From ffcd06fe92353ff95a2323c951ece564559989d3 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Wed, 12 Jun 2024 11:00:17 -0500
Subject: [PATCH] snapshot...
---
modules/contrail/util.py | 60 ++++++++++++++++++++--------------------
1 file changed, 30 insertions(+), 30 deletions(-)
diff --git a/modules/contrail/util.py b/modules/contrail/util.py
index c37dc5c2..36c07d8a 100644
--- a/modules/contrail/util.py
+++ b/modules/contrail/util.py
@@ -7,9 +7,9 @@ import xarray as xr
import pandas as pd
import rasterio
from PIL import Image
-import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import h5py
+from util.GFSDataset import GFSData
from util.util import get_grid_values_all
from util.gfs_reader import get_volume, volume_np_to_xr
from util.geos_nav import GEOSNavigation
@@ -48,8 +48,8 @@ def get_contrail_mask_image(image, thresh=0.157):
def extract(mask_image, image_ts, clavrx_path):
# The GFS file closest to image_ts
gfs_file, _, _ = gfs_files.get_file(image_ts)
- xr_dataset = xr.open_dataset(gfs_file)
+ # Open the CLAVRx file
clvrx_h5f = h5py.File(clavrx_path, 'r')
cloud_top_press = get_grid_values_all(clvrx_h5f, 'cld_press_acha').flatten()
clvrx_lons = get_grid_values_all(clvrx_h5f, 'longitude').flatten()
@@ -78,30 +78,32 @@ def extract(mask_image, image_ts, clavrx_path):
lon_range = [np.min(contrail_lons), np.max(contrail_lons)]
lat_range = [np.min(contrail_lats), np.max(contrail_lats)]
- uwind_3d = get_volume(xr_dataset, 'u-wind', 'm s-1', lon_range=lon_range, lat_range=lat_range)
- vwind_3d = get_volume(xr_dataset, 'v-wind', 'm s-1', lon_range=lon_range, lat_range=lat_range)
- temp_3d = get_volume(xr_dataset, 'temperature', 'degK', lon_range=lon_range, lat_range=lat_range)
- rh_3d = get_volume(xr_dataset, 'rh', '%', lon_range=lon_range, lat_range=lat_range)
-
- uwind_3d = uwind_3d.transpose('Pressure', 'Latitude', 'Longitude')
- vwind_3d = vwind_3d.transpose('Pressure', 'Latitude', 'Longitude')
- temp_3d = temp_3d.transpose('Pressure', 'Latitude', 'Longitude')
- rh_3d = rh_3d.transpose('Pressure', 'Latitude', 'Longitude')
-
- 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 add 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, lat_range=lat_range)
- vert_shear_3d = vert_shear_3d / units.hPa
-
- # Form a new Dataset for interpolation below
- da_dct = {'horz_shear_3d': horz_shear_3d, 'static_3d': static_3d, 'horz_wind_spd_3d': horz_wind_spd_3d,
- 'vert_shear_3d': vert_shear_3d, 'temp_3d': temp_3d, 'rh_3d': rh_3d}
- xr_ds = xr.Dataset(da_dct)
+ # create an instance of the file data accessor and initialize the region of interest
+ with GFSData(gfs_file, lon_range=lon_range, lat_range=lat_range) as gfs_dataset:
+ uwind_3d = gfs_dataset.get_volume('u-wind', 'm s-1')
+ vwind_3d = gfs_dataset.get_volume('v-wind', 'm s-1')
+ temp_3d = gfs_dataset.get_volume('temperature', 'degK')
+ rh_3d = gfs_dataset.get_volume('rh', '%')
+
+ uwind_3d = uwind_3d.transpose('Pressure', 'Latitude', 'Longitude')
+ vwind_3d = vwind_3d.transpose('Pressure', 'Latitude', 'Longitude')
+ temp_3d = temp_3d.transpose('Pressure', 'Latitude', 'Longitude')
+ rh_3d = rh_3d.transpose('Pressure', 'Latitude', 'Longitude')
+
+ 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 add 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 = gfs_dataset.volume_np_to_xr(vert_shear_3d, ['Pressure', 'Latitude', 'Longitude'])
+ vert_shear_3d = vert_shear_3d / units.hPa
+
+ # Form a new Dataset for interpolation below
+ da_dct = {'horz_shear_3d': horz_shear_3d, 'static_3d': static_3d, 'horz_wind_spd_3d': horz_wind_spd_3d,
+ 'vert_shear_3d': vert_shear_3d, 'temp_3d': temp_3d, 'rh_3d': rh_3d}
+ xr_ds = xr.Dataset(da_dct)
all_list = []
levels_dict = {press_bins[key]: [] for key in bins_dict.keys()}
@@ -147,13 +149,11 @@ def extract(mask_image, image_ts, clavrx_path):
columns=["pressure_level", "pressure", "lat", "lon", "temperature", "relative_humidity",
"horz_shear_deform", "static_stability", "horz_wind_speed", "vert_wind_shear"])
- xr_dataset.close()
-
return all_df
-def analyze(dataFrame, column, value):
- result_df = dataFrame[dataFrame[column] == value] # get rows where column has a certain value
+def analyze(pd_df, column, value):
+ result_df = pd_df[pd_df[column] == value] # get rows where column has a certain value
mean = result_df.mean() # calculate mean for other columns
stddev = result_df.std() # calculate standard deviation for other columns
--
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