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Raycast sphere sampling mockup
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| scanradius = 50000.0 | |
| set point distance = 256.0 | |
| num_pts = 610352 | |
| scantime = 2441798s = 40696.6m = 678.3h | |
| too many points to run k nearest neighbors | |
| -- | |
| scanradius = 25000.0 | |
| set point distance = 256.0 | |
| num_pts = 152588 | |
| scantime = 305224s = 5087.1m = 84.8h | |
| too many points to run k nearest neighbors | |
| --- | |
| scanradius = 5000.0 | |
| set point distance = 256.0 | |
| num_pts = 6104 | |
| scantime = 2441s = 40.7m = 0.7h | |
| average point distance = 226.89593558252824 |
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| from numpy import pi, cos, sin, arccos, arange, round, column_stack, sort, mean | |
| import mpl_toolkits.mplot3d | |
| import matplotlib.pyplot as pp | |
| scanradius = 50000.0 | |
| pointdistance = 256.0 | |
| print("scanradius =", scanradius) | |
| print("set point distance =", pointdistance) | |
| num_pts = int(round((4.0 / ((pointdistance/2)**2)) * scanradius**2)) | |
| scantime = (num_pts * scanradius) / (2083 * 6) | |
| print("num_pts =", num_pts) | |
| print("scantime = %ds = %.1fm = %.1fh" % (scantime, scantime / 60.0, scantime / 3600.0)) | |
| indices = arange(0, num_pts, dtype=float) + 0.5 | |
| phi = arccos(1 - 2*indices/num_pts) | |
| theta = pi * (1 + 5**0.5) * indices | |
| x = scanradius * cos(theta) * sin(phi) | |
| y = scanradius * sin(theta) * sin(phi) | |
| z = scanradius * cos(phi); | |
| ##pp.figure().add_subplot(111, projection='3d').scatter(x, y, z); | |
| ##pp.show() | |
| from scipy.spatial import distance | |
| if num_pts < 10000: | |
| points = column_stack((x,y,z)) | |
| all_distances = distance.squareform(distance.pdist(points)) | |
| closest_distances = sort(all_distances, axis=1)[:, 1:4] | |
| average_distance = mean(mean(closest_distances, axis=1)) | |
| print("average point distance =", average_distance) | |
| else: | |
| print("too many points to run k nearest neighbors") |
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