TNO Intern

Commit 2b2605d0 authored by Arjo Segers's avatar Arjo Segers
Browse files

Fix longitude/latitude arrays with no-data values, interpolate between surrounding locations.

parent 957a65a0

src/cso/cso_viirs.py

+29 −7
Original line number Diff line number Diff line
@@ -4,6 +4,9 @@ 
# 2025-02, Arjo Segers

#   Initial version based on "cso_viirs.py".

#

# 2025-09, Arjo Segers

#   Fix longitude/latitude arrays with no-data values, interpolate between surrounding locations.

#





########################################################################
@@ -166,12 +169,6 @@ class VIIRS_File(object): 
        # store:

        self.filenames = filenames



        ## check ...

        # if not os.path.isfile(filename):

        #    logging.error("file not found : %s" % filename)

        #    raise Exception

        ## endif



        # open:

        with xarray.open_mfdataset(

            self.filenames, concat_dim="Idx_Atrack", combine="nested"
@@ -1035,6 +1032,9 @@ class CSO_VIIRS_File(cso_file.CSO_File): 
        Correct locations of the odd scan lines,

        which seem to become dis-located towards the edge of the swath.



        Sometimes no-data values are found (-999), this is problematic to define the footprint bounds.

        Therfore, these are filled by interpolation.



        Arguments:



        * ``values`` : array of shape ``(ny,nx)`` with longitudes or latitudes
@@ -1050,6 +1050,28 @@ class CSO_VIIRS_File(cso_file.CSO_File): 
        # shape:

        ny, nx = values.shape



        # check ...

        if numpy.any( values < -180.0 ):

            # info ..

            logging.warning(f"        replace no-data values in pixel locations ...")

            # modules;

            import scipy

            # loop over pixels:

            for i in range(nx):

                # pixels without data:

                jj, = numpy.where( values[:,i] < -180.0 )

                # any?

                if len(jj) > 0:

                    # valid values:

                    jj1, = numpy.where( values[:,i] >= -180.0 )

                    # interpolator, linear:

                    spl = scipy.interpolate.make_interp_spline( jj1, values[jj1,i], k=1 )

                    # replace:

                    values[jj,i] = spl( jj )

                #endif

            #endfor

        #endif



        # target arrays:

        xx = numpy.zeros((ny, nx))