Matplotlib重叠注释

问题描述

我想在图形中用一些文本注释这些条，但是如果这些条靠得很近并且具有可比较的高度，则这些注释在ea上方.其他，因此很难读取(注释的坐标是从钢筋的位置和高度获取的.)

I want to annotate the bars in a graph with some text but if the bars are close together and have comparable height, the annotations are above ea. other and thus hard to read (the coordinates for the annotations were taken from the bar position and height).

如果发生碰撞，是否可以转移其中一个?

Is there a way to shift one of them if there is a collision?

编辑:条形图有时很细且很靠近，因此垂直对齐并不能解决问题...

The bars are very thin and very close sometimes so just aligning vertically doesn't solve the problem...

图片可能会澄清一些问题:

A picture might clarify things:

推荐答案

我写了一个快速的解决方案，它针对所有其他注释，根据默认边界框检查每个注释位置.如果发生碰撞，它将其位置更改为下一个可用的无碰撞位置.它还带有漂亮的箭头.

I've written a quick solution, which checks each annotation position against default bounding boxes for all the other annotations. If there is a collision it changes its position to the next available collision free place. It also puts in nice arrows.

对于一个非常极端的示例，它将产生以下结果(所有数字均不重叠):

For a fairly extreme example, it will produce this (none of the numbers overlap):

代替此:

这是代码:

import numpy as np
import matplotlib.pyplot as plt
from numpy.random import *

def get_text_positions(x_data, y_data, txt_width, txt_height):
    a = zip(y_data, x_data)
    text_positions = y_data.copy()
    for index, (y, x) in enumerate(a):
        local_text_positions = [i for i in a if i[0] > (y - txt_height)
                            and (abs(i[1] - x) < txt_width * 2) and i != (y,x)]
        if local_text_positions:
            sorted_ltp = sorted(local_text_positions)
            if abs(sorted_ltp[0][0] - y) < txt_height: #True == collision
                differ = np.diff(sorted_ltp, axis=0)
                a[index] = (sorted_ltp[-1][0] + txt_height, a[index][1])
                text_positions[index] = sorted_ltp[-1][0] + txt_height
                for k, (j, m) in enumerate(differ):
                    #j is the vertical distance between words
                    if j > txt_height * 2: #if True then room to fit a word in
                        a[index] = (sorted_ltp[k][0] + txt_height, a[index][1])
                        text_positions[index] = sorted_ltp[k][0] + txt_height
                        break
    return text_positions

def text_plotter(x_data, y_data, text_positions, axis,txt_width,txt_height):
    for x,y,t in zip(x_data, y_data, text_positions):
        axis.text(x - txt_width, 1.01*t, '%d'%int(y),rotation=0, color='blue')
        if y != t:
            axis.arrow(x, t,0,y-t, color='red',alpha=0.3, width=txt_width*0.1,
                       head_width=txt_width, head_length=txt_height*0.5,
                       zorder=0,length_includes_head=True)

下面是生成这些图的代码，显示了用法:

Here is the code producing these plots, showing the usage:

#random test data:
x_data = random_sample(100)
y_data = random_integers(10,50,(100))

#GOOD PLOT:
fig2 = plt.figure()
ax2 = fig2.add_subplot(111)
ax2.bar(x_data, y_data,width=0.00001)
#set the bbox for the text. Increase txt_width for wider text.
txt_height = 0.04*(plt.ylim()[1] - plt.ylim()[0])
txt_width = 0.02*(plt.xlim()[1] - plt.xlim()[0])
#Get the corrected text positions, then write the text.
text_positions = get_text_positions(x_data, y_data, txt_width, txt_height)
text_plotter(x_data, y_data, text_positions, ax2, txt_width, txt_height)

plt.ylim(0,max(text_positions)+2*txt_height)
plt.xlim(-0.1,1.1)

#BAD PLOT:
fig = plt.figure()
ax = fig.add_subplot(111)
ax.bar(x_data, y_data, width=0.0001)
#write the text:
for x,y in zip(x_data, y_data):
    ax.text(x - txt_width, 1.01*y, '%d'%int(y),rotation=0)
plt.ylim(0,max(text_positions)+2*txt_height)
plt.xlim(-0.1,1.1)

plt.show()

这篇关于Matplotlib重叠注释的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！