I have some 3D trajectories I want to plot.
Since they vary a lot in XY, but much less in Z, the default plot3 is misleading, because it automatically scales axes.
I've been told to use axes equal but it has no effect (see the commented line where I used it).
I came up with this code, which in my opinion is very long to achieve a so simple task:
[D,rate]=read_vicon_ascii('csvdata/a1-0.csv');
% or replace above line with D=csvread('stackoverflow-31289872.csv');
% get stackoverflow-31289872.csv at https://drive.google.com/file/d/0B5GjKiDZk3F5UHlVQUxKeFo4SG8/view?pli=1
% indices of X,Y,Z columns
X = 1+(2:3:14);
Y = 2+(2:3:14);
Z = 3+(2:3:14);
Bounds = [ min(min(D(:,X))) max(max(D(:,X)))
min(min(D(:,Y))) max(max(D(:,Y)))
min(min(D(:,Z))) max(max(D(:,Z))) ];
MaxDelta = max(Bounds(:,2)-Bounds(:,1));
SquareBounds = Bounds;
for xyz=1:3
Delta = SquareBounds(xyz,2) - SquareBounds(xyz,1);
SquareBounds(xyz,:) = SquareBounds(xyz,:) + (MaxDelta - Delta) * [-0.5 0.5];
end
figure
hold on
for i=1:5
plot3(D(:,X(i)),D(:,Y(i)),D(:,Z(i)),'r-')
end
xlim(SquareBounds(1,:))
ylim(SquareBounds(2,:))
zlim(SquareBounds(3,:))
%axes equal
hold off
Is there any way to make it better. (or a correct usage of axes equal if that does what is supoosed to do?)
Related
I've found this answer, but I can't complete my work. I wanted to plot more precisely the functions I am studying, without overcoloring my function with black ink... meaning reducing the number of mesh lines. I precise that the functions are complex.
I tried to add to my already existing code the work written at the link above.
This is what I've done:
r = (0:0.35:15)'; % create a matrix of complex inputs
theta = pi*(-2:0.04:2);
z = r*exp(1i*theta);
w = z.^2;
figure('Name','Graphique complexe','units','normalized','outerposition',[0.08 0.1 0.8 0.55]);
s = surf(real(z),imag(z),imag(w),real(w)); % visualize the complex function using surf
s.EdgeColor = 'none';
x=s.XData;
y=s.YData;
z=s.ZData;
x=x(1,:);
y=y(:,1);
% Divide the lengths by the number of lines needed
xnumlines = 10; % 10 lines
ynumlines = 10; % 10 partitions
xspacing = round(length(x)/xnumlines);
yspacing = round(length(y)/ynumlines);
hold on
for i = 1:yspacing:length(y)
Y1 = y(i)*ones(size(x)); % a constant vector
Z1 = z(i,:);
plot3(x,Y1,Z1,'-k');
end
% Plotting lines in the Y-Z plane
for i = 1:xspacing:length(x)
X2 = x(i)*ones(size(y)); % a constant vector
Z2 = z(:,i);
plot3(X2,y,Z2,'-k');
end
hold off
But the problem is that the mesh is still invisible. How to fix this? Where is the problem?
And maybe, instead of drawing a grid, perhaps it is possible to draw circles and radiuses like originally on the graph?
I found an old script of mine where I did more or less what you're looking for. I adapted it to the radial plot you have here.
There are two tricks in this script:
The surface plot contains all the data, but because there is no mesh drawn, it is hard to see the details in this surface (your data is quite smooth, this is particularly true for a more bumpy surface, so I added some noise to the data to show this off). To improve the visibility, we use interpolation for the color, and add a light source.
The mesh drawn is a subsampled version of the original data. Because the original data is radial, the XData and YData properties are not a rectangular grid, and therefore one cannot just take the first row and column of these arrays. Instead, we use the full matrices, but subsample rows for drawing the circles and subsample columns for drawing the radii.
% create a matrix of complex inputs
% (similar to OP, but with more data points)
r = linspace(0,15,101).';
theta = linspace(-pi,pi,101);
z = r * exp(1i*theta);
w = z.^2;
figure, hold on
% visualize the complex function using surf
% (similar to OP, but with a little bit of noise added to Z)
s = surf(real(z),imag(z),imag(w)+5*rand(size(w)),real(w));
s.EdgeColor = 'none';
s.FaceColor = 'interp';
% get data back from figure
x = s.XData;
y = s.YData;
z = s.ZData;
% draw circles -- loop written to make sure the outer circle is drawn
for ii=size(x,1):-10:1
plot3(x(ii,:),y(ii,:),z(ii,:),'k-');
end
% draw radii
for ii=1:5:size(x,2)
plot3(x(:,ii),y(:,ii),z(:,ii),'k-');
end
% set axis properties for better 3D viewing of data
set(gca,'box','on','projection','perspective')
set(gca,'DataAspectRatio',[1,1,40])
view(-10,26)
% add lighting
h = camlight('left');
lighting gouraud
material dull
How about this approach?
