"""
=========================
Interpolations for imshow
=========================

This example displays the difference between interpolation methods for
:meth:`~.axes.Axes.imshow`.

If *interpolation* is None, it defaults to the :rc:`image.interpolation`.
If the interpolation is ``'none'``, then no interpolation is performed for the
Agg, ps and pdf backends. Other backends will default to ``'antialiased'``.

For the Agg, ps and pdf backends, ``interpolation = 'none'`` works well when a
big image is scaled down, while ``interpolation = 'nearest'`` works well when
a small image is scaled up.

See :doc:`/gallery/images_contours_and_fields/image_antialiasing` for a
discussion on the default ``interpolation="antialiased"`` option.
"""

import matplotlib.pyplot as plt
import numpy as np

methods = [None, 'none', 'nearest', 'bilinear', 'bicubic', 'spline16',
           'spline36', 'hanning', 'hamming', 'hermite', 'kaiser', 'quadric',
           'catrom', 'gaussian', 'bessel', 'mitchell', 'sinc', 'lanczos']

# Fixing random state for reproducibility
np.random.seed(19680801)

grid = np.random.rand(4, 4)

fig, axs = plt.subplots(nrows=3, ncols=6, figsize=(9, 6),
                        subplot_kw={'xticks': [], 'yticks': []})

for ax, interp_method in zip(axs.flat, methods):
    ax.imshow(grid, interpolation=interp_method, cmap='viridis')
    ax.set_title(str(interp_method))

plt.tight_layout()
plt.show()

#############################################################################
#
# ------------
#
# References
# """"""""""
#
# The use of the following functions and methods is shown
# in this example:

import matplotlib
matplotlib.axes.Axes.imshow
matplotlib.pyplot.imshow
