Consider the following data points and plots
a = randi(50,1,200);
b = randi(50,1,200);
figure;scatter(a,b,'.')
figure;plot(a,b,'.')
When we run the following code , we receive exactly the same plots for a against b , my question is why should we even use or to rephrase again in what conditions scatter plot has advantage over plot function ? because plot seem to have more formatting options that the scatter function
plot has a concept of the order of the points mattering so you can use it to make line plots. plot also allows you to specify the input x and y values as either vectors or matrices or allows you to input multiple x and y vectors both of which allow you to plot multiple series at once:
whereas scatter only allows you to input 1 x and 1 y and they both have to be vectors. However, 'scatter' allows you to specify an area and colour vector to affect the points individually i.e.
Related
I have a T x 2 Matrix, where in the second column I have some daily financial returns and in the first column I have an indicator, which can assume integer values in the interval [1, 9].
I want to extract 9 different conditional distributions of my returns, conditioned on the values assumed by the indicator. At this point, I want to plot the conditional densities through a Gaussian smoothing with the function 'ksdensity' and plot them in the same 3D plot. The final output should be similar to this one: Image
I tried to reach this result by adapting the answer I found at this thread: Function.
Now suppose that x = axis of returns, y = axis of indicator possible values, z = smoothed conditional densities.
My problem is that, while in the example the meshgrid required for all the values of y have the same values of x by construction, I have different values of x (the returns) because of the conditioning.
First split your returns data into 9 vectors, according to the indicator variable. You can use accumarray for that. Then run ksdensity on each vector separately. Then plot those outputs.
Let's say we have the following data:
A1= [41.3251
18.2350
9.9891
36.1722
50.8702
32.1519
44.6284
60.0892
58.1297
34.7482
34.6447
6.7361
1.2960
1.9778
2.0422];
A2=[86.3924
86.4882
86.1717
85.8506
85.8634
86.1267
86.4304
86.6406
86.5022
86.1384
86.5500
86.2765
86.7044
86.8075
86.9007];
When I plot the above data using plot(A1,A2);, I get this graph:
Is there any way to make the graph look smooth like a cubic plot?
Yes you can. You can interpolate in between the keypoints. This will require a bit of trickery though. Blindly using interpolation with any of MATLAB's commands won't work because they require that the independent axes (the x-axis in your case) to increase. You can't do this with your data currently... at least out of the box. Therefore you'll have to create a dummy list of values that span from 1 up to as many elements as there are in A1 (or A2 as they're both equal in size) to create an independent axis and interpolate both arrays independently by specifying the dummy list with a finer spacing in resolution. This finer spacing is controlled by the total number of new points you want to introduce in the plot. These points will be defined within the range of the dummy list but the spacing in between each point will decrease as you increase the total number of new points. As a general rule, the more points you add the less spacing there will be and so the plot should be more smooth. Once you do that, plot the final values together.
Here's some code for you to run. We will be using interp1 to perform the interpolation for us and most of the work. The function linspace creates the finer grid of points in the dummy list to facilitate the interpolation. N would be the total number of desired points you want to plot. I've made it 500 for now meaning that 500 points will be used for interpolation using your original data. Experiment by increasing (or decreasing) the total number of points and seeing what effect this has in the smoothness of your data.
I'll also be using the Piecewise Cubic Hermite Interpolating Polynomial or pchip as the method of interpolation, which is basically cubic spline interpolation if you want to get technical. Assuming that A1 and A2 are already created:
%// Specify number of interpolating points
N = 500;
%// Specify dummy list of points
D = 1 : numel(A1);
%// Generate finer grid of points
NN = linspace(1, numel(A1), N);
%// Interpolate each set of points independently
A1interp = interp1(D, A1, NN, 'pchip');
A2interp = interp1(D, A2, NN, 'pchip');
%// Plot the data
plot(A1interp, A2interp);
I now get the following:
Am trying to plot 2 variable of different size length in matlab GUI using push button,
but because the variables are of different length it will not work,is there a way i can make it to plot.
d= pdist([x,y,z],'euclidean') ; % value of my distance
dd= 1:10:d; % interval and end 'd' value
FSL=-120; %value of free space loss get from the GUI
DFSL= 1:10:FSL %interval and end at FSL value
plot(dd,DFSL)
The plot code didnt work coming back with an error "
Error using plot
Vectors must be the same lengths"
You can plot vectors of two different lengths, but not against each other. You have used the syntax
plot(x,y)
which means for every element in vector x, there should be a corresponding element in vector y. In your case, you do not have this, hence the error.
You can plot like this though:
plot(x)
figure;
plot(y)
If you are looking to plot them in a single plot, subplot will be useful.
I am trying to plot 3 vectors onto matlab GUI in a serial object's callback.
I want to plot this on axes handle but the problem is it only plot last vector;
plot(handles.axes1,sensor1,'r');
plot(handles.axes1,sensor2,'b');
plot(handles.axes1,sensor3,'g');
I searched on internet and find that this issue can be solved with hold on and hold of feature so I tried this
plot(handles.axes1,sensor1,'r');
hold on ;
plot(handles.axes1,sensor2,'b');
plot(handles.axes1,sensor3,'g');
hold off;
but in this case a new figure is opened(dont know why) and again only the last plot is drawn.
I am stucked. If any one have idea of what would be the issue?
Thanks
I'm not sure why your first try using "hold" didn't work. Seems like it should have.
But in any case, you can get the desired behavior in a single command:
plot(handles.axes1,length(sensor1),sensor1,'r',...
length(sensor2),sensor2,'b',...
length(sensor3),sensor3,'g');
This specifies both an X = length(sensor_) and a Y = sensor_ to the plot command. When you only give plot a Y input, it assumes an X of length(Y). But you can't combine multiple traces in a single plot command by giving only the Y input for each, because it will try to treat the inputs as X,Y pairs.
As the vectors are the same length we can simply combine them as the columns of a matrix and then plot the matrix
plot(handles.axes1,[sensor1',sensor2',sensor3'])
However these will have the default colour order. Without specifying x values setting colors within the plot command is tricky. However (luckily) the default order starts:
blue,green,red...
so swapping the column order will plot the lines with the colours requested
plot(handles.axes1,[sensor2',sensor3',sensor1'])
(this assumes the vectors are rows, if they are columns don't transpose them)
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.