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Mini Shell
Mini Shell
from __future__ import division, absolute_import, print_function
import sys
import platform
import warnings
import fnmatch
import itertools
from numpy.testing.utils import _gen_alignment_data
import numpy.core.umath as ncu
from numpy.core import umath_tests as ncu_tests
import numpy as np
from numpy.testing import (
TestCase, run_module_suite, assert_, assert_equal, assert_raises,
assert_raises_regex, assert_array_equal, assert_almost_equal,
assert_array_almost_equal, dec, assert_allclose, assert_no_warnings,
suppress_warnings
)
def on_powerpc():
""" True if we are running on a Power PC platform."""
return platform.processor() == 'powerpc' or \
platform.machine().startswith('ppc')
class _FilterInvalids(object):
def setUp(self):
self.olderr = np.seterr(invalid='ignore')
def tearDown(self):
np.seterr(**self.olderr)
class TestConstants(TestCase):
def test_pi(self):
assert_allclose(ncu.pi, 3.141592653589793, 1e-15)
def test_e(self):
assert_allclose(ncu.e, 2.718281828459045, 1e-15)
def test_euler_gamma(self):
assert_allclose(ncu.euler_gamma, 0.5772156649015329, 1e-15)
class TestOut(TestCase):
def test_out_subok(self):
for subok in (True, False):
a = np.array(0.5)
o = np.empty(())
r = np.add(a, 2, o, subok=subok)
assert_(r is o)
r = np.add(a, 2, out=o, subok=subok)
assert_(r is o)
r = np.add(a, 2, out=(o,), subok=subok)
assert_(r is o)
d = np.array(5.7)
o1 = np.empty(())
o2 = np.empty((), dtype=np.int32)
r1, r2 = np.frexp(d, o1, None, subok=subok)
assert_(r1 is o1)
r1, r2 = np.frexp(d, None, o2, subok=subok)
assert_(r2 is o2)
r1, r2 = np.frexp(d, o1, o2, subok=subok)
assert_(r1 is o1)
assert_(r2 is o2)
r1, r2 = np.frexp(d, out=(o1, None), subok=subok)
assert_(r1 is o1)
r1, r2 = np.frexp(d, out=(None, o2), subok=subok)
assert_(r2 is o2)
r1, r2 = np.frexp(d, out=(o1, o2), subok=subok)
assert_(r1 is o1)
assert_(r2 is o2)
with warnings.catch_warnings(record=True) as w:
warnings.filterwarnings('always', '', DeprecationWarning)
r1, r2 = np.frexp(d, out=o1, subok=subok)
assert_(r1 is o1)
assert_(w[0].category is DeprecationWarning)
assert_raises(ValueError, np.add, a, 2, o, o, subok=subok)
assert_raises(ValueError, np.add, a, 2, o, out=o, subok=subok)
assert_raises(ValueError, np.add, a, 2, None, out=o, subok=subok)
assert_raises(ValueError, np.add, a, 2, out=(o, o), subok=subok)
assert_raises(ValueError, np.add, a, 2, out=(), subok=subok)
assert_raises(TypeError, np.add, a, 2, [], subok=subok)
assert_raises(TypeError, np.add, a, 2, out=[], subok=subok)
assert_raises(TypeError, np.add, a, 2, out=([],), subok=subok)
o.flags.writeable = False
assert_raises(ValueError, np.add, a, 2, o, subok=subok)
assert_raises(ValueError, np.add, a, 2, out=o, subok=subok)
assert_raises(ValueError, np.add, a, 2, out=(o,), subok=subok)
def test_out_wrap_subok(self):
class ArrayWrap(np.ndarray):
__array_priority__ = 10
def __new__(cls, arr):
return np.asarray(arr).view(cls).copy()
def __array_wrap__(self, arr, context):
return arr.view(type(self))
for subok in (True, False):
a = ArrayWrap([0.5])
r = np.add(a, 2, subok=subok)
if subok:
assert_(isinstance(r, ArrayWrap))
else:
assert_(type(r) == np.ndarray)
r = np.add(a, 2, None, subok=subok)
if subok:
assert_(isinstance(r, ArrayWrap))
else:
assert_(type(r) == np.ndarray)
r = np.add(a, 2, out=None, subok=subok)
if subok:
assert_(isinstance(r, ArrayWrap))
else:
assert_(type(r) == np.ndarray)
r = np.add(a, 2, out=(None,), subok=subok)
if subok:
assert_(isinstance(r, ArrayWrap))
else:
assert_(type(r) == np.ndarray)
d = ArrayWrap([5.7])
o1 = np.empty((1,))
o2 = np.empty((1,), dtype=np.int32)
r1, r2 = np.frexp(d, o1, subok=subok)
if subok:
assert_(isinstance(r2, ArrayWrap))
else:
assert_(type(r2) == np.ndarray)
r1, r2 = np.frexp(d, o1, None, subok=subok)
if subok:
assert_(isinstance(r2, ArrayWrap))
else:
assert_(type(r2) == np.ndarray)
r1, r2 = np.frexp(d, None, o2, subok=subok)
if subok:
assert_(isinstance(r1, ArrayWrap))
else:
assert_(type(r1) == np.ndarray)
r1, r2 = np.frexp(d, out=(o1, None), subok=subok)
if subok:
assert_(isinstance(r2, ArrayWrap))
else:
assert_(type(r2) == np.ndarray)
r1, r2 = np.frexp(d, out=(None, o2), subok=subok)
if subok:
assert_(isinstance(r1, ArrayWrap))
else:
assert_(type(r1) == np.ndarray)
with warnings.catch_warnings(record=True) as w:
warnings.filterwarnings('always', '', DeprecationWarning)
r1, r2 = np.frexp(d, out=o1, subok=subok)
if subok:
assert_(isinstance(r2, ArrayWrap))
else:
assert_(type(r2) == np.ndarray)
assert_(w[0].category is DeprecationWarning)
class TestComparisons(TestCase):
def test_ignore_object_identity_in_equal(self):
# Check error raised when comparing identical objects whose comparison
# is not a simple boolean, e.g., arrays that are compared elementwise.
a = np.array([np.array([1, 2, 3]), None], dtype=object)
assert_raises(ValueError, np.equal, a, a)
# Check error raised when comparing identical non-comparable objects.
class FunkyType(object):
def __eq__(self, other):
raise TypeError("I won't compare")
a = np.array([FunkyType()])
assert_raises(TypeError, np.equal, a, a)
# Check identity doesn't override comparison mismatch.
a = np.array([np.nan], dtype=object)
assert_equal(np.equal(a, a), [False])
def test_ignore_object_identity_in_not_equal(self):
# Check error raised when comparing identical objects whose comparison
# is not a simple boolean, e.g., arrays that are compared elementwise.
a = np.array([np.array([1, 2, 3]), None], dtype=object)
assert_raises(ValueError, np.not_equal, a, a)
# Check error raised when comparing identical non-comparable objects.
class FunkyType(object):
def __ne__(self, other):
raise TypeError("I won't compare")
a = np.array([FunkyType()])
assert_raises(TypeError, np.not_equal, a, a)
# Check identity doesn't override comparison mismatch.
a = np.array([np.nan], dtype=object)
assert_equal(np.not_equal(a, a), [True])
class TestDivision(TestCase):
def test_division_int(self):
# int division should follow Python
x = np.array([5, 10, 90, 100, -5, -10, -90, -100, -120])
if 5 / 10 == 0.5:
assert_equal(x / 100, [0.05, 0.1, 0.9, 1,
-0.05, -0.1, -0.9, -1, -1.2])
else:
assert_equal(x / 100, [0, 0, 0, 1, -1, -1, -1, -1, -2])
assert_equal(x // 100, [0, 0, 0, 1, -1, -1, -1, -1, -2])
assert_equal(x % 100, [5, 10, 90, 0, 95, 90, 10, 0, 80])
def test_division_complex(self):
# check that implementation is correct
msg = "Complex division implementation check"
x = np.array([1. + 1.*1j, 1. + .5*1j, 1. + 2.*1j], dtype=np.complex128)
assert_almost_equal(x**2/x, x, err_msg=msg)
# check overflow, underflow
msg = "Complex division overflow/underflow check"
x = np.array([1.e+110, 1.e-110], dtype=np.complex128)
y = x**2/x
assert_almost_equal(y/x, [1, 1], err_msg=msg)
def test_zero_division_complex(self):
with np.errstate(invalid="ignore", divide="ignore"):
x = np.array([0.0], dtype=np.complex128)
y = 1.0/x
assert_(np.isinf(y)[0])
y = complex(np.inf, np.nan)/x
assert_(np.isinf(y)[0])
y = complex(np.nan, np.inf)/x
assert_(np.isinf(y)[0])
y = complex(np.inf, np.inf)/x
assert_(np.isinf(y)[0])
y = 0.0/x
assert_(np.isnan(y)[0])
def test_floor_division_complex(self):
# check that implementation is correct
msg = "Complex floor division implementation check"
x = np.array([.9 + 1j, -.1 + 1j, .9 + .5*1j, .9 + 2.*1j], dtype=np.complex128)
y = np.array([0., -1., 0., 0.], dtype=np.complex128)
assert_equal(np.floor_divide(x**2, x), y, err_msg=msg)
# check overflow, underflow
msg = "Complex floor division overflow/underflow check"
x = np.array([1.e+110, 1.e-110], dtype=np.complex128)
y = np.floor_divide(x**2, x)
assert_equal(y, [1.e+110, 0], err_msg=msg)
def floor_divide_and_remainder(x, y):
return (np.floor_divide(x, y), np.remainder(x, y))
def _signs(dt):
if dt in np.typecodes['UnsignedInteger']:
return (+1,)
else:
return (+1, -1)
class TestRemainder(TestCase):
def test_remainder_basic(self):
dt = np.typecodes['AllInteger'] + np.typecodes['Float']
for op in [floor_divide_and_remainder, np.divmod]:
for dt1, dt2 in itertools.product(dt, dt):
for sg1, sg2 in itertools.product(_signs(dt1), _signs(dt2)):
fmt = 'op: %s, dt1: %s, dt2: %s, sg1: %s, sg2: %s'
msg = fmt % (op.__name__, dt1, dt2, sg1, sg2)
a = np.array(sg1*71, dtype=dt1)
b = np.array(sg2*19, dtype=dt2)
div, rem = op(a, b)
assert_equal(div*b + rem, a, err_msg=msg)
if sg2 == -1:
assert_(b < rem <= 0, msg)
else:
assert_(b > rem >= 0, msg)
def test_float_remainder_exact(self):
# test that float results are exact for small integers. This also
# holds for the same integers scaled by powers of two.
nlst = list(range(-127, 0))
plst = list(range(1, 128))
dividend = nlst + [0] + plst
divisor = nlst + plst
arg = list(itertools.product(dividend, divisor))
tgt = list(divmod(*t) for t in arg)
a, b = np.array(arg, dtype=int).T
# convert exact integer results from Python to float so that
# signed zero can be used, it is checked.
tgtdiv, tgtrem = np.array(tgt, dtype=float).T
tgtdiv = np.where((tgtdiv == 0.0) & ((b < 0) ^ (a < 0)), -0.0, tgtdiv)
tgtrem = np.where((tgtrem == 0.0) & (b < 0), -0.0, tgtrem)
for op in [floor_divide_and_remainder, np.divmod]:
for dt in np.typecodes['Float']:
msg = 'op: %s, dtype: %s' % (op.__name__, dt)
fa = a.astype(dt)
fb = b.astype(dt)
div, rem = op(fa, fb)
assert_equal(div, tgtdiv, err_msg=msg)
assert_equal(rem, tgtrem, err_msg=msg)
def test_float_remainder_roundoff(self):
# gh-6127
dt = np.typecodes['Float']
for op in [floor_divide_and_remainder, np.divmod]:
for dt1, dt2 in itertools.product(dt, dt):
for sg1, sg2 in itertools.product((+1, -1), (+1, -1)):
fmt = 'op: %s, dt1: %s, dt2: %s, sg1: %s, sg2: %s'
msg = fmt % (op.__name__, dt1, dt2, sg1, sg2)
a = np.array(sg1*78*6e-8, dtype=dt1)
b = np.array(sg2*6e-8, dtype=dt2)
div, rem = op(a, b)
# Equal assertion should hold when fmod is used
assert_equal(div*b + rem, a, err_msg=msg)
if sg2 == -1:
assert_(b < rem <= 0, msg)
else:
assert_(b > rem >= 0, msg)
def test_float_remainder_corner_cases(self):
# Check remainder magnitude.
for dt in np.typecodes['Float']:
b = np.array(1.0, dtype=dt)
a = np.nextafter(np.array(0.0, dtype=dt), -b)
rem = np.remainder(a, b)
assert_(rem <= b, 'dt: %s' % dt)
rem = np.remainder(-a, -b)
assert_(rem >= -b, 'dt: %s' % dt)
# Check nans, inf
with suppress_warnings() as sup:
sup.filter(RuntimeWarning, "invalid value encountered in remainder")
for dt in np.typecodes['Float']:
fone = np.array(1.0, dtype=dt)
fzer = np.array(0.0, dtype=dt)
finf = np.array(np.inf, dtype=dt)
fnan = np.array(np.nan, dtype=dt)
rem = np.remainder(fone, fzer)
assert_(np.isnan(rem), 'dt: %s, rem: %s' % (dt, rem))
# MSVC 2008 returns NaN here, so disable the check.
#rem = np.remainder(fone, finf)
#assert_(rem == fone, 'dt: %s, rem: %s' % (dt, rem))
rem = np.remainder(fone, fnan)
assert_(np.isnan(rem), 'dt: %s, rem: %s' % (dt, rem))
rem = np.remainder(finf, fone)
assert_(np.isnan(rem), 'dt: %s, rem: %s' % (dt, rem))
class TestCbrt(TestCase):
def test_cbrt_scalar(self):
assert_almost_equal((np.cbrt(np.float32(-2.5)**3)), -2.5)
def test_cbrt(self):
x = np.array([1., 2., -3., np.inf, -np.inf])
assert_almost_equal(np.cbrt(x**3), x)
assert_(np.isnan(np.cbrt(np.nan)))
assert_equal(np.cbrt(np.inf), np.inf)
assert_equal(np.cbrt(-np.inf), -np.inf)
class TestPower(TestCase):
def test_power_float(self):
x = np.array([1., 2., 3.])
