def test_hidden(self):
shape = (2, )
a = self.array(shape, intent.hide, None)
assert_(a.arr.shape == shape)
assert_(a.arr_equal(a.arr, zeros(shape, dtype=self.type.dtype)))
shape = (2, 3)
a = self.array(shape, intent.hide, None)
assert_(a.arr.shape == shape)
assert_(a.arr_equal(a.arr, zeros(shape, dtype=self.type.dtype)))
assert_(a.arr.flags['FORTRAN'] and not a.arr.flags['CONTIGUOUS'])
shape = (2, 3)
a = self.array(shape, intent.c.hide, None)
assert_(a.arr.shape == shape)
assert_(a.arr_equal(a.arr, zeros(shape, dtype=self.type.dtype)))
assert_(not a.arr.flags['FORTRAN'] and a.arr.flags['CONTIGUOUS'])
shape = (-1, 3)
try:
a = self.array(shape, intent.hide, None)
except ValueError as msg:
if not str(msg).startswith('failed to create intent'
'(cache|hide)|optional array'):
raise
else:
raise SystemError('intent(hide) should have failed'
' on undefined dimensions')
def test_mem_masked_where(self):
# Ticket #62
from numpy1.ma import masked_where, MaskType
a = np.zeros((1, 1))
b = np.zeros(a.shape, MaskType)
c = masked_where(b, a)
a - c
def test_scalar_return_type(self):
# Full scalar indices should return scalars and object
# arrays should not call PyArray_Return on their items
class Zero(object):
# The most basic valid indexing
def __index__(self):
return 0
z = Zero()
class ArrayLike(object):
# Simple array, should behave like the array
def __array__(self):
return np.array(0)
a = np.zeros(())
assert_(isinstance(a[()], np.float_))
a = np.zeros(1)
assert_(isinstance(a[z], np.float_))
a = np.zeros((1, 1))
assert_(isinstance(a[z, np.array(0)], np.float_))
assert_(isinstance(a[z, ArrayLike()], np.float_))
# And object arrays do not call it too often:
b = np.array(0)
a = np.array(0, dtype=object)
a[()] = b
assert_(isinstance(a[()], np.ndarray))
a = np.array([b, None])
assert_(isinstance(a[z], np.ndarray))
a = np.array([[b, None]])
assert_(isinstance(a[z, np.array(0)], np.ndarray))
assert_(isinstance(a[z, ArrayLike()], np.ndarray))
def test_dtype(self):
dt = np.intc
p = ndpointer(dtype=dt)
assert_(p.from_param(np.array([1], dt)))
dt = '<i4'
p = ndpointer(dtype=dt)
assert_(p.from_param(np.array([1], dt)))
dt = np.dtype('>i4')
p = ndpointer(dtype=dt)
p.from_param(np.array([1], dt))
assert_raises(TypeError, p.from_param,
np.array([1], dt.newbyteorder('swap')))
dtnames = ['x', 'y']
dtformats = [np.intc, np.float64]
dtdescr = {'names': dtnames, 'formats': dtformats}
dt = np.dtype(dtdescr)
p = ndpointer(dtype=dt)
assert_(p.from_param(np.zeros((10,), dt)))
samedt = np.dtype(dtdescr)
p = ndpointer(dtype=samedt)
assert_(p.from_param(np.zeros((10,), dt)))
dt2 = np.dtype(dtdescr, align=True)
if dt.itemsize != dt2.itemsize:
assert_raises(TypeError, p.from_param, np.zeros((10,), dt2))
else:
assert_(p.from_param(np.zeros((10,), dt2)))
def test_broadcast_to_succeeds():
data = [
[np.array(0), (0, ), np.array(0)],
[np.array(0), (1, ), np.zeros(1)],
[np.array(0), (3, ), np.zeros(3)],
[np.ones(1), (1, ), np.ones(1)],
[np.ones(1), (2, ), np.ones(2)],
[np.ones(1), (1, 2, 3), np.ones((1, 2, 3))],
[np.arange(3), (3, ), np.arange(3)],
[np.arange(3), (1, 3),
np.arange(3).reshape(1, -1)],
[np.arange(3), (2, 3),
np.array([[0, 1, 2], [0, 1, 2]])],
# test if shape is not a tuple
[np.ones(0), 0, np.ones(0)],
[np.ones(1), 1, np.ones(1)],
[np.ones(1), 2, np.ones(2)],
# these cases with size 0 are strange, but they reproduce the behavior
# of broadcasting with ufuncs (see test_same_as_ufunc above)
[np.ones(1), (0, ), np.ones(0)],
[np.ones((1, 2)), (0, 2), np.ones((0, 2))],
