def main():
#----------------- 1.parameters for ACCELEROMETER -----------------------------
sensor_ref = 9.81
sensor_res = 10
noise_window = 20; # noise_window used to eliminate first and last noise_window rows
noise_threshold = 40; # noise_threshold: max standard deviation value of data
#files contain raw data
file_z_down = "data\\acc_z_down.txt"
file_z_up = "data\\acc_z_up.txt"
file_y_down = "data\\acc_y_down.txt"
file_y_up = "data\\acc_y_up.txt"
file_x_down = "data\\acc_x_down.txt"
file_x_up = "data\\acc_x_up.txt"
# ------------------------1. read raw measurements from log file ------------------------
# measurements is an array of x,y,z
#
meas_z_down = accelerometer_utils.read_log(file_z_down)
meas_z_up = accelerometer_utils.read_log(file_z_up)
meas_y_down = accelerometer_utils.read_log(file_y_down)
meas_y_up = accelerometer_utils.read_log(file_y_up)
meas_x_down = accelerometer_utils.read_log(file_x_down)
meas_x_up = accelerometer_utils.read_log(file_x_up)
if (len(meas_z_down) == 0 or len(meas_z_up) == 0 or len(meas_y_down) == 0 or len(meas_y_up) == 0 or len(meas_x_down) == 0 or len(meas_x_up) == 0):
print("Error: found zero in log file!")
sys.exit(1)
else:
print("found "+str(len(meas_z_down))+" records after read log")
#2. low pass filter
smooth_z_down = accelerometer_utils.lowpass_filter(meas_z_down)
smooth_z_up = accelerometer_utils.lowpass_filter(meas_z_up)
smooth_y_down = accelerometer_utils.lowpass_filter(meas_y_down)
smooth_y_up = accelerometer_utils.lowpass_filter(meas_y_up)
smooth_x_down = accelerometer_utils.lowpass_filter(meas_x_down)
smooth_x_up = accelerometer_utils.lowpass_filter(meas_x_up)
if(len(smooth_z_down) == 0 or len(smooth_z_up) == 0 or len(smooth_y_down) == 0 or len(smooth_y_up) == 0 or len(smooth_x_down) == 0 or len(smooth_x_up) == 0):
print("Error: found zero after low pass filter.")
sys.exit()
else:
print ("found" + str(len(smooth_z_down)) + " after low pass filter.")
accelerometer_utils.plot_lowpass_filter(True, meas_z_up, smooth_z_up)
#3. filter out noisy measurements by cutting the samples that have large standard deviation
# right now we will not use it.
#flt_meas, flt_idx = accelerometer_utils.filter_meas(measurements, noise_window, noise_threshold)
#print("remaining " + str(len(flt_meas)) + " after low pass")
#if len(flt_meas) == 0:
# print("Error: found zero IMU in log file after low pass!") #+options.sensor+"_RAW measurements for aircraft with id "+options.ac_id+" in log file after low pass!")
# sys.exit(1)
#4. parameters declare
# Parametric function: 'v' is the parameter vector, 'x' the independent varible
fp = lambda v, x: numpy.dot(x, v) # v[0]/(x**v[1])*sin(v[2]*x)
# Error function
e = lambda v, x, y: (fp(v,x)-y)
# Initial parameter value
v0 = [[1, 0, 0],
[0, 1, 0],
[0, 0, 1],
[1, 1, 1]]
# Optimal value of y
y = [[0, 0, 1],
[0, 0, -1],
[0, 1, 0],
[0, -1, 0],
[1, 0, 0],
[-1, 0, 0]]
#normalize smooth measures
x = []
norm = accelerometer_utils.normalize_mean_meas(smooth_z_down)
x.append([norm[0], norm[1], norm[2], 1])
norm = accelerometer_utils.normalize_mean_meas(smooth_z_up)
x.append([norm[0], norm[1], norm[2], 1])
norm = accelerometer_utils.normalize_mean_meas(smooth_y_down)
x.append([norm[0], norm[1], norm[2], 1])
norm = accelerometer_utils.normalize_mean_meas(smooth_y_up)
x.append([norm[0], norm[1], norm[2], 1])
norm = accelerometer_utils.normalize_mean_meas(smooth_x_down)
x.append([norm[0], norm[1], norm[2], 1])
norm = accelerometer_utils.normalize_mean_meas(smooth_x_up)
