Find area of two overlapping circles using monte carlo method - performance

Actually i have two intersecting circles as specified in the figure
i want to find the area of each part separately using Monte carlo method in Matlab .
The code doesn't draw the rectangle or the circles correctly so
i guess what is wrong is my calculation for the x and y and i am not much aware about the geometry equations for solving it so i need help about the equations.
this is my code so far :
n=1000;
%supposing that a rectangle will contain both circles so :
% the mid point of the distance between 2 circles will be (0,6)
% then by adding the radius of the left and right circles the total distance
% will be 27 , 11 from the left and 16 from the right
% width of rectangle = 24
x=27.*rand(n-1)-11;
y=24.*rand(n-1)+2;
count=0;
for i=1:n
if((x(i))^2+(y(i))^2<=25 && (x(i))^2+(y(i)-12)^2<=100)
count=count+1;
figure(2);
plot(x(i),y(i),'b+')
hold on
elseif(~(x(i))^2+(y(i))^2<=25 &&(x(i))^2+(y(i)-12)^2<=100)
figure(2);
plot(x(i),y(i),'y+')
hold on
else
figure(2);
plot(x(i),y(i),'r+')
end
end

Here are the errors I found:
x = 27*rand(n,1)-5
y = 24*rand(n,1)-12
The rectangle extents were incorrect, and if you use rand(n-1) will give you a (n-1) by (n-1) matrix.
and
first If:
(x(i))^2+(y(i))^2<=25 && (x(i)-12)^2+(y(i))^2<=100
the center of the large circle is at x=12 not y=12
Second If:
~(x(i))^2+(y(i))^2<=25 &&(x(i)-12)^2+(y(i))^2<=100
This code can be improved by using logical indexing.
For example, using R, you could do (Matlab code is left as an excercise):
n = 10000
x = 27*runif(n)-5
y = 24*runif(n)-12
plot(x,y)
r = (x^2 + y^2)<=25 & ((x-12)^2 + y^2)<=100
g = (x^2 + y^2)<=25
b = ((x-12)^2 + y^2)<=100
points(x[g],y[g],col="green")
points(x[b],y[b],col="blue")
points(x[r],y[r],col="red")
which gives:

Here is my generic solution for any two circles (without any hardcoded value):
function [ P ] = circles_intersection_area( k1, k2, N )
%CIRCLES_INTERSECTION_AREA Summary...
% Adnan A.
x1 = k1(1);
y1 = k1(2);
r1 = k1(3);
x2 = k2(1);
y2 = k2(2);
r2 = k2(3);
if sqrt((x1-x2)*(x1-x2) + (y1-y2)*(y1-y2)) >= (r1 + r2)
% no intersection
P = 0;
return
end
% Wrapper rectangle config
a_min = x1 - r1 - 2*r2;
a_max = x1 + r1 + 2*r2;
b_min = y1 - r1 - 2*r2;
b_max = y1 + r1 + 2*r2;
% Monte Carlo algorithm
n = 0;
for i = 1:N
rand_x = unifrnd(a_min, a_max);
rand_y = unifrnd(b_min, b_max);
if sqrt((rand_x - x1)^2 + (rand_y - y1)^2) < r1 && sqrt((rand_x - x2)^2 + (rand_y - y2)^2) < r2
% is a point in the both of circles
n = n + 1;
plot(rand_x,rand_y, 'go-');
hold on;
else
plot(rand_x,rand_y, 'ko-');
hold on;
end
end
P = (a_max - a_min) * (b_max - b_min) * n / N;
end
Call it like: circles_intersection_area([-0.4,0,1], [0.4,0,1], 10000) where the first param is the first circle (x,y,r) and the second param is the second circle.

