{
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
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 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "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, PointObservation, TrackObservation, Connector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "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": [
    "## Connect model and observations and extract\n",
    "We connect 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": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<Connector> with \n",
       " -<PointConnector> obs=HKNA_Hm0(n=564) :: 2 models=[SW_1, SW_2]\n",
       " -<PointConnector> obs=EPL_Hm0(n=95) :: 2 models=[SW_1, SW_2]\n",
       " -<TrackConnector> obs=c2_Hm0(n=298) :: 2 models=[SW_1, SW_2]\n",
       " -<PointConnector> obs=HKNA_wind(n=1484) :: 2 models=[SW_1, SW_2]\n",
       " -<PointConnector> obs=F16_wind(n=312) :: 2 models=[SW_1, SW_2]\n",
       " -<TrackConnector> obs=c2_wind(n=298) :: 2 models=[SW_1, SW_2]"
      ]
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "con = Connector()\n",
    "con.add([o1, o2, o3], [mr1['Sign. Wave Height'], mr2['Sign. Wave Height']])\n",
    "con.add([wind1, wind2, wind3], [mr1['Wind speed'], mr2['Wind speed']])\n",
    "con"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc = con.extract()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "cc.n_variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "['Significant_wave_height', 'Wind_speed']"
      ]
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "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": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                                             n   bias   rmse  urmse    mae  \\\n",
       "model observation variable                                                   \n",
       "SW_1  EPL_Hm0     Significant_wave_height   67 -0.067  0.224  0.213  0.189   \n",
       "      F16_wind    Wind_speed                67  2.102  2.775  1.812  2.197   \n",
       "      HKNA_Hm0    Significant_wave_height  386 -0.194  0.352  0.293  0.252   \n",
       "      HKNA_wind   Wind_speed               277 -0.881  1.276  0.923  1.023   \n",
       "      c2_Hm0      Significant_wave_height  113 -0.001  0.352  0.352  0.295   \n",
       "      c2_wind     Wind_speed               113  0.409  0.638  0.490  0.506   \n",
       "SW_2  EPL_Hm0     Significant_wave_height   67 -0.000  0.232  0.232  0.198   \n",
       "      F16_wind    Wind_speed                67  2.102  2.775  1.812  2.197   \n",
       "      HKNA_Hm0    Significant_wave_height  386 -0.100  0.293  0.275  0.214   \n",
       "      HKNA_wind   Wind_speed               277 -0.881  1.276  0.923  1.023   \n",
       "      c2_Hm0      Significant_wave_height  113  0.081  0.430  0.422  0.357   \n",
       "      c2_wind     Wind_speed               113  0.409  0.638  0.490  0.506   \n",
       "\n",
       "                                              cc     si     r2  \n",
       "model observation variable                                      \n",
       "SW_1  EPL_Hm0     Significant_wave_height  0.970  0.078  0.933  \n",
       "      F16_wind    Wind_speed               0.825  0.137  0.230  \n",
       "      HKNA_Hm0    Significant_wave_height  0.971  0.089  0.905  \n",
       "      HKNA_wind   Wind_speed               0.963  0.065  0.861  \n",
       "      c2_Hm0      Significant_wave_height  0.974  0.119  0.900  \n",
       "      c2_wind     Wind_speed               0.960  0.050  0.867  \n",
       "SW_2  EPL_Hm0     Significant_wave_height  0.970  0.085  0.927  \n",
       "      F16_wind    Wind_speed               0.825  0.137  0.230  \n",
       "      HKNA_Hm0    Significant_wave_height  0.971  0.083  0.934  \n",
       "      HKNA_wind   Wind_speed               0.963  0.065  0.861  \n",
       "      c2_Hm0      Significant_wave_height  0.974  0.142  0.850  \n",
