{
    "cells": [
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# Metocean gridded spatial skill\n",
                "\n",
                "Demonstrate plotting functionality etc by the SpatialSkill class"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "metadata": {},
            "outputs": [],
            "source": [
                "import pandas as pd\n",
                "import modelskill as ms"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "metadata": {},
            "outputs": [
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": [
                        "C:\\Users\\jem\\Source\\modelskill\\modelskill\\timeseries\\_track.py:134: UserWarning: Removed 22 duplicate timestamps with keep=first\n",
                        "  warnings.warn(\n"
                    ]
                }
            ],
            "source": [
                "fn = '../tests/testdata/NorthSeaHD_and_windspeed.dfsu'\n",
                "mr = ms.model_result(fn, name='HD', item=0)\n",
                "fn = '../tests/testdata/altimetry_NorthSea_20171027.csv'\n",
                "df = pd.read_csv(fn, index_col=0, parse_dates=True)\n",
                "o1 = ms.TrackObservation(df, item=2, name='alti')\n",
                "cc = ms.match(o1, mr)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "metadata": {},
            "outputs": [],
            "source": [
                "ss = cc.gridded_skill()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "metadata": {},
            "outputs": [
                {
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                            "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
                            "Dimensions:      (y: 5, x: 5)\n",
                            "Coordinates:\n",
                            "  * y            (y) float64 50.6 51.66 52.7 53.75 54.8\n",
                            "  * x            (x) float64 -0.436 1.543 3.517 5.492 7.466\n",
                            "    observation  &lt;U4 &#x27;alti&#x27;\n",
                            "Data variables:\n",
                            "    n            (y, x) int32 3 17 0 0 0 0 49 9 0 ... 70 33 15 27 37 79 0 20 75\n",
                            "    bias         (y, x) float64 -0.02626 -0.188 nan nan ... nan -0.2475 -0.1156\n",
                            "    rmse         (y, x) float64 0.03291 0.2105 nan nan ... nan 0.2635 0.1475\n",
                            "    urmse        (y, x) float64 0.01984 0.09468 nan nan ... nan 0.09066 0.09174\n",
                            "    mae          (y, x) float64 0.02626 0.1923 nan nan ... nan 0.2475 0.1217\n",
                            "    cc           (y, x) float64 0.9973 0.996 nan nan ... nan 0.6293 0.9842\n",
                            "    si           (y, x) float64 0.04669 0.08355 nan nan ... nan 0.0844 0.1475\n",
                            "    r2           (y, x) float64 -1.061 0.9003 nan nan ... nan -18.78 0.9078</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-c3ef2758-7aea-4428-80f8-42c9694bfff9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c3ef2758-7aea-4428-80f8-42c9694bfff9' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 5</li><li><span class='xr-has-index'>x</span>: 5</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5f2c8f82-02ab-4c38-b610-1842f3fb480d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5f2c8f82-02ab-4c38-b610-1842f3fb480d' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>50.6 51.66 52.7 53.75 54.8</div><input id='attrs-c759affa-922b-4632-942b-6a68e04e99e6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c759affa-922b-4632-942b-6a68e04e99e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-395f5754-4e6c-42de-b687-565be5744c3b' class='xr-var-data-in' type='checkbox'><label for='data-395f5754-4e6c-42de-b687-565be5744c3b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees north</dd></dl></div><div class='xr-var-data'><pre>array([50.6045, 51.655 , 52.703 , 53.751 , 54.799 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.436 1.543 3.517 5.492 7.466</div><input id='attrs-aeaeac37-d81a-487d-bc90-6e7d2d044462' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aeaeac37-d81a-487d-bc90-6e7d2d044462' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0080297f-15d2-4cbf-92e2-b535e89e3b8f' class='xr-var-data-in' type='checkbox'><label for='data-0080297f-15d2-4cbf-92e2-b535e89e3b8f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees east</dd></dl></div><div class='xr-var-data'><pre>array([-0.436 ,  1.543 ,  3.517 ,  5.4915,  7.466 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>observation</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U4</div><div class='xr-var-preview xr-preview'>&#x27;alti&#x27;</div><input id='attrs-0d074c40-b9d3-447d-a09d-8c0d190b4fa0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0d074c40-b9d3-447d-a09d-8c0d190b4fa0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4138d13d-10f9-4ee1-b7b5-ff4194e5a9de' class='xr-var-data-in' type='checkbox'><label for='data-4138d13d-10f9-4ee1-b7b5-ff4194e5a9de' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;alti&#x27;, dtype=&#x27;&lt;U4&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d3118993-883b-4f46-9544-8f0114ab7cc9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d3118993-883b-4f46-9544-8f0114ab7cc9' class='xr-section-summary' >Data variables: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>n</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>3 17 0 0 0 0 ... 