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cadquery-freecad-module/Libs/numpy/core/tests/test_nditer.py

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from __future__ import division, absolute_import, print_function
import sys
import warnings
import numpy as np
from numpy import array, arange, nditer, all
from numpy.core.multiarray_tests import test_nditer_too_large
from numpy.testing import (
run_module_suite, assert_, assert_equal, assert_array_equal,
assert_raises, assert_warns, dec, HAS_REFCOUNT, suppress_warnings
)
def iter_multi_index(i):
ret = []
while not i.finished:
ret.append(i.multi_index)
i.iternext()
return ret
def iter_indices(i):
ret = []
while not i.finished:
ret.append(i.index)
i.iternext()
return ret
def iter_iterindices(i):
ret = []
while not i.finished:
ret.append(i.iterindex)
i.iternext()
return ret
@dec.skipif(not HAS_REFCOUNT, "python does not have sys.getrefcount")
def test_iter_refcount():
# Make sure the iterator doesn't leak
# Basic
a = arange(6)
dt = np.dtype('f4').newbyteorder()
rc_a = sys.getrefcount(a)
rc_dt = sys.getrefcount(dt)
it = nditer(a, [],
[['readwrite', 'updateifcopy']],
casting='unsafe',
op_dtypes=[dt])
assert_(not it.iterationneedsapi)
assert_(sys.getrefcount(a) > rc_a)
assert_(sys.getrefcount(dt) > rc_dt)
it = None
assert_equal(sys.getrefcount(a), rc_a)
assert_equal(sys.getrefcount(dt), rc_dt)
# With a copy
a = arange(6, dtype='f4')
dt = np.dtype('f4')
rc_a = sys.getrefcount(a)
rc_dt = sys.getrefcount(dt)
it = nditer(a, [],
[['readwrite']],
op_dtypes=[dt])
rc2_a = sys.getrefcount(a)
rc2_dt = sys.getrefcount(dt)
it2 = it.copy()
assert_(sys.getrefcount(a) > rc2_a)
assert_(sys.getrefcount(dt) > rc2_dt)
it = None
assert_equal(sys.getrefcount(a), rc2_a)
assert_equal(sys.getrefcount(dt), rc2_dt)
it2 = None
assert_equal(sys.getrefcount(a), rc_a)
assert_equal(sys.getrefcount(dt), rc_dt)
del it2 # avoid pyflakes unused variable warning
def test_iter_best_order():
# The iterator should always find the iteration order
# with increasing memory addresses
# Test the ordering for 1-D to 5-D shapes
for shape in [(5,), (3, 4), (2, 3, 4), (2, 3, 4, 3), (2, 3, 2, 2, 3)]:
a = arange(np.prod(shape))
# Test each combination of positive and negative strides
for dirs in range(2**len(shape)):
dirs_index = [slice(None)]*len(shape)
for bit in range(len(shape)):
if ((2**bit) & dirs):
dirs_index[bit] = slice(None, None, -1)
dirs_index = tuple(dirs_index)
aview = a.reshape(shape)[dirs_index]
# C-order
i = nditer(aview, [], [['readonly']])
assert_equal([x for x in i], a)
# Fortran-order
i = nditer(aview.T, [], [['readonly']])
assert_equal([x for x in i], a)
# Other order
if len(shape) > 2:
i = nditer(aview.swapaxes(0, 1), [], [['readonly']])
assert_equal([x for x in i], a)
def test_iter_c_order():
# Test forcing C order
# Test the ordering for 1-D to 5-D shapes
for shape in [(5,), (3, 4), (2, 3, 4), (2, 3, 4, 3), (2, 3, 2, 2, 3)]:
a = arange(np.prod(shape))
# Test each combination of positive and negative strides
for dirs in range(2**len(shape)):
dirs_index = [slice(None)]*len(shape)
for bit in range(len(shape)):
if ((2**bit) & dirs):
dirs_index[bit] = slice(None, None, -1)
dirs_index = tuple(dirs_index)
aview = a.reshape(shape)[dirs_index]
# C-order
i = nditer(aview, order='C')
assert_equal([x for x in i], aview.ravel(order='C'))
# Fortran-order
i = nditer(aview.T, order='C')
assert_equal([x for x in i], aview.T.ravel(order='C'))
# Other order
if len(shape) > 2:
i = nditer(aview.swapaxes(0, 1), order='C')
assert_equal([x for x in i],
aview.swapaxes(0, 1).ravel(order='C'))
def test_iter_f_order():
# Test forcing F order
# Test the ordering for 1-D to 5-D shapes
for shape in [(5,), (3, 4), (2, 3, 4), (2, 3, 4, 3), (2, 3, 2, 2, 3)]:
a = arange(np.prod(shape))
# Test each combination of positive and negative strides
for dirs in range(2**len(shape)):
dirs_index = [slice(None)]*len(shape)
for bit in range(len(shape)):
if ((2**bit) & dirs):
dirs_index[bit] = slice(None, None, -1)
dirs_index = tuple(dirs_index)
aview = a.reshape(shape)[dirs_index]
# C-order
i = nditer(aview, order='F')
assert_equal([x for x in i], aview.ravel(order='F'))
# Fortran-order
i = nditer(aview.T, order='F')
assert_equal([x for x in i], aview.T.ravel(order='F'))
# Other order
if len(shape) > 2:
i = nditer(aview.swapaxes(0, 1), order='F')
assert_equal([x for x in i],
aview.swapaxes(0, 1).ravel(order='F'))
def test_iter_c_or_f_order():
# Test forcing any contiguous (C or F) order
# Test the ordering for 1-D to 5-D shapes
for shape in [(5,), (3, 4), (2, 3, 4), (2, 3, 4, 3), (2, 3, 2, 2, 3)]:
a = arange(np.prod(shape))
# Test each combination of positive and negative strides
for dirs in range(2**len(shape)):
dirs_index = [slice(None)]*len(shape)
for bit in range(len(shape)):
if ((2**bit) & dirs):
dirs_index[bit] = slice(None, None, -1)
dirs_index = tuple(dirs_index)
aview = a.reshape(shape)[dirs_index]
# C-order
i = nditer(aview, order='A')
assert_equal([x for x in i], aview.ravel(order='A'))
# Fortran-order
i = nditer(aview.T, order='A')
assert_equal([x for x in i], aview.T.ravel(order='A'))
# Other order
if len(shape) > 2:
i = nditer(aview.swapaxes(0, 1), order='A')
assert_equal([x for x in i],
aview.swapaxes(0, 1).ravel(order='A'))
def test_iter_best_order_multi_index_1d():
# The multi-indices should be correct with any reordering
a = arange(4)
# 1D order
i = nditer(a, ['multi_index'], [['readonly']])
assert_equal(iter_multi_index(i), [(0,), (1,), (2,), (3,)])
# 1D reversed order
i = nditer(a[::-1], ['multi_index'], [['readonly']])
assert_equal(iter_multi_index(i), [(3,), (2,), (1,), (0,)])
def test_iter_best_order_multi_index_2d():
# The multi-indices should be correct with any reordering
a = arange(6)
# 2D C-order
i = nditer(a.reshape(2, 3), ['multi_index'], [['readonly']])
assert_equal(iter_multi_index(i), [(0, 0), (0, 1), (0, 2), (1, 0), (1, 1), (1, 2)])
# 2D Fortran-order
i = nditer(a.reshape(2, 3).copy(order='F'), ['multi_index'], [['readonly']])
assert_equal(iter_multi_index(i), [(0, 0), (1, 0), (0, 1), (1, 1), (0, 2), (1, 2)])
# 2D reversed C-order
i = nditer(a.reshape(2, 3)[::-1], ['multi_index'], [['readonly']])
assert_equal(iter_multi_index(i), [(1, 0), (1, 1), (1, 2), (0, 0), (0, 1), (0, 2)])
i = nditer(a.reshape(2, 3)[:, ::-1], ['multi_index'], [['readonly']])
assert_equal(iter_multi_index(i), [(0, 2), (0, 1), (0, 0), (1, 2), (1, 1), (1, 0)])
i = nditer(a.reshape(2, 3)[::-1, ::-1], ['multi_index'], [['readonly']])
assert_equal(iter_multi_index(i), [(1, 2), (1, 1), (1, 0), (0, 2), (0, 1), (0, 0)])
# 2D reversed Fortran-order
i = nditer(a.reshape(2, 3).copy(order='F')[::-1], ['multi_index'], [['readonly']])
assert_equal(iter_multi_index(i), [(1, 0), (0, 0), (1, 1), (0, 1), (1, 2), (0, 2)])
i = nditer(a.reshape(2, 3).copy(order='F')[:, ::-1],
['multi_index'], [['readonly']])
assert_equal(iter_multi_index(i), [(0, 2), (1, 2), (0, 1), (1, 1), (0, 0), (1, 0)])
i = nditer(a.reshape(2, 3).copy(order='F')[::-1, ::-1],
['multi_index'], [['readonly']])
assert_equal(iter_multi_index(i), [(1, 2), (0, 2), (1, 1), (0, 1), (1, 0), (0, 0)])
def test_iter_best_order_multi_index_3d():
# The multi-indices should be correct with any reordering
a = arange(12)
# 3D C-order
i = nditer(a.reshape(2, 3, 2), ['multi_index'], [['readonly']])
assert_equal(iter_multi_index(i),
[(0, 0, 0), (0, 0, 1), (0, 1, 0), (0, 1, 1), (0, 2, 0), (0, 2, 1),
(1, 0, 0), (1, 0, 1), (1, 1, 0), (1, 1, 1), (1, 2, 0), (1, 2, 1)])
# 3D Fortran-order
i = nditer(a.reshape(2, 3, 2).copy(order='F'), ['multi_index'], [['readonly']])
assert_equal(iter_multi_index(i),
[(0, 0, 0), (1, 0, 0), (0, 1, 0), (1, 1, 0), (0, 2, 0), (1, 2, 0),
(0, 0, 1), (1, 0, 1), (0, 1, 1), (1, 1, 1), (0, 2, 1), (1, 2, 1)])
# 3D reversed C-order
i = nditer(a.reshape(2, 3, 2)[::-1], ['multi_index'], [['readonly']])
assert_equal(iter_multi_index(i),
[(1, 0, 0), (1, 0, 1), (1, 1, 0), (1, 1, 1), (1, 2, 0), (1, 2, 1),
(0, 0, 0), (0, 0, 1), (0, 1, 0), (0, 1, 1), (0, 2, 0), (0, 2, 1)])
i = nditer(a.reshape(2, 3, 2)[:, ::-1], ['multi_index'], [['readonly']])
assert_equal(iter_multi_index(i),
[(0, 2, 0), (0, 2, 1), (0, 1, 0), (0, 1, 1), (0, 0, 0), (0, 0, 1),
(1, 2, 0), (1, 2, 1), (1, 1, 0), (1, 1, 1), (1, 0, 0), (1, 0, 1)])
i = nditer(a.reshape(2, 3, 2)[:,:, ::-1], ['multi_index'], [['readonly']])
assert_equal(iter_multi_index(i),
[(0, 0, 1), (0, 0, 0), (0, 1, 1), (0, 1, 0), (0, 2, 1), (0, 2, 0),
(1, 0, 1), (1, 0, 0), (1, 1, 1), (1, 1, 0), (1, 2, 1), (1, 2, 0)])
# 3D reversed Fortran-order
i = nditer(a.reshape(2, 3, 2).copy(order='F')[::-1],
['multi_index'], [['readonly']])
assert_equal(iter_multi_index(i),
[(1, 0, 0), (0, 0, 0), (1, 1, 0), (0, 1, 0), (1, 2, 0), (0, 2, 0),
(1, 0, 1), (0, 0, 1), (1, 1, 1), (0, 1, 1), (1, 2, 1), (0, 2, 1)])
i = nditer(a.reshape(2, 3, 2).copy(order='F')[:, ::-1],
['multi_index'], [['readonly']])
assert_equal(iter_multi_index(i),
[(0, 2, 0), (1, 2, 0), (0, 1, 0), (1, 1, 0), (0, 0, 0), (1, 0, 0),
(0, 2, 1), (1, 2, 1), (0, 1, 1), (1, 1, 1), (0, 0, 1), (1, 0, 1)])
i = nditer(a.reshape(2, 3, 2).copy(order='F')[:,:, ::-1],
['multi_index'], [['readonly']])
assert_equal(iter_multi_index(i),
[(0, 0, 1), (1, 0, 1), (0, 1, 1), (1, 1, 1), (0, 2, 1), (1, 2, 1),
(0, 0, 0), (1, 0, 0), (0, 1, 0), (1, 1, 0), (0, 2, 0), (1, 2, 0)])
def test_iter_best_order_c_index_1d():
# The C index should be correct with any reordering
a = arange(4)
# 1D order
i = nditer(a, ['c_index'], [['readonly']])
assert_equal(iter_indices(i), [0, 1, 2, 3])
# 1D reversed order
i = nditer(a[::-1], ['c_index'], [['readonly']])
assert_equal(iter_indices(i), [3, 2, 1, 0])
def test_iter_best_order_c_index_2d():
# The C index should be correct with any reordering
a = arange(6)
# 2D C-order
i = nditer(a.reshape(2, 3), ['c_index'], [['readonly']])
assert_equal(iter_indices(i), [0, 1, 2, 3, 4, 5])
# 2D Fortran-order
i = nditer(a.reshape(2, 3).copy(order='F'),
['c_index'], [['readonly']])
assert_equal(iter_indices(i), [0, 3, 1, 4, 2, 5])
# 2D reversed C-order
i = nditer(a.reshape(2, 3)[::-1], ['c_index'], [['readonly']])
assert_equal(iter_indices(i), [3, 4, 5, 0, 1, 2])
i = nditer(a.reshape(2, 3)[:, ::-1], ['c_index'], [['readonly']])
assert_equal(iter_indices(i), [2, 1, 0, 5, 4, 3])
i = nditer(a.reshape(2, 3)[::-1, ::-1], ['c_index'], [['readonly']])
assert_equal(iter_indices(i), [5, 4, 3, 2, 1, 0])
# 2D reversed Fortran-order
i = nditer(a.reshape(2, 3).copy(order='F')[::-1],
['c_index'], [['readonly']])
assert_equal(iter_indices(i), [3, 0, 4, 1, 5, 2])
i = nditer(a.reshape(2, 3).copy(order='F')[:, ::-1],
['c_index'], [['readonly']])
assert_equal(iter_indices(i), [2, 5, 1, 4, 0, 3])
i = nditer(a.reshape(2, 3).copy(order='F')[::-1, ::-1],
['c_index'], [['readonly']])
assert_equal(iter_indices(i), [5, 2, 4, 1, 3, 0])
def test_iter_best_order_c_index_3d():
# The C index should be correct with any reordering
a = arange(12)
# 3D C-order
i = nditer(a.reshape(2, 3, 2), ['c_index'], [['readonly']])
assert_equal(iter_indices(i),
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])
# 3D Fortran-order
i = nditer(a.reshape(2, 3, 2).copy(order='F'),
['c_index'], [['readonly']])
assert_equal(iter_indices(i),
[0, 6, 2, 8, 4, 10, 1, 7, 3, 9, 5, 11])
# 3D reversed C-order
i = nditer(a.reshape(2, 3, 2)[::-1], ['c_index'], [['readonly']])
assert_equal(iter_indices(i),
[6, 7, 8, 9, 10, 11, 0, 1, 2, 3, 4, 5])
