在 Python 上使用 PyQtChart 或 pyqtgraph 与 PyQt5 绘制流数据图表的最佳方法？

Question

我正在流式传输我想要高效绘制图表的 TimeSeries（在小型计算机上实时显示 20 多个图表）。 我已经在 PyQt5 上尝试过 PyQtChart 和 pyqtgraph，但是对于这两个库，我最终会为收到的每个数据重新绘制整个图表，这感觉不是最佳的。 我选择了 PyQtChart，因为它可以更好地处理 DatetimeSeries，但很高兴被证明是错误的（并分享 pyqtgraph 代码，只是不想让帖子太大）。

下面是我使用随机数据使用 PyQtChart 的工作代码，以便您可以运行它：

import sys
from random import randint
from typing import Union

from PyQt5.QtChart import (QChart, QChartView, QLineSeries, QDateTimeAxis, QValueAxis)
from PyQt5.QtCore import Qt, QDateTime, QTimer
from PyQt5.QtWidgets import QApplication
from PyQt5.QtWidgets import (QWidget, QGridLayout)


class Window(QWidget):
    def __init__(self, window_name: str = 'Ticker'):
        QWidget.__init__(self)
        # GUI
        self.setGeometry(200, 200, 600, 400)
        self.window_name: str = window_name
        self.setWindowTitle(self.window_name)
        layout = QGridLayout(self)

        # change the color of the window
        self.setStyleSheet('background-color:black')

        # Series
        self.high_dataset = QLineSeries()
        self.low_dataset = QLineSeries()
        self.mid_dataset = QLineSeries()

        self.low_of_day: Union[float, None] = 5
        self.high_of_day: Union[float, None] = 15

        # Y Axis
        self.time_axis_y = QValueAxis()
        self.time_axis_y.setLabelFormat("%.2f")
        self.time_axis_y.setTitleText("Price")
        # X Axis
        self.time_axis_x = QDateTimeAxis()
        self.time_axis_x.setFormat("hh:mm:ss")
        self.time_axis_x.setTitleText("Datetime")

        # Events
        self.qt_timer = QTimer()
        # QChart
        self.chart = QChart()
        self.chart.addSeries(self.mid_dataset)
        self.chart.addSeries(self.high_dataset)
        self.chart.addSeries(self.low_dataset)

        self.chart.setTitle("Barchart Percent Example")
        self.chart.setTheme(QChart.ChartThemeDark)

        # https://linuxtut.com/fr/35fb93c7ca35f9665d9f/

        self.chart.legend().setVisible(True)
        self.chart.legend().setAlignment(Qt.AlignBottom)

        self.chartview = QChartView(self.chart)

        # using -1 to span through all rows available in the window
        layout.addWidget(self.chartview, 2, 0, -1, 3)

        self.chartview.setChart(self.chart)

    def set_yaxis(self):
        # Y Axis Settings
        self.time_axis_y.setRange(int(self.low_of_day * .9), int(self.high_of_day * 1.1))

        self.chart.addAxis(self.time_axis_y, Qt.AlignLeft)

        self.mid_dataset.attachAxis(self.time_axis_y)
        self.high_dataset.attachAxis(self.time_axis_y)
        self.low_dataset.attachAxis(self.time_axis_y)

    def set_xaxis(self):
        # X Axis Settings
        self.chart.removeAxis(self.time_axis_x)

        self.time_axis_x = QDateTimeAxis()
        self.time_axis_x.setFormat("hh:mm:ss")
        self.time_axis_x.setTitleText("Datetime")
        self.chart.addAxis(self.time_axis_x, Qt.AlignBottom)

        self.mid_dataset.attachAxis(self.time_axis_x)
        self.high_dataset.attachAxis(self.time_axis_x)
        self.low_dataset.attachAxis(self.time_axis_x)

    def start_app(self):
        self.qt_timer.timeout.connect(self.retrieveStream, )
        time_to_wait: int = 500  # milliseconds
        self.qt_timer.start(time_to_wait)

    def retrieveStream(self):
        date_px = QDateTime()
        date_px = date_px.currentDateTime().toMSecsSinceEpoch()
        print(date_px)

