Cleanup auto data selection

Author: Jaeyoung-Lim

Tools/parametric_model/generate_parametric_model.py

@@ -77,39 +77,35 @@ def start_model_estimation(config, log_path, data_selection="none", plot=False):
                     "directory and models/__init__.py?".format(model_class)
         raise AttributeError(error_str)
 
-    visual_dataframe_selector_config_dict = {
-    "x_axis_col": "timestamp",
-    "sub_plt1_data": ["q0", "q1", "q2", "q3"],
-    "sub_plt2_data": ["u0", "u1", "u2", "u3"],
-    "sub_plt3_data": []
-    }
-
-    if data_handler.estimate_forces == True:
-        visual_dataframe_selector_config_dict["sub_plt3_data"].append("fisher_information_force")
-
-    if data_handler.estimate_moments == True:
-        visual_dataframe_selector_config_dict["sub_plt3_data"].append("fisher_information_rot")
-
     model.load_dataframes(data_df)
     model.prepare_regression_matrices()
     model.compute_fisher_information()
 
     # Interactive data selection
     if data_selection=="interactive":
         from visual_dataframe_selector.data_selector import select_visual_data
-        model.data_df = select_visual_data(model.data_df,visual_dataframe_selector_config_dict)
-        model.n_samples = model.data_df.shape[0]
-    # Automatic data selection (WIP)
-    elif data_selection=="auto":
+        visual_dataframe_selector_config_dict = {
+        "x_axis_col": "timestamp",
+        "sub_plt1_data": ["q0", "q1", "q2", "q3"],
+        "sub_plt2_data": ["u0", "u1", "u2", "u3"],
+        "sub_plt3_data": []
+        }
+
+        if data_handler.estimate_forces == True:
+            visual_dataframe_selector_config_dict["sub_plt3_data"].append("fisher_information_force")
+
+        if data_handler.estimate_moments == True:
+            visual_dataframe_selector_config_dict["sub_plt3_data"].append("fisher_information_rot")
+
+        model.load_dataframes(select_visual_data(model.data_df,visual_dataframe_selector_config_dict))
+    elif data_selection=="auto":     # Automatic data selection (WIP)
         from active_dataframe_selector.data_selector import ActiveDataSelector
         # The goal is to identify automatically the most relevant parts of a log.
         # Currently the draft is designed to choose the most informative 10% of the logs with regards to
         # force and moment parameters. This threshold is currently not validated at all and the percentage
         # can vary drastically from log to log. 
-
         data_selector = ActiveDataSelector(model.data_df)
-        model.data_df = data_selector.select_dataframes(10)
-        model.n_samples = model.data_df.shape[0]
+        model.load_dataframes(data_selector.select_dataframes(10))
 
     model.estimate_model()