Without using For loop.
n = 100000;
data = rand(2,n);
data = data*2*30 - 30;
x = data(1,:);
y = data(2,:);
plot(x,y,'ro');
inside5 = find(x.^2 + y.^2 <=25);
hold on
plot (x(inside5),y(inside5),'bo');
hold on
inside12 = find(x.^2 + (y-12).^2<=144);
plot (x(inside12),y(inside12),'g');
hold on
insidefinal1 = find(x.^2 + y.^2 <=25 & x.^2 + (y-12).^2>=144);
insidefinal2 = find(x.^2 + y.^2 >=25 & x.^2 + (y-12).^2<=144);
% plot(x(insidefinal1),y(insidefinal1),'bo');
hold on
% plot(x(insidefinal2),y(insidefinal2),'ro');
insidefinal3 = find(x.^2 + y.^2 <=25 & x.^2 + (y-12).^2<=144);
% plot(x(insidefinal3),y(insidefinal3),'ro');
area1=(60^2)*(length(insidefinal1)/n);
area3=(60^2)*(length(insidefinal2)/n);
area2= (60^2)*(length(insidefinal3)/n);

Related

Diamond-Square algorithm: How do I determine what tiles I need to run a diamond function and what tiles I need to run a square function on?

I'm working on a diamond-square heightmap generator and I've been stuck on a certain part for a while now.
I'm having trouble determining which tiles I need to run a square() function on and which tiles I need to run a diamond() function on.
I took a look at this guide: http://www.playfuljs.com/realistic-terrain-in-130-lines/ and I took their for loop they're using as an example, but it didn't seem to work at all.
The preferred language for the answer is Lua (or just kindly point me in the right direction). I just need someone to tell me what I need to do to get a for loop that works for both diamond and square functions.
-- height constraints
local min_height = 10
local max_height = 100
-- the grid
local K = 4
local M = 2^K -- the field is cyclic integer grid 0 <= x,y < M (x=M is the same point as x=0)
local heights = {} -- min_height <= heights[x][y] <= max_height
for x = 0, M-1 do
heights[x] = {}
end
-- set corners height (all 4 corners are the same point because of cyclic field)
heights[0][0] = (min_height + max_height) / 2
local delta_height = (max_height - min_height) * 0.264
local side = M
local sqrt2 = 2^0.5
repeat
local dbl_side = side
side = side/2
-- squares
for x = side, M, dbl_side do
for y = side, M, dbl_side do
local sum =
heights[(x-side)%M][(y-side)%M]
+ heights[(x-side)%M][(y+side)%M]
+ heights[(x+side)%M][(y-side)%M]
+ heights[(x+side)%M][(y+side)%M]
heights[x][y] = sum/4 + (2*math.random()-1) * delta_height
end
end
delta_height = delta_height / sqrt2
-- diamonds
for x = 0, M-1, side do
for y = (x+side) % dbl_side, M-1, dbl_side do
local sum =
heights[(x-side)%M][y]
+ heights[x][(y-side)%M]
+ heights[x][(y+side)%M]
+ heights[(x+side)%M][y]
heights[x][y] = sum/4 + (2*math.random()-1) * delta_height
end
end
delta_height = delta_height / sqrt2
until side == 1
-- draw field
for x = 0, M-1 do
local s = ''
for y = 0, M-1 do
s = s..' '..tostring(math.floor(heights[x][y]))
end
print(s)
end