[X,Y,Z] = peaks(500) ;
surf(X,Y,Z) ;
shading interp ;
colorbar
hold on
miss = 10 ; % enter the number of lines you want to miss
plot3(X(1:miss:end,1:miss:end),Y(1:miss:end,1:miss:end),Z(1:miss:end,1:miss:end),'k') ;
plot3(X(1:miss:end,1:miss:end)',Y(1:miss:end,1:miss:end)',Z(1:miss:end,1:miss:end)','k') ;
I have a spectral data (1000 variables on xaxis, and peak intensities as y) and a list of peaks of interest at various specific x locations (a matrix called Peak) which I obtained from a function I made. Here, I would like to draw a line from the maximum value of each peaks to the xaxis - or, eventually, place a vertical arrow above each peaks but I read it is quite troublesome, so just a vertical line is welcome. However, using the following code, I get "Error using line Value must be a vector of numeric type". Any thoughts?
X = spectra;
[Peak,intensity]=PeakDetection(X);
nrow = length(Peak);
Peak2=Peak; % to put inside the real xaxis value
plot(xaxis,X);
hold on
for i = 1 : nbrow
Peak2(:,i) = round(xaxis(:,i)); % to get the real xaxis value and round it
xline = Peak2(:,i);
line('XData',xline,'YData',X,'Color','red','LineWidth',2);
end
hold off
Simple annotation:
Here is a simple way to annotate the peaks:
plot(x,y,x_peak,y_peak+0.1,'v','MarkerFaceColor','r');
where x and y is your data, and x_peak and y_peak is the coordinates of the peaks you want to annotate. The add of 0.1 is just for a better placing of the annotation and should be calibrated for your data.
For example (with some arbitrary data):
x = 1:1000;
y = sin(0.01*x).*cos(0.05*x);
[y_peak,x_peak] = PeakDetection(y); % this is just a sketch based on your code...
plot(x,y,x_peak,y_peak+0.1,'v','MarkerFaceColor','r');
the result:
Line annotation:
This is just a little bit more complicated because we need 4 values for each line. Again, assuming x_peak and y_peak as before:
plot(x,y);
hold on
ax = gca;
ymin = ax.YLim(1);
plot([x_peak;x_peak],[ymin*ones(1,numel(y_peak));y_peak],'r')
% you could write instead:
% line([x_peak;x_peak],[ymin*ones(1,numel(y_peak));y_peak],'Color','r')
% but I prefer the PLOT function.
hold off
and the result:
Arrow annotation:
If you really want those arrows, then you need to first convert the peak location to the normalized figure units. Here how to do that:
plot(x,y);
ylim([-1.5 1.5]) % only for a better look of the arrows
peaks = [x_peak.' y_peak.'];
ax = gca;
% This prat converts the axis unites to the figure normalized unites
% AX is a handle to the figure
% PEAKS is a n-by-2 matrix, where the first column is the x values and the
% second is the y values
pos = ax.Position;
% NORMPEAKS is a matrix in the same size of PEAKS, but with all the values
% converted to normalized units
normpx = pos(3)*((peaks(:,1)-ax.XLim(1))./range(ax.XLim))+ pos(1);
normpy = pos(4)*((peaks(:,2)-ax.YLim(1))./range(ax.YLim))+ pos(2);
normpeaks = [normpx normpy];
for k = 1:size(normpeaks,1)
annotation('arrow',[normpeaks(k,1) normpeaks(k,1)],...
[normpeaks(k,2)+0.1 normpeaks(k,2)],...
'Color','red','LineWidth',2)
end
and the result:
I am looking to create a simple circle graph within MATLAB in which the model shows the point moving along the circle with radius and angular velocity defined by the user.
Angular velocity in RADIANS/SEC
I am relatively new at MATLAB coding so any help would be very useful!
I tried this code:
r=1;
t = 0:.01:2*pi;
x = r*cos(t);
y = r*sin(t);
comet(x,y);
But when I change the 0.01 value the point doesn't move faster, it just skips more of the curve, also i'm unsure if the increments are in radians.
Thanks for your time
Edited version: See edit history for previous version.
