Cleanup for version 8.
This commit is contained in:
Juan Pablo Caram
parent
04d028ecc0
commit
fe61447887
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@ -14,7 +14,7 @@ from shapely.geometry.base import BaseGeometry
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from numpy import arctan2, Inf, array, sqrt, pi, ceil, sin, cos, sign, dot
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from numpy.linalg import solve
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from mpl_toolkits.axes_grid.anchored_artists import AnchoredDrawingArea
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#from mpl_toolkits.axes_grid.anchored_artists import AnchoredDrawingArea
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from rtree import index as rtindex
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404
camlib.py
404
camlib.py
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@ -17,8 +17,8 @@ import re
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import collections
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import numpy as np
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import matplotlib
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import matplotlib.pyplot as plt
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from scipy.spatial import Delaunay, KDTree
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#import matplotlib.pyplot as plt
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#from scipy.spatial import Delaunay, KDTree
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from rtree import index as rtindex
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@ -2982,206 +2982,206 @@ def parse_gerber_number(strnumber, frac_digits):
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return int(strnumber)*(10**(-frac_digits))
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def voronoi(P):
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"""
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Returns a list of all edges of the voronoi diagram for the given input points.
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"""
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delauny = Delaunay(P)
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triangles = delauny.points[delauny.vertices]
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circum_centers = np.array([triangle_csc(tri) for tri in triangles])
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long_lines_endpoints = []
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lineIndices = []
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for i, triangle in enumerate(triangles):
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circum_center = circum_centers[i]
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for j, neighbor in enumerate(delauny.neighbors[i]):
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if neighbor != -1:
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lineIndices.append((i, neighbor))
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else:
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ps = triangle[(j+1)%3] - triangle[(j-1)%3]
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ps = np.array((ps[1], -ps[0]))
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middle = (triangle[(j+1)%3] + triangle[(j-1)%3]) * 0.5
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di = middle - triangle[j]
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ps /= np.linalg.norm(ps)
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di /= np.linalg.norm(di)
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if np.dot(di, ps) < 0.0:
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ps *= -1000.0
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else:
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ps *= 1000.0
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long_lines_endpoints.append(circum_center + ps)
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lineIndices.append((i, len(circum_centers) + len(long_lines_endpoints)-1))
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vertices = np.vstack((circum_centers, long_lines_endpoints))
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# filter out any duplicate lines
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lineIndicesSorted = np.sort(lineIndices) # make (1,2) and (2,1) both (1,2)
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lineIndicesTupled = [tuple(row) for row in lineIndicesSorted]
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lineIndicesUnique = np.unique(lineIndicesTupled)
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return vertices, lineIndicesUnique
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def triangle_csc(pts):
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rows, cols = pts.shape
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A = np.bmat([[2 * np.dot(pts, pts.T), np.ones((rows, 1))],
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[np.ones((1, rows)), np.zeros((1, 1))]])
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b = np.hstack((np.sum(pts * pts, axis=1), np.ones((1))))
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x = np.linalg.solve(A,b)
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bary_coords = x[:-1]
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return np.sum(pts * np.tile(bary_coords.reshape((pts.shape[0], 1)), (1, pts.shape[1])), axis=0)
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def voronoi_cell_lines(points, vertices, lineIndices):
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"""
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Returns a mapping from a voronoi cell to its edges.
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:param points: shape (m,2)
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:param vertices: shape (n,2)
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:param lineIndices: shape (o,2)
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:rtype: dict point index -> list of shape (n,2) with vertex indices
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"""
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kd = KDTree(points)
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cells = collections.defaultdict(list)
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for i1, i2 in lineIndices:
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v1, v2 = vertices[i1], vertices[i2]
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mid = (v1+v2)/2
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_, (p1Idx, p2Idx) = kd.query(mid, 2)
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cells[p1Idx].append((i1, i2))
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cells[p2Idx].append((i1, i2))
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return cells
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def voronoi_edges2polygons(cells):
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"""
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Transforms cell edges into polygons.
