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NelleVNelleV
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PEP8 compliance on the delaunay module
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‎lib/matplotlib/delaunay/interpolate.py‎

Lines changed: 31 additions & 24 deletions
Original file line numberDiff line numberDiff line change
@@ -1,11 +1,13 @@
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from __future__ import print_function
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import numpy as np
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from matplotlib._delaunay import compute_planes, linear_interpolate_grid, nn_interpolate_grid
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from matplotlib._delaunay import compute_planes, linear_interpolate_grid
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from matplotlib._delaunay import nn_interpolate_grid
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from matplotlib._delaunay import nn_interpolate_unstructured
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__all__ = ['LinearInterpolator', 'NNInterpolator']
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def slice2gridspec(key):
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"""Convert a 2-tuple of slices to start,stop,steps for x and y.
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@@ -34,6 +36,7 @@ def slice2gridspec(key):
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return x0, x1, xstep, y0, y1, ystep
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class LinearInterpolator(object):
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"""Interpolate a function defined on the nodes of a triangulation by
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using the planes defined by the three function values at each corner of
@@ -60,10 +63,10 @@ class LinearInterpolator(object):
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Linear Interpolation
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--------------------
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Given the Delauany triangulation (or indeed *any* complete triangulation) we
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can interpolate values inside the convex hull by locating the enclosing
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triangle of the interpolation point and returning the value at that point of
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the plane defined by the three node values.
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Given the Delauany triangulation (or indeed *any* complete triangulation)
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we can interpolate values inside the convex hull by locating the enclosing
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triangle of the interpolation point and returning the value at that point
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of the plane defined by the three node values.
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f = planes[tri,0]*x + planes[tri,1]*y + planes[tri,2]
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@@ -81,11 +84,14 @@ def __init__(self, triangulation, z, default_value=np.nan):
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def __getitem__(self, key):
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x0, x1, xstep, y0, y1, ystep = slice2gridspec(key)
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grid = linear_interpolate_grid(x0, x1, xstep, y0, y1, ystep, self.default_value,
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grid = linear_interpolate_grid(
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x0, x1, xstep, y0, y1, ystep, self.default_value,
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self.planes, self.triangulation.x, self.triangulation.y,
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self.triangulation.triangle_nodes, self.triangulation.triangle_neighbors)
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self.triangulation.triangle_nodes,
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self.triangulation.triangle_neighbors)
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return grid
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class NNInterpolator(object):
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"""Interpolate a function defined on the nodes of a triangulation by
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the natural neighbors method.
@@ -109,23 +115,23 @@ class NNInterpolator(object):
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-------------------------------
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One feature of the Delaunay triangulation is that for each triangle, its
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circumcircle contains no other point (although in degenerate cases, like
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squares, other points may be *on* the circumcircle). One can also construct
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what is called the Voronoi diagram from a Delaunay triangulation by
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connecting the circumcenters of the triangles to those of their neighbors to
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form a tesselation of irregular polygons covering the plane and containing
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only one node from the triangulation. Each point in one node's Voronoi
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polygon is closer to that node than any other node.
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squares, other points may be *on* the circumcircle). One can also
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construct what is called the Voronoi diagram from a Delaunay triangulation
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by connecting the circumcenters of the triangles to those of their
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neighbors to form a tesselation of irregular polygons covering the plane
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and containing only one node from the triangulation. Each point in one
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node's Voronoi polygon is closer to that node than any other node.
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To compute the Natural Neighbors interpolant, we consider adding the
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interpolation point to the triangulation. We define the natural neighbors of
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this point as the set of nodes participating in Delaunay triangles whose
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circumcircles contain the point. To restore the Delaunay-ness of the
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interpolation point to the triangulation. We define the natural neighbors
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of this point as the set of nodes participating in Delaunay triangles
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whose circumcircles contain the point. To restore the Delaunay-ness of the
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triangulation, one would only have to alter those triangles and Voronoi
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polygons. The new Voronooi diagram would have a polygon around the inserted
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point. This polygon would "steal" area from the original Voronoi polygons.
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For each node i in the natural neighbors set, we compute the area stolen
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from its original Voronoi polygon, stolen[i]. We define the natural
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neighbors coordinates
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polygons. The new Voronoi diagram would have a polygon around the
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inserted point. This polygon would "steal" area from the original Voronoi
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polygons. For each node i in the natural neighbors set, we compute the
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area stolen from its original Voronoi polygon, stolen[i]. We define the
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natural neighbors coordinates
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phi[i] = stolen[i] / sum(stolen,axis=0)
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@@ -134,8 +140,8 @@ class NNInterpolator(object):
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The interpolated surface is C1-continuous except at the nodes themselves
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across the convex hull of the input points. One can find the set of points
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that a given node will affect by computing the union of the areas covered by
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the circumcircles of each Delaunay triangle that node participates in.
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that a given node will affect by computing the union of the areas covered
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by the circumcircles of each Delaunay triangle that node participates in.
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"""
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def __init__(self, triangulation, z, default_value=np.nan):
@@ -145,7 +151,8 @@ def __init__(self, triangulation, z, default_value=np.nan):
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def __getitem__(self, key):
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x0, x1, xstep, y0, y1, ystep = slice2gridspec(key)
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grid = nn_interpolate_grid(x0, x1, xstep, y0, y1, ystep, self.default_value,
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grid = nn_interpolate_grid(
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x0, x1, xstep, y0, y1, ystep, self.default_value,
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self.triangulation.x, self.triangulation.y, self.z,
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self.triangulation.circumcenters,
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self.triangulation.triangle_nodes,

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