{
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    {
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      "source": [
        "\n# Xarray Plotting Examples\n\nThis is an example of how to use some different aspects\nof ACT's plotting tools as well as Xarray's tools.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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      "source": [
        "import matplotlib.pyplot as plt\nfrom arm_test_data import DATASETS\n\nimport act\n\n# Set up plot space ahead of time\nfig, ax = plt.subplots(3, figsize=(10, 7))\n\n# Plotting up high-temporal resolution 2D data can be very slow at times.\n# In order to increase the speed, the data can be resampled to a courser\n# resolution prior to plotting.  Using Xarray's resample and selecting\n# the nearest neighbor will greatly increase the speed.\nfilename_ceil = DATASETS.fetch('sgpceilC1.b1.20190101.000000.nc')\nds = act.io.arm.read_arm_netcdf(filename_ceil)\nds = ds.resample(time='1min').nearest()\n\n# These data can be plotted up using the existing xarray functionality\n# which is quick and easy\nds['backscatter'].plot(x='time', ax=ax[0])\n\n# or using ACT\ndisplay = act.plotting.TimeSeriesDisplay(ds)\ndisplay.assign_to_figure_axis(fig, ax[1])\ndisplay.plot('backscatter')\n\n# When using ACT, the axis object can also be manipulated using normal\n# matplotlib calls for more personalized customizations\ndisplay = act.plotting.TimeSeriesDisplay(ds)\ndisplay.assign_to_figure_axis(fig, ax[2])\ndisplay.plot('backscatter')\ndisplay.axes[-1].set_ylim([0, 1500])\n\nplt.show()"
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