{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width:1000 px\">\n",
    "\n",
    "<div style=\"float:right; width:98 px; height:98px;\"><img src=\"https://pbs.twimg.com/profile_images/1187259618/unidata_logo_rgb_sm_400x400.png\" alt=\"Unidata Logo\" style=\"height: 98px;\"></div>\n",
    "\n",
    "<h1>Using Siphon to get NEXRAD Level 3 data from a TDS</h1>\n",
    "<h3>Unidata Python Workshop</h3>\n",
    "\n",
    "<div style=\"clear:both\"></div>\n",
    "</div>\n",
    "\n",
    "<hr style=\"height:2px;\">\n",
    "\n",
    "<div style=\"float:right; width:250 px\"><img src=\"https://upload.wikimedia.org/wikipedia/commons/4/4d/Siphoning.JPG\" alt=\"Siphoning\" style=\"height: 300px;\"></div>\n",
    "\n",
    "### Objectives\n",
    "1. Learn more about Siphon\n",
    "2. Use the RadarServer class to retrieve radar data from a TDS\n",
    "3. Plot this data using numpy arrays and matplotlib\n",
    "\n",
    "In this example, we'll focus on interacting with the Radar Query Service to retrieve radar data.\n",
    "\n",
    "**But first!**\n",
    "Bookmark these resources for when you want to use Siphon later!\n",
    "+ [latest Siphon documentation](http://siphon.readthedocs.org/en/latest/)\n",
    "+ [Siphon github repo](https://github.com/Unidata/siphon)\n",
    "+ [TDS documentation](http://www.unidata.ucar.edu/software/thredds/v4.6/tds/TDS.html)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Querying the server"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we point at the top level of the Radar Query Service (the \"Radar Server\") to see what radar collections are available:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['NEXRAD Level II Radar for Case Study CCS039',\n",
       " 'NEXRAD Level II Radar from IDD',\n",
       " 'NEXRAD Level III Radar for Case Study CCS039',\n",
       " 'NEXRAD Level III Radar from IDD',\n",
       " 'TDWR Level III Radar from IDD']"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from siphon.catalog import TDSCatalog\n",
    "cat = TDSCatalog('http://thredds.ucar.edu/thredds/radarServer/catalog.xml')\n",
    "list(cat.catalog_refs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we create an instance of the `RadarServer` object to point at one of these collections. This downloads some top level metadata and sets things up so we can easily query the server."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from siphon.radarserver import RadarServer\n",
    "rs = RadarServer(cat.catalog_refs['NEXRAD Level III Radar from IDD'].href)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use rs.variables to see a list of radar products available to view from this access URL."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['DAA', 'DHR', 'DOD', 'DPA', 'DPR', 'DSD', 'DSP', 'DTA', 'DU3', 'DU6', 'DVL', 'EET', 'HHC', 'N0C', 'N0H', 'N0K', 'N0M', 'N0Q', 'N0R', 'N0S', 'N0U', 'N0V', 'N0X', 'N0Z', 'N1C', 'N1H', 'N1K', 'N1M', 'N1P', 'N1Q', 'N1S', 'N1U', 'N1X', 'N2C', 'N2H', 'N2K', 'N2M', 'N2Q', 'N2S', 'N2U', 'N2X', 'N3C', 'N3H', 'N3K', 'N3M', 'N3Q', 'N3S', 'N3U', 'N3X', 'NAC', 'NAH', 'NAK', 'NAM', 'NAQ', 'NAU', 'NAX', 'NBC', 'NBH', 'NBK', 'NBM', 'NBQ', 'NBU', 'NBX', 'NCR', 'NET', 'NMD', 'NST', 'NTP', 'NVL', 'NVW', 'OHA', 'PTA']\n"
     ]
    }
   ],
   "source": [
    "print(sorted(rs.variables))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you're not a NEXRAD radar expert, there is more information available within the metadata downloaded from the server. (**NOTE:** Only the codes above are valid for queries.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DAA/Digital Accumulation Array',\n",
       " 'DHR/Digital Hybrid Scan Reflectivity',\n",
       " 'DOD/Digital One-Hour Difference Accumulation',\n",
       " 'DPA/Digital Precipitation Array',\n",
       " 'DPR/Digital Instantaneous Precipitation Rate',\n",
       " 'DSD/Digital Storm Total Difference Accumulation',\n",
       " 'DSP/Digital Storm Total Precipitation',\n",
       " 'DTA/Digital Storm Total Accumulation',\n",
       " 'DU3/Digital 3-hour Accumulation',\n",
