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process_sensor_caldata.py
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#! /usr/bin/env python3
from __future__ import print_function
import argparse
import os
import math
import matplotlib.pyplot as plt
import numpy as np
from scipy.signal import medfilt
from pyulog import *
"""
Reads in IMU data from a static thermal calibration test and performs a curve fit of gyro, accel and baro bias vs temperature
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
2) Set all CAL_ACC*, CAL_GYR*, and CAL_MAG* parameters to defaults
3) Set the parameter SDLOG_MODE to 2, and SDLOG_PROFILE "Thermal calibration" bit (2) to enable logging of sensor data for calibration and power off
4) Cold soak the board for 30 minutes
5) Move to a warm dry, still air, constant pressure environment.
6) Apply power for 45 minutes, keeping the board still.
7) Remove power and extract the .ulog file
8) 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) 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) TODO - we need a way for user to reliably tell when parameters have all been changed and saved.
11) After parameters have finished loading, set SDLOG_MODE and SDLOG_PROFILE to their respective values prior to step 4) and remove power.
12) 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 summary plots in a pdf file named <inputfilename>.pdf
"""
def resampleWithDeltaX(x,y):
xMin = np.amin(x)
xMax = np.amax(x)
nbInterval = 2000
interval = (xMax-xMin)/nbInterval
resampledY = np.zeros(nbInterval)
resampledX = np.zeros(nbInterval)
resampledCount = np.zeros(nbInterval)
for idx in range(0,len(x)):
if x[idx]<xMin:
binIdx = 0
elif x[idx]<xMax:
binIdx = int((x[idx]-xMin)/(interval))
else:
binIdx = nbInterval-1
resampledY[binIdx] += y[idx]
resampledX[binIdx] += x[idx]
resampledCount[binIdx] += 1
idxNotEmpty = np.where(resampledCount != 0)
resampledCount = resampledCount[idxNotEmpty]
resampledY = resampledY[idxNotEmpty]
resampledX = resampledX[idxNotEmpty]
resampledY /= resampledCount
resampledX /= resampledCount
return resampledX,resampledY
def median_filter(data):
return medfilt(data, 31)
parser = argparse.ArgumentParser(description='Reads in IMU data from a static thermal calibration test and performs a curve fit of gyro, accel and baro bias vs temperature')
parser.add_argument('filename', metavar='file.ulg', help='ULog input file')
parser.add_argument('--no_resample', dest='noResample', action='store_const',
const=True, default=False, help='skip resampling and use raw data')
def is_valid_directory(parser, arg):
if os.path.isdir(arg):
# Directory exists so return the directory
return arg
else:
parser.error('The directory {} does not exist'.format(arg))
args = parser.parse_args()
ulog_file_name = args.filename
noResample = args.noResample
ulog = ULog(ulog_file_name, None)
data = ulog.data_list
# extract accel data
num_accels = 0
for d in data:
if d.name == 'sensor_accel':
if d.multi_id == 0:
sensor_accel_0 = d.data
print('found accel 0 data')
num_accels += 1
elif d.multi_id == 1:
sensor_accel_1 = d.data
print('found accel 1 data')
num_accels += 1
elif d.multi_id == 2:
sensor_accel_2 = d.data
print('found accel 2 data')
num_accels += 1
elif d.multi_id == 3:
sensor_accel_3 = d.data
print('found accel 3 data')
num_accels += 1
# extract gyro data
num_gyros = 0
for d in data:
if d.name == 'sensor_gyro':
if d.multi_id == 0:
sensor_gyro_0 = d.data
print('found gyro 0 data')
num_gyros += 1
elif d.multi_id == 1:
sensor_gyro_1 = d.data
print('found gyro 1 data')
num_gyros += 1
elif d.multi_id == 2:
sensor_gyro_2 = d.data
print('found gyro 2 data')
num_gyros += 1
elif d.multi_id == 3:
sensor_gyro_3 = d.data
print('found gyro 3 data')
num_gyros += 1
# extract mag data
num_mags = 0
for d in data:
if d.name == 'sensor_mag':
if d.multi_id == 0:
