def test_intp(self):
# Ticket #99
i_width = np.int_(0).nbytes * 2 - 1
np.intp('0x' + 'f' * i_width, 16)
assert_raises(OverflowError, np.intp, '0x' + 'f' * (i_width + 1), 16)
assert_raises(ValueError, np.intp, '0x1', 32)
assert_equal(255, np.intp('0xFF', 16))
def test_array_richcompare_legacy_weirdness(self):
# It doesn't really work to use assert_deprecated here, b/c part of
# the point of assert_deprecated is to check that when warnings are
# set to "error" mode then the error is propagated -- which is good!
# But here we are testing a bunch of code that is deprecated *because*
# it has the habit of swallowing up errors and converting them into
# different warnings. So assert_warns will have to be sufficient.
assert_warns(FutureWarning, lambda: np.arange(2) == "a")
assert_warns(FutureWarning, lambda: np.arange(2) != "a")
# No warning for scalar comparisons
with warnings.catch_warnings():
warnings.filterwarnings("error")
assert_(not (np.array(0) == "a"))
assert_(np.array(0) != "a")
assert_(not (np.int16(0) == "a"))
assert_(np.int16(0) != "a")
for arg1 in [np.asarray(0), np.int16(0)]:
struct = np.zeros(2, dtype="i4,i4")
for arg2 in [struct, "a"]:
for f in [operator.lt, operator.le, operator.gt, operator.ge]:
if sys.version_info[0] >= 3:
# py3
with warnings.catch_warnings() as l:
warnings.filterwarnings("always")
assert_raises(TypeError, f, arg1, arg2)
assert_(not l)
else:
# py2
assert_warns(DeprecationWarning, f, arg1, arg2)
def test_nan(self):
# Test that nan is 'far' from small, tiny, inf, max and min
for dt in [np.float32, np.float64]:
if dt == np.float32:
maxulp = 1e6
else:
maxulp = 1e12
inf = np.array([np.inf]).astype(dt)
nan = np.array([np.nan]).astype(dt)
big = np.array([np.finfo(dt).max])
tiny = np.array([np.finfo(dt).tiny])
zero = np.array([np.PZERO]).astype(dt)
nzero = np.array([np.NZERO]).astype(dt)
assert_raises(
AssertionError,
lambda: assert_array_max_ulp(nan, inf, maxulp=maxulp))
assert_raises(
AssertionError,
lambda: assert_array_max_ulp(nan, big, maxulp=maxulp))
assert_raises(
AssertionError,
lambda: assert_array_max_ulp(nan, tiny, maxulp=maxulp))
assert_raises(
AssertionError,
lambda: assert_array_max_ulp(nan, zero, maxulp=maxulp))
assert_raises(
AssertionError,
lambda: assert_array_max_ulp(nan, nzero, maxulp=maxulp))
def test_datetime_memoryview(self):
# gh-11656
# Values verified with v1.13.3, shape is not () as in test_scalar_dim
def as_dict(m):
return dict(strides=m.strides,
shape=m.shape,
itemsize=m.itemsize,
ndim=m.ndim,
format=m.format)
dt1 = np.datetime64('2016-01-01')
dt2 = np.datetime64('2017-01-01')
expected = {
'strides': (1, ),
'itemsize': 1,
'ndim': 1,
'shape': (8, ),
'format': 'B'
}
v = memoryview(dt1)
res = as_dict(v)
assert_equal(res, expected)
