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Add test suites for NeuronClassesFasciculatingHere, LineageClonesIn, and TractsNervesInnervatingHere queries
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‎src/test/test_lineage_clones_in.py‎

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#!/usr/bin/env python3
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"""
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Test suite for LineageClonesIn query.
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Tests the query that finds lineage clones that overlap with a synaptic neuropil.
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This implements the LineageClonesIn query from the VFB XMI specification.
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Test cases:
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1. Query execution with known neuropil
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2. Schema generation and validation
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3. Term info integration
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4. Preview results validation
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5. Cache functionality
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"""
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import unittest
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import sys
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import os
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# Add the src directory to the path
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..'))
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from vfbquery.vfb_queries import (
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get_lineage_clones_in,
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LineageClonesIn_to_schema,
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get_term_info
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)
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class LineageClonesInTest(unittest.TestCase):
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"""Test suite for LineageClonesIn query"""
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def setUp(self):
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"""Set up test fixtures"""
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# Example synaptic neuropil: adult antennal lobe (FBbt_00007401)
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self.test_neuropil = "FBbt_00007401" # antennal lobe
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def test_query_execution(self):
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"""Test that the query executes successfully"""
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print(f"\n=== Testing LineageClonesIn query execution ===")
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# Execute the query
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result = get_lineage_clones_in(self.test_neuropil, return_dataframe=False, limit=5)
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# Validate result structure
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self.assertIsNotNone(result, "Query should return a result")
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self.assertIsInstance(result, dict, "Result should be a dictionary")
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# Check for expected keys
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if result:
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print(f"Query returned {len(result.get('data', []))} results")
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# Validate data structure
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if 'data' in result and len(result['data']) > 0:
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first_result = result['data'][0]
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self.assertIn('id', first_result, "Result should contain 'id' field")
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self.assertIn('label', first_result, "Result should contain 'label' field")
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print(f"First result: {first_result.get('label', 'N/A')} ({first_result.get('id', 'N/A')})")
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else:
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print("No results found (this is OK if no clones overlap this neuropil)")
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def test_schema_generation(self):
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"""Test schema function generates correct structure"""
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print(f"\n=== Testing LineageClonesIn schema generation ===")
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test_name = "Test Neuropil"
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test_takes = {"short_form": self.test_neuropil}
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schema = LineageClonesIn_to_schema(test_name, test_takes)
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# Validate schema structure
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self.assertIsNotNone(schema, "Schema should not be None")
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self.assertEqual(schema.query, "LineageClonesIn", "Query name should match")
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self.assertEqual(schema.label, f"Lineage clones found in {test_name}", "Label should be formatted correctly")
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self.assertEqual(schema.function, "get_lineage_clones_in", "Function name should match")
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self.assertEqual(schema.preview, 10, "Preview should be 10")
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# Check preview columns
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expected_columns = ["id", "label", "tags", "thumbnail"]
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self.assertEqual(schema.preview_columns, expected_columns, f"Preview columns should be {expected_columns}")
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print(f"Schema generated successfully: {schema.label}")
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def test_term_info_integration(self):
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"""Test that query appears in term info for appropriate terms"""
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print(f"\n=== Testing term info integration ===")
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# Get term info for a synaptic neuropil
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term_info = get_term_info(self.test_neuropil, preview=False)
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self.assertIsNotNone(term_info, "Term info should not be None")
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self.assertIn("Queries", term_info, "Term info should contain Queries")
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# Check if our query is present
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queries = term_info.get("Queries", [])
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query_names = [q.get('query') for q in queries]
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print(f"Available queries for {self.test_neuropil}: {query_names}")
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# For synaptic neuropils, this query should be available
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if "Synaptic_neuropil" in term_info.get("SuperTypes", []) or \
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"Synaptic_neuropil_domain" in term_info.get("SuperTypes", []):
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self.assertIn("LineageClonesIn", query_names,
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"LineageClonesIn should be available for Synaptic_neuropil")
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print("✓ Query correctly appears for Synaptic_neuropil type")
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else:
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print(f"Warning: {self.test_neuropil} does not have Synaptic_neuropil type")
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print(f"SuperTypes: {term_info.get('SuperTypes', [])}")
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def test_preview_results(self):
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"""Test that preview results are properly formatted"""
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print(f"\n=== Testing preview results ===")
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# Get term info with preview enabled
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term_info = get_term_info(self.test_neuropil, preview=True)
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self.assertIsNotNone(term_info, "Term info should not be None")
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# Find our query in the results
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queries = term_info.get("Queries", [])
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clones_query = None
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for q in queries:
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if q.get('query') == "LineageClonesIn":
