Rolling window for averaging using MATLAB - matlab

I have the following code, pasted below. I would like to change it to only average the 10 most recently filtered images and not the entire group of filtered images. The line I think I need to change is: Yout(k,p,q) = (Yout(k,p,q) + (y.^2))/2;, but how do I do it?
j=1;
K = 1:3600;
window = zeros(1,10);
Yout = zeros(10,column,row);
figure;
y = 0; %# Preallocate memory for output
%Load one image
for i = 1:length(K)
disp(i)
str = int2str(i);
str1 = strcat(str,'.mat');
load(str1);
D{i}(:,:) = A(:,:);
%Go through the columns and rows
for p = 1:column
for q = 1:row
if(mean2(D{i}(p,q))==0)
x = 0;
else
if(i == 1)
meanvalue = mean2(D{i}(p,q));
end
%Calculate the temporal mean value based on previous ones.
meanvalue = (meanvalue+D{i}(p,q))/2;
x = double(D{i}(p,q)/meanvalue);
end
%Filtering for 10 bands, based on the previous state
for k = 1:10
[y, ZState{k}] = filter(bCoeff{k},aCoeff{k},x,ZState{k});
Yout(k,p,q) = (Yout(k,p,q) + (y.^2))/2;
end
end
end
% for k = 2:10
% subplot(5,2,k)
% subimage(Yout(k)*5000, [0 100]);
% colormap jet
% end
% pause(0.01);
end
disp('Done Loading...')

The best way to do this (in my opinion) would be to use a circular-buffer to store your images. In a circular-, or ring-buffer, the oldest data element in the array is overwritten by the newest element pushed in to the array. The basics of making such a structure are described in the short Mathworks video Implementing a simple circular buffer.
For each iteration of you main loop that deals with a single image, just load a new image into the circular-buffer and then use MATLAB's built in mean function to take the average efficiently.
If you need to apply a window function to the data, then make a temporary copy of the frames multiplied by the window function and take the average of the copy at each iteration of the loop.

The line
Yout(k,p,q) = (Yout(k,p,q) + (y.^2))/2;
calculates a kind of Moving Average for each of the 10 bands over all your images.
This line calculates a moving average of meanvalue over your images:
meanvalue=(meanvalue+D{i}(p,q))/2;
For both you will want to add a buffer structure that keeps only the last 10 images.
To simplify it, you can also just keep all in memory. Here is an example for Yout:
Change this line: (Add one dimension)
Yout = zeros(3600,10,column,row);
And change this:
for q = 1:row
[...]
%filtering for 10 bands, based on the previous state
for k = 1:10
[y, ZState{k}] = filter(bCoeff{k},aCoeff{k},x,ZState{k});
Yout(i,k,p,q) = y.^2;
end
YoutAvg = zeros(10,column,row);
start = max(0, i-10+1);
for avgImg = start:i
YoutAvg(k,p,q) = (YoutAvg(k,p,q) + Yout(avgImg,k,p,q))/2;
end
end
Then to display use
subimage(Yout(k)*5000, [0 100]);
You would do sth. similar for meanvalue

