GeoCase

Two pixels out of a hundred are NoData, and your mean elevation is off by 200 metres.

Here is the code. It looks fine, it passes review, and it is wrong:

def mean_elevation(array):
    return float(array.mean())

Point it at a GeoCase case that ships with the package:

import pytest


@pytest.mark.geocase_case("geotiff_nodata_small")
def test_mean_elevation_ignores_nodata(geocase_case):
    array, _profile, nodata = geocase_case.read(1)
    valid = array[array != nodata]
    assert mean_elevation(array) == pytest.approx(float(valid.mean()), rel=0.01)

E   assert -152.8628387451172 == 48.07874298095703 ± 0.480787

The file carries an explicit -9999 NoData sentinel in 2 of its 100 pixels. array.mean() averages the sentinel in and reports −152.9 m for terrain whose real mean is 48.1 m. Nothing raises and nothing warns — the number is simply wrong, and it stays wrong all the way into the report. The only thing that catches it is a test file that actually has NoData in it.

That is what GeoCase is: 166 curated vector, raster and NetCDF files, each one built around a failure mode that survives code review.

Most of them are about geometry, CRS and georeferencing conventions — the assumptions a codebase makes about where a pixel or a vertex actually is:

• a rotated geotransform, where the shortcut inverse matrix everyone writes is wrong, and wrong quietly;
• a bottom-up raster with a positive e term, read upside down without a single warning;
• pixel-is-area versus pixel-is-point anchoring, a half-pixel shift that survives review;
• geometry crossing the antimeridian and coming back as a ring around the globe, or a footprint reaching lon 180.22 that sends a tile request to a tile that does not exist;
• a CRS mismatch between two layers that overlay perfectly on screen;
• EPSG axis order flipping latitude and longitude;
• NoData averaged into a statistic, as above.

Radiometric conventions for Sentinel-1 and Sentinel-2 — scale factors, BOA offsets, dB conversion — are one vertical inside that, machine-checked against geofacts. They are not the thesis.

If your codebase moves pixels and geometry around and reads them with plain GDAL, you are the audience this corpus serves best.

Works with plain GDAL

case.primary_path is an ordinary filesystem path. gdal.Open just works, and the base install needs only pydantic, pyyaml and geofacts — no rasterio, no geopandas, no xarray:

import geocase
from osgeo import gdal

case = geocase.load_case("rotated_two_islands")
ds = gdal.Open(str(case.primary_path))
print(ds.GetGeoTransform())

The extras exist for the convenience loaders (case.load(), case.read()), not for reading the files. Enumerating, selecting and resolving cases needs no optional dependency at all.

The georeferencing-conventions suite is the shortest path from install to a real finding:

@pytest.mark.geocase_suite("georeferencing-conventions")
def test_georeferencing(geocase) -> None:
    ...

The realistic alternative is not "no tests" — it is the test_data/sample.tif someone exported once and the numpy arrays each test improvises. Those pass because they were chosen by the same person who wrote the code, so they encode the same assumptions. GeoCase cases were not, and they ship as a versioned dependency instead of a folder nobody remembers the provenance of. (They also give a coding assistant something to reach for instead of inventing a fixture that agrees with the bug.)

Status: 1.0.0 is available on PyPI — pip install geocase. The compatibility promise covers two surfaces — the pytest workflow (fixtures and markers) and the import geocase public API. 166 bundled cases, 5.1 MB. Remote dataset transport is deferred to v1.1; see the changelog.

Quick Start

1) Install

For local development in this repo:

pip install -e ".[dev]"

There are two supported install shapes, and picking the right one matters.

Greenfield — no geospatial stack yet, let pip build one:

pip install "geocase[all]"

You already have a working geo stack — GDAL, geopandas, rasterio, whether from conda, system packages or a --system-site-packages venv:

pip install geocase

Plain geocase is enough to enumerate, select and resolve every case, because primary_path is just a path and you already have the readers. Use it. [all] re-resolves numpy and pandas and can shadow or break a working stack — it is for greenfield environments only.

A conda-forge feedstock does not exist yet. When one does, installing from it will not carry the extras, since bundling GDAL would make the conda package far heavier than the PyPI equivalent:

conda install -c conda-forge geocase

GeoCase depends on geofacts at runtime — a zero-dependency table of geospatial product facts (radiometric constants, CRS conventions) that the raster presets are machine-checked against. Everything else is optional and gated behind extras.

2) Write a test with GeoCase markers

import pytest


@pytest.mark.geocase_case("dateline_crossing_polygon")
def test_vector_case_loads(geocase_case) -> None:
    gdf = geocase_case.load()
    assert geocase_case.id == "dateline_crossing_polygon"
    assert gdf.crs is not None


@pytest.mark.geocase_select(category="raster")
def test_all_raster_cases_have_pixels(geocase) -> None:
    data, _, _ = geocase.read(1)
    assert data.size > 0

3) Run tests

pytest -v

Run only GeoCase-marked tests:

pytest -m "geocase_case or geocase_suite or geocase_select" -v

CI Jobs

This repository uses GitHub Actions, defined in .github/workflows/.

ci.yml runs on pushes to main and on pull requests:

• catalog — catalog integrity checks (build_case_index.py smoke check, validate_catalog.py, fixture and checksum gates, generated-page freshness)
• tests — the whole tests/ directory on Python 3.11 and 3.14, reporting coverage (not gated)
• lint — ruff format --check and ruff check over src and tests
• typecheck — mypy src
• docs — mkdocs build --strict

release.yml runs only on vX.Y.Z tags; see Releasing.

Local equivalents:

python scripts/build_case_index.py --check
python scripts/validate_catalog.py

python -m pytest tests -q
ruff format --check src tests && ruff check src tests

Core Concepts

• @pytest.mark.geocase_case(...): select explicit case IDs.
• @pytest.mark.geocase_suite(...): use named suites.
• @pytest.mark.geocase_select(...): select by metadata (category, format, geometry_type, tags, risk_types_any, etc.).
• geocase: auto-parameterized fixture (one invocation per resolved case).
• geocase_case: convenience fixture for exactly one resolved case.
• CLI tooling is optional; the primary workflow is plain pytest.

If a GeoCase marker is missing, resolves no cases, refers to an unknown suite, or geocase_case resolves more than one case, the plugin now raises a focused pytest.UsageError that explains what to fix.

Learn More

• docs/getting-started.md
• docs/testing-your-function-with-geocase.md
• docs/case-discovery.md
• docs/assertions-reference.md
• docs/examples-index.md
• docs/benchmark/quickstart.md — the LLM benchmark built on the catalog. Experimental: not part of the v1.0 compatibility promise and not published to the docs site.
• src/geocase/raster/ — geocase.raster, a dependency-free raster primitive plus Sentinel-1/2 presets
• docs/plans/development-plan.md
• docs/contributing/workflow.md
• docs/design/case-recommendation-service.md