10382572278

Committed 14 Aug 2024 06:29AM CUT coverage: 98.177% (+0.004%) from 98.173%

Build # 10382572278

Build Type

push

github

Committed by

web-flow

Commit Message

Merge pull request #8 from p2p-ld/constructable

Make NDArray callable as a functional validator

Run Details

3 of 3 new or added lines in 1 file covered. (100.0%)

808 of 823 relevant lines covered (98.18%)

9.75 hits per line

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100.0

/src/numpydantic/interface/numpy.py

"""
Interface to numpy arrays
"""

from typing import Any

from numpydantic.interface.interface import Interface

try:
    import numpy as np
    from numpy import ndarray

    ENABLED = True

except ImportError:  # pragma: no cover
    ENABLED = False
    ndarray = None
    np = None


class NumpyInterface(Interface):
    """
    Numpy :class:`~numpy.ndarray` s!
    """

    input_types = (ndarray, list)
    return_type = ndarray
    priority = -999
    """
    The numpy interface is usually the interface of last resort.
    We want to use any more specific interface that we might have,
    because the numpy interface checks for anything that could be coerced
    to a numpy array (see :meth:`.NumpyInterface.check` )
    """

    @classmethod
    def check(cls, array: Any) -> bool:
        """
        Check that this is in fact a numpy ndarray or something that can be
        coerced to one
        """
        if isinstance(array, ndarray):
            return True
        else:
            try:
                _ = np.array(array)
                return True
            except Exception:
                return False

    def before_validation(self, array: Any) -> ndarray:
        """
        Coerce to an ndarray. We have already checked if coercion is possible
        in :meth:`.check`
        """
        if not isinstance(array, ndarray):
            array = np.array(array)
        return array

    @classmethod
    def enabled(cls) -> bool:
        """Check that numpy is present in the environment"""
        return ENABLED

1	"""
2	Interface to numpy arrays
3	"""
4
5	from typing import Any	10✔
6
7	from numpydantic.interface.interface import Interface	10✔
8
9	try:	10✔
10	import numpy as np	10✔
11	from numpy import ndarray	10✔
12
13	ENABLED = True	10✔
14
15	except ImportError: # pragma: no cover
16	ENABLED = False
17	ndarray = None
18	np = None
19
20
21	class NumpyInterface(Interface):	10✔
22	"""
23	Numpy :class:`~numpy.ndarray` s!
24	"""
25
26	input_types = (ndarray, list)	10✔
27	return_type = ndarray	10✔
28	priority = -999	10✔
29	"""	6✔
30	The numpy interface is usually the interface of last resort.
31	We want to use any more specific interface that we might have,
32	because the numpy interface checks for anything that could be coerced
33	to a numpy array (see :meth:`.NumpyInterface.check` )
34	"""
35
36	@classmethod	10✔
37	def check(cls, array: Any) -> bool:	10✔
38	"""
39	Check that this is in fact a numpy ndarray or something that can be
40	coerced to one
41	"""
42	if isinstance(array, ndarray):	10✔
43	return True	10✔
44	else:
45	try:	10✔
46	_ = np.array(array)	10✔
47	return True	10✔
48	except Exception:	10✔
49	return False	10✔
50
51	def before_validation(self, array: Any) -> ndarray:	10✔
52	"""
53	Coerce to an ndarray. We have already checked if coercion is possible
54	in :meth:`.check`
55	"""
56	if not isinstance(array, ndarray):	10✔
57	array = np.array(array)	10✔
58	return array	10✔
59
60	@classmethod	10✔
61	def enabled(cls) -> bool:	10✔
62	"""Check that numpy is present in the environment"""
63	return ENABLED	10✔

p2p-ld / numpydantic / 10382572278

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