269 lines
8.9 KiB
Python
Executable file
269 lines
8.9 KiB
Python
Executable file
#!/usr/bin/env python
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#encoding: utf-8
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import sys
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import turtle
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import argparse
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import matplotlib.pyplot as plot
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from itertools import ifilterfalse as filter_if_not
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import ants
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import utils
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from utils import LOG,LOGN
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import hull
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import uberplot
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import shortpath
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import lindenmayer
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import triangulation
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import voronoi
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parser = argparse.ArgumentParser()
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parser.add_argument('-p', "--penrose", help="Do not compute the Penrose tiling but load it from a file",
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default=None, action='store', type=str, metavar="SEGMENTS")
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parser.add_argument( '-d', '--depth', help="Recursive depth of the Lindenmayer computations = size of the Penrose tiling",
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default=1, type=int, metavar="DEPTH")
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parser.add_argument('-t', "--notsp", help="Do not compute the TSP",
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default=False, action='store_true')
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parser.add_argument('-r', "--tour", help="Load several TSP tour from a file",
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default=[None], action='store', type=str, nargs="*", metavar="POINTS")
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parser.add_argument('-m', "--pheromones", help="Load a pheromones matrix from a file",
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default=None, action='store', type=str, metavar="MATRIX")
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parser.add_argument('-g', "--triangulation", help="Do not compute the Delaunay triangulation but load it from a file",
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default=None, action='store', type=str, metavar="SEGMENTS")
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parser.add_argument('-v', "--voronoi", help="Do not compute the Voronoï diagram but load it from a file",
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default=None, action='store', type=str, metavar="POINTS")
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args = parser.parse_args()
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error_codes = {"NOTSP":100}
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depth = args.depth
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LOGN( "depth",depth )
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########################################################################
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# PENROSE TILING
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########################################################################
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penrose_segments = set()
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if args.penrose:
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LOGN( "Load the penrose tiling" )
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penrose_segments = utils.load_segments(args.penrose)
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else:
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LOGN( "Draw the penrose tiling" )
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segment_size = 10
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float_rounding = 10
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ttl = turtle.Turtle()
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ttl.speed('fastest')
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penrose = lindenmayer.DumpTurtleLSystem(ttl,
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axiom="[X]++[X]++[X]++[X]++[X]",
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rules={
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'F': "",
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'W': "YF++ZF----XF[-YF----WF]++",
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'X': "+YF--ZF[---WF--XF]+",
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'Y': "-WF++XF[+++YF++ZF]-",
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'Z': "--YF++++WF[+ZF++++XF]--XF"
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},
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angle=36, heading=0, size=segment_size, rounding=float_rounding )
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# actually do something
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penrose.draw( depth )
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# save this intermediate step
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LOGN( "\tsegments",len(penrose.segments) )
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with open("d%i_penrose.segments" % depth, "w") as fd:
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fd.write( str(penrose) )
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penrose_segments = penrose.segments
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########################################################################
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# TSP
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########################################################################
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trajs = []
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if args.tour != [None]:
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for tour in args.tour:
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trajs.append( utils.load_points(tour) )
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if args.notsp:
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if args.tour == [None] or not args.pheromones:
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LOGN( "If you do not want to solve the TSP, you must provide a solution tour (--tour) and a pheromones matrix (--pheromones)" )
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sys.exit(error_codes["NO-TSP"])
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if args.pheromones:
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phero = utils.load_matrix(args.pheromones)
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else:
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LOGN( "Solve the TSP with an Ant Colony Algorithm" )
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LOGN( "\tConvert the segment list into an adjacency list graph" )
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G = utils.adjacency_from_set( penrose_segments )
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LOGN( "\tCompute a tour" )
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max_it = 10
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num_ants = 10 #* depth
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decay = 0.1
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w_heur = 2.5
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w_local_phero = 0.1
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c_greed = 0.9
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w_history = 1.0
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best,phero = ants.search( G, max_it, num_ants, decay, w_heur, w_local_phero, w_history, c_greed, cost_func = ants.graph_distance )
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LOGN( "\tTransform the resulting nodes permutation into a path on the graph" )
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# by finding the shortest path between two cities.
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traj = []
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for start,end in utils.tour(best["permutation"]):
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p,c = shortpath.astar( G, start, end )
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traj += p
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trajs.append(traj)
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with open("d%i_tour.points" % depth, "w") as fd:
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for p in traj:
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fd.write("%f %f\n" % p)
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with open("d%i_pheromones.mat" % depth, "w") as fd:
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for row in phero:
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key = "%f,%f:" % row
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line = key
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for k in phero[row]:
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val = phero[row][k]
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line += "%f,%f=%f " % (k[0],k[1],val)
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fd.write( line + "\n" )
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########################################################################
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# TRIANGULATION
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########################################################################
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if args.triangulation:
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triangulation_edges = utils.load_segments(args.triangulation)
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else:
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LOGN( "Compute the triangulation of the penrose vertices" )
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points = utils.vertices_of(penrose_segments)
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triangles = triangulation.delaunay_bowyer_watson( points, do_plot = False )
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LOGN( "\tCompute the convex hull of",len(points),"points" )
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# Should convert the set into a list
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hull = hull.convex_hull( list(points) )
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hull_edges = list(utils.tour(hull))
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LOGN( "\t\tHull of",len(hull_edges),"edges" )
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LOGN( "\tRemove triangles that are not sub-parts of the Penrose tiling" )
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# def adjoin_hull(triangle):
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# """Return True if the given triangle has at least one edge that is in the set hull_edges."""