       "      c2_wind     Wind_speed               0.960  0.050  0.867  "
      ],
      "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>67</td>\n      <td>-0.067</td>\n      <td>0.224</td>\n      <td>0.213</td>\n      <td>0.189</td>\n      <td>0.970</td>\n      <td>0.078</td>\n      <td>0.933</td>\n    </tr>\n    <tr>\n      <th>F16_wind</th>\n      <th>Wind_speed</th>\n      <td>67</td>\n      <td>2.102</td>\n      <td>2.775</td>\n      <td>1.812</td>\n      <td>2.197</td>\n      <td>0.825</td>\n      <td>0.137</td>\n      <td>0.230</td>\n    </tr>\n    <tr>\n      <th>HKNA_Hm0</th>\n      <th>Significant_wave_height</th>\n      <td>386</td>\n      <td>-0.194</td>\n      <td>0.352</td>\n      <td>0.293</td>\n      <td>0.252</td>\n      <td>0.971</td>\n      <td>0.089</td>\n      <td>0.905</td>\n    </tr>\n    <tr>\n      <th>HKNA_wind</th>\n      <th>Wind_speed</th>\n      <td>277</td>\n      <td>-0.881</td>\n      <td>1.276</td>\n      <td>0.923</td>\n      <td>1.023</td>\n      <td>0.963</td>\n      <td>0.065</td>\n      <td>0.861</td>\n    </tr>\n    <tr>\n      <th>c2_Hm0</th>\n      <th>Significant_wave_height</th>\n      <td>113</td>\n      <td>-0.001</td>\n      <td>0.352</td>\n      <td>0.352</td>\n      <td>0.295</td>\n      <td>0.974</td>\n      <td>0.119</td>\n      <td>0.900</td>\n    </tr>\n    <tr>\n      <th>c2_wind</th>\n      <th>Wind_speed</th>\n      <td>113</td>\n      <td>0.409</td>\n      <td>0.638</td>\n      <td>0.490</td>\n      <td>0.506</td>\n      <td>0.960</td>\n      <td>0.050</td>\n      <td>0.867</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>67</td>\n      <td>-0.000</td>\n      <td>0.232</td>\n      <td>0.232</td>\n      <td>0.198</td>\n      <td>0.970</td>\n      <td>0.085</td>\n      <td>0.927</td>\n    </tr>\n    <tr>\n      <th>F16_wind</th>\n      <th>Wind_speed</th>\n      <td>67</td>\n      <td>2.102</td>\n      <td>2.775</td>\n      <td>1.812</td>\n      <td>2.197</td>\n      <td>0.825</td>\n      <td>0.137</td>\n      <td>0.230</td>\n    </tr>\n    <tr>\n      <th>HKNA_Hm0</th>\n      <th>Significant_wave_height</th>\n      <td>386</td>\n      <td>-0.100</td>\n      <td>0.293</td>\n      <td>0.275</td>\n      <td>0.214</td>\n      <td>0.971</td>\n      <td>0.083</td>\n      <td>0.934</td>\n    </tr>\n    <tr>\n      <th>HKNA_wind</th>\n      <th>Wind_speed</th>\n      <td>277</td>\n      <td>-0.881</td>\n      <td>1.276</td>\n      <td>0.923</td>\n      <td>1.023</td>\n      <td>0.963</td>\n      <td>0.065</td>\n      <td>0.861</td>\n    </tr>\n    <tr>\n      <th>c2_Hm0</th>\n      <th>Significant_wave_height</th>\n      <td>113</td>\n      <td>0.081</td>\n      <td>0.430</td>\n      <td>0.422</td>\n      <td>0.357</td>\n      <td>0.974</td>\n      <td>0.142</td>\n      <td>0.850</td>\n    </tr>\n    <tr>\n      <th>c2_wind</th>\n      <th>Wind_speed</th>\n      <td>113</td>\n      <td>0.409</td>\n      <td>0.638</td>\n      <td>0.490</td>\n      <td>0.506</td>\n      <td>0.960</td>\n      <td>0.050</td>\n      <td>0.867</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "s = cc.skill()\n",
    "s.round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "FrozenList(['model', 'observation', 'variable'])"
      ]
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "source": [
    "s.index.names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
