImportAssetFromSignedUrl': { 'AssetName': 'string', 'DataSetId': 'string', 'Md5Hash': 'string', 'RevisionId': 'string', 'SignedUrl': 'string', 'SignedUrlExpiresAt': datetime(2015, 1, 1) }, 'ImportAssetsFromS3': { 'AssetSources': [ { 'Bucket': 'string', 'Key': 'string' }, ], 'DataSetId': 'string', 'RevisionId': 'string' } }, 'Errors': [ { 'Code': 'ACCESS_DENIED_EXCEPTION'|'INTERNAL_SERVER_EXCEPTION'|'MALWARE_DETECTED'|'RESOURCE_NOT_FOUND_EXCEPTION'|'SERVICE_QUOTA_EXCEEDED_EXCEPTION'|'VALIDATION_EXCEPTION'|'MALWARE_SCAN_ENCRYPTED_FILE', 'Details': { 'ImportAssetFromSignedUrlJobErrorDetails': { 'AssetName': 'string' }, 'ImportAssetsFromS3JobErrorDetails': [ { 'Bucket': 'string', 'Key': 'string' }, ] }, 'LimitName': 'Assets per revision'|'Asset size in GB', 'LimitValue': 123.0, 'Message': 'string', 'ResourceId': 'string', 'ResourceType': 'REVISION'|'ASSET' }, ], 'Id': 'string', 'State': 'WAITING'|'IN_PROGRESS'|'ERROR'|'COMPLETED'|'CANCELLED'|'TIMED_OUT', 'Type': 'IMPORT_ASSETS_FROM_S3'|'IMPORT_ASSET_FROM_SIGNED_URL'|'EXPORT_ASSETS_TO_S3'|'EXPORT_ASSET_TO_SIGNED_URL', 'UpdatedAt': datetime(2015, 1, 1) } :returns: DataExchange.Client.exceptions.ResourceNotFoundException DataExchange.Client.exceptions.ThrottlingException DataExchange.Client.exceptions.ValidationException DataExchange.Client.exceptions.InternalServerException DataExchange.Client.exceptions.AccessDeniedException """ pass def create_revision(Comment=None, DataSetId=None, Tags=None): """ This operation creates a revision for a data set. See also: AWS API Documentation Exceptions :example: response = client.create_revision( Comment='string', DataSetId='string', Tags={ 'string': 'string' } ) :type Comment: string :param Comment: An optional comment about the revision. :type DataSetId: string :param DataSetId: [REQUIRED]\nThe unique identifier for a data set.\n :type Tags: dict :param Tags: A revision tag is an optional label that you can assign to a revision when you create it. Each tag consists of a key and an optional value, both of which you define. When you use tagging, you can also use tag-based access control in IAM policies to control access to these data sets and revisions.\n\n(string) --\n(string) --\n\n\n\n :rtype: dict ReturnsResponse Syntax { 'Arn': 'string', 'Comment': 'string', 'CreatedAt': datetime(2015, 1, 1), 'DataSetId': 'string', 'Finalized': True|False, 'Id': 'string', 'SourceId': 'string', 'Tags': { 'string': 'string' }, 'UpdatedAt': datetime(2015, 1, 1) } Response Structure (dict) -- 201 response Arn (string) -- The ARN for the revision Comment (string) -- An optional comment about the revision. CreatedAt (datetime) -- The date and time that the revision was created, in ISO 8601 format. DataSetId (string) -- The unique identifier for the data set associated with this revision. Finalized (boolean) -- To publish a revision to a data set in a product, the revision must first be finalized. Finalizing a revision tells AWS Data Exchange that your changes to the assets in the revision are complete. After it\'s in this read-only state, you can publish the revision to your products. Finalized revisions can be published through the AWS Data Exchange console or the AWS Marketplace Catalog API, using the StartChangeSet AWS Marketplace Catalog API action. When using the API, revisions are uniquely identified by their ARN. Id (string) -- The unique identifier for the revision. SourceId (string) -- The revision ID of the owned revision corresponding to the entitled revision being viewed. This parameter is returned when a revision owner is viewing the entitled copy of its owned revision. Tags (dict) -- The tags for the revision. (string) -- (string) -- UpdatedAt (datetime) -- The date and time that the revision was last updated, in ISO 8601 format. Exceptions DataExchange.Client.exceptions.ResourceNotFoundException DataExchange.Client.exceptions.ThrottlingException DataExchange.Client.exceptions.ValidationException DataExchange.Client.exceptions.InternalServerException DataExchange.Client.exceptions.AccessDeniedException :return: { 'Arn': 'string', 'Comment': 'string', 'CreatedAt': datetime(2015, 1, 1), 'DataSetId': 'string', 'Finalized': True|False, 'Id': 'string', 'SourceId': 'string', 'Tags': { 'string': 'string' }, 'UpdatedAt': datetime(2015, 1, 1) } :returns: (string) -- (string) -- """ pass def delete_asset(AssetId=None, DataSetId=None, RevisionId=None): """ This operation deletes an asset. See also: AWS API Documentation Exceptions :example: response = client.delete_asset( AssetId='string', DataSetId='string', RevisionId='string' ) :type AssetId: string :param AssetId: [REQUIRED]\nThe unique identifier for an asset.\n :type DataSetId: string :param DataSetId: [REQUIRED]\nThe unique identifier for a data set.\n :type RevisionId: string :param RevisionId: [REQUIRED]\nThe unique identifier for a revision.\n :returns: DataExchange.Client.exceptions.ValidationException DataExchange.Client.exceptions.InternalServerException DataExchange.Client.exceptions.AccessDeniedException DataExchange.Client.exceptions.ResourceNotFoundException DataExchange.Client.exceptions.ThrottlingException DataExchange.Client.exceptions.ConflictException """ pass def delete_data_set(DataSetId=None): """ This operation deletes a data set. See also: AWS API Documentation Exceptions :example: response = client.delete_data_set( DataSetId='string' ) :type DataSetId: string :param DataSetId: [REQUIRED]\nThe unique identifier for a data set.\n """ pass def delete_revision(DataSetId=None, RevisionId=None): """ This operation deletes a revision. See also: AWS API Documentation Exceptions :example: response = client.delete_revision( DataSetId='string', RevisionId='string' ) :type DataSetId: string :param DataSetId: [REQUIRED]\nThe unique identifier for a data set.\n :type RevisionId: string :param RevisionId: [REQUIRED]\nThe unique identifier for a revision.\n :returns: DataExchange.Client.exceptions.ValidationException DataExchange.Client.exceptions.InternalServerException DataExchange.Client.exceptions.AccessDeniedException DataExchange.Client.exceptions.ResourceNotFoundException DataExchange.Client.exceptions.ThrottlingException DataExchange.Client.exceptions.ConflictException """ pass def generate_presigned_url(ClientMethod=None, Params=None, ExpiresIn=None, HttpMethod=None): """ Generate a presigned url given a client, its method, and arguments :type ClientMethod: string :param ClientMethod: The client method to presign for :type Params: dict :param Params: The parameters normally passed to\nClientMethod. :type ExpiresIn: int :param ExpiresIn: The number of seconds the presigned url is valid\nfor. By default it expires in an hour (3600 seconds) :type HttpMethod: string :param HttpMethod: The http method to use on the generated url. By\ndefault, the http method is whatever is used in the method\'s model. """ pass def get_asset(AssetId=None, DataSetId=None, RevisionId=None): """ This operation returns information about an asset. See also: AWS API Documentation Exceptions :example: response = client.get_asset( AssetId='string', DataSetId='string', RevisionId='string' ) :type AssetId: string :param AssetId: [REQUIRED]\nThe unique identifier for an asset.\n :type DataSetId: string :param DataSetId: [REQUIRED]\nThe unique identifier for a data set.\n :type RevisionId: string :param RevisionId: [REQUIRED]\nThe unique identifier for a revision.\n :rtype: dict ReturnsResponse Syntax { 'Arn': 'string', 'AssetDetails': { 'S3SnapshotAsset': { 'Size': 123.0 } }, 'AssetType': 'S3_SNAPSHOT', 'CreatedAt': datetime(2015, 1, 1), 'DataSetId': 'string', 'Id': 'string', 'Name': 'string', 'RevisionId': 'string', 'SourceId': 'string', 'UpdatedAt': datetime(2015, 1, 1) } Response Structure (dict) -- 200 response Arn (string) -- The ARN for the asset. AssetDetails (dict) -- Information about the asset, including its size. S3SnapshotAsset (dict) -- The S3 object that is the asset. Size (float) -- The size of the S3 object that is the object. AssetType (string) -- The type of file your data is stored in. Currently, the supported asset type is S3_SNAPSHOT. CreatedAt (datetime) -- The date and time that the asset was created, in ISO 8601 format. DataSetId (string) -- The unique identifier for the data set associated with this asset. Id (string) -- The unique identifier for the asset. Name (string) -- The name of the asset When importing from Amazon S3, the S3 object key is used as the asset name. When exporting to Amazon S3, the asset name is used as default target S3 object key. RevisionId (string) -- The unique identifier for the revision associated with this asset. SourceId (string) -- The asset ID of the owned asset corresponding to the entitled asset being viewed. This parameter is returned when an asset owner is viewing the entitled copy of its owned asset. UpdatedAt (datetime) -- The date and time that the asset was last updated, in ISO 8601 format. Exceptions DataExchange.Client.exceptions.ResourceNotFoundException DataExchange.Client.exceptions.ThrottlingException DataExchange.Client.exceptions.ValidationException DataExchange.Client.exceptions.InternalServerException :return: { 'Arn': 'string', 'AssetDetails': { 'S3SnapshotAsset': { 'Size': 123.0 } }, 'AssetType': 'S3_SNAPSHOT', 'CreatedAt': datetime(2015, 1, 1), 'DataSetId': 'string', 'Id': 'string', 'Name': 'string', 'RevisionId': 'string', 'SourceId': 'string', 'UpdatedAt': datetime(2015, 1, 1) } :returns: DataExchange.Client.exceptions.ResourceNotFoundException DataExchange.Client.exceptions.ThrottlingException DataExchange.Client.exceptions.ValidationException DataExchange.Client.exceptions.InternalServerException """ pass def get_data_set(DataSetId=None): """ This operation returns information about a data set. See also: AWS API Documentation Exceptions :example: response = client.get_data_set( DataSetId='string' ) :type DataSetId: string :param DataSetId: [REQUIRED]\nThe unique identifier for a data set.\n :rtype: dict ReturnsResponse Syntax{ 'Arn': 'string', 'AssetType': 'S3_SNAPSHOT', 'CreatedAt': datetime(2015, 1, 1), 'Description': 'string', 'Id': 'string', 'Name': 'string', 'Origin': 'OWNED'|'ENTITLED', 'OriginDetails': { 'ProductId': 'string' }, 'SourceId': 'string', 'Tags': { 'string': 'string' }, 'UpdatedAt': datetime(2015, 1, 1) } Response Structure (dict) --200 response Arn (string) --The ARN for the data set. AssetType (string) --The type of file your data is stored in. Currently, the supported asset type is S3_SNAPSHOT. CreatedAt (datetime) --The date and time that the data set was created, in ISO 8601 format. Description (string) --The description for the data set. Id (string) --The unique identifier for the data set. Name (string) --The name of the data set. Origin (string) --A property that defines the data set as OWNED by the account (for providers) or ENTITLED to the account (for subscribers). OriginDetails (dict) --If the origin of this data set is ENTITLED, includes the details for the product on AWS Marketplace. ProductId (string) -- SourceId (string) --The data set ID of the owned data set corresponding to the entitled data set being viewed. This parameter is returned when a data set owner is viewing the entitled copy of its owned data set. Tags (dict) --The tags for the data set. (string) -- (string) -- UpdatedAt (datetime) --The date and time that the data set was last updated, in ISO 8601 format. Exceptions DataExchange.Client.exceptions.ResourceNotFoundException DataExchange.Client.exceptions.ThrottlingException DataExchange.Client.exceptions.ValidationException DataExchange.Client.exceptions.InternalServerException :return: { 'Arn': 'string', 'AssetType': 'S3_SNAPSHOT', 'CreatedAt': datetime(2015, 1, 1), 'Description': 'string', 'Id': 'string', 'Name': 'string', 'Origin': 'OWNED'|'ENTITLED', 'OriginDetails': { 'ProductId': 'string' }, 'SourceId': 'string', 'Tags': { 'string': 'string' }, 'UpdatedAt': datetime(2015, 1, 1) } :returns: (string) -- (string) -- """ pass def get_job(JobId=None): """ This operation returns information about a job. See also: AWS API Documentation Exceptions :example: response = client.get_job( JobId='string' ) :type JobId: string :param JobId: [REQUIRED]\nThe unique identifier for a job.\n :rtype: dict ReturnsResponse Syntax{ 'Arn': 'string', 'CreatedAt': datetime(2015, 1, 1), 'Details': { 'ExportAssetToSignedUrl': { 'AssetId': 'string', 'DataSetId': 'string', 'RevisionId': 'string', 'SignedUrl': 'string', 'SignedUrlExpiresAt': datetime(2015, 1, 1) }, 'ExportAssetsToS3': { 'AssetDestinations': [ { 'AssetId': 'string', 'Bucket': 'string', 'Key': 'string' }, ], 'DataSetId': 'string', 'Encryption': { 'KmsKeyArn': 'string', 'Type': 'aws:kms'|'AES256' }, 'RevisionId': 'string' }, 'ImportAssetFromSignedUrl': { 'AssetName': 'string', 'DataSetId': 'string', 'Md5Hash': 'string', 'RevisionId': 'string', 'SignedUrl': 'string', 'SignedUrlExpiresAt': datetime(2015, 1, 1) }, 'ImportAssetsFromS3': { 'AssetSources': [ { 'Bucket': 'string', 'Key': 'string' }, ], 'DataSetId': 'string', 'RevisionId': 'string' } }, 'Errors': [ { 'Code': 'ACCESS_DENIED_EXCEPTION'|'INTERNAL_SERVER_EXCEPTION'|'MALWARE_DETECTED'|'RESOURCE_NOT_FOUND_EXCEPTION'|'SERVICE_QUOTA_EXCEEDED_EXCEPTION'|'VALIDATION_EXCEPTION'|'MALWARE_SCAN_ENCRYPTED_FILE', 'Details': { 'ImportAssetFromSignedUrlJobErrorDetails': { 'AssetName': 'string' }, 'ImportAssetsFromS3JobErrorDetails': [ { 'Bucket': 'string', 'Key': 'string' }, ] }, 'LimitName': 'Assets per revision'|'Asset size in GB', 'LimitValue': 123.0, 'Message': 'string', 'ResourceId': 'string', 'ResourceType': 'REVISION'|'ASSET' }, ], 'Id': 'string', 'State': 'WAITING'|'IN_PROGRESS'|'ERROR'|'COMPLETED'|'CANCELLED'|'TIMED_OUT', 'Type': 'IMPORT_ASSETS_FROM_S3'|'IMPORT_ASSET_FROM_SIGNED_URL'|'EXPORT_ASSETS_TO_S3'|'EXPORT_ASSET_TO_SIGNED_URL', 'UpdatedAt': datetime(2015, 1, 1) } Response Structure (dict) --200 response Arn (string) --The ARN for the job. CreatedAt (datetime) --The date and time that the job was created, in ISO 8601 format. Details (dict) --Details about the job. ExportAssetToSignedUrl (dict) --Details for the export to signed URL response. AssetId (string) --The unique identifier for the asset associated with this export job. DataSetId (string) --The unique identifier for the data set associated with this export job. RevisionId (string) --The unique identifier for the revision associated with this export response. SignedUrl (string) --The signed URL for the export request. SignedUrlExpiresAt (datetime) --The date and time that the signed URL expires, in ISO 8601 format. ExportAssetsToS3 (dict) --Details for the export to Amazon S3 response. AssetDestinations (list) --The destination in Amazon S3 where the asset is exported. (dict) --The destination for the asset. AssetId (string) --The unique identifier for the asset. Bucket (string) --The S3 bucket that is the destination for the asset. Key (string) --The name of the object in Amazon S3 for the asset. DataSetId (string) --The unique identifier for the data set associated with this export job. Encryption (dict) --Encryption configuration of the export job. KmsKeyArn (string) --The Amazon Resource Name (ARN) of the the AWS KMS key you want to use to encrypt the Amazon S3 objects. This parameter is required if you choose aws:kms as an encryption type. Type (string) --The type of server side encryption used for encrypting the objects in Amazon S3. RevisionId (string) --The unique identifier for the revision associated with this export response. ImportAssetFromSignedUrl (dict) --Details for the import from signed URL response. AssetName (string) --The name for the asset associated with this import response. DataSetId (string) --The unique identifier for the data set associated with this import job. Md5Hash (string) --The Base64-encoded Md5 hash for the asset, used to ensure the integrity of the file at that location. RevisionId (string) --The unique identifier for the revision associated with this import response. SignedUrl (string) --The signed URL. SignedUrlExpiresAt (datetime) --The time and date at which the signed URL expires, in ISO 8601 format. ImportAssetsFromS3 (dict) --Details for the import from Amazon S3 response. AssetSources (list) --Is a list of Amazon S3 bucket and object key pairs. (dict) --The source of the assets. Bucket (string) --The S3 bucket that\'s part of the source of the asset. Key (string) --The name of the object in Amazon S3 for the asset. DataSetId (string) --The unique identifier for the data set associated with this import job. RevisionId (string) --The unique identifier for the revision associated with this import response. Errors (list) --The errors associated with jobs. (dict) -- An error that occurred with the job request. Code (string) -- The code for the job error. Details (dict) -- ImportAssetFromSignedUrlJobErrorDetails (dict) -- AssetName (string) --The name of the asset. When importing from Amazon S3, the S3 object key is used as the asset name. When exporting to Amazon S3, the asset name is used as default target S3 object key. ImportAssetsFromS3JobErrorDetails (list) --The list of sources for the assets. (dict) --The source of the assets. Bucket (string) --The S3 bucket that\'s part of the source of the asset. Key (string) --The name of the object in Amazon S3 for the asset. LimitName (string) --The name of the limit that was reached. LimitValue (float) -- The value of the exceeded limit. Message (string) -- The message related to the job error. ResourceId (string) -- The unique identifier for the resource related to the error. ResourceType (string) -- The type of resource related to the error. Id (string) --The unique identifier for the job. State (string) --The state of the job. Type (string) --The job type. UpdatedAt (datetime) --The date and time that the job was last updated, in ISO 8601 format. Exceptions DataExchange.Client.exceptions.ResourceNotFoundException DataExchange.Client.exceptions.ThrottlingException DataExchange.Client.exceptions.ValidationException DataExchange.Client.exceptions.InternalServerException :return: { 'Arn': 'string', 'CreatedAt': datetime(2015, 1, 1), 'Details': { 'ExportAssetToSignedUrl': { 'AssetId': 'string', 'DataSetId': 'string', 'RevisionId': 'string', 'SignedUrl': 'string', 'SignedUrlExpiresAt': datetime(2015, 1, 1) }, 'ExportAssetsToS3': { 'AssetDestinations': [ { 'AssetId': 'string', 'Bucket': 'string', 'Key': 'string' }, ], 'DataSetId': 'string', 'Encryption': { 'KmsKeyArn': 'string', 'Type': 'aws:kms'|'AES256' }, 'RevisionId': 'string' }, 'ImportAssetFromSignedUrl': { 'AssetName': 'string', 'DataSetId': 'string', 'Md5Hash': 'string', 'RevisionId': 'string', 'SignedUrl': 'string', 'SignedUrlExpiresAt': datetime(2015, 1, 1) }, 'ImportAssetsFromS3': { 'AssetSources': [ { 'Bucket': 'string', 'Key': 'string' }, ], 'DataSetId': 'string', 'RevisionId': 'string' } }, 'Errors': [ { 'Code': 'ACCESS_DENIED_EXCEPTION'|'INTERNAL_SERVER_EXCEPTION'|'MALWARE_DETECTED'|'RESOURCE_NOT_FOUND_EXCEPTION'|'SERVICE_QUOTA_EXCEEDED_EXCEPTION'|'VALIDATION_EXCEPTION'|'MALWARE_SCAN_ENCRYPTED_FILE', 'Details': { 'ImportAssetFromSignedUrlJobErrorDetails': { 'AssetName': 'string' }, 'ImportAssetsFromS3JobErrorDetails': [ { 'Bucket': 'string', 'Key': 'string' }, ] }, 'LimitName': 'Assets per revision'|'Asset size in GB', 'LimitValue': 123.0, 'Message': 'string', 'ResourceId': 'string', 'ResourceType': 'REVISION'|'ASSET' }, ], 'Id': 'string', 'State': 'WAITING'|'IN_PROGRESS'|'ERROR'|'COMPLETED'|'CANCELLED'|'TIMED_OUT', 'Type': 'IMPORT_ASSETS_FROM_S3'|'IMPORT_ASSET_FROM_SIGNED_URL'|'EXPORT_ASSETS_TO_S3'|'EXPORT_ASSET_TO_SIGNED_URL', 'UpdatedAt': datetime(2015, 1, 1) } """ pass def get_paginator(operation_name=None): """ Create a paginator for an operation. :type operation_name: string :param operation_name: The operation name. This is the same name\nas the method name on the client. For example, if the\nmethod name is create_foo, and you\'d normally invoke the\noperation as client.create_foo(**kwargs), if the\ncreate_foo operation can be paginated, you can use the\ncall client.get_paginator('create_foo'). :rtype: L{botocore.paginate.Paginator} ReturnsA paginator object. """ pass def get_revision(DataSetId=None, RevisionId=None): """ This operation returns information about a revision. See also: AWS API Documentation Exceptions :example: response = client.get_revision( DataSetId='string', RevisionId='string' ) :type DataSetId: string :param DataSetId: [REQUIRED]\nThe unique identifier for a data set.\n :type RevisionId: string :param RevisionId: [REQUIRED]\nThe unique identifier for a revision.\n :rtype: dict ReturnsResponse Syntax { 'Arn': 'string', 'Comment': 'string', 'CreatedAt': datetime(2015, 1, 1), 'DataSetId': 'string', 'Finalized': True|False, 'Id': 'string', 'SourceId': 'string', 'Tags': { 'string': 'string' }, 'UpdatedAt': datetime(2015, 1, 1) } Response Structure (dict) -- 200 response Arn (string) -- The ARN for the revision Comment (string) -- An optional comment about the revision. CreatedAt (datetime) -- The date and time that the revision was created, in ISO 8601 format. DataSetId (string) -- The unique identifier for the data set associated with this revision. Finalized (boolean) -- To publish a revision to a data set in a product, the revision must first be finalized. Finalizing a revision tells AWS Data Exchange that your changes to the assets in the revision are complete. After it\'s in this read-only state, you can publish the revision to your products. Finalized revisions can be published through the AWS Data Exchange console or the AWS Marketplace Catalog API, using the StartChangeSet AWS Marketplace Catalog API action. When using the API, revisions are uniquely identified by their ARN. Id (string) -- The unique identifier for the revision. SourceId (string) -- The revision ID of the owned revision corresponding to the entitled revision being viewed. This parameter is returned when a revision owner is viewing the entitled copy of its owned revision. Tags (dict) -- The tags for the revision. (string) -- (string) -- UpdatedAt (datetime) -- The date and time that the revision was last updated, in ISO 8601 format. Exceptions DataExchange.Client.exceptions.ResourceNotFoundException DataExchange.Client.exceptions.ThrottlingException DataExchange.Client.exceptions.ValidationException DataExchange.Client.exceptions.InternalServerException :return: { 'Arn': 'string', 'Comment': 'string', 'CreatedAt': datetime(2015, 1, 1), 'DataSetId': 'string', 'Finalized': True|False, 'Id': 'string', 'SourceId': 'string', 'Tags': { 'string': 'string' }, 'UpdatedAt': datetime(2015, 1, 1) } :returns: (string) -- (string) -- """ pass def get_waiter(waiter_name=None): """ Returns an object that can wait for some condition. :type waiter_name: str :param waiter_name: The name of the waiter to get. See the waiters\nsection of the service docs for a list of available waiters. :rtype: botocore.waiter.Waiter """ pass def list_data_set_revisions(DataSetId=None, MaxResults=None, NextToken=None): """ This operation lists a data set\'s revisions sorted by CreatedAt in descending order. See also: AWS API Documentation Exceptions :example: response = client.list_data_set_revisions( DataSetId='string', MaxResults=123, NextToken='string' ) :type DataSetId: string :param DataSetId: [REQUIRED]\nThe unique identifier for a data set.\n :type MaxResults: integer :param MaxResults: The maximum number of results returned by a single call. :type NextToken: string :param NextToken: The token value retrieved from a previous call to access the next page of results. :rtype: dict ReturnsResponse Syntax { 'NextToken': 'string', 'Revisions': [ { 'Arn': 'string', 'Comment': 'string', 'CreatedAt': datetime(2015, 1, 1), 'DataSetId': 'string', 'Finalized': True|False, 'Id': 'string', 'SourceId': 'string', 'UpdatedAt': datetime(2015, 1, 1) }, ] } Response Structure (dict) -- 200 response NextToken (string) -- The token value retrieved from a previous call to access the next page of results. Revisions (list) -- The asset objects listed by the request. (dict) -- A revision is a container for one or more assets. Arn (string) -- The ARN for the revision. Comment (string) -- An optional comment about the revision. CreatedAt (datetime) -- The date and time that the revision was created, in ISO 8601 format. DataSetId (string) -- The unique identifier for the data set associated with this revision. Finalized (boolean) -- To publish a revision to a data set in a product, the revision must first be finalized. Finalizing a revision tells AWS Data Exchange that your changes to the assets in the revision are complete. After it\'s in this read-only state, you can publish the revision to your products. Finalized revisions can be published through the AWS Data Exchange console or the AWS Marketplace Catalog API, using the StartChangeSet AWS Marketplace Catalog API action. When using the API, revisions are uniquely identified by their ARN. Id (string) -- The unique identifier for the revision. SourceId (string) -- The revision ID of the owned revision corresponding to the entitled revision being viewed. This parameter is returned when a revision owner is viewing the entitled copy of its owned revision. UpdatedAt (datetime) -- The date and time that the revision was last updated, in ISO 8601 format. Exceptions DataExchange.Client.exceptions.ResourceNotFoundException DataExchange.Client.exceptions.ThrottlingException DataExchange.Client.exceptions.ValidationException DataExchange.Client.exceptions.InternalServerException :return: { 'NextToken': 'string', 'Revisions': [ { 'Arn': 'string', 'Comment': 'string', 'CreatedAt': datetime(2015, 1, 1), 'DataSetId': 'string', 'Finalized': True|False, 'Id': 'string', 'SourceId': 'string', 'UpdatedAt': datetime(2015, 1, 1) }, ] } :returns: DataExchange.Client.exceptions.ResourceNotFoundException DataExchange.Client.exceptions.ThrottlingException DataExchange.Client.exceptions.ValidationException DataExchange.Client.exceptions.InternalServerException """ pass def list_data_sets(MaxResults=None, NextToken=None, Origin=None): """ This operation lists your data sets. When listing by origin OWNED, results are sorted by CreatedAt in descending order. When listing by origin ENTITLED, there is no order and the maxResults parameter is ignored. See also: AWS API Documentation Exceptions :example: response = client.list_data_sets( MaxResults=123, NextToken='string', Origin='string' ) :type MaxResults: integer :param MaxResults: The maximum number of results returned by a single call. :type NextToken: string :param NextToken: The token value retrieved from a previous call to access the next page of results. :type Origin: string :param Origin: A property that defines the data set as OWNED by the account (for providers) or ENTITLED to the account (for subscribers). :rtype: dict ReturnsResponse Syntax { 'DataSets': [ { 'Arn': 'string', 'AssetType': 'S3_SNAPSHOT', 'CreatedAt': datetime(2015, 1, 1), 'Description': 'string', 'Id': 'string', 'Name': 'string', 'Origin': 'OWNED'|'ENTITLED', 'OriginDetails': { 'ProductId': 'string' }, 'SourceId': 'string', 'UpdatedAt': datetime(2015, 1, 1) }, ], 'NextToken': 'string' } Response Structure (dict) -- 200 response DataSets (list) -- The data set objects listed by the request. (dict) -- A data set is an AWS resource with one or more revisions. Arn (string) -- The ARN for the data set. AssetType (string) -- The type of file your data is stored in. Currently, the supported asset type is S3_SNAPSHOT. CreatedAt (datetime) -- The date and time that the data set was created, in ISO 8601 format. Description (string) -- The description for the data set. Id (string) -- The unique identifier for the data set. Name (string) -- The name of the data set. Origin (string) -- A property that defines the data set as OWNED by the account (for providers) or ENTITLED to the account (for subscribers). OriginDetails (dict) -- If the origin of this data set is ENTITLED, includes the details for the product on AWS Marketplace. ProductId (string) -- SourceId (string) -- The data set ID of the owned data set corresponding to the entitled data set being viewed. This parameter is returned when a data set owner is viewing the entitled copy of its owned data set. UpdatedAt (datetime) -- The date and time that the data set was last updated, in ISO 8601 format. NextToken (string) -- The token value retrieved from a previous call to access the next page of results. Exceptions DataExchange.Client.exceptions.ResourceNotFoundException DataExchange.Client.exceptions.ThrottlingException DataExchange.Client.exceptions.ValidationException DataExchange.Client.exceptions.InternalServerException :return: { 'DataSets': [ { 'Arn': 'string', 'AssetType': 'S3_SNAPSHOT', 'CreatedAt': datetime(2015, 1, 1), 'Description': 'string', 'Id': 'string', 'Name': 'string', 'Origin': 'OWNED'|'ENTITLED', 'OriginDetails': { 'ProductId': 'string' }, 'SourceId': 'string', 'UpdatedAt': datetime(2015, 1, 1) }, ], 'NextToken': 'string' } :returns: ProductId (string) -- """ pass def list_jobs(DataSetId=None, MaxResults=None, NextToken=None, RevisionId=None): """ This operation lists your jobs sorted by CreatedAt in descending order. See also: AWS API Documentation Exceptions :example: response = client.list_jobs( DataSetId='string', MaxResults=123, NextToken='string', RevisionId='string' ) :type DataSetId: string :param DataSetId: The unique identifier for a data set. :type MaxResults: integer :param MaxResults: The maximum number of results returned by a single call. :type NextToken: string :param NextToken: The token value retrieved from a previous call to access the next page of results. :type RevisionId: string :param RevisionId: The unique identifier for a revision. :rtype: dict ReturnsResponse Syntax { 'Jobs': [ { 'Arn': 'string', 'CreatedAt': datetime(2015, 1, 1), 'Details': { 'ExportAssetToSignedUrl': { 'AssetId': 'string', 'DataSetId': 'string', 'RevisionId': 'string', 'SignedUrl': 'string', 'SignedUrlExpiresAt': datetime(2015, 1, 1) }, 'ExportAssetsToS3': { 'AssetDestinations': [ { 'AssetId': 'string', 'Bucket': 'string', 'Key': 'string' }, ], 'DataSetId': 'string', 'Encryption': { 'KmsKeyArn': 'string', 'Type': 'aws:kms'|'AES256' }, 'RevisionId': 'string' }, 'ImportAssetFromSignedUrl': { 'AssetName': 'string', 'DataSetId': 'string', 'Md5Hash': 'string', 'RevisionId': 'string', 'SignedUrl': 'string', 'SignedUrlExpiresAt': datetime(2015, 1, 1) }, 'ImportAssetsFromS3': { 'AssetSources': [ { 'Bucket': 'string', 'Key': 'string' }, ], 'DataSetId': 'string', 'RevisionId': 'string' } }, 'Errors': [ { 'Code': 'ACCESS_DENIED_EXCEPTION'|'INTERNAL_SERVER_EXCEPTION'|'MALWARE_DETECTED'|'RESOURCE_NOT_FOUND_EXCEPTION'|'SERVICE_QUOTA_EXCEEDED_EXCEPTION'|'VALIDATION_EXCEPTION'|'MALWARE_SCAN_ENCRYPTED_FILE', 'Details': { 'ImportAssetFromSignedUrlJobErrorDetails': { 'AssetName': 'string' }, 'ImportAssetsFromS3JobErrorDetails': [ { 'Bucket': 'string', 'Key': 'string' }, ] }, 'LimitName': 'Assets per revision'|'Asset size in GB', 'LimitValue': 123.0, 'Message': 'string', 'ResourceId': 'string', 'ResourceType': 'REVISION'|'ASSET' }, ], 'Id': 'string', 'State': 'WAITING'|'IN_PROGRESS'|'ERROR'|'COMPLETED'|'CANCELLED'|'TIMED_OUT', 'Type': 'IMPORT_ASSETS_FROM_S3'|'IMPORT_ASSET_FROM_SIGNED_URL'|'EXPORT_ASSETS_TO_S3'|'EXPORT_ASSET_TO_SIGNED_URL', 'UpdatedAt': datetime(2015, 1, 1) }, ], 'NextToken': 'string' } Response Structure (dict) -- 200 response Jobs (list) -- The jobs listed by the request. (dict) -- AWS Data Exchange Jobs are asynchronous import or export operations used to create or copy assets. A data set owner can both import and export as they see fit. Someone with an entitlement to a data set can only export. Jobs are deleted 90 days after they are created. Arn (string) -- The ARN for the job. CreatedAt (datetime) -- The date and time that the job was created, in ISO 8601 format. Details (dict) -- Details of the operation to be performed by the job, such as export destination details or import source details. ExportAssetToSignedUrl (dict) -- Details for the export to signed URL response. AssetId (string) -- The unique identifier for the asset associated with this export job. DataSetId (string) -- The unique identifier for the data set associated with this export job. RevisionId (string) -- The unique identifier for the revision associated with this export response. SignedUrl (string) -- The signed URL for the export request. SignedUrlExpiresAt (datetime) -- The date and time that the signed URL expires, in ISO 8601 format. ExportAssetsToS3 (dict) -- Details for the export to Amazon S3 response. AssetDestinations (list) -- The destination in Amazon S3 where the asset is exported. (dict) -- The destination for the asset. AssetId (string) -- The unique identifier for the asset. Bucket (string) -- The S3 bucket that is the destination for the asset. Key (string) -- The name of the object in Amazon S3 for the asset. DataSetId (string) -- The unique identifier for the data set associated with this export job. Encryption (dict) -- Encryption configuration of the export job. KmsKeyArn (string) -- The Amazon Resource Name (ARN) of the the AWS KMS key you want to use to encrypt the Amazon S3 objects. This parameter is required if you choose aws:kms as an encryption type. Type (string) -- The type of server side encryption used for encrypting the objects in Amazon S3. RevisionId (string) -- The unique identifier for the revision associated with this export response. ImportAssetFromSignedUrl (dict) -- Details for the import from signed URL response. AssetName (string) -- The name for the asset associated with this import response. DataSetId (string) -- The unique identifier for the data set associated with this import job. Md5Hash (string) -- The Base64-encoded Md5 hash for the asset, used to ensure the integrity of the file at that location. RevisionId (string) -- The unique identifier for the revision associated with this import response. SignedUrl (string) -- The signed URL. SignedUrlExpiresAt (datetime) -- The time and date at which the signed URL expires, in ISO 8601 format. ImportAssetsFromS3 (dict) -- Details for the import from Amazon S3 response. AssetSources (list) -- Is a list of Amazon S3 bucket and object key pairs. (dict) -- The source of the assets. Bucket (string) -- The S3 bucket that\'s part of the source of the asset. Key (string) -- The name of the object in Amazon S3 for the asset. DataSetId (string) -- The unique identifier for the data set associated with this import job. RevisionId (string) -- The unique identifier for the revision associated with this import response. Errors (list) -- Errors for jobs. (dict) -- An error that occurred with the job request. Code (string) -- The code for the job error. Details (dict) -- ImportAssetFromSignedUrlJobErrorDetails (dict) -- AssetName (string) -- The name of the asset. When importing from Amazon S3, the S3 object key is used as the asset name. When exporting to Amazon S3, the asset name is used as default target S3 object key. ImportAssetsFromS3JobErrorDetails (list) -- The list of sources for the assets. (dict) -- The source of the assets. Bucket (string) -- The S3 bucket that\'s part of the source of the asset. Key (string) -- The name of the object in Amazon S3 for the asset. LimitName (string) -- The name of the limit that was reached. LimitValue (float) -- The value of the exceeded limit. Message (string) -- The message related to the job error. ResourceId (string) -- The unique identifier for the resource related to the error. ResourceType (string) -- The type of resource related to the error. Id (string) -- The unique identifier for the job. State (string) -- The state of the job. Type (string) -- The job type. UpdatedAt (datetime) -- The date and time that the job was last updated, in ISO 8601 format. NextToken (string) -- The token value retrieved from a previous call to access the next page of results. Exceptions DataExchange.Client.exceptions.ResourceNotFoundException DataExchange.Client.exceptions.ThrottlingException DataExchange.Client.exceptions.ValidationException DataExchange.Client.exceptions.InternalServerException :return: { 'Jobs': [ { 'Arn': 'string', 'CreatedAt': datetime(2015, 1, 1), 'Details': { 'ExportAssetToSignedUrl': { 'AssetId': 'string', 'DataSetId': 'string', 'RevisionId': 'string', 'SignedUrl': 'string', 'SignedUrlExpiresAt': datetime(2015, 1, 1) }, 'ExportAssetsToS3': { 'AssetDestinations': [ { 'AssetId': 'string', 'Bucket': 'string', 'Key': 'string' }, ], 'DataSetId': 'string', 'Encryption': { 'KmsKeyArn': 'string', 'Type': 'aws:kms'|'AES256' }, 'RevisionId': 'string' }, 'ImportAssetFromSignedUrl': { 'AssetName': 'string', 'DataSetId': 'string', 'Md5Hash': 'string', 'RevisionId': 'string', 'SignedUrl': 'string', 'SignedUrlExpiresAt': datetime(2015, 1, 1) }, 'ImportAssetsFromS3': { 'AssetSources': [ { 'Bucket': 'string', 'Key': 'string' }, ], 'DataSetId': 'string', 'RevisionId': 'string' } }, 'Errors': [ { 'Code': 'ACCESS_DENIED_EXCEPTION'|'INTERNAL_SERVER_EXCEPTION'|'MALWARE_DETECTED'|'RESOURCE_NOT_FOUND_EXCEPTION'|'SERVICE_QUOTA_EXCEEDED_EXCEPTION'|'VALIDATION_EXCEPTION'|'MALWARE_SCAN_ENCRYPTED_FILE', 'Details': { 'ImportAssetFromSignedUrlJobErrorDetails': { 'AssetName': 'string' }, 'ImportAssetsFromS3JobErrorDetails': [ { 'Bucket': 'string', 'Key': 'string' }, ] }, 'LimitName': 'Assets per revision'|'Asset size in GB', 'LimitValue': 123.0, 'Message': 'string', 'ResourceId': 'string', 'ResourceType': 'REVISION'|'ASSET' }, ], 'Id': 'string', 'State': 'WAITING'|'IN_PROGRESS'|'ERROR'|'COMPLETED'|'CANCELLED'|'TIMED_OUT', 'Type': 'IMPORT_ASSETS_FROM_S3'|'IMPORT_ASSET_FROM_SIGNED_URL'|'EXPORT_ASSETS_TO_S3'|'EXPORT_ASSET_TO_SIGNED_URL', 'UpdatedAt': datetime(2015, 1, 1) }, ], 'NextToken': 'string' } :returns: DataExchange.Client.exceptions.ResourceNotFoundException DataExchange.Client.exceptions.ThrottlingException DataExchange.Client.exceptions.ValidationException DataExchange.Client.exceptions.InternalServerException """ pass def list_revision_assets(DataSetId=None, MaxResults=None, NextToken=None, RevisionId=None): """ This operation lists a revision\'s assets sorted alphabetically in descending order. See also: AWS API Documentation Exceptions :example: response = client.list_revision_assets( DataSetId='string', MaxResults=123, NextToken='string', RevisionId='string' ) :type DataSetId: string :param DataSetId: [REQUIRED]\nThe unique identifier for a data set.\n :type MaxResults: integer :param MaxResults: The maximum number of results returned by a single call. :type NextToken: string :param NextToken: The token value retrieved from a previous call to access the next page of results. :type RevisionId: string :param RevisionId: [REQUIRED]\nThe unique identifier for a revision.\n :rtype: dict ReturnsResponse Syntax { 'Assets': [ { 'Arn': 'string', 'AssetDetails': { 'S3SnapshotAsset': { 'Size': 123.0 } }, 'AssetType': 'S3_SNAPSHOT', 'CreatedAt': datetime(2015, 1, 1), 'DataSetId': 'string', 'Id': 'string', 'Name': 'string', 'RevisionId': 'string', 'SourceId': 'string', 'UpdatedAt': datetime(2015, 1, 1) }, ], 'NextToken': 'string' } Response Structure (dict) -- 200 response Assets (list) -- The asset objects listed by the request. (dict) -- An asset in AWS Data Exchange is a piece of data that can be stored as an S3 object. The asset can be a structured data file, an image file, or some other data file. When you create an import job for your files, you create an asset in AWS Data Exchange for each of those files. Arn (string) -- The ARN for the asset. AssetDetails (dict) -- Information about the asset, including its size. S3SnapshotAsset (dict) -- The S3 object that is the asset. Size (float) -- The size of the S3 object that is the object. AssetType (string) -- The type of file your data is stored in. Currently, the supported asset type is S3_SNAPSHOT. CreatedAt (datetime) -- The date and time that the asset was created, in ISO 8601 format. DataSetId (string) -- The unique identifier for the data set associated with this asset. Id (string) -- The unique identifier for the asset. Name (string) -- The name of the asset. When importing from Amazon S3, the S3 object key is used as the asset name. When exporting to Amazon S3, the asset name is used as default target S3 object key. RevisionId (string) -- The unique identifier for the revision associated with this asset. SourceId (string) -- The asset ID of the owned asset corresponding to the entitled asset being viewed. This parameter is returned when an asset owner is viewing the entitled copy of its owned asset. UpdatedAt (datetime) -- The date and time that the asset was last updated, in ISO 8601 format. NextToken (string) -- The token value retrieved from a previous call to access the next page of results. Exceptions DataExchange.Client.exceptions.ResourceNotFoundException DataExchange.Client.exceptions.ThrottlingException DataExchange.Client.exceptions.ValidationException DataExchange.Client.exceptions.InternalServerException :return: { 'Assets': [ { 'Arn': 'string', 'AssetDetails': { 'S3SnapshotAsset': { 'Size': 123.0 } }, 'AssetType': 'S3_SNAPSHOT', 'CreatedAt': datetime(2015, 1, 1), 'DataSetId': 'string', 'Id': 'string', 'Name': 'string', 'RevisionId': 'string', 'SourceId': 'string', 'UpdatedAt': datetime(2015, 1, 1) }, ], 'NextToken': 'string' } :returns: DataExchange.Client.exceptions.ResourceNotFoundException DataExchange.Client.exceptions.ThrottlingException DataExchange.Client.exceptions.ValidationException DataExchange.Client.exceptions.InternalServerException """ pass def list_tags_for_resource(ResourceArn=None): """ This operation lists the tags on the resource. See also: AWS API Documentation :example: response = client.list_tags_for_resource( ResourceArn='string' ) :type ResourceArn: string :param ResourceArn: [REQUIRED]\nAn Amazon Resource Name (ARN) that uniquely identifies an AWS resource.\n :rtype: dict ReturnsResponse Syntax{ 'Tags': { 'string': 'string' } } Response Structure (dict) --200 response Tags (dict) -- A label that consists of a customer-defined key and an optional value. (string) -- (string) -- :return: { 'Tags': { 'string': 'string' } } """ pass def start_job(JobId=None): """ This operation starts a job. See also: AWS API Documentation Exceptions :example: response = client.start_job( JobId='string' ) :type JobId: string :param JobId: [REQUIRED]\nThe unique identifier for a job.\n :rtype: dict ReturnsResponse Syntax{} Response Structure (dict) --202 response Exceptions DataExchange.Client.exceptions.ValidationException DataExchange.Client.exceptions.InternalServerException DataExchange.Client.exceptions.AccessDeniedException DataExchange.Client.exceptions.ResourceNotFoundException DataExchange.Client.exceptions.ThrottlingException DataExchange.Client.exceptions.ConflictException :return: {} """ pass def tag_resource(ResourceArn=None, Tags=None): """ This operation tags a resource. See also: AWS API Documentation :example: response = client.tag_resource( ResourceArn='string', Tags={ 'string': 'string' } ) :type ResourceArn: string :param ResourceArn: [REQUIRED]\nAn Amazon Resource Name (ARN) that uniquely identifies an AWS resource.\n :type Tags: dict :param Tags: [REQUIRED] A label that consists of a customer-defined key and an optional value.\n\n(string) --\n(string) --\n\n\n\n """ pass def untag_resource(ResourceArn=None, TagKeys=None): """ This operation removes one or more tags from a resource. See also: AWS API Documentation :example: response = client.untag_resource( ResourceArn='string', TagKeys=[ 'string', ] ) :type ResourceArn: string :param ResourceArn: [REQUIRED]\nAn Amazon Resource Name (ARN) that uniquely identifies an AWS resource.\n :type TagKeys: list :param TagKeys: [REQUIRED] The key tags.\n\n(string) --\n\n """ pass def update_asset(AssetId=None, DataSetId=None, Name=None, RevisionId=None): """ This operation updates an asset. See also: AWS API Documentation Exceptions :example: response = client.update_asset( AssetId='string', DataSetId='string', Name='string', RevisionId='string' ) :type AssetId: string :param AssetId: [REQUIRED]\nThe unique identifier for an asset.\n :type DataSetId: string :param DataSetId: [REQUIRED]\nThe unique identifier for a data set.\n :type Name: string :param Name: [REQUIRED]\nThe name of the asset. When importing from Amazon S3, the S3 object key is used as the asset name. When exporting to Amazon S3, the asset name is used as default target S3 object key.\n :type RevisionId: string :param RevisionId: [REQUIRED]\nThe unique identifier for a revision.\n :rtype: dict ReturnsResponse Syntax { 'Arn': 'string', 'AssetDetails': { 'S3SnapshotAsset': { 'Size': 123.0 } }, 'AssetType': 'S3_SNAPSHOT', 'CreatedAt': datetime(2015, 1, 1), 'DataSetId': 'string', 'Id': 'string', 'Name': 'string', 'RevisionId': 'string', 'SourceId': 'string', 'UpdatedAt': datetime(2015, 1, 1) } Response Structure (dict) -- 200 response Arn (string) -- The ARN for the asset. AssetDetails (dict) -- Information about the asset, including its size. S3SnapshotAsset (dict) -- The S3 object that is the asset. Size (float) -- The size of the S3 object that is the object. AssetType (string) -- The type of file your data is stored in. Currently, the supported asset type is S3_SNAPSHOT. CreatedAt (datetime) -- The date and time that the asset was created, in ISO 8601 format. DataSetId (string) -- The unique identifier for the data set associated with this asset. Id (string) -- The unique identifier for the asset. Name (string) -- The name of the asset When importing from Amazon S3, the S3 object key is used as the asset name. When exporting to Amazon S3, the asset name is used as default target S3 object key. RevisionId (string) -- The unique identifier for the revision associated with this asset. SourceId (string) -- The asset ID of the owned asset corresponding to the entitled asset being viewed. This parameter is returned when an asset owner is viewing the entitled copy of its owned asset. UpdatedAt (datetime) -- The date and time that the asset was last updated, in ISO 8601 format. Exceptions DataExchange.Client.exceptions.ValidationException DataExchange.Client.exceptions.InternalServerException DataExchange.Client.exceptions.AccessDeniedException DataExchange.Client.exceptions.ResourceNotFoundException DataExchange.Client.exceptions.ThrottlingException DataExchange.Client.exceptions.ConflictException :return: { 'Arn': 'string', 'AssetDetails': { 'S3SnapshotAsset': { 'Size': 123.0 } }, 'AssetType': 'S3_SNAPSHOT', 'CreatedAt': datetime(2015, 1, 1), 'DataSetId': 'string', 'Id': 'string', 'Name': 'string', 'RevisionId': 'string', 'SourceId': 'string', 'UpdatedAt': datetime(2015, 1, 1) } :returns: DataExchange.Client.exceptions.ValidationException DataExchange.Client.exceptions.InternalServerException DataExchange.Client.exceptions.AccessDeniedException DataExchange.Client.exceptions.ResourceNotFoundException DataExchange.Client.exceptions.ThrottlingException DataExchange.Client.exceptions.ConflictException """ pass def update_data_set(DataSetId=None, Description=None, Name=None): """ This operation updates a data set. See also: AWS API Documentation Exceptions :example: response = client.update_data_set( DataSetId='string', Description='string', Name='string' ) :type DataSetId: string :param DataSetId: [REQUIRED]\nThe unique identifier for a data set.\n :type Description: string :param Description: The description for the data set. :type Name: string :param Name: The name of the data set. :rtype: dict ReturnsResponse Syntax { 'Arn': 'string', 'AssetType': 'S3_SNAPSHOT', 'CreatedAt': datetime(2015, 1, 1), 'Description': 'string', 'Id': 'string', 'Name': 'string', 'Origin': 'OWNED'|'ENTITLED', 'OriginDetails': { 'ProductId': 'string' }, 'SourceId': 'string', 'UpdatedAt': datetime(2015, 1, 1) } Response Structure (dict) -- 200 response Arn (string) -- The ARN for the data set. AssetType (string) -- The type of file your data is stored in. Currently, the supported asset type is S3_SNAPSHOT. CreatedAt (datetime) -- The date and time that the data set was created, in ISO 8601 format. Description (string) -- The description for the data set. Id (string) -- The unique identifier for the data set. Name (string) -- The name of the data set. Origin (string) -- A property that defines the data set as OWNED by the account (for providers) or ENTITLED to the account (for subscribers). OriginDetails (dict) -- If the origin of this data set is ENTITLED, includes the details for the product on AWS Marketplace. ProductId (string) -- SourceId (string) -- The data set ID of the owned data set corresponding to the entitled data set being viewed. This parameter is returned when a data set owner is viewing the entitled copy of its owned data set. UpdatedAt (datetime) -- The date and time that the data set was last updated, in ISO 8601 format. Exceptions DataExchange.Client.exceptions.ResourceNotFoundException DataExchange.Client.exceptions.ThrottlingException DataExchange.Client.exceptions.ValidationException DataExchange.Client.exceptions.InternalServerException DataExchange.Client.exceptions.AccessDeniedException :return: { 'Arn': 'string', 'AssetType': 'S3_SNAPSHOT', 'CreatedAt': datetime(2015, 1, 1), 'Description': 'string', 'Id': 'string', 'Name': 'string', 'Origin': 'OWNED'|'ENTITLED', 'OriginDetails': { 'ProductId': 'string' }, 'SourceId': 'string', 'UpdatedAt': datetime(2015, 1, 1) } :returns: ProductId (string) -- """ pass def update_revision(Comment=None, DataSetId=None, Finalized=None, RevisionId=None): """ This operation updates a revision. See also: AWS API Documentation Exceptions :example: response = client.update_revision( Comment='string', DataSetId='string', Finalized=True|False, RevisionId='string' ) :type Comment: string :param Comment: An optional comment about the revision. :type DataSetId: string :param DataSetId: [REQUIRED]\nThe unique identifier for a data set.\n :type Finalized: boolean :param Finalized: Finalizing a revision tells AWS Data Exchange that your changes to the assets in the revision are complete. After it\'s in this read-only state, you can publish the revision to your products. :type RevisionId: string :param RevisionId: [REQUIRED]\nThe unique identifier for a revision.\n :rtype: dict ReturnsResponse Syntax { 'Arn': 'string', 'Comment': 'string', 'CreatedAt': datetime(2015, 1, 1), 'DataSetId': 'string', 'Finalized': True|False, 'Id': 'string', 'SourceId': 'string', 'UpdatedAt': datetime(2015, 1, 1) } Response Structure (dict) -- 200 response Arn (string) -- The ARN for the revision. Comment (string) -- An optional comment about the revision. CreatedAt (datetime) -- The date and time that the revision was created, in ISO 8601 format. DataSetId (string) -- The unique identifier for the data set associated with this revision. Finalized (boolean) -- To publish a revision to a data set in a product, the revision must first be finalized. Finalizing a revision tells AWS Data Exchange that changes to the assets in the revision are complete. After it\'s in this read-only state, you can publish the revision to your products. Finalized revisions can be published through the AWS Data Exchange console or the AWS Marketplace Catalog API, using the StartChangeSet AWS Marketplace Catalog API action. When using the API, revisions are uniquely identified by their ARN. Id (string) -- The unique identifier for the revision. SourceId (string) -- The revision ID of the owned revision corresponding to the entitled revision being viewed. This parameter is returned when a revision owner is viewing the entitled copy of its owned revision. UpdatedAt (datetime) -- The date and time that the revision was last updated, in ISO 8601 format. Exceptions DataExchange.Client.exceptions.ValidationException DataExchange.Client.exceptions.InternalServerException DataExchange.Client.exceptions.AccessDeniedException DataExchange.Client.exceptions.ResourceNotFoundException DataExchange.Client.exceptions.ThrottlingException DataExchange.Client.exceptions.ConflictException :return: { 'Arn': 'string', 'Comment': 'string', 'CreatedAt': datetime(2015, 1, 1), 'DataSetId': 'string', 'Finalized': True|False, 'Id': 'string', 'SourceId': 'string', 'UpdatedAt': datetime(2015, 1, 1) } :returns: DataExchange.Client.exceptions.ValidationException DataExchange.Client.exceptions.InternalServerException DataExchange.Client.exceptions.AccessDeniedException DataExchange.Client.exceptions.ResourceNotFoundException DataExchange.Client.exceptions.ThrottlingException DataExchange.Client.exceptions.ConflictException """ pass

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45591fb5a391b212b2d77aa545b08af2dd457d22

163

py

Python

src/codeacademy/cars.py

mketiku/python-tutorials

57f05ed78d5391b0c551c7e064a1b2f4304a3c82

[ "MIT" ]

1

2022-03-30T00:22:21.000Z

src/codeacademy/cars.py

mketiku/python-tutorials

57f05ed78d5391b0c551c7e064a1b2f4304a3c82

[ "MIT" ]

null

src/codeacademy/cars.py

mketiku/python-tutorials

57f05ed78d5391b0c551c7e064a1b2f4304a3c82

[ "MIT" ]

null

MyGarage = "Ferrari" , "Toyota" , "Honda" for each_car in MyGarage , SamsGarage: print (each_car) for each_car in MyGarage + SamsGarage: print (each_car)

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4594aa5e1d5140e7daf1f5d4c10e70e5b91144aa

4,607

py

Python

torpido/wavelet/wavelets/sym16.py

AP-Atul/Torpido

a646b4d6de7f2e2c96de4c64ce3113f53e3931c2

[ "Unlicense" ]

21

2020-12-23T07:13:10.000Z

2022-01-12T10:32:22.000Z

wavelet/wavelets/sym16.py

AP-Atul/wavelets-ext

00ced22462c369584ebd32f9b5f357f092de0142

[ "MIT" ]

2

2020-12-30T10:45:42.000Z

2021-09-25T09:52:00.000Z

wavelet/wavelets/sym16.py

AP-Atul/wavelets-ext

00ced22462c369584ebd32f9b5f357f092de0142

[ "MIT" ]

1

2021-02-06T21:39:41.000Z

""" Symlet 16 wavelet """ class Symlet16: """ Properties ---------- near symmetric, orthogonal, biorthogonal All values are from http://wavelets.pybytes.com/wavelet/sym16/ """ __name__ = "Symlet Wavelet 16" __motherWaveletLength__ = 32 # length of the mother wavelet __transformWaveletLength__ = 2 # minimum wavelength of input signal # decomposition filter # low-pass decompositionLowFilter = [ 6.230006701220761e-06, -3.113556407621969e-06, -0.00010943147929529757, 2.8078582128442894e-05, 0.0008523547108047095, -0.0001084456223089688, -0.0038809122526038786, 0.0007182119788317892, 0.012666731659857348, -0.0031265171722710075, -0.031051202843553064, 0.004869274404904607, 0.032333091610663785, -0.06698304907021778, -0.034574228416972504, 0.39712293362064416, 0.7565249878756971, 0.47534280601152273, -0.054040601387606135, -0.15959219218520598, 0.03072113906330156, 0.07803785290341991, -0.003510275068374009, -0.024952758046290123, 0.001359844742484172, 0.0069377611308027096, -0.00022211647621176323, -0.0013387206066921965, 3.656592483348223e-05, 0.00016545679579108483, -5.396483179315242e-06, -1.0797982104319795e-05, ] # high-pass decompositionHighFilter = [ 1.0797982104319795e-05, -5.396483179315242e-06, -0.00016545679579108483, 3.656592483348223e-05, 0.0013387206066921965, -0.00022211647621176323, -0.0069377611308027096, 0.001359844742484172, 0.024952758046290123, -0.003510275068374009, -0.07803785290341991, 0.03072113906330156, 0.15959219218520598, -0.054040601387606135, -0.47534280601152273, 0.7565249878756971, -0.39712293362064416, -0.034574228416972504, 0.06698304907021778, 0.032333091610663785, -0.004869274404904607, -0.031051202843553064, 0.0031265171722710075, 0.012666731659857348, -0.0007182119788317892, -0.0038809122526038786, 0.0001084456223089688, 0.0008523547108047095, -2.8078582128442894e-05, -0.00010943147929529757, 3.113556407621969e-06, 6.230006701220761e-06, ] # reconstruction filters # low pass reconstructionLowFilter = [ -1.0797982104319795e-05, -5.396483179315242e-06, 0.00016545679579108483, 3.656592483348223e-05, -0.0013387206066921965, -0.00022211647621176323, 0.0069377611308027096, 0.001359844742484172, -0.024952758046290123, -0.003510275068374009, 0.07803785290341991, 0.03072113906330156, -0.15959219218520598, -0.054040601387606135, 0.47534280601152273, 0.7565249878756971, 0.39712293362064416, -0.034574228416972504, -0.06698304907021778, 0.032333091610663785, 0.004869274404904607, -0.031051202843553064, -0.0031265171722710075, 0.012666731659857348, 0.0007182119788317892, -0.0038809122526038786, -0.0001084456223089688, 0.0008523547108047095, 2.8078582128442894e-05, -0.00010943147929529757, -3.113556407621969e-06, 6.230006701220761e-06, ] # high-pass reconstructionHighFilter = [ 6.230006701220761e-06, 3.113556407621969e-06, -0.00010943147929529757, -2.8078582128442894e-05, 0.0008523547108047095, 0.0001084456223089688, -0.0038809122526038786, -0.0007182119788317892, 0.012666731659857348, 0.0031265171722710075, -0.031051202843553064, -0.004869274404904607, 0.032333091610663785, 0.06698304907021778, -0.034574228416972504, -0.39712293362064416, 0.7565249878756971, -0.47534280601152273, -0.054040601387606135, 0.15959219218520598, 0.03072113906330156, -0.07803785290341991, -0.003510275068374009, 0.024952758046290123, 0.001359844742484172, -0.0069377611308027096, -0.00022211647621176323, 0.0013387206066921965, 3.656592483348223e-05, -0.00016545679579108483, -5.396483179315242e-06, 1.0797982104319795e-05, ]

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9

45cab71be33d658b084e8f81f4d3901bd0c7dae6

206

py

Python

model/third_party/HMNet/ThirdParty/ROUGE/pyrouge/utils/log.py

NickSchoelkopf/SummerTime

9a89aab8e1544e3c52c043b9c47ab325e665e11e

[ "Apache-2.0" ]

178

2021-07-07T23:46:20.000Z

2022-03-26T17:47:21.000Z

model/third_party/HMNet/ThirdParty/ROUGE/pyrouge/utils/log.py

NickSchoelkopf/SummerTime

9a89aab8e1544e3c52c043b9c47ab325e665e11e

[ "Apache-2.0" ]

77

2021-06-18T21:44:53.000Z

2022-02-20T00:23:06.000Z

model/third_party/HMNet/ThirdParty/ROUGE/pyrouge/utils/log.py

NickSchoelkopf/SummerTime

9a89aab8e1544e3c52c043b9c47ab325e665e11e

[ "Apache-2.0" ]

19

2021-06-18T22:24:47.000Z

2022-03-16T12:53:50.000Z

import logging def get_console_logger(name, level=logging.WARNING): return logging.getLogger("pyrouge") def get_global_console_logger(level=logging.WARNING): return logging.getLogger("pyrouge")

20.6

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1

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7

45df4ea695e4bc0eeed1e7c57bfb7e0dfb88369b

51,248

py

Python

pyredis/commands.py

adityagrg/pyredis-Python2

136b40062152599630171c1002b95a13135b8059

[ "MIT" ]

null

pyredis/commands.py

adityagrg/pyredis-Python2

136b40062152599630171c1002b95a13135b8059

[ "MIT" ]

null

pyredis/commands.py

adityagrg/pyredis-Python2

136b40062152599630171c1002b95a13135b8059

[ "MIT" ]

null

__author__ = u'adityagrg' __all__ = [ u'Connection', u'Hash', u'HyperLogLog', u'Key', u'List', u'Publish', u'Scripting', u'Set', u'SSet', u'String', u'Subscribe', u'Transaction' ] class BaseCommand(object): def __init__(self): self._cluster = False def execute(self, *args, **kwargs): raise NotImplemented class Connection(BaseCommand): def __init__(self): super(Connection, self).__init__() def echo(self, *args, shard_key=None, sock=None): u""" Execute ECHO Command, consult Redis documentation for details. :param shard_key: (optional) Should be set to the key name you try to work with. Can not be used if sock is set. Only used if used with a Cluster Client :type shard_key: string :param sock: (optional) The string representation of a socket, the command should be executed against. For example: "testhost_6379" Can not be used if shard_key is set. Only used if used with a Cluster Client :type sock: string :return: result, exception """ if self._cluster: return self.execute(u'ECHO', *args, shard_key=shard_key, sock=sock) return self.execute(u'ECHO', *args) def ping(self, shard_key=None, sock=None): u""" Execute PING Command, consult Redis documentation for details. :param shard_key: (optional) Should be set to the key name you try to work with. Can not be used if sock is set. Only used if used with a Cluster Client :type shard_key: string :param sock: (optional) The string representation of a socket, the command should be executed against. For example: "testhost_6379" Can not be used if shard_key is set. Only used if used with a Cluster Client :type sock: string :return: result,exception """ if self._cluster: return self.execute(u'PING', shard_key=shard_key, sock=sock) return self.execute(u'PING') class Geo(BaseCommand): def __init__(self): super(Geo, self).__init__() def geoadd(self, *args): u""" Execute GEOADD Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'GEOADD', *args, shard_key=args[0]) return self.execute(u'GEOADD', *args) def geodist(self, *args): u""" Execute GEODIST Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'GEODIST', *args, shard_key=args[0]) return self.execute(u'GEODIST', *args) def geohash(self, *args): u""" Execute GEOHASH Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'GEOHASH', *args, shard_key=args[0]) return self.execute(u'GEOHASH', *args) def georadius(self, *args): u""" Execute GEORADIUS Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'GEORADIUS', *args, shard_key=args[0]) return self.execute(u'GEORADIUS', *args) def geopos(self, *args): u""" Execute GEOPOS Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'GEOPOS', *args, shard_key=args[0]) return self.execute(u'GEOPOS', *args) def georadiusbymember(self, *args): u""" Execute GEORADIUSBYMEMBER Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'GEORADIUSBYMEMBER', *args, shard_key=args[0]) return self.execute(u'GEORADIUSBYMEMBER', *args) class Key(BaseCommand): def __init__(self): super(Key, self).__init__() def delete(self, *args): u""" Execute DEL Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'DEL', *args, shard_key=args[0]) return self.execute(u'DEL', *args) def dump(self, *args): u""" Execute DUMP Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'DUMP', *args, shard_key=args[0]) return self.execute(u'DUMP', *args) def exists(self, *args): u""" Execute EXISTS Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'EXISTS', *args, shard_key=args[0]) return self.execute(u'EXISTS', *args) def expire(self, *args): u""" Execute EXPIRE Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'EXPIRE', *args, shard_key=args[0]) return self.execute(u'EXPIRE', *args) def expireat(self, *args): u""" Execute EXPIREAT Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'EXPIREAT') return self.execute(u'EXPIREAT', *args) def keys(self, *args, shard_key=None, sock=None): u""" Execute KEYS Command, consult Redis documentation for details. :param shard_key: (optional) Should be set to the key name you try to work with. Can not be used if sock is set. Only used if used with a Cluster Client :type shard_key: string :param sock: (optional) The string representation of a socket, the command should be executed against. For example: "testhost_6379" Can not be used if shard_key is set. Only used if used with a Cluster Client :type sock: string :return: result, exception """ if self._cluster: return self.execute(u'KEYS', *args, shard_key=shard_key, sock=sock) return self.execute(u'KEYS', *args) def migrate(self, *args): u""" Execute MIGRATE Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: raise NotImplemented return self.execute(u'MIGRATE', *args) def move(self, *args): u""" Execute MOVE Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'MOVE', *args, shard_key=args[0]) return self.execute(u'MOVE', *args) def object(self, *args, shard_key=None, sock=None): u""" Execute OBJECT Command, consult Redis documentation for details. :param shard_key: (optional) Should be set to the key name you try to work with. Can not be used if sock is set. Only used if used with a Cluster Client :type shard_key: string :param sock: (optional) The string representation of a socket, the command should be executed against. For example: "testhost_6379" Can not be used if shard_key is set. Only used if used with a Cluster Client :type sock: string :return: result, exception """ if self._cluster: return self.execute(u'DEL', *args, shard_key=shard_key, sock=sock) return self.execute(u'OBJECT', *args) def persist(self, *args): u""" Execute PERSIST Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'PERSIST', *args, shard_key=args[0]) return self.execute(u'PERSIST', *args) def pexpire(self, *args): u""" Execute PEXPIRE Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'PEXPIRE', *args, shard_key=args[0]) return self.execute(u'PEXPIRE', *args) def pexpireat(self, *args): u""" Execute PEXPIREAT Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'PEXPIREAT', *args, shard_key=args[0]) return self.execute(u'PEXPIREAT', *args) def pttl(self, *args): u""" Execute PTTL Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'PTTL', *args, shard_key=args[0]) return self.execute(u'PTTL', *args) def randomkey(self, *args, shard_key=None, sock=None): u""" Execute RANDOMKEY Command, consult Redis documentation for details. :param shard_key: (optional) Should be set to the key name you try to work with. Can not be used if sock is set. Only used if used with a Cluster Client :type shard_key: string :param sock: (optional) The string representation of a socket, the command should be executed against. For example: "testhost_6379" Can not be used if shard_key is set. Only used if used with a Cluster Client :type sock: string :return: result, exception """ if self._cluster: return self.execute(u'RANDOMKEY', *args, shard_key=shard_key, sock=sock) return self.execute(u'RANDOMKEY', *args) def rename(self, *args): u""" Execute RENAME Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'RENAME', *args, shard_key=args[0]) return self.execute(u'RENAME', *args) def renamenx(self, *args): u""" Execute RENAMENX Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'RENAMENX', *args, shard_key=args[0]) return self.execute(u'RENAMENX', *args) def restore(self, *args): u""" Execute RESTORE Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'RESTORE', *args, shard_key=args[0]) return self.execute(u'RESTORE', *args) def scan(self, *args, shard_key=None, sock=None): u""" Execute SCAN Command, consult Redis documentation for details. :param shard_key: (optional) Should be set to the key name you try to work with. Can not be used if sock is set. Only used if used with a Cluster Client :type shard_key: string :param sock: (optional) The string representation of a socket, the command should be executed against. For example: "testhost_6379" Can not be used if shard_key is set. Only used if used with a Cluster Client :type sock: string :return: result, exception """ if self._cluster: return self.execute(u'SCAN', *args, shard_key=shard_key, sock=sock) return self.execute(u'SCAN', *args) def sort(self, *args): u""" Execute SORT Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'SORT', *args, shard_key=args[0]) return self.execute(u'SORT', *args) def ttl(self, *args): u""" Execute TTL Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'TTL', *args, shard_key=args[0]) return self.execute(u'TTL', *args) def type(self, *args): u""" Execute TYPE Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'TYPE', *args, shard_key=args[0]) return self.execute(u'TYPE', *args) def wait(self, *args): u""" Execute WAIT Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'WAIT', *args, shard_key=args[0]) return self.execute(u'WAIT', *args) class String(BaseCommand): def __init__(self): super(String, self).__init__() def append(self, *args): u""" Execute APPEND Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'APPEND', *args, shard_key=args[0]) return self.execute(u'APPEND', *args) def bitcount(self, *args): u""" Execute BITCOUNT Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'BITCOUNT', *args, shard_key=args[0]) return self.execute(u'BITCOUNT', *args) def bitfield(self, *args): u""" Execute BITFIELD Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'BITFIELD', *args, shard_key=args[0]) return self.execute(u'BITFIELD', *args) def bitop(self, *args): u""" Execute BITOP Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'BITOP', *args, shard_key=args[1]) return self.execute(u'BITOP', *args) def bitpos(self, *args): u""" Execute BITPOS Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'BITPOS', *args, shard_key=args[0]) return self.execute(u'BITPOS', *args) def decr(self, *args): u""" Execute DECR Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'DECR', *args, shard_key=args[0]) return self.execute(u'DECR', *args) def decrby(self, *args): u""" Execute DECRBY Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'DECRBY', *args, shard_key=args[0]) return self.execute(u'DECRBY', *args) def get(self, *args): u""" Execute GET Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'GET', *args, shard_key=args[0]) return self.execute(u'GET', *args) def getbit(self, *args): u""" Execute GETBIT Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'GETBIT', *args, shard_key=args[0]) return self.execute(u'GETBIT', *args) def getrange(self, *args): u""" Execute GETRANGE Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'GETRANGE', *args, shard_key=args[0]) return self.execute(u'GETRANGE', *args) def getset(self, *args): u""" Execute GETSET Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'GETSET', *args, shard_key=args[0]) return self.execute(u'GETSET', *args) def incr(self, *args): u""" Execute INCR Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'INCR', *args, shard_key=args[0]) return self.execute(u'INCR', *args) def incrby(self, *args): u""" Execute INCRBY Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'INCRBY', *args, shard_key=args[0]) return self.execute(u'INCRBY', *args) def incrbyfloat(self, *args): u""" Execute INCRBYFLOAT Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'INCRBYFLOAT', *args, shard_key=args[0]) return self.execute(u'INCRBYFLOAT', *args) def mget(self, *args): u""" Execute MGET Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'MGET', *args, shard_key=args[0]) return self.execute(u'MGET', *args) def mset(self, *args): u""" Execute MSET Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'MSET', *args, shard_key=args[0]) return self.execute(u'MSET', *args) def msetnx(self, *args): u""" Execute MSETNX Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'MSETNX', *args, shard_key=args[0]) return self.execute(u'MSETNX', *args) def psetex(self, *args): u""" Execute PSETEX Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'PSETEX', *args, shard_key=args[0]) return self.execute(u'PSETEX', *args) def set(self, *args): u""" Execute SET Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'SET', *args, shard_key=args[0]) return self.execute(u'SET', *args) def setbit(self, *args): u""" Execute SETBIT Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'SETBIT', *args, shard_key=args[0]) return self.execute(u'SETBIT', *args) def setex(self, *args): u""" Execute SETEX Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'SETEX', *args, shard_key=args[0]) return self.execute(u'SETEX', *args) def setnx(self, *args): u""" Execute SETNX Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'SETNX', *args, shard_key=args[0]) return self.execute(u'SETNX', *args) def setrange(self, *args): u""" Execute SETRANGE Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'SETRANGE', *args, shard_key=args[0]) return self.execute(u'SETRANGE', *args) def strlen(self, *args): u""" Execute STRLEN Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'STRLEN', *args, shard_key=args[0]) return self.execute(u'STRLEN', *args) class Hash(BaseCommand): def __init__(self): super(Hash, self).__init__() def hdel(self, *args): u""" Execute HDEL Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'HDEL', *args, shard_key=args[0]) return self.execute(u'HDEL', *args) def hexists(self, *args): u""" Execute HEXISTS Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'HEXISTS', *args, shard_key=args[0]) return self.execute(u'HEXISTS', *args) def hget(self, *args): u""" Execute HGET Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'HGET', *args, shard_key=args[0]) return self.execute(u'HGET', *args) def hgetall(self, *args): u""" Execute HGETALL Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'HGETALL', *args, shard_key=args[0]) return self.execute(u'HGETALL', *args) def hincrby(self, *args): u""" Execute HINCRBY Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'HINCRBY', *args, shard_key=args[0]) return self.execute(u'HINCRBY', *args) def hincrbyfloat(self, *args): u""" Execute HINCRBYFLOAT Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'HINCRBYFLOAT', *args, shard_key=args[0]) return self.execute(u'HINCRBYFLOAT', *args) def hkeys(self, *args): u""" Execute HKEYS Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'HKEYS', *args, shard_key=args[0]) return self.execute(u'HKEYS', *args) def hlen(self, *args): u""" Execute HLEN Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'HLEN', *args, shard_key=args[0]) return self.execute(u'HLEN', *args) def hmget(self, *args): u""" Execute HMGET Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'HMGET', *args, shard_key=args[0]) return self.execute(u'HMGET', *args) def hmset(self, *args): u""" Execute HMSET Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'HMSET', *args, shard_key=args[0]) return self.execute(u'HMSET', *args) def hset(self, *args): u""" Execute HSET Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'HSET', *args, shard_key=args[0]) return self.execute(u'HSET', *args) def hsetnx(self, *args): u""" Execute HSETNX Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'HSETNX', *args, shard_key=args[0]) return self.execute(u'HSETNX', *args) def hstrlen(self, *args): u""" Execute HSTRLEN Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'HSTRLEN', *args, shard_key=args[0]) return self.execute(u'HSTRLEN', *args) def hvals(self, *args): u""" Execute HVALS Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'HVALS', *args, shard_key=args[0]) return self.execute(u'HVALS', *args) def hscan(self, *args): u""" Execute HSCAN Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'HSCAN', *args, shard_key=args[0]) return self.execute(u'HSCAN', *args) class List(BaseCommand): def __init__(self): super(List, self).__init__() def blpop(self, *args): u""" Execute BLPOP Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'BLPOP', *args, shard_key=args[0]) return self.execute(u'BLPOP', *args) def brpop(self, *args): u""" Execute BRPOP Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'BRPOP', *args, shard_key=args[0]) return self.execute(u'BRPOP', *args) def brpoplpush(self, *args): u""" Execute BRPOPPUSH Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'BRPOPPUSH', *args, shard_key=args[0]) return self.execute(u'BRPOPPUSH', *args) def lindex(self, *args): u""" Execute LINDEX Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'LINDEX', *args, shard_key=args[0]) return self.execute(u'LINDEX', *args) def linsert(self, *args): u""" Execute LINSERT Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'LINSERT', *args, shard_key=args[0]) return self.execute(u'LINSERT', *args) def llen(self, *args): u""" Execute LLEN Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'LLEN', *args, shard_key=args[0]) return self.execute(u'LLEN', *args) def lpop(self, *args): u""" Execute LPOP Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'LPOP', *args, shard_key=args[0]) return self.execute(u'LPOP', *args) def lpush(self, *args): u""" Execute LPUSH Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'LPUSH', *args, shard_key=args[0]) return self.execute(u'LPUSH', *args) def lpushx(self, *args): u""" Execute LPUSHX Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'LPUSHX', *args, shard_key=args[0]) return self.execute(u'LPUSHX', *args) def lrange(self, *args): u""" Execute LRANGE Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'LRANGE', *args, shard_key=args[0]) return self.execute(u'LRANGE', *args) def lrem(self, *args): u""" Execute LREM Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'LREM', *args, shard_key=args[0]) return self.execute(u'LREM', *args) def lset(self, *args): u""" Execute LSET Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'LSET', *args, shard_key=args[0]) return self.execute(u'LSET', *args) def ltrim(self, *args): u""" Execute LTRIM Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'LTRIM', *args, shard_key=args[0]) return self.execute(u'LTRIM', *args) def rpop(self, *args): u""" Execute RPOP Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'RPOP', *args, shard_key=args[0]) return self.execute(u'RPOP', *args) def rpoplpush(self, *args): u""" Execute RPOPLPUSH Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'RPOPLPUSH', *args, shard_key=args[0]) return self.execute(u'RPOPLPUSH', *args) def rpush(self, *args): u""" Execute RPUSH Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'RPUSH', *args, shard_key=args[0]) return self.execute(u'RPUSH', *args) def rpushx(self, *args): u""" Execute RPUSHX Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'RPUSHX', *args, shard_key=args[0]) return self.execute(u'RPUSHX', *args) class Set(BaseCommand): def __init__(self): super(Set, self).__init__() def sadd(self, *args): u""" Execute SADD Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'SADD', *args, shard_key=args[0]) return self.execute(u'SADD', *args) def scard(self, *args): u""" Execute SCARD Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'SCARD', *args, shard_key=args[0]) return self.execute(u'SCARD', *args) def sdiff(self, *args): u""" Execute SDIFF Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'SDIFF', *args, shard_key=args[0]) return self.execute(u'SDIFF', *args) def sdiffstore(self, *args): u""" Execute SDIFFSTORE Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'SDIFFSTORE', *args, shard_key=args[0]) return self.execute(u'SDIFFSTORE', *args) def sinter(self, *args): u""" Execute SINTER Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'SINTER', *args, shard_key=args[0]) return self.execute(u'SINTER', *args) def sinterstore(self, *args): u""" Execute SINTERSTORE Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'SINTERSTORE', *args, shard_key=args[0]) return self.execute(u'SINTERSTORE', *args) def sismember(self, *args): u""" Execute SISMEMBER Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'SISMEMBER', *args, shard_key=args[0]) return self.execute(u'SISMEMBER', *args) def smembers(self, *args): u""" Execute SMEMBERS Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'SMEMBERS', *args, shard_key=args[0]) return self.execute(u'SMEMBERS', *args) def smove(self, *args): u""" Execute SMOVE Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'SMOVE', *args, shard_key=args[0]) return self.execute(u'SMOVE', *args) def spop(self, *args): u""" Execute SPOP Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'SPOP', *args, shard_key=args[0]) return self.execute(u'SPOP', *args) def srandmember(self, *args): u""" Execute SRANDMEMBER Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'SRANDMEMBER', *args, shard_key=args[0]) return self.execute(u'SRANDMEMBER', *args) def srem(self, *args): u""" Execute SREM Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'SREM', *args, shard_key=args[0]) return self.execute(u'SREM', *args) def sunion(self, *args): u""" Execute SUNION Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'SUNION', *args, shard_key=args[0]) return self.execute(u'SUNION', *args) def sunoinstore(self, *args): u""" Execute SUNIONSTORE Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'SUNIONSTORE', *args, shard_key=args[0]) return self.execute(u'SUNIONSTORE', *args) def sscan(self, *args): u""" Execute SSCAN Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'SSCAN', *args, shard_key=args[0]) return self.execute(u'SSCAN', *args) class SSet(BaseCommand): def __init__(self): super(SSet, self).__init__() def zadd(self, *args): u""" Execute ZADD Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'ZADD', *args, shard_key=args[0]) return self.execute(u'ZADD', *args) def zcard(self, *args): u""" Execute ZCARD Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'ZCARD', *args, shard_key=args[0]) return self.execute(u'ZCARD', *args) def zcount(self, *args): u""" Execute ZCOUNT Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'ZCOUNT', *args, shard_key=args[0]) return self.execute(u'ZCOUNT', *args) def zincrby(self, *args): u""" Execute ZINCRBY Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'ZINCRBY', *args, shard_key=args[0]) return self.execute(u'ZINCRBY', *args) def zinterstore(self, *args): u""" Execute ZINTERSTORE Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'ZINTERSTORE', *args, shard_key=args[0]) return self.execute(u'ZINTERSTORE', *args) def zlexcount(self, *args): u""" Execute ZLEXCOUNT Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'ZLEXCOUNT', *args, shard_key=args[0]) return self.execute(u'ZLEXCOUNT', *args) def zrange(self, *args): u""" Execute ZRANGE Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'ZRANGE', *args, shard_key=args[0]) return self.execute(u'ZRANGE', *args) def zrangebylex(self, *args): u""" Execute ZRANGEBYLEX Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'ZRANGEBYLEX', *args, shard_key=args[0]) return self.execute(u'ZRANGEBYLEX', *args) def zrangebyscore(self, *args): u""" Execute ZRANGEBYSCORE Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'ZRANGEBYSCORE', *args, shard_key=args[0]) return self.execute(u'ZRANGEBYSCORE', *args) def zrank(self, *args): u""" Execute ZRANK Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'ZRANK', *args, shard_key=args[0]) return self.execute(u'ZRANK', *args) def zrem(self, *args): u""" Execute ZREM Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'ZREM', *args, shard_key=args[0]) return self.execute(u'ZREM', *args) def zremrangebylex(self, *args): u""" Execute ZREMRANGEBYLEX Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'ZREMRANGEBYLEX', *args, shard_key=args[0]) return self.execute(u'ZREMRANGEBYLEX', *args) def zremrangebyrank(self, *args): u""" Execute ZREMRANGEBYRANK Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'ZREMRANGEBYRANK', *args, shard_key=args[0]) return self.execute(u'ZREMRANGEBYRANK', *args) def zremrangebyscrore(self, *args): u""" Execute ZREMRANGEBYSCORE Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'ZREMRANGEBYSCORE', *args, shard_key=args[0]) return self.execute(u'ZREMRANGEBYSCORE', *args) def zrevrange(self, *args): u""" Execute ZREVRANGE Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'ZREVRANGE', *args, shard_key=args[0]) return self.execute(u'ZREVRANGE', *args) def zrevrangebylex(self, *args): u""" Execute ZREVRANGEBYLEX Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'ZREVRANGEBYLEX', *args, shard_key=args[0]) return self.execute(u'ZREVRANGEBYLEX', *args) def zrevrangebyscore(self, *args): u""" Execute ZREVRANGEBYSCORE Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'ZREVRANGEBYSCORE', *args, shard_key=args[0]) return self.execute(u'ZREVRANGEBYSCORE', *args) def zrevrank(self, *args): u""" Execute ZREVRANK Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'ZREVRANK', *args, shard_key=args[0]) return self.execute(u'ZREVRANK', *args) def zscore(self, *args): u""" Execute ZSCORE Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'ZSCORE', *args, shard_key=args[0]) return self.execute(u'ZSCORE', *args) def zunionstore(self, *args): u""" Execute ZUNIONSTORE Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'ZUNIONSTORE', *args, shard_key=args[0]) return self.execute(u'ZUNIONSTORE', *args) def zscan(self, *args): u""" Execute ZSCAN Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'ZSCAN', *args, shard_key=args[0]) return self.execute(u'ZSCAN', *args) class HyperLogLog(BaseCommand): def __init__(self): super(HyperLogLog, self).__init__() def pfadd(self, *args): u""" Execute PFADD Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'PFADD', *args, shard_key=args[0]) return self.execute(u'PFADD', *args) def pfcount(self, *args): u""" Execute PFCOUNT Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'PFCOUNT', *args, shard_key=args[0]) return self.execute(u'PFCOUNT', *args) def pfmerge(self, *args): u""" Execute PFMERGE Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'PFMERGE', *args, shard_key=args[0]) return self.execute(u'PFMERGE', *args) class Publish(BaseCommand): def __init__(self): super(Publish, self).__init__() def publish(self, *args): u""" Execute PUBLISH Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: raise NotImplemented return self.execute(u'PUBLISH', *args) class Subscribe(object): def write(self, *args): raise NotImplemented def psubscribe(self, *args): u""" Execute PSUBSCRIBE Command, consult Redis documentation for details. :return: result, exception """ return self.write(u'PSUBSCRIBE', *args) def punsubscribe(self, *args): u""" Execute PUNSUBSCRIBE Command, consult Redis documentation for details. :return: result, exception """ return self.write(u'PUNSUBSCRIBE', *args) def subscribe(self, *args): u""" Execute SUBSCRIBE Command, consult Redis documentation for details. :return: result, exception """ return self.write(u'SUBSCRIBE', *args) def unsubscribe(self, *args): u""" Execute UNSUBSCRIBE Command, consult Redis documentation for details. :return: result, exception """ return self.write(u'UNSUBSCRIBE', *args) class Transaction(BaseCommand): def __init__(self): super(Transaction, self).__init__() def discard(self, *args, shard_key=None, sock=None): u""" Execute DISCARD Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'DISCARD', *args, shard_key=shard_key, sock=sock) return self.execute(u'DISCARD', *args) def exec(self, *args, shard_key=None, sock=None): u""" Execute EXEC Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'EXEC', *args, shard_key=shard_key, sock=sock) return self.execute(u'EXEC', *args) def multi(self, *args, shard_key=None, sock=None): u""" Execute MULTI Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'MULTI', *args, shard_key=shard_key, sock=sock) return self.execute(u'MULTI', *args) def unwatch(self, *args, shard_key=None, sock=None): u""" Execute UNWATCH Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'UNWATCH', *args, shard_key=shard_key, sock=sock) return self.execute(u'UNWATCH', *args) def watch(self, *args): u""" Execute WATCH Command, consult Redis documentation for details. :return: result, exception """ if self._cluster: return self.execute(u'WATCH', *args, shard_key=args[0]) return self.execute(u'WATCH', *args) class Scripting(BaseCommand): def __init__(self): super(Scripting, self).__init__() def eval(self, *args, shard_key=None, sock=None): u""" Execute EVAL Command, consult Redis documentation for details. :param shard_key: (optional) Should be set to the key name you try to work with. Can not be used if sock is set. Only used if used with a Cluster Client :type shard_key: string :param sock: (optional) The string representation of a socket, the command should be executed against. For example: "testhost_6379" Can not be used if shard_key is set. Only used if used with a Cluster Client :type sock: string :return: result, exception """ if self._cluster: return self.execute(u'EVAL', *args, shard_key=shard_key, sock=sock) return self.execute(u'EVAL', *args) def evalsha(self, *args, shard_key=None, sock=None): u""" Execute EVALSHA Command, consult Redis documentation for details. :param shard_key: (optional) Should be set to the key name you try to work with. Can not be used if sock is set. Only used if used with a Cluster Client :type shard_key: string :param sock: (optional) The string representation of a socket, the command should be executed against. For example: "testhost_6379" Can not be used if shard_key is set. Only used if used with a Cluster Client :type sock: string :return: result, exception """ if self._cluster: return self.execute(u'EVALSHA', *args, shard_key=shard_key, sock=sock) return self.execute(u'EVALSHA', *args) def script_debug(self, *args, shard_key=None, sock=None): u""" Execute SCRIPT DEBUG Command, consult Redis documentation for details. :param shard_key: (optional) Should be set to the key name you try to work with. Can not be used if sock is set. Only used if used with a Cluster Client :type shard_key: string :param sock: (optional) The string representation of a socket, the command should be executed against. For example: "testhost_6379" Can not be used if shard_key is set. Only used if used with a Cluster Client :type sock: string :return: result, exception """ if self._cluster: return self.execute(u'SCRIPT', u'DEBUG', *args, shard_key=shard_key, sock=sock) return self.execute(u'SCRIPT', u'DEBUG', *args) def script_exists(self, *args, shard_key=None, sock=None): u""" Execute SCRIPT EXISTS Command, consult Redis documentation for details. :param shard_key: (optional) Should be set to the key name you try to work with. Can not be used if sock is set. Only used if used with a Cluster Client :type shard_key: string :param sock: (optional) The string representation of a socket, the command should be executed against. For example: "testhost_6379" Can not be used if shard_key is set. Only used if used with a Cluster Client :type sock: string :return: result, exception """ if self._cluster: return self.execute(u'SCRIPT', u'EXISTS', *args, shard_key=shard_key, sock=sock) return self.execute(u'SCRIPT', u'EXISTS', *args) def script_flush(self, *args, shard_key=None, sock=None): u""" Execute SCRIPT FLUSH Command, consult Redis documentation for details. :param shard_key: (optional) Should be set to the key name you try to work with. Can not be used if sock is set. Only used if used with a Cluster Client :type shard_key: string :param sock: (optional) The string representation of a socket, the command should be executed against. For example: "testhost_6379" Can not be used if shard_key is set. Only used if used with a Cluster Client :type sock: string :return: result, exception """ if self._cluster: return self.execute(u'SCRIPT', u'FLUSH', *args, shard_key=shard_key, sock=sock) return self.execute(u'SCRIPT', u'FLUSH', *args) def script_kill(self, *args, shard_key=None, sock=None): u""" Execute SCRIPT KILL Command, consult Redis documentation for details. :param shard_key: (optional) Should be set to the key name you try to work with. Can not be used if sock is set. Only used if used with a Cluster Client :type shard_key: string :param sock: (optional) The string representation of a socket, the command should be executed against. For example: "testhost_6379" Can not be used if shard_key is set. Only used if used with a Cluster Client :type sock: string :return: result, exception """ if self._cluster: return self.execute(u'SCRIPT', u'KILL', *args, shard_key=shard_key, sock=sock) return self.execute(u'SCRIPT', u'KILL', *args) def script_load(self, *args, shard_key=None, sock=None): u""" Execute SCRIPT LOAD Command, consult Redis documentation for details. :param shard_key: (optional) Should be set to the key name you try to work with. Can not be used if sock is set. Only used if used with a Cluster Client :type shard_key: string :param sock: (optional) The string representation of a socket, the command should be executed against. For example: "testhost_6379" Can not be used if shard_key is set. Only used if used with a Cluster Client :type sock: string :return: result, exception """ if self._cluster: return self.execute(u'SCRIPT', u'LOAD', *args, shard_key=shard_key, sock=sock) return self.execute(u'SCRIPT', u'LOAD', *args)

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9

b3172bc2888915a92d181dfabf2abca5773f2e8e

766

py

Python

2016/test_q3.py

matthewelse/british-informatics-olympiad

4fcc3b264903af01555ad4cd2eb51ea7196f2057

[ "MIT" ]

11

2020-11-11T09:28:44.000Z

2022-03-01T19:20:39.000Z

2016/test_q3.py

matthewelse/british-informatics-olympiad

4fcc3b264903af01555ad4cd2eb51ea7196f2057

[ "MIT" ]

5

2020-11-30T04:06:52.000Z

2022-01-04T09:57:07.000Z

2016/test_q3.py

matthewelse/british-informatics-olympiad

4fcc3b264903af01555ad4cd2eb51ea7196f2057

[ "MIT" ]

4

2020-12-06T11:07:24.000Z

2021-12-31T16:46:51.000Z

"""Test cases for 2016 Q3""" import q3 def test_case0(): assert q3.solve(100, 2, 13) == 4 def test_case1(): assert q3.solve(20, 2, 3) == 2 def test_case2(): assert q3.solve(20, 2, 13) == 4 def test_case3(): assert q3.solve(100, 73, 89) == 2 def test_case4(): assert q3.solve(100, 19, 97) == 7 def test_case5(): assert q3.solve(1000, 3, 971) == 9 def test_case6(): assert q3.solve(2000, 977, 997) == 4 def test_case7(): assert q3.solve(5000, 83, 3643) == 10 def test_case8(): assert q3.solve(614700, 3643, 90149) == 18 def test_case9(): assert q3.solve(987654, 3643, 90149) == 16 def test_case10(): assert q3.solve(1000000, 2, 968137) == 18 def test_case11(): assert q3.solve(1000000, 993851, 995387) == 3

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b33d2026da1b575cff3401d186f1ce5814e04bae

30,676

py

Python

custompackage/.ipynb_checkpoints/traintestloop-checkpoint.py

ilennaj/ktree_constraints

2a25e93c9b4f113caf633b08abb3e48e1c566c59

[ "CC0-1.0" ]

4

2021-03-11T21:46:41.000Z

2021-12-01T06:32:42.000Z

custompackage/.ipynb_checkpoints/traintestloop-checkpoint.py

ilennaj/ktree_constraints

2a25e93c9b4f113caf633b08abb3e48e1c566c59

[ "CC0-1.0" ]

null

custompackage/.ipynb_checkpoints/traintestloop-checkpoint.py

ilennaj/ktree_constraints

2a25e93c9b4f113caf633b08abb3e48e1c566c59

[ "CC0-1.0" ]

1

2021-08-12T19:32:37.000Z

import torch from torch.utils.data import DataLoader import torch.optim as optim import torch.nn as nn import numpy as np import math import time from torch.optim.optimizer import required from torch.utils.data.dataset import random_split import torch.nn.functional as F import torch.optim as optim from torch.optim import Optimizer from pytorchtools import EarlyStopping def train_test_ktree(model, trainloader, validloader, testloader, epochs=10, randorder=False, patience=60): ''' Trains and tests k-tree models Inputs: model, trainloader, validloader, testloader, epochs, randorder, patience Outputs: train loss_curve, train acc_curve, test ave_loss, test accuracy, trained model ''' # Initialize loss function and optimizer # criterion = nn.BCELoss() criterion = nn.BCEWithLogitsLoss() optimizer = optim.Adam(model.parameters(), lr=0.001) # to track training loss and accuracy as model trains loss_curve = [] acc_curve = [] # to track the validation loss as the model trains valid_losses = [] # to track the average validation loss per epoch as the model trains avg_valid_losses = [] # if randorder == True, generate the randomizer index array for randomizing the input image pixel order if randorder == True: ordering = torch.randperm(len(trainloader.dataset.tensors[0][0])) # Initialize early stopping object early_stopping = EarlyStopping(patience=patience, verbose=False) for epoch in range(epochs): # loop over the dataset multiple times ###################### # train the model # ###################### running_loss = 0.0 running_acc = 0.0 model.train() for i, data in enumerate(trainloader): # get the inputs; data is a list of [inputs, labels] inputs, labels, _ = data if randorder == True: # Randomize pixel order inputs = inputs[:,ordering].cuda() else: inputs = inputs.cuda() labels = labels.cuda() # zero the parameter gradients optimizer.zero_grad() # forward + backward + optimize outputs = model(inputs) # if torch.max(outputs) == 1: # print('max output 1') # print(torch.unique(outputs)) loss = criterion(outputs + 1e-8, labels.float().reshape(-1,1)) if torch.sum(torch.isnan(loss)) > 0: break loss.backward() #### # Freeze select weights by zeroing out gradients for child in model.children(): for param in child.parameters(): for freeze_mask in model.freeze_mask_set: if hasattr(param.grad, 'shape'): if param.grad.shape == freeze_mask.shape: param.grad[freeze_mask] = 0 optimizer.step() # print statistics running_loss += loss.item() running_acc += ((outputs > 0) == labels.float().reshape(-1,1)).sum().item()/trainloader.batch_size # Generate loss and accuracy curves by saving average every 4th minibatch if (i % 4) == 3: loss_curve.append(running_loss/4) acc_curve.append(running_acc/4) running_loss = 0.0 running_acc = 0.0 if torch.sum(torch.isnan(loss)) > 0: print('loss is nan, now testing') break ###################### # validate the model # ###################### model.eval() # prep model for evaluation for _, data in enumerate(validloader): inputs, labels, _ = data if randorder == True: # Randomize pixel order inputs = inputs[:,ordering].cuda() else: inputs = inputs.cuda() labels = labels.cuda() # forward pass: compute predicted outputs by passing inputs to the model output = model(inputs) # calculate the loss loss = criterion(output + 1e-8, labels.float().reshape(-1,1)) # record validation loss valid_losses.append(loss.item()) valid_loss = np.average(valid_losses) # early_stopping needs the validation loss to check if it has decreased, # and if it has, it will make a checkpoint of the current model early_stopping(valid_loss, model) if early_stopping.early_stop: print("Early stopping") break # load the last checkpoint with the best model # model.load_state_dict(torch.load('checkpoint.pt')) print('Finished Training, %d epochs' % (epoch+1)) ###################### # test the model # ###################### correct = 0 total = 0 with torch.no_grad(): for data in testloader: images, labels, _ = data if randorder == True: # Randomize pixel order images = images[:,ordering].cuda() else: images = images.cuda() labels = labels.cuda() # forward pass: compute predicted outputs by passing inputs to the model outputs = model(images) # calculate the loss loss = criterion(outputs + 1e-8, labels.float().reshape(-1,1)) # Sum up correct labelings predicted = outputs > 0 total += labels.size(0) correct += (predicted == labels.float().reshape(-1,1)).sum().item() # Calculate test accuracy accuracy = correct/total print('Accuracy of the network on the test images: %2f %%' % ( 100 * accuracy)) print('final outputs:', torch.unique(outputs)) if randorder == True: return(loss_curve, acc_curve, loss, accuracy, model, ordering) else: return(loss_curve, acc_curve, loss, accuracy, model) def train_test_fc(model, trainloader, validloader, testloader, epochs=10, patience=60, lr=0.001): ''' Trains and tests fcnn models Inputs: model, trainloader, validloader, testloader, epochs, patience Outputs: train loss_curve, train acc_curve, test ave_loss, test accuracy, trained model ''' t = Timer() t.start() # Initialize loss function and optimizer # criterion = nn.BCELoss() criterion = nn.BCEWithLogitsLoss() optimizer = optim.Adam(model.parameters(), lr=lr) # to track the validation loss as the model trains valid_losses = [] # to track the average validation loss per epoch as the model trains avg_valid_losses = [] # to track training loss and accuracy as model trains loss_curve = [] acc_curve = [] # Initialize early stopping object early_stopping = EarlyStopping(patience=patience, verbose=False) for epoch in range(epochs): # loop over the dataset multiple times ###################### # train the model # ###################### running_loss = 0.0 running_acc = 0.0 model.train() for i, data in enumerate(trainloader): # get the inputs; data is a list of [inputs, labels] inputs, labels, _ = data inputs = inputs.cuda() labels = labels.cuda() # zero the parameter gradients optimizer.zero_grad() # forward + backward + optimize outputs = model(inputs) loss = criterion(outputs + 1e-10, labels.float().reshape(-1,1)) loss.backward() optimizer.step() # print statistics running_loss += loss.item() # running_acc += (torch.round(outputs) == labels.float().reshape(-1,1)).sum().item()/trainloader.batch_size running_acc += ((outputs > 0) == labels.float().reshape(-1,1)).sum().item()/trainloader.batch_size if i % 4 == 3: # Generate loss and accuracy curves by saving average every 4th minibatch loss_curve.append(running_loss/4) acc_curve.append(running_acc/4) running_loss = 0.0 running_acc = 0.0 ###################### # validate the model # ###################### model.eval() # prep model for evaluation for _, data in enumerate(validloader): inputs, labels, _ = data inputs = inputs.cuda() labels = labels.cuda() # forward pass: compute predicted outputs by passing inputs to the model output = model(inputs) # calculate the loss loss = criterion(output + 1e-8, labels.float().reshape(-1,1)) # record validation loss valid_losses.append(loss.item()) valid_loss = np.average(valid_losses) # early_stopping needs the validation loss to check if it has decresed, # and if it has, it will make a checkpoint of the current model early_stopping(valid_loss, model) if early_stopping.early_stop: print("Early stopping") break # load the last checkpoint with the best model # model.load_state_dict(torch.load('checkpoint.pt')) print('Finished Training, %d epochs' % (epoch+1)) correct = 0 all_loss = 0 total = 0 with torch.no_grad(): for data in testloader: images, labels, _ = data images = images.cuda() labels = labels.cuda() # forward pass: compute predicted outputs by passing inputs to the model outputs = model(images) # calculate the loss loss = criterion(outputs + 1e-8, labels.float().reshape(-1,1)) # Sum up correct labelings predicted = outputs > 0 total += labels.size(0) correct += (predicted == labels.float().reshape(-1,1)).sum().item() all_loss += loss # Calculate test accuracy accuracy = correct/total # Calculate average loss ave_loss = all_loss.item()/total if ave_loss > 1000000: print('ave_loss = ', ave_loss) ave_loss = 4 print('ave_loss = ', ave_loss) print('Accuracy of the network on the 10000 test images: %4f %%' % ( 100 * accuracy)) t.stop() return(loss_curve, acc_curve, ave_loss, accuracy, model) class TimerError(Exception): """A custom exception used to report errors in use of Timer class""" class Timer: def __init__(self): self._start_time = None def start(self): """Start a new timer""" if self._start_time is not None: raise TimerError(f"Timer is running. Use .stop() to stop it") self._start_time = time.perf_counter() def stop(self): """Stop the timer, and report the elapsed time""" if self._start_time is None: raise TimerError(f"Timer is not running. Use .start() to start it") elapsed_time = time.perf_counter() - self._start_time self._start_time = None print(f"Elapsed time: {elapsed_time:0.4f} seconds") def train_test_ktree_sparse(model, trainloader, validloader, testloader, epochs=10, randorder=False, patience=60, lr=0.001): ''' Trains and tests k-tree models Inputs: model, trainloader, validloader, testloader, epochs, randorder, patience Outputs: train loss_curve, train acc_curve, test ave_loss, test accuracy, trained model ''' t = Timer() t.start() # Initialize loss function and optimizer criterion = nn.BCEWithLogitsLoss() optimizer = optim.Adam(model.parameters(), lr=lr) # to track training loss and accuracy as model trains loss_curve = [] acc_curve = [] # to track the validation loss as the model trains valid_losses = [] # to track the average validation loss per epoch as the model trains avg_valid_losses = [] # if randorder == True, generate the randomizer index array for randomizing the input image pixel order if randorder == True: ordering = torch.randperm(len(trainloader.dataset.tensors[0][0])) # Initialize early stopping object early_stopping = EarlyStopping(patience=patience, verbose=False) for epoch in range(epochs): # loop over the dataset multiple times ###################### # train the model # ###################### running_loss = 0.0 running_acc = 0.0 model.train() for i, data in enumerate(trainloader): # get the inputs; data is a list of [inputs, labels] inputs, labels, _ = data if randorder == True: # Randomize pixel order inputs = inputs[:,ordering].cuda() else: inputs = inputs.cuda() labels = labels.cuda() # zero the parameter gradients optimizer.zero_grad() # forward + backward + optimize outputs = model(inputs) loss = criterion(outputs + 1e-8, labels.float().reshape(-1,1)) if torch.sum(torch.isnan(loss)) > 0: break loss.backward() optimizer.step() # print statistics running_loss += loss.item() running_acc += ((outputs > 0) == labels.float().reshape(-1,1)).sum().item()/trainloader.batch_size # Generate loss and accuracy curves by saving average every 4th minibatch if (i % 4) == 3: loss_curve.append(running_loss/4) acc_curve.append(running_acc/4) running_loss = 0.0 running_acc = 0.0 if torch.sum(torch.isnan(loss)) > 0 or torch.sum(torch.isnan(outputs)) > 0: print('loss is nan, now testing') break ###################### # validate the model # ###################### model.eval() # prep model for evaluation for _, data in enumerate(validloader): inputs, labels, _ = data if randorder == True: # Randomize pixel order inputs = inputs[:,ordering].cuda() else: inputs = inputs.cuda() labels = labels.cuda() # forward pass: compute predicted outputs by passing inputs to the model output = model(inputs) # calculate the loss loss = criterion(output + 1e-8, labels.float().reshape(-1,1)) # record validation loss valid_losses.append(loss.item()) valid_loss = np.average(valid_losses) # early_stopping needs the validation loss to check if it has decreased, # and if it has, it will make a checkpoint of the current model if epoch > 200: early_stopping(valid_loss, model) if early_stopping.early_stop: print("Early stopping") break # if torch.sum(torch.isnan(loss)) > 0 or torch.sum(torch.isnan(outputs)) > 0: # loss = 10 # accuracy = 0.5 # return(loss_curve, acc_curve, loss, accuracy, model) # load the last checkpoint with the best model # model.load_state_dict(torch.load('checkpoint.pt')) print('Finished Training, %d epochs' % (epoch+1)) ###################### # test the model # ###################### correct = 0 total = 0 all_loss = 0 with torch.no_grad(): for data in testloader: images, labels, _ = data if randorder == True: # Randomize pixel order images = images[:,ordering].cuda() else: images = images.cuda() labels = labels.cuda() # forward pass: compute predicted outputs by passing inputs to the model outputs = model(images) # calculate the loss loss = criterion(outputs + 1e-8, labels.float().reshape(-1,1)) # Sum up correct labelings predicted = outputs > 0 total += labels.size(0) correct += (predicted == labels.float().reshape(-1,1)).sum().item() all_loss += loss # Calculate test accuracy accuracy = correct/total # Calculate average loss ave_loss = all_loss.item()/total print('Accuracy of the network on the test images: %2f %%' % ( 100 * accuracy)) # print('final outputs:', torch.unique(outputs)) t.stop() # if torch.sum(torch.isnan(torch.Tensor(ave_loss))) > 0 or torch.sum(torch.isnan(outputs)) > 0: # ave_loss = 10 # # accuracy = 0.5 # return(loss_curve, acc_curve, ave_loss, accuracy, model) if np.sum(np.isnan(np.array(ave_loss))) > 0: print('nan ave_loss = ', ave_loss) ave_loss = 4 print('ave_loss = ', ave_loss) if ave_loss > 4: print('big ave_loss = ', ave_loss) ave_loss = 4 print('ave_loss = ', ave_loss) if randorder == True: return(loss_curve, acc_curve, ave_loss, accuracy, model, ordering) else: return(loss_curve, acc_curve, ave_loss, accuracy, model) def train_test_ktree_sparse_debug(model, trainloader, validloader, testloader, epochs=10, randorder=False, patience=60, lr=0.001): loss_curve = [] acc_curve = [] ave_loss = 4 accuracy = 1 model = [] return(loss_curve, acc_curve, ave_loss, accuracy, model) def train_test_ktree_multistage(model, trainloader, validloader, testloader, epochs=10, randorder=False, patience=60, lr=0.001, multistage=True, stages=[0,1,2]): ''' Trains and tests k-tree models Inputs: model, trainloader, validloader, testloader, epochs, randorder, patience Outputs: train loss_curve, train acc_curve, test ave_loss, test accuracy, trained model ''' t = Timer() t.start() syn_layers = [] for syn_name in model.syn_names: for syn_layer in list(model._modules[syn_name].parameters()): syn_layers.append(syn_layer) den_layers = [] for repeat in range(model.Repeats): for den_name in model.names[repeat]: for den_layer in list(model._modules[den_name].parameters()): den_layers.append(den_layer) sqgl_nonlin = list(model.sqgl.parameters()) # Initialize loss function criterion = nn.BCEWithLogitsLoss() # if randorder == True, generate the randomizer index array for randomizing the input image pixel order if randorder == True: ordering = torch.randperm(len(trainloader.dataset.tensors[0][0])) if multistage == False: stages = [3] for stage in stages: # Initialize loss function and optimizer if stage == 0: optimizer = optim.Adam(syn_layers, lr=lr) elif stage == 1: optimizer = optim.Adam(den_layers, lr=lr) elif stage == 2: optimizer = optim.Adam(sqgl_nonlin, lr=lr) else: optimizer = optim.Adam(model.parameters(), lr=lr) # to track training loss and accuracy as model trains loss_curve = [] acc_curve = [] # to track the validation loss as the model trains valid_losses = [] # to track the average validation loss per epoch as the model trains avg_valid_losses = [] # Initialize early stopping object early_stopping = EarlyStopping(patience=patience, verbose=False) for epoch in range(epochs): # loop over the dataset multiple times ###################### # train the model # ###################### running_loss = 0.0 running_acc = 0.0 model.train() for i, data in enumerate(trainloader): # get the inputs; data is a list of [inputs, labels] inputs, labels, _ = data if randorder == True: # Randomize pixel order inputs = inputs[:,ordering].cuda() else: inputs = inputs.cuda() labels = labels.cuda() # zero the parameter gradients optimizer.zero_grad() # forward + backward + optimize outputs = model(inputs) loss = criterion(outputs + 1e-8, labels.float().reshape(-1,1)) if torch.sum(torch.isnan(loss)) > 0: break loss.backward() optimizer.step() # print statistics running_loss += loss.item() running_acc += ((outputs > 0) == labels.float().reshape(-1,1)).sum().item()/trainloader.batch_size # Generate loss and accuracy curves by saving average every 4th minibatch if (i % 4) == 3: loss_curve.append(running_loss/4) acc_curve.append(running_acc/4) running_loss = 0.0 running_acc = 0.0 if torch.sum(torch.isnan(loss)) > 0 or torch.sum(torch.isnan(outputs)) > 0: print('loss is nan, now testing') break ###################### # validate the model # ###################### model.eval() # prep model for evaluation for _, data in enumerate(validloader): inputs, labels, _ = data if randorder == True: # Randomize pixel order inputs = inputs[:,ordering].cuda() else: inputs = inputs.cuda() labels = labels.cuda() # forward pass: compute predicted outputs by passing inputs to the model output = model(inputs) # calculate the loss loss = criterion(output + 1e-8, labels.float().reshape(-1,1)) # record validation loss valid_losses.append(loss.item()) valid_loss = np.average(valid_losses) # early_stopping needs the validation loss to check if it has decreased, # and if it has, it will make a checkpoint of the current model if epoch > 200: early_stopping(valid_loss, model) if early_stopping.early_stop: print("Early stopping") break if torch.sum(torch.isnan(loss)) > 0 or torch.sum(torch.isnan(outputs)) > 0: loss = 10 accuracy = 0.5 return(loss_curve, acc_curve, loss, accuracy, model) # load the last checkpoint with the best model # model.load_state_dict(torch.load('checkpoint.pt')) print('Finished Training, %d epochs' % (epoch+1)) ###################### # test the model # ###################### correct = 0 total = 0 with torch.no_grad(): for data in testloader: images, labels, _ = data if randorder == True: # Randomize pixel order images = images[:,ordering].cuda() else: images = images.cuda() labels = labels.cuda() # forward pass: compute predicted outputs by passing inputs to the model outputs = model(images) # calculate the loss loss = criterion(outputs + 1e-8, labels.float().reshape(-1,1)) # Sum up correct labelings predicted = outputs > 0 total += labels.size(0) correct += (predicted == labels.float().reshape(-1,1)).sum().item() # Calculate test accuracy accuracy = correct/total print('Accuracy of the network on the test images: %2f %%' % ( 100 * accuracy)) # print('final outputs:', torch.unique(outputs)) t.stop() if torch.sum(torch.isnan(loss)) > 0 or torch.sum(torch.isnan(outputs)) > 0: loss = 10 # accuracy = 0.5 return(loss_curve, acc_curve, loss, accuracy, model) if randorder == True: return(loss_curve, acc_curve, loss, accuracy, model, ordering) else: return(loss_curve, acc_curve, loss, accuracy, model) def train_test_ktree_synapse(model, trainloader, validloader, testloader, epochs=10, randorder=False, patience=60, lr=0.001): ''' Trains and tests k-tree models Inputs: model, trainloader, validloader, testloader, epochs, randorder, patience Outputs: train loss_curve, train acc_curve, test ave_loss, test accuracy, trained model ''' t = Timer() t.start() # Initialize loss function and optimizer criterion = nn.BCEWithLogitsLoss() optimizer = optim.Adam(model.parameters(), lr=lr) # to track training loss and accuracy as model trains loss_curve = [] acc_curve = [] # to track the validation loss as the model trains valid_losses = [] # to track the average validation loss per epoch as the model trains avg_valid_losses = [] # if randorder == True, generate the randomizer index array for randomizing the input image pixel order if randorder == True: ordering = torch.randperm(len(trainloader.dataset.tensors[0][0])) # Initialize early stopping object early_stopping = EarlyStopping(patience=patience, verbose=False) for epoch in range(epochs): # loop over the dataset multiple times ###################### # train the model # ###################### running_loss = 0.0 running_acc = 0.0 model.train() for i, data in enumerate(trainloader): # get the inputs; data is a list of [inputs, labels] inputs, labels, _ = data if randorder == True: # Randomize pixel order inputs = inputs[:,ordering].cuda() else: inputs = inputs.cuda() labels = labels.cuda() # zero the parameter gradients optimizer.zero_grad() # forward + backward + optimize outputs, loss_model = model(inputs) loss_pred = criterion(outputs + 1e-8, labels.float().reshape(-1,1)) loss = loss_pred + loss_model if torch.sum(torch.isnan(loss)) > 0: break loss.backward() optimizer.step() # print statistics running_loss += loss.item() running_acc += ((outputs > 0) == labels.float().reshape(-1,1)).sum().item()/trainloader.batch_size # Generate loss and accuracy curves by saving average every 4th minibatch if (i % 4) == 3: loss_curve.append(running_loss/4) acc_curve.append(running_acc/4) running_loss = 0.0 running_acc = 0.0 if torch.sum(torch.isnan(loss)) > 0 or torch.sum(torch.isnan(outputs)) > 0: print('loss is nan, now testing') break ###################### # validate the model # ###################### model.eval() # prep model for evaluation for _, data in enumerate(validloader): inputs, labels, _ = data if randorder == True: # Randomize pixel order inputs = inputs[:,ordering].cuda() else: inputs = inputs.cuda() labels = labels.cuda() # forward pass: compute predicted outputs by passing inputs to the model output, loss_model = model(inputs) # calculate the loss loss_pred = criterion(output + 1e-8, labels.float().reshape(-1,1)) loss = loss_pred + loss_model # record validation loss valid_losses.append(loss.item()) valid_loss = np.average(valid_losses) # early_stopping needs the validation loss to check if it has decreased, # and if it has, it will make a checkpoint of the current model if epoch > 200: early_stopping(valid_loss, model) if early_stopping.early_stop: print("Early stopping") break print('Finished Training, %d epochs' % (epoch+1)) ###################### # test the model # ###################### correct = 0 total = 0 all_loss = 0 with torch.no_grad(): for data in testloader: images, labels, _ = data if randorder == True: # Randomize pixel order images = images[:,ordering].cuda() else: images = images.cuda() labels = labels.cuda() # forward pass: compute predicted outputs by passing inputs to the model outputs, loss_model = model(images) # calculate the loss loss_pred = criterion(outputs + 1e-8, labels.float().reshape(-1,1)) loss = loss_pred + loss_model # Sum up correct labelings predicted = outputs > 0 total += labels.size(0) correct += (predicted == labels.float().reshape(-1,1)).sum().item() all_loss += loss # Calculate test accuracy accuracy = correct/total # Calculate average loss ave_loss = all_loss.item()/total print('Accuracy of the network on the test images: %2f %%' % ( 100 * accuracy)) # print('final outputs:', torch.unique(outputs)) t.stop() if np.sum(np.isnan(np.array(ave_loss))) > 0: print('nan ave_loss = ', ave_loss) ave_loss = 4 print('ave_loss = ', ave_loss) if ave_loss > 4: print('big ave_loss = ', ave_loss) ave_loss = 4 print('ave_loss = ', ave_loss) if randorder == True: return(loss_curve, acc_curve, ave_loss, accuracy, model, ordering) else: return(loss_curve, acc_curve, ave_loss, accuracy, model)

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36.69378

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b344e3d7e916f327ffefd9feda6a2ac051d96066

22,219

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Python

swagger_client/api/voice_endpoints_api.py

networthdata/generated-swagger-client

41dd3fb02b322ed1d39cbaef6b4091ae6cab0d0b

[ "MIT" ]

null

swagger_client/api/voice_endpoints_api.py

networthdata/generated-swagger-client

41dd3fb02b322ed1d39cbaef6b4091ae6cab0d0b

[ "MIT" ]

null

swagger_client/api/voice_endpoints_api.py

networthdata/generated-swagger-client

41dd3fb02b322ed1d39cbaef6b4091ae6cab0d0b

[ "MIT" ]

null

# coding: utf-8 """ Speech Services API v2.0 Speech Services API v2.0. # noqa: E501 OpenAPI spec version: v2.0 Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import re # noqa: F401 # python 2 and python 3 compatibility library import six from swagger_client.api_client import ApiClient class VoiceEndpointsApi(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. Ref: https://github.com/swagger-api/swagger-codegen """ def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client def create_voice_deployment(self, endpoint, **kwargs): # noqa: E501 """Creates a new voice endpoint object. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.create_voice_deployment(endpoint, async_req=True) >>> result = thread.get() :param async_req bool :param EndpointDefinition endpoint: (required) :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.create_voice_deployment_with_http_info(endpoint, **kwargs) # noqa: E501 else: (data) = self.create_voice_deployment_with_http_info(endpoint, **kwargs) # noqa: E501 return data def create_voice_deployment_with_http_info(self, endpoint, **kwargs): # noqa: E501 """Creates a new voice endpoint object. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.create_voice_deployment_with_http_info(endpoint, async_req=True) >>> result = thread.get() :param async_req bool :param EndpointDefinition endpoint: (required) :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['endpoint'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method create_voice_deployment" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'endpoint' is set if ('endpoint' not in params or params['endpoint'] is None): raise ValueError("Missing the required parameter `endpoint` when calling `create_voice_deployment`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'endpoint' in params: body_params = params['endpoint'] # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['subscription_key', 'token'] # noqa: E501 return self.api_client.call_api( '/api/texttospeech/v2.0/endpoints', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def delete_deployment(self, id, **kwargs): # noqa: E501 """Delete the specified voice endpoint. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_deployment(id, async_req=True) >>> result = thread.get() :param async_req bool :param str id: The id of voice endpoint. (required) :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.delete_deployment_with_http_info(id, **kwargs) # noqa: E501 else: (data) = self.delete_deployment_with_http_info(id, **kwargs) # noqa: E501 return data def delete_deployment_with_http_info(self, id, **kwargs): # noqa: E501 """Delete the specified voice endpoint. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_deployment_with_http_info(id, async_req=True) >>> result = thread.get() :param async_req bool :param str id: The id of voice endpoint. (required) :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method delete_deployment" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'id' is set if ('id' not in params or params['id'] is None): raise ValueError("Missing the required parameter `id` when calling `delete_deployment`") # noqa: E501 collection_formats = {} path_params = {} if 'id' in params: path_params['id'] = params['id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # Authentication setting auth_settings = ['subscription_key', 'token'] # noqa: E501 return self.api_client.call_api( '/api/texttospeech/v2.0/endpoints/{id}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_supported_locales_for_voice_endpoints(self, **kwargs): # noqa: E501 """Gets a list of supported locales for custom voice endpoints. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_supported_locales_for_voice_endpoints(async_req=True) >>> result = thread.get() :param async_req bool :return: list[str] If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_supported_locales_for_voice_endpoints_with_http_info(**kwargs) # noqa: E501 else: (data) = self.get_supported_locales_for_voice_endpoints_with_http_info(**kwargs) # noqa: E501 return data def get_supported_locales_for_voice_endpoints_with_http_info(self, **kwargs): # noqa: E501 """Gets a list of supported locales for custom voice endpoints. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_supported_locales_for_voice_endpoints_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool :return: list[str] If the method is called asynchronously, returns the request thread. """ all_params = [] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_supported_locales_for_voice_endpoints" % key ) params[key] = val del params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['subscription_key', 'token'] # noqa: E501 return self.api_client.call_api( '/api/texttospeech/v2.0/endpoints/locales', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[str]', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_voice_deployment(self, id, **kwargs): # noqa: E501 """Gets the details of a custom voice endpoint. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_voice_deployment(id, async_req=True) >>> result = thread.get() :param async_req bool :param str id: (required) :return: Endpoint If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_voice_deployment_with_http_info(id, **kwargs) # noqa: E501 else: (data) = self.get_voice_deployment_with_http_info(id, **kwargs) # noqa: E501 return data def get_voice_deployment_with_http_info(self, id, **kwargs): # noqa: E501 """Gets the details of a custom voice endpoint. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_voice_deployment_with_http_info(id, async_req=True) >>> result = thread.get() :param async_req bool :param str id: (required) :return: Endpoint If the method is called asynchronously, returns the request thread. """ all_params = ['id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_voice_deployment" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'id' is set if ('id' not in params or params['id'] is None): raise ValueError("Missing the required parameter `id` when calling `get_voice_deployment`") # noqa: E501 collection_formats = {} path_params = {} if 'id' in params: path_params['id'] = params['id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['subscription_key', 'token'] # noqa: E501 return self.api_client.call_api( '/api/texttospeech/v2.0/endpoints/{id}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='Endpoint', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_voice_deployments(self, **kwargs): # noqa: E501 """Gets a list of voice endpoint details. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_voice_deployments(async_req=True) >>> result = thread.get() :param async_req bool :return: list[Endpoint] If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_voice_deployments_with_http_info(**kwargs) # noqa: E501 else: (data) = self.get_voice_deployments_with_http_info(**kwargs) # noqa: E501 return data def get_voice_deployments_with_http_info(self, **kwargs): # noqa: E501 """Gets a list of voice endpoint details. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_voice_deployments_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool :return: list[Endpoint] If the method is called asynchronously, returns the request thread. """ all_params = [] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_voice_deployments" % key ) params[key] = val del params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['subscription_key', 'token'] # noqa: E501 return self.api_client.call_api( '/api/texttospeech/v2.0/endpoints', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[Endpoint]', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def update_voice_endpoint(self, id, endpoint_update, **kwargs): # noqa: E501 """Updates the name and description of the endpoint identified by the given ID. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.update_voice_endpoint(id, endpoint_update, async_req=True) >>> result = thread.get() :param async_req bool :param str id: The identifier of the endpoint. (required) :param EndpointMetadataUpdate endpoint_update: The updated values for the endpoint. (required) :return: Endpoint If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.update_voice_endpoint_with_http_info(id, endpoint_update, **kwargs) # noqa: E501 else: (data) = self.update_voice_endpoint_with_http_info(id, endpoint_update, **kwargs) # noqa: E501 return data def update_voice_endpoint_with_http_info(self, id, endpoint_update, **kwargs): # noqa: E501 """Updates the name and description of the endpoint identified by the given ID. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.update_voice_endpoint_with_http_info(id, endpoint_update, async_req=True) >>> result = thread.get() :param async_req bool :param str id: The identifier of the endpoint. (required) :param EndpointMetadataUpdate endpoint_update: The updated values for the endpoint. (required) :return: Endpoint If the method is called asynchronously, returns the request thread. """ all_params = ['id', 'endpoint_update'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method update_voice_endpoint" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'id' is set if ('id' not in params or params['id'] is None): raise ValueError("Missing the required parameter `id` when calling `update_voice_endpoint`") # noqa: E501 # verify the required parameter 'endpoint_update' is set if ('endpoint_update' not in params or params['endpoint_update'] is None): raise ValueError("Missing the required parameter `endpoint_update` when calling `update_voice_endpoint`") # noqa: E501 collection_formats = {} path_params = {} if 'id' in params: path_params['id'] = params['id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'endpoint_update' in params: body_params = params['endpoint_update'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['subscription_key', 'token'] # noqa: E501 return self.api_client.call_api( '/api/texttospeech/v2.0/endpoints/{id}', 'PATCH', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='Endpoint', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats)

37.981197

131

0.609253

2,540

22,219

5.071654

0.06811

0.044093

0.026083

0.033535

0.952492

0.944729

0.922372

0.908555

0.905372

0.889769

0

0.015261

0.301049

22,219

584

132

38.046233

0.814231

0.315901

0

0.78481

1

0

0.180073

0.057606

0

1

0.041139

false

0

0.012658

0

0.113924

0

null

0

1

0

1

0

null

0

8

b353fbc71b9583a9dc441db20de7d702c692ef36

3,217

py

Python

lib_pypy/_pypy_winbase_cffi.py

SeraphRoy/PyPy-Functional

e825dce7f7c484fa666566974a93ed5d59fb73be

[ "Apache-2.0", "OpenSSL" ]

null

lib_pypy/_pypy_winbase_cffi.py

SeraphRoy/PyPy-Functional

e825dce7f7c484fa666566974a93ed5d59fb73be

[ "Apache-2.0", "OpenSSL" ]

null

lib_pypy/_pypy_winbase_cffi.py

SeraphRoy/PyPy-Functional

e825dce7f7c484fa666566974a93ed5d59fb73be

[ "Apache-2.0", "OpenSSL" ]

null

# auto-generated file import _cffi_backend ffi = _cffi_backend.FFI('_pypy_winbase_cffi', _version = 0x2601, _types = b'\x00\x00\x01\x0D\x00\x00\x07\x01\x00\x00\x00\x0F\x00\x00\x01\x0D\x00\x00\x07\x01\x00\x00\x07\x01\x00\x00\x00\x0F\x00\x00\x01\x0D\x00\x00\x07\x01\x00\x00\x07\x01\x00\x00\x09\x01\x00\x00\x00\x0F\x00\x00\x01\x0D\x00\x00\x19\x01\x00\x00\x07\x01\x00\x00\x00\x0F\x00\x00\x01\x0D\x00\x00\x00\x0F\x00\x00\x01\x0D\x00\x00\x50\x03\x00\x00\x13\x11\x00\x00\x53\x03\x00\x00\x15\x11\x00\x00\x07\x01\x00\x00\x0A\x01\x00\x00\x13\x11\x00\x00\x13\x11\x00\x00\x4F\x03\x00\x00\x4E\x03\x00\x00\x02\x0F\x00\x00\x01\x0D\x00\x00\x15\x03\x00\x00\x1F\x11\x00\x00\x15\x11\x00\x00\x0A\x01\x00\x00\x02\x0F\x00\x00\x01\x0D\x00\x00\x15\x11\x00\x00\x02\x0F\x00\x00\x01\x0D\x00\x00\x15\x11\x00\x00\x08\x01\x00\x00\x02\x0F\x00\x00\x01\x0D\x00\x00\x15\x11\x00\x00\x18\x03\x00\x00\x02\x0F\x00\x00\x01\x0D\x00\x00\x15\x11\x00\x00\x15\x11\x00\x00\x15\x11\x00\x00\x1F\x11\x00\x00\x0A\x01\x00\x00\x07\x01\x00\x00\x0A\x01\x00\x00\x02\x0F\x00\x00\x0D\x0D\x00\x00\x07\x01\x00\x00\x00\x0F\x00\x00\x18\x0D\x00\x00\x15\x11\x00\x00\x0A\x01\x00\x00\x02\x0F\x00\x00\x18\x0D\x00\x00\x02\x0F\x00\x00\x42\x0D\x00\x00\x06\x01\x00\x00\x00\x0F\x00\x00\x42\x0D\x00\x00\x00\x0F\x00\x00\x42\x0D\x00\x00\x10\x01\x00\x00\x00\x0F\x00\x00\x15\x0D\x00\x00\x0A\x01\x00\x00\x02\x0F\x00\x00\x15\x0D\x00\x00\x02\x0F\x00\x00\x00\x09\x00\x00\x01\x09\x00\x00\x02\x01\x00\x00\x52\x03\x00\x00\x04\x01\x00\x00\x00\x01', _globals = (b'\x00\x00\x24\x23CloseHandle',0,b'\x00\x00\x1E\x23CreatePipe',0,b'\x00\x00\x12\x23CreateProcessA',0,b'\x00\x00\x2F\x23DuplicateHandle',0,b'\x00\x00\x4C\x23GetCurrentProcess',0,b'\x00\x00\x2B\x23GetExitCodeProcess',0,b'\x00\x00\x49\x23GetStdHandle',0,b'\x00\x00\x3F\x23GetVersion',0,b'\x00\x00\x27\x23TerminateProcess',0,b'\x00\x00\x3B\x23WaitForSingleObject',0,b'\x00\x00\x38\x23_get_osfhandle',0,b'\x00\x00\x10\x23_getch',0,b'\x00\x00\x10\x23_getche',0,b'\x00\x00\x44\x23_getwch',0,b'\x00\x00\x44\x23_getwche',0,b'\x00\x00\x10\x23_kbhit',0,b'\x00\x00\x07\x23_locking',0,b'\x00\x00\x0C\x23_open_osfhandle',0,b'\x00\x00\x00\x23_putch',0,b'\x00\x00\x46\x23_putwch',0,b'\x00\x00\x03\x23_setmode',0,b'\x00\x00\x00\x23_ungetch',0,b'\x00\x00\x41\x23_ungetwch',0), _struct_unions = ((b'\x00\x00\x00\x4E\x00\x00\x00\x02$PROCESS_INFORMATION',b'\x00\x00\x15\x11hProcess',b'\x00\x00\x15\x11hThread',b'\x00\x00\x18\x11dwProcessId',b'\x00\x00\x18\x11dwThreadId'),(b'\x00\x00\x00\x4F\x00\x00\x00\x02$STARTUPINFO',b'\x00\x00\x18\x11cb',b'\x00\x00\x13\x11lpReserved',b'\x00\x00\x13\x11lpDesktop',b'\x00\x00\x13\x11lpTitle',b'\x00\x00\x18\x11dwX',b'\x00\x00\x18\x11dwY',b'\x00\x00\x18\x11dwXSize',b'\x00\x00\x18\x11dwYSize',b'\x00\x00\x18\x11dwXCountChars',b'\x00\x00\x18\x11dwYCountChars',b'\x00\x00\x18\x11dwFillAttribute',b'\x00\x00\x18\x11dwFlags',b'\x00\x00\x42\x11wShowWindow',b'\x00\x00\x42\x11cbReserved2',b'\x00\x00\x51\x11lpReserved2',b'\x00\x00\x15\x11hStdInput',b'\x00\x00\x15\x11hStdOutput',b'\x00\x00\x15\x11hStdError')), _typenames = (b'\x00\x00\x00\x1CLPPROCESS_INFORMATION',b'\x00\x00\x00\x1BLPSTARTUPINFO',b'\x00\x00\x00\x4EPROCESS_INFORMATION',b'\x00\x00\x00\x4FSTARTUPINFO',b'\x00\x00\x00\x42wint_t'), )

292.454545

1,362

0.746348

669

3,217

3.541106

0.171898

0.405234

0.156606

0.074293

0.442803

0.417476

0.348248

0.300127

0

0.339026

0.017097

3,217

10

1,363

321.7

0.410183

0.005906

0

1

0.125

0.870057

0.84683

0

1

0.001883

0

1

0

false

0

0.125

0

0.125

0

null

1

0

1

0

1

0

1

0

1

null

1

0

9

2fa396459e724642621cc4ff04a6aa8d8ba9fa64

85

py

Python

emmet-api/emmet/api/core/__init__.py

acrutt/emmet

e98100c9932f145a3ad3087ddb7aa9b779d9a191

[ "BSD-3-Clause-LBNL" ]

null

emmet-api/emmet/api/core/__init__.py

acrutt/emmet

e98100c9932f145a3ad3087ddb7aa9b779d9a191

[ "BSD-3-Clause-LBNL" ]

null

emmet-api/emmet/api/core/__init__.py

acrutt/emmet

e98100c9932f145a3ad3087ddb7aa9b779d9a191

[ "BSD-3-Clause-LBNL" ]

null

from emmet.api.core.api import MAPI from emmet.api.core.settings import MAPISettings

28.333333

48

0.835294

14

85

5.071429

0.571429

0.253521

0.338028

0.450704

0

0.094118

85

2

49

42.5

0.922078

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

8

2fce595e80ba3933ef68fcf5dc4f3e9a4685ed27

84

py

Python

evkit/sensors/__init__.py

joel99/midlevel-reps

f0b4a4d8ccf09a0488cd18af24723172aff99446

[ "MIT" ]

120

2019-04-22T04:45:28.000Z

2022-03-23T01:53:17.000Z

evkit/sensors/__init__.py

joel99/midlevel-reps

f0b4a4d8ccf09a0488cd18af24723172aff99446

[ "MIT" ]

14

2019-06-12T08:21:21.000Z

2021-08-25T15:36:58.000Z

evkit/sensors/__init__.py

joel99/midlevel-reps

f0b4a4d8ccf09a0488cd18af24723172aff99446

[ "MIT" ]

19

2019-06-19T07:00:36.000Z

2022-03-24T07:18:30.000Z

from .sensorpack import SensorPack as SensorDict from .sensorpack import SensorPack

28

48

0.857143

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8

2fedd7e44704c7122d83f162db7ac464b6c70102

702

py

Python

Packages/Patterns_Package/symbols/line_symbols/Left_faced_equilataral_angle_Triangle.py

saribalarakeshreddy/Python-3.9.0

25b4c74feb2a27b91e69aa82becde23e356e82c4

[ "MIT" ]

null

Packages/Patterns_Package/symbols/line_symbols/Left_faced_equilataral_angle_Triangle.py

saribalarakeshreddy/Python-3.9.0

25b4c74feb2a27b91e69aa82becde23e356e82c4

[ "MIT" ]

null

Packages/Patterns_Package/symbols/line_symbols/Left_faced_equilataral_angle_Triangle.py

saribalarakeshreddy/Python-3.9.0

25b4c74feb2a27b91e69aa82becde23e356e82c4

[ "MIT" ]

null

def for_Left_faced_equilataral_angle_Triangle(): """ pattern for : Left_faced_equilataral_angle_Triangle using for loop""" for i in range(7): for j in range(4): if j==3 or i+j==3 or i-j==3: print('*',end=' ') else: print(' ',end=' ') print() def while_Left_faced_equilataral_angle_Triangle(): """ pattern for : Left_faced_equilataral_angle_Triangle using while loop""" i=0 while i<7: j=0 while j<4: if j==3 or i+j==3 or i-j==3: print('*',end=' ') else: print(' ',end=' ') j+=1 i+=1 print()

31.909091

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702

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0.316456

0.743671

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0.390313

702

22

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0

null

0

1

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1

0

null

0

7

ffa4d3dc99f3c109cf97474ea0ec14440aa63c2c

76

py

Python

torchreid/utils/__init__.py

Bhaskers-Blu-Org2/Semantics-Aligned-Representation-Learning-for-Person-Re-identification

e53715dd40be81b2215f4a530bde0c76bf1f378d

[ "MIT" ]

25

2020-03-17T10:21:05.000Z

2022-03-16T20:05:41.000Z

torchreid/utils/__init__.py

wencoast/Semantics-Aligned-Representation-Learning-for-Person-Re-identification

e53715dd40be81b2215f4a530bde0c76bf1f378d

[ "MIT" ]

5

2020-03-29T18:05:49.000Z

2020-11-15T17:03:20.000Z

torchreid/utils/__init__.py

wencoast/Semantics-Aligned-Representation-Learning-for-Person-Re-identification

e53715dd40be81b2215f4a530bde0c76bf1f378d

[ "MIT" ]

