Added a .gitignore file. So far it ignores autosave matlab files. Added...

Added a .gitignore file. So far it ignores autosave matlab files. Added several functions and scripts to start analyzing the IMU data and characterizing the noise. They are rough starts at this point.

controls/.gitignore

+*.asv
\ No newline at end of file

controls/MATLAB/IMU/accelerometer_noise_analysis.m

+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. 
+quantized_accel_noise_gauss_anal( ax , 'x-axis')
+quantized_accel_noise_gauss_anal( ay , 'y-axis')
+quantized_accel_noise_gauss_anal( az , 'z-axis')
\ No newline at end of file

controls/MATLAB/IMU/load_IMU_data_from_csv.m

+function data = load_IMU_data_from_csv( filename )
+% load_IMU_data_from_csv load data from csv file into workspace 
+%
+%SYNTAX
+% data = load_IMU_data_from_csv( filename )
+% 
+%DESCRIPTION
+% ...to be written...
+%
+%
+%Inputs:
+% filename - string specifying the file 
+%
+%Options:
+% none 
+%
+%Outputs:
+% data - IMU data loaded into a structure (specify more later)  
+%
+%EXAMPLES
+%
+%NOTES
+% This function currently assumes the csv format that was used as of the 
+% date: 11/15/2016 (m/d/y) 
+%
+%AUTHORS
+% Matt Rich - m87rich@iastate.edu
+%
+%DEVELOPERS
+%
+
+%
+%DEVELOPMENT NOTES
+%
+% dates are in m-d-y format 
+%
+% Initial bare bones function. 
+% - Matt Rich 11-15-2016
+%
+delimiter = ',';
+formatSpec = '%f%f%f%f%f%f%f%[^\n\r]';
+fileID = fopen(filename,'r');
+dataArray = textscan(fileID, formatSpec, 'Delimiter', delimiter, 'EmptyValue' ,NaN, 'ReturnOnError', false);
+fclose(fileID);
+data.t = dataArray{:, 1};
+data.ax = dataArray{:, 2};
+data.ay = dataArray{:, 3};
+data.az = dataArray{:, 4};
+data.gx = dataArray{:, 5};
+data.gy = dataArray{:, 6};
+data.gz = dataArray{:, 7};
+
+
+end
+

