54 lines
2.2 KiB
Python
54 lines
2.2 KiB
Python
from __future__ import print_function
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import numpy as np
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import math
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import poisson
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import array
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def main():
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# user defined options
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disk = True # this parameter defines if we look for Poisson-like distribution on a disk/sphere (center at 0, radius 1) or in a square/box (0-1 on x and y)
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repeatPattern = True # this parameter defines if we look for "repeating" pattern so if we should maximize distances also with pattern repetitions
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num_points = 2 # number of points we are looking for
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num_iterations = 4 # number of iterations in which we take average minimum squared distances between points and try to maximize them
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first_point_zero = False # should be first point zero (useful if we already have such sample) or random
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iterations_per_point = 128 # iterations per point trying to look for a new point with larger distance
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sorting_buckets = 0 # if this option is > 0, then sequence will be optimized for tiled cache locality in n x n tiles (x followed by y)
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num_dim = 3 # 1, 2, 3 dimensional version
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num_rotations = 1 # number of rotations of pattern to check against
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poisson_generator = poisson.PoissonGenerator(num_dim, disk, repeatPattern, first_point_zero)
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points = poisson_generator.find_point_set(num_points, num_iterations, iterations_per_point, num_rotations)
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print(points)
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print("")
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print("")
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print("")
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#normalize vectors
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norm_points = []
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for vector in points:
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norm_points.append(vector/np.linalg.norm(vector))
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print(norm_points)
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#ensure all vectors are pointing upwards
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final_points = []
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for vector in norm_points:
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if np.dot(vector, [0,1,0]) < 0:
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final_points.append(-vector)
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else:
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final_points.append(vector)
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print("")
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print("")
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print("")
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print(final_points)
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print("")
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print("")
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print("")
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#format output
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print("vec3 starPositions[{}] = vec3[](".format(num_points))
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for vector in final_points:
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print(" vec3({}, {}, {}),".format(vector[0], vector[1], vector[2]))
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print(");")
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if __name__ == '__main__':
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main()
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