Author: Ronan Lamy <ronan.l...@gmail.com>
Branch: issue-2148
Changeset: r79980:4da2050ae801
Date: 2015-10-04 03:28 +0100
http://bitbucket.org/pypy/pypy/changeset/4da2050ae801/
Log: Move find_shape_and_elems() to ctors.py, since it's only used there
diff --git a/pypy/module/micronumpy/ctors.py b/pypy/module/micronumpy/ctors.py
--- a/pypy/module/micronumpy/ctors.py
+++ b/pypy/module/micronumpy/ctors.py
@@ -3,10 +3,12 @@
from rpython.rlib.buffer import SubBuffer
from rpython.rlib.rstring import strip_spaces
from rpython.rtyper.lltypesystem import lltype, rffi
+
from pypy.module.micronumpy import descriptor, loop, support
from pypy.module.micronumpy.base import (
W_NDimArray, convert_to_array, W_NumpyObject)
from pypy.module.micronumpy.converters import shape_converter
+from . import constants as NPY
def build_scalar(space, w_dtype, w_state):
@@ -82,7 +84,6 @@
return w_res
def _array(space, w_object, w_dtype=None, copy=True, w_order=None,
subok=False):
- from pypy.module.micronumpy import strides
# for anything that isn't already an array, try __array__ method first
if not isinstance(w_object, W_NDimArray):
@@ -143,7 +144,7 @@
w_base=w_base, start=imp.start)
else:
# not an array
- shape, elems_w = strides.find_shape_and_elems(space, w_object, dtype)
+ shape, elems_w = find_shape_and_elems(space, w_object, dtype)
if dtype is None and space.isinstance_w(w_object, space.w_buffer):
dtype = descriptor.get_dtype_cache(space).w_uint8dtype
if dtype is None or (dtype.is_str_or_unicode() and dtype.elsize < 1):
@@ -165,7 +166,6 @@
def numpify(space, w_object):
"""Convert the object to a W_NumpyObject"""
# XXX: code duplication with _array()
- from pypy.module.micronumpy import strides
if isinstance(w_object, W_NumpyObject):
return w_object
# for anything that isn't already an array, try __array__ method first
@@ -173,7 +173,7 @@
if w_array is not None:
return w_array
- shape, elems_w = strides.find_shape_and_elems(space, w_object, None)
+ shape, elems_w = find_shape_and_elems(space, w_object, None)
dtype = find_dtype_for_seq(space, elems_w, None)
if dtype is None:
dtype = descriptor.get_dtype_cache(space).w_float64dtype
@@ -188,6 +188,64 @@
loop.assign(space, w_arr, elems_w)
return w_arr
+def find_shape_and_elems(space, w_iterable, dtype):
+ isstr = space.isinstance_w(w_iterable, space.w_str)
+ if not support.issequence_w(space, w_iterable) or isstr:
+ if dtype is None or dtype.char != NPY.CHARLTR:
+ return [], [w_iterable]
+ is_rec_type = dtype is not None and dtype.is_record()
+ if is_rec_type and is_single_elem(space, w_iterable, is_rec_type):
+ return [], [w_iterable]
+ if isinstance(w_iterable, W_NDimArray) and w_iterable.is_scalar():
+ return [], [w_iterable]
+ return _find_shape_and_elems(space, w_iterable, is_rec_type)
+
+
+def _find_shape_and_elems(space, w_iterable, is_rec_type):
+ from pypy.objspace.std.bufferobject import W_Buffer
+ shape = [space.len_w(w_iterable)]
+ if space.isinstance_w(w_iterable, space.w_buffer):
+ batch = [space.wrap(0)] * shape[0]
+ for i in range(shape[0]):
+ batch[i] = space.ord(space.getitem(w_iterable, space.wrap(i)))
+ else:
+ batch = space.listview(w_iterable)
+ while True:
+ if not batch:
+ return shape[:], []
+ if is_single_elem(space, batch[0], is_rec_type):
+ for w_elem in batch:
+ if not is_single_elem(space, w_elem, is_rec_type):
+ raise OperationError(space.w_ValueError, space.wrap(
+ "setting an array element with a sequence"))
+ return shape[:], batch
+ new_batch = []
+ size = space.len_w(batch[0])
+ for w_elem in batch:
+ if (is_single_elem(space, w_elem, is_rec_type) or
+ space.len_w(w_elem) != size):
+ raise OperationError(space.w_ValueError, space.wrap(
+ "setting an array element with a sequence"))
+ w_array = space.lookup(w_elem, '__array__')
+ if w_array is not None:
+ # Make sure we call the array implementation of listview,
+ # since for some ndarray subclasses (matrix, for instance)
+ # listview does not reduce but rather returns the same class