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# for (p,q) in utils.tour(list(triangle)):
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# if (p,q) in hull_edges or (q,p) in hull_edges:
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# return True
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# return False
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def acute_triangle(triangle):
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"""Return True if the center of the circumcircle of the given triangle lies inside the triangle.
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That is if the triangle is acute."""
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return triangulation.in_triangle( triangulation.circumcircle(triangle)[0], triangle )
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# FIXME at depth 3, some triangles have an edge in the convex hull...
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# Filter out edges that are in hull_edges
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# tri_nohull = list(filter_if_not( adjoin_hull, triangles ))
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# Filter out triangles that are obtuse
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# triangulated = list(filter_if_not( acute_triangle, tri_nohull ))
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triangulated = list(filter_if_not( acute_triangle, triangles ))
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LOGN( "\t\tRemoved", len(triangles)-len(triangulated), "triangles" )
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triangulation_edges = triangulation.edges_of( triangulated )
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with open("d%i_triangulation.segments" % depth, "w") as fd:
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for p0,p1 in triangulation_edges:
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fd.write("%f %f %f %f\n" % (p0[0],p0[1],p1[0],p1[1]) )
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########################################################################
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# VORONOÏ
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########################################################################
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if args.voronoi:
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# voronoi_centers = utils.load_points(args.voronoi)
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pass
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else:
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# LOGN( "Compute the nodes of the Voronoï diagram" )
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voronoi_tri_graph = voronoi.dual(triangulated)
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voronoi_tri_edges = voronoi.edges_of( voronoi.dual(triangulated) )
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voronoi_tri_centers = voronoi_tri_graph.keys()
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voronoi_graph = voronoi.merge_enclosed( voronoi_tri_graph, penrose_segments )
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voronoi_edges = voronoi.edges_of( voronoi_graph )
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voronoi_centers = voronoi_graph.keys()
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# with open("d%i_voronoi_centers.points" % depth, "w") as fd:
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# for p in voronoi_centers:
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# fd.write( "%f %f\n" % (p[0],p[1]) )
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########################################################################
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# PLOT
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########################################################################
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LOGN( "Plot the resulting tour" )
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fig = plot.figure()
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ax = fig.add_subplot(111)
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LOGN( "\tpheromones",len(phero) )#,"x",len(phero[traj[0]]) )
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maxph=0
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for i in phero:
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maxph = max( maxph, max(phero[i].values()))
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# ant colony
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# pheromones
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for i in phero:
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for j in phero[i]:
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if i == j:
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continue
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nph = phero[i][j]/maxph
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seg = [(i,j)]
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# LOGN( nph,seg )
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uberplot.plot_segments( ax, seg, edgecolor="blue", alpha=0.01*nph, linewidth=1*nph )
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# uberplot.scatter_segments( ax, seg, color="red", alpha=0.5, linewidth=nph )
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for traj in trajs:
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LOGN( "\ttraj",len(traj) )
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# best tour
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uberplot.plot_segments( ax, utils.tour(traj), edgecolor="red", alpha=0.9, linewidth=3 )
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LOGN( "\ttiling",len(penrose_segments) )
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tcol = "black"
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uberplot.plot_segments( ax, penrose_segments, edgecolor=tcol, alpha=0.9, linewidth=2 )
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uberplot.scatter_segments( ax, penrose_segments, edgecolor=tcol, alpha=0.9, linewidth=1 )
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# triangulation
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uberplot.plot_segments( ax, triangulation_edges, edgecolor="green", alpha=0.2, linewidth=1 )
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# Voronoï
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uberplot.scatter_points( ax, voronoi_centers, edgecolor="magenta", facecolor="white", s=200, alpha=1 )
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uberplot.plot_segments( ax, voronoi_edges, edgecolor="magenta", alpha=1, linewidth=1 )
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ax.set_aspect('equal')
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# transparent background in SVG
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fig.patch.set_visible(False)
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ax.axis('off')
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plot.savefig("ubergeekism.svg", dpi=600)
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ax.axis('off')
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fig.patch.set_visible(True)
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fig.patch.set_facecolor('white')
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plot.savefig("ubergeekism.png", dpi=600)
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plot.show()
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