I have data which contains peaks that I want to detect with the function findpeaks from the Signal Processing Toolbox, in Matlab.
You will understand my problem with some images :
-> Here you have my data in red, and the finding peaks in blue ; with this code :
[peakHeight, peakLocation] = findpeaks(data,'minPeakHeight',4.3);
I wanted to only keep the first ones (yellow circles).
Sometimes, the first peak in my "red block" is not at the yellow circles location, like this :
I tried many parameters of findpeaks, like 'MinPeakProminence','Threshold' and 'MinPeakDistance', but the best I obtain is this, with findpeaks(data,'minPeakHeight',4.3,'MinPeakProminence',4); :
As my peaks in my data are not really flat, I don't always keep the first good ones.
In my research I found and tried this : islocalmax(data,'FlatSelection','first');, but it doesn't work well too.
To sum up, I want to have one peak for every "red block". And these peaks need to be just after the rise.
So please, have you some idea to solve my problem ? Thanks :)
It looks like you just need to detect the points where the input data crosses some threshold, in your example this threshold might be 4.
For example:
% Create some dummy data
x = linspace(0,9,1000);
y = rand(size(x))/10 + 4.5*(mod(round(x),3)==1);
y = movmean(y, [3, 3] );
% Set a threshold
thresh = 4;
% Check when the signal exceeds this threshold
overThresh = y > thresh;
% Get the indicies where the threshold exceeding region starts
bRisingEdge = [false, diff(overThresh)>0];
% Get the x & y values at the rising edge
yRising = y(bRisingEdge);
xRising = x(bRisingEdge);
% Plot the results to confirm
figure(1); clf; plot( x, y, 'r' );
hold on; plot( xRising, yRising, 'ko', 'markersize', 10 );
Related
I want to add something to make my phase portrait more understandable. Nevertheless, I can't find anything (I found this
https://se.mathworks.com/help/matlab/ref/colorspec.html
https://se.mathworks.com/matlabcentral/fileexchange/11611-linear-2d-plot-with-rainbow-color
https://se.mathworks.com/help/symbolic/mupad_ref/linecolortype.html
) but it is not what I need.
I would really like to see the color of the line of the phase portrait changing depending of if it is at the beginning or at the end of the simulation.
I found this idea which seems great :
I don't understand at all what he has done (the code is I suppose written here:
https://blogs.mathworks.com/pick/2008/08/15/colors-for-your-multi-line-plots/ )
but It would be great if I can plot a one line function which color varies depending of the time. If moreover, like on the picture, I can have have a scale on the right: it would be awesome.
So for now, I have that :
data = readtable('test.txt');
figure('Name','Phase' , 'units','normalized','outerposition',[(8/100) (0.3- 16/100) 0.5 0.7]);
hold on
plot(data{:,2},data{:,3}, 'k.', 'LineWidth',1.5 );
plot(data{:,4},data{:,5}, 'r.', 'LineWidth',1.5 );
xL = xlim;
yL = ylim;
line([0 0], yL); %x-axis
line(xL, [0 0]); %y-axis
title(['Phase portrait'])
xlabel('f')
ylabel('f '' ')
hold off
I read the values of the function in a .txt file, and then I plot the 2nd/3rd columns and 4/5th columns. The first column is the time evoluting.
Do you have any idea :)?
Thank you!
There are several ways to go about this to be honest.
However it makes a bit easier if you let us know what your time data is.
Do you plot your time data on the x (or y) axis or is it a different additional data set. Should it be an additional data set then you can consider it like z-data, plotted on the Z-axis or/and as a color.
