def test_nans_infs(self):
with np.errstate(all='ignore'):
# Check some of the ufuncs
assert_equal(np.isnan(self.all_f16), np.isnan(self.all_f32))
assert_equal(np.isinf(self.all_f16), np.isinf(self.all_f32))
assert_equal(np.isfinite(self.all_f16), np.isfinite(self.all_f32))
assert_equal(np.signbit(self.all_f16), np.signbit(self.all_f32))
assert_equal(np.spacing(float16(65504)), np.inf)
# Check comparisons of all values with NaN
nan = float16(np.nan)
assert_(not (self.all_f16 == nan).any())
assert_(not (nan == self.all_f16).any())
assert_((self.all_f16 != nan).all())
assert_((nan != self.all_f16).all())
assert_(not (self.all_f16 < nan).any())
assert_(not (nan < self.all_f16).any())
assert_(not (self.all_f16 <= nan).any())
assert_(not (nan <= self.all_f16).any())
assert_(not (self.all_f16 > nan).any())
assert_(not (nan > self.all_f16).any())
assert_(not (self.all_f16 >= nan).any())
assert_(not (nan >= self.all_f16).any())
def test_float_modulus_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 = operator.mod(a, b)
assert_(rem <= b, 'dt: %s' % dt)
rem = operator.mod(-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 = operator.mod(fone, fzer)
assert_(np.isnan(rem), 'dt: %s' % dt)
# MSVC 2008 returns NaN here, so disable the check.
#rem = operator.mod(fone, finf)
#assert_(rem == fone, 'dt: %s' % dt)
rem = operator.mod(fone, fnan)
assert_(np.isnan(rem), 'dt: %s' % dt)
rem = operator.mod(finf, fone)
assert_(np.isnan(rem), 'dt: %s' % dt)
def assert_array_compare(self, comparison, x, y, err_msg='', header='',
fill_value=True):
"""
Assert that a comparison of two masked arrays is satisfied elementwise.
"""
xf = self.filled(x)
yf = self.filled(y)
m = self.mask_or(self.getmask(x), self.getmask(y))
x = self.filled(self.masked_array(xf, mask=m), fill_value)
y = self.filled(self.masked_array(yf, mask=m), fill_value)
if (x.dtype.char != "O"):
x = x.astype(float_)
if isinstance(x, np.ndarray) and x.size > 1:
x[np.isnan(x)] = 0
elif np.isnan(x):
x = 0
if (y.dtype.char != "O"):
y = y.astype(float_)
if isinstance(y, np.ndarray) and y.size > 1:
y[np.isnan(y)] = 0
elif np.isnan(y):
y = 0
try:
cond = (x.shape == () or y.shape == ()) or x.shape == y.shape
if not cond:
msg = build_err_msg([x, y],
err_msg
+ '\n(shapes %s, %s mismatch)' % (x.shape,
y.shape),
header=header,
names=('x', 'y'))
assert cond, msg
val = comparison(x, y)
if m is not self.nomask and fill_value:
val = self.masked_array(val, mask=m)
if isinstance(val, bool):
cond = val
reduced = [0]
else:
reduced = val.ravel()
cond = reduced.all()
reduced = reduced.tolist()
if not cond:
match = 100-100.0*reduced.count(1)/len(reduced)
msg = build_err_msg([x, y],
err_msg
+ '\n(mismatch %s%%)' % (match,),
header=header,
names=('x', 'y'))
assert cond, msg
except ValueError:
msg = build_err_msg([x, y], err_msg, header=header, names=('x', 'y'))
raise ValueError(msg)
def test_allnans(self):
mat = np.array([np.nan]*9).reshape(3, 3)
for f in self.nanfuncs:
for axis in [None, 0, 1]:
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
assert_(np.isnan(f(mat, axis=axis)).all())
assert_(len(w) == 1)
assert_(issubclass(w[0].category, RuntimeWarning))
# Check scalar
assert_(np.isnan(f(np.nan)))
assert_(len(w) == 2)
assert_(issubclass(w[0].category, RuntimeWarning))
def _check_ninf_nan(dummy):
msgform = "csqrt(-inf, nan) is (%f, %f), expected (nan, +-inf)"
z = np.sqrt(np.array(complex(-np.inf, np.nan)))
#Fixme: ugly workaround for isinf bug.