[np.ones((2, 1)), (2, 0), np.ones((2, 0))],
]
for input_array, shape, expected in data:
actual = broadcast_to(input_array, shape)
assert_array_equal(expected, actual)
def test_empty(self):
mat = np.zeros((0, 3))
for f in self.nanfuncs:
for axis in [0, None]:
assert_raises(ValueError, f, mat, axis=axis)
for axis in [1]:
res = f(mat, axis=axis)
assert_equal(res, np.zeros(0))
def test_methods(self):
"cases from methods.c"
a = np.zeros((3, 3))
assert_raises(IndexError, lambda: a.item(100))
assert_raises(IndexError, lambda: a.itemset(100, 1))
a = np.zeros((0, 3))
assert_raises(IndexError, lambda: a.item(100))
assert_raises(IndexError, lambda: a.itemset(100, 1))
def test_put_exceptions(self):
a = np.zeros((5, 5))
assert_raises(IndexError, a.put, 100, 0)
a = np.zeros((5, 5), dtype=object)
assert_raises(IndexError, a.put, 100, 0)
a = np.zeros((5, 5, 0))
assert_raises(IndexError, a.put, 100, 0)
a = np.zeros((5, 5, 0), dtype=object)
assert_raises(IndexError, a.put, 100, 0)
def test_zeros0D(self):
"""Check creation of 0-dimensional objects"""
h = np.zeros((), dtype=self._descr)
assert_(normalize_descr(self._descr) == h.dtype.descr)
assert_(h.dtype.fields['x'][0].name[:4] == 'void')
assert_(h.dtype.fields['x'][0].char == 'V')
assert_(h.dtype.fields['x'][0].type == np.void)
# A small check that data is ok
assert_equal(h['z'], np.zeros((), dtype='u1'))
def test_zerosMD(self):
"""Check creation of multi-dimensional objects"""
h = np.zeros((2, 3), dtype=self._descr)
assert_(normalize_descr(self._descr) == h.dtype.descr)
assert_(h.dtype['z'].name == 'uint8')
assert_(h.dtype['z'].char == 'B')
assert_(h.dtype['z'].type == np.uint8)
# A small check that data is ok
assert_equal(h['z'], np.zeros((2, 3), dtype='u1'))
def test_lower_align(self):
# check data that is not aligned to element size
# i.e doubles are aligned to 4 bytes on i386
d = np.zeros(23 * 8, dtype=np.int8)[4:-4].view(np.float64)
o = np.zeros(23 * 8, dtype=np.int8)[4:-4].view(np.float64)
assert_almost_equal(d + d, d * 2)
np.add(d, d, out=o)
np.add(np.ones_like(d), d, out=o)
np.add(d, np.ones_like(d), out=o)
np.add(np.ones_like(d), d)
np.add(d, np.ones_like(d))
def test_take_from_object(self):
# Check exception taking from object array
d = np.zeros(5, dtype=object)
assert_raises(IndexError, d.take, [6])
# Check exception taking from 0-d array
d = np.zeros((5, 0), dtype=object)
assert_raises(IndexError, d.take, [1], axis=1)
assert_raises(IndexError, d.take, [0], axis=1)
assert_raises(IndexError, d.take, [0])
assert_raises(IndexError, d.take, [0], mode='wrap')
assert_raises(IndexError, d.take, [0], mode='clip')
def test_empty(self):
for f, tgt_value in zip(self.nanfuncs, [0, 1]):
mat = np.zeros((0, 3))
tgt = tgt_value*np.ones((0, 3))
res = f(mat, axis=0)
assert_equal(res, tgt)
tgt = mat
res = f(mat, axis=1)
assert_equal(res, tgt)
tgt = np.zeros((0))
res = f(mat, axis=None)
assert_equal(res, tgt)
def test_ptp(self):
(x, X, XX, m, mx, mX, mXX,) = self.d
(n, m) = X.shape
assert_equal(mx.ptp(), mx.compressed().ptp())
rows = np.zeros(n, np.float_)
cols = np.zeros(m, np.float_)
for k in range(m):
cols[k] = mX[:, k].compressed().ptp()
for k in range(n):
rows[k] = mX[k].compressed().ptp()
assert_(eq(mX.ptp(0), cols))
assert_(eq(mX.ptp(1), rows))
def test_normal_types(self):
for op in (operator.eq, operator.ne):
# Broadcasting errors:
self.assert_deprecated(op, args=(np.zeros(3), []))
a = np.zeros(3, dtype='i,i')
# (warning is issued a couple of times here)
self.assert_deprecated(op, args=(a, a[:-1]), num=None)
