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Tools: fix errors in thermal calibration script

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This commit is contained in:
Paul Riseborough
2018-01-09 09:44:01 +11:00
committed by Lorenz Meier
parent d375880c4b
commit b2046ffd6b
1 changed files with 393 additions and 393 deletions
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786
Tools/process_sensor_caldata.py
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@@ -16,7 +16,7 @@ Data can be gathered using the following sequence:
1) Power up the board and set the TC_A_ENABLE, TC_B_ENABLE and TC_G_ENABLE parameters to 1 1) Power up the board and set the TC_A_ENABLE, TC_B_ENABLE and TC_G_ENABLE parameters to 1
2) Set all CAL_GYR and CAL_ACC parameters to defaults 2) Set all CAL_GYR and CAL_ACC parameters to defaults
3) Set the SYS_LOGGER parameter to 1 to use the new system logger 3) Set the SYS_LOGGER parameter to 1 to use the new system logger
4) Set the SDLOG_MODE parameter to 2, and SDLOG_PROFILE parameter to 2 to enable logging of sensor data for calibration and power off 4) Set the SDLOG_MODE parameter to 2, and SDLOG_PROFILE parameter to 4 to enable logging of sensor data for calibration and power off
5) Cold soak the board for 30 minutes 5) Cold soak the board for 30 minutes
6) Move to a warm dry, still air, constant pressure environment. 6) Move to a warm dry, still air, constant pressure environment.
7) Apply power for 45 minutes, keeping the board still. 7) Apply power for 45 minutes, keeping the board still.
@@ -24,7 +24,7 @@ Data can be gathered using the following sequence:
9) Open a terminal window in the Firmware/Tools directory and run the python calibration script script file: 'python process_sensor_caldata.py <full path name to .ulog file> 9) Open a terminal window in the Firmware/Tools directory and run the python calibration script script file: 'python process_sensor_caldata.py <full path name to .ulog file>
10) Power the board, connect QGC and load the parameter from the generated .params file onto the board using QGC. Due to the number of parameters, loading them may take some time. 10) Power the board, connect QGC and load the parameter from the generated .params file onto the board using QGC. Due to the number of parameters, loading them may take some time.
11) TODO - we need a way for user to reliably tell when parameters have all been changed and saved. 11) TODO - we need a way for user to reliably tell when parameters have all been changed and saved.
12) After parameters have finished loading, set SDLOG_MODE to 1 to re-enable normal logging and remove power. 12) After parameters have finished loading, set SDLOG_MODE and SDLOG_PROFILE to their respective values prior to step 4) and remove power.
13) Power the board and perform a normal gyro and accelerometer sensor calibration using QGC. The board must be repowered after this step before flying due to large parameter changes and the thermal compensation parameters only being read on startup. 13) Power the board and perform a normal gyro and accelerometer sensor calibration using QGC. The board must be repowered after this step before flying due to large parameter changes and the thermal compensation parameters only being read on startup.
Outputs thermal compensation parameters in a file named <inputfilename>.params which can be loaded onto the board using QGroundControl Outputs thermal compensation parameters in a file named <inputfilename>.params which can be loaded onto the board using QGroundControl
@@ -56,16 +56,16 @@ for d in data:
if sensor_instance == 0: if sensor_instance == 0:
sensor_gyro_0 = d.data sensor_gyro_0 = d.data
print('found gyro 0 data') print('found gyro 0 data')
num_gyros = 1 num_gyros = 1
if sensor_instance == 1: if sensor_instance == 1:
sensor_gyro_1 = d.data sensor_gyro_1 = d.data
print('found gyro 1 data') print('found gyro 1 data')
num_gyros = 2 num_gyros = 2
if sensor_instance == 2: if sensor_instance == 2:
sensor_gyro_2 = d.data sensor_gyro_2 = d.data
print('found gyro 2 data') print('found gyro 2 data')
num_gyros = 3 num_gyros = 3
sensor_instance = sensor_instance +1 sensor_instance = sensor_instance +1
# extract accel data # extract accel data
sensor_instance = 0 sensor_instance = 0
@@ -75,15 +75,15 @@ for d in data:
if sensor_instance == 0: if sensor_instance == 0:
sensor_accel_0 = d.data sensor_accel_0 = d.data
print('found accel 0 data') print('found accel 0 data')
num_accels = 1 num_accels = 1
if sensor_instance == 1: if sensor_instance == 1:
sensor_accel_1 = d.data sensor_accel_1 = d.data
print('found accel 1 data') print('found accel 1 data')
num_accels = 2 num_accels = 2
if sensor_instance == 2: if sensor_instance == 2:
sensor_accel_2 = d.data sensor_accel_2 = d.data
print('found accel 2 data') print('found accel 2 data')
num_accels = 3 num_accels = 3
sensor_instance = sensor_instance +1 sensor_instance = sensor_instance +1
# extract baro data # extract baro data
@@ -94,11 +94,11 @@ for d in data:
if sensor_instance == 0: if sensor_instance == 0:
sensor_baro_0 = d.data sensor_baro_0 = d.data
print('found baro 0 data') print('found baro 0 data')
num_baros = 1 num_baros = 1
if sensor_instance == 1: if sensor_instance == 1:
sensor_baro_1 = d.data sensor_baro_1 = d.data
print('found baro 1 data') print('found baro 1 data')
num_baros = 2 num_baros = 2
sensor_instance = sensor_instance +1 sensor_instance = sensor_instance +1
# open file to save plots to PDF # open file to save plots to PDF
@@ -133,72 +133,72 @@ gyro_0_params = {
# curve fit the data for gyro 0 corrections # curve fit the data for gyro 0 corrections
if num_gyros >= 1: if num_gyros >= 1:
gyro_0_params['TC_G0_ID'] = int(np.median(sensor_gyro_0['device_id'])) gyro_0_params['TC_G0_ID'] = int(np.median(sensor_gyro_0['device_id']))
# find the min, max and reference temperature # find the min, max and reference temperature
gyro_0_params['TC_G0_TMIN'] = np.amin(sensor_gyro_0['temperature']) gyro_0_params['TC_G0_TMIN'] = np.amin(sensor_gyro_0['temperature'])
gyro_0_params['TC_G0_TMAX'] = np.amax(sensor_gyro_0['temperature']) gyro_0_params['TC_G0_TMAX'] = np.amax(sensor_gyro_0['temperature'])
gyro_0_params['TC_G0_TREF'] = 0.5 * (gyro_0_params['TC_G0_TMIN'] + gyro_0_params['TC_G0_TMAX']) gyro_0_params['TC_G0_TREF'] = 0.5 * (gyro_0_params['TC_G0_TMIN'] + gyro_0_params['TC_G0_TMAX'])
temp_rel = sensor_gyro_0['temperature'] - gyro_0_params['TC_G0_TREF'] temp_rel = sensor_gyro_0['temperature'] - gyro_0_params['TC_G0_TREF']
temp_rel_resample = np.linspace(gyro_0_params['TC_G0_TMIN']-gyro_0_params['TC_G0_TREF'], gyro_0_params['TC_G0_TMAX']-gyro_0_params['TC_G0_TREF'], 100) temp_rel_resample = np.linspace(gyro_0_params['TC_G0_TMIN']-gyro_0_params['TC_G0_TREF'], gyro_0_params['TC_G0_TMAX']-gyro_0_params['TC_G0_TREF'], 100)
temp_resample = temp_rel_resample + gyro_0_params['TC_G0_TREF'] temp_resample = temp_rel_resample + gyro_0_params['TC_G0_TREF']
# fit X axis # fit X axis
coef_gyro_0_x = np.polyfit(temp_rel,sensor_gyro_0['x'],3) coef_gyro_0_x = np.polyfit(temp_rel,sensor_gyro_0['x'],3)
gyro_0_params['TC_G0_X3_0'] = coef_gyro_0_x[0] gyro_0_params['TC_G0_X3_0'] = coef_gyro_0_x[0]
gyro_0_params['TC_G0_X2_0'] = coef_gyro_0_x[1] gyro_0_params['TC_G0_X2_0'] = coef_gyro_0_x[1]
gyro_0_params['TC_G0_X1_0'] = coef_gyro_0_x[2] gyro_0_params['TC_G0_X1_0'] = coef_gyro_0_x[2]
gyro_0_params['TC_G0_X0_0'] = coef_gyro_0_x[3] gyro_0_params['TC_G0_X0_0'] = coef_gyro_0_x[3]
fit_coef_gyro_0_x = np.poly1d(coef_gyro_0_x) fit_coef_gyro_0_x = np.poly1d(coef_gyro_0_x)
gyro_0_x_resample = fit_coef_gyro_0_x(temp_rel_resample) gyro_0_x_resample = fit_coef_gyro_0_x(temp_rel_resample)
# fit Y axis # fit Y axis
coef_gyro_0_y = np.polyfit(temp_rel,sensor_gyro_0['y'],3) coef_gyro_0_y = np.polyfit(temp_rel,sensor_gyro_0['y'],3)
gyro_0_params['TC_G0_X3_1'] = coef_gyro_0_y[0] gyro_0_params['TC_G0_X3_1'] = coef_gyro_0_y[0]
gyro_0_params['TC_G0_X2_1'] = coef_gyro_0_y[1] gyro_0_params['TC_G0_X2_1'] = coef_gyro_0_y[1]
gyro_0_params['TC_G0_X1_1'] = coef_gyro_0_y[2] gyro_0_params['TC_G0_X1_1'] = coef_gyro_0_y[2]
gyro_0_params['TC_G0_X0_1'] = coef_gyro_0_y[3] gyro_0_params['TC_G0_X0_1'] = coef_gyro_0_y[3]
fit_coef_gyro_0_y = np.poly1d(coef_gyro_0_y) fit_coef_gyro_0_y = np.poly1d(coef_gyro_0_y)
gyro_0_y_resample = fit_coef_gyro_0_y(temp_rel_resample) gyro_0_y_resample = fit_coef_gyro_0_y(temp_rel_resample)
# fit Z axis # fit Z axis
coef_gyro_0_z = np.polyfit(temp_rel,sensor_gyro_0['z'],3) coef_gyro_0_z = np.polyfit(temp_rel,sensor_gyro_0['z'],3)
gyro_0_params['TC_G0_X3_2'] = coef_gyro_0_z[0] gyro_0_params['TC_G0_X3_2'] = coef_gyro_0_z[0]
gyro_0_params['TC_G0_X2_2'] = coef_gyro_0_z[1] gyro_0_params['TC_G0_X2_2'] = coef_gyro_0_z[1]
gyro_0_params['TC_G0_X1_2'] = coef_gyro_0_z[2] gyro_0_params['TC_G0_X1_2'] = coef_gyro_0_z[2]
gyro_0_params['TC_G0_X0_2'] = coef_gyro_0_z[3] gyro_0_params['TC_G0_X0_2'] = coef_gyro_0_z[3]
fit_coef_gyro_0_z = np.poly1d(coef_gyro_0_z) fit_coef_gyro_0_z = np.poly1d(coef_gyro_0_z)
gyro_0_z_resample = fit_coef_gyro_0_z(temp_rel_resample) gyro_0_z_resample = fit_coef_gyro_0_z(temp_rel_resample)
# gyro0 vs temperature # gyro0 vs temperature
plt.figure(1,figsize=(20,13)) plt.figure(1,figsize=(20,13))
# draw plots # draw plots
plt.subplot(3,1,1) plt.subplot(3,1,1)
plt.plot(sensor_gyro_0['temperature'],sensor_gyro_0['x'],'b') plt.plot(sensor_gyro_0['temperature'],sensor_gyro_0['x'],'b')
plt.plot(temp_resample,gyro_0_x_resample,'r') plt.plot(temp_resample,gyro_0_x_resample,'r')
plt.title('Gyro 0 Bias vs Temperature') plt.title('Gyro 0 Bias vs Temperature')
plt.ylabel('X bias (rad/s)') plt.ylabel('X bias (rad/s)')
plt.xlabel('temperature (degC)') plt.xlabel('temperature (degC)')
plt.grid() plt.grid()
# draw plots # draw plots
plt.subplot(3,1,2) plt.subplot(3,1,2)
plt.plot(sensor_gyro_0['temperature'],sensor_gyro_0['y'],'b') plt.plot(sensor_gyro_0['temperature'],sensor_gyro_0['y'],'b')
plt.plot(temp_resample,gyro_0_y_resample,'r') plt.plot(temp_resample,gyro_0_y_resample,'r')
plt.ylabel('Y bias (rad/s)') plt.ylabel('Y bias (rad/s)')
plt.xlabel('temperature (degC)') plt.xlabel('temperature (degC)')
plt.grid() plt.grid()
# draw plots # draw plots
plt.subplot(3,1,3) plt.subplot(3,1,3)