with np.errstate(invalid='ignore'):
if not (np.isnan(z.real) and np.isinf(z.imag)):
raise AssertionError(msgform % (z.real, z.imag))
def test_zero_division(self):
with np.errstate(all="ignore"):
for t in [np.complex64, np.complex128]:
a = t(0.0)
b = t(1.0)
assert_(np.isinf(b / a))
b = t(complex(np.inf, np.inf))
assert_(np.isinf(b / a))
b = t(complex(np.inf, np.nan))
assert_(np.isinf(b / a))
b = t(complex(np.nan, np.inf))
assert_(np.isinf(b / a))
b = t(complex(np.nan, np.nan))
assert_(np.isnan(b / a))
b = t(0.)
assert_(np.isnan(b / a))
def test_allnans(self):
mat = np.array([np.nan]*9).reshape(3, 3)
for axis in [None, 0, 1]:
with suppress_warnings() as sup:
sup.record(RuntimeWarning)
assert_(np.isnan(np.nanmedian(mat, axis=axis)).all())
if axis is None:
assert_(len(sup.log) == 1)
else:
assert_(len(sup.log) == 3)
# Check scalar
assert_(np.isnan(np.nanmedian(np.nan)))
if axis is None:
assert_(len(sup.log) == 2)
else:
assert_(len(sup.log) == 4)
def test_retstep(self):
y = linspace(0, 1, 2, retstep=True)
assert_(isinstance(y, tuple) and len(y) == 2)
for num in (0, 1):
for ept in (False, True):
y = linspace(0, 1, num, endpoint=ept, retstep=True)
assert_(
isinstance(y, tuple) and len(y) == 2 and len(y[0]) == num
and isnan(y[1]), 'num={0}, endpoint={1}'.format(num, ept))
def test_allnans(self):
mat = np.array([np.nan]*9).reshape(3, 3)
for axis in [None, 0, 1]:
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
assert_(np.isnan(np.nanpercentile(mat, 60, axis=axis)).all())
if axis is None:
assert_(len(w) == 1)
else:
assert_(len(w) == 3)
assert_(issubclass(w[0].category, RuntimeWarning))
# Check scalar
assert_(np.isnan(np.nanpercentile(np.nan, 60)))
if axis is None:
assert_(len(w) == 2)
else:
assert_(len(w) == 4)
assert_(issubclass(w[0].category, RuntimeWarning))
def test_subclass(self):
class MyNDArray(np.ndarray):
pass
# Check that it works and that type and
# shape are preserved
mine = np.eye(3).view(MyNDArray)
for f in self.nanfuncs:
res = f(mine, axis=0)
assert_(isinstance(res, MyNDArray))
assert_(res.shape == (3,))
res = f(mine, axis=1)
assert_(isinstance(res, MyNDArray))
assert_(res.shape == (3,))
res = f(mine)
assert_(res.shape == ())
# check that rows of nan are dealt with for subclasses (#4628)
mine[1] = np.nan
for f in self.nanfuncs:
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
res = f(mine, axis=0)
assert_(isinstance(res, MyNDArray))
assert_(not np.any(np.isnan(res)))
assert_(len(w) == 0)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
res = f(mine, axis=1)
assert_(isinstance(res, MyNDArray))
assert_(np.isnan(res[1]) and not np.isnan(res[0])
and not np.isnan(res[2]))
assert_(len(w) == 1, 'no warning raised')
assert_(issubclass(w[0].category, RuntimeWarning))
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
res = f(mine)
assert_(res.shape == ())
assert_(res != np.nan)
assert_(len(w) == 0)
def test_result_values(self):
for f, fcmp in zip(self.nanfuncs, [np.greater, np.less]):
for row in _ndat:
with suppress_warnings() as sup:
