{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tutorial on how to use S-grids with time-evolving depth dimensions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some hydrodynamic models (such as SWASH) have time-evolving depth dimensions, for example because they follow the waves on the free surface. Parcels can work with these types of models, but it is a bit involved to set up. That is why we explain here how to run Parcels on `FieldSets` with time-evoloving depth dimensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from parcels import FieldSet, ParticleSet, JITParticle, AdvectionRK4, ParticleFile, plotTrajectoriesFile\n",
    "import numpy as np\n",
    "from datetime import timedelta as delta\n",
    "from os import path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we use sample data from the SWASH model. We first set the `filenames` and `variables`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "filenames = path.join('SWASH_data', 'field_*.nc')\n",
    "variables = {'U': 'cross-shore velocity',\n",
    "             'V': 'along-shore velocity',\n",
    "             'depth_u': 'time varying depth_u'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, the first key step when reading time-evolving depth dimensions is that we specify `depth` as **`'not_yet_set'`** in the `dimensions` dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dimensions = {'U': {'lon': 'x', 'lat': 'y', 'depth': 'not_yet_set', 'time': 't'},\n",
    "              'V': {'lon': 'x', 'lat': 'y', 'depth': 'not_yet_set', 'time': 't'},\n",
    "              'depth_u': {'lon': 'x', 'lat': 'y', 'depth': 'not_yet_set', 'time': 't'}}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, _after_ we create the `FieldSet` object, we set the `depth` dimension of the relevant `Fields` to `fieldset.depth_u` and `fieldset.depth_w`, using the `Field.set_depth_from_field()` method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Casting lon data to np.float32\n",
      "WARNING: Casting lat data to np.float32\n",
      "WARNING: Flipping lat data from North-South to South-North\n",
      "WARNING: Casting depth data to np.float32\n"
     ]
    }
   ],
   "source": [
    "fieldset = FieldSet.from_netcdf(filenames, variables, dimensions, mesh='flat', allow_time_extrapolation=True)\n",
    "fieldset.U.set_depth_from_field(fieldset.depth_u)\n",
    "fieldset.V.set_depth_from_field(fieldset.depth_u)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can create a ParticleSet, run those and plot them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Compiled JITParticleAdvectionRK4 ==> /var/folders/r2/8593q8z93kd7t4j9kbb_f7p00000gr/T/parcels-504/6c662dabb803077cfcabb1b87d3c87bb_0.so\n",
      "WARNING: Casting field data to np.float32\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pset = ParticleSet(fieldset, JITParticle, lon=9.5, lat=12.5, depth=-0.1)\n",
    "pfile = pset.ParticleFile(\"SwashParticles\", outputdt=delta(seconds=0.05))\n",
    "pset.execute(AdvectionRK4, dt=delta(seconds=0.005), output_file=pfile)\n",
    "\n",
    "pfile.export()  # export the trajectory data to a netcdf file\n",
    "plotTrajectoriesFile('SwashParticles.nc');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that, even though we use 2-dimensional `AdvectionRK4`, the particle still moves down, because the grid itself moves down"
   ]
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}