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347 lines
10 KiB
Python
347 lines
10 KiB
Python
"""
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Array API Inspection namespace
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This is the namespace for inspection functions as defined by the array API
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standard. See
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https://data-apis.org/array-api/latest/API_specification/inspection.html for
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more details.
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"""
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from numpy._core import (
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bool,
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complex64,
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complex128,
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dtype,
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float32,
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float64,
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int8,
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int16,
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int32,
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int64,
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intp,
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uint8,
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uint16,
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uint32,
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uint64,
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)
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class __array_namespace_info__:
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"""
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Get the array API inspection namespace for NumPy.
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The array API inspection namespace defines the following functions:
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- capabilities()
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- default_device()
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- default_dtypes()
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- dtypes()
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- devices()
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See
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https://data-apis.org/array-api/latest/API_specification/inspection.html
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for more details.
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Returns
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-------
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info : ModuleType
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The array API inspection namespace for NumPy.
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Examples
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--------
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>>> info = np.__array_namespace_info__()
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>>> info.default_dtypes()
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{'real floating': numpy.float64,
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'complex floating': numpy.complex128,
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'integral': numpy.int64,
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'indexing': numpy.int64}
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"""
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__module__ = 'numpy'
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def capabilities(self):
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"""
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Return a dictionary of array API library capabilities.
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The resulting dictionary has the following keys:
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- **"boolean indexing"**: boolean indicating whether an array library
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supports boolean indexing. Always ``True`` for NumPy.
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- **"data-dependent shapes"**: boolean indicating whether an array
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library supports data-dependent output shapes. Always ``True`` for
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NumPy.
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See
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https://data-apis.org/array-api/latest/API_specification/generated/array_api.info.capabilities.html
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for more details.
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See Also
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--------
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__array_namespace_info__.default_device,
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__array_namespace_info__.default_dtypes,
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__array_namespace_info__.dtypes,
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__array_namespace_info__.devices
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Returns
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-------
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capabilities : dict
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A dictionary of array API library capabilities.
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Examples
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--------
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>>> info = np.__array_namespace_info__()
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>>> info.capabilities()
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{'boolean indexing': True,
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'data-dependent shapes': True,
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'max dimensions': 64}
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"""
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return {
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"boolean indexing": True,
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"data-dependent shapes": True,
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"max dimensions": 64,
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}
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def default_device(self):
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"""
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The default device used for new NumPy arrays.
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For NumPy, this always returns ``'cpu'``.
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See Also
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--------
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__array_namespace_info__.capabilities,
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__array_namespace_info__.default_dtypes,
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__array_namespace_info__.dtypes,
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__array_namespace_info__.devices
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Returns
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-------
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device : str
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The default device used for new NumPy arrays.
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Examples
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--------
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>>> info = np.__array_namespace_info__()
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>>> info.default_device()
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'cpu'
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"""
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return "cpu"
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def default_dtypes(self, *, device=None):
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"""
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The default data types used for new NumPy arrays.
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For NumPy, this always returns the following dictionary:
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- **"real floating"**: ``numpy.float64``
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- **"complex floating"**: ``numpy.complex128``
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- **"integral"**: ``numpy.intp``
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- **"indexing"**: ``numpy.intp``
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Parameters
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----------
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device : str, optional
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The device to get the default data types for. For NumPy, only
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``'cpu'`` is allowed.
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Returns
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-------
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dtypes : dict
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A dictionary describing the default data types used for new NumPy
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arrays.
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See Also
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--------
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__array_namespace_info__.capabilities,
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__array_namespace_info__.default_device,
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__array_namespace_info__.dtypes,
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__array_namespace_info__.devices
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Examples
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--------
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>>> info = np.__array_namespace_info__()
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>>> info.default_dtypes()
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{'real floating': numpy.float64,
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'complex floating': numpy.complex128,
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'integral': numpy.int64,
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'indexing': numpy.int64}
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"""
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if device not in ["cpu", None]:
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raise ValueError(
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'Device not understood. Only "cpu" is allowed, but received:'
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f' {device}'
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)
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return {
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"real floating": dtype(float64),
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"complex floating": dtype(complex128),
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"integral": dtype(intp),
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"indexing": dtype(intp),
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}
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def dtypes(self, *, device=None, kind=None):
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"""
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The array API data types supported by NumPy.
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Note that this function only returns data types that are defined by
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the array API.
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Parameters
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----------
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device : str, optional
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The device to get the data types for. For NumPy, only ``'cpu'`` is
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allowed.
