{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SWAN sensitivity example\n",
    "\n",
    "In this notebook we will load the base config options from yaml file and define\n",
    "different SWAN workspaces for different source terms to simulate sensitivity testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import os\n",
    "from copy import deepcopy\n",
    "from pathlib import Path\n",
    "import yaml\n",
    "import shutil\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Workspace basepath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "workdir = Path(\"example_sensitivity\")\n",
    "shutil.rmtree(workdir, ignore_errors=True)\n",
    "workdir.mkdir()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instantiate model\n",
    "\n",
    "Use a fully-defined config from yaml to instantiate ModelRun with the runtime parameters\n",
    "and the config definition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Uncoment below to view the contents of the yaml file\n",
    "\n",
    "# !cat example_sensitivity.yml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SwanConfigComponents(model_type='swanconfig', template='/source/csiro/rompy/rompy/templates/swancomp', checkout='main', cgrid=REGULAR(model_type='regular', spectrum=SPECTRUM(model_type='spectrum', mdc=36, flow=0.04, fhigh=1.0, msc=None, dir1=None, dir2=None), grid=GRIDREGULAR(model_type='gridregular', xp=110.0, yp=-35.2, alp=4.0, xlen=7.5, ylen=12.5, mx=14, my=24, suffix='c')), startup=STARTUP(model_type='startup', project=PROJECT(model_type='project', name='Test sensitivity', nr='run1', title1='Source terms sensitivity testing', title2=None, title3=None), set=SET(model_type='set', level=0.0, nor=None, depmin=0.05, maxmes=None, maxerr=None, grav=None, rho=None, cdcap=None, inrhog=None, hsrerr=None, direction_convention='nautical', pwtail=None, froudmax=None, icewind=None), mode=MODE(model_type='mode', kind='nonstationary', dim='twodimensional'), coordinates=COORDINATES(model_type='coordinates', kind=SPHERICAL(model_type='spherical', projection='ccm'), reapeating=False)), inpgrid=DataInterface(model_type='data_interface', bottom=SwanDataGrid(model_type='data_grid', id='data', source=SourceIntake(model_type='intake', dataset_id='gebco', catalog_uri='../../tests/data/catalog.yaml', kwargs={}), filter=Filter(sort={}, subset={}, crop={}, timenorm={}, rename={}, derived={}), variables=['elevation'], coords=DatasetCoords(t='time', x='lon', y='lat', z='depth'), crop_data=True, buffer=1.0, z1='elevation', z2=None, var=<GridOptions.BOTTOM: 'bottom'>, fac=-1.0), input=[SwanDataGrid(model_type='data_grid', id='data', source=SourceIntake(model_type='intake', dataset_id='era5', catalog_uri='../../tests/data/catalog.yaml', kwargs={}), filter=Filter(sort={'coords': ['latitude']}, subset={}, crop={}, timenorm={}, rename={}, derived={}), variables=['u10', 'v10'], coords=DatasetCoords(t='time', x='longitude', y='latitude', z='depth'), crop_data=True, buffer=2.0, z1='u10', z2='v10', var=<GridOptions.WIND: 'wind'>, fac=1.0)]), boundary=BOUNDSPEC(model_type='boundspec', shapespec=SHAPESPEC(model_type='shapespec', shape=TMA(model_type='tma', gamma=3.3, d=12.0), per_type='peak', dspr_type='degrees'), location=SIDE(model_type='side', side='west', direction='ccw'), data=CONSTANTPAR(model_type='constantpar', hs=2.0, per=12.0, dir=255.0, dd=25.0)), initial=INITIAL(model_type='initial', kind=DEFAULT(model_type='default')), physics=PHYSICS(model_type='physics', gen=GEN3(model_type='gen3', source_terms=KOMEN(model_type='komen', wind_drag='wu', agrow=False, a=None, cds2=2.3e-05, stpm=0.00302)), sswell=None, negatinp=None, wcapping=None, quadrupl=None, breaking=None, friction=FRICTION_JONSWAP(model_type='jonswap', cfjon=0.038), triad=None, vegetation=None, mud=None, sice=None, turbulence=None, bragg=None, limiter=None, obstacle=None, setup=None, diffraction=None, surfbeat=None, scat=None, deactivate=None), prop=PROP(model_type='prop', scheme=BSBT(model_type='bsbt')), numeric=NUMERIC(model_type='numeric', stop=STOPC(model_type='stopc', dabs=0.02, drel=0.02, curvat=0.02, npnts=98.0, mode=STAT(model_type='stat', mxitst=3, alfa=None), limiter=None), dirimpl=None, sigimpl=None, ctheta=None, csigma=None, setup=None), output=OUTPUT(model_type='output', frame=None, group=None, curve=None, ray=None, isoline=None, points=POINTS(model_type='points', sname='pts', xp=[114.0, 112.5, 115.0], yp=[-34.0, -26.0, -30.0]), ngrid=None, quantity=QUANTITIES(model_type='quantities', quantities=[QUANTITY(model_type='quantity', output=[<BlockOptions.DEPTH: 'depth'>, <BlockOptions.HSIGN: 'hsign'>, <BlockOptions.TPS: 'tps'>, <BlockOptions.DIR: 'dir'>, <BlockOptions.TM01: 'tm01'>], short=None, long=None, lexp=None, hexp=None, excv=-9.0, power=None, ref=None, fswell=None, noswll=None, fmin=None, fmax=None, coord=None), QUANTITY(model_type='quantity', output=[<BlockOptions.HSWELL: 'hswell'>], short=None, long=None, lexp=None, hexp=None, excv=None, power=None, ref=None, fswell=0.125, noswll=None, fmin=None, fmax=None, coord=None)]), output_options=None, block=BLOCK(model_type='block', sname='COMPGRID', fname='swangrid.nc', times=TimeRangeOpen(model_type='open', tbeg=datetime.datetime(1970, 1, 1, 0, 0), delt=datetime.timedelta(seconds=3600), tfmt=1, dfmt='hr', suffix='blk'), header=None, idla=None, output=[<BlockOptions.DEPTH: 'depth'>, <BlockOptions.WIND: 'wind'>, <BlockOptions.HSIGN: 'hsign'>, <BlockOptions.TPS: 'tps'>, <BlockOptions.DIR: 'dir'>], unit=None), table=TABLE(model_type='table', sname='pts', fname='swantable.txt', times=TimeRangeOpen(model_type='open', tbeg=datetime.datetime(1970, 1, 1, 0, 0), delt=datetime.timedelta(seconds=3600), tfmt=1, dfmt='hr', suffix='tbl'), format='header', output=[<BlockOptions.TIME: 'time'>, <BlockOptions.HSIGN: 'hsign'>, <BlockOptions.HSWELL: 'hswell'>, <BlockOptions.DIR: 'dir'>, <BlockOptions.TPS: 'tps'>, <BlockOptions.TM01: 'tm01'>]), specout=SPECOUT(model_type='specout', sname='pts', fname='swanspec.nc', times=TimeRangeOpen(model_type='open', tbeg=datetime.datetime(1970, 1, 1, 0, 0), delt=datetime.timedelta(seconds=3600), tfmt=1, dfmt='hr', suffix='spc'), dim=SPEC2D(model_type='spec2d'), freq=ABS(model_type='abs')), nestout=None, test=None), lockup=LOCKUP(model_type='lockup', compute=COMPUTE_STAT(model_type='stat', times=NONSTATIONARY(model_type='nonstationary', tbeg=datetime.datetime(1970, 1, 1, 0, 0), delt=datetime.timedelta(seconds=3600), tfmt=1, dfmt='sec', suffix='', tend=datetime.datetime(1970, 1, 2, 0, 0)), hotfile=HOTFILE(model_type='hotfile', fname=PosixPath('hotfile.txt'), format='free'), hottimes=[1, -1], suffix='_%Y%m%dT%H%M%S')))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from rompy.swan.config import SwanConfigComponents\n",
    "\n",
    "conf_dict = yaml.load(open(\"example_sensitivity.yml\"), Loader=yaml.Loader)\n",
    "config = SwanConfigComponents(**conf_dict)\n",
    "config"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examine components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PROJECT name='Test sensitivity' nr='run1' title1='Source terms sensitivity testing'\n"
     ]
    }
   ],
   "source": [
    "# Project\n",
    "\n",
    "print(config.startup.project.render())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GEN3 KOMEN cds2=2.3e-05 stpm=0.00302 DRAG WU\n"
     ]
    }
   ],
   "source": [
    "# Source terms\n",
    "\n",
    "print(config.physics.gen.render())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sensitivity config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_experiment(config, source_terms):\n",
    "    \"\"\"Return a new config object for a given experiment\"\"\"\n",
    "    new_config = deepcopy(config)\n",
    "    new_config.startup.project.title2 = f\"Experiment {source_terms.model_type.upper()}\"\n",
    "    new_config.physics.gen.source_terms = source_terms\n",
    "    return new_config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TimeRange(start=datetime.datetime(2023, 1, 1, 0, 0), end=datetime.datetime(2023, 1, 2, 0, 0), duration=datetime.timedelta(days=1), interval=datetime.timedelta(seconds=3600), include_end=True)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from rompy.core.time import TimeRange\n",
    "\n",
    "period = TimeRange(\n",
    "    start=\"2023-01-01T00:00:00\",\n",
    "    end=\"2023-01-02T00:00:00\",\n",
    "    interval=\"1h\"\n",
    ")\n",
    "\n",
    "period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "PROJECT name='Test sensitivity' nr='run1' title1='Source terms sensitivity testing' title2='Experiment KOMEN'\n",
