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      "source": [
        "\n# Contourf and log color scale\n\nDemonstrate use of a log color scale in contourf\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\nimport numpy as np\nfrom numpy import ma\n\nfrom matplotlib import cm, ticker\n\nN = 100\nx = np.linspace(-3.0, 3.0, N)\ny = np.linspace(-2.0, 2.0, N)\n\nX, Y = np.meshgrid(x, y)\n\n# A low hump with a spike coming out.\n# Needs to have z/colour axis on a log scale, so we see both hump and spike.\n# A linear scale only shows the spike.\nZ1 = np.exp(-X**2 - Y**2)\nZ2 = np.exp(-(X * 10)**2 - (Y * 10)**2)\nz = Z1 + 50 * Z2\n\n# Put in some negative values (lower left corner) to cause trouble with logs:\nz[:5, :5] = -1\n\n# The following is not strictly essential, but it will eliminate\n# a warning.  Comment it out to see the warning.\nz = ma.masked_where(z <= 0, z)\n\n\n# Automatic selection of levels works; setting the\n# log locator tells contourf to use a log scale:\nfig, ax = plt.subplots()\ncs = ax.contourf(X, Y, z, locator=ticker.LogLocator(), cmap=cm.PuBu_r)\n\n# Alternatively, you can manually set the levels\n# and the norm:\n# lev_exp = np.arange(np.floor(np.log10(z.min())-1),\n#                    np.ceil(np.log10(z.max())+1))\n# levs = np.power(10, lev_exp)\n# cs = ax.contourf(X, Y, z, levs, norm=colors.LogNorm())\n\ncbar = fig.colorbar(cs)\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.contourf` / `matplotlib.pyplot.contourf`\n   - `matplotlib.figure.Figure.colorbar` / `matplotlib.pyplot.colorbar`\n   - `matplotlib.axes.Axes.legend` / `matplotlib.pyplot.legend`\n   - `matplotlib.ticker.LogLocator`\n\n"
      ]
    }
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