{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Postprocessing XBeach 2D\n",
    "\n",
    "Postprocessing 2D XBeach model output with the Python toolbox "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import default modules\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "import os\n",
    "\n",
    "# method to import xbtools with try routine\n",
    "try:\n",
    "    import xbTools\n",
    "except ImportError:\n",
    "    print('**no xbTools installation found in environment, adding parent path of notebook to see if it works')\n",
    "    sys.path.append(os.path.abspath(os.path.join('..', '..', 'xbeach-toolbox')))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the toolbox and set the xbeach styleshoot for plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "## import xbeach tools\n",
    "#sys.path.append(os.path.abspath(os.path.join('..')))\n",
    "from xbTools.xbeachpost import XBeachModelAnalysis"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data\n",
    "Specify path and load model from folder, in this example the xb-2D model prepared by the xbeach-setup-2D is used!\n",
    "\n",
    "Please note that you should run this model before this script can be executed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = XBeachModelAnalysis('test_run_1', 'xb-2D\\\\')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get metadata from XBlog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'wavemodel': 'surfbeat',\n",
       " 'cyclic': '0 (no record found, default value used)',\n",
       " 'swave': '1 (no record found, default value used)',\n",
       " 'single_dir': '1 (no record found, default value used)',\n",
       " 'lwave': '1 (no record found, default value used)',\n",
       " 'flow': '1 (no record found, default value used)',\n",
       " 'sedtrans': '1 (no record found, default value used)',\n",
       " 'morphology': '0',\n",
       " 'avalanching': '0 (no record found, default value used)',\n",
       " 'gwflow': '0 (no record found, default value used)',\n",
       " 'ships': '0 (no record found, default value used)',\n",
       " 'vegetation': '0 (no record found, default value used)',\n",
       " 'setbathy': '0 (no record found, default value used)',\n",
       " 'viscosity': '1 (no record found, default value used)',\n",
       " 'advection': '1 (no record found, default value used)',\n",
       " 'wind': '0 (no record found, default value used)',\n",
       " 'gridform': 'xbeach (no record found, default value used)',\n",
       " 'xori': '.0000',\n",
       " 'yori': '.0000',\n",
       " 'alfa': '10.0000',\n",
       " 'nx': '174',\n",
       " 'ny': '180',\n",
       " 'posdwn': '-1.0000',\n",
       " 'depfile': 'bed.dep',\n",
       " 'vardx': '1',\n",
       " 'dx': '-1.0000 (no record found, default value used)',\n",
       " 'dy': '-1.0000 (no record found, default value used)',\n",
       " 'xfile': 'x.grd',\n",
       " 'yfile': 'y.grd',\n",
       " 'nz': '1 (no record found, default value used)',\n",
       " 'thetamin': '-90.0000',\n",
       " 'thetamax': '90.0000',\n",
       " 'thetanaut': '0',\n",
       " 'r': ' 1',\n",
       " 'dtheta_s': '10.0000',\n",
       " 'CFL': '.7000 (no record found, default value used)',\n",
       " 'dtset': '.0000 (no record found, default value used)',\n",
       " 'tstop': '3600.0000',\n",
       " 'maxdtfac': '50.0000 (no record found, default value used)',\n",
       " 'rho': '1025.0000 (no record found, default value used)',\n",
       " 'g': '9.8100 (no record found, default value used)',\n",
       " 'depthscale': '1.0000 (no record found, default value used)',\n",
       " 'zsinitfile': ' None specified',\n",
       " 'wbctype': 'jonstable',\n",
       " 'bcfile': 'jonstable.txt',\n",
       " 'taper': '100.0000 (no record found, default value used)',\n",