Related

Updating histogram in a for-loop without growing y-data

I have had zero luck finding this elsewhere on the site, so here's my problem. I loop through about a thousand mat files, each with about 10,000 points of data. I'm trying to create an overall histogram of this data, but it's not very feasible to concatenate all this data to give to hist.
I was hoping to be able to create an N and Bin variable each loop using hist (y), then N and Bin would be recalculated on the next loop iteration by using hist(y_new). And so on and so on. That way the source data doesn't grow and when the loop finally ends, I can just use bar(). If this method wouldn't work, then I am very open-minded to other solutions.
Also, it is probably not safe to assume that the x data will remain constant throughout each iteration. I'm using 2012a.
Thanks for any help!!
I think the best solution here is to loop through your files twice: once to set the bins and once to do the histogram. But, if this is impossible in your case, here's a one shot solution that requires you to set the bin width beforehand.
clear; close all;
rng('default') % for reproducibility
% make example data
N = 10; % number of data files
M = 5; % length of data files
xs = cell(1,N);
for i = 1:N
xs{i} = trnd(1,1,M);
end
% parameters
width = 2;
% main
for i = 1:length(xs)
x = xs{i}; % "load data"
range = [min(x) max(x)];
binsPos = 0:width:range(2)+width;
binsNeg = fliplr( 0:-width:range(1)-width );
newBins = [binsNeg(1:end-1) binsPos];
newCounts = histc(x, newBins);
newCounts(end) = []; % last bin should always be zero, see help histc
if i == 1
counts = newCounts;
bins = newBins;
else
% combine new and old counts
allBins = min(bins(1), newBins(1)) : width : max(bins(end), newBins(end));
allCounts = zeros(1,length(allBins)-1);
allCounts(find(allBins==bins(1)) : find(allBins==bins(end-1))) = counts;
allCounts(find(allBins==newBins(1)) : find(allBins==newBins(end-1))) = ...
allCounts(find(allBins==newBins(1)) : find(allBins==newBins(end-1))) + newCounts;
bins = allBins;
counts = allCounts;
end
end
% check
figure
bar(bins(1:end-1) + width/2, counts)
xFull = [xs{:}];
[fullCounts] = histc(xFull, bins);
fullCounts(end) = [];
figure
bar(bins(1:end-1) + width/2, fullCounts)

How to add standrad deviation and moving average

What I want to is:
I got folder with 32 txt files and 1 excle file, each file contain some data in two columns: time, level.
I already managed to pull the data from the folder and open each file in Matlab and get the data from it. What I need to do is create plot for each data file.
each of the 32 plots should have:
Change in average over time
Standard deviation
With both of this things I am straggling can't make it work.
also I need to make another plot this time the plot should have the average over each minute from all the 32 files.
here is my code until now:
clc,clear;
myDir = 'my path';
dirInfo = dir([myDir,'*.txt']);
filenames = {dirInfo.name};
N = numel(filenames);
data=cell(N,1);
for i=1:N
fid = fopen([myDir,filenames{i}] );
data{i} = textscan(fid,'%f %f','headerlines',2);
fclose(fid);
temp1=data{i,1};
time=temp1{1};
level=temp1{2};
Average(i)=mean(level(1:find(time>60)));
AverageVec=ones(length(time),1).*Average(i);
Standard=std(level);
figure(i);
plot(time,level);
xlim([0 60]);
hold on
plot(time, AverageVec);
hold on
plot(time, Standard);
legend('Level','Average','Standard Deviation')
end
the main problam with this code is that i get only average over all the 60 sec not moving average, and the standard deviation returns nothing.
few things you need to know:
*temp1 is 1x2 cell
*time and level are 22973x1 double.
Apperently you need an alternative to movmean and movstd since they where introduced in 2016a. I combined the suggestion from #bla with two loops that correct for the edge effects.
function [movmean,movstd] = moving_ms(vec,k)
if mod(k,2)==0,k=k+1;end
L = length(vec);
movmean=conv(vec,ones(k,1)./k,'same');
% correct edges
n=(k-1)/2;
movmean(1) = mean(vec(1:n+1));
N=n;
for ct = 2:n
movmean(ct) = movmean(ct-1) + (vec(ct+n) - movmean(ct-1))/N;
N=N+1;
end
movmean(L) = mean(vec((L-n):L));
N=n;
for ct = (L-1):-1:(L-n)
movmean(ct) = movmean(ct+1) + (vec(ct-n) - movmean(ct+1))/N;
N=N+1;
end
%mov variance
movstd = nan(size(vec));
for ct = 1:n
movstd(ct) = sum((vec(1:n+ct)-movmean(ct)).^2);
movstd(ct) = movstd(ct)/(n+ct-1);
end
for ct = n+1:(L-n)
movstd(ct) = sum((vec((ct-n):(ct+n))-movmean(ct)).^2);
movstd(ct) = movstd(ct)/(k-1);
end
for ct = (L-n):L
movstd(ct) = sum((vec((ct-n):L)-movmean(ct)).^2);
movstd(ct) = movstd(ct)/(L-ct+n);
end
movstd=sqrt(movstd);
Someone with matlab >=2016a can compare them using:
v=rand(1,1E3);m1 = movmean(v,101);s1=movstd(v,101);
[m2,s2] = moving_ms(v,101);
x=1:1E3;figure(1);clf;
subplot(1,2,1);plot(x,m1,x,m2);
subplot(1,2,2);plot(x,s1,x,s2);
It should show a single red line since the blue line is overlapped.

