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Mini Shell
Mini Shell
from __future__ import division, absolute_import, print_function
import sys
import itertools
import numpy as np
from numpy.testing import (run_module_suite, assert_, assert_raises, assert_equal,
assert_array_equal, assert_allclose, dec)
from numpy.core.multiarray_tests import solve_diophantine, internal_overlap
from numpy.core import umath_tests
from numpy.lib.stride_tricks import as_strided
from numpy.compat import long
if sys.version_info[0] >= 3:
xrange = range
ndims = 2
size = 10
shape = tuple([size] * ndims)
MAY_SHARE_BOUNDS = 0
MAY_SHARE_EXACT = -1
def _indices_for_nelems(nelems):
"""Returns slices of length nelems, from start onwards, in direction sign."""
if nelems == 0:
return [size // 2] # int index
res = []
for step in (1, 2):
for sign in (-1, 1):
start = size // 2 - nelems * step * sign // 2
stop = start + nelems * step * sign
res.append(slice(start, stop, step * sign))
return res
def _indices_for_axis():
"""Returns (src, dst) pairs of indices."""
res = []
for nelems in (0, 2, 3):
ind = _indices_for_nelems(nelems)
# no itertools.product available in Py2.4
res.extend([(a, b) for a in ind for b in ind]) # all assignments of size "nelems"
return res
def _indices(ndims):
"""Returns ((axis0_src, axis0_dst), (axis1_src, axis1_dst), ... ) index pairs."""
ind = _indices_for_axis()
# no itertools.product available in Py2.4
res = [[]]
for i in range(ndims):
newres = []
for elem in ind:
for others in res:
newres.append([elem] + others)
res = newres
return res
def _check_assignment(srcidx, dstidx):
"""Check assignment arr[dstidx] = arr[srcidx] works."""
arr = np.arange(np.product(shape)).reshape(shape)
cpy = arr.copy()
cpy[dstidx] = arr[srcidx]
arr[dstidx] = arr[srcidx]
assert_(np.all(arr == cpy),
'assigning arr[%s] = arr[%s]' % (dstidx, srcidx))
def test_overlapping_assignments():
# Test automatically generated assignments which overlap in memory.
inds = _indices(ndims)
for ind in inds:
srcidx = tuple([a[0] for a in ind])
dstidx = tuple([a[1] for a in ind])
yield _check_assignment, srcidx, dstidx
@dec.slow
def test_diophantine_fuzz():
# Fuzz test the diophantine solver
rng = np.random.RandomState(1234)
max_int = np.iinfo(np.intp).max
for ndim in range(10):
feasible_count = 0
infeasible_count = 0
min_count = 500//(ndim + 1)
while min(feasible_count, infeasible_count) < min_count:
# Ensure big and small integer problems
A_max = 1 + rng.randint(0, 11, dtype=np.intp)**6
U_max = rng.randint(0, 11, dtype=np.intp)**6
A_max = min(max_int, A_max)
U_max = min(max_int-1, U_max)
A = tuple(int(rng.randint(1, A_max+1, dtype=np.intp))
for j in range(ndim))
U = tuple(int(rng.randint(0, U_max+2, dtype=np.intp))
for j in range(ndim))
b_ub = min(max_int-2, sum(a*ub for a, ub in zip(A, U)))
b = rng.randint(-1, b_ub+2, dtype=np.intp)
if ndim == 0 and feasible_count < min_count:
b = 0
X = solve_diophantine(A, U, b)
if X is None:
# Check the simplified decision problem agrees
X_simplified = solve_diophantine(A, U, b, simplify=1)
