"""Dataset data model tests.""" import json import os import platform import pytest from pyDataverse.models import Dataset from ..conftest import test_config def read_file(filename, mode="r"): """Read in a file. Parameters ---------- filename : str Filename with full path. mode : str Read mode of file. Defaults to `r`. See more at https://docs.python.org/3.5/library/functions.html#open Returns ------- str Returns data as string. """ with open(filename, mode) as f: return f.read() def write_json(filename, data, mode="w", encoding="utf-8"): """Write data to a json file. Parameters ---------- filename : str Filename with full path. data : dict Data to be written in the json file. mode : str Write mode of file. Defaults to `w`. See more at https://docs.python.org/3/library/functions.html#open """ with open(filename, mode, encoding=encoding) as f: json.dump(data, f, indent=2) def data_object(): """Get Dataset object. Returns ------- pydataverse.models.Dataset :class:`Dataset` object. """ return Dataset() def dict_flat_set_min(): """Get flat dict for set() of minimum Dataset. Returns ------- dict Flat dict with minimum Dataset data. """ return { "title": "Darwin's Finches", "author": [{"authorName": "Finch, Fiona", "authorAffiliation": "Birds Inc."}], "datasetContact": [ { "datasetContactEmail": "finch@mailinator.com", "datasetContactName": "Finch, Fiona", } ], "dsDescription": [ { "dsDescriptionValue": "Darwin's finches (also known as the Galápagos finches) are a group of about fifteen species of passerine birds." } ], "subject": ["Medicine, Health and Life Sciences"], "citation_displayName": "Citation Metadata", } def dict_flat_set_full(): """Get flat dict for set() of full Dataset. Returns ------- dict Flat dict with full Dataset data. """ return { "license": "CC0", "termsOfUse": "CC0 Waiver", "termsOfAccess": "Terms of Access", "fileAccessRequest": True, "protocol": "doi", "authority": "10.11587", "identifier": "6AQBYW", "citation_displayName": "Citation Metadata", "title": "Replication Data for: Title", "subtitle": "Subtitle", "alternativeTitle": "Alternative Title", "alternativeURL": "http://AlternativeURL.org", "otherId": [ {"otherIdAgency": "OtherIDAgency1", "otherIdValue": "OtherIDIdentifier1"} ], "author": [ { "authorName": "LastAuthor1, FirstAuthor1", "authorAffiliation": "AuthorAffiliation1", "authorIdentifierScheme": "ORCID", "authorIdentifier": "AuthorIdentifier1", } ], "datasetContact": [ { "datasetContactName": "LastContact1, FirstContact1", "datasetContactAffiliation": "ContactAffiliation1", "datasetContactEmail": "ContactEmail1@mailinator.com", } ], "dsDescription": [ { "dsDescriptionValue": "DescriptionText2", "dsDescriptionDate": "1000-02-02", } ], "subject": [ "Agricultural Sciences", "Business and Management", "Engineering", "Law", ], "keyword": [ { "keywordValue": "KeywordTerm1", "keywordVocabulary": "KeywordVocabulary1", "keywordVocabularyURI": "http://KeywordVocabularyURL1.org", } ], "topicClassification": [ { "topicClassValue": "Topic Class Value1", "topicClassVocab": "Topic Classification Vocabulary", "topicClassVocabURI": "https://topic.class/vocab/uri", } ], "publication": [ { "publicationCitation": "RelatedPublicationCitation1", "publicationIDType": "ark", "publicationIDNumber": "RelatedPublicationIDNumber1", "publicationURL": "http://RelatedPublicationURL1.org", } ], "notesText": "Notes1", "producer": [ { "producerName": "LastProducer1, FirstProducer1", "producerAffiliation": "ProducerAffiliation1", "producerAbbreviation": "ProducerAbbreviation1", "producerURL": "http://ProducerURL1.org", "producerLogoURL": "http://ProducerLogoURL1.org", } ], "productionDate": "1003-01-01", "productionPlace": "ProductionPlace", "contributor": [ { "contributorType": "Data Collector", "contributorName": "LastContributor1, FirstContributor1", } ], "grantNumber": [ { "grantNumberAgency": "GrantInformationGrantAgency1", "grantNumberValue": "GrantInformationGrantNumber1", } ], "distributor": [ { "distributorName": "LastDistributor1, FirstDistributor1", "distributorAffiliation": "DistributorAffiliation1", "distributorAbbreviation": "DistributorAbbreviation1", "distributorURL": "http://DistributorURL1.org", "distributorLogoURL": "http://DistributorLogoURL1.org", } ], "distributionDate": "1004-01-01", "depositor": "LastDepositor, FirstDepositor", "dateOfDeposit": "1002-01-01", "timePeriodCovered": [ { "timePeriodCoveredStart": "1005-01-01", "timePeriodCoveredEnd": "1005-01-02", } ], "dateOfCollection": [ {"dateOfCollectionStart": "1006-01-01", "dateOfCollectionEnd": "1006-01-01"} ], "kindOfData": ["KindOfData1", "KindOfData2"], "language": ["German"], "series": { "seriesName": "SeriesName", "seriesInformation": "SeriesInformation", }, "software": [ {"softwareName": "SoftwareName1", "softwareVersion": "SoftwareVersion1"} ], "relatedMaterial": ["RelatedMaterial1", "RelatedMaterial2"], "relatedDatasets": ["RelatedDatasets1", "RelatedDatasets2"], "otherReferences": ["OtherReferences1", "OtherReferences2"], "dataSources": ["DataSources1", "DataSources2"], "originOfSources": "OriginOfSources", "characteristicOfSources": "CharacteristicOfSourcesNoted", "accessToSources": "DocumentationAndAccessToSources", "geospatial_displayName": "Geospatial Metadata", "geographicCoverage": [ { "country": "Afghanistan", "state": "GeographicCoverageStateProvince1", "city": "GeographicCoverageCity1", "otherGeographicCoverage": "GeographicCoverageOther1", } ], "geographicUnit": ["GeographicUnit1", "GeographicUnit2"], "geographicBoundingBox": [ { "westLongitude": "10", "eastLongitude": "20", "northLongitude": "30", "southLongitude": "40", } ], "socialscience_displayName": "Social Science and Humanities Metadata", "unitOfAnalysis": ["UnitOfAnalysis1", "UnitOfAnalysis2"], "universe": ["Universe1", "Universe2"], "timeMethod": "TimeMethod", "dataCollector": "LastDataCollector1, FirstDataCollector1", "collectorTraining": "CollectorTraining", "frequencyOfDataCollection": "Frequency", "samplingProcedure": "SamplingProcedure", "targetSampleSize": { "targetSampleActualSize": "100", "targetSampleSizeFormula": "TargetSampleSizeFormula", }, "deviationsFromSampleDesign": "MajorDeviationsForSampleDesign", "collectionMode": "CollectionMode", "researchInstrument": "TypeOfResearchInstrument", "dataCollectionSituation": "CharacteristicsOfDataCollectionSituation", "actionsToMinimizeLoss": "ActionsToMinimizeLosses", "controlOperations": "ControlOperations", "weighting": "Weighting", "cleaningOperations": "CleaningOperations", "datasetLevelErrorNotes": "StudyLevelErrorNotes", "responseRate": "ResponseRate", "samplingErrorEstimates": "EstimatesOfSamplingError", "otherDataAppraisal": "OtherFormsOfDataAppraisal", "socialScienceNotes": { "socialScienceNotesType": "NotesType", "socialScienceNotesSubject": "NotesSubject", "socialScienceNotesText": "NotesText", }, "journal_displayName": "Journal Metadata", "journalVolumeIssue": [ { "journalVolume": "JournalVolume1", "journalIssue": "JournalIssue1", "journalPubDate": "1008-01-01", } ], "journalArticleType": "abstract", } def object_data_init(): """Get dictionary for Dataset with initial attributes. Returns ------- dict Dictionary of init data attributes set. """ return { "_Dataset_default_json_format": "dataverse_upload", "_Dataset_default_json_schema_filename": test_config[ "dataset_upload_schema_filename" ], "_Dataset_allowed_json_formats": [ "dataverse_upload", "dataverse_download", "dspace", "custom", ], "_Dataset_json_dataverse_upload_attr": json_dataverse_upload_attr(), "_internal_attributes": [], } def object_data_min(): """Get dictionary for Dataset with minimum attributes. Returns ------- pyDataverse.Dataset :class:`Dataset` with minimum attributes set. """ return { "title": "Darwin's Finches", "author": [{"authorName": "Finch, Fiona", "authorAffiliation": "Birds Inc."