我目前正在创建一个带有3个matplotlib颜色Map的Qt窗口。
这个过程是这样的：
1.用户将图的x和y值输入到GUI中（每个图将具有相同的x和y值）。
1.这些值将被转换，然后被发送到微处理器，该微处理器将与DAC和ADC两者通信（仪器“填充”发生在DAC和ADC之间的“后端”上，即频谱分析仪进行噪声测量）。
1.对于每个x和y点，我们将为每个色图接收一个z值。强调：这意味着每个子图都需要同时更新。
我目前正在编写一个脚本，当数据被获取时，它将在Qt窗口中实时绘制这些颜色图。我可以找到容易获得的资源，就如何实时绘制线图;但是，我无法找到任何颜色Map表。以下是我到目前为止拥有的代码：

import sys
import time
import random

import numpy as np

from matplotlib.backends.qt_compat import QtWidgets
from matplotlib.backends.backend_qt5agg import (
    FigureCanvas, NavigationToolbar2QT as NavigationToolbar)
from matplotlib.figure import Figure
from matplotlib.colors import ListedColormap, LinearSegmentedColormap
import matplotlib as mpl
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable

# Quick note, this will need to be a widget class in the final version so it can be run from the kalamari main window
# but I think that will just change how the class is initalized

class ApplicationWindow(QtWidgets.QMainWindow):
    def __init__(self):
        # set up the window
        super().__init__()
        self._main = QtWidgets.QWidget()
        self.setCentralWidget(self._main)
        # It seems as though this layout is what is going to allow me to
        # orient all 3 subplots onto the same y axis
        layout = QtWidgets.QVBoxLayout(self._main)

        # create seperate canvas objects
        first_canvas = FigureCanvas(Figure(figsize=(9, 6)))
        layout.addWidget(NavigationToolbar(first_canvas, self))
        layout.addWidget(first_canvas)

        second_canvas = FigureCanvas(Figure(figsize=(9, 3)))
        layout.addWidget(second_canvas)
        layout.addWidget(NavigationToolbar(second_canvas, self))

        # add subplots to the first canvas
        self._first_axs = first_canvas.figure.subplots(1, 3)

        # create data and colormap

        # Here I replace X and Y with coordinate vectors which are just np.linspace
        x = np.linspace(0, 10, 10)
        y = np.linspace(0, 10, 10)
        # For the final version you can use x for flux and y for biases and then have
        # each row of z be the voltages, such that:
        # z[i,j] is V(bias[j], flux[i])
        # Then here is the data I will use to determine the color,
        # this needs to have the same dimension as the coordinates
        z = np.random.rand(10, 10)

        custom_cmap = mpl.colors.LinearSegmentedColormap.from_list(
            "custom", ["#00008B", "blue", "cyan", "green", "yellow", "orange", "red", "#8B0000"])

        # access each subplot using regular indexing
        self._first_axs[0].set_title(
            'I' + u'\u209B' + u'\u2092' + u'\u209C', size=40)
        self._first_axs[1].set_title(
            'dI' + u'\u209B' + u'\u2092' + u'\u209C' + '/dt', size=40)
        self._first_axs[2].set_title('Noise', size=40)

        # plot data and create colorbars
        self.plot1 = self._first_axs[0].contourf(
            x, y, z, levels=20, cmap=custom_cmap)
        self.plot2 = self._first_axs[1].contourf(
            x, y, z, levels=20, cmap=custom_cmap)
        self.plot3 = self._first_axs[2].contourf(
            x, y, z, levels=20, cmap=custom_cmap)
        self.cbar1 = first_canvas.figure.colorbar(
            self.plot1, ax=self._first_axs[0], orientation='horizontal')
        self.cbar2 = first_canvas.figure.colorbar(
            self.plot2, ax=self._first_axs[1], orientation='horizontal')
        self.cbar3 = first_canvas.figure.colorbar(
            self.plot3, ax=self._first_axs[2], orientation='horizontal')

        # make the second canvas a dynamic plot
        self._second_ax = second_canvas.figure.subplots()
        t = list(range(50))
        self.yData = [random.randint(0, 10) for i in range(50)]

        # Set up a Line2D.
        self._line, = self._second_ax.plot(t, self.yData)
        self._timer = second_canvas.new_timer(50)
        self._timer.add_callback(self._update_canvas)
        self._timer.start()

    def _update_canvas(self):
        t = list(range(50))
        self.yData = self.yData[1:] + [random.randint(0, 10)]
        # set line data
        self._line.set_data(t, self.yData)
        self._line.figure.canvas.draw()

if __name__ == "__main__":
    # Check for open QApplication.instance()
    qapp = QtWidgets.QApplication.instance()
    if not qapp:
        qapp = QtWidgets.QApplication(sys.argv)

    # run it!
    app = ApplicationWindow()
    app.show()
    app.activateWindow()
    app.raise_()
    qapp.exec()

这段代码目前会产生一个Qt窗口和两个独立的“canvases”。第一个画布是3个颜色Map表，第二个是一个实时图形的线图。

热门图

• 这些是静态的，但我希望它们是动态的。
  *是否有人能够帮助我确定如何在收集数据时实时绘制这些色图？

底部图

• 这是实时运行的
• 我不需要线图来实现我的目的。它只是作为实时绘图方法的参考。