Is there a formula or algorithm used to calculate row and column positions of random x and y points on a coordinate system?
The entire coordinate system can be skewed in both the x and y at an unknown angle, so the first row would be considered to be the top most row of x and y coordinates. There can also be missing x and y coordinates as well within the entire array.
Row and Column Layout
I have considered trying a concave hull algorithm, then connecting each end point with a line, and finally getting each point closest to the lines but this seems overly complicated.
Related
So I'm attempting to make a wall system like the Sims. While it is 3D, it can be simplified to 2D (think a bird's eye view). A wall is defined by two points: a starting point an an ending point and the walls themselves have directions and can be simplified to unit vectors. Walls are also made with rectangular prisms, each of these prisms start at one point and end at another. So, bearing this in mind, sometimes walls have to be extended, otherwise there will be a small gap in the convex corner of the wall
if one of the points is shared between the walls. Any number of walls can be placed in a single position, so all of this in mind, a grid point's data could contain something like this (using Lua), each table represents a vector2, using a table for ease of visualization, this code won't actually work:
local gridPoint = {
{x = 1, y = 0},
{x = 0.7071, y = 0.7071},
{x = 0, y = 1},
{x = 0.7071, y = -0.7071},
}
Here's a visual of what the points might look like, where the intersection is the grid point and the walls are the vectors extending from the intersection, assuming each of the vectors have a length of 1 (excuse my terrible drawing skills). Since dot products flip when greater than 180 degrees, the largest angle would always be <=180.
So now, to get the angle between any two of points, we can just do math.deg(math.acos(v1:dot(v2))) to get the angle between these two points in degrees, where v1 is vector 1, v2 is vector 2, and dot is a function that returns the dot product of v1 and v2.
So up to this point, everything is fine. The issue is that I have to create two loops which goes through every single combination of possible dot products which I'm not sure is the best method of finding the largest angle, this is #gridPoint^2 possible combinations which is fine at lower numbers, but this number of possible combination increases exponentially with each new wall.
Is there an easier way of doing this?
This problem is equivalent to finding of the farthest pair of points, where all the points lie at unit circle.
Sort points - it is simpler to separate points with positive y (angles 0..Pi) into the first list, and points with negative y into the second list,sort them by X-coordinate, then join the first list and reversed second one. This stage takes O(nlogn) time
Then perform searching of the farthest points - fix the first point index (A[i]), walk through the list until squared distance A[i]-A[j+1] becomes less than squared distance A[i]-A[j]. So we find the farthest point A[j] for A[i]. Now increment i and search the farthest point for it - starting from j.
Repeat until j exceeds n.
This stage is linear because we move i and j only in forward direction. Essentially this is a method of rotating calipers, so you can get some implementation elsewhere.
Imagine you have a grid of sample points of a function z = f(x, y) where 1 < x < N and 1 < y < N. The formula is not given, but just the raw data, that could be for example the grey level of an image.
I would like to find, given a point A, whose x and y coordinates are given (and z is known from the data, so A is a vertex of the surface) a number M of points that lie on the circumference of the circle with center in A and radius R that are a good approximation of a circular "cloth" draped on the imaginary surface described by the data points. Imagine also that the edges of the surface are a triangle mesh.
The biggest constraint in the approximation is that the sum of the length of the edges of the resulting polygon is constantly R * 2 * PI, so that moving the A point across the surface would just change the M points but never the sum of their reciprocal distances. The draping doesn't need to be perfect, it would be nice though to be as close as possible to the surface., or always on one side of the surface, above or below.
Could anybody give me a pointer to something to read about this? Is this a known problem?
I feel that the problem is not completely formulated, I'd already like some help to give a complete description of it.
If I draw a line from let's say: (2,3) to (42,28), how can I get all points on the line in a Point list? I tried using the slope, but I can't seem to get the hang of it.
To be clear: I would like all the pixels that the line covers. So I can make the line 'clickable'.
This is a math question. The equation of a line is:
y = mx + c
So you need to figure out the gradient (m) and the intercept (c) and then plug in values for x to get values for y.
But what do you mean by "all the points on a line"? There is an infinite number of points if x and y are real numbers.
You can use the formula (x-x1)/(x1-x2) = (y-y1)/(y1-y2). And you know the points with x values ranging from 2 to 42 are on the line and their associated y values have to be found. If any of the resulting y value is not an integer then it should be approximated rightly. And if two consecutive y values differ by more than 1 then the missing y values should be mapped to the last x value.