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:param cells: as returned from voronoi_cell_lines
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:rtype: dict point index -> list of vertex indices which form a polygon
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"""
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# first, close the outer cells
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for pIdx, lineIndices_ in cells.items():
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dangling_lines = []
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for i1, i2 in lineIndices_:
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connections = filter(lambda (i1_, i2_): (i1, i2) != (i1_, i2_) and (i1 == i1_ or i1 == i2_ or i2 == i1_ or i2 == i2_), lineIndices_)
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assert 1 <= len(connections) <= 2
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if len(connections) == 1:
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dangling_lines.append((i1, i2))
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assert len(dangling_lines) in [0, 2]
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if len(dangling_lines) == 2:
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(i11, i12), (i21, i22) = dangling_lines
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# determine which line ends are unconnected
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connected = filter(lambda (i1,i2): (i1,i2) != (i11,i12) and (i1 == i11 or i2 == i11), lineIndices_)
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i11Unconnected = len(connected) == 0
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connected = filter(lambda (i1,i2): (i1,i2) != (i21,i22) and (i1 == i21 or i2 == i21), lineIndices_)
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i21Unconnected = len(connected) == 0
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startIdx = i11 if i11Unconnected else i12
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endIdx = i21 if i21Unconnected else i22
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cells[pIdx].append((startIdx, endIdx))
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# then, form polygons by storing vertex indices in (counter-)clockwise order
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polys = dict()
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for pIdx, lineIndices_ in cells.items():
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# get a directed graph which contains both directions and arbitrarily follow one of both
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directedGraph = lineIndices_ + [(i2, i1) for (i1, i2) in lineIndices_]
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directedGraphMap = collections.defaultdict(list)
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for (i1, i2) in directedGraph:
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directedGraphMap[i1].append(i2)
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orderedEdges = []
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currentEdge = directedGraph[0]
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while len(orderedEdges) < len(lineIndices_):
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i1 = currentEdge[1]
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i2 = directedGraphMap[i1][0] if directedGraphMap[i1][0] != currentEdge[0] else directedGraphMap[i1][1]
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nextEdge = (i1, i2)
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orderedEdges.append(nextEdge)
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currentEdge = nextEdge
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polys[pIdx] = [i1 for (i1, i2) in orderedEdges]
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return polys
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def voronoi_polygons(points):
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"""
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Returns the voronoi polygon for each input point.
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:param points: shape (n,2)
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:rtype: list of n polygons where each polygon is an array of vertices
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"""
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vertices, lineIndices = voronoi(points)
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cells = voronoi_cell_lines(points, vertices, lineIndices)
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polys = voronoi_edges2polygons(cells)
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polylist = []
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for i in xrange(len(points)):
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poly = vertices[np.asarray(polys[i])]
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polylist.append(poly)
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return polylist
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class Zprofile:
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def __init__(self):
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# data contains lists of [x, y, z]
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self.data = []
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# Computed voronoi polygons (shapely)
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self.polygons = []
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pass
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def plot_polygons(self):
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axes = plt.subplot(1, 1, 1)
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plt.axis([-0.05, 1.05, -0.05, 1.05])
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for poly in self.polygons:
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p = PolygonPatch(poly, facecolor=np.random.rand(3, 1), alpha=0.3)
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axes.add_patch(p)
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def init_from_csv(self, filename):
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pass
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def init_from_string(self, zpstring):
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pass
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def init_from_list(self, zplist):
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self.data = zplist
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def generate_polygons(self):
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self.polygons = [Polygon(p) for p in voronoi_polygons(array([[x[0], x[1]] for x in self.data]))]
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def normalize(self, origin):
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pass
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def paste(self, path):
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"""
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Return a list of dictionaries containing the parts of the original
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path and their z-axis offset.
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"""
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# At most one region/polygon will contain the path
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containing = [i for i in range(len(self.polygons)) if self.polygons[i].contains(path)]
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if len(containing) > 0:
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return [{"path": path, "z": self.data[containing[0]][2]}]
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# All region indexes that intersect with the path
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crossing = [i for i in range(len(self.polygons)) if self.polygons[i].intersects(path)]
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return [{"path": path.intersection(self.polygons[i]),
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"z": self.data[i][2]} for i in crossing]
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# def voronoi(P):
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# """
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# Returns a list of all edges of the voronoi diagram for the given input points.