plt.plot(sensor_gyro_0['temperature'],sensor_gyro_0['z'],'b') plt.plot(sensor_gyro_0['temperature'],sensor_gyro_0['z'],'b')
plt.plot(temp_resample,gyro_0_z_resample,'r') plt.plot(temp_resample,gyro_0_z_resample,'r')
plt.ylabel('Z bias (rad/s)') plt.ylabel('Z bias (rad/s)')
plt.xlabel('temperature (degC)') plt.xlabel('temperature (degC)')
plt.grid() plt.grid()
pp.savefig() pp.savefig()
################################################################################# #################################################################################
@@ -229,72 +229,72 @@ gyro_1_params = {
# curve fit the data for gyro 1 corrections # curve fit the data for gyro 1 corrections
if num_gyros >= 2: if num_gyros >= 2:
gyro_1_params['TC_G1_ID'] = int(np.median(sensor_gyro_1['device_id'])) gyro_1_params['TC_G1_ID'] = int(np.median(sensor_gyro_1['device_id']))
# find the min, max and reference temperature # find the min, max and reference temperature
gyro_1_params['TC_G1_TMIN'] = np.amin(sensor_gyro_1['temperature']) gyro_1_params['TC_G1_TMIN'] = np.amin(sensor_gyro_1['temperature'])
gyro_1_params['TC_G1_TMAX'] = np.amax(sensor_gyro_1['temperature']) gyro_1_params['TC_G1_TMAX'] = np.amax(sensor_gyro_1['temperature'])
gyro_1_params['TC_G1_TREF'] = 0.5 * (gyro_1_params['TC_G1_TMIN'] + gyro_1_params['TC_G1_TMAX']) gyro_1_params['TC_G1_TREF'] = 0.5 * (gyro_1_params['TC_G1_TMIN'] + gyro_1_params['TC_G1_TMAX'])
temp_rel = sensor_gyro_1['temperature'] - gyro_1_params['TC_G1_TREF'] temp_rel = sensor_gyro_1['temperature'] - gyro_1_params['TC_G1_TREF']
temp_rel_resample = np.linspace(gyro_1_params['TC_G1_TMIN']-gyro_1_params['TC_G1_TREF'], gyro_1_params['TC_G1_TMAX']-gyro_1_params['TC_G1_TREF'], 100) temp_rel_resample = np.linspace(gyro_1_params['TC_G1_TMIN']-gyro_1_params['TC_G1_TREF'], gyro_1_params['TC_G1_TMAX']-gyro_1_params['TC_G1_TREF'], 100)
temp_resample = temp_rel_resample + gyro_1_params['TC_G1_TREF'] temp_resample = temp_rel_resample + gyro_1_params['TC_G1_TREF']
# fit X axis # fit X axis
coef_gyro_1_x = np.polyfit(temp_rel,sensor_gyro_1['x'],3) coef_gyro_1_x = np.polyfit(temp_rel,sensor_gyro_1['x'],3)
gyro_1_params['TC_G1_X3_0'] = coef_gyro_1_x[0] gyro_1_params['TC_G1_X3_0'] = coef_gyro_1_x[0]
gyro_1_params['TC_G1_X2_0'] = coef_gyro_1_x[1] gyro_1_params['TC_G1_X2_0'] = coef_gyro_1_x[1]
gyro_1_params['TC_G1_X1_0'] = coef_gyro_1_x[2] gyro_1_params['TC_G1_X1_0'] = coef_gyro_1_x[2]
gyro_1_params['TC_G1_X0_0'] = coef_gyro_1_x[3] gyro_1_params['TC_G1_X0_0'] = coef_gyro_1_x[3]
fit_coef_gyro_1_x = np.poly1d(coef_gyro_1_x) fit_coef_gyro_1_x = np.poly1d(coef_gyro_1_x)
gyro_1_x_resample = fit_coef_gyro_1_x(temp_rel_resample) gyro_1_x_resample = fit_coef_gyro_1_x(temp_rel_resample)
# fit Y axis # fit Y axis
coef_gyro_1_y = np.polyfit(temp_rel,sensor_gyro_1['y'],3) coef_gyro_1_y = np.polyfit(temp_rel,sensor_gyro_1['y'],3)
gyro_1_params['TC_G1_X3_1'] = coef_gyro_1_y[0] gyro_1_params['TC_G1_X3_1'] = coef_gyro_1_y[0]
gyro_1_params['TC_G1_X2_1'] = coef_gyro_1_y[1] gyro_1_params['TC_G1_X2_1'] = coef_gyro_1_y[1]
gyro_1_params['TC_G1_X1_1'] = coef_gyro_1_y[2] gyro_1_params['TC_G1_X1_1'] = coef_gyro_1_y[2]
gyro_1_params['TC_G1_X0_1'] = coef_gyro_1_y[3] gyro_1_params['TC_G1_X0_1'] = coef_gyro_1_y[3]
fit_coef_gyro_1_y = np.poly1d(coef_gyro_1_y) fit_coef_gyro_1_y = np.poly1d(coef_gyro_1_y)
gyro_1_y_resample = fit_coef_gyro_1_y(temp_rel_resample) gyro_1_y_resample = fit_coef_gyro_1_y(temp_rel_resample)
# fit Z axis # fit Z axis
coef_gyro_1_z = np.polyfit(temp_rel,sensor_gyro_1['z'],3) coef_gyro_1_z = np.polyfit(temp_rel,sensor_gyro_1['z'],3)
gyro_1_params['TC_G1_X3_2'] = coef_gyro_1_z[0] gyro_1_params['TC_G1_X3_2'] = coef_gyro_1_z[0]
gyro_1_params['TC_G1_X2_2'] = coef_gyro_1_z[1] gyro_1_params['TC_G1_X2_2'] = coef_gyro_1_z[1]
gyro_1_params['TC_G1_X1_2'] = coef_gyro_1_z[2] gyro_1_params['TC_G1_X1_2'] = coef_gyro_1_z[2]
gyro_1_params['TC_G1_X0_2'] = coef_gyro_1_z[3] gyro_1_params['TC_G1_X0_2'] = coef_gyro_1_z[3]
fit_coef_gyro_1_z = np.poly1d(coef_gyro_1_z) fit_coef_gyro_1_z = np.poly1d(coef_gyro_1_z)
gyro_1_z_resample = fit_coef_gyro_1_z(temp_rel_resample) gyro_1_z_resample = fit_coef_gyro_1_z(temp_rel_resample)
# gyro1 vs temperature # gyro1 vs temperature
plt.figure(2,figsize=(20,13)) plt.figure(2,figsize=(20,13))
# draw plots # draw plots
plt.subplot(3,1,1) plt.subplot(3,1,1)
plt.plot(sensor_gyro_1['temperature'],sensor_gyro_1['x'],'b') plt.plot(sensor_gyro_1['temperature'],sensor_gyro_1['x'],'b')
plt.plot(temp_resample,gyro_1_x_resample,'r') plt.plot(temp_resample,gyro_1_x_resample,'r')
plt.title('Gyro 1 Bias vs Temperature') plt.title('Gyro 1 Bias vs Temperature')
plt.ylabel('X bias (rad/s)') plt.ylabel('X bias (rad/s)')
plt.xlabel('temperature (degC)') plt.xlabel('temperature (degC)')
plt.grid() plt.grid()
# draw plots # draw plots
plt.subplot(3,1,2) plt.subplot(3,1,2)
plt.plot(sensor_gyro_1['temperature'],sensor_gyro_1['y'],'b') plt.plot(sensor_gyro_1['temperature'],sensor_gyro_1['y'],'b')
plt.plot(temp_resample,gyro_1_y_resample,'r') plt.plot(temp_resample,gyro_1_y_resample,'r')
