partially successfully break the graph into parts

serial_plotter.py

@@ -26,9 +26,40 @@ class SerialPlotter:
         self.colors = ['blue', 'orange', 'green', 'yellow']
 
         # TODO: make the figure size an UI option and pass into the settings.json
-        self.fig, self.axs = plt.subplots(1, 1, figsize=(9, 6))
+        #self.fig, self.axs = plt.subplots(figsize=(9, 6))
-
+        # self.fig_1 = plt.figure(1)
-        self.fig.canvas.mpl_connect('close_event', self.event_close)
+        # self.axs_1 = plt.axes()
+        
+        # self.fig_2 = plt.figure(2)
+        # self.axs_2 = plt.axes()
+        if (self.sensors == 1) :
+            self.fig_1, self.axs_1 = plt.subplots()
+            
+            self.fig_1.canvas.mpl_connect('close_event', self.event_close)
+        elif (self.sensors == 2) :
+            self.fig_1, self.axs_1 = plt.subplots()
+            self.fig_2, self.axs_2 = plt.subplots()
+            
+            self.fig_1.canvas.mpl_connect('close_event', self.event_close)
+            self.fig_2.canvas.mpl_connect('close_event', self.event_close)
+        elif (self.sensors == 3) :
+            self.fig_1, self.axs_1 = plt.subplots()
+            self.fig_2, self.axs_2 = plt.subplots()
+            self.fig_3, self.axs_3 = plt.subplots()
+            
+            self.fig_1.canvas.mpl_connect('close_event', self.event_close)
+            self.fig_2.canvas.mpl_connect('close_event', self.event_close)
+            self.fig_3.canvas.mpl_connect('close_event', self.event_close)
+        elif (self.sensors == 4) :
+            self.fig_1, self.axs_1 = plt.subplots()
+            self.fig_2, self.axs_2 = plt.subplots()
+            self.fig_3, self.axs_3 = plt.subplots()
+            self.fig_4, self.axs_4 = plt.subplots()
+            
+            self.fig_1.canvas.mpl_connect('close_event', self.event_close)
+            self.fig_2.canvas.mpl_connect('close_event', self.event_close)
+            self.fig_3.canvas.mpl_connect('close_event', self.event_close)
+            self.fig_4.canvas.mpl_connect('close_event', self.event_close)   
 
         self.timeStamps = {}
         self.sensorsData = {}
@@ -40,7 +71,7 @@ class SerialPlotter:
 
     def event_close(self, event) -> None:
         """
-        Actiions need to be executed when the graph has closed. Start a new .csv file to get read for new graph and
+        Actions need to be executed when the graph has closed. Start a new .csv file to get read for new graph and
         bring back the UI, if exist
         """
         file = self.settings["file_name"]
@@ -48,7 +79,7 @@ class SerialPlotter:
         if self.parent:
             self.parent.Show()
 
-    def animation(self, t: int) -> None:
+    def animation_1(self, t) -> None:
         """
         render a frame of the animated graph. 
         """
@@ -61,44 +92,222 @@ class SerialPlotter:
             if len(ndata) > 0:
                 row = ndata[-1]
                 i = 0
-                while i < self.sensors:
+                if 0 < self.windowsize < len(self.timeStamps[i]):
-                    # shift all data left by 1 index, pop out the leftmost value, if windowsize is not 0 (infinite)
+                    self.timeStamps[i].pop(0)
-                    # TODO: make sure the two lists have the same size
+                    self.sensorsData[i].pop(0)
-                    # if self.windowsize > 0 and len(self.timeStamps[i]) > self.windowsize
-                    if 0 < self.windowsize < len(self.timeStamps[i]):
-                        self.timeStamps[i].pop(0)
-                        self.sensorsData[i].pop(0)
 
                     # self.timeStamps[i].append(datetime.now().strftime('%H:%M:%S'))  # version 1
 
                     # version 2, if we decide to go with this one change the list type to list[int] from list[str]
-                    self.timeStamps[i].append(str(self.timeElapsed))
+                self.timeStamps[i].append(str(self.timeElapsed))
 
