如何在 PyQt5 GUI 中快速绘制 matplotlib 实时绘图

Question

如何在 PyQt5 GUI 中快速绘制 matplotlib 实时绘图

K.M*_*ier 5 python plot matplotlib python-3.x pyqt5

几年前，我已经尝试过matplotlib在 GUI 中嵌入实时绘图PyQt5。实时绘图显示从传感器捕获的实时数据流、某些过程……我已经成功了，您可以在此处阅读相关帖子：

现在我需要再次做同样的事情。我记得我以前的方法有效，但无法跟上快速的数据流。我在互联网上找到了一些示例代码，我想将它们呈现给您。其中一个显然比另一个快，但我不知道为什么。我想获得更多见解。我相信更深入的了解将使我能够保持互动PyQt5和matplotlib高效。

1.第一个例子

这个例子基于这篇文章：
https://matplotlib.org/3.1.1/gallery/user_interfaces/embedding_in_qt_sgskip.html
该文章来自官方matplotlib网站，并解释了如何在PyQt5窗口中嵌入matplotlib图形。

我对示例代码做了一些细微的调整，但基本原理仍然是相同的。请将以下代码复制粘贴到 Python 文件中并运行：

#####################################################################################
#                                                                                   #
#                PLOT A LIVE GRAPH IN A PYQT WINDOW                                 #
#                EXAMPLE 1                                                          #
#               ------------------------------------                                #
# This code is inspired on:                                                         #
# https://matplotlib.org/3.1.1/gallery/user_interfaces/embedding_in_qt_sgskip.html  #
#                                                                                   #
#####################################################################################

from __future__ import annotations
from typing import *
import sys
import os
from matplotlib.backends.qt_compat import QtCore, QtWidgets
# from PyQt5 import QtWidgets, QtCore
from matplotlib.backends.backend_qt5agg import FigureCanvas
# from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
import matplotlib as mpl
import numpy as np

class ApplicationWindow(QtWidgets.QMainWindow):
    '''
    The PyQt5 main window.

    '''
    def __init__(self):
        super().__init__()
        # 1. Window settings
        self.setGeometry(300, 300, 800, 400)
        self.setWindowTitle("Matplotlib live plot in PyQt - example 1")
        self.frm = QtWidgets.QFrame(self)
        self.frm.setStyleSheet("QWidget { background-color: #eeeeec; }")
        self.lyt = QtWidgets.QVBoxLayout()
        self.frm.setLayout(self.lyt)
        self.setCentralWidget(self.frm)

        # 2. Place the matplotlib figure
        self.myFig = MyFigureCanvas(x_len=200, y_range=[0, 100], interval=20)
        self.lyt.addWidget(self.myFig)

        # 3. Show
        self.show()
        return

class MyFigureCanvas(FigureCanvas):
    '''
    This is the FigureCanvas in which the live plot is drawn.

    '''
    def __init__(self, x_len:int, y_range:List, interval:int) -> None:
        '''
        :param x_len:       The nr of data points shown in one plot.
        :param y_range:     Range on y-axis.
        :param interval:    Get a new datapoint every .. milliseconds.

        '''
        super().__init__(mpl.figure.Figure())
        # Range settings
        self._x_len_ = x_len
        self._y_range_ = y_range

        # Store two lists _x_ and _y_
        self._x_ = list(range(0, x_len))
        self._y_ = [0] * x_len

        # Store a figure ax
        self._ax_ = self.figure.subplots()

        # Initiate the timer
        self._timer_ = self.new_timer(interval, [(self._update_canvas_, (), {})])
        self._timer_.start()
        return

    def _update_canvas_(self) -> None:
        '''
        This function gets called regularly by the timer.

        '''
        self._y_.append(round(get_next_datapoint(), 2))     # Add new datapoint
        self._y_ = self._y_[-self._x_len_:]                 # Truncate list _y_
        self._ax_.clear()                                   # Clear ax
        self._ax_.plot(self._x_, self._y_)                  # Plot y(x)
        self._ax_.set_ylim(ymin=self._y_range_[0], ymax=self._y_range_[1])
        self.draw()
        return

# Data source
# ------------
n = np.linspace(0, 499, 500)
d = 50 + 25 * (np.sin(n / 8.3)) + 10 * (np.sin(n / 7.5)) - 5 * (np.sin(n / 1.5))
i = 0
def get_next_datapoint():
    global i
    i += 1
    if i > 499:
        i = 0
    return d[i]

if __name__ == "__main__":
    qapp = QtWidgets.QApplication(sys.argv)
    app = ApplicationWindow()
    qapp.exec_()

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您应该看到以下窗口：

2.第二个例子

我在这里找到了另一个实时matplotlib图表的例子： https:
//learn.sparkfun.com/tutorials/graph-sensor-data-with-python-and-matplotlib/speeding-up-the-plot-animation
但是，作者没有不用PyQt5来嵌入他的现场情节。因此，我对代码进行了一些修改，以在PyQt5窗口中绘制绘图：

