def test_errcall(self):
def foo(*args):
print(args)
olderrcall = np.geterrcall()
with np.errstate(call=foo):
assert_(np.geterrcall() is foo, 'call is not foo')
with np.errstate(call=None):
assert_(np.geterrcall() is None, 'call is not None')
assert_(np.geterrcall() is olderrcall, 'call is not olderrcall')
def test_invalid(self):
with np.errstate(all='raise', under='ignore'):
a = -np.arange(3)
# This should work
with np.errstate(invalid='ignore'):
np.sqrt(a)
# While this should fail!
try:
np.sqrt(a)
except FloatingPointError:
pass
else:
self.fail("Did not raise an invalid error")
def test_divide(self):
with np.errstate(all='raise', under='ignore'):
a = -np.arange(3)
# This should work
with np.errstate(divide='ignore'):
a // 0
# While this should fail!
try:
a // 0
except FloatingPointError:
pass
else:
self.fail("Did not raise divide by zero error")
def test_complex_bad2(self):
with np.errstate(divide='ignore', invalid='ignore'):
v = 1 + 1j
v += np.array(-1 + 1.j) / 0.
vals = nan_to_num(v)
assert_all(np.isfinite(vals))
assert_equal(type(vals), np.complex_)
def test_testUfuncs1(self):
# Test various functions such as sin, cos.
(x, y, a10, m1, m2, xm, ym, z, zm, xf, s) = self.d
assert_(eq(np.cos(x), cos(xm)))
assert_(eq(np.cosh(x), cosh(xm)))
assert_(eq(np.sin(x), sin(xm)))
assert_(eq(np.sinh(x), sinh(xm)))
assert_(eq(np.tan(x), tan(xm)))
assert_(eq(np.tanh(x), tanh(xm)))
with np.errstate(divide='ignore', invalid='ignore'):
assert_(eq(np.sqrt(abs(x)), sqrt(xm)))
assert_(eq(np.log(abs(x)), log(xm)))
assert_(eq(np.log10(abs(x)), log10(xm)))
assert_(eq(np.exp(x), exp(xm)))
assert_(eq(np.arcsin(z), arcsin(zm)))
assert_(eq(np.arccos(z), arccos(zm)))
assert_(eq(np.arctan(z), arctan(zm)))
assert_(eq(np.arctan2(x, y), arctan2(xm, ym)))
assert_(eq(np.absolute(x), absolute(xm)))
assert_(eq(np.equal(x, y), equal(xm, ym)))
assert_(eq(np.not_equal(x, y), not_equal(xm, ym)))
assert_(eq(np.less(x, y), less(xm, ym)))
assert_(eq(np.greater(x, y), greater(xm, ym)))
assert_(eq(np.less_equal(x, y), less_equal(xm, ym)))
assert_(eq(np.greater_equal(x, y), greater_equal(xm, ym)))
assert_(eq(np.conjugate(x), conjugate(xm)))
assert_(eq(np.concatenate((x, y)), concatenate((xm, ym))))
assert_(eq(np.concatenate((x, y)), concatenate((x, y))))
assert_(eq(np.concatenate((x, y)), concatenate((xm, y))))
assert_(eq(np.concatenate((x, y, x)), concatenate((x, ym, x))))
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_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_branches(self):
with np.errstate(all="ignore"):
for t in [np.complex64, np.complex128]:
# tupled (numerator, denominator, expected)
# for testing as expected == numerator/denominator
data = list()
# trigger branch: real(fabs(denom)) > imag(fabs(denom))
# followed by else condition as neither are == 0
data.append(((2.0, 1.0), (2.0, 1.0), (1.0, 0.0)))
# trigger branch: real(fabs(denom)) > imag(fabs(denom))
# followed by if condition as both are == 0
# is performed in test_zero_division(), so this is skipped
# trigger else if branch: real(fabs(denom)) < imag(fabs(denom))
data.append(((1.0, 2.0), (1.0, 2.0), (1.0, 0.0)))
for cases in data:
n = cases[0]
d = cases[1]
ex = cases[2]
result = t(complex(n[0], n[1])) / t(complex(d[0], d[1]))
# check real and imag parts separately to avoid comparison
# in array context, which does not account for signed zeros
assert_equal(result.real, ex[0])
assert_equal(result.imag, ex[1])
def test_complex_bad(self):
with np.errstate(divide='ignore', invalid='ignore'):
v = 1 + 1j
v += np.array(0 + 1.j) / 0.
