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