sensor_mag_0 = d.data
print('found mag 0 data')
num_mags += 1
elif d.multi_id == 1:
sensor_mag_1 = d.data
print('found mag 1 data')
num_mags += 1
elif d.multi_id == 2:
sensor_mag_2 = d.data
print('found mag 2 data')
num_mags += 1
elif d.multi_id == 3:
sensor_mag_3 = d.data
print('found mag 3 data')
num_mags += 1
# extract baro data
num_baros = 0
for d in data:
if d.name == 'sensor_baro':
if d.multi_id == 0:
sensor_baro_0 = d.data
print('found baro 0 data')
num_baros += 1
elif d.multi_id == 1:
sensor_baro_1 = d.data
print('found baro 1 data')
num_baros += 1
elif d.multi_id == 2:
sensor_baro_2 = d.data
print('found baro 2 data')
num_baros += 1
elif d.multi_id == 3:
sensor_baro_3 = d.data
print('found baro 3 data')
num_baros += 1
# open file to save plots to PDF
from matplotlib.backends.backend_pdf import PdfPages
output_plot_filename = ulog_file_name + ".pdf"
pp = PdfPages(output_plot_filename)
#################################################################################
#################################################################################
# define data dictionary of accel 0 thermal correction parameters
accel_0_params = {
'TC_A0_ID':0,
'TC_A0_TMIN':0.0,
'TC_A0_TMAX':0.0,
'TC_A0_TREF':0.0,
'TC_A0_X0_0':0.0,
'TC_A0_X1_0':0.0,
'TC_A0_X2_0':0.0,
'TC_A0_X3_0':0.0,
'TC_A0_X0_1':0.0,
'TC_A0_X1_1':0.0,
'TC_A0_X2_1':0.0,
'TC_A0_X3_1':0.0,
'TC_A0_X0_2':0.0,
'TC_A0_X1_2':0.0,
'TC_A0_X2_2':0.0,
'TC_A0_X3_2':0.0
}
# curve fit the data for accel 0 corrections
if num_accels >= 1 and not math.isnan(sensor_accel_0['temperature'][0]):
accel_0_params['TC_A0_ID'] = int(np.median(sensor_accel_0['device_id']))
# find the min, max and reference 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_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_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']
sensor_accel_0['x'] = median_filter(sensor_accel_0['x'])
sensor_accel_0['y'] = median_filter(sensor_accel_0['y'])
sensor_accel_0['z'] = median_filter(sensor_accel_0['z'])
# fit X axis
correction_x = sensor_accel_0['x'] - np.median(sensor_accel_0['x'])
if noResample:
coef_accel_0_x = np.polyfit(temp_rel,correction_x, 3)
else:
temp, sens = resampleWithDeltaX(temp_rel,correction_x)
coef_accel_0_x = np.polyfit(temp, sens, 3)
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_X1_0'] = coef_accel_0_x[2]
accel_0_params['TC_A0_X0_0'] = coef_accel_0_x[3]
fit_coef_accel_0_x = np.poly1d(coef_accel_0_x)
correction_x_resample = fit_coef_accel_0_x(temp_rel_resample)
# fit Y axis
correction_y = sensor_accel_0['y'] - np.median(sensor_accel_0['y'])
if noResample:
coef_accel_0_y = np.polyfit(temp_rel, correction_y, 3)
else:
temp, sens = resampleWithDeltaX(temp_rel,correction_y)
coef_accel_0_y = np.polyfit(temp, sens, 3)
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_X1_1'] = coef_accel_0_y[2]
accel_0_params['TC_A0_X0_1'] = coef_accel_0_y[3]
fit_coef_accel_0_y = np.poly1d(coef_accel_0_y)
correction_y_resample = fit_coef_accel_0_y(temp_rel_resample)
# fit Z axis
correction_z = sensor_accel_0['z'] - np.median(sensor_accel_0['z'])
if noResample:
coef_accel_0_z = np.polyfit(temp_rel,correction_z, 3)
else:
temp, sens = resampleWithDeltaX(temp_rel,correction_z)
coef_accel_0_z = np.polyfit(temp, sens, 3)
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_X1_2'] = coef_accel_0_z[2]
accel_0_params['TC_A0_X0_2'] = coef_accel_0_z[3]
fit_coef_accel_0_z = np.poly1d(coef_accel_0_z)
correction_z_resample = fit_coef_accel_0_z(temp_rel_resample)
# accel 0 vs temperature
plt.figure(1,figsize=(20,13))
# draw plots
plt.subplot(3,1,1)
plt.plot(sensor_accel_0['temperature'],correction_x,'b')
plt.plot(temp_resample,correction_x_resample,'r')
plt.title('Accel 0 ({}) Bias vs Temperature'.format(accel_0_params['TC_A0_ID']))
plt.ylabel('X bias (m/s/s)')
plt.xlabel('temperature (degC)')
