I need to find out the intersecting points of two circles. I have the center points and the radius of each circle. I need to do it in MATLAB. Any help will be appreciated.
Assume a triangle ABC, where A and B are the centers of the circle, and C is one or the other intersection point. a, b, and c are the sides opposite the corresponding corners. alpha, beta, and gamma are the angles associated with A, B, and C, respectively.
Then, b^2+c^2 - 2*bccos(alpha) = a^2. Knowing alpha (or its cosine), you can find the location of C.
A = [0 0]; %# center of the first circle
B = [1 0]; %# center of the second circle
a = 0.7; %# radius of the SECOND circle
b = 0.9; %# radius of the FIRST circle
c = norm(A-B); %# distance between circles
cosAlpha = (b^2+c^2-a^2)/(2*b*c);
u_AB = (B - A)/c; %# unit vector from first to second center
pu_AB = [u_AB(2), -u_AB(1)]; %# perpendicular vector to unit vector
%# use the cosine of alpha to calculate the length of the
%# vector along and perpendicular to AB that leads to the
%# intersection point
intersect_1 = A + u_AB * (b*cosAlpha) + pu_AB * (b*sqrt(1-cosAlpha^2));
intersect_2 = A + u_AB * (b*cosAlpha) - pu_AB * (b*sqrt(1-cosAlpha^2));
intersect_1 =
0.66 -0.61188
intersect_2 =
0.66 0.61188
Find the equations of the circles. Make sure to account for the negative of the square root or else you will just have a semi circle.
Set the equations of the two circles equal to eachother.
Here is a simple code using two File Exchange submissions: first - to draw circles, second - to find intersections (links below).
clf
N=30; % circle resolution as the number of points
hold on
% draw 1st circle at (0,0) radius 5 and get X and Y data
H1=circle([0 0],5,N);
X1=get(H1,'XData');
Y1=get(H1,'YData');
% draw 2nd circle at (2,5) radius 3 and get X and Y data
H2=circle([2 5],3,N);
X2=get(H2,'XData');
Y2=get(H2,'YData');
% find intersection points
[x,y]=intersections(X1,Y1,X2,Y2,0);
% and plot them as red o's
plot(x,y,'ro')
hold off
axis equal
CIRCLE
Fast and Robust Curve Intersections
Function CIRCCIRC does this for you.
[xout,yout] = circcirc(x1,y1,r1,x2,y2,r2)
This will give you the two intersection points.
http://www.mathworks.nl/help/map/ref/circcirc.html
I am pasting Roger Stafford's answer here. (See link) This also prints if there is no intersection between circles. I think you can figure it the geometrical relations in the code.
% P1 and P2 are column vectors r1 and r2 are their respective radius.
% P1 = [x1;y1]; P2 = [x2;y2];
d2 = sum((P2-P1).^2);
P0 = (P1+P2)/2+(r1^2-r2^2)/d2/2*(P2-P1);
t = ((r1+r2)^2-d2)*(d2-(r2-r1)^2);
if t <= 0
fprintf('The circles don''t intersect.\n')
else
T = sqrt(t)/d2/2*[0 -1;1 0]*(P2-P1);
Pa = P0 + T; % Pa and Pb are circles' intersection points
Pb = P0 - T;
end
Pint = [Pa, Pb];
Related
How to distribute the points to be like Fig.A
This matlab code for Fig. B :
N = 30; % number of points
r = 0.5; % r = radius
d = 50; % dimension
C_point = 0; % center point
figure, clf
C = ones(1, d) * C_point;
C_rep = repmat( C,N,1);
X = randn(N,d);
s2 = sum(X.^2,2) ;
radius = r * (rand(N,1).^(1/d));
X = X.*repmat(radius./sqrt(s2),1,d) + C_rep;
%% Plot 2D
t = linspace(0, 2*pi, 100);
x = r*cos(t) + C(1);
y = r*sin(t) + C(2);
plot(x,y,'b')
hold on
plot(C(1),C(2),'b.', 'MarkerSize', 10) % center point
hold on
plot(X(:,1), X(:,2),'r.','markersize',10);
axis equal;rotate3d off; rotate3d on;drawnow;shg;
hold on
ax = axis;
Source of the code
What I should change to be like fig. A
The OP's code computes points uniformly distributed within a d-dimensional box, projects those onto a d-dimensional sphere, then samples the radius to move them inside the d-dimensional ball. This is perfect except that the points inside the box, when projected onto the sphere, do not form a uniform distribution on that sphere. If instead you find random points distributed in a Gaussian distribution, you are guaranteed uniform angle distribution.
