comments and code cleanup

Logging, and threading cleanup

csv_logging.py

@@ -9,9 +9,9 @@ from tkinter import filedialog
 from tkinter import messagebox
 import User_Interface as ui
 
-log_data = pd.DataFrame()  # pandas data frame containing logged data
+log_data = pd.DataFrame()  # pandas data frame containing the logged data, in-program representation of csv/excel data
 unsaved_data = False  # Bool to indicate if there is unsaved data, set to True each time a datapoint is logged
-zero_time = datetime.now()
+zero_time = datetime.now()  # set reference for timestamps in log file, reset when log_data is cleared and restarted
 
 # create dictionary with all value handles that could be logged
 # Key: String that is displayed in UI and column headers. Also serves as handle to access dictionary elements.
@@ -39,20 +39,23 @@ def triple_list(key_list):  # creates list with each entry of key_list tripled w
     return new_list
 
 
-def log_datapoint(key_list):  # ToDo: comments
-    global log_data
-    global unsaved_data
-    date = datetime.now().date()
-    time = datetime.now().strftime("%H:%M:%S,%f")
-    t = (datetime.now() - zero_time).total_seconds()
-    data = [[date, time, t]]
-    for key in key_list:
-        for axis in g.AXES:
-            data[0].append(getattr(axis, axis_data_dict[key]))  # get value
-    column_names = ["Date", "Time", "t (s)", *triple_list(key_list)]
-    new_row = pd.DataFrame(data, columns=column_names)
-    log_data = log_data.append(new_row, ignore_index=True)
-    unsaved_data = True
+def log_datapoint(key_list):  # logs a single row of data into the log_data DataFrame
+    # key_list determines what data is logged
+    global log_data  # get global dataframe with logged data
+    global unsaved_data  # get global variable that indicates if there is unsaved data
+    date = datetime.now().date()  # get current date
+    time = datetime.now().strftime("%H:%M:%S,%f")  # get string with current time in correct format
+    t = (datetime.now() - zero_time).total_seconds()  # calculate timestamp relative to the start of the logging
+    data = [[date, time, t]]  # initialize new data row with timestamps
+    for key in key_list:  # go through the list telling us what data to log
+        for axis in g.AXES:  # log this data for each axis
+            # get relevant value from the correct AXIS object and append to new data row:
+            data[0].append(getattr(axis, axis_data_dict[key]))
+
+    column_names = ["Date", "Time", "t (s)", *triple_list(key_list)]  # create list with the correct column headers
+    new_row = pd.DataFrame(data, columns=column_names)  # create data frame containing the new row
+    log_data = log_data.append(new_row, ignore_index=True)  # append the new data frame to the logged data
+    unsaved_data = True  # tell other program parts that there is now unsaved data
 
 
 def select_file():  # select a file to write logs to
@@ -90,4 +93,4 @@ def write_to_file(dataframe, filepath):
 
 def clear_logged_data():  # clears all logged data from data frame
     global log_data  # get global variable
-    log_data = pd.DataFrame()  # reset to an empty data frame, i.e. clear all logged data
+    log_data = pd.DataFrame()  # reset to an empty data frame, i.e. clearing all logged data