I have one vector (newdata) consisting of 4100 lines and one column. To be exact, those elements are the counts of a spectrum. What I want is to reproduce the spectrum using MATLAB. That's why I created a new vector:
channels=[1:size(newdata,1)];
I tried to plot the spectrum (using channel in x axis and newdata as a weight) by typing:
hist(channels,newdata)
But I got an error
??? Error using ==> histc
Edges vector must be monotonically non-decreasing.
Error in ==> hist at 86
nn = histc(y,[-inf bins],1);
How can I draw the desired spectrum?
try plotting using the bar command
bar( channels, newData );
Related
I need to generate some random 2D points (for example 30 points) near the y=x line, insert them in a matrix, plot it and then calculate the SVD of the matrix. But since I'm new to MATLAB I don't know how can I generate my desired matrix.
Since this looks like homework I'll just post some general ideas here.
randi can be used to get semi-random integers. Using that you can create a 2D matrix by duplicating the array and putting them together. Thus: generate a 30x1 column and duplicate it to a 30x2 column. All rows will have the same two entries, i.e. x=y.
Noise can be added to this by creating a 30x2 matrix of random numbers, use rand for that and simply add that to the previously created matrix.
Check the documentation on svd to see how the singular-value decomposition works, it's fairly straight-forward if you know your linear algebra.
Finally for plotting you can use various tools such as image, imagesc, plot, surf and scatter, try them and see which works best for you.
Here is a quick example I made: https://saturnapi.com/fullstack/2d-points-randomly-near-line
%// Welcome to Saturn's MATLAB-Octave API.
%// Delete the sample code below these comments and write your own!'
x = 13 + 6.*rand(20,1);
y = x*0.7 + 0.5*rand(20,1);
[X,Y] = meshgrid(x,y)
figure(1);
plot(x,y,'.');
%// Print plot as PNG with resultion of 60 pixels per inch
print("MyPNG.png", "-dpng", "-r60");
I have to plot 10 frequency distributions on one graph. In order to keep things tidy, I would like to avoid making a histogram with bins and would prefer having lines that follow the contour of each histogram plot.
I tried the following
[counts, bins] = hist(data);
plot(bins, counts)
But this gives me a very inexact and jagged line.
I read about ksdensity, which gives me a nice curve, but it changes the scaling of my y-axis and I need to be able to read the frequencies from the y-axis.
Can you recommend anything else?
You're using the default number of bins for your histogram and, I will assume, for your kernel density estimation calculations.
Depending on how many data points you have, that will certainly not be optimal, as you've discovered. The first thing to try is to calculate the optimum bin width to give the smoothest curve while simultaneously preserving the underlying PDF as best as possible. (see also here, here, and here);
If you still don't like how smooth the resulting plot is, you could try using the bins output from hist as a further input to ksdensity. Perhaps something like this:
[kcounts,kbins] = ksdensity(data,bins,'npoints',length(bins));
I don't have your data, so you may have to play with the parameters a bit to get exactly what you want.
Alternatively, you could try fitting a spline through the points that you get from hist and plotting that instead.
Some code:
data = randn(1,1e4);
optN = sshist(data);
figure(1)
[N,Center] = hist(data);
[Nopt,CenterOpt] = hist(data,optN);
[f,xi] = ksdensity(data,CenterOpt);
dN = mode(diff(Center));
dNopt = mode(diff(CenterOpt));
plot(Center,N/dN,'.-',CenterOpt,Nopt/dNopt,'.-',xi,f*length(data),'.-')
legend('Default','Optimum','ksdensity')
The result:
Note that the "optimum" bin width preserves some of the fine structure of the distribution (I had to run this a couple times to get the spikes) while the ksdensity gives a smooth curve. Depending on what you're looking for in your data, that may be either good or bad.
How about interpolating with splines?
nbins = 10; %// number of bins for original histogram
n_interp = 500; %// number of values for interpolation
[counts, bins] = hist(data, nbins);
bins_interp = linspace(bins(1), bins(end), n_interp);
counts_interp = interp1(bins, counts, bins_interp, 'spline');
plot(bins, counts) %// original histogram
figure
plot(bins_interp, counts_interp) %// interpolated histogram
Example: let
data = randn(1,1e4);
Original histogram:
Interpolated:
Following your code, the y axis in the above figures gives the count, not the probability density. To get probability density you need to normalize:
normalization = 1/(bins(2)-bins(1))/sum(counts);
plot(bins, counts*normalization) %// original histogram
plot(bins_interp, counts_interp*normalization) %// interpolated histogram
Check: total area should be approximately 1:
>> trapz(bins_interp, counts_interp*normalization)
ans =
1.0009
I create a matlab gui and have some element in it with some axes. I plot one of my desire plot in ploter1 ( first axes ) using
plot(handles.ploter1,xx); title(handles.ploter1,'Waveform');
and it is ok,but I want use specgram and plot specgram result in another axes by I dont know how can do it :(
I test
specgram(wav,N,fs,hamming(N/4),round(0.9*N/4));xlabel('time, s');
or
specgram(handles.ploter2,wav,N,fs,hamming(N/4),round(0.9*N/4));xlabel('time, s');
but return me error or nothing !!!
please help me. thank you very much
EDIT
as mentioned in the comments by bdecaf, what should work, is to set the current axes:
axes(handles.ploter2);
now, when using just
spectrogram(x,window,noverlap,F)]
the plot should be on the specified axes. If not, try:
hold on
before!
