Changed csv plots to show instantaneous field changes

csv_threading.py

@@ -4,6 +4,7 @@
 # import packages:
 import time
 import pandas
+import numpy as np
 from threading import *
 from tkinter import messagebox
 import matplotlib.pyplot as plt
@@ -13,6 +14,7 @@ import User_Interface as ui
 import cage_func as func
 import globals as g
 
+import traceback  # ToDo: remove!
 
 class ExecCSVThread(Thread):
     # main class for executing a CSV sequence
@@ -28,7 +30,7 @@ class ExecCSVThread(Thread):
         self.__stop_event = Event()  # event which can be set to stop the thread execution if needed
 
     def run(self):  # called to start the execution of the thread
-        ui.ui_print("Starting Sequence Execution...")
+        ui.ui_print("\nStarting Sequence Execution...")
         self.execute_sequence(self.array, 0.1, self.parent, self.controller)  # run sequence
         # when the sequence has ended, reset buttons on the UI:
         if not g.exitFlag:  # main window is open
@@ -130,7 +132,6 @@ def check_array_ok(array):
 
 def plot_field_sequence(array, width, height):  # create plot of fixed size (pixels) from array
     # ToDo (optional): polar plots, plots of angle...
-    # ToDo (optional): show graphs as steps (as performed by test stand)
     fig_dpi = 100  # set figure resolution (dots per inch)
     px = 1/fig_dpi  # get pixel to inch size conversion
     figure = plt.Figure(figsize=(width*px, height*px), dpi=fig_dpi)  # create figure with correct size
@@ -140,9 +141,21 @@ def plot_field_sequence(array, width, height):  # create plot of fixed size (pix
 
     figure.suptitle("Magnetic Field Sequence")  # set figure title
 
-    t = array[:, 0]  # extract time column
+    # modify data to show instantaneous jumps in field to reflect test stand operation
+    new_array = np.array([[0, 0, 0, 0]], dtype=float)  # initialize modified array, zeros to show start from no fields
+
+    last_vals = [0, 0, 0]  # [x,y,z] field values from last data point (zero here), used to create step in data
+    for row in array[:, 0:4]:  # go through each row in the original array
+        # create extra datapoint at current timestamp, with field values from last, this creates "step" in plot:
+        new_array = np.append(new_array, [[row[0], *last_vals]], axis=0)
+        new_array = np.append(new_array, [row], axis=0)  # add actual datapoint for current timestamp
+        last_vals = row[1:4]  # save values from current timestamp for next
+    new_array = np.append(new_array, [[new_array[-1, 0], 0, 0, 0]], axis=0)  # append last datapoint with 0 fields
+
+    # extract data and plot:
+    t = new_array[:, 0]  # extract time column
     for i in [0, 1, 2]:  # go through all three axes
-        data = array[:, i + 1] * 1e6  # extract field column of this axis and convert to microtesla
+        data = new_array[:, i + 1] * 1e6  # extract field column of this axis and convert to microtesla
         max_val = g.AXES[i].max_comp_field[1] * 1e6  # get limits of achievable field
         min_val = g.AXES[i].max_comp_field[0] * 1e6
         plot = axes[i]  # get appropriate subplot