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Make a sphere with equidistant vertices

I'm trying to make a spherical burst of rays for the purpose of checking collision, but having specific interactions happen based upon what or where each ray hit. Hence why I'm using rays rather then something simpler such as OverlapSphere.
The reason I'm looking for how to make a sphere is because I can use the same math for my rays, by having them go to the vertices of where the sphere would be. But every way I can find for making a sphere has the lines get closer the near to the poles, which makes sense, as its pretty easy to do. But as you can imagine, its not that useful for my current project.
TL;DR:
How do I make a sphere with equidistant vertices? If its not perfectly equidistant its fine, it just needs to pretty close. If this happens, it would be great if you could give how much the difference would be, and where, if applicable.
Extra notes:
I've looked at this and this, but the math is way over my head, so what I've been looking for might've just been staring me in the face this whole time.

You could use an icosphere. As the vertices are distributed on equilateral triangles, your vertices are guaranteed to be equidistant.
To construct the icosphere, first you make an icosahedron and then split the faces recursively in smaller triangles as explained in this article.

Are you aware that the sphere given to you by Unity is in fact designed
with this exact goal in mind?
ie, the entire raison d'etre of the sphere built-in to Unity is that the points are fairly smoothly space ...... roughly equidistant, as you phrase it.
To bring up such a sphere in Unity, just do this:
You can then instantly get access to the verts, as you know
Mesh mesh = GetComponent<MeshFilter>().mesh;
Vector3[] vv = mesh.vertices;
int kVerts=vv.Length
for (int i=0; i<kVerts; ++i)
Debug.Log ... vv[i]
Note you can easily check "which part of the sphere" they are on by (for example) checking how far they are from your "cities" (or whatever) or just check (for example) the z values to see which hemisphere they are in .. et cetera.
Furthermore...
Please note. Regarding your overall reason for wanting to do this:
but having specific interactions happen based upon what or where each ray hit
Note that it could not be easier to do this using PhysX. (The completely built-in game physics in Unity.) Indeed, I have never, ever, looked at a collision without doing something "specific" depending on "where it hit!"
You can for example get the point where the contact was with http://docs.unity3d.com/ScriptReference/RaycastHit-point.html
It's worth noting it is absolutely inconceivable one could write something approaching the performance of PhysX in casual programming.
I hope this makes things easier!

slice the sphere into N circles
compute perimeter of it
divide it by the same angle that create the slice
this gives you the number of vertexes
and also angle step inside circle
cast rays
This is how I coded it in C++ + OpenGL:
// draw unit sphere points (r=1 center=(0,0,0)) ... your rays directions
int ia,na,ib,nb;
double x,y,z,r;
double a,b,da,db;
na=16; // number of slices
da=M_PI/double(na-1); // latitude angle step
for (a=-0.5*M_PI,ia=0;ia<na;ia++,a+=da) // slice sphere to circles in xy planes
{
r=cos(a); // radius of actual circle in xy plane
z=sin(a); // height of actual circle in xy plane
nb=ceil(2.0*M_PI*r/da);
db=2.0*M_PI/double(nb); // longitude angle step
if ((ia==0)||(ia==na-1)) { nb=1; db=0.0; } // handle edge cases
for (b=0.0,ib=0;ib<nb;ib++,b+=db) // cut circle to vertexes
{
x=r*cos(b); // compute x,y of vertex
y=r*sin(b);
// this just draw the ray direction (x,y,z) as line in OpenGL
// so you can ignore this
// instead add the ray cast of yours
double w=1.2;
glBegin(GL_LINES);
glColor3f(1.0,1.0,1.0); glVertex3d(x,y,z);
glColor3f(0.0,0.0,0.0); glVertex3d(w*x,w*y,w*z);
glEnd();
}
}
This is how it looks like:
R,G,B lines are the sphere coordinate system axises X,Y,Z
White-ish lines are your Vertexes (White) + direction (Gray)
[Notes]
do not forget to include math.h
and replace the OpenGL stuff with yours

If you want 4, 6, 8, 12 or 20 vertices then you can have exactly equidistant vertices as the Platonic solid which all fit inside a sphere. The actual coordinates of these should be easy to get. For other numbers of vertices you can use other polyhedra and scale the verties so they lie on a sphere. If you need lots of points then a geodesic dome might be a good base. The C60 bucky-ball could be a good base with 60 points. For most of these you should be able to find 3D models from which you can extract coordinates.