First compute points with a Gaussian distribution in d dimensions (I do all here with minimal changes to the OP's code):
N = 1000; % number of points
r = 0.5; % r = radius
d = 3; % dimension
C_point = 0; % center point
C = ones(1,d) * C_point;
C_rep = repmat(C,N,1);
X = randn(N,d);
Note that I use randn, not rand. randn creates a Gaussian distribution.
Next we normalize the vectors so the points move to the sphere:
nX = sqrt(sum(X.^2,2));
X = X./repmat(nX,1,d);
These points are uniformly distributed, which you can verify by scatter3(X(:,1),X(:,2),X(:,3)); axis equal and turning the display around (a 2D rendering doesn't do it justice). This is the reason I set d=3 above, and N=1000. I wanted to be able to plot the points and see lots of them.
Next we compute, as you already did, a random distance to the origin, and correct it for the dimensionality:
radius = r * (rand(N,1).^(1/d));
X = X.*repmat(radius,1,d) + C_rep;
X now is distributed uniformly in the ball. Again, scatter3(X(:,1),X(:,2),X(:,3)); axis equal shows this.
However, if you set d=50 and then plot only two dimensions of your data, you will not see the data filling the circle. And you will not see a uniform distribution either. This is because you are projecting a 50-D ball onto 2 dimensions, this simply does not work. You either have to trust the math, or you have to slice the data:
figure, hold on
t = linspace(0, 2*pi, 100);
x = r*cos(t) + C(1);
y = r*sin(t) + C(2);
plot(x,y,'b')
plot(C(1),C(2),'b.', 'MarkerSize', 10) % center point
axis equal
I = all(abs(X(:,3:d))<0.1,2);
plot(X(I,1), X(I,2),'r.','markersize',10);
The I there indexes points that are close to the origin in dimensions perpendicular to the first two shown. Again, with d=50 you will have very few points there, so you will need to set N very large! To see the same density of points as in the case above, for every dimension you add, you need to multiply N by 10. So for d=5 you'd have N=1000*10*10=1e5, and for d=50 you'd need N=1e50. That is totally impossible to compute, of course.
I have a piecewise function, where domain changes for each case. The function is as follows:
For
(x,y)greater than Divider v= f(x,y) (A1)
(x,y)less than Divider v = g(x,y) (A2)
The location of the divider changes with tilt angle of the rectangle given in figures 1 and 2.Figure 1 & 2 The divider will always be a bisector of the rectangle. For example, the divider makes an angle (alpha + 90) with the horizontal.
If the rectangle makes an angle 0, it's easy to implement above functions as I can create meshgrid from
x =B to C & y = A to D for A1
x =A to B & y = A to D for A2
However, when the angles for the rectangle are different, I can't figure out how to create the mesh to calculate the function v using the algorithm A1 and A2 above.
I was thinking of using some inequality and using the equation of the line (as I have the co-ordinates for the center of the rectangle and the angle of tilt). But, I can't seem to think of a way to do it for all angles (for example , slope of pi/2 as in the first figure, yields infinity). Even if I do create some kind of inequality, I can't create a mesh.
