I am trying to add Gaussian fit to the histogram in MATLAB, but I do not know how to apply the fit to one particular peak only.
http://postimg.org/image/phms61rdh/ first plot
http://postimg.org/image/sindrn8er/ second plot
I am also posting part of the code I have been using:
data_Rb = (importdata('path\name.txt'));
counts_Rb = data_Rb.data(:,3);
figure
hist(counts_Rb, nbins);
xlim([0 120]);
title('Rubidium');
histfit(counts_Rb,1000);
pd=fitdist(counts_Rb(:),'normal')
x=0:0.001:120;
PDF=pdf(pd,x); %PDF is a vector of y values: it's our fit
PDF=PDF/max(PDF); %nor
y=ylim;
PDF=PDF*y(2);
hold on
plot(x,PDF,'r-','LineWidth',2);
hold off
These two blocks give me two different Gaussians as shown in the first picture. I do not understand why they fit data so badly: is it because of the tail on the RHS?
In the second plot I need to apply Gaussian fit to the last peak only. How should I do it?
And finally, after applying the fit, fit results are outputed onto the screen. Is there a function to save them into an array or so for later use?
Thanks in advance!
Regarding your last question, see How can I access the fitted distribution parameters from the HISTFIT function in Statistiics Toolbox ?
There is a workaround for this isssue by making a copy of the HISTFIT
function and modifying the code to pass out the "pd" variable. One way
to do this is to change the "h" output on the first line of the code
to "varargout" and add the following to the end of the file:
h = [hh; hh1];
argout={h,pd};
if nargout > length(argout)
error('Too many output arguments.');
end
[varargout{1:nargout}]=argout{1:nargout};
Related
I am working on calculating the SNR of given signals.
length = linspace(-2*pi,2*pi);
signal_1 = sin(length);
plot(signal_1);
noise_1 = 0.5*rand(size(signal_1));
plot(noise_1);
snr_22 = snr(signal_1,noise_1);
But it shows only the noise figure (attached below) whenever I run this, nothing else. But it should also show signal figures and SNR in decibels. Can anyone help me out? I am using Matlab R2015b.
As the documentation on snr() shows:
snr(___) with no output arguments plots the spectrum of the signal in the current figure window and labels its main features. It uses different colors to draw the fundamental component, the DC value and the harmonics, and the noise. The SNR appears above the plot. This functionality works for all syntaxes listed above except snr(x,y).
emphasis mine.
In other words, call figure; snr(signal_1,noise_1, __);, i.e. with at least one other input argument and no output argument, at the end of your code to have the spectrum appear. Otherwise, take a look at the contents of snr_22 and plot it by hand.
So... I am working on part b and I haven no idea what this question is asking me to do. Does it want me to plot the actual Q(x) equation graph with part a graphs?
It appears as if you need to plot the pdf(x) and cdf(x) and compare to the estimated pdf and cdf you obtained for the different values of t.
I'm just getting started with matlab and I'm trying to plot some graph with it.
The problem is I don't know how to get the average data out of 10 plot().
Can anyone guide me for it? Thank you :)
Assuming you don't have access to the original data you used for doing the plots:
plot_data = get(get(gca,'Children'),'YData'); % cell array of all "y" data of plots
average = mean(cell2mat(plot_data));
In order for this to work, you have to use this code right after doing the plots, that is, without plotting to any other figure (gca is a handle to the current axes).
Assume your data is stored row-wise in a m x n matrix A, with n columns corresponding to different values of the continuous error, and m rows corresponding to different curves. Then to inspect the mean over the curves just use
Amean = mean(A,1);
plot(Amean)
Please take a look at this link: it solve my problem getting the average plot.
https://www.mathworks.com/matlabcentral/fileexchange/27134-plot-average-line
After downloading the files just put those script on your working folder and add this line to your script.
plotAverage(gca,[],'userobustmean',0)
So, I've fitted an exponential curve to some data points using 'fit' and now I want to get the equation of the fitted curve in the Legend in the graph. How can I do that? I want to have an equation on the form y=Ce^-xt in the legend. Can I get the coefficients, C and x from the fitted curve and then put the equation inside the legend? Or can I get the whole equation written out in some way? I have many plotted graphs so I would be grateful if the method is not so much time consuming.
Edit: Perhaps I was unclear. The main problem is how I should get out the coefficients from the fitted line I've plotted. Then I want to have the equation inside the legend in my graph. Do I have to take out the coefficients by hand (and how can it be done?) or can Matlab write it straight out like in, for example, excel?
The documentation explains that you can obtain the coefficients derived from a fit executed as c = fit(...), as
coef=coeffvalues(c)
C=coef(1)
x=coef(2)
You can create your own legend as illustrated by the following example.
