from __future__ import division, absolute_import, print_function
import warnings
import itertools
import pytest
import numpy as np
import numpy.core._umath_tests as umt
import numpy.linalg._umath_linalg as uml
import numpy.core._operand_flag_tests as opflag_tests
import numpy.core._rational_tests as _rational_tests
from numpy.testing import (
assert_, assert_equal, assert_raises, assert_array_equal,
assert_almost_equal, assert_array_almost_equal, assert_no_warnings,
assert_allclose,
)
from numpy.compat import pickle
class TestUfuncKwargs(object):
def test_kwarg_exact(self):
assert_raises(TypeError, np.add, 1, 2, castingx='safe')
assert_raises(TypeError, np.add, 1, 2, dtypex=int)
assert_raises(TypeError, np.add, 1, 2, extobjx=[4096])
assert_raises(TypeError, np.add, 1, 2, outx=None)
assert_raises(TypeError, np.add, 1, 2, sigx='ii->i')
assert_raises(TypeError, np.add, 1, 2, signaturex='ii->i')
assert_raises(TypeError, np.add, 1, 2, subokx=False)
assert_raises(TypeError, np.add, 1, 2, wherex=[True])
def test_sig_signature(self):
assert_raises(ValueError, np.add, 1, 2, sig='ii->i',
signature='ii->i')
def test_sig_dtype(self):
assert_raises(RuntimeError, np.add, 1, 2, sig='ii->i',
dtype=int)
assert_raises(RuntimeError, np.add, 1, 2, signature='ii->i',
dtype=int)
def test_extobj_refcount(self):
# Should not segfault with USE_DEBUG.
assert_raises(TypeError, np.add, 1, 2, extobj=[4096], parrot=True)
class TestUfuncGenericLoops(object):
"""Test generic loops.
The loops to be tested are:
PyUFunc_ff_f_As_dd_d
PyUFunc_ff_f
PyUFunc_dd_d
PyUFunc_gg_g
PyUFunc_FF_F_As_DD_D
PyUFunc_DD_D
PyUFunc_FF_F
PyUFunc_GG_G
PyUFunc_OO_O
PyUFunc_OO_O_method
PyUFunc_f_f_As_d_d
PyUFunc_d_d
PyUFunc_f_f
PyUFunc_g_g
PyUFunc_F_F_As_D_D
PyUFunc_F_F
PyUFunc_D_D
PyUFunc_G_G
PyUFunc_O_O
PyUFunc_O_O_method
PyUFunc_On_Om
Where:
f -- float
d -- double
g -- long double
F -- complex float
D -- complex double
G -- complex long double
O -- python object
It is difficult to assure that each of these loops is entered from the
Python level as the special cased loops are a moving target and the
corresponding types are architecture dependent. We probably need to
define C level testing ufuncs to get at them. For the time being, I've
just looked at the signatures registered in the build directory to find
relevant functions.
"""
np_dtypes = [
(np.single, np.single), (np.single, np.double),
(np.csingle, np.csingle), (np.csingle, np.cdouble),
(np.double, np.double), (np.longdouble, np.longdouble),
(np.cdouble, np.cdouble), (np.clongdouble, np.clongdouble)]
@pytest.mark.parametrize('input_dtype,output_dtype', np_dtypes)
def test_unary_PyUFunc(self, input_dtype, output_dtype, f=np.exp, x=0, y=1):
xs = np.full(10, input_dtype(x), dtype=output_dtype)
ys = f(xs)[::2]
assert_allclose(ys, y)
assert_equal(ys.dtype, output_dtype)
def f2(x, y):
return x**y
@pytest.mark.parametrize('input_dtype,output_dtype', np_dtypes)
def test_binary_PyUFunc(self, input_dtype, output_dtype, f=f2, x=0, y=1):
xs = np.full(10, input_dtype(x), dtype=output_dtype)
ys = f(xs, xs)[::2]
assert_allclose(ys, y)
assert_equal(ys.dtype, output_dtype)
# class to use in testing object method loops
class foo(object):
def conjugate(self):
return np.bool_(1)
def logical_xor(self, obj):
return np.bool_(1)
def test_unary_PyUFunc_O_O(self):
x = np.ones(10, dtype=object)
assert_(np.all(np.abs(x) == 1))
def test_unary_PyUFunc_O_O_method(self, foo=foo):
x = np.full(10, foo(), dtype=object)
assert_(np.all(np.conjugate(x) == True))
def test_binary_PyUFunc_OO_O(self):
x = np.ones(10, dtype=object)
assert_(np.all(np.add(x, x) == 2))
def test_binary_PyUFunc_OO_O_method(self, foo=foo):
x = np.full(10, foo(), dtype=object)
assert_(np.all(np.logical_xor(x, x)))
def test_binary_PyUFunc_On_Om_method(self, foo=foo):
x = np.full((10, 2, 3), foo(), dtype=object)
assert_(np.all(np.logical_xor(x, x)))
class TestUfunc(object):
def test_pickle(self):
for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
assert_(pickle.loads(pickle.dumps(np.sin,
protocol=proto)) is np.sin)
# Check that ufunc not defined in the top level numpy namespace
# such as numpy.core._rational_tests.test_add can also be pickled
res = pickle.loads(pickle.dumps(_rational_tests.test_add,
protocol=proto))
assert_(res is _rational_tests.test_add)
def test_pickle_withstring(self):
astring = (b"cnumpy.core\n_ufunc_reconstruct\np0\n"
b"(S'numpy.core.umath'\np1\nS'cos'\np2\ntp3\nRp4\n.")
assert_(pickle.loads(astring) is np.cos)
def test_reduceat_shifting_sum(self):
L = 6
x = np.arange(L)
idx = np.array(list(zip(np.arange(L - 2), np.arange(L - 2) + 2))).ravel()
assert_array_equal(np.add.reduceat(x, idx)[::2], [1, 3, 5, 7])
def test_all_ufunc(self):
"""Try to check presence and results of all ufuncs.
The list of ufuncs comes from generate_umath.py and is as follows:
===== ==== ============= =============== ========================
done args function types notes
===== ==== ============= =============== ========================
n 1 conjugate nums + O
n 1 absolute nums + O complex -> real
n 1 negative nums + O
n 1 sign nums + O -> int
n 1 invert bool + ints + O flts raise an error
n 1 degrees real + M cmplx raise an error
n 1 radians real + M cmplx raise an error
n 1 arccos flts + M
n 1 arccosh flts + M
n 1 arcsin flts + M
n 1 arcsinh flts + M
n 1 arctan flts + M
n 1 arctanh flts + M
n 1 cos flts + M
n 1 sin flts + M
n 1 tan flts + M
n 1 cosh flts + M
n 1 sinh flts + M
n 1 tanh flts + M
n 1 exp flts + M
n 1 expm1 flts + M
n 1 log flts + M
n 1 log10 flts + M
n 1 log1p flts + M
n 1 sqrt flts + M real x < 0 raises error
n 1 ceil real + M
n 1 trunc real + M
n 1 floor real + M
n 1 fabs real + M
n 1 rint flts + M
n 1 isnan flts -> bool
n 1 isinf flts -> bool
n 1 isfinite flts -> bool
n 1 signbit real -> bool
n 1 modf real -> (frac, int)
n 1 logical_not bool + nums + M -> bool
n 2 left_shift ints + O flts raise an error
n 2 right_shift ints + O flts raise an error
n 2 add bool + nums + O boolean + is ||
n 2 subtract bool + nums + O boolean - is ^
n 2 multiply bool + nums + O boolean * is &
n 2 divide nums + O
n 2 floor_divide nums + O
n 2 true_divide nums + O bBhH -> f, iIlLqQ -> d
n 2 fmod nums + M
n 2 power nums + O
n 2 greater bool + nums + O -> bool
n 2 greater_equal bool + nums + O -> bool
n 2 less bool + nums + O -> bool
n 2 less_equal bool + nums + O -> bool
n 2 equal bool + nums + O -> bool
n 2 not_equal bool + nums + O -> bool
n 2 logical_and bool + nums + M -> bool
n 2 logical_or bool + nums + M -> bool
n 2 logical_xor bool + nums + M -> bool
n 2 maximum bool + nums + O
n 2 minimum bool + nums + O
n 2 bitwise_and bool + ints + O flts raise an error
n 2 bitwise_or bool + ints + O flts raise an error
n 2 bitwise_xor bool + ints + O flts raise an error
n 2 arctan2 real + M
n 2 remainder ints + real + O
n 2 hypot real + M
===== ==== ============= =============== ========================
Types other than those listed will be accepted, but they are cast to
the smallest compatible type for which the function is defined. The
casting rules are:
bool -> int8 -> float32
ints -> double
"""
pass
# from include/numpy/ufuncobject.h
size_inferred = 2
can_ignore = 4
def test_signature0(self):
# the arguments to test_signature are: nin, nout, core_signature
enabled, num_dims, ixs, flags, sizes = umt.test_signature(
2, 1, "(i),(i)->()")
assert_equal(enabled, 1)
assert_equal(num_dims, (1, 1, 0))
assert_equal(ixs, (0, 0))
assert_equal(flags, (self.size_inferred,))
assert_equal(sizes, (-1,))
def test_signature1(self):
# empty core signature; treat as plain ufunc (with trivial core)
enabled, num_dims, ixs, flags, sizes = umt.test_signature(
2, 1, "(),()->()")
assert_equal(enabled, 0)
assert_equal(num_dims, (0, 0, 0))
assert_equal(ixs, ())
assert_equal(flags, ())
assert_equal(sizes, ())
def test_signature2(self):
# more complicated names for variables
enabled, num_dims, ixs, flags, sizes = umt.test_signature(
2, 1, "(i1,i2),(J_1)->(_kAB)")
assert_equal(enabled, 1)
assert_equal(num_dims, (2, 1, 1))
assert_equal(ixs, (0, 1, 2, 3))
assert_equal(flags, (self.size_inferred,)*4)
assert_equal(sizes, (-1, -1, -1, -1))
def test_signature3(self):
enabled, num_dims, ixs, flags, sizes = umt.test_signature(
2, 1, u"(i1, i12), (J_1)->(i12, i2)")
assert_equal(enabled, 1)
assert_equal(num_dims, (2, 1, 2))
assert_equal(ixs, (0, 1, 2, 1, 3))
assert_equal(flags, (self.size_inferred,)*4)
assert_equal(sizes, (-1, -1, -1, -1))
def test_signature4(self):
# matrix_multiply signature from _umath_tests
enabled, num_dims, ixs, flags, sizes = umt.test_signature(
2, 1, "(n,k),(k,m)->(n,m)")
assert_equal(enabled, 1)
assert_equal(num_dims, (2, 2, 2))
assert_equal(ixs, (0, 1, 1, 2, 0, 2))
assert_equal(flags, (self.size_inferred,)*3)
assert_equal(sizes, (-1, -1, -1))
def test_signature5(self):
# matmul signature from _umath_tests
enabled, num_dims, ixs, flags, sizes = umt.test_signature(
2, 1, "(n?,k),(k,m?)->(n?,m?)")