Below is an example of what you can do by making a 3D plot but displaying it in 2D, this allows you to add the colorbar without too many problems.
x=0:5;
y=0:5;
z=rand(1,6); %random data to simulate your time
xx=[x' x']; %this allows you to plot the data using surf in 3d
yy=[y' y']; %same as for xx
z1=zeros(size(xx)); % we don't need z-data so we're making it a matrix of zeros
zc=[z' z']; %input here your time data values, if x/y then you can just use those instead of z
hs=surf(xx,yy,z1,zc,'EdgeColor','interp') %// color binded to "z" values, choose interp for interpolated/gradual color changes, flat makes it sudden
colormap('hsv') %choose your colormap or make it yourself
view(2) %// view(0,90)
hcb=colorbar; %add a colorbar
I found this, thanks to another user on stackoverflaw.
data = readtable('4ressorspendule.txt');
n = numel(data.Var1);
c = size(data,2);
figure('Name','Phase' , 'units','normalized','outerposition',[(8/100) (0.3 - 16/100) 0.5 0.7]);
for i=1:n
hold on
plot(data{i,2},data{i,3},'.','Color',[1 (1-i/n) 0] ,'MarkerSize',4);
plot(data{i,4},data{i,5},'.','Color',[0 (i/n) (1-i/n)],'MarkerSize',4);
end
xL = xlim;
yL = ylim;
line([0 0], yL); %x-axis
line(xL, [0 0]); %y-axis
title(['Phase portrait'])
xlabel('f')
ylabel('f '' ')
hold off
I haven't used MATLAB in a while and I am stuck on a small detail. I would really appreciate it if someone could help me out!
So I am trying to plot a transfer function using a specific function called freqs but I can't figure out how I can label specific points on the graph.
b = [0 0 10.0455]; % Numerator coefficients
a = [(1/139344) (1/183.75) 1]; % Denominator coefficients
w = logspace(-3,5); % Frequency vector
freqs(b,a,w)
grid on
I want to mark values at points x=600 Hz and 7500 Hz with a marker or to be more specific, points (600,20) and (7500,-71), both of which should lie on the curve. For some reason, freqs doesn't let me do that.
freqs is very limited when you want to rely on it plotting the frequency response for you. Basically, you have no control on how to modify the graph on top of what MATLAB generates for you.
Instead, generate the output response in a vector yourself, then plot the magnitude and phase of the output yourself so that you have full control. If you specify an output when calling freqs, you will get the response of the system.
With this, you can find the magnitude of the output by abs and the phase by angle. BTW, (600,20) and (7500,-71) make absolutely no sense unless you're talking about magnitude in dB.... which I will assume is the case for the moment.
As such, we can reproduce the plot that freqs gives by the following. The key is to use semilogx to get a semi-logarithmic graph on the x-axis. On top of this, declare those points that you want to mark on the magnitude, so (600,20) and (7500,-71):
%// Your code:
b = [0 0 10.0455]; % Numerator coefficients
a = [(1/139344) (1/183.75) 1]; % Denominator coefficients
w = logspace(-3,5); % Frequency vector
%// New code
h = freqs(b,a,w); %// Output of freqs
mag = 20*log10(abs(h)); %// Magnitude in dB
pha = (180/pi)*angle(h); %// Phase in degrees
%// Declare points
wpt = [600, 7500];
mpt = [20, -71];
%// Plot the magnitude as well as markers
figure;
subplot(2,1,1);
semilogx(w, mag, wpt, mpt, 'r.');
xlabel('Frequency');
ylabel('Magnitude (dB)');
grid;
%// Plot phase
subplot(2,1,2);
semilogx(w, pha);
xlabel('Frequency');
ylabel('Phase (Degrees)');
grid;
We get this:
If you check what freqs generates for you, you'll see that we get the same thing, but the magnitude is in gain (V/V) instead of dB. If you want it in V/V, then just plot the magnitude without the 20*log10() call. Using your data, the markers I plotted are not on the graph (wpt and mpt), so adjust the points to whatever you see fit.
There are a couple issues before we attempt to answer your question. First, there is no data-point at 600Hz or 7500Hz. These frequencies fall between data-points when graphed using the freqs command. See the image below, with datatips added interactively. I copy-pasted your code to generate this data.
