Beispiel #1
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 def test_metadata_clean_function(self):
     test1 = "abc\tdef2"
     test2 = """
         abc\tdef!
         """
     self.assertEqual(MetadataValidator.clean_value(test1), "abc def2")
     self.assertEqual(MetadataValidator.clean_value(test2), "abc def")
Beispiel #2
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 def validate_metadata(self, data):
     validator = MetadataValidator()
     try:
         validator.validate(data)
     except MetadataValidationException as e:
         raise serializers.ValidationError(e)
     return data
Beispiel #3
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 def validate_metadata(self, data):
     validator = MetadataValidator()
     try:
         validator.validate(data)
     except MetadataValidationException as e:
         print("ERROR in validation")
         return data
     return data
Beispiel #4
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def generate_sample_data_content(files, pipeline_name, pipeline_github,
                                 pipeline_version):
    result = "SAMPLE_ID\tREQUEST_ID\tPROJECT_ID\tPATIENT_ID\tCOLLAB_ID\tSAMPLE_TYPE\tGENE_PANEL\tONCOTREE_CODE\tSAMPLE_CLASS\tSPECIMEN_PRESERVATION_TYPE\tSEX\tTISSUE_SITE\tIGO_ID\tRUN_MODE\tPIPELINE\tPIPELINE_GITHUB_LINK\tPIPELINE_VERSION\n"
    ret_str = "metadata__sampleId"
    query = Q(file__file_group_id=settings.IMPORT_FILE_GROUP)
    query |= Q(file__file_group__slug="origin-unknown")
    query |= Q(file__file_group__slug="fero-legacy-data")
    query = query & Q(file__path__in=files)
    samples = FileRepository.filter(
        q=query).order_by(ret_str).distinct(ret_str).all()
    for sample in samples:
        metadata = sample.metadata
        result += "{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\n".format(
            metadata.get(
                "cmoSampleName",
                format_sample_name(metadata["sampleName"],
                                   metadata["specimenType"])),
            metadata["requestId"],
            get_project_id(metadata["requestId"]),
            metadata["patientId"],
            metadata["investigatorSampleId"],
            MetadataValidator.clean_value(metadata["sampleClass"]),
            MetadataValidator.clean_value(metadata["recipe"]),
            MetadataValidator.clean_value(metadata["oncoTreeCode"]),
            MetadataValidator.clean_value(metadata["specimenType"]),
            MetadataValidator.clean_value(metadata["preservation"]),
            MetadataValidator.clean_value(metadata["sex"]),
            MetadataValidator.clean_value(metadata["tissueLocation"]),
            metadata["sampleId"],
            MetadataValidator.clean_value(metadata["runMode"]),
            pipeline_name,
            pipeline_github,
            pipeline_version,
        )
    return result