       " 'DU6/Digital 24-hour Accumulation',\n",
       " 'DVL/Digital Vertically Integrated Liquid',\n",
       " 'EET/Enchanced Echo Tops',\n",
       " 'HHC/Hybrid Hydrometeor Classification',\n",
       " 'N0C/Correlation Coefficient Tilt 1',\n",
       " 'N0H/Hydrometeor Classification Tilt 1',\n",
       " 'N0K/Specific Differential Phase Tilt 1',\n",
       " 'N0M/Melting Layer Tilt 1',\n",
       " 'N0Q/Base Reflectivity DR Tilt 1',\n",
       " 'N0R/Base Reflectivity 124nm',\n",
       " 'N0S/Storm-Rel Mean Vel Tilt 1',\n",
       " 'N0U/Radial Velocity DV Tilt 1',\n",
       " 'N0V/Radial Velocity 124nm',\n",
       " 'N0X/Differential Reflectivity Tilt 1',\n",
       " 'N0Z/Base Reflecitvity 248nm',\n",
       " 'N1C/Correlation Coefficient Tilt 3',\n",
       " 'N1H/Hydrometeor Classification Tilt 3',\n",
       " 'N1K/Specific Differential Phase Tilt 3',\n",
       " 'N1M/Melting Layer Tilt 3',\n",
       " 'N1P/1-hour Rainfall',\n",
       " 'N1Q/Base Reflectivity DR Tilt 3',\n",
       " 'N1S/Storm-Rel Mean Vel Tilt 2',\n",
       " 'N1U/Radial Velocity DV Tilt 3',\n",
       " 'N1X/Differential Reflectivity Tilt 3',\n",
       " 'N2C/Correlation Coefficient Tilt 5',\n",
       " 'N2H/Hydrometeor Classification Tilt 5',\n",
       " 'N2K/Specific Differential Phase Tilt 5',\n",
       " 'N2M/Melting Layer Tilt 5',\n",
       " 'N2Q/Base Reflectivity DR Tilt 5',\n",
       " 'N2S/Storm-Rel Mean Vel Tilt 3',\n",
       " 'N2U/Radial Velocity DV Tilt 5',\n",
       " 'N2X/Differential Reflectivity Tilt 5',\n",
       " 'N3C/Correlation Coefficient Tilt 6',\n",
       " 'N3H/Hydrometeor Classification Tilt 6',\n",
       " 'N3K/Specific Differential Phase Tilt 6',\n",
       " 'N3M/Melting Layer Tilt 6',\n",
       " 'N3Q/Base Reflectivity DR Tilt 6',\n",
       " 'N3S/Storm-Rel Mean Vel Tilt 4',\n",
       " 'N3U/Radial Velocity DV Tilt 6',\n",
       " 'N3X/Differential Reflectivity Tilt 6',\n",
       " 'NAC/Correlation Coefficient Tilt 2',\n",
       " 'NAH/Hydrometeor Classification Tilt 2',\n",
       " 'NAK/Specific Differential Phase Tilt 2',\n",
       " 'NAM/Melting Layer Tilt 2',\n",
       " 'NAQ/Base Reflectivity DR Tilt 2',\n",
       " 'NAU/Radial Velocity DV Tilt 2',\n",
       " 'NAX/Differential Reflectivity Tilt 2',\n",
       " 'NBC/Correlation Coefficient Tilt 4',\n",
       " 'NBH/Hydrometeor Classification Tilt 4',\n",
       " 'NBK/Specific Differential Phase Tilt 4',\n",
       " 'NBM/Melting Layer Tilt 4',\n",
       " 'NBQ/Base Reflectivity DR Tilt 4',\n",
       " 'NBU/Radial Velocity DV Tilt 4',\n",
       " 'NBX/Differential Reflectivity Tilt 4',\n",
       " 'NCR/Composite Reflectivity',\n",
       " 'NET/Echo Tops',\n",
       " 'NMD/Mesocyclone',\n",
       " 'NST/Storm Tracking Information',\n",
       " 'NTP/Storm Total Rainfall',\n",
       " 'NVL/Vertically Integrated Liquid',\n",
       " 'NVW/Velocity Azimuth Display Wind Profile',\n",
       " 'OHA/One Hour Accumulation',\n",
       " 'PTA/Storm Total Accumulation']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(rs.metadata['variables'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also see a list of the stations. Each station has associated location information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ABC', 'ABR', 'ABX', 'ACG', 'AEC', 'AHG', 'AIH', 'AKC', 'AKQ', 'AMA', 'AMX', 'APD', 'APX', 'ARX', 'ATX', 'BBX', 'BGM', 'BHX', 'BIS', 'BLX', 'BMX', 'BOX', 'BRO', 'BUF', 'BYX', 'CAE', 'CBW', 'CBX', 'CCX', 'CLE', 'CLX', 'CRP', 'CXX', 'CYS', 'DAX', 'DDC', 'DFX', 'DGX', 'DIX', 'DLH', 'DMX', 'DOX', 'DTX', 'DVN', 'DYX', 'EAX', 'EMX', 'ENX', 'EOX', 'EPZ', 'ESX', 'EVX', 'EWX', 'EYX', 'FCX', 'FDR', 'FDX', 'FFC', 'FSD', 'FSX', 'FTG', 'FWS', 'GGW', 'GJX', 'GLD', 'GRB', 'GRK', 'GRR', 'GSP', 'GUA', 'GWX', 'GYX', 'HDX', 'HGX', 'HKI', 'HKM', 'HMO', 'HNX', 'HPX', 'HTX', 'HWA', 'ICT', 'ICX', 'ILN', 'ILX', 'IND', 'INX', 'IWA', 'IWX', 'JAX', 'JGX', 'JKL', 'JUA', 'LBB', 'LCH', 'LIX', 'LNX', 'LOT', 'LRX', 'LSX', 'LTX', 'LVX', 'LWX', 'LZK', 'MAF', 'MAX', 'MBX', 'MHX', 'MKX', 'MLB', 'MOB', 'MPX', 'MQT', 'MRX', 'MSX', 'MTX', 'MUX', 'MVX', 'MXX', 'NKX', 'NQA', 'OAX', 'OHX', 'OKX', 'OTX', 'PAH', 'PBZ', 'PDT', 'POE', 'PUX', 'RAX', 'RGX', 'RIW', 'RLX', 'RTX', 'SFX', 'SGF', 'SHV', 'SJT', 'SOX', 'SRX', 'TBW', 'TFX', 'TLH', 'TLX', 'TWX', 'TYX', 'UDX', 'UEX', 'VAX', 'VBX', 'VNX', 'VTX', 'VWX', 'YUX']\n"
