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from typing import Type |
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import numpy as np |
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from nptyping._ndarray_meta import _NDArray |
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class NDArray(np.ndarray, _NDArray): |
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""" |
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NDArray is a representation of numpy.ndarray. |
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An Array with any dimensions of any size and any type: |
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>>> NDArray |
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NDArray[(typing.Any, ...), typing.Any] |
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An array with 1 dimension of any size and any type: |
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>>> from typing import Any |
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>>> NDArray[Any] |
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NDArray[(typing.Any,), typing.Any] |
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An array with 1 dimension of size 3 and any type: |
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>>> NDArray[3] |
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NDArray[(3,), typing.Any] |
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An array with any dimensions of any size and type int: |
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>>> NDArray[int] |
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NDArray[(typing.Any, ...), int] |
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An array with 1 dimension of size 3 and type int: |
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>>> NDArray[3, int] |
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NDArray[(3,), int] |
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An array with any dimensions of size 3 and type int: |
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>>> NDArray[(3, ...), int] |
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NDArray[(3, ...), int] |
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An array with 3 dimensions of sizes 3, 3, 5 and type int: |
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>>> NDArray[(3, 3, 5), int] |
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NDArray[(3, 3, 5), int] |
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""" |
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@staticmethod |
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def type_of(arr: np.ndarray) -> Type['NDArray']: |
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""" |
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Return an nptyping.NDArray type T such that isinstance(arr, T). |
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>>> NDArray.type_of(np.array([[1, 2], [3, 4.0]])) |
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NDArray[(2, 2), float64] |
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:param arr: any numpy.ndarray. |
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:return: a nptyping.NDArray. |
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""" |
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return NDArray[arr.shape, arr.dtype] |
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ramonhagenaars /
nptyping
