From abd2c65f432270370fb51b9c1d4d39e7b09c0ced Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Tue, 28 May 2024 13:03:32 -0500
Subject: [PATCH] snapshot...
---
modules/contrail/util.py | 12 ++++++++++--
1 file changed, 10 insertions(+), 2 deletions(-)
diff --git a/modules/contrail/util.py b/modules/contrail/util.py
index bb1e7d43..4ab55fb9 100644
--- a/modules/contrail/util.py
+++ b/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()
--
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