i = nditer(a.reshape(2, 3, 2)[:, ::-1], ['c_index'], [['readonly']])
assert_equal(iter_indices(i),
[4, 5, 2, 3, 0, 1, 10, 11, 8, 9, 6, 7])
i = nditer(a.reshape(2, 3, 2)[:,:, ::-1], ['c_index'], [['readonly']])
assert_equal(iter_indices(i),
[1, 0, 3, 2, 5, 4, 7, 6, 9, 8, 11, 10])
# 3D reversed Fortran-order
i = nditer(a.reshape(2, 3, 2).copy(order='F')[::-1],
['c_index'], [['readonly']])
assert_equal(iter_indices(i),
[6, 0, 8, 2, 10, 4, 7, 1, 9, 3, 11, 5])
i = nditer(a.reshape(2, 3, 2).copy(order='F')[:, ::-1],
['c_index'], [['readonly']])
assert_equal(iter_indices(i),
[4, 10, 2, 8, 0, 6, 5, 11, 3, 9, 1, 7])
i = nditer(a.reshape(2, 3, 2).copy(order='F')[:,:, ::-1],
['c_index'], [['readonly']])
assert_equal(iter_indices(i),
[1, 7, 3, 9, 5, 11, 0, 6, 2, 8, 4, 10])
def test_iter_best_order_f_index_1d():
# The Fortran index should be correct with any reordering
a = arange(4)
# 1D order
i = nditer(a, ['f_index'], [['readonly']])
assert_equal(iter_indices(i), [0, 1, 2, 3])
# 1D reversed order
i = nditer(a[::-1], ['f_index'], [['readonly']])
assert_equal(iter_indices(i), [3, 2, 1, 0])
def test_iter_best_order_f_index_2d():
# The Fortran index should be correct with any reordering
a = arange(6)
# 2D C-order
i = nditer(a.reshape(2, 3), ['f_index'], [['readonly']])
assert_equal(iter_indices(i), [0, 2, 4, 1, 3, 5])
# 2D Fortran-order
i = nditer(a.reshape(2, 3).copy(order='F'),
['f_index'], [['readonly']])
assert_equal(iter_indices(i), [0, 1, 2, 3, 4, 5])
# 2D reversed C-order
i = nditer(a.reshape(2, 3)[::-1], ['f_index'], [['readonly']])
assert_equal(iter_indices(i), [1, 3, 5, 0, 2, 4])
i = nditer(a.reshape(2, 3)[:, ::-1], ['f_index'], [['readonly']])
assert_equal(iter_indices(i), [4, 2, 0, 5, 3, 1])
i = nditer(a.reshape(2, 3)[::-1, ::-1], ['f_index'], [['readonly']])
assert_equal(iter_indices(i), [5, 3, 1, 4, 2, 0])
# 2D reversed Fortran-order
i = nditer(a.reshape(2, 3).copy(order='F')[::-1],
['f_index'], [['readonly']])
assert_equal(iter_indices(i), [1, 0, 3, 2, 5, 4])
i = nditer(a.reshape(2, 3).copy(order='F')[:, ::-1],
['f_index'], [['readonly']])
assert_equal(iter_indices(i), [4, 5, 2, 3, 0, 1])
i = nditer(a.reshape(2, 3).copy(order='F')[::-1, ::-1],
['f_index'], [['readonly']])
assert_equal(iter_indices(i), [5, 4, 3, 2, 1, 0])
def test_iter_best_order_f_index_3d():
# The Fortran index should be correct with any reordering
a = arange(12)
# 3D C-order
i = nditer(a.reshape(2, 3, 2), ['f_index'], [['readonly']])
assert_equal(iter_indices(i),
[0, 6, 2, 8, 4, 10, 1, 7, 3, 9, 5, 11])
# 3D Fortran-order
i = nditer(a.reshape(2, 3, 2).copy(order='F'),
['f_index'], [['readonly']])
assert_equal(iter_indices(i),
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])
# 3D reversed C-order
i = nditer(a.reshape(2, 3, 2)[::-1], ['f_index'], [['readonly']])
assert_equal(iter_indices(i),
[1, 7, 3, 9, 5, 11, 0, 6, 2, 8, 4, 10])
i = nditer(a.reshape(2, 3, 2)[:, ::-1], ['f_index'], [['readonly']])
assert_equal(iter_indices(i),
[4, 10, 2, 8, 0, 6, 5, 11, 3, 9, 1, 7])
i = nditer(a.reshape(2, 3, 2)[:,:, ::-1], ['f_index'], [['readonly']])
assert_equal(iter_indices(i),
[6, 0, 8, 2, 10, 4, 7, 1, 9, 3, 11, 5])
# 3D reversed Fortran-order
i = nditer(a.reshape(2, 3, 2).copy(order='F')[::-1],
['f_index'], [['readonly']])
assert_equal(iter_indices(i),
[1, 0, 3, 2, 5, 4, 7, 6, 9, 8, 11, 10])
i = nditer(a.reshape(2, 3, 2).copy(order='F')[:, ::-1],
['f_index'], [['readonly']])
assert_equal(iter_indices(i),
[4, 5, 2, 3, 0, 1, 10, 11, 8, 9, 6, 7])
i = nditer(a.reshape(2, 3, 2).copy(order='F')[:,:, ::-1],
['f_index'], [['readonly']])
assert_equal(iter_indices(i),
[6, 7, 8, 9, 10, 11, 0, 1, 2, 3, 4, 5])
def test_iter_no_inner_full_coalesce():
# Check no_inner iterators which coalesce into a single inner loop
for shape in [(5,), (3, 4), (2, 3, 4), (2, 3, 4, 3), (2, 3, 2, 2, 3)]:
size = np.prod(shape)
a = arange(size)
# Test each combination of forward and backwards indexing
for dirs in range(2**len(shape)):
dirs_index = [slice(None)]*len(shape)
for bit in range(len(shape)):
if ((2**bit) & dirs):
dirs_index[bit] = slice(None, None, -1)
dirs_index = tuple(dirs_index)
aview = a.reshape(shape)[dirs_index]
# C-order
i = nditer(aview, ['external_loop'], [['readonly']])
assert_equal(i.ndim, 1)
assert_equal(i[0].shape, (size,))
# Fortran-order
i = nditer(aview.T, ['external_loop'], [['readonly']])
assert_equal(i.ndim, 1)
assert_equal(i[0].shape, (size,))
# Other order
if len(shape) > 2:
i = nditer(aview.swapaxes(0, 1),
['external_loop'], [['readonly']])
assert_equal(i.ndim, 1)
assert_equal(i[0].shape, (size,))
def test_iter_no_inner_dim_coalescing():
# Check no_inner iterators whose dimensions may not coalesce completely
# Skipping the last element in a dimension prevents coalescing
# with the next-bigger dimension
a = arange(24).reshape(2, 3, 4)[:,:, :-1]
i = nditer(a, ['external_loop'], [['readonly']])
assert_equal(i.ndim, 2)
assert_equal(i[0].shape, (3,))
a = arange(24).reshape(2, 3, 4)[:, :-1,:]
i = nditer(a, ['external_loop'], [['readonly']])
assert_equal(i.ndim, 2)
assert_equal(i[0].shape, (8,))
a = arange(24).reshape(2, 3, 4)[:-1,:,:]
i = nditer(a, ['external_loop'], [['readonly']])
assert_equal(i.ndim, 1)
assert_equal(i[0].shape, (12,))
# Even with lots of 1-sized dimensions, should still coalesce
a = arange(24).reshape(1, 1, 2, 1, 1, 3, 1, 1, 4, 1, 1)
i = nditer(a, ['external_loop'], [['readonly']])
assert_equal(i.ndim, 1)
assert_equal(i[0].shape, (24,))
def test_iter_dim_coalescing():
# Check that the correct number of dimensions are coalesced
# Tracking a multi-index disables coalescing
a = arange(24).reshape(2, 3, 4)
i = nditer(a, ['multi_index'], [['readonly']])
assert_equal(i.ndim, 3)
# A tracked index can allow coalescing if it's compatible with the array
a3d = arange(24).reshape(2, 3, 4)
i = nditer(a3d, ['c_index'], [['readonly']])
assert_equal(i.ndim, 1)
i = nditer(a3d.swapaxes(0, 1), ['c_index'], [['readonly']])
assert_equal(i.ndim, 3)
i = nditer(a3d.T, ['c_index'], [['readonly']])
assert_equal(i.ndim, 3)
i = nditer(a3d.T, ['f_index'], [['readonly']])
assert_equal(i.ndim, 1)
i = nditer(a3d.T.swapaxes(0, 1), ['f_index'], [['readonly']])
assert_equal(i.ndim, 3)
# When C or F order is forced, coalescing may still occur
a3d = arange(24).reshape(2, 3, 4)
i = nditer(a3d, order='C')
assert_equal(i.ndim, 1)
i = nditer(a3d.T, order='C')
assert_equal(i.ndim, 3)
i = nditer(a3d, order='F')
assert_equal(i.ndim, 3)
i = nditer(a3d.T, order='F')
assert_equal(i.ndim, 1)
i = nditer(a3d, order='A')
assert_equal(i.ndim, 1)
i = nditer(a3d.T, order='A')
assert_equal(i.ndim, 1)
def test_iter_broadcasting():
# Standard NumPy broadcasting rules
# 1D with scalar
i = nditer([arange(6), np.int32(2)], ['multi_index'], [['readonly']]*2)
assert_equal(i.itersize, 6)
assert_equal(i.shape, (6,))
# 2D with scalar
i = nditer([arange(6).reshape(2, 3), np.int32(2)],
['multi_index'], [['readonly']]*2)
assert_equal(i.itersize, 6)
assert_equal(i.shape, (2, 3))
# 2D with 1D
i = nditer([arange(6).reshape(2, 3), arange(3)],
['multi_index'], [['readonly']]*2)
assert_equal(i.itersize, 6)
assert_equal(i.shape, (2, 3))
i = nditer([arange(2).reshape(2, 1), arange(3)],
['multi_index'], [['readonly']]*2)
assert_equal(i.itersize, 6)
assert_equal(i.shape, (2, 3))
# 2D with 2D
i = nditer([arange(2).reshape(2, 1), arange(3).reshape(1, 3)],
['multi_index'], [['readonly']]*2)
assert_equal(i.itersize, 6)
assert_equal(i.shape, (2, 3))
# 3D with scalar
i = nditer([np.int32(2), arange(24).reshape(4, 2, 3)],
['multi_index'], [['readonly']]*2)
assert_equal(i.itersize, 24)
assert_equal(i.shape, (4, 2, 3))
# 3D with 1D
i = nditer([arange(3), arange(24).reshape(4, 2, 3)],
['multi_index'], [['readonly']]*2)
assert_equal(i.itersize, 24)
assert_equal(i.shape, (4, 2, 3))
i = nditer([arange(3), arange(8).reshape(4, 2, 1)],
['multi_index'], [['readonly']]*2)
assert_equal(i.itersize, 24)
assert_equal(i.shape, (4, 2, 3))
# 3D with 2D
i = nditer([arange(6).reshape(2, 3), arange(24).reshape(4, 2, 3)],
['multi_index'], [['readonly']]*2)
assert_equal(i.itersize, 24)
assert_equal(i.shape, (4, 2, 3))
i = nditer([arange(2).reshape(2, 1), arange(24).reshape(4, 2, 3)],
['multi_index'], [['readonly']]*2)
assert_equal(i.itersize, 24)
assert_equal(i.shape, (4, 2, 3))
i = nditer([arange(3).reshape(1, 3), arange(8).reshape(4, 2, 1)],
['multi_index'], [['readonly']]*2)
assert_equal(i.itersize, 24)
assert_equal(i.shape, (4, 2, 3))
# 3D with 3D
i = nditer([arange(2).reshape(1, 2, 1), arange(3).reshape(1, 1, 3),
arange(4).reshape(4, 1, 1)],
['multi_index'], [['readonly']]*3)
assert_equal(i.itersize, 24)
assert_equal(i.shape, (4, 2, 3))
i = nditer([arange(6).reshape(1, 2, 3), arange(4).reshape(4, 1, 1)],
['multi_index'], [['readonly']]*2)
assert_equal(i.itersize, 24)
assert_equal(i.shape, (4, 2, 3))
i = nditer([arange(24).reshape(4, 2, 3), arange(12).reshape(4, 1, 3)],
['multi_index'], [['readonly']]*2)
assert_equal(i.itersize, 24)
assert_equal(i.shape, (4, 2, 3))
def test_iter_itershape():
# Check that allocated outputs work with a specified shape
a = np.arange(6, dtype='i2').reshape(2, 3)
i = nditer([a, None], [], [['readonly'], ['writeonly', 'allocate']],
op_axes=[[0, 1, None], None],
itershape=(-1, -1, 4))
assert_equal(i.operands[1].shape, (2, 3, 4))
assert_equal(i.operands[1].strides, (24, 8, 2))
i = nditer([a.T, None], [], [['readonly'], ['writeonly', 'allocate']],
op_axes=[[0, 1, None], None],
itershape=(-1, -1, 4))
assert_equal(i.operands[1].shape, (3, 2, 4))
assert_equal(i.operands[1].strides, (8, 24, 2))
i = nditer([a.T, None], [], [['readonly'], ['writeonly', 'allocate']],
order='F',
op_axes=[[0, 1, None], None],
itershape=(-1, -1, 4))
assert_equal(i.operands[1].shape, (3, 2, 4))
assert_equal(i.operands[1].strides, (2, 6, 12))
# If we specify 1 in the itershape, it shouldn't allow broadcasting
# of that dimension to a bigger value
assert_raises(ValueError, nditer, [a, None], [],
[['readonly'], ['writeonly', 'allocate']],
op_axes=[[0, 1, None], None],
itershape=(-1, 1, 4))
# Test bug that for no op_axes but itershape, they are NULLed correctly
i = np.nditer([np.ones(2), None, None], itershape=(2,))
def test_iter_broadcasting_errors():
# Check that errors are thrown for bad broadcasting shapes
# 1D with 1D
assert_raises(ValueError, nditer, [arange(2), arange(3)],
[], [['readonly']]*2)
# 2D with 1D
assert_raises(ValueError, nditer,
[arange(6).reshape(2, 3), arange(2)],
[], [['readonly']]*2)
# 2D with 2D
assert_raises(ValueError, nditer,
[arange(6).reshape(2, 3), arange(9).reshape(3, 3)],
[], [['readonly']]*2)
assert_raises(ValueError, nditer,
[arange(6).reshape(2, 3), arange(4).reshape(2, 2)],
[], [['readonly']]*2)
# 3D with 3D
assert_raises(ValueError, nditer,
[arange(36).reshape(3, 3, 4), arange(24).reshape(2, 3, 4)],
[], [['readonly']]*2)
assert_raises(ValueError, nditer,
[arange(8).reshape(2, 4, 1), arange(24).reshape(2, 3, 4)],
[], [['readonly']]*2)
# Verify that the error message mentions the right shapes
try:
nditer([arange(2).reshape(1, 2, 1),
arange(3).reshape(1, 3),
arange(6).reshape(2, 3)],
[],
[['readonly'], ['readonly'], ['writeonly', 'no_broadcast']])
raise AssertionError('Should have raised a broadcast error')
except ValueError as e:
msg = str(e)
# The message should contain the shape of the 3rd operand
assert_(msg.find('(2,3)') >= 0,
'Message "%s" doesn\'t contain operand shape (2,3)' % msg)