Second, it does not appear that either (600,20) or (7500,-71) lie on the curves, at least with the data as you entered above.
One solution is to use plot a marker on the desired position, and use a "text" object to add a string describing the point. I put together a script using your data, to generate this figure:
The code is as follows:
b = [0 0 10.0455];
a = [(1/139344) (1/183.75) 1];
w = logspace(-3,5);
freqs(b,a,w)
grid on
figureHandle = gcf;
figureChildren = get ( figureHandle , 'children' ); % The children this returns may vary.
axes1Handle = figureChildren(1);
axes2Handle = figureChildren(2);
axes1Children = get(axes1Handle,'children'); % This should be a "line" object.
axes2Children = get(axes2Handle,'children'); % This should be a "line" object.
axes1XData = get(axes1Children,'xdata');
axes1YData = get(axes1Children,'ydata');
axes2XData = get(axes2Children,'xdata');
axes2YData = get(axes2Children,'ydata');
hold(axes1Handle,'on');
plot(axes1Handle,axes1XData(40),axes1YData(40),'m*');
pointString1 = ['(',num2str(axes1XData(40)),',',num2str(axes1YData(40)),')'];
handleText1 = text(axes1XData(40),axes1YData(40),pointString1,'parent',axes1Handle);
hold(axes2Handle,'on');
plot(axes2Handle,axes2XData(40),axes2YData(40),'m*');
pointString2 = ['(',num2str(axes2XData(40)),',',num2str(axes2YData(40)),')'];
handleText2 = text(axes2XData(40),axes2YData(40),pointString2,'parent',axes2Handle);
I was trying to do histogram image comparison between two RGB images which includes heads of the same persons and non-heads to see the correlation between them. The reason I am doing this is because after performing scanning using HOG to check whether the scanning window is a head or not, I am now trying to track the same head throughout consequent frames and also I want to remove some clear false positives.
I currently tried both RGB and HSV histogram comparison and used Euclidean Distance to check the difference between the histograms. The following is the code I wrote:
%RGB histogram comparison
%clear all;
img1 = imread('testImages/samehead_1.png');
img2 = imread('testImages/samehead_2.png');
img1 = rgb2hsv(img1);
img2 = rgb2hsv(img2);
%% calculate number of bins = root(pixels);
[rows, cols] = size(img1);
no_of_pixels = rows * cols;
%no_of_bins = floor(sqrt(no_of_pixels));
no_of_bins = 256;
%% obtain Histogram for each colour
% -----1st Image---------
rHist1 = imhist(img1(:,:,1), no_of_bins);
gHist1 = imhist(img1(:,:,2), no_of_bins);
bHist1 = imhist(img1(:,:,3), no_of_bins);
hFig = figure;
hold on;
h(1) = stem(1:256, rHist1);
h(2) = stem(1:256 + 1/3, gHist1);
h(3) = stem(1:256 + 2/3, bHist1);
set(h, 'marker', 'none')
set(h(1), 'color', [1 0 0])
set(h(2), 'color', [0 1 0])
set(h(3), 'color', [0 0 1])
hold off;
% -----2nd Image---------
rHist2 = imhist(img2(:,:,1), no_of_bins);
gHist2 = imhist(img2(:,:,2), no_of_bins);
bHist2 = imhist(img2(:,:,3), no_of_bins);
hFig = figure;
hold on;
h(1) = stem(1:256, rHist2);
h(2) = stem(1:256 + 1/3, gHist2);
h(3) = stem(1:256 + 2/3, bHist2);
set(h, 'marker', 'none')
set(h(1), 'color', [1 0 0])
set(h(2), 'color', [0 1 0])
set(h(3), 'color', [0 0 1])
%% concatenate values of a histogram in 3D matrix
% -----1st Image---------
M1(:,1) = rHist1;
M1(:,2) = gHist1;
M1(:,3) = bHist1;
% -----2nd Image---------
M2(:,1) = rHist2;
M2(:,2) = gHist2;
M2(:,3) = bHist2;
%% normalise Histogram
% -----1st Image---------
M1 = M1./no_of_pixels;
% -----2nd Image---------
M2 = M2./no_of_pixels;
%% Calculate Euclidean distance between the two histograms
E_distance = sqrt(sum((M2-M1).^2));
The E_distance consists of an array containing 3 distances which refer to the red histogram difference, green and blue.