assert_equal(x**0, [1., 1., 1.])
assert_equal(x**1, x)
assert_equal(x**2, [1., 4., 9.])
y = x.copy()
y **= 2
assert_equal(y, [1., 4., 9.])
assert_almost_equal(x**(-1), [1., 0.5, 1./3])
assert_almost_equal(x**(0.5), [1., ncu.sqrt(2), ncu.sqrt(3)])
for out, inp, msg in _gen_alignment_data(dtype=np.float32,
type='unary',
max_size=11):
exp = [ncu.sqrt(i) for i in inp]
assert_almost_equal(inp**(0.5), exp, err_msg=msg)
np.sqrt(inp, out=out)
assert_equal(out, exp, err_msg=msg)
for out, inp, msg in _gen_alignment_data(dtype=np.float64,
type='unary',
max_size=7):
exp = [ncu.sqrt(i) for i in inp]
assert_almost_equal(inp**(0.5), exp, err_msg=msg)
np.sqrt(inp, out=out)
assert_equal(out, exp, err_msg=msg)
def test_power_complex(self):
x = np.array([1+2j, 2+3j, 3+4j])
assert_equal(x**0, [1., 1., 1.])
assert_equal(x**1, x)
assert_almost_equal(x**2, [-3+4j, -5+12j, -7+24j])
assert_almost_equal(x**3, [(1+2j)**3, (2+3j)**3, (3+4j)**3])
assert_almost_equal(x**4, [(1+2j)**4, (2+3j)**4, (3+4j)**4])
assert_almost_equal(x**(-1), [1/(1+2j), 1/(2+3j), 1/(3+4j)])
assert_almost_equal(x**(-2), [1/(1+2j)**2, 1/(2+3j)**2, 1/(3+4j)**2])
assert_almost_equal(x**(-3), [(-11+2j)/125, (-46-9j)/2197,
(-117-44j)/15625])
assert_almost_equal(x**(0.5), [ncu.sqrt(1+2j), ncu.sqrt(2+3j),
ncu.sqrt(3+4j)])
norm = 1./((x**14)[0])
assert_almost_equal(x**14 * norm,
[i * norm for i in [-76443+16124j, 23161315+58317492j,
5583548873 + 2465133864j]])
# Ticket #836
def assert_complex_equal(x, y):
assert_array_equal(x.real, y.real)
assert_array_equal(x.imag, y.imag)
for z in [complex(0, np.inf), complex(1, np.inf)]:
z = np.array([z], dtype=np.complex_)
with np.errstate(invalid="ignore"):
assert_complex_equal(z**1, z)
assert_complex_equal(z**2, z*z)
assert_complex_equal(z**3, z*z*z)
def test_power_zero(self):
# ticket #1271
zero = np.array([0j])
one = np.array([1+0j])
cnan = np.array([complex(np.nan, np.nan)])
# FIXME cinf not tested.
#cinf = np.array([complex(np.inf, 0)])
def assert_complex_equal(x, y):
x, y = np.asarray(x), np.asarray(y)
assert_array_equal(x.real, y.real)
assert_array_equal(x.imag, y.imag)
# positive powers
for p in [0.33, 0.5, 1, 1.5, 2, 3, 4, 5, 6.6]:
assert_complex_equal(np.power(zero, p), zero)
# zero power
assert_complex_equal(np.power(zero, 0), one)
with np.errstate(invalid="ignore"):
assert_complex_equal(np.power(zero, 0+1j), cnan)
# negative power
for p in [0.33, 0.5, 1, 1.5, 2, 3, 4, 5, 6.6]:
assert_complex_equal(np.power(zero, -p), cnan)
assert_complex_equal(np.power(zero, -1+0.2j), cnan)
def test_fast_power(self):
x = np.array([1, 2, 3], np.int16)
res = x**2.0
assert_((x**2.00001).dtype is res.dtype)
assert_array_equal(res, [1, 4, 9])
# check the inplace operation on the casted copy doesn't mess with x
assert_(not np.may_share_memory(res, x))
assert_array_equal(x, [1, 2, 3])
# Check that the fast path ignores 1-element not 0-d arrays
res = x ** np.array([[[2]]])
assert_equal(res.shape, (1, 1, 3))
def test_integer_power(self):
a = np.array([15, 15], 'i8')
b = np.power(a, a)
assert_equal(b, [437893890380859375, 437893890380859375])
def test_integer_power_with_integer_zero_exponent(self):
dtypes = np.typecodes['Integer']
for dt in dtypes:
arr = np.arange(-10, 10, dtype=dt)
assert_equal(np.power(arr, 0), np.ones_like(arr))
dtypes = np.typecodes['UnsignedInteger']
for dt in dtypes:
arr = np.arange(10, dtype=dt)
assert_equal(np.power(arr, 0), np.ones_like(arr))
def test_integer_power_of_1(self):
dtypes = np.typecodes['AllInteger']
for dt in dtypes:
arr = np.arange(10, dtype=dt)
assert_equal(np.power(1, arr), np.ones_like(arr))
def test_integer_power_of_zero(self):
dtypes = np.typecodes['AllInteger']
for dt in dtypes:
arr = np.arange(1, 10, dtype=dt)
assert_equal(np.power(0, arr), np.zeros_like(arr))
def test_integer_to_negative_power(self):
dtypes = np.typecodes['Integer']
for dt in dtypes:
a = np.array([0, 1, 2, 3], dtype=dt)
b = np.array([0, 1, 2, -3], dtype=dt)
one = np.array(1, dtype=dt)
minusone = np.array(-1, dtype=dt)
assert_raises(ValueError, np.power, a, b)
assert_raises(ValueError, np.power, a, minusone)
assert_raises(ValueError, np.power, one, b)
assert_raises(ValueError, np.power, one, minusone)
class TestFloat_power(TestCase):
def test_type_conversion(self):
arg_type = '?bhilBHILefdgFDG'
res_type = 'ddddddddddddgDDG'
for dtin, dtout in zip(arg_type, res_type):
msg = "dtin: %s, dtout: %s" % (dtin, dtout)
arg = np.ones(1, dtype=dtin)
res = np.float_power(arg, arg)
assert_(res.dtype.name == np.dtype(dtout).name, msg)
class TestLog2(TestCase):
def test_log2_values(self):
x = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]
y = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
for dt in ['f', 'd', 'g']:
xf = np.array(x, dtype=dt)
yf = np.array(y, dtype=dt)
assert_almost_equal(np.log2(xf), yf)
def test_log2_ints(self):
# a good log2 implementation should provide this,
# might fail on OS with bad libm
for i in range(1, 65):
v = np.log2(2.**i)
assert_equal(v, float(i), err_msg='at exponent %d' % i)
def test_log2_special(self):
assert_equal(np.log2(1.), 0.)
assert_equal(np.log2(np.inf), np.inf)
assert_(np.isnan(np.log2(np.nan)))
with warnings.catch_warnings(record=True) as w:
warnings.filterwarnings('always', '', RuntimeWarning)
assert_(np.isnan(np.log2(-1.)))
assert_(np.isnan(np.log2(-np.inf)))
assert_equal(np.log2(0.), -np.inf)
assert_(w[0].category is RuntimeWarning)
assert_(w[1].category is RuntimeWarning)
assert_(w[2].category is RuntimeWarning)
class TestExp2(TestCase):
def test_exp2_values(self):
x = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]
y = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
for dt in ['f', 'd', 'g']:
xf = np.array(x, dtype=dt)
yf = np.array(y, dtype=dt)
assert_almost_equal(np.exp2(yf), xf)
class TestLogAddExp2(_FilterInvalids):
# Need test for intermediate precisions
def test_logaddexp2_values(self):
x = [1, 2, 3, 4, 5]
y = [5, 4, 3, 2, 1]
z = [6, 6, 6, 6, 6]
for dt, dec_ in zip(['f', 'd', 'g'], [6, 15, 15]):
xf = np.log2(np.array(x, dtype=dt))
yf = np.log2(np.array(y, dtype=dt))
zf = np.log2(np.array(z, dtype=dt))
assert_almost_equal(np.logaddexp2(xf, yf), zf, decimal=dec_)
def test_logaddexp2_range(self):
x = [1000000, -1000000, 1000200, -1000200]
y = [1000200, -1000200, 1000000, -1000000]
z = [1000200, -1000000, 1000200, -1000000]
for dt in ['f', 'd', 'g']:
logxf = np.array(x, dtype=dt)
logyf = np.array(y, dtype=dt)
logzf = np.array(z, dtype=dt)
assert_almost_equal(np.logaddexp2(logxf, logyf), logzf)
def test_inf(self):
inf = np.inf
x = [inf, -inf, inf, -inf, inf, 1, -inf, 1]
y = [inf, inf, -inf, -inf, 1, inf, 1, -inf]
z = [inf, inf, inf, -inf, inf, inf, 1, 1]
with np.errstate(invalid='raise'):
for dt in ['f', 'd', 'g']:
logxf = np.array(x, dtype=dt)
logyf = np.array(y, dtype=dt)
logzf = np.array(z, dtype=dt)
assert_equal(np.logaddexp2(logxf, logyf), logzf)
def test_nan(self):
assert_(np.isnan(np.logaddexp2(np.nan, np.inf)))
assert_(np.isnan(np.logaddexp2(np.inf, np.nan)))
assert_(np.isnan(np.logaddexp2(np.nan, 0)))
assert_(np.isnan(np.logaddexp2(0, np.nan)))
assert_(np.isnan(np.logaddexp2(np.nan, np.nan)))
class TestLog(TestCase):
def test_log_values(self):
x = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]
y = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
for dt in ['f', 'd', 'g']:
log2_ = 0.69314718055994530943
xf = np.array(x, dtype=dt)
yf = np.array(y, dtype=dt)*log2_
assert_almost_equal(np.log(xf), yf)
class TestExp(TestCase):
def test_exp_values(self):
x = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]
y = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
for dt in ['f', 'd', 'g']:
log2_ = 0.69314718055994530943
xf = np.array(x, dtype=dt)
yf = np.array(y, dtype=dt)*log2_
assert_almost_equal(np.exp(yf), xf)
class TestLogAddExp(_FilterInvalids):
def test_logaddexp_values(self):
x = [1, 2, 3, 4, 5]
y = [5, 4, 3, 2, 1]
z = [6, 6, 6, 6, 6]
for dt, dec_ in zip(['f', 'd', 'g'], [6, 15, 15]):
xf = np.log(np.array(x, dtype=dt))
yf = np.log(np.array(y, dtype=dt))
zf = np.log(np.array(z, dtype=dt))
assert_almost_equal(np.logaddexp(xf, yf), zf, decimal=dec_)
def test_logaddexp_range(self):
x = [1000000, -1000000, 1000200, -1000200]
y = [1000200, -1000200, 1000000, -1000000]
z = [1000200, -1000000, 1000200, -1000000]
for dt in ['f', 'd', 'g']:
logxf = np.array(x, dtype=dt)
logyf = np.array(y, dtype=dt)
logzf = np.array(z, dtype=dt)
assert_almost_equal(np.logaddexp(logxf, logyf), logzf)
def test_inf(self):
inf = np.inf
x = [inf, -inf, inf, -inf, inf, 1, -inf, 1]
y = [inf, inf, -inf, -inf, 1, inf, 1, -inf]
z = [inf, inf, inf, -inf, inf, inf, 1, 1]
with np.errstate(invalid='raise'):
for dt in ['f', 'd', 'g']:
logxf = np.array(x, dtype=dt)
logyf = np.array(y, dtype=dt)
logzf = np.array(z, dtype=dt)
assert_equal(np.logaddexp(logxf, logyf), logzf)
def test_nan(self):
assert_(np.isnan(np.logaddexp(np.nan, np.inf)))
assert_(np.isnan(np.logaddexp(np.inf, np.nan)))
assert_(np.isnan(np.logaddexp(np.nan, 0)))
assert_(np.isnan(np.logaddexp(0, np.nan)))
assert_(np.isnan(np.logaddexp(np.nan, np.nan)))
class TestLog1p(TestCase):
def test_log1p(self):
assert_almost_equal(ncu.log1p(0.2), ncu.log(1.2))
assert_almost_equal(ncu.log1p(1e-6), ncu.log(1+1e-6))
def test_special(self):
with np.errstate(invalid="ignore", divide="ignore"):
assert_equal(ncu.log1p(np.nan), np.nan)
assert_equal(ncu.log1p(np.inf), np.inf)
assert_equal(ncu.log1p(-1.), -np.inf)
assert_equal(ncu.log1p(-2.), np.nan)
assert_equal(ncu.log1p(-np.inf), np.nan)
class TestExpm1(TestCase):
def test_expm1(self):
assert_almost_equal(ncu.expm1(0.2), ncu.exp(0.2)-1)
assert_almost_equal(ncu.expm1(1e-6), ncu.exp(1e-6)-1)
def test_special(self):
assert_equal(ncu.expm1(np.inf), np.inf)
assert_equal(ncu.expm1(0.), 0.)
