numpydantic/tests/test_shape.py

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from typing import Any
import numpy as np
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import pytest
from pydantic import BaseModel, ValidationError
from numpydantic import NDArray, Shape
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pytestmark = pytest.mark.shape
@pytest.mark.parametrize(
"shape,valid",
[
((2, 6), True),
((2, 7), True),
((3, 6), True),
((3, 7), True),
((4, 6), True),
((4, 7), True),
((1, 6), False),
((5, 6), False),
((2, 5), False),
((2, 8), False),
],
)
def test_shape_range(shape, valid):
"""Specify a dimension with a range of possible sizes"""
class MyModel(BaseModel):
array: NDArray[Shape["2-4, 6-7"], Any]
if valid:
_ = MyModel(array=np.zeros(shape, dtype=np.uint8))
else:
with pytest.raises(ValidationError):
_ = MyModel(array=np.zeros(shape, dtype=np.uint8))
@pytest.mark.parametrize(
"shape,valid",
[
((2, 5), True),
((10, 5), True),
((2, 2), True),
((1, 5), False),
((2, 6), False),
],
)
def test_shape_wildcard(shape, valid):
"""Specify an open-ended minimum or maximum size for a given dimension"""
class MyModel(BaseModel):
array: NDArray[Shape["2-*, *-5"], Any]
if valid:
_ = MyModel(array=np.zeros(shape, dtype=np.uint8))
else:
with pytest.raises(ValidationError):
_ = MyModel(array=np.zeros(shape, dtype=np.uint8))
def test_range_shape_schema():
"""
Range shapes should correctly generate JSON Schema
"""
class MyModel(BaseModel):
array_range: NDArray[Shape["2-4"], Any]
array_range_min: NDArray[Shape["2-*"], Any]
array_range_max: NDArray[Shape["*-4"], Any]
schema = MyModel.model_json_schema()
assert schema["properties"]["array_range"]["minItems"] == 2
assert schema["properties"]["array_range"]["maxItems"] == 4
assert schema["properties"]["array_range_min"]["minItems"] == 2
assert "maxItems" not in schema["properties"]["array_range_min"]
assert schema["properties"]["array_range_max"]["maxItems"] == 4
assert "minItems" not in schema["properties"]["array_range_max"]