def rolling_fltr(dem, f=np.nanmedian, size=3, circular=True, origmask=False):
"""General rolling filter (default operator is median filter)
Can input any function f
Efficient for smaller arrays, correclty handles NaN, fills gaps
"""
print("Applying rolling filter: %s with size %s" % (f.__name__, size))
dem = malib.checkma(dem)
#Convert to float32 so we can fill with nan
dem = dem.astype(np.float32)
newshp = (dem.size, size * size)
#Force a step size of 1
t = malib.sliding_window_padded(dem.filled(np.nan), (size, size), (1, 1))
if circular:
if size > 3:
mask = circular_mask(size)
t[:, mask] = np.nan
t = t.reshape(newshp)
out = f(t, axis=1).reshape(dem.shape)
out = np.ma.fix_invalid(out).astype(dem.dtype)
out.set_fill_value(dem.fill_value)
if origmask:
out = np.ma.array(out, mask=np.ma.getmaskarray(dem))
return out
def rolling_fltr(dem, f=np.nanmedian, size=3, circular=True):
"""General rolling filter (default operator is median filter)
Can input any function f
Efficient for smaller arrays, correclty handles NaN, fills gaps
"""
dem = malib.checkma(dem)
newshp = (dem.size, size*size)
#Force a step size of 1
t = malib.sliding_window_padded(dem.filled(np.nan), (size, size), (1, 1))
if circular:
mask = circular_mask(size)
t[:,mask] = np.nan
t = t.reshape(newshp)
out = f(t, axis=1).reshape(dem.shape)
out = np.ma.fix_invalid(out)
out.set_fill_value(dem.fill_value)
return out