plt.grid()
# draw plots
plt.subplot(3,1,2)
plt.plot(sensor_accel_0['temperature'],correction_y,'b')
plt.plot(temp_resample,correction_y_resample,'r')
plt.ylabel('Y bias (m/s/s)')
plt.xlabel('temperature (degC)')
plt.grid()
# draw plots
plt.subplot(3,1,3)
plt.plot(sensor_accel_0['temperature'],correction_z,'b')
plt.plot(temp_resample,correction_z_resample,'r')
plt.ylabel('Z bias (m/s/s)')
plt.xlabel('temperature (degC)')
plt.grid()
pp.savefig()
#################################################################################
#################################################################################
# define data dictionary of accel 1 thermal correction parameters
accel_1_params = {
'TC_A1_ID':0,
'TC_A1_TMIN':0.0,
'TC_A1_TMAX':0.0,
'TC_A1_TREF':0.0,
'TC_A1_X0_0':0.0,
'TC_A1_X1_0':0.0,
'TC_A1_X2_0':0.0,
'TC_A1_X3_0':0.0,
'TC_A1_X0_1':0.0,
'TC_A1_X1_1':0.0,
'TC_A1_X2_1':0.0,
'TC_A1_X3_1':0.0,
'TC_A1_X0_2':0.0,
'TC_A1_X1_2':0.0,
'TC_A1_X2_2':0.0,
'TC_A1_X3_2':0.0
}
# curve fit the data for accel 1 corrections
if num_accels >= 2 and not math.isnan(sensor_accel_1['temperature'][0]):
accel_1_params['TC_A1_ID'] = int(np.median(sensor_accel_1['device_id']))
# find the min, max and reference 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_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_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']
sensor_accel_1['x'] = median_filter(sensor_accel_1['x'])
sensor_accel_1['y'] = median_filter(sensor_accel_1['y'])
sensor_accel_1['z'] = median_filter(sensor_accel_1['z'])
# fit X axis
correction_x = sensor_accel_1['x'] - np.median(sensor_accel_1['x'])
if noResample:
coef_accel_1_x = np.polyfit(temp_rel, correction_x, 3)
else:
temp, sens = resampleWithDeltaX(temp_rel, correction_x)
coef_accel_1_x = np.polyfit(temp, sens, 3)
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_X1_0'] = coef_accel_1_x[2]
accel_1_params['TC_A1_X0_0'] = coef_accel_1_x[3]
fit_coef_accel_1_x = np.poly1d(coef_accel_1_x)
correction_x_resample = fit_coef_accel_1_x(temp_rel_resample)
# fit Y axis
correction_y = sensor_accel_1['y'] - np.median(sensor_accel_1['y'])
if noResample:
coef_accel_1_y = np.polyfit(temp_rel,correction_y,3)
else:
temp, sens = resampleWithDeltaX(temp_rel,correction_y)
coef_accel_1_y = np.polyfit(temp, sens ,3)
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_X1_1'] = coef_accel_1_y[2]
accel_1_params['TC_A1_X0_1'] = coef_accel_1_y[3]
fit_coef_accel_1_y = np.poly1d(coef_accel_1_y)
correction_y_resample = fit_coef_accel_1_y(temp_rel_resample)
# fit Z axis
correction_z = sensor_accel_1['z'] - np.median(sensor_accel_1['z'])
if noResample:
coef_accel_1_z = np.polyfit(temp_rel,correction_z, 3)
else:
temp, sens = resampleWithDeltaX(temp_rel,correction_z)
coef_accel_1_z = np.polyfit(temp, sens, 3)
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_X1_2'] = coef_accel_1_z[2]
accel_1_params['TC_A1_X0_2'] = coef_accel_1_z[3]
fit_coef_accel_1_z = np.poly1d(coef_accel_1_z)
correction_z_resample = fit_coef_accel_1_z(temp_rel_resample)
# accel 1 vs temperature
plt.figure(2,figsize=(20,13))
# draw plots
plt.subplot(3,1,1)
plt.plot(sensor_accel_1['temperature'],correction_x,'b')
plt.plot(temp_resample,correction_x_resample,'r')
plt.title('Accel 1 ({}) Bias vs Temperature'.format(accel_1_params['TC_A1_ID']))
plt.ylabel('X bias (m/s/s)')
plt.xlabel('temperature (degC)')
plt.grid()
# draw plots
plt.subplot(3,1,2)
plt.plot(sensor_accel_1['temperature'],correction_y,'b')
plt.plot(temp_resample,correction_y_resample,'r')
plt.ylabel('Y bias (m/s/s)')
plt.xlabel('temperature (degC)')
plt.grid()
# draw plots
plt.subplot(3,1,3)
plt.plot(sensor_accel_1['temperature'],correction_z,'b')
plt.plot(temp_resample,correction_z_resample,'r')
plt.ylabel('Z bias (m/s/s)')
plt.xlabel('temperature (degC)')
plt.grid()
pp.savefig()