27 37 79 0 20 75</div><input id='attrs-987c275e-d0b6-44e7-8170-739c402a33aa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-987c275e-d0b6-44e7-8170-739c402a33aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29d961a0-8577-48ee-8c21-2bcbf1a0ddd7' class='xr-var-data-in' type='checkbox'><label for='data-29d961a0-8577-48ee-8c21-2bcbf1a0ddd7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Number of observations</dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><pre>array([[ 3, 17,  0,  0,  0],\n",
                            "       [ 0, 49,  9,  0,  0],\n",
                            "       [ 0, 34, 51,  0,  0],\n",
                            "       [13, 70, 33, 15, 27],\n",
                            "       [37, 79,  0, 20, 75]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bias</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.02626 -0.188 ... -0.2475 -0.1156</div><input id='attrs-e5f8c4a6-0746-49dc-ab9b-4b4c15b23a0a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e5f8c4a6-0746-49dc-ab9b-4b4c15b23a0a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3080f402-73d5-4318-ab71-944746619d61' class='xr-var-data-in' type='checkbox'><label for='data-3080f402-73d5-4318-ab71-944746619d61' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-0.02626242, -0.18803033,         nan,         nan,         nan],\n",
                            "       [        nan, -0.00739744,  0.03850896,         nan,         nan],\n",
                            "       [        nan, -0.02572273, -0.03130381,         nan,         nan],\n",
                            "       [-0.03491047, -0.06608473, -0.0943395 , -0.18522225,  0.06944564],\n",
                            "       [-0.02363357, -0.0668018 ,         nan, -0.2474566 , -0.11555013]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rmse</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.03291 0.2105 ... 0.2635 0.1475</div><input id='attrs-d35c16e3-a35c-44a8-b6be-5d7fef3a7e50' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d35c16e3-a35c-44a8-b6be-5d7fef3a7e50' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8393128e-5761-4eb5-9254-418e81ed5324' class='xr-var-data-in' type='checkbox'><label for='data-8393128e-5761-4eb5-9254-418e81ed5324' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.03291127, 0.2105243 ,        nan,        nan,        nan],\n",
                            "       [       nan, 0.0597115 , 0.05467727,        nan,        nan],\n",
                            "       [       nan, 0.06665436, 0.0790099 ,        nan,        nan],\n",
                            "       [0.07787699, 0.10411702, 0.1019227 , 0.19039907, 0.09739114],\n",
                            "       [0.04853201, 0.08421489,        nan, 0.26354011, 0.1475401 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>urmse</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.01984 0.09468 ... 0.09066 0.09174</div><input id='attrs-2660c643-3a33-4292-a7ab-1204079976e0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2660c643-3a33-4292-a7ab-1204079976e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1999b6f1-fbb9-49f0-84c1-9fbc1a75e3d7' class='xr-var-data-in' type='checkbox'><label for='data-1999b6f1-fbb9-49f0-84c1-9fbc1a75e3d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.01983525, 0.0946841 ,        nan,        nan,        nan],\n",
                            "       [       nan, 0.05925151, 0.03881577,        nan,        nan],\n",
                            "       [       nan, 0.06149101, 0.07254403,        nan,        nan],\n",
                            "       [0.06961382, 0.08045596, 0.03857843, 0.04409675, 0.06828131],\n",
                            "       [0.0423888 , 0.05128027,        nan, 0.09065662, 0.09174011]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mae</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.02626 0.1923 ... 0.2475 0.1217</div><input id='attrs-b208c0e2-5945-4bab-bfba-641e19d6b706' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b208c0e2-5945-4bab-bfba-641e19d6b706' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76b1999a-19c0-4573-8bd2-b173668262b5' class='xr-var-data-in' type='checkbox'><label for='data-76b1999a-19c0-4573-8bd2-b173668262b5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.02626242, 0.19233748,        nan,        nan,        nan],\n",
