{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SW_DutchCoast\n",
    "\n",
    "Validate MIKE 21 Spectral wave model for the Dutch coast."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndentationError",
     "evalue": "unindent does not match any outer indentation level (__init__.py, line 56)",
     "output_type": "error",
     "traceback": [
      "Traceback \u001b[0;36m(most recent call last)\u001b[0m:\n",
      "\u001b[0m  File \u001b[1;32m~/.local/lib/python3.11/site-packages/IPython/core/interactiveshell.py:3433\u001b[0m in \u001b[1;35mrun_code\u001b[0m\n    exec(code_obj, self.user_global_ns, self.user_ns)\u001b[0m\n",
      "\u001b[0;36m  Cell \u001b[0;32mIn[2], line 1\u001b[0;36m\n\u001b[0;31m    import modelskill as ms\u001b[0;36m\n",
      "\u001b[0;36m  File \u001b[0;32m~/src/modelskill/modelskill/__init__.py:56\u001b[0;36m\u001b[0m\n\u001b[0;31m    return ComparerCollection.load(fn)\u001b[0m\n\u001b[0m                                      ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m unindent does not match any outer indentation level\n"
     ]
    }
   ],
   "source": [
    "import modelskill as ms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib_inline.backend_inline import set_matplotlib_formats\n",
    "set_matplotlib_formats('png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fn = '../tests/testdata/SW/HKZN_local_2017_DutchCoast.dfsu'\n",
    "mr = ms.ModelResult(fn, name='HKZN_local', item=0)\n",
    "mr.data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "o1 = ms.PointObservation('../tests/testdata/SW/HKNA_Hm0.dfs0', item=0, x=4.2420, y=52.6887, name=\"HKNA\")\n",
    "o2 = ms.PointObservation(\"../tests/testdata/SW/eur_Hm0.dfs0\", item=0, x=3.2760, y=51.9990, name=\"EPL\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "o1.plot.hist();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "o1.plot(); "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "o3 = ms.TrackObservation(\"../tests/testdata/SW/Alti_c2_Dutch.dfs0\", item=3, name=\"c2\")\n",
    "o3.data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ms.plotting.spatial_overview([o1, o2, o3], mr);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ms.plotting.temporal_coverage([o1, o2, o3], mr);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ms.plotting.temporal_coverage([o1, o2, o3], mr, limit_to_model_period=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare observations and model result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc = ms.compare([o1, o2, o3], mr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save the comparison to a file\n",
    "\n",
    "Extracting model results¶ can be a time-consuming process. Therefore, it is recommended to save the results to a file. This can be done using the save method of the comparison object.\n",
    "\n",
    "Each comparer can be saved as a NetCDF file, and a `ComparerCollection` can be saved as a zip file, preferably with the extension `.msk`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc.save(\"SW_DutchCoast.msk\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc2 = ms.load(\"SW_DutchCoast.msk\")\n",
    "cc2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc.skill().style()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc.save"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc[\"c2\"].skill(metrics=\"mean_absolute_error\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc[\"HKNA\"].plot.timeseries(figsize=(10,5));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc[\"HKNA\"].plot.timeseries(width=1000, backend=\"plotly\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc[\"EPL\"].plot.scatter(figsize=(8,8), show_hist=True); "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc[\"EPL\"].plot.scatter(show_hist=True, backend='plotly', width=600, height=600); #,xlim=[-1,11])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc[\"c2\"].hist(bins=20);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc[\"HKNA\"].plot.scatter(bins=0.25, cmap=\"viridis\");"
   ]
  }
 ],
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