{
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    {
      "cell_type": "markdown",
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      "source": [
        "\n# Interpolations for imshow\n\nThis example displays the difference between interpolation methods for\n`~.axes.Axes.imshow`.\n\nIf *interpolation* is None, it defaults to the :rc:`image.interpolation`.\nIf the interpolation is ``'none'``, then no interpolation is performed for the\nAgg, ps and pdf backends. Other backends will default to ``'auto'``.\n\nFor the Agg, ps and pdf backends, ``interpolation='none'`` works well when a\nbig image is scaled down, while ``interpolation='nearest'`` works well when\na small image is scaled up.\n\nSee :doc:`/gallery/images_contours_and_fields/image_antialiasing` for a\ndiscussion on the default ``interpolation='auto'`` option.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\nimport numpy as np\n\nmethods = [None, 'none', 'nearest', 'bilinear', 'bicubic', 'spline16',\n           'spline36', 'hanning', 'hamming', 'hermite', 'kaiser', 'quadric',\n           'catrom', 'gaussian', 'bessel', 'mitchell', 'sinc', 'lanczos']\n\n# Fixing random state for reproducibility\nnp.random.seed(19680801)\n\ngrid = np.random.rand(4, 4)\n\nfig, axs = plt.subplots(nrows=3, ncols=6, figsize=(9, 6),\n                        subplot_kw={'xticks': [], 'yticks': []})\n\nfor ax, interp_method in zip(axs.flat, methods):\n    ax.imshow(grid, interpolation=interp_method, cmap='viridis')\n    ax.set_title(str(interp_method))\n\nplt.tight_layout()\nplt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        ".. admonition:: References\n\n   The use of the following functions, methods, classes and modules is shown\n   in this example:\n\n   - `matplotlib.axes.Axes.imshow` / `matplotlib.pyplot.imshow`\n\n"
      ]
    }
  ],
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