assert_equal(ncu.expm1(-0.), -0.)
assert_equal(ncu.expm1(np.inf), np.inf)
assert_equal(ncu.expm1(-np.inf), -1.)
class TestHypot(TestCase, object):
def test_simple(self):
assert_almost_equal(ncu.hypot(1, 1), ncu.sqrt(2))
assert_almost_equal(ncu.hypot(0, 0), 0)
def test_reduce(self):
assert_almost_equal(ncu.hypot.reduce([3.0, 4.0]), 5.0)
assert_almost_equal(ncu.hypot.reduce([3.0, 4.0, 0]), 5.0)
assert_almost_equal(ncu.hypot.reduce([9.0, 12.0, 20.0]), 25.0)
assert_equal(ncu.hypot.reduce([]), 0.0)
def assert_hypot_isnan(x, y):
with np.errstate(invalid='ignore'):
assert_(np.isnan(ncu.hypot(x, y)),
"hypot(%s, %s) is %s, not nan" % (x, y, ncu.hypot(x, y)))
def assert_hypot_isinf(x, y):
with np.errstate(invalid='ignore'):
assert_(np.isinf(ncu.hypot(x, y)),
"hypot(%s, %s) is %s, not inf" % (x, y, ncu.hypot(x, y)))
class TestHypotSpecialValues(TestCase):
def test_nan_outputs(self):
assert_hypot_isnan(np.nan, np.nan)
assert_hypot_isnan(np.nan, 1)
def test_nan_outputs2(self):
assert_hypot_isinf(np.nan, np.inf)
assert_hypot_isinf(np.inf, np.nan)
assert_hypot_isinf(np.inf, 0)
assert_hypot_isinf(0, np.inf)
assert_hypot_isinf(np.inf, np.inf)
assert_hypot_isinf(np.inf, 23.0)
def test_no_fpe(self):
assert_no_warnings(ncu.hypot, np.inf, 0)
def assert_arctan2_isnan(x, y):
assert_(np.isnan(ncu.arctan2(x, y)), "arctan(%s, %s) is %s, not nan" % (x, y, ncu.arctan2(x, y)))
def assert_arctan2_ispinf(x, y):
assert_((np.isinf(ncu.arctan2(x, y)) and ncu.arctan2(x, y) > 0), "arctan(%s, %s) is %s, not +inf" % (x, y, ncu.arctan2(x, y)))
def assert_arctan2_isninf(x, y):
assert_((np.isinf(ncu.arctan2(x, y)) and ncu.arctan2(x, y) < 0), "arctan(%s, %s) is %s, not -inf" % (x, y, ncu.arctan2(x, y)))
def assert_arctan2_ispzero(x, y):
assert_((ncu.arctan2(x, y) == 0 and not np.signbit(ncu.arctan2(x, y))), "arctan(%s, %s) is %s, not +0" % (x, y, ncu.arctan2(x, y)))
def assert_arctan2_isnzero(x, y):
assert_((ncu.arctan2(x, y) == 0 and np.signbit(ncu.arctan2(x, y))), "arctan(%s, %s) is %s, not -0" % (x, y, ncu.arctan2(x, y)))
class TestArctan2SpecialValues(TestCase):
def test_one_one(self):
# atan2(1, 1) returns pi/4.
assert_almost_equal(ncu.arctan2(1, 1), 0.25 * np.pi)
assert_almost_equal(ncu.arctan2(-1, 1), -0.25 * np.pi)
assert_almost_equal(ncu.arctan2(1, -1), 0.75 * np.pi)
def test_zero_nzero(self):
# atan2(+-0, -0) returns +-pi.
assert_almost_equal(ncu.arctan2(np.PZERO, np.NZERO), np.pi)
assert_almost_equal(ncu.arctan2(np.NZERO, np.NZERO), -np.pi)
def test_zero_pzero(self):
# atan2(+-0, +0) returns +-0.
assert_arctan2_ispzero(np.PZERO, np.PZERO)
assert_arctan2_isnzero(np.NZERO, np.PZERO)
def test_zero_negative(self):
# atan2(+-0, x) returns +-pi for x < 0.
assert_almost_equal(ncu.arctan2(np.PZERO, -1), np.pi)
assert_almost_equal(ncu.arctan2(np.NZERO, -1), -np.pi)
def test_zero_positive(self):
# atan2(+-0, x) returns +-0 for x > 0.
assert_arctan2_ispzero(np.PZERO, 1)
assert_arctan2_isnzero(np.NZERO, 1)
def test_positive_zero(self):
# atan2(y, +-0) returns +pi/2 for y > 0.
assert_almost_equal(ncu.arctan2(1, np.PZERO), 0.5 * np.pi)
assert_almost_equal(ncu.arctan2(1, np.NZERO), 0.5 * np.pi)
def test_negative_zero(self):
# atan2(y, +-0) returns -pi/2 for y < 0.
assert_almost_equal(ncu.arctan2(-1, np.PZERO), -0.5 * np.pi)
assert_almost_equal(ncu.arctan2(-1, np.NZERO), -0.5 * np.pi)
def test_any_ninf(self):
# atan2(+-y, -infinity) returns +-pi for finite y > 0.
assert_almost_equal(ncu.arctan2(1, np.NINF), np.pi)
assert_almost_equal(ncu.arctan2(-1, np.NINF), -np.pi)
def test_any_pinf(self):
# atan2(+-y, +infinity) returns +-0 for finite y > 0.
assert_arctan2_ispzero(1, np.inf)
assert_arctan2_isnzero(-1, np.inf)
def test_inf_any(self):
# atan2(+-infinity, x) returns +-pi/2 for finite x.
assert_almost_equal(ncu.arctan2( np.inf, 1), 0.5 * np.pi)
assert_almost_equal(ncu.arctan2(-np.inf, 1), -0.5 * np.pi)
def test_inf_ninf(self):
# atan2(+-infinity, -infinity) returns +-3*pi/4.
assert_almost_equal(ncu.arctan2( np.inf, -np.inf), 0.75 * np.pi)
assert_almost_equal(ncu.arctan2(-np.inf, -np.inf), -0.75 * np.pi)
def test_inf_pinf(self):
# atan2(+-infinity, +infinity) returns +-pi/4.
assert_almost_equal(ncu.arctan2( np.inf, np.inf), 0.25 * np.pi)
assert_almost_equal(ncu.arctan2(-np.inf, np.inf), -0.25 * np.pi)
def test_nan_any(self):
# atan2(nan, x) returns nan for any x, including inf
assert_arctan2_isnan(np.nan, np.inf)
assert_arctan2_isnan(np.inf, np.nan)
assert_arctan2_isnan(np.nan, np.nan)
class TestLdexp(TestCase):
def _check_ldexp(self, tp):
assert_almost_equal(ncu.ldexp(np.array(2., np.float32),
np.array(3, tp)), 16.)
assert_almost_equal(ncu.ldexp(np.array(2., np.float64),
np.array(3, tp)), 16.)
assert_almost_equal(ncu.ldexp(np.array(2., np.longdouble),
np.array(3, tp)), 16.)
def test_ldexp(self):
# The default Python int type should work
assert_almost_equal(ncu.ldexp(2., 3), 16.)
# The following int types should all be accepted
self._check_ldexp(np.int8)
self._check_ldexp(np.int16)
self._check_ldexp(np.int32)
self._check_ldexp('i')
self._check_ldexp('l')
def test_ldexp_overflow(self):
# silence warning emitted on overflow
with np.errstate(over="ignore"):
imax = np.iinfo(np.dtype('l')).max
imin = np.iinfo(np.dtype('l')).min
assert_equal(ncu.ldexp(2., imax), np.inf)
assert_equal(ncu.ldexp(2., imin), 0)
class TestMaximum(_FilterInvalids):
def test_reduce(self):
dflt = np.typecodes['AllFloat']
dint = np.typecodes['AllInteger']
seq1 = np.arange(11)
seq2 = seq1[::-1]
func = np.maximum.reduce
for dt in dint:
tmp1 = seq1.astype(dt)
tmp2 = seq2.astype(dt)
assert_equal(func(tmp1), 10)
assert_equal(func(tmp2), 10)
for dt in dflt:
tmp1 = seq1.astype(dt)
tmp2 = seq2.astype(dt)
assert_equal(func(tmp1), 10)
assert_equal(func(tmp2), 10)
tmp1[::2] = np.nan
tmp2[::2] = np.nan
assert_equal(func(tmp1), np.nan)
assert_equal(func(tmp2), np.nan)
def test_reduce_complex(self):
assert_equal(np.maximum.reduce([1, 2j]), 1)
assert_equal(np.maximum.reduce([1+3j, 2j]), 1+3j)
def test_float_nans(self):
nan = np.nan
arg1 = np.array([0, nan, nan])
arg2 = np.array([nan, 0, nan])
out = np.array([nan, nan, nan])
assert_equal(np.maximum(arg1, arg2), out)
def test_object_nans(self):
# Multiple checks to give this a chance to
# fail if cmp is used instead of rich compare.
# Failure cannot be guaranteed.
for i in range(1):
x = np.array(float('nan'), np.object)
y = 1.0
z = np.array(float('nan'), np.object)
assert_(np.maximum(x, y) == 1.0)
assert_(np.maximum(z, y) == 1.0)
def test_complex_nans(self):
nan = np.nan
for cnan in [complex(nan, 0), complex(0, nan), complex(nan, nan)]:
arg1 = np.array([0, cnan, cnan], dtype=np.complex)
arg2 = np.array([cnan, 0, cnan], dtype=np.complex)
out = np.array([nan, nan, nan], dtype=np.complex)
assert_equal(np.maximum(arg1, arg2), out)
def test_object_array(self):
arg1 = np.arange(5, dtype=np.object)
arg2 = arg1 + 1
assert_equal(np.maximum(arg1, arg2), arg2)
class TestMinimum(_FilterInvalids):
def test_reduce(self):
dflt = np.typecodes['AllFloat']
dint = np.typecodes['AllInteger']
seq1 = np.arange(11)
seq2 = seq1[::-1]
func = np.minimum.reduce
for dt in dint:
tmp1 = seq1.astype(dt)
tmp2 = seq2.astype(dt)
assert_equal(func(tmp1), 0)
assert_equal(func(tmp2), 0)
for dt in dflt:
tmp1 = seq1.astype(dt)
tmp2 = seq2.astype(dt)
assert_equal(func(tmp1), 0)
assert_equal(func(tmp2), 0)
tmp1[::2] = np.nan
tmp2[::2] = np.nan
assert_equal(func(tmp1), np.nan)
assert_equal(func(tmp2), np.nan)
def test_reduce_complex(self):
assert_equal(np.minimum.reduce([1, 2j]), 2j)
assert_equal(np.minimum.reduce([1+3j, 2j]), 2j)
def test_float_nans(self):
nan = np.nan
arg1 = np.array([0, nan, nan])
arg2 = np.array([nan, 0, nan])
out = np.array([nan, nan, nan])
assert_equal(np.minimum(arg1, arg2), out)
def test_object_nans(self):