}], "datasetContact": [ { "datasetContactEmail": "finch@mailinator.com", "datasetContactName": "Finch, Fiona", } ], "dsDescription": [ { "dsDescriptionValue": "Darwin's finches (also known as the Galápagos finches) are a group of about fifteen species of passerine birds." } ], "subject": ["Medicine, Health and Life Sciences"], "citation_displayName": "Citation Metadata", } def object_data_full(): """Get dictionary for Dataset with full attributes. Returns ------- pyDataverse.Dataset :class:`Dataset` with full attributes set. """ return { "license": "CC0", "termsOfUse": "CC0 Waiver", "termsOfAccess": "Terms of Access", "fileAccessRequest": True, "protocol": "doi", "authority": "10.11587", "identifier": "6AQBYW", "citation_displayName": "Citation Metadata", "title": "Replication Data for: Title", "subtitle": "Subtitle", "alternativeTitle": "Alternative Title", "alternativeURL": "http://AlternativeURL.org", "otherId": [ {"otherIdAgency": "OtherIDAgency1", "otherIdValue": "OtherIDIdentifier1"} ], "author": [ { "authorName": "LastAuthor1, FirstAuthor1", "authorAffiliation": "AuthorAffiliation1", "authorIdentifierScheme": "ORCID", "authorIdentifier": "AuthorIdentifier1", } ], "datasetContact": [ { "datasetContactName": "LastContact1, FirstContact1", "datasetContactAffiliation": "ContactAffiliation1", "datasetContactEmail": "ContactEmail1@mailinator.com", } ], "dsDescription": [ { "dsDescriptionValue": "DescriptionText2", "dsDescriptionDate": "1000-02-02", } ], "subject": [ "Agricultural Sciences", "Business and Management", "Engineering", "Law", ], "keyword": [ { "keywordValue": "KeywordTerm1", "keywordVocabulary": "KeywordVocabulary1", "keywordVocabularyURI": "http://KeywordVocabularyURL1.org", } ], "topicClassification": [ { "topicClassValue": "Topic Class Value1", "topicClassVocab": "Topic Classification Vocabulary", "topicClassVocabURI": "https://topic.class/vocab/uri", } ], "publication": [ { "publicationCitation": "RelatedPublicationCitation1", "publicationIDType": "ark", "publicationIDNumber": "RelatedPublicationIDNumber1", "publicationURL": "http://RelatedPublicationURL1.org", } ], "notesText": "Notes1", "producer": [ { "producerName": "LastProducer1, FirstProducer1", "producerAffiliation": "ProducerAffiliation1", "producerAbbreviation": "ProducerAbbreviation1", "producerURL": "http://ProducerURL1.org", "producerLogoURL": "http://ProducerLogoURL1.org", } ], "productionDate": "1003-01-01", "productionPlace": "ProductionPlace", "contributor": [ { "contributorType": "Data Collector", "contributorName": "LastContributor1, FirstContributor1", } ], "grantNumber": [ { "grantNumberAgency": "GrantInformationGrantAgency1", "grantNumberValue": "GrantInformationGrantNumber1", } ], "distributor": [ { "distributorName": "LastDistributor1, FirstDistributor1", "distributorAffiliation": "DistributorAffiliation1", "distributorAbbreviation": "DistributorAbbreviation1", "distributorURL": "http://DistributorURL1.org", "distributorLogoURL": "http://DistributorLogoURL1.org", } ], "distributionDate": "1004-01-01", "depositor": "LastDepositor, FirstDepositor", "dateOfDeposit": "1002-01-01", "timePeriodCovered": [ { "timePeriodCoveredStart": "1005-01-01", "timePeriodCoveredEnd": "1005-01-02", } ], "dateOfCollection": [ {"dateOfCollectionStart": "1006-01-01", "dateOfCollectionEnd": "1006-01-01"} ], "kindOfData": ["KindOfData1", "KindOfData2"], "language": ["German"], "series": { "seriesName": "SeriesName", "seriesInformation": "SeriesInformation", }, "software": [ {"softwareName": "SoftwareName1", "softwareVersion": "SoftwareVersion1"} ], "relatedMaterial": ["RelatedMaterial1", "RelatedMaterial2"], "relatedDatasets": ["RelatedDatasets1", "RelatedDatasets2"], "otherReferences": ["OtherReferences1", "OtherReferences2"], "dataSources": ["DataSources1", "DataSources2"], "originOfSources": "OriginOfSources", "characteristicOfSources": "CharacteristicOfSourcesNoted", "accessToSources": "DocumentationAndAccessToSources", "geospatial_displayName": "Geospatial Metadata", "geographicCoverage": [ { "country": "Afghanistan", "state": "GeographicCoverageStateProvince1", "city": "GeographicCoverageCity1", "otherGeographicCoverage": "GeographicCoverageOther1", } ], "geographicUnit": ["GeographicUnit1", "GeographicUnit2"], "geographicBoundingBox": [ { "westLongitude": "10", "eastLongitude": "20", "northLongitude": "30", "southLongitude": "40", } ], "socialscience_displayName": "Social Science and Humanities Metadata", "unitOfAnalysis": ["UnitOfAnalysis1", "UnitOfAnalysis2"], "universe": ["Universe1", "Universe2"], "timeMethod": "TimeMethod", "dataCollector": "LastDataCollector1, FirstDataCollector1", "collectorTraining": "CollectorTraining", "frequencyOfDataCollection": "Frequency", "samplingProcedure": "SamplingProcedure", "targetSampleSize": { "targetSampleActualSize": "100", "targetSampleSizeFormula": "TargetSampleSizeFormula", }, "deviationsFromSampleDesign": "MajorDeviationsForSampleDesign", "collectionMode": "CollectionMode", "researchInstrument": "TypeOfResearchInstrument", "dataCollectionSituation": "CharacteristicsOfDataCollectionSituation", "actionsToMinimizeLoss": "ActionsToMinimizeLosses", "controlOperations": "ControlOperations", "weighting": "Weighting", "cleaningOperations": "CleaningOperations", "datasetLevelErrorNotes": "StudyLevelErrorNotes", "responseRate": "ResponseRate", "samplingErrorEstimates": "EstimatesOfSamplingError", "otherDataAppraisal": "OtherFormsOfDataAppraisal", "socialScienceNotes": { "socialScienceNotesType": "NotesType", "socialScienceNotesSubject": "NotesSubject", "socialScienceNotesText": "NotesText", }, "journal_displayName": "Journal Metadata", "journalVolumeIssue": [ { "journalVolume": "JournalVolume1", "journalIssue": "JournalIssue1", "journalPubDate": "1008-01-01", } ], "journalArticleType": "abstract", } def dict_flat_get_min(): """Get flat dict for :func:`get` with minimum data of Dataset. Returns ------- dict Minimum Dataset dictionary returned by :func:`get`. """ return { "title": "Darwin's Finches", "author": [{"authorName": "Finch, Fiona", "authorAffiliation": "Birds Inc."}], "datasetContact": [ { "datasetContactEmail": "finch@mailinator.com", "datasetContactName": "Finch, Fiona", } ], "dsDescription": [ { "dsDescriptionValue": "Darwin's finches (also known as the Galápagos finches) are a group of about fifteen species of passerine birds." }, ], "subject": ["Medicine, Health and Life Sciences"], "citation_displayName": "Citation Metadata", } def dict_flat_get_full(): """Get flat dict for :func:`get` of full data of Dataset. Returns ------- dict Full Datafile dictionary returned by :func:`get`. """ return { "license": "CC0", "termsOfUse": "CC0 Waiver", "termsOfAccess": "Terms of Access", "fileAccessRequest": True, "protocol": "doi", "authority": "10.11587", "identifier": "6AQBYW", "title": "Replication Data for: Title", "subtitle": "Subtitle", "alternativeTitle": "Alternative Title", "alternativeURL": "http://AlternativeURL.org", "otherId": [ {"otherIdAgency": "OtherIDAgency1", "otherIdValue": "OtherIDIdentifier1"} ], "author": [ { "authorName": "LastAuthor1, FirstAuthor1", "authorAffiliation": "AuthorAffiliation1", "authorIdentifierScheme": "ORCID", "authorIdentifier": "AuthorIdentifier1", } ], "datasetContact": [ { "datasetContactName": "LastContact1, FirstContact1", "datasetContactAffiliation": "ContactAffiliation1", "datasetContactEmail": "ContactEmail1@mailinator.com", } ], "dsDescription": [ { "dsDescriptionValue": "DescriptionText2", "dsDescriptionDate": "1000-02-02", } ], "subject": [ "Agricultural Sciences", "Business and Management", "Engineering", "Law", ], "keyword": [ { "keywordValue": "KeywordTerm1", "keywordVocabulary": "KeywordVocabulary1", "keywordVocabularyURI": "http://KeywordVocabularyURL1.org", } ], "topicClassification": [ { "topicClassValue": "Topic Class Value1", "topicClassVocab": "Topic Classification Vocabulary", "topicClassVocabURI": "https://topic.class/vocab/uri", } ], "publication": [ { "publicationCitation": "RelatedPublicationCitation1", "publicationIDType": "ark", "publicationIDNumber": "RelatedPublicationIDNumber1", "publicationURL": "http://RelatedPublicationURL1.org", } ], "notesText": "Notes1", "producer": [ { "producerName": "LastProducer1, FirstProducer1", "producerAffiliation": "ProducerAffiliation1", "producerAbbreviation": "ProducerAbbreviation1", "producerURL": "http://ProducerURL1.org", "producerLogoURL": "http://ProducerLogoURL1.org", } ], "productionDate": "1003-01-01", "productionPlace": "ProductionPlace", "contributor": [ { "contributorType": "Data Collector", "contributorName": "LastContributor1, FirstContributor1", } ], "grantNumber": [ { "grantNumberAgency": "GrantInformationGrantAgency1", "grantNumberValue": "GrantInformationGrantNumber1", } ], "distributor": [ { "distributorName": "LastDistributor1, FirstDistributor1", "distributorAffiliation": "DistributorAffiliation1", "distributorAbbreviation": "DistributorAbbreviation1", "distributorURL": "http://DistributorURL1.org", "distributorLogoURL": "http://DistributorLogoURL1.org", } ], "distributionDate": "1004-01-01", "depositor": "LastDepositor, FirstDepositor", "dateOfDeposit": "1002-01-01", "timePeriodCovered": [ { "timePeriodCoveredStart": "1005-01-01", "timePeriodCoveredEnd": "1005-01-02", } ], "dateOfCollection": [ {"dateOfCollectionStart": "1006-01-01", "dateOfCollectionEnd": "1006-01-01"} ], "kindOfData": ["KindOfData1", "KindOfData2"], "language": ["German"], "series": { "seriesName": "SeriesName", "seriesInformation": "SeriesInformation", }, "software": [ {"softwareName": "SoftwareName1", "softwareVersion": "SoftwareVersion1"} ], "relatedMaterial": ["RelatedMaterial1", "RelatedMaterial2"], "relatedDatasets": ["RelatedDatasets1", "RelatedDatasets2"], "otherReferences": ["OtherReferences1", "OtherReferences2"], "dataSources": ["DataSources1", "DataSources2"], "originOfSources": "OriginOfSources", "characteristicOfSources": "CharacteristicOfSourcesNoted", "accessToSources": "DocumentationAndAccessToSources", "geospatial_displayName": "Geospatial Metadata", "geographicCoverage": [ { "country": "Afghanistan", "state": "GeographicCoverageStateProvince1", "city": "GeographicCoverageCity1", "otherGeographicCoverage": "GeographicCoverageOther1", } ], "geographicUnit": ["GeographicUnit1", "GeographicUnit2"], "geographicBoundingBox": [ { "westLongitude": "10", "eastLongitude": "20", "northLongitude": "30", "southLongitude": "40", } ], "socialscience_displayName": "Social Science and Humanities Metadata", "unitOfAnalysis": ["UnitOfAnalysis1", "UnitOfAnalysis2"], "universe": ["Universe1", "Universe2"], "timeMethod": "TimeMethod", "dataCollector": "LastDataCollector1, FirstDataCollector1", "collectorTraining": "CollectorTraining", "frequencyOfDataCollection": "Frequency", "samplingProcedure": "SamplingProcedure", "targetSampleSize": { "targetSampleActualSize": "100", "targetSampleSizeFormula": "TargetSampleSizeFormula", }, "deviationsFromSampleDesign": "MajorDeviationsForSampleDesign", "collectionMode": "CollectionMode", "researchInstrument": "TypeOfResearchInstrument", "dataCollectionSituation": "CharacteristicsOfDataCollectionSituation", "actionsToMinimizeLoss": "ActionsToMinimizeLosses", "controlOperations": "ControlOperations", "weighting": "Weighting", "cleaningOperations": "CleaningOperations", "datasetLevelErrorNotes": "StudyLevelErrorNotes", "responseRate": "ResponseRate", "samplingErrorEstimates": "EstimatesOfSamplingError", "otherDataAppraisal": "OtherFormsOfDataAppraisal", "socialScienceNotes": { "socialScienceNotesType": "NotesType", "socialScienceNotesSubject": "NotesSubject", "socialScienceNotesText": "NotesText", }, "journal_displayName": "Journal