I have a vector that I want to print a histogram of of data for. This data ranges from -100 to +100. The amount of data around the outer edges is insignificant and therefore I don't want to see it. I am most interested in showing data from -20 to +20.
1.) How can I limit that window to print on my histogram?
The amount of data I have at 0 outnumbers of the amount of data I have anywhere in the dataset by a minimum of 10:1. When I print the histogram, the layout of element frequency is lost because it is outnumbered by 0.
2.) Is there a way that I can scale the number of 0 values to be three times the number of -1 entries?
I am expecting an exponential drop of this dataset (in general) and therefore three times the frequency of -1 would easily allow me to see the frequency of the other data.
You can use something like
binCenters = -20:5:20;
[N,X] = hist(V,binCenters);
N = N./scalingVector;
bar(X(2:end-1),N(2:end-1));
Note that the code excludes the extremes of N and X from the bar plot, since they contain the number of values smaller than -20 and larger than 20. Also, by building scalingVector appropriately, you can scale N as you please.
You could also just toss out any values outside the [-20,20] range by using
subsetData=data(abs(data)<=20)
1) You can limit the histogram range you see on the plot by just setting the X axes limits:
xlim([-20 20])
Setting bins in hist command is good, but remember thatall the values outside the bins will fall into the most left and right bin. So you will need to set the axes limits anyway.
2) If there is a big difference between values in different bins, one way is to transform values on Y axes to log scale. Unfortunately just setting Y axes to log (set(gca,'YScale','log')) does not work for bar plot. Calculate the histogram with hist or histc (depending on whether you want to specify bins centers or edges) and log2 the values:
[y, xbin] = hist(data);
bar(xbin, log2(y) ,'hist')
Histogram has a few different methods of calling it. I strongly recommend you read the documentation on the function (doc hist)
What you are looking for is to put in a custom range in the histogram bin. It depends a bit on how many bins you want, but something like this will work.
Data=randn(1000,1)*20;
hist(Data,-20:20);
You could, if you want to, change the frequency of the binning as well. You could also change the axis so that you only focus on the range from -20 to 20, using a xaxis([-20 20]) command. You could also ignore the bin at 0, by using an yaxis and limiting the values to exclude the 0 bin. Without knowing what you want exactly, I can only give you suggestions.
Related
I have generated a random matrix. I want to show it on a plot and see different values with different colors.
phi = zeros(10,10);
for i= 1:length(phi)
for j=1:length(phi)
phi(i,j) = .5 + .2*(.5-rand);
end
end
Image(phi)
I have been using colorbar and caxis to set the range of colorbar, I already have read some other posts, but it does not work in my case and I don't know why. I want to set the colorbar from zero 0 to 1 with 100 units.
Although, I have tried
Image(phi*10)
which give me something similar to what I want but it does not show difference between close numbers like 0.42 and 0.45.
Thanks in advance for your help.
First, no need for the for loop, this will do the same:
phi=.5+.2.*(.5-rand(10));
imagesc(phi);
second, use:
colormap(jet(16));
colorbar;
this will discretize your colormap to how many levels (or bin intensities) you want, I chose 16, but you can play with it and see.It also uses the maximum and minimum values as the extreme points of the colormap.
Last, if this is still not enough and you need a higher dynamic range, you will need to edit the colormap or combine several colormaps. One way to do this for example is by combining b\w with color, for example
colormap([jet(8); bone(8)] );
etc...
I have a graph like this and I want to determine the number of peaks. Since it's a wave function the whole graph has many peaks that is why I was unsuccefull in finding the number of peaks using functions such as findpeaks for the graph below it returns a number of peaks around 3000 whereas I want to have the number 7.
My idea was to do a for or while loop that counts the number of instances where the average is higher than 0.5. So ultimately I want a function that iterates in segments over the graph returns the number of peaks and the range of y index values for which this peak occurs (I think the best way to do this would to save them to a zeros matrix).
link of file data: https://www.dropbox.com/s/vv8vqv28mqzfr9l/Example_data.mat?dl=0
Do you mean you are trying to count the 'on' parts of your data?
You're on the right track using findpeaks. If you scroll to the bottom of the documentation you'll see that you can actually tweak the routine in various ways, for example specifying the minimum distance between peaks, or the minimum difference between a point and its neighbour before it is considered a peak.
By defining the minimum distance between peaks, I detected the following 7 peaks. Code is included below. Alternatively you can play around with the other parameters you can pass into findpeaks.
The only other thing to note is that I took the absolute value of your data.
load('Example_data.mat')
indx = 1:numel(number11);
[pks, locs] = findpeaks(abs(number11), indx, 'MinPeakDistance', 0.25e4);
hold on
plot(number11)
plot(locs,pks, 'rx')
disp(numel(pks))
I have two "graphs" which differ from each other at a maximum of 0.006 , if one doesn't inspect the graph one might think that there is a large difference . How can i make them look more close to each other. I would want the scale not to change if I zoom the picture ,how can i do that
Look at the y axis , these two graphs are close to each other but look far from each other if you really don't consider the y axis
If you don't care about changing the y values when plotting, you need to make sure that each graph is plotted with respect to the same scale. One thing you could try is to normalize both plots so that the y values both fall within a certain dynamic range. One popular range is simply [0,1]. As such, given your two signals y1 and y2 that you want to plot, do something like this:
y1_new = (y1 - min(y1)) / (max(y1) - min(y1));
y2_new = (y2 - min(y2)) / (max(y2) - min(y2));
You would then plot y1_new and y2_new on the same figure. The above transformation is a very standard way of taking a set of points or input data and transforming it so that the minimum is 0 and the maximum is 1.
