1145 lines
42 KiB
Python
1145 lines
42 KiB
Python
from __future__ import division, print_function, absolute_import
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import os
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import copy
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import pytest
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import numpy as np
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from numpy.testing import (assert_equal, assert_almost_equal,
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assert_, assert_allclose, assert_array_equal)
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import pytest
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from pytest import raises as assert_raises
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from scipy._lib.six import xrange
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import scipy.spatial.qhull as qhull
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from scipy.spatial import cKDTree as KDTree
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from scipy.spatial import Voronoi
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import itertools
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def sorted_tuple(x):
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return tuple(sorted(x))
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def sorted_unique_tuple(x):
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return tuple(np.unique(x))
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def assert_unordered_tuple_list_equal(a, b, tpl=tuple):
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if isinstance(a, np.ndarray):
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a = a.tolist()
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if isinstance(b, np.ndarray):
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b = b.tolist()
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a = list(map(tpl, a))
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a.sort()
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b = list(map(tpl, b))
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b.sort()
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assert_equal(a, b)
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np.random.seed(1234)
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points = [(0,0), (0,1), (1,0), (1,1), (0.5, 0.5), (0.5, 1.5)]
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pathological_data_1 = np.array([
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[-3.14,-3.14], [-3.14,-2.36], [-3.14,-1.57], [-3.14,-0.79],
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[-3.14,0.0], [-3.14,0.79], [-3.14,1.57], [-3.14,2.36],
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[-3.14,3.14], [-2.36,-3.14], [-2.36,-2.36], [-2.36,-1.57],
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[-2.36,-0.79], [-2.36,0.0], [-2.36,0.79], [-2.36,1.57],
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[-2.36,2.36], [-2.36,3.14], [-1.57,-0.79], [-1.57,0.79],
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[-1.57,-1.57], [-1.57,0.0], [-1.57,1.57], [-1.57,-3.14],
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[-1.57,-2.36], [-1.57,2.36], [-1.57,3.14], [-0.79,-1.57],
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[-0.79,1.57], [-0.79,-3.14], [-0.79,-2.36], [-0.79,-0.79],
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[-0.79,0.0], [-0.79,0.79], [-0.79,2.36], [-0.79,3.14],
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[0.0,-3.14], [0.0,-2.36], [0.0,-1.57], [0.0,-0.79], [0.0,0.0],
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[0.0,0.79], [0.0,1.57], [0.0,2.36], [0.0,3.14], [0.79,-3.14],
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[0.79,-2.36], [0.79,-0.79], [0.79,0.0], [0.79,0.79],
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[0.79,2.36], [0.79,3.14], [0.79,-1.57], [0.79,1.57],
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[1.57,-3.14], [1.57,-2.36], [1.57,2.36], [1.57,3.14],
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[1.57,-1.57], [1.57,0.0], [1.57,1.57], [1.57,-0.79],
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[1.57,0.79], [2.36,-3.14], [2.36,-2.36], [2.36,-1.57],
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[2.36,-0.79], [2.36,0.0], [2.36,0.79], [2.36,1.57],
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[2.36,2.36], [2.36,3.14], [3.14,-3.14], [3.14,-2.36],
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[3.14,-1.57], [3.14,-0.79], [3.14,0.0], [3.14,0.79],
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[3.14,1.57], [3.14,2.36], [3.14,3.14],
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])
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pathological_data_2 = np.array([
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[-1, -1], [-1, 0], [-1, 1],
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[0, -1], [0, 0], [0, 1],
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[1, -1 - np.finfo(np.float_).eps], [1, 0], [1, 1],
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])
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bug_2850_chunks = [np.random.rand(10, 2),
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np.array([[0,0], [0,1], [1,0], [1,1]]) # add corners
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]
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# same with some additional chunks
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bug_2850_chunks_2 = (bug_2850_chunks +
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[np.random.rand(10, 2),
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0.25 + np.array([[0,0], [0,1], [1,0], [1,1]])])
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DATASETS = {
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'some-points': np.asarray(points),
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'random-2d': np.random.rand(30, 2),
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'random-3d': np.random.rand(30, 3),
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'random-4d': np.random.rand(30, 4),
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'random-5d': np.random.rand(30, 5),
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'random-6d': np.random.rand(10, 6),
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'random-7d': np.random.rand(10, 7),
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'random-8d': np.random.rand(10, 8),
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'pathological-1': pathological_data_1,
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'pathological-2': pathological_data_2
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}
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INCREMENTAL_DATASETS = {
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'bug-2850': (bug_2850_chunks, None),
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'bug-2850-2': (bug_2850_chunks_2, None),
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}
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def _add_inc_data(name, chunksize):
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"""
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Generate incremental datasets from basic data sets
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"""
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points = DATASETS[name]
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ndim = points.shape[1]
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opts = None
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nmin = ndim + 2
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if name == 'some-points':
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# since Qz is not allowed, use QJ
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opts = 'QJ Pp'
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elif name == 'pathological-1':
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# include enough points so that we get different x-coordinates
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nmin = 12
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chunks = [points[:nmin]]
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for j in xrange(nmin, len(points), chunksize):
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chunks.append(points[j:j+chunksize])
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new_name = "%s-chunk-%d" % (name, chunksize)
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assert new_name not in INCREMENTAL_DATASETS
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INCREMENTAL_DATASETS[new_name] = (chunks, opts)
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for name in DATASETS:
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for chunksize in 1, 4, 16:
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_add_inc_data(name, chunksize)
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class Test_Qhull(object):
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def test_swapping(self):
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# Check that Qhull state swapping works
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x = qhull._Qhull(b'v',
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np.array([[0,0],[0,1],[1,0],[1,1.],[0.5,0.5]]),
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b'Qz')
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xd = copy.deepcopy(x.get_voronoi_diagram())
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y = qhull._Qhull(b'v',
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np.array([[0,0],[0,1],[1,0],[1,2.]]),
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b'Qz')
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yd = copy.deepcopy(y.get_voronoi_diagram())
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xd2 = copy.deepcopy(x.get_voronoi_diagram())
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x.close()
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yd2 = copy.deepcopy(y.get_voronoi_diagram())
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y.close()
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assert_raises(RuntimeError, x.get_voronoi_diagram)
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assert_raises(RuntimeError, y.get_voronoi_diagram)
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assert_allclose(xd[0], xd2[0])
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assert_unordered_tuple_list_equal(xd[1], xd2[1], tpl=sorted_tuple)
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assert_unordered_tuple_list_equal(xd[2], xd2[2], tpl=sorted_tuple)
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assert_unordered_tuple_list_equal(xd[3], xd2[3], tpl=sorted_tuple)
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assert_array_equal(xd[4], xd2[4])
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assert_allclose(yd[0], yd2[0])
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assert_unordered_tuple_list_equal(yd[1], yd2[1], tpl=sorted_tuple)
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assert_unordered_tuple_list_equal(yd[2], yd2[2], tpl=sorted_tuple)
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assert_unordered_tuple_list_equal(yd[3], yd2[3], tpl=sorted_tuple)
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assert_array_equal(yd[4], yd2[4])
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x.close()
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assert_raises(RuntimeError, x.get_voronoi_diagram)
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y.close()
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assert_raises(RuntimeError, y.get_voronoi_diagram)
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def test_issue_8051(self):
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points = np.array([[0, 0], [0, 1], [0, 2], [1, 0], [1, 1], [1, 2],[2, 0], [2, 1], [2, 2]])
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Voronoi(points)
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class TestUtilities(object):
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"""
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Check that utility functions work.