#################################################################################
#################################################################################
# define data dictionary of accel 2 thermal correction parameters
accel_2_params = {
'TC_A2_ID':0,
'TC_A2_TMIN':0.0,
'TC_A2_TMAX':0.0,
'TC_A2_TREF':0.0,
'TC_A2_X0_0':0.0,
'TC_A2_X1_0':0.0,
'TC_A2_X2_0':0.0,
'TC_A2_X3_0':0.0,
'TC_A2_X0_1':0.0,
'TC_A2_X1_1':0.0,
'TC_A2_X2_1':0.0,
'TC_A2_X3_1':0.0,
'TC_A2_X0_2':0.0,
'TC_A2_X1_2':0.0,
'TC_A2_X2_2':0.0,
'TC_A2_X3_2':0.0
}
# curve fit the data for accel 2 corrections
if num_accels >= 3 and not math.isnan(sensor_accel_2['temperature'][0]):
accel_2_params['TC_A2_ID'] = int(np.median(sensor_accel_2['device_id']))
# find the min, max and reference 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_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_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']
sensor_accel_2['x'] = median_filter(sensor_accel_2['x'])
sensor_accel_2['y'] = median_filter(sensor_accel_2['y'])
sensor_accel_2['z'] = median_filter(sensor_accel_2['z'])
# fit X axis
correction_x = sensor_accel_2['x'] - np.median(sensor_accel_2['x'])
if noResample:
coef_accel_2_x = np.polyfit(temp_rel,correction_x, 3)
else:
temp, sens = resampleWithDeltaX(temp_rel, correction_x)
coef_accel_2_x = np.polyfit(temp, sens, 3)
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_X1_0'] = coef_accel_2_x[2]
accel_2_params['TC_A2_X0_0'] = coef_accel_2_x[3]
fit_coef_accel_2_x = np.poly1d(coef_accel_2_x)
correction_x_resample = fit_coef_accel_2_x(temp_rel_resample)
# fit Y axis
correction_y = sensor_accel_2['y'] - np.median(sensor_accel_2['y'])
if noResample:
coef_accel_2_y = np.polyfit(temp_rel,correction_y,3)
else:
temp, sens = resampleWithDeltaX(temp_rel,correction_y)
coef_accel_2_y = np.polyfit(temp, sens ,3)
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_X1_1'] = coef_accel_2_y[2]
accel_2_params['TC_A2_X0_1'] = coef_accel_2_y[3]
fit_coef_accel_2_y = np.poly1d(coef_accel_2_y)
correction_y_resample = fit_coef_accel_2_y(temp_rel_resample)
# fit Z axis
correction_z = sensor_accel_2['z'] - np.median(sensor_accel_2['z'])
if noResample:
coef_accel_2_z = np.polyfit(temp_rel,correction_z,3)
else:
temp, sens = resampleWithDeltaX(temp_rel,correction_z)
coef_accel_2_z = np.polyfit(temp, sens ,3)
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_X1_2'] = coef_accel_2_z[2]
accel_2_params['TC_A2_X0_2'] = coef_accel_2_z[3]
fit_coef_accel_2_z = np.poly1d(coef_accel_2_z)
correction_z_resample = fit_coef_accel_2_z(temp_rel_resample)
# accel 2 vs temperature
plt.figure(3,figsize=(20,13))
# draw plots
plt.subplot(3,1,1)
plt.plot(sensor_accel_2['temperature'],correction_x,'b')
plt.plot(temp_resample,correction_x_resample,'r')
plt.title('Accel 2 ({}) Bias vs Temperature'.format(accel_2_params['TC_A2_ID']))
plt.ylabel('X bias (m/s/s)')
plt.xlabel('temperature (degC)')
plt.grid()
# draw plots
plt.subplot(3,1,2)
plt.plot(sensor_accel_2['temperature'],correction_y,'b')
plt.plot(temp_resample,correction_y_resample,'r')
plt.ylabel('Y bias (m/s/s)')
plt.xlabel('temperature (degC)')
plt.grid()
# draw plots
plt.subplot(3,1,3)
plt.plot(sensor_accel_2['temperature'],correction_z,'b')
plt.plot(temp_resample,correction_z_resample,'r')
plt.ylabel('Z bias (m/s/s)')
plt.xlabel('temperature (degC)')
plt.grid()
pp.savefig()
#################################################################################
#################################################################################
# define data dictionary of accel 3 thermal correction parameters
accel_3_params = {
'TC_A3_ID':0,
'TC_A3_TMIN':0.0,
'TC_A3_TMAX':0.0,
'TC_A3_TREF':0.0,
'TC_A3_X0_0':0.0,
'TC_A3_X1_0':0.0,
'TC_A3_X2_0':0.0,
'TC_A3_X3_0':0.0,
'TC_A3_X0_1':0.0,
'TC_A3_X1_1':0.0,
'TC_A3_X2_1':0.0,
'TC_A3_X3_1':0.0,
