def detrend_discontinuous_linear(t, x, max_gap=0, recenter=True):
"""
Just removed the linear signal
Args:
t: time data (should be continuous)
x: x data. may be gappy (has nans)
max_gap: the largest gap allowable for interpolation
recenter: if True, with output will have the same mean as the input
Returns:
The detrended data as a pd Series. if the original data was discontinuous, there will be fewer entries in the
returned Series, but the index will still match up
"""
sub_tees, sub_exes = continuous_subsets(t, x, max_gap=max_gap, repair=True)
d_exes = [dt(sub) for sub in sub_exes]
tees = []
exes = []
for sub_t, sub_x in zip(sub_tees, d_exes):
tees.extend(sub_t)
exes.extend(sub_x)
if recenter:
exes = exes + (np.nanmean(x) - np.mean(exes))
c = crush_series(t, x, tees, exes)
return c
def detrend(image):
'''
Remove linear trends from an image.
Performs a 2 axis linear detrend using scipy.signal.detrend
Parameters
----------
image : xarray.DataArray
Image to process
Returns
-------
image : xarray.DataArray
Image with linear trends removed
'''
return copy_metadata(image, dt(dt(image, image.dims.index('x')), image.dims.index('y')))
def __init__(self, ts, detrend=False):
if len(ts) != 0:
m, n = ts.shape[0], ts.shape[2]
if detrend:
for i in range(m):
for j in range(n):
ts[i, :, j] = dt(ts[i, :, j])
self.ts = ts