plt.ylabel('Y bias (rad/s)') plt.ylabel('Y bias (rad/s)')
plt.xlabel('temperature (degC)') plt.xlabel('temperature (degC)')
plt.grid() plt.grid()
# draw plots # draw plots
plt.subplot(3,1,3) plt.subplot(3,1,3)
plt.plot(sensor_gyro_1['temperature'],sensor_gyro_1['z'],'b') plt.plot(sensor_gyro_1['temperature'],sensor_gyro_1['z'],'b')
plt.plot(temp_resample,gyro_1_z_resample,'r') plt.plot(temp_resample,gyro_1_z_resample,'r')
plt.ylabel('Z bias (rad/s)') plt.ylabel('Z bias (rad/s)')
plt.xlabel('temperature (degC)') plt.xlabel('temperature (degC)')
plt.grid() plt.grid()
pp.savefig() pp.savefig()
################################################################################# #################################################################################
@@ -325,72 +325,72 @@ gyro_2_params = {
# curve fit the data for gyro 2 corrections # curve fit the data for gyro 2 corrections
if num_gyros >= 3: if num_gyros >= 3:
gyro_2_params['TC_G2_ID'] = int(np.median(sensor_gyro_2['device_id'])) gyro_2_params['TC_G2_ID'] = int(np.median(sensor_gyro_2['device_id']))
# find the min, max and reference temperature # find the min, max and reference temperature
gyro_2_params['TC_G2_TMIN'] = np.amin(sensor_gyro_2['temperature']) gyro_2_params['TC_G2_TMIN'] = np.amin(sensor_gyro_2['temperature'])
gyro_2_params['TC_G2_TMAX'] = np.amax(sensor_gyro_2['temperature']) gyro_2_params['TC_G2_TMAX'] = np.amax(sensor_gyro_2['temperature'])
gyro_2_params['TC_G2_TREF'] = 0.5 * (gyro_2_params['TC_G2_TMIN'] + gyro_2_params['TC_G2_TMAX']) gyro_2_params['TC_G2_TREF'] = 0.5 * (gyro_2_params['TC_G2_TMIN'] + gyro_2_params['TC_G2_TMAX'])
temp_rel = sensor_gyro_2['temperature'] - gyro_2_params['TC_G2_TREF'] temp_rel = sensor_gyro_2['temperature'] - gyro_2_params['TC_G2_TREF']
temp_rel_resample = np.linspace(gyro_2_params['TC_G2_TMIN']-gyro_2_params['TC_G2_TREF'], gyro_2_params['TC_G2_TMAX']-gyro_2_params['TC_G2_TREF'], 100) temp_rel_resample = np.linspace(gyro_2_params['TC_G2_TMIN']-gyro_2_params['TC_G2_TREF'], gyro_2_params['TC_G2_TMAX']-gyro_2_params['TC_G2_TREF'], 100)
temp_resample = temp_rel_resample + gyro_2_params['TC_G2_TREF'] temp_resample = temp_rel_resample + gyro_2_params['TC_G2_TREF']
# fit X axis # fit X axis
coef_gyro_2_x = np.polyfit(temp_rel,sensor_gyro_2['x'],3) coef_gyro_2_x = np.polyfit(temp_rel,sensor_gyro_2['x'],3)
gyro_2_params['TC_G2_X3_0'] = coef_gyro_2_x[0] gyro_2_params['TC_G2_X3_0'] = coef_gyro_2_x[0]
gyro_2_params['TC_G2_X2_0'] = coef_gyro_2_x[1] gyro_2_params['TC_G2_X2_0'] = coef_gyro_2_x[1]
gyro_2_params['TC_G2_X1_0'] = coef_gyro_2_x[2] gyro_2_params['TC_G2_X1_0'] = coef_gyro_2_x[2]
gyro_2_params['TC_G2_X0_0'] = coef_gyro_2_x[3] gyro_2_params['TC_G2_X0_0'] = coef_gyro_2_x[3]
fit_coef_gyro_2_x = np.poly1d(coef_gyro_2_x) fit_coef_gyro_2_x = np.poly1d(coef_gyro_2_x)
gyro_2_x_resample = fit_coef_gyro_2_x(temp_rel_resample) gyro_2_x_resample = fit_coef_gyro_2_x(temp_rel_resample)
# fit Y axis # fit Y axis
coef_gyro_2_y = np.polyfit(temp_rel,sensor_gyro_2['y'],3) coef_gyro_2_y = np.polyfit(temp_rel,sensor_gyro_2['y'],3)
gyro_2_params['TC_G2_X3_1'] = coef_gyro_2_y[0] gyro_2_params['TC_G2_X3_1'] = coef_gyro_2_y[0]
gyro_2_params['TC_G2_X2_1'] = coef_gyro_2_y[1] gyro_2_params['TC_G2_X2_1'] = coef_gyro_2_y[1]
gyro_2_params['TC_G2_X1_1'] = coef_gyro_2_y[2] gyro_2_params['TC_G2_X1_1'] = coef_gyro_2_y[2]
gyro_2_params['TC_G2_X0_1'] = coef_gyro_2_y[3] gyro_2_params['TC_G2_X0_1'] = coef_gyro_2_y[3]
fit_coef_gyro_2_y = np.poly1d(coef_gyro_2_y) fit_coef_gyro_2_y = np.poly1d(coef_gyro_2_y)
gyro_2_y_resample = fit_coef_gyro_2_y(temp_rel_resample) gyro_2_y_resample = fit_coef_gyro_2_y(temp_rel_resample)
# fit Z axis # fit Z axis
coef_gyro_2_z = np.polyfit(temp_rel,sensor_gyro_2['z'],3) coef_gyro_2_z = np.polyfit(temp_rel,sensor_gyro_2['z'],3)
gyro_2_params['TC_G2_X3_2'] = coef_gyro_2_z[0] gyro_2_params['TC_G2_X3_2'] = coef_gyro_2_z[0]
gyro_2_params['TC_G2_X2_2'] = coef_gyro_2_z[1] gyro_2_params['TC_G2_X2_2'] = coef_gyro_2_z[1]
gyro_2_params['TC_G2_X1_2'] = coef_gyro_2_z[2] gyro_2_params['TC_G2_X1_2'] = coef_gyro_2_z[2]
gyro_2_params['TC_G2_X0_2'] = coef_gyro_2_z[3] gyro_2_params['TC_G2_X0_2'] = coef_gyro_2_z[3]
fit_coef_gyro_2_z = np.poly1d(coef_gyro_2_z) fit_coef_gyro_2_z = np.poly1d(coef_gyro_2_z)
gyro_2_z_resample = fit_coef_gyro_2_z(temp_rel_resample) gyro_2_z_resample = fit_coef_gyro_2_z(temp_rel_resample)
# gyro2 vs temperature # gyro2 vs temperature
plt.figure(3,figsize=(20,13)) plt.figure(3,figsize=(20,13))
# draw plots # draw plots
plt.subplot(3,1,1) plt.subplot(3,1,1)
plt.plot(sensor_gyro_2['temperature'],sensor_gyro_2['x'],'b') plt.plot(sensor_gyro_2['temperature'],sensor_gyro_2['x'],'b')
plt.plot(temp_resample,gyro_2_x_resample,'r') plt.plot(temp_resample,gyro_2_x_resample,'r')
plt.title('Gyro 2 Bias vs Temperature') plt.title('Gyro 2 Bias vs Temperature')
plt.ylabel('X bias (rad/s)') plt.ylabel('X bias (rad/s)')
plt.xlabel('temperature (degC)') plt.xlabel('temperature (degC)')
plt.grid() plt.grid()
# draw plots # draw plots
plt.subplot(3,1,2) plt.subplot(3,1,2)
plt.plot(sensor_gyro_2['temperature'],sensor_gyro_2['y'],'b') plt.plot(sensor_gyro_2['temperature'],sensor_gyro_2['y'],'b')
