forked from Archive/PX4-Autopilot
Streamline python script for temp cal. (#6416)
* Streamline python script for temp cal. * Simplify file generation for temp calibration.
This commit is contained in:
James Goppert
GitHub
parent
1abd629461
commit
b86380086e
|
|
@ -1,2 +1,3 @@
|
|||
parameters.wiki
|
||||
parameters.xml
|
||||
*.pdf
|
||||
|
|
|
|||
|
|
@ -1,4 +1,29 @@
|
|||
#! /usr/bin/env python
|
||||
"""
|
||||
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) Set the TC_A_ENABLE, TC_B_ENABLE and TC_G_ENABLE parameters to 0 to
|
||||
thermal compensation and reboot
|
||||
2) Perform a gyro and accel cal
|
||||
2) Set the SYS_LOGGER parameter to 1 to use the new system logger
|
||||
3) Set the SDLOG_MODE parameter to 3 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 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 script file directory
|
||||
9) Run the script file 'python process_sensor_caldata.py
|
||||
<full path name to .ulog file>
|
||||
|
||||
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
|
||||
|
||||
"""
|
||||
|
||||
from __future__ import print_function
|
||||
|
||||
|
|
@ -7,815 +32,174 @@ import os
|
|||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
|
||||
from pyulog import *
|
||||
import pyulog
|
||||
|
||||
class Param(dict):
|
||||
def __init__(self, name, val):
|
||||
"""
|
||||
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) Set the TC_A_ENABLE, TC_B_ENABLE and TC_G_ENABLE parameters to 0 to thermal compensation and reboot
|
||||
2) Perform a gyro and accel cal
|
||||
2) Set the SYS_LOGGER parameter to 1 to use the new system logger
|
||||
3) Set the SDLOG_MODE parameter to 3 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 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 script file directory
|
||||
9) Run the script file 'python process_sensor_caldata.py <full path name to .ulog file>
|
||||
|
||||
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
|
||||
Initialize a param dict
|
||||
"""
|
||||
self.name = name
|
||||
self.val = val
|
||||
|
||||
def temp_calibration(data, topic, fields, units, label):
|
||||
"""
|
||||
Performe a temperature calibration on a sensor.
|
||||
"""
|
||||
|
||||
parser = argparse.ArgumentParser(description='Analyse the sensor_gyro message data')
|
||||
parser.add_argument('filename', metavar='file.ulg', help='ULog input file')
|
||||
# pylint: disable=no-member
|
||||
params = {}
|
||||
|
||||
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))
|
||||
int_params = ['ID']
|
||||
float_params = [
|
||||
'TMIN', 'TMAX', 'TREF',
|
||||
'X0_0', 'X1_0', 'X2_0', 'X3_0',
|
||||
'X0_1', 'X1_1', 'X2_1', 'X3_1',
|
||||
'X0_2', 'X1_2', 'X2_2', 'X3_2',
|
||||
'SCL_0', 'SCL_1', 'SCL_2'
|
||||
]
|
||||
|
||||
args = parser.parse_args()
|
||||
ulog_file_name = args.filename
|
||||
# define data dictionary of thermal correction parameters
|
||||
for field in int_params:
|
||||
params[field] = {
|
||||
'val': 0,
|
||||
'type': 'INT',
|
||||
}
|
||||
|
||||
ulog = ULog(ulog_file_name, None)
|
||||
data = ulog.data_list
|
||||
for field in float_params: params[field] = {
|
||||
'val': 0,
|
||||
'type': 'FLOAT',
|
||||
}
|
||||
|
||||
# define constants
|
||||
gravity = 9.80665
|
||||
# curve fit the data for corrections - note
|
||||
# corrections have oppsite sign to sensor bias
|
||||
try:
|
||||
params['ID']['val'] = int(np.median(data['device_id']))
|
||||
except:
|
||||
print('no device id')
|
||||
pass
|
||||
|
||||
# extract gyro data
|
||||
sensor_instance = 0
|
||||
for d in data:
|
||||
if d.name == 'sensor_gyro':
|
||||
if sensor_instance == 0:
|
||||
sensor_gyro_0 = d.data
|
||||
print('found gyro 0 data')
|
||||
if sensor_instance == 1:
|
||||
sensor_gyro_1 = d.data
|
||||
print('found gyro 1 data')
|
||||
if sensor_instance == 2:
|
||||
