{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c556d9fb-d6ec-4142-b9f1-a12140850776",
   "metadata": {},
   "source": [
    "# Example Use of the `DeleteParticle` Kernel\n",
    "\n",
    "This notebook shows how the `moacean_parcels.kernels.DeleteParticle` kernel\n",
    "can be used in `ParticleSet.execute()` as a custom recovery kernel that\n",
    "deletes any particles that cause kernel errors.\n",
    "\n",
    "This notebook is based on one created by Birgit Rogalla to explore the\n",
    "possible fate of material released on to the ocean surface from the\n",
    "MV Zim Kingston as it burned on 24-Oct-2021.\n",
    "Fortunately,\n",
    "there was no significant release of material from the Zim Kingston,\n",
    "so the particle tracks calculated here are just another demonstration\n",
    "of the strong ebb/flood currents along the coastline near Victoria."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b6f3c616-54be-4ab0-87dc-58d35820e7b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import timedelta\n",
    "from pathlib import Path\n",
    "\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature\n",
    "import matplotlib.cm\n",
    "import matplotlib.colors\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from parcels import FieldSet, ParticleSet, JITParticle, AdvectionRK4_3D, ErrorCode\n",
    "import xarray as xr\n",
    "\n",
    "from moacean_parcels.kernels import DeleteParticle\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a71a97f1-ff27-42dd-94f4-898a8e2261fa",
   "metadata": {},
   "source": [
    "Set up SalishSeaCast ocean model fields,\n",
    "and geo-reference arrays from the model mesh mask,\n",
    "then create a `parcels.FieldSet` from them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1979e62c-2af0-4b79-9d1f-12959e1f26ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "nowcast = Path(\"/results/SalishSea/nowcast-blue.201905/24oct21/\")\n",
    "forecast = Path(\"/results/SalishSea/forecast.201905/24oct21/\")\n",
    "\n",
    "nowcastU = nowcast / \"SalishSea_1h_20211024_20211024_grid_U.nc\"\n",
    "nowcastV = nowcast / \"SalishSea_1h_20211024_20211024_grid_V.nc\"\n",
    "nowcastW = nowcast / \"SalishSea_1h_20211024_20211024_grid_W.nc\"\n",
    "\n",
    "forecastU = forecast / \"SalishSea_1h_20211025_20211026_grid_U.nc\"\n",
    "forecastV = forecast / \"SalishSea_1h_20211025_20211026_grid_V.nc\"\n",
    "forecastW = forecast / \"SalishSea_1h_20211025_20211026_grid_W.nc\"\n",
    "\n",
    "files_U = [nowcastU, forecastU]\n",
    "files_V = [nowcastV, forecastV]\n",
    "files_W = [nowcastW, forecastW]\n",
    "\n",
    "mesh_mask = Path(\"/media/doug/warehouse/MEOPAR/grid/mesh_mask201702.nc\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d404f0f8",
   "metadata": {},
   "source": [
    "The `VisibleDeprecationWarning` messages in the output of this cell are a known issue\n",
    "that will perhaps be resolved in the next release of OceanParcels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c503706f-416b-40b0-b577-8ac425582d91",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/media/doug/warehouse/conda_envs/moacean-parcels/lib/python3.9/site-packages/parcels/field.py:241: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  timeslices = np.array(timeslices)\n",
      "/media/doug/warehouse/conda_envs/moacean-parcels/lib/python3.9/site-packages/parcels/field.py:243: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  dataFiles = np.concatenate(np.array(dataFiles))\n",
      "/media/doug/warehouse/conda_envs/moacean-parcels/lib/python3.9/site-packages/parcels/field.py:241: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  timeslices = np.array(timeslices)\n",
      "/media/doug/warehouse/conda_envs/moacean-parcels/lib/python3.9/site-packages/parcels/field.py:243: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  dataFiles = np.concatenate(np.array(dataFiles))\n",
      "/media/doug/warehouse/conda_envs/moacean-parcels/lib/python3.9/site-packages/parcels/field.py:241: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  timeslices = np.array(timeslices)\n",
