在matplotlib中，在散点图中的数据点周围绘制平滑多边形

在matplotlib中，在散点图中的数据点周围绘制平滑多边形

在这里，您有一个例子。我被写成主要思想，但是显然，您可以做得更好。

简短说明：

1）您需要计算凸包（http://en.wikipedia.org/wiki/Convex_hull）

2）使用船体，您可以对其进行缩放以将所有数据保留在其中。

3）您必须对结果曲线进行插值。

第一部分在http://wiki.scipy.org/Cookbook/Finding_Convex_Hull中完成。第二个是微不足道的。第三种非常笼统，您可以执行任何方法，有许多不同的方法可以执行相同的 *** 作。

希望对您有帮助…

#Taken from http://wiki.scipy.org/Cookbook/Finding_Convex_Hullimport numpy as n, pylab as p, timedef _angle_to_point(point, centre):    '''calculate angle in 2-D between points and x axis'''    delta = point - centre    res = n.arctan(delta[1] / delta[0])    if delta[0] < 0:        res += n.pi    return resdef _draw_triangle(p1, p2, p3, **kwargs):    tmp = n.vstack((p1,p2,p3))    x,y = [x[0] for x in zip(tmp.transpose())]    p.fill(x,y, **kwargs)def area_of_triangle(p1, p2, p3):    '''calculate area of any triangle given co-ordinates of the corners'''    return n.linalg.norm(n.cross((p2 - p1), (p3 - p1)))/2.def convex_hull(points, graphic=False, smidgen=0.0075):    '''    Calculate subset of points that make a convex hull around points    Recursively eliminates points that lie inside two neighbouring points until only convex hull is remaining.    :Parameters:    points : ndarray (2 x m)    array of points for which to find hull    graphic : bool    use pylab to show progress?    smidgen : float    offset for graphic number labels - useful values depend on your data range    :Returns:    hull_points : ndarray (2 x n)    convex hull surrounding points    '''    if graphic:        p.clf()        p.plot(points[0], points[1], 'ro')    n_pts = points.shape[1]    assert(n_pts > 5)    centre = points.mean(1)    if graphic: p.plot((centre[0],),(centre[1],),'bo')    angles = n.apply_along_axis(_angle_to_point, 0, points, centre)    pts_ord = points[:,angles.argsort()]    if graphic:        for i in xrange(n_pts): p.text(pts_ord[0,i] + smidgen, pts_ord[1,i] + smidgen,         '%d' % i)    pts = [x[0] for x in zip(pts_ord.transpose())]    prev_pts = len(pts) + 1    k = 0    while prev_pts > n_pts:        prev_pts = n_pts        n_pts = len(pts)        if graphic: p.gca().patches = []        i = -2        while i < (n_pts - 2): Aij = area_of_triangle(centre, pts[i],     pts[(i + 1) % n_pts]) Ajk = area_of_triangle(centre, pts[(i + 1) % n_pts],   pts[(i + 2) % n_pts]) Aik = area_of_triangle(centre, pts[i],     pts[(i + 2) % n_pts]) if graphic:     _draw_triangle(centre, pts[i], pts[(i + 1) % n_pts],          facecolor='blue', alpha = 0.2)     _draw_triangle(centre, pts[(i + 1) % n_pts],          pts[(i + 2) % n_pts],          facecolor='green', alpha = 0.2)     _draw_triangle(centre, pts[i], pts[(i + 2) % n_pts],          facecolor='red', alpha = 0.2) if Aij + Ajk < Aik:     if graphic: p.plot((pts[i + 1][0],),(pts[i + 1][1],),'go')     del pts[i+1] i += 1 n_pts = len(pts)        k += 1    return n.asarray(pts)if __name__ == "__main__":    import scipy.interpolate as interpolate#    fig = p.figure(figsize=(10,10))    theta = 2*n.pi*n.random.rand(1000)    r = n.random.rand(1000)**0.5    x,y = r*p.cos(theta),r*p.sin(theta)    points = n.ndarray((2,len(x)))    points[0,:],points[1,:] = x,y    scale = 1.03    hull_pts = scale*convex_hull(points)    p.plot(x,y,'ko')    x,y = [],[]    convex = scale*hull_pts    for point in convex:        x.append(point[0])        y.append(point[1])    x.append(convex[0][0])    y.append(convex[0][1])    x,y = n.array(x),n.array(y)#Taken from https://stackoverflow.com/questions/14344099/numpy-scipy-smooth-spline-representation-of-an-arbitrary-contour-flength    nt = n.linspace(0, 1, 100)    t = n.zeros(x.shape)    t[1:] = n.sqrt((x[1:] - x[:-1])**2 + (y[1:] - y[:-1])**2)    t = n.cumsum(t)    t /= t[-1]    x2 = interpolate.spline(t, x, nt)    y2 = interpolate.spline(t, y, nt)    p.plot(x2, y2,'r--',linewidth=2)    p.show()