                            "       [       nan, 0.04864544, 0.04781144,        nan,        nan],\n",
                            "       [       nan, 0.05425773, 0.06353798,        nan,        nan],\n",
                            "       [0.06834444, 0.09011237, 0.0944789 , 0.18522225, 0.07906528],\n",
                            "       [0.04031912, 0.07316076,        nan, 0.2474566 , 0.12166717]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cc</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9973 0.996 nan ... 0.6293 0.9842</div><input id='attrs-98151a6c-253c-435c-a666-f3eeb4a02cb7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-98151a6c-253c-435c-a666-f3eeb4a02cb7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-111c6fb8-ac93-4379-a346-13a56efefb7e' class='xr-var-data-in' type='checkbox'><label for='data-111c6fb8-ac93-4379-a346-13a56efefb7e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.99728268, 0.99598751,        nan,        nan,        nan],\n",
                            "       [       nan, 0.9694353 , 0.93948231,        nan,        nan],\n",
                            "       [       nan, 0.97019491, 0.93311056,        nan,        nan],\n",
                            "       [0.85687731, 0.96794641, 0.99112518, 0.45645075, 0.99681521],\n",
                            "       [0.99839868, 0.98678894,        nan, 0.62927256, 0.98424795]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>si</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.04669 0.08355 ... 0.0844 0.1475</div><input id='attrs-08c06a40-e8e0-4215-9d28-e390316260c2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-08c06a40-e8e0-4215-9d28-e390316260c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3405eb28-48b6-48b6-98cf-86e333a9a42a' class='xr-var-data-in' type='checkbox'><label for='data-3405eb28-48b6-48b6-98cf-86e333a9a42a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.04668582, 0.08355029,        nan,        nan,        nan],\n",
                            "       [       nan, 0.31219543, 0.0370097 ,        nan,        nan],\n",
                            "       [       nan, 0.13777501, 0.09086265,        nan,        nan],\n",
                            "       [0.06799246, 0.11873341, 0.05001289, 0.03785259, 0.08204733],\n",
                            "       [0.04734921, 0.08309096,        nan, 0.08439573, 0.1475146 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>r2</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.061 0.9003 nan ... -18.78 0.9078</div><input id='attrs-d0d675c2-46cf-4fad-883a-ba8c126b3741' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d0d675c2-46cf-4fad-883a-ba8c126b3741' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-75565578-e48e-4168-abba-2c20610b59b4' class='xr-var-data-in' type='checkbox'><label for='data-75565578-e48e-4168-abba-2c20610b59b4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ -1.06054742,   0.90028343,          nan,          nan,\n",
                            "                 nan],\n",
                            "       [         nan,   0.93500673,   0.76627265,          nan,\n",
                            "                 nan],\n",
                            "       [         nan,   0.91312387,   0.84573422,          nan,\n",
                            "                 nan],\n",
                            "       [  0.65889097,   0.89012941,   0.84239658, -15.96469414,\n",
                            "          0.98694282],\n",
                            "       [  0.99031492,   0.92493705,          nan, -18.77585743,\n",
                            "          0.90783476]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e36266e8-dced-4d32-9aa0-08955d62a060' class='xr-section-summary-in' type='checkbox'  ><label for='section-e36266e8-dced-4d32-9aa0-08955d62a060' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a43a7021-3b62-4137-b55c-9639a8cbd0d8' class='xr-index-data-in' type='checkbox'/><label for='index-a43a7021-3b62-4137-b55c-9639a8cbd0d8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CategoricalIndex([50.6045, 51.655, 52.703, 53.751, 54.799], categories=[50.6045, 51.6550, 52.7030, 53.7510, 54.7990], ordered=True, dtype=&#x27;category&#x27;, name=&#x27;y&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9e23f60b-49a6-489c-8a68-610c8cf51e26' class='xr-index-data-in' type='checkbox'/><label for='index-9e23f60b-49a6-489c-8a68-610c8cf51e26' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CategoricalIndex([-0.43599999999999994, 1.543, 3.5169999999999995, 5.4915,\n",
                            "                  7.465999999999999],\n",
