python-awips/examples/notebooks/.ipynb_checkpoints/Gridded_Data-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDEX Grid Inventory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AVN211\n",
      "AVN225\n",
      "DGEX\n",
      "ECMF-Global\n",
      "ECMF1\n",
      "ECMF10\n",
      "ECMF11\n",
      "ECMF12\n",
      "ECMF2\n",
      "ECMF3\n",
      "ECMF4\n",
      "ECMF5\n",
      "ECMF6\n",
      "ECMF7\n",
      "ECMF8\n",
      "ECMF9\n",
      "ETA\n",
      "GFS\n",
      "GFS40\n",
      "GFSGuide\n",
      "GFSLAMP5\n",
      "HPCGuide\n",
      "HPCqpfNDFD\n",
      "HRRR\n",
      "LAMP2p5\n",
      "MRMS_1000\n",
      "NAM12\n",
      "NAM40\n",
      "NCWF\n",
      "NOHRSC-SNOW\n",
      "NamDNG\n",
      "NamDNG5\n",
      "QPE-MSR\n",
      "RAP13\n",
      "RAP40\n",
      "RTMA\n",
      "RTMA5\n",
      "URMA25\n",
      "estofsPR\n",
      "estofsUS\n"
     ]
    }
   ],
   "source": [
    "from awips.dataaccess import DataAccessLayer\n",
    "\n",
    "# Set host\n",
    "DataAccessLayer.changeEDEXHost(\"edex-cloud.unidata.ucar.edu\")\n",
    "\n",
    "# Init data request\n",
    "request = DataAccessLayer.newDataRequest()\n",
    "\n",
    "# Set datatype\n",
    "request.setDatatype(\"grid\")\n",
    "\n",
    "# Get a list of all available models\n",
    "available_grids = DataAccessLayer.getAvailableLocationNames(request)\n",
    "\n",
    "# Sort\n",
    "available_grids.sort()\n",
    "\n",
    "for grid in available_grids:\n",
    "    print grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**LocationNames** is different for different plugins\n",
    "- radar is icao\n",
    "- satellite is sector\n",
    "\n",
    "\n",
    "## Requesting a Grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time : 2016-02-23 15:00:00 (12)\n",
      "Model: RAP40\n",
      "Unit : %\n",
      "Parm : RH\n",
      "(151, 113)\n",
      "[[ 93.05456543  93.05456543  87.05456543 ...,  73.05456543  72.05456543\n",
      "   71.05456543]\n",
      " [ 70.05456543  70.05456543  67.05456543 ...,  69.05456543  46.05456924\n",
      "   37.05456924]\n",
      " [ 40.05456924  56.05456924  68.05456543 ...,  51.05456924  73.05456543\n",
      "   74.05456543]\n",
      " ..., \n",
      " [ 65.05456543  62.05456924  63.05456924 ...,  67.05456543  65.05456543\n",
      "   46.05456924]\n",
      " [ 48.05456924  59.05456924  62.05456924 ...,   4.05456877   5.05456877\n",
      "    5.05456877]\n",
      " [  7.05456877   8.05456829  10.05456829 ...,  91.05456543  95.05456543\n",
      "   95.05456543]]\n",
      "lat array = [[ 54.24940109  54.35071945  54.45080566 ...,  57.9545517   57.91926193\n",
      "   57.88272858]\n",
      " [ 57.84495163  57.80593109  57.76566696 ...,  58.07667542  58.08861542\n",
      "   58.09931183]\n",
      " [ 58.10876846  58.11697769  58.12394714 ...,  56.40270996  56.46187973\n",
      "   56.51980972]\n",
      " ..., \n",
      " [ 19.93209648  19.89832115  19.86351395 ...,  20.054636    20.06362152\n",
      "   20.07156372]\n",
      " [ 20.0784626   20.08431816  20.08912849 ...,  18.58354759  18.63155174\n",
