snapshot...

modules/contrail/util.py

@@ -52,7 +52,6 @@ def extract(mask_image, image_ts, clavrx_path):
     clvrx_lats = get_grid_values_all(clvrx_h5f, 'latitude').flatten()
 
     contrail_idxs = (mask_image == 1).flatten()
-    print('number of contrail pixels: ', np.sum(contrail_idxs))
 
     # Assuming GOES FD for now -------------------
     # elems, lines = np.meshgrid(np.arange(5424), np.arange(5424))
@@ -121,6 +120,8 @@ def extract(mask_image, image_ts, clavrx_path):
         print('working on pressure level: ', bins[key])
         for c_idx in bins_dict[key]:
             lon = contrail_lons[c_idx]
+            if lon < 0:  # Match GFS convention
+                lon += 360.0
             lat = contrail_lats[c_idx]
             press = contrail_press[c_idx]
 
@@ -129,6 +130,9 @@ def extract(mask_image, image_ts, clavrx_path):
             horz_wind_spd_value = horz_wind_spd_3d.interp(Pressure=press, Longitude=lon, Latitude=lat, method='nearest')
             vert_shear_value = vert_shear_3d.interp(Pressure=press, Longitude=lon, Latitude=lat, method='nearest')
 
+            # tmp = horz_shear_3d.sel(Pressure=press, method='nearest')
+            # tmp = tmp.sel(Longitude=lon, Latitude=lat, method='nearest')
+
             voxel_dict[key].append((press, lat, lon, horz_shear_value, static_value, horz_wind_spd_value, vert_shear_value))
 
     # Create pandas DataFrame for each list of tuples in voxel_dict