      "KOMEN DRAG WU\n",
      "\n",
      "PROJECT name='Test sensitivity' nr='run1' title1='Source terms sensitivity testing' title2='Experiment JANSSEN'\n",
      "JANSSEN DRAG WU\n",
      "\n",
      "PROJECT name='Test sensitivity' nr='run1' title1='Source terms sensitivity testing' title2='Experiment WESTHUYSEN'\n",
      "WESTHUYSEN DRAG WU\n",
      "\n",
      "PROJECT name='Test sensitivity' nr='run1' title1='Source terms sensitivity testing' title2='Experiment ST6C1'\n",
      "ST6 a1sds=4.7e-07 a2sds=6.6e-06 p1sds=4.0 p2sds=4.0 UP HWANG VECTAU U10PROXY windscaling=28.0 AGROW\n",
      "\n",
      "PROJECT name='Test sensitivity' nr='run1' title1='Source terms sensitivity testing' title2='Experiment ST6C2'\n",
      "ST6 a1sds=4.7e-07 a2sds=6.6e-06 p1sds=4.0 p2sds=4.0 UP FAN VECTAU U10PROXY windscaling=28.0 AGROW\n",
      "\n",
      "PROJECT name='Test sensitivity' nr='run1' title1='Source terms sensitivity testing' title2='Experiment ST6C3'\n",
      "ST6 a1sds=2.8e-06 a2sds=3.5e-05 p1sds=4.0 p2sds=4.0 UP HWANG VECTAU U10PROXY windscaling=32.0 AGROW\n",
      "\n",
      "PROJECT name='Test sensitivity' nr='run1' title1='Source terms sensitivity testing' title2='Experiment ST6C4'\n",
      "ST6 a1sds=2.8e-06 a2sds=3.5e-05 p1sds=4.0 p2sds=4.0 UP HWANG VECTAU U10PROXY windscaling=32.0 DEBIAS cdfac=0.89 AGROW\n",
      "\n",
      "PROJECT name='Test sensitivity' nr='run1' title1='Source terms sensitivity testing' title2='Experiment ST6C5'\n",
      "ST6 a1sds=6.5e-06 a2sds=8.5e-05 p1sds=4.0 p2sds=4.0 UP HWANG VECTAU U10PROXY windscaling=35.0 DEBIAS cdfac=0.89 AGROW\n"
     ]
    }
   ],
   "source": [
    "from rompy.model import ModelRun\n",
    "from rompy.swan.subcomponents.physics import KOMEN, JANSSEN, WESTHUYSEN, ST6C1, ST6C2, ST6C3, ST6C4, ST6C5\n",
    "runs = []\n",
    "for component in [KOMEN, JANSSEN, WESTHUYSEN, ST6C1, ST6C2, ST6C3, ST6C4, ST6C5]:\n",
    "    source_terms = component()\n",
    "    run_id = f\"{source_terms.model_type.lower()}\"\n",
    "    new_config = set_experiment(config, source_terms=source_terms)\n",
    "\n",
    "    print(f\"\\n{new_config.startup.project.render()}\")\n",
    "    print(f\"{new_config.physics.gen.source_terms.render()}\")\n",
    "\n",
    "    runs.append(ModelRun(\n",
    "        run_id=run_id,\n",
    "        config=new_config,\n",
    "        period=period,\n",
    "        output_dir=str(workdir)),\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate workspaces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:rompy.model:\n",
      "INFO:rompy.model:-----------------------------------------------------\n",
      "INFO:rompy.model:Model settings:\n",
      "INFO:rompy.model:\n",
      "run_id: komen\n",
      "period: \n",
      "\tStart: 2023-01-01 00:00:00\n",
      "\tEnd: 2023-01-02 00:00:00\n",
      "\tDuration: 1 day, 0:00:00\n",
      "\tInterval: 1:00:00\n",
      "\tInclude End: True\n",
      "\n",
      "output_dir: example_sensitivity\n",
      "config: <class 'rompy.swan.config.SwanConfigComponents'>\n",
      "\n",
      "INFO:rompy.model:-----------------------------------------------------\n",
      "INFO:rompy.model:Generating model input files in example_sensitivity\n",
      "INFO:rompy.swan.data:\tWriting bottom to example_sensitivity/komen/bottom.grd\n",
      "INFO:rompy.swan.data:\tWriting wind to example_sensitivity/komen/wind.grd\n",
      "INFO:rompy.model:\n",
      "INFO:rompy.model:Successfully generated project in example_sensitivity\n",
      "INFO:rompy.model:-----------------------------------------------------\n",
      "INFO:rompy.model:\n",
      "INFO:rompy.model:-----------------------------------------------------\n",
      "INFO:rompy.model:Model settings:\n",
      "INFO:rompy.model:\n",
      "run_id: janssen\n",
      "period: \n",
      "\tStart: 2023-01-01 00:00:00\n",
      "\tEnd: 2023-01-02 00:00:00\n",
      "\tDuration: 1 day, 0:00:00\n",
      "\tInterval: 1:00:00\n",
      "\tInclude End: True\n",
      "\n",
      "output_dir: example_sensitivity\n",
      "config: <class 'rompy.swan.config.SwanConfigComponents'>\n",
      "\n",
      "INFO:rompy.model:-----------------------------------------------------\n",
      "INFO:rompy.model:Generating model input files in example_sensitivity\n",
      "INFO:rompy.swan.data:\tWriting bottom to example_sensitivity/janssen/bottom.grd\n",
      "INFO:rompy.swan.data:\tWriting wind to example_sensitivity/janssen/wind.grd\n",
      "INFO:rompy.model:\n",
      "INFO:rompy.model:Successfully generated project in example_sensitivity\n",
      "INFO:rompy.model:-----------------------------------------------------\n",
      "INFO:rompy.model:\n",
      "INFO:rompy.model:-----------------------------------------------------\n",
      "INFO:rompy.model:Model settings:\n",
      "INFO:rompy.model:\n",
      "run_id: westhuysen\n",
      "period: \n",
      "\tStart: 2023-01-01 00:00:00\n",
      "\tEnd: 2023-01-02 00:00:00\n",
      "\tDuration: 1 day, 0:00:00\n",
      "\tInterval: 1:00:00\n",
      "\tInclude End: True\n",
      "\n",
      "output_dir: example_sensitivity\n",
      "config: <class 'rompy.swan.config.SwanConfigComponents'>\n",
      "\n",
      "INFO:rompy.model:-----------------------------------------------------\n",
      "INFO:rompy.model:Generating model input files in example_sensitivity\n",
      "INFO:rompy.swan.data:\tWriting bottom to example_sensitivity/westhuysen/bottom.grd\n",
      "INFO:rompy.swan.data:\tWriting wind to example_sensitivity/westhuysen/wind.grd\n",
      "INFO:rompy.model:\n",
      "INFO:rompy.model:Successfully generated project in example_sensitivity\n",
      "INFO:rompy.model:-----------------------------------------------------\n",
      "INFO:rompy.model:\n",
      "INFO:rompy.model:-----------------------------------------------------\n",
      "INFO:rompy.model:Model settings:\n",
      "INFO:rompy.model:\n",
      "run_id: st6c1\n",
      "period: \n",
      "\tStart: 2023-01-01 00:00:00\n",
      "\tEnd: 2023-01-02 00:00:00\n",
      "\tDuration: 1 day, 0:00:00\n",
      "\tInterval: 1:00:00\n",
      "\tInclude End: True\n",
      "\n",
      "output_dir: example_sensitivity\n",
      "config: <class 'rompy.swan.config.SwanConfigComponents'>\n",
      "\n",
      "INFO:rompy.model:-----------------------------------------------------\n",
      "INFO:rompy.model:Generating model input files in example_sensitivity\n",
      "INFO:rompy.swan.data:\tWriting bottom to example_sensitivity/st6c1/bottom.grd\n",
      "INFO:rompy.swan.data:\tWriting wind to example_sensitivity/st6c1/wind.grd\n",
      "INFO:rompy.model:\n",
      "INFO:rompy.model:Successfully generated project in example_sensitivity\n",
      "INFO:rompy.model:-----------------------------------------------------\n",
      "INFO:rompy.model:\n",
      "INFO:rompy.model:-----------------------------------------------------\n",
      "INFO:rompy.model:Model settings:\n",
      "INFO:rompy.model:\n",
      "run_id: st6c2\n",
      "period: \n",
      "\tStart: 2023-01-01 00:00:00\n",
      "\tEnd: 2023-01-02 00:00:00\n",
      "\tDuration: 1 day, 0:00:00\n",
      "\tInterval: 1:00:00\n",
      "\tInclude End: True\n",
      "\n",
      "output_dir: example_sensitivity\n",
      "config: <class 'rompy.swan.config.SwanConfigComponents'>\n",
      "\n",
      "INFO:rompy.model:-----------------------------------------------------\n",
      "INFO:rompy.model:Generating model input files in example_sensitivity\n",
      "INFO:rompy.swan.data:\tWriting bottom to example_sensitivity/st6c2/bottom.grd\n",
      "INFO:rompy.swan.data:\tWriting wind to example_sensitivity/st6c2/wind.grd\n",
      "INFO:rompy.model:\n",
      "INFO:rompy.model:Successfully generated project in example_sensitivity\n",
      "INFO:rompy.model:-----------------------------------------------------\n",
      "INFO:rompy.model:\n",
      "INFO:rompy.model:-----------------------------------------------------\n",
      "INFO:rompy.model:Model settings:\n",
      "INFO:rompy.model:\n",
      "run_id: st6c3\n",
      "period: \n",
      "\tStart: 2023-01-01 00:00:00\n",
      "\tEnd: 2023-01-02 00:00:00\n",
      "\tDuration: 1 day, 0:00:00\n",
      "\tInterval: 1:00:00\n",
      "\tInclude End: True\n",
      "\n",
      "output_dir: example_sensitivity\n",
      "config: <class 'rompy.swan.config.SwanConfigComponents'>\n",
      "\n",
