問題描述
幾年前，我已經嘗試在 PyQt5 GUI 中嵌入實時 matplotlib 圖.實時圖顯示了從傳感器捕獲的實時數據流、一些過程……我得到了它的工作，您可以在此處閱讀相關帖子:
2.第二個例子
我在這里找到了另一個實時
matplotlib圖示例:
這沒什么大不了的，但我只是想知道為什么.解決方案第二種情況(使用
FuncAnimation)更快，因為它使用blitting"，這可以避免重繪幀之間不會改變的東西.matplotlib 網站上提供的嵌入 qt 的示例沒有考慮速度，因此性能較差.您會注意到它在每次迭代時調用
ax.clear()和ax.plot()，導致每次都重新繪制整個畫布.如果要使用與FuncAnimation中的代碼相同的代碼(也就是說，創(chuàng)建一個 Axes 和一個藝術家，并更新藝術家中的數據，而不是每次都創(chuàng)建一個新藝術家)你應該得到非常接近我相信的相同性能.Some years ago, I already experimented with embedding live
matplotlibplots in aPyQt5GUI. Live plots show a data-stream real-time, captured from a sensor, some process, ... I got that working, and you can read the related posts here:Matplotlib animation inside your own GUI
How do I plot in real-time in a while loop using matplotlib?
Now I need to do the same thing again. I remember my previous approach worked, but couldn't keep up with fast datastreams. I found a couple of example codes on the internet, that I'd like to present to you. One of them is clearly faster than the other, but I don't know why. I'd like to gain more insights. I believe a deeper understanding will enable me to keep my interactions with
PyQt5andmatplotlibefficient.1. First example
This example is based on this article:
https://matplotlib.org/3.1.1/gallery/user_interfaces/embedding_in_qt_sgskip.html
The article is from the officialmatplotlibwebsite, and explains how to embed a matplotlib figure in aPyQt5window.I did a few minor adjustments to the example code, but the basics are still the same. Please copy-paste the code below to a Python file and run it:
##################################################################################### # # # 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_()You should see the following window:
2. Second example
I found another example of live
matplotlibgraphs here:
https://learn.sparkfun.com/tutorials/graph-sensor-data-with-python-and-matplotlib/speeding-up-the-plot-animation
However, the author doesn't usePyQt5to embed his live plot. Therefore, I've modified the code a bit, to get the plot in aPyQt5window:##################################################################################### # # # 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_()The resulting live plot is exactly the same. However, if you start playing around with the
intervalparameter from theMyFigureCanvas()constructor, you will notice that the first example won't be able to follow. The second example can go much faster.3. Questions
I've got a couple of questions I'd like to present to you:
The
QtCoreandQtWidgetsclasses can be imported like this:
from matplotlib.backends.qt_compat import QtCore, QtWidgets
or like this:
from PyQt5 import QtWidgets, QtCore
Both work equally well. Is there a reason to prefer one over the other?
The
FigureCanvascan be imported like this:
from matplotlib.backends.backend_qt5agg import FigureCanvas
or like this:from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
But I already figured out why. Thebackend_qt5aggfile seems to defineFigureCanvasas an alias forFigureCanvasQTAgg.
Why exactly is the second example so much faster than the first one? Honestly, it surprises me. The first example is based on a webpage from the official matplotlib website. I'd expect that one to be better.
Do you have any suggestions to make the second example even faster?
4. Edits
Based on the webpage:
https://bastibe.de/2013-05-30-speeding-up-matplotlib.html
I modified the first example to increase its speed. Please have a look at the code:##################################################################################### # # # 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_()The result is pretty amazing. The modifications make the first example definitely much faster! However, I don't know if this makes the first example equally fast now to the second example. They're certainly close to each other. Anyone an idea who wins?
Also, I noticed that one vertical line on the left, and one horizontal line on top is missing: It's not a big deal, but I just wonder why.
解決方案The second case (using
FuncAnimation) is faster because it uses "blitting", which avoids redrawing things that do not change between frames.The example provided on the matplotlib website for embedding in qt was not written with speed in mind, hence the poorer performance. You'll notice that it calls
ax.clear()andax.plot()at each iteration, causing the whole canvas to be redrawn everytime. If you were to use the same code as in the code withFuncAnimation(that is to say, create an Axes and an artist, and update the data in the artist instead of creating a new artists every time) you should get pretty close to the same performance I believe.這篇關于如何在 PyQt5 GUI 中制作快速 matplotlib 實時繪圖的文章就介紹到這了，希望我們推薦的答案對大家有所幫助，也希望大家多多支持html5模板網！
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