1Please help me with this problem. I have wasted a lot of time on this. It seems to be out of my reach
%% Constants
Angle1=0;
Angle1=Angle1.*pi./180;
rect_center=0; % in m
rect_length=5; % in m
rect_width=1; % in m
rect_strength=1.8401e-06;
Angle2=0;
Angle2 =Angle2.*pi./180;
%% This code calculates the outer coordinates of the rectangle by using the central point
% the following code calculates the vertices
vertexA=rect_center+(-rect_width./2.*exp(1i.*1.5708)-rect_length./2).*exp(1i.*Angle2);
vertexA=[vertexA,vertexA+2.*(rect_width./2.*exp(1i.*1.5708)).*exp(1i.*Angle2)];
vertexB=rect_center+(-rect_width./2.*exp(1i.*1.5708)+rect_length./2).*exp(1i.*Angle2);
vertexB=[vertexB,vertexB+2.*(rect_width./2.*exp(1i.*1.5708)).*exp(1i.*Angle2)];
za1=vertexA(1:numel(vertexA)/2);
za2=vertexA(1+numel(vertexA)/2:numel(vertexA));
zb1=vertexB(1:numel(vertexB)/2);
zb2=vertexB(1+numel(vertexB)/2:numel(vertexB));
arg1=exp(-1i.*Angle2);
%% This Section makes the two equations necessary for making the graphs
syms var_z
% Equation 1
Eqn1(var_z)=1.5844e-07.*exp(-1i.*Angle1).*var_z./9.8692e-13;
% subparts of the Equation 2
A = 1.0133e+12.*(-1i.*rect_strength.*exp(-1i*Angle2)./(2*pi.*rect_length.*rect_width*0.2));
ZA1 = var_z+za1-2*rect_center;
ZA2 = var_z+za2-2*rect_center;
ZB1 = var_z+zb1-2*rect_center;
ZB2 = var_z+zb2-2*rect_center;
ZAA2 = log(abs(ZA2)) + 1i*mod(angle(ZA2),2*pi);
ZAA1 = log(abs(ZA1)) + 1i*mod(angle(ZA1),2*pi);
ZBB1 = log(abs(ZB1)) + 1i*mod(angle(ZB1),2*pi);
ZBB2 = log(abs(ZB2)) + 1i*mod(angle(ZB2),2*pi);
%Equation 2 ; this is used for the left side of the center
Eqn2= A*(ZA2*(log(ZA2)-1)-(ZA1*(log(ZA1)-1))+(ZB1*(log(ZB1)-1))-(ZB2*(log(ZB2)-1)));
%Equation 3 ; this is used for the right side of the center
Eqn3 = A.*(ZA2*(ZAA2-1)-(ZA1*(ZAA1-1))+(ZB1*(ZBB1-1))-(ZB2*(ZBB2-1)));
%Equation 4 :Add Equation 2 and Equation 1; this is used for the left side of the center
Eqn4 = matlabFunction(Eqn1+Eqn2,'vars',var_z);
%Equation 5: Add Equation 3 and Equation 1; this is used for the right side of the center
Eqn5 = matlabFunction(Eqn1+Eqn3,'vars',var_z);
%% Prepare for making the plots
minx=-10; %min x coordinate
maxx=10; %max x coordinate
nr_x=1000; %nr x points
miny=-10; %min y coordinate
maxy=10; %max y coordinate
nr_y=1000; %nr y points
%This vector starts from left corner (minx) to the middle of the plot surface,
%The middle of the plot surface lies at the center of the rectange
%created earlier
xvec1=minx:(rect_center-minx)/(0.5*nr_x-1):rect_center;
%This vector starts from middle to the right corner (maxx) of the plot surface,
%The middle of the plot surface lies at the center of the rectange
%created earlier
xvec2=rect_center:(maxx-rect_center)/(0.5*nr_x-1):maxx;
%the y vectors start from miny to maxy
yvec1=miny:(maxy-miny)/(nr_y-1):maxy;
yvec2=miny:(maxy-miny)/(nr_y-1):maxy;
% create mesh from above vectors
[x1,y1]=meshgrid(xvec1,yvec1);
[x2,y2]=meshgrid(xvec2,yvec2);
z1=x1+1i*y1;
z2=x2+1i*y2;
% Calculate the above function using equation 4 and equation 5 using the mesh created above
r1 = -real(Eqn5(z1));
r2 = -real(Eqn4(z2));
%Combine the calculated functions
Result = [r1 r2];
%Combine the grids
x = [x1 x2];
y = [y1 y2];
% plot contours
[c,h]=contourf(x,y,Result(:,:,1),50,'LineWidth',1);
% plot the outerboundary of the rectangle
line_x=real([vertexA;vertexB]);
line_y=imag([vertexA;vertexB]);
line(line_x,line_y,'color','r','linestyle',':','linewidth',5)
The final Figure is supposed to look like this.Final Expected Figure.
