{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tutorial on how to use Parcels on NEMO curvilinear grids"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parcels also supports [curvilinear grids](https://www.nemo-ocean.eu/doc/node108.html) such as those used in the [NEMO models](https://www.nemo-ocean.eu/).\n",
    "\n",
    "We will be using the example data in the `NemoCurvilinear_data/` directory. These fiels are a purely zonal flow on an aqua-planet (so zonal-velocity is 1 m/s and meridional-velocity is 0 m/s everywhere, and no land). However, because of the curvilinear grid, the `U` and `V` fields vary north of 26N."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from parcels import FieldSet, ParticleSet, JITParticle, ParticleFile, plotTrajectoriesFile\n",
    "from parcels import AdvectionRK4\n",
    "import numpy as np\n",
    "from datetime import timedelta as delta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can create a `FieldSet` just like we do for normal grids. Note that we need to provide an extra filename for the `mesh_mask` file that holds information on the Curvilinear grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Generating rotation angles fields in file: NemoCurvilinear_data_rotation_angles.nc\n",
      "WARNING: Casting lon data to np.float32\n",
      "WARNING: Casting lat data to np.float32\n",
      "WARNING: Casting time data to np.float64\n",
      "WARNING: Casting depth data to np.float32\n"
     ]
    }
   ],
   "source": [
    "filenames = {'U': 'NemoCurvilinear_data/U_purely_zonal-ORCA025_grid_U.nc4',\n",
    "             'V': 'NemoCurvilinear_data/V_purely_zonal-ORCA025_grid_V.nc4',\n",
    "             'mesh_mask': 'NemoCurvilinear_data/mesh_mask.nc4'}\n",
    "variables = {'U': 'U',\n",
    "             'V': 'V'}\n",
    "dimensions = {'U': {'lon': 'nav_lon_u', 'lat': 'nav_lat_u'},\n",
    "              'V': {'lon': 'nav_lon_v', 'lat': 'nav_lat_v'}}\n",
    "field_set = FieldSet.from_nemo(filenames, variables, dimensions, mesh='spherical', allow_time_extrapolation=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the `FieldSet.from_nemo()` function assumes some default values for the `mesh_mask` file. If your `mesh_mask` file has different variables and dimensions, you can set these with a dictionary such as the default one below, and add that as `dimensions=dimensions` to the call of `FieldSet.from_nemo()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "variables['mesh_mask'] = {'cosU': 'cosU',\n",
    "                          'sinU': 'sinU',\n",
    "                          'cosV': 'cosV',\n",
    "                          'sinV': 'sinV'}\n",
    "dimensions['mesh_mask'] = {'U': {'lon': 'glamu', 'lat': 'gphiu'},\n",
    "                           'V': {'lon': 'glamv', 'lat': 'gphiv'},\n",
    "                           'F': {'lon': 'glamf', 'lat': 'gphif'}}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we can plot the `U` field. Note that we need to rescale the `grid.lon` so that it is monotonic (needed for plotting purposes only; Parcels itself can handle non-monotonic grids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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HcRaNmTotM/g4cJ6kcyWtEWThdePnsXPN7JwoK98D/F9m9l/byprZ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\ncMHvOI6zYrjgdxzHWTFc8DuO46wY/z/sehZXnSPxLQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10284a590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Remap the longitudes because field.show() requires monotonic longitudes\n",
    "field_set.U.grid.lon[field_set.U.grid.lon<73] += 360\n",
    "field_set.U.show()\n",
    "\n",
    "# Now reverse the remapping above\n",
    "field_set.U.grid.lon[field_set.U.grid.lon > (72+360)] -=360"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you see above, the `U` field indeed is 1 m/s south of 50N, but varies with longitude and latitude north of that\n",
    "\n",
    "Now we can run particles as normal. Parcels will take care to rotate the `U` and `V` fields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Compiled JITParticleAdvectionRK4periodicBC ==> /var/folders/r2/8593q8z93kd7t4j9kbb_f7p00000gr/T/parcels-504/ae256881993532bad8b3758037987412.so\n"
     ]
    }
   ],
   "source": [
    "# Start 20 particles on a meridional line at 180W\n",
    "npart = 20\n",
    "lonp = -180 * np.ones(npart)\n",
    "latp = [i for i in np.linspace(-70, 88, npart)]\n",
    "\n",
    "# Create a periodic boundary condition kernel\n",
    "def periodicBC(particle, pieldSet, time, dt):\n",
    "    if particle.lon > 180:\n",
    "        particle.lon -= 360\n",
    "\n",
    "pset = ParticleSet.from_list(field_set, JITParticle, lon=lonp, lat=latp)\n",
    "pfile = ParticleFile(\"nemo_particles\", pset, outputdt=delta(days=1))\n",
    "kernels = pset.Kernel(AdvectionRK4) + periodicBC\n",
    "pset.execute(kernels, runtime=delta(days=50), dt=delta(hours=6),\n",
    "             output_file=pfile)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then we can plot these trajectories. As expected, all trajectories go exactly zonal and due to the curvature of the earth, ones at higher latitude move more degrees eastward (even though the distance in km is equal for all particles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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REsszFF3ZlEWc3cR9NfAE8DiFf3TTS8Wd8efhQ8CzSVwXZR1PUVxzKFx981jy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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116083650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plotTrajectoriesFile(\"nemo_particles.nc\")"
   ]
  }
 ],
 "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": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}