如何使用 matplotlib 实时绘制不断增长的数据文件？答案

【问题标题】：How can I plot a growing data file in real-time with matplotlib?如何使用 matplotlib 实时绘制不断增长的数据文件？
【发布时间】：2017-05-28 11:09:48
【问题描述】：

我正在尝试实时绘制一个文件 (datos.txt)，该文件将不断从 pH 传感器获取新数据。

如here 所示，我可以绘制一个数据文件，但我仍然无法实时绘制。我正在使用以下代码：

import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from datetime import datetime
import numpy as np    

# Converter function
datefunc = lambda x: mdates.date2num(datetime.strptime(x, '%d-%m-%Y %H:%M:%S'))

# Read data from 'file.dat'
dates, levels = np.genfromtxt('/home/ramiro/Programas/pythonProgs/datos.txt',    # Data to be read
                              delimiter=19,  # First column is 19 characters wide
                              converters={0: datefunc}, # Formatting of column 0
                              dtype=float,   # All values are floats
                              unpack=True)   # Unpack to several variables

fig = plt.figure()
ax = fig.add_subplot(111)

# Configure x-ticks
ax.set_xticks(dates) # Tickmark + label at every plotted point
ax.xaxis.set_major_formatter(mdates.DateFormatter('%d/%m/%Y %H:%M'))

ax.locator_params(axis='x',nbins=10)
ax.plot_date(dates, levels, ls='-', marker='o')
ax.set_title('Hora')
ax.set_ylabel('pH')
ax.grid(True)

# Format the x-axis for dates (label formatting, rotation)
fig.autofmt_xdate(rotation=45)
fig.tight_layout()

plt.show()

我看过一些实时绘图的例子，但我不知道如何让我的工作

【问题讨论】：

有a question about plotting csv data in real time。请告诉我们它在多大程度上不能解决问题。在你的问题中具体。你到底想达到什么目的？更新后的数据应该如何绘制？

标签： python datetime matplotlib plot

【解决方案1】：

您可以将您的情节包装成animate 函数，如下所示：

import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import matplotlib.animation as animation
from datetime import datetime
import numpy as np    

def animate(i, fig, ax):
    # Converter function
    datefunc = lambda x: mdates.date2num(datetime.strptime(x, '%d-%m-%Y %H:%M:%S'))

    # Read data from 'file.dat'
    dates, levels = np.genfromtxt('/home/ramiro/Programas/pythonProgs/datos.txt',    # Data to be read
                                  delimiter=19,  # First column is 19 characters wide
                                  converters={0: datefunc}, # Formatting of column 0
                                  dtype=float,   # All values are floats
                                  unpack=True)   # Unpack to several variables

    # Configure x-ticks
    ax.set_xticks(dates) # Tickmark + label at every plotted point
    ax.xaxis.set_major_formatter(mdates.DateFormatter('%d/%m/%Y %H:%M'))

    ax.locator_params(axis='x',nbins=10)
    ax.plot_date(dates, levels, 'k', ls='-', marker='o')
    ax.set_title('Hora')
    ax.set_ylabel('pH')
    ax.grid(True)

    # Format the x-axis for dates (label formatting, rotation)
    fig.autofmt_xdate(rotation=45)
    fig.tight_layout()

fig = plt.figure()
ax = fig.add_subplot(111)
ani = animation.FuncAnimation(fig, animate, fargs=(fig, ax), interval=1000)
plt.show()

这将每秒重新读取并显示您的情节。

【讨论】：