     ]
    }
   ],
   "source": [
    "print(sorted(rs.stations))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Station(id='TLX', elevation=370.0, latitude=35.32, longitude=-97.27, name='OKLAHOMA_CITY/Norman')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs.stations['TLX']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we'll create a new query object to help request the data. Using the chaining methods, let's ask for reflectivity data at the lowest tilt (NOQ) from radar TLX (Oklahoma City) for the current time. We see that when the query is represented as a string, it shows the encoded URL."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "var=N0Q&time=2017-04-19T20%3A56%3A49.425145&stn=TLX"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "query = rs.query()\n",
    "query.stations('TLX').time(datetime.utcnow()).variables('N0Q')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The query also supports time range queries, queries for closest to a lon/lat point, or getting all radars within a lon/lat box.\n",
    "\n",
    "We can use the RadarServer instance to check our query, to make sure we have required parameters and that we have chosen valid station(s) and variable(s)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs.validate_query(query)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make the request, which returns an instance of TDSCatalog. This handles parsing the catalog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "catalog = rs.get_catalog(query)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can look at the datasets on the catalog to see what data we found by the query. We find one NIDS file in the return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('Level3_TLX_N0Q_20170419_2051.nids',\n",
       "              <siphon.catalog.Dataset at 0x10fd3d748>)])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "catalog.datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise: Querying the radar server\n",
    "\n",
    "We'll work through doing some more queries on the radar server. Some useful links:\n",
    "- RadarQuery [documentation](https://siphon.readthedocs.org/en/latest/api/radarserver.html#siphon.radarserver.RadarQuery)\n",
    "- Documentation on Python's [datetime.timedelta](https://docs.python.org/3.5/library/datetime.html#timedelta-objects)\n",
    "\n",
    "See if you can write Python code for the following queries:\n",
    "\n",
    "Get ZDR (differential reflectivity) for 3 days ago from the radar nearest to Hays, KS (lon -99.324403, lat 38.874929). **No map necessary!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get base reflectivity for the last two hours from all of the radars in Wyoming (call it the bounding box with lower left corner 41.008717, -111.056360 and upper right corner 44.981008, -104.042719)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pulling out the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can pull that dataset out of the dictionary and look at the available access URLs. We see URLs for OPeNDAP, CDMRemote, and HTTPServer (direct download)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'CdmRemote': 'http://thredds.ucar.edu/thredds/cdmremote/nexrad/level3/IDD/N0Q/TLX/20170419/Level3_TLX_N0Q_20170419_2051.nids',\n",
       " 'HTTPServer': 'http://thredds.ucar.edu/thredds/fileServer/nexrad/level3/IDD/N0Q/TLX/20170419/Level3_TLX_N0Q_20170419_2051.nids',\n",
       " 'OPENDAP': 'http://thredds.ucar.edu/thredds/dodsC/nexrad/level3/IDD/N0Q/TLX/20170419/Level3_TLX_N0Q_20170419_2051.nids'}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = list(catalog.datasets.values())[0]\n",
