python-awips/examples/notebooks/Grid_Levels_and_Parameters.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## List Available Parameters for a Grid Name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AV\n",
      "BLI\n",
      "CAPE\n",
      "CFRZR6hr\n",
      "CICEP6hr\n",
      "CIn\n",
      "CP6hr\n",
      "CRAIN6hr\n",
      "CSNOW6hr\n",
      "GH\n",
      "P\n",
      "P6hr\n",
      "PMSL\n",
      "PVV\n",
      "PW\n",
      "RH\n",
      "SLI\n",
      "T\n",
      "TP6hr\n",
      "VSS\n",
      "WEASD\n",
      "WGH\n",
      "uW\n",
      "vW\n"
     ]
    }
   ],
   "source": [
    "from awips.dataaccess import DataAccessLayer\n",
    "\n",
    "# Select HRRR\n",
    "DataAccessLayer.changeEDEXHost(\"edex-cloud.unidata.ucar.edu\")\n",
    "request = DataAccessLayer.newDataRequest()\n",
    "request.setDatatype(\"grid\")\n",
    "request.setLocationNames(\"GFS40\")\n",
    "\n",
    "# Print parm list\n",
    "available_parms = DataAccessLayer.getAvailableParameters(request)\n",
    "available_parms.sort()\n",
    "for parm in available_parms:\n",
    "    print parm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## List Available Levels for Parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000.0MB\n",
      "950.0MB\n",
      "925.0MB\n",
      "900.0MB\n",
      "875.0MB\n",
      "850.0MB\n",
      "825.0MB\n",
      "800.0MB\n",
      "775.0MB\n",
      "725.0MB\n",
      "600.0MB\n",
      "575.0MB\n",
      "0.0_30.0BL\n",
      "60.0_90.0BL\n",
      "90.0_120.0BL\n",
      "0.5PV\n",
      "2.0PV\n",
      "30.0_60.0BL\n",
      "1.0PV\n",
      "750.0MB\n",
      "120.0_150.0BL\n",
      "975.0MB\n",
      "700.0MB\n",
      "675.0MB\n",
      "650.0MB\n",
      "625.0MB\n",
      "550.0MB\n",
      "525.0MB\n",
      "500.0MB\n",
      "450.0MB\n",
      "400.0MB\n",
      "300.0MB\n",
      "250.0MB\n",
      "200.0MB\n",
      "150.0MB\n",
      "100.0MB\n",
      "0.0TROP\n",
      "1.5PV\n",
      "150.0_180.0BL\n",
      "350.0MB\n",
      "10.0FHAG\n",
      "0.0MAXW\n"
     ]
    }
   ],
   "source": [
    "# Set parm to u-wind\n",
    "request.setParameters(\"uW\")\n",
    "\n",
    "# Print level list\n",
    "available_levels = DataAccessLayer.getAvailableLevels(request)\n",
    "available_levels.sort()\n",
    "for level in available_levels:\n",
    "    print level"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Construct Wind Field from U and V Components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time : 2016-04-20 18:00:00 (240)\n",
      "Model: GFS40\n",
      "Unit : m*sec^-1\n",
      "Parms : vW vW\n",
      "(185, 129)\n",
      "windArray = [[ 1.47078204  1.69705617  0.69296461 ...,  9.390378    9.14996147   8.55599213] [ 8.23072243  8.20243835  8.31557465 ...,  1.48492408  0.56568539   0.39597979] [ 0.49497473  0.52325904  0.1979899  ...,  2.67286372  2.63043714   2.65872145] ...,  [ 2.17788887  2.20617294  2.13546252 ...,  1.01823378  0.62225395   0.39597979] [ 0.02828427  0.8768124   1.51320839 ...,  6.47709799  6.68922997   6.84479332] [ 6.92964649  7.02864122  6.98621511 ...,  0.91923875  1.24450791   1.28693426]] knot\n"
     ]
    }
   ],
   "source": [
    "import numpy\n",
    "from metpy.units import units\n",
    "\n",
    "# Set level for u-wind\n",
    "request.setLevels(\"10.0FHAG\")\n",
    "t = DataAccessLayer.getAvailableTimes(request)\n",
    "# Select last time for u-wind\n",
    "response = DataAccessLayer.getGridData(request, [t[-1]])\n",
    "data_uw = response[-1]\n",
    "lons,lats = data_uw.getLatLonCoords()\n",
    "\n",
    "# Select v-wind\n",
    "request.setParameters(\"vW\")\n",
    "# Select last time for v-wind\n",
    "response = DataAccessLayer.getGridData(request, [t[-1]])\n",
    "data_uv = response[-1]\n",
    "\n",
    "# Print \n",
    "print 'Time :', t[-1]\n",
    "print 'Model:', data_uv.getLocationName()\n",
    "print 'Unit :', data_uv.getUnit()\n",
    "print 'Parms :', data_uw.getParameter(), data_uv.getParameter()\n",
    "print data_uv.getRawData().shape\n",
    "\n",
    "# Calculate total wind speed\n",
    "spd = numpy.sqrt( data_uw.getRawData()**2 + data_uv.getRawData()**2 )\n",
    "spd = spd * units.knot\n",
    "print \"windArray =\", spd\n",
    "\n",
    "data = data_uw"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting a Grid with Basemap\n",
    "\n",
    "Using **matplotlib**, **numpy**, and **basemap**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd4164c2110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "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",
    "plt.figure(figsize=(12, 12), dpi=100)\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()), spd.flatten())\n",
    "rdata = tli(rlons, rlats)\n",
    "#cs = map.contourf(rlons, rlats, rdata, latlon=True)\n",
    "cs = map.contourf(rlons, rlats, rdata, latlon=True, vmin=0, vmax=20, cmap='BuPu')\n",
    "\n",
    "# Add colorbar\n",
    "cbar = map.colorbar(cs,location='bottom',pad=\"5%\")\n",
    "\n",
    "cbar.set_label(\"Wind Speed (Knots)\")\n",
    "\n",
    "# Show plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "or use **pcolormesh** rather than **contourf**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAqwAAAFMCAYAAAAObu4vAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n7p3bVKpaXbympABYhGt
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd410082390>"
      ]
     },
     "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=20, cmap='BuPu')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting a Grid with Cartopy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAArMAAAHvCAYAAABHQ1cKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nXyuv2UkjuUj5AjAa9ev
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd4183d5e10>"
      ]
     },
     "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=20, cmap='BuPu')\n",
    "ax.coastlines()\n",
    "ax.gridlines()\n",
    "\n",
    "# add colorbar\n",
    "cbar = plt.colorbar(orientation='horizontal')\n",
    "cbar.set_label(\"Wind Speed (Knots)\")\n",
    "plt.show()"
   ]
  },
  {
   "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": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}