How to force MATLAB function area to hold on in figure

I'm working on this function which gets axis handler and data, and is supposed to plot it correctly in the axis. The function is called in for loop. It's supposed to draw the multiple data in one figure. My resulted figure is shown below.
There are only two correctly plotted graphs (those with four colors). Others miss areas plotted before the final area (red area is the last plotted area in each graph). But the script is same for every axis. So where can be the mistake? The whole function is written below.
function [] = powerSpectrumSmooth(axis,signal,fs)
N= length(signal);
samplesPer1Hz = N/fs;
delta = int16(3.5*samplesPer1Hz); %last sample of delta frequncies
theta = int16(7.5*samplesPer1Hz); %last sample of theta frequncies
alpha = int16(13*samplesPer1Hz); %last sample of alpha frequncies
beta = int16(30*samplesPer1Hz); %last sample of beta frequncies
x=fft(double(signal));
powerSpectrum = 20*log10(abs(real(x)));
smoothPS=smooth(powerSpectrum,51);
PSmin=min(powerSpectrum(1:beta));
y1=[(smoothPS(1:delta)); zeros(beta-delta,1)+PSmin];
y2=[zeros(delta-1,1)+PSmin; (smoothPS(delta:theta)); zeros(beta-theta,1)+PSmin];
y3=[zeros(theta-1,1)+PSmin; (smoothPS(theta:alpha)); zeros(beta-alpha,1)+PSmin];
y4=[zeros(alpha-1,1)+PSmin; (smoothPS(alpha:beta))];
a1=area(axis,1:beta,y1);
set(a1,'FaceColor','yellow')
hold on
a2=area(axis,1:beta,y2);
set(a2,'FaceColor','blue')
a3=area(axis,1:beta,y3);
set(a3,'FaceColor','green')
a4=area(axis,1:beta,y4);
set(a4,'FaceColor','red')
ADDED
And here is the function which calls the function above.
function [] = drawPowerSpectrum(axesContainer,dataContainer,fs)
size = length(axesContainer);
for l=1:size
powerSpectrumSmooth(axesContainer{l},dataContainer{l},fs)
set(axesContainer{l},'XTickLabel','')
set(axesContainer{l},'YTickLabel','')
uistack(axesContainer{l}, 'top');
end
ADDED 29th July
Here is a script which reproduces the error, so you can run it in your computer. Before running it again you might need to clear variables.
len = 9;
axesContainer = cell(len,1);
x = [0.1,0.4,0.7,0.1,0.4,0.7,0.1,0.4,0.7];
y = [0.1,0.1,0.1,0.4,0.4,0.4,0.7,0.7,0.7];
figure(1)
for i=1:len
axesContainer{i} = axes('Position',[x(i),y(i),0.2,0.2]);
end
dataContainer = cell(len,1);
N = 1500;
for i=1:len
dataContainer{i} = rand(1,N)*100;
end
for l=1:len
y1=[(dataContainer{l}(1:N/4)) zeros(1,3*N/4)];
y2=[zeros(1,N/4) (dataContainer{l}(N/4+1:(2*N/4))) zeros(1,2*N/4)];
y3=[zeros(1,2*N/4) (dataContainer{l}(2*N/4+1:3*N/4)) zeros(1,N/4)];
y4=[zeros(1,3*N/4) (dataContainer{l}(3*N/4+1:N))];
axes=axesContainer{l};
a1=area(axes,1:N,y1);
set(a1,'FaceColor','yellow')
