snapshot...

modules/icing/util.py

@@ -1037,6 +1037,7 @@ def run_icing_predict_image(clvrx_dir='/Users/tomrink/data/clavrx/RadC/', output
                             prob_thresh=0.5, satellite='GOES16', domain='CONUS', day_night='AUTO',
                             l1b_andor_l2='BOTH', use_flight_altitude=True, flight_levels=[0, 1, 2, 3, 4],
                             res_fac=1, model_type='CNN', extent=[-105, -70, 15, 50], pirep_file=None, icing_dict=None,
+                            data_extent=None,
                             obs_lons=None, obs_lats=None, obs_times=None, obs_alt=None, obs_intensity=None):
     if model_type == 'CNN':
         import deeplearning.icing_cnn as icing_cnn
@@ -1089,7 +1090,8 @@ def run_icing_predict_image(clvrx_dir='/Users/tomrink/data/clavrx/RadC/', output
         ts = dto.timestamp()
         clvrx_str_time = dto.strftime('%Y-%m-%d_%H:%M')
 
-        data_dct, ll, cc = make_for_full_domain_predict(h5f, name_list=train_params, satellite=satellite, domain=domain, res_fac=res_fac)
+        data_dct, ll, cc = make_for_full_domain_predict(h5f, name_list=train_params, satellite=satellite, domain=domain,
+                                                        res_fac=res_fac, data_extent=data_extent)
 
         if fidx == 0:
             num_elems = len(cc)
@@ -1098,7 +1100,8 @@ def run_icing_predict_image(clvrx_dir='/Users/tomrink/data/clavrx/RadC/', output
 
         ancil_data_dct, _, _ = make_for_full_domain_predict(h5f, name_list=
                             ['solar_zenith_angle', 'sensor_zenith_angle', 'cld_height_acha', 'cld_geo_thick'],
-                            satellite=satellite, domain=domain, res_fac=res_fac)
+                            satellite=satellite, domain=domain, res_fac=res_fac,
+                            data_extent=data_extent)
 
         satzen = ancil_data_dct['sensor_zenith_angle']
         solzen = ancil_data_dct['solar_zenith_angle']