# Multiple checks to give this a chance to
# fail if cmp is used instead of rich compare.
# Failure cannot be guaranteed.
for i in range(1):
x = np.array(float('nan'), np.object)
y = 1.0
z = np.array(float('nan'), np.object)
assert_(np.minimum(x, y) == 1.0)
assert_(np.minimum(z, y) == 1.0)
def test_complex_nans(self):
nan = np.nan
for cnan in [complex(nan, 0), complex(0, nan), complex(nan, nan)]:
arg1 = np.array([0, cnan, cnan], dtype=np.complex)
arg2 = np.array([cnan, 0, cnan], dtype=np.complex)
out = np.array([nan, nan, nan], dtype=np.complex)
assert_equal(np.minimum(arg1, arg2), out)
def test_object_array(self):
arg1 = np.arange(5, dtype=np.object)
arg2 = arg1 + 1
assert_equal(np.minimum(arg1, arg2), arg1)
class TestFmax(_FilterInvalids):
def test_reduce(self):
dflt = np.typecodes['AllFloat']
dint = np.typecodes['AllInteger']
seq1 = np.arange(11)
seq2 = seq1[::-1]
func = np.fmax.reduce
for dt in dint:
tmp1 = seq1.astype(dt)
tmp2 = seq2.astype(dt)
assert_equal(func(tmp1), 10)
assert_equal(func(tmp2), 10)
for dt in dflt:
tmp1 = seq1.astype(dt)
tmp2 = seq2.astype(dt)
assert_equal(func(tmp1), 10)
assert_equal(func(tmp2), 10)
tmp1[::2] = np.nan
tmp2[::2] = np.nan
assert_equal(func(tmp1), 9)
assert_equal(func(tmp2), 9)
def test_reduce_complex(self):
assert_equal(np.fmax.reduce([1, 2j]), 1)
assert_equal(np.fmax.reduce([1+3j, 2j]), 1+3j)
def test_float_nans(self):
nan = np.nan
arg1 = np.array([0, nan, nan])
arg2 = np.array([nan, 0, nan])
out = np.array([0, 0, nan])
assert_equal(np.fmax(arg1, arg2), out)
def test_complex_nans(self):
nan = np.nan
for cnan in [complex(nan, 0), complex(0, nan), complex(nan, nan)]:
arg1 = np.array([0, cnan, cnan], dtype=np.complex)
arg2 = np.array([cnan, 0, cnan], dtype=np.complex)
out = np.array([0, 0, nan], dtype=np.complex)
assert_equal(np.fmax(arg1, arg2), out)
class TestFmin(_FilterInvalids):
def test_reduce(self):
dflt = np.typecodes['AllFloat']
dint = np.typecodes['AllInteger']
seq1 = np.arange(11)
seq2 = seq1[::-1]
func = np.fmin.reduce
for dt in dint:
tmp1 = seq1.astype(dt)
tmp2 = seq2.astype(dt)
assert_equal(func(tmp1), 0)
assert_equal(func(tmp2), 0)
for dt in dflt:
tmp1 = seq1.astype(dt)
tmp2 = seq2.astype(dt)
assert_equal(func(tmp1), 0)
assert_equal(func(tmp2), 0)
tmp1[::2] = np.nan
tmp2[::2] = np.nan
assert_equal(func(tmp1), 1)
assert_equal(func(tmp2), 1)
def test_reduce_complex(self):
assert_equal(np.fmin.reduce([1, 2j]), 2j)
assert_equal(np.fmin.reduce([1+3j, 2j]), 2j)
def test_float_nans(self):
nan = np.nan
arg1 = np.array([0, nan, nan])
arg2 = np.array([nan, 0, nan])
out = np.array([0, 0, nan])
assert_equal(np.fmin(arg1, arg2), out)
def test_complex_nans(self):
nan = np.nan
for cnan in [complex(nan, 0), complex(0, nan), complex(nan, nan)]:
arg1 = np.array([0, cnan, cnan], dtype=np.complex)
arg2 = np.array([cnan, 0, cnan], dtype=np.complex)
out = np.array([0, 0, nan], dtype=np.complex)
assert_equal(np.fmin(arg1, arg2), out)
class TestBool(TestCase):
def test_exceptions(self):
a = np.ones(1, dtype=np.bool_)
assert_raises(TypeError, np.negative, a)
assert_raises(TypeError, np.positive, a)
def test_truth_table_logical(self):
# 2, 3 and 4 serves as true values
input1 = [0, 0, 3, 2]
input2 = [0, 4, 0, 2]
typecodes = (np.typecodes['AllFloat']
+ np.typecodes['AllInteger']
+ '?') # boolean
for dtype in map(np.dtype, typecodes):
arg1 = np.asarray(input1, dtype=dtype)
arg2 = np.asarray(input2, dtype=dtype)
# OR
out = [False, True, True, True]
for func in (np.logical_or, np.maximum):
assert_equal(func(arg1, arg2).astype(bool), out)
# AND
out = [False, False, False, True]
for func in (np.logical_and, np.minimum):
assert_equal(func(arg1, arg2).astype(bool), out)
# XOR
out = [False, True, True, False]
for func in (np.logical_xor, np.not_equal):
assert_equal(func(arg1, arg2).astype(bool), out)
def test_truth_table_bitwise(self):
arg1 = [False, False, True, True]
arg2 = [False, True, False, True]
out = [False, True, True, True]
assert_equal(np.bitwise_or(arg1, arg2), out)
out = [False, False, False, True]
assert_equal(np.bitwise_and(arg1, arg2), out)
out = [False, True, True, False]
assert_equal(np.bitwise_xor(arg1, arg2), out)
def test_reduce(self):
none = np.array([0, 0, 0, 0], bool)
some = np.array([1, 0, 1, 1], bool)
every = np.array([1, 1, 1, 1], bool)
empty = np.array([], bool)
arrs = [none, some, every, empty]
for arr in arrs:
assert_equal(np.logical_and.reduce(arr), all(arr))
for arr in arrs:
assert_equal(np.logical_or.reduce(arr), any(arr))
for arr in arrs:
assert_equal(np.logical_xor.reduce(arr), arr.sum() % 2 == 1)
class TestBitwiseUFuncs(TestCase):
bitwise_types = [np.dtype(c) for c in '?' + 'bBhHiIlLqQ' + 'O']
def test_values(self):
for dt in self.bitwise_types:
zeros = np.array([0], dtype=dt)
ones = np.array([-1], dtype=dt)
msg = "dt = '%s'" % dt.char
assert_equal(np.bitwise_not(zeros), ones, err_msg=msg)
assert_equal(np.bitwise_not(ones), zeros, err_msg=msg)
assert_equal(np.bitwise_or(zeros, zeros), zeros, err_msg=msg)
assert_equal(np.bitwise_or(zeros, ones), ones, err_msg=msg)
assert_equal(np.bitwise_or(ones, zeros), ones, err_msg=msg)
assert_equal(np.bitwise_or(ones, ones), ones, err_msg=msg)
assert_equal(np.bitwise_xor(zeros, zeros), zeros, err_msg=msg)
assert_equal(np.bitwise_xor(zeros, ones), ones, err_msg=msg)
assert_equal(np.bitwise_xor(ones, zeros), ones, err_msg=msg)
assert_equal(np.bitwise_xor(ones, ones), zeros, err_msg=msg)
assert_equal(np.bitwise_and(zeros, zeros), zeros, err_msg=msg)
assert_equal(np.bitwise_and(zeros, ones), zeros, err_msg=msg)
assert_equal(np.bitwise_and(ones, zeros), zeros, err_msg=msg)
assert_equal(np.bitwise_and(ones, ones), ones, err_msg=msg)
def test_types(self):
for dt in self.bitwise_types:
zeros = np.array([0], dtype=dt)
ones = np.array([-1], dtype=dt)
msg = "dt = '%s'" % dt.char
assert_(np.bitwise_not(zeros).dtype == dt, msg)
assert_(np.bitwise_or(zeros, zeros).dtype == dt, msg)
assert_(np.bitwise_xor(zeros, zeros).dtype == dt, msg)
assert_(np.bitwise_and(zeros, zeros).dtype == dt, msg)
def test_identity(self):
assert_(np.bitwise_or.identity == 0, 'bitwise_or')
assert_(np.bitwise_xor.identity == 0, 'bitwise_xor')
assert_(np.bitwise_and.identity == -1, 'bitwise_and')
def test_reduction(self):
binary_funcs = (np.bitwise_or, np.bitwise_xor, np.bitwise_and)
for dt in self.bitwise_types:
zeros = np.array([0], dtype=dt)
ones = np.array([-1], dtype=dt)
for f in binary_funcs:
msg = "dt: '%s', f: '%s'" % (dt, f)
assert_equal(f.reduce(zeros), zeros, err_msg=msg)
assert_equal(f.reduce(ones), ones, err_msg=msg)
# Test empty reduction, no object dtype
for dt in self.bitwise_types[:-1]:
# No object array types
empty = np.array([], dtype=dt)
for f in binary_funcs:
msg = "dt: '%s', f: '%s'" % (dt, f)
tgt = np.array(f.identity, dtype=dt)
res = f.reduce(empty)
assert_equal(res, tgt, err_msg=msg)
assert_(res.dtype == tgt.dtype, msg)