hold on
a2=area(axes,1:N,y2);
set(a2,'FaceColor','blue')
hold on
a3=area(axes,1:N,y3);
set(a3,'FaceColor','green')
hold on
a4=area(axes,1:N,y4);
set(a4,'FaceColor','red')
set(axes,'XTickLabel','')
set(axes,'YTickLabel','')
end
My result of this script is plotted below:
Again only one picture contains all areas.
It looks like that every call to plot(axes,data) deletes whatever was written in axes.
Important note: Do not use a variable name the same as a function. Do not call something sin ,plot or axes!! I changed it to axs.
To solve the problem I just used the classic subplot instead of creating the axes as you did:
len = 9;
axesContainer = cell(len,1);
x = [0.1,0.4,0.7,0.1,0.4,0.7,0.1,0.4,0.7];
y = [0.1,0.1,0.1,0.4,0.4,0.4,0.7,0.7,0.7];
figure(1)
dataContainer = cell(len,1);
N = 1500;
for i=1:len
dataContainer{i} = rand(1,N)*100;
end
for l=1:len
y1=[(dataContainer{l}(1:N/4)) zeros(1,3*N/4)];
y2=[zeros(1,N/4) (dataContainer{l}(N/4+1:(2*N/4))) zeros(1,2*N/4)];
y3=[zeros(1,2*N/4) (dataContainer{l}(2*N/4+1:3*N/4)) zeros(1,N/4)];
y4=[zeros(1,3*N/4) (dataContainer{l}(3*N/4+1:N))];
axs=subplot(3,3,l);
a1=area(axs,1:N,y1);
set(a1,'FaceColor','yellow')
hold on
a2=area(axs,1:N,y2);
set(a2,'FaceColor','blue')
hold on
a3=area(axs,1:N,y3);
set(a3,'FaceColor','green')
hold on
a4=area(axs,1:N,y4);
set(a4,'FaceColor','red')
set(axs,'XTickLabel','')
set(axs,'YTickLabel','')
axis tight % this is to beautify it.
end
As far as I know, you can still save the axs variable in an axescontainer and then modify the properties you want (like location).
I found out how to do what I needed.
len = 8;
axesContainer = cell(len,1);
x = [0.1,0.4,0.7,0.1,0.4,0.7,0.1,0.4];
y = [0.1,0.1,0.1,0.4,0.4,0.4,0.7,0.7];
figure(1)
for i=1:len
axesContainer{i} = axes('Position',[x(i),y(i),0.2,0.2]);
end
dataContainer = cell(len,1);
N = 1500;
for i=1:len
dataContainer{i} = rand(1,N)*100;
end
for l=1:len
y1=[(dataContainer{l}(1:N/4)) zeros(1,3*N/4)];
y2=[zeros(1,N/4) (dataContainer{l}(N/4+1:(2*N/4))) zeros(1,2*N/4)];
y3=[zeros(1,2*N/4) (dataContainer{l}(2*N/4+1:3*N/4)) zeros(1,N/4)];
y4=[zeros(1,3*N/4) (dataContainer{l}(3*N/4+1:N))];
axes=axesContainer{l};
Y=[y1',y2',y3',y4'];
a=area(axes,Y);
set(axes,'XTickLabel','')
set(axes,'YTickLabel','')
end
The area is supposed to work with matrices like this. The tricky part is, that the signal in every next column is not plotted absolutely, but relatively to the data in previous column. That means, if at time 1 the data in first column has value 1 and data in second column has value 4, the second column data is ploted at value 5. Source: http://www.mathworks.com/help/matlab/ref/area.html

