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close all; clear all; clc;
filename = 'imu.csv';
data = load_IMU_data_from_csv( filename ) ;
t = data.t;
ax = data.ax;
ay = data.ay;
az = data.az;
gx = data.gx;
gy = data.gy;
gz = data.gz;
% analyze the noise (deviation from mean) on each axis and compare the
% emperical distribution to one generated from a quantized Gaussian
% distrubtuion with the same variance.

Matt Rich
committed
quantized_noise_gauss_anal( ax , 'Accelerometer' , 'x-axis' , 'gs' )
quantized_noise_gauss_anal( ay , 'Accelerometer' , 'y-axis', 'gs' )
quantized_noise_gauss_anal( az , 'Accelerometer' , 'z-axis', 'gs' )
quantized_noise_gauss_anal( gx , 'Gyroscope' , 'x-axis' , 'rad/s')
quantized_noise_gauss_anal( gy , 'Gyroscope' , 'y-axis' , 'rad/s' )
quantized_noise_gauss_anal( gz , 'Gyroscope' , 'z-axis' , 'rad/s' )