I have made a function that allows a user to draw several points and interpolate those points. I want the function to calculate the centre of mass using this vector :
I think I should therefore first calculate the area of the figure (I drew this example to illustrate the function output). I want to do this using Green's theorem
However since I'm quite a beginner in MATLAB I'm stuck at how to implement this formula in order to find the centre of mass. I'm also not sure how to get the data as my output so far is only the x- and y cordinates of the points.
function draw
fig = figure();
hax = axes('Parent', fig);
axis(hax, 'manual')
[x,y] = getpts();
M = [x y]
T = cumsum(sqrt([0,diff(x')].^2 + [0,diff(y')].^2));
T_i = linspace(T(1),T(end),1000);
X_i = interp1(T,x',T_i,'cubic');
Y_i = interp1(T,y',T_i,'cubic');
plot(x,y,'b.',X_i,Y_i,'r-')
end
The Center Of Mass for a 2D coordinate system should just be the mean of the interpolated x-coordinates and y-coordinates. The Interpolation should give you evenly spaced coordinates which you can use to your advantage. So simply add to your existing function:
CenterOfMass= [mean(X_i),mean(Y_i)]
plot(x,y,'b.',X_i,Y_i,'r-')
hold on
plot(CenterOfMass(1),CenterOfMass(2),'ro')
should give you the center of mass assuming that all points are weighted equally.
Related
I have generated a rectangular matrix with the azimouth angle changing with rows and the radius changing as you change column. These are meant to represent the relative velocities experienced by a rotating helicopter blade. This produces a matrix called Vmat. I want to plot this to appears in a circle (representing the rotation of the blade)
So far I have tried
[R,T] = meshgrid(r,az);
[x,y] = pol2cart(T,R);
surf(x,y,Vmat(r,az));
which should produce a contoured surface showing velocity as it changes with azimouth angle and radius but it comes up with dimension errors.
I don't mind if it is a 2d contour plot or 3d plot i guess both would be written in a similar way.
Thanks
James
The error is in writing Vmat(r,az), presuming that these are actual values of radius and azimuth, not indexes into your radius and azimuth. If you want to take only a subset of Vmat that's a slightly different matter, but this should work:
[R,T] = meshgrid(r,az); % creates a grid in polar coordinates
[x,y] = pol2cart(T,R); % changes those to cartesian for surf
surf(x,y,Vmat);
Alternatively you could do a contour plot:
h = polar([0 2*pi], [0 max(r)]); % set up polar axes with right scale
delete(h) % remove line
hold on
contour(x,y,Vmat);
I have a distribution of points inside a circle.
So, I draw a circular grid inside that circle. I want to find the number of points inside each cell of the circular gird.
Is there a way to implement that easily. or maybe drawing a grid is not necessary?
My goal is plotting the distribution.
Any help is highly appreciated.
Thanks in advance.
If X,Y are the coordinates of the points in your circle, the distances from the center can be obtained with
(edit: T/H #horchler)
d = sqrt(sum([X(:)-X0 Y(:)-Y0].^2,2));
where X0, Y0 are the coordinates of the center of the circle.
You can then compute a radial distribution using hist:
figure, hist(d)
or if you just want the distribution and bins
[distr bins] = hist(d);
By "circular Grid" I understand a grid in azimuth and modulus. I suggest you convert to polar coordinates:
z = x + j*y; % x, y are vectors woth x, y coordinates of the points
az = angle(z); % note that this gives azimuth in radians
mod = abs(z);
and then apply some kind ot 2D histogram to az and mod, for example using this function. (Please note it is a user-contributed file. I haven't tested it myself).
I have a 2d image, I have locations where local minimas occurs.
I want to measure the width of the valleys "leading" to those minimas.
I need either the radii of the circles or ellipses fitted to these valley.
An example attached here, dark red lines on the peaks contours is what I wish to find.
Thanks.
I am partially extending the answer of #Lucas.
Given a threshold t I would consider the points P_m that are below t and closer to a certain point m of minimum of your f (given a characteristic scale length r).
(You said your data are noisy; to distinguish minima and talk about wells, you need to estimate such r. In your example it can be for instance r=4, i.e. half the distance between the minima).
Then you have to consider a metric for each well region P_m, say for example
metric(P_m) = .5 * mean{ maximum vertical diameter of P_m ,
maximum horizontal diameter of P_m}.
In your picture metric(P_m) = 2 for both wells.