        mid_px = randint(int((self.low_of_day + 2) * 100), int((self.high_of_day - 2) * 100)) / 100

        self.mid_dataset.append(date_px, mid_px)
        self.low_dataset.append(date_px, self.low_of_day)
        self.high_dataset.append(date_px, self.high_of_day)

        print(f"epoch: {date_px}, mid: {mid_px:.2f}")

        self.update()

    def update(self):
        print("updating chart")

        self.chart.removeSeries(self.mid_dataset)
        self.chart.removeSeries(self.low_dataset)
        self.chart.removeSeries(self.high_dataset)

        self.chart.addSeries(self.mid_dataset)
        self.chart.addSeries(self.high_dataset)
        self.chart.addSeries(self.low_dataset)

        self.set_yaxis()
        self.set_xaxis()


if __name__ == '__main__':
    app = QApplication(sys.argv)
    window = Window()
    window.show()
    window.start_app()

    sys.exit(app.exec_())

此代码最大的担忧是：

1. “更新”方法基本上重新绘制图表的每个元素=>我更喜欢双端队列，刷新/更新/重新触发类型的解决方案
2. QLineSeries 似乎没有像双端队列集合那样的 maxLen，因此我最终可能会得到大量数据（理想情况下希望运行三个以上的 QLineSeries）

除此之外，我将很高兴收到关于如何优化此代码的任何见解。 我是 Qt/Asyncio/Threading 的新手，非常渴望学习。

最佳

编辑图表现在更新，无需重新绘制所有内容 让我知道是否有更好的方法，或者需要改进的代码，因为我是 Qt 的新手。

感谢下面的回答（@domarm），我纠​​正了更新图表的方式，下面的链接让我意识到我需要在每次刷新时设置轴的最小最大值，以便数据在范围内。

使用轴更新 Qchart

import sys
from datetime import datetime
from random import randint
from typing import Union, Optional

from PyQt5.QtChart import (QChart, QChartView, QLineSeries, QDateTimeAxis, QValueAxis)
from PyQt5.QtCore import (Qt, QDateTime, QTimer, QPointF)
from PyQt5.QtGui import QFont
from PyQt5.QtWidgets import (QWidget, QGridLayout, QLabel, QApplication)


# https://doc.qt.io/qt-5/qtcharts-modeldata-example.html

class Window(QWidget):
    running = False

    def __init__(self, window_name: str = 'Chart',
                 chart_title: Optional[str] = None,
                 geometry_ratio: int = 2,
                 histo_tick_size: int = 200):
        QWidget.__init__(self)
        # GUI
        self.window_wideness: int = 300
        self.histo_tick_size: int = histo_tick_size
        self.setGeometry(200,
                         200,
                         int(self.window_wideness * geometry_ratio),
                         self.window_wideness
                         )
        self.window_name: str = window_name
        self.setWindowTitle(self.window_name)
        self.label_color: str = 'grey'
        self.text_color: str = 'white'
        # Layout
        layout = QGridLayout(self)

        # Gui components
        bold_font = QFont()
        bold_font.setBold(True)

        self.label_last_px = QLabel('-', self)
        self.label_last_px.setFont(bold_font)
        self.label_last_px.setStyleSheet("QLabel { color : blue; }")
        layout.addWidget(self.label_last_px)

        # change the color of the window
        self.setStyleSheet('background-color:black')
        # QChart
        self.chart = QChart()
        if chart_title:
            self.chart.setTitle(chart_title)
        # Series
        self.high_dataset = QLineSeries(self.chart)
        self.high_dataset.setName("High")

        self.low_dataset = QLineSeries(self.chart)
        self.low_dataset.setName("Low")

        self.mid_dataset = QLineSeries(self.chart)
        self.mid_dataset.setName("Mid")

        self.low_of_day: Union[float, None] = 5
        self.high_of_day: Union[float, None] = 15
        self.last_data_point: dict = {"last_date": None, "mid_px": None, "low_px": None, "high_px": None}

        # Y Axis
        self.time_axis_y = QValueAxis()
        self.time_axis_y.setLabelFormat("%.2f")
        self.time_axis_y.setTitleText("Price")