v = memoryview(dt2 - dt1)
res = as_dict(v)
assert_equal(res, expected)
dt = np.dtype([('a', 'uint16'), ('b', 'M8[s]')])
a = np.empty(1, dt)
# Fails to create a PEP 3118 valid buffer
assert_raises((ValueError, BufferError), memoryview, a[0])
def test_pad_too_many_axes(self):
arr = np.arange(30).reshape(5, 6)
# Attempt to pad using a 3D array equivalent
bad_shape = (((3,), (4,), (5,)), ((0,), (1,), (2,)))
assert_raises(ValueError, pad, arr, bad_shape,
mode='constant')
def test_extended_axis_invalid(self):
d = np.ones((3, 5, 7, 11))
assert_raises(np.AxisError, np.nanpercentile, d, q=5, axis=-5)
assert_raises(np.AxisError, np.nanpercentile, d, q=5, axis=(0, -5))
assert_raises(np.AxisError, np.nanpercentile, d, q=5, axis=4)
assert_raises(np.AxisError, np.nanpercentile, d, q=5, axis=(0, 4))
assert_raises(ValueError, np.nanpercentile, d, q=5, axis=(1, 1))
def test_dtype(self):
dt = np.intc
p = ndpointer(dtype=dt)
assert_(p.from_param(np.array([1], dt)))
dt = '<i4'
p = ndpointer(dtype=dt)
assert_(p.from_param(np.array([1], dt)))
dt = np.dtype('>i4')
p = ndpointer(dtype=dt)
p.from_param(np.array([1], dt))
assert_raises(TypeError, p.from_param,
np.array([1], dt.newbyteorder('swap')))
dtnames = ['x', 'y']
dtformats = [np.intc, np.float64]
dtdescr = {'names': dtnames, 'formats': dtformats}
dt = np.dtype(dtdescr)
p = ndpointer(dtype=dt)
assert_(p.from_param(np.zeros((10,), dt)))
samedt = np.dtype(dtdescr)
p = ndpointer(dtype=samedt)
assert_(p.from_param(np.zeros((10,), dt)))
dt2 = np.dtype(dtdescr, align=True)
if dt.itemsize != dt2.itemsize:
assert_raises(TypeError, p.from_param, np.zeros((10,), dt2))
else:
assert_(p.from_param(np.zeros((10,), dt2)))
def test_iter_allocate_output_subtype():
# Make sure that the subtype with priority wins
# 2018-04-29: moved here from core.tests.test_nditer, given the
# matrix specific shape test.
# matrix vs ndarray
a = np.matrix([[1, 2], [3, 4]])
b = np.arange(4).reshape(2, 2).T
i = np.nditer([a, b, None], [],
[['readonly'], ['readonly'], ['writeonly', 'allocate']])
assert_(type(i.operands[2]) is np.matrix)
assert_(type(i.operands[2]) is not np.ndarray)
assert_equal(i.operands[2].shape, (2, 2))
# matrix always wants things to be 2D
b = np.arange(4).reshape(1, 2, 2)
assert_raises(RuntimeError, np.nditer, [a, b, None], [],
[['readonly'], ['readonly'], ['writeonly', 'allocate']])
# but if subtypes are disabled, the result can still work
i = np.nditer([a, b, None], [],
[['readonly'], ['readonly'],
['writeonly', 'allocate', 'no_subtype']])
assert_(type(i.operands[2]) is np.ndarray)
assert_(type(i.operands[2]) is not np.matrix)
assert_equal(i.operands[2].shape, (1, 2, 2))
def test_opt_out(self):
class OptOut(object):
"""Object that opts out of __array_ufunc__."""
__array_ufunc__ = None
def __add__(self, other):
return self
def __radd__(self, other):
return self
array_like = ArrayLike(1)
opt_out = OptOut()
# supported operations
assert_(array_like + opt_out is opt_out)
assert_(opt_out + array_like is opt_out)
# not supported
with assert_raises(TypeError):
# don't use the Python default, array_like = array_like + opt_out
array_like += opt_out
with assert_raises(TypeError):
array_like - opt_out
with assert_raises(TypeError):
opt_out - array_like
def test_hermeder(self):
# check exceptions
assert_raises(ValueError, herme.hermeder, [0], .5)
assert_raises(ValueError, herme.hermeder, [0], -1)
# check that zeroth derivative does nothing
for i in range(5):
tgt = [0] * i + [1]
res = herme.hermeder(tgt, m=0)
assert_equal(trim(res), trim(tgt))
# check that derivation is the inverse of integration
for i in range(5):
for j in range(2, 5):
tgt = [0] * i + [1]
res = herme.hermeder(herme.hermeint(tgt, m=j), m=j)
assert_almost_equal(trim(res), trim(tgt))
# check derivation with scaling
for i in range(5):
for j in range(2, 5):
tgt = [0] * i + [1]
res = herme.hermeder(herme.hermeint(tgt, m=j, scl=2),
m=j,
scl=.5)
assert_almost_equal(trim(res), trim(tgt))
def test_incorrect_methods(self):
"""
Check a Value Error is thrown when an unknown string is passed in
"""
check_list = ['mad', 'freeman', 'histograms', 'IQR']
for estimator in check_list:
assert_raises(ValueError, histogram, [1, 2, 3], estimator)
def test_seq_repeat(self):
# Test that basic sequences get repeated when multiplied with
# numpy integers. And errors are raised when multiplied with others.
# Some of this behaviour may be controversial and could be open for
# change.
accepted_types = set(np.typecodes["AllInteger"])
deprecated_types = set('?')
forbidden_types = (set(np.typecodes["All"]) - accepted_types -
deprecated_types)
forbidden_types -= set('V') # can't default-construct void scalars
for seq_type in (list, tuple):
seq = seq_type([1, 2, 3])
for numpy_type in accepted_types:
i = np.dtype(numpy_type).type(2)
assert_equal(seq * i, seq * int(i))
assert_equal(i * seq, int(i) * seq)
for numpy_type in deprecated_types:
i = np.dtype(numpy_type).type()
assert_equal(
assert_warns(DeprecationWarning, operator.mul, seq, i),
seq * int(i))
assert_equal(
assert_warns(DeprecationWarning, operator.mul, i, seq),
int(i) * seq)
for numpy_type in forbidden_types:
i = np.dtype(numpy_type).type()
assert_raises(TypeError, operator.mul, seq, i)
assert_raises(TypeError, operator.mul, i, seq)
def test_inf(self):
a = np.array([[1., 2.], [3., 4.]])
b = a.copy()
a[0, 0] = np.inf
assert_raises(AssertionError, lambda: self._assert_func(a, b))
b[0, 0] = -np.inf
assert_raises(AssertionError, lambda: self._assert_func(a, b))
def test_basic(self):
y = linspace(0, 10)
assert_(len(y) == 50)
y = linspace(2, 10, num=100)
assert_(y[-1] == 10)
y = linspace(2, 10, endpoint=0)
assert_(y[-1] < 10)
assert_raises(ValueError, linspace, 0, 10, num=-1)
def test_trimcoef(self):
coef = [2, -1, 1, 0]
# Test exceptions
assert_raises(ValueError, pu.trimcoef, coef, -1)
# Test results
assert_equal(pu.trimcoef(coef), coef[:-1])
assert_equal(pu.trimcoef(coef, 1), coef[:-3])
assert_equal(pu.trimcoef(coef, 2), [0])
def test_basic(self):
assert_raises(ValueError, fliplr, ones(4))
a = get_mat(4)
b = a[:, ::-1]
assert_equal(fliplr(a), b)
a = [[0, 1, 2], [3, 4, 5]]
b = [[2, 1, 0], [5, 4, 3]]
assert_equal(fliplr(a), b)
def test_int_from_infinite_longdouble___int__(self):
x = np.longdouble(np.inf)
assert_raises(OverflowError, x.__int__)
with suppress_warnings() as sup:
sup.record(np.ComplexWarning)
x = np.clongdouble(np.inf)
assert_raises(OverflowError, x.__int__)
assert_equal(len(sup.log), 1)
def test_simple_arrays(self):
x = np.array([1234.22])
y = np.array([1234.23])
self._assert_func(x, y, significant=5)
self._assert_func(x, y, significant=6)
assert_raises(AssertionError,
lambda: self._assert_func(x, y, significant=7))
def test_simple(self):
x = np.array([1234.2222])
y = np.array([1234.2223])
self._assert_func(x, y, decimal=3)
self._assert_func(x, y, decimal=4)
assert_raises(AssertionError,
lambda: self._assert_func(x, y, decimal=5))
def test_no_postfix(self):
assert_raises(ValueError,
join_by,
'a',
self.a,
self.b,
r1postfix='',
r2postfix='')
def test_InvalidHTTP(self):
url = invalid_httpurl()
assert_raises(IOError, self.ds.open, url)
try:
self.ds.open(url)
except IOError as e:
# Regression test for bug fixed in r4342.
assert_(e.errno is None)
def test_empty(self):
mat = np.zeros((0, 3))
for f in self.nanfuncs:
for axis in [0, None]:
assert_raises(ValueError, f, mat, axis=axis)
for axis in [1]:
res = f(mat, axis=axis)
assert_equal(res, np.zeros(0))
def test_basic(self):
a = np.arange(10)
assert_raises(IndexError, a.__getitem__, [0.5, 1.5])
assert_raises(IndexError, a.__getitem__, (['1', '2'],))
# The following is valid
a.__getitem__([])
def test_non_integer_sequence_multiplication(self):
# NumPy scalar sequence multiply should not work with non-integers
def mult(a, b):
return a * b
assert_raises(TypeError, mult, [1], np.float_(3))
# following should be OK
mult([1], np.int_(3))
def test_truncate(Poly):
p = Poly([1, 2, 3])
assert_raises(ValueError, p.truncate, .5)
assert_raises(ValueError, p.truncate, 0)
assert_equal(len(p.truncate(4)), 3)
assert_equal(len(p.truncate(3)), 3)
assert_equal(len(p.truncate(2)), 2)
assert_equal(len(p.truncate(1)), 1)
def test_cutdeg(Poly):
p = Poly([1, 2, 3])
assert_raises(ValueError, p.cutdeg, .5)
assert_raises(ValueError, p.cutdeg, -1)
assert_equal(len(p.cutdeg(3)), 3)
assert_equal(len(p.cutdeg(2)), 3)
assert_equal(len(p.cutdeg(1)), 2)
assert_equal(len(p.cutdeg(0)), 1)
def test_to_64():
with exc_iter(INT128_VALUES) as it:
for a, in it:
if not (INT64_MIN <= a <= INT64_MAX):
assert_raises(OverflowError, mt.extint_to_64, a)
else:
b = mt.extint_to_64(a)
if a != b:
assert_equal(b, a)
def test_constant_both(self):
# non-contiguous should raise error
x = np.arange(6, dtype=np.float64)[::2]
assert_raises(ValueError, self.module.foo, x)
# check values with contiguous array
x = np.arange(3, dtype=np.float64)
self.module.foo(x)
assert_equal(x, [0 + 1 * 3 * 3 + 2 * 3 * 3, 1 * 3, 2 * 3])
def test_ndim(self):
p = ndpointer(ndim=0)
assert_(p.from_param(np.array(1)))
assert_raises(TypeError, p.from_param, np.array([1]))
p = ndpointer(ndim=1)
assert_raises(TypeError, p.from_param, np.array(1))
assert_(p.from_param(np.array([1])))
p = ndpointer(ndim=2)
assert_(p.from_param(np.array([[1]])))
def test_flags(self):
x = np.array([[1, 2], [3, 4]], order='F')
p = ndpointer(flags='FORTRAN')
assert_(p.from_param(x))
p = ndpointer(flags='CONTIGUOUS')
assert_raises(TypeError, p.from_param, x)
p = ndpointer(flags=x.flags.num)
assert_(p.from_param(x))
assert_raises(TypeError, p.from_param, np.array([[1, 2], [3, 4]]))