assert_(X_simplified is None, (A, U, b, X_simplified))
# Check no solution exists (provided the problem is
# small enough so that brute force checking doesn't
# take too long)
try:
ranges = tuple(xrange(0, a*ub+1, a) for a, ub in zip(A, U))
except OverflowError:
# xrange on 32-bit Python 2 may overflow
continue
size = 1
for r in ranges:
size *= len(r)
if size < 100000:
assert_(not any(sum(w) == b for w in itertools.product(*ranges)))
infeasible_count += 1
else:
# Check the simplified decision problem agrees
X_simplified = solve_diophantine(A, U, b, simplify=1)
assert_(X_simplified is not None, (A, U, b, X_simplified))
# Check validity
assert_(sum(a*x for a, x in zip(A, X)) == b)
assert_(all(0 <= x <= ub for x, ub in zip(X, U)))
feasible_count += 1
def test_diophantine_overflow():
# Smoke test integer overflow detection
max_intp = np.iinfo(np.intp).max
max_int64 = np.iinfo(np.int64).max
if max_int64 <= max_intp:
# Check that the algorithm works internally in 128-bit;
# solving this problem requires large intermediate numbers
A = (max_int64//2, max_int64//2 - 10)
U = (max_int64//2, max_int64//2 - 10)
b = 2*(max_int64//2) - 10
assert_equal(solve_diophantine(A, U, b), (1, 1))
def check_may_share_memory_exact(a, b):
got = np.may_share_memory(a, b, max_work=MAY_SHARE_EXACT)
assert_equal(np.may_share_memory(a, b),
np.may_share_memory(a, b, max_work=MAY_SHARE_BOUNDS))
a.fill(0)
b.fill(0)
a.fill(1)
exact = b.any()
err_msg = ""
if got != exact:
err_msg = " " + "\n ".join([
"base_a - base_b = %r" % (a.__array_interface__['data'][0] - b.__array_interface__['data'][0],),
"shape_a = %r" % (a.shape,),
"shape_b = %r" % (b.shape,),
"strides_a = %r" % (a.strides,),
"strides_b = %r" % (b.strides,),
"size_a = %r" % (a.size,),
"size_b = %r" % (b.size,)
])
assert_equal(got, exact, err_msg=err_msg)
def test_may_share_memory_manual():
# Manual test cases for may_share_memory
# Base arrays
xs0 = [
np.zeros([13, 21, 23, 22], dtype=np.int8),
np.zeros([13, 21, 23*2, 22], dtype=np.int8)[:,:,::2,:]
]
# Generate all negative stride combinations
xs = []
for x in xs0:
for ss in itertools.product(*(([slice(None), slice(None, None, -1)],)*4)):
xp = x[ss]
xs.append(xp)
for x in xs:
# The default is a simple extent check
assert_(np.may_share_memory(x[:,0,:], x[:,1,:]))
assert_(np.may_share_memory(x[:,0,:], x[:,1,:], max_work=None))
# Exact checks
check_may_share_memory_exact(x[:,0,:], x[:,1,:])
check_may_share_memory_exact(x[:,::7], x[:,3::3])
try:
xp = x.ravel()
if xp.flags.owndata:
continue
xp = xp.view(np.int16)
except ValueError:
continue
# 0-size arrays cannot overlap
check_may_share_memory_exact(x.ravel()[6:6],
xp.reshape(13, 21, 23, 11)[:,::7])
# Test itemsize is dealt with
check_may_share_memory_exact(x[:,::7],
xp.reshape(13, 21, 23, 11))
check_may_share_memory_exact(x[:,::7],
xp.reshape(13, 21, 23, 11)[:,3::3])
check_may_share_memory_exact(x.ravel()[6:7],
xp.reshape(13, 21, 23, 11)[:,::7])
# Check unit size
x = np.zeros([1], dtype=np.int8)
check_may_share_memory_exact(x, x)
check_may_share_memory_exact(x, x.copy())
