AlkantarClanX12
Your IP : 3.16.76.102
import numpy as np
import numpy.core as nx
import numpy.lib.ufunclike as ufl
from numpy.testing import (
assert_, assert_equal, assert_array_equal, assert_warns, assert_raises
)
class TestUfunclike:
def test_isposinf(self):
a = nx.array([nx.inf, -nx.inf, nx.nan, 0.0, 3.0, -3.0])
out = nx.zeros(a.shape, bool)
tgt = nx.array([True, False, False, False, False, False])
res = ufl.isposinf(a)
assert_equal(res, tgt)
res = ufl.isposinf(a, out)
assert_equal(res, tgt)
assert_equal(out, tgt)
a = a.astype(np.complex_)
with assert_raises(TypeError):
ufl.isposinf(a)
def test_isneginf(self):
a = nx.array([nx.inf, -nx.inf, nx.nan, 0.0, 3.0, -3.0])
out = nx.zeros(a.shape, bool)
tgt = nx.array([False, True, False, False, False, False])
res = ufl.isneginf(a)
assert_equal(res, tgt)
res = ufl.isneginf(a, out)
assert_equal(res, tgt)
assert_equal(out, tgt)
a = a.astype(np.complex_)
with assert_raises(TypeError):
ufl.isneginf(a)
def test_fix(self):
a = nx.array([[1.0, 1.1, 1.5, 1.8], [-1.0, -1.1, -1.5, -1.8]])
out = nx.zeros(a.shape, float)
tgt = nx.array([[1., 1., 1., 1.], [-1., -1., -1., -1.]])
res = ufl.fix(a)
assert_equal(res, tgt)
res = ufl.fix(a, out)
assert_equal(res, tgt)
assert_equal(out, tgt)
assert_equal(ufl.fix(3.14), 3)
def test_fix_with_subclass(self):
class MyArray(nx.ndarray):
def __new__(cls, data, metadata=None):
res = nx.array(data, copy=True).view(cls)
res.metadata = metadata
return res
def __array_wrap__(self, obj, context=None):
if isinstance(obj, MyArray):
obj.metadata = self.metadata
return obj
def __array_finalize__(self, obj):
self.metadata = getattr(obj, 'metadata', None)
return self
a = nx.array([1.1, -1.1])
m = MyArray(a, metadata='foo')
f = ufl.fix(m)
assert_array_equal(f, nx.array([1, -1]))
assert_(isinstance(f, MyArray))
assert_equal(f.metadata, 'foo')
# check 0d arrays don't decay to scalars
m0d = m[0,...]
m0d.metadata = 'bar'
f0d = ufl.fix(m0d)
assert_(isinstance(f0d, MyArray))
assert_equal(f0d.metadata, 'bar')
def test_scalar(self):
x = np.inf
actual = np.isposinf(x)
expected = np.True_
assert_equal(actual, expected)
assert_equal(type(actual), type(expected))
x = -3.4
actual = np.fix(x)
expected = np.float64(-3.0)
assert_equal(actual, expected)
assert_equal(type(actual), type(expected))
out = np.array(0.0)
actual = np.fix(x, out=out)
assert_(actual is out)