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"""
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def test_find_simplex(self):
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# Simple check that simplex finding works
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points = np.array([(0,0), (0,1), (1,1), (1,0)], dtype=np.double)
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tri = qhull.Delaunay(points)
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# +---+
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# |\ 0|
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# | \ |
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# |1 \|
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# +---+
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assert_equal(tri.vertices, [[1, 3, 2], [3, 1, 0]])
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for p in [(0.25, 0.25, 1),
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(0.75, 0.75, 0),
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(0.3, 0.2, 1)]:
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i = tri.find_simplex(p[:2])
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assert_equal(i, p[2], err_msg='%r' % (p,))
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j = qhull.tsearch(tri, p[:2])
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assert_equal(i, j)
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def test_plane_distance(self):
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# Compare plane distance from hyperplane equations obtained from Qhull
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# to manually computed plane equations
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x = np.array([(0,0), (1, 1), (1, 0), (0.99189033, 0.37674127),
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(0.99440079, 0.45182168)], dtype=np.double)
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p = np.array([0.99966555, 0.15685619], dtype=np.double)
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tri = qhull.Delaunay(x)
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z = tri.lift_points(x)
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pz = tri.lift_points(p)
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dist = tri.plane_distance(p)
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for j, v in enumerate(tri.vertices):
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x1 = z[v[0]]
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x2 = z[v[1]]
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x3 = z[v[2]]
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n = np.cross(x1 - x3, x2 - x3)
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n /= np.sqrt(np.dot(n, n))
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n *= -np.sign(n[2])
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d = np.dot(n, pz - x3)
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assert_almost_equal(dist[j], d)
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def test_convex_hull(self):
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# Simple check that the convex hull seems to works
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points = np.array([(0,0), (0,1), (1,1), (1,0)], dtype=np.double)
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tri = qhull.Delaunay(points)
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# +---+
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# |\ 0|
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# | \ |
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# |1 \|
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# +---+
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assert_equal(tri.convex_hull, [[3, 2], [1, 2], [1, 0], [3, 0]])
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def test_volume_area(self):
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#Basic check that we get back the correct volume and area for a cube
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points = np.array([(0, 0, 0), (0, 1, 0), (1, 0, 0), (1, 1, 0),
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(0, 0, 1), (0, 1, 1), (1, 0, 1), (1, 1, 1)])
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hull = qhull.ConvexHull(points)
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assert_allclose(hull.volume, 1., rtol=1e-14,
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err_msg="Volume of cube is incorrect")
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assert_allclose(hull.area, 6., rtol=1e-14,
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err_msg="Area of cube is incorrect")
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def test_random_volume_area(self):
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#Test that the results for a random 10-point convex are
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#coherent with the output of qconvex Qt s FA
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points = np.array([(0.362568364506, 0.472712355305, 0.347003084477),
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(0.733731893414, 0.634480295684, 0.950513180209),
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(0.511239955611, 0.876839441267, 0.418047827863),
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(0.0765906233393, 0.527373281342, 0.6509863541),
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(0.146694972056, 0.596725793348, 0.894860986685),
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(0.513808585741, 0.069576205858, 0.530890338876),
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(0.512343805118, 0.663537132612, 0.037689295973),
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(0.47282965018, 0.462176697655, 0.14061843691),
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(0.240584597123, 0.778660020591, 0.722913476339),
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(0.951271745935, 0.967000673944, 0.890661319684)])
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hull = qhull.ConvexHull(points)
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assert_allclose(hull.volume, 0.14562013, rtol=1e-07,
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err_msg="Volume of random polyhedron is incorrect")
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assert_allclose(hull.area, 1.6670425, rtol=1e-07,
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err_msg="Area of random polyhedron is incorrect")
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def test_incremental_volume_area_random_input(self):
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"""Test that incremental mode gives the same volume/area as
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non-incremental mode and incremental mode with restart"""
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nr_points = 20
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dim = 3
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points = np.random.random((nr_points, dim))
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inc_hull = qhull.ConvexHull(points[:dim+1, :], incremental=True)
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inc_restart_hull = qhull.ConvexHull(points[:dim+1, :], incremental=True)
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for i in range(dim+1, nr_points):
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hull = qhull.ConvexHull(points[:i+1, :])
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inc_hull.add_points(points[i:i+1, :])
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inc_restart_hull.add_points(points[i:i+1, :], restart=True)
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assert_allclose(hull.volume, inc_hull.volume, rtol=1e-7)
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assert_allclose(hull.volume, inc_restart_hull.volume, rtol=1e-7)
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assert_allclose(hull.area, inc_hull.area, rtol=1e-7)
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assert_allclose(hull.area, inc_restart_hull.area, rtol=1e-7)
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def _check_barycentric_transforms(self, tri, err_msg="",
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unit_cube=False,
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unit_cube_tol=0):
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"""Check that a triangulation has reasonable barycentric transforms"""
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vertices = tri.points[tri.vertices]