vals = nan_to_num(v)
# !! This is actually (unexpectedly) zero
assert_all(np.isfinite(vals))
assert_equal(type(vals), np.complex_)
def check_complex_value(f, x1, y1, x2, y2, exact=True):
z1 = np.array([complex(x1, y1)])
z2 = complex(x2, y2)
with np.errstate(invalid='ignore'):
if exact:
assert_equal(f(z1), z2)
else:
assert_almost_equal(f(z1), z2)
def test_generic(self):
with np.errstate(divide='ignore', invalid='ignore'):
vals = nan_to_num(np.array((-1., 0, 1)) / 0.)
assert_all(vals[0] < -1e10) and assert_all(np.isfinite(vals[0]))
assert_(vals[1] == 0)
assert_all(vals[2] > 1e10) and assert_all(np.isfinite(vals[2]))
assert_equal(type(vals), np.ndarray)
# perform the same test but in-place
with np.errstate(divide='ignore', invalid='ignore'):
vals = np.array((-1., 0, 1)) / 0.
result = nan_to_num(vals, copy=False)
assert_(result is vals)
assert_all(vals[0] < -1e10) and assert_all(np.isfinite(vals[0]))
assert_(vals[1] == 0)
assert_all(vals[2] > 1e10) and assert_all(np.isfinite(vals[2]))
assert_equal(type(vals), np.ndarray)
def test_underlow(self):
# Regression test for #759:
# instantiating MachAr for dtype = np.float96 raises spurious warning.
with errstate(all='raise'):
try:
self._run_machar_highprec()
except FloatingPointError as e:
msg = "Caught %s exception, should not have been raised." % e
raise AssertionError(msg)
def test_inf_edges(self):
# Test using +/-inf bin edges works. See #1788.
with np.errstate(invalid='ignore'):
x = np.arange(6).reshape(3, 2)
expected = np.array([[1, 0], [0, 1], [0, 1]])
h, e = np.histogramdd(x, bins=[3, [-np.inf, 2, 10]])
assert_allclose(h, expected)
h, e = np.histogramdd(x, bins=[3, np.array([-1, 2, np.inf])])
assert_allclose(h, expected)
h, e = np.histogramdd(x, bins=[3, [-np.inf, 3, np.inf]])
assert_allclose(h, expected)
def test_testScalarArithmetic(self):
xm = array(0, mask=1)
#TODO FIXME: Find out what the following raises a warning in r8247
with np.errstate(divide='ignore'):
assert_((1 / array(0)).mask)
assert_((1 + xm).mask)
assert_((-xm).mask)
assert_((-xm).mask)
assert_(maximum(xm, xm).mask)
assert_(minimum(xm, xm).mask)
assert_(xm.filled().dtype is xm._data.dtype)
x = array(0, mask=0)
assert_(x.filled() == x._data)
assert_equal(str(xm), str(masked_print_option))
def test_testArithmetic(self):
# Test of basic arithmetic.
(x, y, a10, m1, m2, xm, ym, z, zm, xf, s) = self.d
a2d = array([[1, 2], [0, 4]])
a2dm = masked_array(a2d, [[0, 0], [1, 0]])
assert_(eq(a2d * a2d, a2d * a2dm))
assert_(eq(a2d + a2d, a2d + a2dm))
assert_(eq(a2d - a2d, a2d - a2dm))
for s in [(12,), (4, 3), (2, 6)]:
x = x.reshape(s)
y = y.reshape(s)
xm = xm.reshape(s)
ym = ym.reshape(s)
xf = xf.reshape(s)
assert_(eq(-x, -xm))
assert_(eq(x + y, xm + ym))
assert_(eq(x - y, xm - ym))
assert_(eq(x * y, xm * ym))
with np.errstate(divide='ignore', invalid='ignore'):
assert_(eq(x / y, xm / ym))
assert_(eq(a10 + y, a10 + ym))
assert_(eq(a10 - y, a10 - ym))
assert_(eq(a10 * y, a10 * ym))
with np.errstate(divide='ignore', invalid='ignore'):
assert_(eq(a10 / y, a10 / ym))
assert_(eq(x + a10, xm + a10))
assert_(eq(x - a10, xm - a10))
assert_(eq(x * a10, xm * a10))
assert_(eq(x / a10, xm / a10))
assert_(eq(x ** 2, xm ** 2))
assert_(eq(abs(x) ** 2.5, abs(xm) ** 2.5))
assert_(eq(x ** y, xm ** ym))
assert_(eq(np.add(x, y), add(xm, ym)))
assert_(eq(np.subtract(x, y), subtract(xm, ym)))
assert_(eq(np.multiply(x, y), multiply(xm, ym)))
with np.errstate(divide='ignore', invalid='ignore'):