sup.filter(RuntimeWarning, "invalid value encountered in")
ind = f(row)
val = row[ind]
# comparing with NaN is tricky as the result
# is always false except for NaN != NaN
assert_(not np.isnan(val))
assert_(not fcmp(val, row).any())
assert_(not np.equal(val, row[:ind]).any())
def test_mirr(self):
val = [-4500, -800, 800, 800, 600, 600, 800, 800, 700, 3000]
assert_almost_equal(np.mirr(val, 0.08, 0.055), 0.0666, 4)
val = [-120000, 39000, 30000, 21000, 37000, 46000]
assert_almost_equal(np.mirr(val, 0.10, 0.12), 0.126094, 6)
val = [100, 200, -50, 300, -200]
assert_almost_equal(np.mirr(val, 0.05, 0.06), 0.3428, 4)
val = [39000, 30000, 21000, 37000, 46000]
assert_(np.isnan(np.mirr(val, 0.10, 0.12)))
def test_nanfunctions_matrices():
# Check that it works and that type and
# shape are preserved
# 2018-04-29: moved here from core.tests.test_nanfunctions
mat = np.matrix(np.eye(3))
for f in [np.nanmin, np.nanmax]:
res = f(mat, axis=0)
assert_(isinstance(res, np.matrix))
assert_(res.shape == (1, 3))
res = f(mat, axis=1)
assert_(isinstance(res, np.matrix))
assert_(res.shape == (3, 1))
res = f(mat)
assert_(np.isscalar(res))
# check that rows of nan are dealt with for subclasses (#4628)
mat[1] = np.nan
for f in [np.nanmin, np.nanmax]:
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
res = f(mat, axis=0)
assert_(isinstance(res, np.matrix))
assert_(not np.any(np.isnan(res)))
assert_(len(w) == 0)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
res = f(mat, axis=1)
assert_(isinstance(res, np.matrix))
assert_(np.isnan(res[1, 0]) and not np.isnan(res[0, 0])
and not np.isnan(res[2, 0]))
assert_(len(w) == 1, 'no warning raised')
assert_(issubclass(w[0].category, RuntimeWarning))
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
res = f(mat)
assert_(np.isscalar(res))
assert_(res != np.nan)
assert_(len(w) == 0)
def test_empty(self):
mat = np.zeros((0, 3))
for axis in [0, None]:
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
assert_(np.isnan(np.nanpercentile(mat, 40, axis=axis)).all())
assert_(len(w) == 1)
assert_(issubclass(w[0].category, RuntimeWarning))
for axis in [1]:
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
assert_equal(np.nanpercentile(mat, 40, axis=axis), np.zeros([]))
assert_(len(w) == 0)
def test_small_large(self):
# test the small and large code paths, current cutoff 400 elements
for s in [5, 20, 51, 200, 1000]:
d = np.random.randn(4, s)
# Randomly set some elements to NaN:
w = np.random.randint(0, d.size, size=d.size // 5)
d.ravel()[w] = np.nan
d[:,0] = 1. # ensure at least one good value
# use normal median without nans to compare
tgt = []
for x in d:
nonan = np.compress(~np.isnan(x), x)
tgt.append(np.median(nonan, overwrite_input=True))
assert_array_equal(np.nanmedian(d, axis=-1), tgt)
def test_ddof_too_big(self):
nanfuncs = [np.nanvar, np.nanstd]
stdfuncs = [np.var, np.std]
dsize = [len(d) for d in _rdat]
for nf, rf in zip(nanfuncs, stdfuncs):
for ddof in range(5):
with suppress_warnings() as sup:
sup.record(RuntimeWarning)
