/usr/lib/python2.7/dist-packages/pyopencl/compyte/array.py is in python-pyopencl 2017.2.2-1.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 | from __future__ import division
__copyright__ = "Copyright (C) 2011 Andreas Kloeckner"
__license__ = """
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
"""
import numpy as np
def f_contiguous_strides(itemsize, shape):
if shape:
strides = [itemsize]
for s in shape[:-1]:
strides.append(strides[-1]*s)
return tuple(strides)
else:
return ()
def c_contiguous_strides(itemsize, shape):
if shape:
strides = [itemsize]
for s in shape[:0:-1]:
strides.append(strides[-1]*s)
return tuple(strides[::-1])
else:
return ()
def equal_strides(strides1, strides2, shape):
if len(strides1) != len(strides2) or len(strides2) != len(shape):
return False
for s, st1, st2 in zip(shape, strides1, strides2):
if s != 1 and st1 != st2:
return False
return True
def is_f_contiguous_strides(strides, itemsize, shape):
return equal_strides(strides, f_contiguous_strides(itemsize, shape), shape)
def is_c_contiguous_strides(strides, itemsize, shape):
return equal_strides(strides, c_contiguous_strides(itemsize, shape), shape)
class ArrayFlags:
def __init__(self, ary):
self.f_contiguous = is_f_contiguous_strides(
ary.strides, ary.dtype.itemsize, ary.shape)
self.c_contiguous = is_c_contiguous_strides(
ary.strides, ary.dtype.itemsize, ary.shape)
self.forc = self.f_contiguous or self.c_contiguous
def get_common_dtype(obj1, obj2, allow_double):
# Yes, numpy behaves differently depending on whether
# we're dealing with arrays or scalars.
zero1 = np.zeros(1, dtype=obj1.dtype)
try:
zero2 = np.zeros(1, dtype=obj2.dtype)
except AttributeError:
zero2 = obj2
result = (zero1 + zero2).dtype
if not allow_double:
if result == np.float64:
result = np.dtype(np.float32)
elif result == np.complex128:
result = np.dtype(np.complex64)
return result
def bound(a):
high = a.bytes
low = a.bytes
for stri, shp in zip(a.strides, a.shape):
if stri < 0:
low += (stri)*(shp-1)
else:
high += (stri)*(shp-1)
return low, high
def may_share_memory(a, b):
# When this is called with a an ndarray and b
# a sparse matrix, numpy.may_share_memory fails.
if a is b:
return True
if a.__class__ is b.__class__:
a_l, a_h = bound(a)
b_l, b_h = bound(b)
if b_l >= a_h or a_l >= b_h:
return False
return True
else:
return False
# {{{ as_strided implementation
try:
from numpy.lib.stride_tricks import as_strided as _as_strided
_test_dtype = np.dtype(
[("a", np.float64), ("b", np.float64)], align=True)
_test_result = _as_strided(np.zeros(10, dtype=_test_dtype))
if _test_result.dtype != _test_dtype:
raise RuntimeError("numpy's as_strided is broken")
as_strided = _as_strided
except:
# stolen from numpy to be compatible with older versions of numpy
class _DummyArray(object):
""" Dummy object that just exists to hang __array_interface__ dictionaries
and possibly keep alive a reference to a base array.
"""
def __init__(self, interface, base=None):
self.__array_interface__ = interface
self.base = base
def as_strided(x, shape=None, strides=None):
""" Make an ndarray from the given array with the given shape and strides.
"""
# work around Numpy bug 1873 (reported by Irwin Zaid)
# Since this is stolen from numpy, this implementation has the same bug.
# http://projects.scipy.org/numpy/ticket/1873
# == https://github.com/numpy/numpy/issues/2466
# Do not recreate the array if nothing need to be changed.
# This fixes a lot of errors on pypy since DummyArray hack does not
# currently (2014/May/17) on pypy.
if ((shape is None or x.shape == shape) and
(strides is None or x.strides == strides)):
return x
if not x.dtype.isbuiltin:
if shape is None:
shape = x.shape
strides = tuple(strides)
from pytools import product
if strides is not None and shape is not None \
and product(shape) == product(x.shape) \
and x.flags.forc:
# Workaround: If we're being asked to do what amounts to a
# contiguous reshape, at least do that.
if strides == f_contiguous_strides(x.dtype.itemsize, shape):
# **dict is a workaround for Python 2.5 syntax.
result = x.reshape(-1).reshape(*shape, **dict(order="F"))
assert result.strides == strides
return result
elif strides == c_contiguous_strides(x.dtype.itemsize, shape):
# **dict is a workaround for Python 2.5 syntax.
result = x.reshape(-1).reshape(*shape, **dict(order="C"))
assert result.strides == strides
return result
raise NotImplementedError(
"as_strided won't work on non-builtin arrays for now. "
"See https://github.com/numpy/numpy/issues/2466")
interface = dict(x.__array_interface__)
if shape is not None:
interface['shape'] = tuple(shape)
if strides is not None:
interface['strides'] = tuple(strides)
return np.asarray(_DummyArray(interface, base=x))
# }}}
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