{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# IMPROVE Data\n\nThis example shows how to get IMPROVE data for the\nsystem located at ARM's Southern Great Plains site.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n\nimport act\n\n# Pull the data using the site_id from IMPROVE\n# https://views.cira.colostate.edu/adms/Pub/SiteSummary.aspx?dsidse=10001&siidse=244\n# Data from the IMPROVE station next to ARM's BNF deployment\n# can be found using site_id='21'\nds = act.discovery.get_improve_data(site_id='244', start_date='1/1/2023', end_date='12/31/2023')\n\n# Remove all data that's set to the FillValue\nds = ds.where(ds['aluminum_fine'] != ds['aluminum_fine'].attrs['_FillValue'])\n\ndisplay = act.plotting.TimeSeriesDisplay(ds, figsize=(10, 6))\ndisplay.plot('aluminum_fine')\n\n# Print out the known problems documented by IMPROVE\nprint(ds.attrs['site_problems'])\n\n# Write out the data to netCDF and csv\nds.to_netcdf('./sgpimprove.20230101.nc')\nds.to_dataframe().to_csv('sgpimprove.20230101.csv')\n\nplt.show()"
      ]
    }
  ],
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      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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      "file_extension": ".py",
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      "pygments_lexer": "ipython3",
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