'TC_A3_X0_2':0.0,
'TC_A3_X1_2':0.0,
'TC_A3_X2_2':0.0,
'TC_A3_X3_2':0.0
}
# curve fit the data for accel 2 corrections
if num_accels >= 4 and not math.isnan(sensor_accel_3['temperature'][0]):
accel_3_params['TC_A3_ID'] = int(np.median(sensor_accel_3['device_id']))
# find the min, max and reference temperature
accel_3_params['TC_A3_TMIN'] = np.amin(sensor_accel_3['temperature'])
accel_3_params['TC_A3_TMAX'] = np.amax(sensor_accel_3['temperature'])
accel_3_params['TC_A3_TREF'] = 0.5 * (accel_3_params['TC_A3_TMIN'] + accel_3_params['TC_A3_TMAX'])
temp_rel = sensor_accel_3['temperature'] - accel_3_params['TC_A3_TREF']
temp_rel_resample = np.linspace(accel_3_params['TC_A3_TMIN']-accel_3_params['TC_A3_TREF'], accel_3_params['TC_A3_TMAX']-accel_3_params['TC_A3_TREF'], 100)
temp_resample = temp_rel_resample + accel_3_params['TC_A3_TREF']
sensor_accel_3['x'] = median_filter(sensor_accel_3['x'])
sensor_accel_3['y'] = median_filter(sensor_accel_3['y'])
sensor_accel_3['z'] = median_filter(sensor_accel_3['z'])
# fit X axis
correction_x = sensor_accel_3['x'] - np.median(sensor_accel_3['x'])
coef_accel_3_x = np.polyfit(temp_rel, correction_x, 3)
accel_3_params['TC_A3_X3_0'] = coef_accel_3_x[0]
accel_3_params['TC_A3_X2_0'] = coef_accel_3_x[1]
accel_3_params['TC_A3_X1_0'] = coef_accel_3_x[2]
accel_3_params['TC_A3_X0_0'] = coef_accel_3_x[3]
fit_coef_accel_3_x = np.poly1d(coef_accel_3_x)
correction_x_resample = fit_coef_accel_3_x(temp_rel_resample)
# fit Y axis
correction_y = sensor_accel_3['y'] - np.median(sensor_accel_3['y'])
coef_accel_3_y = np.polyfit(temp_rel, correction_y, 3)
accel_3_params['TC_A3_X3_1'] = coef_accel_3_y[0]
accel_3_params['TC_A3_X2_1'] = coef_accel_3_y[1]
accel_3_params['TC_A3_X1_1'] = coef_accel_3_y[2]
accel_3_params['TC_A3_X0_1'] = coef_accel_3_y[3]
fit_coef_accel_3_y = np.poly1d(coef_accel_3_y)
correction_y_resample = fit_coef_accel_3_y(temp_rel_resample)
# fit Z axis
correction_z = sensor_accel_3['z'] - np.median(sensor_accel_3['z'])
coef_accel_3_z = np.polyfit(temp_rel, correction_z, 3)
accel_3_params['TC_A3_X3_2'] = coef_accel_3_z[0]
accel_3_params['TC_A3_X2_2'] = coef_accel_3_z[1]
accel_3_params['TC_A3_X1_2'] = coef_accel_3_z[2]
accel_3_params['TC_A3_X0_2'] = coef_accel_3_z[3]
fit_coef_accel_3_z = np.poly1d(coef_accel_3_z)
correction_z_resample = fit_coef_accel_3_z(temp_rel_resample)
# accel 3 vs temperature
plt.figure(4,figsize=(20,13))
# draw plots
plt.subplot(3,1,1)
plt.plot(sensor_accel_3['temperature'],correction_x,'b')
plt.plot(temp_resample,correction_x_resample,'r')
plt.title('Accel 3 ({}) Bias vs Temperature'.format(accel_3_params['TC_A3_ID']))
plt.ylabel('X bias (m/s/s)')
plt.xlabel('temperature (degC)')
plt.grid()
# draw plots
plt.subplot(3,1,2)
plt.plot(sensor_accel_3['temperature'],correction_y,'b')
plt.plot(temp_resample,correction_y_resample,'r')
plt.ylabel('Y bias (m/s/s)')
plt.xlabel('temperature (degC)')
plt.grid()
# draw plots
plt.subplot(3,1,3)
plt.plot(sensor_accel_3['temperature'],correction_z,'b')
plt.plot(temp_resample,correction_z_resample,'r')
plt.ylabel('Z bias (m/s/s)')
plt.xlabel('temperature (degC)')
plt.grid()
pp.savefig()
#################################################################################
#################################################################################
# define data dictionary of gyro 0 thermal correction parameters
gyro_0_params = {
'TC_G0_ID':0,
'TC_G0_TMIN':0.0,
'TC_G0_TMAX':0.0,
'TC_G0_TREF':0.0,
'TC_G0_X0_0':0.0,
'TC_G0_X1_0':0.0,
'TC_G0_X2_0':0.0,
'TC_G0_X3_0':0.0,
'TC_G0_X0_1':0.0,
'TC_G0_X1_1':0.0,
'TC_G0_X2_1':0.0,
'TC_G0_X3_1':0.0,
'TC_G0_X0_2':0.0,
'TC_G0_X1_2':0.0,
'TC_G0_X2_2':0.0,
'TC_G0_X3_2':0.0
}
# curve fit the data for gyro 0 corrections
if num_gyros >= 1:
#if the gyro has no temperature data, use the corresponding accel instance or the primary baro temp.
