Unified test runner and testing infrastructure for the GEO-INFER ecosystem. Use when running cross-module tests, configuring test categories, setting up test fixtures for spatial data, or analyzing test results.
Resources
15Install
npx skillscat add activeinferenceinstitute/geo-infer/geo-infer-test Install via the SkillsCat registry.
About this skill
This skill provides a unified test runner and infrastructure for the GEO-INFER ecosystem. It simplifies testing across multiple modules by offering category filtering, spatial data fixtures, and automated reporting. Use it to execute cross-module tests, manage geospatial test data, or analyze performance and integration results within the ecosystem.
SKILL.md
GEO-INFER-TEST
Instructions
Core Capabilities
- Unified test runner:
run_unified_tests.pyfor all 44 modules - Category filtering: unit, integration, system, performance
- Module filtering: Test individual or groups of modules
- Result reporting: JUnit XML + HTML reports per module
- Fixtures: Shared spatial test data and coordinate generators
Usage
# Run all tests
uv run python GEO-INFER-TEST/run_unified_tests.py
# Run specific module
uv run python GEO-INFER-TEST/run_unified_tests.py --module MATH
# Run by category
uv run python GEO-INFER-TEST/run_unified_tests.py --category integration
# Direct pytest
uv run python -m pytest GEO-INFER-MATH/tests/unit/ -v --tb=shortPytest Markers
unit, integration, system, performance, geospatial, api, slow, fast
Examples
# Spatial test fixture: generate realistic test coordinates
import numpy as np
def make_test_coordinates(n=100, center=(45.5, -122.6), spread=0.1):
"""Generate n random lat/lng pairs around a center point."""
lats = np.random.normal(center[0], spread, n)
lngs = np.random.normal(center[1], spread, n)
return list(zip(lats.clip(-90, 90), lngs.clip(-180, 180)))
coords = make_test_coordinates(200)# Property-based test example with Hypothesis
from hypothesis import given, strategies as st
import numpy as np
@given(
lat=st.floats(min_value=-90, max_value=90),
lng=st.floats(min_value=-180, max_value=180),
resolution=st.integers(min_value=0, max_value=15)
)
def test_h3_roundtrip(lat, lng, resolution):
"""H3 cell encoding/decoding preserves approximate coordinates."""
import h3
cell = h3.latlng_to_cell(lat, lng, resolution)
rlat, rlng = h3.cell_to_latlng(cell)
assert abs(rlat - lat) < 1.0 # Within ~1 degree# Run specific test categories
uv run python -m pytest GEO-INFER-MATH/tests/ -m "unit and not slow" -v
uv run python -m pytest GEO-INFER-SPACE/tests/ -m "geospatial" -v
uv run python -m pytest GEO-INFER-ACT/tests/ -m "integration" -v --tb=shortGuidelines
- Use real objects in tests
- Property-based tests (Hypothesis) in ≥10 modules
- Results saved to
.geo-infer-test-results/{MODULE}_results.xml
Integrations
- EXAMPLES → Example code treated as integration tests
- OPS → CI/CD test results feed monitoring
- All modules → Unified test runner spans all 44 modules