contour

I would like to do a 3D contour plot using Mayavi in exactly the same way as the third figure on this page (a hydrogen electron cloud model) : http://www.sethanil.com/python-for-reseach/5 I have a set of data points which I created using my own model which I would like to use. The data points are stored in a multi-dimensional numpy array like so: XYZV = [[1, 2, 3, 4], [6, 7, 8, 9], ... [4, 5, 6, 7]] The data points are not uniformly spread in XYZ space and not stored in any particular order. I think the example uses a meshgrid to generate the data points - I have looked this up but totally don

Given a set of (x, y, z) coordinates, how would I go about creating a contour map? Would be nice to know how to implement in d3 but wouldn't mind trying to implement it myself if I had some direction. For d3 users, can I create a contour map using d3.geom.contour() and jasondavies' conrec.js: https://github.com/jasondavies/conrec.js Essentially, I'd like to replicate this contour map using d3.js: http://beaugunderson.com/routes/ It looks like this would be very easy with conrec.js . If you pass the data in the form that you have it, you can get a list of paths by calling .contourList() on the

As I present here (in 2D), I'm wondering if how I could "scale" an image input to be plotted to a range in the plot. To be more clear, this is what I need: I have a 400 * 400 image that is generated based on a function which interval is -1..1. So, I do a translate to save this data, like this: x = Utils.translate(pos_x, 0, self.width, -1, 1) y = Utils.translate(pos_y, 0, self.height, -1, 1) data = Utils.map_position_to_function(x, y) I.e, first I map its position to my range and then I calculate de f(x, y) based on this "new position" and save the data. Also, as I save this image, I do a

I have a lot of data on a scatter graph- the end result will have several million points. This is too many to make sense of on a scatter graph- I want to turn it into a 2D histogram, and plot a contour plot, as described here https://micropore.wordpress.com/2011/10/01/2d-density-plot-or-2d-histogram/ This is similar to the code I'm using, and has the same problem import numpy import matplotlib.pyplot as pyplot def convert_edges_to_centres(edges): centres = numpy.empty(len(edges)-1) for i in range(len(centres)): centres[i] = (edges[i+1] - edges[i])/2 + edges[i] return centres x = numpy.random

This is based from this question , which is focusing more on OpenCV C++ so I decided to make this question. This is one part of my program: vector<vector<Point> > contours; vector<vector<Point> > largest_contours; double largest_area = 0; for(int i= 0; i < contours.size(); i++){ double area = contourArea(contours[i]); if(area >= largest_area){ largest_area = area; largest_contours = contours[i]; <---THIS is the problem } } Basically the program will do: Scans every contours detected in the image sequences/video Labels the contours as contours[i] Calculates the area of every contours Compares