{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import modelskill as ms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model skill visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ComparerCollection>\n",
       "Comparer: HKZA\n",
       "Comparer: HKZA_2\n",
       "Comparer: HKZA_3\n",
       "Comparer: HKNA\n",
       "Comparer: HKNA_2\n",
       "Comparer: HKNA_3\n",
       "Comparer: EPL\n",
       "Comparer: EPL_2\n",
       "Comparer: EPL_3"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fn = 'data/SW/HKZN_local_2017_DutchCoast.dfsu'\n",
    "mr = ms.model_result(fn, name='HKZN_local', item=0)\n",
    "\n",
    "obs = [ms.PointObservation('data/SW/HKZA_Hm0.dfs0', item=0, x=3.9, y=52.7, name=\"HKZA\"),\n",
    "        ms.PointObservation('data/SW/HKZA_Hm0.dfs0', item=0, x=3.8, y=52.5, name=\"HKZA_2\"),\n",
    "        ms.PointObservation('data/SW/HKZA_Hm0.dfs0', item=0, x=3.5, y=52.6, name=\"HKZA_3\"),\n",
    "        ms.PointObservation('data/SW/HKNA_Hm0.dfs0', item=0, x=4.2420, y=52.6887, name=\"HKNA\"),\n",
    "        ms.PointObservation('data/SW/HKNA_Hm0.dfs0', item=0, x=4.2, y=52.6, name=\"HKNA_2\"),\n",
    "        ms.PointObservation('data/SW/HKNA_Hm0.dfs0', item=0, x=4.3, y=52.7, name=\"HKNA_3\"),\n",
    "        ms.PointObservation(\"data/SW/eur_Hm0.dfs0\", item=0, x=3.2760, y=51.9990, name=\"EPL\"),\n",
    "        ms.PointObservation(\"data/SW/eur_Hm0.dfs0\", item=0, x=3.2, y=51.9, name=\"EPL_2\"),\n",
    "        ms.PointObservation(\"data/SW/eur_Hm0.dfs0\", item=0, x=3.3, y=51.95, name=\"EPL_3\")\n",
    "]\n",
    "\n",
    "cc = ms.match(obs=obs, mod=mr)\n",
    "cc\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n</th>\n",
       "      <th>bias</th>\n",
       "      <th>rmse</th>\n",
       "      <th>urmse</th>\n",
       "      <th>mae</th>\n",
       "      <th>cc</th>\n",
       "      <th>si</th>\n",
       "      <th>r2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>observation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>EPL</th>\n",
       "      <td>66</td>\n",
       "      <td>-0.066028</td>\n",
       "      <td>0.224919</td>\n",
       "      <td>0.215009</td>\n",
       "      <td>0.189791</td>\n",
       "      <td>0.969512</td>\n",
       "      <td>0.082773</td>\n",
       "      <td>0.932082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EPL_2</th>\n",
       "      <td>66</td>\n",
       "      <td>-0.176137</td>\n",
       "      <td>0.278117</td>\n",
       "      <td>0.215233</td>\n",
       "      <td>0.240594</td>\n",
       "      <td>0.968521</td>\n",
       "      <td>0.082859</td>\n",
       "      <td>0.896154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EPL_3</th>\n",
       "      <td>66</td>\n",
       "      <td>-0.066028</td>\n",
       "      <td>0.224919</td>\n",
       "      <td>0.215009</td>\n",
       "      <td>0.189791</td>\n",
       "      <td>0.969512</td>\n",
       "      <td>0.082773</td>\n",
       "      <td>0.932082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HKNA</th>\n",
       "      <td>386</td>\n",
       "      <td>-0.194260</td>\n",
       "      <td>0.351964</td>\n",
       "      <td>0.293499</td>\n",
       "      <td>0.251839</td>\n",
       "      <td>0.971194</td>\n",
       "      <td>0.094489</td>\n",
       "      <td>0.905300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HKNA_2</th>\n",
       "      <td>386</td>\n",
       "      <td>-0.296194</td>\n",
       "      <td>0.430783</td>\n",
       "      <td>0.312799</td>\n",
       "      <td>0.325512</td>\n",
       "      <td>0.969579</td>\n",
       "      <td>0.100702</td>\n",
       "      <td>0.858137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HKNA_3</th>\n",
       "      <td>386</td>\n",
       "      <td>-0.135648</td>\n",
       "      <td>0.298130</td>\n",