def iter_random_view_pairs(x, same_steps=True, equal_size=False):
rng = np.random.RandomState(1234)
if equal_size and same_steps:
raise ValueError()
def random_slice(n, step):
start = rng.randint(0, n+1, dtype=np.intp)
stop = rng.randint(start, n+1, dtype=np.intp)
if rng.randint(0, 2, dtype=np.intp) == 0:
stop, start = start, stop
step *= -1
return slice(start, stop, step)
def random_slice_fixed_size(n, step, size):
start = rng.randint(0, n+1 - size*step)
stop = start + (size-1)*step + 1
if rng.randint(0, 2) == 0:
stop, start = start-1, stop-1
if stop < 0:
stop = None
step *= -1
return slice(start, stop, step)
# First a few regular views
yield x, x
for j in range(1, 7, 3):
yield x[j:], x[:-j]
yield x[...,j:], x[...,:-j]
# An array with zero stride internal overlap
strides = list(x.strides)
strides[0] = 0
xp = as_strided(x, shape=x.shape, strides=strides)
yield x, xp
yield xp, xp
# An array with non-zero stride internal overlap
strides = list(x.strides)
if strides[0] > 1:
strides[0] = 1
xp = as_strided(x, shape=x.shape, strides=strides)
yield x, xp
yield xp, xp
# Then discontiguous views
while True:
steps = tuple(rng.randint(1, 11, dtype=np.intp)
if rng.randint(0, 5, dtype=np.intp) == 0 else 1
for j in range(x.ndim))
s1 = tuple(random_slice(p, s) for p, s in zip(x.shape, steps))
t1 = np.arange(x.ndim)
rng.shuffle(t1)
if equal_size:
t2 = t1
else:
t2 = np.arange(x.ndim)
rng.shuffle(t2)
a = x[s1]
if equal_size:
if a.size == 0:
continue
steps2 = tuple(rng.randint(1, max(2, p//(1+pa)))
if rng.randint(0, 5) == 0 else 1
for p, s, pa in zip(x.shape, s1, a.shape))
s2 = tuple(random_slice_fixed_size(p, s, pa)
for p, s, pa in zip(x.shape, steps2, a.shape))
elif same_steps:
steps2 = steps
else:
steps2 = tuple(rng.randint(1, 11, dtype=np.intp)
if rng.randint(0, 5, dtype=np.intp) == 0 else 1
for j in range(x.ndim))
if not equal_size:
s2 = tuple(random_slice(p, s) for p, s in zip(x.shape, steps2))
a = a.transpose(t1)
b = x[s2].transpose(t2)
yield a, b
def check_may_share_memory_easy_fuzz(get_max_work, same_steps, min_count):
# Check that overlap problems with common strides are solved with
# little work.
x = np.zeros([17,34,71,97], dtype=np.int16)
feasible = 0
infeasible = 0
pair_iter = iter_random_view_pairs(x, same_steps)
while min(feasible, infeasible) < min_count:
a, b = next(pair_iter)
bounds_overlap = np.may_share_memory(a, b)
may_share_answer = np.may_share_memory(a, b)
easy_answer = np.may_share_memory(a, b, max_work=get_max_work(a, b))
exact_answer = np.may_share_memory(a, b, max_work=MAY_SHARE_EXACT)
if easy_answer != exact_answer:
# assert_equal is slow...
assert_equal(easy_answer, exact_answer)
if may_share_answer != bounds_overlap:
assert_equal(may_share_answer, bounds_overlap)
if bounds_overlap:
if exact_answer:
feasible += 1
else:
infeasible += 1
@dec.slow
def test_may_share_memory_easy_fuzz():
# Check that overlap problems with common strides are always
# solved with little work.
check_may_share_memory_easy_fuzz(get_max_work=lambda a, b: 1,
same_steps=True,
min_count=2000)
@dec.slow
def test_may_share_memory_harder_fuzz():