      "/media/doug/warehouse/conda_envs/moacean-parcels/lib/python3.9/site-packages/parcels/field.py:243: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  dataFiles = np.concatenate(np.array(dataFiles))\n"
     ]
    }
   ],
   "source": [
    "filenames = {\n",
    "    \"U\": {\"lon\": mesh_mask, \"lat\": mesh_mask, \"depth\": files_W[0], \"data\": files_U},\n",
    "    \"V\": {\"lon\": mesh_mask, \"lat\": mesh_mask, \"depth\": files_W[0], \"data\": files_V},\n",
    "    \"W\": {\"lon\": mesh_mask, \"lat\": mesh_mask, \"depth\": files_W[0], \"data\": files_W},\n",
    "}\n",
    "\n",
    "dimensions = {\n",
    "    \"U\": {\"lon\": \"glamf\", \"lat\": \"gphif\", \"depth\": \"depthw\", \"time\": \"time_counter\"},\n",
    "    \"V\": {\"lon\": \"glamf\", \"lat\": \"gphif\", \"depth\": \"depthw\", \"time\": \"time_counter\"},\n",
    "    \"W\": {\"lon\": \"glamf\", \"lat\": \"gphif\", \"depth\": \"depthw\", \"time\": \"time_counter\"},\n",
    "}\n",
    "\n",
    "variables = {\"U\": \"vozocrtx\", \"V\": \"vomecrty\", \"W\": \"vovecrtz\"}\n",
    "\n",
    "fieldset = FieldSet.from_nemo(filenames, variables, dimensions)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "961061df",
   "metadata": {},
   "source": [
    "Set up a collection of particles to be released at the ship's AIS reported location\n",
    "and release the particles at 1 hour intervals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "af37caec-5069-471d-b259-8e6df0e4e81a",
   "metadata": {},
   "outputs": [],
   "source": [
    "release_lon = -123.36337  # east\n",
    "release_lat = 48.33805  # north\n",
    "release_depth = 1  # metres\n",
    "\n",
    "pset = ParticleSet.from_list(\n",
    "    fieldset=fieldset,  # the fields on which the particles are advected\n",
    "    pclass=JITParticle,  # the type of particles (JITParticle or ScipyParticle)\n",
    "    lon=release_lon,  # a vector of release longitudes\n",
    "    lat=release_lat,  # a vector of release latitudes\n",
    "    depth=release_depth,  # a vector of release depths\n",
    "    repeatdt=timedelta(hours=1),  # repeat release of particles every hour\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be5384e3",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d07182f1-06ab-487b-bfc7-4da9c529a7a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "kernels = pset.Kernel(AdvectionRK4_3D)\n",
    "\n",
    "recovery_kernels = {ErrorCode.ErrorOutOfBounds: DeleteParticle}\n",
    "\n",
    "output_file = pset.ParticleFile(\n",
    "    name=\"/tmp/NEMO3D_fire_RK43D.nc\", outputdt=timedelta(hours=1)\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe0df30c",
   "metadata": {},
   "source": [
    "Execute the particles simulation and save the output file.\n",
    "\n",
    "Notice the messages like:\n",
    "\n",
    "    Particle [7] lost!! (lon, lat: -123.26292897202094, 48.37882576325017, depth: 4.015960466473189, time: 39600.0s)\n",
    "\n",
    "in the cell output.\n",
    "Those are from the `DeleteParticle` recovery kernel doing its job.\n",
    "\n",
    "Without `recovery=recovery_kernels` in the `.execute()` call,\n",
    "the simulation would stop when particle 7 goes out of bounds through the surface,\n",
    "and no output file would be produced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "046a58c8-acf2-482a-ad37-6a574e8065d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Compiled ArrayJITParticleAdvectionRK4_3D ==> /tmp/parcels-1000/lib49a737f4d032488557d7f6ad96f65175_0.so\n",
      "INFO: Temporary output files are stored in /tmp/out-ULAGARIP.\n",
      "INFO: You can use \"parcels_convert_npydir_to_netcdf /tmp/out-ULAGARIP\" to convert these to a NetCDF file during the run.\n",
