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