{
    "cells": [
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# Metocean track comparison\n",
                "\n",
                "Comparing MIKE 21 HD dfsu model result with satellite track observation of surface elevation. \n",
                "\n",
                "This notebook also includes gridded spatial skill assessments. "
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "metadata": {},
            "outputs": [],
            "source": [
                "import pandas as pd\n",
                "import matplotlib.pyplot as plt\n",
                "from matplotlib_inline.backend_inline import set_matplotlib_formats\n",
                "import modelskill as ms\n",
                "set_matplotlib_formats('png')"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Extract track data"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "<DfsuModelResult> 'HD'"
                        ]
                    },
                    "execution_count": 2,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "fn = '../tests/testdata/NorthSeaHD_and_windspeed.dfsu'\n",
                "mr = ms.model_result(fn, name='HD', item=0)\n",
                "mr"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "In this case, the track observations are stored in a csv file, which we can read in using pandas.\n",
                "Any file format that can be read into a pandas dataframe can be used here."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "metadata": {},
            "outputs": [],
            "source": [
                "fn = '../tests/testdata/altimetry_NorthSea_20171027.csv'\n",
                "df = pd.read_csv(fn, index_col=0, parse_dates=True)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr style=\"text-align: right;\">\n",
                            "      <th></th>\n",
                            "      <th>lon</th>\n",
                            "      <th>lat</th>\n",
                            "      <th>surface_elevation</th>\n",
                            "      <th>significant_wave_height</th>\n",
                            "      <th>wind_speed</th>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>date</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>2017-10-26 04:37:37</th>\n",
                            "      <td>8.757272</td>\n",
                            "      <td>53.926136</td>\n",
                            "      <td>1.6449</td>\n",
                            "      <td>0.426</td>\n",
                            "      <td>6.100000</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2017-10-26 04:37:54</th>\n",
                            "      <td>8.221631</td>\n",
                            "      <td>54.948459</td>\n",
                            "      <td>1.1200</td>\n",
                            "      <td>1.634</td>\n",
                            "      <td>9.030000</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2017-10-26 04:37:55</th>\n",
                            "      <td>8.189390</td>\n",
                            "      <td>55.008547</td>\n",
                            "      <td>1.0882</td>\n",
                            "      <td>1.717</td>\n",
                            "      <td>9.370000</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2017-10-26 04:37:56</th>\n",
                            "      <td>8.157065</td>\n",
                            "      <td>55.068627</td>\n",
                            "      <td>1.0309</td>\n",
                            "      <td>1.869</td>\n",
                            "      <td>9.559999</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2017-10-26 04:37:58</th>\n",
                            "      <td>8.124656</td>\n",
                            "      <td>55.128700</td>\n",
                            "      <td>1.0369</td>\n",
                            "      <td>1.939</td>\n",
                            "      <td>9.980000</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "                          lon        lat  surface_elevation  \\\n",
                            "date                                                          \n",
                            "2017-10-26 04:37:37  8.757272  53.926136             1.6449   \n",
                            "2017-10-26 04:37:54  8.221631  54.948459             1.1200   \n",
                            "2017-10-26 04:37:55  8.189390  55.008547             1.0882   \n",
                            "2017-10-26 04:37:56  8.157065  55.068627             1.0309   \n",
                            "2017-10-26 04:37:58  8.124656  55.128700             1.0369   \n",
                            "\n",
                            "                     significant_wave_height  wind_speed  \n",
                            "date                                                      \n",
                            "2017-10-26 04:37:37                    0.426    6.100000  \n",
                            "2017-10-26 04:37:54                    1.634    9.030000  \n",
                            "2017-10-26 04:37:55                    1.717    9.370000  \n",
                            "2017-10-26 04:37:56                    1.869    9.559999  \n",
                            "2017-10-26 04:37:58                    1.939    9.980000  "
                        ]
                    },
                    "execution_count": 4,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "df.head()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "Quantity(name='Surface Elevation', unit='m')"
                        ]
                    },
                    "execution_count": 5,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "mr.quantity"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "metadata": {},
            "outputs": [
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": [
                        "C:\\Users\\jem\\Source\\modelskill\\modelskill\\timeseries\\_track.py:135: UserWarning: Removed 22 duplicate timestamps with keep=first\n",
                        "  warnings.warn(\n"
                    ]
                },
                {
                    "data": {
                        "text/plain": [
                            "TrackObservation: alti, n=1093"
                        ]
                    },
                    "execution_count": 6,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "# the dataframe doesn't include the metadata on which quantity it contains, we add this manually, consistent with the model result\n",
                "o1 = ms.TrackObservation(df, item=\"surface_elevation\", name='alti', quantity=ms.Quantity(name=\"Surface Elevation\", unit=\"meter\")) \n",
                "o1"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "<Axes: title={'center': 'Spatial coverage'}>"
                        ]
                    },
                    "execution_count": 7,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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                        "text/plain": [
                            "<Figure size 640x480 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "ms.plotting.spatial_overview(o1, mr)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "<Comparer>\n",
                            "Quantity: Surface Elevation [meter]\n",
                            "Observation: alti, n_points=532\n",
                            " Model: HD, rmse=0.115"
                        ]
                    },
                    "execution_count": 8,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "cmp = ms.match(o1, mr)\n",
                "cmp"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "metadata": {},
            "outputs": [
                {
                    "data": {
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                            "body[data-theme=dark],\n",
                            "body.vscode-dark {\n",
                            "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
                            "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
                            "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
                            "  --xr-border-color: #1F1F1F;\n",
                            "  --xr-disabled-color: #515151;\n",
                            "  --xr-background-color: #111111;\n",
                            "  --xr-background-color-row-even: #111111;\n",
                            "  --xr-background-color-row-odd: #313131;\n",
                            "}\n",
                            "\n",
                            ".xr-wrap {\n",
                            "  display: block !important;\n",
                            "  min-width: 300px;\n",
                            "  max-width: 700px;\n",
                            "}\n",
                            "\n",
                            ".xr-text-repr-fallback {\n",
                            "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
                            "  display: none;\n",
                            "}\n",
                            "\n",
                            ".xr-header {\n",
                            "  padding-top: 6px;\n",
                            "  padding-bottom: 6px;\n",
                            "  margin-bottom: 4px;\n",
                            "  border-bottom: solid 1px var(--xr-border-color);\n",
                            "}\n",
                            "\n",
                            ".xr-header > div,\n",
                            ".xr-header > ul {\n",
                            "  display: inline;\n",
                            "  margin-top: 0;\n",
                            "  margin-bottom: 0;\n",
                            "}\n",
                            "\n",
                            ".xr-obj-type,\n",
                            ".xr-array-name {\n",
                            "  margin-left: 2px;\n",
                            "  margin-right: 10px;\n",
                            "}\n",
                            "\n",
                            ".xr-obj-type {\n",
                            "  color: var(--xr-font-color2);\n",
                            "}\n",
                            "\n",
                            ".xr-sections {\n",
                            "  padding-left: 0 !important;\n",
                            "  display: grid;\n",
                            "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
                            "}\n",
                            "\n",
                            ".xr-section-item {\n",
                            "  display: contents;\n",
                            "}\n",
                            "\n",
                            ".xr-section-item input {\n",
                            "  display: none;\n",
                            "}\n",
                            "\n",
                            ".xr-section-item input + label {\n",
                            "  color: var(--xr-disabled-color);\n",
                            "}\n",
                            "\n",
                            ".xr-section-item input:enabled + label {\n",
                            "  cursor: pointer;\n",
                            "  color: var(--xr-font-color2);\n",
                            "}\n",
                            "\n",
                            ".xr-section-item input:enabled + label:hover {\n",
                            "  color: var(--xr-font-color0);\n",
                            "}\n",
                            "\n",
                            ".xr-section-summary {\n",
                            "  grid-column: 1;\n",
                            "  color: var(--xr-font-color2);\n",
                            "  font-weight: 500;\n",
                            "}\n",
                            "\n",
                            ".xr-section-summary > span {\n",
                            "  display: inline-block;\n",
                            "  padding-left: 0.5em;\n",
                            "}\n",
                            "\n",
                            ".xr-section-summary-in:disabled + label {\n",
                            "  color: var(--xr-font-color2);\n",
                            "}\n",
                            "\n",
                            ".xr-section-summary-in + label:before {\n",
                            "  display: inline-block;\n",
                            "  content: '►';\n",
                            "  font-size: 11px;\n",
                            "  width: 15px;\n",
                            "  text-align: center;\n",
                            "}\n",
                            "\n",
                            ".xr-section-summary-in:disabled + label:before {\n",
                            "  color: var(--xr-disabled-color);\n",
                            "}\n",
                            "\n",
