{
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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 },
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 "cells": [
  {
   "source": [
    "# Multi variable comparison\n",
    "Assessing both wave height and wind speed at the same time"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from fmskill import ModelResult, ModelResultCollection, PointObservation, TrackObservation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "source": [
    "## Define observations\n",
    "Below, the observations will take the default variable names from the eum type of the item. Alternatively, the user can give another variable name by providing the `variable_name` argument."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# wave height\n",
    "o1 = PointObservation('../tests/testdata/SW/HKNA_Hm0.dfs0', item=0, x=4.2420, y=52.6887, name=\"HKNA_Hm0\")\n",
    "o2 = PointObservation(\"../tests/testdata/SW/eur_Hm0.dfs0\", item=0, x=3.2760, y=51.9990, name=\"EPL_Hm0\")\n",
    "o3 = TrackObservation(\"../tests/testdata/SW/Alti_c2_Dutch.dfs0\", item=3, name=\"c2_Hm0\")\n",
    "\n",
    "# wind speed\n",
    "wind1 = PointObservation('../tests/testdata/SW/HKNA_wind.dfs0', item=0, x=4.2420, y=52.6887, name=\"HKNA_wind\")\n",
    "wind2 = PointObservation('../tests/testdata/SW/F16_wind.dfs0', item=0, x=4.01222, y=54.1167, name=\"F16_wind\")\n",
    "wind3 = TrackObservation(\"../tests/testdata/SW/Alti_c2_Dutch.dfs0\", item=2, name=\"c2_wind\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "'Significant_wave_height'"
      ]
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "o1.variable_name"
   ]
  },
  {
   "source": [
    "## Define model results\n",
    "Two different model results are defined."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "mr1 = ModelResult('../tests/testdata/SW/HKZN_local_2017_DutchCoast.dfsu', name='SW_1')\n",
    "mr2 = ModelResult('../tests/testdata/SW/HKZN_local_2017_DutchCoast_v2.dfsu', name='SW_2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[Sign. Wave Height <Significant wave height> (meter),\n",
       " Max. Wave Height <Maximum wave height> (meter),\n",
       " Peak Wave Period <Wave period> (second),\n",
       " Wave Period, T01 <Wave period> (second),\n",
       " Wave Period, T02 <Wave period> (second),\n",
       " Peak Wave Direction <Wave direction> (radian),\n",
       " Mean Wave Direction <Mean Wave Direction> (degree),\n",
       " Dir. Stand. Deviation <Directional Standard Deviation> (degree),\n",
       " x-comp. of wave height vector <u velocity component> (meter per sec),\n",
       " y-comp. of wave height vector <v velocity component> (meter per sec),\n",
       " Surface elevation <Surface Elevation> (meter),\n",
       " Current velocity, U <u velocity component> (meter per sec),\n",
       " Current velocity, V <v velocity component> (meter per sec),\n",
       " Wind speed <Wind speed> (meter per sec),\n",
       " Wind direction <Wind Direction> (degree)]"
      ]
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "mr1.dfs.items"
   ]
  },
  {
   "source": [
    "## Associate model and observations and extract\n",
    "We match the observation item and model item by refering to the item name in the ModelResult. Item number can also be used."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "mr = ModelResultCollection([mr1, mr2])\n",
    "#mr = mr1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'HKNA_Hm0': PointObservation: HKNA_Hm0, x=4.242, y=52.6887,\n",
       " 'EPL_Hm0': PointObservation: EPL_Hm0, x=3.276, y=51.999,\n",
       " 'c2_Hm0': TrackObservation: c2_Hm0, n=298,\n",
       " 'HKNA_wind': PointObservation: HKNA_wind, x=4.242, y=52.6887,\n",
       " 'F16_wind': PointObservation: F16_wind, x=4.01222, y=54.1167,\n",
       " 'c2_wind': TrackObservation: c2_wind, n=298}"
