Test if point is in some rectangle
I have a large collection of rectangles, all of the same size. I am generating random points that should not fall in these rectangles, so what I wish to do is test if the generated point lies in one of the rectangles, and if it does, generate a new point.
Using R-trees seem to work, but they are really meant for rectangles and not points. I could use a modified version of a R-tree algorithm which works with points too, but I'd rather not reinvent the wheel, if there is already some better solution. I'm not very familiar with data-structures, so maybe there already exists some structure that works for my problem?
In summary, basically what I'm asking is if anyone knows of a good algorithm, that works in Python, that can be used to check if a point lies in any rectangle in a given set of rectangles.
edit: This is in 2D and the rectangles are not rotated.
Answer
This Reddit thread addresses your problem:
If your universe is integer, or if the level of precision is well known and is not too high, you can use abelsson's suggestion from the thread, using O(1) lookup using coloring:
As usual you can trade space for time.. here is a O(1) lookup with very low constant. init: Create a bitmap large enough to envelop all rectangles with sufficient precision, initialize it to black. Color all pixels containing any rectangle white. O(1) lookup: is the point (x,y) white? If so, a rectangle was hit.
I recommend you go to that post and fully read ModernRonin's answer which is the most accepted one. I pasted it here:
First, the micro problem. You have an arbitrarily rotated rectangle, and a point. Is the point inside the rectangle?
There are many ways to do this. But the best, I think, is using the 2d vector cross product. First, make sure the points of the rectangle are stored in clockwise order. Then do the vector cross product with 1) the vector formed by the two points of the side and 2) a vector from the first point of the side to the test point. Check the sign of the result - positive is inside (to the right of) the side, negative is outside. If it's inside all four sides, it's inside the rectangle. Or equivalently, if it's outside any of the sides, it's outside the rectangle. More explanation here.
This method will take 3 subtracts per vector * times 2 vectors per side, plus one cross product per side which is three multiplies and two adds. 11 flops per side, 44 flops per rectangle.
If you don't like the cross product, then you could do something like: figure out the inscribed and circumscribed circles for each rectangle, check if the point inside the inscribed one. If so, it's in the rectangle as well. If not, check if it's outside the circumscribed rectangle. If so, it's outside the rectangle as well. If it falls between the two circles, you're f****d and you have to check it the hard way.
Finding if a point is inside a circle in 2d takes two subtractions and two squarings (= multiplies), and then you compare distance squared to avoid having to do a square root. That's 4 flops, times two circles is 8 flops - but sometimes you still won't know. Also this assumes that you don't pay any CPU time to compute the circumscribed or inscribed circles, which may or may not be true depending on how much pre-computation you're willing to do on your rectangle set.
In any event, it's probably not a great idea to test the point against every rectangle, especially if you have a hundred million of them.
Which brings us to the macro problem. How to avoid testing the point against every single rectangle in the set? In 2D, this is probably a quad-tree problem. In 3d, what generic_handle said - an octree. Off the top of my head, I would probably implement it as a B+ tree. It's tempting to use d = 5, so that each node can have up to 4 children, since that maps so nicely onto the quad-tree abstraction. But if the set of rectangles is too big to fit into main memory (not very likely these days), then having nodes the same size as disk blocks is probably the way to go.
Watch out for annoying degenerate cases, like some data set that has ten thousand nearly identical rectangles with centers at the same exact point. :P
Why is this problem important? It's useful in computer graphics, to check if a ray intersects a polygon. I.e., did that sniper rifle shot you just made hit the person you were shooting at? It's also used in real-time map software, like say GPS units. GPS tells you the coordinates you're at, but the map software has to find where that point is in a huge amount of map data, and do it several times per second.
Again, credit to ModernRonin...