Current File : //opt/cloudlinux/venv/lib64/python3.11/site-packages/numpy/_globals.py
"""
Module defining global singleton classes.
This module raises a RuntimeError if an attempt to reload it is made. In that
way the identities of the classes defined here are fixed and will remain so
even if numpy itself is reloaded. In particular, a function like the following
will still work correctly after numpy is reloaded::
def foo(arg=np._NoValue):
if arg is np._NoValue:
...
That was not the case when the singleton classes were defined in the numpy
``__init__.py`` file. See gh-7844 for a discussion of the reload problem that
motivated this module.
"""
import enum
from ._utils import set_module as _set_module
__all__ = ['_NoValue', '_CopyMode']
# Disallow reloading this module so as to preserve the identities of the
# classes defined here.
if '_is_loaded' in globals():
raise RuntimeError('Reloading numpy._globals is not allowed')
_is_loaded = True
class _NoValueType:
"""Special keyword value.
The instance of this class may be used as the default value assigned to a
keyword if no other obvious default (e.g., `None`) is suitable,
Common reasons for using this keyword are:
- A new keyword is added to a function, and that function forwards its
inputs to another function or method which can be defined outside of
NumPy. For example, ``np.std(x)`` calls ``x.std``, so when a ``keepdims``
keyword was added that could only be forwarded if the user explicitly
specified ``keepdims``; downstream array libraries may not have added
the same keyword, so adding ``x.std(..., keepdims=keepdims)``
unconditionally could have broken previously working code.
- A keyword is being deprecated, and a deprecation warning must only be
emitted when the keyword is used.
"""
__instance = None
def __new__(cls):
# ensure that only one instance exists
if not cls.__instance:
cls.__instance = super().__new__(cls)
return cls.__instance
def __repr__(self):
return "<no value>"
_NoValue = _NoValueType()
@_set_module("numpy")
class _CopyMode(enum.Enum):
"""
An enumeration for the copy modes supported
by numpy.copy() and numpy.array(). The following three modes are supported,
- ALWAYS: This means that a deep copy of the input
array will always be taken.
- IF_NEEDED: This means that a deep copy of the input
array will be taken only if necessary.
- NEVER: This means that the deep copy will never be taken.
If a copy cannot be avoided then a `ValueError` will be
raised.
Note that the buffer-protocol could in theory do copies. NumPy currently
assumes an object exporting the buffer protocol will never do this.
"""
ALWAYS = True
IF_NEEDED = False
NEVER = 2
def __bool__(self):
# For backwards compatibility
if self == _CopyMode.ALWAYS:
return True
if self == _CopyMode.IF_NEEDED:
return False
raise ValueError(f"{self} is neither True nor False.")
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