Give that a whirl and see how it goes!
A maximum difference of .006 is rather significant, when the maximal value of one of the curve is .001.
If the y axis ran from say 1000.000 to 1000.007, you would have a valid claim that the difference is negligible.
I have about 10000 floating point data, and have read them into a single row matrix.
Now I would like to plot them and show their distribution, would there be some simple functions to do that?
plot() actually plots value with respect to data number...which is not what I want
bar() is similar to what I want, but actually I would like to lower the sample rate and merge neighbor bars which are close enough (e.g. one bar for 0.50-0.55, and one bar for 0.55-0.60, etc) instead of having one single bar for every single data sample.
would there be a function to calculate this distribution by dividing the range into small steps, and outputting the prob density in each step?
Thank you!
hist() would be best. It plots a histogram, with a lot of options which you can see by doc hist, or by checking the Matlab website. Options include a specified number of bins, or a range of bins. This will plot a histogram of 1000 normally random points, with 50 bins.
hist(randn(1000,1),50)
I'm trying to find some peaks in Matlab, but the function findpeaks.m doesn't have the width option. The peaks I want to be detected are in the balls. All the detected are in the red squares. As you can see they have a low width. Any help?
here's the code I use:
[pk,lo] = findpeaks(ecg);
lo2 = zeros(size(lo));
for m = 1:length(lo) - 1
if (ecg(m) - ecg(m+1)) > 0.025
lo2(m) = lo(m);
end
end
p = find(lo2 == 0);
lo2(p) = [];
figure, plot(ecg);
hold on
plot(lo, ecg(lo), 'rs');
By the looks of it you want to characterise each peak in terms of amplitude and width, so that you can apply thresholds (or simmilar) to these values to select only those meeting your criteria (tall and thin).
One way you could do this is to fit a normal distribution to each peak, pegging the mean and amplitude to the value you have found already, and using an optimisation function to find the standard deviation (width of normal distribution).
So, you would need a function which calculates a representation of your data based on the sum of all the gaussian distributions you have, and an error function (mean squared error perhaps) then you just need to throw this into one of matlabs inbuilt optimisation/minimisation functions.
The optimal set of standard deviation parameters would give you the widths of each peak, or at least a good approximation.
Another method, based on Adiel's comment and which is perhaps more appropriate since it looks like you are working on ecg data, would be to also find the local minima (troughs) as well as the peaks. From this you could construct an approximate measure of 'thinness' by taking the x-axis distance between the troughs on either side of a given peak.
You need to define a peak width first, determine how narrow you want your peaks to be and then select them accordingly.
For instance, you can define the width of a peak as the difference between the x-coordinates at which the y-coordinates equal to half of the peak's value (see here). Another approach, (which seems more appropriate here) is to measure the gradient at fixed distances from the peak itself, and selecting the peaks accordingly. In MATLAB, you'll probably use a gradient filter for that :
g = conv(ecg, [-1 0 1], 'same'); %// Gradient filter
idx = g(lo) > thr); %// Indices of narrow peaks
lo = lo(idx);
where thr is the threshold value that you need to determine for yourself. Lower threshold values mean more tolerance for wider peaks.
You need to define what it means to be a peak of interest, and what you mean by the width of that peak. Once you do those things, you are a step ahead.
Perhaps you might locate each peak using find peaks. Then locate the troughs, one of which should lie between each pair of peaks. A trough is simply a peak of -y. Make sure you worry about the first and last peaks/troughs.
Next, define the half height points as the location midway in height between each peak and trough. This can be done using a reverse linear interpolation on the curve.
Finally, the width at half height might be simply the distance (on the x axis) between those two half height points.
Thinking pragmatically, I suppose you could use something along the lines of this simple brute-force approach:
[peaks , peakLocations] = findpeaks(+X);
[troughs, troughLocations] = findpeaks(-X);
width = zeros(size(peaks));
for ii = 1:numel(peaks)
trough_before = troughLocations( ...
find(troughLocations < peakLocations(ii), 1,'last') );
trough_after = troughLocations( ...
find(troughLocations > peakLocations(ii), 1,'first') );
width(ii) = trough_after - trough_before;
end
This will find the distance between the two troughs surrounding a peak of interest.
Use the 'MinPeakHeight' option in findpeaks() to pre-prune your data. By the looks of it, there is no automatic way to extract the peaks you want (unless you somehow have explicit indices to them). Meaning, you'll have to select them manually.
Now of course, there will be many more details that will have to be dealt with, but given the shape of your data set, I think the underlying idea here can nicely solve your problem.