                            ".xr-section-summary-in:checked + label:before {\n",
                            "  content: '▼';\n",
                            "}\n",
                            "\n",
                            ".xr-section-summary-in:checked + label > span {\n",
                            "  display: none;\n",
                            "}\n",
                            "\n",
                            ".xr-section-summary,\n",
                            ".xr-section-inline-details {\n",
                            "  padding-top: 4px;\n",
                            "  padding-bottom: 4px;\n",
                            "}\n",
                            "\n",
                            ".xr-section-inline-details {\n",
                            "  grid-column: 2 / -1;\n",
                            "}\n",
                            "\n",
                            ".xr-section-details {\n",
                            "  display: none;\n",
                            "  grid-column: 1 / -1;\n",
                            "  margin-bottom: 5px;\n",
                            "}\n",
                            "\n",
                            ".xr-section-summary-in:checked ~ .xr-section-details {\n",
                            "  display: contents;\n",
                            "}\n",
                            "\n",
                            ".xr-array-wrap {\n",
                            "  grid-column: 1 / -1;\n",
                            "  display: grid;\n",
                            "  grid-template-columns: 20px auto;\n",
                            "}\n",
                            "\n",
                            ".xr-array-wrap > label {\n",
                            "  grid-column: 1;\n",
                            "  vertical-align: top;\n",
                            "}\n",
                            "\n",
                            ".xr-preview {\n",
                            "  color: var(--xr-font-color3);\n",
                            "}\n",
                            "\n",
                            ".xr-array-preview,\n",
                            ".xr-array-data {\n",
                            "  padding: 0 5px !important;\n",
                            "  grid-column: 2;\n",
                            "}\n",
                            "\n",
                            ".xr-array-data,\n",
                            ".xr-array-in:checked ~ .xr-array-preview {\n",
                            "  display: none;\n",
                            "}\n",
                            "\n",
                            ".xr-array-in:checked ~ .xr-array-data,\n",
                            ".xr-array-preview {\n",
                            "  display: inline-block;\n",
                            "}\n",
                            "\n",
                            ".xr-dim-list {\n",
                            "  display: inline-block !important;\n",
                            "  list-style: none;\n",
                            "  padding: 0 !important;\n",
                            "  margin: 0;\n",
                            "}\n",
                            "\n",
                            ".xr-dim-list li {\n",
                            "  display: inline-block;\n",
                            "  padding: 0;\n",
                            "  margin: 0;\n",
                            "}\n",
                            "\n",
                            ".xr-dim-list:before {\n",
                            "  content: '(';\n",
                            "}\n",
                            "\n",
                            ".xr-dim-list:after {\n",
                            "  content: ')';\n",
                            "}\n",
                            "\n",
                            ".xr-dim-list li:not(:last-child):after {\n",
                            "  content: ',';\n",
                            "  padding-right: 5px;\n",
                            "}\n",
                            "\n",
                            ".xr-has-index {\n",
                            "  font-weight: bold;\n",
                            "}\n",
                            "\n",
                            ".xr-var-list,\n",
                            ".xr-var-item {\n",
                            "  display: contents;\n",
                            "}\n",
                            "\n",
                            ".xr-var-item > div,\n",
                            ".xr-var-item label,\n",
                            ".xr-var-item > .xr-var-name span {\n",
                            "  background-color: var(--xr-background-color-row-even);\n",
                            "  margin-bottom: 0;\n",
                            "}\n",
                            "\n",
                            ".xr-var-item > .xr-var-name:hover span {\n",
                            "  padding-right: 5px;\n",
                            "}\n",
                            "\n",
                            ".xr-var-list > li:nth-child(odd) > div,\n",
                            ".xr-var-list > li:nth-child(odd) > label,\n",
                            ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
                            "  background-color: var(--xr-background-color-row-odd);\n",
                            "}\n",
                            "\n",
                            ".xr-var-name {\n",
                            "  grid-column: 1;\n",
                            "}\n",
                            "\n",
                            ".xr-var-dims {\n",
                            "  grid-column: 2;\n",
                            "}\n",
                            "\n",
                            ".xr-var-dtype {\n",
                            "  grid-column: 3;\n",
                            "  text-align: right;\n",
                            "  color: var(--xr-font-color2);\n",
                            "}\n",
                            "\n",
                            ".xr-var-preview {\n",
                            "  grid-column: 4;\n",
                            "}\n",
                            "\n",
                            ".xr-index-preview {\n",
                            "  grid-column: 2 / 5;\n",
                            "  color: var(--xr-font-color2);\n",
                            "}\n",
                            "\n",
                            ".xr-var-name,\n",
                            ".xr-var-dims,\n",
                            ".xr-var-dtype,\n",
                            ".xr-preview,\n",
                            ".xr-attrs dt {\n",
                            "  white-space: nowrap;\n",
                            "  overflow: hidden;\n",
                            "  text-overflow: ellipsis;\n",
                            "  padding-right: 10px;\n",
                            "}\n",
                            "\n",
                            ".xr-var-name:hover,\n",
                            ".xr-var-dims:hover,\n",
                            ".xr-var-dtype:hover,\n",
                            ".xr-attrs dt:hover {\n",
                            "  overflow: visible;\n",
                            "  width: auto;\n",
                            "  z-index: 1;\n",
                            "}\n",
                            "\n",
                            ".xr-var-attrs,\n",
                            ".xr-var-data,\n",
                            ".xr-index-data {\n",
                            "  display: none;\n",
                            "  background-color: var(--xr-background-color) !important;\n",
                            "  padding-bottom: 5px !important;\n",
                            "}\n",
                            "\n",
                            ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
                            ".xr-var-data-in:checked ~ .xr-var-data,\n",
                            ".xr-index-data-in:checked ~ .xr-index-data {\n",
                            "  display: block;\n",
                            "}\n",
                            "\n",
                            ".xr-var-data > table {\n",
                            "  float: right;\n",
                            "}\n",
                            "\n",
                            ".xr-var-name span,\n",
                            ".xr-var-data,\n",
                            ".xr-index-name div,\n",
                            ".xr-index-data,\n",
                            ".xr-attrs {\n",
                            "  padding-left: 25px !important;\n",
                            "}\n",
                            "\n",
                            ".xr-attrs,\n",
                            ".xr-var-attrs,\n",
                            ".xr-var-data,\n",
                            ".xr-index-data {\n",
                            "  grid-column: 1 / -1;\n",
                            "}\n",
                            "\n",
                            "dl.xr-attrs {\n",
                            "  padding: 0;\n",
                            "  margin: 0;\n",
                            "  display: grid;\n",
                            "  grid-template-columns: 125px auto;\n",
                            "}\n",
                            "\n",
                            ".xr-attrs dt,\n",
                            ".xr-attrs dd {\n",
                            "  padding: 0;\n",
                            "  margin: 0;\n",
                            "  float: left;\n",
                            "  padding-right: 10px;\n",
                            "  width: auto;\n",
                            "}\n",
                            "\n",
                            ".xr-attrs dt {\n",
                            "  font-weight: normal;\n",
                            "  grid-column: 1;\n",
                            "}\n",
                            "\n",
                            ".xr-attrs dt:hover span {\n",
                            "  display: inline-block;\n",
                            "  background: var(--xr-background-color);\n",
                            "  padding-right: 10px;\n",
                            "}\n",
                            "\n",
                            ".xr-attrs dd {\n",
                            "  grid-column: 2;\n",
                            "  white-space: pre-wrap;\n",
                            "  word-break: break-all;\n",
                            "}\n",
                            "\n",
                            ".xr-icon-database,\n",
                            ".xr-icon-file-text2,\n",
                            ".xr-no-icon {\n",
                            "  display: inline-block;\n",
                            "  vertical-align: middle;\n",
                            "  width: 1em;\n",
                            "  height: 1.5em !important;\n",
                            "  stroke-width: 0;\n",
                            "  stroke: currentColor;\n",
                            "  fill: currentColor;\n",
                            "}\n",
                            "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
                            "Dimensions:      (time: 532)\n",
                            "Coordinates:\n",
                            "  * time         (time) datetime64[ns] 2017-10-27T10:45:19 ... 2017-10-29T13:...\n",
                            "    x            (time) float64 1.262 1.231 1.2 1.168 ... 6.908 6.971 7.034\n",
                            "    y            (time) float64 55.3 55.24 55.18 55.13 ... 55.24 55.28 55.32\n",
                            "    z            float64 nan\n",
                            "Data variables:\n",
                            "    Observation  (time) float64 0.3778 0.4375 0.4489 ... 0.8562 0.8368 0.8218\n",
                            "    HD           (time) float32 0.3699 0.356 0.3559 ... 0.7068 0.7068 0.685\n",
                            "Attributes:\n",
                            "    gtype:               track\n",
                            "    modelskill_version:  1.0.dev23\n",
                            "    weight:              1.0\n",
                            "    name:                alti</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-78740652-0b64-43b8-81cb-1cf73073647b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-78740652-0b64-43b8-81cb-1cf73073647b' 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'>time</span>: 532</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-140f59b2-49b7-4f38-966b-1c296c08cd25' class='xr-section-summary-in' type='checkbox'  checked><label for='section-140f59b2-49b7-4f38-966b-1c296c08cd25' class='xr-section-summary' >Coordinates: <span>(4)</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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2017-10-27T10:45:19 ... 2017-10-...</div><input id='attrs-ec92ca8c-ce44-4a03-b0a6-9d370699d1a3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ec92ca8c-ce44-4a03-b0a6-9d370699d1a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-867e9dcf-ab4f-469b-9910-b94e487a03e5' class='xr-var-data-in' type='checkbox'><label for='data-867e9dcf-ab4f-469b-9910-b94e487a03e5' 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;2017-10-27T10:45:19.000000000&#x27;, &#x27;2017-10-27T10:45:20.000000000&#x27;,\n",
