I need ideas to resize my axes to have a much more airy graph to better visualize and calculate the gain between the different curves.
I used the code : axis([0 6 1e-3 1e0]) or xlim([0 6]); ylim([1e-3 1e0])
I would like to have for example my curve with: xlim([0:0.2:6]) (just the idea, otherwise it's wrong on matlab).
Thank you!
If I understand what you want, you need more XTicks in the x limits mentioned. After you plot just:
set(gca,'XTick',0:0.2:6)
another way is to write:
h=plot(.... whatever you plot...
h.XTick=0:0.2:6
Logarithmic Plot:
To create the axes the function xticks() and yticks() can be used to set the intervals, start and endpoints. xticks() and yticks() essentially take vectors that define all the ticks on the scales/axes. Just in case you'd like to also edit the interval along the y-axis. This vector can be created by raising each element in the vector (-3,1:0) to be an exponent with a base of 10. Finally, setting gca (the current axis) to logarithmic will allow the vertical spacing between the ticks to be evenly distributed.
axis([0 6 1e-3 1e0]);
Start = 0; Interval = 0.2; End = 6;
X_Vector = (Start: Interval: End);
xticks(X_Vector);
Y_Vector = 10.^(-3: 1: 0);
yticks(Y_Vector);
set(gca, 'YScale', 'log');
title("Logarithmic Plot");
grid;
Ran using MATLAB R2019b
Related
I want to represent data with 2 variables in 2D format. The value is represented by color and the 2 variables as the 2 axis. I am using the contourf function to plot my data:
clc; clear;
load('dataM.mat')
cMap=jet(256); %set the colomap using the "jet" scale
F2=figure(1);
[c,h]=contourf(xrow,ycol,BDmatrix,50);
set(h, 'edgecolor','none');
xlim([0.0352 0.3872]);
ylim([0.0352 0.3872]);
colormap(cMap);
cb=colorbar;
caxis([0.7 0.96]);
% box on;
hold on;
Both xrow and ycol are 6x6 matrices representing the coordinates. BDmatrix is the 6x6 matrix representing the corresponding data. However, what I get is this:
The following is the xrow and yrow matices:
The following is the BDmatrix matices:
Would it be possible for the contour color to vary smoothly rather than appearing as straight lines joining the data points? The problem of this figure is the coarse-granularity which is not appealing. I have tried to replace contourf with imagec but it seems not working. I am using MATLAB R2015b.
You can interpolate your data.
newpoints = 100;
[xq,yq] = meshgrid(...
linspace(min(min(xrow,[],2)),max(max(xrow,[],2)),newpoints ),...
linspace(min(min(ycol,[],1)),max(max(ycol,[],1)),newpoints )...
);
BDmatrixq = interp2(xrow,ycol,BDmatrix,xq,yq,'cubic');
[c,h]=contourf(xq,yq,BDmatrixq);
Choose the "smoothness" of the new plot via the parameter newpoints.
To reduce the Color edges, you can increase the number of value-steps. By default this is 10. The following code increases the number of value-steps to 50:
[c,h]=contourf(xq,yq,BDmatrixq,50);
A 3D-surf plot would be more suitable for very smooth color-shading. Just rotate it to a top-down view. The surf plot is also much faster than the contour plot with a lot of value-steps.
f = figure;
ax = axes('Parent',f);
h = surf(xq,yq,BDmatrixq,'Parent',ax);
set(h, 'edgecolor','none');
view(ax,[0,90]);
colormap(Jet);
colorbar;
Note 1: Cubic interpolation is not shape-preserving. That means, the interpolated shape can have maxima which are greater than the maximum values of the original BDmatrix (and minima which are less). If BDmatrix has noisy values, the interpolation might be bad.
Note 2: If you generated xrow and yrow by yourself (and know the limits), than you do not need that min-max-extraction what I did.
Note 3: After adding screenshots of your data matrices to your original posting, one can see, that xrow and ycol come from an ndgrid generator. So we also must use this here in order to be consistent. Since interp2 needs meshgrid we have to switch to griddedInterpolant:
[xq,yq] = ndgrid(...
linspace(min(min(xrow,[],1)),max(max(xrow,[],1)),newpoints ),...
linspace(min(min(ycol,[],2)),max(max(ycol,[],2)),newpoints )...
);
F = griddedInterpolant(xrow,ycol,BDmatrix,'cubic');
BDmatrixq = F(xq,yq);
I have two different functions of time x(t) and y(t). I want to plot x(t) vs y(t) in Matlab . The plot needs to be divided into a 40x40 grid stretching from min and max values of signal in each direction. I then need to calculate the number of grid boxes occupied in the plot. Please suggest a convenient way to implement this in Matlab.
I've tried the following code (neglect the upper and lower limits of axis):
NrGrid = 20; % Number Of Grids
x = linspace(0, 100, NrGrid+1);
[X,Y] = meshgrid(x);
figure(1)
plot(X,Y,'k')
hold on
plot(Y,X,'k')
hold off
set(gca, 'Box','off', 'XTick',[], 'YTick',[])
axis square
In my understanding, the code only divides the plot into grids. how do I count the number of grids that are occupied?