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sc = 1/(tri.ndim + 1.0)
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centroids = vertices.sum(axis=1) * sc
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# Either: (i) the simplex has a `nan` barycentric transform,
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# or, (ii) the centroid is in the simplex
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def barycentric_transform(tr, x):
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ndim = tr.shape[1]
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r = tr[:,-1,:]
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Tinv = tr[:,:-1,:]
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return np.einsum('ijk,ik->ij', Tinv, x - r)
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eps = np.finfo(float).eps
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c = barycentric_transform(tri.transform, centroids)
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olderr = np.seterr(invalid="ignore")
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try:
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ok = np.isnan(c).all(axis=1) | (abs(c - sc)/sc < 0.1).all(axis=1)
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finally:
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np.seterr(**olderr)
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assert_(ok.all(), "%s %s" % (err_msg, np.nonzero(~ok)))
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# Invalid simplices must be (nearly) zero volume
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q = vertices[:,:-1,:] - vertices[:,-1,None,:]
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volume = np.array([np.linalg.det(q[k,:,:])
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for k in range(tri.nsimplex)])
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ok = np.isfinite(tri.transform[:,0,0]) | (volume < np.sqrt(eps))
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assert_(ok.all(), "%s %s" % (err_msg, np.nonzero(~ok)))
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# Also, find_simplex for the centroid should end up in some
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# simplex for the non-degenerate cases
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j = tri.find_simplex(centroids)
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ok = (j != -1) | np.isnan(tri.transform[:,0,0])
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assert_(ok.all(), "%s %s" % (err_msg, np.nonzero(~ok)))
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if unit_cube:
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# If in unit cube, no interior point should be marked out of hull
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at_boundary = (centroids <= unit_cube_tol).any(axis=1)
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at_boundary |= (centroids >= 1 - unit_cube_tol).any(axis=1)
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ok = (j != -1) | at_boundary
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assert_(ok.all(), "%s %s" % (err_msg, np.nonzero(~ok)))
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def test_degenerate_barycentric_transforms(self):
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# The triangulation should not produce invalid barycentric
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# transforms that stump the simplex finding
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data = np.load(os.path.join(os.path.dirname(__file__), 'data',
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'degenerate_pointset.npz'))
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points = data['c']
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data.close()
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tri = qhull.Delaunay(points)
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# Check that there are not too many invalid simplices
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bad_count = np.isnan(tri.transform[:,0,0]).sum()
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assert_(bad_count < 23, bad_count)
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# Check the transforms
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self._check_barycentric_transforms(tri)
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@pytest.mark.slow
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def test_more_barycentric_transforms(self):
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# Triangulate some "nasty" grids
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eps = np.finfo(float).eps
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npoints = {2: 70, 3: 11, 4: 5, 5: 3}
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for ndim in xrange(2, 6):
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# Generate an uniform grid in n-d unit cube
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x = np.linspace(0, 1, npoints[ndim])
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grid = np.c_[list(map(np.ravel, np.broadcast_arrays(*np.ix_(*([x]*ndim)))))].T
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err_msg = "ndim=%d" % ndim
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# Check using regular grid
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tri = qhull.Delaunay(grid)
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self._check_barycentric_transforms(tri, err_msg=err_msg,
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unit_cube=True)
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# Check with eps-perturbations
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np.random.seed(1234)
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m = (np.random.rand(grid.shape[0]) < 0.2)
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grid[m,:] += 2*eps*(np.random.rand(*grid[m,:].shape) - 0.5)
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tri = qhull.Delaunay(grid)
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self._check_barycentric_transforms(tri, err_msg=err_msg,
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unit_cube=True,
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unit_cube_tol=2*eps)
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# Check with duplicated data
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tri = qhull.Delaunay(np.r_[grid, grid])
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self._check_barycentric_transforms(tri, err_msg=err_msg,
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unit_cube=True,
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unit_cube_tol=2*eps)
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class TestVertexNeighborVertices(object):
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def _check(self, tri):
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expected = [set() for j in range(tri.points.shape[0])]
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for s in tri.simplices:
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for a in s:
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for b in s:
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if a != b:
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expected[a].add(b)
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indptr, indices = tri.vertex_neighbor_vertices
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got = [set(map(int, indices[indptr[j]:indptr[j+1]]))
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for j in range(tri.points.shape[0])]
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assert_equal(got, expected, err_msg="%r != %r" % (got, expected))
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def test_triangle(self):
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points = np.array([(0,0), (0,1), (1,0)], dtype=np.double)
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tri = qhull.Delaunay(points)
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self._check(tri)
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def test_rectangle(self):
|
|
points = np.array([(0,0), (0,1), (1,1), (1,0)], dtype=np.double)
|
|
tri = qhull.Delaunay(points)
|
|
self._check(tri)
|
|
|
|
def test_complicated(self):
|
|
points = np.array([(0,0), (0,1), (1,1), (1,0),
|
|
(0.5, 0.5), (0.9, 0.5)], dtype=np.double)
|
|
tri = qhull.Delaunay(points)
|
|
self._check(tri)
|
|
|
|
|
|
class TestDelaunay(object):
|
|
"""
|
|
Check that triangulation works.
