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      "source": [
        "\n# Cloud Base Height Retrievals\n\nThis example shows how to calculate the cloud base heights\nusing the sobel edge detection method.  This can be used\nfor vertical radar and lidar data.\n\nAuthor: Adam Theisen\n"
      ]
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      "source": [
        "import numpy as np\nfrom arm_test_data import DATASETS\nfrom matplotlib import pyplot as plt\n\nimport act\n\n# Read Ceilometer data for an example\nfilename_ceil = DATASETS.fetch('sgpceilC1.b1.20190101.000000.nc')\nds = act.io.arm.read_arm_netcdf(filename_ceil)\n\nds = act.retrievals.cbh.generic_sobel_cbh(\n    ds, variable='backscatter', height_dim='range', var_thresh=1000.0, fill_na=0.0\n)\n\n# Plot the cloud base height data\ndisplay = act.plotting.TimeSeriesDisplay(ds, subplot_shape=(1, 2), figsize=(16, 6))\ndisplay.plot('backscatter', subplot_index=(0, 0))\ntitle = 'SGP Ceilometer with Lidar-Calculated CBH Overplotted'\ndisplay.plot('first_cbh', subplot_index=(0, 0), color='k', set_title=title)\n\ndisplay.plot('backscatter', subplot_index=(0, 1))\ntitle = 'SGP Ceilometer with CBH Overplotted'\ndisplay.plot('cbh_sobel_backscatter', color='k', subplot_index=(0, 1), set_title=title)\n\ndiff = ds['first_cbh'].values - ds['cbh_sobel_backscatter'].values\n\nprint(\"Average difference between ceilomter and sobel heights \", np.nanmean(diff))\n\nds.close()\nplt.show()"
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