使用matplotlib在鼠标上的散点图上用x，y坐标以外的其他标签标记点 [英] Using matplotlib to label points on a scatter plot on mouse over with some label other than x,y coordinates

问题描述

我正在尝试使用DataCursor方法( https://stackoverflow.com/a/4674445/1301710 )使用matplotlib标记点.我有数千点，并希望在鼠标悬停时看到它们的标签.但是，有两个区别:一，我正在绘制散点图，二，我想标记每个点的名称，而不仅仅是x，y坐标.

I am trying to use the DataCursor approach (https://stackoverflow.com/a/4674445/1301710) to label points using matplotlib. I have several thousand points and would like to see their label on mouseover. However, there are two differences: one, I'm making a scatter plot and two, I want to label names for each point and not just the x,y coordinates.

这是我的代码

import os
import matplotlib.pyplot as plt

class DataCursor(object):
text_template = 'x: %0.2f\ny: %0.2f'
x, y = 0.0, 0.0
xoffset, yoffset = -20, 20
text_template = 'x: %0.2f\ny: %0.2f'

def __init__(self, ax, labels,x,y):
    self.ax = ax
self.xlist = x
self.ylist = y
self.labels = labels
    self.annotation = ax.annotate(self.text_template, 
            xy=(self.x, self.y), xytext=(self.xoffset, self.yoffset), 
            textcoords='offset points', ha='right', va='bottom',
            bbox=dict(boxstyle='round,pad=0.5', fc='yellow', alpha=0.5),
            arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0')
            )
    self.annotation.set_visible(False)

def __call__(self, event):
    self.event = event
    xdata, ydata = event.artist.get_data()
    #self.x, self.y = xdata[event.ind], ydata[event.ind]
    self.x, self.y = event.mouseevent.xdata, event.mouseevent.ydata
self.label = self.labels[self.xlist.index(self.x)]
    if self.x is not None:
        self.annotation.xy = self.x, self.y
        self.annotation.set_text(self.label)
        self.annotation.set_visible(True)
        event.canvas.draw()

def process():
#code to make ht_dict here
# ht_dict has the following format: 'ht1' = [nov14count, nov21count] where each key is a string and each value is a list of two integers

print("Start making scatter plot..")
hts = []
nov14 = []
nov21 = []
for key in ht_dict.keys():
    nov14.append(ht_dict[key][0])
    nov21.append(ht_dict[key][1])
hts.append(key)
fig = plt.figure()
scatter = plt.scatter(nov14, nov21)


fig.canvas.mpl_connect('pick_event', DataCursor(plt.gca(), hts, nov14, nov21))
scatter.set_picker(5)
plt.show()  

process()

我遇到以下错误:

AttributeError: 'CircleCollection' object has no attribute 'get_data'

我希望能够在鼠标悬停在nov14和nov21列表中分别存储在相同索引处的x和y坐标上看到hts列表中存储的字符串.我不确定该如何处理该错误，将不胜感激. 我遇到的另一个问题是(通过尝试对DataCursor线程中现有图进行更改)，由于我当前正在使用索引of来获取标签，这会给我一个列表错误中不存在的值，因为单击的值可能与列表中的值不完全相同.您对显示某个点的标签/名称有更好的建议吗?

I want to be able to see the string stored in the list hts on mouseover at the x and y coordinates stored in nov14 and nov21 lists respectively at the same indices. I'm not sure what to make of this error and would appreciate any help. Another question I have is (from trying out changes on the existing plot in the DataCursor thread) that using index of to get back label as I am currently doing will give me a value doesn't exist in list error since the clicked on value may not be exactly same as the value in the list. Do you have any suggestions of a better way to display some label/name about a point?

任何可以阅读的文档指南或指针，将不胜感激.

Any guidance or pointers to documentation where I could read up would be appreciated.

谢谢！

推荐答案

采用

Adopting the approach for annotating using the label, given in the examples section on the documentation page of the mpldatacursor, you could do something along these lines (plot a single point with each scatter plot to be able to set an individual label for each point):

import matplotlib.pyplot as plt
from mpldatacursor import datacursor
import random

fig, ax = plt.subplots()
ax.set_title('Click on a dot to display its label')

# Plot a number of random dots
for i in range(1, 1000):
    ax.scatter([random.random()], [random.random()], label='$ID: {}$'.format(i))

# Use a DataCursor to interactively display the label for a selected line...
datacursor(formatter='{label}'.format)

plt.show()

不幸的是，它效率很低，也就是说，几乎不能使用超过1000点的数据.

Unfortunately it's rather inefficient, i.e., hardly usable with more than, say, 1000 points.

结果示例图片:

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