-                    self.sensorsData[i].append(row[i])
+                self.sensorsData[i].append(row[i])
                     # plot a line
                     # round the number to scientific notation
-                    self.axs.plot(self.timeStamps[i], self.sensorsData[i], color=self.colors[i], 
+                self.axs_1.plot(self.timeStamps[i], self.sensorsData[i], color=self.colors[i], 
                                   label=f'sensor {i + 1}, latest: {np.format_float_scientific(self.sensorsData[i][-1], precision = 2)} $\Omega$')
-                    
                     # TODO: make the font size an UI option from the settings.json
                     # Make the font size of values placed on x-axis and y-aixs equal to 16 (large enough)
-                    plt.xticks(fontsize = max(self.fontSize, 10))
+                #plt.xticks(fontsize = max(self.fontSize, 10))
-                    plt.yticks(fontsize = max(self.fontSize, 10))
+                #plt.yticks(fontsize = max(self.fontSize, 10))
+                self.axs_1.tick_params(axis='both', which='both', labelsize=max(self.fontSize, 10))
                     
                     # make the label of two axes large enough
-                    self.axs.set_xlabel('Time (seconds)', fontsize = max(self.fontSize, 10))
+                self.axs_1.set_xlabel('Time (seconds)', fontsize = max(self.fontSize, 10))
-                    self.axs.set_ylabel(u'Resistance ($\Omega$)', fontsize = max(self.fontSize, 10))
+                self.axs_1.set_ylabel(u'Resistance ($\Omega$)', fontsize = max(self.fontSize, 10))
-                    i += 1
+                #i += 1
             self.timeElapsed += 1  # increment time
 
             # Acknowledgement: https://stackoverflow.com/a/13589144 (can you imagine how long it took me to find this before ChatGPT?)
-            handles, labels = self.axs.get_legend_handles_labels()
+            #handles, labels = self.axs.get_legend_handles_labels()
+            #by_label = dict(zip(labels, handles))
+            #self.axs.legend(by_label.values(), by_label.keys(), loc='best', fontsize = max(self.fontSize, 10)) # Make the legend on graph large enough
+            
+            # handles, labels = self.axs_1.get_legend_handles_labels()
+            # by_label = dict(zip(labels, handles))
+            # self.axs_1.legend(by_label.values(), by_label.keys(), loc='best', fontsize = max(self.fontSize, 10))
+            
+            handles, labels = plt.gca().get_legend_handles_labels()
             by_label = dict(zip(labels, handles))
-            self.axs.legend(by_label.values(), by_label.keys(), loc='best', fontsize = max(self.fontSize, 10)) # Make the legend on graph large enough
+            plt.legend(by_label.values(), by_label.keys(), loc='best', fontsize = max(self.fontSize, 10))
         except:
             traceback.print_exc()
 