Here is the pseudo code (tried to capture the crux of the algorithm)
prevY = y1
for(i=x1+1;i<=x2;++i)
{
y = computeY(i);
if(diff(y,prevY)>1) dump_points(prevY,y,i);
prevY = y;
dump_point(i,y);
}
dump_points(prevY,y2,x2);
I am probably not covering all the cases here (esp. not the corner ones). But the idea is that for one value of x there would could be many values of y and vice versa depending on the slope of the line. The algorithm should consider this and generate all the points.
I have a line that I must do calculations on for each grid square the line passes through.
I have used the Superline algorithm to get all these grid squares. This gives me an array of X,Y coordinates to check.
Now, here is where I am stuck, I need to be able to calculate the distance traveled through each of the grid squares... As in, on a line not on either 90 degree or 45 degree angles, each grid square accommodates a different 'length' of the total line.
Image example here, need 10 reputation to post images
As you can see, some squares have much more 'line length' in them than others - this is what I need to find.
How do I work this out for each grid square? I've been at this for a while and request the help of the Stack Overflowers!
There may be some clever way to do this that is faster and easier, but you could always hack through it like this:
You know the distance formula: s=sqrt((x2-x1)^2+(y2-y1)^2). To apply this, you must find the x and y co-ordinates of the points where the line intersects the edges of each grid cell. You can do this by plugging the x and y co-ordinates of the boundaries of the cell into the equation of the line and solve for x or y as appropriate.
That is, each cell extends from some point (x0,y0) to (x0+1,y0+1). So we need to find y(x0), y(x0+1), x(y0), and x(y0+1). For each of these, the x or y value found may or may not be within the ranges for that co-ordinate for that cell. Specifically, two of them will be and two won't. The two that are correspond to the edges that the line passes through, and the two that aren't are edges that it doesn't pass through.
Okay, maybe this sounds pretty confusing, so let's work through an example.
Let's say your line has the equation x=2/3 * y. You want to know where it intersects the edges of the cell extending from (1,0) to (2,1).
Plug in x=1 and you get y=2/3. 2/3 is in the legal range for y -- 0 to 1 -- so (1,2/3) is a point on the edge where the line intersects this cell. Namely, the left edge.
Plug in x=2 and you get y=4/3. 4/3 is outside the range for y. So the line does not pass through the right edge.
Plug in y=0 and you get x=0. 0 is not in the range for x, so the line does not pass through the bottom edge.
Plug in y=1 and you get x=3/2. 3/2 is in the legal range for x, so (3/2,1) is another intersection point, on the top edge.
Thus, the two points where the line intersects the edges of the cell are (1,2/3) and (3/2,1). Plug these into the distance formula and you'll get the length of the line segement through this cell, namely sqrt((1-3/2)^2+(2/3-1)^2)=sqrt(1/4+1/9)=sqrt(13/36). You can approximate that to any desired level of precision.
To do this in a program you'd need something like: (I'll use pseudo code because I don't know what language you're using)
// Assuming y=mx+b
function y(x)
return mx+b
function x(y)
return (y-b)/m
// cellx, celly are co-ordinates of lower left corner of cell
// Upper right must therefore be cellx+1, celly+1
function segLength(cellx, celly)
// We'll create two arrays pointx and pointy to hold co-ordinates of intersect points
// n is index into these arrays
// In an object-oriented language, we'd create an array of point objects, but whatever
n=0
y1=y(cellx)
if y1>=celly and y1<=celly+1
pointx[n]=cellx
pointy[n]=y1
n=n+1
y2=y(cellx+1)
if y2>=celly and y2<=celly+1
pointx[n]=cellx+1
pointy[n]=y2
n=n+1
x1=x(celly)
if x1>=cellx and x1<=cellx+1
pointx[n]=x1
pointy[n]=celly
n=n+1
x2=x(celly+1)
if x2>=cellx and x2<=cellx+1
pointx[n]=x2
pointy[n]=celly+1
n=n+1
if n==0
return "Error: line does not intersect this cell"
else if n==2
return sqrt((pointx[0]-pointx[1])^2+(pointy[0]-pointy[1])^2)
else
return "Error: Impossible condition"
Well, I'm sure you could make the code a little cleaner, but that's the idea.
have a look at Siddon's algorithm: "Fast calculation of the exact radiological path for a three-dimensional CT array"
unfortunately you need a subscription to read the original paper, but it is fairly well described in this paper
Siddon's algorithm is an O(n) algorithm for finding the length of intersection of a line with each pixel/voxel in a regular 2d/3d grid.
