{
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# Quantitative Precipitation Forecast (QPF)\nThis example demonstrates how to perform QPF using radar data\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import xarray as xr\nimport swirlspy.qpf.qpfrover as rov\nimport numpy as np\nimport os\nfrom swirlspy.rad.cinrad import read_cinrad\nfrom swirlspy.rad.xrnd import xrnd\n\nimport os.path\n\ntry:\n    THIS_DIR = os.path.dirname(os.path.abspath(__file__))\nexcept NameError:\n    import sys\n    THIS_DIR = os.path.dirname(os.path.abspath(sys.argv[0]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Initialising\n\nRead data from files (in this example, the files are\nin cinrad format) and storing data as xarrays.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "file_type = 'cinrad-cband'\nvalid_time = '201805150624'\nxllcenter = 113.35\nyllcenter = 23.0\nsteps = 6\ntime_step_size = 5\n# radar_radius = 256\n\nos.chdir(THIS_DIR + \"/../tests/test_qpf\")\nfile_name1 = \"Z_RADR_I_Z9290_20180515062400_O_DOR_CB_CAP.bin\"\nfile_name2 = \"Z_RADR_I_Z9290_20180515062900_O_DOR_CB_CAP.bin\"\n\n# Reading file data, storing as xarray\nxarray1 = read_cinrad(file_name1, 113.35, 23.0,\n                      480, 480, '201805150624', 5, 'aeqd', 256, 'c-band')\nxarray2 = read_cinrad(file_name2, 113.35, 23.0,\n                      480, 480, '201805150629', 5, 'aeqd', 256, 'c-band')\nqpf_xarray = xrnd(xarray1, xarray2)\nos.chdir(THIS_DIR)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Extrapolation\nPerform rover extrapolation\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "# Calling rover\nmotion_u, motion_v, reflectivity = rov.rover_qpf(qpf_xarray, xllcenter,\n                                                 yllcenter, valid_time, steps,\n                                                 time_step_size, THIS_DIR)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Generating radar plots\n\nHere we generate radar colour-images based on forecasted\ndata over the next six timesteps.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "rov.rover_map(reflectivity, THIS_DIR)"
      ]
    }
  ],
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    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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        "version": 3
      },
      "file_extension": ".py",
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      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.15"
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