# Empty object arrays use the identity. Note that the types may
# differ, the actual type used is determined by the assign_identity
# function and is not the same as the type returned by the identity
# method.
for f in binary_funcs:
msg = "dt: '%s'" % (f,)
empty = np.array([], dtype=object)
tgt = f.identity
res = f.reduce(empty)
assert_equal(res, tgt, err_msg=msg)
# Non-empty object arrays do not use the identity
for f in binary_funcs:
msg = "dt: '%s'" % (f,)
btype = np.array([True], dtype=object)
assert_(type(f.reduce(btype)) is bool, msg)
class TestInt(TestCase):
def test_logical_not(self):
x = np.ones(10, dtype=np.int16)
o = np.ones(10 * 2, dtype=np.bool)
tgt = o.copy()
tgt[::2] = False
os = o[::2]
assert_array_equal(np.logical_not(x, out=os), False)
assert_array_equal(o, tgt)
class TestFloatingPoint(TestCase):
def test_floating_point(self):
assert_equal(ncu.FLOATING_POINT_SUPPORT, 1)
class TestDegrees(TestCase):
def test_degrees(self):
assert_almost_equal(ncu.degrees(np.pi), 180.0)
assert_almost_equal(ncu.degrees(-0.5*np.pi), -90.0)
class TestRadians(TestCase):
def test_radians(self):
assert_almost_equal(ncu.radians(180.0), np.pi)
assert_almost_equal(ncu.radians(-90.0), -0.5*np.pi)
class TestHeavside(TestCase):
def test_heaviside(self):
x = np.array([[-30.0, -0.1, 0.0, 0.2], [7.5, np.nan, np.inf, -np.inf]])
expectedhalf = np.array([[0.0, 0.0, 0.5, 1.0], [1.0, np.nan, 1.0, 0.0]])
expected1 = expectedhalf.copy()
expected1[0, 2] = 1
h = ncu.heaviside(x, 0.5)
assert_equal(h, expectedhalf)
h = ncu.heaviside(x, 1.0)
assert_equal(h, expected1)
x = x.astype(np.float32)
h = ncu.heaviside(x, np.float32(0.5))
assert_equal(h, expectedhalf.astype(np.float32))
h = ncu.heaviside(x, np.float32(1.0))
assert_equal(h, expected1.astype(np.float32))
class TestSign(TestCase):
def test_sign(self):
a = np.array([np.inf, -np.inf, np.nan, 0.0, 3.0, -3.0])
out = np.zeros(a.shape)
tgt = np.array([1., -1., np.nan, 0.0, 1.0, -1.0])
with np.errstate(invalid='ignore'):
res = ncu.sign(a)
assert_equal(res, tgt)
res = ncu.sign(a, out)
assert_equal(res, tgt)
assert_equal(out, tgt)
def test_sign_dtype_object(self):
# In reference to github issue #6229
foo = np.array([-.1, 0, .1])
a = np.sign(foo.astype(np.object))
b = np.sign(foo)
assert_array_equal(a, b)
def test_sign_dtype_nan_object(self):
# In reference to github issue #6229
def test_nan():
foo = np.array([np.nan])
a = np.sign(foo.astype(np.object))
assert_raises(TypeError, test_nan)
class TestMinMax(TestCase):
def test_minmax_blocked(self):
# simd tests on max/min, test all alignments, slow but important
# for 2 * vz + 2 * (vs - 1) + 1 (unrolled once)
for dt, sz in [(np.float32, 15), (np.float64, 7)]:
for out, inp, msg in _gen_alignment_data(dtype=dt, type='unary',
max_size=sz):
for i in range(inp.size):
inp[:] = np.arange(inp.size, dtype=dt)
inp[i] = np.nan
emsg = lambda: '%r\n%s' % (inp, msg)
assert_(np.isnan(inp.max()), msg=emsg)
assert_(np.isnan(inp.min()), msg=emsg)
inp[i] = 1e10
assert_equal(inp.max(), 1e10, err_msg=msg)
inp[i] = -1e10
assert_equal(inp.min(), -1e10, err_msg=msg)
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)
assert_equal(d.max(), d[0])
assert_equal(d.min(), d[0])
class TestAbsoluteNegative(TestCase):
def test_abs_neg_blocked(self):
# simd tests on abs, test all alignments for vz + 2 * (vs - 1) + 1
for dt, sz in [(np.float32, 11), (np.float64, 5)]:
for out, inp, msg in _gen_alignment_data(dtype=dt, type='unary',
max_size=sz):
tgt = [ncu.absolute(i) for i in inp]
np.absolute(inp, out=out)
assert_equal(out, tgt, err_msg=msg)
self.assertTrue((out >= 0).all())
tgt = [-1*(i) for i in inp]
np.negative(inp, out=out)
assert_equal(out, tgt, err_msg=msg)
for v in [np.nan, -np.inf, np.inf]:
for i in range(inp.size):
d = np.arange(inp.size, dtype=dt)
inp[:] = -d
inp[i] = v
d[i] = -v if v == -np.inf else v
assert_array_equal(np.abs(inp), d, err_msg=msg)
np.abs(inp, out=out)
assert_array_equal(out, d, err_msg=msg)
assert_array_equal(-inp, -1*inp, err_msg=msg)
d = -1 * inp
np.negative(inp, out=out)
assert_array_equal(out, d, err_msg=msg)
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)
assert_equal(np.abs(d), d)
assert_equal(np.negative(d), -d)
np.negative(d, out=d)
np.negative(np.ones_like(d), out=d)
np.abs(d, out=d)
np.abs(np.ones_like(d), out=d)
class TestPositive(TestCase):
def test_valid(self):
valid_dtypes = [int, float, complex, object]
for dtype in valid_dtypes:
x = np.arange(5, dtype=dtype)
result = np.positive(x)
assert_equal(x, result, err_msg=str(dtype))
def test_invalid(self):
with assert_raises(TypeError):
np.positive(True)
with assert_raises(TypeError):
np.positive(np.datetime64('2000-01-01'))
with assert_raises(TypeError):
np.positive(np.array(['foo'], dtype=str))
with assert_raises(TypeError):
np.positive(np.array(['bar'], dtype=object))
class TestSpecialMethods(TestCase):
def test_wrap(self):
class with_wrap(object):
def __array__(self):
return np.zeros(1)
def __array_wrap__(self, arr, context):
r = with_wrap()
r.arr = arr
r.context = context
return r
a = with_wrap()
x = ncu.minimum(a, a)
assert_equal(x.arr, np.zeros(1))
func, args, i = x.context
self.assertTrue(func is ncu.minimum)
self.assertEqual(len(args), 2)
assert_equal(args[0], a)
assert_equal(args[1], a)
self.assertEqual(i, 0)
def test_wrap_with_iterable(self):
# test fix for bug #1026:
class with_wrap(np.ndarray):
__array_priority__ = 10
def __new__(cls):
return np.asarray(1).view(cls).copy()
def __array_wrap__(self, arr, context):
return arr.view(type(self))
a = with_wrap()
x = ncu.multiply(a, (1, 2, 3))
self.assertTrue(isinstance(x, with_wrap))
assert_array_equal(x, np.array((1, 2, 3)))
def test_priority_with_scalar(self):
# test fix for bug #826:
class A(np.ndarray):
__array_priority__ = 10
def __new__(cls):
return np.asarray(1.0, 'float64').view(cls).copy()
a = A()
x = np.float64(1)*a
self.assertTrue(isinstance(x, A))
assert_array_equal(x, np.array(1))
def test_old_wrap(self):
class with_wrap(object):
def __array__(self):
return np.zeros(1)
def __array_wrap__(self, arr):
r = with_wrap()
r.arr = arr
return r
a = with_wrap()
x = ncu.minimum(a, a)
assert_equal(x.arr, np.zeros(1))
def test_priority(self):
class A(object):
def __array__(self):
return np.zeros(1)
def __array_wrap__(self, arr, context):
r = type(self)()
r.arr = arr
r.context = context
return r
class B(A):
__array_priority__ = 20.
class C(A):
__array_priority__ = 40.
x = np.zeros(1)
a = A()
b = B()
c = C()
f = ncu.minimum
self.assertTrue(type(f(x, x)) is np.ndarray)
self.assertTrue(type(f(x, a)) is A)
self.assertTrue(type(f(x, b)) is B)
self.assertTrue(type(f(x, c)) is C)
self.assertTrue(type(f(a, x)) is A)
self.assertTrue(type(f(b, x)) is B)
self.assertTrue(type(f(c, x)) is C)
self.assertTrue(type(f(a, a)) is A)
self.assertTrue(type(f(a, b)) is B)
self.assertTrue(type(f(b, a)) is B)
self.assertTrue(type(f(b, b)) is B)
self.assertTrue(type(f(b, c)) is C)
self.assertTrue(type(f(c, b)) is C)
self.assertTrue(type(f(c, c)) is C)
self.assertTrue(type(ncu.exp(a) is A))
self.assertTrue(type(ncu.exp(b) is B))
self.assertTrue(type(ncu.exp(c) is C))
def test_failing_wrap(self):
class A(object):
def __array__(self):
return np.zeros(1)
def __array_wrap__(self, arr, context):
raise RuntimeError
a = A()
self.assertRaises(RuntimeError, ncu.maximum, a, a)
def test_none_wrap(self):
# Tests that issue #8507 is resolved. Previously, this would segfault
class A(object):
def __array__(self):
return np.zeros(1)
def __array_wrap__(self, arr, context=None):
return None
a = A()
assert_equal(ncu.maximum(a, a), None)
def test_default_prepare(self):
class with_wrap(object):
__array_priority__ = 10
def __array__(self):
return np.zeros(1)
def __array_wrap__(self, arr, context):
return arr
a = with_wrap()
x = ncu.minimum(a, a)
assert_equal(x, np.zeros(1))
assert_equal(type(x), np.ndarray)
def test_prepare(self):
class with_prepare(np.ndarray):
__array_priority__ = 10
def __array_prepare__(self, arr, context):
# make sure we can return a new
return np.array(arr).view(type=with_prepare)
a = np.array(1).view(type=with_prepare)
x = np.add(a, a)
assert_equal(x, np.array(2))
assert_equal(type(x), with_prepare)
def test_prepare_out(self):
class with_prepare(np.ndarray):
__array_priority__ = 10
def __array_prepare__(self, arr, context):
return np.array(arr).view(type=with_prepare)
a = np.array([1]).view(type=with_prepare)
x = np.add(a, a, a)
# Returned array is new, because of the strange
# __array_prepare__ above
assert_(not np.shares_memory(x, a))
assert_equal(x, np.array([2]))
assert_equal(type(x), with_prepare)
def test_failing_prepare(self):
class A(object):
def __array__(self):
return np.zeros(1)
def __array_prepare__(self, arr, context=None):
raise RuntimeError
a = A()
self.assertRaises(RuntimeError, ncu.maximum, a, a)
def test_array_with_context(self):
class A(object):
def __array__(self, dtype=None, context=None):
func, args, i = context
self.func = func
self.args = args
self.i = i
return np.zeros(1)
class B(object):
def __array__(self, dtype=None):
return np.zeros(1, dtype)
class C(object):
def __array__(self):
return np.zeros(1)
a = A()
ncu.maximum(np.zeros(1), a)
self.assertTrue(a.func is ncu.maximum)
assert_equal(a.args[0], 0)
self.assertTrue(a.args[1] is a)
self.assertTrue(a.i == 1)
assert_equal(ncu.maximum(a, B()), 0)
assert_equal(ncu.maximum(a, C()), 0)
def test_ufunc_override(self):
class A(object):
def __array_ufunc__(self, func, method, *inputs, **kwargs):
return self, func, method, inputs, kwargs
a = A()
b = np.matrix([1])
res0 = np.multiply(a, b)
res1 = np.multiply(b, b, out=a)
# self
assert_equal(res0[0], a)
assert_equal(res1[0], a)
assert_equal(res0[1], np.multiply)
assert_equal(res1[1], np.multiply)
assert_equal(res0[2], '__call__')
assert_equal(res1[2], '__call__')
assert_equal(res0[3], (a, b))
assert_equal(res1[3], (b, b))
assert_equal(res0[4], {})
assert_equal(res1[4], {'out': (a,)})
def test_ufunc_override_mro(self):
# Some multi arg functions for testing.
def tres_mul(a, b, c):
return a * b * c
def quatro_mul(a, b, c, d):
return a * b * c * d
# Make these into ufuncs.
three_mul_ufunc = np.frompyfunc(tres_mul, 3, 1)
four_mul_ufunc = np.frompyfunc(quatro_mul, 4, 1)
class A(object):
def __array_ufunc__(self, func, method, *inputs, **kwargs):
return "A"
class ASub(A):
def __array_ufunc__(self, func, method, *inputs, **kwargs):
return "ASub"
class B(object):
def __array_ufunc__(self, func, method, *inputs, **kwargs):
return "B"
class C(object):
def __array_ufunc__(self, func, method, *inputs, **kwargs):
return NotImplemented
class CSub(C):
def __array_ufunc__(self, func, method, *inputs, **kwargs):
return NotImplemented
a = A()
a_sub = ASub()
b = B()
c = C()
c_sub = CSub()
# Standard
res = np.multiply(a, a_sub)
assert_equal(res, "ASub")
res = np.multiply(a_sub, b)
assert_equal(res, "ASub")
# With 1 NotImplemented
res = np.multiply(c, a)
assert_equal(res, "A")
# Both NotImplemented.
assert_raises(TypeError, np.multiply, c, c_sub)
assert_raises(TypeError, np.multiply, c_sub, c)
assert_raises(TypeError, np.multiply, 2, c)
# Ternary testing.
assert_equal(three_mul_ufunc(a, 1, 2), "A")
assert_equal(three_mul_ufunc(1, a, 2), "A")
assert_equal(three_mul_ufunc(1, 2, a), "A")
assert_equal(three_mul_ufunc(a, a, 6), "A")
assert_equal(three_mul_ufunc(a, 2, a), "A")
assert_equal(three_mul_ufunc(a, 2, b), "A")
assert_equal(three_mul_ufunc(a, 2, a_sub), "ASub")
assert_equal(three_mul_ufunc(a, a_sub, 3), "ASub")
assert_equal(three_mul_ufunc(c, a_sub, 3), "ASub")
assert_equal(three_mul_ufunc(1, a_sub, c), "ASub")
assert_equal(three_mul_ufunc(a, b, c), "A")
assert_equal(three_mul_ufunc(a, b, c_sub), "A")
assert_equal(three_mul_ufunc(1, 2, b), "B")
assert_raises(TypeError, three_mul_ufunc, 1, 2, c)
assert_raises(TypeError, three_mul_ufunc, c_sub, 2, c)
assert_raises(TypeError, three_mul_ufunc, c_sub, 2, 3)