The Problem is:
When I compare the histogram of a non-head(eg. a bag) with that of a head..there is a clear difference in the error. So this is acceptable and can help me to remove the false positive.
However! When I am trying to check whether the two images are heads of the same person, this technique did not help at all as the head of another person gave a less Euclidean distance than that of the same person.
Can someone explain to me if I am doing this correctly, or maybe any guidance of what I should do?
PS: I got the idea of the LAB histogram comparison from this paper (Affinity Measures section): People Looking at each other
Color histogram similarity may be used as a good clue for tracking by detection, but don't count on it to disambiguate all possible matches between people-people and people-non-people.
According to your code, there is one thing you can do to improve the comparison: currently, you are working with per-channel histograms. This is not discriminative enough because you do not know when R-G-B components co-occur (e.g. you know how many times the red channel is in range 64-96 and how many times the blue is in range 32-64, but not when these occur simultaneously). To rectify this, you must work with 3D histograms, counting the co-occurrence of colors). For a discretization of 8 bins per channel, your histograms will have 8^3=512 bins.
Other suggestions for improvement:
Weighted assignment to neighboring bins according to interpolation weights. This eliminates the discontinuities introduced by bin quantization
Hierarchical splitting of detection window into cells (1 cell, 4 cells, 16 cells, etc), each with its own histogram, where the histograms of different levels and cells are concatenated. This allows catching local color details, like the color of a shirt, or even more local, a shirt pocket/sleeve.
Working with the Earth Mover's Distance (EMD) instead of the Euclidean metric for comparing histograms. This reduces color quantization effects (differences in histograms are weighted by color-space distance instead of equal weights), and allows for some error in the localization of cells within the detection window.
Use other cues for tracking. You'll be surprised how much the similarity between the HoG descriptors of your detections helps disambiguate matches!
I have a set of data with over 4000 points. I want to exclude grooves from them, ideally from the point from which they start. The data look for example like this:
The problem with this is the noise I get at the top of the plateaus. I have an idea, in which I would take an average value of the most common within some boundaries (again, ideally sth like the red line here:
and then I would construct a temporary matrix, which would fill up one by one with Y if they are less than this average. If the Y(i) would rise above average, the matrix would find its minima and compare it with the global minima. If the temporary matrix's minima wouldn't be sth like 80% of the global minima, it would be discarded as noise.
I've tried using mean(Y), interpolating and fitting it in a polynomial (the green line) - none of those method would cut it to the point I would be satisfied.
I need this to be extremely robust and it doesn't need to be quick. The top and bottom values can vary a lot, as well as the shape of the plateaus. The groove width is more or less the same.
Do you have any ideas? Again, the point is to extract the values that would make the groove.
How about a median filter?
Let's define some noisy data similar to yours, and plot it in blue:
x = .2*sin((0:9999)/1000); %// signal
x(1000:1099) = x(1000:1099) + sin((0:99)/50*pi); %// noise: spike
x(5000:5199) = x(5000:5199) - sin((0:199)/100*pi); %// noise: wider spike
x = x + .05*sin((0:9999)/10); %// noise: high-freq ripple
plot(x)
Now apply the median filter (using medfilt2 from the Image Processing Toolbox) and plot in red. The parameter k controls the filter memory. It should chosen to be large compared to noise variations, and small compared to signal variations:
k = 500; %// filter memory. Choose as needed
y = medfilt2(x,[1 k]);
hold on
plot(y, 'r', 'linewidth', 2)
In case you don't have the image processing toolbox and can't use medfilt2 a method that's more manual. Skip the extreme values, and do a curve fit with sin1 as curve type. Note that this will only work if the signal is in fact a sine wave!