    "ds.access_urls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll use the CDMRemote reader in Siphon and pass it the appropriate access URL. (This will all behave identically to using the 'OPENDAP' access, if we replace the `Dataset` from Siphon with that from `netCDF4`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from siphon.cdmr import Dataset\n",
    "data = Dataset(ds.access_urls['CdmRemote'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The CDMRemote reader provides an interface that is almost identical to the usual python NetCDF interface. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['elevation',\n",
       " 'azimuth',\n",
       " 'gate',\n",
       " 'latitude',\n",
       " 'longitude',\n",
       " 'altitude',\n",
       " 'rays_time',\n",
       " 'BaseReflectivityDR_RAW',\n",
       " 'BaseReflectivityDR']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(data.variables)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We pull out the variables we need for azimuth and range, as well as the data itself."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rng = data.variables['gate'][:]\n",
    "az = data.variables['azimuth'][:]\n",
    "ref = data.variables['BaseReflectivityDR'][:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then convert the polar coordinates to Cartesian using numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "x = rng * np.sin(np.deg2rad(az))[:, None]\n",
    "y = rng * np.cos(np.deg2rad(az))[:, None]\n",
    "ref = np.ma.array(ref, mask=np.isnan(ref))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we plot them up using matplotlib and cartopy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JBAKWhKPdrszQ4I3rwdqEPcThw4exY8cOt81wFeYJM5yEecL+Cke7bYMeXvGE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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1174b1ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy\n",
    "from metpy.plots import ctables  # For NWS colortable\n",
    "\n",
    "# Create projection centered on the radar. This allows us to use x\n",
    "# and y relative to the radar.\n",
    "proj = cartopy.crs.LambertConformal(central_longitude=data.RadarLongitude,\n",
    "                                    central_latitude=data.RadarLatitude)\n",
    "\n",
    "# New figure with specified projection\n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "ax = fig.add_subplot(1, 1, 1, projection=proj)\n",
    "\n",
    "# Grab state borders\n",
    "state_borders = cartopy.feature.NaturalEarthFeature(\n",
    "    category='cultural', name='admin_1_states_provinces_lakes',\n",
    "    scale='50m', facecolor='none')\n",
    "ax.add_feature(state_borders, edgecolor='black', linewidth=2, zorder=2)\n",
    "\n",
    "# Counties\n",
    "counties = cartopy.io.shapereader.Reader('../../data/counties.shp')\n",
    "ax.add_geometries(counties.geometries(), cartopy.crs.PlateCarree(),\n",
    "                  facecolor='None', edgecolor='grey', zorder=1)\n",
    "\n",
    "# Set limits in lat/lon space\n",
    "ax.set_extent([data.RadarLongitude - 2.5, data.RadarLongitude + 2.5,\n",
    "               data.RadarLatitude - 2.5, data.RadarLatitude + 2.5])\n",
    "\n",
    "# Get the NWS typical reflectivity color table, along with an appropriate norm that\n",
    "# starts at 5 dBz and has steps in 5 dBz increments\n",
    "norm, cmap = ctables.registry.get_with_steps('NWSReflectivity', 5, 5)\n",
    "mesh = ax.pcolormesh(x, y, ref, cmap=cmap, norm=norm, zorder=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise: Your turn to plot!\n",
    "\n",
    "Try making your own plot of radar data. Various options here, but this is pretty open-ended. Some options to inspire you:\n",
    "- Try working with Level II Data (All variables are three-dimensional and in a single file)\n",
    "- Try plotting Storm Total Precipitation or Digital Accumulation for an area that recently had a bunch of rain\n",
    "- Maybe plot Doppler velocity or some dual-pol data (ZDR or correlation coefficient) for recent severe weather. Bonus points for making a multi-panel plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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