Metadata", "journalVolumeIssue": [ { "journalVolume": "JournalVolume1", "journalIssue": "JournalIssue1", "journalPubDate": "1008-01-01", } ], "journalArticleType": "abstract", "citation_displayName": "Citation Metadata", } def json_upload_min(): """Get JSON string of minimum Dataset. Returns ------- str JSON string. """ return read_file(test_config["dataset_upload_min_filename"]) def json_upload_full(): """Get JSON string of full Dataset. Returns ------- str JSON string. """ return read_file(test_config["dataset_upload_full_filename"]) def json_dataverse_upload_attr(): """List of attributes import or export in format `dataverse_upload`. Returns ------- list List of attributes, which will be used for import and export. """ return [ "license", "termsOfUse", "termsOfAccess", "fileAccessRequest", "protocol", "authority", "identifier", "citation_displayName", "title", "subtitle", "alternativeTitle", "alternativeURL", "otherId", "author", "datasetContact", "dsDescription", "subject", "keyword", "topicClassification", "publication", "notesText", "producer", "productionDate", "productionPlace", "contributor", "grantNumber", "distributor", "distributionDate", "depositor", "dateOfDeposit", "timePeriodCovered", "dateOfCollection", "kindOfData", "language", "series", "software", "relatedMaterial", "relatedDatasets", "otherReferences", "dataSources", "originOfSources", "characteristicOfSources", "accessToSources", "geospatial_displayName", "geographicCoverage", "geographicUnit", "geographicBoundingBox", "socialscience_displayName", "unitOfAnalysis", "universe", "timeMethod", "dataCollector", "collectorTraining", "frequencyOfDataCollection", "samplingProcedure", "targetSampleSize", "deviationsFromSampleDesign", "collectionMode", "researchInstrument", "dataCollectionSituation", "actionsToMinimizeLoss", "controlOperations", "weighting", "cleaningOperations", "datasetLevelErrorNotes", "responseRate", "samplingErrorEstimates", "otherDataAppraisal", "socialScienceNotes", "journal_displayName", "journalVolumeIssue", "journalArticleType", ] def json_dataverse_upload_required_attr(): """List of attributes required for `dataverse_upload` JSON. Returns ------- list List of attributes, which will be used for import and export. """ return ["title", "author", "datasetContact", "dsDescription", "subject"] class TestDatasetGeneric(object): """Generic tests for Dataset().""" def test_dataset_set_and_get_valid(self): """Test Dataset.get() with valid data.""" data = [ ((dict_flat_set_min(), object_data_min()), dict_flat_get_min()), ((dict_flat_set_full(), object_data_full()), dict_flat_get_full()), (({}, {}), {}), ] pdv = data_object() pdv.set(dict_flat_set_min()) assert isinstance(pdv.get(), dict) for input, data_eval in data: pdv = data_object() pdv.set(input[0]) data = pdv.get() for key, val in data_eval.items(): assert data[key] == input[1][key] == data_eval[key] assert len(data) == len(input[1]) == len(data_eval) def test_dataset_set_invalid(self): """Test Dataset.set() with invalid data.""" # invalid data for data in test_config["invalid_set_types"]: with pytest.raises(AssertionError): pdv = data_object() pdv.set(data) def test_dataset_validate_json_valid(self): """Test Dataset.validate_json() with valid data.""" data = [ ((dict_flat_set_min(), {}), True), ((dict_flat_set_full(), {}), True), ((dict_flat_set_min(), {"data_format": "dataverse_upload"}), True), ( ( dict_flat_set_min(), { "data_format": "dataverse_upload", "filename_schema": test_config[ "dataset_upload_schema_filename" ], }, ), True, ), ( ( dict_flat_set_min(), {"filename_schema": test_config["dataset_upload_schema_filename"]}, ), True, ), ] for input, data_eval in data: pdv = data_object() pdv.set(input[0]) assert pdv.validate_json() == data_eval class TestDatasetSpecific(object): """Specific tests for Dataset().""" def test_dataset_from_json_valid(self): """Test Dataset.from_json() with valid data.""" data = [ (({json_upload_min()}, {}), object_data_min()), (({json_upload_full()}, {}), object_data_full()), ( ({json_upload_min()}, {"data_format": "dataverse_upload"}), object_data_min(), ), (({json_upload_min()}, {"validate": False}), object_data_min()), ( ( {json_upload_min()}, {"filename_schema": "", "validate": False}, ), object_data_min(), ), ( ( {json_upload_min()}, {"filename_schema": "wrong", "validate": False}, ), object_data_min(), ), ( ( {json_upload_min()}, { "filename_schema": test_config[ "dataset_upload_schema_filename" ], "validate": True, }, ), object_data_min(), ), ] for input, data_eval in data: pdv = data_object() args = input[0] kwargs = input[1] pdv.from_json(*args, **kwargs) for key, val in data_eval.items(): assert getattr(pdv, key) == data_eval[key] assert len(pdv.__dict__) - len(object_data_init()) == len(data_eval) def test_dataset_to_json_valid(self): """Test Dataset.json() with valid data.""" data = [ ((dict_flat_set_min(), {}), json.loads(json_upload_min())), ((dict_flat_set_full(), {}), json.loads(json_upload_full())), ( (dict_flat_set_min(), {"data_format": "dataverse_upload"}), json.loads(json_upload_min()), ), ( (dict_flat_set_min(), {"validate": False}), json.loads(json_upload_min()), ), ( ( dict_flat_set_min(), {"filename_schema": "", "validate": False}, ), json.loads(json_upload_min()), ), ( ( dict_flat_set_min(), {"filename_schema": "wrong", "validate": False}, ), json.loads(json_upload_min()), ), ( ( dict_flat_set_min(), { "filename_schema": test_config[ "dataset_upload_schema_filename" ], "validate": True, }, ), json.loads(json_upload_min()), ), ] pdv = data_object() pdv.set(dict_flat_set_min()) assert isinstance(pdv.json(), str) # TODO: recursevily test values of lists and dicts for input, data_eval in data: pdv = data_object() pdv.set(input[0]) kwargs = input[1] data = json.loads(pdv.json(**kwargs)) assert data assert isinstance(data, dict) assert len(data) == len(data_eval) assert len(data["datasetVersion"]["metadataBlocks"]["citation"]) == len( data_eval["datasetVersion"]["metadataBlocks"]["citation"] ) assert len( data["datasetVersion"]["metadataBlocks"]["citation"]["fields"] ) == len( data_eval["datasetVersion"]["metadataBlocks"]["citation"]["fields"] ) def test_dataset_init_valid(self): """Test Dataset.__init__() with valid data.""" # specific data = [ (Dataset(), {}), (Dataset(dict_flat_set_min()), object_data_min()), (Dataset(dict_flat_set_full()), object_data_full()), (Dataset({}), {}), ] for pdv, data_eval in data: for key, val in data_eval.items(): assert getattr(pdv, key) == data_eval[key] assert len(pdv.