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