# -*- coding: utf-8 -*-
# NumPy-related utilities
# Copyright (C) 2018 Nexedi SA and Contributors.
# Kirill Smelkov
#
# This program is free software: you can Use, Study, Modify and Redistribute
# it under the terms of the GNU General Public License version 3, or (at your
# option) any later version, as published by the Free Software Foundation.
#
# You can also Link and Combine this program with other software covered by
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import numpy as np
from numpy.lib import stride_tricks as npst
# _as_strided is similar to numpy.lib.stride_tricks.as_strided, but allows to
# set all shape/stridev/dtype in one go.
#
# It must be used with extreme care, because if there is math error in the
# arguments, the resulting array can cover wrong memory. Bugs here thus can
# lead to mysterious crashes.
def _as_strided(arr, shape, stridev, dtype):
# the code below is very close to
#
# a = stride_tricks.as_strided(arr, shape=shape, strides=stridev)
#
# but we don't use as_strided() because we also have to change dtype
# with shape and strides in one go - else changing dtype after either
# via a.dtype = ..., or via a.view(dtype=...) can raise errors like
#
# "When changing to a larger dtype, its size must be a
# divisor of the total size in bytes of the last axis
# of the array."
aiface = dict(arr.__array_interface__)
aiface['shape'] = shape
aiface['strides'] = stridev
# type: for now we only care that itemsize is the same
aiface['typestr'] = '|V%d' % dtype.itemsize
aiface['descr'] = [('', aiface['typestr'])]
a = np.asarray(npst.DummyArray(aiface, base=arr))
# restore full dtype - it should not raise here, since itemsize is the same
a.dtype = dtype
# restore full array type (mimics subok=True)
if type(a) is not type(arr):
a = a.view(type=type(arr))
# we are done
return a
# structured creates view of the array interpreting its minor axis as fully covered by dtype.
#
# The minor axis of the array must be C-contiguous and be of dtype.itemsize in size.
#
# Structured is similar to arr.view(dtype) + corresponding reshape, but does
# not have limitations of ndarray.view(). For example:
#
# In [1]: a = np.arange(3*3, dtype=np.int32).reshape((3,3))
#
# In [2]: a
# Out[2]:
# array([[0, 1, 2],
# [3, 4, 5],
# [6, 7, 8]], dtype=int32)
#
# In [3]: b = a[:2,:2]
#
# In [4]: b
# Out[4]:
# array([[0, 1],
# [3, 4]], dtype=int32)
#
# In [5]: dtxy = np.dtype([('x', np.int32), ('y', np.int32)])
#
# In [6]: dtxy
# Out[6]: dtype([('x', ' in ()
# ----> 1 b.view(dtxy)
#
# ValueError: To change to a dtype of a different size, the array must be C-contiguous
#
# In [8]: structured(b, dtxy)
# Out[8]: array([(0, 1), (3, 4)], dtype=[('x', '