subscribe not work after upgrading RxPy from 1.x to 3.x

Question

I am using Python 3.7.3.

I try to upgrade RxPy from 1.6.1 (1.x) to 3.0.0a3 (3.x).

Old code using RxPy 1.x

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)

If you run the code you can see a real-time CPU monitor chart.

However, after I installed the new RxPy by

pip3 install --pre rx

with new code below, it only shows white one without any dynamic chart.

And the function update_plot actually never ran. Any idea?

New code using RxPy 3.x

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

Time units are now in seconds

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