                            "       &#x27;2017-10-27T10:45:21.000000000&#x27;, ..., &#x27;2017-10-29T13:10:42.000000000&#x27;,\n",
                            "       &#x27;2017-10-29T13:10:43.000000000&#x27;, &#x27;2017-10-29T13:10:44.000000000&#x27;],\n",
                            "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.262 1.231 1.2 ... 6.971 7.034</div><input id='attrs-4f2631bc-70c3-4a3b-93b5-ed95e3a2e7b0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4f2631bc-70c3-4a3b-93b5-ed95e3a2e7b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-287e8d5f-7800-453c-8e4b-571683150343' class='xr-var-data-in' type='checkbox'><label for='data-287e8d5f-7800-453c-8e4b-571683150343' 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([ 1.2624630e+00,  1.2309900e+00,  1.1995940e+00,  1.1682770e+00,\n",
                            "        1.1370370e+00,  1.1058750e+00,  1.0747890e+00,  1.0437800e+00,\n",
                            "        1.0128470e+00,  9.8198900e-01,  9.5120700e-01,  9.2050000e-01,\n",
                            "        8.8986800e-01,  8.5931000e-01,  8.2882500e-01,  7.9841400e-01,\n",
                            "        7.6807700e-01,  7.3781200e-01,  7.0762000e-01,  6.7750000e-01,\n",
                            "        6.4745100e-01,  6.1747500e-01,  5.8756900e-01,  5.5773400e-01,\n",
                            "        5.2796900e-01,  4.9827500e-01,  4.6865000e-01,  4.3909500e-01,\n",
                            "        4.0960900e-01,  3.8019200e-01,  3.5084300e-01,  3.2156200e-01,\n",
                            "        2.4319076e+00,  2.4228545e+00,  2.4137889e+00,  2.4047105e+00,\n",
                            "        2.3956195e+00,  2.3773988e+00,  2.3682692e+00,  2.3591266e+00,\n",
                            "        2.3499710e+00,  2.3408023e+00,  2.3316204e+00,  2.3224254e+00,\n",
                            "        2.3132170e+00,  2.3039954e+00,  2.2947604e+00,  2.2855120e+00,\n",
                            "        2.2762500e+00,  2.2669744e+00,  2.2576852e+00,  2.2483823e+00,\n",
                            "        2.2390656e+00,  2.2203907e+00,  2.2110324e+00,  2.2016601e+00,\n",
                            "        2.1922736e+00,  2.1828730e+00,  2.1734582e+00,  2.1640292e+00,\n",
                            "        2.1545858e+00,  2.1451279e+00,  2.1356556e+00,  2.1261688e+00,\n",
                            "        2.1166673e+00,  2.1071511e+00,  2.0976202e+00,  2.0880744e+00,\n",
                            "        2.0785138e+00,  2.0689381e+00,  2.0497417e+00,  2.0401207e+00,\n",
                            "        2.0304845e+00,  2.0208330e+00,  2.0111660e+00,  2.0014836e+00,\n",
                            "        1.9917857e+00,  1.9820721e+00,  1.9723428e+00,  1.9625978e+00,\n",
                            "...\n",
                            "        2.3503190e+00,  2.4043610e+00,  2.4585100e+00,  2.5127660e+00,\n",
                            "        2.5671290e+00,  2.6216000e+00,  2.6761780e+00,  2.7308640e+00,\n",
                            "        2.7856590e+00,  2.8405620e+00,  2.8955740e+00,  2.9506950e+00,\n",
                            "        3.0059250e+00,  3.0612650e+00,  3.1167160e+00,  3.1722760e+00,\n",
                            "        3.2279470e+00,  3.2837290e+00,  3.3396210e+00,  3.3956260e+00,\n",
                            "        3.4517420e+00,  3.5079700e+00,  3.5643100e+00,  3.6207620e+00,\n",
                            "        3.6773280e+00,  3.7340060e+00,  3.7907980e+00,  3.8477040e+00,\n",
                            "        3.9047240e+00,  3.9618570e+00,  4.0191060e+00,  4.0764690e+00,\n",
                            "        4.1339470e+00,  4.1915410e+00,  4.2492510e+00,  4.3070760e+00,\n",
                            "        4.3650180e+00,  4.4230760e+00,  4.4812520e+00,  4.5395440e+00,\n",
                            "        4.5979540e+00,  4.6564820e+00,  4.7151270e+00,  4.7738910e+00,\n",
                            "        4.8327740e+00,  4.8917750e+00,  4.9508960e+00,  5.0101360e+00,\n",
                            "        5.0694970e+00,  5.1289770e+00,  5.1885780e+00,  5.2482990e+00,\n",
                            "        5.3081410e+00,  5.3681050e+00,  5.4281900e+00,  5.4883980e+00,\n",
                            "        5.5487270e+00,  5.6091790e+00,  5.6697540e+00,  5.7304520e+00,\n",
                            "        5.7912730e+00,  5.8522180e+00,  5.9132870e+00,  5.9744800e+00,\n",
                            "        6.0357980e+00,  6.0972410e+00,  6.1588090e+00,  6.2823220e+00,\n",
                            "        6.3442670e+00,  6.4063390e+00,  6.4685380e+00,  6.5308630e+00,\n",
                            "        6.5933160e+00,  6.6558970e+00,  6.7186060e+00,  6.7814420e+00,\n",
                            "        6.8444080e+00,  6.9075020e+00,  6.9707260e+00,  7.0340790e+00])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>55.3 55.24 55.18 ... 55.28 55.32</div><input id='attrs-eeae631b-66b2-4ba0-b486-06e6ff2e98c9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eeae631b-66b2-4ba0-b486-06e6ff2e98c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd14be61-329e-48c6-9ed3-81ccb79e890f' class='xr-var-data-in' type='checkbox'><label for='data-dd14be61-329e-48c6-9ed3-81ccb79e890f' 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([55.298639 , 55.241321 , 55.183996 , 55.126665 , 55.069327 ,\n",
                            "       55.011982 , 54.954631 , 54.897273 , 54.839909 , 54.782539 ,\n",
                            "       54.725162 , 54.667778 , 54.610388 , 54.552992 , 54.49559  ,\n",
                            "       54.438181 , 54.380766 , 54.323345 , 54.265917 , 54.208484 ,\n",
                            "       54.151044 , 54.093598 , 54.036146 , 53.978688 , 53.921224 ,\n",
                            "       53.863753 , 53.806277 , 53.748795 , 53.691307 , 53.633813 ,\n",
                            "       53.576313 , 53.518807 , 51.1964343, 51.2533518, 51.3102683,\n",
                            "       51.367184 , 51.4240987, 51.537926 , 51.5948382, 51.6517496,\n",
                            "       51.7086602, 51.76557  , 51.8224787, 51.8793867, 51.936294 ,\n",
                            "       51.9932003, 52.0501051, 52.1070096, 52.1639132, 52.2208162,\n",
                            "       52.2777181, 52.3346193, 52.3915194, 52.5053174, 52.5622149,\n",
                            "       52.6191117, 52.6760077, 52.7329027, 52.7897968, 52.8466901,\n",
                            "       52.9035824, 52.960474 , 53.0173645, 53.074254 , 53.1311428,\n",
                            "       53.1880309, 53.2449181, 53.3018044, 53.3586897, 53.4155743,\n",
                            "       53.5293406, 53.5862226, 53.6431035, 53.6999837, 53.7568629,\n",
                            "       53.8137413, 53.8706187, 53.9274953, 53.9843709, 54.0412454,\n",
                            "       54.098119 , 54.1549921, 54.2118642, 54.2687355, 54.3256057,\n",
                            "       54.382475 , 54.4962113, 54.553078 , 54.6099438, 54.6668088,\n",
                            "       54.7236728, 54.780536 , 54.8373982, 54.8942595, 54.95112  ,\n",
                            "       55.0079794, 55.0648375, 55.1216952, 55.1785519, 55.2354079,\n",
                            "...\n",
                            "       51.455426 , 51.496692 , 51.53793  , 51.57914  , 51.620322 ,\n",
                            "       51.661477 , 51.702604 , 51.743702 , 51.784773 , 51.825815 ,\n",
                            "       51.86683  , 51.907815 , 51.948773 , 51.989701 , 52.030601 ,\n",
                            "       52.071473 , 52.112315 , 52.153129 , 52.193913 , 52.234668 ,\n",
                            "       52.275395 , 52.316091 , 52.356759 , 52.397397 , 52.438005 ,\n",
                            "       52.478584 , 52.519132 , 52.559651 , 52.60014  , 52.640599 ,\n",
                            "       52.681028 , 52.721426 , 52.761794 , 52.802132 , 52.842439 ,\n",
                            "       52.882715 , 52.922961 , 52.963176 , 53.003359 , 53.043512 ,\n",
                            "       53.083633 , 53.123724 , 53.163782 , 53.20381  , 53.243806 ,\n",
                            "       53.283769 , 53.323702 , 53.363602 , 53.40347  , 53.443307 ,\n",
                            "       53.483111 , 53.522882 , 53.562622 , 53.602329 , 53.642003 ,\n",
                            "       53.681644 , 53.721253 , 53.760828 , 53.800371 , 53.83988  ,\n",
                            "       53.879356 , 53.918799 , 53.958208 , 53.997584 , 54.036926 ,\n",
                            "       54.076234 , 54.115508 , 54.154748 , 54.193954 , 54.233126 ,\n",
                            "       54.272263 , 54.311366 , 54.350434 , 54.389467 , 54.428466 ,\n",
                            "       54.46743  , 54.506358 , 54.545252 , 54.58411  , 54.622932 ,\n",
                            "       54.661719 , 54.700471 , 54.739187 , 54.777867 , 54.855118 ,\n",
                            "       54.89369  , 54.932225 , 54.970723 , 55.009185 , 55.047611 ,\n",
                            "       55.085999 , 55.124351 , 55.162665 , 55.200942 , 55.239182 ,\n",
                            "       55.277385 , 55.31555  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan</div><input id='attrs-50cc31f8-e479-4c28-b391-963820449c4a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-50cc31f8-e479-4c28-b391-963820449c4a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b0cb18be-9fc8-43a7-9dec-dac4e1f3d2c3' class='xr-var-data-in' type='checkbox'><label for='data-b0cb18be-9fc8-43a7-9dec-dac4e1f3d2c3' 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(nan)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-caa44824-5fdd-46ce-bb89-ba03b5cce7bd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-caa44824-5fdd-46ce-bb89-ba03b5cce7bd' class='xr-section-summary' >Data variables: <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-var-name'><span>Observation</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.3778 0.4375 ... 0.8368 0.8218</div><input id='attrs-604b8db7-cce8-4354-a6fb-d92531e323ab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-604b8db7-cce8-4354-a6fb-d92531e323ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c1adb1c4-d41d-48ec-b09a-4f0b915117b6' class='xr-var-data-in' type='checkbox'><label for='data-c1adb1c4-d41d-48ec-b09a-4f0b915117b6' 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>Surface Elevation</dd><dt><span>units :</span></dt><dd>meter</dd><dt><span>kind :</span></dt><dd>observation</dd><dt><span>color :</span></dt><dd>#d62728</dd></dl></div><div class='xr-var-data'><pre>array([ 0.3778,  0.4375,  0.4489,  0.4711,  0.4678,  0.5024,  0.5429,\n",
                            "        0.584 ,  0.5625,  0.6037,  0.6133,  0.6338,  0.6482,  0.6385,\n",
                            "        0.6824,  0.6905,  0.6954,  0.7481,  0.7695,  0.7657,  0.7927,\n",
                            "        0.8042,  0.8435,  0.8758,  0.9098,  0.9281,  1.0135,  1.0442,\n",
                            "        1.1025,  1.1779,  1.2357,  1.2884, -0.5083, -0.506 , -0.437 ,\n",
                            "       -0.4558, -0.4174, -0.3721, -0.339 , -0.302 , -0.25  , -0.1807,\n",
                            "       -0.1276, -0.1098, -0.0382,  0.0054,  0.0501,  0.1032,  0.1238,\n",
                            "        0.1738,  0.1864,  0.265 ,  0.3251,  0.3698,  0.3665,  0.3852,\n",
                            "        0.3896,  0.4671,  0.4414,  0.471 ,  0.4546,  0.4844,  0.4338,\n",
                            "        0.487 ,  0.458 ,  0.4572,  0.4592,  0.4119,  0.4301,  0.4158,\n",
                            "        0.3859,  0.4234,  0.3773,  0.3467,  0.3258,  0.2946,  0.3025,\n",
                            "        0.2804,  0.2625,  0.2852,  0.2762,  0.2485,  0.2137,  0.2281,\n",
                            "        0.17  ,  0.1838,  0.205 ,  0.1952,  0.162 ,  0.1314,  0.1402,\n",
                            "        0.1288,  0.0977,  0.1088,  0.0799,  0.021 ,  0.0065, -0.0273,\n",
                            "        0.0117, -0.0234, -0.0074,  0.3918,  0.3286,  0.3577,  0.3811,\n",
                            "        0.3698,  0.3304,  0.3576,  0.3842,  0.3406,  0.3589,  0.3941,\n",
                            "        0.3512,  0.3411,  0.3555,  0.3418,  0.2989,  0.292 ,  0.3341,\n",
                            "        0.3608,  0.3604,  0.3558,  0.3067,  0.3621,  0.2993,  0.2834,\n",