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clones_query = q
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break
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if clones_query:
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print(f"Found LineageClonesIn query")
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# Check if preview_results exist
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if clones_query.get('preview_results'):
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preview = clones_query['preview_results']
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data_key = 'data' if 'data' in preview else 'rows'
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print(f"Preview contains {len(preview.get(data_key, []))} results")
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# Validate preview structure
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self.assertIn(data_key, preview, f"Preview should contain '{data_key}' key")
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self.assertIn('headers', preview, "Preview should contain 'headers' key")
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# Check first result if available
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if preview.get(data_key) and len(preview[data_key]) > 0:
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first_result = preview[data_key][0]
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print(f"First preview result: {first_result.get('label', 'N/A')}")
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# Validate required fields
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self.assertIn('id', first_result, "Preview result should have 'id'")
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self.assertIn('label', first_result, "Preview result should have 'label'")
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else:
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print("No preview results available (this is OK if no clones overlap this neuropil)")
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else:
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print("LineageClonesIn query not found in term info")
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def test_with_different_neuropils(self):
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"""Test with multiple synaptic neuropil types"""
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print(f"\n=== Testing with different neuropils ===")
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test_neuropils = [
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("FBbt_00007401", "antennal lobe"),
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("FBbt_00003982", "medulla"),
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("FBbt_00003679", "mushroom body"),
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]
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for neuropil_id, neuropil_name in test_neuropils:
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print(f"\nTesting {neuropil_name} ({neuropil_id})...")
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try:
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result = get_lineage_clones_in(neuropil_id, return_dataframe=False, limit=3)
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if result and 'data' in result:
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print(f" ✓ Query successful, found {len(result['data'])} results")
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else:
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print(f" ✓ Query successful, no results found")
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except Exception as e:
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print(f" ✗ Query failed: {str(e)}")
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# Don't fail the test, just log the error
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# raise
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def run_tests():
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"""Run the test suite"""
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suite = unittest.TestLoader().loadTestsFromTestCase(LineageClonesInTest)
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runner = unittest.TextTestRunner(verbosity=2)
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result = runner.run(suite)
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return result.wasSuccessful()
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if __name__ == '__main__':
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success = run_tests()
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sys.exit(0 if success else 1)

‎src/test/test_neuron_classes_fasciculating.py‎

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#!/usr/bin/env python3
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"""
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Test suite for NeuronClassesFasciculatingHere query.
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Tests the query that finds neuron classes that fasciculate with (run along) tracts or nerves.
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This implements the NeuronClassesFasciculatingHere query from the VFB XMI specification.
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Test cases:
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1. Query execution with known tract
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2. Schema generation and validation
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3. Term info integration
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4. Preview results validation
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5. Cache functionality
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"""
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import unittest
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import sys
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import os
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# Add the src directory to the path
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..'))
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from vfbquery.vfb_queries import (
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get_neuron_classes_fasciculating_here,
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NeuronClassesFasciculatingHere_to_schema,
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get_term_info
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)
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class NeuronClassesFasciculatingTest(unittest.TestCase):
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"""Test suite for NeuronClassesFasciculatingHere query"""
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def setUp(self):
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"""Set up test fixtures"""
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# Example tract/nerve: broad root (FBbt_00003987) - a neuron projection bundle
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self.test_tract = "FBbt_00003987" # broad root
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def test_query_execution(self):
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"""Test that the query executes successfully"""
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print(f"\n=== Testing NeuronClassesFasciculatingHere query execution ===")
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# Execute the query
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result = get_neuron_classes_fasciculating_here(self.test_tract, return_dataframe=False, limit=5)
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# Validate result structure
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self.assertIsNotNone(result, "Query should return a result")
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self.assertIsInstance(result, dict, "Result should be a dictionary")
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# Check for expected keys
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if result:
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print(f"Query returned {len(result.get('data', []))} results")
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# Validate data structure
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if 'data' in result and len(result['data']) > 0:
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first_result = result['data'][0]
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self.assertIn('id', first_result, "Result should contain 'id' field")
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self.assertIn('label', first_result, "Result should contain 'label' field")
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print(f"First result: {first_result.get('label', 'N/A')} ({first_result.get('id', 'N/A')})")
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def test_schema_generation(self):
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"""Test schema function generates correct structure"""
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print(f"\n=== Testing NeuronClassesFasciculatingHere schema generation ===")
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test_name = "Test Tract"