assert_(eq(np.divide(x, y), divide(xm, ym)))
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_known_types():
# Test we are correctly compiling parameters for known types
for ftype, ma_like in ((np.float16, _float16_ma),
(np.float32, _float32_ma), (np.float64,
_float64_ma)):
assert_ma_equal(_discovered_machar(ftype), ma_like)
# Suppress warning for broken discovery of double double on PPC
with np.errstate(all='ignore'):
ld_ma = _discovered_machar(np.longdouble)
bytes = np.dtype(np.longdouble).itemsize
if (ld_ma.it, ld_ma.maxexp) == (63, 16384) and bytes in (12, 16):
# 80-bit extended precision
assert_ma_equal(ld_ma, _float80_ma)
elif (ld_ma.it, ld_ma.maxexp) == (112, 16384) and bytes == 16:
# IEE 754 128-bit
assert_ma_equal(ld_ma, _float128_ma)
def test_unary_gufunc_fuzz(self):
shapes = [7, 13, 8, 21, 29, 32]
gufunc = _umath_tests.euclidean_pdist
rng = np.random.RandomState(1234)
for ndim in range(2, 6):
x = rng.rand(*shapes[:ndim])
it = iter_random_view_pairs(x, same_steps=False, equal_size=True)
min_count = 500 // (ndim + 1)**2
overlapping = 0
while overlapping < min_count:
a, b = next(it)
if min(a.shape[-2:]) < 2 or min(b.shape[-2:]) < 2 or a.shape[-1] < 2:
continue
# Ensure the shapes are so that euclidean_pdist is happy
if b.shape[-1] > b.shape[-2]:
b = b[...,0,:]
else:
b = b[...,:,0]
n = a.shape[-2]
p = n * (n - 1) // 2
if p <= b.shape[-1] and p > 0:
b = b[...,:p]
else:
n = max(2, int(np.sqrt(b.shape[-1]))//2)
p = n * (n - 1) // 2
a = a[...,:n,:]
b = b[...,:p]
# Call
if np.shares_memory(a, b):
overlapping += 1
with np.errstate(over='ignore', invalid='ignore'):
assert_copy_equivalent(gufunc, [a], out=b)
def test_signed_zeros(self):
with np.errstate(all="ignore"):
for t in [np.complex64, np.complex128]:
# tupled (numerator, denominator, expected)
# for testing as expected == numerator/denominator
data = (((0.0, -1.0), (0.0, 1.0), (-1.0, -0.0)),
((0.0, -1.0), (0.0, -1.0),
(1.0, -0.0)), ((0.0, -1.0), (-0.0, -1.0), (1.0, 0.0)),
((0.0, -1.0), (-0.0, 1.0),
(-1.0, 0.0)), ((0.0, 1.0), (0.0, -1.0), (-1.0, 0.0)),
((0.0, -1.0), (0.0, -1.0), (1.0, -0.0)),
((-0.0, -1.0), (0.0, -1.0), (1.0, -0.0)),
((-0.0, 1.0), (0.0, -1.0), (-1.0, -0.0)))
for cases in data:
n = cases[0]
d = cases[1]
ex = cases[2]
result = t(complex(n[0], n[1])) / t(complex(d[0], d[1]))
# check real and imag parts separately to avoid comparison
# in array context, which does not account for signed zeros
assert_equal(result.real, ex[0])
assert_equal(result.imag, ex[1])
def test_testUfuncRegression(self):
f_invalid_ignore = [
'sqrt', 'arctanh', 'arcsin', 'arccos',
'arccosh', 'arctanh', 'log', 'log10', 'divide',
'true_divide', 'floor_divide', 'remainder', 'fmod']
for f in ['sqrt', 'log', 'log10', 'exp', 'conjugate',
'sin', 'cos', 'tan',
'arcsin', 'arccos', 'arctan',
'sinh', 'cosh', 'tanh',
'arcsinh',
'arccosh',
'arctanh',
'absolute', 'fabs', 'negative',
'floor', 'ceil',
'logical_not',
'add', 'subtract', 'multiply',
'divide', 'true_divide', 'floor_divide',
'remainder', 'fmod', 'hypot', 'arctan2',
'equal', 'not_equal', 'less_equal', 'greater_equal',
'less', 'greater',
'logical_and', 'logical_or', 'logical_xor']:
try:
uf = getattr(umath, f)
except AttributeError:
uf = getattr(fromnumeric, f)
mf = getattr(np.ma, f)
args = self.d[:uf.nin]
with np.errstate():
if f in f_invalid_ignore:
np.seterr(invalid='ignore')
if f in ['arctanh', 'log', 'log10']:
np.seterr(divide='ignore')
ur = uf(*args)
mr = mf(*args)
assert_(eq(ur.filled(0), mr.filled(0), f))