x = linspace(0,3*pi,1000);
y1 = sin(x) + rand()*sin(100*x).*(mod(round(10*x),5)<3);
y2 = 20*(mod(round(5*x),5) == 0).*sin(20*x);
y = y1 + y2; %// A messy sine-wave
yy = y; %// Store the messy sine-wave
[~, idx] = sort(y);
y(idx(1:round(0.15*end))) = y(idx(round(0.15*end))); %// Flatten out the smallest values
y(idx(round(0.85*end):end)) = y(idx(round(0.85*end)));%// Flatten out the largest values
[foo goodness output] = fit(x.',y.', 'sin1'); %// Do a curve fit
plot(foo,x,y) %// Plot it
hold on
plot(x,yy,'black')
Might not be perfect, but it's a step in the right direction.
I have a problem dealing with 3rd dimension plot for three variables.
I have three matrices: Temperature, Humidity and Power. During one year, at every hour, each one of the above were measured. So, we have for each matrix 365*24 = 8760 points. Then, one average point is taken every day. So,
Tavg = 365 X 1
Havg = 365 X 1
Pavg = 365 X 1
In electrical point of veiw, the power depends on the temperature and humidity. I want to discover this relation using a three dimensional plot.
I tried using mesh, meshz, surf, plot3, and many other commands in MATLAB but unfortunately I couldn't get what I want. For example, let us take first 10 days. Here, every day is represented by average temperature, average humidity and average power.
Tavg = [18.6275
17.7386
15.4330
15.4404
16.4487
17.4735
19.4582
20.6670
19.8246
16.4810];
Havg = [75.7105
65.0892
40.7025
45.5119
47.9225
62.8814
48.1127
62.1248
73.0119
60.4168];
Pavg = [13.0921
13.7083
13.4703
13.7500
13.7023
10.6311
13.5000
12.6250
13.7083
12.9286];
How do I represent these matrices by three dimension plot?
The challenge is that the 3-D surface plotting functions (mesh, surf, etc.) are looking for a 2-D matrix of z values. So to use them you need to construct such a matrix from the data.
Currently the data is sea of points in 3-D space, so, you have to map these points to a surface. A simple approach to this is to divide up the X-Y (temperature-humidity) plane into bins and then take the average of all of the Z (power) data. Here is some sample code for this that uses accumarray() to compute the averages for each bin:
% Specify bin sizes
Tbin = 3;
Hbin = 20;
% Create binned average array
% First create a two column array of bin indexes to use as subscripts
subs = [round(Havg/Hbin)+1, round(Tavg/Tbin)+1];
% Now create the Z (power) estimate as the average value in each bin
Pest = accumarray(subs,Pavg,[],#mean);
% And the corresponding X (temp) & Y (humidity) vectors
Tval = Tbin/2:Tbin:size(Pest,2)*Tbin;
Hval = Hbin/2:Hbin:size(Pest,1)*Hbin;
% And create the plot
figure(1)
surf(Tval, Hval, Pest)
xlabel('Temperature')
ylabel('Humidity')
zlabel('Power')
title('Simple binned average')
xlim([14 24])
ylim([40 80])
The graph is a bit coarse (can't post image yet, since I am new) because we only have a few data points. We can enhance the visualization by removing any empty bins by setting their value to NaN. Also the binning approach hides any variation in the Z (power) data so we can also overlay the orgional point cloud using plot3 without drawing connecting lines. (Again no image b/c I am new)
Additional code for the final plot:
%% Expanded Plot
% Remove zeros (useful with enough valid data)
%Pest(Pest == 0) = NaN;
% First the original points
figure(2)
plot3(Tavg, Havg, Pavg, '.')