sensor_gyro_2 = d.data
|
||||
print('found gyro 2 data')
|
||||
sensor_instance = sensor_instance +1
|
||||
# find the min, max and reference temperature
|
||||
params['TMIN']['val'] = float(np.amin(data['temperature']))
|
||||
params['TMAX']['val'] = float(np.amax(data['temperature']))
|
||||
params['TREF']['val'] = float(0.5 * (params['TMIN']['val'] + params['TMAX']['val']))
|
||||
temp_rel = data['temperature'] - params['TREF']['val']
|
||||
temp_rel_resample = np.linspace(
|
||||
float(params['TMIN']['val'] - params['TREF']['val']),
|
||||
float(params['TMAX']['val'] - params['TREF']['val']), 100)
|
||||
temp_resample = temp_rel_resample + params['TREF']['val']
|
||||
|
||||
# extract accel data
|
||||
sensor_instance = 0
|
||||
for d in data:
|
||||
if d.name == 'sensor_accel':
|
||||
if sensor_instance == 0:
|
||||
sensor_accel_0 = d.data
|
||||
print('found accel 0 data')
|
||||
if sensor_instance == 1:
|
||||
sensor_accel_1 = d.data
|
||||
print('found accel 1 data')
|
||||
if sensor_instance == 2:
|
||||
sensor_accel_2 = d.data
|
||||
print('found accel 2 data')
|
||||
sensor_instance = sensor_instance +1
|
||||
for i, field in enumerate(fields):
|
||||
coef = np.polyfit(temp_rel, -data[field], 3)
|
||||
for j in range(3):
|
||||
params['X{:d}_{:d}'.format(3-j, i)]['val'] = float(coef[j])
|
||||
fit_coef = np.poly1d(coef)
|
||||
resample = fit_coef(temp_rel_resample)
|
||||
|
||||
# extract baro data
|
||||
sensor_instance = 0
|
||||
for d in data:
|
||||
if d.name == 'sensor_baro':
|
||||
if sensor_instance == 0:
|
||||
sensor_baro_0 = d.data
|
||||
print('found baro 0 data')
|
||||
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')
|
||||
sensor_instance = sensor_instance +1
|
||||
# draw plots
|
||||
plt.subplot(len(fields), 1, i + 1)
|
||||
plt.plot(data['temperature'], data[field], 'b')
|
||||
plt.plot(temp_resample, -resample, 'r')
|
||||
plt.title('{:s} Bias vs Temperature'.format(topic))
|
||||
plt.ylabel('{:s} bias {:s}'.format(field, units))
|
||||
plt.xlabel('temperature (degC)')
|
||||
plt.grid()
|
||||
|
||||
return params
|
||||
|
||||
|
||||
def process_file(log_path, out_path, template_path):
|
||||
"""
|
||||
Command line interface to temperature calibration.
|
||||
"""
|
||||
log = pyulog.ULog(log_path, 'sensor_gyro, sensor_accel, sensor_baro')
|
||||
data = {}
|
||||
for d in log.data_list:
|
||||
data['{:s}_{:d}'.format(d.name, d.multi_id)] = d.data
|
||||
|
||||
params = {}
|
||||
|
||||
# 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)
|
||||
# from matplotlib.backends.backend_pdf import PdfPages
|
||||
# output_plot_filename = ulog_file_name + ".pdf"
|
||||
# pp = PdfPages(output_plot_filename)
|
||||
|
||||
#################################################################################
|
||||
configs = [
|
||||
{
|
||||
'msg': 'sensor_gyro',
|
||||
'fields': ['x', 'y', 'z'],
|
||||
'units': 'rad/s',
|
||||
'label': 'TC_G'
|
||||
},
|
||||
{
|
||||
'msg': 'sensor_accel',
|
||||
'fields': ['x', 'y', 'z'],
|
||||
'units': 'm/s^2',
|
||||
'label': 'TC_A'
|
||||
},
|
||||
{
|
||||
'msg': 'sensor_baro',
|
||||
'fields': ['pressure'],
|
||||
'units': 'm',
|
||||
'label': 'TC_B'
|
||||
},
|
||||
]
|
||||
|
||||
# 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,
|
||||
'TC_G0_SCL_0':1.0,
|
||||
'TC_G0_SCL_1':1.0,
|
||||
'TC_G0_SCL_2':1.0
|
||||
for config in configs:
|
||||
for d in log.data_list:
|
||||
if d.name == config['msg']:
|
||||
plt.figure(figsize=(20, 13))
|
||||
topic = '{:s}_{:d}'.format(d.name, d.multi_id)
|
||||
print('found {:s} data'.format(topic))
|
||||
label='{:s}{:d}'.format(
|
||||
config['label'], d.multi_id)
|
||||
params[topic] = {
|
||||
'params': temp_calibration(
|
||||
data=d.data, topic=topic,
|
||||
fields=config['fields'],
|
||||
units=config['units'],
|
||||
label=label),
|