       " 'nmax': '.8000 (no record found, default value used)',\n",
       " 'nonhspectrum': '0 (no record found, default value used)',\n",
       " 'Hrms': '1.0000 (no record found, default value used)',\n",
       " 'Tm01': '10.0000 (no record found, default value used)',\n",
       " 'Trep': '10.0000 (no record found, default value used)',\n",
       " 'dir0': '270.0000 (no record found, default value used)',\n",
       " 'm': '10 (no record found, default value used)',\n",
       " 'lateralwave': 'neumann (no record found, default value used)',\n",
       " 'random': '1 (no record found, default value used)',\n",
       " 'fcutoff': '.0000 (no record found, default value used)',\n",
       " 'trepfac': '.0100 (no record found, default value used)',\n",
       " 'sprdthr': '.0800 (no record found, default value used)',\n",
       " 'Tm01switch': '0 (no record found, default value used)',\n",
       " 'nspectrumloc': '1 (no record found, default value used)',\n",
       " 'wbcEvarreduce': '1.0000 (no record found, default value used)',\n",
       " 'front': 'abs_2d (no record found, default value used)',\n",
       " 'left': 'neumann (no record found, default value used)',\n",
       " 'right': 'neumann (no record found, default value used)',\n",
       " 'back': 'abs_2d (no record found, default value used)',\n",
       " 'ARC': '1 (no record found, default value used)',\n",
       " 'order': '2.0000 (no record found, default value used)',\n",
       " 'highcomp': '0 (no record found, default value used)',\n",
       " 'freewave': '0 (no record found, default value used)',\n",
       " 'epsi': '-1.0000 (no record found, default value used)',\n",
       " 'tidetype': 'velocity (no record found, default value used)',\n",
       " 'tideloc': '0 (no record found, default value used)',\n",
       " 'zs0': '.0000',\n",
       " 'disch_loc_file': ' None specified',\n",
       " 'disch_timeseries_file': ' None specified',\n",
       " 'ndischarge': '0 (no record found, default value used)',\n",
       " 'ntdischarge': '0 (no record found, default value used)',\n",
       " 'break': 'roelvink_daly (no record found, default value used)',\n",
       " 'gamma': '.4600 (no record found, default value used)',\n",
       " 'gamma2': '.3400 (no record found, default value used)',\n",
       " 'gammax': '2.0000 (no record found, default value used)',\n",
       " 'alpha': '1.3800 (no record found, default value used)',\n",
       " 'n': '10.0000 (no record found, default value used)',\n",
       " 'delta': '.0000 (no record found, default value used)',\n",
       " 'deltahmin': '.1000 (no record found, default value used)',\n",
       " 'fw': '.0000 (no record found, default value used)',\n",
       " 'fwfile': ' None specified',\n",
       " 'fwcutoff': '1000.0000 (no record found, default value used)',\n",
       " 'breakerdelay': '1.0000 (no record found, default value used)',\n",
       " 'roller': '1 (no record found, default value used)',\n",
       " 'rfb': '0 (no record found, default value used)',\n",
       " 'beta': '.0800 (no record found, default value used)',\n",
       " 'wci': '0 (no record found, default value used)',\n",
       " 'hwci': '.1000 (no record found, default value used)',\n",
       " 'hwcimax': '100.0000 (no record found, default value used)',\n",
       " 'cats': '4.0000 (no record found, default value used)',\n",
       " 'bedfriction': 'manning (no record found, default value used)',\n",
       " 'bedfricfile': ' None specified',\n",
       " 'bedfriccoef': '.0200 (no record found, default value used)',\n",
       " 'maxcf': '.0400 (no record found, default value used)',\n",
       " 'nuh': '.1000 (no record found, default value used)',\n",
       " 'nuhfac': '.0000 (no record found, default value used)',\n",
       " 'smag': '1 (no record found, default value used)',\n",