                            "        0.3294,  0.3093,  0.2776,  0.245 ,  0.2482,  0.2867,  0.1857,\n",
                            "        0.2494,  0.2225,  1.219 ,  1.2172,  1.2268,  1.2307,  1.2273,\n",
                            "...\n",
                            "        0.183 ,  1.2829,  1.3124,  1.2756,  1.2202,  1.204 ,  1.1605,\n",
                            "        1.2002,  1.2039,  1.2307,  1.2151,  1.1634,  1.0952,  1.1142,\n",
                            "        1.0802,  1.0727,  1.0536,  1.0119,  0.9656,  0.9728,  0.9813,\n",
                            "        0.9698,  0.9979,  0.9672,  0.9242,  0.9098,  0.8882,  0.8759,\n",
                            "        0.8368,  0.8731,  0.8064,  0.8199,  0.8675,  0.8544,  0.8069,\n",
                            "        0.7771,  0.753 ,  0.6954, -0.4108, -0.4066, -0.4572, -0.4903,\n",
                            "       -0.4522, -0.0665, -0.122 , -0.0804, -0.0264, -0.0309,  0.0175,\n",
                            "        0.0301,  0.0643,  0.0861,  0.0877,  0.1356,  0.1878,  0.2797,\n",
                            "        0.2345,  0.3212,  0.3309,  0.3368,  0.3873,  0.4347,  0.4313,\n",
                            "        0.4716,  0.473 ,  0.5719,  0.5185,  0.5664,  0.6304,  0.6318,\n",
                            "        0.7146,  0.737 ,  0.6474,  0.6977,  0.7506,  0.8584,  0.8072,\n",
                            "        0.8446,  0.8694,  0.8546,  0.8971,  0.9012,  0.9497,  0.9861,\n",
                            "        1.0477,  0.996 ,  1.0681,  1.0409,  1.0564,  1.0139,  1.0568,\n",
                            "        1.1473,  1.1096,  1.1158,  1.0753,  1.1388,  1.1439,  1.1677,\n",
                            "        1.1982,  1.1331,  1.2567,  1.1535,  1.2455,  1.1809,  1.2228,\n",
                            "        1.2054,  1.1702,  1.1167,  1.1228,  1.1234,  1.1102,  1.1485,\n",
                            "        1.1599,  1.1248,  1.1867,  1.0446,  1.094 ,  1.0638,  1.0441,\n",
                            "        1.1125,  1.1013,  1.0331,  1.1585,  1.0049,  1.0158,  1.0632,\n",
                            "        0.9968,  1.0605,  1.0035,  1.0703,  1.0284,  1.0866,  0.9857,\n",
                            "        0.9519,  0.9803,  0.9243,  0.9137,  0.8562,  0.8368,  0.8218])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HD</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.3699 0.356 ... 0.7068 0.685</div><input id='attrs-8093f668-28bf-486c-bc8a-69f8529a1949' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8093f668-28bf-486c-bc8a-69f8529a1949' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2bfe85fe-bbd3-4742-a17b-376d4e0733b1' class='xr-var-data-in' type='checkbox'><label for='data-2bfe85fe-bbd3-4742-a17b-376d4e0733b1' 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>name :</span></dt><dd>HD</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>eumType :</span></dt><dd>EUMType.Surface_Elevation</dd><dt><span>eumUnit :</span></dt><dd>EUMUnit.meter</dd><dt><span>long_name :</span></dt><dd>Surface Elevation</dd><dt><span>kind :</span></dt><dd>model</dd><dt><span>color :</span></dt><dd>#5ad45a</dd></dl></div><div class='xr-var-data'><pre>array([ 3.69931817e-01,  3.55970353e-01,  3.55910420e-01,  3.85431111e-01,\n",
                            "        4.25780147e-01,  4.25716728e-01,  4.25653338e-01,  4.59940106e-01,\n",
                            "        4.59872961e-01,  4.59805846e-01,  5.35151005e-01,  5.35086930e-01,\n",
                            "        5.53068757e-01,  5.53002000e-01,  5.91679752e-01,  5.91613472e-01,\n",
                            "        6.14721179e-01,  6.14651024e-01,  6.92026496e-01,  6.91959083e-01,\n",
                            "        7.34060585e-01,  7.33989537e-01,  7.33918488e-01,  8.07319760e-01,\n",
                            "        8.50688815e-01,  8.50617766e-01,  9.51076686e-01,  9.51010704e-01,\n",
                            "        9.50944722e-01,  1.15824819e+00,  1.15820146e+00,  1.15815473e+00,\n",
                            "       -5.56605160e-01, -5.56446075e-01, -4.88739014e-01, -4.88595307e-01,\n",
                            "       -4.88451600e-01, -3.41673672e-01, -3.41565728e-01, -3.41457784e-01,\n",
                            "       -2.42190570e-01, -2.42096007e-01, -2.42001429e-01, -1.15995137e-02,\n",
                            "       -1.32777486e-02, -1.32092135e-02, -1.31406784e-02,  6.84411526e-02,\n",
                            "        1.56138435e-01,  1.56195790e-01,  1.56253159e-01,  2.38782272e-01,\n",
                            "        2.29255423e-01,  3.29792768e-01,  3.29814523e-01,  3.29836279e-01,\n",
                            "        3.30011189e-01,  4.00271535e-01,  4.00268555e-01,  4.00265574e-01,\n",
                            "        4.00262564e-01,  4.23327267e-01,  4.23296958e-01,  4.23266619e-01,\n",
                            "        3.92616272e-01,  3.92576545e-01,  3.90274525e-01,  3.90222490e-01,\n",
                            "        3.46917361e-01,  3.46859008e-01,  3.03083003e-01,  3.03013802e-01,\n",
                            "        2.79559374e-01,  2.79490113e-01,  2.37180129e-01,  1.90825284e-01,\n",
                            "        1.90746069e-01,  1.90666869e-01,  1.65146843e-01,  1.52750924e-01,\n",
                            "...\n",
                            "        5.06607115e-01,  5.37447989e-01,  5.37445962e-01,  5.83109856e-01,\n",
                            "        5.83105445e-01,  6.11486018e-01,  6.11477613e-01,  5.91955543e-01,\n",
                            "        6.55335128e-01,  6.55320168e-01,  6.55305266e-01,  6.55290365e-01,\n",
                            "        7.75347829e-01,  7.75327146e-01,  7.75306463e-01,  7.33285964e-01,\n",
                            "        8.11623633e-01,  8.11599314e-01,  8.11574996e-01,  8.44816923e-01,\n",
                            "        8.82582068e-01,  8.82555127e-01,  9.07505155e-01,  9.07477558e-01,\n",
                            "        9.39957857e-01,  9.39929783e-01,  9.62679744e-01,  9.78279293e-01,\n",
                            "        9.93009984e-01,  9.92986023e-01,  1.01252019e+00,  1.01249659e+00,\n",
                            "        1.02222669e+00,  1.02220666e+00,  1.02218652e+00,  9.93419528e-01,\n",
                            "        9.93408680e-01,  1.01949608e+00,  1.01948714e+00,  1.01947832e+00,\n",
                            "        9.99739408e-01,  9.99734640e-01,  1.00588322e+00,  9.79266524e-01,\n",
                            "        9.79267716e-01,  9.73056495e-01,  9.73062456e-01,  9.24553573e-01,\n",
                            "        9.24561977e-01,  8.91650498e-01,  8.91657352e-01,  8.29208255e-01,\n",
                            "        8.36624622e-01,  8.36636007e-01,  7.95329869e-01,  8.00262630e-01,\n",
                            "        8.00277770e-01,  8.00292969e-01,  7.71054685e-01,  7.71070004e-01,\n",
                            "        7.71085262e-01,  7.71100581e-01,  7.48452067e-01,  7.19396591e-01,\n",
                            "        7.19412446e-01,  7.19428241e-01,  7.26109326e-01,  7.26125777e-01,\n",
                            "        7.26142287e-01,  7.46161699e-01,  7.23324955e-01,  7.23343730e-01,\n",
                            "        7.06753433e-01,  7.06772268e-01,  7.06791103e-01,  6.84967458e-01],\n",
                            "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b98c28be-fabb-4102-bd0a-015964dea48c' class='xr-section-summary-in' type='checkbox'  ><label for='section-b98c28be-fabb-4102-bd0a-015964dea48c' class='xr-section-summary' >Indexes: <span>(1)</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>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-46403018-f209-4924-97e3-6515ec4903d6' class='xr-index-data-in' type='checkbox'/><label for='index-46403018-f209-4924-97e3-6515ec4903d6' 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(DatetimeIndex([&#x27;2017-10-27 10:45:19&#x27;, &#x27;2017-10-27 10:45:20&#x27;,\n",
                            "               &#x27;2017-10-27 10:45:21&#x27;, &#x27;2017-10-27 10:45:22&#x27;,\n",
                            "               &#x27;2017-10-27 10:45:23&#x27;, &#x27;2017-10-27 10:45:24&#x27;,\n",
                            "               &#x27;2017-10-27 10:45:25&#x27;, &#x27;2017-10-27 10:45:26&#x27;,\n",
                            "               &#x27;2017-10-27 10:45:27&#x27;, &#x27;2017-10-27 10:45:28&#x27;,\n",
                            "               ...\n",
                            "               &#x27;2017-10-29 13:10:35&#x27;, &#x27;2017-10-29 13:10:36&#x27;,\n",
                            "               &#x27;2017-10-29 13:10:37&#x27;, &#x27;2017-10-29 13:10:38&#x27;,\n",
                            "               &#x27;2017-10-29 13:10:39&#x27;, &#x27;2017-10-29 13:10:40&#x27;,\n",
                            "               &#x27;2017-10-29 13:10:41&#x27;, &#x27;2017-10-29 13:10:42&#x27;,\n",
                            "               &#x27;2017-10-29 13:10:43&#x27;, &#x27;2017-10-29 13:10:44&#x27;],\n",
                            "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=532, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-46632dca-7d7d-4636-8847-00dcefd0ae3e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-46632dca-7d7d-4636-8847-00dcefd0ae3e' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>gtype :</span></dt><dd>track</dd><dt><span>modelskill_version :</span></dt><dd>1.0.dev23</dd><dt><span>weight :</span></dt><dd>1.0</dd><dt><span>name :</span></dt><dd>alti</dd></dl></div></li></ul></div></div>"
                        ],
                        "text/plain": [
                            "<xarray.Dataset>\n",
                            "Dimensions:      (time: 532)\n",
                            "Coordinates:\n",
                            "  * time         (time) datetime64[ns] 2017-10-27T10:45:19 ... 2017-10-29T13:...\n",
                            "    x            (time) float64 1.262 1.231 1.2 1.168 ... 6.908 6.971 7.034\n",
                            "    y            (time) float64 55.3 55.24 55.18 55.13 ... 55.24 55.28 55.32\n",
                            "    z            float64 nan\n",
                            "Data variables:\n",
                            "    Observation  (time) float64 0.3778 0.4375 0.4489 ... 0.8562 0.8368 0.8218\n",
                            "    HD           (time) float32 0.3699 0.356 0.3559 ... 0.7068 0.7068 0.685\n",
                            "Attributes:\n",
                            "    gtype:               track\n",
                            "    modelskill_version:  1.0.dev23\n",
                            "    weight:              1.0\n",
                            "    name:                alti"
                        ]
                    },
                    "execution_count": 9,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "cmp.data"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 10,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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                        "text/plain": [
                            "<Figure size 800x800 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "cmp.plot.scatter();"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Extract track from dfs0\n",
                "ModelResult is now a dfs0"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 11,
            "metadata": {},
            "outputs": [
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": [
                        "C:\\Users\\jem\\Source\\modelskill\\modelskill\\timeseries\\_track.py:135: UserWarning: Removed 22 duplicate timestamps with keep=first\n",
                        "  warnings.warn(\n"
                    ]
                },
                {
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                            "\n",
                            ".xr-section-summary-in:disabled + label:before {\n",
                            "  color: var(--xr-disabled-color);\n",
                            "}\n",
                            "\n",
                            ".xr-section-summary-in:checked + label:before {\n",
                            "  content: '▼';\n",
                            "}\n",
                            "\n",
                            ".xr-section-summary-in:checked + label > span {\n",
                            "  display: none;\n",
                            "}\n",
                            "\n",
                            ".xr-section-summary,\n",
                            ".xr-section-inline-details {\n",
                            "  padding-top: 4px;\n",
                            "  padding-bottom: 4px;\n",
                            "}\n",
                            "\n",
                            ".xr-section-inline-details {\n",
                            "  grid-column: 2 / -1;\n",
