{
    "cells": [
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# Combine comparers\n",
                "ModelSkill comparers can be combined using `merge`."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "metadata": {},
            "outputs": [],
            "source": [
                "import modelskill as ms"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Observations"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "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\")\n",
                "o3 = ms.TrackObservation(\"../tests/testdata/SW/Alti_c2_Dutch.dfs0\", item=3, name=\"c2\")"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Model Results"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "<GridModelResult>: CMEMS\n",
                            "Time: 2017-10-28 00:00:00 - 2017-10-29 18:00:00\n",
                            "Quantity: Spectral significant wave height (Hm0) [m]"
                        ]
                    },
                    "execution_count": 3,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "fn = \"../tests/testdata/SW/CMEMS_DutchCoast_*.nc\"\n",
                "mr1 = ms.model_result(fn, name='CMEMS', item='VHM0')\n",
                "mr1"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "metadata": {},
            "outputs": [],
            "source": [
                "mr2 = ms.model_result('../tests/testdata/SW/HKZN_local_2017_DutchCoast.dfsu', name='MIKE21SW', item=0)"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Match\n",
                "Notice that the two ModelResults doesn't cover the exact same period."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "<Axes: >"
                        ]
                    },
                    "execution_count": 5,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 700x225 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "ms.plotting.temporal_coverage(obs=[o1,o2,o3], mod=[mr1,mr2], limit_to_model_period=False)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "metadata": {},
            "outputs": [],
            "source": [
                "cc1 = ms.match(obs=[o1,o2,o3], mod=mr1)#, name='SW')\n",
                "cc2 = ms.match(obs=[o1,o2,o3], mod=mr2)# , name='SW')"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "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>HKNA</th>\n",
                            "      <td>242</td>\n",
                            "      <td>-0.724389</td>\n",
                            "      <td>0.864553</td>\n",
                            "      <td>0.471924</td>\n",
                            "      <td>0.724429</td>\n",
                            "      <td>0.906446</td>\n",
                            "      <td>0.126675</td>\n",
                            "      <td>0.251523</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>EPL</th>\n",
                            "      <td>43</td>\n",
                            "      <td>-0.433137</td>\n",
                            "      <td>0.511635</td>\n",
                            "      <td>0.272329</td>\n",
                            "      <td>0.435782</td>\n",
                            "      <td>0.920654</td>\n",
                            "      <td>0.089302</td>\n",
                            "      <td>0.460214</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>c2</th>\n",
                            "      <td>39</td>\n",
                            "      <td>-0.302117</td>\n",
                            "      <td>0.472280</td>\n",
                            "      <td>0.363006</td>\n",
                            "      <td>0.397694</td>\n",
                            "      <td>0.935287</td>\n",
                            "      <td>0.091603</td>\n",
                            "      <td>0.745605</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "               n      bias      rmse     urmse       mae        cc        si  \\\n",
                            "observation                                                                    \n",
                            "HKNA         242 -0.724389  0.864553  0.471924  0.724429  0.906446  0.126675   \n",
                            "EPL           43 -0.433137  0.511635  0.272329  0.435782  0.920654  0.089302   \n",
                            "c2            39 -0.302117  0.472280  0.363006  0.397694  0.935287  0.091603   \n",
                            "\n",
                            "                   r2  \n",
                            "observation            \n",
                            "HKNA         0.251523  \n",
                            "EPL          0.460214  \n",
                            "c2           0.745605  "
                        ]
                    },
                    "execution_count": 7,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "cc1.skill()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "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>HKNA</th>\n",
                            "      <td>386</td>\n",
                            "      <td>-0.202413</td>\n",
                            "      <td>0.355195</td>\n",
                            "      <td>0.291877</td>\n",
                            "      <td>0.255866</td>\n",
                            "      <td>0.971708</td>\n",
                            "      <td>0.093967</td>\n",
                            "      <td>0.903554</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>EPL</th>\n",
                            "      <td>67</td>\n",
                            "      <td>-0.071238</td>\n",
                            "      <td>0.224923</td>\n",
                            "      <td>0.213344</td>\n",
                            "      <td>0.189455</td>\n",
                            "      <td>0.969760</td>\n",
                            "      <td>0.082482</td>\n",
                            "      <td>0.931793</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>c2</th>\n",
                            "      <td>113</td>\n",
                            "      <td>-0.004701</td>\n",
                            "      <td>0.352470</td>\n",
                            "      <td>0.352439</td>\n",
                            "      <td>0.294758</td>\n",
                            "      <td>0.975050</td>\n",
                            "      <td>0.128010</td>\n",