-    def plotting(self) -> FuncAnimation:
+    def animation_2(self, t: int) -> None:
+        """
+        render a frame of the animated graph. 
+        """
+        # Some of them copied from the old code, so even Eric may not 100% know what they do. ChatGPT may give a better explaination
+        try:
+            plt.cla()  # clear previous frame
+            # read the last line from the .csv file, the data start from the second column so omit index #0
+            file = open(self.settings["file_name"], "r")
+            ndata = np.array([np.asarray(line.split(", ")[1:], dtype=np.float32) for line in islice(file, 1, None)])#read from the second row of the file ignoring headers
+            if len(ndata) > 0:
+                row = ndata[-1]
+                i = 1
+                if 0 < self.windowsize < len(self.timeStamps[i]):
+                    self.timeStamps[i].pop(0)
+                    self.sensorsData[i].pop(0)
+
+                    # self.timeStamps[i].append(datetime.now().strftime('%H:%M:%S'))  # version 1
+
+                    # version 2, if we decide to go with this one change the list type to list[int] from list[str]
+                self.timeStamps[i].append(str(self.timeElapsed))
+
+                self.sensorsData[i].append(row[i])
+                    # plot a line
+                    # round the number to scientific notation
+                self.axs_2.plot(self.timeStamps[i], self.sensorsData[i], color=self.colors[i], 
+                                  label=f'sensor {i + 1}, latest: {np.format_float_scientific(self.sensorsData[i][-1], precision = 2)} $\Omega$')
+                    # TODO: make the font size an UI option from the settings.json
+                    # Make the font size of values placed on x-axis and y-aixs equal to 16 (large enough)
+                #self.axs_2.set_xticks(fontsize = max(self.fontSize, 10))
+                #self.axs_2.set_yticks(fontsize = max(self.fontSize, 10))
+                self.axs_2.tick_params(axis='both', which='both', labelsize=max(self.fontSize, 10))
+                    
+                    # make the label of two axes large enough
+                self.axs_2.set_xlabel('Time (seconds)', fontsize = max(self.fontSize, 10))
+                self.axs_2.set_ylabel(u'Resistance ($\Omega$)', fontsize = max(self.fontSize, 10))
+                #i += 1
+            self.timeElapsed += 1  # increment time
+
+            # Acknowledgement: https://stackoverflow.com/a/13589144 (can you imagine how long it took me to find this before ChatGPT?)
+            #handles, labels = self.axs.get_legend_handles_labels()
+            #by_label = dict(zip(labels, handles))
+            #self.axs.legend(by_label.values(), by_label.keys(), loc='best', fontsize = max(self.fontSize, 10)) # Make the legend on graph large enough
+            
+            # handles, labels = self.axs_2.get_legend_handles_labels()
+            # by_label = dict(zip(labels, handles))
+            # self.axs_2.legend(by_label.values(), by_label.keys(), loc='best', fontsize = max(self.fontSize, 10))
+            handles, labels = plt.gca().get_legend_handles_labels()
+            by_label = dict(zip(labels, handles))
+            plt.legend(by_label.values(), by_label.keys(), loc='best', fontsize = max(self.fontSize, 10))
+        except:
+            traceback.print_exc()
+            
+    def animation_3(self, t: int) -> None:
+        """
+        render a frame of the animated graph. 
+        """
+        # Some of them copied from the old code, so even Eric may not 100% know what they do. ChatGPT may give a better explaination
+        try:
+            plt.cla()  # clear previous frame
+            # read the last line from the .csv file, the data start from the second column so omit index #0
+            file = open(self.settings["file_name"], "r")
+            ndata = np.array([np.asarray(line.split(", ")[1:], dtype=np.float32) for line in islice(file, 1, None)])#read from the second row of the file ignoring headers
+            if len(ndata) > 0:
+                row = ndata[-1]
+                i = 2
+                if 0 < self.windowsize < len(self.timeStamps[i]):
+                    self.timeStamps[i].pop(0)
+                    self.sensorsData[i].pop(0)
+
+                    # self.timeStamps[i].append(datetime.now().strftime('%H:%M:%S'))  # version 1
+
+                    # version 2, if we decide to go with this one change the list type to list[int] from list[str]
+                self.timeStamps[i].append(str(self.timeElapsed))
+
+                self.sensorsData[i].append(row[i])
+                    # plot a line
+                    # round the number to scientific notation
+                self.axs_3.plot(self.timeStamps[i], self.sensorsData[i], color=self.colors[i], 
+                                  label=f'sensor {i + 1}, latest: {np.format_float_scientific(self.sensorsData[i][-1], precision = 2)} $\Omega$')
+                    # TODO: make the font size an UI option from the settings.json
+                    # Make the font size of values placed on x-axis and y-aixs equal to 16 (large enough)
+                #self.axs_2.set_xticks(fontsize = max(self.fontSize, 10))
+                #self.axs_2.set_yticks(fontsize = max(self.fontSize, 10))
+                self.axs_3.tick_params(axis='both', which='both', labelsize=max(self.fontSize, 10))
+                    
+                    # make the label of two axes large enough
+                self.axs_3.set_xlabel('Time (seconds)', fontsize = max(self.fontSize, 10))