      ]
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "source": [
    "mr.add_observation(o1, item='Sign. Wave Height')\n",
    "mr.add_observation(o2, item='Sign. Wave Height')\n",
    "mr.add_observation(o3, item='Sign. Wave Height')\n",
    "mr.add_observation(wind1, item='Wind speed')\n",
    "mr.add_observation(wind2, item='Wind speed')\n",
    "mr.add_observation(wind3, item='Wind speed')\n",
    "mr.observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc = mr.extract() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "cc.n_variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "['Significant_wave_height', 'Wind_speed']"
      ]
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "cc.var_names"
   ]
  },
  {
   "source": [
    "## Analysis\n",
    "Now that the result has been extracted, we can do analysis. Multiple variables means an extra level in the multi-index of the skill dataframe. "
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                                             n      bias      rmse     urmse  \\\n",
       "model observation variable                                                     \n",
       "SW_1  EPL_Hm0     Significant_wave_height   66 -0.075335  0.216357  0.202817   \n",
       "      F16_wind    Wind_speed                67  2.102049  2.775330  1.812140   \n",
       "      HKNA_Hm0    Significant_wave_height  385 -0.195266  0.352283  0.293214   \n",
       "      HKNA_wind   Wind_speed               277 -0.880928  1.276179  0.923363   \n",
       "      c2_Hm0      Significant_wave_height  113 -0.001210  0.351796  0.351794   \n",
       "      c2_wind     Wind_speed               113  0.408558  0.637718  0.489657   \n",
       "SW_2  EPL_Hm0     Significant_wave_height   66 -0.007782  0.225996  0.225862   \n",
       "      F16_wind    Wind_speed                67  2.102049  2.775330  1.812140   \n",
       "      HKNA_Hm0    Significant_wave_height  385 -0.101189  0.293247  0.275235   \n",
       "      HKNA_wind   Wind_speed               277 -0.880928  1.276179  0.923363   \n",
       "      c2_Hm0      Significant_wave_height  113  0.081431  0.430268  0.422492   \n",
       "      c2_wind     Wind_speed               113  0.408558  0.637718  0.489657   \n",
       "\n",
       "                                                mae        cc        si  \\\n",
       "model observation variable                                                \n",
       "SW_1  EPL_Hm0     Significant_wave_height  0.183641  0.972467  0.073902   \n",
       "      F16_wind    Wind_speed               2.196785  0.824620  0.136781   \n",
       "      HKNA_Hm0    Significant_wave_height  0.251992  0.971082  0.088488   \n",
       "      HKNA_wind   Wind_speed               1.023092  0.962845  0.064528   \n",
       "      c2_Hm0      Significant_wave_height  0.294585  0.974335  0.118511   \n",
       "      c2_wind     Wind_speed               0.505665  0.960284  0.049998   \n",
       "SW_2  EPL_Hm0     Significant_wave_height  0.193719  0.972467  0.082298   \n",
       "      F16_wind    Wind_speed               2.196785  0.824620  0.136781   \n",
       "      HKNA_Hm0    Significant_wave_height  0.214476  0.971082  0.083062   \n",
       "      HKNA_wind   Wind_speed               1.023092  0.962845  0.064528   \n",
       "      c2_Hm0      Significant_wave_height  0.357138  0.974335  0.142327   \n",
       "      c2_wind     Wind_speed               0.505665  0.960284  0.049998   \n",
       "\n",
       "                                                 r2  \n",
       "model observation variable                           \n",
       "SW_1  EPL_Hm0     Significant_wave_height  0.934093  \n",
       "      F16_wind    Wind_speed               0.229514  \n",
       "      HKNA_Hm0    Significant_wave_height  0.904815  \n",