sup.filter(np.ComplexWarning)
tgt = [ddof >= d for d in dsize]
res = nf(_ndat, axis=1, ddof=ddof)
assert_equal(np.isnan(res), tgt)
if any(tgt):
assert_(len(sup.log) == 1)
else:
assert_(len(sup.log) == 0)
def test_mirr_decimal(self):
val = [Decimal('-4500'), Decimal('-800'), Decimal('800'), Decimal('800'),
Decimal('600'), Decimal('600'), Decimal('800'), Decimal('800'),
Decimal('700'), Decimal('3000')]
assert_equal(np.mirr(val, Decimal('0.08'), Decimal('0.055')),
Decimal('0.066597175031553548874239618'))
val = [Decimal('-120000'), Decimal('39000'), Decimal('30000'),
Decimal('21000'), Decimal('37000'), Decimal('46000')]
assert_equal(np.mirr(val, Decimal('0.10'), Decimal('0.12')), Decimal('0.126094130365905145828421880'))
val = [Decimal('100'), Decimal('200'), Decimal('-50'),
Decimal('300'), Decimal('-200')]
assert_equal(np.mirr(val, Decimal('0.05'), Decimal('0.06')), Decimal('0.342823387842176663647819868'))
val = [Decimal('39000'), Decimal('30000'), Decimal('21000'), Decimal('37000'), Decimal('46000')]
assert_(np.isnan(np.mirr(val, Decimal('0.10'), Decimal('0.12'))))
def _median_nancheck(data, result, axis, out):
"""
Utility function to check median result from data for NaN values at the end
and return NaN in that case. Input result can also be a MaskedArray.
Parameters
----------
data : array
Input data to median function
result : Array or MaskedArray
Result of median function
axis : {int, sequence of int, None}, optional
Axis or axes along which the median was computed.
out : ndarray, optional
Output array in which to place the result.
Returns
-------
median : scalar or ndarray
Median or NaN in axes which contained NaN in the input.
"""
if data.size == 0:
return result
data = np.moveaxis(data, axis, -1)
n = np.isnan(data[..., -1])
# masked NaN values are ok
if np.ma.isMaskedArray(n):
n = n.filled(False)
if result.ndim == 0:
if n == True:
warnings.warn("Invalid value encountered in median",
RuntimeWarning,
stacklevel=3)
if out is not None:
out[...] = data.dtype.type(np.nan)
result = out
else:
result = data.dtype.type(np.nan)
elif np.count_nonzero(n.ravel()) > 0:
warnings.warn("Invalid value encountered in median for" +
" %d results" % np.count_nonzero(n.ravel()),
RuntimeWarning,
stacklevel=3)
result[n] = np.nan
return result
def test_goodvalues(self):
z = np.array((-1., 0., 1.))
res = np.isnan(z) == 0
assert_all(np.all(res, axis=0))
def _check_inf_inf(dummy):
msgform = "cexp(inf, inf) is (%f, %f), expected (+-inf, nan)"
with np.errstate(invalid='ignore'):
z = f(np.array(complex(np.inf, np.inf)))
if not np.isinf(z.real) or not np.isnan(z.imag):
raise AssertionError(msgform % (z.real, z.imag))
def test_half_ufuncs(self):
"""Test the various ufuncs"""
a = np.array([0, 1, 2, 4, 2], dtype=float16)
b = np.array([-2, 5, 1, 4, 3], dtype=float16)
c = np.array([0, -1, -np.inf, np.nan, 6], dtype=float16)
assert_equal(np.add(a, b), [-2, 6, 3, 8, 5])
assert_equal(np.subtract(a, b), [2, -4, 1, 0, -1])
assert_equal(np.multiply(a, b), [0, 5, 2, 16, 6])