       "      <td>0.265482</td>\n",
       "      <td>0.210063</td>\n",
       "      <td>0.975813</td>\n",
       "      <td>0.085469</td>\n",
       "      <td>0.932054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HKZA</th>\n",
       "      <td>397</td>\n",
       "      <td>0.051763</td>\n",
       "      <td>0.285233</td>\n",
       "      <td>0.280497</td>\n",
       "      <td>0.222436</td>\n",
       "      <td>0.965938</td>\n",
       "      <td>0.096780</td>\n",
       "      <td>0.930484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HKZA_2</th>\n",
       "      <td>397</td>\n",
       "      <td>-0.006792</td>\n",
       "      <td>0.281183</td>\n",
       "      <td>0.281101</td>\n",
       "      <td>0.219259</td>\n",
       "      <td>0.966268</td>\n",
       "      <td>0.096988</td>\n",
       "      <td>0.932444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HKZA_3</th>\n",
       "      <td>397</td>\n",
       "      <td>-0.011184</td>\n",
       "      <td>0.278949</td>\n",
       "      <td>0.278725</td>\n",
       "      <td>0.218145</td>\n",
       "      <td>0.966900</td>\n",
       "      <td>0.096168</td>\n",
       "      <td>0.933514</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               n      bias      rmse     urmse       mae        cc        si  \\\n",
       "observation                                                                    \n",
       "EPL           66 -0.066028  0.224919  0.215009  0.189791  0.969512  0.082773   \n",
       "EPL_2         66 -0.176137  0.278117  0.215233  0.240594  0.968521  0.082859   \n",
       "EPL_3         66 -0.066028  0.224919  0.215009  0.189791  0.969512  0.082773   \n",
       "HKNA         386 -0.194260  0.351964  0.293499  0.251839  0.971194  0.094489   \n",
       "HKNA_2       386 -0.296194  0.430783  0.312799  0.325512  0.969579  0.100702   \n",
       "HKNA_3       386 -0.135648  0.298130  0.265482  0.210063  0.975813  0.085469   \n",
       "HKZA         397  0.051763  0.285233  0.280497  0.222436  0.965938  0.096780   \n",
       "HKZA_2       397 -0.006792  0.281183  0.281101  0.219259  0.966268  0.096988   \n",
       "HKZA_3       397 -0.011184  0.278949  0.278725  0.218145  0.966900  0.096168   \n",
       "\n",
       "                   r2  \n",
       "observation            \n",
       "EPL          0.932082  \n",
       "EPL_2        0.896154  \n",
       "EPL_3        0.932082  \n",
       "HKNA         0.905300  \n",
       "HKNA_2       0.858137  \n",
       "HKNA_3       0.932054  \n",
       "HKZA         0.930484  \n",
       "HKZA_2       0.932444  \n",
       "HKZA_3       0.933514  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc.skill()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "modelskill.skill.SkillTable"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = cc.skill()\n",
    "type(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s.rmse.plot.bar();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s['urmse'].plot.line();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Custom plot\n",
    "\n",
    "All skill statistics are available in a dataframe, and in case you need a tailor-made plot, you can get data and use matplotlib to get exactly what you need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = s.to_dataframe().sort_values('bias')\n",
    "x = df.bias\n",
    "y = df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(6,6))\n",
    "plt.scatter(x,y,marker='D',c='red',s=100)\n",
    "plt.xlim(-0.35,0.35)\n",
    "plt.axvline(0,linestyle='--')\n",
    "plt.xlabel(\"Bias (m)\")\n",
    "plt.title(mr.name)\n",
    "plt.grid()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "f4041ee05ab07c15354d6207e763f17a216c3f5ccf08906343c2b4fd3fa7a6fb"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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
}