plt.plot(temp_resample,gyro_2_y_resample,'r') plt.plot(temp_resample,gyro_2_y_resample,'r')
plt.ylabel('Y bias (rad/s)') plt.ylabel('Y bias (rad/s)')
plt.xlabel('temperature (degC)') plt.xlabel('temperature (degC)')
plt.grid() plt.grid()
# draw plots # draw plots
plt.subplot(3,1,3) plt.subplot(3,1,3)
plt.plot(sensor_gyro_2['temperature'],sensor_gyro_2['z'],'b') plt.plot(sensor_gyro_2['temperature'],sensor_gyro_2['z'],'b')
plt.plot(temp_resample,gyro_2_z_resample,'r') plt.plot(temp_resample,gyro_2_z_resample,'r')
plt.ylabel('Z bias (rad/s)') plt.ylabel('Z bias (rad/s)')
plt.xlabel('temperature (degC)') plt.xlabel('temperature (degC)')
plt.grid() plt.grid()
pp.savefig() pp.savefig()
################################################################################# #################################################################################
@@ -421,75 +421,75 @@ accel_0_params = {
# curve fit the data for accel 0 corrections # curve fit the data for accel 0 corrections
if num_accels >= 1: if num_accels >= 1:
accel_0_params['TC_A0_ID'] = int(np.median(sensor_accel_0['device_id'])) accel_0_params['TC_A0_ID'] = int(np.median(sensor_accel_0['device_id']))
# find the min, max and reference temperature # find the min, max and reference temperature
accel_0_params['TC_A0_TMIN'] = np.amin(sensor_accel_0['temperature']) accel_0_params['TC_A0_TMIN'] = np.amin(sensor_accel_0['temperature'])
accel_0_params['TC_A0_TMAX'] = np.amax(sensor_accel_0['temperature']) accel_0_params['TC_A0_TMAX'] = np.amax(sensor_accel_0['temperature'])
accel_0_params['TC_A0_TREF'] = 0.5 * (accel_0_params['TC_A0_TMIN'] + accel_0_params['TC_A0_TMAX']) accel_0_params['TC_A0_TREF'] = 0.5 * (accel_0_params['TC_A0_TMIN'] + accel_0_params['TC_A0_TMAX'])
temp_rel = sensor_accel_0['temperature'] - accel_0_params['TC_A0_TREF'] temp_rel = sensor_accel_0['temperature'] - accel_0_params['TC_A0_TREF']
temp_rel_resample = np.linspace(accel_0_params['TC_A0_TMIN']-accel_0_params['TC_A0_TREF'], accel_0_params['TC_A0_TMAX']-accel_0_params['TC_A0_TREF'], 100) temp_rel_resample = np.linspace(accel_0_params['TC_A0_TMIN']-accel_0_params['TC_A0_TREF'], accel_0_params['TC_A0_TMAX']-accel_0_params['TC_A0_TREF'], 100)
temp_resample = temp_rel_resample + accel_0_params['TC_A0_TREF'] temp_resample = temp_rel_resample + accel_0_params['TC_A0_TREF']
# fit X axis # fit X axis
correction_x = sensor_accel_0['x'] - np.median(sensor_accel_0['x']) correction_x = sensor_accel_0['x'] - np.median(sensor_accel_0['x'])
coef_accel_0_x = np.polyfit(temp_rel,correction_x,3) coef_accel_0_x = np.polyfit(temp_rel,correction_x,3)
accel_0_params['TC_A0_X3_0'] = coef_accel_0_x[0] accel_0_params['TC_A0_X3_0'] = coef_accel_0_x[0]
accel_0_params['TC_A0_X2_0'] = coef_accel_0_x[1] accel_0_params['TC_A0_X2_0'] = coef_accel_0_x[1]
accel_0_params['TC_A0_X1_0'] = coef_accel_0_x[2] accel_0_params['TC_A0_X1_0'] = coef_accel_0_x[2]
accel_0_params['TC_A0_X0_0'] = coef_accel_0_x[3] accel_0_params['TC_A0_X0_0'] = coef_accel_0_x[3]
fit_coef_accel_0_x = np.poly1d(coef_accel_0_x) fit_coef_accel_0_x = np.poly1d(coef_accel_0_x)
correction_x_resample = fit_coef_accel_0_x(temp_rel_resample) correction_x_resample = fit_coef_accel_0_x(temp_rel_resample)
# fit Y axis # fit Y axis
correction_y = sensor_accel_0['y']-np.median(sensor_accel_0['y']) correction_y = sensor_accel_0['y']-np.median(sensor_accel_0['y'])
coef_accel_0_y = np.polyfit(temp_rel,correction_y,3) coef_accel_0_y = np.polyfit(temp_rel,correction_y,3)
accel_0_params['TC_A0_X3_1'] = coef_accel_0_y[0] accel_0_params['TC_A0_X3_1'] = coef_accel_0_y[0]
accel_0_params['TC_A0_X2_1'] = coef_accel_0_y[1] accel_0_params['TC_A0_X2_1'] = coef_accel_0_y[1]
accel_0_params['TC_A0_X1_1'] = coef_accel_0_y[2] accel_0_params['TC_A0_X1_1'] = coef_accel_0_y[2]
accel_0_params['TC_A0_X0_1'] = coef_accel_0_y[3] accel_0_params['TC_A0_X0_1'] = coef_accel_0_y[3]
fit_coef_accel_0_y = np.poly1d(coef_accel_0_y) fit_coef_accel_0_y = np.poly1d(coef_accel_0_y)
correction_y_resample = fit_coef_accel_0_y(temp_rel_resample) correction_y_resample = fit_coef_accel_0_y(temp_rel_resample)
# fit Z axis # fit Z axis
correction_z = sensor_accel_0['z']-np.median(sensor_accel_0['z']) correction_z = sensor_accel_0['z']-np.median(sensor_accel_0['z'])
coef_accel_0_z = np.polyfit(temp_rel,correction_z,3) coef_accel_0_z = np.polyfit(temp_rel,correction_z,3)
accel_0_params['TC_A0_X3_2'] = coef_accel_0_z[0] accel_0_params['TC_A0_X3_2'] = coef_accel_0_z[0]
accel_0_params['TC_A0_X2_2'] = coef_accel_0_z[1] accel_0_params['TC_A0_X2_2'] = coef_accel_0_z[1]
accel_0_params['TC_A0_X1_2'] = coef_accel_0_z[2] accel_0_params['TC_A0_X1_2'] = coef_accel_0_z[2]
accel_0_params['TC_A0_X0_2'] = coef_accel_0_z[3] accel_0_params['TC_A0_X0_2'] = coef_accel_0_z[3]
fit_coef_accel_0_z = np.poly1d(coef_accel_0_z) fit_coef_accel_0_z = np.poly1d(coef_accel_0_z)
correction_z_resample = fit_coef_accel_0_z(temp_rel_resample) correction_z_resample = fit_coef_accel_0_z(temp_rel_resample)
# accel 0 vs temperature # accel 0 vs temperature
plt.figure(4,figsize=(20,13)) plt.figure(4,figsize=(20,13))