+ w_elem = space.get_and_call_function(w_array, w_elem,
space.w_None)
+ new_batch += space.listview(w_elem)
+ shape.append(size)
+ batch = new_batch
+
+def is_single_elem(space, w_elem, is_rec_type):
+ if (is_rec_type and space.isinstance_w(w_elem, space.w_tuple)):
+ return True
+ if (space.isinstance_w(w_elem, space.w_tuple) or
+ space.isinstance_w(w_elem, space.w_list)):
+ return False
+ if isinstance(w_elem, W_NDimArray) and not w_elem.is_scalar():
+ return False
+ return True
+
def _dtype_guess(space, dtype, w_elem):
from .casting import scalar2dtype, find_binop_result_dtype
if isinstance(w_elem, W_NDimArray) and w_elem.is_scalar():
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
@@ -189,67 +189,6 @@
return rstrides, rbackstrides
-def is_single_elem(space, w_elem, is_rec_type):
- if (is_rec_type and space.isinstance_w(w_elem, space.w_tuple)):
- return True
- if (space.isinstance_w(w_elem, space.w_tuple) or
- space.isinstance_w(w_elem, space.w_list)):
- return False
- if isinstance(w_elem, W_NDimArray) and not w_elem.is_scalar():
- return False
- return True
-
-
-def find_shape_and_elems(space, w_iterable, dtype):
- isstr = space.isinstance_w(w_iterable, space.w_str)
- if not support.issequence_w(space, w_iterable) or isstr:
- if dtype is None or dtype.char != NPY.CHARLTR:
- return [], [w_iterable]
- is_rec_type = dtype is not None and dtype.is_record()
- if is_rec_type and is_single_elem(space, w_iterable, is_rec_type):
- return [], [w_iterable]
- if isinstance(w_iterable, W_NDimArray) and w_iterable.is_scalar():
- return [], [w_iterable]
- return _find_shape_and_elems(space, w_iterable, is_rec_type)
-
-
-def _find_shape_and_elems(space, w_iterable, is_rec_type):
- from pypy.objspace.std.bufferobject import W_Buffer
- shape = [space.len_w(w_iterable)]
- if space.isinstance_w(w_iterable, space.w_buffer):
- batch = [space.wrap(0)] * shape[0]
- for i in range(shape[0]):
- batch[i] = space.ord(space.getitem(w_iterable, space.wrap(i)))
- else:
- batch = space.listview(w_iterable)
- while True:
- if not batch:
- return shape[:], []
- if is_single_elem(space, batch[0], is_rec_type):
- for w_elem in batch:
- if not is_single_elem(space, w_elem, is_rec_type):
- raise OperationError(space.w_ValueError, space.wrap(
- "setting an array element with a sequence"))
- return shape[:], batch
- new_batch = []
- size = space.len_w(batch[0])
- for w_elem in batch:
- if (is_single_elem(space, w_elem, is_rec_type) or
- space.len_w(w_elem) != size):
- raise OperationError(space.w_ValueError, space.wrap(
- "setting an array element with a sequence"))
- w_array = space.lookup(w_elem, '__array__')
- if w_array is not None:
- # Make sure we call the array implementation of listview,
- # since for some ndarray subclasses (matrix, for instance)
- # listview does not reduce but rather returns the same class
- w_elem = space.get_and_call_function(w_array, w_elem,
space.w_None)
- new_batch += space.listview(w_elem)
- shape.append(size)
- batch = new_batch
-
-
-
@jit.unroll_safe
def shape_agreement(space, shape1, w_arr2, broadcast_down=True):
if w_arr2 is None:
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
@@ -169,7 +169,7 @@
[1, 1, 1, 105, 105]
def test_find_shape(self):
- from pypy.module.micronumpy.strides import find_shape_and_elems
+ from pypy.module.micronumpy.ctors import find_shape_and_elems
space = self.space
shape, elems = find_shape_and_elems(space,
@@ -2485,7 +2485,7 @@
x = array([(u'a', 'b')], dtype=t)
x['a'] = u'1'
assert str(x) == "[(u'1', 'b')]"
-
+
def test_boolean_indexing(self):
import numpy as np
@@ -2709,7 +2709,7 @@
"input array from shape (3,1) into shape (3)"
a[:, 1] = b[:,0] > 0.5
assert (a == [[0, 1], [0, 1], [0, 1]]).all()
-
+
def test_ufunc(self):
from numpy import array
@@ -3868,7 +3868,7 @@
assert a[0]['y'] == 2
assert a[1]['y'] == 1
-
+
a = array([(1, [])], dtype=[('a', int32), ('b', int32, 0)])
assert a['b'].shape == (1, 0)
b = loads(dumps(a))
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