|
|
|
|
"""
|
|
def test_masked_array_fails(self):
|
|
masked_array = np.ma.masked_all(1)
|
|
assert_raises(ValueError, qhull.Delaunay, masked_array)
|
|
|
|
def test_array_with_nans_fails(self):
|
|
points_with_nan = np.array([(0,0), (0,1), (1,1), (1,np.nan)], dtype=np.double)
|
|
assert_raises(ValueError, qhull.Delaunay, points_with_nan)
|
|
|
|
def test_nd_simplex(self):
|
|
# simple smoke test: triangulate a n-dimensional simplex
|
|
for nd in xrange(2, 8):
|
|
points = np.zeros((nd+1, nd))
|
|
for j in xrange(nd):
|
|
points[j,j] = 1.0
|
|
points[-1,:] = 1.0
|
|
|
|
tri = qhull.Delaunay(points)
|
|
|
|
tri.vertices.sort()
|
|
|
|
assert_equal(tri.vertices, np.arange(nd+1, dtype=int)[None,:])
|
|
assert_equal(tri.neighbors, -1 + np.zeros((nd+1), dtype=int)[None,:])
|
|
|
|
def test_2d_square(self):
|
|
# simple smoke test: 2d square
|
|
points = np.array([(0,0), (0,1), (1,1), (1,0)], dtype=np.double)
|
|
tri = qhull.Delaunay(points)
|
|
|
|
assert_equal(tri.vertices, [[1, 3, 2], [3, 1, 0]])
|
|
assert_equal(tri.neighbors, [[-1, -1, 1], [-1, -1, 0]])
|
|
|
|
def test_duplicate_points(self):
|
|
x = np.array([0, 1, 0, 1], dtype=np.float64)
|
|
y = np.array([0, 0, 1, 1], dtype=np.float64)
|
|
|
|
xp = np.r_[x, x]
|
|
yp = np.r_[y, y]
|
|
|
|
# shouldn't fail on duplicate points
|
|
tri = qhull.Delaunay(np.c_[x, y])
|
|
tri2 = qhull.Delaunay(np.c_[xp, yp])
|
|
|
|
def test_pathological(self):
|
|
# both should succeed
|
|
points = DATASETS['pathological-1']
|
|
tri = qhull.Delaunay(points)
|
|
assert_equal(tri.points[tri.vertices].max(), points.max())
|
|
assert_equal(tri.points[tri.vertices].min(), points.min())
|
|
|
|
points = DATASETS['pathological-2']
|
|
tri = qhull.Delaunay(points)
|
|
assert_equal(tri.points[tri.vertices].max(), points.max())
|
|
assert_equal(tri.points[tri.vertices].min(), points.min())
|
|
|
|
def test_joggle(self):
|
|
# Check that the option QJ indeed guarantees that all input points
|
|
# occur as vertices of the triangulation
|
|
|
|
points = np.random.rand(10, 2)
|
|
points = np.r_[points, points] # duplicate input data
|
|
|
|
tri = qhull.Delaunay(points, qhull_options="QJ Qbb Pp")
|
|
assert_array_equal(np.unique(tri.simplices.ravel()),
|
|
np.arange(len(points)))
|
|
|
|
def test_coplanar(self):
|
|
# Check that the coplanar point output option indeed works
|
|
points = np.random.rand(10, 2)
|
|
points = np.r_[points, points] # duplicate input data
|
|
|
|
tri = qhull.Delaunay(points)
|
|
|
|
assert_(len(np.unique(tri.simplices.ravel())) == len(points)//2)
|
|
assert_(len(tri.coplanar) == len(points)//2)
|
|
|
|
assert_(len(np.unique(tri.coplanar[:,2])) == len(points)//2)
|
|
|
|
assert_(np.all(tri.vertex_to_simplex >= 0))
|
|
|
|
def test_furthest_site(self):
|
|
points = [(0, 0), (0, 1), (1, 0), (0.5, 0.5), (1.1, 1.1)]
|
|
tri = qhull.Delaunay(points, furthest_site=True)
|
|
|
|
expected = np.array([(1, 4, 0), (4, 2, 0)]) # from Qhull
|
|
assert_array_equal(tri.simplices, expected)
|
|
|
|
@pytest.mark.parametrize("name", sorted(INCREMENTAL_DATASETS))
|
|
def test_incremental(self, name):
|
|
# Test incremental construction of the triangulation
|
|
|
|
chunks, opts = INCREMENTAL_DATASETS[name]
|
|
points = np.concatenate(chunks, axis=0)
|
|
|
|
obj = qhull.Delaunay(chunks[0], incremental=True,
|
|
qhull_options=opts)
|
|
for chunk in chunks[1:]:
|
|
obj.add_points(chunk)
|
|
|
|
obj2 = qhull.Delaunay(points)
|
|
|
|
obj3 = qhull.Delaunay(chunks[0], incremental=True,
|
|
qhull_options=opts)
|
|
if len(chunks) > 1:
|
|
obj3.add_points(np.concatenate(chunks[1:], axis=0),
|
|
restart=True)
|
|
|
|
# Check that the incremental mode agrees with upfront mode
|
|
if name.startswith('pathological'):
|
|
# XXX: These produce valid but different triangulations.
|
|
# They look OK when plotted, but how to check them?
|
|
|
|
assert_array_equal(np.unique(obj.simplices.ravel()),
|
|
np.arange(points.shape[0]))
|
|
assert_array_equal(np.unique(obj2.simplices.ravel()),
|
|
np.arange(points.shape[0]))
|
|
else:
|
|
assert_unordered_tuple_list_equal(obj.simplices, obj2.simplices,
|
|
tpl=sorted_tuple)
|
|
|
|
assert_unordered_tuple_list_equal(obj2.simplices, obj3.simplices,
|
|
tpl=sorted_tuple)
|
|
|
|
|
|
def assert_hulls_equal(points, facets_1, facets_2):
|
|
# Check that two convex hulls constructed from the same point set
|
|
# are equal
|
|
|
|
facets_1 = set(map(sorted_tuple, facets_1))
|
|
facets_2 = set(map(sorted_tuple, facets_2))
|
|
|
|
if facets_1 != facets_2 and points.shape[1] == 2:
|
|
# The direct check fails for the pathological cases
|
|
# --- then the convex hull from Delaunay differs (due
|
|
# to rounding error etc.) from the hull computed
|
|
# otherwise, by the question whether (tricoplanar)
|
|
# points that lie almost exactly on the hull are
|
|
# included as vertices of the hull or not.
|
|
#
|
|
# So we check the result, and accept it if the Delaunay
|
|
# hull line segments are a subset of the usual hull.
|
|
|
|
eps = 1000 * np.finfo(float).eps
|
|
|
|
for a, b in facets_1:
|
|
for ap, bp in facets_2:
|
|
t = points[bp] - points[ap]
|
|
t /= np.linalg.norm(t) # tangent
|
|
n = np.array([-t[1], t[0]]) # normal
|
|
|
|
# check that the two line segments are parallel
|
|
# to the same line
|
|
c1 = np.dot(n, points[b] - points[ap])
|
|
c2 = np.dot(n, points[a] - points[ap])
|
|
if not np.allclose(np.dot(c1, n), 0):
|
|
continue
|
|
if not np.allclose(np.dot(c2, n), 0):
|
|
continue
|
|
|
|
# Check that the segment (a, b) is contained in (ap, bp)
|
|
c1 = np.dot(t, points[a] - points[ap])
|
|
c2 = np.dot(t, points[b] - points[ap])
|
|
c3 = np.dot(t, points[bp] - points[ap])
|
|
if c1 < -eps or c1 > c3 + eps:
|
|
continue
|
|
if c2 < -eps or c2 > c3 + eps:
|
|
continue
|
|
|
|
# OK:
|
|
break
|
|
else:
|
|
raise AssertionError("comparison fails")
|
|
|
|
# it was OK
|
|
return
|
|
|
|
assert_equal(facets_1, facets_2)
|
|
|
|
|
|
class TestConvexHull:
|
|
def test_masked_array_fails(self):
|
|
masked_array = np.ma.masked_all(1)
|
|
assert_raises(ValueError, qhull.ConvexHull, masked_array)
|
|
|
|
def test_array_with_nans_fails(self):
|
|
points_with_nan = np.array([(0,0), (1,1), (2,np.nan)], dtype=np.double)
|
|
assert_raises(ValueError, qhull.ConvexHull, points_with_nan)
|
|
|
|
@pytest.mark.parametrize("name", sorted(DATASETS))
|
|
def test_hull_consistency_tri(self, name):
|
|
# Check that a convex hull returned by qhull in ndim
|
|
# and the hull constructed from ndim delaunay agree
|
|
points = DATASETS[name]
|
|
|
|
tri = qhull.Delaunay(points)
|
|
hull = qhull.ConvexHull(points)
|
|
|
|
assert_hulls_equal(points, tri.convex_hull, hull.simplices)
|
|
|
|
# Check that the hull extremes are as expected
|
|
if points.shape[1] == 2:
|
|
assert_equal(np.unique(hull.simplices), np.sort(hull.vertices))
|
|
else:
|
|
assert_equal(np.unique(hull.simplices), hull.vertices)
|
|
|
|
@pytest.mark.parametrize("name", sorted(INCREMENTAL_DATASETS))
|
|
def test_incremental(self, name):
|
|
# Test incremental construction of the convex hull
|
|
chunks, _ = INCREMENTAL_DATASETS[name]
|
|
points = np.concatenate(chunks, axis=0)
|
|
|
|
obj = qhull.ConvexHull(chunks[0], incremental=True)
|
|
for chunk in chunks[1:]:
|
|
obj.add_points(chunk)
|
|
|
|
obj2 = qhull.ConvexHull(points)
|
|
|
|
obj3 = qhull.ConvexHull(chunks[0], incremental=True)
|
|
if len(chunks) > 1:
|
|
obj3.add_points(np.concatenate(chunks[1:], axis=0),
|
|
restart=True)
|
|
|
|
# Check that the incremental mode agrees with upfront mode
|
|
assert_hulls_equal(points, obj.simplices, obj2.simplices)
|
|
assert_hulls_equal(points, obj.simplices, obj3.simplices)
|
|
|
|
def test_vertices_2d(self):
|
|
# The vertices should be in counterclockwise order in 2-D
|
|
np.random.seed(1234)
|
|
points = np.random.rand(30, 2)
|
|
|
|
hull = qhull.ConvexHull(points)
|
|
assert_equal(np.unique(hull.simplices), np.sort(hull.vertices))
|
|
|
|
# Check counterclockwiseness
|
|
x, y = hull.points[hull.vertices].T
|
|
angle = np.arctan2(y - y.mean(), x - x.mean())
|
|
assert_(np.all(np.diff(np.unwrap(angle)) > 0))
|
|
|
|
def test_volume_area(self):
|
|
# Basic check that we get back the correct volume and area for a cube
|
|
points = np.array([(0, 0, 0), (0, 1, 0), (1, 0, 0), (1, 1, 0),
|
|
(0, 0, 1), (0, 1, 1), (1, 0, 1), (1, 1, 1)])
|
|
tri = qhull.ConvexHull(points)
|
|
|
|
assert_allclose(tri.volume, 1., rtol=1e-14)
|
|
assert_allclose(tri.area, 6., rtol=1e-14)
|
|
|
|
@pytest.mark.parametrize("incremental", [False, True])
|
|
def test_good2d(self, incremental):
|
|
# Make sure the QGn option gives the correct value of "good".
|
|
points = np.array([[0.2, 0.2],
|
|
[0.2, 0.4],
|
|
[0.4, 0.4],
|
|
[0.4, 0.2],
|
|
[0.3, 0.6]])
|
|
hull = qhull.ConvexHull(points=points,
|
|
incremental=incremental,
|
|
qhull_options='QG4')
|
|
expected = np.array([False, True, False, False], dtype=bool)
|
|
actual = hull.good
|
|
assert_equal(actual, expected)
|
|
|
|
@pytest.mark.parametrize("visibility", [
|
|
"QG4", # visible=True
|
|
"QG-4", # visible=False
|
|
])
|
|
@pytest.mark.parametrize("new_gen, expected", [
|
|
# add generator that places QG4 inside hull
|
|
# so all facets are invisible
|
|
(np.array([[0.3, 0.7]]),
|
|
np.array([False, False, False, False, False], dtype=bool)),
|
|
# adding a generator on the opposite side of the square
|
|
# should preserve the single visible facet & add one invisible
|
|
# facet
|
|
(np.array([[0.3, -0.7]]),
|
|
np.array([False, True, False, False, False], dtype=bool)),
|
|
# split the visible facet on top of the square into two
|
|
# visible facets, with visibility at the end of the array
|
|
# because add_points concatenates
|
|
(np.array([[0.3, 0.41]]),
|
|
np.array([False, False, False, True, True], dtype=bool)),
