python - 如何通过回调更新 PyQt 中的散点图？

Question

我有这个程序，当用户单击按钮时，我想用用户给出的新数据更新先前绘制的图。我想做的是向用户展示分类器系统的决策边界图，当用户添加新数据时，我想相应地更新我的散点图。这是我的代码:

from matplotlib.backends.backend_qt5agg import (
    FigureCanvasQTAgg,
    FigureManagerQT,
)
from PyQt5 import QtWidgets
import matplotlib.pyplot as plt
from matplotlib.figure import Figure
import numpy as np
class CustomFigureCanvas(FigureCanvasQTAgg):
    def __init__(self, parent=None, cmap_name="coolwarm"):
        fig = Figure()
        self.color_map = plt.get_cmap(cmap_name)
        self.axes = fig.add_subplot(111)
        super().__init__(fig)
        self.setParent(parent)
        self.setBaseSize(300, 300)
        self.setMaximumSize(400, 400)
        self.setMinimumSize(250, 250)
        self.setSizePolicy(
            QtWidgets.QSizePolicy.MinimumExpanding,
            QtWidgets.QSizePolicy.MinimumExpanding,
        )

    def set_clf_2d(self, clf_2d):
        self.clf = clf_2d

    def plot_new_datapoints(self, x2D):
        self.add_datapoint(x2D)

    @staticmethod
    def _make_meshgrid(x, y, h=0.02):
        x_min, x_max = x.min() - 1, x.max() + 1
        y_min, y_max = y.min() - 1, y.max() + 1
        XX, YY = np.meshgrid(
            np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)
        )
        return XX, YY

    def _plot_contours(self, xx, yy, **params):
        """Plot the decision boundaries for a classifier.

        Parameters
        ----------
        ax: matplotlib axes object
        clf: a classifier
        xx: meshgrid ndarray
        yy: meshgrid ndarray
        params: dictionary of params to pass to contourf, optional
        """
        Z = self.clf.predict(np.c_[xx.ravel(), yy.ravel()])
        Z = Z.reshape(xx.shape)
        self.axes.contourf(xx, yy, Z, **params)

    def plot_data(self, x2D, y):
        """plots the given array and the decision function bounday.

        Arguments:
            x2D {np.array} -- [2d array]
            y {np.array} -- [1d array]
        """

        x0, x1 = x2D[:, 0], x2D[:, 1]
        xx, yy = CustomFigureCanvas._make_meshgrid(x0, x1)
        labels = ["Cognitive", "Not Cognitive"]
        colors = ["r", "b"]
        self.axes.clear()
        self._plot_contours(xx, yy, cmap=self.color_map, alpha=0.8)
        target_ids = [0, 1]

        for i, c, label in zip(target_ids, colors, labels):
            print(i, label)
            self.axes.scatter(
                x0[y == i],
                x1[y == i],
                color=c,
                label=label,
                marker="o",
                s=(15, 15),
            )

        self.axes.set_xlim(xx.min(), xx.max())
        self.axes.set_ylim(yy.min(), yy.max())
        self.axes.set_title("2D Representation using PCA")
        self.axes.legend(fontsize=8)
        self.axes.plot()

    def add_datapoint(self, x2d):
        """Adds a new datapoint to the plot

        Arguments:
            x2d {a 2d single point, [x,y]} -- [np.array with shape (1,2)]
            axes {plt.axes} -- [description]

        """
        print(x2d, type(x2d))
        self.axes.scatter(
            x2d[:, 0],
            x2d[:, 1],
            color="k",
            label="Current Text",
            marker="o",
            s=(15, 15),
        )
        self.axes.legend(fontsize=8)
        self.axes.plot()

我当前遇到的问题是，在调用_plot_contours之后，绘图将不会更新。在阅读了 matplotlib 中的“可更新”图形后，我看到一些建议使用 plt.ion() 来生成可更新的图形。还有一些关于使用 FuncAnimation 类的建议，但这并不完全是我需要的解决方案，因为它不依赖于用户的按钮单击回调，而是在给定的时间间隔内刷新绘图。

编辑:这是重现我遇到的问题的最小代码:

import sys
from PyQt5 import QtWidgets

from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.backends.backend_qt5agg import (
    NavigationToolbar2QT as NavigationToolbar,
)
from matplotlib.figure import Figure
from custom_figure_canvas import CustomFigureCanvas

import random
import numpy as np
from sklearn.svm import SVC


class Window(QtWidgets.QDialog):
    def __init__(self, parent=None):
        super(Window, self).__init__(parent)

        # a figure instance to plot on
        self.figure = Figure()

        # this is the Canvas Widget that displays the `figure`
        # it takes the `figure` instance as a parameter to __init__
        self.canvas = CustomFigureCanvas(parent=self)

        # this is the Navigation widget
        # it takes the Canvas widget and a parent
        self.toolbar = NavigationToolbar(self.canvas, self)

        # Just some button connected to `plot` method
        self.button = QtWidgets.QPushButton("Plot")
        self.button.clicked.connect(self.plot)

        # set the layout
        layout = QtWidgets.QVBoxLayout()
        layout.addWidget(self.toolbar)
        layout.addWidget(self.canvas)
        layout.addWidget(self.button)
        self.setLayout(layout)

    def plot(self):

        x2D = np.random.rand(50, 2)
        y = np.random.randint(0, 2, size=(50,))

        x2D_train = np.random.rand(50, 2)
        y_train = np.random.randint(0, 2, size=(50,))

        clf = SVC()
        clf.fit(x2D_train, y_train)

        print(x2D)
        self.canvas.set_clf_2d(clf)
        self.canvas.plot_data(x2D, y)


if __name__ == "__main__":
    app = QtWidgets.QApplication(sys.argv)

    main = Window()
    main.show()

    sys.exit(app.exec_())

Answer 1

对于使用 Qt 的 matplotlib，您必须刷新绘图，为此您可以使用以下方法:

self.axes.figure.canvas.draw_idle()

或者

self.axes.figure.canvas.draw()

就您而言:

# ...

def _plot_contours(self, xx, yy, **params):
    # ...
    self.axes.contourf(xx, yy, Z, **params)
    self.axes.figure.canvas.draw()

def plot_data(self, x2D, y):
    # ...
    self.axes.plot()
    self.axes.figure.canvas.draw()

# ...

输出: