File size: 7,557 Bytes
7885a28 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 |
import numpy as np
from scipy._lib.decorator import decorator as _decorator
__all__ = ['delaunay_plot_2d', 'convex_hull_plot_2d', 'voronoi_plot_2d']
@_decorator
def _held_figure(func, obj, ax=None, **kw):
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 = getattr(ax, 'ishold', lambda: True)()
if was_held:
return func(obj, ax=ax, **kw)
try:
ax.hold(True)
return func(obj, ax=ax, **kw)
finally:
ax.hold(was_held)
def _adjust_bounds(ax, points):
margin = 0.1 * np.ptp(points, 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])
@_held_figure
def delaunay_plot_2d(tri, ax=None):
"""
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.
Examples
--------
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from scipy.spatial import Delaunay, delaunay_plot_2d
The Delaunay triangulation of a set of random points:
>>> rng = np.random.default_rng()
>>> points = rng.random((30, 2))
>>> tri = Delaunay(points)
Plot it:
>>> _ = delaunay_plot_2d(tri)
>>> plt.show()
"""
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
@_held_figure
def convex_hull_plot_2d(hull, ax=None):
"""
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.
Examples
--------
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from scipy.spatial import ConvexHull, convex_hull_plot_2d
The convex hull of a random set of points:
>>> rng = np.random.default_rng()
>>> points = rng.random((30, 2))
>>> hull = ConvexHull(points)
Plot it:
>>> _ = convex_hull_plot_2d(hull)
>>> plt.show()
"""
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
@_held_figure
def voronoi_plot_2d(vor, ax=None, **kw):
"""
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. For degenerate input, including collinearity and
other violations of general position, it may be preferable to
calculate the Voronoi diagram with Qhull options ``QJ`` for random
joggling, or ``Qt`` to enforce triangulated output. Otherwise, some
Voronoi regions may not be visible.
Examples
--------
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from scipy.spatial import Voronoi, voronoi_plot_2d
Create a set of points for the example:
>>> rng = np.random.default_rng()
>>> points = rng.random((10,2))
Generate the Voronoi diagram for the points:
>>> vor = Voronoi(points)
Use `voronoi_plot_2d` to plot the diagram:
>>> fig = voronoi_plot_2d(vor)
Use `voronoi_plot_2d` to plot the diagram again, with some settings
customized:
>>> 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 = np.ptp(vor.points, 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
if (vor.furthest_site):
direction = -direction
aspect_factor = abs(ptp_bound.max() / ptp_bound.min())
far_point = vor.vertices[i] + direction * ptp_bound.max() * aspect_factor
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
|