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https://github.com/p2p-ld/numpydantic.git
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64 lines
1.9 KiB
Python
64 lines
1.9 KiB
Python
from pathlib import Path
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from typing import Callable, Tuple, Union
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import numpy as np
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import pytest
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import zarr
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from numpydantic.interface.hdf5 import H5ArrayPath
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from numpydantic.interface.zarr import ZarrArrayPath
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from numpydantic.testing import ValidationCase
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from numpydantic.testing.interfaces import HDF5Case, HDF5CompoundCase, VideoCase
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@pytest.fixture(scope="function")
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def hdf5_array(
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request, tmp_output_dir_func
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) -> Callable[[Tuple[int, ...], Union[np.dtype, type]], H5ArrayPath]:
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def _hdf5_array(
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shape: Tuple[int, ...] = (10, 10),
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dtype: Union[np.dtype, type] = float,
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compound: bool = False,
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) -> H5ArrayPath:
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if compound:
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array: H5ArrayPath = HDF5CompoundCase.make_array(
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shape, dtype, tmp_output_dir_func
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)
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return array
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else:
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return HDF5Case.make_array(shape, dtype, tmp_output_dir_func)
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return _hdf5_array
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@pytest.fixture(scope="function")
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def zarr_nested_array(tmp_output_dir_func) -> ZarrArrayPath:
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"""Zarr array within a nested array"""
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file = tmp_output_dir_func / "nested.zarr"
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path = "a/b/c"
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root = zarr.open(str(file), mode="w")
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_ = root.zeros(path, shape=(100, 100), chunks=(10, 10))
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return ZarrArrayPath(file=file, path=path)
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@pytest.fixture(scope="function")
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def zarr_array(tmp_output_dir_func) -> Path:
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file = tmp_output_dir_func / "array.zarr"
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array = zarr.open(str(file), mode="w", shape=(100, 100), chunks=(10, 10))
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array[:] = 0
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return file
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@pytest.fixture(scope="function")
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def avi_video(tmp_output_dir_func) -> Callable[[Tuple[int, int], int, bool], Path]:
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def _make_video(shape=(100, 50), frames=10, is_color=True) -> Path:
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shape = (frames, *shape)
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if is_color:
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shape = (*shape, 3)
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return VideoCase.array_from_case(
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ValidationCase(shape=shape, dtype=np.uint8), tmp_output_dir_func
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)
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return _make_video
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