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        "\n# Skew-T plot of a sounding\n\nThis example shows how to make a Skew-T plot from a sounding\nand calculate stability indicies.\n"
      ]
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      "source": [
        "import xarray as xr\nfrom arm_test_data import DATASETS\nfrom matplotlib import pyplot as plt\n\nimport act\n\n# Make sure attributes are retained\nxr.set_options(keep_attrs=True)\n\n# Read data\nfilename_sonde = DATASETS.fetch('sgpsondewnpnC1.b1.20190101.053200.cdf')\nsonde_ds = act.io.arm.read_arm_netcdf(filename_sonde)\n\nprint(list(sonde_ds))\n# Calculate stability indicies\nsonde_ds = act.retrievals.calculate_stability_indicies(\n    sonde_ds, temp_name='tdry', td_name='dp', p_name='pres'\n)\nprint(sonde_ds['lifted_index'])\n\n# Set up plot\nskewt = act.plotting.SkewTDisplay(sonde_ds, figsize=(15, 10))\n\n# Add data\nskewt.plot_from_u_and_v('u_wind', 'v_wind', 'pres', 'tdry', 'dp')\n\nplt.show()\n# One could also add options like adiabats and mixing lines\nskewt = act.plotting.SkewTDisplay(sonde_ds, figsize=(15, 10))\nskewt.plot_from_u_and_v(\n    'u_wind',\n    'v_wind',\n    'pres',\n    'tdry',\n    'dp',\n    plot_dry_adiabats=True,\n    plot_moist_adiabats=True,\n    plot_mixing_lines=True,\n)\nplt.show()\nsonde_ds.close()"
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