# Quaternary testing.
assert_equal(four_mul_ufunc(a, 1, 2, 3), "A")
assert_equal(four_mul_ufunc(1, a, 2, 3), "A")
assert_equal(four_mul_ufunc(1, 1, a, 3), "A")
assert_equal(four_mul_ufunc(1, 1, 2, a), "A")
assert_equal(four_mul_ufunc(a, b, 2, 3), "A")
assert_equal(four_mul_ufunc(1, a, 2, b), "A")
assert_equal(four_mul_ufunc(b, 1, a, 3), "B")
assert_equal(four_mul_ufunc(a_sub, 1, 2, a), "ASub")
assert_equal(four_mul_ufunc(a, 1, 2, a_sub), "ASub")
assert_raises(TypeError, four_mul_ufunc, 1, 2, 3, c)
assert_raises(TypeError, four_mul_ufunc, 1, 2, c_sub, c)
assert_raises(TypeError, four_mul_ufunc, 1, c, c_sub, c)
def test_ufunc_override_methods(self):
class A(object):
def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
return self, ufunc, method, inputs, kwargs
# __call__
a = A()
res = np.multiply.__call__(1, a, foo='bar', answer=42)
assert_equal(res[0], a)
assert_equal(res[1], np.multiply)
assert_equal(res[2], '__call__')
assert_equal(res[3], (1, a))
assert_equal(res[4], {'foo': 'bar', 'answer': 42})
# __call__, wrong args
assert_raises(TypeError, np.multiply, a)
assert_raises(TypeError, np.multiply, a, a, a, a)
assert_raises(TypeError, np.multiply, a, a, sig='a', signature='a')
# reduce, positional args
res = np.multiply.reduce(a, 'axis0', 'dtype0', 'out0', 'keep0')
assert_equal(res[0], a)
assert_equal(res[1], np.multiply)
assert_equal(res[2], 'reduce')
assert_equal(res[3], (a,))
assert_equal(res[4], {'dtype':'dtype0',
'out': ('out0',),
'keepdims': 'keep0',
'axis': 'axis0'})
# reduce, kwargs
res = np.multiply.reduce(a, axis='axis0', dtype='dtype0', out='out0',
keepdims='keep0')
assert_equal(res[0], a)
assert_equal(res[1], np.multiply)
assert_equal(res[2], 'reduce')
assert_equal(res[3], (a,))
assert_equal(res[4], {'dtype':'dtype0',
'out': ('out0',),
'keepdims': 'keep0',
'axis': 'axis0'})
# reduce, output equal to None removed, but not other explicit ones,
# even if they are at their default value.
res = np.multiply.reduce(a, 0, None, None, False)
assert_equal(res[4], {'axis': 0, 'dtype': None, 'keepdims': False})
res = np.multiply.reduce(a, out=None, axis=0, keepdims=True)
assert_equal(res[4], {'axis': 0, 'keepdims': True})
res = np.multiply.reduce(a, None, out=(None,), dtype=None)
assert_equal(res[4], {'axis': None, 'dtype': None})
# reduce, wrong args
assert_raises(ValueError, np.multiply.reduce, a, out=())
assert_raises(ValueError, np.multiply.reduce, a, out=('out0', 'out1'))
assert_raises(TypeError, np.multiply.reduce, a, 'axis0', axis='axis0')
# accumulate, pos args
res = np.multiply.accumulate(a, 'axis0', 'dtype0', 'out0')
assert_equal(res[0], a)
assert_equal(res[1], np.multiply)
assert_equal(res[2], 'accumulate')
assert_equal(res[3], (a,))
assert_equal(res[4], {'dtype':'dtype0',
'out': ('out0',),
'axis': 'axis0'})
# accumulate, kwargs
res = np.multiply.accumulate(a, axis='axis0', dtype='dtype0',
out='out0')
assert_equal(res[0], a)
assert_equal(res[1], np.multiply)
assert_equal(res[2], 'accumulate')
assert_equal(res[3], (a,))
assert_equal(res[4], {'dtype':'dtype0',
'out': ('out0',),
'axis': 'axis0'})
# accumulate, output equal to None removed.
res = np.multiply.accumulate(a, 0, None, None)
assert_equal(res[4], {'axis': 0, 'dtype': None})
res = np.multiply.accumulate(a, out=None, axis=0, dtype='dtype1')
assert_equal(res[4], {'axis': 0, 'dtype': 'dtype1'})
res = np.multiply.accumulate(a, None, out=(None,), dtype=None)
assert_equal(res[4], {'axis': None, 'dtype': None})
# accumulate, wrong args
assert_raises(ValueError, np.multiply.accumulate, a, out=())
assert_raises(ValueError, np.multiply.accumulate, a,
out=('out0', 'out1'))
assert_raises(TypeError, np.multiply.accumulate, a,
'axis0', axis='axis0')
# reduceat, pos args
res = np.multiply.reduceat(a, [4, 2], 'axis0', 'dtype0', 'out0')
assert_equal(res[0], a)
assert_equal(res[1], np.multiply)
assert_equal(res[2], 'reduceat')
assert_equal(res[3], (a, [4, 2]))
assert_equal(res[4], {'dtype':'dtype0',
'out': ('out0',),
'axis': 'axis0'})
# reduceat, kwargs
res = np.multiply.reduceat(a, [4, 2], axis='axis0', dtype='dtype0',
out='out0')
assert_equal(res[0], a)
assert_equal(res[1], np.multiply)
assert_equal(res[2], 'reduceat')
assert_equal(res[3], (a, [4, 2]))
assert_equal(res[4], {'dtype':'dtype0',
'out': ('out0',),
'axis': 'axis0'})
# reduceat, output equal to None removed.
res = np.multiply.reduceat(a, [4, 2], 0, None, None)
assert_equal(res[4], {'axis': 0, 'dtype': None})
res = np.multiply.reduceat(a, [4, 2], axis=None, out=None, dtype='dt')
assert_equal(res[4], {'axis': None, 'dtype': 'dt'})
res = np.multiply.reduceat(a, [4, 2], None, None, out=(None,))
assert_equal(res[4], {'axis': None, 'dtype': None})
# reduceat, wrong args
assert_raises(ValueError, np.multiply.reduce, a, [4, 2], out=())
assert_raises(ValueError, np.multiply.reduce, a, [4, 2],
out=('out0', 'out1'))
assert_raises(TypeError, np.multiply.reduce, a, [4, 2],
'axis0', axis='axis0')
# outer
res = np.multiply.outer(a, 42)
assert_equal(res[0], a)
assert_equal(res[1], np.multiply)
assert_equal(res[2], 'outer')
assert_equal(res[3], (a, 42))
assert_equal(res[4], {})
# outer, wrong args
assert_raises(TypeError, np.multiply.outer, a)
assert_raises(TypeError, np.multiply.outer, a, a, a, a)
# at
res = np.multiply.at(a, [4, 2], 'b0')
assert_equal(res[0], a)
assert_equal(res[1], np.multiply)
assert_equal(res[2], 'at')
assert_equal(res[3], (a, [4, 2], 'b0'))
# at, wrong args
assert_raises(TypeError, np.multiply.at, a)
assert_raises(TypeError, np.multiply.at, a, a, a, a)
def test_ufunc_override_out(self):
class A(object):
def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
return kwargs
class B(object):
def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
return kwargs
a = A()
b = B()
res0 = np.multiply(a, b, 'out_arg')
res1 = np.multiply(a, b, out='out_arg')
res2 = np.multiply(2, b, 'out_arg')
res3 = np.multiply(3, b, out='out_arg')
res4 = np.multiply(a, 4, 'out_arg')
res5 = np.multiply(a, 5, out='out_arg')
assert_equal(res0['out'][0], 'out_arg')
assert_equal(res1['out'][0], 'out_arg')
assert_equal(res2['out'][0], 'out_arg')
assert_equal(res3['out'][0], 'out_arg')
assert_equal(res4['out'][0], 'out_arg')
assert_equal(res5['out'][0], 'out_arg')
# ufuncs with multiple output modf and frexp.
res6 = np.modf(a, 'out0', 'out1')
res7 = np.frexp(a, 'out0', 'out1')
assert_equal(res6['out'][0], 'out0')
assert_equal(res6['out'][1], 'out1')
assert_equal(res7['out'][0], 'out0')
assert_equal(res7['out'][1], 'out1')
# While we're at it, check that default output is never passed on.
assert_(np.sin(a, None) == {})
assert_(np.sin(a, out=None) == {})
assert_(np.sin(a, out=(None,)) == {})
assert_(np.modf(a, None) == {})
assert_(np.modf(a, None, None) == {})
assert_(np.modf(a, out=(None, None)) == {})
with warnings.catch_warnings(record=True) as w:
warnings.filterwarnings('always', '', DeprecationWarning)
assert_(np.modf(a, out=None) == {})
assert_(w[0].category is DeprecationWarning)
# don't give positional and output argument, or too many arguments.
# wrong number of arguments in the tuple is an error too.
assert_raises(TypeError, np.multiply, a, b, 'one', out='two')
assert_raises(TypeError, np.multiply, a, b, 'one', 'two')
assert_raises(ValueError, np.multiply, a, b, out=('one', 'two'))
assert_raises(ValueError, np.multiply, a, out=())
assert_raises(TypeError, np.modf, a, 'one', out=('two', 'three'))
assert_raises(TypeError, np.modf, a, 'one', 'two', 'three')
assert_raises(ValueError, np.modf, a, out=('one', 'two', 'three'))
assert_raises(ValueError, np.modf, a, out=('one',))
def test_ufunc_override_exception(self):
class A(object):
def __array_ufunc__(self, *a, **kwargs):
raise ValueError("oops")
a = A()
assert_raises(ValueError, np.negative, 1, out=a)
assert_raises(ValueError, np.negative, a)
assert_raises(ValueError, np.divide, 1., a)
def test_ufunc_override_not_implemented(self):
class A(object):
def __array_ufunc__(self, *args, **kwargs):
return NotImplemented
msg = ("operand type(s) all returned NotImplemented from "
"__array_ufunc__(<ufunc 'negative'>, '__call__', <*>): 'A'")
with assert_raises_regex(TypeError, fnmatch.translate(msg)):
np.negative(A())
msg = ("operand type(s) all returned NotImplemented from "
"__array_ufunc__(<ufunc 'add'>, '__call__', <*>, <object *>, "
"out=(1,)): 'A', 'object', 'int'")
with assert_raises_regex(TypeError, fnmatch.translate(msg)):
np.add(A(), object(), out=1)
def test_ufunc_override_disabled(self):
class OptOut(object):
__array_ufunc__ = None
opt_out = OptOut()
# ufuncs always raise
msg = "operand 'OptOut' does not support ufuncs"
with assert_raises_regex(TypeError, msg):
np.add(opt_out, 1)
with assert_raises_regex(TypeError, msg):
np.add(1, opt_out)
with assert_raises_regex(TypeError, msg):
np.negative(opt_out)
# opt-outs still hold even when other arguments have pathological
# __array_ufunc__ implementations
class GreedyArray(object):
def __array_ufunc__(self, *args, **kwargs):
return self
greedy = GreedyArray()
assert_(np.negative(greedy) is greedy)
with assert_raises_regex(TypeError, msg):
np.add(greedy, opt_out)
with assert_raises_regex(TypeError, msg):
np.add(greedy, 1, out=opt_out)
def test_gufunc_override(self):
# gufunc are just ufunc instances, but follow a different path,
# so check __array_ufunc__ overrides them properly.
class A(object):
def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
return self, ufunc, method, inputs, kwargs
inner1d = ncu_tests.inner1d
a = A()
res = inner1d(a, a)
assert_equal(res[0], a)
assert_equal(res[1], inner1d)
assert_equal(res[2], '__call__')
assert_equal(res[3], (a, a))
assert_equal(res[4], {})
res = inner1d(1, 1, out=a)
assert_equal(res[0], a)
assert_equal(res[1], inner1d)
assert_equal(res[2], '__call__')
assert_equal(res[3], (1, 1))
assert_equal(res[4], {'out': (a,)})
# wrong number of arguments in the tuple is an error too.
assert_raises(TypeError, inner1d, a, out='two')
assert_raises(TypeError, inner1d, a, a, 'one', out='two')
assert_raises(TypeError, inner1d, a, a, 'one', 'two')
assert_raises(ValueError, inner1d, a, a, out=('one', 'two'))
assert_raises(ValueError, inner1d, a, a, out=())
def test_ufunc_override_with_super(self):
# NOTE: this class is given as an example in doc/subclassing.py;
# if you make any changes here, do update it there too.
class A(np.ndarray):
def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
args = []
in_no = []
for i, input_ in enumerate(inputs):
if isinstance(input_, A):
in_no.append(i)
args.append(input_.view(np.ndarray))
else:
args.append(input_)
outputs = kwargs.pop('out', None)
out_no = []
if outputs:
out_args = []
for j, output in enumerate(outputs):
if isinstance(output, A):
out_no.append(j)
out_args.append(output.view(np.ndarray))
else:
out_args.append(output)
kwargs['out'] = tuple(out_args)
else:
outputs = (None,) * ufunc.nout
info = {}
if in_no:
info['inputs'] = in_no
if out_no:
info['outputs'] = out_no
results = super(A, self).__array_ufunc__(ufunc, method,
*args, **kwargs)
if results is NotImplemented:
return NotImplemented
if method == 'at':
if isinstance(inputs[0], A):
inputs[0].info = info
return
if ufunc.nout == 1:
results = (results,)
results = tuple((np.asarray(result).view(A)
if output is None else output)
for result, output in zip(results, outputs))
if results and isinstance(results[0], A):
results[0].info = info
return results[0] if len(results) == 1 else results
class B(object):
def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
if any(isinstance(input_, A) for input_ in inputs):
return "A!"
else:
return NotImplemented
d = np.arange(5.)