On the whole, in terms of pseudo-code you may consider
M := set of local minima of f
for_each(minimum m in M){
P_m += {p : d(p,m) < r and f(r)<t} % say that += is the push operation in a Stack
}
radius_of_region_around(m) = metric(P_m); %
I would suggest making a list of points that describe the values at the edge of your ellipse, perhaps by finding all the points where it crosses a threshold.
above = data > threshold
apply a simple edge detector
edges = EdgeDetector(above)
find coordinates of edges
[row,col] = find(edges)
Then apply this ellipse fitter http://www.mathworks.com/matlabcentral/fileexchange/3215-fitellipse
I'm assuming here you have access to the x, y and z data and are not processing a given JPG (or so) image. Then, you can use the function contourc to your advantage:
% plot some example function
figure(1), clf, hold on
[x,y,z] = peaks;
surf(x,y,z+10,'edgecolor', 'none')
grid on, view(44,24)
% generate contour matrix. The last entry is a 2-element vector, the last
% element of which is to ensure the right algorithm gets called (so leave
% it untouched), and the first element is your threshold.
C = contourc(x(1,:), y(:,1), z, [-4 max(z(:))+1]);
% plot the selected points
plot(C(1,2:end), C(2,2:end), 'r.')
Then use this superfast ellipse fitting tool to fit an ellipse through those points and find all the parameters of the ellipse you desire.
I suggest you read help contourc and doc contourc to find out why the above works, and what else you can use it for.
I've run simulations which have given me data points corresponding to X number of different radii, and Y number of angles each one was evaluated at. This means that I have X times Y data points which I need to plot.
I am currently plotting it in an non-ideal fashion: I am using the x and y axes as the r and theta axes. This means that my data appears as a sinusoidal trend which increases with radius on a Cartesian grid, not the circle which it physically represents. This is how I am currently plotting my data:
surf(r_val, th_val, v_val);
What I wish to do is plot my data on a cylindrical axis, such like that of the function polar(), but in R3 space. I would rather not download a toolbox, or modify the existing polar function; if there is no other solution then I will obviously end up doing this anyways.
Thanks for your help!
G.
Also, I am using Matlab 2012a
EDIT:
r_val = 1x8 vector containing unique radii
th_val = 1x16 vector containing unique angles
v_val = 8x16 matrix containing voltages corresponding to each position
NOTE: (after answered)
The truly ideal solution does not exist to this problem, as Matlab currently supports no true polar axes methods. Resource found here.
You should transform your coordinates to Cartesian coordinates before plotting them. MATLAB has builtin functions for perfroming coordiante transformations. See, for example pol2cart, which transforms polar or cylindrical coordinates to Cartesian coordinates. In your case you would simply use something like:
[x, y] = pol2cart(th_val, r_val);
surf(x, y, v_val);
Edit: Given that th_val and r_val are vectors of differing lengths it is necessary to first create a grid of points before calling pol2cart, along the lines of:
[R, T] = meshgrid(r_val, th_val);
[x, y] = pol2cart(T, R);
surf(x, y, v_val);
I have a script to create scatter plots (using gscatter) based on x-y data (discrete data points, not continuous) produced by another script. Since these data points are actually the locations of certain objects in a circular space, adding polar grid lines will make the plots more meaningful.
Does anyone know how to show polar grid lines on a Cartesian scatter plot, or am I better off using polar plots?
I once made this script for drawing a polar coordinate system on top of a regular plot. Perhaps it can be useful for you. It is based on this script but simplified to only draw the coordinate system and no data. If this wasn't what you were looking for, check out the linked script, perhaps it can help as well.
Be sure to tweak the radius as needed! I usually disable the axis but it's up to you to fix that if you need another look :)
R=6000; %radius
S=10; %num circ.lines
N=10; %num ang.lines
sect_width=2*pi/N;
offset_angle=0:sect_width:2*pi-sect_width;
%------------------
r=linspace(0,R,S+1);
w=0:.01:2*pi;
clf %remove if needed
hold on
axis equal
for n=2:length(r)
plot(real(r(n)*exp(j*w)),imag(r(n)*exp(j*w)),'k--')
end
for n=1:length(offset_angle)
plot(real([0 R]*exp(j*offset_angle(n))),imag([0 R]*exp(j*offset_angle(n))),'k-')
end
%------------------
You can always use the pol2cart function to generate the polar grid lines.
For example:
function DrawGridLines
x = randn(10);
y = randn(10);
figure;scatter(x(:),y(:));
hold on ;
for angle = 0:20:(360-20)
[x1,y1] = pol2cart( angle / 180 * pi , [0 2]);
plot(x1,y1,'r')
end
for rho = 0:0.1:2
[x1,y1] = pol2cart( 0:0.01:2*pi , rho);
plot(x1,y1,'b')
end
axis equal
end