MoV code clean up

README.md

@@ -276,6 +276,22 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
 
 This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification.
 
+## Disclaimer
+This document was prepared as an account of work sponsored by an
+agency of the United States government. Neither the United States government
+nor Lawrence Livermore National Security, LLC, nor any of their employees
+makes any warranty, expressed or implied, or assumes any legal liability or
+responsibility for the accuracy, completeness, or usefulness of any
+information, apparatus, product, or process disclosed, or represents that its
+use would not infringe privately owned rights. Reference herein to any specific
+commercial product, process, or service by trade name, trademark, manufacturer,
+or otherwise does not necessarily constitute or imply its endorsement,
+recommendation, or favoring by the United States government or Lawrence
+Livermore National Security, LLC. The views and opinions of authors expressed
+herein do not necessarily state or reflect those of the United States
+government or Lawrence Livermore National Security, LLC, and shall not be used
+for advertising or product endorsement purposes.
+
 
 
 

pcmdi_metrics/variability_mode/lib/calc_stat.py

@@ -27,22 +27,51 @@ def calc_stats_save_dict(
     debug=False,
 ):
     """
-    NOTE: Calculate statistics and save numbers to dictionary for JSON.
-    Input
-    - mode: [str] name of variability mode
-    - dict_head: [dict] subset of dictionary
-    - eof: [2d field] linear regressed eof pattern (eof domain)
-    - eof_lr: [2d field] linear regressed eof pattern (global)
-    - slope: [2d field] slope from above linear regression (bring it here to calculate rmsc)
-    - pc: [1d field] principle component time series
-    - stdv_pc: [float] standard deviation of principle component time series
-    - frac: [1 number field] fraction of explained variance
-    - eof_obs: [2d field] eof pattern over subdomain from observation
-    - eof_lr_obs: [2d field] eof pattern over globe (linear regressed) from observation
-    - stdv_pc_obs: [float] standard deviation of principle component time series of observation
-    - obs_compare: [bool] calculate statistics against given observation (default=True)
-    - method: [string] 'eof' or 'cbf'
-    - debug: [bool]
+    Calculate statistics and save results to a dictionary for JSON output.
+
+    This function computes various statistics based on the provided model and observational
+    data, and stores the results in a dictionary format suitable for JSON serialization.
+
+    Parameters
+    ----------
+    mode : str
+        The name of the variability mode.
+    dict_head : dict
+        A subset of the dictionary to which results will be added.
+    model_ds : xr.Dataset
+        The dataset containing model data.
+    model_data_var : str
+        The variable name in the model dataset.
+    eof : xr.Dataset
+        The linear regressed EOF pattern for the EOF domain (2D field).
+    eof_lr : xr.Dataset
+        The linear regressed EOF pattern for the global domain (2D field).
+    pc : array-like
+        The principal component time series (1D field).
+    stdv_pc : float
+        The standard deviation of the principal component time series.
+    frac : float
+        The fraction of explained variance.
+    regions_specs : dict, optional
+        Specifications for regions, if applicable. Default is None.
+    obs_ds : xr.Dataset, optional
+        The dataset containing observational data. Default is None.
+    eof_obs : optional
+        The EOF pattern over the subdomain from observations (2D field). Default is None.
+    eof_lr_obs : optional
+        The linear regressed EOF pattern over the globe from observations (2D field). Default is None.
+    stdv_pc_obs : float, optional
+        The standard deviation of the principal component time series of observations. Default is None.
+    obs_compare : bool, optional
+        Whether to calculate statistics against the given observations. Default is True.
+    method : str, optional
+        The method to use, either 'eof' or 'cbf'. Default is 'eof'.
+    debug : bool, optional
+        A flag to enable debugging output. Default is False.
+
+    Returns
+    -------
+    None
     """
 
     # Add to dictionary for json output
@@ -60,20 +89,14 @@ def calc_stats_save_dict(
     # Note: '_glo' indicates statistics calculated over global domain
     # . . . . . . . . . . . . . . . . . . . . . . . . .
     if obs_compare:
-        # ref_grid_global = get_grid(eof_lr_obs)
         ref_grid_global = get_grid(obs_ds)
         # Regrid (interpolation, model grid to ref grid)
         debug_print("regrid (global) start", debug)
-        # eof_model_global = eof_lr.regrid(eof_lr,
-        #    ref_grid_global, regridTool="regrid2", mkCyclic=True
-        # )
-        # eof_model_global = regrid(eof_lr, ref_grid_global)
         eof_model_global = regrid(
             model_ds, data_var=model_data_var, target_grid=ref_grid_global
         )[model_data_var]
         debug_print("regrid end", debug)
         # Extract subdomain
-        # eof_model = eof_model_global(region_subdomain)
         eof_model = region_subset(eof_model_global, mode, regions_specs=regions_specs)
 
         # Spatial correlation weighted by area ('generate' option for weights)
@@ -86,9 +109,7 @@ def calc_stats_save_dict(
             # Double check for arbitrary sign control
             if cor < 0:
                 debug_print("eof pattern pcor < 0, flip sign!", debug)
-                # variables_to_flip_sign = [eof, pc, eof_lr, eof_model_global, eof_model]
-                # for variable in variables_to_flip_sign:
-                #    variable *= -1
+
                 eof.values = eof.values * -1
                 pc.values = pc.values * -1
                 eof_lr.values = eof_lr.values * -1