util.py

    if use_nan:
        max_lvl = np.where(max_lvl == -1, np.nan, max_lvl)
    if has_time:
        max_lvl = max_lvl.reshape((1, y.shape[0], x.shape[0]))

    icing_pred_ds = rootgrp.createVariable('max_icing_probability_level', 'i2', var_dim_list)
    icing_pred_ds.setncattr('coordinates', geo_coords)
    icing_pred_ds.setncattr('grid_mapping', 'Projection')
    icing_pred_ds.setncattr('missing', -1)
    icing_pred_ds[:,] = max_lvl

    if bt_10_4 is not None:
        bt_ds = rootgrp.createVariable('bt_10_4', 'f4', var_dim_list)
        bt_ds.setncattr('coordinates', geo_coords)
        bt_ds.setncattr('grid_mapping', 'Projection')
        bt_ds[:,] = bt_10_4

    lon_ds = rootgrp.createVariable('longitude', 'f4', [dim_1_name, dim_0_name])
    lon_ds.units = 'degrees_east'
    lon_ds[:,] = lons

    lat_ds = rootgrp.createVariable('latitude', 'f4', [dim_1_name, dim_0_name])
    lat_ds.units = 'degrees_north'
    lat_ds[:,] = lats

    cf_nav_dct = get_cf_nav_parameters(satellite, domain)

    if satellite == 'H08':
        long_name = 'Himawari Imagery Projection'
    elif satellite == 'GOES16':
        long_name = 'GOES-16/17 Imagery Projection'

    proj_ds = rootgrp.createVariable('Projection', 'b')
    proj_ds.setncattr('long_name', long_name)
    proj_ds.setncattr('grid_mapping_name', 'geostationary')
    proj_ds.setncattr('sweep_angle_axis', cf_nav_dct['sweep_angle_axis'])
    proj_ds.setncattr('semi_major_axis', cf_nav_dct['semi_major_axis'])
    proj_ds.setncattr('semi_minor_axis', cf_nav_dct['semi_minor_axis'])
    proj_ds.setncattr('inverse_flattening', cf_nav_dct['inverse_flattening'])
    proj_ds.setncattr('perspective_point_height', cf_nav_dct['perspective_point_height'])
    proj_ds.setncattr('latitude_of_projection_origin', cf_nav_dct['latitude_of_projection_origin'])
    proj_ds.setncattr('longitude_of_projection_origin', cf_nav_dct['longitude_of_projection_origin'])

    if x is not None:
        x_ds = rootgrp.createVariable(dim_0_name, 'f8', [dim_0_name])
        x_ds.units = 'rad'
        x_ds.setncattr('axis', 'X')
        x_ds.setncattr('standard_name', 'projection_x_coordinate')
        x_ds.setncattr('long_name', 'fixed grid viewing angle')
        x_ds.setncattr('scale_factor', cf_nav_dct['x_scale_factor'])
        x_ds.setncattr('add_offset', cf_nav_dct['x_add_offset'])
        x_ds[:] = x

        y_ds = rootgrp.createVariable(dim_1_name, 'f8', [dim_1_name])
        y_ds.units = 'rad'
        y_ds.setncattr('axis', 'Y')
        y_ds.setncattr('standard_name', 'projection_y_coordinate')
        y_ds.setncattr('long_name', 'fixed grid viewing angle')
        y_ds.setncattr('scale_factor', cf_nav_dct['y_scale_factor'])
        y_ds.setncattr('add_offset', cf_nav_dct['y_add_offset'])
        y_ds[:] = y

    if elems is not None:
        elem_ds = rootgrp.createVariable('elems', 'i2', [dim_0_name])
        elem_ds[:] = elems
        line_ds = rootgrp.createVariable('lines', 'i2', [dim_1_name])
        line_ds[:] = lines
        pass

    rootgrp.close()


def write_icing_file_nc4_viirs(clvrx_str_time, output_dir, preds_dct, probs_dct, lons, lats,
                               has_time=False, use_nan=False, prob_thresh=0.5, bt_10_4=None):
    outfile_name = output_dir + 'icing_prediction_'+clvrx_str_time+'.nc'
    rootgrp = Dataset(outfile_name, 'w', format='NETCDF4')

    rootgrp.setncattr('Conventions', 'CF-1.7')

    dim_0_name = 'x'
    dim_1_name = 'y'
    time_dim_name = 'time'
    geo_coords = 'longitude latitude'

