def assert_zeroish_mean_per_segment(wf):
data = wf.data
values = data.values
finite_segs = get_finite_segments(values)
for bp in finite_segs.bp:
ar = finite_segs.flat[bp[0]:bp[1]]
seg_mean = np.nanmean(ar)
assert np.allclose(seg_mean, 0)
def test_no_nan(self):
"""very simple test with No Nans"""
ar = np.arange(100).reshape(10, 10)
nns = get_finite_segments(ar)
# no Nans so flat should be the same size
assert nns.flat.size == ar.size
for ind in nns.ind:
# each segment should span exactly one row
assert ind[0][1] - ind[0][0] == 1
# and 10 columns
assert ind[1][1] - ind[1][0] == 10
def _linear_detrend_with_nan(self, values, finite, method):
""" Apply linear detrend to data which have NaNs. """
# init array for output
out = values.copy()
# get finite segments
nns = get_finite_segments(values, finite=finite)
detrended = scipy.signal.detrend(nns.flat,
type=method,
bp=nns.bp[:, 1])
assert np.shape(detrended) == np.shape(nns.flat)
# put humpty dumpty back together again
for flat_ind, ind in zip(nns.bp, nns.ind):
# get slice out of detrended result
ar = detrended[flat_ind[0]:flat_ind[1]]
out[ind[0][0]:ind[0][1], ind[1][0]:ind[1][1]] = ar
return out
def test_with_nan(self):
"""test with one NaN."""
# get test data with one NaN in first row
ar = np.arange(100).reshape(10, 10).astype(float)
ar[0, 5] = np.NaN
ar[-1, -1] = np.Inf
nns = get_finite_segments(ar)
assert nns.flat.size == ar.size - 2
# there should now be 11 segments (2 from first row)
assert len(nns.bp) == 11
# first row should have two segments
first, second = nns.ind[0], nns.ind[1]
assert first[1][1] - first[1][0] <= 5
assert second[1][1] - second[1][0] <= 5
# last row should only be 9 long
last = nns.ind[-1]
assert last[1][1] - last[1][0] == 9
def test_all_nan(self):
"""ensure all NaN raises."""
ar = np.empty(100).reshape(10, 10) * np.NaN
with pytest.raises(ValueError):
get_finite_segments(ar)