       "      HKNA_wind   Wind_speed               0.860548  \n",
       "      c2_Hm0      Significant_wave_height  0.899507  \n",
       "      c2_wind     Wind_speed               0.867103  \n",
       "SW_2  EPL_Hm0     Significant_wave_height  0.928089  \n",
       "      F16_wind    Wind_speed               0.229514  \n",
       "      HKNA_Hm0    Significant_wave_height  0.934045  \n",
       "      HKNA_wind   Wind_speed               0.860548  \n",
       "      c2_Hm0      Significant_wave_height  0.849675  \n",
       "      c2_wind     Wind_speed               0.867103  "
      ],
      "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></th>\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>model</th>\n      <th>observation</th>\n      <th>variable</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 rowspan=\"6\" valign=\"top\">SW_1</th>\n      <th>EPL_Hm0</th>\n      <th>Significant_wave_height</th>\n      <td>66</td>\n      <td>-0.075335</td>\n      <td>0.216357</td>\n      <td>0.202817</td>\n      <td>0.183641</td>\n      <td>0.972467</td>\n      <td>0.073902</td>\n      <td>0.934093</td>\n    </tr>\n    <tr>\n      <th>F16_wind</th>\n      <th>Wind_speed</th>\n      <td>67</td>\n      <td>2.102049</td>\n      <td>2.775330</td>\n      <td>1.812140</td>\n      <td>2.196785</td>\n      <td>0.824620</td>\n      <td>0.136781</td>\n      <td>0.229514</td>\n    </tr>\n    <tr>\n      <th>HKNA_Hm0</th>\n      <th>Significant_wave_height</th>\n      <td>385</td>\n      <td>-0.195266</td>\n      <td>0.352283</td>\n      <td>0.293214</td>\n      <td>0.251992</td>\n      <td>0.971082</td>\n      <td>0.088488</td>\n      <td>0.904815</td>\n    </tr>\n    <tr>\n      <th>HKNA_wind</th>\n      <th>Wind_speed</th>\n      <td>277</td>\n      <td>-0.880928</td>\n      <td>1.276179</td>\n      <td>0.923363</td>\n      <td>1.023092</td>\n      <td>0.962845</td>\n      <td>0.064528</td>\n      <td>0.860548</td>\n    </tr>\n    <tr>\n      <th>c2_Hm0</th>\n      <th>Significant_wave_height</th>\n      <td>113</td>\n      <td>-0.001210</td>\n      <td>0.351796</td>\n      <td>0.351794</td>\n      <td>0.294585</td>\n      <td>0.974335</td>\n      <td>0.118511</td>\n      <td>0.899507</td>\n    </tr>\n    <tr>\n      <th>c2_wind</th>\n      <th>Wind_speed</th>\n      <td>113</td>\n      <td>0.408558</td>\n      <td>0.637718</td>\n      <td>0.489657</td>\n      <td>0.505665</td>\n      <td>0.960284</td>\n      <td>0.049998</td>\n      <td>0.867103</td>\n    </tr>\n    <tr>\n      <th rowspan=\"6\" valign=\"top\">SW_2</th>\n      <th>EPL_Hm0</th>\n      <th>Significant_wave_height</th>\n      <td>66</td>\n      <td>-0.007782</td>\n      <td>0.225996</td>\n      <td>0.225862</td>\n      <td>0.193719</td>\n      <td>0.972467</td>\n      <td>0.082298</td>\n      <td>0.928089</td>\n    </tr>\n    <tr>\n      <th>F16_wind</th>\n      <th>Wind_speed</th>\n      <td>67</td>\n      <td>2.102049</td>\n      <td>2.775330</td>\n      <td>1.812140</td>\n      <td>2.196785</td>\n      <td>0.824620</td>\n      <td>0.136781</td>\n      <td>0.229514</td>\n    </tr>\n    <tr>\n      <th>HKNA_Hm0</th>\n      <th>Significant_wave_height</th>\n      <td>385</td>\n      <td>-0.101189</td>\n      <td>0.293247</td>\n      <td>0.275235</td>\n      <td>0.214476</td>\n      <td>0.971082</td>\n      <td>0.083062</td>\n      <td>0.934045</td>\n    </tr>\n    <tr>\n      <th>HKNA_wind</th>\n      <th>Wind_speed</th>\n      <td>277</td>\n      <td>-0.880928</td>\n      <td>1.276179</td>\n      <td>0.923363</td>\n      <td>1.023092</td>\n      <td>0.962845</td>\n      <td>0.064528</td>\n      <td>0.860548</td>\n    </tr>\n    <tr>\n      <th>c2_Hm0</th>\n      <th>Significant_wave_height</th>\n      <td>113</td>\n      <td>0.081431</td>\n      <td>0.430268</td>\n      <td>0.422492</td>\n      <td>0.357138</td>\n      <td>0.974335</td>\n      <td>0.142327</td>\n      <td>0.849675</td>\n    </tr>\n    <tr>\n      <th>c2_wind</th>\n      <th>Wind_speed</th>\n      <td>113</td>\n      <td>0.408558</td>\n      <td>0.637718</td>\n      <td>0.489657</td>\n      <td>0.505665</td>\n      <td>0.960284</td>\n      <td>0.049998</td>\n      <td>0.867103</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "source": [
