Author: mattip <matti.pi...@gmail.com>
Branch: nditer-buffered
Changeset: r78604:f5d927f06929
Date: 2015-07-20 08:44 +0300
http://bitbucket.org/pypy/pypy/changeset/f5d927f06929/
Log: improve test by comparing to upstream, move things around until part
of test passes
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
@@ -18,7 +18,7 @@
from pypy.module.micronumpy.converters import multi_axis_converter, \
order_converter, shape_converter, searchside_converter
from pypy.module.micronumpy.flagsobj import W_FlagsObject
-from pypy.module.micronumpy.strides import get_shape_from_iterable, \
+from pypy.module.micronumpy.strideops import get_shape_from_iterable, \
shape_agreement, shape_agreement_multiple, is_c_contiguous, is_f_contiguous
from pypy.module.micronumpy.casting import can_cast_array
diff --git a/pypy/module/micronumpy/nditer.py b/pypy/module/micronumpy/nditer.py
--- a/pypy/module/micronumpy/nditer.py
+++ b/pypy/module/micronumpy/nditer.py
@@ -7,8 +7,7 @@
from pypy.module.micronumpy.base import W_NDimArray, convert_to_array,
W_NumpyObject
from pypy.module.micronumpy.descriptor import decode_w_dtype
from pypy.module.micronumpy.iterators import ArrayIter
-from pypy.module.micronumpy.strides import (calculate_broadcast_strides,
- shape_agreement,
shape_agreement_multiple)
+from pypy.module.micronumpy import strideops
from pypy.module.micronumpy.casting import find_binop_result_dtype
@@ -213,6 +212,15 @@
def get_iter(space, order, arr, shape, dtype, op_flags, base):
imp = arr.implementation
backward = is_backward(imp, order)
+ if len(shape) == 1:
+ min_dim = 0
+ min_stride = 0xefffffff
+ for i in range(len(imp.shape)):
+ if imp.strides[i] < min_stride:
+ min_dim = i
+ min_stride = imp.strides[i]
+ return ConcreteIter(imp, imp.get_size(), shape, [imp.strides[i]],
+ [imp.backstrides[i]], op_flags, base)
if arr.is_scalar():
return ConcreteIter(imp, 1, [], [], [], op_flags, base)
if (abs(imp.strides[0]) < abs(imp.strides[-1]) and not backward) or \
@@ -220,19 +228,20 @@
# flip the strides. Is this always true for multidimension?
strides = imp.strides[:]
backstrides = imp.backstrides[:]
- shape = imp.shape[:]
strides.reverse()
backstrides.reverse()
- shape.reverse()
+ _shape = imp.shape[:]
+ _shape.reverse()
else:
strides = imp.strides
backstrides = imp.backstrides
- r = calculate_broadcast_strides(strides, backstrides, imp.shape,
+ _shape = imp.shape
+ r = strideops.calculate_broadcast_strides(strides, backstrides, _shape,
shape, backward)
if len(shape) != len(r[0]):
# shape can be shorter when using an external loop, just return a view
- return ConcreteIter(imp, imp.get_size(), imp.shape, r[0], r[1],
op_flags, base)
- return ConcreteIter(imp, imp.get_size(), shape, r[0], r[1], op_flags, base)
+ return ConcreteIter(imp, imp.get_size(), _shape, r[0], r[1], op_flags,
base)
+ return ConcreteIter(imp, imp.get_size(), _shape, r[0], r[1], op_flags,
base)
def calculate_ndim(op_in, oa_ndim):
if oa_ndim >=0:
@@ -413,15 +422,15 @@
outargs = [i for i in range(len(self.seq))
if self.seq[i] is None or self.op_flags[i].rw == 'w']
if len(outargs) > 0:
- out_shape = shape_agreement_multiple(space, [self.seq[i] for i in
outargs])
+ out_shape = strideops.shape_agreement_multiple(space, [self.seq[i]
for i in outargs])
else:
out_shape = None
if space.isinstance_w(w_itershape, space.w_tuple) or \
space.isinstance_w(w_itershape, space.w_list):
self.shape = [space.int_w(i) for i in space.listview(w_itershape)]
else:
- self.shape = shape_agreement_multiple(space, self.seq,
- shape=out_shape)
+ self.shape = strideops.shape_agreement_multiple(space, self.seq,
+ shape=out_shape)
if len(outargs) > 0:
# Make None operands writeonly and flagged for allocation
if len(self.dtypes) > 0:
@@ -443,7 +452,7 @@
else:
if not self.op_flags[i].broadcast:
# Raises if ooutput cannot be broadcast
- shape_agreement(space, self.shape, self.seq[i], False)
+ strideops.shape_agreement(space, self.shape,
self.seq[i], False)
if self.tracked_index != "":
if self.order == "K":
@@ -453,7 +462,6 @@
else:
backward = self.order != self.tracked_index
