#matlab bmes

%% BMES 202: code 2_1 

%% CODE 

function [data, c_head, csv_file]= ca02_fun1(csv_file) 

  % If user does not provide an input file name (nargin), prompt the user to select a csv file [uigetfile]

...and create a variable to hold the full file name (file name as a function output (csv_file [1xN] character array)).

    if nargin == 0

    [filename, path]=uigetfile('*.csv','Choose file','AG_1p_test1.csv');

    csv_file=fullfile(path, filename);

    end

  % Read in the data [csv_read] (data Mx3 numeric array) and the header information

...(c_head [2x1] cell array) [fopen, fgetl, fclose, !!  can't use uiimport], and provide these as function outputs.

  data=csvread(csv_file,2,0);

fileID=fopen(csv_file,'rt');

fgetl(fileID);

  c_head(1,1)={fgetl(fileID)};

c_head(2,1)={fgetl(fileID)};

fclose(fileID);

  % In figure1, visualize the raw data (time vs load, time vs displacement) [plot, subplot]

  figure(1);

subplot(2,1,1);

plot(data(:,1),data(:,2),'-')

xlabel('Time (sec)'), ylabel('Force (N)'), title('Time (sec) vs Force (N)')

  subplot(2,1,2);

plot(data(:,1),data(:,3),'-')

xlabel('Time (sec)'), ylabel('Displacement (mm)'), title('Time (sec) vs Displacement (mm)')

return;

%% BMES 202: code 2_2

%% CODE

function [dt_hold]  = ca02_fun2(data)  

% Create a new figure (figure 2), and plot time versus load (make sure load is positive)

figure(2)

plot(data(:,1),data(:,2),'-');

xlabel('Time (sec)'), ylabel('Load (N)');

title('Time (sec) vs Load (N)');

  % Prompt the user (msgbox, uiwait) to select the beginning of the hold [ginput]

uiwait(msgbox('Please select the beginning of the hold'));

[x y]=ginput(1);

  % Find the rows of the hold data based on the point the user selected [find]

rows=find(data(:,1)> x & -data(:,2)> y);

  % Create an output variable which holds the data in only the rows from step 8 (dt_hold)

dt_hold=data(rows,:);

% Overlay the 'hold' data to figure 2.

figure(2)

hold on

plot(dt_hold(:,1),-dt_hold(:,2)),'r-')

return;

  %% BMES 202: code 2_3

%% CODE

function [fit_parms, d_err] = ca02_fun3(dt_hold)  

  %(Analysis Function)

  % In a new figure (figure 3), plot the time vs displacement data (be sure to 'shift' the time data so that

...it starts at 0, and displacement is positive)

figure(3);

dt_new=dt_hold-dt_hold(1);

plot(dt_new(:,1),-dt_new(:,3),'-');

xlabel('TIme (sec)'), ylabel('Displacement (mm)')

  % Set the upper and lower limits on the parameters for your equation

ul=dt_new(1); %upper limit

ll=dt_new(end,1); %lower limit

  % Set the initial guess for the parameters a, b, c

a=-dt_new(1,3); %at t=0 and y=a

b=-dt_new(end,3)-(-dt_new(1,3)); %scaling factor: last-first value

  max_tc=a+0.632*b;

%The time that fits this description

possible_values=find(-dt_new(:,3)>max_tc*(1-1e-4) & -dt_new(:,3)<max_tc*(1+1e-4));

%therefore

c=dt_new(possible_values(1),1);

  % Fit the equation to the 'hold' displacement/time data [lsqcurvefit] and provide the 

...best-fit parameters as outputs of your function.

    fit_parms=lsqcurvefit(@creep_fun1 ,[a b c], dt_new(:,1), -dt_new(:,3), [0 0 0], 1.5*[a b c]);

  y_data= fit_parms(1)+fit_parms(2)*(1-exp(-dt_new(:,1)/fit_parms(3)));%from the equation given

  %plot

hold on;

plot(dt_new(:,1),-y_data,'ro')

  % Calculate the estimated displacement from the best-fit parameters, and overlay that data onto figure 3.

% Calculate the error of the fit (difference in estimate and actual displacement) [diff]

d_err=diff([y_data -dt_new(:,3)],2);

  return

%% BMES 202: code 2_4 

%% CODE

  function ca02_fun4()

% Call functions 1-3 in sequence

[data, c_head, csv_file]= ca02_fun1();

[dt_hold] = ca02_fun2(data);

[fit_parms, d_err] = ca02_fun3(dt_hold);

  % Calculate the root-mean-square error (RMSE)

rmse = rms(d_err)

% Take the output from functions 1-3, and format the following parameters 

% to screen for copy & paste (sprintf, disp):  'Filename, a(mm), b (mm), c

% (sec), RMSE' (RMSE is root-mean-square-error)

  [pth name ext] = fileparts(csv_file);

  out(1, 1) = {sprintf('%%Filename\ta(mm)\tb(mm)\tc(sec)\tRMSE')};

  % The first line of the output file sets up the headers for the variables

% that are being reported in addition to their units. They are formatted

% using sprintf.

  out(2, 1) = {sprintf('%s\t%.2f\t%.2f\t%.2f\t%.4f', [name ext], ...

    fit_parms(1), fit_parms(2), fit_parms(3), rmse)}; 

  % The second line of the output file prints the filename and other

% variables with the desired number of significant figures.

char(out) % The output is converted to characters and ready for 

  % copy-and-paste to Excel.

return