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248 lines
7.6 KiB
Python
248 lines
7.6 KiB
Python
"""
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Exceptions and Warnings
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=======================
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General exceptions used by NumPy. Note that some exceptions may be module
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specific, such as linear algebra errors.
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.. versionadded:: NumPy 1.25
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The exceptions module is new in NumPy 1.25. Older exceptions remain
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available through the main NumPy namespace for compatibility.
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.. currentmodule:: numpy.exceptions
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Warnings
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--------
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.. autosummary::
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:toctree: generated/
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ComplexWarning Given when converting complex to real.
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VisibleDeprecationWarning Same as a DeprecationWarning, but more visible.
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RankWarning Issued when the design matrix is rank deficient.
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Exceptions
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----------
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.. autosummary::
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:toctree: generated/
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AxisError Given when an axis was invalid.
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DTypePromotionError Given when no common dtype could be found.
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TooHardError Error specific to `numpy.shares_memory`.
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"""
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__all__ = [
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"ComplexWarning", "VisibleDeprecationWarning", "ModuleDeprecationWarning",
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"TooHardError", "AxisError", "DTypePromotionError"]
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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 ComplexWarning(RuntimeWarning):
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"""
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The warning raised when casting a complex dtype to a real dtype.
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As implemented, casting a complex number to a real discards its imaginary
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part, but this behavior may not be what the user actually wants.
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"""
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pass
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class ModuleDeprecationWarning(DeprecationWarning):
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"""Module deprecation warning.
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.. warning::
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This warning should not be used, since nose testing is not relevant
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anymore.
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The nose tester turns ordinary Deprecation warnings into test failures.
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That makes it hard to deprecate whole modules, because they get
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imported by default. So this is a special Deprecation warning that the
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nose tester will let pass without making tests fail.
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"""
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pass
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class VisibleDeprecationWarning(UserWarning):
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"""Visible deprecation warning.
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By default, python will not show deprecation warnings, so this class
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can be used when a very visible warning is helpful, for example because
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the usage is most likely a user bug.
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"""
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pass
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class RankWarning(RuntimeWarning):
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"""Matrix rank warning.
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Issued by polynomial functions when the design matrix is rank deficient.
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"""
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pass
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# Exception used in shares_memory()
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class TooHardError(RuntimeError):
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"""``max_work`` was exceeded.
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This is raised whenever the maximum number of candidate solutions
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to consider specified by the ``max_work`` parameter is exceeded.
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Assigning a finite number to ``max_work`` may have caused the operation
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to fail.
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"""
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pass
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class AxisError(ValueError, IndexError):
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"""Axis supplied was invalid.
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This is raised whenever an ``axis`` parameter is specified that is larger
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than the number of array dimensions.
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For compatibility with code written against older numpy versions, which
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raised a mixture of :exc:`ValueError` and :exc:`IndexError` for this
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situation, this exception subclasses both to ensure that
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``except ValueError`` and ``except IndexError`` statements continue
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to catch ``AxisError``.
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Parameters
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----------
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axis : int or str
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The out of bounds axis or a custom exception message.
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If an axis is provided, then `ndim` should be specified as well.
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ndim : int, optional
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The number of array dimensions.
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msg_prefix : str, optional
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A prefix for the exception message.
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Attributes
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----------
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axis : int, optional
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The out of bounds axis or ``None`` if a custom exception
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message was provided. This should be the axis as passed by
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the user, before any normalization to resolve negative indices.
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.. versionadded:: 1.22
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ndim : int, optional
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The number of array dimensions or ``None`` if a custom exception
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message was provided.
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.. versionadded:: 1.22
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Examples
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--------
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>>> import numpy as np
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>>> array_1d = np.arange(10)
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>>> np.cumsum(array_1d, axis=1)
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Traceback (most recent call last):
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...
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numpy.exceptions.AxisError: axis 1 is out of bounds for array of dimension 1
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Negative axes are preserved:
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>>> np.cumsum(array_1d, axis=-2)
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Traceback (most recent call last):
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...
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numpy.exceptions.AxisError: axis -2 is out of bounds for array of dimension 1
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The class constructor generally takes the axis and arrays'
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dimensionality as arguments:
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>>> print(np.exceptions.AxisError(2, 1, msg_prefix='error'))
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error: axis 2 is out of bounds for array of dimension 1
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Alternatively, a custom exception message can be passed:
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>>> print(np.exceptions.AxisError('Custom error message'))
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Custom error message
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"""
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__slots__ = ("_msg", "axis", "ndim")
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def __init__(self, axis, ndim=None, msg_prefix=None):
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if ndim is msg_prefix is None:
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# single-argument form: directly set the error message
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self._msg = axis
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self.axis = None
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self.ndim = None
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else:
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self._msg = msg_prefix
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self.axis = axis
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self.ndim = ndim
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def __str__(self):
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axis = self.axis
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ndim = self.ndim
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if axis is ndim is None:
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return self._msg
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else:
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msg = f"axis {axis} is out of bounds for array of dimension {ndim}"
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if self._msg is not None:
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msg = f"{self._msg}: {msg}"
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return msg
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class DTypePromotionError(TypeError):
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"""Multiple DTypes could not be converted to a common one.
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This exception derives from ``TypeError`` and is raised whenever dtypes
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cannot be converted to a single common one. This can be because they
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are of a different category/class or incompatible instances of the same
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one (see Examples).
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Notes
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-----
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Many functions will use promotion to find the correct result and
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implementation. For these functions the error will typically be chained
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with a more specific error indicating that no implementation was found
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for the input dtypes.
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Typically promotion should be considered "invalid" between the dtypes of
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two arrays when `arr1 == arr2` can safely return all ``False`` because the
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dtypes are fundamentally different.
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Examples
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--------
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Datetimes and complex numbers are incompatible classes and cannot be
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promoted:
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>>> import numpy as np
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>>> np.result_type(np.dtype("M8[s]"), np.complex128) # doctest: +IGNORE_EXCEPTION_DETAIL
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Traceback (most recent call last):
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...
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DTypePromotionError: The DType <class 'numpy.dtype[datetime64]'> could not
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be promoted by <class 'numpy.dtype[complex128]'>. This means that no common
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DType exists for the given inputs. For example they cannot be stored in a
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single array unless the dtype is `object`. The full list of DTypes is:
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(<class 'numpy.dtype[datetime64]'>, <class 'numpy.dtype[complex128]'>)
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For example for structured dtypes, the structure can mismatch and the
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same ``DTypePromotionError`` is given when two structured dtypes with
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a mismatch in their number of fields is given:
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>>> dtype1 = np.dtype([("field1", np.float64), ("field2", np.int64)])
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>>> dtype2 = np.dtype([("field1", np.float64)])
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>>> np.promote_types(dtype1, dtype2) # doctest: +IGNORE_EXCEPTION_DETAIL
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Traceback (most recent call last):
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...
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DTypePromotionError: field names `('field1', 'field2')` and `('field1',)`
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mismatch.
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""" # noqa: E501
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pass
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