surface

I have a cloud of points scattered in a 2D Euclidean space. I would like to calculate the area inside the polygon linking the most extreme (=peripheral) points of the cloud. In other words, I would like to estimate the area covered by the cloud in this space. Is there a formula in R? Thanks a lot for any response Julien This is called the convex-hull problem; R built-in chull function should do the work. To count area, you may use a formula from here . EDIT: Even better; splancs package has areapl function. So the function solving your problem should look like this: cha<-function(x,y){ chull(x

I would like to plot a surface with a colormap, wireframe and contours using matplotlib . Something like this: Notice that I am not asking about the contours that lie in the plane parallel to xy but the ones that are 3D and white in the image. If I go the naïve way and plot all these things I cannot see the contours (see code and image below). import numpy as np from mpl_toolkits.mplot3d import axes3d import matplotlib.pyplot as plt fig = plt.figure() ax = fig.add_subplot(111, projection="3d") X, Y = np.mgrid[-1:1:30j, -1:1:30j] Z = np.sin(np.pi*X)*np.sin(np.pi*Y) ax.plot_surface(X, Y, Z, cmap

I have a set of 3d points that approximate a surface. Each point, however, are subject to some error. Furthermore, the set of points contain a lot more points than is actually needed to represent the underlying surface. What I am looking for is an algorithm to create a new (much smaller) set of points representing a simplified, smoother version of the surface (pardon for not having a better definition than "simplified, smoother"). The underlying surface is not a mathematical one so I'm not hoping to fit the data set to some mathematical function. Instead of dealing with it as a point cloud, I

I'm trying to plot the trajectory of a photon orbiting a black hole. I'd like the photon to go behind the black hole and then in front of it (stable circular orbit). However I can't plot that, my photon is either in the foreground or in the background. I've tried using zorder but it didn't work fig = plt.figure() ax = fig.add_subplot(111, projection="3d") ax.set_aspect('equal') # black surface u = np.linspace(0, 2*np.pi, 100) v = np.linspace(0, 2*np.pi, 100) x = rs * np.outer(np.cos(u), np.sin(v)) y = rs * np.outer(np.sin(u), np.sin(v)) z = rs * np.outer(np.ones(np.size(u)), np.cos(v)) # plot

I have a 3d plot of a disk, here is the code: ri = 100 ra = 300 h=20 # input xy coordinates xy = np.array([[ri,0],[ra,0],[ra,h],[ri,h],[ri,0]]) # radial component is x values of input r = xy[:,0] # angular component is one revolution of 30 steps phi = np.linspace(0, 2*np.pi, 50) # create grid R,Phi = np.meshgrid(r,phi) # transform to cartesian coordinates X = R*np.cos(Phi) Y = R*np.sin(Phi) # Z values are y values, repeated 30 times Z = np.tile(xy[:,1],len(Y)).reshape(Y.shape) fig = plt.figure() ax = fig.add_subplot(1, 1, 1, projection='3d') ax.set_zlim(0,200) ax.plot_surface(X, Y, Z, alpha=0