Kaczmarz animation

i am asking for help.. I want to animate the Kaczmarz method on Matlab. It's method allows to find solution of system of equations by the serial projecting solution vector on hyperplanes, which which is given by the eqations of system.
And i want make animation of this vector moving (like the point is going on the projected vectors).
%% System of equations
% 2x + 3y = 4;
% x - y = 2;
% 6x + y = 15;
%%
A = [2 3;1 -1; 6 1];
f = [4; 2; 15];
resh = pinv(A)*f
x = -10:0.1:10;
e1 = (1 - 2*x)/3;
e2 = (x - 2);
e3 = 15 - 6*x;
plot(x,e1)
grid on
%
axis([0 4 -2 2])
hold on
plot(x,e2)
hold on
plot(x,e3)
hold on
precision = 0.001; % точность
iteration = 100; % количество итераций
lambda = 0.75; % лямбда
[m,n] = size(A);
x = zeros(n,1);
%count of norms
for i = 1:m
nrm(i) = norm(A(i,:));
end
for i = 1:1:iteration
j = mod(i-1,m) + 1;
if (nrm(j) <= 0), continue, end;
predx = x;
x = x + ((f(j) - A(j,:)*x)*A(j,:)')/(nrm(j))^2;
p = plot(x);
set(p)
%pause 0.04;
hold on;
if(norm(predx - x) <= precision), break, end
end
I wrote the code for this method, by don't imagine how make the animation, how I can use the set function.
In your code there are a lot of redundant and random pieces. Do not call hold on more than once, it does nothing. Also set(p) does nothing, you want to set some ps properties to something, then you use set.
Also, you are plotting the result, but not the "change". The change is a line between the previous and current, and that is the only reason you'd want to have a variable such as predx, to plot. SO USE IT!
Anyway, this following code plots your algorithm. I added a repeated line to plot in green and then delete, so you can see what the last step does. I also changed the plots in the begging to just plot in red so its more clear what is each of the things.
Change your loop for:
for i = 1:1:iteration
j = mod(i-1,m) + 1;
if (nrm(j) <= 0), continue, end;
predx = x;
x = x + ((f(j) - A(j,:)*x)*A(j,:)')/(nrm(j))^2;
plot([predx(1) x(1)],[predx(2) x(2)],'b'); %plot line
c=plot([predx(1) x(1)],[predx(2) x(2)],'g'); %plot it in green
pause(0.1)
children = get(gca, 'children'); %delete the green line
delete(children(1));
drawnow
% hold on;
if(norm(predx - x) <= precision), break, end
end
This will show:

Quadratic Equation From Points

I need to implement a function that finds the trajectory of a projectile and I have three points - origin, destination and the point of maximum height.
I need to find the correct quadratic function that includes these points.
I'm having a hard time figuring out what to do. Where should I start?
Assume you have your origin and destination on the y-axis,namely x1 and x2. If not you can shift them later.
a*x*x + b*x + c = 0//equation
x1*x2=(c/a);
c = (x1*x2)*a;
x1+x2=(-b/a);
b = (x1+x2)/(-a);
a*((x1+x2)/2)^2 + b*((x1+x2)/2) + c = h//max height
let X=(x1+x2)/2;
a*X*X + ((2*X)/(-a))*X + (x1*x2)*a - h = 0;
Now you can iterate through a=0 until the above equation is true as you have all the values X ,x1 , x2 and h.
double eqn = (-h),a=0;//a=0.Assuming you have declared x1,x2 and X already
while(eqn!=0)
{
a++;
eqn = a*X*X + ((2*X)/(-a))*X + (x1*x2)*a - h;
}
b = (x1+x2)/(-a);
c = (x1*x2)*a;
Thus you got all your coeffecients.

MatLab code speed and optimization. How to improve?

