使用matplotlib

Question

我正在尝试使用matplotlib实时绘制数据。到目前为止，我仅成功显示了静态图形窗口并让自己感到沮丧。我根据此处的其他答案尝试了许多细微的更改，但似乎没有任何效果。我的代码如下所示。任何帮助表示赞赏。

import matplotlib.pyplot as plt
import numpy as np

class Plotter():
    def __init__(self):
        self.error = []
        self.fig, self.ax = plt.subplots()
        self.line, = self.ax.plot(range(len(self.error)), self.error)

    def start_plot(self):
        plt.ion()


    def end_plot(self):
        self.error = []

    def update(self, worker):
        self.error.append(worker.GetMetricValue())
        self.line.set_ydata(self.error)
        return self.line

    def update_plot(self, worker):
        self.error.append(worker.Get_metric_value())
        self.ax.set_ylim(np.min(self.error)*1.1, (np.max(self.error)+0.1)*1.1)
        self.line.set_ydata(self.error)
        self.line.set_xdata(range(len(self.error)))
        self.fig.canvas.draw()
        plt.pause(0.1)
        #self.fig.canvas.flush_events()

    def get_error(self):
        return self.error



class WorkerClass():
    def __init__(self):
        pass

    def Get_metric_value(self):
        return np.random.rand()


def main():
    worker = WorkerClass()
    pltr = Plotter()
    pltr.start_plot()
    for i in range(10):
        print("iteration {}".format(i))
        pltr.update_plot(worker)


if __name__ == '__main__':
    main()

Answer 1

这是该代码的更正版本。我建议对动画使用FuncAnimation，因为它更稳定并且可以在图形的事件循环中运行。

import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
import numpy as np

class Plotter():
    def __init__(self):
        self.worker = WorkerClass()
        self.error = []
        self.fig, self.ax = plt.subplots()
        self.line, = self.ax.plot(range(len(self.error)), self.error)

    def start_plot(self):
        self.ani = FuncAnimation(self.fig, self.update_plot, frames=10, 
                                 interval=100, repeat=False)
        plt.show()

    def end_plot(self):
        self.error = []

    def update_plot(self, i):
        self.error.append(self.worker.Get_metric_value())
        self.line.set_data(range(len(self.error)), self.error)
        self.ax.relim()
        self.ax.autoscale_view()

    def get_error(self):
        return self.error


class WorkerClass():
    def __init__(self):
        pass

    def Get_metric_value(self):
        return np.random.rand()


def main():

    pltr = Plotter()
    pltr.start_plot()

if __name__ == '__main__':
    main()