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Update Tools API openAPI contract and regenerate VCF results pydantic model #50
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,164 @@ | ||
| import unittest | ||
| from pydantic import ValidationError | ||
|
|
||
| from vep.models.vcf_results_model import ( | ||
| PaginationMetadata, | ||
| PredictedIntergenicConsequence, | ||
| PredictedTranscriptConsequence, | ||
| FeatureType, | ||
| Strand, | ||
| ReferenceVariantAllele, | ||
| Location, | ||
| AlternativeVariantAllele, | ||
| Variant, | ||
| Metadata, | ||
| VepResultsResponse, | ||
| ) | ||
|
|
||
|
|
||
| class TestVCFResultModel(unittest.TestCase): | ||
|
|
||
| def test_predicted_intergenic_consequence(self): | ||
| consequence = PredictedIntergenicConsequence( | ||
| feature_type=None, | ||
| consequences=["intergenic_variant"] | ||
| ) | ||
| self.assertIsNone(consequence.feature_type) | ||
| self.assertEqual(consequence.consequences, ["intergenic_variant"]) | ||
|
|
||
| # Missing consequences should raise a ValidationError | ||
| with self.assertRaises(ValidationError): | ||
| PredictedIntergenicConsequence(feature_type=None) | ||
|
|
||
| def test_predicted_transcript_consequence(self): | ||
| consequence = PredictedTranscriptConsequence( | ||
| feature_type=FeatureType.transcript, | ||
| stable_id="ENST00000367770.8", | ||
| gene_stable_id="ENSG00000157764.13", | ||
| gene_symbol=None, | ||
| biotype="protein_coding", | ||
| is_canonical=True, | ||
| consequences=["missense_variant"], | ||
| strand=Strand.forward, | ||
| ) | ||
| self.assertIsNone(consequence.gene_symbol) | ||
|
|
||
| # Valid instance without explicitly setting gene_symbol (default=None) | ||
| consequence_no_symbol = PredictedTranscriptConsequence( | ||
| feature_type=FeatureType.transcript, | ||
| stable_id="ENST00000367770.8", | ||
| gene_stable_id="ENSG00000157764.13", | ||
| biotype="protein_coding", | ||
| is_canonical=True, | ||
| consequences=["missense_variant"], | ||
| strand=Strand.forward, | ||
| ) | ||
| self.assertIsNone(consequence_no_symbol.gene_symbol) | ||
|
|
||
| def test_alternative_variant_allele(self): | ||
| alternative_allele = AlternativeVariantAllele( | ||
| allele_sequence="A", | ||
| allele_type="insertion", | ||
| representative_population_allele_frequency=None, | ||
| predicted_molecular_consequences=[ | ||
| PredictedIntergenicConsequence( | ||
| feature_type=None, | ||
| consequences=["intergenic_variant"], | ||
| ) | ||
| ], | ||
| ) | ||
| self.assertIsNone(alternative_allele.representative_population_allele_frequency) | ||
|
|
||
| # Valid instance without explicitly setting representative_population_allele_frequency (default=None) | ||
| alternative_allele_no_freq = AlternativeVariantAllele( | ||
| allele_sequence="A", | ||
| allele_type="insertion", | ||
| predicted_molecular_consequences=[ | ||
| PredictedIntergenicConsequence( | ||
| feature_type=None, | ||
| consequences=["intergenic_variant"], | ||
| ) | ||
| ], | ||
| ) | ||
| self.assertIsNone(alternative_allele_no_freq.representative_population_allele_frequency) | ||
|
|
||
| def test_variant(self): | ||
| variant = Variant( | ||
| name=None, | ||
| allele_type="SNP", | ||
| location=Location(region_name="1", start=10000, end=10001), | ||
| reference_allele=ReferenceVariantAllele(allele_sequence="C"), | ||
| alternative_alleles=[ | ||
| AlternativeVariantAllele( | ||
| allele_sequence="T", | ||
| allele_type="SNP", | ||
| predicted_molecular_consequences=[ | ||
| PredictedIntergenicConsequence( | ||
| feature_type=None, | ||
| consequences=["intergenic_variant"] | ||
| ) | ||
| ] | ||
| ) | ||
| ] | ||
| ) | ||
| self.assertIsNone(variant.name) | ||
|
|
||
| # Valid instance without explicitly setting name (default=None) | ||
| variant_no_name = Variant( | ||
| allele_type="SNP", | ||
| location=Location(region_name="1", start=10000, end=10001), | ||
| reference_allele=ReferenceVariantAllele(allele_sequence="C"), | ||
| alternative_alleles=[ | ||
| AlternativeVariantAllele( | ||
| allele_sequence="T", | ||
| allele_type="SNP", | ||
| predicted_molecular_consequences=[ | ||
| PredictedIntergenicConsequence( | ||
| feature_type=None, | ||
| consequences=["intergenic_variant"] | ||
| ) | ||
| ] | ||
| ) | ||
| ] | ||
| ) | ||
| self.assertIsNone(variant_no_name.name) | ||
|
|
||
| def test_metadata_required(self): | ||
| metadata = Metadata( | ||
| pagination=PaginationMetadata(page=1, per_page=10, total=100) | ||
| ) | ||
| self.assertEqual(metadata.pagination.page, 1) | ||
|
|
||
| # Invalid instance without pagination - should raise ValidationError | ||
| with self.assertRaises(ValidationError): | ||
| Metadata() | ||
|
|
||
| def test_vep_results_response(self): | ||
| metadata = Metadata( | ||
| pagination=PaginationMetadata(page=1, per_page=10, total=100) | ||
| ) | ||
| variant = Variant( | ||
| name=None, | ||
| allele_type="SNV", | ||
| location=Location(region_name="1", start=10000, end=10001), | ||
| reference_allele=ReferenceVariantAllele(allele_sequence="A"), | ||
| alternative_alleles=[ | ||
| AlternativeVariantAllele( | ||
| allele_sequence="T", | ||
| allele_type="SNP", | ||
| predicted_molecular_consequences=[ | ||
| PredictedIntergenicConsequence( | ||
| feature_type=None, | ||
| consequences=["intergenic_variant"], | ||
| ) | ||
| ] | ||
| ) | ||
| ], | ||
| ) | ||
| response = VepResultsResponse(metadata=metadata, variants=[variant]) | ||
| self.assertEqual(response.metadata.pagination.page, 1) | ||
| self.assertEqual(len(response.variants), 1) | ||
|
|
||
| # Invalid instance without metadata - should raise ValidationError | ||
| with self.assertRaises(ValidationError): | ||
| VepResultsResponse(variants=[variant]) | ||
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Seeing now for the first time how the
PredictedIntergenicConsequenceclass is instantiated, I think passing both these arguments to it is redundant (it must be the result of it being auto-generated from the schema). If the contract is that thePredictedIntergenicConsequenceclass will always serialise to:{ "feature_type": null, "consequences": ["intergenic_variant"] }and if the caller of the class always knows that it is calling the
PredictedIntergenicConsequenceclass, then there shouldn't be any need to pass any data into it. This class can have thefeature_typefield set always toNone, and theconsequencesfield set always to["intergenic_variant"].