Could someone please run this for me and tell me how long it takes for you? It took my laptop 60s. I can't tell if it's my laptop that's crappy or my code. Probably both.
I just started learning MatLab, so I'm not yet familiar with which functions are better than others for specific tasks. If you have any suggestions on how I could improve this code, it would be greatly appreciated.
function gbp
clear; clc;
zi = 0; % initial position
zf = 100; % final position
Ei = 1; % initial electric field
c = 3*10^8; % speed of light
epsilon = 8.86*10^-12; % permittivity of free space
lambda = 1064*10^-9; % wavelength
k = 2*pi/lambda; % wave number
wi = 1.78*10^-3; % initial waist width (minimum spot size)
zr = (pi*wi^2)/lambda; % Rayleigh range
Ri = zi + zr^2/zi; % initial radius of curvature
qi = 1/(1/Ri-1i*lambda/(pi*wi^2)); % initial complex beam parameter
Psii = atan(real(qi)/imag(qi)); % Gouy phase
mat = [1 zf; 0 1]; % transformation matrix
A = mat(1,1); B = mat(1,2); C = mat(2,1); D = mat(2,2);
qf = (A*qi + B)/(C*qi + D); % final complex beam parameter
wf = sqrt(-lambda/pi*(1/imag(1/qf))); % final spot size
Rf = 1/real(1/qf); % final radius of curvature
Psif = atan(real(qf)/imag(qf)); % final Gouy phase
% Hermite - Gaussian modes function
u = #(z, x, n, w, R, Psi) (2/pi)^(1/4)*sqrt(exp(1i*(2*n+1)*Psi)/(2^n*factorial(n)*w))*...
hermiteH(n,sqrt(2)*x/w).*exp(-x.^2*(1/w^2+1i*k/(2*R))-1i*k*z);
% Complex amplitude coefficients function
a = #(n) exp(1i*k*zi)*integral(#(x) Ei.*conj(u(zi, x, n, wi, Ri, Psii)),-2*wi,2*wi);
%----------------------------------------------------------------------------
xlisti = -0.1:1/10000:0.1; % initial x-axis range
xlistf = -0.1:1/10000:0.1; % final x-axis range
nlist = 0:2:20; % modes range
function Eiplot
Efieldi = zeros(size(xlisti));
for nr = nlist
Efieldi = Efieldi + a(nr).*u(zi, xlisti, nr, wi, Ri, Psii)*exp(-1i*k*zi);
end
Ii = 1/2*c*epsilon*arrayfun(#(x)x.*conj(x),Efieldi);
end
function Efplot
Efieldf = zeros(size(xlistf));
for nr = nlist
Efieldf = Efieldf + a(nr).*u(zf, xlistf, nr, wf, Rf, Psif)*exp(-1i*k*zf);
end
If = 1/2*c*epsilon*arrayfun(#(x)x.*conj(x),Efieldf);
end
Eiplot
Efplot
plot(xlisti,real(Ii),xlistf,real(If))
xlabel('x(m)') % x-axis label
ylabel('I(W/m^2)') % y-axis label
end
The cost is coming from the calls to hermiteH -- for every call, this creates a new function using symbolic variables, then evaluates the function at your input. The key to speeding this up is to pre-compute the hermite polynomial functions then evaluate those rather than create them from scratch each time (speedup from ~26 seconds to around 0.75 secs on my computer).
With the changes:
function gbp
x = sym('x');
zi = 0; % initial position
zf = 100; % final position
Ei = 1; % initial electric field
c = 3*10^8; % speed of light
epsilon = 8.86*10^-12; % permittivity of free space
lambda = 1064*10^-9; % wavelength
k = 2*pi/lambda; % wave number
wi = 1.78*10^-3; % initial waist width (minimum spot size)
zr = (pi*wi^2)/lambda; % Rayleigh range
Ri = zi + zr^2/zi; % initial radius of curvature
qi = 1/(1/Ri-1i*lambda/(pi*wi^2)); % initial complex beam parameter
Psii = atan(real(qi)/imag(qi)); % Gouy phase
mat = [1 zf; 0 1]; % transformation matrix
A = mat(1,1); B = mat(1,2); C = mat(2,1); D = mat(2,2);
qf = (A*qi + B)/(C*qi + D); % final complex beam parameter
wf = sqrt(-lambda/pi*(1/imag(1/qf))); % final spot size
Rf = 1/real(1/qf); % final radius of curvature
Psif = atan(real(qf)/imag(qf)); % final Gouy phase
% Hermite - Gaussian modes function
nlist = 0:2:20; % modes range
% precompute hermite polynomials for nlist
hermites = {};
for n = nlist
if n == 0
hermites{n + 1} = #(x)1.0;
else
hermites{n + 1} = matlabFunction(hermiteH(n, x));
end
end
u = #(z, x, n, w, R, Psi) (2/pi)^(1/4)*sqrt(exp(1i*(2*n+1)*Psi)/(2^n*factorial(n)*w))*...
hermites{n + 1}(sqrt(2)*x/w).*exp(-x.^2*(1/w^2+1i*k/(2*R))-1i*k*z);
% Complex amplitude coefficients function
a = #(n) exp(1i*k*zi)*integral(#(x) Ei.*conj(u(zi, x, n, wi, Ri, Psii)),-2*wi,2*wi);
%----------------------------------------------------------------------------
xlisti = -0.1:1/10000:0.1; % initial x-axis range
xlistf = -0.1:1/10000:0.1; % final x-axis range
function Eiplot
Efieldi = zeros(size(xlisti));
for nr = nlist
Efieldi = Efieldi + a(nr).*u(zi, xlisti, nr, wi, Ri, Psii)*exp(-1i*k*zi);
end
Ii = 1/2*c*epsilon*arrayfun(#(x)x.*conj(x),Efieldi);
end
function Efplot
Efieldf = zeros(size(xlistf));
for nr = nlist
Efieldf = Efieldf + a(nr).*u(zf, xlistf, nr, wf, Rf, Psif)*exp(-1i*k*zf);
end
If = 1/2*c*epsilon*arrayfun(#(x)x.*conj(x),Efieldf);
end
Eiplot
Efplot
plot(xlisti,real(Ii),xlistf,real(If))
xlabel('x(m)') % x-axis label
ylabel('I(W/m^2)') % y-axis label
end