    "s = cc.skill()\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "FrozenList(['model', 'observation', 'variable'])"
      ]
     },
     "metadata": {},
     "execution_count": 13
    }
   ],
   "source": [
    "s.index.names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
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</tr>    <tr>        <th class=\"index_name level0\" >model</th>        <th class=\"index_name level1\" >observation</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n                <tr>\n                        <th id=\"T_82f02_level0_row0\" class=\"row_heading level0 row0\" rowspan=\"3\">SW_1</th>\n                        <th id=\"T_82f02_level1_row0\" class=\"row_heading level1 row0\" >EPL_Hm0</th>\n                        <td id=\"T_82f02_row0_col0\" class=\"data row0 col0\" >Significant_wave_height</td>\n                        <td id=\"T_82f02_row0_col1\" class=\"data row0 col1\" >66</td>\n                        <td id=\"T_82f02_row0_col2\" class=\"data row0 col2\" >-0.075</td>\n                        <td id=\"T_82f02_row0_col3\" class=\"data row0 col3\" >0.216</td>\n                        <td id=\"T_82f02_row0_col4\" class=\"data row0 col4\" >0.203</td>\n                        <td id=\"T_82f02_row0_col5\" class=\"data row0 col5\" >0.184</td>\n                        <td id=\"T_82f02_row0_col6\" class=\"data row0 col6\" >0.972</td>\n                        <td id=\"T_82f02_row0_col7\" class=\"data row0 col7\" >0.074</td>\n                        <td id=\"T_82f02_row0_col8\" class=\"data row0 col8\" >0.934</td>\n            </tr>\n            <tr>\n                                <th id=\"T_82f02_level1_row1\" class=\"row_heading level1 row1\" >HKNA_Hm0</th>\n                        <td id=\"T_82f02_row1_col0\" class=\"data row1 col0\" >Significant_wave_height</td>\n                        <td id=\"T_82f02_row1_col1\" class=\"data row1 col1\" >385</td>\n                        <td id=\"T_82f02_row1_col2\" class=\"data row1 col2\" >-0.195</td>\n                        <td id=\"T_82f02_row1_col3\" class=\"data row1 col3\" >0.352</td>\n                        <td id=\"T_82f02_row1_col4\" class=\"data row1 col4\" >0.293</td>\n                        <td id=\"T_82f02_row1_col5\" class=\"data row1 col5\" >0.252</td>\n                        <td id=\"T_82f02_row1_col6\" class=\"data row1 col6\" >0.971</td>\n                        <td id=\"T_82f02_row1_col7\" class=\"data row1 col7\" >0.088</td>\n                        <td id=\"T_82f02_row1_col8\" class=\"data row1 col8\" >0.905</td>\n            </tr>\n            <tr>\n                                <th id=\"T_82f02_level1_row2\" class=\"row_heading level1 row2\" >c2_Hm0</th>\n                        <td id=\"T_82f02_row2_col0\" class=\"data row2 col0\" >Significant_wave_height</td>\n                        <td id=\"T_82f02_row2_col1\" class=\"data row2 col1\" >113</td>\n                        <td id=\"T_82f02_row2_col2\" class=\"data row2 col2\" >-0.001</td>\n                        <td id=\"T_82f02_row2_col3\" class=\"data row2 col3\" >0.352</td>\n                        <td id=\"T_82f02_row2_col4\" class=\"data row2 col4\" >0.352</td>\n                        <td id=\"T_82f02_row2_col5\" class=\"data row2 col5\" >0.295</td>\n                        <td id=\"T_82f02_row2_col6\" class=\"data row2 col6\" >0.974</td>\n                        <td id=\"T_82f02_row2_col7\" class=\"data row2 col7\" >0.119</td>\n                        <td