        # X Axis
        self.time_axis_x = QDateTimeAxis()
        self.time_axis_x.setTitleText("Datetime")

        # Events
        self.qt_timer = QTimer()

        self.chart.setTheme(QChart.ChartThemeDark)
        self.chart.addSeries(self.mid_dataset)
        self.chart.addSeries(self.low_dataset)
        self.chart.addSeries(self.high_dataset)
        # https://linuxtut.com/fr/35fb93c7ca35f9665d9f/

        self.chart.legend().setVisible(True)
        # self.chart.legend().setAlignment(Qt.AlignBottom)

        self.chartview = QChartView(self.chart)
        # self.chartview.chart().setAxisX(self.axisX, self.mid_dataset)

        # using -1 to span through all rows available in the window
        layout.addWidget(self.chartview, 2, 0, -1, 3)

        self.chartview.setChart(self.chart)

    def set_yaxis(self):
        # Y Axis Settings
        self.time_axis_y.setRange(int(self.low_of_day * .9), int(self.high_of_day * 1.1))

        self.chart.addAxis(self.time_axis_y, Qt.AlignLeft)

        self.mid_dataset.attachAxis(self.time_axis_y)
        self.high_dataset.attachAxis(self.time_axis_y)
        self.low_dataset.attachAxis(self.time_axis_y)

    def set_xaxis(self):
        # X Axis Settings
        self.chart.removeAxis(self.time_axis_x)
        # X Axis
        self.time_axis_x = QDateTimeAxis()
        self.time_axis_x.setFormat("hh:mm:ss")
        self.time_axis_x.setTitleText("Datetime")

        point_first: QPointF = self.mid_dataset.at(0)
        point_last: QPointF = self.mid_dataset.at(len(self.mid_dataset) - 1)

        # needs to be updated each time for chart to render
        # https://stackoverflow.com/questions/57079698/qdatetimeaxis-series-are-not-displayed
        self.time_axis_x.setMin(QDateTime().fromMSecsSinceEpoch(point_first.x()).addSecs(0))
        self.time_axis_x.setMax(QDateTime().fromMSecsSinceEpoch(point_last.x()).addSecs(0))

        self.chart.addAxis(self.time_axis_x, Qt.AlignBottom)

        self.mid_dataset.attachAxis(self.time_axis_x)
        self.high_dataset.attachAxis(self.time_axis_x)
        self.low_dataset.attachAxis(self.time_axis_x)

    def _update_label_last_px(self):
        last_point: QPointF = self.mid_dataset.at(self.mid_dataset.count() - 1)
        last_date: datetime = datetime.fromtimestamp(last_point.x() / 1000)
        last_price = last_point.y()
        self.label_last_px.setText(f"Date time: {last_date.strftime('%d-%m-%y %H:%M %S')}  "
                                   f"Price: {last_price:.2f}")

    def start_app(self):
        """Start Thread generator"""
        # This method is supposed to stream data but not the issue, problem is that chart is not updating
        self.qt_timer.timeout.connect(self.update, )
        time_to_wait: int = 250  # milliseconds
        self.qt_timer.start(time_to_wait)

    def update(self):
        """ Update chart and Label with the latest data in Series"""
        print("updating chart")
        self._update_label_last_px()
        # date_px = QDateTime()
        # self.last_data_point['last_date'] = date_px.currentDateTime().toMSecsSinceEpoch()

        date_px = datetime.now().timestamp() * 1000
        self.last_data_point['last_date'] = date_px
        # Make up a price
        self.last_data_point['mid_px'] = randint(int((self.low_of_day + 2) * 100),
                                                 int((self.high_of_day - 2) * 100)) / 100
        self.last_data_point['low_date'] = self.low_of_day
        self.last_data_point['high_date'] = self.high_of_day
        print(self.last_data_point)

