bqplot和ipywidget滑块，最大递归深度

Question

如果我执行下面的Notebook，一切似乎都很好。然而，当我移动滑块时，笔记本的响应性几乎降到了零，一段时间后，我得到了以下错误：

RecursionError: maximum recursion depth exceeded in comparison

你能告诉我为什么吗？

# We'll start with bqplot's matplotlib inspired API

from bqplot import pyplot as plt

# Let's begin by importing some libraries we'll need
import numpy as np

import ipywidgets as wi

def make_data(x, sigma_noise):
    """
    Generates a sine wave

    :param x: x value, scalar or vector between 0 and ..
    :param sigma_noise: standard deviation of the noise added to the data
    """
    y = np.sin(x)
    noise = np.random.normal(loc=0, scale=sigma_noise, size=len(x))
    y_noise = y + noise   
    return y_noise


def update_plot(message):
    y_noise = make_data(x, slider.value)
    plot_1.y = y_noise

x = np.linspace(0, 10)
y = make_data(x, noise)

figure = plt.figure(title='Test', animation_duration=100)
#figure.animation_duration = 250
plot_1 = plt.scatter(x, y)
plot_1.observe(update_plot, ['x','y'])

slider = wi.FloatSlider(description='noise', value=0.001, min=0, max=1)
slider.observe(update_plot, 'value')

wi.VBox([slider, figure])

编辑: DougR提供的解决方案有效。另外，我找到了另一个解决方案:用figure.observe(update_plot, ['x','y'])代替plot_1.observe(update_plot, ['x','y'])

Answer 1

您在plot1图上有一个观察调用，该调用随后更新plot1图...这导致了递归循环。如果将If语句放入函数中，则可以对其进行设置，使其仅在第一次操作后运行。

# We'll start with bqplot's matplotlib inspired API

from bqplot import pyplot as plt

# Let's begin by importing some libraries we'll need
import numpy as np

import ipywidgets as wi

def make_data(x, sigma_noise):
    """
    Generates a sine wave

    :param x: x value, scalar or vector between 0 and ..
    :param sigma_noise: standard deviation of the noise added to the data
    """
    y = np.sin(x)
    noise = np.random.normal(loc=0, scale=sigma_noise, size=len(x))
    y_noise = y + noise   
    return y_noise


def update_plot(message):
#     print(message)
    if message['name'] == 'value':
        y_noise = make_data(x, slider.value)
        plot_1.y = y_noise

noise = 2
x = np.linspace(0, 10)
y = make_data(x, noise)

figure = plt.figure(title='Test', animation_duration=100)
#figure.animation_duration = 250
plot_1 = plt.scatter(x, y)
plot_1.observe(update_plot, ['x','y'])

slider = wi.FloatSlider(description='noise', value=0.001, min=0, max=1)
slider.observe(update_plot, 'value')

wi.VBox([slider, figure])