如何在matplotlib中更新图? [英] How to update a plot in matplotlib?

问题描述

我在这里重新绘制图形时遇到问题.我允许用户在时间刻度(x轴)中指定单位，然后重新计算并调用此函数plots().我希望该图可以简单地更新，而不是在图上追加另一个图.

I'm having issues with redrawing the figure here. I allow the user to specify the units in the time scale (x-axis) and then I recalculate and call this function plots(). I want the plot to simply update, not append another plot to the figure.

def plots():
    global vlgaBuffSorted
    cntr()

    result = collections.defaultdict(list)
    for d in vlgaBuffSorted:
        result[d['event']].append(d)

    result_list = result.values()

    f = Figure()
    graph1 = f.add_subplot(211)
    graph2 = f.add_subplot(212,sharex=graph1)

    for item in result_list:
        tL = []
        vgsL = []
        vdsL = []
        isubL = []
        for dict in item:
            tL.append(dict['time'])
            vgsL.append(dict['vgs'])
            vdsL.append(dict['vds'])
            isubL.append(dict['isub'])
        graph1.plot(tL,vdsL,'bo',label='a')
        graph1.plot(tL,vgsL,'rp',label='b')
        graph2.plot(tL,isubL,'b-',label='c')

    plotCanvas = FigureCanvasTkAgg(f, pltFrame)
    toolbar = NavigationToolbar2TkAgg(plotCanvas, pltFrame)
    toolbar.pack(side=BOTTOM)
    plotCanvas.get_tk_widget().pack(side=TOP)

推荐答案

您基本上有两个选择:

1. 精确执行当前操作，但是在重新配置数据之前先调用graph1.clear()和graph2.clear().这是最慢但最简单，最可靠的选项.

1. Do exactly what you're currently doing, but call graph1.clear() and graph2.clear() before replotting the data. This is the slowest, but most simplest and most robust option.

除了重新绘制外，您还可以更新绘图对象的数据.您需要在代码中进行一些更改，但这应该比每次重新绘制都快得多.但是，您要绘制的数据的形状无法更改，并且如果数据范围正在更改，则需要手动重置x和y轴限制.

Instead of replotting, you can just update the data of the plot objects. You'll need to make some changes in your code, but this should be much, much faster than replotting things every time. However, the shape of the data that you're plotting can't change, and if the range of your data is changing, you'll need to manually reset the x and y axis limits.

举第二个选择的例子:

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 6*np.pi, 100)
y = np.sin(x)

# You probably won't need this if you're embedding things in a tkinter plot...
plt.ion()

fig = plt.figure()
ax = fig.add_subplot(111)
line1, = ax.plot(x, y, 'r-') # Returns a tuple of line objects, thus the comma

for phase in np.linspace(0, 10*np.pi, 500):
    line1.set_ydata(np.sin(x + phase))
    fig.canvas.draw()
    fig.canvas.flush_events()

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