I'm not sure which angle defines the dividing line so I assume it's Angle1. It looks like logical indexing is the way to go here. Instead of creating two separate mesh grids we simply create the entire mesh grid then partition it into two sets and operate on each independently.
%% Prepare for making the plots
minx=-10; %min x coordinate
maxx=10; %max x coordinate
nr_x=1000; %nr x points
miny=-10; %min y coordinate
maxy=10; %max y coordinate
nr_y=1000; %nr y points
% create full mesh grid
xvec=linspace(minx,maxx,nr_x);
yvec=linspace(miny,maxy,nr_y);
[x,y]=meshgrid(xvec,yvec);
% Partition mesh based on divider line
% Assumes the line passes through (ox,oy) with normal vector defined by Angle1
ox = rect_center;
oy = rect_center;
a = cos(Angle1);
b = sin(Angle1);
c = -(a*ox + b*oy);
% use logical indexing to opperate on the appropriate parts of the mesh
idx1 = a*x + b*y + c < 0;
idx2 = ~idx1;
z = zeros(size(x));
z(idx1) = x(idx1) + 1i*y(idx1);
z(idx2) = x(idx2) + 1i*y(idx2);
% Calculate the above function using equation 4 and equation 5
% using the mesh created above
Result = zeros(size(z));
Result(idx1) = -real(Eqn5(z(idx1)));
Result(idx2) = -real(Eqn4(z(idx2)));
For example with Angle1 = 45 and Angle2 = 45 we get the following indexing
>> contourf(x,y,idx1);
>> line(line_x,line_y,'color','r','linestyle',':','linewidth',5);
where the yellow region uses Eqn5 and the blue region uses Eqn4. This agrees with the example you posted but I don't know what the resulting contour map for other cases is supposed to look like.
Hope this helps.
Given unit circle, and a set of M smaller circles of radius r. Find the maximum radius of the smaller circles that allows them all to fit inside the unit circle without overlap.
I have the following circles packing in polygon example link
I want to change equations that say that all circles are inside the polygon
theta = 2*pi/N; % Angle covered by each side of the polygon
phi = theta*(0:N-1)'; % Direction of the normal to the side of the polygon
polyEq = ( [cos(phi) sin(phi)]*x <= cdist-r );
to equations that say that all circles are inside the circle, but i don't know how. Can somebody help me?
Kind regards.
In your case, there are no "sides of a polygon" so there is no analogue to theta and you'll need to change every place theta is referenced, and all variables that reference theta (like phi).
Something like the following should work. I've just copy-pasted the code from your link, gotten rid of theta and phi, redefined cdist and polyEq, and made it plot a unit circle in the answer instead of a polygon. Please ask if any of these choices are unclear.