12

2020-03-17T06:27:29.000Z

2021-09-13T12:48:12.000Z

from __future__ import absolute_import from __future__ import print_function

38

0.907895

10

76

5.9

0.6

0.338983

0.542373

0

0.092105

76

2

39

38

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true

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0

null

0

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0

1

0

1

0

8

925a39819c88bcb51ccc8b0a8c8496c4d9809ec1

26,448

py

Python

adaptiveleak/unit_tests/utils/message.py

tejaskannan/adaptive-sensor-security

4c6dd1eb55eb30a8330c4bf3537e06c7d7802c0b

[ "Apache-2.0" ]

null

adaptiveleak/unit_tests/utils/message.py

tejaskannan/adaptive-sensor-security

4c6dd1eb55eb30a8330c4bf3537e06c7d7802c0b

[ "Apache-2.0" ]

null

adaptiveleak/unit_tests/utils/message.py

tejaskannan/adaptive-sensor-security

4c6dd1eb55eb30a8330c4bf3537e06c7d7802c0b

[ "Apache-2.0" ]

null

import unittest import numpy as np import h5py from sklearn.metrics import mean_absolute_error from adaptiveleak.utils import message from adaptiveleak.utils.constants import SMALL_NUMBER from adaptiveleak.utils.data_utils import pad_to_length, create_groups, select_range_shifts_array from adaptiveleak.utils.shifting import merge_shift_groups class TestByte(unittest.TestCase): def test_encode_decode_six(self): measurements = np.array([[0.25, -0.125, 0.75], [-0.125, 0.625, -0.5]]) precision = 6 width = 8 seq_length = 8 collected_indices = [0, 3] encoded = message.encode_standard_measurements(measurements=measurements, precision=precision, width=width, collected_indices=collected_indices, seq_length=seq_length, should_compress=False) recovered, indices, _ = message.decode_standard_measurements(byte_str=encoded, num_features=measurements.shape[1], seq_length=seq_length, width=width, precision=precision, should_compress=False) # Check recovered values self.assertTrue(np.all(np.isclose(measurements, recovered))) # Check indices self.assertEqual(len(indices), 2) self.assertEqual(indices[0], collected_indices[0]) self.assertEqual(indices[1], collected_indices[1]) def test_encode_decode_two(self): measurements = np.array([[0.25, -0.125, 0.75], [-0.125, 0.625, -0.5]]) precision = 2 width = 4 seq_length = 8 collected_indices = [0, 4] encoded = message.encode_standard_measurements(measurements=measurements, precision=precision, width=width, collected_indices=collected_indices, seq_length=seq_length, should_compress=False) recovered, indices, _ = message.decode_standard_measurements(byte_str=encoded, num_features=measurements.shape[1], seq_length=seq_length, width=width, precision=precision, should_compress=False) expected = np.array([[0.25, 0.0, 0.75], [0.0, 0.5, -0.5]]) # Check recovered values self.assertTrue(np.all(np.isclose(expected, recovered))) # Check indices self.assertEqual(len(indices), 2) self.assertEqual(indices[0], collected_indices[0]) self.assertEqual(indices[1], collected_indices[1]) def test_encode_decode_ten(self): measurements = np.array([[0.25, -0.125, (1.0 / 512.0)], [-0.125, 0.625, -0.5]]) precision = 10 width = 13 seq_length = 8 collected_indices = [0, 6] encoded = message.encode_standard_measurements(measurements=measurements, precision=precision, width=width, collected_indices=collected_indices, seq_length=seq_length, should_compress=False) recovered, indices, _ = message.decode_standard_measurements(byte_str=encoded, num_features=measurements.shape[1], seq_length=seq_length, width=width, precision=precision, should_compress=False) # Check recovered values self.assertTrue(np.all(np.isclose(measurements, recovered))) # Check indices self.assertEqual(len(indices), 2) self.assertEqual(indices[0], collected_indices[0]) self.assertEqual(indices[1], collected_indices[1]) def test_encode_decode_two_compressed(self): measurements = np.array([[0.25, -0.125, 0.75], [-0.125, 0.625, -0.5]]) precision = 2 width = 4 seq_length = 8 collected_indices = [0, 4] encoded = message.encode_standard_measurements(measurements=measurements, precision=precision, width=width, collected_indices=collected_indices, seq_length=seq_length, should_compress=True) recovered, indices, _ = message.decode_standard_measurements(byte_str=encoded, num_features=measurements.shape[1], seq_length=seq_length, width=width, precision=precision, should_compress=True) expected = np.array([[0.25, 0.0, 0.75], [0.0, 0.5, -0.5]]) # Check recovered values self.assertTrue(np.all(np.isclose(expected, recovered))) # Check indices self.assertEqual(len(indices), 2) self.assertEqual(indices[0], collected_indices[0]) self.assertEqual(indices[1], collected_indices[1]) def test_encode_decode_six_compressed(self): measurements = np.array([[1.25, -0.125, -0.75], [1.125, -0.625, -0.5]]) precision = 4 width = 6 seq_length = 8 collected_indices = [0, 4] encoded = message.encode_standard_measurements(measurements=measurements, precision=precision, width=width, collected_indices=collected_indices, seq_length=seq_length, should_compress=True) recovered, indices, _ = message.decode_standard_measurements(byte_str=encoded, num_features=measurements.shape[1], seq_length=seq_length, width=width, precision=precision, should_compress=True) # Check recovered values self.assertTrue(np.all(np.isclose(measurements, recovered))) # Check indices self.assertEqual(len(indices), 2) self.assertEqual(indices[0], collected_indices[0]) self.assertEqual(indices[1], collected_indices[1]) class TestGroupWidths(unittest.TestCase): def test_encode_decode_widths(self): widths = [16, 5, 9, 12] shifts = [7, 0, 4, 6] reps = [10, 4, 3, 6] encoded = message.encode_shifts(widths=widths, shifts=shifts, reps=reps, num_shift_bits=4, min_width=5) rec_shifts, rec_widths, rec_reps, num_bytes = message.decode_shifts(encoded=encoded, num_shift_bits=4, min_width=5) self.assertEqual(rec_widths, widths) self.assertEqual(rec_shifts, shifts) self.assertEqual(rec_reps, reps) class TestStable(unittest.TestCase): def test_encode_decode_two_groups(self): measurements = np.array([[0.25, -0.125, 0.75], [-0.125, 0.625, -0.5]]) non_fractional = 2 seq_length = 8 collected_indices = [0, 1] widths = [5, 5] shifts = [-2, -1] sizes = [3, 3] encoded = message.encode_stable_measurements(measurements=measurements, collected_indices=collected_indices, seq_length=seq_length, widths=widths, shifts=shifts, group_sizes=sizes, non_fractional=non_fractional) decoded, indices, widths = message.decode_stable_measurements(encoded=encoded, seq_length=seq_length, num_features=measurements.shape[1], non_fractional=non_fractional) # Check recovered values error = mean_absolute_error(y_true=measurements, y_pred=decoded) self.assertLess(error, SMALL_NUMBER) # Check the returned width self.assertEqual(widths, [5, 5]) # Check indices self.assertEqual(indices, collected_indices) def test_encode_decode_two_groups_truncated(self): measurements = np.array([[0.25, -0.125, 0.75], [-0.125, 0.625, -0.5]]) non_fractional = 2 seq_length = 8 collected_indices = [0, 5] widths = [5, 5] shifts = [-1, -1] sizes = [3, 3] encoded = message.encode_stable_measurements(measurements=measurements, collected_indices=collected_indices, seq_length=seq_length, widths=widths, shifts=shifts, group_sizes=sizes, non_fractional=non_fractional) decoded, indices, widths = message.decode_stable_measurements(encoded=encoded, seq_length=seq_length, num_features=measurements.shape[1], non_fractional=non_fractional) # Check recovered values error = mean_absolute_error(y_true=measurements, y_pred=decoded) self.assertLess(error, 0.03) # Check the widths self.assertEqual(widths, [5, 5]) # Check indices self.assertEqual(indices, collected_indices) def test_encode_decode_two_groups_truncated_signed(self): measurements = np.array([[0.25, -0.125, -0.75], [0.125, -0.625, -0.5]]) non_fractional = 2 seq_length = 8 collected_indices = [0, 5] widths = [5, 5] shifts = [-1, -1] sizes = [2, 4] encoded = message.encode_stable_measurements(measurements=measurements, collected_indices=collected_indices, seq_length=seq_length, widths=widths, shifts=shifts, group_sizes=sizes, non_fractional=non_fractional) decoded, indices, widths = message.decode_stable_measurements(encoded=encoded, seq_length=seq_length, num_features=measurements.shape[1], non_fractional=non_fractional) # Check recovered values error = mean_absolute_error(y_true=measurements, y_pred=decoded) self.assertLess(error, 0.002) # Check the width self.assertEqual(widths, [5, 5]) # Check indices self.assertEqual(indices, collected_indices) def test_encode_decode_three_groups(self): measurements = np.array([[0.25, -0.125, 0.75], [-0.25, 0.625, -0.5]]) non_fractional = 4 seq_length = 8 collected_indices = [0, 7] widths = [5, 6, 5] shifts = [-1, -2, 0] sizes = [2, 3, 1] encoded = message.encode_stable_measurements(measurements=measurements, collected_indices=collected_indices, seq_length=seq_length, widths=widths, shifts=shifts, group_sizes=sizes, non_fractional=non_fractional) decoded, indices, widths = message.decode_stable_measurements(encoded=encoded, seq_length=seq_length, num_features=measurements.shape[1], non_fractional=non_fractional) # Check recovered values error = mean_absolute_error(y_true=measurements, y_pred=decoded) self.assertLess(error, SMALL_NUMBER) # Check widths self.assertEqual(widths, [5, 6, 5]) # Check indices self.assertEqual(indices, collected_indices) def test_encode_decode_small_padded(self): measurements = np.array([[0.25, -0.125, 0.75], [-0.125, 0.625, -0.5]]) non_fractional = 2 seq_length = 8 collected_indices = [0, 1] widths = [5, 5] shifts = [-2, -1] sizes = [3, 3] encoded = message.encode_stable_measurements(measurements=measurements, collected_indices=collected_indices, seq_length=seq_length, widths=widths, shifts=shifts, group_sizes=sizes, non_fractional=non_fractional) encoded = pad_to_length(encoded, length=len(encoded) + 7) decoded, indices, widths = message.decode_stable_measurements(encoded=encoded, seq_length=seq_length, num_features=measurements.shape[1], non_fractional=non_fractional) # Check recovered values error = mean_absolute_error(y_true=measurements, y_pred=decoded) self.assertLess(error, SMALL_NUMBER) # Check the returned width self.assertEqual(widths, [5, 5]) # Check indices self.assertEqual(indices, collected_indices) def test_encode_decode_large(self): # Load the data with h5py.File('../../datasets/uci_har/train/data.h5', 'r') as fin: inputs = fin['inputs'][0] # [50, 6] width = 8 seq_length = inputs.shape[0] collected_indices = list(range(seq_length)) non_fractional = 3 flattened = inputs.T.reshape(-1) # Set the shifts shifts = select_range_shifts_array(measurements=flattened, old_width=16, old_precision=13, new_width=width, num_range_bits=3) merged_shifts, sizes = merge_shift_groups(values=flattened, shifts=shifts, max_num_groups=6) # Set the widths using the number of groups group_widths = [width for _ in sizes] # Encode and Decode the message encoded = message.encode_stable_measurements(measurements=inputs, collected_indices=collected_indices, seq_length=seq_length, widths=group_widths, group_sizes=sizes, shifts=merged_shifts, non_fractional=non_fractional) decoded, indices, widths = message.decode_stable_measurements(encoded=encoded, seq_length=seq_length, num_features=inputs.shape[1], non_fractional=non_fractional) error = mean_absolute_error(y_true=inputs, y_pred=decoded) self.assertLessEqual(error, 0.01) self.assertEqual(widths, group_widths) def test_encode_decode_large_two(self): # Load the data with h5py.File('../../datasets/uci_har/train/data.h5', 'r') as fin: inputs = fin['inputs'][495] # [50, 6] width = 8 seq_length = inputs.shape[0] collected_indices = list(range(seq_length)) non_fractional = 3 flattened = inputs.T.reshape(-1) # Set the shifts shifts = select_range_shifts_array(measurements=flattened, old_width=16, old_precision=13, new_width=width, num_range_bits=3) merged_shifts, sizes = merge_shift_groups(values=flattened, shifts=shifts, max_num_groups=6) # Set the widths using the number of groups group_widths = [width for _ in sizes] # Encode and Decode the message encoded = message.encode_stable_measurements(measurements=inputs, collected_indices=collected_indices, seq_length=seq_length, widths=group_widths, group_sizes=sizes, shifts=merged_shifts, non_fractional=non_fractional) decoded, indices, widths = message.decode_stable_measurements(encoded=encoded, seq_length=seq_length, num_features=inputs.shape[1], non_fractional=non_fractional) error = mean_absolute_error(y_true=inputs, y_pred=decoded) self.assertLessEqual(error, 0.01) self.assertEqual(widths, group_widths) def test_encode_decode_large_tight(self): # Load the data with h5py.File('../../datasets/uci_har/train/data.h5', 'r') as fin: inputs = fin['inputs'][495] # [50, 6] width = 5 seq_length = inputs.shape[0] collected_indices = list(range(seq_length)) non_fractional = 3 flattened = inputs.T.reshape(-1) # Set the shifts shifts = select_range_shifts_array(measurements=flattened, old_width=16, old_precision=13, new_width=width, num_range_bits=3) merged_shifts, sizes = merge_shift_groups(values=flattened, shifts=shifts, max_num_groups=6) # Set the widths using the number of groups group_widths = [width for _ in sizes] # Encode and Decode the message encoded = message.encode_stable_measurements(measurements=inputs, collected_indices=collected_indices, seq_length=seq_length, widths=group_widths, group_sizes=sizes, shifts=merged_shifts, non_fractional=non_fractional) decoded, indices, widths = message.decode_stable_measurements(encoded=encoded, seq_length=seq_length, num_features=inputs.shape[1], non_fractional=non_fractional) error = mean_absolute_error(y_true=inputs, y_pred=decoded) self.assertLessEqual(error, 0.062) self.assertEqual(widths, group_widths) def test_encode_decode_large_padded(self): # Load the data with h5py.File('../../datasets/uci_har/train/data.h5', 'r') as fin: inputs = fin['inputs'][495] # [50, 6] width = 8 seq_length = inputs.shape[0] collected_indices = list(range(seq_length)) non_fractional = 3 flattened = inputs.T.reshape(-1) # Set the shifts shifts = select_range_shifts_array(measurements=flattened, old_width=16, old_precision=13, new_width=width, num_range_bits=3) merged_shifts, sizes = merge_shift_groups(values=flattened, shifts=shifts, max_num_groups=6) # Set the widths using the number of groups group_widths = [width for _ in sizes] # Encode and Decode the message encoded = message.encode_stable_measurements(measurements=inputs, collected_indices=collected_indices, seq_length=seq_length, widths=group_widths, group_sizes=sizes, shifts=merged_shifts, non_fractional=non_fractional) encoded = pad_to_length(encoded, length=len(encoded) + 12) decoded, indices, widths = message.decode_stable_measurements(encoded=encoded, seq_length=seq_length, num_features=inputs.shape[1], non_fractional=non_fractional) error = mean_absolute_error(y_true=inputs, y_pred=decoded) self.assertLessEqual(error, 0.01) self.assertEqual(widths, group_widths) class TestDeltaEncode(unittest.TestCase): def test_encode(self): measurements = np.array([[10.0, 10.0], [12.0, 12.0], [12.5, 11.5]]) encoded = message.delta_encode(measurements) expected = np.array([[10.0, 10.0], [2.0, 2.0], [0.5, -0.5]]) self.assertTrue(np.all(np.isclose(encoded, expected))) def test_decode(self): encoded = np.array([[10.0, 10.0], [2.0, 2.0], [0.5, -0.5]]) recovered = message.delta_decode(encoded) expected = np.array([[10.0, 10.0], [12.0, 12.0], [12.5, 11.5]]) self.assertTrue(np.all(np.isclose(recovered, expected))) def test_single_feature(self): rand = np.random.RandomState(seed=3489) seq_length = 7 measurements = rand.uniform(low=-2.0, high=2.0, size=(seq_length, 1)) encoded = message.delta_encode(measurements) recovered = message.delta_decode(encoded) self.assertTrue(np.all(np.isclose(recovered, measurements))) def test_many_features(self): rand = np.random.RandomState(seed=3489) seq_length = 12 num_features = 5 measurements = rand.uniform(low=-2.0, high=2.0, size=(seq_length, num_features)) encoded = message.delta_encode(measurements) recovered = message.delta_decode(encoded) self.assertTrue(np.all(np.isclose(recovered, measurements))) if __name__ == '__main__': unittest.main()

46.4

123

0.457123

2,256

26,448

5.135195

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0.888476

0.87363

0.87104

0.863444

0

0.039834

0.470357

26,448

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0

null

0

1

0

1

0

null

0

7

928896db4fa020128dd99e341598b1422f0d2587

127

py

Python

testprojectD-rice-d058558a4d4f/rice/api/__init__.py

YuanXMjoy/rice

05e908eea8c9189c3b392d2d57e5653191bf1da9

[ "MIT" ]

null

testprojectD-rice-d058558a4d4f/rice/api/__init__.py

YuanXMjoy/rice

05e908eea8c9189c3b392d2d57e5653191bf1da9

[ "MIT" ]

null

testprojectD-rice-d058558a4d4f/rice/api/__init__.py

YuanXMjoy/rice

05e908eea8c9189c3b392d2d57e5653191bf1da9

[ "MIT" ]

null

from flask import Blueprint api = Blueprint('api', __name__) from . import rice, order, login, change_password, change_phone

21.166667

63

0.771654

17

127

5.411765

0.705882

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5

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null

0

1

0

1

0

7

2b96ced1b97eaf0217dd776e5ad366bc76bab8f0

15,667

py

Python

omega_miya/database/model/cooldown.py

rinrini001/omega-miya

53a6683fccb0618e306abe9e103cec78445f3796

[ "MIT" ]

120

2021-04-20T13:20:46.000Z

2022-03-26T05:43:21.000Z

omega_miya/database/model/cooldown.py

rinrini001/omega-miya

53a6683fccb0618e306abe9e103cec78445f3796

[ "MIT" ]

57

2021-04-20T08:10:14.000Z

2022-03-28T01:55:14.000Z

omega_miya/database/model/cooldown.py

rinrini001/omega-miya

53a6683fccb0618e306abe9e103cec78445f3796

[ "MIT" ]

32

2021-04-21T01:57:17.000Z

2022-03-01T18:06:34.000Z

from omega_miya.database.database import BaseDB from omega_miya.database.class_result import Result from omega_miya.database.tables import CoolDownEvent from datetime import datetime from sqlalchemy.future import select from sqlalchemy.orm.exc import NoResultFound, MultipleResultsFound class DBCoolDownEvent(object): global_group_type: str = 'global_group' global_user_type: str = 'global_user' group_type: str = 'group' user_type: str = 'user' @classmethod async def add_global_group_cool_down_event( cls, group_id: int, stop_at: datetime, description: str = None) -> Result.IntResult: """ :return: result = 0: Success result = -1: Error """ async_session = BaseDB().get_async_session() async with async_session() as session: try: async with session.begin(): try: session_result = await session.execute( select(CoolDownEvent). where(CoolDownEvent.event_type == cls.global_group_type). where(CoolDownEvent.group_id == group_id) ) exist_event = session_result.scalar_one() exist_event.stop_at = stop_at exist_event.description = description exist_event.updated_at = datetime.now() result = Result.IntResult(error=False, info='Success upgraded', result=0) except NoResultFound: new_event = CoolDownEvent( event_type=cls.global_group_type, group_id=group_id, stop_at=stop_at, description=description, created_at=datetime.now()) session.add(new_event) result = Result.IntResult(error=False, info='Success added', result=0) await session.commit() except MultipleResultsFound: await session.rollback() result = Result.IntResult(error=True, info='MultipleResultsFound', result=-1) except Exception as e: await session.rollback() result = Result.IntResult(error=True, info=repr(e), result=-1) return result @classmethod async def check_global_group_cool_down_event(cls, group_id: int) -> Result.IntResult: """ :return: result = 2: Success with CoolDown Event expired result = 1: Success with CoolDown Event exist result = 0: Success with CoolDown Event not found result = -1: Error """ async_session = BaseDB().get_async_session() async with async_session() as session: async with session.begin(): try: session_result = await session.execute( select(CoolDownEvent). where(CoolDownEvent.event_type == cls.global_group_type). where(CoolDownEvent.group_id == group_id) ) event = session_result.scalar_one() stop_at = event.stop_at if datetime.now() > stop_at: result = Result.IntResult(error=False, info='Success, CoolDown expired', result=2) else: result = Result.IntResult(error=False, info=f'CoolDown until: {stop_at}', result=1) except NoResultFound: result = Result.IntResult(error=False, info='NoResultFound', result=0) except MultipleResultsFound: result = Result.IntResult(error=True, info='MultipleResultsFound', result=-1) except Exception as e: result = Result.IntResult(error=True, info=repr(e), result=-1) return result @classmethod async def add_global_user_cool_down_event( cls, user_id: int, stop_at: datetime, description: str = None) -> Result.IntResult: """ :return: result = 0: Success result = -1: Error """ async_session = BaseDB().get_async_session() async with async_session() as session: try: async with session.begin(): try: session_result = await session.execute( select(CoolDownEvent). where(CoolDownEvent.event_type == cls.global_user_type). where(CoolDownEvent.user_id == user_id) ) exist_event = session_result.scalar_one() exist_event.stop_at = stop_at exist_event.description = description exist_event.updated_at = datetime.now() result = Result.IntResult(error=False, info='Success upgraded', result=0) except NoResultFound: new_event = CoolDownEvent( event_type=cls.global_user_type, user_id=user_id, stop_at=stop_at, description=description, created_at=datetime.now()) session.add(new_event) result = Result.IntResult(error=False, info='Success added', result=0) await session.commit() except MultipleResultsFound: await session.rollback() result = Result.IntResult(error=True, info='MultipleResultsFound', result=-1) except Exception as e: await session.rollback() result = Result.IntResult(error=True, info=repr(e), result=-1) return result @classmethod async def check_global_user_cool_down_event(cls, user_id: int) -> Result.IntResult: """ :return: result = 2: Success with CoolDown Event expired result = 1: Success with CoolDown Event exist result = 0: Success with CoolDown Event not found result = -1: Error """ async_session = BaseDB().get_async_session() async with async_session() as session: async with session.begin(): try: session_result = await session.execute( select(CoolDownEvent). where(CoolDownEvent.event_type == cls.global_user_type). where(CoolDownEvent.user_id == user_id) ) event = session_result.scalar_one() stop_at = event.stop_at if datetime.now() > stop_at: result = Result.IntResult(error=False, info='Success, CoolDown expired', result=2) else: result = Result.IntResult(error=False, info=f'CoolDown until: {stop_at}', result=1) except NoResultFound: result = Result.IntResult(error=False, info='NoResultFound', result=0) except MultipleResultsFound: result = Result.IntResult(error=True, info='MultipleResultsFound', result=-1) except Exception as e: result = Result.IntResult(error=True, info=repr(e), result=-1) return result @classmethod async def add_group_cool_down_event( cls, plugin: str, group_id: int, stop_at: datetime, description: str = None) -> Result.IntResult: """ :return: result = 0: Success result = -1: Error """ async_session = BaseDB().get_async_session() async with async_session() as session: try: async with session.begin(): try: session_result = await session.execute( select(CoolDownEvent). where(CoolDownEvent.event_type == cls.group_type). where(CoolDownEvent.plugin == plugin). where(CoolDownEvent.group_id == group_id) ) exist_event = session_result.scalar_one() exist_event.stop_at = stop_at exist_event.description = description exist_event.updated_at = datetime.now() result = Result.IntResult(error=False, info='Success upgraded', result=0) except NoResultFound: new_event = CoolDownEvent( event_type=cls.group_type, plugin=plugin, group_id=group_id, stop_at=stop_at, description=description, created_at=datetime.now()) session.add(new_event) result = Result.IntResult(error=False, info='Success added', result=0) await session.commit() except MultipleResultsFound: await session.rollback() result = Result.IntResult(error=True, info='MultipleResultsFound', result=-1) except Exception as e: await session.rollback() result = Result.IntResult(error=True, info=repr(e), result=-1) return result @classmethod async def check_group_cool_down_event(cls, plugin: str, group_id: int) -> Result.IntResult: """ :return: result = 2: Success with CoolDown Event expired result = 1: Success with CoolDown Event exist result = 0: Success with CoolDown Event not found result = -1: Error """ async_session = BaseDB().get_async_session() async with async_session() as session: async with session.begin(): try: session_result = await session.execute( select(CoolDownEvent). where(CoolDownEvent.event_type == cls.group_type). where(CoolDownEvent.plugin == plugin). where(CoolDownEvent.group_id == group_id) ) event = session_result.scalar_one() stop_at = event.stop_at if datetime.now() > stop_at: result = Result.IntResult(error=False, info='Success, CoolDown expired', result=2) else: result = Result.IntResult(error=False, info=f'CoolDown until: {stop_at}', result=1) except NoResultFound: result = Result.IntResult(error=False, info='NoResultFound', result=0) except MultipleResultsFound: result = Result.IntResult(error=True, info='MultipleResultsFound', result=-1) except Exception as e: result = Result.IntResult(error=True, info=repr(e), result=-1) return result @classmethod async def add_user_cool_down_event( cls, plugin: str, user_id: int, stop_at: datetime, description: str = None) -> Result.IntResult: """ :return: result = 0: Success result = -1: Error """ async_session = BaseDB().get_async_session() async with async_session() as session: try: async with session.begin(): try: session_result = await session.execute( select(CoolDownEvent). where(CoolDownEvent.event_type == cls.user_type). where(CoolDownEvent.plugin == plugin). where(CoolDownEvent.user_id == user_id) ) exist_event = session_result.scalar_one() exist_event.stop_at = stop_at exist_event.description = description exist_event.updated_at = datetime.now() result = Result.IntResult(error=False, info='Success upgraded', result=0) except NoResultFound: new_event = CoolDownEvent( event_type=cls.user_type, plugin=plugin, user_id=user_id, stop_at=stop_at, description=description, created_at=datetime.now()) session.add(new_event) result = Result.IntResult(error=False, info='Success added', result=0) await session.commit() except MultipleResultsFound: await session.rollback() result = Result.IntResult(error=True, info='MultipleResultsFound', result=-1) except Exception as e: await session.rollback() result = Result.IntResult(error=True, info=repr(e), result=-1) return result @classmethod async def check_user_cool_down_event(cls, plugin: str, user_id: int) -> Result.IntResult: """ :return: result = 2: Success with CoolDown Event expired result = 1: Success with CoolDown Event exist result = 0: Success with CoolDown Event not found result = -1: Error """ async_session = BaseDB().get_async_session() async with async_session() as session: async with session.begin(): try: session_result = await session.execute( select(CoolDownEvent). where(CoolDownEvent.event_type == cls.user_type). where(CoolDownEvent.plugin == plugin). where(CoolDownEvent.user_id == user_id) ) event = session_result.scalar_one() stop_at = event.stop_at if datetime.now() > stop_at: result = Result.IntResult(error=False, info='Success, CoolDown expired', result=2) else: result = Result.IntResult(error=False, info=f'CoolDown until: {stop_at}', result=1) except NoResultFound: result = Result.IntResult(error=False, info='NoResultFound', result=0) except MultipleResultsFound: result = Result.IntResult(error=True, info='MultipleResultsFound', result=-1) except Exception as e: result = Result.IntResult(error=True, info=repr(e), result=-1) return result @classmethod async def clear_time_out_event(cls) -> Result.DictResult: async_session = BaseDB().get_async_session() async with async_session() as session: async with session.begin(): session_result = await session.execute( select(CoolDownEvent).order_by(CoolDownEvent.id) ) events = session_result.scalars().all() failed_events = [] for event in events: try: if datetime.now() >= event.stop_at: await session.delete(event) await session.commit() except Exception as e: await session.rollback() failed_events.append((event, e)) continue return Result.DictResult(error=False, info='Tasks completed', result={'all': events, 'failed': failed_events})

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py

Python

packs/chatops_tests/actions/test_aliases_with_slack.py

winem/st2tests

1d52733bb2b51c9e0cdcdec5759b56c9822cbdd1

[ "Apache-2.0" ]

4

2015-08-26T12:06:30.000Z

2017-11-04T16:15:07.000Z

packs/chatops_tests/actions/test_aliases_with_slack.py

winem/st2tests

1d52733bb2b51c9e0cdcdec5759b56c9822cbdd1

[ "Apache-2.0" ]

90

2015-06-06T01:16:20.000Z

2021-10-30T12:10:39.000Z

packs/chatops_tests/actions/test_aliases_with_slack.py

winem/st2tests

1d52733bb2b51c9e0cdcdec5759b56c9822cbdd1

[ "Apache-2.0" ]

14

2015-06-15T01:48:04.000Z

2022-01-06T03:23:45.000Z

from __future__ import absolute_import, print_function, unicode_literals import os import time import unittest2 from slackclient import SlackClient # REQUIRED environment variables: # * WEBSOCKET_CLIENT_CA_BUNDLE # - Should be set to: # /etc/pki/ca-trust/extracted/pem/tls-ca-bundle.pem # for RHEL7 systems # - Unnecessary for systems with Python 2.7.9+ (eg: Ubuntu 16.04 and later) # - Not directly used by this script, it is used to specify the certificate # bundle for root certificates loaded by the websocket Python package # * SLACK_CHANNEL # - the Slack channel to connect to # * SLACK_BOT_USERNAME # - the Slack username for the StackStorm bot # - this should be set to the same username as the SLACK_BOT_API_TOKEN # * SLACK_USER_USERNAME # - the Slack username for the Python script impersonating a user # - this should be set to the same username as the SLACK_USER_API_TOKEN Slackbot, below # * SLACK_USER_API_TOKEN # - the Slack API token for the Python script that impersonates a user # - THIS MUST BE DIFFERENT THAN SLACK_BOT_API_TOKEN # OPTIONAL environment variables: # # * SLACK_WAIT_FOR_MESSAGES_TIMEOUT # - Should be set to the number of seconds it is guaranteed to take the ST2 # IUT to respond # - Used to timeout while waiting for responses, and used to wait long enough # to assume a non-response for tests that don't expect responses # - Default: 120 def ignore_username(userid): # Remove 'user_typing' messages, since they are almost certainly # caused by a human typing in the channel. Otherwise, the number of # messages can be erroneously inflated. def filter_messages(message): if message['type'] != 'message': return False elif message.get('user') == userid: return False else: return True return filter_messages class SlackEndToEndTestCase(unittest2.TestCase): maxDiff = None @classmethod def setUpClass(cls): cls.WAIT_FOR_MESSAGES_TIMEOUT = int(os.environ.get('SLACK_WAIT_FOR_MESSAGES_TIMEOUT', 120)) cls.SLACK_CHANNEL = os.environ['SLACK_CHANNEL'] cls.SLACK_BOT_USERNAME = os.environ['SLACK_BOT_USERNAME'] cls.SLACK_USER_API_TOKEN = os.environ['SLACK_USER_API_TOKEN'] cls.SLACK_USER_USERNAME = os.environ['SLACK_USER_USERNAME'] # This token is for the bot that impersonates a user cls.client = SlackClient(connect=True, token=cls.SLACK_USER_API_TOKEN) cls.channel = cls.SLACK_CHANNEL cls.bot_username = cls.SLACK_BOT_USERNAME cls.username = cls.SLACK_USER_USERNAME cls.userid = cls.get_user_id(cls.username) cls.filter = staticmethod(ignore_username(cls.userid)) cls.client.api_call( "chat.postMessage", channel=cls.channel, text="`===== BEGINNING ChatOps End-to-End Tests =====`", as_user=True) # Connect as the bot cls.client.rtm_connect() @classmethod def tearDownClass(cls): cls.client.api_call( "chat.postMessage", channel=cls.channel, text="`===== FINISHED ChatOps End-to-End Tests =====`", as_user=True) @classmethod def get_user_id(cls, username): for user in cls.client.api_call("users.list").get('members'): if user.get('real_name') == username: return user.get('id') def test_non_response(self): post_message_response = self.client.api_call( "chat.postMessage", channel=self.channel, text="This message should not prompt a response from the bot", as_user=True) messages = [] for i in range(self.WAIT_FOR_MESSAGES_TIMEOUT): if len(messages) >= 2: break time.sleep(1) all_messages = self.client.rtm_read() filtered_messages = filter(self.filter, all_messages) if filtered_messages: messages.extend(filtered_messages) self.assertListEqual(messages, []) if len(messages) != 0: time.sleep(self.WAIT_FOR_MESSAGES_TIMEOUT) # Drain the event buffer self.client.rtm_read() def test_help_shortcut(self): post_message_response = self.client.api_call( "chat.postMessage", channel=self.channel, text="!help", as_user=True) messages = [] for i in range(self.WAIT_FOR_MESSAGES_TIMEOUT): if len(messages) >= 2: break time.sleep(1) all_messages = self.client.rtm_read() filtered_messages = filter(self.filter, all_messages) if filtered_messages: messages.extend(filtered_messages) self.assertEqual(2, len(messages)) if len(messages) != 2: time.sleep(self.WAIT_FOR_MESSAGES_TIMEOUT) # Help commands for 'unused' action alias should returns 105. combined_text = messages[0]['text'] + "\n" + messages[1]['text'] number_of_unused_commands = len(list(filter(lambda line: line.startswith('![unused]'), combined_text.split('\n')))) self.assertEqual(number_of_unused_commands, 105) # Drain the event buffer self.client.rtm_read() def test_help_longcut(self): post_message_response = self.client.api_call( "chat.postMessage", channel=self.channel, text="@{bot_user} help".format(bot_user=self.bot_username), as_user=True, link_names=True) messages = [] for i in range(self.WAIT_FOR_MESSAGES_TIMEOUT): if len(messages) >= 2: break time.sleep(1) all_messages = self.client.rtm_read() filtered_messages = filter(self.filter, all_messages) if filtered_messages: messages.extend(filtered_messages) self.assertEqual(2, len(messages)) if len(messages) != 2: time.sleep(self.WAIT_FOR_MESSAGES_TIMEOUT) # Help commands for 'unused' action alias should returns 105 combined_text = messages[0]['text'] + "\n" + messages[1]['text'] number_of_unused_commands = len(list(filter(lambda line: line.startswith('![unused]'), combined_text.split('\n')))) self.assertEqual(number_of_unused_commands, 105) # Drain the event buffer self.client.rtm_read() def test_run_command_on_localhost(self): post_message_response = self.client.api_call( "chat.postMessage", channel=self.channel, text="!remote run date on localhost", as_user=True) messages = [] for i in range(self.WAIT_FOR_MESSAGES_TIMEOUT): if len(messages) >= 2: break time.sleep(1) all_messages = self.client.rtm_read() filtered_messages = filter(self.filter, all_messages) if filtered_messages: messages.extend(filtered_messages) self.assertEqual(2, len(messages)) if len(messages) != 2: time.sleep(self.WAIT_FOR_MESSAGES_TIMEOUT) # Test for ack self.assertIn("details available at", messages[0]['text']) # Test for response self.assertIsNotNone(messages[1].get('bot_id')) self.assertIsNotNone(messages[1].get('attachments')) self.assertGreater(len(messages[1]['attachments']), 0) self.assertIsNotNone(messages[1]['attachments'][0].get('text')) # Check the pretext self.assertRegex(messages[1]['attachments'][0]['pretext'], r'<@{userid}>'.format(userid=self.userid)) # Test attachment msg_text = messages[1]['attachments'][0]['text'] self.assertRegex(msg_text, r'Action core\.remote completed\.') self.assertRegex(msg_text, r'status\s*:\s*succeeded') self.assertRegex(msg_text, r'execution\s*:\s*[0-9a-fA-F]{24}') self.assertRegex(msg_text, r'web_url\s*:\s*') # The time can be an integer or a float, and might contain non-ASCII # characters like mu (Unicode 03BC), which gets converted to \u03BC. # So instead of strictly specifying those, we have a very relaxed # regex to capture the execution duration. self.assertRegex(msg_text, r'Took \d+.*s to complete\.') self.assertRegex(msg_text, r'result\s*:\s*') self.assertRegex(msg_text, r'localhost\s*:\s*') self.assertRegex(msg_text, r'stdout\s*:\s*') # Drain the event buffer self.client.rtm_read() def test_run_command_on_localhost_with_bad_argument(self): post_message_response = self.client.api_call( "chat.postMessage", channel=self.channel, text="!pack get pack=example", as_user=True) messages = [] for i in range(self.WAIT_FOR_MESSAGES_TIMEOUT): if len(messages) >= 1: break time.sleep(1) all_messages = self.client.rtm_read() filtered_messages = filter(self.filter, all_messages) if filtered_messages: messages.extend(filtered_messages) self.assertEqual(1, len(messages)) if len(messages) != 1: time.sleep(self.WAIT_FOR_MESSAGES_TIMEOUT) # Test for response self.assertIsNotNone(messages[0].get('bot_id')) self.assertIsNotNone(messages[0].get('attachments')) self.assertGreater(len(messages[0]['attachments']), 0) self.assertIsNotNone(messages[0]['attachments'][0].get('color')) self.assertEqual(messages[0]['attachments'][0]['color'], 'F35A00') self.assertIsNotNone(messages[0]['attachments'][0].get('text')) # Check the pretext msg_pretext = messages[0]['attachments'][0]['pretext'] self.assertRegex(msg_pretext, r"<@{userid}>: I'm sorry, Dave. I'm afraid I can't do that. ".format(userid=self.userid)) # Test attachment msg_text = messages[0]['attachments'][0]['text'] self.assertRegex(msg_text, r"Command \"pack get pack=example\" doesn't match format string \"pack get \{\{ pack \}\}\"") # Drain the event buffer self.client.rtm_read() def test_run_exact_command_on_localhost(self): post_message_response = self.client.api_call( "chat.postMessage", channel=self.channel, text="!remote run \"echo ChatOps run exact command on localhost\" on localhost", as_user=True) messages = [] for i in range(self.WAIT_FOR_MESSAGES_TIMEOUT): if len(messages) >= 2: break time.sleep(1) all_messages = self.client.rtm_read() filtered_messages = filter(self.filter, all_messages) if filtered_messages: messages.extend(filtered_messages) self.assertEqual(2, len(messages)) if len(messages) != 2: time.sleep(self.WAIT_FOR_MESSAGES_TIMEOUT) # Test for ack self.assertIn("details available at", messages[0]['text']) # Test for response self.assertIsNotNone(messages[1].get('bot_id')) self.assertIsNotNone(messages[1].get('attachments')) self.assertGreater(len(messages[1]['attachments']), 0) self.assertIsNotNone(messages[1]['attachments'][0].get('text')) # Check the pretext # This test depends a bit on the hubot-stackstorm adapter self.assertRegex(messages[1]['attachments'][0]['pretext'], r'<@{userid}>'.format(userid=self.userid)) # Test attachment msg_text = messages[1]['attachments'][0]['text'] self.assertRegex(msg_text, r'Action core\.remote completed\.') self.assertRegex(msg_text, r'status\s*:\s*succeeded') self.assertRegex(msg_text, r'execution\s*:\s*[0-9a-fA-F]{24}') self.assertRegex(msg_text, r'web_url\s*:\s*') # The time can be an integer or a float, and might contain non-ASCII # characters like mu (Unicode 03BC), which gets converted to \u03BC. # So instead of strictly specifying those, we have a very relaxed # regex to capture the execution duration. self.assertRegex(msg_text, r'Took \d+.*s to complete\.') self.assertRegex(msg_text, r'result\s*:\s*') self.assertRegex(msg_text, r'localhost\s*:\s*') self.assertRegex(msg_text, r'stdout\s*:\s*ChatOps run exact command on localhost') # Drain the event buffer self.client.rtm_read() def test_run_exact_command_on_multiple_hosts(self): post_message_response = self.client.api_call( "chat.postMessage", channel=self.channel, text="!remote run \"echo ChatOps run exact command on multiple hosts\" on localhost,127.0.0.1", as_user=True) messages = [] for i in range(self.WAIT_FOR_MESSAGES_TIMEOUT): if len(messages) >= 2: break time.sleep(1) all_messages = self.client.rtm_read() filtered_messages = filter(self.filter, all_messages) if filtered_messages: messages.extend(filtered_messages) self.assertEqual(2, len(messages)) if len(messages) != 2: time.sleep(self.WAIT_FOR_MESSAGES_TIMEOUT) # Test for ack self.assertIn("details available at", messages[0]['text']) # Test for response self.assertIsNotNone(messages[1].get('bot_id')) self.assertIsNotNone(messages[1].get('attachments')) self.assertGreater(len(messages[1]['attachments']), 0) self.assertIsNotNone(messages[1]['attachments'][0].get('text')) # Check the pretext self.assertRegex(messages[1]['attachments'][0]['pretext'], r'<@{userid}>'.format(userid=self.userid)) # Test attachment msg_text = messages[1]['attachments'][0]['text'] self.assertRegex(msg_text, r'Action core\.remote completed\.') self.assertRegex(msg_text, r'status\s*:\s*succeeded') self.assertRegex(msg_text, r'execution\s*:\s*[0-9a-fA-F]{24}') self.assertRegex(msg_text, r'web_url\s*:\s*') # The time can be an integer or a float, and might contain non-ASCII # characters like mu (Unicode 03BC), which gets converted to \u03BC. # So instead of strictly specifying those, we have a very relaxed # regex to capture the execution duration. self.assertRegex(msg_text, r'Took \d+.*s to complete\.') self.assertRegex(msg_text, r'result\s*:\s*') self.assertRegex(msg_text, r'localhost\s*:\s*\n\s*stdout\s*:\s*ChatOps run exact command on multiple hosts') self.assertRegex(msg_text, r'127.0.0.1\s*:\s*\n\s*stdout\s*:\s*ChatOps run exact command on multiple hosts') # Drain the event buffer self.client.rtm_read() def test_run_command_on_default_hosts(self): post_message_response = self.client.api_call( "chat.postMessage", channel=self.channel, text="!default run \"echo ChatOps run command on default hosts\"", as_user=True) messages = [] for i in range(self.WAIT_FOR_MESSAGES_TIMEOUT): if len(messages) >= 2: break time.sleep(1) all_messages = self.client.rtm_read() filtered_messages = filter(self.filter, all_messages) if filtered_messages: messages.extend(filtered_messages) self.assertEqual(2, len(messages)) if len(messages) != 2: time.sleep(self.WAIT_FOR_MESSAGES_TIMEOUT) # Test for ack self.assertIn("details available at", messages[0]['text']) # Test for response self.assertIsNotNone(messages[1].get('bot_id')) self.assertIsNotNone(messages[1].get('attachments')) self.assertGreater(len(messages[1]['attachments']), 0) self.assertIsNotNone(messages[1]['attachments'][0].get('text')) # Check the pretext self.assertRegex(messages[1]['attachments'][0]['pretext'], r'<@{userid}>'.format(userid=self.userid)) # Test attachment msg_text = messages[1]['attachments'][0]['text'] self.assertRegex(msg_text, r'Action core\.remote completed\.') self.assertRegex(msg_text, r'status\s*:\s*succeeded') self.assertRegex(msg_text, r'execution\s*:\s*[0-9a-fA-F]{24}') # The time can be an integer or a float, and might contain non-ASCII # characters like mu (Unicode 03BC), which gets converted to \u03BC. # So instead of strictly specifying those, we have a very relaxed # regex to capture the execution duration. self.assertRegex(msg_text, r'Took \d+.*s to complete\.') # Drain the event buffer self.client.rtm_read() def test_run_command_with_regex_and_default_parameter(self): post_message_response = self.client.api_call( "chat.postMessage", channel=self.channel, text="!regex run \"echo ChatOps run command with regex\".", as_user=True) messages = [] for i in range(self.WAIT_FOR_MESSAGES_TIMEOUT): if len(messages) >= 2: break time.sleep(1) all_messages = self.client.rtm_read() filtered_messages = filter(self.filter, all_messages) if filtered_messages: messages.extend(filtered_messages) self.assertEqual(2, len(messages)) if len(messages) != 2: time.sleep(self.WAIT_FOR_MESSAGES_TIMEOUT) # Test for ack self.assertIn("details available at", messages[0]['text']) # Test for response self.assertIsNotNone(messages[1].get('bot_id')) self.assertIsNotNone(messages[1].get('attachments')) self.assertGreater(len(messages[1]['attachments']), 0) self.assertIsNotNone(messages[1]['attachments'][0].get('text')) # Check the pretext self.assertRegex(messages[1]['attachments'][0]['pretext'], r'<@{userid}>'.format(userid=self.userid)) # Test attachment msg_text = messages[1]['attachments'][0]['text'] self.assertRegex(msg_text, r'Action core\.remote completed\.') self.assertRegex(msg_text, r'status\s*:\s*succeeded') self.assertRegex(msg_text, r'execution\s*:\s*[0-9a-fA-F]{24}') # The time can be an integer or a float, and might contain non-ASCII # characters like mu (Unicode 03BC), which gets converted to \u03BC. # So instead of strictly specifying those, we have a very relaxed # regex to capture the execution duration. self.assertRegex(msg_text, r'Took \d+.*s to complete\.') # Drain the event buffer self.client.rtm_read() def test_execute_command_with_regex_and_default_parameter(self): post_message_response = self.client.api_call( "chat.postMessage", channel=self.channel, text="!regex execute \"echo ChatOps execute command on default hosts\"!", as_user=True) messages = [] for i in range(self.WAIT_FOR_MESSAGES_TIMEOUT): if len(messages) >= 2: break time.sleep(1) all_messages = self.client.rtm_read() filtered_messages = filter(self.filter, all_messages) if filtered_messages: messages.extend(filtered_messages) self.assertEqual(2, len(messages)) if len(messages) != 2: time.sleep(self.WAIT_FOR_MESSAGES_TIMEOUT) # Test for ack self.assertIn("details available at", messages[0]['text']) # Test for response self.assertIsNotNone(messages[1].get('bot_id')) self.assertIsNotNone(messages[1].get('attachments')) self.assertGreater(len(messages[1]['attachments']), 0) self.assertIsNotNone(messages[1]['attachments'][0].get('text')) # Check the pretext self.assertRegex(messages[1]['attachments'][0]['pretext'], r'<@{userid}>'.format(userid=self.userid)) # Test attachment msg_text = messages[1]['attachments'][0]['text'] self.assertRegex(msg_text, r'Action core\.remote completed\.') self.assertRegex(msg_text, r'status\s*:\s*succeeded') self.assertRegex(msg_text, r'execution\s*:\s*[0-9a-fA-F]{24}') # The time can be an integer or a float, and might contain non-ASCII # characters like mu (Unicode 03BC), which gets converted to \u03BC. # So instead of strictly specifying those, we have a very relaxed # regex to capture the execution duration. self.assertRegex(msg_text, r'Took \d+.*s to complete\.') # Drain the event buffer self.client.rtm_read() def test_run_command_with_extra_parameter(self): post_message_response = self.client.api_call( "chat.postMessage", channel=self.channel, text="!extra run \"echo ChatOps run command with extra parameter\" on localhost timeout=120", as_user=True) messages = [] for i in range(self.WAIT_FOR_MESSAGES_TIMEOUT): if len(messages) >= 2: break time.sleep(1) all_messages = self.client.rtm_read() filtered_messages = filter(self.filter, all_messages) if filtered_messages: messages.extend(filtered_messages) self.assertEqual(2, len(messages)) if len(messages) != 2: time.sleep(self.WAIT_FOR_MESSAGES_TIMEOUT) # Test for ack self.assertIn("details available at", messages[0]['text']) # Test for response self.assertIsNotNone(messages[1].get('bot_id')) self.assertIsNotNone(messages[1].get('attachments')) self.assertGreater(len(messages[1]['attachments']), 0) self.assertIsNotNone(messages[1]['attachments'][0].get('text')) # Check the pretext self.assertRegex(messages[1]['attachments'][0]['pretext'], r'<@{userid}>'.format(userid=self.userid)) # Test attachment msg_text = messages[1]['attachments'][0]['text'] self.assertRegex(msg_text, r'Action core\.remote completed\.') self.assertRegex(msg_text, r'status\s*:\s*succeeded') self.assertRegex(msg_text, r'execution\s*:\s*[0-9a-fA-F]{24}') # The time can be an integer or a float, and might contain non-ASCII # characters like mu (Unicode 03BC), which gets converted to \u03BC. # So instead of strictly specifying those, we have a very relaxed # regex to capture the execution duration. self.assertRegex(msg_text, r'Took \d+.*s to complete\.') # Drain the event buffer self.client.rtm_read() def test_weird_run_remote_command_with_parameter(self): post_message_response = self.client.api_call( "chat.postMessage", channel=self.channel, text="!weird run remote command \"echo ChatOps run weird command\" on localhost", as_user=True) messages = [] for i in range(self.WAIT_FOR_MESSAGES_TIMEOUT): if len(messages) >= 2: break time.sleep(1) all_messages = self.client.rtm_read() filtered_messages = filter(self.filter, all_messages) if filtered_messages: messages.extend(filtered_messages) self.assertEqual(2, len(messages)) if len(messages) != 2: time.sleep(self.WAIT_FOR_MESSAGES_TIMEOUT) # Test for ack self.assertIn("details available at", messages[0]['text']) # Test for response self.assertIsNotNone(messages[1].get('bot_id')) self.assertIsNotNone(messages[1].get('attachments')) self.assertGreater(len(messages[1]['attachments']), 0) self.assertIsNotNone(messages[1]['attachments'][0].get('text')) # Check the pretext self.assertRegex(messages[1]['attachments'][0]['pretext'], r'<@{userid}>'.format(userid=self.userid)) # Test attachment msg_text = messages[1]['attachments'][0]['text'] self.assertRegex(msg_text, r'Action core\.remote completed\.') self.assertRegex(msg_text, r'status\s*:\s*succeeded') self.assertRegex(msg_text, r'execution\s*:\s*[0-9a-fA-F]{24}') # The time can be an integer or a float, and might contain non-ASCII # characters like mu (Unicode 03BC), which gets converted to \u03BC. # So instead of strictly specifying those, we have a very relaxed # regex to capture the execution duration. self.assertRegex(msg_text, r'Took \d+.*s to complete\.') # Drain the event buffer self.client.rtm_read() def test_weird_run_remote_command_with_ssh(self): post_message_response = self.client.api_call( "chat.postMessage", channel=self.channel, text="!weird ssh to hosts localhost and run command \"echo ChatOps run weird command with SSH\"", as_user=True) messages = [] for i in range(self.WAIT_FOR_MESSAGES_TIMEOUT): if len(messages) >= 2: break time.sleep(1) all_messages = self.client.rtm_read() filtered_messages = filter(self.filter, all_messages) if filtered_messages: messages.extend(filtered_messages) self.assertEqual(2, len(messages)) if len(messages) != 2: time.sleep(self.WAIT_FOR_MESSAGES_TIMEOUT) # Test for ack self.assertIn("details available at", messages[0]['text']) # Test for response self.assertIsNotNone(messages[1].get('bot_id')) self.assertIsNotNone(messages[1].get('attachments')) self.assertGreater(len(messages[1]['attachments']), 0) self.assertIsNotNone(messages[1]['attachments'][0].get('text')) # Check the pretext self.assertRegex(messages[1]['attachments'][0]['pretext'], r'<@{userid}>'.format(userid=self.userid)) # Test attachment msg_text = messages[1]['attachments'][0]['text'] self.assertRegex(msg_text, r'Action core\.remote completed\.') self.assertRegex(msg_text, r'status\s*:\s*succeeded') self.assertRegex(msg_text, r'execution\s*:\s*[0-9a-fA-F]{24}') # The time can be an integer or a float, and might contain non-ASCII # characters like mu (Unicode 03BC), which gets converted to \u03BC. # So instead of strictly specifying those, we have a very relaxed # regex to capture the execution duration. self.assertRegex(msg_text, r'Took \d+.*s to complete\.') # Drain the event buffer self.client.rtm_read() def test_weird_omg_just_run_command(self): post_message_response = self.client.api_call( "chat.postMessage", channel=self.channel, text="!weird OMG st2 just run this command \"echo ChatOps run weird OMG command\" on ma boxes localhost already", as_user=True) messages = [] for i in range(self.WAIT_FOR_MESSAGES_TIMEOUT): if len(messages) >= 2: break time.sleep(1) all_messages = self.client.rtm_read() filtered_messages = filter(self.filter, all_messages) if filtered_messages: messages.extend(filtered_messages) self.assertEqual(2, len(messages)) if len(messages) != 2: time.sleep(self.WAIT_FOR_MESSAGES_TIMEOUT) # Test for ack self.assertIn("details available at", messages[0]['text']) # Test for response self.assertIsNotNone(messages[1].get('bot_id')) self.assertIsNotNone(messages[1].get('attachments')) self.assertGreater(len(messages[1]['attachments']), 0) self.assertIsNotNone(messages[1]['attachments'][0].get('text')) # Check the pretext self.assertRegex(messages[1]['attachments'][0]['pretext'], r'<@{userid}>'.format(userid=self.userid)) # Test attachment msg_text = messages[1]['attachments'][0]['text'] self.assertRegex(msg_text, r'Action core\.remote completed\.') self.assertRegex(msg_text, r'status\s*:\s*succeeded') self.assertRegex(msg_text, r'execution\s*:\s*[0-9a-fA-F]{24}') # The time can be an integer or a float, and might contain non-ASCII # characters like mu (Unicode 03BC), which gets converted to \u03BC. # So instead of strictly specifying those, we have a very relaxed # regex to capture the execution duration. self.assertRegex(msg_text, r'Took \d+.*s to complete\.') # Drain the event buffer self.client.rtm_read() def test_custom_ack(self): post_message_response = self.client.api_call( "chat.postMessage", channel=self.channel, text="!custom-ack run \"echo ChatOps run command with custom ack\" on localhost", as_user=True) messages = [] for i in range(self.WAIT_FOR_MESSAGES_TIMEOUT): if len(messages) >= 2: break time.sleep(1) all_messages = self.client.rtm_read() filtered_messages = filter(self.filter, all_messages) if filtered_messages: messages.extend(filtered_messages) self.assertEqual(2, len(messages)) if len(messages) != 2: time.sleep(self.WAIT_FOR_MESSAGES_TIMEOUT) # Test for response self.assertIsNotNone(messages[0].get('bot_id')) self.assertEqual(messages[0].get('text'), 'Running the command(s) for you') # Drain the event buffer self.client.rtm_read() def test_disabled_ack(self): post_message_response = self.client.api_call( "chat.postMessage", channel=self.channel, text="!disabled-custom-ack run \"echo ChatOps run command with disabled ack\" on localhost", as_user=True) messages = [] for i in range(self.WAIT_FOR_MESSAGES_TIMEOUT): if len(messages) >= 1: break time.sleep(1) all_messages = self.client.rtm_read() filtered_messages = filter(self.filter, all_messages) if filtered_messages: messages.extend(filtered_messages) self.assertEqual(1, len(messages)) if len(messages) != 1: time.sleep(self.WAIT_FOR_MESSAGES_TIMEOUT) # Test for response self.assertIsNotNone(messages[0].get('bot_id')) self.assertIsNotNone(messages[0].get('attachments')) self.assertGreater(len(messages[0]['attachments']), 0) self.assertIsNotNone(messages[0]['attachments'][0].get('text')) # Check the pretext self.assertRegex(messages[0]['attachments'][0]['pretext'], r'<@{userid}>'.format(userid=self.userid)) # Test attachment msg_text = messages[0]['attachments'][0]['text'] self.assertRegex(msg_text, r'Action core\.remote completed\.') self.assertRegex(msg_text, r'status\s*:\s*succeeded') self.assertRegex(msg_text, r'execution\s*:\s*[0-9a-fA-F]{24}') # The time can be an integer or a float, and might contain non-ASCII # characters like mu (Unicode 03BC), which gets converted to \u03BC. # So instead of strictly specifying those, we have a very relaxed # regex to capture the execution duration. self.assertRegex(msg_text, r'Took \d+.*s to complete\.') # Drain the event buffer self.client.rtm_read() def test_disabled_ack_with_bad_command(self): post_message_response = self.client.api_call( "chat.postMessage", channel=self.channel, text="!disabled-custom-ack run \"echof ChatOps run bad command\" on localhost", as_user=True) messages = [] for i in range(self.WAIT_FOR_MESSAGES_TIMEOUT): if len(messages) >= 1: break time.sleep(1) all_messages = self.client.rtm_read() filtered_messages = filter(self.filter, all_messages) if filtered_messages: messages.extend(filtered_messages) self.assertEqual(1, len(messages)) if len(messages) != 1: time.sleep(self.WAIT_FOR_MESSAGES_TIMEOUT) # Test for response self.assertIsNotNone(messages[0].get('bot_id')) self.assertIsNotNone(messages[0].get('attachments')) self.assertGreater(len(messages[0]['attachments']), 0) self.assertIsNotNone(messages[0]['attachments'][0].get('text')) # Check the pretext self.assertRegex(messages[0]['attachments'][0]['pretext'], r'<@{userid}>'.format(userid=self.userid)) # Test attachment msg_text = messages[0]['attachments'][0]['text'] self.assertRegex(msg_text, r'Action core\.remote completed\.') self.assertRegex(msg_text, r'status\s*:\s*failed') self.assertRegex(msg_text, r'execution\s*:\s*[0-9a-fA-F]{24}') # The time can be an integer or a float, and might contain non-ASCII # characters like mu (Unicode 03BC), which gets converted to \u03BC. # So instead of strictly specifying those, we have a very relaxed # regex to capture the execution duration. self.assertRegex(msg_text, r'Took \d+.*s to complete\.') self.assertRegex(msg_text, r'stderr\s*:.*sh:.*echof:.*not found') self.assertRegex(msg_text, r'return_code\s*:\s*\d+') # Drain the event buffer self.client.rtm_read() def test_alias_with_custom_result_format(self): post_message_response = self.client.api_call( "chat.postMessage", channel=self.channel, text="!custom-format run \"echo ChatOps run command with custom result format\" on localhost", as_user=True) messages = [] for i in range(self.WAIT_FOR_MESSAGES_TIMEOUT): if len(messages) >= 2: break time.sleep(1) all_messages = self.client.rtm_read() filtered_messages = filter(self.filter, all_messages) if filtered_messages: messages.extend(filtered_messages) self.assertEqual(2, len(messages)) if len(messages) != 2: time.sleep(self.WAIT_FOR_MESSAGES_TIMEOUT) # Test for ack self.assertIn("details available at", messages[0]['text']) # Test for response self.assertIsNotNone(messages[1].get('bot_id')) self.assertIsNotNone(messages[1].get('attachments')) self.assertGreater(len(messages[1]['attachments']), 0) self.assertIsNotNone(messages[1]['attachments'][0].get('text')) # Check the pretext self.assertRegex(messages[1]['attachments'][0]['pretext'], r'<@{userid}>'.format(userid=self.userid)) # Test fallback self.assertEqual(messages[1]['attachments'][0]['text'], messages[1]['attachments'][0]['fallback']) # Test attachment msg_text = messages[1]['attachments'][0]['text'] expected_text = ('Ran command `echo ChatOps run command with custom result format` on `1` host.\n' '\n' 'Details are as follows:\n' 'Host: `localhost`\n' ' ---> stdout: ChatOps run command with custom result format\n' ' ---> stderr: \n') self.assertEqual(msg_text, expected_text) # Drain the event buffer self.client.rtm_read() def test_alias_with_custom_result_format_and_multiple_hosts(self): post_message_response = self.client.api_call( "chat.postMessage", channel=self.channel, text="!custom-format run \"echo ChatOps run command with custom result format on multiple hosts\" on localhost,127.0.0.1", as_user=True) messages = [] for i in range(self.WAIT_FOR_MESSAGES_TIMEOUT): if len(messages) >= 2: break time.sleep(1) all_messages = self.client.rtm_read() filtered_messages = filter(self.filter, all_messages) if filtered_messages: messages.extend(filtered_messages) self.assertEqual(2, len(messages)) if len(messages) != 2: time.sleep(self.WAIT_FOR_MESSAGES_TIMEOUT) # Test for ack self.assertIn("details available at", messages[0]['text']) # Test for response self.assertIsNotNone(messages[1].get('bot_id')) self.assertIsNotNone(messages[1].get('attachments')) self.assertGreater(len(messages[1]['attachments']), 0) self.assertIsNotNone(messages[1]['attachments'][0].get('text')) # Check the pretext self.assertRegex(messages[1]['attachments'][0]['pretext'], r'<@{userid}>'.format(userid=self.userid)) # Test fallback self.assertEqual(messages[1]['attachments'][0]['text'], messages[1]['attachments'][0]['fallback']) # Test attachment msg_text = messages[1]['attachments'][0]['text'] expected_report = 'Ran command `echo ChatOps run command with custom result format on multiple hosts` on `2` hosts.\n' expected_details = 'Details are as follows:\n' expected_127_0_0_1 = ('Host: `127.0.0.1`\n' ' ---> stdout: ChatOps run command with custom result format on multiple hosts\n' ' ---> stderr: \n') expected_localhost = ('Host: `localhost`\n' ' ---> stdout: ChatOps run command with custom result format on multiple hosts\n' ' ---> stderr: \n') self.assertIn(expected_report, msg_text) self.assertIn(expected_details, msg_text) self.assertIn(expected_127_0_0_1, msg_text) self.assertIn(expected_localhost, msg_text) # Drain the event buffer self.client.rtm_read() def test_alias_with_disabled_result(self): post_message_response = self.client.api_call( "chat.postMessage", channel=self.channel, text="!disabled-result run \"echo ChatOps run command with disabled result\" on localhost", as_user=True) messages = [] # Wait for longer here since we want to test that it does _not_ # emit a result for i in range(self.WAIT_FOR_MESSAGES_TIMEOUT): if len(messages) >= 2: break time.sleep(1) all_messages = self.client.rtm_read() filtered_messages = filter(self.filter, all_messages) if filtered_messages: messages.extend(filtered_messages) self.assertEqual(1, len(messages)) if len(messages) != 1: time.sleep(self.WAIT_FOR_MESSAGES_TIMEOUT) # Test for ack self.assertIn("details available at", messages[0]['text']) # Drain the event buffer self.client.rtm_read() def test_attachment_and_plaintext_backup(self): post_message_response = self.client.api_call( "chat.postMessage", channel=self.channel, text="!plaintext-and-attachment run \"echo ChatOps run exact command with custom result format with plaintext and attachment\" on localhost", as_user=True) messages = [] for i in range(self.WAIT_FOR_MESSAGES_TIMEOUT): if len(messages) >= 2: break time.sleep(1) all_messages = self.client.rtm_read() filtered_messages = filter(self.filter, all_messages) if filtered_messages: messages.extend(filtered_messages) self.assertEqual(2, len(messages)) if len(messages) != 2: time.sleep(self.WAIT_FOR_MESSAGES_TIMEOUT) # Test for ack self.assertIn("details available at", messages[0]['text']) # Test for response self.assertIsNotNone(messages[1].get('bot_id')) self.assertIsNotNone(messages[1].get('attachments')) self.assertGreater(len(messages[1]['attachments']), 0) self.assertIsNotNone(messages[1]['attachments'][0].get('text')) # Check the pretext self.assertEqual(messages[1]['attachments'][0]['pretext'], '<@{userid}>: action completed! '.format(userid=self.userid)) # Test attachment self.assertEqual(messages[1]['attachments'][0]['fallback'], messages[1]['attachments'][0]['text']) # Drain the event buffer self.client.rtm_read() def test_fields_parameter(self): post_message_response = self.client.api_call( "chat.postMessage", channel=self.channel, text="!kitten pic", as_user=True) messages = [] for i in range(self.WAIT_FOR_MESSAGES_TIMEOUT): if len(messages) >= 2: break time.sleep(1) all_messages = self.client.rtm_read() filtered_messages = filter(self.filter, all_messages) if filtered_messages: messages.extend(filtered_messages) self.assertEqual(2, len(messages)) if len(messages) != 2: time.sleep(self.WAIT_FOR_MESSAGES_TIMEOUT) # Test for ack self.assertIn("details available at", messages[0]['text']) # Test for response self.assertIsNotNone(messages[1].get('bot_id')) self.assertIsNotNone(messages[1].get('attachments')) self.assertGreater(len(messages[1]['attachments']), 0) self.assertIsNotNone(messages[1]['attachments'][0].get('text')) # Check the pretext self.assertEqual(messages[1]['attachments'][0]['pretext'], r'<@{userid}>: your kittens are here! '.format(userid=self.userid)) # Test fallback self.assertEqual(messages[1]['attachments'][0]['fallback'], messages[1]['attachments'][0]['text']) # Test attachment self.assertEqual(messages[1]['attachments'][0]['text'], ' Regards from the Box Kingdom.') self.assertEqual(messages[1]['attachments'][0]['fields'], [ { 'short': True, 'title': 'Kitten headcount', 'value': 'Eight.', }, { 'short': True, 'title': 'Number of boxes', 'value': 'A bunch', }, ]) self.assertEqual(messages[1]['attachments'][0]['image_url'], 'http://i.imgur.com/Gb9kAYK.jpg') self.assertEqual(messages[1]['attachments'][0]['color'], '00AA00') # Drain the event buffer self.client.rtm_read() def test_jinja_input_parameters(self): post_message_response = self.client.api_call( "chat.postMessage", channel=self.channel, text="!say Hello in #88CCEE", as_user=True) messages = [] for i in range(self.WAIT_FOR_MESSAGES_TIMEOUT): if len(messages) >= 2: break time.sleep(1) all_messages = self.client.rtm_read() filtered_messages = filter(self.filter, all_messages) if filtered_messages: messages.extend(filtered_messages) self.assertEqual(2, len(messages)) if len(messages) != 2: time.sleep(self.WAIT_FOR_MESSAGES_TIMEOUT) # Test for ack self.assertIn("details available at", messages[0]['text']) # Test for response self.assertIsNotNone(messages[1].get('bot_id')) self.assertIsNotNone(messages[1].get('attachments')) self.assertGreater(len(messages[1]['attachments']), 0) self.assertIsNotNone(messages[1]['attachments'][0].get('text')) # Check the pretext self.assertEqual(messages[1]['attachments'][0]['pretext'], r'<@{userid}>: '.format(userid=self.userid)) # Test fallback self.assertEqual(messages[1]['attachments'][0]['fallback'], messages[1]['attachments'][0]['text']) # Test attachment msg_text = messages[1]['attachments'][0]['text'] self.assertRegex(msg_text, r'Action core\.noop completed\.') self.assertRegex(msg_text, r'status\s*:\s*succeeded') self.assertRegex(msg_text, r'execution\s*:\s*[0-9a-fA-F]{24}') # The time can be an integer or a float, and might contain non-ASCII # characters like mu (Unicode 03BC), which gets converted to \u03BC. # So instead of strictly specifying those, we have a very relaxed # regex to capture the execution duration. self.assertRegex(msg_text, r'Took \d+.*s to complete\.') self.assertEqual(messages[1]['attachments'][0]['color'], '88CCEE') # Drain the event buffer self.client.rtm_read() try: from st2common.runners.base_action import Action class SlackEndToEndTestAction(Action): def run(self, *args, **kwargs): suite = unittest2.TestLoader().loadTestsFromTestCase(SlackEndToEndTestCase) return unittest2.TextTestRunner().run(suite) except ImportError: pass if __name__ == '__main__': unittest2.main()

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4.934759

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0.036712

0.053174

0.055833

0.878792

0.860837

0.848272

0.842044

0.833813

0

0.01529

0.270289

46,158

1,194

154

38.658291

0.799893

0.142099

0

0.811575

0

0.005384

0.160514

0.021669

0

0.292059

1

0.039031

false

0.001346

0.009421

0

0.060565

0.001346

0

null

0

1

0

1

0

null

0

7

ecf8c326b8cacff903228286c6469dfe3705476b

169

py

Python

app/views/__init__.py

sangjeedondrub/git-webhook

e272d4cc7c3961ef8d0e33a317fa282047e56fe4

[ "MIT" ]

2

2018-07-30T05:51:00.000Z

2019-06-19T11:15:11.000Z

app/views/__init__.py

ncuhome/git-webhook

c33bbf99502fea46c6ceed1ec45a48069c533f1a

[ "MIT" ]

null

app/views/__init__.py

ncuhome/git-webhook

c33bbf99502fea46c6ceed1ec45a48069c533f1a

[ "MIT" ]

2

2016-11-21T02:38:25.000Z

2019-06-19T11:15:24.000Z

# -*- coding: utf-8 -*- from app.views import common from app.views import webhook from app.views import server from app.views import history from app.views import api

21.125

29

0.763314

28

169

4.607143

0.428571

0.271318

0.465116

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30

24.142857

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null

0

1

0

1

0

1

0

8

a63e8f5d7066d5dbb4a2743e902064af918ee663

113

py

Python

util/util.py

calebcodesgud/Crypto

f56b29390f56bb7c9caef09a25012631ab890164

[ "MIT" ]

null

util/util.py

calebcodesgud/Crypto

f56b29390f56bb7c9caef09a25012631ab890164

[ "MIT" ]

null

util/util.py

calebcodesgud/Crypto

f56b29390f56bb7c9caef09a25012631ab890164

[ "MIT" ]

null

def ISO_date_reformat(min_date, max_date): return f'{min_date}'.split(' ')[0], f'{max_date}'.split(' ')[0]

37.666667

67

0.637168

19

113

3.473684

0.526316

0.212121

0.30303

0

0.020202

0.123894

113

3

67

37.666667

0.646465

0

0.198198

0

1

0.5

false

0

0.5

1

0

1

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null

1

0

1

0

null

0

1

0

1

0

7

a66ba3bf5edbfaa0fb8e44a0e81c68bc0992b3ec

3,676

py

Python

tests/rules/test_protocol.py

bockstaller/europarl-crawler

5d4497da068cfe5dc10d250b232b0473821b4877

[ "MIT" ]

null

tests/rules/test_protocol.py

bockstaller/europarl-crawler

5d4497da068cfe5dc10d250b232b0473821b4877

[ "MIT" ]

null

tests/rules/test_protocol.py

bockstaller/europarl-crawler

5d4497da068cfe5dc10d250b232b0473821b4877

[ "MIT" ]

null

from datetime import date import pytest from europarl.rules.protocol import ProtocolEnHtmlRule, ProtocolEnPdfRule @pytest.mark.parametrize( "date,expected", [ ( date(year=2019, month=8, day=1), "https://europarl.europa.eu/doceo/document/PV-9-2019-08-01_EN.pdf", ), ( date(year=2014, month=8, day=1), "https://europarl.europa.eu/doceo/document/PV-8-2014-08-01_EN.pdf", ), ( date(year=2009, month=8, day=1), "https://europarl.europa.eu/doceo/document/PV-7-2009-08-01_EN.pdf", ), ( date(year=2004, month=8, day=1), "https://europarl.europa.eu/doceo/document/PV-6-2004-08-01_EN.pdf", ), ( date(year=1999, month=8, day=1), "https://europarl.europa.eu/doceo/document/PV-5-1999-08-01_EN.pdf", ), ( date(year=1994, month=8, day=1), "https://europarl.europa.eu/doceo/document/PV-4-1994-08-01_EN.pdf", ), ( date(year=1989, month=8, day=1), "https://europarl.europa.eu/doceo/document/PV-3-1989-08-01_EN.pdf", ), ( date(year=1984, month=8, day=1), "https://europarl.europa.eu/doceo/document/PV-2-1984-08-01_EN.pdf", ), ( date(year=1979, month=8, day=1), "https://europarl.europa.eu/doceo/document/PV-1-1979-08-01_EN.pdf", ), ( date(year=1950, month=8, day=1), "https://europarl.europa.eu/doceo/document/PV-0-1950-08-01_EN.pdf", ), ( date(year=2025, month=8, day=1), "https://europarl.europa.eu/doceo/document/PV-0-2025-08-01_EN.pdf", ), ], ) def test_get_url_protocol_en_pdf(date, expected): assert ProtocolEnPdfRule.url(date=date) == expected @pytest.mark.parametrize( "date,expected", [ ( date(year=2019, month=8, day=1), "https://europarl.europa.eu/doceo/document/PV-9-2019-08-01_EN.html", ), ( date(year=2014, month=8, day=1), "https://europarl.europa.eu/doceo/document/PV-8-2014-08-01_EN.html", ), ( date(year=2009, month=8, day=1), "https://europarl.europa.eu/doceo/document/PV-7-2009-08-01_EN.html", ), ( date(year=2004, month=8, day=1), "https://europarl.europa.eu/doceo/document/PV-6-2004-08-01_EN.html", ), ( date(year=1999, month=8, day=1), "https://europarl.europa.eu/doceo/document/PV-5-1999-08-01_EN.html", ), ( date(year=1994, month=8, day=1), "https://europarl.europa.eu/doceo/document/PV-4-1994-08-01_EN.html", ), ( date(year=1989, month=8, day=1), "https://europarl.europa.eu/doceo/document/PV-3-1989-08-01_EN.html", ), ( date(year=1984, month=8, day=1), "https://europarl.europa.eu/doceo/document/PV-2-1984-08-01_EN.html", ), ( date(year=1979, month=8, day=1), "https://europarl.europa.eu/doceo/document/PV-1-1979-08-01_EN.html", ), ( date(year=1950, month=8, day=1), "https://europarl.europa.eu/doceo/document/PV-0-1950-08-01_EN.html", ), ( date(year=2025, month=8, day=1), "https://europarl.europa.eu/doceo/document/PV-0-2025-08-01_EN.html", ), ], ) def test_get_url_protocol_en_html(date, expected): assert ProtocolEnHtmlRule.url(date) == expected

32.821429

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0.53074

492

3,676

3.900407

0.107724

0.091714

0.103179

0.114643

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3,676

111

81

33.117117

0.613277

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0.47619

0

0.209524

0.39309

0

0.019048

1

0.019048

false

0

0.028571

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null

0

1

0

1

0

null

0

7

a6e63fbfeb3d6ad5bd2ca0b65c9e93042ef5cbb7

9,923

py

Python

src/abaqus/Load/ConcentratedForce.py

Haiiliin/PyAbaqus

f20db6ebea19b73059fe875a53be370253381078

[ "MIT" ]

7

2022-01-21T09:15:45.000Z

2022-02-15T09:31:58.000Z

src/abaqus/Load/ConcentratedForce.py

Haiiliin/PyAbaqus

f20db6ebea19b73059fe875a53be370253381078

[ "MIT" ]

null

src/abaqus/Load/ConcentratedForce.py

Haiiliin/PyAbaqus

f20db6ebea19b73059fe875a53be370253381078

[ "MIT" ]

null

import typing from abaqusConstants import * from .Load import Load from ..Region.Region import Region class ConcentratedForce(Load): """The ConcentratedForce object defines a concentrated force. The ConcentratedForce object is derived from the Load object. Attributes ---------- name: str A String specifying the load repository key. distributionType: SymbolicConstant A SymbolicConstant specifying how the load is distributed spatially. Possible values are UNIFORM and FIELD. The default value is UNIFORM. follower: Boolean A Boolean specifying whether the direction of the force rotates with the rotation at each node of the region. You should provide the **follower** argument only if it is valid for the specified step. The default value is OFF. localCsys: int None or a :py:class:`~abaqus.Datum.DatumCsys.DatumCsys` object specifying the local coordinate system of the load's degrees of freedom. If **localCsys=None**, the degrees of freedom are defined in the global coordinate system. When this member is queried, it returns an Int. The default value is None. field: str A String specifying the name of the :py:class:`~abaqus.Field.AnalyticalField.AnalyticalField` object associated with this load. The **field** argument applies only when **distributionType=FIELD**. The default value is an empty string. region: Region A :py:class:`~abaqus.Region.Region.Region` object specifying the region to which the load is applied. Notes ----- This object can be accessed by: .. code-block:: python import load mdb.models[name].loads[name] """ # A String specifying the load repository key. name: str = '' # A SymbolicConstant specifying how the load is distributed spatially. Possible values are # UNIFORM and FIELD. The default value is UNIFORM. distributionType: SymbolicConstant = UNIFORM # A Boolean specifying whether the direction of the force rotates with the rotation at # each node of the region. You should provide the *follower* argument only if it is valid # for the specified step. The default value is OFF. follower: Boolean = OFF # None or a DatumCsys object specifying the local coordinate system of the load's degrees # of freedom. If *localCsys*=None, the degrees of freedom are defined in the global # coordinate system. When this member is queried, it returns an Int. The default value is # None. localCsys: int = None # A String specifying the name of the AnalyticalField object associated with this load. # The *field* argument applies only when *distributionType*=FIELD. The default value is an # empty string. field: str = '' # A Region object specifying the region to which the load is applied. region: Region = Region() def __init__(self, name: str, createStepName: str, region: Region, distributionType: SymbolicConstant = UNIFORM, field: str = '', cf1: float = None, cf2: float = None, cf3: float = None, amplitude: str = UNSET, follower: Boolean = OFF, localCsys: int = None): """This method creates a ConcentratedForce object. Notes ----- This function can be accessed by: .. code-block:: python mdb.models[name].ConcentratedForce Parameters ---------- name A String specifying the load repository key. createStepName A String specifying the name of the step in which the load is created. region A Region object specifying the region to which the load is applied. distributionType A SymbolicConstant specifying how the load is distributed spatially. Possible values are UNIFORM and FIELD. The default value is UNIFORM. field A String specifying the name of the AnalyticalField object associated with this load. The *field* argument applies only when *distributionType*=FIELD. The default value is an empty string. cf1 A Float or a Complex specifying the concentrated force component in the 1-direction. Although *cf1*, *cf2*, and *cf3* are optional arguments, at least one of them must be nonzero. cf2 A Float or a Complex specifying the concentrated force component in the 2-direction. cf3 A Float or a Complex specifying the concentrated force component in the 3-direction. amplitude A String or the SymbolicConstant UNSET specifying the name of the amplitude reference. UNSET should be used if the load has no amplitude reference. The default value is UNSET. You should provide the *amplitude* argument only if it is valid for the specified step. follower A Boolean specifying whether the direction of the force rotates with the rotation at each node of the region. You should provide the *follower* argument only if it is valid for the specified step. The default value is OFF. localCsys None or a DatumCsys object specifying the local coordinate system of the load's degrees of freedom. If *localCsys*=None, the degrees of freedom are defined in the global coordinate system. When this member is queried, it returns an Int. The default value is None. Returns ------- A ConcentratedForce object. """ super().__init__() pass def setValues(self, distributionType: SymbolicConstant = UNIFORM, field: str = '', cf1: float = None, cf2: float = None, cf3: float = None, amplitude: str = UNSET, follower: Boolean = OFF, localCsys: int = None): """This method modifies the data for an existing ConcentratedForce object in the step where it is created. Parameters ---------- distributionType A SymbolicConstant specifying how the load is distributed spatially. Possible values are UNIFORM and FIELD. The default value is UNIFORM. field A String specifying the name of the AnalyticalField object associated with this load. The *field* argument applies only when *distributionType*=FIELD. The default value is an empty string. cf1 A Float or a Complex specifying the concentrated force component in the 1-direction. Although *cf1*, *cf2*, and *cf3* are optional arguments, at least one of them must be nonzero. cf2 A Float or a Complex specifying the concentrated force component in the 2-direction. cf3 A Float or a Complex specifying the concentrated force component in the 3-direction. amplitude A String or the SymbolicConstant UNSET specifying the name of the amplitude reference. UNSET should be used if the load has no amplitude reference. The default value is UNSET. You should provide the *amplitude* argument only if it is valid for the specified step. follower A Boolean specifying whether the direction of the force rotates with the rotation at each node of the region. You should provide the *follower* argument only if it is valid for the specified step. The default value is OFF. localCsys None or a DatumCsys object specifying the local coordinate system of the load's degrees of freedom. If *localCsys*=None, the degrees of freedom are defined in the global coordinate system. When this member is queried, it returns an Int. The default value is None. """ pass def setValuesInStep(self, stepName: str, cf1: typing.Union[SymbolicConstant, float] = None, cf2: typing.Union[SymbolicConstant, float] = None, cf3: typing.Union[SymbolicConstant, float] = None, amplitude: str = ''): """This method modifies the propagating data for an existing ConcentratedForce object in the specified step. Parameters ---------- stepName A String specifying the name of the step in which the load is modified. cf1 A Float, a Complex, or the SymbolicConstant UNCHANGED specifying the concentrated force component in the 1-direction. UNCHANGED should be used if the concentrated force component is propagated from the previous analysis step. cf2 A Float, a Complex, or the SymbolicConstant UNCHANGED specifying the concentrated force component in the 2-direction. UNCHANGED should be used if the concentrated force component is propagated from the previous analysis step. cf3 A Float, a Complex, or the SymbolicConstant UNCHANGED specifying the concentrated force component in the 3-direction. UNCHANGED should be used if the concentrated force component is propagated from the previous analysis step. amplitude A String or a SymbolicConstant specifying the name of the amplitude reference. Possible values for the SymbolicConstant are UNCHANGED and FREED. UNCHANGED should be used if the amplitude is propagated from the previous analysis step. FREED should be used if the load is changed to have no amplitude reference. You should provide the *amplitude* argument only if it is valid for the specified step. """ pass

49.368159

135

0.657059

1,245

9,923

5.230522

0.121285

0.055897

0.041462

0.04699

0.834152

0.816646

0.809429

0.777948

0.751536

0.751075

0

0.004722

0.295777

9,923

200

136

49.615

0.927161

0.762068

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0.259259

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1

0.111111

false

0.111111

0.148148

0

0.518519

0

null

0

1

0

null

0

1

0

1

0

8

a6ec5503877df686540d013092246767da80bd92

81

py

Python

1_languages/python/src/operators_comparison.py

praisetompane/3_programming

dd3e2e89a36a613d895fdbdd9c03845cb648fddf

[ "MIT" ]

null

1_languages/python/src/operators_comparison.py

praisetompane/3_programming

dd3e2e89a36a613d895fdbdd9c03845cb648fddf

[ "MIT" ]

null

1_languages/python/src/operators_comparison.py

praisetompane/3_programming

dd3e2e89a36a613d895fdbdd9c03845cb648fddf

[ "MIT" ]

null

print(3 > 2) print(3 >= 2) print(3 < 2) print(3 <= 2) print(3 == 2) print(3 != 2)

13.5

13

0.518519

18

81

2.333333

0.166667

0.857143

1

1.428571

1

0

0.1875

0.209877

81

6

14

13.5

0.46875

0

1

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true

0

1

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null

1

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1

0

1

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null

0

1

0

1

0

14

472e0fd8384cd046d555ba2b796972cb88a4e15b

5,991

py

Python

example_main.py

Rdataflow/pyFINT

f31a523ad21fc45c2b4480a1bd75263d99201848

[ "MIT" ]

null

example_main.py

Rdataflow/pyFINT

f31a523ad21fc45c2b4480a1bd75263d99201848

[ "MIT" ]

2

2021-12-21T08:20:36.000Z

2022-01-26T15:39:57.000Z

example_main.py

Rdataflow/pyFINT

f31a523ad21fc45c2b4480a1bd75263d99201848

[ "MIT" ]

1

2022-01-25T21:35:37.000Z

###################################################################### # Copyright (C) 2021 ecorisQ # Use of this source code is governed by an MIT-style license that can be found in the LICENSE # file or at https://opensource.org/licenses/MIT. # # Author: Christoph Schaller, BFH-HAFL, December 2020 # # Script for demonstrating the use of pyFINT. The script expects # input rasters of 1m resolution ###################################################################### import os from datetime import timedelta import time from pyfintcontroller import * # Default entry point if __name__ == "__main__": start_time = time.time() #Expected resolution is 1m #One Filter example #One 1.5m resize example #Path to output folder working_dir = os.path.join(os.getcwd(), "output") #Paths to input rasters #Vegetation Height Model/Normalised Surface Model nsm_file = os.path.join(os.getcwd(), "sample_data/VHM_1m.tif") # # Standard Detection with 1m input VHM without resizing of filtering # fint_controller = pyFintController() fint_controller.set_working_dir(working_dir) #Whether to allow the use of altitude in DBH calculation (requires DTM) fint_controller.m_altitude_allowed = False #NSM/VHM used for detection fint_controller.set_normalized_model_file_name(nsm_file,None) #Set the function for calculating the DBH, whether to allow altitude in calculation fint_controller.set_dbh_function("2.52*H^0.84", False) #Whether to randomize the DBH value and the degree of deviation in percent fint_controller.set_diameter_randomization(False,20) #Minimum height of a pixel to be considered for a local maxima fint_controller.set_minimum_height(1) #Minimum height for a detected maxima to be consideres as a tree fint_controller.set_minimum_detection_height(4) #Tell the controller to run the detection fint_controller.run_process() # # Detection with 1m input VHM resized to 1.5m # fint_controller = pyFintController() fint_controller.set_working_dir(working_dir) #Whether to allow the use of altitude in DBH calculation (requires DEM) fint_controller.m_altitude_allowed = False #NSM/VHM used for detection fint_controller.set_normalized_model_file_name(nsm_file,None) #Set the function for calculating the DBH, whether to allow altitude in calculation fint_controller.set_dbh_function("2.52*H^0.84", False) #Whether to randomize the DBH value and the degree of deviation in percent fint_controller.set_diameter_randomization(False,20) #Minimum height of a pixel to be considered for a local maxima fint_controller.set_minimum_height(1) #Minimum height for a detected maxima to be consideres as a tree fint_controller.set_minimum_detection_height(4) #Tell the controller to resize the input tho the specified resolution with the given method #Supported methods basing on gdal: ["near", "bilinear", "cubic", "cubicspline", "lanczos", "average", "mode", "max", "min", "med", "q1", "q3"] fint_controller.set_resize_resolution(1.5,"bilinear") #Tell the controller to run the detection fint_controller.run_process() # # Detection with 1m input VHM and with Gauss filter sigma=2 and radius=3 # fint_controller = pyFintController() fint_controller.set_working_dir(working_dir) #Whether to allow the use of altitude in DBH calculation (requires DEM) fint_controller.m_altitude_allowed = False #NSM/VHM used for detection fint_controller.set_normalized_model_file_name(nsm_file,None) #Set the function for calculating the DBH, whether to allow altitude in calculation fint_controller.set_dbh_function("2.52*H^0.84", False) #Whether to randomize the DBH value and the degree of deviation in percent fint_controller.set_diameter_randomization(False,20) #Minimum height of a pixel to be considered for a local maxima fint_controller.set_minimum_height(1) #Minimum height for a detected maxima to be consideres as a tree fint_controller.set_minimum_detection_height(4) #Tell the controller to apply a Gauss filter of the given strength and radius; radius needs to be an odd number fint_controller.set_gauss_filter(size = 3, sigma = 2) #Tell the controller to run the detection fint_controller.run_process() # # Detection with 1m input VHM with resizing to 1.5 as well as with Gauss filter sigma=2 and radius=3 # fint_controller = pyFintController() fint_controller.set_working_dir(working_dir) #Whether to allow the use of altitude in DBH calculation (requires DEM) fint_controller.m_altitude_allowed = False #NSM/VHM used for detection fint_controller.set_normalized_model_file_name(nsm_file,None) #Set the function for calculating the DBH, whether to allow altitude in calculation fint_controller.set_dbh_function("2.52*H^0.84", False) #Whether to randomize the DBH value and the degree of deviation in percent fint_controller.set_diameter_randomization(False,20) #Minimum height of a pixel to be considered for a local maxima fint_controller.set_minimum_height(1) #Minimum height for a detected maxima to be consideres as a tree fint_controller.set_minimum_detection_height(4) #Tell the controller to resize the input tho the specified resolution with the given method #Supported methods basing on gdal: ["near", "bilinear", "cubic", "cubicspline", "lanczos", "average", "mode", "max", "min", "med", "q1", "q3"] fint_controller.set_resize_resolution(1.5,"bilinear") #Tell the controller to apply a Gauss filter of the given strength and radius; radius needs to be an odd number fint_controller.set_gauss_filter(size = 3, sigma = 2) #Tell the controller to run the detection fint_controller.run_process() print("TOTAL PROCESSING TIME: %s (h:min:sec)" % str(timedelta(seconds=(time.time() - start_time))))

47.547619

146

0.734101

869

5,991

4.897583

0.205984

0.131579

0.111842

0.045113

0.838111

0.824248

0

0.015593

0.175764

5,991

125

147

47.928

0.846294

0.519947

0

0.816327

0

0.049738

0.008227

0

1

0

false

0

0.081633

0

0.081633

0.020408

0

null

0

1

0

1

0

null

0

7

472fa965788c99514d37ebbd2567d0fa88f9cd88

133

py

Python

quest/plugins/base/__init__.py

sdc50/quest

95fee57e6fb177c4d32c5e6cffbde61333f81b7d

[ "BSD-3-Clause" ]

12

2018-03-26T19:59:54.000Z

2022-02-02T01:21:09.000Z

quest/plugins/base/__init__.py

sdc50/quest

95fee57e6fb177c4d32c5e6cffbde61333f81b7d

[ "BSD-3-Clause" ]

110

2018-02-08T19:56:15.000Z

2019-05-30T20:55:09.000Z

quest/plugins/base/__init__.py

sdc50/quest

95fee57e6fb177c4d32c5e6cffbde61333f81b7d

[ "BSD-3-Clause" ]

10

2018-02-08T20:31:43.000Z

2020-08-05T18:45:01.000Z

from .tool_base import * from .io_base import * from .provider_base import * from .publish_base import * from .service_base import *

22.166667

28

0.774436

20

133

4.9

0.4

0.510204

0.571429

0

0.150376

133

5

29

26.6

0.867257

0

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0

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0

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null

0

1

0

1

0

1

0

7

5b50f4a9d7e41fb807c3398da8f229fdcec68391

30,185

py

Python

VNN/experiment/model.py

arthuraleksandrovich/vector_ann

35158b5f9741646c362ef2c069be3503186975c9

[ "MIT" ]

null

VNN/experiment/model.py

arthuraleksandrovich/vector_ann

35158b5f9741646c362ef2c069be3503186975c9

[ "MIT" ]

null

VNN/experiment/model.py

arthuraleksandrovich/vector_ann

35158b5f9741646c362ef2c069be3503186975c9

[ "MIT" ]

null

"""Models for experimenting""" import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers from network import vlayers from network import vlayers_conv def get_scalar_model(dataset_shapes, hidden_layer_units=(2,), activation='relu', output_activation=None, \ kernel_initializer='random_normal', bias_initializer='random_normal', \ optimizer=keras.optimizers.RMSprop(), loss=keras.losses.MeanSquaredError(), metrics=[keras.metrics.MeanSquaredError()]): """Scalar network, standard tensorflow implementation""" if output_activation is None: output_activation = activation input_dims = dataset_shapes[0] output_dims = dataset_shapes[1] # Create model inputs = keras.Input(shape=input_dims) x = inputs for h in hidden_layer_units: x = layers.Dense(h, activation=activation, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer)(x) outputs = layers.Dense(output_dims[-1], activation=output_activation, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer)(x) model = keras.Model(inputs=inputs, outputs=outputs) model.compile( optimizer=optimizer, loss=loss, metrics=metrics ) return model def get_vector_model(dataset_shapes, fractal_depth=1, hidden_layer_units=(2,), inner_hidden_layer_units=(2,), \ activation='relu', output_activation=None, \ weight_type="unique", weight_initializer='random_normal', \ optimizer=keras.optimizers.RMSprop(), loss=keras.losses.MeanSquaredError(), metrics=[keras.metrics.MeanSquaredError()]): """Vector network""" if output_activation is None: output_activation = activation input_dims = dataset_shapes[0] output_dims = dataset_shapes[1] # Create model inputs = keras.Input(shape=input_dims) x = vlayers.VInput(hidden_layer_units[0] if len(hidden_layer_units) > 0 else output_dims[-1], weight_initializer='random_normal')(inputs) if len(hidden_layer_units) > 0: for h in hidden_layer_units[1:] + (output_dims[-1],): if fractal_depth < 1: x = vlayers.VDense(h, activation=activation, weight_initializer=weight_initializer)(x) else: x = vlayers.VFractal(h, depth=fractal_depth, hidden_layer_units=inner_hidden_layer_units, activation=activation, weight_initializer=weight_initializer, weight_type=weight_type)(x) outputs = vlayers.VOutput(activation=output_activation)(x) model = keras.Model(inputs=inputs, outputs=outputs) model.compile( optimizer=optimizer, loss=loss, metrics=metrics ) return model def get_scalar_conv_model1(dataset_shapes, optimizer=keras.optimizers.SGD()): input_dims = dataset_shapes[0] output_dims = dataset_shapes[1] # Create model first_filter = 7 inputs = keras.Input(shape=input_dims) x = layers.Conv2D(output_dims[0], first_filter, activation='relu', strides=(1,1), padding='valid', kernel_initializer='random_normal', bias_initializer='random_normal' )(inputs) x = layers.ReLU()(x) x = layers.MaxPool2D(pool_size=(input_dims[0]-first_filter+1, input_dims[1]-first_filter+1), strides=(1,1), padding='valid')(x) x = layers.Activation('sigmoid')(x) outputs = layers.Flatten()(x) model = keras.Model(inputs=inputs, outputs=outputs) model.compile( optimizer=optimizer, loss=keras.losses.MeanSquaredError(), metrics=[keras.metrics.MeanSquaredError()] ) return model def get_vector_conv_model1(dataset_shapes, shared_inner_nets, optimizer=keras.optimizers.SGD()): input_dims = dataset_shapes[0] output_dims = dataset_shapes[1] # Create model first_filter = 7 inputs = keras.Input(shape=input_dims) x = vlayers_conv.VInputConv((first_filter,first_filter), num_filters=output_dims[0], kernel_type="convolution", strides=(1,1), padding_type='valid' )(inputs) x = vlayers_conv.VConvFractal((input_dims[0]-first_filter+1, input_dims[1]-first_filter+1), kernel_type="pooling", strides=(1,1), padding_type='valid', layer_type="convolution", activation="relu", depth=1, shared_inner_nets=shared_inner_nets, hidden_layer_units=(2,) )(x) x = vlayers_conv.VOutputConv(layer_type="pooling", pooling="max")(x) outputs = layers.Flatten()(x) model = keras.Model(inputs=inputs, outputs=outputs) model.compile( optimizer=optimizer, loss=keras.losses.MeanSquaredError(), metrics=[keras.metrics.MeanSquaredError()] ) return model def get_scalar_conv_model2(dataset_shapes, optimizer=keras.optimizers.SGD()): input_dims = dataset_shapes[0] output_dims = dataset_shapes[1] # Create model first_filter = 3 second_filter = 5 third_filter = input_dims[0] - first_filter - second_filter + 2 first_filter_num = 5 inputs = keras.Input(shape=input_dims) x = layers.Conv2D(first_filter_num, first_filter, activation='relu', strides=(1,1), padding='valid', kernel_initializer='random_normal', bias_initializer='random_normal' )(inputs) x = layers.ReLU()(x) x = layers.Conv2D(output_dims[0], second_filter, activation='relu', strides=(1,1), padding='valid', kernel_initializer='random_normal', bias_initializer='random_normal' )(x) x = layers.ReLU()(x) x = layers.MaxPool2D(pool_size=(third_filter,third_filter), strides=(1,1), padding='valid')(x) x = layers.Activation('sigmoid')(x) outputs = layers.Flatten()(x) model = keras.Model(inputs=inputs, outputs=outputs) model.compile( optimizer=optimizer, loss=keras.losses.MeanSquaredError(), metrics=[keras.metrics.MeanSquaredError()] ) return model def get_vector_conv_model2(dataset_shapes, shared_inner_nets, optimizer=keras.optimizers.SGD()): input_dims = dataset_shapes[0] output_dims = dataset_shapes[1] # Create model first_filter = 3 second_filter = 5 third_filter = input_dims[0] - first_filter - second_filter + 2 first_filter_num = 5 inputs = keras.Input(shape=input_dims) x = vlayers_conv.VInputConv((first_filter,first_filter), num_filters=first_filter_num, kernel_type="convolution", strides=(1,1), padding_type='valid' )(inputs) x = vlayers_conv.VConvFractal((second_filter,second_filter), kernel_type="convolution", num_filters=output_dims[0], strides=(1,1), padding_type='valid', layer_type="convolution", activation="relu", depth=1, shared_inner_nets=shared_inner_nets, hidden_layer_units=(2,) )(x) x = vlayers_conv.VConv((third_filter,third_filter), kernel_type="pooling", strides=(1,1), padding_type='valid', layer_type="convolution", activation="relu" )(x) x = vlayers_conv.VOutputConv(layer_type="pooling", pooling="max")(x) outputs = layers.Flatten()(x) model = keras.Model(inputs=inputs, outputs=outputs) model.compile( optimizer=optimizer, loss=keras.losses.MeanSquaredError(), metrics=[keras.metrics.MeanSquaredError()] ) return model def get_scalar_conv_model3(dataset_shapes, optimizer=keras.optimizers.SGD()): input_dims = dataset_shapes[0] output_dims = dataset_shapes[1] # Settings first_filter_num = 5 first_filter_dim = 3 second_filter_num = output_dims[0] second_filter_dim = ((input_dims[0] // 4) - (first_filter_dim - 1)) // 2 inputs = keras.Input(shape=input_dims) # Decrease input twice x = layers.AveragePooling2D(pool_size=(4,4), strides=(4,4), padding='valid')(inputs) # First convolutional layer x = layers.Conv2D(first_filter_num, first_filter_dim, activation='relu', strides=(1,1), padding='valid', kernel_initializer='random_normal', bias_initializer='random_normal' )(x) x = layers.ReLU()(x) # First pooling layer x = layers.MaxPool2D(pool_size=(2,2), strides=(2,2), padding='valid')(x) # Second convolutional layer x = layers.Conv2D(second_filter_num, second_filter_dim, activation='relu', strides=(1,1), padding='valid', kernel_initializer='random_normal', bias_initializer='random_normal' )(x) x = layers.Activation('softmax')(x) outputs = layers.Flatten()(x) model = keras.Model(inputs=inputs, outputs=outputs) model.compile( optimizer=optimizer, loss=keras.losses.MeanSquaredError(), metrics=[keras.metrics.MeanSquaredError()] ) return model def get_vector_conv_model3(dataset_shapes, shared_inner_nets, optimizer=keras.optimizers.SGD()): input_dims = dataset_shapes[0] output_dims = dataset_shapes[1] # Settings first_filter_num = 5 first_filter_dim = 3 second_filter_num = output_dims[0] second_filter_dim = ((input_dims[0] // 4) - (first_filter_dim - 1)) // 2 inputs = keras.Input(shape=input_dims) # Decrease input x = layers.AveragePooling2D(pool_size=(4,4), strides=(4,4), padding='valid')(inputs) # First convolutional layer x = vlayers_conv.VInputConv((first_filter_dim,first_filter_dim), num_filters=first_filter_num, kernel_type="convolution", strides=(1,1), padding_type='valid' )(x) x = vlayers_conv.VConvFractal((2,2), kernel_type="pooling", strides=(2,2), padding_type='valid', layer_type="convolution", weight_initializer="random_normal", activation="relu", depth=1, shared_inner_nets=shared_inner_nets, hidden_layer_units=(2,) )(x) # First pooling layer x = vlayers_conv.VOutputConv(layer_type="pooling", pooling="max")(x) # Second convolutional layer x = layers.Conv2D(second_filter_num, second_filter_dim, activation='relu', strides=(1,1), padding='valid', kernel_initializer='random_normal', bias_initializer='random_normal' )(x) x = layers.Activation('softmax')(x) outputs = layers.Flatten()(x) model = keras.Model(inputs=inputs, outputs=outputs) model.compile( optimizer=optimizer, loss=keras.losses.MeanSquaredError(), metrics=[keras.metrics.MeanSquaredError()] ) return model def get_scalar_conv_model4(dataset_shapes, optimizer=keras.optimizers.SGD()): input_dims = dataset_shapes[0] output_dims = dataset_shapes[1] # Settings first_filter_num = 5 first_filter_dim = 3 second_filter_num = output_dims[0] second_filter_dim = 3 third_filter_dim = (input_dims[0] // 4) - (first_filter_dim - 1) - (second_filter_dim - 1) inputs = keras.Input(shape=input_dims) # Decrease input twice x = layers.AveragePooling2D(pool_size=(4,4), strides=(4,4), padding='valid')(inputs) # First convolutional layer x = layers.Conv2D(first_filter_num, first_filter_dim, activation='relu', strides=(1,1), padding='valid', kernel_initializer='random_normal', bias_initializer='random_normal' )(x) x = layers.ReLU()(x) # Second convolutional layer x = layers.Conv2D(second_filter_num, second_filter_dim, activation='relu', strides=(1,1), padding='valid', kernel_initializer='random_normal', bias_initializer='random_normal' )(x) x = layers.ReLU()(x) # First pooling layer x = layers.MaxPool2D(pool_size=(third_filter_dim,third_filter_dim), strides=(1,1), padding='valid')(x) x = layers.Activation('softmax')(x) outputs = layers.Flatten()(x) model = keras.Model(inputs=inputs, outputs=outputs) model.compile( optimizer=optimizer, loss=keras.losses.MeanSquaredError(), metrics=[keras.metrics.MeanSquaredError()] ) return model def get_vector_conv_model4(dataset_shapes, shared_inner_nets, optimizer=keras.optimizers.SGD()): input_dims = dataset_shapes[0] output_dims = dataset_shapes[1] # Settings first_filter_num = 5 first_filter_dim = 3 second_filter_num = output_dims[0] second_filter_dim = 3 third_filter_dim = (input_dims[0] // 4) - (first_filter_dim - 1) - (second_filter_dim - 1) inputs = keras.Input(shape=input_dims) # Decrease input x = layers.AveragePooling2D(pool_size=(4,4), strides=(4,4), padding='valid')(inputs) # First convolutional layer x = vlayers_conv.VInputConv((first_filter_dim,first_filter_dim), num_filters=first_filter_num, kernel_type="convolution", strides=(1,1), padding_type='valid' )(x) x = vlayers_conv.VConvFractal((second_filter_dim,second_filter_dim), num_filters=second_filter_num, kernel_type="convolution", strides=(1,1), padding_type='valid', layer_type="convolution", weight_initializer="random_normal", activation="relu", depth=1, shared_inner_nets=shared_inner_nets, hidden_layer_units=(2,) )(x) # Second convolutional layer x = vlayers_conv.VOutputConv(layer_type="convolution", activation="relu")(x) # First pooling layer x = layers.MaxPool2D(pool_size=(third_filter_dim,third_filter_dim), strides=(1,1), padding='valid')(x) x = layers.Activation('softmax')(x) outputs = layers.Flatten()(x) x = layers.Activation('softmax')(x) outputs = layers.Flatten()(x) model = keras.Model(inputs=inputs, outputs=outputs) model.compile( optimizer=optimizer, loss=keras.losses.MeanSquaredError(), metrics=[keras.metrics.MeanSquaredError()] ) return model def get_le_net_5(dataset_shapes, optimizer=keras.optimizers.SGD()): """Modified and simplified LeNet-5 ReLU layers after each convolution layer are added Subsampling layers are replaced with MaxPool C3 is simple conv layer In fully connected layers sigmoid is replaced with ReLU, Gaussian connection is replaced with softmax Unmodified model's source: Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), pp. 2278–2324, 1998.""" input_dims = dataset_shapes[0] # Initially 32x32x1 output_dims = dataset_shapes[1] # Initially 10 inputs = keras.Input(shape=input_dims) x = inputs # Pad input if input_dims[0] < 32: padding = (32 - input_dims[0]) // 2 x = layers.ZeroPadding2D(padding=padding)(x) # C1 x = layers.Conv2D(6, 5, activation='relu', strides=(1,1), padding='valid', kernel_initializer='random_normal', bias_initializer='random_normal' )(x) x = layers.ReLU()(x) # S2 x = layers.MaxPool2D(pool_size=(2,2), strides=(2,2), padding='valid')(x) # C3 x = layers.Conv2D(16, 5, activation='relu', strides=(1,1), padding='valid', kernel_initializer='random_normal', bias_initializer='random_normal' )(x) # S4 x = layers.MaxPool2D(pool_size=(2,2), strides=(2,2), padding='valid')(x) # C5 x = layers.Conv2D(120, 5, activation='relu', strides=(1,1), padding='valid', kernel_initializer='random_normal', bias_initializer='random_normal' )(x) x = layers.Flatten()(x) # F6 x = layers.Dense(84, activation='relu', kernel_initializer='random_normal', bias_initializer='random_normal')(x) # Output outputs = layers.Dense(output_dims[0], activation='softmax', kernel_initializer='random_normal', bias_initializer='random_normal')(x) model = keras.Model(inputs=inputs, outputs=outputs) model.compile( optimizer=optimizer, loss=keras.losses.MeanSquaredError(), metrics=[keras.metrics.MeanSquaredError()] ) return model def get_le_net_5_without_fully_connected(dataset_shapes, optimizer=keras.optimizers.SGD()): input_dims = dataset_shapes[0] # Initially 32x32x1 output_dims = dataset_shapes[1] # Initially 10 inputs = keras.Input(shape=input_dims) x = inputs # Pad input if input_dims[0] < 32: padding = (32 - input_dims[0]) // 2 x = layers.ZeroPadding2D(padding=padding)(x) # C1 x = layers.Conv2D(6, 5, activation='relu', strides=(1,1), padding='valid', kernel_initializer='random_normal', bias_initializer='random_normal' )(x) x = layers.ReLU()(x) # S2 x = layers.MaxPool2D(pool_size=(2,2), strides=(2,2), padding='valid')(x) # C3 x = layers.Conv2D(16, 5, activation='relu', strides=(1,1), padding='valid', kernel_initializer='random_normal', bias_initializer='random_normal' )(x) # S4 x = layers.MaxPool2D(pool_size=(2,2), strides=(2,2), padding='valid')(x) # C5 x = layers.Conv2D(output_dims[0], 5, activation='softmax', strides=(1,1), padding='valid', kernel_initializer='random_normal', bias_initializer='random_normal' )(x) outputs = layers.Flatten()(x) model = keras.Model(inputs=inputs, outputs=outputs) model.compile( optimizer=optimizer, loss=keras.losses.MeanSquaredError(), metrics=[keras.metrics.MeanSquaredError()] ) return model def get_le_net_5_fractal1(dataset_shapes, shared_inner_nets, optimizer=keras.optimizers.SGD()): input_dims = dataset_shapes[0] # Initially 32x32x1 output_dims = dataset_shapes[1] # Initially 10 inputs = keras.Input(shape=input_dims) x = inputs # Pad input if input_dims[0] < 32: padding = (32 - input_dims[0]) // 2 x = layers.ZeroPadding2D(padding=padding)(x) # C1 # x = layers.Conv2D(6, 5, # activation='relu', # strides=(1,1), # padding='valid', # kernel_initializer='random_normal', # bias_initializer='random_normal' # )(x) # x = layers.ReLU()(x) x = vlayers_conv.VInputConv((5,5), num_filters=6, kernel_type="convolution", strides=(1,1), padding_type='valid' )(x) x = vlayers_conv.VConvFractal((2,2), kernel_type="pooling", strides=(2,2), padding_type='valid', layer_type="convolution", weight_initializer="random_normal", activation="relu", depth=1, shared_inner_nets=shared_inner_nets, hidden_layer_units=(2,) )(x) # S2 # x = layers.MaxPool2D(pool_size=(2,2), strides=(2,2), padding='valid')(x) x = vlayers_conv.VOutputConv(layer_type="pooling", pooling="max")(x) # C3 x = layers.Conv2D(16, 5, activation='relu', strides=(1,1), padding='valid', kernel_initializer='random_normal', bias_initializer='random_normal' )(x) # S4 x = layers.MaxPool2D(pool_size=(2,2), strides=(2,2), padding='valid')(x) # C5 x = layers.Conv2D(output_dims[0], 5, activation='softmax', strides=(1,1), padding='valid', kernel_initializer='random_normal', bias_initializer='random_normal' )(x) outputs = layers.Flatten()(x) model = keras.Model(inputs=inputs, outputs=outputs) model.compile( optimizer=optimizer, loss=keras.losses.MeanSquaredError(), metrics=[keras.metrics.MeanSquaredError()] ) return model def get_le_net_5_fractal2(dataset_shapes, shared_inner_nets, optimizer=keras.optimizers.SGD()): input_dims = dataset_shapes[0] # Initially 32x32x1 output_dims = dataset_shapes[1] # Initially 10 inputs = keras.Input(shape=input_dims) x = inputs # Pad input if input_dims[0] < 32: padding = (32 - input_dims[0]) // 2 x = layers.ZeroPadding2D(padding=padding)(x) # C1 x = layers.Conv2D(6, 5, activation='relu', strides=(1,1), padding='valid', kernel_initializer='random_normal', bias_initializer='random_normal' )(x) x = layers.ReLU()(x) # S2 x = layers.MaxPool2D(pool_size=(2,2), strides=(2,2), padding='valid')(x) # C3 # x = layers.Conv2D(16, 5, # activation='relu', # strides=(1,1), # padding='valid', # kernel_initializer='random_normal', # bias_initializer='random_normal' # )(x) x = vlayers_conv.VInputConv((5,5), num_filters=16, kernel_type="convolution", strides=(1,1), padding_type='valid' )(x) x = vlayers_conv.VConvFractal((2,2), kernel_type="pooling", strides=(2,2), padding_type='valid', layer_type="convolution", weight_initializer="random_normal", activation="relu", depth=1, shared_inner_nets=shared_inner_nets, hidden_layer_units=(2,) )(x) # S4 # x = layers.MaxPool2D(pool_size=(2,2), strides=(2,2), padding='valid')(x) x = vlayers_conv.VOutputConv(layer_type="pooling", pooling="max")(x) # C5 x = layers.Conv2D(output_dims[0], 5, activation='softmax', strides=(1,1), padding='valid', kernel_initializer='random_normal', bias_initializer='random_normal' )(x) outputs = layers.Flatten()(x) model = keras.Model(inputs=inputs, outputs=outputs) model.compile( optimizer=optimizer, loss=keras.losses.MeanSquaredError(), metrics=[keras.metrics.MeanSquaredError()] ) return model def get_le_net_5_fractal3(dataset_shapes, shared_inner_nets, optimizer=keras.optimizers.SGD()): input_dims = dataset_shapes[0] # Initially 32x32x1 output_dims = dataset_shapes[1] # Initially 10 inputs = keras.Input(shape=input_dims) x = inputs # Pad input if input_dims[0] < 32: padding = (32 - input_dims[0]) // 2 x = layers.ZeroPadding2D(padding=padding)(x) x = vlayers_conv.VInputConv((5,5), num_filters=6, kernel_type="convolution", strides=(1,1), padding_type='valid' )(x) x = vlayers_conv.VConvFractal((2,2), kernel_type="pooling", strides=(2,2), padding_type='valid', layer_type="convolution", weight_initializer="random_normal", activation="relu", depth=1, shared_inner_nets=shared_inner_nets, hidden_layer_units=(2,) )(x) # S2 x = vlayers_conv.VOutputConv(layer_type="pooling", pooling="max")(x) # C5 x = layers.Conv2D(output_dims[0], 14, activation='softmax', strides=(1,1), padding='valid', kernel_initializer='random_normal', bias_initializer='random_normal' )(x) outputs = layers.Flatten()(x) model = keras.Model(inputs=inputs, outputs=outputs) model.compile( optimizer=optimizer, loss=keras.losses.MeanSquaredError(), metrics=[keras.metrics.MeanSquaredError()] ) return model def get_le_net_5_fractal4(dataset_shapes, shared_inner_nets, optimizer=keras.optimizers.SGD()): input_dims = dataset_shapes[0] # Initially 32x32x1 output_dims = dataset_shapes[1] # Initially 10 inputs = keras.Input(shape=input_dims) x = inputs # Pad input if input_dims[0] < 32: padding = (32 - input_dims[0]) // 2 x = layers.ZeroPadding2D(padding=padding)(x) # C1 x = vlayers_conv.VInputConv((5,5), num_filters=1, kernel_type="convolution", strides=(1,1), padding_type='valid' )(x) x = vlayers_conv.VConvFractal((2,2), kernel_type="pooling", strides=(2,2), padding_type='valid', layer_type="convolution", weight_initializer="random_normal", activation="relu", depth=1, shared_inner_nets=shared_inner_nets, hidden_layer_units=(2,) )(x) # S2 x = vlayers_conv.VOutputConv(layer_type="pooling", pooling="max")(x) # C3 x = layers.Conv2D(16, 5, activation='relu', strides=(1,1), padding='valid', kernel_initializer='random_normal', bias_initializer='random_normal' )(x) # S4 x = layers.MaxPool2D(pool_size=(2,2), strides=(2,2), padding='valid')(x) # C5 x = layers.Conv2D(output_dims[0], 5, activation='softmax', strides=(1,1), padding='valid', kernel_initializer='random_normal', bias_initializer='random_normal' )(x) outputs = layers.Flatten()(x) model = keras.Model(inputs=inputs, outputs=outputs) model.compile( optimizer=optimizer, loss=keras.losses.MeanSquaredError(), metrics=[keras.metrics.MeanSquaredError()] ) return model def get_le_net_5_fractal5(dataset_shapes, shared_inner_nets, hidden_layer_units=(2,), activation="relu", optimizer=keras.optimizers.SGD()): input_dims = dataset_shapes[0] # Initially 32x32x1 output_dims = dataset_shapes[1] # Initially 10 inputs = keras.Input(shape=input_dims) x = inputs # Pad input if input_dims[0] < 32: padding = (32 - input_dims[0]) // 2 x = layers.ZeroPadding2D(padding=padding)(x) # C1 x = vlayers_conv.VInputConv((5,5), num_filters=6, kernel_type="convolution", strides=(1,1), padding_type='valid' )(x) x = vlayers_conv.VConvFractal((2,2), kernel_type="pooling", strides=(2,2), padding_type='valid', layer_type="convolution", weight_initializer="random_normal", activation=activation, depth=1, shared_inner_nets=shared_inner_nets, hidden_layer_units=hidden_layer_units )(x) # S2 x = vlayers_conv.VConv((5,5), layer_type="pooling", pooling="max", num_filters=16, kernel_type="convolution", strides=(1,1), padding_type='valid', weight_initializer="random_normal" )(x) # C3 x = vlayers_conv.VConvFractal((2,2), kernel_type="pooling", strides=(2,2), padding_type='valid', layer_type="convolution", weight_initializer="random_normal", activation=activation, depth=1, shared_inner_nets=shared_inner_nets, hidden_layer_units=hidden_layer_units )(x) # S4 # x = vlayers_conv.VOutputConv(layer_type="pooling", pooling="max")(x) x = vlayers_conv.VConv((5,5), layer_type="pooling", pooling="max", num_filters=output_dims[0], kernel_type="convolution", strides=(1,1), padding_type='valid', weight_initializer="random_normal" )(x) # C5 x = vlayers_conv.VOutputConv(layer_type="convolution", activation='softmax')(x) outputs = layers.Flatten()(x) model = keras.Model(inputs=inputs, outputs=outputs) model.compile( optimizer=optimizer, loss=keras.losses.MeanSquaredError(), metrics=[keras.metrics.MeanSquaredError()] ) return model def get_le_net_5_fractal6(dataset_shapes, shared_inner_nets, hidden_layer_units=(2,), activation="relu", optimizer=keras.optimizers.SGD()): input_dims = dataset_shapes[0] # Initially 32x32x1 output_dims = dataset_shapes[1] # Initially 10 inputs = keras.Input(shape=input_dims) x = inputs # Pad input if input_dims[0] < 32: padding = (32 - input_dims[0]) // 2 x = layers.ZeroPadding2D(padding=padding)(x) # C1 x = vlayers_conv.VInputConv((5,5), num_filters=6, kernel_type="convolution", strides=(1,1), padding_type='valid' )(x) x = vlayers_conv.VConvFractal((2,2), kernel_type="convolution", num_filters = 6, strides=(2,2), padding_type='valid', layer_type="convolution", weight_initializer="random_normal", activation=activation, depth=1, shared_inner_nets=shared_inner_nets, hidden_layer_units=hidden_layer_units )(x) # S2 x = vlayers_conv.VConv((5,5), layer_type="convolution", activation=activation, num_filters=16, kernel_type="convolution", strides=(1,1), padding_type='valid', weight_initializer="random_normal" )(x) # C3 x = vlayers_conv.VConvFractal((2,2), kernel_type="convolution", num_filters = 16, strides=(2,2), padding_type='valid', layer_type="convolution", weight_initializer="random_normal", activation=activation, depth=1, shared_inner_nets=shared_inner_nets, hidden_layer_units=hidden_layer_units )(x) # S4 x = vlayers_conv.VConv((5,5), layer_type="convolution", activation=activation, num_filters=output_dims[0], kernel_type="convolution", strides=(1,1), padding_type='valid', weight_initializer="random_normal" )(x) # C5 x = vlayers_conv.VOutputConv(layer_type="convolution", activation='softmax')(x) outputs = layers.Flatten()(x) model = keras.Model(inputs=inputs, outputs=outputs) model.compile( optimizer=optimizer, loss=keras.losses.MeanSquaredError(), metrics=[keras.metrics.MeanSquaredError()] ) return model

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5b5c2dd193bd1480b5f5b6ce263a09884f843d48

19,417

py

Python

sdk/python/pulumi_aws/devicefarm/test_grid_project.py

chivandikwa/pulumi-aws

19c08bf9dcb90544450ffa4eec7bf6751058fde2

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

null

sdk/python/pulumi_aws/devicefarm/test_grid_project.py

chivandikwa/pulumi-aws

19c08bf9dcb90544450ffa4eec7bf6751058fde2

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

null

sdk/python/pulumi_aws/devicefarm/test_grid_project.py

chivandikwa/pulumi-aws

19c08bf9dcb90544450ffa4eec7bf6751058fde2

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

null

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from .. import _utilities from . import outputs from ._inputs import * __all__ = ['TestGridProjectArgs', 'TestGridProject'] @pulumi.input_type class TestGridProjectArgs: def __init__(__self__, *, description: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None, tags_all: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None, vpc_config: Optional[pulumi.Input['TestGridProjectVpcConfigArgs']] = None): """ The set of arguments for constructing a TestGridProject resource. :param pulumi.Input[str] description: Human-readable description of the project. :param pulumi.Input[str] name: The name of the Selenium testing project. :param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: A map of tags to assign to the resource. If configured with a provider [`default_tags` configuration block](https://www.terraform.io/docs/providers/aws/index.html#default_tags-configuration-block) present, tags with matching keys will overwrite those defined at the provider-level. :param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags_all: A map of tags assigned to the resource, including those inherited from the provider [`default_tags` configuration block](https://www.terraform.io/docs/providers/aws/index.html#default_tags-configuration-block). :param pulumi.Input['TestGridProjectVpcConfigArgs'] vpc_config: The VPC security groups and subnets that are attached to a project. See VPC Config below. """ if description is not None: pulumi.set(__self__, "description", description) if name is not None: pulumi.set(__self__, "name", name) if tags is not None: pulumi.set(__self__, "tags", tags) if tags_all is not None: pulumi.set(__self__, "tags_all", tags_all) if vpc_config is not None: pulumi.set(__self__, "vpc_config", vpc_config) @property @pulumi.getter def description(self) -> Optional[pulumi.Input[str]]: """ Human-readable description of the project. """ return pulumi.get(self, "description") @description.setter def description(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "description", value) @property @pulumi.getter def name(self) -> Optional[pulumi.Input[str]]: """ The name of the Selenium testing project. """ return pulumi.get(self, "name") @name.setter def name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "name", value) @property @pulumi.getter def tags(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]: """ A map of tags to assign to the resource. If configured with a provider [`default_tags` configuration block](https://www.terraform.io/docs/providers/aws/index.html#default_tags-configuration-block) present, tags with matching keys will overwrite those defined at the provider-level. """ return pulumi.get(self, "tags") @tags.setter def tags(self, value: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]): pulumi.set(self, "tags", value) @property @pulumi.getter(name="tagsAll") def tags_all(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]: """ A map of tags assigned to the resource, including those inherited from the provider [`default_tags` configuration block](https://www.terraform.io/docs/providers/aws/index.html#default_tags-configuration-block). """ return pulumi.get(self, "tags_all") @tags_all.setter def tags_all(self, value: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]): pulumi.set(self, "tags_all", value) @property @pulumi.getter(name="vpcConfig") def vpc_config(self) -> Optional[pulumi.Input['TestGridProjectVpcConfigArgs']]: """ The VPC security groups and subnets that are attached to a project. See VPC Config below. """ return pulumi.get(self, "vpc_config") @vpc_config.setter def vpc_config(self, value: Optional[pulumi.Input['TestGridProjectVpcConfigArgs']]): pulumi.set(self, "vpc_config", value) @pulumi.input_type class _TestGridProjectState: def __init__(__self__, *, arn: Optional[pulumi.Input[str]] = None, description: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None, tags_all: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None, vpc_config: Optional[pulumi.Input['TestGridProjectVpcConfigArgs']] = None): """ Input properties used for looking up and filtering TestGridProject resources. :param pulumi.Input[str] arn: The Amazon Resource Name of this Test Grid Project. :param pulumi.Input[str] description: Human-readable description of the project. :param pulumi.Input[str] name: The name of the Selenium testing project. :param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: A map of tags to assign to the resource. If configured with a provider [`default_tags` configuration block](https://www.terraform.io/docs/providers/aws/index.html#default_tags-configuration-block) present, tags with matching keys will overwrite those defined at the provider-level. :param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags_all: A map of tags assigned to the resource, including those inherited from the provider [`default_tags` configuration block](https://www.terraform.io/docs/providers/aws/index.html#default_tags-configuration-block). :param pulumi.Input['TestGridProjectVpcConfigArgs'] vpc_config: The VPC security groups and subnets that are attached to a project. See VPC Config below. """ if arn is not None: pulumi.set(__self__, "arn", arn) if description is not None: pulumi.set(__self__, "description", description) if name is not None: pulumi.set(__self__, "name", name) if tags is not None: pulumi.set(__self__, "tags", tags) if tags_all is not None: pulumi.set(__self__, "tags_all", tags_all) if vpc_config is not None: pulumi.set(__self__, "vpc_config", vpc_config) @property @pulumi.getter def arn(self) -> Optional[pulumi.Input[str]]: """ The Amazon Resource Name of this Test Grid Project. """ return pulumi.get(self, "arn") @arn.setter def arn(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "arn", value) @property @pulumi.getter def description(self) -> Optional[pulumi.Input[str]]: """ Human-readable description of the project. """ return pulumi.get(self, "description") @description.setter def description(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "description", value) @property @pulumi.getter def name(self) -> Optional[pulumi.Input[str]]: """ The name of the Selenium testing project. """ return pulumi.get(self, "name") @name.setter def name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "name", value) @property @pulumi.getter def tags(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]: """ A map of tags to assign to the resource. If configured with a provider [`default_tags` configuration block](https://www.terraform.io/docs/providers/aws/index.html#default_tags-configuration-block) present, tags with matching keys will overwrite those defined at the provider-level. """ return pulumi.get(self, "tags") @tags.setter def tags(self, value: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]): pulumi.set(self, "tags", value) @property @pulumi.getter(name="tagsAll") def tags_all(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]: """ A map of tags assigned to the resource, including those inherited from the provider [`default_tags` configuration block](https://www.terraform.io/docs/providers/aws/index.html#default_tags-configuration-block). """ return pulumi.get(self, "tags_all") @tags_all.setter def tags_all(self, value: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]): pulumi.set(self, "tags_all", value) @property @pulumi.getter(name="vpcConfig") def vpc_config(self) -> Optional[pulumi.Input['TestGridProjectVpcConfigArgs']]: """ The VPC security groups and subnets that are attached to a project. See VPC Config below. """ return pulumi.get(self, "vpc_config") @vpc_config.setter def vpc_config(self, value: Optional[pulumi.Input['TestGridProjectVpcConfigArgs']]): pulumi.set(self, "vpc_config", value) class TestGridProject(pulumi.CustomResource): @overload def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, description: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None, tags_all: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None, vpc_config: Optional[pulumi.Input[pulumi.InputType['TestGridProjectVpcConfigArgs']]] = None, __props__=None): """ Provides a resource to manage AWS Device Farm Test Grid Projects. > **NOTE:** AWS currently has limited regional support for Device Farm (e.g., `us-west-2`). See [AWS Device Farm endpoints and quotas](https://docs.aws.amazon.com/general/latest/gr/devicefarm.html) for information on supported regions. ## Import DeviceFarm Test Grid Projects can be imported by their arn ```sh $ pulumi import aws:devicefarm/testGridProject:TestGridProject example arn:aws:devicefarm:us-west-2:123456789012:testgrid-project:4fa784c7-ccb4-4dbf-ba4f-02198320daa1 ``` :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] description: Human-readable description of the project. :param pulumi.Input[str] name: The name of the Selenium testing project. :param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: A map of tags to assign to the resource. If configured with a provider [`default_tags` configuration block](https://www.terraform.io/docs/providers/aws/index.html#default_tags-configuration-block) present, tags with matching keys will overwrite those defined at the provider-level. :param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags_all: A map of tags assigned to the resource, including those inherited from the provider [`default_tags` configuration block](https://www.terraform.io/docs/providers/aws/index.html#default_tags-configuration-block). :param pulumi.Input[pulumi.InputType['TestGridProjectVpcConfigArgs']] vpc_config: The VPC security groups and subnets that are attached to a project. See VPC Config below. """ ... @overload def __init__(__self__, resource_name: str, args: Optional[TestGridProjectArgs] = None, opts: Optional[pulumi.ResourceOptions] = None): """ Provides a resource to manage AWS Device Farm Test Grid Projects. > **NOTE:** AWS currently has limited regional support for Device Farm (e.g., `us-west-2`). See [AWS Device Farm endpoints and quotas](https://docs.aws.amazon.com/general/latest/gr/devicefarm.html) for information on supported regions. ## Import DeviceFarm Test Grid Projects can be imported by their arn ```sh $ pulumi import aws:devicefarm/testGridProject:TestGridProject example arn:aws:devicefarm:us-west-2:123456789012:testgrid-project:4fa784c7-ccb4-4dbf-ba4f-02198320daa1 ``` :param str resource_name: The name of the resource. :param TestGridProjectArgs args: The arguments to use to populate this resource's properties. :param pulumi.ResourceOptions opts: Options for the resource. """ ... def __init__(__self__, resource_name: str, *args, **kwargs): resource_args, opts = _utilities.get_resource_args_opts(TestGridProjectArgs, pulumi.ResourceOptions, *args, **kwargs) if resource_args is not None: __self__._internal_init(resource_name, opts, **resource_args.__dict__) else: __self__._internal_init(resource_name, *args, **kwargs) def _internal_init(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, description: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None, tags_all: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None, vpc_config: Optional[pulumi.Input[pulumi.InputType['TestGridProjectVpcConfigArgs']]] = None, __props__=None): if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = _utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = TestGridProjectArgs.__new__(TestGridProjectArgs) __props__.__dict__["description"] = description __props__.__dict__["name"] = name __props__.__dict__["tags"] = tags __props__.__dict__["tags_all"] = tags_all __props__.__dict__["vpc_config"] = vpc_config __props__.__dict__["arn"] = None super(TestGridProject, __self__).__init__( 'aws:devicefarm/testGridProject:TestGridProject', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None, arn: Optional[pulumi.Input[str]] = None, description: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None, tags_all: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None, vpc_config: Optional[pulumi.Input[pulumi.InputType['TestGridProjectVpcConfigArgs']]] = None) -> 'TestGridProject': """ Get an existing TestGridProject resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param pulumi.Input[str] id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] arn: The Amazon Resource Name of this Test Grid Project. :param pulumi.Input[str] description: Human-readable description of the project. :param pulumi.Input[str] name: The name of the Selenium testing project. :param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: A map of tags to assign to the resource. If configured with a provider [`default_tags` configuration block](https://www.terraform.io/docs/providers/aws/index.html#default_tags-configuration-block) present, tags with matching keys will overwrite those defined at the provider-level. :param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags_all: A map of tags assigned to the resource, including those inherited from the provider [`default_tags` configuration block](https://www.terraform.io/docs/providers/aws/index.html#default_tags-configuration-block). :param pulumi.Input[pulumi.InputType['TestGridProjectVpcConfigArgs']] vpc_config: The VPC security groups and subnets that are attached to a project. See VPC Config below. """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = _TestGridProjectState.__new__(_TestGridProjectState) __props__.__dict__["arn"] = arn __props__.__dict__["description"] = description __props__.__dict__["name"] = name __props__.__dict__["tags"] = tags __props__.__dict__["tags_all"] = tags_all __props__.__dict__["vpc_config"] = vpc_config return TestGridProject(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter def arn(self) -> pulumi.Output[str]: """ The Amazon Resource Name of this Test Grid Project. """ return pulumi.get(self, "arn") @property @pulumi.getter def description(self) -> pulumi.Output[Optional[str]]: """ Human-readable description of the project. """ return pulumi.get(self, "description") @property @pulumi.getter def name(self) -> pulumi.Output[str]: """ The name of the Selenium testing project. """ return pulumi.get(self, "name") @property @pulumi.getter def tags(self) -> pulumi.Output[Optional[Mapping[str, str]]]: """ A map of tags to assign to the resource. If configured with a provider [`default_tags` configuration block](https://www.terraform.io/docs/providers/aws/index.html#default_tags-configuration-block) present, tags with matching keys will overwrite those defined at the provider-level. """ return pulumi.get(self, "tags") @property @pulumi.getter(name="tagsAll") def tags_all(self) -> pulumi.Output[Mapping[str, str]]: """ A map of tags assigned to the resource, including those inherited from the provider [`default_tags` configuration block](https://www.terraform.io/docs/providers/aws/index.html#default_tags-configuration-block). """ return pulumi.get(self, "tags_all") @property @pulumi.getter(name="vpcConfig") def vpc_config(self) -> pulumi.Output[Optional['outputs.TestGridProjectVpcConfig']]: """ The VPC security groups and subnets that are attached to a project. See VPC Config below. """ return pulumi.get(self, "vpc_config")

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false

0.004525

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0

null

0

1

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1

0

null

0

8

5b7eef74c9e604a935d921b723e9dd99f1c3dcce

2,654

py

Python

pyaz/network/application_gateway/client_cert/__init__.py

py-az-cli/py-az-cli

9a7dc44e360c096a5a2f15595353e9dad88a9792

[ "MIT" ]

null

pyaz/network/application_gateway/client_cert/__init__.py

py-az-cli/py-az-cli

9a7dc44e360c096a5a2f15595353e9dad88a9792

[ "MIT" ]

null

pyaz/network/application_gateway/client_cert/__init__.py

py-az-cli/py-az-cli

9a7dc44e360c096a5a2f15595353e9dad88a9792

[ "MIT" ]

1

2022-02-03T09:12:01.000Z

from .... pyaz_utils import _call_az def add(data, gateway_name, name, resource_group): ''' Add trusted client certificate of the application gateway. Required Parameters: - data -- Certificate public data. - gateway_name -- Name of the application gateway. - name -- Name of the trusted client certificate that is unique within an Application Gateway - resource_group -- Name of resource group. You can configure the default group using `az configure --defaults group=<name>` ''' return _call_az("az network application-gateway client-cert add", locals()) def remove(gateway_name, name, resource_group): ''' Remove an existing trusted client certificate of the application gateway. Required Parameters: - gateway_name -- Name of the application gateway. - name -- Name of the trusted client certificate that is unique within an Application Gateway - resource_group -- Name of resource group. You can configure the default group using `az configure --defaults group=<name>` ''' return _call_az("az network application-gateway client-cert remove", locals()) def list(gateway_name, resource_group): ''' List the existing trusted client certificate of the application gateway. Required Parameters: - gateway_name -- Name of the application gateway. - resource_group -- Name of resource group. You can configure the default group using `az configure --defaults group=<name>` ''' return _call_az("az network application-gateway client-cert list", locals()) def show(gateway_name, name, resource_group): ''' Show an existing trusted client certificate of the application gateway. Required Parameters: - gateway_name -- Name of the application gateway. - name -- Name of the trusted client certificate that is unique within an Application Gateway - resource_group -- Name of resource group. You can configure the default group using `az configure --defaults group=<name>` ''' return _call_az("az network application-gateway client-cert show", locals()) def update(data, gateway_name, name, resource_group): ''' Update trusted client certificate of the application gateway. Required Parameters: - data -- Certificate public data. - gateway_name -- Name of the application gateway. - name -- Name of the trusted client certificate that is unique within an Application Gateway - resource_group -- Name of resource group. You can configure the default group using `az configure --defaults group=<name>` ''' return _call_az("az network application-gateway client-cert update", locals())

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0.102686

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7

5b93dd0a7f2266c3611c141873d0d7500f35fb36

3,252

py

Python

tests/slo/backup_quality/predicate/test_sli_table_newer_modification_predicate.py

Morgenz/bbq

f0fd3f626841c610aee80ad08a61123b7cccb775

[ "Apache-2.0" ]

41

2018-05-08T11:54:37.000Z

2022-02-09T21:19:17.000Z

tests/slo/backup_quality/predicate/test_sli_table_newer_modification_predicate.py

Morgenz/bbq

f0fd3f626841c610aee80ad08a61123b7cccb775

[ "Apache-2.0" ]

139

2018-06-07T13:45:21.000Z

2021-04-30T20:44:06.000Z

tests/slo/backup_quality/predicate/test_sli_table_newer_modification_predicate.py

Morgenz/bbq

f0fd3f626841c610aee80ad08a61123b7cccb775

[ "Apache-2.0" ]

5

2019-09-11T12:28:24.000Z

2022-02-04T21:38:29.000Z

import unittest from mock import Mock, patch from src.commons.big_query.big_query import BigQuery from src.slo.backup_quality.predicate.sli_table_newer_modification_predicate import \ SLITableNewerModificationPredicate class TestSLITableNewerModificationPredicate(unittest.TestCase): @patch('src.commons.big_query.big_query.BigQuery.__init__', Mock(return_value=None)) @patch('src.commons.big_query.big_query.BigQuery.get_table', Mock(return_value={'projectId': 'p', 'lastModifiedTime': '1618522714837', 'schema': {'fields': []}})) def test_should_return_true_if_get_table_has_newer_modification_time_than_census(self): # given sli_table = { "snapshotTime": None, "projectId": 'p', "datasetId": 'd', "tableId": 'd', "partitionId": None, "creationTime": None, "lastModifiedTime": float('1.518522714837E9'), "backupCreated": None, "backupLastModified": None, "xDays": 4 } # when is_modified = SLITableNewerModificationPredicate(BigQuery()).is_modified_since_last_census_snapshot(sli_table) # then self.assertTrue(is_modified) @patch('src.commons.big_query.big_query.BigQuery.__init__', Mock(return_value=None)) @patch('src.commons.big_query.big_query.BigQuery.get_table', Mock(return_value={'projectId': 'p', 'lastModifiedTime': '1518522714837', 'schema': {'fields': []}})) def test_should_return_false_if_get_table_has_the_same_modification_time_than_census(self): # given sli_table = { "snapshotTime": None, "projectId": 'p', "datasetId": 'd', "tableId": 'd', "partitionId": None, "creationTime": None, "lastModifiedTime": float('1.518522714837E9'), "backupCreated": None, "backupLastModified": None, "xDays": 4 } # when is_modified = SLITableNewerModificationPredicate(BigQuery()).is_modified_since_last_census_snapshot(sli_table) # then self.assertFalse(is_modified) @patch('src.commons.big_query.big_query.BigQuery.__init__', Mock(return_value=None)) @patch('src.commons.big_query.big_query.BigQuery.get_table', Mock(return_value={'projectId': 'p', 'lastModifiedTime': '1418522714837', 'schema': {'fields': []}})) def test_should_return_false_if_get_table_has_the_older_modification_time_than_census(self): # given sli_table = { "snapshotTime": None, "projectId": 'p', "datasetId": 'd', "tableId": 'd', "partitionId": None, "creationTime": None, "lastModifiedTime": float('1.518522714837E9'), "backupCreated": None, "backupLastModified": None, "xDays": 4 } # when is_modified = SLITableNewerModificationPredicate(BigQuery()).is_modified_since_last_census_snapshot(sli_table) # then self.assertFalse(is_modified)

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null

0

1

0

1

0

null

0

7

5b9f88ef8d854e5ded4dacd5315333903a5bfde7

34

py

Python

tests/test_toy_projpy.py

anasm-17/toy_projpy

c751ddf0a52f98437fffc3eec6c691df67f3406f

[ "MIT" ]

null

tests/test_toy_projpy.py

anasm-17/toy_projpy

c751ddf0a52f98437fffc3eec6c691df67f3406f

[ "MIT" ]

null

tests/test_toy_projpy.py

anasm-17/toy_projpy

c751ddf0a52f98437fffc3eec6c691df67f3406f

[ "MIT" ]

null

from toy_projpy import toy_projpy

17

33

0.882353

6

34

4.666667

0.666667

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34

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null

0

1

0

1

0

1

0

7

5beb38a7a1d81e467ec5c3095ad8036b4f41a819

18,337

py

Python

directdm/num/single_nucleon_form_factors.py

DirectDM/directdm-py

9e940703bc4e5b2266ce2c93c27abee755c0cbaf

[ "MIT" ]

5

2017-09-09T16:22:00.000Z

2021-11-17T07:31:11.000Z

directdm/num/single_nucleon_form_factors.py

DirectDM/directdm-py

9e940703bc4e5b2266ce2c93c27abee755c0cbaf

[ "MIT" ]

2

2018-04-17T16:43:27.000Z

2018-04-19T12:34:54.000Z

directdm/num/single_nucleon_form_factors.py

DirectDM/directdm-py

9e940703bc4e5b2266ce2c93c27abee755c0cbaf

[ "MIT" ]