    dim_1_len, dim_0_len = lons.shape

    dim_0 = rootgrp.createDimension(dim_0_name, size=dim_0_len)
    dim_1 = rootgrp.createDimension(dim_1_name, size=dim_1_len)
    dim_time = rootgrp.createDimension(time_dim_name, size=1)

    tvar = rootgrp.createVariable('time', 'f8', time_dim_name)
    tvar[0] = get_timestamp(clvrx_str_time)
    tvar.units = 'seconds since 1970-01-01 00:00:00'

    if not has_time:
        var_dim_list = [dim_1_name, dim_0_name]
    else:
        var_dim_list = [time_dim_name, dim_1_name, dim_0_name]

    prob_s = []

    flt_lvls = list(preds_dct.keys())
    for flvl in flt_lvls:
        preds = preds_dct[flvl]

        icing_pred_ds = rootgrp.createVariable('icing_prediction_level_'+flt_level_ranges_str[flvl], 'i2', var_dim_list)
        icing_pred_ds.setncattr('coordinates', geo_coords)
        icing_pred_ds.setncattr('grid_mapping', 'Projection')
        icing_pred_ds.setncattr('missing', -1)
        if has_time:
            preds = preds.reshape((1, dim_1_len, dim_0_len))
        icing_pred_ds[:,] = preds

    for flvl in flt_lvls:
        probs = probs_dct[flvl]
        prob_s.append(probs)

        icing_prob_ds = rootgrp.createVariable('icing_probability_level_'+flt_level_ranges_str[flvl], 'f4', var_dim_list)
        icing_prob_ds.setncattr('coordinates', geo_coords)
        icing_prob_ds.setncattr('grid_mapping', 'Projection')
        if not use_nan:
            icing_prob_ds.setncattr('missing', -1.0)
        else:
            icing_prob_ds.setncattr('missing', np.nan)
        if has_time:
            probs = probs.reshape((1, dim_1_len, dim_0_len))
        if use_nan:
            probs = np.where(probs < prob_thresh, np.nan, probs)
        icing_prob_ds[:,] = probs

    prob_s = np.stack(prob_s, axis=-1)
    max_prob = np.max(prob_s, axis=2)
    if use_nan:
        max_prob = np.where(max_prob < prob_thresh, np.nan, max_prob)
    if has_time:
        max_prob = max_prob.reshape(1, dim_1_len, dim_0_len)

    icing_prob_ds = rootgrp.createVariable('max_icing_probability_column', 'f4', var_dim_list)
    icing_prob_ds.setncattr('coordinates', geo_coords)
    icing_prob_ds.setncattr('grid_mapping', 'Projection')
    if not use_nan:
        icing_prob_ds.setncattr('missing', -1.0)
    else:
        icing_prob_ds.setncattr('missing', np.nan)
    icing_prob_ds[:,] = max_prob

    prob_s = np.where(prob_s < prob_thresh, -1.0, prob_s)
    max_lvl = np.where(np.all(prob_s == -1, axis=2), -1, np.argmax(prob_s, axis=2))
    if use_nan:
        max_lvl = np.where(max_lvl == -1, np.nan, max_lvl)
    if has_time:
        max_lvl = max_lvl.reshape((1, dim_1_len, dim_0_len))

    icing_pred_ds = rootgrp.createVariable('max_icing_probability_level', 'i2', var_dim_list)
    icing_pred_ds.setncattr('coordinates', geo_coords)
    icing_pred_ds.setncattr('grid_mapping', 'Projection')
    icing_pred_ds.setncattr('missing', -1)
    icing_pred_ds[:,] = max_lvl

    if bt_10_4 is not None:
        bt_ds = rootgrp.createVariable('bt_10_4', 'f4', var_dim_list)
        bt_ds.setncattr('coordinates', geo_coords)
        bt_ds.setncattr('grid_mapping', 'Projection')
        bt_ds[:,] = bt_10_4

    lon_ds = rootgrp.createVariable('longitude', 'f4', [dim_1_name, dim_0_name])
    lon_ds.units = 'degrees_east'
    lon_ds[:,] = lons

    lat_ds = rootgrp.createVariable('latitude', 'f4', [dim_1_name, dim_0_name])
    lat_ds.units = 'degrees_north'
    lat_ds[:,] = lats

    proj_ds = rootgrp.createVariable('Projection', 'b')
    proj_ds.setncattr('grid_mapping_name', 'latitude_longitude')

    rootgrp.close()