assert_(eqmask(ur.mask, mr.mask))
def test_ind(self):
with np.errstate(divide='ignore', invalid='ignore'):
assert_all(np.isinf(np.array((0., )) / 0.) == 0)
def test_half_fpe(self):
with np.errstate(all='raise'):
sx16 = np.array((1e-4, ), dtype=float16)
bx16 = np.array((1e4, ), dtype=float16)
sy16 = float16(1e-4)
by16 = float16(1e4)
# Underflow errors
assert_raises_fpe('underflow', lambda a, b: a * b, sx16, sx16)
assert_raises_fpe('underflow', lambda a, b: a * b, sx16, sy16)
assert_raises_fpe('underflow', lambda a, b: a * b, sy16, sx16)
assert_raises_fpe('underflow', lambda a, b: a * b, sy16, sy16)
assert_raises_fpe('underflow', lambda a, b: a / b, sx16, bx16)
assert_raises_fpe('underflow', lambda a, b: a / b, sx16, by16)
assert_raises_fpe('underflow', lambda a, b: a / b, sy16, bx16)
assert_raises_fpe('underflow', lambda a, b: a / b, sy16, by16)
assert_raises_fpe('underflow', lambda a, b: a / b,
float16(2.**-14), float16(2**11))
assert_raises_fpe('underflow', lambda a, b: a / b,
float16(-2.**-14), float16(2**11))
assert_raises_fpe('underflow', lambda a, b: a / b,
float16(2.**-14 + 2**-24), float16(2))
assert_raises_fpe('underflow', lambda a, b: a / b,
float16(-2.**-14 - 2**-24), float16(2))
assert_raises_fpe('underflow', lambda a, b: a / b,
float16(2.**-14 + 2**-23), float16(4))
# Overflow errors
assert_raises_fpe('overflow', lambda a, b: a * b, bx16, bx16)
assert_raises_fpe('overflow', lambda a, b: a * b, bx16, by16)
assert_raises_fpe('overflow', lambda a, b: a * b, by16, bx16)
assert_raises_fpe('overflow', lambda a, b: a * b, by16, by16)
assert_raises_fpe('overflow', lambda a, b: a / b, bx16, sx16)
assert_raises_fpe('overflow', lambda a, b: a / b, bx16, sy16)
assert_raises_fpe('overflow', lambda a, b: a / b, by16, sx16)
assert_raises_fpe('overflow', lambda a, b: a / b, by16, sy16)
assert_raises_fpe('overflow', lambda a, b: a + b, float16(65504),
float16(17))
assert_raises_fpe('overflow', lambda a, b: a - b, float16(-65504),
float16(17))
assert_raises_fpe('overflow', np.nextafter, float16(65504),
float16(np.inf))
assert_raises_fpe('overflow', np.nextafter, float16(-65504),
float16(-np.inf))
assert_raises_fpe('overflow', np.spacing, float16(65504))
# Invalid value errors
assert_raises_fpe('invalid', np.divide, float16(np.inf),
float16(np.inf))
assert_raises_fpe('invalid', np.spacing, float16(np.inf))
assert_raises_fpe('invalid', np.spacing, float16(np.nan))
assert_raises_fpe('invalid', np.nextafter, float16(np.inf),
float16(0))
assert_raises_fpe('invalid', np.nextafter, float16(-np.inf),
float16(0))
assert_raises_fpe('invalid', np.nextafter, float16(0),
float16(np.nan))
# These should not raise
float16(65472) + float16(32)
float16(2**-13) / float16(2)
float16(2**-14) / float16(2**10)
np.spacing(float16(-65504))
np.nextafter(float16(65504), float16(-np.inf))
np.nextafter(float16(-65504), float16(np.inf))
float16(2**-14) / float16(2**10)
float16(-2**-14) / float16(2**10)
float16(2**-14 + 2**-23) / float16(2)
float16(-2**-14 - 2**-23) / float16(2)
def test_posinf(self):
with np.errstate(divide='ignore'):
assert_all(np.isnan(np.array((1., )) / 0.) == 0)
def test_complex1(self):
with np.errstate(divide='ignore', invalid='ignore'):
assert_all(np.isnan(np.array(0 + 0j) / 0.) == 1)
def test_neginf(self):
with np.errstate(divide='ignore', invalid='ignore'):
assert_all(np.isfinite(np.array((-1., )) / 0.) == 0)
def nper(rate, pmt, pv, fv=0, when='end'):
"""
Compute the number of periodic payments.