Radius = 10;
AngularVelocity = 5; % in deg / s
AngleStep = 0.1
Angles = AngleStep : AngleStep : 2*pi;
CircleX = [Radius]; % empty array
CircleY = [0]; % empty array
%Initial zero-angle plot whose data we'll keep updating in the for loop:
a = plot([CircleX,CircleX], [CircleY,CircleY], 'r:');
hold on;
b = plot(CircleX, CircleY, 'o', 'markeredgecolor', 'k', 'markerfacecolor','g');
axis([-Radius, +Radius, -Radius, +Radius]); % make sure the axis is fixed
axis equal; % make x and y pixels of equal size so it "looks" a circle!
hold off;
for t = Angles
CircleX(end+1) = Radius * cos (t); % append point at end of CircleX array
CircleY(end+1) = Radius * sin (t); % append point at end of Circley array
set(a,'xdata',CircleX,'ydata',CircleY); % update plot 'a' data
set(b,'xdata', CircleX(end), 'ydata', CircleY(end)); % update plot 'b' data
drawnow; % ensure intermediate frames are shown!
pause(AngleStep/AngularVelocity) % pause the right amount of time!
end
This edit has made two changes compared to the previous version:
Instead of redrawing, now we're updating the data of an existing plot. This is generally faster as matlab doesn't have to redraw axes objects (i.e. the containers that hold the plot)
I increased AngleStep from 0.01 to 0.1. This means there's 10 times less angles to draw, so you can afford to draw then 10 times slower, therefore it becomes less likely that matlab will be unable to draw because of overhead. Having said that, this is at the cost of a less perfect circle. Try with AngleStep=1 to see what I mean.
I need to plot the roots onto a transfer function H(z) overlaying a unit circle, giving enough room to see all points. I'm able to get the roots from H(z) when it is given in the form zeros = [z0 z1 z2...], poles = [p0 p1 p2]. Using Matlab's roots function, I'm able to get the pole and zero locations. My Matlab code so far is
function zplot(b, a)
b_roots = roots(b);
a_roots = roots(a);
hold on
rectangle('Position',[-1 -1 2 2],'Curvature',[1 1]);
plot(b_roots,'x blue');
plot(a_roots,'o blue');
axis %need axis to be equal and +10percent of maximum value
hold off
end
So far, I can plot the roots and the unit circle, but I need help with adjusting the axes so that they are 1) equal to each other and 2) 10% more than the highest value. I'm not sure how to go about doing this part. I tried making a variable lim_max = max(b_roots,a_roots) but it ended up being an array, and wouldn't work in the axis([-lim_max lim_max -lim_max lim_max]) function. I need the plot to scale to +10% with the inputs as they change.
Side note (not necessary): is there a way for it to look like a circle when I plot it, because right now it ends up looking like an oval most of the time. I can readjust the screen, which is fine, but if there's an easy way to do that, I'd also like to know.
Set axis equal and calculate min/max:
function zplot(b, a)
b_roots = roots(b);
a_roots = roots(a);
xlimits = [min(min([real(a_roots);real(b_roots)])), max(max([real(a_roots);real(b_roots)]))];
ylimits = [min(min([imag(a_roots);imag(b_roots)])), max(max([imag(a_roots);imag(b_roots)]))];
hold on
rectangle('Position',[-1 -1 2 2],'Curvature',[1 1]);
plot(b_roots,'x black');
plot(a_roots,'o blue');
axis equal;
xlim(1.1*xlimits);
ylim(1.1*ylimits);
hold off
end
Use the following code. This will 1) find the maximum overall limits of x and y axes 2) set those limits equal to each other 3) plot those limits +10%
b_roots = roots(b);
a_roots = roots(a);
x_min = min(min([real(a_roots);real(b_roots)]));
x_max = max(max([real(a_roots);real(b_roots)]));
y_min = min(min([imag(a_roots);imag(b_roots)]));
y_max = max(max([imag(a_roots);imag(b_roots)]));
%get the magnitude of the overall minimum value
min_lim = abs(min(x_min,y_min));
%abs may not be necessary
max_lim = abs(max(x_max,y_max));
%set high and low limits equal to each other from negative to positive
eq_limit = [-max(min_lim,max_lim),max(min_lim,max_lim)];
hold on
rectangle('Position',[-1 -1 2 2],'Curvature',[1 1]);
plot(b_roots,'x black');
plot(a_roots,'o blue');
axis equal;
xlim(1.1*eq_limit);
ylim(1.1*eq_limit);
hold off
Thanks to #M.S. for their answer and help.
I have a simple loglog curve as above. Is there some function in Matlab which can fit this curve by segmented lines and show the starting and end points of these line segments ? I have checked the curve fitting toolbox in matlab. They seems to do curve fitting by either one line or some functions. I do not want to curve fitting by one line only.
If there is no direct function, any alternative to achieve the same goal is fine with me. My goal is to fit the curve by segmented lines and get locations of the end points of these segments .
First of all, your problem is not called curve fitting. Curve fitting is when you have data, and you find the best function that describes it, in some sense. You, on the other hand, want to create a piecewise linear approximation of your function.
