From 0c2000b03747174f047ec1fa7c05f3afd7fff097 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Tue, 28 May 2024 14:00:38 -0500
Subject: [PATCH] snapshot...
---
modules/contrail/util.py | 5 +++--
1 file changed, 3 insertions(+), 2 deletions(-)
diff --git a/modules/contrail/util.py b/modules/contrail/util.py
index 4ab55fb9..a5c47e91 100644
--- a/modules/contrail/util.py
+++ b/modules/contrail/util.py
@@ -114,7 +114,7 @@ def extract(mask_image, image_ts, clavrx_path):
# 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'])
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)
+ vert_shear_3d = vert_shear_3d / units.hPa
voxel_dict = {key: [] for key in bins_dict.keys()}
for key in bins_dict.keys():
@@ -134,7 +134,8 @@ def extract(mask_image, image_ts, clavrx_path):
# 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"])
+ df = pd.DataFrame(v, columns=["press", "lat", "lon", "horz_shear_value", "static_value", "horz_wind_spd_value", "vert_shear_value"])
+ voxel_dict_df[k] = df
xr_dataset.close()
--
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