I think the easiest way to control points on a sphere is by using spherical coordinates. Then you can control position of points around the sphere by using two angles (rho and phi) and the radius.
Example code for filling points uniformly around a rotating sphere (for fun):
var time = 1; // Increment this variable every frame to see the rotation
var count = 1000;
for (int i = 0; i < count; i++)
{
var rho = time + i;
var phi = 2 * Math.PI * i / count;
var x = (float)(radius * Math.Sin(phi) * Math.Cos(rho));
var z = (float)(radius * Math.Sin(phi) * Math.Sin(rho));
var y = (float)(radius * Math.Cos(phi));
Draw(x, y, z); // your drawing code for rendering the point
}

As some answers have already suggested, use an icosahedron based solution. The source for this is quite easy to come by (and I have written my own several times) but I find the excellent Primitives Pro plugin extremely handy under many other circumstances, and always use their sphere instead of the built-in Unity one.
Link to Primitives Pro component
Primitives Pro options

Algorithm to generate equally distributed points in a polygon

I am looking for an algorithm to generate equally distributed points inside a polygon.
Here is the scenario:
I have a polygon specified by the coordinates of the points at the corners (x, y) for each point. And I have the number of points to generate inside the polygon.
For example lets say I have a polygon containing 5 points: (1, 1) ; (1, 2) ; (2, 3) ; (3, 2) ; and (3, 1)
And I need to generate 20 equally distanced points inside that polygon.
Note: Some polygons may not support equally distributed points, but I'm looking to distribute the points in a way to cover all the region of the polygon with as much consistency as possible. (what i mean is I don't want a part with a lot more points than another)
Is there an algorithm to do so? or maybe a library
I am working on a C# application, but any language is ok, since I only need the algorithm and I can translate it.
Thanks a lot for any help

The simple approach I use is:
Triangulate the polygon. Ear clipping is entirely adequate, as all you need is a dissection of the polygon into a set of non-overlapping triangles.
Compute the area of each triangle. Sample from each triangle proportionally to the area of that triangle relative to the whole. This costs only a single uniform random number per sample.
Once a point is determined to have come from a given triangle, sample uniformly over the triangle. This is itself easier than you might think.
So really it all comes down to how do you sample within a triangle. This is easily enough done. A triangle is defined by 3 vertices. I'll call them P1, P2, P3.
Pick ANY edge of the triangle. Generate a point (P4) that lies uniformly along that edge. Thus if P1 and P2 are the coordinates of the corresponding end points, then P will be a uniformly sampled point along that edge, if r has uniform distribution on the interval [0,1].
P4 = (1-r)*P1 + r*P2
Next, sample along the line segment between P3 and P4, but do so non-uniformly. If s is a uniform random number on the interval [0,1], then
P5 = (1-sqrt(s))*P3 + sqrt(s)*P4
r and s are independent pseudo-random numbers of course. Then P5 will be randomly sampled, uniform over the triangle.
The nice thing is it needs no rejection scheme to implement, so long, thin polygons are not a problem. And for each sample, the cost is only in the need to generate three random numbers per event. Since ear clipping is rather simply done and an efficient task, the sampling will be efficient, even for nasty looking polygons or non-convex polygons.

An easy way to do this is this:
Calculate the bounding box
Generate points in that box
Discard all points not in the polygon of interest
This approach generates a certain amount of wasted points. For a triangle, it is never more than 50%. For arbitrary polygons this can be arbitrarily high so you need to see if it works for you.
For arbitrary polys you can decompose the polygon into triangles first which allows you to get to a guaranteed upper bound of wasted points: 50%.
For equally distanced points, generate points from a space-filling curve (and discard all points that are not in the polygon).