# The message should contain the broadcast shape
assert_(msg.find('(1,2,3)') >= 0,
'Message "%s" doesn\'t contain broadcast shape (1,2,3)' % msg)
try:
nditer([arange(6).reshape(2, 3), arange(2)],
[],
[['readonly'], ['readonly']],
op_axes=[[0, 1], [0, np.newaxis]],
itershape=(4, 3))
raise AssertionError('Should have raised a broadcast error')
except ValueError as e:
msg = str(e)
# The message should contain "shape->remappedshape" for each operand
assert_(msg.find('(2,3)->(2,3)') >= 0,
'Message "%s" doesn\'t contain operand shape (2,3)->(2,3)' % msg)
assert_(msg.find('(2,)->(2,newaxis)') >= 0,
('Message "%s" doesn\'t contain remapped operand shape' +
'(2,)->(2,newaxis)') % msg)
# The message should contain the itershape parameter
assert_(msg.find('(4,3)') >= 0,
'Message "%s" doesn\'t contain itershape parameter (4,3)' % msg)
try:
nditer([np.zeros((2, 1, 1)), np.zeros((2,))],
[],
[['writeonly', 'no_broadcast'], ['readonly']])
raise AssertionError('Should have raised a broadcast error')
except ValueError as e:
msg = str(e)
# The message should contain the shape of the bad operand
assert_(msg.find('(2,1,1)') >= 0,
'Message "%s" doesn\'t contain operand shape (2,1,1)' % msg)
# The message should contain the broadcast shape
assert_(msg.find('(2,1,2)') >= 0,
'Message "%s" doesn\'t contain the broadcast shape (2,1,2)' % msg)
def test_iter_flags_errors():
# Check that bad combinations of flags produce errors
a = arange(6)
# Not enough operands
assert_raises(ValueError, nditer, [], [], [])
# Too many operands
assert_raises(ValueError, nditer, [a]*100, [], [['readonly']]*100)
# Bad global flag
assert_raises(ValueError, nditer, [a], ['bad flag'], [['readonly']])
# Bad op flag
assert_raises(ValueError, nditer, [a], [], [['readonly', 'bad flag']])
# Bad order parameter
assert_raises(ValueError, nditer, [a], [], [['readonly']], order='G')
# Bad casting parameter
assert_raises(ValueError, nditer, [a], [], [['readonly']], casting='noon')
# op_flags must match ops
assert_raises(ValueError, nditer, [a]*3, [], [['readonly']]*2)
# Cannot track both a C and an F index
assert_raises(ValueError, nditer, a,
['c_index', 'f_index'], [['readonly']])
# Inner iteration and multi-indices/indices are incompatible
assert_raises(ValueError, nditer, a,
['external_loop', 'multi_index'], [['readonly']])
assert_raises(ValueError, nditer, a,
['external_loop', 'c_index'], [['readonly']])
assert_raises(ValueError, nditer, a,
['external_loop', 'f_index'], [['readonly']])
# Must specify exactly one of readwrite/readonly/writeonly per operand
assert_raises(ValueError, nditer, a, [], [[]])
assert_raises(ValueError, nditer, a, [], [['readonly', 'writeonly']])
assert_raises(ValueError, nditer, a, [], [['readonly', 'readwrite']])
assert_raises(ValueError, nditer, a, [], [['writeonly', 'readwrite']])
assert_raises(ValueError, nditer, a,
[], [['readonly', 'writeonly', 'readwrite']])
# Python scalars are always readonly
assert_raises(TypeError, nditer, 1.5, [], [['writeonly']])
assert_raises(TypeError, nditer, 1.5, [], [['readwrite']])
# Array scalars are always readonly
assert_raises(TypeError, nditer, np.int32(1), [], [['writeonly']])
assert_raises(TypeError, nditer, np.int32(1), [], [['readwrite']])
# Check readonly array
a.flags.writeable = False
assert_raises(ValueError, nditer, a, [], [['writeonly']])
assert_raises(ValueError, nditer, a, [], [['readwrite']])
a.flags.writeable = True
# Multi-indices available only with the multi_index flag
i = nditer(arange(6), [], [['readonly']])
assert_raises(ValueError, lambda i:i.multi_index, i)
# Index available only with an index flag
assert_raises(ValueError, lambda i:i.index, i)
# GotoCoords and GotoIndex incompatible with buffering or no_inner
def assign_multi_index(i):
i.multi_index = (0,)
def assign_index(i):
i.index = 0
def assign_iterindex(i):
i.iterindex = 0
def assign_iterrange(i):
i.iterrange = (0, 1)
i = nditer(arange(6), ['external_loop'])
assert_raises(ValueError, assign_multi_index, i)
assert_raises(ValueError, assign_index, i)
assert_raises(ValueError, assign_iterindex, i)
assert_raises(ValueError, assign_iterrange, i)
i = nditer(arange(6), ['buffered'])
assert_raises(ValueError, assign_multi_index, i)
assert_raises(ValueError, assign_index, i)
assert_raises(ValueError, assign_iterrange, i)
# Can't iterate if size is zero
assert_raises(ValueError, nditer, np.array([]))
def test_iter_slice():
a, b, c = np.arange(3), np.arange(3), np.arange(3.)
i = nditer([a, b, c], [], ['readwrite'])
i[0:2] = (3, 3)
assert_equal(a, [3, 1, 2])
assert_equal(b, [3, 1, 2])
assert_equal(c, [0, 1, 2])
i[1] = 12
assert_equal(i[0:2], [3, 12])
def test_iter_nbo_align_contig():
# Check that byte order, alignment, and contig changes work
# Byte order change by requesting a specific dtype
a = np.arange(6, dtype='f4')
au = a.byteswap().newbyteorder()
assert_(a.dtype.byteorder != au.dtype.byteorder)
i = nditer(au, [], [['readwrite', 'updateifcopy']],
casting='equiv',
op_dtypes=[np.dtype('f4')])
assert_equal(i.dtypes[0].byteorder, a.dtype.byteorder)
assert_equal(i.operands[0].dtype.byteorder, a.dtype.byteorder)
assert_equal(i.operands[0], a)
i.operands[0][:] = 2
i = None
assert_equal(au, [2]*6)
# Byte order change by requesting NBO
a = np.arange(6, dtype='f4')
au = a.byteswap().newbyteorder()
assert_(a.dtype.byteorder != au.dtype.byteorder)
i = nditer(au, [], [['readwrite', 'updateifcopy', 'nbo']], casting='equiv')
assert_equal(i.dtypes[0].byteorder, a.dtype.byteorder)
assert_equal(i.operands[0].dtype.byteorder, a.dtype.byteorder)
assert_equal(i.operands[0], a)
i.operands[0][:] = 2
i = None
assert_equal(au, [2]*6)
# Unaligned input
a = np.zeros((6*4+1,), dtype='i1')[1:]
a.dtype = 'f4'
a[:] = np.arange(6, dtype='f4')
assert_(not a.flags.aligned)
# Without 'aligned', shouldn't copy
i = nditer(a, [], [['readonly']])
assert_(not i.operands[0].flags.aligned)
assert_equal(i.operands[0], a)
# With 'aligned', should make a copy
i = nditer(a, [], [['readwrite', 'updateifcopy', 'aligned']])
assert_(i.operands[0].flags.aligned)
assert_equal(i.operands[0], a)
i.operands[0][:] = 3
i = None
assert_equal(a, [3]*6)
# Discontiguous input
a = arange(12)
# If it is contiguous, shouldn't copy
i = nditer(a[:6], [], [['readonly']])
assert_(i.operands[0].flags.contiguous)
assert_equal(i.operands[0], a[:6])
# If it isn't contiguous, should buffer
i = nditer(a[::2], ['buffered', 'external_loop'],
[['readonly', 'contig']],
buffersize=10)
assert_(i[0].flags.contiguous)
assert_equal(i[0], a[::2])
def test_iter_array_cast():
# Check that arrays are cast as requested
# No cast 'f4' -> 'f4'
a = np.arange(6, dtype='f4').reshape(2, 3)
i = nditer(a, [], [['readwrite']], op_dtypes=[np.dtype('f4')])
assert_equal(i.operands[0], a)
assert_equal(i.operands[0].dtype, np.dtype('f4'))
# Byte-order cast '<f4' -> '>f4'
a = np.arange(6, dtype='<f4').reshape(2, 3)
i = nditer(a, [], [['readwrite', 'updateifcopy']],
casting='equiv',
op_dtypes=[np.dtype('>f4')])
assert_equal(i.operands[0], a)
assert_equal(i.operands[0].dtype, np.dtype('>f4'))
# Safe case 'f4' -> 'f8'
a = np.arange(24, dtype='f4').reshape(2, 3, 4).swapaxes(1, 2)
i = nditer(a, [], [['readonly', 'copy']],
casting='safe',
op_dtypes=[np.dtype('f8')])
assert_equal(i.operands[0], a)
assert_equal(i.operands[0].dtype, np.dtype('f8'))
# The memory layout of the temporary should match a (a is (48,4,16))
# except negative strides get flipped to positive strides.
assert_equal(i.operands[0].strides, (96, 8, 32))
a = a[::-1,:, ::-1]
i = nditer(a, [], [['readonly', 'copy']],
casting='safe',
op_dtypes=[np.dtype('f8')])
assert_equal(i.operands[0], a)
assert_equal(i.operands[0].dtype, np.dtype('f8'))
assert_equal(i.operands[0].strides, (96, 8, 32))
# Same-kind cast 'f8' -> 'f4' -> 'f8'
a = np.arange(24, dtype='f8').reshape(2, 3, 4).T
i = nditer(a, [],
[['readwrite', 'updateifcopy']],
casting='same_kind',
op_dtypes=[np.dtype('f4')])
assert_equal(i.operands[0], a)
assert_equal(i.operands[0].dtype, np.dtype('f4'))
assert_equal(i.operands[0].strides, (4, 16, 48))
# Check that UPDATEIFCOPY is activated
i.operands[0][2, 1, 1] = -12.5
assert_(a[2, 1, 1] != -12.5)
i = None
assert_equal(a[2, 1, 1], -12.5)
a = np.arange(6, dtype='i4')[::-2]
i = nditer(a, [],
[['writeonly', 'updateifcopy']],
casting='unsafe',
op_dtypes=[np.dtype('f4')])
assert_equal(i.operands[0].dtype, np.dtype('f4'))
# Even though the stride was negative in 'a', it
# becomes positive in the temporary
assert_equal(i.operands[0].strides, (4,))
i.operands[0][:] = [1, 2, 3]
i = None
assert_equal(a, [1, 2, 3])
def test_iter_array_cast_errors():
# Check that invalid casts are caught
# Need to enable copying for casts to occur
assert_raises(TypeError, nditer, arange(2, dtype='f4'), [],
[['readonly']], op_dtypes=[np.dtype('f8')])
# Also need to allow casting for casts to occur
assert_raises(TypeError, nditer, arange(2, dtype='f4'), [],
[['readonly', 'copy']], casting='no',
op_dtypes=[np.dtype('f8')])
assert_raises(TypeError, nditer, arange(2, dtype='f4'), [],
[['readonly', 'copy']], casting='equiv',
op_dtypes=[np.dtype('f8')])
assert_raises(TypeError, nditer, arange(2, dtype='f8'), [],
[['writeonly', 'updateifcopy']],
casting='no',
op_dtypes=[np.dtype('f4')])
assert_raises(TypeError, nditer, arange(2, dtype='f8'), [],
[['writeonly', 'updateifcopy']],
casting='equiv',
op_dtypes=[np.dtype('f4')])
# '<f4' -> '>f4' should not work with casting='no'
assert_raises(TypeError, nditer, arange(2, dtype='<f4'), [],
[['readonly', 'copy']], casting='no',
op_dtypes=[np.dtype('>f4')])
# 'f4' -> 'f8' is a safe cast, but 'f8' -> 'f4' isn't
assert_raises(TypeError, nditer, arange(2, dtype='f4'), [],
[['readwrite', 'updateifcopy']],
casting='safe',
op_dtypes=[np.dtype('f8')])
assert_raises(TypeError, nditer, arange(2, dtype='f8'), [],
[['readwrite', 'updateifcopy']],
casting='safe',
op_dtypes=[np.dtype('f4')])
# 'f4' -> 'i4' is neither a safe nor a same-kind cast
assert_raises(TypeError, nditer, arange(2, dtype='f4'), [],
[['readonly', 'copy']],
casting='same_kind',
op_dtypes=[np.dtype('i4')])
assert_raises(TypeError, nditer, arange(2, dtype='i4'), [],
[['writeonly', 'updateifcopy']],
casting='same_kind',
op_dtypes=[np.dtype('f4')])
def test_iter_scalar_cast():
# Check that scalars are cast as requested
# No cast 'f4' -> 'f4'
i = nditer(np.float32(2.5), [], [['readonly']],
op_dtypes=[np.dtype('f4')])
assert_equal(i.dtypes[0], np.dtype('f4'))
assert_equal(i.value.dtype, np.dtype('f4'))
assert_equal(i.value, 2.5)
# Safe cast 'f4' -> 'f8'
i = nditer(np.float32(2.5), [],
[['readonly', 'copy']],
casting='safe',
op_dtypes=[np.dtype('f8')])
assert_equal(i.dtypes[0], np.dtype('f8'))
assert_equal(i.value.dtype, np.dtype('f8'))
assert_equal(i.value, 2.5)
# Same-kind cast 'f8' -> 'f4'
i = nditer(np.float64(2.5), [],
[['readonly', 'copy']],
casting='same_kind',
op_dtypes=[np.dtype('f4')])
assert_equal(i.dtypes[0], np.dtype('f4'))
assert_equal(i.value.dtype, np.dtype('f4'))
assert_equal(i.value, 2.5)
# Unsafe cast 'f8' -> 'i4'
i = nditer(np.float64(3.0), [],
[['readonly', 'copy']],
casting='unsafe',
op_dtypes=[np.dtype('i4')])
assert_equal(i.dtypes[0], np.dtype('i4'))
assert_equal(i.value.dtype, np.dtype('i4'))
assert_equal(i.value, 3)
# Readonly scalars may be cast even without setting COPY or BUFFERED
i = nditer(3, [], [['readonly']], op_dtypes=[np.dtype('f8')])
assert_equal(i[0].dtype, np.dtype('f8'))
assert_equal(i[0], 3.)