2

2018-05-10T17:39:57.000Z

2018-09-19T16:40:07.000Z

#!/usr/bin/env python3 class F1: def __init__(self, quark, nucleon, input_dict): """ The nuclear form factor F1 Return the nuclear form factor F1 Arguments --------- quark = 'u', 'd', 's' -- the quark flavor (up, down, strange) nucleon = 'p', 'n' -- the nucleon (proton or neutron) """ self.quark = quark self.nucleon = nucleon self.ip = input_dict def value_zero_mom(self): """ Return the value of the form factor at zero momentum transfer """ if self.nucleon == 'p': if self.quark == 'u': return 2 if self.quark == 'd': return 1 if self.quark == 's': return 0 if self.nucleon == 'n': if self.quark == 'u': return 1 if self.quark == 'd': return 2 if self.quark == 's': return 0 def first_deriv_zero_mom(self): """ Return the value of the first derivative of the form factor w.r.t. q^2 at zero momentum transfer (only strange quark) """ if self.nucleon == 'p': if self.quark == 's': return self.ip['rs2'] / 6 if self.nucleon == 'n': if self.quark == 's': return self.ip['rs2'] / 6 class F2(object): def __init__(self, quark, nucleon, input_dict): """ The nuclear form factor F2 Return the nuclear form factor F2 Arguments --------- quark = 'u', 'd', 's' -- the quark flavor (up, down, strange) nucleon = 'p', 'n' -- the nucleon (proton or neutron) input_dict (optional) -- a dictionary of hadronic input parameters (default is Num_input().input_parameters) """ self.quark = quark self.nucleon = nucleon self.ip = input_dict def value_zero_mom(self): """ Return the value of the form factor at zero momentum transfer """ if self.nucleon == 'p': if self.quark == 'u': return 2*self.ip['ap'] + self.ip['an'] + self.ip['F2sp'] if self.quark == 'd': return 2*self.ip['an'] + self.ip['ap'] + self.ip['F2sp'] if self.quark == 's': return self.ip['F2sp'] if self.nucleon == 'n': if self.quark == 'u': return 2*self.ip['an'] + self.ip['ap'] + self.ip['F2sp'] if self.quark == 'd': return 2*self.ip['ap'] + self.ip['an'] + self.ip['F2sp'] if self.quark == 's': return self.ip['F2sp'] class FA(object): def __init__(self, quark, nucleon, input_dict): """ The nuclear form factor FA at zero momentum transfer Return the nuclear form factor FA, evaluated at zero momentum transfer. Arguments --------- quark = 'u', 'd', 's' -- the quark flavor (up, down, strange) nucleon = 'p', 'n' -- the nucleon (proton or neutron) input_dict (optional) -- a dictionary of hadronic input parameters (default is Num_input().input_parameters) """ self.quark = quark self.nucleon = nucleon self.ip = input_dict def value_zero_mom(self): """ Return the value of the form factor at zero momentum transfer """ if self.nucleon == 'p': if self.quark == 'u': return self.ip['Deltaup'] if self.quark == 'd': return self.ip['Deltadp'] if self.quark == 's': return self.ip['Deltas'] if self.nucleon == 'n': if self.quark == 'u': return self.ip['Deltaun'] if self.quark == 'd': return self.ip['Deltadn'] if self.quark == 's': return self.ip['Deltas'] class FPprimed(object): def __init__(self, quark, nucleon, input_dict): """ The nuclear form factor FPprimed Return the nuclear form factor FPprimed Arguments --------- quark = 'u', 'd', 's' -- the quark flavor (up, down, strange) nucleon = 'p', 'n' -- the nucleon (proton or neutron) input_dict (optional) -- a dictionary of hadronic input parameters (default is Num_input().input_parameters) """ self.quark = quark self.nucleon = nucleon self.ip = input_dict def value_pion_pole(self): """ Return the coefficient of the pion pole The pion pole is given, in terms of the spatial momentum q, by 1 / (q^2 + mpi0^2) """ self.mN = (self.ip['mproton'] + self.ip['mneutron'])/2 if self.nucleon == 'p': if self.quark == 'u': return self.mN**2 * 2 * self.ip['gA'] if self.quark == 'd': return - self.mN**2 * 2 * self.ip['gA'] if self.quark == 's': return 0 if self.nucleon == 'n': if self.quark == 'u': return - self.mN**2 * 2 * self.ip['gA'] if self.quark == 'd': return self.mN**2 * 2 * self.ip['gA'] if self.quark == 's': return 0 def value_eta_pole(self): """ Return the coefficient of the pion pole The eta pole is given, in terms of the spatial momentum q, by 1 / (q^2 + meta^2) """ self.mN = (self.ip['mproton'] + self.ip['mneutron'])/2 if self.nucleon == 'p': if self.quark == 'u': return self.mN**2 * 2 * (self.ip['Deltaup'] + self.ip['Deltadp'] - 2*self.ip['Deltas'])/3 if self.quark == 'd': return self.mN**2 * 2 * (self.ip['Deltaup'] + self.ip['Deltadp'] - 2*self.ip['Deltas'])/3 if self.quark == 's': return - self.mN**2 * 4 * (self.ip['Deltaup'] + self.ip['Deltadp'] - 2*self.ip['Deltas'])/3 if self.nucleon == 'n': if self.quark == 'u': return self.mN**2 * 2 * (self.ip['Deltaup'] + self.ip['Deltadp'] - 2*self.ip['Deltas'])/3 if self.quark == 'd': return self.mN**2 * 2 * (self.ip['Deltaup'] + self.ip['Deltadp'] - 2*self.ip['Deltas'])/3 if self.quark == 's': return - self.mN**2 * 4 * (self.ip['Deltaup'] + self.ip['Deltadp'] - 2*self.ip['Deltas'])/3 class FS(object): def __init__(self, quark, nucleon, input_dict): """ The nuclear form factor FS Return the nuclear form factor FS Arguments --------- quark = 'u', 'd', 's' -- the quark flavor (up, down, strange) nucleon = 'p', 'n' -- the nucleon (proton or neutron) input_dict (optional) -- a dictionary of hadronic input parameters (default is Num_input().input_parameters) """ self.quark = quark self.nucleon = nucleon self.ip = input_dict def value_zero_mom(self): """ Return the value of the form factor at zero momentum transfer """ if self.nucleon == 'p': if self.quark == 'u': return self.ip['sigmaup'] if self.quark == 'd': return self.ip['sigmadp'] if self.quark == 's': return self.ip['sigmas'] if self.nucleon == 'n': if self.quark == 'u': return self.ip['sigmaun'] if self.quark == 'd': return self.ip['sigmadn'] if self.quark == 's': return self.ip['sigmas'] class FP(object): def __init__(self, quark, nucleon, input_dict): """ The nuclear form factor FP Return the nuclear form factor FP Arguments --------- quark = 'u', 'd', 's' -- the quark flavor (up, down, strange) nucleon = 'p', 'n' -- the nucleon (proton or neutron) input_dict (optional) -- a dictionary of hadronic input parameters (default is Num_input().input_parameters) """ self.quark = quark self.nucleon = nucleon self.ip = input_dict def value_pion_pole(self): """ Return the coefficient of the pion pole The pion pole is given, in terms of the spatial momentum q, by 1 / (q^2 + mpi0^2) """ self.mN = (self.ip['mproton'] + self.ip['mneutron'])/2 if self.nucleon == 'p': if self.quark == 'u': return self.mN**2 * self.ip['gA'] * self.ip['B0mu'] / self.mN if self.quark == 'd': return - self.mN**2 * self.ip['gA'] * self.ip['B0md'] / self.mN if self.quark == 's': return 0 if self.nucleon == 'n': if self.quark == 'u': return - self.mN**2 * self.ip['gA'] * self.ip['B0mu'] / self.mN if self.quark == 'd': return self.mN**2 * self.ip['gA'] * self.ip['B0md'] / self.mN if self.quark == 's': return 0 def value_eta_pole(self): """ Return the coefficient of the pion pole The eta pole is given, in terms of the spatial momentum q, by 1 / (q^2 + meta^2) """ self.mN = (self.ip['mproton'] + self.ip['mneutron'])/2 if self.nucleon == 'p': if self.quark == 'u': return self.mN**2 * (self.ip['Deltaup'] + self.ip['Deltadp'] - 2*self.ip['Deltas'])/3/self.mN * self.ip['B0mu'] if self.quark == 'd': return self.mN**2 * (self.ip['Deltaup'] + self.ip['Deltadp'] - 2*self.ip['Deltas'])/3/self.mN * self.ip['B0md'] if self.quark == 's': return - 2 * self.mN**2 * (self.ip['Deltaup'] + self.ip['Deltadp'] - 2*self.ip['Deltas'])/3/self.mN * self.ip['B0ms'] if self.nucleon == 'n': if self.quark == 'u': return self.mN**2 * (self.ip['Deltaup'] + self.ip['Deltadp'] - 2*self.ip['Deltas'])/3/self.mN * self.ip['B0mu'] if self.quark == 'd': return self.mN**2 * (self.ip['Deltaup'] + self.ip['Deltadp'] - 2*self.ip['Deltas'])/3/self.mN * self.ip['B0md'] if self.quark == 's': return - 2 * self.mN**2 * (self.ip['Deltaup'] + self.ip['Deltadp'] - 2*self.ip['Deltas'])/3/self.mN * self.ip['B0ms'] class FT0(object): def __init__(self, quark, nucleon, input_dict): """ The nuclear form factor FT0 Return the nuclear form factor FT0 Arguments --------- quark = 'u', 'd', 's' -- the quark flavor (up, down, strange) nucleon = 'p', 'n' -- the nucleon (proton or neutron) input_dict (optional) -- a dictionary of hadronic input parameters (default is Num_input().input_parameters) """ self.quark = quark self.nucleon = nucleon self.ip = input_dict def value_zero_mom(self): """ Return the value of the form factor at zero momentum transfer """ if self.nucleon == 'p': if self.quark == 'u': return self.ip['mu_at_2GeV'] * self.ip['gTu'] if self.quark == 'd': return self.ip['md_at_2GeV'] * self.ip['gTd'] if self.quark == 's': return self.ip['ms_at_2GeV'] * self.ip['gTs'] if self.nucleon == 'n': if self.quark == 'u': return self.ip['mu_at_2GeV'] * self.ip['gTd'] if self.quark == 'd': return self.ip['md_at_2GeV'] * self.ip['gTu'] if self.quark == 's': return self.ip['ms_at_2GeV'] * self.ip['gTs'] class FT1(object): def __init__(self, quark, nucleon, input_dict): """ The nuclear form factor FT1 Return the nuclear form factor FT1 Arguments --------- quark = 'u', 'd', 's' -- the quark flavor (up, down, strange) nucleon = 'p', 'n' -- the nucleon (proton or neutron) input_dict (optional) -- a dictionary of hadronic input parameters (default is Num_input().input_parameters) """ self.quark = quark self.nucleon = nucleon self.ip = input_dict def value_zero_mom(self): """ Return the value of the form factor at zero momentum transfer """ if self.nucleon == 'p': if self.quark == 'u': return - self.ip['mu_at_2GeV'] * self.ip['BT10up'] if self.quark == 'd': return - self.ip['md_at_2GeV'] * self.ip['BT10dp'] if self.quark == 's': return - self.ip['ms_at_2GeV'] * self.ip['BT10s'] if self.nucleon == 'n': if self.quark == 'u': return - self.ip['mu_at_2GeV'] * self.ip['BT10un'] if self.quark == 'd': return - self.ip['md_at_2GeV'] * self.ip['BT10dn'] if self.quark == 's': return - self.ip['ms_at_2GeV'] * self.ip['BT10s'] class FG(object): def __init__(self, nucleon, input_dict): """ The nuclear form factor FG Return the nuclear form factor FG Arguments --------- nucleon = 'p', 'n' -- the nucleon (proton or neutron) input_dict (optional) -- a dictionary of hadronic input parameters (default is Num_input().input_parameters) """ self.nucleon = nucleon self.ip = input_dict def value_zero_mom(self): """ Return the value of the form factor at zero momentum transfer """ if self.nucleon == 'p': return -2*self.ip['mG']/27 if self.nucleon == 'n': return -2*self.ip['mG']/27 class FGtilde(object): def __init__(self, nucleon, input_dict): """ The nuclear form factor FGtilde Return the nuclear form factor FGtilde Arguments --------- nucleon = 'p', 'n' -- the nucleon (proton or neutron) input_dict (optional) -- a dictionary of hadronic input parameters (default is Num_input().input_parameters) """ self.nucleon = nucleon self.ip = input_dict def value_zero_mom(self): """ Return the value of the form factor at zero momentum transfer """ self.mtilde = 1/(1/self.ip['mu_at_2GeV'] + 1/self.ip['md_at_2GeV'] + 1/self.ip['ms_at_2GeV']) self.mN = (self.ip['mproton'] + self.ip['mneutron'])/2 if self.nucleon == 'p': return -self.mN * self.mtilde * (self.ip['Deltaup']/self.ip['mu_at_2GeV']\ + self.ip['Deltadp']/self.ip['md_at_2GeV']\ + self.ip['Deltas']/self.ip['ms_at_2GeV']) if self.nucleon == 'n': return -self.mN * self.mtilde * (self.ip['Deltaun']/self.ip['mu_at_2GeV']\ + self.ip['Deltadn']/self.ip['md_at_2GeV']\ + self.ip['Deltas']/self.ip['ms_at_2GeV']) def value_pion_pole(self): """ Return the coefficient of the pion pole The pion pole is given, in terms of the spatial momentum q, by q^2 / (q^2 + mpi0^2) """ self.mtilde = 1/(1/self.ip['mu_at_2GeV'] + 1/self.ip['md_at_2GeV'] + 1/self.ip['ms_at_2GeV']) self.mN = (self.ip['mproton'] + self.ip['mneutron'])/2 if self.nucleon == 'p': return self.mN * self.mtilde * self.ip['gA'] * (1/self.ip['mu_at_2GeV'] - 1/self.ip['md_at_2GeV']) / 2 if self.nucleon == 'n': return - self.mN * self.mtilde * self.ip['gA'] * (1/self.ip['mu_at_2GeV'] - 1/self.ip['md_at_2GeV']) / 2 def value_eta_pole(self): """ Return the coefficient of the eta pole The eta pole is given, in terms of the spatial momentum q, by q^2 / (q^2 + meta^2) """ self.mtilde = 1/(1/self.ip['mu_at_2GeV'] + 1/self.ip['md_at_2GeV'] + 1/self.ip['ms_at_2GeV']) self.mN = (self.ip['mproton'] + self.ip['mneutron'])/2 if self.nucleon == 'p': return self.mN * self.mtilde * (self.ip['Deltaup'] + self.ip['Deltadp'] - 2*self.ip['Deltas'])\ * (1/self.ip['mu_at_2GeV'] + 1/self.ip['md_at_2GeV'] - 2/self.ip['ms_at_2GeV']) / 6 if self.nucleon == 'n': return self.mN * self.mtilde * (self.ip['Deltaun'] + self.ip['Deltadn'] - 2*self.ip['Deltas'])\ * (1/self.ip['mu_at_2GeV'] + 1/self.ip['md_at_2GeV'] - 2/self.ip['ms_at_2GeV']) / 6 class FTwist2: def __init__(self, flavor, nucleon, input_dict): """ The twist-two nuclear form factors Return the twist-two nuclear form factors Arguments --------- flavor = 'u', 'd', 's', 'g' -- the "quark" flavor (up, down, strange, or gluon contribution) nucleon = 'p', 'n' -- the nucleon (proton or neutron) input_dict (optional) -- a dictionary of hadronic input parameters (default is Num_input().input_parameters) """ self.flavor = flavor self.nucleon = nucleon self.ip = input_dict def value_zero_mom(self): """ Return the value of the form factor at zero momentum transfer """ self.mp = self.ip['mproton'] self.mn = self.ip['mneutron'] if self.nucleon == 'p': if self.flavor == 'u': return 3/4 * self.mp * self.ip['f2up'] if self.flavor == 'd': return 3/4 * self.mp * self.ip['f2dp'] if self.flavor == 's': return 3/4 * self.mp * self.ip['f2sp'] if self.flavor == 'g': return 3/4 * self.mp * self.ip['f2g'] if self.nucleon == 'n': if self.flavor == 'u': return 3/4 * self.mn * self.ip['f2un'] if self.flavor == 'd': return 3/4 * self.mn * self.ip['f2dn'] if self.flavor == 's': return 3/4 * self.mn * self.ip['f2sn'] if self.flavor == 'g': return 3/4 * self.mn * self.ip['f2g']

35.536822

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0.503572

2,368

18,337

3.813767

0.056588

0.120917

0.075518

0.029233

0.944524

0.903333

0.884952

0.858598

0.818514

0.809434

0

0.019987

0.345149

18,337

515

134

35.605825

0.732095

0.273

0

0.805243

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false

0

0.434457

0

null

0

1

0

1

0

null

0

7

75129e33dd7897210069bec3c1355b219353861a

6,749

py

Python

hackersinresidence/webapp/migrations/0007_auto_20171221_0729.py

noisebridge/hackersinresidence

2a1ae32ef6f49614b295a32933974e34266c5411

[ "MIT" ]

4

2018-03-12T22:46:13.000Z

2019-07-20T01:58:37.000Z

hackersinresidence/webapp/migrations/0007_auto_20171221_0729.py

noisebridge/hackersinresidence

2a1ae32ef6f49614b295a32933974e34266c5411

[ "MIT" ]

10

2018-02-18T09:07:15.000Z

2018-02-25T22:18:37.000Z

hackersinresidence/webapp/migrations/0007_auto_20171221_0729.py

noisebridge/hackersinresidence

2a1ae32ef6f49614b295a32933974e34266c5411

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Generated by Django 1.11.4 on 2017-12-21 07:29 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('webapp', '0006_auto_20171205_1929'), ] operations = [ migrations.AddField( model_name='opportunity', name='offer_additional_detail', field=models.TextField(blank=True, null=True), ), migrations.AddField( model_name='opportunity', name='offer_food_checkbox', field=models.BooleanField(default=False), ), migrations.AddField( model_name='opportunity', name='offer_food_detail', field=models.CharField(blank=True, max_length=256, null=True), ), migrations.AddField( model_name='opportunity', name='offer_housing_checkbox', field=models.BooleanField(default=False), ), migrations.AddField( model_name='opportunity', name='offer_housing_detail', field=models.CharField(blank=True, max_length=256, null=True), ), migrations.AddField( model_name='opportunity', name='offer_stipend_checkbox', field=models.BooleanField(default=False), ), migrations.AddField( model_name='opportunity', name='offer_stipend_detail', field=models.CharField(blank=True, max_length=256, null=True), ), migrations.AddField( model_name='opportunity', name='offer_studio_checkbox', field=models.BooleanField(default=False), ), migrations.AddField( model_name='opportunity', name='offer_studio_detail', field=models.CharField(blank=True, max_length=256, null=True), ), migrations.AddField( model_name='opportunity', name='offer_tools_checkbox', field=models.BooleanField(default=False), ), migrations.AddField( model_name='opportunity', name='offer_tools_detail', field=models.CharField(blank=True, max_length=256, null=True), ), migrations.AddField( model_name='opportunity', name='offer_travel_checkbox', field=models.BooleanField(default=False), ), migrations.AddField( model_name='opportunity', name='offer_travel_detail', field=models.CharField(blank=True, max_length=256, null=True), ), migrations.AddField( model_name='opportunity', name='require_class_checkbox', field=models.BooleanField(default=False), ), migrations.AddField( model_name='opportunity', name='require_class_detail', field=models.CharField(blank=True, max_length=256, null=True), ), migrations.AddField( model_name='opportunity', name='require_date_detail', field=models.TextField(blank=True, null=True), ), migrations.AddField( model_name='opportunity', name='require_end_date', field=models.DateField(blank=True, help_text='Latest date the residency can end', null=True), ), migrations.AddField( model_name='opportunity', name='require_hackathon_checkbox', field=models.BooleanField(default=False), ), migrations.AddField( model_name='opportunity', name='require_hackathon_detail', field=models.CharField(blank=True, max_length=256, null=True), ), migrations.AddField( model_name='opportunity', name='require_language', field=models.CharField(blank=True, max_length=256, null=True), ), migrations.AddField( model_name='opportunity', name='require_maximum_stay', field=models.CharField(blank=True, help_text='Minimum required length of stay', max_length=256, null=True), ), migrations.AddField( model_name='opportunity', name='require_mentoring_checkbox', field=models.BooleanField(default=False), ), migrations.AddField( model_name='opportunity', name='require_mentoring_detail', field=models.CharField(blank=True, max_length=256, null=True), ), migrations.AddField( model_name='opportunity', name='require_minimum_stay', field=models.CharField(blank=True, help_text='Minimum required length of stay', max_length=256, null=True), ), migrations.AddField( model_name='opportunity', name='require_other_requirements', field=models.TextField(blank=True, null=True), ), migrations.AddField( model_name='opportunity', name='require_presentation_checkbox', field=models.BooleanField(default=False), ), migrations.AddField( model_name='opportunity', name='require_presentation_detail', field=models.CharField(blank=True, max_length=256, null=True), ), migrations.AddField( model_name='opportunity', name='require_start_date', field=models.DateField(blank=True, help_text='Earliest date the residency can start', null=True), ), migrations.AddField( model_name='opportunity', name='require_talk_checkbox', field=models.BooleanField(default=False), ), migrations.AddField( model_name='opportunity', name='require_talk_detail', field=models.CharField(blank=True, max_length=256, null=True), ), migrations.AddField( model_name='opportunity', name='require_workshop_checkbox', field=models.BooleanField(default=False), ), migrations.AddField( model_name='opportunity', name='require_workshop_detail', field=models.CharField(blank=True, max_length=256, null=True), ), migrations.AlterField( model_name='opportunity', name='description', field=models.TextField(blank=True, null=True), ), migrations.AlterField( model_name='opportunity', name='expiration_date', field=models.DateField(blank=True, null=True), ), ]

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0

null

0

1

0

1

0

null

0

9

f32c8e95743fd38ffb623c448ca2952d8310f5c7

46

py

Python

NavigationSystem/__init__.py

CallumJHays/g26-egb320-2019

6dde6b5d2f72fac3928c5042a27dc50e978c3425

[ "MIT" ]

null

NavigationSystem/__init__.py

CallumJHays/g26-egb320-2019

6dde6b5d2f72fac3928c5042a27dc50e978c3425

[ "MIT" ]

null

NavigationSystem/__init__.py

CallumJHays/g26-egb320-2019

6dde6b5d2f72fac3928c5042a27dc50e978c3425

[ "MIT" ]

null

from .NavigationSystem import NavigationSystem

46

0.913043

4

46

10.5

0.75

0

0.065217

46

1

46

0.976744

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1

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1

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0

null

0

1

0

1

0

1

0

7

f32ecbb312d7d9c0f8996936d71ce79cd9ef4ab1

119

py

Python

utils/__init__.py

Untesler/autotrading

4ea15dd89960ce14caa1e09119769a027730c02e

[ "MIT" ]

null

utils/__init__.py

Untesler/autotrading

4ea15dd89960ce14caa1e09119769a027730c02e

[ "MIT" ]

null

utils/__init__.py

Untesler/autotrading

4ea15dd89960ce14caa1e09119769a027730c02e

[ "MIT" ]

null

from utils.predictor import * from utils.smoother import * from utils.preprocessstock import * from utils.obv import *

23.8

35

0.798319

16

119

5.9375

0.4375

0.378947

0.473684

0

0.134454

119

4

36

29.75

0.92233

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true

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null

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0

null

0

1

0

1

0

1

0

7

f341b8ac3d886894bdd3bb1a5297ed0905f3c132

132

py

Python

lib/sshfdpass/actions/tcp4.py

pasztor/sshfdpass

81d135021191a272eefb5a53610c1a78e3496ee5

[ "MIT" ]

1

2020-02-27T12:36:19.000Z

lib/sshfdpass/actions/tcp4.py

pasztor/sshfdpass

81d135021191a272eefb5a53610c1a78e3496ee5

[ "MIT" ]

null

lib/sshfdpass/actions/tcp4.py

pasztor/sshfdpass

81d135021191a272eefb5a53610c1a78e3496ee5

[ "MIT" ]

null

import sshfdpass.actions.tcp class Action(sshfdpass.actions.tcp.Action): def _defaults(self): return dict(aforder='4')

22

43

0.719697

17

132

5.529412

0.764706

0.340426

0.404255

0

0.009009

0.159091

132

5

44

26.4

0.837838

0

0.007576

0

1

0.25

false

0.5

0.25

1

0

1

0

null

1

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1

0

null

0

1

0

1

0

1

0

8

f355b878c0f5146fbad39e4db768c31bb8a511db

180

py

Python

src/boost_histogram/utils.py

HDembinski/boost-histogram

6071588d8b58504938f72818d22ff3ce2a5b45dc

[ "BSD-3-Clause" ]

null

src/boost_histogram/utils.py

HDembinski/boost-histogram

6071588d8b58504938f72818d22ff3ce2a5b45dc

[ "BSD-3-Clause" ]

null

src/boost_histogram/utils.py

HDembinski/boost-histogram

6071588d8b58504938f72818d22ff3ce2a5b45dc

[ "BSD-3-Clause" ]

null

from __future__ import absolute_import, division, print_function del absolute_import, division, print_function __all__ = ("set_family",) from ._internal.utils import set_family

22.5

64

0.822222

23

180

5.782609

0.565217

0.210526

0.330827

0.406015

0.526316

0

0.111111

180

7

65

25.714286

0.83125

0

0.055556

0

1

0

false

0

0.75

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0.75

0.5

1

0

null

1

0

1

0

null

0

1

0

1

0

7

f38a94fa36c647cb06a59b34e6eaada1e29550a3

6,960

py

Python

lib/SystemEventLogLib/eventing_service_events.py

mhocouchbase/testrunner

10faf6955a905dee9a254daf90352881d4687735

[ "Apache-2.0" ]

null

lib/SystemEventLogLib/eventing_service_events.py

mhocouchbase/testrunner

10faf6955a905dee9a254daf90352881d4687735

[ "Apache-2.0" ]

null

lib/SystemEventLogLib/eventing_service_events.py

mhocouchbase/testrunner

10faf6955a905dee9a254daf90352881d4687735

[ "Apache-2.0" ]

null

from SystemEventLogLib.Events import Event from constants.cb_constants.system_event_log import Eventing class EventingServiceEvents(object): @staticmethod def producer_startup(node): return { Event.Fields.EVENT_ID: Eventing.ProducerStartup, Event.Fields.COMPONENT: Event.Component.EVENTING, Event.Fields.DESCRIPTION: "eventing-producer process startup", Event.Fields.SEVERITY: Event.Severity.INFO, Event.Fields.SUB_COMPONENT: "eventing-producer", Event.Fields.NODE_NAME: node } @staticmethod def consumer_startup(node): return { Event.Fields.EVENT_ID: Eventing.ConsumerStartup, Event.Fields.COMPONENT: Event.Component.EVENTING, Event.Fields.DESCRIPTION: "eventing-producer process startup", Event.Fields.SEVERITY: Event.Severity.INFO, Event.Fields.SUB_COMPONENT: "eventing-producer", Event.Fields.NODE_NAME: node } @staticmethod def consumer_crash(node): return { Event.Fields.EVENT_ID: Eventing.ConsumerCrash, Event.Fields.COMPONENT: Event.Component.EVENTING, Event.Fields.DESCRIPTION: "eventing-producer process startup", Event.Fields.SEVERITY: Event.Severity.INFO, Event.Fields.SUB_COMPONENT: "eventing-producer", Event.Fields.NODE_NAME: node } @staticmethod def start_tracing(node): return { Event.Fields.EVENT_ID: Eventing.StartTracing, Event.Fields.COMPONENT: Event.Component.EVENTING, Event.Fields.DESCRIPTION: "Tracing started", Event.Fields.SEVERITY: Event.Severity.INFO, Event.Fields.SUB_COMPONENT: "eventing-producer", Event.Fields.NODE_NAME: node } @staticmethod def stop_tracing(node): return { Event.Fields.EVENT_ID: Eventing.StopTracing, Event.Fields.COMPONENT: Event.Component.EVENTING, Event.Fields.DESCRIPTION: "Tracing stopped", Event.Fields.SEVERITY: Event.Severity.INFO, Event.Fields.SUB_COMPONENT: "eventing-producer", Event.Fields.NODE_NAME: node } @staticmethod def start_debugger(node, appname): return { Event.Fields.EVENT_ID: Eventing.StartDebugger, Event.Fields.COMPONENT: Event.Component.EVENTING, Event.Fields.DESCRIPTION: "Debugger started", Event.Fields.SEVERITY: Event.Severity.INFO, Event.Fields.SUB_COMPONENT: "eventing-producer", Event.Fields.NODE_NAME: node, Event.Fields.EXTRA_ATTRS: {"appName": appname} } @staticmethod def stop_debugger(node, appname): return { Event.Fields.EVENT_ID: Eventing.StopDebugger, Event.Fields.COMPONENT: Event.Component.EVENTING, Event.Fields.DESCRIPTION: "Debugger stopped", Event.Fields.SEVERITY: Event.Severity.INFO, Event.Fields.SUB_COMPONENT: "eventing-producer", Event.Fields.NODE_NAME: node, Event.Fields.EXTRA_ATTRS: {"appName": appname} } @staticmethod def create_function(node, appname): return { Event.Fields.EVENT_ID: Eventing.CreateFunction, Event.Fields.COMPONENT: Event.Component.EVENTING, Event.Fields.DESCRIPTION: "Create Function", Event.Fields.SEVERITY: Event.Severity.INFO, Event.Fields.SUB_COMPONENT: "eventing-producer", Event.Fields.NODE_NAME: node, Event.Fields.EXTRA_ATTRS: {"appName": appname} } @staticmethod def delete_function(node, appname): return { Event.Fields.EVENT_ID: Eventing.DeleteFunction, Event.Fields.COMPONENT: Event.Component.EVENTING, Event.Fields.DESCRIPTION: "Delete Function", Event.Fields.SEVERITY: Event.Severity.INFO, Event.Fields.SUB_COMPONENT: "eventing-producer", Event.Fields.NODE_NAME: node, Event.Fields.EXTRA_ATTRS: {"appName": appname} } @staticmethod def import_functions(node): return { Event.Fields.EVENT_ID: Eventing.ImportFunctions, Event.Fields.COMPONENT: Event.Component.EVENTING, Event.Fields.DESCRIPTION: "Import Functions", Event.Fields.SEVERITY: Event.Severity.INFO, Event.Fields.SUB_COMPONENT: "eventing-producer", Event.Fields.NODE_NAME: node } @staticmethod def export_functions(node): return { Event.Fields.EVENT_ID: Eventing.ExportFunctions, Event.Fields.COMPONENT: Event.Component.EVENTING, Event.Fields.DESCRIPTION: "Export Functions", Event.Fields.SEVERITY: Event.Severity.INFO, Event.Fields.SUB_COMPONENT: "eventing-producer", Event.Fields.NODE_NAME: node } @staticmethod def deploy_function(node, appname): return { Event.Fields.EVENT_ID: Eventing.DeployFunction, Event.Fields.COMPONENT: Event.Component.EVENTING, Event.Fields.DESCRIPTION: "Function deployed", Event.Fields.SEVERITY: Event.Severity.INFO, Event.Fields.SUB_COMPONENT: "eventing-producer", Event.Fields.NODE_NAME: node, Event.Fields.EXTRA_ATTRS: {"appName": appname} } @staticmethod def undeploy_function(node, appname): return { Event.Fields.EVENT_ID: Eventing.UndeployFunction, Event.Fields.COMPONENT: Event.Component.EVENTING, Event.Fields.DESCRIPTION: "Function undeployed", Event.Fields.SEVERITY: Event.Severity.INFO, Event.Fields.SUB_COMPONENT: "eventing-producer", Event.Fields.NODE_NAME: node, Event.Fields.EXTRA_ATTRS: {"appName": appname} } @staticmethod def resume_function(node, appname): return { Event.Fields.EVENT_ID: Eventing.ResumeFunction, Event.Fields.COMPONENT: Event.Component.EVENTING, Event.Fields.DESCRIPTION: "Function resumed", Event.Fields.SEVERITY: Event.Severity.INFO, Event.Fields.SUB_COMPONENT: "eventing-producer", Event.Fields.NODE_NAME: node, Event.Fields.EXTRA_ATTRS: {"appName": appname} } @staticmethod def pause_function(node, appname): return { Event.Fields.EVENT_ID: Eventing.PauseFunction, Event.Fields.COMPONENT: Event.Component.EVENTING, Event.Fields.DESCRIPTION: "Function paused", Event.Fields.SEVERITY: Event.Severity.INFO, Event.Fields.SUB_COMPONENT: "eventing-producer", Event.Fields.NODE_NAME: node, Event.Fields.EXTRA_ATTRS: {"appName": appname} }

39.322034

74

0.632902

679

6,960

6.382916

0.098675

0.248731

0.058837

0.076142

0.892017

0.88371

0.823258

0.670051

0

0.270259

6,960

177

75

39.322034

0.853318

0

0.627329

0

0.086338

0

1

0.093168

false

0

0.031056

0.093168

0.223602

0

null

1

0

1

0

null

0

7

caf10f8f4f26e98a338c344dd037e7129872b8e5

269

py

Python

public/course/NLP/Hockey/sample.py

Caterpie-poke/playground

da78cf88154587c555d69a131a418fb999c14409

[ "MIT" ]

null

public/course/NLP/Hockey/sample.py

Caterpie-poke/playground

da78cf88154587c555d69a131a418fb999c14409

[ "MIT" ]

3

2019-11-08T04:08:51.000Z

2020-03-02T14:01:38.000Z

public/course/NLP/Hockey/sample.py

Caterpie-poke/playground

da78cf88154587c555d69a131a418fb999c14409

[ "MIT" ]

3

2019-10-28T02:49:41.000Z

2019-12-01T09:01:42.000Z

from puppy2d import * Rectangle(0, 500, 800, 50, isStatic=True) Rectangle(0, -500, 800, 50, isStatic=True) Rectangle(400, 0, 50, 1000, isStatic=True) Rectangle(-400, 0, 50, 1000, isStatic=True) Circle(0, 300, 50) Circle(0, -300, 50) Circle(0, 0, 25, restitution=0.9)

24.454545

43

0.69145

46

269

4.043478

0.369565

0.258065

0.33871

0.172043

0.822581

0.655914

0.397849

0

0.244635

0.133829

269

10

44

26.9

0.553648

0

1

0

true

0

0.125

0

0.125

0

null

1

0

1

0

null

0

1

0

7

1b2c94bd0141a5d0ed28cb6031353270e7a9cb5a

24,197

py

Python

sense/client/profile_api.py

sdn-sense/sense-o-py-client

d686c4a2e084fbb6d8ff3b00c6f73db63965f9c6

[ "MIT" ]

null

sense/client/profile_api.py

sdn-sense/sense-o-py-client

d686c4a2e084fbb6d8ff3b00c6f73db63965f9c6

[ "MIT" ]

22

2020-08-27T21:57:47.000Z

2022-03-15T14:57:28.000Z

sense/client/profile_api.py

sdn-sense/sense-o-py-client

d686c4a2e084fbb6d8ff3b00c6f73db63965f9c6

[ "MIT" ]

1

2021-03-30T06:30:20.000Z

# coding: utf-8 """ SENSE-O Northbound Intent API StackV SENSE-O Northbound REST API Documentation # noqa: E501 OpenAPI spec version: 2.0.2 Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import six from sense.client.requestwrapper import RequestWrapper class ProfileApi(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. Ref: https://github.com/swagger-api/swagger-codegen """ def __init__(self, req_wrapper=None): if req_wrapper is None: self.client = RequestWrapper() else: self.client = req_wrapper if 'SI_UUID' in self.client.config: self.si_uuid = self.client.config['SI_UUID'] else: self.si_uuid = None def profile_list(self, **kwargs): """Get skimmed profile data # noqa: E501 Retrieves the list of profiles the user is permitted to use without any JSON data. # noqa: E501 This method makes a synchronous HTTP request by default. :param async_req bool :return: list[SlimProfile] If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.profile_get_with_http_info(**kwargs) # noqa: E501 else: (data) = self.profile_get_with_http_info(**kwargs) # noqa: E501 return data def profile_get_with_http_info(self, **kwargs): """Get skimmed profile data # noqa: E501 Retrieves the list of profiles the user is permitted to use without any JSON data. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.profile_get_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool :return: list[SlimProfile] If the method is called asynchronously, returns the request thread. """ all_params = [] all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError("Got an unexpected keyword argument '%s'" " to method profile_get" % key) params[key] = val del params['kwargs'] return self.client.request('GET', f'/profile') def profile_describe(self, uuid, **kwargs): # noqa: E501 """Get single profile # noqa: E501 Retrieves the specified profile. # noqa: E501 This method makes a synchronous HTTP request by default. :param async_req bool :param str uuid: Profile UUID. (required) :return: FullProfile If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.profile_uuid_get_with_http_info(uuid, **kwargs) # noqa: E501 else: (data) = self.profile_uuid_get_with_http_info( uuid, **kwargs) # noqa: E501 return data def profile_uuid_get_with_http_info(self, uuid, **kwargs): # noqa: E501 """Get single profile # noqa: E501 Retrieves the specified profile. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.profile_uuid_get_with_http_info(uuid, async_req=True) >>> result = thread.get() :param async_req bool :param str uuid: Profile UUID. (required) :return: FullProfile If the method is called asynchronously, returns the request thread. """ all_params = ['uuid'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError("Got an unexpected keyword argument '%s'" " to method profile_uuid_get" % key) params[key] = val del params['kwargs'] # verify the required parameter 'uuid' is set if ('uuid' not in params or params['uuid'] is None): raise ValueError( "Missing the required parameter `uuid` when calling `profile_uuid_get`" ) # noqa: E501 return self.client.request('GET', f'/profile/' + uuid) def profile_create(self, body, **kwargs): # noqa: E501 """Create a profile # noqa: E501 Builds and saves a new profile, using provided starting data. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.profile_create(body, async_req=True) >>> result = thread.get() :param async_req bool :param ProfileManifest body: Profile creation manifest. (required) :return: str If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.profile_post_with_http_info(body, **kwargs) # noqa: E501 else: (data) = self.profile_post_with_http_info(body, **kwargs) # noqa: E501 return data def profile_post_with_http_info(self, body, **kwargs): # noqa: E501 """Create a profile # noqa: E501 Builds and saves a new profile, using provided starting data. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.profile_post_with_http_info(body, async_req=True) >>> result = thread.get() :param async_req bool :param ProfileManifest body: Profile creation manifest. (required) :return: str If the method is called asynchronously, returns the request thread. """ all_params = ['body'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError("Got an unexpected keyword argument '%s'" " to method profile_post" % key) params[key] = val del params['kwargs'] # verify the required parameter 'body' is set if ('body' not in params or params['body'] is None): raise ValueError( "Missing the required parameter `body` when calling `profile_post`" ) # noqa: E501 body_params = None if 'body' in params: body_params = params['body'] return self.client.request('POST', f'/profile', body_params=body_params) def profile_delete(self, uuid, **kwargs): # noqa: E501 """Delete profile # noqa: E501 Deletes the specified profile. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.profile_delete(uuid, async_req=True) >>> result = thread.get() :param async_req bool :param str uuid: Profile UUID. (required) :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.profile_uuid_delete_with_http_info( uuid, **kwargs) # noqa: E501 else: (data) = self.profile_uuid_delete_with_http_info( uuid, **kwargs) # noqa: E501 return data def profile_uuid_delete_with_http_info(self, uuid, **kwargs): # noqa: E501 """Delete profile # noqa: E501 Deletes the specified profile. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.profile_uuid_delete_with_http_info(uuid, async_req=True) >>> result = thread.get() :param async_req bool :param str uuid: Profile UUID. (required) :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['uuid'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError("Got an unexpected keyword argument '%s'" " to method profile_uuid_delete" % key) params[key] = val del params['kwargs'] # verify the required parameter 'uuid' is set if ('uuid' not in params or params['uuid'] is None): raise ValueError( "Missing the required parameter `uuid` when calling `profile_uuid_delete`" ) # noqa: E501 return self.client.request('DELETE', f'/profile/{uuid}') def profile_add_licenses(self, body, uuid, **kwargs): # noqa: E501 """Add new license # noqa: E501 Assigns a new license to a user, giving them access to execute the specified profile (and potentially edit as well). # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.profile_add_licenses(body, uuid, async_req=True) >>> result = thread.get() :param async_req bool :param ProfileLicense body: License object. (required) :param str uuid: Profile UUID. (required) :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.profile_uuid_licenses_post_with_http_info( body, uuid, **kwargs) # noqa: E501 else: (data) = self.profile_uuid_licenses_post_with_http_info( body, uuid, **kwargs) # noqa: E501 return data def profile_uuid_licenses_post_with_http_info(self, body, uuid, **kwargs): # noqa: E501 """Add new license # noqa: E501 Assigns a new license to a user, giving them access to execute the specified profile (and potentially edit as well). # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.profile_uuid_licenses_post_with_http_info(body, uuid, async_req=True) >>> result = thread.get() :param async_req bool :param ProfileLicense body: License object. (required) :param str uuid: Profile UUID. (required) :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['body', 'uuid'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError("Got an unexpected keyword argument '%s'" " to method profile_uuid_licenses_post" % key) params[key] = val del params['kwargs'] # verify the required parameter 'body' is set if ('body' not in params or params['body'] is None): raise ValueError( "Missing the required parameter `body` when calling `profile_uuid_licenses_post`" ) # noqa: E501 # verify the required parameter 'uuid' is set if ('uuid' not in params or params['uuid'] is None): raise ValueError( "Missing the required parameter `uuid` when calling `profile_uuid_licenses_post`" ) # noqa: E501 path_params = {} if 'uuid' in params: path_params['uuid'] = params['uuid'] # noqa: E501 body_params = None if 'body' in params: body_params = params['body'] return self.client.request('POST', f'/profile/{uuid}/licenses', body_params=body_params) def profile_update_licenses(self, body, uuid, **kwargs): # noqa: E501 """Edit existing license # noqa: E501 Edits an existing license to a user. Setting the remaining field to 0 will delete the license. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.profile_update_licenses(body, uuid, async_req=True) >>> result = thread.get() :param async_req bool :param ProfileLicense body: License object. (required) :param str uuid: Profile UUID. (required) :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.profile_uuid_licenses_put_with_http_info( body, uuid, **kwargs) # noqa: E501 else: (data) = self.profile_uuid_licenses_put_with_http_info( body, uuid, **kwargs) # noqa: E501 return data def profile_uuid_licenses_put_with_http_info(self, body, uuid, **kwargs): # noqa: E501 """Edit existing license # noqa: E501 Edits an existing license to a user. Setting the remaining field to 0 will delete the license. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.profile_uuid_licenses_put_with_http_info(body, uuid, async_req=True) >>> result = thread.get() :param async_req bool :param ProfileLicense body: License object. (required) :param str uuid: Profile UUID. (required) :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['body', 'uuid'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError("Got an unexpected keyword argument '%s'" " to method profile_uuid_licenses_put" % key) params[key] = val del params['kwargs'] # verify the required parameter 'body' is set if ('body' not in params or params['body'] is None): raise ValueError( "Missing the required parameter `body` when calling `profile_uuid_licenses_put`" ) # noqa: E501 # verify the required parameter 'uuid' is set if ('uuid' not in params or params['uuid'] is None): raise ValueError( "Missing the required parameter `uuid` when calling `profile_uuid_licenses_put`" ) # noqa: E501 path_params = {} if 'uuid' in params: path_params['uuid'] = params['uuid'] # noqa: E501 body_params = None if 'body' in params: body_params = params['body'] return self.client.request('PUT', f'/profile/{uuid}/licenses', body_params=body_params) def profile_update(self, body, uuid, **kwargs): # noqa: E501 """Edit a profile # noqa: E501 Submits an updated version of a profile for saving. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.profile_uuid_put(body, uuid, async_req=True) >>> result = thread.get() :param async_req bool :param ProfileManifest body: Profile creation manifest. (required) :param str uuid: Profile UUID. (required) :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.profile_uuid_put_with_http_info(body, uuid, **kwargs) # noqa: E501 else: (data) = self.profile_uuid_put_with_http_info( body, uuid, **kwargs) # noqa: E501 return data def profile_uuid_put_with_http_info(self, body, uuid, **kwargs): # noqa: E501 """Edit a profile # noqa: E501 Submits an updated version of a profile for saving. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.profile_uuid_put_with_http_info(body, uuid, async_req=True) >>> result = thread.get() :param async_req bool :param ProfileManifest body: Profile creation manifest. (required) :param str uuid: Profile UUID. (required) :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['body', 'uuid'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError("Got an unexpected keyword argument '%s'" " to method profile_uuid_put" % key) params[key] = val del params['kwargs'] # verify the required parameter 'body' is set if ('body' not in params or params['body'] is None): raise ValueError( "Missing the required parameter `body` when calling `profile_uuid_put`" ) # noqa: E501 # verify the required parameter 'uuid' is set if ('uuid' not in params or params['uuid'] is None): raise ValueError( "Missing the required parameter `uuid` when calling `profile_uuid_put`" ) # noqa: E501 path_params = {} if 'uuid' in params: path_params['uuid'] = params['uuid'] # noqa: E501 body_params = None if 'body' in params: body_params = params['body'] return self.client.request('PUT', f'/profile/{uuid}', body_params=body_params) def profile_get_uses(self, uuid, username, **kwargs): # noqa: E501 """Get license usage # noqa: E501 Retrieves the remaining number of tickets or slots for allocations. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.profile_uuid_uses_username_get(uuid, username, async_req=True) >>> result = thread.get() :param async_req bool :param str uuid: Profile UUID. (required) :param str username: Username of licensed user. (required) :return: float If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.profile_uuid_uses_username_get_with_http_info( uuid, username, **kwargs) # noqa: E501 else: (data) = self.profile_uuid_uses_username_get_with_http_info( uuid, username, **kwargs) # noqa: E501 return data def profile_uuid_uses_username_get_with_http_info(self, uuid, username, **kwargs): # noqa: E501 """Get license usage # noqa: E501 Retrieves the remaining number of tickets or slots for allocations. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.profile_uuid_uses_username_get_with_http_info(uuid, username, async_req=True) >>> result = thread.get() :param async_req bool :param str uuid: Profile UUID. (required) :param str username: Username of licensed user. (required) :return: float If the method is called asynchronously, returns the request thread. """ all_params = ['uuid', 'username'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError("Got an unexpected keyword argument '%s'" " to method profile_uuid_uses_username_get" % key) params[key] = val del params['kwargs'] # verify the required parameter 'uuid' is set if ('uuid' not in params or params['uuid'] is None): raise ValueError( "Missing the required parameter `uuid` when calling `profile_uuid_uses_username_get`" ) # noqa: E501 # verify the required parameter 'username' is set if ('username' not in params or params['username'] is None): raise ValueError( "Missing the required parameter `username` when calling `profile_uuid_uses_username_get`" ) # noqa: E501 path_params = {} if 'uuid' in params: path_params['uuid'] = params['uuid'] # noqa: E501 if 'username' in params: path_params['username'] = params['username'] # noqa: E501 return self.client.request('GET', f'/profile/{uuid}/uses/{username}')

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1b8b4b00286874edd0587b50f4598e236128cfa5

28,074

py

Python

sdk/python/pulumi_consul/autopilot_config.py

pulumi/pulumi-consul

5b66c5b97fda6b5433bfb4d4173c999e468c82e8

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

3

2019-11-12T12:21:18.000Z

2021-07-31T08:17:22.000Z

sdk/python/pulumi_consul/autopilot_config.py

pulumi/pulumi-consul

5b66c5b97fda6b5433bfb4d4173c999e468c82e8

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

38

2019-11-21T15:19:33.000Z

2022-03-31T15:24:11.000Z

sdk/python/pulumi_consul/autopilot_config.py

pulumi/pulumi-consul

5b66c5b97fda6b5433bfb4d4173c999e468c82e8

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

2

2020-11-24T12:23:13.000Z

2021-12-06T17:33:31.000Z

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from . import _utilities __all__ = ['AutopilotConfigArgs', 'AutopilotConfig'] @pulumi.input_type class AutopilotConfigArgs: def __init__(__self__, *, cleanup_dead_servers: Optional[pulumi.Input[bool]] = None, datacenter: Optional[pulumi.Input[str]] = None, disable_upgrade_migration: Optional[pulumi.Input[bool]] = None, last_contact_threshold: Optional[pulumi.Input[str]] = None, max_trailing_logs: Optional[pulumi.Input[int]] = None, redundancy_zone_tag: Optional[pulumi.Input[str]] = None, server_stabilization_time: Optional[pulumi.Input[str]] = None, upgrade_version_tag: Optional[pulumi.Input[str]] = None): """ The set of arguments for constructing a AutopilotConfig resource. :param pulumi.Input[bool] cleanup_dead_servers: Whether to remove failing servers when a replacement comes online. Defaults to true. :param pulumi.Input[str] datacenter: The datacenter to use. This overrides the agent's default datacenter and the datacenter in the provider setup. :param pulumi.Input[bool] disable_upgrade_migration: Whether to disable [upgrade migrations](https://www.consul.io/docs/guides/autopilot.html#redundancy-zones). Defaults to false. :param pulumi.Input[str] last_contact_threshold: The time after which a server is considered as unhealthy and will be removed. Defaults to `"200ms"`. :param pulumi.Input[int] max_trailing_logs: The maximum number of Raft log entries a server can trail the leader. Defaults to 250. :param pulumi.Input[str] redundancy_zone_tag: The [redundancy zone](https://www.consul.io/docs/guides/autopilot.html#redundancy-zones) tag to use. Consul will try to keep one voting server by zone to take advantage of isolated failure domains. Defaults to an empty string. :param pulumi.Input[str] server_stabilization_time: The period to wait for a server to be healthy and stable before being promoted to a full, voting member. Defaults to `"10s"`. :param pulumi.Input[str] upgrade_version_tag: The tag to override the version information used during a migration. Defaults to an empty string. """ if cleanup_dead_servers is not None: pulumi.set(__self__, "cleanup_dead_servers", cleanup_dead_servers) if datacenter is not None: pulumi.set(__self__, "datacenter", datacenter) if disable_upgrade_migration is not None: pulumi.set(__self__, "disable_upgrade_migration", disable_upgrade_migration) if last_contact_threshold is not None: pulumi.set(__self__, "last_contact_threshold", last_contact_threshold) if max_trailing_logs is not None: pulumi.set(__self__, "max_trailing_logs", max_trailing_logs) if redundancy_zone_tag is not None: pulumi.set(__self__, "redundancy_zone_tag", redundancy_zone_tag) if server_stabilization_time is not None: pulumi.set(__self__, "server_stabilization_time", server_stabilization_time) if upgrade_version_tag is not None: pulumi.set(__self__, "upgrade_version_tag", upgrade_version_tag) @property @pulumi.getter(name="cleanupDeadServers") def cleanup_dead_servers(self) -> Optional[pulumi.Input[bool]]: """ Whether to remove failing servers when a replacement comes online. Defaults to true. """ return pulumi.get(self, "cleanup_dead_servers") @cleanup_dead_servers.setter def cleanup_dead_servers(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "cleanup_dead_servers", value) @property @pulumi.getter def datacenter(self) -> Optional[pulumi.Input[str]]: """ The datacenter to use. This overrides the agent's default datacenter and the datacenter in the provider setup. """ return pulumi.get(self, "datacenter") @datacenter.setter def datacenter(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "datacenter", value) @property @pulumi.getter(name="disableUpgradeMigration") def disable_upgrade_migration(self) -> Optional[pulumi.Input[bool]]: """ Whether to disable [upgrade migrations](https://www.consul.io/docs/guides/autopilot.html#redundancy-zones). Defaults to false. """ return pulumi.get(self, "disable_upgrade_migration") @disable_upgrade_migration.setter def disable_upgrade_migration(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "disable_upgrade_migration", value) @property @pulumi.getter(name="lastContactThreshold") def last_contact_threshold(self) -> Optional[pulumi.Input[str]]: """ The time after which a server is considered as unhealthy and will be removed. Defaults to `"200ms"`. """ return pulumi.get(self, "last_contact_threshold") @last_contact_threshold.setter def last_contact_threshold(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "last_contact_threshold", value) @property @pulumi.getter(name="maxTrailingLogs") def max_trailing_logs(self) -> Optional[pulumi.Input[int]]: """ The maximum number of Raft log entries a server can trail the leader. Defaults to 250. """ return pulumi.get(self, "max_trailing_logs") @max_trailing_logs.setter def max_trailing_logs(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "max_trailing_logs", value) @property @pulumi.getter(name="redundancyZoneTag") def redundancy_zone_tag(self) -> Optional[pulumi.Input[str]]: """ The [redundancy zone](https://www.consul.io/docs/guides/autopilot.html#redundancy-zones) tag to use. Consul will try to keep one voting server by zone to take advantage of isolated failure domains. Defaults to an empty string. """ return pulumi.get(self, "redundancy_zone_tag") @redundancy_zone_tag.setter def redundancy_zone_tag(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "redundancy_zone_tag", value) @property @pulumi.getter(name="serverStabilizationTime") def server_stabilization_time(self) -> Optional[pulumi.Input[str]]: """ The period to wait for a server to be healthy and stable before being promoted to a full, voting member. Defaults to `"10s"`. """ return pulumi.get(self, "server_stabilization_time") @server_stabilization_time.setter def server_stabilization_time(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "server_stabilization_time", value) @property @pulumi.getter(name="upgradeVersionTag") def upgrade_version_tag(self) -> Optional[pulumi.Input[str]]: """ The tag to override the version information used during a migration. Defaults to an empty string. """ return pulumi.get(self, "upgrade_version_tag") @upgrade_version_tag.setter def upgrade_version_tag(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "upgrade_version_tag", value) @pulumi.input_type class _AutopilotConfigState: def __init__(__self__, *, cleanup_dead_servers: Optional[pulumi.Input[bool]] = None, datacenter: Optional[pulumi.Input[str]] = None, disable_upgrade_migration: Optional[pulumi.Input[bool]] = None, last_contact_threshold: Optional[pulumi.Input[str]] = None, max_trailing_logs: Optional[pulumi.Input[int]] = None, redundancy_zone_tag: Optional[pulumi.Input[str]] = None, server_stabilization_time: Optional[pulumi.Input[str]] = None, upgrade_version_tag: Optional[pulumi.Input[str]] = None): """ Input properties used for looking up and filtering AutopilotConfig resources. :param pulumi.Input[bool] cleanup_dead_servers: Whether to remove failing servers when a replacement comes online. Defaults to true. :param pulumi.Input[str] datacenter: The datacenter to use. This overrides the agent's default datacenter and the datacenter in the provider setup. :param pulumi.Input[bool] disable_upgrade_migration: Whether to disable [upgrade migrations](https://www.consul.io/docs/guides/autopilot.html#redundancy-zones). Defaults to false. :param pulumi.Input[str] last_contact_threshold: The time after which a server is considered as unhealthy and will be removed. Defaults to `"200ms"`. :param pulumi.Input[int] max_trailing_logs: The maximum number of Raft log entries a server can trail the leader. Defaults to 250. :param pulumi.Input[str] redundancy_zone_tag: The [redundancy zone](https://www.consul.io/docs/guides/autopilot.html#redundancy-zones) tag to use. Consul will try to keep one voting server by zone to take advantage of isolated failure domains. Defaults to an empty string. :param pulumi.Input[str] server_stabilization_time: The period to wait for a server to be healthy and stable before being promoted to a full, voting member. Defaults to `"10s"`. :param pulumi.Input[str] upgrade_version_tag: The tag to override the version information used during a migration. Defaults to an empty string. """ if cleanup_dead_servers is not None: pulumi.set(__self__, "cleanup_dead_servers", cleanup_dead_servers) if datacenter is not None: pulumi.set(__self__, "datacenter", datacenter) if disable_upgrade_migration is not None: pulumi.set(__self__, "disable_upgrade_migration", disable_upgrade_migration) if last_contact_threshold is not None: pulumi.set(__self__, "last_contact_threshold", last_contact_threshold) if max_trailing_logs is not None: pulumi.set(__self__, "max_trailing_logs", max_trailing_logs) if redundancy_zone_tag is not None: pulumi.set(__self__, "redundancy_zone_tag", redundancy_zone_tag) if server_stabilization_time is not None: pulumi.set(__self__, "server_stabilization_time", server_stabilization_time) if upgrade_version_tag is not None: pulumi.set(__self__, "upgrade_version_tag", upgrade_version_tag) @property @pulumi.getter(name="cleanupDeadServers") def cleanup_dead_servers(self) -> Optional[pulumi.Input[bool]]: """ Whether to remove failing servers when a replacement comes online. Defaults to true. """ return pulumi.get(self, "cleanup_dead_servers") @cleanup_dead_servers.setter def cleanup_dead_servers(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "cleanup_dead_servers", value) @property @pulumi.getter def datacenter(self) -> Optional[pulumi.Input[str]]: """ The datacenter to use. This overrides the agent's default datacenter and the datacenter in the provider setup. """ return pulumi.get(self, "datacenter") @datacenter.setter def datacenter(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "datacenter", value) @property @pulumi.getter(name="disableUpgradeMigration") def disable_upgrade_migration(self) -> Optional[pulumi.Input[bool]]: """ Whether to disable [upgrade migrations](https://www.consul.io/docs/guides/autopilot.html#redundancy-zones). Defaults to false. """ return pulumi.get(self, "disable_upgrade_migration") @disable_upgrade_migration.setter def disable_upgrade_migration(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "disable_upgrade_migration", value) @property @pulumi.getter(name="lastContactThreshold") def last_contact_threshold(self) -> Optional[pulumi.Input[str]]: """ The time after which a server is considered as unhealthy and will be removed. Defaults to `"200ms"`. """ return pulumi.get(self, "last_contact_threshold") @last_contact_threshold.setter def last_contact_threshold(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "last_contact_threshold", value) @property @pulumi.getter(name="maxTrailingLogs") def max_trailing_logs(self) -> Optional[pulumi.Input[int]]: """ The maximum number of Raft log entries a server can trail the leader. Defaults to 250. """ return pulumi.get(self, "max_trailing_logs") @max_trailing_logs.setter def max_trailing_logs(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "max_trailing_logs", value) @property @pulumi.getter(name="redundancyZoneTag") def redundancy_zone_tag(self) -> Optional[pulumi.Input[str]]: """ The [redundancy zone](https://www.consul.io/docs/guides/autopilot.html#redundancy-zones) tag to use. Consul will try to keep one voting server by zone to take advantage of isolated failure domains. Defaults to an empty string. """ return pulumi.get(self, "redundancy_zone_tag") @redundancy_zone_tag.setter def redundancy_zone_tag(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "redundancy_zone_tag", value) @property @pulumi.getter(name="serverStabilizationTime") def server_stabilization_time(self) -> Optional[pulumi.Input[str]]: """ The period to wait for a server to be healthy and stable before being promoted to a full, voting member. Defaults to `"10s"`. """ return pulumi.get(self, "server_stabilization_time") @server_stabilization_time.setter def server_stabilization_time(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "server_stabilization_time", value) @property @pulumi.getter(name="upgradeVersionTag") def upgrade_version_tag(self) -> Optional[pulumi.Input[str]]: """ The tag to override the version information used during a migration. Defaults to an empty string. """ return pulumi.get(self, "upgrade_version_tag") @upgrade_version_tag.setter def upgrade_version_tag(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "upgrade_version_tag", value) class AutopilotConfig(pulumi.CustomResource): @overload def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, cleanup_dead_servers: Optional[pulumi.Input[bool]] = None, datacenter: Optional[pulumi.Input[str]] = None, disable_upgrade_migration: Optional[pulumi.Input[bool]] = None, last_contact_threshold: Optional[pulumi.Input[str]] = None, max_trailing_logs: Optional[pulumi.Input[int]] = None, redundancy_zone_tag: Optional[pulumi.Input[str]] = None, server_stabilization_time: Optional[pulumi.Input[str]] = None, upgrade_version_tag: Optional[pulumi.Input[str]] = None, __props__=None): """ Provides access to the [Autopilot Configuration](https://www.consul.io/docs/guides/autopilot.html) of Consul to automatically manage Consul servers. It includes to automatically cleanup dead servers, monitor the status of the Raft cluster and stable server introduction. ## Example Usage ```python import pulumi import pulumi_consul as consul config = consul.AutopilotConfig("config", cleanup_dead_servers=False, last_contact_threshold="1s", max_trailing_logs=500) ``` :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[bool] cleanup_dead_servers: Whether to remove failing servers when a replacement comes online. Defaults to true. :param pulumi.Input[str] datacenter: The datacenter to use. This overrides the agent's default datacenter and the datacenter in the provider setup. :param pulumi.Input[bool] disable_upgrade_migration: Whether to disable [upgrade migrations](https://www.consul.io/docs/guides/autopilot.html#redundancy-zones). Defaults to false. :param pulumi.Input[str] last_contact_threshold: The time after which a server is considered as unhealthy and will be removed. Defaults to `"200ms"`. :param pulumi.Input[int] max_trailing_logs: The maximum number of Raft log entries a server can trail the leader. Defaults to 250. :param pulumi.Input[str] redundancy_zone_tag: The [redundancy zone](https://www.consul.io/docs/guides/autopilot.html#redundancy-zones) tag to use. Consul will try to keep one voting server by zone to take advantage of isolated failure domains. Defaults to an empty string. :param pulumi.Input[str] server_stabilization_time: The period to wait for a server to be healthy and stable before being promoted to a full, voting member. Defaults to `"10s"`. :param pulumi.Input[str] upgrade_version_tag: The tag to override the version information used during a migration. Defaults to an empty string. """ ... @overload def __init__(__self__, resource_name: str, args: Optional[AutopilotConfigArgs] = None, opts: Optional[pulumi.ResourceOptions] = None): """ Provides access to the [Autopilot Configuration](https://www.consul.io/docs/guides/autopilot.html) of Consul to automatically manage Consul servers. It includes to automatically cleanup dead servers, monitor the status of the Raft cluster and stable server introduction. ## Example Usage ```python import pulumi import pulumi_consul as consul config = consul.AutopilotConfig("config", cleanup_dead_servers=False, last_contact_threshold="1s", max_trailing_logs=500) ``` :param str resource_name: The name of the resource. :param AutopilotConfigArgs args: The arguments to use to populate this resource's properties. :param pulumi.ResourceOptions opts: Options for the resource. """ ... def __init__(__self__, resource_name: str, *args, **kwargs): resource_args, opts = _utilities.get_resource_args_opts(AutopilotConfigArgs, pulumi.ResourceOptions, *args, **kwargs) if resource_args is not None: __self__._internal_init(resource_name, opts, **resource_args.__dict__) else: __self__._internal_init(resource_name, *args, **kwargs) def _internal_init(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, cleanup_dead_servers: Optional[pulumi.Input[bool]] = None, datacenter: Optional[pulumi.Input[str]] = None, disable_upgrade_migration: Optional[pulumi.Input[bool]] = None, last_contact_threshold: Optional[pulumi.Input[str]] = None, max_trailing_logs: Optional[pulumi.Input[int]] = None, redundancy_zone_tag: Optional[pulumi.Input[str]] = None, server_stabilization_time: Optional[pulumi.Input[str]] = None, upgrade_version_tag: Optional[pulumi.Input[str]] = None, __props__=None): if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = _utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = AutopilotConfigArgs.__new__(AutopilotConfigArgs) __props__.__dict__["cleanup_dead_servers"] = cleanup_dead_servers __props__.__dict__["datacenter"] = datacenter __props__.__dict__["disable_upgrade_migration"] = disable_upgrade_migration __props__.__dict__["last_contact_threshold"] = last_contact_threshold __props__.__dict__["max_trailing_logs"] = max_trailing_logs __props__.__dict__["redundancy_zone_tag"] = redundancy_zone_tag __props__.__dict__["server_stabilization_time"] = server_stabilization_time __props__.__dict__["upgrade_version_tag"] = upgrade_version_tag super(AutopilotConfig, __self__).__init__( 'consul:index/autopilotConfig:AutopilotConfig', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None, cleanup_dead_servers: Optional[pulumi.Input[bool]] = None, datacenter: Optional[pulumi.Input[str]] = None, disable_upgrade_migration: Optional[pulumi.Input[bool]] = None, last_contact_threshold: Optional[pulumi.Input[str]] = None, max_trailing_logs: Optional[pulumi.Input[int]] = None, redundancy_zone_tag: Optional[pulumi.Input[str]] = None, server_stabilization_time: Optional[pulumi.Input[str]] = None, upgrade_version_tag: Optional[pulumi.Input[str]] = None) -> 'AutopilotConfig': """ Get an existing AutopilotConfig resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param pulumi.Input[str] id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[bool] cleanup_dead_servers: Whether to remove failing servers when a replacement comes online. Defaults to true. :param pulumi.Input[str] datacenter: The datacenter to use. This overrides the agent's default datacenter and the datacenter in the provider setup. :param pulumi.Input[bool] disable_upgrade_migration: Whether to disable [upgrade migrations](https://www.consul.io/docs/guides/autopilot.html#redundancy-zones). Defaults to false. :param pulumi.Input[str] last_contact_threshold: The time after which a server is considered as unhealthy and will be removed. Defaults to `"200ms"`. :param pulumi.Input[int] max_trailing_logs: The maximum number of Raft log entries a server can trail the leader. Defaults to 250. :param pulumi.Input[str] redundancy_zone_tag: The [redundancy zone](https://www.consul.io/docs/guides/autopilot.html#redundancy-zones) tag to use. Consul will try to keep one voting server by zone to take advantage of isolated failure domains. Defaults to an empty string. :param pulumi.Input[str] server_stabilization_time: The period to wait for a server to be healthy and stable before being promoted to a full, voting member. Defaults to `"10s"`. :param pulumi.Input[str] upgrade_version_tag: The tag to override the version information used during a migration. Defaults to an empty string. """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = _AutopilotConfigState.__new__(_AutopilotConfigState) __props__.__dict__["cleanup_dead_servers"] = cleanup_dead_servers __props__.__dict__["datacenter"] = datacenter __props__.__dict__["disable_upgrade_migration"] = disable_upgrade_migration __props__.__dict__["last_contact_threshold"] = last_contact_threshold __props__.__dict__["max_trailing_logs"] = max_trailing_logs __props__.__dict__["redundancy_zone_tag"] = redundancy_zone_tag __props__.__dict__["server_stabilization_time"] = server_stabilization_time __props__.__dict__["upgrade_version_tag"] = upgrade_version_tag return AutopilotConfig(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter(name="cleanupDeadServers") def cleanup_dead_servers(self) -> pulumi.Output[Optional[bool]]: """ Whether to remove failing servers when a replacement comes online. Defaults to true. """ return pulumi.get(self, "cleanup_dead_servers") @property @pulumi.getter def datacenter(self) -> pulumi.Output[Optional[str]]: """ The datacenter to use. This overrides the agent's default datacenter and the datacenter in the provider setup. """ return pulumi.get(self, "datacenter") @property @pulumi.getter(name="disableUpgradeMigration") def disable_upgrade_migration(self) -> pulumi.Output[Optional[bool]]: """ Whether to disable [upgrade migrations](https://www.consul.io/docs/guides/autopilot.html#redundancy-zones). Defaults to false. """ return pulumi.get(self, "disable_upgrade_migration") @property @pulumi.getter(name="lastContactThreshold") def last_contact_threshold(self) -> pulumi.Output[Optional[str]]: """ The time after which a server is considered as unhealthy and will be removed. Defaults to `"200ms"`. """ return pulumi.get(self, "last_contact_threshold") @property @pulumi.getter(name="maxTrailingLogs") def max_trailing_logs(self) -> pulumi.Output[Optional[int]]: """ The maximum number of Raft log entries a server can trail the leader. Defaults to 250. """ return pulumi.get(self, "max_trailing_logs") @property @pulumi.getter(name="redundancyZoneTag") def redundancy_zone_tag(self) -> pulumi.Output[Optional[str]]: """ The [redundancy zone](https://www.consul.io/docs/guides/autopilot.html#redundancy-zones) tag to use. Consul will try to keep one voting server by zone to take advantage of isolated failure domains. Defaults to an empty string. """ return pulumi.get(self, "redundancy_zone_tag") @property @pulumi.getter(name="serverStabilizationTime") def server_stabilization_time(self) -> pulumi.Output[Optional[str]]: """ The period to wait for a server to be healthy and stable before being promoted to a full, voting member. Defaults to `"10s"`. """ return pulumi.get(self, "server_stabilization_time") @property @pulumi.getter(name="upgradeVersionTag") def upgrade_version_tag(self) -> pulumi.Output[Optional[str]]: """ The tag to override the version information used during a migration. Defaults to an empty string. """ return pulumi.get(self, "upgrade_version_tag")

48.487047

168

0.672971

3,339

28,074

5.423181

0.068583

0.065606

0.075547

0.054672

0.907334

0.901149

0.895792

0.887011

0.884195

0

0.00304

0.238334

28,074

578

169

48.570934

0.843801

0.37305

0

0.83391

1

0

0.130095

0.052366

0

1

0.16263

false

0.00346

0.017301

0

0.276817

0

null

0

1

0

1

0

null

0

8

1bc4f58877774c4d2a942683d70d34207bdc21f2

148

py

Python

src/tensora/__init__.py

amirmolavi/tensora

521a18abbb9689852b533b0d75b92ccd6bcd5245

[ "MIT" ]

3

2019-04-24T01:47:20.000Z

2021-06-13T10:40:38.000Z

src/tensora/__init__.py

amirmolavi/tensora

521a18abbb9689852b533b0d75b92ccd6bcd5245

[ "MIT" ]

5

2021-01-22T18:02:41.000Z

2021-02-20T21:11:04.000Z

src/tensora/__init__.py

amirmolavi/tensora

521a18abbb9689852b533b0d75b92ccd6bcd5245

[ "MIT" ]

2

2021-01-24T23:05:43.000Z

2021-04-19T18:54:55.000Z

from .format import Mode, Format # noqa: F401 from .tensor import Tensor # noqa: F401 from .function import tensor_method, evaluate # noqa: F401

37

59

0.75

21

148

5.238095

0.47619

0.218182

0

0.07377

0.175676

148

3

60

49.333333

0.827869

0.216216

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

7

1bc5d095dcb686c9c6e8b6eed1065a40a157c58c

131

py

Python

tenant_schemas/tests/__init__.py

Jragon/django-tenants-rls

99a336a0d1ee83c70b6224a583ee8e3b8ee5c930

[ "MIT" ]

null

tenant_schemas/tests/__init__.py

Jragon/django-tenants-rls

99a336a0d1ee83c70b6224a583ee8e3b8ee5c930

[ "MIT" ]

null

tenant_schemas/tests/__init__.py

Jragon/django-tenants-rls

99a336a0d1ee83c70b6224a583ee8e3b8ee5c930

[ "MIT" ]

null

from .test_cache import * from .test_log import * from .test_routes import * from .test_tenants import * from .test_utils import *

21.833333

27

0.770992

20

131

4.8

0.4

0.416667

0.583333

0

0.152672

131

5

28

26.2

0.864865

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

7

940c95bf7ba7e46f7a1d4f4a7a6adc1630333a73

7,218

py

Python

tests/unit/db/postgres/test_unloader.py

ellyteitsworth/records-mover

21cd56efc2d23cfff04ec1fdf582e5229546c418

[ "Apache-2.0" ]

null

tests/unit/db/postgres/test_unloader.py

ellyteitsworth/records-mover

21cd56efc2d23cfff04ec1fdf582e5229546c418

[ "Apache-2.0" ]

null

tests/unit/db/postgres/test_unloader.py

ellyteitsworth/records-mover

21cd56efc2d23cfff04ec1fdf582e5229546c418

[ "Apache-2.0" ]

null

import unittest from records_mover.db.postgres.unloader import PostgresUnloader from records_mover.records import DelimitedRecordsFormat from mock import MagicMock, Mock, patch, ANY class TestPostgresUnloader(unittest.TestCase): def setUp(self): self.mock_url_resolver = Mock(name='url_resolver') self.mock_db = MagicMock(name='db') self.unloader = PostgresUnloader(self.mock_db) @patch('records_mover.db.postgres.unloader.quote_value') @patch('records_mover.db.postgres.unloader.copy_to') @patch('records_mover.db.postgres.unloader.complain_on_unhandled_hints') @patch('records_mover.db.postgres.unloader.Table') @patch('records_mover.db.postgres.unloader.postgres_copy_to_options') def test_unload(self, mock_postgres_copy_to_options, mock_Table, mock_complain_on_unhandled_hints, mock_copy_to, mock_quote_value): mock_schema = Mock(name='schema') mock_table = Mock(name='table') mock_unload_plan = Mock(name='unload_plan') mock_directory = MagicMock(name='directory') mock_records_format = Mock(name='records_format', spec=DelimitedRecordsFormat) mock_records_format.hints = {} mock_unload_plan.records_format = mock_records_format mock_date_output_style = "DATE_OUTPUT_STYLE" mock_date_order_style = "DATE_ORDER_STYLE" mock_postgres_options = { 'abc': 123 } mock_postgres_copy_to_options.return_value = ( mock_date_output_style, mock_date_order_style, mock_postgres_options, ) mock_quote_value.return_value = "ABC" self.unloader.unload(mock_schema, mock_table, mock_unload_plan, mock_directory) mock_processing_instructions = mock_unload_plan.processing_instructions mock_unhandled_hints = set(mock_records_format.hints.keys()) mock_complain_on_unhandled_hints.\ assert_called_with(mock_processing_instructions.fail_if_dont_understand, mock_unhandled_hints, mock_records_format.hints) mock_table_obj = mock_Table.return_value mock_Table.assert_called_with(mock_table, ANY, schema=mock_schema, autoload=True, autoload_with=self.mock_db) mock_conn = self.mock_db.engine.begin.return_value.__enter__.return_value mock_quote_value.assert_called_with(mock_conn, 'DATE_OUTPUT_STYLE, DATE_ORDER_STYLE') mock_conn.execute.assert_called_with('SET LOCAL DateStyle = ABC') mock_fileobj = mock_directory.loc.file_in_this_directory.return_value.open.\ return_value.__enter__.return_value mock_copy_to.assert_called_with(mock_table_obj.select.return_value, mock_fileobj, mock_conn, abc=123) @patch('records_mover.db.postgres.unloader.quote_value') @patch('records_mover.db.postgres.unloader.copy_to') @patch('records_mover.db.postgres.unloader.complain_on_unhandled_hints') @patch('records_mover.db.postgres.unloader.Table') @patch('records_mover.db.postgres.unloader.postgres_copy_to_options') def test_unload_default_date_order_style(self, mock_postgres_copy_to_options, mock_Table, mock_complain_on_unhandled_hints, mock_copy_to, mock_quote_value): mock_schema = Mock(name='schema') mock_table = Mock(name='table') mock_unload_plan = Mock(name='unload_plan') mock_directory = MagicMock(name='directory') mock_records_format = Mock(name='records_format', spec=DelimitedRecordsFormat) mock_records_format.hints = {} mock_unload_plan.records_format = mock_records_format mock_date_output_style = "DATE_OUTPUT_STYLE" mock_date_order_style = None mock_postgres_options = { 'abc': 123 } mock_postgres_copy_to_options.return_value = ( mock_date_output_style, mock_date_order_style, mock_postgres_options, ) mock_quote_value.return_value = "ABC" self.unloader.unload(mock_schema, mock_table, mock_unload_plan, mock_directory) mock_processing_instructions = mock_unload_plan.processing_instructions mock_unhandled_hints = set(mock_records_format.hints.keys()) mock_complain_on_unhandled_hints.\ assert_called_with(mock_processing_instructions.fail_if_dont_understand, mock_unhandled_hints, mock_records_format.hints) mock_table_obj = mock_Table.return_value mock_Table.assert_called_with(mock_table, ANY, schema=mock_schema, autoload=True, autoload_with=self.mock_db) mock_conn = self.mock_db.engine.begin.return_value.__enter__.return_value mock_quote_value.assert_called_with(mock_conn, 'DATE_OUTPUT_STYLE, MDY') mock_conn.execute.assert_called_with('SET LOCAL DateStyle = ABC') mock_fileobj = mock_directory.loc.file_in_this_directory.return_value.open.\ return_value.__enter__.return_value mock_copy_to.assert_called_with(mock_table_obj.select.return_value, mock_fileobj, mock_conn, abc=123) @patch('records_mover.db.postgres.unloader.complain_on_unhandled_hints') @patch('records_mover.db.postgres.unloader.postgres_copy_to_options') def test_can_unload_this_format_true(self, mock_postgres_copy_to_options, mock_complain_on_unhandled_hints): source_records_format = Mock(name='source_records_format', spec=DelimitedRecordsFormat) source_records_format.hints = {} out = self.unloader.can_unload_this_format(source_records_format) self.assertTrue(out) def test_best_records_format(self): self.assertEqual(DelimitedRecordsFormat(variant='bluelabs', hints={ 'compression': None }), self.unloader.best_records_format())

50.125

93

0.592823

730

7,218

5.384932

0.119178

0.066141

0.046299

0.072755

0.840753

0.823455

0.819893

0.811498

0

0.002523

0.341092

7,218

143

94

50.475524

0.824012

0

0.77037

0

0.129122

0.088667

0

0.088889

1

0.037037

false

0

0.02963

0

0.074074

0

null

0

1

0

1

0

null

0

7

945daf87d28a2ed629a052cfc6bed1b2c979d89a

109

py

Python

technocup/2017/elimination_round_3/place.py

dluschan/olymp

dfbf4352dbc7f6fd7563e7bd19aff6fd67fb50b7

[ "MIT" ]

null

technocup/2017/elimination_round_3/place.py

dluschan/olymp

dfbf4352dbc7f6fd7563e7bd19aff6fd67fb50b7

[ "MIT" ]

null

technocup/2017/elimination_round_3/place.py

dluschan/olymp

dfbf4352dbc7f6fd7563e7bd19aff6fd67fb50b7

[ "MIT" ]

1

2018-09-14T18:50:48.000Z

n, m, k = map(int, input().split()) print((k + 2*m - 1) // (2*m), (k % 2*m + 1) // 2, 'L' if k % 2 else 'R')

36.333333

72

0.412844

25

109

1.8

0.56

0.133333

0.177778

0.222222

0

0.085366

0.247706

109

2

73

54.5

0.463415

0

0.018349

0

1

0

true

0

0.5

1

0

1

null

0

1

0

1

0

1

0

null

0

1

0

1

0

7

947c9256fca1d8ebc606d4a9dc7634d3a31aed08

20,200

py

Python

test.py

kushalkolar/NuSeT

cea354634fe432027f5103752f323791b78e3afe

[ "MIT" ]

18

2019-10-17T23:47:03.000Z

2022-03-28T14:37:02.000Z

test.py

kushalkolar/NuSeT

cea354634fe432027f5103752f323791b78e3afe

[ "MIT" ]

4

2020-08-18T20:02:03.000Z

2022-02-23T00:16:45.000Z

test.py

kushalkolar/NuSeT

cea354634fe432027f5103752f323791b78e3afe

[ "MIT" ]

8

2019-11-22T06:20:57.000Z

2021-11-08T20:32:57.000Z

import tensorflow as tf import numpy as np import csv from PIL import Image from tqdm import tqdm from skimage.transform import rescale from model_layers.models import UNET from model_layers.model_RPN import RPN from model_layers.anchor_size import anchor_size from model_layers.rpn_target import RPNTarget from model_layers.rpn_proposal import RPNProposal from model_layers.rpn_loss import RPNLoss from model_layers.seg_loss import segmentation_loss from model_layers.marker_watershed import marker_watershed from model_layers.compute_metrics import compute_metrics from utils.load_data import load_data_test from utils.tf_utils import optimizer_fun from utils.anchors import generate_anchors_reference from utils.generate_anchors import generate_anchors from utils.test import generate_gt_boxes from utils.normalization import whole_image_norm, foreground_norm, clean_image from utils.losses import smooth_l1_loss from utils.image_vis import draw_rpn_bbox_pred, draw_gt_boxes, draw_top_nms_proposals, draw_rpn_bbox_targets, draw_rpn_bbox_pred_only # inspired from https://github.com/tryolabs/luminoth/blob/master/luminoth/models/fasterrcnn/rpn_test.py def test(params, self): """Predict masks for all images in a given directory, and save them Args: params (dict): the parameters of the network """ # Get the testing parameters perform_watershed = params['watershed'] bbox_min_score = params['min_score'] nms_thresh = params['nms_threshold'] postProcess = params['postProcess'] resize_scale = params['scale_ratio'] # Load the data # x_test, y_test: test images and corresponding labels x_id, x_test = load_data_test(self.batch_seg_path) # pred_dict and pred_dict_final save all the temp variables pred_dict_final = {} train_initial = tf.placeholder(dtype=tf.float32, shape=[1, None, None, 1]) input_shape = tf.shape(train_initial) input_height = input_shape[1] input_width = input_shape[2] im_shape = tf.cast([input_height, input_width], tf.float32) # number of classes needed to be classified, for our case this equals to 2 # (foreground and background) nb_classes = 2 # feed the initial image to U-Net, we expect 2 outputs: # 1. feat_map of shape (?,hf,wf,1024), which will be passed to the # region proposal network # 2. final_logits of shape(?,h,w,2), which is the prediction from U-net with tf.variable_scope('model_U-Net') as scope: final_logits, feat_map = UNET(nb_classes, train_initial) # The final_logits has 2 channels for foreground/background softmax scores, # then we get prediction with larger score for each pixel pred_masks = tf.argmax(final_logits, axis=3) pred_masks = tf.reshape(pred_masks,[input_height,input_width]) pred_masks = tf.to_float(pred_masks) # Dynamic anchor base size calculated from median cell lengths base_size = anchor_size(tf.reshape(pred_masks,[input_height,input_width])) # scales and ratios are used to generate different anchors scales = np.array([ 0.5, 1, 2]) ratios = np.array([ 0.125, 0.25, 0.5, 1, 2, 4, 8]) # stride is to control how sparse we want to place anchors across the image # stride = 16 means to place an anchor every 16 pixels on the original image stride = 16 # Generate the anchor reference with respect to the original image ref_anchors = generate_anchors_reference(base_size, ratios, scales) num_ref_anchors = scales.shape[0] * ratios.shape[0] feat_height = input_height / stride feat_width = input_width / stride # Generate all the anchors based on ref_anchors all_anchors = generate_anchors(ref_anchors, stride, [feat_height,feat_width]) num_anchors = all_anchors.shape[0] with tf.variable_scope('model_RPN') as scope: prediction_dict = RPN(feat_map, num_ref_anchors) # Get the tensors from the dict rpn_cls_prob = prediction_dict['rpn_cls_prob'] rpn_bbox_pred = prediction_dict['rpn_bbox_pred'] proposal_prediction = RPNProposal(rpn_cls_prob, rpn_bbox_pred, all_anchors, im_shape, nms_thresh) pred_dict_final['all_anchors'] = tf.cast(all_anchors, tf.float32) prediction_dict['proposals'] = proposal_prediction['proposals'] prediction_dict['scores'] = proposal_prediction['scores'] pred_dict_final['rpn_prediction'] = prediction_dict scores = pred_dict_final['rpn_prediction']['scores'] proposals = pred_dict_final['rpn_prediction']['proposals'] pred_masks_watershed = tf.to_float(marker_watershed(scores, proposals, pred_masks, min_score = bbox_min_score)) # start point for testing, and end point for graph sess = tf.Session() sess.run(tf.global_variables_initializer()) num_batches_test = len(x_test) saver = tf.train.Saver() masks1 = [] # Restore the per-image normalization model from the trained network saver.restore(sess,'./Network/whole_norm.ckpt') sess.run(tf.local_variables_initializer()) for j in tqdm(range(0,num_batches_test)): # whole image normalization batch_data = x_test[j] batch_data_shape = batch_data.shape image = np.reshape(batch_data, [batch_data_shape[0],batch_data_shape[1]]) if resize_scale != 1: image = rescale(image, self.params['scale_ratio'], anti_aliasing=True) # Clip the height and width to be 16-fold imheight, imwidth = image.shape imheight = imheight//16*16 imwidth = imwidth//16*16 image = image[:imheight, :imwidth] image_normalized_wn = whole_image_norm(image) image_normalized_wn = np.reshape(image_normalized_wn, [1,imheight,imwidth,1]) masks = sess.run(pred_masks, feed_dict={train_initial:image_normalized_wn}) if not self.usingCL: self.progress_var.set(j/2/num_batches_test*100) self.window.update() # First pass, get the coarse masks, and normalize the image on masks masks1.append(masks) # Restore the foreground normalization model from the trained network saver.restore(sess,'./Network/foreground.ckpt') sess.run(tf.local_variables_initializer()) for j in tqdm(range(0,num_batches_test)): batch_data = x_test[j] batch_data_shape = batch_data.shape image = np.reshape(batch_data, [batch_data_shape[0],batch_data_shape[1]]) if resize_scale != 1: image = rescale(image, self.params['scale_ratio']) # Clip the height and width to be 16-fold imheight, imwidth = image.shape imheight = imheight//16*16 imwidth = imwidth//16*16 image = image[:imheight, :imwidth] # Final pass, foreground normalization to get final masks image_normalized_fg = foreground_norm(image, masks1[j]) image_normalized_fg = np.reshape(image_normalized_fg, [1,imheight,imwidth,1]) # If adding watershed, we save the watershed masks separately if perform_watershed == 'yes': masks_watershed = sess.run(pred_masks_watershed, feed_dict={train_initial:image_normalized_fg}) if postProcess == 'yes': masks_watershed = clean_image(masks_watershed) # Revert the scale to original display if resize_scale != 1: masks_watershed = rescale(masks_watershed, 1/self.params['scale_ratio']) I8 = (((masks_watershed - masks_watershed.min()) / (masks_watershed.max() - masks_watershed.min())) * 255).astype(np.uint8) img = Image.fromarray(I8) img.save(self.batch_seg_path + x_id[j] + '_masks_watershed.png') else: masks = sess.run(pred_masks, feed_dict={train_initial:image_normalized_fg}) if postProcess == 'yes': masks = clean_image(masks) # enable these 2 lines if your want to see the detection result #image_pil = draw_top_nms_proposals(pred_dict, batch_data, min_score=bbox_min_score, draw_gt=False) #image_pil.save(str(j)+'_pred.png') # Revert the scale to original display if resize_scale != 1: masks = rescale(masks, 1/self.params['scale_ratio']) I8 = (((masks - masks.min()) / (masks.max() - masks.min())) * 255).astype(np.uint8) img = Image.fromarray(I8) img.save(self.batch_seg_path + x_id[j] + '_masks.png') if not self.usingCL: self.progress_var.set(50 + j/2/num_batches_test*100) self.window.update() sess.close() # This function is similar to the function above, but only for one image that is # displayed on NuSeT GUI def test_single_img(params, x_test): """input the image, return the segmented mask Args: params (dict): the parameters of the network x_test: the input image in numpy array """ # Get the testing parameters perform_watershed = params['watershed'] bbox_min_score = params['min_score'] nms_thresh = params['nms_threshold'] postProcess = params['postProcess'] # pred_dict and pred_dict_final save all the temp variables pred_dict_final = {} train_initial = tf.placeholder(dtype=tf.float32, shape=[1, None, None, 1]) input_shape = tf.shape(train_initial) input_height = input_shape[1] input_width = input_shape[2] im_shape = tf.cast([input_height, input_width], tf.float32) # number of classes needed to be classified, for our case this equals to 2 # (foreground and background) nb_classes = 2 # feed the initial image to U-Net, we expect 2 outputs: # 1. feat_map of shape (?,32,32,1024), which will be passed to the # region proposal network # 2. final_logits of shape(?,512,512,2), which is the prediction from U-net with tf.variable_scope('model_U-Net') as scope: final_logits, feat_map = UNET(nb_classes, train_initial) # The final_logits has 2 channels for foreground/background softmax scores, # then we get prediction with larger score for each pixel pred_masks = tf.argmax(final_logits, axis=3) pred_masks = tf.reshape(pred_masks,[input_height,input_width]) pred_masks = tf.to_float(pred_masks) # Dynamic anchor base size calculated from median cell lengths base_size = anchor_size(tf.reshape(pred_masks,[input_height,input_width])) # scales and ratios are used to generate different anchors scales = np.array([ 0.5, 1, 2]) ratios = np.array([ 0.125, 0.25, 0.5, 1, 2, 4, 8]) # stride is to control how sparse we want to place anchors across the image # stride = 16 means to place an anchor every 16 pixels on the original image stride = 16 # Generate the anchor reference with respect to the original image ref_anchors = generate_anchors_reference(base_size, ratios, scales) num_ref_anchors = scales.shape[0] * ratios.shape[0] feat_height = input_height / stride feat_width = input_width / stride # Generate all the anchors based on ref_anchors all_anchors = generate_anchors(ref_anchors, stride, [feat_height,feat_width]) num_anchors = all_anchors.shape[0] with tf.variable_scope('model_RPN') as scope: prediction_dict = RPN(feat_map, num_ref_anchors) # Get the tensors from the dict rpn_cls_prob = prediction_dict['rpn_cls_prob'] rpn_bbox_pred = prediction_dict['rpn_bbox_pred'] proposal_prediction = RPNProposal(rpn_cls_prob, rpn_bbox_pred, all_anchors, im_shape, nms_thresh) pred_dict_final['all_anchors'] = tf.cast(all_anchors, tf.float32) prediction_dict['proposals'] = proposal_prediction['proposals'] prediction_dict['scores'] = proposal_prediction['scores'] pred_dict_final['rpn_prediction'] = prediction_dict scores = pred_dict_final['rpn_prediction']['scores'] proposals = pred_dict_final['rpn_prediction']['proposals'] pred_masks_watershed = tf.to_float(marker_watershed(scores, proposals, pred_masks, min_score = bbox_min_score)) # start point for testing, and end point for graph sess = tf.Session() sess.run(tf.global_variables_initializer()) num_batches_test = len(x_test) saver = tf.train.Saver() masks1 = [] # Restore the per-image normalization model from the trained network saver.restore(sess,'./Network/whole_norm.ckpt') sess.run(tf.local_variables_initializer()) for j in tqdm(range(0,num_batches_test)): # whole image normalization batch_data = x_test[j] batch_data_shape = batch_data.shape image_normalized_wn = whole_image_norm(batch_data) image_normalized_wn = np.reshape(image_normalized_wn, [1,batch_data_shape[0],batch_data_shape[1],1]) masks = sess.run(pred_masks, feed_dict={train_initial:image_normalized_wn}) # First pass, get the coarse masks, and normalize the image on masks masks1.append(masks) # Restore the foreground normalization model from the trained network saver.restore(sess,'./Network/foreground.ckpt') #saver.restore(sess,'./Network/fg_norm_weights_fluorescent/'+str(30)+'.ckpt') sess.run(tf.local_variables_initializer()) for j in tqdm(range(0,num_batches_test)): batch_data = x_test[j] batch_data_shape = batch_data.shape image = np.reshape(batch_data, [batch_data_shape[0],batch_data_shape[1]]) # Final pass, foreground normalization to get final masks image_normalized_fg = foreground_norm(image, masks1[j]) image_normalized_fg = np.reshape(image_normalized_fg, [1,batch_data_shape[0],batch_data_shape[1],1]) # If adding watershed, we save the watershed masks separately if perform_watershed == 'yes': masks = sess.run(pred_masks_watershed, feed_dict={train_initial:image_normalized_fg}) if postProcess == 'yes': masks = clean_image(masks) else: masks = sess.run(pred_masks, feed_dict={train_initial:image_normalized_fg}) if postProcess == 'yes': masks = clean_image(masks) sess.close() return masks def test_UNet(params, self): """Predict masks for all images in a given directory, and save them Args: params (dict): the parameters of the network """ postProcess = params['postProcess'] resize_scale = params['scale_ratio'] # Load the data # x_test, y_test: test images and corresponding labels x_id, x_test = load_data_test(self.batch_seg_path) # pred_dict and pred_dict_final save all the temp variables pred_dict_final = {} train_initial = tf.placeholder(dtype=tf.float32, shape=[1, None, None, 1]) input_shape = tf.shape(train_initial) input_height = input_shape[1] input_width = input_shape[2] im_shape = tf.cast([input_height, input_width], tf.float32) # number of classes needed to be classified, for our case this equals to 2 # (foreground and background) nb_classes = 2 # feed the initial image to U-Net, we expect 2 outputs: # 1. feat_map of shape (?,hf,wf,1024), which will be passed to the # region proposal network # 2. final_logits of shape(?,h,w,2), which is the prediction from U-net with tf.variable_scope('model_U-Net') as scope: final_logits, feat_map = UNET(nb_classes, train_initial) # The final_logits has 2 channels for foreground/background softmax scores, # then we get prediction with larger score for each pixel pred_masks = tf.argmax(final_logits, axis=3) pred_masks = tf.reshape(pred_masks,[input_height,input_width]) pred_masks = tf.to_float(pred_masks) # start point for testing, and end point for graph sess = tf.Session() sess.run(tf.global_variables_initializer()) num_batches_test = len(x_test) saver = tf.train.Saver() # Restore the per-image normalization model from the trained network saver.restore(sess,'./Network/UNet.ckpt') sess.run(tf.local_variables_initializer()) for j in tqdm(range(0,num_batches_test)): # whole image normalization batch_data = x_test[j] batch_data_shape = batch_data.shape image = np.reshape(batch_data, [batch_data_shape[0],batch_data_shape[1]]) if resize_scale != 1: image = rescale(image, self.params['scale_ratio'], anti_aliasing=True) # Clip the height and width to be 16-fold imheight, imwidth = image.shape imheight = imheight//16*16 imwidth = imwidth//16*16 image = image[:imheight, :imwidth] image_normalized_wn = whole_image_norm(image) image_normalized_wn = np.reshape(image_normalized_wn, [1,imheight,imwidth,1]) masks = sess.run(pred_masks, feed_dict={train_initial:image_normalized_wn}) if not self.usingCL: self.progress_var.set(j/num_batches_test*100) self.window.update() if postProcess == 'yes': masks = clean_image(masks) # Revert the scale to original display if resize_scale != 1: masks = rescale(masks, 1/self.params['scale_ratio']) I8 = (((masks - masks.min()) / (masks.max() - masks.min())) * 255).astype(np.uint8) img = Image.fromarray(I8) img.save(self.batch_seg_path + x_id[j] + '_masks.png') sess.close() # This function is similar to the function above, but only for one image that is # displayed on NuSeT GUI def test_single_img_UNet(params, x_test): """input the image, return the segmented mask Args: params (dict): the parameters of the network x_test: the input image in numpy array """ # Get the testing parameters postProcess = params['postProcess'] # pred_dict and pred_dict_final save all the temp variables pred_dict_final = {} train_initial = tf.placeholder(dtype=tf.float32, shape=[1, None, None, 1]) input_shape = tf.shape(train_initial) input_height = input_shape[1] input_width = input_shape[2] im_shape = tf.cast([input_height, input_width], tf.float32) # number of classes needed to be classified, for our case this equals to 2 # (foreground and background) nb_classes = 2 # feed the initial image to U-Net, we expect 2 outputs: # 1. feat_map of shape (?,32,32,1024), which will be passed to the # region proposal network # 2. final_logits of shape(?,512,512,2), which is the prediction from U-net with tf.variable_scope('model_U-Net') as scope: final_logits, feat_map = UNET(nb_classes, train_initial) # The final_logits has 2 channels for foreground/background softmax scores, # then we get prediction with larger score for each pixel pred_masks = tf.argmax(final_logits, axis=3) pred_masks = tf.reshape(pred_masks,[input_height,input_width]) pred_masks = tf.to_float(pred_masks) # start point for testing, and end point for graph sess = tf.Session() sess.run(tf.global_variables_initializer()) num_batches_test = len(x_test) saver = tf.train.Saver() masks1 = [] # Restore the per-image normalization model from the trained network saver.restore(sess,'./Network/UNet.ckpt') sess.run(tf.local_variables_initializer()) for j in tqdm(range(0,num_batches_test)): # whole image normalization batch_data = x_test[j] batch_data_shape = batch_data.shape image_normalized_wn = whole_image_norm(batch_data) image_normalized_wn = np.reshape(image_normalized_wn, [1,batch_data_shape[0],batch_data_shape[1],1]) masks = sess.run(pred_masks, feed_dict={train_initial:image_normalized_wn}) if postProcess == 'yes': masks = clean_image(masks) sess.close() return masks

38.697318

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0.882947

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0.226287

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521

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38.771593

0.825133

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null

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1

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1

0

null

0

7

846a6de367f961a87b8b506bb0aad795c93373d5

331

py

Python

authentication/forms.py

darkcloudb/DJANGO-twitterclone

1c6c05ce09fa8f780b35927a6c28d3dcdf74e640

[ "MIT" ]

null

authentication/forms.py

darkcloudb/DJANGO-twitterclone

1c6c05ce09fa8f780b35927a6c28d3dcdf74e640

[ "MIT" ]

null

authentication/forms.py

darkcloudb/DJANGO-twitterclone

1c6c05ce09fa8f780b35927a6c28d3dcdf74e640

[ "MIT" ]

null

from django import forms class LoginForm(forms.Form): username = forms.CharField(max_length=30) password = forms.CharField(widget=forms.PasswordInput) class SignUpForm(forms.Form): username = forms.CharField(max_length=30) password = forms.CharField(widget=forms.PasswordInput) # email = forms.EmailField()

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8

84afab2f163d6f2d83a9d7686cf213e1c2dac5c2

114,271

py

Python

kmip/tests/unit/core/objects/test_objects.py

openstack/deb-python-kmip

f86134878b5f558b39f51e67a6e6ba5a0b03e222

[ "Apache-2.0" ]

12

2016-09-14T21:59:10.000Z

2020-03-11T07:37:25.000Z

kmip/tests/unit/core/objects/test_objects.py

openstack/deb-python-kmip

f86134878b5f558b39f51e67a6e6ba5a0b03e222

[ "Apache-2.0" ]

null

kmip/tests/unit/core/objects/test_objects.py

openstack/deb-python-kmip

f86134878b5f558b39f51e67a6e6ba5a0b03e222

[ "Apache-2.0" ]

null

# Copyright (c) 2015 The Johns Hopkins University/Applied Physics Laboratory # 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. from six import string_types import testtools from testtools import TestCase from kmip.core import attributes from kmip.core import enums from kmip.core.enums import AttributeType from kmip.core.enums import BlockCipherMode from kmip.core.enums import HashingAlgorithm as HashingAlgorithmEnum from kmip.core.enums import KeyRoleType from kmip.core.enums import PaddingMethod from kmip.core.enums import Tags from kmip.core.factories.attributes import AttributeValueFactory from kmip.core import objects from kmip.core.objects import Attribute from kmip.core.objects import ExtensionName from kmip.core.objects import ExtensionTag from kmip.core.objects import ExtensionType from kmip.core.objects import KeyMaterialStruct from kmip.core import utils from kmip.core.utils import BytearrayStream class TestAttributeClass(TestCase): """ A test suite for the Attribute class """ def setUp(self): super(TestAttributeClass, self).setUp() name_a = 'CRYPTOGRAPHIC PARAMETERS' name_b = 'CRYPTOGRAPHIC ALGORITHM' self.attribute_name_a = Attribute.AttributeName(name_a) self.attribute_name_b = Attribute.AttributeName(name_b) self.factory = AttributeValueFactory() self.attribute_value_a = self.factory.create_attribute_value( AttributeType.CRYPTOGRAPHIC_PARAMETERS, {'block_cipher_mode': BlockCipherMode.CBC, 'padding_method': PaddingMethod.PKCS5, 'hashing_algorithm': HashingAlgorithmEnum.SHA_1, 'key_role_type': KeyRoleType.BDK}) self.attribute_value_b = self.factory.create_attribute_value( AttributeType.CRYPTOGRAPHIC_PARAMETERS, {'block_cipher_mode': BlockCipherMode.CCM, 'padding_method': PaddingMethod.PKCS5, 'hashing_algorithm': HashingAlgorithmEnum.SHA_1, 'key_role_type': KeyRoleType.BDK}) index_a = 2 index_b = 3 self.attribute_index_a = Attribute.AttributeIndex(index_a) self.attribute_index_b = Attribute.AttributeIndex(index_b) self.attributeObj_a = Attribute( attribute_name=self.attribute_name_a, attribute_value=self.attribute_value_a, attribute_index=self.attribute_index_a) self.attributeObj_b = Attribute( attribute_name=self.attribute_name_b, attribute_value=self.attribute_value_a, attribute_index=self.attribute_index_a) self.attributeObj_c = Attribute( attribute_name=self.attribute_name_a, attribute_value=self.attribute_value_b, attribute_index=self.attribute_index_a) self.attributeObj_d = Attribute( attribute_name=self.attribute_name_a, attribute_value=self.attribute_value_a, attribute_index=self.attribute_index_b) self.key_req_with_crypt_params = BytearrayStream(( b'\x42\x00\x08\x01\x00\x00\x00\x78\x42\x00\x0a\x07\x00\x00\x00\x18' b'\x43\x52\x59\x50\x54\x4f\x47\x52\x41\x50\x48\x49\x43\x20\x50\x41' b'\x52\x41\x4d\x45\x54\x45\x52\x53' b'\x42\x00\x09\x02\x00\x00\x00\x04\x00\x00\x00\x02\x00\x00\x00\x00' b'\x42\x00\x0b\x01\x00\x00\x00\x40' b'\x42\x00\x11\x05\x00\x00\x00\x04\x00\x00\x00\x01\x00\x00\x00\x00' b'\x42\x00\x5f\x05\x00\x00\x00\x04\x00\x00\x00\x03\x00\x00\x00\x00' b'\x42\x00\x38\x05\x00\x00\x00\x04\x00\x00\x00\x04\x00\x00\x00\x00' b'\x42\x00\x83\x05\x00\x00\x00\x04\x00\x00\x00\x01\x00\x00\x00\x00' )) def tearDown(self): super(TestAttributeClass, self).tearDown() def test_read(self): attrObj = Attribute() attrObj.read(self.key_req_with_crypt_params) self.assertEqual(self.attributeObj_a, attrObj) def test_write(self): attrObj = Attribute(self.attribute_name_a, self.attribute_index_a, self.attribute_value_a) ostream = BytearrayStream() attrObj.write(ostream) self.assertEqual(self.key_req_with_crypt_params, ostream) def test_equal_on_equal(self): self.assertFalse(self.attributeObj_a == self.attributeObj_b) self.assertFalse(self.attributeObj_a == self.attributeObj_c) self.assertFalse(self.attributeObj_a == self.attributeObj_d) def test_not_equal_on_not_equal(self): self.assertTrue(self.attributeObj_a != self.attributeObj_b) class TestKeyMaterialStruct(TestCase): """ A test suite for the KeyMaterialStruct. A placeholder test suite. Should be removed when KeyMaterialStruct is removed from the code base. """ def setUp(self): super(TestKeyMaterialStruct, self).setUp() def tearDown(self): super(TestKeyMaterialStruct, self).tearDown() def test_valid_tag(self): """ Test that the KeyMaterialStruct tag is valid. """ struct = KeyMaterialStruct() self.assertEqual(Tags.KEY_MATERIAL, struct.tag) class TestExtensionName(TestCase): """ A test suite for the ExtensionName class. Since ExtensionName is a simple wrapper for the TextString primitive, only a few tests pertaining to construction are needed. """ def setUp(self): super(TestExtensionName, self).setUp() def tearDown(self): super(TestExtensionName, self).tearDown() def _test_init(self, value): if (isinstance(value, string_types)) or (value is None): extension_name = ExtensionName(value) if value is None: value = '' msg = "expected {0}, observed {1}".format( value, extension_name.value) self.assertEqual(value, extension_name.value, msg) else: self.assertRaises(TypeError, ExtensionName, value) def test_init_with_none(self): """ Test that an ExtensionName object can be constructed with no specified value. """ self._test_init(None) def test_init_with_valid(self): """ Test that an ExtensionName object can be constructed with a valid string value. """ self._test_init("valid") def test_init_with_invalid(self): """ Test that a TypeError exception is raised when a non-string value is used to construct an ExtensionName object. """ self._test_init(0) class TestExtensionTag(TestCase): """ A test suite for the ExtensionTag class. Since ExtensionTag is a simple wrapper for the Integer primitive, only a few tests pertaining to construction are needed. """ def setUp(self): super(TestExtensionTag, self).setUp() def tearDown(self): super(TestExtensionTag, self).tearDown() def _test_init(self, value): if (isinstance(value, int)) or (value is None): extension_tag = ExtensionTag(value) if value is None: value = 0 msg = "expected {0}, observed {1}".format( value, extension_tag.value) self.assertEqual(value, extension_tag.value, msg) else: self.assertRaises(TypeError, ExtensionTag, value) def test_init_with_none(self): """ Test that an ExtensionTag object can be constructed with no specified value. """ self._test_init(None) def test_init_with_valid(self): """ Test that an ExtensionTag object can be constructed with a valid integer value. """ self._test_init(0) def test_init_with_invalid(self): """ Test that a TypeError exception is raised when a non-integer value is used to construct an ExtensionName object. """ self._test_init("invalid") class TestExtensionType(TestCase): """ A test suite for the ExtensionType class. Since ExtensionType is a simple wrapper for the Integer primitive, only a few tests pertaining to construction are needed. """ def setUp(self): super(TestExtensionType, self).setUp() def tearDown(self): super(TestExtensionType, self).tearDown() def _test_init(self, value): if (isinstance(value, int)) or (value is None): extension_type = ExtensionType(value) if value is None: value = 0 msg = "expected {0}, observed {1}".format( value, extension_type.value) self.assertEqual(value, extension_type.value, msg) else: self.assertRaises(TypeError, ExtensionType, value) def test_init_with_none(self): """ Test that an ExtensionType object can be constructed with no specified value. """ self._test_init(None) def test_init_with_valid(self): """ Test that an ExtensionType object can be constructed with a valid integer value. """ self._test_init(0) def test_init_with_invalid(self): """ Test that a TypeError exception is raised when a non-string value is used to construct an ExtensionType object. """ self._test_init("invalid") class TestEncryptionKeyInformation(testtools.TestCase): """ Test suite for the EncryptionKeyInformation struct. """ def setUp(self): super(TestEncryptionKeyInformation, self).setUp() # Encoding obtained from the KMIP 1.1 testing document, Section 14.1. # # This encoding matches the following set of values: # Unique Identifier - 100182d5-72b8-47aa-8383-4d97d512e98a # Cryptographic Parameters # Block Cipher Mode - NIST_KEY_WRAP self.full_encoding = BytearrayStream( b'\x42\x00\x36\x01\x00\x00\x00\x48' b'\x42\x00\x94\x07\x00\x00\x00\x24' b'\x31\x30\x30\x31\x38\x32\x64\x35\x2D\x37\x32\x62\x38\x2D\x34\x37' b'\x61\x61\x2D\x38\x33\x38\x33\x2D\x34\x64\x39\x37\x64\x35\x31\x32' b'\x65\x39\x38\x61\x00\x00\x00\x00' b'\x42\x00\x2B\x01\x00\x00\x00\x10' b'\x42\x00\x11\x05\x00\x00\x00\x04\x00\x00\x00\x0D\x00\x00\x00\x00' ) # Adapted from the full encoding above. This encoding matches the # following set of values: # Unique Identifier - 100182d5-72b8-47aa-8383-4d97d512e98a self.partial_encoding = BytearrayStream( b'\x42\x00\x36\x01\x00\x00\x00\x30' b'\x42\x00\x94\x07\x00\x00\x00\x24' b'\x31\x30\x30\x31\x38\x32\x64\x35\x2D\x37\x32\x62\x38\x2D\x34\x37' b'\x61\x61\x2D\x38\x33\x38\x33\x2D\x34\x64\x39\x37\x64\x35\x31\x32' b'\x65\x39\x38\x61\x00\x00\x00\x00' ) self.empty_encoding = BytearrayStream( b'\x42\x00\x36\x01\x00\x00\x00\x00' ) def tearDown(self): super(TestEncryptionKeyInformation, self).tearDown() def test_init(self): """ Test that an EncryptionKeyInformation struct can be constructed with no arguments. """ encryption_key_information = objects.EncryptionKeyInformation() self.assertEqual(None, encryption_key_information.unique_identifier) self.assertEqual( None, encryption_key_information.cryptographic_parameters ) def test_init_with_args(self): """ Test that an EncryptionKeyInformation struct can be constructed with valid values. """ cryptographic_parameters = attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.CTR) encryption_key_information = objects.EncryptionKeyInformation( unique_identifier="00000000-1111-2222-3333-444444444444", cryptographic_parameters=cryptographic_parameters ) self.assertEqual( "00000000-1111-2222-3333-444444444444", encryption_key_information.unique_identifier ) self.assertIsInstance( encryption_key_information.cryptographic_parameters, attributes.CryptographicParameters ) parameters = encryption_key_information.cryptographic_parameters self.assertEqual( enums.BlockCipherMode.CTR, parameters.block_cipher_mode ) def test_invalid_unique_identifier(self): """ Test that a TypeError is raised when an invalid value is used to set the unique identifier of an EncryptionKeyInformation struct. """ kwargs = {'unique_identifier': 0} self.assertRaisesRegexp( TypeError, "Unique identifier must be a string.", objects.EncryptionKeyInformation, **kwargs ) encryption_key_information = objects.EncryptionKeyInformation() args = (encryption_key_information, 'unique_identifier', 0) self.assertRaisesRegexp( TypeError, "Unique identifier must be a string.", setattr, *args ) def test_invalid_cryptographic_parameters(self): """ Test that a TypeError is raised when an invalid value is used to set the cryptographic parameters of an EncryptionKeyInformation struct. """ kwargs = {'cryptographic_parameters': 'invalid'} self.assertRaisesRegexp( TypeError, "Cryptographic parameters must be a CryptographicParameters " "struct.", objects.EncryptionKeyInformation, **kwargs ) encryption_key_information = objects.EncryptionKeyInformation() args = ( encryption_key_information, 'cryptographic_parameters', 'invalid' ) self.assertRaisesRegexp( TypeError, "Cryptographic parameters must be a CryptographicParameters " "struct.", setattr, *args ) def test_read(self): """ Test that an EncryptionKeyInformation struct can be read from a data stream. """ encryption_key_information = objects.EncryptionKeyInformation() self.assertEqual(None, encryption_key_information.unique_identifier) self.assertEqual( None, encryption_key_information.cryptographic_parameters ) encryption_key_information.read(self.full_encoding) self.assertEqual( "100182d5-72b8-47aa-8383-4d97d512e98a", encryption_key_information.unique_identifier ) self.assertIsInstance( encryption_key_information.cryptographic_parameters, attributes.CryptographicParameters ) cryptographic_parameters = \ encryption_key_information.cryptographic_parameters self.assertEqual( enums.BlockCipherMode.NIST_KEY_WRAP, cryptographic_parameters.block_cipher_mode ) def test_read_partial(self): """ Test that an EncryptionKeyInformation struct can be read from a partial data stream. """ encryption_key_information = objects.EncryptionKeyInformation() self.assertEqual(None, encryption_key_information.unique_identifier) self.assertEqual( None, encryption_key_information.cryptographic_parameters ) encryption_key_information.read(self.partial_encoding) self.assertEqual( "100182d5-72b8-47aa-8383-4d97d512e98a", encryption_key_information.unique_identifier ) self.assertEqual( None, encryption_key_information.cryptographic_parameters ) def test_read_invalid(self): """ Test that a ValueError gets raised when a required EncryptionKeyInformation field is missing from the struct encoding. """ encryption_key_information = objects.EncryptionKeyInformation() args = (self.empty_encoding,) self.assertRaisesRegexp( ValueError, "Invalid struct missing the unique identifier attribute.", encryption_key_information.read, *args ) def test_write(self): """ Test that an EncryptionKeyInformation struct can be written to a data stream. """ cryptographic_parameters = attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.NIST_KEY_WRAP ) encryption_key_information = objects.EncryptionKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a", cryptographic_parameters=cryptographic_parameters ) stream = BytearrayStream() encryption_key_information.write(stream) self.assertEqual(len(self.full_encoding), len(stream)) self.assertEqual(str(self.full_encoding), str(stream)) def test_write_partial(self): """ Test that a partially defined EncryptionKeyInformation struct can be written to a data stream. """ encryption_key_information = objects.EncryptionKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a" ) stream = BytearrayStream() encryption_key_information.write(stream) self.assertEqual(len(self.partial_encoding), len(stream)) self.assertEqual(str(self.partial_encoding), str(stream)) def test_write_invalid(self): """ Test that a ValueError gets raised when a required EncryptionKeyInformation field is missing when encoding the struct. """ encryption_key_information = objects.EncryptionKeyInformation() stream = utils.BytearrayStream() args = (stream,) self.assertRaisesRegexp( ValueError, "Invalid struct missing the unique identifier attribute.", encryption_key_information.write, *args ) def test_equal_on_equal(self): """ Test that the equality operator returns True when comparing two EncryptionKeyInformation structs with the same data. """ a = objects.EncryptionKeyInformation() b = objects.EncryptionKeyInformation() self.assertTrue(a == b) self.assertTrue(b == a) a = objects.EncryptionKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.CBC ) ) b = objects.EncryptionKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.CBC ) ) self.assertTrue(a == b) self.assertTrue(b == a) def test_equal_on_not_equal_unique_identifier(self): """ Test that the equality operator returns False when comparing two EncryptionKeyInformation structs with different unique identifiers. """ a = objects.EncryptionKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a" ) b = objects.EncryptionKeyInformation( unique_identifier="00000000-1111-2222-3333-444444444444" ) self.assertFalse(a == b) self.assertFalse(b == a) def test_equal_on_not_equal_cryptographic_parameters(self): """ Test that the equality operator returns False when comparing two EncryptionKeyInformation structs with different cryptographic parameters. """ a = objects.EncryptionKeyInformation( cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.CBC ) ) b = objects.EncryptionKeyInformation( cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.GCM ) ) self.assertFalse(a == b) self.assertFalse(b == a) def test_equal_on_type_mismatch(self): """ Test that the equality operator returns False when comparing two EncryptionKeyInformation structs with different types. """ a = objects.EncryptionKeyInformation() b = 'invalid' self.assertFalse(a == b) self.assertFalse(b == a) def test_not_equal_on_equal(self): """ Test that the inequality operator returns False when comparing two EncryptionKeyInformation structs with the same data. """ a = objects.EncryptionKeyInformation() b = objects.EncryptionKeyInformation() self.assertFalse(a != b) self.assertFalse(b != a) a = objects.EncryptionKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.CBC ) ) b = objects.EncryptionKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.CBC ) ) self.assertFalse(a != b) self.assertFalse(b != a) def test_not_equal_on_not_equal_unique_identifier(self): """ Test that the inequality operator returns True when comparing two EncryptionKeyInformation structs with different unique identifiers. """ a = objects.EncryptionKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a" ) b = objects.EncryptionKeyInformation( unique_identifier="00000000-1111-2222-3333-444444444444" ) self.assertTrue(a != b) self.assertTrue(b != a) def test_not_equal_on_not_equal_cryptographic_parameters(self): """ Test that the inequality operator returns True when comparing two EncryptionKeyInformation structs with different cryptographic parameters. """ a = objects.EncryptionKeyInformation( cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.CBC ) ) b = objects.EncryptionKeyInformation( cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.GCM ) ) self.assertTrue(a != b) self.assertTrue(b != a) def test_not_equal_on_type_mismatch(self): """ Test that the inequality operator returns True when comparing two EncryptionKeyInformation structs with different types. """ a = objects.EncryptionKeyInformation() b = 'invalid' self.assertTrue(a != b) self.assertTrue(b != a) def test_repr(self): """ Test that repr can be applied to an EncryptionKeyInformation struct. """ encryption_key_information = objects.EncryptionKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.CBC ) ) expected = ( "EncryptionKeyInformation(" "unique_identifier='100182d5-72b8-47aa-8383-4d97d512e98a', " "cryptographic_parameters=CryptographicParameters(" "block_cipher_mode=BlockCipherMode.CBC, " "padding_method=None, " "hashing_algorithm=None, " "key_role_type=None, " "digital_signature_algorithm=None, " "cryptographic_algorithm=None, " "random_iv=None, " "iv_length=None, " "tag_length=None, " "fixed_field_length=None, " "invocation_field_length=None, " "counter_length=None, " "initial_counter_value=None))" ) observed = repr(encryption_key_information) self.assertEqual(expected, observed) def test_str(self): """ Test that str can be applied to an EncryptionKeyInformation struct. """ cryptographic_parameters = attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.CBC ) encryption_key_information = objects.EncryptionKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a", cryptographic_parameters=cryptographic_parameters ) expected = str({ 'unique_identifier': "100182d5-72b8-47aa-8383-4d97d512e98a", 'cryptographic_parameters': cryptographic_parameters }) observed = str(encryption_key_information) self.assertEqual(expected, observed) class TestMACSignatureKeyInformation(testtools.TestCase): """ Test suite for the MACSignatureKeyInformation struct. """ def setUp(self): super(TestMACSignatureKeyInformation, self).setUp() # Encoding obtained in part from the KMIP 1.1 testing document, # Section 14.1. The rest of the encoding was built by hand. # # This encoding matches the following set of values: # Unique Identifier - 100182d5-72b8-47aa-8383-4d97d512e98a # Cryptographic Parameters # Block Cipher Mode - NIST_KEY_WRAP self.full_encoding = BytearrayStream( b'\x42\x00\x4E\x01\x00\x00\x00\x48' b'\x42\x00\x94\x07\x00\x00\x00\x24' b'\x31\x30\x30\x31\x38\x32\x64\x35\x2D\x37\x32\x62\x38\x2D\x34\x37' b'\x61\x61\x2D\x38\x33\x38\x33\x2D\x34\x64\x39\x37\x64\x35\x31\x32' b'\x65\x39\x38\x61\x00\x00\x00\x00' b'\x42\x00\x2B\x01\x00\x00\x00\x10' b'\x42\x00\x11\x05\x00\x00\x00\x04\x00\x00\x00\x0D\x00\x00\x00\x00' ) # Adapted from the full encoding above. This encoding matches the # following set of values: # Unique Identifier - 100182d5-72b8-47aa-8383-4d97d512e98a self.partial_encoding = BytearrayStream( b'\x42\x00\x4E\x01\x00\x00\x00\x30' b'\x42\x00\x94\x07\x00\x00\x00\x24' b'\x31\x30\x30\x31\x38\x32\x64\x35\x2D\x37\x32\x62\x38\x2D\x34\x37' b'\x61\x61\x2D\x38\x33\x38\x33\x2D\x34\x64\x39\x37\x64\x35\x31\x32' b'\x65\x39\x38\x61\x00\x00\x00\x00' ) self.empty_encoding = BytearrayStream( b'\x42\x00\x4E\x01\x00\x00\x00\x00' ) def tearDown(self): super(TestMACSignatureKeyInformation, self).tearDown() def test_init(self): """ Test that a MACSignatureKeyInformation struct can be constructed with no arguments. """ mac_signature_key_information = objects.MACSignatureKeyInformation() self.assertEqual( None, mac_signature_key_information.unique_identifier ) self.assertEqual( None, mac_signature_key_information.cryptographic_parameters ) def test_init_with_args(self): """ Test that a MACSignatureKeyInformation struct can be constructed with valid values. """ cryptographic_parameters = attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.CTR) mac_signature_key_information = objects.MACSignatureKeyInformation( unique_identifier="00000000-1111-2222-3333-444444444444", cryptographic_parameters=cryptographic_parameters ) self.assertEqual( "00000000-1111-2222-3333-444444444444", mac_signature_key_information.unique_identifier ) self.assertIsInstance( mac_signature_key_information.cryptographic_parameters, attributes.CryptographicParameters ) parameters = mac_signature_key_information.cryptographic_parameters self.assertEqual( enums.BlockCipherMode.CTR, parameters.block_cipher_mode ) def test_invalid_unique_identifier(self): """ Test that a TypeError is raised when an invalid value is used to set the unique identifier of a MACSignatureKeyInformation struct. """ kwargs = {'unique_identifier': 0} self.assertRaisesRegexp( TypeError, "Unique identifier must be a string.", objects.MACSignatureKeyInformation, **kwargs ) args = (objects.MACSignatureKeyInformation(), 'unique_identifier', 0) self.assertRaisesRegexp( TypeError, "Unique identifier must be a string.", setattr, *args ) def test_invalid_cryptographic_parameters(self): """ Test that a TypeError is raised when an invalid value is used to set the cryptographic parameters of a MACSignatureKeyInformation struct. """ kwargs = {'cryptographic_parameters': 'invalid'} self.assertRaisesRegexp( TypeError, "Cryptographic parameters must be a CryptographicParameters " "struct.", objects.MACSignatureKeyInformation, **kwargs ) args = ( objects.MACSignatureKeyInformation(), 'cryptographic_parameters', 'invalid' ) self.assertRaisesRegexp( TypeError, "Cryptographic parameters must be a CryptographicParameters " "struct.", setattr, *args ) def test_read(self): """ Test that a MACSignatureKeyInformation struct can be read from a data stream. """ mac_signature_key_information = objects.MACSignatureKeyInformation() self.assertEqual( None, mac_signature_key_information.unique_identifier ) self.assertEqual( None, mac_signature_key_information.cryptographic_parameters ) mac_signature_key_information.read(self.full_encoding) self.assertEqual( "100182d5-72b8-47aa-8383-4d97d512e98a", mac_signature_key_information.unique_identifier ) self.assertIsInstance( mac_signature_key_information.cryptographic_parameters, attributes.CryptographicParameters ) cryptographic_parameters = \ mac_signature_key_information.cryptographic_parameters self.assertEqual( enums.BlockCipherMode.NIST_KEY_WRAP, cryptographic_parameters.block_cipher_mode ) def test_read_partial(self): """ Test that a MACSignatureKeyInformation struct can be read from a partial data stream. """ mac_signature_key_information = objects.MACSignatureKeyInformation() self.assertEqual( None, mac_signature_key_information.unique_identifier ) self.assertEqual( None, mac_signature_key_information.cryptographic_parameters ) mac_signature_key_information.read(self.partial_encoding) self.assertEqual( "100182d5-72b8-47aa-8383-4d97d512e98a", mac_signature_key_information.unique_identifier ) self.assertEqual( None, mac_signature_key_information.cryptographic_parameters ) def test_read_invalid(self): """ Test that a ValueError gets raised when a required MACSignatureKeyInformation field is missing from the struct encoding. """ mac_signature_key_information = objects.MACSignatureKeyInformation() args = (self.empty_encoding,) self.assertRaisesRegexp( ValueError, "Invalid struct missing the unique identifier attribute.", mac_signature_key_information.read, *args ) def test_write(self): """ Test that a MACSignatureKeyInformation struct can be written to a data stream. """ cryptographic_parameters = attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.NIST_KEY_WRAP ) mac_signature_key_information = objects.MACSignatureKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a", cryptographic_parameters=cryptographic_parameters ) stream = BytearrayStream() mac_signature_key_information.write(stream) self.assertEqual(len(self.full_encoding), len(stream)) self.assertEqual(str(self.full_encoding), str(stream)) def test_write_partial(self): """ Test that a partially defined MACSignatureKeyInformation struct can be written to a data stream. """ mac_signature_key_information = objects.MACSignatureKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a" ) stream = BytearrayStream() mac_signature_key_information.write(stream) self.assertEqual(len(self.partial_encoding), len(stream)) self.assertEqual(str(self.partial_encoding), str(stream)) def test_write_invalid(self): """ Test that a ValueError gets raised when a required MACSignatureKeyInformation field is missing when encoding the struct. """ mac_signature_key_information = objects.MACSignatureKeyInformation() stream = utils.BytearrayStream() args = (stream,) self.assertRaisesRegexp( ValueError, "Invalid struct missing the unique identifier attribute.", mac_signature_key_information.write, *args ) def test_equal_on_equal(self): """ Test that the equality operator returns True when comparing two MACSignatureKeyInformation structs with the same data. """ a = objects.MACSignatureKeyInformation() b = objects.MACSignatureKeyInformation() self.assertTrue(a == b) self.assertTrue(b == a) a = objects.MACSignatureKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.CBC ) ) b = objects.MACSignatureKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.CBC ) ) self.assertTrue(a == b) self.assertTrue(b == a) def test_equal_on_not_equal_unique_identifier(self): """ Test that the equality operator returns False when comparing two MACSignatureKeyInformation structs with different unique identifiers. """ a = objects.MACSignatureKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a" ) b = objects.MACSignatureKeyInformation( unique_identifier="00000000-1111-2222-3333-444444444444" ) self.assertFalse(a == b) self.assertFalse(b == a) def test_equal_on_not_equal_cryptographic_parameters(self): """ Test that the equality operator returns False when comparing two MACSignatureKeyInformation structs with different cryptographic parameters. """ a = objects.MACSignatureKeyInformation( cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.CBC ) ) b = objects.MACSignatureKeyInformation( cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.GCM ) ) self.assertFalse(a == b) self.assertFalse(b == a) def test_equal_on_type_mismatch(self): """ Test that the equality operator returns False when comparing two MACSignatureKeyInformation structs with different types. """ a = objects.MACSignatureKeyInformation() b = 'invalid' self.assertFalse(a == b) self.assertFalse(b == a) def test_not_equal_on_equal(self): """ Test that the inequality operator returns False when comparing two MACSignatureKeyInformation structs with the same data. """ a = objects.MACSignatureKeyInformation() b = objects.MACSignatureKeyInformation() self.assertFalse(a != b) self.assertFalse(b != a) a = objects.MACSignatureKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.CBC ) ) b = objects.MACSignatureKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.CBC ) ) self.assertFalse(a != b) self.assertFalse(b != a) def test_not_equal_on_not_equal_unique_identifier(self): """ Test that the inequality operator returns True when comparing two MACSignatureKeyInformation structs with different unique identifiers. """ a = objects.MACSignatureKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a" ) b = objects.MACSignatureKeyInformation( unique_identifier="00000000-1111-2222-3333-444444444444" ) self.assertTrue(a != b) self.assertTrue(b != a) def test_not_equal_on_not_equal_cryptographic_parameters(self): """ Test that the inequality operator returns True when comparing two MACSignatureKeyInformation structs with different cryptographic parameters. """ a = objects.MACSignatureKeyInformation( cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.CBC ) ) b = objects.MACSignatureKeyInformation( cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.GCM ) ) self.assertTrue(a != b) self.assertTrue(b != a) def test_not_equal_on_type_mismatch(self): """ Test that the inequality operator returns True when comparing two MACSignatureKeyInformation structs with different types. """ a = objects.MACSignatureKeyInformation() b = 'invalid' self.assertTrue(a != b) self.assertTrue(b != a) def test_repr(self): """ Test that repr can be applied to an MACSignatureKeyInformation struct. """ mac_signature_key_information = objects.MACSignatureKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.CBC ) ) expected = ( "MACSignatureKeyInformation(" "unique_identifier='100182d5-72b8-47aa-8383-4d97d512e98a', " "cryptographic_parameters=CryptographicParameters(" "block_cipher_mode=BlockCipherMode.CBC, " "padding_method=None, " "hashing_algorithm=None, " "key_role_type=None, " "digital_signature_algorithm=None, " "cryptographic_algorithm=None, " "random_iv=None, " "iv_length=None, " "tag_length=None, " "fixed_field_length=None, " "invocation_field_length=None, " "counter_length=None, " "initial_counter_value=None))" ) observed = repr(mac_signature_key_information) self.assertEqual(expected, observed) def test_str(self): """ Test that str can be applied to a MACSignatureKeyInformation struct. """ cryptographic_parameters = attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.CBC ) mac_signature_key_information = objects.MACSignatureKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a", cryptographic_parameters=cryptographic_parameters ) expected = str({ 'unique_identifier': "100182d5-72b8-47aa-8383-4d97d512e98a", 'cryptographic_parameters': cryptographic_parameters }) observed = str(mac_signature_key_information) self.assertEqual(expected, observed) class TestKeyWrappingData(testtools.TestCase): """ Test suite for the KeyWrappingData struct. """ def setUp(self): super(TestKeyWrappingData, self).setUp() # Encoding obtained in part from the KMIP 1.1 testing document, # Sections 14.1. The rest was built by hand. # # This encoding matches the following set of values: # # Wrapping Method - ENCRYPT # Encryption Key Information # Unique Identifier - 100182d5-72b8-47aa-8383-4d97d512e98a # Cryptographic Parameters # Block Cipher Mode - NIST_KEY_WRAP # MAC/Signature Key Information # Unique Identifier - 100182d5-72b8-47aa-8383-4d97d512e98a # Cryptographic Parameters # Block Cipher Mode - NIST_KEY_WRAP # MAC/Signature - 0x0123456789ABCDEF # IV/Counter/Nonce - 0x01 # Encoding Option - NO_ENCODING self.full_encoding = BytearrayStream( b'\x42\x00\x46\x01\x00\x00\x00\xE0' b'\x42\x00\x9E\x05\x00\x00\x00\x04\x00\x00\x00\x01\x00\x00\x00\x00' b'\x42\x00\x36\x01\x00\x00\x00\x48' b'\x42\x00\x94\x07\x00\x00\x00\x24' b'\x31\x30\x30\x31\x38\x32\x64\x35\x2D\x37\x32\x62\x38\x2D\x34\x37' b'\x61\x61\x2D\x38\x33\x38\x33\x2D\x34\x64\x39\x37\x64\x35\x31\x32' b'\x65\x39\x38\x61\x00\x00\x00\x00' b'\x42\x00\x2B\x01\x00\x00\x00\x10' b'\x42\x00\x11\x05\x00\x00\x00\x04\x00\x00\x00\x0D\x00\x00\x00\x00' b'\x42\x00\x4E\x01\x00\x00\x00\x48' b'\x42\x00\x94\x07\x00\x00\x00\x24' b'\x31\x30\x30\x31\x38\x32\x64\x35\x2D\x37\x32\x62\x38\x2D\x34\x37' b'\x61\x61\x2D\x38\x33\x38\x33\x2D\x34\x64\x39\x37\x64\x35\x31\x32' b'\x65\x39\x38\x61\x00\x00\x00\x00' b'\x42\x00\x2B\x01\x00\x00\x00\x10' b'\x42\x00\x11\x05\x00\x00\x00\x04\x00\x00\x00\x0D\x00\x00\x00\x00' b'\x42\x00\x4D\x08\x00\x00\x00\x08\x01\x23\x45\x67\x89\xAB\xCD\xEF' b'\x42\x00\x3D\x08\x00\x00\x00\x01\x01\x00\x00\x00\x00\x00\x00\x00' b'\x42\x00\xA3\x05\x00\x00\x00\x04\x00\x00\x00\x01\x00\x00\x00\x00' ) # Encoding obtained from the KMIP 1.1 testing document, Section 14.1. # This encoding matches the following set of values: # # Wrapping Method - ENCRYPT # Encryption Key Information # Unique Identifier - 100182d5-72b8-47aa-8383-4d97d512e98a # Cryptographic Parameters # Block Cipher Mode - NIST_KEY_WRAP # Encoding Option - NO_ENCODING self.partial_encoding = BytearrayStream( b'\x42\x00\x46\x01\x00\x00\x00\x70' b'\x42\x00\x9E\x05\x00\x00\x00\x04\x00\x00\x00\x01\x00\x00\x00\x00' b'\x42\x00\x36\x01\x00\x00\x00\x48' b'\x42\x00\x94\x07\x00\x00\x00\x24' b'\x31\x30\x30\x31\x38\x32\x64\x35\x2D\x37\x32\x62\x38\x2D\x34\x37' b'\x61\x61\x2D\x38\x33\x38\x33\x2D\x34\x64\x39\x37\x64\x35\x31\x32' b'\x65\x39\x38\x61\x00\x00\x00\x00' b'\x42\x00\x2B\x01\x00\x00\x00\x10' b'\x42\x00\x11\x05\x00\x00\x00\x04\x00\x00\x00\x0D\x00\x00\x00\x00' b'\x42\x00\xA3\x05\x00\x00\x00\x04\x00\x00\x00\x01\x00\x00\x00\x00' ) self.empty_encoding = BytearrayStream( b'\x42\x00\x46\x01\x00\x00\x00\x00' ) def tearDown(self): super(TestKeyWrappingData, self).tearDown() def test_init(self): """ Test that a KeyWrappingData struct can be constructed with no arguments. """ key_wrapping_data = objects.KeyWrappingData() self.assertEqual(None, key_wrapping_data.wrapping_method) self.assertEqual(None, key_wrapping_data.encryption_key_information) self.assertEqual(None, key_wrapping_data.mac_signature_key_information) self.assertEqual(None, key_wrapping_data.mac_signature) self.assertEqual(None, key_wrapping_data.iv_counter_nonce) self.assertEqual(None, key_wrapping_data.encoding_option) def test_init_with_args(self): """ Test that a KeyWrappingData struct can be constructed with valid values. """ key_wrapping_data = objects.KeyWrappingData( wrapping_method=enums.WrappingMethod.ENCRYPT, encryption_key_information=objects.EncryptionKeyInformation( unique_identifier="12345678-9012-3456-7890-123456789012", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.CTR ) ), mac_signature_key_information=objects.MACSignatureKeyInformation( unique_identifier="00000000-1111-2222-3333-444444444444", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.NIST_KEY_WRAP ) ), mac_signature=b'\x01', iv_counter_nonce=b'\x02', encoding_option=enums.EncodingOption.TTLV_ENCODING ) self.assertEqual( enums.WrappingMethod.ENCRYPT, key_wrapping_data.wrapping_method ) self.assertIsInstance( key_wrapping_data.encryption_key_information, objects.EncryptionKeyInformation ) e = key_wrapping_data.encryption_key_information self.assertEqual( "12345678-9012-3456-7890-123456789012", e.unique_identifier ) self.assertIsInstance( e.cryptographic_parameters, attributes.CryptographicParameters ) self.assertEqual( enums.BlockCipherMode.CTR, e.cryptographic_parameters.block_cipher_mode ) self.assertIsInstance( key_wrapping_data.mac_signature_key_information, objects.MACSignatureKeyInformation ) m = key_wrapping_data.mac_signature_key_information self.assertEqual( "00000000-1111-2222-3333-444444444444", m.unique_identifier ) self.assertIsInstance( m.cryptographic_parameters, attributes.CryptographicParameters ) self.assertEqual( enums.BlockCipherMode.NIST_KEY_WRAP, m.cryptographic_parameters.block_cipher_mode ) self.assertEqual(b'\x01', key_wrapping_data.mac_signature) self.assertEqual(b'\x02', key_wrapping_data.iv_counter_nonce) self.assertEqual( enums.EncodingOption.TTLV_ENCODING, key_wrapping_data.encoding_option ) def test_invalid_wrapping_method(self): """ Test that a TypeError is raised when an invalid value is used to set the wrapping method of a KeyWrappingData struct. """ kwargs = {'wrapping_method': 'invalid'} self.assertRaisesRegexp( TypeError, "Wrapping method must be a WrappingMethod enumeration.", objects.KeyWrappingData, **kwargs ) args = (objects.KeyWrappingData(), 'wrapping_method', 0) self.assertRaisesRegexp( TypeError, "Wrapping method must be a WrappingMethod enumeration.", setattr, *args ) def test_invalid_encryption_key_information(self): """ Test that a TypeError is raised when an invalid value is used to set the encryption key information of a KeyWrappingData struct. """ kwargs = {'encryption_key_information': 'invalid'} self.assertRaisesRegexp( TypeError, "Encryption key information must be an EncryptionKeyInformation " "struct.", objects.KeyWrappingData, **kwargs ) args = ( objects.KeyWrappingData(), 'encryption_key_information', 'invalid' ) self.assertRaisesRegexp( TypeError, "Encryption key information must be an EncryptionKeyInformation " "struct.", setattr, *args ) def test_invalid_mac_signature_key_information(self): """ Test that a TypeError is raised when an invalid value is used to set the MAC/signature key information of a KeyWrappingData struct. """ kwargs = {'mac_signature_key_information': 'invalid'} self.assertRaisesRegexp( TypeError, "MAC/signature key information must be an " "MACSignatureKeyInformation struct.", objects.KeyWrappingData, **kwargs ) args = ( objects.KeyWrappingData(), 'mac_signature_key_information', 'invalid' ) self.assertRaisesRegexp( TypeError, "MAC/signature key information must be an " "MACSignatureKeyInformation struct.", setattr, *args ) def test_invalid_mac_signature(self): """ Test that a TypeError is raised when an invalid value is used to set the MAC/signature of a KeyWrappingData struct. """ kwargs = {'mac_signature': 0} self.assertRaisesRegexp( TypeError, "MAC/signature must be bytes.", objects.KeyWrappingData, **kwargs ) args = ( objects.KeyWrappingData(), 'mac_signature', 0 ) self.assertRaisesRegexp( TypeError, "MAC/signature must be bytes.", setattr, *args ) def test_invalid_iv_counter_nonce(self): """ Test that a TypeError is raised when an invalid value is used to set the IV/counter/nonce of a KeyWrappingData struct. """ kwargs = {'iv_counter_nonce': 0} self.assertRaisesRegexp( TypeError, "IV/counter/nonce must be bytes.", objects.KeyWrappingData, **kwargs ) args = ( objects.KeyWrappingData(), 'iv_counter_nonce', 0 ) self.assertRaisesRegexp( TypeError, "IV/counter/nonce must be bytes.", setattr, *args ) def test_invalid_encoding_option(self): """ Test that a TypeError is raised when an invalid value is used to set the encoding option of a KeyWrappingData struct. """ kwargs = {'encoding_option': 'invalid'} self.assertRaisesRegexp( TypeError, "Encoding option must be an EncodingOption enumeration.", objects.KeyWrappingData, **kwargs ) args = ( objects.KeyWrappingData(), 'encoding_option', 'invalid' ) self.assertRaisesRegexp( TypeError, "Encoding option must be an EncodingOption enumeration.", setattr, *args ) def test_read(self): """ Test that a KeyWrappingData struct can be read from a data stream. """ key_wrapping_data = objects.KeyWrappingData() self.assertEqual(None, key_wrapping_data.wrapping_method) self.assertEqual(None, key_wrapping_data.encryption_key_information) self.assertEqual(None, key_wrapping_data.mac_signature_key_information) self.assertEqual(None, key_wrapping_data.mac_signature) self.assertEqual(None, key_wrapping_data.iv_counter_nonce) self.assertEqual(None, key_wrapping_data.encoding_option) key_wrapping_data.read(self.full_encoding) self.assertEqual( enums.WrappingMethod.ENCRYPT, key_wrapping_data.wrapping_method ) self.assertIsInstance( key_wrapping_data.encryption_key_information, objects.EncryptionKeyInformation ) e = key_wrapping_data.encryption_key_information self.assertEqual( "100182d5-72b8-47aa-8383-4d97d512e98a", e.unique_identifier ) self.assertIsInstance( e.cryptographic_parameters, attributes.CryptographicParameters ) self.assertEqual( enums.BlockCipherMode.NIST_KEY_WRAP, e.cryptographic_parameters.block_cipher_mode ) self.assertIsInstance( key_wrapping_data.mac_signature_key_information, objects.MACSignatureKeyInformation ) m = key_wrapping_data.mac_signature_key_information self.assertEqual( "100182d5-72b8-47aa-8383-4d97d512e98a", m.unique_identifier ) self.assertIsInstance( m.cryptographic_parameters, attributes.CryptographicParameters ) self.assertEqual( enums.BlockCipherMode.NIST_KEY_WRAP, m.cryptographic_parameters.block_cipher_mode ) self.assertEqual( b'\x01\x23\x45\x67\x89\xAB\xCD\xEF', key_wrapping_data.mac_signature ) self.assertEqual( b'\x01', key_wrapping_data.iv_counter_nonce ) self.assertEqual( enums.EncodingOption.NO_ENCODING, key_wrapping_data.encoding_option ) def test_read_partial(self): """ Test that a KeyWrappingData struct can be read from a partial data stream. """ key_wrapping_data = objects.KeyWrappingData() self.assertEqual(None, key_wrapping_data.wrapping_method) self.assertEqual(None, key_wrapping_data.encryption_key_information) self.assertEqual(None, key_wrapping_data.mac_signature_key_information) self.assertEqual(None, key_wrapping_data.mac_signature) self.assertEqual(None, key_wrapping_data.iv_counter_nonce) self.assertEqual(None, key_wrapping_data.encoding_option) key_wrapping_data.read(self.partial_encoding) self.assertEqual( enums.WrappingMethod.ENCRYPT, key_wrapping_data.wrapping_method ) self.assertIsInstance( key_wrapping_data.encryption_key_information, objects.EncryptionKeyInformation ) e = key_wrapping_data.encryption_key_information self.assertEqual( "100182d5-72b8-47aa-8383-4d97d512e98a", e.unique_identifier ) self.assertIsInstance( e.cryptographic_parameters, attributes.CryptographicParameters ) self.assertEqual( enums.BlockCipherMode.NIST_KEY_WRAP, e.cryptographic_parameters.block_cipher_mode ) self.assertIsNone(key_wrapping_data.mac_signature_key_information) self.assertIsNone(key_wrapping_data.mac_signature) self.assertIsNone(key_wrapping_data.iv_counter_nonce) self.assertEqual( enums.EncodingOption.NO_ENCODING, key_wrapping_data.encoding_option ) def test_read_invalid(self): """ Test that a ValueError gets raised when a required KeyWrappingData field is missing from the struct encoding. """ key_wrapping_data = objects.KeyWrappingData() args = (self.empty_encoding,) self.assertRaisesRegexp( ValueError, "Invalid struct missing the wrapping method attribute.", key_wrapping_data.read, *args ) def test_write(self): """ Test that a KeyWrappingData struct can be written to a data stream. """ key_wrapping_data = objects.KeyWrappingData( wrapping_method=enums.WrappingMethod.ENCRYPT, encryption_key_information=objects.EncryptionKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.NIST_KEY_WRAP ) ), mac_signature_key_information=objects.MACSignatureKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.NIST_KEY_WRAP ) ), mac_signature=b'\x01\x23\x45\x67\x89\xAB\xCD\xEF', iv_counter_nonce=b'\x01', encoding_option=enums.EncodingOption.NO_ENCODING ) stream = BytearrayStream() key_wrapping_data.write(stream) self.assertEqual(len(self.full_encoding), len(stream)) self.assertEqual(str(self.full_encoding), str(stream)) def test_write_partial(self): """ Test that a partially defined KeyWrappingData struct can be written to a data stream. """ key_wrapping_data = objects.KeyWrappingData( wrapping_method=enums.WrappingMethod.ENCRYPT, encryption_key_information=objects.EncryptionKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.NIST_KEY_WRAP ) ), encoding_option=enums.EncodingOption.NO_ENCODING ) stream = BytearrayStream() key_wrapping_data.write(stream) self.assertEqual(len(self.partial_encoding), len(stream)) self.assertEqual(str(self.partial_encoding), str(stream)) def test_write_invalid(self): """ Test that a ValueError gets raised when a required KeyWrappingData field is missing when encoding the struct. """ key_wrapping_data = objects.KeyWrappingData() stream = utils.BytearrayStream() args = (stream,) self.assertRaisesRegexp( ValueError, "Invalid struct missing the wrapping method attribute.", key_wrapping_data.write, *args ) def test_equal_on_equal(self): """ Test that the equality operator returns True when comparing two KeyWrappingData structs with the same data. """ a = objects.KeyWrappingData() b = objects.KeyWrappingData() self.assertTrue(a == b) self.assertTrue(b == a) a = objects.KeyWrappingData( wrapping_method=enums.WrappingMethod.ENCRYPT, encryption_key_information=objects.EncryptionKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.NIST_KEY_WRAP ) ), mac_signature_key_information=objects.MACSignatureKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.NIST_KEY_WRAP ) ), mac_signature=b'\x01\x01\x01\x01\x01\x01\x01\x01', iv_counter_nonce=b'\x01', encoding_option=enums.EncodingOption.NO_ENCODING ) b = objects.KeyWrappingData( wrapping_method=enums.WrappingMethod.ENCRYPT, encryption_key_information=objects.EncryptionKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.NIST_KEY_WRAP ) ), mac_signature_key_information=objects.MACSignatureKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.NIST_KEY_WRAP ) ), mac_signature=b'\x01\x01\x01\x01\x01\x01\x01\x01', iv_counter_nonce=b'\x01', encoding_option=enums.EncodingOption.NO_ENCODING ) self.assertTrue(a == b) self.assertTrue(b == a) def test_equal_on_not_equal_wrapping_method(self): """ Test that the equality operator returns False when comparing two KeyWrappingData structs with different wrapping methods. """ a = objects.KeyWrappingData( wrapping_method=enums.WrappingMethod.ENCRYPT ) b = objects.KeyWrappingData( wrapping_method=enums.WrappingMethod.MAC_SIGN ) self.assertFalse(a == b) self.assertFalse(b == a) def test_equal_on_not_equal_encryption_key_information(self): """ Test that the equality operator returns False when comparing two KeyWrappingData structs with different encryption key information. """ a = objects.KeyWrappingData( encryption_key_information=objects.EncryptionKeyInformation( unique_identifier="100182d5-72b8-ffff-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.CBC ) ) ) b = objects.KeyWrappingData( encryption_key_information=objects.EncryptionKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.NIST_KEY_WRAP ) ) ) self.assertFalse(a == b) self.assertFalse(b == a) def test_equal_on_not_equal_mac_signature_key_information(self): """ Test that the equality operator returns False when comparing two KeyWrappingData structs with different MAC/signature key information. """ a = objects.KeyWrappingData( mac_signature_key_information=objects.MACSignatureKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.NIST_KEY_WRAP ) ) ) b = objects.KeyWrappingData( mac_signature_key_information=objects.MACSignatureKeyInformation( unique_identifier="100182d5-72b8-ffff-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.CBC ) ) ) self.assertFalse(a == b) self.assertFalse(b == a) def test_equal_on_not_equal_mac_signatures(self): """ Test that the equality operator returns False when comparing two KeyWrappingData structs with different MAC/signatures. """ a = objects.KeyWrappingData(mac_signature=b'\x01') b = objects.KeyWrappingData(mac_signature=b'\x10') self.assertFalse(a == b) self.assertFalse(b == a) def test_equal_on_not_equal_iv_counter_nonce(self): """ Test that the equality operator returns False when comparing two KeyWrappingData structs with different IV/counter/nonces. """ a = objects.KeyWrappingData(iv_counter_nonce=b'\x01') b = objects.KeyWrappingData(iv_counter_nonce=b'\x10') self.assertFalse(a == b) self.assertFalse(b == a) def test_equal_on_not_equal_encoding_option(self): """ Test that the equality operator returns False when comparing two KeyWrappingData structs with different encoding options. """ a = objects.KeyWrappingData( encoding_option=enums.EncodingOption.NO_ENCODING ) b = objects.KeyWrappingData( encoding_option=enums.EncodingOption.TTLV_ENCODING ) self.assertFalse(a == b) self.assertFalse(b == a) def test_equal_on_type_mismatch(self): """ Test that the equality operator returns False when comparing two KeyWrappingData structs with different types. """ a = objects.KeyWrappingData() b = 'invalid' self.assertFalse(a == b) self.assertFalse(b == a) def test_not_equal_on_equal(self): """ Test that the inequality operator returns False when comparing two KeyWrappingData structs with the same data. """ a = objects.KeyWrappingData() b = objects.KeyWrappingData() self.assertFalse(a != b) self.assertFalse(b != a) a = objects.KeyWrappingData( wrapping_method=enums.WrappingMethod.ENCRYPT, encryption_key_information=objects.EncryptionKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.NIST_KEY_WRAP ) ), mac_signature_key_information=objects.MACSignatureKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.NIST_KEY_WRAP ) ), mac_signature=b'\x01\x01\x01\x01\x01\x01\x01\x01', iv_counter_nonce=b'\x01', encoding_option=enums.EncodingOption.NO_ENCODING ) b = objects.KeyWrappingData( wrapping_method=enums.WrappingMethod.ENCRYPT, encryption_key_information=objects.EncryptionKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.NIST_KEY_WRAP ) ), mac_signature_key_information=objects.MACSignatureKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.NIST_KEY_WRAP ) ), mac_signature=b'\x01\x01\x01\x01\x01\x01\x01\x01', iv_counter_nonce=b'\x01', encoding_option=enums.EncodingOption.NO_ENCODING ) self.assertFalse(a != b) self.assertFalse(b != a) def test_not_equal_on_not_equal_wrapping_method(self): """ Test that the inequality operator returns True when comparing two KeyWrappingData structs with different wrapping methods. """ a = objects.KeyWrappingData( wrapping_method=enums.WrappingMethod.ENCRYPT ) b = objects.KeyWrappingData( wrapping_method=enums.WrappingMethod.MAC_SIGN ) self.assertTrue(a != b) self.assertTrue(b != a) def test_not_equal_on_not_equal_encryption_key_information(self): """ Test that the inequality operator returns True when comparing two KeyWrappingData structs with different encryption key information. """ a = objects.KeyWrappingData( encryption_key_information=objects.EncryptionKeyInformation( unique_identifier="100182d5-72b8-ffff-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.CBC ) ) ) b = objects.KeyWrappingData( encryption_key_information=objects.EncryptionKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.NIST_KEY_WRAP ) ) ) self.assertTrue(a != b) self.assertTrue(b != a) def test_not_equal_on_not_equal_mac_signature_key_information(self): """ Test that the inequality operator returns True when comparing two KeyWrappingData structs with different MAC/signature key information. """ a = objects.KeyWrappingData( mac_signature_key_information=objects.MACSignatureKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.NIST_KEY_WRAP ) ) ) b = objects.KeyWrappingData( mac_signature_key_information=objects.MACSignatureKeyInformation( unique_identifier="100182d5-72b8-ffff-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.CBC ) ) ) self.assertTrue(a != b) self.assertTrue(b != a) def test_not_equal_on_not_equal_mac_signatures(self): """ Test that the inequality operator returns True when comparing two KeyWrappingData structs with different MAC/signatures. """ a = objects.KeyWrappingData(mac_signature=b'\x01') b = objects.KeyWrappingData(mac_signature=b'\x10') self.assertTrue(a != b) self.assertTrue(b != a) def test_not_equal_on_not_equal_iv_counter_nonce(self): """ Test that the inequality operator returns True when comparing two KeyWrappingData structs with different IV/counter/nonces. """ a = objects.KeyWrappingData(iv_counter_nonce=b'\x01') b = objects.KeyWrappingData(iv_counter_nonce=b'\x10') self.assertTrue(a != b) self.assertTrue(b != a) def test_not_equal_on_not_equal_encoding_option(self): """ Test that the inequality operator returns True when comparing two KeyWrappingData structs with different encoding options. """ a = objects.KeyWrappingData( encoding_option=enums.EncodingOption.NO_ENCODING ) b = objects.KeyWrappingData( encoding_option=enums.EncodingOption.TTLV_ENCODING ) self.assertTrue(a != b) self.assertTrue(b != a) def test_not_equal_on_type_mismatch(self): """ Test that the inequality operator returns True when comparing two KeyWrappingData structs with different types. """ a = objects.KeyWrappingData() b = 'invalid' self.assertTrue(a != b) self.assertTrue(b != a) def test_repr(self): """ Test that repr can be applied to an KeyWrappingData struct. """ key_wrapping_data = objects.KeyWrappingData( wrapping_method=enums.WrappingMethod.ENCRYPT, encryption_key_information=objects.EncryptionKeyInformation( unique_identifier="100182d5-72b8-ffff-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.NIST_KEY_WRAP ) ), mac_signature_key_information=objects.MACSignatureKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.CBC ) ), mac_signature=b'\x01\x01\x02\x02\x03\x03\x04\x04', iv_counter_nonce=b'\xFF', encoding_option=enums.EncodingOption.TTLV_ENCODING ) expected = ( "KeyWrappingData(" "wrapping_method=WrappingMethod.ENCRYPT, " "encryption_key_information=EncryptionKeyInformation(" "unique_identifier='100182d5-72b8-ffff-8383-4d97d512e98a', " "cryptographic_parameters=CryptographicParameters(" "block_cipher_mode=BlockCipherMode.NIST_KEY_WRAP, " "padding_method=None, " "hashing_algorithm=None, " "key_role_type=None, " "digital_signature_algorithm=None, " "cryptographic_algorithm=None, " "random_iv=None, " "iv_length=None, " "tag_length=None, " "fixed_field_length=None, " "invocation_field_length=None, " "counter_length=None, " "initial_counter_value=None)), " "mac_signature_key_information=MACSignatureKeyInformation(" "unique_identifier='100182d5-72b8-47aa-8383-4d97d512e98a', " "cryptographic_parameters=CryptographicParameters(" "block_cipher_mode=BlockCipherMode.CBC, " "padding_method=None, " "hashing_algorithm=None, " "key_role_type=None, " "digital_signature_algorithm=None, " "cryptographic_algorithm=None, " "random_iv=None, " "iv_length=None, " "tag_length=None, " "fixed_field_length=None, " "invocation_field_length=None, " "counter_length=None, " "initial_counter_value=None)), " "mac_signature={0}, " "iv_counter_nonce={1}, " "encoding_option=EncodingOption.TTLV_ENCODING)".format( b'\x01\x01\x02\x02\x03\x03\x04\x04', b'\xFF' ) ) observed = repr(key_wrapping_data) self.assertEqual(expected, observed) def test_str(self): """ Test that str can be applied to a KeyWrappingData struct. """ key_wrapping_data = objects.KeyWrappingData( wrapping_method=enums.WrappingMethod.ENCRYPT, encryption_key_information=objects.EncryptionKeyInformation( unique_identifier="100182d5-72b8-ffff-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.NIST_KEY_WRAP ) ), mac_signature_key_information=objects.MACSignatureKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.CBC ) ), mac_signature=b'\x01\x01\x02\x02\x03\x03\x04\x04', iv_counter_nonce=b'\xFF', encoding_option=enums.EncodingOption.TTLV_ENCODING ) expected = str({ 'wrapping_method': enums.WrappingMethod.ENCRYPT, 'encryption_key_information': objects.EncryptionKeyInformation( unique_identifier="100182d5-72b8-ffff-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.NIST_KEY_WRAP ) ), 'mac_signature_key_information': objects.MACSignatureKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.CBC ) ), 'mac_signature': b'\x01\x01\x02\x02\x03\x03\x04\x04', 'iv_counter_nonce': b'\xFF', 'encoding_option': enums.EncodingOption.TTLV_ENCODING }) observed = str(key_wrapping_data) self.assertEqual(expected, observed) class TestKeyWrappingSpecification(testtools.TestCase): """ Test suite for the KeyWrappingSpecification struct. """ def setUp(self): super(TestKeyWrappingSpecification, self).setUp() # Encoding obtained in part from the KMIP 1.1 testing document, # Sections 14.1 and 14.2. The rest was built by hand. # # This encoding matches the following set of values: # # Wrapping Method - Encrypt # Encryption Key Information # Unique Identifier - 100182d5-72b8-47aa-8383-4d97d512e98a # Cryptographic Parameters # Block Cipher Mode - NIST_KEY_WRAP # MAC/Signature Key Information # Unique Identifier - 100182d5-72b8-47aa-8383-4d97d512e98a # Cryptographic Parameters # Block Cipher Mode - NIST_KEY_WRAP # Attribute Names # Cryptographic Usage Mask # Encoding Option - NO_ENCODING self.full_encoding = BytearrayStream( b'\x42\x00\x47\x01\x00\x00\x00\xE0' b'\x42\x00\x9E\x05\x00\x00\x00\x04\x00\x00\x00\x01\x00\x00\x00\x00' b'\x42\x00\x36\x01\x00\x00\x00\x48' b'\x42\x00\x94\x07\x00\x00\x00\x24' b'\x31\x30\x30\x31\x38\x32\x64\x35\x2D\x37\x32\x62\x38\x2D\x34\x37' b'\x61\x61\x2D\x38\x33\x38\x33\x2D\x34\x64\x39\x37\x64\x35\x31\x32' b'\x65\x39\x38\x61\x00\x00\x00\x00' b'\x42\x00\x2B\x01\x00\x00\x00\x10' b'\x42\x00\x11\x05\x00\x00\x00\x04\x00\x00\x00\x0D\x00\x00\x00\x00' b'\x42\x00\x4E\x01\x00\x00\x00\x48' b'\x42\x00\x94\x07\x00\x00\x00\x24' b'\x31\x30\x30\x31\x38\x32\x64\x35\x2D\x37\x32\x62\x38\x2D\x34\x37' b'\x61\x61\x2D\x38\x33\x38\x33\x2D\x34\x64\x39\x37\x64\x35\x31\x32' b'\x65\x39\x38\x61\x00\x00\x00\x00' b'\x42\x00\x2B\x01\x00\x00\x00\x10' b'\x42\x00\x11\x05\x00\x00\x00\x04\x00\x00\x00\x0D\x00\x00\x00\x00' b'\x42\x00\x0A\x07\x00\x00\x00\x18' b'\x43\x72\x79\x70\x74\x6F\x67\x72\x61\x70\x68\x69\x63\x20\x55\x73' b'\x61\x67\x65\x20\x4D\x61\x73\x6B' b'\x42\x00\xA3\x05\x00\x00\x00\x04\x00\x00\x00\x01\x00\x00\x00\x00' ) # Adapted from the full encoding above. This encoding matches the # following set of values: # # Wrapping Method - Encrypt # Encryption Key Information # Unique Identifier - 100182d5-72b8-47aa-8383-4d97d512e98a # Cryptographic Parameters # Block Cipher Mode - NIST_KEY_WRAP self.partial_encoding = BytearrayStream( b'\x42\x00\x47\x01\x00\x00\x00\x60' b'\x42\x00\x9E\x05\x00\x00\x00\x04\x00\x00\x00\x01\x00\x00\x00\x00' b'\x42\x00\x36\x01\x00\x00\x00\x48' b'\x42\x00\x94\x07\x00\x00\x00\x24' b'\x31\x30\x30\x31\x38\x32\x64\x35\x2D\x37\x32\x62\x38\x2D\x34\x37' b'\x61\x61\x2D\x38\x33\x38\x33\x2D\x34\x64\x39\x37\x64\x35\x31\x32' b'\x65\x39\x38\x61\x00\x00\x00\x00' b'\x42\x00\x2B\x01\x00\x00\x00\x10' b'\x42\x00\x11\x05\x00\x00\x00\x04\x00\x00\x00\x0D\x00\x00\x00\x00' ) self.empty_encoding = BytearrayStream( b'\x42\x00\x47\x01\x00\x00\x00\x00' ) def tearDown(self): super(TestKeyWrappingSpecification, self).tearDown() def test_init(self): """ Test that a KeyWrappingSpecification struct can be constructed with no arguments. """ key_wrapping_specification = objects.KeyWrappingSpecification() self.assertEqual(None, key_wrapping_specification.wrapping_method) self.assertEqual( None, key_wrapping_specification.encryption_key_information ) self.assertEqual( None, key_wrapping_specification.mac_signature_key_information ) self.assertEqual(None, key_wrapping_specification.attribute_names) self.assertEqual(None, key_wrapping_specification.encoding_option) def test_init_with_args(self): """ Test that a KeyWrappingSpecification struct can be constructed with valid values. """ encryption_key_information = objects.EncryptionKeyInformation( unique_identifier="12345678-9012-3456-7890-123456789012", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.CTR ) ) mac_signature_key_information = objects.MACSignatureKeyInformation( unique_identifier="00000000-1111-2222-3333-444444444444", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.NIST_KEY_WRAP ) ) key_wrapping_specification = objects.KeyWrappingSpecification( wrapping_method=enums.WrappingMethod.ENCRYPT, encryption_key_information=encryption_key_information, mac_signature_key_information=mac_signature_key_information, attribute_names=[ 'Cryptographic Algorithm', 'Cryptographic Length', 'Cryptographic Usage Mask' ], encoding_option=enums.EncodingOption.TTLV_ENCODING ) self.assertEqual( enums.WrappingMethod.ENCRYPT, key_wrapping_specification.wrapping_method ) self.assertIsInstance( key_wrapping_specification.encryption_key_information, objects.EncryptionKeyInformation ) e = key_wrapping_specification.encryption_key_information self.assertEqual( "12345678-9012-3456-7890-123456789012", e.unique_identifier ) self.assertIsInstance( e.cryptographic_parameters, attributes.CryptographicParameters ) self.assertEqual( enums.BlockCipherMode.CTR, e.cryptographic_parameters.block_cipher_mode ) self.assertIsInstance( key_wrapping_specification.mac_signature_key_information, objects.MACSignatureKeyInformation ) m = key_wrapping_specification.mac_signature_key_information self.assertEqual( "00000000-1111-2222-3333-444444444444", m.unique_identifier ) self.assertIsInstance( m.cryptographic_parameters, attributes.CryptographicParameters ) self.assertEqual( enums.BlockCipherMode.NIST_KEY_WRAP, m.cryptographic_parameters.block_cipher_mode ) self.assertIsInstance( key_wrapping_specification.attribute_names, list ) self.assertEqual(3, len(key_wrapping_specification.attribute_names)) self.assertEqual( 'Cryptographic Algorithm', key_wrapping_specification.attribute_names[0] ) self.assertEqual( 'Cryptographic Length', key_wrapping_specification.attribute_names[1] ) self.assertEqual( 'Cryptographic Usage Mask', key_wrapping_specification.attribute_names[2] ) self.assertEqual( enums.EncodingOption.TTLV_ENCODING, key_wrapping_specification.encoding_option ) def test_invalid_wrapping_method(self): """ Test that a TypeError is raised when an invalid value is used to set the wrapping method of a KeyWrappingSpecification struct. """ kwargs = {'wrapping_method': 'invalid'} self.assertRaisesRegexp( TypeError, "Wrapping method must be a WrappingMethod enumeration.", objects.KeyWrappingSpecification, **kwargs ) args = (objects.KeyWrappingSpecification(), 'wrapping_method', 0) self.assertRaisesRegexp( TypeError, "Wrapping method must be a WrappingMethod enumeration.", setattr, *args ) def test_invalid_encryption_key_information(self): """ Test that a TypeError is raised when an invalid value is used to set the encryption key information of a KeyWrappingSpecification struct. """ kwargs = {'encryption_key_information': 'invalid'} self.assertRaisesRegexp( TypeError, "Encryption key information must be an EncryptionKeyInformation " "struct.", objects.KeyWrappingSpecification, **kwargs ) args = ( objects.KeyWrappingSpecification(), 'encryption_key_information', 'invalid' ) self.assertRaisesRegexp( TypeError, "Encryption key information must be an EncryptionKeyInformation " "struct.", setattr, *args ) def test_invalid_mac_signature_key_information(self): """ Test that a TypeError is raised when an invalid value is used to set the MAC/signature key information of a KeyWrappingSpecification struct. """ kwargs = {'mac_signature_key_information': 'invalid'} self.assertRaisesRegexp( TypeError, "MAC/signature key information must be an " "MACSignatureKeyInformation struct.", objects.KeyWrappingSpecification, **kwargs ) args = ( objects.KeyWrappingSpecification(), 'mac_signature_key_information', 'invalid' ) self.assertRaisesRegexp( TypeError, "MAC/signature key information must be an " "MACSignatureKeyInformation struct.", setattr, *args ) def test_invalid_attribute_names(self): """ Test that a TypeError is raised when an invalid value is used to set the attribute names of a KeyWrappingSpecification struct. """ kwargs = {'attribute_names': 'invalid'} self.assertRaisesRegexp( TypeError, "Attribute names must be a list of strings.", objects.KeyWrappingSpecification, **kwargs ) args = ( objects.KeyWrappingSpecification(), 'attribute_names', ['valid', 0] ) self.assertRaisesRegexp( TypeError, "Attribute names must be a list of strings.", setattr, *args ) def test_invalid_encoding_option(self): """ Test that a TypeError is raised when an invalid value is used to set the encoding option of a KeyWrappingSpecification struct. """ kwargs = {'encoding_option': 'invalid'} self.assertRaisesRegexp( TypeError, "Encoding option must be an EncodingOption enumeration.", objects.KeyWrappingSpecification, **kwargs ) args = ( objects.KeyWrappingSpecification(), 'encoding_option', 'invalid' ) self.assertRaisesRegexp( TypeError, "Encoding option must be an EncodingOption enumeration.", setattr, *args ) def test_read(self): """ Test that a KeyWrappingSpecification struct can be read from a data stream. """ key_wrapping_specification = objects.KeyWrappingSpecification() self.assertEqual(None, key_wrapping_specification.wrapping_method) self.assertEqual( None, key_wrapping_specification.encryption_key_information ) self.assertEqual( None, key_wrapping_specification.mac_signature_key_information ) self.assertEqual(None, key_wrapping_specification.attribute_names) self.assertEqual(None, key_wrapping_specification.encoding_option) key_wrapping_specification.read(self.full_encoding) self.assertEqual( enums.WrappingMethod.ENCRYPT, key_wrapping_specification.wrapping_method ) self.assertIsInstance( key_wrapping_specification.encryption_key_information, objects.EncryptionKeyInformation ) e = key_wrapping_specification.encryption_key_information self.assertEqual( "100182d5-72b8-47aa-8383-4d97d512e98a", e.unique_identifier ) self.assertIsInstance( e.cryptographic_parameters, attributes.CryptographicParameters ) self.assertEqual( enums.BlockCipherMode.NIST_KEY_WRAP, e.cryptographic_parameters.block_cipher_mode ) self.assertIsInstance( key_wrapping_specification.mac_signature_key_information, objects.MACSignatureKeyInformation ) m = key_wrapping_specification.mac_signature_key_information self.assertEqual( "100182d5-72b8-47aa-8383-4d97d512e98a", m.unique_identifier ) self.assertIsInstance( m.cryptographic_parameters, attributes.CryptographicParameters ) self.assertEqual( enums.BlockCipherMode.NIST_KEY_WRAP, m.cryptographic_parameters.block_cipher_mode ) self.assertIsInstance( key_wrapping_specification.attribute_names, list ) self.assertEqual( 'Cryptographic Usage Mask', key_wrapping_specification.attribute_names[0] ) self.assertEqual( enums.EncodingOption.NO_ENCODING, key_wrapping_specification.encoding_option ) def test_read_partial(self): """ Test that a KeyWrappingSpecification struct can be read from a partial data stream. """ key_wrapping_specification = objects.KeyWrappingSpecification() self.assertEqual(None, key_wrapping_specification.wrapping_method) self.assertEqual( None, key_wrapping_specification.encryption_key_information ) self.assertEqual( None, key_wrapping_specification.mac_signature_key_information ) self.assertEqual(None, key_wrapping_specification.attribute_names) self.assertEqual(None, key_wrapping_specification.encoding_option) key_wrapping_specification.read(self.partial_encoding) self.assertEqual( enums.WrappingMethod.ENCRYPT, key_wrapping_specification.wrapping_method ) self.assertIsInstance( key_wrapping_specification.encryption_key_information, objects.EncryptionKeyInformation ) e = key_wrapping_specification.encryption_key_information self.assertEqual( "100182d5-72b8-47aa-8383-4d97d512e98a", e.unique_identifier ) self.assertIsInstance( e.cryptographic_parameters, attributes.CryptographicParameters ) self.assertEqual( enums.BlockCipherMode.NIST_KEY_WRAP, e.cryptographic_parameters.block_cipher_mode ) self.assertIsNone( key_wrapping_specification.mac_signature_key_information ) self.assertIsNone( key_wrapping_specification.attribute_names ) self.assertIsNone( key_wrapping_specification.encoding_option ) def test_read_invalid(self): """ Test that a ValueError gets raised when a required MACSignatureKeyInformation field is missing from the struct encoding. """ key_wrapping_specification = objects.KeyWrappingSpecification() args = (self.empty_encoding,) self.assertRaisesRegexp( ValueError, "Invalid struct missing the wrapping method attribute.", key_wrapping_specification.read, *args ) def test_write(self): """ Test that a KeyWrappingSpecification struct can be written to a data stream. """ key_wrapping_specification = objects.KeyWrappingSpecification( wrapping_method=enums.WrappingMethod.ENCRYPT, encryption_key_information=objects.EncryptionKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.NIST_KEY_WRAP ) ), mac_signature_key_information=objects.MACSignatureKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.NIST_KEY_WRAP ) ), attribute_names=['Cryptographic Usage Mask'], encoding_option=enums.EncodingOption.NO_ENCODING ) stream = BytearrayStream() key_wrapping_specification.write(stream) self.assertEqual(len(self.full_encoding), len(stream)) self.assertEqual(str(self.full_encoding), str(stream)) def test_write_partial(self): """ Test that a partially defined KeyWrappingSpecification struct can be written to a data stream. """ key_wrapping_specification = objects.KeyWrappingSpecification( wrapping_method=enums.WrappingMethod.ENCRYPT, encryption_key_information=objects.EncryptionKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.NIST_KEY_WRAP ) ) ) stream = BytearrayStream() key_wrapping_specification.write(stream) self.assertEqual(len(self.partial_encoding), len(stream)) self.assertEqual(str(self.partial_encoding), str(stream)) def test_write_invalid(self): """ Test that a ValueError gets raised when a required KeyWrappingSpecification field is missing when encoding the struct. """ key_wrapping_specification = objects.KeyWrappingSpecification() stream = utils.BytearrayStream() args = (stream,) self.assertRaisesRegexp( ValueError, "Invalid struct missing the wrapping method attribute.", key_wrapping_specification.write, *args ) def test_equal_on_equal(self): """ Test that the equality operator returns True when comparing two KeyWrappingSpecification structs with the same data. """ a = objects.KeyWrappingSpecification() b = objects.KeyWrappingSpecification() self.assertTrue(a == b) self.assertTrue(b == a) a = objects.KeyWrappingSpecification( wrapping_method=enums.WrappingMethod.ENCRYPT, encryption_key_information=objects.EncryptionKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.NIST_KEY_WRAP ) ), attribute_names=['Cryptographic Usage Mask'], encoding_option=enums.EncodingOption.NO_ENCODING ) b = objects.KeyWrappingSpecification( wrapping_method=enums.WrappingMethod.ENCRYPT, encryption_key_information=objects.EncryptionKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.NIST_KEY_WRAP ) ), attribute_names=['Cryptographic Usage Mask'], encoding_option=enums.EncodingOption.NO_ENCODING ) self.assertTrue(a == b) self.assertTrue(b == a) def test_equal_on_not_equal_wrapping_method(self): """ Test that the equality operator returns False when comparing two KeyWrappingSpecification structs with different wrapping methods. """ a = objects.KeyWrappingSpecification( wrapping_method=enums.WrappingMethod.ENCRYPT ) b = objects.KeyWrappingSpecification( wrapping_method=enums.WrappingMethod.MAC_SIGN ) self.assertFalse(a == b) self.assertFalse(b == a) def test_equal_on_not_equal_encryption_key_information(self): """ Test that the equality operator returns False when comparing two KeyWrappingSpecification structs with different encryption key information. """ a = objects.KeyWrappingSpecification( encryption_key_information=objects.EncryptionKeyInformation( unique_identifier="100182d5-72b8-ffff-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.CBC ) ) ) b = objects.KeyWrappingSpecification( encryption_key_information=objects.EncryptionKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.NIST_KEY_WRAP ) ) ) self.assertFalse(a == b) self.assertFalse(b == a) def test_equal_on_not_equal_mac_signature_key_information(self): """ Test that the equality operator returns False when comparing two KeyWrappingSpecification structs with different MAC/signature key information. """ a = objects.KeyWrappingSpecification( mac_signature_key_information=objects.MACSignatureKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.NIST_KEY_WRAP ) ) ) b = objects.KeyWrappingSpecification( mac_signature_key_information=objects.MACSignatureKeyInformation( unique_identifier="100182d5-72b8-ffff-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.CBC ) ) ) self.assertFalse(a == b) self.assertFalse(b == a) def test_equal_on_not_equal_attribute_names(self): """ Test that the equality operator returns False when comparing two KeyWrappingSpecification structs with different attribute names. """ a = objects.KeyWrappingSpecification( attribute_names=[ 'Cryptographic Algorithm', 'Cryptographic Length' ] ) b = objects.KeyWrappingSpecification( attribute_names=['Cryptographic Usage Mask'] ) self.assertFalse(a == b) self.assertFalse(b == a) def test_equal_on_not_equal_encoding_option(self): """ Test that the equality operator returns False when comparing two KeyWrappingSpecification structs with different encoding options. """ a = objects.KeyWrappingSpecification( encoding_option=enums.EncodingOption.NO_ENCODING ) b = objects.KeyWrappingSpecification( encoding_option=enums.EncodingOption.TTLV_ENCODING ) self.assertFalse(a == b) self.assertFalse(b == a) def test_equal_on_type_mismatch(self): """ Test that the equality operator returns False when comparing two KeyWrappingSpecification structs with different types. """ a = objects.KeyWrappingSpecification() b = 'invalid' self.assertFalse(a == b) self.assertFalse(b == a) def test_not_equal_on_equal(self): """ Test that the inequality operator returns False when comparing two KeyWrappingSpecification structs with the same data. """ a = objects.KeyWrappingSpecification() b = objects.KeyWrappingSpecification() self.assertFalse(a != b) self.assertFalse(b != a) a = objects.KeyWrappingSpecification( wrapping_method=enums.WrappingMethod.ENCRYPT, encryption_key_information=objects.EncryptionKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.NIST_KEY_WRAP ) ), attribute_names=['Cryptographic Usage Mask'], encoding_option=enums.EncodingOption.NO_ENCODING ) b = objects.KeyWrappingSpecification( wrapping_method=enums.WrappingMethod.ENCRYPT, encryption_key_information=objects.EncryptionKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.NIST_KEY_WRAP ) ), attribute_names=['Cryptographic Usage Mask'], encoding_option=enums.EncodingOption.NO_ENCODING ) self.assertFalse(a != b) self.assertFalse(b != a) def test_not_equal_on_not_equal_wrapping_method(self): """ Test that the inequality operator returns True when comparing two KeyWrappingSpecification structs with different wrapping methods. """ a = objects.KeyWrappingSpecification( wrapping_method=enums.WrappingMethod.ENCRYPT ) b = objects.KeyWrappingSpecification( wrapping_method=enums.WrappingMethod.MAC_SIGN ) self.assertTrue(a != b) self.assertTrue(b != a) def test_not_equal_on_not_equal_encryption_key_information(self): """ Test that the inequality operator returns True when comparing two KeyWrappingSpecification structs with different encryption key information. """ a = objects.KeyWrappingSpecification( encryption_key_information=objects.EncryptionKeyInformation( unique_identifier="100182d5-72b8-ffff-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.CBC ) ) ) b = objects.KeyWrappingSpecification( encryption_key_information=objects.EncryptionKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.NIST_KEY_WRAP ) ) ) self.assertTrue(a != b) self.assertTrue(b != a) def test_not_equal_on_not_equal_mac_signature_key_information(self): """ Test that the inequality operator returns True when comparing two KeyWrappingSpecification structs with different MAC/signature key information. """ a = objects.KeyWrappingSpecification( mac_signature_key_information=objects.MACSignatureKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.NIST_KEY_WRAP ) ) ) b = objects.KeyWrappingSpecification( mac_signature_key_information=objects.MACSignatureKeyInformation( unique_identifier="100182d5-72b8-ffff-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.CBC ) ) ) self.assertTrue(a != b) self.assertTrue(b != a) def test_not_equal_on_not_equal_attribute_names(self): """ Test that the inequality operator returns True when comparing two KeyWrappingSpecification structs with different attribute names. """ a = objects.KeyWrappingSpecification( attribute_names=[ 'Cryptographic Algorithm', 'Cryptographic Length' ] ) b = objects.KeyWrappingSpecification( attribute_names=['Cryptographic Usage Mask'] ) self.assertTrue(a != b) self.assertTrue(b != a) def test_not_equal_on_not_equal_encoding_option(self): """ Test that the inequality operator returns True when comparing two KeyWrappingSpecification structs with different encoding options. """ a = objects.KeyWrappingSpecification( encoding_option=enums.EncodingOption.NO_ENCODING ) b = objects.KeyWrappingSpecification( encoding_option=enums.EncodingOption.TTLV_ENCODING ) self.assertTrue(a != b) self.assertTrue(b != a) def test_not_equal_on_type_mismatch(self): """ Test that the inequality operator returns True when comparing two KeyWrappingSpecification structs with different types. """ a = objects.KeyWrappingSpecification() b = 'invalid' self.assertTrue(a != b) self.assertTrue(b != a) def test_repr(self): """ Test that repr can be applied to an KeyWrappingSpecification struct. """ key_wrapping_specification = objects.KeyWrappingSpecification( wrapping_method=enums.WrappingMethod.ENCRYPT, encryption_key_information=objects.EncryptionKeyInformation( unique_identifier="100182d5-72b8-ffff-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.NIST_KEY_WRAP ) ), mac_signature_key_information=objects.MACSignatureKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.CBC ) ), attribute_names=[ 'Cryptographic Algorithm', 'Cryptographic Length' ], encoding_option=enums.EncodingOption.TTLV_ENCODING ) expected = ( "KeyWrappingSpecification(" "wrapping_method=WrappingMethod.ENCRYPT, " "encryption_key_information=EncryptionKeyInformation(" "unique_identifier='100182d5-72b8-ffff-8383-4d97d512e98a', " "cryptographic_parameters=CryptographicParameters(" "block_cipher_mode=BlockCipherMode.NIST_KEY_WRAP, " "padding_method=None, " "hashing_algorithm=None, " "key_role_type=None, " "digital_signature_algorithm=None, " "cryptographic_algorithm=None, " "random_iv=None, " "iv_length=None, " "tag_length=None, " "fixed_field_length=None, " "invocation_field_length=None, " "counter_length=None, " "initial_counter_value=None)), " "mac_signature_key_information=MACSignatureKeyInformation(" "unique_identifier='100182d5-72b8-47aa-8383-4d97d512e98a', " "cryptographic_parameters=CryptographicParameters(" "block_cipher_mode=BlockCipherMode.CBC, " "padding_method=None, " "hashing_algorithm=None, " "key_role_type=None, " "digital_signature_algorithm=None, " "cryptographic_algorithm=None, " "random_iv=None, " "iv_length=None, " "tag_length=None, " "fixed_field_length=None, " "invocation_field_length=None, " "counter_length=None, " "initial_counter_value=None)), " "attribute_names=[" "'Cryptographic Algorithm', 'Cryptographic Length'], " "encoding_option=EncodingOption.TTLV_ENCODING)" ) observed = repr(key_wrapping_specification) self.assertEqual(expected, observed) def test_str(self): """ Test that str can be applied to a KeyWrappingSpecification struct. """ key_wrapping_specification = objects.KeyWrappingSpecification( wrapping_method=enums.WrappingMethod.ENCRYPT, encryption_key_information=objects.EncryptionKeyInformation( unique_identifier="100182d5-72b8-ffff-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.NIST_KEY_WRAP ) ), mac_signature_key_information=objects.MACSignatureKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.CBC ) ), attribute_names=[ 'Cryptographic Algorithm', 'Cryptographic Length' ], encoding_option=enums.EncodingOption.TTLV_ENCODING ) expected = str({ 'wrapping_method': enums.WrappingMethod.ENCRYPT, 'encryption_key_information': objects.EncryptionKeyInformation( unique_identifier="100182d5-72b8-ffff-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.NIST_KEY_WRAP ) ), 'mac_signature_key_information': objects.MACSignatureKeyInformation( unique_identifier="100182d5-72b8-47aa-8383-4d97d512e98a", cryptographic_parameters=attributes.CryptographicParameters( block_cipher_mode=enums.BlockCipherMode.CBC ) ), 'attribute_names': [ 'Cryptographic Algorithm', 'Cryptographic Length' ], 'encoding_option': enums.EncodingOption.TTLV_ENCODING }) observed = str(key_wrapping_specification) self.assertEqual(expected, observed)