       " 'wearth': '.0417 (no record found, default value used)',\n",
       " 'lat': '.0000 (no record found, default value used)',\n",
       " 'form': 'vanthiel_vanrijn (no record found, default value use',\n",
       " 'waveform': 'vanthiel (no record found, default value used)',\n",
       " 'sws': '1 (no record found, default value used)',\n",
       " 'lws': '1 (no record found, default value used)',\n",
       " 'BRfac': '1.0000 (no record found, default value used)',\n",
       " 'facSk': '.1500 (no record found, default value used)',\n",
       " 'facAs': '.2000 (no record found, default value used)',\n",
       " 'facua': '.1750 (no record found, default value used)',\n",
       " 'Tbfac': '1.0000 (no record found, default value used)',\n",
       " 'turb': 'bore_averaged (no record found, default value used)',\n",
       " 'turbadv': 'none (no record found, default value used)',\n",
       " 'sus': '1 (no record found, default value used)',\n",
       " 'bed': '1 (no record found, default value used)',\n",
       " 'bulk': '0 (no record found, default value used)',\n",
       " 'facsl': '.1500 (no record found, default value used)',\n",
       " 'z0': '.0060 (no record found, default value used)',\n",
       " 'smax': '-1.0000 (no record found, default value used)',\n",
       " 'bdslpeffmag': 'roelvink_total (no record found, default value used)',\n",
       " 'bdslpeffini': 'none (no record found, default value used)',\n",
       " 'bdslpeffdir': 'none (no record found, default value used)',\n",
       " 'reposeangle': '30.0000 (no record found, default value used)',\n",
       " 'tsfac': '.1000 (no record found, default value used)',\n",
       " 'Tsmin': '.5000 (no record found, default value used)',\n",
       " 'facDc': '1.0000 (no record found, default value used)',\n",
       " 'lwt': '0 (no record found, default value used)',\n",
       " 'betad': '1.0000 (no record found, default value used)',\n",
       " 'fallvelred': '0 (no record found, default value used)',\n",
       " 'dilatancy': '0 (no record found, default value used)',\n",
       " 'alfaD50': '.4000 (no record found, default value used)',\n",
       " 'ngd': '1 (no record found, default value used)',\n",
       " 'nd': '3 (no record found, default value used)',\n",
       " 'por': '.4000 (no record found, default value used)',\n",
       " 'D50': '.0002 (no record found, default value used)',\n",
       " 'D90': '.0003 (no record found, default value used)',\n",
       " 'rhos': '2650.0000 (no record found, default value used)',\n",
       " 'dzg': '.1000 (no record found, default value used)',\n",
       " 'dzg1': '.1000 (no record found, default value used)',\n",
       " 'dzg2': '.1000 (no record found, default value used)',\n",
       " 'dzg3': '.1000 (no record found, default value used)',\n",
       " 'sedcal': '1.0000 (no record found, default value used)',\n",
       " 'ucrcal': '1.0000 (no record found, default value used)',\n",
       " 'timings': '1 (no record found, default value used)',\n",
       " 'tunits': ' None specified',\n",
       " 'tstart': '.0000 (no record found, default value used)',\n",
       " 'tint': '900.0000 (no record found, default value used)',\n",
       " 'tsglobal': ' None specified',\n",
       " 'tintg': '900.0000 (no record found, default value used)',\n",
       " 'tspoints': ' None specified',\n",
       " 'tintp': '1.0000 (no record found, default value used)',\n",
       " 'tsmean': ' None specified',\n",
       " 'tintm': '3600.0000 (no record found, default value used)',\n",
       " 'nglobalvar': '3',\n",
       " 'npoints': '4',\n",
       " 'nrugauge': '0 (no record found, default value used)',\n",
       " 'npointvar': '2',\n",
       " 'nrugdepth': '1 (no record found, default value used)',\n",
       " 'rugdepth': '.0000 (no record found, default value used)',\n",