assert_equal(enabled, 1)
assert_equal(num_dims, (2, 2, 2))
assert_equal(ixs, (0, 1, 1, 2, 0, 2))
assert_equal(flags, (self.size_inferred | self.can_ignore,
self.size_inferred,
self.size_inferred | self.can_ignore))
assert_equal(sizes, (-1, -1, -1))
def test_signature6(self):
enabled, num_dims, ixs, flags, sizes = umt.test_signature(
1, 1, "(3)->()")
assert_equal(enabled, 1)
assert_equal(num_dims, (1, 0))
assert_equal(ixs, (0,))
assert_equal(flags, (0,))
assert_equal(sizes, (3,))
def test_signature7(self):
enabled, num_dims, ixs, flags, sizes = umt.test_signature(
3, 1, "(3),(03,3),(n)->(9)")
assert_equal(enabled, 1)
assert_equal(num_dims, (1, 2, 1, 1))
assert_equal(ixs, (0, 0, 0, 1, 2))
assert_equal(flags, (0, self.size_inferred, 0))
assert_equal(sizes, (3, -1, 9))
def test_signature8(self):
enabled, num_dims, ixs, flags, sizes = umt.test_signature(
3, 1, "(3?),(3?,3?),(n)->(9)")
assert_equal(enabled, 1)
assert_equal(num_dims, (1, 2, 1, 1))
assert_equal(ixs, (0, 0, 0, 1, 2))
assert_equal(flags, (self.can_ignore, self.size_inferred, 0))
assert_equal(sizes, (3, -1, 9))
def test_signature_failure_extra_parenthesis(self):
with assert_raises(ValueError):
umt.test_signature(2, 1, "((i)),(i)->()")
def test_signature_failure_mismatching_parenthesis(self):
with assert_raises(ValueError):
umt.test_signature(2, 1, "(i),)i(->()")
def test_signature_failure_signature_missing_input_arg(self):
with assert_raises(ValueError):
umt.test_signature(2, 1, "(i),->()")
def test_signature_failure_signature_missing_output_arg(self):
with assert_raises(ValueError):
umt.test_signature(2, 2, "(i),(i)->()")
def test_get_signature(self):
assert_equal(umt.inner1d.signature, "(i),(i)->()")
def test_forced_sig(self):
a = 0.5*np.arange(3, dtype='f8')
assert_equal(np.add(a, 0.5), [0.5, 1, 1.5])
assert_equal(np.add(a, 0.5, sig='i', casting='unsafe'), [0, 0, 1])
assert_equal(np.add(a, 0.5, sig='ii->i', casting='unsafe'), [0, 0, 1])
assert_equal(np.add(a, 0.5, sig=('i4',), casting='unsafe'), [0, 0, 1])
assert_equal(np.add(a, 0.5, sig=('i4', 'i4', 'i4'),
casting='unsafe'), [0, 0, 1])
b = np.zeros((3,), dtype='f8')
np.add(a, 0.5, out=b)
assert_equal(b, [0.5, 1, 1.5])
b[:] = 0
np.add(a, 0.5, sig='i', out=b, casting='unsafe')
assert_equal(b, [0, 0, 1])
b[:] = 0
np.add(a, 0.5, sig='ii->i', out=b, casting='unsafe')
assert_equal(b, [0, 0, 1])
b[:] = 0
np.add(a, 0.5, sig=('i4',), out=b, casting='unsafe')
assert_equal(b, [0, 0, 1])
b[:] = 0
np.add(a, 0.5, sig=('i4', 'i4', 'i4'), out=b, casting='unsafe')
assert_equal(b, [0, 0, 1])
def test_true_divide(self):
a = np.array(10)
b = np.array(20)
tgt = np.array(0.5)
for tc in 'bhilqBHILQefdgFDG':
dt = np.dtype(tc)
aa = a.astype(dt)
bb = b.astype(dt)
# Check result value and dtype.
for x, y in itertools.product([aa, -aa], [bb, -bb]):
# Check with no output type specified
if tc in 'FDG':
tgt = complex(x)/complex(y)
else:
tgt = float(x)/float(y)
res = np.true_divide(x, y)
rtol = max(np.finfo(res).resolution, 1e-15)
assert_allclose(res, tgt, rtol=rtol)
if tc in 'bhilqBHILQ':
assert_(res.dtype.name == 'float64')
else:
assert_(res.dtype.name == dt.name )
# Check with output type specified. This also checks for the
# incorrect casts in issue gh-3484 because the unary '-' does
# not change types, even for unsigned types, Hence casts in the
# ufunc from signed to unsigned and vice versa will lead to
# errors in the values.
for tcout in 'bhilqBHILQ':
dtout = np.dtype(tcout)
assert_raises(TypeError, np.true_divide, x, y, dtype=dtout)
for tcout in 'efdg':
dtout = np.dtype(tcout)
if tc in 'FDG':
# Casting complex to float is not allowed
assert_raises(TypeError, np.true_divide, x, y, dtype=dtout)
else:
tgt = float(x)/float(y)
rtol = max(np.finfo(dtout).resolution, 1e-15)
atol = max(np.finfo(dtout).tiny, 3e-308)
# Some test values result in invalid for float16.
with np.errstate(invalid='ignore'):
res = np.true_divide(x, y, dtype=dtout)
if not np.isfinite(res) and tcout == 'e':
continue
assert_allclose(res, tgt, rtol=rtol, atol=atol)
assert_(res.dtype.name == dtout.name)
for tcout in 'FDG':
dtout = np.dtype(tcout)
tgt = complex(x)/complex(y)
rtol = max(np.finfo(dtout).resolution, 1e-15)
atol = max(np.finfo(dtout).tiny, 3e-308)
res = np.true_divide(x, y, dtype=dtout)
if not np.isfinite(res):
continue
assert_allclose(res, tgt, rtol=rtol, atol=atol)
assert_(res.dtype.name == dtout.name)
# Check booleans
a = np.ones((), dtype=np.bool_)
res = np.true_divide(a, a)
assert_(res == 1.0)
assert_(res.dtype.name == 'float64')
res = np.true_divide(~a, a)
assert_(res == 0.0)
assert_(res.dtype.name == 'float64')
def test_sum_stability(self):
a = np.ones(500, dtype=np.float32)
assert_almost_equal((a / 10.).sum() - a.size / 10., 0, 4)
a = np.ones(500, dtype=np.float64)
assert_almost_equal((a / 10.).sum() - a.size / 10., 0, 13)
def test_sum(self):
for dt in (int, np.float16, np.float32, np.float64, np.longdouble):
for v in (0, 1, 2, 7, 8, 9, 15, 16, 19, 127,
128, 1024, 1235):
tgt = dt(v * (v + 1) / 2)
d = np.arange(1, v + 1, dtype=dt)
# warning if sum overflows, which it does in float16
overflow = not np.isfinite(tgt)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
assert_almost_equal(np.sum(d), tgt)
assert_equal(len(w), 1 * overflow)
assert_almost_equal(np.sum(d[::-1]), tgt)
assert_equal(len(w), 2 * overflow)
d = np.ones(500, dtype=dt)
assert_almost_equal(np.sum(d[::2]), 250.)
assert_almost_equal(np.sum(d[1::2]), 250.)
assert_almost_equal(np.sum(d[::3]), 167.)
assert_almost_equal(np.sum(d[1::3]), 167.)
assert_almost_equal(np.sum(d[::-2]), 250.)
assert_almost_equal(np.sum(d[-1::-2]), 250.)
assert_almost_equal(np.sum(d[::-3]), 167.)
assert_almost_equal(np.sum(d[-1::-3]), 167.)
# sum with first reduction entry != 0
d = np.ones((1,), dtype=dt)
d += d
assert_almost_equal(d, 2.)
def test_sum_complex(self):
for dt in (np.complex64, np.complex128, np.clongdouble):
for v in (0, 1, 2, 7, 8, 9, 15, 16, 19, 127,
128, 1024, 1235):
tgt = dt(v * (v + 1) / 2) - dt((v * (v + 1) / 2) * 1j)
d = np.empty(v, dtype=dt)
d.real = np.arange(1, v + 1)
d.imag = -np.arange(1, v + 1)
assert_almost_equal(np.sum(d), tgt)
assert_almost_equal(np.sum(d[::-1]), tgt)
d = np.ones(500, dtype=dt) + 1j
assert_almost_equal(np.sum(d[::2]), 250. + 250j)
assert_almost_equal(np.sum(d[1::2]), 250. + 250j)
assert_almost_equal(np.sum(d[::3]), 167. + 167j)
assert_almost_equal(np.sum(d[1::3]), 167. + 167j)
assert_almost_equal(np.sum(d[::-2]), 250. + 250j)
assert_almost_equal(np.sum(d[-1::-2]), 250. + 250j)
assert_almost_equal(np.sum(d[::-3]), 167. + 167j)
assert_almost_equal(np.sum(d[-1::-3]), 167. + 167j)
# sum with first reduction entry != 0
d = np.ones((1,), dtype=dt) + 1j
d += d
assert_almost_equal(d, 2. + 2j)
def test_sum_initial(self):
# Integer, single axis
assert_equal(np.sum([3], initial=2), 5)
# Floating point
assert_almost_equal(np.sum([0.2], initial=0.1), 0.3)
# Multiple non-adjacent axes
assert_equal(np.sum(np.ones((2, 3, 5), dtype=np.int64), axis=(0, 2), initial=2),
[12, 12, 12])
def test_sum_where(self):
# More extensive tests done in test_reduction_with_where.
assert_equal(np.sum([[1., 2.], [3., 4.]], where=[True, False]), 4.)
