From 7965ac52b8b88bf14a0f8bac32b2f6c20d6eabce Mon Sep 17 00:00:00 2001
From: kaklise <kaklise@sandia.gov>
Date: Thu, 15 Feb 2024 15:27:31 -0800
Subject: [PATCH] removed group_data function

---
 pyomo/contrib/parmest/parmest.py | 39 --------------------------------
 1 file changed, 39 deletions(-)

diff --git a/pyomo/contrib/parmest/parmest.py b/pyomo/contrib/parmest/parmest.py
index 2e44b278423..83b24e39327 100644
--- a/pyomo/contrib/parmest/parmest.py
+++ b/pyomo/contrib/parmest/parmest.py
@@ -16,7 +16,6 @@
 # TODO: move use_mpisppy to a Pyomo configuration option
 
 # Redesign TODOS
-# TODO: remove group_data,this is only used in 1 example and should be handled by the user in Experiment
 # TODO: _treemaker is not used in parmest, the code could be moved to scenario tree if needed
 # TODO: Create additional built in objective expressions in an Enum class which includes SSE (see SSE function below)
 # TODO: Clean up the use of theta_names through out the code.  The Experiment returns the CUID of each theta and this can be used directly (instead of the name)
@@ -272,44 +271,6 @@ def _experiment_instance_creation_callback(
 
 #     return m
 
-
-# def group_data(data, groupby_column_name, use_mean=None):
-#     """
-#     Group data by scenario
-
-#     Parameters
-#     ----------
-#     data: DataFrame
-#         Data
-#     groupby_column_name: strings
-#         Name of data column which contains scenario numbers
-#     use_mean: list of column names or None, optional
-#         Name of data columns which should be reduced to a single value per
-#         scenario by taking the mean
-
-#     Returns
-#     ----------
-#     grouped_data: list of dictionaries
-#         Grouped data
-#     """
-#     if use_mean is None:
-#         use_mean_list = []
-#     else:
-#         use_mean_list = use_mean
-
-#     grouped_data = []
-#     for exp_num, group in data.groupby(data[groupby_column_name]):
-#         d = {}
-#         for col in group.columns:
-#             if col in use_mean_list:
-#                 d[col] = group[col].mean()
-#             else:
-#                 d[col] = list(group[col])
-#         grouped_data.append(d)
-
-#     return grouped_data
-
-
 def SSE(model):
     expr = sum((y - yhat) ** 2 for y, yhat in model.experiment_outputs.items())
     return expr