       " 'nmeanvar': '1',\n",
       " 'outputformat': 'netcdf (no record found, default value used)',\n",
       " 'outputprecision': 'double (no record found, default value used)',\n",
       " 'ncfilename': ' None specified',\n",
       " 'remdryoutput': '1 (no record found, default value used)',\n",
       " 'projection': ' None specified',\n",
       " 'rotate': '1 (no record found, default value used)',\n",
       " 'scheme': 'warmbeam (no record found, default value used)',\n",
       " 'wavint': '600.0000 (no record found, default value used)',\n",
       " 'maxerror': '.0001 (no record found, default value used)',\n",
       " 'maxerror_angle': '1.0000 (no record found, default value used)',\n",
       " 'maxiter': '500 (no record found, default value used)',\n",
       " 'truncationtype': 'absolute (no record found, default value used)',\n",
       " 'eps': '.0050 (no record found, default value used)',\n",
       " 'eps_sd': '.5000 (no record found, default value used)',\n",
       " 'umin': '.0000 (no record found, default value used)',\n",
       " 'secorder': '0 (no record found, default value used)',\n",
       " 'thetanum': '1.0000 (no record found, default value used)',\n",
       " 'cmax': '.1000 (no record found, default value used)',\n",
       " 'dtlimTs': '5.0000 (no record found, default value used)'}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.metadata"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Additional settings\n",
    "Not necessary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "results.set_save_fig(False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Coordinate settings\n",
    "Plot in local coords instead of global coords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "results.set_plot_localcoords(False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Kilometer setting option\n",
    "plot in kilometers instead of m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "results.set_plot_km_coords(False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set starttime\n",
    "if not specified time from netcdf is used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#results.set_globalstarttime('2021-10-11T13:00:00')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Change units\n",
    "m to m+msl / NAP etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "results.set_unitdict({'zb':['m+msl']}) "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the xbeach model set-up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "results.load_model_setup()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load XBeach model output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading variable zs from file\n",
      "loading variable point_zs from file\n"
     ]
    }
   ],
   "source": [
    "zs = results.load_modeloutput('zs')\n",
    "\n",
    "ny, nx = results.var['globalx'].shape\n",
    "\n",
    "results.load_modeloutput('point_zs')\n",
    "\n",
    "zs = results.get_modeloutput('zs')\n",
    "point_zs = results.get_modeloutput('point_zs')\n",
    "\n",
    "[t, zs] = results.get_modeloutput_by_station('zs','point001')\n",
    "\n",
    "#results.fig_check_tide_bc()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting 2D fields and 1D transects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading variable zb from file\n",
