281 lines
8.4 KiB
Python
281 lines
8.4 KiB
Python
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"""
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Various richly-typed exceptions, that also help us deal with string formatting
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in python where it's easier.
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By putting the formatting in `__str__`, we also avoid paying the cost for
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users who silence the exceptions.
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"""
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from numpy.core.overrides import set_module
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def _unpack_tuple(tup):
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if len(tup) == 1:
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return tup[0]
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else:
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return tup
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def _display_as_base(cls):
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"""
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A decorator that makes an exception class look like its base.
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We use this to hide subclasses that are implementation details - the user
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should catch the base type, which is what the traceback will show them.
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Classes decorated with this decorator are subject to removal without a
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deprecation warning.
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"""
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assert issubclass(cls, Exception)
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cls.__name__ = cls.__base__.__name__
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return cls
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class UFuncTypeError(TypeError):
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""" Base class for all ufunc exceptions """
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def __init__(self, ufunc):
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self.ufunc = ufunc
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@_display_as_base
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class _UFuncBinaryResolutionError(UFuncTypeError):
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""" Thrown when a binary resolution fails """
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def __init__(self, ufunc, dtypes):
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super().__init__(ufunc)
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self.dtypes = tuple(dtypes)
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assert len(self.dtypes) == 2
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def __str__(self):
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return (
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"ufunc {!r} cannot use operands with types {!r} and {!r}"
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).format(
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self.ufunc.__name__, *self.dtypes
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)
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@_display_as_base
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class _UFuncNoLoopError(UFuncTypeError):
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""" Thrown when a ufunc loop cannot be found """
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def __init__(self, ufunc, dtypes):
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super().__init__(ufunc)
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self.dtypes = tuple(dtypes)
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def __str__(self):
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return (
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"ufunc {!r} did not contain a loop with signature matching types "
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"{!r} -> {!r}"
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).format(
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self.ufunc.__name__,
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_unpack_tuple(self.dtypes[:self.ufunc.nin]),
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_unpack_tuple(self.dtypes[self.ufunc.nin:])
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)
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@_display_as_base
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class _UFuncCastingError(UFuncTypeError):
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def __init__(self, ufunc, casting, from_, to):
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super().__init__(ufunc)
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self.casting = casting
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self.from_ = from_
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self.to = to
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@_display_as_base
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class _UFuncInputCastingError(_UFuncCastingError):
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""" Thrown when a ufunc input cannot be casted """
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def __init__(self, ufunc, casting, from_, to, i):
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super().__init__(ufunc, casting, from_, to)
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self.in_i = i
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def __str__(self):
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# only show the number if more than one input exists
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i_str = "{} ".format(self.in_i) if self.ufunc.nin != 1 else ""
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return (
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"Cannot cast ufunc {!r} input {}from {!r} to {!r} with casting "
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"rule {!r}"
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).format(
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self.ufunc.__name__, i_str, self.from_, self.to, self.casting
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)
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@_display_as_base
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class _UFuncOutputCastingError(_UFuncCastingError):
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""" Thrown when a ufunc output cannot be casted """
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def __init__(self, ufunc, casting, from_, to, i):
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super().__init__(ufunc, casting, from_, to)
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self.out_i = i
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def __str__(self):
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# only show the number if more than one output exists
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i_str = "{} ".format(self.out_i) if self.ufunc.nout != 1 else ""
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return (
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"Cannot cast ufunc {!r} output {}from {!r} to {!r} with casting "
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"rule {!r}"
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).format(
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self.ufunc.__name__, i_str, self.from_, self.to, self.casting
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)
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# Exception used in shares_memory()
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@set_module('numpy')
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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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@set_module('numpy')
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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 `ValueError` and `IndexError` for this situation, this
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exception subclasses both to ensure that ``except ValueError`` and
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``except IndexError`` statements continue to catch `AxisError`.
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.. versionadded:: 1.13
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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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>>> 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.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.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.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.AxisError('Custom error message'))
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Custom error message
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"""
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__slots__ = ("axis", "ndim", "_msg")
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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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@_display_as_base
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class _ArrayMemoryError(MemoryError):
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""" Thrown when an array cannot be allocated"""
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def __init__(self, shape, dtype):
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self.shape = shape
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self.dtype = dtype
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@property
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def _total_size(self):
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num_bytes = self.dtype.itemsize
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for dim in self.shape:
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num_bytes *= dim
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return num_bytes
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@staticmethod
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def _size_to_string(num_bytes):
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""" Convert a number of bytes into a binary size string """
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# https://en.wikipedia.org/wiki/Binary_prefix
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LOG2_STEP = 10
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STEP = 1024
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units = ['bytes', 'KiB', 'MiB', 'GiB', 'TiB', 'PiB', 'EiB']
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unit_i = max(num_bytes.bit_length() - 1, 1) // LOG2_STEP
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unit_val = 1 << (unit_i * LOG2_STEP)
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n_units = num_bytes / unit_val
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del unit_val
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# ensure we pick a unit that is correct after rounding
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if round(n_units) == STEP:
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unit_i += 1
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n_units /= STEP
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# deal with sizes so large that we don't have units for them
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if unit_i >= len(units):
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new_unit_i = len(units) - 1
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n_units *= 1 << ((unit_i - new_unit_i) * LOG2_STEP)
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unit_i = new_unit_i
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unit_name = units[unit_i]
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# format with a sensible number of digits
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if unit_i == 0:
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# no decimal point on bytes
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return '{:.0f} {}'.format(n_units, unit_name)
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elif round(n_units) < 1000:
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# 3 significant figures, if none are dropped to the left of the .
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return '{:#.3g} {}'.format(n_units, unit_name)
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else:
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# just give all the digits otherwise
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return '{:#.0f} {}'.format(n_units, unit_name)
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def __str__(self):
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size_str = self._size_to_string(self._total_size)
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return (
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"Unable to allocate {} for an array with shape {} and data type {}"
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.format(size_str, self.shape, self.dtype)
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)
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