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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
(
dtype
,
bool
,
intp
,
int8
,
int16
,
int32
,
int64
,
uint8
,
uint16
,
uint32
,
uint64
,
float32
,
float64
,
complex64
,
complex128
,
)
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}
"""
return
{
"boolean indexing"
:
True
,
"data-dependent shapes"
:
True
,
# 'max rank' will be part of the 2024.12 standard
# "max rank": 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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