                            "}\n",
                            "\n",
                            ".xr-section-details {\n",
                            "  display: none;\n",
                            "  grid-column: 1 / -1;\n",
                            "  margin-bottom: 5px;\n",
                            "}\n",
                            "\n",
                            ".xr-section-summary-in:checked ~ .xr-section-details {\n",
                            "  display: contents;\n",
                            "}\n",
                            "\n",
                            ".xr-array-wrap {\n",
                            "  grid-column: 1 / -1;\n",
                            "  display: grid;\n",
                            "  grid-template-columns: 20px auto;\n",
                            "}\n",
                            "\n",
                            ".xr-array-wrap > label {\n",
                            "  grid-column: 1;\n",
                            "  vertical-align: top;\n",
                            "}\n",
                            "\n",
                            ".xr-preview {\n",
                            "  color: var(--xr-font-color3);\n",
                            "}\n",
                            "\n",
                            ".xr-array-preview,\n",
                            ".xr-array-data {\n",
                            "  padding: 0 5px !important;\n",
                            "  grid-column: 2;\n",
                            "}\n",
                            "\n",
                            ".xr-array-data,\n",
                            ".xr-array-in:checked ~ .xr-array-preview {\n",
                            "  display: none;\n",
                            "}\n",
                            "\n",
                            ".xr-array-in:checked ~ .xr-array-data,\n",
                            ".xr-array-preview {\n",
                            "  display: inline-block;\n",
                            "}\n",
                            "\n",
                            ".xr-dim-list {\n",
                            "  display: inline-block !important;\n",
                            "  list-style: none;\n",
                            "  padding: 0 !important;\n",
                            "  margin: 0;\n",
                            "}\n",
                            "\n",
                            ".xr-dim-list li {\n",
                            "  display: inline-block;\n",
                            "  padding: 0;\n",
                            "  margin: 0;\n",
                            "}\n",
                            "\n",
                            ".xr-dim-list:before {\n",
                            "  content: '(';\n",
                            "}\n",
                            "\n",
                            ".xr-dim-list:after {\n",
                            "  content: ')';\n",
                            "}\n",
                            "\n",
                            ".xr-dim-list li:not(:last-child):after {\n",
                            "  content: ',';\n",
                            "  padding-right: 5px;\n",
                            "}\n",
                            "\n",
                            ".xr-has-index {\n",
                            "  font-weight: bold;\n",
                            "}\n",
                            "\n",
                            ".xr-var-list,\n",
                            ".xr-var-item {\n",
                            "  display: contents;\n",
                            "}\n",
                            "\n",
                            ".xr-var-item > div,\n",
                            ".xr-var-item label,\n",
                            ".xr-var-item > .xr-var-name span {\n",
                            "  background-color: var(--xr-background-color-row-even);\n",
                            "  margin-bottom: 0;\n",
                            "}\n",
                            "\n",
                            ".xr-var-item > .xr-var-name:hover span {\n",
                            "  padding-right: 5px;\n",
                            "}\n",
                            "\n",
                            ".xr-var-list > li:nth-child(odd) > div,\n",
                            ".xr-var-list > li:nth-child(odd) > label,\n",
                            ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
                            "  background-color: var(--xr-background-color-row-odd);\n",
                            "}\n",
                            "\n",
                            ".xr-var-name {\n",
                            "  grid-column: 1;\n",
                            "}\n",
                            "\n",
                            ".xr-var-dims {\n",
                            "  grid-column: 2;\n",
                            "}\n",
                            "\n",
                            ".xr-var-dtype {\n",
                            "  grid-column: 3;\n",
                            "  text-align: right;\n",
                            "  color: var(--xr-font-color2);\n",
                            "}\n",
                            "\n",
                            ".xr-var-preview {\n",
                            "  grid-column: 4;\n",
                            "}\n",
                            "\n",
                            ".xr-index-preview {\n",
                            "  grid-column: 2 / 5;\n",
                            "  color: var(--xr-font-color2);\n",
                            "}\n",
                            "\n",
                            ".xr-var-name,\n",
                            ".xr-var-dims,\n",
                            ".xr-var-dtype,\n",
                            ".xr-preview,\n",
                            ".xr-attrs dt {\n",
                            "  white-space: nowrap;\n",
                            "  overflow: hidden;\n",
                            "  text-overflow: ellipsis;\n",
                            "  padding-right: 10px;\n",
                            "}\n",
                            "\n",
                            ".xr-var-name:hover,\n",
                            ".xr-var-dims:hover,\n",
                            ".xr-var-dtype:hover,\n",
                            ".xr-attrs dt:hover {\n",
                            "  overflow: visible;\n",
                            "  width: auto;\n",
                            "  z-index: 1;\n",
                            "}\n",
                            "\n",
                            ".xr-var-attrs,\n",
                            ".xr-var-data,\n",
                            ".xr-index-data {\n",
                            "  display: none;\n",
                            "  background-color: var(--xr-background-color) !important;\n",
                            "  padding-bottom: 5px !important;\n",
                            "}\n",
                            "\n",
                            ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
                            ".xr-var-data-in:checked ~ .xr-var-data,\n",
                            ".xr-index-data-in:checked ~ .xr-index-data {\n",
                            "  display: block;\n",
                            "}\n",
                            "\n",
                            ".xr-var-data > table {\n",
                            "  float: right;\n",
                            "}\n",
                            "\n",
                            ".xr-var-name span,\n",
                            ".xr-var-data,\n",
                            ".xr-index-name div,\n",
                            ".xr-index-data,\n",
                            ".xr-attrs {\n",
                            "  padding-left: 25px !important;\n",
                            "}\n",
                            "\n",
                            ".xr-attrs,\n",
                            ".xr-var-attrs,\n",
                            ".xr-var-data,\n",
                            ".xr-index-data {\n",
                            "  grid-column: 1 / -1;\n",
                            "}\n",
                            "\n",
                            "dl.xr-attrs {\n",
                            "  padding: 0;\n",
                            "  margin: 0;\n",
                            "  display: grid;\n",
                            "  grid-template-columns: 125px auto;\n",
                            "}\n",
                            "\n",
                            ".xr-attrs dt,\n",
                            ".xr-attrs dd {\n",
                            "  padding: 0;\n",
                            "  margin: 0;\n",
                            "  float: left;\n",
                            "  padding-right: 10px;\n",
                            "  width: auto;\n",
                            "}\n",
                            "\n",
                            ".xr-attrs dt {\n",
                            "  font-weight: normal;\n",
                            "  grid-column: 1;\n",
                            "}\n",
                            "\n",
                            ".xr-attrs dt:hover span {\n",
                            "  display: inline-block;\n",
                            "  background: var(--xr-background-color);\n",
                            "  padding-right: 10px;\n",
                            "}\n",
                            "\n",
                            ".xr-attrs dd {\n",
                            "  grid-column: 2;\n",
                            "  white-space: pre-wrap;\n",
                            "  word-break: break-all;\n",
                            "}\n",
                            "\n",
                            ".xr-icon-database,\n",
                            ".xr-icon-file-text2,\n",
                            ".xr-no-icon {\n",
                            "  display: inline-block;\n",
                            "  vertical-align: middle;\n",
                            "  width: 1em;\n",
                            "  height: 1.5em !important;\n",
                            "  stroke-width: 0;\n",
                            "  stroke: currentColor;\n",
                            "  fill: currentColor;\n",
                            "}\n",
                            "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
                            "Dimensions:  (time: 1093)\n",
                            "Coordinates:\n",
                            "  * time     (time) datetime64[ns] 2017-10-26T04:37:37 ... 2017-10-30T20:54:47\n",
                            "    x        (time) float64 8.757 8.222 8.189 8.157 ... 3.672 3.64 3.609 3.577\n",
                            "    y        (time) float64 53.93 54.95 55.01 55.07 ... 55.42 55.48 55.54 55.59\n",
                            "    z        float64 nan\n",
                            "Data variables:\n",
                            "    HD       (time) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan\n",
                            "Attributes:\n",
                            "    gtype:               track\n",
                            "    modelskill_version:  1.0.dev23</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-4a26cf2f-f2a3-4dbd-9b27-5a121c687e95' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4a26cf2f-f2a3-4dbd-9b27-5a121c687e95' 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'>time</span>: 1093</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-32543950-d60e-488b-8223-13f1fbe38e7f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-32543950-d60e-488b-8223-13f1fbe38e7f' class='xr-section-summary' >Coordinates: <span>(4)</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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2017-10-26T04:37:37 ... 2017-10-...</div><input id='attrs-25b03435-429b-4b7d-9b6e-df5ce167a1b9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-25b03435-429b-4b7d-9b6e-df5ce167a1b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6198da88-57d2-427f-849d-83aa20d16136' class='xr-var-data-in' type='checkbox'><label for='data-6198da88-57d2-427f-849d-83aa20d16136' 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;2017-10-26T04:37:37.000000000&#x27;, &#x27;2017-10-26T04:37:54.000000000&#x27;,\n",
                            "       &#x27;2017-10-26T04:37:55.000000000&#x27;, ..., &#x27;2017-10-30T20:54:45.000000000&#x27;,\n",
                            "       &#x27;2017-10-30T20:54:46.000000000&#x27;, &#x27;2017-10-30T20:54:47.000000000&#x27;],\n",