M = 19; % number of circles to fit
toms r % radius of circles
x = tom('x', 2, M); % coordinates of circle centers
clear pi % 3.1415...
cdist = 1; % radius of unit circle
%%%%%% equations saying all circles are inside of unit circle
polyEq = (sqrt(x(1,:).^2 + x(2,:).^2) + r <= cdist);
% create a set of equations that say that no circles overlap
circEq = cell(M-1,1);
for i=1:M-1
circEq{i} = ( sqrt(sum((x(:,i+1:end)-repmat(x(:,i),1,M-i)).^2)) >= 2*r );
end
% starting guess
x0 = { r == 0.5*sqrt(1/M), x == 0.3*randn(size(x)) };
% solve the problem, maximizing r
options = struct;
% try multiple starting guesses and choose best result
options.solver = 'multimin';
options.xInit = 30; % number of different starting guesses
solution = ezsolve(-r,{polyEq,circEq},x0,options);
% plot result
x_vals = (0:100)./100;
plot(sqrt(1 - x_vals.^2),'-') % top of unit circle
plot(-1.*sqrt(1 - x_vals.^2),'-') % bottom of unit circle
axis image
hold on
alpha = linspace(0,2*pi,100);
cx = solution.r*cos(alpha);
cy = solution.r*sin(alpha);
for i=1:M
plot(solution.x(1,i)+cx,solution.x(2,i)+cy) % circle number i
end
hold off
title(['Maximum radius = ' num2str(solution.r)]);
I am required to plot a circle in matlab and mark its center and generate a random coordinate inside the circle and the negative of this coordinate and measure the distance between these two points
I tried this
x = linspace(-sqrt(10),sqrt(10));
y1 = sqrt(10-x.^2);
y2 = -sqrt(10-x.^2);
plot(x,y1,x,y2)
axis equal
to make the circle and its ok but i don't know how to proceed to generate the random coordinate ad its negative and measure the distance between them
Add this to your code -
%%// Choose from 100 random point pairs
N = 100;
%%// Radius of circle
radius = sqrt(10);
%%// Create a random point matrix Nx2
pt1 = [ radius*2*(rand(N,1)-0.5) radius*2*(rand(N,1)-0.5)];
%%// Select the first pair that lies inside circle
pt1 = pt1(find(sqrt( pt1(:,1).^2 + pt1(:,2).^2 )<radius,1),:);
%%// Negative of the point, i.e. on the other side of the center of the circle but equidistant from the center
pt2 = -pt1;
%%// Distance between the two points
dist1 = sqrt((pt1(1)-pt2(1)).^2 + (pt1(2)-pt2(2)).^2);
%%// Overlay the center and the two points on the circle plot
hold on
text(0,0,'C') %%// Center
text(pt1(1),pt1(2),'P') %%// First point
text(pt2(1),pt2(2),'MP') %%// Second point (Mirror Point, MP)
Plot
You can use the rand() function to produce a random distance from center, as well as to produce a random angle. You can then convert to x,y coordinates, and negate them to get the negative coordinates. Finally, you can use the distance formula to calculate the distance between the two coordinates. Here is some sample code:
x = linspace(-sqrt(10),sqrt(10));
y1 = sqrt(10-x.^2);
y2 = -sqrt(10-x.^2);
plot(x,y1,x,y2)
axis equal
hold on
r_max = sqrt(10); %set to radius of circle
r = rand(1)*r_max; %produces a random vector whose length is <=radius
theta = rand(1)*2*pi; % produces a random angle
x_coord = r*cos(theta); %calculate x coord
y_coord = r*sin(theta); % calculate y coord
x_coord_neg = -1*x_coord; % negate x coord
y_coord_neg = -1*y_coord; % negate y coord
plot(x_coord,y_coord, 'x')
plot(x_coord_neg,y_coord_neg, 'rx')
dist = sqrt((x_coord - x_coord_neg)^2 + (y_coord - y_coord_neg)^2) % calculate distance
Not sure if you actually want "negative coordinates" or the complex conjugate of the coordinates. In the case of the latter, you would just negate y to get the complex conjugate.
I would like to reproduce the following figure in MATLAB:
There are two classes of points with X and Y coordinates. I'd like to surround each class with an ellipse with one parameter of standard deviation, which determine how far the ellipse will go along the axis.
