mirror of
https://github.com/p2p-ld/numpydantic.git
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121 lines
3.6 KiB
Python
121 lines
3.6 KiB
Python
import pytest
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from typing import Union, Optional, Any
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import json
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import numpy as np
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from pydantic import BaseModel, ValidationError, Field
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from nptyping import Shape, Number
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from numpydantic import NDArray
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from numpydantic.exceptions import ShapeError, DtypeError
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# from .fixtures import tmp_output_dir_func
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def test_ndarray_type():
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class Model(BaseModel):
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array: NDArray[Shape["2 x, * y"], Number]
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array_any: Optional[NDArray[Any, Any]] = None
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schema = Model.model_json_schema()
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assert schema["properties"]["array"]["items"] == {
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"items": {"type": "number"},
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"type": "array",
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}
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assert schema["properties"]["array"]["maxItems"] == 2
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assert schema["properties"]["array"]["minItems"] == 2
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# models should instantiate correctly!
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instance = Model(array=np.zeros((2, 3)))
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with pytest.raises(ValidationError):
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instance = Model(array=np.zeros((4, 6)))
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with pytest.raises(DtypeError):
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instance = Model(array=np.ones((2, 3), dtype=bool))
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instance = Model(array=np.zeros((2, 3)), array_any=np.ones((3, 4, 5)))
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def test_ndarray_union():
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class Model(BaseModel):
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array: Optional[
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Union[
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NDArray[Shape["* x, * y"], Number],
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NDArray[Shape["* x, * y, 3 r_g_b"], Number],
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NDArray[Shape["* x, * y, 3 r_g_b, 4 r_g_b_a"], Number],
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]
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] = Field(None)
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instance = Model()
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instance = Model(array=np.random.random((5, 10)))
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instance = Model(array=np.random.random((5, 10, 3)))
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instance = Model(array=np.random.random((5, 10, 3, 4)))
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with pytest.raises(ValidationError):
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instance = Model(array=np.random.random((5,)))
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with pytest.raises(ValidationError):
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instance = Model(array=np.random.random((5, 10, 4)))
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with pytest.raises(ValidationError):
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instance = Model(array=np.random.random((5, 10, 3, 6)))
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with pytest.raises(ValidationError):
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instance = Model(array=np.random.random((5, 10, 4, 6)))
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def test_ndarray_coercion():
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"""
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Coerce lists to arrays
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"""
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class Model(BaseModel):
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array: NDArray[Shape["* x"], Number]
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amod = Model(array=[1, 2, 3, 4.5])
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assert np.allclose(amod.array, np.array([1, 2, 3, 4.5]))
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with pytest.raises(DtypeError):
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amod = Model(array=["a", "b", "c"])
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def test_ndarray_serialize():
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"""
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Arrays should be dumped to a list when using json, but kept as ndarray otherwise
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"""
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class Model(BaseModel):
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array: NDArray[Any, Number]
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mod = Model(array=np.random.random((3, 3)))
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mod_str = mod.model_dump_json()
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mod_json = json.loads(mod_str)
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assert isinstance(mod_json["array"], list)
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# but when we just dump to a dict we don't coerce
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mod_dict = mod.model_dump()
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assert isinstance(mod_dict["array"], np.ndarray)
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# def test_ndarray_proxy(tmp_output_dir_func):
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# h5f_source = tmp_output_dir_func / 'test.h5'
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# with h5py.File(h5f_source, 'w') as h5f:
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# dset_good = h5f.create_dataset('/data', data=np.random.random((1024,1024,3)))
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# dset_bad = h5f.create_dataset('/data_bad', data=np.random.random((1024, 1024, 4)))
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#
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# class Model(BaseModel):
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# array: NDArray[Shape["* x, * y, 3 z"], Number]
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#
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# mod = Model(array=NDArrayProxy(h5f_file=h5f_source, path='/data'))
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# subarray = mod.array[0:5, 0:5, :]
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# assert isinstance(subarray, np.ndarray)
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# assert isinstance(subarray.sum(), float)
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# assert mod.array.name == '/data'
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#
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# with pytest.raises(NotImplementedError):
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# mod.array[0] = 5
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#
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# with pytest.raises(ValidationError):
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# mod = Model(array=NDArrayProxy(h5f_file=h5f_source, path='/data_bad'))
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