|
|
# with our current Qhull options, coplanarity will not count
|
|
# for visibility; this case shifts one visible & one invisible
|
|
# facet & adds a coplanar facet
|
|
# simplex at index position 2 is the shifted visible facet
|
|
# the final simplex is the coplanar facet
|
|
(np.array([[0.5, 0.6], [0.6, 0.6]]),
|
|
np.array([False, False, True, False, False], dtype=bool)),
|
|
# place the new generator such that it envelops the query
|
|
# point within the convex hull, but only just barely within
|
|
# the double precision limit
|
|
# NOTE: testing exact degeneracy is less predictable than this
|
|
# scenario, perhaps because of the default Qt option we have
|
|
# enabled for Qhull to handle precision matters
|
|
(np.array([[0.3, 0.6 + 1e-16]]),
|
|
np.array([False, False, False, False, False], dtype=bool)),
|
|
])
|
|
def test_good2d_incremental_changes(self, new_gen, expected,
|
|
visibility):
|
|
# use the usual square convex hull
|
|
# generators from test_good2d
|
|
points = np.array([[0.2, 0.2],
|
|
[0.2, 0.4],
|
|
[0.4, 0.4],
|
|
[0.4, 0.2],
|
|
[0.3, 0.6]])
|
|
hull = qhull.ConvexHull(points=points,
|
|
incremental=True,
|
|
qhull_options=visibility)
|
|
hull.add_points(new_gen)
|
|
actual = hull.good
|
|
if '-' in visibility:
|
|
expected = np.invert(expected)
|
|
assert_equal(actual, expected)
|
|
|
|
@pytest.mark.parametrize("incremental", [False, True])
|
|
def test_good2d_no_option(self, incremental):
|
|
# handle case where good attribue doesn't exist
|
|
# because Qgn or Qg-n wasn't specified
|
|
points = np.array([[0.2, 0.2],
|
|
[0.2, 0.4],
|
|
[0.4, 0.4],
|
|
[0.4, 0.2],
|
|
[0.3, 0.6]])
|
|
hull = qhull.ConvexHull(points=points,
|
|
incremental=incremental)
|
|
actual = hull.good
|
|
assert actual is None
|
|
# preserve None after incremental addition
|
|
if incremental:
|
|
hull.add_points(np.zeros((1, 2)))
|
|
actual = hull.good
|
|
assert actual is None
|
|
|
|
@pytest.mark.parametrize("incremental", [False, True])
|
|
def test_good2d_inside(self, incremental):
|
|
# Make sure the QGn option gives the correct value of "good".
|
|
# When point n is inside the convex hull of the rest, good is
|
|
# all False.
|
|
points = np.array([[0.2, 0.2],
|
|
[0.2, 0.4],
|
|
[0.4, 0.4],
|
|
[0.4, 0.2],
|
|
[0.3, 0.3]])
|
|
hull = qhull.ConvexHull(points=points,
|
|
incremental=incremental,
|
|
qhull_options='QG4')
|
|
expected = np.array([False, False, False, False], dtype=bool)
|
|
actual = hull.good
|
|
assert_equal(actual, expected)
|
|
|
|
@pytest.mark.parametrize("incremental", [False, True])
|
|
def test_good3d(self, incremental):
|
|
# Make sure the QGn option gives the correct value of "good"
|
|
# for a 3d figure
|
|
points = np.array([[0.0, 0.0, 0.0],
|
|
[0.90029516, -0.39187448, 0.18948093],
|
|
[0.48676420, -0.72627633, 0.48536925],
|
|
[0.57651530, -0.81179274, -0.09285832],
|
|
[0.67846893, -0.71119562, 0.18406710]])
|
|
hull = qhull.ConvexHull(points=points,
|
|
incremental=incremental,
|
|
qhull_options='QG0')
|
|
expected = np.array([True, False, False, False], dtype=bool)
|
|
assert_equal(hull.good, expected)
|
|
|
|
class TestVoronoi:
|
|
def test_masked_array_fails(self):
|
|
masked_array = np.ma.masked_all(1)
|
|
assert_raises(ValueError, qhull.Voronoi, masked_array)
|
|
|
|
def test_simple(self):
|
|
# Simple case with known Voronoi diagram
|
|
points = [(0, 0), (0, 1), (0, 2),
|
|
(1, 0), (1, 1), (1, 2),
|
|
(2, 0), (2, 1), (2, 2)]
|
|
|
|
# qhull v o Fv Qbb Qc Qz < dat
|
|
output = """
|
|
2
|
|
5 10 1
|
|
-10.101 -10.101
|
|
0.5 0.5
|
|
0.5 1.5
|
|
1.5 0.5
|
|
1.5 1.5
|
|
2 0 1
|
|
3 2 0 1
|
|
2 0 2
|
|
3 3 0 1
|
|
4 1 2 4 3
|
|
3 4 0 2
|
|
2 0 3
|
|
3 4 0 3
|
|
2 0 4
|
|
0
|
|
12
|
|
4 0 3 0 1
|
|
4 0 1 0 1
|
|
4 1 4 1 2
|
|
4 1 2 0 2
|
|
4 2 5 0 2
|
|
4 3 4 1 3
|
|
4 3 6 0 3
|
|
4 4 5 2 4
|
|
4 4 7 3 4
|
|
4 5 8 0 4
|
|
4 6 7 0 3
|
|
4 7 8 0 4
|
|
"""
|
|
self._compare_qvoronoi(points, output)
|
|
|
|
def _compare_qvoronoi(self, points, output, **kw):
|
|
"""Compare to output from 'qvoronoi o Fv < data' to Voronoi()"""
|
|
|
|
# Parse output
|
|
output = [list(map(float, x.split())) for x in output.strip().splitlines()]
|
|
nvertex = int(output[1][0])
|
|
vertices = list(map(tuple, output[3:2+nvertex])) # exclude inf
|
|
nregion = int(output[1][1])
|
|
regions = [[int(y)-1 for y in x[1:]]
|
|
for x in output[2+nvertex:2+nvertex+nregion]]
|
|
nridge = int(output[2+nvertex+nregion][0])
|
|
ridge_points = [[int(y) for y in x[1:3]]
|
|
for x in output[3+nvertex+nregion:]]
|
|
ridge_vertices = [[int(y)-1 for y in x[3:]]
|
|
for x in output[3+nvertex+nregion:]]
|
|
|
|
# Compare results
|
|
vor = qhull.Voronoi(points, **kw)
|
|
|
|
def sorttuple(x):
|
|
return tuple(sorted(x))
|
|
|
|
assert_allclose(vor.vertices, vertices)
|
|
assert_equal(set(map(tuple, vor.regions)),
|
|
set(map(tuple, regions)))
|
|
|
|
p1 = list(zip(list(map(sorttuple, ridge_points)), list(map(sorttuple, ridge_vertices))))
|
|
p2 = list(zip(list(map(sorttuple, vor.ridge_points.tolist())),
|
|
list(map(sorttuple, vor.ridge_vertices))))
|
|
p1.sort()
|
|
p2.sort()
|
|
|
|
assert_equal(p1, p2)
|
|
|
|
@pytest.mark.parametrize("name", sorted(DATASETS))
|
|
def test_ridges(self, name):