if math.isnan(sensor_gyro_0['temperature'][0]):
if not math.isnan(sensor_accel_0['temperature'][0]):
sensor_gyro_0['temperature'] = sensor_accel_0['temperature']
elif not math.isnan(sensor_baro_0['temperature'][0]):
sensor_gyro_0['temperature'] = sensor_baro_0['temperature']
if not math.isnan(sensor_gyro_0['temperature'][0]):
gyro_0_params['TC_G0_ID'] = int(np.median(sensor_gyro_0['device_id']))
# find the min, max and reference 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_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_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']
sensor_gyro_0['x'] = median_filter(sensor_gyro_0['x'])
sensor_gyro_0['y'] = median_filter(sensor_gyro_0['y'])
sensor_gyro_0['z'] = median_filter(sensor_gyro_0['z'])
# fit X axis
if noResample:
coef_gyro_0_x = np.polyfit(temp_rel, sensor_gyro_0['x'], 3)
else:
temp, sens = resampleWithDeltaX(temp_rel, sensor_gyro_0['x'])
coef_gyro_0_x = np.polyfit(temp, sens, 3)
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_X1_0'] = coef_gyro_0_x[2]
gyro_0_params['TC_G0_X0_0'] = coef_gyro_0_x[3]
fit_coef_gyro_0_x = np.poly1d(coef_gyro_0_x)
gyro_0_x_resample = fit_coef_gyro_0_x(temp_rel_resample)
# fit Y axis
if noResample:
coef_gyro_0_y = np.polyfit(temp_rel, sensor_gyro_0['y'], 3)
else:
temp, sens = resampleWithDeltaX(temp_rel,sensor_gyro_0['y'])
coef_gyro_0_y = np.polyfit(temp, sens, 3)
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_X1_1'] = coef_gyro_0_y[2]
gyro_0_params['TC_G0_X0_1'] = coef_gyro_0_y[3]
fit_coef_gyro_0_y = np.poly1d(coef_gyro_0_y)
gyro_0_y_resample = fit_coef_gyro_0_y(temp_rel_resample)
# fit Z axis
if noResample:
coef_gyro_0_z = np.polyfit(temp_rel, sensor_gyro_0['z'],3)
else:
temp, sens = resampleWithDeltaX(temp_rel, sensor_gyro_0['z'])
coef_gyro_0_z = np.polyfit(temp, sens ,3)
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_X1_2'] = coef_gyro_0_z[2]
gyro_0_params['TC_G0_X0_2'] = coef_gyro_0_z[3]
fit_coef_gyro_0_z = np.poly1d(coef_gyro_0_z)
gyro_0_z_resample = fit_coef_gyro_0_z(temp_rel_resample)
# gyro0 vs temperature
plt.figure(5,figsize=(20,13))
# draw plots
plt.subplot(3,1,1)
plt.plot(sensor_gyro_0['temperature'],sensor_gyro_0['x'],'b')
plt.plot(temp_resample,gyro_0_x_resample,'r')
plt.title('Gyro 0 ({}) Bias vs Temperature'.format(gyro_0_params['TC_G0_ID']))
plt.ylabel('X bias (rad/s)')
plt.xlabel('temperature (degC)')
plt.grid()
# draw plots
plt.subplot(3,1,2)
plt.plot(sensor_gyro_0['temperature'],sensor_gyro_0['y'],'b')
plt.plot(temp_resample,gyro_0_y_resample,'r')
plt.ylabel('Y bias (rad/s)')
plt.xlabel('temperature (degC)')
plt.grid()
# draw plots
plt.subplot(3,1,3)
plt.plot(sensor_gyro_0['temperature'],sensor_gyro_0['z'],'b')
plt.plot(temp_resample,gyro_0_z_resample,'r')
plt.ylabel('Z bias (rad/s)')
plt.xlabel('temperature (degC)')
plt.grid()
pp.savefig()
#################################################################################
#################################################################################
# define data dictionary of gyro 1 thermal correction parameters
gyro_1_params = {
'TC_G1_ID':0,
'TC_G1_TMIN':0.0,
'TC_G1_TMAX':0.0,
'TC_G1_TREF':0.0,
'TC_G1_X0_0':0.0,
'TC_G1_X1_0':0.0,
'TC_G1_X2_0':0.0,
'TC_G1_X3_0':0.0,
'TC_G1_X0_1':0.0,
'TC_G1_X1_1':0.0,
'TC_G1_X2_1':0.0,
'TC_G1_X3_1':0.0,
'TC_G1_X0_2':0.0,
'TC_G1_X1_2':0.0,
'TC_G1_X2_2':0.0,
'TC_G1_X3_2':0.0
}
# curve fit the data for gyro 1 corrections
if num_gyros >= 2:
#if the gyro has no temperature data, use the corresponding accel instance or the primary baro temp.
