use MeasurementPlot class in prototype script

Python_script/prototype.py

@@ -11,7 +11,7 @@ import climate_chamber
 import VNA
 import virtual_time
 import json
-
+import MeasurementPlot
 
 TEMPERATURE_STABLE = 0x1
 HUMIDITY_STABLE = 0x2
@@ -61,6 +61,8 @@ class Measurements:
         self.vna.create_new_trace("Trace3", "S21")
         self.vna.create_new_trace("Trace4", "S22")
 
+        self.measurement_plot = MeasurementPlot.MeasurementPlot()
+
     def perform_measurements(self):
         with open(self.output, mode='w', newline='') as csv_file:
             fieldnames = ['TIMESTAMP', 'TARGET_TEMPERATURE', 'READBACK_TEMPERATURE', 'TARGET_HUMIDITY',
@@ -71,7 +73,7 @@ class Measurements:
             writer = csv.DictWriter(csv_file, fieldnames=fieldnames)
             writer.writeheader()
             self.data_collection = []
-            self.subplot_tuple = plt.subplots(2, figsize=(12, 10))
+            plt.ion()
 
             with open(self.sweep_file) as file:
                 try:
@@ -136,8 +138,7 @@ class Measurements:
                                 print('Setpoint: ' + str(next_temp) + ' ' + str(next_hum) + ' | Temp: ' + data.temp +
                                       ' °C' + ' | Humid: ' + data.hum + '%'
                                       + ' | soaking read nr' + str(len(magnitudes_queue)))
-                                self.write_data(writer, next_temp, next_hum, data, self.cook_up_equi_indicator())#,
-                                                #data_frame, subplot_tuple)
+                                self.write_data(writer, next_temp, next_hum, data, self.cook_up_equi_indicator())
 
                                 if self.temperature_stable and self.humidity_stable and self.magnitude_stable and\
                                         self.phase_stable:
@@ -201,13 +202,13 @@ class Measurements:
 
         return MeasurementData(int(self.clock.time()), temp, hum, power, frequency, s11, s21, s12, s22)
 
-    def write_data(self, writer, target_temp, target_hum, data, equi_indicator):#, data_frame, subplot_tuple):
+    def write_data(self, writer, target_temp, target_hum, data, equi_indicator):
         measurement = {
             'TIMESTAMP': data.timestamp,
             'TARGET_TEMPERATURE': target_temp,
-            'READBACK_TEMPERATURE': data.temp,
+            'READBACK_TEMPERATURE': float(data.temp),
             'TARGET_HUMIDITY': target_hum,
-            'READBACK_HUMIDITY': data.hum,
+            'READBACK_HUMIDITY': float(data.hum),
             'RF_POWER': data.power,
             'RF_FREQUENCY': data.frequency,
             'EQUILIBRIUM_INDICATOR': equi_indicator,
@@ -223,7 +224,7 @@ class Measurements:
         writer.writerow(measurement)
         self.data_collection.append(measurement)
         data_frame = pd.DataFrame(self.data_collection)
-        plot_measurement(data_frame, False, False, self.subplot_tuple)
+        self.measurement_plot.draw(data_frame)
 
     def current_milli_time(self):
         return int(round(self.clock.time() * 1000))
@@ -282,63 +283,12 @@ class Measurements:
         return (target_hum-self.max_delta_hum <= float(readback_hum)) and \
                (float(readback_hum) <= target_hum+self.max_delta_hum)
 
-# plot a single measurement (phase, magnitude, temp and hum against time) from a pandas data frame
-def plot_measurement(data_frame, show_blocking_plot, save_pdf=True, subplot_tuple = (None, None)):
-    fig, ax1 = subplot_tuple
-    if fig is None or ax1 is None:
-        fig, ax1 = plt.subplots(2, figsize=(12, 10))
-
-    path_collection01 = ax1[0].scatter(data_frame.TIMESTAMP, data_frame.S21_PHASE, c='red', marker='<', label='Phase')
-    twin2_0 = ax1[0].twinx()
-    path_collection02 = twin2_0.scatter(data_frame.TIMESTAMP, data_frame.S21_MAGNITUDE, c='#3120E0', marker='4',
-                                        label='Magnitude')
-    twin3_0 = ax1[0].twinx()
-    twin3_0.spines['right'].set_position(('outward', 40))
-    path_collection03 = twin3_0.scatter(data_frame.TIMESTAMP, data_frame.EQUILIBRIUM_INDICATOR, c='black', marker=".",
-                                        label='Equilibrium_Indicator')
-    ax1[0].set_xlabel("TIMESTAMP")
-    ax1[0].set_ylabel("PHASE", color='red')
-    twin2_0.set_ylabel("MAGNITUDE", color='#3120E0')
-    twin3_0.set_ylabel("EQUILIBRIUM_INDICATOR", color='black')
-
-    ax1[0].grid(True, linestyle=":")
-    all_path_collections = [path_collection01, path_collection02, path_collection03]
-    labels = [pc.get_label() for pc in all_path_collections]
-    ax1[0].legend(all_path_collections, labels, loc='lower right')
-
-    path_collection11 = ax1[1].scatter(data_frame.TIMESTAMP, data_frame.READBACK_TEMPERATURE, c='blue', marker='p',
-                                       label="Temperature")
-    twin2_1 = ax1[1].twinx()
-    path_collection12 = twin2_1.scatter(data_frame.TIMESTAMP, data_frame.READBACK_HUMIDITY, c='green', marker="*",
-                                        label="Humidity")
-    twin3_1 = ax1[1].twinx()
-    twin3_1.spines['right'].set_position(('outward', 40))
-    path_collection13 = twin3_1.scatter(data_frame.TIMESTAMP, data_frame.EQUILIBRIUM_INDICATOR, c='black', marker=".",
-                                        label="Equilibrium_Indicator")
-    ax1[1].set_xlabel("TIMESTAMP")
-    ax1[1].set_ylabel("TEMPERATURE ", color='blue')
-    twin2_1.set_ylabel("HUMIDITY", color='green')
-    twin3_1.set_ylabel("EQUILIBRIUM_INDICATOR", color='black')
-    ax1[1].grid(True, linestyle=":")
-    all_path_collections = [path_collection11, path_collection12, path_collection13]
-    labels = [pc.get_label() for pc in all_path_collections]
-    ax1[1].legend(all_path_collections, labels, loc='lower right')
-
-    if save_pdf:
-        fig.savefig(output_basename + '_graph.pdf')
-
-    # turn interactive on if we are not supposed to block, otherwise turn it off
-    if show_blocking_plot:
-        plt.ioff()
-    else:
-        plt.ion()
-        fig.canvas.draw()
-        fig.canvas.flush_events()
-    plt.show()
-
 def plot_output(output_basename, show_blocking_plot, save_pdf=True):
     data_frame = pd.read_csv(output_basename+'.csv')
-    plot_measurement(data_frame, show_blocking_plot, save_pdf)
+    if show_blocking_plot:
+        plt.ioff()
+    plot = MeasurementPlot.MeasurementPlot()
+    plot.draw(data_frame, output_basename + '_graph.pdf')
     
 if __name__ == '__main__':
     parser = ArgumentParser()