# draw plots # draw plots
plt.subplot(3,1,1) plt.subplot(3,1,1)
plt.plot(sensor_accel_0['temperature'],correction_x,'b') plt.plot(sensor_accel_0['temperature'],correction_x,'b')
plt.plot(temp_resample,correction_x_resample,'r') plt.plot(temp_resample,correction_x_resample,'r')
plt.title('Accel 0 Bias vs Temperature') plt.title('Accel 0 Bias vs Temperature')
plt.ylabel('X bias (m/s/s)') plt.ylabel('X bias (m/s/s)')
plt.xlabel('temperature (degC)') plt.xlabel('temperature (degC)')
plt.grid() plt.grid()
# draw plots # draw plots
plt.subplot(3,1,2) plt.subplot(3,1,2)
plt.plot(sensor_accel_0['temperature'],correction_y,'b') plt.plot(sensor_accel_0['temperature'],correction_y,'b')
plt.plot(temp_resample,correction_y_resample,'r') plt.plot(temp_resample,correction_y_resample,'r')
plt.ylabel('Y bias (m/s/s)') plt.ylabel('Y bias (m/s/s)')
plt.xlabel('temperature (degC)') plt.xlabel('temperature (degC)')
plt.grid() plt.grid()
# draw plots # draw plots
plt.subplot(3,1,3) plt.subplot(3,1,3)
plt.plot(sensor_accel_0['temperature'],correction_z,'b') plt.plot(sensor_accel_0['temperature'],correction_z,'b')
plt.plot(temp_resample,correction_z_resample,'r') plt.plot(temp_resample,correction_z_resample,'r')
plt.ylabel('Z bias (m/s/s)') plt.ylabel('Z bias (m/s/s)')
plt.xlabel('temperature (degC)') plt.xlabel('temperature (degC)')
plt.grid() plt.grid()
pp.savefig() pp.savefig()
################################################################################# #################################################################################
@@ -520,75 +520,75 @@ accel_1_params = {
# curve fit the data for accel 1 corrections # curve fit the data for accel 1 corrections
if num_accels >= 2: if num_accels >= 2:
accel_1_params['TC_A1_ID'] = int(np.median(sensor_accel_1['device_id'])) accel_1_params['TC_A1_ID'] = int(np.median(sensor_accel_1['device_id']))
# find the min, max and reference temperature # find the min, max and reference temperature
accel_1_params['TC_A1_TMIN'] = np.amin(sensor_accel_1['temperature']) accel_1_params['TC_A1_TMIN'] = np.amin(sensor_accel_1['temperature'])
accel_1_params['TC_A1_TMAX'] = np.amax(sensor_accel_1['temperature']) accel_1_params['TC_A1_TMAX'] = np.amax(sensor_accel_1['temperature'])
accel_1_params['TC_A1_TREF'] = 0.5 * (accel_1_params['TC_A1_TMIN'] + accel_1_params['TC_A1_TMAX']) accel_1_params['TC_A1_TREF'] = 0.5 * (accel_1_params['TC_A1_TMIN'] + accel_1_params['TC_A1_TMAX'])
temp_rel = sensor_accel_1['temperature'] - accel_1_params['TC_A1_TREF'] temp_rel = sensor_accel_1['temperature'] - accel_1_params['TC_A1_TREF']
temp_rel_resample = np.linspace(accel_1_params['TC_A1_TMIN']-accel_1_params['TC_A1_TREF'], accel_1_params['TC_A1_TMAX']-accel_1_params['TC_A1_TREF'], 100) temp_rel_resample = np.linspace(accel_1_params['TC_A1_TMIN']-accel_1_params['TC_A1_TREF'], accel_1_params['TC_A1_TMAX']-accel_1_params['TC_A1_TREF'], 100)
temp_resample = temp_rel_resample + accel_1_params['TC_A1_TREF'] temp_resample = temp_rel_resample + accel_1_params['TC_A1_TREF']
# fit X axis # fit X axis
correction_x = sensor_accel_1['x']-np.median(sensor_accel_1['x']) correction_x = sensor_accel_1['x']-np.median(sensor_accel_1['x'])
coef_accel_1_x = np.polyfit(temp_rel,correction_x,3) coef_accel_1_x = np.polyfit(temp_rel,correction_x,3)
accel_1_params['TC_A1_X3_0'] = coef_accel_1_x[0] accel_1_params['TC_A1_X3_0'] = coef_accel_1_x[0]
accel_1_params['TC_A1_X2_0'] = coef_accel_1_x[1] accel_1_params['TC_A1_X2_0'] = coef_accel_1_x[1]
accel_1_params['TC_A1_X1_0'] = coef_accel_1_x[2] accel_1_params['TC_A1_X1_0'] = coef_accel_1_x[2]
accel_1_params['TC_A1_X0_0'] = coef_accel_1_x[3] accel_1_params['TC_A1_X0_0'] = coef_accel_1_x[3]
fit_coef_accel_1_x = np.poly1d(coef_accel_1_x) fit_coef_accel_1_x = np.poly1d(coef_accel_1_x)
correction_x_resample = fit_coef_accel_1_x(temp_rel_resample) correction_x_resample = fit_coef_accel_1_x(temp_rel_resample)
# fit Y axis # fit Y axis
correction_y = sensor_accel_1['y']-np.median(sensor_accel_1['y']) correction_y = sensor_accel_1['y']-np.median(sensor_accel_1['y'])
coef_accel_1_y = np.polyfit(temp_rel,correction_y,3) coef_accel_1_y = np.polyfit(temp_rel,correction_y,3)
accel_1_params['TC_A1_X3_1'] = coef_accel_1_y[0] accel_1_params['TC_A1_X3_1'] = coef_accel_1_y[0]
accel_1_params['TC_A1_X2_1'] = coef_accel_1_y[1] accel_1_params['TC_A1_X2_1'] = coef_accel_1_y[1]
accel_1_params['TC_A1_X1_1'] = coef_accel_1_y[2] accel_1_params['TC_A1_X1_1'] = coef_accel_1_y[2]
accel_1_params['TC_A1_X0_1'] = coef_accel_1_y[3] accel_1_params['TC_A1_X0_1'] = coef_accel_1_y[3]
fit_coef_accel_1_y = np.poly1d(coef_accel_1_y) fit_coef_accel_1_y = np.poly1d(coef_accel_1_y)
correction_y_resample = fit_coef_accel_1_y(temp_rel_resample) correction_y_resample = fit_coef_accel_1_y(temp_rel_resample)
# fit Z axis # fit Z axis
correction_z = (sensor_accel_1['z'])-np.median(sensor_accel_1['z']) correction_z = (sensor_accel_1['z'])-np.median(sensor_accel_1['z'])
coef_accel_1_z = np.polyfit(temp_rel,correction_z,3) coef_accel_1_z = np.polyfit(temp_rel,correction_z,3)