# Overlap problems with not necessarily common strides take more
# work.
#
# The work bound below can't be reduced much. Harder problems can
# also exist but not be detected here, as the set of problems
# comes from RNG.
check_may_share_memory_easy_fuzz(get_max_work=lambda a, b: max(a.size, b.size)//2,
same_steps=False,
min_count=2000)
def test_shares_memory_api():
x = np.zeros([4, 5, 6], dtype=np.int8)
assert_equal(np.shares_memory(x, x), True)
assert_equal(np.shares_memory(x, x.copy()), False)
a = x[:,::2,::3]
b = x[:,::3,::2]
assert_equal(np.shares_memory(a, b), True)
assert_equal(np.shares_memory(a, b, max_work=None), True)
assert_raises(np.TooHardError, np.shares_memory, a, b, max_work=1)
assert_raises(np.TooHardError, np.shares_memory, a, b, max_work=long(1))
def test_may_share_memory_bad_max_work():
x = np.zeros([1])
assert_raises(OverflowError, np.may_share_memory, x, x, max_work=10**100)
assert_raises(OverflowError, np.shares_memory, x, x, max_work=10**100)
def test_internal_overlap_diophantine():
def check(A, U, exists=None):
X = solve_diophantine(A, U, 0, require_ub_nontrivial=1)
if exists is None:
exists = (X is not None)
if X is not None:
assert_(sum(a*x for a, x in zip(A, X)) == sum(a*u//2 for a, u in zip(A, U)))
assert_(all(0 <= x <= u for x, u in zip(X, U)))
assert_(any(x != u//2 for x, u in zip(X, U)))
if exists:
assert_(X is not None, repr(X))
else:
assert_(X is None, repr(X))
# Smoke tests
check((3, 2), (2*2, 3*2), exists=True)
check((3*2, 2), (15*2, (3-1)*2), exists=False)
def test_internal_overlap_slices():
# Slicing an array never generates internal overlap
x = np.zeros([17,34,71,97], dtype=np.int16)
rng = np.random.RandomState(1234)
def random_slice(n, step):
start = rng.randint(0, n+1, dtype=np.intp)
stop = rng.randint(start, n+1, dtype=np.intp)
if rng.randint(0, 2, dtype=np.intp) == 0:
stop, start = start, stop
step *= -1
return slice(start, stop, step)
cases = 0
min_count = 5000
while cases < min_count:
steps = tuple(rng.randint(1, 11, dtype=np.intp)
if rng.randint(0, 5, dtype=np.intp) == 0 else 1
for j in range(x.ndim))
t1 = np.arange(x.ndim)
rng.shuffle(t1)
s1 = tuple(random_slice(p, s) for p, s in zip(x.shape, steps))
a = x[s1].transpose(t1)
assert_(not internal_overlap(a))
cases += 1
def check_internal_overlap(a, manual_expected=None):
got = internal_overlap(a)
# Brute-force check
m = set()
ranges = tuple(xrange(n) for n in a.shape)
for v in itertools.product(*ranges):
offset = sum(s*w for s, w in zip(a.strides, v))
if offset in m:
expected = True
break
else:
m.add(offset)
else:
expected = False
# Compare
if got != expected:
assert_equal(got, expected, err_msg=repr((a.strides, a.shape)))
if manual_expected is not None and expected != manual_expected:
assert_equal(expected, manual_expected)
return got
def test_internal_overlap_manual():
# Stride tricks can construct arrays with internal overlap
# We don't care about memory bounds, the array is not
# read/write accessed
x = np.arange(1).astype(np.int8)
# Check low-dimensional special cases
check_internal_overlap(x, False) # 1-dim
check_internal_overlap(x.reshape([]), False) # 0-dim
a = as_strided(x, strides=(3, 4), shape=(4, 4))
check_internal_overlap(a, False)