def test_iter_scalar_cast_errors():
# Check that invalid casts are caught
# Need to allow copying/buffering for write casts of scalars to occur
assert_raises(TypeError, nditer, np.float32(2), [],
[['readwrite']], op_dtypes=[np.dtype('f8')])
assert_raises(TypeError, nditer, 2.5, [],
[['readwrite']], op_dtypes=[np.dtype('f4')])
# 'f8' -> 'f4' isn't a safe cast if the value would overflow
assert_raises(TypeError, nditer, np.float64(1e60), [],
[['readonly']],
casting='safe',
op_dtypes=[np.dtype('f4')])
# 'f4' -> 'i4' is neither a safe nor a same-kind cast
assert_raises(TypeError, nditer, np.float32(2), [],
[['readonly']],
casting='same_kind',
op_dtypes=[np.dtype('i4')])
def test_iter_object_arrays_basic():
# Check that object arrays work
obj = {'a':3,'b':'d'}
a = np.array([[1, 2, 3], None, obj, None], dtype='O')
if HAS_REFCOUNT:
rc = sys.getrefcount(obj)
# Need to allow references for object arrays
assert_raises(TypeError, nditer, a)
if HAS_REFCOUNT:
assert_equal(sys.getrefcount(obj), rc)
i = nditer(a, ['refs_ok'], ['readonly'])
vals = [x_[()] for x_ in i]
assert_equal(np.array(vals, dtype='O'), a)
vals, i, x = [None]*3
if HAS_REFCOUNT:
assert_equal(sys.getrefcount(obj), rc)
i = nditer(a.reshape(2, 2).T, ['refs_ok', 'buffered'],
['readonly'], order='C')
assert_(i.iterationneedsapi)
vals = [x_[()] for x_ in i]
assert_equal(np.array(vals, dtype='O'), a.reshape(2, 2).ravel(order='F'))
vals, i, x = [None]*3
if HAS_REFCOUNT:
assert_equal(sys.getrefcount(obj), rc)
i = nditer(a.reshape(2, 2).T, ['refs_ok', 'buffered'],
['readwrite'], order='C')
for x in i:
x[...] = None
vals, i, x = [None]*3
if HAS_REFCOUNT:
assert_(sys.getrefcount(obj) == rc-1)
assert_equal(a, np.array([None]*4, dtype='O'))
def test_iter_object_arrays_conversions():
# Conversions to/from objects
a = np.arange(6, dtype='O')
i = nditer(a, ['refs_ok', 'buffered'], ['readwrite'],
casting='unsafe', op_dtypes='i4')
for x in i:
x[...] += 1
assert_equal(a, np.arange(6)+1)
a = np.arange(6, dtype='i4')
i = nditer(a, ['refs_ok', 'buffered'], ['readwrite'],
casting='unsafe', op_dtypes='O')
for x in i:
x[...] += 1
assert_equal(a, np.arange(6)+1)
# Non-contiguous object array
a = np.zeros((6,), dtype=[('p', 'i1'), ('a', 'O')])
a = a['a']
a[:] = np.arange(6)
i = nditer(a, ['refs_ok', 'buffered'], ['readwrite'],
casting='unsafe', op_dtypes='i4')
for x in i:
x[...] += 1
assert_equal(a, np.arange(6)+1)
#Non-contiguous value array
a = np.zeros((6,), dtype=[('p', 'i1'), ('a', 'i4')])
a = a['a']
a[:] = np.arange(6) + 98172488
i = nditer(a, ['refs_ok', 'buffered'], ['readwrite'],
casting='unsafe', op_dtypes='O')
ob = i[0][()]
if HAS_REFCOUNT:
rc = sys.getrefcount(ob)
for x in i:
x[...] += 1
if HAS_REFCOUNT:
assert_(sys.getrefcount(ob) == rc-1)
assert_equal(a, np.arange(6)+98172489)
def test_iter_common_dtype():
# Check that the iterator finds a common data type correctly
i = nditer([array([3], dtype='f4'), array([0], dtype='f8')],
['common_dtype'],
[['readonly', 'copy']]*2,
casting='safe')
assert_equal(i.dtypes[0], np.dtype('f8'))
assert_equal(i.dtypes[1], np.dtype('f8'))
i = nditer([array([3], dtype='i4'), array([0], dtype='f4')],
['common_dtype'],
[['readonly', 'copy']]*2,
casting='safe')
assert_equal(i.dtypes[0], np.dtype('f8'))
assert_equal(i.dtypes[1], np.dtype('f8'))
i = nditer([array([3], dtype='f4'), array(0, dtype='f8')],
['common_dtype'],
[['readonly', 'copy']]*2,
casting='same_kind')
assert_equal(i.dtypes[0], np.dtype('f4'))
assert_equal(i.dtypes[1], np.dtype('f4'))
i = nditer([array([3], dtype='u4'), array(0, dtype='i4')],
['common_dtype'],
[['readonly', 'copy']]*2,
casting='safe')
assert_equal(i.dtypes[0], np.dtype('u4'))
assert_equal(i.dtypes[1], np.dtype('u4'))
i = nditer([array([3], dtype='u4'), array(-12, dtype='i4')],
['common_dtype'],
[['readonly', 'copy']]*2,
casting='safe')
assert_equal(i.dtypes[0], np.dtype('i8'))
assert_equal(i.dtypes[1], np.dtype('i8'))
i = nditer([array([3], dtype='u4'), array(-12, dtype='i4'),
array([2j], dtype='c8'), array([9], dtype='f8')],
['common_dtype'],
[['readonly', 'copy']]*4,
casting='safe')
assert_equal(i.dtypes[0], np.dtype('c16'))
assert_equal(i.dtypes[1], np.dtype('c16'))
assert_equal(i.dtypes[2], np.dtype('c16'))
assert_equal(i.dtypes[3], np.dtype('c16'))
assert_equal(i.value, (3, -12, 2j, 9))
# When allocating outputs, other outputs aren't factored in
i = nditer([array([3], dtype='i4'), None, array([2j], dtype='c16')], [],
[['readonly', 'copy'],
['writeonly', 'allocate'],
['writeonly']],
casting='safe')
assert_equal(i.dtypes[0], np.dtype('i4'))
assert_equal(i.dtypes[1], np.dtype('i4'))
assert_equal(i.dtypes[2], np.dtype('c16'))
# But, if common data types are requested, they are
i = nditer([array([3], dtype='i4'), None, array([2j], dtype='c16')],
['common_dtype'],
[['readonly', 'copy'],
['writeonly', 'allocate'],
['writeonly']],
casting='safe')
assert_equal(i.dtypes[0], np.dtype('c16'))
assert_equal(i.dtypes[1], np.dtype('c16'))
assert_equal(i.dtypes[2], np.dtype('c16'))
def test_iter_copy_if_overlap():
# Ensure the iterator makes copies on read/write overlap, if requested
# Copy not needed, 1 op
for flag in ['readonly', 'writeonly', 'readwrite']:
a = arange(10)
i = nditer([a], ['copy_if_overlap'], [[flag]])
assert_(i.operands[0] is a)
# Copy needed, 2 ops, read-write overlap
x = arange(10)
a = x[1:]
b = x[:-1]
i = nditer([a, b], ['copy_if_overlap'], [['readonly'], ['readwrite']])
assert_(not np.shares_memory(*i.operands))
# Copy not needed with elementwise, 2 ops, exactly same arrays
x = arange(10)
a = x
b = x
i = nditer([a, b], ['copy_if_overlap'], [['readonly', 'overlap_assume_elementwise'],
['readwrite', 'overlap_assume_elementwise']])
assert_(i.operands[0] is a and i.operands[1] is b)
i = nditer([a, b], ['copy_if_overlap'], [['readonly'], ['readwrite']])
assert_(i.operands[0] is a and not np.shares_memory(i.operands[1], b))
# Copy not needed, 2 ops, no overlap
x = arange(10)
a = x[::2]
b = x[1::2]
i = nditer([a, b], ['copy_if_overlap'], [['readonly'], ['writeonly']])
assert_(i.operands[0] is a and i.operands[1] is b)
# Copy needed, 2 ops, read-write overlap
x = arange(4, dtype=np.int8)
a = x[3:]
b = x.view(np.int32)[:1]
i = nditer([a, b], ['copy_if_overlap'], [['readonly'], ['writeonly']])
assert_(not np.shares_memory(*i.operands))
# Copy needed, 3 ops, read-write overlap
for flag in ['writeonly', 'readwrite']:
x = np.ones([10, 10])
a = x
b = x.T
c = x
i = nditer([a, b, c], ['copy_if_overlap'],
[['readonly'], ['readonly'], [flag]])
a2, b2, c2 = i.operands
assert_(not np.shares_memory(a2, c2))
assert_(not np.shares_memory(b2, c2))
# Copy not needed, 3 ops, read-only overlap
x = np.ones([10, 10])
a = x
b = x.T
c = x
i = nditer([a, b, c], ['copy_if_overlap'],
[['readonly'], ['readonly'], ['readonly']])
a2, b2, c2 = i.operands
assert_(a is a2)
assert_(b is b2)
assert_(c is c2)
# Copy not needed, 3 ops, read-only overlap
x = np.ones([10, 10])
a = x
b = np.ones([10, 10])
c = x.T
i = nditer([a, b, c], ['copy_if_overlap'],
[['readonly'], ['writeonly'], ['readonly']])
a2, b2, c2 = i.operands
assert_(a is a2)
assert_(b is b2)
assert_(c is c2)
# Copy not needed, 3 ops, write-only overlap
x = np.arange(7)
a = x[:3]
b = x[3:6]
c = x[4:7]
i = nditer([a, b, c], ['copy_if_overlap'],
[['readonly'], ['writeonly'], ['writeonly']])
a2, b2, c2 = i.operands
assert_(a is a2)
assert_(b is b2)
assert_(c is c2)
def test_iter_op_axes():
# Check that custom axes work
# Reverse the axes
a = arange(6).reshape(2, 3)
i = nditer([a, a.T], [], [['readonly']]*2, op_axes=[[0, 1], [1, 0]])
assert_(all([x == y for (x, y) in i]))
a = arange(24).reshape(2, 3, 4)
i = nditer([a.T, a], [], [['readonly']]*2, op_axes=[[2, 1, 0], None])
assert_(all([x == y for (x, y) in i]))
# Broadcast 1D to any dimension
a = arange(1, 31).reshape(2, 3, 5)
b = arange(1, 3)
i = nditer([a, b], [], [['readonly']]*2, op_axes=[None, [0, -1, -1]])
assert_equal([x*y for (x, y) in i], (a*b.reshape(2, 1, 1)).ravel())
b = arange(1, 4)
i = nditer([a, b], [], [['readonly']]*2, op_axes=[None, [-1, 0, -1]])
assert_equal([x*y for (x, y) in i], (a*b.reshape(1, 3, 1)).ravel())
b = arange(1, 6)
i = nditer([a, b], [], [['readonly']]*2,
op_axes=[None, [np.newaxis, np.newaxis, 0]])
assert_equal([x*y for (x, y) in i], (a*b.reshape(1, 1, 5)).ravel())
# Inner product-style broadcasting
a = arange(24).reshape(2, 3, 4)
b = arange(40).reshape(5, 2, 4)
i = nditer([a, b], ['multi_index'], [['readonly']]*2,
op_axes=[[0, 1, -1, -1], [-1, -1, 0, 1]])
assert_equal(i.shape, (2, 3, 5, 2))
# Matrix product-style broadcasting
a = arange(12).reshape(3, 4)
b = arange(20).reshape(4, 5)
i = nditer([a, b], ['multi_index'], [['readonly']]*2,
op_axes=[[0, -1], [-1, 1]])
assert_equal(i.shape, (3, 5))
def test_iter_op_axes_errors():
# Check that custom axes throws errors for bad inputs
# Wrong number of items in op_axes
a = arange(6).reshape(2, 3)
assert_raises(ValueError, nditer, [a, a], [], [['readonly']]*2,
op_axes=[[0], [1], [0]])
# Out of bounds items in op_axes
assert_raises(ValueError, nditer, [a, a], [], [['readonly']]*2,
op_axes=[[2, 1], [0, 1]])
assert_raises(ValueError, nditer, [a, a], [], [['readonly']]*2,
op_axes=[[0, 1], [2, -1]])
# Duplicate items in op_axes
assert_raises(ValueError, nditer, [a, a], [], [['readonly']]*2,
op_axes=[[0, 0], [0, 1]])
assert_raises(ValueError, nditer, [a, a], [], [['readonly']]*2,
op_axes=[[0, 1], [1, 1]])
# Different sized arrays in op_axes
assert_raises(ValueError, nditer, [a, a], [], [['readonly']]*2,
op_axes=[[0, 1], [0, 1, 0]])
# Non-broadcastable dimensions in the result
assert_raises(ValueError, nditer, [a, a], [], [['readonly']]*2,
op_axes=[[0, 1], [1, 0]])
def test_iter_copy():
# Check that copying the iterator works correctly
a = arange(24).reshape(2, 3, 4)
# Simple iterator
i = nditer(a)
j = i.copy()
assert_equal([x[()] for x in i], [x[()] for x in j])
i.iterindex = 3
j = i.copy()
assert_equal([x[()] for x in i], [x[()] for x in j])
# Buffered iterator
i = nditer(a, ['buffered', 'ranged'], order='F', buffersize=3)
j = i.copy()
assert_equal([x[()] for x in i], [x[()] for x in j])
i.iterindex = 3
j = i.copy()
assert_equal([x[()] for x in i], [x[()] for x in j])
i.iterrange = (3, 9)
j = i.copy()
assert_equal([x[()] for x in i], [x[()] for x in j])
i.iterrange = (2, 18)
next(i)
next(i)
j = i.copy()
assert_equal([x[()] for x in i], [x[()] for x in j])
# Casting iterator
i = nditer(a, ['buffered'], order='F', casting='unsafe',
op_dtypes='f8', buffersize=5)
j = i.copy()
i = None
assert_equal([x[()] for x in j], a.ravel(order='F'))
a = arange(24, dtype='<i4').reshape(2, 3, 4)
i = nditer(a, ['buffered'], order='F', casting='unsafe',
op_dtypes='>f8', buffersize=5)
j = i.copy()
i = None
assert_equal([x[()] for x in j], a.ravel(order='F'))
def test_iter_allocate_output_simple():
# Check that the iterator will properly allocate outputs
# Simple case
a = arange(6)
i = nditer([a, None], [], [['readonly'], ['writeonly', 'allocate']],
op_dtypes=[None, np.dtype('f4')])
assert_equal(i.operands[1].shape, a.shape)
assert_equal(i.operands[1].dtype, np.dtype('f4'))
def test_iter_allocate_output_buffered_readwrite():
# Allocated output with buffering + delay_bufalloc
a = arange(6)
i = nditer([a, None], ['buffered', 'delay_bufalloc'],
[['readonly'], ['allocate', 'readwrite']])
i.operands[1][:] = 1
i.reset()
for x in i:
x[1][...] += x[0][...]