accel_1_params['TC_A1_X3_2'] = coef_accel_1_z[0] accel_1_params['TC_A1_X3_2'] = coef_accel_1_z[0]
accel_1_params['TC_A1_X2_2'] = coef_accel_1_z[1] accel_1_params['TC_A1_X2_2'] = coef_accel_1_z[1]
accel_1_params['TC_A1_X1_2'] = coef_accel_1_z[2] accel_1_params['TC_A1_X1_2'] = coef_accel_1_z[2]
accel_1_params['TC_A1_X0_2'] = coef_accel_1_z[3] accel_1_params['TC_A1_X0_2'] = coef_accel_1_z[3]
fit_coef_accel_1_z = np.poly1d(coef_accel_1_z) fit_coef_accel_1_z = np.poly1d(coef_accel_1_z)
correction_z_resample = fit_coef_accel_1_z(temp_rel_resample) correction_z_resample = fit_coef_accel_1_z(temp_rel_resample)
# accel 1 vs temperature # accel 1 vs temperature
plt.figure(5,figsize=(20,13)) plt.figure(5,figsize=(20,13))
# draw plots # draw plots
plt.subplot(3,1,1) plt.subplot(3,1,1)
plt.plot(sensor_accel_1['temperature'],correction_x,'b') plt.plot(sensor_accel_1['temperature'],correction_x,'b')
plt.plot(temp_resample,correction_x_resample,'r') plt.plot(temp_resample,correction_x_resample,'r')
plt.title('Accel 1 Bias vs Temperature') plt.title('Accel 1 Bias vs Temperature')
plt.ylabel('X bias (m/s/s)') plt.ylabel('X bias (m/s/s)')
plt.xlabel('temperature (degC)') plt.xlabel('temperature (degC)')
plt.grid() plt.grid()
# draw plots # draw plots
plt.subplot(3,1,2) plt.subplot(3,1,2)
plt.plot(sensor_accel_1['temperature'],correction_y,'b') plt.plot(sensor_accel_1['temperature'],correction_y,'b')
plt.plot(temp_resample,correction_y_resample,'r') plt.plot(temp_resample,correction_y_resample,'r')
plt.ylabel('Y bias (m/s/s)') plt.ylabel('Y bias (m/s/s)')
plt.xlabel('temperature (degC)') plt.xlabel('temperature (degC)')
plt.grid() plt.grid()
# draw plots # draw plots
plt.subplot(3,1,3) plt.subplot(3,1,3)
plt.plot(sensor_accel_1['temperature'],correction_z,'b') plt.plot(sensor_accel_1['temperature'],correction_z,'b')
plt.plot(temp_resample,correction_z_resample,'r') plt.plot(temp_resample,correction_z_resample,'r')
plt.ylabel('Z bias (m/s/s)') plt.ylabel('Z bias (m/s/s)')
plt.xlabel('temperature (degC)') plt.xlabel('temperature (degC)')
plt.grid() plt.grid()
pp.savefig() pp.savefig()
################################################################################# #################################################################################
@@ -619,75 +619,75 @@ accel_2_params = {
# curve fit the data for accel 2 corrections # curve fit the data for accel 2 corrections
if num_accels >= 3: if num_accels >= 3:
accel_2_params['TC_A2_ID'] = int(np.median(sensor_accel_2['device_id'])) accel_2_params['TC_A2_ID'] = int(np.median(sensor_accel_2['device_id']))
# find the min, max and reference temperature # find the min, max and reference temperature
accel_2_params['TC_A2_TMIN'] = np.amin(sensor_accel_2['temperature']) accel_2_params['TC_A2_TMIN'] = np.amin(sensor_accel_2['temperature'])
accel_2_params['TC_A2_TMAX'] = np.amax(sensor_accel_2['temperature']) accel_2_params['TC_A2_TMAX'] = np.amax(sensor_accel_2['temperature'])
accel_2_params['TC_A2_TREF'] = 0.5 * (accel_2_params['TC_A2_TMIN'] + accel_2_params['TC_A2_TMAX']) accel_2_params['TC_A2_TREF'] = 0.5 * (accel_2_params['TC_A2_TMIN'] + accel_2_params['TC_A2_TMAX'])
temp_rel = sensor_accel_2['temperature'] - accel_2_params['TC_A2_TREF'] temp_rel = sensor_accel_2['temperature'] - accel_2_params['TC_A2_TREF']
temp_rel_resample = np.linspace(accel_2_params['TC_A2_TMIN']-accel_2_params['TC_A2_TREF'], accel_2_params['TC_A2_TMAX']-accel_2_params['TC_A2_TREF'], 100) temp_rel_resample = np.linspace(accel_2_params['TC_A2_TMIN']-accel_2_params['TC_A2_TREF'], accel_2_params['TC_A2_TMAX']-accel_2_params['TC_A2_TREF'], 100)
temp_resample = temp_rel_resample + accel_2_params['TC_A2_TREF'] temp_resample = temp_rel_resample + accel_2_params['TC_A2_TREF']
# fit X axis # fit X axis
correction_x = sensor_accel_2['x']-np.median(sensor_accel_2['x']) correction_x = sensor_accel_2['x']-np.median(sensor_accel_2['x'])
coef_accel_2_x = np.polyfit(temp_rel,correction_x,3) coef_accel_2_x = np.polyfit(temp_rel,correction_x,3)
accel_2_params['TC_A2_X3_0'] = coef_accel_2_x[0] accel_2_params['TC_A2_X3_0'] = coef_accel_2_x[0]
accel_2_params['TC_A2_X2_0'] = coef_accel_2_x[1] accel_2_params['TC_A2_X2_0'] = coef_accel_2_x[1]
accel_2_params['TC_A2_X1_0'] = coef_accel_2_x[2] accel_2_params['TC_A2_X1_0'] = coef_accel_2_x[2]
accel_2_params['TC_A2_X0_0'] = coef_accel_2_x[3] accel_2_params['TC_A2_X0_0'] = coef_accel_2_x[3]
fit_coef_accel_2_x = np.poly1d(coef_accel_2_x) fit_coef_accel_2_x = np.poly1d(coef_accel_2_x)
correction_x_resample = fit_coef_accel_2_x(temp_rel_resample) correction_x_resample = fit_coef_accel_2_x(temp_rel_resample)
# fit Y axis # fit Y axis
correction_y = sensor_accel_2['y']-np.median(sensor_accel_2['y']) correction_y = sensor_accel_2['y']-np.median(sensor_accel_2['y'])
coef_accel_2_y = np.polyfit(temp_rel,correction_y,3) coef_accel_2_y = np.polyfit(temp_rel,correction_y,3)
accel_2_params['TC_A2_X3_1'] = coef_accel_2_y[0] accel_2_params['TC_A2_X3_1'] = coef_accel_2_y[0]
accel_2_params['TC_A2_X2_1'] = coef_accel_2_y[1] accel_2_params['TC_A2_X2_1'] = coef_accel_2_y[1]
accel_2_params['TC_A2_X1_1'] = coef_accel_2_y[2] accel_2_params['TC_A2_X1_1'] = coef_accel_2_y[2]
