def test_nanfunctions_matrices_general():
# 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.nanargmin, np.nanargmax, np.nansum, np.nanprod,
np.nanmean, np.nanvar, np.nanstd):
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))
for f in np.nancumsum, np.nancumprod:
res = f(mat, axis=0)
assert_(isinstance(res, np.matrix))
assert_(res.shape == (3, 3))
res = f(mat, axis=1)
assert_(isinstance(res, np.matrix))
assert_(res.shape == (3, 3))
res = f(mat)
assert_(isinstance(res, np.matrix))
assert_(res.shape == (1, 3*3))
def test_basic(self):
assert_(np.isscalar(3))
assert_(not np.isscalar([3]))
assert_(not np.isscalar((3, )))
assert_(np.isscalar(3j))
assert_(np.isscalar(long(10)))
assert_(np.isscalar(4.0))
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_ufunc_return_ndarray(self):
fp = memmap(self.tmpfp, dtype=self.dtype, shape=self.shape)
fp[:] = self.data
with suppress_warnings() as sup:
sup.filter(FutureWarning, "np.average currently does not preserve")
for unary_op in [sum, average, product]:
result = unary_op(fp)
assert_(isscalar(result))
assert_(result.__class__ is self.data[0, 0].__class__)
assert_(unary_op(fp, axis=0).__class__ is ndarray)
assert_(unary_op(fp, axis=1).__class__ is ndarray)
for binary_op in [add, subtract, multiply]:
assert_(binary_op(fp, self.data).__class__ is ndarray)
assert_(binary_op(self.data, fp).__class__ is ndarray)
assert_(binary_op(fp, fp).__class__ is ndarray)
fp += 1
assert (fp.__class__ is memmap)
add(fp, 1, out=fp)
assert (fp.__class__ is memmap)
def test_scalar(self):
assert_equal(np.nanpercentile(0., 100), 0.)
a = np.arange(6)
r = np.nanpercentile(a, 50, axis=0)
assert_equal(r, 2.5)
assert_(np.isscalar(r))