From ce3f9443a98cb129d13af2875c64defad220d7dc Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Wed, 29 May 2024 13:41:20 -0500
Subject: [PATCH] snapshot...
---
modules/contrail/util.py | 29 ++++++++++-------------------
1 file changed, 10 insertions(+), 19 deletions(-)
diff --git a/modules/contrail/util.py b/modules/contrail/util.py
index e1b09b2b..7db8ddec 100644
--- a/modules/contrail/util.py
+++ b/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
--
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