      "   18.67854691]\n",
      " [ 18.72453308  18.76950836  18.81346893 ...,  17.49624634  17.42861557\n",
      "   17.36001205]]\n",
      "lon array = [[-139.83120728 -139.32348633 -138.81448364 ...,  -79.26060486\n",
      "   -78.70166016  -78.14326477]\n",
      " [ -77.58544922  -77.02822876  -76.47161865 ..., -100.70157623\n",
      "  -100.13801575  -99.57427216]\n",
      " [ -99.01037598  -98.44634247  -97.88218689 ..., -121.69165039\n",
      "  -121.15060425 -120.60871887]\n",
      " ..., \n",
      " [ -82.65139008  -82.26644897  -81.88170624 ...,  -98.52494049\n",
      "   -98.13802338  -97.75105286]\n",
      " [ -97.36403656  -96.97698212  -96.58989716 ..., -113.07767487\n",
      "  -112.69831085 -112.31866455]\n",
      " [-111.93874359 -111.5585556  -111.17810822 ...,  -69.85433197\n",
      "   -69.48160553  -69.10926819]]\n"
     ]
    }
   ],
   "source": [
    "# Grid request\n",
    "request.setLocationNames('RAP40')\n",
    "request.setParameters(\"RH\")\n",
    "request.setLevels(\"850MB\")\n",
    "\n",
    "# Get available times\n",
    "t = DataAccessLayer.getAvailableTimes(request)\n",
    "\n",
    "# Select last available time [-1]\n",
    "response = DataAccessLayer.getGridData(request, [t[0]])\n",
    "data = response[0]\n",
    "lon,lat = data.getLatLonCoords()\n",
    "\n",
    "# Print info\n",
    "print 'Time :', t[-1]\n",
    "print 'Model:', data.getLocationName()\n",
    "print 'Unit :', data.getUnit()\n",
    "print 'Parm :', data.getParameter()\n",
    "\n",
    "# Print data array\n",
    "print data.getRawData().shape\n",
    "print data.getRawData()\n",
    "print \"lat array =\", lat\n",
    "print \"lon array =\", lon\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting a Grid with Basemap\n",
    "\n",
    "Using **matplotlib**, **numpy**, and **basemap**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b11499e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.tri as mtri\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.transforms import offset_copy\n",
    "from mpl_toolkits.basemap import Basemap, cm\n",
    "import numpy as np\n",
    "from numpy import linspace, transpose\n",
    "from numpy import meshgrid\n",
    "\n",
    "\n",
    "plt.figure(figsize=(12, 12), dpi=100)\n",
    "lons,lats = data.getLatLonCoords()\n",
    "\n",
    "map = Basemap(projection='cyl',\n",
    "      resolution = 'c',\n",
    "      llcrnrlon = lons.min(), llcrnrlat = lats.min(),\n",
    "      urcrnrlon =lons.max(), urcrnrlat = lats.max()\n",
    ")\n",
    "map.drawcoastlines()\n",
    "map.drawstates()\n",
    "map.drawcountries()\n",
    "\n",
    "# \n",
    "# We have to reproject our grid, see https://stackoverflow.com/questions/31822553/m\n",
    "#\n",
    "x = linspace(0, map.urcrnrx, data.getRawData().shape[1])\n",
    "y = linspace(0, map.urcrnry, data.getRawData().shape[0])\n",
    "xx, yy = meshgrid(x, y)\n",
    "ngrid = len(x)\n",
    "rlons = np.repeat(np.linspace(np.min(lons), np.max(lons), ngrid),\n",
    "          ngrid).reshape(ngrid, ngrid)\n",