||||
'label': label
|
||||
}
|
||||
|
||||
# curve fit the data for gyro 0 corrections - note corrections have oppsite sign to sensor bias
|
||||
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']
|
||||
|
||||
# fit X axis
|
||||
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_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
|
||||
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_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
|
||||
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_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(1,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')
|
||||
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,
|
||||
'TC_G1_SCL_0':1.0,
|
||||
'TC_G1_SCL_1':1.0,
|
||||
'TC_G1_SCL_2':1.0
|
||||
}
|
||||
|
||||
# curve fit the data for gyro 1 corrections - note corrections have oppsite sign to sensor bias
|
||||
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']
|
||||
|
||||
# fit X axis
|
||||
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_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
|
||||
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_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
|
||||
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_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(2,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')
|
||||
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,
|
||||
'TC_G2_SCL_0':1.0,
|
||||
'TC_G2_SCL_1':1.0,
|
||||
'TC_G2_SCL_2':1.0
|
||||
}
|
||||
|
||||
# curve fit the data for gyro 2 corrections - note corrections have oppsite sign to sensor bias
|
||||
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']
|
||||
|
||||
# fit X axis
|
||||
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_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
|
||||
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_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
|
||||
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_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(3,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')
|
||||
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()
|
||||
|
||||
#################################################################################
|
||||
|
||||
#################################################################################
|
||||
|
||||
# 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,
|
||||
'TC_A0_SCL_0':1.0,
|
||||
'TC_A0_SCL_1':1.0,
|
||||
'TC_A0_SCL_2':1.0
|
||||
}
|
||||
|
||||
# curve fit the data for accel 0 corrections - note corrections have oppsite sign to sensor bias
|
||||
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']
|
||||
|
||||
# fit X axis
|
||||
correction_x = np.median(sensor_accel_0['x'])-sensor_accel_0['x']
|
||||
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_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 = np.median(sensor_accel_0['y'])-sensor_accel_0['y']
|
||||
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_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 = np.median(sensor_accel_0['z'])-sensor_accel_0['z']
|
||||
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_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(4,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')
|
||||
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,
|
||||
'TC_A1_SCL_0':1.0,
|
||||
'TC_A1_SCL_1':1.0,
|
||||
'TC_A1_SCL_2':1.0
|
||||
}
|
||||
|
||||
# curve fit the data for accel 1 corrections - note corrections have oppsite sign to sensor bias
|
||||
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']
|
||||
|
||||
# fit X axis
|
||||
correction_x = np.median(sensor_accel_1['x'])-sensor_accel_1['x']
|
||||
