add distillation of diag file statistics to ocnanalvrfy task

scripts/exgdas_global_marine_analysis_vrfy.py

@@ -25,6 +25,7 @@
 import cartopy.crs as ccrs
 import gen_eva_obs_yaml
 import marine_eva_post
+import diag_statistics
 import subprocess
 from datetime import datetime, timedelta
 
@@ -324,3 +325,5 @@ def plot_zonal_slice(config):
     infile = os.path.join('evayamls', file)
     print('running eva on', infile)
     subprocess.run(['eva', infile], check=True)
+
+diag_statistics.get_diag_stats()

ush/diag_statistics.py

@@ -0,0 +1,87 @@
+#!/usr/bin/env python3
+# diag_statistics.py
+# in ocnanalvrfy task, untar and unzip ocean-relevant diag files from
+# atmospheric analysis, calculate means, and write them as ascii to arcdir
+import os
+import numpy as np
+import pandas as pd
+from netCDF4 import Dataset
+import tarfile
+import gzip
+import shutil
+
+variables = ['sst', 't']
+
+data = os.getenv('DATA')
+pdy = os.getenv('PDY')
+cyc = os.getenv('cyc')
+comout = os.getenv('COM_ATMOS_ANALYSIS')
+arcdir = os.getenv('ARCDIR')
+
+
+def get_diag_stats():
+
+    tarfilename = 'gdas.t' + cyc + 'z.cnvstat'
+
+    os.chdir(data)
+
+    for var in variables:
+
+        diagfilename = 'diag_conv_' + var + '_ges.' + pdy + cyc + '.nc4'
+        zipfilename = diagfilename + '.gz'
+        outfilename = 'cnvstat.' + var + '.gdas.' + pdy + cyc
+
+        with tarfile.open(os.path.join(comout, tarfilename), "r") as tf:
+            tf.extract(member=zipfilename)
+        with gzip.open(zipfilename, 'rb') as f_in:
+            with open(diagfilename, 'wb') as f_out:
+                shutil.copyfileobj(f_in, f_out)
+
+        # The following is lifted from PyGSI/src/pyGSI/diags.py
+
+        df_dict = {}
+
+        # Open netCDF file, store data into dictionary
+        with Dataset(diagfilename, mode='r') as f:
+            for var in f.variables:
+                # Station_ID and Observation_Class variables need
+                # to be converted from byte string to string
+                if var in ['Station_ID', 'Observation_Class']:
+                    diagdata = f.variables[var][:]
+                    diagdata = [i.tobytes(fill_value='/////', order='C')
+                                for i in diagdata]
+                    diagdata = np.array(
+                        [''.join(i.decode('UTF-8', 'ignore').split())
+                         for i in diagdata])
+                    df_dict[var] = diagdata
+
+                # Grab variables with only 'nobs' dimension
+                elif len(f.variables[var].shape) == 1:
+                    df_dict[var] = f.variables[var][:]
+
+        # Create pandas dataframe from dict
+        df = pd.DataFrame(df_dict)
+
+        # looking at the used observations, maybe variations on this later
+        df = df[df.Analysis_Use_Flag == 1.0]
+
+        # this is a crude filter to obtain surface t observations, hopefully
+        # catching at least most of the airborn observations and passing the
+        # sea observations (with much else)
+        # TODO: This needs to be refined, possibly using obtype
+        df = df[df.Station_Elevation <= 10.0]
+
+        meaned_cols = ['Obs_Minus_Forecast_unadjusted',
+                       'Obs_Minus_Forecast_adjusted']
+
+        df[meaned_cols] = np.abs(df[meaned_cols])
+
+        means = df.groupby(['Observation_Type'])[meaned_cols].mean()
+        mean_all = df[meaned_cols].mean()
+        mean_all.name = 'All'
+        means = pd.concat([means, mean_all.to_frame().T])
+        means.index.name = 'obtype'
+
+        outfilenamepath = os.path.join(arcdir, outfilename)
+        print('writing ', outfilenamepath)
+        means.to_csv(outfilenamepath)