Uniform sampling of intersection area of two disks
Given 2D uniform variable we can generate a uniform distribution in a unit-disk as discussed here.
My problem is similar in that i wish to uniformly sample the intersection area of two intersecting disks where one disk is always the unit-disk and the other can be freely moved and resized like here
I was trying to split the area into two regions (as depicted above) and sample each region individual based on the respected disk. My approach is based on uniform disk algorithm cited above. To sample the first region right of the center line I would restrict theta to be within the two intersection points. Next r would need to be projected based on that theta such that the points are pushed in the area between our mid line and the radius of the disk. The python sample code can be found here.
u = unifrom2D()
A;B; // Intersection points
for p in allPoints
theta = u.x * (getTheta(A) - getTheta(B)) + getTheta(B)
r = sqrt(u.y + (1- u.y)*length2(lineIntersection(theta)))
p = (r * cos(theta), r * sin(theta))
However this approach is rather expensive and further fails to preserve uniformity. Just to clarify i do not want to use rejection sampling.
I am not sure if this is better than rejection sampling, but here is a solution for uniform sampling of a circle segment (with center angle <= pi) involving the numerical computation of an inverse function. (The uniform sampling of the intersection of two circles can then be composed of the sampling of segments, sectors and triangles - depending on how the intersection can be split into simpler figures.)
First we need to know how to generate a random value Z with given distribution F , ie we want
P(Z < x) = F(x) <=> (x = F^-1(y))
P(Z < F^-1(y)) = F(F^-1(y)) = y <=> (F is monotonous)
P(F(Z) < y) = y
This means: if Z has the requested distribution F , then F(Z) is distributed uniformly. The other way round:
Z = F^-1(Y),
where Y is distributed uniformly in [0,1] , has the requested distribution.
If F is of the form
/ 0, x < a
F(x) = | (F0(x)-F0(a)) / (F0(b)-F0(a)), a <= x <= b
1, b < x
then we can choose a Y0 uniformly in [F(a),F(b)] and set Z = F0^-1(Y0) .
We choose to parametrize the segment by (theta,r) , where the center angle theta is measured from one segment side. When the segment's center angle is alpha , the area of the segment intersected with a sector of angle theta starting where the segment starts is (for the unit circle, theta in [0,alpha/2] )
F0_theta(theta) = 0.5*(theta - d*(s - d*tan(alpha/2-theta)))
where s = AB/2 = sin(alpha/2) and d = dist(M,AB) = cos(alpha/2) (the distance of the circle center to the segment). (The case alpha/2 <= theta <= alpha is symmetric and not considered here.) We need a random theta with P(theta < x) = F_theta(x) . The inverse of F_theta cannot be computed symbolically - it must be determined by some optimization algorithm (eg Newton-Raphson).
Once theta is fixed we need a random radius r in the range
[r_min, 1], r_min = d/cos(alpha/2-theta).
For x in [0, 1-r_min] the distribution must be
F0_r(x) = (x+r_min)^2 - r_min^2 = x^2 + 2*x*r_min.
Here the inverse can be computed symbolically:
F0_r^-1(y) = -r_min + sqrt(r_min^2+y)
Here is an implementation in Python for proof of concept:
from math import sin,cos,tan,sqrt
from scipy.optimize import newton
# area of segment of unit circle
# alpha: center angle of segment (0 <= alpha <= pi)
def segmentArea(alpha):
return 0.5*(alpha - sin(alpha))
# generate a function that gives the area of a segment of a unit circle
# intersected with a sector of given angle, where the sector starts at one end of the segment.
# The returned function is valid for [0,alpha/2].
# For theta=alpha/2 the returned function gives half of the segment area.
# alpha: center angle of segment (0 <= alpha <= pi)
def segmentAreaByAngle_gen(alpha):
alpha_2 = 0.5*alpha
s,d = sin(alpha_2),cos(alpha_2)
return lambda theta: 0.5*(theta - d*(s - d*tan(alpha_2-theta)))
# generate derivative function generated by segmentAreaByAngle_gen
def segmentAreaByAngleDeriv_gen(alpha):
alpha_2 = 0.5*alpha
d = cos(alpha_2)
return lambda theta: (lambda dr = d/cos(alpha_2-theta): 0.5*(1 - dr*dr))()
# generate inverse of function generated by segmentAreaByAngle_gen
def segmentAreaByAngleInv_gen(alpha):
x0 = sqrt(0.5*segmentArea(alpha)) # initial guess by approximating half of segment with right-angled triangle
return lambda area: newton(lambda theta: segmentAreaByAngle_gen(alpha)(theta) - area, x0, segmentAreaByAngleDeriv_gen(alpha))
# for a segment of the unit circle in canonical position
# (i.e. symmetric to x-axis, on positive side of x-axis)
# generate uniformly distributed random point in upper half
def randomPointInSegmentHalf(alpha):
FInv = segmentAreaByAngleInv_gen(alpha)
areaRandom = random.uniform(0,0.5*segmentArea(alpha))
thetaRandom = FInv(areaRandom)
alpha_2 = 0.5*alpha
d = cos(alpha_2)
rMin = d/cos(alpha_2-thetaRandom)
secAreaRandom = random.uniform(0, 1-rMin*rMin)
rRandom = sqrt(rMin*rMin + secAreaRandom)
return rRandom*cos(alpha_2-thetaRandom), rRandom*sin(alpha_2-thetaRandom)
The visualisation seems to verify uniform distribution (of the upper half of a segment with center angle pi/2 ):
import matplotlib.pyplot as plot
segmentPoints = [randomPointInSegmentHalf(pi/2) for _ in range(500)]
plot.scatter(*zip(*segmentPoints))
plot.show()
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