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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.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Geospatial Active Inference framework with 44 modules for ecological, civic, and commercial spatial analysis. Use when working with geospatial data, Active Inference, Bayesian modeling, H3 hexagonal indexing, spatial statistics, or any domain-specific geographic analysis (agriculture, health, economics, risk, climate, energy, transport, marine, forestry, water).
Statistical Parametric Mapping for geospatial data. Use when performing GLM-based spatial analysis, random field theory corrections, cluster-level inference, or neuroimaging-style statistical mapping on geographic datasets.
Ant Colony Optimization and swarm intelligence for geospatial problems. Use when solving spatial optimization with ACO, PSO, ABC algorithms, implementing stigmergic coordination, or optimizing geographic routing and resource allocation with bio-inspired methods.
H3 hexagonal spatial indexing and multi-backend spatial operations. Use when working with H3 cells, spatial indexing, coordinate systems, raster/vector operations, or any spatial backend dispatch (H3, SRAI, PostGIS).
Generative geospatial art and cartographic visualization. Use when creating artistic map visualizations, generative spatial art, interactive cartographic displays, animation sequences, or aesthetically-focused geographic rendering.
Bayesian inference and probabilistic modeling for geospatial data. Use when building hierarchical models, computing posteriors with PyMC or TFP, performing variational inference, model comparison (LOO/WAIC/DIC), or spatial Gaussian processes.
| name | geo-infer-test |
| description | 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. |
| prerequisites | {"required":[],"recommended":[]} |
| difficulty | beginner |
| estimated_time | 30min |
| examples_dir | ../GEO-INFER-EXAMPLES/examples/ |
run_unified_tests.py for all 44 modules# 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=short
unit, integration, system, performance, geospatial, api, slow, fast
# 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=short
test-results/{MODULE}_results.xml