I suggest the following strategy:
Split manually into sections. The section size should depend on the derivative, large derivative -> small section
Sample the function at the nodes between the sections
Find a linear interpolation that passes through the points mentioned above.
Here is an example of a code that does that. You can see that the red line (interpolation) is very close to the original function, despite the small amount of sections. This happens due to the adaptive section size.
function fitLogLog()
x = 2:1000;
y = log(log(x));
%# Find section sizes, by using an inverse of the approximation of the derivative
numOfSections = 20;
indexes = round(linspace(1,numel(y),numOfSections));
derivativeApprox = diff(y(indexes));
inverseDerivative = 1./derivativeApprox;
weightOfSection = inverseDerivative/sum(inverseDerivative);
totalRange = max(x(:))-min(x(:));
sectionSize = weightOfSection.* totalRange;
%# The relevant nodes
xNodes = x(1) + [ 0 cumsum(sectionSize)];
yNodes = log(log(xNodes));
figure;plot(x,y);
hold on;
plot (xNodes,yNodes,'r');
scatter (xNodes,yNodes,'r');
legend('log(log(x))','adaptive linear interpolation');
end
Andrey's adaptive solution provides a more accurate overall fit. If what you want is segments of a fixed length, however, then here is something that should work, using a method that also returns a complete set of all the fitted values. Could be vectorized if speed is needed.
Nsamp = 1000; %number of data samples on x-axis
x = [1:Nsamp]; %this is your x-axis
Nlines = 5; %number of lines to fit
fx = exp(-10*x/Nsamp); %generate something like your current data, f(x)
gx = NaN(size(fx)); %this will hold your fitted lines, g(x)
joins = round(linspace(1, Nsamp, Nlines+1)); %define equally spaced breaks along the x-axis
dx = diff(x(joins)); %x-change
df = diff(fx(joins)); %f(x)-change
m = df./dx; %gradient for each section
for i = 1:Nlines
x1 = joins(i); %start point
x2 = joins(i+1); %end point
gx(x1:x2) = fx(x1) + m(i)*(0:dx(i)); %compute line segment
end
subplot(2,1,1)
h(1,:) = plot(x, fx, 'b', x, gx, 'k', joins, gx(joins), 'ro');
title('Normal Plot')
subplot(2,1,2)
h(2,:) = loglog(x, fx, 'b', x, gx, 'k', joins, gx(joins), 'ro');
title('Log Log Plot')
for ip = 1:2
subplot(2,1,ip)
set(h(ip,:), 'LineWidth', 2)
legend('Data', 'Piecewise Linear', 'Location', 'NorthEastOutside')
legend boxoff
end
This is not an exact answer to this question, but since I arrived here based on a search, I'd like to answer the related question of how to create (not fit) a piecewise linear function that is intended to represent the mean (or median, or some other other function) of interval data in a scatter plot.
First, a related but more sophisticated alternative using regression, which apparently has some MATLAB code listed on the wikipedia page, is Multivariate adaptive regression splines.
The solution here is to just calculate the mean on overlapping intervals to get points
function [x, y] = intervalAggregate(Xdata, Ydata, aggFun, intStep, intOverlap)
% intOverlap in [0, 1); 0 for no overlap of intervals, etc.
% intStep this is the size of the interval being aggregated.
minX = min(Xdata);
maxX = max(Xdata);
minY = min(Ydata);
maxY = max(Ydata);
intInc = intOverlap*intStep; %How far we advance each iteraction.
if intOverlap <= 0
intInc = intStep;
end
nInt = ceil((maxX-minX)/intInc); %Number of aggregations
parfor i = 1:nInt
xStart = minX + (i-1)*intInc;
xEnd = xStart + intStep;
intervalIndices = find((Xdata >= xStart) & (Xdata <= xEnd));
x(i) = aggFun(Xdata(intervalIndices));
y(i) = aggFun(Ydata(intervalIndices));
end
For instance, to calculate the mean over some paired X and Y data I had handy with intervals of length 0.1 having roughly 1/3 overlap with each other (see scatter image):
[x,y] = intervalAggregate(Xdat, Ydat, #mean, 0.1, 0.333)
x =
Columns 1 through 8
0.0552 0.0868 0.1170 0.1475 0.1844 0.2173 0.2498 0.2834
Columns 9 through 15
0.3182 0.3561 0.3875 0.4178 0.4494 0.4671 0.4822
y =
Columns 1 through 8
0.9992 0.9983 0.9971 0.9955 0.9927 0.9905 0.9876 0.9846
Columns 9 through 15
0.9803 0.9750 0.9707 0.9653 0.9598 0.9560 0.9537
We see that as x increases, y tends to decrease slightly. From there, it is easy enough to draw line segments and/or perform some other kind of smoothing.
(Note that I did not attempt to vectorize this solution; a much faster version could be assumed if Xdata is sorted.)