#####################################################################################
#                                                                                   #
#                PLOT A LIVE GRAPH IN A PYQT WINDOW                                 #
#                EXAMPLE 2                                                          #
#               ------------------------------------                                #
# This code is inspired on:                                                         #
# https://learn.sparkfun.com/tutorials/graph-sensor-data-with-python-and-matplotlib/speeding-up-the-plot-animation  #
#                                                                                   #
#####################################################################################

from __future__ import annotations
from typing import *
import sys
import os
from matplotlib.backends.qt_compat import QtCore, QtWidgets
# from PyQt5 import QtWidgets, QtCore
from matplotlib.backends.backend_qt5agg import FigureCanvas
# from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
import matplotlib as mpl
import matplotlib.figure as mpl_fig
import matplotlib.animation as anim
import numpy as np

class ApplicationWindow(QtWidgets.QMainWindow):
    '''
    The PyQt5 main window.

    '''
    def __init__(self):
        super().__init__()
        # 1. Window settings
        self.setGeometry(300, 300, 800, 400)
        self.setWindowTitle("Matplotlib live plot in PyQt - example 2")
        self.frm = QtWidgets.QFrame(self)
        self.frm.setStyleSheet("QWidget { background-color: #eeeeec; }")
        self.lyt = QtWidgets.QVBoxLayout()
        self.frm.setLayout(self.lyt)
        self.setCentralWidget(self.frm)

        # 2. Place the matplotlib figure
        self.myFig = MyFigureCanvas(x_len=200, y_range=[0, 100], interval=20)
        self.lyt.addWidget(self.myFig)

        # 3. Show
        self.show()
        return

class MyFigureCanvas(FigureCanvas, anim.FuncAnimation):
    '''
    This is the FigureCanvas in which the live plot is drawn.

    '''
    def __init__(self, x_len:int, y_range:List, interval:int) -> None:
        '''
        :param x_len:       The nr of data points shown in one plot.
        :param y_range:     Range on y-axis.
        :param interval:    Get a new datapoint every .. milliseconds.

        '''
        FigureCanvas.__init__(self, mpl_fig.Figure())
        # Range settings
        self._x_len_ = x_len
        self._y_range_ = y_range

        # Store two lists _x_ and _y_
        x = list(range(0, x_len))
        y = [0] * x_len

        # Store a figure and ax
        self._ax_  = self.figure.subplots()
        self._ax_.set_ylim(ymin=self._y_range_[0], ymax=self._y_range_[1])
        self._line_, = self._ax_.plot(x, y)

        # Call superclass constructors
        anim.FuncAnimation.__init__(self, self.figure, self._update_canvas_, fargs=(y,), interval=interval, blit=True)
        return

    def _update_canvas_(self, i, y) -> None:
        '''
        This function gets called regularly by the timer.

        '''
        y.append(round(get_next_datapoint(), 2))     # Add new datapoint
        y = y[-self._x_len_:]                        # Truncate list _y_
        self._line_.set_ydata(y)
        return self._line_,

# Data source
# ------------
n = np.linspace(0, 499, 500)
d = 50 + 25 * (np.sin(n / 8.3)) + 10 * (np.sin(n / 7.5)) - 5 * (np.sin(n / 1.5))
i = 0
def get_next_datapoint():
    global i
    i += 1
    if i > 499:
        i = 0
    return d[i]

if __name__ == "__main__":
    qapp = QtWidgets.QApplication(sys.argv)
    app = ApplicationWindow()
    qapp.exec_()

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生成的实时情节完全相同。但是，如果您开始使用构造函数interval中的参数MyFigureCanvas()，您会注意到第一个示例将无法遵循。第二个例子可以运行得更快。

3. 问题

我有几个问题想向您提出：

和QtCore类QtWidgets可以像这样导入：
from matplotlib.backends.qt_compat import QtCore, QtWidgets
或像这样：
from PyQt5 import QtWidgets, QtCore
两者工作得同样好。有理由选择其中一种而不是另一种吗？
可以FigureCanvas这样导入：
from matplotlib.backends.backend_qt5agg import FigureCanvas
或这样： from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
但我已经找出原因了。该backend_qt5agg文件似乎定义FigureCanvas为的别名FigureCanvasQTAgg。
为什么第二个例子比第一个例子快得多？老实说，这让我感到惊讶。第一个示例基于 matplotlib 官方网站的网页。我希望那个会更好。
您有什么建议可以使第二个示例更快吗？

4. 编辑

基于网页：
https://bastibe.de/2013-05-30-speeding-up-matplotlib.html
我修改了第一个示例以提高其速度。请看一下代码：