assert_equal(np.sum([[1., 2.], [3., 4.]], axis=0, initial=5.,
where=[True, False]), [9., 5.])
def test_inner1d(self):
a = np.arange(6).reshape((2, 3))
assert_array_equal(umt.inner1d(a, a), np.sum(a*a, axis=-1))
a = np.arange(6)
assert_array_equal(umt.inner1d(a, a), np.sum(a*a))
def test_broadcast(self):
msg = "broadcast"
a = np.arange(4).reshape((2, 1, 2))
b = np.arange(4).reshape((1, 2, 2))
assert_array_equal(umt.inner1d(a, b), np.sum(a*b, axis=-1), err_msg=msg)
msg = "extend & broadcast loop dimensions"
b = np.arange(4).reshape((2, 2))
assert_array_equal(umt.inner1d(a, b), np.sum(a*b, axis=-1), err_msg=msg)
# Broadcast in core dimensions should fail
a = np.arange(8).reshape((4, 2))
b = np.arange(4).reshape((4, 1))
assert_raises(ValueError, umt.inner1d, a, b)
# Extend core dimensions should fail
a = np.arange(8).reshape((4, 2))
b = np.array(7)
assert_raises(ValueError, umt.inner1d, a, b)
# Broadcast should fail
a = np.arange(2).reshape((2, 1, 1))
b = np.arange(3).reshape((3, 1, 1))
assert_raises(ValueError, umt.inner1d, a, b)
# Writing to a broadcasted array with overlap should warn, gh-2705
a = np.arange(2)
b = np.arange(4).reshape((2, 2))
u, v = np.broadcast_arrays(a, b)
assert_equal(u.strides[0], 0)
x = u + v
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
u += v
assert_equal(len(w), 1)
assert_(x[0,0] != u[0, 0])
def test_type_cast(self):
msg = "type cast"
a = np.arange(6, dtype='short').reshape((2, 3))
assert_array_equal(umt.inner1d(a, a), np.sum(a*a, axis=-1),
err_msg=msg)
msg = "type cast on one argument"
a = np.arange(6).reshape((2, 3))
b = a + 0.1
assert_array_almost_equal(umt.inner1d(a, b), np.sum(a*b, axis=-1),
err_msg=msg)
def test_endian(self):
msg = "big endian"
a = np.arange(6, dtype='>i4').reshape((2, 3))
assert_array_equal(umt.inner1d(a, a), np.sum(a*a, axis=-1),
err_msg=msg)
msg = "little endian"
a = np.arange(6, dtype='<i4').reshape((2, 3))
assert_array_equal(umt.inner1d(a, a), np.sum(a*a, axis=-1),
err_msg=msg)
# Output should always be native-endian
Ba = np.arange(1, dtype='>f8')
La = np.arange(1, dtype='<f8')
assert_equal((Ba+Ba).dtype, np.dtype('f8'))
assert_equal((Ba+La).dtype, np.dtype('f8'))
assert_equal((La+Ba).dtype, np.dtype('f8'))
assert_equal((La+La).dtype, np.dtype('f8'))
assert_equal(np.absolute(La).dtype, np.dtype('f8'))
assert_equal(np.absolute(Ba).dtype, np.dtype('f8'))
assert_equal(np.negative(La).dtype, np.dtype('f8'))
assert_equal(np.negative(Ba).dtype, np.dtype('f8'))
def test_incontiguous_array(self):
msg = "incontiguous memory layout of array"
x = np.arange(64).reshape((2, 2, 2, 2, 2, 2))
a = x[:, 0,:, 0,:, 0]
b = x[:, 1,:, 1,:, 1]
a[0, 0, 0] = -1
msg2 = "make sure it references to the original array"
assert_equal(x[0, 0, 0, 0, 0, 0], -1, err_msg=msg2)
assert_array_equal(umt.inner1d(a, b), np.sum(a*b, axis=-1), err_msg=msg)
x = np.arange(24).reshape(2, 3, 4)
a = x.T
b = x.T
a[0, 0, 0] = -1
assert_equal(x[0, 0, 0], -1, err_msg=msg2)
assert_array_equal(umt.inner1d(a, b), np.sum(a*b, axis=-1), err_msg=msg)
def test_output_argument(self):
msg = "output argument"
a = np.arange(12).reshape((2, 3, 2))
b = np.arange(4).reshape((2, 1, 2)) + 1
c = np.zeros((2, 3), dtype='int')
umt.inner1d(a, b, c)
assert_array_equal(c, np.sum(a*b, axis=-1), err_msg=msg)
c[:] = -1
umt.inner1d(a, b, out=c)
assert_array_equal(c, np.sum(a*b, axis=-1), err_msg=msg)
msg = "output argument with type cast"
c = np.zeros((2, 3), dtype='int16')
umt.inner1d(a, b, c)
assert_array_equal(c, np.sum(a*b, axis=-1), err_msg=msg)
c[:] = -1
umt.inner1d(a, b, out=c)
assert_array_equal(c, np.sum(a*b, axis=-1), err_msg=msg)
msg = "output argument with incontiguous layout"
c = np.zeros((2, 3, 4), dtype='int16')
umt.inner1d(a, b, c[..., 0])
assert_array_equal(c[..., 0], np.sum(a*b, axis=-1), err_msg=msg)
c[:] = -1
umt.inner1d(a, b, out=c[..., 0])
assert_array_equal(c[..., 0], np.sum(a*b, axis=-1), err_msg=msg)
def test_axes_argument(self):
# inner1d signature: '(i),(i)->()'
inner1d = umt.inner1d
a = np.arange(27.).reshape((3, 3, 3))
b = np.arange(10., 19.).reshape((3, 1, 3))
# basic tests on inputs (outputs tested below with matrix_multiply).
c = inner1d(a, b)
assert_array_equal(c, (a * b).sum(-1))
# default
c = inner1d(a, b, axes=[(-1,), (-1,), ()])
assert_array_equal(c, (a * b).sum(-1))
# integers ok for single axis.
c = inner1d(a, b, axes=[-1, -1, ()])
assert_array_equal(c, (a * b).sum(-1))
# mix fine
c = inner1d(a, b, axes=[(-1,), -1, ()])
assert_array_equal(c, (a * b).sum(-1))
# can omit last axis.
c = inner1d(a, b, axes=[-1, -1])
assert_array_equal(c, (a * b).sum(-1))
# can pass in other types of integer (with __index__ protocol)
c = inner1d(a, b, axes=[np.int8(-1), np.array(-1, dtype=np.int32)])
assert_array_equal(c, (a * b).sum(-1))
# swap some axes
c = inner1d(a, b, axes=[0, 0])
assert_array_equal(c, (a * b).sum(0))
c = inner1d(a, b, axes=[0, 2])
assert_array_equal(c, (a.transpose(1, 2, 0) * b).sum(-1))
# Check errors for improperly constructed axes arguments.
# should have list.
assert_raises(TypeError, inner1d, a, b, axes=-1)
# needs enough elements
assert_raises(ValueError, inner1d, a, b, axes=[-1])
# should pass in indices.
assert_raises(TypeError, inner1d, a, b, axes=[-1.0, -1.0])
assert_raises(TypeError, inner1d, a, b, axes=[(-1.0,), -1])
assert_raises(TypeError, inner1d, a, b, axes=[None, 1])
# cannot pass an index unless there is only one dimension
# (output is wrong in this case)
assert_raises(TypeError, inner1d, a, b, axes=[-1, -1, -1])
# or pass in generally the wrong number of axes
assert_raises(ValueError, inner1d, a, b, axes=[-1, -1, (-1,)])
assert_raises(ValueError, inner1d, a, b, axes=[-1, (-2, -1), ()])
# axes need to have same length.
assert_raises(ValueError, inner1d, a, b, axes=[0, 1])
# matrix_multiply signature: '(m,n),(n,p)->(m,p)'
mm = umt.matrix_multiply
a = np.arange(12).reshape((2, 3, 2))
b = np.arange(8).reshape((2, 2, 2, 1)) + 1
# Sanity check.
c = mm(a, b)
assert_array_equal(c, np.matmul(a, b))
# Default axes.
c = mm(a, b, axes=[(-2, -1), (-2, -1), (-2, -1)])
assert_array_equal(c, np.matmul(a, b))
# Default with explicit axes.
c = mm(a, b, axes=[(1, 2), (2, 3), (2, 3)])
assert_array_equal(c, np.matmul(a, b))
# swap some axes.
c = mm(a, b, axes=[(0, -1), (1, 2), (-2, -1)])
assert_array_equal(c, np.matmul(a.transpose(1, 0, 2),
b.transpose(0, 3, 1, 2)))
# Default with output array.
c = np.empty((2, 2, 3, 1))
d = mm(a, b, out=c, axes=[(1, 2), (2, 3), (2, 3)])
assert_(c is d)
assert_array_equal(c, np.matmul(a, b))
# Transposed output array
c = np.empty((1, 2, 2, 3))
d = mm(a, b, out=c, axes=[(-2, -1), (-2, -1), (3, 0)])
assert_(c is d)
assert_array_equal(c, np.matmul(a, b).transpose(3, 0, 1, 2))
