在matplotlib中的數據點周圍繪製一個平滑的多邊形

Question

我有一堆有兩組數據的交叉圖，並且一直在尋找一種用平滑的多邊形輪廓突出顯示它們的繪製區域的matploltib方式。

目前我只是使用Adobe Illustrator和修改保存的情節，但這並不理想。例如：

我會很感激任何指針/鏈接的例子。

乾杯

Answer 1

在這裏，你有一個例子。我寫了主要想法，但很明顯，你可以做得更好。

一個簡短的說明：

1）你需要計算凸包（http://en.wikipedia.org/wiki/Convex_hull）

2）與船身，你可以擴展它來存放所有數據中。

3）您必須插入結果曲線。

第一部分在http://wiki.scipy.org/Cookbook/Finding_Convex_Hull完成。第二個是微不足道的。第三個是非常普遍的，你可以執行任何方法，有很多不同的方法來做同樣的事情。我採取@ Jaime的方法（Smooth spline representation of an arbitrary contour, f(length) --> x,y），我認爲這是一個非常好的方法。

我希望它幫你...

#Taken from http://wiki.scipy.org/Cookbook/Finding_Convex_Hull 

import numpy as n, pylab as p, time 

def _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 res 

def _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() 

有一些有用的文件，如：

http://repositorium.sdum.uminho.pt/bitstream/1822/6429/1/ConcaveHull_ACM_MYS.pdf

此外，您可以嘗試使用：http://resources.arcgis.com/en/help/main/10.1/index.html#//007000000013000000

我不'對arcgis一無所知，但看起來很好。