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_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 = np.median(sensor_accel_1['y'])-sensor_accel_1['y']
|
||||
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_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 = np.median(sensor_accel_1['z'])-(sensor_accel_1['z'])
|
||||
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_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(5,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')
|
||||
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,
|
||||
'TC_A2_SCL_0':1.0,
|
||||
'TC_A2_SCL_1':1.0,
|
||||
'TC_A2_SCL_2':1.0
|
||||
}
|
||||
|
||||
# curve fit the data for accel 2 corrections - note corrections have oppsite sign to sensor bias
|
||||
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']
|
||||
|
||||
# fit X axis
|
||||
correction_x = np.median(sensor_accel_2['x'])-sensor_accel_2['x']
|
||||
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_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 = np.median(sensor_accel_2['y'])-sensor_accel_2['y']
|
||||
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_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 = np.median(sensor_accel_2['z'])-sensor_accel_2['z']
|
||||
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_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(6,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')
|
||||
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 baro 0 thermal correction parameters
|
||||
baro_0_params = {
|
||||
'TC_B0_ID':0,
|
||||
'TC_B0_TMIN':0.0,
|
||||
'TC_B0_TMAX':0.0,
|
||||
'TC_B0_TREF':0.0,
|
||||
'TC_B0_X0':0.0,
|
||||
'TC_B0_X1':0.0,
|
||||
'TC_B0_X2':0.0,
|
||||
'TC_B0_X3':0.0,
|
||||
'TC_B0_X4':0.0,
|
||||
'TC_B0_X5':0.0,
|
||||
'TC_B0_SCL':1.0,
|
||||
}
|
||||
|
||||
# curve fit the data for baro 0 corrections - note corrections have oppsite sign to sensor bias
|
||||
baro_0_params['TC_B0_ID'] = int(np.median(sensor_baro_0['device_id']))
|
||||
|
||||
# find the min, max and reference temperature
|
||||
baro_0_params['TC_B0_TMIN'] = np.amin(sensor_baro_0['temperature'])
|
||||
baro_0_params['TC_B0_TMAX'] = np.amax(sensor_baro_0['temperature'])
|
||||
baro_0_params['TC_B0_TREF'] = 0.5 * (baro_0_params['TC_B0_TMIN'] + baro_0_params['TC_B0_TMAX'])
|
||||
temp_rel = sensor_baro_0['temperature'] - baro_0_params['TC_B0_TREF']
|
||||
temp_rel_resample = np.linspace(baro_0_params['TC_B0_TMIN']-baro_0_params['TC_B0_TREF'], baro_0_params['TC_B0_TMAX']-baro_0_params['TC_B0_TREF'], 100)
|
||||
temp_resample = temp_rel_resample + baro_0_params['TC_B0_TREF']
|
||||
|
||||
# fit data
|
||||
median_pressure =100*np.median(sensor_baro_0['pressure']);
|
||||
coef_baro_0_x = np.polyfit(temp_rel,median_pressure-100*sensor_baro_0['pressure'],5) # convert from hPa to Pa
|
||||
baro_0_params['TC_B0_X5'] = coef_baro_0_x[0]
|
||||
baro_0_params['TC_B0_X4'] = coef_baro_0_x[1]
|
||||
baro_0_params['TC_B0_X3'] = coef_baro_0_x[2]
|
||||
baro_0_params['TC_B0_X2'] = coef_baro_0_x[3]
|
||||
baro_0_params['TC_B0_X1'] = coef_baro_0_x[4]
|
||||
baro_0_params['TC_B0_X0'] = coef_baro_0_x[5]
|
||||
fit_coef_baro_0_x = np.poly1d(coef_baro_0_x)
|
||||
baro_0_x_resample = fit_coef_baro_0_x(temp_rel_resample)
|
||||
|
||||
# baro 0 vs temperature
|
||||
plt.figure(7,figsize=(20,13))
|
||||
|
||||
# draw plots
|
||||
plt.plot(sensor_baro_0['temperature'],100*sensor_baro_0['pressure']-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.xlabel('temperature (degC)')
|
||||
plt.grid()
|
||||
|
||||
pp.savefig()
|
||||
|
||||
#################################################################################
|
||||
|
||||
# close the pdf file
|
||||
pp.close()
|
||||
|
||||
# clase all figures
|
||||
plt.close("all")
|
||||
|
||||
# write correction parameters to file
|
||||
test_results_filename = ulog_file_name + ".params"