      "INFO:rompy.model:-----------------------------------------------------\n",
      "INFO:rompy.model:Generating model input files in example_sensitivity\n",
      "INFO:rompy.swan.data:\tWriting bottom to example_sensitivity/st6c3/bottom.grd\n",
      "INFO:rompy.swan.data:\tWriting wind to example_sensitivity/st6c3/wind.grd\n",
      "INFO:rompy.model:\n",
      "INFO:rompy.model:Successfully generated project in example_sensitivity\n",
      "INFO:rompy.model:-----------------------------------------------------\n",
      "INFO:rompy.model:\n",
      "INFO:rompy.model:-----------------------------------------------------\n",
      "INFO:rompy.model:Model settings:\n",
      "INFO:rompy.model:\n",
      "run_id: st6c4\n",
      "period: \n",
      "\tStart: 2023-01-01 00:00:00\n",
      "\tEnd: 2023-01-02 00:00:00\n",
      "\tDuration: 1 day, 0:00:00\n",
      "\tInterval: 1:00:00\n",
      "\tInclude End: True\n",
      "\n",
      "output_dir: example_sensitivity\n",
      "config: <class 'rompy.swan.config.SwanConfigComponents'>\n",
      "\n",
      "INFO:rompy.model:-----------------------------------------------------\n",
      "INFO:rompy.model:Generating model input files in example_sensitivity\n",
      "INFO:rompy.swan.data:\tWriting bottom to example_sensitivity/st6c4/bottom.grd\n",
      "INFO:rompy.swan.data:\tWriting wind to example_sensitivity/st6c4/wind.grd\n",
      "INFO:rompy.model:\n",
      "INFO:rompy.model:Successfully generated project in example_sensitivity\n",
      "INFO:rompy.model:-----------------------------------------------------\n",
      "INFO:rompy.model:\n",
      "INFO:rompy.model:-----------------------------------------------------\n",
      "INFO:rompy.model:Model settings:\n",
      "INFO:rompy.model:\n",
      "run_id: st6c5\n",
      "period: \n",
      "\tStart: 2023-01-01 00:00:00\n",
      "\tEnd: 2023-01-02 00:00:00\n",
      "\tDuration: 1 day, 0:00:00\n",
      "\tInterval: 1:00:00\n",
      "\tInclude End: True\n",
      "\n",
      "output_dir: example_sensitivity\n",
      "config: <class 'rompy.swan.config.SwanConfigComponents'>\n",
      "\n",
      "INFO:rompy.model:-----------------------------------------------------\n",
      "INFO:rompy.model:Generating model input files in example_sensitivity\n",
      "INFO:rompy.swan.data:\tWriting bottom to example_sensitivity/st6c5/bottom.grd\n",
      "INFO:rompy.swan.data:\tWriting wind to example_sensitivity/st6c5/wind.grd\n",
      "INFO:rompy.model:\n",
      "INFO:rompy.model:Successfully generated project in example_sensitivity\n",
      "INFO:rompy.model:-----------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "for run in runs:\n",
    "    run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check the workspace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[PosixPath('example_sensitivity/janssen'),\n",
       " PosixPath('example_sensitivity/komen'),\n",
       " PosixPath('example_sensitivity/st6c1'),\n",
       " PosixPath('example_sensitivity/st6c2'),\n",
       " PosixPath('example_sensitivity/st6c3'),\n",
       " PosixPath('example_sensitivity/st6c4'),\n",
       " PosixPath('example_sensitivity/st6c5'),\n",
       " PosixPath('example_sensitivity/westhuysen')]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modeldirs = sorted(workdir.glob(\"*\"))\n",
    "modeldirs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[PosixPath('example_sensitivity/janssen/INPUT'),\n",
       " PosixPath('example_sensitivity/janssen/bottom.grd'),\n",
       " PosixPath('example_sensitivity/janssen/wind.grd')]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(modeldirs[0].glob(\"*\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "! Rompy SwanConfig\n",
      "! Template: /source/csiro/rompy/rompy/templates/swancomp\n",
      "! Generated: 2023-11-09 19:10:43.112490 on rafael-XPS by rguedes\n",
      "\n",
      "\n",
      "! Startup -------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "\n",
      "PROJECT name='Test sensitivity' nr='run1' title1='Source terms sensitivity testing' title2='Experiment JANSSEN'\n",
      "\n",
      "SET level=0.0 depmin=0.05 NAUTICAL\n",
      "\n",
      "MODE NONSTATIONARY TWODIMENSIONAL\n",
      "\n",
      "COORDINATES SPHERICAL CCM\n",
      "\n",
      "! Computational Grid --------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n",
      "CGRID REGULAR xpc=110.0 ypc=-35.2 alpc=4.0 xlenc=7.5 ylenc=12.5 mxc=14 myc=24 CIRCLE mdc=36 flow=0.04 fhigh=1.0\n",
      "\n",
      "! Input Grids ---------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "\n",
      "INPGRID BOTTOM REG 109.0 -36.0 0.0 9 14 1.0 1.0 EXC -99.0\n",
      "READINP BOTTOM -1.0 'bottom.grd' 3 FREE\n",
      "\n",
      "INPGRID WIND REG 110.0 -35.0 0.0 1 2 5.0 5.0 EXC -99.0 NONSTATION 20230101.000000 6.0 HR\n",
      "READINP WIND 1.0 'wind.grd' 3 0 1 0 FREE\n",
      "\n",
      "\n",
      "\n",
      "! Boundary and Initial conditions -------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "\n",
      "BOUND SHAPESPEC TMA gamma=3.3 d=12.0 PEAK DSPR DEGREES\n",
      "BOUNDSPEC SIDE WEST CCW CONSTANT PAR hs=2.0 per=12.0 dir=255.0 dd=25.0\n",
      "\n",
      "INITIAL DEFAULT\n",
      "\n",
      "\n",
      "! Physics -------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "\n",
      "GEN3 JANSSEN DRAG WU\n",
      "\n",
      "FRICTION JONSWAP CONSTANT cfjon=0.038\n",
      "\n",
      "\n",
      "! Numerics ------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "\n",
      "PROP BSBT\n",
      "\n",
      "NUMERIC STOPC dabs=0.02 drel=0.02 curvat=0.02 npnts=98.0 STATIONARY mxitst=3\n",
      "\n",
      "\n",
      "! Output --------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "\n",
      "POINTS sname='pts' &\n",
      "    xp=114.0 yp=-34.0 &\n",
      "    xp=112.5 yp=-26.0 &\n",
      "    xp=115.0 yp=-30.0\n",
      "\n",
      "QUANTITY DEPTH HSIGN TPS DIR TM01 excv=-9.0\n",
      "\n",
      "QUANTITY HSWELL fswell=0.125\n",
      "\n",
      "BLOCK sname='COMPGRID' fname='swangrid.nc' &\n",
      "    DEPTH &\n",
      "    WIND &\n",
      "    HSIGN &\n",
      "    TPS &\n",
      "    DIR &\n",
      "    OUTPUT tbegblk=20230101.000000 deltblk=1.0 HR\n",
      "\n",
      "TABLE sname='pts' HEADER fname='swantable.txt' &\n",
      "    TIME &\n",
      "    HSIGN &\n",
      "    HSWELL &\n",
      "    DIR &\n",
      "    TPS &\n",
      "    TM01 &\n",
      "    OUTPUT tbegtbl=20230101.000000 delttbl=1.0 HR\n",
      "\n",
      "SPECOUT sname='pts' SPEC2D ABS fname='swanspec.nc' OUTPUT tbegspc=20230101.000000 deltspc=1.0 HR\n",
      "\n",
      "\n",
      "! Lockup --------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "\n",
      "COMPUTE STATIONARY time=20230101.000000\n",
      "COMPUTE STATIONARY time=20230101.010000\n",
      "HOTFILE fname='hotfile_20230101T010000.txt' FREE\n",
      "COMPUTE STATIONARY time=20230101.020000\n",
      "COMPUTE STATIONARY time=20230101.030000\n",
      "COMPUTE STATIONARY time=20230101.040000\n",
      "COMPUTE STATIONARY time=20230101.050000\n",
      "COMPUTE STATIONARY time=20230101.060000\n",
      "COMPUTE STATIONARY time=20230101.070000\n",
      "COMPUTE STATIONARY time=20230101.080000\n",
      "COMPUTE STATIONARY time=20230101.090000\n",
      "COMPUTE STATIONARY time=20230101.100000\n",
      "COMPUTE STATIONARY time=20230101.110000\n",
      "COMPUTE STATIONARY time=20230101.120000\n",
      "COMPUTE STATIONARY time=20230101.130000\n",
      "COMPUTE STATIONARY time=20230101.140000\n",
      "COMPUTE STATIONARY time=20230101.150000\n",
      "COMPUTE STATIONARY time=20230101.160000\n",
      "COMPUTE STATIONARY time=20230101.170000\n",
      "COMPUTE STATIONARY time=20230101.180000\n",
      "COMPUTE STATIONARY time=20230101.190000\n",
      "COMPUTE STATIONARY time=20230101.200000\n",
      "COMPUTE STATIONARY time=20230101.210000\n",
      "COMPUTE STATIONARY time=20230101.220000\n",
      "COMPUTE STATIONARY time=20230101.230000\n",
      "COMPUTE STATIONARY time=20230102.000000\n",
      "HOTFILE fname='hotfile_20230102T000000.txt' FREE\n",
      "STOP\n"
     ]
    }
   ],
   "source": [
    "input = modeldirs[0] / \"INPUT\"\n",
    "print(input.read_text())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "docker run -v ./example_sensitivity/janssen:/home oceanum/swan:4141 swan.exe > example_sensitivity/janssen/swan.log\n",
      "docker run -v ./example_sensitivity/komen:/home oceanum/swan:4141 swan.exe > example_sensitivity/komen/swan.log\n",