Trilateration and locating the point (x,y,z)

I want to find the coordinate of an unknown node which lie somewhere in the space which has its reference distance away from 3 or more nodes which all of them have known coordinate.
This problem is exactly like Trilateration as described here Trilateration.
However, I don't understand the part about "Preliminary and final computations" (refer to the wikipedia site). I don't get where I could find P1, P2 and P3 just so I can put to those equation?
Thanks
Trilateration is the process of finding the center of the area of intersection of three spheres. The center point and radius of each of the three spheres must be known.
Let's consider your three example centerpoints P1 [-1,1], P2 [1,1], and P3 [-1,-1]. The first requirement is that P1' be at the origin, so let us adjust the points accordingly by adding an offset vector V [1,-1] to all three:
P1' = P1 + V = [0, 0]
P2' = P2 + V = [2, 0]
P3' = P3 + V = [0,-2]
Note: Adjusted points are denoted by the ' (prime) annotation.
P2' must also lie on the x-axis. In this case it already does, so no adjustment is necessary.
We will assume the radius of each sphere to be 2.
Now we have 3 equations (given) and 3 unknowns (X, Y, Z of center-of-intersection point).
Solve for P4'x:
x = (r1^2 - r2^2 + d^2) / 2d //(d,0) are coords of P2'
x = (2^2 - 2^2 + 2^2) / 2*2
x = 1
Solve for P4'y:
y = (r1^2 - r3^2 + i^2 + j^2) / 2j - (i/j)x //(i,j) are coords of P3'
y = (2^2 - 2^2 + 0 + -2^2) / 2*-2 - 0
y = -1
Ignore z for 2D problems.
P4' = [1,-1]
Now we translate back to original coordinate space by subtracting the offset vector V:
P4 = P4' - V = [0,0]
The solution point, P4, lies at the origin as expected.
The second half of the article is describing a method of representing a set of points where P1 is not at the origin or P2 is not on the x-axis such that they fit those constraints. I prefer to think of it instead as a translation, but both methods will result in the same solution.
Edit: Rotating P2' to the x-axis
If P2' does not lie on the x-axis after translating P1 to the origin, we must perform a rotation on the view.
First, let's create some new vectors to use as an example:
P1 = [2,3]
P2 = [3,4]
P3 = [5,2]
Remember, we must first translate P1 to the origin. As always, the offset vector, V, is -P1. In this case, V = [-2,-3]
P1' = P1 + V = [2,3] + [-2,-3] = [0, 0]
P2' = P2 + V = [3,4] + [-2,-3] = [1, 1]
P3' = P3 + V = [5,2] + [-2,-3] = [3,-1]
To determine the angle of rotation, we must find the angle between P2' and [1,0] (the x-axis).
We can use the dot product equality:
A dot B = ||A|| ||B|| cos(theta)
When B is [1,0], this can be simplified: A dot B is always just the X component of A, and ||B|| (the magnitude of B) is always a multiplication by 1, and can therefore be ignored.
We now have Ax = ||A|| cos(theta), which we can rearrange to our final equation:
theta = acos(Ax / ||A||)