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kind : str or tuple of str, optional
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The kind of data types to return. If ``None``, all data types are
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returned. If a string, only data types of that kind are returned.
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If a tuple, a dictionary containing the union of the given kinds
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is returned. The following kinds are supported:
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- ``'bool'``: boolean data types (i.e., ``bool``).
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- ``'signed integer'``: signed integer data types (i.e., ``int8``,
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``int16``, ``int32``, ``int64``).
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- ``'unsigned integer'``: unsigned integer data types (i.e.,
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``uint8``, ``uint16``, ``uint32``, ``uint64``).
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- ``'integral'``: integer data types. Shorthand for ``('signed
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integer', 'unsigned integer')``.
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- ``'real floating'``: real-valued floating-point data types
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(i.e., ``float32``, ``float64``).
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- ``'complex floating'``: complex floating-point data types (i.e.,
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``complex64``, ``complex128``).
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- ``'numeric'``: numeric data types. Shorthand for ``('integral',
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'real floating', 'complex floating')``.
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Returns
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-------
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dtypes : dict
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A dictionary mapping the names of data types to the corresponding
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NumPy data types.
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See Also
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--------
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__array_namespace_info__.capabilities,
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__array_namespace_info__.default_device,
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__array_namespace_info__.default_dtypes,
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__array_namespace_info__.devices
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Examples
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--------
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>>> info = np.__array_namespace_info__()
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>>> info.dtypes(kind='signed integer')
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{'int8': numpy.int8,
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'int16': numpy.int16,
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'int32': numpy.int32,
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'int64': numpy.int64}
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"""
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if device not in ["cpu", None]:
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raise ValueError(
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'Device not understood. Only "cpu" is allowed, but received:'
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f' {device}'
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)
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if kind is None:
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return {
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"bool": dtype(bool),
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"int8": dtype(int8),
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"int16": dtype(int16),
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"int32": dtype(int32),
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"int64": dtype(int64),
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"uint8": dtype(uint8),
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"uint16": dtype(uint16),
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"uint32": dtype(uint32),
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"uint64": dtype(uint64),
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"float32": dtype(float32),
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"float64": dtype(float64),
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"complex64": dtype(complex64),
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"complex128": dtype(complex128),
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}
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if kind == "bool":
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return {"bool": bool}
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if kind == "signed integer":
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return {
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"int8": dtype(int8),
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"int16": dtype(int16),
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"int32": dtype(int32),
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"int64": dtype(int64),
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}
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if kind == "unsigned integer":
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return {
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"uint8": dtype(uint8),
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"uint16": dtype(uint16),
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"uint32": dtype(uint32),
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"uint64": dtype(uint64),
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}
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if kind == "integral":
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return {
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"int8": dtype(int8),
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"int16": dtype(int16),
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"int32": dtype(int32),
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"int64": dtype(int64),
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"uint8": dtype(uint8),
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"uint16": dtype(uint16),
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"uint32": dtype(uint32),
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"uint64": dtype(uint64),
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}
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if kind == "real floating":
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return {
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"float32": dtype(float32),
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"float64": dtype(float64),
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}
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if kind == "complex floating":
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return {
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"complex64": dtype(complex64),
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"complex128": dtype(complex128),
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}
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if kind == "numeric":
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return {
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"int8": dtype(int8),
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"int16": dtype(int16),
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"int32": dtype(int32),
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"int64": dtype(int64),
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"uint8": dtype(uint8),
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"uint16": dtype(uint16),
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"uint32": dtype(uint32),
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"uint64": dtype(uint64),
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"float32": dtype(float32),
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"float64": dtype(float64),
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"complex64": dtype(complex64),
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"complex128": dtype(complex128),
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}
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if isinstance(kind, tuple):
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res = {}
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for k in kind:
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res.update(self.dtypes(kind=k))
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return res
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raise ValueError(f"unsupported kind: {kind!r}")
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def devices(self):
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"""
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The devices supported by NumPy.
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For NumPy, this always returns ``['cpu']``.
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Returns
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-------
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devices : list of str
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The devices supported by NumPy.
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See Also
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--------
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__array_namespace_info__.capabilities,
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__array_namespace_info__.default_device,
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__array_namespace_info__.default_dtypes,
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__array_namespace_info__.dtypes
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Examples
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--------
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>>> info = np.__array_namespace_info__()
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>>> info.devices()
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['cpu']
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"""
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return ["cpu"]
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