:class:`decimal.Decimal` type is not supported.
Parameters
----------
rate : array_like
Rate of interest (per period)
pmt : array_like
Payment
pv : array_like
Present value
fv : array_like, optional
Future value
when : {{'begin', 1}, {'end', 0}}, {string, int}, optional
When payments are due ('begin' (1) or 'end' (0))
Notes
-----
The number of periods ``nper`` is computed by solving the equation::
fv + pv*(1+rate)**nper + pmt*(1+rate*when)/rate*((1+rate)**nper-1) = 0
but if ``rate = 0`` then::
fv + pv + pmt*nper = 0
Examples
--------
If you only had $150/month to pay towards the loan, how long would it take
to pay-off a loan of $8,000 at 7% annual interest?
>>> print(round(np.nper(0.07/12, -150, 8000), 5))
64.07335
So, over 64 months would be required to pay off the loan.
The same analysis could be done with several different interest rates
and/or payments and/or total amounts to produce an entire table.
>>> np.nper(*(np.ogrid[0.07/12: 0.08/12: 0.01/12,
... -150 : -99 : 50 ,
... 8000 : 9001 : 1000]))
array([[[ 64.07334877, 74.06368256],
[ 108.07548412, 127.99022654]],
[[ 66.12443902, 76.87897353],
[ 114.70165583, 137.90124779]]])
"""
when = _convert_when(when)
(rate, pmt, pv, fv, when) = map(np.asarray, [rate, pmt, pv, fv, when])
use_zero_rate = False
with np.errstate(divide="raise"):
try:
z = pmt * (1 + rate * when) / rate
except FloatingPointError:
use_zero_rate = True
if use_zero_rate:
return (-fv + pv) / pmt
else:
A = -(fv + pv) / (pmt + 0)
B = np.log((-fv + z) / (pv + z)) / np.log(1 + rate)
return np.where(rate == 0, A, B)
def test_unary_ufunc_1d_manual(self):
# Exercise branches in PyArray_EQUIVALENTLY_ITERABLE
def check(a, b):
a_orig = a.copy()
b_orig = b.copy()
b0 = b.copy()
c1 = ufunc(a, out=b0)
c2 = ufunc(a, out=b)
assert_array_equal(c1, c2)
# Trigger "fancy ufunc loop" code path
mask = view_element_first_byte(b).view(np.bool_)
a[...] = a_orig
b[...] = b_orig
c1 = ufunc(a, out=b.copy(), where=mask.copy()).copy()
a[...] = a_orig
b[...] = b_orig
c2 = ufunc(a, out=b, where=mask.copy()).copy()
# Also, mask overlapping with output
a[...] = a_orig
b[...] = b_orig
c3 = ufunc(a, out=b, where=mask).copy()
assert_array_equal(c1, c2)
assert_array_equal(c1, c3)
dtypes = [np.int8, np.int16, np.int32, np.int64, np.float32,
np.float64, np.complex64, np.complex128]
dtypes = [np.dtype(x) for x in dtypes]
for dtype in dtypes:
if np.issubdtype(dtype, np.integer):
ufunc = np.invert
else:
ufunc = np.reciprocal
n = 1000
k = 10
indices = [
np.index_exp[:n],
np.index_exp[k:k+n],
np.index_exp[n-1::-1],
np.index_exp[k+n-1:k-1:-1],
np.index_exp[:2*n:2],
np.index_exp[k:k+2*n:2],
np.index_exp[2*n-1::-2],
np.index_exp[k+2*n-1:k-1:-2],
]
for xi, yi in itertools.product(indices, indices):
v = np.arange(1, 1 + n*2 + k, dtype=dtype)
x = v[xi]
y = v[yi]
with np.errstate(all='ignore'):
check(x, y)
# Scalar cases
check(x[:1], y)
check(x[-1:], y)
check(x[:1].reshape([]), y)
check(x[-1:].reshape([]), y)
def test_complex1(self):
with np.errstate(divide='ignore', invalid='ignore'):
assert_all(np.isfinite(np.array(1 + 1j) / 0.) == 0)
def test_neginf_scalar(self):
with np.errstate(divide='ignore', invalid='ignore'):
assert_all(np.isinf(np.array(-1.) / 0.) == 1)
def test_generic(self):
with np.errstate(divide='ignore', invalid='ignore'):
vals = isneginf(np.array((-1., 0, 1)) / 0.)
assert_(vals[0] == 1)
assert_(vals[1] == 0)
assert_(vals[2] == 0)
