python - 你如何实时 plot 颜色图？

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()

import numpy as np import matplotlib.pyplot as plt rng = np.random.default_rng() N = 25 x = np.linspace(-1, 1, N) y = np.linspace(-1, 1, N) Niter = 25 fig, ax = plt.subplots() ax.set_aspect(1) for _ in range(Niter): ax.cla() z = rng.uniform(size=(N,N)) ax.contourf(x, y, z) fig.canvas.draw() renderer = fig.canvas.renderer ax.draw(renderer) plt.pause(0.01)