assert_equal(np.divide(a, b), [0, 0.199951171875, 2, 1, 0.66650390625])
assert_equal(np.equal(a, b), [False, False, False, True, False])
assert_equal(np.not_equal(a, b), [True, True, True, False, True])
assert_equal(np.less(a, b), [False, True, False, False, True])
assert_equal(np.less_equal(a, b), [False, True, False, True, True])
assert_equal(np.greater(a, b), [True, False, True, False, False])
assert_equal(np.greater_equal(a, b), [True, False, True, True, False])
assert_equal(np.logical_and(a, b), [False, True, True, True, True])
assert_equal(np.logical_or(a, b), [True, True, True, True, True])
assert_equal(np.logical_xor(a, b), [True, False, False, False, False])
assert_equal(np.logical_not(a), [True, False, False, False, False])
assert_equal(np.isnan(c), [False, False, False, True, False])
assert_equal(np.isinf(c), [False, False, True, False, False])
assert_equal(np.isfinite(c), [True, True, False, False, True])
assert_equal(np.signbit(b), [True, False, False, False, False])
assert_equal(np.copysign(b, a), [2, 5, 1, 4, 3])
assert_equal(np.maximum(a, b), [0, 5, 2, 4, 3])
x = np.maximum(b, c)
assert_(np.isnan(x[3]))
x[3] = 0
assert_equal(x, [0, 5, 1, 0, 6])
assert_equal(np.minimum(a, b), [-2, 1, 1, 4, 2])
x = np.minimum(b, c)
assert_(np.isnan(x[3]))
x[3] = 0
assert_equal(x, [-2, -1, -np.inf, 0, 3])
assert_equal(np.fmax(a, b), [0, 5, 2, 4, 3])
assert_equal(np.fmax(b, c), [0, 5, 1, 4, 6])
assert_equal(np.fmin(a, b), [-2, 1, 1, 4, 2])
assert_equal(np.fmin(b, c), [-2, -1, -np.inf, 4, 3])
assert_equal(np.floor_divide(a, b), [0, 0, 2, 1, 0])
assert_equal(np.remainder(a, b), [0, 1, 0, 0, 2])
assert_equal(np.divmod(a, b), ([0, 0, 2, 1, 0], [0, 1, 0, 0, 2]))
assert_equal(np.square(b), [4, 25, 1, 16, 9])
assert_equal(np.reciprocal(b),
[-0.5, 0.199951171875, 1, 0.25, 0.333251953125])
assert_equal(np.ones_like(b), [1, 1, 1, 1, 1])
assert_equal(np.conjugate(b), b)
assert_equal(np.absolute(b), [2, 5, 1, 4, 3])
assert_equal(np.negative(b), [2, -5, -1, -4, -3])
assert_equal(np.positive(b), b)
assert_equal(np.sign(b), [-1, 1, 1, 1, 1])
assert_equal(np.modf(b), ([0, 0, 0, 0, 0], b))
assert_equal(np.frexp(b),
([-0.5, 0.625, 0.5, 0.5, 0.75], [2, 3, 1, 3, 2]))
assert_equal(np.ldexp(b, [0, 1, 2, 4, 2]), [-2, 10, 4, 64, 12])
def test_neginf(self):
with np.errstate(divide='ignore'):
assert_all(np.isnan(np.array((-1., )) / 0.) == 0)
def test_ind(self):
with np.errstate(divide='ignore', invalid='ignore'):
assert_all(np.isnan(np.array((0., )) / 0.) == 1)
def test_beta_small_parameters(self):
# Test that beta with small a and b parameters does not produce
# NaNs due to roundoff errors causing 0 / 0, gh-5851
np.random.seed(1234567890)
x = np.random.beta(0.0001, 0.0001, size=100)
assert_(not np.any(np.isnan(x)), 'Nans in np.random.beta')
def test_complex1(self):
with np.errstate(divide='ignore', invalid='ignore'):
assert_all(np.isnan(np.array(0 + 0j) / 0.) == 1)
def test_complex(self):
assert_all(np.isnan(1 + 1j) == 0)
def test_integer(self):
assert_all(np.isnan(1) == 0)
