snapshot...

modules/contrail/util.py

@@ -70,20 +70,6 @@ def extract(mask_image, image_ts, clavrx_path):
     for bin_num in np.unique(binned_indexes):
         bins_dict[bin_num] = np.where(binned_indexes == bin_num)[0]
 
-    # # This does point by point computation of model parameters for each contrail pixel
-    # voxel_dict = {key: [] for key in bins_dict.keys()}
-    # for key in bins_dict.keys():
-    #     print('working on pressure level: ', bins[key])
-    #     for c_idx in bins_dict[key]:
-    #         lon = contrail_lons[c_idx]
-    #         lat = contrail_lats[c_idx]
-    #         press = contrail_press[c_idx]
-    #
-    #         wind_3d = get_voxel_s(xr_dataset, ['u-wind','v-wind'], lon, lat, press)
-    #         if wind_3d is not None:
-    #             voxel_dict[key].append(wind_3d)
-    # ------------------------------------------------------------------------------------
-
     # This section will compute model parameters in bulk for the region then pull for each contrail pixel
     lon_range = [np.min(contrail_lons), np.max(contrail_lons)]
     lat_range = [np.min(contrail_lats), np.max(contrail_lats)]
@@ -124,22 +110,28 @@ def extract(mask_image, image_ts, clavrx_path):
             static_value = static_3d.interp(Pressure=press, Longitude=lon, Latitude=lat, method='nearest').item(0)
             horz_wind_spd_value = horz_wind_spd_3d.interp(Pressure=press, Longitude=lon, Latitude=lat, method='nearest').item(0)
             vert_shear_value = vert_shear_3d.interp(Pressure=press, Longitude=lon, Latitude=lat, method='nearest').item(0)
+            temp_value = temp_3d.interp(Pressure=press, Longitude=lon, Latitude=lat, method='nearest').item(0)
+            rh_value = rh_3d.interp(Pressure=press, Longitude=lon, Latitude=lat, method='nearest').item(0)
 
             # tmp = horz_shear_3d.sel(Pressure=press, method='nearest')
             # tmp = tmp.sel(Longitude=lon, Latitude=lat, method='nearest')
 
-            voxel_dict[key].append((press_level, press, lat, lon, horz_shear_value, static_value, horz_wind_spd_value, vert_shear_value))
-            all_list.append((press_level, press, lat, lon, horz_shear_value, static_value, horz_wind_spd_value, vert_shear_value))
+            voxel_dict[key].append((press_level, press, lat, lon, temp_value, rh_value, horz_shear_value, static_value, horz_wind_spd_value, vert_shear_value))
+            all_list.append((press_level, press, lat, lon, temp_value, rh_value, 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 = {}
     for k, v in voxel_dict.items():
         print(k, len(v))
-        df = pd.DataFrame(v, columns=["pressure_level", "pressure", "lat", "lon", "horz_shear_deform", "static_stability", "horz_wind_speed", "vert_wind_shear"])
+        df = pd.DataFrame(v,
+                          columns=["pressure_level", "pressure", "lat", "lon", "temperature", "relative_humidity",
+                                   "horz_shear_deform", "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_level", "pressure", "lat", "lon", "horz_shear_deform", "static_stability", "horz_wind_speed", "vert_wind_shear"])
+    all_df = pd.DataFrame(all_list,
+                          columns=["pressure_level", "pressure", "lat", "lon", "temperature", "relative_humidity",
+                                   "horz_shear_deform", "static_stability", "horz_wind_speed", "vert_wind_shear"])
 
     xr_dataset.close()
 
@@ -148,7 +140,6 @@ def extract(mask_image, image_ts, clavrx_path):
 
 def analyze(dataFrame, column, value):
     result_df = dataFrame[dataFrame[column] == value]  # get rows where column has a certain value
-    print(result_df.head())
 
     mean = result_df.mean()  # calculate mean for other columns
     stddev = result_df.std()  # calculate standard deviation for other columns