Use the Euclidean Distance.
sqrt((x2-x1)^2 + (y2-y1)^2)
This gives the actual distance in units between points (x1,y1) and (x2,y2)
You can fairly simply find this for each square.
You have the slope of the line m = (y2-y1)/(x2-x1).
You have the starting point:
(x1,y2)
What is the y position at x1 + 1? (i.e. starting at the next square)
Assuming you set your starting point to 0 the equation of this line is simply:
y_n = mx_n
so y_n = (y2-y1)/(x2-x1) * x_n
Then the coordinates at the first square are (x1,y1) and at the nth point:
(1, ((y2-y1)/(x2-x1))*1)
(2, ((y2-y1)/(x2-x1))*2)
(3, ((y2-y1)/(x2-x1))*3)
...
(n, ((y2-y1)/(x2-x1))*n)
Then the distance through the nth square is:
sqrt((x_n+1 - x_n)^2 + (y_n+1 - y_n)^2)
Problem: Suppose you have a collection of points in the 2D plane. I want to know if this set of points sits on a regular grid (if they are a subset of a 2D lattice). I would like some ideas on how to do this.
For now, let's say I'm only interested in whether these points form an axis-aligned rectangular grid (that the underlying lattice is rectangular, aligned with the x and y axes), and that it is a complete rectangle (the subset of the lattice has a rectangular boundary with no holes). Any solutions must be quite efficient (better than O(N^2)), since N can be hundreds of thousands or millions.
Context: I wrote a 2D vector field plot generator which works for an arbitrarily sampled vector field. In the case that the sampling is on a regular grid, there are simpler/more efficient interpolation schemes for generating the plot, and I would like to know when I can use this special case. The special case is sufficiently better that it merits doing. The program is written in C.
This might be dumb but if your points were to lie on a regular grid, then wouldn't peaks in the Fourier transform of the coordinates all be exact multiples of the grid resolution? You could do a separate Fourier transform the X and Y coordinates. If theres no holes on grid then the FT would be a delta function I think. FFT is O(nlog(n)).
p.s. I would have left this as a comment but my rep is too low..
Not quite sure if this is what you are after but for a collection of 2d points on a plane you can always fit them on a rectangular grid (down to the precision of your points anyway), the problem may be the grid they fit to may be too sparsly populated by the points to provide any benefit to your algorithm.
to find a rectangular grid that fits a set of points you essentially need to find the GCD of all the x coordinates and the GCD of all the y coordinates with the origin at xmin,ymin this should be O( n (log n)^2) I think.
How you decide if this grid is then too sparse is not clear however
If the points all come only from intersections on the grid then the hough transform of your set of points might help you. If you find that two mutually perpendicular sets of lines occur most often (meaning you find peaks at four values of theta all 90 degrees apart) and you find repeating peaks in gamma space then you have a grid. Otherwise not.
Here's a solution that works in O(ND log N), where N is the number of points and D is the number of dimensions (2 in your case).
Allocate D arrays with space for N numbers: X, Y, Z, etc. (Time: O(ND))
Iterate through your point list and add the x-coordinate to list X, the y-coordinate to list Y, etc. (Time: O(ND))
Sort each of the new lists. (Time: O(ND log N))
Count the number of unique values in each list and make sure the difference between successive unique values is the same across the whole list. (Time: O(ND))
If
the unique values in each dimension are equally spaced, and
if the product of the number of unique values of each coordinate is equal to the number of original points (length(uniq(X))*length(uniq(Y))* ... == N,
then the points are in a regular rectangular grid.
Let's say a grid is defined by an orientation Or (within 0 and 90 deg) and a resolution Res. You could compute a cost function that evaluate if a grid (Or, Res) sticks to your points. For example, you could compute the average distance of each point to its closest point of the grid.
Your problem is then to find the (Or, Res) pair that minimize the cost function. In order to narrow the search space and improve the , some a heuristic to test "good" candidate grids could be used.
This approach is the same as the one used in the Hough transform proposed by jilles. The (Or, Res) space is comparable to the Hough's gamma space.