OLD
specgram or spectogram does not have a parameter for the plot. You have to define it later on.
I suggest to get the result first by:
[S,F,T]=spectrogram(x,window,noverlap,F)]
and then plot it on a specific axes:
plot(handles.ploter2, S,F)
But I am not sure about which parameter you want to plot. Please take a look at the docs.
From the docs:
[S,F,T] = spectrogram(...) returns a vector of frequencies, F, and a vector of times, T, at which the spectrogram is computed. F has length equal to the number of rows of S. T has length k (defined above) and the values in T correspond to the center of each segment.
[S,F,T] = spectrogram(x,window,noverlap,F) uses a vector F of frequencies in Hz. F must be a vector with at least two elements. This case computes the spectrogram at the frequencies in F using the Goertzel algorithm. The specified frequencies are rounded to the nearest DFT bin commensurate with the signal's resolution. In all other syntax cases where nfft or a default for nfft is used, the short-time Fourier transform is used. The F vector returned is a vector of the rounded frequencies. T is a vector of times at which the spectrogram is computed. The length of F is equal to the number of rows of S. The length of T is equal to k, as defined above and each value corresponds to the center of each segment.
[S,F,T] = spectrogram(x,window,noverlap,F,fs) uses a vector F of frequencies in Hz as above and uses the fs sampling frequency in Hz. If fs is specified as empty [], it defaults to 1 Hz.
I have the following matlab code for approximating a differential equation via the Euler-method:
% Eulermethod
a=0;
b=0.6;
Steps=6;
dt=(b-a)/Steps;
x=zeros(Steps+1,1);
x(1,1)=1;
y=zeros(Steps+1,1);
for i=1:Steps
x(i+1,1)=x(i,1)+dt*(x(i,1)*x(i,1)+1);
end
plot(x)
I want to be able to plot the solution plot for several different values of Steps in one plot and have the x-axis go from 0 to 0.6 instead of from for example 1 to 100 000 etc. Can this be done?
If you use the hold on command this will allow you achieve multiple plots on the same figure. Similarly, if you separate your data into x and y vectors, you can plot them against eachother by passing 2 vectors to plot instead of just one. For example
figure
hold on
for i=1:m
x = [];
y = [];
%% code to populate your vectors
plot(x,y)
end
You should now see all your plots simultanesously on the same figure. If you want x to be composed of n equally spaced elements between 0 and 0.6, you could use the linspace command:
x = linspace(0.0,0.6,n);
In order to distinguish your plots, you can pass an extra paramter to the function .For example
plot(x,y,'r+')
will plot the data as a series of red + symbols.
Plot can take more arguments: plot(x_axis,values, modifiers);
If x-axis is a vector of M elements, values can be a matrix of MxN elements, each of which are drawn with a separate color.
I have various plots (with hold on) as show in the following figure:
I would like to know how to find equations of these six curves in Matlab. Thanks.
I found interactive fitting tool in Matlab simple and helpful, though somewhat limited in scope:
The graph above seems to be linear interpolation. Given vectors X and Y of data, where X contains the arguments and Y the function points, you could do
f = interp1(X, Y, x)
to get the linearly interpolated value f(x). For example if the data is
X = [0 1 2 3 4 5];
Y = [0 1 4 9 16 25];
then
y = interp1(X, Y, 1.5)
should give you a very rough approximation to 1.5^2. interp1 will match the graph exactly, but you might be interested in fancier curve-fitting operations, like spline approximations etc.
Does rxns stand for reactions? In that case, your curves are most likely exponential. An exponential function has the form: y = a*exp(b * x) . In your case, y is the width of mixing zone, and x is the time in years. Now, all you need to do is run exponential regression in Matlab to find the optimal values of parameters a and b, and you'll have your equations.
The advice, though there might be better answer, from me is: try to see the rate of increase in the curve. For example, cubic is more representative than quadratic if the rate of increase seems fast and find the polynomial and compute the deviation error. For irregular curves, you might try spline fitting. I guess there is also a toolbox in matlab for spline fitting.
There is a way to extract information with the current figure handle (gcf) from you graph.
For example, you can get the series that were plotted in a graph:
% Some figure is created and data are plotted on it
figure;
hold on;
A = [ 1 2 3 4 5 7] % Dummy data
B = A.*A % Some other dummy data
plot(A,B);
plot(A.*3,B-1);
% Those three lines of code will get you series that were plotted on your graph
lh=findall(gcf,'type','line'); % Extract the plotted line from the figure handle
xp=get(lh,'xdata'); % Extract the Xs
yp=get(lh,'ydata'); % Extract the Ys
There must be other informations that you can get from the "findall(gcf,...)" methods.