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# Enabling the `widget` backend.
# This requires jupyter-matplotlib a.k.a. ipympl.
# ipympl can be install via pip or conda.
%matplotlib widget
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import matplotlib.pyplot as plt
import numpy as np
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# When using the `widget` backend from ipympl,
# fig.canvas is a proper Jupyter interactive widget, which can be embedded in
# Layout classes like HBox and Vbox.
# One can bound figure attributes to other widget values.
from ipywidgets import HBox, FloatSlider
plt.ioff()
plt.clf()
slider = FloatSlider(
orientation='vertical',
value=1.0,
min=0.02,
max=2.0
)
fig = plt.figure(3)
x = np.linspace(0, 20, 500)
lines = plt.plot(x, np.sin(slider.value * x))
def update_lines(change):
lines[0].set_data(x, np.sin(change.new * x))
fig.canvas.draw()
fig.canvas.flush_events()
slider.observe(update_lines, names='value')
HBox([slider, fig.canvas])