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0481b565feae0501b91ed08bf219cd44b07aab36

152

py

Python

plugins/netmiko/komand_netmiko/actions/__init__.py

lukaszlaszuk/insightconnect-plugins

8c6ce323bfbb12c55f8b5a9c08975d25eb9f8892

[ "MIT" ]

46

2019-06-05T20:47:58.000Z

2022-03-29T10:18:01.000Z

plugins/netmiko/komand_netmiko/actions/__init__.py

lukaszlaszuk/insightconnect-plugins

8c6ce323bfbb12c55f8b5a9c08975d25eb9f8892

[ "MIT" ]

386

2019-06-07T20:20:39.000Z

2022-03-30T17:35:01.000Z

plugins/netmiko/komand_netmiko/actions/__init__.py

lukaszlaszuk/insightconnect-plugins

8c6ce323bfbb12c55f8b5a9c08975d25eb9f8892

[ "MIT" ]

43

2019-07-09T14:13:58.000Z

2022-03-28T12:04:46.000Z

# GENERATED BY KOMAND SDK - DO NOT EDIT from .configuration_commands.action import ConfigurationCommands from .show_commands.action import ShowCommands

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7

04c39917a382b74f20d5e9f540d0b7a860688f90

10,898

py

Python

kbsbot/context_management/tests/test_utils.py

astandre/cb-context-management-ms

031ea7bd9a7770d5617cb9145d7fc94a0730495a

[ "MIT" ]

null

kbsbot/context_management/tests/test_utils.py

astandre/cb-context-management-ms

031ea7bd9a7770d5617cb9145d7fc94a0730495a

[ "MIT" ]

null

kbsbot/context_management/tests/test_utils.py

astandre/cb-context-management-ms

031ea7bd9a7770d5617cb9145d7fc94a0730495a

[ "MIT" ]

null

import unittest from kbsbot.context_management.utils import * class TestUtils(unittest.TestCase): def test_get_entities(self): local_interactions = [ { "date": "Mon, 03 Feb 2020 14:33:41 GMT", "input": { "context": { "entities": [], "intent": None }, "user_input": "Cuando inicia el curso de huertos familiares" }, "output": { "answer": { "answer_type": "text", "text": " El curso denominado emprendimiento y generación de ideas se oferta con la finalidad de desarrollar conocimientos, identificar y potenciar oportunidades, para emprender e innovar en el ámbito personal, social, laboral o productivo." }, "context": { "entities": [ { "type": "http://127.0.0.1/ockb/course/ontology/Course", "value": "http://127.0.0.1/ockb/resources/EAIG5" } ], "intent": "http://127.0.0.1/ockb/resources/ObtenerInformacion" } } , "social_network": 1, "user": 1 }, { "date": "Mon, 03 Feb 2020 14:35:47 GMT", "input": { "context": { "entities": [], "intent": None }, "user_input": "Cuando inicia el curso de huertos familiares" }, "output": { "answer": { "answer_type": "text", "text": " El curso denominado emprendimiento y generación de ideas se oferta con la finalidad de desarrollar conocimientos, identificar y potenciar oportunidades, para emprender e innovar en el ámbito personal, social, laboral o productivo." }, "context": { "entities": [ { "type": "http://127.0.0.1/ockb/course/ontology/Course", "value": "http://127.0.0.1/ockb/resources/EAIG5" } ], "intent": "http://127.0.0.1/ockb/resources/ObtenerInformacion" } } , "social_network": 1, "user": 1 }, { "date": "Mon, 03 Feb 2020 14:37:44 GMT", "input": { "context": { "entities": [], "intent": None }, "user_input": "Cuando inicia el curso de huertos familiares" }, "output": {}, "social_network": 1, "user": 1 }, { "date": "Mon, 03 Feb 2020 14:39:16 GMT", "input": { "context": { "entities": [], "intent": None }, "user_input": "Cuando inicia el curso de huertos familiares" }, "output": { "answer": { "answer_type": "text", "text": " El curso denominado emprendimiento y generación de ideas se oferta con la finalidad de desarrollar conocimientos, identificar y potenciar oportunidades, para emprender e innovar en el ámbito personal, social, laboral o productivo." }, "context": { "entities": [ { "type": "http://127.0.0.1/ockb/course/ontology/Course", "value": "http://127.0.0.1/ockb/resources/EAIG5" } ], "intent": "http://127.0.0.1/ockb/resources/ObtenerInformacion" } }, "social_network": 1, "user": 1 }, { "date": "Mon, 03 Feb 2020 14:40:42 GMT", "input": { "context": { "entities": [], "intent": None }, "user_input": "Que cursos hay" }, "output": { "answer": { "answer_type": "text", "text": " El curso denominado emprendimiento y generación de ideas se oferta con la finalidad de desarrollar conocimientos, identificar y potenciar oportunidades, para emprender e innovar en el ámbito personal, social, laboral o productivo." }, "context": { "entities": [ { "type": "http://127.0.0.1/ockb/course/ontology/Course", "value": "http://127.0.0.1/ockb/resources/EAIG5" } ], "intent": "http://127.0.0.1/ockb/resources/ObtenerInformacion" } }, "social_network": 1, "user": 1 } ] requires = ["http://127.0.0.1/ockb/course/ontology/Course"] result = get_entities(local_interactions, requires) self.assertTrue(len(result) > 0) local_interactions = [ { "date": "Mon, 03 Feb 2020 14:33:41 GMT", "input": { "context": { "entities": [], "intent": None }, "user_input": "Cuando inicia el curso de huertos familiares" }, "output": { "answer": { "answer_type": "text", "text": " El curso denominado emprendimiento y generación de ideas se oferta con la finalidad de desarrollar conocimientos, identificar y potenciar oportunidades, para emprender e innovar en el ámbito personal, social, laboral o productivo." }, "context": { "entities": [ ], "intent": "http://127.0.0.1/ockb/resources/ObtenerInformacion" } } , "social_network": 1, "user": 1 }, { "date": "Mon, 03 Feb 2020 14:35:47 GMT", "input": { "context": { "entities": [], "intent": None }, "user_input": "Cuando inicia el curso de huertos familiares" }, "output": { "answer": { "answer_type": "text", "text": " El curso denominado emprendimiento y generación de ideas se oferta con la finalidad de desarrollar conocimientos, identificar y potenciar oportunidades, para emprender e innovar en el ámbito personal, social, laboral o productivo." }, "context": { "entities": [ ], "intent": "http://127.0.0.1/ockb/resources/ObtenerInformacion" } } , "social_network": 1, "user": 1 }, { "date": "Mon, 03 Feb 2020 14:37:44 GMT", "input": { "context": { "entities": [], "intent": None }, "user_input": "Cuando inicia el curso de huertos familiares" }, "output": {}, "social_network": 1, "user": 1 }, { "date": "Mon, 03 Feb 2020 14:39:16 GMT", "input": { "context": { "entities": [], "intent": None }, "user_input": "Cuando inicia el curso de huertos familiares" }, "output": { "answer": { "answer_type": "text", "text": " El curso denominado emprendimiento y generación de ideas se oferta con la finalidad de desarrollar conocimientos, identificar y potenciar oportunidades, para emprender e innovar en el ámbito personal, social, laboral o productivo." }, "context": { "entities": [ ], "intent": "http://127.0.0.1/ockb/resources/ObtenerInformacion" } }, "social_network": 1, "user": 1 }, { "date": "Mon, 03 Feb 2020 14:40:42 GMT", "input": { "context": { "entities": [], "intent": None }, "user_input": "Que cursos hay" }, "output": { "answer": { "answer_type": "text", "text": " El curso denominado emprendimiento y generación de ideas se oferta con la finalidad de desarrollar conocimientos, identificar y potenciar oportunidades, para emprender e innovar en el ámbito personal, social, laboral o productivo." }, "context": { "entities": [ ], "intent": "http://127.0.0.1/ockb/resources/ObtenerInformacion" } }, "social_network": 1, "user": 1 } ] requires = ["http://127.0.0.1/ockb/course/ontology/Course"] result = get_entities(local_interactions, requires) self.assertTrue(len(result) == 0)

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b6ba1aa4e056bd3d9565770507c3ae49bf367a18

29,451

py

Python

src/guidelines/external_contig.py

noemiefedon/BELLA

ca86e5cd6f593478235c64aa4d0409b0e78dbcbb

[ "MIT" ]

null

src/guidelines/external_contig.py

noemiefedon/BELLA

ca86e5cd6f593478235c64aa4d0409b0e78dbcbb

[ "MIT" ]

null

src/guidelines/external_contig.py

noemiefedon/BELLA

ca86e5cd6f593478235c64aa4d0409b0e78dbcbb

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Function to ensure external sorting for contiguity 'across adjacent sublaminates' Created on Mon Jan 29 12:00:18 2018 @author: Noemie Fedon """ import numpy as np def external_contig(angle, n_plies_group, constraints, ss_before, angle2 = None): ''' returns only the stacking sequences that satisfy constraints concerning contiguity at the junction with an adjacent group of plies, but not within the group of plies OUTPUTS - angle: the selected sublaminate stacking sequences line by line - angle2: the selected sublaminate stacking sequences line by line if a second sublaminate is given as input for angle2 INPUTS - angle: the first sublaminate stacking sequences - angle:2 matrix storing the second sublaminate stacking sequences - ss_before is the stacking sequence of the sublaminate adjacent to the first sublaminate ''' if angle.ndim == 1: angle = angle.reshape((1, angle.size)) ss_beforeLength = ss_before.size # CHECK FOR CORRECT INPUTS SIZE if n_plies_group > angle.shape[1]: raise Exception('The input set of angles have fewer elements that what is asked to be checked') if angle2 is None: # TO ENSURE CONTIGUITY if constraints.contig: # To ensure the contiguity constraint at the junction of ply groups if ss_beforeLength>=1: if constraints.n_contig ==2: if n_plies_group>1: a = angle.shape[0] for ii in range(a)[::-1]: if angle[ii, 0] == ss_before[-1] \ and angle[ii, 1] == ss_before[-1]: angle = np.delete(angle, np.s_[ii], axis=0) continue elif constraints.n_contig == 3: if n_plies_group>2: a = angle.shape[0] for ii in range(a)[::-1]: if angle[ii, 0] == ss_before[-1] \ and angle[ii, 1] == ss_before[-1] \ and angle[ii, 2] == ss_before[-1]: angle = np.delete(angle, np.s_[ii], axis=0) continue elif constraints.n_contig == 4: if n_plies_group>3: a = angle.shape[0] for ii in range(a)[::-1]: if angle[ii, 0] == ss_before[-1] \ and angle[ii, 1] == ss_before[-1] \ and angle[ii, 2] == ss_before[-1] \ and angle[ii, 3] == ss_before[-1]: angle = np.delete(angle, np.s_[ii], axis=0) continue elif constraints.n_contig == 5: if n_plies_group>4: a = angle.shape[0] for ii in range(a)[::-1]: if angle[ii, 0] == ss_before[-1] \ and angle[ii, 1] == ss_before[-1] \ and angle[ii, 2] == ss_before[-1] \ and angle[ii, 3] == ss_before[-1]\ and angle[ii, 4] == ss_before[-1]: angle = np.delete(angle, np.s_[ii], axis=0) continue elif constraints.n_contig == 6: if n_plies_group>5: a = angle.shape[0] for ii in range(a)[::-1]: if angle[ii, 0] == ss_before[-1] \ and angle[ii, 1] == ss_before[-1] \ and angle[ii, 2] == ss_before[-1] \ and angle[ii, 3] == ss_before[-1] \ and angle[ii, 4] == ss_before[-1] \ and angle[ii, 5] == ss_before[-1]: angle = np.delete(angle, np.s_[ii], axis=0) continue else: raise Exception( 'constraints.n_contig must be 2, 3, 4 or 5') if ss_beforeLength>=2: if constraints.n_contig ==2: a = angle.shape[0] for ii in range(a)[::-1]: if angle[ii, 0] == ss_before[-1] \ and angle[ii, 0] == ss_before[-2]: angle = np.delete(angle, np.s_[ii], axis=0) continue elif constraints.n_contig == 3: if n_plies_group>1: a = angle.shape[0] for ii in range(a)[::-1]: if angle[ii, 0] == ss_before[-1] \ and angle[ii, 1] == ss_before[-1] \ and ss_before[-2] == ss_before[-1]: angle = np.delete(angle, np.s_[ii], axis=0) continue elif constraints.n_contig == 4: if n_plies_group>2: a = angle.shape[0] for ii in range(a)[::-1]: if angle[ii, 0] == ss_before[-1] \ and angle[ii, 1] == ss_before[-1] \ and angle[ii, 2] == ss_before[-1] \ and ss_before[-2] == ss_before[-1]: angle = np.delete(angle, np.s_[ii], axis=0) continue elif constraints.n_contig == 5: if n_plies_group>3: a = angle.shape[0] for ii in range(a)[::-1]: if angle[ii, 0] == ss_before[-1] \ and angle[ii, 1] == ss_before[-1] \ and angle[ii, 2] == ss_before[-1] \ and ss_before[-2] == ss_before[-1] \ and angle[ii, 3] == ss_before[-1]: angle = np.delete(angle, np.s_[ii], axis=0) continue elif constraints.n_contig == 6: if n_plies_group>4: a = angle.shape[0] for ii in range(a)[::-1]: if angle[ii, 0] == ss_before[-1] \ and angle[ii, 1] == ss_before[-1] \ and angle[ii, 2] == ss_before[-1] \ and ss_before[-2] == ss_before[-1] \ and angle[ii, 3] == ss_before[-1] \ and angle[ii, 4] == ss_before[-1]: angle = np.delete(angle, np.s_[ii], axis=0) continue else: raise Exception( 'constraints.n_contig must be 2, 3, 4 or 5') if ss_beforeLength>=3: if constraints.n_contig == 2: pass elif constraints.n_contig == 3: a = angle.shape[0] for ii in range(a)[::-1]: if angle[ii, 0] == ss_before[-1] \ and ss_before[-3] == ss_before[-1] \ and ss_before[-2] == ss_before[-1]: angle = np.delete(angle, np.s_[ii], axis=0) continue elif constraints.n_contig == 4: if n_plies_group>1: a = angle.shape[0] for ii in range(a)[::-1]: if angle[ii, 0] == ss_before[-1] \ and angle[ii, 1] == ss_before[-1] \ and ss_before[-3] == ss_before[-1] \ and ss_before[-2] == ss_before[-1]: angle = np.delete(angle, np.s_[ii], axis=0) continue elif constraints.n_contig == 5: if n_plies_group>2: a = angle.shape[0] for ii in range(a)[::-1]: if angle[ii, 0] == ss_before[-1] \ and angle[ii, 1] == ss_before[-1] \ and ss_before[-3] == ss_before[-1] \ and ss_before[-2] == ss_before[-1] \ and angle[ii, 2] == ss_before[-1]: angle = np.delete(angle, np.s_[ii], axis=0) continue elif constraints.n_contig == 6: if n_plies_group>3: a = angle.shape[0] for ii in range(a)[::-1]: if angle[ii, 0] == ss_before[-1] \ and angle[ii, 1] == ss_before[-1] \ and ss_before[-3] == ss_before[-1] \ and ss_before[-2] == ss_before[-1] \ and angle[ii, 2] == ss_before[-1] \ and angle[ii, 3] == ss_before[-1]: angle = np.delete(angle, np.s_[ii], axis=0) continue else: raise Exception( 'constraints.n_contig must be 2, 3, 4 or 5') if ss_beforeLength>=4: if constraints.n_contig ==2: pass elif constraints.n_contig == 3: pass elif constraints.n_contig == 4: a = angle.shape[0] for ii in range(a)[::-1]: if angle[ii, 0] == ss_before[-1] \ and ss_before[-4] == ss_before[-1] \ and ss_before[-3] == ss_before[-1] \ and ss_before[-2] == ss_before[-1]: angle = np.delete(angle, np.s_[ii], axis=0) continue elif constraints.n_contig == 5: if n_plies_group>1: a = angle.shape[0] for ii in range(a)[::-1]: if angle[ii, 0] == ss_before[-1] \ and ss_before[-4] == ss_before[-1] \ and ss_before[-3] == ss_before[-1] \ and ss_before[-2] == ss_before[-1] \ and angle[ii, 1] == ss_before[-1]: angle = np.delete(angle, np.s_[ii], axis=0) continue elif constraints.n_contig == 6: if n_plies_group>2: a = angle.shape[0] for ii in range(a)[::-1]: if angle[ii, 0] == ss_before[-1] \ and ss_before[-4] == ss_before[-1] \ and ss_before[-3] == ss_before[-1] \ and ss_before[-2] == ss_before[-1] \ and angle[ii, 1] == ss_before[-1] \ and angle[ii, 2] == ss_before[-1]: angle = np.delete(angle, np.s_[ii], axis=0) continue else: raise Exception( 'constraints.n_contig must be 2, 3, 4 or 5') if ss_beforeLength>=5: if constraints.n_contig ==2: pass elif constraints.n_contig == 3: pass elif constraints.n_contig == 4: pass elif constraints.n_contig == 5: a = angle.shape[0] for ii in range(a)[::-1]: if angle[ii, 0] == ss_before[-1] \ and ss_before[-4] == ss_before[-1] \ and ss_before[-3] == ss_before[-1] \ and ss_before[-2] == ss_before[-1] \ and angle[ii, 0] == ss_before[-5]: angle = np.delete(angle, np.s_[ii], axis=0) continue elif constraints.n_contig == 6: if n_plies_group>1: a = angle.shape[0] for ii in range(a)[::-1]: if angle[ii, 0] == ss_before[-1] \ and ss_before[-4] == ss_before[-1] \ and ss_before[-3] == ss_before[-1] \ and ss_before[-2] == ss_before[-1] \ and angle[ii, 0] == ss_before[-5] \ and angle[ii, 1] == ss_before[-5]: angle = np.delete(angle, np.s_[ii], axis=0) continue else: raise Exception( 'constraints.n_contig must be 2, 3, 4 or 5') if ss_beforeLength>=6: if constraints.n_contig == 2: pass elif constraints.n_contig == 3: pass elif constraints.n_contig == 4: pass elif constraints.n_contig == 5: pass elif constraints.n_contig == 6: a = angle.shape[0] for ii in range(a)[::-1]: if angle[ii, 0] == ss_before[-1] \ and ss_before[-4] == ss_before[-1] \ and ss_before[-3] == ss_before[-1] \ and ss_before[-2] == ss_before[-1] \ and angle[ii, 0] == ss_before[-5] \ and ss_before[-6] == ss_before[-5]: angle = np.delete(angle, np.s_[ii], axis=0) continue else: raise Exception( 'constraints.n_contig must be 2, 3, 4 or 5') else: # TO ENSURE CONTIGUITY if constraints.contig: # To ensure the contiguity constraint at the junction of ply groups if ss_beforeLength>=1: if constraints.n_contig ==2: if n_plies_group>1: a = angle.shape[0] for ii in range(a)[::-1]: if angle[ii, 0] == ss_before[-1] \ and angle[ii, 1] == ss_before[-1]: angle = np.delete(angle, np.s_[ii], axis=0) continue elif constraints.n_contig == 3: if n_plies_group>2: a = angle.shape[0] for ii in range(a)[::-1]: if angle[ii, 0] == ss_before[-1] \ and angle[ii, 1] == ss_before[-1] \ and angle[ii, 2] == ss_before[-1]: angle = np.delete(angle, np.s_[ii], axis=0) continue elif constraints.n_contig == 4: if n_plies_group>3: a = angle.shape[0] for ii in range(a)[::-1]: if angle[ii, 0] == ss_before[-1] \ and angle[ii, 1] == ss_before[-1] \ and angle[ii, 2] == ss_before[-1] \ and angle[ii, 3] == ss_before[-1]: angle = np.delete(angle, np.s_[ii], axis=0) continue elif constraints.n_contig == 5: if n_plies_group>4: a = angle.shape[0] for ii in range(a)[::-1]: if angle[ii, 0] == ss_before[-1] \ and angle[ii, 1] == ss_before[-1] \ and angle[ii, 2] == ss_before[-1] \ and angle[ii, 3] == ss_before[-1]\ and angle[ii, 4] == ss_before[-1]: angle = np.delete(angle, np.s_[ii], axis=0) continue elif constraints.n_contig == 6: if n_plies_group>5: a = angle.shape[0] for ii in range(a)[::-1]: if angle[ii, 0] == ss_before[-1] \ and angle[ii, 1] == ss_before[-1] \ and angle[ii, 2] == ss_before[-1] \ and angle[ii, 3] == ss_before[-1] \ and angle[ii, 4] == ss_before[-1] \ and angle[ii, 5] == ss_before[-1]: angle = np.delete(angle, np.s_[ii], axis=0) continue else: raise Exception( 'constraints.n_contig must be 2, 3, 4 or 5') if ss_beforeLength>=2: if constraints.n_contig ==2: a = angle.shape[0] for ii in range(a)[::-1]: if angle[ii, 0] == ss_before[-1] \ and angle[ii, 0] == ss_before[-2]: angle = np.delete(angle, np.s_[ii], axis=0) continue elif constraints.n_contig == 3: if n_plies_group>1: a = angle.shape[0] for ii in range(a)[::-1]: if angle[ii, 0] == ss_before[-1] \ and angle[ii, 1] == ss_before[-1] \ and ss_before[-2] == ss_before[-1]: angle = np.delete(angle, np.s_[ii], axis=0) continue elif constraints.n_contig == 4: if n_plies_group>2: a = angle.shape[0] for ii in range(a)[::-1]: if angle[ii, 0] == ss_before[-1] \ and angle[ii, 1] == ss_before[-1] \ and angle[ii, 2] == ss_before[-1] \ and ss_before[-2] == ss_before[-1]: angle = np.delete(angle, np.s_[ii], axis=0) continue elif constraints.n_contig == 5: if n_plies_group>3: a = angle.shape[0] for ii in range(a)[::-1]: if angle[ii, 0] == ss_before[-1] \ and angle[ii, 1] == ss_before[-1] \ and angle[ii, 2] == ss_before[-1] \ and ss_before[-2] == ss_before[-1] \ and angle[ii, 3] == ss_before[-1]: angle = np.delete(angle, np.s_[ii], axis=0) continue elif constraints.n_contig == 6: if n_plies_group>4: a = angle.shape[0] for ii in range(a)[::-1]: if angle[ii, 0] == ss_before[-1] \ and angle[ii, 1] == ss_before[-1] \ and angle[ii, 2] == ss_before[-1] \ and ss_before[-2] == ss_before[-1] \ and angle[ii, 3] == ss_before[-1] \ and angle[ii, 4] == ss_before[-1]: angle = np.delete(angle, np.s_[ii], axis=0) continue else: raise Exception( 'constraints.n_contig must be 2, 3, 4 or 5') if ss_beforeLength>=3: if constraints.n_contig == 2: pass elif constraints.n_contig == 3: a = angle.shape[0] for ii in range(a)[::-1]: if angle[ii, 0] == ss_before[-1] \ and ss_before[-3] == ss_before[-1] \ and ss_before[-2] == ss_before[-1]: angle = np.delete(angle, np.s_[ii], axis=0) continue elif constraints.n_contig == 4: if n_plies_group>1: a = angle.shape[0] for ii in range(a)[::-1]: if angle[ii, 0] == ss_before[-1] \ and angle[ii, 1] == ss_before[-1] \ and ss_before[-3] == ss_before[-1] \ and ss_before[-2] == ss_before[-1]: angle = np.delete(angle, np.s_[ii], axis=0) continue elif constraints.n_contig == 5: if n_plies_group>2: a = angle.shape[0] for ii in range(a)[::-1]: if angle[ii, 0] == ss_before[-1] \ and angle[ii, 1] == ss_before[-1] \ and ss_before[-3] == ss_before[-1] \ and ss_before[-2] == ss_before[-1] \ and angle[ii, 2] == ss_before[-1]: angle = np.delete(angle, np.s_[ii], axis=0) continue elif constraints.n_contig == 6: if n_plies_group>3: a = angle.shape[0] for ii in range(a)[::-1]: if angle[ii, 0] == ss_before[-1] \ and angle[ii, 1] == ss_before[-1] \ and ss_before[-3] == ss_before[-1] \ and ss_before[-2] == ss_before[-1] \ and angle[ii, 2] == ss_before[-1] \ and angle[ii, 3] == ss_before[-1]: angle = np.delete(angle, np.s_[ii], axis=0) continue else: raise Exception( 'constraints.n_contig must be 2, 3, 4 or 5') if ss_beforeLength>=4: if constraints.n_contig ==2: pass elif constraints.n_contig == 3: pass elif constraints.n_contig == 4: a = angle.shape[0] for ii in range(a)[::-1]: if angle[ii, 0] == ss_before[-1] \ and ss_before[-4] == ss_before[-1] \ and ss_before[-3] == ss_before[-1] \ and ss_before[-2] == ss_before[-1]: angle = np.delete(angle, np.s_[ii], axis=0) continue elif constraints.n_contig == 5: if n_plies_group>1: a = angle.shape[0] for ii in range(a)[::-1]: if angle[ii, 0] == ss_before[-1] \ and ss_before[-4] == ss_before[-1] \ and ss_before[-3] == ss_before[-1] \ and ss_before[-2] == ss_before[-1] \ and angle[ii, 1] == ss_before[-1]: angle = np.delete(angle, np.s_[ii], axis=0) continue elif constraints.n_contig == 6: if n_plies_group>2: a = angle.shape[0] for ii in range(a)[::-1]: if angle[ii, 0] == ss_before[-1] \ and ss_before[-4] == ss_before[-1] \ and ss_before[-3] == ss_before[-1] \ and ss_before[-2] == ss_before[-1] \ and angle[ii, 1] == ss_before[-1] \ and angle[ii, 2] == ss_before[-1]: angle = np.delete(angle, np.s_[ii], axis=0) continue else: raise Exception( 'constraints.n_contig must be 2, 3, 4 or 5') if ss_beforeLength>=5: if constraints.n_contig ==2: pass elif constraints.n_contig == 3: pass elif constraints.n_contig == 4: pass elif constraints.n_contig == 5: a = angle.shape[0] for ii in range(a)[::-1]: if angle[ii, 0] == ss_before[-1] \ and ss_before[-4] == ss_before[-1] \ and ss_before[-3] == ss_before[-1] \ and ss_before[-2] == ss_before[-1] \ and angle[ii, 0] == ss_before[-5]: angle = np.delete(angle, np.s_[ii], axis=0) continue elif constraints.n_contig == 6: if n_plies_group>1: a = angle.shape[0] for ii in range(a)[::-1]: if angle[ii, 0] == ss_before[-1] \ and ss_before[-4] == ss_before[-1] \ and ss_before[-3] == ss_before[-1] \ and ss_before[-2] == ss_before[-1] \ and angle[ii, 0] == ss_before[-5] \ and angle[ii, 1] == ss_before[-5]: angle = np.delete(angle, np.s_[ii], axis=0) continue else: raise Exception( 'constraints.n_contig must be 2, 3, 4 or 5') if ss_beforeLength>=6: if constraints.n_contig == 2: pass elif constraints.n_contig == 3: pass elif constraints.n_contig == 4: pass elif constraints.n_contig == 5: pass elif constraints.n_contig == 6: a = angle.shape[0] for ii in range(a)[::-1]: if angle[ii, 0] == ss_before[-1] \ and ss_before[-4] == ss_before[-1] \ and ss_before[-3] == ss_before[-1] \ and ss_before[-2] == ss_before[-1] \ and angle[ii, 0] == ss_before[-5] \ and ss_before[-6] == ss_before[-5]: angle = np.delete(angle, np.s_[ii], axis=0) continue else: raise Exception( 'constraints.n_contig must be 2, 3, 4 or 5') return angle, angle2 if __name__ == "__main__": 'Test' import sys sys.path.append(r'C:\BELLA') from src.LAYLA_V02.constraints import Constraints from src.divers.pretty_print import print_ss, print_list_ss constraints = Constraints() constraints.contig = True constraints.n_contig = 2 print('*** Test for the function external_contig ***\n') print('Input stacking sequences:\n') ss = np.array([[-45, -45, 0, 45, 90], [0, 45, 45, 45, 45],[0, 0, 0, 45, 45]]) print_list_ss(ss) print('Stacking sequence of adajacent sublaminate:\n') ss_before = np.array([-45]) print_ss(ss_before) n_plies_group = 5 middle_ply = 0 test, _ = external_contig(ss, n_plies_group, constraints, ss_before, ss) if test.shape[0]: print('Stacking sequences satisfying the rule:\n') print_list_ss(test) else: print('No stacking sequence satisfy the rule\n')

40.014946

104

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3,078

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0.190934

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29,451

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40.014946

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b6bb15d9d1c1238ff8ec41f15e5776493ca189dc

525,541

py

Python

causal_networkx/ci/tests/testdata.py

adam2392/causal-networkx

aba5355d2e900b30dd3d99916981674f3c0074e9

[ "BSD-3-Clause" ]

null

causal_networkx/ci/tests/testdata.py

adam2392/causal-networkx

aba5355d2e900b30dd3d99916981674f3c0074e9

[ "BSD-3-Clause" ]

null

causal_networkx/ci/tests/testdata.py

adam2392/causal-networkx

aba5355d2e900b30dd3d99916981674f3c0074e9

[ "BSD-3-Clause" ]

null

"""The data included in this file is the same data distributed with the pcalg package for R developed by Markus Kalisch, Alain Hauser, Martin Maechler, Diego Colombo, Doris Entner, Patrik Hoyer, Antti Hyttinen, and Jonas Peters. License: GPLv2. 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1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, ] bin_answer = [ 9.95394730483e-256, 6.9654995846e-250, 2.70986437702e-244, 1.66028307209e-137, 4.5256578439e-134, ] dis_data = [ 2, 0, 0, 2, 1, 2, 1, 1, 2, 1, 1, 0, 1, 3, 0, 1, 0, 2, 2, 1, 1, 0, 0, 2, 1, 2, 1, 1, 0, 1, 2, 1, 0, 2, 1, 0, 0, 1, 2, 1, 2, 0, 2, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 3, 1, 0, 1, 0, 2, 1, 0, 1, 1, 2, 1, 0, 0, 0, 2, 1, 1, 0, 1, 2, 1, 2, 0, 2, 2, 1, 2, 0, 1, 3, 1, 0, 0, 0, 3, 0, 2, 0, 1, 2, 0, 2, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 2, 1, 2, 0, 1, 0, 0, 0, 0, 0, 3, 1, 0, 0, 1, 0, 1, 0, 0, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 2, 0, 1, 2, 1, 2, 0, 1, 3, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 2, 0, 2, 3, 1, 0, 0, 1, 3, 1, 0, 0, 2, 2, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 1, 3, 1, 0, 0, 0, 2, 1, 2, 0, 2, 2, 1, 2, 0, 1, 0, 1, 0, 1, 1, 2, 1, 1, 0, 2, 3, 1, 1, 0, 2, 0, 1, 0, 0, 0, 2, 0, 2, 1, 2, 2, 1, 2, 1, 1, 2, 0, 2, 1, 2, 2, 1, 1, 1, 1, 2, 1, 1, 0, 2, 2, 1, 0, 1, 1, 2, 1, 1, 0, 1, 2, 1, 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0, 2, 1, 2, 0, 2, 0, 1, 1, 0, 0, 0, 1, 2, 0, 1, 0, 1, 2, 0, 1, 3, 0, 1, 0, 0, 2, 1, 1, 0, 2, 0, 1, 2, 0, 2, 3, 1, 2, 0, 1, 3, 1, 1, 0, 1, 1, 1, 0, 1, 0, 2, 1, 1, 0, 2, 3, 1, 2, 0, 2, 0, 1, 2, 0, 2, 3, 1, 1, 0, 0, 0, 1, 1, 1, 2, 1, 0, 1, 0, 2, 2, 1, 1, 0, 2, 2, 0, 2, 0, 0, 2, 0, 2, 0, 2, 1, 1, 0, 0, 0, 2, 1, 2, 1, 1, 2, 1, 2, 0, 2, 0, 1, 1, 0, 1, 2, 1, 2, 0, 1, 2, 1, ] dis_answer = [ 0.355124470003, 1.24045554632e-05, 0.00571262073676, 0.775345672281, 0.0992360700386, ]

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Python

tests/test_main.py

cacrespo/pylexis

5f9b1b04e33b59e6b040023ffe8c1a657d38bdcc

[ "MIT" ]

3

2022-01-08T20:20:06.000Z

2022-01-09T21:58:39.000Z

tests/test_main.py

cacrespo/pylexis

5f9b1b04e33b59e6b040023ffe8c1a657d38bdcc

[ "MIT" ]

9

2021-12-30T13:04:21.000Z

2022-02-09T23:03:21.000Z

tests/test_main.py

cacrespo/pylexis

5f9b1b04e33b59e6b040023ffe8c1a657d38bdcc

[ "MIT" ]

null

import pytest def test_TODO(): pass

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py

Python

ufdl-speech-app/src/ufdl/speech_app/routers/__init__.py

waikato-ufdl/ufdl-backend

776fc906c61eba6c2f2e6324758e7b8a323e30d7

[ "Apache-2.0" ]

null

ufdl-speech-app/src/ufdl/speech_app/routers/__init__.py

waikato-ufdl/ufdl-backend

776fc906c61eba6c2f2e6324758e7b8a323e30d7

[ "Apache-2.0" ]

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2022-02-10T10:35:15.000Z

ufdl-speech-app/src/ufdl/speech_app/routers/__init__.py

waikato-ufdl/ufdl-backend

776fc906c61eba6c2f2e6324758e7b8a323e30d7

[ "Apache-2.0" ]

null

from ._UFDLSpeechRouter import UFDLSpeechRouter

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Python

models/cifarnet.py

CrispyHarder/deep-weight-prior

b87e61d6ad590c61b90e188ec86bfb956073be65

[ "MIT" ]

41

2019-02-12T10:15:19.000Z

2021-02-14T00:04:47.000Z

models/cifarnet.py

CrispyHarder/deep-weight-prior

b87e61d6ad590c61b90e188ec86bfb956073be65

[ "MIT" ]

1

2020-10-25T21:18:59.000Z

2020-10-27T23:20:34.000Z

models/cifarnet.py

CrispyHarder/deep-weight-prior

b87e61d6ad590c61b90e188ec86bfb956073be65

[ "MIT" ]

8

2019-08-26T01:55:26.000Z

2021-01-23T22:18:35.000Z

import torch from models import bayes from torch import nn from collections import OrderedDict import utils from torch import distributions as dist import numpy as np class Flatten(nn.Module): def forward(self, x): return x.view(x.size(0), -1) class CIFARNet(bayes._BayesNet): def __init__(self, cfg, device=None, n_classes=10, do=[], k=1., vae_list=None, **kwargs): super(CIFARNet, self).__init__(**kwargs) self.device = device self.cfg = cfg d1, d2, d3 = map(int, [128 * k, 256 * k, 512 * k]) if cfg == 'vanilla': # 3x32x32 self.features = nn.Sequential(OrderedDict([ ('conv1', nn.Conv2d(3, d1, 7)), # 128x26x26 ('bn1', nn.BatchNorm2d(d1)), ('relu1', nn.LeakyReLU()), ('maxpool', nn.MaxPool2d(2)), # 128x13x13 ('conv2', nn.Conv2d(d1, d2, 5)), # 256x9x9 ('bn2', nn.BatchNorm2d(d2)), ('relu2', nn.LeakyReLU()), ('conv3', nn.Conv2d(d2, d2, 5)), # 256x5x5 ('bn3', nn.BatchNorm2d(d2)), ('relu3', nn.LeakyReLU()), ('conv4', nn.Conv2d(d2, 512, 5)), # 512x1x1 ('bn4', nn.BatchNorm2d(512)), ('relu4', nn.LeakyReLU()), ('flatten', Flatten()), # 512 ])) elif cfg == 'bayes': # 3x32x32 self.features = nn.Sequential(OrderedDict([ ('conv1', bayes.BayesConv2d(3, d1, 7)), # 128x26x26 ('bn1', nn.BatchNorm2d(d1)), ('relu1', nn.LeakyReLU()), ('maxpool', nn.MaxPool2d(2)), # 128x13x13 ('conv2', bayes.BayesConv2d(d1, d2, 5)), # 256x9x9 ('bn2', nn.BatchNorm2d(d2)), ('relu2', nn.LeakyReLU()), ('conv3', bayes.BayesConv2d(d2, d2, 5)), # 256x5x5 ('bn3', nn.BatchNorm2d(d2)), ('relu3', nn.LeakyReLU()), ('conv4', bayes.BayesConv2d(d2, 512, 5)), # 512x1x1 ('bn4', nn.BatchNorm2d(512)), ('relu4', nn.LeakyReLU()), ('flatten', Flatten()), # 512 ])) elif cfg == 'bayes1110': # 3x32x32 self.features = nn.Sequential(OrderedDict([ ('conv1', bayes.BayesConv2d(3, d1, 7)), # 128x26x26 ('bn1', nn.BatchNorm2d(d1)), ('relu1', nn.LeakyReLU()), ('maxpool', nn.MaxPool2d(2)), # 128x13x13 ('conv2', bayes.BayesConv2d(d1, d2, 5)), # 256x9x9 ('bn2', nn.BatchNorm2d(d2)), ('relu2', nn.LeakyReLU()), ('conv3', bayes.BayesConv2d(d2, d2, 5)), # 256x5x5 ('bn3', nn.BatchNorm2d(d2)), ('relu3', nn.LeakyReLU()), ('conv4', nn.Conv2d(d2, 512, 5)), # 512x1x1 ('bn4', nn.BatchNorm2d(512)), ('relu4', nn.LeakyReLU()), ('flatten', Flatten()), # 512 ])) elif cfg == 'bayes1100': # 3x32x32 self.features = nn.Sequential(OrderedDict([ ('conv1', bayes.BayesConv2d(3, d1, 7)), # 128x26x26 ('bn1', nn.BatchNorm2d(d1)), ('relu1', nn.LeakyReLU()), ('maxpool', nn.MaxPool2d(2)), # 128x13x13 ('conv2', bayes.BayesConv2d(d1, d2, 5)), # 256x9x9 ('bn2', nn.BatchNorm2d(d2)), ('relu2', nn.LeakyReLU()), ('conv3', nn.Conv2d(d2, d2, 5)), # 256x5x5 ('bn3', nn.BatchNorm2d(d2)), ('relu3', nn.LeakyReLU()), ('conv4', nn.Conv2d(d2, 512, 5)), # 512x1x1 ('bn4', nn.BatchNorm2d(512)), ('relu4', nn.LeakyReLU()), ('flatten', Flatten()), # 512 ])) elif cfg == 'bayes1000': # 3x32x32 self.features = nn.Sequential(OrderedDict([ ('conv1', bayes.BayesConv2d(3, d1, 7)), # 128x26x26 ('bn1', nn.BatchNorm2d(d1)), ('relu1', nn.LeakyReLU()), ('maxpool', nn.MaxPool2d(2)), # 128x13x13 ('conv2', nn.Conv2d(d1, d2, 5)), # 256x9x9 ('bn2', nn.BatchNorm2d(d2)), ('relu2', nn.LeakyReLU()), ('conv3', nn.Conv2d(d2, d2, 5)), # 256x5x5 ('bn3', nn.BatchNorm2d(d2)), ('relu3', nn.LeakyReLU()), ('conv4', nn.Conv2d(d2, 512, 5)), # 512x1x1 ('bn4', nn.BatchNorm2d(512)), ('relu4', nn.LeakyReLU()), ('flatten', Flatten()), # 512 ])) elif cfg == 'bayes-mtrunca': # 3x32x32 self.features = nn.Sequential(OrderedDict([ ('conv1', bayes.MuTruncAlphaFFGConv2d(3, d1, 7)), # 128x26x26 ('bn1', nn.BatchNorm2d(d1)), ('relu1', nn.LeakyReLU()), ('maxpool', nn.MaxPool2d(2)), # 128x13x13 ('conv2', bayes.MuTruncAlphaFFGConv2d(d1, d2, 5)), # 256x9x9 ('bn2', nn.BatchNorm2d(d2)), ('relu2', nn.LeakyReLU()), ('conv3', bayes.MuTruncAlphaFFGConv2d(d2, d2, 5)), # 256x5x5 ('bn3', nn.BatchNorm2d(d2)), ('relu3', nn.LeakyReLU()), ('conv4', bayes.MuTruncAlphaFFGConv2d(d2, 512, 5)), # 512x1x1 ('bn4', nn.BatchNorm2d(512)), ('relu4', nn.LeakyReLU()), ('flatten', Flatten()), # 512 ])) elif cfg == 'bayes1100-mtrunca': # 3x32x32 self.features = nn.Sequential(OrderedDict([ ('conv1', bayes.MuTruncAlphaFFGConv2d(3, d1, 7)), # 128x26x26 ('bn1', nn.BatchNorm2d(d1)), ('relu1', nn.LeakyReLU()), ('maxpool', nn.MaxPool2d(2)), # 128x13x13 ('conv2', bayes.MuTruncAlphaFFGConv2d(d1, d2, 5)), # 256x9x9 ('bn2', nn.BatchNorm2d(d2)), ('relu2', nn.LeakyReLU()), ('conv3', nn.Conv2d(d2, d2, 5)), # 256x5x5 ('bn3', nn.BatchNorm2d(d2)), ('relu3', nn.LeakyReLU()), ('conv4', nn.Conv2d(d2, 512, 5)), # 512x1x1 ('bn4', nn.BatchNorm2d(512)), ('relu4', nn.LeakyReLU()), ('flatten', Flatten()), # 512 ])) elif cfg == 'bayes1000-mtrunca': # 3x32x32 self.features = nn.Sequential(OrderedDict([ ('conv1', bayes.MuTruncAlphaFFGConv2d(3, d1, 7)), # 128x26x26 ('bn1', nn.BatchNorm2d(d1)), ('relu1', nn.LeakyReLU()), ('maxpool', nn.MaxPool2d(2)), # 128x13x13 ('conv2', nn.Conv2d(d1, d2, 5)), # 256x9x9 ('bn2', nn.BatchNorm2d(d2)), ('relu2', nn.LeakyReLU()), ('conv3', nn.Conv2d(d2, d2, 5)), # 256x5x5 ('bn3', nn.BatchNorm2d(d2)), ('relu3', nn.LeakyReLU()), ('conv4', nn.Conv2d(d2, 512, 5)), # 512x1x1 ('bn4', nn.BatchNorm2d(512)), ('relu4', nn.LeakyReLU()), ('flatten', Flatten()), # 512 ])) elif cfg == 'vanilla-do': # 3x32x32 self.features = nn.Sequential(OrderedDict([ ('conv1', nn.Conv2d(3, d1, 7)), # 128x26x26 ('bn1', nn.BatchNorm2d(d1)), ('relu1', nn.LeakyReLU()), ('maxpool', nn.MaxPool2d(2)), # 128x13x13 ('conv2', nn.Conv2d(d1, d2, 5)), # 256x9x9 ('bn2', nn.BatchNorm2d(d2)), ('relu2', nn.LeakyReLU()), ('conv3', nn.Conv2d(d2, d2, 5)), # 256x5x5 ('bn3', nn.BatchNorm2d(d2)), ('relu3', nn.LeakyReLU()), ('conv4', nn.Conv2d(d2, 512, 5)), # 512x1x1 ('bn4', nn.BatchNorm2d(512)), ('relu4', nn.LeakyReLU()), ('flatten', Flatten()), # 512 ])) else: raise NotImplementedError self.classifier = nn.Sequential(OrderedDict([ ('fc1', nn.Linear(512, 512)), ('bn1', nn.BatchNorm1d(512)), ('relu1', nn.LeakyReLU()), ('linear', nn.Linear(512, n_classes)) ])) if self.device: self.to(self.device) def forward(self, input): return self.classifier(self.features(input)) def weights_init(self, init_list, vae_list, flow_list=None, pretrained=None, filters_list=None): self.apply(utils.weight_init(module=nn.Conv2d, initf=nn.init.xavier_normal_)) self.apply(utils.weight_init(module=nn.Linear, initf=nn.init.xavier_normal_)) self.apply(utils.weight_init(module=bayes.LogScaleConv2d, initf=utils.const_init(-10.))) self.apply(utils.weight_init(module=bayes.LogScaleLinear, initf=utils.const_init(-10.))) if len(init_list) > 0 and init_list[0] == 'pretrained': assert len(init_list) == 1 w_pretrained = torch.load(pretrained) for k, v in w_pretrained.items(): if k in self.state_dict(): self.state_dict()[k].data.copy_(v) else: tokens = k.split('.') self.state_dict()['.'.join(tokens[:2] + ['mean'] + tokens[-1:])].data.copy_(v) return convs = [self.features.conv1, self.features.conv2, self.features.conv3, self.features.conv4] for i, m in enumerate(convs): init = init_list[i] if i < len(init_list) else 'xavier' w = m.mean.weight if isinstance(m, bayes._Bayes) else m.weight if init == 'vae': vae_path = vae_list[i] vae = utils.load_vae(vae_path, device=self.device) z = torch.randn(w.size(0) * w.size(1), vae.encoder.z_dim, 1, 1).to(vae.device) x = vae.decode(z)[0] w.data = x.reshape(w.shape) elif init == 'flow': flow_path = flow_list[i] flow = utils.load_flow(flow_path, device=self.device) utils.flow_init(flow)(w) elif init == 'xavier': pass elif init == 'filters': filters = np.load(filters_list[i]) filters = np.concatenate([filters]*10) N = np.prod(w.shape[:2]) filters = filters[np.random.permutation(len(filters))[:N]] w.data = torch.from_numpy(filters.reshape(*w.shape)).to(self.device) elif init == 'recon': filters = np.load(filters_list[i]) filters = np.concatenate([filters]*10) N = np.prod(w.shape[:2]) filters = filters[np.random.permutation(len(filters))[:N]] vae_path = vae_list[i] vae = utils.load_vae(vae_path, device=self.device) filters = vae(torch.from_numpy(filters).to(self.device))[1][0] w.data = filters.reshape_as(w) else: raise NotImplementedError('no {} init'.format(init)) def set_prior(self, prior_list, dwp_samples, vae_list, flow_list=None): convs = [self.features.conv1, self.features.conv2, self.features.conv3, self.features.conv4] for i, m in enumerate(convs): if not isinstance(m, bayes._Bayes): continue if prior_list[i] == 'vae': vae = utils.load_vae(vae_list[i], self.device) vae = nn.DataParallel(vae) for p in vae.parameters(): p.requires_grad = False m.kl_function = utils.kl_dwp(vae, n_tries=dwp_samples) elif prior_list[i] == 'flow': flow = utils.load_flow(flow_list[i], self.device) for p in flow.parameters(): p.requires_grad = False m.kl_function = utils.kl_flow(flow, n_tries=dwp_samples) elif prior_list[i] == 'sn': m.kl_function = utils.kl_normal m.prior = dist.Normal(torch.FloatTensor([0.]).to(self.device), torch.FloatTensor([1.]).to(self.device)) elif prior_list[i] == 'loguniform': if self.cfg in ['bayes-mtrunca', 'bayes1100-mtrunca', 'bayes1000-mtrunca']: m.kl_function = utils.kl_loguniform_with_trunc_alpha else: raise NotImplementedError elif prior_list[i] == 'no': pass else: raise NotImplementedError class CIFARNetNew(bayes._BayesNet): def __init__(self, cfg, device=None, n_classes=10, do=[], k=1., vae_list=None, logvar=-10., **kwargs): super(CIFARNetNew, self).__init__(**kwargs) self.device = device self.cfg = cfg self.vaes = [] d1, d2, d3 = map(int, [128 * k, 256 * k, 512 * k]) if cfg in ['vanilla', 'vanilla-nofc', 'vanilla-do']: # 3x32x32 self.features = nn.Sequential(OrderedDict([ ('conv1', nn.Conv2d(3, d1, 7)), # 128x26x26 ('relu1', nn.LeakyReLU()), ('maxpool', nn.MaxPool2d(2)), # 128x13x13 ('conv2', nn.Conv2d(d1, d2, 5)), # 256x9x9 ('relu2', nn.LeakyReLU()), ('conv3', nn.Conv2d(d2, d2, 5)), # 256x5x5 ('relu3', nn.LeakyReLU()), ('flatten', Flatten()), # 512 ])) elif cfg == 'bayes111' or cfg == 'bayes111-nofc': # 3x32x32 self.features = nn.Sequential(OrderedDict([ ('conv1', bayes.BayesConv2d(3, d1, 7)), # 128x26x26 ('relu1', nn.LeakyReLU()), ('maxpool', nn.MaxPool2d(2)), # 128x13x13 ('conv2', bayes.BayesConv2d(d1, d2, 5)), # 256x9x9 ('relu2', nn.LeakyReLU()), ('conv3', bayes.BayesConv2d(d2, d2, 5)), # 256x5x5 ('relu3', nn.LeakyReLU()), ('flatten', Flatten()), # 512 ])) elif cfg == 'bayes111-mutrunca': # 3x32x32 self.features = nn.Sequential(OrderedDict([ ('conv1', bayes.MuTruncAlphaFFGConv2d(3, d1, 7)), # 128x26x26 ('relu1', nn.LeakyReLU()), ('maxpool', nn.MaxPool2d(2)), # 128x13x13 ('conv2', bayes.MuTruncAlphaFFGConv2d(d1, d2, 5)), # 256x9x9 ('relu2', nn.LeakyReLU()), ('conv3', bayes.MuTruncAlphaFFGConv2d(d2, d2, 5)), # 256x5x5 ('relu3', nn.LeakyReLU()), ('flatten', Flatten()), # 512 ])) elif cfg == 'bayes110': # 3x32x32 self.features = nn.Sequential(OrderedDict([ ('conv1', bayes.BayesConv2d(3, d1, 7)), # 128x26x26 ('relu1', nn.LeakyReLU()), ('maxpool', nn.MaxPool2d(2)), # 128x13x13 ('conv2', bayes.BayesConv2d(d1, d2, 5)), # 256x9x9 ('relu2', nn.LeakyReLU()), ('conv3', nn.Conv2d(d2, d2, 5)), # 256x5x5 ('relu3', nn.LeakyReLU()), ('flatten', Flatten()), # 512 ])) elif cfg == 'bayes100' or cfg == 'bayes100-nofc': # 3x32x32 self.features = nn.Sequential(OrderedDict([ ('conv1', bayes.BayesConv2d(3, d1, 7)), # 128x26x26 ('relu1', nn.LeakyReLU()), ('maxpool', nn.MaxPool2d(2)), # 128x13x13 ('conv2', nn.Conv2d(d1, d2, 5)), # 256x9x9 ('relu2', nn.LeakyReLU()), ('conv3', nn.Conv2d(d2, d2, 5)), # 256x5x5 ('relu3', nn.LeakyReLU()), ('flatten', Flatten()), # 512 ])) else: raise NotImplementedError if 'nofc' in self.cfg: self.classifier = nn.Sequential(OrderedDict([ ('linear', nn.Linear(d2 * 25, n_classes)) ])) print('====> CIFARNetNew without FC!!!!!') elif 'do' in self.cfg: self.classifier = nn.Sequential(OrderedDict([ ('do1', nn.Dropout(0.5)), ('fc1', nn.Linear(d2 * 25, 512)), ('relu1', nn.LeakyReLU()), ('do2', nn.Dropout(0.2)), ('linear', nn.Linear(512, n_classes)) ])) else: self.classifier = nn.Sequential(OrderedDict([ ('fc1', nn.Linear(d2 * 25, 512)), ('relu1', nn.LeakyReLU()), ('linear', nn.Linear(512, n_classes)) ])) if self.device: self.to(self.device) def forward(self, input): return self.classifier(self.features(input)) def weights_init(self, init_list, vae_list, flow_list=None, pretrained=None, filters_list=None, logvar=-10.): if len(init_list) == 1 and init_list[0] == 'no': return self.apply(utils.weight_init(module=nn.Conv2d, initf=nn.init.xavier_normal_)) self.apply(utils.weight_init(module=nn.Linear, initf=nn.init.xavier_normal_)) self.apply(utils.weight_init(module=bayes.LogScaleConv2d, initf=utils.const_init(logvar))) self.apply(utils.weight_init(module=bayes.LogScaleLinear, initf=utils.const_init(logvar))) if len(init_list) > 0 and init_list[0] == 'pretrained': assert len(init_list) == 1 w_pretrained = torch.load(pretrained) for k, v in w_pretrained.items(): if k in self.state_dict(): self.state_dict()[k].data.copy_(v) else: tokens = k.split('.') self.state_dict()['.'.join(tokens[:2] + ['mean'] + tokens[-1:])].data.copy_(v) return convs = [self.features.conv1, self.features.conv2, self.features.conv3] for i, m in enumerate(convs): init = init_list[i] if i < len(init_list) else 'xavier' w = m.mean.weight if isinstance(m, bayes._Bayes) else m.weight if init == 'vae': vae_path = vae_list[i] vae = utils.load_vae(vae_path, device=self.device) z = torch.randn(w.size(0) * w.size(1), vae.encoder.z_dim, 1, 1).to(vae.device) x = vae.decode(z)[0] w.data = x.reshape(w.shape) elif init == 'flow': flow_path = flow_list[i] flow = utils.load_flow(flow_path, device=self.device) utils.flow_init(flow)(w) elif init == 'xavier' or init == 'no': pass elif init == 'filters': filters = np.load(filters_list[i]) filters = np.concatenate([filters]*10) N = np.prod(w.shape[:2]) filters = filters[np.random.permutation(len(filters))[:N]] w.data = torch.from_numpy(filters.reshape(*w.shape)).to(self.device) elif init == 'recon': filters = np.load(filters_list[i]) filters = np.concatenate([filters]*10) N = np.prod(w.shape[:2]) filters = filters[np.random.permutation(len(filters))[:N]] vae_path = vae_list[i] vae = utils.load_vae(vae_path, device=self.device) filters = vae(torch.from_numpy(filters).to(self.device))[1][0] w.data = filters.reshape_as(w) else: raise NotImplementedError('no {} init'.format(init)) def set_prior(self, prior_list, dwp_samples, vae_list, flow_list=None): convs = [self.features.conv1, self.features.conv2, self.features.conv3] for i, m in enumerate(convs): if not isinstance(m, bayes._Bayes): continue if prior_list[i] == 'vae': vae = utils.load_vae(vae_list[i], self.device) vae = nn.DataParallel(vae) self.vaes.append(vae) for p in vae.parameters(): p.requires_grad = False m.kl_function = utils.kl_dwp(vae, n_tries=dwp_samples) elif prior_list[i] == 'flow': flow = utils.load_flow(flow_list[i], self.device) for p in flow.parameters(): p.requires_grad = False m.kl_function = utils.kl_flow(flow, n_tries=dwp_samples) elif prior_list[i] == 'sn': m.kl_function = utils.kl_normal m.prior = dist.Normal(torch.FloatTensor([0.]).to(self.device), torch.FloatTensor([1.]).to(self.device)) elif prior_list[i] == 'loguniform': if self.cfg in ['bayes111-mutrunca']: m.kl_function = utils.kl_loguniform_with_trunc_alpha else: raise NotImplementedError elif prior_list[i] == 'no': pass else: raise NotImplementedError def set_dwp_regularizer(self, vae_list): for path in vae_list: vae = utils.load_vae(path, device=self.device) for p in vae.parameters(): p.requires_grad = False self.vaes.append(vae) def get_dwp_reg(self, backward=False, n_tries=1, weight=1., target='elbo'): modules = [self.features.conv1, self.features.conv2, self.features.conv3] reg = 0. for m, vae in zip(modules, self.vaes): reg += utils.dwp_regularizer(vae, m, n_tries=n_tries, backward=backward, weight=weight, target=target) return reg

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b68b33186122cb9074f12603b9c49710c042b8dc

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Python

memsource_cli/api/machine_translation_settings_api.py

unofficial-memsource/memsource-cli-client

a6639506b74e95476da87f4375953448b76ea90c

[ "Apache-2.0" ]

16

2019-09-25T00:20:38.000Z

2021-05-04T05:56:10.000Z

memsource_cli/api/machine_translation_settings_api.py

zerodayz/memsource-cli-client

c2574f1467539a49e6637c874e88d75c7ef789b3

[ "Apache-2.0" ]

26

2019-09-30T14:00:03.000Z

2021-05-12T11:15:18.000Z

memsource_cli/api/machine_translation_settings_api.py

zerodayz/memsource-cli-client

c2574f1467539a49e6637c874e88d75c7ef789b3

[ "Apache-2.0" ]

1

2021-05-24T16:19:14.000Z

# coding: utf-8 """ Memsource REST API Welcome to Memsource's API documentation. To view our legacy APIs please [visit our documentation](https://wiki.memsource.com/wiki/Memsource_API) and for more information about our new APIs, [visit our blog](https://www.memsource.com/blog/2017/10/24/introducing-rest-apis-qa-with-the-memsource-api-team/). If you have any questions, please contact [Memsource Support](<mailto:support@memsource.com>). # noqa: E501 OpenAPI spec version: Latest Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import re # noqa: F401 # python 2 and python 3 compatibility library import six from memsource_cli.api_client import ApiClient class MachineTranslationSettingsApi(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. Ref: https://github.com/swagger-api/swagger-codegen """ def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client def get_list(self, **kwargs): # noqa: E501 """List machine translate settings # noqa: E501 # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_list(async_req=True) >>> result = thread.get() :param async_req bool :param int page_number: Page number, starting with 0, default 0 :param int page_size: Page size, accepts values between 1 and 50, default 50 :return: PageDtoMachineTranslateSettingsPbmDto If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_list_with_http_info(**kwargs) # noqa: E501 else: (data) = self.get_list_with_http_info(**kwargs) # noqa: E501 return data def get_list_with_http_info(self, **kwargs): # noqa: E501 """List machine translate settings # noqa: E501 # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_list_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool :param int page_number: Page number, starting with 0, default 0 :param int page_size: Page size, accepts values between 1 and 50, default 50 :return: PageDtoMachineTranslateSettingsPbmDto If the method is called asynchronously, returns the request thread. """ all_params = ['page_number', 'page_size'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_list" % key ) params[key] = val del params['kwargs'] if 'page_number' in params and params['page_number'] < 0: # noqa: E501 raise ValueError("Invalid value for parameter `page_number` when calling `get_list`, must be a value greater than or equal to `0`") # noqa: E501 if 'page_size' in params and params['page_size'] > 50: # noqa: E501 raise ValueError("Invalid value for parameter `page_size` when calling `get_list`, must be a value less than or equal to `50`") # noqa: E501 if 'page_size' in params and params['page_size'] < 1: # noqa: E501 raise ValueError("Invalid value for parameter `page_size` when calling `get_list`, must be a value greater than or equal to `1`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] if 'page_number' in params: query_params.append(('pageNumber', params['page_number'])) # noqa: E501 if 'page_size' in params: query_params.append(('pageSize', params['page_size'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/api2/v1/machineTranslateSettings', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PageDtoMachineTranslateSettingsPbmDto', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_machine_translate_settings_for_project_template(self, project_template_id, **kwargs): # noqa: E501 """Get machine translate settings # noqa: E501 # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_machine_translate_settings_for_project_template(project_template_id, async_req=True) >>> result = thread.get() :param async_req bool :param str project_template_id: (required) :return: MTSettingsPerLanguageListDto If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_machine_translate_settings_for_project_template_with_http_info(project_template_id, **kwargs) # noqa: E501 else: (data) = self.get_machine_translate_settings_for_project_template_with_http_info(project_template_id, **kwargs) # noqa: E501 return data def get_machine_translate_settings_for_project_template_with_http_info(self, project_template_id, **kwargs): # noqa: E501 """Get machine translate settings # noqa: E501 # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_machine_translate_settings_for_project_template_with_http_info(project_template_id, async_req=True) >>> result = thread.get() :param async_req bool :param str project_template_id: (required) :return: MTSettingsPerLanguageListDto If the method is called asynchronously, returns the request thread. """ all_params = ['project_template_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_machine_translate_settings_for_project_template" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'project_template_id' is set if ('project_template_id' not in params or params['project_template_id'] is None): raise ValueError("Missing the required parameter `project_template_id` when calling `get_machine_translate_settings_for_project_template`") # noqa: E501 collection_formats = {} path_params = {} if 'project_template_id' in params: path_params['projectTemplateId'] = params['project_template_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/api2/v1/projectTemplates/{projectTemplateId}/mtSettings', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='MTSettingsPerLanguageListDto', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_mt_settings(self, id, **kwargs): # noqa: E501 """Get machine translate settings # noqa: E501 # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_mt_settings(id, async_req=True) >>> result = thread.get() :param async_req bool :param int id: (required) :return: MachineTranslateSettingsPbmDto If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_mt_settings_with_http_info(id, **kwargs) # noqa: E501 else: (data) = self.get_mt_settings_with_http_info(id, **kwargs) # noqa: E501 return data def get_mt_settings_with_http_info(self, id, **kwargs): # noqa: E501 """Get machine translate settings # noqa: E501 # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_mt_settings_with_http_info(id, async_req=True) >>> result = thread.get() :param async_req bool :param int id: (required) :return: MachineTranslateSettingsPbmDto If the method is called asynchronously, returns the request thread. """ all_params = ['id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_mt_settings" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'id' is set if ('id' not in params or params['id'] is None): raise ValueError("Missing the required parameter `id` when calling `get_mt_settings`") # noqa: E501 collection_formats = {} path_params = {} if 'id' in params: path_params['id'] = params['id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/api2/v1/machineTranslateSettings/{id}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='MachineTranslateSettingsPbmDto', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_status(self, id, **kwargs): # noqa: E501 """Get status of machine translate engine # noqa: E501 # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_status(id, async_req=True) >>> result = thread.get() :param async_req bool :param int id: (required) :return: MachineTranslateStatusDto If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_status_with_http_info(id, **kwargs) # noqa: E501 else: (data) = self.get_status_with_http_info(id, **kwargs) # noqa: E501 return data def get_status_with_http_info(self, id, **kwargs): # noqa: E501 """Get status of machine translate engine # noqa: E501 # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_status_with_http_info(id, async_req=True) >>> result = thread.get() :param async_req bool :param int id: (required) :return: MachineTranslateStatusDto If the method is called asynchronously, returns the request thread. """ all_params = ['id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_status" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'id' is set if ('id' not in params or params['id'] is None): raise ValueError("Missing the required parameter `id` when calling `get_status`") # noqa: E501 collection_formats = {} path_params = {} if 'id' in params: path_params['id'] = params['id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/api2/v1/machineTranslateSettings/{id}/status', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='MachineTranslateStatusDto', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_translation_resources(self, project_uid, job_uid, **kwargs): # noqa: E501 """Get translation resources # noqa: E501 # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_translation_resources(project_uid, job_uid, async_req=True) >>> result = thread.get() :param async_req bool :param str project_uid: (required) :param str job_uid: (required) :return: TranslationResourcesDto If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_translation_resources_with_http_info(project_uid, job_uid, **kwargs) # noqa: E501 else: (data) = self.get_translation_resources_with_http_info(project_uid, job_uid, **kwargs) # noqa: E501 return data def get_translation_resources_with_http_info(self, project_uid, job_uid, **kwargs): # noqa: E501 """Get translation resources # noqa: E501 # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_translation_resources_with_http_info(project_uid, job_uid, async_req=True) >>> result = thread.get() :param async_req bool :param str project_uid: (required) :param str job_uid: (required) :return: TranslationResourcesDto If the method is called asynchronously, returns the request thread. """ all_params = ['project_uid', 'job_uid'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_translation_resources" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'project_uid' is set if ('project_uid' not in params or params['project_uid'] is None): raise ValueError("Missing the required parameter `project_uid` when calling `get_translation_resources`") # noqa: E501 # verify the required parameter 'job_uid' is set if ('job_uid' not in params or params['job_uid'] is None): raise ValueError("Missing the required parameter `job_uid` when calling `get_translation_resources`") # noqa: E501 collection_formats = {} path_params = {} if 'project_uid' in params: path_params['projectUid'] = params['project_uid'] # noqa: E501 if 'job_uid' in params: path_params['jobUid'] = params['job_uid'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/api2/v1/projects/{projectUid}/jobs/{jobUid}/translationResources', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='TranslationResourcesDto', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats)

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null

0

1

0

1

0

null

0

7

b69a1ac9e4bd19cae9c76c9d055daa994f7badc3

1,954

py

Python

source/00/logo.py

schef/schef.github.io

ac6fc70e5077deeeb8233ede89e0895fdc2a0d05

[ "MIT" ]

null

source/00/logo.py

schef/schef.github.io

ac6fc70e5077deeeb8233ede89e0895fdc2a0d05

[ "MIT" ]

null

source/00/logo.py

schef/schef.github.io

ac6fc70e5077deeeb8233ede89e0895fdc2a0d05

[ "MIT" ]

null

def print_logo(): print(" dP dP ") print(" 88 88 ") print(" .d8888b. 88d888b. .d8888b. .d8888b. 88 dP dP d8888P .d8888b. ") print(" 88' `88 88' `88 Y8ooooo. 88' `88 88 88 88 88 88ooood8 ") print(" 88. .88 88. .88 88 88. .88 88 88. .88 88 88. ... ") print(" `88888P8 88Y888P' `88888P' `88888P' dP `88888P' dP `88888P' ") print(" 88 ") print(" dP ") print(" oo dP dP ") print(" 88 88 ") print(" 88d888b. dP d8888P .d8888b. 88d888b. ") print(" 88' `88 88 88 88' `"" 88' `88 ") print(" 88. .88 88 88 88. ... 88 88 ") print(" 88Y888P' dP dP `88888P' dP dP ") print(" 88 ") print(" dP ") print(" dP oo oo ") print(" 88 ") print(" d8888P 88d888b. .d8888b. dP 88d888b. dP 88d888b. .d8888b. ") print(" 88 88' `88 88' `88 88 88' `88 88 88' `88 88' `88 ") print(" 88 88 88. .88 88 88 88 88 88 88 88. .88 ") print(" dP dP `88888P8 dP dP dP dP dP dP `8888P88 ") print(" .88 ") print(" d8888P ")

75.153846

85

0.265609

155

1,954

3.341935

0.096774

0.432432

0.544402

0.617761

0.511583

0.415058

0.274131

0.254826

0

0.386591

0.641249

1,954

25

86

78.16

0.352354

0

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true

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1

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null

0

1

0

1

0

9

fcb12093f0403df0fd625ae693b03a886413fd2c

2,524

py

Python

frontstage_api/controllers/collection_exercise_controller.py

ONSdigital/ras-frontstage-api

7bb32a85868e2a241b8a0331b884155a36450669

[ "MIT" ]

2

2018-03-05T11:58:51.000Z

2018-03-06T12:33:59.000Z

frontstage_api/controllers/collection_exercise_controller.py

ONSdigital/ras-frontstage-api

7bb32a85868e2a241b8a0331b884155a36450669

[ "MIT" ]

34

2017-10-17T10:50:18.000Z

2018-07-31T09:04:40.000Z

frontstage_api/controllers/collection_exercise_controller.py

ONSdigital/ras-frontstage-api

7bb32a85868e2a241b8a0331b884155a36450669

[ "MIT" ]

1

2021-04-11T08:14:40.000Z

import logging from structlog import wrap_logger from frontstage_api import app from frontstage_api.common.request_handler import request_handler from frontstage_api.exceptions.exceptions import ApiError logger = wrap_logger(logging.getLogger(__name__)) def get_collection_exercise(collection_exercise_id): logger.debug('Retrieving collection exercise', collection_exercise_id=collection_exercise_id) url = f"{app.config['RM_COLLECTION_EXERCISE_SERVICE']}/collectionexercises/{collection_exercise_id}" response = request_handler('GET', url, auth=app.config['BASIC_AUTH']) if response.status_code != 200: raise ApiError(url=url, status_code=response.status_code, description='Failed to retrieve collection exercise', collection_exercise_id=collection_exercise_id) logger.debug('Successfully retrieved collection exercise', collection_exercise_id=collection_exercise_id) return response.json() def get_collection_exercise_events(collection_exercise_id): logger.debug('Retrieving collection exercise events', collection_exercise_id=collection_exercise_id) url = f"{app.config['RM_COLLECTION_EXERCISE_SERVICE']}/collectionexercises/{collection_exercise_id}/events" response = request_handler('GET', url, auth=app.config['BASIC_AUTH']) if response.status_code != 200: raise ApiError(url=url, status_code=response.status_code, description='Failed to retrieve collection exercise events', collection_exercise_id=collection_exercise_id) logger.debug('Successfully retrieved collection exercise events', collection_exercise_id=collection_exercise_id) return response.json() def get_collection_exercise_event(collection_exercise_id, tag): logger.debug('Retrieving collection exercise event', collection_exercise_id=collection_exercise_id, tag=tag) url = f"{app.config['RM_COLLECTION_EXERCISE_SERVICE']}/collectionexercises/{collection_exercise_id}/events/{tag}" response = request_handler('GET', url, auth=app.config['BASIC_AUTH']) if response.status_code != 200: raise ApiError(url=url, status_code=response.status_code, description='Failed to retrieve collection exercise event', collection_exercise_id=collection_exercise_id, tag=tag) logger.debug('Successfully retrieved collection exercise event', collection_exercise_id=collection_exercise_id, tag=tag) return response.json()

45.890909

124

0.763867

295

2,524

6.227119

0.162712

0.382145

0.261296

0.146979

0.868263

0.847033

0.812738

0.758302

0.738704

0

0.004208

0.152536

2,524

54

125

46.740741

0.854605

0

0.388889

0

0.277734

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0

1

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false

0

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0

null

1

0

1

0

null

0

8

fcbc7a20d39af03d92f63f4afee57967173ff4a4

13,428

py

Python

bootstrap_create_figures.py

Warvito/Normative-modelling-using-deep-autoencoders

54972ca7b503f023438dde1d08b5cfdbdc5a84a0

[ "MIT" ]

12

2020-02-10T10:12:09.000Z

2022-02-20T11:45:01.000Z

bootstrap_create_figures.py

Warvito/Normative-modelling-using-deep-autoencoders

54972ca7b503f023438dde1d08b5cfdbdc5a84a0

[ "MIT" ]

2

2021-11-10T19:40:21.000Z

2022-02-09T23:34:33.000Z

bootstrap_create_figures.py

Warvito/Normative-modelling-using-deep-autoencoders

54972ca7b503f023438dde1d08b5cfdbdc5a84a0

[ "MIT" ]

4

2020-08-31T04:52:10.000Z

2021-07-06T11:17:11.000Z

#!/usr/bin/env python3 """ Script to create Figure 2 of the paper. """ from pathlib import Path import pandas as pd import matplotlib.pyplot as plt import numpy as np from tqdm import tqdm from utils import load_dataset PROJECT_ROOT = Path.cwd() def main(): """Create elements for figure 2 of the paper""" # ---------------------------------------------------------------------------- n_bootstrap = 1000 model_name = 'supervised_aae' outputs_dir = PROJECT_ROOT / 'outputs' bootstrap_dir = outputs_dir / 'bootstrap_analysis' model_dir = bootstrap_dir / model_name # ---------------------------------------------------------------------------- dataset_name = 'ADNI' participants_path = PROJECT_ROOT / 'data' / dataset_name / 'participants.tsv' freesurfer_path = PROJECT_ROOT / 'data' / dataset_name / 'freesurferData.csv' ids_path = PROJECT_ROOT / 'outputs' / (dataset_name + '_homogeneous_ids.csv') adni_df = load_dataset(participants_path, ids_path, freesurfer_path) mean_adni_list = [] for i_bootstrap in tqdm(range(n_bootstrap)): bootstrap_model_dir = model_dir / '{:03d}'.format(i_bootstrap) output_dataset_dir = bootstrap_model_dir / dataset_name output_dataset_dir.mkdir(exist_ok=True) reconstruction_error_df = pd.read_csv(output_dataset_dir / 'reconstruction_error.csv') error_hc = reconstruction_error_df.loc[adni_df['Diagn'] == 1]['Reconstruction error'] error_emci = reconstruction_error_df.loc[adni_df['Diagn'] == 27]['Reconstruction error'] error_lmci = reconstruction_error_df.loc[adni_df['Diagn'] == 28]['Reconstruction error'] error_ad = reconstruction_error_df.loc[adni_df['Diagn'] == 17]['Reconstruction error'] mean_adni_list.append([error_hc.mean(), error_emci.mean(), error_lmci.mean(), error_ad.mean()]) mean_adni_list = np.array(mean_adni_list) plt.hlines(range(4), np.percentile(mean_adni_list, 2.5, axis=0), np.percentile(mean_adni_list, 97.5, axis=0)) plt.plot(np.mean(mean_adni_list, axis=0), range(4), 's', color='k') plt.savefig(bootstrap_dir / 'ADNI.eps', format='eps') plt.close() plt.clf() results = pd.DataFrame(columns={'Measure', 'HC', 'EMCI', 'LMCI', 'AD'}) results = results.append({'Measure': 'Mean', 'HC': np.mean(mean_adni_list, axis=0)[0], 'EMCI': np.mean(mean_adni_list, axis=0)[1], 'LMCI': np.mean(mean_adni_list, axis=0)[2], 'AD': np.mean(mean_adni_list, axis=0)[3], }, ignore_index=True) results = results.append({'Measure': 'Lower', 'HC': np.percentile(mean_adni_list, 2.5, axis=0)[0], 'EMCI': np.percentile(mean_adni_list, 2.5, axis=0)[1], 'LMCI': np.percentile(mean_adni_list, 2.5, axis=0)[2], 'AD': np.percentile(mean_adni_list, 2.5, axis=0)[3], }, ignore_index=True) results = results.append({'Measure': 'Upper', 'HC': np.percentile(mean_adni_list, 97.5, axis=0)[0], 'EMCI': np.percentile(mean_adni_list, 97.5, axis=0)[1], 'LMCI': np.percentile(mean_adni_list, 97.5, axis=0)[2], 'AD': np.percentile(mean_adni_list, 97.5, axis=0)[3], }, ignore_index=True) results.to_csv(bootstrap_dir / dataset_name / 'deviations.csv', index=False) # ---------------------------------------------------------------------------- dataset_name = 'AIBL' participants_path = PROJECT_ROOT / 'data' / dataset_name / 'participants.tsv' freesurfer_path = PROJECT_ROOT / 'data' / dataset_name / 'freesurferData.csv' ids_path = PROJECT_ROOT / 'outputs' / (dataset_name + '_homogeneous_ids.csv') brescia_df = load_dataset(participants_path, ids_path, freesurfer_path) mean_brescia_list = [] for i_bootstrap in tqdm(range(n_bootstrap)): bootstrap_model_dir = model_dir / '{:03d}'.format(i_bootstrap) output_dataset_dir = bootstrap_model_dir / dataset_name output_dataset_dir.mkdir(exist_ok=True) reconstruction_error_df = pd.read_csv(output_dataset_dir / 'reconstruction_error.csv') error_hc = reconstruction_error_df.loc[brescia_df['Diagn'] == 1]['Reconstruction error'] error_mci = reconstruction_error_df.loc[brescia_df['Diagn'] == 18]['Reconstruction error'] error_ad = reconstruction_error_df.loc[brescia_df['Diagn'] == 17]['Reconstruction error'] mean_brescia_list.append([error_hc.mean(), error_mci.mean(), error_ad.mean()]) mean_brescia_list = np.array(mean_brescia_list) plt.hlines(range(3), np.percentile(mean_brescia_list, 2.5, axis=0), np.percentile(mean_brescia_list, 97.5, axis=0)) plt.plot(np.mean(mean_brescia_list, axis=0), range(3), 's', color='k') plt.savefig(bootstrap_dir / 'AIBL.eps', format='eps') plt.close() plt.clf() results = pd.DataFrame(columns={'Measure', 'HC', 'MCI', 'AD'}) results = results.append({'Measure': 'Mean', 'HC': np.mean(mean_brescia_list, axis=0)[0], 'MCI': np.mean(mean_brescia_list, axis=0)[1], 'AD': np.mean(mean_brescia_list, axis=0)[2], }, ignore_index=True) results = results.append({'Measure': 'Lower', 'HC': np.percentile(mean_brescia_list, 2.5, axis=0)[0], 'MCI': np.percentile(mean_brescia_list, 2.5, axis=0)[1], 'AD': np.percentile(mean_brescia_list, 2.5, axis=0)[2], }, ignore_index=True) results = results.append({'Measure': 'Upper', 'HC': np.percentile(mean_brescia_list, 97.5, axis=0)[0], 'MCI': np.percentile(mean_brescia_list, 97.5, axis=0)[1], 'AD': np.percentile(mean_brescia_list, 97.5, axis=0)[2], }, ignore_index=True) results.to_csv(bootstrap_dir / dataset_name / 'deviations.csv', index=False) # ---------------------------------------------------------------------------- dataset_name = 'TOMC' participants_path = PROJECT_ROOT / 'data' / dataset_name / 'participants.tsv' freesurfer_path = PROJECT_ROOT / 'data' / dataset_name / 'freesurferData.csv' ids_path = PROJECT_ROOT / 'outputs' / (dataset_name + '_homogeneous_ids.csv') brescia_df = load_dataset(participants_path, ids_path, freesurfer_path) mean_brescia_list = [] for i_bootstrap in tqdm(range(n_bootstrap)): bootstrap_model_dir = model_dir / '{:03d}'.format(i_bootstrap) output_dataset_dir = bootstrap_model_dir / dataset_name output_dataset_dir.mkdir(exist_ok=True) reconstruction_error_df = pd.read_csv(output_dataset_dir / 'reconstruction_error.csv') error_hc = reconstruction_error_df.loc[brescia_df['Diagn'] == 1]['Reconstruction error'] error_mci = reconstruction_error_df.loc[brescia_df['Diagn'] == 18]['Reconstruction error'] error_ad = reconstruction_error_df.loc[brescia_df['Diagn'] == 17]['Reconstruction error'] mean_brescia_list.append([error_hc.mean(), error_mci.mean(), error_ad.mean()]) mean_brescia_list = np.array(mean_brescia_list) plt.hlines(range(3), np.percentile(mean_brescia_list, 2.5, axis=0), np.percentile(mean_brescia_list, 97.5, axis=0)) plt.plot(np.mean(mean_brescia_list, axis=0), range(3), 's', color='k') plt.savefig(bootstrap_dir / 'TOMC.eps', format='eps') plt.close() plt.clf() results = pd.DataFrame(columns={'Measure', 'HC', 'MCI', 'AD'}) results = results.append({'Measure': 'Mean', 'HC': np.mean(mean_brescia_list, axis=0)[0], 'MCI': np.mean(mean_brescia_list, axis=0)[1], 'AD': np.mean(mean_brescia_list, axis=0)[2], }, ignore_index=True) results = results.append({'Measure': 'Lower', 'HC': np.percentile(mean_brescia_list, 2.5, axis=0)[0], 'MCI': np.percentile(mean_brescia_list, 2.5, axis=0)[1], 'AD': np.percentile(mean_brescia_list, 2.5, axis=0)[2], }, ignore_index=True) results = results.append({'Measure': 'Upper', 'HC': np.percentile(mean_brescia_list, 97.5, axis=0)[0], 'MCI': np.percentile(mean_brescia_list, 97.5, axis=0)[1], 'AD': np.percentile(mean_brescia_list, 97.5, axis=0)[2], }, ignore_index=True) results.to_csv(bootstrap_dir / dataset_name / 'deviations.csv', index=False) # ---------------------------------------------------------------------------- dataset_name = 'OASIS1' participants_path = PROJECT_ROOT / 'data' / dataset_name / 'participants.tsv' freesurfer_path = PROJECT_ROOT / 'data' / dataset_name / 'freesurferData.csv' ids_path = PROJECT_ROOT / 'outputs' / (dataset_name + '_homogeneous_ids.csv') oasis1_df = load_dataset(participants_path, ids_path, freesurfer_path) mean_oasis1_list = [] for i_bootstrap in tqdm(range(n_bootstrap)): bootstrap_model_dir = model_dir / '{:03d}'.format(i_bootstrap) output_dataset_dir = bootstrap_model_dir / dataset_name output_dataset_dir.mkdir(exist_ok=True) reconstruction_error_df = pd.read_csv(output_dataset_dir / 'reconstruction_error.csv') error_hc = reconstruction_error_df.loc[oasis1_df['Diagn'] == 1]['Reconstruction error'] error_ad = reconstruction_error_df.loc[oasis1_df['Diagn'] == 17]['Reconstruction error'] mean_oasis1_list.append([error_hc.mean(), error_ad.mean()]) mean_oasis1_list = np.array(mean_oasis1_list) plt.hlines(range(2), np.percentile(mean_oasis1_list, 2.5, axis=0), np.percentile(mean_oasis1_list, 97.5, axis=0)) plt.plot(np.mean(mean_oasis1_list, axis=0), range(2), 's', color='k') plt.savefig(bootstrap_dir / 'OASIS1.eps', format='eps') plt.close() plt.clf() results = pd.DataFrame(columns={'Measure', 'HC', 'AD'}) results = results.append({'Measure': 'Mean', 'HC': np.mean(mean_oasis1_list, axis=0)[0], 'AD': np.mean(mean_oasis1_list, axis=0)[1], }, ignore_index=True) results = results.append({'Measure': 'Lower', 'HC': np.percentile(mean_oasis1_list, 2.5, axis=0)[0], 'AD': np.percentile(mean_oasis1_list, 2.5, axis=0)[1], }, ignore_index=True) results = results.append({'Measure': 'Upper', 'HC': np.percentile(mean_oasis1_list, 97.5, axis=0)[0], 'AD': np.percentile(mean_oasis1_list, 97.5, axis=0)[1], }, ignore_index=True) results.to_csv(bootstrap_dir / dataset_name / 'deviations.csv', index=False) # ---------------------------------------------------------------------------- dataset_name = 'MIRIAD' participants_path = PROJECT_ROOT / 'data' / dataset_name / 'participants.tsv' freesurfer_path = PROJECT_ROOT / 'data' / dataset_name / 'freesurferData.csv' ids_path = PROJECT_ROOT / 'outputs' / (dataset_name + '_homogeneous_ids.csv') oasis1_df = load_dataset(participants_path, ids_path, freesurfer_path) mean_oasis1_list = [] for i_bootstrap in tqdm(range(n_bootstrap)): bootstrap_model_dir = model_dir / '{:03d}'.format(i_bootstrap) output_dataset_dir = bootstrap_model_dir / dataset_name output_dataset_dir.mkdir(exist_ok=True) reconstruction_error_df = pd.read_csv(output_dataset_dir / 'reconstruction_error.csv') error_hc = reconstruction_error_df.loc[oasis1_df['Diagn'] == 1]['Reconstruction error'] error_ad = reconstruction_error_df.loc[oasis1_df['Diagn'] == 17]['Reconstruction error'] mean_oasis1_list.append([error_hc.mean(), error_ad.mean()]) mean_oasis1_list = np.array(mean_oasis1_list) plt.hlines(range(2), np.percentile(mean_oasis1_list, 2.5, axis=0), np.percentile(mean_oasis1_list, 97.5, axis=0)) plt.plot(np.mean(mean_oasis1_list, axis=0), range(2), 's', color='k') plt.savefig(bootstrap_dir / 'MIRIAD.eps', format='eps') plt.close() plt.clf() results = pd.DataFrame(columns={'Measure', 'HC', 'AD'}) results = results.append({'Measure': 'Mean', 'HC': np.mean(mean_oasis1_list, axis=0)[0], 'AD': np.mean(mean_oasis1_list, axis=0)[1], }, ignore_index=True) results = results.append({'Measure': 'Lower', 'HC': np.percentile(mean_oasis1_list, 2.5, axis=0)[0], 'AD': np.percentile(mean_oasis1_list, 2.5, axis=0)[1], }, ignore_index=True) results = results.append({'Measure': 'Upper', 'HC': np.percentile(mean_oasis1_list, 97.5, axis=0)[0], 'AD': np.percentile(mean_oasis1_list, 97.5, axis=0)[1], }, ignore_index=True) results.to_csv(bootstrap_dir / dataset_name / 'deviations.csv', index=False) if __name__ == "__main__": main()

51.448276

108

0.60076

1,678

13,428

4.530393

0.06913

0.03749

0.079979

0.024993

0.931465

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false

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0

null

0

1

0

1

0

null

0

7

1e2200a230b5fa1ee0c0616792bdc35f92d07455

282

py

Python

tests/test_get_stable_release_version_number.py

kant/blender-downloader

0acaec48d384a8951056d463a8939167e30ea1d4

[ "BSD-3-Clause" ]

1

2021-02-14T00:49:15.000Z

tests/test_get_stable_release_version_number.py

kant/blender-downloader

0acaec48d384a8951056d463a8939167e30ea1d4

[ "BSD-3-Clause" ]

22

2021-02-13T20:51:33.000Z

2022-01-11T17:24:39.000Z

tests/test_get_stable_release_version_number.py

kant/blender-downloader

0acaec48d384a8951056d463a8939167e30ea1d4

[ "BSD-3-Clause" ]

2

2021-06-22T09:38:29.000Z

2022-01-01T21:37:02.000Z

"""Test that the stable version number can be retrieved from Blender website.""" import re from blender_downloader import get_stable_release_version_number def test_get_stable_release_version_number(): assert re.match(r"^\d+\.\d+\.\d+", get_stable_release_version_number())

