numpydantic/tests/test_interface/test_zarr.py
sneakers-the-rat 1187b37b2d
hoo boy. working combinatoric testing.
Split out annotation dtype and shape, swap out all interface tests, fix numpy and dask model casting, make merging models more efficient, correctly parameterize and mark tests!
2024-10-10 23:56:45 -07:00

129 lines
3.6 KiB
Python

import json
import numpy as np
import pytest
from numpydantic.interface import ZarrInterface
from numpydantic.interface.zarr import ZarrArrayPath
from numpydantic.testing.cases import ZarrCase, ZarrDirCase, ZarrNestedCase, ZarrZipCase
from numpydantic.testing.helpers import InterfaceCase
pytestmark = pytest.mark.zarr
@pytest.fixture(
params=[ZarrCase, ZarrZipCase, ZarrDirCase, ZarrNestedCase],
)
def zarr_case(request) -> InterfaceCase:
return request.param
@pytest.fixture(
params=[
None, # use the default store
"dir_array",
"zip_array",
"nested_dir_array",
],
ids=["MutableMapping", "DirectoryStore", "ZipStore", "NestedDirectoryStore"],
)
def store(request):
if isinstance(request.param, str):
return request.getfixturevalue(request.param)
else:
return request.param
def test_zarr_enabled():
assert ZarrInterface.enabled()
def test_zarr_check(interface_cases, tmp_output_dir_func):
"""
We should only use the zarr interface for zarr-like things
"""
array = interface_cases.make_array(path=tmp_output_dir_func)
if interface_cases.interface is ZarrInterface:
assert ZarrInterface.check(array)
else:
assert not ZarrInterface.check(array)
@pytest.mark.shape
def test_zarr_shape(shape_cases, zarr_case):
shape_cases.interface = zarr_case
shape_cases.validate_case()
@pytest.mark.dtype
def test_zarr_dtype(dtype_cases, zarr_case):
dtype_cases.interface = zarr_case
if dtype_cases.skip():
pytest.skip()
dtype_cases.validate_case()
@pytest.mark.parametrize("array", ["zarr_nested_array", "zarr_array"])
def test_zarr_from_tuple(array, model_blank, request):
"""Should be able to do the same validation logic from tuples as an input"""
array = request.getfixturevalue(array)
if isinstance(array, ZarrArrayPath):
_ = model_blank(array=(array.file, array.path))
else:
_ = model_blank(array=(array,))
def test_zarr_from_path(zarr_array, model_blank):
"""Should be able to just pass a path"""
_ = model_blank(array=zarr_array)
def test_zarr_array_path_from_iterable(zarr_array):
"""Construct a zarr array path from some iterable!!!"""
# from a single path
apath = ZarrArrayPath.from_iterable((zarr_array,))
assert apath.file == zarr_array
assert apath.path is None
inner_path = "/test/array"
apath = ZarrArrayPath.from_iterable((zarr_array, inner_path))
assert apath.file == zarr_array
assert apath.path == inner_path
@pytest.mark.serialization
@pytest.mark.parametrize("dump_array", [True, False])
@pytest.mark.parametrize("roundtrip", [True, False])
def test_zarr_to_json(zarr_case, model_blank, roundtrip, dump_array, tmp_path):
expected_fields = (
"Type",
"Data type",
"Shape",
"Chunk shape",
"Compressor",
"Store type",
"hexdigest",
)
lol_array = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=int)
array = zarr_case.make_array(array=lol_array, dtype=int, path=tmp_path)
instance = model_blank(array=array)
context = {"dump_array": dump_array}
as_json = json.loads(
instance.model_dump_json(round_trip=roundtrip, context=context)
)["array"]
if roundtrip:
if dump_array:
assert np.array_equal(as_json["value"], lol_array)
else:
if as_json.get("file", False):
assert "array" not in as_json
for field in expected_fields:
assert field in as_json["info"]
assert len(as_json["info"]["hexdigest"]) == 40
else:
assert np.array_equal(as_json, lol_array)