or in our case:
theta = acos(P2'x / ||P2'||)
We calculate the magnitude of P2' using ||A|| = sqrt(Ax + Ay + Az)
||P2'|| = sqrt(1 + 1 + 0) = sqrt(2)
Plugging that in we can solve for theta
theta = acos(1 / sqrt(2)) = 45 degrees
Now let's use the rotation matrix to rotate the scene by -45 degrees.
Since P2'y is positive, and the rotation matrix rotates counter-clockwise, we'll use a negative rotation to align P2 to the x-axis (if P2'y is negative, don't negate theta).
R(theta) = [cos(theta) -sin(theta)]
[sin(theta) cos(theta)]
R(-45) = [cos(-45) -sin(-45)]
[sin(-45) cos(-45)]
We'll use double prime notation, '', to denote vectors which have been both translated and rotated.
P1'' = [0,0] (no need to calculate this one)
P2'' = [1 cos(-45) - 1 sin(-45)] = [sqrt(2)] = [1.414]
[1 sin(-45) + 1 cos(-45)] = [0] = [0]
P3'' = [3 cos(-45) - (-1) sin(-45)] = [sqrt(2)] = [ 1.414]
[3 sin(-45) + (-1) cos(-45)] = [-2*sqrt(2)] = [-2.828]
Now you can use P1'', P2'', and P3'' to solve for P4''. Apply the reverse rotation to P4'' to get P4', then the reverse translation to get P4, your center point.
To undo the rotation, multiply P4'' by R(-theta), in this case R(45). To undo the translation, subtract the offset vector V, which is the same as adding P1 (assuming you used -P1 as your V originally).
This is the algorithm I use in a 3D printer firmware. It avoids rotating the coordinate system, but it may not be the best.
There are 2 solutions to the trilateration problem. To get the second one, replace "- sqrtf" by "+ sqrtf" in the quadratic equation solution.
Obviously you can use doubles instead of floats if you have enough processor power and memory.
// Primary parameters
float anchorA[3], anchorB[3], anchorC[3]; // XYZ coordinates of the anchors
// Derived parameters
float Da2, Db2, Dc2;
float Xab, Xbc, Xca;
float Yab, Ybc, Yca;
float Zab, Zbc, Zca;
float P, Q, R, P2, U, A;
...
inline float fsquare(float f) { return f * f; }
...
// Precompute the derived parameters - they don't change unless the anchor positions change.
Da2 = fsquare(anchorA[0]) + fsquare(anchorA[1]) + fsquare(anchorA[2]);
Db2 = fsquare(anchorB[0]) + fsquare(anchorB[1]) + fsquare(anchorB[2]);
Dc2 = fsquare(anchorC[0]) + fsquare(anchorC[1]) + fsquare(anchorC[2]);
Xab = anchorA[0] - anchorB[0];
Xbc = anchorB[0] - anchorC[0];
Xca = anchorC[0] - anchorA[0];
Yab = anchorA[1] - anchorB[1];
Ybc = anchorB[1] - anchorC[1];
Yca = anchorC[1] - anchorA[1];
Zab = anchorB[2] - anchorC[2];
Zbc = anchorB[2] - anchorC[2];
Zca = anchorC[2] - anchorA[2];
P = ( anchorB[0] * Yca
- anchorA[0] * anchorC[1]
+ anchorA[1] * anchorC[0]
- anchorB[1] * Xca
) * 2;
P2 = fsquare(P);
Q = ( anchorB[1] * Zca
- anchorA[1] * anchorC[2]
+ anchorA[2] * anchorC[1]
- anchorB[2] * Yca
) * 2;
R = - ( anchorB[0] * Zca
+ anchorA[0] * anchorC[2]
+ anchorA[2] * anchorC[0]
- anchorB[2] * Xca
) * 2;