      "docker run -v ./example_sensitivity/st6c1:/home oceanum/swan:4141 swan.exe > example_sensitivity/st6c1/swan.log\n",
      "docker run -v ./example_sensitivity/st6c2:/home oceanum/swan:4141 swan.exe > example_sensitivity/st6c2/swan.log\n",
      "docker run -v ./example_sensitivity/st6c3:/home oceanum/swan:4141 swan.exe > example_sensitivity/st6c3/swan.log\n",
      "docker run -v ./example_sensitivity/st6c4:/home oceanum/swan:4141 swan.exe > example_sensitivity/st6c4/swan.log\n",
      "docker run -v ./example_sensitivity/st6c5:/home oceanum/swan:4141 swan.exe > example_sensitivity/st6c5/swan.log\n",
      "docker run -v ./example_sensitivity/westhuysen:/home oceanum/swan:4141 swan.exe > example_sensitivity/westhuysen/swan.log\n"
     ]
    }
   ],
   "source": [
    "for modeldir in modeldirs:\n",
    "    cmd = [\"docker\", \"run\", \"-v\", f\"./{modeldir}:/home\", \"oceanum/swan:4141\", \"swan.exe\", \">\", f\"{modeldir}/swan.log\"]\n",
    "    print(\" \".join(cmd))\n",
    "    os.system(\" \".join(cmd))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[PosixPath('example_sensitivity/janssen/swangrid.nc'), PosixPath('example_sensitivity/janssen/swanspec.nc')]\n",
      "[PosixPath('example_sensitivity/komen/swangrid.nc'), PosixPath('example_sensitivity/komen/swanspec.nc')]\n",
      "[PosixPath('example_sensitivity/st6c1/swangrid.nc'), PosixPath('example_sensitivity/st6c1/swanspec.nc')]\n",
      "[PosixPath('example_sensitivity/st6c2/swangrid.nc'), PosixPath('example_sensitivity/st6c2/swanspec.nc')]\n",
      "[PosixPath('example_sensitivity/st6c3/swangrid.nc'), PosixPath('example_sensitivity/st6c3/swanspec.nc')]\n",
      "[PosixPath('example_sensitivity/st6c4/swangrid.nc'), PosixPath('example_sensitivity/st6c4/swanspec.nc')]\n",
      "[PosixPath('example_sensitivity/st6c5/swangrid.nc'), PosixPath('example_sensitivity/st6c5/swanspec.nc')]\n",
      "[PosixPath('example_sensitivity/westhuysen/swangrid.nc'), PosixPath('example_sensitivity/westhuysen/swanspec.nc')]\n"
     ]
    }
   ],
   "source": [
    "# Check for output files\n",
    "for modeldir in modeldirs:\n",
    "    print(sorted(modeldir.glob(\"*.nc\")))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "from wavespectra import read_ncswan, read_swan\n",
    "from wavespectra.core.swan import read_tab\n",
    "\n",
    "pd.set_option(\"display.notebook_repr_html\", False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_gridded_output(run):\n",
    "    \"\"\"Read mean gridded output from a model run\"\"\"\n",
    "    modeldir = Path(run.output_dir) / run.run_id\n",
    "    dset = xr.open_dataset(modeldir / run.config.output.block.fname)\n",
    "    return dset.mean(\"time\")\n",
    "\n",
    "\n",
    "def read_spectra_output(run):\n",
    "    \"\"\"Read mean spectra output from a model run\"\"\"\n",
    "    modeldir = Path(run.output_dir) / run.run_id\n",
    "    dset = read_ncswan(modeldir / run.config.output.specout.fname)\n",
    "    return dset.mean(\"time\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
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       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:       (yc: 25, xc: 15, source_terms: 8)\n",
       "Coordinates:\n",
       "    longitude     (yc, xc) float32 110.0 110.5 111.1 111.6 ... 115.5 116.1 116.6\n",
       "    latitude      (yc, xc) float32 -35.2 -35.16 -35.13 ... -22.28 -22.24 -22.21\n",
       "  * source_terms  (source_terms) &lt;U10 &#x27;komen&#x27; &#x27;janssen&#x27; ... &#x27;st6c4&#x27; &#x27;st6c5&#x27;\n",
       "Dimensions without coordinates: yc, xc\n",
       "Data variables:\n",
       "    depth         (source_terms, yc, xc) float32 4.824e+03 4.16e+03 ... nan nan\n",
       "    xwnd          (source_terms, yc, xc) float32 -0.8986 -1.029 ... nan nan\n",
       "    ywnd          (source_terms, yc, xc) float32 6.718 6.65 6.582 ... nan nan\n",
       "    hs            (source_terms, yc, xc) float32 1.019 0.6307 0.6288 ... nan nan\n",
       "    tps           (source_terms, yc, xc) float32 11.66 11.66 11.66 ... nan nan\n",
       "    theta0        (source_terms, yc, xc) float32 255.5 291.2 291.5 ... nan nan</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-ed5a100b-cbe4-4d72-8c24-e8b4b8b7d869' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ed5a100b-cbe4-4d72-8c24-e8b4b8b7d869' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>yc</span>: 25</li><li><span>xc</span>: 15</li><li><span class='xr-has-index'>source_terms</span>: 8</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-fdbe83d6-f1f9-49a5-a1f9-e8ad0d588f40' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fdbe83d6-f1f9-49a5-a1f9-e8ad0d588f40' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(yc, xc)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>110.0 110.5 111.1 ... 116.1 116.6</div><input id='attrs-a8cc8890-4804-449a-8bca-7863c6d2fc6e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a8cc8890-4804-449a-8bca-7863c6d2fc6e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e817149c-9494-42d8-84e0-6d977a7a8254' class='xr-var-data-in' type='checkbox'><label for='data-e817149c-9494-42d8-84e0-6d977a7a8254' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([[110.      , 110.53441 , 111.06882 , 111.603226, 112.137634,\n",
       "        112.67204 , 113.20646 , 113.74087 , 114.275276, 114.809685,\n",
       "        115.34409 , 115.8785  , 116.41291 , 116.94732 , 117.48173 ],\n",
       "       [109.96367 , 110.49808 , 111.032486, 111.566895, 112.1013  ,\n",
       "        112.63571 , 113.17013 , 113.70454 , 114.238945, 114.77335 ,\n",
       "        115.30776 , 115.84217 , 116.37658 , 116.91099 , 117.4454  ],\n",
       "       [109.92734 , 110.46175 , 110.996155, 111.53056 , 112.06497 ,\n",
       "        112.59938 , 113.13379 , 113.668205, 114.202614, 114.73702 ,\n",
       "        115.27143 , 115.80584 , 116.34025 , 116.87466 , 117.409065],\n",
       "       [109.89101 , 110.425415, 110.95982 , 111.49423 , 112.02864 ,\n",
       "        112.56305 , 113.09746 , 113.631874, 114.16628 , 114.70069 ,\n",
       "        115.2351  , 115.76951 , 116.30392 , 116.838326, 117.372734],\n",
       "       [109.854675, 110.389084, 110.92349 , 111.4579  , 111.99231 ,\n",
       "        112.52672 , 113.06113 , 113.59554 , 114.12995 , 114.66436 ,\n",
       "        115.19877 , 115.73318 , 116.267586, 116.801994, 117.3364  ],\n",
       "       [109.818344, 110.35275 , 110.88716 , 111.42157 , 111.95598 ,\n",
       "        112.49039 , 113.024796, 113.559204, 114.09362 , 114.62803 ,\n",
       "        115.16244 , 115.696846, 116.231255, 116.76566 , 117.30007 ],\n",
       "       [109.78201 , 110.31642 , 110.85083 , 111.38524 , 111.91965 ,\n",
       "        112.454056, 112.988464, 113.52287 , 114.05728 , 114.5917  ,\n",
       "...\n",
       "        114.690125, 115.22453 , 115.75894 , 116.29335 , 116.82777 ],\n",
       "       [109.3097  , 109.84411 , 110.37852 , 110.91293 , 111.44734 ,\n",
       "        111.98175 , 112.51616 , 113.05057 , 113.58498 , 114.119385,\n",
       "        114.65379 , 115.1882  , 115.72261 , 116.25702 , 116.791435],\n",
       "       [109.27337 , 109.80778 , 110.342186, 110.876595, 111.41101 ,\n",
       "        111.94542 , 112.47983 , 113.01424 , 113.548645, 114.08305 ,\n",
       "        114.61746 , 115.15187 , 115.68628 , 116.22069 , 116.755104],\n",
       "       [109.23704 , 109.77145 , 110.305855, 110.84026 , 111.37467 ,\n",
       "        111.90909 , 112.4435  , 112.977905, 113.512314, 114.04672 ,\n",
       "        114.58113 , 115.11554 , 115.64995 , 116.18436 , 116.718765],\n",
       "       [109.20071 , 109.735115, 110.26952 , 110.80393 , 111.33834 ,\n",
       "        111.87276 , 112.407166, 112.941574, 113.47598 , 114.01039 ,\n",
       "        114.5448  , 115.07921 , 115.61362 , 116.148026, 116.682434],\n",
       "       [109.164375, 109.698784, 110.23319 , 110.7676  , 111.30201 ,\n",
       "        111.836426, 112.370834, 112.90524 , 113.43965 , 113.97406 ,\n",
       "        114.50847 , 115.04288 , 115.577286, 116.111694, 116.6461  ],\n",
       "       [109.128044, 109.66245 , 110.19686 , 110.73127 , 111.26568 ,\n",
       "        111.80009 , 112.3345  , 112.86891 , 113.40332 , 113.93773 ,\n",