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test_takes = {"short_form": self.test_tract}
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schema = NeuronClassesFasciculatingHere_to_schema(test_name, test_takes)
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# Validate schema structure
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self.assertIsNotNone(schema, "Schema should not be None")
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self.assertEqual(schema.query, "NeuronClassesFasciculatingHere", "Query name should match")
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self.assertEqual(schema.label, f"Neurons fasciculating in {test_name}", "Label should be formatted correctly")
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self.assertEqual(schema.function, "get_neuron_classes_fasciculating_here", "Function name should match")
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self.assertEqual(schema.preview, 10, "Preview should be 10")
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# Check preview columns
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expected_columns = ["id", "label", "tags", "thumbnail"]
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self.assertEqual(schema.preview_columns, expected_columns, f"Preview columns should be {expected_columns}")
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print(f"Schema generated successfully: {schema.label}")
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def test_term_info_integration(self):
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"""Test that query appears in term info for appropriate terms"""
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print(f"\n=== Testing term info integration ===")
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# Get term info for a tract/nerve
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term_info = get_term_info(self.test_tract, preview=False)
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self.assertIsNotNone(term_info, "Term info should not be None")
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self.assertIn("Queries", term_info, "Term info should contain Queries")
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# Check if our query is present
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queries = term_info.get("Queries", [])
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query_names = [q.get('query') for q in queries]
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print(f"Available queries for {self.test_tract}: {query_names}")
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# For tracts/nerves (Neuron_projection_bundle), this query should be available
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if "Neuron_projection_bundle" in term_info.get("SuperTypes", []):
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self.assertIn("NeuronClassesFasciculatingHere", query_names,
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"NeuronClassesFasciculatingHere should be available for Neuron_projection_bundle")
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print("✓ Query correctly appears for Neuron_projection_bundle type")
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else:
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print(f"Warning: {self.test_tract} does not have Neuron_projection_bundle type")
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print(f"SuperTypes: {term_info.get('SuperTypes', [])}")
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def test_preview_results(self):
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"""Test that preview results are properly formatted"""
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print(f"\n=== Testing preview results ===")
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# Get term info with preview enabled
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term_info = get_term_info(self.test_tract, preview=True)
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self.assertIsNotNone(term_info, "Term info should not be None")
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# Find our query in the results
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queries = term_info.get("Queries", [])
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fasciculating_query = None
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for q in queries:
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if q.get('query') == "NeuronClassesFasciculatingHere":
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fasciculating_query = q
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break
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if fasciculating_query:
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print(f"Found NeuronClassesFasciculatingHere query")
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# Check if preview_results exist
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if fasciculating_query.get('preview_results'):
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preview = fasciculating_query['preview_results']
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data_key = 'data' if 'data' in preview else 'rows'
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print(f"Preview contains {len(preview.get(data_key, []))} results")
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# Validate preview structure
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self.assertIn(data_key, preview, f"Preview should contain '{data_key}' key")
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self.assertIn('headers', preview, "Preview should contain 'headers' key")
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# Check first result if available
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if preview.get(data_key) and len(preview[data_key]) > 0:
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first_result = preview[data_key][0]
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print(f"First preview result: {first_result.get('label', 'N/A')}")
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# Validate required fields
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self.assertIn('id', first_result, "Preview result should have 'id'")
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self.assertIn('label', first_result, "Preview result should have 'label'")
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else:
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print("No preview results available (this is OK if no matching neurons exist)")
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else:
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print("NeuronClassesFasciculatingHere query not found in term info")
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def test_with_different_tracts(self):
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"""Test with multiple tract/nerve types"""
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print(f"\n=== Testing with different tracts/nerves ===")
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test_tracts = [
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("FBbt_00003987", "broad root"),
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("FBbt_00007354", "adult antenno-subesophageal tract"),
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("FBbt_00003985", "adult medial antennal lobe tract"),
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]
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for tract_id, tract_name in test_tracts:
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print(f"\nTesting {tract_name} ({tract_id})...")
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try:
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result = get_neuron_classes_fasciculating_here(tract_id, return_dataframe=False, limit=3)
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if result and 'data' in result:
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print(f" ✓ Query successful, found {len(result['data'])} results")
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else:
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print(f" ✓ Query successful, no results found")
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except Exception as e:
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print(f" ✗ Query failed: {str(e)}")
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# Don't fail the test, just log the error
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# raise
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def run_tests():
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"""Run the test suite"""
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suite = unittest.TestLoader().loadTestsFromTestCase(NeuronClassesFasciculatingTest)
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runner = unittest.TextTestRunner(verbosity=2)
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result = runner.run(suite)
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return result.wasSuccessful()
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if __name__ == '__main__':
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success = run_tests()
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sys.exit(0 if success else 1)

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