x.append([norm[0], norm[1], norm[2], 1])
#calculate directly
raw_params = accelerometer_utils.cal_raw_params(x, y) # x is raw measures, y is expected measures (both are normalized)
calibration_params = accelerometer_utils.cal_calibration_params(raw_params)
print "misalignment matrix:"
print calibration_params[0]
print "scale factors:"
print calibration_params[1]
print "offset:"
print calibration_params[2]
def main():
#files contain raw data
file_z_down = "data\\acc_z_down.txt"
file_z_up = "data\\acc_z_up.txt"
file_y_down = "data\\acc_y_down.txt"
file_y_up = "data\\acc_y_up.txt"
file_x_down = "data\\acc_x_down.txt"
file_x_up = "data\\acc_x_up.txt"
# ------------------------1. read raw measurements from log file ------------------------
# measurements is an array of x,y,z
#
meas_z_down = accelerometer_utils.read_log(file_z_down)
meas_z_up = accelerometer_utils.read_log(file_z_up)
meas_y_down = accelerometer_utils.read_log(file_y_down)
meas_y_up = accelerometer_utils.read_log(file_y_up)
meas_x_down = accelerometer_utils.read_log(file_x_down)
meas_x_up = accelerometer_utils.read_log(file_x_up)
if (len(meas_z_down) == 0 or len(meas_z_up) == 0 or len(meas_y_down) == 0 or len(meas_y_up) == 0 or len(meas_x_down) == 0 or len(meas_x_up) == 0):
print("Error: found zero in log file!")
sys.exit(1)
else:
print("found "+str(len(meas_z_down))+" records after read log")
##2. low pass filter --> RIGHT NOW WE DONT USE IT
#smooth_z_down = accelerometer_utils.lowpass_filter(meas_z_down)
#smooth_z_up = accelerometer_utils.lowpass_filter(meas_z_up)
#smooth_y_down = accelerometer_utils.lowpass_filter(meas_y_down)
#smooth_y_up = accelerometer_utils.lowpass_filter(meas_y_up)
#smooth_x_down = accelerometer_utils.lowpass_filter(meas_x_down)
#smooth_x_up = accelerometer_utils.lowpass_filter(meas_x_up)
#if(len(smooth_z_down) == 0 or len(smooth_z_up) == 0 or len(smooth_y_down) == 0 or len(smooth_y_up) == 0 or len(smooth_x_down) == 0 or len(smooth_x_up) == 0):
# print("Error: found zero after low pass filter.")
# sys.exit()
#else:
# print ("found" + str(len(smooth_z_down)) + " after low pass filter.")
# accelerometer_utils.plot_lowpass_filter(True, meas_z_up, smooth_z_up)
smooth_z_down = meas_z_down
smooth_z_up = meas_z_up
smooth_y_down = meas_y_down
smooth_y_up = meas_y_up
smooth_x_down = meas_x_down
smooth_x_up = meas_x_up
#3. calculate parameter matrix directly
# Optimal value of y
y = [[0, 0, 1],
[0, 0, -1],
[0, 1, 0],
[0, -1, 0],
[1, 0, 0],
[-1, 0, 0]]
#normalize smooth measures
x = []
norm = accelerometer_utils.normalize_mean_meas(smooth_z_down)
x.append([norm[0], norm[1], norm[2], 1])
norm = accelerometer_utils.normalize_mean_meas(smooth_z_up)
x.append([norm[0], norm[1], norm[2], 1])
norm = accelerometer_utils.normalize_mean_meas(smooth_y_down)
x.append([norm[0], norm[1], norm[2], 1])
norm = accelerometer_utils.normalize_mean_meas(smooth_y_up)
x.append([norm[0], norm[1], norm[2], 1])
norm = accelerometer_utils.normalize_mean_meas(smooth_x_down)
x.append([norm[0], norm[1], norm[2], 1])
norm = accelerometer_utils.normalize_mean_meas(smooth_x_up)
x.append([norm[0], norm[1], norm[2], 1])
#calculate directly
raw_params = accelerometer_utils.cal_raw_params(x, y) # x is raw measures, y is expected measures (both are normalized)
calibration_params = accelerometer_utils.cal_calibration_params(raw_params)
print "misalignment matrix:"
print calibration_params[0]
print "scale factors:"
print calibration_params[1]
print "offset:"
print calibration_params[2]