Defining C and x as the parameters from your fits,
% simulate and plot some data
t= [0:0.1:1];
C = 0.9;
x = 1;
figure, hold on
plot(t,C*exp(-x*t)+ 0.1*randn(1,length(t)),'ok', 'MarkerFaceColor','k')
plot(t,C*exp(-x*t),':k')
axis([-.1 1.2 0 1.2])
% here we add the "legend"
line([0.62 0.7],[1 1],'Linestyle',':','color','k')
text(0.6,1,[ ' ' num2str(C,'%.2f'),' exp(-' num2str(x,'%.2f') ' t) ' ],'EdgeColor','k')
set(gca,'box','on')
Example output:
You may have to adjust number formatting and the size of the text box to suit your needs.
Here is a piece of code that will display the fitted equation in the legend box. You can reduce the amount of digits in the legend by manipulating the sprintf option: %f to %3.2f for example.
%some data
load census
s = fitoptions('Method','NonlinearLeastSquares','Lower',[0,0],'Upper',[Inf,max(cdate)],'Startpoint',[1 1]);
f = fittype('a*(x-b)^n','problem','n','options',s);
%fit, plot and legend
[c2,gof2] = fit(cdate,pop,f,'problem',2)
plot(c2,'m');
legend(sprintf('%f * (x - %f)^%d',c2.a,c2.b,c2.n));
The command disp(c2) will display in the command window what is stored in the fit object. Also, by enabling the "datatip in edit mode" option (Matlab preferences, then Editor, then Display), you will have an instant view on the data stored by putting your mouse cursor over an object.
function [S]=evaFit(ffit)
S=sprintf('y=%s',formula(ffit));
S2='';
N=coeffnames(ffit);
V=coeffvalues(ffit);
for i= 1:numcoeffs(ffit)
S2=sprintf('%s %c=%f',S2, string(N(i)), V(i));
end
S=sprintf('%s%s',S, S2);
end
I have a Matlab figure I want to use in a paper. This figure contains multiple cdfplots.
Now the problem is that I cannot use the markers because the become very dense in the plot.
If i want to make the samples sparse I have to drop some samples from the cdfplot which will result in a different cdfplot line.
How can I add enough markers while maintaining the actual line?
One method is to get XData/YData properties from your curves follow solution (1) from #ephsmith and set it back. Here is an example for one curve.
y = evrnd(0,3,100,1); %# random data
%# original data
subplot(1,2,1)
h = cdfplot(y);
set(h,'Marker','*','MarkerSize',8,'MarkerEdgeColor','r','LineStyle','none')
%# reduced data
subplot(1,2,2)
h = cdfplot(y);
set(h,'Marker','*','MarkerSize',8,'MarkerEdgeColor','r','LineStyle','none')
xdata = get(h,'XData');
ydata = get(h,'YData');
set(h,'XData',xdata(1:5:end));
set(h,'YData',ydata(1:5:end));
Another method is to calculate empirical CDF separately using ECDF function, then reduce the results before plotting with PLOT.
y = evrnd(0,3,100,1); %# random data
[f, x] = ecdf(y);
%# original data
subplot(1,2,1)
plot(x,f,'*')
%# reduced data
subplot(1,2,2)
plot(x(1:5:end),f(1:5:end),'r*')
Result
I know this is potentially unnecessary given MATLAB's built-in functions (in the Statistics Toolbox anyway) but it may be of use to other viewers who do not have access to the toolbox.
The empirical CMF (CDF) is essentially the cumulative sum of the empirical PMF. The latter is attainable in MATLAB via the hist function. In order to get a nice approximation to the empirical PMF, the number of bins must be selected appropriately. In the following example, I assume that 64 bins is good enough for your data.
%# compute a histogram with 64 bins for the data points stored in y
[f,x]=hist(y,64);
%# convert the frequency points in f to proportions
f = f./sum(f);
%# compute the cumulative sum of the empirical PMF
cmf = cumsum(f);
Now you can choose how many points you'd like to plot by using the reduced data example given by yuk.
n=20 ; % number of total data markers in the curve graph
M_n = round(linspace(1,numel(y),n)) ; % indices of markers
% plot the whole line, and markers for selected data points
plot(x,y,'b-',y(M_n),y(M_n),'rs')
verry simple.....
try reducing the marker size.
x = rand(10000,1);
y = x + rand(10000,1);
plot(x,y,'b.','markersize',1);
For publishing purposes I tend to use the plot tools on the figure window. This allow you to tweak all of the plot parameters and immediately see the result.
If the problem is that you have too many data points, you can:
1). Plot using every nth sample of the data. Experiment to find an n that results in the look you want.
2). I typically fit curves to my data and add a few sparsely placed markers to plots of the fits to differentiate the curves.
Honestly, for publishing purposes I have always found that choosing different 'LineStyle' or 'LineWidth' properties for the lines gives much cleaner results than using different markers. This would also be a lot easier than trying to downsample your data, and for plots made with CDFPLOT I find that markers simply occlude the stairstep nature of the lines.