You can use Lloyd’s algorithm:
https://en.m.wikipedia.org/wiki/Lloyd%27s_algorithm

You can try the {spatialEco} package (https://cran.r-project.org/web/packages/spatialEco/index.html)
and apply the function sample.poly (https://www.rdocumentation.org/packages/spatialEco/versions/1.3-2/topics/sample.poly)
You can try this code:
library(rgeos)
library(spatialEco)
mypoly = readWKT("POLYGON((1 1,5 1,5 5,1 5,1 1))")
plot(mypoly)
points = sample.poly(mypoly, n= 20, type = "regular")
#points2 = sample.poly(mypoly, n= 20, type = "stratified")
#another type which may answer your problem
plot(points, col="red", add=T)

The easy answer comes from an easier question: How to generate a given number of randomly distributed points from the uniform distribution that will all fit inside a given polygon?
The easy answer is this: find the bounding box of your polygon (let's say it's [a,b] x [c,d]), then keep generating pairs of real numbers, one from U(a,b), the other from U(b,c), until you have n coordinate pairs that fit inside your polygon. This is simple to program, but, if your polygon is very jagged, or thin and skewed, very wasteful and slow.
For a better answer, find the smallest rotated rectangular bounding box, and do the above in transformed coordinates.

Genettic algorithms can do it rather quickly
Reffer to GENETIC ALGORITHMS FOR GRAPH LAYOUTS WITH GEOMETRIC CONSTRAINTS
You can use Force-Directed Graph for that...
Look at http://en.wikipedia.org/wiki/Force-based_algorithms_(graph_drawing)
it defiantly can throw you a bone.
I didn't try it ever,
but i remmember there is a possiblity to set a Fix for some Vertices in the Graph
Your Algorithm will eventually be like
Create a Graph G = Closed Path of the Vertices in V
Fix the Vertecies in place
Add N Verticies to the Graph and Fully connect them with Edges with equal tension value 1.0
Run_force_graph(G)
Scale Graph to bounded Box of
Though it wont be absolute because some convex shapes may produce wiered results (take a Star)
LASTLY: didn't read , but it seems relevant by the title and abstract
take a look at Consistent Graph Layout for Weighted Graphs
Hope this helps...

A better answer comes from a better question. Suppose you want to put a set of n watchtowers to cover a polygon. You could see this as an optimization problem: find the 2n coordinates of the n points that will minimize a cost function (or maximize a value function) that fits your goal. One possible cost function could calculate, for each point, the distance to its closest neighbor or the boundary of the polygon, whichever is less, and calculate the variance of this sequence as a measure of "non-uniformity". You could use a random set of n points, obtained as above, as your initial solution.
I've seen such a "watchtower problem" in some book. Algorithms, calculus, or optimization.
#Youssef: sorry about the delay; a friend came, and a network hiccuped.
#others: have some patience, don't be so trigger-happy.

Creating a equilateral triangular grid over geometry

I need to create a equilateral triangular grid that fits a given geometry.
I have an image containing the geometry, it might include holes or thin paths. and i need to create a grid similar to this image:
The circles are variable in diameter, and need to cover the entire geometry. the points does not have to be on the geometry.

You can think of the triangular grid as being an oblique rectangular grid
This enables you to store the state of each circle in a 2-dimensional matrix, for instance, and to use simple nested loops for processing. Of cause then you will have to translate these logical coordinates to the geometry plane coordinates for drawing.
const double Sin30 = 0.5;
static readonly double Cos30 = Math.Cos(30*Math.PI/180);
for (int xLogical = 0; xLogical < NX; xLogical++) {
for (int yLogical = 0; yLogical < NY; yLogical++) {
double xGeo = GridDistance * xLogical * Cos30;
double yGeo = GridDistance * (yLogical + xLogical * Sin30);
...
}
}

I am assuming this is to create a 2D meshing tool. If it is, and it is homework, I suggest doing it yourself as you will get alot out of it. If it is not a meshing problem what I will have to say should help you regardless...
To do this, use the grid node centres to generate your equilaterals. If you don't have the centre points to start with you will need to look at first selecting an orientation for your object and then creating these (rectangular based) grid nodes (you will have to work out a way of testing whether these points actually lie inside your object boundaries). You can then construct your equilateral triangles using these points. Note. You again will have to deal with edge detection to get half decent accuracy.
To go a bit further that just equilaterals, and get a more accurate mesh, you will have to look into anisotropic mesh adaptation (AMA) using triangulation. This will be a lot harder than the basic approach outlined above - but fun!
Check out this link to a 2D tet-mesh generator using AMA. The paper this code is based on is:
V. Dolejsi: Anisotropic mesh adaptation for finite volume and finite element methods on triangular meshes
Computing and Visualisation in Science, 1:165-178, 1998.