# Check errors for improperly constructed axes arguments.
# wrong argument
assert_raises(TypeError, mm, a, b, axis=1)
# axes should be list
assert_raises(TypeError, mm, a, b, axes=1)
assert_raises(TypeError, mm, a, b, axes=((-2, -1), (-2, -1), (-2, -1)))
# list needs to have right length
assert_raises(ValueError, mm, a, b, axes=[])
assert_raises(ValueError, mm, a, b, axes=[(-2, -1)])
# list should contain tuples for multiple axes
assert_raises(TypeError, mm, a, b, axes=[-1, -1, -1])
assert_raises(TypeError, mm, a, b, axes=[(-2, -1), (-2, -1), -1])
assert_raises(TypeError,
mm, a, b, axes=[[-2, -1], [-2, -1], [-2, -1]])
assert_raises(TypeError,
mm, a, b, axes=[(-2, -1), (-2, -1), [-2, -1]])
assert_raises(TypeError, mm, a, b, axes=[(-2, -1), (-2, -1), None])
# tuples should not have duplicated values
assert_raises(ValueError, mm, a, b, axes=[(-2, -1), (-2, -1), (-2, -2)])
# arrays should have enough axes.
z = np.zeros((2, 2))
assert_raises(ValueError, mm, z, z[0])
assert_raises(ValueError, mm, z, z, out=z[:, 0])
assert_raises(ValueError, mm, z[1], z, axes=[0, 1])
assert_raises(ValueError, mm, z, z, out=z[0], axes=[0, 1])
# Regular ufuncs should not accept axes.
assert_raises(TypeError, np.add, 1., 1., axes=[0])
# should be able to deal with bad unrelated kwargs.
assert_raises(TypeError, mm, z, z, axes=[0, 1], parrot=True)
def test_axis_argument(self):
# inner1d signature: '(i),(i)->()'
inner1d = umt.inner1d
a = np.arange(27.).reshape((3, 3, 3))
b = np.arange(10., 19.).reshape((3, 1, 3))
c = inner1d(a, b)
assert_array_equal(c, (a * b).sum(-1))
c = inner1d(a, b, axis=-1)
assert_array_equal(c, (a * b).sum(-1))
out = np.zeros_like(c)
d = inner1d(a, b, axis=-1, out=out)
assert_(d is out)
assert_array_equal(d, c)
c = inner1d(a, b, axis=0)
assert_array_equal(c, (a * b).sum(0))
# Sanity checks on innerwt and cumsum.
a = np.arange(6).reshape((2, 3))
b = np.arange(10, 16).reshape((2, 3))
w = np.arange(20, 26).reshape((2, 3))
assert_array_equal(umt.innerwt(a, b, w, axis=0),
np.sum(a * b * w, axis=0))
assert_array_equal(umt.cumsum(a, axis=0), np.cumsum(a, axis=0))
assert_array_equal(umt.cumsum(a, axis=-1), np.cumsum(a, axis=-1))
out = np.empty_like(a)
b = umt.cumsum(a, out=out, axis=0)
assert_(out is b)
assert_array_equal(b, np.cumsum(a, axis=0))
b = umt.cumsum(a, out=out, axis=1)
assert_(out is b)
assert_array_equal(b, np.cumsum(a, axis=-1))
# Check errors.
# Cannot pass in both axis and axes.
assert_raises(TypeError, inner1d, a, b, axis=0, axes=[0, 0])
# Not an integer.
assert_raises(TypeError, inner1d, a, b, axis=[0])
# more than 1 core dimensions.
mm = umt.matrix_multiply
assert_raises(TypeError, mm, a, b, axis=1)
# Output wrong size in axis.
out = np.empty((1, 2, 3), dtype=a.dtype)
assert_raises(ValueError, umt.cumsum, a, out=out, axis=0)
# Regular ufuncs should not accept axis.
assert_raises(TypeError, np.add, 1., 1., axis=0)
def test_keepdims_argument(self):
# inner1d signature: '(i),(i)->()'
inner1d = umt.inner1d
a = np.arange(27.).reshape((3, 3, 3))
b = np.arange(10., 19.).reshape((3, 1, 3))
c = inner1d(a, b)
assert_array_equal(c, (a * b).sum(-1))
c = inner1d(a, b, keepdims=False)
assert_array_equal(c, (a * b).sum(-1))
c = inner1d(a, b, keepdims=True)
assert_array_equal(c, (a * b).sum(-1, keepdims=True))
out = np.zeros_like(c)
d = inner1d(a, b, keepdims=True, out=out)
assert_(d is out)
assert_array_equal(d, c)
# Now combined with axis and axes.
c = inner1d(a, b, axis=-1, keepdims=False)
assert_array_equal(c, (a * b).sum(-1, keepdims=False))
c = inner1d(a, b, axis=-1, keepdims=True)
assert_array_equal(c, (a * b).sum(-1, keepdims=True))
c = inner1d(a, b, axis=0, keepdims=False)
assert_array_equal(c, (a * b).sum(0, keepdims=False))
c = inner1d(a, b, axis=0, keepdims=True)
assert_array_equal(c, (a * b).sum(0, keepdims=True))
c = inner1d(a, b, axes=[(-1,), (-1,), ()], keepdims=False)
assert_array_equal(c, (a * b).sum(-1))
c = inner1d(a, b, axes=[(-1,), (-1,), (-1,)], keepdims=True)
assert_array_equal(c, (a * b).sum(-1, keepdims=True))
c = inner1d(a, b, axes=[0, 0], keepdims=False)
assert_array_equal(c, (a * b).sum(0))
c = inner1d(a, b, axes=[0, 0, 0], keepdims=True)
assert_array_equal(c, (a * b).sum(0, keepdims=True))
c = inner1d(a, b, axes=[0, 2], keepdims=False)
assert_array_equal(c, (a.transpose(1, 2, 0) * b).sum(-1))
c = inner1d(a, b, axes=[0, 2], keepdims=True)
assert_array_equal(c, (a.transpose(1, 2, 0) * b).sum(-1,
keepdims=True))
c = inner1d(a, b, axes=[0, 2, 2], keepdims=True)
assert_array_equal(c, (a.transpose(1, 2, 0) * b).sum(-1,
keepdims=True))
c = inner1d(a, b, axes=[0, 2, 0], keepdims=True)
assert_array_equal(c, (a * b.transpose(2, 0, 1)).sum(0, keepdims=True))
# Hardly useful, but should work.
c = inner1d(a, b, axes=[0, 2, 1], keepdims=True)
assert_array_equal(c, (a.transpose(1, 0, 2) * b.transpose(0, 2, 1))
.sum(1, keepdims=True))
# Check with two core dimensions.
a = np.eye(3) * np.arange(4.)[:, np.newaxis, np.newaxis]
expected = uml.det(a)
c = uml.det(a, keepdims=False)
assert_array_equal(c, expected)
c = uml.det(a, keepdims=True)
assert_array_equal(c, expected[:, np.newaxis, np.newaxis])
a = np.eye(3) * np.arange(4.)[:, np.newaxis, np.newaxis]
expected_s, expected_l = uml.slogdet(a)
cs, cl = uml.slogdet(a, keepdims=False)
assert_array_equal(cs, expected_s)
assert_array_equal(cl, expected_l)
cs, cl = uml.slogdet(a, keepdims=True)
assert_array_equal(cs, expected_s[:, np.newaxis, np.newaxis])
assert_array_equal(cl, expected_l[:, np.newaxis, np.newaxis])
# Sanity check on innerwt.
a = np.arange(6).reshape((2, 3))
b = np.arange(10, 16).reshape((2, 3))
w = np.arange(20, 26).reshape((2, 3))
assert_array_equal(umt.innerwt(a, b, w, keepdims=True),
np.sum(a * b * w, axis=-1, keepdims=True))
assert_array_equal(umt.innerwt(a, b, w, axis=0, keepdims=True),
np.sum(a * b * w, axis=0, keepdims=True))
# Check errors.
# Not a boolean
assert_raises(TypeError, inner1d, a, b, keepdims='true')
# More than 1 core dimension, and core output dimensions.
mm = umt.matrix_multiply
assert_raises(TypeError, mm, a, b, keepdims=True)
assert_raises(TypeError, mm, a, b, keepdims=False)
# Regular ufuncs should not accept keepdims.
assert_raises(TypeError, np.add, 1., 1., keepdims=False)
def test_innerwt(self):
a = np.arange(6).reshape((2, 3))
b = np.arange(10, 16).reshape((2, 3))
w = np.arange(20, 26).reshape((2, 3))
assert_array_equal(umt.innerwt(a, b, w), np.sum(a*b*w, axis=-1))
a = np.arange(100, 124).reshape((2, 3, 4))
b = np.arange(200, 224).reshape((2, 3, 4))
w = np.arange(300, 324).reshape((2, 3, 4))
assert_array_equal(umt.innerwt(a, b, w), np.sum(a*b*w, axis=-1))
def test_innerwt_empty(self):
"""Test generalized ufunc with zero-sized operands"""
a = np.array([], dtype='f8')
b = np.array([], dtype='f8')
w = np.array([], dtype='f8')
assert_array_equal(umt.innerwt(a, b, w), np.sum(a*b*w, axis=-1))
def test_cross1d(self):
"""Test with fixed-sized signature."""
a = np.eye(3)
assert_array_equal(umt.cross1d(a, a), np.zeros((3, 3)))
out = np.zeros((3, 3))
result = umt.cross1d(a[0], a, out)
assert_(result is out)
assert_array_equal(result, np.vstack((np.zeros(3), a[2], -a[1])))
assert_raises(ValueError, umt.cross1d, np.eye(4), np.eye(4))
assert_raises(ValueError, umt.cross1d, a, np.arange(4.))
