_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):
    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)
        )