snapshot...

modules/contrail/util.py

@@ -4,6 +4,7 @@ from datetime import timezone
 
 import numpy as np
 import xarray as xr
+import pandas as pd
 import rasterio
 from PIL import Image
 import matplotlib.pyplot as plt
@@ -108,8 +109,10 @@ def extract(mask_image, image_ts, clavrx_path):
     horz_shear_3d = shearing_deformation(uwind_3d, vwind_3d)
     static_3d = static_stability(temp_3d.coords['Pressure'] * units.hPa, temp_3d)
     horz_wind_spd_3d = wind_speed(uwind_3d, vwind_3d)
+
+    # This one's a bit more work: `first_derivative` only returns a ndarray with no units, so we use the
+    # helper function to create a DataArray and units via metpy's pint support
     vert_shear_3d = first_derivative(horz_wind_spd_3d, axis=0, x=temp_3d.coords['Pressure'])
-    print(horz_wind_spd_3d.shape, vert_shear_3d.shape)
     vert_shear_3d = volume_np_to_xr(vert_shear_3d, ['Pressure', 'Latitude', 'Longitude'], lon_range=[lon_range[0], lon_range[1]], lat_range=[lat_range[0], lat_range[1]])
     vert_shear_3d = vert_shear_3d * units.meter / (units.second * units.hPa)
 
@@ -126,7 +129,12 @@ 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')
 
-            voxel_dict[key].append((horz_shear_value, static_value, horz_wind_spd_value, vert_shear_value))
+            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
+    voxel_dict_df = {}
+    for k, v in voxel_dict.items():
+        voxel_dict_df[k] = pd.DataFrame(v, columns=["press", "lat", "lon", "horz_shear_value", "static_value", "horz_wind_spd_value", "vert_shear_value"])
 
     xr_dataset.close()