assert_raises(ValueError, umt.cross1d, a, np.arange(3.), np.zeros((3, 4)))
def test_can_ignore_signature(self):
# Comparing the effects of ? in signature:
# matrix_multiply: (m,n),(n,p)->(m,p) # all must be there.
# matmul: (m?,n),(n,p?)->(m?,p?) # allow missing m, p.
mat = np.arange(12).reshape((2, 3, 2))
single_vec = np.arange(2)
col_vec = single_vec[:, np.newaxis]
col_vec_array = np.arange(8).reshape((2, 2, 2, 1)) + 1
# matrix @ single column vector with proper dimension
mm_col_vec = umt.matrix_multiply(mat, col_vec)
# matmul does the same thing
matmul_col_vec = umt.matmul(mat, col_vec)
assert_array_equal(matmul_col_vec, mm_col_vec)
# matrix @ vector without dimension making it a column vector.
# matrix multiply fails -> missing core dim.
assert_raises(ValueError, umt.matrix_multiply, mat, single_vec)
# matmul mimicker passes, and returns a vector.
matmul_col = umt.matmul(mat, single_vec)
assert_array_equal(matmul_col, mm_col_vec.squeeze())
# Now with a column array: same as for column vector,
# broadcasting sensibly.
mm_col_vec = umt.matrix_multiply(mat, col_vec_array)
matmul_col_vec = umt.matmul(mat, col_vec_array)
assert_array_equal(matmul_col_vec, mm_col_vec)
# As above, but for row vector
single_vec = np.arange(3)
row_vec = single_vec[np.newaxis, :]
row_vec_array = np.arange(24).reshape((4, 2, 1, 1, 3)) + 1
# row vector @ matrix
mm_row_vec = umt.matrix_multiply(row_vec, mat)
matmul_row_vec = umt.matmul(row_vec, mat)
assert_array_equal(matmul_row_vec, mm_row_vec)
# single row vector @ matrix
assert_raises(ValueError, umt.matrix_multiply, single_vec, mat)
matmul_row = umt.matmul(single_vec, mat)
assert_array_equal(matmul_row, mm_row_vec.squeeze())
# row vector array @ matrix
mm_row_vec = umt.matrix_multiply(row_vec_array, mat)
matmul_row_vec = umt.matmul(row_vec_array, mat)
assert_array_equal(matmul_row_vec, mm_row_vec)
# Now for vector combinations
# row vector @ column vector
col_vec = row_vec.T
col_vec_array = row_vec_array.swapaxes(-2, -1)
mm_row_col_vec = umt.matrix_multiply(row_vec, col_vec)
matmul_row_col_vec = umt.matmul(row_vec, col_vec)
assert_array_equal(matmul_row_col_vec, mm_row_col_vec)
# single row vector @ single col vector
assert_raises(ValueError, umt.matrix_multiply, single_vec, single_vec)
matmul_row_col = umt.matmul(single_vec, single_vec)
assert_array_equal(matmul_row_col, mm_row_col_vec.squeeze())
# row vector array @ matrix
mm_row_col_array = umt.matrix_multiply(row_vec_array, col_vec_array)
matmul_row_col_array = umt.matmul(row_vec_array, col_vec_array)
assert_array_equal(matmul_row_col_array, mm_row_col_array)
# Finally, check that things are *not* squeezed if one gives an
# output.
out = np.zeros_like(mm_row_col_array)
out = umt.matrix_multiply(row_vec_array, col_vec_array, out=out)
assert_array_equal(out, mm_row_col_array)
out[:] = 0
out = umt.matmul(row_vec_array, col_vec_array, out=out)
assert_array_equal(out, mm_row_col_array)
# And check one cannot put missing dimensions back.
out = np.zeros_like(mm_row_col_vec)
assert_raises(ValueError, umt.matrix_multiply, single_vec, single_vec,
out)
# But fine for matmul, since it is just a broadcast.
out = umt.matmul(single_vec, single_vec, out)
assert_array_equal(out, mm_row_col_vec.squeeze())
def test_matrix_multiply(self):
self.compare_matrix_multiply_results(np.int64)
self.compare_matrix_multiply_results(np.double)
def test_matrix_multiply_umath_empty(self):
res = umt.matrix_multiply(np.ones((0, 10)), np.ones((10, 0)))
assert_array_equal(res, np.zeros((0, 0)))
res = umt.matrix_multiply(np.ones((10, 0)), np.ones((0, 10)))
assert_array_equal(res, np.zeros((10, 10)))
def compare_matrix_multiply_results(self, tp):
d1 = np.array(np.random.rand(2, 3, 4), dtype=tp)
d2 = np.array(np.random.rand(2, 3, 4), dtype=tp)
msg = "matrix multiply on type %s" % d1.dtype.name
def permute_n(n):
if n == 1:
return ([0],)
ret = ()
base = permute_n(n-1)
for perm in base:
for i in range(n):
new = perm + [n-1]
new[n-1] = new[i]
new[i] = n-1
ret += (new,)
return ret
def slice_n(n):
if n == 0:
return ((),)
ret = ()
base = slice_n(n-1)
for sl in base:
ret += (sl+(slice(None),),)
ret += (sl+(slice(0, 1),),)
return ret
def broadcastable(s1, s2):
return s1 == s2 or s1 == 1 or s2 == 1
permute_3 = permute_n(3)
slice_3 = slice_n(3) + ((slice(None, None, -1),)*3,)
ref = True
for p1 in permute_3:
for p2 in permute_3:
for s1 in slice_3:
for s2 in slice_3:
a1 = d1.transpose(p1)[s1]
a2 = d2.transpose(p2)[s2]
ref = ref and a1.base is not None
ref = ref and a2.base is not None
if (a1.shape[-1] == a2.shape[-2] and
broadcastable(a1.shape[0], a2.shape[0])):
assert_array_almost_equal(
umt.matrix_multiply(a1, a2),
np.sum(a2[..., np.newaxis].swapaxes(-3, -1) *
a1[..., np.newaxis,:], axis=-1),
err_msg=msg + ' %s %s' % (str(a1.shape),
str(a2.shape)))
assert_equal(ref, True, err_msg="reference check")
def test_euclidean_pdist(self):
a = np.arange(12, dtype=float).reshape(4, 3)
out = np.empty((a.shape[0] * (a.shape[0] - 1) // 2,), dtype=a.dtype)
umt.euclidean_pdist(a, out)
b = np.sqrt(np.sum((a[:, None] - a)**2, axis=-1))
b = b[~np.tri(a.shape[0], dtype=bool)]
assert_almost_equal(out, b)
# An output array is required to determine p with signature (n,d)->(p)
assert_raises(ValueError, umt.euclidean_pdist, a)
def test_cumsum(self):
a = np.arange(10)
result = umt.cumsum(a)
assert_array_equal(result, a.cumsum())
def test_object_logical(self):
a = np.array([3, None, True, False, "test", ""], dtype=object)
assert_equal(np.logical_or(a, None),
np.array([x or None for x in a], dtype=object))
assert_equal(np.logical_or(a, True),
np.array([x or True for x in a], dtype=object))
assert_equal(np.logical_or(a, 12),
np.array([x or 12 for x in a], dtype=object))
assert_equal(np.logical_or(a, "blah"),
np.array([x or "blah" for x in a], dtype=object))
assert_equal(np.logical_and(a, None),
np.array([x and None for x in a], dtype=object))
assert_equal(np.logical_and(a, True),
np.array([x and True for x in a], dtype=object))
assert_equal(np.logical_and(a, 12),
np.array([x and 12 for x in a], dtype=object))
assert_equal(np.logical_and(a, "blah"),
np.array([x and "blah" for x in a], dtype=object))
assert_equal(np.logical_not(a),
np.array([not x for x in a], dtype=object))
assert_equal(np.logical_or.reduce(a), 3)
assert_equal(np.logical_and.reduce(a), None)
def test_object_comparison(self):
class HasComparisons(object):
def __eq__(self, other):
return '=='
arr0d = np.array(HasComparisons())
assert_equal(arr0d == arr0d, True)
assert_equal(np.equal(arr0d, arr0d), True) # normal behavior is a cast
arr1d = np.array([HasComparisons()])
assert_equal(arr1d == arr1d, np.array([True]))
assert_equal(np.equal(arr1d, arr1d), np.array([True])) # normal behavior is a cast
assert_equal(np.equal(arr1d, arr1d, dtype=object), np.array(['==']))
def test_object_array_reduction(self):
# Reductions on object arrays
a = np.array(['a', 'b', 'c'], dtype=object)
assert_equal(np.sum(a), 'abc')
assert_equal(np.max(a), 'c')
assert_equal(np.min(a), 'a')
a = np.array([True, False, True], dtype=object)
assert_equal(np.sum(a), 2)
assert_equal(np.prod(a), 0)
assert_equal(np.any(a), True)
assert_equal(np.all(a), False)
assert_equal(np.max(a), True)
assert_equal(np.min(a), False)
assert_equal(np.array([[1]], dtype=object).sum(), 1)
assert_equal(np.array([[[1, 2]]], dtype=object).sum((0, 1)), [1, 2])
assert_equal(np.array([1], dtype=object).sum(initial=1), 2)
assert_equal(np.array([[1], [2, 3]], dtype=object)
.sum(initial=[0], where=[False, True]), [0, 2, 3])
def test_object_array_accumulate_inplace(self):
# Checks that in-place accumulates work, see also gh-7402
arr = np.ones(4, dtype=object)
arr[:] = [[1] for i in range(4)]
# Twice reproduced also for tuples:
np.add.accumulate(arr, out=arr)
np.add.accumulate(arr, out=arr)
assert_array_equal(arr, np.array([[1]*i for i in [1, 3, 6, 10]]))
# And the same if the axis argument is used
arr = np.ones((2, 4), dtype=object)
arr[0, :] = [[2] for i in range(4)]
np.add.accumulate(arr, out=arr, axis=-1)
np.add.accumulate(arr, out=arr, axis=-1)
assert_array_equal(arr[0, :], np.array([[2]*i for i in [1, 3, 6, 10]]))
