def test_multiple_percentiles(self):
perc = [50, 100]
mat = np.ones((4, 3))
nan_mat = np.nan * mat
# For checking consistency in higher dimensional case
large_mat = np.ones((3, 4, 5))
large_mat[:, 0:2:4, :] = 0
large_mat[:, :, 3:] *= 2
for axis in [None, 0, 1]:
for keepdim in [False, True]:
with suppress_warnings() as sup:
sup.filter(RuntimeWarning, "All-NaN slice encountered")
val = np.percentile(mat, perc, axis=axis, keepdims=keepdim)
nan_val = np.nanpercentile(nan_mat, perc, axis=axis,
keepdims=keepdim)
assert_equal(nan_val.shape, val.shape)
val = np.percentile(large_mat, perc, axis=axis,
keepdims=keepdim)
nan_val = np.nanpercentile(large_mat, perc, axis=axis,
keepdims=keepdim)
assert_equal(nan_val, val)
megamat = np.ones((3, 4, 5, 6))
assert_equal(np.nanpercentile(megamat, perc, axis=(1, 2)).shape, (2, 3, 6))
def test_result_values(self):
tgt = [np.percentile(d, 28) for d in _rdat]
res = np.nanpercentile(_ndat, 28, axis=1)
assert_almost_equal(res, tgt)
# Transpose the array to fit the output convention of numpy1.percentile
tgt = np.transpose([np.percentile(d, (28, 98)) for d in _rdat])
res = np.nanpercentile(_ndat, (28, 98), axis=1)
assert_almost_equal(res, tgt)
def test_regression(self):
ar = np.arange(24).reshape(2, 3, 4).astype(float)
ar[0][1] = np.nan
assert_equal(np.nanquantile(ar, q=0.5), np.nanpercentile(ar, q=50))
assert_equal(np.nanquantile(ar, q=0.5, axis=0),
np.nanpercentile(ar, q=50, axis=0))
assert_equal(np.nanquantile(ar, q=0.5, axis=1),
np.nanpercentile(ar, q=50, axis=1))
assert_equal(np.nanquantile(ar, q=[0.5], axis=1),
np.nanpercentile(ar, q=[50], axis=1))
assert_equal(np.nanquantile(ar, q=[0.25, 0.5, 0.75], axis=1),
np.nanpercentile(ar, q=[25, 50, 75], axis=1))
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_out(self):
mat = np.random.rand(3, 3)
nan_mat = np.insert(mat, [0, 2], np.nan, axis=1)
resout = np.zeros(3)
tgt = np.percentile(mat, 42, axis=1)
res = np.nanpercentile(nan_mat, 42, axis=1, out=resout)
assert_almost_equal(res, resout)
assert_almost_equal(res, tgt)
# 0-d output:
resout = np.zeros(())
tgt = np.percentile(mat, 42, axis=None)
res = np.nanpercentile(nan_mat, 42, axis=None, out=resout)
assert_almost_equal(res, resout)
assert_almost_equal(res, tgt)
res = np.nanpercentile(nan_mat, 42, axis=(0, 1), out=resout)
assert_almost_equal(res, resout)
assert_almost_equal(res, tgt)
def test_keepdims(self):
mat = np.eye(3)
for axis in [None, 0, 1]:
tgt = np.percentile(mat, 70, axis=axis, out=None,
overwrite_input=False)
res = np.nanpercentile(mat, 70, axis=axis, out=None,
overwrite_input=False)
assert_(res.ndim == tgt.ndim)
d = np.ones((3, 5, 7, 11))
# Randomly set some elements to NaN:
w = np.random.random((4, 200)) * np.array(d.shape)[:, None]
w = w.astype(np.intp)
d[tuple(w)] = np.nan
with suppress_warnings() as sup:
sup.filter(RuntimeWarning)
res = np.nanpercentile(d, 90, axis=None, keepdims=True)
assert_equal(res.shape, (1, 1, 1, 1))
res = np.nanpercentile(d, 90, axis=(0, 1), keepdims=True)
assert_equal(res.shape, (1, 1, 7, 11))
res = np.nanpercentile(d, 90, axis=(0, 3), keepdims=True)
assert_equal(res.shape, (1, 5, 7, 1))
res = np.nanpercentile(d, 90, axis=(1,), keepdims=True)
assert_equal(res.shape, (3, 1, 7, 11))
res = np.nanpercentile(d, 90, axis=(0, 1, 2, 3), keepdims=True)
assert_equal(res.shape, (1, 1, 1, 1))
res = np.nanpercentile(d, 90, axis=(0, 1, 3), keepdims=True)
assert_equal(res.shape, (1, 1, 7, 1))
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_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))
def test_mutation(self):
# Check that passed array is not modified.
ndat = _ndat.copy()
np.nanpercentile(ndat, 30)
assert_equal(ndat, _ndat)