      "loading variable H from file\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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iMrAZ4VmAnyXvjBfd2ON+PT5HxQWkXu8bs1LzEXk2BHheVa/K8vyhNPxad6aAUXGBVdsXL7YDXnnllcGRSOSH++yzT+3ee++dLDlNj0KM0yhOysvL6+vq6ppNTysifwe2SnJoEnArfi5Hg/fr8E/HmTIwR21KV4ADNqXBfVBGM8ITWMyHBMe/AG4kweINRDeVYD2LdxGUk52bICkisgW++sEDrehmAd4F0RX/o7Kg1QNLQVD8cWe80PbD/0HuAfRYsmRJzeOPP96ltra24vHHHycajVJRUVExZsyYrl27duWggw4CoLq6mqqqKsLhcL6Ha5QIqnpoOu2CXCmPZ3sd59yv8H/frwN/CYVCjZaGhkKhden0U+oCvABv2TbrPoiRKLIi4sgwwi0Q8VuAs4Cf5PAR/8Dg/blsO0j4gZkF/DPX7odgcqwv3oc+AJ8zYQe86NcD3SORSFl1dTU1NTXcfvvtnZrGhNTV1fHQQw8BcNddd1FW5h9COnfuzPnnn8+qVatMjI1WISJbJ7ghRwIuy67uxevJ8/jo1D7AOdl0VNIC3MSyzUhMye4R/R68AOdyZn4IPmKrOlXDlgj+LaYBp4pIparWpDypCUGuhF1osGoH4K3aWGKaShpKJEGwDjcSiTBhwgRqahpfUkQoLy8nJsb19fWNPgOsW7eOK6+8EoCKigqGDx/OsGHDTIiNbJgiImH8U/EHwC+y7KdPKBTqC+Cc+wutWF9f0gIMrRLTbHgN+BofFPBgjvocAryoqutTtkzNs/hf6gE0saiDcN2YeJbh/Wg7BK/++MmxbfFWreJXIiTOAjc7ORazfGOUl3sXb6dOneLWbY8ePZgyZQq1tbVxUa6rq0NV42K8fv16pk+fzty5c5k6daqJsJERqnpijrqqjX0IhUJ1zmVrSHcAAW5LVDUqIgvxAtxqROR7eGvz0lz0h0/pWQ8cQoIAO+d+iy9xVJvQto6GVSGJfycZh++uXr0a8BZvZWVlsy6F3r17x/2+4IW7R48eTJ48mfXrG35/1q1bx80338w+++zDvvvua0JstDV7Oedi1WYE6BpsC6ChUCjtlKSSr/wsaRTlFPxk11H4FQTjVHVpcOyI4Fg5cKeqTk7zmt+qarfULfOHiPwK+BOwnap+2Mq+hgGP4WvnpZsPOVWfLwO1qnpAsPzrBnyU4EYtnpgFkUiEuXPnMmPGDPr06cPQoUOzEsxYP7Nnz6a2tpbEv9ny8nJGjBjBiBEjTIg7AP3794/W1NSUjOGYTwE+EL++9r5mBPgovL/0KHzplhtVdT8RKceX0zkM+AhYDByvqivSuGYxCPBeQAT4mare38q+rgPOADZV1bRmVdPo8xrgvEGDBn3/tttuuxMfzpvzf7NEv6+IcMstt3DAAQekPjFFn9XV1Xz88cfMmDHDhLgDUswC7JwbBJwQCoXOSPecstRNsiOw2P7bQpPheHFWVX0J2FREtsYnwn5HVd8L/J7Tgrbthdfx950LN8QQfABJTsQ34B9AxbBhwxbif/zy8oOV6PcVEd58881W9xkOh5kwYQLDhw+nsrKSIGc0ANFolBkzZjB+/HimTZvGnXfeSSQSafU1DaMlnHNh59wU59wH+CCsjP7QC/lLsg2Q+Ij+UbAv2f7EcvWNSFKUs6Coar2IPEcrBVhEDsWvNLgrJwMLmDJlysqLLrpIly9fvvtRRx2Vt3+vnXfeOf65c+fOcb9uLgiHw0ydOjWpW6Kuro6rrroKEaGiooIRI0bYqgkjpzjndsFnFzwe+BKf8lVCoVDG3/lCClayOGptYX9SkhTlLAbmAyNFZAdV/SDTk4PsZ4/j/y3GishfcrF21zm3/ZFHHvn8ww8/zOLFi/P6f//KK69QVlbGiSeeyKGHHppzAQyHw4TDYY455pi4EEej0fiqCVWltraW6dOnM2vWLEaOHMnuu+9u64mNXPAmfg3wMaFQ6B0A59yvWz4lOYUU4I/wy5pi/BCf4KJzM/vbE/OD9z+IyPVZiOdQGlYblJOD0GHnXN9gXJsOGDBAbr75Zv785z8zePDgnIvRF198wfTp0zn66KM577zzctp3UxKFOLZqYsqUKdTU1DSyiqdPnw7QyDKOCXKPHj1MmI1MGI23gOc75/6Gd5NmlQY1b5NwACKyA/B4M5NwPwbOpGES7iZV3VdEKvCTcIcAH+Mn4U5Q1eVpXK/gk3DBOPYH/g9vua8DMkp+E1jAz+PF97tMz2+Kc24wvjbcxgCPPPIIV1xxRXxZWK7X1P7xj3/k/vvvZ86cOWy//fY56zddWlo10RLl5eUcfvjhVFVVFZ0gRyIRHnvsMdatW8dPfvKTohlXW1NMk3BBTuoReFfEwfgIuVmhUGheun20eCMiMiqNPtYlS2qcRlHOJ/Hi+w5+Gdr44FidiJyJr3tWDtyVjvgWGUNIncKyWYKotS+BfwOnt1J8h+ODQuLLzL766qvYdaitraW6ujpnX+iFCxfywAMPMHDgwIKILyR3T9TV1cUDOpojGo3y5JNP8uST/s+5oqKCkSNHFtyHHIlEOPnkk6mt9cu0n376ae66664OK8LFQigU+hafo+WBIEfwsfjc4bkRYGAqfh1qS+b1gXgxbUQaRTkVv8Qq2bEnk/XZjliAT8xTQRaJeUSkJ76o6ORWiu+p+HW+iUUn2W+//bjllluor6+nU6dOOZsgi0QinHPOOdTV1bF48WIikUhBRSKZe+LNN99sJMgigqrG3xOJuS5mz55d0Mm8GTNmxMUXyPmPppEZzrlLWji8IJO+UgnwU6raYro2EflrJhfsCAQW7MXAZODsLES0f/C+JJvrO+cEuAw4jybiC16YRowYwcyZM7nxxhtz9kVevHgxdXU+KVQ0Gi0akYgJcYxEQY75gJsTZiA+mVeIEOhp06Yxe/bsRvtEJKerSoyMSTbZ3w0f0LQ5cHm6HbUowKo6NlUH6bTpoEzDC3A2lXf7410YmRbgjKWBvA1foLLZ6Lajjz6amTNnxgUzF2y8sc9BJCI5taxzTVNBjpHMUk70Ia9bt45bb72V008/vU1EOBKJcPXVVzfa1717d2pqanjrrbcsXWeBCIVC18U+O+c2xudXGY//zl/X3HnJSMuZHUSn/RifmCV+jqpen8nFOhj/Ar6ioUpzJlQBK1V1dSYnOecqgUfxEwIthhb37duXiooKqqurOfDAA1tqmjYrVqygsrKSk08+mf3337/dCUMyS3nu3LnMmjUr7gJ48cUXWbp0aZtYwjfeeOMG0X7fffcd0Wg0r5OoRmoCn+9E4Kf4ybd+oVAo4+oa6UbCzQXG4c3rjRNeRjMEPu5XyU6A+5Nh+knn3CZ4/9MhpJHXoUuXLvTt25clS7LycmzAd999x7x58zjyyCP55S9/WRKCEA6H+d3vfsddd93FwIENBQ7WrVvH3Llz83bdSCTCueeeS3V1w59AeXk5Q4YMaSTIqkpNTU1ex2JsiHPuD/jVWauBvqFQ6LJsxBfSF+AfquooVb1UVX8fe2VzwQ7Gq0BfEUnbDSEiW+GjAdNWxiCV5GK82G/g822O/v37s2LFCtauXZu6cQqeffZZvv32W4YNG9bqvoqNcDjML3/5Szp1avhvnDlzZl5CnWM5NObNa5hIFxFGjx7N+PHj6dy5czxZPXgRnj17toVdty3n4us0Xgx84pz7JnitTsiSlhbpCvBTIvKjTEdp8Co+SfluGZwTm4BLywJ2zu0UXKcXGVYJ7t+/P3V1dSxbtiyT05IyZ84cttlmG/r375+6cTskNnEZyz9RV1fHDTfckHPhmzNnTqPcyTE3wzHHHBMPwT7rrLPYf//9421iE55G2xAKhcpCoVDXUCi0cSgU2iThtXEmqSghfQF+CZglIt+JyDcislpEMlL6DkpsEi0TN0QVfgIukqqhc25vvFD3JIvJvnA4TFlZWavdEPPnz2fRokVUVVU1ss5KjWHDhjVKArRkyRImTJiQMxGORCLMmjUrvl1RUcGxxx7byMcbS0h0+umnx9sV84RnMSEiY0RkuYjUi0hVk2MXisg7IrJSRA5vqzGl+225Dl9UcSNV3URVN1bVjJS+g7ISH8mWiQD3B95Q1TUtNXLOHYyPltuULLPade/end12261VAhyJRJg4cSKqylNPPVXSj8IxC3TgwIFxEa6pqeHWW2/NyX3HlsGBt3xHjRrFJZdcktSfHg6H6du3L1tssYVNwqWPA0bhCxPEEZE++NDiPfDpWW8JFh6k16lzh2U7oHS/uG8DTvMZt1yCqGoUWEbmFnCLiuic+wk+WU+rw6779+/PsmXLGlWcyITq6uoN1v6WMuFwmNNPP53KygZvz6JFizjllFNaJcKRSKTRet9OnTpxzDHHtHjOPvvsw9dff80ee+yR9XU7Eqr6hqquTHJoODBNVWtU9X18dO6+GXR9bbZjSleA/w0sCMz0ibFXthftYLwKhCUxeW0ziMgPgK1pwf/rnDsHuJsMJttaon///tTU1DB58uSsBGS77bYDin/tby6JWcL77uu/o4kh3dly3333EY1GAf9vmU5i+V122YW6ujquu+66kn7ySCQajZaJSHXC69TUZ6WkudS4eSfdpBbvB6/OtFB80UjKq8Bp+Emy91K0PSF43yABexDdNhmfwCgn4gs+Vy/Ao48+mlWk17/+9S8Axo0bx8EHH9xhHoXD4TBnnXVWPEdDfX09PXr0yKqv6upqnnnmmfh2OtYvEF+S9uCDDzJjxowO4YooLy+vr6ura/ZXXkT+ji8o25RJqvpYc6cl2dfi075z7m4a8r1s55yL5+0OhUItRg8nkpYA25KzVpE4EdesAAcZ0K4JNm8QkddjIczOuQp8yfuR5Lh228qV/oks28Q8zzzzDHvuuScTJ3a8B6JwOMyFF17IFVdcgapyzTXX0Lt374z+/SKRCJMmTYpvp2v9Anz88cdAfpIqtVdU9dAsTmsuNW5L3JPw+QB8MEbGtOiCEJHLUnXQUhsROSKYVXxHRH6b5PhvRCQSvJyIREVks+DYByLyenCsPTsWX8cn5knlBx6Kz/4GDRnUcM51xScmyrn4AlRVVcXLxFdUVGTkQvjwww9ZsWIFhx2W9RxEu2fVqlXxCbna2lpuuummtN0BsSxnn3ziv+tlZWXxJWfpsN9++8Wv3VHcP3liDnCciFSKSC+gN/BKSyeEQqHnYi9gdZPttEnlA56Q6PNN8joXP3u4AcEs4s3AkUAf4PhgtjGOqv5BVcOqGgYuBJ5T1cQ6cgcFx9vtX1ZQz+2fwLGBldscCwnKWhNkUHPObQq8AAwmD+IL3or7zW9+A8AZZ5yRsfULdGgBrqqqonPnznEhXLx4cdoTcolZzsrKyhgwYEBGboRwOMygQYPYaKONOoT7obWIyMggNe5A4AkReRogSHf7CLAC+BtwRjCBni7ZzWCTWoCn0jj0uOmre9AmGZkW1zweeCj9obcPAtHdHtgV+EcLIhzLsPQIcMjrr7/+L2ApEAK65HOMY8aMoWvXrvFH2nSZN28eoVCIbbZpk/mKoiRxaVqMWNKelkQ4lmA9RkVFRVZJfgYMGMDatWvZdtttUzfu4KjqLFX9oapWqmpPVT084dhVqrqTqu6qqk9l0m8oFBqQ7ZhSZUNrje837eKaIrIRfv3dmYmXB+aJiAK3B7Xf2iNDaXDyd6b55Oyxmu0XvP76613xvuPv0QZlozp37syAAQN44YUX4rlxU/HRRx+xfPlyfv3rrEphlRSxpWlLly5l3To/f7po0aIWk/ZMnjw5PomWid+3KbvuuivgE/eMGjXKrOA2xDlXBUzCG1gVBE+woVBoz3T7yGfYUiYzi8cA/9fE/TBIVfvhXRhniEjSlF0icmpsSQpFUBU5CQtoeESJ0nzC5kHAx6+++upWwMvAFrTh/RxwwAF8/PHHvP/++2m1v++++wDM8gqIWcL77edtjOYS5UQiEcaOHcvy5Q1FXtJd9ZCM2Prt2bNnt3otspExD+CXhI7Ga9jRwXva5PMLnsnM4nE0cT+o6ifB+2ciMgvv0ljY9MQirYocJ0jOfgi+TMnCFpKzD9pmm23eqaioeJY8+XtbYvDgwQA8//zz7Ljjji22jUQiTJs2DYCLLrqI73//+2Z54UX4zDPPZOnSpfE8wtOnT+fTTz+lZ8+elJeX8/DDDzfKaNYa6xfgrbfeAmwlRIH4PBQKzWlNB/kU4MVA72BW8WO8yJ7QtJGI9MDXUBubsK8bUKaqq4PPPyKDLPPFhqq+KCJPAQNERJpGFIrIdsC2Y8eO3YrsEri3mq233pqdd96Z559/npNOOqnFtk8++WSjahH2pW8glrTn0UcfRVVRVRYu3MBuABon2smWWP6N+vr6jFexGK3mUufcncA/gHgGpVAoNDPdDlItQ/uziNzU3Kulc1W1Du/TfRp4A3hEVZeLyGkiclpC05HAPFVNtF57Ai+IyGv45SBPqOrf0r2pIuVZ/FPATk0PnHzyyZMB+vXrVxDxjTF48GCWLFnCt9+2/CDx2WefAX7m3pY/bUjTpD3JKC8v3yDRTjaEw2FOOeUUAIYMGZJ1P0ZWjAfC+PmrY2hwQ6RNKgs4cf3t7/GVjdMmWXHNoCJy4vY9NF7UjKq+B+yVybXaAfOD94PwseY458qAP61Zs2ZM165d2WWXXQo2OPACfPfdd/Pyyy9z8MEHJ22zdu1aXn75ZQYNGkRVVZWVxElCzB88d+7cDco+iQjl5eVcdNFFjBkzJifX2203n+30mWeeYeHChbYkre3YKxQK9W1NB6lWQcSjO0TkV4nbRsasBD7FC/BU51wnvN/7yNdee61ir732oqKisHOI4XCYLl26MHXqVDbbbLOkX+J58+axZs0aTjnllJLN/ZsLYuWNYmWNVJXdd9+dVatW5fxH64MPPgAaT/yZALcJLznn+oRCoRXZdpDJN94yobUCVVURmQ8ctHDhwm6bbbbZk0DVmjVrNnr77bf5xS9+Ueghsnz5ctavX49zjgkTJnDnnXdu8EWeMWMGvXr1ol+/foUZZDujuQKguSQWzRiNRuMVMmIJ3I28cgBwknPufbwPuKiWoRkbMh/Y6vPPP38Vv6pjo2XLllFfX18UX5bq6ur45Nr69es3yO71zjvvEIlEGD16dFprhY22IRwOM3x4Q4xTbW2t1YlrG47Ahy3/iHwsQxOR1TRYvhslVMEQvFFnSdkz4Pjjj1/50EMPsXTp0l677rprBfgVBSJSFJUkqqqqqKysZN26dagqO+3UeL7wtttuo6ysjF69ehVohEZzjBw5ksceeyxuBc+YMYPddtstZ35mY0NCodA/W9tHi9/6WOWL4FWR8NkqYmSIcy504YUXzuzZsyeLFy+uAG9xzpkzB1XlrLPOKvgi+tjk0UknnURFRQVPPdUQkTlt2jSefvpp6uvrOffccws+VqMx4XCYUaNGxbej0ShXX321/T/lAefc0ly0geKMHCs5nHMHAE+KSPd9992XBQsWcPHFF/Pss88W3XramM+yW7du3HLLLey5554sXryY+fPnx9sUy1iNxgwbNoyZM2fGE7tHo1GbkMsPuzvnWqpkK0BayaFNgPOMc24YfrXDRgA9e/Zk9erV8UQsFRUVqGrRrac9+eSTefTRR7n2Wl9tJVbxor6+vujGanjC4TCTJk3iyiuvpL6+3lwR+SOdKudpZVMzAc4jzrlTgRtIqGCROHlVXl7OqFGj2HrrrYtuPW1lZSX7779/vE5ZWVkZI0eOLMqxGg2MGTOGN954g+nTpwPeCr7qqqsyThRvNE8ufL8xCj/zU4I458Q5dxnwJ5qUDzrwwAPp0qUL5eXl8SQsEyZMKMovx+jRo6msrGwXYzUaGDZsWKNJ3Wg0yqWXXmr+4CJESqnQsYh8q6qtrhTcGoLottvweS+SjiUSiVBdXd0uLMn2NFajgXPOOYdnn3220b7y8nImTZrUrt0R/fv3j9bU1JTMk7sJcA5xzlUC04FDKEBGM8OIEYlEGDduXHxCLkZZWRkXX3xxuxXh1giwiIwBLgN2B/ZV1epg/