def test_object_array_reduceat_inplace(self):
# Checks that in-place reduceats work, see also gh-7465
arr = np.empty(4, dtype=object)
arr[:] = [[1] for i in range(4)]
out = np.empty(4, dtype=object)
out[:] = [[1] for i in range(4)]
np.add.reduceat(arr, np.arange(4), out=arr)
np.add.reduceat(arr, np.arange(4), out=arr)
assert_array_equal(arr, out)
# And the same if the axis argument is used
arr = np.ones((2, 4), dtype=object)
arr[0, :] = [[2] for i in range(4)]
out = np.ones((2, 4), dtype=object)
out[0, :] = [[2] for i in range(4)]
np.add.reduceat(arr, np.arange(4), out=arr, axis=-1)
np.add.reduceat(arr, np.arange(4), out=arr, axis=-1)
assert_array_equal(arr, out)
def test_zerosize_reduction(self):
# Test with default dtype and object dtype
for a in [[], np.array([], dtype=object)]:
assert_equal(np.sum(a), 0)
assert_equal(np.prod(a), 1)
assert_equal(np.any(a), False)
assert_equal(np.all(a), True)
assert_raises(ValueError, np.max, a)
assert_raises(ValueError, np.min, a)
def test_axis_out_of_bounds(self):
a = np.array([False, False])
assert_raises(np.AxisError, a.all, axis=1)
a = np.array([False, False])
assert_raises(np.AxisError, a.all, axis=-2)
a = np.array([False, False])
assert_raises(np.AxisError, a.any, axis=1)
a = np.array([False, False])
assert_raises(np.AxisError, a.any, axis=-2)
def test_scalar_reduction(self):
# The functions 'sum', 'prod', etc allow specifying axis=0
# even for scalars
assert_equal(np.sum(3, axis=0), 3)
assert_equal(np.prod(3.5, axis=0), 3.5)
assert_equal(np.any(True, axis=0), True)
assert_equal(np.all(False, axis=0), False)
assert_equal(np.max(3, axis=0), 3)
assert_equal(np.min(2.5, axis=0), 2.5)
# Check scalar behaviour for ufuncs without an identity
assert_equal(np.power.reduce(3), 3)
# Make sure that scalars are coming out from this operation
assert_(type(np.prod(np.float32(2.5), axis=0)) is np.float32)
assert_(type(np.sum(np.float32(2.5), axis=0)) is np.float32)
assert_(type(np.max(np.float32(2.5), axis=0)) is np.float32)
assert_(type(np.min(np.float32(2.5), axis=0)) is np.float32)
# check if scalars/0-d arrays get cast
assert_(type(np.any(0, axis=0)) is np.bool_)
# assert that 0-d arrays get wrapped
class MyArray(np.ndarray):
pass
a = np.array(1).view(MyArray)
assert_(type(np.any(a)) is MyArray)
def test_casting_out_param(self):
# Test that it's possible to do casts on output
a = np.ones((200, 100), np.int64)
b = np.ones((200, 100), np.int64)
c = np.ones((200, 100), np.float64)
np.add(a, b, out=c)
assert_equal(c, 2)
a = np.zeros(65536)
b = np.zeros(65536, dtype=np.float32)
np.subtract(a, 0, out=b)
assert_equal(b, 0)
def test_where_param(self):
# Test that the where= ufunc parameter works with regular arrays
a = np.arange(7)
b = np.ones(7)
c = np.zeros(7)
np.add(a, b, out=c, where=(a % 2 == 1))
assert_equal(c, [0, 2, 0, 4, 0, 6, 0])
a = np.arange(4).reshape(2, 2) + 2
np.power(a, [2, 3], out=a, where=[[0, 1], [1, 0]])
assert_equal(a, [[2, 27], [16, 5]])
# Broadcasting the where= parameter
np.subtract(a, 2, out=a, where=[True, False])
assert_equal(a, [[0, 27], [14, 5]])
def test_where_param_buffer_output(self):
# This test is temporarily skipped because it requires
# adding masking features to the nditer to work properly
# With casting on output
a = np.ones(10, np.int64)
b = np.ones(10, np.int64)
c = 1.5 * np.ones(10, np.float64)
np.add(a, b, out=c, where=[1, 0, 0, 1, 0, 0, 1, 1, 1, 0])
assert_equal(c, [2, 1.5, 1.5, 2, 1.5, 1.5, 2, 2, 2, 1.5])
def test_where_param_alloc(self):
# With casting and allocated output
a = np.array([1], dtype=np.int64)
m = np.array([True], dtype=bool)
assert_equal(np.sqrt(a, where=m), [1])
# No casting and allocated output
a = np.array([1], dtype=np.float64)
m = np.array([True], dtype=bool)
assert_equal(np.sqrt(a, where=m), [1])
def check_identityless_reduction(self, a):
# np.minimum.reduce is an identityless reduction
# Verify that it sees the zero at various positions
a[...] = 1
a[1, 0, 0] = 0
assert_equal(np.minimum.reduce(a, axis=None), 0)
assert_equal(np.minimum.reduce(a, axis=(0, 1)), [0, 1, 1, 1])
assert_equal(np.minimum.reduce(a, axis=(0, 2)), [0, 1, 1])
assert_equal(np.minimum.reduce(a, axis=(1, 2)), [1, 0])
assert_equal(np.minimum.reduce(a, axis=0),
[[0, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]])
assert_equal(np.minimum.reduce(a, axis=1),
[[1, 1, 1, 1], [0, 1, 1, 1]])
assert_equal(np.minimum.reduce(a, axis=2),
[[1, 1, 1], [0, 1, 1]])
assert_equal(np.minimum.reduce(a, axis=()), a)
a[...] = 1
a[0, 1, 0] = 0
assert_equal(np.minimum.reduce(a, axis=None), 0)
assert_equal(np.minimum.reduce(a, axis=(0, 1)), [0, 1, 1, 1])
assert_equal(np.minimum.reduce(a, axis=(0, 2)), [1, 0, 1])
assert_equal(np.minimum.reduce(a, axis=(1, 2)), [0, 1])
assert_equal(np.minimum.reduce(a, axis=0),
[[1, 1, 1, 1], [0, 1, 1, 1], [1, 1, 1, 1]])
assert_equal(np.minimum.reduce(a, axis=1),
[[0, 1, 1, 1], [1, 1, 1, 1]])
assert_equal(np.minimum.reduce(a, axis=2),
[[1, 0, 1], [1, 1, 1]])
assert_equal(np.minimum.reduce(a, axis=()), a)
a[...] = 1
a[0, 0, 1] = 0
assert_equal(np.minimum.reduce(a, axis=None), 0)
assert_equal(np.minimum.reduce(a, axis=(0, 1)), [1, 0, 1, 1])
assert_equal(np.minimum.reduce(a, axis=(0, 2)), [0, 1, 1])
assert_equal(np.minimum.reduce(a, axis=(1, 2)), [0, 1])
assert_equal(np.minimum.reduce(a, axis=0),
[[1, 0, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]])
assert_equal(np.minimum.reduce(a, axis=1),
[[1, 0, 1, 1], [1, 1, 1, 1]])
assert_equal(np.minimum.reduce(a, axis=2),
[[0, 1, 1], [1, 1, 1]])
assert_equal(np.minimum.reduce(a, axis=()), a)
def test_identityless_reduction_corder(self):
a = np.empty((2, 3, 4), order='C')
self.check_identityless_reduction(a)
def test_identityless_reduction_forder(self):
a = np.empty((2, 3, 4), order='F')
self.check_identityless_reduction(a)
def test_identityless_reduction_otherorder(self):
a = np.empty((2, 4, 3), order='C').swapaxes(1, 2)
self.check_identityless_reduction(a)
def test_identityless_reduction_noncontig(self):
a = np.empty((3, 5, 4), order='C').swapaxes(1, 2)
a = a[1:, 1:, 1:]
self.check_identityless_reduction(a)
def test_identityless_reduction_noncontig_unaligned(self):
a = np.empty((3*4*5*8 + 1,), dtype='i1')
a = a[1:].view(dtype='f8')
a.shape = (3, 4, 5)
a = a[1:, 1:, 1:]
self.check_identityless_reduction(a)
def test_initial_reduction(self):
# np.minimum.reduce is an identityless reduction
# For cases like np.maximum(np.abs(...), initial=0)
# More generally, a supremum over non-negative numbers.
assert_equal(np.maximum.reduce([], initial=0), 0)
# For cases like reduction of an empty array over the reals.
assert_equal(np.minimum.reduce([], initial=np.inf), np.inf)
assert_equal(np.maximum.reduce([], initial=-np.inf), -np.inf)
# Random tests
assert_equal(np.minimum.reduce([5], initial=4), 4)
assert_equal(np.maximum.reduce([4], initial=5), 5)
assert_equal(np.maximum.reduce([5], initial=4), 5)
assert_equal(np.minimum.reduce([4], initial=5), 4)
# Check initial=None raises ValueError for both types of ufunc reductions
assert_raises(ValueError, np.minimum.reduce, [], initial=None)
assert_raises(ValueError, np.add.reduce, [], initial=None)
# Check that np._NoValue gives default behavior.
assert_equal(np.add.reduce([], initial=np._NoValue), 0)
# Check that initial kwarg behaves as intended for dtype=object
a = np.array([10], dtype=object)
res = np.add.reduce(a, initial=5)
assert_equal(res, 15)
@pytest.mark.parametrize('axis', (0, 1, None))
@pytest.mark.parametrize('where', (np.array([False, True, True]),
np.array([[True], [False], [True]]),
np.array([[True, False, False],
[False, True, False],
[False, True, True]])))
def test_reduction_with_where(self, axis, where):
a = np.arange(9.).reshape(3, 3)
a_copy = a.copy()
a_check = np.zeros_like(a)
np.positive(a, out=a_check, where=where)
res = np.add.reduce(a, axis=axis, where=where)
check = a_check.sum(axis)
assert_equal(res, check)
# Check we do not overwrite elements of a internally.
assert_array_equal(a, a_copy)
@pytest.mark.parametrize(('axis', 'where'),
((0, np.array([True, False, True])),
(1, [True, True, False]),
(None, True)))
@pytest.mark.parametrize('initial', (-np.inf, 5.))