# Element comparison error (numpy array can't be compared).
a = np.array([1, np.array([1, 2, 3])], dtype=object)
b = np.array([1, np.array([1, 2, 3])], dtype=object)
self.assert_deprecated(op, args=(a, b), num=None)
def test_empty(self):
mat = np.zeros((0, 3))
for axis in [0, None]:
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
assert_(np.isnan(np.nanpercentile(mat, 40, axis=axis)).all())
assert_(len(w) == 1)
assert_(issubclass(w[0].category, RuntimeWarning))
for axis in [1]:
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
assert_equal(np.nanpercentile(mat, 40, axis=axis), np.zeros([]))
assert_(len(w) == 0)
def test_boolean_assignment_needs_api(self):
# See also gh-7666
# This caused a segfault on Python 2 due to the GIL not being
# held when the iterator does not need it, but the transfer function
# does
arr = np.zeros(1000)
indx = np.zeros(1000, dtype=bool)
indx[:100] = True
arr[indx] = np.ones(100, dtype=object)
expected = np.zeros(1000)
expected[:100] = 1
assert_array_equal(arr, expected)
def test_vector(self):
vals = (100 * arange(5)).astype('l')
b = zeros((5, 5))
for k in range(5):
b[k, k] = vals[k]
assert_equal(diag(vals), b)
b = zeros((7, 7))
c = b.copy()
for k in range(5):
b[k, k + 2] = vals[k]
c[k + 2, k] = vals[k]
assert_equal(diag(vals, k=2), b)
assert_equal(diag(vals, k=-2), c)
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 test_stack():
# non-iterable input
assert_raises(TypeError, stack, 1)
# 0d input
for input_ in [(1, 2, 3),
[np.int32(1), np.int32(2), np.int32(3)],
[np.array(1), np.array(2), np.array(3)]]:
assert_array_equal(stack(input_), [1, 2, 3])
# 1d input examples
a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
r1 = array([[1, 2, 3], [4, 5, 6]])
assert_array_equal(np.stack((a, b)), r1)
assert_array_equal(np.stack((a, b), axis=1), r1.T)
# all input types
assert_array_equal(np.stack(list([a, b])), r1)
assert_array_equal(np.stack(array([a, b])), r1)
# all shapes for 1d input
arrays = [np.random.randn(3) for _ in range(10)]
axes = [0, 1, -1, -2]
expected_shapes = [(10, 3), (3, 10), (3, 10), (10, 3)]
for axis, expected_shape in zip(axes, expected_shapes):
assert_equal(np.stack(arrays, axis).shape, expected_shape)
assert_raises_regex(np.AxisError, 'out of bounds', stack, arrays, axis=2)
assert_raises_regex(np.AxisError, 'out of bounds', stack, arrays, axis=-3)
# all shapes for 2d input
arrays = [np.random.randn(3, 4) for _ in range(10)]
axes = [0, 1, 2, -1, -2, -3]
expected_shapes = [(10, 3, 4), (3, 10, 4), (3, 4, 10),
(3, 4, 10), (3, 10, 4), (10, 3, 4)]
for axis, expected_shape in zip(axes, expected_shapes):
assert_equal(np.stack(arrays, axis).shape, expected_shape)
# empty arrays
assert_(stack([[], [], []]).shape == (3, 0))
assert_(stack([[], [], []], axis=1).shape == (0, 3))
# edge cases
assert_raises_regex(ValueError, 'need at least one array', stack, [])