intermittent loops in matlab

Hello again logical friends!
I’m aware this is quite an involved question so please bear with me! I think I’ve managed to get it down to two specifics:- I need two loops which I can’t seem to get working…
Firstly; The variable rollers(1).ink is a (12x1) vector containing ink values. This program shares the ink equally between rollers at each connection. I’m attempting to get rollers(1).ink to interact with rollers(2) only at specific timesteps. The ink should transfer into the system once for every full revolution i.e. nTimesSteps = each multiple of nBins_max. The ink should not transfer back to rollers(1).ink as the system rotates – it should only introduce ink to the system once per revolution and not take any back out. Currently I’ve set rollers(1).ink = ones but only for testing. I’m truly stuck here!
Secondly; The reason it needs to do this is because at the end of the sim I also wish to remove ink in the form of a printed image. The image should be a reflection of the ink on the last roller in my system and half of this value should be removed from the last roller and taken out of the system at each revolution. The ink remaining on the last roller should be recycled and ‘re-split’ in the system ready for the next rotation.
So…I think it’s around the loop beginning line86 where I need to do all this stuff. In pseudo, for the intermittent in-feed I’ve been trying something like:
For k = 1:nTimeSteps
While nTimesSteps = mod(nTimeSteps, nBins_max) == 0 % This should only output when nTimeSteps is a whole multiple of nBins_max i.e. one full revolution
‘Give me the ink on each segment at each time step in a matrix’
End
The output for averageAmountOfInk is the exact format I would like to return this data except I don’t really need the average, just the actual value at each moment in time. I keep getting errors for dimensional mismatches when I try to re-create this using something like:
For m = 1:nTimeSteps
For n = 1:N
Rollers(m,n) = rollers(n).ink’;
End
End
I’ll post the full code below if anyone is interested to see what it does currently. There’s a function at the end also which of course needs to be saved out to a separate file.
I’ve posted variations of this question a couple of times but I’m fully aware it’s quite a tricky one and I’m finding it difficult to get my intent across over the internets!
If anyone has any ideas/advice/general insults about my lack of programming skills then feel free to reply!
%% Simple roller train
% # Single forme roller
% # Ink film thickness = 1 micron
clc
clear all
clf
% # Initial state
C = [0,70; % # Roller centres (x, y)
10,70;
21,61;
11,48;
21,34;
27,16;
0,0
];
R = [5.6,4.42,9.8,6.65,10.59,8.4,23]; % # Roller radii (r)
% # Direction of rotation (clockwise = -1, anticlockwise = 1)
rotDir = [1,-1,1,-1,1,-1,1]';
N = numel(R); % # Amount of rollers
% # Find connected rollers
isconn = #(m, n)(sum(([1, -1] * C([m, n], :)).^2)...
-sum(R([m, n])).^2 < eps);
[Y, X] = meshgrid(1:N, 1:N);
conn = reshape(arrayfun(isconn, X(:), Y(:)), N, N) - eye(N);
% # Number of bins for biggest roller
nBins_max = 50;
nBins = round(nBins_max*R/max(R))';
% # Initialize roller struct
rollers = struct('position',{}','ink',{}','connections',{}',...
'rotDirection',{}');
% # Initialise matrices for roller properties
for ii = 1:N
rollers(ii).ink = zeros(1,nBins(ii));
rollers(ii).rotDirection = rotDir(ii);
rollers(ii).connections = zeros(1,nBins(ii));
rollers(ii).position = 1:nBins(ii);
end
for ii = 1:N
for jj = 1:N
if(ii~=jj)
if(conn(ii,jj) == 1)
connInd = getConnectionIndex(C,ii,jj,nBins(ii));
rollers(ii).connections(connInd) = jj;
end
end
end
end
% # Initialize averageAmountOfInk and calculate initial distribution
nTimeSteps = 1*nBins_max;
averageAmountOfInk = zeros(nTimeSteps,N);
inkPerSeg = zeros(nTimeSteps,N);
for ii = 1:N
averageAmountOfInk(1,ii) = mean(rollers(ii).ink);
end
% # Iterate through timesteps
for tt = 1:nTimeSteps
rollers(1).ink = ones(1,nBins(1));
% # Rotate all rollers
for ii = 1:N
rollers(ii).ink(:) = ...
circshift(rollers(ii).ink(:),rollers(ii).rotDirection);
end
% # Update all roller-connections
for ii = 1:N
for jj = 1:nBins(ii)
if(rollers(ii).connections(jj) ~= 0)
index1 = rollers(ii).connections(jj);
index2 = find(ii == rollers(index1).connections);
ink1 = rollers(ii).ink(jj);
ink2 = rollers(index1).ink(index2);
rollers(ii).ink(jj) = (ink1+ink2)/2;
rollers(index1).ink(index2) = (ink1+ink2)/2;
end
end
end
% # Calculate average amount of ink on each roller
for ii = 1:N
averageAmountOfInk(tt,ii) = sum(rollers(ii).ink);
end
end
image(5:20) = (rollers(7).ink(5:20))./2;
inkPerSeg1 = [rollers(1).ink]';
inkPerSeg2 = [rollers(2).ink]';
inkPerSeg3 = [rollers(3).ink]';
inkPerSeg4 = [rollers(4).ink]';
inkPerSeg5 = [rollers(5).ink]';
inkPerSeg6 = [rollers(6).ink]';
inkPerSeg7 = [rollers(7).ink]';
This is an extended comment rather than a proper answer, but the comment box is a bit too small ...
Your code overwhelms me, I can't see the wood for the trees. I suggest that you eliminate all the stuff we don't need to see to help you with your immediate problem (all those lines drawing figures for example) -- I think it will help you to debug your code yourself to put all that stuff into functions or scripts.
Your code snippet
For k = 1:nTimeSteps
While nTimesSteps = mod(nTimeSteps, nBins_max) == 0
‘Give me the ink on each segment at each time step in a matrix’
End
might be (I don't quite understand your use of the while statement, the word While is not a Matlab keyword, and as you have written it the value returned by the statement doesn't change from iteration to iteration) equivalent to
For k = 1:nBins_max:nTimeSteps
‘Give me the ink on each segment at each time step in a matrix’
End
You seem to have missed an essential feature of Matlab's colon operator ...
1:8 = [1 2 3 4 5 6 7 8]
but
1:2:8 = [1 3 5 7]
that is, the second number in the triplet is the stride between successive elements.
Your matrix conn has a 1 at the (row,col) where rollers are connected, and a 0 elsewhere. You can find the row and column indices of all the 1s like this:
[ri,ci] = find(conn==1)
You could then pick up the (row,col) locations of the 1s without the nest of loops and if statements that begins
for ii = 1:N
for jj = 1:N
if(ii~=jj)
if(conn(ii,jj) == 1)
I could go on, but won't, that's enough for one comment.