The figure was created with another software and I don't exactly understand how it calculates the ellipse.
Here is the data I'm using for this figure. The 1st column is class, 2nd - X, 3rd - Y. I can use gscatter to draw the points itself.
A = [
0 0.89287 1.54987
0 0.69933 1.81970
0 0.84022 1.28598
0 0.79523 1.16012
0 0.61266 1.12835
0 0.39950 0.37942
0 0.54807 1.66173
0 0.50882 1.43175
0 0.68840 1.58589
0 0.59572 1.29311
1 1.00787 1.09905
1 1.23724 0.98834
1 1.02175 0.67245
1 0.88458 0.36003
1 0.66582 1.22097
1 1.24408 0.59735
1 1.03421 0.88595
1 1.66279 0.84183
];
gscatter(A(:,2),A(:,3),A(:,1))
FYI, here is the SO question on how to draw ellipse. So, we just need to know all the parameters to draw it.
Update:
I agree that the center can be calculated as the means of X and Y coordinates. Probably I have to use principal component analysis (PRINCOMP) for each class to determine the angle and shape. Still thinking...
Consider the code:
%# generate data
num = 50;
X = [ mvnrnd([0.5 1.5], [0.025 0.03 ; 0.03 0.16], num) ; ...
mvnrnd([1 1], [0.09 -0.01 ; -0.01 0.08], num) ];
G = [1*ones(num,1) ; 2*ones(num,1)];
gscatter(X(:,1), X(:,2), G)
axis equal, hold on
for k=1:2
%# indices of points in this group
idx = ( G == k );
%# substract mean
Mu = mean( X(idx,:) );
X0 = bsxfun(#minus, X(idx,:), Mu);
%# eigen decomposition [sorted by eigen values]
[V D] = eig( X0'*X0 ./ (sum(idx)-1) ); %#' cov(X0)
[D order] = sort(diag(D), 'descend');
D = diag(D);
V = V(:, order);
t = linspace(0,2*pi,100);
e = [cos(t) ; sin(t)]; %# unit circle
VV = V*sqrt(D); %# scale eigenvectors
e = bsxfun(#plus, VV*e, Mu'); %#' project circle back to orig space
%# plot cov and major/minor axes
plot(e(1,:), e(2,:), 'Color','k');
%#quiver(Mu(1),Mu(2), VV(1,1),VV(2,1), 'Color','k')
%#quiver(Mu(1),Mu(2), VV(1,2),VV(2,2), 'Color','k')
end
EDIT
If you want the ellipse to represent a specific level of standard deviation, the correct way of doing is by scaling the covariance matrix:
STD = 2; %# 2 standard deviations
conf = 2*normcdf(STD)-1; %# covers around 95% of population
scale = chi2inv(conf,2); %# inverse chi-squared with dof=#dimensions
Cov = cov(X0) * scale;
[V D] = eig(Cov);
I'd try the following approach:
Calculate the x-y centroid for the center of the ellipse (x,y in the linked question)
Calculate the linear regression fit line to get the orientation of the ellipse's major axis (angle)
Calculate the standard deviation in the x and y axes
Translate the x-y standard deviations so they're orthogonal to the fit line (a,b)
I'll assume there is only one set of points given in a single matrix, e.g.
B = A(1:10,2:3);
you can reproduce this procedure for each data set.
Compute the center of the ellipsoid, which is the mean of the points. Matlab function: mean
Center your data. Matlab function bsxfun
Compute the principal axis of the ellipsoid and their respective magnitude. Matlab function: eig
The successive steps are illustrated below:
Center = mean(B,1);
Centered_data = bsxfun(#minus,B,Center);
[AX,MAG] = eig(Centered_data' * Centered_data);
The columns of AX contain the vectors describing the principal axis of the ellipsoid while the diagonal of MAG contains information on their magnitude.
To plot the ellipsoid, scale each principal axis with the square root of its magnitude.