id=\"T_82f02_row2_col8\" class=\"data row2 col8\" >0.900</td>\n            </tr>\n            <tr>\n                        <th id=\"T_82f02_level0_row3\" class=\"row_heading level0 row3\" rowspan=\"3\">SW_2</th>\n                        <th id=\"T_82f02_level1_row3\" class=\"row_heading level1 row3\" >EPL_Hm0</th>\n                        <td id=\"T_82f02_row3_col0\" class=\"data row3 col0\" >Significant_wave_height</td>\n                        <td id=\"T_82f02_row3_col1\" class=\"data row3 col1\" >66</td>\n                        <td id=\"T_82f02_row3_col2\" class=\"data row3 col2\" >-0.008</td>\n                        <td id=\"T_82f02_row3_col3\" class=\"data row3 col3\" >0.226</td>\n                        <td id=\"T_82f02_row3_col4\" class=\"data row3 col4\" >0.226</td>\n                        <td id=\"T_82f02_row3_col5\" class=\"data row3 col5\" >0.194</td>\n                        <td id=\"T_82f02_row3_col6\" class=\"data row3 col6\" >0.972</td>\n                        <td id=\"T_82f02_row3_col7\" class=\"data row3 col7\" >0.082</td>\n                        <td id=\"T_82f02_row3_col8\" class=\"data row3 col8\" >0.928</td>\n            </tr>\n            <tr>\n                                <th id=\"T_82f02_level1_row4\" class=\"row_heading level1 row4\" >HKNA_Hm0</th>\n                        <td id=\"T_82f02_row4_col0\" class=\"data row4 col0\" >Significant_wave_height</td>\n                        <td id=\"T_82f02_row4_col1\" class=\"data row4 col1\" >385</td>\n                        <td id=\"T_82f02_row4_col2\" class=\"data row4 col2\" >-0.101</td>\n                        <td id=\"T_82f02_row4_col3\" class=\"data row4 col3\" >0.293</td>\n                        <td id=\"T_82f02_row4_col4\" class=\"data row4 col4\" >0.275</td>\n                        <td id=\"T_82f02_row4_col5\" class=\"data row4 col5\" >0.214</td>\n                        <td id=\"T_82f02_row4_col6\" class=\"data row4 col6\" >0.971</td>\n                        <td id=\"T_82f02_row4_col7\" class=\"data row4 col7\" >0.083</td>\n                        <td id=\"T_82f02_row4_col8\" class=\"data row4 col8\" >0.934</td>\n            </tr>\n            <tr>\n                                <th id=\"T_82f02_level1_row5\" class=\"row_heading level1 row5\" >c2_Hm0</th>\n                        <td id=\"T_82f02_row5_col0\" class=\"data row5 col0\" >Significant_wave_height</td>\n                        <td id=\"T_82f02_row5_col1\" class=\"data row5 col1\" >113</td>\n                        <td id=\"T_82f02_row5_col2\" class=\"data row5 col2\" >0.081</td>\n                        <td id=\"T_82f02_row5_col3\" class=\"data row5 col3\" >0.430</td>\n                        <td id=\"T_82f02_row5_col4\" class=\"data row5 col4\" >0.422</td>\n                        <td id=\"T_82f02_row5_col5\" class=\"data row5 col5\" >0.357</td>\n                        <td id=\"T_82f02_row5_col6\" class=\"data row5 col6\" >0.974</td>\n                        <td id=\"T_82f02_row5_col7\" class=\"data row5 col7\" >0.142</td>\n                        <td id=\"T_82f02_row5_col8\" class=\"data row5 col8\" >0.850</td>\n            </tr>\n    </tbody></table>"
     },
     "metadata": {},
     "execution_count": 14
    }
   ],
   "source": [
    "s = cc.skill(variable='Significant_wave_height')\n",
    "s.style()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x19ec0cf5520>"
      ],