                            "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>8.757 8.222 8.189 ... 3.609 3.577</div><input id='attrs-cf2001fc-8369-41c1-a78f-eb0b344edf2e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cf2001fc-8369-41c1-a78f-eb0b344edf2e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c0c8288-b40f-4e99-a720-ff22db6e3bb2' class='xr-var-data-in' type='checkbox'><label for='data-3c0c8288-b40f-4e99-a720-ff22db6e3bb2' 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>name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>undefined</dd><dt><span>eumType :</span></dt><dd>EUMType.Undefined</dd><dt><span>eumUnit :</span></dt><dd>EUMUnit.undefined</dd></dl></div><div class='xr-var-data'><pre>array([8.75727177, 8.22163105, 8.18939018, ..., 3.64042497, 3.60862303,\n",
                            "       3.57674098])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>53.93 54.95 55.01 ... 55.54 55.59</div><input id='attrs-c8dbe5c8-8a0a-4536-9717-5aa9507cfb53' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c8dbe5c8-8a0a-4536-9717-5aa9507cfb53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-12cf44a9-4283-4840-aecb-e4b6d642c3c4' class='xr-var-data-in' type='checkbox'><label for='data-12cf44a9-4283-4840-aecb-e4b6d642c3c4' 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>name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>undefined</dd><dt><span>eumType :</span></dt><dd>EUMType.Undefined</dd><dt><span>eumUnit :</span></dt><dd>EUMUnit.undefined</dd></dl></div><div class='xr-var-data'><pre>array([53.92613602, 54.94845963, 55.00854874, ..., 55.47812653,\n",
                            "       55.53541946, 55.59270096])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan</div><input id='attrs-291ca0c6-150d-4562-8ab2-f6f1573b66b0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-291ca0c6-150d-4562-8ab2-f6f1573b66b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d46d070d-1434-4309-afcf-f28b5801ddc9' class='xr-var-data-in' type='checkbox'><label for='data-d46d070d-1434-4309-afcf-f28b5801ddc9' 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(nan)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8f85cd4a-5bea-4125-911f-4532d7d381cf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8f85cd4a-5bea-4125-911f-4532d7d381cf' class='xr-section-summary' >Data variables: <span>(1)</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>HD</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-64eeec70-aace-46d2-b154-fcc11bd16c3f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-64eeec70-aace-46d2-b154-fcc11bd16c3f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c0390d0-d502-4617-8f47-1bd24b3a6c15' class='xr-var-data-in' type='checkbox'><label for='data-3c0390d0-d502-4617-8f47-1bd24b3a6c15' 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>name :</span></dt><dd>Model_surface_elevation</dd><dt><span>units :</span></dt><dd>undefined</dd><dt><span>eumType :</span></dt><dd>EUMType.Undefined</dd><dt><span>eumUnit :</span></dt><dd>EUMUnit.undefined</dd><dt><span>long_name :</span></dt><dd>Undefined</dd><dt><span>kind :</span></dt><dd>model</dd><dt><span>color :</span></dt><dd>#5ad45a</dd></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ..., nan, nan, nan])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-48d4870d-f80e-42fa-ba9c-7cdbe57a1caa' class='xr-section-summary-in' type='checkbox'  ><label for='section-48d4870d-f80e-42fa-ba9c-7cdbe57a1caa' class='xr-section-summary' >Indexes: <span>(1)</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>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-bd64438b-d8f5-4349-a5c2-39c392c7674f' class='xr-index-data-in' type='checkbox'/><label for='index-bd64438b-d8f5-4349-a5c2-39c392c7674f' 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(DatetimeIndex([&#x27;2017-10-26 04:37:37&#x27;, &#x27;2017-10-26 04:37:54&#x27;,\n",
                            "               &#x27;2017-10-26 04:37:55&#x27;, &#x27;2017-10-26 04:37:56&#x27;,\n",
                            "               &#x27;2017-10-26 04:37:58&#x27;, &#x27;2017-10-26 04:37:59&#x27;,\n",
                            "               &#x27;2017-10-26 04:38:00&#x27;, &#x27;2017-10-26 04:38:01&#x27;,\n",
                            "               &#x27;2017-10-26 04:38:02&#x27;, &#x27;2017-10-26 04:38:03&#x27;,\n",
                            "               ...\n",
                            "               &#x27;2017-10-30 20:54:38&#x27;, &#x27;2017-10-30 20:54:39&#x27;,\n",
                            "               &#x27;2017-10-30 20:54:40&#x27;, &#x27;2017-10-30 20:54:41&#x27;,\n",
                            "               &#x27;2017-10-30 20:54:42&#x27;, &#x27;2017-10-30 20:54:43&#x27;,\n",
                            "               &#x27;2017-10-30 20:54:44&#x27;, &#x27;2017-10-30 20:54:45&#x27;,\n",
                            "               &#x27;2017-10-30 20:54:46&#x27;, &#x27;2017-10-30 20:54:47&#x27;],\n",
                            "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=1093, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a88ba697-11f9-45da-ae19-bcafc5f8f6b8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a88ba697-11f9-45da-ae19-bcafc5f8f6b8' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>gtype :</span></dt><dd>track</dd><dt><span>modelskill_version :</span></dt><dd>1.0.dev23</dd></dl></div></li></ul></div></div>"
                        ],
                        "text/plain": [
                            "<xarray.Dataset>\n",
                            "Dimensions:  (time: 1093)\n",
                            "Coordinates:\n",
                            "  * time     (time) datetime64[ns] 2017-10-26T04:37:37 ... 2017-10-30T20:54:47\n",
                            "    x        (time) float64 8.757 8.222 8.189 8.157 ... 3.672 3.64 3.609 3.577\n",
                            "    y        (time) float64 53.93 54.95 55.01 55.07 ... 55.42 55.48 55.54 55.59\n",
                            "    z        float64 nan\n",
                            "Data variables:\n",
                            "    HD       (time) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan\n",
                            "Attributes:\n",
                            "    gtype:               track\n",
                            "    modelskill_version:  1.0.dev23"
                        ]
                    },
                    "execution_count": 11,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "fn = '../tests/testdata/NorthSeaHD_extracted_track.dfs0'\n",
                "mr = ms.TrackModelResult(fn, name='HD', item=2)  # explicitly define type as Track\n",
                "mr.data"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 12,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "<TrackModelResult> 'HD' (n_points: 1093)"
                        ]
                    },
                    "execution_count": 12,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "mr"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 13,
            "metadata": {},
            "outputs": [
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": [
                        "C:\\Users\\jem\\Source\\modelskill\\modelskill\\timeseries\\_track.py:135: UserWarning: Removed 22 duplicate timestamps with keep=first\n",
                        "  warnings.warn(\n"
                    ]
                }
            ],
            "source": [
                "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')"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 14,
            "metadata": {},
            "outputs": [
                {
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                            "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
                            "Dimensions:  (time: 1093)\n",
                            "Coordinates:\n",
                            "  * time     (time) datetime64[ns] 2017-10-26T04:37:37 ... 2017-10-30T20:54:47\n",
                            "    x        (time) float64 8.757 8.222 8.189 8.157 ... 3.672 3.64 3.609 3.577\n",
                            "    y        (time) float64 53.93 54.95 55.01 55.07 ... 55.42 55.48 55.54 55.59\n",
                            "    z        float64 nan\n",
                            "Data variables:\n",
                            "    alti     (time) float64 1.645 1.12 1.088 1.031 ... 0.0364 0.0283 0.0601\n",
                            "Attributes:\n",
                            "    gtype:               track\n",
                            "    modelskill_version:  1.0.dev23\n",
                            "    weight:              1.0</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-a8b5304e-5dd6-45f1-b874-b84c503a72cd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a8b5304e-5dd6-45f1-b874-b84c503a72cd' 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'>time</span>: 1093</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7f9f3fc0-48a4-418c-ad78-3a169b56b57e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7f9f3fc0-48a4-418c-ad78-3a169b56b57e' class='xr-section-summary' >Coordinates: <span>(4)</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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2017-10-26T04:37:37 ... 2017-10-...</div><input id='attrs-2f191cfa-9010-4816-b80b-0eb52d9040e9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2f191cfa-9010-4816-b80b-0eb52d9040e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-abff493b-f37d-4e31-8832-5744c9308305' class='xr-var-data-in' type='checkbox'><label for='data-abff493b-f37d-4e31-8832-5744c9308305' 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;2017-10-26T04:37:37.000000000&#x27;, &#x27;2017-10-26T04:37:54.000000000&#x27;,\n",
                            "       &#x27;2017-10-26T04:37:55.000000000&#x27;, ..., &#x27;2017-10-30T20:54:45.000000000&#x27;,\n",
                            "       &#x27;2017-10-30T20:54:46.000000000&#x27;, &#x27;2017-10-30T20:54:47.000000000&#x27;],\n",
                            "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>8.757 8.222 8.189 ... 3.609 3.577</div><input id='attrs-af7d60ad-164b-40e9-9ec5-35f801f2723e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-af7d60ad-164b-40e9-9ec5-35f801f2723e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74362e92-35ad-48f8-823a-7bdfa3e476ce' class='xr-var-data-in' type='checkbox'><label for='data-74362e92-35ad-48f8-823a-7bdfa3e476ce' 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([8.757272, 8.221631, 8.18939 , ..., 3.640425, 3.608623, 3.576741])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>53.93 54.95 55.01 ... 55.54 55.59</div><input id='attrs-059199aa-e254-45ec-9bab-56b2a8c07d20' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-059199aa-e254-45ec-9bab-56b2a8c07d20' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c7b61b2e-c624-426f-9cb9-827d86279cf6' class='xr-var-data-in' type='checkbox'><label for='data-c7b61b2e-c624-426f-9cb9-827d86279cf6' 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([53.926136, 54.948459, 55.008547, ..., 55.478128, 55.535418,\n",
                            "       55.592701])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan</div><input id='attrs-659b778c-3c6c-4d16-95e4-35fdc34b4b07' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-659b778c-3c6c-4d16-95e4-35fdc34b4b07' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ec8a9a8-8bca-4e85-8019-a161ea268a44' class='xr-var-data-in' type='checkbox'><label for='data-7ec8a9a8-8bca-4e85-8019-a161ea268a44' 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(nan)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-abcdfb5f-25ff-4f7b-bfab-ea0b703739b4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-abcdfb5f-25ff-4f7b-bfab-ea0b703739b4' class='xr-section-summary' >Data variables: <span>(1)</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>alti</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.645 1.12 1.088 ... 0.0283 0.0601</div><input id='attrs-b9eb8a15-0c2b-4cdd-a1bf-caa10b485a86' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b9eb8a15-0c2b-4cdd-a1bf-caa10b485a86' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a7065d5-3e12-44a4-b6bf-75f6ce484d84' class='xr-var-data-in' type='checkbox'><label for='data-5a7065d5-3e12-44a4-b6bf-75f6ce484d84' 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></dd><dt><span>units :</span></dt><dd></dd><dt><span>kind :</span></dt><dd>observation</dd><dt><span>color :</span></dt><dd>#d62728</dd></dl></div><div class='xr-var-data'><pre>array([1.6449, 1.12  , 1.0882, ..., 0.0364, 0.0283, 0.0601])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-67a27bbc-8727-4b2a-870b-a1d116e6b37d' class='xr-section-summary-in' type='checkbox'  ><label for='section-67a27bbc-8727-4b2a-870b-a1d116e6b37d' class='xr-section-summary' >Indexes: <span>(1)</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>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-13e726ac-a328-419c-871e-459079800375' class='xr-index-data-in' type='checkbox'/><label for='index-13e726ac-a328-419c-871e-459079800375' 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(DatetimeIndex([&#x27;2017-10-26 04:37:37&#x27;, &#x27;2017-10-26 04:37:54&#x27;,\n",
                            "               &#x27;2017-10-26 04:37:55&#x27;, &#x27;2017-10-26 04:37:56&#x27;,\n",