                            "                 categories=[-0.4360, 1.5430, 3.5170, 5.4915, 7.4660], ordered=True, dtype=&#x27;category&#x27;, name=&#x27;x&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-23b1a937-fe6c-45ef-9fb3-90bf1a5af078' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-23b1a937-fe6c-45ef-9fb3-90bf1a5af078' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
                        ],
                        "text/plain": [
                            "<xarray.Dataset>\n",
                            "Dimensions:      (y: 5, x: 5)\n",
                            "Coordinates:\n",
                            "  * y            (y) float64 50.6 51.66 52.7 53.75 54.8\n",
                            "  * x            (x) float64 -0.436 1.543 3.517 5.492 7.466\n",
                            "    observation  <U4 'alti'\n",
                            "Data variables:\n",
                            "    n            (y, x) int32 3 17 0 0 0 0 49 9 0 ... 70 33 15 27 37 79 0 20 75\n",
                            "    bias         (y, x) float64 -0.02626 -0.188 nan nan ... nan -0.2475 -0.1156\n",
                            "    rmse         (y, x) float64 0.03291 0.2105 nan nan ... nan 0.2635 0.1475\n",
                            "    urmse        (y, x) float64 0.01984 0.09468 nan nan ... nan 0.09066 0.09174\n",
                            "    mae          (y, x) float64 0.02626 0.1923 nan nan ... nan 0.2475 0.1217\n",
                            "    cc           (y, x) float64 0.9973 0.996 nan nan ... nan 0.6293 0.9842\n",
                            "    si           (y, x) float64 0.04669 0.08355 nan nan ... nan 0.0844 0.1475\n",
                            "    r2           (y, x) float64 -1.061 0.9003 nan nan ... nan -18.78 0.9078"
                        ]
                    },
                    "execution_count": 4,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "ss"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "['n', 'bias', 'rmse', 'urmse', 'mae', 'cc', 'si', 'r2']"
                        ]
                    },
                    "execution_count": 5,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "ss.metrics"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 640x480 with 2 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "ss.rmse.plot();"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 300x300 with 2 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "ss['n'].plot(figsize=(3,3));"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Multi models"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "metadata": {},
            "outputs": [],
            "source": [
                "fn = \"../tests/testdata/SW/HKZN_local_2017_DutchCoast.dfsu\"\n",
                "mr1 = ms.model_result(fn, name=\"SW_1\", item=0)\n",
                "fn = \"../tests/testdata/SW/HKZN_local_2017_DutchCoast_v2.dfsu\"\n",
                "mr2 = ms.model_result(fn, name=\"SW_2\", item=0)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "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\")\n",
                "cc = ms.match(obs=[o1, o2, o3], mod=[mr1, mr2])"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## gridded_skill\n",
                "Group by model"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 10,
            "metadata": {},
            "outputs": [],
            "source": [
                "ss = cc.gridded_skill(by='model', bins=6)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 11,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "['SW_1', 'SW_2']"
                        ]
                    },
                    "execution_count": 11,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "ss.mod_names"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 12,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 700x300 with 3 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "ss.bias.plot();"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 13,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 640x480 with 2 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "ss.rmse.plot(model='SW_1', cmap='YlOrRd');"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": []
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "base",
            "language": "python",
            "name": "python3"
        },
        "language_info": {
            "codemirror_mode": {
                "name": "ipython",
                "version": 3
            },
            "file_extension": ".py",
            "mimetype": "text/x-python",
            "name": "python",
            "nbconvert_exporter": "python",
            "pygments_lexer": "ipython3",
            "version": "3.10.12"
        },
        "orig_nbformat": 2
    },
    "nbformat": 4,
    "nbformat_minor": 2
}