      " 27% (39600.0 of 144000.0) |###          | Elapsed Time: 0:00:33 ETA:   0:02:04"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Particle [7] lost!! (lon, lat: -123.26292897202094, 48.37882576325017, depth: 4.015960466473189, time: 39600.0s)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 30% (43200.0 of 144000.0) |###          | Elapsed Time: 0:00:38 ETA:   0:02:21"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Particle [3] lost!! (lon, lat: -123.2525273677497, 48.37970223071624, depth: 14.762369210121506, time: 43200.0s)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 32% (46800.0 of 144000.0) |####         | Elapsed Time: 0:00:43 ETA:   0:02:25"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Particle [12] lost!! (lon, lat: -123.33204734557782, 48.35583601088063, depth: 2.9548517280782107, time: 46800.0s)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 35% (50400.0 of 144000.0) |####         | Elapsed Time: 0:00:48 ETA:   0:02:16"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Particle [6] lost!! (lon, lat: -123.15848183473733, 48.47756111759809, depth: 53.51842788129579, time: 50400.0s)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 40% (57600.0 of 144000.0) |#####        | Elapsed Time: 0:00:59 ETA:   0:02:03"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Particle [4] lost!! (lon, lat: -123.14468251460809, 48.48316094082082, depth: 1.4058619025670396, time: 57600.0s)\n",
      "Particle [8] lost!! (lon, lat: -123.11958089183733, 48.43985826125179, depth: 5.273367492351099, time: 57600.0s)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 42% (61200.0 of 144000.0) |#####        | Elapsed Time: 0:01:05 ETA:   0:02:04"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Particle [5] lost!! (lon, lat: -123.13589383771998, 48.48299015991886, depth: 12.88297560729552, time: 61200.0s)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 65% (93600.0 of 144000.0) |########     | Elapsed Time: 0:01:44 ETA:   0:01:05"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Particle [13] lost!! (lon, lat: -123.47211933406058, 48.29754074282731, depth: 7.926310052767803, time: 93600.0s)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 67% (97200.0 of 144000.0) |########     | Elapsed Time: 0:01:49 ETA:   0:01:10"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Particle [17] lost!! (lon, lat: -123.47956585671241, 48.29225104874882, depth: 4.024776245138128, time: 97200.0s)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 70% (100800.0 of 144000.0) |########    | Elapsed Time: 0:01:56 ETA:   0:01:12"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Particle [0] lost!! (lon, lat: -123.14912970742583, 48.452954980847544, depth: 21.58061466377694, time: 100800.0s)\n",
      "Particle [1] lost!! (lon, lat: -123.14912970742583, 48.452954980847544, depth: 21.58061466377694, time: 100800.0s)\n",
      "Particle [18] lost!! (lon, lat: -123.53270965389399, 48.29803571034548, depth: 10.141196306154598, time: 100800.0s)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 72% (104400.0 of 144000.0) |########    | Elapsed Time: 0:02:01 ETA:   0:00:56"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Particle [22] lost!! (lon, lat: -123.48151894316764, 48.35280690805819, depth: 3.278420927177649, time: 104400.0s)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 75% (108000.0 of 144000.0) |#########   | Elapsed Time: 0:02:05 ETA:   0:00:47"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Particle [24] lost!! (lon, lat: -123.50796832025296, 48.32143854696556, depth: 1.7636030350113288, time: 108000.0s)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 77% (111600.0 of 144000.0) |#########   | Elapsed Time: 0:02:11 ETA:   0:00:48"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Particle [11] lost!! (lon, lat: -123.52292639751084, 48.31817624325199, depth: 3.9794909236443345, time: 111600.0s)\n",
      "Particle [21] lost!! (lon, lat: -123.52667460833842, 48.316025800916854, depth: 3.5541201288433513, time: 111600.0s)\n",
      "Particle [23] lost!! (lon, lat: -123.51800953561721, 48.2982119628578, depth: -1.7027146872569574, time: 111600.0s)\n",