                            "      <td>0.899121</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "               n      bias      rmse     urmse       mae        cc        si  \\\n",
                            "observation                                                                    \n",
                            "HKNA         386 -0.202413  0.355195  0.291877  0.255866  0.971708  0.093967   \n",
                            "EPL           67 -0.071238  0.224923  0.213344  0.189455  0.969760  0.082482   \n",
                            "c2           113 -0.004701  0.352470  0.352439  0.294758  0.975050  0.128010   \n",
                            "\n",
                            "                   r2  \n",
                            "observation            \n",
                            "HKNA         0.903554  \n",
                            "EPL          0.931793  \n",
                            "c2           0.899121  "
                        ]
                    },
                    "execution_count": 8,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "cc2.skill()"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Merge the two Comparers"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "metadata": {},
            "outputs": [],
            "source": [
                "cc = cc1.merge(cc2)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 10,
            "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></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></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=\"3\" valign=\"top\">CMEMS</th>\n",
                            "      <th>HKNA</th>\n",
                            "      <td>242</td>\n",
                            "      <td>-0.724389</td>\n",
                            "      <td>0.864553</td>\n",
                            "      <td>0.471924</td>\n",
                            "      <td>0.724429</td>\n",
                            "      <td>0.906446</td>\n",
                            "      <td>0.126675</td>\n",
                            "      <td>0.251523</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>EPL</th>\n",
                            "      <td>43</td>\n",
                            "      <td>-0.433137</td>\n",
                            "      <td>0.511635</td>\n",
                            "      <td>0.272329</td>\n",
                            "      <td>0.435782</td>\n",
                            "      <td>0.920654</td>\n",
                            "      <td>0.089302</td>\n",
                            "      <td>0.460214</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>c2</th>\n",
                            "      <td>39</td>\n",
                            "      <td>-0.302117</td>\n",
                            "      <td>0.472280</td>\n",
                            "      <td>0.363006</td>\n",
                            "      <td>0.397694</td>\n",
                            "      <td>0.935287</td>\n",
                            "      <td>0.091603</td>\n",
                            "      <td>0.745605</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th rowspan=\"3\" valign=\"top\">MIKE21SW</th>\n",
                            "      <th>HKNA</th>\n",
                            "      <td>242</td>\n",
                            "      <td>-0.239775</td>\n",
                            "      <td>0.414822</td>\n",
                            "      <td>0.338504</td>\n",
                            "      <td>0.300389</td>\n",
                            "      <td>0.949700</td>\n",
                            "      <td>0.090862</td>\n",
                            "      <td>0.827687</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>EPL</th>\n",
                            "      <td>43</td>\n",
                            "      <td>-0.084324</td>\n",
                            "      <td>0.207253</td>\n",
                            "      <td>0.189323</td>\n",
                            "      <td>0.175296</td>\n",
                            "      <td>0.972958</td>\n",
                            "      <td>0.062083</td>\n",
                            "      <td>0.911427</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>c2</th>\n",
                            "      <td>39</td>\n",
                            "      <td>0.319975</td>\n",
                            "      <td>0.401442</td>\n",
                            "      <td>0.242429</td>\n",
                            "      <td>0.350164</td>\n",
                            "      <td>0.966030</td>\n",
                            "      <td>0.061176</td>\n",
                            "      <td>0.816197</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "                        n      bias      rmse     urmse       mae        cc  \\\n",
                            "model    observation                                                          \n",
                            "CMEMS    HKNA         242 -0.724389  0.864553  0.471924  0.724429  0.906446   \n",
                            "         EPL           43 -0.433137  0.511635  0.272329  0.435782  0.920654   \n",
                            "         c2            39 -0.302117  0.472280  0.363006  0.397694  0.935287   \n",
                            "MIKE21SW HKNA         242 -0.239775  0.414822  0.338504  0.300389  0.949700   \n",
                            "         EPL           43 -0.084324  0.207253  0.189323  0.175296  0.972958   \n",
                            "         c2            39  0.319975  0.401442  0.242429  0.350164  0.966030   \n",
                            "\n",
                            "                            si        r2  \n",
                            "model    observation                      \n",
                            "CMEMS    HKNA         0.126675  0.251523  \n",
                            "         EPL          0.089302  0.460214  \n",
                            "         c2           0.091603  0.745605  \n",
                            "MIKE21SW HKNA         0.090862  0.827687  \n",
                            "         EPL          0.062083  0.911427  \n",
                            "         c2           0.061176  0.816197  "
                        ]
                    },
                    "execution_count": 10,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "cc.skill()"
            ]
        }
    ],
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