docs for chgres_cold2fv3.py

docs/requirements.txt

@@ -2,7 +2,6 @@ sphinxcontrib-bibtex
 sphinx_rtd_theme
 docutils==0.16
 numpy
-xarray
-matplotlib
 netCDF4
+xarray
 matplotlib

ush/chgres_cold2fv3.py

@@ -1,3 +1,13 @@
+"""The purpose of this code is to convert a cold start file from
+chgres_cube into a FV3 warm start (restart) file, enabling data
+blending operators that require a warm start format. The code performs
+essential transformations, including wind rotation and vertical
+remapping of atmospheric scalars and winds, ensuring the cold start
+data aligns with the FV3 model's restart format. Reason: The global 6
+h background from chgres_cube is a cold start and must be converted to
+a warm start file to be able to blend with the 1 h EnKF restarts.
+
+"""
 import numpy as np
 from netCDF4 import Dataset
 import remap_dwinds
@@ -6,6 +16,29 @@
 import sys
 
 def nan_check(arr, name, check_id):
+ """NAN check function.
+
+ This function checks a given array for NaN (Not a Number) values,
+ counts the total number of NaNs, and prints their indices if any
+ are found. It helps identify problematic data in the array that
+ may disrupt further processing.
+
+ After all nan_checks are done, if there is at least one nan then
+ the code fails. I put a note right after the vertical remapping of
+ the wind that says "Perform a NaN check - sometimes will get NaNs
+ at this point". I don't think it should fail. I think it was more
+ of an issue in development and I kept the check there just to be
+ safe.
+
+ Parameters:
+ arr: numpy array to be checked for NaN values.
+ name: string used to label the array in the output for identification.
+ check_id: integer or string identifier to differentiate between multiple checks in the output.
+
+ Returns:
+ nan_count: integer representing the total number of NaN values found in the array.
+
+ """
  nan_count = 0
  nan_count = np.sum(np.isnan(arr))
  print(f"coldstartwinds({check_id}) nan_count({name}): {nan_count}")