|
|
# Check that the ridges computed by Voronoi indeed separate
|
|
# the regions of nearest neighborhood, by comparing the result
|
|
# to KDTree.
|
|
|
|
points = DATASETS[name]
|
|
|
|
tree = KDTree(points)
|
|
vor = qhull.Voronoi(points)
|
|
|
|
for p, v in vor.ridge_dict.items():
|
|
# consider only finite ridges
|
|
if not np.all(np.asarray(v) >= 0):
|
|
continue
|
|
|
|
ridge_midpoint = vor.vertices[v].mean(axis=0)
|
|
d = 1e-6 * (points[p[0]] - ridge_midpoint)
|
|
|
|
dist, k = tree.query(ridge_midpoint + d, k=1)
|
|
assert_equal(k, p[0])
|
|
|
|
dist, k = tree.query(ridge_midpoint - d, k=1)
|
|
assert_equal(k, p[1])
|
|
|
|
def test_furthest_site(self):
|
|
points = [(0, 0), (0, 1), (1, 0), (0.5, 0.5), (1.1, 1.1)]
|
|
|
|
# qhull v o Fv Qbb Qc Qu < dat
|
|
output = """
|
|
2
|
|
3 5 1
|
|
-10.101 -10.101
|
|
0.6000000000000001 0.5
|
|
0.5 0.6000000000000001
|
|
3 0 2 1
|
|
2 0 1
|
|
2 0 2
|
|
0
|
|
3 0 2 1
|
|
5
|
|
4 0 2 0 2
|
|
4 0 4 1 2
|
|
4 0 1 0 1
|
|
4 1 4 0 1
|
|
4 2 4 0 2
|
|
"""
|
|
self._compare_qvoronoi(points, output, furthest_site=True)
|
|
|
|
def test_furthest_site_flag(self):
|
|
points = [(0, 0), (0, 1), (1, 0), (0.5, 0.5), (1.1, 1.1)]
|
|
|
|
vor = Voronoi(points)
|
|
assert_equal(vor.furthest_site,False)
|
|
vor = Voronoi(points,furthest_site=True)
|
|
assert_equal(vor.furthest_site,True)
|
|
|
|
@pytest.mark.parametrize("name", sorted(INCREMENTAL_DATASETS))
|
|
def test_incremental(self, name):
|
|
# Test incremental construction of the triangulation
|
|
|
|
if INCREMENTAL_DATASETS[name][0][0].shape[1] > 3:
|
|
# too slow (testing of the result --- qhull is still fast)
|
|
return
|
|
|
|
chunks, opts = INCREMENTAL_DATASETS[name]
|
|
points = np.concatenate(chunks, axis=0)
|
|
|
|
obj = qhull.Voronoi(chunks[0], incremental=True,
|
|
qhull_options=opts)
|
|
for chunk in chunks[1:]:
|
|
obj.add_points(chunk)
|
|
|
|
obj2 = qhull.Voronoi(points)
|
|
|
|
obj3 = qhull.Voronoi(chunks[0], incremental=True,
|
|
qhull_options=opts)
|
|
if len(chunks) > 1:
|
|
obj3.add_points(np.concatenate(chunks[1:], axis=0),
|
|
restart=True)
|
|
|
|
# -- Check that the incremental mode agrees with upfront mode
|
|
assert_equal(len(obj.point_region), len(obj2.point_region))
|
|
assert_equal(len(obj.point_region), len(obj3.point_region))
|
|
|
|
# The vertices may be in different order or duplicated in
|
|
# the incremental map
|
|
for objx in obj, obj3:
|
|
vertex_map = {-1: -1}
|
|
for i, v in enumerate(objx.vertices):
|
|
for j, v2 in enumerate(obj2.vertices):
|
|
if np.allclose(v, v2):
|
|
vertex_map[i] = j
|
|
|
|
def remap(x):
|
|
if hasattr(x, '__len__'):
|
|
return tuple(set([remap(y) for y in x]))
|
|
try:
|
|
return vertex_map[x]
|
|
except KeyError:
|
|
raise AssertionError("incremental result has spurious vertex at %r"
|
|
% (objx.vertices[x],))
|
|
|
|
def simplified(x):
|
|
items = set(map(sorted_tuple, x))
|
|
if () in items:
|
|
items.remove(())
|
|
items = [x for x in items if len(x) > 1]
|
|
items.sort()
|
|
return items
|
|
|
|
assert_equal(
|
|
simplified(remap(objx.regions)),
|
|
simplified(obj2.regions)
|
|
)
|
|
assert_equal(
|
|
simplified(remap(objx.ridge_vertices)),
|
|
simplified(obj2.ridge_vertices)
|
|
)
|
|
|
|
# XXX: compare ridge_points --- not clear exactly how to do this
|
|
|
|
|
|
class Test_HalfspaceIntersection(object):
|
|
def assert_unordered_allclose(self, arr1, arr2, rtol=1e-7):
|
|
"""Check that every line in arr1 is only once in arr2"""
|
|
assert_equal(arr1.shape, arr2.shape)
|
|
|
|
truths = np.zeros((arr1.shape[0],), dtype=bool)
|
|
for l1 in arr1:
|
|
indexes = np.nonzero((abs(arr2 - l1) < rtol).all(axis=1))[0]
|
|
assert_equal(indexes.shape, (1,))
|
|
truths[indexes[0]] = True
|
|
assert_(truths.all())
|
|
|
|
def test_cube_halfspace_intersection(self):
|
|
halfspaces = np.array([[-1.0, 0.0, 0.0],
|
|
[0.0, -1.0, 0.0],
|
|
[1.0, 0.0, -1.0],
|
|
[0.0, 1.0, -1.0]])
|
|
feasible_point = np.array([0.5, 0.5])
|
|
|
|
points = np.array([[0.0, 0.0], [1.0, 0.0], [0.0, 1.0], [1.0, 1.0]])
|
|
|
|
hull = qhull.HalfspaceIntersection(halfspaces, feasible_point)
|
|
|
|
assert_allclose(hull.intersections, points)
|
|
|
|
def test_self_dual_polytope_intersection(self):
|
|
fname = os.path.join(os.path.dirname(__file__), 'data',
|
|
'selfdual-4d-polytope.txt')
|
|
ineqs = np.genfromtxt(fname)
|
|
halfspaces = -np.hstack((ineqs[:, 1:], ineqs[:, :1]))
|
|
|
|
feas_point = np.array([0., 0., 0., 0.])