if math.isnan(sensor_gyro_1['temperature'][0]):
if not math.isnan(sensor_accel_1['temperature'][0]):
sensor_gyro_1['temperature'] = sensor_accel_1['temperature']
elif not math.isnan(sensor_baro_0['temperature'][0]):
sensor_gyro_1['temperature'] = sensor_baro_0['temperature']
if not math.isnan(sensor_gyro_1['temperature'][0]):
gyro_1_params['TC_G1_ID'] = int(np.median(sensor_gyro_1['device_id']))
# find the min, max and reference 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_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_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']
sensor_gyro_1['x'] = median_filter(sensor_gyro_1['x'])
sensor_gyro_1['y'] = median_filter(sensor_gyro_1['y'])
sensor_gyro_1['z'] = median_filter(sensor_gyro_1['z'])
# fit X axis
if noResample:
coef_gyro_1_x = np.polyfit(temp_rel,sensor_gyro_1['x'],3)
else:
temp, sens = resampleWithDeltaX(temp_rel,sensor_gyro_1['x'])
coef_gyro_1_x = np.polyfit(temp, sens ,3)
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_X1_0'] = coef_gyro_1_x[2]
gyro_1_params['TC_G1_X0_0'] = coef_gyro_1_x[3]
fit_coef_gyro_1_x = np.poly1d(coef_gyro_1_x)
gyro_1_x_resample = fit_coef_gyro_1_x(temp_rel_resample)
# fit Y axis
if noResample:
coef_gyro_1_y = np.polyfit(temp_rel,sensor_gyro_1['y'],3)
else:
temp, sens = resampleWithDeltaX(temp_rel,sensor_gyro_1['y'])
coef_gyro_1_y = np.polyfit(temp, sens ,3)
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_X1_1'] = coef_gyro_1_y[2]
gyro_1_params['TC_G1_X0_1'] = coef_gyro_1_y[3]
fit_coef_gyro_1_y = np.poly1d(coef_gyro_1_y)
gyro_1_y_resample = fit_coef_gyro_1_y(temp_rel_resample)
# fit Z axis
if noResample:
coef_gyro_1_z = np.polyfit(temp_rel,sensor_gyro_1['z'],3)
else:
temp, sens = resampleWithDeltaX(temp_rel,sensor_gyro_1['z'])
coef_gyro_1_z = np.polyfit(temp, sens ,3)
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_X1_2'] = coef_gyro_1_z[2]
gyro_1_params['TC_G1_X0_2'] = coef_gyro_1_z[3]
fit_coef_gyro_1_z = np.poly1d(coef_gyro_1_z)
gyro_1_z_resample = fit_coef_gyro_1_z(temp_rel_resample)
# gyro1 vs temperature
plt.figure(6,figsize=(20,13))
# draw plots
plt.subplot(3,1,1)
plt.plot(sensor_gyro_1['temperature'],sensor_gyro_1['x'],'b')
plt.plot(temp_resample,gyro_1_x_resample,'r')
plt.title('Gyro 1 ({}) Bias vs Temperature'.format(gyro_1_params['TC_G1_ID']))
plt.ylabel('X bias (rad/s)')
plt.xlabel('temperature (degC)')
plt.grid()
# draw plots
plt.subplot(3,1,2)
plt.plot(sensor_gyro_1['temperature'],sensor_gyro_1['y'],'b')
plt.plot(temp_resample,gyro_1_y_resample,'r')
plt.ylabel('Y bias (rad/s)')
plt.xlabel('temperature (degC)')
plt.grid()
# draw plots
plt.subplot(3,1,3)
plt.plot(sensor_gyro_1['temperature'],sensor_gyro_1['z'],'b')
plt.plot(temp_resample,gyro_1_z_resample,'r')
plt.ylabel('Z bias (rad/s)')
plt.xlabel('temperature (degC)')
plt.grid()
pp.savefig()
#################################################################################
#################################################################################
# define data dictionary of gyro 2 thermal correction parameters
gyro_2_params = {
'TC_G2_ID':0,
'TC_G2_TMIN':0.0,
'TC_G2_TMAX':0.0,
'TC_G2_TREF':0.0,
'TC_G2_X0_0':0.0,
'TC_G2_X1_0':0.0,
'TC_G2_X2_0':0.0,
'TC_G2_X3_0':0.0,
'TC_G2_X0_1':0.0,
'TC_G2_X1_1':0.0,
'TC_G2_X2_1':0.0,
'TC_G2_X3_1':0.0,
'TC_G2_X0_2':0.0,
'TC_G2_X1_2':0.0,
'TC_G2_X2_2':0.0,
'TC_G2_X3_2':0.0
}
# curve fit the data for gyro 2 corrections
if num_gyros >= 3:
#if the gyro has no temperature data, use the corresponding accel instance or the primary baro temp.