hold on
% And now our estimate
% The use of 'FaceColor' 'Interp' uses colors that "bleed" down the face
% rather than only coloring the faces away from the origin
surfc(Tval, Hval, Pest, 'FaceColor', 'Interp')
% Make this plot semi-transparent to see the original dots anb back side
alpha(0.5)
xlabel('Temperature')
ylabel('Humidity')
zlabel('Power')
grid on
title('Nicer binned average')
xlim([14 24])
ylim([40 80])
I think you're asking for a surface fit for your data. The Curve Fitting Toolbox handles this nicely:
% Fit model to data.
ft = fittype( 'poly11' );
fitresult = fit( [Tavg, Havg], Pavg, ft);
% Plot fit with data.
plot( fitresult, [xData, yData], zData );
legend( 'fit 1', 'Pavg vs. Tavg, Havg', 'Location', 'NorthEast' );
xlabel( 'Tavg' );
ylabel( 'Havg' );
zlabel( 'Pavg' );
grid on
If you don't have the Curve Fitting Toolbox, you can use the backslash operator:
% Find the coefficients.
const = ones(size(Tavg));
coeff = [Tavg Havg const] \ Pavg;
% Plot the original data points
clf
plot3(Tavg,Havg,Pavg,'r.','MarkerSize',20);
hold on
% Plot the surface.
[xx, yy] = meshgrid( ...
linspace(min(Tavg),max(Tavg)) , ...
linspace(min(Havg),max(Havg)) );
zz = coeff(1) * xx + coeff(2) * yy + coeff(3);
surf(xx,yy,zz)
title(sprintf('z=(%f)*x+(%f)*y+(%f)',coeff))
grid on
axis tight
Both of these fit a linear polynomial surface, i.e. a plane, but you'll probably want to use something more complicated. Both of these techniques can be adapted to this situation. There's more information on this subject at mathworks.com: How can I determine the equation of the best-fit line, plane, or N-D surface using MATLAB?.
You might want to look at Delaunay triangulation:
tri = delaunay(Tavg, Havg);
trisurf(tri, Tavg, Havg, Pavg);
Using your example data, this code generates an interesting 'surface'. But I believe this is another way of doing what you want.
You might also try the GridFit tool by John D'Errico from MATLAB Central. This tool produces a surface similar to interpolating between the data points (as is done by MATLAB's griddata) but with cleaner results because it smooths the resulting surface. Conceptually multiple datapoints for nearby or overlapping X,Y coordinates are averaged to produce a smooth result rather than noisy "ripples." The tool also allows for some extrapolation beyond the data points. Here is a code example (assuming the GridFit Tool has already been installed):
%Establish points for surface
num_points = 20;
Tval = linspace(min(Tavg),max(Tavg),num_points);
Hval = linspace(min(Havg),max(Havg),num_points);
%Do the fancy fitting with smoothing
Pest = gridfit(Tavg, Havg, Pavg, Tval, Hval);
%Plot results
figure(5)
surfc(XI,YI,Pest, 'FaceColor', 'Interp')
To produce an even nicer plot, you can add labels, some transparancy and overlay the original points:
alpha(0.5)
hold on
plot3(Tavg,Havg,Pavg,'.')
xlabel('Temperature')
ylabel('Humidity')
zlabel('Power')
grid on
title('GridFit')
PS: #upperBound: Thanks for the Delaunay triangulation tip. That seems like the way to go if you want to go through each of the points. I am a newbie so can't comment yet.
Below is your solution:
Save/write the Myplot3D function
function [x,y,V]=Myplot3D(X,Y,Z)
x=linspace(X(1),X(end),100);
y=linspace(Y(1),Y(end),100);
[Xt,Yt]=meshgrid(x,y);
V=griddata(X,Y,Z,Xt,Yt);
Call the following from your command line (or script)
[Tavg_new,Pavg_new,V]=Myplot3D(Tavg,Pavg,Havg);
surf(Tavg_new,Pavg_new,V)
colormap jet;
xlabel('Temperature')
ylabel('Power/Pressure')
zlabel('Humidity')