def test_reduction_with_where_and_initial(self, axis, where, initial):
a = np.arange(9.).reshape(3, 3)
a_copy = a.copy()
a_check = np.full(a.shape, -np.inf)
np.positive(a, out=a_check, where=where)
res = np.maximum.reduce(a, axis=axis, where=where, initial=initial)
check = a_check.max(axis, initial=initial)
assert_equal(res, check)
def test_reduction_where_initial_needed(self):
a = np.arange(9.).reshape(3, 3)
m = [False, True, False]
assert_raises(ValueError, np.maximum.reduce, a, where=m)
def test_identityless_reduction_nonreorderable(self):
a = np.array([[8.0, 2.0, 2.0], [1.0, 0.5, 0.25]])
res = np.divide.reduce(a, axis=0)
assert_equal(res, [8.0, 4.0, 8.0])
res = np.divide.reduce(a, axis=1)
assert_equal(res, [2.0, 8.0])
res = np.divide.reduce(a, axis=())
assert_equal(res, a)
assert_raises(ValueError, np.divide.reduce, a, axis=(0, 1))
def test_reduce_zero_axis(self):
# If we have a n x m array and do a reduction with axis=1, then we are
# doing n reductions, and each reduction takes an m-element array. For
# a reduction operation without an identity, then:
# n > 0, m > 0: fine
# n = 0, m > 0: fine, doing 0 reductions of m-element arrays
# n > 0, m = 0: can't reduce a 0-element array, ValueError
# n = 0, m = 0: can't reduce a 0-element array, ValueError (for
# consistency with the above case)
# This test doesn't actually look at return values, it just checks to
# make sure that error we get an error in exactly those cases where we
# expect one, and assumes the calculations themselves are done
# correctly.
def ok(f, *args, **kwargs):
f(*args, **kwargs)
def err(f, *args, **kwargs):
assert_raises(ValueError, f, *args, **kwargs)
def t(expect, func, n, m):
expect(func, np.zeros((n, m)), axis=1)
expect(func, np.zeros((m, n)), axis=0)
expect(func, np.zeros((n // 2, n // 2, m)), axis=2)
expect(func, np.zeros((n // 2, m, n // 2)), axis=1)
expect(func, np.zeros((n, m // 2, m // 2)), axis=(1, 2))
expect(func, np.zeros((m // 2, n, m // 2)), axis=(0, 2))
expect(func, np.zeros((m // 3, m // 3, m // 3,
n // 2, n // 2)),
axis=(0, 1, 2))
# Check what happens if the inner (resp. outer) dimensions are a
# mix of zero and non-zero:
expect(func, np.zeros((10, m, n)), axis=(0, 1))
expect(func, np.zeros((10, n, m)), axis=(0, 2))
expect(func, np.zeros((m, 10, n)), axis=0)
expect(func, np.zeros((10, m, n)), axis=1)
expect(func, np.zeros((10, n, m)), axis=2)
# np.maximum is just an arbitrary ufunc with no reduction identity
assert_equal(np.maximum.identity, None)
t(ok, np.maximum.reduce, 30, 30)
t(ok, np.maximum.reduce, 0, 30)
t(err, np.maximum.reduce, 30, 0)
t(err, np.maximum.reduce, 0, 0)
err(np.maximum.reduce, [])
np.maximum.reduce(np.zeros((0, 0)), axis=())
# all of the combinations are fine for a reduction that has an
# identity
t(ok, np.add.reduce, 30, 30)
t(ok, np.add.reduce, 0, 30)
t(ok, np.add.reduce, 30, 0)
t(ok, np.add.reduce, 0, 0)
np.add.reduce([])
np.add.reduce(np.zeros((0, 0)), axis=())
# OTOH, accumulate always makes sense for any combination of n and m,
# because it maps an m-element array to an m-element array. These
# tests are simpler because accumulate doesn't accept multiple axes.
for uf in (np.maximum, np.add):
uf.accumulate(np.zeros((30, 0)), axis=0)
uf.accumulate(np.zeros((0, 30)), axis=0)
uf.accumulate(np.zeros((30, 30)), axis=0)
uf.accumulate(np.zeros((0, 0)), axis=0)
def test_safe_casting(self):
# In old versions of numpy, in-place operations used the 'unsafe'
# casting rules. In versions >= 1.10, 'same_kind' is the
# default and an exception is raised instead of a warning.
# when 'same_kind' is not satisfied.
a = np.array([1, 2, 3], dtype=int)
# Non-in-place addition is fine
assert_array_equal(assert_no_warnings(np.add, a, 1.1),
[2.1, 3.1, 4.1])
assert_raises(TypeError, np.add, a, 1.1, out=a)
def add_inplace(a, b):
a += b
assert_raises(TypeError, add_inplace, a, 1.1)
# Make sure that explicitly overriding the exception is allowed:
assert_no_warnings(np.add, a, 1.1, out=a, casting="unsafe")
assert_array_equal(a, [2, 3, 4])
def test_ufunc_custom_out(self):
# Test ufunc with built in input types and custom output type
a = np.array([0, 1, 2], dtype='i8')
b = np.array([0, 1, 2], dtype='i8')
c = np.empty(3, dtype=_rational_tests.rational)
# Output must be specified so numpy knows what
# ufunc signature to look for
result = _rational_tests.test_add(a, b, c)
target = np.array([0, 2, 4], dtype=_rational_tests.rational)
assert_equal(result, target)
# no output type should raise TypeError
with assert_raises(TypeError):
_rational_tests.test_add(a, b)
def test_operand_flags(self):
a = np.arange(16, dtype='l').reshape(4, 4)
b = np.arange(9, dtype='l').reshape(3, 3)
opflag_tests.inplace_add(a[:-1, :-1], b)
assert_equal(a, np.array([[0, 2, 4, 3], [7, 9, 11, 7],
[14, 16, 18, 11], [12, 13, 14, 15]], dtype='l'))
a = np.array(0)
opflag_tests.inplace_add(a, 3)
assert_equal(a, 3)
opflag_tests.inplace_add(a, [3, 4])
assert_equal(a, 10)
def test_struct_ufunc(self):
import numpy.core._struct_ufunc_tests as struct_ufunc
a = np.array([(1, 2, 3)], dtype='u8,u8,u8')
b = np.array([(1, 2, 3)], dtype='u8,u8,u8')
result = struct_ufunc.add_triplet(a, b)
assert_equal(result, np.array([(2, 4, 6)], dtype='u8,u8,u8'))
assert_raises(RuntimeError, struct_ufunc.register_fail)
def test_custom_ufunc(self):
a = np.array(
[_rational_tests.rational(1, 2),
_rational_tests.rational(1, 3),
_rational_tests.rational(1, 4)],
dtype=_rational_tests.rational)
b = np.array(
[_rational_tests.rational(1, 2),
_rational_tests.rational(1, 3),
_rational_tests.rational(1, 4)],
dtype=_rational_tests.rational)
result = _rational_tests.test_add_rationals(a, b)
expected = np.array(
[_rational_tests.rational(1),
_rational_tests.rational(2, 3),
_rational_tests.rational(1, 2)],
dtype=_rational_tests.rational)
assert_equal(result, expected)
def test_custom_ufunc_forced_sig(self):
# gh-9351 - looking for a non-first userloop would previously hang
with assert_raises(TypeError):
np.multiply(_rational_tests.rational(1), 1,
signature=(_rational_tests.rational, int, None))
def test_custom_array_like(self):
class MyThing(object):
__array_priority__ = 1000
rmul_count = 0
getitem_count = 0
def __init__(self, shape):
self.shape = shape
def __len__(self):
return self.shape[0]
def __getitem__(self, i):
MyThing.getitem_count += 1
if not isinstance(i, tuple):
i = (i,)
if len(i) > self.ndim:
raise IndexError("boo")
return MyThing(self.shape[len(i):])
def __rmul__(self, other):
MyThing.rmul_count += 1
return self
np.float64(5)*MyThing((3, 3))
assert_(MyThing.rmul_count == 1, MyThing.rmul_count)
assert_(MyThing.getitem_count <= 2, MyThing.getitem_count)
def test_inplace_fancy_indexing(self):
a = np.arange(10)
np.add.at(a, [2, 5, 2], 1)
assert_equal(a, [0, 1, 4, 3, 4, 6, 6, 7, 8, 9])
a = np.arange(10)
b = np.array([100, 100, 100])
np.add.at(a, [2, 5, 2], b)
assert_equal(a, [0, 1, 202, 3, 4, 105, 6, 7, 8, 9])
a = np.arange(9).reshape(3, 3)
b = np.array([[100, 100, 100], [200, 200, 200], [300, 300, 300]])
np.add.at(a, (slice(None), [1, 2, 1]), b)
assert_equal(a, [[0, 201, 102], [3, 404, 205], [6, 607, 308]])
a = np.arange(27).reshape(3, 3, 3)
b = np.array([100, 200, 300])
np.add.at(a, (slice(None), slice(None), [1, 2, 1]), b)
assert_equal(a,
[[[0, 401, 202],
[3, 404, 205],
[6, 407, 208]],
[[9, 410, 211],
[12, 413, 214],
[15, 416, 217]],
[[18, 419, 220],
[21, 422, 223],
[24, 425, 226]]])
a = np.arange(9).reshape(3, 3)
b = np.array([[100, 100, 100], [200, 200, 200], [300, 300, 300]])
np.add.at(a, ([1, 2, 1], slice(None)), b)
assert_equal(a, [[0, 1, 2], [403, 404, 405], [206, 207, 208]])
a = np.arange(27).reshape(3, 3, 3)
b = np.array([100, 200, 300])
np.add.at(a, (slice(None), [1, 2, 1], slice(None)), b)
assert_equal(a,
[[[0, 1, 2],
[203, 404, 605],
[106, 207, 308]],
[[9, 10, 11],
[212, 413, 614],
[115, 216, 317]],
[[18, 19, 20],
[221, 422, 623],
[124, 225, 326]]])
a = np.arange(9).reshape(3, 3)
b = np.array([100, 200, 300])
np.add.at(a, (0, [1, 2, 1]), b)
assert_equal(a, [[0, 401, 202], [3, 4, 5], [6, 7, 8]])
a = np.arange(27).reshape(3, 3, 3)
b = np.array([100, 200, 300])
np.add.at(a, ([1, 2, 1], 0, slice(None)), b)
assert_equal(a,
[[[0, 1, 2],
[3, 4, 5],
[6, 7, 8]],
[[209, 410, 611],
[12, 13, 14],
[15, 16, 17]],
[[118, 219, 320],
[21, 22, 23],
[24, 25, 26]]])
a = np.arange(27).reshape(3, 3, 3)
b = np.array([100, 200, 300])
np.add.at(a, (slice(None), slice(None), slice(None)), b)
assert_equal(a,
[[[100, 201, 302],
[103, 204, 305],
[106, 207, 308]],
[[109, 210, 311],
[112, 213, 314],
