voronoi

I want to calculate Thiessen weights to compute areal rainfall from number of point measurements. I am using R and thanks to some previous question in the same topic, I got to know that I can use deldir . But the problem is my boundary polygon is not a rectangle; it's an irregular polygon (it's a catchment boundary derived using ArcGIS). But in deldir the boundary can only be a rectangle. Are there any other packages where I can calculate Thiessen weights of an area covered by an irregular boundary? Given below are my measurement points (meas_points) and coordinates of a (simplified) boundary

I would like to compute the mean and standard deviation of the areas of a set of Voronoi regions in 2D (if the region extends to infinity, I'll just clip it to the unit square). However if possible I would like to do this computation from the Delaunay Triangulation without explicitly computing the Voronoi regions? Is this even possible, or is it better to just compute the Voronoi diagram explicitly? In order to calculate the voronoi region of a vertex you need to iterate the 1-ring around it. Then the area of the region is defined as: A = 1/8 * (sum for every adjacent vertex p_i) { (cot alpha

Is it possible to color the scipy.spatial.Voronoi diagram? I know it is. But now my goal is to color each cell according to a color scale to represent a physical quantity. As in the image below (PRL 107, 155704 (2011)): And I would also like to know if it is possible to calculate the area of each cell, because it is a quantity that I would like to calculate The Hagen Color scale: Actually the link you provide gives the code needed to colorize the Voronoi diagram. In order to assign each cell a color representing a physical quantity, you need to map the values of this physical quantity to a

I am trying to find a way to calculate a 2d Power Diagram in Python. For this I want to make use of the fact that a 2d power diagram can be interpreted as the intersection of a regular 3d voronoi diagram with a plane . With the SciPy Voronoi module I can calculate a 3d Voronoi diagram - is there a possibility to intersect it with a plane and convert it to a 2d diagram? There isn't yet a power diagram capability inside of SciPy. Converting a 3D Voronoi diagram into a 2D power diagram is likely to be difficult and, at least within Python, slow. To work around this, I've developed a C++ program

Is there a simple way to find out if a point is inside a voronoi cell? For example, the following code generates something like the diagram below: using namespace boost::polygon; point_data<int> p1(0, 0); point_data<int> p2(-10, 10); point_data<int> p3(-10, -10); point_data<int> p4(10, -10); point_data<int> p5(10, 10); std::vector<point_data<int>> pts = { p1, p2, p3, p4, p5 }; construct_voronoi(pts.begin(), pts.end(), vd); In this case, how can I found out if the point (5,5) is inside the central cell? I could create a polygon out of each cell and find out using a point in polygon algorithm ,

I am working with an algorithm that, for each iteration, needs to find which region of a Voronoi diagram a set of arbirary coordinats belong to. that is, which region each coordinate is located within. (We can assume that all coordinates will belong to a region, if that makes any difference.) I don't have any code that works in Python yet, but the the pseudo code looks something like this: ## we are in two dimensions and we have 0<x<1, 0<y<1. for i in xrange(1000): XY = get_random_points_in_domain() XY_candidates = get_random_points_in_domain() vor = Voronoi(XY) # for instance scipy.spatial

Background I'm trying to visualize the results of a kmeans clustering procedure on the following data using voronoi polygons on a US map. Here is the code I've been running so far: input <- read.csv("LatLong.csv", header = T, sep = ",") # K Means Clustering set.seed(123) km <- kmeans(input, 17) cent <- data.frame(km$centers) # Visualization states <- map_data("state") StateMap <- ggplot() + geom_polygon(data = states, aes(x = long, y = lat, group = group), col = "white") # Voronoi V <- deldir(cent$long, cent$lat) ll <-apply(V$dirsgs, 1, FUN = function(x){ readWKT(sprintf("LINESTRING(%s %s, %s