Alexander Minges
eb270cba9b
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162 lines
No EOL
6.6 KiB
Python
162 lines
No EOL
6.6 KiB
Python
import json
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import os
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import pytest
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from unittest.mock import MagicMock, patch
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from doi2dataset import MetadataProcessor
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@pytest.fixture
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def openalex_data():
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"""Load the saved JSON response from the file 'srep45389.json'"""
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json_path = os.path.join(os.path.dirname(__file__), "srep45389.json")
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with open(json_path, "r", encoding="utf-8") as f:
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data = json.load(f)
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return data
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@pytest.fixture
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def metadata_processor():
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"""Create a MetadataProcessor instance with mocked dependencies"""
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doi = "10.1038/srep45389"
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processor = MetadataProcessor(doi=doi, upload=False, progress=False)
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return processor
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def test_build_metadata_basic_fields(metadata_processor, openalex_data, monkeypatch):
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"""Test that _build_metadata correctly extracts basic metadata fields"""
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# Mock the console to avoid print errors
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metadata_processor.console = MagicMock()
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# Mock the Abstract related methods and objects to avoid console errors
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abstract_mock = MagicMock()
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abstract_mock.text = "This is a sample abstract"
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abstract_mock.source = "openalex"
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monkeypatch.setattr("doi2dataset.AbstractProcessor.get_abstract", lambda *args, **kwargs: abstract_mock)
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# Mock the _fetch_data method to return our test data
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metadata_processor._fetch_data = MagicMock(return_value=openalex_data)
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# Mock methods that might cause issues in isolation
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metadata_processor._build_description = MagicMock(return_value="Test description")
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metadata_processor._get_involved_pis = MagicMock(return_value=[])
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metadata_processor._build_organization_metadata = MagicMock(return_value={})
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# Call the method we're testing
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metadata = metadata_processor._build_metadata(openalex_data)
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# Verify the basic metadata fields were extracted correctly
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assert metadata is not None
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assert 'datasetVersion' in metadata
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# Examine the fields inside datasetVersion.metadataBlocks
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assert 'metadataBlocks' in metadata['datasetVersion']
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citation = metadata['datasetVersion']['metadataBlocks'].get('citation', {})
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# Check fields in citation section
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assert 'fields' in citation
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fields = citation['fields']
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# Check for basic metadata fields in a more flexible way
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field_names = [field.get('typeName') for field in fields]
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assert 'title' in field_names
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assert 'subject' in field_names
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assert 'dsDescription' in field_names # Description is named 'dsDescription' in the schema
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def test_build_metadata_authors(metadata_processor, openalex_data, monkeypatch):
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"""Test that _build_metadata correctly processes author information"""
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# Mock the console to avoid print errors
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metadata_processor.console = MagicMock()
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# Mock the Abstract related methods and objects to avoid console errors
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abstract_mock = MagicMock()
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abstract_mock.text = "This is a sample abstract"
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abstract_mock.source = "openalex"
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monkeypatch.setattr("doi2dataset.AbstractProcessor.get_abstract", lambda *args, **kwargs: abstract_mock)
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# Mock the _fetch_data method to return our test data
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metadata_processor._fetch_data = MagicMock(return_value=openalex_data)
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# Mock methods that might cause issues in isolation
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metadata_processor._build_description = MagicMock(return_value="Test description")
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metadata_processor._get_involved_pis = MagicMock(return_value=[])
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metadata_processor._build_organization_metadata = MagicMock(return_value={})
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# Call the method we're testing
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metadata = metadata_processor._build_metadata(openalex_data)
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# Examine the fields inside datasetVersion.metadataBlocks
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assert 'metadataBlocks' in metadata['datasetVersion']
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citation = metadata['datasetVersion']['metadataBlocks'].get('citation', {})
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# Check fields in citation section
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assert 'fields' in citation
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fields = citation['fields']
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# Check for author and datasetContact fields
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field_names = [field.get('typeName') for field in fields]
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assert 'author' in field_names
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assert 'datasetContact' in field_names
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# Verify these are compound fields with actual entries
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for field in fields:
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if field.get('typeName') == 'author':
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assert 'value' in field
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assert isinstance(field['value'], list)
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assert len(field['value']) > 0
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if field.get('typeName') == 'datasetContact':
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assert 'value' in field
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assert isinstance(field['value'], list)
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# The datasetContact might be empty in test environment
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# Just check it exists rather than asserting length
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def test_build_metadata_keywords_and_topics(metadata_processor, openalex_data, monkeypatch):
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"""Test that _build_metadata correctly extracts keywords and topics"""
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# Mock the console to avoid print errors
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metadata_processor.console = MagicMock()
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# Mock the Abstract related methods and objects to avoid console errors
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abstract_mock = MagicMock()
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abstract_mock.text = "This is a sample abstract"
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abstract_mock.source = "openalex"
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monkeypatch.setattr("doi2dataset.AbstractProcessor.get_abstract", lambda *args, **kwargs: abstract_mock)
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# Mock the _fetch_data method to return our test data
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metadata_processor._fetch_data = MagicMock(return_value=openalex_data)
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# Mock methods that might cause issues in isolation
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metadata_processor._build_description = MagicMock(return_value="Test description")
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metadata_processor._get_involved_pis = MagicMock(return_value=[])
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metadata_processor._build_organization_metadata = MagicMock(return_value={})
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# Call the method we're testing
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metadata = metadata_processor._build_metadata(openalex_data)
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# Examine the fields inside datasetVersion.metadataBlocks
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assert 'metadataBlocks' in metadata['datasetVersion']
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citation = metadata['datasetVersion']['metadataBlocks'].get('citation', {})
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# Check fields in citation section
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assert 'fields' in citation
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fields = citation['fields']
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# Check for keyword and subject fields
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field_names = [field.get('typeName') for field in fields]
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# If keywords exist, verify structure
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if 'keyword' in field_names:
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for field in fields:
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if field.get('typeName') == 'keyword':
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assert 'value' in field
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assert isinstance(field['value'], list)
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# Check for subject field which should definitely exist
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assert 'subject' in field_names
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for field in fields:
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if field.get('typeName') == 'subject':
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assert 'value' in field
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assert isinstance(field['value'], list)
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assert len(field['value']) > 0 |