几年前,我已经尝试过实时嵌入 matplotlib
情节在 PyQt5
图形用户界面。实时绘图显示实时数据流,从传感器捕获,一些过程,......我得到了工作,你可以在这里阅读相关帖子:
现在我需要再次做同样的事情。我记得我以前的方法有效,但跟不上快速的数据流。我在互联网上找到了几个示例代码,我想向您展示。其中一个明显比另一个快,但我不知道为什么。我想获得更多的见解。我相信更深入的了解将使我能够保持与
PyQt5
的互动。和 matplotlib
高效的。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
文章来自官方
matplotlib
网站,并解释了如何在 PyQt5
中嵌入 matplotlib 图 window 。我对示例代码做了一些小的调整,但基本原理仍然相同。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_()
您应该会看到以下窗口:
2. 第二个例子
我发现了另一个活的例子
matplotlib
图表在这里:https://learn.sparkfun.com/tutorials/graph-sensor-data-with-python-and-matplotlib/speeding-up-the-plot-animation
但是,作者没有使用
PyQt5
嵌入他的现场情节。因此,我稍微修改了代码,以便在 PyQt5
中获得绘图。 window :#####################################################################################
# #
# 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_()
生成的实时绘图完全相同。但是,如果您开始玩弄
interval
来自 MyFigureCanvas()
的参数构造函数,您会注意到第一个示例将无法遵循。第二个例子可以更快。3. 问题
我有几个问题想向您提出:
QtCore
和 QtWidgets
类可以这样导入:from matplotlib.backends.qt_compat import QtCore, QtWidgets
或者像这样:from PyQt5 import QtWidgets, QtCore
两者都同样有效。是否有理由偏爱其中之一?FigureCanvas
可以这样导入:from matplotlib.backends.backend_qt5agg import FigureCanvas
或者像这样:from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
但我已经知道为什么了。 backend_qt5agg
文件似乎定义了 FigureCanvas
作为 FigureCanvasQTAgg
的别名.4. 编辑
基于网页:
https://bastibe.de/2013-05-30-speeding-up-matplotlib.html
我修改了第一个例子 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_()
结果非常惊人。修改使 第一个例子 肯定要快得多!但是,我不知道这是否使 第一个例子 现在同样快到 第二个例子 .他们肯定彼此接近。谁知道谁赢了?
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。
最佳答案
第二种情况(使用 FuncAnimation
)更快,因为 it uses "blitting" ,这避免了重绘在帧之间不会改变的东西。
matplotlib 网站上提供的用于嵌入 qt 的示例没有考虑速度,因此性能较差。你会注意到它调用了 ax.clear()
和 ax.plot()
在每次迭代中,导致每次都重新绘制整个 Canvas 。如果您要使用与 FuncAnimation
相同的代码(也就是说,创建一个 Axes 和一个艺术家,并更新艺术家中的数据而不是每次都创建一个新的艺术家)你应该非常接近我相信的相同表现。
关于python - 如何在 PyQt5 GUI 中制作快速的 matplotlib 实时绘图,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/57891219/