      "text/html": "<style  type=\"text/css\" >\n#T_48b01_row0_col3,#T_48b01_row0_col5,#T_48b01_row0_col6,#T_48b01_row0_col7,#T_48b01_row0_col8,#T_48b01_row0_col9{\n            text-decoration:  underline;\n             font-style:  italic;\n             font-weight:  bold;\n        }#T_48b01_row0_col4{\n            background-color:  #fff7ec;\n            color:  #000000;\n            text-decoration:  underline;\n             font-style:  italic;\n             font-weight:  bold;\n        }#T_48b01_row1_col4{\n            background-color:  #7f0000;\n            color:  #f1f1f1;\n        }</style><table id=\"T_48b01_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >observation</th>        <th class=\"col_heading level0 col1\" >variable</th>        <th class=\"col_heading level0 col2\" >n</th>        <th class=\"col_heading level0 col3\" >bias</th>        <th class=\"col_heading level0 col4\" >rmse</th>        <th class=\"col_heading level0 col5\" >urmse</th>        <th class=\"col_heading level0 col6\" >mae</th>        <th class=\"col_heading level0 col7\" >cc</th>        <th class=\"col_heading level0 col8\" >si</th>        <th class=\"col_heading level0 col9\" >r2</th>    </tr>    <tr>        <th class=\"index_name level0\" >model</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n                <tr>\n                        <th id=\"T_48b01_level0_row0\" class=\"row_heading level0 row0\" >SW_1</th>\n                        <td id=\"T_48b01_row0_col0\" class=\"data row0 col0\" >c2_Hm0</td>\n                        <td id=\"T_48b01_row0_col1\" class=\"data row0 col1\" >Significant_wave_height</td>\n                        <td id=\"T_48b01_row0_col2\" class=\"data row0 col2\" >113</td>\n                        <td id=\"T_48b01_row0_col3\" class=\"data row0 col3\" >-0.001</td>\n                        <td id=\"T_48b01_row0_col4\" class=\"data row0 col4\" >0.352</td>\n                        <td id=\"T_48b01_row0_col5\" class=\"data row0 col5\" >0.352</td>\n                        <td id=\"T_48b01_row0_col6\" class=\"data row0 col6\" >0.295</td>\n                        <td id=\"T_48b01_row0_col7\" class=\"data row0 col7\" >0.974</td>\n                        <td id=\"T_48b01_row0_col8\" class=\"data row0 col8\" >0.119</td>\n                        <td id=\"T_48b01_row0_col9\" class=\"data row0 col9\" >0.900</td>\n            </tr>\n            <tr>\n                        <th id=\"T_48b01_level0_row1\" class=\"row_heading level0 row1\" >SW_2</th>\n                        <td id=\"T_48b01_row1_col0\" class=\"data row1 col0\" >c2_Hm0</td>\n                        <td id=\"T_48b01_row1_col1\" class=\"data row1 col1\" >Significant_wave_height</td>\n                        <td id=\"T_48b01_row1_col2\" class=\"data row1 col2\" >113</td>\n                        <td id=\"T_48b01_row1_col3\" class=\"data row1 col3\" >0.081</td>\n                        <td id=\"T_48b01_row1_col4\" class=\"data row1 col4\" >0.430</td>\n                        <td id=\"T_48b01_row1_col5\" class=\"data row1 col5\" >0.422</td>\n                        <td id=\"T_48b01_row1_col6\" class=\"data row1 col6\" >0.357</td>\n                        <td id=\"T_48b01_row1_col7\" class=\"data row1 col7\" >0.974</td>\n                        <td id=\"T_48b01_row1_col8\" class=\"data row1 col8\" >0.142</td>\n                        <td id=\"T_48b01_row1_col9\" class=\"data row1 col9\" >0.850</td>\n            </tr>\n    </tbody></table>"
     },
     "metadata": {},
     "execution_count": 15
    }
   ],
   "source": [
    "s.sel(observation='c2_Hm0').style(columns='rmse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "s.plot_line('rmse', ylabel='Hm0 RMSE [m]');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 576x576 with 2 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "cc.scatter(model=1)"
   ]
  },
  {
   "source": [
    "## mean skill\n",
    "The `mean_skill()` method will return a weighted average of the skill score per model and variable. You can get the \"normal\" mean_skill (per model) by selecting a specific variable either by id or name."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                                 n      bias      rmse     urmse       mae  \\\n",
       "model variable                                                               \n",