a = as_strided(x, strides=(3, 4), shape=(5, 4))
check_internal_overlap(a, True)
a = as_strided(x, strides=(0,), shape=(0,))
check_internal_overlap(a, False)
a = as_strided(x, strides=(0,), shape=(1,))
check_internal_overlap(a, False)
a = as_strided(x, strides=(0,), shape=(2,))
check_internal_overlap(a, True)
a = as_strided(x, strides=(0, -9993), shape=(87, 22))
check_internal_overlap(a, True)
a = as_strided(x, strides=(0, -9993), shape=(1, 22))
check_internal_overlap(a, False)
a = as_strided(x, strides=(0, -9993), shape=(0, 22))
check_internal_overlap(a, False)
def test_internal_overlap_fuzz():
# Fuzz check; the brute-force check is fairly slow
x = np.arange(1).astype(np.int8)
overlap = 0
no_overlap = 0
min_count = 100
rng = np.random.RandomState(1234)
while min(overlap, no_overlap) < min_count:
ndim = rng.randint(1, 4, dtype=np.intp)
strides = tuple(rng.randint(-1000, 1000, dtype=np.intp)
for j in range(ndim))
shape = tuple(rng.randint(1, 30, dtype=np.intp)
for j in range(ndim))
a = as_strided(x, strides=strides, shape=shape)
result = check_internal_overlap(a)
if result:
overlap += 1
else:
no_overlap += 1
def test_non_ndarray_inputs():
# Regression check for gh-5604
class MyArray(object):
def __init__(self, data):
self.data = data
@property
def __array_interface__(self):
return self.data.__array_interface__
class MyArray2(object):
def __init__(self, data):
self.data = data
def __array__(self):
return self.data
for cls in [MyArray, MyArray2]:
x = np.arange(5)
assert_(np.may_share_memory(cls(x[::2]), x[1::2]))
assert_(not np.shares_memory(cls(x[::2]), x[1::2]))
assert_(np.shares_memory(cls(x[1::3]), x[::2]))
assert_(np.may_share_memory(cls(x[1::3]), x[::2]))
def view_element_first_byte(x):
"""Construct an array viewing the first byte of each element of `x`"""
from numpy.lib.stride_tricks import DummyArray
interface = dict(x.__array_interface__)
interface['typestr'] = '|b1'
interface['descr'] = [('', '|b1')]
return np.asarray(DummyArray(interface, x))
def assert_copy_equivalent(operation, args, out, **kwargs):
"""
Check that operation(*args, out=out) produces results
equivalent to out[...] = operation(*args, out=out.copy())
"""
kwargs['out'] = out
kwargs2 = dict(kwargs)
kwargs2['out'] = out.copy()
out_orig = out.copy()
out[...] = operation(*args, **kwargs2)
expected = out.copy()
out[...] = out_orig
got = operation(*args, **kwargs).copy()
if (got != expected).any():
assert_equal(got, expected)
class TestUFunc(object):
"""
Test ufunc call memory overlap handling
"""
def check_unary_fuzz(self, operation, get_out_axis_size, dtype=np.int16,
count=5000):
shapes = [7, 13, 8, 21, 29, 32]
rng = np.random.RandomState(1234)
for ndim in range(1, 6):
x = rng.randint(0, 2**16, size=shapes[:ndim]).astype(dtype)
it = iter_random_view_pairs(x, same_steps=False, equal_size=True)
min_count = count // (ndim + 1)**2
overlapping = 0
while overlapping < min_count:
a, b = next(it)
a_orig = a.copy()
b_orig = b.copy()
if get_out_axis_size is None:
assert_copy_equivalent(operation, [a], out=b)
if np.shares_memory(a, b):
overlapping += 1
else:
for axis in itertools.chain(range(ndim), [None]):
a[...] = a_orig
b[...] = b_orig
# Determine size for reduction axis (None if scalar)
outsize, scalarize = get_out_axis_size(a, b, axis)