#####################################################################################
#                                                                                   #
#                PLOT A LIVE GRAPH IN A PYQT WINDOW                                 #
#                EXAMPLE 1 (modified for extra speed)                               #
#               --------------------------------------                              #
# This code is inspired on:                                                         #
# https://matplotlib.org/3.1.1/gallery/user_interfaces/embedding_in_qt_sgskip.html  #
# and on:                                                                           #
# https://bastibe.de/2013-05-30-speeding-up-matplotlib.html                         #
#                                                                                   #
#####################################################################################

from __future__ import annotations
from typing import *
import sys
import os
from matplotlib.backends.qt_compat import QtCore, QtWidgets
# from PyQt5 import QtWidgets, QtCore
from matplotlib.backends.backend_qt5agg import FigureCanvas
# from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
import matplotlib as mpl
import numpy as np

class ApplicationWindow(QtWidgets.QMainWindow):
    '''
    The PyQt5 main window.

    '''
    def __init__(self):
        super().__init__()
        # 1. Window settings
        self.setGeometry(300, 300, 800, 400)
        self.setWindowTitle("Matplotlib live plot in PyQt - example 1 (modified for extra speed)")
        self.frm = QtWidgets.QFrame(self)
        self.frm.setStyleSheet("QWidget { background-color: #eeeeec; }")
        self.lyt = QtWidgets.QVBoxLayout()
        self.frm.setLayout(self.lyt)
        self.setCentralWidget(self.frm)

        # 2. Place the matplotlib figure
        self.myFig = MyFigureCanvas(x_len=200, y_range=[0, 100], interval=1)
        self.lyt.addWidget(self.myFig)

        # 3. Show
        self.show()
        return

class MyFigureCanvas(FigureCanvas):
    '''
    This is the FigureCanvas in which the live plot is drawn.

    '''
    def __init__(self, x_len:int, y_range:List, interval:int) -> None:
        '''
        :param x_len:       The nr of data points shown in one plot.
        :param y_range:     Range on y-axis.
        :param interval:    Get a new datapoint every .. milliseconds.

        '''
        super().__init__(mpl.figure.Figure())
        # Range settings
        self._x_len_ = x_len
        self._y_range_ = y_range

        # Store two lists _x_ and _y_
        self._x_ = list(range(0, x_len))
        self._y_ = [0] * x_len

        # Store a figure ax
        self._ax_ = self.figure.subplots()
        self._ax_.set_ylim(ymin=self._y_range_[0], ymax=self._y_range_[1]) # added
        self._line_, = self._ax_.plot(self._x_, self._y_)                  # added
        self.draw()                                                        # added

        # Initiate the timer
        self._timer_ = self.new_timer(interval, [(self._update_canvas_, (), {})])
        self._timer_.start()
        return

    def _update_canvas_(self) -> None:
        '''
        This function gets called regularly by the timer.

        '''
        self._y_.append(round(get_next_datapoint(), 2))     # Add new datapoint
        self._y_ = self._y_[-self._x_len_:]                 # Truncate list y

        # Previous code
        # --------------
        # self._ax_.clear()                                   # Clear ax
        # self._ax_.plot(self._x_, self._y_)                  # Plot y(x)
        # self._ax_.set_ylim(ymin=self._y_range_[0], ymax=self._y_range_[1])
        # self.draw()

        # New code
        # ---------
        self._line_.set_ydata(self._y_)
        self._ax_.draw_artist(self._ax_.patch)
        self._ax_.draw_artist(self._line_)
        self.update()
        self.flush_events()
        return

# Data source
# ------------
n = np.linspace(0, 499, 500)
d = 50 + 25 * (np.sin(n / 8.3)) + 10 * (np.sin(n / 7.5)) - 5 * (np.sin(n / 1.5))
i = 0
def get_next_datapoint():
    global i
    i += 1
    if i > 499:
        i = 0
    return d[i]

if __name__ == "__main__":
    qapp = QtWidgets.QApplication(sys.argv)
    app = ApplicationWindow()
    qapp.exec_()

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结果非常惊人。这些修改使得第一个示例绝对更快！但是，我不知道这是否使第一个示例现在与第二个示例同样快。他们彼此之间当然很亲近。有人知道谁赢了吗？

另外，我注意到左侧的一条垂直线和顶部的一条水平线丢失了：这没什么大不了的，但我只是想知道为什么。

Answer 1

Diz*_*ahi 5

第二种情况（使用FuncAnimation）更快，因为它使用“blitting”，这可以避免重绘帧之间不发生变化的内容。

matplotlib 网站上提供的用于嵌入 qt 的示例并未考虑到速度，因此性能较差。您会注意到它在每次迭代时调用ax.clear()and ax.plot()，导致每次都重新绘制整个画布。如果您要使用与代码中相同的代码FuncAnimation（也就是说，创建一个轴和一个艺术家，并更新艺术家中的数据而不是每次创建一个新的艺术家），您应该非常接近相同的代码性能我相信。

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