237 lines
7.3 KiB
Python
237 lines
7.3 KiB
Python
import numpy as np
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try:
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from scipy.sparse.linalg import spsolve
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from scipy.sparse import coo_matrix, eye
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except ImportError:
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pass
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from . import triangles
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def filter_laplacian(mesh,
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lamb=0.5,
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iterations=10,
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implicit_time_integration=False,
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volume_constraint=True,
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laplacian_operator=None):
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"""
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Smooth a mesh in-place using laplacian smoothing.
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Articles
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1 - "Improved Laplacian Smoothing of Noisy Surface Meshes"
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J. Vollmer, R. Mencl, and H. Muller
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2 - "Implicit Fairing of Irregular Meshes using Diffusion
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and Curvature Flow". M. Desbrun, M. Meyer,
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P. Schroder, A.H.B. Caltech
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Parameters
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------------
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mesh : trimesh.Trimesh
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Mesh to be smoothed in place
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lamb : float
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Diffusion speed constant
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If 0.0, no diffusion
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If > 0.0, diffusion occurs
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implicit_time_integration: boolean
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if False: explicit time integration
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-lamb <= 1.0 - Stability Limit (Article 1)
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if True: implicit time integration
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-lamb no limit (Article 2)
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iterations : int
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Number of passes to run filter
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laplacian_operator : None or scipy.sparse.coo.coo_matrix
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Sparse matrix laplacian operator
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Will be autogenerated if None
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"""
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# if the laplacian operator was not passed create it here
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if laplacian_operator is None:
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laplacian_operator = laplacian_calculation(mesh)
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# save initial volume
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if volume_constraint:
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vol_ini = mesh.volume
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# get mesh vertices and faces as vanilla numpy array
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vertices = mesh.vertices.copy().view(np.ndarray)
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faces = mesh.faces.copy().view(np.ndarray)
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# Set matrix for linear system of equations
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if implicit_time_integration:
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dlap = laplacian_operator.shape[0]
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AA = eye(dlap) + lamb * (eye(dlap) - laplacian_operator)
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# Number of passes
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for _index in range(iterations):
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# Classic Explicit Time Integration - Article 1
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if not implicit_time_integration:
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dot = laplacian_operator.dot(vertices) - vertices
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vertices += lamb * dot
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# Implicit Time Integration - Article 2
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else:
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vertices = spsolve(AA, vertices)
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# volume constraint
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if volume_constraint:
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# find the volume with new vertex positions
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vol_new = triangles.mass_properties(
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vertices[faces], skip_inertia=True)["volume"]
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# scale by volume ratio
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vertices *= ((vol_ini / vol_new) ** (1.0 / 3.0))
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# assign modified vertices back to mesh
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mesh.vertices = vertices
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return mesh
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def filter_humphrey(mesh,
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alpha=0.1,
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beta=0.5,
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iterations=10,
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laplacian_operator=None):
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"""
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Smooth a mesh in-place using laplacian smoothing
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and Humphrey filtering.
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Articles
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"Improved Laplacian Smoothing of Noisy Surface Meshes"
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J. Vollmer, R. Mencl, and H. Muller
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Parameters
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------------
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mesh : trimesh.Trimesh
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Mesh to be smoothed in place
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alpha : float
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Controls shrinkage, range is 0.0 - 1.0
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If 0.0, not considered
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If 1.0, no smoothing
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beta : float
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Controls how aggressive smoothing is
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If 0.0, no smoothing
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If 1.0, full aggressiveness
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iterations : int
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Number of passes to run filter
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laplacian_operator : None or scipy.sparse.coo.coo_matrix
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Sparse matrix laplacian operator
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Will be autogenerated if None
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"""
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# if the laplacian operator was not passed create it here
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if laplacian_operator is None:
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laplacian_operator = laplacian_calculation(mesh)
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# get mesh vertices as vanilla numpy array
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vertices = mesh.vertices.copy().view(np.ndarray)
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# save original unmodified vertices
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original = vertices.copy()
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# run through iterations of filter
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for _index in range(iterations):
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vert_q = vertices.copy()
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vertices = laplacian_operator.dot(vertices)
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vert_b = vertices - (alpha * original + (1.0 - alpha) * vert_q)
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vertices -= (beta * vert_b + (1.0 - beta) *
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laplacian_operator.dot(vert_b))
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# assign modified vertices back to mesh
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mesh.vertices = vertices
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return mesh
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def filter_taubin(mesh,
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lamb=0.5,
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nu=0.5,
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iterations=10,
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laplacian_operator=None):
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"""
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Smooth a mesh in-place using laplacian smoothing
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and taubin filtering.
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Articles
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"Improved Laplacian Smoothing of Noisy Surface Meshes"
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J. Vollmer, R. Mencl, and H. Muller
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Parameters
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------------
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mesh : trimesh.Trimesh
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Mesh to be smoothed in place.
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lamb : float
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Controls shrinkage, range is 0.0 - 1.0
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nu : float
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Controls dilation, range is 0.0 - 1.0
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Nu shall be between 0.0 < 1.0/lambda - 1.0/nu < 0.1
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iterations : int
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Number of passes to run the filter
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laplacian_operator : None or scipy.sparse.coo.coo_matrix
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Sparse matrix laplacian operator
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Will be autogenerated if None
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"""
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# if the laplacian operator was not passed create it here
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if laplacian_operator is None:
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laplacian_operator = laplacian_calculation(mesh)
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# get mesh vertices as vanilla numpy array
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vertices = mesh.vertices.copy().view(np.ndarray)
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# run through multiple passes of the filter
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for index in range(iterations):
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# do a sparse dot product on the vertices
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dot = laplacian_operator.dot(vertices) - vertices
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# alternate shrinkage and dilation
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if index % 2 == 0:
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vertices += lamb * dot
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else:
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vertices -= nu * dot
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# assign updated vertices back to mesh
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mesh.vertices = vertices
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return mesh
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def laplacian_calculation(mesh, equal_weight=True):
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"""
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Calculate a sparse matrix for laplacian operations.
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Parameters
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-------------
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mesh : trimesh.Trimesh
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Input geometry
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equal_weight : bool
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If True, all neighbors will be considered equally
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If False, all neighbors will be weighted by inverse distance
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Returns
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----------
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laplacian : scipy.sparse.coo.coo_matrix
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Laplacian operator
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"""
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# get the vertex neighbors from the cache
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neighbors = mesh.vertex_neighbors
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# avoid hitting crc checks in loops
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vertices = mesh.vertices.view(np.ndarray)
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# stack neighbors to 1D arrays
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col = np.concatenate(neighbors)
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row = np.concatenate([[i] * len(n)
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for i, n in enumerate(neighbors)])
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if equal_weight:
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# equal weights for each neighbor
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data = np.concatenate([[1.0 / len(n)] * len(n)
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for n in neighbors])
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else:
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# umbrella weights, distance-weighted
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# use dot product of ones to replace array.sum(axis=1)
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ones = np.ones(3)
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# the distance from verticesex to neighbors
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norms = [1.0 / np.sqrt(np.dot((vertices[i] - vertices[n]) ** 2, ones))
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for i, n in enumerate(neighbors)]
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# normalize group and stack into single array
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data = np.concatenate([i / i.sum() for i in norms])
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# create the sparse matrix
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matrix = coo_matrix((data, (row, col)),
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shape=[len(vertices)] * 2)
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return matrix
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