if outsize == 'skip':
continue
# Slice b to get an output array of the correct size
sl = [slice(None)] * ndim
if axis is None:
if outsize is None:
sl = [slice(0, 1)] + [0]*(ndim - 1)
else:
sl = [slice(0, outsize)] + [0]*(ndim - 1)
else:
if outsize is None:
k = b.shape[axis]//2
if ndim == 1:
sl[axis] = slice(k, k + 1)
else:
sl[axis] = k
else:
assert b.shape[axis] >= outsize
sl[axis] = slice(0, outsize)
b_out = b[tuple(sl)]
if scalarize:
b_out = b_out.reshape([])
if np.shares_memory(a, b_out):
overlapping += 1
# Check result
assert_copy_equivalent(operation, [a], out=b_out, axis=axis)
@dec.slow
def test_unary_ufunc_call_fuzz(self):
self.check_unary_fuzz(np.invert, None, np.int16)
def test_binary_ufunc_accumulate_fuzz(self):
def get_out_axis_size(a, b, axis):
if axis is None:
if a.ndim == 1:
return a.size, False
else:
return 'skip', False # accumulate doesn't support this
else:
return a.shape[axis], False
self.check_unary_fuzz(np.add.accumulate, get_out_axis_size,
dtype=np.int16, count=500)
def test_binary_ufunc_reduce_fuzz(self):
def get_out_axis_size(a, b, axis):
return None, (axis is None or a.ndim == 1)
self.check_unary_fuzz(np.add.reduce, get_out_axis_size,
dtype=np.int16, count=500)
def test_binary_ufunc_reduceat_fuzz(self):
def get_out_axis_size(a, b, axis):
if axis is None:
if a.ndim == 1:
return a.size, False
else:
return 'skip', False # reduceat doesn't support this
else:
return a.shape[axis], False
def do_reduceat(a, out, axis):
if axis is None:
size = len(a)
step = size//len(out)
else:
size = a.shape[axis]
step = a.shape[axis] // out.shape[axis]
idx = np.arange(0, size, step)
return np.add.reduceat(a, idx, out=out, axis=axis)
self.check_unary_fuzz(do_reduceat, get_out_axis_size,
dtype=np.int16, count=500)
def test_binary_ufunc_reduceat_manual(self):
def check(ufunc, a, ind, out):
c1 = ufunc.reduceat(a.copy(), ind.copy(), out=out.copy())
c2 = ufunc.reduceat(a, ind, out=out)
assert_array_equal(c1, c2)
# Exactly same input/output arrays
a = np.arange(10000, dtype=np.int16)
check(np.add, a, a[::-1].copy(), a)
# Overlap with index
a = np.arange(10000, dtype=np.int16)
check(np.add, a, a[::-1], a)
def test_unary_gufunc_fuzz(self):
shapes = [7, 13, 8, 21, 29, 32]
gufunc = umath_tests.euclidean_pdist
rng = np.random.RandomState(1234)
for ndim in range(2, 6):
x = rng.rand(*shapes[:ndim])
it = iter_random_view_pairs(x, same_steps=False, equal_size=True)
min_count = 500 // (ndim + 1)**2
overlapping = 0
while overlapping < min_count:
a, b = next(it)
if min(a.shape[-2:]) < 2 or min(b.shape[-2:]) < 2 or a.shape[-1] < 2:
continue
# Ensure the shapes are so that euclidean_pdist is happy
if b.shape[-1] > b.shape[-2]:
b = b[...,0,:]
else:
b = b[...,:,0]
n = a.shape[-2]
p = n * (n - 1) // 2
if p <= b.shape[-1] and p > 0:
b = b[...,:p]
else:
n = max(2, int(np.sqrt(b.shape[-1]))//2)
p = n * (n - 1) // 2
a = a[...,:n,:]
b = b[...,:p]
# Call
if np.shares_memory(a, b):
overlapping += 1
with np.errstate(over='ignore', invalid='ignore'):
assert_copy_equivalent(gufunc, [a], out=b)
def test_ufunc_at_manual(self):
def check(ufunc, a, ind, b=None):
a0 = a.copy()
if b is None:
ufunc.at(a0, ind.copy())
c1 = a0.copy()
ufunc.at(a, ind)
