{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# Extract a radar column above a point\n\nGiven a radar and a point, extract the column of radar data values above\na point\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "# Author: Maxwell Grover (mgrover@anl.gov)\n# License: BSD 3 clause\n\nimport cartopy.crs as ccrs\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nimport pyart\nfrom pyart.testing import get_test_data\n\n# Read in some test data\nfilename = get_test_data(\"swx_20120520_0641.nc\")\nradar = pyart.io.read(filename)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Plot the first sweep and our desired point**\n\nLet's visualize our radar data from a single sweep, and plot\nthe location of our desired point on a map.\nThis will provide some context as to where we are extracting our\ncolumn of values.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "site_lon = -97.73  # longitude in degrees\nsite_lat = 36.41  # latitdue in degrees\n\n# Setup the RadarMapDisplay and add our projection\ndisplay = pyart.graph.RadarMapDisplay(radar)\nax = plt.subplot(111, projection=ccrs.PlateCarree())\n\n# Visualize the reflectivity field, using the lowest sweep with\n# latitude and longitude lines\ndisplay.plot_ppi_map(\n    \"reflectivity_horizontal\",\n    0,\n    ax=ax,\n    vmin=-32,\n    vmax=64.0,\n    lon_lines=np.arange(-98, -97, 0.2),\n    lat_lines=np.arange(36, 37, 0.2),\n)\n\n# Plot our site location on top of the radar image\nax.scatter(site_lon, site_lat, color=\"black\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Now that we have our point defined, and our radar object, we can use the following\nutility function in Py-ART to subset a column\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "ds = pyart.util.columnsect.get_field_location(radar, site_lat, site_lon)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "This function returns an xarray dataset, with all of our data fields!\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "print(ds)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Visualize the Reflectivity Values in the Column**\n\nLet's visualize the reflectivity values in the column\nabove our point, which is stored in our new dataset\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "ds.corrected_reflectivity_horizontal.plot(y=\"height\")"
      ]
    }
  ],
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      "display_name": "Python 3",
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      "file_extension": ".py",
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