python-awips/examples/notebooks/Grid_Levels_and_Parameters.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This examples covers the callable methods of the Python AWIPS DAF when working with gridded data.  We start with a connection to an EDEX server, then query data types, then grid names, parameters, levels, and other information. Finally the gridded data is plotted for its domain using Matplotlib and Cartopy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DataAccessLayer.changeEDEXHost()\n",
    "\n",
    "After DataAccessLayer is imported from the package `awips.dataaccess`, the first step is to define the EDEX data server hostname (`edex-cloud.unidata.ucar.edu` for these examples)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from awips.dataaccess import DataAccessLayer\n",
    "DataAccessLayer.changeEDEXHost(\"edex-cloud.unidata.ucar.edu\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DataAccessLayer.getSupportedDatatypes()\n",
    "\n",
    "getSupportedDatatypes() returns a list of available data types offered by the EDEX server defined above. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['acars',\n",
       " 'airep',\n",
       " 'binlightning',\n",
       " 'bufrmosavn',\n",
       " 'bufrmoseta',\n",
       " 'bufrmosgfs',\n",
       " 'bufrmoshpc',\n",
       " 'bufrmoslamp',\n",
       " 'bufrmosmrf',\n",
       " 'bufrua',\n",
       " 'climate',\n",
       " 'common_obs_spatial',\n",
       " 'ffmp',\n",
       " 'gfe',\n",
       " 'grid',\n",
       " 'hydro',\n",
       " 'ldadmesonet',\n",
       " 'maps',\n",
       " 'modelsounding',\n",
       " 'obs',\n",
       " 'pirep',\n",
       " 'practicewarning',\n",
       " 'profiler',\n",
       " 'radar',\n",
       " 'radar_spatial',\n",
       " 'satellite',\n",
       " 'sfcobs',\n",
       " 'topo',\n",
       " 'warning']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTypes = DataAccessLayer.getSupportedDatatypes()\n",
    "list(dataTypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DataAccessLayer.newDataRequest()\n",
    "\n",
    "Now create a new data request, and set the data type to **grid** and \"locationName\" to **RAP40** with **setDataType()** and **setLocationNames()**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "request = DataAccessLayer.newDataRequest()\n",
    "request.setDatatype(\"grid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DataAccessLayer.getAvailableLocationNames()\n",
    "\n",
    "With datatype set to \"grid\", we can query all available grid names with **getAvailableLocationNames()**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['CMC',\n",
       " 'DGEX',\n",
       " 'ESTOFS',\n",
       " 'ETSS',\n",
       " 'FFG-ALR',\n",
       " 'FFG-FWR',\n",
       " 'FFG-KRF',\n",
       " 'FFG-MSR',\n",
       " 'FFG-ORN',\n",
       " 'FFG-PTR',\n",
       " 'FFG-RHA',\n",
       " 'FFG-RSA',\n",
       " 'FFG-STR',\n",
       " 'FFG-TAR',\n",
       " 'FFG-TIR',\n",
       " 'FFG-TUA',\n",
       " 'FNMOC-FAROP',\n",
       " 'FNMOC-NCODA',\n",
       " 'FNMOC-WW3',\n",
       " 'GFS',\n",
       " 'GFS20',\n",
       " 'GribModel:7:14:108',\n",
       " 'HFR-EAST_6KM',\n",
       " 'HFR-EAST_PR_6KM',\n",
       " 'HFR-US_EAST_DELAWARE_1KM',\n",
       " 'HFR-US_EAST_FLORIDA_2KM',\n",
       " 'HFR-US_EAST_NORTH_2KM',\n",
       " 'HFR-US_EAST_SOUTH_2KM',\n",
       " 'HFR-US_EAST_VIRGINIA_1KM',\n",
       " 'HFR-US_HAWAII_1KM',\n",
       " 'HFR-US_HAWAII_2KM',\n",
       " 'HFR-US_HAWAII_6KM',\n",
       " 'HFR-US_WEST_500M',\n",
       " 'HFR-US_WEST_CENCAL_2KM',\n",
       " 'HFR-US_WEST_LOSANGELES_1KM',\n",
       " 'HFR-US_WEST_LOSOSOS_1KM',\n",
       " 'HFR-US_WEST_NORTH_2KM',\n",
       " 'HFR-US_WEST_SANFRAN_1KM',\n",
       " 'HFR-US_WEST_SOCAL_2KM',\n",
       " 'HFR-US_WEST_WASHINGTON_1KM',\n",
       " 'HFR-WEST_6KM',\n",
       " 'HPCGuide',\n",
       " 'HPCqpf',\n",
       " 'HPCqpfNDFD',\n",
       " 'HRRR',\n",
       " 'LAMP2p5',\n",
       " 'MOSGuide',\n",
       " 'MOSGuideExtended',\n",
       " 'MRMS_0500',\n",
       " 'MRMS_1000',\n",
       " 'NAM12',\n",
       " 'NAM40',\n",
       " 'NAVGEM',\n",
       " 'NCWF',\n",
       " 'NOHRSC-SNOW',\n",
       " 'NamDNG',\n",
       " 'QPE-ALR',\n",
       " 'QPE-FWR',\n",
       " 'QPE-KRF',\n",
       " 'QPE-MSR',\n",
       " 'QPE-ORN',\n",
