def compute(self, data):
"""
:param xarray.Dataset data:
:return: xarray.Dataset
"""
# We need to reshape our data into Y, X, Band, Time
squashed_together_dimensions, normal_datacube_dimensions = self._vars_to_transpose(
data)
squashed = data.to_array(dim='variable').transpose(
*squashed_together_dimensions)
assert squashed.dims == squashed_together_dimensions
# Grab a copy of the coordinates we need for creating the output DataArray
output_coords = copy(squashed.coords)
if 'time' in output_coords:
del output_coords['time']
if 'source' in output_coords:
del output_coords['source']
# Call Dale's function here
squashed = gmpcm(squashed.data, num_threads=self.num_threads)
all_zeros = (squashed == 0.).all(axis=-1)
squashed[all_zeros] = np.nan
# Jam the raw numpy array back into a pleasantly labelled DataArray
output_dims = squashed_together_dimensions[:-1]
as_datarray = xarray.DataArray(squashed,
dims=output_dims,
coords=output_coords)
return as_datarray.transpose(
*normal_datacube_dimensions).to_dataset(dim='variable')
def compute(self, data):
"""
:param xarray.Dataset data:
:return: xarray.Dataset
"""
# We need to reshape our data into Y, X, Band, Time
squashed_together_dimensions, output_dimensions = self._vars_to_transpose(
data)
squashed = data.to_array(dim='variable').transpose(
*squashed_together_dimensions)
assert squashed.dims == squashed_together_dimensions
# Grab a copy of the coordinates we need for creating the output DataArray
output_coords = copy(squashed.coords)
if 'variable' in output_coords:
del output_coords['variable']
if 'time' in output_coords:
del output_coords['time']
if 'source' in output_coords:
del output_coords['source']
# Call Dale's geometric median & spectral mad functions here
gm = pcm.gmpcm(squashed.data)
squashed = pcm.smad(squashed.data, gm)
# Jam the raw numpy array back into a pleasantly labelled DataArray
as_datarray = xarray.DataArray(squashed,
dims=output_dimensions,
coords=output_coords)
return as_datarray.transpose(*output_dimensions).to_dataset(
name='smad')