       "SW_1  Significant_wave_height  564 -0.090604  0.306812  0.282609  0.243406   \n",
       "      Wind_speed               457  0.543226  1.563076  1.075053  1.241847   \n",
       "SW_2  Significant_wave_height  564 -0.009180  0.316503  0.307863  0.255111   \n",
       "      Wind_speed               457  0.543226  1.563076  1.075053  1.241847   \n",
       "\n",
       "                                     cc        si        r2  \n",
       "model variable                                               \n",
       "SW_1  Significant_wave_height  0.972628  0.093634  0.912805  \n",
       "      Wind_speed               0.915916  0.083769  0.652388  \n",
       "SW_2  Significant_wave_height  0.972628  0.102563  0.903936  \n",
       "      Wind_speed               0.915916  0.083769  0.652388  "
      ],
      "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></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>model</th>\n      <th>variable</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 rowspan=\"2\" valign=\"top\">SW_1</th>\n      <th>Significant_wave_height</th>\n      <td>564</td>\n      <td>-0.090604</td>\n      <td>0.306812</td>\n      <td>0.282609</td>\n      <td>0.243406</td>\n      <td>0.972628</td>\n      <td>0.093634</td>\n      <td>0.912805</td>\n    </tr>\n    <tr>\n      <th>Wind_speed</th>\n      <td>457</td>\n      <td>0.543226</td>\n      <td>1.563076</td>\n      <td>1.075053</td>\n      <td>1.241847</td>\n      <td>0.915916</td>\n      <td>0.083769</td>\n      <td>0.652388</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">SW_2</th>\n      <th>Significant_wave_height</th>\n      <td>564</td>\n      <td>-0.009180</td>\n      <td>0.316503</td>\n      <td>0.307863</td>\n      <td>0.255111</td>\n      <td>0.972628</td>\n      <td>0.102563</td>\n      <td>0.903936</td>\n    </tr>\n    <tr>\n      <th>Wind_speed</th>\n      <td>457</td>\n      <td>0.543226</td>\n      <td>1.563076</td>\n      <td>1.075053</td>\n      <td>1.241847</td>\n      <td>0.915916</td>\n      <td>0.083769</td>\n      <td>0.652388</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 18
    }
   ],
   "source": [
    "s = cc.mean_skill()\n",
    "s"
   ]
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "s.plot_bar('si', title='scatter index');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                        model    n      bias      rmse     urmse       mae  \\\n",
       "variable                                                                     \n",
       "Significant_wave_height  SW_2  564 -0.009180  0.316503  0.307863  0.255111   \n",
       "Wind_speed               SW_2  457  0.543226  1.563076  1.075053  1.241847   \n",
       "\n",
       "                               cc        si        r2  \n",
       "variable                                               \n",
       "Significant_wave_height  0.972628  0.102563  0.903936  \n",
       "Wind_speed               0.915916  0.083769  0.652388  "
      ],
      "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>model</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>variable</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      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Significant_wave_height</th>\n      <td>SW_2</td>\n      <td>564</td>\n      <td>-0.009180</td>\n      <td>0.316503</td>\n      <td>0.307863</td>\n      <td>0.255111</td>\n      <td>0.972628</td>\n      <td>0.102563</td>\n      <td>0.903936</td>\n    </tr>\n    <tr>\n      <th>Wind_speed</th>\n      <td>SW_2</td>\n      <td>457</td>\n      <td>0.543226</td>\n      <td>1.563076</td>\n      <td>1.075053</td>\n      <td>1.241847</td>\n      <td>0.915916</td>\n      <td>0.083769</td>\n      <td>0.652388</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 20
    }
   ],
   "source": [
    "cc.mean_skill(model='SW_2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x19ec1072c70>"
