bigarray: Factor-out our custom numpy.lib.stride_tricks.as_strided-alike into lib/xnumpy.py

We are going to use this code in another place, so move this out to
dommon place as a preparatory step first.

On a related note: Since ArrayRef is generic and quite independent from
BigArray (it only supports it, but equally it supports just other - e.g.
plain arrays), the proper place for it might be also to be lib/xnumpy.py .
We might get to this topic a bit later.

bigarray/__init__.py

@@ -38,8 +38,8 @@ of physical RAM.
 
 from __future__ import print_function
 from wendelin.lib.calc import mul
+from wendelin.lib.xnumpy import _as_strided
 from numpy import ndarray, dtype, sign, newaxis, asarray, argmax, uint8
-from numpy.lib.stride_tricks import DummyArray
 import logging
 
 
@@ -618,29 +618,7 @@ class ArrayRef(object):
         #
         # it is also safe because we checked .shape and .stridev not to escape
         # from bchild data buffer.
-        #
-        # the code below is very close to
-        #
-        #   a = stride_tricks.as_strided(bchild_z0, shape=self.shape, strides=self.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(bchild_z0.__array_interface__)
-        aiface['shape']   = tuple(self.shape)
-        aiface['strides'] = tuple(self.stridev)
-        # type: for now we only care that itemsize is the same
-        aiface['typestr'] = '|V%d' % self.dtype.itemsize
-        aiface['descr']   = [('', aiface['typestr'])]
-
-        a = asarray(DummyArray(aiface, base=bchild_z0))
-
-        # restore full dtype - it should not raise here, since itemsize is the same
-        a.dtype = self.dtype
+        a = _as_strided(bchild_z0, tuple(self.shape), tuple(self.stridev), self.dtype)
 
         # restore full array type
         a = a.view(type=self.atype)

lib/xnumpy.py

+# -*- coding: utf-8 -*-
+# NumPy-related utilities
+# Copyright (C) 2018  Nexedi SA and Contributors.
+#                     Kirill Smelkov <kirr@nexedi.com>
+#
+# 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
+# the terms of any of the Free Software licenses or any of the Open Source
+# Initiative approved licenses and Convey the resulting work. Corresponding
+# source of such a combination shall include the source code for all other
+# software used.
+#
+# This program is distributed WITHOUT ANY WARRANTY; without even the implied
+# warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+#
+# See COPYING file for full licensing terms.
+# See https://www.nexedi.com/licensing for rationale and options.
+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