snapshot...

modules/contrail/util.py

@@ -115,6 +115,7 @@ def extract(mask_image, image_ts, clavrx_path):
     vert_shear_3d = volume_np_to_xr(vert_shear_3d, ['Pressure', 'Latitude', 'Longitude'], lon_range=lon_range, lat_range=lat_range)
     vert_shear_3d = vert_shear_3d / units.hPa
 
+    all_list = []
     voxel_dict = {key: [] for key in bins_dict.keys()}
     for key in bins_dict.keys():
         print('working on pressure level: ', bins[key])
@@ -134,6 +135,7 @@ def extract(mask_image, image_ts, clavrx_path):
             # 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))
+            all_list.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
     voxel_dict_df = {}
@@ -141,8 +143,13 @@ def extract(mask_image, image_ts, clavrx_path):
         df = pd.DataFrame(v, columns=["pressure", "lat", "lon", "horz_wind_shear", "static_stability", "horz_wind_speed", "vert_wind_shear"])
         voxel_dict_df[k] = df
 
+    # Create a DataFrame for all tuples
+    all_df = pd.DataFrame(all_list, columns=["pressure", "lat", "lon", "horz_wind_shear", "static_stability", "horz_wind_speed", "vert_wind_shear"])
+
     xr_dataset.close()
 
+    return all_df
+