271 lines
7.1 KiB
Python
271 lines
7.1 KiB
Python
import numpy as np
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from scipy._lib.decorator import decorator as _decorator
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__all__ = ['delaunay_plot_2d', 'convex_hull_plot_2d', 'voronoi_plot_2d']
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@_decorator
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def _held_figure(func, obj, ax=None, **kw):
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import matplotlib.pyplot as plt
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if ax is None:
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fig = plt.figure()
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ax = fig.gca()
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return func(obj, ax=ax, **kw)
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# As of matplotlib 2.0, the "hold" mechanism is deprecated.
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# When matplotlib 1.x is no longer supported, this check can be removed.
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was_held = getattr(ax, 'ishold', lambda: True)()
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if was_held:
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return func(obj, ax=ax, **kw)
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try:
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ax.hold(True)
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return func(obj, ax=ax, **kw)
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finally:
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ax.hold(was_held)
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def _adjust_bounds(ax, points):
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margin = 0.1 * np.ptp(points, axis=0)
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xy_min = points.min(axis=0) - margin
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xy_max = points.max(axis=0) + margin
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ax.set_xlim(xy_min[0], xy_max[0])
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ax.set_ylim(xy_min[1], xy_max[1])
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@_held_figure
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def delaunay_plot_2d(tri, ax=None):
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"""
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Plot the given Delaunay triangulation in 2-D
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Parameters
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----------
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tri : scipy.spatial.Delaunay instance
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Triangulation to plot
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ax : matplotlib.axes.Axes instance, optional
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Axes to plot on
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Returns
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-------
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fig : matplotlib.figure.Figure instance
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Figure for the plot
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See Also
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--------
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Delaunay
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matplotlib.pyplot.triplot
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Notes
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-----
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Requires Matplotlib.
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Examples
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--------
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>>> import numpy as np
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>>> import matplotlib.pyplot as plt
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>>> from scipy.spatial import Delaunay, delaunay_plot_2d
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The Delaunay triangulation of a set of random points:
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>>> rng = np.random.default_rng()
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>>> points = rng.random((30, 2))
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>>> tri = Delaunay(points)
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Plot it:
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>>> _ = delaunay_plot_2d(tri)
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>>> plt.show()
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"""
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if tri.points.shape[1] != 2:
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raise ValueError("Delaunay triangulation is not 2-D")
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x, y = tri.points.T
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ax.plot(x, y, 'o')
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ax.triplot(x, y, tri.simplices.copy())
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_adjust_bounds(ax, tri.points)
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return ax.figure
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@_held_figure
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def convex_hull_plot_2d(hull, ax=None):
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"""
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Plot the given convex hull diagram in 2-D
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Parameters
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----------
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hull : scipy.spatial.ConvexHull instance
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Convex hull to plot
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ax : matplotlib.axes.Axes instance, optional
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Axes to plot on
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Returns
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-------
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fig : matplotlib.figure.Figure instance
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Figure for the plot
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See Also
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--------
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ConvexHull
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Notes
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-----
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Requires Matplotlib.
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Examples
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--------
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>>> import numpy as np
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>>> import matplotlib.pyplot as plt
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>>> from scipy.spatial import ConvexHull, convex_hull_plot_2d
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The convex hull of a random set of points:
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>>> rng = np.random.default_rng()
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>>> points = rng.random((30, 2))
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>>> hull = ConvexHull(points)
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Plot it:
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>>> _ = convex_hull_plot_2d(hull)
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>>> plt.show()
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"""
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from matplotlib.collections import LineCollection
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if hull.points.shape[1] != 2:
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raise ValueError("Convex hull is not 2-D")
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ax.plot(hull.points[:, 0], hull.points[:, 1], 'o')
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line_segments = [hull.points[simplex] for simplex in hull.simplices]
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ax.add_collection(LineCollection(line_segments,
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colors='k',
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linestyle='solid'))
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_adjust_bounds(ax, hull.points)
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return ax.figure
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@_held_figure
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def voronoi_plot_2d(vor, ax=None, **kw):
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"""
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Plot the given Voronoi diagram in 2-D
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Parameters
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----------
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vor : scipy.spatial.Voronoi instance
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Diagram to plot
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ax : matplotlib.axes.Axes instance, optional
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Axes to plot on
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show_points : bool, optional
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Add the Voronoi points to the plot.
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show_vertices : bool, optional
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Add the Voronoi vertices to the plot.
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line_colors : string, optional
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Specifies the line color for polygon boundaries
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line_width : float, optional
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Specifies the line width for polygon boundaries
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line_alpha : float, optional
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Specifies the line alpha for polygon boundaries
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point_size : float, optional
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Specifies the size of points
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Returns
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-------
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fig : matplotlib.figure.Figure instance
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Figure for the plot
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See Also
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--------
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Voronoi
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Notes
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-----
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Requires Matplotlib.
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Examples
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--------
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>>> import numpy as np
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>>> import matplotlib.pyplot as plt
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>>> from scipy.spatial import Voronoi, voronoi_plot_2d
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Create a set of points for the example:
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>>> rng = np.random.default_rng()
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>>> points = rng.random((10,2))
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Generate the Voronoi diagram for the points:
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>>> vor = Voronoi(points)
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Use `voronoi_plot_2d` to plot the diagram:
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>>> fig = voronoi_plot_2d(vor)
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Use `voronoi_plot_2d` to plot the diagram again, with some settings
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customized:
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>>> fig = voronoi_plot_2d(vor, show_vertices=False, line_colors='orange',
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... line_width=2, line_alpha=0.6, point_size=2)
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>>> plt.show()
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"""
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from matplotlib.collections import LineCollection
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if vor.points.shape[1] != 2:
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raise ValueError("Voronoi diagram is not 2-D")
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if kw.get('show_points', True):
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point_size = kw.get('point_size', None)
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ax.plot(vor.points[:, 0], vor.points[:, 1], '.', markersize=point_size)
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if kw.get('show_vertices', True):
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ax.plot(vor.vertices[:, 0], vor.vertices[:, 1], 'o')
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line_colors = kw.get('line_colors', 'k')
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line_width = kw.get('line_width', 1.0)
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line_alpha = kw.get('line_alpha', 1.0)
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center = vor.points.mean(axis=0)
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ptp_bound = np.ptp(vor.points, axis=0)
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finite_segments = []
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infinite_segments = []
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for pointidx, simplex in zip(vor.ridge_points, vor.ridge_vertices):
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simplex = np.asarray(simplex)
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if np.all(simplex >= 0):
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finite_segments.append(vor.vertices[simplex])
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else:
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i = simplex[simplex >= 0][0] # finite end Voronoi vertex
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t = vor.points[pointidx[1]] - vor.points[pointidx[0]] # tangent
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t /= np.linalg.norm(t)
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n = np.array([-t[1], t[0]]) # normal
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midpoint = vor.points[pointidx].mean(axis=0)
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direction = np.sign(np.dot(midpoint - center, n)) * n
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if (vor.furthest_site):
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direction = -direction
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aspect_factor = abs(ptp_bound.max() / ptp_bound.min())
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far_point = vor.vertices[i] + direction * ptp_bound.max() * aspect_factor
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infinite_segments.append([vor.vertices[i], far_point])
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ax.add_collection(LineCollection(finite_segments,
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colors=line_colors,
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lw=line_width,
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alpha=line_alpha,
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linestyle='solid'))
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ax.add_collection(LineCollection(infinite_segments,
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colors=line_colors,
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lw=line_width,
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alpha=line_alpha,
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linestyle='dashed'))
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_adjust_bounds(ax, vor.points)
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return ax.figure
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