                            "               &#x27;2017-10-26 04:37:58&#x27;, &#x27;2017-10-26 04:37:59&#x27;,\n",
                            "               &#x27;2017-10-26 04:38:00&#x27;, &#x27;2017-10-26 04:38:01&#x27;,\n",
                            "               &#x27;2017-10-26 04:38:02&#x27;, &#x27;2017-10-26 04:38:03&#x27;,\n",
                            "               ...\n",
                            "               &#x27;2017-10-30 20:54:38&#x27;, &#x27;2017-10-30 20:54:39&#x27;,\n",
                            "               &#x27;2017-10-30 20:54:40&#x27;, &#x27;2017-10-30 20:54:41&#x27;,\n",
                            "               &#x27;2017-10-30 20:54:42&#x27;, &#x27;2017-10-30 20:54:43&#x27;,\n",
                            "               &#x27;2017-10-30 20:54:44&#x27;, &#x27;2017-10-30 20:54:45&#x27;,\n",
                            "               &#x27;2017-10-30 20:54:46&#x27;, &#x27;2017-10-30 20:54:47&#x27;],\n",
                            "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=1093, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0ca71878-c919-4105-af76-ae7e59c042c8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0ca71878-c919-4105-af76-ae7e59c042c8' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>gtype :</span></dt><dd>track</dd><dt><span>modelskill_version :</span></dt><dd>1.0.dev23</dd><dt><span>weight :</span></dt><dd>1.0</dd></dl></div></li></ul></div></div>"
                        ],
                        "text/plain": [
                            "<xarray.Dataset>\n",
                            "Dimensions:  (time: 1093)\n",
                            "Coordinates:\n",
                            "  * time     (time) datetime64[ns] 2017-10-26T04:37:37 ... 2017-10-30T20:54:47\n",
                            "    x        (time) float64 8.757 8.222 8.189 8.157 ... 3.672 3.64 3.609 3.577\n",
                            "    y        (time) float64 53.93 54.95 55.01 55.07 ... 55.42 55.48 55.54 55.59\n",
                            "    z        float64 nan\n",
                            "Data variables:\n",
                            "    alti     (time) float64 1.645 1.12 1.088 1.031 ... 0.0364 0.0283 0.0601\n",
                            "Attributes:\n",
                            "    gtype:               track\n",
                            "    modelskill_version:  1.0.dev23\n",
                            "    weight:              1.0"
                        ]
                    },
                    "execution_count": 14,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "o1.data"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 15,
            "metadata": {},
            "outputs": [
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": [
                        "C:\\Users\\jem\\Source\\modelskill\\modelskill\\matching.py:269: FutureWarning: compare is deprecated. Use match instead.\n",
                        "  warnings.warn(\"compare is deprecated. Use match instead.\", FutureWarning)\n"
                    ]
                }
            ],
            "source": [
                "cmp = ms.compare(o1, mr)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 16,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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                        "text/plain": [
                            "<Figure size 800x800 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "cmp.plot.scatter();"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Gridded skill\n",
                "\n",
                "Load model, load observation, add observation to model and extract."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 17,
            "metadata": {},
            "outputs": [
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": [
                        "C:\\Users\\jem\\Source\\modelskill\\modelskill\\timeseries\\_track.py:135: UserWarning: Removed 22 duplicate timestamps with keep=first\n",
                        "  warnings.warn(\n"
                    ]
                },
                {
                    "data": {
                        "text/plain": [
                            "<Comparer>\n",
                            "Quantity:  []\n",
                            "Observation: alti, n_points=532\n",
                            " Model: HD, rmse=0.115"
                        ]
                    },
                    "execution_count": 17,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "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",
                "cmp = ms.match(o1, mr)\n",
                "cmp"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Get metrics binned by a regular spatial grid, returns xarray Dataset"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 18,
            "metadata": {},
            "outputs": [],
            "source": [
                "gs = cmp.gridded_skill(metrics=['bias'])"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 19,
            "metadata": {},
            "outputs": [
                {
                    "data": {
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                            "}\n",
                            "\n",
                            ".xr-section-summary-in + label:before {\n",
                            "  display: inline-block;\n",
                            "  content: '►';\n",
                            "  font-size: 11px;\n",
                            "  width: 15px;\n",
                            "  text-align: center;\n",
                            "}\n",
                            "\n",
                            ".xr-section-summary-in:disabled + label:before {\n",
                            "  color: var(--xr-disabled-color);\n",
                            "}\n",
                            "\n",
                            ".xr-section-summary-in:checked + label:before {\n",
                            "  content: '▼';\n",
                            "}\n",
                            "\n",
                            ".xr-section-summary-in:checked + label > span {\n",
                            "  display: none;\n",
                            "}\n",
                            "\n",
                            ".xr-section-summary,\n",
                            ".xr-section-inline-details {\n",
                            "  padding-top: 4px;\n",
                            "  padding-bottom: 4px;\n",
                            "}\n",
                            "\n",
                            ".xr-section-inline-details {\n",
                            "  grid-column: 2 / -1;\n",
                            "}\n",
                            "\n",
                            ".xr-section-details {\n",
                            "  display: none;\n",
                            "  grid-column: 1 / -1;\n",
                            "  margin-bottom: 5px;\n",
                            "}\n",
                            "\n",
                            ".xr-section-summary-in:checked ~ .xr-section-details {\n",
                            "  display: contents;\n",
                            "}\n",
                            "\n",
                            ".xr-array-wrap {\n",
                            "  grid-column: 1 / -1;\n",
                            "  display: grid;\n",
                            "  grid-template-columns: 20px auto;\n",
                            "}\n",
                            "\n",
                            ".xr-array-wrap > label {\n",
                            "  grid-column: 1;\n",
                            "  vertical-align: top;\n",
                            "}\n",
                            "\n",
                            ".xr-preview {\n",
                            "  color: var(--xr-font-color3);\n",
                            "}\n",
                            "\n",
                            ".xr-array-preview,\n",
                            ".xr-array-data {\n",
                            "  padding: 0 5px !important;\n",
                            "  grid-column: 2;\n",
                            "}\n",
                            "\n",
                            ".xr-array-data,\n",
                            ".xr-array-in:checked ~ .xr-array-preview {\n",
                            "  display: none;\n",
                            "}\n",
                            "\n",
                            ".xr-array-in:checked ~ .xr-array-data,\n",
                            ".xr-array-preview {\n",
                            "  display: inline-block;\n",
                            "}\n",
                            "\n",
                            ".xr-dim-list {\n",
                            "  display: inline-block !important;\n",
                            "  list-style: none;\n",
                            "  padding: 0 !important;\n",
                            "  margin: 0;\n",
                            "}\n",
                            "\n",
                            ".xr-dim-list li {\n",
                            "  display: inline-block;\n",
                            "  padding: 0;\n",
                            "  margin: 0;\n",
                            "}\n",
                            "\n",
                            ".xr-dim-list:before {\n",
                            "  content: '(';\n",
                            "}\n",
                            "\n",
                            ".xr-dim-list:after {\n",
                            "  content: ')';\n",
                            "}\n",
                            "\n",
                            ".xr-dim-list li:not(:last-child):after {\n",
                            "  content: ',';\n",
                            "  padding-right: 5px;\n",
                            "}\n",
                            "\n",
                            ".xr-has-index {\n",
                            "  font-weight: bold;\n",
                            "}\n",
                            "\n",
                            ".xr-var-list,\n",
                            ".xr-var-item {\n",
                            "  display: contents;\n",
                            "}\n",
                            "\n",
                            ".xr-var-item > div,\n",
                            ".xr-var-item label,\n",
                            ".xr-var-item > .xr-var-name span {\n",
                            "  background-color: var(--xr-background-color-row-even);\n",
                            "  margin-bottom: 0;\n",
                            "}\n",
                            "\n",
                            ".xr-var-item > .xr-var-name:hover span {\n",
                            "  padding-right: 5px;\n",
                            "}\n",
                            "\n",
                            ".xr-var-list > li:nth-child(odd) > div,\n",
                            ".xr-var-list > li:nth-child(odd) > label,\n",
                            ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
                            "  background-color: var(--xr-background-color-row-odd);\n",
                            "}\n",
                            "\n",
                            ".xr-var-name {\n",
                            "  grid-column: 1;\n",
                            "}\n",
                            "\n",
                            ".xr-var-dims {\n",
                            "  grid-column: 2;\n",
                            "}\n",
                            "\n",
                            ".xr-var-dtype {\n",
                            "  grid-column: 3;\n",
                            "  text-align: right;\n",
                            "  color: var(--xr-font-color2);\n",
                            "}\n",
                            "\n",
                            ".xr-var-preview {\n",
                            "  grid-column: 4;\n",
                            "}\n",
                            "\n",
                            ".xr-index-preview {\n",
                            "  grid-column: 2 / 5;\n",
                            "  color: var(--xr-font-color2);\n",
                            "}\n",
                            "\n",
                            ".xr-var-name,\n",
                            ".xr-var-dims,\n",
                            ".xr-var-dtype,\n",
                            ".xr-preview,\n",
                            ".xr-attrs dt {\n",
                            "  white-space: nowrap;\n",
                            "  overflow: hidden;\n",
                            "  text-overflow: ellipsis;\n",
                            "  padding-right: 10px;\n",
                            "}\n",
                            "\n",
                            ".xr-var-name:hover,\n",
                            ".xr-var-dims:hover,\n",