if math.isnan(sensor_gyro_2['temperature'][0]):
if not math.isnan(sensor_accel_2['temperature'][0]):
sensor_gyro_2['temperature'] = sensor_accel_2['temperature']
elif not math.isnan(sensor_baro_0['temperature'][0]):
sensor_gyro_2['temperature'] = sensor_baro_0['temperature']
if not math.isnan(sensor_gyro_2['temperature'][0]):
gyro_2_params['TC_G2_ID'] = int(np.median(sensor_gyro_2['device_id']))
# find the min, max and reference 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_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_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']
sensor_gyro_2['x'] = median_filter(sensor_gyro_2['x'])
sensor_gyro_2['y'] = median_filter(sensor_gyro_2['y'])
sensor_gyro_2['z'] = median_filter(sensor_gyro_2['z'])
# fit X axis
if noResample:
coef_gyro_2_x = np.polyfit(temp_rel,sensor_gyro_2['x'],3)
else:
temp, sens = resampleWithDeltaX(temp_rel,sensor_gyro_2['x'])
coef_gyro_2_x = np.polyfit(temp, sens ,3)
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_X1_0'] = coef_gyro_2_x[2]
gyro_2_params['TC_G2_X0_0'] = coef_gyro_2_x[3]
fit_coef_gyro_2_x = np.poly1d(coef_gyro_2_x)
gyro_2_x_resample = fit_coef_gyro_2_x(temp_rel_resample)
# fit Y axis
if noResample:
coef_gyro_2_y = np.polyfit(temp_rel, sensor_gyro_2['y'], 3)
else:
temp, sens = resampleWithDeltaX(temp_rel, sensor_gyro_2['y'])
coef_gyro_2_y = np.polyfit(temp, sens, 3)
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_X1_1'] = coef_gyro_2_y[2]
gyro_2_params['TC_G2_X0_1'] = coef_gyro_2_y[3]
fit_coef_gyro_2_y = np.poly1d(coef_gyro_2_y)
gyro_2_y_resample = fit_coef_gyro_2_y(temp_rel_resample)
# fit Z axis
if noResample:
coef_gyro_2_z = np.polyfit(temp_rel,sensor_gyro_2['z'], 3)
else:
temp, sens = resampleWithDeltaX(temp_rel,sensor_gyro_2['z'])
coef_gyro_2_z = np.polyfit(temp, sens, 3)
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_X1_2'] = coef_gyro_2_z[2]
gyro_2_params['TC_G2_X0_2'] = coef_gyro_2_z[3]
fit_coef_gyro_2_z = np.poly1d(coef_gyro_2_z)
gyro_2_z_resample = fit_coef_gyro_2_z(temp_rel_resample)
# gyro2 vs temperature
plt.figure(7,figsize=(20,13))
# draw plots
plt.subplot(3,1,1)
plt.plot(sensor_gyro_2['temperature'],sensor_gyro_2['x'],'b')
plt.plot(temp_resample,gyro_2_x_resample,'r')
plt.title('Gyro 2 ({}) Bias vs Temperature'.format(gyro_2_params['TC_G2_ID']))
plt.ylabel('X bias (rad/s)')
plt.xlabel('temperature (degC)')
plt.grid()
# draw plots
plt.subplot(3,1,2)
plt.plot(sensor_gyro_2['temperature'],sensor_gyro_2['y'],'b')
plt.plot(temp_resample,gyro_2_y_resample,'r')
plt.ylabel('Y bias (rad/s)')
plt.xlabel('temperature (degC)')
plt.grid()
# draw plots
plt.subplot(3,1,3)
plt.plot(sensor_gyro_2['temperature'],sensor_gyro_2['z'],'b')
plt.plot(temp_resample,gyro_2_z_resample,'r')
plt.ylabel('Z bias (rad/s)')
plt.xlabel('temperature (degC)')
plt.grid()
pp.savefig()