       "        114.47214 , 115.006546, 115.540955, 116.07536 , 116.60977 ]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(yc, xc)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-35.2 -35.16 ... -22.24 -22.21</div><input id='attrs-e738a3f8-aac9-4472-9304-fd7d6aa0a34b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e738a3f8-aac9-4472-9304-fd7d6aa0a34b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c5496cc9-d00a-45d6-8f10-9ff2b6b33022' class='xr-var-data-in' type='checkbox'><label for='data-c5496cc9-d00a-45d6-8f10-9ff2b6b33022' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([[-35.2     , -35.162632, -35.125263, -35.08789 , -35.05052 ,\n",
       "        -35.013153, -34.975784, -34.938416, -34.901043, -34.863674,\n",
       "        -34.826305, -34.788937, -34.751568, -34.714195, -34.676826],\n",
       "       [-34.680435, -34.643066, -34.605698, -34.56833 , -34.530956,\n",
       "        -34.493587, -34.45622 , -34.41885 , -34.38148 , -34.34411 ,\n",
       "        -34.30674 , -34.26937 , -34.232002, -34.194633, -34.15726 ],\n",
       "       [-34.160873, -34.1235  , -34.086132, -34.048763, -34.011395,\n",
       "        -33.974022, -33.936653, -33.899284, -33.861916, -33.824547,\n",
       "        -33.787178, -33.749805, -33.712437, -33.675068, -33.6377  ],\n",
       "       [-33.641308, -33.60394 , -33.566566, -33.529198, -33.49183 ,\n",
       "        -33.45446 , -33.41709 , -33.37972 , -33.34235 , -33.30498 ,\n",
       "        -33.267612, -33.230244, -33.19287 , -33.155502, -33.118134],\n",
       "       [-33.121742, -33.084373, -33.047005, -33.009632, -32.972263,\n",
       "        -32.934895, -32.897526, -32.860157, -32.822784, -32.785416,\n",
       "        -32.748047, -32.71068 , -32.67331 , -32.635937, -32.598568],\n",
       "       [-32.602177, -32.564808, -32.52744 , -32.49007 , -32.452698,\n",
       "        -32.41533 , -32.37796 , -32.34059 , -32.303223, -32.265854,\n",
       "        -32.22848 , -32.191113, -32.153744, -32.116375, -32.079002],\n",
       "       [-32.08261 , -32.045242, -32.007874, -31.970505, -31.933134,\n",
       "        -31.895765, -31.858397, -31.821026, -31.783657, -31.746286,\n",
       "...\n",
       "        -25.474142, -25.436771, -25.399403, -25.362034, -25.324665],\n",
       "       [-25.328274, -25.290903, -25.253532, -25.216164, -25.178795,\n",
       "        -25.141426, -25.104055, -25.066685, -25.029316, -24.991947,\n",
       "        -24.954578, -24.917208, -24.879837, -24.842468, -24.8051  ],\n",
       "       [-24.808708, -24.77134 , -24.73397 , -24.696602, -24.659231,\n",
       "        -24.62186 , -24.584492, -24.547123, -24.509754, -24.472383,\n",
       "        -24.435013, -24.397644, -24.360275, -24.322906, -24.285536],\n",
       "       [-24.289143, -24.251774, -24.214405, -24.177036, -24.139666,\n",
       "        -24.102295, -24.064926, -24.027557, -23.990189, -23.952818,\n",
       "        -23.915447, -23.878078, -23.84071 , -23.80334 , -23.76597 ],\n",
       "       [-23.769579, -23.732208, -23.69484 , -23.65747 , -23.620102,\n",
       "        -23.582731, -23.54536 , -23.507992, -23.470623, -23.433254,\n",
       "        -23.395884, -23.358513, -23.321144, -23.283775, -23.246407],\n",
       "       [-23.250015, -23.212646, -23.175276, -23.137907, -23.100536,\n",
       "        -23.063168, -23.025799, -22.988428, -22.95106 , -22.913689,\n",
       "        -22.87632 , -22.838951, -22.80158 , -22.764212, -22.726841],\n",
       "       [-22.73045 , -22.69308 , -22.65571 , -22.618341, -22.58097 ,\n",
       "        -22.543602, -22.506233, -22.468863, -22.431494, -22.394123,\n",
       "        -22.356754, -22.319386, -22.282015, -22.244646, -22.207275]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>source_terms</span></div><div class='xr-var-dims'>(source_terms)</div><div class='xr-var-dtype'>&lt;U10</div><div class='xr-var-preview xr-preview'>&#x27;komen&#x27; &#x27;janssen&#x27; ... &#x27;st6c5&#x27;</div><input id='attrs-9bae3b31-432b-4f67-92a5-cbcee9b8f3d9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9bae3b31-432b-4f67-92a5-cbcee9b8f3d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3f56793-db0e-4ce0-8a4c-bcb900444a5e' class='xr-var-data-in' type='checkbox'><label for='data-c3f56793-db0e-4ce0-8a4c-bcb900444a5e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;komen&#x27;, &#x27;janssen&#x27;, &#x27;westhuysen&#x27;, &#x27;st6c1&#x27;, &#x27;st6c2&#x27;, &#x27;st6c3&#x27;, &#x27;st6c4&#x27;,\n",
       "       &#x27;st6c5&#x27;], dtype=&#x27;&lt;U10&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-53bac059-e369-4d50-80f0-e713cc5d76a3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-53bac059-e369-4d50-80f0-e713cc5d76a3' class='xr-section-summary' >Data variables: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>depth</span></div><div class='xr-var-dims'>(source_terms, yc, xc)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>4.824e+03 4.16e+03 ... nan nan</div><input id='attrs-45348fef-15b5-4ee3-8468-e816923633e5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-45348fef-15b5-4ee3-8468-e816923633e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad8c7c9f-a37b-4984-9b4a-434bf69d4944' class='xr-var-data-in' type='checkbox'><label for='data-ad8c7c9f-a37b-4984-9b4a-434bf69d4944' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[4823.951 , 4160.235 , 3517.904 , ...,       nan,       nan,\n",
       "               nan],\n",
       "        [4081.297 , 3441.2422, 2892.3357, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [3056.671 , 2631.195 , 2384.4294, ...,       nan,       nan,\n",
       "               nan],\n",
       "        ...,\n",
       "        [3246.6487, 4145.945 , 4225.1567, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [3510.1592, 4227.9146, 4563.9614, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [3835.8284, 4350.438 , 4695.601 , ...,       nan,       nan,\n",
       "               nan]],\n",
       "\n",
       "       [[4823.951 , 4160.235 , 3517.904 , ...,       nan,       nan,\n",
       "               nan],\n",
       "        [4081.297 , 3441.2422, 2892.3357, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [3056.671 , 2631.195 , 2384.4294, ...,       nan,       nan,\n",
       "               nan],\n",
       "...\n",
       "        [3246.6487, 4145.945 , 4225.1567, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [3510.1592, 4227.9146, 4563.9614, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [3835.8284, 4350.438 , 4695.601 , ...,       nan,       nan,\n",
       "               nan]],\n",
       "\n",
       "       [[4823.951 , 4160.235 , 3517.904 , ...,       nan,       nan,\n",
       "               nan],\n",
       "        [4081.297 , 3441.2422, 2892.3357, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [3056.671 , 2631.195 , 2384.4294, ...,       nan,       nan,\n",
       "               nan],\n",
       "        ...,\n",
       "        [3246.6487, 4145.945 , 4225.1567, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [3510.1592, 4227.9146, 4563.9614, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [3835.8284, 4350.438 , 4695.601 , ...,       nan,       nan,\n",
       "               nan]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xwnd</span></div><div class='xr-var-dims'>(source_terms, yc, xc)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.8986 -1.029 -1.157 ... nan nan</div><input id='attrs-b25a6357-19b8-4178-b90b-c7bcd53c64e2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b25a6357-19b8-4178-b90b-c7bcd53c64e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b8a9d44-4219-4eb8-b71e-e6fa05a988e0' class='xr-var-data-in' type='checkbox'><label for='data-6b8a9d44-4219-4eb8-b71e-e6fa05a988e0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-0.8985871, -1.0285959, -1.157384 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        [-1.1927854, -1.3132724, -1.432905 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        [-1.6694846, -1.7384562, -1.8047427, ...,        nan,\n",
       "                nan,        nan],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[-0.8985871, -1.0285959, -1.157384 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        [-1.1927854, -1.3132724, -1.432905 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        [-1.6694846, -1.7384562, -1.8047427, ...,        nan,\n",
       "                nan,        nan],\n",
       "...\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[-0.8985871, -1.0285959, -1.157384 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        [-1.1927854, -1.3132724, -1.432905 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        [-1.6694846, -1.7384562, -1.8047427, ...,        nan,\n",