[115, 216, 317]],
[[118, 219, 320],
[121, 222, 323],
[124, 225, 326]]])
a = np.arange(10)
np.negative.at(a, [2, 5, 2])
assert_equal(a, [0, 1, 2, 3, 4, -5, 6, 7, 8, 9])
# Test 0-dim array
a = np.array(0)
np.add.at(a, (), 1)
assert_equal(a, 1)
assert_raises(IndexError, np.add.at, a, 0, 1)
assert_raises(IndexError, np.add.at, a, [], 1)
# Test mixed dtypes
a = np.arange(10)
np.power.at(a, [1, 2, 3, 2], 3.5)
assert_equal(a, np.array([0, 1, 4414, 46, 4, 5, 6, 7, 8, 9]))
# Test boolean indexing and boolean ufuncs
a = np.arange(10)
index = a % 2 == 0
np.equal.at(a, index, [0, 2, 4, 6, 8])
assert_equal(a, [1, 1, 1, 3, 1, 5, 1, 7, 1, 9])
# Test unary operator
a = np.arange(10, dtype='u4')
np.invert.at(a, [2, 5, 2])
assert_equal(a, [0, 1, 2, 3, 4, 5 ^ 0xffffffff, 6, 7, 8, 9])
# Test empty subspace
orig = np.arange(4)
a = orig[:, None][:, 0:0]
np.add.at(a, [0, 1], 3)
assert_array_equal(orig, np.arange(4))
# Test with swapped byte order
index = np.array([1, 2, 1], np.dtype('i').newbyteorder())
values = np.array([1, 2, 3, 4], np.dtype('f').newbyteorder())
np.add.at(values, index, 3)
assert_array_equal(values, [1, 8, 6, 4])
# Test exception thrown
values = np.array(['a', 1], dtype=object)
assert_raises(TypeError, np.add.at, values, [0, 1], 1)
assert_array_equal(values, np.array(['a', 1], dtype=object))
# Test multiple output ufuncs raise error, gh-5665
assert_raises(ValueError, np.modf.at, np.arange(10), [1])
def test_reduce_arguments(self):
f = np.add.reduce
d = np.ones((5,2), dtype=int)
o = np.ones((2,), dtype=d.dtype)
r = o * 5
assert_equal(f(d), r)
# a, axis=0, dtype=None, out=None, keepdims=False
assert_equal(f(d, axis=0), r)
assert_equal(f(d, 0), r)
assert_equal(f(d, 0, dtype=None), r)
assert_equal(f(d, 0, dtype='i'), r)
assert_equal(f(d, 0, 'i'), r)
assert_equal(f(d, 0, None), r)
assert_equal(f(d, 0, None, out=None), r)
assert_equal(f(d, 0, None, out=o), r)
assert_equal(f(d, 0, None, o), r)
assert_equal(f(d, 0, None, None), r)
assert_equal(f(d, 0, None, None, keepdims=False), r)
assert_equal(f(d, 0, None, None, True), r.reshape((1,) + r.shape))
assert_equal(f(d, 0, None, None, False, 0), r)
assert_equal(f(d, 0, None, None, False, initial=0), r)
assert_equal(f(d, 0, None, None, False, 0, True), r)
assert_equal(f(d, 0, None, None, False, 0, where=True), r)
# multiple keywords
assert_equal(f(d, axis=0, dtype=None, out=None, keepdims=False), r)
assert_equal(f(d, 0, dtype=None, out=None, keepdims=False), r)
assert_equal(f(d, 0, None, out=None, keepdims=False), r)
assert_equal(f(d, 0, None, out=None, keepdims=False, initial=0,
where=True), r)
# too little
assert_raises(TypeError, f)
# too much
assert_raises(TypeError, f, d, 0, None, None, False, 0, True, 1)
# invalid axis
assert_raises(TypeError, f, d, "invalid")
assert_raises(TypeError, f, d, axis="invalid")
assert_raises(TypeError, f, d, axis="invalid", dtype=None,
keepdims=True)
# invalid dtype
assert_raises(TypeError, f, d, 0, "invalid")
assert_raises(TypeError, f, d, dtype="invalid")
assert_raises(TypeError, f, d, dtype="invalid", out=None)
# invalid out
assert_raises(TypeError, f, d, 0, None, "invalid")
assert_raises(TypeError, f, d, out="invalid")
assert_raises(TypeError, f, d, out="invalid", dtype=None)
# keepdims boolean, no invalid value
# assert_raises(TypeError, f, d, 0, None, None, "invalid")
# assert_raises(TypeError, f, d, keepdims="invalid", axis=0, dtype=None)
# invalid mix
assert_raises(TypeError, f, d, 0, keepdims="invalid", dtype="invalid",
out=None)
# invalid keyord
assert_raises(TypeError, f, d, axis=0, dtype=None, invalid=0)
assert_raises(TypeError, f, d, invalid=0)
assert_raises(TypeError, f, d, 0, keepdims=True, invalid="invalid",
out=None)
assert_raises(TypeError, f, d, axis=0, dtype=None, keepdims=True,
out=None, invalid=0)
assert_raises(TypeError, f, d, axis=0, dtype=None,
out=None, invalid=0)
def test_structured_equal(self):
# https://github.com/numpy/numpy/issues/4855
class MyA(np.ndarray):
def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
return getattr(ufunc, method)(*(input.view(np.ndarray)
for input in inputs), **kwargs)
a = np.arange(12.).reshape(4,3)
ra = a.view(dtype=('f8,f8,f8')).squeeze()
mra = ra.view(MyA)
target = np.array([ True, False, False, False], dtype=bool)
assert_equal(np.all(target == (mra == ra[0])), True)
def test_scalar_equal(self):
# Scalar comparisons should always work, without deprecation warnings.
# even when the ufunc fails.
a = np.array(0.)
b = np.array('a')
assert_(a != b)
assert_(b != a)
assert_(not (a == b))
assert_(not (b == a))
def test_NotImplemented_not_returned(self):
# See gh-5964 and gh-2091. Some of these functions are not operator
# related and were fixed for other reasons in the past.
binary_funcs = [
np.power, np.add, np.subtract, np.multiply, np.divide,
np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
np.logical_and, np.logical_or, np.logical_xor, np.maximum,
np.minimum, np.mod,
np.greater, np.greater_equal, np.less, np.less_equal,
np.equal, np.not_equal]
a = np.array('1')
b = 1
c = np.array([1., 2.])
for f in binary_funcs:
assert_raises(TypeError, f, a, b)
assert_raises(TypeError, f, c, a)
def test_reduce_noncontig_output(self):
# Check that reduction deals with non-contiguous output arrays
# appropriately.
#
# gh-8036
x = np.arange(7*13*8, dtype=np.int16).reshape(7, 13, 8)
x = x[4:6,1:11:6,1:5].transpose(1, 2, 0)
y_base = np.arange(4*4, dtype=np.int16).reshape(4, 4)
y = y_base[::2,:]
y_base_copy = y_base.copy()
r0 = np.add.reduce(x, out=y.copy(), axis=2)
r1 = np.add.reduce(x, out=y, axis=2)
# The results should match, and y_base shouldn't get clobbered
assert_equal(r0, r1)
assert_equal(y_base[1,:], y_base_copy[1,:])
assert_equal(y_base[3,:], y_base_copy[3,:])
def test_no_doc_string(self):
# gh-9337
assert_('\n' not in umt.inner1d_no_doc.__doc__)
def test_invalid_args(self):
# gh-7961
exc = pytest.raises(TypeError, np.sqrt, None)
# minimally check the exception text
assert exc.match('loop of ufunc does not support')
@pytest.mark.parametrize('nat', [np.datetime64('nat'), np.timedelta64('nat')])
def test_nat_is_not_finite(self, nat):
try:
assert not np.isfinite(nat)
except TypeError:
pass # ok, just not implemented
@pytest.mark.parametrize('nat', [np.datetime64('nat'), np.timedelta64('nat')])
def test_nat_is_nan(self, nat):
try:
assert np.isnan(nat)
except TypeError:
pass # ok, just not implemented
@pytest.mark.parametrize('nat', [np.datetime64('nat'), np.timedelta64('nat')])
def test_nat_is_not_inf(self, nat):
try:
assert not np.isinf(nat)
except TypeError:
pass # ok, just not implemented
@pytest.mark.parametrize('ufunc', [getattr(np, x) for x in dir(np)
if isinstance(getattr(np, x), np.ufunc)])
def test_ufunc_types(ufunc):
'''
Check all ufuncs that the correct type is returned. Avoid
object and boolean types since many operations are not defined for
for them.
Choose the shape so even dot and matmul will succeed
'''
for typ in ufunc.types:
# types is a list of strings like ii->i
if 'O' in typ or '?' in typ:
continue
inp, out = typ.split('->')
args = [np.ones((3, 3), t) for t in inp]
with warnings.catch_warnings(record=True):
warnings.filterwarnings("always")
res = ufunc(*args)
if isinstance(res, tuple):
outs = tuple(out)
assert len(res) == len(outs)
for r, t in zip(res, outs):
assert r.dtype == np.dtype(t)
else:
assert res.dtype == np.dtype(out)
@pytest.mark.parametrize('ufunc', [getattr(np, x) for x in dir(np)
if isinstance(getattr(np, x), np.ufunc)])
def test_ufunc_noncontiguous(ufunc):
'''
Check that contiguous and non-contiguous calls to ufuncs
have the same results for values in range(9)
'''
for typ in ufunc.types:
# types is a list of strings like ii->i
if any(set('O?mM') & set(typ)):
# bool, object, datetime are too irregular for this simple test
continue
inp, out = typ.split('->')
args_c = [np.empty(6, t) for t in inp]
args_n = [np.empty(18, t)[::3] for t in inp]
for a in args_c:
a.flat = range(1,7)
for a in args_n:
a.flat = range(1,7)
with warnings.catch_warnings(record=True):
warnings.filterwarnings("always")
res_c = ufunc(*args_c)
res_n = ufunc(*args_n)
if len(out) == 1:
res_c = (res_c,)
res_n = (res_n,)
for c_ar, n_ar in zip(res_c, res_n):
dt = c_ar.dtype
if np.issubdtype(dt, np.floating):
# for floating point results allow a small fuss in comparisons
# since different algorithms (libm vs. intrinsics) can be used
# for different input strides
res_eps = np.finfo(dt).eps
tol = 2*res_eps
assert_allclose(res_c, res_n, atol=tol, rtol=tol)
else:
assert_equal(c_ar, n_ar)