       " 'QPE-TIR',\n",
       " 'QPE-TUA',\n",
       " 'RAP13',\n",
       " 'RAP20',\n",
       " 'RAP40',\n",
       " 'RFCqpf',\n",
       " 'RTMA',\n",
       " 'UKMET-Global',\n",
       " 'UKMET-MODEL1',\n",
       " 'URMA25',\n",
       " 'WaveWatch']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "available_grids = DataAccessLayer.getAvailableLocationNames(request)\n",
    "available_grids.sort()\n",
    "list(available_grids)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set grid name with `setLocationNames()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "request.setLocationNames(\"RAP13\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# List Available Parameters for a Grid\n",
    "\n",
    "### DataAccessLayer.getAvailableParameters()\n",
    "\n",
    "After datatype and model name (locationName) are set, you can query all available parameters with **getAvailableParameters()**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['BLI',\n",
       " 'CAPE',\n",
       " 'CFRZR',\n",
       " 'CICEP',\n",
       " 'CIn',\n",
       " 'CP1hr',\n",
       " 'CRAIN',\n",
       " 'CSNOW',\n",
       " 'GH',\n",
       " 'HPBL',\n",
       " 'Heli',\n",
       " 'LgSP1hr',\n",
       " 'MMSP',\n",
       " 'P',\n",
       " 'PR',\n",
       " 'PVV',\n",
       " 'PW',\n",
       " 'REFC',\n",
       " 'RH',\n",
       " 'SLI',\n",
       " 'SnD',\n",
       " 'T',\n",
       " 'USTM',\n",
       " 'VSTM',\n",
       " 'Vis',\n",
       " 'WEASD',\n",
       " 'WEASD1hr',\n",
       " 'WGS',\n",
       " 'uW',\n",
       " 'vW']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "availableParms = DataAccessLayer.getAvailableParameters(request)\n",
    "availableParms.sort()\n",
    "list(availableParms)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### setParameters()\n",
    "\n",
    "\n",
    "set the request parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "request.setParameters(\"T\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## List Available Levels for Parameter\n",
    "\n",
    "Using **DataAccessLayer.getAvailableLevels()**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "150.0MB\n",
      "575.0MB\n",
      "90.0_120.0BL\n",
      "925.0MB\n",
      "400.0MB\n",
      "900.0MB\n",
      "700.0MB\n",
      "100.0MB\n",
      "60.0_90.0BL\n",
      "975.0MB\n",
      "450.0MB\n",
      "225.0MB\n",
      "550.0MB\n",
      "750.0MB\n",
      "425.0MB\n",
      "350.0MB\n",
      "525.0MB\n",
      "500.0MB\n",
      "2.0FHAG\n",
      "675.0MB\n",
      "150.0_180.0BL\n",
      "200.0MB\n",
      "0.0TROP\n",
      "375.0MB\n",
      "1000.0MB\n",
      "30.0_60.0BL\n",
      "650.0MB\n",
      "950.0MB\n",
      "825.0MB\n",
      "475.0MB\n",
      "125.0MB\n",
      "800.0MB\n",
      "775.0MB\n",
      "625.0MB\n",
      "600.0MB\n",
      "275.0MB\n",
      "300.0MB\n",
      "725.0MB\n",
      "325.0MB\n",
      "250.0MB\n",
      "0.0_30.0BL\n",
      "875.0MB\n",
      "120.0_150.0BL\n",
      "850.0MB\n",
      "175.0MB\n"
     ]
    }
   ],
   "source": [
    "availableLevels = DataAccessLayer.getAvailableLevels(request)\n",
    "for level in availableLevels:\n",
    "    print(level)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **0.0SFC** is the Surface level\n",
    "* **FHAG** stands for Fixed Height Above Ground (in meters)\n",
    "* **NTAT** stands for Nominal Top of the ATmosphere\n",
    "* **BL** stands for Boundary Layer, where **0.0_30.0BL** reads as *0-30 mb above ground level*  \n",
    "* **TROP** is the Tropopause level\n",
    "\n",
    "### request.setLevels()\n",
    "\n",
    "For this example we will use Surface Temperature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "request.setLevels(\"2.0FHAG\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DataAccessLayer.getAvailableTimes()\n",
    "\n",
    "* **getAvailableTimes(request, True)** will return an object of *run times* - formatted as `YYYY-MM-DD HH:MM:SS`\n",
    "* **getAvailableTimes(request)** will return an object of all times - formatted as `YYYY-MM-DD HH:MM:SS (F:ff)`\n",
    "* **getForecastRun(cycle, times)** will return a DataTime array for a single forecast cycle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<DataTime instance: 2017-02-01 16:00:00 (1) >,\n",
       " <DataTime instance: 2017-02-01 16:00:00 (2) >,\n",