    "rlats = np.repeat(np.linspace(np.min(lats), np.max(lats), ngrid),\n",
    "          ngrid).reshape(ngrid, ngrid).T\n",
    "tli = mtri.LinearTriInterpolator(mtri.Triangulation(lons.flatten(),\n",
    "          lats.flatten()), data.getRawData().flatten())\n",
    "rdata = tli(rlons, rlats)\n",
    "cs = map.contourf(rlons, rlats, rdata, latlon=True, vmin=0, vmax=100, cmap='YlGn')\n",
    "\n",
    "# add colorbar.\n",
    "cbar = map.colorbar(cs,location='bottom',pad=\"5%\")\n",
    "cbar.set_label(data.getParameter() + data.getUnit() )\n",
    "\n",
    "# Show plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "or use **pcolormesh** rather than **contourf**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAACXYAAATxCAYAAACREFnnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/nuphroAwNraGhs3blS
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b1abbb790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 12), dpi=100)\n",
    "map = Basemap(projection='cyl',\n",
    "      resolution = 'c',\n",
    "      llcrnrlon = lons.min(), llcrnrlat = lats.min(),\n",
    "      urcrnrlon =lons.max(), urcrnrlat = lats.max()\n",
    ")\n",
    "map.drawcoastlines()\n",
    "map.drawstates()\n",
    "map.drawcountries()\n",
    "cs = map.pcolormesh(rlons, rlats, rdata, latlon=True, vmin=0, vmax=100, cmap='YlGn')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting a Grid with Cartopy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAACYEAAAcvCAYAAAB6XKnFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAnNwAAJzcBVAXW8gAAIABJREFUeJzsnXmYFOW9tp+Z6Z6FfRPcAAFRgwKCCwooghpJNEpQMXgO\nLqhIhBMkuCQiJhpcIRJP0CCfUY7LcYu7cTtoQAQFF0AxKquAoiyyjDBb90x/fzRVU11dy1tVb209\nz31dfc3U/nZXd1X1W3c/v6JMJgNCCCGEEEIIIYQQQgghhBBCCCGEEEIIIfGkOOwGEEIIIYQQQggh\nhBBCCCGEEEIIIYQQQghxDyUwQgghhBBCCCGEEEIIIYQQQgghhBBCCIkxlMAIIYQQQgghhBBCCCGE\nEEIIIYQQQgghJMZQAiOEEEIIIYQQQgghhBBCCCGEEEIIIYSQGEMJjBBCCCGEEEIIIYQQQgghhBBC\nCCGEEEJiDCUwQgghhBBCCCGEEEIIIYQQQgghhBBCCIkxlMAIIYQQQgghhBBCCCGEEEIIIYQQQggh\nJMZQAiOEEEIIIYQQQgghhBBCCCGEEEIIIYSQGEMJjBBCCCGEEEIIIYQQQgghhBBCCCGEEEJiDCUw\nQgghhBBCCCGEEEIIIYQQQgghhBBCCIkxlMAIIYQQQgghhBBCCCGEEEIIIYQQQgghJMZQAiOEEEII\nIYQQQgghhBBCCCGEEEIIIYSQGEMJjBBCCCGEEEIIIYQQQgghhBBCCCGEEEJiDCUwQgghhBBCCCGE\nEEIIIYQQQgghhBBCCIkxlMAIIYQQQgghhBBCCCGEEEIIIYQQQgghJMZQAiOEEEIIIYQQQgghhBBC\nCCGEEEIIIYSQGEMJjBBCCCGEEEIIIYQQQgghhBBCCCGEEEJiDCUwQgghhBBCCCGEEEIIIYQQQggh\nhBBCCIkxlMAIIYQQQgghhBBCCCGEEEIIIYQQQgghJMZQAiOEEEIIIYQQQgghhBBCCCGEEEIIIYSQ\nGEMJjBBCCCGEEEIIIYQQQgghhBBCCCGEEEJiDCUwQgghhBBCCCGEEEIIIYQQQgghhBBCCIkxibAb\noKWoqGgOgN5ht4MQQgghhBBCCCGEEEIIIYQQQgghhBBCJPJZJpMZ79fKIyWBISuADQy7EYQQQggh\nhBBCCCGEEEIIIYQQQgghhBASF1gOkhBCCCGEEEIIIYQQQgghhBBCCCGEEEJiTNSSwFRatGiBPn36\nhN2MQKmvr0dlZSUqKyuxZ88e7N27V3jZRCKBVq1aoVWrVmjdujWaNWuGoqIiH1tLCCGEEEIIIYQQ\nQghxSyaTwfLly1FTUwMAKCoqwnHHHYfS0tKQW0YIIYSIU1NTg/Xr12P37t1501q3bo0ePXqgvLw8\nhJYRQgqd2tpafPnll9i3b1/O+Pbt26Nnz54oLrbOw8lkMtizZw+2bt2KnTt3IpPJmM7brl07dOrU\nCW3atOE9+AIlk8lg7dq12L59e874bt264aCDDgqpVaRQ+PTTTx35P16IrATWp08fLF68OOxmhMq+\nffvw/vvvY+HChVi4cCGWLl2Kuro6w3nT6TR27tyJnTt3AsheWJ9yyik49dRTMWTIEPTv3x+JRGR3\ntytWrlwJAOjbt2/ILSGEEELE4fmLEEJIHOH5ixBC/GHevHm4/PLLAWRvOgwePBizZs0KuVWFA89f\nhBASDJlMBk8//TSuvfZabN26VR2/Z88efP7555g6dSpuuOEGlJWVhdjK+MDzFyHiVFVV4corr8ST\nTz6pjvvhhx9w6KGH4sUXX8Rhhx2Wt8y6deswb948zJs3D998843punv16oUrrrgC//mf/4mOHTv6\n0XwSMerr6zFu3Dg8/PDD6rgNGzZg4sSJ+O1vfxtiy+IBz1/mDBo0CEuWLAlkW0VWRmvQFBUVLQYw\nEAAGDhzY5CUwPdXV1Vi6dKkqhb3//vvqLwXtaNGiBQYNGoQhQ4ZgyJAhOP7442P/q8LOnTsDADZv\n3hxySwghhBBxeP4ihBASR3j+IoQQf0ilUujZsyc2btwIAKioqMDGjRtxwAEHhNyywoDnL0IICZbd\nu3dj6tSp+Nvf/paXqHPkkUfib3/7G4YOHRpS6+IDz1+EOCOTyWDGjBn43e9+l3Ps6dChA/7xj39g\nyJAhqKqqwnPPPYeHH34YCxYsMF1Xq1atMHr0aIwdOxYnnHACU7+aIA0NDbjmmmvw4IMP5oy/8847\n8bvf/S6kVsUDnr/M0UlgSzKZzCC/tkUJLMbU1tbio48+UqWwxYsX58VdmlFRUYGBAwfi1FNPRb9+\n/VBWVoZEIuHoUVJSkjeuuLg4sJMhDyKEEELiCM9fhBBC4gjPX4QQ4h8PPPAAJkyYoA7fdNNNuP32\n20NsUeHA8xchhITD0qVLMX78eKxYsSJv2iWXXIKZM2dSeLaA5y9C3PHaa6/h4osvxp49e9RxiUQC\nI0aMwFtvvYXKykrTZU877TSMHTsW559/Ppo1axZEc0mEyWQy+M1vfoPZs2fnjL/tttswbdq0kFoV\nfXj+MocSGCiBuSGVSuGTTz5RpbD33nvP8mTmF15FMtHHE088geLiYtxxxx1o27Yt2rVrh7Zt2+Y8\nCq0EJiGEkPjDi2BCCCFxhOcvQgjxj5qaGnTr1g3ff/89gGz6wMaNG9GmTZuQWxZ/eP4ihJDwSKfT\n+Otf/4pp06blBRi0bdsW99xzD8aOHYvi4uKQWhhdeP4ixD1fffUVzjvvPHz11Ve28x566KG47LLL\ncNlll6FHjx4BtI7EiUwmgylTpmDWrFk546dNm4Zbb72VKXEG8PxlDiUwUAKTQX19PVauXKlKYe++\n+y527doVdrMCpWXLlqoQppfEtMP6aa1bt0ZJSUnYzSeEEFKAvPrqqwCAc845J+SWEEIIIeLw/EUI\nIf4yc+ZMXH/99erw9OnTMXXq1BBbVBjw/EUIIeGzefNmTJo0CS+88ELetEGDBmHOnDk45phjQmhZ\ndOH5ixBv7NmzBxdffDFee+21vGnJZBIjRozA2LFjceaZZ/J+MLEkk8ngd7/7He65556c8VOnTsX0\n6dNDalV04fnLHEpgoATmBw0NDVi1apUqhC1cuBDbt28Pu1mRpKioCK1bt1alsAMOOACXXHIJLr74\n4rCbRgghhBBCCCGEEEIKjL1796Jr167YuXMnAKB9+/bYuHEjmjdvHnLLCCGEEDm88sormDhxIjZt\n2pQzPpFI4Le//S1uueUWnvcIIdKor6/HtGnTcNdddyGTyaBPnz644oorcPHFF6NDhw5hN4/EiEwm\ng1tuuSVP+poxYwauu+66kFpF4gYlMFACC4JMJoOvvvoKW7ZsQX19PdLptNDDybyyl1eWTaVS2Ldv\nH4J+/7799tsYNmxYoNskhBBCCCGEEEIIIYXPn/70J9xyyy3q8L333ovJkyeH2CJCCCFELvv27cOt\nt96Ke++9F/X19TnTunbtivvvvx9nn312SK0jhBQimzZtQl1dHXr06MHyfcQTt912G/7whz/kjHv4\n4Ydx+eWXh9QiEicogYESGLGnoaEBe/bswa5du7Bz507s2rVLfdgNV1ZWutpm7969sXz5ckaDEkII\nIYQQQgghhBCp7Nq1C127dsWPP/4IADjooIOwfv16lJeXh9wyQgghRC6ffvopxo8fj/fffz9v2siR\nI/HQQw+hbdu2IbSMEEIIMWfq1Km444471OHi4mI899xzGDFiRIitInGAEhgogRF/SafT2L17t5A0\n9tlnn2HdunXqsg8++CDGjRsXYusJIYQQQghp2lwxf5L6f006bTv/vlSd7Tza9dSksv+n6xrHpfb/\nn6pL5UxL1Wb/1tVq5k03ZMft/wsAqVTj/wra6aLzAEDGYD7SSFGy2Ld1lybE1p00aYN+ef18RutP\n6saVliWE2pCzDoFlEqXZeZKlyf1/Eznjy5P7/yayf5snS3OGK5IJVCSSOeMAoNzgR1QVifxx2mUU\nmhnMp6UqnZseoT8eVOu
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b1acc0b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import iris\n",
    "import cartopy.crs as ccrs\n",
    "from cartopy import config\n",
    "\n",
    "lon,lat = data.getLatLonCoords()\n",
    "plt.figure(figsize=(12, 12), dpi=100)\n",
    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "cs = plt.contourf(rlons, rlats, rdata, 60, transform=ccrs.PlateCarree(), vmin=0, vmax=100, cmap='YlGn')\n",
    "ax.coastlines()\n",
    "ax.gridlines()\n",
    "\n",
    "# add colorbar\n",
    "cbar = plt.colorbar(orientation='horizontal')\n",
    "cbar.set_label(data.getParameter() + data.getUnit() )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Mar 15 16 22:52:00 , Mar 15 16 22:52:00 )\n",
