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        "\n# Demo CurveLinear Grid2\n\nCustom grid and ticklines.\n\nThis example demonstrates how to use GridHelperCurveLinear to define\ncustom grids and ticklines by applying a transformation on the grid.\nAs showcase on the plot, a 5x5 matrix is displayed on the Axes.\n"
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        "import matplotlib.pyplot as plt\nimport numpy as np\n\nfrom mpl_toolkits.axisartist.axislines import Axes\nfrom mpl_toolkits.axisartist.grid_finder import (ExtremeFinderSimple,\n                                                 MaxNLocator)\nfrom mpl_toolkits.axisartist.grid_helper_curvelinear import \\\n    GridHelperCurveLinear\n\n\ndef curvelinear_test1(fig):\n    \"\"\"Grid for custom transform.\"\"\"\n\n    def tr(x, y):\n        return np.sign(x)*abs(x)**.5, y\n\n    def inv_tr(x, y):\n        return np.sign(x)*x**2, y\n\n    grid_helper = GridHelperCurveLinear(\n        (tr, inv_tr),\n        extreme_finder=ExtremeFinderSimple(20, 20),\n        # better tick density\n        grid_locator1=MaxNLocator(nbins=6), grid_locator2=MaxNLocator(nbins=6))\n\n    ax1 = fig.add_subplot(axes_class=Axes, grid_helper=grid_helper)\n    # ax1 will have a ticks and gridlines defined by the given\n    # transform (+ transData of the Axes). Note that the transform of the Axes\n    # itself (i.e., transData) is not affected by the given transform.\n\n    ax1.imshow(np.arange(25).reshape(5, 5),\n               vmax=50, cmap=plt.cm.gray_r, origin=\"lower\")\n\n\nif __name__ == \"__main__\":\n    fig = plt.figure(figsize=(7, 4))\n    curvelinear_test1(fig)\n    plt.show()"
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