image-match/python/py/Lib/site-packages/scipy/spatial/_plotutils.py

import numpy as np

__all__ = ['delaunay_plot_2d', 'convex_hull_plot_2d', 'voronoi_plot_2d']


def _get_axes():
    import matplotlib.pyplot as plt

    return plt.figure().gca()


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])


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 = ax or _get_axes()
    ax.plot(x, y, 'o')
    ax.triplot(x, y, tri.simplices.copy())

    _adjust_bounds(ax, tri.points)

    return ax.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 = ax or _get_axes()
    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


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")

    ax = ax or _get_axes()

    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