assert_equal(i.operands[1], a+1)
def test_iter_allocate_output_itorder():
# The allocated output should match the iteration order
# C-order input, best iteration order
a = arange(6, dtype='i4').reshape(2, 3)
i = nditer([a, None], [], [['readonly'], ['writeonly', 'allocate']],
op_dtypes=[None, np.dtype('f4')])
assert_equal(i.operands[1].shape, a.shape)
assert_equal(i.operands[1].strides, a.strides)
assert_equal(i.operands[1].dtype, np.dtype('f4'))
# F-order input, best iteration order
a = arange(24, dtype='i4').reshape(2, 3, 4).T
i = nditer([a, None], [], [['readonly'], ['writeonly', 'allocate']],
op_dtypes=[None, np.dtype('f4')])
assert_equal(i.operands[1].shape, a.shape)
assert_equal(i.operands[1].strides, a.strides)
assert_equal(i.operands[1].dtype, np.dtype('f4'))
# Non-contiguous input, C iteration order
a = arange(24, dtype='i4').reshape(2, 3, 4).swapaxes(0, 1)
i = nditer([a, None], [],
[['readonly'], ['writeonly', 'allocate']],
order='C',
op_dtypes=[None, np.dtype('f4')])
assert_equal(i.operands[1].shape, a.shape)
assert_equal(i.operands[1].strides, (32, 16, 4))
assert_equal(i.operands[1].dtype, np.dtype('f4'))
def test_iter_allocate_output_opaxes():
# Specifying op_axes should work
a = arange(24, dtype='i4').reshape(2, 3, 4)
i = nditer([None, a], [], [['writeonly', 'allocate'], ['readonly']],
op_dtypes=[np.dtype('u4'), None],
op_axes=[[1, 2, 0], None])
assert_equal(i.operands[0].shape, (4, 2, 3))
assert_equal(i.operands[0].strides, (4, 48, 16))
assert_equal(i.operands[0].dtype, np.dtype('u4'))
def test_iter_allocate_output_types_promotion():
# Check type promotion of automatic outputs
i = nditer([array([3], dtype='f4'), array([0], dtype='f8'), None], [],
[['readonly']]*2+[['writeonly', 'allocate']])
assert_equal(i.dtypes[2], np.dtype('f8'))
i = nditer([array([3], dtype='i4'), array([0], dtype='f4'), None], [],
[['readonly']]*2+[['writeonly', 'allocate']])
assert_equal(i.dtypes[2], np.dtype('f8'))
i = nditer([array([3], dtype='f4'), array(0, dtype='f8'), None], [],
[['readonly']]*2+[['writeonly', 'allocate']])
assert_equal(i.dtypes[2], np.dtype('f4'))
i = nditer([array([3], dtype='u4'), array(0, dtype='i4'), None], [],
[['readonly']]*2+[['writeonly', 'allocate']])
assert_equal(i.dtypes[2], np.dtype('u4'))
i = nditer([array([3], dtype='u4'), array(-12, dtype='i4'), None], [],
[['readonly']]*2+[['writeonly', 'allocate']])
assert_equal(i.dtypes[2], np.dtype('i8'))
def test_iter_allocate_output_types_byte_order():
# Verify the rules for byte order changes
# When there's just one input, the output type exactly matches
a = array([3], dtype='u4').newbyteorder()
i = nditer([a, None], [],
[['readonly'], ['writeonly', 'allocate']])
assert_equal(i.dtypes[0], i.dtypes[1])
# With two or more inputs, the output type is in native byte order
i = nditer([a, a, None], [],
[['readonly'], ['readonly'], ['writeonly', 'allocate']])
assert_(i.dtypes[0] != i.dtypes[2])
assert_equal(i.dtypes[0].newbyteorder('='), i.dtypes[2])
def test_iter_allocate_output_types_scalar():
# If the inputs are all scalars, the output should be a scalar
i = nditer([None, 1, 2.3, np.float32(12), np.complex128(3)], [],
[['writeonly', 'allocate']] + [['readonly']]*4)
assert_equal(i.operands[0].dtype, np.dtype('complex128'))
assert_equal(i.operands[0].ndim, 0)
def test_iter_allocate_output_subtype():
# Make sure that the subtype with priority wins
# matrix vs ndarray
a = np.matrix([[1, 2], [3, 4]])
b = np.arange(4).reshape(2, 2).T
i = nditer([a, b, None], [],
[['readonly'], ['readonly'], ['writeonly', 'allocate']])
assert_equal(type(a), type(i.operands[2]))
assert_(type(b) != type(i.operands[2]))
assert_equal(i.operands[2].shape, (2, 2))
# matrix always wants things to be 2D
b = np.arange(4).reshape(1, 2, 2)
assert_raises(RuntimeError, nditer, [a, b, None], [],
[['readonly'], ['readonly'], ['writeonly', 'allocate']])
# but if subtypes are disabled, the result can still work
i = nditer([a, b, None], [],
[['readonly'], ['readonly'], ['writeonly', 'allocate', 'no_subtype']])
assert_equal(type(b), type(i.operands[2]))
assert_(type(a) != type(i.operands[2]))
assert_equal(i.operands[2].shape, (1, 2, 2))
def test_iter_allocate_output_errors():
# Check that the iterator will throw errors for bad output allocations
# Need an input if no output data type is specified
a = arange(6)
assert_raises(TypeError, nditer, [a, None], [],
[['writeonly'], ['writeonly', 'allocate']])
# Allocated output should be flagged for writing
assert_raises(ValueError, nditer, [a, None], [],
[['readonly'], ['allocate', 'readonly']])
# Allocated output can't have buffering without delayed bufalloc
assert_raises(ValueError, nditer, [a, None], ['buffered'],
['allocate', 'readwrite'])
# Must specify at least one input
assert_raises(ValueError, nditer, [None, None], [],
[['writeonly', 'allocate'],
['writeonly', 'allocate']],
op_dtypes=[np.dtype('f4'), np.dtype('f4')])
# If using op_axes, must specify all the axes
a = arange(24, dtype='i4').reshape(2, 3, 4)
assert_raises(ValueError, nditer, [a, None], [],
[['readonly'], ['writeonly', 'allocate']],
op_dtypes=[None, np.dtype('f4')],
op_axes=[None, [0, np.newaxis, 1]])
# If using op_axes, the axes must be within bounds
assert_raises(ValueError, nditer, [a, None], [],
[['readonly'], ['writeonly', 'allocate']],
op_dtypes=[None, np.dtype('f4')],
op_axes=[None, [0, 3, 1]])
# If using op_axes, there can't be duplicates
assert_raises(ValueError, nditer, [a, None], [],
[['readonly'], ['writeonly', 'allocate']],
op_dtypes=[None, np.dtype('f4')],
op_axes=[None, [0, 2, 1, 0]])
def test_iter_remove_axis():
a = arange(24).reshape(2, 3, 4)
i = nditer(a, ['multi_index'])
i.remove_axis(1)
assert_equal([x for x in i], a[:, 0,:].ravel())
a = a[::-1,:,:]
i = nditer(a, ['multi_index'])
i.remove_axis(0)
assert_equal([x for x in i], a[0,:,:].ravel())
def test_iter_remove_multi_index_inner_loop():
# Check that removing multi-index support works
a = arange(24).reshape(2, 3, 4)
i = nditer(a, ['multi_index'])
assert_equal(i.ndim, 3)
assert_equal(i.shape, (2, 3, 4))
assert_equal(i.itviews[0].shape, (2, 3, 4))
# Removing the multi-index tracking causes all dimensions to coalesce
before = [x for x in i]
i.remove_multi_index()
after = [x for x in i]
assert_equal(before, after)
assert_equal(i.ndim, 1)
assert_raises(ValueError, lambda i:i.shape, i)
assert_equal(i.itviews[0].shape, (24,))
# Removing the inner loop means there's just one iteration
i.reset()
assert_equal(i.itersize, 24)
assert_equal(i[0].shape, tuple())
i.enable_external_loop()
assert_equal(i.itersize, 24)
assert_equal(i[0].shape, (24,))
assert_equal(i.value, arange(24))
def test_iter_iterindex():
# Make sure iterindex works
buffersize = 5
a = arange(24).reshape(4, 3, 2)
for flags in ([], ['buffered']):
i = nditer(a, flags, buffersize=buffersize)
assert_equal(iter_iterindices(i), list(range(24)))
i.iterindex = 2
assert_equal(iter_iterindices(i), list(range(2, 24)))
i = nditer(a, flags, order='F', buffersize=buffersize)
assert_equal(iter_iterindices(i), list(range(24)))
i.iterindex = 5
assert_equal(iter_iterindices(i), list(range(5, 24)))
i = nditer(a[::-1], flags, order='F', buffersize=buffersize)
assert_equal(iter_iterindices(i), list(range(24)))
i.iterindex = 9
assert_equal(iter_iterindices(i), list(range(9, 24)))
i = nditer(a[::-1, ::-1], flags, order='C', buffersize=buffersize)
assert_equal(iter_iterindices(i), list(range(24)))
i.iterindex = 13
assert_equal(iter_iterindices(i), list(range(13, 24)))
i = nditer(a[::1, ::-1], flags, buffersize=buffersize)
assert_equal(iter_iterindices(i), list(range(24)))
i.iterindex = 23
assert_equal(iter_iterindices(i), list(range(23, 24)))
i.reset()
i.iterindex = 2
assert_equal(iter_iterindices(i), list(range(2, 24)))
def test_iter_iterrange():
# Make sure getting and resetting the iterrange works
buffersize = 5
a = arange(24, dtype='i4').reshape(4, 3, 2)
a_fort = a.ravel(order='F')
i = nditer(a, ['ranged'], ['readonly'], order='F',
buffersize=buffersize)
assert_equal(i.iterrange, (0, 24))
assert_equal([x[()] for x in i], a_fort)
for r in [(0, 24), (1, 2), (3, 24), (5, 5), (0, 20), (23, 24)]:
i.iterrange = r
assert_equal(i.iterrange, r)
assert_equal([x[()] for x in i], a_fort[r[0]:r[1]])
i = nditer(a, ['ranged', 'buffered'], ['readonly'], order='F',
op_dtypes='f8', buffersize=buffersize)
assert_equal(i.iterrange, (0, 24))
assert_equal([x[()] for x in i], a_fort)
for r in [(0, 24), (1, 2), (3, 24), (5, 5), (0, 20), (23, 24)]:
i.iterrange = r
assert_equal(i.iterrange, r)
assert_equal([x[()] for x in i], a_fort[r[0]:r[1]])
def get_array(i):
val = np.array([], dtype='f8')
for x in i:
val = np.concatenate((val, x))
return val
i = nditer(a, ['ranged', 'buffered', 'external_loop'],
['readonly'], order='F',
op_dtypes='f8', buffersize=buffersize)
assert_equal(i.iterrange, (0, 24))
assert_equal(get_array(i), a_fort)
for r in [(0, 24), (1, 2), (3, 24), (5, 5), (0, 20), (23, 24)]:
i.iterrange = r
assert_equal(i.iterrange, r)
assert_equal(get_array(i), a_fort[r[0]:r[1]])
def test_iter_buffering():
# Test buffering with several buffer sizes and types
arrays = []
# F-order swapped array
arrays.append(np.arange(24,
dtype='c16').reshape(2, 3, 4).T.newbyteorder().byteswap())
# Contiguous 1-dimensional array
arrays.append(np.arange(10, dtype='f4'))
# Unaligned array
a = np.zeros((4*16+1,), dtype='i1')[1:]
a.dtype = 'i4'
a[:] = np.arange(16, dtype='i4')
arrays.append(a)
# 4-D F-order array
arrays.append(np.arange(120, dtype='i4').reshape(5, 3, 2, 4).T)
for a in arrays:
for buffersize in (1, 2, 3, 5, 8, 11, 16, 1024):
vals = []
i = nditer(a, ['buffered', 'external_loop'],
[['readonly', 'nbo', 'aligned']],
order='C',
casting='equiv',
buffersize=buffersize)
while not i.finished:
assert_(i[0].size <= buffersize)
vals.append(i[0].copy())
i.iternext()
assert_equal(np.concatenate(vals), a.ravel(order='C'))
def test_iter_write_buffering():
# Test that buffering of writes is working
# F-order swapped array
a = np.arange(24).reshape(2, 3, 4).T.newbyteorder().byteswap()
i = nditer(a, ['buffered'],
[['readwrite', 'nbo', 'aligned']],
casting='equiv',
order='C',
buffersize=16)
x = 0
while not i.finished:
i[0] = x
x += 1
i.iternext()
assert_equal(a.ravel(order='C'), np.arange(24))
def test_iter_buffering_delayed_alloc():
# Test that delaying buffer allocation works
a = np.arange(6)