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      "text/html": "<style  type=\"text/css\" >\n#T_b5c63_row0_col2{\n            background-color:  #a9c6fd;\n            color:  #000000;\n        }#T_b5c63_row0_col3,#T_b5c63_row0_col4,#T_b5c63_row0_col5,#T_b5c63_row0_col7,#T_b5c63_row2_col6,#T_b5c63_row4_col8{\n            background-color:  #fff7ec;\n            color:  #000000;\n            text-decoration:  underline;\n             font-style:  italic;\n             font-weight:  bold;\n        }#T_b5c63_row0_col6,#T_b5c63_row3_col6,#T_b5c63_row5_col3,#T_b5c63_row5_col4,#T_b5c63_row5_col5,#T_b5c63_row5_col7,#T_b5c63_row5_col8{\n            background-color:  #7f0000;\n            color:  #f1f1f1;\n        }#T_b5c63_row0_col8{\n            background-color:  #fff5e6;\n            color:  #000000;\n        }#T_b5c63_row1_col2{\n            background-color:  #3b4cc0;\n            color:  #f1f1f1;\n        }#T_b5c63_row1_col3{\n            background-color:  #ef6548;\n            color:  #000000;\n        }#T_b5c63_row1_col4{\n     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color:  #000000;\n        }#T_b5c63_row4_col4{\n            background-color:  #fdcb95;\n            color:  #000000;\n        }#T_b5c63_row4_col5{\n            background-color:  #fee4bf;\n            color:  #000000;\n        }#T_b5c63_row4_col7{\n            background-color:  #feeed7;\n            color:  #000000;\n        }#T_b5c63_row5_col2{\n            background-color:  #f7a98b;\n            color:  #000000;\n        }#T_b5c63_row5_col6{\n            background-color:  #fff7ec;\n            color:  #000000;\n        }</style><table id=\"T_b5c63_\" ><thead>    <tr>        <th class=\"blank\" ></th>        <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=\"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_b5c63_level0_row0\" class=\"row_heading level0 row0\" rowspan=\"3\">SW_1</th>\n                        <th id=\"T_b5c63_level1_row0\" class=\"row_heading level1 row0\" >EPL_Hm0</th>\n                        <td id=\"T_b5c63_row0_col0\" class=\"data row0 col0\" >Significant_wave_height</td>\n                        <td id=\"T_b5c63_row0_col1\" class=\"data row0 col1\" >67</td>\n                        <td id=\"T_b5c63_row0_col2\" class=\"data row0 col2\" >-0.067</td>\n                        <td id=\"T_b5c63_row0_col3\" class=\"data row0 col3\" >0.224</td>\n                        <td id=\"T_b5c63_row0_col4\" class=\"data row0 col4\" >0.213</td>\n                        <td id=\"T_b5c63_row0_col5\" class=\"data row0 col5\" >0.189</td>\n                        <td id=\"T_b5c63_row0_col6\" class=\"data row0 col6\" >0.970</td>\n                        <td id=\"T_b5c63_row0_col7\" class=\"data row0 col7\" >0.078</td>\n                        <td id=\"T_b5c63_row0_col8\" class=\"data row0 col8\" >0.933</td>\n            </tr>\n            <tr>\n                                <th id=\"T_b5c63_level1_row1\" class=\"row_heading level1 row1\" >HKNA_Hm0</th>\n                        <td id=\"T_b5c63_row1_col0\" class=\"data row1 col0\" >Significant_wave_height</td>\n                        <td id=\"T_b5c63_row1_col1\" class=\"data row1 col1\" >386</td>\n                        <td id=\"T_b5c63_row1_col2\" class=\"data row1 col2\" >-0.194</td>\n                        <td id=\"T_b5c63_row1_col3\" class=\"data row1 col3\" >0.352</td>\n                        <td id=\"T_b5c63_row1_col4\" class=\"data row1 col4\" >0.293</td>\n                        <td id=\"T_b5c63_row1_col5\" class=\"data row1 col5\" >0.252</td>\n                        <td id=\"T_b5c63_row1_col6\" class=\"data row1 col6\" >0.971</td>\n                        <td id=\"T_b5c63_row1_col7\" class=\"data row1 col7\" >0.089</td>\n                        <td id=\"T_b5c63_row1_col8\" class=\"data row1 col8\" >0.905</td>\n            </tr>\n            <tr>\n                                <th id=\"T_b5c63_level1_row2\" class=\"row_heading level1 row2\" >c2_Hm0</th>\n                        <td id=\"T_b5c63_row2_col0\" class=\"data row2 col0\" >Significant_wave_height</td>\n                        <td id=\"T_b5c63_row2_col1\" class=\"data row2 col1\" >113</td>\n                        <td id=\"T_b5c63_row2_col2\" class=\"data row2 col2\" >-0.001</td>\n                        <td id=\"T_b5c63_row2_col3\" class=\"data row2 col3\" >0.352</td>\n                        <td id=\"T_b5c63_row2_col4\" class=\"data row2 col4\" >0.352</td>\n                        <td id=\"T_b5c63_row2_col5\" class=\"data row2 col5\" >0.295</td>\n                        <td id=\"T_b5c63_row2_col6\" class=\"data row2 col6\" >0.974</td>\n                        <td id=\"T_b5c63_row2_col7\" class=\"data row2 col7\" >0.119</td>\n                        <td id=\"T_b5c63_row2_col8\" class=\"data row2 col8\" >0.900</td>\n            </tr>\n            <tr>\n                        <th id=\"T_b5c63_level0_row3\" class=\"row_heading level0 row3\" rowspan=\"3\">SW_2</th>\n                        <th id=\"T_b5c63_level1_row3\" class=\"row_heading level1 row3\" >EPL_Hm0</th>\n                        <td id=\"T_b5c63_row3_col0\" class=\"data row3 col0\" >Significant_wave_height</td>\n                        <td id=\"T_b5c63_row3_col1\" class=\"data row3 col1\" >67</td>\n                        <td id=\"T_b5c63_row3_col2\" class=\"data row3 col2\" >-0.000</td>\n                        <td id=\"T_b5c63_row3_col3\" class=\"data row3 col3\" >0.232</td>\n                        <td id=\"T_b5c63_row3_col4\" class=\"data row3 col4\" >0.232</td>\n                        <td id=\"T_b5c63_row3_col5\" class=\"data row3 col5\" >0.198</td>\n                        <td id=\"T_b5c63_row3_col6\" class=\"data row3 col6\" >0.970</td>\n                        <td id=\"T_b5c63_row3_col7\" class=\"data row3 col7\" >0.085</td>\n                        <td id=\"T_b5c63_row3_col8\" class=\"data row3 col8\" >0.927</td>\n            </tr>\n            <tr>\n                                <th id=\"T_b5c63_level1_row4\" class=\"row_heading level1 row4\" >HKNA_Hm0</th>\n                        <td id=\"T_b5c63_row4_col0\" class=\"data row4 col0\" >Significant_wave_height</td>\n                        <td id=\"T_b5c63_row4_col1\" class=\"data row4 col1\" >386</td>\n                        <td id=\"T_b5c63_row4_col2\" class=\"data row4 col2\" >-0.100</td>\n                        <td id=\"T_b5c63_row4_col3\" class=\"data row4 col3\" >0.293</td>\n                        <td id=\"T_b5c63_row4_col4\" class=\"data row4 col4\" >0.275</td>\n                        <td id=\"T_b5c63_row4_col5\" class=\"data row4 col5\" >0.214</td>\n                        <td id=\"T_b5c63_row4_col6\" class=\"data row4 col6\" >0.971</td>\n                        <td id=\"T_b5c63_row4_col7\" class=\"data row4 col7\" >0.083</td>\n                        <td id=\"T_b5c63_row4_col8\" class=\"data row4 col8\" >0.934</td>\n            </tr>\n            <tr>\n                                <th id=\"T_b5c63_level1_row5\" class=\"row_heading level1 row5\" >c2_Hm0</th>\n                        <td id=\"T_b5c63_row5_col0\" class=\"data row5 col0\" >Significant_wave_height</td>\n                        <td id=\"T_b5c63_row5_col1\" class=\"data row5 col1\" >113</td>\n                        <td id=\"T_b5c63_row5_col2\" class=\"data row5 col2\" >0.081</td>\n                        <td id=\"T_b5c63_row5_col3\" class=\"data row5 col3\" >0.430</td>\n                        <td id=\"T_b5c63_row5_col4\" class=\"data row5 col4\" >0.422</td>\n                        <td id=\"T_b5c63_row5_col5\" class=\"data row5 col5\" >0.357</td>\n                        <td id=\"T_b5c63_row5_col6\" class=\"data row5 col6\" >0.974</td>\n                        <td id=\"T_b5c63_row5_col7\" class=\"data row5 col7\" >0.142</td>\n                        <td id=\"T_b5c63_row5_col8\" class=\"data row5 col8\" >0.850</td>\n            </tr>\n    </tbody></table>"
     },
     "metadata": {},
     "execution_count": 13
    }
   ],
   "source": [
    "s = cc.skill(variable='Significant_wave_height')\n",
    "s.style()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2173bd089d0>"
      ],