|
|
hs = qhull.HalfspaceIntersection(halfspaces, feas_point)
|
|
|
|
assert_equal(hs.intersections.shape, (24, 4))
|
|
|
|
assert_almost_equal(hs.dual_volume, 32.0)
|
|
assert_equal(len(hs.dual_facets), 24)
|
|
for facet in hs.dual_facets:
|
|
assert_equal(len(facet), 6)
|
|
|
|
dists = halfspaces[:, -1] + halfspaces[:, :-1].dot(feas_point)
|
|
self.assert_unordered_allclose((halfspaces[:, :-1].T/dists).T, hs.dual_points)
|
|
|
|
points = itertools.permutations([0., 0., 0.5, -0.5])
|
|
for point in points:
|
|
assert_equal(np.sum((hs.intersections == point).all(axis=1)), 1)
|
|
|
|
def test_wrong_feasible_point(self):
|
|
halfspaces = np.array([[-1.0, 0.0, 0.0],
|
|
[0.0, -1.0, 0.0],
|
|
[1.0, 0.0, -1.0],
|
|
[0.0, 1.0, -1.0]])
|
|
feasible_point = np.array([0.5, 0.5, 0.5])
|
|
#Feasible point is (ndim,) instead of (ndim-1,)
|
|
assert_raises(ValueError, qhull.HalfspaceIntersection, halfspaces, feasible_point)
|
|
feasible_point = np.array([[0.5], [0.5]])
|
|
#Feasible point is (ndim-1, 1) instead of (ndim-1,)
|
|
assert_raises(ValueError, qhull.HalfspaceIntersection, halfspaces, feasible_point)
|
|
feasible_point = np.array([[0.5, 0.5]])
|
|
#Feasible point is (1, ndim-1) instead of (ndim-1,)
|
|
assert_raises(ValueError, qhull.HalfspaceIntersection, halfspaces, feasible_point)
|
|
|
|
feasible_point = np.array([-0.5, -0.5])
|
|
#Feasible point is outside feasible region
|
|
assert_raises(qhull.QhullError, qhull.HalfspaceIntersection, halfspaces, feasible_point)
|
|
|
|
def test_incremental(self):
|
|
#Cube
|
|
halfspaces = np.array([[0., 0., -1., -0.5],
|
|
[0., -1., 0., -0.5],
|
|
[-1., 0., 0., -0.5],
|
|
[1., 0., 0., -0.5],
|
|
[0., 1., 0., -0.5],
|
|
[0., 0., 1., -0.5]])
|
|
#Cut each summit
|
|
extra_normals = np.array([[1., 1., 1.],
|
|
[1., 1., -1.],
|
|
[1., -1., 1.],
|
|
[1, -1., -1.]])
|
|
offsets = np.array([[-1.]]*8)
|
|
extra_halfspaces = np.hstack((np.vstack((extra_normals, -extra_normals)),
|
|
offsets))
|
|
|
|
feas_point = np.array([0., 0., 0.])
|
|
|
|
inc_hs = qhull.HalfspaceIntersection(halfspaces, feas_point, incremental=True)
|
|
|
|
inc_res_hs = qhull.HalfspaceIntersection(halfspaces, feas_point, incremental=True)
|
|
|
|
for i, ehs in enumerate(extra_halfspaces):
|
|
inc_hs.add_halfspaces(ehs[np.newaxis, :])
|
|
|
|
inc_res_hs.add_halfspaces(ehs[np.newaxis, :], restart=True)
|
|
|
|
total = np.vstack((halfspaces, extra_halfspaces[:i+1, :]))
|
|
|
|
hs = qhull.HalfspaceIntersection(total, feas_point)
|
|
|
|
assert_allclose(inc_hs.halfspaces, inc_res_hs.halfspaces)
|
|
assert_allclose(inc_hs.halfspaces, hs.halfspaces)
|
|
|
|
#Direct computation and restart should have points in same order
|
|
assert_allclose(hs.intersections, inc_res_hs.intersections)
|
|
#Incremental will have points in different order than direct computation
|
|
self.assert_unordered_allclose(inc_hs.intersections, hs.intersections)
|
|
|
|
inc_hs.close()
|
|
|
|
def test_cube(self):
|
|
# Halfspaces of the cube:
|
|
halfspaces = np.array([[-1., 0., 0., 0.], # x >= 0
|
|
[1., 0., 0., -1.], # x <= 1
|
|
[0., -1., 0., 0.], # y >= 0
|
|
[0., 1., 0., -1.], # y <= 1
|
|
[0., 0., -1., 0.], # z >= 0
|
|
[0., 0., 1., -1.]]) # z <= 1
|
|
point = np.array([0.5, 0.5, 0.5])
|
|
|
|
hs = qhull.HalfspaceIntersection(halfspaces, point)
|
|
|
|
# qhalf H0.5,0.5,0.5 o < input.txt
|
|
qhalf_points = np.array([
|
|
[-2, 0, 0],
|
|
[2, 0, 0],
|
|
[0, -2, 0],
|
|
[0, 2, 0],
|
|
[0, 0, -2],
|
|
[0, 0, 2]])
|
|
qhalf_facets = [
|
|
[2, 4, 0],
|
|
[4, 2, 1],
|
|
[5, 2, 0],
|
|
[2, 5, 1],
|
|
[3, 4, 1],
|
|
[4, 3, 0],
|
|
[5, 3, 1],
|
|
[3, 5, 0]]
|
|
|
|
assert len(qhalf_facets) == len(hs.dual_facets)
|
|
for a, b in zip(qhalf_facets, hs.dual_facets):
|
|
assert set(a) == set(b) # facet orientation can differ
|
|
|
|
assert_allclose(hs.dual_points, qhalf_points)
|