assert_raises_regex(ValueError, 'must have the same shape',
stack, [1, np.arange(3)])
assert_raises_regex(ValueError, 'must have the same shape',
stack, [np.arange(3), 1])
assert_raises_regex(ValueError, 'must have the same shape',
stack, [np.arange(3), 1], axis=1)
assert_raises_regex(ValueError, 'must have the same shape',
stack, [np.zeros((3, 3)), np.zeros(3)], axis=1)
assert_raises_regex(ValueError, 'must have the same shape',
stack, [np.arange(2), np.arange(3)])
def test_diag_indices():
di = diag_indices(4)
a = np.array([[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16]])
a[di] = 100
assert_array_equal(
a, np.array([[100, 2, 3, 4],
[5, 100, 7, 8],
[9, 10, 100, 12],
[13, 14, 15, 100]])
)
# Now, we create indices to manipulate a 3-d array:
d3 = diag_indices(2, 3)
# And use it to set the diagonal of a zeros array to 1:
a = np.zeros((2, 2, 2), int)
a[d3] = 1
assert_array_equal(
a, np.array([[[1, 0],
[0, 0]],
[[0, 0],
[0, 1]]])
)
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_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
def test_different_field_order(self):
# gh-8940
a = np.zeros(3, dtype=[('a', 'i4'), ('b', 'f4'), ('c', 'u1')])
b = np.ones(3, dtype=[('c', 'u1'), ('b', 'f4'), ('a', 'i4')])
# this should not give a FutureWarning:
j = join_by(['c', 'b'], a, b, jointype='inner', usemask=False)
assert_equal(j.dtype.names, ['b', 'c', 'a1', 'a2'])
def test_weights(self):
v = np.random.rand(100)
w = np.ones(100) * 5
a, b = histogram(v)
na, nb = histogram(v, density=True)
wa, wb = histogram(v, weights=w)
nwa, nwb = histogram(v, weights=w, density=True)
assert_array_almost_equal(a * 5, wa)
assert_array_almost_equal(na, nwa)
# Check weights are properly applied.
v = np.linspace(0, 10, 10)
w = np.concatenate((np.zeros(5), np.ones(5)))
wa, wb = histogram(v, bins=np.arange(11), weights=w)
assert_array_almost_equal(wa, w)
# Check with integer weights
wa, wb = histogram([1, 2, 2, 4], bins=4, weights=[4, 3, 2, 1])
assert_array_equal(wa, [4, 5, 0, 1])
wa, wb = histogram([1, 2, 2, 4],
bins=4,
weights=[4, 3, 2, 1],
density=True)
assert_array_almost_equal(wa, np.array([4, 5, 0, 1]) / 10. / 3. * 4)
# Check weights with non-uniform bin widths
a, b = histogram(np.arange(9), [0, 1, 3, 6, 10],
weights=[2, 1, 1, 1, 1, 1, 1, 1, 1],
density=True)
assert_almost_equal(a, [.2, .1, .1, .075])
def test_simple(self):
x = np.array([[-.5, .5, 1.5], [-.5, 1.5, 2.5], [-.5, 2.5, .5],
[.5, .5, 1.5], [.5, 1.5, 2.5], [.5, 2.5, 2.5]])
H, edges = histogramdd(x, (2, 3, 3), range=[[-1, 1], [0, 3], [0, 3]])
answer = np.array([[[0, 1, 0], [0, 0, 1], [1, 0, 0]],
[[0, 1, 0], [0, 0, 1], [0, 0, 1]]])
assert_array_equal(H, answer)
# Check normalization
ed = [[-2, 0, 2], [0, 1, 2, 3], [0, 1, 2, 3]]
H, edges = histogramdd(x, bins=ed, density=True)
assert_(np.all(H == answer / 12.))