__dict__) - len(object_data_init()) == len(data_eval) def test_dataset_init_invalid(self): """Test Dataset.init() with invalid data.""" pdv = Dataset() # invalid data for data in test_config["invalid_set_types"]: with pytest.raises(AssertionError): pdv.set(data) def test_dataset_from_json_invalid(self): """Test Dataset.from_json() with invalid data.""" # invalid data for data in test_config["invalid_json_data_types"]: with pytest.raises(AssertionError): pdv = data_object() pdv.from_json(data, validate=False) if int(platform.python_version_tuple()[1]) >= 5: for json_string in test_config["invalid_json_strings"]: with pytest.raises(json.decoder.JSONDecodeError): pdv = data_object() pdv.from_json(json_string, validate=False) else: for json_string in test_config["invalid_json_strings"]: with pytest.raises(ValueError): pdv = data_object() pdv.from_json(json_string, validate=False) # invalid `filename_schema` for filename_schema in test_config["invalid_filename_strings"]: with pytest.raises(FileNotFoundError): pdv = data_object() pdv.from_json(json_upload_min(), filename_schema=filename_schema) for filename_schema in test_config["invalid_filename_types"]: with pytest.raises(AssertionError): pdv = data_object() pdv.from_json(json_upload_min(), filename_schema=filename_schema) # invalid `data_format` for data_format in ( test_config["invalid_data_format_types"] + test_config["invalid_data_format_strings"] ): with pytest.raises(AssertionError): pdv = data_object() pdv.from_json( json_upload_min(), data_format=data_format, validate=False ) # invalid `validate` for validate in test_config["invalid_validate_types"]: with pytest.raises(AssertionError): pdv = data_object() pdv.from_json(json_upload_min(), validate=validate) def test_dataset_to_json_invalid(self): """Test Dataset.json() with non-valid data.""" # invalid `filename_schema` for filename_schema in test_config["invalid_filename_strings"]: with pytest.raises(FileNotFoundError): obj = data_object() obj.json(filename_schema=filename_schema) for filename_schema in test_config["invalid_filename_types"]: with pytest.raises(AssertionError): pdv = data_object() pdv.json(filename_schema=filename_schema) # invalid `data_format` for data_format in ( test_config["invalid_data_format_types"] + test_config["invalid_data_format_strings"] ): with pytest.raises(AssertionError): pdv = data_object() pdv.set(dict_flat_set_min()) pdv.json(data_format=data_format, validate=False) # invalid `validate` for validate in test_config["invalid_validate_types"]: with pytest.raises(AssertionError): pdv = data_object() pdv.set(dict_flat_set_min()) pdv.json(validate=validate) def test_dataset_validate_json_invalid(self): """Test Dataset.validate_json() with non-valid data.""" # invalid `filename_schema` for filename_schema in test_config["invalid_filename_strings"]: with pytest.raises(FileNotFoundError): pdv = data_object() pdv.set(dict_flat_set_min()) pdv.validate_json(filename_schema=filename_schema) for filename_schema in test_config["invalid_filename_types"]: with pytest.raises(AssertionError): pdv = data_object() pdv.set(dict_flat_set_min()) pdv.validate_json(filename_schema=filename_schema) if not os.environ.get("TRAVIS"): class TestDatasetSpecificTravisNot(object): """Generic tests for Dataset(), not running on Travis (no file-write permissions).""" def test_dataset_to_json_from_json_valid(self): """Test Dataset to JSON from JSON with valid data.""" data = [ (dict_flat_set_min(), {}), (dict_flat_set_full(), {}), (dict_flat_set_min(), {"data_format": "dataverse_upload"}), (dict_flat_set_min(), {"validate": False}), ( dict_flat_set_min(), {"filename_schema": "wrong", "validate": False}, ), ( dict_flat_set_min(), { "filename_schema": test_config[ "dataset_upload_schema_filename" ], "validate": True, }, ), ] for data_set, kwargs_from in data: kwargs = {} pdv_start = data_object() pdv_start.set(data_set) if "validate" in kwargs_from: if not kwargs_from["validate"]: kwargs = {"validate": False} write_json( test_config["dataset_json_output_filename"], json.loads(pdv_start.json(**kwargs)), ) pdv_end = data_object() kwargs = kwargs_from pdv_end.from_json( read_file(test_config["dataset_json_output_filename"]), **kwargs ) for key, val in pdv_end.get().items(): assert getattr(pdv_start, key) == getattr(pdv_end, key) assert len(pdv_start.__dict__) == len( pdv_end.__dict__, )