      "Particle [25] lost!! (lon, lat: -123.51585717591021, 48.293119725350664, depth: -1.1635998159581504, time: 111600.0s)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 95% (136800.0 of 144000.0) |########### | Elapsed Time: 0:02:43 ETA:   0:00:08"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Particle [30] lost!! (lon, lat: -123.18812986382724, 48.4204735240998, depth: 5.0363781721680425, time: 136800.0s)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 97% (140400.0 of 144000.0) |########### | Elapsed Time: 0:02:48 ETA:   0:00:05"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Particle [27] lost!! (lon, lat: -123.1567717104626, 48.44724911699148, depth: 24.865415518230293, time: 140400.0s)\n",
      "Particle [32] lost!! (lon, lat: -123.1565265097133, 48.471734766433364, depth: 62.17914843271137, time: 140400.0s)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (144000.0 of 144000.0) |############| Elapsed Time: 0:02:49 Time:  0:02:49\n",
      "/media/doug/warehouse/conda_envs/moacean-parcels/lib/python3.9/site-packages/numpy/lib/arraysetops.py:270: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  ar = np.asanyarray(ar)\n"
     ]
    }
   ],
   "source": [
    "pset.execute(\n",
    "    kernels,\n",
    "    runtime=timedelta(hours=40),  # duration of particles simulation\n",
    "    dt=timedelta(hours=1),  # particles simulation time step\n",
    "    recovery=recovery_kernels,\n",
    "    output_file=output_file,\n",
    ")\n",
    "\n",
    "output_file.export()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99d57c93-6506-4b48-8721-82375fa49956",
   "metadata": {},
   "source": [
    "Visualize the particle tracks coloured by particle release time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "76480bf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Figure and axes set-up\n",
    "fig, ax = plt.subplots(figsize=(12, 8), subplot_kw={\"projection\": ccrs.PlateCarree()})\n",
    "ax.set_title(\"Particle Tracks Coloured by Release Time\")\n",
    "ax.set_extent((-123.8, -122.75, 48, 48.75))\n",
    "ax.coastlines(resolution=\"10m\", color=\"black\", linewidth=1)\n",
    "ax.add_feature(cartopy.feature.LAND)\n",
    "\n",
    "# Mark location of particles release\n",
    "ax.plot(release_lon, release_lat, marker=\"*\", color=\"red\", markersize=8, transform=ccrs.Geodetic())\n",
    "\n",
    "with xr.open_dataset(output_file.name) as ds:\n",
    "    # Colour-map-based collection of colours to show particle release times\n",
    "    traj_hrs = len(ds.trajectory.traj)\n",
    "    cmap = matplotlib.cm.plasma\n",
    "    cmap_norm = matplotlib.colors.Normalize(0, traj_hrs)\n",
    "    colours_time = cmap(np.linspace(0, 1, traj_hrs))\n",
    "\n",
    "    for particle in range(traj_hrs):\n",
    "        # Particle track\n",
    "        ax.plot(\n",
    "            ds.lon[particle],\n",
    "            ds.lat[particle],\n",
    "            linestyle=\"-\",\n",
    "            linewidth=0.7,\n",
    "            color=colours_time[particle],\n",
    "            transform=ccrs.Geodetic(),\n",
    "        )\n",
    "        # Last location on particle track\n",
    "        # May be end of simulations, but may also be when particle went out of bounds\n",
    "        last_obs = (\n",
    "            len(ds.trajectory[particle][np.isfinite(ds.trajectory[particle])]) - 1\n",
    "        )\n",
    "        ax.plot(\n",
    "            ds.lon[particle][last_obs],\n",
    "            ds.lat[particle][last_obs],\n",
    "            marker=\".\",\n",
    "            color=\"white\",\n",
    "            markersize=8,\n",
    "            markeredgecolor=\"black\",\n",
    "            transform=ccrs.Geodetic(),\n",
    "        )\n",
    "\n",
    "    # Colour Bar\n",
    "    cbar = fig.colorbar(\n",
    "        matplotlib.cm.ScalarMappable(cmap_norm, cmap),\n",
    "        ax=ax,\n",
    "        shrink=0.9,\n",
    "        label=\"Release Time [hr]\",\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a58b2ea",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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