      "430\n",
      "86\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b1148c350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from metpy.calc import get_wind_components\n",
    "from metpy.cbook import get_test_data\n",
    "from metpy.plots import StationPlot, StationPlotLayout, simple_layout\n",
    "from metpy.units import units\n",
    "\n",
    "# Initialize\n",
    "data,latitude,longitude,stationName,temperature,dewpoint,seaLevelPress,windDir,windSpeed = [],[],[],[],[],[],[],[],[]\n",
    "request = DataAccessLayer.newDataRequest()\n",
    "request.setDatatype(\"obs\")\n",
    "\n",
    "#\n",
    "# we need to set one station to query latest time.  this is hack-y and should be fixed\n",
    "# because when you DON'T set a location name, you tend to get a single observation\n",
    "# that came in a second ago, so your \"latest data for the last time for all stations\"\n",
    "# data array consists of one village in Peru and time-matching is suspect right now.\n",
    "#\n",
    "# So here take a known US station (OKC) and hope/assume that a lot of other stations \n",
    "# are also reporting (and that this is a 00/20/40 ob). \n",
    "#\n",
    "request.setLocationNames(\"KOKC\")\n",
    "datatimes = DataAccessLayer.getAvailableTimes(request)\n",
    "\n",
    "# Get most recent time for location\n",
    "time = datatimes[-1].validPeriod\n",
    "\n",
    "# \"presWeather\",\"skyCover\",\"skyLayerBase\"\n",
    "# are multi-dimensional(??) and returned seperately (not sure why yet)... deal with those later\n",
    "request.setParameters(\"presWeather\",\"skyCover\", \"skyLayerBase\",\"stationName\",\"temperature\",\"dewpoint\",\"windDir\",\"windSpeed\",\n",
    "                      \"seaLevelPress\",\"longitude\",\"latitude\")\n",
    "request.setLocationNames()\n",
    "response = DataAccessLayer.getGeometryData(request,times=time)\n",
    "print time\n",
    "PRES_PARAMS = set([\"presWeather\"])\n",
    "SKY_PARAMS = set([\"skyCover\", \"skyLayerBase\"])\n",
    "# Build ordered arrays\n",
    "wx,cvr,bas=[],[],[]\n",
    "for ob in response:\n",
    "    #print ob.getParameters()\n",
    "    if set(ob.getParameters()) & PRES_PARAMS :\n",
    "        wx.append(ob.getString(\"presWeather\"))\n",
    "        continue\n",
    "    if set(ob.getParameters()) & SKY_PARAMS :\n",
    "        cvr.append(ob.getString(\"skyCover\"))\n",
    "        bas.append(ob.getNumber(\"skyLayerBase\"))\n",
    "        continue\n",
    "    latitude.append(float(ob.getString(\"latitude\")))\n",
    "    longitude.append(float(ob.getString(\"longitude\")))\n",
    "    #stationName.append(ob.getString(\"stationName\"))\n",
    "    temperature.append(float(ob.getString(\"temperature\")))\n",
    "    dewpoint.append(float(ob.getString(\"dewpoint\")))\n",
    "    seaLevelPress.append(float(ob.getString(\"seaLevelPress\")))\n",
    "    windDir.append(float(ob.getString(\"windDir\")))\n",
    "    windSpeed.append(float(ob.getString(\"windSpeed\")))\n",
    "    \n",
    "    \n",
    "print len(wx)\n",
