From 50d55f65116e1ea46bd86357d715f9230a1cc411 Mon Sep 17 00:00:00 2001
From: Edward Hartnett <38856240+edwardhartnett@users.noreply.github.com>
Date: Thu, 10 Oct 2024 13:06:06 -0600
Subject: [PATCH] documentation for blending_fv3.py (#517)

* added docs to blending_fv3.py
* added requirements
---------
---
 docs/requirements.txt |  1 +
 ush/blending_fv3.py   | 38 ++++++++++++++++++++++++++++++++++++++
 2 files changed, 39 insertions(+)

diff --git a/docs/requirements.txt b/docs/requirements.txt
index 817841744..c0691fdcb 100644
--- a/docs/requirements.txt
+++ b/docs/requirements.txt
@@ -3,5 +3,6 @@ sphinx_rtd_theme
 docutils==0.16
 numpy
 netCDF4
+raymond
 xarray
 matplotlib
diff --git a/ush/blending_fv3.py b/ush/blending_fv3.py
index 374d6ea0f..41d9e56cf 100755
--- a/ush/blending_fv3.py
+++ b/ush/blending_fv3.py
@@ -1,9 +1,42 @@
+"""This script performs blending between regional and global weather
+forecast model restarts using the Fortran module raymond. The Raymond
+filter is a sixth-order tangent low-pass implicit filter and can be
+controlled via the cutoff length scale (Lx).
+
+"""
 import numpy as np
 from netCDF4 import Dataset
 import raymond
 import sys
 
 def check_file_nans(test_nc, vars_fg, vars_bg, name):
+    """Check for NaN values in specified variables of a netCDF file.
+
+    This function iterates over a list of variables and checks for NaN values in the provided
+    netCDF file. It prints the count of NaNs found for each variable and indicates whether
+    any NaNs were detected.
+
+    Again, if there are any NaNs found, I wanted to catch that here
+    instead of later when the model is running. I don't think there is
+    any reason to expect NaNs.
+
+    Parameters:
+    test_nc: Dataset
+    The test netCDF file containing the variables to be checked for NaN values.
+    vars_fg: list of str
+    A list of variable names from the regional model (foreground) to check.
+    vars_bg: list of str
+    A corresponding list of variable names from the global model (background) to check.
+    name: str
+    A string representing the context (e.g., 'glb' for global or 'reg' for regional)
+    to identify the source of the variables being checked.
+
+    Returns:
+    bool
+    Returns True if any NaN values are found in the specified variables;
+    otherwise, returns False.
+
+    """
     nans = False
     for (var_fg, var_bg) in zip(vars_fg, vars_bg):
         i = vars_fg.index(var_fg)
@@ -20,6 +53,11 @@ def check_file_nans(test_nc, vars_fg, vars_bg, name):
         return nans
 
 def err_check(err):
+    """
+    Check for error.
+
+    err: Error code.
+    """
     if err > 0:
         print(f"An error ocurred in {sys.argv[0]}. Blending failed!!!")
         print(f"err={err}")