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from __future__ import annotations
from typing import TYPE_CHECKING, Any, Final
import pytest
from numpydoc.docscrape import NumpyDocString
import zarr
from zarr.api import asynchronous, synchronous
if TYPE_CHECKING:
from collections.abc import Callable
MATCHED_EXPORT_NAMES: Final[tuple[str, ...]] = tuple(
sorted(set(synchronous.__all__) | set(asynchronous.__all__))
)
"""A sorted tuple of names that are exported by both the sync and async APIs."""
MATCHED_CALLABLE_NAMES: Final[tuple[str, ...]] = tuple(
x for x in MATCHED_EXPORT_NAMES if callable(getattr(synchronous, x))
)
"""A sorted tuple of callable names that are exported by both the sync and async APIs."""
@pytest.mark.parametrize("callable_name", MATCHED_CALLABLE_NAMES)
def test_docstrings_match(callable_name: str) -> None:
"""
Tests that the docstrings for the sync and async define identical parameters.
"""
callable_a = getattr(synchronous, callable_name)
callable_b = getattr(asynchronous, callable_name)
if callable_a.__doc__ is None:
assert callable_b.__doc__ is None
else:
params_a = NumpyDocString(callable_a.__doc__)["Parameters"]
params_b = NumpyDocString(callable_b.__doc__)["Parameters"]
mismatch = []
for idx, (a, b) in enumerate(zip(params_a, params_b, strict=False)):
if a != b:
mismatch.append((idx, (a, b)))
assert mismatch == []
@pytest.mark.parametrize(
("parameter_name", "array_creation_routines"),
[
(
("store", "path"),
(
asynchronous.create_array,
synchronous.create_array,
asynchronous.create_group,
synchronous.create_group,
zarr.AsyncGroup.create_array,
zarr.Group.create_array,
),
),
(
(
"store",
"path",
),
(
asynchronous.create,
synchronous.create,
zarr.Group.create,
zarr.AsyncArray.create,
zarr.Array.create,
),
),
(
(
(
"filters",
"codecs",
"compressors",
"compressor",
"chunks",
"shape",
"dtype",
"shardsfill_value",
)
),
(
asynchronous.create,
synchronous.create,
asynchronous.create_array,
synchronous.create_array,
zarr.AsyncGroup.create_array,
zarr.Group.create_array,
zarr.AsyncGroup.create_dataset,
zarr.Group.create_dataset,
),
),
],
ids=str,
)
def test_docstring_consistent_parameters(
parameter_name: str, array_creation_routines: tuple[Callable[[Any], Any], ...]
) -> None:
"""
Tests that array and group creation routines document the same parameters consistently.
This test inspects the docstrings of sets of callables and generates two dicts:
- a dict where the keys are parameter descriptions and the values are the names of the routines with those
descriptions
- a dict where the keys are parameter types and the values are the names of the routines with those types
If each dict has just 1 value, then the parameter description and type in the docstring must be
identical across different routines. But if these dicts have multiple values, then there must be
routines that use the same parameter but document it differently, which will trigger a test failure.
"""
descs: dict[tuple[str, ...], tuple[str, ...]] = {}
types: dict[str, tuple[str, ...]] = {}
for routine in array_creation_routines:
key = f"{routine.__module__}.{routine.__qualname__}"
docstring = NumpyDocString(routine.__doc__)
param_dict = {d.name: d for d in docstring["Parameters"]}
if parameter_name in param_dict:
val = param_dict[parameter_name]
if tuple(val.desc) in descs:
descs[tuple(val.desc)] = descs[tuple(val.desc)] + (key,)
else:
descs[tuple(val.desc)] = (key,)
if val.type in types:
types[val.type] = types[val.type] + (key,)
else:
types[val.type] = (key,)
assert len(descs) <= 1
assert len(types) <= 1
|