U = (anchorA[2] * P2) + (anchorA[0] * Q * P) + (anchorA[1] * R * P);
A = (P2 + fsquare(Q) + fsquare(R)) * 2;
...
// Calculate Cartesian coordinates given the distances to the anchors (La, Lb and Lc)
// First calculate PQRST such that x = (Qz + S)/P, y = (Rz + T)/P.
// P, Q and R depend only on the anchor positions, so they are pre-computed
const float S = - Yab * (fsquare(Lc) - Dc2)
- Yca * (fsquare(Lb) - Db2)
- Ybc * (fsquare(La) - Da2);
const float T = - Xab * (fsquare(Lc) - Dc2)
+ Xca * (fsquare(Lb) - Db2)
+ Xbc * (fsquare(La) - Da2);
// Calculate quadratic equation coefficients
const float halfB = (S * Q) - (R * T) - U;
const float C = fsquare(S) + fsquare(T) + (anchorA[1] * T - anchorA[0] * S) * P * 2 + (Da2 - fsquare(La)) * P2;
// Solve the quadratic equation for z
float z = (- halfB - sqrtf(fsquare(halfB) - A * C))/A;
// Substitute back for X and Y
float x = (Q * z + S)/P;
float y = (R * z + T)/P;
Here are the Wikipedia calculations, presented in an OpenSCAD script, which I think helps to understand the problem in a visual wayand provides an easy way to check that the results are correct. Example output from the script
// Trilateration example
// from Wikipedia
//
// pA, pB and pC are the centres of the spheres
// If necessary the spheres must be translated
// and rotated so that:
// -- all z values are 0
// -- pA is at the origin
pA = [0,0,0];
// -- pB is on the x axis
pB = [10,0,0];
pC = [9,7,0];
// rA , rB and rC are the radii of the spheres
rA = 9;
rB = 5;
rC = 7;
if ( pA != [0,0,0]){
echo ("ERROR: pA must be at the origin");
assert(false);
}
if ( (pB[2] !=0 ) || pC[2] !=0){
echo("ERROR: all sphere centers must be in z = 0 plane");
assert(false);
}
if (pB[1] != 0){
echo("pB centre must be on the x axis");
assert(false);
}
// show the spheres
module spheres(){
translate (pA){
sphere(r= rA, $fn = rA * 10);
}
translate(pB){
sphere(r = rB, $fn = rB * 10);
}
translate(pC){
sphere (r = rC, $fn = rC * 10);
}
}
function unit_vector( v) = v / norm(v);
ex = unit_vector(pB - pA) ;
echo(ex = ex);
i = ex * ( pC - pA);
echo (i = i);
ey = unit_vector(pC - pA - i * ex);
echo (ey = ey);
d = norm(pB - pA);
echo (d = d);
j = ey * ( pC - pA);
echo (j = j);
x = (pow(rA,2) - pow(rB,2) + pow(d,2)) / (2 * d);
echo( x = x);
// size of the cube to subtract to show
// the intersection of the spheres
cube_size = [10,10,10];
if ( ((d - rA) >= rB) || ( rB >= ( d + rA)) ){
echo ("Error Y not solvable");
}else{
y = (( pow(rA,2) - pow(rC,2) + pow(i,2) + pow(j,2)) / (2 * j))
- ( i / j) * x;
echo(y = y);
zpow2 = pow(rA,2) - pow(x,2) - pow(y,2);
if ( zpow2 < 0){
echo ("z not solvable");
}else{
z = sqrt(zpow2);
echo (z = z);
// subtract a cube with one of its corners
// at the point where the sphers intersect
difference(){
spheres();
translate ([x,y - cube_size[1],z]){
cube(cube_size);
}
}
translate ([x,y - cube_size[1],z]){
%cube(cube_size);
}
}
}

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