"""

======================================

Thresholding an Image with 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 numpy as np

import matplotlib.pyplot as plt

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`