intersection

问题 So, I'd like to figure out a function that allows you to determine if two cubes of arbitrary rotation and size intersect. If the cubes are not arbitrary in their rotation (but locked to a particular axis) the intersection is simple; you check if they intersect in all three dimensions by checking their bounds to see if they cross or are within one another in all three dimensions. If they cross or are within in only two, they do not intersect. This method can be used to determine if the

问题 I am trying to use fsolve as quoted here : http://glowingpython.blogspot.gr/2011/05/hot-to-find-intersection-of-two.html, On order to find the intersection between two curves. Both curves basically are two arrays of floats. The first of them is a one dimension array Pmech ( Pmech(x) ) and the second is a two dimension array Pair ( Pair(x,y) ) The x - axis is common for both arrays ,so what i want to do is for every y to see where Pair and Pmech intersect. I am aware of the fact that fsolve()

问题 How to find whether a line intercepted in a polygon 回答1: The question is a little bit ambiguous but let's try anyway: Assume the points (x,y) on the line are defined by the equation Ax + By + C = 0. Then we can obviously determine if a point (x,y) is on the line by evaluating Ax + By + C. If the point is not on the line then the sign of Ax + By + C tells us on which side of the line the point is. Hence by inspecting the signs of the expression Ax + By + C for each vertex (x,y) of the polygon,

问题 In short, I have to generate a constrained delaunay triangulation from a set of 2D points that carry additional information. Since there could be intersecting constraints, I need that the triangulation properly interpolates the satellite data whenever it generates the new point of intersection. I tried with CGAL (C++) to define a custom kernel following the example here, but I've failed to compile it succesfully, basically because there is something fundamental of the underlying generic

问题 In my current project I need to calculate the intersection area of triangles and the unit squares in an infinite grid. For every triangle (given by three pairs of floating point numbers) I need to know the area (in the interval (0,1] ) it has in common with every square it intersects. Right now I convert both (the triangle and the square) to polygons and use Sutherland-Hodgman polygon clipping to calculate the intersection polygon, which I then use to calculate its area. This approach now

问题 In my current project I need to calculate the intersection area of triangles and the unit squares in an infinite grid. For every triangle (given by three pairs of floating point numbers) I need to know the area (in the interval (0,1] ) it has in common with every square it intersects. Right now I convert both (the triangle and the square) to polygons and use Sutherland-Hodgman polygon clipping to calculate the intersection polygon, which I then use to calculate its area. This approach now

问题 I have three tables with differing genomic intervals. Here is an example: > a chr interval.start interval.end names 1 chr1 5 10 a 2 chr1 6 10 b 3 chr2 7 10 c 4 chr3 8 10 d > b chr interval.start interval.end names 1 chr1 6 15 e 2 chr1 7 15 f 3 chr1 8 15 g > c chr interval.start interval.end names 1 chr1 7 12 h 2 chr1 8 12 i 3 chr5 9 12 j 4 chr10 10 12 k 5 chr20 11 12 l I am trying to find the common intervals between all tables after converting info to GRanges. Essentially I want to do

问题 Starting from a shapefile containing a fairly large number (about 20000) of potentially partially-overlapping polygons, I'd need to extract all the sub-polygons originated by intersecting their different "boundaries". In practice, starting from some mock-up data: library(tibble) library(dplyr) library(sf) ncircles <- 9 rmax <- 120 x_limits <- c(-70,70) y_limits <- c(-30,30) set.seed(100) xy <- data.frame( id = paste0("id_", 1:ncircles), x = runif(ncircles, min(x_limits), max(x_limits)), y =

问题 I have a large number of user IDs (integers), potentially millions. These users all belong to various groups (sets of integers), such that there are on the order of 10 million groups. To simplify my example and get to the essence of it, let's assume that all groups contain 20 user IDs. I want to find all pairs of integer sets that have an intersection of 15 or greater. Should I compare every pair of sets? (If I keep a data structure that maps userIDs to set membership, this would not be

问题 I'm doing some set operations in Python, and I noticed something odd.. >> set([1,2,3]) | set([2,3,4]) set([1, 2, 3, 4]) >> set().union(*[[1,2,3], [2,3,4]]) set([1, 2, 3, 4]) That's good, expected behaviour - but with intersection: >> set([1,2,3]) & set([2,3,4]) set([2, 3]) >> set().intersection(*[[1,2,3], [2,3,4]]) set([]) Am I losing my mind here? Why isn't set.intersection() operating as I'd expect it to? How can I do the intersection of many sets as I did with union (assuming the [[1,2,3],