      "text/html": "<style  type=\"text/css\" >\n#T_41957_row0_col3,#T_41957_row0_col5,#T_41957_row0_col6,#T_41957_row0_col7,#T_41957_row0_col8,#T_41957_row0_col9{\n            text-decoration:  underline;\n             font-style:  italic;\n             font-weight:  bold;\n        }#T_41957_row0_col4{\n            background-color:  #fff7ec;\n            color:  #000000;\n            text-decoration:  underline;\n             font-style:  italic;\n             font-weight:  bold;\n        }#T_41957_row1_col4{\n            background-color:  #7f0000;\n            color:  #f1f1f1;\n        }</style><table id=\"T_41957_\" ><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_41957_level0_row0\" class=\"row_heading level0 row0\" >SW_1</th>\n                        <td id=\"T_41957_row0_col0\" class=\"data row0 col0\" >c2_Hm0</td>\n                        <td id=\"T_41957_row0_col1\" class=\"data row0 col1\" >Significant_wave_height</td>\n                        <td id=\"T_41957_row0_col2\" class=\"data row0 col2\" >113</td>\n                        <td id=\"T_41957_row0_col3\" class=\"data row0 col3\" >-0.001</td>\n                        <td id=\"T_41957_row0_col4\" class=\"data row0 col4\" >0.352</td>\n                        <td id=\"T_41957_row0_col5\" class=\"data row0 col5\" >0.352</td>\n                        <td id=\"T_41957_row0_col6\" class=\"data row0 col6\" >0.295</td>\n                        <td id=\"T_41957_row0_col7\" class=\"data row0 col7\" >0.974</td>\n                        <td id=\"T_41957_row0_col8\" class=\"data row0 col8\" >0.119</td>\n                        <td id=\"T_41957_row0_col9\" class=\"data row0 col9\" >0.900</td>\n            </tr>\n            <tr>\n                        <th id=\"T_41957_level0_row1\" class=\"row_heading level0 row1\" >SW_2</th>\n                        <td id=\"T_41957_row1_col0\" class=\"data row1 col0\" >c2_Hm0</td>\n                        <td id=\"T_41957_row1_col1\" class=\"data row1 col1\" >Significant_wave_height</td>\n                        <td id=\"T_41957_row1_col2\" class=\"data row1 col2\" >113</td>\n                        <td id=\"T_41957_row1_col3\" class=\"data row1 col3\" >0.081</td>\n                        <td id=\"T_41957_row1_col4\" class=\"data row1 col4\" >0.430</td>\n                        <td id=\"T_41957_row1_col5\" class=\"data row1 col5\" >0.422</td>\n                        <td id=\"T_41957_row1_col6\" class=\"data row1 col6\" >0.357</td>\n                        <td id=\"T_41957_row1_col7\" class=\"data row1 col7\" >0.974</td>\n                        <td id=\"T_41957_row1_col8\" class=\"data row1 col8\" >0.142</td>\n                        <td id=\"T_41957_row1_col9\" class=\"data row1 col9\" >0.850</td>\n            </tr>\n    </tbody></table>"
     },
     "metadata": {},
     "execution_count": 14
    }
   ],
   "source": [
    "s.sel(observation='c2_Hm0').style(columns='rmse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": 16,
   "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": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                                 n      bias      rmse     urmse       mae  \\\n",
       "model variable                                                               \n",
       "SW_1  Significant_wave_height  566 -0.087356  0.309119  0.286247  0.244979   \n",
       "      Wind_speed               457  0.543226  1.563076  1.075053  1.241847   \n",
       "SW_2  Significant_wave_height  566 -0.006398  0.318593  0.310086  0.256618   \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  0.971791  0.095158  0.912468  \n",
       "      Wind_speed               0.915916  0.083769  0.652388  \n",
       "SW_2  Significant_wave_height  0.971791  0.103590  0.903722  \n",
       "      Wind_speed               0.915916  0.083769  0.652388  "
      ],
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     },
     "metadata": {},
     "execution_count": 17
    }
   ],
   "source": [
    "s = cc.mean_skill()\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "s.plot_bar('si', title='scatter index');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                        model    n      bias      rmse     urmse       mae  \\\n",