Fast algorithm to find the x closest points to a given point on a plane

I would like to find a fast algorithm in order to find the x closest points to a given point on a plane.
We are actually dealing with not too many points (between 1,000 and 100,000), but I need the x closest points for every of these points. (where x usually will be between 5 and 20.)
I need to write it in C#.
A bit more context about the use case: These points are coordinates on a map. (I know, this means we are not exactly talking about a plane, but I hope to avoid dealing with projection issues.) In the end points that have many other points close to them should be displayed in red, points that have not too many points close to them should be displayed green. Between these two extremees the points are on a color gradient.

What you need is a data structure appropriate for organizing points in a plane. The K-D-Tree is often used in such situations. See k-d tree on Wikipedia.
Here, I found a general description of Geometric Algorithms
UPDATE
I ported a Java implementation of a KD-tree to C#. Please see User:Ojd/KD-Tree on RoboWiki. You can download the code there or you can download CySoft.Collections.zip directly from my homepage (only download, no docu).

For a given point (not all of them) and as the number of points is not extreme, you could calculate the distance from each point:
var points = new List<Point>();
Point source = ...
....
var closestPoints = points.Where(point => point != source).
OrderBy(point => NotReallyDistanceButShouldDo(source, point)).
Take(20);
private double NotReallyDistanceButShouldDo(Point source, Point target)
{
return Math.Pow(target.X - source.X, 2) + Math.Pow(target.Y - source.Y, 2);
}
(I've used x = 20)
The calculation are based on doubles so the fpu should be able to do a decent job here.
Note that you might get better performance if Point is a class rather than a struct.

You need to create a distance function, then calculate distance for every point and sort the results, and take the first x.
If the results must be 100% accurate then you can use the standard distance function:
d = SQRT((x2 - x1)^2 + (y2 - y1)^2)

To make this more efficent. lets say the distance is k. Take all points with x coordinates between x-k and x+k. similarly take, y-k and y+k. So you have removed all excess coordinates. now make distance by (x-x1)^2 + (y-y1)^2. Make a min heap of k elements on them , and add them to the heap if new point < min(heap). You now have the k minimum elements in the heap.

How to fill a square with smaller squares/rectangles?

In my office at work, we are not allowed to paint the walls, so I have decided to frame out squares and rectangles, attach some nice fabric to them, and arrange them on the wall.
I am trying to write a method which will take my input dimensions (9' x 8' 8") and min/max size (1' x 3', 2', 4', etc..) and generate a random pattern of squares and rectangles to fill the wall. I tried doing this by hand, but I'm just not happy with the layout that I got, and it takes about 35 minutes each time I want to 'randomize' the layout.

One solution is to start with x*y squares and randomly merge squares together to form rectangles. You'll want to give differing weights to different size squares to keep the algorithm from just ending up with loads of tiny rectangles (i.e. large rectangles should probably have a higher chance of being picked for merging until they get too big).

Sounds like a Treemap

Another idea:
1. Randomly generate points on the wall
Use as many points as the number of rectangles you want
Introduce sampling bias to get cooler patterns
2. Build the kd-tree of these points
The kd-tree will split the space in a number of rectangles. There might be too much structure for what you want, but its still a neat geeky algorithm.
(see: http://en.wikipedia.org/wiki/Kd-tree)
Edit: Just looked at JTreeMap, looks a bit like this is what its doing.

If you're talking on a pure programing problem ;) There is a technique called Bin Packing that tries to pack a number of bins into the smallest area possible. There's loads of material out there:
http://en.wikipedia.org/wiki/Bin_packing_problem
http://mathworld.wolfram.com/Bin-PackingProblem.html
http://www.cs.sunysb.edu/~algorith/files/bin-packing.shtml
So you 'could' create a load of random squares and run it through a bin packer to generate your pattern.
I've not implemented a bin packing algorithm myself but I've seen it done by a colleague for a Nike website. Best of luck

Since you can pick the size of the rectangles, this is not a hard problem.
I'd say you can do something as simple as:
Pick an (x,y) coordinate that is not currently inside a rectangle.
Pick a second (x,y) coordinate so that when you draw a rectangle between
the two coordinates, it won't overlap anything. The bounding box of
valid points is just bounded by the nearest rectangles' walls.
Draw that rectangle.
Repeat until, say, you have 90% of the area covered. At that point you
can either stop, or fill in the remaining holes with as big rectangles
as possible.
It might be interesting to parametrize the generation of points, and then make a genetic algorithm. The fitness function will be how much you like the arrangement - it would draw hundreds of arrangements for you, and you would rate them on a scale of 1-10. It would then take the best ones and tweak those, and repeat until you get an arrangement you really like.