+                self.axs_3.set_ylabel(u'Resistance ($\Omega$)', fontsize = max(self.fontSize, 10))
+                #i += 1
+            self.timeElapsed += 1  # increment time
+
+            # Acknowledgement: https://stackoverflow.com/a/13589144 (can you imagine how long it took me to find this before ChatGPT?)
+            #handles, labels = self.axs.get_legend_handles_labels()
+            #by_label = dict(zip(labels, handles))
+            #self.axs.legend(by_label.values(), by_label.keys(), loc='best', fontsize = max(self.fontSize, 10)) # Make the legend on graph large enough
+            
+            # handles, labels = self.axs_2.get_legend_handles_labels()
+            # by_label = dict(zip(labels, handles))
+            # self.axs_2.legend(by_label.values(), by_label.keys(), loc='best', fontsize = max(self.fontSize, 10))
+            handles, labels = plt.gca().get_legend_handles_labels()
+            by_label = dict(zip(labels, handles))
+            plt.legend(by_label.values(), by_label.keys(), loc='best', fontsize = max(self.fontSize, 10))
+        except:
+            traceback.print_exc()
+            
+    def animation_4(self, t: int) -> None:
+        """
+        render a frame of the animated graph. 
+        """
+        # Some of them copied from the old code, so even Eric may not 100% know what they do. ChatGPT may give a better explaination
+        try:
+            plt.cla()  # clear previous frame
+            # read the last line from the .csv file, the data start from the second column so omit index #0
+            file = open(self.settings["file_name"], "r")
+            ndata = np.array([np.asarray(line.split(", ")[1:], dtype=np.float32) for line in islice(file, 1, None)])#read from the second row of the file ignoring headers
+            if len(ndata) > 0:
+                row = ndata[-1]
+                i = 3
+                if 0 < self.windowsize < len(self.timeStamps[i]):
+                    self.timeStamps[i].pop(0)
+                    self.sensorsData[i].pop(0)
+
+                    # self.timeStamps[i].append(datetime.now().strftime('%H:%M:%S'))  # version 1
+
+                    # version 2, if we decide to go with this one change the list type to list[int] from list[str]
+                self.timeStamps[i].append(str(self.timeElapsed))
+
+                self.sensorsData[i].append(row[i])
+                    # plot a line
+                    # round the number to scientific notation
+                self.axs_4.plot(self.timeStamps[i], self.sensorsData[i], color=self.colors[i], 
+                                  label=f'sensor {i + 1}, latest: {np.format_float_scientific(self.sensorsData[i][-1], precision = 2)} $\Omega$')
+                    # TODO: make the font size an UI option from the settings.json
+                    # Make the font size of values placed on x-axis and y-aixs equal to 16 (large enough)
+                #self.axs_2.set_xticks(fontsize = max(self.fontSize, 10))
+                #self.axs_2.set_yticks(fontsize = max(self.fontSize, 10))
+                self.axs_4.tick_params(axis='both', which='both', labelsize=max(self.fontSize, 10))
+                    
+                    # make the label of two axes large enough
+                self.axs_4.set_xlabel('Time (seconds)', fontsize = max(self.fontSize, 10))
+                self.axs_4.set_ylabel(u'Resistance ($\Omega$)', fontsize = max(self.fontSize, 10))
+                #i += 1
+            self.timeElapsed += 1  # increment time
+
+            # Acknowledgement: https://stackoverflow.com/a/13589144 (can you imagine how long it took me to find this before ChatGPT?)
+            #handles, labels = self.axs.get_legend_handles_labels()
+            #by_label = dict(zip(labels, handles))
+            #self.axs.legend(by_label.values(), by_label.keys(), loc='best', fontsize = max(self.fontSize, 10)) # Make the legend on graph large enough
+            
+            # handles, labels = self.axs_2.get_legend_handles_labels()
+            # by_label = dict(zip(labels, handles))
+            # self.axs_2.legend(by_label.values(), by_label.keys(), loc='best', fontsize = max(self.fontSize, 10))
+            handles, labels = plt.gca().get_legend_handles_labels()
+            by_label = dict(zip(labels, handles))
+            plt.legend(by_label.values(), by_label.keys(), loc='best', fontsize = max(self.fontSize, 10))
+        except:
+            traceback.print_exc()
+            
+    def plotting_1(self) -> FuncAnimation:
         """ animate the dynamic plot """
-        ani = FuncAnimation(self.fig, self.animation, blit=False, interval=self.delay * 1000, save_count=60)
+        ani_1 = FuncAnimation(self.fig_1, self.animation_1, blit=False, interval=self.delay * 1000, save_count=60)
-        return ani
+        return ani_1
+
+    def plotting_2(self) -> FuncAnimation:
+        """ animate the dynamic plot """
+        ani_2 = FuncAnimation(self.fig_2, self.animation_2, blit=False, interval=self.delay * 1000, save_count=60)
+        return ani_2
+        
+    def plotting_3(self) -> FuncAnimation:
+        """ animate the dynamic plot """
+        ani_3 = FuncAnimation(self.fig_3, self.animation_3, blit=False, interval=self.delay * 1000, save_count=60)
+        return ani_3
+    
+    def plotting_4(self) -> FuncAnimation:
+        """ animate the dynamic plot """
+        ani_4 = FuncAnimation(self.fig_3, self.animation_3, blit=False, interval=self.delay * 1000, save_count=60)
+        return ani_4