28.2

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282

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0.25

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282

9

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31.333333

0.828685

0.262411

0

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0.25

1

0.25

true

0

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1

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1

0

1

0

8

1e66869116b61d2c59c52e1ffb6ad8d78d9154d9

51,734

py

Python

20210803/main.py

Brook1711/openda1

1d67912083ecf60b04daa6d9cf377339d179b1aa

[ "Apache-2.0" ]

null

20210803/main.py

Brook1711/openda1

1d67912083ecf60b04daa6d9cf377339d179b1aa

[ "Apache-2.0" ]

null

20210803/main.py

Brook1711/openda1

1d67912083ecf60b04daa6d9cf377339d179b1aa

[ "Apache-2.0" ]

null

import pandas as pd import json import numpy as np import ast from datetime import datetime import plotly.graph_objs as go from plotly.offline import plot import plotly.offline as offline from pandas.core.indexes import interval import re from pathlib import Path import os class data_analysis: def __init__(self, df, name = 'default') -> None: self.accuracy_list, self.addition_list =0, 0 self.name = name self.with_successrate = [0, 1] self.df = df self.problem_num = len(ast.literal_eval(self.df.loc[0, 'task_answers'])) self.row_num = len(self.df) self.df.insert(len(self.df.columns), 'ans', self.remove_str()) self.df.insert(len(self.df.columns), 'interval', self.get_interval()[0]) self.df.insert(len(self.df.columns), 'day', self.get_interval()[1]) self.df.insert(len(self.df.columns), 'start_hour', self.get_interval()[2]) self.df.insert(len(self.df.columns), 'end_hour', self.get_interval()[3]) self.df.insert(len(self.df.columns), 'start_time_float', self.get_interval()[4]) if -1 in self.df.sort_values(by='start_hour', ascending=True).groupby('start_hour').groups.keys(): self.df = self.df.drop(list(self.df.sort_values(by='start_hour', ascending=True).groupby('start_hour').groups[-1])) self.df = self.df.reset_index(drop=True) self.row_num = len(self.df) self.ndf = pd.DataFrame(self.create_new_df()) self.ndf_list = self.divide_ndf() self.group_list = self.group_by() self.count_df_list = self.count_group() # self.addition_list, self.success_df,self.problem_num_list = self.get_addition() # self.output_df = 0 # self.output() print('init complete') def remove_time_error(self): df_rm_time_err = 0 return df_rm_time_err def remove_str_per_row(self, data_per_row): frame_list = ast.literal_eval(data_per_row) frame_dic_list = [] for index in range(len(frame_list)): if frame_list[index] == '': frame_dic_list.append({'frame':'0'}) else: frame_dic_list.append(json.loads(frame_list[index])) return frame_dic_list def remove_str(self): ndf_ans_8_list = [] ndf_rm_frame = [] for i in range(self.row_num): dic_temp = self.remove_str_per_row(self.df.loc[i,'task_answers']) ndf_ans_8_list.append(dic_temp) new_dic_list = [] for dic in dic_temp: dic = dic['frame'] new_dic = dic new_dic_list.append(new_dic) ndf_rm_frame.append(new_dic_list) return ndf_rm_frame def get_interval(self): interval_list = [] day_list = [] start_hour_list = [] stop_hour_list = [] start_time_list = [] for i in range(len(self.df)): interval_list.append(self.get_interval_per_row(i)[0]) day_list.append(self.get_interval_per_row(i)[1]) start_hour_list.append(self.get_interval_per_row(i)[2]) stop_hour_list.append(self.get_interval_per_row(i)[3]) start_time_list.append(self.get_interval_per_row(i)[4]) return [interval_list, day_list, start_hour_list, stop_hour_list, start_time_list] def get_interval_per_row(self, index): row_data = self.df.loc[index,:] start_time = row_data['start_time'] if start_time != start_time: return -1, -1, -1, -1, -1 start_time = datetime.strptime(start_time,"%Y-%m-%dT%H:%M:%S+08:00") expire_time = row_data['expire_time'] expire_time = datetime.strptime(expire_time,"%Y-%m-%dT%H:%M:%S+08:00") stop_time = row_data['stop_time'] if stop_time != stop_time: return -1, -1, -1, -1, -1 stop_time = datetime.strptime(stop_time,"%Y-%m-%dT%H:%M:%S+08:00") total_sec = (stop_time - start_time).seconds return [total_sec, str(start_time.month)+str(start_time.day), start_time.hour, stop_time.hour, start_time.hour+start_time.minute/60.0] def create_new_df(self): twoD_list = [] for row in range(self.row_num): ans_dic_list = self.df.loc[row, 'ans'] twoD_list.append(ans_dic_list) return twoD_list def divide_ndf(self): ndf_list = [] for i in range(len(self.ndf.columns)): ndf_list.append(pd.DataFrame(self.ndf.loc[:,i])) return ndf_list def group_by_per_problem(self, index): df_temp = self.ndf_list[index] df_str_list = [] for j in range(len(df_temp)): ndf_index_j = df_temp.iloc[j, 0] if ndf_index_j == None: df_str_list.append(str(None)) else: df_str_list.append(self.content_to_str(ndf_index_j)) df_temp.insert(1, 'ans_str', df_str_list) df_per_problom = df_temp.groupby('ans_str') return df_per_problom def content_to_str(self, data): if data == None or type(data) == str: return str(None) elif type(data) == type([]): return self.data_to_str(data) elif 'data' in data.keys(): return self.data_to_str(data['data']) else: return self.data_to_str(data) def data_to_str(self, data): if type(data) == type({}): return str(list(data.values())) else: return str(data) def main_df_process(self): return 0 def group_by(self): group_list = [] for i in range(self.problem_num): df_temp = self.group_by_per_problem(i) group_list.append(df_temp) return group_list def count_group(self): count_df_list = [] for group in self.group_list: count_df_list.append(group.count()) return count_df_list def plot(self): data = [go.Histogram(x=list(self.df.loc[:,'interval']))] layout={"title": "学生用时分布", "xaxis_title": "学生用时，单位秒", "yaxis_title": "学生个数", # x轴坐标倾斜60度 "xaxis": {"tickangle": 60} } fig = go.Figure(data=data,layout=layout) plot(fig,filename="./plot/"+self.name+"/time.html",auto_open=False,image='png',image_height=800,image_width=1500) # offline.iplot(fig) return 0 def plot_problem(self): data = [go.Bar(x = list(range(self.problem_num)), y = [len(list(group)) for group in self.group_list])] layout={"title": "不同题目的编码数量", "xaxis_title": "题目编号", "yaxis_title": "编码个数", # x轴坐标倾斜60度 "xaxis": {"tickangle": 60} } fig = go.Figure(data=data,layout=layout) plot(fig,filename="./plot/"+self.name+"/plot_problem.html",auto_open=False,image='png',image_height=800,image_width=1500) # offline.iplot(fig) return 0 def output(self): tar_path = './output/added_columns/'+self.name if not os.path.exists(tar_path): os.makedirs(tar_path) self.df.to_excel(tar_path+'/' +'data_added_columns.xlsx') def calculate_acc(self): accuracy_list = [] addition_list = [] for i, df in enumerate(self.count_df_list): print(i) additional_infor_df = pd.DataFrame({'list':[ast.literal_eval(index) for index in df.index]}) additional_infor_df.insert(len(additional_infor_df.columns), 'count', list( df.iloc[:, 0])) # additional_infor_df.to_excel('./output/all'+'/' +str(i) + '_count.xlsx') if i in [0, 1]: drop_index_list = [] for j in range(len(additional_infor_df)): if additional_infor_df.iloc[j,0]== None: drop_index_list.append(j) elif len(additional_infor_df.iloc[j,0]) != 4: drop_index_list.append(j) additional_infor_df = additional_infor_df.drop(drop_index_list) additional_infor_df = additional_infor_df.reset_index(drop=True) additional_infor_df.insert(len(additional_infor_df.columns), 'ratio', [int(l)*100/float(self.row_num) for l in list( additional_infor_df.loc[:, 'count'])]) additional_infor_df.insert(len(additional_infor_df.columns), 'success', ['0' if l==None else str(l[0]) for l in additional_infor_df.iloc[:,0] ]) if len(list(additional_infor_df.groupby('success'))) == 2: if list(additional_infor_df.groupby('success'))[1][0] == '1': accuracy_list.append(additional_infor_df.groupby('success')['count'].sum().iloc[1]/self.row_num) else: accuracy_list.append(additional_infor_df.groupby('success')['count'].sum().iloc[0]/self.row_num) elif len(list(additional_infor_df.groupby('success'))) == 1: if list(additional_infor_df.groupby('success'))[0][0] == '1': accuracy_list.append(1.0) else: accuracy_list.append(0.0) else: accuracy_list.append(0.0) elif i in [2,3]: drop_index_list = [] for j in range(len(additional_infor_df)): if additional_infor_df.iloc[j,0]== None: drop_index_list.append(j) elif len(additional_infor_df.iloc[j,0]) == 0: drop_index_list.append(j) elif type(additional_infor_df.iloc[j,0][0]) != str: drop_index_list.append(j) additional_infor_df = additional_infor_df.drop(drop_index_list) additional_infor_df = additional_infor_df.reset_index(drop=True) additional_infor_df.insert(len(additional_infor_df.columns), 'ratio', [int(l)*100/float(self.row_num) for l in list( additional_infor_df.loc[:, 'count'])]) additional_infor_df.insert(len(additional_infor_df.columns), 'success', ['1' if l!= None and len(l)!=0 and l[0]=='00' else '0' for l in additional_infor_df.iloc[:,0] ]) if len(list(additional_infor_df.groupby('success'))) == 2: if list(additional_infor_df.groupby('success'))[1][0] == '1': accuracy_list.append(additional_infor_df.groupby('success')['count'].sum().iloc[1]/self.row_num) else: accuracy_list.append(additional_infor_df.groupby('success')['count'].sum().iloc[0]/self.row_num) elif len(list(additional_infor_df.groupby('success'))) == 1: if list(additional_infor_df.groupby('success'))[0][0] == '1': accuracy_list.append(1.0) else: accuracy_list.append(0.0) else: accuracy_list.append(0.0) elif i in [5]: drop_index_list = [] for j in range(len(additional_infor_df)): if additional_infor_df.iloc[j,0]== None: drop_index_list.append(j) elif len(additional_infor_df.iloc[j,0]) == 0: drop_index_list.append(j) elif type(additional_infor_df.iloc[j,0][0]) != str: drop_index_list.append(j) additional_infor_df = additional_infor_df.drop(drop_index_list) additional_infor_df = additional_infor_df.reset_index(drop=True) additional_infor_df.insert(len(additional_infor_df.columns), 'ratio', [int(l)*100/float(self.row_num) for l in list( additional_infor_df.loc[:, 'count'])]) additional_infor_df.insert(len(additional_infor_df.columns), 'success', ['1' if l!= None and len(l)==2 and l[0]+l[1]=='B_AC_A' else '0' for l in additional_infor_df.iloc[:,0] ]) if len(list(additional_infor_df.groupby('success'))) == 2: if list(additional_infor_df.groupby('success'))[1][0] == '1': accuracy_list.append(additional_infor_df.groupby('success')['count'].sum().iloc[1]/self.row_num) else: accuracy_list.append(additional_infor_df.groupby('success')['count'].sum().iloc[0]/self.row_num) elif len(list(additional_infor_df.groupby('success'))) == 1: if list(additional_infor_df.groupby('success'))[0][0] == '1': accuracy_list.append(1.0) else: accuracy_list.append(0.0) else: accuracy_list.append(0.0) elif i in [6]: drop_index_list = [] for j in range(len(additional_infor_df)): if additional_infor_df.iloc[j,0]== None: drop_index_list.append(j) elif len(additional_infor_df.iloc[j,0]) == 0: drop_index_list.append(j) elif type(additional_infor_df.iloc[j,0][0]) != str: drop_index_list.append(j) additional_infor_df = additional_infor_df.drop(drop_index_list) additional_infor_df = additional_infor_df.reset_index(drop=True) additional_infor_df.insert(len(additional_infor_df.columns), 'ratio', [int(l)*100/float(self.row_num) for l in list( additional_infor_df.loc[:, 'count'])]) additional_infor_df.insert(len(additional_infor_df.columns), 'success', ['1' if l!= None and len(l)==5 and l[0]+l[1]+l[2]+l[3]+l[4]=='B_AC_AG_FD_BE_B' else '0' for l in additional_infor_df.iloc[:,0] ]) if len(list(additional_infor_df.groupby('success'))) == 2: if list(additional_infor_df.groupby('success'))[1][0] == '1': accuracy_list.append(additional_infor_df.groupby('success')['count'].sum().iloc[1]/self.row_num) else: accuracy_list.append(additional_infor_df.groupby('success')['count'].sum().iloc[0]/self.row_num) elif len(list(additional_infor_df.groupby('success'))) == 1: if list(additional_infor_df.groupby('success'))[0][0] == '1': accuracy_list.append(1.0) else: accuracy_list.append(0.0) else: accuracy_list.append(0.0) elif i in [7]: drop_index_list = [] for j in range(len(additional_infor_df)): if additional_infor_df.iloc[j,0]== None: drop_index_list.append(j) elif len(additional_infor_df.iloc[j,0]) != 4: drop_index_list.append(j) elif type(additional_infor_df.iloc[j,0][0]) != list or type(additional_infor_df.iloc[j,0][1]) != list or type(additional_infor_df.iloc[j,0][2]) != list or type(additional_infor_df.iloc[j,0][3]) != list: drop_index_list.append(j) additional_infor_df = additional_infor_df.drop(drop_index_list) additional_infor_df = additional_infor_df.reset_index(drop=True) additional_infor_df.insert(len(additional_infor_df.columns), 'ratio', [int(l)*100/float(self.row_num) for l in list( additional_infor_df.loc[:, 'count'])]) additional_infor_df.insert(len(additional_infor_df.columns), 'success', ['1' if l!= None and len(l)==4 and [[0,1],[0,2],[1,2]] in l and [[0,4],[0,5],[1,5]] in l and [[0,6],[0,7],[1,7]] in l and [[0,10],[0,11],[1,11]] else '0' for l in additional_infor_df.iloc[:,0] ]) if len(list(additional_infor_df.groupby('success'))) == 2: if list(additional_infor_df.groupby('success'))[1][0] == '1': accuracy_list.append(additional_infor_df.groupby('success')['count'].sum().iloc[1]/self.row_num) else: accuracy_list.append(additional_infor_df.groupby('success')['count'].sum().iloc[0]/self.row_num) elif len(list(additional_infor_df.groupby('success'))) == 1: if list(additional_infor_df.groupby('success'))[0][0] == '1': accuracy_list.append(1.0) else: accuracy_list.append(0.0) else: accuracy_list.append(0.0) elif i in [8] : drop_index_list = [] for j in range(len(additional_infor_df)): if additional_infor_df.iloc[j,0]== None: drop_index_list.append(j) elif len(additional_infor_df.iloc[j,0]) != 5: drop_index_list.append(j) elif type(additional_infor_df.iloc[j,0][0]) != list or type(additional_infor_df.iloc[j,0][1]) != list or type(additional_infor_df.iloc[j,0][2]) != list or type(additional_infor_df.iloc[j,0][3]) != list or type(additional_infor_df.iloc[j,0][4]) != list: drop_index_list.append(j) additional_infor_df = additional_infor_df.drop(drop_index_list) additional_infor_df = additional_infor_df.reset_index(drop=True) additional_infor_df.insert(len(additional_infor_df.columns), 'ratio', [int(l)*100/float(self.row_num) for l in list( additional_infor_df.loc[:, 'count'])]) verify_list = [[[1, 0], [2, 0], [3, 0], [3, 1]], [[0, 1], [1, 1], [2, 1], [2, 2]], [[3, 2], [4, 2], [5, 2], [5, 3]], [[2, 3], [3, 3], [4, 3], [4, 4]], [[0, 4], [1, 4], [2, 4], [2, 5]]] additional_infor_df.insert(len(additional_infor_df.columns), 'success', ['1' if l!= None and len(l)==5 and verify_list[0] in l and verify_list[1] in l and verify_list[2] in l and verify_list[3] in l and verify_list[4] in l else '0' for l in additional_infor_df.iloc[:,0] ]) if len(list(additional_infor_df.groupby('success'))) == 2: if list(additional_infor_df.groupby('success'))[1][0] == '1': accuracy_list.append(additional_infor_df.groupby('success')['count'].sum().iloc[1]/self.row_num) else: accuracy_list.append(additional_infor_df.groupby('success')['count'].sum().iloc[0]/self.row_num) elif len(list(additional_infor_df.groupby('success'))) == 1: if list(additional_infor_df.groupby('success'))[0][0] == '1': accuracy_list.append(1.0) else: accuracy_list.append(0.0) else: accuracy_list.append(0.0) elif i in [9]: drop_index_list = [] for j in range(len(additional_infor_df)): if additional_infor_df.iloc[j,0]== None: drop_index_list.append(j) elif len(additional_infor_df.iloc[j,0]) == 0: drop_index_list.append(j) elif type(additional_infor_df.iloc[j,0][0]) != str: drop_index_list.append(j) additional_infor_df = additional_infor_df.drop(drop_index_list) additional_infor_df = additional_infor_df.reset_index(drop=True) for row in range(len(additional_infor_df)): list_temp = additional_infor_df.loc[row,'list'] if list_temp!=None and len(list_temp)==2: if list_temp[0][0:2] > list_temp[0][-2:]: list_temp[0] = list_temp[0][-2:] + '_' + list_temp[0][0:2] if list_temp[1][0:2] > list_temp[1][-2:]: list_temp[1] = list_temp[1][-2:] + '_' + list_temp[1][0:2] if list_temp[0] > list_temp[1]: additional_infor_df._set_value(row,'list', str([list_temp[1], list_temp[0]])) else: additional_infor_df._set_value(row,'list', str(list_temp)) else: additional_infor_df._set_value(row,'list', str(list_temp)) grouped = additional_infor_df.groupby('list')['count'].sum() additional_infor_df = pd.DataFrame({'list':[ast.literal_eval(index) for index in grouped.index]}) additional_infor_df.insert(len(additional_infor_df.columns), 'count', list( grouped.iloc[:])) verify_list = [['02_09', '05_06'],['02_05', '06_09'],['02_06', '05_09']] additional_infor_df.insert(len(additional_infor_df.columns), 'ratio', [int(l)*100/float(self.row_num) for l in list( grouped.iloc[:])]) additional_infor_df.insert(len(additional_infor_df.columns), 'success', ['1' if l!= None and len(l)==2 and l in verify_list else '0' for l in additional_infor_df.iloc[:,0] ]) if len(list(additional_infor_df.groupby('success'))) == 2: if list(additional_infor_df.groupby('success'))[1][0] == '1': accuracy_list.append(additional_infor_df.groupby('success')['count'].sum().iloc[1]/self.row_num) else: accuracy_list.append(additional_infor_df.groupby('success')['count'].sum().iloc[0]/self.row_num) elif len(list(additional_infor_df.groupby('success'))) == 1: if list(additional_infor_df.groupby('success'))[0][0] == '1': accuracy_list.append(1.0) else: accuracy_list.append(0.0) else: accuracy_list.append(0.0) elif i in [10]: drop_index_list = [] for j in range(len(additional_infor_df)): if additional_infor_df.iloc[j,0]== None: drop_index_list.append(j) elif len(additional_infor_df.iloc[j,0]) == 0: drop_index_list.append(j) elif type(additional_infor_df.iloc[j,0][0]) != str: drop_index_list.append(j) additional_infor_df = additional_infor_df.drop(drop_index_list) additional_infor_df = additional_infor_df.reset_index(drop=True) for row in range(len(additional_infor_df)): list_temp = additional_infor_df.loc[row, 'list'] if list_temp!=None: list_temp = [[int(rebuild_i) for rebuild_i in re.findall(r"\d+", rebuild)] for rebuild in list_temp] for list_mem in list_temp: list_mem.sort() list_temp.sort() additional_infor_df._set_value(row,'list', str([str(list_str[0]) + '_' + str(list_str[1]) for list_str in list_temp])) else: additional_infor_df._set_value(row,'list', str(list_temp)) grouped = additional_infor_df.groupby('list')['count'].sum() additional_infor_df = pd.DataFrame({'list':[ast.literal_eval(index) for index in grouped.index]}) additional_infor_df.insert(len(additional_infor_df.columns), 'count', list( grouped.iloc[:])) verify_list = ['2','6','12','14','15','16'] additional_infor_df.insert(len(additional_infor_df.columns), 'ratio', [int(l)*100/float(self.row_num) for l in list( grouped.iloc[:])]) additional_infor_df.insert(len(additional_infor_df.columns), 'success', ['1' if l!= None and len(l)==3 and len(set([re.findall(r"\d+",l[0])[0], re.findall(r"\d+",l[0])[1], re.findall(r"\d+",l[1])[0], re.findall(r"\d+",l[1])[1], re.findall(r"\d+",l[2])[0],re.findall(r"\d+",l[2])[1]]))==6 and re.findall(r"\d+",l[0])[0] in verify_list and re.findall(r"\d+",l[0])[1] in verify_list and re.findall(r"\d+",l[1])[0] in verify_list and re.findall(r"\d+",l[1])[1] in verify_list and re.findall(r"\d+",l[2])[0] in verify_list and re.findall(r"\d+",l[2])[1] in verify_list else '0' for l in additional_infor_df.iloc[:,0] ]) if len(list(additional_infor_df.groupby('success'))) == 2: if list(additional_infor_df.groupby('success'))[1][0] == '1': accuracy_list.append(additional_infor_df.groupby('success')['count'].sum().iloc[1]/self.row_num) else: accuracy_list.append(additional_infor_df.groupby('success')['count'].sum().iloc[0]/self.row_num) elif len(list(additional_infor_df.groupby('success'))) == 1: if list(additional_infor_df.groupby('success'))[0][0] == '1': accuracy_list.append(1.0) else: accuracy_list.append(0.0) else: accuracy_list.append(0.0) elif i in [11]: drop_index_list = [] for j in range(len(additional_infor_df)): if additional_infor_df.iloc[j,0]== None: drop_index_list.append(j) elif len(additional_infor_df.iloc[j,0]) == 0: drop_index_list.append(j) elif type(additional_infor_df.iloc[j,0][0]) != list: drop_index_list.append(j) elif len(additional_infor_df.iloc[j,0][0]) == 0: drop_index_list.append(j) elif None in additional_infor_df.iloc[j,0][0]: drop_index_list.append(j) additional_infor_df = additional_infor_df.drop(drop_index_list) additional_infor_df = additional_infor_df.reset_index(drop=True) for row in range(len(additional_infor_df)): list_temp = additional_infor_df.loc[row, 'list'] if list_temp!=None: additional_infor_df._set_value(row,'list', "".join(re.findall(r"\d+", str(list_temp)))) else: additional_infor_df._set_value(row,'list', '') grouped = additional_infor_df.groupby('list')['count'].sum() additional_infor_df = pd.DataFrame({'list':[index for index in grouped.index]}) additional_infor_df.insert(len(additional_infor_df.columns), 'count', list( grouped.iloc[:])) additional_infor_df.insert(len(additional_infor_df.columns), 'ratio', [int(l)*100/float(self.row_num) for l in list( grouped.iloc[:])]) verify_str = '0012210224' additional_infor_df.insert(len(additional_infor_df.columns), 'success', ['1' if l==verify_str else '0' for l in additional_infor_df.iloc[:,0] ]) if len(list(additional_infor_df.groupby('success'))) == 2: if list(additional_infor_df.groupby('success'))[1][0] == '1': accuracy_list.append(additional_infor_df.groupby('success')['count'].sum().iloc[1]/self.row_num) else: accuracy_list.append(additional_infor_df.groupby('success')['count'].sum().iloc[0]/self.row_num) elif len(list(additional_infor_df.groupby('success'))) == 1: if list(additional_infor_df.groupby('success'))[0][0] == '1': accuracy_list.append(1.0) else: accuracy_list.append(0.0) else: accuracy_list.append(0.0) elif i in [12]: drop_index_list = [] for j in range(len(additional_infor_df)): if additional_infor_df.iloc[j,0]== None: drop_index_list.append(j) elif len(additional_infor_df.iloc[j,0]) == 0: drop_index_list.append(j) elif type(additional_infor_df.iloc[j,0][0]) != list: drop_index_list.append(j) elif len(additional_infor_df.iloc[j,0][0]) == 0: drop_index_list.append(j) elif None in additional_infor_df.iloc[j,0][0]: drop_index_list.append(j) additional_infor_df = additional_infor_df.drop(drop_index_list) additional_infor_df = additional_infor_df.reset_index(drop=True) for row in range(len(additional_infor_df)): list_temp = additional_infor_df.loc[row, 'list'] if list_temp!=None: additional_infor_df._set_value(row,'list', "".join(re.findall(r"\d+", str(list_temp)))) else: additional_infor_df._set_value(row,'list', '') grouped = additional_infor_df.groupby('list')['count'].sum() additional_infor_df = pd.DataFrame({'list':[index for index in grouped.index]}) additional_infor_df.insert(len(additional_infor_df.columns), 'count', list( grouped.iloc[:])) additional_infor_df.insert(len(additional_infor_df.columns), 'ratio', [int(l)*100/float(self.row_num) for l in list( grouped.iloc[:])]) verify_str = '2213110425' additional_infor_df.insert(len(additional_infor_df.columns), 'success', ['1' if l==verify_str else '0' for l in additional_infor_df.iloc[:,0] ]) if len(list(additional_infor_df.groupby('success'))) == 2: if list(additional_infor_df.groupby('success'))[1][0] == '1': accuracy_list.append(additional_infor_df.groupby('success')['count'].sum().iloc[1]/self.row_num) else: accuracy_list.append(additional_infor_df.groupby('success')['count'].sum().iloc[0]/self.row_num) elif len(list(additional_infor_df.groupby('success'))) == 1: if list(additional_infor_df.groupby('success'))[0][0] == '1': accuracy_list.append(1.0) else: accuracy_list.append(0.0) else: accuracy_list.append(0.0) elif i in [13]: drop_index_list = [] for j in range(len(additional_infor_df)): if additional_infor_df.iloc[j,0]== None: drop_index_list.append(j) elif type(additional_infor_df.iloc[j,0]) != list: drop_index_list.append(j) elif len(additional_infor_df.iloc[j,0]) != 2: drop_index_list.append(j) else: for n in additional_infor_df.iloc[j,0]: if type(n) != int: drop_index_list.append(j) break additional_infor_df = additional_infor_df.drop(drop_index_list) additional_infor_df = additional_infor_df.reset_index(drop=True) for row in range(len(additional_infor_df)): list_temp = additional_infor_df.loc[row, 'list'] if list_temp!=None: additional_infor_df._set_value(row,'list', "".join(re.findall(r"\d+", str(list_temp)))) else: additional_infor_df._set_value(row,'list', '') grouped = additional_infor_df.groupby('list')['count'].sum() additional_infor_df = pd.DataFrame({'list':[index for index in grouped.index]}) additional_infor_df.insert(len(additional_infor_df.columns), 'count', list( grouped.iloc[:])) verify_str = '21' additional_infor_df.insert(len(additional_infor_df.columns), 'ratio', [int(l)*100/float(self.row_num) for l in list( grouped.iloc[:])]) additional_infor_df.insert(len(additional_infor_df.columns), 'success', ['1' if l==verify_str else '0' for l in additional_infor_df.iloc[:,0] ]) if len(list(additional_infor_df.groupby('success'))) == 2: if list(additional_infor_df.groupby('success'))[1][0] == '1': accuracy_list.append(additional_infor_df.groupby('success')['count'].sum().iloc[1]/self.row_num) else: accuracy_list.append(additional_infor_df.groupby('success')['count'].sum().iloc[0]/self.row_num) elif len(list(additional_infor_df.groupby('success'))) == 1: if list(additional_infor_df.groupby('success'))[0][0] == '1': accuracy_list.append(1.0) else: accuracy_list.append(0.0) else: accuracy_list.append(0.0) elif i in [14]: drop_index_list = [] for j in range(len(additional_infor_df)): if additional_infor_df.iloc[j,0]== None: drop_index_list.append(j) elif type(additional_infor_df.iloc[j,0]) != list: drop_index_list.append(j) elif len(additional_infor_df.iloc[j,0]) != 6: drop_index_list.append(j) else: for n in additional_infor_df.iloc[j,0]: if type(n) != int: drop_index_list.append(j) break additional_infor_df = additional_infor_df.drop(drop_index_list) additional_infor_df = additional_infor_df.reset_index(drop=True) for row in range(len(additional_infor_df)): list_temp = additional_infor_df.loc[row, 'list'] if list_temp!=None: additional_infor_df._set_value(row,'list', "".join(re.findall(r"\d+", str(list_temp)))) else: additional_infor_df._set_value(row,'list', '') grouped = additional_infor_df.groupby('list')['count'].sum() additional_infor_df = pd.DataFrame({'list':[index for index in grouped.index]}) additional_infor_df.insert(len(additional_infor_df.columns), 'count', list( grouped.iloc[:])) additional_infor_df.insert(len(additional_infor_df.columns), 'ratio', [int(l)*100/float(self.row_num) for l in list( grouped.iloc[:])]) verify_str = '121223' additional_infor_df.insert(len(additional_infor_df.columns), 'success', ['1' if l==verify_str else '0' for l in additional_infor_df.iloc[:,0] ]) if len(list(additional_infor_df.groupby('success'))) == 2: if list(additional_infor_df.groupby('success'))[1][0] == '1': accuracy_list.append(additional_infor_df.groupby('success')['count'].sum().iloc[1]/self.row_num) else: accuracy_list.append(additional_infor_df.groupby('success')['count'].sum().iloc[0]/self.row_num) elif len(list(additional_infor_df.groupby('success'))) == 1: if list(additional_infor_df.groupby('success'))[0][0] == '1': accuracy_list.append(1.0) else: accuracy_list.append(0.0) else: accuracy_list.append(0.0) elif i in [15]: drop_index_list = [] for j in range(len(additional_infor_df)): if additional_infor_df.iloc[j,0]== None: drop_index_list.append(j) elif type(additional_infor_df.iloc[j,0]) != list: drop_index_list.append(j) elif len(additional_infor_df.iloc[j,0])==0 or type(additional_infor_df.iloc[j,0][0]) != list: drop_index_list.append(j) elif len(additional_infor_df.iloc[j,0][0])==0 or type(additional_infor_df.iloc[j,0][0][0]) != list: drop_index_list.append(j) elif len(additional_infor_df.iloc[j,0][0][0])!=3: drop_index_list.append(j) else: for n in additional_infor_df.iloc[j,0][0][0]: if type(n) != int: drop_index_list.append(j) break additional_infor_df = additional_infor_df.drop(drop_index_list) additional_infor_df = additional_infor_df.reset_index(drop=True) for row in range(len(additional_infor_df)): list_temp = additional_infor_df.loc[row, 'list'] if list_temp!=None: additional_infor_df._set_value(row,'list', "".join(re.findall(r"\d+", str(list_temp)))) else: additional_infor_df._set_value(row,'list', '') grouped = additional_infor_df.groupby('list')['count'].sum() additional_infor_df = pd.DataFrame({'list':[index for index in grouped.index]}) additional_infor_df.insert(len(additional_infor_df.columns), 'count', list( grouped.iloc[:])) additional_infor_df.insert(len(additional_infor_df.columns), 'ratio', [int(l)*100/float(self.row_num) for l in list( grouped.iloc[:])]) verify_list = ['0', '2'] additional_infor_df.insert(len(additional_infor_df.columns), 'success', ['1' if len(l)==3 and l[0] in verify_list and l[1] in verify_list and l[2] in verify_list else '0' for l in additional_infor_df.iloc[:,0] ]) if len(list(additional_infor_df.groupby('success'))) == 2: if list(additional_infor_df.groupby('success'))[1][0] == '1': accuracy_list.append(additional_infor_df.groupby('success')['count'].sum().iloc[1]/self.row_num) else: accuracy_list.append(additional_infor_df.groupby('success')['count'].sum().iloc[0]/self.row_num) elif len(list(additional_infor_df.groupby('success'))) == 1: if list(additional_infor_df.groupby('success'))[0][0] == '1': accuracy_list.append(1.0) else: accuracy_list.append(0.0) else: accuracy_list.append(0.0) elif i in [16]: drop_index_list = [] for j in range(len(additional_infor_df)): if additional_infor_df.iloc[j,0]== None: drop_index_list.append(j) elif type(additional_infor_df.iloc[j,0]) != list: drop_index_list.append(j) elif len(additional_infor_df.iloc[j,0])==0 or type(additional_infor_df.iloc[j,0][0]) != list: drop_index_list.append(j) elif len(additional_infor_df.iloc[j,0][0])==0 or type(additional_infor_df.iloc[j,0][0][0]) != list: drop_index_list.append(j) elif len(additional_infor_df.iloc[j,0][0][0])!=6: drop_index_list.append(j) else: for n in additional_infor_df.iloc[j,0][0][0]: if type(n) != int: drop_index_list.append(j) break additional_infor_df = additional_infor_df.drop(drop_index_list) additional_infor_df = additional_infor_df.reset_index(drop=True) for row in range(len(additional_infor_df)): list_temp = additional_infor_df.loc[row, 'list'] if list_temp!=None: additional_infor_df._set_value(row,'list', "".join(re.findall(r"\d+", str(list_temp)))) else: additional_infor_df._set_value(row,'list', '') grouped = additional_infor_df.groupby('list')['count'].sum() additional_infor_df = pd.DataFrame({'list':[index for index in grouped.index]}) additional_infor_df.insert(len(additional_infor_df.columns), 'count', list( grouped.iloc[:])) additional_infor_df.insert(len(additional_infor_df.columns), 'ratio', [int(l)*100/float(self.row_num) for l in list( grouped.iloc[:])]) verify_str = '000000' additional_infor_df.insert(len(additional_infor_df.columns), 'success', ['1' if l.replace('2','0')==verify_str else '0' for l in additional_infor_df.iloc[:,0] ]) if len(list(additional_infor_df.groupby('success'))) == 2: if list(additional_infor_df.groupby('success'))[1][0] == '1': accuracy_list.append(additional_infor_df.groupby('success')['count'].sum().iloc[1]/self.row_num) else: accuracy_list.append(additional_infor_df.groupby('success')['count'].sum().iloc[0]/self.row_num) elif len(list(additional_infor_df.groupby('success'))) == 1: if list(additional_infor_df.groupby('success'))[0][0] == '1': accuracy_list.append(1.0) else: accuracy_list.append(0.0) else: accuracy_list.append(0.0) elif i in [17]: drop_index_list = [] for j in range(len(additional_infor_df)): if additional_infor_df.iloc[j,0]== None: drop_index_list.append(j) elif type(additional_infor_df.iloc[j,0]) != list: drop_index_list.append(j) elif len(additional_infor_df.iloc[j,0])==0 or type(additional_infor_df.iloc[j,0][0]) != list: drop_index_list.append(j) elif len(additional_infor_df.iloc[j,0][0])!=7: drop_index_list.append(j) else: for n in additional_infor_df.iloc[j,0][0]: if n not in [0, 1]: drop_index_list.append(j) break additional_infor_df = additional_infor_df.drop(drop_index_list) additional_infor_df = additional_infor_df.reset_index(drop=True) for row in range(len(additional_infor_df)): list_temp = additional_infor_df.loc[row, 'list'] if list_temp!=None: additional_infor_df._set_value(row,'list', "".join(re.findall(r"\d+", str(list_temp)))) else: additional_infor_df._set_value(row,'list', '') grouped = additional_infor_df.groupby('list')['count'].sum() additional_infor_df = pd.DataFrame({'list':[index for index in grouped.index]}) additional_infor_df.insert(len(additional_infor_df.columns), 'count', list( grouped.iloc[:])) additional_infor_df.insert(len(additional_infor_df.columns), 'ratio', [int(l)*100/float(self.row_num) for l in list( grouped.iloc[:])]) verify_str = '1010011001' additional_infor_df.insert(len(additional_infor_df.columns), 'success', ['1' if l==verify_str else '0' for l in additional_infor_df.iloc[:,0] ]) if len(list(additional_infor_df.groupby('success'))) == 2: if list(additional_infor_df.groupby('success'))[1][0] == '1': accuracy_list.append(additional_infor_df.groupby('success')['count'].sum().iloc[1]/self.row_num) else: accuracy_list.append(additional_infor_df.groupby('success')['count'].sum().iloc[0]/self.row_num) elif len(list(additional_infor_df.groupby('success'))) == 1: if list(additional_infor_df.groupby('success'))[0][0] == '1': accuracy_list.append(1.0) else: accuracy_list.append(0.0) else: accuracy_list.append(0.0) elif i in [18]: drop_index_list = [] for j in range(len(additional_infor_df)): if additional_infor_df.iloc[j,0]== None: drop_index_list.append(j) elif type(additional_infor_df.iloc[j,0]) != list: drop_index_list.append(j) elif len(additional_infor_df.iloc[j,0])==0 or type(additional_infor_df.iloc[j,0][0]) != list: drop_index_list.append(j) elif len(additional_infor_df.iloc[j,0][0])<1: drop_index_list.append(j) else: for n in additional_infor_df.iloc[j,0][0]: if n not in [0, 1]: drop_index_list.append(j) break additional_infor_df = additional_infor_df.drop(drop_index_list) additional_infor_df = additional_infor_df.reset_index(drop=True) for row in range(len(additional_infor_df)): list_temp = additional_infor_df.loc[row, 'list'] if list_temp!=None: additional_infor_df._set_value(row,'list', str(list_temp)) else: additional_infor_df._set_value(row,'list', '') grouped = additional_infor_df.groupby('list')['count'].sum() additional_infor_df = pd.DataFrame({'list':[index for index in grouped.index]}) additional_infor_df.insert(len(additional_infor_df.columns), 'count', list( grouped.iloc[:])) additional_infor_df.insert(len(additional_infor_df.columns), 'ratio', [int(l)*100/float(self.row_num) for l in list( grouped.iloc[:])]) verify_list = ['[[0, 0, 1, 1, 0, 1, 0], [[1, 0], [0]]]','[[1, 1, 0, 0, 1, 0, 1], [[0], [1, 0]]]'] additional_infor_df.insert(len(additional_infor_df.columns), 'success', ['1' if l in verify_list else '0' for l in additional_infor_df.iloc[:,0] ]) if len(list(additional_infor_df.groupby('success'))) == 2: if list(additional_infor_df.groupby('success'))[1][0] == '1': accuracy_list.append(additional_infor_df.groupby('success')['count'].sum().iloc[1]/self.row_num) else: accuracy_list.append(additional_infor_df.groupby('success')['count'].sum().iloc[0]/self.row_num) elif len(list(additional_infor_df.groupby('success'))) == 1: if list(additional_infor_df.groupby('success'))[0][0] == '1': accuracy_list.append(1.0) else: accuracy_list.append(0.0) else: accuracy_list.append(0.0) elif i in [19]: drop_index_list = [] for j in range(len(additional_infor_df)): if additional_infor_df.iloc[j,0]== None: drop_index_list.append(j) elif type(additional_infor_df.iloc[j,0]) != list: drop_index_list.append(j) elif len(additional_infor_df.iloc[j,0])!=4 or type(additional_infor_df.iloc[j,0][0]) != list or type(additional_infor_df.iloc[j,0][1]) != int or type(additional_infor_df.iloc[j,0][2]) != int: drop_index_list.append(j) else: for n in additional_infor_df.iloc[j,0][0]: if n not in [0, 1, 2]: drop_index_list.append(j) break additional_infor_df = additional_infor_df.drop(drop_index_list) additional_infor_df = additional_infor_df.reset_index(drop=True) for row in range(len(additional_infor_df)): list_temp = additional_infor_df.loc[row, 'list'] if list_temp!=None: additional_infor_df._set_value(row,'list', str(list_temp)) else: additional_infor_df._set_value(row,'list', '') grouped = additional_infor_df.groupby('list')['count'].sum() additional_infor_df = pd.DataFrame({'list':[index for index in grouped.index]}) additional_infor_df.insert(len(additional_infor_df.columns), 'count', list( grouped.iloc[:])) additional_infor_df.insert(len(additional_infor_df.columns), 'ratio', [int(l)*100/float(self.row_num) for l in list( grouped.iloc[:])]) verify_str = '[[1, 0, 1, 2], 1, 16, True]' additional_infor_df.insert(len(additional_infor_df.columns), 'success', ['1' if l==verify_str else '0' for l in additional_infor_df.iloc[:,0] ]) if len(list(additional_infor_df.groupby('success'))) == 2: if list(additional_infor_df.groupby('success'))[1][0] == '1': accuracy_list.append(additional_infor_df.groupby('success')['count'].sum().iloc[1]/self.row_num) else: accuracy_list.append(additional_infor_df.groupby('success')['count'].sum().iloc[0]/self.row_num) elif len(list(additional_infor_df.groupby('success'))) == 1: if list(additional_infor_df.groupby('success'))[0][0] == '1': accuracy_list.append(1.0) else: accuracy_list.append(0.0) else: accuracy_list.append(0.0) if i not in [4, 20,21]: addition_list.append(additional_infor_df) tar_path = './output/acc/'+self.name+'_acc' if not os.path.exists(tar_path): os.makedirs(tar_path) additional_infor_df.to_excel(tar_path+'/' +str(i) + '_acc.xlsx') self.accuracy_list, self.addition_list = accuracy_list, addition_list return accuracy_list, addition_list if __name__ == '__main__': df = pd.read_excel('./data/ticket_user_mianyang.xlsx') # df_junior = pd.read_excel('./data/junior.xlsx') # df_senior = pd.read_excel('./data/senior.xlsx') data_entity = data_analysis(df) # data_entity_junior = data_analysis(df = df_junior, name = 'junior') # data_entity_senior = data_analysis(df = df_senior, name = 'senior')

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1

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1

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null

0

9

1ec7979de0fc17ac4271cfc3d5c8691949f08e08

14,457

py

Python

components/exp-test-agent/tests/loop/test_agent_loop.py

sfahad1414/AGENT

84069edc96b6190bb03ffd5099cbc8966061a563

[ "Apache-2.0" ]

15

2020-05-06T16:17:56.000Z

2022-03-30T12:25:16.000Z

components/exp-test-agent/tests/loop/test_agent_loop.py

dionny/AGENT

8a833406b590e23623fcc67db99f6f964d002396

[ "Apache-2.0" ]

2

2021-08-25T16:17:16.000Z

2022-02-10T06:35:58.000Z

components/exp-test-agent/tests/loop/test_agent_loop.py

dionny/AGENT

8a833406b590e23623fcc67db99f6f964d002396

[ "Apache-2.0" ]

7

2020-04-07T18:47:55.000Z

2022-03-30T12:14:58.000Z

from abstraction.actionable_state import ActionableState from loop.agent_loop import AgentLoop from unittest.mock import Mock, patch @patch(AgentLoop.__module__ + '.threading.Thread') def test_agent_start(thread): # Arrange. sut_url = "TEST" runner_url = "TEST" form_expert_client = Mock() runner_client = Mock() page_analysis_client = Mock() flow_generator_client = Mock() loop = AgentLoop(sut_url, runner_url, form_expert_client, runner_client, page_analysis_client, flow_generator_client) thread_mock = Mock() thread.return_value = thread_mock # Act. loop.start() # Assert. assert thread.called assert thread_mock.start.called @patch.dict(AgentLoop.__module__ + '.general_memory', {'SESSION_STOPPED': False}, clear=True) def test_loop_start(): # Arrange. sut_url = "TEST" runner_url = "TEST" form_expert_client = Mock() runner_client = Mock() page_analysis_client = Mock() flow_generator_client = Mock() loop = AgentLoop(sut_url, runner_url, form_expert_client, runner_client, page_analysis_client, flow_generator_client) # Act. with patch(AgentLoop.__module__ + '.AgentLoop.loop_iteration') as loop_iteration: with patch(AgentLoop.__module__ + '.AgentLoop.loop_end') as loop_end: loop.loop_start() # Assert. assert runner_client.launch.called_with(sut_url) assert loop_iteration.call_count == AgentLoop.NUM_ITERATIONS assert loop_end.called @patch.dict(AgentLoop.__module__ + '.general_memory', {'SESSION_STOPPED': True}, clear=True) def test_session_stop(): # Arrange. sut_url = "TEST" runner_url = "TEST" form_expert_client = Mock() runner_client = Mock() page_analysis_client = Mock() flow_generator_client = Mock() loop = AgentLoop(sut_url, runner_url, form_expert_client, runner_client, page_analysis_client, flow_generator_client) # Act. with patch(AgentLoop.__module__ + '.AgentLoop.loop_iteration') as loop_iteration: with patch(AgentLoop.__module__ + '.AgentLoop.loop_end') as loop_end: loop.loop_start() # Assert. assert runner_client.launch.called_with(sut_url) assert not loop_iteration.called assert loop_end.called @patch.dict(AgentLoop.__module__ + '.general_memory', {'SESSION_STOPPED': False}, clear=True) def test_runner_unable_to_launch(): # Arrange. sut_url = "TEST" runner_url = "TEST" form_expert_client = Mock() runner_client = Mock() page_analysis_client = Mock() flow_generator_client = Mock() runner_client.launch.return_value = False loop = AgentLoop(sut_url, runner_url, form_expert_client, runner_client, page_analysis_client, flow_generator_client) # Act. with patch(AgentLoop.__module__ + '.AgentLoop.loop_iteration') as loop_iteration: with patch(AgentLoop.__module__ + '.AgentLoop.loop_end') as loop_end: loop.loop_start() # Assert. assert runner_client.launch.called_with(sut_url) assert not loop_iteration.called assert not loop_end.called def test_loop_end(): # Arrange. sut_url = "TEST" runner_url = "TEST" form_expert_client = Mock() runner_client = Mock() page_analysis_client = Mock() flow_generator_client = Mock() loop = AgentLoop(sut_url, runner_url, form_expert_client, runner_client, page_analysis_client, flow_generator_client) # Act. loop.loop_end() # Assert. assert runner_client.quit.called @patch.dict(AgentLoop.__module__ + '.general_memory', {'SESSION_STOPPED': False}, clear=True) def test_loop_lifecycle(): # Arrange. sut_url = "TEST" runner_url = "TEST" form_expert_client = Mock() runner_client = Mock() page_analysis_client = Mock() flow_generator_client = Mock() loop = AgentLoop(sut_url, runner_url, form_expert_client, runner_client, page_analysis_client, flow_generator_client) # Act. with patch(AgentLoop.__module__ + '.AgentLoop.loop_iteration') as loop_iteration: loop.loop_start() # Assert. assert runner_client.launch.called_with(sut_url) assert loop_iteration.call_count == AgentLoop.NUM_ITERATIONS assert runner_client.quit.called @patch.dict(AgentLoop.__module__ + '.general_memory', {'SESSION_STOPPED': False}, clear=True) def test_loop_iteration_exception(): # Arrange. sut_url = "TEST" runner_url = "TEST" form_expert_client = Mock() runner_client = Mock() page_analysis_client = Mock() flow_generator_client = Mock() loop = AgentLoop(sut_url, runner_url, form_expert_client, runner_client, page_analysis_client, flow_generator_client) # Act. with patch(AgentLoop.__module__ + '.AgentLoop.loop_iteration') as loop_iteration: with patch(AgentLoop.__module__ + '.AgentLoop.loop_end') as loop_end: def exception_side_effect(): raise Exception('test') loop_iteration.side_effect = exception_side_effect loop.loop_start() # Assert. assert runner_client.launch.called_with(sut_url) assert loop_iteration.call_count == 1 assert loop_end.called def test_loop_iteration_happy_path(): # Arrange. sut_url = "TEST" runner_url = "TEST" form_expert_client = Mock() runner_client = Mock() page_analysis_client = Mock() flow_generator_client = Mock() page_analysis = { 'analysis': { 'labelCandidates': ['Label_FirstName'], 'COMMIT': ['C1'], 'ERRORMESSAGE': ['E1'], 'errorMessages': ['E1'] } } page_analysis_client.run_analysis.return_value = page_analysis widget_first_name_label = { 'key': 'Label_FirstName', 'label': 'FirstNameLabel', 'actions': [], 'selector': '#lblFirstName', 'properties': { 'tagName': 'LABEL', 'text': 'First Name', 'x': 10, 'y': 10 } } widget_first_name = { 'key': 'FIRSTNAME', 'label': 'FirstName', 'actions': ['set'], 'selector': '#firstName', 'properties': { 'tagName': 'INPUT', 'x': 20, 'y': 20 } } widget_save = { 'key': 'C1', 'label': 'Save', 'actions': ['click'], 'selector': '#save', 'properties': { 'tagName': 'BUTTON', 'x': 40, 'y': 40 } } widget_error = { 'key': 'E1', 'label': 'Error', 'properties': { 'tagName': 'LABEL', 'x': 60, 'y': 60 } } target_concrete_state = { 'widgets': { 'Label_FirstName': widget_first_name_label, 'FIRSTNAME': widget_first_name, 'C1': widget_save, 'E1': widget_error } } flow_generator_client.generate_flow.return_value = "OBSERVE TEXTBOX FIRSTNAME " \ "TRY VALID FIRSTNAME " \ "CLICK COMMIT " \ "NOTOBSERVE ERRORMESSAGE" runner_client.concrete_state.return_value = target_concrete_state flow_publisher = Mock() flow_executor = Mock() loop = AgentLoop(sut_url, runner_url, form_expert_client, runner_client, page_analysis_client, flow_generator_client, flow_publisher, flow_executor) # Act. with patch(AgentLoop.__module__ + '.PriorityMemory') as memory_mock: actual_memory_mock = Mock() memory_mock.return_value = actual_memory_mock loop.loop_iteration() # Assert. assert flow_generator_client.generate_flow.called assert flow_publisher.publish.call_count == 2 assert flow_executor.execute.call_count == 1 assert not actual_memory_mock.in_memory.called def test_loop_iteration_no_generated_test_flows_should_explore(): # Arrange. sut_url = "TEST" runner_url = "TEST" form_expert_client = Mock() runner_client = Mock() page_analysis_client = Mock() flow_generator_client = Mock() page_analysis = { 'analysis': { 'labelCandidates': ['Label_FirstName'], 'COMMIT': ['C1'], 'ERRORMESSAGE': ['E1'], 'errorMessages': ['E1'] } } page_analysis_client.run_analysis.return_value = page_analysis widget_first_name_label = { 'key': 'Label_FirstName', 'label': 'FirstNameLabel', 'actions': [], 'selector': '#lblFirstName', 'properties': { 'tagName': 'LABEL', 'text': 'First Name', 'x': 10, 'y': 10 } } widget_first_name = { 'key': 'FIRSTNAME', 'label': 'FirstName', 'actions': ['set'], 'selector': '#firstName', 'properties': { 'tagName': 'INPUT', 'x': 20, 'y': 20 } } widget_save = { 'key': 'C1', 'label': 'Save', 'actions': ['click'], 'selector': '#save', 'properties': { 'tagName': 'BUTTON', 'x': 40, 'y': 40 } } widget_error = { 'key': 'E1', 'label': 'Error', 'properties': { 'tagName': 'LABEL', 'x': 60, 'y': 60 } } target_concrete_state = { 'widgets': { 'Label_FirstName': widget_first_name_label, 'FIRSTNAME': widget_first_name, 'C1': widget_save, 'E1': widget_error } } flow_generator_client.generate_flow.return_value = None runner_client.concrete_state.return_value = target_concrete_state flow_publisher = Mock() flow_executor = Mock() # Act. with patch(AgentLoop.__module__ + '.PriorityMemory') as memory_mock: actual_memory_mock = Mock() memory_mock.return_value = actual_memory_mock actual_memory_mock.choose_widget.return_value = widget_first_name loop = AgentLoop(sut_url, runner_url, form_expert_client, runner_client, page_analysis_client, flow_generator_client, flow_publisher, flow_executor) loop.loop_iteration() # Assert. assert flow_generator_client.generate_flow.called assert not flow_publisher.publish.called assert not flow_executor.execute.called assert actual_memory_mock.in_memory.called assert actual_memory_mock.update_memory.called assert form_expert_client.get_concrete_value.called assert runner_client.perform_action.called class ConcreteTestFlowStub: def __init__(self): self.hash = 0 def calculate_hash(self): pass @patch.dict(AgentLoop.__module__ + '.celery_memory', {'HASH': [ConcreteTestFlowStub()]}, clear=True) def test_loop_iteration_no_generated_test_flow_but_flows_in_queue(): # Arrange. sut_url = "TEST" runner_url = "TEST" form_expert_client = Mock() runner_client = Mock() page_analysis_client = Mock() flow_generator_client = Mock() page_analysis = { 'analysis': { 'labelCandidates': ['Label_FirstName'], 'COMMIT': ['C1'], 'ERRORMESSAGE': ['E1'], 'errorMessages': ['E1'] } } page_analysis_client.run_analysis.return_value = page_analysis widget_first_name_label = { 'key': 'Label_FirstName', 'label': 'FirstNameLabel', 'actions': [], 'selector': '#lblFirstName', 'properties': { 'tagName': 'LABEL', 'text': 'First Name', 'x': 10, 'y': 10 } } widget_first_name = { 'key': 'FIRSTNAME', 'label': 'FirstName', 'actions': ['set'], 'selector': '#firstName', 'properties': { 'tagName': 'INPUT', 'x': 20, 'y': 20 } } widget_save = { 'key': 'C1', 'label': 'Save', 'actions': ['click'], 'selector': '#save', 'properties': { 'tagName': 'BUTTON', 'x': 40, 'y': 40 } } widget_error = { 'key': 'E1', 'label': 'Error', 'properties': { 'tagName': 'LABEL', 'x': 60, 'y': 60 } } target_concrete_state = { 'widgets': { 'Label_FirstName': widget_first_name_label, 'FIRSTNAME': widget_first_name, 'C1': widget_save, 'E1': widget_error } } abstract_state = ActionableState() abstract_state.add_static_widget(widget_first_name_label) abstract_state.add_widget(widget_first_name) abstract_state.add_widget(widget_save) abstract_state.add_widget(widget_error) abstract_state.hash = "HASH" flow_generator_client.generate_flow.return_value = None runner_client.concrete_state.return_value = target_concrete_state flow_publisher = Mock() flow_executor = Mock() # Act. with patch(AgentLoop.__module__ + '.StateAbstracter') as state_abstracter: with patch(AgentLoop.__module__ + '.PriorityMemory') as memory_mock: actual_memory_mock = Mock() memory_mock.return_value = actual_memory_mock actual_mapper = Mock() state_abstracter.return_value = actual_mapper actual_mapper.process.return_value = abstract_state loop = AgentLoop(sut_url, runner_url, form_expert_client, runner_client, page_analysis_client, flow_generator_client, flow_publisher, flow_executor) loop.loop_iteration() # Assert. assert flow_generator_client.generate_flow.called assert not flow_publisher.publish.called assert flow_executor.execute.called assert not actual_memory_mock.in_memory.called assert not actual_memory_mock.update_memory.called assert not form_expert_client.get_concrete_value.called assert not runner_client.perform_action.called

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null

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1

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1

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null

0

7

1ed0478960f290427614380e0bea489b36313f29

2,596

py

Python

bidaf/tests/check_results.py

davidgolub/QuestionGeneration

6b31e1a8855774230051093ca24ba0a7750a6712

[ "MIT" ]

117

2017-09-06T23:25:59.000Z

2021-06-29T12:24:26.000Z

bidaf/tests/check_results.py

davidgolub/QuestionGeneration

6b31e1a8855774230051093ca24ba0a7750a6712

[ "MIT" ]

14

2017-12-06T21:08:28.000Z

2020-06-22T06:03:23.000Z

bidaf/tests/check_results.py

davidgolub/QuestionGeneration

6b31e1a8855774230051093ca24ba0a7750a6712

[ "MIT" ]

33

2017-10-06T05:16:07.000Z

2021-05-10T00:30:13.000Z

import pickle import gzip for path in [42, 43, 44, 45, 46, 47, 48, 49, 51, 52, 53, 54]: save_path = 'out/basic/19/eval/test-0%s000.pklz' % path#'out/basic/06/eval/dev-040000.pklz'#'out/basic/12/eval/dev-047000.pklz'#out/basic/10/eval/dev-053000.pklz'#'out/basic/09/eval/dev-042000.pklz' #'out/basic/06/eval/dev-040000.pklz' f = gzip.open(save_path,'rb') res= pickle.load(f) f.close() print(save_path) print(res['f1']) print(res['acc']) #restore the object #out/basic/19/eval for path in ['041000']: save_path = 'out/basic/17/eval/test-%s.pklz' % path#'out/basic/06/eval/dev-040000.pklz'#'out/basic/12/eval/dev-047000.pklz'#out/basic/10/eval/dev-053000.pklz'#'out/basic/09/eval/dev-042000.pklz' #'out/basic/06/eval/dev-040000.pklz' f = gzip.open(save_path,'rb') res= pickle.load(f) f.close() print(save_path) print(res['f1']) print(res['acc']) for path in ['041000', '042000', '043000', '044000', '045000']: save_path = 'out/basic/14/eval/test-%s.pklz' % path#'out/basic/06/eval/dev-040000.pklz'#'out/basic/12/eval/dev-047000.pklz'#out/basic/10/eval/dev-053000.pklz'#'out/basic/09/eval/dev-042000.pklz' #'out/basic/06/eval/dev-040000.pklz' f = gzip.open(save_path,'rb') res= pickle.load(f) f.close() print(save_path) print(res['f1']) print(res['acc']) # out/basic/25/eval for path in ['044000', '045000', '046000', '047000', '048000', '049000', '050000', '051000', '052000']: save_path = 'out/basic/14/eval/dev-%s.pklz' % path#'out/basic/06/eval/dev-040000.pklz'#'out/basic/12/eval/dev-047000.pklz'#out/basic/10/eval/dev-053000.pklz'#'out/basic/09/eval/dev-042000.pklz' #'out/basic/06/eval/dev-040000.pklz' f = gzip.open(save_path,'rb') res= pickle.load(f) f.close() print(save_path) print(res['f1']) print(res['acc']) for path in ['041000', '042000']: save_path = 'out/basic/18/eval/dev-%s.pklz' % path#'out/basic/06/eval/dev-040000.pklz'#'out/basic/12/eval/dev-047000.pklz'#out/basic/10/eval/dev-053000.pklz'#'out/basic/09/eval/dev-042000.pklz' #'out/basic/06/eval/dev-040000.pklz' f = gzip.open(save_path,'rb') res= pickle.load(f) f.close() print(save_path) print(res['f1']) print(res['acc']) for path in ['041000', '042000', '043000', '044000', '045000', '046000', '047000', '048000', '049000']: save_path = 'out/basic/17/eval/dev-%s.pklz' % path#'out/basic/06/eval/dev-040000.pklz'#'out/basic/12/eval/dev-047000.pklz'#out/basic/10/eval/dev-053000.pklz'#'out/basic/09/eval/dev-042000.pklz' #'out/basic/06/eval/dev-040000.pklz' f = gzip.open(save_path,'rb') res= pickle.load(f) f.close() print(save_path) print(res['f1']) print(res['acc'])

39.333333

236

0.684515

468

2,596

3.758547

0.134615

0.172825

0.163729

0.095509

0.891984

0.816941

0

0.186924

0.080894

2,596

66

237

39.333333

0.550293

0.424884

0

0.72

0

0.259411

0.123888

0

1

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false

0

0.04

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0.04

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0

null

0

1

0

1

0

null

0

7

1ed35ed5dcd7ea859b179eac8530879a11250d2c

13,603

py

Python

tests/test_finder.py

TrustedMercury/filter-profanity

7c38dbba19e341ad72068338952ff07dc8037e37

[ "MIT" ]

8

2020-08-25T01:33:29.000Z

2021-02-21T12:01:03.000Z

tests/test_finder.py

TrustedMercury/filter-profanity

7c38dbba19e341ad72068338952ff07dc8037e37

[ "MIT" ]

2

2020-10-20T13:05:05.000Z

2020-10-21T00:19:32.000Z

tests/test_finder.py

TrustedMercury/filter-profanity

7c38dbba19e341ad72068338952ff07dc8037e37

[ "MIT" ]

3

2020-10-20T12:10:15.000Z

2020-12-05T00:36:10.000Z

import unittest from profanity import get_profanity class TestFinder(unittest.TestCase): def test_finder_false_small(self): self.assertEqual( get_profanity( "This doesn't have any profanity, why do I bother"), [] ) def test_finder_true_small(self): self.assertEqual( get_profanity("This code is definitely not sh!t"), ['sh!t'] ) def test_finder_false_large(self): self.assertEqual( get_profanity( "lorem ipsum dolor sit amet i write this text by hand, poetry ain't my skill more likely it is to kill!. what the heck am i doing with my life? i thought to myself, but then i thought - maybe there is a point in all of this? a hidden treasure? no, there is not."), [] ) def test_finder_no_duplicates_large(self): self.assertEqual( get_profanity( "Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut fringilla erat ut cursus suscipit. Curabitur odio metus, varius vitae felis eget, euismod tincidunt felis. Quisque justo nisl, gravida ut pulvinar sed, suscipit lobortis nibh. Aliquam vestibulum eleifend est, et dapibus leo aliquam id. Morbi porta sodales mauris, in fermentum sapien blandit nec. Cras turpis massa, efficitur eu euismod id, euismod ac ex. Ut luctus justo lectus, eget lacinia diam semper ac. Aliquam erat volutpat. sh!t habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas. Sed pharetra metus eu quam aliquet, quis tempor eros vulputate.Sed orci purus, aliquet sed facilisis ut, mollis et nisl. Curabitur finibus porttitor nisl vitae vestibulum. Duis tincidunt tempor maximus. Vestibulum in ante porttitor, luctus purus in, tempor lacus. Integer tempus, lorem eget rhoncus tristique, tortor orci laoreet felis, ac dapibus magna purus ac lectus. Integer blandit sit amet velit sed cursus. Proin justo ex, luctus sit amet sapien elementum, iaculis interdum lacus. Proin nec turpis quis leo fringilla consequat. Aliquam erat volutpat. Nam accumsan lorem ut justo consectetur, vel pulvinar risus congue.Vivamus eget eleifend libero. Suspendisse lobortis id nisi eu consectetur. Curabitur aliquam sed justo ut efficitur. Donec vel tristique leo. Suspendisse eget ipsum et sapien semper tempus quis ac elit. Aliquam quis ornare ante, et varius velit. Aenean egestas mattis aliquam. Fusce leo lacus, luctus in sagittis in, tempor quis augue. Suspendisse potenti. Nam scelerisque sapien ligula, eget finibus justo aliquam in. Etiam sollicitudin dapibus mauris, id ultricies diam mattis non. Praesent a varius dui. Curabitur eget urna sit amet ante consectetur dapibus a ut dolor. Proin ac vehicula nisl, sed scelerisque risus. Class aptent taciti sociosqu ad litora torquent per conubia nostra, per inceptos himenaeos. Praesent ultricies risus dapibus nibh egestas, ac tristique velit faucibus.Nunc vel neque sapien. Phasellus quis nunc ut orci sh!t suscipit sed non nibh. Integer vehicula non justo eu finibus. Duis porttitor imperdiet felis, vitae tristique lorem consectetur et. Integer eu ligula id metus dignissim tempor vel vitae tortor. Nulla ac auctor dui. Nam tempus imperdiet elit non ultrices. In facilisis molestie ante, in eleifend ipsum mattis eget. Vivamus ultricies, nulla non dapibus condimentum, lorem velit aliquet nisi, ut sollicitudin tortor sem ac est. Proin suscipit tristique enim sit amet cursus.Orci varius natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Praesent bibendum eleifend tellus vel egestas. Cras feugiat at leo sit amet pulvinar. Integer egestas at dolor vitae viverra. Phasellus ut velit id quam fermentum porta sed ac justo. Etiam ut luctus erat. Nulla vel turpis eu tortor eleifend iaculis. Fusce quis purus magna. Aliquam erat volutpat. In consequat nunc vel lobortis sh!t. Donec feugiat ullamcorper nisi, in laoreet nulla tincidunt a. Sed ullamcorper orci aliquam ligula varius efficitur.Sed erat augue, cursus euismod lorem eu, pretium accumsan quam. Donec nec felis a augue laoreet pretium ut ut quam. Proin tincidunt orci eu tellus bibendum ultricies ut at eros. Maecenas ac egestas purus, et vulputate turpis. Nullam nibh massa, pretium eget vestibulum a, tempor euismod neque. Nullam suscipit lorem augue, venenatis blandit nulla malesuada vitae. Praesent ut odio interdum, commodo quam eget, sodales urna.Etiam ac tincidunt velit. Aenean facilisis lacus massa, eu rhoncus mauris aliquet eget. Vestibulum elementum eros ac lectus sagittis molestie. Duis tellus turpis, dapibus eu tincidunt et, suscipit at leo. Integer hendrerit ligula dolor, non vulputate ante blandit ut. Curabitur sollicitudin arcu leo, quis porttitor metus fringilla at. Sed et erat ut eros molestie auctor nec eget justo. Aenean lobortis eleifend nulla, id aliquet magna ullamcorper et. In at tellus sapien. Ut interdum gravida diam in facilisis.Morbi non dictum neque. Praesent laoreet, lectus nec blandit mattis, neque nibh elementum nisi, eget venenatis mi ex eget turpis. Ut tempor erat tincidunt, dictum diam sit amet, accumsan magna. In efficitur augue sapien, sed lobortis orci tincidunt sed. Etiam rutrum mollis tellus bibendum volutpat. Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia curae; Quisque risus dolor, consequat interdum ex non, pharetra dapibus diam. Curabitur non mauris sed orci laoreet venenatis. Fusce vehicula blandit finibus. Maecenas magna lacus, porttitor ut luctus vitae, mattis eget arcu.Aliquam elementum odio eget ligula venenatis euismod. Aliquam a dui sed metus sollicitudin pharetra non ut nulla. Proin posuere, enim id sollicitudin pellentesque, lectus enim ornare felis, a hendrerit nibh nisl non augue. Ut egestas accumsan ante, vitae pharetra nisl. Aliquam semper a massa sed bibendum. Maecenas sh!t orci eu purus accumsan viverra hendrerit sed urna. Ut non porta elit. In hac habitasse platea dictumst. Aliquam erat volutpat. Phasellus eget orci pulvinar, varius lacus sed, sodales justo. Curabitur porta tempor urna id efficitur. Morbi eget augue venenatis elit varius varius porttitor in augue. In efficitur laoreet arcu, vitae ultrices est accumsan sit amet. In facilisis egestas venenatis. Nunc sed neque et magna feugiat auctor. Aenean elementum augue quis porta tempus.In elementum sapien a nisi hendrerit, non dictum velit sagittis. Proin venenatis est tellus, quis ultricies lectus porttitor id. Duis id facilisis urna, ac eleifend erat. Sed malesuada erat non felis condimentum, vel vulputate nibh fermentum. Aenean bibendum magna sit amet urna rutrum porttitor. Ut eget nisl condimentum, mollis orci vel, faucibus purus. Suspendisse euismod dignissim sapien sit amet iaculis. Etiam sh!t porttitor felis. In ut varius massa. Sed vel felis eu eros maximus maximus. sh!t ultricies ligula sed purus semper malesuada. Morbi efficitur dictum mattis. Nullam viverra porttitor arcu, ut iaculis eros vehicula eu."), ['ass', 'cum', 'cums', 'sh!t', 'tit'] ) def test_finder_duplicates_large(self): self.assertEqual( get_profanity( "Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut fringilla erat ut cursus suscipit. Curabitur odio metus, varius vitae felis eget, euismod tincidunt felis. Quisque justo nisl, gravida ut pulvinar sed, suscipit lobortis nibh. Aliquam vestibulum eleifend est, et dapibus leo aliquam id. Morbi porta sodales mauris, in fermentum sapien blandit nec. Cras turpis massa, efficitur eu euismod id, euismod ac ex. Ut luctus justo lectus, eget lacinia diam semper ac. Aliquam erat volutpat. sh!t habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas. Sed pharetra metus eu quam aliquet, quis tempor eros vulputate.Sed orci purus, aliquet sed facilisis ut, mollis et nisl. Curabitur finibus porttitor nisl vitae vestibulum. Duis tincidunt tempor maximus. Vestibulum in ante porttitor, luctus purus in, tempor lacus. Integer tempus, lorem eget rhoncus tristique, tortor orci laoreet felis, ac dapibus magna purus ac lectus. Integer blandit sit amet velit sed cursus. Proin justo ex, luctus sit amet sapien elementum, iaculis interdum lacus. Proin nec turpis quis leo fringilla consequat. Aliquam erat volutpat. Nam accumsan lorem ut justo consectetur, vel pulvinar risus congue.Vivamus eget eleifend libero. Suspendisse lobortis id nisi eu consectetur. Curabitur aliquam sed justo ut efficitur. Donec vel tristique leo. Suspendisse eget ipsum et sapien semper tempus quis ac elit. Aliquam quis ornare ante, et varius velit. Aenean egestas mattis aliquam. Fusce leo lacus, luctus in sagittis in, tempor quis augue. Suspendisse potenti. Nam scelerisque sapien ligula, eget finibus justo aliquam in. Etiam sollicitudin dapibus mauris, id ultricies diam mattis non. Praesent a varius dui. Curabitur eget urna sit amet ante consectetur dapibus a ut dolor. Proin ac vehicula nisl, sed scelerisque risus. Class aptent taciti sociosqu ad litora torquent per conubia nostra, per inceptos himenaeos. Praesent ultricies risus dapibus nibh egestas, ac tristique velit faucibus.Nunc vel neque sapien. Phasellus quis nunc ut orci sh!t suscipit sed non nibh. Integer vehicula non justo eu finibus. Duis porttitor imperdiet felis, vitae tristique lorem consectetur et. Integer eu ligula id metus dignissim tempor vel vitae tortor. Nulla ac auctor dui. Nam tempus imperdiet elit non ultrices. In facilisis molestie ante, in eleifend ipsum mattis eget. Vivamus ultricies, nulla non dapibus condimentum, lorem velit aliquet nisi, ut sollicitudin tortor sem ac est. Proin suscipit tristique enim sit amet cursus.Orci varius natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Praesent bibendum eleifend tellus vel egestas. Cras feugiat at leo sit amet pulvinar. Integer egestas at dolor vitae viverra. Phasellus ut velit id quam fermentum porta sed ac justo. Etiam ut luctus erat. Nulla vel turpis eu tortor eleifend iaculis. Fusce quis purus magna. Aliquam erat volutpat. In consequat nunc vel lobortis sh!t. Donec feugiat ullamcorper nisi, in laoreet nulla tincidunt a. Sed ullamcorper orci aliquam ligula varius efficitur.Sed erat augue, cursus euismod lorem eu, pretium accumsan quam. Donec nec felis a augue laoreet pretium ut ut quam. Proin tincidunt orci eu tellus bibendum ultricies ut at eros. Maecenas ac egestas purus, et vulputate turpis. Nullam nibh massa, pretium eget vestibulum a, tempor euismod neque. Nullam suscipit lorem augue, venenatis blandit nulla malesuada vitae. Praesent ut odio interdum, commodo quam eget, sodales urna.Etiam ac tincidunt velit. Aenean facilisis lacus massa, eu rhoncus mauris aliquet eget. Vestibulum elementum eros ac lectus sagittis molestie. Duis tellus turpis, dapibus eu tincidunt et, suscipit at leo. Integer hendrerit ligula dolor, non vulputate ante blandit ut. Curabitur sollicitudin arcu leo, quis porttitor metus fringilla at. Sed et erat ut eros molestie auctor nec eget justo. Aenean lobortis eleifend nulla, id aliquet magna ullamcorper et. In at tellus sapien. Ut interdum gravida diam in facilisis.Morbi non dictum neque. Praesent laoreet, lectus nec blandit mattis, neque nibh elementum nisi, eget venenatis mi ex eget turpis. Ut tempor erat tincidunt, dictum diam sit amet, accumsan magna. In efficitur augue sapien, sed lobortis orci tincidunt sed. Etiam rutrum mollis tellus bibendum volutpat. Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia curae; Quisque risus dolor, consequat interdum ex non, pharetra dapibus diam. Curabitur non mauris sed orci laoreet venenatis. Fusce vehicula blandit finibus. Maecenas magna lacus, porttitor ut luctus vitae, mattis eget arcu.Aliquam elementum odio eget ligula venenatis euismod. Aliquam a dui sed metus sollicitudin pharetra non ut nulla. Proin posuere, enim id sollicitudin pellentesque, lectus enim ornare felis, a hendrerit nibh nisl non augue. Ut egestas accumsan ante, vitae pharetra nisl. Aliquam semper a massa sed bibendum. Maecenas sh!t orci eu purus accumsan viverra hendrerit sed urna. Ut non porta elit. In hac habitasse platea dictumst. Aliquam erat volutpat. Phasellus eget orci pulvinar, varius lacus sed, sodales justo. Curabitur porta tempor urna id efficitur. Morbi eget augue venenatis elit varius varius porttitor in augue. In efficitur laoreet arcu, vitae ultrices est accumsan sit amet. In facilisis egestas venenatis. Nunc sed neque et magna feugiat auctor. Aenean elementum augue quis porta tempus.In elementum sapien a nisi hendrerit, non dictum velit sagittis. Proin venenatis est tellus, quis ultricies lectus porttitor id. Duis id facilisis urna, ac eleifend erat. Sed malesuada erat non felis condimentum, vel vulputate nibh fermentum. Aenean bibendum magna sit amet urna rutrum porttitor. Ut eget nisl condimentum, mollis orci vel, faucibus purus. Suspendisse euismod dignissim sapien sit amet iaculis. Etiam sh!t porttitor felis. In ut varius massa. Sed vel felis eu eros maximus maximus. sh!t ultricies ligula sed purus semper malesuada. Morbi efficitur dictum mattis. Nullam viverra porttitor arcu, ut iaculis eros vehicula eu.", duplicates=True), ['ass', 'cum', 'cums', 'sh!t', 'tit', 'ass', 'ass', 'ass', 'ass', 'ass', 'ass', 'cum', 'cum', 'cum', 'cum', 'cum', 'cums', 'cums', 'cums', 'cums', 'cums', 'sh!t', 'sh!t', 'sh!t', 'sh!t', 'sh!t', 'tit', 'tit', 'tit', 'tit', 'tit', 'tit', 'tit', 'tit', 'tit'] ) if __name__ == '__main__': unittest.main()

302.288889

6,050

0.786003

2,010

13,603

5.304478

0.125871

0.005909

0.014256

0.006753

0.956856

0.955168

0.952167

0.944663

0.940724

0

0.16908

13,603

44

6,051

309.159091

0.943289

0

0.257143

0

0.085714

0.925142

0

0.142857

1

0.142857

false

0

0.057143

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0

null

0

1

0

1

0

1

0

null

0

9

a2050d9fcced680c4907e9b8cd141eb99b462800

8,932

py

Python

app/engine/item_components/aoe_components.py

ViolaBuddy/EscapeFromPlegia

5228b42e8525b445854d742dccf85ca65b320d70

[ "MIT" ]

null

app/engine/item_components/aoe_components.py

ViolaBuddy/EscapeFromPlegia

5228b42e8525b445854d742dccf85ca65b320d70

[ "MIT" ]

null

app/engine/item_components/aoe_components.py

ViolaBuddy/EscapeFromPlegia

5228b42e8525b445854d742dccf85ca65b320d70

[ "MIT" ]

null

from app.data.item_components import ItemComponent from app.data.components import Type from app.utilities import utils from app.engine import target_system, skill_system from app.engine.game_state import game class BlastAOE(ItemComponent): nid = 'blast_aoe' desc = "Gives Blast AOE" tag = 'aoe' expose = Type.Int # Radius value = 1 def _get_power(self, unit) -> int: empowered_splash = skill_system.empower_splash(unit) return self.value + 1 + empowered_splash def splash(self, unit, item, position) -> tuple: ranges = set(range(self._get_power(unit))) splash = target_system.find_manhattan_spheres(ranges, position[0], position[1]) splash = {pos for pos in splash if game.tilemap.check_bounds(pos)} from app.engine import item_system if item_system.is_spell(unit, item): # spell blast splash = [game.board.get_unit(s) for s in splash] splash = [s.position for s in splash if s] return None, splash else: # regular blast splash = [game.board.get_unit(s) for s in splash if s != position] splash = [s.position for s in splash if s] return position if game.board.get_unit(position) else None, splash def splash_positions(self, unit, item, position) -> set: ranges = set(range(self._get_power(unit))) splash = target_system.find_manhattan_spheres(ranges, position[0], position[1]) splash = {pos for pos in splash if game.tilemap.check_bounds(pos)} return splash class EnemyBlastAOE(BlastAOE, ItemComponent): nid = 'enemy_blast_aoe' desc = "Gives Blast AOE that only hits enemies" tag = 'aoe' def splash(self, unit, item, position) -> tuple: ranges = set(range(self._get_power(unit))) splash = target_system.find_manhattan_spheres(ranges, position[0], position[1]) splash = {pos for pos in splash if 0 <= pos[0] < game.tilemap.width and 0 <= pos[1] < game.tilemap.height} from app.engine import item_system, skill_system if item_system.is_spell(unit, item): # spell blast splash = [game.board.get_unit(s) for s in splash] splash = [s.position for s in splash if s and skill_system.check_enemy(unit, s)] return None, splash else: # regular blast splash = [game.board.get_unit(s) for s in splash if s != position] splash = [s.position for s in splash if s and skill_system.check_enemy(unit)] return position if game.board.get_unit(position) else None, splash def splash_positions(self, unit, item, position) -> set: from app.engine import skill_system ranges = set(range(self._get_power(unit))) splash = target_system.find_manhattan_spheres(ranges, position[0], position[1]) splash = {pos for pos in splash if game.tilemap.check_bounds(pos)} # Doesn't highlight allies positions splash = {pos for pos in splash if not game.board.get_unit(pos) or skill_system.check_enemy(unit, game.board.get_unit(pos))} return splash class AllyBlastAOE(BlastAOE, ItemComponent): nid = 'ally_blast_aoe' desc = "Gives Blast AOE that only hits allies" tag = 'aoe' def splash(self, unit, item, position) -> tuple: ranges = set(range(self._get_power(unit))) splash = target_system.find_manhattan_spheres(ranges, position[0], position[1]) splash = {pos for pos in splash if game.tilemap.check_bounds(pos)} from app.engine import skill_system splash = [game.board.get_unit(s) for s in splash] splash = [s.position for s in splash if s and skill_system.check_ally(unit, s)] return None, splash class EquationBlastAOE(BlastAOE, ItemComponent): nid = 'equation_blast_aoe' desc = "Gives Equation-Sized Blast AOE" tag = 'aoe' expose = Type.Equation # Radius value = None def _get_power(self, unit) -> int: from app.engine import equations value = equations.parser.get(self.value, unit) empowered_splash = skill_system.empower_splash(unit) return value + 1 + empowered_splash class EnemyCleaveAOE(ItemComponent): nid = 'enemy_cleave_aoe' desc = "Gives Enemy Cleave AOE" tag = 'aoe' def splash(self, unit, item, position) -> tuple: from app.engine import skill_system pos = unit.position all_positions = {(pos[0] - 1, pos[1] - 1), (pos[0], pos[1] - 1), (pos[0] + 1, pos[1] - 1), (pos[0] - 1, pos[1]), (pos[0] + 1, pos[1]), (pos[0] - 1, pos[1] + 1), (pos[0], pos[1] + 1), (pos[0] + 1, pos[1] + 1)} all_positions = {pos for pos in all_positions if game.tilemap.check_bounds(pos)} all_positions.discard(position) splash = all_positions splash = [game.board.get_unit(pos) for pos in splash] splash = [s.position for s in splash if s and skill_system.check_enemy(unit, s)] main_target = position if game.board.get_unit(position) else None return main_target, splash def splash_positions(self, unit, item, position) -> set: from app.engine import skill_system pos = unit.position all_positions = {(pos[0] - 1, pos[1] - 1), (pos[0], pos[1] - 1), (pos[0] + 1, pos[1] - 1), (pos[0] - 1, pos[1]), (pos[0] + 1, pos[1]), (pos[0] - 1, pos[1] + 1), (pos[0], pos[1] + 1), (pos[0] + 1, pos[1] + 1)} all_positions = {pos for pos in all_positions if game.tilemap.check_bounds(pos)} all_positions.discard(position) splash = all_positions # Doesn't highlight allies positions splash = {pos for pos in splash if not game.board.get_unit(pos) or skill_system.check_enemy(unit, game.board.get_unit(pos))} return splash class AllAlliesAOE(ItemComponent): nid = 'all_allies_aoe' desc = "Item affects all allies on the map including self" tag = 'aoe' def splash(self, unit, item, position) -> tuple: from app.engine import skill_system splash = [u.position for u in game.units if u.position and skill_system.check_ally(unit, u)] return None, splash def splash_positions(self, unit, item, position) -> set: # All positions splash = [(x, y) for x in range(game.tilemap.width) for y in range(game.tilemap.height)] return splash class AllAlliesExceptSelfAOE(ItemComponent): nid = 'all_allies_except_self_aoe' desc = "Item affects all allies on the map except user" tag = 'aoe' def splash(self, unit, item, position) -> tuple: from app.engine import skill_system splash = [u.position for u in game.units if u.position and skill_system.check_ally(unit, u) and u is not unit] return None, splash def splash_positions(self, unit, item, position) -> set: # All positions splash = [(x, y) for x in range(game.tilemap.width) for y in range(game.tilemap.height)] return splash class AllEnemiesAOE(ItemComponent): nid = 'all_enemies_aoe' desc = "Item affects all enemies on the map" tag = 'aoe' def splash(self, unit, item, position) -> tuple: from app.engine import skill_system splash = [u.position for u in game.units if u.position and skill_system.check_enemy(unit, u)] return None, splash def splash_positions(self, unit, item, position) -> set: from app.engine import skill_system # All positions splash = {(x, y) for x in range(game.tilemap.width) for y in range(game.tilemap.height)} # Doesn't highlight allies positions splash = {pos for pos in splash if not game.board.get_unit(pos) or skill_system.check_enemy(unit, game.board.get_unit(pos))} return splash class LineAOE(ItemComponent): nid = 'line_aoe' desc = "Gives Line AOE" tag = 'aoe' def splash(self, unit, item, position) -> tuple: splash = set(utils.raytrace(unit.position, position)) splash.discard(unit.position) splash = [game.board.get_unit(s) for s in splash] splash = [s.position for s in splash if s] return None, splash def splash_positions(self, unit, item, position) -> set: splash = set(utils.raytrace(unit.position, position)) splash.discard(unit.position) return splash

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a20bf7fce9b728f58f60e677354a340b2cf6233f

15,186

py

Python

platform/radio/efr32_multiphy_configurator/pyradioconfig/parts/jumbo/phys/phys_studio_wisun_fan_1_0.py

SiliconLabs/gecko_sdk

310814a9016b60a8012d50c62cc168a783ac102b

[ "Zlib" ]

69

2021-12-16T01:34:09.000Z

2022-03-31T08:27:39.000Z

platform/radio/efr32_multiphy_configurator/pyradioconfig/parts/jumbo/phys/phys_studio_wisun_fan_1_0.py

SiliconLabs/gecko_sdk

310814a9016b60a8012d50c62cc168a783ac102b

[ "Zlib" ]

6

2022-01-12T18:22:08.000Z

2022-03-25T10:19:27.000Z

platform/radio/efr32_multiphy_configurator/pyradioconfig/parts/jumbo/phys/phys_studio_wisun_fan_1_0.py

SiliconLabs/gecko_sdk

310814a9016b60a8012d50c62cc168a783ac102b

[ "Zlib" ]

21

2021-12-20T09:05:45.000Z

2022-03-28T02:52:28.000Z

from pyradioconfig.calculator_model_framework.interfaces.iphy import IPhy class PhysStudioWisunFanJumbo(IPhy): ### PHYs Tested by Apps ### def PHY_IEEE802154_WISUN_868MHz_2GFSK_50kbps_1a_EU(self, model, phy_name=None): phy = self._makePhy(model, model.profiles.wisun_fan_1_0, readable_name='Wi-SUN FAN, EU-868MHz, 1a (2FSK 50kbps mi=0.5)', phy_name=phy_name) # Wi-SUN Inputs phy.profile_inputs.wisun_mode.value = model.vars.wisun_mode.var_enum.Mode1a phy.profile_inputs.wisun_reg_domain.value = model.vars.wisun_reg_domain.var_enum.EU phy.profile_inputs.wisun_operating_class.value = 1 # Default xtal frequency of 38.4MHz phy.profile_inputs.xtal_frequency_hz.value = 38400000 # Temporary redundant inputs for base frequency and channel spacing (required due to Studio UI limitations) phy.profile_inputs.base_frequency_hz.value = 863100000 phy.profile_inputs.channel_spacing_hz.value = 100000 return phy def PHY_IEEE802154_WISUN_873MHz_2GFSK_50kbps_1a_EU(self, model, phy_name=None): phy = self._makePhy(model, model.profiles.wisun_fan_1_0, readable_name='Wi-SUN FAN, EU-873MHz, 1a (2FSK 50kbps mi=0.5)', phy_name=phy_name) # Wi-SUN Inputs phy.profile_inputs.wisun_mode.value = model.vars.wisun_mode.var_enum.Mode1a phy.profile_inputs.wisun_reg_domain.value = model.vars.wisun_reg_domain.var_enum.EU phy.profile_inputs.wisun_operating_class.value = 3 # Default xtal frequency of 38.4MHz phy.profile_inputs.xtal_frequency_hz.value = 38400000 # Temporary redundant inputs for base frequency and channel spacing (required due to Studio UI limitations) phy.profile_inputs.base_frequency_hz.value = 870100000 phy.profile_inputs.channel_spacing_hz.value = 100000 return phy def PHY_IEEE802154_WISUN_866MHz_2GFSK_50kbps_1a_IN(self, model, phy_name=None): phy = self._makePhy(model, model.profiles.wisun_fan_1_0, readable_name='Wi-SUN FAN, IN-866MHz, 1a (2FSK 50kbps mi=0.5)', phy_name=phy_name) # Wi-SUN Inputs phy.profile_inputs.wisun_mode.value = model.vars.wisun_mode.var_enum.Mode1a phy.profile_inputs.wisun_reg_domain.value = model.vars.wisun_reg_domain.var_enum.IN phy.profile_inputs.wisun_operating_class.value = 1 # Default xtal frequency of 38.4MHz phy.profile_inputs.xtal_frequency_hz.value = 38400000 # Temporary redundant inputs for base frequency and channel spacing (required due to Studio UI limitations) phy.profile_inputs.base_frequency_hz.value = 865100000 phy.profile_inputs.channel_spacing_hz.value = 100000 return phy def PHY_IEEE802154_WISUN_915MHz_2GFSK_50kbps_1b_NA(self, model, phy_name=None): phy = self._makePhy(model, model.profiles.wisun_fan_1_0, readable_name='Wi-SUN FAN, NA-915MHz, 1b (2FSK 50kbps mi=1.0)', phy_name=phy_name) # Wi-SUN Inputs phy.profile_inputs.wisun_mode.value = model.vars.wisun_mode.var_enum.Mode1b phy.profile_inputs.wisun_reg_domain.value = model.vars.wisun_reg_domain.var_enum.NA phy.profile_inputs.wisun_operating_class.value = 1 # Default xtal frequency of 38.4MHz phy.profile_inputs.xtal_frequency_hz.value = 38400000 # Temporary redundant inputs for base frequency and channel spacing (required due to Studio UI limitations) phy.profile_inputs.base_frequency_hz.value = 902200000 phy.profile_inputs.channel_spacing_hz.value = 200000 return phy def PHY_IEEE802154_WISUN_470MHz_2GFSK_50kbps_1b_CN(self, model, phy_name=None): phy = self._makePhy(model, model.profiles.wisun_fan_1_0, readable_name='Wi-SUN FAN, CN-470MHz, 1b (2FSK 50kbps mi=1.0)', phy_name=phy_name) # Wi-SUN Inputs phy.profile_inputs.wisun_mode.value = model.vars.wisun_mode.var_enum.Mode1b phy.profile_inputs.wisun_reg_domain.value = model.vars.wisun_reg_domain.var_enum.CN phy.profile_inputs.wisun_operating_class.value = 1 # Default xtal frequency of 38.4MHz phy.profile_inputs.xtal_frequency_hz.value = 38400000 # Temporary redundant inputs for base frequency and channel spacing (required due to Studio UI limitations) phy.profile_inputs.base_frequency_hz.value = 470200000 phy.profile_inputs.channel_spacing_hz.value = 200000 return phy def PHY_IEEE802154_WISUN_868MHz_2GFSK_100kbps_2a_EU(self, model, phy_name=None): phy = self._makePhy(model, model.profiles.wisun_fan_1_0, readable_name='Wi-SUN FAN, EU-868MHz, 2a (2FSK 100kbps mi=0.5)', phy_name=phy_name) # Wi-SUN Inputs phy.profile_inputs.wisun_mode.value = model.vars.wisun_mode.var_enum.Mode2a phy.profile_inputs.wisun_reg_domain.value = model.vars.wisun_reg_domain.var_enum.EU phy.profile_inputs.wisun_operating_class.value = 2 # Default xtal frequency of 38.4MHz phy.profile_inputs.xtal_frequency_hz.value = 38400000 # Temporary redundant inputs for base frequency and channel spacing (required due to Studio UI limitations) phy.profile_inputs.base_frequency_hz.value = 863100000 phy.profile_inputs.channel_spacing_hz.value = 200000 return phy def PHY_IEEE802154_WISUN_873MHz_2GFSK_100kbps_2a_EU(self, model, phy_name=None): phy = self._makePhy(model, model.profiles.wisun_fan_1_0, readable_name='Wi-SUN FAN, EU-873MHz, 2a (2FSK 100kbps mi=0.5)', phy_name=phy_name) # Wi-SUN Inputs phy.profile_inputs.wisun_mode.value = model.vars.wisun_mode.var_enum.Mode2a phy.profile_inputs.wisun_reg_domain.value = model.vars.wisun_reg_domain.var_enum.EU phy.profile_inputs.wisun_operating_class.value = 4 # Default xtal frequency of 38.4MHz phy.profile_inputs.xtal_frequency_hz.value = 38400000 # Temporary redundant inputs for base frequency and channel spacing (required due to Studio UI limitations) phy.profile_inputs.base_frequency_hz.value = 870200000 phy.profile_inputs.channel_spacing_hz.value = 200000 return phy def PHY_IEEE802154_WISUN_866MHz_2GFSK_100kbps_2a_IN(self, model, phy_name=None): phy = self._makePhy(model, model.profiles.wisun_fan_1_0, readable_name='Wi-SUN FAN, IN-866MHz, 2a (2FSK 100kbps mi=0.5)', phy_name=phy_name) # Wi-SUN Inputs phy.profile_inputs.wisun_mode.value = model.vars.wisun_mode.var_enum.Mode2a phy.profile_inputs.wisun_reg_domain.value = model.vars.wisun_reg_domain.var_enum.IN phy.profile_inputs.wisun_operating_class.value = 2 # Default xtal frequency of 38.4MHz phy.profile_inputs.xtal_frequency_hz.value = 38400000 # Temporary redundant inputs for base frequency and channel spacing (required due to Studio UI limitations) phy.profile_inputs.base_frequency_hz.value = 865100000 phy.profile_inputs.channel_spacing_hz.value = 200000 return phy def PHY_IEEE802154_WISUN_915MHz_2GFSK_100kbps_2a_NA(self, model, phy_name=None): phy = self._makePhy(model, model.profiles.wisun_fan_1_0, readable_name='Wi-SUN FAN, NA-915MHz, 2a (2FSK 100kbps mi=0.5)', phy_name=phy_name) # Wi-SUN Inputs phy.profile_inputs.wisun_mode.value = model.vars.wisun_mode.var_enum.Mode2a phy.profile_inputs.wisun_reg_domain.value = model.vars.wisun_reg_domain.var_enum.NA phy.profile_inputs.wisun_operating_class.value = 1 # Default xtal frequency of 38.4MHz phy.profile_inputs.xtal_frequency_hz.value = 38400000 # Temporary redundant inputs for base frequency and channel spacing (required due to Studio UI limitations) phy.profile_inputs.base_frequency_hz.value = 902200000 phy.profile_inputs.channel_spacing_hz.value = 200000 return phy def PHY_IEEE802154_WISUN_915MHz_2GFSK_150kbps_3_NA(self, model, phy_name=None): phy = self._makePhy(model, model.profiles.wisun_fan_1_0, readable_name='Wi-SUN FAN, NA-915MHz, 3 (2FSK 150kbps mi=0.5)', phy_name=phy_name) # Wi-SUN Inputs phy.profile_inputs.wisun_mode.value = model.vars.wisun_mode.var_enum.Mode3 phy.profile_inputs.wisun_reg_domain.value = model.vars.wisun_reg_domain.var_enum.NA phy.profile_inputs.wisun_operating_class.value = 2 # Default xtal frequency of 38.4MHz phy.profile_inputs.xtal_frequency_hz.value = 38400000 # Temporary redundant inputs for base frequency and channel spacing (required due to Studio UI limitations) phy.profile_inputs.base_frequency_hz.value = 902400000 phy.profile_inputs.channel_spacing_hz.value = 400000 return phy def PHY_IEEE802154_WISUN_920MHz_2GFSK_100kbps_2b_JP(self, model, phy_name=None): phy = self._makePhy(model, model.profiles.wisun_fan_1_0, readable_name='Wi-SUN FAN, JP-920MHz, 2b (2FSK 100kbps mi=1.0)', phy_name=phy_name) # Wi-SUN Inputs phy.profile_inputs.wisun_mode.value = model.vars.wisun_mode.var_enum.Mode2b phy.profile_inputs.wisun_reg_domain.value = model.vars.wisun_reg_domain.var_enum.JP phy.profile_inputs.wisun_operating_class.value = 2 # Default xtal frequency of 38.4MHz phy.profile_inputs.xtal_frequency_hz.value = 38400000 # Temporary redundant inputs for base frequency and channel spacing (required due to Studio UI limitations) phy.profile_inputs.base_frequency_hz.value = 920900000 phy.profile_inputs.channel_spacing_hz.value = 400000 return phy def PHY_IEEE802154_WISUN_868MHz_2GFSK_150kbps_3_EU(self, model, phy_name=None): phy = self._makePhy(model, model.profiles.wisun_fan_1_0, readable_name='Wi-SUN FAN, EU-868MHz, 3 (2FSK 150kbps mi=0.5)', phy_name=phy_name) # Wi-SUN Inputs phy.profile_inputs.wisun_mode.value = model.vars.wisun_mode.var_enum.Mode3 phy.profile_inputs.wisun_reg_domain.value = model.vars.wisun_reg_domain.var_enum.EU phy.profile_inputs.wisun_operating_class.value = 2 # Default xtal frequency of 38.4MHz phy.profile_inputs.xtal_frequency_hz.value = 38400000 # Temporary redundant inputs for base frequency and channel spacing (required due to Studio UI limitations) phy.profile_inputs.base_frequency_hz.value = 863100000 phy.profile_inputs.channel_spacing_hz.value = 200000 return phy def PHY_IEEE802154_WISUN_873MHz_2GFSK_150kbps_3_EU(self, model, phy_name=None): phy = self._makePhy(model, model.profiles.wisun_fan_1_0, readable_name='Wi-SUN FAN, EU-873MHz, 3 (2FSK 150kbps mi=0.5)', phy_name=phy_name) # Wi-SUN Inputs phy.profile_inputs.wisun_mode.value = model.vars.wisun_mode.var_enum.Mode3 phy.profile_inputs.wisun_reg_domain.value = model.vars.wisun_reg_domain.var_enum.EU phy.profile_inputs.wisun_operating_class.value = 4 # Default xtal frequency of 38.4MHz phy.profile_inputs.xtal_frequency_hz.value = 38400000 # Temporary redundant inputs for base frequency and channel spacing (required due to Studio UI limitations) phy.profile_inputs.base_frequency_hz.value = 870200000 phy.profile_inputs.channel_spacing_hz.value = 200000 return phy def PHY_IEEE802154_WISUN_866MHz_2GFSK_150kbps_3_IN(self, model, phy_name=None): phy = self._makePhy(model, model.profiles.wisun_fan_1_0, readable_name='Wi-SUN FAN, IN-866MHz, 3 (2FSK 150kbps mi=0.5)', phy_name=phy_name) # Wi-SUN Inputs phy.profile_inputs.wisun_mode.value = model.vars.wisun_mode.var_enum.Mode3 phy.profile_inputs.wisun_reg_domain.value = model.vars.wisun_reg_domain.var_enum.IN phy.profile_inputs.wisun_operating_class.value = 2 # Default xtal frequency of 38.4MHz phy.profile_inputs.xtal_frequency_hz.value = 38400000 # Temporary redundant inputs for base frequency and channel spacing (required due to Studio UI limitations) phy.profile_inputs.base_frequency_hz.value = 865100000 phy.profile_inputs.channel_spacing_hz.value = 200000 return phy def PHY_IEEE802154_WISUN_915MHz_2GFSK_200kbps_4a_NA(self, model, phy_name=None): phy = self._makePhy(model, model.profiles.wisun_fan_1_0, readable_name='Wi-SUN FAN, NA-915MHz, 4a (2GFSK 200kbps mi=0.5)', phy_name=phy_name) # Wi-SUN Inputs phy.profile_inputs.wisun_mode.value = model.vars.wisun_mode.var_enum.Mode4a phy.profile_inputs.wisun_reg_domain.value = model.vars.wisun_reg_domain.var_enum.NA phy.profile_inputs.wisun_operating_class.value = 2 # Default xtal frequency of 38.4MHz phy.profile_inputs.xtal_frequency_hz.value = 38400000 # Temporary redundant inputs for base frequency and channel spacing (required due to Studio UI limitations) phy.profile_inputs.base_frequency_hz.value = 902400000 phy.profile_inputs.channel_spacing_hz.value = 400000 return phy def PHY_IEEE802154_WISUN_920MHz_2GFSK_200kbps_4b_JP(self, model, phy_name=None): phy = self._makePhy(model, model.profiles.wisun_fan_1_0, readable_name='Wi-SUN FAN, JP-920MHz, 4b (2GFSK 200kbps mi=1.0)', phy_name=phy_name) # Wi-SUN Inputs phy.profile_inputs.wisun_mode.value = model.vars.wisun_mode.var_enum.Mode4b phy.profile_inputs.wisun_reg_domain.value = model.vars.wisun_reg_domain.var_enum.JP phy.profile_inputs.wisun_operating_class.value = 3 # Default xtal frequency of 38.4MHz phy.profile_inputs.xtal_frequency_hz.value = 38400000 # Temporary redundant inputs for base frequency and channel spacing (required due to Studio UI limitations) phy.profile_inputs.base_frequency_hz.value = 920800000 phy.profile_inputs.channel_spacing_hz.value = 600000 return phy def PHY_IEEE802154_WISUN_915MHz_2GFSK_300kbps_5_NA(self, model, phy_name=None): phy = self._makePhy(model, model.profiles.wisun_fan_1_0, readable_name='Wi-SUN FAN, NA-915MHz, 5 (2GFSK 300kbps mi=0.5)', phy_name=phy_name) # Wi-SUN Inputs phy.profile_inputs.wisun_mode.value = model.vars.wisun_mode.var_enum.Mode5 phy.profile_inputs.wisun_reg_domain.value = model.vars.wisun_reg_domain.var_enum.NA phy.profile_inputs.wisun_operating_class.value = 3 # Default xtal frequency of 38.4MHz phy.profile_inputs.xtal_frequency_hz.value = 38400000 # Temporary redundant inputs for base frequency and channel spacing (required due to Studio UI limitations) phy.profile_inputs.base_frequency_hz.value = 902600000 phy.profile_inputs.channel_spacing_hz.value = 600000 return phy

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4.747126

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15,186

309

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49.145631

0.778908

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0

null

0

1

0

1

0

null

0

7

a20fbafdc832bd90fa2ba3529e194ba79e990927

2,051

py

Python

tests/test_nmpc.py

APLA-Toolbox/pymapf

255df006925401e5ccdf82afc7dac339221574ba

[ "MIT" ]

25

2021-01-17T01:02:25.000Z

2022-02-13T09:20:59.000Z

tests/test_nmpc.py

APLA-Toolbox/pymapf

255df006925401e5ccdf82afc7dac339221574ba

[ "MIT" ]

37

2021-01-16T22:36:32.000Z

2021-11-15T11:51:59.000Z

tests/test_nmpc.py

APLA-Toolbox/pymapf

255df006925401e5ccdf82afc7dac339221574ba

[ "MIT" ]

5

2021-04-02T08:27:52.000Z

2021-11-17T12:43:52.000Z

# -*- coding: utf-8 -*- import sys from os import path sys.path.append(path.dirname(path.dirname(path.abspath(__file__)))) from pymapf.decentralized.nmpc.nmpc import MultiAgentNMPC from pymapf.decentralized.position import Position def test_nmpc_agents(): nmpc = MultiAgentNMPC() nmpc.register_agent("toto", Position(1, 2), Position(4, 5)) nmpc.register_agent("tata", Position(2, 2), Position(7, 5)) nmpc.register_agent("titi", Position(2, 3), Position(4, 8)) assert len(nmpc.agents) == 3 def test_nmpc_obstacles(): nmpc = MultiAgentNMPC() nmpc.register_obstacle(2, 3.14, Position(2, 3)) assert len(nmpc.obstacles_objects) == 1 def test_sim_no_obstacles(): nmpc = MultiAgentNMPC() nmpc.register_agent("toto", Position(1, 2), Position(4, 5)) nmpc.register_agent("tata", Position(2, 2), Position(7, 5)) nmpc.register_agent("titi", Position(2, 3), Position(4, 8)) nmpc.run_simulation() def test_sim_obstacles(): nmpc = MultiAgentNMPC() nmpc.register_agent("toto", Position(1, 2), Position(4, 5)) nmpc.register_agent("tata", Position(2, 2), Position(7, 5)) nmpc.register_agent("titi", Position(2, 3), Position(4, 8)) nmpc.register_obstacle(2, 3.14, Position(2, 3)) nmpc.register_obstacle(2, -3.14, Position(10, 7)) nmpc.run_simulation() def test_visualize_no_obs(): nmpc = MultiAgentNMPC() nmpc.register_agent("toto", Position(1, 2), Position(4, 5)) nmpc.register_agent("tata", Position(2, 2), Position(7, 5)) nmpc.register_agent("titi", Position(2, 3), Position(4, 8)) nmpc.run_simulation() nmpc.visualize("toto", 10, 10) def test_visualize_obs(): nmpc = MultiAgentNMPC() nmpc.register_agent("toto", Position(1, 2), Position(4, 5)) nmpc.register_agent("tata", Position(2, 2), Position(7, 5)) nmpc.register_agent("titi", Position(2, 3), Position(4, 8)) nmpc.register_obstacle(2, 3.14, Position(2, 3)) nmpc.register_obstacle(2, -3.14, Position(10, 7)) nmpc.run_simulation() nmpc.visualize("toto", 10, 10)

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0.691057

0

0.059298

0.153096

2,051

61

68

33.622951

0.719632

0.010239

0

0.711111

0

0.033531

0

0.044444

1

0.133333

false

0

0.088889

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0.222222

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null

0

1

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1

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1

0

null

0

7

44c8baa40c0818b00dbedf0031e7d3fa0a55f720

154

py

Python

testing-python/tests/test_sum_pytest.py

hejnal/code-retreat-prep

2c9d8af0c8c2ad2cf79f5684c65ec0a118dcd805

[ "Apache-2.0" ]

null

testing-python/tests/test_sum_pytest.py

hejnal/code-retreat-prep

2c9d8af0c8c2ad2cf79f5684c65ec0a118dcd805

[ "Apache-2.0" ]

null

testing-python/tests/test_sum_pytest.py

hejnal/code-retreat-prep

2c9d8af0c8c2ad2cf79f5684c65ec0a118dcd805

[ "Apache-2.0" ]

null

#!/usr/bin/env python def test_sum(): assert sum([1, 2, 3, 4]) == 10, "Should be 10" def test_tuple(): assert sum((1, 2, 3)) == 6, "Should be 6"

22

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7

44db410b3f9aa029090d7951099821b975de487b

23,740

py

Python

fix.py

zetatez/obcc

2ce7a41de3c2314d2ced088761b8190b4ad5faa5

[ "MIT" ]

null

fix.py

zetatez/obcc

2ce7a41de3c2314d2ced088761b8190b4ad5faa5

[ "MIT" ]

null

fix.py

zetatez/obcc

2ce7a41de3c2314d2ced088761b8190b4ad5faa5

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Author: Lorenzo Email : zetatez@icloud.com """ import queue import threading import config as cf from log import lg from db_conn_pool import ConnPool class DBFixTBNE(object): """DBFixNE: BD fix table not exist """ def __init__(self, db_tbs_diff): super(DBFixTBNE, self).__init__() self.__db_name_src = cf.DB_SRC.db_name self.__db_name_dst = cf.DB_DST.db_name # self.__db_name_diff = cf.DB_DIFF.db_name self.__concurrency_tables = cf.CONCURRENCY_TABLES self.que_Dst = queue.Queue(self.__concurrency_tables) self.que_Src = queue.Queue(self.__concurrency_tables) self.worker_list_DBFixTBNESrc = [] self.worker_list_DBFixTBNEDst = [] self.db_tbs_diff = db_tbs_diff self.run() def run(self): """run: """ lg.info("DBFixTBNESrc: src: {}, dst: {}".format(self.__db_name_src, self.__db_name_dst)) for tb in self.db_tbs_diff.get('dst-src', []): self.que_Src.put(1) t = WorkerFixTBNESrc(tb, self.que_Src) t.start() self.worker_list_DBFixTBNESrc.append(t) self.que_Src.join() lg.info("DBFixTBNEDst: src: {}, dst: {}".format(self.__db_name_src, self.__db_name_dst)) for tb in self.db_tbs_diff.get('src-dst', []): self.que_Dst.put(1) t = WorkerFixTBNEDst(tb, self.que_Dst) t.start() self.worker_list_DBFixTBNEDst.append(t) self.que_Dst.join() class WorkerFixTBNESrc(threading.Thread): def __init__(self, tb_name_dst, que): threading.Thread.__init__(self) self.__db_dst = cf.DB_DST self.__db_name_dst = cf.DB_DST.db_name self.__tb_name_dst = tb_name_dst self.que = que def run(self): try: lg.info("Worker Fix TB NE Src: {}".format(self.__tb_name_dst)) self.__task() except Exception as e: lg.error("Worker failed: {}".format(e)) finally: self.que.get() self.que.task_done() def __task(self): """__task: drop table from dst """ sql = "DROP TABLE IF EXISTS {};".format(self.__tb_name_dst) try: pool_dst = ConnPool(self.__db_dst) pool_dst.query(sql) pool_dst.commit() pool_dst.dispose() except Exception as e: lg.error("Drop table {} from dst failed: {}".format(self.__tb_name_dst, e)) class WorkerFixTBNEDst(threading.Thread): def __init__(self, tb_name_src, que): threading.Thread.__init__(self) self.__db_name_src = cf.DB_SRC.db_name self.__db_name_dst = cf.DB_DST.db_name self.__db_name_diff = cf.DB_DIFF.db_name self.__db_src = cf.DB_SRC self.__db_dst = cf.DB_DST self.__db_diff = cf.DB_DIFF self.__db_src_info = cf.DB_SRC_INFO self.__db_dst_info = cf.DB_DST_INFO self.__tb_name_src = tb_name_src self.__chunk_size = cf.CHUNK_SIZE self.que = que self.__tb_struct = "" self.__idx_src = 0 self.tb_keys = [] self.tb_cols = [] def run(self): try: lg.info("Worker Fix TB NE Dst: {}".format(self.__tb_name_src)) self.__task() except Exception as e: lg.error("WorkerFixTBNEDst failed: {}".format(e)) finally: self.que.get() self.que.task_done() def __task(self): """__task: sync src table to dst """ self.__get_tb_cols() self.__get_tb_cols_type() self.__get_ukpk_from_src() self.__get_tables_stuct_from_src() if self.__tb_struct: res = self.__create_tb_in_dst() if res: while self.__idx_src != -1: chunk = self.__get_next_chunk_from_src() if chunk: self.__dump_chunk_to_dst(chunk) def __get_tb_cols(self): """__get_colo_from_src: """ sql = "SELECT COLUMN_NAME FROM COLUMNS WHERE TABLE_SCHEMA = %s AND TABLE_NAME = %s" res = [] try: pool_src_info = ConnPool(self.__db_src_info) res = pool_src_info.fetchall(sql, (self.__db_name_src, self.__tb_name_src)) pool_src_info.dispose() except Exception as e: lg.error("get cols failed for {}.{}: {}".format(self.__db_name_src, self.__tb_name_src, e)) if len(res) == 0: lg.error("get cols failed for {}.{}".format(self.__db_name_src, self.__tb_name_src)) self.tb_cols = [] else: self.tb_cols = [x[0].decode() for x in res] def __get_tb_cols_type(self): """__get_tb_cols_type: """ sql = "select column_name, data_type from columns where table_schema='{}' and table_name='{}'".format(self.__db_name_src, self.__tb_name_src) res = [] try: pool_dst_info = ConnPool(self.__db_src_info) res = pool_dst_info.fetchall(sql) pool_dst_info.dispose() except Exception as e: lg.error("Get tb cols type failed for {}.{}: {}".format(self.__db_name_src, self.__tb_name_src, e)) if len(res) == 0: lg.error("Get tb cols type failed for {}.{}".format(self.__db_name_src, self.__tb_name_src)) self.tb_cols_mp = {} else: self.tb_cols_mp = dict([[y.decode() for y in x] for x in res]) def __get_ukpk_from_src(self): """_get_ukpk_from_src: """ sql_keys = "show index from " + self.__tb_name_src + " where Key_name='PRIMARY'" res = [] try: pool_src = ConnPool(self.__db_src) res = pool_src.fetchall(sql_keys) if len(res) == 0: sql_keys = "show index from " + self.__tb_name_src + " where Non_unique=0" res = pool_src.fetchall(sql_keys) pool_src.dispose() except Exception as e: lg.error("Get uk or pk failed for {}.{}: {}".format(self.__db_name_src, self.__tb_name_src, e)) if len(res) == 0: lg.error("Get uk or pk failed for {}.{}".format(self.__db_name_src, self.__tb_name_src)) self.tb_keys = [] else: self.tb_keys = [x[4].decode() for x in res] def __get_tables_stuct_from_src(self): """__get_tables_stuct_from_src: """ sql = "show create table {}".format(self.__tb_name_src) res = "" try: pool_src = ConnPool(self.__db_src) res = pool_src.fetchall(sql) pool_src.dispose() except Exception as e: lg.error("Get table struct failed: {}".format(e)) res = [x[1].decode().replace("\n", "") for x in res] if not res: lg.error("Get table struct failed for table: {}".format(self.__tb_name_src)) self.__tb_struct = res[0] if res else "" def __create_tb_in_dst(self): """__create_tb_in_dst: """ if self.__tb_struct: try: pool_dst = ConnPool(self.__db_dst) pool_dst.query(self.__tb_struct) pool_dst.commit() pool_dst.dispose() except Exception as e: lg.error("Create table {} in dst failed: {}".format(self.__tb_name_src, e)) return False return True else: return False def __get_next_chunk_from_src(self): """get_next_chunk_from_src """ sql = "select {} from {} order by {} asc limit %s,%s".format(",".join(self.tb_cols), self.__tb_name_src, " asc,".join(self.tb_cols)) res = [] try: pool_src = ConnPool(self.__db_src) res = pool_src.fetchall(sql, (self.__idx_src, self.__chunk_size)) pool_src.dispose() except Exception as e: lg.error("get next chunk from src failed for {}.{}: {}".format(self.__db_name_src, self.__tb_name_src, e)) self.__idx_src = self.__idx_src + self.__chunk_size if len(res) == self.__chunk_size else -1 ress = [] if len(res) == 0: lg.error("get next chunk from src failed for {}.{}".format(self.__db_name_src, self.__tb_name_src)) ress = [] else: for x in res: l = [] for y in x: if isinstance(y, bytearray): l.append(y.decode()) elif isinstance(y, int) or isinstance(y, float): l.append(y) else: # anything else not be known by far l.append(y) ress.append(l) return ress def __dump_chunk_to_dst(self, chunk_from_src): """__dump_chunk_to_dst: """ def parser(row): """parser: """ values = "" for key, val in zip(self.tb_cols, row): key_type = self.tb_cols_mp.get(key, "") if key_type == "int" or key_type == "float": values = values + str(val) + "," elif key_type == "str" or key_type == 'char': values = values + "'" + str(val) + "'" + "," else: values = values + "'" + str(val) + "'" + "," values = values.strip(",") return values sql = "insert into {}({}) values({})".format(self.__tb_name_src, ','.join(self.tb_cols), "{}") try: pool_dst = ConnPool(self.__db_dst) for row in chunk_from_src: values = parser(row) sql_exec = sql.format(values) pool_dst.query(sql_exec) pool_dst.commit() pool_dst.dispose() except Exception as e: lg.error("dump to dst failed for {}.{}: {}".format(self.__db_name_dst, self.__tb_name_dst, e)) class DBFixTBUB(object): """DBFixTBUB: db fix table unbalance """ def __init__(self, diff): super(DBFixTBUB, self).__init__() self.__db_name_src = cf.DB_SRC.db_name self.__db_name_dst = cf.DB_DST.db_name # self.__db_name_diff = cf.DB_DIFF.db_name self.__concurrency_tables = cf.CONCURRENCY_TABLES self.que_Dst = queue.Queue(self.__concurrency_tables) self.que_Src = queue.Queue(self.__concurrency_tables) self.worker_list_DBFixTBUBSrc = [] self.worker_list_DBFixTBUBDst = [] self.diff = diff self.run() def run(self): """run: """ lg.info("DBFixTBUB DST: src: {}, dst: {}".format(self.__db_name_src, self.__db_name_dst)) for d in self.diff: if d.get('ct_dst',0) != 0: self.que_Dst.put(1) t = WorkerFixTBUBDst(d.get("tb_dst"), self.que_Dst) t.start() self.worker_list_DBFixTBUBDst.append(t) self.que_Dst.join() lg.info("DBFixTBUB SRC: src: {}, dst: {}".format(self.__db_name_src, self.__db_name_dst)) for d in self.diff: if d.get('ct_src',0) != 0: self.que_Src.put(1) t = WorkerFixTBUBSrc(d.get("tb_src"), d.get("tb_dst"), self.que_Src) t.start() self.worker_list_DBFixTBUBSrc.append(t) self.que_Src.join() class WorkerFixTBUBDst(threading.Thread): def __init__(self, tb_name_dst, que): threading.Thread.__init__(self) self.__db_name_src = cf.DB_SRC.db_name self.__db_name_dst = cf.DB_DST.db_name self.__db_name_diff = cf.DB_DIFF.db_name self.__db_src = cf.DB_SRC self.__db_dst = cf.DB_DST self.__db_diff = cf.DB_DIFF self.__db_src_info = cf.DB_SRC_INFO self.__db_dst_info = cf.DB_DST_INFO self.__chunk_size = cf.CHUNK_SIZE self.__tb_name_diff = cf.DB_DIFF_TABLE self.__tb_name_dst = tb_name_dst self.que = que self.__idx_diff = 0 self.tb_cols_mp = {} def run(self): try: self.__task() except Exception as e: lg.error("Worker failed: {}".format(e)) finally: self.que.get() self.que.task_done() def __task(self): """__task: delete from dst """ lg.info("Worker Fix TB UB DST: dst: {}.{}".format(self.__db_name_dst, self.__tb_name_dst)) self.__get_tb_cols_type() while self.__idx_diff != -1: chunk_from_diff = self.__get_next_chunk_from_diff() self.__delete_from_dst_with_chunk_from_diff(chunk_from_diff) def __get_tb_cols_type(self): """__get_tb_cols_type: """ sql = "select column_name, data_type from columns where table_schema='{}' and table_name='{}'".format(self.__db_name_dst, self.__tb_name_dst) res = [] try: pool_dst_info = ConnPool(self.__db_dst_info) res = pool_dst_info.fetchall(sql) pool_dst_info.dispose() except Exception as e: lg.error("Get tb cols type failed for {}.{}: {}".format(self.__db_name_dst, self.__tb_name_dst, e)) if len(res) == 0: lg.error("Get tb cols type failed for {}.{}".format(self.__db_name_dst, self.__tb_name_dst)) self.tb_cols_mp = {} else: self.tb_cols_mp = dict([[y.decode() for y in x] for x in res]) def __get_next_chunk_from_diff(self): """__get_next_chunk_from_diff: """ sql = "select db_name,tb_name,tb_keys,tb_keys_val from {} where flag = {} order by db_name asc, tb_name asc, tb_keys asc, tb_keys_val asc limit %s,%s".format(self.__tb_name_diff, 0) res = [] try: pool_src = ConnPool(self.__db_diff) res = pool_src.fetchall(sql, (self.__idx_diff, self.__chunk_size)) pool_src.dispose() except Exception as e: lg.error("get next chunk from diff failed for {}.{}: {}".format(self.__db_name_diff, self.__tb_name_diff, e)) self.__idx_diff = self.__idx_diff + self.__chunk_size if len(res) == self.__chunk_size else -1 if len(res) == 0: lg.error("get next chunk from diff failed for {}.{}".format(self.__db_name_diff, self.__tb_name_diff)) res = [] else: res = [[y.decode() for y in x] for x in res] return res def __delete_from_dst_with_chunk_from_diff(self, chunk_from_diff): """ __delete_from_dst_with_chunk_from_diff: """ def parser(row): """parser: """ where_cond = "" db_name, tb_name, tb_keys, tb_keys_val = row keys_list = tb_keys.split("#") keys_val_list = tb_keys_val.split("#") for key, val in zip(keys_list, keys_val_list): key_type = self.tb_cols_mp.get(key, "") if key_type == "int": where_cond = where_cond + key + "=" + val + "," elif key_type == "float": where_cond = where_cond + key + "=" + val + "," elif key_type == "str" or key_type == 'char': where_cond = where_cond + key + "='" + val + "'," pass where_cond = where_cond.strip(",") return where_cond try: pool_dst = ConnPool(self.__db_dst) for row in chunk_from_diff: # lg.error(row) where_cond = parser(row) # lg.error(where_cond) sql = "delete from {} where {};".format(self.__tb_name_dst, where_cond) pool_dst.query(sql) pool_dst.commit() pool_dst.dispose() except Exception as e: lg.error("delete from dst table {} failed: {}".format(self.__tb_name_dst, e)) class WorkerFixTBUBSrc(threading.Thread): def __init__(self, tb_name_src, tb_name_dst, que): threading.Thread.__init__(self) self.__db_name_src = cf.DB_SRC.db_name self.__db_name_dst = cf.DB_DST.db_name self.__db_name_diff = cf.DB_DIFF.db_name self.__db_src = cf.DB_SRC self.__db_dst = cf.DB_DST self.__db_diff = cf.DB_DIFF self.__db_src_info = cf.DB_SRC_INFO self.__db_dst_info = cf.DB_DST_INFO self.__chunk_size = cf.CHUNK_SIZE self.__tb_name_diff = cf.DB_DIFF_TABLE self.__tb_name_src = tb_name_src self.__tb_name_dst = tb_name_dst self.que = que self.__idx_diff = 0 self.tb_cols_mp = {} self.tb_cols = [] def run(self): try: self.__task() except Exception as e: lg.error("Worker failed: {}".format(e)) finally: self.que.get() self.que.task_done() def __task(self): """__task: task """ lg.info("Worker Fix TB UB Src: src:{}.{} dst: {}.{}".format(self.__db_name_src, self.__tb_name_src, self.__db_name_dst, self.__tb_name_dst)) self.__get_tb_cols() self.__get_tb_cols_type() while self.__idx_diff != -1: chunk_from_diff = self.__get_next_chunk_from_diff() chunk_from_src = self.__get_chunk_from_src_with_chunk_from_diff(chunk_from_diff) self.__dump_chunk_to_dst(chunk_from_src) def __get_tb_cols(self): """__get_colo: """ sql = "SELECT COLUMN_NAME FROM COLUMNS WHERE TABLE_SCHEMA = %s AND TABLE_NAME = %s" res = [] try: pool_src_info = ConnPool(self.__db_src_info) res = pool_src_info.fetchall(sql, (self.__db_name_src, self.__tb_name_src)) pool_src_info.dispose() except Exception as e: lg.error("get cols failed for {}.{}: {}".format(self.__db_name_src, self.__tb_name_src, e)) if len(res) == 0: lg.error("get cols failed for {}.{}".format(self.__db_name_src, self.__tb_name_src)) self.tb_cols = [] else: self.tb_cols = [x[0].decode() for x in res] def __get_tb_cols_type(self): """__get_tb_cols_type: """ sql = "select column_name, data_type from columns where table_schema='{}' and table_name='{}'".format(self.__db_name_src, self.__tb_name_src) res = [] try: pool_dst_info = ConnPool(self.__db_src_info) res = pool_dst_info.fetchall(sql) pool_dst_info.dispose() except Exception as e: lg.error("Get tb cols type failed for {}.{}: {}".format(self.__db_name_src, self.__tb_name_src, e)) if len(res) == 0: lg.error("Get tb cols type failed for {}.{}".format(self.__db_name_src, self.__tb_name_src)) self.tb_cols_mp = {} else: self.tb_cols_mp = dict([[y.decode() for y in x] for x in res]) def __get_next_chunk_from_diff(self): """__get_next_chunk_from_diff: """ sql = "select db_name,tb_name,tb_keys,tb_keys_val from {} where flag = {} order by db_name asc, tb_name asc, tb_keys asc, tb_keys_val asc limit %s,%s".format(self.__tb_name_diff, 1) res = [] try: pool_src = ConnPool(self.__db_diff) res = pool_src.fetchall(sql, (self.__idx_diff, self.__chunk_size)) pool_src.dispose() except Exception as e: lg.error("get next chunk from diff failed for {}.{}: {}".format(self.__db_name_diff, self.__tb_name_diff, e)) self.__idx_diff = self.__idx_diff + self.__chunk_size if len(res) == self.__chunk_size else -1 if len(res) == 0: lg.error("get next chunk from diff failed for {}.{}".format(self.__db_name_diff, self.__tb_name_diff)) res = [] else: res = [[y.decode() for y in x] for x in res] return res def __get_chunk_from_src_with_chunk_from_diff(self, chunk): """__get_chunk_from_src_with_chunk_from_diff: """ def parser(row): """parser: """ db_name, tb_name, tb_keys, tb_keys_val = row where_cond = "" keys_list = tb_keys.split("#") keys_val_list = tb_keys_val.split("#") for key, val in zip(keys_list, keys_val_list): true_key = self.tb_cols_mp.get(key, "") if true_key == "int": where_cond = key + "=" + val + "," elif true_key == "float": where_cond = key + "=" + val + "," elif true_key == "str" or true_key == 'char': where_cond = key + "='" + val + "'," where_cond = where_cond.strip(",") return where_cond sql = "select {} from {} where {}".format(','.join(self.tb_cols), self.__tb_name_src, "{}") ck = [] try: pool_src = ConnPool(self.__db_src) for row in chunk: res = [] where_cond = parser(row) sql_exec = sql.format(where_cond) res = pool_src.fetchone(sql_exec) if len(res) == 0: lg.error("get row failed for {}.{}".format(self.__db_name_src, self.__tb_name_src)) else: ress = [] for y in res: if isinstance(y, bytearray): ress.append(y.decode()) elif isinstance(y, int) or isinstance(y, float): ress.append(y) else: # anything else not be known by far ress.append(y) ck.append(ress) pool_src.dispose() except Exception as e: lg.error("get chunk failed for {}.{}: {}".format(self.__db_name_src, self.__tb_name_src, e)) if not ck: lg.error("get chunk failed for {}.{}".format(self.__db_name_src, self.__tb_name_src)) return ck def __dump_chunk_to_dst(self, chunk_from_src): """__dump_chunk_to_dst: """ def parser(row): """parser: """ values = "" for key, val in zip(self.tb_cols, row): key_type = self.tb_cols_mp.get(key, "") if key_type == "int" or key_type == "float": values = values + str(val) + "," elif key_type == "str" or key_type == 'char': values = values + "'" + str(val) + "'" + "," else: values = values + "'" + str(val) + "'" + "," values = values.strip(",") return values sql = "replace into {}({}) values({})".format(self.__tb_name_dst, ','.join(self.tb_cols), "{}") try: pool_dst = ConnPool(self.__db_dst) for row in chunk_from_src: values = parser(row) sql_exec = sql.format(values) pool_dst.query(sql_exec) pool_dst.commit() pool_dst.dispose() except Exception as e: lg.error("dump to dst failed for {}.{}: {}".format(self.__db_name_dst, self.__tb_name_dst, e)) if __name__ == '__main__': pass

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null

0

7

44fd4dcd8a9fe0f2a10c52831c8a746d6da78721

2,893

py

Python

trianglelib/utils.py

leimao/Sphinx-Python-TriangleLib

de1d9a606f619818226d2b62fa0b67e38d0b5243

[ "MIT" ]

2

2020-08-03T11:10:59.000Z

2021-07-17T17:07:35.000Z

trianglelib/utils.py

leimao/Sphinx-Python-TriangleLib

de1d9a606f619818226d2b62fa0b67e38d0b5243

[ "MIT" ]

null

trianglelib/utils.py

leimao/Sphinx-Python-TriangleLib

de1d9a606f619818226d2b62fa0b67e38d0b5243

[ "MIT" ]

null

""" Routines to test triangle properties without explicit instantiation. """ from trianglelib.shape import Triangle def _make_triangle(a, b, c): try: return Triangle(a, b, c) except ValueError: return None def is_triangle(a, b, c): """ Return whether lengths `a`, `b`, `c` can be the sides of a triangle. :param a: side length one :type a: :class:`float` :param b: side length two :type b: :class:`float` :param c: side length three :type c: :class:`float` :return: whether lengths `a`, `b`, `c` can be the sides of a triangle :rtype: :class:`bool` """ t = _make_triangle(a, b, c) return (t is not None) def is_equilateral(a, b, c): """ Return whether lengths `a`, `b`, and `c` are an equilateral triangle. :param a: side length one :type a: :class:`float` :param b: side length two :type b: :class:`float` :param c: side length three :type c: :class:`float` :return: whether lengths `a`, `b`, and `c` are an equilateral triangle :rtype: :class:`bool` """ t = _make_triangle(a, b, c) return (t is not None) and t.is_equilateral() def is_isosceles(a, b, c): """ Return whether lengths `a`, `b`, and `c` are an isosceles triangle. :param a: side length one :type a: :class:`float` :param b: side length two :type b: :class:`float` :param c: side length three :type c: :class:`float` :return: whether lengths `a`, `b`, and `c` are an isosceles triangle :rtype: :class:`bool` """ t = _make_triangle(a, b, c) return (t is not None) and t.is_isosceles() def compute_perimeter(a, b, c): """ Return the perimeter of the triangle with side lengths `a`, `b`, and `c`. If the three lengths provided cannot be the sides of a triangle, then the perimeter 0 is returned. :param a: side length one :type a: :class:`float` :param b: side length two :type b: :class:`float` :param c: side length three :type c: :class:`float` :return: perimeter. If the three lengths provided cannot be the sides of a triangle, then the perimeter 0 is returned. :rtype: :class:`float` """ t = _make_triangle(a, b, c) return 0 if (t is None) else t.perimeter() def compute_area(a, b, c): """ Return the area of the triangle with side lengths `a`, `b`, and `c`. If the three lengths provided cannot be the sides of a triangle, then the area 0 is returned. :param a: side length one :type a: :class:`float` :param b: side length two :type b: :class:`float` :param c: side length three :type c: :class:`float` :return: area. If the three lengths provided cannot be the sides of a triangle, then the perimeter 0 is returned. :rtype: :class:`float` """ t = _make_triangle(a, b, c) return 0 if (t is None) else t.area()

29.520408

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null

0

1

0

1

0

8

782e752e1ebc0e4fc5b1e1f49beb7ab4a20d6568

116

py

Python

codify/__init__.py

thorwhalen/codify

7a95e9d0acb0541f641d4a3163cbe388ce70c775

[ "MIT" ]

1

2022-01-19T13:14:51.000Z

codify/__init__.py

thorwhalen/codify

7a95e9d0acb0541f641d4a3163cbe388ce70c775

[ "MIT" ]

null

codify/__init__.py

thorwhalen/codify

7a95e9d0acb0541f641d4a3163cbe388ce70c775

[ "MIT" ]

null

from codify.qr_coding import qr_object, qrcode_img_of, qrcode_img_of_sha256 from codify.util import bytes_to_sha256

38.666667

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null

0

1

0

1

0

1

0

7

7835789e231702961ad98d488208609543d0ac6c

2,804

py

Python

tests/test_run_order.py

pluto-py/engine

81e9973b194189382b75d24da39ee914dfa03c0b

[ "MIT" ]

2

2020-09-08T16:49:55.000Z

2020-10-18T20:18:01.000Z

tests/test_run_order.py

malyvsen/Pluto.py

81e9973b194189382b75d24da39ee914dfa03c0b

[ "MIT" ]

null

tests/test_run_order.py

malyvsen/Pluto.py

81e9973b194189382b75d24da39ee914dfa03c0b

[ "MIT" ]

null

from pluto.notebook import Notebook from pluto.run_order import RunOrder from pluto.cell import Cell from pluto.errors import NameConflictError, CycleError class TestRunOrder: def test_independent(self): notebook = Notebook(cells=[ Cell(code='a = 0'), Cell(code='b = 1') ]) run_order = RunOrder.from_notebook(notebook) assert run_order.order == notebook.cells assert run_order.errors == {} def test_basic_ordered(self): notebook = Notebook(cells=[ Cell(code='a = 0'), Cell(code='b = a + 1') ]) run_order = RunOrder.from_notebook(notebook) assert run_order.order == notebook.cells assert run_order.errors == {} def test_basic_unordered(self): notebook = Notebook(cells=[ Cell(code='b = a + 0'), Cell(code='a = 1') ]) run_order = RunOrder.from_notebook(notebook) assert run_order.order == list(reversed(notebook.cells)) assert run_order.errors == {} def test_name_conflict(self): notebook = Notebook(cells=[ Cell(code='a = 0'), Cell(code='a = 1') ]) run_order = RunOrder.from_notebook(notebook) assert run_order.order == [] assert run_order.errors == { notebook.cells[0]: [NameConflictError('a')], notebook.cells[1]: [NameConflictError('a')] } def test_name_conflict_consequence(self): notebook = Notebook(cells=[ Cell(code='a = 0'), Cell(code='a = 1\nb = 1'), Cell(code='b * 2') ]) run_order = RunOrder.from_notebook(notebook) assert run_order.order == [notebook.cells[2]] assert run_order.errors == { notebook.cells[0]: [NameConflictError('a')], notebook.cells[1]: [NameConflictError('a')] } def test_cycle(self): notebook = Notebook(cells=[ Cell(code='a = b - 0'), Cell(code='b = a + 1') ]) run_order = RunOrder.from_notebook(notebook) assert run_order.order == [] assert run_order.errors == { notebook.cells[0]: [CycleError(notebook.cells)], notebook.cells[1]: [CycleError(notebook.cells)] } def test_cycle_consequence(self): notebook = Notebook(cells=[ Cell(code='a = b - 0'), Cell(code='b = a + 1\nc = -1'), Cell(code='c // 2') ]) run_order = RunOrder.from_notebook(notebook) assert run_order.order == [notebook.cells[2]] assert run_order.errors == { notebook.cells[0]: [CycleError(notebook.cells[:2])], notebook.cells[1]: [CycleError(notebook.cells[:2])] }

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0.82643

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0.715319

0

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2,804

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31.155556

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0

null

1

0

1

0

null

0

7

7894dd02bb19f3c4531a92f2dee8725c76d45ab8

11,822

py

Python

lino_book/projects/lydia/tests/dumps/18.12.0/ledger_voucher.py

lino-framework/lino_book

4eab916832cd8f48ff1b9fc8c2789f0b437da0f8

[ "BSD-2-Clause" ]

3

2016-08-25T05:58:09.000Z

2019-12-05T11:13:45.000Z

lino_book/projects/lydia/tests/dumps/18.12.0/ledger_voucher.py

lino-framework/lino_book

4eab916832cd8f48ff1b9fc8c2789f0b437da0f8

[ "BSD-2-Clause" ]

18

2016-11-12T21:38:58.000Z

2019-12-03T17:54:38.000Z

lino_book/projects/lydia/tests/dumps/18.12.0/ledger_voucher.py

lino-framework/lino_book

4eab916832cd8f48ff1b9fc8c2789f0b437da0f8

[ "BSD-2-Clause" ]

9

2016-10-15T11:12:33.000Z

2021-09-22T04:37:37.000Z

# -*- coding: UTF-8 -*- logger.info("Loading 132 objects to table ledger_voucher...") # fields: id, user, journal, entry_date, voucher_date, accounting_period, number, narration, state loader.save(create_ledger_voucher(1,4,1,date(2015,1,7),date(2015,1,6),1,1,u'','20')) loader.save(create_ledger_voucher(2,5,1,date(2015,1,8),date(2015,1,7),1,2,u'','20')) loader.save(create_ledger_voucher(3,6,1,date(2015,1,9),date(2015,1,8),1,3,u'','20')) loader.save(create_ledger_voucher(4,3,1,date(2015,1,10),date(2015,1,9),1,4,u'','20')) loader.save(create_ledger_voucher(5,2,1,date(2015,1,11),date(2015,1,10),1,5,u'','20')) loader.save(create_ledger_voucher(6,1,1,date(2015,2,7),date(2015,2,6),2,6,u'','20')) loader.save(create_ledger_voucher(7,4,1,date(2015,2,8),date(2015,2,7),2,7,u'','20')) loader.save(create_ledger_voucher(8,5,1,date(2015,2,9),date(2015,2,8),2,8,u'','20')) loader.save(create_ledger_voucher(9,6,1,date(2015,2,10),date(2015,2,9),2,9,u'','20')) loader.save(create_ledger_voucher(10,3,1,date(2015,3,7),date(2015,3,6),3,10,u'','20')) loader.save(create_ledger_voucher(11,2,1,date(2015,4,7),date(2015,4,6),4,11,u'','20')) loader.save(create_ledger_voucher(12,1,1,date(2015,4,8),date(2015,4,7),4,12,u'','20')) loader.save(create_ledger_voucher(13,4,1,date(2015,4,9),date(2015,4,8),4,13,u'','20')) loader.save(create_ledger_voucher(14,5,1,date(2015,4,10),date(2015,4,9),4,14,u'','20')) loader.save(create_ledger_voucher(15,6,1,date(2015,4,11),date(2015,4,10),4,15,u'','20')) loader.save(create_ledger_voucher(16,3,1,date(2015,4,12),date(2015,4,11),4,16,u'','20')) loader.save(create_ledger_voucher(17,2,1,date(2015,4,13),date(2015,4,12),4,17,u'','20')) loader.save(create_ledger_voucher(18,1,1,date(2015,4,14),date(2015,4,13),4,18,u'','20')) loader.save(create_ledger_voucher(19,4,1,date(2015,5,7),date(2015,5,6),5,19,u'','20')) loader.save(create_ledger_voucher(20,5,1,date(2015,5,8),date(2015,5,7),5,20,u'','20')) loader.save(create_ledger_voucher(21,6,1,date(2015,5,9),date(2015,5,8),5,21,u'','20')) loader.save(create_ledger_voucher(22,3,1,date(2015,5,10),date(2015,5,9),5,22,u'','20')) loader.save(create_ledger_voucher(23,2,1,date(2015,5,11),date(2015,5,10),5,23,u'','20')) loader.save(create_ledger_voucher(24,1,1,date(2015,5,12),date(2015,5,11),5,24,u'','20')) loader.save(create_ledger_voucher(25,6,1,date(2015,1,1),date(2014,12,31),1,25,u'','20')) loader.save(create_ledger_voucher(26,6,1,date(2015,1,1),date(2014,12,31),1,26,u'','20')) loader.save(create_ledger_voucher(27,6,1,date(2015,1,1),date(2014,12,31),1,27,u'','20')) loader.save(create_ledger_voucher(28,6,1,date(2015,1,1),date(2014,12,31),1,28,u'','20')) loader.save(create_ledger_voucher(29,6,1,date(2015,1,1),date(2014,12,31),1,29,u'','20')) loader.save(create_ledger_voucher(30,6,1,date(2015,1,1),date(2014,12,31),1,30,u'','20')) loader.save(create_ledger_voucher(31,6,1,date(2015,1,1),date(2014,12,31),1,31,u'','20')) loader.save(create_ledger_voucher(32,6,1,date(2015,1,1),date(2014,12,31),1,32,u'','20')) loader.save(create_ledger_voucher(33,6,1,date(2015,1,1),date(2014,12,31),1,33,u'','20')) loader.save(create_ledger_voucher(34,6,1,date(2015,1,1),date(2014,12,31),1,34,u'','20')) loader.save(create_ledger_voucher(35,6,1,date(2015,1,1),date(2014,12,31),1,35,u'','20')) loader.save(create_ledger_voucher(36,6,1,date(2015,1,1),date(2014,12,31),1,36,u'','20')) loader.save(create_ledger_voucher(37,6,1,date(2015,1,1),date(2014,12,31),1,37,u'','20')) loader.save(create_ledger_voucher(38,6,1,date(2015,1,1),date(2014,12,31),1,38,u'','20')) loader.save(create_ledger_voucher(39,6,1,date(2015,1,1),date(2014,12,31),1,39,u'','20')) loader.save(create_ledger_voucher(40,6,1,date(2015,1,1),date(2014,12,31),1,40,u'','20')) loader.save(create_ledger_voucher(41,6,1,date(2015,1,1),date(2014,12,31),1,41,u'','20')) loader.save(create_ledger_voucher(42,6,1,date(2015,1,1),date(2014,12,31),1,42,u'','20')) loader.save(create_ledger_voucher(43,6,1,date(2015,1,1),date(2014,12,31),1,43,u'','20')) loader.save(create_ledger_voucher(44,6,1,date(2015,1,1),date(2014,12,31),1,44,u'','20')) loader.save(create_ledger_voucher(45,6,1,date(2015,1,1),date(2014,12,31),1,45,u'','20')) loader.save(create_ledger_voucher(46,6,1,date(2015,1,1),date(2014,12,31),1,46,u'','20')) loader.save(create_ledger_voucher(47,6,1,date(2015,1,1),date(2014,12,31),1,47,u'','20')) loader.save(create_ledger_voucher(48,6,1,date(2015,2,1),date(2015,1,31),2,48,u'','20')) loader.save(create_ledger_voucher(49,6,1,date(2015,2,1),date(2015,1,31),2,49,u'','20')) loader.save(create_ledger_voucher(50,6,1,date(2015,2,1),date(2015,1,31),2,50,u'','20')) loader.save(create_ledger_voucher(51,6,1,date(2015,2,1),date(2015,1,31),2,51,u'','20')) loader.save(create_ledger_voucher(52,6,1,date(2015,2,1),date(2015,1,31),2,52,u'','20')) loader.save(create_ledger_voucher(53,6,1,date(2015,2,1),date(2015,1,31),2,53,u'','20')) loader.save(create_ledger_voucher(54,6,1,date(2015,2,1),date(2015,1,31),2,54,u'','20')) loader.save(create_ledger_voucher(55,6,1,date(2015,2,1),date(2015,1,31),2,55,u'','20')) loader.save(create_ledger_voucher(56,6,1,date(2015,2,1),date(2015,1,31),2,56,u'','20')) loader.save(create_ledger_voucher(57,6,1,date(2015,2,1),date(2015,1,31),2,57,u'','20')) loader.save(create_ledger_voucher(58,6,1,date(2015,2,1),date(2015,1,31),2,58,u'','20')) loader.save(create_ledger_voucher(59,6,1,date(2015,2,1),date(2015,1,31),2,59,u'','20')) loader.save(create_ledger_voucher(60,6,1,date(2015,2,1),date(2015,1,31),2,60,u'','20')) loader.save(create_ledger_voucher(61,6,1,date(2015,2,1),date(2015,1,31),2,61,u'','20')) loader.save(create_ledger_voucher(62,6,1,date(2015,2,1),date(2015,1,31),2,62,u'','20')) loader.save(create_ledger_voucher(63,6,1,date(2015,2,1),date(2015,1,31),2,63,u'','20')) loader.save(create_ledger_voucher(64,6,1,date(2015,2,1),date(2015,1,31),2,64,u'','20')) loader.save(create_ledger_voucher(65,6,1,date(2015,2,1),date(2015,1,31),2,65,u'','20')) loader.save(create_ledger_voucher(66,6,1,date(2015,2,1),date(2015,1,31),2,66,u'','20')) loader.save(create_ledger_voucher(67,6,1,date(2015,2,1),date(2015,1,31),2,67,u'','20')) loader.save(create_ledger_voucher(68,6,1,date(2015,2,1),date(2015,1,31),2,68,u'','20')) loader.save(create_ledger_voucher(69,6,1,date(2015,2,1),date(2015,1,31),2,69,u'','20')) loader.save(create_ledger_voucher(70,6,1,date(2015,2,1),date(2015,1,31),2,70,u'','20')) loader.save(create_ledger_voucher(71,6,1,date(2015,3,1),date(2015,2,28),3,71,u'','20')) loader.save(create_ledger_voucher(72,6,1,date(2015,3,1),date(2015,2,28),3,72,u'','20')) loader.save(create_ledger_voucher(73,6,1,date(2015,3,1),date(2015,2,28),3,73,u'','20')) loader.save(create_ledger_voucher(74,6,1,date(2015,3,1),date(2015,2,28),3,74,u'','20')) loader.save(create_ledger_voucher(75,6,1,date(2015,3,1),date(2015,2,28),3,75,u'','20')) loader.save(create_ledger_voucher(76,6,1,date(2015,3,1),date(2015,2,28),3,76,u'','20')) loader.save(create_ledger_voucher(77,6,1,date(2015,3,1),date(2015,2,28),3,77,u'','20')) loader.save(create_ledger_voucher(78,6,1,date(2015,3,1),date(2015,2,28),3,78,u'','20')) loader.save(create_ledger_voucher(79,6,1,date(2015,3,1),date(2015,2,28),3,79,u'','20')) loader.save(create_ledger_voucher(80,6,1,date(2015,3,1),date(2015,2,28),3,80,u'','20')) loader.save(create_ledger_voucher(81,6,1,date(2015,3,1),date(2015,2,28),3,81,u'','20')) loader.save(create_ledger_voucher(82,6,1,date(2015,3,1),date(2015,2,28),3,82,u'','20')) loader.save(create_ledger_voucher(83,6,1,date(2015,3,1),date(2015,2,28),3,83,u'','20')) loader.save(create_ledger_voucher(84,6,1,date(2015,3,1),date(2015,2,28),3,84,u'','20')) loader.save(create_ledger_voucher(85,6,1,date(2015,3,1),date(2015,2,28),3,85,u'','20')) loader.save(create_ledger_voucher(86,6,1,date(2015,3,1),date(2015,2,28),3,86,u'','20')) loader.save(create_ledger_voucher(87,4,3,date(2015,1,3),date(2015,1,2),1,1,u'','20')) loader.save(create_ledger_voucher(88,5,3,date(2015,1,4),date(2015,1,3),1,2,u'','20')) loader.save(create_ledger_voucher(89,6,3,date(2015,1,5),date(2015,1,4),1,3,u'','20')) loader.save(create_ledger_voucher(90,3,3,date(2015,1,6),date(2015,1,5),1,4,u'','20')) loader.save(create_ledger_voucher(91,2,3,date(2015,1,7),date(2015,1,6),1,5,u'','20')) loader.save(create_ledger_voucher(92,1,3,date(2015,1,8),date(2015,1,7),1,6,u'','20')) loader.save(create_ledger_voucher(93,4,3,date(2015,1,9),date(2015,1,8),1,7,u'','20')) loader.save(create_ledger_voucher(94,5,3,date(2015,2,3),date(2015,2,2),2,8,u'','20')) loader.save(create_ledger_voucher(95,6,3,date(2015,2,4),date(2015,2,3),2,9,u'','20')) loader.save(create_ledger_voucher(96,3,3,date(2015,2,5),date(2015,2,4),2,10,u'','20')) loader.save(create_ledger_voucher(97,2,3,date(2015,2,6),date(2015,2,5),2,11,u'','20')) loader.save(create_ledger_voucher(98,1,3,date(2015,2,7),date(2015,2,6),2,12,u'','20')) loader.save(create_ledger_voucher(99,4,3,date(2015,2,8),date(2015,2,7),2,13,u'','20')) loader.save(create_ledger_voucher(100,5,3,date(2015,2,9),date(2015,2,8),2,14,u'','20')) loader.save(create_ledger_voucher(101,6,3,date(2015,3,3),date(2015,3,2),3,15,u'','20')) loader.save(create_ledger_voucher(102,3,3,date(2015,3,4),date(2015,3,3),3,16,u'','20')) loader.save(create_ledger_voucher(103,2,3,date(2015,3,5),date(2015,3,4),3,17,u'','20')) loader.save(create_ledger_voucher(104,1,3,date(2015,3,6),date(2015,3,5),3,18,u'','20')) loader.save(create_ledger_voucher(105,4,3,date(2015,3,7),date(2015,3,6),3,19,u'','20')) loader.save(create_ledger_voucher(106,5,3,date(2015,3,8),date(2015,3,7),3,20,u'','20')) loader.save(create_ledger_voucher(107,6,3,date(2015,3,9),date(2015,3,8),3,21,u'','20')) loader.save(create_ledger_voucher(108,3,3,date(2015,4,3),date(2015,4,2),4,22,u'','20')) loader.save(create_ledger_voucher(109,2,3,date(2015,4,4),date(2015,4,3),4,23,u'','20')) loader.save(create_ledger_voucher(110,1,3,date(2015,4,5),date(2015,4,4),4,24,u'','20')) loader.save(create_ledger_voucher(111,4,3,date(2015,4,6),date(2015,4,5),4,25,u'','20')) loader.save(create_ledger_voucher(112,5,3,date(2015,4,7),date(2015,4,6),4,26,u'','20')) loader.save(create_ledger_voucher(113,6,3,date(2015,4,8),date(2015,4,7),4,27,u'','20')) loader.save(create_ledger_voucher(114,3,3,date(2015,4,9),date(2015,4,8),4,28,u'','20')) loader.save(create_ledger_voucher(115,2,3,date(2015,5,3),date(2015,5,2),5,29,u'','20')) loader.save(create_ledger_voucher(116,1,3,date(2015,5,4),date(2015,5,3),5,30,u'','20')) loader.save(create_ledger_voucher(117,4,3,date(2015,5,5),date(2015,5,4),5,31,u'','20')) loader.save(create_ledger_voucher(118,5,3,date(2015,5,6),date(2015,5,5),5,32,u'','20')) loader.save(create_ledger_voucher(119,6,3,date(2015,5,7),date(2015,5,6),5,33,u'','20')) loader.save(create_ledger_voucher(120,3,3,date(2015,5,8),date(2015,5,7),5,34,u'','20')) loader.save(create_ledger_voucher(121,2,3,date(2015,5,9),date(2015,5,8),5,35,u'','20')) loader.save(create_ledger_voucher(122,4,8,date(2015,1,31),date(2015,1,31),1,1,u'','20')) loader.save(create_ledger_voucher(123,5,8,date(2015,2,28),date(2015,2,28),2,2,u'','20')) loader.save(create_ledger_voucher(124,6,8,date(2015,3,28),date(2015,3,28),3,3,u'','20')) loader.save(create_ledger_voucher(125,4,4,date(2015,1,13),date(2015,1,13),1,1,u'','20')) loader.save(create_ledger_voucher(126,5,4,date(2015,2,13),date(2015,2,13),2,2,u'','20')) loader.save(create_ledger_voucher(127,6,4,date(2015,3,13),date(2015,3,13),3,3,u'','20')) loader.save(create_ledger_voucher(128,3,4,date(2015,4,13),date(2015,4,13),4,4,u'','20')) loader.save(create_ledger_voucher(129,2,6,date(2015,1,21),date(2015,1,21),1,1,u'','20')) loader.save(create_ledger_voucher(130,1,6,date(2015,2,21),date(2015,2,21),2,2,u'','20')) loader.save(create_ledger_voucher(131,4,6,date(2015,3,21),date(2015,3,21),3,3,u'','20')) loader.save(create_ledger_voucher(132,5,6,date(2015,4,21),date(2015,4,21),4,4,u'','20')) loader.flush_deferred_objects()

85.666667

98

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3.022106

0.060322

0.239028

0.26184

0.36003

0.831267

0.798289

0.590999

0.351351

0.18733

0

0.248243

0.013196

11,822

137

99

86.291971

0.443168

0.009981

0

0.026493

0

1

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true

0

null

1

0

1

0

1

0

null

0

1

0

9

152a88a1825453ea748e41d3f18227011e093066

40,277

py

Python

lang/python/github/com/metaprov/modelaapi/services/model/v1/model_pb2_grpc.py

metaprov/modeldapi

ee05693832051dcd990ee4f061715d7ae0787340

[ "Apache-2.0" ]

5

2022-02-18T03:40:10.000Z

2022-03-01T16:11:24.000Z

lang/python/github/com/metaprov/modelaapi/services/model/v1/model_pb2_grpc.py

metaprov/modeldapi

ee05693832051dcd990ee4f061715d7ae0787340

[ "Apache-2.0" ]

1

2022-01-07T19:59:25.000Z

2022-02-04T01:21:14.000Z

lang/python/github/com/metaprov/modelaapi/services/model/v1/model_pb2_grpc.py

metaprov/modeldapi

ee05693832051dcd990ee4f061715d7ae0787340

[ "Apache-2.0" ]