controls/MATLAB/IMU/microcart_imu_analysis_1.m

+% function microcart_imu_analysis_1
+
+close all;  clc; 
+filename = 'imu.csv';
+delimiter = ',';
+formatSpec = '%f%f%f%f%f%f%f%[^\n\r]';
+fileID = fopen(filename,'r');
+dataArray = textscan(fileID, formatSpec, 'Delimiter', delimiter, 'EmptyValue' ,NaN, 'ReturnOnError', false);
+fclose(fileID);
+t = dataArray{:, 1};
+ax = dataArray{:, 2};
+ay = dataArray{:, 3};
+az = dataArray{:, 4};
+gx = dataArray{:, 5};
+gy = dataArray{:, 6};
+gz = dataArray{:, 7};
+clearvars filename delimiter formatSpec fileID dataArray ans;
+
+% magnitude of acceleration in gs 
+a = ( ax.^2 + ay.^2 + az.^2 ).^0.5 ; 
+
+% magnitude of deviation from ideal 
+ea1 = 1 - a ;
+
+% magnitude of deviation from mean 
+ea2 = a - mean(a) ; 
+
+% RMS error 
+ea1rms = sqrt(1/length(ea1)*(ea1')*ea1) ; 
+ea2rms = sqrt(1/length(ea2)*(ea2')*ea2) ; 
+
+subplot(3,1,1); 
+plot(t, a , t , ones(length(t),1)); axis([min(t),max(t),min([a;1])-0.01,max([a;1])+0.01]); grid;
+xlabel('Time (s)'); ylabel('Acceleration (gs)'); 
+legend('Measured','Ideal'); 
+
+
+subplot(3,1,2); 
+plot(t, ea1 ); axis([min(t),max(t),min(ea1)-0.01,max(ea1)+0.01]); grid;
+xlabel('Time (s)'); ylabel('Error From Ideal (gs)'); 
+
+subplot(3,1,3); 
+plot(t, ea2 ); axis([min(t),max(t),min(ea2)-0.01,max(ea2)+0.01]); grid;
+xlabel('Time (s)'); ylabel('Error From Mean (gs)'); 
+
+% 
+figure; 
+X = fft(ea2); 
+N = length(ea2); 
+fs = 1/(t(2)-t(1)); 
+F = ([-N/2:N/2-1]/N)*fs; %frequency axis for plotting FFTs 
+% subplot(2,1,1); 
+plot(F,fftshift(abs(X))); grid; 
+xlabel('frequency (Hz)');
+ylabel(['|FFT(x)|']);
+% subplot(2,1,2); 
+% plot(F,20*log10(fftshift(abs(X)))); grid; 
+% xlabel('frequency (Hz)');
+% ylabel('|FFT(x)| dB');
+suptitle('x = Error From Mean (gs)'); 
+
+
+eax = ax - mean(ax); 
+
+vx = var(ax); 
+sigmax = sqrt(vx);  
+
+figure; 
+
+bins = unique(eax) ; 
+dbins = bins(2)-bins(1); % the quantization levels consistent 
+
+bins_right = bins + dbins/2; 
+bins_left = bins - dbins/2; 
+cgauss_right = normcdf(bins_right,0,sigmax); 
+cgauss_left = normcdf(bins_left,0,sigmax); 
+
+empf = length(eax)*(cgauss_right-cgauss_left); 
+stairs(bins_left,empf,'r','LineWidth',1) ; 
+xlabel('Error From Mean (gs)'); 
+ylabel('Occurences'); 
+
+grid; 
+hold on; 
+h = hist(eax,bins); 
+stem(bins,h); 
+
+legend(['Quantized Gaussian Noise: N(0,',num2str(vx),')'],'Emperical Distribution')
+
+
+quantized_accel_noise_gauss_anal( ax )
+
+
+
+% end
+
+
+

controls/MATLAB/IMU/quantized_accel_noise_gauss_anal.m

+function varargout = quantized_accel_noise_gauss_anal( a , axis )
+% quantized_accel_noise_gauss_anal analysis for quantized acelerometer data 
+%
+%SYNTAX
+% quantized_accel_noise_gauss_anal( a , axis )
+% 
+%DESCRIPTION
+% ...to be written...
+%
+%
+%Inputs:
+%    a - vector of a single axis accelerometer reading 
+% axis - string specifying the axis label 
+%
+%Options:
+% none 
+%
+%Outputs:
+% none 
+%
+%EXAMPLES
+%
+%NOTES
+% This function is only really meaningful for a static (not moving) test. 
+%
+%AUTHORS
+% Matt Rich - m87rich@iastate.edu
+%
+%DEVELOPERS
+%
+
+%
+%DEVELOPMENT NOTES
+%
+% dates are in m-d-y format 
+%
+% Initial bare bones function. 
+% - Matt Rich 11-15-2016
+%
+
+mu = mean(a); 
+v = var(a); 
+sigma = sqrt(v); 
+
+ea = a - mu; 
+
+mue = mean(ea); %calculate the mean of the error from mean
+
+
+figure; 
+
+bins = unique(ea) ; 
+dbins = bins(2)-bins(1); % assuming the quantization levels consistent 
+
+bins_right = bins + dbins/2; 
+bins_left = bins - dbins/2; 
+cgauss_right = normcdf(bins_right,mue,sigma); 
+cgauss_left = normcdf(bins_left,mue,sigma); 
+
+emp_dist = length(ea)*(cgauss_right-cgauss_left); 
+stairs(bins_left,emp_dist,'r','LineWidth',1) ; 
+xlabel('Error From Mean (gs)'); 
+ylabel('Occurences'); 
+grid; 
+hold on; 
+h = hist(ea,bins); 
+stem(bins,h); 
+legend(['Quantized Gaussian Noise: N(',num2str(mue),',',num2str(v),')'],'Emperical Distribution'); 
+title(['Accelerometer deviation from mean on ',axis]); 
+
+
+end
+