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"""
=================================
Image scaling using a RangeSlider
=================================
Using the RangeSlider widget to control the thresholding of an image.
The RangeSlider widget can be used similarly to the `.widgets.Slider`
widget. The major difference is that RangeSlider's ``val`` attribute
is a tuple of floats ``(lower val, upper val)`` rather than a single float.
See :doc:`/gallery/widgets/slider_demo` for an example of using
a ``Slider`` to control a single float.
See :doc:`/gallery/widgets/slider_snap_demo` for an example of having
the ``Slider`` snap to discrete values.
"""
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.widgets import RangeSlider
# generate a fake image
np.random.seed(19680801)
N = 128
img = np.random.randn(N, N)
fig, axs = plt.subplots(1, 2, figsize=(10, 5))
fig.subplots_adjust(bottom=0.25)
im = axs[0].imshow(img)
axs[1].hist(img.flatten(), bins='auto')
axs[1].set_title('Histogram of pixel intensities')
# Create the RangeSlider
slider_ax = fig.add_axes([0.20, 0.1, 0.60, 0.03])
slider = RangeSlider(slider_ax, "Threshold", img.min(), img.max())
# Create the Vertical lines on the histogram
lower_limit_line = axs[1].axvline(slider.val[0], color='k')
upper_limit_line = axs[1].axvline(slider.val[1], color='k')
def update(val):
# The val passed to a callback by the RangeSlider will
# be a tuple of (min, max)
# Update the image's colormap
im.norm.vmin = val[0]
im.norm.vmax = val[1]
# Update the position of the vertical lines
lower_limit_line.set_xdata([val[0], val[0]])
upper_limit_line.set_xdata([val[1], val[1]])
# Redraw the figure to ensure it updates
fig.canvas.draw_idle()
slider.on_changed(update)
plt.show()
# %%
#
# .. admonition:: References
#
# The use of the following functions, methods, classes and modules is shown
# in this example:
#
# - `matplotlib.widgets.RangeSlider`
|
