From 9629fc320357da7b6d7782978aa5bd5ed67b676c Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Wed, 29 May 2024 13:17:36 -0500
Subject: [PATCH] snapshot...
---
modules/contrail/util.py | 30 +++++++++++++++++++-----------
1 file changed, 19 insertions(+), 11 deletions(-)
diff --git a/modules/contrail/util.py b/modules/contrail/util.py
index 720037b2..e26af9a8 100644
--- a/modules/contrail/util.py
+++ b/modules/contrail/util.py
@@ -111,7 +111,8 @@ def extract(mask_image, image_ts, clavrx_path):
all_list = []
voxel_dict = {key: [] for key in bins_dict.keys()}
for key in bins_dict.keys():
- print('working on pressure level: ', bins[key])
+ press_level = bins[key]
+ print('working on pressure level: ', press_level)
for c_idx in bins_dict[key]:
press = contrail_press[c_idx]
lat = contrail_lats[c_idx]
@@ -119,34 +120,41 @@ def extract(mask_image, image_ts, clavrx_path):
if lon < 0: # Match GFS convention
lon += 360.0
- horz_shear_value = horz_shear_3d.interp(Pressure=press, Longitude=lon, Latitude=lat, method='nearest')
- static_value = static_3d.interp(Pressure=press, Longitude=lon, Latitude=lat, method='nearest')
- 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')
+ horz_shear_value = horz_shear_3d.interp(Pressure=press, Longitude=lon, Latitude=lat, method='nearest').item(0)
+ 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)
# tmp = horz_shear_3d.sel(Pressure=press, method='nearest')
# 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))
+ 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))
# 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", "lat", "lon", "horz_wind_shear", "static_stability", "horz_wind_speed", "vert_wind_shear"])
+ df = pd.DataFrame(v, columns=["pressure_level", "pressure", "lat", "lon", "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", "lat", "lon", "horz_wind_shear", "static_stability", "horz_wind_speed", "vert_wind_shear"])
+ all_df = pd.DataFrame(all_list, columns=["pressure_level", "pressure", "lat", "lon", "horz_shear_deform", "static_stability", "horz_wind_speed", "vert_wind_shear"])
xr_dataset.close()
return all_df
-def analyze(dataFrame):
- pass
+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
+
+ print("Mean:", mean)
+ print("Std deviation:", stddev)
--
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