1

2022-03-25T10:21:43.000Z

# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT! """Client and server classes corresponding to protobuf-defined services.""" import grpc from github.com.metaprov.modelaapi.services.model.v1 import model_pb2 as github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2 class ModelServiceStub(object): """Missing associated documentation comment in .proto file.""" def __init__(self, channel): """Constructor. Args: channel: A grpc.Channel. """ self.ListModels = channel.unary_unary( '/github.com.metaprov.modelaapi.services.model.v1.ModelService/ListModels', request_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.ListModelsRequest.SerializeToString, response_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.ListModelsResponse.FromString, ) self.CreateModel = channel.unary_unary( '/github.com.metaprov.modelaapi.services.model.v1.ModelService/CreateModel', request_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.CreateModelRequest.SerializeToString, response_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.CreateModelResponse.FromString, ) self.GetModel = channel.unary_unary( '/github.com.metaprov.modelaapi.services.model.v1.ModelService/GetModel', request_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.GetModelRequest.SerializeToString, response_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.GetModelResponse.FromString, ) self.UpdateModel = channel.unary_unary( '/github.com.metaprov.modelaapi.services.model.v1.ModelService/UpdateModel', request_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.UpdateModelRequest.SerializeToString, response_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.UpdateModelResponse.FromString, ) self.DeleteModel = channel.unary_unary( '/github.com.metaprov.modelaapi.services.model.v1.ModelService/DeleteModel', request_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.DeleteModelRequest.SerializeToString, response_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.DeleteModelResponse.FromString, ) self.DeployModel = channel.unary_unary( '/github.com.metaprov.modelaapi.services.model.v1.ModelService/DeployModel', request_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.DeployModelRequest.SerializeToString, response_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.DeployModelResponse.FromString, ) self.PublishModel = channel.unary_unary( '/github.com.metaprov.modelaapi.services.model.v1.ModelService/PublishModel', request_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.PublishModelRequest.SerializeToString, response_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.PublishModelResponse.FromString, ) self.CreateModelProfile = channel.unary_unary( '/github.com.metaprov.modelaapi.services.model.v1.ModelService/CreateModelProfile', request_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.CreateModelProfileRequest.SerializeToString, response_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.CreateModelProfileResponse.FromString, ) self.GetModelProfile = channel.unary_unary( '/github.com.metaprov.modelaapi.services.model.v1.ModelService/GetModelProfile', request_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.GetModelProfileRequest.SerializeToString, response_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.GetModelProfileResponse.FromString, ) self.GetModelMisclass = channel.unary_unary( '/github.com.metaprov.modelaapi.services.model.v1.ModelService/GetModelMisclass', request_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.GetMisclassRequest.SerializeToString, response_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.GetMisclassResponse.FromString, ) self.GetModelLogs = channel.unary_unary( '/github.com.metaprov.modelaapi.services.model.v1.ModelService/GetModelLogs', request_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.GetModelLogsRequest.SerializeToString, response_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.GetModelLogsResponse.FromString, ) self.AbortModel = channel.unary_unary( '/github.com.metaprov.modelaapi.services.model.v1.ModelService/AbortModel', request_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.AbortModelRequest.SerializeToString, response_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.AbortModelResponse.FromString, ) self.PauseModel = channel.unary_unary( '/github.com.metaprov.modelaapi.services.model.v1.ModelService/PauseModel', request_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.PauseModelRequest.SerializeToString, response_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.PauseModelResponse.FromString, ) self.ResumeModel = channel.unary_unary( '/github.com.metaprov.modelaapi.services.model.v1.ModelService/ResumeModel', request_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.ResumeModelRequest.SerializeToString, response_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.ResumeModelResponse.FromString, ) self.CompareModels = channel.unary_unary( '/github.com.metaprov.modelaapi.services.model.v1.ModelService/CompareModels', request_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.CompareModelsRequest.SerializeToString, response_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.CompareModelsResponse.FromString, ) self.CompileModel = channel.unary_unary( '/github.com.metaprov.modelaapi.services.model.v1.ModelService/CompileModel', request_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.CompileModelRequest.SerializeToString, response_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.CompileModelResponse.FromString, ) self.DownloadModel = channel.unary_unary( '/github.com.metaprov.modelaapi.services.model.v1.ModelService/DownloadModel', request_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.DownloadModelRequest.SerializeToString, response_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.DownloadModelResponse.FromString, ) self.FlagModel = channel.unary_unary( '/github.com.metaprov.modelaapi.services.model.v1.ModelService/FlagModel', request_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.FlagModelRequest.SerializeToString, response_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.FlagModelResponse.FromString, ) self.TestModel = channel.unary_unary( '/github.com.metaprov.modelaapi.services.model.v1.ModelService/TestModel', request_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.TestModelRequest.SerializeToString, response_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.TestModelResponse.FromString, ) class ModelServiceServicer(object): """Missing associated documentation comment in .proto file.""" def ListModels(self, request, context): """Missing associated documentation comment in .proto file.""" context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def CreateModel(self, request, context): """Missing associated documentation comment in .proto file.""" context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def GetModel(self, request, context): """Missing associated documentation comment in .proto file.""" context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def UpdateModel(self, request, context): """Missing associated documentation comment in .proto file.""" context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def DeleteModel(self, request, context): """Missing associated documentation comment in .proto file.""" context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def DeployModel(self, request, context): """Missing associated documentation comment in .proto file.""" context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def PublishModel(self, request, context): """Missing associated documentation comment in .proto file.""" context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def CreateModelProfile(self, request, context): """Missing associated documentation comment in .proto file.""" context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def GetModelProfile(self, request, context): """Missing associated documentation comment in .proto file.""" context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def GetModelMisclass(self, request, context): """Missing associated documentation comment in .proto file.""" context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def GetModelLogs(self, request, context): """Missing associated documentation comment in .proto file.""" context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def AbortModel(self, request, context): """Missing associated documentation comment in .proto file.""" context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def PauseModel(self, request, context): """Missing associated documentation comment in .proto file.""" context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def ResumeModel(self, request, context): """Missing associated documentation comment in .proto file.""" context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def CompareModels(self, request, context): """Missing associated documentation comment in .proto file.""" context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def CompileModel(self, request, context): """Missing associated documentation comment in .proto file.""" context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def DownloadModel(self, request, context): """Missing associated documentation comment in .proto file.""" context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def FlagModel(self, request, context): """Flag model """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def TestModel(self, request, context): """Mark the model to test """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def add_ModelServiceServicer_to_server(servicer, server): rpc_method_handlers = { 'ListModels': grpc.unary_unary_rpc_method_handler( servicer.ListModels, request_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.ListModelsRequest.FromString, response_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.ListModelsResponse.SerializeToString, ), 'CreateModel': grpc.unary_unary_rpc_method_handler( servicer.CreateModel, request_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.CreateModelRequest.FromString, response_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.CreateModelResponse.SerializeToString, ), 'GetModel': grpc.unary_unary_rpc_method_handler( servicer.GetModel, request_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.GetModelRequest.FromString, response_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.GetModelResponse.SerializeToString, ), 'UpdateModel': grpc.unary_unary_rpc_method_handler( servicer.UpdateModel, request_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.UpdateModelRequest.FromString, response_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.UpdateModelResponse.SerializeToString, ), 'DeleteModel': grpc.unary_unary_rpc_method_handler( servicer.DeleteModel, request_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.DeleteModelRequest.FromString, response_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.DeleteModelResponse.SerializeToString, ), 'DeployModel': grpc.unary_unary_rpc_method_handler( servicer.DeployModel, request_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.DeployModelRequest.FromString, response_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.DeployModelResponse.SerializeToString, ), 'PublishModel': grpc.unary_unary_rpc_method_handler( servicer.PublishModel, request_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.PublishModelRequest.FromString, response_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.PublishModelResponse.SerializeToString, ), 'CreateModelProfile': grpc.unary_unary_rpc_method_handler( servicer.CreateModelProfile, request_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.CreateModelProfileRequest.FromString, response_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.CreateModelProfileResponse.SerializeToString, ), 'GetModelProfile': grpc.unary_unary_rpc_method_handler( servicer.GetModelProfile, request_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.GetModelProfileRequest.FromString, response_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.GetModelProfileResponse.SerializeToString, ), 'GetModelMisclass': grpc.unary_unary_rpc_method_handler( servicer.GetModelMisclass, request_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.GetMisclassRequest.FromString, response_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.GetMisclassResponse.SerializeToString, ), 'GetModelLogs': grpc.unary_unary_rpc_method_handler( servicer.GetModelLogs, request_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.GetModelLogsRequest.FromString, response_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.GetModelLogsResponse.SerializeToString, ), 'AbortModel': grpc.unary_unary_rpc_method_handler( servicer.AbortModel, request_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.AbortModelRequest.FromString, response_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.AbortModelResponse.SerializeToString, ), 'PauseModel': grpc.unary_unary_rpc_method_handler( servicer.PauseModel, request_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.PauseModelRequest.FromString, response_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.PauseModelResponse.SerializeToString, ), 'ResumeModel': grpc.unary_unary_rpc_method_handler( servicer.ResumeModel, request_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.ResumeModelRequest.FromString, response_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.ResumeModelResponse.SerializeToString, ), 'CompareModels': grpc.unary_unary_rpc_method_handler( servicer.CompareModels, request_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.CompareModelsRequest.FromString, response_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.CompareModelsResponse.SerializeToString, ), 'CompileModel': grpc.unary_unary_rpc_method_handler( servicer.CompileModel, request_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.CompileModelRequest.FromString, response_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.CompileModelResponse.SerializeToString, ), 'DownloadModel': grpc.unary_unary_rpc_method_handler( servicer.DownloadModel, request_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.DownloadModelRequest.FromString, response_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.DownloadModelResponse.SerializeToString, ), 'FlagModel': grpc.unary_unary_rpc_method_handler( servicer.FlagModel, request_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.FlagModelRequest.FromString, response_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.FlagModelResponse.SerializeToString, ), 'TestModel': grpc.unary_unary_rpc_method_handler( servicer.TestModel, request_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.TestModelRequest.FromString, response_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.TestModelResponse.SerializeToString, ), } generic_handler = grpc.method_handlers_generic_handler( 'github.com.metaprov.modelaapi.services.model.v1.ModelService', rpc_method_handlers) server.add_generic_rpc_handlers((generic_handler,)) # This class is part of an EXPERIMENTAL API. class ModelService(object): """Missing associated documentation comment in .proto file.""" @staticmethod def ListModels(request, target, options=(), channel_credentials=None, call_credentials=None, insecure=False, compression=None, wait_for_ready=None, timeout=None, metadata=None): return grpc.experimental.unary_unary(request, target, '/github.com.metaprov.modelaapi.services.model.v1.ModelService/ListModels', github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.ListModelsRequest.SerializeToString, github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.ListModelsResponse.FromString, options, channel_credentials, insecure, call_credentials, compression, wait_for_ready, timeout, metadata) @staticmethod def CreateModel(request, target, options=(), channel_credentials=None, call_credentials=None, insecure=False, compression=None, wait_for_ready=None, timeout=None, metadata=None): return grpc.experimental.unary_unary(request, target, '/github.com.metaprov.modelaapi.services.model.v1.ModelService/CreateModel', github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.CreateModelRequest.SerializeToString, github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.CreateModelResponse.FromString, options, channel_credentials, insecure, call_credentials, compression, wait_for_ready, timeout, metadata) @staticmethod def GetModel(request, target, options=(), channel_credentials=None, call_credentials=None, insecure=False, compression=None, wait_for_ready=None, timeout=None, metadata=None): return grpc.experimental.unary_unary(request, target, '/github.com.metaprov.modelaapi.services.model.v1.ModelService/GetModel', github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.GetModelRequest.SerializeToString, github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.GetModelResponse.FromString, options, channel_credentials, insecure, call_credentials, compression, wait_for_ready, timeout, metadata) @staticmethod def UpdateModel(request, target, options=(), channel_credentials=None, call_credentials=None, insecure=False, compression=None, wait_for_ready=None, timeout=None, metadata=None): return grpc.experimental.unary_unary(request, target, '/github.com.metaprov.modelaapi.services.model.v1.ModelService/UpdateModel', github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.UpdateModelRequest.SerializeToString, github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.UpdateModelResponse.FromString, options, channel_credentials, insecure, call_credentials, compression, wait_for_ready, timeout, metadata) @staticmethod def DeleteModel(request, target, options=(), channel_credentials=None, call_credentials=None, insecure=False, compression=None, wait_for_ready=None, timeout=None, metadata=None): return grpc.experimental.unary_unary(request, target, '/github.com.metaprov.modelaapi.services.model.v1.ModelService/DeleteModel', github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.DeleteModelRequest.SerializeToString, github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.DeleteModelResponse.FromString, options, channel_credentials, insecure, call_credentials, compression, wait_for_ready, timeout, metadata) @staticmethod def DeployModel(request, target, options=(), channel_credentials=None, call_credentials=None, insecure=False, compression=None, wait_for_ready=None, timeout=None, metadata=None): return grpc.experimental.unary_unary(request, target, '/github.com.metaprov.modelaapi.services.model.v1.ModelService/DeployModel', github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.DeployModelRequest.SerializeToString, github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.DeployModelResponse.FromString, options, channel_credentials, insecure, call_credentials, compression, wait_for_ready, timeout, metadata) @staticmethod def PublishModel(request, target, options=(), channel_credentials=None, call_credentials=None, insecure=False, compression=None, wait_for_ready=None, timeout=None, metadata=None): return grpc.experimental.unary_unary(request, target, '/github.com.metaprov.modelaapi.services.model.v1.ModelService/PublishModel', github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.PublishModelRequest.SerializeToString, github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.PublishModelResponse.FromString, options, channel_credentials, insecure, call_credentials, compression, wait_for_ready, timeout, metadata) @staticmethod def CreateModelProfile(request, target, options=(), channel_credentials=None, call_credentials=None, insecure=False, compression=None, wait_for_ready=None, timeout=None, metadata=None): return grpc.experimental.unary_unary(request, target, '/github.com.metaprov.modelaapi.services.model.v1.ModelService/CreateModelProfile', github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.CreateModelProfileRequest.SerializeToString, github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.CreateModelProfileResponse.FromString, options, channel_credentials, insecure, call_credentials, compression, wait_for_ready, timeout, metadata) @staticmethod def GetModelProfile(request, target, options=(), channel_credentials=None, call_credentials=None, insecure=False, compression=None, wait_for_ready=None, timeout=None, metadata=None): return grpc.experimental.unary_unary(request, target, '/github.com.metaprov.modelaapi.services.model.v1.ModelService/GetModelProfile', github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.GetModelProfileRequest.SerializeToString, github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.GetModelProfileResponse.FromString, options, channel_credentials, insecure, call_credentials, compression, wait_for_ready, timeout, metadata) @staticmethod def GetModelMisclass(request, target, options=(), channel_credentials=None, call_credentials=None, insecure=False, compression=None, wait_for_ready=None, timeout=None, metadata=None): return grpc.experimental.unary_unary(request, target, '/github.com.metaprov.modelaapi.services.model.v1.ModelService/GetModelMisclass', github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.GetMisclassRequest.SerializeToString, github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.GetMisclassResponse.FromString, options, channel_credentials, insecure, call_credentials, compression, wait_for_ready, timeout, metadata) @staticmethod def GetModelLogs(request, target, options=(), channel_credentials=None, call_credentials=None, insecure=False, compression=None, wait_for_ready=None, timeout=None, metadata=None): return grpc.experimental.unary_unary(request, target, '/github.com.metaprov.modelaapi.services.model.v1.ModelService/GetModelLogs', github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.GetModelLogsRequest.SerializeToString, github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.GetModelLogsResponse.FromString, options, channel_credentials, insecure, call_credentials, compression, wait_for_ready, timeout, metadata) @staticmethod def AbortModel(request, target, options=(), channel_credentials=None, call_credentials=None, insecure=False, compression=None, wait_for_ready=None, timeout=None, metadata=None): return grpc.experimental.unary_unary(request, target, '/github.com.metaprov.modelaapi.services.model.v1.ModelService/AbortModel', github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.AbortModelRequest.SerializeToString, github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.AbortModelResponse.FromString, options, channel_credentials, insecure, call_credentials, compression, wait_for_ready, timeout, metadata) @staticmethod def PauseModel(request, target, options=(), channel_credentials=None, call_credentials=None, insecure=False, compression=None, wait_for_ready=None, timeout=None, metadata=None): return grpc.experimental.unary_unary(request, target, '/github.com.metaprov.modelaapi.services.model.v1.ModelService/PauseModel', github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.PauseModelRequest.SerializeToString, github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.PauseModelResponse.FromString, options, channel_credentials, insecure, call_credentials, compression, wait_for_ready, timeout, metadata) @staticmethod def ResumeModel(request, target, options=(), channel_credentials=None, call_credentials=None, insecure=False, compression=None, wait_for_ready=None, timeout=None, metadata=None): return grpc.experimental.unary_unary(request, target, '/github.com.metaprov.modelaapi.services.model.v1.ModelService/ResumeModel', github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.ResumeModelRequest.SerializeToString, github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.ResumeModelResponse.FromString, options, channel_credentials, insecure, call_credentials, compression, wait_for_ready, timeout, metadata) @staticmethod def CompareModels(request, target, options=(), channel_credentials=None, call_credentials=None, insecure=False, compression=None, wait_for_ready=None, timeout=None, metadata=None): return grpc.experimental.unary_unary(request, target, '/github.com.metaprov.modelaapi.services.model.v1.ModelService/CompareModels', github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.CompareModelsRequest.SerializeToString, github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.CompareModelsResponse.FromString, options, channel_credentials, insecure, call_credentials, compression, wait_for_ready, timeout, metadata) @staticmethod def CompileModel(request, target, options=(), channel_credentials=None, call_credentials=None, insecure=False, compression=None, wait_for_ready=None, timeout=None, metadata=None): return grpc.experimental.unary_unary(request, target, '/github.com.metaprov.modelaapi.services.model.v1.ModelService/CompileModel', github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.CompileModelRequest.SerializeToString, github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.CompileModelResponse.FromString, options, channel_credentials, insecure, call_credentials, compression, wait_for_ready, timeout, metadata) @staticmethod def DownloadModel(request, target, options=(), channel_credentials=None, call_credentials=None, insecure=False, compression=None, wait_for_ready=None, timeout=None, metadata=None): return grpc.experimental.unary_unary(request, target, '/github.com.metaprov.modelaapi.services.model.v1.ModelService/DownloadModel', github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.DownloadModelRequest.SerializeToString, github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.DownloadModelResponse.FromString, options, channel_credentials, insecure, call_credentials, compression, wait_for_ready, timeout, metadata) @staticmethod def FlagModel(request, target, options=(), channel_credentials=None, call_credentials=None, insecure=False, compression=None, wait_for_ready=None, timeout=None, metadata=None): return grpc.experimental.unary_unary(request, target, '/github.com.metaprov.modelaapi.services.model.v1.ModelService/FlagModel', github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.FlagModelRequest.SerializeToString, github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.FlagModelResponse.FromString, options, channel_credentials, insecure, call_credentials, compression, wait_for_ready, timeout, metadata) @staticmethod def TestModel(request, target, options=(), channel_credentials=None, call_credentials=None, insecure=False, compression=None, wait_for_ready=None, timeout=None, metadata=None): return grpc.experimental.unary_unary(request, target, '/github.com.metaprov.modelaapi.services.model.v1.ModelService/TestModel', github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.TestModelRequest.SerializeToString, github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_model_dot_v1_dot_model__pb2.TestModelResponse.FromString, options, channel_credentials, insecure, call_credentials, compression, wait_for_ready, timeout, metadata)

60.749623

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4,306

40,277

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0.035996

0.067335

0.050501

0.063127

0.925199

0.897277

0.893362

0.872942

0

0.008461

0.204732

40,277

662

173

60.84139

0.84465

0.03533

0

0.522337

1

0

0.102204

0.073888

0

1

0.068729

false

0

0.003436

0.032646

0.109966

0

null

0

1

0

1

0

null

0

7

1595e7f69f89cad593cabd4d8b56f829193bbd8e

151

py

Python

clge/project_creator_files/sounds.py

RafayelGardishyan/CLGE

12afb3612963c7631406c3693cdcff4442379c1c

[ "MIT" ]

2

2018-02-20T06:13:44.000Z

2019-10-31T21:55:00.000Z

clge/project_creator_files/sounds.py

RafayelGardishyan/CLGE

12afb3612963c7631406c3693cdcff4442379c1c

[ "MIT" ]

6

2018-02-03T12:33:35.000Z

2018-09-10T17:27:57.000Z

clge/project_creator_files/sounds.py

RafayelGardishyan/CLGE

12afb3612963c7631406c3693cdcff4442379c1c

[ "MIT" ]

1

2018-02-13T14:02:28.000Z

from settings import SOUND_FOLDER_PREFIX as sfp from clge import AudioPlayer def get_sounds(): return {"sound1": AudioPlayer(sfp + "sound1.wav")}

25.166667

54

0.761589

21

151

5.333333

0.761905

0

0.015504

0.145695

151

5

55

30.2

0.852713

0

0.10596

0

1

0.25

true

0

0.5

0.25

1

0

1

0

null

0

1

0

null

0

1

0

1

0

7

15b88fd3127ec5d507d43f469f861215906edce3

6,327

py

Python

studybuddyfinder/migrations/0025_auto_20201112_1840.py

SindhuMente/CS3240-StudyBuddyFinder

c3c2f2b80b8351df9255e44194bce6503f984183

[ "MIT" ]

2

2020-12-10T02:39:00.000Z

2021-03-16T23:32:46.000Z

studybuddyfinder/migrations/0025_auto_20201112_1840.py

SindhuMente/CS3240-StudyBuddyFinder

c3c2f2b80b8351df9255e44194bce6503f984183

[ "MIT" ]

null

studybuddyfinder/migrations/0025_auto_20201112_1840.py

SindhuMente/CS3240-StudyBuddyFinder

c3c2f2b80b8351df9255e44194bce6503f984183

[ "MIT" ]

null

# Generated by Django 3.1.1 on 2020-11-12 23:40 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('studybuddyfinder', '0024_auto_20201112_0023'), ] operations = [ migrations.RenameField( model_name='calendar', old_name='fri', new_name='frifts', ), migrations.RemoveField( model_name='calendar', name='mon', ), migrations.RemoveField( model_name='calendar', name='sat', ), migrations.RemoveField( model_name='calendar', name='sun', ), migrations.RemoveField( model_name='calendar', name='thurs', ), migrations.RemoveField( model_name='calendar', name='tues', ), migrations.RemoveField( model_name='calendar', name='wed', ), migrations.AddField( model_name='calendar', name='friste', field=models.BooleanField(default=False), ), migrations.AddField( model_name='calendar', name='frittf', field=models.BooleanField(default=False), ), migrations.AddField( model_name='calendar', name='frittt', field=models.BooleanField(default=False), ), migrations.AddField( model_name='calendar', name='fritttwo', field=models.BooleanField(default=False), ), migrations.AddField( model_name='calendar', name='monfts', field=models.BooleanField(default=False), ), migrations.AddField( model_name='calendar', name='monste', field=models.BooleanField(default=False), ), migrations.AddField( model_name='calendar', name='monttf', field=models.BooleanField(default=False), ), migrations.AddField( model_name='calendar', name='monttt', field=models.BooleanField(default=False), ), migrations.AddField( model_name='calendar', name='montttwo', field=models.BooleanField(default=False), ), migrations.AddField( model_name='calendar', name='satfts', field=models.BooleanField(default=False), ), migrations.AddField( model_name='calendar', name='satste', field=models.BooleanField(default=False), ), migrations.AddField( model_name='calendar', name='satttf', field=models.BooleanField(default=False), ), migrations.AddField( model_name='calendar', name='satttt', field=models.BooleanField(default=False), ), migrations.AddField( model_name='calendar', name='sattttwo', field=models.BooleanField(default=False), ), migrations.AddField( model_name='calendar', name='sunfts', field=models.BooleanField(default=False), ), migrations.AddField( model_name='calendar', name='sunste', field=models.BooleanField(default=False), ), migrations.AddField( model_name='calendar', name='sunttf', field=models.BooleanField(default=False), ), migrations.AddField( model_name='calendar', name='sunttt', field=models.BooleanField(default=False), ), migrations.AddField( model_name='calendar', name='suntttwo', field=models.BooleanField(default=False), ), migrations.AddField( model_name='calendar', name='thursfts', field=models.BooleanField(default=False), ), migrations.AddField( model_name='calendar', name='thursste', field=models.BooleanField(default=False), ), migrations.AddField( model_name='calendar', name='thursttf', field=models.BooleanField(default=False), ), migrations.AddField( model_name='calendar', name='thursttt', field=models.BooleanField(default=False), ), migrations.AddField( model_name='calendar', name='thurstttwo', field=models.BooleanField(default=False), ), migrations.AddField( model_name='calendar', name='tuesfts', field=models.BooleanField(default=False), ), migrations.AddField( model_name='calendar', name='tuesste', field=models.BooleanField(default=False), ), migrations.AddField( model_name='calendar', name='tuesttf', field=models.BooleanField(default=False), ), migrations.AddField( model_name='calendar', name='tuesttt', field=models.BooleanField(default=False), ), migrations.AddField( model_name='calendar', name='tuestttwo', field=models.BooleanField(default=False), ), migrations.AddField( model_name='calendar', name='wedfts', field=models.BooleanField(default=False), ), migrations.AddField( model_name='calendar', name='wedste', field=models.BooleanField(default=False), ), migrations.AddField( model_name='calendar', name='wedttf', field=models.BooleanField(default=False), ), migrations.AddField( model_name='calendar', name='wedttt', field=models.BooleanField(default=False), ), migrations.AddField( model_name='calendar', name='wedtttwo', field=models.BooleanField(default=False), ), ]

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0.214991

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null

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1

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null

0

10

ec5cdaa5f765e746b19371eaba50b7d646a5efb6

45

py

Python

WeatherPy/api_keys.py

shujams/python-api-challenge

193aa5c6fd0fb23be7431d6fe5f9afe654693de5

[ "MIT" ]

null

WeatherPy/api_keys.py

shujams/python-api-challenge

193aa5c6fd0fb23be7431d6fe5f9afe654693de5

[ "MIT" ]

null

WeatherPy/api_keys.py

shujams/python-api-challenge

193aa5c6fd0fb23be7431d6fe5f9afe654693de5

[ "MIT" ]

null

api_key = "99f4aaf8e92e82c523a3af11a8829066"

22.5

44

0.866667

3

45

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1

0

1

null

0

8

01d326571f2703129bfc0f61cbdc0393e1594594

37,068

py

Python

scripts/create_figures.py

duguyue100/spikefuel

e06713b62c0bc7f881dd75a5a4842723cce4aaab

[ "MIT" ]

12

2016-05-12T09:58:19.000Z

2021-04-10T02:46:21.000Z

scripts/create_figures.py

colinshane/spikefuel

e06713b62c0bc7f881dd75a5a4842723cce4aaab

[ "MIT" ]

1

2019-07-08T03:50:02.000Z

2019-07-09T07:22:18.000Z

scripts/create_figures.py

colinshane/spikefuel

e06713b62c0bc7f881dd75a5a4842723cce4aaab

[ "MIT" ]

10

2016-04-09T01:58:22.000Z

2020-06-07T05:13:46.000Z

"""Create figures for visualization purposes. Author: Yuhuang Hu Email : duguyue100@gmail.com """ import os from os.path import join import h5py import cPickle as pickle import numpy as np from moviepy.editor import ImageSequenceClip import cv2 import matplotlib import matplotlib.pylab as plt from spikefuel import dvsproc, gui, tools, helpers # matplotlib.rcParams.update({'font.size': 100}) # options: # "vot", "tracking", "ucf50", "caltech256" # "caltech256-identity-wrong-files" # "vot-ps", "tracking-ps", "ucf50-ps", "caltech256-ps" # "event-frequency" # "mnist-dvs", "mnist-dvs-ps", "nmnist", "ncaltech101", "ncaltech101-ps" # "white-test" # "vot-dvs-figure" "vot-figure" "tracking-dvs-figure" "tracking-figure" # "ucf50-figure", "ucf50-dvs-figure" # "gui-show" option = "y-time-figure" data_path = os.environ["SPIKEFUEL_DATA"] stats_path = os.path.join(data_path, "sf_data") if option == "ucf50-dvs-figure": ucf50_fn = "INI_UCF50_30fps_20160424.hdf5" ucf50_path = join(data_path, ucf50_fn) ucf50_db = h5py.File(ucf50_path, mode="r") ucf50_stats_path = os.path.join(stats_path, "ucf50_stats.pkl") vid_num = 10 f = file(ucf50_stats_path, mode="r") ucf50_stats = pickle.load(f) f.close() ucf50_list = ucf50_stats["ucf50_list"] cn = "RopeClimbing" vid_name = ucf50_stats[cn][vid_num-1] vid_n, vid_ex = os.path.splitext(vid_name) seq_save_path = os.path.join(data_path, "all_imgs", "ucf50_dvs_figs") num_frames = int(ucf50_db[cn][vid_n].attrs["num_frames"]) timestamps = ucf50_db[cn][vid_n]["timestamps"][()] x_pos = ucf50_db[cn][vid_n]["x_pos"][()] y_pos = ucf50_db[cn][vid_n]["y_pos"][()] pol = ucf50_db[cn][vid_n]["pol"][()] (timestamps, x_pos, y_pos, pol) = dvsproc.clean_up_events(timestamps, x_pos, y_pos, pol, window=1000) frames, fs, _ = dvsproc.gen_dvs_frames(timestamps, x_pos, y_pos, pol, num_frames, fs=3) new_frames = [] for frame in frames: tmp_frame = (((frame+fs)/float(2*fs))*255).astype(np.uint8) new_frames.append(tmp_frame) for i in xrange(len(new_frames)): img_name = join(seq_save_path, "%08d" % (i+1,)+".png") cv2.imwrite(img_name, new_frames[i]) print "Sequence %s is saved at %s" % (vid_name, seq_save_path) if option == "ucf50-figure": ucf50_path = join(data_path, "UCF50", "UCF50") ucf50_stats_path = os.path.join(stats_path, "ucf50_stats.pkl") vid_num = 10 f = file(ucf50_stats_path, mode="r") ucf50_stats = pickle.load(f) f.close() ucf50_list = ucf50_stats["ucf50_list"] cn = "RopeClimbing" seq_save_path = os.path.join(data_path, "all_imgs", "ucf50_figs") vid_name = ucf50_stats[cn][vid_num-1] frames, num_frames = helpers.read_video(join(ucf50_path, cn, vid_name)) for i in xrange(num_frames): img_name = join(seq_save_path, "%08d" % (i+1,)+".png") cv2.imwrite(img_name, frames[i]) print "Sequence %s is saved at %s" % (vid_name, seq_save_path) if option == "tracking-figure": tracking_path = os.path.join(data_path, "TrackingDataset") tracking_stats_path = os.path.join(stats_path, "tracking_stats.pkl") f = file(tracking_stats_path, mode="r") tracking_stats = pickle.load(f) f.close() pl = tracking_stats["primary_list"] sl = tracking_stats["secondary_list"] pc = pl[6] sc = sl[pc][3] print sc seq_save_path = os.path.join(data_path, "all_imgs", "tracking_figs") frames = [] for img_name in tracking_stats[sc]: img_path = join(tracking_path, pc, sc, img_name) frames.append(cv2.imread(img_path)) gt_path = os.path.join(tracking_path, pc, sc, "groundtruth.txt") gt = np.loadtxt(gt_path, dtype=np.float32, delimiter=",") gt = helpers.trans_groundtruth(gt, method="size") gt = np.reshape(gt, (gt.shape[0], 4, 2)) print "[MESSAGE] Images are loaded" new_frames = gui.draw_poly_box_sequence(frames, gt) new_frames = gui.rescale_image_sequence(new_frames, 270, 360, [0, 0, 0]) for i in xrange(len(new_frames)): img_name = join(seq_save_path, "%08d" % (i+1,)+".png") cv2.imwrite(img_name, new_frames[i]) print "Sequence %s is saved at %s" % (sc, seq_save_path) if option == "tracking-dvs-figure": tracking_fn = "INI_TrackingDataset_30fps_20160424.hdf5" tracking_path = os.path.join(data_path, tracking_fn) tracking_db = h5py.File(tracking_path, mode="r") tracking_stats_path = os.path.join(stats_path, "tracking_stats.pkl") f = file(tracking_stats_path, mode="r") tracking_stats = pickle.load(f) f.close() pl = tracking_stats["primary_list"] sl = tracking_stats["secondary_list"] pc = pl[1] sc = sl[pc][7] print sc seq_save_path = os.path.join(data_path, "all_imgs", "tracking_dvs_figs") num_frames = int(tracking_db[pc][sc].attrs["num_frames"]) timestamps = tracking_db[pc][sc]["timestamps"][()] x_pos = tracking_db[pc][sc]["x_pos"][()] y_pos = tracking_db[pc][sc]["y_pos"][()] pol = tracking_db[pc][sc]["pol"][()] bounding_box = tracking_db[pc][sc]["bounding_box"][()] gt = bounding_box[:, 1:] gt = np.reshape(gt, (gt.shape[0], 4, 2)) (timestamps, x_pos, y_pos, pol) = dvsproc.clean_up_events(timestamps, x_pos, y_pos, pol, window=1000) frames, fs, _ = dvsproc.gen_dvs_frames(timestamps, x_pos, y_pos, pol, num_frames, fs=3) new_frames = [] for frame in frames: tmp_frame = (((frame+fs) / float(2*fs))*255).astype(np.uint8) new_frames.append(tmp_frame) rgb_frames = [] height = new_frames[0].shape[0] width = new_frames[0].shape[1] for frame in new_frames: temp_frame = np.zeros((height, width, 3)) temp_frame[:, :, 0] = frame temp_frame[:, :, 1] = frame temp_frame[:, :, 2] = frame rgb_frames.append(temp_frame) new_frames = gui.draw_poly_box_sequence(rgb_frames, gt, color=[0, 0, 255]) for i in xrange(len(new_frames)): img_name = join(seq_save_path, "%08d" % (i+1,)+".png") cv2.imwrite(img_name, new_frames[i]) print "Sequence %s is saved at %s" % (sc, seq_save_path) if option == "vot-figure": vot_path = os.path.join(data_path, "vot2015") vot_stats_path = os.path.join(stats_path, "vot_stats.pkl") # load vot stats f = file(vot_stats_path, mode="r") vot_stats = pickle.load(f) f.close() vot_list = vot_stats['vot_list'] num_frames = vot_stats['num_frames'] no_seq = 0 vidseq = vot_list[no_seq] seq_save_path = join(data_path, "all_imgs", "vot_figs") list_path = join(vot_path, vidseq) img_list = tools.create_vot_image_list(list_path, num_frames[no_seq]) gts = np.loadtxt(join(list_path, "groundtruth.txt"), dtype=np.float32, delimiter=",") gts = np.reshape(gts, (gts.shape[0], 4, 2)) print "[MESSAGE] Ground truths and image lists are loaded." frames = [] for img_name in img_list: frames.append(cv2.imread(img_name)) print "[MESSAGE] Images are loaded" new_frames = gui.draw_poly_box_sequence(frames, gts) for i in xrange(len(new_frames)): img_name = join(seq_save_path, "%08d" % (i+1,)+".png") cv2.imwrite(img_name, new_frames[i]) print "Sequence %s is saved at %s" % (vidseq, seq_save_path) if option == "vot-dvs-figure": vot_fn = "INI_VOT_30fps_20160424.hdf5" vot_path = os.path.join(data_path, vot_fn) vot_db = h5py.File(vot_path, mode="r") vot_stats_path = os.path.join(stats_path, "vot_stats.pkl") # load vot stats f = file(vot_stats_path, mode="r") vot_stats = pickle.load(f) f.close() vot_list = vot_stats['vot_list'] num_frames = vot_stats['num_frames'] vidseq = vot_list[1] seq_save_path = join(data_path, "all_imgs", "vot_dvs_figs") num_frames = int(vot_db[vidseq].attrs["num_frames"]) timestamps = vot_db[vidseq]["timestamps"][()] x_pos = vot_db[vidseq]["x_pos"][()] y_pos = vot_db[vidseq]["y_pos"][()] pol = vot_db[vidseq]["pol"][()] bounding_box = vot_db[vidseq]["bounding_box"][()] gt = bounding_box[:, 1:] gt = np.reshape(gt, (gt.shape[0], 4, 2)) (timestamps, x_pos, y_pos, pol) = dvsproc.clean_up_events(timestamps, x_pos, y_pos, pol, window=1000) frames, fs, _ = dvsproc.gen_dvs_frames(timestamps, x_pos, y_pos, pol, num_frames, fs=3) new_frames = [] for frame in frames: tmp_frame = (((frame+fs)/float(2*fs))*255).astype(np.uint8) new_frames.append(tmp_frame) rgb_frames = [] height = new_frames[0].shape[0] width = new_frames[0].shape[1] for frame in new_frames: temp_frame = np.zeros((height, width, 3)) temp_frame[:, :, 0] = frame temp_frame[:, :, 1] = frame temp_frame[:, :, 2] = frame rgb_frames.append(temp_frame) new_frames = gui.draw_poly_box_sequence(rgb_frames, gt, color=[0, 0, 255]) for i in xrange(len(new_frames)): img_name = join(seq_save_path, "%08d" % (i+1,)+".png") cv2.imwrite(img_name, new_frames[i]) print "Sequence %s is saved at %s" % (vidseq, seq_save_path) if option == "white-test": test_path = os.path.join(data_path, "test.aedat") (timestamps, xaddr, yaddr, pol) = dvsproc.loadaerdat(test_path) event_arr = dvsproc.cal_event_count(timestamps) event_freq = dvsproc.cal_event_freq(event_arr, window=1000) plt.figure(figsize=(18, 8)) plt.plot(event_freq[:, 0]/1e3, event_freq[:, 1], linewidth=2) plt.xlabel("Time (s)") plt.ylabel("Event Frequency") plt.savefig(os.path.join(data_path, "event_freq.pdf")) timestamps = timestamps-timestamps[0] timestamps = timestamps[:10000] tend = timestamps[-1] vv = np.zeros((tend+1,)) for i in xrange(timestamps.shape[0]): vv[timestamps[i]] += 1 fs = 1e6 L = vv.shape[0] vv = vv - np.sum(vv)/L NFFT = int(2**np.ceil(np.log2(L))) ff = np.fft.fft(vv, NFFT)/L f = fs/2*(np.arange(NFFT/2)/float(NFFT/2)) f_draw = f ff_draw = 2*np.abs(ff[:NFFT/2]) plt.figure(figsize=(24, 10)) # plt.ylim([0, 3e-3]) plt.xlim([0, 100]) plt.grid(True) plt.plot(f_draw, ff_draw, 'b', linewidth=2) plt.ticklabel_format(style='sci', axis='y', scilimits=(0, 0)) plt.xticks(np.arange(0, 100+1, 10)) plt.xlabel("Frequency [Hz]") plt.ylabel("Events") plt.savefig(os.path.join(data_path, "white_test_ps.pdf")) if option == "caltech256-identity-wrong-files": caltech_fn = "Caltech256_10fps_20160411.hdf5" caltech_path = os.path.join(data_path, caltech_fn) caltech_db = h5py.File(caltech_path, mode="r") caltech_stats_path = os.path.join(stats_path, "caltech256_stats.pkl") img_num = 30 f = file(caltech_stats_path, mode="r") caltech_stats = pickle.load(f) f.close() caltech_list = caltech_stats["caltech256_list"] cn = caltech_list[62] img_name = caltech_stats[cn][63 - 1] print img_name img_n, img_ex = os.path.splitext(img_name) seq_save_path = os.path.join(data_path, "caltech256_figs_exp", img_n + ".gif") if not os.path.isfile(seq_save_path): num_frames = int(caltech_db[cn][img_n].attrs["num_frames"]) print "Number of frames: ", num_frames timestamps = caltech_db[cn][img_n]["timestamps"][()] x_pos = caltech_db[cn][img_n]["x_pos"][()] y_pos = caltech_db[cn][img_n]["y_pos"][()] pol = caltech_db[cn][img_n]["pol"][()] print timestamps print x_pos print y_pos.shape print pol.shape (timestamps, x_pos, y_pos, pol) = dvsproc.clean_up_events(timestamps, x_pos, y_pos, pol, window=1000) frames, fs, _ = dvsproc.gen_dvs_frames(timestamps, x_pos, y_pos, pol, num_frames, fs=3) print "Length of produced frames: ", len(frames) new_frames = [] for frame in frames: tmp_frame = (((frame+fs)/float(2*fs))*255).astype(np.uint8) new_frames.append(tmp_frame) clip = ImageSequenceClip(new_frames, fps=20) clip.write_gif(seq_save_path, fps=30) print "Sequence %s is saved at %s" % (img_name, seq_save_path) elif option == "caltech256-ps": caltech_fn = "INI_Caltech256_10fps_20160424.hdf5" caltech_path = os.path.join(data_path, caltech_fn) caltech_db = h5py.File(caltech_path, mode="r") caltech_stats_path = os.path.join(stats_path, "caltech256_stats.pkl") caltech_save_path = os.path.join(data_path, "caltech256_ps.eps") img_num = 60 f = file(caltech_stats_path, mode="r") caltech_stats = pickle.load(f) f.close() caltech_list = caltech_stats["caltech256_list"] cn = caltech_list[0] img_name = caltech_stats[cn][img_num-1] img_n, img_ex = os.path.splitext(img_name) timestamps = caltech_db[cn][img_n]["timestamps"][()] print "[MESSAGE] DATA IS LOADED." tend = timestamps[-1] vv = np.zeros(tend+1) for i in xrange(timestamps.shape[0]): vv[timestamps[i]] += 1 fs = 1e6 L = vv.shape[0] vv = vv - np.sum(vv)/L NFFT = int(2**np.ceil(np.log2(L))) ff = np.fft.fft(vv, NFFT)/L f = fs/2*(np.arange(NFFT/2)/float(NFFT/2)) f_draw = f ff_draw = 2*np.abs(ff[:NFFT/2]) plt.figure(figsize=(50, 45)) # plt.ylim([0, 3e-3]) plt.xlim([0, 100]) # plt.grid(True) plt.plot(f_draw, ff_draw, 'b', linewidth=10) plt.ticklabel_format(style='sci', axis='y', scilimits=(0, 0)) plt.xticks(np.arange(0, 100+1, 20)) plt.yticks(np.arange(0, 2.0e-1, 0.3e-1)) plt.xlabel("Frequency [Hz]", fontsize=150) # plt.ylabel("Events", fontsize=100) plt.savefig(caltech_save_path, format="eps", dpi=1200, bbox_inches='tight', pad_inches=0.5) # plt.show() print "[MESSAGE] Power Spectrum is saved at %s" % (caltech_save_path) elif option == "ucf50-ps": ucf50_fn = "INI_UCF50_30fps_20160424.hdf5" ucf50_path = os.path.join(data_path, ucf50_fn) ucf50_db = h5py.File(ucf50_path, mode="r") ucf50_stats_path = os.path.join(stats_path, "ucf50_stats.pkl") vid_num = 11 ucf50_save_path = os.path.join(data_path, "ucf50_ps.eps") f = file(ucf50_stats_path, mode="r") ucf50_stats = pickle.load(f) f.close() ucf50_list = ucf50_stats["ucf50_list"] cn = ucf50_list[0] vid_name = ucf50_stats[cn][vid_num-1] vid_n, vid_ex = os.path.splitext(vid_name) timestamps = ucf50_db[cn][vid_n]["timestamps"][()] print "[MESSAGE] DATA IS LOADED." tend = timestamps[-1] vv = np.zeros(tend+1) for i in xrange(timestamps.shape[0]): vv[timestamps[i]] += 1 fs = 1e6 L = vv.shape[0] vv = vv - np.sum(vv)/L NFFT = int(2**np.ceil(np.log2(L))) ff = np.fft.fft(vv, NFFT)/L f = fs/2*(np.arange(NFFT/2)/float(NFFT/2)) f_draw = f ff_draw = 2*np.abs(ff[:NFFT/2]) plt.figure(figsize=(50, 45)) # plt.ylim([0, 3e-3]) plt.xlim([0, 100]) # plt.grid(True) plt.plot(f_draw, ff_draw, 'b', linewidth=10) plt.ticklabel_format(style='sci', axis='y', scilimits=(0, 0)) plt.xticks(np.arange(0, 100+1, 20)) plt.yticks(np.arange(0, 2.5e-1, 0.4e-1)) plt.xlabel("Frequency [Hz]", fontsize=150) # plt.ylabel("Events", fontsize=100) plt.savefig(ucf50_save_path, format="eps", dpi=1200, bbox_inches='tight', pad_inches=0.5) # plt.show() print "[MESSAGE] Power Spectrum is saved at %s" % (ucf50_save_path) elif option == "tracking-ps": tracking_fn = "INI_TrackingDataset_30fps_20160424.hdf5" tracking_path = os.path.join(data_path, tracking_fn) tracking_db = h5py.File(tracking_path, mode="r") tracking_stats_path = os.path.join(stats_path, "tracking_stats.pkl") tracking_save_path = os.path.join(data_path, "tracking_ps.eps") f = file(tracking_stats_path, mode="r") tracking_stats = pickle.load(f) f.close() pl = tracking_stats["primary_list"] sl = tracking_stats["secondary_list"] pc = pl[0] sc = sl[pc][1] timestamps = tracking_db[pc][sc]["timestamps"][()] print "[MESSAGE] DATA IS LOADED." tend = timestamps[-1] vv = np.zeros(tend+1) for i in xrange(timestamps.shape[0]): vv[timestamps[i]] += 1 fs = 1e6 L = vv.shape[0] vv = vv - np.sum(vv)/L NFFT = int(2**np.ceil(np.log2(L))) ff = np.fft.fft(vv, NFFT)/L f = fs/2*(np.arange(NFFT/2)/float(NFFT/2)) f_draw = f ff_draw = 2*np.abs(ff[:NFFT/2]) plt.figure(figsize=(50, 45)) # plt.ylim([0, 3e-3]) plt.xlim([0, 100]) # plt.grid(True) plt.plot(f_draw, ff_draw, 'b', linewidth=10) plt.ticklabel_format(style='sci', axis='y', scilimits=(0, 0)) plt.xticks(np.arange(0, 100+1, 20)) plt.yticks(np.arange(0, 7e-2+2e-2, 1.5e-2)) plt.xlabel("Frequency [Hz]", fontsize=150) # plt.ylabel("Events", fontsize=100) plt.savefig(tracking_save_path, format="eps", dpi=1200, bbox_inches='tight', pad_inches=0.5) print "[MESSAGE] Power Spectrum is saved at %s" % (tracking_save_path) elif option == "vot-ps": vot_fn = "INI_VOT_30fps_20160424.hdf5" vot_path = os.path.join(data_path, vot_fn) vot_db = h5py.File(vot_path, mode="r") vot_stats_path = os.path.join(stats_path, "vot_stats.pkl") vot_save_path = os.path.join(data_path, "vot_ps.eps") # load vot stats f = file(vot_stats_path, mode="r") vot_stats = pickle.load(f) f.close() vot_list = vot_stats['vot_list'] num_frames = vot_stats['num_frames'] vidseq = vot_list[9] timestamps = vot_db[vidseq]["timestamps"][()] print "[MESSAGE] DATA IS LOADED." tend = timestamps[-1] vv = np.zeros(tend+1) for i in xrange(timestamps.shape[0]): vv[timestamps[i]] += 1 fs = 1e6 L = vv.shape[0] vv = vv - np.sum(vv)/L NFFT = int(2**np.ceil(np.log2(L))) ff = np.fft.fft(vv, NFFT)/L f = fs/2*(np.arange(NFFT/2)/float(NFFT/2)) f_draw = f ff_draw = 2*np.abs(ff[:NFFT/2]) plt.figure(figsize=(50, 45)) # plt.ylim([0, 3e-3]) plt.xlim([0, 100]) # plt.grid(True) plt.plot(f_draw, ff_draw, 'b', linewidth=10) plt.ticklabel_format(style='sci', axis='y', scilimits=(0, 0)) plt.xticks(np.arange(0, 100+1, 20)) plt.yticks(np.arange(0, 1.8e-1+0.3e-1, 0.3e-1)) plt.xlabel("Frequency [Hz]", fontsize=150) # plt.ylabel("Events", fontsize=100) plt.savefig(vot_save_path, format="eps", dpi=1200, bbox_inches='tight', pad_inches=0.5) # plt.show() print "[MESSAGE] Power Spectrum is saved at %s" % (vot_save_path) elif option == "event-frequency": vot_fn = "INI_VOT_30fps_20160424.hdf5" vot_path = os.path.join(data_path, vot_fn) vot_db = h5py.File(vot_path, mode="r") vot_stats_path = os.path.join(stats_path, "vot_stats.pkl") # load vot stats f = file(vot_stats_path, mode="r") vot_stats = pickle.load(f) f.close() vot_list = vot_stats['vot_list'] num_frames = vot_stats['num_frames'] vidseq = vot_list[2] timestamps = vot_db[vidseq]["timestamps"][()] event_arr = dvsproc.cal_event_count(timestamps) event_freq = dvsproc.cal_event_freq(event_arr, window=1000) plt.figure(figsize=(54, 24)) plt.plot(event_freq[:, 0]/1e6, event_freq[:, 1], linewidth=10) plt.xlabel("Time (s)", fontsize=100) plt.ylabel("Event Frequency", fontsize=100) plt.savefig(os.path.join(data_path, "event_freq.eps"), format="eps", dpi=1200, bbox_inches='tight', pad_inches=0.5) elif option == "mnist-dvs": mnist_path = os.path.join(data_path, "MNIST_DVS") for i in xrange(10): base_path = os.path.join(mnist_path, str(i)) s4_path = os.path.join(base_path, "mnist_"+str(i)+"_scale04.aedat") s8_path = os.path.join(base_path, "mnist_"+str(i)+"_scale08.aedat") s16_path = os.path.join(base_path, "mnist_"+str(i)+"_scale16.aedat") for p in [s4_path, s8_path, s16_path]: p_n, p_ex = os.path.splitext(p) (timestamps, xaddr, yaddr, pol) = dvsproc.loadaerdat(p, camera='DVS128') frames, fs, _ = dvsproc.gen_dvs_frames(timestamps, xaddr, yaddr, pol, num_frames=10, fs=5, platform="linux2", device="DVS128") frame = ((frames[1]+fs)/float(2*fs)*256).astype(np.uint8) cv2.imwrite(p_n+".png", frame) print "[MESSAGE] Image for recording %s is generated" % p elif option == "mnist-dvs-ps": mnist_path = os.path.join(data_path, "MNIST_DVS") mnist_save_path = os.path.join(mnist_path, "ps_mnist_dvs.pdf") i = 4 base_path = os.path.join(mnist_path, str(i)) s4_path = os.path.join(base_path, "mnist_"+str(i)+"_scale04.aedat") s8_path = os.path.join(base_path, "mnist_"+str(i)+"_scale08.aedat") s16_path = os.path.join(base_path, "mnist_"+str(i)+"_scale16.aedat") (timestamps, xaddr, yaddr, pol) = dvsproc.loadaerdat(s4_path, camera='DVS128') print "[MESSAGE] DATA IS LOADED." tend = timestamps[-1] vv = np.zeros(tend+1) for i in xrange(timestamps.shape[0]): vv[timestamps[i]] += 1 fs = 1e6 L = vv.shape[0] vv = vv - np.sum(vv)/L NFFT = int(2**np.ceil(np.log2(L))) ff = np.fft.fft(vv, NFFT)/L f = fs/2*(np.arange(NFFT/2)/float(NFFT/2)) f_draw = f[:450] ff_draw = 2*np.abs(ff[:450]) plt.figure(figsize=(18, 8)) plt.ylim([0, 3e-3]) plt.xlim([0, 100]) plt.grid(True) plt.plot(f_draw, ff_draw, 'b', linewidth=2) plt.ticklabel_format(style='sci', axis='y', scilimits=(0, 0)) plt.xticks(np.arange(np.min(f_draw), np.max(f_draw)+1, 10)) plt.xlabel("Frequency [Hz]") plt.ylabel("Events") plt.savefig(mnist_save_path) # plt.show() print "[MESSAGE] Power Spectrum is saved at %s" % (mnist_save_path) elif option == "nmnist": nmnist_path = os.path.join(data_path, "N_MNIST") for i in xrange(10): file_path = os.path.join(nmnist_path, str(i)+".bin") f_n, f_ex = os.path.splitext(file_path) print "[MESSAGE] Loading %s" % (file_path) file_handle = open(file_path, 'rb') raw_data = np.fromfile(file_handle, dtype=np.uint8) file_handle.close() raw_data = np.uint16(raw_data) all_y = raw_data[1::5] all_x = raw_data[0::5] all_p = (raw_data[2::5] & 128) >> 7 all_ts = ((raw_data[2::5] & 127) << 16) | \ (raw_data[3::5] << 8) | (raw_data[4::5]) frames, fs, _ = dvsproc.gen_dvs_frames(all_ts, all_x, all_y, all_p, 3, fs=3, platform="linux2", device="ATIS") frame = frames[1] frame = ((frame[:28, :28]+fs)/float(2*fs)*256).astype(np.uint8) cv2.imwrite(f_n+".png", frame) print "[MESSAGE] Image for recording %s is generated" % (file_path) elif option == "ncaltech101": n_caltech_path = os.path.join(data_path, "N_Caltech101") for i in xrange(16): file_path = os.path.join(n_caltech_path, "image_"+"%04d" % (i+1,)+".bin") f_n, f_ex = os.path.splitext(file_path) print "[MESSAGE] Loading %s" % (file_path) file_handle = open(file_path, 'rb') raw_data = np.fromfile(file_handle, dtype=np.uint8) file_handle.close() raw_data = np.uint16(raw_data) all_y = raw_data[1::5] all_x = raw_data[0::5] all_p = (raw_data[2::5] & 128) >> 7 all_ts = ((raw_data[2::5] & 127) << 16) | \ (raw_data[3::5] << 8) | (raw_data[4::5]) max_y = np.max(all_y) max_x = np.max(all_x) frames, fs, _ = dvsproc.gen_dvs_frames(all_ts, all_x, all_y, all_p, 3, fs=3, platform="linux2", device="ATIS") frame = frames[2][:max_y, :max_x] frame = ((frame+fs)/float(2*fs)*256).astype(np.uint8) cv2.imwrite(f_n+".png", frame) print "[MESSAGE] Image for recording %s is generated" % (file_path) elif option == "ncaltech101-ps": n_caltech_path = os.path.join(data_path, "N_Caltech101") n_caltech_save_path = os.path.join(n_caltech_path, "ps_ncaltech101.pdf") timestamps = np.array([]) for i in xrange(100): file_path = os.path.join(n_caltech_path, "image_" + "%04d" % (i + 1,) + ".bin") print "[MESSAGE] Loading %s" % (file_path) file_handle = open(file_path, 'rb') raw_data = np.fromfile(file_handle, dtype=np.uint8) file_handle.close() raw_data = np.uint16(raw_data) all_ts = ((raw_data[2::5] & 127) << 16) | \ (raw_data[3::5] << 8) | (raw_data[4::5]) all_ts = all_ts.astype(np.float64) if not timestamps.size: timestamps = all_ts else: # all_ts -= all_ts[0] all_ts += timestamps[-1] timestamps = np.hstack((timestamps, all_ts)) num_data = timestamps.shape[0] tend = timestamps[-1] vv = np.zeros(tend+1) for i in xrange(num_data): if timestamps[i] < tend: vv[timestamps[i]] += 1 fs = 1e6 L = vv.shape[0] vv = vv - np.sum(vv)/L NFFT = int(2**np.ceil(np.log2(L))) ff = np.fft.fft(vv, NFFT)/L f = fs/2*(np.arange(NFFT/2)/float(NFFT/2)) f_draw = f ff_draw = 2*np.abs(ff[:NFFT/2]) plt.figure(figsize=(18, 8)) # plt.ylim([0, 2e-5]) plt.xlim([0, 100]) plt.grid(True) plt.plot(f_draw, ff_draw, 'b', linewidth=2) plt.ticklabel_format(style='sci', axis='y', scilimits=(0, 0)) plt.xticks(np.arange(0, 100+1, 10)) plt.xlabel("Frequency [Hz]") plt.ylabel("Events") plt.savefig(n_caltech_save_path) # plt.show() print "[MESSAGE] Power Spectrum is saved at %s" % (n_caltech_save_path) elif option == "vot": # Load VOT Challenge Dataset vot_fn = "VOT_30fps_20160409.hdf5" vot_path = os.path.join(data_path, vot_fn) vot_db = h5py.File(vot_path, mode="r") vot_stats_path = os.path.join(stats_path, "vot_stats.pkl") # load vot stats f = file(vot_stats_path, mode="r") vot_stats = pickle.load(f) f.close() vot_list = vot_stats['vot_list'] num_frames = vot_stats['num_frames'] for vidseq in vot_list: seq_save_path = os.path.join(data_path, "vot_gifs", vidseq+".gif") if not os.path.isfile(seq_save_path): num_frames = int(vot_db[vidseq].attrs["num_frames"]) timestamps = vot_db[vidseq]["timestamps"][()] x_pos = vot_db[vidseq]["x_pos"][()] y_pos = vot_db[vidseq]["y_pos"][()] pol = vot_db[vidseq]["pol"][()] bounding_box = vot_db[vidseq]["bounding_box"][()] gt = bounding_box[:, 1:] gt = np.reshape(gt, (gt.shape[0], 4, 2)) (timestamps, x_pos, y_pos, pol) = dvsproc.clean_up_events(timestamps, x_pos, y_pos, pol, window=1000) frames, fs, _ = dvsproc.gen_dvs_frames(timestamps, x_pos, y_pos, pol, num_frames, fs=3) new_frames = [] for frame in frames: tmp_frame = (((frame+fs)/float(2*fs))*255).astype(np.uint8) new_frames.append(tmp_frame) new_frames = gui.draw_poly_box_sequence(new_frames, gt, color=[0, 255, 0]) clip = ImageSequenceClip(new_frames, fps=20) clip.write_gif(seq_save_path, fps=30) print "Sequence %s is saved at %s" % (vidseq, seq_save_path) elif option == "tracking": tracking_fn = "TrackingDataset_30fps_20160401.hdf5" tracking_path = os.path.join(data_path, tracking_fn) tracking_db = h5py.File(tracking_path, mode="r") tracking_stats_path = os.path.join(stats_path, "tracking_stats.pkl") f = file(tracking_stats_path, mode="r") tracking_stats = pickle.load(f) f.close() pl = tracking_stats["primary_list"] sl = tracking_stats["secondary_list"] for pc in pl: # remove sequence Kalal until I got more memory if pc != "Kalal": for sc in sl[pc]: seq_save_path = os.path.join(data_path, "tracking_gifs", sc+".gif") if not os.path.isfile(seq_save_path): num_frames = int(tracking_db[pc][sc].attrs["num_frames"]) timestamps = tracking_db[pc][sc]["timestamps"][()] x_pos = tracking_db[pc][sc]["x_pos"][()] y_pos = tracking_db[pc][sc]["y_pos"][()] pol = tracking_db[pc][sc]["pol"][()] bounding_box = tracking_db[pc][sc]["bounding_box"][()] gt = bounding_box[:, 1:] gt = np.reshape(gt, (gt.shape[0], 4, 2)) (timestamps, x_pos, y_pos, pol) = dvsproc.clean_up_events(timestamps, x_pos, y_pos, pol, window=1000) frames, fs, _ = dvsproc.gen_dvs_frames(timestamps, x_pos, y_pos, pol, num_frames, fs=3) new_frames = [] for frame in frames: tmp_frame = (((frame+fs) / float(2*fs))*255).astype(np.uint8) new_frames.append(tmp_frame) new_frames = gui.draw_poly_box_sequence(new_frames, gt, color=[0, 255, 0]) clip = ImageSequenceClip(new_frames, fps=20) clip.write_gif(seq_save_path, fps=30) print "Sequence %s is saved at %s" % (sc, seq_save_path) elif option == "ucf50": ucf50_fn = "UCF50_30fps_20160409.hdf5" ucf50_path = os.path.join(data_path, ucf50_fn) ucf50_db = h5py.File(ucf50_path, mode="r") ucf50_stats_path = os.path.join(stats_path, "ucf50_stats.pkl") vid_num = 10 f = file(ucf50_stats_path, mode="r") ucf50_stats = pickle.load(f) f.close() ucf50_list = ucf50_stats["ucf50_list"] for cn in ucf50_list: vid_name = ucf50_stats[cn][vid_num-1] vid_n, vid_ex = os.path.splitext(vid_name) seq_save_path = os.path.join(data_path, "ucf50_gifs", vid_n+".gif") if not os.path.isfile(seq_save_path): num_frames = int(ucf50_db[cn][vid_n].attrs["num_frames"]) timestamps = ucf50_db[cn][vid_n]["timestamps"][()] x_pos = ucf50_db[cn][vid_n]["x_pos"][()] y_pos = ucf50_db[cn][vid_n]["y_pos"][()] pol = ucf50_db[cn][vid_n]["pol"][()] (timestamps, x_pos, y_pos, pol) = dvsproc.clean_up_events(timestamps, x_pos, y_pos, pol, window=1000) frames, fs, _ = dvsproc.gen_dvs_frames(timestamps, x_pos, y_pos, pol, num_frames, fs=3) new_frames = [] for frame in frames: tmp_frame = (((frame+fs)/float(2*fs))*255).astype(np.uint8) new_frames.append(tmp_frame) clip = ImageSequenceClip(new_frames, fps=20) clip.write_gif(seq_save_path, fps=30) print "Sequence %s is saved at %s" % (vid_name, seq_save_path) elif option == "caltech256": caltech_fn = "Caltech256_10fps_20160411.hdf5" caltech_path = os.path.join(data_path, caltech_fn) caltech_db = h5py.File(caltech_path, mode="r") caltech_stats_path = os.path.join(stats_path, "caltech256_stats.pkl") img_num = 30 f = file(caltech_stats_path, mode="r") caltech_stats = pickle.load(f) f.close() caltech_list = caltech_stats["caltech256_list"] for cn in caltech_list: img_name = caltech_stats[cn][img_num-1] img_n, img_ex = os.path.splitext(img_name) seq_save_path = os.path.join(data_path, "caltech256_figs_exp", img_n+".gif") if not os.path.isfile(seq_save_path): num_frames = int(caltech_db[cn][img_n].attrs["num_frames"]) print "Number of frames: ", num_frames timestamps = caltech_db[cn][img_n]["timestamps"][()] x_pos = caltech_db[cn][img_n]["x_pos"][()] y_pos = caltech_db[cn][img_n]["y_pos"][()] pol = caltech_db[cn][img_n]["pol"][()] (timestamps, x_pos, y_pos, pol) = dvsproc.clean_up_events(timestamps, x_pos, y_pos, pol, window=1000) frames, fs, _ = dvsproc.gen_dvs_frames(timestamps, x_pos, y_pos, pol, num_frames, fs=3) print "Length of produced frames: ", len(frames) new_frames = [] for frame in frames: tmp_frame = (((frame+fs)/float(2*fs))*255).astype(np.uint8) new_frames.append(tmp_frame) clip = ImageSequenceClip(new_frames, fps=20) clip.write_gif(seq_save_path, fps=30) print "Sequence %s is saved at %s" % (img_name, seq_save_path) if option == "gui-show": # Put text on screen image_path = os.path.join(data_path, "vot2015", "motocross1", "00000106.jpg") frame = cv2.imread(image_path) win_w = 720 win_h = 540 scale = 0.9 window_title = "DVS-VOT-EXP" bg_color = [127, 127, 127] message = "Experiment Setup Calibration" # Check if input window is 4:3 if float(win_h)/float(win_w) != 0.75: raise ValueError("the input window is not in ratio 4:3") # get stats of smaller window swin_h = int(scale*win_h) swin_w = int(scale*win_w) frame = gui.rescale_image(frame, swin_h, swin_w, color=bg_color) window = np.ones((win_h, win_w, 3))*bg_color diff_y = (win_h-swin_h)/2 diff_x = (win_w-swin_w)/2 window[diff_y:swin_h+diff_y, diff_x:swin_w+diff_x, :] = frame window = np.array(window, dtype=np.uint8) flag = True while (1): # draw such window if flag is True: temp_win = window.copy() cv2.rectangle(temp_win, (diff_x, diff_y), (diff_x+swin_w, diff_y+swin_h), color=[0, 255, 0], thickness=2) flag = True elif flag is False: temp_win = window.copy() flag = True cv2.imshow(window_title, temp_win) k = cv2.waitKey(delay=10) & 0xFF if k == 27: break print "[MESSAGE] Experiment setup calibration is finished." if option == "y-time-figure": ucf50_fn = "INI_UCF50_30fps_20160424.hdf5" ucf50_path = join(data_path, ucf50_fn) ucf50_db = h5py.File(ucf50_path, mode="r") ucf50_stats_path = os.path.join(stats_path, "ucf50_stats.pkl") vid_num = 50 f = file(ucf50_stats_path, mode="r") ucf50_stats = pickle.load(f) f.close() ucf50_list = ucf50_stats["ucf50_list"] cn = "Drumming" vid_name = ucf50_stats[cn][vid_num-1] vid_n, vid_ex = os.path.splitext(vid_name) seq_save_path = os.path.join(data_path, "all_imgs", "ucf50_dvs_figs") num_frames = int(ucf50_db[cn][vid_n].attrs["num_frames"]) timestamps = ucf50_db[cn][vid_n]["timestamps"][()] x_pos = ucf50_db[cn][vid_n]["x_pos"][()] y_pos = ucf50_db[cn][vid_n]["y_pos"][()] pol = ucf50_db[cn][vid_n]["pol"][()] time = timestamps[3000:4000] x_idx = x_pos[3000:4000] y_idx = y_pos[3000:4000] plt.figure(figsize=(30, 6)) plt.plot(time/1e3, y_idx, ".", linewidth=2) plt.ylim([0, 180]) plt.xlabel("Time (ms)") plt.ylabel("y") plt.savefig(os.path.join(data_path, "y-time-figure.png")) from mpl_toolkits.mplot3d import Axes3D fig = plt.figure(figsize=(30, 15)) ax = fig.gca(projection='3d') ax.plot(time/1e3, x_idx, y_idx, ".", linewidth=2) ax.set_xlabel('Time (ms)') ax.set_ylabel('X') ax.set_zlabel('Y') fig.savefig(os.path.join(data_path, "x-y-time-figure.png"))

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0

null

0

1

0

8

174a6026e044b743b43a3f936d0634b389d53b10

18,213

py

Python

purchases/migrations/0001_initial.py

rossm6/accounts

74633ce4038806222048d85ef9dfe97a957a6a71

[ "MIT" ]

11

2021-01-23T01:09:54.000Z

2021-01-25T07:16:30.000Z

purchases/migrations/0001_initial.py

rossm6/accounts

74633ce4038806222048d85ef9dfe97a957a6a71

[ "MIT" ]

7

2021-04-06T18:19:10.000Z

2021-09-22T19:45:03.000Z

purchases/migrations/0001_initial.py

rossm6/accounts

74633ce4038806222048d85ef9dfe97a957a6a71

[ "MIT" ]

3

2021-01-23T18:55:32.000Z

2021-02-16T17:47:59.000Z

# Generated by Django 3.1.3 on 2021-01-01 15:00 import accountancy.fields import accountancy.mixins import accountancy.models from django.conf import settings from django.db import migrations, models import django.db.models.deletion import purchases.models import simple_history.models class Migration(migrations.Migration): initial = True dependencies = [ ('nominals', '0001_initial'), ('contacts', '0001_initial'), ('controls', '0001_initial'), ('cashbook', '0001_initial'), migrations.swappable_dependency(settings.AUTH_USER_MODEL), ('vat', '__first__'), ] operations = [ migrations.CreateModel( name='PurchaseHeader', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('ref', models.CharField(max_length=20)), ('goods', accountancy.fields.UIDecimalField(blank=True, decimal_places=2, max_digits=10, null=True)), ('discount', accountancy.fields.UIDecimalField(blank=True, decimal_places=2, max_digits=10, null=True)), ('vat', accountancy.fields.UIDecimalField(blank=True, decimal_places=2, max_digits=10, null=True)), ('total', accountancy.fields.UIDecimalField(blank=True, decimal_places=2, max_digits=10, null=True)), ('paid', accountancy.fields.UIDecimalField(blank=True, decimal_places=2, max_digits=10, null=True)), ('due', accountancy.fields.UIDecimalField(blank=True, decimal_places=2, max_digits=10, null=True)), ('date', models.DateField()), ('due_date', models.DateField(blank=True, null=True)), ('status', models.CharField(choices=[('c', 'cleared'), ('v', 'void')], default='c', max_length=2)), ('created', models.DateTimeField(auto_now_add=True)), ('type', models.CharField(choices=[('pbi', 'Brought Forward Invoice'), ('pbc', 'Brought Forward Credit Note'), ('pbp', 'Brought Forward Payment'), ('pbr', 'Brought Forward Refund'), ('pp', 'Payment'), ('pr', 'Refund'), ('pi', 'Invoice'), ('pc', 'Credit Note')], max_length=3)), ('cash_book', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='cashbook.cashbook')), ], options={ 'permissions': [('view_transactions_enquiry', 'Can view transactions'), ('view_age_creditors_report', 'Can view aged creditors report'), ('create_brought_forward_invoice_transaction', 'Can create brought forward invoice'), ('create_brought_forward_credit_note_transaction', 'Can create brought forward credit note'), ('create_brought_forward_payment_transaction', 'Can create brought forward payment'), ('create_brought_forward_refund_transaction', 'Can create brought forward refund'), ('create_invoice_transaction', 'Can create invoice'), ('create_credit_note_transaction', 'Can create credit note'), ('create_payment_transaction', 'Can create payment'), ('create_refund_transaction', 'Can create refund'), ('edit_brought_forward_invoice_transaction', 'Can edit brought forward invoice'), ('edit_brought_forward_credit_note_transaction', 'Can edit brought forward credit note'), ('edit_brought_forward_payment_transaction', 'Can edit brought forward payment'), ('edit_brought_forward_refund_transaction', 'Can edit brought forward refund'), ('edit_invoice_transaction', 'Can edit invoice'), ('edit_credit_note_transaction', 'Can edit credit note'), ('edit_payment_transaction', 'Can edit payment'), ('edit_refund_transaction', 'Can edit refund'), ('view_brought_forward_invoice_transaction', 'Can view brought forward invoice'), ('view_brought_forward_credit_note_transaction', 'Can view brought forward credit note'), ('view_brought_forward_payment_transaction', 'Can view brought forward payment'), ('view_brought_forward_refund_transaction', 'Can view brought forward refund'), ('view_invoice_transaction', 'Can view invoice'), ('view_credit_note_transaction', 'Can view credit note'), ('view_payment_transaction', 'Can view payment'), ('view_refund_transaction', 'Can view refund'), ('void_brought_forward_invoice_transaction', 'Can void brought forward invoice'), ('void_brought_forward_credit_note_transaction', 'Can void brought forward credit note'), ('void_brought_forward_payment_transaction', 'Can void brought forward payment'), ('void_brought_forward_refund_transaction', 'Can void brought forward refund'), ('void_invoice_transaction', 'Can void invoice'), ('void_credit_note_transaction', 'Can void credit note'), ('void_payment_transaction', 'Can void payment'), ('void_refund_transaction', 'Can void refund')], }, bases=(purchases.models.ModuleTransactions, accountancy.mixins.AuditMixin, accountancy.models.TransactionBase, models.Model), ), migrations.CreateModel( name='Supplier', fields=[ ], options={ 'proxy': True, 'indexes': [], 'constraints': [], }, bases=('contacts.contact',), ), migrations.CreateModel( name='PurchaseMatching', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created', models.DateField(auto_now_add=True)), ('value', accountancy.fields.AccountsDecimalField(blank=True, decimal_places=2, default=0, max_digits=10)), ('matched_by_type', models.CharField(choices=[('pbi', 'Brought Forward Invoice'), ('pbc', 'Brought Forward Credit Note'), ('pbp', 'Brought Forward Payment'), ('pbr', 'Brought Forward Refund'), ('pp', 'Payment'), ('pr', 'Refund'), ('pi', 'Invoice'), ('pc', 'Credit Note')], max_length=3)), ('matched_to_type', models.CharField(choices=[('pbi', 'Brought Forward Invoice'), ('pbc', 'Brought Forward Credit Note'), ('pbp', 'Brought Forward Payment'), ('pbr', 'Brought Forward Refund'), ('pp', 'Payment'), ('pr', 'Refund'), ('pi', 'Invoice'), ('pc', 'Credit Note')], max_length=3)), ('matched_by', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='matched_by_these', to='purchases.purchaseheader')), ('matched_to', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='matched_to_these', to='purchases.purchaseheader')), ('period', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to='controls.period')), ], options={ 'abstract': False, }, bases=(accountancy.mixins.AuditMixin, models.Model), ), migrations.CreateModel( name='PurchaseLine', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('line_no', models.IntegerField()), ('description', models.CharField(max_length=100)), ('goods', accountancy.fields.UIDecimalField(blank=True, decimal_places=2, max_digits=10, null=True)), ('vat', accountancy.fields.UIDecimalField(blank=True, decimal_places=2, max_digits=10, null=True)), ('type', models.CharField(choices=[('pbi', 'Brought Forward Invoice'), ('pbc', 'Brought Forward Credit Note'), ('pbp', 'Brought Forward Payment'), ('pbr', 'Brought Forward Refund'), ('pp', 'Payment'), ('pr', 'Refund'), ('pi', 'Invoice'), ('pc', 'Credit Note')], max_length=3)), ('goods_nominal_transaction', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='purchase_good_line', to='nominals.nominaltransaction')), ('header', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='purchases.purchaseheader')), ('nominal', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='nominals.nominal')), ('total_nominal_transaction', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='purchase_total_line', to='nominals.nominaltransaction')), ('vat_code', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to='vat.vat', verbose_name='Vat Code')), ('vat_nominal_transaction', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='purchase_vat_line', to='nominals.nominaltransaction')), ('vat_transaction', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='purchase_line_vat_transaction', to='vat.vattransaction')), ], options={ 'ordering': ['line_no'], }, bases=(purchases.models.ModuleTransactions, accountancy.mixins.AuditMixin, accountancy.models.TransactionBase, models.Model), ), migrations.AddField( model_name='purchaseheader', name='matched_to', field=models.ManyToManyField(through='purchases.PurchaseMatching', to='purchases.PurchaseHeader'), ), migrations.AddField( model_name='purchaseheader', name='period', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to='controls.period'), ), migrations.AddField( model_name='purchaseheader', name='supplier', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='purchases.supplier'), ), migrations.CreateModel( name='HistoricalPurchaseMatching', fields=[ ('id', models.IntegerField(auto_created=True, blank=True, db_index=True, verbose_name='ID')), ('created', models.DateField(blank=True, editable=False)), ('value', accountancy.fields.AccountsDecimalField(blank=True, decimal_places=2, default=0, max_digits=10)), ('matched_by_type', models.CharField(choices=[('pbi', 'Brought Forward Invoice'), ('pbc', 'Brought Forward Credit Note'), ('pbp', 'Brought Forward Payment'), ('pbr', 'Brought Forward Refund'), ('pp', 'Payment'), ('pr', 'Refund'), ('pi', 'Invoice'), ('pc', 'Credit Note')], max_length=3)), ('matched_to_type', models.CharField(choices=[('pbi', 'Brought Forward Invoice'), ('pbc', 'Brought Forward Credit Note'), ('pbp', 'Brought Forward Payment'), ('pbr', 'Brought Forward Refund'), ('pp', 'Payment'), ('pr', 'Refund'), ('pi', 'Invoice'), ('pc', 'Credit Note')], max_length=3)), ('history_id', models.AutoField(primary_key=True, serialize=False)), ('history_date', models.DateTimeField()), ('history_change_reason', models.CharField(max_length=100, null=True)), ('history_type', models.CharField(choices=[('+', 'Created'), ('~', 'Changed'), ('-', 'Deleted')], max_length=1)), ('history_user', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to=settings.AUTH_USER_MODEL)), ('matched_by', models.ForeignKey(blank=True, db_constraint=False, null=True, on_delete=django.db.models.deletion.DO_NOTHING, related_name='+', to='purchases.purchaseheader')), ('matched_to', models.ForeignKey(blank=True, db_constraint=False, null=True, on_delete=django.db.models.deletion.DO_NOTHING, related_name='+', to='purchases.purchaseheader')), ('period', models.ForeignKey(blank=True, db_constraint=False, null=True, on_delete=django.db.models.deletion.DO_NOTHING, related_name='+', to='controls.period')), ], options={ 'verbose_name': 'historical purchase matching', 'ordering': ('-history_date', '-history_id'), 'get_latest_by': 'history_date', }, bases=(simple_history.models.HistoricalChanges, models.Model), ), migrations.CreateModel( name='HistoricalPurchaseLine', fields=[ ('id', models.IntegerField(auto_created=True, blank=True, db_index=True, verbose_name='ID')), ('line_no', models.IntegerField()), ('description', models.CharField(max_length=100)), ('goods', accountancy.fields.UIDecimalField(blank=True, decimal_places=2, max_digits=10, null=True)), ('vat', accountancy.fields.UIDecimalField(blank=True, decimal_places=2, max_digits=10, null=True)), ('type', models.CharField(choices=[('pbi', 'Brought Forward Invoice'), ('pbc', 'Brought Forward Credit Note'), ('pbp', 'Brought Forward Payment'), ('pbr', 'Brought Forward Refund'), ('pp', 'Payment'), ('pr', 'Refund'), ('pi', 'Invoice'), ('pc', 'Credit Note')], max_length=3)), ('history_id', models.AutoField(primary_key=True, serialize=False)), ('history_date', models.DateTimeField()), ('history_change_reason', models.CharField(max_length=100, null=True)), ('history_type', models.CharField(choices=[('+', 'Created'), ('~', 'Changed'), ('-', 'Deleted')], max_length=1)), ('goods_nominal_transaction', models.ForeignKey(blank=True, db_constraint=False, null=True, on_delete=django.db.models.deletion.DO_NOTHING, related_name='+', to='nominals.nominaltransaction')), ('header', models.ForeignKey(blank=True, db_constraint=False, null=True, on_delete=django.db.models.deletion.DO_NOTHING, related_name='+', to='purchases.purchaseheader')), ('history_user', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to=settings.AUTH_USER_MODEL)), ('nominal', models.ForeignKey(blank=True, db_constraint=False, null=True, on_delete=django.db.models.deletion.DO_NOTHING, related_name='+', to='nominals.nominal')), ('total_nominal_transaction', models.ForeignKey(blank=True, db_constraint=False, null=True, on_delete=django.db.models.deletion.DO_NOTHING, related_name='+', to='nominals.nominaltransaction')), ('vat_code', models.ForeignKey(blank=True, db_constraint=False, null=True, on_delete=django.db.models.deletion.DO_NOTHING, related_name='+', to='vat.vat', verbose_name='Vat Code')), ('vat_nominal_transaction', models.ForeignKey(blank=True, db_constraint=False, null=True, on_delete=django.db.models.deletion.DO_NOTHING, related_name='+', to='nominals.nominaltransaction')), ('vat_transaction', models.ForeignKey(blank=True, db_constraint=False, null=True, on_delete=django.db.models.deletion.DO_NOTHING, related_name='+', to='vat.vattransaction')), ], options={ 'verbose_name': 'historical purchase line', 'ordering': ('-history_date', '-history_id'), 'get_latest_by': 'history_date', }, bases=(simple_history.models.HistoricalChanges, models.Model), ), migrations.CreateModel( name='HistoricalPurchaseHeader', fields=[ ('id', models.IntegerField(auto_created=True, blank=True, db_index=True, verbose_name='ID')), ('ref', models.CharField(max_length=20)), ('goods', accountancy.fields.UIDecimalField(blank=True, decimal_places=2, max_digits=10, null=True)), ('discount', accountancy.fields.UIDecimalField(blank=True, decimal_places=2, max_digits=10, null=True)), ('vat', accountancy.fields.UIDecimalField(blank=True, decimal_places=2, max_digits=10, null=True)), ('total', accountancy.fields.UIDecimalField(blank=True, decimal_places=2, max_digits=10, null=True)), ('paid', accountancy.fields.UIDecimalField(blank=True, decimal_places=2, max_digits=10, null=True)), ('due', accountancy.fields.UIDecimalField(blank=True, decimal_places=2, max_digits=10, null=True)), ('date', models.DateField()), ('due_date', models.DateField(blank=True, null=True)), ('status', models.CharField(choices=[('c', 'cleared'), ('v', 'void')], default='c', max_length=2)), ('created', models.DateTimeField(blank=True, editable=False)), ('type', models.CharField(choices=[('pbi', 'Brought Forward Invoice'), ('pbc', 'Brought Forward Credit Note'), ('pbp', 'Brought Forward Payment'), ('pbr', 'Brought Forward Refund'), ('pp', 'Payment'), ('pr', 'Refund'), ('pi', 'Invoice'), ('pc', 'Credit Note')], max_length=3)), ('history_id', models.AutoField(primary_key=True, serialize=False)), ('history_date', models.DateTimeField()), ('history_change_reason', models.CharField(max_length=100, null=True)), ('history_type', models.CharField(choices=[('+', 'Created'), ('~', 'Changed'), ('-', 'Deleted')], max_length=1)), ('cash_book', models.ForeignKey(blank=True, db_constraint=False, null=True, on_delete=django.db.models.deletion.DO_NOTHING, related_name='+', to='cashbook.cashbook')), ('history_user', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to=settings.AUTH_USER_MODEL)), ('period', models.ForeignKey(blank=True, db_constraint=False, null=True, on_delete=django.db.models.deletion.DO_NOTHING, related_name='+', to='controls.period')), ('supplier', models.ForeignKey(blank=True, db_constraint=False, null=True, on_delete=django.db.models.deletion.DO_NOTHING, related_name='+', to='purchases.supplier')), ], options={ 'verbose_name': 'historical purchase header', 'ordering': ('-history_date', '-history_id'), 'get_latest_by': 'history_date', }, bases=(simple_history.models.HistoricalChanges, models.Model), ), ]

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0

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1

0

1

0

null

0

8

1763531b636a995563a45bfe2dadd8f98afbca84

6,526

py

Python

pinax/stripe/migrations/0014_blank_with_null.py

lock8/pinax-stripe

50e846e41718646e85219d31676566ebc3fea477

[ "MIT" ]

null

pinax/stripe/migrations/0014_blank_with_null.py

lock8/pinax-stripe

50e846e41718646e85219d31676566ebc3fea477

[ "MIT" ]

114

2017-10-18T09:14:02.000Z

2019-01-24T19:03:01.000Z

pinax/stripe/migrations/0014_blank_with_null.py

lock8/pinax-stripe

50e846e41718646e85219d31676566ebc3fea477

[ "MIT" ]

1

2017-10-20T08:13:09.000Z

# -*- coding: utf-8 -*- # Generated by Django 1.11.6 on 2017-11-23 15:43 from __future__ import unicode_literals from django.db import migrations, models import django.db.models.deletion import jsonfield.fields class Migration(migrations.Migration): dependencies = [ ('pinax_stripe', '0013_charge_outcome'), ] operations = [ migrations.AlterField( model_name='account', name='tos_acceptance_date', field=models.DateField(blank=True, null=True), ), migrations.AlterField( model_name='charge', name='amount', field=models.DecimalField(blank=True, decimal_places=2, max_digits=9, null=True), ), migrations.AlterField( model_name='charge', name='amount_refunded', field=models.DecimalField(blank=True, decimal_places=2, max_digits=9, null=True), ), migrations.AlterField( model_name='charge', name='customer', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='charges', to='pinax_stripe.Customer'), ), migrations.AlterField( model_name='charge', name='invoice', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='charges', to='pinax_stripe.Invoice'), ), migrations.AlterField( model_name='charge', name='source', field=models.CharField(blank=True, max_length=100), ), migrations.AlterField( model_name='coupon', name='amount_off', field=models.DecimalField(blank=True, decimal_places=2, max_digits=9, null=True), ), migrations.AlterField( model_name='coupon', name='duration_in_months', field=models.PositiveIntegerField(blank=True, null=True), ), migrations.AlterField( model_name='coupon', name='max_redemptions', field=models.PositiveIntegerField(blank=True, null=True), ), migrations.AlterField( model_name='coupon', name='percent_off', field=models.PositiveIntegerField(blank=True, null=True), ), migrations.AlterField( model_name='coupon', name='redeem_by', field=models.DateTimeField(blank=True, null=True), ), migrations.AlterField( model_name='coupon', name='times_redeemed', field=models.PositiveIntegerField(blank=True, null=True), ), migrations.AlterField( model_name='customer', name='date_purged', field=models.DateTimeField(blank=True, editable=False, null=True), ), migrations.AlterField( model_name='discount', name='customer', field=models.OneToOneField(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='pinax_stripe.Customer'), ), migrations.AlterField( model_name='discount', name='end', field=models.DateTimeField(blank=True, null=True), ), migrations.AlterField( model_name='discount', name='start', field=models.DateTimeField(blank=True, null=True), ), migrations.AlterField( model_name='discount', name='subscription', field=models.OneToOneField(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='pinax_stripe.Subscription'), ), migrations.AlterField( model_name='event', name='customer', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='pinax_stripe.Customer'), ), migrations.AlterField( model_name='eventprocessingexception', name='event', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='pinax_stripe.Event'), ), migrations.AlterField( model_name='invoice', name='attempt_count', field=models.PositiveIntegerField(blank=True, null=True), ), migrations.AlterField( model_name='invoice', name='charge', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='invoices', to='pinax_stripe.Charge'), ), migrations.AlterField( model_name='invoice', name='subscription', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='pinax_stripe.Subscription'), ), migrations.AlterField( model_name='invoice', name='tax', field=models.DecimalField(blank=True, decimal_places=2, max_digits=9, null=True), ), migrations.AlterField( model_name='invoice', name='tax_percent', field=models.DecimalField(blank=True, decimal_places=2, max_digits=9, null=True), ), migrations.AlterField( model_name='invoice', name='webhooks_delivered_at', field=models.DateTimeField(blank=True, null=True), ), migrations.AlterField( model_name='invoiceitem', name='plan', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='pinax_stripe.Plan'), ), migrations.AlterField( model_name='invoiceitem', name='quantity', field=models.IntegerField(blank=True, null=True), ), migrations.AlterField( model_name='invoiceitem', name='subscription', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='pinax_stripe.Subscription'), ), migrations.AlterField( model_name='plan', name='trial_period_days', field=models.IntegerField(blank=True, null=True), ), migrations.AlterField( model_name='subscription', name='application_fee_percent', field=models.DecimalField(blank=True, decimal_places=2, default=None, max_digits=3, null=True), ), ]

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null

0

9

bd58916cc69b575e1c82db88c216ca06999b7d20

24,004

py

Python

code/generateTmatrix.py

AdityaMate/collapsing_bandits

2aecccc6fd986f869088438ea5eba7bbfd5c1e91

[ "MIT" ]

6

2020-11-27T10:33:54.000Z

2022-02-28T11:13:34.000Z

code/generateTmatrix.py

AdityaMate/collapsing_bandits

2aecccc6fd986f869088438ea5eba7bbfd5c1e91

[ "MIT" ]

null

code/generateTmatrix.py

AdityaMate/collapsing_bandits

2aecccc6fd986f869088438ea5eba7bbfd5c1e91

[ "MIT" ]

1

2021-09-15T05:21:47.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Nov 3 15:24:48 2020 @author: adityamate, killian-34 """ import numpy as np import pandas as pd import time import pomdp from itertools import combinations from whittle import * from utils import * import os import argparse import tqdm def computeAverageTmatrixFromData(N, file_root='.', epsilon=0.005): """ Generates a Nx2x2x2 T matrix indexed as: T[patient_number][action][current_state][next_state] action=0 denotes passive action, a=1 is active action State 0 denotes NA and state 1 denotes A """ fname = os.path.join(file_root, 'data/patient_T_matrices.npy') real = np.load(fname) T=np.zeros((N,2,2,2)) #Passive action transition probabilities penalty_pass_00=0 penalty_pass_11=0 #Active action transition probabilities benefit_act_00=0 benefit_act_11=0 avg = real.mean(axis=0) # for i in range(N): T_base = np.zeros((2,2)) T_base[0,0] = avg[0] T_base[1,1] = avg[1] T_base[0,1] = 1 - T_base[0,0] T_base[1,0] = 1 - T_base[1,1] T_base = smooth_real_probs(T_base, epsilon) shift = 0.05 # Patient responds well to call benefit_act_00=np.random.uniform(low=0., high=shift) # will subtract from prob of staying 0,0 benefit_act_11= benefit_act_00 + np.random.uniform(low=0., high=shift) # will add to prob of staying 1,1 # add benefit_act_00 to benefit_act_11 to guarantee the p11>p01 condition # Patient does well on their own, low penalty for not calling penalty_pass_11=np.random.uniform(low=0., high=shift) # will sub from prob of staying 1,1 penalty_pass_00=penalty_pass_11+np.random.uniform(low=0., high=shift) # will add to prob of staying 0,0 T_pass = np.copy(T_base) T_act = np.copy(T_base) T_act[0,0] = max(0, T_act[0,0] - benefit_act_00) T_act[1,1] = min(1, T_act[1,1] + benefit_act_11) T_pass[0,0] = min(1, T_pass[0,0] + penalty_pass_00) T_pass[1,1] = max(0, T_pass[1,1] - penalty_pass_11) T_pass[0,1] = 1 - T_pass[0,0] T_pass[1,0] = 1 - T_pass[1,1] T_act[0,1] = 1 - T_act[0,0] T_act[1,0] = 1 - T_act[1,1] T_pass = epsilon_clip(T_pass, epsilon) T_act = epsilon_clip(T_act, epsilon) #print(T_pass) #print(T_act) #print() if not verify_T_matrix(np.array([T_pass, T_act])): print("T matrix invalid\n",np.array([T_pass, T_act])) raise ValueError() for i in range(N): T[i,0]=T_pass T[i,1]=T_act return T # See page 7 of: # https://projects.iq.harvard.edu/files/teamcore/files/2016_15_teamcore_aamas2016_eve_yundi.pdf def specialTmatrix(N, kfrac=10, distribution=[0.5, 0.5], delta=0.02, option=2, badf=50): option =3 if option==0: T=np.zeros((N,2,2,2)) patient_descriptions=[] T_p_01=[0.3, 0.3] T_p_11=[0.97, 0.1] T_a_01=[0.3, 0.9] T_a_11=[0.97, 0.97] for i in range(N): index=np.random.choice(range(len(distribution)), p=distribution) T[i][0][0][1]=np.random.uniform(T_p_01[index]-delta, T_p_01[index]+delta) T[i][0][1][1]=np.random.uniform(T_p_11[index]-delta, T_p_11[index]+delta) T[i][1][0][1]=np.random.uniform(T_a_01[index]-delta, T_a_01[index]+delta) T[i][1][1][1]=np.random.uniform(T_a_11[index]-delta, T_a_11[index]+delta) return T elif option==1: T=np.zeros((N,2,2,2)) k=int(kfrac*N/100.) # Myopic wants to pull type 2 ''' type1 = np.array( [[[0.9, 0.1], [0.6, 0.41]], [[0.6, 0.4], [0.3, 0.7]]]) type2 = np.array( [[[0.9, 0.1], [0.6, 0.4]], [[0.6, 0.4], [0.3, 0.7]]]) ''' type1 = np.array( [[[0.6, 0.4], [0.29, 0.71]], [[0.35, 0.65], [0.05, 0.95]]]) type2 = np.array( [[[0.6, 0.4], [0.3, 0.7]], [[0.35, 0.65], [0.05, 0.95]]]) for i in range(k): T[i] = type2 for j in range(k, N): type1 = np.array( [[[0.6, 0.4], [0.29, 0.71+ j*0.001]], [[0.35, 0.65], [0.05, 0.95]]]) T[j]=type1 print ("Returning T matrix: ") print ("N: ", N, "k: ", k) print ("shape: ", T.shape) return T elif option==2: T=np.zeros((N,2,2,2)) type1= [[[0.97, 0.03], [0.03, 0.97]], [[0.96, 0.04], [0.01, 0.99]]] type2 = [[[0.25, 0.75], [0.03, 0.97]], [[0.23, 0.77], [0.01 , 0.99 ]]] T[0]=type1 T[1]=type2 return T elif option==3: shift1= 0.05 shift2= 0.05 shift3= 0.05 shift4= 0.05 epsilon=0.01 T=np.zeros((N,2,2,2)) type1= [[[0.97, 0.03], [0.03, 0.97]], [[0.96, 0.04], [0.01, 0.99]]] ###### Bad patient type2 = [[[0.25, 0.75], [0.03, 0.97]], [[0.23, 0.77], [0.01 , 0.99 ]]] ##### Good patient (self-healing) for i in range(N): types=[type1, type2] type_choice=types[np.random.choice([0, 1],p=[badf/100., 1-(badf/100.)])] T[i]=np.array(type_choice) # add benefit_act_00 to benefit_act_11 to guarantee the p11>p01 condition benefit_act_00=np.random.uniform(low=0., high=shift1) # will subtract from prob of staying 0,0 benefit_act_11= benefit_act_00 + np.random.uniform(low=0., high=shift2) # will add to prob of staying 1,1 # Patient does well on their own, low penalty for not calling penalty_pass_11=np.random.uniform(low=0., high=shift3) # will sub from prob of staying 1,1 penalty_pass_00=penalty_pass_11+np.random.uniform(low=0., high=shift4) # will add to prob of staying 0,0 T[i][1][0][0]= max(0, T[i][1][0][0] - benefit_act_00) T[i][1][1][1]= min(1, T[i][1][1][1] + benefit_act_11) T[i][0][0][0]= min(1, T[i][0][0][0] + penalty_pass_00) T[i][0][1][1]= max(0, T[i][0][1][1] - penalty_pass_11) T[i][0][0][1]= 1- T[i][0][0][0] T[i][0][1][0]= 1- T[i][0][1][1] T[i][1][0][1]= 1- T[i][1][0][0] T[i][1][1][0]= 1- T[i][1][1][1] T[i][0]=epsilon_clip(T[i][0], epsilon) T[i][1]=epsilon_clip(T[i][1], epsilon) return T def generateYundiMyopicFailTmatrix(): # Return a randomly generated T matrix (not unformly random because of sorting) T=np.zeros((2,2,2,2)) # T[0] = [[[0.95, 0.05], # [0.05, 0.95]], # [[0.99, 0.01], # [0.1, 0.9]]] # T[1] = [[[0.4, 0.6], # [0.1, 0.9]], # [[0.7, 0.3], # [0.4, 0.6]]] T[0] = [[[0.99, 0.01], [0.1, 0.9]], [[0.95, 0.05], [0.05, 0.95]]] T[1] = [[[0.7, 0.3], [0.4, 0.6]], [[0.4, 0.6], [0.1, 0.9]]] return T def generateRandomTmatrix(N, random_stream): # Return a randomly generated T matrix (not unformly random because of sorting) T=np.zeros((N,2,2,2)) for i in range(N): p_pass_01, p_pass_11, p_act_01, p_act_11=sorted(random_stream.uniform(size=4)) T[i,0]=np.array([[1-p_pass_01, p_pass_01],[1-p_pass_11, p_pass_11]]) T[i,1]=np.array([[1-p_act_01, p_act_01],[1-p_act_11, p_act_11]]) return T def generateTmatrix(N, responsive_patient_fraction=0.4, range_pass_00=(0.8,1.0), range_pass_11=(0.6,0.9), range_act_g_00=(0,0.2),range_act_g_11=(0.9,1.0), range_act_b_00=(0.6,0.8), range_act_b_11=(0.9,1.0)): # p_act01 < p01/(p01+p10) """ Generates a Nx2x2x2 T matrix indexed as: T[patient_number][action][current_state][next_state] action=0 denotes passive action, a=1 is active action State 0 denotes NA and state 1 denotes A """ T=np.zeros((N,2,2,2)) #Passive action transition probabilities p_pass_00=np.random.uniform(low=range_pass_00[0], high=range_pass_00[1], size=N) p_pass_11=np.random.uniform(low=range_pass_11[0], high=range_pass_11[1], size=N) #Active action transition probabilities #responsive_patient_fraction=0.4 p_act_00=np.zeros(N) p_act_11=np.zeros(N) for i in range(N): if np.random.binomial(1,responsive_patient_fraction)==1: # Patient responds well to call p_act_00[i]=np.random.uniform(low=range_act_g_00[0], high=range_act_g_00[1]) p_act_11[i]=np.random.uniform(low=range_act_g_11[0], high=range_act_g_11[1]) else: # Patient doesn't respond well to call p_act_00[i]=np.random.uniform(low=range_act_b_00[0], high=range_act_b_00[1]) p_act_11[i]=np.random.uniform(low=range_act_b_11[0], high=range_act_b_11[1]) for i in range(N): T[i,0]=np.array([[p_pass_00[i], 1-p_pass_00[i]],[1-p_pass_11[i],p_pass_11[i]]]) T[i,1]=np.array([[p_act_00[i], 1-p_act_00[i]],[1-p_act_11[i],p_act_11[i]]]) #print (T[:20]) return T # guaranteed to generate 'bad patients' according to the definition here: # p_act01 < p01/(p01+p10) == bad # as well as good patients according to the same. # we only want to consider bottom chain bad patients because top chain bad patients # would mean our action has negative effect on them which isn't realistic. # but this gives bad separation from myopic def generateTmatrixBadf(N, responsive_patient_fraction=0.4, range_pass_00=(0.6,0.8), range_pass_11=(0.6,0.89), range_act_g_00=(0,0.2),range_act_g_11=(0.9,1.0), range_act_b_00=(0.7,0.9), range_act_b_11=(0.9,1.0)): # print("p_act01 < p01/(p01+p10)") """ Generates a Nx2x2x2 T matrix indexed as: T[patient_number][action][current_state][next_state] action=0 denotes passive action, a=1 is active action State 0 denotes NA and state 1 denotes A """ T=np.zeros((N,2,2,2)) #Passive action transition probabilities p_pass_00=np.random.uniform(low=range_pass_00[0], high=range_pass_00[1], size=N) p_pass_11=np.random.uniform(low=range_pass_11[0], high=range_pass_11[1], size=N) #Active action transition probabilities #responsive_patient_fraction=0.4 p_act_00=np.zeros(N) p_act_11=np.zeros(N) for i in range(N): if np.random.binomial(1,responsive_patient_fraction)==1: # Patient responds well to call p_act_00[i]=np.random.uniform(low=range_act_g_00[0], high=range_act_g_00[1]) p_act_11[i]=np.random.uniform(low=range_act_g_11[0], high=range_act_g_11[1]) p_act01 = 1-p_act_00[i] p01 = 1-p_pass_00[i] p10 = 1-p_pass_11[i] if p_act01 < p01/(p01+p10): raise ValueError("Intended good patient was bad.") else: # Patient doesn't respond well to call p_act_00[i]=np.random.uniform(low=range_act_b_00[0], high=range_act_b_00[1]) p_act_11[i]=np.random.uniform(low=range_act_b_11[0], high=range_act_b_11[1]) p_act01 = 1-p_act_00[i] p01 = 1-p_pass_00[i] p10 = 1-p_pass_11[i] if not (p_act01 < p01/(p01+p10)): raise ValueError("Intended bad patient was good.") for i in range(N): T[i,0]=np.array([[p_pass_00[i], 1-p_pass_00[i]],[1-p_pass_11[i],p_pass_11[i]]]) T[i,1]=np.array([[p_act_00[i], 1-p_act_00[i]],[1-p_act_11[i],p_act_11[i]]]) #print (T[:20]) return T # guaranteed to generate 'bad patients' according to the definition here: # p_act01 < p01/(p01+p10) == bad # as well as good patients according to the same. # we only want to consider bottom chain bad patients because top chain bad patients # would mean our action has negative effect on them which isn't realistic. # but this gives bad separation from myopic def generateTmatrixFullRandom(N,badf=0.2): # print("p_act01 < p01/(p01+p10)") """ Generates a Nx2x2x2 T matrix indexed as: T[patient_number][action][current_state][next_state] action=0 denotes passive action, a=1 is active action State 0 denotes NA and state 1 denotes A """ T=np.zeros((N,2,2,2)) for i in range(N): should_be_bad_patient = np.random.binomial(1,badf)==1 valid = False while not valid: this_T = np.random.dirichlet([1,1],size=(2,2)) if should_be_bad_patient: p_act01 = this_T[1][0][1] p01 = this_T[0][0][1] p10 = this_T[0][1][0] is_bad_patient = p_act01 < p01/(p01+p10) is_valid_matrix = verify_T_matrix(this_T) valid = is_bad_patient and is_valid_matrix else: p_act01 = this_T[1][0][1] p01 = this_T[0][0][1] p10 = this_T[0][1][0] is_bad_patient = p_act01 < p01/(p01+p10) is_valid_matrix = verify_T_matrix(this_T) valid = (not is_bad_patient) and is_valid_matrix if should_be_bad_patient != (p_act01 < p01/(p01+p10)): raise ValueError("Mismatch") T[i] = this_T # print (T) # 1/0 return T def generateTmatrixNIBandIB(N,thresh_opt_frac=1, beta=0.5, quick_check=False): """ Generates a Nx2x2x2 T matrix indexed as: T[patient_number][action][current_state][next_state] action=0 denotes passive action, a=1 is active action State 0 denotes NA and state 1 denotes A """ T=np.zeros((N,2,2,2)) thres_opt_patients=np.random.choice([i for i in range(N)],size=int(thresh_opt_frac*N), replace=False) for i in range(N): valid = False while not valid: this_T = np.random.dirichlet([1,1],size=(2,2)) valid = verify_T_matrix(this_T) if valid and thresh_opt_frac is not None: satisfies_condition=False if i in thres_opt_patients: # Threshold opt patient satisfies_condition=isThresholdOptimal(this_T,beta, quick_check=quick_check) else: # Reverse Threshold opt patient satisfies_condition=isReverseThresholdOptimal(this_T,beta, quick_check=quick_check) valid=satisfies_condition T[i] = this_T # print (T) # 1/0 return T def generateTmatrixNIBandIBFast(N): """ Generates a Nx2x2x2 T matrix indexed as: T[patient_number][action][current_state][next_state] action=0 denotes passive action, a=1 is active action State 0 denotes NA and state 1 denotes A """ T=np.zeros((N,2,2,2)) for i in range(N): valid = False while not valid: this_T = np.random.dirichlet([1,1],size=(2,2)) valid = verify_T_matrix(this_T) T[i] = this_T # print (T) # 1/0 return T # there are only 41 of 8350 cases where # p11 < p10 results from not (p11=0.0 or p10=1.0) def smooth_real_probs(T, epsilon): # T = epsilon_clip(T, epsilon) if T[1,1] < T[0,1]: # make p11 and p01 equal so we can properly simulate # action effects # If it looks like this, make t01 = t11 # [[0.0, 1.0], # [0.01, 0.99]]] # If it looks like this, make t11 = t01 # [[0.95, 0.05], # [1.0, 0.0]]] if T[0,1] >= 0.5: T[0,1] = T[1,1] else: T[1,1] = T[0,1] T[0,0] = 1- T[0,1] T[1,0] = 1- T[1,1] return T def generateTmatrixReal(N, file_root='.', responsive_patient_fraction=0.4, epsilon=0.005, shift1=0,shift2=0,shift3=0,shift4=0, intervention_effect=0.05, thresh_opt_frac=None, beta=0.5, quick_check=False): """ Generates a Nx2x2x2 T matrix indexed as: T[patient_number][action][current_state][next_state] action=0 denotes passive action, a=1 is active action State 0 denotes NA and state 1 denotes A """ fname = os.path.join(file_root+'/data/', 'patient_T_matrices.npy') real = np.load(fname) T=np.zeros((N,2,2,2)) #Passive action transition probabilities penalty_pass_00=0 penalty_pass_11=0 #Active action transition probabilities benefit_act_00=0 benefit_act_11=0 if thresh_opt_frac is None: choices = np.random.choice(np.arange(real.shape[0]), N, replace=True) else: thres_opt_patients=np.random.choice([i for i in range(N)],size=int(thresh_opt_frac*N), replace=False) i=0 while i < N: if thresh_opt_frac is None: choice = choices[i] else: choice=np.random.choice(np.arange(real.shape[0]), 1, replace=True)[0] T_base = np.zeros((2,2)) T_base[0,0] = real[choice][0] T_base[1,1] = real[choice][1] T_base[0,1] = 1 - T_base[0,0] T_base[1,0] = 1 - T_base[1,1] T_base = smooth_real_probs(T_base, epsilon) shift = intervention_effect # Patient responds well to call benefit_act_00=np.random.uniform(low=0., high=shift) # will subtract from prob of staying 0,0 benefit_act_11= benefit_act_00 + np.random.uniform(low=0., high=shift) # will add to prob of staying 1,1 # add benefit_act_00 to benefit_act_11 to guarantee the p11>p01 condition # Patient does well on their own, low penalty for not calling penalty_pass_11=np.random.uniform(low=0., high=shift) # will sub from prob of staying 1,1 penalty_pass_00=penalty_pass_11+np.random.uniform(low=0., high=shift) # will add to prob of staying 0,0 ''' For perturbation experiment only. TEMPORARY CODE below. ''' """ benefit_act_00=np.random.uniform(low=0., high=shift1) # will subtract from prob of staying 0,0 benefit_act_11= benefit_act_00 + np.random.uniform(low=0., high=shift2) # will add to prob of staying 1,1 # add benefit_act_00 to benefit_act_11 to guarantee the p11>p01 condition # Patient does well on their own, low penalty for not calling penalty_pass_11=np.random.uniform(low=0., high=shift3) # will sub from prob of staying 1,1 penalty_pass_00=penalty_pass_11+np.random.uniform(low=0., high=shift4) # will add to prob of staying 0,0 """ T_pass = np.copy(T_base) T_act = np.copy(T_base) T_act[0,0] = max(0, T_act[0,0] - benefit_act_00) T_act[1,1] = min(1, T_act[1,1] + benefit_act_11) T_pass[0,0] = min(1, T_pass[0,0] + penalty_pass_00) T_pass[1,1] = max(0, T_pass[1,1] - penalty_pass_11) T_pass[0,1] = 1 - T_pass[0,0] T_pass[1,0] = 1 - T_pass[1,1] T_act[0,1] = 1 - T_act[0,0] T_act[1,0] = 1 - T_act[1,1] T_pass = epsilon_clip(T_pass, epsilon) T_act = epsilon_clip(T_act, epsilon) #print(T_pass) #print(T_act) #print() if not verify_T_matrix(np.array([T_pass, T_act])): print("T matrix invalid\n",np.array([T_pass, T_act])) raise ValueError() if thresh_opt_frac is None: satisfies_condition=True else: satisfies_condition=False if i in thres_opt_patients: # Threshold opt patient satisfies_condition=isThresholdOptimal([T_pass,T_act],beta, quick_check=quick_check) else: # Reverse Threshold opt patient satisfies_condition=isReverseThresholdOptimal([T_pass,T_act],beta, quick_check=quick_check) if satisfies_condition: T[i,0]=T_pass T[i,1]=T_act i+=1 return T def generateTmatrixRealNoReplace(N, file_root='.', epsilon=0.005, shift1=0,shift2=0,shift3=0,shift4=0, intervention_effect=0.05): """ Generates a Nx2x2x2 T matrix indexed as: T[patient_number][action][current_state][next_state] action=0 denotes passive action, a=1 is active action State 0 denotes NA and state 1 denotes A """ fname = os.path.join(file_root, 'data/patient_T_matrices.npy') real = np.load(fname) T=np.zeros((N,2,2,2)) #Passive action transition probabilities penalty_pass_00=0 penalty_pass_11=0 #Active action transition probabilities benefit_act_00=0 benefit_act_11=0 choices = np.random.choice(np.arange(real.shape[0]), N, replace=False) for i,choice in enumerate(choices): T_base = np.zeros((2,2)) T_base[0,0] = real[choice][0] T_base[1,1] = real[choice][1] T_base[0,1] = 1 - T_base[0,0] T_base[1,0] = 1 - T_base[1,1] T_base = smooth_real_probs(T_base, epsilon) shift = intervention_effect # Patient responds well to call benefit_act_00=np.random.uniform(low=0., high=shift) # will subtract from prob of staying 0,0 benefit_act_11= benefit_act_00 + np.random.uniform(low=0., high=shift) # will add to prob of staying 1,1 # add benefit_act_00 to benefit_act_11 to guarantee the p11>p01 condition # Patient does well on their own, low penalty for not calling penalty_pass_11=np.random.uniform(low=0., high=shift) # will sub from prob of staying 1,1 penalty_pass_00=penalty_pass_11+np.random.uniform(low=0., high=shift) # will add to prob of staying 0,0 ''' For perturbation experiment only. TEMPORARY CODE below. ''' """ benefit_act_00=np.random.uniform(low=0., high=shift1) # will subtract from prob of staying 0,0 benefit_act_11= benefit_act_00 + np.random.uniform(low=0., high=shift2) # will add to prob of staying 1,1 # add benefit_act_00 to benefit_act_11 to guarantee the p11>p01 condition # Patient does well on their own, low penalty for not calling penalty_pass_11=np.random.uniform(low=0., high=shift3) # will sub from prob of staying 1,1 penalty_pass_00=penalty_pass_11+np.random.uniform(low=0., high=shift4) # will add to prob of staying 0,0 """ T_pass = np.copy(T_base) T_act = np.copy(T_base) T_act[0,0] = max(0, T_act[0,0] - benefit_act_00) T_act[1,1] = min(1, T_act[1,1] + benefit_act_11) T_pass[0,0] = min(1, T_pass[0,0] + penalty_pass_00) T_pass[1,1] = max(0, T_pass[1,1] - penalty_pass_11) T_pass[0,1] = 1 - T_pass[0,0] T_pass[1,0] = 1 - T_pass[1,1] T_act[0,1] = 1 - T_act[0,0] T_act[1,0] = 1 - T_act[1,1] T_pass = epsilon_clip(T_pass, epsilon) T_act = epsilon_clip(T_act, epsilon) #print(T_pass) #print(T_act) #print() if not verify_T_matrix(np.array([T_pass, T_act])): print("T matrix invalid\n",np.array([T_pass, T_act])) raise ValueError() T[i,0]=T_pass T[i,1]=T_act return T

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Python

fonts/DejaVuSansMono_16.py

ironss/micropython-lib

61719636dad9aaa581c8e39e71ccc515e75c2d43

[ "MIT" ]

null

fonts/DejaVuSansMono_16.py

ironss/micropython-lib

61719636dad9aaa581c8e39e71ccc515e75c2d43

[ "MIT" ]

null

fonts/DejaVuSansMono_16.py

ironss/micropython-lib

61719636dad9aaa581c8e39e71ccc515e75c2d43

[ "MIT" ]

2

2019-09-24T13:36:55.000Z

2020-04-18T02:05:38.000Z

# Code generated by font-to-py.py. # Font: DejaVuSansMono.ttf version = '0.26' def height(): return 16 def max_width(): return 9 def hmap(): return False def reverse(): return False def monospaced(): return False def min_ch(): return 32 def max_ch(): return 126 _font =\ b'\x09\x00\x04\x00\x02\x00\xc2\x0d\x42\x00\x22\x00\x1c\x00\x00\x00'\ b'\x00\x00\x00\x00\x09\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00'\ b'\x00\x00\x00\x00\x00\x00\x00\x00\x09\x00\xfe\x0c\x00\x00\x00\x00'\ b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x09\x00\x1e\x00'\ b'\x00\x00\x00\x00\x1e\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00'\ b'\x09\x00\x00\x01\x10\x09\xd0\x07\x7c\x01\x16\x0d\xd0\x07\x7c\x01'\ b'\x16\x01\x10\x00\x09\x00\x38\x04\x4c\x08\x44\x08\xff\x3f\x84\x08'\ b'\x84\x08\x08\x07\x00\x00\x00\x00\x09\x00\x1c\x00\xa2\x00\xa2\x00'\ b'\x62\x00\x5c\x07\xc0\x08\xa0\x08\xa0\x08\x00\x07\x09\x00\xc0\x03'\ b'\x7c\x04\x32\x08\x62\x08\x82\x09\x02\x07\xc0\x09\x00\x00\x00\x00'\ b'\x09\x00\x1e\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00'\ b'\x00\x00\x00\x00\x09\x00\xf0\x07\x0c\x18\x02\x20\x00\x00\x00\x00'\ b'\x00\x00\x00\x00\x00\x00\x00\x00\x09\x00\x02\x20\x0c\x18\xf0\x07'\ b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x09\x00\x24\x00'\ b'\x28\x00\x18\x00\x7e\x00\x18\x00\x28\x00\x24\x00\x00\x00\x00\x00'\ b'\x09\x00\x80\x00\x80\x00\x80\x00\xf0\x07\x80\x00\x80\x00\x80\x00'\ b'\x00\x00\x00\x00\x09\x00\x00\x20\x00\x1c\x00\x0c\x00\x00\x00\x00'\ b'\x00\x00\x00\x00\x00\x00\x00\x00\x09\x00\x80\x00\x80\x00\x80\x00'\ b'\x80\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x09\x00\x00\x0c'\ b'\x00\x0c\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00'\ b'\x09\x00\x00\x10\x00\x0c\x00\x03\xc0\x00\x30\x00\x0c\x00\x02\x00'\ b'\x00\x00\x00\x00\x09\x00\xf8\x03\x04\x04\x02\x08\x62\x08\x62\x08'\ b'\x04\x04\xf8\x03\x00\x00\x00\x00\x09\x00\x04\x08\x02\x08\xfe\x0f'\ b'\x00\x08\x00\x08\x00\x00\x00\x00\x00\x00\x00\x00\x09\x00\x0c\x08'\ b'\x06\x0c\x02\x0a\x02\x09\x82\x08\x44\x08\x3c\x08\x00\x00\x00\x00'\ b'\x09\x00\x04\x04\x02\x08\x42\x08\x42\x08\x42\x08\xa4\x0c\xbc\x07'\ b'\x00\x00\x00\x00\x09\x00\x80\x01\x60\x01\x30\x01\x0c\x01\x06\x01'\ b'\xfe\x0f\x00\x01\x00\x00\x00\x00\x09\x00\x7e\x04\x22\x08\x22\x08'\ b'\x22\x08\x22\x08\x42\x04\x80\x03\x00\x00\x00\x00\x09\x00\xf0\x03'\ b'\x4c\x04\x26\x08\x22\x08\x22\x08\x62\x0c\xc4\x07\x00\x00\x00\x00'\ b'\x09\x00\x02\x00\x02\x08\x02\x06\x82\x01\x62\x00\x1e\x00\x06\x00'\ b'\x00\x00\x00\x00\x09\x00\xbc\x07\xa6\x0c\x42\x08\x42\x08\x42\x08'\ b'\xa6\x0c\xbc\x07\x00\x00\x00\x00\x09\x00\x7c\x04\xc6\x08\x82\x08'\ b'\x82\x08\x82\x0c\x44\x06\xf8\x01\x00\x00\x00\x00\x09\x00\x30\x0c'\ b'\x30\x0c\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00'\ b'\x09\x00\x00\x20\x30\x1c\x30\x0c\x00\x00\x00\x00\x00\x00\x00\x00'\ b'\x00\x00\x00\x00\x09\x00\x80\x00\xc0\x01\x40\x01\x40\x01\x20\x02'\ b'\x20\x02\x20\x02\x10\x04\x00\x00\x09\x00\x20\x01\x20\x01\x20\x01'\ b'\x20\x01\x20\x01\x20\x01\x20\x01\x20\x01\x00\x00\x09\x00\x10\x04'\ b'\x20\x02\x20\x02\x20\x02\x40\x01\x40\x01\xc0\x01\x80\x00\x00\x00'\ b'\x09\x00\x04\x00\x02\x00\xc2\x0d\x42\x00\x22\x00\x1c\x00\x00\x00'\ b'\x00\x00\x00\x00\x09\x00\xe0\x07\x18\x18\x0c\x10\xc4\x23\x24\x24'\ b'\x2c\x24\xf8\x07\x00\x00\x00\x00\x09\x00\x00\x0c\xc0\x03\x3c\x01'\ b'\x02\x01\x3c\x01\xc0\x03\x00\x0c\x00\x00\x00\x00\x09\x00\xfe\x0f'\ b'\x42\x08\x42\x08\x42\x08\x42\x08\xe6\x0c\xbc\x07\x00\x00\x00\x00'\ b'\x09\x00\xf0\x01\x0c\x06\x02\x08\x02\x08\x02\x08\x02\x08\x04\x04'\ b'\x00\x00\x00\x00\x09\x00\xfe\x0f\x02\x08\x02\x08\x02\x08\x06\x0c'\ b'\x0c\x06\xf0\x01\x00\x00\x00\x00\x09\x00\xfe\x0f\x42\x08\x42\x08'\ b'\x42\x08\x42\x08\x42\x08\x42\x08\x00\x00\x00\x00\x09\x00\xfe\x0f'\ b'\x42\x00\x42\x00\x42\x00\x42\x00\x42\x00\x42\x00\x00\x00\x00\x00'\ b'\x09\x00\xf0\x01\x0c\x06\x02\x08\x02\x08\x42\x08\x42\x08\xc4\x07'\ b'\x00\x00\x00\x00\x09\x00\xfe\x0f\x40\x00\x40\x00\x40\x00\x40\x00'\ b'\x40\x00\xfe\x0f\x00\x00\x00\x00\x09\x00\x02\x08\x02\x08\xfe\x0f'\ b'\x02\x08\x02\x08\x00\x00\x00\x00\x00\x00\x00\x00\x09\x00\x00\x04'\ b'\x00\x08\x02\x08\x02\x08\x02\x0c\xfe\x07\x00\x00\x00\x00\x00\x00'\ b'\x09\x00\xfe\x0f\x40\x00\x20\x00\xd0\x00\x08\x01\x04\x06\x02\x08'\ b'\x00\x00\x00\x00\x09\x00\xfe\x0f\x00\x08\x00\x08\x00\x08\x00\x08'\ b'\x00\x08\x00\x08\x00\x00\x00\x00\x09\x00\xfe\x0f\x0e\x00\x70\x00'\ b'\x80\x00\x70\x00\x0e\x00\xfe\x0f\x00\x00\x00\x00\x09\x00\xfe\x0f'\ b'\x06\x00\x38\x00\xe0\x00\x00\x03\x00\x0c\xfe\x0f\x00\x00\x00\x00'\ b'\x09\x00\xf8\x03\x04\x04\x02\x08\x02\x08\x02\x08\x04\x04\xf8\x03'\ b'\x00\x00\x00\x00\x09\x00\xfe\x0f\x82\x00\x82\x00\x82\x00\x82\x00'\ b'\xc4\x00\x7c\x00\x00\x00\x00\x00\x09\x00\xf8\x03\x04\x04\x02\x08'\ b'\x02\x08\x02\x18\x04\x3c\xf8\x03\x00\x00\x00\x00\x09\x00\xfe\x0f'\ b'\x42\x00\x42\x00\x42\x00\x42\x00\xa6\x00\x3c\x07\x00\x08\x00\x00'\ b'\x09\x00\x3c\x04\x24\x0c\x42\x08\x42\x08\x42\x08\x86\x0c\x84\x07'\ b'\x00\x00\x00\x00\x09\x00\x02\x00\x02\x00\x02\x00\xfe\x0f\x02\x00'\ b'\x02\x00\x02\x00\x00\x00\x00\x00\x09\x00\xfe\x07\x00\x0c\x00\x08'\ b'\x00\x08\x00\x08\x00\x0c\xfe\x07\x00\x00\x00\x00\x09\x00\x06\x00'\ b'\x78\x00\x80\x07\x00\x08\x80\x07\x78\x00\x06\x00\x00\x00\x00\x00'\ b'\x09\x00\x0e\x00\xf0\x03\x00\x0c\xe0\x03\x10\x00\xe0\x03\x00\x0c'\ b'\xf0\x03\x0e\x00\x09\x00\x02\x08\x0c\x06\xb0\x01\x40\x00\xb0\x01'\ b'\x0c\x06\x02\x08\x00\x00\x00\x00\x09\x00\x02\x00\x06\x00\x18\x00'\ b'\x30\x00\xc0\x0f\x20\x00\x18\x00\x06\x00\x02\x00\x09\x00\x02\x0c'\ b'\x02\x0a\x82\x09\x42\x08\x32\x08\x0a\x08\x06\x08\x00\x00\x00\x00'\ b'\x09\x00\xfe\x3f\x02\x20\x02\x20\x00\x00\x00\x00\x00\x00\x00\x00'\ b'\x00\x00\x00\x00\x09\x00\x02\x00\x0c\x00\x30\x00\xc0\x00\x00\x03'\ b'\x00\x0c\x00\x10\x00\x00\x00\x00\x09\x00\x02\x20\x02\x20\xfe\x3f'\ b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x09\x00\x10\x00'\ b'\x18\x00\x0c\x00\x06\x00\x06\x00\x0c\x00\x18\x00\x10\x00\x00\x00'\ b'\x09\x00\x00\x10\x00\x10\x00\x10\x00\x10\x00\x10\x00\x10\x00\x10'\ b'\x00\x10\x00\x10\x09\x00\x01\x00\x03\x00\x06\x00\x04\x00\x00\x00'\ b'\x00\x00\x00\x00\x00\x00\x00\x00\x09\x00\x00\x07\xa0\x09\x90\x08'\ b'\x90\x08\x90\x08\x90\x04\xe0\x0f\x00\x00\x00\x00\x09\x00\xfe\x0f'\ b'\x20\x04\x10\x08\x10\x08\x10\x08\x20\x04\xc0\x03\x00\x00\x00\x00'\ b'\x09\x00\xc0\x03\x20\x04\x10\x08\x10\x08\x10\x08\x20\x04\x00\x00'\ b'\x00\x00\x00\x00\x09\x00\xc0\x03\x20\x04\x10\x08\x10\x08\x10\x08'\ b'\x20\x04\xfe\x0f\x00\x00\x00\x00\x09\x00\xc0\x03\x20\x05\x10\x09'\ b'\x10\x09\x10\x09\x20\x09\xc0\x05\x00\x00\x00\x00\x09\x00\x10\x00'\ b'\x10\x00\xfc\x0f\x12\x00\x12\x00\x12\x00\x00\x00\x00\x00\x00\x00'\ b'\x09\x00\xc0\x03\x20\x24\x10\x48\x10\x48\x10\x48\x20\x64\xf0\x3f'\ b'\x00\x00\x00\x00\x09\x00\xfe\x0f\x20\x00\x10\x00\x10\x00\x10\x00'\ b'\x30\x00\xe0\x0f\x00\x00\x00\x00\x09\x00\x00\x08\x10\x08\x10\x08'\ b'\xf6\x0f\x00\x08\x00\x08\x00\x08\x00\x00\x00\x00\x09\x00\x00\x40'\ b'\x10\x40\x10\x40\xf6\x3f\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00'\ b'\x09\x00\xfe\x0f\x80\x00\xc0\x00\x20\x01\x20\x02\x10\x04\x00\x08'\ b'\x00\x00\x00\x00\x09\x00\x02\x00\x02\x00\x02\x00\xfe\x07\x00\x08'\ b'\x00\x08\x00\x08\x00\x00\x00\x00\x09\x00\xf0\x0f\x10\x00\x10\x00'\ b'\xf0\x0f\x10\x00\x10\x00\xe0\x0f\x00\x00\x00\x00\x09\x00\xf0\x0f'\ b'\x20\x00\x10\x00\x10\x00\x10\x00\x30\x00\xe0\x0f\x00\x00\x00\x00'\ b'\x09\x00\xc0\x03\x20\x04\x10\x08\x10\x08\x10\x08\x20\x04\xc0\x03'\ b'\x00\x00\x00\x00\x09\x00\xf0\x7f\x20\x04\x10\x08\x10\x08\x10\x08'\ b'\x20\x04\xc0\x03\x00\x00\x00\x00\x09\x00\xc0\x03\x20\x04\x10\x08'\ b'\x10\x08\x10\x08\x20\x04\xf0\x7f\x00\x00\x00\x00\x09\x00\xf0\x0f'\ b'\x20\x00\x10\x00\x10\x00\x10\x00\x20\x00\x00\x00\x00\x00\x00\x00'\ b'\x09\x00\xe0\x04\x90\x08\x90\x08\x90\x08\x10\x09\x10\x09\x20\x07'\ b'\x00\x00\x00\x00\x09\x00\x10\x00\x10\x00\xfc\x07\x10\x08\x10\x08'\ b'\x10\x08\x00\x00\x00\x00\x00\x00\x09\x00\xf0\x07\x00\x0c\x00\x08'\ b'\x00\x08\x00\x08\x00\x04\xf0\x0f\x00\x00\x00\x00\x09\x00\x10\x00'\ b'\xe0\x00\x00\x07\x00\x08\x00\x07\xe0\x00\x10\x00\x00\x00\x00\x00'\ b'\x09\x00\x30\x00\xc0\x03\x00\x0c\x00\x03\xc0\x00\x00\x03\x00\x0c'\ b'\xc0\x03\x30\x00\x09\x00\x10\x08\x30\x0c\x40\x02\x80\x01\x40\x02'\ b'\x30\x0c\x10\x08\x00\x00\x00\x00\x09\x00\x10\x00\xe0\x40\x00\x43'\ b'\x00\x3c\x00\x07\xe0\x00\x10\x00\x00\x00\x00\x00\x09\x00\x10\x0c'\ b'\x10\x0a\x10\x09\x10\x09\x90\x08\x50\x08\x30\x08\x00\x00\x00\x00'\ b'\x09\x00\x80\x00\x80\x00\x7c\x3f\x02\x40\x02\x40\x00\x00\x00\x00'\ b'\x00\x00\x00\x00\x09\x00\xfe\xff\x00\x00\x00\x00\x00\x00\x00\x00'\ b'\x00\x00\x00\x00\x00\x00\x00\x00\x09\x00\x02\x40\x02\x40\x7c\x3f'\ b'\x80\x00\x80\x00\x00\x00\x00\x00\x00\x00\x00\x00\x09\x00\x80\x00'\ b'\x40\x00\x40\x00\x40\x00\x80\x00\x80\x00\x80\x00\x40\x00\x00\x00'\ _index =\ b'\x00\x00\x14\x00\x28\x00\x3c\x00\x50\x00\x64\x00\x78\x00\x8c\x00'\ b'\xa0\x00\xb4\x00\xc8\x00\xdc\x00\xf0\x00\x04\x01\x18\x01\x2c\x01'\ b'\x40\x01\x54\x01\x68\x01\x7c\x01\x90\x01\xa4\x01\xb8\x01\xcc\x01'\ b'\xe0\x01\xf4\x01\x08\x02\x1c\x02\x30\x02\x44\x02\x58\x02\x6c\x02'\ b'\x80\x02\x94\x02\xa8\x02\xbc\x02\xd0\x02\xe4\x02\xf8\x02\x0c\x03'\ b'\x20\x03\x34\x03\x48\x03\x5c\x03\x70\x03\x84\x03\x98\x03\xac\x03'\ b'\xc0\x03\xd4\x03\xe8\x03\xfc\x03\x10\x04\x24\x04\x38\x04\x4c\x04'\ b'\x60\x04\x74\x04\x88\x04\x9c\x04\xb0\x04\xc4\x04\xd8\x04\xec\x04'\ b'\x00\x05\x14\x05\x28\x05\x3c\x05\x50\x05\x64\x05\x78\x05\x8c\x05'\ b'\xa0\x05\xb4\x05\xc8\x05\xdc\x05\xf0\x05\x04\x06\x18\x06\x2c\x06'\ b'\x40\x06\x54\x06\x68\x06\x7c\x06\x90\x06\xa4\x06\xb8\x06\xcc\x06'\ b'\xe0\x06\xf4\x06\x08\x07\x1c\x07\x30\x07\x44\x07\x58\x07\x6c\x07'\ b'\x80\x07' _mvfont = memoryview(_font) def _chr_addr(ordch): offset = 2 * (ordch - 32) return int.from_bytes(_index[offset:offset + 2], 'little') def get_width(s): width = 0 for ch in s: ordch = ord(ch) ordch = ordch + 1 if ordch >= 32 and ordch <= 126 else 32 offset = _chr_addr(ordch) width += int.from_bytes(_font[offset:offset + 2], 'little') return width def get_ch(ch): ordch = ord(ch) ordch = ordch + 1 if ordch >= 32 and ordch <= 126 else 32 offset = _chr_addr(ordch) width = int.from_bytes(_font[offset:offset + 2], 'little') next_offs = _chr_addr(ordch +1) return _mvfont[offset + 2:next_offs], width

54.403226

68

0.701453

2,396

10,119

2.951586

0.06177

0.386878

0.427602

0.400452

0.634615

0.599972

0.553733

0.509333

0.414027

0.357466

0

0.401973

0.038245

10,119

185

69

54.697297

0.324702

0.005633

0

0.065089

1

0.781065

0.842911

0.839928

0

1

0

1

0.059172

false

0

0.04142

0.118343

0

null

1

0

1

0

1

0

1

null

1

0

10

bdd4d6f2788703591e8228c84761525ba7fb6ecc

9,020

py

Python

tests/test_complex.py

gasparka/pyha

60d9bbfd6075e7548d670d05317d64bc2a1a19ee

[ "Apache-2.0" ]

6

2017-05-18T18:57:07.000Z

2020-08-06T11:23:34.000Z

tests/test_complex.py

gasparka/pyha

60d9bbfd6075e7548d670d05317d64bc2a1a19ee

[ "Apache-2.0" ]

607

2017-05-10T12:51:54.000Z

2022-03-31T18:08:15.000Z

tests/test_complex.py

gasparka/pyha

60d9bbfd6075e7548d670d05317d64bc2a1a19ee

[ "Apache-2.0" ]

1

2019-03-20T13:57:46.000Z

from pyha import Hardware, sims_close, Complex, hardware_sims_equal, scalb, simulate import numpy as np from pyha.common.shift_register import ShiftRegister def test_loopback(): class T(Hardware): def main(self, x): return x dut = T() inp = np.random.uniform(-1, 1, 2) + np.random.uniform(-1, 1, 2) * 1j sims = simulate(dut, inp, simulations=['HARDWARE', 'RTL']) assert hardware_sims_equal(sims) assert sims_close(sims) def test_register(): class T(Hardware): def __init__(self): self.DELAY = 1 self.reg = Complex() # TODO: this should resize to 0, -17?? def main(self, x): self.reg = x return self.reg dut = T() inp = np.random.uniform(-1, 1, 256) + np.random.uniform(-1, 1, 256) * 1j sims = simulate(dut, inp) assert hardware_sims_equal(sims) assert sims_close(sims) def test_loopback_negative_left(): class T(Hardware): def main(self, x): return x dut = T() inp = np.random.uniform(-1, 1, 256) + np.random.uniform(-1, 1, 256) * 1j inp *= 0.05 sims = simulate(dut, inp, input_types=[Complex(0, -3, -20)], conversion_path='/tmp/pyha_output') assert hardware_sims_equal(sims) assert sims_close(sims) def test_old_shiftreg(): class T(Hardware): def __init__(self): self.reg = [Complex() for _ in range(16)] self.DELAY = 1 def main(self, x): self.reg = [x] + self.reg[:-1] return self.reg[-1] dut = T() inp = np.random.uniform(-1, 1, 256) + np.random.uniform(-1, 1, 256) * 1j sims = simulate(dut, inp, simulations=['HARDWARE', 'RTL', 'NETLIST']) assert hardware_sims_equal(sims) assert sims_close(sims) def test_new_shiftreg(): class T(Hardware): def __init__(self): self.reg = ShiftRegister([Complex() for _ in range(16)]) self.DELAY = 1 def main(self, x): self.reg.push_next(x) return self.reg.peek() dut = T() inp = np.random.uniform(-1, 1, 256) + np.random.uniform(-1, 1, 256) * 1j sims = simulate(dut, inp, simulations=['HARDWARE', 'RTL', 'NETLIST']) assert hardware_sims_equal(sims) assert sims_close(sims) def test_multiply(): class T(Hardware): def main(self, a, b): return a * b dut = T() a = np.random.uniform(-1, 1, 256) + np.random.uniform(-1, 1, 256) * 1j b = np.random.uniform(-1, 1, 256) + np.random.uniform(-1, 1, 256) * 1j sims = simulate(dut, a, b) assert hardware_sims_equal(sims) assert sims_close(sims) def test_multiply_negative_left(): class T(Hardware): def main(self, a, b): return a * b dut = T() a = np.random.uniform(-0.01, 0.01, 256) + np.random.uniform(-0.01, 0.01, 256) * 1j b = np.random.uniform(-0.01, 0.01, 256) + np.random.uniform(-0.01, 0.01, 256) * 1j sims = simulate(dut, a, b, input_types=[Complex(0, -3, -20), Complex(0, -3, -20)], conversion_path='/tmp/pyha_output/src') assert hardware_sims_equal(sims) assert sims_close(sims) def test_add(): class T(Hardware): def main(self, a, b): return a + b dut = T() a = np.random.uniform(-1, 1, 256) + np.random.uniform(-1, 1, 256) * 1j b = np.random.uniform(-1, 1, 256) + np.random.uniform(-1, 1, 256) * 1j sims = simulate(dut, a, b) assert hardware_sims_equal(sims) assert sims_close(sims) def test_sub(): class T(Hardware): def main(self, a, b): return a - b dut = T() a = np.random.uniform(-1, 1, 256) + np.random.uniform(-1, 1, 256) * 1j b = np.random.uniform(-1, 1, 256) + np.random.uniform(-1, 1, 256) * 1j sims = simulate(dut, a, b) assert hardware_sims_equal(sims) assert sims_close(sims) def test_rshift(): class T(Hardware): def main(self, a, b): return a >> b dut = T() a = np.random.uniform(-1, 1, 256) + np.random.uniform(-1, 1, 256) * 1j b = np.random.randint(0, 17, 256) sims = simulate(dut, a, b) assert hardware_sims_equal(sims) assert sims_close(sims) def test_lshift(): class T(Hardware): def main(self, a, b): return a << b dut = T() a = np.random.uniform(-1, 1, 256) + np.random.uniform(-1, 1, 256) * 1j b = np.random.randint(0, 17, 256) sims = simulate(dut, a, b) assert hardware_sims_equal(sims) # assert sims_close(sims) def test_scalb(): class T(Hardware): def __init__(self, scalbi): self.SCALB_I = scalbi def main(self, a): # ret = scalb(a, b) return scalb(a, self.SCALB_I) dut = T(-1) a = [0.125 + 0.25j] sims = simulate(dut, a) assert hardware_sims_equal(sims) assert sims_close(sims) dut = T(0) sims = simulate(dut, a) assert hardware_sims_equal(sims) assert sims_close(sims) dut = T(1) sims = simulate(dut, a) assert hardware_sims_equal(sims) assert sims_close(sims) def test_scalb_bug(): """ Result with negative integer bits were mishandled.. """ # TODO: probably not fully resolved... class T(Hardware): def __init__(self, scalbi): self.SCALB_I = scalbi def main(self, a): ret = scalb(a, self.SCALB_I) return ret dut = T(-1) a = [0.125 + 0.25j] sims = simulate(dut, a) assert hardware_sims_equal(sims) assert sims_close(sims) def test_part_access(): class T(Hardware): def main(self, a): return a.real, a.imag dut = T() a = np.random.uniform(-1, 1, 256) + np.random.uniform(-1, 1, 256) * 1j sims = simulate(dut, a) assert hardware_sims_equal(sims) assert sims_close(sims) def test_part_access_submod(): """ Bug: 'a.elem' was merged to 'aelem', see https://github.com/PyCQA/redbaron/issues/161 """ class A(Hardware): def __init__(self, elem): self.elem = elem class T(Hardware): def main(self, a): return a.elem.real, a.elem.imag dut = T() a = np.random.uniform(-1, 1, 256) + np.random.uniform(-1, 1, 256) * 1j a = [A(x) for x in a] sims = simulate(dut, a) assert hardware_sims_equal(sims) assert sims_close(sims) def test_add_float(): class T(Hardware): def main(self, a, b): return a + b dut = T() a = np.random.uniform(-1, 1, 256) + np.random.uniform(-1, 1, 256) * 1j b = np.random.uniform(-1, 1, 256) sims = simulate(dut, a, b) assert hardware_sims_equal(sims) assert sims_close(sims) def test_sub_float(): class T(Hardware): def main(self, a, b): return a - b dut = T() a = np.random.uniform(-1, 1, 256) + np.random.uniform(-1, 1, 256) * 1j b = np.random.uniform(-1, 1, 256) sims = simulate(dut, a, b) assert hardware_sims_equal(sims) assert sims_close(sims) def test_sub_uneven_types(): """ Failed if a and b were different sizes, bug was in minimum function, that acted as maximum """ class T(Hardware): def main(self, a, b): return a - b dut = T() a = np.random.uniform(-1, 1, 256) + np.random.uniform(-1, 1, 256) * 1j b = np.random.uniform(-1, 1, 256) sims = simulate(dut, a, b, input_types=[Complex(0, 0, -17), Complex(0, 0, -18)], simulations=['HARDWARE', 'RTL'], conversion_path='/home/gaspar/git/pyhacores/playground') assert hardware_sims_equal(sims) assert sims_close(sims) def test_mult_float(): class T(Hardware): def main(self, a, b): return a * b dut = T() a = np.random.uniform(-1, 1, 256) + np.random.uniform(-1, 1, 256) * 1j b = np.random.uniform(-1, 1, 256) sims = simulate(dut, a, b) assert hardware_sims_equal(sims) assert sims_close(sims) def test_floatconst_operations(): class T(Hardware): def main(self, a): q = a + 0.24 w = a - 0.2 e = a * 0.4 return q, w, e dut = T() a = np.random.uniform(-1, 1, 256) + np.random.uniform(-1, 1, 256) * 1j sims = simulate(dut, a) assert hardware_sims_equal(sims) assert sims_close(sims) def test_complex_constants(): class T(Hardware): def __init__(self): self.DELAY = 1 self.reg = Complex(0, 0, -17) self.reg2 = Complex(0, 0, -17) self.reg3 = Complex(0, 0, -17) def main(self, x): self.reg = self.reg + x - (x * x) # this was incorrectly parsed as complex constant! self.reg2 = 0.0 + 0.5j self.reg3 = 0.0 + 0.5 * 1j return self.reg, self.reg2 dut = T() inputs = [0 + 0j, 0.1 + 0.2j, -0.1 + 0.3j] sims = simulate(dut, inputs, simulations=['HARDWARE', 'RTL'], conversion_path='/home/gaspar/git/pyhacores/playground') assert sims_close(sims)

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da0fb74ade327eeeca4e5304acee9f105a33a3d5

14,912

py

Python

devel/lib/python2.7/dist-packages/interbotix_xs_sdk/srv/_OperatingModes.py

Jam-cpu/Masters-Project---Final

0b266b1f117a579b96507249f0a128d0e3cc082a

[ "BSD-3-Clause-Clear" ]

null

devel/lib/python2.7/dist-packages/interbotix_xs_sdk/srv/_OperatingModes.py

Jam-cpu/Masters-Project---Final

0b266b1f117a579b96507249f0a128d0e3cc082a

[ "BSD-3-Clause-Clear" ]

null

devel/lib/python2.7/dist-packages/interbotix_xs_sdk/srv/_OperatingModes.py

Jam-cpu/Masters-Project---Final

0b266b1f117a579b96507249f0a128d0e3cc082a

[ "BSD-3-Clause-Clear" ]

null

# This Python file uses the following encoding: utf-8 """autogenerated by genpy from interbotix_xs_sdk/OperatingModesRequest.msg. Do not edit.""" import codecs import sys python3 = True if sys.hexversion > 0x03000000 else False import genpy import struct class OperatingModesRequest(genpy.Message): _md5sum = "cb68bef3d517c840b0a5cc0f73d64e36" _type = "interbotix_xs_sdk/OperatingModesRequest" _has_header = False # flag to mark the presence of a Header object _full_text = """# Set Operating Modes # # To get familiar with the various operating modes, go to... # http://emanual.robotis.com/docs/en/software/dynamixel/dynamixel_workbench/ # ...click on a motor model, and scroll down to the 'Operating Mode' section. # # There are 6 valid operating modes. They are... # "position" - allows up to 1 complete joint revolution (perfect for arm joints); units are in radians # "ext_position" - allows up to 512 joint revolutions; units are in radians # "velocity" - allows infinite number of rotations (perfect for wheeled robots); units are in rad/s # "current" - allows infinite number of rotations (perfect for grippers); units are in milliamps # "current_based_position" - allows up to 512 joint revolutions; units are in radians # "pwm" - allows infinite number of rotations (perfect for grippers); units are in PWM # # Note that the interbotix_xs_sdk offers one other 'pseudo' operating mode that can be useful in controlling Interbotix Grippers - called "linear_position". # Behind the scenes, it uses the "position" operating mode mentioned above. The main difference is that with this mode, a desired linear distance [m] # between the two gripper fingers can be commanded. In the "position" mode though, only the angular position of the motor can be commanded. # # There are 2 valid profile types - either 'time' or 'velocity'. Depending on which is chosen, the following parameters behave differently. # # 1) profile_velocity: acts as a pass-through to the Profile_Velocity register and operates in one of two ways. If # 'profile_type' is set to 'velocity', this parameter describes the max velocity limit for the specified joint(s); # for example, if doing 'position' control, setting this to '131' would be equivalent to a limit of 3.14 rad/s; if # 'profile_type' is set to 'time', this parameter sets the time span (in milliseconds) that it should take for the # specified joint(s) to move; to have an 'infinite' max limit, set to '0'. # # 2) profile_acceleration: acts as a pass-through to the Profile_Acceleration register and operates in one of two ways. If # 'profile_type' is set to 'velocity', this parameter describes the max acceleration limit for the specified joint(s); # for example, if doing 'position' or 'velocity' control, setting this to '15' would be equivalent to a limit of 5.6 rad/s^2; # if 'profile_type' is set to 'time', this parameter sets the time span (in milliseconds) that it should take for the # specified joint(s) to accelerate; to have an 'infinite' max limit, set to '0'. string cmd_type # set to 'group' if commanding a joint group or 'single' if commanding a single joint string name # name of the group if commanding a joint group or joint if commanding a single joint string mode # desired operating mode as described above string profile_type # desired 'profile' type - either 'time' or 'velocity' as described above int32 profile_velocity # desired velocity profile as explained above - only used in 'position' or the 'ext_position' control modes int32 profile_acceleration # desired acceleration profile as explained above - used in all modes except for 'current' and 'pwm' control """ __slots__ = ['cmd_type','name','mode','profile_type','profile_velocity','profile_acceleration'] _slot_types = ['string','string','string','string','int32','int32'] def __init__(self, *args, **kwds): """ Constructor. Any message fields that are implicitly/explicitly set to None will be assigned a default value. The recommend use is keyword arguments as this is more robust to future message changes. You cannot mix in-order arguments and keyword arguments. The available fields are: cmd_type,name,mode,profile_type,profile_velocity,profile_acceleration :param args: complete set of field values, in .msg order :param kwds: use keyword arguments corresponding to message field names to set specific fields. """ if args or kwds: super(OperatingModesRequest, self).__init__(*args, **kwds) # message fields cannot be None, assign default values for those that are if self.cmd_type is None: self.cmd_type = '' if self.name is None: self.name = '' if self.mode is None: self.mode = '' if self.profile_type is None: self.profile_type = '' if self.profile_velocity is None: self.profile_velocity = 0 if self.profile_acceleration is None: self.profile_acceleration = 0 else: self.cmd_type = '' self.name = '' self.mode = '' self.profile_type = '' self.profile_velocity = 0 self.profile_acceleration = 0 def _get_types(self): """ internal API method """ return self._slot_types def serialize(self, buff): """ serialize message into buffer :param buff: buffer, ``StringIO`` """ try: _x = self.cmd_type length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.Struct('<I%ss'%length).pack(length, _x)) _x = self.name length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.Struct('<I%ss'%length).pack(length, _x)) _x = self.mode length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.Struct('<I%ss'%length).pack(length, _x)) _x = self.profile_type length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.Struct('<I%ss'%length).pack(length, _x)) _x = self buff.write(_get_struct_2i().pack(_x.profile_velocity, _x.profile_acceleration)) except struct.error as se: self._check_types(struct.error("%s: '%s' when writing '%s'" % (type(se), str(se), str(locals().get('_x', self))))) except TypeError as te: self._check_types(ValueError("%s: '%s' when writing '%s'" % (type(te), str(te), str(locals().get('_x', self))))) def deserialize(self, str): """ unpack serialized message in str into this message instance :param str: byte array of serialized message, ``str`` """ if python3: codecs.lookup_error("rosmsg").msg_type = self._type try: end = 0 start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.cmd_type = str[start:end].decode('utf-8', 'rosmsg') else: self.cmd_type = str[start:end] start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.name = str[start:end].decode('utf-8', 'rosmsg') else: self.name = str[start:end] start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.mode = str[start:end].decode('utf-8', 'rosmsg') else: self.mode = str[start:end] start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.profile_type = str[start:end].decode('utf-8', 'rosmsg') else: self.profile_type = str[start:end] _x = self start = end end += 8 (_x.profile_velocity, _x.profile_acceleration,) = _get_struct_2i().unpack(str[start:end]) return self except struct.error as e: raise genpy.DeserializationError(e) # most likely buffer underfill def serialize_numpy(self, buff, numpy): """ serialize message with numpy array types into buffer :param buff: buffer, ``StringIO`` :param numpy: numpy python module """ try: _x = self.cmd_type length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.Struct('<I%ss'%length).pack(length, _x)) _x = self.name length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.Struct('<I%ss'%length).pack(length, _x)) _x = self.mode length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.Struct('<I%ss'%length).pack(length, _x)) _x = self.profile_type length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.Struct('<I%ss'%length).pack(length, _x)) _x = self buff.write(_get_struct_2i().pack(_x.profile_velocity, _x.profile_acceleration)) except struct.error as se: self._check_types(struct.error("%s: '%s' when writing '%s'" % (type(se), str(se), str(locals().get('_x', self))))) except TypeError as te: self._check_types(ValueError("%s: '%s' when writing '%s'" % (type(te), str(te), str(locals().get('_x', self))))) def deserialize_numpy(self, str, numpy): """ unpack serialized message in str into this message instance using numpy for array types :param str: byte array of serialized message, ``str`` :param numpy: numpy python module """ if python3: codecs.lookup_error("rosmsg").msg_type = self._type try: end = 0 start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.cmd_type = str[start:end].decode('utf-8', 'rosmsg') else: self.cmd_type = str[start:end] start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.name = str[start:end].decode('utf-8', 'rosmsg') else: self.name = str[start:end] start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.mode = str[start:end].decode('utf-8', 'rosmsg') else: self.mode = str[start:end] start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.profile_type = str[start:end].decode('utf-8', 'rosmsg') else: self.profile_type = str[start:end] _x = self start = end end += 8 (_x.profile_velocity, _x.profile_acceleration,) = _get_struct_2i().unpack(str[start:end]) return self except struct.error as e: raise genpy.DeserializationError(e) # most likely buffer underfill _struct_I = genpy.struct_I def _get_struct_I(): global _struct_I return _struct_I _struct_2i = None def _get_struct_2i(): global _struct_2i if _struct_2i is None: _struct_2i = struct.Struct("<2i") return _struct_2i # This Python file uses the following encoding: utf-8 """autogenerated by genpy from interbotix_xs_sdk/OperatingModesResponse.msg. Do not edit.""" import codecs import sys python3 = True if sys.hexversion > 0x03000000 else False import genpy import struct class OperatingModesResponse(genpy.Message): _md5sum = "d41d8cd98f00b204e9800998ecf8427e" _type = "interbotix_xs_sdk/OperatingModesResponse" _has_header = False # flag to mark the presence of a Header object _full_text = """ """ __slots__ = [] _slot_types = [] def __init__(self, *args, **kwds): """ Constructor. Any message fields that are implicitly/explicitly set to None will be assigned a default value. The recommend use is keyword arguments as this is more robust to future message changes. You cannot mix in-order arguments and keyword arguments. The available fields are: :param args: complete set of field values, in .msg order :param kwds: use keyword arguments corresponding to message field names to set specific fields. """ if args or kwds: super(OperatingModesResponse, self).__init__(*args, **kwds) def _get_types(self): """ internal API method """ return self._slot_types def serialize(self, buff): """ serialize message into buffer :param buff: buffer, ``StringIO`` """ try: pass except struct.error as se: self._check_types(struct.error("%s: '%s' when writing '%s'" % (type(se), str(se), str(locals().get('_x', self))))) except TypeError as te: self._check_types(ValueError("%s: '%s' when writing '%s'" % (type(te), str(te), str(locals().get('_x', self))))) def deserialize(self, str): """ unpack serialized message in str into this message instance :param str: byte array of serialized message, ``str`` """ if python3: codecs.lookup_error("rosmsg").msg_type = self._type try: end = 0 return self except struct.error as e: raise genpy.DeserializationError(e) # most likely buffer underfill def serialize_numpy(self, buff, numpy): """ serialize message with numpy array types into buffer :param buff: buffer, ``StringIO`` :param numpy: numpy python module """ try: pass except struct.error as se: self._check_types(struct.error("%s: '%s' when writing '%s'" % (type(se), str(se), str(locals().get('_x', self))))) except TypeError as te: self._check_types(ValueError("%s: '%s' when writing '%s'" % (type(te), str(te), str(locals().get('_x', self))))) def deserialize_numpy(self, str, numpy): """ unpack serialized message in str into this message instance using numpy for array types :param str: byte array of serialized message, ``str`` :param numpy: numpy python module """ if python3: codecs.lookup_error("rosmsg").msg_type = self._type try: end = 0 return self except struct.error as e: raise genpy.DeserializationError(e) # most likely buffer underfill _struct_I = genpy.struct_I def _get_struct_I(): global _struct_I return _struct_I class OperatingModes(object): _type = 'interbotix_xs_sdk/OperatingModes' _md5sum = 'cb68bef3d517c840b0a5cc0f73d64e36' _request_class = OperatingModesRequest _response_class = OperatingModesResponse