       "                nan,        nan],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ywnd</span></div><div class='xr-var-dims'>(source_terms, yc, xc)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>6.718 6.65 6.582 ... nan nan nan</div><input id='attrs-74237775-1d2f-4f6c-9f27-8f3cc819f9a7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-74237775-1d2f-4f6c-9f27-8f3cc819f9a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03d65f8a-76eb-4cb3-b920-6731a09b9c06' class='xr-var-data-in' type='checkbox'><label for='data-03d65f8a-76eb-4cb3-b920-6731a09b9c06' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[6.7184663, 6.650105 , 6.5822325, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [6.903531 , 6.849573 , 6.785363 , ...,       nan,       nan,\n",
       "               nan],\n",
       "        [7.2056627, 7.13828  , 7.054905 , ...,       nan,       nan,\n",
       "               nan],\n",
       "        ...,\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan]],\n",
       "\n",
       "       [[6.7184663, 6.650105 , 6.5822325, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [6.903531 , 6.849573 , 6.785363 , ...,       nan,       nan,\n",
       "               nan],\n",
       "        [7.2056627, 7.13828  , 7.054905 , ...,       nan,       nan,\n",
       "               nan],\n",
       "...\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan]],\n",
       "\n",
       "       [[6.7184663, 6.650105 , 6.5822325, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [6.903531 , 6.849573 , 6.785363 , ...,       nan,       nan,\n",
       "               nan],\n",
       "        [7.2056627, 7.13828  , 7.054905 , ...,       nan,       nan,\n",
       "               nan],\n",
       "        ...,\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hs</span></div><div class='xr-var-dims'>(source_terms, yc, xc)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.019 0.6307 0.6288 ... nan nan nan</div><input id='attrs-a7a75978-8458-48e0-93b4-bf8fc5ce3d34' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a7a75978-8458-48e0-93b4-bf8fc5ce3d34' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5902cda4-fa5b-4631-83b1-834a7c60800b' class='xr-var-data-in' type='checkbox'><label for='data-5902cda4-fa5b-4631-83b1-834a7c60800b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[1.0192268 , 0.63066536, 0.6288341 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        [1.1814324 , 1.3336895 , 1.2929169 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        [1.2695091 , 1.4816737 , 1.4532303 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        ...,\n",
       "        [1.7042453 , 1.9786367 , 1.9581891 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        [1.6415908 , 1.8393509 , 1.8341626 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        [1.5887021 , 1.671926  , 1.694815  , ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[1.0107424 , 0.6126289 , 0.60542625, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [1.1889399 , 1.32725   , 1.2820215 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        [1.2861719 , 1.4874724 , 1.4557636 , ...,        nan,\n",
       "                nan,        nan],\n",
       "...\n",
       "        [1.6818448 , 1.9637436 , 1.9407938 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        [1.6280103 , 1.8407547 , 1.8310803 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        [1.574511  , 1.67745   , 1.6956695 , ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[1.0049133 , 0.5968502 , 0.5881523 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        [1.1108131 , 1.1534166 , 1.10535   , ...,        nan,\n",
       "                nan,        nan],\n",
       "        [1.1872003 , 1.3097018 , 1.2791528 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        ...,\n",
       "        [1.7535931 , 2.0571308 , 2.0438855 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        [1.6953642 , 1.9304483 , 1.9248636 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        [1.6400958 , 1.7745602 , 1.7801449 , ...,        nan,\n",
       "                nan,        nan]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tps</span></div><div class='xr-var-dims'>(source_terms, yc, xc)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>11.66 11.66 11.66 ... nan nan nan</div><input id='attrs-7979d5c3-8cbd-4246-9140-b67ce6cd8396' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7979d5c3-8cbd-4246-9140-b67ce6cd8396' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f3701a8-8c85-4af3-ac6f-7fa509f82e18' class='xr-var-data-in' type='checkbox'><label for='data-6f3701a8-8c85-4af3-ac6f-7fa509f82e18' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[11.661121 , 11.661121 , 11.661732 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        [11.661121 , 11.661793 , 11.66222  , ...,        nan,\n",
       "                nan,        nan],\n",
       "        [11.661121 , 11.662037 , 11.662708 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        ...,\n",
       "        [ 7.6884065,  7.8873553,  7.903043 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        [ 7.7627187,  7.927702 ,  7.9547405, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [ 7.8102965,  7.9473553,  7.978271 , ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[11.661121 , 11.66164  , 11.662373 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        [11.661121 , 11.662556 , 11.663898 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        [11.661121 , 11.663197 , 10.905086 , ...,        nan,\n",
       "                nan,        nan],\n",
       "...\n",
       "        [ 7.538561 ,  7.665639 ,  7.6623135, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [ 8.1478   ,  7.7109585,  7.7162695, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [ 8.539263 ,  7.7229834,  7.735404 , ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[11.661121 , 11.661884 , 11.662403 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        [11.661121 , 11.661884 , 11.66222  , ...,        nan,\n",
       "                nan,        nan],\n",
       "        [11.661121 , 11.661945 , 11.6628   , ...,        nan,\n",
       "                nan,        nan],\n",
       "        ...,\n",
       "        [ 7.7295146,  7.864436 ,  7.868037 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        [ 7.7735524,  7.9105506,  7.9117393, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [ 7.8086786,  7.9306006,  7.932705 , ...,        nan,\n",
       "                nan,        nan]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>theta0</span></div><div class='xr-var-dims'>(source_terms, yc, xc)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>255.5 291.2 291.5 ... nan nan nan</div><input id='attrs-44601082-7ec5-4b29-b289-fe589a48ab83' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-44601082-7ec5-4b29-b289-fe589a48ab83' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3cb3e0c1-4db9-477a-a98b-27518a58a112' class='xr-var-data-in' type='checkbox'><label for='data-3cb3e0c1-4db9-477a-a98b-27518a58a112' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[255.51137, 291.2079 , 291.5412 , ...,       nan,       nan,\n",
       "               nan],\n",
       "        [232.16513, 207.59586, 205.65694, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [215.15535, 197.54132, 198.0902 , ...,       nan,       nan,\n",
       "               nan],\n",
       "        ...,\n",
       "        [178.59105, 182.74272, 189.30594, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [183.33075, 184.87103, 189.97305, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [187.59137, 180.92574, 186.74869, ...,       nan,       nan,\n",
       "               nan]],\n",
       "\n",
       "       [[255.24213, 287.9488 , 287.68384, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [229.47887, 206.4798 , 204.27303, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [212.41219, 196.26465, 195.89618, ...,       nan,       nan,\n",
       "               nan],\n",
       "...\n",
       "        [178.39902, 182.91827, 189.82843, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [182.74205, 184.25159, 189.59729, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [187.41913, 180.35092, 186.54213, ...,       nan,       nan,\n",
       "               nan]],\n",
       "\n",