b = np.arange(1, dtype='f4')
i = nditer([a, b], ['buffered', 'delay_bufalloc', 'multi_index', 'reduce_ok'],
['readwrite'],
casting='unsafe',
op_dtypes='f4')
assert_(i.has_delayed_bufalloc)
assert_raises(ValueError, lambda i:i.multi_index, i)
assert_raises(ValueError, lambda i:i[0], i)
assert_raises(ValueError, lambda i:i[0:2], i)
def assign_iter(i):
i[0] = 0
assert_raises(ValueError, assign_iter, i)
i.reset()
assert_(not i.has_delayed_bufalloc)
assert_equal(i.multi_index, (0,))
assert_equal(i[0], 0)
i[1] = 1
assert_equal(i[0:2], [0, 1])
assert_equal([[x[0][()], x[1][()]] for x in i], list(zip(range(6), [1]*6)))
def test_iter_buffered_cast_simple():
# Test that buffering can handle a simple cast
a = np.arange(10, dtype='f4')
i = nditer(a, ['buffered', 'external_loop'],
[['readwrite', 'nbo', 'aligned']],
casting='same_kind',
op_dtypes=[np.dtype('f8')],
buffersize=3)
for v in i:
v[...] *= 2
assert_equal(a, 2*np.arange(10, dtype='f4'))
def test_iter_buffered_cast_byteswapped():
# Test that buffering can handle a cast which requires swap->cast->swap
a = np.arange(10, dtype='f4').newbyteorder().byteswap()
i = nditer(a, ['buffered', 'external_loop'],
[['readwrite', 'nbo', 'aligned']],
casting='same_kind',
op_dtypes=[np.dtype('f8').newbyteorder()],
buffersize=3)
for v in i:
v[...] *= 2
assert_equal(a, 2*np.arange(10, dtype='f4'))
with suppress_warnings() as sup:
sup.filter(np.ComplexWarning)
a = np.arange(10, dtype='f8').newbyteorder().byteswap()
i = nditer(a, ['buffered', 'external_loop'],
[['readwrite', 'nbo', 'aligned']],
casting='unsafe',
op_dtypes=[np.dtype('c8').newbyteorder()],
buffersize=3)
for v in i:
v[...] *= 2
assert_equal(a, 2*np.arange(10, dtype='f8'))
def test_iter_buffered_cast_byteswapped_complex():
# Test that buffering can handle a cast which requires swap->cast->copy
a = np.arange(10, dtype='c8').newbyteorder().byteswap()
a += 2j
i = nditer(a, ['buffered', 'external_loop'],
[['readwrite', 'nbo', 'aligned']],
casting='same_kind',
op_dtypes=[np.dtype('c16')],
buffersize=3)
for v in i:
v[...] *= 2
assert_equal(a, 2*np.arange(10, dtype='c8') + 4j)
a = np.arange(10, dtype='c8')
a += 2j
i = nditer(a, ['buffered', 'external_loop'],
[['readwrite', 'nbo', 'aligned']],
casting='same_kind',
op_dtypes=[np.dtype('c16').newbyteorder()],
buffersize=3)
for v in i:
v[...] *= 2
assert_equal(a, 2*np.arange(10, dtype='c8') + 4j)
a = np.arange(10, dtype=np.clongdouble).newbyteorder().byteswap()
a += 2j
i = nditer(a, ['buffered', 'external_loop'],
[['readwrite', 'nbo', 'aligned']],
casting='same_kind',
op_dtypes=[np.dtype('c16')],
buffersize=3)
for v in i:
v[...] *= 2
assert_equal(a, 2*np.arange(10, dtype=np.clongdouble) + 4j)
a = np.arange(10, dtype=np.longdouble).newbyteorder().byteswap()
i = nditer(a, ['buffered', 'external_loop'],
[['readwrite', 'nbo', 'aligned']],
casting='same_kind',
op_dtypes=[np.dtype('f4')],
buffersize=7)
for v in i:
v[...] *= 2
assert_equal(a, 2*np.arange(10, dtype=np.longdouble))
def test_iter_buffered_cast_structured_type():
# Tests buffering of structured types
# simple -> struct type (duplicates the value)
sdt = [('a', 'f4'), ('b', 'i8'), ('c', 'c8', (2, 3)), ('d', 'O')]
a = np.arange(3, dtype='f4') + 0.5
i = nditer(a, ['buffered', 'refs_ok'], ['readonly'],
casting='unsafe',
op_dtypes=sdt)
vals = [np.array(x) for x in i]
assert_equal(vals[0]['a'], 0.5)
assert_equal(vals[0]['b'], 0)
assert_equal(vals[0]['c'], [[(0.5)]*3]*2)
assert_equal(vals[0]['d'], 0.5)
assert_equal(vals[1]['a'], 1.5)
assert_equal(vals[1]['b'], 1)
assert_equal(vals[1]['c'], [[(1.5)]*3]*2)
assert_equal(vals[1]['d'], 1.5)
assert_equal(vals[0].dtype, np.dtype(sdt))
# object -> struct type
sdt = [('a', 'f4'), ('b', 'i8'), ('c', 'c8', (2, 3)), ('d', 'O')]
a = np.zeros((3,), dtype='O')
a[0] = (0.5, 0.5, [[0.5, 0.5, 0.5], [0.5, 0.5, 0.5]], 0.5)
a[1] = (1.5, 1.5, [[1.5, 1.5, 1.5], [1.5, 1.5, 1.5]], 1.5)
a[2] = (2.5, 2.5, [[2.5, 2.5, 2.5], [2.5, 2.5, 2.5]], 2.5)
if HAS_REFCOUNT:
rc = sys.getrefcount(a[0])
i = nditer(a, ['buffered', 'refs_ok'], ['readonly'],
casting='unsafe',
op_dtypes=sdt)
vals = [x.copy() for x in i]
assert_equal(vals[0]['a'], 0.5)
assert_equal(vals[0]['b'], 0)
assert_equal(vals[0]['c'], [[(0.5)]*3]*2)
assert_equal(vals[0]['d'], 0.5)
assert_equal(vals[1]['a'], 1.5)
assert_equal(vals[1]['b'], 1)
assert_equal(vals[1]['c'], [[(1.5)]*3]*2)
assert_equal(vals[1]['d'], 1.5)
assert_equal(vals[0].dtype, np.dtype(sdt))
vals, i, x = [None]*3
if HAS_REFCOUNT:
assert_equal(sys.getrefcount(a[0]), rc)
# single-field struct type -> simple
sdt = [('a', 'f4')]
a = np.array([(5.5,), (8,)], dtype=sdt)
i = nditer(a, ['buffered', 'refs_ok'], ['readonly'],
casting='unsafe',
op_dtypes='i4')
assert_equal([x_[()] for x_ in i], [5, 8])
# make sure multi-field struct type -> simple doesn't work
sdt = [('a', 'f4'), ('b', 'i8'), ('d', 'O')]
a = np.array([(5.5, 7, 'test'), (8, 10, 11)], dtype=sdt)
assert_raises(ValueError, lambda: (
nditer(a, ['buffered', 'refs_ok'], ['readonly'],
casting='unsafe',
op_dtypes='i4')))
# struct type -> struct type (field-wise copy)
sdt1 = [('a', 'f4'), ('b', 'i8'), ('d', 'O')]
sdt2 = [('d', 'u2'), ('a', 'O'), ('b', 'f8')]
a = np.array([(1, 2, 3), (4, 5, 6)], dtype=sdt1)
i = nditer(a, ['buffered', 'refs_ok'], ['readonly'],
casting='unsafe',
op_dtypes=sdt2)
assert_equal(i[0].dtype, np.dtype(sdt2))
assert_equal([np.array(x_) for x_ in i],
[np.array((1, 2, 3), dtype=sdt2),
np.array((4, 5, 6), dtype=sdt2)])
# make sure struct type -> struct type with different
# number of fields fails
sdt1 = [('a', 'f4'), ('b', 'i8'), ('d', 'O')]
sdt2 = [('b', 'O'), ('a', 'f8')]
a = np.array([(1, 2, 3), (4, 5, 6)], dtype=sdt1)
assert_raises(ValueError, lambda : (
nditer(a, ['buffered', 'refs_ok'], ['readwrite'],
casting='unsafe',
op_dtypes=sdt2)))
def test_iter_buffered_cast_subarray():
# Tests buffering of subarrays
# one element -> many (copies it to all)
sdt1 = [('a', 'f4')]
sdt2 = [('a', 'f8', (3, 2, 2))]
a = np.zeros((6,), dtype=sdt1)
a['a'] = np.arange(6)
i = nditer(a, ['buffered', 'refs_ok'], ['readonly'],
casting='unsafe',
op_dtypes=sdt2)
assert_equal(i[0].dtype, np.dtype(sdt2))
for x, count in zip(i, list(range(6))):
assert_(np.all(x['a'] == count))
# one element -> many -> back (copies it to all)
sdt1 = [('a', 'O', (1, 1))]
sdt2 = [('a', 'O', (3, 2, 2))]
a = np.zeros((6,), dtype=sdt1)
a['a'][:, 0, 0] = np.arange(6)
i = nditer(a, ['buffered', 'refs_ok'], ['readwrite'],
casting='unsafe',
op_dtypes=sdt2)
assert_equal(i[0].dtype, np.dtype(sdt2))
count = 0
for x in i:
assert_(np.all(x['a'] == count))
x['a'][0] += 2
count += 1
assert_equal(a['a'], np.arange(6).reshape(6, 1, 1)+2)
# many -> one element -> back (copies just element 0)
sdt1 = [('a', 'O', (3, 2, 2))]
sdt2 = [('a', 'O', (1,))]
a = np.zeros((6,), dtype=sdt1)
a['a'][:, 0, 0, 0] = np.arange(6)
i = nditer(a, ['buffered', 'refs_ok'], ['readwrite'],
casting='unsafe',
op_dtypes=sdt2)
assert_equal(i[0].dtype, np.dtype(sdt2))
count = 0
for x in i:
assert_equal(x['a'], count)
x['a'] += 2
count += 1
assert_equal(a['a'], np.arange(6).reshape(6, 1, 1, 1)*np.ones((1, 3, 2, 2))+2)
# many -> one element -> back (copies just element 0)
sdt1 = [('a', 'f8', (3, 2, 2))]
sdt2 = [('a', 'O', (1,))]
a = np.zeros((6,), dtype=sdt1)
a['a'][:, 0, 0, 0] = np.arange(6)
i = nditer(a, ['buffered', 'refs_ok'], ['readonly'],
casting='unsafe',
op_dtypes=sdt2)
assert_equal(i[0].dtype, np.dtype(sdt2))
count = 0
for x in i:
assert_equal(x['a'], count)
count += 1
# many -> one element (copies just element 0)
sdt1 = [('a', 'O', (3, 2, 2))]
sdt2 = [('a', 'f4', (1,))]
a = np.zeros((6,), dtype=sdt1)
a['a'][:, 0, 0, 0] = np.arange(6)
i = nditer(a, ['buffered', 'refs_ok'], ['readonly'],
casting='unsafe',
op_dtypes=sdt2)
assert_equal(i[0].dtype, np.dtype(sdt2))
count = 0
for x in i:
assert_equal(x['a'], count)
count += 1
# many -> matching shape (straightforward copy)
sdt1 = [('a', 'O', (3, 2, 2))]
sdt2 = [('a', 'f4', (3, 2, 2))]
a = np.zeros((6,), dtype=sdt1)
a['a'] = np.arange(6*3*2*2).reshape(6, 3, 2, 2)
i = nditer(a, ['buffered', 'refs_ok'], ['readonly'],
casting='unsafe',
op_dtypes=sdt2)
assert_equal(i[0].dtype, np.dtype(sdt2))
count = 0
for x in i:
assert_equal(x['a'], a[count]['a'])
count += 1
# vector -> smaller vector (truncates)
sdt1 = [('a', 'f8', (6,))]
sdt2 = [('a', 'f4', (2,))]
a = np.zeros((6,), dtype=sdt1)
a['a'] = np.arange(6*6).reshape(6, 6)
i = nditer(a, ['buffered', 'refs_ok'], ['readonly'],
casting='unsafe',
op_dtypes=sdt2)
assert_equal(i[0].dtype, np.dtype(sdt2))
count = 0
for x in i:
assert_equal(x['a'], a[count]['a'][:2])
count += 1
# vector -> bigger vector (pads with zeros)
sdt1 = [('a', 'f8', (2,))]
sdt2 = [('a', 'f4', (6,))]
a = np.zeros((6,), dtype=sdt1)
a['a'] = np.arange(6*2).reshape(6, 2)
i = nditer(a, ['buffered', 'refs_ok'], ['readonly'],
casting='unsafe',
op_dtypes=sdt2)
assert_equal(i[0].dtype, np.dtype(sdt2))
count = 0
for x in i:
assert_equal(x['a'][:2], a[count]['a'])
assert_equal(x['a'][2:], [0, 0, 0, 0])
count += 1
# vector -> matrix (broadcasts)
sdt1 = [('a', 'f8', (2,))]
sdt2 = [('a', 'f4', (2, 2))]
a = np.zeros((6,), dtype=sdt1)
a['a'] = np.arange(6*2).reshape(6, 2)
i = nditer(a, ['buffered', 'refs_ok'], ['readonly'],
casting='unsafe',
op_dtypes=sdt2)
assert_equal(i[0].dtype, np.dtype(sdt2))
count = 0
for x in i:
assert_equal(x['a'][0], a[count]['a'])
assert_equal(x['a'][1], a[count]['a'])
count += 1
# vector -> matrix (broadcasts and zero-pads)
sdt1 = [('a', 'f8', (2, 1))]
sdt2 = [('a', 'f4', (3, 2))]
a = np.zeros((6,), dtype=sdt1)
a['a'] = np.arange(6*2).reshape(6, 2, 1)
i = nditer(a, ['buffered', 'refs_ok'], ['readonly'],
casting='unsafe',
op_dtypes=sdt2)
assert_equal(i[0].dtype, np.dtype(sdt2))
count = 0
for x in i:
assert_equal(x['a'][:2, 0], a[count]['a'][:, 0])
assert_equal(x['a'][:2, 1], a[count]['a'][:, 0])
assert_equal(x['a'][2,:], [0, 0])
count += 1
# matrix -> matrix (truncates and zero-pads)
sdt1 = [('a', 'f8', (2, 3))]
sdt2 = [('a', 'f4', (3, 2))]
a = np.zeros((6,), dtype=sdt1)
a['a'] = np.arange(6*2*3).reshape(6, 2, 3)
i = nditer(a, ['buffered', 'refs_ok'], ['readonly'],
casting='unsafe',
op_dtypes=sdt2)
assert_equal(i[0].dtype, np.dtype(sdt2))
count = 0
for x in i:
assert_equal(x['a'][:2, 0], a[count]['a'][:, 0])
assert_equal(x['a'][:2, 1], a[count]['a'][:, 1])
assert_equal(x['a'][2,:], [0, 0])
count += 1
def test_iter_buffering_badwriteback():
