Matplotlib 中的内联标签 [英] Inline labels in Matplotlib

问题描述

在 Matplotlib 中，制作图例并不太难(example_legend()，如下)，但我认为将标签正确放置在绘制的曲线上是更好的风格(如 example_inline()，如下).这可能非常繁琐，因为我必须手动指定坐标，而且如果我重新格式化绘图，我可能必须重新定位标签.有没有办法在 Matplotlib 中的曲线上自动生成标签?能够以与曲线角度相对应的角度定向文本的奖励积分.

In Matplotlib, it's not too tough to make a legend (example_legend(), below), but I think it's better style to put labels right on the curves being plotted (as in example_inline(), below). This can be very fiddly, because I have to specify coordinates by hand, and, if I re-format the plot, I probably have to reposition the labels. Is there a way to automatically generate labels on curves in Matplotlib? Bonus points for being able to orient the text at an angle corresponding to the angle of the curve.

import numpy as np
import matplotlib.pyplot as plt

def example_legend():
    plt.clf()
    x = np.linspace(0, 1, 101)
    y1 = np.sin(x * np.pi / 2)
    y2 = np.cos(x * np.pi / 2)
    plt.plot(x, y1, label='sin')
    plt.plot(x, y2, label='cos')
    plt.legend()

def example_inline():
    plt.clf()
    x = np.linspace(0, 1, 101)
    y1 = np.sin(x * np.pi / 2)
    y2 = np.cos(x * np.pi / 2)
    plt.plot(x, y1, label='sin')
    plt.plot(x, y2, label='cos')
    plt.text(0.08, 0.2, 'sin')
    plt.text(0.9, 0.2, 'cos')

推荐答案

好问题，不久前我已经尝试过这个，但没有经常使用它，因为它仍然不是防弹的.我将绘图区域划分为一个 32x32 的网格，并根据以下规则计算了每条线标签最佳位置的势场":

Nice question, a while ago I've experimented a bit with this, but haven't used it a lot because it's still not bulletproof. I divided the plot area into a 32x32 grid and calculated a 'potential field' for the best position of a label for each line according the following rules:

• 空白是放置标签的好地方
• 标签应该靠近对应的线
• 标签应远离其他行

代码是这样的:

import matplotlib.pyplot as plt
import numpy as np
from scipy import ndimage


def my_legend(axis = None):

    if axis == None:
        axis = plt.gca()

    N = 32
    Nlines = len(axis.lines)
    print Nlines

    xmin, xmax = axis.get_xlim()
    ymin, ymax = axis.get_ylim()

    # the 'point of presence' matrix
    pop = np.zeros((Nlines, N, N), dtype=np.float)    

    for l in range(Nlines):
        # get xy data and scale it to the NxN squares
        xy = axis.lines[l].get_xydata()
        xy = (xy - [xmin,ymin]) / ([xmax-xmin, ymax-ymin]) * N
        xy = xy.astype(np.int32)
        # mask stuff outside plot        
        mask = (xy[:,0] >= 0) & (xy[:,0] < N) & (xy[:,1] >= 0) & (xy[:,1] < N)
        xy = xy[mask]
        # add to pop
        for p in xy:
            pop[l][tuple(p)] = 1.0

    # find whitespace, nice place for labels
    ws = 1.0 - (np.sum(pop, axis=0) > 0) * 1.0 
    # don't use the borders
    ws[:,0]   = 0
    ws[:,N-1] = 0
    ws[0,:]   = 0  
    ws[N-1,:] = 0  

    # blur the pop's
    for l in range(Nlines):
        pop[l] = ndimage.gaussian_filter(pop[l], sigma=N/5)

    for l in range(Nlines):
        # positive weights for current line, negative weight for others....
        w = -0.3 * np.ones(Nlines, dtype=np.float)
        w[l] = 0.5

        # calculate a field         
        p = ws + np.sum(w[:, np.newaxis, np.newaxis] * pop, axis=0)
        plt.figure()
        plt.imshow(p, interpolation='nearest')
        plt.title(axis.lines[l].get_label())

        pos = np.argmax(p)  # note, argmax flattens the array first 
        best_x, best_y =  (pos / N, pos % N) 
        x = xmin + (xmax-xmin) * best_x / N       
        y = ymin + (ymax-ymin) * best_y / N       


        axis.text(x, y, axis.lines[l].get_label(), 
                  horizontalalignment='center',
                  verticalalignment='center')


plt.close('all')

x = np.linspace(0, 1, 101)
y1 = np.sin(x * np.pi / 2)
y2 = np.cos(x * np.pi / 2)
y3 = x * x
plt.plot(x, y1, 'b', label='blue')
plt.plot(x, y2, 'r', label='red')
plt.plot(x, y3, 'g', label='green')
my_legend()
plt.show()

结果图:

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