{
 "metadata": {
  "name": "jarkus_plot_transect"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from netCDF4 import Dataset,date2index,num2date\n",
      "import matplotlib.pyplot as plt\n",
      "from matplotlib.ticker import MaxNLocator\n",
      "import os\n",
      "import numpy as np\n",
      "import shapely.geometry\n",
      "from descartes import PolygonPatch\n",
      "import datetime"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "url = 'http://dtvirt5.deltares.nl:8080/thredds/dodsC/opendap/rijkswaterstaat/jarkus/profiles/jarkus135_12-Feb-2013.nc'\n",
      "ids = (9011825,) # specify as tuple\n",
      "year = 2008\n",
      "\n",
      "try:\n",
      "    ds = Dataset(url)\n",
      "    id = ds.variables['id'][:]\n",
      "    aidx = np.nonzero(np.in1d(id, ids))[0]\n",
      "    x = ds.variables['cross_shore'][:]\n",
      "    altitude = ds.variables['altitude'][:,aidx,:]\n",
      "    time = ds.variables['time']\n",
      "    t = map(lambda t: t.year, num2date(time, units=time.units))\n",
      "    mhw = ds.variables['mean_high_water'][:]\n",
      "finally:\n",
      "    ds.close()\n",
      "print 'z',altitude.shape\n",
      "print 'x',x.shape\n",
      "print 'id',id.shape\n",
      "print 'aidx',aidx\n",
      "print 't', t"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "z (48, 1, 1925)\n",
        "x (1925,)\n",
        "id (2178,)\n",
        "aidx [1439]\n",
        "t [1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012]\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def interpolate_nans(data, left=None, right=None):\n",
      "    idx = ~np.isnan(data)\n",
      "    x = np.arange(len(idx))\n",
      "    if np.any(idx):\n",
      "        data = np.interp(x, x[idx], data[idx], left=left, right=left)\n",
      "    return data\n",
      "\n",
      "# interpolate in cross-shore direction\n",
      "z = np.apply_along_axis(lambda d: interpolate_nans(d, left=np.nan, right=np.nan), 2, altitude.filled(np.nan))\n",
      "# interpolate in time, extrapolate with the first resp. last available value\n",
      "z = np.apply_along_axis(interpolate_nans, 0, z)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def xz2shapes(x, z, min_z=None):\n",
      "    idx = ~np.isnan(z)\n",
      "    # cross-shore profiles\n",
      "    xz = np.c_[x[idx], z[idx]]\n",
      "    # LineString of profile\n",
      "    line = shapely.geometry.LineString(xz)\n",
      "    # retreive bounds\n",
      "    minx, miny, maxx, maxy = line.bounds\n",
      "    if not min_z:\n",
      "        min_z = miny\n",
      "    # coords of polygon\n",
      "    coords = np.r_[xz,[[maxx, min_z-.0001],[minx, min_z-.0001], xz[0,:]]]\n",
      "    # create polygon\n",
      "    poly = shapely.geometry.Polygon(coords.astype('float')).intersection(shapely.geometry.box(minx, min_z, maxx, maxy))\n",
      "    \n",
      "    return line,poly"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tidx = np.nonzero(np.in1d(t,(2011,)))[0]\n",
      "l,p = zip(*map(lambda zi: xz2shapes(x,zi.squeeze()), z))\n",
      "\n",
      "minx, miny, maxx, maxy = l[tidx].bounds\n",
      "hlines = map(lambda level: shapely.geometry.LineString([(minx, level), (maxx, level)]), (-4,mhw[aidx],3,8,maxy))\n",
      "crossings = map(lambda c: c.intersection(l[tidx]).bounds[2:], hlines)\n",
      "vbox = shapely.geometry.box(crossings[0][0], -4, crossings[2][0], 3)\n",
      "volume = vbox.intersection(p[tidx])\n",
      "slope = shapely.geometry.LineString(crossings[:3:2])\n",
      "\n",
      "fig,ax = plt.subplots(1,1)\n",
      "fig.set_figwidth(5)\n",
      "fig.set_figheight(3)\n",
      "\n",
      "ax.set_title('transect %i'%ids[0], fontsize=11)\n",
