delete some unnecessary logging info

dynamics_learning/dynamics_learning/testing/__init__.py

@@ -137,18 +137,13 @@ class Dataset:
             if date == "20240719_141438":
                 continue
             dates.append(date)
-
-        logger.info(dates)
-        self.most_recent_file = max(
-            dates
-        )  # FIXME this is not returning the most recent file
+        self.most_recent_file = max(dates)
         logger.info(f"Most recent file is set to {self.most_recent_file}.")
         return self.most_recent_file
 
     def _delete_files(self, files, most_recent_file: None):
         logger.info(f"Most recent file is {most_recent_file}.")
         for file in files:
-            logger.info(f"Checking {file.name}.")
             if most_recent_file:
                 if most_recent_file not in file.name:
                     local_file_path = Path(self.local_path + "/" + file.name)
@@ -190,7 +185,6 @@ class Dataset:
                     for file in files
                     if file.metadata_form()["Robot UUID"][0] == ROBOT_UUID
                 ]
-                logger.info(f"Filtered files: {filtered_files}")
                 if filtered_files == []:
                     logger.critical("No files found with given robot_uuid.")
                     sys.exit(1)
@@ -201,7 +195,6 @@ class Dataset:
                     for file in files
                     if file.metadata_form()["Robot UUID"][0] != ROBOT_UUID
                 ]
-                logger.info(f"Files to delete: {delete_files}")
 
                 if filtered_files:
                     # check for the most recent file
@@ -211,7 +204,6 @@ class Dataset:
 
                 # delete all files with wrong robot_uuid
                 if delete_files:
-                    logger.log("I think this needs fixing")
                     self._delete_files(delete_files, None)
                 return self.analysis_file
 
@@ -442,7 +434,7 @@ LSTM Implementation Output: Concluded
                     color=rwth_style.green,
                 )
 
-                axs[joint_number // num_col, joint_number % num_col].scatter(  # FIXME
+                axs[joint_number // num_col, joint_number % num_col].scatter(
                     t_meas[window_size - 1 :],
                     y_pred[:, joint_number],
                     label="Prediction based on command trajectory (NN)",