# Check that H has the correct shape.
H, edges = histogramdd(x, (2, 3, 4),
range=[[-1, 1], [0, 3], [0, 4]],
density=True)
answer = np.array([[[0, 1, 0, 0], [0, 0, 1, 0], [1, 0, 0, 0]],
[[0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 1, 0]]])
assert_array_almost_equal(H, answer / 6., 4)
# Check that a sequence of arrays is accepted and H has the correct
# shape.
z = [np.squeeze(y) for y in np.split(x, 3, axis=1)]
H, edges = histogramdd(z,
bins=(4, 3, 2),
range=[[-2, 2], [0, 3], [0, 2]])
answer = np.array([[[0, 0], [0, 0], [0, 0]], [[0, 1], [0, 0], [1, 0]],
[[0, 1], [0, 0], [0, 0]], [[0, 0], [0, 0], [0, 0]]])
assert_array_equal(H, answer)
Z = np.zeros((5, 5, 5))
Z[list(range(5)), list(range(5)), list(range(5))] = 1.
H, edges = histogramdd([np.arange(5), np.arange(5), np.arange(5)], 5)
assert_array_equal(H, Z)
def test_valuesSD(self):
# Check assignment of single-dimensional objects with values
ua = np.zeros((2,), dtype='U%s' % self.ulen)
ua[0] = self.ucs_value*self.ulen
self.content_check(ua, ua[0], 4*self.ulen*2)
ua[1] = self.ucs_value*self.ulen
self.content_check(ua, ua[1], 4*self.ulen*2)
def test_valuesMD(self):
# Check assignment of multi-dimensional objects with values
ua = np.zeros((2, 3, 4), dtype='U%s' % self.ulen)
ua[0, 0, 0] = self.ucs_value*self.ulen
self.content_check(ua, ua[0, 0, 0], 4*self.ulen*2*3*4)
ua[-1, -1, -1] = self.ucs_value*self.ulen
self.content_check(ua, ua[-1, -1, -1], 4*self.ulen*2*3*4)
def setup(self):
self.a = np.arange(np.prod([3, 1, 5, 6])).reshape(3, 1, 5, 6)
self.b = np.empty((3, 0, 5, 6))
self.complex_indices = ['skip', Ellipsis,
0,
# Boolean indices, up to 3-d for some special cases of eating up
# dimensions, also need to test all False
np.array([True, False, False]),
np.array([[True, False], [False, True]]),
np.array([[[False, False], [False, False]]]),
# Some slices:
slice(-5, 5, 2),
slice(1, 1, 100),
slice(4, -1, -2),
slice(None, None, -3),
# Some Fancy indexes:
np.empty((0, 1, 1), dtype=np.intp), # empty and can be broadcast
np.array([0, 1, -2]),
np.array([[2], [0], [1]]),
np.array([[0, -1], [0, 1]], dtype=np.dtype('intp').newbyteorder()),
np.array([2, -1], dtype=np.int8),
np.zeros([1]*31, dtype=int), # trigger too large array.
np.array([0., 1.])] # invalid datatype
# Some simpler indices that still cover a bit more
self.simple_indices = [Ellipsis, None, -1, [1], np.array([True]),
'skip']
# Very simple ones to fill the rest:
self.fill_indices = [slice(None, None), 0]
def test_nested(self):
one = np.array([1, 1, 1])
two = np.array([[2, 2, 2], [2, 2, 2], [2, 2, 2]])
three = np.array([3, 3, 3])
four = np.array([4, 4, 4])
five = np.array(5)
six = np.array([6, 6, 6, 6, 6])
zero = np.zeros((2, 6))
result = np.block([
[
np.block([
[one],
[three],
[four]
]),
two
],
[five, six],
[zero]
])
expected = np.array([[1, 1, 1, 2, 2, 2],
[3, 3, 3, 2, 2, 2],
[4, 4, 4, 2, 2, 2],
[5, 6, 6, 6, 6, 6],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0]])
assert_equal(result, expected)
