Source code for numpy.exceptions

Exceptions and Warnings (:mod:`numpy.exceptions`)

General exceptions used by NumPy.  Note that some exceptions may be module
specific, such as linear algebra errors.

.. versionadded:: NumPy 1.25

    The exceptions module is new in NumPy 1.25.  Older exceptions remain
    available through the main NumPy namespace for compatibility.

.. currentmodule:: numpy.exceptions

.. autosummary::
   :toctree: generated/

   ComplexWarning             Given when converting complex to real.
   VisibleDeprecationWarning  Same as a DeprecationWarning, but more visible.

.. autosummary::
   :toctree: generated/

    AxisError          Given when an axis was invalid.
    DTypePromotionError   Given when no common dtype could be found.
    TooHardError       Error specific to `numpy.shares_memory`.


__all__ = [
    "ComplexWarning", "VisibleDeprecationWarning", "ModuleDeprecationWarning",
    "TooHardError", "AxisError", "DTypePromotionError"]

# 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

[docs] class ComplexWarning(RuntimeWarning): """ The warning raised when casting a complex dtype to a real dtype. As implemented, casting a complex number to a real discards its imaginary part, but this behavior may not be what the user actually wants. """ pass
class ModuleDeprecationWarning(DeprecationWarning): """Module deprecation warning. .. warning:: This warning should not be used, since nose testing is not relevant anymore. The nose tester turns ordinary Deprecation warnings into test failures. That makes it hard to deprecate whole modules, because they get imported by default. So this is a special Deprecation warning that the nose tester will let pass without making tests fail. """ class VisibleDeprecationWarning(UserWarning): """Visible deprecation warning. By default, python will not show deprecation warnings, so this class can be used when a very visible warning is helpful, for example because the usage is most likely a user bug. """ # Exception used in shares_memory() class TooHardError(RuntimeError): """max_work was exceeded. This is raised whenever the maximum number of candidate solutions to consider specified by the ``max_work`` parameter is exceeded. Assigning a finite number to max_work may have caused the operation to fail. """ pass class AxisError(ValueError, IndexError): """Axis supplied was invalid. This is raised whenever an ``axis`` parameter is specified that is larger than the number of array dimensions. For compatibility with code written against older numpy versions, which raised a mixture of `ValueError` and `IndexError` for this situation, this exception subclasses both to ensure that ``except ValueError`` and ``except IndexError`` statements continue to catch `AxisError`. .. versionadded:: 1.13 Parameters ---------- axis : int or str The out of bounds axis or a custom exception message. If an axis is provided, then `ndim` should be specified as well. ndim : int, optional The number of array dimensions. msg_prefix : str, optional A prefix for the exception message. Attributes ---------- axis : int, optional The out of bounds axis or ``None`` if a custom exception message was provided. This should be the axis as passed by the user, before any normalization to resolve negative indices. .. versionadded:: 1.22 ndim : int, optional The number of array dimensions or ``None`` if a custom exception message was provided. .. versionadded:: 1.22 Examples -------- >>> array_1d = np.arange(10) >>> np.cumsum(array_1d, axis=1) Traceback (most recent call last): ... numpy.exceptions.AxisError: axis 1 is out of bounds for array of dimension 1 Negative axes are preserved: >>> np.cumsum(array_1d, axis=-2) Traceback (most recent call last): ... numpy.exceptions.AxisError: axis -2 is out of bounds for array of dimension 1 The class constructor generally takes the axis and arrays' dimensionality as arguments: >>> print(np.AxisError(2, 1, msg_prefix='error')) error: axis 2 is out of bounds for array of dimension 1 Alternatively, a custom exception message can be passed: >>> print(np.AxisError('Custom error message')) Custom error message """ __slots__ = ("axis", "ndim", "_msg") def __init__(self, axis, ndim=None, msg_prefix=None): if ndim is msg_prefix is None: # single-argument form: directly set the error message self._msg = axis self.axis = None self.ndim = None else: self._msg = msg_prefix self.axis = axis self.ndim = ndim def __str__(self): axis = self.axis ndim = self.ndim if axis is ndim is None: return self._msg else: msg = f"axis {axis} is out of bounds for array of dimension {ndim}" if self._msg is not None: msg = f"{self._msg}: {msg}" return msg class DTypePromotionError(TypeError): """Multiple DTypes could not be converted to a common one. This exception derives from ``TypeError`` and is raised whenever dtypes cannot be converted to a single common one. This can be because they are of a different category/class or incompatible instances of the same one (see Examples). Notes ----- Many functions will use promotion to find the correct result and implementation. For these functions the error will typically be chained with a more specific error indicating that no implementation was found for the input dtypes. Typically promotion should be considered "invalid" between the dtypes of two arrays when `arr1 == arr2` can safely return all ``False`` because the dtypes are fundamentally different. Examples -------- Datetimes and complex numbers are incompatible classes and cannot be promoted: >>> np.result_type(np.dtype("M8[s]"), np.complex128) DTypePromotionError: The DType <class 'numpy.dtype[datetime64]'> could not be promoted by <class 'numpy.dtype[complex128]'>. This means that no common DType exists for the given inputs. For example they cannot be stored in a single array unless the dtype is `object`. The full list of DTypes is: (<class 'numpy.dtype[datetime64]'>, <class 'numpy.dtype[complex128]'>) For example for structured dtypes, the structure can mismatch and the same ``DTypePromotionError`` is given when two structured dtypes with a mismatch in their number of fields is given: >>> dtype1 = np.dtype([("field1", np.float64), ("field2", np.int64)]) >>> dtype2 = np.dtype([("field1", np.float64)]) >>> np.promote_types(dtype1, dtype2) DTypePromotionError: field names `('field1', 'field2')` and `('field1',)` mismatch. """ pass