Plotting only part of csv if csv has more than 1000 plot points

src/csv_threading.py

@@ -61,16 +61,21 @@ class ExecCSVThread(Thread):
         return self._stop_event.is_set()
 
     def execute_sequence(self, array, delay, parent, controller):
+        # Initialize plot
+        ui_print("Initializing plots...")
+        figure, avx_lines = display_plot(parent)
+
         # main execution method of the class
         # runs through array with times and desired fields and commands test bench accordingly
 
         # array format: [time (s), xField (T), yField (T), zField (T)]
+        ui_print("Initializing cage...")
         self.cage_dev.idle()  # sets outputs on PSUs to 0 and Arduino pins to LOW before starting
         t_zero = time.time()  # set reference time for start of run
 
         # Check if everything is properly connected:
-        all_connected = (parent.xy_override.get() or g.CAGE_DEVICE.psu1 is not None) and\
-                        (parent.z_override.get() or g.CAGE_DEVICE.psu2 is not None) and\
+        all_connected = (parent.xy_override.get() or g.CAGE_DEVICE.psu1 is not None) and \
+                        (parent.z_override.get() or g.CAGE_DEVICE.psu2 is not None) and \
                         (parent.arduino_override.get() or g.CAGE_DEVICE.arduino is not None)
         compensate_field = parent.compensated_field_var.get()
 
@@ -80,9 +85,7 @@ class ExecCSVThread(Thread):
             messagebox.showwarning("Device Error!", "Required devices are not present, sequence aborted.")
             return
 
-        # Initialize plot
-        figure, avx_lines = display_plot(parent)
-
+        ui_print("Starting csv replay...")
         i = 0  # index of the current array row
         while i < len(array):
             if self.stopped or g.exit_flag:
@@ -113,10 +116,14 @@ class ExecCSVThread(Thread):
 
                 # Update figure
                 try:
-                    for j in range(4):
-                        avx_lines[j].set_data([t, t], [0, 1])
-                    # print("The next line might crash the programm, outcomment if necessary")
-                    parent.plot_canvas.draw()  # equivalent to matplotlib.show()
+                    if abs(t - target_t) < 0.2:
+                        for j in range(4):
+                            avx_lines[j].set_data([t, t], [0, 1])
+                        # print("The next line might crash the programm, outcomment if necessary")
+                        parent.plot_canvas.draw()  # equivalent to matplotlib.show()
+                    else:
+                        ui_print("Update rate of plot slows down field generation and is thus skipped.")
+
                 except DeviceAccessError as e:
                     ui_print("Failed to update figure: ", e)
 
@@ -125,7 +132,6 @@ class ExecCSVThread(Thread):
             elif t <= target_t - delay - 0.02:  # is there enough time to sleep before the next row?
                 time.sleep(delay)  # sleep to give other threads time to run
 
-
         ui_print("Sequence executed, powering down channels.")
 
 
@@ -152,7 +158,7 @@ def check_array_ok(array):
             data_point = array[row_idx, i + 1]  # extract data for this axis from array
             if data_point > max_val or data_point < min_val:
                 # Out of bounds
-                warnings.append({'row': row_idx+1, 'axis': g.AXIS_NAMES[i]})
+                warnings.append({'row': row_idx + 1, 'axis': g.AXIS_NAMES[i]})
 
     # show warning pop-up if values are exceeding limits
     nr_warnings = len(warnings)
@@ -194,7 +200,7 @@ def display_plot(parent):  # create plot of fixed size (pixels) from array
                  axes[2].axvline(x=0, color="r"), axes[3].axvline(x=0, color="r")]
     # Show new plot
     parent.plot_canvas = FigureCanvasTkAgg(figure, parent.plot_frame)  # create canvas to draw figure on
-    #print("The next line might crash the programm, outcomment if necessary")
+    # print("The next line might crash the programm, outcomment if necessary")
     parent.plot_canvas.draw()  # equivalent to matplotlib.show()
     parent.plot_canvas.get_tk_widget().grid(row=0, column=0, sticky="nesw")  # place canvas in the UI
 
@@ -204,8 +210,8 @@ def display_plot(parent):  # create plot of fixed size (pixels) from array
 def plot_field_sequence(array, width, height):  # create plot of fixed size (pixels) from array
     # ToDo (optional): polar plots, plots of angle...
     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
+    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
 
     # noinspection PyTypeChecker,SpellCheckingInspection
     axes = figure.subplots(4, sharex=True, sharey=False, gridspec_kw={'hspace': 0.4})  # make subplots with shared axes
@@ -215,8 +221,18 @@ def plot_field_sequence(array, width, height):  # create plot of fixed size (pix
     # modify data to show instantaneous jumps in field to reflect test bench operation
     new_array = np.array([[0, 0, 0, 0, 0]], dtype=float)  # initialize modified array, zeros to show start from no field
 
+    length = len(array[:, 0])
+    max_length = 1000
+    if length > max_length:
+        n = round(length/max_length)  # plot every n-th element
+        ui_print("Array contains {:d} elements more than the maximum amount of {:d}. "
+                 "To improve display only every {:d} element is plotted!"
+                 .format(length, max_length, n))
+    else:
+        n = 1  # plot every element
+
     last_values = [0, 0, 0, 0]  # [x,y,z, rr] field values / rot rate from last data point (zero here)
-    for row in array[:, 0:5]:  # go through each row in the original array
+    for row in array[::n, 0:5]:  # go through each row in the original array
         # create extra datapoint at current timestamp, with field values from last to create a "step" in the plot:
         new_array = np.append(new_array, [[row[0], *last_values]], axis=0)
         new_array = np.append(new_array, [row], axis=0)  # add actual datapoint for current timestamp