# 1 input, 1 output
a = np.arange(5.).view(A)
b = np.sin(a)
check = np.sin(d)
assert_(np.all(check == b))
assert_equal(b.info, {'inputs': [0]})
b = np.sin(d, out=(a,))
assert_(np.all(check == b))
assert_equal(b.info, {'outputs': [0]})
assert_(b is a)
a = np.arange(5.).view(A)
b = np.sin(a, out=a)
assert_(np.all(check == b))
assert_equal(b.info, {'inputs': [0], 'outputs': [0]})
# 1 input, 2 outputs
a = np.arange(5.).view(A)
b1, b2 = np.modf(a)
assert_equal(b1.info, {'inputs': [0]})
b1, b2 = np.modf(d, out=(None, a))
assert_(b2 is a)
assert_equal(b1.info, {'outputs': [1]})
a = np.arange(5.).view(A)
b = np.arange(5.).view(A)
c1, c2 = np.modf(a, out=(a, b))
assert_(c1 is a)
assert_(c2 is b)
assert_equal(c1.info, {'inputs': [0], 'outputs': [0, 1]})
# 2 input, 1 output
a = np.arange(5.).view(A)
b = np.arange(5.).view(A)
c = np.add(a, b, out=a)
assert_(c is a)
assert_equal(c.info, {'inputs': [0, 1], 'outputs': [0]})
# some tests with a non-ndarray subclass
a = np.arange(5.)
b = B()
assert_(a.__array_ufunc__(np.add, '__call__', a, b) is NotImplemented)
assert_(b.__array_ufunc__(np.add, '__call__', a, b) is NotImplemented)
assert_raises(TypeError, np.add, a, b)
a = a.view(A)
assert_(a.__array_ufunc__(np.add, '__call__', a, b) is NotImplemented)
assert_(b.__array_ufunc__(np.add, '__call__', a, b) == "A!")
assert_(np.add(a, b) == "A!")
# regression check for gh-9102 -- tests ufunc.reduce implicitly.
d = np.array([[1, 2, 3], [1, 2, 3]])
a = d.view(A)
c = a.any()
check = d.any()
assert_equal(c, check)
assert_(c.info, {'inputs': [0]})
c = a.max()
check = d.max()
assert_equal(c, check)
assert_(c.info, {'inputs': [0]})
b = np.array(0).view(A)
c = a.max(out=b)
assert_equal(c, check)
assert_(c is b)
assert_(c.info, {'inputs': [0], 'outputs': [0]})
check = a.max(axis=0)
b = np.zeros_like(check).view(A)
c = a.max(axis=0, out=b)
assert_equal(c, check)
assert_(c is b)
assert_(c.info, {'inputs': [0], 'outputs': [0]})
# simple explicit tests of reduce, accumulate, reduceat
check = np.add.reduce(d, axis=1)
c = np.add.reduce(a, axis=1)
assert_equal(c, check)
assert_(c.info, {'inputs': [0]})
b = np.zeros_like(c)
c = np.add.reduce(a, 1, None, b)
assert_equal(c, check)
assert_(c is b)
assert_(c.info, {'inputs': [0], 'outputs': [0]})
check = np.add.accumulate(d, axis=0)
c = np.add.accumulate(a, axis=0)
assert_equal(c, check)
assert_(c.info, {'inputs': [0]})
b = np.zeros_like(c)
c = np.add.accumulate(a, 0, None, b)
assert_equal(c, check)
assert_(c is b)
assert_(c.info, {'inputs': [0], 'outputs': [0]})
indices = [0, 2, 1]
check = np.add.reduceat(d, indices, axis=1)
c = np.add.reduceat(a, indices, axis=1)
assert_equal(c, check)
assert_(c.info, {'inputs': [0]})
b = np.zeros_like(c)
c = np.add.reduceat(a, indices, 1, None, b)
assert_equal(c, check)
assert_(c is b)
assert_(c.info, {'inputs': [0], 'outputs': [0]})
# and a few tests for at
d = np.array([[1, 2, 3], [1, 2, 3]])
check = d.copy()
a = d.copy().view(A)
np.add.at(check, ([0, 1], [0, 2]), 1.)
np.add.at(a, ([0, 1], [0, 2]), 1.)
assert_equal(a, check)
assert_(a.info, {'inputs': [0]})
b = np.array(1.).view(A)
a = d.copy().view(A)
np.add.at(a, ([0, 1], [0, 2]), b)
assert_equal(a, check)
assert_(a.info, {'inputs': [0, 2]})
class TestChoose(TestCase):
def test_mixed(self):
c = np.array([True, True])
a = np.array([True, True])
assert_equal(np.choose(c, (a, 1)), np.array([1, 1]))
def is_longdouble_finfo_bogus():
info = np.finfo(np.longcomplex)
return not np.isfinite(np.log10(info.tiny/info.eps))
class TestComplexFunctions(object):
funcs = [np.arcsin, np.arccos, np.arctan, np.arcsinh, np.arccosh,
np.arctanh, np.sin, np.cos, np.tan, np.exp,
np.exp2, np.log, np.sqrt, np.log10, np.log2,
np.log1p]
def test_it(self):
for f in self.funcs:
if f is np.arccosh:
x = 1.5
else:
x = .5
fr = f(x)
fz = f(np.complex(x))
assert_almost_equal(fz.real, fr, err_msg='real part %s' % f)
assert_almost_equal(fz.imag, 0., err_msg='imag part %s' % f)
def test_precisions_consistent(self):
z = 1 + 1j
for f in self.funcs:
fcf = f(np.csingle(z))
fcd = f(np.cdouble(z))
fcl = f(np.clongdouble(z))
assert_almost_equal(fcf, fcd, decimal=6, err_msg='fch-fcd %s' % f)
assert_almost_equal(fcl, fcd, decimal=15, err_msg='fch-fcl %s' % f)
def test_branch_cuts(self):
# check branch cuts and continuity on them
yield _check_branch_cut, np.log, -0.5, 1j, 1, -1, True
yield _check_branch_cut, np.log2, -0.5, 1j, 1, -1, True
yield _check_branch_cut, np.log10, -0.5, 1j, 1, -1, True
yield _check_branch_cut, np.log1p, -1.5, 1j, 1, -1, True
yield _check_branch_cut, np.sqrt, -0.5, 1j, 1, -1, True
yield _check_branch_cut, np.arcsin, [ -2, 2], [1j, 1j], 1, -1, True
yield _check_branch_cut, np.arccos, [ -2, 2], [1j, 1j], 1, -1, True
yield _check_branch_cut, np.arctan, [0-2j, 2j], [1, 1], -1, 1, True
yield _check_branch_cut, np.arcsinh, [0-2j, 2j], [1, 1], -1, 1, True
yield _check_branch_cut, np.arccosh, [ -1, 0.5], [1j, 1j], 1, -1, True
yield _check_branch_cut, np.arctanh, [ -2, 2], [1j, 1j], 1, -1, True
# check against bogus branch cuts: assert continuity between quadrants
yield _check_branch_cut, np.arcsin, [0-2j, 2j], [ 1, 1], 1, 1
yield _check_branch_cut, np.arccos, [0-2j, 2j], [ 1, 1], 1, 1
yield _check_branch_cut, np.arctan, [ -2, 2], [1j, 1j], 1, 1
yield _check_branch_cut, np.arcsinh, [ -2, 2, 0], [1j, 1j, 1], 1, 1
yield _check_branch_cut, np.arccosh, [0-2j, 2j, 2], [1, 1, 1j], 1, 1
yield _check_branch_cut, np.arctanh, [0-2j, 2j, 0], [1, 1, 1j], 1, 1
def test_branch_cuts_complex64(self):
# check branch cuts and continuity on them
yield _check_branch_cut, np.log, -0.5, 1j, 1, -1, True, np.complex64
yield _check_branch_cut, np.log2, -0.5, 1j, 1, -1, True, np.complex64
yield _check_branch_cut, np.log10, -0.5, 1j, 1, -1, True, np.complex64
yield _check_branch_cut, np.log1p, -1.5, 1j, 1, -1, True, np.complex64
yield _check_branch_cut, np.sqrt, -0.5, 1j, 1, -1, True, np.complex64
yield _check_branch_cut, np.arcsin, [ -2, 2], [1j, 1j], 1, -1, True, np.complex64
yield _check_branch_cut, np.arccos, [ -2, 2], [1j, 1j], 1, -1, True, np.complex64
yield _check_branch_cut, np.arctan, [0-2j, 2j], [1, 1], -1, 1, True, np.complex64
yield _check_branch_cut, np.arcsinh, [0-2j, 2j], [1, 1], -1, 1, True, np.complex64
yield _check_branch_cut, np.arccosh, [ -1, 0.5], [1j, 1j], 1, -1, True, np.complex64
yield _check_branch_cut, np.arctanh, [ -2, 2], [1j, 1j], 1, -1, True, np.complex64
# check against bogus branch cuts: assert continuity between quadrants
yield _check_branch_cut, np.arcsin, [0-2j, 2j], [ 1, 1], 1, 1, False, np.complex64
yield _check_branch_cut, np.arccos, [0-2j, 2j], [ 1, 1], 1, 1, False, np.complex64
yield _check_branch_cut, np.arctan, [ -2, 2], [1j, 1j], 1, 1, False, np.complex64
yield _check_branch_cut, np.arcsinh, [ -2, 2, 0], [1j, 1j, 1], 1, 1, False, np.complex64
yield _check_branch_cut, np.arccosh, [0-2j, 2j, 2], [1, 1, 1j], 1, 1, False, np.complex64
yield _check_branch_cut, np.arctanh, [0-2j, 2j, 0], [1, 1, 1j], 1, 1, False, np.complex64
def test_against_cmath(self):
import cmath
points = [-1-1j, -1+1j, +1-1j, +1+1j]
name_map = {'arcsin': 'asin', 'arccos': 'acos', 'arctan': 'atan',
'arcsinh': 'asinh', 'arccosh': 'acosh', 'arctanh': 'atanh'}
atol = 4*np.finfo(np.complex).eps
for func in self.funcs:
fname = func.__name__.split('.')[-1]
cname = name_map.get(fname, fname)
try:
cfunc = getattr(cmath, cname)
except AttributeError:
continue
for p in points:
a = complex(func(np.complex_(p)))
b = cfunc(p)
assert_(abs(a - b) < atol, "%s %s: %s; cmath: %s" % (fname, p, a, b))
def check_loss_of_precision(self, dtype):
"""Check loss of precision in complex arc* functions"""
# Check against known-good functions
info = np.finfo(dtype)
real_dtype = dtype(0.).real.dtype
eps = info.eps
def check(x, rtol):
x = x.astype(real_dtype)
z = x.astype(dtype)
d = np.absolute(np.arcsinh(x)/np.arcsinh(z).real - 1)
assert_(np.all(d < rtol), (np.argmax(d), x[np.argmax(d)], d.max(),
'arcsinh'))
z = (1j*x).astype(dtype)
d = np.absolute(np.arcsinh(x)/np.arcsin(z).imag - 1)
assert_(np.all(d < rtol), (np.argmax(d), x[np.argmax(d)], d.max(),
'arcsin'))
z = x.astype(dtype)
d = np.absolute(np.arctanh(x)/np.arctanh(z).real - 1)
assert_(np.all(d < rtol), (np.argmax(d), x[np.argmax(d)], d.max(),
'arctanh'))
z = (1j*x).astype(dtype)
d = np.absolute(np.arctanh(x)/np.arctan(z).imag - 1)
assert_(np.all(d < rtol), (np.argmax(d), x[np.argmax(d)], d.max(),
'arctan'))
# The switchover was chosen as 1e-3; hence there can be up to
# ~eps/1e-3 of relative cancellation error before it
x_series = np.logspace(-20, -3.001, 200)
x_basic = np.logspace(-2.999, 0, 10, endpoint=False)
if dtype is np.longcomplex:
# It's not guaranteed that the system-provided arc functions
# are accurate down to a few epsilons. (Eg. on Linux 64-bit)
# So, give more leeway for long complex tests here:
check(x_series, 50*eps)
else:
check(x_series, 2.1*eps)
check(x_basic, 2*eps/1e-3)
# Check a few points
z = np.array([1e-5*(1+1j)], dtype=dtype)
p = 9.999999999333333333e-6 + 1.000000000066666666e-5j
d = np.absolute(1-np.arctanh(z)/p)
assert_(np.all(d < 1e-15))
p = 1.0000000000333333333e-5 + 9.999999999666666667e-6j
d = np.absolute(1-np.arcsinh(z)/p)
assert_(np.all(d < 1e-15))
p = 9.999999999333333333e-6j + 1.000000000066666666e-5
d = np.absolute(1-np.arctan(z)/p)
assert_(np.all(d < 1e-15))
p = 1.0000000000333333333e-5j + 9.999999999666666667e-6
d = np.absolute(1-np.arcsin(z)/p)
assert_(np.all(d < 1e-15))
# Check continuity across switchover points
def check(func, z0, d=1):
z0 = np.asarray(z0, dtype=dtype)
zp = z0 + abs(z0) * d * eps * 2
zm = z0 - abs(z0) * d * eps * 2
assert_(np.all(zp != zm), (zp, zm))
# NB: the cancellation error at the switchover is at least eps
good = (abs(func(zp) - func(zm)) < 2*eps)
assert_(np.all(good), (func, z0[~good]))
for func in (np.arcsinh, np.arcsinh, np.arcsin, np.arctanh, np.arctan):
pts = [rp+1j*ip for rp in (-1e-3, 0, 1e-3) for ip in(-1e-3, 0, 1e-3)
if rp != 0 or ip != 0]
check(func, pts, 1)
check(func, pts, 1j)
check(func, pts, 1+1j)
def test_loss_of_precision(self):
for dtype in [np.complex64, np.complex_]:
yield self.check_loss_of_precision, dtype
@dec.knownfailureif(is_longdouble_finfo_bogus(), "Bogus long double finfo")
def test_loss_of_precision_longcomplex(self):
self.check_loss_of_precision(np.longcomplex)
class TestAttributes(TestCase):
def test_attributes(self):
add = ncu.add
assert_equal(add.__name__, 'add')
self.assertTrue(add.ntypes >= 18) # don't fail if types added
self.assertTrue('ii->i' in add.types)
assert_equal(add.nin, 2)
assert_equal(add.nout, 1)
assert_equal(add.identity, 0)
def test_doc(self):
# don't bother checking the long list of kwargs, which are likely to
# change
assert_(ncu.add.__doc__.startswith(
"add(x1, x2, /, out=None, *, where=True"))
assert_(ncu.frexp.__doc__.startswith(
"frexp(x[, out1, out2], / [, out=(None, None)], *, where=True"))
class TestSubclass(TestCase):
def test_subclass_op(self):
class simple(np.ndarray):
def __new__(subtype, shape):
self = np.ndarray.__new__(subtype, shape, dtype=object)
self.fill(0)
return self
a = simple((3, 4))
assert_equal(a+a, a)
def _check_branch_cut(f, x0, dx, re_sign=1, im_sign=-1, sig_zero_ok=False,
dtype=np.complex):
"""
Check for a branch cut in a function.