        # Feed datasets and simulate deque
        # https://www.qtcentre.org/threads/67774-Dynamically-updating-QChart
        if self.mid_dataset.count() > self.histo_tick_size:
            self.mid_dataset.remove(0)
            self.low_dataset.remove(0)
            self.high_dataset.remove(0)

        self.mid_dataset.append(self.last_data_point['last_date'], self.last_data_point['mid_px'])
        self.low_dataset.append(self.last_data_point['last_date'], self.last_data_point['low_date'])
        self.high_dataset.append(self.last_data_point['last_date'], self.last_data_point['high_date'])
        self.set_xaxis()
        self.set_yaxis()


if __name__ == '__main__':
    app = QApplication(sys.argv)
    window = Window()
    window.show()
    window.start_app()

    sys.exit(app.exec())

Answer 1

您可以使用 pglive 包来绘制实时流中的数据。它基于 pyqtgraph，可以轻松处理 ~100Hz 的数据速率。
它使用DataConnector，它将数据存储在deque中，并使用pyqt信号来更新线程安全的绘图。如果您的输入数据以高速率更新，您还可以设置以 Hz 为单位的更新速率。

还有一些额外的功能可用，例如引导线或十字准线，这使得在鼠标光标下显示精确值变得容易。

以下是基于您输入的示例代码：

import sys
import time
from random import randint
from threading import Thread
from time import sleep
from typing import Union

from PyQt5.QtWidgets import QWidget, QApplication, QGridLayout
from pglive.kwargs import Axis
from pglive.sources.data_connector import DataConnector
from pglive.sources.live_axis import LiveAxis
from pglive.sources.live_plot import LiveLinePlot
from pglive.sources.live_plot_widget import LivePlotWidget


class Window(QWidget):
    running = False

    def __init__(self, parent=None):
        super().__init__(parent)
        layout = QGridLayout(self)
        self.low_of_day: Union[float, None] = 5
        self.high_of_day: Union[float, None] = 15

        # Create one curve pre dataset
        high_plot = LiveLinePlot(pen="blue")
        low_plot = LiveLinePlot(pen="orange")
        mid_plot = LiveLinePlot(pen="green")

        # Data connectors for each plot with dequeue of 600 points
        self.high_connector = DataConnector(high_plot, max_points=600)
        self.low_connector = DataConnector(low_plot, max_points=600)
        self.mid_connector = DataConnector(mid_plot, max_points=600)

        # Setup bottom axis with TIME tick format
        # You can use Axis.DATETIME to show date as well
        bottom_axis = LiveAxis("bottom", **{Axis.TICK_FORMAT: Axis.TIME})

        # Create plot itself
        self.chart_view = LivePlotWidget(title="Line Plot - Time series @ 2Hz", axisItems={'bottom': bottom_axis})
        # Show grid
        self.chart_view.showGrid(x=True, y=True, alpha=0.3)
        # Set labels
        self.chart_view.setLabel('bottom', 'Datetime', units="s")
        self.chart_view.setLabel('left', 'Price')
        # Add all three curves
        self.chart_view.addItem(mid_plot)
        self.chart_view.addItem(low_plot)
        self.chart_view.addItem(high_plot)

        # using -1 to span through all rows available in the window
        layout.addWidget(self.chart_view, 2, 0, -1, 3)

    def update(self):
        """Generate data at 2Hz"""
        while self.running:
            timestamp = time.time()

            mid_px = randint(int((self.low_of_day + 2) * 100), int((self.high_of_day - 2) * 100)) / 100

            self.mid_connector.cb_append_data_point(mid_px, timestamp)
            self.low_connector.cb_append_data_point(self.low_of_day, timestamp)
            self.high_connector.cb_append_data_point(self.high_of_day, timestamp)

            print(f"epoch: {timestamp}, mid: {mid_px:.2f}")
            sleep(0.5)

    def start_app(self):
        """Start Thread generator"""
        self.running = True
        Thread(target=self.update).start()


if __name__ == '__main__':
    app = QApplication(sys.argv)
    window = Window()
    window.show()
    window.start_app()
    app.exec()
    window.running = False

以下是它的动态外观：

pyqtgraph 的一个小缺点是对情节外观的定制有点尴尬。但这是因为 pqytgraph 是为了速度而构建的。 pglive 还解决了您缺乏时间和日期时间格式的问题。

肯定还有其他好的软件包可以处理这个问题，但如果您的目标是获得良好的性能，那么这可能是一个不错的选择。