       " <DataTime instance: 2017-02-01 16:00:00 (3) >,\n",
       " <DataTime instance: 2017-02-01 16:00:00 (4) >,\n",
       " <DataTime instance: 2017-02-01 16:00:00 (6) >,\n",
       " <DataTime instance: 2017-02-01 16:00:00 (15) >,\n",
       " <DataTime instance: 2017-02-01 16:00:00 (16) >,\n",
       " <DataTime instance: 2017-02-01 16:00:00 (17) >,\n",
       " <DataTime instance: 2017-02-01 16:00:00 (18) >,\n",
       " <DataTime instance: 2017-02-01 16:00:00 (19) >,\n",
       " <DataTime instance: 2017-02-01 16:00:00 (20) >,\n",
       " <DataTime instance: 2017-02-01 16:00:00 (21) >]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cycles = DataAccessLayer.getAvailableTimes(request, True)\n",
    "times = DataAccessLayer.getAvailableTimes(request)\n",
    "fcstRun = DataAccessLayer.getForecastRun(cycles[-1], times)\n",
    "list(fcstRun)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Request a Grid\n",
    "\n",
    "### DataAccessLayer.getGridData()\n",
    "\n",
    "Now that we have our `request` and DataTime `fcstRun` arrays ready, it's time to request the data array from EDEX."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Time :', '2017-02-01 16:00:00 (21)')\n",
      "('Model:', 'RAP13')\n",
      "('Parm :', 'T')\n",
      "('Unit :', 'K')\n",
      "(337, 451)\n"
     ]
    }
   ],
   "source": [
    "response = DataAccessLayer.getGridData(request, [fcstRun[-1]])\n",
    "for grid in response:\n",
    "    data = grid.getRawData()\n",
    "    lons, lats = grid.getLatLonCoords()\n",
    "    print('Time :', str(grid.getDataTime()))\n",
    "\n",
    "print('Model:', str(grid.getLocationName()))\n",
    "print('Parm :', str(grid.getParameter()))\n",
    "print('Unit :', str(grid.getUnit()))\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot a Grid with Matplotlib and Cartopy\n",
    "\n",
    "## 1. pcolormesh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnwAAAHqCAYAAACeOpOVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nHEzHjh1N2zLzySefsGn
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f214250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cfeature\n",
    "from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n",
    "import numpy as np\n",
    "import numpy.ma as ma\n",
    "from scipy.io import loadmat\n",
    "def make_map(bbox, projection=ccrs.PlateCarree()):\n",
    "    fig, ax = plt.subplots(figsize=(16, 9),\n",
    "                           subplot_kw=dict(projection=projection))\n",
    "    ax.set_extent(bbox)\n",
    "    ax.coastlines(resolution='50m')\n",
    "    gl = ax.gridlines(draw_labels=True)\n",
    "    gl.xlabels_top = gl.ylabels_right = False\n",
    "    gl.xformatter = LONGITUDE_FORMATTER\n",
    "    gl.yformatter = LATITUDE_FORMATTER\n",
    "    return fig, ax\n",
    "\n",
    "cmap = plt.get_cmap('rainbow')\n",
    "bbox = [lons.min(), lons.max(), lats.min(), lats.max()]\n",
    "fig, ax = make_map(bbox=bbox)\n",
    "cs = ax.pcolormesh(lons, lats, data, cmap=cmap)\n",
    "cbar = fig.colorbar(cs, extend='both', shrink=0.5, orientation='horizontal')\n",
    "cbar.set_label(str(grid.getLocationName()) +\" \" \\\n",
    "               + str(grid.getLevel()) + \" \" \\\n",
    "               + str(grid.getParameter()) \\\n",
    "               + \" (\" + str(grid.getUnit()) + \") \" \\\n",
    "               + \"valid \" + str(grid.getDataTime().getRefTime()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. contourf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnwAAAHsCAYAAABIY3CIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nePAALy8vHB0d+eyzzxg
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f0f6910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2, ax2 = make_map(bbox=bbox)\n",
    "cs2 = ax2.contourf(lons, lats, data, 80, cmap=cmap,\n",
    "                  vmin=data.min(), vmax=data.max())\n",
    "cbar2 = fig2.colorbar(cs2, extend='both', shrink=0.5, orientation='horizontal')\n",
    "cbar2.set_label(str(grid.getLocationName()) +\" \" \\\n",
    "               + str(grid.getLevel()) + \" \" \\\n",
    "               + str(grid.getParameter()) \\\n",
    "               + \" (\" + str(grid.getUnit()) + \") \" \\\n",
    "               + \"valid \" + str(grid.getDataTime().getRefTime()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}