diff --git a/Tools/process_sensor_caldata.py b/Tools/process_sensor_caldata.py
index d8bc05d1df..e7cebafcf3 100644
--- a/Tools/process_sensor_caldata.py
+++ b/Tools/process_sensor_caldata.py
@@ -88,17 +88,17 @@ for d in data:
 
 # extract baro data
 sensor_instance = 0
+num_baros = 0
 for d in data:
     if d.name == 'sensor_baro':
         if sensor_instance == 0:
             sensor_baro_0 = d.data
             print('found baro 0 data')
+        num_baros = 1
         if sensor_instance == 1:
             sensor_baro_1 = d.data
             print('found baro 1 data')
-        if sensor_instance == 2:
-            sensor_baro_2 = d.data
-            print('found baro 2 data')
+        num_baros = 2
         sensor_instance = sensor_instance +1
 
 # open file to save plots to PDF
@@ -738,12 +738,65 @@ plt.figure(7,figsize=(20,13))
 plt.plot(sensor_baro_0['temperature'],100*sensor_baro_0['pressure']-100*median_pressure,'b')
 plt.plot(temp_resample,baro_0_x_resample,'r')
 plt.title('Baro 0 Bias vs Temperature')
-plt.ylabel('X bias (Pa)')
+plt.ylabel('Z bias (Pa)')
 plt.xlabel('temperature (degC)')
 plt.grid()
 
 pp.savefig()
 
+# define data dictionary of baro 1 thermal correction  parameters
+baro_1_params = {
+'TC_B1_ID':0,
+'TC_B1_TMIN':0.0,
+'TC_B1_TMAX':0.0,
+'TC_B1_TREF':0.0,
+'TC_B1_X0':0.0,
+'TC_B1_X1':0.0,
+'TC_B1_X2':0.0,
+'TC_B1_X3':0.0,
+'TC_B1_X4':0.0,
+'TC_B1_X5':0.0,
+'TC_B1_SCL':1.0,
+}
+
+if num_baros >= 2:
+
+	# curve fit the data for baro 0 corrections
+	baro_1_params['TC_B1_ID'] = int(np.median(sensor_baro_1['device_id']))
+
+	# find the min, max and reference temperature
+	baro_1_params['TC_B1_TMIN'] = np.amin(sensor_baro_1['temperature'])
+	baro_1_params['TC_B1_TMAX'] = np.amax(sensor_baro_1['temperature'])
+	baro_1_params['TC_B1_TREF'] = 0.5 * (baro_1_params['TC_B1_TMIN'] + baro_1_params['TC_B1_TMAX'])
+	temp_rel = sensor_baro_1['temperature'] - baro_1_params['TC_B1_TREF']
+	temp_rel_resample = np.linspace(baro_1_params['TC_B1_TMIN']-baro_1_params['TC_B1_TREF'], baro_1_params['TC_B1_TMAX']-baro_1_params['TC_B1_TREF'], 100)
+	temp_resample = temp_rel_resample + baro_1_params['TC_B1_TREF']
+
+	# fit data
+	median_pressure = np.median(sensor_baro_1['pressure']);
+	coef_baro_1_x = np.polyfit(temp_rel,100*(sensor_baro_1['pressure']-median_pressure),5) # convert from hPa to Pa
+	baro_1_params['TC_B1_X5'] = coef_baro_1_x[0]
+	baro_1_params['TC_B1_X4'] = coef_baro_1_x[1]
+	baro_1_params['TC_B1_X3'] = coef_baro_1_x[2]
+	baro_1_params['TC_B1_X2'] = coef_baro_1_x[3]
+	baro_1_params['TC_B1_X1'] = coef_baro_1_x[4]
+	baro_1_params['TC_B1_X0'] = coef_baro_1_x[5]
+	fit_coef_baro_1_x = np.poly1d(coef_baro_1_x)
+	baro_1_x_resample = fit_coef_baro_1_x(temp_rel_resample)
+
+	# baro 1 vs temperature
+	plt.figure(8,figsize=(20,13))
+
+	# draw plots
+	plt.plot(sensor_baro_1['temperature'],100*sensor_baro_1['pressure']-100*median_pressure,'b')
+	plt.plot(temp_resample,baro_1_x_resample,'r')
+	plt.title('Baro 1 Bias vs Temperature')
+	plt.ylabel('Z bias (Pa)')
+	plt.xlabel('temperature (degC)')
+	plt.grid()
+
+	pp.savefig()
+
 #################################################################################
 
 # close the pdf file
@@ -790,15 +843,25 @@ for key in key_list_accel:
     file.write("1"+"\t"+"1"+"\t"+key+"\t"+str(accel_2_params[key])+"\t"+type+"\n")
 
 # baro 0 corrections
-key_list_accel = list(baro_0_params.keys())
-key_list_accel.sort
-for key in key_list_accel:
+key_list_baro = list(baro_0_params.keys())
+key_list_baro.sort
+for key in key_list_baro:
     if key == 'TC_B0_ID':
         type = "6"
     else:
         type = "9"
     file.write("1"+"\t"+"1"+"\t"+key+"\t"+str(baro_0_params[key])+"\t"+type+"\n")
 
+# baro 1 corrections
+key_list_baro = list(baro_1_params.keys())
+key_list_baro.sort
+for key in key_list_baro:
+    if key == 'TC_B1_ID':
+        type = "6"
+    else:
+        type = "9"
+    file.write("1"+"\t"+"1"+"\t"+key+"\t"+str(baro_1_params[key])+"\t"+type+"\n")
+
 # gyro 0 corrections
 key_list_gyro = list(gyro_0_params.keys())
 key_list_gyro.sort()