# Writing back from a buffer cannot combine elements
# a needs write buffering, but had a broadcast dimension
a = np.arange(6).reshape(2, 3, 1)
b = np.arange(12).reshape(2, 3, 2)
assert_raises(ValueError, nditer, [a, b],
['buffered', 'external_loop'],
[['readwrite'], ['writeonly']],
order='C')
# But if a is readonly, it's fine
nditer([a, b], ['buffered', 'external_loop'],
[['readonly'], ['writeonly']],
order='C')
# If a has just one element, it's fine too (constant 0 stride, a reduction)
a = np.arange(1).reshape(1, 1, 1)
nditer([a, b], ['buffered', 'external_loop', 'reduce_ok'],
[['readwrite'], ['writeonly']],
order='C')
# check that it fails on other dimensions too
a = np.arange(6).reshape(1, 3, 2)
assert_raises(ValueError, nditer, [a, b],
['buffered', 'external_loop'],
[['readwrite'], ['writeonly']],
order='C')
a = np.arange(4).reshape(2, 1, 2)
assert_raises(ValueError, nditer, [a, b],
['buffered', 'external_loop'],
[['readwrite'], ['writeonly']],
order='C')
def test_iter_buffering_string():
# Safe casting disallows shrinking strings
a = np.array(['abc', 'a', 'abcd'], dtype=np.bytes_)
assert_equal(a.dtype, np.dtype('S4'))
assert_raises(TypeError, nditer, a, ['buffered'], ['readonly'],
op_dtypes='S2')
i = nditer(a, ['buffered'], ['readonly'], op_dtypes='S6')
assert_equal(i[0], b'abc')
assert_equal(i[0].dtype, np.dtype('S6'))
a = np.array(['abc', 'a', 'abcd'], dtype=np.unicode)
assert_equal(a.dtype, np.dtype('U4'))
assert_raises(TypeError, nditer, a, ['buffered'], ['readonly'],
op_dtypes='U2')
i = nditer(a, ['buffered'], ['readonly'], op_dtypes='U6')
assert_equal(i[0], u'abc')
assert_equal(i[0].dtype, np.dtype('U6'))
def test_iter_buffering_growinner():
# Test that the inner loop grows when no buffering is needed
a = np.arange(30)
i = nditer(a, ['buffered', 'growinner', 'external_loop'],
buffersize=5)
# Should end up with just one inner loop here
assert_equal(i[0].size, a.size)
@dec.slow
def test_iter_buffered_reduce_reuse():
# large enough array for all views, including negative strides.
a = np.arange(2*3**5)[3**5:3**5+1]
flags = ['buffered', 'delay_bufalloc', 'multi_index', 'reduce_ok', 'refs_ok']
op_flags = [('readonly',), ('readwrite', 'allocate')]
op_axes_list = [[(0, 1, 2), (0, 1, -1)], [(0, 1, 2), (0, -1, -1)]]
# wrong dtype to force buffering
op_dtypes = [float, a.dtype]
def get_params():
for xs in range(-3**2, 3**2 + 1):
for ys in range(xs, 3**2 + 1):
for op_axes in op_axes_list:
# last stride is reduced and because of that not
# important for this test, as it is the inner stride.
strides = (xs * a.itemsize, ys * a.itemsize, a.itemsize)
arr = np.lib.stride_tricks.as_strided(a, (3, 3, 3), strides)
for skip in [0, 1]:
yield arr, op_axes, skip
for arr, op_axes, skip in get_params():
nditer2 = np.nditer([arr.copy(), None],
op_axes=op_axes, flags=flags, op_flags=op_flags,
op_dtypes=op_dtypes)
nditer2.operands[-1][...] = 0
nditer2.reset()
nditer2.iterindex = skip
for (a2_in, b2_in) in nditer2:
b2_in += a2_in.astype(np.int_)
comp_res = nditer2.operands[-1]
for bufsize in range(0, 3**3):
nditer1 = np.nditer([arr, None],
op_axes=op_axes, flags=flags, op_flags=op_flags,
buffersize=bufsize, op_dtypes=op_dtypes)
nditer1.operands[-1][...] = 0
nditer1.reset()
nditer1.iterindex = skip
for (a1_in, b1_in) in nditer1:
b1_in += a1_in.astype(np.int_)
res = nditer1.operands[-1]
assert_array_equal(res, comp_res)
def test_iter_no_broadcast():
# Test that the no_broadcast flag works
a = np.arange(24).reshape(2, 3, 4)
b = np.arange(6).reshape(2, 3, 1)
c = np.arange(12).reshape(3, 4)
nditer([a, b, c], [],
[['readonly', 'no_broadcast'],
['readonly'], ['readonly']])
assert_raises(ValueError, nditer, [a, b, c], [],
[['readonly'], ['readonly', 'no_broadcast'], ['readonly']])
assert_raises(ValueError, nditer, [a, b, c], [],
[['readonly'], ['readonly'], ['readonly', 'no_broadcast']])
class TestIterNested(object):
def test_basic(self):
# Test nested iteration basic usage
a = arange(12).reshape(2, 3, 2)
i, j = np.nested_iters(a, [[0], [1, 2]])
vals = []
for x in i:
vals.append([y for y in j])
assert_equal(vals, [[0, 1, 2, 3, 4, 5], [6, 7, 8, 9, 10, 11]])
i, j = np.nested_iters(a, [[0, 1], [2]])
vals = []
for x in i:
vals.append([y for y in j])
assert_equal(vals, [[0, 1], [2, 3], [4, 5], [6, 7], [8, 9], [10, 11]])
i, j = np.nested_iters(a, [[0, 2], [1]])
vals = []
for x in i:
vals.append([y for y in j])
assert_equal(vals, [[0, 2, 4], [1, 3, 5], [6, 8, 10], [7, 9, 11]])
def test_reorder(self):
# Test nested iteration basic usage
a = arange(12).reshape(2, 3, 2)
# In 'K' order (default), it gets reordered
i, j = np.nested_iters(a, [[0], [2, 1]])
vals = []
for x in i:
vals.append([y for y in j])
assert_equal(vals, [[0, 1, 2, 3, 4, 5], [6, 7, 8, 9, 10, 11]])
i, j = np.nested_iters(a, [[1, 0], [2]])
vals = []
for x in i:
vals.append([y for y in j])
assert_equal(vals, [[0, 1], [2, 3], [4, 5], [6, 7], [8, 9], [10, 11]])
i, j = np.nested_iters(a, [[2, 0], [1]])
vals = []
for x in i:
vals.append([y for y in j])
assert_equal(vals, [[0, 2, 4], [1, 3, 5], [6, 8, 10], [7, 9, 11]])
# In 'C' order, it doesn't
i, j = np.nested_iters(a, [[0], [2, 1]], order='C')
vals = []
for x in i:
vals.append([y for y in j])
assert_equal(vals, [[0, 2, 4, 1, 3, 5], [6, 8, 10, 7, 9, 11]])
i, j = np.nested_iters(a, [[1, 0], [2]], order='C')
vals = []
for x in i:
vals.append([y for y in j])
assert_equal(vals, [[0, 1], [6, 7], [2, 3], [8, 9], [4, 5], [10, 11]])
i, j = np.nested_iters(a, [[2, 0], [1]], order='C')
vals = []
for x in i:
vals.append([y for y in j])
assert_equal(vals, [[0, 2, 4], [6, 8, 10], [1, 3, 5], [7, 9, 11]])
def test_flip_axes(self):
# Test nested iteration with negative axes
a = arange(12).reshape(2, 3, 2)[::-1, ::-1, ::-1]
# In 'K' order (default), the axes all get flipped
i, j = np.nested_iters(a, [[0], [1, 2]])
vals = []
for x in i:
vals.append([y for y in j])
assert_equal(vals, [[0, 1, 2, 3, 4, 5], [6, 7, 8, 9, 10, 11]])
i, j = np.nested_iters(a, [[0, 1], [2]])
vals = []
for x in i:
vals.append([y for y in j])
assert_equal(vals, [[0, 1], [2, 3], [4, 5], [6, 7], [8, 9], [10, 11]])
i, j = np.nested_iters(a, [[0, 2], [1]])
vals = []
for x in i:
vals.append([y for y in j])
assert_equal(vals, [[0, 2, 4], [1, 3, 5], [6, 8, 10], [7, 9, 11]])
# In 'C' order, flipping axes is disabled
i, j = np.nested_iters(a, [[0], [1, 2]], order='C')
vals = []
for x in i:
vals.append([y for y in j])
assert_equal(vals, [[11, 10, 9, 8, 7, 6], [5, 4, 3, 2, 1, 0]])
i, j = np.nested_iters(a, [[0, 1], [2]], order='C')
vals = []
for x in i:
vals.append([y for y in j])
assert_equal(vals, [[11, 10], [9, 8], [7, 6], [5, 4], [3, 2], [1, 0]])
i, j = np.nested_iters(a, [[0, 2], [1]], order='C')
vals = []
for x in i:
vals.append([y for y in j])
assert_equal(vals, [[11, 9, 7], [10, 8, 6], [5, 3, 1], [4, 2, 0]])
def test_broadcast(self):
# Test nested iteration with broadcasting
a = arange(2).reshape(2, 1)
b = arange(3).reshape(1, 3)
i, j = np.nested_iters([a, b], [[0], [1]])
vals = []
for x in i:
vals.append([y for y in j])
assert_equal(vals, [[[0, 0], [0, 1], [0, 2]], [[1, 0], [1, 1], [1, 2]]])
i, j = np.nested_iters([a, b], [[1], [0]])
vals = []
for x in i:
vals.append([y for y in j])
assert_equal(vals, [[[0, 0], [1, 0]], [[0, 1], [1, 1]], [[0, 2], [1, 2]]])
def test_dtype_copy(self):
# Test nested iteration with a copy to change dtype
# copy
a = arange(6, dtype='i4').reshape(2, 3)
i, j = np.nested_iters(a, [[0], [1]],
op_flags=['readonly', 'copy'],
op_dtypes='f8')
assert_equal(j[0].dtype, np.dtype('f8'))
vals = []
for x in i:
vals.append([y for y in j])
assert_equal(vals, [[0, 1, 2], [3, 4, 5]])
vals = None
# updateifcopy
a = arange(6, dtype='f4').reshape(2, 3)
i, j = np.nested_iters(a, [[0], [1]],
op_flags=['readwrite', 'updateifcopy'],
casting='same_kind',
op_dtypes='f8')
assert_equal(j[0].dtype, np.dtype('f8'))
for x in i:
for y in j:
y[...] += 1
assert_equal(a, [[0, 1, 2], [3, 4, 5]])
i, j, x, y = (None,)*4 # force the updateifcopy
assert_equal(a, [[1, 2, 3], [4, 5, 6]])
def test_dtype_buffered(self):
# Test nested iteration with buffering to change dtype
a = arange(6, dtype='f4').reshape(2, 3)
i, j = np.nested_iters(a, [[0], [1]],
flags=['buffered'],
op_flags=['readwrite'],
casting='same_kind',
op_dtypes='f8')
assert_equal(j[0].dtype, np.dtype('f8'))
for x in i:
for y in j:
y[...] += 1
assert_equal(a, [[1, 2, 3], [4, 5, 6]])
def test_0d(self):
a = np.arange(12).reshape(2, 3, 2)
i, j = np.nested_iters(a, [[], [1, 0, 2]])
vals = []
for x in i:
vals.append([y for y in j])
assert_equal(vals, [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]])
i, j = np.nested_iters(a, [[1, 0, 2], []])
vals = []
for x in i:
vals.append([y for y in j])
assert_equal(vals, [[0], [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11]])
i, j, k = np.nested_iters(a, [[2, 0], [], [1]])
vals = []
for x in i:
for y in j:
vals.append([z for z in k])
assert_equal(vals, [[0, 2, 4], [1, 3, 5], [6, 8, 10], [7, 9, 11]])
def test_iter_reduction_error():
a = np.arange(6)
assert_raises(ValueError, nditer, [a, None], [],
[['readonly'], ['readwrite', 'allocate']],
op_axes=[[0], [-1]])
a = np.arange(6).reshape(2, 3)
assert_raises(ValueError, nditer, [a, None], ['external_loop'],
[['readonly'], ['readwrite', 'allocate']],
op_axes=[[0, 1], [-1, -1]])
def test_iter_reduction():
# Test doing reductions with the iterator
a = np.arange(6)
i = nditer([a, None], ['reduce_ok'],
[['readonly'], ['readwrite', 'allocate']],
op_axes=[[0], [-1]])
# Need to initialize the output operand to the addition unit
i.operands[1][...] = 0
# Do the reduction
for x, y in i:
y[...] += x
# Since no axes were specified, should have allocated a scalar
assert_equal(i.operands[1].ndim, 0)
assert_equal(i.operands[1], np.sum(a))
a = np.arange(6).reshape(2, 3)
i = nditer([a, None], ['reduce_ok', 'external_loop'],
[['readonly'], ['readwrite', 'allocate']],
op_axes=[[0, 1], [-1, -1]])
# Need to initialize the output operand to the addition unit
i.operands[1][...] = 0
# Reduction shape/strides for the output
assert_equal(i[1].shape, (6,))
assert_equal(i[1].strides, (0,))
# Do the reduction
for x, y in i:
# Use a for loop instead of ``y[...] += x``
# (equivalent to ``y[...] = y[...].copy() + x``),
# because y has zero strides we use for the reduction
for j in range(len(y)):
y[j] += x[j]
# Since no axes were specified, should have allocated a scalar
assert_equal(i.operands[1].ndim, 0)