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# """
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# delauny = Delaunay(P)
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# triangles = delauny.points[delauny.vertices]
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#
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# circum_centers = np.array([triangle_csc(tri) for tri in triangles])
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# long_lines_endpoints = []
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#
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# lineIndices = []
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# for i, triangle in enumerate(triangles):
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# circum_center = circum_centers[i]
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# for j, neighbor in enumerate(delauny.neighbors[i]):
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# if neighbor != -1:
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# lineIndices.append((i, neighbor))
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# else:
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# ps = triangle[(j+1)%3] - triangle[(j-1)%3]
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# ps = np.array((ps[1], -ps[0]))
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#
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# middle = (triangle[(j+1)%3] + triangle[(j-1)%3]) * 0.5
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# di = middle - triangle[j]
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#
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# ps /= np.linalg.norm(ps)
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# di /= np.linalg.norm(di)
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#
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# if np.dot(di, ps) < 0.0:
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# ps *= -1000.0
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# else:
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# ps *= 1000.0
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#
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# long_lines_endpoints.append(circum_center + ps)
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# lineIndices.append((i, len(circum_centers) + len(long_lines_endpoints)-1))
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#
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# vertices = np.vstack((circum_centers, long_lines_endpoints))
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#
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# # filter out any duplicate lines
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# lineIndicesSorted = np.sort(lineIndices) # make (1,2) and (2,1) both (1,2)
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# lineIndicesTupled = [tuple(row) for row in lineIndicesSorted]
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# lineIndicesUnique = np.unique(lineIndicesTupled)
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#
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# return vertices, lineIndicesUnique
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#
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#
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# def triangle_csc(pts):
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# rows, cols = pts.shape
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#
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# A = np.bmat([[2 * np.dot(pts, pts.T), np.ones((rows, 1))],
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# [np.ones((1, rows)), np.zeros((1, 1))]])
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#
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# b = np.hstack((np.sum(pts * pts, axis=1), np.ones((1))))
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# x = np.linalg.solve(A,b)
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# bary_coords = x[:-1]
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# return np.sum(pts * np.tile(bary_coords.reshape((pts.shape[0], 1)), (1, pts.shape[1])), axis=0)
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#
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#
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# def voronoi_cell_lines(points, vertices, lineIndices):
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# """
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# Returns a mapping from a voronoi cell to its edges.
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#
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# :param points: shape (m,2)
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# :param vertices: shape (n,2)
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# :param lineIndices: shape (o,2)
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# :rtype: dict point index -> list of shape (n,2) with vertex indices
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# """
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# kd = KDTree(points)
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#
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# cells = collections.defaultdict(list)
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# for i1, i2 in lineIndices:
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# v1, v2 = vertices[i1], vertices[i2]
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# mid = (v1+v2)/2
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# _, (p1Idx, p2Idx) = kd.query(mid, 2)
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# cells[p1Idx].append((i1, i2))
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# cells[p2Idx].append((i1, i2))
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#
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# return cells
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#
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#
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# def voronoi_edges2polygons(cells):
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# """
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# Transforms cell edges into polygons.