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7

e519899586bbce4511a7ae0a743f8707d1f98f97

17,541

py

Python

idaes/generic_models/unit_models/column_models/tests/test_solvent_condenser.py

michaelbynum/idaes-pse

b9c7bc21d0d411657cbe448c40afdc96c41e3465

[ "RSA-MD" ]

1

2019-02-21T22:03:48.000Z

idaes/generic_models/unit_models/column_models/tests/test_solvent_condenser.py

michaelbynum/idaes-pse

b9c7bc21d0d411657cbe448c40afdc96c41e3465

[ "RSA-MD" ]

1

2021-02-27T00:40:54.000Z

2021-03-01T13:51:55.000Z

idaes/generic_models/unit_models/column_models/tests/test_solvent_condenser.py

michaelbynum/idaes-pse

b9c7bc21d0d411657cbe448c40afdc96c41e3465

[ "RSA-MD" ]

1

2021-09-10T16:00:58.000Z

################################################################################# # The Institute for the Design of Advanced Energy Systems Integrated Platform # Framework (IDAES IP) was produced under the DOE Institute for the # Design of Advanced Energy Systems (IDAES), and is copyright (c) 2018-2021 # by the software owners: The Regents of the University of California, through # Lawrence Berkeley National Laboratory, National Technology & Engineering # Solutions of Sandia, LLC, Carnegie Mellon University, West Virginia University # Research Corporation, et al. All rights reserved. # # Please see the files COPYRIGHT.md and LICENSE.md for full copyright and # license information. ################################################################################# """ Tests for solvent condenser unit model. Authors: Andrew Lee """ import pytest from pyomo.environ import (ConcreteModel, Constraint, Param, TerminationCondition, SolverStatus, units, value) from pyomo.util.check_units import (assert_units_consistent, assert_units_equivalent) from idaes.core import FlowsheetBlock from idaes.generic_models.properties.core.generic.generic_property import ( GenericParameterBlock) from idaes.core.util.model_statistics import (degrees_of_freedom, number_variables, number_total_constraints, number_unused_variables) from idaes.core.util.testing import initialization_tester from idaes.core.util import get_solver, scaling as iscale from idaes.generic_models.unit_models.column_models.solvent_condenser import ( SolventCondenser) from idaes.power_generation.carbon_capture.mea_solvent_system.properties.MEA_solvent \ import configuration as aqueous_mea from idaes.power_generation.carbon_capture.mea_solvent_system.properties.MEA_vapor \ import wet_co2 # ----------------------------------------------------------------------------- # Get default solver for testing solver = get_solver() # ----------------------------------------------------------------------------- class TestStripperVaporFlow(object): @pytest.fixture(scope="class") def model(self): m = ConcreteModel() m.fs = FlowsheetBlock(default={"dynamic": False}) m.fs.liquid_properties = GenericParameterBlock(default=aqueous_mea) m.fs.vapor_properties = GenericParameterBlock(default=wet_co2) m.fs.unit = SolventCondenser(default={ "liquid_property_package": m.fs.liquid_properties, "vapor_property_package": m.fs.vapor_properties}) m.fs.unit.inlet.flow_mol[0].fix(1.1117) m.fs.unit.inlet.temperature[0].fix(339.33) m.fs.unit.inlet.pressure[0].fix(184360) m.fs.unit.inlet.mole_frac_comp[0, "CO2"].fix(0.8817) m.fs.unit.inlet.mole_frac_comp[0, "H2O"].fix(0.1183) m.fs.unit.reflux.flow_mol[0].fix(0.1083) iscale.set_scaling_factor( m.fs.unit.vapor_phase.properties_out[0].fug_phase_comp[ "Vap", "CO2"], 1e-5) iscale.set_scaling_factor( m.fs.unit.vapor_phase.properties_out[0].fug_phase_comp[ "Vap", "H2O"], 1e-3) iscale.calculate_scaling_factors(m.fs.unit) return m @pytest.mark.build @pytest.mark.unit def test_build(self, model): assert hasattr(model.fs.unit, "inlet") assert len(model.fs.unit.inlet.vars) == 4 assert hasattr(model.fs.unit.inlet, "flow_mol") assert hasattr(model.fs.unit.inlet, "mole_frac_comp") assert hasattr(model.fs.unit.inlet, "temperature") assert hasattr(model.fs.unit.inlet, "pressure") assert hasattr(model.fs.unit, "reflux") assert len(model.fs.unit.reflux.vars) == 4 assert hasattr(model.fs.unit.reflux, "flow_mol") assert hasattr(model.fs.unit.reflux, "mole_frac_comp") assert hasattr(model.fs.unit.reflux, "temperature") assert hasattr(model.fs.unit.reflux, "pressure") assert hasattr(model.fs.unit, "vapor_outlet") assert len(model.fs.unit.vapor_outlet.vars) == 4 assert hasattr(model.fs.unit.vapor_outlet, "flow_mol") assert hasattr(model.fs.unit.vapor_outlet, "mole_frac_comp") assert hasattr(model.fs.unit.vapor_outlet, "temperature") assert hasattr(model.fs.unit.vapor_outlet, "pressure") assert isinstance(model.fs.unit.unit_material_balance, Constraint) assert isinstance(model.fs.unit.unit_enthalpy_balance, Constraint) assert isinstance(model.fs.unit.unit_temperature_equality, Constraint) assert isinstance(model.fs.unit.unit_pressure_balance, Constraint) assert isinstance(model.fs.unit.zero_flow_param, Param) assert number_variables(model.fs.unit) == 55 assert number_total_constraints(model.fs.unit) == 49 assert number_unused_variables(model.fs.unit) == 0 @pytest.mark.component def test_units(self, model): assert_units_consistent(model) assert_units_equivalent(model.fs.unit.heat_duty[0], units.W) @pytest.mark.unit def test_dof(self, model): assert degrees_of_freedom(model) == 0 @pytest.mark.solver @pytest.mark.skipif(solver is None, reason="Solver not available") @pytest.mark.component def test_initialize(self, model): initialization_tester(model) # @pytest.mark.solver @pytest.mark.skipif(solver is None, reason="Solver not available") @pytest.mark.component def test_solve(self, model): results = solver.solve(model) # Check for optimal solution assert results.solver.termination_condition == \ TerminationCondition.optimal assert results.solver.status == SolverStatus.ok @pytest.mark.solver @pytest.mark.skipif(solver is None, reason="Solver not available") @pytest.mark.component def test_solution(self, model): assert (pytest.approx(0.1083, rel=1e-5) == value(model.fs.unit.reflux.flow_mol[0])) assert (pytest.approx(0, abs=1e-3) == value(model.fs.unit.reflux.mole_frac_comp[0, 'CO2'])) assert (pytest.approx(0, abs=1e-3) == value(model.fs.unit.reflux.mole_frac_comp[0, 'MEA'])) assert (pytest.approx(1, rel=1e-3) == value(model.fs.unit.reflux.mole_frac_comp[0, 'H2O'])) assert (pytest.approx(184360, rel=1e-5) == value(model.fs.unit.reflux.pressure[0])) assert (pytest.approx(303.244, rel=1e-5) == value(model.fs.unit.reflux.temperature[0])) assert (pytest.approx(1.0034, rel=1e-5) == value(model.fs.unit.vapor_outlet.flow_mol[0])) assert (pytest.approx(0.976758, rel=1e-5) == value(model.fs.unit.vapor_outlet.mole_frac_comp[0, 'CO2'])) assert (pytest.approx(0.0232423, rel=1e-5) == value(model.fs.unit.vapor_outlet.mole_frac_comp[0, 'H2O'])) assert (pytest.approx(184360, rel=1e-5) == value(model.fs.unit.vapor_outlet.pressure[0])) assert (pytest.approx(303.244, rel=1e-5) == value(model.fs.unit.vapor_outlet.temperature[0])) assert (pytest.approx(-6264.72, rel=1e-5) == value(model.fs.unit.heat_duty[0])) @pytest.mark.solver @pytest.mark.skipif(solver is None, reason="Solver not available") @pytest.mark.component def test_conservation(self, model): assert abs(value(model.fs.unit.inlet.flow_mol[0] - model.fs.unit.reflux.flow_mol[0] - model.fs.unit.vapor_outlet.flow_mol[0])) <= 1e-6 assert (abs(value(model.fs.unit.inlet.flow_mol[0] * model.fs.unit.inlet.mole_frac_comp[0, "CO2"] - model.fs.unit.reflux.flow_mol[0] * model.fs.unit.reflux.mole_frac_comp[0, "CO2"] - model.fs.unit.vapor_outlet.flow_mol[0] * model.fs.unit.vapor_outlet.mole_frac_comp[0, "CO2"])) <= 1e-6) assert (abs(value(model.fs.unit.inlet.flow_mol[0] * model.fs.unit.inlet.mole_frac_comp[0, "H2O"] - model.fs.unit.reflux.flow_mol[0] * model.fs.unit.reflux.mole_frac_comp[0, "H2O"] - model.fs.unit.vapor_outlet.flow_mol[0] * model.fs.unit.vapor_outlet.mole_frac_comp[0, "H2O"])) <= 1e-6) assert (abs(value(model.fs.unit.reflux.flow_mol[0] * model.fs.unit.reflux.mole_frac_comp[0, "MEA"])) <= 1e-6) assert abs(value( model.fs.unit.vapor_phase.properties_in[0]._enthalpy_flow_term[ "Vap"] - model.fs.unit.vapor_phase.properties_out[0]._enthalpy_flow_term[ "Vap"] - model.fs.unit.liquid_phase[0]._enthalpy_flow_term["Liq"] + model.fs.unit.heat_duty[0])) <= 1e-6 # ----------------------------------------------------------------------------- class TestStripperHeatDuty(object): @pytest.fixture(scope="class") def model(self): m = ConcreteModel() m.fs = FlowsheetBlock(default={"dynamic": False}) m.fs.liquid_properties = GenericParameterBlock(default=aqueous_mea) m.fs.vapor_properties = GenericParameterBlock(default=wet_co2) m.fs.unit = SolventCondenser(default={ "liquid_property_package": m.fs.liquid_properties, "vapor_property_package": m.fs.vapor_properties}) m.fs.unit.inlet.flow_mol[0].fix(1.1117) m.fs.unit.inlet.temperature[0].fix(339.33) m.fs.unit.inlet.pressure[0].fix(184360) m.fs.unit.inlet.mole_frac_comp[0, "CO2"].fix(0.8817) m.fs.unit.inlet.mole_frac_comp[0, "H2O"].fix(0.1183) m.fs.unit.heat_duty.fix(-6264) return m @pytest.mark.build @pytest.mark.unit def test_build(self, model): assert hasattr(model.fs.unit, "inlet") assert len(model.fs.unit.inlet.vars) == 4 assert hasattr(model.fs.unit.inlet, "flow_mol") assert hasattr(model.fs.unit.inlet, "mole_frac_comp") assert hasattr(model.fs.unit.inlet, "temperature") assert hasattr(model.fs.unit.inlet, "pressure") assert hasattr(model.fs.unit, "reflux") assert len(model.fs.unit.reflux.vars) == 4 assert hasattr(model.fs.unit.reflux, "flow_mol") assert hasattr(model.fs.unit.reflux, "mole_frac_comp") assert hasattr(model.fs.unit.reflux, "temperature") assert hasattr(model.fs.unit.reflux, "pressure") assert hasattr(model.fs.unit, "vapor_outlet") assert len(model.fs.unit.vapor_outlet.vars) == 4 assert hasattr(model.fs.unit.vapor_outlet, "flow_mol") assert hasattr(model.fs.unit.vapor_outlet, "mole_frac_comp") assert hasattr(model.fs.unit.vapor_outlet, "temperature") assert hasattr(model.fs.unit.vapor_outlet, "pressure") assert isinstance(model.fs.unit.unit_material_balance, Constraint) assert isinstance(model.fs.unit.unit_enthalpy_balance, Constraint) assert isinstance(model.fs.unit.unit_temperature_equality, Constraint) assert isinstance(model.fs.unit.unit_pressure_balance, Constraint) assert isinstance(model.fs.unit.zero_flow_param, Param) assert number_variables(model.fs.unit) == 55 assert number_total_constraints(model.fs.unit) == 49 assert number_unused_variables(model.fs.unit) == 0 @pytest.mark.component def test_units(self, model): assert_units_consistent(model) assert_units_equivalent(model.fs.unit.heat_duty[0], units.W) @pytest.mark.unit def test_dof(self, model): assert degrees_of_freedom(model) == 0 @pytest.mark.solver @pytest.mark.skipif(solver is None, reason="Solver not available") @pytest.mark.component def test_initialize(self, model): initialization_tester(model) # @pytest.mark.solver @pytest.mark.skipif(solver is None, reason="Solver not available") @pytest.mark.component def test_solve(self, model): results = solver.solve(model) # Check for optimal solution assert results.solver.termination_condition == \ TerminationCondition.optimal assert results.solver.status == SolverStatus.ok @pytest.mark.solver @pytest.mark.skipif(solver is None, reason="Solver not available") @pytest.mark.component def test_solution(self, model): assert (pytest.approx(0.108291, rel=1e-5) == value(model.fs.unit.reflux.flow_mol[0])) assert (pytest.approx(0, abs=1e-3) == value(model.fs.unit.reflux.mole_frac_comp[0, 'CO2'])) assert (pytest.approx(0, abs=1e-3) == value(model.fs.unit.reflux.mole_frac_comp[0, 'MEA'])) assert (pytest.approx(1, rel=1e-3) == value(model.fs.unit.reflux.mole_frac_comp[0, 'H2O'])) assert (pytest.approx(184360, rel=1e-5) == value(model.fs.unit.reflux.pressure[0])) assert (pytest.approx(303.250, rel=1e-5) == value(model.fs.unit.reflux.temperature[0])) assert (pytest.approx(1.0034, rel=1e-5) == value(model.fs.unit.vapor_outlet.flow_mol[0])) assert (pytest.approx(0.976758, rel=1e-5) == value(model.fs.unit.vapor_outlet.mole_frac_comp[0, 'CO2'])) assert (pytest.approx(0.0232509, rel=1e-5) == value(model.fs.unit.vapor_outlet.mole_frac_comp[0, 'H2O'])) assert (pytest.approx(184360, rel=1e-5) == value(model.fs.unit.vapor_outlet.pressure[0])) assert (pytest.approx(303.250, rel=1e-5) == value(model.fs.unit.vapor_outlet.temperature[0])) assert (pytest.approx(-6264, rel=1e-5) == value(model.fs.unit.heat_duty[0])) @pytest.mark.solver @pytest.mark.skipif(solver is None, reason="Solver not available") @pytest.mark.component def test_conservation(self, model): assert abs(value(model.fs.unit.inlet.flow_mol[0] - model.fs.unit.reflux.flow_mol[0] - model.fs.unit.vapor_outlet.flow_mol[0])) <= 1e-6 assert (abs(value(model.fs.unit.inlet.flow_mol[0] * model.fs.unit.inlet.mole_frac_comp[0, "CO2"] - model.fs.unit.reflux.flow_mol[0] * model.fs.unit.reflux.mole_frac_comp[0, "CO2"] - model.fs.unit.vapor_outlet.flow_mol[0] * model.fs.unit.vapor_outlet.mole_frac_comp[0, "CO2"])) <= 1e-6) assert (abs(value(model.fs.unit.inlet.flow_mol[0] * model.fs.unit.inlet.mole_frac_comp[0, "H2O"] - model.fs.unit.reflux.flow_mol[0] * model.fs.unit.reflux.mole_frac_comp[0, "H2O"] - model.fs.unit.vapor_outlet.flow_mol[0] * model.fs.unit.vapor_outlet.mole_frac_comp[0, "H2O"])) <= 1e-6) assert (abs(value(model.fs.unit.reflux.flow_mol[0] * model.fs.unit.reflux.mole_frac_comp[0, "MEA"])) <= 1e-6) assert abs(value( model.fs.unit.vapor_phase.properties_in[0]._enthalpy_flow_term[ "Vap"] - model.fs.unit.vapor_phase.properties_out[0]._enthalpy_flow_term[ "Vap"] - model.fs.unit.liquid_phase[0]._enthalpy_flow_term["Liq"] + model.fs.unit.heat_duty[0])) <= 1e-6 @pytest.mark.component def test_scaling(self, model): iscale.set_scaling_factor( model.fs.unit.vapor_phase.properties_out[0].fug_phase_comp[ "Vap", "CO2"], 1e-5) iscale.set_scaling_factor( model.fs.unit.vapor_phase.properties_out[0].fug_phase_comp[ "Vap", "H2O"], 1e-3) iscale.calculate_scaling_factors(model.fs.unit) assert iscale.get_constraint_transform_applied_scaling_factor( model.fs.unit.unit_material_balance[0, "CO2"]) == 1 assert iscale.get_constraint_transform_applied_scaling_factor( model.fs.unit.unit_material_balance[0, "H2O"]) == 1 assert iscale.get_constraint_transform_applied_scaling_factor( model.fs.unit.unit_material_balance[0, "MEA"]) == 1e8 assert iscale.get_constraint_transform_applied_scaling_factor( model.fs.unit.unit_phase_equilibrium[0, "CO2"]) == 1e-5 assert iscale.get_constraint_transform_applied_scaling_factor( model.fs.unit.unit_phase_equilibrium[0, "H2O"]) == 1e-3 assert iscale.get_constraint_transform_applied_scaling_factor( model.fs.unit.unit_temperature_equality[0]) == 1e-2 assert iscale.get_constraint_transform_applied_scaling_factor( model.fs.unit.unit_enthalpy_balance[0]) == 1 assert iscale.get_constraint_transform_applied_scaling_factor( model.fs.unit.unit_pressure_balance[0]) == 1e-5

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Python

tests/pulses/sequences/test_eval_simplify_sequences.py

jerjohste/exopy_pulses

844660082331f8972039a085397a92c9a06a46af

[ "BSD-3-Clause" ]

2

2016-02-09T20:23:16.000Z

2017-09-04T10:18:45.000Z

tests/pulses/sequences/test_eval_simplify_sequences.py

jerjohste/exopy_pulses

844660082331f8972039a085397a92c9a06a46af

[ "BSD-3-Clause" ]

15

2015-12-14T21:58:50.000Z

2017-10-12T07:04:33.000Z

tests/pulses/sequences/test_eval_simplify_sequences.py

jerjohste/exopy_pulses

844660082331f8972039a085397a92c9a06a46af

[ "BSD-3-Clause" ]

2

2016-02-09T20:23:16.000Z

2017-09-07T09:41:36.000Z

# -*- coding: utf-8 -*- # ----------------------------------------------------------------------------- # Copyright 2015-2018 by ExopyPulses Authors, see AUTHORS for more details. # # Distributed under the terms of the BSD license. # # The full license is in the file LICENCE, distributed with this software. # ----------------------------------------------------------------------------- """Test evaluating and simplifying base sequences """ from collections import OrderedDict import pytest from exopy_pulses.pulses.pulse import Pulse from exopy_pulses.pulses.shapes.square_shape import SquareShape from exopy_pulses.pulses.sequences.base_sequences\ import RootSequence, BaseSequence from exopy_pulses.testing.context import DummyContext @pytest.fixture def root(): root = RootSequence() context = DummyContext(sampling=0.5) root.context = context return root def add_children(seq, children): """Add a sequence of item to a BaseSequence. """ for i, c in enumerate(children): seq.add_child_item(i, c) def test_sequence_compilation1(root): """Test compiling a flat sequence. """ root.external_vars = OrderedDict({'a': 1.5}) root.local_vars = OrderedDict({'b': '2*{a}'}) pulse1 = Pulse(def_1='1.0', def_2='{a}', kind='Analogical', shape=SquareShape(amplitude='0.5', _cache={'amplitude': 1.0})) pulse2 = Pulse(def_1='{a} + 1.0', def_2='3.0') pulse3 = Pulse(def_1='{2_stop} + 0.5', def_2='10 + {b}') add_children(root, (pulse1, pulse2, pulse3)) res, missings, errors = root.evaluate_sequence() print(errors) pulses = root.items assert res assert len(pulses) == 3 assert pulses[0].start == 1.0 assert pulses[0].stop == 1.5 assert pulses[0].duration == 0.5 assert pulses[0].shape._cache['amplitude'] == 0.5 assert pulses[1].start == 2.5 assert pulses[1].stop == 3.0 assert pulses[1].duration == 0.5 assert pulses[2].start == 3.5 assert pulses[2].stop == 13.0 assert pulses[2].duration == 9.5 def test_sequence_compilation1bis(root): """Compiles two times a sequence while changing a parameter to make sure the cache is cleaned in between Also validate that the context cache is cleaned """ root.external_vars = OrderedDict({'a': 1.5}) pulse1 = Pulse(def_1='1.0', def_2='{a}') pulse2 = Pulse(def_1='{a} + 1.0', def_2='4.0') pulse3 = Pulse(def_1='{2_stop} + 0.5', def_2='10') add_children(root, (pulse1, pulse2, pulse3)) res, _, _ = root.evaluate_sequence() pulses = root.items context = root.context assert not context._cache context._cache = {'a': 1} assert res assert len(pulses) == 3 assert pulses[0].stop == 1.5 root.external_vars = OrderedDict({'a': 2.}) res = root.evaluate_sequence() pulses = root.items context = root.context assert not context._cache assert res assert len(pulses) == 3 assert pulses[0].stop == 2. def test_sequence_compilation2(root): """Test compiling a flat sequence of fixed duration. """ root.external_vars = OrderedDict({'a': 1.5}) root.time_constrained = True root.sequence_duration = '10.0' pulse1 = Pulse(def_1='1.0', def_2='{a}') pulse2 = Pulse(def_1='{a} + 1.0', def_2='3.0') pulse3 = Pulse(def_1='{2_stop} + 0.5', def_2='{sequence_end}') add_children(root, (pulse1, pulse2, pulse3)) res, missings, errors = root.evaluate_sequence() print(errors) pulses = root.items assert res assert len(pulses) == 3 assert pulses[0].start == 1.0 assert pulses[0].stop == 1.5 assert pulses[0].duration == 0.5 assert pulses[1].start == 2.5 assert pulses[1].stop == 3.0 assert pulses[1].duration == 0.5 assert pulses[2].start == 3.5 assert pulses[2].stop == 10.0 assert pulses[2].duration == 6.5 def test_sequence_compilation2bis(root): """Test compiling a flat sequence of fixed duration but with a pulse stopping too late. """ root.external_vars = OrderedDict({'a': 1.5}) root.time_constrained = True root.sequence_duration = '10.0' pulse1 = Pulse(def_1='1.0', def_2='{a}') pulse2 = Pulse(def_1='{a} + 1.0', def_2='3.0') pulse3 = Pulse(def_1='{2_stop} + 0.5', def_2='{sequence_end} + 1') add_children(root, (pulse1, pulse2, pulse3)) res, missings, errors = root.evaluate_sequence() assert not res assert 'root-stop' in errors def test_sequence_compilation3(root): """Test compiling a flat sequence in two passes. """ root.external_vars = OrderedDict({'a': 1.5}) pulse1 = Pulse(def_1='1.0', def_2='{2_start} - 1.0') pulse2 = Pulse(def_1='{a} + 1.0', def_2='3.0') pulse3 = Pulse(def_1='{2_stop} + 0.5', def_2='10') add_children(root, (pulse1, pulse2, pulse3)) res, _, _ = root.evaluate_sequence() pulses = root.items assert res assert len(pulses) == 3 assert pulses[0].start == 1.0 assert pulses[0].stop == 1.5 assert pulses[0].duration == 0.5 assert pulses[1].start == 2.5 assert pulses[1].stop == 3.0 assert pulses[1].duration == 0.5 assert pulses[2].start == 3.5 assert pulses[2].stop == 10.0 assert pulses[2].duration == 6.5 def test_sequence_compilation4(root): """Test compiling a flat sequence with circular references. """ root.external_vars = OrderedDict({'a': 1.5}) pulse1 = Pulse(def_1='1.0', def_2='{2_start} - 1.0') pulse2 = Pulse(def_1='{1_stop} + 1.0', def_2='3.0') pulse3 = Pulse(def_1='{2_stop} + 0.5', def_2='10') add_children(root, (pulse1, pulse2, pulse3)) res, missings, errors = root.evaluate_sequence() assert not res assert len(missings) == 2 assert '1_stop' in missings assert '2_start' in missings assert len(errors) == 0 def test_sequence_compilation5(root): """Test compiling a flat sequence with evaluation errors. missing global """ root.time_constrained = True root.sequence_duration = '10.0' pulse1 = Pulse(def_1='1.0', def_2='{a}') pulse2 = Pulse(def_1='{a} + 1.0', def_2='3.0') pulse3 = Pulse(def_1='{2_stop} + 0.5', def_2='{sequence_end}') add_children(root, (pulse1, pulse2, pulse3)) res, missings, errors = root.evaluate_sequence() assert not res assert len(missings) == 1 assert 'a' in missings assert len(errors) == 0 def test_sequence_compilation6(root): """Test compiling a flat sequence with evaluation errors. wrong string value """ root.external_vars = OrderedDict({'a': 1.5}) root.time_constrained = True root.sequence_duration = '*10.0*' pulse1 = Pulse(def_1='1.0', def_2='{a}') pulse2 = Pulse(def_1='{a} +* 1.0', def_2='3.0') pulse3 = Pulse(def_1='{2_stop} + 0.5', def_2='10.0') add_children(root, (pulse1, pulse2, pulse3)) res, missings, errors = root.evaluate_sequence() assert not res assert not missings assert len(errors) == 2 assert '2_start' in errors assert 'root_seq_duration' in errors def test_sequence_compilation6bis(root): """Test compiling a flat sequence with evaluation errors. local vars of root """ root.time_constrained = True root.sequence_duration = '10.0' root.external_vars = OrderedDict({'a': 1.5}) root.local_vars = OrderedDict({'b': '2*{a}+', 'c': '{dummy}'}) pulse1 = Pulse(def_1='1.0', def_2='{a}') pulse2 = Pulse(def_1='{a} + 1.0', def_2='3.0') pulse3 = Pulse(def_1='{2_stop} + 0.5', def_2='{sequence_end}') add_children(root, (pulse1, pulse2, pulse3)) res, missings, errors = root.evaluate_sequence() assert not res assert 'dummy' in missings assert 'root_b' in errors def test_sequence_compilation6ter(root): """Test compiling a flat sequence with evaluation errors. wrong string value """ root.external_vars = OrderedDict({'a': 1.5}) root.time_constrained = True root.sequence_duration = '10.0*{dummy}' pulse1 = Pulse(def_1='1.0', def_2='{a}') pulse2 = Pulse(def_1='{a} +* 1.0', def_2='3.0') pulse3 = Pulse(def_1='{2_stop} + 0.5', def_2='10.0') add_children(root, (pulse1, pulse2, pulse3)) res, missings, errors = root.evaluate_sequence() assert not res assert 'dummy' in missings assert len(errors) == 1 assert '2_start' in errors def test_sequence_compilation7(root): """Test compiling a nested sequence with a disabled item """ root.external_vars = OrderedDict({'a': 1.5}) pulse1 = Pulse(def_1='1.0', def_2='{a}') pulse2 = Pulse(def_1='{a} + 1.0', def_2='3.0') pulse3 = Pulse(def_1='{3_stop} + 0.5', def_2='10.0') pulse3bis = Pulse(def_1='{3_stop} + 0.5', def_2='10.0', enabled=False) pulse4 = Pulse(def_1='2.0', def_2='0.5', def_mode='Start/Duration') pulse5 = Pulse(def_1='3.0', def_2='0.5', def_mode='Start/Duration') sequence2 = BaseSequence() add_children(sequence2, (pulse3, pulse3bis)) sequence1 = BaseSequence() add_children(sequence1, (pulse2, sequence2, pulse4)) add_children(root, (pulse1, sequence1, pulse5)) res, missings, errors = root.evaluate_sequence() pulses = root.simplify_sequence() assert res assert len(pulses) == 5 assert pulses[0] is pulse1 assert pulses[0].start == 1.0 assert pulses[0].stop == 1.5 assert pulses[0].duration == 0.5 assert pulses[1] is pulse2 assert pulses[1].start == 2.5 assert pulses[1].stop == 3.0 assert pulses[1].duration == 0.5 assert pulses[2] is pulse3 assert pulses[2].start == 3.5 assert pulses[2].stop == 10.0 assert pulses[2].duration == 6.5 assert pulses[3] is pulse4 assert pulses[3].start == 2.0 assert pulses[3].stop == 2.5 assert pulses[3].duration == 0.5 assert pulses[4] is pulse5 assert pulses[4].start == 3.0 assert pulses[4].stop == 3.5 assert pulses[4].duration == 0.5 def test_sequence_compilation8(root): """Test compiling a nested sequence in two passes on the external sequence. """ root.external_vars = OrderedDict({'a': 1.5}) pulse1 = Pulse(def_1='1.0', def_2='{7_start} - 1.0') pulse2 = Pulse(def_1='{a} + 1.0', def_2='3.0') pulse3 = Pulse(def_1='{3_stop} + 0.5', def_2='10.0') pulse4 = Pulse(def_1='2.0', def_2='0.5', def_mode='Start/Duration') pulse5 = Pulse(def_1='3.0', def_2='0.5', def_mode='Start/Duration') sequence2 = BaseSequence() sequence2.add_child_item(0, pulse3) sequence1 = BaseSequence() add_children(sequence1, (pulse2, sequence2, pulse4)) add_children(root, (pulse1, sequence1, pulse5)) res, missings, errors = root.evaluate_sequence() pulses = root.simplify_sequence() assert res assert len(pulses) == 5 assert pulses[0] is pulse1 assert pulses[0].start == 1.0 assert pulses[0].stop == 2.0 assert pulses[0].duration == 1.0 assert pulses[1] is pulse2 assert pulses[1].start == 2.5 assert pulses[1].stop == 3.0 assert pulses[1].duration == 0.5 assert pulses[2] is pulse3 assert pulses[2].start == 3.5 assert pulses[2].stop == 10.0 assert pulses[2].duration == 6.5 assert pulses[3] is pulse4 assert pulses[3].start == 2.0 assert pulses[3].stop == 2.5 assert pulses[3].duration == 0.5 assert pulses[4] is pulse5 assert pulses[4].start == 3.0 assert pulses[4].stop == 3.5 assert pulses[4].duration == 0.5 def test_sequence_compilation9(root): """Test compiling a nested sequence in multi passes. """ root.external_vars = OrderedDict({'a': 1.5}) pulse1 = Pulse(def_1='1.0', def_2='{7_start} - 1.0') pulse2 = Pulse(def_1='{a} + 1.0', def_2='{6_start} + 1.0') pulse3 = Pulse(def_1='{3_stop} + 0.5', def_2='10.0') pulse4 = Pulse(def_1='2.0', def_2='0.5', def_mode='Start/Duration') pulse5 = Pulse(def_1='3.0', def_2='0.5', def_mode='Start/Duration') sequence2 = BaseSequence() sequence2.add_child_item(0, pulse3) sequence1 = BaseSequence() add_children(sequence1, (pulse2, sequence2, pulse4)) add_children(root, (pulse1, sequence1, pulse5)) res, missings, errors = root.evaluate_sequence() pulses = root.simplify_sequence() assert res assert len(pulses) == 5 assert pulses[0] is pulse1 assert pulses[0].start == 1.0 assert pulses[0].stop == 2.0 assert pulses[0].duration == 1.0 assert pulses[1] is pulse2 assert pulses[1].start == 2.5 assert pulses[1].stop == 3.0 assert pulses[1].duration == 0.5 assert pulses[2] is pulse3 assert pulses[2].start == 3.5 assert pulses[2].stop == 10.0 assert pulses[2].duration == 6.5 assert pulses[3] is pulse4 assert pulses[3].start == 2.0 assert pulses[3].stop == 2.5 assert pulses[3].duration == 0.5 assert pulses[4] is pulse5 assert pulses[4].start == 3.0 assert pulses[4].stop == 3.5 assert pulses[4].duration == 0.5 def test_sequence_compilation10(root): """Test compiling a nested sequence with circular reference in the deep one. """ root.external_vars = OrderedDict({'a': 1.5}) pulse1 = Pulse(def_1='1.0', def_2='{7_start} - 1.0') pulse2 = Pulse(def_1='{a} + 1.0', def_2='{6_start} + 1.0') pulse3 = Pulse(def_1='{3_stop} + 0.5', def_2='10.0') pulse4 = Pulse(def_1='2.0', def_2='0.5', def_mode='Start/Duration') pulse5 = Pulse(def_1='{1_stop}', def_2='0.5', def_mode='Start/Duration') sequence2 = BaseSequence() sequence2.add_child_item(0, pulse3) sequence1 = BaseSequence() add_children(sequence1, (pulse2, sequence2, pulse4)) add_children(root, (pulse1, sequence1, pulse5)) res, missings, errors = root.evaluate_sequence() assert not res assert len(missings) == 2 assert '7_start' in missings assert '1_stop' in missings assert not errors def test_sequence_compilation11(root): """Test compiling a nested sequence with circular reference in the deep one. """ root.external_vars = OrderedDict({'a': 1.5}) pulse1 = Pulse(def_1='1.0', def_2='{7_start} - 1.0') pulse2 = Pulse(def_1='{a} + 1.0', def_2='{6_start} + 1.0') pulse3 = Pulse(def_1='{3_stop} + *0.5', def_2='10.0') pulse4 = Pulse(def_1='2.0', def_2='0.5', def_mode='Start/Duration') pulse5 = Pulse(def_1='3.0', def_2='0.5', def_mode='Start/Duration') sequence2 = BaseSequence() sequence2.add_child_item(0, pulse3) sequence1 = BaseSequence() add_children(sequence1, (pulse2, sequence2, pulse4)) add_children(root, (pulse1, sequence1, pulse5)) res, missings, errors = root.evaluate_sequence() assert not res assert len(errors) == 1 assert '5_start' in errors def test_sequence_compilation12(root): """Test compiling a nested sequence using local vars. """ root.external_vars = OrderedDict({'a': 1.5}) pulse1 = Pulse(def_1='1.0', def_2='{a}') pulse2 = Pulse(def_1='{a} + 1.0', def_2='3.0') pulse3 = Pulse(def_1='{3_stop} + 0.5', def_2='{b}') pulse4 = Pulse(def_1='2.0', def_2='0.5', def_mode='Start/Duration') pulse5 = Pulse(def_1='3.0', def_2='0.5', def_mode='Start/Duration') sequence2 = BaseSequence(local_vars=OrderedDict({'b': '2**2'})) sequence2.add_child_item(0, pulse3) sequence1 = BaseSequence() add_children(sequence1, (pulse2, sequence2, pulse4)) add_children(root, (pulse1, sequence1, pulse5)) res, missings, errors = root.evaluate_sequence() print(missings, errors) pulses = root.simplify_sequence() assert res assert len(pulses) == 5 assert pulses[0] is pulse1 assert pulses[0].start == 1.0 assert pulses[0].stop == 1.5 assert pulses[0].duration == 0.5 assert pulses[1] is pulse2 assert pulses[1].start == 2.5 assert pulses[1].stop == 3.0 assert pulses[1].duration == 0.5 assert pulses[2] is pulse3 assert pulses[2].start == 3.5 assert pulses[2].stop == 4 assert pulses[2].duration == 0.5 assert pulses[3] is pulse4 assert pulses[3].start == 2.0 assert pulses[3].stop == 2.5 assert pulses[3].duration == 0.5 assert pulses[4] is pulse5 assert pulses[4].start == 3.0 assert pulses[4].stop == 3.5 assert pulses[4].duration == 0.5 def test_sequence_compilation13(root): """Test compiling a nested sequence with wrong local vars definitions. """ root.external_vars = OrderedDict({'a': 1.5}) pulse1 = Pulse(def_1='1.0', def_2='{a}') pulse2 = Pulse(def_1='{a} + 1.0', def_2='3.0') pulse3 = Pulse(def_1='{3_stop} + 0.5', def_2='{b}') pulse4 = Pulse(def_1='2.0', def_2='0.5', def_mode='Start/Duration') pulse5 = Pulse(def_1='3.0', def_2='0.5', def_mode='Start/Duration') sequence2 = BaseSequence(local_vars=OrderedDict({'b': '2**', 'c': '{dummy}'})) sequence2.add_child_item(0, pulse3) sequence1 = BaseSequence() add_children(sequence1, (pulse2, sequence2, pulse4)) add_children(root, (pulse1, sequence1, pulse5)) res, missings, errors = root.evaluate_sequence() assert not res assert len(missings) == 2 assert 'b' in missings assert 'dummy' in missings assert '4_b' in errors def test_sequence_compilation14(root): """Test the locality of local vars. """ root.external_vars = OrderedDict({'a': 1.5}) pulse1 = Pulse(def_1='1.0', def_2='{a}') pulse2 = Pulse(def_1='{a} + 1.0', def_2='3.0') pulse3 = Pulse(def_1='{3_stop} + 0.5', def_2='{b}') pulse4 = Pulse(def_1='2.0', def_2='0.5', def_mode='Start/Duration') pulse5 = Pulse(def_1='3.0', def_2='{b}', def_mode='Start/Duration') sequence2 = BaseSequence(local_vars=OrderedDict({'b': '2**2'})) sequence2.add_child_item(0, pulse3) sequence1 = BaseSequence() add_children(sequence1, (pulse2, sequence2, pulse4)) add_children(root, (pulse1, sequence1, pulse5)) res, missings, errors = root.evaluate_sequence() assert not res assert len(missings) == 1 assert 'b' in missings assert not errors # No test of the evaluation errors on the defs as this is handled # at the Item level and tested in the test of the Pulses. def test_sequence_compilation15(root): """Test compiling a nested sequence with internal fixed length. """ root.external_vars = OrderedDict({'a': 1.5}) pulse1 = Pulse(def_1='1.0', def_2='{a}') pulse2 = Pulse(def_1='{a} + 1.0', def_2='3.0') pulse3 = Pulse(def_1='{4_start} + 0.5', def_2='{4_start}+{4_duration}-0.5') pulse4 = Pulse(def_1='2.0', def_2='0.5', def_mode='Start/Duration') pulse5 = Pulse(def_1='3.0', def_2='0.5', def_mode='Start/Duration') sequence2 = BaseSequence(time_constrained=True, def_1='{3_stop} + 0.5', def_2='6') sequence2.add_child_item(0, pulse3) sequence1 = BaseSequence() add_children(sequence1, (pulse2, sequence2, pulse4)) add_children(root, (pulse1, sequence1, pulse5)) res, missings, errors = root.evaluate_sequence() pulses = root.simplify_sequence() assert res assert len(pulses) == 5 assert pulses[0] is pulse1 assert pulses[0].start == 1.0 assert pulses[0].stop == 1.5 assert pulses[0].duration == 0.5 assert pulses[1] is pulse2 assert pulses[1].start == 2.5 assert pulses[1].stop == 3.0 assert pulses[1].duration == 0.5 assert pulses[2] is pulse3 assert pulses[2].start == 4 assert pulses[2].stop == 5.5 assert pulses[2].duration == 1.5 assert pulses[3] is pulse4 assert pulses[3].start == 2.0 assert pulses[3].stop == 2.5 assert pulses[3].duration == 0.5 assert pulses[4] is pulse5 assert pulses[4].start == 3.0 assert pulses[4].stop == 3.5 assert pulses[4].duration == 0.5 def test_sequence_compilation16(root): """Test compiling a nested sequence with internal fixed length but incoherent pulse start. """ root.external_vars = OrderedDict({'a': 1.5}) pulse1 = Pulse(def_1='1.0', def_2='{a}') pulse2 = Pulse(def_1='{a} + 1.0', def_2='3.0') pulse3 = Pulse(def_1='{4_start} - 0.5', def_2='{4_start}+{4_duration}-0.5') pulse4 = Pulse(def_1='2.0', def_2='0.5', def_mode='Start/Duration') pulse5 = Pulse(def_1='3.0', def_2='0.5', def_mode='Start/Duration') sequence2 = BaseSequence(time_constrained=True, def_1='{3_stop} + 0.5', def_2='6', name='test') sequence2.add_child_item(0, pulse3) sequence1 = BaseSequence() add_children(sequence1, (pulse2, sequence2, pulse4)) add_children(root, (pulse1, sequence1, pulse5)) res, missings, errors = root.evaluate_sequence() assert not res assert 'test-start' in errors def test_sequence_compilation17(root): """Test compiling a nested sequence with internal fixed length but incoherent pulse stop. """ root.external_vars = OrderedDict({'a': 1.5}) pulse1 = Pulse(def_1='1.0', def_2='{a}') pulse2 = Pulse(def_1='{a} + 1.0', def_2='3.0') pulse3 = Pulse(def_1='{4_start} + 0.5', def_2='{4_start}+{4_duration}+0.5') pulse4 = Pulse(def_1='2.0', def_2='0.5', def_mode='Start/Duration') pulse5 = Pulse(def_1='3.0', def_2='0.5', def_mode='Start/Duration') sequence2 = BaseSequence(time_constrained=True, def_1='{3_stop} + 0.5', def_2='6', name='test') sequence2.add_child_item(0, pulse3) sequence1 = BaseSequence() add_children(sequence1, (pulse2, sequence2, pulse4)) add_children(root, (pulse1, sequence1, pulse5)) res, missings, errors = root.evaluate_sequence() assert not res assert not missings assert 'test-stop' in errors def test_sequence_compilation18(root): """Test compiling a nested fixed duration sequence. """ root.external_vars = OrderedDict({'a': 1.5}) root.time_constrained = True root.sequence_duration = '100' pulse1 = Pulse(def_1='1.0', def_2='{a}') pulse2 = Pulse(def_1='{a} + 1.0', def_2='3.0') pulse3 = Pulse(def_1='{4_start} + 0.5', def_2='{4_start}+{4_duration}-0.5') pulse4 = Pulse(def_1='2.0', def_2='0.5', def_mode='Start/Duration') pulse5 = Pulse(def_1='3.0', def_2='0.5', def_mode='Start/Duration') sequence2 = BaseSequence(time_constrained=True, def_1='{3_stop} + 0.5', def_2='6', name='test') sequence2.add_child_item(0, pulse3) sequence1 = BaseSequence() add_children(sequence1, (pulse2, sequence2, pulse4)) add_children(root, (pulse1, sequence1, pulse5)) res, missings, errors = root.evaluate_sequence() print(errors) assert res root.sequence_duration = '1' res, missings, errors = root.evaluate_sequence() assert not res assert 'root-stop' in errors

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antonOO/vmware-openapi-generator-1

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[ "MIT" ]

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2018-12-07T18:54:25.000Z

2021-12-06T23:10:41.000Z

lib/__init__.py

antonOO/vmware-openapi-generator-1

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2019-01-07T08:38:36.000Z

2021-04-28T15:52:51.000Z

lib/__init__.py

antonOO/vmware-openapi-generator-1

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from .api_endpoint.api_metadata_processor import ApiMetadataProcessor from .rest_endpoint.rest_metadata_processor import RestMetadataProcessor

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install/app_store/tk-multi-workfiles/v0.7.4/python/tk_multi_workfiles/ui/resources_rc.py

JoanAzpeitia/lp_sg

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bundle_cache/app_store/tk-multi-workfiles/v0.7.4/python/tk_multi_workfiles/ui/resources_rc.py

ColinKennedy/tk-config-default2-respawn

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bundle_cache/app_store/tk-multi-workfiles/v0.7.4/python/tk_multi_workfiles/ui/resources_rc.py

ColinKennedy/tk-config-default2-respawn

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# -*- coding: utf-8 -*- # Resource object code # # by: The Resource Compiler for PySide (Qt v4.7.4) # # WARNING! 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Python

src/graph_transpiler/webdnn/frontend/tensorflow/ops/gen_spectral_ops.py

steerapi/webdnn

1df51cc094e5a528cfd3452c264905708eadb491

[ "MIT" ]

1

2021-04-09T15:55:35.000Z

src/graph_transpiler/webdnn/frontend/tensorflow/ops/gen_spectral_ops.py

steerapi/webdnn

1df51cc094e5a528cfd3452c264905708eadb491

[ "MIT" ]

null

src/graph_transpiler/webdnn/frontend/tensorflow/ops/gen_spectral_ops.py

steerapi/webdnn

1df51cc094e5a528cfd3452c264905708eadb491

[ "MIT" ]

null

import tensorflow as tf from webdnn.frontend.tensorflow.converter import TensorFlowConverter @TensorFlowConverter.register_handler("BatchFFT") def batch_fft_handler(converter: TensorFlowConverter, tf_op: "tf.Operation"): raise NotImplementedError(f"[TensorFlowConverter] {tf_op.type} is not supported yet.") @TensorFlowConverter.register_handler("BatchFFT2D") def batch_fft2_d_handler(converter: TensorFlowConverter, tf_op: "tf.Operation"): raise NotImplementedError(f"[TensorFlowConverter] {tf_op.type} is not supported yet.") @TensorFlowConverter.register_handler("BatchFFT3D") def batch_fft3_d_handler(converter: TensorFlowConverter, tf_op: "tf.Operation"): raise NotImplementedError(f"[TensorFlowConverter] {tf_op.type} is not supported yet.") @TensorFlowConverter.register_handler("BatchIFFT") def batch_ifft_handler(converter: TensorFlowConverter, tf_op: "tf.Operation"): raise NotImplementedError(f"[TensorFlowConverter] {tf_op.type} is not supported yet.") @TensorFlowConverter.register_handler("BatchIFFT2D") def batch_ifft2_d_handler(converter: TensorFlowConverter, tf_op: "tf.Operation"): raise NotImplementedError(f"[TensorFlowConverter] {tf_op.type} is not supported yet.") @TensorFlowConverter.register_handler("BatchIFFT3D") def batch_ifft3_d_handler(converter: TensorFlowConverter, tf_op: "tf.Operation"): raise NotImplementedError(f"[TensorFlowConverter] {tf_op.type} is not supported yet.") @TensorFlowConverter.register_handler("FFT") def fft_handler(converter: TensorFlowConverter, tf_op: "tf.Operation"): raise NotImplementedError(f"[TensorFlowConverter] {tf_op.type} is not supported yet.") @TensorFlowConverter.register_handler("FFT2D") def fft2_d_handler(converter: TensorFlowConverter, tf_op: "tf.Operation"): raise NotImplementedError(f"[TensorFlowConverter] {tf_op.type} is not supported yet.") @TensorFlowConverter.register_handler("FFT3D") def fft3_d_handler(converter: TensorFlowConverter, tf_op: "tf.Operation"): raise NotImplementedError(f"[TensorFlowConverter] {tf_op.type} is not supported yet.") @TensorFlowConverter.register_handler("IFFT") def ifft_handler(converter: TensorFlowConverter, tf_op: "tf.Operation"): raise NotImplementedError(f"[TensorFlowConverter] {tf_op.type} is not supported yet.") @TensorFlowConverter.register_handler("IFFT2D") def ifft2_d_handler(converter: TensorFlowConverter, tf_op: "tf.Operation"): raise NotImplementedError(f"[TensorFlowConverter] {tf_op.type} is not supported yet.") @TensorFlowConverter.register_handler("IFFT3D") def ifft3_d_handler(converter: TensorFlowConverter, tf_op: "tf.Operation"): raise NotImplementedError(f"[TensorFlowConverter] {tf_op.type} is not supported yet.") @TensorFlowConverter.register_handler("IRFFT") def irfft_handler(converter: TensorFlowConverter, tf_op: "tf.Operation"): raise NotImplementedError(f"[TensorFlowConverter] {tf_op.type} is not supported yet.") @TensorFlowConverter.register_handler("IRFFT2D") def irfft2_d_handler(converter: TensorFlowConverter, tf_op: "tf.Operation"): raise NotImplementedError(f"[TensorFlowConverter] {tf_op.type} is not supported yet.") @TensorFlowConverter.register_handler("IRFFT3D") def irfft3_d_handler(converter: TensorFlowConverter, tf_op: "tf.Operation"): raise NotImplementedError(f"[TensorFlowConverter] {tf_op.type} is not supported yet.") @TensorFlowConverter.register_handler("RFFT") def rfft_handler(converter: TensorFlowConverter, tf_op: "tf.Operation"): raise NotImplementedError(f"[TensorFlowConverter] {tf_op.type} is not supported yet.") @TensorFlowConverter.register_handler("RFFT2D") def rfft2_d_handler(converter: TensorFlowConverter, tf_op: "tf.Operation"): raise NotImplementedError(f"[TensorFlowConverter] {tf_op.type} is not supported yet.") @TensorFlowConverter.register_handler("RFFT3D") def rfft3_d_handler(converter: TensorFlowConverter, tf_op: "tf.Operation"): raise NotImplementedError(f"[TensorFlowConverter] {tf_op.type} is not supported yet.")

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10

00bf01d0081cf21b9b584a14f75c742856ed45f7

220

py

Python

tests/__main__.py

dipietrantonio/pdf4py

1b09fe720a30902d295db85c295a8758768840ad

[ "MIT" ]

null

tests/__main__.py

dipietrantonio/pdf4py

1b09fe720a30902d295db85c295a8758768840ad

[ "MIT" ]

null

tests/__main__.py

dipietrantonio/pdf4py

1b09fe720a30902d295db85c295a8758768840ad

[ "MIT" ]

1

2021-12-22T07:46:34.000Z

import unittest from .functional_tests import * from .unit_tests import * from .aes_unit_tests import * from .decrypt_unit_tests import * from .decoders_unit_tests import * if __name__ == "__main__": unittest.main()

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Python

join.py

AGH-Narzedzia-Informatyczne/Project_Labyrinth

317f744500fb73f9b8961ec725904cae00aadb92

[ "MIT" ]

1

2020-12-16T14:32:23.000Z

join.py

Pandoors/Project_Labyrinth

317f744500fb73f9b8961ec725904cae00aadb92

[ "MIT" ]

5

2020-11-22T19:34:42.000Z

2020-12-10T23:57:38.000Z

join.py

Pandoors/Project_Labyrinth

317f744500fb73f9b8961ec725904cae00aadb92

[ "MIT" ]

5

2020-12-16T14:31:48.000Z

2020-12-16T14:32:17.000Z

# POPRZEDNI UI. ZOSTAŁ JEDNAK ODRZUCONY I PRZESZLIŚMY NA TKINTER. # import labirynth_generator as bk_lab # import PyMazeDFS # import PyMazeBFS # import DFS_generator.mazeToGraphic as mazeToGraphic # import prim as prim # import binary_tree as bintree # # import PySimpleGUI as sg # # sg.theme('Topanga') # # # def bartek(): # layout = [[sg.Text("Labirynt generwany algorytmem BFS wersja 2", justification='center', font='Helvetica 15')], # [sg.Text('Podaj wymiar N labiryntu:', justification='center', font='Helvetica 15'), # sg.InputText(size=(8, 5), font='Helvetica 18')], # [sg.Button("Generuj labirynt NxN", size=(15, 1), font='Helvetica 18')], # [sg.Button("Zamknij okno", size=(10, 1), font='Helvetica 18')] # ] # # Create the Window # window = sg.Window('Generator labiryntów', layout, element_justification='c') # # Event Loop to process "events" # while True: # event, values = window.read() # print(event, values) # if event in (None, 'Zamknij okno'): # break # if event in (None, 'Generuj labirynt NxN'): # bk_lab.generate(int(values[0])) # window.close() # # # def hania(): # layout = [[sg.Text("Labirynt generwany algorytmem Prima", justification='center', font='Helvetica 15')], # [sg.Text('Podaj wymiar N labiryntu:', justification='center', font='Helvetica 15'), # sg.InputText(size=(8, 5), font='Helvetica 18')], # [sg.Button("Generuj labirynt NxN", size=(15, 1), font='Helvetica 18')], # [sg.Button("Zamknij okno", size=(10, 1), font='Helvetica 18')] # ] # # Create the Window # window = sg.Window('Generator labiryntów', layout, element_justification='c') # # Event Loop to process "events" # while True: # event, values = window.read() # print(event, values) # if event in (None, 'Zamknij okno'): # break # if event in (None, 'Generuj labirynt NxN'): # prim.hania_prim(int(values[0])) # window.close() # # # def pawel(): # layout = [[sg.Text("Labirynt generwany algorytmem BFS wersja 2", justification='center', font='Helvetica 15')], # [sg.Text('Podaj wymiar N labiryntu:', justification='center', font='Helvetica 15'), # sg.InputText(size=(8, 5), font='Helvetica 18')], # [sg.Button("Generuj labirynt NxN", size=(15, 1), font='Helvetica 18')], # [sg.Button("Zamknij okno", size=(10, 1), font='Helvetica 18')] # ] # # Create the Window # window = sg.Window('Generator labiryntów', layout, element_justification='c') # # Event Loop to process "events" # while True: # event, values = window.read() # print(event, values) # if event in (None, 'Zamknij okno'): # break # if event in (None, 'Generuj labirynt NxN'): # bintree.pawel_tree(int(values[0])) # window.close() # # # def lukasz_hex(): # layout = [[sg.Text("Labirynt generowany algorytmem DFS ", justification='center', font='Helvetica 15')], # [sg.Text('Podaj promien labiryntu:', justification='center', font='Helvetica 15'), # sg.InputText(size=(8, 5), font='Helvetica 18')], # [sg.Button("Generuj labirynt", size=(15, 1), font='Helvetica 18')], # [sg.Button("Zamknij okno", size=(10, 1), font='Helvetica 18')] # ] # # Create the Window # window = sg.Window('Generator labiryntów', layout, element_justification='c') # # Event Loop to process "events" # while True: # event, values = window.read() # print(event, values) # if event in (None, 'Zamknij okno'): # break # if event in (None, 'Generuj labirynt'): # mazeToGraphic.generate(int(values[0])) # window.close() # # # def konrad_dfs(): # layout = [[sg.Text("Labirynt generowany algorytmem DFS", justification='center', font='Helvetica 15')], # [sg.Text('Podaj wymiar N labiryntu:', justification='center', font='Helvetica 15'), # sg.InputText(size=(8, 5), font='Helvetica 18')], # [sg.Button("Generuj labirynt NxN", size=(15, 1), font='Helvetica 18')], # [sg.Button("Zamknij okno", size=(10, 1), font='Helvetica 18')] # ] # # Create the Window # window = sg.Window('Generator labiryntów', layout, element_justification='c') # # Event Loop to process "events" # while True: # event, values = window.read() # print(event, values) # if event in (None, 'Zamknij okno'): # break # if event in (None, 'Generuj labirynt NxN'): # PyMazeDFS.generate(int(values[0])) # window.close() # # # def konrad_bfs(): # layout = [[sg.Text("Labirynt generowany algorytmem BFS", justification='center', font='Helvetica 15')], # [sg.Text('Podaj wymiar N labiryntu:', justification='center', font='Helvetica 15'), # sg.InputText(size=(8, 5), font='Helvetica 18')], # [sg.Button("Generuj labirynt NxN", size=(15, 1), font='Helvetica 18')], # [sg.Button("Zamknij okno", size=(10, 1), font='Helvetica 18')] # ] # # Create the Window # window = sg.Window('Generator labiryntów', layout, element_justification='c') # # Event Loop to process "events" # while True: # event, values = window.read() # print(event, values) # if event in (None, 'Zamknij okno') or None: # break # if event in (None, 'Generuj labirynt NxN'): # PyMazeBFS.generate(int(values[0])) # window.close() # # # layout = [ # [sg.Text("Witaj! Wybierz jeden z poniższych generatorów labiryntów.", justification='center', font='Helvetica 15')], # [sg.Text("Naciśnięcie jednego z poniższych przycisków otworzy nowe okno konfiguracji", justification='center', # font='Helvetica 15')], # [sg.Button('DFS', size=(15, 1), font='Helvetica 20')], # [sg.Button('BFS', size=(15, 1), font='Helvetica 20')], # [sg.Button('BFS wersja 2', size=(15, 1), font='Helvetica 20')], # [sg.Button('HEX DFS', size=(15, 1), font='Helvetica 20')], # [sg.Button('Pawel', size=(15, 1), font='Helvetica 20')], # [sg.Button('Prim', size=(15, 1), font='Helvetica 20')], # [sg.Button("Zakończ program", size=(10, 1), font='Helvetica 18')] # ] # # Create the Window # window = sg.Window('Wybór generatora', layout, element_justification='c') # # Event Loop to process "events" # while True: # event, values = window.read() # print(event, values) # if event in (None, 'Zakończ program') or None: # break # # if event in (None, 'DFS'): # konrad_dfs() # if event in (None, 'BFS wersja 2'): # bartek() # if event in (None, 'BFS'): # konrad_bfs() # if event in (None, 'HEX DFS'): # lukasz_hex() # if event in (None, 'Pawel'): # pawel() # if event in (None, 'Prim'): # hania() # window.close()

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8

dabfeb1dd189f05dc07e5e35a2c9e0bf5df22970

4,883

py

Python

matroids/construct/rank_function.py

PotassiumIodide/matroid-theory-in-python

51c06ba728c9d9002234fe98b1bc84bffb86a0cb

[ "MIT" ]

2

2020-11-27T09:51:49.000Z

2021-11-10T07:16:34.000Z

matroids/construct/rank_function.py

PotassiumIodide/matroid-theory-in-python

51c06ba728c9d9002234fe98b1bc84bffb86a0cb

[ "MIT" ]

1

2020-11-16T07:22:29.000Z

matroids/construct/rank_function.py

PotassiumIodide/matroid-theory-in-python

51c06ba728c9d9002234fe98b1bc84bffb86a0cb

[ "MIT" ]

null

from typing import Callable, TypeVar from matroids.core.set_operator import powset import matroids.construct.independent_sets as independent_sets import matroids.construct.circuits as circuits T = TypeVar('T') def from_independent_matroid(matroid: tuple[set[T], list[set[T]]]) -> Callable[[set[T]], int]: """Construct a rank function from a matroid defined by independent sets. Args: matroid (tuple[set[T], list[set[T]]]): A matroid defined by independent sets. Returns: Callable[set[T], int]: The rank function of a given matroid. """ E, Is = matroid # r(X) = max{|I|: I ∈ Is, I ⊆ X}, ∀X ⊆ E. return lambda X: max(map(len, (I for I in Is if I <= X))) def from_dependent_matroid(matroid: tuple[set[T], list[set[T]]]) -> Callable[[set[T]], int]: """Construct a rank function from a matroid defined by dependent sets. Args: matroid (tuple[set[T], list[set[T]]]): A matroid defined by dependent sets. Returns: Callable[[set[T]], int]: The rank function of a given matroid. """ E, _ = matroid return from_independent_matroid((E, independent_sets.from_dependent_matroid(matroid))) def from_bases_matroid(matroid: tuple[set[T], list[set[T]]]) -> Callable[[set[T]], int]: """Construct a rank function from a matroid defined by bases. Args: matroid (tuple[set[T], list[set[T]]]): A matroid defined by bases. Returns: Callable[[set[T]], int]: A rank function of a given matroid. """ E, _ = matroid return from_independent_matroid((E, independent_sets.from_bases_matroid(matroid))) def from_circuits_matroid(matroid: tuple[set[T], list[set[T]]]) -> Callable[[set[T]], int]: """Construct a rank function from a matroid defined by circuits. Args: matroid (tuple[set[T], list[set[T]]]): A matroid defined by circuits. Returns: Callable[[set[T]], int]: The rank function of a given matroid. """ E, _ = matroid return from_independent_matroid((E, independent_sets.from_circuits_matroid(matroid))) def from_nulity_matroid(matroid: tuple[set[T], Callable[[set[T]], int]]) -> Callable[[set[T]], int]: """Construct a rank function from a matroid defined by a nulity function. Args: matroid (tuple[set[T], Callable[[set[T]], set[T]]]): A matroid defined by a nulity function. Returns: Callable[[set[T]], int]: The rank function of a given matroid. """ E, n = matroid # r(X) = |X| - n(X), ∀X ⊆ E. return lambda X: len(X) - n(X) def from_closure_matroid(matroid: tuple[set[T], Callable[[set[T]], set[T]]]) -> Callable[[set[T]], int]: """Construct a rank function from a matroid defined by a closure function. Args: matroid (tuple[set[T], Callable[[set[T]], set[T]]]): A matroid defined by a closure function. Returns: Callable[[set[T]], int]: The rank function of a given matroid. """ E, cl = matroid # r(X) = min{ |I| : X ⊆ cl(I) }, ∀X ⊆ E. return lambda X: min(len(I) for I in powset(E) if X <= cl(I)) def from_flats_matroid(matroid: tuple[set[T], list[set[T]]]) -> Callable[[set[T]], int]: """Construct a rank function from a matroid defined by flats. Args: matroid (tuple[set[T], list[set[T]]]): A matroid defined by flats. Returns: Callable[[set[T]], int]: The rank function of a given matroid. """ E, _ = matroid return from_independent_matroid((E, independent_sets.from_flats_matroid(matroid))) def from_open_matroid(matroid: tuple[set[T], list[set[T]]]) -> Callable[[set[T]], int]: """Construct a rank function from a matroid defined by open sets. Args: matroid (tuple[set[T], list[set[T]]]): A matroid defined by open sets. Returns: Callable[[set[T]], int]: The rank function of a given matroid. """ E, _ = matroid return from_independent_matroid((E, independent_sets.from_open_matroid(matroid))) def from_hyperplanes_matroid(matroid: tuple[set[T], list[set[T]]]) -> Callable[[set[T]], int]: """Construct a rank function from a matroid defined by hyperplanes. Args: matroid (tuple[set[T], list[set[T]]]): A matroid defined by hyperplanes. Returns: Callable[[set[T]], int]: The rank function of a given matroid. """ E, _ = matroid return from_independent_matroid((E, independent_sets.from_hyperplanes_matroid(matroid))) def from_spanning_matroid(matroid: tuple[set[T], list[set[T]]]) -> Callable[[set[T]], int]: """Construct a rank function from a matroid defined by spanning sets. Args: matroid (tuple[set[T], list[set[T]]]): A matroid defined by spanning sets. Returns: Callable[[set[T]], int]: The rank function of a given matroid. """ E, _ = matroid return from_independent_matroid((E, independent_sets.from_spanning_matroid(matroid)))

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dae39b1cd0672fc282c2e533d12eeda144d4116e

21,245

py

Python

Code/train.py

skywolf829/ASMRSR

3faae231864abe9df6a06a444bd9610368090413

[ "MIT" ]

4

2021-04-12T02:57:31.000Z

2021-06-29T09:34:34.000Z

Code/train.py

skywolf829/ASMRSR

3faae231864abe9df6a06a444bd9610368090413

[ "MIT" ]

null

Code/train.py

skywolf829/ASMRSR

3faae231864abe9df6a06a444bd9610368090413

[ "MIT" ]

null

from utility_functions import to_pixel_samples, to_img, PSNR, make_coord, make_residual_weight_grid import torch from torch.nn.parallel import DistributedDataParallel as DDP import torch.distributed as dist import torch.multiprocessing as mp import torch.nn as nn import torch.nn.functional as F from torch.utils.tensorboard import SummaryWriter import time import torch.optim as optim import os from models import save_model import numpy as np class Trainer(): def __init__(self, opt): self.opt = opt def train_distributed(self, rank, model, opt, dataset): opt['device'] = "cuda:" + str(rank) dist.init_process_group( backend='nccl', init_method='env://', world_size = self.opt['num_nodes'] * self.opt['gpus_per_node'], rank=rank ) model = model.to(rank) model = DDP(model, device_ids=[rank]) model_optim = optim.Adam(model.parameters(), lr=self.opt["g_lr"], betas=(self.opt["beta_1"],self.opt["beta_2"])) optim_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer=model_optim, milestones=[200, 400, 600, 800],gamma=self.opt['gamma']) if(rank == 0): writer = SummaryWriter(os.path.join('tensorboard',opt['save_name'])) start_time = time.time() train_sampler = torch.utils.data.distributed.DistributedSampler(dataset, num_replicas=opt['num_nodes']*opt['gpus_per_node'], rank=rank) dataloader = torch.utils.data.DataLoader( dataset=dataset, shuffle=False, num_workers=opt["num_workers"], pin_memory=True, sampler=train_sampler ) L1loss = nn.L1Loss().to(opt["device"]) step = 0 for epoch in range(opt['epoch_number'], opt['epochs']): opt["epoch_number"] = epoch for batch_num, real_hr in enumerate(dataloader): model.zero_grad() real_hr = real_hr.to(self.opt['device']) if(rank == 0): hr_im = torch.from_numpy(np.transpose(to_img(real_hr, self.opt['mode']), [2, 0, 1])[0:3]).unsqueeze(0) real_shape = real_hr.shape #print(real_hr.dtype) #print("Full shape : " + str(real_hr.shape)) if(model.upscaling_model.continuous): scale_factor = torch.rand([1], device=real_hr.device, dtype=real_hr.dtype) * \ (self.opt['scale_factor_end'] - self.opt['scale_factor_start']) + \ self.opt['scale_factor_start'] else: scale_factor = (1/self.opt['spatial_downscale_ratio']) #scale_factor = 1 #print("Scale factor: " + str(scale_factor)) real_lr = F.interpolate(real_hr, scale_factor=(1/scale_factor), mode = "bilinear" if "2D" in self.opt['mode'] else "trilinear", align_corners=False, recompute_scale_factor=False) if(rank == 0): lr_im = torch.from_numpy(np.transpose(to_img(real_lr, self.opt['mode']), [2, 0, 1])[0:3]).unsqueeze(0) lr_im = F.interpolate(lr_im, size=hr_im.shape[2:], mode='nearest') if(model.upscaling_model.continuous): hr_coords, real_hr = to_pixel_samples(real_hr, flatten=False) cell_sizes = torch.ones_like(hr_coords) for i in range(cell_sizes.shape[-1]): cell_sizes[:,:,i] *= 2 / real_shape[2+i] lr_upscaled = model(real_lr, hr_coords, cell_sizes) if("2D" in self.opt['mode']): lr_upscaled = lr_upscaled.permute(2, 0, 1).unsqueeze(0) else: lr_upscaled = lr_upscaled.permute(3, 0, 1, 2).unsqueeze(0) lr_upscaled = torch.flatten(lr_upscaled,start_dim=1, end_dim=-1).permute(1,0) else: lr_upscaled = model(real_lr) if(rank == 0): sr_im = torch.from_numpy(np.transpose(to_img(lr_upscaled, self.opt['mode']),[2, 0, 1])[0:3]).unsqueeze(0) L1 = L1loss(lr_upscaled, real_hr) L1.backward() model_optim.step() optim_scheduler.step() psnr = PSNR(lr_upscaled, real_hr) if(rank == 0 and step % self.opt['save_every'] == 0): print("Epoch %i batch %i, sf: x%0.02f, L1: %0.04f, PSNR (dB): %0.02f" % \ (epoch, batch_num, scale_factor, L1.item(), psnr.item())) writer.add_scalar('L1', L1.item(), step) writer.add_images("LR, SR, HR", torch.cat([lr_im, sr_im, hr_im]), global_step=step) step += 1 if(rank == 0 and epoch % self.opt['save_every'] == 0): save_model(model, self.opt) print("Saved model") end_time = time.time() total_time = start_time - end_time if(rank == 0): print("Time to train: " + str(total_time)) save_model(model, self.opt) print("Saved model") def train_single(self, model, dataset): model = model.to(self.opt['device']) if not self.opt['fine_tuning']: model_optim = optim.Adam(model.parameters(), lr=self.opt["g_lr"], betas=(self.opt["beta_1"],self.opt["beta_2"])) else: for param in model.feature_extractor.parameters(): param.requires_grad = False model_optim = optim.Adam(model.upscaling_model.parameters(), lr=self.opt["g_lr"], betas=(self.opt["beta_1"],self.opt["beta_2"])) optim_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer=model_optim, milestones=[200, 400, 600, 800],gamma=self.opt['gamma']) writer = SummaryWriter(os.path.join('tensorboard',self.opt['save_name'])) start_time = time.time() dataloader = torch.utils.data.DataLoader( dataset=dataset, shuffle=True, num_workers=self.opt["num_workers"], pin_memory=True ) L1loss = nn.L1Loss().to(self.opt["device"]) step = 0 for epoch in range(self.opt['epoch_number'], self.opt['epochs']): self.opt["epoch_number"] = epoch for batch_num, real_hr in enumerate(dataloader): model.zero_grad() real_hr = real_hr.to(self.opt['device']) hr_im = torch.from_numpy(np.transpose(to_img(real_hr, self.opt['mode']), [2, 0, 1])[0:3]).unsqueeze(0) real_shape = real_hr.shape if(model.upscaling_model.continuous): scale_factor = torch.rand([1], device=real_hr.device, dtype=real_hr.dtype) * \ (self.opt['scale_factor_end'] - self.opt['scale_factor_start']) + \ self.opt['scale_factor_start'] else: scale_factor = (1/self.opt['spatial_downscale_ratio']) real_lr = F.interpolate(real_hr, scale_factor=(1/scale_factor), mode = "bilinear" if "2D" in self.opt['mode'] else "trilinear", align_corners=False, recompute_scale_factor=False) lr_im = torch.from_numpy(np.transpose(to_img(real_lr, self.opt['mode']), [2, 0, 1])[0:3]).unsqueeze(0) lr_im = F.interpolate(lr_im, mode='nearest', size=hr_im.shape[2:]) if(model.upscaling_model.continuous): hr_coords, real_hr = to_pixel_samples(real_hr, flatten=False) cell_sizes = torch.ones_like(hr_coords) for i in range(cell_sizes.shape[-1]): cell_sizes[:,:,i] *= 2 / real_shape[2+i] lr_upscaled = model(real_lr, hr_coords, cell_sizes) if("2D" in self.opt['mode']): lr_upscaled = lr_upscaled.permute(2, 0, 1).unsqueeze(0) else: lr_upscaled = lr_upscaled.permute(3, 0, 1, 2).unsqueeze(0) sr_im = torch.from_numpy(np.transpose(to_img(lr_upscaled, self.opt['mode']),[2, 0, 1])[0:3]).unsqueeze(0) lr_upscaled = torch.flatten(lr_upscaled,start_dim=1, end_dim=-1).permute(1,0) else: lr_upscaled = model(real_lr) sr_im = torch.from_numpy(np.transpose(to_img(lr_upscaled, self.opt['mode']),[2, 0, 1])[0:3]).unsqueeze(0) L1 = L1loss(lr_upscaled, real_hr) L1.backward() model_optim.step() optim_scheduler.step() psnr = PSNR(lr_upscaled, real_hr) if(step % self.opt['save_every'] == 0): print("Epoch %i batch %i, sf: x%0.02f, L1: %0.04f, PSNR (dB): %0.02f" % \ (epoch, batch_num, scale_factor, L1.item(), psnr.item())) writer.add_scalar('L1', L1.item(), step) writer.add_images("LR, SR, HR", torch.cat([lr_im, sr_im, hr_im]), global_step=step) step += 1 if(epoch % self.opt['save_every'] == 0): save_model(model, self.opt) print("Saved model") end_time = time.time() total_time = start_time - end_time print("Time to train: " + str(total_time)) save_model(model, self.opt) print("Saved model") def train(self, model, dataset): torch.manual_seed(0b10101010101010101010101010101010) if(self.opt['train_distributed']): print("Training distributed across " + str(self.opt['gpus_per_node']) + " GPUs") os.environ['MASTER_ADDR'] = '127.0.0.1' os.environ['MASTER_PORT'] = '29500' mp.spawn(self.train_distributed, args=(model,self.opt,dataset), nprocs=self.opt['gpus_per_node'], join=True) else: print("Training on " + self.opt['device']) self.train_single(model, dataset) class ImplicitNetworkTrainer(): def __init__(self, opt): self.opt = opt def train_single(self, model, dataset): model = model.to(self.opt['device']) model_optim = optim.Adam(model.parameters(), lr=self.opt["g_lr"], betas=(self.opt["beta_1"],self.opt["beta_2"])) optim_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer=model_optim, milestones=[200, 400, 600, 800],gamma=self.opt['gamma']) writer = SummaryWriter(os.path.join('tensorboard',self.opt['save_name'])) start_time = time.time() dataloader = torch.utils.data.DataLoader( dataset=dataset, shuffle=True, num_workers=self.opt["num_workers"], pin_memory=True, batch_size=self.opt['minibatch'] ) L1loss = nn.L1Loss().to(self.opt["device"]) step = 0 for epoch in range(self.opt['epoch_number'], self.opt['epochs']): self.opt["epoch_number"] = epoch for batch_num, inout in enumerate(dataloader): model.zero_grad() in_coords, out_vals = inout in_coords = in_coords.to(self.opt['device']) out_vals = out_vals.to(self.opt['device']) recovered_data = model(in_coords) L1 = L1loss(recovered_data, out_vals) L1.backward() model_optim.step() optim_scheduler.step() psnr = PSNR(recovered_data, out_vals) if(step % self.opt['save_every'] == 0): print("Epoch %i batch %i, sf: L1: %0.04f, PSNR (dB): %0.02f" % \ (epoch, batch_num, L1.item(), psnr.item())) writer.add_scalar('L1', L1.item(), step) step += 1 if(epoch % self.opt['save_every'] == 0): save_model(model, self.opt) print("Saved model") end_time = time.time() total_time = start_time - end_time print("Time to train: " + str(total_time)) save_model(model, self.opt) print("Saved model") def train(self, model, dataset): torch.manual_seed(0b10101010101010101010101010101010) if(self.opt['train_distributed']): print("Training distributed across " + str(self.opt['gpus_per_node']) + " GPUs") os.environ['MASTER_ADDR'] = '127.0.0.1' os.environ['MASTER_PORT'] = '29500' mp.spawn(self.train_distributed, args=(model,self.opt,dataset), nprocs=self.opt['gpus_per_node'], join=True) else: print("Training on " + self.opt['device']) self.train_single(model, dataset) class TemporalTrainer(): def __init__(self, opt): self.opt = opt def train_distributed(self, rank, model, opt, dataset): opt['device'] = "cuda:" + str(rank) dist.init_process_group( backend='nccl', init_method='env://', world_size = self.opt['num_nodes'] * self.opt['gpus_per_node'], rank=rank ) model = model.to(rank) model = DDP(model, device_ids=[rank]) model_optim = optim.Adam(model.parameters(), lr=self.opt["g_lr"], betas=(self.opt["beta_1"],self.opt["beta_2"])) optim_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer=model_optim, milestones=[200, 400, 600, 800],gamma=self.opt['gamma']) if(rank == 0): writer = SummaryWriter(os.path.join('tensorboard',opt['save_name'])) start_time = time.time() train_sampler = torch.utils.data.distributed.DistributedSampler(dataset, num_replicas=opt['num_nodes']*opt['gpus_per_node'], rank=rank) dataloader = torch.utils.data.DataLoader( dataset=dataset, shuffle=False, num_workers=opt["num_workers"], pin_memory=True, sampler=train_sampler ) L1loss = nn.L1Loss().to(opt["device"]) step = 0 for epoch in range(opt['epoch_number'], opt['epochs']): opt["epoch_number"] = epoch for batch_num, real_hr in enumerate(dataloader): model.zero_grad() real_hr = real_hr.to(self.opt['device']) real_shape = real_hr.shape scale_factor = torch.rand([1], device=real_hr.device, dtype=real_hr.dtype) * \ (self.opt['scale_factor_end'] - self.opt['scale_factor_start']) + \ self.opt['scale_factor_start'] real_lr = F.interpolate(real_hr, scale_factor=(1/scale_factor), mode = "bilinear" if "2D" in self.opt['mode'] else "trilinear", align_corners=False, recompute_scale_factor=False) hr_coords, real_hr = to_pixel_samples(real_hr, flatten=False) cell_sizes = torch.ones_like(hr_coords) for i in range(cell_sizes.shape[-1]): cell_sizes[:,:,i] *= 2 / real_shape[2+i] lr_upscaled = model(real_lr, hr_coords, cell_sizes) lr_upscaled = lr_upscaled.permute(3, 0, 1, 2).unsqueeze(0) L1 = L1loss(torch.flatten(lr_upscaled,start_dim=1, end_dim=-1).permute(1,0), real_hr) L1.backward() model_optim.step() optim_scheduler.step() psnr = PSNR(torch.flatten(lr_upscaled,start_dim=1, end_dim=-1).permute(1,0), real_hr) if(rank == 0 and step % self.opt['save_every'] == 0): print("Epoch %i batch %i, sf: x%0.02f, L1: %0.04f, PSNR (dB): %0.02f" % \ (epoch, batch_num, scale_factor, L1.item(), psnr.item())) writer.add_scalar('L1', L1.item(), step) writer.add_images("LR, SR, HR", torch.cat([lr_im, sr_im, hr_im]), global_step=step) step += 1 if(rank == 0 and epoch % self.opt['save_every'] == 0): save_model(model, self.opt) print("Saved model") end_time = time.time() total_time = start_time - end_time if(rank == 0): print("Time to train: " + str(total_time)) save_model(model, self.opt) print("Saved model") def train_single(self, model, dataset): model = model.to(self.opt['device']) model_optim = optim.Adam(model.parameters(), lr=self.opt["g_lr"], betas=(self.opt["beta_1"],self.opt["beta_2"])) optim_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer=model_optim, milestones=[200, 400, 600, 800],gamma=self.opt['gamma']) writer = SummaryWriter(os.path.join('tensorboard',self.opt['save_name'])) start_time = time.time() dataloader = torch.utils.data.DataLoader( dataset=dataset, shuffle=True, num_workers=self.opt["num_workers"], pin_memory=True ) L1loss = nn.L1Loss().to(self.opt["device"]) step = 0 for epoch in range(self.opt['epoch_number'], self.opt['epochs']): self.opt["epoch_number"] = epoch for batch_num, real_hr in enumerate(dataloader): model.zero_grad() real_hr = real_hr.to(self.opt['device']) real_shape = real_hr.shape scale_factor = torch.rand([1], device=real_hr.device, dtype=real_hr.dtype) * \ (self.opt['scale_factor_end'] - self.opt['scale_factor_start']) + \ self.opt['scale_factor_start'] s = [real_hr.shape[2], real_hr.shape[3], round((real_hr.shape[4]/scale_factor).item())] real_lr = F.interpolate(real_hr, size=s, mode='trilinear', align_corners=False) hr_coords, real_hr = to_pixel_samples(real_hr, flatten=False) cell_sizes = torch.ones_like(hr_coords) for i in range(cell_sizes.shape[-1]): cell_sizes[:,:,i] *= 2 / real_shape[2+i] lr_upscaled = model(real_lr, hr_coords, cell_sizes) lr_upscaled = lr_upscaled.permute(3, 0, 1, 2).unsqueeze(0) L1 = L1loss(torch.flatten(lr_upscaled,start_dim=1, end_dim=-1).permute(1,0), real_hr) L1.backward() model_optim.step() optim_scheduler.step() psnr = PSNR(torch.flatten(lr_upscaled,start_dim=1, end_dim=-1).permute(1,0), real_hr) if(step % self.opt['save_every'] == 0 or True): print("Epoch %i batch %i, sf: x%0.02f, L1: %0.04f, PSNR (dB): %0.02f" % \ (epoch, batch_num, scale_factor, L1.item(), psnr.item())) #writer.add_scalar('L1', L1.item(), step) step += 1 if(epoch % self.opt['save_every'] == 0): save_model(model, self.opt) print("Saved model") end_time = time.time() total_time = start_time - end_time print("Time to train: " + str(total_time)) save_model(model, self.opt) print("Saved model") def train(self, model, dataset): torch.manual_seed(0b10101010101010101010101010101010) if(self.opt['train_distributed']): print("Training distributed across " + str(self.opt['gpus_per_node']) + " GPUs") os.environ['MASTER_ADDR'] = '127.0.0.1' os.environ['MASTER_PORT'] = '29500' mp.spawn(self.train_distributed, args=(model,self.opt,dataset), nprocs=self.opt['gpus_per_node'], join=True) else: print("Training on " + self.opt['device']) self.train_single(model, dataset)

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0.899387

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null

0

1

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1

0

null

0

7

dafda4e1c0d82ff6427d77ee3473f5fe21b464f7

6,248

py

Python

system/indy-node-tests/TestViewChangeSuite.py

ashcherbakov/indy-test-automation

23fc7bebee87da6cbf16aa58e2d29d38cfc642cd

[ "Apache-2.0" ]

null

system/indy-node-tests/TestViewChangeSuite.py

ashcherbakov/indy-test-automation

23fc7bebee87da6cbf16aa58e2d29d38cfc642cd

[ "Apache-2.0" ]

null

system/indy-node-tests/TestViewChangeSuite.py

ashcherbakov/indy-test-automation

23fc7bebee87da6cbf16aa58e2d29d38cfc642cd

[ "Apache-2.0" ]

null

import pytest import asyncio from system.utils import * @pytest.mark.usefixtures('docker_setup_and_teardown') @pytest.mark.usefixtures('check_no_failures_fixture') class TestViewChangeSuite: @pytest.mark.asyncio async def test_vc_by_restart_primary( self, pool_handler, wallet_handler, get_default_trustee, nodes_num ): trustee_did, _ = get_default_trustee await ensure_pool_is_functional(pool_handler, wallet_handler, trustee_did) primary_before, _, _ = await get_primary(pool_handler, wallet_handler, trustee_did) p1 = NodeHost(primary_before) p1.stop_service() await ensure_primary_changed(pool_handler, wallet_handler, trustee_did, primary_before) await ensure_pool_is_functional(pool_handler, wallet_handler, trustee_did) p1.start_service() await ensure_pool_is_in_sync(nodes_num=nodes_num) await ensure_pool_is_functional(pool_handler, wallet_handler, trustee_did) @pytest.mark.skip('INDY-2023') @pytest.mark.asyncio async def test_vc_by_demotion_primary( self, pool_handler, wallet_handler, get_default_trustee, nodes_num ): trustee_did, _ = get_default_trustee await ensure_pool_is_functional(pool_handler, wallet_handler, trustee_did) primary_before, primary_alias, primary_did = await get_primary(pool_handler, wallet_handler, trustee_did) await eventually(demote_node, pool_handler, wallet_handler, trustee_did, primary_alias, primary_did) primary_next = await ensure_primary_changed(pool_handler, wallet_handler, trustee_did, primary_before) await ensure_pool_is_functional(pool_handler, wallet_handler, trustee_did) await eventually(promote_node, pool_handler, wallet_handler, trustee_did, primary_alias, primary_did) await ensure_primary_changed(pool_handler, wallet_handler, trustee_did, primary_next) await ensure_pool_is_in_sync(nodes_num=nodes_num) await ensure_pool_is_functional(pool_handler, wallet_handler, trustee_did) @pytest.mark.asyncio async def test_vc_by_demotion_last( self, pool_handler, wallet_handler, get_default_trustee, nodes_num ): _alias = 'Node7' _did = 'BM8dTooz5uykCbYSAAFwKNkYfT4koomBHsSWHTDtkjhW' trustee_did, _ = get_default_trustee await ensure_pool_is_functional(pool_handler, wallet_handler, trustee_did) primary_first, _, _ = await get_primary(pool_handler, wallet_handler, trustee_did) await eventually(demote_node, pool_handler, wallet_handler, trustee_did, _alias, _did) primary_next = await ensure_primary_changed(pool_handler, wallet_handler, trustee_did, primary_first) await ensure_pool_is_functional(pool_handler, wallet_handler, trustee_did) await eventually(promote_node, pool_handler, wallet_handler, trustee_did, _alias, _did) await ensure_primary_changed(pool_handler, wallet_handler, trustee_did, primary_next) await ensure_pool_is_in_sync(nodes_num=nodes_num) await ensure_pool_is_functional(pool_handler, wallet_handler, trustee_did) @pytest.mark.nodes_num(8) @pytest.mark.asyncio async def test_demotion_of_backup_primary_with_restart_with_vc( self, pool_handler, wallet_handler, get_default_trustee, nodes_num ): R0_PRIMARY_ID = 1 R1_PRIMARY_ID = 2 R2_PRIMARY_ID = 3 hosts = [NodeHost(node_id + 1) for node_id in range(nodes_num)] trustee_did, _ = get_default_trustee await check_pool_is_functional(pool_handler, wallet_handler, trustee_did) pool_info = get_pool_info(str(R0_PRIMARY_ID)) primary_r2_alias = get_node_alias(R2_PRIMARY_ID) primary_r2_did = get_node_did(primary_r2_alias, pool_info=pool_info) await eventually(demote_node, pool_handler, wallet_handler, trustee_did, primary_r2_alias, primary_r2_did) await ensure_primary_changed(pool_handler, wallet_handler, trustee_did, str(R0_PRIMARY_ID)) await ensure_pool_is_functional(pool_handler, wallet_handler, trustee_did) restart_pool(hosts) await ensure_pool_is_in_sync(node_ids=[h.id for h in hosts if h.id != R2_PRIMARY_ID]) await ensure_pool_is_functional(pool_handler, wallet_handler, trustee_did) @pytest.mark.nodes_num(8) @pytest.mark.asyncio async def test_demotion_of_backup_primary_with_restart_without_vc( self, pool_handler, wallet_handler, get_default_trustee, nodes_num ): R0_PRIMARY_ID = 1 R1_PRIMARY_ID = 2 R2_PRIMARY_ID = 3 hosts = [NodeHost(node_id + 1) for node_id in range(nodes_num)] trustee_did, _ = get_default_trustee await check_pool_is_functional(pool_handler, wallet_handler, trustee_did) pool_info = get_pool_info(str(R0_PRIMARY_ID)) host2 = hosts[R1_PRIMARY_ID - 1] host2.stop_service() primary_r2_alias = get_node_alias(R2_PRIMARY_ID) primary_r2_did = get_node_did(primary_r2_alias, pool_info=pool_info) await eventually(demote_node, pool_handler, wallet_handler, trustee_did, primary_r2_alias, primary_r2_did) restart_pool(hosts) await ensure_pool_is_in_sync(node_ids=[h.id for h in hosts if h.id != R2_PRIMARY_ID]) await ensure_pool_is_functional(pool_handler, wallet_handler, trustee_did) @pytest.mark.nodes_num(4) @pytest.mark.asyncio async def test_multiple_vcs( self, pool_handler, wallet_handler, get_default_trustee, nodes_num ): trustee_did, _ = get_default_trustee for i in range(10): primary, alias, target_did = await get_primary(pool_handler, wallet_handler, trustee_did) p = NodeHost(primary) p.stop_service() await ensure_primary_changed(pool_handler, wallet_handler, trustee_did, primary) await ensure_pool_is_functional(pool_handler, wallet_handler, trustee_did) p.start_service() await ensure_pool_is_in_sync(nodes_num=nodes_num) await ensure_state_root_hashes_are_in_sync(pool_handler, wallet_handler, trustee_did) await ensure_pool_is_functional(pool_handler, wallet_handler, trustee_did)

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0.111111

0.103505

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null

0

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null

0

7

979da7da0ef01e9552b446bd485ddf5844b2f75e

10,871

py

Python

ImageSource.py

puwow/pydatalib

b4cb309135996cbefedbe17931bf00d0b1cdcf34

[ "Apache-2.0" ]

1

2021-03-10T17:07:57.000Z

ImageSource.py

puwow/pydatalib

b4cb309135996cbefedbe17931bf00d0b1cdcf34

[ "Apache-2.0" ]

null

ImageSource.py

puwow/pydatalib

b4cb309135996cbefedbe17931bf00d0b1cdcf34

[ "Apache-2.0" ]

null

#-*- coding:utf-8 -*- import wx import base64 class ImageSource: images={'address': b'iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAArUlEQVQ4T2NkwAEEJt92+JCregCXPEycEZsCoak3GxgYmer//WVwJGQIXgMYGBkaGP79w6rmXbZ6A8hyvAYwMjEE/P/HsAGHN069y1I1BxsgPO129n8GhinoCv8xMDgwMTDgDQdGmH+x2QIygJmBYS7QcGUs8hAXEDKA6f8/RwyX/WM6AAtcgl74kKV6EF9U4o9GoBcoNIAFaIAiBS6gNCFRnBKHsAGgeCc2OwMAC51skx9KbRIAAAAASUVORK5CYII=', 'bank': b'iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAA/ElEQVQ4T2NkoBAw4tIvMvmGyT9mlnYGhv9MQDXV77JUT2BTi9UAwel30hn//+8BauCBavoGNWQCuiEoBvBOeSLMyvi9i4GRIQmHy1ayMjLmvcxUeQWThxsgNP22O+N/hqn/GRiU8QbLf4aHQHX5b3NUN4LUgQ0QnHanlZHhfxUp4QnU2Pk2S7WCUWDybQcmZob9pGiGqf33l8ER7gXBabf3M/7/d/BdtnoDzFBgyIPlhabd/g9S/CFX9YDQ1JsN/xmZ7N9nqTrCvQDxxqgBFIUBKNQZmRnqQbHw7x/TAVCggqIWFPIYbKZ/DqBY+P+XoREUK9RLB+QkJJAeAJsqzo3nhQh9AAAAAElFTkSuQmCC', 'car': b'iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAABSElEQVQ4T62SMS8EQRTH39uVKDR27xothkRCwwe4i1Ki0IlGeZnLqTQqrtLoLruoVKJSCe2dD6AhSG5OIlpuV4JEws4zI0yysSY5a5pJJu/98vu/eQg5D+bshxTAC9tzALhqh9JWzMeOv2tSgEIo9gnwVCbUzoI4fTCMBLNdzpZ+ALzGzSS68jzizBrLDwVR4kzFtZELDTHFhUBsEpIvEzywRUCXFhzCl26VrRmAv93ZA6LlngZKsh5Vxzc+DbSWApwooWvlVFFPA7/AngDxEKTsV/eijmsAMoHyY421vFA01WMpC0AArZiz8mBDlBwXmilAboOe8n8VGwOthC6sa3UdxQbT6joKJVDXkc03eoEI1ES4AzjzwEfPsiD+jpgACZeq6Ugt03xqD4q7t0OS3ryowq5sBsWwM/36TnfPK+w+BfjLDP4F8AFe/IURxOanEgAAAABJRU5ErkJggg==', 'company': b'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', 'creditcard': b'iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAhUlEQVQ4T2NkoBAwUqifgToGKE794MbAyFxJimsYGZl672VybwG7QGHa5ytAhjYpBjAwMD64n8WjCDZAzcjiP2maIapvnTvBSB0DzBd8IeiCV98wldzP4oW4AGSAODf+CLn8+h+GL+EGKE77TNAF2MKIegZQHI0UJyRy0gBMD3XyAiUuAACjwzIRHNfLcQAAAABJRU5ErkJggg==', 'time': b'iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAABNklEQVQ4T2NkoBAwouvnnflMhPXvl3IGBkY9hv8M+mB5RoaLDAz/L/1m5un8nC71BlkPigGC0+7ZMjL82QDUIYTdYf/f/WdgCXifpXQYJg83QHjqrYT/jIzzkTX+Z2A4AHEAgwOKrf//J77NVlsAlWNgEJh+XYHpH/MVBkZGbmSF77JUGQWn3d6PbgDD//9f/zH91fmQqfkA7ALh6Xc6////X4bubJwGgFzFyNj1NlOlHGLAtNubgM71JckABobNb7NU/cAGCE27/RRISZFiAFDtM6ALpQkaAAlcpnpgNCqgWYAwAJcXgN7aBPRsFyfTh/M//vGXAtNFA1L0IbyAKxDhioGGMPz5O/8/M9N1ZDF4IOKKRuyJCSiKHo3gmMCSkHAZwIiekGAKKUrKMEMoykw4/YxHAgCZwJsRKwobkgAAAABJRU5ErkJggg==', 'pcard': b'iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAA7UlEQVQ4T6WTsQ7BUBSG/1OJF0AHk6cws1rFxGA2GKWbToiRxMBgYTH0FXgbUuIBSHr8bVQaKmlzO93e/P93/3POvQLDTwz9+ABsRwcIYGcCWvD9maxCbQSwR7rjqpvJHIsUe38uPSk72rAUx1zmtzgQNNMAHhQXJmpRVwu1Cpy+D2D0RirAsmBfpnKtONoXxTYyKtyfhAI3FRAUUL1N5ExAh4BDvgSKOw3L4hOLRxFrrtvZE9DMrpaSUTkdl70YJ3sQ1h5rvkvwuLFMAjidOv+H7MHms8/a/wFyTzJKYHyR4mONrnLu7AmD8Wt8AQSOamnPBqgAAAAAAElFTkSuQmCC', 'network': b'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', 'money': b'iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAABHUlEQVQ4T2NkoBAw4tMvMPm2A0j+Q67qAVzqcBogNPVOCQPj/26wxv+Mpe+yVXqwGYJigNDUmw3/GZnsQQqBEmDbYeA/AwPYFYyMDAfeZao2wsRRDBCcdns/isb//yAKGZnqkQ16n6XqSIwBE/79/78QpJCJkTEeSBWAfQN0CVYDkP38/z/D699/GbTZmP9lMzAx/Qc5WWjanftA7QroYQL2gsi0O8b/GP6fQfbzuyxVRuFpN13+MzACw4SxBj0AmRgYTd5kqZzFaQBEw/8WRob/B99mqe8RmnYb6HoEQDEAJIwSbQyMD95lqSiCYgUk9+sv01RWZoarwBgQxeoFmLlosUBaIIIMoTgaKU5IyIFEVlJGjyqKMhO6Ybj4AFEXjBHT0bEiAAAAAElFTkSuQmCC', 'work': b'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', 'coord': b'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', 'person': b'iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAABTklEQVQ4T6WTQS8DQRiG3285ObEVJ8fdEhLREP0H/AOuwkV30Zu4bfcmbsVuXYgr/4B/UCEVCaG7RyfR5eTEfmaLVrvTLbGn2fne75l33pkh/POjTv39JS+jAJmoHgKVl5xekWmlALXkWWDaALjvs4leQbwd5HS7HRIDqI43C8Kp1BljLjD1s5+1GCDl+msM3pHbpfWaoe0mAoT9JTAOpA4Iy2Ibh4mAQdefCsEXMoACmn4ytMtEQFRU970xEf2JGI5/iW+gYD5Y0W+7hphyqotMlBfCyTbxFTEXa2b6qKMD1bkvgBQr8W5xaAfmSOFb0ziFpPTjtpun0QCorsd/udWBodd7mw72qllWqCjmsl1AZQo5X1tNl1sA9SaLe9Uhf0uMFgR6uAXEeBD/x8Gjtgmb3mIZtK864N5NEHpmonnG+/mzMXotc9bxNf42jw+ewWUR/TFDoAAAAABJRU5ErkJggg==', 'platform': b'iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAABGklEQVQ4T2NkoBAwwvQLzbit9e83gxgx5jGxMrx6l6F6DaQWbIDA5NsOTMwM+4nRDFPz7y+D44dc1QNgA4Sm3mxgYGSqJ9KAf0B1TAz//zW+y1ZvwDDgPwPDAcb//w5iGMbIJA2UUwFqcADLYTEgBCgsC/RU37tM1UZsrhGafrue4T9DGtCgWyBL4C4AhQGyBpDfsBmATR1mGECdhtUFyGGFKwxgfiPdACQdIL/hNABNHcILIAkmJmD4MDDgDURwDAAD+/+/J1ijkeH//+X//jHOQncBM9N/BaDpbgyMjJG4opHYhAQxGzkQBaff9QHG62YiUyJEPyOT7/tM5S2IzDTtThrD/79SRBnCyPzsXZYK2JtwA4jSiEURAG7bkBHc7fayAAAAAElFTkSuQmCC', 'doc': b'iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAuklEQVQ4T2NkAIG+Dz0M/xmCgSwFMJ8weMDAyLCWoUighJGh/70Cwz/G+4T1YFHB9F+RkaH7vQMDE+N+LNKfGP799weLMzFuBJJ8GGr+/XfEZ8BZhmIBE7Cm3g9ngKQxqQYwAF3QCHVBPVYvEnAB4WABG0AhgIQBMyN2JxIy/O//RnyBSEg7KIzwxsKoAYRDABqI/V8lGf79fkaMegw1TKxSkITU82EqMHuGAlmiRBr0Gpj9VzOUCGQDAO0RVEruCSX5AAAAAElFTkSuQmCC', 'phone': b'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', 'book': b'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', 'color': b'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', 'qrcode': b'iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAeklEQVQ4T2NkoBAwUqifAW6AwOTbDkxM/xyQDfz3j+kAiI9N/EOuKlgOboDQ1JsNDIxM9Sgu+v+vEczHIv4uW71huBlAcSDiMgAW2riim3AsMDOtQtH8918YiE9aLGCxfhAagCsQmVgZXmELwHcZqtdQUiK5mYri3AgAOhFwEYz/MPEAAAAASUVORK5CYII=', 'vdata': b'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', 'code': b'iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAY0lEQVQ4T2NkoBAwUqifgVFw2u39IEMY//87CDPsXbZ6A0z8fZaqI4gNk//PyGQPUgcXF5p2+z9Y4/9/jcgGwMTfZakygtkweUameqCGAyADQOIQyVEDRsNgwNMBxXmB0twIAF0ZIjhG0cdqAAAAAElFTkSuQmCC', 'file': b'iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAzklEQVQ4T2NkgAKhqTcbGBiZ6mF8GP2fgeEA4x8Gfwbm/zPeZatFocszwg2YdhuoFjv495fBkZGZAWz4+yxVR2RVJBkAVOwAchGyIUQZALQxDIhDgZpFQbYz/v//HOYdYg0AaUv/9/f/LbDz/7Bc+FCo+AFsGDFhgC1gYd7AZsDRd1mqNtiCU2ja7SNAcWuQHFANWC9WF4ACCpsBoECEidPUAMq9gCtBIYvj9QIo5SErZmJm2I9uKG0NoNgL5BmAIztjNez/v8Z32eoNIDkAMTpyES6bUNMAAAAASUVORK5CYII='} @classmethod def get_bitmap( cls, key ): if cls.images.get(key) is not None: return wx.Bitmap().FromPNGData( base64.b64decode(cls.images.get(key))) else: return wx.Bitmap().FromPNGData( base64.b64decode(cls.images.get("vdata"))) if __name__ == '__main__': print(ImageSource.get_bitmap("car"))

724.733333

10,451

0.938

412

10,871

24.725728

0.815534

0.086581

0.003534

0.002945

0.010209

0

0.127376

0.012786

10,871

15

10,452

724.733333

0.821841

0.00184

0

1.333333

0.946267

0.934562

0

1

0

1

0.083333

false

0

0.166667

0

0.583333

0.083333

0

1

null

0

1

0

1

null

1

0

1

0

8

8af9a971d56324defec0953d995fe55adc6ca21e

118,605

py

Python

openapi-python-client/openapi_client/api/external_task_api.py

yanavasileva/camunda-bpm-examples

051f8f28c62845e68ce4059ab64264c5a0bdc009

[ "Apache-2.0" ]

null

openapi-python-client/openapi_client/api/external_task_api.py

yanavasileva/camunda-bpm-examples

051f8f28c62845e68ce4059ab64264c5a0bdc009

[ "Apache-2.0" ]

null

openapi-python-client/openapi_client/api/external_task_api.py

yanavasileva/camunda-bpm-examples

051f8f28c62845e68ce4059ab64264c5a0bdc009

[ "Apache-2.0" ]

null

# coding: utf-8 """ Camunda BPM REST API OpenApi Spec for Camunda BPM REST API. # noqa: E501 The version of the OpenAPI document: 7.13.0 Generated by: https://openapi-generator.tech """ from __future__ import absolute_import import re # noqa: F401 # python 2 and python 3 compatibility library import six from openapi_client.api_client import ApiClient from openapi_client.exceptions import ( # noqa: F401 ApiTypeError, ApiValueError ) class ExternalTaskApi(object): """NOTE: This class is auto generated by OpenAPI Generator Ref: https://openapi-generator.tech Do not edit the class manually. """ def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client def complete_external_task_resource(self, id, **kwargs): # noqa: E501 """complete_external_task_resource # noqa: E501 Completes an external task by id and updates process variables. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.complete_external_task_resource(id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str id: The id of the task to complete. (required) :param CompleteExternalTaskDto complete_external_task_dto: :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.complete_external_task_resource_with_http_info(id, **kwargs) # noqa: E501 def complete_external_task_resource_with_http_info(self, id, **kwargs): # noqa: E501 """complete_external_task_resource # noqa: E501 Completes an external task by id and updates process variables. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.complete_external_task_resource_with_http_info(id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str id: The id of the task to complete. (required) :param CompleteExternalTaskDto complete_external_task_dto: :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: None If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = [ 'id', 'complete_external_task_dto' ] all_params.extend( [ 'async_req', '_return_http_data_only', '_preload_content', '_request_timeout' ] ) for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method complete_external_task_resource" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'id' is set if self.api_client.client_side_validation and ('id' not in local_var_params or # noqa: E501 local_var_params['id'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `id` when calling `complete_external_task_resource`") # noqa: E501 collection_formats = {} path_params = {} if 'id' in local_var_params: path_params['id'] = local_var_params['id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'complete_external_task_dto' in local_var_params: body_params = local_var_params['complete_external_task_dto'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/external-task/{id}/complete', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def extend_lock(self, id, **kwargs): # noqa: E501 """extend_lock # noqa: E501 Extends the timeout of the lock by a given amount of time. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.extend_lock(id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str id: The id of the external task. (required) :param ExtendLockOnExternalTaskDto extend_lock_on_external_task_dto: :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.extend_lock_with_http_info(id, **kwargs) # noqa: E501 def extend_lock_with_http_info(self, id, **kwargs): # noqa: E501 """extend_lock # noqa: E501 Extends the timeout of the lock by a given amount of time. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.extend_lock_with_http_info(id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str id: The id of the external task. (required) :param ExtendLockOnExternalTaskDto extend_lock_on_external_task_dto: :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: None If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = [ 'id', 'extend_lock_on_external_task_dto' ] all_params.extend( [ 'async_req', '_return_http_data_only', '_preload_content', '_request_timeout' ] ) for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method extend_lock" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'id' is set if self.api_client.client_side_validation and ('id' not in local_var_params or # noqa: E501 local_var_params['id'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `id` when calling `extend_lock`") # noqa: E501 collection_formats = {} path_params = {} if 'id' in local_var_params: path_params['id'] = local_var_params['id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'extend_lock_on_external_task_dto' in local_var_params: body_params = local_var_params['extend_lock_on_external_task_dto'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/external-task/{id}/extendLock', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def fetch_and_lock(self, **kwargs): # noqa: E501 """fetch_and_lock # noqa: E501 Fetches and locks a specific number of external tasks for execution by a worker. Query can be restricted to specific task topics and for each task topic an individual lock time can be provided. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.fetch_and_lock(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param FetchExternalTasksDto fetch_external_tasks_dto: :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: list[LockedExternalTaskDto] If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.fetch_and_lock_with_http_info(**kwargs) # noqa: E501 def fetch_and_lock_with_http_info(self, **kwargs): # noqa: E501 """fetch_and_lock # noqa: E501 Fetches and locks a specific number of external tasks for execution by a worker. Query can be restricted to specific task topics and for each task topic an individual lock time can be provided. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.fetch_and_lock_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param FetchExternalTasksDto fetch_external_tasks_dto: :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(list[LockedExternalTaskDto], status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = [ 'fetch_external_tasks_dto' ] all_params.extend( [ 'async_req', '_return_http_data_only', '_preload_content', '_request_timeout' ] ) for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method fetch_and_lock" % key ) local_var_params[key] = val del local_var_params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'fetch_external_tasks_dto' in local_var_params: body_params = local_var_params['fetch_external_tasks_dto'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/external-task/fetchAndLock', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[LockedExternalTaskDto]', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_external_task(self, id, **kwargs): # noqa: E501 """get_external_task # noqa: E501 Retrieves an external task by id, corresponding to the `ExternalTask` interface in the engine. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_external_task(id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str id: The id of the external task to be retrieved. (required) :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: ExternalTaskDto If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.get_external_task_with_http_info(id, **kwargs) # noqa: E501 def get_external_task_with_http_info(self, id, **kwargs): # noqa: E501 """get_external_task # noqa: E501 Retrieves an external task by id, corresponding to the `ExternalTask` interface in the engine. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_external_task_with_http_info(id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str id: The id of the external task to be retrieved. (required) :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(ExternalTaskDto, status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = [ 'id' ] all_params.extend( [ 'async_req', '_return_http_data_only', '_preload_content', '_request_timeout' ] ) for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method get_external_task" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'id' is set if self.api_client.client_side_validation and ('id' not in local_var_params or # noqa: E501 local_var_params['id'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `id` when calling `get_external_task`") # noqa: E501 collection_formats = {} path_params = {} if 'id' in local_var_params: path_params['id'] = local_var_params['id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/external-task/{id}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ExternalTaskDto', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_external_task_error_details(self, id, **kwargs): # noqa: E501 """get_external_task_error_details # noqa: E501 Retrieves the error details in the context of a running external task by id. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_external_task_error_details(id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str id: The id of the external task for which the error details should be retrieved. (required) :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: str If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.get_external_task_error_details_with_http_info(id, **kwargs) # noqa: E501 def get_external_task_error_details_with_http_info(self, id, **kwargs): # noqa: E501 """get_external_task_error_details # noqa: E501 Retrieves the error details in the context of a running external task by id. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_external_task_error_details_with_http_info(id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str id: The id of the external task for which the error details should be retrieved. (required) :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(str, status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = [ 'id' ] all_params.extend( [ 'async_req', '_return_http_data_only', '_preload_content', '_request_timeout' ] ) for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method get_external_task_error_details" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'id' is set if self.api_client.client_side_validation and ('id' not in local_var_params or # noqa: E501 local_var_params['id'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `id` when calling `get_external_task_error_details`") # noqa: E501 collection_formats = {} path_params = {} if 'id' in local_var_params: path_params['id'] = local_var_params['id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['text/plain', 'application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/external-task/{id}/errorDetails', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='str', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_external_tasks(self, **kwargs): # noqa: E501 """get_external_tasks # noqa: E501 Queries for the external tasks that fulfill given parameters. Parameters may be static as well as dynamic runtime properties of executions. The size of the result set can be retrieved by using the [Get External Task Count](https://docs.camunda.org/manual/7.13/reference/rest/external-task/get-query-count/) method. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_external_tasks(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str external_task_id: Filter by an external task's id. :param str external_task_id_in: Filter by the comma-separated list of external task ids. :param str topic_name: Filter by an external task topic. :param str worker_id: Filter by the id of the worker that the task was most recently locked by. :param bool locked: Only include external tasks that are currently locked (i.e., they have a lock time and it has not expired). Value may only be `true`, as `false` matches any external task. :param bool not_locked: Only include external tasks that are currently not locked (i.e., they have no lock or it has expired). Value may only be `true`, as `false` matches any external task. :param bool with_retries_left: Only include external tasks that have a positive (> 0) number of retries (or `null`). Value may only be `true`, as `false` matches any external task. :param bool no_retries_left: Only include external tasks that have 0 retries. Value may only be `true`, as `false` matches any external task. :param datetime lock_expiration_after: Restrict to external tasks that have a lock that expires after a given date. By [default](https://docs.camunda.org/manual/7.13/reference/rest/overview/date-format/), the date must have the format `yyyy-MM-dd'T'HH:mm:ss.SSSZ`, e.g., `2013-01-23T14:42:45.000+0200`. :param datetime lock_expiration_before: Restrict to external tasks that have a lock that expires before a given date. By [default](https://docs.camunda.org/manual/7.13/reference/rest/overview/date-format/), the date must have the format `yyyy-MM-dd'T'HH:mm:ss.SSSZ`, e.g., `2013-01-23T14:42:45.000+0200`. :param str activity_id: Filter by the id of the activity that an external task is created for. :param str activity_id_in: Filter by the comma-separated list of ids of the activities that an external task is created for. :param str execution_id: Filter by the id of the execution that an external task belongs to. :param str process_instance_id: Filter by the id of the process instance that an external task belongs to. :param str process_instance_id_in: Filter by a comma-separated list of process instance ids that an external task may belong to. :param str process_definition_id: Filter by the id of the process definition that an external task belongs to. :param str tenant_id_in: Filter by a comma-separated list of tenant ids. An external task must have one of the given tenant ids. :param bool active: Only include active tasks. Value may only be `true`, as `false` matches any external task. :param bool suspended: Only include suspended tasks. Value may only be `true`, as `false` matches any external task. :param int priority_higher_than_or_equals: Only include jobs with a priority higher than or equal to the given value. Value must be a valid `long` value. :param int priority_lower_than_or_equals: Only include jobs with a priority lower than or equal to the given value. Value must be a valid `long` value. :param str sort_by: Sort the results lexicographically by a given criterion. Must be used in conjunction with the sortOrder parameter. :param str sort_order: Sort the results in a given order. Values may be asc for ascending order or desc for descending order. Must be used in conjunction with the sortBy parameter. :param int first_result: Pagination of results. Specifies the index of the first result to return. :param int max_results: Pagination of results. Specifies the maximum number of results to return. Will return less results if there are no more results left. :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: list[ExternalTaskDto] If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.get_external_tasks_with_http_info(**kwargs) # noqa: E501 def get_external_tasks_with_http_info(self, **kwargs): # noqa: E501 """get_external_tasks # noqa: E501 Queries for the external tasks that fulfill given parameters. Parameters may be static as well as dynamic runtime properties of executions. The size of the result set can be retrieved by using the [Get External Task Count](https://docs.camunda.org/manual/7.13/reference/rest/external-task/get-query-count/) method. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_external_tasks_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str external_task_id: Filter by an external task's id. :param str external_task_id_in: Filter by the comma-separated list of external task ids. :param str topic_name: Filter by an external task topic. :param str worker_id: Filter by the id of the worker that the task was most recently locked by. :param bool locked: Only include external tasks that are currently locked (i.e., they have a lock time and it has not expired). Value may only be `true`, as `false` matches any external task. :param bool not_locked: Only include external tasks that are currently not locked (i.e., they have no lock or it has expired). Value may only be `true`, as `false` matches any external task. :param bool with_retries_left: Only include external tasks that have a positive (> 0) number of retries (or `null`). Value may only be `true`, as `false` matches any external task. :param bool no_retries_left: Only include external tasks that have 0 retries. Value may only be `true`, as `false` matches any external task. :param datetime lock_expiration_after: Restrict to external tasks that have a lock that expires after a given date. By [default](https://docs.camunda.org/manual/7.13/reference/rest/overview/date-format/), the date must have the format `yyyy-MM-dd'T'HH:mm:ss.SSSZ`, e.g., `2013-01-23T14:42:45.000+0200`. :param datetime lock_expiration_before: Restrict to external tasks that have a lock that expires before a given date. By [default](https://docs.camunda.org/manual/7.13/reference/rest/overview/date-format/), the date must have the format `yyyy-MM-dd'T'HH:mm:ss.SSSZ`, e.g., `2013-01-23T14:42:45.000+0200`. :param str activity_id: Filter by the id of the activity that an external task is created for. :param str activity_id_in: Filter by the comma-separated list of ids of the activities that an external task is created for. :param str execution_id: Filter by the id of the execution that an external task belongs to. :param str process_instance_id: Filter by the id of the process instance that an external task belongs to. :param str process_instance_id_in: Filter by a comma-separated list of process instance ids that an external task may belong to. :param str process_definition_id: Filter by the id of the process definition that an external task belongs to. :param str tenant_id_in: Filter by a comma-separated list of tenant ids. An external task must have one of the given tenant ids. :param bool active: Only include active tasks. Value may only be `true`, as `false` matches any external task. :param bool suspended: Only include suspended tasks. Value may only be `true`, as `false` matches any external task. :param int priority_higher_than_or_equals: Only include jobs with a priority higher than or equal to the given value. Value must be a valid `long` value. :param int priority_lower_than_or_equals: Only include jobs with a priority lower than or equal to the given value. Value must be a valid `long` value. :param str sort_by: Sort the results lexicographically by a given criterion. Must be used in conjunction with the sortOrder parameter. :param str sort_order: Sort the results in a given order. Values may be asc for ascending order or desc for descending order. Must be used in conjunction with the sortBy parameter. :param int first_result: Pagination of results. Specifies the index of the first result to return. :param int max_results: Pagination of results. Specifies the maximum number of results to return. Will return less results if there are no more results left. :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(list[ExternalTaskDto], status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = [ 'external_task_id', 'external_task_id_in', 'topic_name', 'worker_id', 'locked', 'not_locked', 'with_retries_left', 'no_retries_left', 'lock_expiration_after', 'lock_expiration_before', 'activity_id', 'activity_id_in', 'execution_id', 'process_instance_id', 'process_instance_id_in', 'process_definition_id', 'tenant_id_in', 'active', 'suspended', 'priority_higher_than_or_equals', 'priority_lower_than_or_equals', 'sort_by', 'sort_order', 'first_result', 'max_results' ] all_params.extend( [ 'async_req', '_return_http_data_only', '_preload_content', '_request_timeout' ] ) for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method get_external_tasks" % key ) local_var_params[key] = val del local_var_params['kwargs'] collection_formats = {} path_params = {} query_params = [] if 'external_task_id' in local_var_params and local_var_params['external_task_id'] is not None: # noqa: E501 query_params.append(('externalTaskId', local_var_params['external_task_id'])) # noqa: E501 if 'external_task_id_in' in local_var_params and local_var_params['external_task_id_in'] is not None: # noqa: E501 query_params.append(('externalTaskIdIn', local_var_params['external_task_id_in'])) # noqa: E501 if 'topic_name' in local_var_params and local_var_params['topic_name'] is not None: # noqa: E501 query_params.append(('topicName', local_var_params['topic_name'])) # noqa: E501 if 'worker_id' in local_var_params and local_var_params['worker_id'] is not None: # noqa: E501 query_params.append(('workerId', local_var_params['worker_id'])) # noqa: E501 if 'locked' in local_var_params and local_var_params['locked'] is not None: # noqa: E501 query_params.append(('locked', local_var_params['locked'])) # noqa: E501 if 'not_locked' in local_var_params and local_var_params['not_locked'] is not None: # noqa: E501 query_params.append(('notLocked', local_var_params['not_locked'])) # noqa: E501 if 'with_retries_left' in local_var_params and local_var_params['with_retries_left'] is not None: # noqa: E501 query_params.append(('withRetriesLeft', local_var_params['with_retries_left'])) # noqa: E501 if 'no_retries_left' in local_var_params and local_var_params['no_retries_left'] is not None: # noqa: E501 query_params.append(('noRetriesLeft', local_var_params['no_retries_left'])) # noqa: E501 if 'lock_expiration_after' in local_var_params and local_var_params['lock_expiration_after'] is not None: # noqa: E501 query_params.append(('lockExpirationAfter', local_var_params['lock_expiration_after'])) # noqa: E501 if 'lock_expiration_before' in local_var_params and local_var_params['lock_expiration_before'] is not None: # noqa: E501 query_params.append(('lockExpirationBefore', local_var_params['lock_expiration_before'])) # noqa: E501 if 'activity_id' in local_var_params and local_var_params['activity_id'] is not None: # noqa: E501 query_params.append(('activityId', local_var_params['activity_id'])) # noqa: E501 if 'activity_id_in' in local_var_params and local_var_params['activity_id_in'] is not None: # noqa: E501 query_params.append(('activityIdIn', local_var_params['activity_id_in'])) # noqa: E501 if 'execution_id' in local_var_params and local_var_params['execution_id'] is not None: # noqa: E501 query_params.append(('executionId', local_var_params['execution_id'])) # noqa: E501 if 'process_instance_id' in local_var_params and local_var_params['process_instance_id'] is not None: # noqa: E501 query_params.append(('processInstanceId', local_var_params['process_instance_id'])) # noqa: E501 if 'process_instance_id_in' in local_var_params and local_var_params['process_instance_id_in'] is not None: # noqa: E501 query_params.append(('processInstanceIdIn', local_var_params['process_instance_id_in'])) # noqa: E501 if 'process_definition_id' in local_var_params and local_var_params['process_definition_id'] is not None: # noqa: E501 query_params.append(('processDefinitionId', local_var_params['process_definition_id'])) # noqa: E501 if 'tenant_id_in' in local_var_params and local_var_params['tenant_id_in'] is not None: # noqa: E501 query_params.append(('tenantIdIn', local_var_params['tenant_id_in'])) # noqa: E501 if 'active' in local_var_params and local_var_params['active'] is not None: # noqa: E501 query_params.append(('active', local_var_params['active'])) # noqa: E501 if 'suspended' in local_var_params and local_var_params['suspended'] is not None: # noqa: E501 query_params.append(('suspended', local_var_params['suspended'])) # noqa: E501 if 'priority_higher_than_or_equals' in local_var_params and local_var_params['priority_higher_than_or_equals'] is not None: # noqa: E501 query_params.append(('priorityHigherThanOrEquals', local_var_params['priority_higher_than_or_equals'])) # noqa: E501 if 'priority_lower_than_or_equals' in local_var_params and local_var_params['priority_lower_than_or_equals'] is not None: # noqa: E501 query_params.append(('priorityLowerThanOrEquals', local_var_params['priority_lower_than_or_equals'])) # noqa: E501 if 'sort_by' in local_var_params and local_var_params['sort_by'] is not None: # noqa: E501 query_params.append(('sortBy', local_var_params['sort_by'])) # noqa: E501 if 'sort_order' in local_var_params and local_var_params['sort_order'] is not None: # noqa: E501 query_params.append(('sortOrder', local_var_params['sort_order'])) # noqa: E501 if 'first_result' in local_var_params and local_var_params['first_result'] is not None: # noqa: E501 query_params.append(('firstResult', local_var_params['first_result'])) # noqa: E501 if 'max_results' in local_var_params and local_var_params['max_results'] is not None: # noqa: E501 query_params.append(('maxResults', local_var_params['max_results'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/external-task', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[ExternalTaskDto]', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_external_tasks_count(self, **kwargs): # noqa: E501 """get_external_tasks_count # noqa: E501 Queries for the number of external tasks that fulfill given parameters. Takes the same parameters as the [Get External Tasks](https://docs.camunda.org/manual/7.13/reference/rest/external-task/get-query/) method. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_external_tasks_count(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str external_task_id: Filter by an external task's id. :param str external_task_id_in: Filter by the comma-separated list of external task ids. :param str topic_name: Filter by an external task topic. :param str worker_id: Filter by the id of the worker that the task was most recently locked by. :param bool locked: Only include external tasks that are currently locked (i.e., they have a lock time and it has not expired). Value may only be `true`, as `false` matches any external task. :param bool not_locked: Only include external tasks that are currently not locked (i.e., they have no lock or it has expired). Value may only be `true`, as `false` matches any external task. :param bool with_retries_left: Only include external tasks that have a positive (> 0) number of retries (or `null`). Value may only be `true`, as `false` matches any external task. :param bool no_retries_left: Only include external tasks that have 0 retries. Value may only be `true`, as `false` matches any external task. :param datetime lock_expiration_after: Restrict to external tasks that have a lock that expires after a given date. By [default](https://docs.camunda.org/manual/7.13/reference/rest/overview/date-format/), the date must have the format `yyyy-MM-dd'T'HH:mm:ss.SSSZ`, e.g., `2013-01-23T14:42:45.000+0200`. :param datetime lock_expiration_before: Restrict to external tasks that have a lock that expires before a given date. By [default](https://docs.camunda.org/manual/7.13/reference/rest/overview/date-format/), the date must have the format `yyyy-MM-dd'T'HH:mm:ss.SSSZ`, e.g., `2013-01-23T14:42:45.000+0200`. :param str activity_id: Filter by the id of the activity that an external task is created for. :param str activity_id_in: Filter by the comma-separated list of ids of the activities that an external task is created for. :param str execution_id: Filter by the id of the execution that an external task belongs to. :param str process_instance_id: Filter by the id of the process instance that an external task belongs to. :param str process_instance_id_in: Filter by a comma-separated list of process instance ids that an external task may belong to. :param str process_definition_id: Filter by the id of the process definition that an external task belongs to. :param str tenant_id_in: Filter by a comma-separated list of tenant ids. An external task must have one of the given tenant ids. :param bool active: Only include active tasks. Value may only be `true`, as `false` matches any external task. :param bool suspended: Only include suspended tasks. Value may only be `true`, as `false` matches any external task. :param int priority_higher_than_or_equals: Only include jobs with a priority higher than or equal to the given value. Value must be a valid `long` value. :param int priority_lower_than_or_equals: Only include jobs with a priority lower than or equal to the given value. Value must be a valid `long` value. :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: CountResultDto If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.get_external_tasks_count_with_http_info(**kwargs) # noqa: E501 def get_external_tasks_count_with_http_info(self, **kwargs): # noqa: E501 """get_external_tasks_count # noqa: E501 Queries for the number of external tasks that fulfill given parameters. Takes the same parameters as the [Get External Tasks](https://docs.camunda.org/manual/7.13/reference/rest/external-task/get-query/) method. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_external_tasks_count_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str external_task_id: Filter by an external task's id. :param str external_task_id_in: Filter by the comma-separated list of external task ids. :param str topic_name: Filter by an external task topic. :param str worker_id: Filter by the id of the worker that the task was most recently locked by. :param bool locked: Only include external tasks that are currently locked (i.e., they have a lock time and it has not expired). Value may only be `true`, as `false` matches any external task. :param bool not_locked: Only include external tasks that are currently not locked (i.e., they have no lock or it has expired). Value may only be `true`, as `false` matches any external task. :param bool with_retries_left: Only include external tasks that have a positive (> 0) number of retries (or `null`). Value may only be `true`, as `false` matches any external task. :param bool no_retries_left: Only include external tasks that have 0 retries. Value may only be `true`, as `false` matches any external task. :param datetime lock_expiration_after: Restrict to external tasks that have a lock that expires after a given date. By [default](https://docs.camunda.org/manual/7.13/reference/rest/overview/date-format/), the date must have the format `yyyy-MM-dd'T'HH:mm:ss.SSSZ`, e.g., `2013-01-23T14:42:45.000+0200`. :param datetime lock_expiration_before: Restrict to external tasks that have a lock that expires before a given date. By [default](https://docs.camunda.org/manual/7.13/reference/rest/overview/date-format/), the date must have the format `yyyy-MM-dd'T'HH:mm:ss.SSSZ`, e.g., `2013-01-23T14:42:45.000+0200`. :param str activity_id: Filter by the id of the activity that an external task is created for. :param str activity_id_in: Filter by the comma-separated list of ids of the activities that an external task is created for. :param str execution_id: Filter by the id of the execution that an external task belongs to. :param str process_instance_id: Filter by the id of the process instance that an external task belongs to. :param str process_instance_id_in: Filter by a comma-separated list of process instance ids that an external task may belong to. :param str process_definition_id: Filter by the id of the process definition that an external task belongs to. :param str tenant_id_in: Filter by a comma-separated list of tenant ids. An external task must have one of the given tenant ids. :param bool active: Only include active tasks. Value may only be `true`, as `false` matches any external task. :param bool suspended: Only include suspended tasks. Value may only be `true`, as `false` matches any external task. :param int priority_higher_than_or_equals: Only include jobs with a priority higher than or equal to the given value. Value must be a valid `long` value. :param int priority_lower_than_or_equals: Only include jobs with a priority lower than or equal to the given value. Value must be a valid `long` value. :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(CountResultDto, status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = [ 'external_task_id', 'external_task_id_in', 'topic_name', 'worker_id', 'locked', 'not_locked', 'with_retries_left', 'no_retries_left', 'lock_expiration_after', 'lock_expiration_before', 'activity_id', 'activity_id_in', 'execution_id', 'process_instance_id', 'process_instance_id_in', 'process_definition_id', 'tenant_id_in', 'active', 'suspended', 'priority_higher_than_or_equals', 'priority_lower_than_or_equals' ] all_params.extend( [ 'async_req', '_return_http_data_only', '_preload_content', '_request_timeout' ] ) for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method get_external_tasks_count" % key ) local_var_params[key] = val del local_var_params['kwargs'] collection_formats = {} path_params = {} query_params = [] if 'external_task_id' in local_var_params and local_var_params['external_task_id'] is not None: # noqa: E501 query_params.append(('externalTaskId', local_var_params['external_task_id'])) # noqa: E501 if 'external_task_id_in' in local_var_params and local_var_params['external_task_id_in'] is not None: # noqa: E501 query_params.append(('externalTaskIdIn', local_var_params['external_task_id_in'])) # noqa: E501 if 'topic_name' in local_var_params and local_var_params['topic_name'] is not None: # noqa: E501 query_params.append(('topicName', local_var_params['topic_name'])) # noqa: E501 if 'worker_id' in local_var_params and local_var_params['worker_id'] is not None: # noqa: E501 query_params.append(('workerId', local_var_params['worker_id'])) # noqa: E501 if 'locked' in local_var_params and local_var_params['locked'] is not None: # noqa: E501 query_params.append(('locked', local_var_params['locked'])) # noqa: E501 if 'not_locked' in local_var_params and local_var_params['not_locked'] is not None: # noqa: E501 query_params.append(('notLocked', local_var_params['not_locked'])) # noqa: E501 if 'with_retries_left' in local_var_params and local_var_params['with_retries_left'] is not None: # noqa: E501 query_params.append(('withRetriesLeft', local_var_params['with_retries_left'])) # noqa: E501 if 'no_retries_left' in local_var_params and local_var_params['no_retries_left'] is not None: # noqa: E501 query_params.append(('noRetriesLeft', local_var_params['no_retries_left'])) # noqa: E501 if 'lock_expiration_after' in local_var_params and local_var_params['lock_expiration_after'] is not None: # noqa: E501 query_params.append(('lockExpirationAfter', local_var_params['lock_expiration_after'])) # noqa: E501 if 'lock_expiration_before' in local_var_params and local_var_params['lock_expiration_before'] is not None: # noqa: E501 query_params.append(('lockExpirationBefore', local_var_params['lock_expiration_before'])) # noqa: E501 if 'activity_id' in local_var_params and local_var_params['activity_id'] is not None: # noqa: E501 query_params.append(('activityId', local_var_params['activity_id'])) # noqa: E501 if 'activity_id_in' in local_var_params and local_var_params['activity_id_in'] is not None: # noqa: E501 query_params.append(('activityIdIn', local_var_params['activity_id_in'])) # noqa: E501 if 'execution_id' in local_var_params and local_var_params['execution_id'] is not None: # noqa: E501 query_params.append(('executionId', local_var_params['execution_id'])) # noqa: E501 if 'process_instance_id' in local_var_params and local_var_params['process_instance_id'] is not None: # noqa: E501 query_params.append(('processInstanceId', local_var_params['process_instance_id'])) # noqa: E501 if 'process_instance_id_in' in local_var_params and local_var_params['process_instance_id_in'] is not None: # noqa: E501 query_params.append(('processInstanceIdIn', local_var_params['process_instance_id_in'])) # noqa: E501 if 'process_definition_id' in local_var_params and local_var_params['process_definition_id'] is not None: # noqa: E501 query_params.append(('processDefinitionId', local_var_params['process_definition_id'])) # noqa: E501 if 'tenant_id_in' in local_var_params and local_var_params['tenant_id_in'] is not None: # noqa: E501 query_params.append(('tenantIdIn', local_var_params['tenant_id_in'])) # noqa: E501 if 'active' in local_var_params and local_var_params['active'] is not None: # noqa: E501 query_params.append(('active', local_var_params['active'])) # noqa: E501 if 'suspended' in local_var_params and local_var_params['suspended'] is not None: # noqa: E501 query_params.append(('suspended', local_var_params['suspended'])) # noqa: E501 if 'priority_higher_than_or_equals' in local_var_params and local_var_params['priority_higher_than_or_equals'] is not None: # noqa: E501 query_params.append(('priorityHigherThanOrEquals', local_var_params['priority_higher_than_or_equals'])) # noqa: E501 if 'priority_lower_than_or_equals' in local_var_params and local_var_params['priority_lower_than_or_equals'] is not None: # noqa: E501 query_params.append(('priorityLowerThanOrEquals', local_var_params['priority_lower_than_or_equals'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/external-task/count', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='CountResultDto', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_topic_names(self, **kwargs): # noqa: E501 """get_topic_names # noqa: E501 Queries for distinct topic names of external tasks that fulfill given parameters. Query can be restricted to only tasks with retries left, tasks that are locked, or tasks that are unlocked. The parameters withLockedTasks and withUnlockedTasks are exclusive. Setting them both to true will return an empty list. Providing no parameters will return a list of all distinct topic names with external tasks. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_topic_names(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param bool with_locked_tasks: Only include external tasks that are currently locked (i.e., they have a lock time and it has not expired). Value may only be `true`, as `false` matches any external task. :param bool with_unlocked_tasks: Only include external tasks that are currently not locked (i.e., they have no lock or it has expired). Value may only be `true`, as `false` matches any external task. :param bool with_retries_left: Only include external tasks that have a positive (> 0) number of retries (or `null`). Value may only be `true`, as `false` matches any external task. :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: list[str] If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.get_topic_names_with_http_info(**kwargs) # noqa: E501 def get_topic_names_with_http_info(self, **kwargs): # noqa: E501 """get_topic_names # noqa: E501 Queries for distinct topic names of external tasks that fulfill given parameters. Query can be restricted to only tasks with retries left, tasks that are locked, or tasks that are unlocked. The parameters withLockedTasks and withUnlockedTasks are exclusive. Setting them both to true will return an empty list. Providing no parameters will return a list of all distinct topic names with external tasks. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_topic_names_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param bool with_locked_tasks: Only include external tasks that are currently locked (i.e., they have a lock time and it has not expired). Value may only be `true`, as `false` matches any external task. :param bool with_unlocked_tasks: Only include external tasks that are currently not locked (i.e., they have no lock or it has expired). Value may only be `true`, as `false` matches any external task. :param bool with_retries_left: Only include external tasks that have a positive (> 0) number of retries (or `null`). Value may only be `true`, as `false` matches any external task. :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(list[str], status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = [ 'with_locked_tasks', 'with_unlocked_tasks', 'with_retries_left' ] all_params.extend( [ 'async_req', '_return_http_data_only', '_preload_content', '_request_timeout' ] ) for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method get_topic_names" % key ) local_var_params[key] = val del local_var_params['kwargs'] collection_formats = {} path_params = {} query_params = [] if 'with_locked_tasks' in local_var_params and local_var_params['with_locked_tasks'] is not None: # noqa: E501 query_params.append(('withLockedTasks', local_var_params['with_locked_tasks'])) # noqa: E501 if 'with_unlocked_tasks' in local_var_params and local_var_params['with_unlocked_tasks'] is not None: # noqa: E501 query_params.append(('withUnlockedTasks', local_var_params['with_unlocked_tasks'])) # noqa: E501 if 'with_retries_left' in local_var_params and local_var_params['with_retries_left'] is not None: # noqa: E501 query_params.append(('withRetriesLeft', local_var_params['with_retries_left'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/external-task/topic-names', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[str]', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def handle_external_task_bpmn_error(self, id, **kwargs): # noqa: E501 """handle_external_task_bpmn_error # noqa: E501 Reports a business error in the context of a running external task by id. The error code must be specified to identify the BPMN error handler. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.handle_external_task_bpmn_error(id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str id: The id of the external task in which context a BPMN error is reported. (required) :param ExternalTaskBpmnError external_task_bpmn_error: :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.handle_external_task_bpmn_error_with_http_info(id, **kwargs) # noqa: E501 def handle_external_task_bpmn_error_with_http_info(self, id, **kwargs): # noqa: E501 """handle_external_task_bpmn_error # noqa: E501 Reports a business error in the context of a running external task by id. The error code must be specified to identify the BPMN error handler. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.handle_external_task_bpmn_error_with_http_info(id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str id: The id of the external task in which context a BPMN error is reported. (required) :param ExternalTaskBpmnError external_task_bpmn_error: :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: None If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = [ 'id', 'external_task_bpmn_error' ] all_params.extend( [ 'async_req', '_return_http_data_only', '_preload_content', '_request_timeout' ] ) for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method handle_external_task_bpmn_error" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'id' is set if self.api_client.client_side_validation and ('id' not in local_var_params or # noqa: E501 local_var_params['id'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `id` when calling `handle_external_task_bpmn_error`") # noqa: E501 collection_formats = {} path_params = {} if 'id' in local_var_params: path_params['id'] = local_var_params['id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'external_task_bpmn_error' in local_var_params: body_params = local_var_params['external_task_bpmn_error'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/external-task/{id}/bpmnError', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def handle_failure(self, id, **kwargs): # noqa: E501 """handle_failure # noqa: E501 Reports a failure to execute an external task by id. A number of retries and a timeout until the task can be retried can be specified. If retries are set to 0, an incident for this task is created. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.handle_failure(id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str id: The id of the external task to report a failure for. (required) :param ExternalTaskFailureDto external_task_failure_dto: :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.handle_failure_with_http_info(id, **kwargs) # noqa: E501 def handle_failure_with_http_info(self, id, **kwargs): # noqa: E501 """handle_failure # noqa: E501 Reports a failure to execute an external task by id. A number of retries and a timeout until the task can be retried can be specified. If retries are set to 0, an incident for this task is created. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.handle_failure_with_http_info(id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str id: The id of the external task to report a failure for. (required) :param ExternalTaskFailureDto external_task_failure_dto: :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: None If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = [ 'id', 'external_task_failure_dto' ] all_params.extend( [ 'async_req', '_return_http_data_only', '_preload_content', '_request_timeout' ] ) for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method handle_failure" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'id' is set if self.api_client.client_side_validation and ('id' not in local_var_params or # noqa: E501 local_var_params['id'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `id` when calling `handle_failure`") # noqa: E501 collection_formats = {} path_params = {} if 'id' in local_var_params: path_params['id'] = local_var_params['id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'external_task_failure_dto' in local_var_params: body_params = local_var_params['external_task_failure_dto'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/external-task/{id}/failure', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def query_external_tasks(self, **kwargs): # noqa: E501 """query_external_tasks # noqa: E501 Queries for external tasks that fulfill given parameters in the form of a JSON object. This method is slightly more powerful than the [Get External Tasks](https://docs.camunda.org/manual/7.13/reference/rest/external-task/get-query/) method because it allows to specify a hierarchical result sorting. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.query_external_tasks(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param int first_result: Pagination of results. Specifies the index of the first result to return. :param int max_results: Pagination of results. Specifies the maximum number of results to return. Will return less results if there are no more results left. :param ExternalTaskQueryDto external_task_query_dto: :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: list[ExternalTaskDto] If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.query_external_tasks_with_http_info(**kwargs) # noqa: E501 def query_external_tasks_with_http_info(self, **kwargs): # noqa: E501 """query_external_tasks # noqa: E501 Queries for external tasks that fulfill given parameters in the form of a JSON object. This method is slightly more powerful than the [Get External Tasks](https://docs.camunda.org/manual/7.13/reference/rest/external-task/get-query/) method because it allows to specify a hierarchical result sorting. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.query_external_tasks_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param int first_result: Pagination of results. Specifies the index of the first result to return. :param int max_results: Pagination of results. Specifies the maximum number of results to return. Will return less results if there are no more results left. :param ExternalTaskQueryDto external_task_query_dto: :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(list[ExternalTaskDto], status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = [ 'first_result', 'max_results', 'external_task_query_dto' ] all_params.extend( [ 'async_req', '_return_http_data_only', '_preload_content', '_request_timeout' ] ) for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method query_external_tasks" % key ) local_var_params[key] = val del local_var_params['kwargs'] collection_formats = {} path_params = {} query_params = [] if 'first_result' in local_var_params and local_var_params['first_result'] is not None: # noqa: E501 query_params.append(('firstResult', local_var_params['first_result'])) # noqa: E501 if 'max_results' in local_var_params and local_var_params['max_results'] is not None: # noqa: E501 query_params.append(('maxResults', local_var_params['max_results'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None if 'external_task_query_dto' in local_var_params: body_params = local_var_params['external_task_query_dto'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/external-task', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[ExternalTaskDto]', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def query_external_tasks_count(self, **kwargs): # noqa: E501 """query_external_tasks_count # noqa: E501 Queries for the number of external tasks that fulfill given parameters. This method takes the same message body as the [Get External Tasks (POST)](https://docs.camunda.org/manual/7.13/reference/rest/external-task/post-query/) method. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.query_external_tasks_count(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param ExternalTaskQueryDto external_task_query_dto: :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: CountResultDto If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.query_external_tasks_count_with_http_info(**kwargs) # noqa: E501 def query_external_tasks_count_with_http_info(self, **kwargs): # noqa: E501 """query_external_tasks_count # noqa: E501 Queries for the number of external tasks that fulfill given parameters. This method takes the same message body as the [Get External Tasks (POST)](https://docs.camunda.org/manual/7.13/reference/rest/external-task/post-query/) method. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.query_external_tasks_count_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param ExternalTaskQueryDto external_task_query_dto: :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(CountResultDto, status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = [ 'external_task_query_dto' ] all_params.extend( [ 'async_req', '_return_http_data_only', '_preload_content', '_request_timeout' ] ) for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method query_external_tasks_count" % key ) local_var_params[key] = val del local_var_params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'external_task_query_dto' in local_var_params: body_params = local_var_params['external_task_query_dto'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/external-task/count', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='CountResultDto', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def set_external_task_resource_priority(self, id, **kwargs): # noqa: E501 """set_external_task_resource_priority # noqa: E501 Sets the priority of an existing external task by id. The default value of a priority is 0. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.set_external_task_resource_priority(id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str id: The id of the external task to set the priority for. (required) :param PriorityDto priority_dto: :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.set_external_task_resource_priority_with_http_info(id, **kwargs) # noqa: E501 def set_external_task_resource_priority_with_http_info(self, id, **kwargs): # noqa: E501 """set_external_task_resource_priority # noqa: E501 Sets the priority of an existing external task by id. The default value of a priority is 0. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.set_external_task_resource_priority_with_http_info(id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str id: The id of the external task to set the priority for. (required) :param PriorityDto priority_dto: :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: None If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = [ 'id', 'priority_dto' ] all_params.extend( [ 'async_req', '_return_http_data_only', '_preload_content', '_request_timeout' ] ) for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method set_external_task_resource_priority" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'id' is set if self.api_client.client_side_validation and ('id' not in local_var_params or # noqa: E501 local_var_params['id'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `id` when calling `set_external_task_resource_priority`") # noqa: E501 collection_formats = {} path_params = {} if 'id' in local_var_params: path_params['id'] = local_var_params['id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'priority_dto' in local_var_params: body_params = local_var_params['priority_dto'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/external-task/{id}/priority', 'PUT', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def set_external_task_resource_retries(self, id, **kwargs): # noqa: E501 """set_external_task_resource_retries # noqa: E501 Sets the number of retries left to execute an external task by id. If retries are set to 0, an incident is created. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.set_external_task_resource_retries(id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str id: The id of the external task to set the number of retries for. (required) :param RetriesDto retries_dto: :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.set_external_task_resource_retries_with_http_info(id, **kwargs) # noqa: E501 def set_external_task_resource_retries_with_http_info(self, id, **kwargs): # noqa: E501 """set_external_task_resource_retries # noqa: E501 Sets the number of retries left to execute an external task by id. If retries are set to 0, an incident is created. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.set_external_task_resource_retries_with_http_info(id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str id: The id of the external task to set the number of retries for. (required) :param RetriesDto retries_dto: :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: None If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = [ 'id', 'retries_dto' ] all_params.extend( [ 'async_req', '_return_http_data_only', '_preload_content', '_request_timeout' ] ) for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method set_external_task_resource_retries" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'id' is set if self.api_client.client_side_validation and ('id' not in local_var_params or # noqa: E501 local_var_params['id'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `id` when calling `set_external_task_resource_retries`") # noqa: E501 collection_formats = {} path_params = {} if 'id' in local_var_params: path_params['id'] = local_var_params['id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'retries_dto' in local_var_params: body_params = local_var_params['retries_dto'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/external-task/{id}/retries', 'PUT', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def set_external_task_retries(self, **kwargs): # noqa: E501 """set_external_task_retries # noqa: E501 Sets the number of retries left to execute external tasks by id synchronously. If retries are set to 0, an incident is created. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.set_external_task_retries(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param SetRetriesForExternalTasksDto set_retries_for_external_tasks_dto: :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.set_external_task_retries_with_http_info(**kwargs) # noqa: E501 def set_external_task_retries_with_http_info(self, **kwargs): # noqa: E501 """set_external_task_retries # noqa: E501 Sets the number of retries left to execute external tasks by id synchronously. If retries are set to 0, an incident is created. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.set_external_task_retries_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param SetRetriesForExternalTasksDto set_retries_for_external_tasks_dto: :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: None If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = [ 'set_retries_for_external_tasks_dto' ] all_params.extend( [ 'async_req', '_return_http_data_only', '_preload_content', '_request_timeout' ] ) for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method set_external_task_retries" % key ) local_var_params[key] = val del local_var_params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'set_retries_for_external_tasks_dto' in local_var_params: body_params = local_var_params['set_retries_for_external_tasks_dto'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/external-task/retries', 'PUT', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def set_external_task_retries_async_operation(self, **kwargs): # noqa: E501 """set_external_task_retries_async_operation # noqa: E501 Sets the number of retries left to execute external tasks by id asynchronously. If retries are set to 0, an incident is created. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.set_external_task_retries_async_operation(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param SetRetriesForExternalTasksDto set_retries_for_external_tasks_dto: :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: BatchDto If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.set_external_task_retries_async_operation_with_http_info(**kwargs) # noqa: E501 def set_external_task_retries_async_operation_with_http_info(self, **kwargs): # noqa: E501 """set_external_task_retries_async_operation # noqa: E501 Sets the number of retries left to execute external tasks by id asynchronously. If retries are set to 0, an incident is created. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.set_external_task_retries_async_operation_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param SetRetriesForExternalTasksDto set_retries_for_external_tasks_dto: :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(BatchDto, status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = [ 'set_retries_for_external_tasks_dto' ] all_params.extend( [ 'async_req', '_return_http_data_only', '_preload_content', '_request_timeout' ] ) for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method set_external_task_retries_async_operation" % key ) local_var_params[key] = val del local_var_params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'set_retries_for_external_tasks_dto' in local_var_params: body_params = local_var_params['set_retries_for_external_tasks_dto'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/external-task/retries-async', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='BatchDto', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def unlock(self, id, **kwargs): # noqa: E501 """unlock # noqa: E501 Unlocks an external task by id. Clears the task's lock expiration time and worker id. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.unlock(id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str id: The id of the external task to unlock. (required) :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.unlock_with_http_info(id, **kwargs) # noqa: E501 def unlock_with_http_info(self, id, **kwargs): # noqa: E501 """unlock # noqa: E501 Unlocks an external task by id. Clears the task's lock expiration time and worker id. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.unlock_with_http_info(id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str id: The id of the external task to unlock. (required) :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: None If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = [ 'id' ] all_params.extend( [ 'async_req', '_return_http_data_only', '_preload_content', '_request_timeout' ] ) for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method unlock" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'id' is set if self.api_client.client_side_validation and ('id' not in local_var_params or # noqa: E501 local_var_params['id'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `id` when calling `unlock`") # noqa: E501 collection_formats = {} path_params = {} if 'id' in local_var_params: path_params['id'] = local_var_params['id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/external-task/{id}/unlock', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats)

