Author: mattip <[email protected]>
Branch:
Changeset: r82688:403a0931e0bc
Date: 2016-03-04 11:58 +0200
http://bitbucket.org/pypy/pypy/changeset/403a0931e0bc/
Log: merge ndarray-setitem-filtered, which fixes issue #1674, issue #1717
diff --git a/pypy/module/micronumpy/concrete.py
b/pypy/module/micronumpy/concrete.py
--- a/pypy/module/micronumpy/concrete.py
+++ b/pypy/module/micronumpy/concrete.py
@@ -298,7 +298,14 @@
except IndexError:
# not a single result
chunks = self._prepare_slice_args(space, w_index)
- return new_view(space, orig_arr, chunks)
+ copy = False
+ if isinstance(chunks[0], BooleanChunk):
+ # numpy compatibility
+ copy = True
+ w_ret = new_view(space, orig_arr, chunks)
+ if copy:
+ w_ret = w_ret.descr_copy(space, space.wrap(w_ret.get_order()))
+ return w_ret
def descr_setitem(self, space, orig_arr, w_index, w_value):
try:
diff --git a/pypy/module/micronumpy/ndarray.py
b/pypy/module/micronumpy/ndarray.py
--- a/pypy/module/micronumpy/ndarray.py
+++ b/pypy/module/micronumpy/ndarray.py
@@ -22,7 +22,8 @@
from pypy.module.micronumpy.flagsobj import W_FlagsObject
from pypy.module.micronumpy.strides import (
get_shape_from_iterable, shape_agreement, shape_agreement_multiple,
- is_c_contiguous, is_f_contiguous, calc_strides, new_view)
+ is_c_contiguous, is_f_contiguous, calc_strides, new_view, BooleanChunk,
+ SliceChunk)
from pypy.module.micronumpy.casting import can_cast_array
from pypy.module.micronumpy.descriptor import get_dtype_cache
@@ -204,7 +205,13 @@
if iter_shape is None:
# w_index is a list of slices, return a view
chunks = self.implementation._prepare_slice_args(space, w_index)
- return new_view(space, self, chunks)
+ copy = False
+ if isinstance(chunks[0], BooleanChunk):
+ copy = True
+ w_ret = new_view(space, self, chunks)
+ if copy:
+ w_ret = w_ret.descr_copy(space, space.wrap(w_ret.get_order()))
+ return w_ret
shape = res_shape + self.get_shape()[len(indexes):]
w_res = W_NDimArray.from_shape(space, shape, self.get_dtype(),
self.get_order(), w_instance=self)
@@ -220,8 +227,24 @@
if iter_shape is None:
# w_index is a list of slices
chunks = self.implementation._prepare_slice_args(space, w_index)
- view = new_view(space, self, chunks)
- view.implementation.setslice(space, val_arr)
+ dim = -1
+ view = self
+ for i, c in enumerate(chunks):
+ if isinstance(c, BooleanChunk):
+ dim = i
+ idx = c.w_idx
+ chunks.pop(i)
+ chunks.insert(0, SliceChunk(space.newslice(space.wrap(0),
+ space.w_None, space.w_None)))
+ break
+ if dim > 0:
+ view = self.implementation.swapaxes(space, self, 0, dim)
+ if dim >= 0:
+ view = new_view(space, self, chunks)
+ view.setitem_filter(space, idx, val_arr)
+ else:
+ view = new_view(space, self, chunks)
+ view.implementation.setslice(space, val_arr)
return
if support.product(iter_shape) == 0:
return
diff --git a/pypy/module/micronumpy/strides.py
b/pypy/module/micronumpy/strides.py
--- a/pypy/module/micronumpy/strides.py
+++ b/pypy/module/micronumpy/strides.py
@@ -97,22 +97,19 @@
# filter by axis dim
filtr = chunks[dim]
assert isinstance(filtr, BooleanChunk)
+ # XXX this creates a new array, and fails in setitem
w_arr = w_arr.getitem_filter(space, filtr.w_idx, axis=dim)
arr = w_arr.implementation
chunks[dim] = SliceChunk(space.newslice(space.wrap(0),
- space.wrap(-1), space.w_None))
+ space.w_None, space.w_None))
r = calculate_slice_strides(space, arr.shape, arr.start,
arr.get_strides(), arr.get_backstrides(), chunks)
else:
r = calculate_slice_strides(space, arr.shape, arr.start,
arr.get_strides(), arr.get_backstrides(), chunks)
shape, start, strides, backstrides = r
- w_ret = W_NDimArray.new_slice(space, start, strides[:], backstrides[:],
+ return W_NDimArray.new_slice(space, start, strides[:], backstrides[:],
shape[:], arr, w_arr)
- if dim == 0:
- # Do not return a view
- return w_ret.descr_copy(space, space.wrap(w_ret.get_order()))
- return w_ret
@jit.unroll_safe
def _extend_shape(old_shape, chunks):
diff --git a/pypy/module/micronumpy/test/test_ndarray.py
b/pypy/module/micronumpy/test/test_ndarray.py
--- a/pypy/module/micronumpy/test/test_ndarray.py
+++ b/pypy/module/micronumpy/test/test_ndarray.py
@@ -2550,8 +2550,10 @@
assert b.base is None
b = a[:, np.array([True, False, True])]
assert b.base is not None
+ a[np.array([True, False]), 0] = 100
b = a[np.array([True, False]), 0]
- assert (b ==[0]).all()
+ assert b.shape == (1,)
+ assert (b ==[100]).all()
def test_scalar_indexing(self):
import numpy as np
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