test.py

@@ -5,6 +5,7 @@ from multiprocessing import *
 import os, sys, json, wx, importlib, warnings
 import serial.tools.list_ports
 import numpy as np
+import matplotlib.pyplot as plt
 
 warnings.filterwarnings("ignore", category=DeprecationWarning)
 
@@ -18,7 +19,7 @@ if '_PYIBoot_SPLASH' in os.environ and importlib.util.find_spec("pyi_splash"):
 HEIGHT = 800
 WIDTH = 800
 
-global ani, t1
+#global ani, t1
 
 # TODO: add the option to change the font size of the graph
 def main():
@@ -76,6 +77,8 @@ def gen_settings(resistors, input_voltage, port, filename, window_size, font_siz
     settings["file_name"] = filename
     settings["delay"] = delay
     settings["font_size"] = frame.fontSize.GetValue()
+    global sensors
+    sensors = len(settings['sensor_ports']) 
     # TODO: get the input voltage from the UI and save it to the settings.json
     open(name, 'w').write(json.dumps(settings, indent=4))
     open(filename, "a", newline="", encoding="utf-8")
@@ -86,7 +89,7 @@ def run(e):
     run the read_arduino.py and Serial Plotter in parallel, but the plotter is in the main thread and other one can be in a separate thread, since
     matplot's FuncAnimation doesn't like to not be in the main thread (Big L for them..)
     """
-    global ani, t1  # the variables to call the plotter and the read_arduino.py, we want them to get tossed around between functions and even programs
+    global ani_1, t1, ani_2, ani_3, ani_4  # the variables to call the plotter and the read_arduino.py, we want them to get tossed around between functions and even programs
     main()
     if 't1' in globals():
         t1.terminate()  # end the previous serial reads, if there is any
@@ -97,8 +100,23 @@ def run(e):
     # run the plotter. Note that we should not put the plotter class, or function, in another process since
     # matplot's FuncAnimation doesn't like that
     plotter = SerialPlotter(frame)
-    ani = plotter.plotting()
+    if (sensors == 1) :
-    plotter.fig.show()
+        ani_1 = plotter.plotting_1()
+    elif (sensors == 2):
+        ani_1 = plotter.plotting_1()
+        ani_2 = plotter.plotting_2()
+    elif (sensors == 3) :
+        ani_1 = plotter.plotting_1()
+        ani_2 = plotter.plotting_2()
+        ani_3 = plotter.plotting_3()
+    elif (sensors == 4) :
+        ani_1 = plotter.plotting_1()
+        ani_2 = plotter.plotting_2()
+        ani_3 = plotter.plotting_3()
+        ani_4 = plotter.plotting_4()
+    #plotter.fig_1.show()
+    #plotter.fig_2.show()
+    plt.show()
 
     frame.btLaunch.SetLabelText("Plot")  # change the text on the "launch" button to "plot"
     if not frame.show_msg.GetValue():