Assert that `x0` lies on a branch cut of function `f` and `f` is
continuous from the direction `dx`.
Parameters
----------
f : func
Function to check
x0 : array-like
Point on branch cut
dx : array-like
Direction to check continuity in
re_sign, im_sign : {1, -1}
Change of sign of the real or imaginary part expected
sig_zero_ok : bool
Whether to check if the branch cut respects signed zero (if applicable)
dtype : dtype
Dtype to check (should be complex)
"""
x0 = np.atleast_1d(x0).astype(dtype)
dx = np.atleast_1d(dx).astype(dtype)
if np.dtype(dtype).char == 'F':
scale = np.finfo(dtype).eps * 1e2
atol = np.float32(1e-2)
else:
scale = np.finfo(dtype).eps * 1e3
atol = 1e-4
y0 = f(x0)
yp = f(x0 + dx*scale*np.absolute(x0)/np.absolute(dx))
ym = f(x0 - dx*scale*np.absolute(x0)/np.absolute(dx))
assert_(np.all(np.absolute(y0.real - yp.real) < atol), (y0, yp))
assert_(np.all(np.absolute(y0.imag - yp.imag) < atol), (y0, yp))
assert_(np.all(np.absolute(y0.real - ym.real*re_sign) < atol), (y0, ym))
assert_(np.all(np.absolute(y0.imag - ym.imag*im_sign) < atol), (y0, ym))
if sig_zero_ok:
# check that signed zeros also work as a displacement
jr = (x0.real == 0) & (dx.real != 0)
ji = (x0.imag == 0) & (dx.imag != 0)
if np.any(jr):
x = x0[jr]
x.real = np.NZERO
ym = f(x)
assert_(np.all(np.absolute(y0[jr].real - ym.real*re_sign) < atol), (y0[jr], ym))
assert_(np.all(np.absolute(y0[jr].imag - ym.imag*im_sign) < atol), (y0[jr], ym))
if np.any(ji):
x = x0[ji]
x.imag = np.NZERO
ym = f(x)
assert_(np.all(np.absolute(y0[ji].real - ym.real*re_sign) < atol), (y0[ji], ym))
assert_(np.all(np.absolute(y0[ji].imag - ym.imag*im_sign) < atol), (y0[ji], ym))
def test_copysign():
assert_(np.copysign(1, -1) == -1)
with np.errstate(divide="ignore"):
assert_(1 / np.copysign(0, -1) < 0)
assert_(1 / np.copysign(0, 1) > 0)
assert_(np.signbit(np.copysign(np.nan, -1)))
assert_(not np.signbit(np.copysign(np.nan, 1)))
def _test_nextafter(t):
one = t(1)
two = t(2)
zero = t(0)
eps = np.finfo(t).eps
assert_(np.nextafter(one, two) - one == eps)
assert_(np.nextafter(one, zero) - one < 0)
assert_(np.isnan(np.nextafter(np.nan, one)))
assert_(np.isnan(np.nextafter(one, np.nan)))
assert_(np.nextafter(one, one) == one)
def test_nextafter():
return _test_nextafter(np.float64)
def test_nextafterf():
return _test_nextafter(np.float32)
@dec.knownfailureif(sys.platform == 'win32',
"Long double support buggy on win32, ticket 1664.")
def test_nextafterl():
return _test_nextafter(np.longdouble)
def test_nextafter_0():
for t, direction in itertools.product(np.sctypes['float'], (1, -1)):
tiny = np.finfo(t).tiny
assert_(0. < direction * np.nextafter(t(0), t(direction)) < tiny)
assert_equal(np.nextafter(t(0), t(direction)) / t(2.1), direction * 0.0)
def _test_spacing(t):
one = t(1)
eps = np.finfo(t).eps
nan = t(np.nan)
inf = t(np.inf)
with np.errstate(invalid='ignore'):
assert_(np.spacing(one) == eps)
assert_(np.isnan(np.spacing(nan)))
assert_(np.isnan(np.spacing(inf)))
assert_(np.isnan(np.spacing(-inf)))
assert_(np.spacing(t(1e30)) != 0)
def test_spacing():
return _test_spacing(np.float64)
def test_spacingf():
return _test_spacing(np.float32)
@dec.knownfailureif(sys.platform == 'win32',
"Long double support buggy on win32, ticket 1664.")
def test_spacingl():
return _test_spacing(np.longdouble)
def test_spacing_gfortran():
# Reference from this fortran file, built with gfortran 4.3.3 on linux
# 32bits:
# PROGRAM test_spacing
# INTEGER, PARAMETER :: SGL = SELECTED_REAL_KIND(p=6, r=37)
# INTEGER, PARAMETER :: DBL = SELECTED_REAL_KIND(p=13, r=200)
#
# WRITE(*,*) spacing(0.00001_DBL)
# WRITE(*,*) spacing(1.0_DBL)
# WRITE(*,*) spacing(1000._DBL)
# WRITE(*,*) spacing(10500._DBL)
#
# WRITE(*,*) spacing(0.00001_SGL)
# WRITE(*,*) spacing(1.0_SGL)
# WRITE(*,*) spacing(1000._SGL)
# WRITE(*,*) spacing(10500._SGL)
# END PROGRAM
ref = {np.float64: [1.69406589450860068E-021,
2.22044604925031308E-016,
1.13686837721616030E-013,
1.81898940354585648E-012],
np.float32: [9.09494702E-13,
1.19209290E-07,
6.10351563E-05,
9.76562500E-04]}
for dt, dec_ in zip([np.float32, np.float64], (10, 20)):
x = np.array([1e-5, 1, 1000, 10500], dtype=dt)
assert_array_almost_equal(np.spacing(x), ref[dt], decimal=dec_)
def test_nextafter_vs_spacing():
# XXX: spacing does not handle long double yet
for t in [np.float32, np.float64]:
for _f in [1, 1e-5, 1000]:
f = t(_f)
f1 = t(_f + 1)
assert_(np.nextafter(f, f1) - f == np.spacing(f))
def test_pos_nan():
"""Check np.nan is a positive nan."""
assert_(np.signbit(np.nan) == 0)
def test_reduceat():
"""Test bug in reduceat when structured arrays are not copied."""
db = np.dtype([('name', 'S11'), ('time', np.int64), ('value', np.float32)])
a = np.empty([100], dtype=db)
a['name'] = 'Simple'
a['time'] = 10
a['value'] = 100
indx = [0, 7, 15, 25]
h2 = []
val1 = indx[0]
for val2 in indx[1:]:
h2.append(np.add.reduce(a['value'][val1:val2]))
val1 = val2
h2.append(np.add.reduce(a['value'][val1:]))
h2 = np.array(h2)
# test buffered -- this should work
h1 = np.add.reduceat(a['value'], indx)
assert_array_almost_equal(h1, h2)
# This is when the error occurs.
# test no buffer
np.setbufsize(32)
h1 = np.add.reduceat(a['value'], indx)
np.setbufsize(np.UFUNC_BUFSIZE_DEFAULT)
assert_array_almost_equal(h1, h2)
def test_reduceat_empty():
"""Reduceat should work with empty arrays"""
indices = np.array([], 'i4')
x = np.array([], 'f8')
result = np.add.reduceat(x, indices)
assert_equal(result.dtype, x.dtype)
assert_equal(result.shape, (0,))
# Another case with a slightly different zero-sized shape
x = np.ones((5, 2))
result = np.add.reduceat(x, [], axis=0)
assert_equal(result.dtype, x.dtype)
assert_equal(result.shape, (0, 2))
result = np.add.reduceat(x, [], axis=1)
assert_equal(result.dtype, x.dtype)
assert_equal(result.shape, (5, 0))
def test_complex_nan_comparisons():
nans = [complex(np.nan, 0), complex(0, np.nan), complex(np.nan, np.nan)]
fins = [complex(1, 0), complex(-1, 0), complex(0, 1), complex(0, -1),
complex(1, 1), complex(-1, -1), complex(0, 0)]
with np.errstate(invalid='ignore'):
for x in nans + fins:
x = np.array([x])
for y in nans + fins:
y = np.array([y])
if np.isfinite(x) and np.isfinite(y):
continue
assert_equal(x < y, False, err_msg="%r < %r" % (x, y))
assert_equal(x > y, False, err_msg="%r > %r" % (x, y))
assert_equal(x <= y, False, err_msg="%r <= %r" % (x, y))
assert_equal(x >= y, False, err_msg="%r >= %r" % (x, y))
assert_equal(x == y, False, err_msg="%r == %r" % (x, y))
def test_rint_big_int():
# np.rint bug for large integer values on Windows 32-bit and MKL
# https://github.com/numpy/numpy/issues/6685
val = 4607998452777363968
# This is exactly representable in floating point
assert_equal(val, int(float(val)))
# Rint should not change the value
assert_equal(val, np.rint(val))
def test_signaling_nan_exceptions():
with assert_no_warnings():
a = np.ndarray(shape=(), dtype='float32', buffer=b'\x00\xe0\xbf\xff')
np.isnan(a)
if __name__ == "__main__":
run_module_suite()
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