Théophile Bastian
62730a03b4
This allows to visualize easily a generated configuration, thus allowing testing
52 lines
1.3 KiB
Python
52 lines
1.3 KiB
Python
import gen_marching_cubes_conf as gen
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import matplotlib.pyplot as plt
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from mpl_toolkits.mplot3d import axes3d
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import random
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def split_pt_list(pts):
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splitted = [[], [], []]
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for point in pts:
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for i in range(3):
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splitted[i].append(point[i])
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return splitted
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def pt_of_edge(edge):
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def avg(val0, val1):
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return (val0 + val1) / 2
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vert0 = edge.vert[0]
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vert1 = edge.vert[1]
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return (avg(vert0[0], vert1[0]),
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avg(vert0[1], vert1[1]),
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avg(vert0[2], vert1[2]))
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def tri_repr(tri, subplt):
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pts = [pt_of_edge(tri[i]) for i in range(3)]
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x_val, y_val, z_val = split_pt_list(pts)
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x_val = [val + random.random() / 10**5 for val in x_val]
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y_val = [val + random.random() / 10**5 for val in y_val]
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subplt.plot_trisurf(x_val, y_val, z_val)
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def display_case(tri_cube):
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figure = plt.figure()
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subplt = figure.add_subplot(111, projection='3d')
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actives = split_pt_list(list(tri_cube.activated))
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inactives = split_pt_list(list(tri_cube.non_activated))
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subplt.scatter3D(actives[0], actives[1], actives[2],
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c='r', marker='o')
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subplt.scatter3D(inactives[0], inactives[1], inactives[2],
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c='b', marker='.')
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for triangle in tri_cube.triangles:
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tri_repr(triangle, subplt)
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plt.show()
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