{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# Layer images with alpha blending\n\nLayer images above one another using alpha blending\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\nimport numpy as np\n\n\ndef func3(x, y):\n    return (1 - x / 2 + x**5 + y**3) * np.exp(-(x**2 + y**2))\n\n\n# make these smaller to increase the resolution\ndx, dy = 0.05, 0.05\n\nx = np.arange(-3.0, 3.0, dx)\ny = np.arange(-3.0, 3.0, dy)\nX, Y = np.meshgrid(x, y)\n\n# when layering multiple images, the images need to have the same\n# extent.  This does not mean they need to have the same shape, but\n# they both need to render to the same coordinate system determined by\n# xmin, xmax, ymin, ymax.  Note if you use different interpolations\n# for the images their apparent extent could be different due to\n# interpolation edge effects\n\nextent = np.min(x), np.max(x), np.min(y), np.max(y)\nfig = plt.figure(frameon=False)\n\nZ1 = np.add.outer(range(8), range(8)) % 2  # chessboard\nim1 = plt.imshow(Z1, cmap=plt.cm.gray, interpolation='nearest',\n                 extent=extent)\n\nZ2 = func3(X, Y)\n\nim2 = plt.imshow(Z2, cmap=plt.cm.viridis, alpha=.9, interpolation='bilinear',\n                 extent=extent)\n\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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      "display_name": "Python 3",
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      "codemirror_mode": {
        "name": "ipython",
        "version": 3
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      "file_extension": ".py",
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      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
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