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#
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# :param cells: as returned from voronoi_cell_lines
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# :rtype: dict point index -> list of vertex indices which form a polygon
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# """
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#
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# # first, close the outer cells
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# for pIdx, lineIndices_ in cells.items():
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# dangling_lines = []
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# for i1, i2 in lineIndices_:
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# connections = filter(lambda (i1_, i2_): (i1, i2) != (i1_, i2_) and (i1 == i1_ or i1 == i2_ or i2 == i1_ or i2 == i2_), lineIndices_)
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# assert 1 <= len(connections) <= 2
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# if len(connections) == 1:
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# dangling_lines.append((i1, i2))
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# assert len(dangling_lines) in [0, 2]
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# if len(dangling_lines) == 2:
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# (i11, i12), (i21, i22) = dangling_lines
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#
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# # determine which line ends are unconnected
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# connected = filter(lambda (i1,i2): (i1,i2) != (i11,i12) and (i1 == i11 or i2 == i11), lineIndices_)
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# i11Unconnected = len(connected) == 0
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#
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# connected = filter(lambda (i1,i2): (i1,i2) != (i21,i22) and (i1 == i21 or i2 == i21), lineIndices_)
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# i21Unconnected = len(connected) == 0
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#
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# startIdx = i11 if i11Unconnected else i12
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# endIdx = i21 if i21Unconnected else i22
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#
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# cells[pIdx].append((startIdx, endIdx))
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#
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# # then, form polygons by storing vertex indices in (counter-)clockwise order
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# polys = dict()
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# for pIdx, lineIndices_ in cells.items():
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# # get a directed graph which contains both directions and arbitrarily follow one of both
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# directedGraph = lineIndices_ + [(i2, i1) for (i1, i2) in lineIndices_]
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# directedGraphMap = collections.defaultdict(list)
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# for (i1, i2) in directedGraph:
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# directedGraphMap[i1].append(i2)
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# orderedEdges = []
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# currentEdge = directedGraph[0]
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# while len(orderedEdges) < len(lineIndices_):
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# i1 = currentEdge[1]
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# i2 = directedGraphMap[i1][0] if directedGraphMap[i1][0] != currentEdge[0] else directedGraphMap[i1][1]
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# nextEdge = (i1, i2)
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# orderedEdges.append(nextEdge)
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# currentEdge = nextEdge
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#
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# polys[pIdx] = [i1 for (i1, i2) in orderedEdges]
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#
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# return polys
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#
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#
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# def voronoi_polygons(points):
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# """
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# Returns the voronoi polygon for each input point.
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#
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# :param points: shape (n,2)
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# :rtype: list of n polygons where each polygon is an array of vertices
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# """
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# vertices, lineIndices = voronoi(points)
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# cells = voronoi_cell_lines(points, vertices, lineIndices)
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# polys = voronoi_edges2polygons(cells)
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# polylist = []
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# for i in xrange(len(points)):
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# poly = vertices[np.asarray(polys[i])]
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# polylist.append(poly)
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# return polylist
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#
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#
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# class Zprofile:
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# def __init__(self):
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#
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# # data contains lists of [x, y, z]
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# self.data = []
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#
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# # Computed voronoi polygons (shapely)
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# self.polygons = []
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# pass
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#
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# def plot_polygons(self):
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# axes = plt.subplot(1, 1, 1)
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#
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# plt.axis([-0.05, 1.05, -0.05, 1.05])
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#
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# for poly in self.polygons:
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# p = PolygonPatch(poly, facecolor=np.random.rand(3, 1), alpha=0.3)
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# axes.add_patch(p)
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#
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# def init_from_csv(self, filename):
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# pass
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#
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# def init_from_string(self, zpstring):
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# pass
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#
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# def init_from_list(self, zplist):
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# self.data = zplist
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#
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# def generate_polygons(self):
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# self.polygons = [Polygon(p) for p in voronoi_polygons(array([[x[0], x[1]] for x in self.data]))]
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#
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# def normalize(self, origin):
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# pass
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#
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# def paste(self, path):
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# """
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# Return a list of dictionaries containing the parts of the original
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# path and their z-axis offset.
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# """
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#
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# # At most one region/polygon will contain the path
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# containing = [i for i in range(len(self.polygons)) if self.polygons[i].contains(path)]
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#
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# if len(containing) > 0:
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# return [{"path": path, "z": self.data[containing[0]][2]}]
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#
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# # All region indexes that intersect with the path
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# crossing = [i for i in range(len(self.polygons)) if self.polygons[i].intersects(path)]
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#
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# return [{"path": path.intersection(self.polygons[i]),
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# "z": self.data[i][2]} for i in crossing]
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def autolist(obj):
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