       "variable                                                                     \n",
       "Significant_wave_height  SW_2  566 -0.006398  0.318593  0.310086  0.256618   \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  0.971791  0.103590  0.903722  \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>566</td>\n      <td>-0.006398</td>\n      <td>0.318593</td>\n      <td>0.310086</td>\n      <td>0.256618</td>\n      <td>0.971791</td>\n      <td>0.103590</td>\n      <td>0.903722</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": 19
    }
   ],
   "source": [
    "cc.mean_skill(model='SW_2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2173c9d0100>"
      ],
      "text/html": "<style  type=\"text/css\" >\n#T_9fab3_row0_col3,#T_9fab3_row0_col4,#T_9fab3_row0_col5,#T_9fab3_row0_col6,#T_9fab3_row0_col7,#T_9fab3_row0_col8,#T_9fab3_row1_col2{\n            text-decoration:  underline;\n             font-style:  italic;\n             font-weight:  bold;\n        }</style><table id=\"T_9fab3_\" ><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_9fab3_level0_row0\" class=\"row_heading level0 row0\" >SW_1</th>\n                        <td id=\"T_9fab3_row0_col0\" class=\"data row0 col0\" >Significant_wave_height</td>\n                        <td id=\"T_9fab3_row0_col1\" class=\"data row0 col1\" >566</td>\n                        <td id=\"T_9fab3_row0_col2\" class=\"data row0 col2\" >-0.087</td>\n                        <td id=\"T_9fab3_row0_col3\" class=\"data row0 col3\" >0.309</td>\n                        <td id=\"T_9fab3_row0_col4\" class=\"data row0 col4\" >0.286</td>\n                        <td id=\"T_9fab3_row0_col5\" class=\"data row0 col5\" >0.245</td>\n                        <td id=\"T_9fab3_row0_col6\" class=\"data row0 col6\" >0.972</td>\n                        <td id=\"T_9fab3_row0_col7\" class=\"data row0 col7\" >0.095</td>\n                        <td id=\"T_9fab3_row0_col8\" class=\"data row0 col8\" >0.912</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9fab3_level0_row1\" class=\"row_heading level0 row1\" >SW_2</th>\n                        <td id=\"T_9fab3_row1_col0\" class=\"data row1 col0\" >Significant_wave_height</td>\n                        <td id=\"T_9fab3_row1_col1\" class=\"data row1 col1\" >566</td>\n                        <td id=\"T_9fab3_row1_col2\" class=\"data row1 col2\" >-0.006</td>\n                        <td id=\"T_9fab3_row1_col3\" class=\"data row1 col3\" >0.319</td>\n                        <td id=\"T_9fab3_row1_col4\" class=\"data row1 col4\" >0.310</td>\n                        <td id=\"T_9fab3_row1_col5\" class=\"data row1 col5\" >0.257</td>\n                        <td id=\"T_9fab3_row1_col6\" class=\"data row1 col6\" >0.972</td>\n                        <td id=\"T_9fab3_row1_col7\" class=\"data row1 col7\" >0.104</td>\n                        <td id=\"T_9fab3_row1_col8\" class=\"data row1 col8\" >0.904</td>\n            </tr>\n    </tbody></table>"
     },
     "metadata": {},
     "execution_count": 20
    }
   ],
   "source": [
    "cc.mean_skill(variable='Significant_wave_height').style(columns=[])"
   ]
  },
  {
   "source": [
    "## score"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'SW_1': 0.9360973364582041, 'SW_2': 0.9408344074670156}"
      ]
     },
     "metadata": {},
     "execution_count": 21
    }
   ],
   "source": [
    "cc.score()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.9360973364582041"
      ]
     },
     "metadata": {},
     "execution_count": 22
    }
   ],
   "source": [
    "cc.score(model='SW_1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'SW_1': 1.563075733625679, 'SW_2': 1.563075733625679}"
      ]
     },
     "metadata": {},
     "execution_count": 23
    }
   ],
   "source": [
    "cc.score(variable='Wind_speed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}