      "ax.set_xlabel('cross-shore coordinate [m]', fontsize=9)\n",
      "ax.set_ylabel('altitude [m]', fontsize=9)\n",
      "ax.spines['right'].set_visible(False)\n",
      "ax.spines['top'].set_visible(False)\n",
      "ax.set_xlim(minx,maxx)\n",
      "ax.set_ylim(miny,maxy+1)\n",
      "ax.spines['right'].set_color('none')\n",
      "ax.spines['top'].set_color('none')\n",
      "ax.xaxis.set_ticks_position('bottom')\n",
      "ax.yaxis.set_ticks_position('left')\n",
      "\n",
      "ax.plot(*l[tidx].xy)\n",
      "ax.add_patch(PolygonPatch(p[tidx], alpha=.4, fc='orange'))\n",
      "pv = ax.add_patch(PolygonPatch(volume, alpha=.4, fc='green'))\n",
      "ax.add_patch(PolygonPatch(vbox, alpha=.1, fc='green'))\n",
      "ax.plot(*slope.xy, ls=':', marker='o', alpha=.8,zorder=10)\n",
      "\n",
      "for c,color,txt in zip(hlines, ('orange','b','orange','k'), ('lower ref. level volume', 'mean high water', 'upper ref. level volume', 'dune height threshold')):\n",
      "    ax.plot(*c.xy, ls='--', color=color)\n",
      "    textxy = np.c_[c.xy][-1]\n",
      "    ax.text(textxy[0], textxy[1]+.2, txt, ha='right')\n",
      "\n",
      "ax.vlines(crossings[-1][0], miny, maxy, linestyles='--')\n",
      "    \n",
      "#ax.set_xlim(ax.get_xlim()[::-1])\n",
      "ax.annotate('4: max. dune height', xy=crossings[-1], xytext=(15, -2), textcoords='offset points',arrowprops=dict(color='black',arrowstyle=\"->\"))\n",
      "ax.annotate('shoreline', xy=crossings[1], xytext=(15, 7), textcoords='offset points',arrowprops=dict(color='black',arrowstyle=\"->\"))\n",
      "ax.annotate('2: slope', xy=array(slope.centroid.xy), xytext=(15, 7), textcoords='offset points',arrowprops=dict(color='black',arrowstyle=\"->\"))\n",
      "ax.annotate('', xy=crossings[1], xytext=(crossings[-1][0], mhw[aidx]), textcoords='data',arrowprops=dict(color='black',arrowstyle=\"<->\"))\n",
      "ax.hlines(crossings[-1][0], -4, crossings[-1][-1], linestyles = 'solid', color='k', zorder=15)\n",
      "ax.text(volume.centroid.xy[0][0], volume.centroid.xy[1][0], '1', ha='center', va='center')\n",
      "ax.text(np.mean((crossings[1][0]-50, crossings[-1][0])), crossings[1][1], 'beach\\nwidth', ha='center',fontsize=7)\n",
      "ax.text(np.mean((crossings[1][0], crossings[-1][0])), crossings[1][1]-.5, '3', ha='center', va='top')\n",
      "\n",
      "ax.xaxis.set_major_locator(MaxNLocator(4))\n",
      "fig.subplots_adjust(bottom=.15,right=.98)\n",
      "fig.savefig('jarkus_plot_transect.png', dpi=300)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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YWGxdU+NkoqKqFVsOkJioTb3ar+7AatXI41miDlOmyHG1jkUiger6yZjXe8Kz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\nxynZ2Njg6upKZGTkW7lekfeTUsWYu3b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/BQsW6C9E3rifa32Sk5PJyMhAq9Xi7+/Pu+++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\nJBmZ/wO6ZIH32AfJgAAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ints = c.intersection(l[tidx])\n",
      "swb = asarray(ints)\n",
      "swb\n",
      "crossings\n",
      "np.c_[c.xy][-1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "array([ 3180.,     8.])"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "zip(*map(lambda c: c.intersection(l[tidx]).bounds[2:], hlines))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "[(1358.9655172413793, 991.8181818181819, 930.3846153846154, 792.0, 680.0),\n",
        " (-4.0, 1.05, 3.0, 8.0, 10.73)]"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax.spines['right']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "<matplotlib.spines.Spine at 0x36446d0>"
       ]
      }
     ],
     "prompt_number": 14
    }
   ],
   "metadata": {}
  }
 ]
}