Reference: I basically need to implement the algorithm in this paper:
http://www.fhv.at/media/pdf/forschung/prozess-und-produktengineering/working-papers/working-papers-2005/detecting-ventricular
Sounds like you want to create a 40x40 matrix and then use a Bresenham line drawing algorithm to connect each of the points (after appropriate scaling) in x(t)/y(t) correspondence in that matrix.
You can then use nnz to count the number of non-zero elements in the matrix.
I managed to get a much simpler solution than the one mentioned by Dave Durbin by quantizing the signal into 40 levels and then comparing it with its shifted region. The code is attached for reference:
function TD=TimeDelay(val,fs)
n=40;
jump=( max(val) + abs(min(val)))/40;
level=zeros(n,1);
level(1)=min(val) + jump;
for i=2:n
level(i)=level(i-1)+jump;
end
level(n)=level(n)+1;
ScaledECG=zeros(size(val));
ScaledECG(val <= level(1))=1;
for j=2:n
ScaledECG( val<=level(j) & val>level(j-1))=j;
end
tau=fs*.5;
N=zeros(n,n);
for k=tau+1:1:length(val)
N(ScaledECG(k-tau),ScaledECG(k))=N(ScaledECG(k-tau),ScaledECG(k)) + 1;
end
N(N>5)=0;
N(N<=5)=1;
TD=sum(sum(N));
I have 3 singals and I'm trying to plot their phasors and their sum. I need to plot them end to end to demonstrate phasor addition. That is, the first phasor must start from the origin. The second phasor must start from the end of the first phasor. The third phasor must start from the end of the second one. In this way, the end point of the third phasor is the resulting phasor (considering that it starts at the origin). Horizontal and vertical axes are the real and imaginary axes, respectively in range of [-30, 30].
I just started using matlab today and this is due the night. I tried using plot, plot2, plot3, compass, and several ways but with all of them i failed. Compass was the closest to success.
I have amplitude and phase values of each phasor.
So how can I accomplish this task? Can you help me to draw two of phasors?
Any help is appreciated.
Thank you!
Related Example: from http://fourier.eng.hmc.edu/e84/lectures/ch3/node2.html
[example by spektre]
The following example should get you started:
First, the three phasors are defined.
% Define three complex numbers by magnitude and phase
ph1 = 20*exp(1i*0.25*pi);
ph2 = 10*exp(1i*0.7*pi);
ph3 = 5*exp(1i*1.2*pi);
Then, using cumsum, a vector containing ph1, ph1+ph2, ph1+ph2+ph3 is calculated.
% Step-wise vector sum
vecs = cumsum([ph1; ph2; ph3]);
vecs = [0; vecs]; % add origin as starting point
The complex numbers are plotted by real and imaginary part.
% Plot
figure;
plot(real(vecs), imag(vecs), '-+');
xlim([-30 30]);
ylim([-30 30]);
xlabel('real part');
ylabel('imaginary part');
grid on;
This produces the following figure:
figure(1); hold on;
ang = [0.1 0.2 0.7] ; % Angles in rad
r = [1 2 4] ; % Vector of radius
start = [0 0]
for i=1:numel(r)
plot([start(1) start(1)+r(i)*cos(ang(i))],[start(2) start(2)+r(i)*sin(ang(i))],'b-+')
start=start+[r(i)*cos(ang(i)) r(i)*sin(ang(i))]
end
plot([0 start(1)],[0 start(2)],'r-')
I'm using the Matlab function "hist" to estimate the probability density function of a realization of a random process I have.
I'm actually:
1) taking the histogram of h0
2) normalizing its area in order to get 1
3) plotting the normalized curve.
The problem is that, no matter how many bins I use, the histogram never start from 0 and never go back to 0 whereas I would really like that kind of behavior.
The code I use is the following:
Nbin = 36;
[n,x0] = hist(h0,Nbin);
edge = find(n~=0,1,'last');
Step = x0(edge)/Nbin;
Scale_factor = sum(Step*n);
PDF_h0 = n/Scale_factor;
hist(h0 ,Nbin) %plot the histogram
figure;
plot(a1,p_rice); %plot the theoretical curve in blue
hold on;
plot(x0, PDF_h0,'red'); %plot the normalized curve obtained from the histogram
And the plots I get are:
If your problem is that the plotted red curve does not go to zero: you can solve that adding initial and final points with y-axis value 0. It seems from your code that the x-axis separation is Step, so it would be:
plot([x0(1)-Step x0 x0(end)+Step], [0 PDF_h0 0], 'red')
I have the following code.
% Generate message signal
t1 = -1:0.1:1;
message_sig = rectpuls(t1);
L = length(message_sig);
figure(2)
stairs(0:L-1:L, 'linewidth',1.5);
axis([0 L -1 2]);
title('Message Signal');
When i run this, the length of my x axis is from 0 to 20.
How can i reduce it to say 0 to 8, while plotting the same bit pattern. Because when i try modulating and add noise, the whole plot (noise modulated signal) is blue and needs to be zoomed alot to see accurately.
So can someone help me with a code that could solve this.
simply use xlim([0,8]), that will restrict the x-axis from going beyond 8, or edit your axis call to be axis([0,8,-1,2])
UPDATE
Assuming you have the image processing toolbox, it is really simple
in = [0,1,0];
imresize(in,[1,8],'nearest');
This will take that pattern and expand it to whatever dimension you want.