Kmean plotting in matlab

I am on a project thumb recognition system on matlab. I implemented Kmean Algorithm and I got results as well. Actually now I want to plot the results like here they done. I am trying but couldn't be able to do so. I am using the following code.
load training.mat; % loaded just to get trainingData variable
labelData = zeros(200,1);
labelData(1:100,:) = 0;
labelData(101:200,:) = 1;
k=2;
[trainCtr, traina] = kmeans(trainingData,k);
trainingResult1=[];
for i=1:k
trainingResult1 = [trainingResult1 sum(trainCtr(1:100)==i)];
end
trainingResult2=[];
for i=1:k
trainingResult2 = [trainingResult2 sum(trainCtr(101:200)==i)];
end
load testing.mat; % loaded just to get testingData variable
c1 = zeros(k,1054);
c1 = traina;
cluster = zeros(200,1);
for j=1:200
testTemp = repmat(testingData(j,1:1054),k,1);
difference = sum((c1 - testTemp).^2, 2);
[value index] = min(difference);
cluster(j,1) = index;
end
testingResult1 = [];
for i=1:k
testingResult1 = [testingResult1 sum(cluster(1:100)==i)];
end
testingResult2 = [];
for i=1:k
testingResult2 = [testingResult2 sum(cluster(101:200)==i)];
end
in above code trainingData is matrix of 200 X 1054 in which 200 are images of thumbs and 1054 are columns. actually each image is of 25 X 42. I reshaped each image in to row matrix (1 X 1050) and 4 other (some features) columns so total of 1054 columns are in each image. Similarly testingData I made it in the similar manner as I made testingData It is also the order of 200 X 1054. Now my Problem is just to plot the results as they did in here.
After selecting 2 features, you can just follow the example. Start a figure, use hold on, and use plot or scatter to plot the centroids and the data points. E.g.
selectedFeatures = [42,43];
plot(trainingData(trainCtr==1,selectedFeatures(1)),
trainingData(trainCtr==1,selectedFeatures(2)),
'r.','MarkerSize',12)
Would plot the selected feature values of the data points in cluster 1.