accel_2_params['TC_A2_X0_1'] = coef_accel_2_y[3] accel_2_params['TC_A2_X0_1'] = coef_accel_2_y[3]
fit_coef_accel_2_y = np.poly1d(coef_accel_2_y) fit_coef_accel_2_y = np.poly1d(coef_accel_2_y)
correction_y_resample = fit_coef_accel_2_y(temp_rel_resample) correction_y_resample = fit_coef_accel_2_y(temp_rel_resample)
# fit Z axis # fit Z axis
correction_z = sensor_accel_2['z']-np.median(sensor_accel_2['z']) correction_z = sensor_accel_2['z']-np.median(sensor_accel_2['z'])
coef_accel_2_z = np.polyfit(temp_rel,correction_z,3) coef_accel_2_z = np.polyfit(temp_rel,correction_z,3)
accel_2_params['TC_A2_X3_2'] = coef_accel_2_z[0] accel_2_params['TC_A2_X3_2'] = coef_accel_2_z[0]
accel_2_params['TC_A2_X2_2'] = coef_accel_2_z[1] accel_2_params['TC_A2_X2_2'] = coef_accel_2_z[1]
accel_2_params['TC_A2_X1_2'] = coef_accel_2_z[2] accel_2_params['TC_A2_X1_2'] = coef_accel_2_z[2]
accel_2_params['TC_A2_X0_2'] = coef_accel_2_z[3] accel_2_params['TC_A2_X0_2'] = coef_accel_2_z[3]
fit_coef_accel_2_z = np.poly1d(coef_accel_2_z) fit_coef_accel_2_z = np.poly1d(coef_accel_2_z)
correction_z_resample = fit_coef_accel_2_z(temp_rel_resample) correction_z_resample = fit_coef_accel_2_z(temp_rel_resample)
# accel 2 vs temperature # accel 2 vs temperature
plt.figure(6,figsize=(20,13)) plt.figure(6,figsize=(20,13))
# draw plots # draw plots
plt.subplot(3,1,1) plt.subplot(3,1,1)
plt.plot(sensor_accel_2['temperature'],correction_x,'b') plt.plot(sensor_accel_2['temperature'],correction_x,'b')
plt.plot(temp_resample,correction_x_resample,'r') plt.plot(temp_resample,correction_x_resample,'r')
plt.title('Accel 2 Bias vs Temperature') plt.title('Accel 2 Bias vs Temperature')
plt.ylabel('X bias (m/s/s)') plt.ylabel('X bias (m/s/s)')
plt.xlabel('temperature (degC)') plt.xlabel('temperature (degC)')
plt.grid() plt.grid()
# draw plots # draw plots
plt.subplot(3,1,2) plt.subplot(3,1,2)
plt.plot(sensor_accel_2['temperature'],correction_y,'b') plt.plot(sensor_accel_2['temperature'],correction_y,'b')
plt.plot(temp_resample,correction_y_resample,'r') plt.plot(temp_resample,correction_y_resample,'r')
plt.ylabel('Y bias (m/s/s)') plt.ylabel('Y bias (m/s/s)')
plt.xlabel('temperature (degC)') plt.xlabel('temperature (degC)')
plt.grid() plt.grid()
# draw plots # draw plots
plt.subplot(3,1,3) plt.subplot(3,1,3)
plt.plot(sensor_accel_2['temperature'],correction_z,'b') plt.plot(sensor_accel_2['temperature'],correction_z,'b')
plt.plot(temp_resample,correction_z_resample,'r') plt.plot(temp_resample,correction_z_resample,'r')
plt.ylabel('Z bias (m/s/s)') plt.ylabel('Z bias (m/s/s)')
plt.xlabel('temperature (degC)') plt.xlabel('temperature (degC)')
plt.grid() plt.grid()
pp.savefig() pp.savefig()
################################################################################# #################################################################################
@@ -761,41 +761,41 @@ baro_1_params = {
if num_baros >= 2: if num_baros >= 2:
# curve fit the data for baro 0 corrections # curve fit the data for baro 0 corrections
baro_1_params['TC_B1_ID'] = int(np.median(sensor_baro_1['device_id'])) baro_1_params['TC_B1_ID'] = int(np.median(sensor_baro_1['device_id']))
# find the min, max and reference temperature # find the min, max and reference temperature
baro_1_params['TC_B1_TMIN'] = np.amin(sensor_baro_1['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_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']) 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 = 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_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'] temp_resample = temp_rel_resample + baro_1_params['TC_B1_TREF']
# fit data # fit data
median_pressure = np.median(sensor_baro_1['pressure']); 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 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_X5'] = coef_baro_1_x[0]
baro_1_params['TC_B1_X4'] = coef_baro_1_x[1] 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_X3'] = coef_baro_1_x[2]
baro_1_params['TC_B1_X2'] = coef_baro_1_x[3] 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_X1'] = coef_baro_1_x[4]
baro_1_params['TC_B1_X0'] = coef_baro_1_x[5] baro_1_params['TC_B1_X0'] = coef_baro_1_x[5]
fit_coef_baro_1_x = np.poly1d(coef_baro_1_x) 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_x_resample = fit_coef_baro_1_x(temp_rel_resample)
# baro 1 vs temperature # baro 1 vs temperature
plt.figure(8,figsize=(20,13)) plt.figure(8,figsize=(20,13))
# draw plots # draw plots
plt.plot(sensor_baro_1['temperature'],100*sensor_baro_1['pressure']-100*median_pressure,'b') 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.plot(temp_resample,baro_1_x_resample,'r')
plt.title('Baro 1 Bias vs Temperature') plt.title('Baro 1 Bias vs Temperature')
plt.ylabel('Z bias (Pa)') plt.ylabel('Z bias (Pa)')
plt.xlabel('temperature (degC)') plt.xlabel('temperature (degC)')
plt.grid() plt.grid()
pp.savefig() pp.savefig()
################################################################################# #################################################################################