python - 将 RxPy 从 1.x 升级到 3.x 后订阅不起作用

Question

我正在使用 Python 3.7.3。

我尝试将 RxPy 从 1.6.1 (1.x) 升级到 3.0.0a3 (3.x)。

使用 RxPy 1.x 的旧代码

from rx import Observable
import psutil
import numpy as np
import pylab as plt

cpu_data = (Observable
            .interval(100)  # Each 100 milliseconds
            .map(lambda x: psutil.cpu_percent())
            .publish())
cpu_data.connect()


def monitor_cpu(npoints):
    lines, = plt.plot([], [])
    plt.xlim(0, npoints)
    plt.ylim(0, 100)

    cpu_data_window = cpu_data.buffer_with_count(npoints, 1)

    def update_plot(cpu_readings):
        lines.set_xdata(np.arange(len(cpu_readings)))
        lines.set_ydata(np.array(cpu_readings))
        plt.draw()

    alertpoints = 4
    high_cpu = (cpu_data
                .buffer_with_count(alertpoints, 1)
                .map(lambda readings: all(r > 20 for r in readings)))

    label = plt.text(1, 1, "normal")

    def update_warning(is_high):
        if is_high:
            label.set_text("high")
        else:
            label.set_text("normal")

    high_cpu.subscribe(update_warning)
    cpu_data_window.subscribe(update_plot)

    plt.show()


if __name__ == '__main__':
    monitor_cpu(10)

如果您运行代码，您可以看到实时 CPU 监控图表。

但是，在我安装了新的 RxPy 之后

pip3 install --pre rx

使用下面的新代码，它只显示白色，没有任何动态图表。

而且该功能update_plot实际上从未运行过。任何想法？

使用 RxPy 3.x 的新代码

from rx import interval, operators as op
import psutil
import numpy as np
import pylab as plt


cpu_data = interval(100).pipe(  # Each 100 milliseconds
    op.map(lambda x: psutil.cpu_percent()),
    op.publish())
cpu_data.connect()


def monitor_cpu(npoints):
    lines, = plt.plot([], [])
    plt.xlim(0, npoints)
    plt.ylim(0, 100)

    cpu_data_window = cpu_data.pipe(
        op.buffer_with_count(npoints, 1))

    def update_plot(cpu_readings):
        print('update')  # here never runs
        lines.set_xdata(np.arange(len(cpu_readings)))
        lines.set_ydata(np.array(cpu_readings))
        plt.draw()

    alertpoints = 4
    high_cpu = cpu_data.pipe(
                op.buffer_with_count(alertpoints, 1),
                op.map(lambda readings: all(r > 20 for r in readings)))

    label = plt.text(1, 1, "normal")

    def update_warning(is_high):
        if is_high:
            label.set_text("high")
        else:
            label.set_text("normal")

    high_cpu.subscribe(update_warning)
    cpu_data_window.subscribe(update_plot)

    plt.show()


if __name__ == '__main__':
    monitor_cpu(10)

Answer 1

时间单位现在以秒为单位

cpu_data = interval(0.1).pipe(  # Each 100 milliseconds
    op.map(lambda x: psutil.cpu_percent()),
    op.publish())
cpu_data.connect()