w74fDWxkvUvqeppyfpoCefcJqFQ6JvULRswF0SOcM5tgg+0OBQTX6PAxCbkYvX+YtTX13PllVfGfcQdDAeMIklaW+DdWHm0LMX3IWCKc+5e51zS3OXJMAHOAc65nvisbf3IYcl4w2gNY8aM4Z577uHggw9uNPlbX1/fIdcIq+obqroydcuseCMUCp0WCoVOopk6mcnIqwCnURV5qIisSqiMfEm65xYLzrkd8eWDdgQqCzwcw2hEOBzmxhtv5He/+10jEY5GoxuEmrcHotFoWawCTvA6NUdd9xKRV0XkOREZnMmJzrmpwGHOubOdc0OBr9M9N2/O7ISqyIfhq2MsFpE5qto0c9Dzqnp0lucWFOfc3vhkzD2wpwmjiBkzZgwffvghd999N+Azp33zTUbuyqKgvLy8vq6urtlF6CLyd2CrJIcmqepjSfYD/BvYTlW/FJH+wGwR2UNVW/wHcs6NBmaGQqFTnHNdgL3xOV42T+tmyO864HhVZAARiVVFTkdEW3Num+CcOwiYSzMrHQyj2Nhkk8bZA+6991623XbbdjshlwxVPTSLc2oIKlqo6hIReRfYhcb50JPxV2C2c25sKBRaByxyzu0WCoXSTm2YT6stWVXkZPXLB4rIayLylIjskeG5BSnK6ZwbAzyBia/RjqiqqmqUc7qpLzgSiXDOOecwfPhwjj32WA4//HCGDx9e8hN2IvL94KkbEdkRn+HsvTROfRN4DpgR5PcGOCuTa+dTsNKpirwU2F5V14jIUcBs/M2nXVG5rYtyOufOBKZgk21GOyMcDnPRRRfFQ5UB6urquPTSS+nUqRMrVyafn7r88stZsmQJkydPbsvh5hwRGQn8Gfg+8ISIRFT1cOBA4HIRqcOHEJ/WpEJ7c2goFLrNObcWmOOcG0Vy7WqWglZFTvSxqOqTInKLiGyRzrltjXNOgKuBszHxNdopMXdDogi/915qY++JJ55gyy23ZOLEiXkdXz5R1VnArCT7ZwAzsujyK4BQKHRfIMJPkOES1Hy6IOJVkUWkM35pRqMSziKylQRTsyKybzCeL9M5ty1xzlUA9+LF19b4Gu2aMWPGMHr06IzPu+eee0reHZEJoVDokITPjwLXk8EEHORRgNOsinws4ILqxzcBx6kn6bn5GmtLOOe64n/ZRmPia5QIw4YN2yBII8YWW2zBrrvuSo8ejTMqqmqHXD+cLqFQ6PFQKLRFJudYKHILOOc2xS8z2x1zOxglxvTp07nqqquIRqOICL169WLs2LGNVkVcf/313HPPPfG81SLC2WefzYQJEwoy5lLLBVEyN5JrnHM/AJ7H+587F3g4hpFzxowZQ+/evVtMthTz+SauH25qGRvZYwKcBOfcLnjx3Qz7NzJKmHSqNieuHy4rK2PVqlV5HlXHwaK3muCc2xef1+H7mPgaRqP1wyJiFnAOMQFOwDl3OD6jWQ8yXM9nGKVKOBzmxBNPBHzwxpQpU2wiLkeYAAc458bi1wjaSgfDaELXrn4OWlXjRVmN1mMCDDjnfoOPprOVDoaRhIEDB8Y/l5eXW1HWHNGhfZxBdNufgFMw8TWMFikrK4tHzxm5ocNawEHyjGl48TW3g2G0QHV1dXwtcG1tLXPnzi3wiEqDDinAzrluwDzgaEx8DSMlVVVV8cg5VWX27Nk2EZcDOpwAO+c2BxYBAzDxNYy0CIfDjBgxIr7dXitqFBsdSoCdc9vhU2DuCnQp8HAMo10xfPjw+GebiMsNHUaAnXN74MV3Gyy02DCyoqysw0hGm1Doopw/FZFlwetFEdkr4dgHIvJ6UKyzVc86zrlBeLfDZkDyFFCGYbRI4kRcXV2duSByQN4EOKGw5pFAH+B4EenTpNn7wBBV3RO4gqCyRQIHqWpYVbN+1nHOHYOfcNsYi24zjKypqqqiU6dO8e32FpIsIn8QkTcDg2+WiGyacOzCwFBcKSKHt9WY8mkBxwtrqup6/JKv4YkNVPVFVf0q2HwJn3ksZzjnJgAPY5NthtFqwuEw5513HtBuQ5KfAUKBwfcWcCFAYBgeB+wBHAHcEqsRl2/yGYiRrLDmfi20/znwVMK2AvNERIHbg9pvGyAipwKnBpudnHPh4PNI4DwswMIwcsa33zaUXYyFJKfKplYsqOq8hM2X8AUhwBuG04LqyO+LyDt4A3JRvsdU6KKcvqHIQXgBPiBh9yBV/UREtgSeEZE3VXXhBh0mFOXs2rWr4quUgrfuzfI1jBxSVVUVj4grKytr85UQ0Wi0rMmc0B3NGWcpOBn/dAzeWHwp4VizVdhzTUGLcgKIyJ7AncCRqvplbL+qfhK8fyYis/C/SBsIcBI2Sd3EMIxsCco4FoTy8vL6urq6ZlVfRP4ObJXk0CRVfSxoMwmoAx6InZakfZuUCsqnAMcLawIf430sJyQ2EJHtgJnAiar6VsL+bkCZqq4OPv8IuDyPYzUMIw2ShSQXkwtCVQ9t6biInISPgD1EG+qxFawKe6GLcl6CryJ6S5PlZj2BF4Jina8AT6jq3/I1VsMw0iMxOTvQrkKSReQI4AJgmKquTTg0BzhORCoDg7E3XnfyTl6zoanqk8CTTfbdlvB5ArBBdT9VfQ/Yq+l+wzAKSzgcZvjw4fHy9MVoBbfA/wKV+DklgJdU9bTAMHwEWIF3TZyhqtG2GJCFtRiGkRGJJe3bU2IeVd1ZVbcNYgvCqnpawrGrVHUnVd1VVZ9qqZ9cYgJsGEZGhMNhhgwZEt+2xDzZYwJsGEbGxGrEgSXmaQ0mwIZhZExFRUV8OVo0GuXtt98u8IjaJybAhmFkTOJytGg0ylVXXdUu/MDFhgmwYRgZE4uIixGNRrngggtMhDPEBNgwjIwJh8MMHTq00b5PPvmEcePGmQhngAmwYRhZMX78+A3CkqPRKHfffXeBRtT+MAE2DCMrwuEw48aN22D//Pnz44EaRsuYABuGkTUTJ05k/PjxjfapKldffbW5ItLABNgwjFYxceJELrnkkkbuiPr6egvOSAMTYMMwWs2YMWM4//zz49sVFRUWnJEGhS7KKSJyU3B8mYj0S/dcwzCKi1AoVOghtDsKXZTzSHzqt974skK3ZnCuYRhFRKLLwfJDpEdBi3IG2/ep5yVgUxHZOs1zDcMoIqqqqujSpQvl5eV06tTJXBBpUOiinMnabJPmuYZhFBHhcJipU6dSXV1NVVVVe8kRXFAKXZSzuTaZFPSMV0VOzNRvGEbbEw6HTXgzIJ8uiHTqLDXXJu0aTap6h6pWqWqVCbBhGM0hIn8QkTeDCf9ZIrJpsH8HEfkuKIsWEZHbUnSVM/IpwPGinCLSGV+Uc06TNnOAnwWrIQYAq1T132meaxiGkQnPACFV3RN4C7gw4di7ySpl5Ju8mYyqWicisaKc5cBdsaKcwfHb8PXijgLeAdYC41s6N19jNQyj9FHVeQmbLwHHFmosMaShMnP7R0S0srKydG4ogWg0Gq/DVWrYvbVPCnFvNTU1AixJ2HWHqt6RaT8iMhd4WFX/KiI7AMvxVvE3wMWq+nwuxptyHCUmwNWqWpJrX+ze2id2b22LiPwd2CrJoUmq+ljQZhJQBYxSVRWRSqC7qn4pIv2B2cAeqvpNvsdrs1aGYZQMqnpoS8dF5CTgaOAQDaxPVa0BaoLPS0TkXWAXIO+RJJYLwjCMDoGIHAFcAAxT1bUJ+78fRN8iIjviI3Pfa4sxlZoFnLEvqB1h99Y+sXsrHv4XqASeCTK3vRSseDgQuFxE6oAocJqq/rctBlRSPmDDMIz2hLkgDMMwCoQJsGEYRoEoCQFu77mDRWRbEZkvIm+IyHIROSfYv5mIPCMibwfv30s458LgfleKyOGFG316iEi5iLwqIo8H2yVxbyKyqYg8GoS4viEiA0vo3n4d/D06EXlIRLqUyr0VDararl/4SLl3gR2BzsBrQJ9CjyvDe9ga6Bd83hi/ILwPMAX4bbD/t8C1wec+wX1WAr2C+y8v9H2kuMeJwIPA48F2SdwbcC8wIfjcGdi0FO4Nn5HwfaBrsP0IMK4U7q2YXqVgAbf73MGq+m9VXRp8Xg28gf8CDMd/wQneRwSfhwPTVLVGVd/Hh3Lv26aDzgAR+SHwY+DOhN3t/t5EZBP8DPpfAFR1vap+TQncW0AF0FVEKoCN8AmxSuXeioJSEODmcgq3S4KwyL2Bl4Ge6pMTEbxvGTRrb/d8A3A+UJ+wrxTubUfgc+DuwL1yp4h0owTuTVU/Bv4I/Av4Nz5R1jxK4N6KiVIQ4LRzBxc7ItIdmAH8SlsOg2w39ywiRwOfqeqSlI2DU5LsK8p7w1uI/YBbVXVv4Fv8Y3lztJt7C3y7w/HuhB8A3URkbEunJNlXlPdWTJSCAKedO7iYEZFOePF9QFVnBrv/E5RoInj/LNjfnu55EDBMRD7Au4cOFpG/Uhr39hHwkaq+HGw/ihfkUri3Q4H3VfVzVa0FZgL7Uxr3VjSUggC3+9zB4sNy/gK8oarXJxyaA5wUfD4JeCxh/3EiUikivfChk6+01XgzQVUvVNUfquoO+P+bZ1V1LKVxb58CH4rIrsGuQ4AVlMC94V0PA0Rko+Dv8xD83EQp3FvxUOhZwFy88DmF38LPvE4q9HiyGP8B+Me1ZUAkeB0FbA78A3g7eN8s4ZxJwf2uBI4s9D2keZ9DaVgFURL3BoTxSVuW4bNofa+E7u33wJuAA+7Hr3AoiXsrlpeFIhuGYRSIUnBBGIZhtEtMgA3DMAqECbBhGEaBMAE2DMMoECbAhmEYBcIE2DAMo0CYABvtBhH5QES2yPM1xonI5yJyZ+rWjc77g4h8KiLn5WtsRulRajXhjAIgIhWqWlfocbREhmN8WFXPzKR/Vf2NiHybxdCMDoxZwEZKRORnIrJMRF4TkfuDffeIyPUiMh+4VkTCIvJS0G5WLFG3iJwtIiuC/dOCfUNEJBK8XhWRjZtcr5uIPBFcz4nITxIOnyUiS0XkdRHZLWi/mYjMDq7xkojsGey/TETuEJF5wH3iq9/OEJHFwWtQGvc+Luh7roi8LyJnisjEYNwvichmOflHNjokZgEbLSIie+BDTAep6hdNBGcX4FBVjYrIMuAsVX1ORC4HLgV+hc8O1ktVa0Rk0+C884AzVPX/ggxw65pc9gjgE1X9cTCGHgnHvlDVfiLyy6CfCfiQ2VdVdYSIHAzchw8RBugPHKCq34nIg8CfVPUFEdkOeBrYPY1/hhA+RWgXfJ7bC1R1bxH5E/AzfLpNw8gYs4CNVBwMPKqqXwBo43Ld0wPx7QFsqqrPBfvvxScqB58j4YEglWHMBfB/wPUicnZwXlPXwOvAoSJyrYgMVtVVCcdimeKWADsEnw/A5ypAVZ8FNk8Q7Tmq+l3w+VDgf0Ukgk8es0lT67sZ5qvqalX9HFgFzE0Y5w7NnmUYKTABNlIhNJ/XNR2f54+Bm/GW6JLAFzsZb7l2BV6KuRJiqOpbQfvXgWtE5JKEwzXBe5SGJ7iWctEmjrEMGKiq4eC1jfoKJKmoSfhcn7Bdjz1FGq3ABNhIxT+A/xGRzcH7W5s2CCzUr0RkcLDrROA5ESkDtlXV+fiKGJsC3UVkJ1V9XVWvxWcSayTAIvIDYK2q/hVflaFfijEuBH4anDsU76ZIltB+HhCfXBORcIp+DSOv2K+30SKqulxErsILahR4FV+csSknAbeJyEbAe8B4fMHUvwbuAMH7X78WkStE5CC8FbsCeKpJX32BP4hIPVALnJ5imJfhywItA9bSkK+2KWcDNwftKvDCfVqKvg0jb1g6SsNIQETGAVWZLkMLzr0MWKOqf8z1uIzSxFwQhtGY74AjswnEAMaSnl/cMACzgA3DMAqGWcCGYRgFwgTYMAyjQJgAG4ZhFAgTYMMwjALx/9TCQ2tN1ewWAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 360x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## change coordinates of plots to local coordinates:\n",
    "#results.set_plot_localcoords(True)\n",
    "## only plot a certain Area Of Interest of the complete grid\n",
    "#results.set_aoi([20,445,20,220])\n",
    "\n",
    "# example usage map plotting\n",
    "fig, ax = results.fig_map_var('zb','[m]', it = 2)\n",
    "\n",
    "#fig, ax = results.fig_map_diffvar('zb', '$\\Delta z_b$ [m]')\n",
    "\n",
    "fig, ax = results.fig_cross_var('H', 2, iy=5, coord=None, plot_ref_bathy=True, zmin=-25)\n",
    "\n",
    "fig, ax = results.fig_profile_change(iy=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Print params of xbeach run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'wavemodel': 'surfbeat',\n",
       " 'wbctype': 'jonstable',\n",
       " 'vardx': 1.0,\n",
       " 'posdwn': '-1',\n",
       " 'nx': 174.0,\n",
       " 'ny': 180.0,\n",
       " 'xori': 0.0,\n",
       " 'yori': 0.0,\n",
       " 'alfa': 10.0,\n",
       " 'xfile': 'x.grd',\n",
       " 'yfile': 'y.grd',\n",
       " 'depfile': 'bed.dep',\n",
       " 'thetamin': '-90',\n",
       " 'thetamax': 90.0,\n",
       " 'thetanaut': 0.0,\n",
       " 'dtheta': 10.0,\n",
       " 'dtheta_s': 10.0,\n",
       " 'Wavemodel': 'surfbeat',\n",
       " 'morphology': 0.0,\n",
       " 'tstop': 3600.0,\n",
       " 'zs0': 0.0,\n",
       " 'bcfile': 'jonstable.txt',\n",
       " 'nglobalvar': 3.0,\n",
       " 'npointvar': 2.0,\n",
       " 'nmeanvar': 1.0,\n",
       " 'npoints': 4.0,\n",
       " 'globalvar': ['zb', 'zs', 'H'],\n",
       " 'meanvar': ['zb'],\n",
       " 'pointvar': ['zs', 'H'],\n",
       " 'points': {'0': (6.0, 0.0), '': (12.0, 0.0)}}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}