Question

我尝试在滚动窗口中使用matplotlib绘制快速数据（每秒1-10个数据点）。

我喜欢熊猫，因为它很简单。

我的问题是：

我的框架是否有效（关于使用pandas，matplotlib＆＃39;＆＃34; plt.draw＆＃34;）？
如果循环运行的时间为1＆＃39; 000＆＃39; 000＆000;数据帧（self.df）会变得太大 - ＆gt;是否最好在某个时刻删除数据收集器，然后从空数据框开始？但那么滚动窗口的连续性呢？
一段时间后，这个例子开始变得很慢。是因为效率低下还是由于内存使用效率低下而导致
控制台显示警告：

MatplotlibDeprecationWarning：使用默认事件循环直到函数特定于此GUI实现warnings.warn（str，mplDeprecation）

我必须照顾好吗？

提前致谢

这是我到目前为止所做的：

import numpy as np
import matplotlib.pyplot as plt; plt.ion()
import pandas as pd
import matplotlib
import matplotlib.gridspec as gridspec
plt.style.use('ggplot')
from pathlib import Path
import datetime
import matplotlib


class tradeScreen(object):

    def __init__(self):
        self.rollingWindow = 100
        self.df = pd.DataFrame(dict(time=np.NaN, bid=np.NaN, ask=np.NaN, limitBuy=np.NaN, limitSell=np.NaN, stopLoss=np.NaN), index=np.arange(self.rollingWindow))
        self.df['time'] = pd.to_datetime(self.df['time'])  # format 'time' as datetime object

        # initialise plot and line
        plt.figure()
        G = gridspec.GridSpec(2, 1)

        self.axes_1 = plt.subplot(G[0, :])
        self.axes_1.set_ylabel('First Panel')

        self.axes_2 = plt.subplot(G[1, :])
        self.axes_2.set_ylabel('Second Panel')

        self.line1, = self.axes_1.plot(self.df['time'], self.df['bid'])
        self.line2, = self.axes_1.plot(self.df['time'], self.df['ask'])
        self.line3, = self.axes_1.plot(self.df['time'], self.df['limitBuy'])
        self.line4, = self.axes_1.plot(self.df['time'], self.df['limitSell'])
        self.line5, = self.axes_2.plot(self.df['time'], self.df['stopLoss'])


    def plotter(self, tick, i):

        df = self.df
        rollingWindow = self.rollingWindow

        current_time = pd.datetime.now()

        df.loc[i, "bid"] = tick["bid"].values.item(0)
        df.loc[i, "ask"] = tick["ask"].values.item(0)
        df.loc[i, "limitBuy"] = tick["limitBuy"].values.item(0)
        df.loc[i, "limitSell"] = tick["limitSell"].values.item(0)
        df.loc[i, "stopLoss"] = tick["stopLoss"].values.item(0)
        df.loc[i, "time"] = current_time

        self.line1.set_data(pd.to_datetime(df['time'][:i].tail(rollingWindow)), df['bid'][:i].tail(rollingWindow))
        self.line2.set_data(pd.to_datetime(df['time'][:i].tail(rollingWindow)), df['ask'][:i].tail(rollingWindow))
        self.line3.set_data(pd.to_datetime(df['time'][:i].tail(rollingWindow)), df['limitBuy'][:i].tail(rollingWindow))
        self.line4.set_data(pd.to_datetime(df['time'][:i].tail(rollingWindow)), df['limitSell'][:i].tail(rollingWindow))
        self.line5.set_data(pd.to_datetime(df['time'][:i].tail(rollingWindow)), df['stopLoss'][:i].tail(rollingWindow))

        self.axes_1.autoscale_view(True, True, True)
        self.axes_1.relim()
        self.axes_2.autoscale_view(True, True, True)
        self.axes_2.relim()

        plt.draw()
        plt.pause(0.00000000000000001)


p = tradeScreen()
i = 0
for i in np.arange(300):

    # generate random data point
    t = pd.datetime.now()
    bid = np.random.rand()
    ask = np.random.rand()
    limitBuy = np.random.rand()
    limitSell = np.random.rand()
    stopLoss = np.random.rand()

    tick = pd.DataFrame(dict(time=t, bid=bid, ask=ask, limitBuy=limitBuy, limitSell=limitSell, stopLoss=stopLoss),
                        index=np.arange(1))

    p.plotter(tick, i)
    i += 1

Answer 1

您可能需要查看Animation模块，以便在matplotlib中制作动画。请参阅此链接以获取解释：http://jakevdp.github.io/blog/2012/08/18/matplotlib-animation-tutorial/

对于后人，该页面上的第一个示例是Animation系统的要点：

"""
Matplotlib Animation Example

author: Jake Vanderplas
email: vanderplas@astro.washington.edu
website: http://jakevdp.github.com
license: BSD
Please feel free to use and modify this, but keep the above information. Thanks!
"""

import numpy as np
from matplotlib import pyplot as plt
from matplotlib import animation

# First set up the figure, the axis, and the plot element we want to animate
fig = plt.figure()
ax = plt.axes(xlim=(0, 2), ylim=(-2, 2))
line, = ax.plot([], [], lw=2)

# initialization function: plot the background of each frame
def init():
    line.set_data([], [])
    return line,

# animation function.  This is called sequentially
def animate(i):
    x = np.linspace(0, 2, 1000)
    y = np.sin(2 * np.pi * (x - 0.01 * i))
    line.set_data(x, y)
    return line,

# call the animator.  blit=True means only re-draw the parts that have changed.
anim = animation.FuncAnimation(fig, animate, init_func=init,
                               frames=200, interval=20, blit=True)

在最后一行使用blitting（blit=True）可能会提高你的表现。但是，AFAIK matplotlib的设计并未考虑到性能，您可以查看更适合此任务的其他库，例如PyQtGraph

在滚动窗口中绘制快速传入的数据

1 个答案: