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        "\n# Consolidation of CoURAGE Data Sources\n\nThis example shows how to use ACT to combine multiple\ndatasets to support ARM's CoURAGE deployment in\nBaltimore, MD.  Example uses ARM, EPA, and ASOS data.\n"
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        "import os\nfrom datetime import datetime\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nimport act\n\n# Get Surface Meteorology data\nlat = (39.04, 39.6)\nlon = (-77.10, -76.04)\ntime_window = [datetime(2024, 12, 15), datetime(2024, 12, 20)]\nasos_dict = act.discovery.get_asos_data(time_window, lat_range=lat, lon_range=lon, regions='MD')\nasos_stations = asos_dict.keys()\n\n# Set up a dictionary to fill with data\ndata_dict = {}\n\n# Fill the dictionary with ASOS data\nfor s in asos_stations:\n    ds = asos_dict[s]\n    ds = ds.where(~np.isnan(ds.tmpf), drop=True)\n    ds['tmpf'].attrs['units'] = 'degF'\n    ds.utils.change_units(variables='tmpf', desired_unit='degC', verbose=True)\n    data_dict[s] = ds\n\n# You need an account and token from https://docs.airnowapi.org/ first\n# And then you can download EPA data\nairnow_token = os.getenv('AIRNOW_API')\nif airnow_token is not None and len(airnow_token) > 0:\n    latlon = '-76.905,39.185,-76.158,39.499'\n\n    ds_airnow = act.discovery.get_airnow_bounded_obs(\n        airnow_token, '2024-12-15T00', '2024-12-20T23', latlon, 'OZONE,PM25', data_type='B'\n    )\n    ds_airnow = act.utils.convert_2d_to_1d(ds_airnow, parse='sites')\n    sites = ds_airnow['sites'].values\n    data_dict['EPA'] = ds_airnow\n    airnow = True\n\n# Place your username and token here\nusername = os.getenv('ARM_USERNAME')\ntoken = os.getenv('ARM_PASSWORD')\n\n# Download ARM data\nif username is not None and token is not None and len(username) > 1:\n    # Example to show how easy it is to download ARM data if a username/token are set\n    sdate = '2024-12-15'\n    edate = '2024-12-20'\n\n    # Download and read ARM MET data\n    results = act.discovery.download_arm_data(username, token, 'crgmetM1.b1', sdate, edate)\n    ds_arm = act.io.arm.read_arm_netcdf(results)\n    data_dict['ARM'] = ds_arm\n\n    # Download and read ARM Ozone data\n    results = act.discovery.download_arm_data(username, token, 'crgaoso3S2.a1', sdate, edate)\n    ds_o3 = act.io.arm.read_arm_netcdf(results, cleanup_qc=True)\n    ds_o3.qcfilter.datafilter('o3', rm_assessments=['Suspect', 'Bad'], del_qc_var=False)\n    data_dict['ARM_O3'] = ds_o3\n\n    # Download and read ARM SMPS data\n    results = act.discovery.download_arm_data(username, token, 'crgaossmpsS2.b1', sdate, edate)\n    ds_smps = act.io.arm.read_arm_netcdf(results)\n    data_dict['ARM_SMPS'] = ds_smps\n\n    # Set up plot and plot all surface temperature data\n    display = act.plotting.TimeSeriesDisplay(data_dict, figsize=(12, 10), subplot_shape=(3,))\n    for k in data_dict.keys():\n        if 'ARM' not in k and k != 'EPA':\n            display.plot('tmpf', dsname=k, label=k, subplot_index=(0,))\n        elif k == 'ARM':\n            display.plot('temp_mean', dsname=k, label=k, subplot_index=(0,))\n            display.day_night_background(dsname='ARM', subplot_index=(0,))\n\n    display.set_yrng([-5, 20], subplot_index=(0,))\n\n    # Plot up ozone data\n    title = 'Comparison of ARM and EPA Ozone Measurements'\n    display.plot('o3', dsname='ARM_O3', label='ARM', subplot_index=(1,))\n    if airnow:\n        display.plot(\n            'OZONE_sites_2',\n            dsname='EPA',\n            label='EPA ' + sites[2],\n            subplot_index=(1,),\n            set_title=title,\n        )\n    display.set_yrng([0, 60], subplot_index=(1,))\n    display.day_night_background(dsname='ARM', subplot_index=(1,))\n\n    # Plot SMPS data\n    title = 'ARM SMPS Concentrations and EPA PM2.5'\n    if airnow:\n        display.plot('PM2.5_sites_0', dsname='EPA', label='EPA ' + sites[0], subplot_index=(2,))\n        display.plot('PM2.5_sites_1', dsname='EPA', label='EPA ' + sites[1], subplot_index=(2,))\n        display.plot(\n            'PM2.5_sites_3',\n            dsname='EPA',\n            label='EPA ' + sites[3],\n            subplot_index=(2,),\n            set_title=title,\n        )\n\n    display.set_yrng([0, 30], subplot_index=(2,))\n    plt.legend(loc=2)\n    ax2 = display.axes[2].twinx()\n    ax2.plot(ds_smps['time'], ds_smps['total_N_conc'], color='purple')\n    ax2.set_ylabel('ARM SMPS (' + ds_smps['total_N_conc'].attrs['units'] + ')')\n    display.day_night_background(dsname='ARM', subplot_index=(2,))\n\n    plt.show()"
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