       "       [[255.1523 , 286.08612, 285.77368, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [242.54309, 226.48305, 225.45906, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [229.49602, 212.3399 , 212.71413, ...,       nan,       nan,\n",
       "               nan],\n",
       "        ...,\n",
       "        [175.19928, 180.99101, 187.77765, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [179.3098 , 182.44803, 187.57178, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [183.65086, 178.93758, 185.23143, ...,       nan,       nan,\n",
       "               nan]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c15d89c2-80c7-4a29-a560-f514fe962489' class='xr-section-summary-in' type='checkbox'  ><label for='section-c15d89c2-80c7-4a29-a560-f514fe962489' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>source_terms</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5becda24-9dfa-41aa-b655-d85c7f48adc1' class='xr-index-data-in' type='checkbox'/><label for='index-5becda24-9dfa-41aa-b655-d85c7f48adc1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;komen&#x27;, &#x27;janssen&#x27;, &#x27;westhuysen&#x27;, &#x27;st6c1&#x27;, &#x27;st6c2&#x27;, &#x27;st6c3&#x27;, &#x27;st6c4&#x27;,\n",
       "       &#x27;st6c5&#x27;],\n",
       "      dtype=&#x27;object&#x27;, name=&#x27;source_terms&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5a68b035-ea35-4587-b777-e0d9a8b1c0a6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5a68b035-ea35-4587-b777-e0d9a8b1c0a6' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:       (yc: 25, xc: 15, source_terms: 8)\n",
       "Coordinates:\n",
       "    longitude     (yc, xc) float32 110.0 110.5 111.1 111.6 ... 115.5 116.1 116.6\n",
       "    latitude      (yc, xc) float32 -35.2 -35.16 -35.13 ... -22.28 -22.24 -22.21\n",
       "  * source_terms  (source_terms) <U10 'komen' 'janssen' ... 'st6c4' 'st6c5'\n",
       "Dimensions without coordinates: yc, xc\n",
       "Data variables:\n",
       "    depth         (source_terms, yc, xc) float32 4.824e+03 4.16e+03 ... nan nan\n",
       "    xwnd          (source_terms, yc, xc) float32 -0.8986 -1.029 ... nan nan\n",
       "    ywnd          (source_terms, yc, xc) float32 6.718 6.65 6.582 ... nan nan\n",
       "    hs            (source_terms, yc, xc) float32 1.019 0.6307 0.6288 ... nan nan\n",
       "    tps           (source_terms, yc, xc) float32 11.66 11.66 11.66 ... nan nan\n",
       "    theta0        (source_terms, yc, xc) float32 255.5 291.2 291.5 ... nan nan"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gridded parameters\n",
    "\n",
    "dsgrid = xr.concat([read_gridded_output(run) for run in runs], dim=\"source_terms\")\n",
    "dsgrid[\"source_terms\"] = [run.run_id for run in runs]\n",
    "dsgrid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:       (source_terms: 8, site: 3, freq: 35, dir: 36)\n",
       "Coordinates:\n",
       "  * freq          (freq) float32 0.04 0.04397 0.04834 ... 0.8275 0.9097 1.0\n",
       "  * dir           (dir) float32 261.0 251.0 241.0 231.0 ... 291.0 281.0 271.0\n",
       "  * site          (site) int64 1 2 3\n",
       "  * source_terms  (source_terms) &lt;U10 &#x27;komen&#x27; &#x27;janssen&#x27; ... &#x27;st6c4&#x27; &#x27;st6c5&#x27;\n",
       "Data variables:\n",
       "    lon           (source_terms, site) float32 dask.array&lt;chunksize=(1, 3), meta=np.ndarray&gt;\n",
       "    lat           (source_terms, site) float32 dask.array&lt;chunksize=(1, 3), meta=np.ndarray&gt;\n",
       "    efth          (source_terms, site, freq, dir) float32 dask.array&lt;chunksize=(1, 3, 35, 36), meta=np.ndarray&gt;\n",
       "    dpt           (source_terms, site) float32 dask.array&lt;chunksize=(1, 3), meta=np.ndarray&gt;\n",
       "    wspd          (source_terms, site) float32 dask.array&lt;chunksize=(1, 3), meta=np.ndarray&gt;\n",
       "    wdir          (source_terms, site) float32 dask.array&lt;chunksize=(1, 3), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-d3e9d812-4f73-45d7-9e0f-58be19f8a80b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d3e9d812-4f73-45d7-9e0f-58be19f8a80b' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>source_terms</span>: 8</li><li><span class='xr-has-index'>site</span>: 3</li><li><span class='xr-has-index'>freq</span>: 35</li><li><span class='xr-has-index'>dir</span>: 36</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e1982a2b-d22b-46d3-965c-30fcac8d5841' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e1982a2b-d22b-46d3-965c-30fcac8d5841' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>freq</span></div><div class='xr-var-dims'>(freq)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.04 0.04397 0.04834 ... 0.9097 1.0</div><input id='attrs-fb4acc9e-ee79-4ead-9512-1cb99df92610' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fb4acc9e-ee79-4ead-9512-1cb99df92610' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1530ae0-3b05-4a0c-a8b6-680d7107bde8' class='xr-var-data-in' type='checkbox'><label for='data-e1530ae0-3b05-4a0c-a8b6-680d7107bde8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>sea_surface_wave_frequency</dd><dt><span>units :</span></dt><dd>Hz</dd></dl></div><div class='xr-var-data'><pre>array([0.04    , 0.043972, 0.048338, 0.053138, 0.058415, 0.064215, 0.070592,\n",
       "       0.077602, 0.085307, 0.093778, 0.103091, 0.113327, 0.124581, 0.136951,\n",
       "       0.15055 , 0.1655  , 0.181934, 0.2     , 0.21986 , 0.241692, 0.265691,\n",
       "       0.292074, 0.321077, 0.35296 , 0.388008, 0.426537, 0.468892, 0.515452,\n",
       "       0.566636, 0.622902, 0.684756, 0.752752, 0.827499, 0.909669, 0.999999],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dir</span></div><div class='xr-var-dims'>(dir)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>261.0 251.0 241.0 ... 281.0 271.0</div><input id='attrs-dc39a296-2ff2-48b4-9bb3-bb3d45c190bd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dc39a296-2ff2-48b4-9bb3-bb3d45c190bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f3f8387-2fd6-487c-a121-e797976b4add' class='xr-var-data-in' type='checkbox'><label for='data-3f3f8387-2fd6-487c-a121-e797976b4add' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>sea_surface_wave_from_direction</dd><dt><span>units :</span></dt><dd>degree</dd></dl></div><div class='xr-var-data'><pre>array([261.      , 251.      , 241.      , 231.      , 221.      , 211.      ,\n",
       "       201.      , 191.      , 181.      , 171.      , 160.99998 , 151.      ,\n",
       "       141.      , 131.      , 120.999985, 110.999985, 100.99999 ,  90.99999 ,\n",
       "        80.99999 ,  70.999985,  60.999985,  50.999985,  40.99999 ,  30.999989,\n",
       "        20.99999 ,  10.999992,   0.999994, 350.99997 , 340.99997 , 330.99997 ,\n",
       "       320.99997 , 310.99997 , 300.99997 , 290.99997 , 281.      , 271.      ],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>site</span></div><div class='xr-var-dims'>(site)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3</div><input id='attrs-36a6d71f-f139-407a-982a-78c4d811d25d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-36a6d71f-f139-407a-982a-78c4d811d25d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42d2e4fe-bca6-4423-80dd-b24856c2665c' class='xr-var-data-in' type='checkbox'><label for='data-42d2e4fe-bca6-4423-80dd-b24856c2665c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>site</dd><dt><span>units :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>array([1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>source_terms</span></div><div class='xr-var-dims'>(source_terms)</div><div class='xr-var-dtype'>&lt;U10</div><div class='xr-var-preview xr-preview'>&#x27;komen&#x27; &#x27;janssen&#x27; ... &#x27;st6c5&#x27;</div><input id='attrs-b4169ec1-b7f0-4e40-89b6-3767e0d3dcd0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b4169ec1-b7f0-4e40-89b6-3767e0d3dcd0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77613676-542c-49e7-8da0-443b2cd43440' class='xr-var-data-in' type='checkbox'><label for='data-77613676-542c-49e7-8da0-443b2cd43440' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;komen&#x27;, &#x27;janssen&#x27;, &#x27;westhuysen&#x27;, &#x27;st6c1&#x27;, &#x27;st6c2&#x27;, &#x27;st6c3&#x27;, &#x27;st6c4&#x27;,\n",