self.index_iter = IndexIterator(self.shape, backward=backward)
-
# handle w_op_dtypes part 2: copy where needed if possible
if len(self.dtypes) > 0:
for i in range(len(self.seq)):
@@ -553,7 +561,7 @@
res.append(self.getitem(it, st))
self.iters[i] = (it, it.next(st))
if len(res) < 2:
- return res[0]
+ return res[0]
return space.newtuple(res)
def iternext(self):
@@ -648,7 +656,9 @@
raise oefmt(space.w_NotImplementedError, "not implemented yet")
def descr_get_shape(self, space):
- raise oefmt(space.w_NotImplementedError, "not implemented yet")
+ if self.done:
+ raise oefmt(space.w_ValueError, "Iterator is past the end")
+ return space.newtuple([space.wrap(i) for i in self.shape])
def descr_get_value(self, space):
raise oefmt(space.w_NotImplementedError, "not implemented yet")
diff --git a/pypy/module/micronumpy/strides.py
b/pypy/module/micronumpy/strideops.py
rename from pypy/module/micronumpy/strides.py
rename to pypy/module/micronumpy/strideops.py
--- a/pypy/module/micronumpy/strides.py
+++ b/pypy/module/micronumpy/strideops.py
@@ -3,7 +3,6 @@
from pypy.module.micronumpy import support, constants as NPY
from pypy.module.micronumpy.base import W_NDimArray
-
# structures to describe slicing
class BaseChunk(object):
diff --git a/pypy/module/micronumpy/support.py
b/pypy/module/micronumpy/support.py
--- a/pypy/module/micronumpy/support.py
+++ b/pypy/module/micronumpy/support.py
@@ -11,7 +11,6 @@
space.isinstance_w(w_obj, space.w_buffer) or
isinstance(w_obj, W_NDimArray))
-
def index_w(space, w_obj):
try:
return space.int_w(space.index(w_obj))
@@ -23,7 +22,6 @@
"ellipsis (`...`), numpy.newaxis (`None`) and integer or "
"boolean arrays are valid indices")
-
@jit.unroll_safe
def product(s):
i = 1
@@ -31,7 +29,6 @@
i = ovfcheck(i * x)
return i
-
def check_and_adjust_index(space, index, size, axis):
if index < -size or index >= size:
if axis >= 0:
diff --git a/pypy/module/micronumpy/test/dummy_module.py
b/pypy/module/micronumpy/test/dummy_module.py
--- a/pypy/module/micronumpy/test/dummy_module.py
+++ b/pypy/module/micronumpy/test/dummy_module.py
@@ -38,3 +38,12 @@
a = zeros(*args, **kwargs)
a.fill(1)
return a
+
+def shape(a):
+ try:
+ result = a.shape
+ except AttributeError:
+ result = asarray(a).shape
+ return result
+
+
diff --git a/pypy/module/micronumpy/test/test_nditer.py
b/pypy/module/micronumpy/test/test_nditer.py
--- a/pypy/module/micronumpy/test/test_nditer.py
+++ b/pypy/module/micronumpy/test/test_nditer.py
@@ -149,16 +149,26 @@
# assert str(exc.value).startswith("Iterator flag EXTERNAL_LOOP
cannot")
def test_buffered(self):
- from numpy import arange, nditer, array
- a = arange(6).reshape(2,3)
- import sys
+ from numpy import arange, nditer, array, shape
+ a = arange(6).reshape(2, 3)
r = []
- for x in nditer(a, flags=['external_loop', 'buffered'], order='F'):
- r.append(x)
- array_r = array(r)
- assert len(array_r.shape) == 2
- assert array_r.shape == (1, 6)
- assert (array_r == [0, 3, 1, 4, 2, 5]).all()
+ x = nditer(a, flags=['buffered'], order='F')
+ assert x.shape == a.shape # maybe a numpy bug, but be compatible
+ for i in x:
+ r.append(i)
+ assert x.shape == a.shape
+ assert r[0].shape == ()
+ raises(ValueError, shape, x)
+ assert len(r) == 6 # buffered flattens the iterator
+ r = []
+ assert a.shape == (2, 3)
+ x = nditer(a, flags=['external_loop', 'buffered'], order='F')
+ assert x.shape == a.shape # maybe a numpy bug, but be compatible
+ for i in x:
+ r.append(i)
+ assert len(r) == 1 # external_loop coalesces the flattened iterator
+ assert r[0].shape == (6,)
+ assert (r[0] == [0, 3, 1, 4, 2, 5]).all() #respect the order
def test_op_dtype(self):
from numpy import arange, nditer, sqrt, array
@@ -186,7 +196,8 @@
import sys
a = arange(6.)
exc = raises(TypeError, nditer, a, flags=['buffered'],
op_dtypes=['float32'])
- assert str(exc.value).startswith("Iterator operand 0 dtype could not
be cast")
+ errstr = str(exc.value)
+ assert errstr.startswith("Iterator operand 0 dtype could not be cast")
r = []
for x in nditer(a, flags=['buffered'], op_dtypes=['float32'],
casting='same_kind'):
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