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Edit File: _exceptions.py
""" Various richly-typed exceptions, that also help us deal with string formatting in python where it's easier. By putting the formatting in `__str__`, we also avoid paying the cost for users who silence the exceptions. """ from numpy.core.overrides import set_module def _unpack_tuple(tup): if len(tup) == 1: return tup[0] else: return tup def _display_as_base(cls): """ A decorator that makes an exception class look like its base. We use this to hide subclasses that are implementation details - the user should catch the base type, which is what the traceback will show them. Classes decorated with this decorator are subject to removal without a deprecation warning. """ assert issubclass(cls, Exception) cls.__name__ = cls.__base__.__name__ return cls class UFuncTypeError(TypeError): """ Base class for all ufunc exceptions """ def __init__(self, ufunc): self.ufunc = ufunc @_display_as_base class _UFuncBinaryResolutionError(UFuncTypeError): """ Thrown when a binary resolution fails """ def __init__(self, ufunc, dtypes): super().__init__(ufunc) self.dtypes = tuple(dtypes) assert len(self.dtypes) == 2 def __str__(self): return ( "ufunc {!r} cannot use operands with types {!r} and {!r}" ).format( self.ufunc.__name__, *self.dtypes ) @_display_as_base class _UFuncNoLoopError(UFuncTypeError): """ Thrown when a ufunc loop cannot be found """ def __init__(self, ufunc, dtypes): super().__init__(ufunc) self.dtypes = tuple(dtypes) def __str__(self): return ( "ufunc {!r} did not contain a loop with signature matching types " "{!r} -> {!r}" ).format( self.ufunc.__name__, _unpack_tuple(self.dtypes[:self.ufunc.nin]), _unpack_tuple(self.dtypes[self.ufunc.nin:]) ) @_display_as_base class _UFuncCastingError(UFuncTypeError): def __init__(self, ufunc, casting, from_, to): super().__init__(ufunc) self.casting = casting self.from_ = from_ self.to = to @_display_as_base class _UFuncInputCastingError(_UFuncCastingError): """ Thrown when a ufunc input cannot be casted """ def __init__(self, ufunc, casting, from_, to, i): super().__init__(ufunc, casting, from_, to) self.in_i = i def __str__(self): # only show the number if more than one input exists i_str = "{} ".format(self.in_i) if self.ufunc.nin != 1 else "" return ( "Cannot cast ufunc {!r} input {}from {!r} to {!r} with casting " "rule {!r}" ).format( self.ufunc.__name__, i_str, self.from_, self.to, self.casting ) @_display_as_base class _UFuncOutputCastingError(_UFuncCastingError): """ Thrown when a ufunc output cannot be casted """ def __init__(self, ufunc, casting, from_, to, i): super().__init__(ufunc, casting, from_, to) self.out_i = i def __str__(self): # only show the number if more than one output exists i_str = "{} ".format(self.out_i) if self.ufunc.nout != 1 else "" return ( "Cannot cast ufunc {!r} output {}from {!r} to {!r} with casting " "rule {!r}" ).format( self.ufunc.__name__, i_str, self.from_, self.to, self.casting ) # Exception used in shares_memory() @set_module('numpy') 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 @set_module('numpy') 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.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.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 @_display_as_base class _ArrayMemoryError(MemoryError): """ Thrown when an array cannot be allocated""" def __init__(self, shape, dtype): self.shape = shape self.dtype = dtype @property def _total_size(self): num_bytes = self.dtype.itemsize for dim in self.shape: num_bytes *= dim return num_bytes @staticmethod def _size_to_string(num_bytes): """ Convert a number of bytes into a binary size string """ # https://en.wikipedia.org/wiki/Binary_prefix LOG2_STEP = 10 STEP = 1024 units = ['bytes', 'KiB', 'MiB', 'GiB', 'TiB', 'PiB', 'EiB'] unit_i = max(num_bytes.bit_length() - 1, 1) // LOG2_STEP unit_val = 1 << (unit_i * LOG2_STEP) n_units = num_bytes / unit_val del unit_val # ensure we pick a unit that is correct after rounding if round(n_units) == STEP: unit_i += 1 n_units /= STEP # deal with sizes so large that we don't have units for them if unit_i >= len(units): new_unit_i = len(units) - 1 n_units *= 1 << ((unit_i - new_unit_i) * LOG2_STEP) unit_i = new_unit_i unit_name = units[unit_i] # format with a sensible number of digits if unit_i == 0: # no decimal point on bytes return '{:.0f} {}'.format(n_units, unit_name) elif round(n_units) < 1000: # 3 significant figures, if none are dropped to the left of the . return '{:#.3g} {}'.format(n_units, unit_name) else: # just give all the digits otherwise return '{:#.0f} {}'.format(n_units, unit_name) def __str__(self): size_str = self._size_to_string(self._total_size) return ( "Unable to allocate {} for an array with shape {} and data type {}" .format(size_str, self.shape, self.dtype) )