Bin packing or square packing?
Bin packing:
http://www.cs.sunysb.edu/~algorith/files/bin-packing.shtml
Square packing:
http://www.maa.org/editorial/mathgames/mathgames_12_01_03.html
This actually sounds more like an old school random square painting demo, circa 8-bit computing days, especially if you don't mind overlaps. But if you want to be especially geeky, create random squares and solve for the packing problem.

Building off Philippe Beaudoin answer.
There are treemap implementations in other languages that you can also use. In Ruby with RubyTreeMap you could do
require 'Treemap'
require 'Treemap/image_output.rb'
root = Treemap::Node.new 0.upto(100){|i| root.new_child(:size => rand) }
output = Treemap::ImageOutput.new do |o|
o.width = 800
o.height = 600
end
output.to_png(root, "C:/output/test.png")
However it sorts the rectangles, so it doesn't look very random, but it could be a start. See rubytreemap.rubyforge.org/docs/index.html for more info

I would generate everything in a spiral slowly going in. If at any point you reach a point where your solution is proven to be 'unsolvable' (IE, can't put any squares in the remaining middle to satisfy the constraints), go to an earlier draft and change some square until you find a happy solution.
Pseudocode would look something like:
public Board GenerateSquares(direction, board, prevSquare)
{
Rectangle[] rs = generateAllPossibleNextRectangles(direction, prevSquare, board);
for(/*all possible next rectangles in some random order*/)){
if(board.add(rs[x]){
//see if you need to change direction)
Board nBoard = GenerateSquares(direction, board, rs[x]);
if(nBoard != null) return nBoard; //done
else board.remove(rs[x]);
}
}
//all possibilities tried, none worked
return null;
}
}

I suggest:
Start by setting up a polygon with four vertices to be eaten in varying size (up to maxside) rectangle lumps:
public double[] fillBoard(double width, double height, double maxside) {
double[] dest = new int[0];
double[] poly = new int[10];
poly[0] = 0; poly[1] = 0; poly[2] = width; poly[3] = 0;
poly[4] = width; poly[5] = height; poly[6] = 0; poly[7] = height;
poly[8] = 0; poly[9] = 0;
...
return dest; /* x,y pairs */
}
Then choose a random vertex, find polygon lines within (inclusive) 2 X maxside of the line.
Find x values of all vertical lines and y values of all horizontal lines. Create ratings for the "goodness" of choosing each x and y value, and equations to generate ratings for values in between the values. Goodness is measured as reducing number of lines in remaining polygon. Generate three options for each range of values between two x coordinates or two y coordinates, using pseudo-random generator. Rate and choose pairs of x and pair of y values on weighted average basis leaning towards good options. Apply new rectangle to list by cutting its shape from the poly array and adding rectangle coordinates to the dest array.
Question does not state a minimum side parameter. But if one is needed, algorithm should (upon hitting a hitch with a gap being too small) not include too small candidates in selection lists (whic will occasionally make them empty) and deselect a number of the surrounding rectangles in a certain radius of the problem with size and perform new regeneration attempts of that area, and hopefully the problem area, until the criteria are met. Recursion can remove progressively larger areas if a smaller relaying of tiles fails.
EDIT
Do some hit testing to eliminate potential overlaps. And eat some spinach before starting the typing. ;)

Define input area;
Draw vertical lines at several random horizontal locations through the entire height;
Draw horizontal lines at several vertical positions through the entire width;
Shift some "columns" up or down by arbitrary amounts;
Shift some "rows" left or right by arbitrary amounts (it may be required to subdivide some cells to obtain full horizontal seams;
Remove seams as aesthetically required.
This graphical method has similarities to Brian's answer.