c2 = a.copy()
else:
ufunc.at(a0, ind.copy(), b.copy())
c1 = a0.copy()
ufunc.at(a, ind, b)
c2 = a.copy()
assert_array_equal(c1, c2)
# Overlap with index
a = np.arange(10000, dtype=np.int16)
check(np.invert, a[::-1], a)
# Overlap with second data array
a = np.arange(100, dtype=np.int16)
ind = np.arange(0, 100, 2, dtype=np.int16)
check(np.add, a, ind, a[25:75])
def test_unary_ufunc_1d_manual(self):
# Exercise branches in PyArray_EQUIVALENTLY_ITERABLE
def check(a, b):
a_orig = a.copy()
b_orig = b.copy()
b0 = b.copy()
c1 = ufunc(a, out=b0)
c2 = ufunc(a, out=b)
assert_array_equal(c1, c2)
# Trigger "fancy ufunc loop" code path
mask = view_element_first_byte(b).view(np.bool_)
a[...] = a_orig
b[...] = b_orig
c1 = ufunc(a, out=b.copy(), where=mask.copy()).copy()
a[...] = a_orig
b[...] = b_orig
c2 = ufunc(a, out=b, where=mask.copy()).copy()
# Also, mask overlapping with output
a[...] = a_orig
b[...] = b_orig
c3 = ufunc(a, out=b, where=mask).copy()
assert_array_equal(c1, c2)
assert_array_equal(c1, c3)
dtypes = [np.int8, np.int16, np.int32, np.int64, np.float32,
np.float64, np.complex64, np.complex128]
dtypes = [np.dtype(x) for x in dtypes]
for dtype in dtypes:
if np.issubdtype(dtype, np.integer):
ufunc = np.invert
else:
ufunc = np.reciprocal
n = 1000
k = 10
indices = [
np.index_exp[:n],
np.index_exp[k:k+n],
np.index_exp[n-1::-1],
np.index_exp[k+n-1:k-1:-1],
np.index_exp[:2*n:2],
np.index_exp[k:k+2*n:2],
np.index_exp[2*n-1::-2],
np.index_exp[k+2*n-1:k-1:-2],
]
for xi, yi in itertools.product(indices, indices):
v = np.arange(1, 1 + n*2 + k, dtype=dtype)
x = v[xi]
y = v[yi]
with np.errstate(all='ignore'):
check(x, y)
# Scalar cases
check(x[:1], y)
check(x[-1:], y)
check(x[:1].reshape([]), y)
check(x[-1:].reshape([]), y)
def test_unary_ufunc_where_same(self):
# Check behavior at wheremask overlap
ufunc = np.invert
def check(a, out, mask):
c1 = ufunc(a, out=out.copy(), where=mask.copy())
c2 = ufunc(a, out=out, where=mask)
assert_array_equal(c1, c2)
# Check behavior with same input and output arrays
x = np.arange(100).astype(np.bool_)
check(x, x, x)
check(x, x.copy(), x)
check(x, x, x.copy())
@dec.slow
def test_binary_ufunc_1d_manual(self):
ufunc = np.add
def check(a, b, c):
c0 = c.copy()
c1 = ufunc(a, b, out=c0)
c2 = ufunc(a, b, out=c)
assert_array_equal(c1, c2)
for dtype in [np.int8, np.int16, np.int32, np.int64,
np.float32, np.float64, np.complex64, np.complex128]:
# Check different data dependency orders
n = 1000
k = 10
indices = []
for p in [1, 2]:
indices.extend([
np.index_exp[:p*n:p],
np.index_exp[k:k+p*n:p],
np.index_exp[p*n-1::-p],
np.index_exp[k+p*n-1:k-1:-p],
])
for x, y, z in itertools.product(indices, indices, indices):
v = np.arange(6*n).astype(dtype)
x = v[x]
y = v[y]
z = v[z]
check(x, y, z)
# Scalar cases
check(x[:1], y, z)
check(x[-1:], y, z)
check(x[:1].reshape([]), y, z)
check(x[-1:].reshape([]), y, z)
check(x, y[:1], z)
check(x, y[-1:], z)
check(x, y[:1].reshape([]), z)
check(x, y[-1:].reshape([]), z)
def test_inplace_op_simple_manual(self):
rng = np.random.RandomState(1234)
x = rng.rand(200, 200) # bigger than bufsize
x += x.T
assert_array_equal(x - x.T, 0)
if __name__ == "__main__":
run_module_suite()
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