    "print len(temperature)\n",
    "\n",
    "\n",
    "# Convert\n",
    "data = dict()\n",
    "data['latitude']  = np.array(latitude)\n",
    "data['longitude'] = np.array(longitude)\n",
    "data['air_temperature'] = np.array(temperature)* units.degC\n",
    "data['dew_point_temperature'] = np.array(dewpoint)* units.degC\n",
    "#data['air_pressure_at_sea_level'] = np.array(seaLevelPress)* units('mbar')\n",
    "u, v = get_wind_components(np.array(windSpeed) * units('knots'),\n",
    "                           np.array(windDir) * units.degree)\n",
    "data['eastward_wind'], data['northward_wind'] = u, v\n",
    "\n",
    "# Convert the fraction value into a code of 0-8, which can be used to pull out\n",
    "# the appropriate symbol\n",
    "#data['cloud_coverage'] = (8 * data_arr['cloud_fraction']).astype(int)\n",
    "\n",
    "# Map weather strings to WMO codes, which we can use to convert to symbols\n",
    "# Only use the first symbol if there are multiple\n",
    "#wx_text = make_string_list(data_arr['weather'])\n",
    "#wx_codes = {'':0, 'HZ':5, 'BR':10, '-DZ':51, 'DZ':53, '+DZ':55,\n",
    "#            '-RA':61, 'RA':63, '+RA':65, '-SN':71, 'SN':73, '+SN':75}\n",
    "#data['present_weather'] = [wx_codes[s.split()[0] if ' ' in s else s] for s in wx]\n",
    "\n",
    "# Set up the map projection\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as feat\n",
    "from matplotlib import rcParams\n",
    "rcParams['savefig.dpi'] = 255\n",
    "proj = ccrs.LambertConformal(central_longitude=-95, central_latitude=35,\n",
    "                             standard_parallels=[35])\n",
    "state_boundaries = feat.NaturalEarthFeature(category='cultural',\n",
    "                                            name='admin_1_states_provinces_lines',\n",
    "                                            scale='110m', facecolor='none')\n",
    "# Create the figure\n",
    "fig = plt.figure(figsize=(20, 10))\n",
    "ax = fig.add_subplot(1, 1, 1, projection=proj)\n",
    "\n",
    "# Add map elements \n",
    "ax.add_feature(feat.LAND, zorder=-1)\n",
    "ax.add_feature(feat.OCEAN, zorder=-1)\n",
    "ax.add_feature(feat.LAKES, zorder=-1)\n",
    "ax.coastlines(resolution='110m', zorder=2, color='black')\n",
    "ax.add_feature(state_boundaries)\n",
    "ax.add_feature(feat.BORDERS, linewidth='2', edgecolor='black')\n",
    "ax.set_extent((-118, -73, 23, 50))\n",
    "\n",
    "# Start the station plot by specifying the axes to draw on, as well as the\n",
    "# lon/lat of the stations (with transform). We also the fontsize to 12 pt.\n",
    "stationplot = StationPlot(ax, data['longitude'], data['latitude'],\n",
    "                          transform=ccrs.PlateCarree(), fontsize=12)\n",
    "\n",
    "# The layout knows where everything should go, and things are standardized using\n",
    "# the names of variables. So the layout pulls arrays out of `data` and plots them\n",
    "# using `stationplot`.\n",
    "simple_layout.plot(stationplot, data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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