assert_equal(i.operands[1], np.sum(a))
# This is a tricky reduction case for the buffering double loop
# to handle
a = np.ones((2, 3, 5))
it1 = nditer([a, None], ['reduce_ok', 'external_loop'],
[['readonly'], ['readwrite', 'allocate']],
op_axes=[None, [0, -1, 1]])
it2 = nditer([a, None], ['reduce_ok', 'external_loop',
'buffered', 'delay_bufalloc'],
[['readonly'], ['readwrite', 'allocate']],
op_axes=[None, [0, -1, 1]], buffersize=10)
it1.operands[1].fill(0)
it2.operands[1].fill(0)
it2.reset()
for x in it1:
x[1][...] += x[0]
for x in it2:
x[1][...] += x[0]
assert_equal(it1.operands[1], it2.operands[1])
assert_equal(it2.operands[1].sum(), a.size)
def test_iter_buffering_reduction():
# Test doing buffered reductions with the iterator
a = np.arange(6)
b = np.array(0., dtype='f8').byteswap().newbyteorder()
i = nditer([a, b], ['reduce_ok', 'buffered'],
[['readonly'], ['readwrite', 'nbo']],
op_axes=[[0], [-1]])
assert_equal(i[1].dtype, np.dtype('f8'))
assert_(i[1].dtype != b.dtype)
# Do the reduction
for x, y in i:
y[...] += x
# Since no axes were specified, should have allocated a scalar
assert_equal(b, np.sum(a))
a = np.arange(6).reshape(2, 3)
b = np.array([0, 0], dtype='f8').byteswap().newbyteorder()
i = nditer([a, b], ['reduce_ok', 'external_loop', 'buffered'],
[['readonly'], ['readwrite', 'nbo']],
op_axes=[[0, 1], [0, -1]])
# Reduction shape/strides for the output
assert_equal(i[1].shape, (3,))
assert_equal(i[1].strides, (0,))
# Do the reduction
for x, y in i:
# Use a for loop instead of ``y[...] += x``
# (equivalent to ``y[...] = y[...].copy() + x``),
# because y has zero strides we use for the reduction
for j in range(len(y)):
y[j] += x[j]
assert_equal(b, np.sum(a, axis=1))
# Iterator inner double loop was wrong on this one
p = np.arange(2) + 1
it = np.nditer([p, None],
['delay_bufalloc', 'reduce_ok', 'buffered', 'external_loop'],
[['readonly'], ['readwrite', 'allocate']],
op_axes=[[-1, 0], [-1, -1]],
itershape=(2, 2))
it.operands[1].fill(0)
it.reset()
assert_equal(it[0], [1, 2, 1, 2])
# Iterator inner loop should take argument contiguity into account
x = np.ones((7, 13, 8), np.int8)[4:6,1:11:6,1:5].transpose(1, 2, 0)
x[...] = np.arange(x.size).reshape(x.shape)
y_base = np.arange(4*4, dtype=np.int8).reshape(4, 4)
y_base_copy = y_base.copy()
y = y_base[::2,:,None]
it = np.nditer([y, x],
['buffered', 'external_loop', 'reduce_ok'],
[['readwrite'], ['readonly']])
for a, b in it:
a.fill(2)
assert_equal(y_base[1::2], y_base_copy[1::2])
assert_equal(y_base[::2], 2)
def test_iter_buffering_reduction_reuse_reduce_loops():
# There was a bug triggering reuse of the reduce loop inappropriately,
# which caused processing to happen in unnecessarily small chunks
# and overran the buffer.
a = np.zeros((2, 7))
b = np.zeros((1, 7))
it = np.nditer([a, b], flags=['reduce_ok', 'external_loop', 'buffered'],
op_flags=[['readonly'], ['readwrite']],
buffersize=5)
bufsizes = []
for x, y in it:
bufsizes.append(x.shape[0])
assert_equal(bufsizes, [5, 2, 5, 2])
assert_equal(sum(bufsizes), a.size)
def test_iter_writemasked_badinput():
a = np.zeros((2, 3))
b = np.zeros((3,))
m = np.array([[True, True, False], [False, True, False]])
m2 = np.array([True, True, False])
m3 = np.array([0, 1, 1], dtype='u1')
mbad1 = np.array([0, 1, 1], dtype='i1')
mbad2 = np.array([0, 1, 1], dtype='f4')
# Need an 'arraymask' if any operand is 'writemasked'
assert_raises(ValueError, nditer, [a, m], [],
[['readwrite', 'writemasked'], ['readonly']])
# A 'writemasked' operand must not be readonly
assert_raises(ValueError, nditer, [a, m], [],
[['readonly', 'writemasked'], ['readonly', 'arraymask']])
# 'writemasked' and 'arraymask' may not be used together
assert_raises(ValueError, nditer, [a, m], [],
[['readonly'], ['readwrite', 'arraymask', 'writemasked']])
# 'arraymask' may only be specified once
assert_raises(ValueError, nditer, [a, m, m2], [],
[['readwrite', 'writemasked'],
['readonly', 'arraymask'],
['readonly', 'arraymask']])
# An 'arraymask' with nothing 'writemasked' also doesn't make sense
assert_raises(ValueError, nditer, [a, m], [],
[['readwrite'], ['readonly', 'arraymask']])
# A writemasked reduction requires a similarly smaller mask
assert_raises(ValueError, nditer, [a, b, m], ['reduce_ok'],
[['readonly'],
['readwrite', 'writemasked'],
['readonly', 'arraymask']])
# But this should work with a smaller/equal mask to the reduction operand
np.nditer([a, b, m2], ['reduce_ok'],
[['readonly'],
['readwrite', 'writemasked'],
['readonly', 'arraymask']])
# The arraymask itself cannot be a reduction
assert_raises(ValueError, nditer, [a, b, m2], ['reduce_ok'],
[['readonly'],
['readwrite', 'writemasked'],
['readwrite', 'arraymask']])
# A uint8 mask is ok too
np.nditer([a, m3], ['buffered'],
[['readwrite', 'writemasked'],
['readonly', 'arraymask']],
op_dtypes=['f4', None],
casting='same_kind')
# An int8 mask isn't ok
assert_raises(TypeError, np.nditer, [a, mbad1], ['buffered'],
[['readwrite', 'writemasked'],
['readonly', 'arraymask']],
op_dtypes=['f4', None],
casting='same_kind')
# A float32 mask isn't ok
assert_raises(TypeError, np.nditer, [a, mbad2], ['buffered'],
[['readwrite', 'writemasked'],
['readonly', 'arraymask']],
op_dtypes=['f4', None],
casting='same_kind')
def test_iter_writemasked():
a = np.zeros((3,), dtype='f8')
msk = np.array([True, True, False])
# When buffering is unused, 'writemasked' effectively does nothing.
# It's up to the user of the iterator to obey the requested semantics.
it = np.nditer([a, msk], [],
[['readwrite', 'writemasked'],
['readonly', 'arraymask']])
for x, m in it:
x[...] = 1
# Because we violated the semantics, all the values became 1
assert_equal(a, [1, 1, 1])
# Even if buffering is enabled, we still may be accessing the array
# directly.
it = np.nditer([a, msk], ['buffered'],
[['readwrite', 'writemasked'],
['readonly', 'arraymask']])
for x, m in it:
x[...] = 2.5
# Because we violated the semantics, all the values became 2.5
assert_equal(a, [2.5, 2.5, 2.5])
# If buffering will definitely happening, for instance because of
# a cast, only the items selected by the mask will be copied back from
# the buffer.
it = np.nditer([a, msk], ['buffered'],
[['readwrite', 'writemasked'],
['readonly', 'arraymask']],
op_dtypes=['i8', None],
casting='unsafe')
for x, m in it:
x[...] = 3
# Even though we violated the semantics, only the selected values
# were copied back
assert_equal(a, [3, 3, 2.5])
def test_iter_non_writable_attribute_deletion():
it = np.nditer(np.ones(2))
attr = ["value", "shape", "operands", "itviews", "has_delayed_bufalloc",
"iterationneedsapi", "has_multi_index", "has_index", "dtypes",
"ndim", "nop", "itersize", "finished"]
for s in attr:
assert_raises(AttributeError, delattr, it, s)
def test_iter_writable_attribute_deletion():
it = np.nditer(np.ones(2))
attr = [ "multi_index", "index", "iterrange", "iterindex"]
for s in attr:
assert_raises(AttributeError, delattr, it, s)
def test_iter_element_deletion():
it = np.nditer(np.ones(3))
try:
del it[1]
del it[1:2]
except TypeError:
pass
except Exception:
raise AssertionError
def test_iter_allocated_array_dtypes():
# If the dtype of an allocated output has a shape, the shape gets
# tacked onto the end of the result.
it = np.nditer(([1, 3, 20], None), op_dtypes=[None, ('i4', (2,))])
for a, b in it:
b[0] = a - 1
b[1] = a + 1
assert_equal(it.operands[1], [[0, 2], [2, 4], [19, 21]])
# Make sure this works for scalars too
it = np.nditer((10, 2, None), op_dtypes=[None, None, ('i4', (2, 2))])
for a, b, c in it:
c[0, 0] = a - b
c[0, 1] = a + b
c[1, 0] = a * b
c[1, 1] = a / b
assert_equal(it.operands[2], [[8, 12], [20, 5]])
def test_0d_iter():
# Basic test for iteration of 0-d arrays:
i = nditer([2, 3], ['multi_index'], [['readonly']]*2)
assert_equal(i.ndim, 0)
assert_equal(next(i), (2, 3))
assert_equal(i.multi_index, ())
assert_equal(i.iterindex, 0)
assert_raises(StopIteration, next, i)
# test reset:
i.reset()
assert_equal(next(i), (2, 3))
assert_raises(StopIteration, next, i)
# test forcing to 0-d
i = nditer(np.arange(5), ['multi_index'], [['readonly']], op_axes=[()])
assert_equal(i.ndim, 0)
assert_equal(len(i), 1)
# note that itershape=(), still behaves like None due to the conversions
# Test a more complex buffered casting case (same as another test above)
sdt = [('a', 'f4'), ('b', 'i8'), ('c', 'c8', (2, 3)), ('d', 'O')]
a = np.array(0.5, dtype='f4')
i = nditer(a, ['buffered', 'refs_ok'], ['readonly'],
casting='unsafe', op_dtypes=sdt)
vals = next(i)
assert_equal(vals['a'], 0.5)
assert_equal(vals['b'], 0)
assert_equal(vals['c'], [[(0.5)]*3]*2)
assert_equal(vals['d'], 0.5)
def test_iter_too_large():
# The total size of the iterator must not exceed the maximum intp due
# to broadcasting. Dividing by 1024 will keep it small enough to
# give a legal array.
size = np.iinfo(np.intp).max // 1024
arr = np.lib.stride_tricks.as_strided(np.zeros(1), (size,), (0,))
assert_raises(ValueError, nditer, (arr, arr[:, None]))
# test the same for multiindex. That may get more interesting when
# removing 0 dimensional axis is allowed (since an iterator can grow then)
assert_raises(ValueError, nditer,
(arr, arr[:, None]), flags=['multi_index'])
def test_iter_too_large_with_multiindex():
# When a multi index is being tracked, the error is delayed this
# checks the delayed error messages and getting below that by
# removing an axis.
base_size = 2**10
num = 1
while base_size**num < np.iinfo(np.intp).max:
num += 1
shape_template = [1, 1] * num
arrays = []
for i in range(num):
shape = shape_template[:]
shape[i * 2] = 2**10
arrays.append(np.empty(shape))
arrays = tuple(arrays)
# arrays are now too large to be broadcast. The different modes test
# different nditer functionality with or without GIL.
for mode in range(6):
assert_raises(ValueError, test_nditer_too_large, arrays, -1, mode)
# but if we do nothing with the nditer, it can be constructed:
test_nditer_too_large(arrays, -1, 7)
# When an axis is removed, things should work again (half the time):
for i in range(num):
for mode in range(6):
# an axis with size 1024 is removed:
test_nditer_too_large(arrays, i*2, mode)
# an axis with size 1 is removed:
assert_raises(ValueError, test_nditer_too_large,
arrays, i*2 + 1, mode)
if __name__ == "__main__":
run_module_suite()
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