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      "source": [
        "\n# pcolormesh\n\n`.axes.Axes.pcolormesh` allows you to generate 2D image-style plots.\nNote that it is faster than the similar `~.axes.Axes.pcolor`.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
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      "source": [
        "import matplotlib.pyplot as plt\nimport numpy as np\n\nfrom matplotlib.colors import BoundaryNorm\nfrom matplotlib.ticker import MaxNLocator"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Basic pcolormesh\n\nWe usually specify a pcolormesh by defining the edge of quadrilaterals and\nthe value of the quadrilateral.  Note that here *x* and *y* each have one\nextra element than Z in the respective dimension.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "np.random.seed(19680801)\nZ = np.random.rand(6, 10)\nx = np.arange(-0.5, 10, 1)  # len = 11\ny = np.arange(4.5, 11, 1)  # len = 7\n\nfig, ax = plt.subplots()\nax.pcolormesh(x, y, Z)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Non-rectilinear pcolormesh\n\nNote that we can also specify matrices for *X* and *Y* and have\nnon-rectilinear quadrilaterals.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
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      "source": [
        "x = np.arange(-0.5, 10, 1)  # len = 11\ny = np.arange(4.5, 11, 1)  # len = 7\nX, Y = np.meshgrid(x, y)\nX = X + 0.2 * Y  # tilt the coordinates.\nY = Y + 0.3 * X\n\nfig, ax = plt.subplots()\nax.pcolormesh(X, Y, Z)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Centered Coordinates\n\nOften a user wants to pass *X* and *Y* with the same sizes as *Z* to\n`.axes.Axes.pcolormesh`. This is also allowed if ``shading='auto'`` is\npassed (default set by :rc:`pcolor.shading`). Pre Matplotlib 3.3,\n``shading='flat'`` would drop the last column and row of *Z*, but now gives\nan error. If this is really what you want, then simply drop the last row and\ncolumn of Z manually:\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "x = np.arange(10)  # len = 10\ny = np.arange(6)  # len = 6\nX, Y = np.meshgrid(x, y)\n\nfig, axs = plt.subplots(2, 1, sharex=True, sharey=True)\naxs[0].pcolormesh(X, Y, Z, vmin=np.min(Z), vmax=np.max(Z), shading='auto')\naxs[0].set_title(\"shading='auto' = 'nearest'\")\naxs[1].pcolormesh(X, Y, Z[:-1, :-1], vmin=np.min(Z), vmax=np.max(Z),\n                  shading='flat')\naxs[1].set_title(\"shading='flat'\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Making levels using Norms\n\nShows how to combine Normalization and Colormap instances to draw\n\"levels\" in `.axes.Axes.pcolor`, `.axes.Axes.pcolormesh`\nand `.axes.Axes.imshow` type plots in a similar\nway to the levels keyword argument to contour/contourf.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "# make these smaller to increase the resolution\ndx, dy = 0.05, 0.05\n\n# generate 2 2d grids for the x & y bounds\ny, x = np.mgrid[slice(1, 5 + dy, dy),\n                slice(1, 5 + dx, dx)]\n\nz = np.sin(x)**10 + np.cos(10 + y*x) * np.cos(x)\n\n# x and y are bounds, so z should be the value *inside* those bounds.\n# Therefore, remove the last value from the z array.\nz = z[:-1, :-1]\nlevels = MaxNLocator(nbins=15).tick_values(z.min(), z.max())\n\n\n# pick the desired colormap, sensible levels, and define a normalization\n# instance which takes data values and translates those into levels.\ncmap = plt.colormaps['PiYG']\nnorm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)\n\nfig, (ax0, ax1) = plt.subplots(nrows=2)\n\nim = ax0.pcolormesh(x, y, z, cmap=cmap, norm=norm)\nfig.colorbar(im, ax=ax0)\nax0.set_title('pcolormesh with levels')\n\n\n# contours are *point* based plots, so convert our bound into point\n# centers\ncf = ax1.contourf(x[:-1, :-1] + dx/2.,\n                  y[:-1, :-1] + dy/2., z, levels=levels,\n                  cmap=cmap)\nfig.colorbar(cf, ax=ax1)\nax1.set_title('contourf with levels')\n\n# adjust spacing between subplots so `ax1` title and `ax0` tick labels\n# don't overlap\nfig.tight_layout()\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.pcolormesh` / `matplotlib.pyplot.pcolormesh`\n   - `matplotlib.axes.Axes.contourf` / `matplotlib.pyplot.contourf`\n   - `matplotlib.figure.Figure.colorbar` / `matplotlib.pyplot.colorbar`\n   - `matplotlib.colors.BoundaryNorm`\n   - `matplotlib.ticker.MaxNLocator`\n\n"
      ]
    }
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