def flip(a, axis):
"""Reverse the order of elements in an array along the given axis.
Note that ``flip`` function has been introduced since NumPy v1.12.
The contents of this document is the same as the original one.
Args:
a (~cupy.ndarray): Input array.
axis (int): Axis in array, which entries are reversed.
Returns:
~cupy.ndarray: Output array.
.. seealso:: :func:`numpy.flip`
"""
a_ndim = a.ndim
if a_ndim < 1:
raise core._AxisError('Input must be >= 1-d')
axis = int(axis)
if not -a_ndim <= axis < a_ndim:
raise core._AxisError('axis must be >= %d and < %d' %
(-a_ndim, a_ndim))
return _flip(a, axis)
def _cum_core(a, axis, dtype, out, kern, batch_kern):
a = cupy.asarray(a)
if out is None:
if dtype is None:
kind = a.dtype.kind
if kind == 'b':
dtype = numpy.dtype('l')
elif kind == 'i' and a.dtype.itemsize < numpy.dtype('l').itemsize:
dtype = numpy.dtype('l')
elif kind == 'u' and a.dtype.itemsize < numpy.dtype('L').itemsize:
dtype = numpy.dtype('L')
else:
dtype = a.dtype
out = a.astype(dtype)
else:
out[...] = a
if axis is None:
out = out.ravel()
elif not (-a.ndim <= axis < a.ndim):
raise core._AxisError('axis(={}) out of bounds'.format(axis))
else:
return _proc_as_batch(batch_kern, out, axis=axis)
pos = 1
while pos < out.size:
kern(pos, out, size=out.size)
pos <<= 1
return out
def _get_positive_axis(ndim, axis):
a = axis
if a < 0:
a += ndim
if a < 0 or a >= ndim:
raise core._AxisError(
'axis {} out of bounds [0, {})'.format(axis, ndim))
return a
def stack(tup, axis=0):
"""Stacks arrays along a new axis.
Args:
tup (sequence of arrays): Arrays to be stacked.
axis (int): Axis along which the arrays are stacked.
Returns:
cupy.ndarray: Stacked array.
.. seealso:: :func:`numpy.stack`
"""
# TODO(okuta) Remove this if exampd_dims is updated
for x in tup:
if not (-x.ndim - 1 <= axis <= x.ndim):
raise core._AxisError('axis {} out of bounds [{}, {}]'.format(
axis, -x.ndim - 1, x.ndim))
return concatenate([cupy.expand_dims(x, axis) for x in tup], axis)
def _cum_core(a, axis, dtype, out, kern, batch_kern, *, op=None):
a = cupy.asarray(a)
if out is None:
if dtype is None:
kind = a.dtype.kind
if kind == 'b':
dtype = numpy.dtype('l')
elif kind == 'i' and a.dtype.itemsize < numpy.dtype('l').itemsize:
dtype = numpy.dtype('l')
elif kind == 'u' and a.dtype.itemsize < numpy.dtype('L').itemsize:
dtype = numpy.dtype('L')
else:
dtype = a.dtype
out = a.astype(dtype)
else:
out[...] = a
if axis is None:
out = out.ravel()
elif not (-a.ndim <= axis < a.ndim):
raise core._AxisError('axis(={}) out of bounds'.format(axis))
else:
return _proc_as_batch(batch_kern, out, axis=axis)
if cupy.cuda.cub_enabled:
# result will be None if the scan is not compatible with CUB
result = cub.cub_scan(out, op)
if result is not None:
return result
pos = 1
while pos < out.size:
kern(pos, out, size=out.size)
pos <<= 1
return out