       "       &#x27;st6c5&#x27;], dtype=&#x27;&lt;U10&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8a111056-d53f-4e0b-a880-a8d87d8e6ec7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8a111056-d53f-4e0b-a880-a8d87d8e6ec7' class='xr-section-summary' >Data variables: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(source_terms, site)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 3), meta=np.ndarray&gt;</div><input id='attrs-86e4c631-cdf0-4b02-b023-2dfef637c207' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-86e4c631-cdf0-4b02-b023-2dfef637c207' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7610c0f-8bf7-45cf-a616-f33b9ee4d209' class='xr-var-data-in' type='checkbox'><label for='data-e7610c0f-8bf7-45cf-a616-f33b9ee4d209' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
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       "                    <tr>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wdir</span></div><div class='xr-var-dims'>(source_terms, site)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 3), meta=np.ndarray&gt;</div><input id='attrs-ed810847-214a-4b8b-8b8e-b9197e4c1066' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ed810847-214a-4b8b-8b8e-b9197e4c1066' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b43b5374-00e6-44a7-aee8-a5514458a44d' class='xr-var-data-in' type='checkbox'><label for='data-b43b5374-00e6-44a7-aee8-a5514458a44d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 96 B </td>\n",
       "                        <td> 12 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (8, 3) </td>\n",
       "                        <td> (1, 3) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 8 chunks in 89 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"95\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"45\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"15\" x2=\"45\" y2=\"15\" />\n",
       "  <line x1=\"0\" y1=\"30\" x2=\"45\" y2=\"30\" />\n",
       "  <line x1=\"0\" y1=\"45\" x2=\"45\" y2=\"45\" />\n",
       "  <line x1=\"0\" y1=\"60\" x2=\"45\" y2=\"60\" />\n",
       "  <line x1=\"0\" y1=\"75\" x2=\"45\" y2=\"75\" />\n",
       "  <line x1=\"0\" y1=\"90\" x2=\"45\" y2=\"90\" />\n",
       "  <line x1=\"0\" y1=\"105\" x2=\"45\" y2=\"105\" />\n",
       "  <line x1=\"0\" y1=\"120\" x2=\"45\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 45.0,0.0 45.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"22.500000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text>\n",
       "  <text x=\"65.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,65.000000,60.000000)\">8</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-6340d49d-fc30-4b8d-8f8a-f364d67d0a87' class='xr-section-summary-in' type='checkbox'  ><label for='section-6340d49d-fc30-4b8d-8f8a-f364d67d0a87' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>freq</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6dba1f14-2a09-415a-9913-018ef4da95eb' class='xr-index-data-in' type='checkbox'/><label for='index-6dba1f14-2a09-415a-9913-018ef4da95eb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0.03999999910593033,   0.0439719632267952,  0.04833833873271942,\n",
       "        0.05313829332590103, 0.058414872735738754,  0.06421541422605515,\n",
       "        0.07059194892644882,  0.07760166376829147,  0.08530744165182114,\n",
       "        0.09377838671207428,  0.10309050232172012,  0.11332729458808899,\n",
       "        0.12458059191703796,  0.13695132732391357,   0.1505504697561264,\n",
       "         0.1655000001192093,  0.18193399906158447,   0.1999998688697815,\n",
       "        0.21985965967178345,  0.24169152975082397,  0.26569128036499023,\n",
       "        0.29207417368888855,   0.3210768699645996,   0.3529595136642456,\n",
       "         0.3880080580711365,     0.42653688788414,  0.46889159083366394,\n",
       "         0.5154520869255066,   0.5666360259056091,   0.6229023933410645,\n",
       "         0.6847560405731201,   0.7527517080307007,    0.827499270439148,\n",
       "         0.9096691608428955,   0.9999985098838806],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;freq&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>dir</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9a01d2bb-6cd7-424d-9dd2-f6efc97dfcc3' class='xr-index-data-in' type='checkbox'/><label for='index-9a01d2bb-6cd7-424d-9dd2-f6efc97dfcc3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([             261.0,              251.0,              241.0,\n",
       "                    231.0,              221.0,              211.0,\n",
       "                    201.0,              191.0,              181.0,\n",
       "                    171.0, 160.99998474121094,              151.0,\n",
       "                    141.0,              131.0, 120.99998474121094,\n",
       "       110.99998474121094, 100.99999237060547,  90.99999237060547,\n",
       "        80.99999237060547,  70.99998474121094,  60.99998474121094,\n",
       "        50.99998474121094,   40.9999885559082, 30.999988555908203,\n",
       "       20.999990463256836, 10.999992370605469, 0.9999943375587463,\n",
       "        350.9999694824219,  340.9999694824219,  330.9999694824219,\n",
       "        320.9999694824219,  310.9999694824219,  300.9999694824219,\n",
       "        290.9999694824219,              281.0,              271.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;dir&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>site</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7728ee21-02ea-48bf-98fd-f9fad245d210' class='xr-index-data-in' type='checkbox'/><label for='index-7728ee21-02ea-48bf-98fd-f9fad245d210' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;site&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>source_terms</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c32c19c8-a778-4683-aba5-af6c8b53f122' class='xr-index-data-in' type='checkbox'/><label for='index-c32c19c8-a778-4683-aba5-af6c8b53f122' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;komen&#x27;, &#x27;janssen&#x27;, &#x27;westhuysen&#x27;, &#x27;st6c1&#x27;, &#x27;st6c2&#x27;, &#x27;st6c3&#x27;, &#x27;st6c4&#x27;,\n",
       "       &#x27;st6c5&#x27;],\n",
       "      dtype=&#x27;object&#x27;, name=&#x27;source_terms&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e1cf7dc6-9ec5-419a-8d93-e96c3d2a967a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e1cf7dc6-9ec5-419a-8d93-e96c3d2a967a' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:       (source_terms: 8, site: 3, freq: 35, dir: 36)\n",
       "Coordinates:\n",
       "  * freq          (freq) float32 0.04 0.04397 0.04834 ... 0.8275 0.9097 1.0\n",
       "  * dir           (dir) float32 261.0 251.0 241.0 231.0 ... 291.0 281.0 271.0\n",
       "  * site          (site) int64 1 2 3\n",
       "  * source_terms  (source_terms) <U10 'komen' 'janssen' ... 'st6c4' 'st6c5'\n",
       "Data variables:\n",
       "    lon           (source_terms, site) float32 dask.array<chunksize=(1, 3), meta=np.ndarray>\n",
       "    lat           (source_terms, site) float32 dask.array<chunksize=(1, 3), meta=np.ndarray>\n",
       "    efth          (source_terms, site, freq, dir) float32 dask.array<chunksize=(1, 3, 35, 36), meta=np.ndarray>\n",
       "    dpt           (source_terms, site) float32 dask.array<chunksize=(1, 3), meta=np.ndarray>\n",
       "    wspd          (source_terms, site) float32 dask.array<chunksize=(1, 3), meta=np.ndarray>\n",
       "    wdir          (source_terms, site) float32 dask.array<chunksize=(1, 3), meta=np.ndarray>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Wave spectra\n",
    "\n",
    "dspec = xr.concat([read_spectra_output(run) for run in runs], dim=\"source_terms\")\n",
    "dspec[\"source_terms\"] = [run.run_id for run in runs]\n",
    "dspec"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gridded parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1300x600 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = dsgrid.hs.plot(\n",
    "    x=\"longitude\",\n",
    "    y=\"latitude\",\n",
    "    col=\"source_terms\",\n",
    "    col_wrap=4,\n",
    "    vmin=0.5,\n",
    "    vmax=3.0,\n",
    "    cmap=\"turbo\",\n",
    "    subplot_kws=dict(projection=ccrs.PlateCarree()),\n",
    "    cbar_kwargs=dict(label=\"Hs (m)\"),\n",
    ")\n",
    "f.map(lambda: plt.gca().coastlines());"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wave spectra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1300x600 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = dspec.isel(site=0).spec.plot(col=\"source_terms\", col_wrap=4, cbar_kwargs={\"label\": \"Ed (m2/Hz/deg)\"})"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "rompy",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}