94 lines
3.4 KiB
Python
94 lines
3.4 KiB
Python
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import numpy as np
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from matplotlib import cbook
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from matplotlib.tri import Triangulation
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class TriFinder:
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"""
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Abstract base class for classes used to find the triangles of a
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Triangulation in which (x, y) points lie.
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Rather than instantiate an object of a class derived from TriFinder, it is
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usually better to use the function `.Triangulation.get_trifinder`.
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Derived classes implement __call__(x, y) where x and y are array-like point
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coordinates of the same shape.
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"""
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def __init__(self, triangulation):
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cbook._check_isinstance(Triangulation, triangulation=triangulation)
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self._triangulation = triangulation
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class TrapezoidMapTriFinder(TriFinder):
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"""
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`~matplotlib.tri.TriFinder` class implemented using the trapezoid
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map algorithm from the book "Computational Geometry, Algorithms and
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Applications", second edition, by M. de Berg, M. van Kreveld, M. Overmars
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and O. Schwarzkopf.
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The triangulation must be valid, i.e. it must not have duplicate points,
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triangles formed from colinear points, or overlapping triangles. The
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algorithm has some tolerance to triangles formed from colinear points, but
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this should not be relied upon.
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"""
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def __init__(self, triangulation):
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from matplotlib import _tri
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TriFinder.__init__(self, triangulation)
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self._cpp_trifinder = _tri.TrapezoidMapTriFinder(
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triangulation.get_cpp_triangulation())
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self._initialize()
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def __call__(self, x, y):
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"""
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Return an array containing the indices of the triangles in which the
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specified *x*, *y* points lie, or -1 for points that do not lie within
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a triangle.
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*x*, *y* are array-like x and y coordinates of the same shape and any
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number of dimensions.
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Returns integer array with the same shape and *x* and *y*.
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"""
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x = np.asarray(x, dtype=np.float64)
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y = np.asarray(y, dtype=np.float64)
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if x.shape != y.shape:
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raise ValueError("x and y must be array-like with the same shape")
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# C++ does the heavy lifting, and expects 1D arrays.
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indices = (self._cpp_trifinder.find_many(x.ravel(), y.ravel())
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.reshape(x.shape))
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return indices
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def _get_tree_stats(self):
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"""
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Return a python list containing the statistics about the node tree:
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0: number of nodes (tree size)
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1: number of unique nodes
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2: number of trapezoids (tree leaf nodes)
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3: number of unique trapezoids
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4: maximum parent count (max number of times a node is repeated in
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tree)
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5: maximum depth of tree (one more than the maximum number of
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comparisons needed to search through the tree)
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6: mean of all trapezoid depths (one more than the average number
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of comparisons needed to search through the tree)
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"""
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return self._cpp_trifinder.get_tree_stats()
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def _initialize(self):
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"""
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Initialize the underlying C++ object. Can be called multiple times if,
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for example, the triangulation is modified.
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"""
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self._cpp_trifinder.initialize()
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def _print_tree(self):
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"""
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Print a text representation of the node tree, which is useful for
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debugging purposes.
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"""
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self._cpp_trifinder.print_tree()
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