52.526572

424

0.626213

14,519

118,605

4.877195

0.027757

0.043044

0.068604

0.021607

0.983237

0.980851

0.977137

0.975145

0.970669

0.966065

0

0.016016

0.301404

118,605

2,257

425

52.549845

0.838614

0.504616

0

0.783685

1

0

0.204043

0.076101

0

1

0.032081

false

0

0.004583

0

0.068744

0

null

0

1

0

1

0

null

0

8

c10c5c8d35d6f9ddb221d59c10828ea892946d95

972

py

Python

img.py

shabidkhan/hamgman

e032d266ed4fb73e2acc7ce4bad1d8adb846ae99

[ "MIT" ]

1

2020-02-19T13:14:17.000Z

img.py

shabidkhan/hamgman

e032d266ed4fb73e2acc7ce4bad1d8adb846ae99

[ "MIT" ]

null

img.py

shabidkhan/hamgman

e032d266ed4fb73e2acc7ce4bad1d8adb846ae99

[ "MIT" ]

1

2020-10-29T19:01:58.000Z

def Show_Image(chance): image=[''' \O/ | / \ XXX ''',''' O /|\ / \ XXX ''',''' | O | /|\ | / \ | XXX =========''',''' +------------+ | O | /|\ | / \ | XXX =========''',''' +-------------+ | | O | /|\ | / \ | XXX =========''',''' +-------------+ | | (O) | /|\ | / \ | XXX =========''',''' +-------------+ | | \(O)/ | | | / \ | =========''',''' +-------------+ | | (O) | ||| | / \ | ========''' ] return image[chance+1]

18.339623

99

0.069959

23

972

2.913043

0.347826

0.358209

0.447761

0.597015

0.373134

0

0.003003

0.657407

972

52

100

18.692308

0.198198

0

0.680851

0

0.793814

0

1

0.021277

false

0

0.042553

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1

0

null

1

0

1

0

1

0

1

0

null

0

7

c14401724d7c008e7f13b49bb431e7e8838d7834

110,983

py

Python

tests/test_format_title.py

SaMeHub/cds_paper_bot

6ea07b7295b3f493d9e8d605fd1b09b0fe283441

[ "MIT" ]

2

2018-03-20T15:59:34.000Z

2018-03-21T00:18:39.000Z

tests/test_format_title.py

SaMeHub/cds_paper_bot

6ea07b7295b3f493d9e8d605fd1b09b0fe283441

[ "MIT" ]

30

2018-01-12T11:36:12.000Z

2021-09-04T07:22:15.000Z

tests/test_format_title.py

SaMeHub/cds_paper_bot

6ea07b7295b3f493d9e8d605fd1b09b0fe283441

[ "MIT" ]

1

2019-07-09T06:45:34.000Z

"""Test title formatting.""" import sys import os import pytest sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))) import cds_paper_bot # pylint: disable=wrong-import-position,import-error class TestFormatTitle(object): """List of titles and what they should look like after formatting.""" @pytest.mark.parametrize( "input_title, expected", [ ("Analysis at $\\sqrt s=13 TeV$", "Analysis at √(s) = 13 TeV"), ("\\sqrt s", "√(s)"), ("Analysis of process $x \\rightarrowy$", "Analysis of process x → y"), ("$x__s$", "x_s"), ("x →y", "x → y"), ("$t\\overline tt$", "ttt"), ("$t\\bar{t}$", "tt̅"), ("$t \\bar{t}$", "tt̅"), ("$t \\overline t$", "tt"), ("\\overline xy", "xy"), ("Bethe--Bloch", "Bethe–Bloch"), ("Bethe---Bloch", "Bethe—Bloch"), ("Energies of 15keV and MeV, 6eV", "Energies of 15 keV and MeV, 6 eV"), ("13TeV", "13 TeV"), ("nonsenseTeV", "nonsenseTeV"), ("13tev", "13tev"), ("50eV", "50 eV"), # pylint: disable=line-too-long,too-many-lines # CMS cms_pas_feed ( "Measurement of differential ${\\mathrm t}\\bar{\\mathrm t}$ production cross sections for high-$p_{\\text{T}}$ top quarks in proton-proton collisions at $\\sqrt{s} = 13\\,\\text{TeV}$", "Measurement of differential tt̅ production cross sections for high-p_T top quarks in proton-proton collisions at √(s) = 13 TeV", ), ( "Search for long-lived particles decaying into displaced jets", "Search for long-lived particles decaying into displaced jets", ), ( "Study of hard color singlet exchange in dijet events with proton-proton collisions at $\\sqrt{s}= 13~\\mathrm{TeV}$", "Study of hard color singlet exchange in dijet events with proton-proton collisions at √(s) = 13 TeV", ), ( "Inclusive search for a highly boosted Higgs boson decaying to a bottom quark-antiquark pair at $\\sqrt{s} = 13~\\mathrm{TeV}$ with $137~\\mathrm{fb}^{-1}$", "Inclusive search for a highly boosted Higgs boson decaying to a bottom quark-antiquark pair at √(s) = 13 TeV with 137 fb⁻¹", ), ( "Observation of heavy triboson production in leptonic final states in proton-proton collisions at $\\sqrt{s}=13~\\mathrm{TeV}$", "Observation of heavy triboson production in leptonic final states in proton-proton collisions at √(s) = 13 TeV", ), ( "Studies of $\\mathrm{W^+W^-}$ production at $\\sqrt{s}=13~\\mathrm{TeV}$", "Studies of W⁺W⁻ production at √(s) = 13 TeV", ), ( "Measurement of the $CP$ violating phase $\\phi_{\\text{s}}$ in the $\\mathrm{B}_s \\to \\mathrm{J}/\\psi\\,\\phi(1020) \\to \\mu^+\\mu^-\\,\\mathrm{K}^+\\mathrm{K}^-$ channel in proton-proton collisions at $\\sqrt{s} = 13~\\mathrm{TeV}$", "Measurement of the CP violating phase ϕ_s in the B_s → J/ψ ϕ(1020) → μ⁺μ⁻ K⁺K⁻ channel in proton-proton collisions at √(s) = 13 TeV", ), ( "Measurements of production cross sections of same-sign WW and WZ boson pairs in association with two jets in proton-proton collisions at sqrts = 13 TeV", "Measurements of production cross sections of same-sign WW and WZ boson pairs in association with two jets in proton-proton collisions at √(s) = 13 TeV", ), ( "Measurement of differential cross sections for single top quark production in association with a W boson at $\\sqrt{s}=13~\\mathrm{TeV}$", "Measurement of differential cross sections for single top quark production in association with a W boson at √(s) = 13 TeV", ), ( "Measurement of the W boson rapidity, helicity, and differential cross sections in pp collisions at $\\sqrt{s}=13~\\mathrm{TeV}$", "Measurement of the W boson rapidity, helicity, and differential cross sections in pp collisions at √(s) = 13 TeV", ), ( "Search for disappearing tracks in proton-proton collisions at $\\sqrt{s} = 13$ TeV", "Search for disappearing tracks in proton-proton collisions at √(s) = 13 TeV", ), ( "Combined Higgs boson production and decay measurements with up to 137 fb-1 of proton-proton collision data at sqrts = 13 TeV", "Combined Higgs boson production and decay measurements with up to 137 fb-1 of proton-proton collision data at √(s) = 13 TeV", ), ( "Search for a light charged Higgs boson in the H$^{\\pm} \\rightarrow$ cs channel at 13 TeV", "Search for a light charged Higgs boson in the H^± → cs channel at 13 TeV", ), ( "Measurement of prompt $\\rm{ D_{s}^{+}}$ production in pp and PbPb collisions at $\\sqrt{s_{_{\\text{NN}}}}$ = 5.02 TeV", "Measurement of prompt D⁺_s production in pp and PbPb collisions at √(s_NN) = 5.02 TeV", ), ( "Extraction of CKM matrix elements in single top quark $t$-channel events in proton-proton collisions at $\\sqrt{s} = 13$ TeV", "Extraction of CKM matrix elements in single top quark t-channel events in proton-proton collisions at √(s) = 13 TeV", ), ( "Nuclear modification factor of isolated photons in PbPb collisions at $\\sqrt{s_{_{\\mathrm{NN}}}} = 5.02~\\mathrm{TeV}$", "Nuclear modification factor of isolated photons in PbPb collisions at √(s_NN) = 5.02 TeV", ), ( "Nuclear modification of $\\Upsilon$ states in pPb collisions at $\\sqrt{s_\\mathrm{NN}} = 5.02~\\mathrm{TeV}$", "Nuclear modification of Υ states in pPb collisions at √(s_NN) = 5.02 TeV", ), ( "Search for strong electromagnetic fields in PbPb collisions at 5.02 TeV via azimuthal anisotropy of $\\mathrm{D^0}$ and $\\mathrm{\\overline{D}^0}$ mesons", "Search for strong electromagnetic fields in PbPb collisions at 5.02 TeV via azimuthal anisotropy of D⁰ and D̅⁰ mesons", ), ( "Studies of charm and beauty long-range correlations in pp and pPb collisions", "Studies of charm and beauty long-range correlations in pp and pPb collisions", ), ( "Evidence for top quark production in nucleus-nucleus collisions", "Evidence for top quark production in nucleus-nucleus collisions", ), ( "Evidence for $\\chi_{c1}$(3872) in PbPb collisions and studies of its prompt production at $\\sqrt{\\smash[b]{s_{_{\\mathrm{NN}}}}}=5.02$ TeV", "Evidence for χ_c1(3872) in PbPb collisions and studies of its prompt production at √(s_NN) = 5.02 TeV", ), ( "Study of quark- and gluon-like jet fractions using jet charge in PbPb and pp collisions at 5.02 TeV", "Study of quark- and gluon-like jet fractions using jet charge in PbPb and pp collisions at 5.02 TeV", ), ( "Measurement of the elliptic flow of $\\Upsilon\\textrm{(1S)}$ and $\\Upsilon\\textrm{(2S)}$ mesons in PbPb collisions at $\\sqrt{\\mathrm{s_{NN}}}=5.02~\\mathrm{TeV}$", "Measurement of the elliptic flow of Υ(1S) and Υ(2S) mesons in PbPb collisions at √(s_NN) = 5.02 TeV", ), ( "Measurement of Jet Nuclear Modification Factor in PbPb Collisions at $\\sqrt{s_{NN}}$ = 5.02 TeV with CMS", "Measurement of Jet Nuclear Modification Factor in PbPb Collisions at √(s_NN) = 5.02 TeV with CMS", ), ( "Measurement of $\\mathrm{b}$ jet shapes in pp collisions at $\\sqrt{s} = 5.02~\\mathrm{TeV}$", "Measurement of b jet shapes in pp collisions at √(s) = 5.02 TeV", ), ( "Measurement of the average very forward energy as a function of the track multiplicity at central rapidities in proton-proton collisions at $\\sqrt{s}=13~\\mathrm{TeV}$", "Measurement of the average very forward energy as a function of the track multiplicity at central rapidities in proton-proton collisions at √(s) = 13 TeV", ), ( "Search for direct $\\tau$ slepton pair production in proton-proton collisions at $\\sqrt{s}=13~\\mathrm{TeV}$", "Search for direct τ slepton pair production in proton-proton collisions at √(s) = 13 TeV", ), ( "Search for heavy resonances in the all-hadronic vector-boson pair final state with a multi-dimensional fit", "Search for heavy resonances in the all-hadronic vector-boson pair final state with a multi-dimensional fit", ), ( "Study of the $\\mathrm{B}^{+}\\rightarrow \\mathrm{J}/\\psi \\bar{\\Lambda} \\mathrm{p}$ decay in proton-proton collisions at $\\sqrt{s}= 8~\\mathrm{TeV}$", "Study of the B⁺ → J/ψΛ̅p decay in proton-proton collisions at √(s) = 8 TeV", ), ( "Search for new physics in multilepton final states in pp collisions at $\\sqrt{s}=13~\\mathrm{TeV}$", "Search for new physics in multilepton final states in pp collisions at √(s) = 13 TeV", ), ( "Measurement of the $\\textrm{pp} \\rightarrow \\textrm{Z}\\textrm{Z}$ production cross section at $\\sqrt{s} = 13$ TeV with the Run 2 data set", "Measurement of the pp → ZZ production cross section at √(s) = 13 TeV with the Run 2 data set", ), ( "Search for standard model production of four top quarks in final states with same-sign and multiple leptons in proton-proton collisions at $\\sqrt{s}=13~\\mathrm{TeV}$", "Search for standard model production of four top quarks in final states with same-sign and multiple leptons in proton-proton collisions at √(s) = 13 TeV", ), ( "Search for a heavy Higgs boson decaying to a pair of W bosons in proton-proton collisions at $\\sqrt{s}=13~\\mathrm{TeV}$", "Search for a heavy Higgs boson decaying to a pair of W bosons in proton-proton collisions at √(s) = 13 TeV", ), ( "Search for heavy Higgs bosons decaying to a top quark pair in proton-proton collisions at $\\sqrt{s} = 13\\,\\mathrm{TeV}$", "Search for heavy Higgs bosons decaying to a top quark pair in proton-proton collisions at √(s) = 13 TeV", ), ( "Measurement of Higgs boson production and decay to the $\\tau\\tau$ final state", "Measurement of Higgs boson production and decay to the ττ final state", ), ( "Measurements of properties of the Higgs boson in the four-lepton final state in proton-proton collisions at $\\sqrt{s}=13~\\mathrm{TeV}$", "Measurements of properties of the Higgs boson in the four-lepton final state in proton-proton collisions at √(s) = 13 TeV", ), ( "First constraints on invisible Higgs boson decays using $\\mathrm{t}\\bar{\\mathrm{t}}\\mathrm{H}$ production at $\\sqrt{s}=13~\\mathrm{TeV}$", "First constraints on invisible Higgs boson decays using tt̅H production at √(s) = 13 TeV", ), ( "Combined search for gauge-mediated supersymmetry with photons in 13 TeV collisions at the CMS experiment", "Combined search for gauge-mediated supersymmetry with photons in 13 TeV collisions at the CMS experiment", ), ( "Evidence for WW production from double-parton interactions in proton-proton collisions at $\\sqrt{s}$ = 13 TeV", "Evidence for WW production from double-parton interactions in proton-proton collisions at √(s) = 13 TeV", ), ( "Search for $\\tau \\to 3\\mu$ decays using $\\tau$ leptons produced in D and B meson decays", "Search for τ → 3μ decays using τ leptons produced in D and B meson decays", ), ( "Search for physics beyond the standard model in events with two same-sign leptons or at least three leptons and jets in proton-proton collisions at $\\sqrt{s}=13~\\mathrm{TeV}$.", "Search for physics beyond the standard model in events with two same-sign leptons or at least three leptons and jets in proton-proton collisions at √(s) = 13 TeV.", ), ( "Searches for new phenomena in events with jets and high values of the $M_{\\mathrm{T2}}$ variable, including signatures with disappearing tracks, in proton-proton collisions at $\\sqrt{s}=13~\\mathrm{TeV}$", "Searches for new phenomena in events with jets and high values of the M_T2 variable, including signatures with disappearing tracks, in proton-proton collisions at √(s) = 13 TeV", ), ( "Search for long-lived particles using delayed jets and missing transverse momentum with proton-proton collisions at $\\sqrt{s}$ = 13 TeV", "Search for long-lived particles using delayed jets and missing transverse momentum with proton-proton collisions at √(s) = 13 TeV", ), ( "Search for excited leptons decaying via contact interaction to two leptons and two jets", "Search for excited leptons decaying via contact interaction to two leptons and two jets", ), ( "Search for a pseudoscalar boson in the mass range from 4 to 15 GeV produced in decays of the 125 GeV Higgs boson in the final states with two muons and two nearby tracks at $\\sqrt{s}=13~\\mathrm{TeV}$", "Search for a pseudoscalar boson in the mass range from 4 to 15 GeV produced in decays of the 125 GeV Higgs boson in the final states with two muons and two nearby tracks at √(s) = 13 TeV", ), ( "Search for boosted quark-antiquark resonances produced in association with a photon at $\\sqrt{s}=13~\\mathrm{TeV}$", "Search for boosted quark-antiquark resonances produced in association with a photon at √(s) = 13 TeV", ), ( "Search for new physics in events with closely collimated photons and gluons", "Search for new physics in events with closely collimated photons and gluons", ), ( "Search for Pair Production of Vector-Like Quarks in the Fully Hadronic Channel", "Search for Pair Production of Vector-Like Quarks in the Fully Hadronic Channel", ), ( "Measurements of Higgs boson production via gluon fusion and vector boson fusion in the diphoton decay channel at $\\sqrt{s} = 13$ TeV", "Measurements of Higgs boson production via gluon fusion and vector boson fusion in the diphoton decay channel at √(s) = 13 TeV", ), ( "Search for a charged Higgs boson decaying into top and bottom quarks in proton-proton collisions at 13TeV in events with electrons or muons", "Search for a charged Higgs boson decaying into top and bottom quarks in proton-proton collisions at 13 TeV in events with electrons or muons", ), # CMS cms_paper_feed ( "Combination of the W boson polarization measurements in top quark decays using ATLAS and CMS data at $\\sqrt{s} = $ 8 TeV", "Combination of the W boson polarization measurements in top quark decays using ATLAS and CMS data at √(s) = 8 TeV", ), ( "Measurements of production cross sections of WZ and same-sign WW boson pairs in association with two jets in proton-proton collisions at $\\sqrt{s} = $ 13 TeV", "Measurements of production cross sections of WZ and same-sign WW boson pairs in association with two jets in proton-proton collisions at √(s) = 13 TeV", ), ( "Measurement of CKM matrix elements in single top quark $t$-channel production in proton-proton collisions at $\\sqrt{s} = $ 13 TeV", "Measurement of CKM matrix elements in single top quark t-channel production in proton-proton collisions at √(s) = 13 TeV", ), ( "Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques", "Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques", ), ( "Search for disappearing tracks in proton-proton collisions at $\\sqrt{s} = $ 13 TeV", "Search for disappearing tracks in proton-proton collisions at √(s) = 13 TeV", ), ( "Measurement of quark- and gluon-like jet fractions using jet charge in PbPb and pp collisions at 5.02 TeV", "Measurement of quark- and gluon-like jet fractions using jet charge in PbPb and pp collisions at 5.02 TeV", ), ( "The production of isolated photons in PbPb and pp collisions at ${\\sqrt {\\smash [b]{s_{_{\\mathrm {NN}}}}}} = $ 5.02 TeV", "The production of isolated photons in PbPb and pp collisions at √(s_NN) = 5.02 TeV", ), ( "Measurements of ${\\mathrm{t\\bar{t}}\\mathrm{H}} $ production and the CP structure of the Yukawa interaction between the Higgs boson and top quark in the diphoton decay channel", "Measurements of tt̅H production and the CP structure of the Yukawa interaction between the Higgs boson and top quark in the diphoton decay channel", ), ( "Measurement of the cross section for $\\mathrm{t\\bar{t}}$ production with additional jets and b jets in pp collisions at $\\sqrt{s} = $ 13 TeV", "Measurement of the cross section for tt̅ production with additional jets and b jets in pp collisions at √(s) = 13 TeV", ), ( "Study of central exclusive $\\pi^{+}\\pi^{-}$ production in proton-proton collisions at $\\sqrt{s} = $ 5.02 and 13 TeV", "Study of central exclusive π⁺π⁻ production in proton-proton collisions at √(s) = 5.02 and 13 TeV", ), ( "Pileup mitigation at CMS in 13 TeV data", "Pileup mitigation at CMS in 13 TeV data", ), ( "Measurement of single-diffractive dijet production in proton-proton collisions at $\\sqrt{s} =$ 8 TeV with the CMS and TOTEM experiments", "Measurement of single-diffractive dijet production in proton-proton collisions at √(s) = 8 TeV with the CMS and TOTEM experiments", ), ( "Measurement of the cross section for electroweak production of a Z boson, a photon and two jets in proton-proton collisions at $\\sqrt{s} = $ 13 TeV and constraints on anomalous quartic couplings", "Measurement of the cross section for electroweak production of a Z boson, a photon and two jets in proton-proton collisions at √(s) = 13 TeV and constraints on anomalous quartic couplings", ), ( "A measurement of the Higgs boson mass in the diphoton decay channel", "A measurement of the Higgs boson mass in the diphoton decay channel", ), ( "Measurement of the $\\Upsilon(\\text{1S}) $ pair production cross section and search for resonances decaying to $\\Upsilon(\\text{1S}) \\mu^{+}\\mu^{-}$ in proton-proton collisions at $\\sqrt{s} = $ 13 TeV", "Measurement of the Υ(1S) pair production cross section and search for resonances decaying to Υ(1S) μ⁺μ⁻ in proton-proton collisions at √(s) = 13 TeV", ), ( "Search for physics beyond the standard model in events with jets and two same-sign or at least three charged leptons in proton-proton collisions at $\\sqrt{s}=$ 13 TeV", "Search for physics beyond the standard model in events with jets and two same-sign or at least three charged leptons in proton-proton collisions at √(s) = 13 TeV", ), ( "Search for charged Higgs bosons decaying into a top and a bottom quark in the all-jet final state of pp collisions at $\\sqrt{s}=$ 13 TeV", "Search for charged Higgs bosons decaying into a top and a bottom quark in the all-jet final state of pp collisions at √(s) = 13 TeV", ), ( "Measurement of the associated production of a Z boson with charm or bottom quark jets in proton-proton collisions at $\\sqrt{s}=$ 13 TeV", "Measurement of the associated production of a Z boson with charm or bottom quark jets in proton-proton collisions at √(s) = 13 TeV", ), ( "Measurements of dose-rate effects in the radiation damage of plastic scintillator tiles using silicon photomultipliers", "Measurements of dose-rate effects in the radiation damage of plastic scintillator tiles using silicon photomultipliers", ), ( "Study of excited $\\Lambda_{\\mathrm{b}}^{0}$ states decaying to $\\Lambda_{\\mathrm{b}}^{0}\\pi^{+}\\pi^{-}$ in proton-proton collisions at $\\sqrt{s} = $ 13 TeV", "Study of excited Λ⁰_b states decaying to Λ⁰_b π⁺π⁻ in proton-proton collisions at √(s) = 13 TeV", ), ( "Search for an excited lepton that decays via a contact interaction to a lepton and two jets in proton-proton collisions at ${\\sqrt{s}} = $ 13 TeV", "Search for an excited lepton that decays via a contact interaction to a lepton and two jets in proton-proton collisions at √(s) = 13 TeV", ), ( "A deep neural network to search for new long-lived particles decaying to jets", "A deep neural network to search for new long-lived particles decaying to jets", ), ( "Measurement of the top quark forward-backward production asymmetry and the anomalous chromoelectric and chromomagnetic moments in pp collisions at $\\sqrt{s} = $ 13 TeV", "Measurement of the top quark forward-backward production asymmetry and the anomalous chromoelectric and chromomagnetic moments in pp collisions at √(s) = 13 TeV", ), ( "Search for direct top squark pair production in events with one lepton, jets, and missing transverse momentum at 13 TeV with the CMS experiment", "Search for direct top squark pair production in events with one lepton, jets, and missing transverse momentum at 13 TeV with the CMS experiment", ), ( "Measurement of the ${\\chi_{\\mathrm{c}1}}$ and ${\\chi_{\\mathrm{c}2}}$ polarizations in proton-proton collisions at $\\sqrt{s} = $ 8 TeV", "Measurement of the χ_c1 and χ_c2 polarizations in proton-proton collisions at √(s) = 8 TeV", ), ( "Extraction and validation of a new set of CMS PYTHIA-8 tunes from underlying-event measurements", "Extraction and validation of a new set of CMS PYTHIA-8 tunes from underlying-event measurements", ), ( "Search for new physics in top quark production in dilepton final states in proton-proton collisions at $\\sqrt{s}$ = 13 TeV", "Search for new physics in top quark production in dilepton final states in proton-proton collisions at √(s) = 13 TeV", ), ( "Search for a low-mass $\\tau^{-}\\tau^{+}$ resonance in association with a bottom quark in proton-proton collisions at $\\sqrt{s} = $ 13 TeV", "Search for a low-mass τ⁻τ⁺ resonance in association with a bottom quark in proton-proton collisions at √(s) = 13 TeV", ), ( "Search for supersymmetry in final states with photons and missing transverse momentum in proton-proton collisions at 13 TeV", "Search for supersymmetry in final states with photons and missing transverse momentum in proton-proton collisions at 13 TeV", ), ( "Constraints on anomalous HVV couplings from the production of Higgs bosons decaying to $\\tau$ lepton pairs", "Constraints on anomalous HVV couplings from the production of Higgs bosons decaying to τ lepton pairs", ), ( "Performance of missing transverse momentum reconstruction in proton-proton collisions at $\\sqrt{s} = $ 13 TeV using the CMS detector", "Performance of missing transverse momentum reconstruction in proton-proton collisions at √(s) = 13 TeV using the CMS detector", ), ( "Search for charged Higgs bosons in the $\\mathrm{H}^{\\pm} \\to \\tau^{\\pm}\\nu_\\tau$ decay channel in proton-proton collisions at $\\sqrt{s} = $ 13 TeV", "Search for charged Higgs bosons in the H^± → τ^±ν_τ decay channel in proton-proton collisions at √(s) = 13 TeV", ), ( "Measurement of electroweak production of a W boson in association with two jets in proton-proton collisions at $\\sqrt{s} = $ 13 TeV", "Measurement of electroweak production of a W boson in association with two jets in proton-proton collisions at √(s) = 13 TeV", ), ( "An embedding technique to determine $\\tau\\tau$ backgrounds in proton-proton collision data", "An embedding technique to determine ττ backgrounds in proton-proton collision data", ), ( "Search for a heavy pseudoscalar boson decaying to a Z and a Higgs boson at $\\sqrt{s} = $ 13 TeV", "Search for a heavy pseudoscalar boson decaying to a Z and a Higgs boson at √(s) = 13 TeV", ), ( "Combinations of single-top-quark production cross-section measurements and $|f_{\\rm LV}V_{tb}|$ determinations at $\\sqrt{s}=7$ and 8 TeV with the ATLAS and CMS experiments", "Combinations of single-top-quark production cross-section measurements and |f_LVV_tb| determinations at √(s) = 7 and 8 TeV with the ATLAS and CMS experiments", ), ( "Azimuthal separation in nearly back-to-back jet topologies in inclusive 2- and 3-jet events in pp collisions at $\\sqrt{s}=$ 13 TeV", "Azimuthal separation in nearly back-to-back jet topologies in inclusive 2- and 3-jet events in pp collisions at √(s) = 13 TeV", ), ( "Pseudorapidity distributions of charged hadrons in xenon-xenon collisions at ${\\sqrt {\\smash [b]{s_{_{\\mathrm {NN}}}}}} = $ 5.44 TeV", "Pseudorapidity distributions of charged hadrons in xenon-xenon collisions at √(s_NN) = 5.44 TeV", ), ( "Measurement of exclusive $\\rho(770)^{0}$ photoproduction in ultraperipheral pPb collisions at ${\\sqrt {\\smash [b]{s_{_{\\mathrm {NN}}}}}} = $ 5.02 TeV", "Measurement of exclusive ρ⁰(770) photoproduction in ultraperipheral pPb collisions at √(s_NN) = 5.02 TeV", ), ( "Observation of two excited $ \\mathrm{B^{+}_{c}} $ states and measurement of the ${\\mathrm{B^{+}_{c}} \\text{(2S)}}$ mass in pp collisions at $\\sqrt{s} = $ 13 TeV", "Observation of two excited B⁺_c states and measurement of the B⁺_c (2S) mass in pp collisions at √(s) = 13 TeV", ), ( "Search for W boson decays to three charged pions", "Search for W boson decays to three charged pions", ), ( "Charged-particle angular correlations in XeXe collisions at ${\\sqrt {\\smash [b]{s_{_{\\mathrm {NN}}}}}} = $ 5.44 TeV", "Charged-particle angular correlations in XeXe collisions at √(s_NN) = 5.44 TeV", ), ( "Search for supersymmetry in events with a photon, jets, b-jets, and missing transverse momentum in proton-proton collisions at 13 TeV", "Search for supersymmetry in events with a photon, jets, b-jets, and missing transverse momentum in proton-proton collisions at 13 TeV", ), ( "Measurement of electroweak WZ boson production and search for new physics in WZ + two jets events in pp collisions at $\\sqrt{s} = $ 13 TeV", "Measurement of electroweak WZ boson production and search for new physics in WZ + two jets events in pp collisions at √(s) = 13 TeV", ), ( "Measurements of the ${{\\mathrm{p}}{\\mathrm{p}}\\to\\mathrm{W}\\mathrm{Z}}$ inclusive and differential production cross section and constraints on charged anomalous triple gauge couplings at ${\\sqrt{s}} = $ 13 TeV", "Measurements of the pp → WZ inclusive and differential production cross section and constraints on charged anomalous triple gauge couplings at √(s) = 13 TeV", ), ( "Search for dark matter produced in association with a single top quark or a top quark pair in proton-proton collisions at $\\sqrt{s} = $ 13 TeV", "Search for dark matter produced in association with a single top quark or a top quark pair in proton-proton collisions at √(s) = 13 TeV", ), ( "Search for the pair production of light top squarks in the $\\mathrm{e}^{\\pm}\\mu^{\\mp}$ final state in proton-proton collisions at $\\sqrt{s} = $ 13 TeV", "Search for the pair production of light top squarks in the e^±μ^∓ final state in proton-proton collisions at √(s) = 13 TeV", ), ( "Measurements of the Higgs boson width and anomalous HVV couplings from on-shell and off-shell production in the four-lepton final state", "Measurements of the Higgs boson width and anomalous HVV couplings from on-shell and off-shell production in the four-lepton final state", ), ( "Measurement of the $ \\mathrm{t\\bar{t}} $ production cross section, the top quark mass, and the strong coupling constant using dilepton events in pp collisions at $\\sqrt{s} = $ 13 TeV", "Measurement of the tt̅ production cross section, the top quark mass, and the strong coupling constant using dilepton events in pp collisions at √(s) = 13 TeV", ), ( "Measurement of the differential Drell-Yan cross section in proton-proton collisions at $\\sqrt{s} = $ 13 TeV", "Measurement of the differential Drell-Yan cross section in proton-proton collisions at √(s) = 13 TeV", ), # CMS cms_pas_feed ( "Measurements of differential Higgs boson production cross sections in the leptonic WW decay mode at $\\sqrt{s} = 13~\\mathrm{TeV}$", "Measurements of differential Higgs boson production cross sections in the leptonic WW decay mode at √(s) = 13 TeV", ), ( "A measurement of the Higgs boson mass in the diphoton decay channel", "A measurement of the Higgs boson mass in the diphoton decay channel", ), ( "A deep neural network for simultaneous estimation of b quark energy and resolution", "A deep neural network for simultaneous estimation of b quark energy and resolution", ), ( "Template measurement of the top quark forward-backward asymmetry and anomalous chromoelectric and chromomagnetic moments in the semileptonic channel at sqrt(s)=13 TeV", "Template measurement of the top quark forward-backward asymmetry and anomalous chromoelectric and chromomagnetic moments in the semileptonic channel at sqrt(s) = 13 TeV", ), ( "Search for supersymmetry in pp collisions at $\\sqrt{s}=13~\\mathrm{TeV}$ with $137~\\mathrm{fb}^{-1}$ in the final state with a single lepton using the sum of masses of large-radius jets", "Search for supersymmetry in pp collisions at √(s) = 13 TeV with 137 fb⁻¹ in the final state with a single lepton using the sum of masses of large-radius jets", ), ( "Measurement of the top quark pair production cross section in the dilepton channel including a $\\tau$ lepton in proton-proton collisions at $\\sqrt{s}=13~\\mathrm{TeV}$", "Measurement of the top quark pair production cross section in the dilepton channel including a τ lepton in proton-proton collisions at √(s) = 13 TeV", ), ( "Search for a narrow resonance decaying to a pair of muons in proton-proton collisions at 13 TeV", "Search for a narrow resonance decaying to a pair of muons in proton-proton collisions at 13 TeV", ), ( "Observation of the $\\Lambda_{\\mathrm{b}} \\to \\mathrm{J}/\\psi \\Lambda \\phi$ decay in proton-proton collisions at $\\sqrt{s}=$ 13 TeV", "Observation of the Λ_b → J/ψΛϕ decay in proton-proton collisions at √(s) = 13 TeV", ), ( "Measurement of properties of Bs0 to mu+mu- decays and search for B0 to mu+mu- with the CMS experiment", "Measurement of properties of Bs0 to mu+mu- decays and search for B0 to mu+mu- with the CMS experiment", ), ( "Search for supersymmetry with a compressed mass spectrum in events with a soft $\\tau$ lepton, a highly energetic jet, and large missing transverse momentum in proton-proton collisions at $\\sqrt{s}=13~\\mathrm{TeV}$", "Search for supersymmetry with a compressed mass spectrum in events with a soft τ lepton, a highly energetic jet, and large missing transverse momentum in proton-proton collisions at √(s) = 13 TeV", ), ( "Measurement of the cross section for $\\mathrm{t}\\bar{\\mathrm{t}}$ production with additional jets and b jets in proton-proton collisions at $\\sqrt{s}=13~\\mathrm{TeV}$", "Measurement of the cross section for tt̅ production with additional jets and b jets in proton-proton collisions at √(s) = 13 TeV", ), ( "Search for a narrow resonance in high-mass dilepton final states in proton-proton collisions using 140$~\\mathrm{fb}^{-1}$ of data at $\\sqrt{s}=13~\\mathrm{TeV}$", "Search for a narrow resonance in high-mass dilepton final states in proton-proton collisions using 140 fb⁻¹ of data at √(s) = 13 TeV", ), ( "Search for dijet resonances in events with three jets from proton-proton collisions at $\\sqrt{s}=13~\\mathrm{TeV}$", "Search for dijet resonances in events with three jets from proton-proton collisions at √(s) = 13 TeV", ), ( "First measurement of the running of the top quark mass", "First measurement of the running of the top quark mass", ), ( "Measurement of the associated production of a Z boson with charm or bottom quark jets in proton-proton collisions at $\\sqrt{s}=13~\\mathrm{TeV}$", "Measurement of the associated production of a Z boson with charm or bottom quark jets in proton-proton collisions at √(s) = 13 TeV", ), ( "Machine learning-based identification of highly Lorentz-boosted hadronically decaying particles at the CMS experiment", "Machine learning-based identification of highly Lorentz-boosted hadronically decaying particles at the CMS experiment", ), ( "Pileup mitigation at CMS in 13 TeV data", "Pileup mitigation at CMS in 13 TeV data", ), ( "Search for the standard model Higgs boson decaying to charm quarks", "Search for the standard model Higgs boson decaying to charm quarks", ), ( "Measurement of the jet mass distribution in highly boosted top quark decays in pp collisions at $\\sqrt{s}=13~\\text{TeV}$", "Measurement of the jet mass distribution in highly boosted top quark decays in pp collisions at √(s) = 13 TeV", ), ( "Search for the resonant production of a pair of Higgs bosons decaying to the bb-barZZ final state", "Search for the resonant production of a pair of Higgs bosons decaying to the bb-barZZ final state", ), ( "Measurement of the dependence of inclusive jet production cross sections on the anti- $k_{\\mathrm{T}}$ distance parameter in proton-proton collisions at sqrt(s) 13 TeV", "Measurement of the dependence of inclusive jet production cross sections on the anti- k_T distance parameter in proton-proton collisions at sqrt(s) 13 TeV", ), ( "Measurement of electroweak production of Z gamma in association with two jets in proton-proton collisions at sqrt(s) = 13 TeV", "Measurement of electroweak production of Z gamma in association with two jets in proton-proton collisions at sqrt(s) = 13 TeV", ), ( "Measurement of the associated production of a W boson and a charm quark at $\\sqrt{s}=8~\\mathrm{TeV}$", "Measurement of the associated production of a W boson and a charm quark at √(s) = 8 TeV", ), ( "Search for direct top squark pair production in events with one lepton, jets and missing transverse energy at 13 TeV", "Search for direct top squark pair production in events with one lepton, jets and missing transverse energy at 13 TeV", ), ( "A search for dijet resonances in proton-proton collisions at $\\sqrt{s}=13~\\mathrm{TeV}$ with a new background prediction method", "A search for dijet resonances in proton-proton collisions at √(s) = 13 TeV with a new background prediction method", ), # CMS cms_paper_feed ( "Bose-Einstein correlations of charged hadrons in proton-proton collisions at $\\sqrt{s} = $ 13 TeV", "Bose-Einstein correlations of charged hadrons in proton-proton collisions at √(s) = 13 TeV", ), ( "Mixed higher-order anisotropic flow and nonlinear response coefficients of charged particles in PbPb collisions at ${\\sqrt {\\smash [b]{s_{_{\\mathrm {NN}}}}}} = $ 2.76 and 5.02 TeV", "Mixed higher-order anisotropic flow and nonlinear response coefficients of charged particles in PbPb collisions at √(s_NN) = 2.76 and 5.02 TeV", ), ( "Strange hadron production in pp and pPb collisions at ${\\sqrt {\\smash [b]{s_{_{\\mathrm {NN}}}}}} = $ 5.02 TeV", "Strange hadron production in pp and pPb collisions at √(s_NN) = 5.02 TeV", ), ( "Study of $\\mathrm{J}/\\psi$ meson production from jet fragmentation in pp collisions at $\\sqrt{s} = $ 8 TeV", "Study of J/ψ meson production from jet fragmentation in pp collisions at √(s) = 8 TeV", ), ( "Search for supersymmetry with a compressed mass spectrum in events with a soft $\\tau$ lepton, a highly energetic jet, and large missing transverse momentum in proton-proton collisions at $\\sqrt{s} =$ 13 TeV", "Search for supersymmetry with a compressed mass spectrum in events with a soft τ lepton, a highly energetic jet, and large missing transverse momentum in proton-proton collisions at √(s) = 13 TeV", ), ( "Calibration of the CMS hadron calorimeters using proton-proton collision data at $\\sqrt{s} = $ 13 TeV", "Calibration of the CMS hadron calorimeters using proton-proton collision data at √(s) = 13 TeV", ), ( "Running of the top quark mass from proton-proton collisions at ${\\sqrt{s}} = $ 13 TeV", "Running of the top quark mass from proton-proton collisions at √(s) = 13 TeV", ), ( "Evidence for WW production from double-parton interactions in proton-proton collisions at $\\sqrt{s} = $ 13 TeV", "Evidence for WW production from double-parton interactions in proton-proton collisions at √(s) = 13 TeV", ), ( "Search for long-lived particles using delayed photons in proton-proton collisions at $\\sqrt{s} = $ 13 TeV", "Search for long-lived particles using delayed photons in proton-proton collisions at √(s) = 13 TeV", ), ( "Measurement of the $\\mathrm{t\\bar{t}}\\mathrm{b\\bar{b}}$ production cross section in the all-jet final state in pp collisions at $\\sqrt{s} = $ 13 TeV", "Measurement of the tt̅bb̅ production cross section in the all-jet final state in pp collisions at √(s) = 13 TeV", ), ( "Search for electroweak production of a vector-like T quark using fully hadronic final states", "Search for electroweak production of a vector-like T quark using fully hadronic final states", ), ( "Measurements of differential Z boson production cross sections in proton-proton collisions at $\\sqrt{s} = $ 13 TeV", "Measurements of differential Z boson production cross sections in proton-proton collisions at √(s) = 13 TeV", ), ( "Search for low mass vector resonances decaying into quark-antiquark pairs in proton-proton collisions at $\\sqrt{s} = $ 13 TeV", "Search for low mass vector resonances decaying into quark-antiquark pairs in proton-proton collisions at √(s) = 13 TeV", ), ( "Searches for physics beyond the standard model with the ${M_{\\mathrm{T2}}}$ variable in hadronic final states with and without disappearing tracks in proton-proton collisions at $\\sqrt{s} = $ 13 TeV", "Searches for physics beyond the standard model with the M_T2 variable in hadronic final states with and without disappearing tracks in proton-proton collisions at √(s) = 13 TeV", ), ( "Search for a charged Higgs boson decaying into top and bottom quarks in proton-proton collisions at $\\sqrt{s} = $ 13 TeV in events with electrons or muons", "Search for a charged Higgs boson decaying into top and bottom quarks in proton-proton collisions at √(s) = 13 TeV in events with electrons or muons", ), ( "Search for supersymmetry using Higgs boson to diphoton decays at $\\sqrt{s} = $ 13 TeV", "Search for supersymmetry using Higgs boson to diphoton decays at √(s) = 13 TeV", ), ( "Search for production of four top quarks in final states with same-sign or multiple leptons in proton-proton collisions at $\\sqrt{s}= $ 13 TeV", "Search for production of four top quarks in final states with same-sign or multiple leptons in proton-proton collisions at √(s) = 13 TeV", ), ( "Search for supersymmetry in proton-proton collisions at 13 TeV in final states with jets and missing transverse momentum", "Search for supersymmetry in proton-proton collisions at 13 TeV in final states with jets and missing transverse momentum", ), ( "Search for dark photons in decays of Higgs bosons produced in association with Z bosons in proton-proton collisions at $\\sqrt{s} = $ 13 TeV", "Search for dark photons in decays of Higgs bosons produced in association with Z bosons in proton-proton collisions at √(s) = 13 TeV", ), ( "Search for dark matter particles produced in association with a Higgs boson in proton-proton collisions at $\\sqrt{s} = $ 13 TeV", "Search for dark matter particles produced in association with a Higgs boson in proton-proton collisions at √(s) = 13 TeV", ), ( "Search for heavy Higgs bosons decaying to a top quark pair in proton-proton collisions at $\\sqrt{s} = $ 13 TeV", "Search for heavy Higgs bosons decaying to a top quark pair in proton-proton collisions at √(s) = 13 TeV", ), ( "Search for direct pair production of supersymmetric partners to the $\\tau$ lepton in proton-proton collisions at $\\sqrt{s} = $ 13 TeV", "Search for direct pair production of supersymmetric partners to the τ lepton in proton-proton collisions at √(s) = 13 TeV", ), ( "Measurement of top quark pair production in association with a Z boson in proton-proton collisions at $\\sqrt{s} = $ 13 TeV", "Measurement of top quark pair production in association with a Z boson in proton-proton collisions at √(s) = 13 TeV", ), ( "Search for anomalous triple gauge couplings in WW and WZ production in lepton + jet events in proton-proton collisions at $\\sqrt{s} = $ 13 TeV", "Search for anomalous triple gauge couplings in WW and WZ production in lepton + jet events in proton-proton collisions at √(s) = 13 TeV", ), # ATLAS atlas_conf_feed ( "Search for bottom-squark pair production with the ATLAS detector in final states containing Higgs bosons, $b$-jets and missing transverse momentum", "Search for bottom-squark pair production with the ATLAS detector in final states containing Higgs bosons, b-jets and missing transverse momentum", ), ( "Search for heavy neutral Higgs bosons produced in association with $b$-quarks and decaying to $b$-quarks at $\\sqrt{s}=13$~TeV with the ATLAS detector", "Search for heavy neutral Higgs bosons produced in association with b-quarks and decaying to b-quarks at √(s) = 13 TeV with the ATLAS detector", ), ( "Measurement of the CP violation phase $\\phi_{s}$ in $B_{s}\\to J/\\psi \\phi$ decays in ATLAS at 13 TeV", "Measurement of the CP violation phase ϕ_s in B_s → J/ψϕ decays in ATLAS at 13 TeV", ), ( "Search for electroweak production of charginos and sleptons decaying in final states with two leptons and missing transverse momentum in $\\sqrt{s}=13$ TeV $pp$ collisions using the ATLAS detector", "Search for electroweak production of charginos and sleptons decaying in final states with two leptons and missing transverse momentum in √(s) = 13 TeV pp collisions using the ATLAS detector", ), ( "Search for New Phenomena in Dijet Events using 139 fb$^{−1}$ of $pp$ collisions at $\\sqrt{s}$ = 13TeV collected with the ATLAS Detector", "Search for New Phenomena in Dijet Events using 139 fb^−1 of pp collisions at √(s) = 13 TeV collected with the ATLAS Detector", ), ( "Search for long-lived, massive particles in events with a displaced vertex and a displaced muon in $pp$ collisions at $\\sqrt{s} = 13$ TeV with the ATLAS detector", "Search for long-lived, massive particles in events with a displaced vertex and a displaced muon in pp collisions at √(s) = 13 TeV with the ATLAS detector", ), ( "Combined measurements of Higgs boson production and decay using up to $80$ fb$^{-1}$ of proton--proton collision data at $\\sqrt{s}=$ 13 TeV collected with the ATLAS experiment", "Combined measurements of Higgs boson production and decay using up to 80 fb⁻¹ of proton–proton collision data at √(s) = 13 TeV collected with the ATLAS experiment", ), ( "Measurement of Higgs boson production in association with a $t\\overline t$ pair in the diphoton decay channel using 139~fb$^{-1}$ of LHC data collected at $\\sqrt{s} = 13$~TeV by the ATLAS experiment", "Measurement of Higgs boson production in association with a tt pair in the diphoton decay channel using 139 fb⁻¹ of LHC data collected at √(s) = 13 TeV by the ATLAS experiment", ), ( "Search for diboson resonances in hadronic final states in 139 fb$^{-1}$ of $pp$ collisions at $\\sqrt{s} = 13$ TeV with the ATLAS detector", "Search for diboson resonances in hadronic final states in 139 fb⁻¹ of pp collisions at √(s) = 13 TeV with the ATLAS detector", ), ( "Observation of light-by-light scattering in ultraperipheral Pb+Pb collisions with the ATLAS detector", "Observation of light-by-light scattering in ultraperipheral Pb+Pb collisions with the ATLAS detector", ), ( "Search for high-mass dilepton resonances using $139\\,\\mathrm{fb}^{-1}$ of $pp$ collision data collected at $\\sqrt{s}=13\\,\\mathrm{TeV}$ with the ATLAS detector", "Search for high-mass dilepton resonances using 139 fb⁻¹ of pp collision data collected at √(s) = 13 TeV with the ATLAS detector", ), ( "Calibration of the $b$-tagging eﬃciency on charm jets using a sample of $W$+$c$ events with $\\sqrt{s}$ = 13 TeV ATLAS data", "Calibration of the b-tagging eﬃciency on charm jets using a sample of W+c events with √(s) = 13 TeV ATLAS data", ), ( "Combination of searches for invisible Higgs boson decays with the ATLAS experiment", "Combination of searches for invisible Higgs boson decays with the ATLAS experiment", ), ( "Measurements of $VH$, $H \\to b\\bar{b}$ production as a function of the vector boson transverse momentum in 13 TeV pp collisions with the ATLAS detector", "Measurements of VH, H → bb̅ production as a function of the vector boson transverse momentum in 13 TeV pp collisions with the ATLAS detector", ), ( "Search for boosted resonances decaying to two b-quarks and produced in association with a jet at $\\sqrt{s}=13$ TeV with the ATLAS detector", "Search for boosted resonances decaying to two b-quarks and produced in association with a jet at √(s) = 13 TeV with the ATLAS detector", ), ( "Constraints on mediator-based dark matter models using $\\sqrt s = 13$ TeV $pp$ collisions at the LHC with the ATLAS detector", "Constraints on mediator-based dark matter models using √(s) = 13 TeV pp collisions at the LHC with the ATLAS detector", ), ( "Dijet azimuthal correlations and conditional yields in $p\\!p$ and $p$+Pb collisions at $\\sqrt{s_{_\\text{NN}}}$~=~5.02 TeV with the ATLAS detector", "Dijet azimuthal correlations and conditional yields in pp and p+Pb collisions at √(s_NN) = 5.02 TeV with the ATLAS detector", ), ( "Search for top quark decays t\\rightarrowHq with 36 fb^{−1} of pp collision data at \\sqrt{s} = 13 TeV with the ATLAS detector", "Search for top quark decays t → Hq with 36 fb^−1 of pp collision data at √(s) = 13 TeV with the ATLAS detector", ), ( "Measurements of inclusive and differential cross-sections of $t\\bar{t}\\gamma$ production in leptonic final states in a fiducial volume at $\\sqrt{s}=13~\\text{TeV}$ in ATLAS", "Measurements of inclusive and differential cross-sections of tt̅γ production in leptonic final states in a fiducial volume at √(s) = 13 TeV in ATLAS", ), ( "Measurement of the $t\\bar{t}W$ and $t\\bar{t}Z$ cross sections in proton–proton collisions at $\\sqrt{s}$ = 13 TeV with the ATLAS detector", "Measurement of the tt̅W and tt̅Z cross sections in proton–proton collisions at √(s) = 13 TeV with the ATLAS detector", ), ( "Study of the rare decays of B0s and B0 into muon pairs from data collected during 2015 and 2016 with the ATLAS detector", "Study of the rare decays of B0s and B0 into muon pairs from data collected during 2015 and 2016 with the ATLAS detector", ), ( "Calibration of the ATLAS $b$-tagging algorithm in $t\\bar{t}$ semi-leptonic events", "Calibration of the ATLAS b-tagging algorithm in tt̅ semi-leptonic events", ), ( "Search for charged lepton-flavour violation in top-quark decays at the LHC with the ATLAS detector", "Search for charged lepton-flavour violation in top-quark decays at the LHC with the ATLAS detector", ), ( "Combination of searches for Higgs boson pairs in $pp$ collisions at 13 TeV with the ATLAS experiment.", "Combination of searches for Higgs boson pairs in pp collisions at 13 TeV with the ATLAS experiment.", ), ( "Search for direct chargino pair production with W-boson mediated decays in events with two leptons and missing transverse momentum at $\\sqrt{s} = 13$ TeV with the ATLAS detector", "Search for direct chargino pair production with W-boson mediated decays in events with two leptons and missing transverse momentum at √(s) = 13 TeV with the ATLAS detector", ), # ATLAS atlas_paper_feed ( "Observation of light-by-light scattering in ultraperipheral Pb+Pb collisions with the ATLAS detector", "Observation of light-by-light scattering in ultraperipheral Pb+Pb collisions with the ATLAS detector", ), ( "Evidence for the production of three massive vector bosons with the ATLAS detector", "Evidence for the production of three massive vector bosons with the ATLAS detector", ), ( "Measurement of the production cross section for a Higgs boson in association with a vector boson in the $H \\rightarrow WW^{\\ast} \\rightarrow \\ell\\nu\\ell\\nu$ channel in $pp$ collisions at $\\sqrt{s} = 13$ TeV with the ATLAS detector", "Measurement of the production cross section for a Higgs boson in association with a vector boson in the H → WW^∗ → ℓνℓν channel in pp collisions at √(s) = 13 TeV with the ATLAS detector", ), ( "Measurements of top-quark pair spin correlations in the $e\\mu$ channel at $\\sqrt{s} = 13$ TeV using $pp$ collisions in the ATLAS detector", "Measurements of top-quark pair spin correlations in the eμ channel at √(s) = 13 TeV using pp collisions in the ATLAS detector", ), ( "Search for high-mass dilepton resonances using 139 fb$^{-1}$ of $pp$ collision data collected at $\\sqrt{s}=13$ TeV with the ATLAS detector", "Search for high-mass dilepton resonances using 139 fb⁻¹ of pp collision data collected at √(s) = 13 TeV with the ATLAS detector", ), ( "Measurement of $VH$, $H\\to b\\bar{b}$ production as a function of the vector-boson transverse momentum in 13 TeV $pp$ collisions with the ATLAS detector", "Measurement of VH, H → bb̅ production as a function of the vector-boson transverse momentum in 13 TeV pp collisions with the ATLAS detector", ), ( "Measurement of jet-substructure observables in top quark, $W$ boson and light jet production in proton-proton collisions at $\\sqrt{s}=13$ TeV with the ATLAS detector", "Measurement of jet-substructure observables in top quark, W boson and light jet production in proton-proton collisions at √(s) = 13 TeV with the ATLAS detector", ), ( "Measurement of prompt photon production in $\\sqrt{s_\\mathrm{NN}} = 8.16$ TeV $p$+Pb collisions with ATLAS", "Measurement of prompt photon production in √(s_NN) = 8.16 TeV p+Pb collisions with ATLAS", ), ( "Constraints on mediator-based dark matter and scalar dark energy models using $\\sqrt{s}= 13$ TeV $pp$ collision data collected by the ATLAS detector", "Constraints on mediator-based dark matter and scalar dark energy models using √(s) = 13 TeV pp collision data collected by the ATLAS detector", ), ( "Search for heavy particles decaying into a top-quark pair in the fully hadronic final state in $pp$ collisions at $\\sqrt{s} =$13 TeV with the ATLAS detector", "Search for heavy particles decaying into a top-quark pair in the fully hadronic final state in pp collisions at √(s) = 13 TeV with the ATLAS detector", ), ( "Comparison of fragmentation functions for light-quark- and gluon-dominated jets from $pp$ and Pb+Pb collisions in ATLAS", "Comparison of fragmentation functions for light-quark- and gluon-dominated jets from pp and Pb+Pb collisions in ATLAS", ), ( "Searches for third-generation scalar leptoquarks in $\\sqrt{s} = 13$ TeV $pp$ collisions with the ATLAS detector", "Searches for third-generation scalar leptoquarks in √(s) = 13 TeV pp collisions with the ATLAS detector", ), ( "Combinations of single-top-quark production cross-section measurements and $|f_{\\rm LV}V_{tb}|$ determinations at $\\sqrt{s}=7$ and 8 TeV with the ATLAS and CMS experiments", "Combinations of single-top-quark production cross-section measurements and |f_LVV_tb| determinations at √(s) = 7 and 8 TeV with the ATLAS and CMS experiments", ), ( "Measurement of the four-lepton invariant mass spectrum in 13 TeV proton-proton collisions with the ATLAS detector", "Measurement of the four-lepton invariant mass spectrum in 13 TeV proton-proton collisions with the ATLAS detector", ), ( "Measurement of $W^{\\pm}Z$ production cross sections and gauge boson polarisation in $pp$ collisions at $\\sqrt{s} = 13$ TeV with the ATLAS detector", "Measurement of W^±Z production cross sections and gauge boson polarisation in pp collisions at √(s) = 13 TeV with the ATLAS detector", ), ( "Electron reconstruction and identification in the ATLAS experiment using the 2015 and 2016 LHC proton-proton collision data at $\\sqrt{s} = 13$ TeV", "Electron reconstruction and identification in the ATLAS experiment using the 2015 and 2016 LHC proton-proton collision data at √(s) = 13 TeV", ), ( "Search for long-lived neutral particles in $pp$ collisions at $\\sqrt{s} = 13$ TeV that decay into displaced hadronic jets in the ATLAS calorimeter", "Search for long-lived neutral particles in pp collisions at √(s) = 13 TeV that decay into displaced hadronic jets in the ATLAS calorimeter", ), ( "Search for heavy charged long-lived particles in the ATLAS detector in 31.6 fb$^{-1}$ of proton-proton collision data at $\\sqrt{s} = 13$ TeV", "Search for heavy charged long-lived particles in the ATLAS detector in 31.6 fb⁻¹ of proton-proton collision data at √(s) = 13 TeV", ), ( "Searches for scalar leptoquarks and differential cross-section measurements in dilepton-dijet events in proton-proton collisions at a centre-of-mass energy of $\\sqrt{s} = 13$ TeV with the ATLAS experiment", "Searches for scalar leptoquarks and differential cross-section measurements in dilepton-dijet events in proton-proton collisions at a centre-of-mass energy of √(s) = 13 TeV with the ATLAS experiment", ), ( "Search for low-mass resonances decaying into two jets and produced in association with a photon using $pp$ collisions at $\\sqrt{s} = 13$ TeV with the ATLAS detector", "Search for low-mass resonances decaying into two jets and produced in association with a photon using pp collisions at √(s) = 13 TeV with the ATLAS detector", ), ( "Dijet azimuthal correlations and conditional yields in $pp$ and $p$+Pb collisions at $\\sqrt{s_{\\rm NN}}$ = 5.02 TeV with the ATLAS detector", "Dijet azimuthal correlations and conditional yields in pp and p+Pb collisions at √(s_NN) = 5.02 TeV with the ATLAS detector", ), ( "Measurement of the ratio of cross sections for inclusive isolated-photon production in $pp$ collisions at $\\sqrt{s}=13$ and $8$ TeV with the ATLAS detector", "Measurement of the ratio of cross sections for inclusive isolated-photon production in pp collisions at √(s) = 13 and 8 TeV with the ATLAS detector", ), ( "Search for scalar resonances decaying into $\\mu^{+}\\mu^{-}$ in events with and without $b$-tagged jets produced in proton-proton collisions at $\\sqrt{s}=13$ TeV with the ATLAS detector", "Search for scalar resonances decaying into μ⁺μ⁻ in events with and without b-tagged jets produced in proton-proton collisions at √(s) = 13 TeV with the ATLAS detector", ), ( "Measurement of the $t\\bar{t}Z$ and $t\\bar{t}W$ cross sections in proton-proton collisions at $\\sqrt{s}=13$ TeV with the ATLAS detector", "Measurement of the tt̅Z and tt̅W cross sections in proton-proton collisions at √(s) = 13 TeV with the ATLAS detector", ), ( "Search for top-quark decays $t \\rightarrow Hq$ with 36 fb$^{-1}$ of $pp$ collision data at $\\sqrt{s}=13$ TeV with the ATLAS detector", "Search for top-quark decays t → Hq with 36 fb⁻¹ of pp collision data at √(s) = 13 TeV with the ATLAS detector", ), # ATLAS atlas_paper_feed ( "Evidence for electroweak production of two jets in association with a $Z\\gamma$ pair in $pp$ collisions at $\\sqrt{s} = 13$ TeV with the ATLAS detector", "Evidence for electroweak production of two jets in association with a Zγ pair in pp collisions at √(s) = 13 TeV with the ATLAS detector", ), ( "Measurement of the $t\\bar{t}$ production cross-section and lepton differential distributions in $e\\mu$ dilepton events from $pp$ collisions at $\\sqrt{s}=13$ TeV with the ATLAS detector", "Measurement of the tt̅ production cross-section and lepton differential distributions in eμ dilepton events from pp collisions at √(s) = 13 TeV with the ATLAS detector", ), ( "Search for new resonances in mass distributions of jet pairs using 139 fb$^{-1}$ of $pp$ collisions at $\\sqrt{s}=13$ TeV with the ATLAS detector", "Search for new resonances in mass distributions of jet pairs using 139 fb⁻¹ of pp collisions at √(s) = 13 TeV with the ATLAS detector", ), ( "Determination of jet calibration and energy resolution in proton-proton collisions at $\\sqrt{s}$ = 8 TeV using the ATLAS detector", "Determination of jet calibration and energy resolution in proton-proton collisions at √(s) = 8 TeV using the ATLAS detector", ), ( "Measurement of $J/\\psi$ production in association with a $W^\\pm$ boson with $pp$ data at 8 TeV", "Measurement of J/ψ production in association with a W^± boson with pp data at 8 TeV", ), ( "Search for the Higgs boson decays $H \\to ee$ and $H \\to e\\mu$ in $pp$ collisions at $\\sqrt{s} = 13$ TeV with the ATLAS detector", "Search for the Higgs boson decays H → ee and H → eμ in pp collisions at √(s) = 13 TeV with the ATLAS detector", ), ( "Search for direct production of electroweakinos in final states with one lepton, missing transverse momentum and a Higgs boson decaying into two $b$-jets in $pp$ collisions at $\\sqrt{s}=13$ TeV with the ATLAS detector", "Search for direct production of electroweakinos in final states with one lepton, missing transverse momentum and a Higgs boson decaying into two b-jets in pp collisions at √(s) = 13 TeV with the ATLAS detector", ), ( "Search for squarks and gluinos in final states with same-sign leptons and jets using 139 fb$^{-1}$ of data collected with the ATLAS detector", "Search for squarks and gluinos in final states with same-sign leptons and jets using 139 fb⁻¹ of data collected with the ATLAS detector", ), ( "Combined measurements of Higgs boson production and decay using up to $80$ fb$^{-1}$ of proton-proton collision data at $\\sqrt{s}=$ 13 TeV collected with the ATLAS experiment", "Combined measurements of Higgs boson production and decay using up to 80 fb⁻¹ of proton-proton collision data at √(s) = 13 TeV collected with the ATLAS experiment", ), ( "Measurement of azimuthal anisotropy of muons from charm and bottom hadrons in $pp$ collisions at $\\sqrt{s}=13$ TeV with the ATLAS detector", "Measurement of azimuthal anisotropy of muons from charm and bottom hadrons in pp collisions at √(s) = 13 TeV with the ATLAS detector", ), ( "Search for light long-lived neutral particles produced in $pp$ collisions at $\\sqrt{s} =$ 13 TeV and decaying into collimated leptons or light hadrons with the ATLAS detector", "Search for light long-lived neutral particles produced in pp collisions at √(s) = 13 TeV and decaying into collimated leptons or light hadrons with the ATLAS detector", ), ( "Performance of electron and photon triggers in ATLAS during LHC Run 2", "Performance of electron and photon triggers in ATLAS during LHC Run 2", ), ( "Search for flavour-changing neutral currents in processes with one top quark and a photon using 81 fb$^{-1}$ of $pp$ collisions at $\\sqrt{s} = 13$ TeV with the ATLAS experiment", "Search for flavour-changing neutral currents in processes with one top quark and a photon using 81 fb⁻¹ of pp collisions at √(s) = 13 TeV with the ATLAS experiment", ), ( "Search for electroweak production of charginos and sleptons decaying into final states with two leptons and missing transverse momentum in $\\sqrt{s}=13$ TeV $pp$ collisions using the ATLAS detector", "Search for electroweak production of charginos and sleptons decaying into final states with two leptons and missing transverse momentum in √(s) = 13 TeV pp collisions using the ATLAS detector", ), ( "Measurements of top-quark pair differential and double-differential cross-sections in the $\\ell$+jets channel with $pp$ collisions at $\\sqrt{s}=13$ TeV using the ATLAS detector", "Measurements of top-quark pair differential and double-differential cross-sections in the ℓ+jets channel with pp collisions at √(s) = 13 TeV using the ATLAS detector", ), ( "Search for non-resonant Higgs boson pair production in the $bb\\ell\\nu\\ell\\nu$ final state with the ATLAS detector in $pp$ collisions at $\\sqrt{s} = 13$ TeV", "Search for non-resonant Higgs boson pair production in the bbℓνℓν final state with the ATLAS detector in pp collisions at √(s) = 13 TeV", ), ( "Measurement of angular and momentum distributions of charged particles within and around jets in Pb+Pb and $pp$ collisions at $\\sqrt{s_{\\mathrm{NN}}} = 5.02$ TeV with the ATLAS detector", "Measurement of angular and momentum distributions of charged particles within and around jets in Pb+Pb and pp collisions at √(s_NN) = 5.02 TeV with the ATLAS detector", ), ( "Search for bottom-squark pair production with the ATLAS detector in final states containing Higgs bosons, $b$-jets and missing transverse momentum", "Search for bottom-squark pair production with the ATLAS detector in final states containing Higgs bosons, b-jets and missing transverse momentum", ), ( "Measurement of the inclusive isolated-photon cross section in $pp$ collisions at $\\sqrt{s}=13$ TeV using 36 fb$^{-1}$ of ATLAS data", "Measurement of the inclusive isolated-photon cross section in pp collisions at √(s) = 13 TeV using 36 fb⁻¹ of ATLAS data", ), ( "Electron and photon performance measurements with the ATLAS detector using the 2015-2017 LHC proton-proton collision data", "Electron and photon performance measurements with the ATLAS detector using the 2015-2017 LHC proton-proton collision data", ), ( "Measurement of $K_S^0$ and $\\Lambda^0$ production in $t \\bar{t}$ dileptonic events in $pp$ collisions at $\\sqrt{s} =$ 7 TeV with the ATLAS detector", "Measurement of K⁰_S and Λ⁰ production in tt̅ dileptonic events in pp collisions at √(s) = 7 TeV with the ATLAS detector", ), ( "Measurement of $W^\\pm$ boson production in Pb+Pb collisions at $\\sqrt{s_\\mathrm{NN}} = 5.02$ TeV with the ATLAS detector", "Measurement of W^± boson production in Pb+Pb collisions at √(s_NN) = 5.02 TeV with the ATLAS detector", ), ( "Search for displaced vertices of oppositely charged leptons from decays of long-lived particles in $pp$ collisions at $\\sqrt{s}$ = 13 TeV with the ATLAS detector", "Search for displaced vertices of oppositely charged leptons from decays of long-lived particles in pp collisions at √(s) = 13 TeV with the ATLAS detector", ), ( "Measurement of the jet mass in high transverse momentum $Z(\\rightarrow b\\overline{b})\\gamma$ production at $\\sqrt{s}= 13$ TeV using the ATLAS detector", "Measurement of the jet mass in high transverse momentum Z( → bb)γ production at √(s) = 13 TeV using the ATLAS detector", ), ( "Measurement of the inclusive cross-section for the production of jets in association with a $Z$ boson in proton-proton collisions at 8 TeV using the ATLAS detector", "Measurement of the inclusive cross-section for the production of jets in association with a Z boson in proton-proton collisions at 8 TeV using the ATLAS detector", ), # ALICE alice_paper_feed ( "One-dimensional charged kaon femtoscopy in p-Pb collisions at $\\sqrt{s_{\\rm NN}}$ = 5.02 TeV", "One-dimensional charged kaon femtoscopy in p-Pb collisions at √(s_NN) = 5.02 TeV", ), ( "Investigations of anisotropic flow using multi-particle azimuthal correlations in pp, p$-$Pb, Xe$-$Xe, and Pb$-$Pb collisions at the LHC", "Investigations of anisotropic flow using multi-particle azimuthal correlations in pp, p-Pb, Xe-Xe, and Pb-Pb collisions at the LHC", ), ( "Multiplicity dependence of (anti-)deuteron production in pp collisions at $\\sqrt{s}$ = 7 TeV", "Multiplicity dependence of (anti-)deuteron production in pp collisions at √(s) = 7 TeV", ), ( "Calibration of the photon spectrometer PHOS of the ALICE experiment", "Calibration of the photon spectrometer PHOS of the ALICE experiment", ), ( "Measurement of D$^0$, D$^+$, D$^*$ and D$_s$ production in pp collisions at $\\sqrt{s}$ = 5.02 TeV", "Measurement of D⁰, D⁺, D* and D_s production in pp collisions at √(s) = 5.02 TeV", ), ( "Real-time data processing in the ALICE High Level Trigger at the LHC", "Real-time data processing in the ALICE High Level Trigger at the LHC", ), ( "Event-shape and multiplicity dependence of freeze-out radii in pp collisions at $\\sqrt{s}$ = 7 TeV", "Event-shape and multiplicity dependence of freeze-out radii in pp collisions at √(s) = 7 TeV", ), ( "Study of J/$\\psi$ azimuthal anisotropy at forward rapidity in Pb-Pb collisions at $\\sqrt{s_{\\rm NN}}$ = 5.02 TeV", "Study of J/ψ azimuthal anisotropy at forward rapidity in Pb-Pb collisions at √(s_NN) = 5.02 TeV", ), ( "Charged-particle pseudorapidity density at mid-rapidity in p-Pb collisions at $\\sqrt{s_{\\rm{NN}}}$ = 8.16 TeV", "Charged-particle pseudorapidity density at mid-rapidity in p-Pb collisions at √(s_NN) = 8.16 TeV", ), ( "Jet fragmentation transverse momentum measurements from di-hadron correlations in $\\sqrt{s}$ = 7 TeV pp and $\\sqrt{s_{\\rm{NN}}}$ = 5.02 TeV p-Pb collisions", "Jet fragmentation transverse momentum measurements from di-hadron correlations in √(s) = 7 TeV pp and √(s_NN) = 5.02 TeV p-Pb collisions", ), ( "$\\Lambda_{\\rm c}^{+}$ production in Pb-Pb collisions at $\\sqrt{s_{\\rm NN}}=5.02$ TeV", "Λ⁺_c production in Pb-Pb collisions at √(s_NN) = 5.02 TeV", ), ( "Event-shape engineering for the D-meson elliptic flow in mid-central Pb-Pb collisions at $\\sqrt{s_{\\rm NN}}=5.02$ TeV", "Event-shape engineering for the D-meson elliptic flow in mid-central Pb-Pb collisions at √(s_NN) = 5.02 TeV", ), ( "Energy dependence of exclusive $J/\\psi$ photoproduction off protons in ultra-peripheral p-Pb collisions at $\\sqrt{s_{\\rm{NN}}}$ = 5.02 TeV", "Energy dependence of exclusive J/ψ photoproduction off protons in ultra-peripheral p-Pb collisions at √(s_NN) = 5.02 TeV", ), ( "Charged jet cross section and fragmentation in proton-proton collisions at $\\sqrt{s}$ = 7 TeV", "Charged jet cross section and fragmentation in proton-proton collisions at √(s) = 7 TeV", ), ( "Measuring $\\rm{K}^{0}\\rm{K}^{\\pm}$ interactions using pp collisions at $\\sqrt{s}$ = 7 TeV", "Measuring K⁰K^± interactions using pp collisions at √(s) = 7 TeV", ), ( "Multiplicity dependence of light-flavor hadron production in pp collisions at $\\sqrt{s}$ = 7 TeV", "Multiplicity dependence of light-flavor hadron production in pp collisions at √(s) = 7 TeV", ), ( "Medium modification of the shape of small-radius jets in central Pb-Pb collisions at $\\sqrt{s_{\\rm{NN}}}$ = 2.76 TeV", "Medium modification of the shape of small-radius jets in central Pb-Pb collisions at √(s_NN) = 2.76 TeV", ), ( "Measurement of dielectron production in central Pb-Pb collisions at $\\sqrt{{\\textit{s}}_{\\mathrm{NN}}}$ = 2.76 TeV", "Measurement of dielectron production in central Pb-Pb collisions at √(s_NN) = 2.76 TeV", ), ( "p-p, p-$\\Lambda$ and $\\Lambda$-$\\Lambda$ correlations studied via femtoscopy in pp reactions at $\\sqrt{s}$ = 7 TeV", "p-p, p-Λ and Λ-Λ correlations studied via femtoscopy in pp reactions at √(s) = 7 TeV", ), ( "Dielectron and heavy-quark production in inelastic and high-multiplicity proton-proton collisions at $\\sqrt{s} = 13$ TeV", "Dielectron and heavy-quark production in inelastic and high-multiplicity proton-proton collisions at √(s) = 13 TeV", ), ( "Centrality and pseudorapidity dependence of the charged-particle multiplicity density in Xe-Xe collisions at $\\sqrt{s_{\\rm NN}}$ = 5.44 TeV", "Centrality and pseudorapidity dependence of the charged-particle multiplicity density in Xe-Xe collisions at √(s_NN) = 5.44 TeV", ), ( "Azimuthal anisotropy of heavy-flavour decay electrons in p-Pb collisions at $\\sqrt{s_{NN}}$ = 5.02 TeV", "Azimuthal anisotropy of heavy-flavour decay electrons in p-Pb collisions at √(s_NN) = 5.02 TeV", ), ( "Non-Flow and Flow studies with differential transverse momentum and number density correlations in p-Pb and Pb-Pb at LHC", "Non-Flow and Flow studies with differential transverse momentum and number density correlations in p-Pb and Pb-Pb at LHC", ), ( "Direct photon elliptic flow in Pb-Pb collisions at $\\sqrt{s_{NN}}$ = 2.76 TeV", "Direct photon elliptic flow in Pb-Pb collisions at √(s_NN) = 2.76 TeV", ), ( "Suppression of $\\Lambda(1520)$ resonance production in central Pb-Pb collisions at $\\sqrt{s_{NN}}$ = 2.76 TeV", "Suppression of Λ(1520) resonance production in central Pb-Pb collisions at √(s_NN) = 2.76 TeV", ), # ALICE alice_paper_feed ( "Production of charged pions, kaons and (anti-)protons in Pb-Pb and inelastic pp collisions at $\\sqrt{s_{\\rm{NN}}}$ = 5.02 TeV", "Production of charged pions, kaons and (anti-)protons in Pb-Pb and inelastic pp collisions at √(s_NN) = 5.02 TeV", ), ( "Measurement of electrons from semileptonic heavy-flavour hadron decays at mid-rapidity in pp and Pb-Pb collisions at $\\sqrt{s_{\\rm{NN}}}$ = 5.02 TeV", "Measurement of electrons from semileptonic heavy-flavour hadron decays at mid-rapidity in pp and Pb-Pb collisions at √(s_NN) = 5.02 TeV", ), ( "Measurement of the (anti-)$^{3}$He elliptic flow in Pb-Pb collisions at $\\sqrt{s_{\\rm NN}}$ = 5.02 TeV", "Measurement of the (anti-)^3He elliptic flow in Pb-Pb collisions at √(s_NN) = 5.02 TeV", ), ( "Measurements of inclusive jet spectra in pp and central Pb–Pb collisions at $\\sqrt{s_{\\rm{NN}}}$ = 5.02 TeV", "Measurements of inclusive jet spectra in pp and central Pb–Pb collisions at √(s_NN) = 5.02 TeV", ), ( "Studies of J/$\\psi$ production at forward rapidity in Pb-Pb collisions at $\\sqrt{s_{\\rm NN}}$ = 5.02 TeV", "Studies of J/ψ production at forward rapidity in Pb-Pb collisions at √(s_NN) = 5.02 TeV", ), ( "Measurement of $\\Lambda$(1520) production in pp collisions at $\\sqrt{s}$ = 7 TeV and p-Pb collisions at $\\sqrt{s_{\\rm{NN}}}$ = 5.02 TeV", "Measurement of Λ(1520) production in pp collisions at √(s) = 7 TeV and p-Pb collisions at √(s_NN) = 5.02 TeV", ), ( "Global polarization of $\\Lambda$ and $\\overline{\\Lambda}$ hyperons in Pb-Pb collisions at the LHC", "Global polarization of Λ and Λ hyperons in Pb-Pb collisions at the LHC", ), ( "Multiplicity dependence of (multi-)strange hadron production in proton-proton collisions at $\\sqrt{s}$ = 13 TeV", "Multiplicity dependence of (multi-)strange hadron production in proton-proton collisions at √(s) = 13 TeV", ), ( "$^{3}_{\\Lambda}\\mathrm{H}$ and $^{3}_{\\overline{\\Lambda}}\\mathrm{\\overline{H}}$ lifetime measurement in Pb-Pb collisions at \\newline $\\sqrt{s_{\\mathrm{NN}}} = $ 5.02 TeV via two-body decay", "^3_ΛH and ^3_ΛH lifetime measurement in Pb-Pb collisions at √(s_NN) = 5.02 TeV via two-body decay", ), ( "Measurement of Υ(1S) elliptic flow at forward rapidity in Pb-Pb collisions at $\\sqrt{s_{NN}}$ = 5.02TeV", "Measurement of Υ(1S) elliptic flow at forward rapidity in Pb-Pb collisions at √(s_NN) = 5.02 TeV", ), ( "Measurement of prompt D$^{0}$, D$^{+}$, D$^{∗+}$, and D$^{+}_{s}$ production in p$-$Pb collisions at $\\sqrt{s_{\\rm NN}}$ = 5.02 TeV", "Measurement of prompt D⁰, D⁺, D*⁺, and D⁺_s production in p-Pb collisions at √(s_NN) = 5.02 TeV", ), ( "Multiplicity dependence of light (anti-)nuclei production in p-Pb collisions at $\\sqrt{s_{\\rm{NN}}}$ = 5.02 TeV", "Multiplicity dependence of light (anti-)nuclei production in p-Pb collisions at √(s_NN) = 5.02 TeV", ), ( "Scattering studies with low-energy kaon-proton femtoscopy in proton-proton collisions at the LHC", "Scattering studies with low-energy kaon-proton femtoscopy in proton-proton collisions at the LHC", ), ( "Measurement of the inclusive isolated photon production cross section in pp collisions at $\\sqrt{s}$ = 7 TeV", "Measurement of the inclusive isolated photon production cross section in pp collisions at √(s) = 7 TeV", ), ( "Inclusive J/$\\psi$ production at mid-rapidity in pp collisions at $\\sqrt{s}$ = 5.02 TeV", "Inclusive J/ψ production at mid-rapidity in pp collisions at √(s) = 5.02 TeV", ), ( "Study of the $\\Lambda$-$\\Lambda$ interaction with femtoscopy correlations in pp and p-Pb collisions at the LHC", "Study of the Λ-Λ interaction with femtoscopy correlations in pp and p-Pb collisions at the LHC", ), ( "Charged-particle production as a function of multiplicity and transverse spherocity in pp collisions at $\\sqrt{s} =5.02$ and 13 TeV", "Charged-particle production as a function of multiplicity and transverse spherocity in pp collisions at √(s) = 5.02 and 13 TeV", ), ( "Exploration of jet substructure using iterative declustering in pp and Pb-Pb collisions at LHC energies", "Exploration of jet substructure using iterative declustering in pp and Pb-Pb collisions at LHC energies", ), ( "Measurement of the production of charm jets tagged with D$^{0}$ mesons in pp collisions at $\\sqrt{s}$= 7 TeV", "Measurement of the production of charm jets tagged with D⁰ mesons in pp collisions at √(s) = 7 TeV", ), ( "First observation of an attractive interaction between a proton and a multi-strange baryon", "First observation of an attractive interaction between a proton and a multi-strange baryon", ), ( "Measurement of jet radial profiles in Pb$-$Pb collisions at $\\sqrt{s_{\\rm NN}}$ = 2.76 TeV", "Measurement of jet radial profiles in Pb-Pb collisions at √(s_NN) = 2.76 TeV", ), ( "Production of muons from heavy-flavour hadron decays in pp collisions at $\\sqrt{s}=5.02$ TeV", "Production of muons from heavy-flavour hadron decays in pp collisions at √(s) = 5.02 TeV", ), ( "Measurement of charged jet cross section in pp collisions at $\\sqrt{s}=5.02$ TeV", "Measurement of charged jet cross section in pp collisions at √(s) = 5.02 TeV", ), ( "Coherent J/$\\psi$ photoproduction at forward rapidity in ultra-peripheral Pb-Pb collisions at $\\sqrt{s_{\\rm{NN}}}=5.02$ TeV", "Coherent J/ψ photoproduction at forward rapidity in ultra-peripheral Pb-Pb collisions at √(s_NN) = 5.02 TeV", ), # LHCb lhcb_paper_feed ( "Measurements of $CP$ asymmetries in charmless four-body $\\Lambda^0_b$ and $\\Xi_b^0$ decays", "Measurements of CP asymmetries in charmless four-body Λ⁰_b and Ξ⁰_b decays", ), ( "Observation of an excited $B_c^+$ state", "Observation of an excited B⁺_c state", ), ( "Near-threshold $D\\bar{D}$ spectroscopy and observation of a new charmonium state", "Near-threshold DD̅ spectroscopy and observation of a new charmonium state", ), ( "Search for lepton-universality violation in $B^+\\to K^+\\ell^+\\ell^-$ decays", "Search for lepton-universality violation in B⁺ → K⁺ℓ⁺ℓ⁻ decays", ), ( "Observation of $C\\!P$ violation in charm decays", "Observation of CP violation in charm decays", ), ( "Measurement of the $CP$-violating phase $\\phi_s$ from $B_{s}^{0}\\to J/\\psi\\pi^+\\pi⁻$ decays in 13 TeV $pp$ collisions", "Measurement of the CP-violating phase ϕ_s from B⁰_s → J/ψπ⁺π⁻ decays in 13 TeV pp collisions", ), ( "Measurement of the mass difference between neutral charm-meson eigenstates", "Measurement of the mass difference between neutral charm-meson eigenstates", ), ( "Search for $CP$ violation in $D^+_s\\to K_S^0\\pi^+$, $D^+\\to K_S^0K^+$ and $D^+\\to\\phi\\pi^+$ decays", "Search for CP violation in D⁺_s → K⁰_S π⁺, D⁺ → K⁰_S K⁺ and D⁺ → ϕπ⁺ decays", ), ( "Amplitude analysis of $B^{0}_{s} \\rightarrow K^{0}_{\\textrm{S}} K^{\\pm}\\pi^{\\mp}$ decays", "Amplitude analysis of B⁰_s → K⁰_S K^±π^∓ decays", ), ( "Measurement of $b$-hadron fractions in 13 TeV $pp$ collisions", "Measurement of b-hadron fractions in 13 TeV pp collisions", ), ( "Dalitz Plot analysis of the $D^+ \\to K^- K^+ K^+$ decay", "Dalitz Plot analysis of the D⁺ → K⁻ K⁺ K⁺ decay", ), ( "Observation of $B^0_{(s)} \\to J/\\psi p \\overline{p}$ decays and precision measurements of the $B^0_{(s)}$ masses", "Observation of B⁰_s → J/ψ pp decays and precision measurements of the B⁰_s masses", ), ( "Measurement of $B^+$, $B^0$ and $\\Lambda_b^0$ production in $p\\mkern 1mu\\mathrm{Pb}$ collisions at $\\sqrt{s_{NN}} = 8.16 \\ \\rm TeV$", "Measurement of B⁺, B⁰ and Λ⁰_b production in p 1muPb collisions at √(s_NN) = 8.16 TeV", ), ( "Measurement of the ratio of branching fractions of the decays $\\Lambda_b^0 \\!\\to \\psi(2S) \\Lambda$ and $\\Lambda_b^0 \\!\\to J\\!/\\!\\psi \\Lambda$", "Measurement of the ratio of branching fractions of the decays Λ⁰_b → ψ(2S) Λ and Λ⁰_b → J/ψΛ", ), ( "Measurement of the mass and production rate of $\\Xi_b^-$ baryons", "Measurement of the mass and production rate of Ξ⁻_b baryons", ), ( "Model-independent observation of exotic contributions to $B^0\\to J/\\psi K^+\\pi^-$ decays", "Model-independent observation of exotic contributions to B⁰ → J/ψ K⁺π⁻ decays", ), ( "Measurement of the branching fraction and $C\\!P$ asymmetry in $B^{+}\\rightarrow J/\\psi \\rho^{+}$ decays", "Measurement of the branching fraction and CP asymmetry in B⁺ → J/ψρ⁺ decays", ), ( "Search for the rare decay $B^{+} \\rightarrow \\mu^{+}\\mu^{-}\\mu^{+}\\nu_{\\mu}$", "Search for the rare decay B⁺ → μ⁺μ⁻μ⁺ν_μ", ), ( "Study of the $B^0\\to \\rho(770)^0 K^*(892)^0$ decay with an amplitude analysis of $B^0\\to (\\pi^+\\pi^-) (K^+\\pi^-)$ decays", "Study of the B⁰ → ρ⁰(770) K*⁰(892) decay with an amplitude analysis of B⁰ → (π⁺π⁻) (K⁺π⁻) decays", ), ( "Search for $CP$ violation through an amplitude analysis of $D^0\\rightarrow K^+ K^- \\pi^+ \\pi^-$ decays", "Search for CP violation through an amplitude analysis of D⁰ → K⁺ K⁻ π⁺ π⁻ decays", ), ( "First measurement of charm production in fixed-target configuration at the LHC", "First measurement of charm production in fixed-target configuration at the LHC", ), ( "Study of $\\Upsilon$ production in $p$Pb collisions at $\\sqrt{s_{NN}}=8.16$ TeV", "Study of Υ production in pPb collisions at √(s_NN) = 8.16 TeV", ), ( "Measurement of the charm-mixing parameter $y_{CP}$", "Measurement of the charm-mixing parameter y_CP", ), ( "Measurement of the branching fractions of the decays $D^+\\rightarrow K^-K^+K^+$, $D^+\\rightarrow \\pi^-\\pi^+K^+$ and $D^+_s\\rightarrow\\pi^-K^+K^+$", "Measurement of the branching fractions of the decays D⁺ → K⁻K⁺K⁺, D⁺ → π⁻π⁺K⁺ and D⁺_s → π⁻K⁺K⁺", ), ( "Observation of two resonances in the $\\Lambda_b^0 \\pi^\\pm$ systems and precise measurement of $\\Sigma_b^\\pm$ and $\\Sigma_b^{*\\pm}$ properties", "Observation of two resonances in the Λ⁰_b π^± systems and precise measurement of Σ^±_b and Σ*^±_b properties", ), # LHCb lhcb_conf_feed ( "Prospects for searches for long-lived particles after the LHCb detector upgrades", "Prospects for searches for long-lived particles after the LHCb detector upgrades", ), ( "LHCb projections for proton-lead collisions during LHC Runs 3 and 4", "LHCb projections for proton-lead collisions during LHC Runs 3 and 4", ), ( "Measurement of $B^+$, $B^0$ and $\\Lambda⁰_b$ production and nuclear modification in $p$Pb collisions at $\\sqrt{s_\\mathrm{NN}}=8.16 ~~\\text {TeV}$", "Measurement of B⁺, B⁰ and Λ⁰_b production and nuclear modification in pPb collisions at √(s_NN) = 8.16 TeV", ), ( "Study of coherent $J/\\psi$ production in lead-lead collisions at $\\sqrt{s_{\\rm NN}} =5\\ \\rm{TeV}$ with the LHCb experiment", "Study of coherent J/ψ production in lead-lead collisions at √(s_NN) = 5 TeV with the LHCb experiment", ), ( "Update of the LHCb combination of the CKM angle $\\gamma$", "Update of the LHCb combination of the CKM angle γ", ), ( "Measurement of CP violation in the $B_s^0 \\to \\phi \\phi$ decay and search for the $B^0 \\to \\phi\\phi$ decay", "Measurement of CP violation in the B⁰_s → ϕϕ decay and search for the B⁰ → ϕϕ decay", ), ( "Prompt $\\Lambda^+_{\\mathrm{c}}$ production in $p\\mathrm{Pb}$ collisions at $\\sqrt{s_{_{\\mathrm{NN}}}} = 5.02\\mathrm{\\,Te\\kern -0.1em V}$", "Prompt Λ⁺_c production in pPb collisions at √(s_NN) = 5.02 TeV", ), ( "Update of the LHCb combination of the CKM angle $\\gamma$ using $B\\to DK$ decays", "Update of the LHCb combination of the CKM angle γ using B → DK decays", ), ( "Measurement of antiproton production in $p$He collisions at $\\sqrt{s_{\\scriptscriptstyle\\rm NN}}=110$ GeV", "Measurement of antiproton production in pHe collisions at √(s_NN) = 110 GeV", ), ( "Measurement of $J/\\psi$ and $D^0$ production in $p$Ar collisions at $\\sqrt{s_{NN}}=110$ GeV", "Measurement of J/ψ and D⁰ production in pAr collisions at √(s_NN) = 110 GeV", ), ( "Measurement of time-dependent $C\\!P$-violating asymmetries in $B^0\\to\\pi^+\\pi^-$ and $B_s^0\\to K^+K^-$ decays at LHCb", "Measurement of time-dependent CP-violating asymmetries in B⁰ → π⁺π⁻ and B⁰_s → K⁺K⁻ decays at LHCb", ), ( "First observation of a baryonic $B_s^0$ decay", "First observation of a baryonic B⁰_s decay", ), ( "Measurement of $C\\!P$ asymmetry in $B_s^0\\to D_s^{\\mp}K^{\\pm}$ decays", "Measurement of CP asymmetry in B⁰_s → D_s^∓K^± decays", ), ( "Study of the decay $B^{\\pm} \\to DK^{*\\pm}$ with two-body $D$ decays", "Study of the decay B^± → DK*^± with two-body D decays", ), ( "Evidence for the rare decay $\\Sigma^+ \\to p \\mu^+ \\mu^-$", "Evidence for the rare decay Σ⁺ → p μ⁺ μ⁻", ), ( "Updated limit for the decay $K_{\\rm\\scriptscriptstyle S}^0\\rightarrow\\mu^+\\mu^-$", "Updated limit for the decay K⁰_S → μ⁺μ⁻", ), ( "Search for the rare decays $B^0_{(s)}\\to\\tau^+\\tau^-$", "Search for the rare decays B⁰_s → τ⁺τ⁻", ), ( "$CP$-violating asymmetries from the decay-time distribution of prompt $D^0 \\to K^+ K^-$ and $D^0 \\to \\pi^+\\pi^-$ decays in the full $\\mbox{LHCb}$ Run 1 data sample. Measurement using unbinned, acceptance corrected decay-time.", "CP-violating asymmetries from the decay-time distribution of prompt D⁰ → K⁺ K⁻ and D⁰ → π⁺π⁻ decays in the full LHCb Run 1 data sample. Measurement using unbinned, acceptance corrected decay-time.", ), ( "$CP$-violating asymmetries from the decay-time distribution of prompt $D^0 \\to K^+K^-$ and $D^0 \\to \\pi^+\\pi^-$ decays in the full LHCb Run~1 data sample. Measurement using yield asymmetries in bins of decay time.", "CP-violating asymmetries from the decay-time distribution of prompt D⁰ → K⁺K⁻ and D⁰ → π⁺π⁻ decays in the full LHCb Run 1 data sample. Measurement using yield asymmetries in bins of decay time.", ), ( "Dalitz plot analysis of the $D^+ \\rightarrow K^- K^+ K^+$ decay with the isobar model", "Dalitz plot analysis of the D⁺ → K⁻ K⁺ K⁺ decay with the isobar model", ), ( "Central exclusive production of $J/\\psi$ and $\\psi(2S)$ mesons in pp collisions at $\\sqrt{s}=13$ TeV", "Central exclusive production of J/ψ and ψ(2S) mesons in pp collisions at √(s) = 13 TeV", ), ( "Search for $H^0 \\rightarrow b \\bar{b}$ or $c \\bar{c}$ in association with a $W$ or $Z$ boson in the forward region of $pp$ collisions", "Search for H⁰ → bb̅ or cc̅ in association with a W or Z boson in the forward region of pp collisions", ), ( "LHCb dimuon and charm mass distributions", "LHCb dimuon and charm mass distributions", ), ( "Search for structure in the $B_s^0\\pi^\\pm$ invariant mass spectrum", "Search for structure in the B⁰_s π^± invariant mass spectrum", ), ( "Study of cold nuclear matter effects using prompt $D^0$ meson production in $p\\mathrm{Pb}$ collisions at LHCb", "Study of cold nuclear matter effects using prompt D⁰ meson production in pPb collisions at LHCb", ), # LHCb lhcb_paper_feed ( "Search for $A' \\to \\mu^+ \\mu^-$ decays", "Search for A' → μ⁺ μ⁻ decays", ), ( "Search for the doubly charmed baryon $\\Xi_{cc}^{+}$", "Search for the doubly charmed baryon Ξ⁺_cc", ), ( "Amplitude analysis of the $B^+ \\to \\pi^+ \\pi^+ \\pi^-$ decay", "Amplitude analysis of the B⁺ → π⁺ π⁺ π⁻ decay", ), ( "Observation of several sources of $CP$ violation in $B^+ \\to \\pi^+ \\pi^+ \\pi^-$ decays", "Observation of several sources of CP violation in B⁺ → π⁺ π⁺ π⁻ decays", ), ( "Measurement of $\\psi(2S)$ production cross-sections in proton-proton collisions at $\\sqrt{s} = 7$ and 13 TeV", "Measurement of ψ(2S) production cross-sections in proton-proton collisions at √(s) = 7 and 13 TeV", ), ( "Measurement of CP violation in the $B_s^0\\rightarrow\\phi\\phi$ decay and search for the $B^0\\rightarrow\\phi\\phi$ decay", "Measurement of CP violation in the B⁰_s → ϕϕ decay and search for the B⁰ → ϕϕ decay", ), ( "Precision measurement of the $\\Lambda_c^+$, $\\Xi_c^+$ and $\\Xi_c^0$ baryon lifetimes", "Precision measurement of the Λ⁺_c, Ξ⁺_c and Ξ⁰_c baryon lifetimes", ), ( "Observation of the $\\Lambda_b^0\\rightarrow \\chi_{c1}(3872)pK^-$ decay", "Observation of the Λ⁰_b → χ_c1(3872)pK⁻ decay", ), ( "Updated measurement of time-dependent CP-violating observables in $B^0_s \\to J/\\psi K^+K^-$ decays", "Updated measurement of time-dependent CP-violating observables in B⁰_s → J/ψ K⁺K⁻ decays", ), ( "Measurement of $C\\!P$ observables in the process $B^0 \\to DK^{*0}$ with two- and four-body $D$ decays", "Measurement of CP observables in the process B⁰ → DK*⁰ with two- and four-body D decays", ), ( "Amplitude analysis of $B^\\pm \\to \\pi^\\pm K^+ K^-$ decays", "Amplitude analysis of B^± → π^± K⁺ K⁻ decays", ), ( "Search for the lepton-flavour-violating decays $B^{0}_{s}\\to\\tau^{\\pm}\\mu^{\\mp}$ and $B^{0}\\to\\tau^{\\pm}\\mu^{\\mp}$", "Search for the lepton-flavour-violating decays B⁰_s → τ^±μ^∓ and B⁰ → τ^±μ^∓", ), ( "Amplitude analysis of the $B^0_{(s)} \\to K^{*0} \\overline{K}^{*0}$ decays and measurement of the branching fraction of the $B^0 \\to K^{*0} \\overline{K}^{*0}$ decay", "Amplitude analysis of the B⁰_s → K*⁰K*⁰ decays and measurement of the branching fraction of the B⁰ → K*⁰K*⁰ decay", ), ( "Measurement of $CP$-violating and mixing-induced observables in $B_s^0 \\to \\phi\\gamma$ decays", "Measurement of CP-violating and mixing-induced observables in B⁰_s → ϕγ decays", ), ( "A search for $\\it{\\Xi}^{++}_{cc} \\rightarrow D^{+} p K^{-} \\pi^{+}$ decays", "A search for Ξ⁺⁺_cc → D⁺ p K⁻π⁺ decays", ), ( "Measurement of charged hadron production in $Z$-tagged jets in proton-proton collisions at $\\sqrt{s}=8$ TeV", "Measurement of charged hadron production in Z-tagged jets in proton-proton collisions at √(s) = 8 TeV", ), ( "Observation of a narrow pentaquark state, $P_c(4312)^+$, and of two-peak structure of the $P_c(4450)^+$", "Observation of a narrow pentaquark state, P⁺_c(4312), and of two-peak structure of the P⁺_c(4450)", ), ( "Measurements of $CP$ asymmetries in charmless four-body $\\Lambda^0_b$ and $\\Xi_b^0$ decays", "Measurements of CP asymmetries in charmless four-body Λ⁰_b and Ξ⁰_b decays", ), ( "Observation of an excited $B_c^+$ state", "Observation of an excited B⁺_c state", ), ( "Near-threshold $D\\bar{D}$ spectroscopy and observation of a new charmonium state", "Near-threshold DD̅ spectroscopy and observation of a new charmonium state", ), ( "Search for lepton-universality violation in $B^+\\to K^+\\ell^+\\ell^-$ decays", "Search for lepton-universality violation in B⁺ → K⁺ℓ⁺ℓ⁻ decays", ), ( "Observation of $C\\!P$ violation in charm decays", "Observation of CP violation in charm decays", ), ( "Measurement of the $CP$-violating phase $\\phi_s$ from $B_{s}^{0}\\to J/\\psi\\pi^+\\pi^-$ decays in 13 TeV $pp$ collisions", "Measurement of the CP-violating phase ϕ_s from B⁰_s → J/ψπ⁺π⁻ decays in 13 TeV pp collisions", ), ( "Measurement of the mass difference between neutral charm-meson eigenstates", "Measurement of the mass difference between neutral charm-meson eigenstates", ), ( "Search for $CP$ violation in $D^+_s\\to K_S^0\\pi^+$, $D^+\\to K_S^0K^+$ and $D^+\\to\\phi\\pi^+$ decays", "Search for CP violation in D⁺_s → K⁰_S π⁺, D⁺ → K⁰_S K⁺ and D⁺ → ϕπ⁺ decays", ), # LHCb lhcb_conf_feed ( "Strong constraints on the $K^0_s \\to \\mu^+ \\mu^-$ branching fraction", "Strong constraints on the K⁰_s → μ⁺ μ⁻ branching fraction", ), ( "Search for time-dependent $CP$ violation in $D^0 \\to K^+ K^-$ and $D^0 \\to \\pi^+ \\pi^-$ decays", "Search for time-dependent CP violation in D⁰ → K⁺ K⁻ and D⁰ → π⁺ π⁻ decays", ), ( "Prospects for searches for long-lived particles after the LHCb detector upgrades", "Prospects for searches for long-lived particles after the LHCb detector upgrades", ), ( "LHCb projections for proton-lead collisions during LHC Runs 3 and 4", "LHCb projections for proton-lead collisions during LHC Runs 3 and 4", ), ( "Measurement of $B^+$, $B^0$ and $\\Lambda_b^0$ production and nuclear modification in $p$Pb collisions at $\\sqrt{s_\\mathrm{NN}}=8.16 ~~\\text {TeV}$", "Measurement of B⁺, B⁰ and Λ⁰_b production and nuclear modification in pPb collisions at √(s_NN) = 8.16 TeV", ), ( "Study of coherent $J/\\psi$ production in lead-lead collisions at $\\sqrt{s_{\\rm NN}} =5\\ \\rm{TeV}$ with the LHCb experiment", "Study of coherent J/ψ production in lead-lead collisions at √(s_NN) = 5 TeV with the LHCb experiment", ), ( "Update of the LHCb combination of the CKM angle $\\gamma$", "Update of the LHCb combination of the CKM angle γ", ), ( "Measurement of CP violation in the $B_s^0 \\to \\phi \\phi$ decay and search for the $B^0 \\to \\phi\\phi$ decay", "Measurement of CP violation in the B⁰_s → ϕϕ decay and search for the B⁰ → ϕϕ decay", ), ( "Prompt $\\Lambda^+_{\\mathrm{c}}$ production in $p\\mathrm{Pb}$ collisions at $\\sqrt{s_{_{\\mathrm{NN}}}} = 5.02\\mathrm{\\,Te\\kern -0.1em V}$", "Prompt Λ⁺_c production in pPb collisions at √(s_NN) = 5.02 TeV", ), ( "Update of the LHCb combination of the CKM angle $\\gamma$ using $B\\to DK$ decays", "Update of the LHCb combination of the CKM angle γ using B → DK decays", ), ( "Measurement of antiproton production in $p$He collisions at $\\sqrt{s_{\\scriptscriptstyle\\rm NN}}=110$ GeV", "Measurement of antiproton production in pHe collisions at √(s_NN) = 110 GeV", ), ( "Measurement of $J/\\psi$ and $D^0$ production in $p$Ar collisions at $\\sqrt{s_{NN}}=110$ GeV", "Measurement of J/ψ and D⁰ production in pAr collisions at √(s_NN) = 110 GeV", ), ( "Measurement of time-dependent $C\\!P$-violating asymmetries in $B^0\\to\\pi^+\\pi^-$ and $B_s^0\\to K^+K^-$ decays at LHCb", "Measurement of time-dependent CP-violating asymmetries in B⁰ → π⁺π⁻ and B⁰_s → K⁺K⁻ decays at LHCb", ), ( "First observation of a baryonic $B_s^0$ decay", "First observation of a baryonic B⁰_s decay", ), ( "Measurement of $C\\!P$ asymmetry in $B_s^0\\to D_s^{\\mp}K^{\\pm}$ decays", "Measurement of CP asymmetry in B⁰_s → D_s^∓K^± decays", ), ( "Study of the decay $B^{\\pm} \\to DK^{*\\pm}$ with two-body $D$ decays", "Study of the decay B^± → DK*^± with two-body D decays", ), ( "Evidence for the rare decay $\\Sigma^+ \\to p \\mu^+ \\mu^-$", "Evidence for the rare decay Σ⁺ → p μ⁺ μ⁻", ), ( "Updated limit for the decay $K_{\\rm\\scriptscriptstyle S}^0\\rightarrow\\mu^+\\mu^-$", "Updated limit for the decay K⁰_S → μ⁺μ⁻", ), ( "Search for the rare decays $B^0_{(s)}\\to\\tau^+\\tau^-$", "Search for the rare decays B⁰_s → τ⁺τ⁻", ), ( "$CP$-violating asymmetries from the decay-time distribution of prompt $D^0 \\to K^+ K^-$ and $D^0 \\to \\pi^+\\pi^-$ decays in the full $\\mbox{LHCb}$ Run 1 data sample. Measurement using unbinned, acceptance corrected decay-time.", "CP-violating asymmetries from the decay-time distribution of prompt D⁰ → K⁺ K⁻ and D⁰ → π⁺π⁻ decays in the full LHCb Run 1 data sample. Measurement using unbinned, acceptance corrected decay-time.", ), ( "$CP$-violating asymmetries from the decay-time distribution of prompt $D^0 \\to K^+K^-$ and $D^0 \\to \\pi^+\\pi^-$ decays in the full LHCb Run~1 data sample. Measurement using yield asymmetries in bins of decay time.", "CP-violating asymmetries from the decay-time distribution of prompt D⁰ → K⁺K⁻ and D⁰ → π⁺π⁻ decays in the full LHCb Run 1 data sample. Measurement using yield asymmetries in bins of decay time.", ), ( "Dalitz plot analysis of the $D^+ \\rightarrow K^- K^+ K^+$ decay with the isobar model", "Dalitz plot analysis of the D⁺ → K⁻ K⁺ K⁺ decay with the isobar model", ), ( "Central exclusive production of $J/\\psi$ and $\\psi(2S)$ mesons in pp collisions at $\\sqrt{s}=13$ TeV", "Central exclusive production of J/ψ and ψ(2S) mesons in pp collisions at √(s) = 13 TeV", ), ( "Search for $H^0 \\rightarrow b \\bar{b}$ or $c \\bar{c}$ in association with a $W$ or $Z$ boson in the forward region of $pp$ collisions", "Search for H⁰ → bb̅ or cc̅ in association with a W or Z boson in the forward region of pp collisions", ), ], ) def test_formatting(self, input_title, expected): """Test the list above.""" new_title = cds_paper_bot.format_title(input_title) assert new_title == expected

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0

8

c194d6fcaa53ad0901bbd02c0843287e5286c3f9

5,805

py

Python

xbox/webapi/api/provider/screenshots/__init__.py

Landcross/xbox-webapi-python

0e3f01254907d929fc52f843a5f3bf53ef2ba876

[ "MIT" ]

122

2018-03-17T05:20:35.000Z

2022-03-30T23:30:14.000Z

xbox/webapi/api/provider/screenshots/__init__.py

Landcross/xbox-webapi-python

0e3f01254907d929fc52f843a5f3bf53ef2ba876

[ "MIT" ]

62

2018-03-27T14:17:11.000Z

2022-03-30T16:36:03.000Z

xbox/webapi/api/provider/screenshots/__init__.py

Landcross/xbox-webapi-python

0e3f01254907d929fc52f843a5f3bf53ef2ba876

[ "MIT" ]

38

2018-05-09T19:17:48.000Z

2022-02-03T06:55:04.000Z

""" Screenshots - Get screenshot info """ from xbox.webapi.api.provider.baseprovider import BaseProvider from xbox.webapi.api.provider.screenshots.models import ScreenshotResponse class ScreenshotsProvider(BaseProvider): SCREENSHOTS_METADATA_URL = "https://screenshotsmetadata.xboxlive.com" HEADERS_SCREENSHOTS_METADATA = {"x-xbl-contract-version": "5"} async def get_recent_community_screenshots_by_title_id( self, title_id: str, **kwargs ) -> ScreenshotResponse: """ Get recent community screenshots by Title Id Args: title_id: Title Id to get screenshots for Returns: :class:`ScreenshotResponse`: Screenshot Response """ url = self.SCREENSHOTS_METADATA_URL + f"/public/titles/{title_id}/screenshots" params = {"qualifier": "created"} resp = await self.client.session.get( url, params=params, headers=self.HEADERS_SCREENSHOTS_METADATA, **kwargs ) resp.raise_for_status() return ScreenshotResponse.parse_raw(await resp.text()) async def get_recent_own_screenshots( self, title_id: str = None, skip_items: int = 0, max_items: int = 25, **kwargs ) -> ScreenshotResponse: """ Get own recent screenshots, optionally filter for title Id Args: title_id: Title ID to filter skip_items: Item count to skip max_items: Maximum item count to load Returns: :class:`ScreenshotResponse`: Screenshot Response """ url = self.SCREENSHOTS_METADATA_URL + "/users/me" if title_id: url += f"/titles/{title_id}" url += "/screenshots" params = {"skipItems": skip_items, "maxItems": max_items} resp = await self.client.session.get( url, params=params, headers=self.HEADERS_SCREENSHOTS_METADATA, **kwargs ) resp.raise_for_status() return ScreenshotResponse.parse_raw(await resp.text()) async def get_recent_screenshots_by_xuid( self, xuid: str, title_id: str = None, skip_items: int = 0, max_items: int = 25, **kwargs, ) -> ScreenshotResponse: """ Get recent screenshots by XUID, optionally filter for title Id Args: xuid: XUID of user to get screenshots from title_id: Optional title id filter skip_items: Item count to skip max_items: Maximum item count to load Returns: :class:`ScreenshotResponse`: Screenshot Response """ url = self.SCREENSHOTS_METADATA_URL + f"/users/xuid({xuid})" if title_id: url += f"/titles/{title_id}" url += "/screenshots" params = {"skipItems": skip_items, "maxItems": max_items} resp = await self.client.session.get( url, params=params, headers=self.HEADERS_SCREENSHOTS_METADATA, **kwargs ) resp.raise_for_status() return ScreenshotResponse.parse_raw(await resp.text()) async def get_saved_community_screenshots_by_title_id( self, title_id: str, **kwargs ) -> ScreenshotResponse: """ Get saved community screenshots by Title Id Args: title_id: Title Id to get screenshots for Returns: :class:`ScreenshotResponse`: Screenshot Response """ url = f"{self.SCREENSHOTS_METADATA_URL}/public/titles/{title_id}/screenshots/saved" params = {"qualifier": "created"} resp = await self.client.session.get( url, params=params, headers=self.HEADERS_SCREENSHOTS_METADATA, **kwargs ) resp.raise_for_status() return ScreenshotResponse.parse_raw(await resp.text()) async def get_saved_own_screenshots( self, title_id: str = None, skip_items: int = 0, max_items: int = 25, **kwargs ) -> ScreenshotResponse: """ Get own saved screenshots, optionally filter for title Id an Args: title_id: Optional Title ID to filter skip_items: Item count to skip max_items: Maximum item count to load Returns: :class:`ScreenshotResponse`: Screenshot Response """ url = self.SCREENSHOTS_METADATA_URL + "/users/me" if title_id: url += f"/titles/{title_id}" url += "/screenshots/saved" params = {"skipItems": skip_items, "maxItems": max_items} resp = await self.client.session.get( url, params=params, headers=self.HEADERS_SCREENSHOTS_METADATA, **kwargs ) resp.raise_for_status() return ScreenshotResponse.parse_raw(await resp.text()) async def get_saved_screenshots_by_xuid( self, xuid: str, title_id: str = None, skip_items: int = 0, max_items: int = 25, **kwargs, ) -> ScreenshotResponse: """ Get saved screenshots by XUID, optionally filter for title Id Args: xuid: XUID of user to get screenshots from title_id: Optional title id filter skip_items: Item count to skip max_items: Maximum item count to load Returns: :class:`ScreenshotResponse`: Screenshot Response """ url = self.SCREENSHOTS_METADATA_URL + f"/users/xuid({xuid})" if title_id: url += f"/titles/{title_id}" url += "/screenshots/saved" params = {"skipItems": skip_items, "maxItems": max_items} resp = await self.client.session.get( url, params=params, headers=self.HEADERS_SCREENSHOTS_METADATA, **kwargs ) resp.raise_for_status() return ScreenshotResponse.parse_raw(await resp.text())

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7

c1bc1cd3407ed26127c070ac914737e0849d27ad

132

py

Python

mortgage_filter/__init__.py

lukavuko/mortgage-filter-package

187d771c441f93b6a5dd2c5bf67ee519d1888430

[ "MIT" ]

null

mortgage_filter/__init__.py

lukavuko/mortgage-filter-package

187d771c441f93b6a5dd2c5bf67ee519d1888430

[ "MIT" ]

null

mortgage_filter/__init__.py

lukavuko/mortgage-filter-package

187d771c441f93b6a5dd2c5bf67ee519d1888430

[ "MIT" ]

null

from mortgage_filter.mortgage_filter import * from mortgage_filter.mortgage_base import * from mortgage_filter.exceptions import *

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7

c1c846d0c0614ce5dff23f2e66a0dbba014a830e

24,535

py

Python

core/arxiv/submission/services/plaintext/tests.py

NeolithEra/arxiv-submission-core

d4f20be62a882d2d5f3d1584eda69e7d90ca2c12

[ "MIT" ]

null

core/arxiv/submission/services/plaintext/tests.py

NeolithEra/arxiv-submission-core

d4f20be62a882d2d5f3d1584eda69e7d90ca2c12

[ "MIT" ]

null

core/arxiv/submission/services/plaintext/tests.py

NeolithEra/arxiv-submission-core

d4f20be62a882d2d5f3d1584eda69e7d90ca2c12

[ "MIT" ]

null

"""Tests for :mod:`arxiv.submission.services.plaintext`.""" from unittest import TestCase, mock from arxiv.integration.api import exceptions, status from . import plaintext mock_app = mock.MagicMock(config={ 'PLAINTEXT_ENDPOINT': 'http://foohost:5432', 'PLAINTEXT_VERIFY': False }) class TestPlainTextService(TestCase): """Tests for :class:`.plaintext.PlainTextService`.""" @mock.patch('arxiv.integration.api.service.current_app', mock_app) @mock.patch('arxiv.integration.api.service.requests.Session') def test_already_in_progress(self, mock_Session): """A plaintext extraction is already in progress.""" mock_post = mock.MagicMock( return_value=mock.MagicMock( status_code=status.SEE_OTHER, json=mock.MagicMock(return_value={}), headers={'Location': '...'} ) ) mock_Session.return_value = mock.MagicMock(post=mock_post) source_id = '132456' service = plaintext.PlainTextService('http://foohost:8123') with self.assertRaises(plaintext.ExtractionInProgress): service.request_extraction(source_id) @mock.patch('arxiv.integration.api.service.current_app', mock_app) @mock.patch('arxiv.integration.api.service.requests.Session') def test_request_extraction(self, mock_Session): """Extraction is successfully requested.""" mock_session = mock.MagicMock(**{ 'post': mock.MagicMock( return_value=mock.MagicMock( status_code=status.ACCEPTED, json=mock.MagicMock(return_value={}), content='', headers={'Location': '/somewhere'} ) ), 'get': mock.MagicMock( return_value=mock.MagicMock( status_code=status.OK, json=mock.MagicMock( return_value={'reason': 'extraction in process'} ), content="{'reason': 'fulltext extraction in process'}", headers={} ) ) }) mock_Session.return_value = mock_session source_id = '132456' service = plaintext.PlainTextService('http://foohost:8123') self.assertIsNone(service.request_extraction(source_id)) self.assertEqual( mock_session.post.call_args[0][0], 'http://foohost:8123/submission/132456' ) @mock.patch('arxiv.integration.api.service.current_app', mock_app) @mock.patch('arxiv.integration.api.service.requests.Session') def test_request_extraction_bad_request(self, mock_Session): """Service returns 400 Bad Request.""" mock_Session.return_value = mock.MagicMock( post=mock.MagicMock( return_value=mock.MagicMock( status_code=status.BAD_REQUEST, json=mock.MagicMock(return_value={ 'reason': 'something is not quite right' }) ) ) ) source_id = '132456' service = plaintext.PlainTextService('foohost', 8000) with self.assertRaises(exceptions.BadRequest): service.request_extraction(source_id) @mock.patch('arxiv.integration.api.service.current_app', mock_app) @mock.patch('arxiv.integration.api.service.requests.Session') def test_request_extraction_server_error(self, mock_Session): """Service returns 500 Internal Server Error.""" mock_Session.return_value = mock.MagicMock( post=mock.MagicMock( return_value=mock.MagicMock( status_code=status.INTERNAL_SERVER_ERROR, json=mock.MagicMock(return_value={ 'reason': 'something is not quite right' }) ) ) ) source_id = '132456' service = plaintext.PlainTextService('foohost', 8000) with self.assertRaises(exceptions.RequestFailed): service.request_extraction(source_id) @mock.patch('arxiv.integration.api.service.current_app', mock_app) @mock.patch('arxiv.integration.api.service.requests.Session') def test_request_extraction_unauthorized(self, mock_Session): """Service returns 401 Unauthorized.""" mock_Session.return_value = mock.MagicMock( post=mock.MagicMock( return_value=mock.MagicMock( status_code=status.UNAUTHORIZED, json=mock.MagicMock(return_value={ 'reason': 'who are you' }) ) ) ) source_id = '132456' service = plaintext.PlainTextService('foohost', 8000) with self.assertRaises(exceptions.RequestUnauthorized): service.request_extraction(source_id) @mock.patch('arxiv.integration.api.service.current_app', mock_app) @mock.patch('arxiv.integration.api.service.requests.Session') def test_request_extraction_forbidden(self, mock_Session): """Service returns 403 Forbidden.""" mock_Session.return_value = mock.MagicMock( post=mock.MagicMock( return_value=mock.MagicMock( status_code=status.FORBIDDEN, json=mock.MagicMock(return_value={ 'reason': 'you do not have sufficient authz' }) ) ) ) source_id = '132456' service = plaintext.PlainTextService('foohost', 8000) with self.assertRaises(exceptions.RequestForbidden): service.request_extraction(source_id) @mock.patch('arxiv.integration.api.service.current_app', mock_app) @mock.patch('arxiv.integration.api.service.requests.Session') def test_extraction_is_complete(self, mock_Session): """Extraction is indeed complete.""" mock_get = mock.MagicMock( return_value=mock.MagicMock( status_code=status.SEE_OTHER, json=mock.MagicMock(return_value={}), headers={'Location': '...'} ) ) mock_Session.return_value = mock.MagicMock(get=mock_get) source_id = '132456' service = plaintext.PlainTextService('http://foohost:8123') self.assertTrue(service.extraction_is_complete(source_id)) self.assertEqual( mock_get.call_args[0][0], 'http://foohost:8123/submission/132456/status' ) @mock.patch('arxiv.integration.api.service.current_app', mock_app) @mock.patch('arxiv.integration.api.service.requests.Session') def test_extraction_in_progress(self, mock_Session): """Extraction is still in progress.""" mock_get = mock.MagicMock( return_value=mock.MagicMock( status_code=status.OK, json=mock.MagicMock(return_value={'status': 'in_progress'}) ) ) mock_Session.return_value = mock.MagicMock(get=mock_get) source_id = '132456' service = plaintext.PlainTextService('http://foohost:8123') self.assertFalse(service.extraction_is_complete(source_id)) self.assertEqual( mock_get.call_args[0][0], 'http://foohost:8123/submission/132456/status' ) @mock.patch('arxiv.integration.api.service.current_app', mock_app) @mock.patch('arxiv.integration.api.service.requests.Session') def test_extraction_failed(self, mock_Session): """Extraction failed.""" mock_get = mock.MagicMock( return_value=mock.MagicMock( status_code=status.OK, json=mock.MagicMock(return_value={'status': 'failed'}) ) ) mock_Session.return_value = mock.MagicMock(get=mock_get) source_id = '132456' service = plaintext.PlainTextService('http://foohost:8123') with self.assertRaises(plaintext.ExtractionFailed): self.assertFalse(service.extraction_is_complete(source_id)) @mock.patch('arxiv.integration.api.service.current_app', mock_app) @mock.patch('arxiv.integration.api.service.requests.Session') def test_complete_unauthorized(self, mock_Session): """Service returns 401 Unauthorized.""" mock_Session.return_value = mock.MagicMock( get=mock.MagicMock( return_value=mock.MagicMock( status_code=status.UNAUTHORIZED, json=mock.MagicMock(return_value={ 'reason': 'who are you' }) ) ) ) source_id = '132456' service = plaintext.PlainTextService('foohost', 8000) with self.assertRaises(exceptions.RequestUnauthorized): service.extraction_is_complete(source_id) @mock.patch('arxiv.integration.api.service.current_app', mock_app) @mock.patch('arxiv.integration.api.service.requests.Session') def test_complete_forbidden(self, mock_Session): """Service returns 403 Forbidden.""" mock_Session.return_value = mock.MagicMock( get=mock.MagicMock( return_value=mock.MagicMock( status_code=status.FORBIDDEN, json=mock.MagicMock(return_value={ 'reason': 'you do not have sufficient authz' }) ) ) ) source_id = '132456' service = plaintext.PlainTextService('foohost', 8000) with self.assertRaises(exceptions.RequestForbidden): service.extraction_is_complete(source_id) @mock.patch('arxiv.integration.api.service.current_app', mock_app) @mock.patch('arxiv.integration.api.service.requests.Session') def test_retrieve_unauthorized(self, mock_Session): """Service returns 401 Unauthorized.""" mock_Session.return_value = mock.MagicMock( get=mock.MagicMock( return_value=mock.MagicMock( status_code=status.UNAUTHORIZED, json=mock.MagicMock(return_value={ 'reason': 'who are you' }) ) ) ) source_id = '132456' service = plaintext.PlainTextService('foohost', 8000) with self.assertRaises(exceptions.RequestUnauthorized): service.retrieve_content(source_id) @mock.patch('arxiv.integration.api.service.current_app', mock_app) @mock.patch('arxiv.integration.api.service.requests.Session') def test_retrieve_forbidden(self, mock_Session): """Service returns 403 Forbidden.""" mock_Session.return_value = mock.MagicMock( get=mock.MagicMock( return_value=mock.MagicMock( status_code=status.FORBIDDEN, json=mock.MagicMock(return_value={ 'reason': 'you do not have sufficient authz' }) ) ) ) source_id = '132456' service = plaintext.PlainTextService('foohost', 8000) with self.assertRaises(exceptions.RequestForbidden): service.retrieve_content(source_id) @mock.patch('arxiv.integration.api.service.current_app', mock_app) @mock.patch('arxiv.integration.api.service.requests.Session') def test_retrieve(self, mock_Session): """Retrieval is successful.""" content = b'thisisthecontent' mock_get = mock.MagicMock( return_value=mock.MagicMock( status_code=status.OK, content=content ) ) mock_Session.return_value = mock.MagicMock(get=mock_get) source_id = '132456' service = plaintext.PlainTextService('http://foohost:8123') self.assertEqual(service.retrieve_content(source_id), content, "Returns binary content as received") self.assertEqual( mock_get.call_args[0][0], 'http://foohost:8123/submission/132456' ) @mock.patch('arxiv.integration.api.service.current_app', mock_app) @mock.patch('arxiv.integration.api.service.requests.Session') def test_retrieve_nonexistant(self, mock_Session): """There is no such plaintext resource.""" mock_get = mock.MagicMock( return_value=mock.MagicMock( status_code=status.NOT_FOUND, json=mock.MagicMock(return_value={'reason': 'no such thing'}) ) ) mock_Session.return_value = mock.MagicMock(get=mock_get) source_id = '132456' service = plaintext.PlainTextService('http://foohost:8123') with self.assertRaises(exceptions.NotFound): service.retrieve_content(source_id) @mock.patch('arxiv.integration.api.service.current_app', mock_app) @mock.patch('arxiv.integration.api.service.requests.Session') def test_retrieve_in_progress(self, mock_Session): """There is no such plaintext resource.""" mock_get = mock.MagicMock( return_value=mock.MagicMock( status_code=status.SEE_OTHER, json=mock.MagicMock(return_value={}), headers={'Location': '...'} ) ) mock_Session.return_value = mock.MagicMock(get=mock_get) source_id = '132456' service = plaintext.PlainTextService('http://foohost:8123') with self.assertRaises(plaintext.ExtractionInProgress): service.retrieve_content(source_id) class TestPlainTextServiceModule(TestCase): """Tests for :mod:`.services.plaintext`.""" def session(self, status_code=status.OK, method="get", json={}, content="", headers={}): """Make a mock session.""" return mock.MagicMock(**{ method: mock.MagicMock( return_value=mock.MagicMock( status_code=status_code, json=mock.MagicMock( return_value=json ), content=content, headers=headers ) ) }) @mock.patch('arxiv.integration.api.service.current_app', mock_app) @mock.patch('arxiv.integration.api.service.requests.Session') def test_already_in_progress(self, mock_Session): """A plaintext extraction is already in progress.""" mock_Session.return_value = self.session( status_code=status.SEE_OTHER, method='post', headers={'Location': '...'} ) source_id = '132456' with self.assertRaises(plaintext.ExtractionInProgress): plaintext.PlainTextService.request_extraction(source_id) @mock.patch('arxiv.integration.api.service.current_app', mock_app) @mock.patch('arxiv.integration.api.service.requests.Session') def test_request_extraction(self, mock_Session): """Extraction is successfully requested.""" mock_session = mock.MagicMock(**{ 'post': mock.MagicMock( return_value=mock.MagicMock( status_code=status.ACCEPTED, json=mock.MagicMock(return_value={}), content='', headers={'Location': '/somewhere'} ) ), 'get': mock.MagicMock( return_value=mock.MagicMock( status_code=status.OK, json=mock.MagicMock( return_value={'reason': 'extraction in process'} ), content="{'reason': 'fulltext extraction in process'}", headers={} ) ) }) mock_Session.return_value = mock_session source_id = '132456' self.assertIsNone( plaintext.PlainTextService.request_extraction(source_id) ) self.assertEqual(mock_session.post.call_args[0][0], 'http://foohost:5432/submission/132456') @mock.patch('arxiv.integration.api.service.current_app', mock_app) @mock.patch('arxiv.integration.api.service.requests.Session') def test_extraction_bad_request(self, mock_Session): """Service returns 400 Bad Request.""" mock_Session.return_value = self.session( status_code=status.BAD_REQUEST, method='post', json={'reason': 'something is not quite right'} ) source_id = '132456' with self.assertRaises(exceptions.BadRequest): plaintext.PlainTextService.request_extraction(source_id) @mock.patch('arxiv.integration.api.service.current_app', mock_app) @mock.patch('arxiv.integration.api.service.requests.Session') def test_extraction_server_error(self, mock_Session): """Service returns 500 Internal Server Error.""" mock_Session.return_value = self.session( status_code=status.INTERNAL_SERVER_ERROR, method='post', json={'reason': 'something is not quite right'} ) source_id = '132456' with self.assertRaises(exceptions.RequestFailed): plaintext.PlainTextService.request_extraction(source_id) @mock.patch('arxiv.integration.api.service.current_app', mock_app) @mock.patch('arxiv.integration.api.service.requests.Session') def test_extraction_unauthorized(self, mock_Session): """Service returns 401 Unauthorized.""" mock_Session.return_value = self.session( status_code=status.UNAUTHORIZED, method='post', json={'reason': 'who are you'} ) source_id = '132456' with self.assertRaises(exceptions.RequestUnauthorized): plaintext.PlainTextService.request_extraction(source_id) @mock.patch('arxiv.integration.api.service.current_app', mock_app) @mock.patch('arxiv.integration.api.service.requests.Session') def test_request_extraction_forbidden(self, mock_Session): """Service returns 403 Forbidden.""" mock_Session.return_value = self.session( status_code=status.FORBIDDEN, method='post', json={'reason': 'you do not have sufficient authz'} ) source_id = '132456' with self.assertRaises(exceptions.RequestForbidden): plaintext.PlainTextService.request_extraction(source_id) @mock.patch('arxiv.integration.api.service.current_app', mock_app) @mock.patch('arxiv.integration.api.service.requests.Session') def test_extraction_is_complete(self, mock_Session): """Extraction is indeed complete.""" mock_session = self.session( status_code=status.SEE_OTHER, headers={'Location': '...'} ) mock_Session.return_value = mock_session source_id = '132456' self.assertTrue(plaintext.PlainTextService.extraction_is_complete(source_id)) self.assertEqual(mock_session.get.call_args[0][0], 'http://foohost:5432/submission/132456/status') @mock.patch('arxiv.integration.api.service.current_app', mock_app) @mock.patch('arxiv.integration.api.service.requests.Session') def test_extraction_in_progress(self, mock_Session): """Extraction is still in progress.""" mock_session = self.session( json={'status': 'in_progress'} ) mock_Session.return_value = mock_session source_id = '132456' self.assertFalse(plaintext.PlainTextService.extraction_is_complete(source_id)) self.assertEqual(mock_session.get.call_args[0][0], 'http://foohost:5432/submission/132456/status') @mock.patch('arxiv.integration.api.service.current_app', mock_app) @mock.patch('arxiv.integration.api.service.requests.Session') def test_extraction_failed(self, mock_Session): """Extraction failed.""" mock_Session.return_value = self.session(json={'status': 'failed'}) source_id = '132456' with self.assertRaises(plaintext.ExtractionFailed): self.assertFalse(plaintext.PlainTextService.extraction_is_complete(source_id)) @mock.patch('arxiv.integration.api.service.current_app', mock_app) @mock.patch('arxiv.integration.api.service.requests.Session') def test_complete_unauthorized(self, mock_Session): """Service returns 401 Unauthorized.""" mock_Session.return_value = self.session( status_code=status.UNAUTHORIZED, json={'reason': 'who are you'} ) source_id = '132456' with self.assertRaises(exceptions.RequestUnauthorized): plaintext.PlainTextService.extraction_is_complete(source_id) @mock.patch('arxiv.integration.api.service.current_app', mock_app) @mock.patch('arxiv.integration.api.service.requests.Session') def test_complete_forbidden(self, mock_Session): """Service returns 403 Forbidden.""" mock_Session.return_value = self.session( status_code=status.FORBIDDEN, json={'reason': 'you do not have sufficient authz'} ) source_id = '132456' with self.assertRaises(exceptions.RequestForbidden): plaintext.PlainTextService.extraction_is_complete(source_id) @mock.patch('arxiv.integration.api.service.current_app', mock_app) @mock.patch('arxiv.integration.api.service.requests.Session') def test_retrieve_unauthorized(self, mock_Session): """Service returns 401 Unauthorized.""" mock_Session.return_value = self.session( status_code=status.UNAUTHORIZED, json={'reason': 'who are you'} ) source_id = '132456' with self.assertRaises(exceptions.RequestUnauthorized): plaintext.PlainTextService.retrieve_content(source_id) @mock.patch('arxiv.integration.api.service.current_app', mock_app) @mock.patch('arxiv.integration.api.service.requests.Session') def test_retrieve_forbidden(self, mock_Session): """Service returns 403 Forbidden.""" mock_Session.return_value = self.session( status_code=status.FORBIDDEN, json={'reason': 'you do not have sufficient authz'} ) source_id = '132456' with self.assertRaises(exceptions.RequestForbidden): plaintext.PlainTextService.retrieve_content(source_id) @mock.patch('arxiv.integration.api.service.current_app', mock_app) @mock.patch('arxiv.integration.api.service.requests.Session') def test_retrieve(self, mock_Session): """Retrieval is successful.""" content = b'thisisthecontent' mock_get = mock.MagicMock( return_value=mock.MagicMock( status_code=status.OK, content=content ) ) mock_Session.return_value = mock.MagicMock(get=mock_get) source_id = '132456' self.assertEqual( plaintext.PlainTextService.retrieve_content(source_id), content, "Returns binary content as received" ) self.assertEqual(mock_get.call_args[0][0], 'http://foohost:5432/submission/132456') @mock.patch('arxiv.integration.api.service.current_app', mock_app) @mock.patch('arxiv.integration.api.service.requests.Session') def test_retrieve_nonexistant(self, mock_Session): """There is no such plaintext resource.""" mock_Session.return_value = self.session( status_code=status.NOT_FOUND, json={'reason': 'no such thing'} ) source_id = '132456' with self.assertRaises(exceptions.NotFound): plaintext.PlainTextService.retrieve_content(source_id) @mock.patch('arxiv.integration.api.service.current_app', mock_app) @mock.patch('arxiv.integration.api.service.requests.Session') def test_retrieve_in_progress(self, mock_Session): """There is no such plaintext resource.""" mock_Session.return_value = self.session( status_code=status.SEE_OTHER, headers={'Location': '...'} ) source_id = '132456' with self.assertRaises(plaintext.ExtractionInProgress): plaintext.PlainTextService.retrieve_content(source_id)

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a9af80f351b858eae02cbda26821ff7ba45d92d0

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py

Python

data.py

xiyuanzh/ESC-GAN

1052e170a6414e20b4a7235b25c63df49361046e

[ "Apache-2.0" ]

9

2022-01-27T19:31:53.000Z

2022-03-30T20:36:48.000Z

data.py

xiyuanzh/ESC-GAN

1052e170a6414e20b4a7235b25c63df49361046e

[ "Apache-2.0" ]

null

data.py

xiyuanzh/ESC-GAN

1052e170a6414e20b4a7235b25c63df49361046e

[ "Apache-2.0" ]

null

from torch.utils.data import Dataset import xarray as xr import numpy as np import torch class Prep(Dataset): def __init__(self, seq_len): dset = xr.open_dataset("./data/CMAP.nc") temp_nc = dset.variables['precip'][:492] #up to year 2019 timespan = len(temp_nc) lat = len(temp_nc[0]) lon = len(temp_nc[0][0]) temp_arr = temp_nc.values mean, var = self.z_norm(temp_arr) temp_arr = (temp_arr - self.mean) / self.var self.valid_mask = (temp_arr == temp_arr) + 0 temp_arr[temp_arr != temp_arr] = 0 #set nan to zero self.temp = torch.from_numpy(temp_arr).unsqueeze(3) self.temp = torch.stack(list(torch.split(self.temp, seq_len))).float() self.valid_mask = torch.stack(list(torch.split(torch.from_numpy(self.valid_mask), seq_len))).unsqueeze(4).float() test_mask = np.load('./data/cmap_mask.npy').reshape((lat, lon, 1)) self.test_mask = torch.from_numpy(test_mask).repeat(len(self.temp), seq_len, 1, 1, 1).float() def __len__(self): return len(self.temp) def __getitem__(self, idx): return self.temp[idx], self.valid_mask[idx], self.test_mask[idx] def z_norm(self, x): self.mean = np.nanmean(x) self.var = np.nanstd(x) return self.mean, self.var def de_z_norm(self, x): return x * self.var + self.mean class Hadcrut(Dataset): def __init__(self, seq_len): dset = xr.open_dataset("./data/HadCRUT.nc") temp_nc = dset.variables['temperature_anomaly'][:2040] # up to year 2019 timespan = len(temp_nc) lat = len(temp_nc[0]) lon = len(temp_nc[0][0]) temp_arr = temp_nc.values mean, var = self.z_norm(temp_arr) temp_arr = (temp_arr - self.mean) / self.var self.valid_mask = (temp_arr == temp_arr) + 0 temp_arr[temp_arr != temp_arr] = 0 #set nan to zero self.temp = torch.from_numpy(temp_arr).unsqueeze(3) self.temp = torch.stack(list(torch.split(self.temp, seq_len))).float() self.valid_mask = torch.stack(list(torch.split(torch.from_numpy(self.valid_mask), seq_len))).unsqueeze(4).float() test_mask = np.load('./data/hadcrut_mask.npy').reshape((lat, lon, 1)) self.test_mask = torch.from_numpy(test_mask).repeat(len(self.temp), seq_len, 1, 1, 1).float() def __len__(self): return len(self.temp) def __getitem__(self, idx): return self.temp[idx], self.valid_mask[idx], self.test_mask[idx] def z_norm(self, x): self.mean = np.nanmean(x) self.var = np.nanstd(x) return self.mean, self.var def de_z_norm(self, x): return x * self.var + self.mean

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