|
||||
file = open(test_results_filename,"w")
|
||||
file.write("# Sensor thermal compensation parameters\n")
|
||||
file.write("#\n")
|
||||
file.write("# Vehicle-Id Component-Id Name Value Type\n")
|
||||
|
||||
# accel 0 corrections
|
||||
key_list_accel = list(accel_0_params.keys())
|
||||
key_list_accel.sort
|
||||
for key in key_list_accel:
|
||||
if key == 'TC_A0_ID':
|
||||
type = "6"
|
||||
else:
|
||||
type = "9"
|
||||
file.write("1"+"\t"+"1"+"\t"+key+"\t"+str(accel_0_params[key])+"\t"+type+"\n")
|
||||
|
||||
# accel 1 corrections
|
||||
key_list_accel = list(accel_1_params.keys())
|
||||
key_list_accel.sort
|
||||
for key in key_list_accel:
|
||||
if key == 'TC_A1_ID':
|
||||
type = "6"
|
||||
else:
|
||||
type = "9"
|
||||
file.write("1"+"\t"+"1"+"\t"+key+"\t"+str(accel_1_params[key])+"\t"+type+"\n")
|
||||
|
||||
# accel 2 corrections
|
||||
key_list_accel = list(accel_2_params.keys())
|
||||
key_list_accel.sort
|
||||
for key in key_list_accel:
|
||||
if key == 'TC_A2_ID':
|
||||
type = "6"
|
||||
else:
|
||||
type = "9"
|
||||
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:
|
||||
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")
|
||||
|
||||
# gyro 0 corrections
|
||||
key_list_gyro = list(gyro_0_params.keys())
|
||||
key_list_gyro.sort()
|
||||
for key in key_list_gyro:
|
||||
if key == 'TC_G0_ID':
|
||||
type = "6"
|
||||
else:
|
||||
type = "9"
|
||||
file.write("1"+"\t"+"1"+"\t"+key+"\t"+str(gyro_0_params[key])+"\t"+type+"\n")
|
||||
|
||||
# gyro 1 corrections
|
||||
key_list_gyro = list(gyro_1_params.keys())
|
||||
key_list_gyro.sort()
|
||||
for key in key_list_gyro:
|
||||
if key == 'TC_G1_ID':
|
||||
type = "6"
|
||||
else:
|
||||
type = "9"
|
||||
file.write("1"+"\t"+"1"+"\t"+key+"\t"+str(gyro_1_params[key])+"\t"+type+"\n")
|
||||
|
||||
# gyro 2 corrections
|
||||
key_list_gyro = list(gyro_2_params.keys())
|
||||
key_list_gyro.sort()
|
||||
for key in key_list_gyro:
|
||||
if key == 'TC_G2_ID':
|
||||
type = "6"
|
||||
else:
|
||||
type = "9"
|
||||
file.write("1"+"\t"+"1"+"\t"+key+"\t"+str(gyro_2_params[key])+"\t"+type+"\n")
|
||||
|
||||
file.close()
|
||||
|
||||
print('Correction parameters written to ' + test_results_filename)
|
||||
print('Plots saved to ' + output_plot_filename)
|
||||
plt.savefig('{:s}_cal.pdf'.format(topic))
|
||||
|
||||
# JSON file generation
|
||||
# import json
|
||||
# print(json.dumps(params, indent=2))
|
||||
|
||||
body = ''
|
||||
for sensor in sorted(params.keys()):
|
||||
for param in sorted(params[sensor]['params'].keys()):
|
||||
label = params[sensor]['label']
|
||||
pdict = params[sensor]['params']
|
||||
if pdict[param]['type'] == 'INT':
|
||||
type_id = 6
|
||||
elif pdict[param]['type'] == 'FLOAT':
|
||||
type_id = 9
|
||||
val = pdict[param]['val']
|
||||
name = '{:s}_{:s}'.format(label, param)
|
||||
body += "1\t1\t{name:20s}\t{val:15g}\t{type_id:5d}\n".format(**locals())
|
||||
|
||||
# simple template file output
|
||||
text = """# Sensor thermal compensation parameters
|
||||
#
|
||||
# Vehicle-Id Component-Id Name Value Type
|
||||
{body:s}
|
||||
""".format(body=body)
|
||||
|
||||
with open(out_path, 'w') as f:
|
||||
f.write(text)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser(
|
||||
description='Analyse the sensor_gyro message data')
|
||||
parser.add_argument('filename', metavar='file.ulg', help='ULog input file')
|
||||
args = parser.parse_args()
|
||||
ulog_file_name = args.filename
|
||||
template_path = os.path.join(os.path.dirname(
|
||||
os.path.realpath(__file__)), 'templates')
|
||||
process_file(log_path=args.filename, out_path=ulog_file_name.replace('ulg', 'params'),
|
||||
template_path=template_path)
|
||||
|
||||
# vim: set et fenc=utf-8 ff=unix sts=0 sw=4 ts=4 :
|
||||
|
|
|
|||
Loading…
Reference in New Issue