      ],
      "text/html": "<style  type=\"text/css\" >\n#T_b5f3c_row0_col3,#T_b5f3c_row0_col4,#T_b5f3c_row0_col5,#T_b5f3c_row0_col6,#T_b5f3c_row0_col7,#T_b5f3c_row0_col8,#T_b5f3c_row1_col2{\n            text-decoration:  underline;\n             font-style:  italic;\n             font-weight:  bold;\n        }</style><table id=\"T_b5f3c_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >variable</th>        <th class=\"col_heading level0 col1\" >n</th>        <th class=\"col_heading level0 col2\" >bias</th>        <th class=\"col_heading level0 col3\" >rmse</th>        <th class=\"col_heading level0 col4\" >urmse</th>        <th class=\"col_heading level0 col5\" >mae</th>        <th class=\"col_heading level0 col6\" >cc</th>        <th class=\"col_heading level0 col7\" >si</th>        <th class=\"col_heading level0 col8\" >r2</th>    </tr>    <tr>        <th class=\"index_name level0\" >model</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n                <tr>\n                        <th id=\"T_b5f3c_level0_row0\" class=\"row_heading level0 row0\" >SW_1</th>\n                        <td id=\"T_b5f3c_row0_col0\" class=\"data row0 col0\" >Significant_wave_height</td>\n                        <td id=\"T_b5f3c_row0_col1\" class=\"data row0 col1\" >564</td>\n                        <td id=\"T_b5f3c_row0_col2\" class=\"data row0 col2\" >-0.091</td>\n                        <td id=\"T_b5f3c_row0_col3\" class=\"data row0 col3\" >0.307</td>\n                        <td id=\"T_b5f3c_row0_col4\" class=\"data row0 col4\" >0.283</td>\n                        <td id=\"T_b5f3c_row0_col5\" class=\"data row0 col5\" >0.243</td>\n                        <td id=\"T_b5f3c_row0_col6\" class=\"data row0 col6\" >0.973</td>\n                        <td id=\"T_b5f3c_row0_col7\" class=\"data row0 col7\" >0.094</td>\n                        <td id=\"T_b5f3c_row0_col8\" class=\"data row0 col8\" >0.913</td>\n            </tr>\n            <tr>\n                        <th id=\"T_b5f3c_level0_row1\" class=\"row_heading level0 row1\" >SW_2</th>\n                        <td id=\"T_b5f3c_row1_col0\" class=\"data row1 col0\" >Significant_wave_height</td>\n                        <td id=\"T_b5f3c_row1_col1\" class=\"data row1 col1\" >564</td>\n                        <td id=\"T_b5f3c_row1_col2\" class=\"data row1 col2\" >-0.009</td>\n                        <td id=\"T_b5f3c_row1_col3\" class=\"data row1 col3\" >0.317</td>\n                        <td id=\"T_b5f3c_row1_col4\" class=\"data row1 col4\" >0.308</td>\n                        <td id=\"T_b5f3c_row1_col5\" class=\"data row1 col5\" >0.255</td>\n                        <td id=\"T_b5f3c_row1_col6\" class=\"data row1 col6\" >0.973</td>\n                        <td id=\"T_b5f3c_row1_col7\" class=\"data row1 col7\" >0.103</td>\n                        <td id=\"T_b5f3c_row1_col8\" class=\"data row1 col8\" >0.904</td>\n            </tr>\n    </tbody></table>"
     },
     "metadata": {},
     "execution_count": 21
    }
   ],
   "source": [
    "cc.mean_skill(variable='Significant_wave_height').style(columns=[])"
   ]
  },
  {
   "source": [
    "## score"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'SW_1': 0.9349438989181208, 'SW_2': 0.9397895582539258}"
      ]
     },
     "metadata": {},
     "execution_count": 22
    }
   ],
   "source": [
    "cc.score()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.9349438989181208"
      ]
     },
     "metadata": {},
     "execution_count": 23
    }
   ],
   "source": [
    "cc.score(model='SW_1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'SW_1': 1.5630757331111964, 'SW_2': 1.5630757331111964}"
      ]
     },
     "metadata": {},
     "execution_count": 24
    }
   ],
   "source": [
    "cc.score(variable='Wind_speed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}