                            ".xr-var-dtype:hover,\n",
                            ".xr-attrs dt:hover {\n",
                            "  overflow: visible;\n",
                            "  width: auto;\n",
                            "  z-index: 1;\n",
                            "}\n",
                            "\n",
                            ".xr-var-attrs,\n",
                            ".xr-var-data,\n",
                            ".xr-index-data {\n",
                            "  display: none;\n",
                            "  background-color: var(--xr-background-color) !important;\n",
                            "  padding-bottom: 5px !important;\n",
                            "}\n",
                            "\n",
                            ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
                            ".xr-var-data-in:checked ~ .xr-var-data,\n",
                            ".xr-index-data-in:checked ~ .xr-index-data {\n",
                            "  display: block;\n",
                            "}\n",
                            "\n",
                            ".xr-var-data > table {\n",
                            "  float: right;\n",
                            "}\n",
                            "\n",
                            ".xr-var-name span,\n",
                            ".xr-var-data,\n",
                            ".xr-index-name div,\n",
                            ".xr-index-data,\n",
                            ".xr-attrs {\n",
                            "  padding-left: 25px !important;\n",
                            "}\n",
                            "\n",
                            ".xr-attrs,\n",
                            ".xr-var-attrs,\n",
                            ".xr-var-data,\n",
                            ".xr-index-data {\n",
                            "  grid-column: 1 / -1;\n",
                            "}\n",
                            "\n",
                            "dl.xr-attrs {\n",
                            "  padding: 0;\n",
                            "  margin: 0;\n",
                            "  display: grid;\n",
                            "  grid-template-columns: 125px auto;\n",
                            "}\n",
                            "\n",
                            ".xr-attrs dt,\n",
                            ".xr-attrs dd {\n",
                            "  padding: 0;\n",
                            "  margin: 0;\n",
                            "  float: left;\n",
                            "  padding-right: 10px;\n",
                            "  width: auto;\n",
                            "}\n",
                            "\n",
                            ".xr-attrs dt {\n",
                            "  font-weight: normal;\n",
                            "  grid-column: 1;\n",
                            "}\n",
                            "\n",
                            ".xr-attrs dt:hover span {\n",
                            "  display: inline-block;\n",
                            "  background: var(--xr-background-color);\n",
                            "  padding-right: 10px;\n",
                            "}\n",
                            "\n",
                            ".xr-attrs dd {\n",
                            "  grid-column: 2;\n",
                            "  white-space: pre-wrap;\n",
                            "  word-break: break-all;\n",
                            "}\n",
                            "\n",
                            ".xr-icon-database,\n",
                            ".xr-icon-file-text2,\n",
                            ".xr-no-icon {\n",
                            "  display: inline-block;\n",
                            "  vertical-align: middle;\n",
                            "  width: 1em;\n",
                            "  height: 1.5em !important;\n",
                            "  stroke-width: 0;\n",
                            "  stroke: currentColor;\n",
                            "  fill: currentColor;\n",
                            "}\n",
                            "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;n&#x27; (y: 5, x: 5)&gt;\n",
                            "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]])\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",
                            "Attributes:\n",
                            "    long_name:  Number of observations\n",
                            "    units:      -</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'n'</div><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><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-532ab63a-83f9-4f5d-abf2-e0a6b06959ad' class='xr-array-in' type='checkbox' checked><label for='section-532ab63a-83f9-4f5d-abf2-e0a6b06959ad' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>3 17 0 0 0 0 49 9 0 0 0 34 51 0 0 13 70 33 15 27 37 79 0 20 75</span></div><div class='xr-array-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></div></li><li class='xr-section-item'><input id='section-bcfd071b-1fcf-4913-baa8-a73ea88e62e7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bcfd071b-1fcf-4913-baa8-a73ea88e62e7' 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-1dd736bd-89c9-4e1a-a90e-b0c0b9cffcd0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1dd736bd-89c9-4e1a-a90e-b0c0b9cffcd0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c709b9b5-93a0-4e6f-bc3b-fa77ae1518b2' class='xr-var-data-in' type='checkbox'><label for='data-c709b9b5-93a0-4e6f-bc3b-fa77ae1518b2' 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-4d0cae08-0591-41de-a424-966f0e920f7d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4d0cae08-0591-41de-a424-966f0e920f7d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-282bfb29-7c7e-4d8b-ac1d-b430d0e5ad69' class='xr-var-data-in' type='checkbox'><label for='data-282bfb29-7c7e-4d8b-ac1d-b430d0e5ad69' 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-499c941a-550c-4acc-80bb-2f4ff9747aa4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-499c941a-550c-4acc-80bb-2f4ff9747aa4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-444f672c-5b87-4019-81be-f0db594c8d58' class='xr-var-data-in' type='checkbox'><label for='data-444f672c-5b87-4019-81be-f0db594c8d58' 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-4a3af47b-1108-4ccc-8e56-ee0a4e4f164c' class='xr-section-summary-in' type='checkbox'  ><label for='section-4a3af47b-1108-4ccc-8e56-ee0a4e4f164c' 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-95f10a48-91fd-4645-8704-3845bb82370f' class='xr-index-data-in' type='checkbox'/><label for='index-95f10a48-91fd-4645-8704-3845bb82370f' 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(Index([50.6045, 51.655, 52.703, 53.751, 54.799], dtype=&#x27;float64&#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-75563e56-b682-471c-a680-e9932fed6607' class='xr-index-data-in' type='checkbox'/><label for='index-75563e56-b682-471c-a680-e9932fed6607' 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(Index([-0.43599999999999994, 1.543, 3.5169999999999995, 5.4915,\n",
                            "       7.465999999999999],\n",
                            "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cc14a124-b0d0-40f9-9576-f2254b82a053' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cc14a124-b0d0-40f9-9576-f2254b82a053' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Number of observations</dd><dt><span>units :</span></dt><dd>-</dd></dl></div></li></ul></div></div>"
                        ],
                        "text/plain": [
                            "<xarray.DataArray 'n' (y: 5, x: 5)>\n",
                            "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]])\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",
                            "Attributes:\n",
                            "    long_name:  Number of observations\n",
                            "    units:      -"
                        ]
                    },
                    "execution_count": 19,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "gs['n'].data"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Plot using xarray - convenient methods coming soon!"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 20,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 1000x500 with 4 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "fig, axes = plt.subplots(ncols=2, nrows=1, figsize = (10, 5))\n",
                "gs.n.plot(ax=axes[0])\n",
                "gs.bias.plot(ax=axes[1]);"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Minimum number of observations"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 21,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 1000x500 with 4 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "gs = cmp.gridded_skill(metrics=['bias'], n_min=25)\n",
                "fig, axes = plt.subplots(ncols=2, nrows=1, figsize=(10, 5))\n",
                "gs.n.plot(ax=axes[0])\n",
                "gs.bias.plot(ax=axes[1]);"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Multiple bins - gridded skill for water level categories\n",
                "\n",
                "Get data from comparer as dataframe and add a water level category as a new column."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 22,
            "metadata": {},
            "outputs": [],
            "source": [
                "dftmp = cmp.data.to_dataframe()\n",
                "dftmp[\"wl category\"] = 'high'\n",
                "dftmp.loc[dftmp['HD']<0, \"wl category\"] = 'low'"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Add the \"wl category\" to the comparer's data structure."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 23,
            "metadata": {},
            "outputs": [
                {
                    "data": {
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                            "</svg>\n",
                            "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
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                            "\n",
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                            ".xr-index-data,\n",
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                            "\n",
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                            ".xr-var-data,\n",
                            ".xr-index-data {\n",
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                            "\n",
                            ".xr-attrs dt,\n",
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                            "\n",
                            ".xr-attrs dt {\n",
                            "  font-weight: normal;\n",
                            "  grid-column: 1;\n",
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                            "\n",
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                            "  white-space: pre-wrap;\n",
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                            "\n",
                            ".xr-icon-database,\n",
                            ".xr-icon-file-text2,\n",
                            ".xr-no-icon {\n",
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                            "  stroke: currentColor;\n",
                            "  fill: currentColor;\n",
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                            "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
                            "Dimensions:      (time: 532)\n",
                            "Coordinates:\n",
                            "  * time         (time) datetime64[ns] 2017-10-27T10:45:19 ... 2017-10-29T13:...\n",
                            "    x            (time) float64 1.262 1.231 1.2 1.168 ... 6.908 6.971 7.034\n",
                            "    y            (time) float64 55.3 55.24 55.18 55.13 ... 55.24 55.28 55.32\n",
                            "    z            float64 nan\n",
                            "Data variables:\n",
                            "    Observation  (time) float64 0.3778 0.4375 0.4489 ... 0.8562 0.8368 0.8218\n",
                            "    HD           (time) float32 0.3699 0.356 0.3559 ... 0.7068 0.7068 0.685\n",
                            "    wl category  (time) object &#x27;high&#x27; &#x27;high&#x27; &#x27;high&#x27; ... &#x27;high&#x27; &#x27;high&#x27; &#x27;high&#x27;\n",
                            "Attributes:\n",
                            "    gtype:               track\n",
                            "    modelskill_version:  1.0.dev23\n",
                            "    weight:              1.0\n",
                            "    name:                alti</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-bb5821ce-2fb3-4467-abb0-c233b8c51ec2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bb5821ce-2fb3-4467-abb0-c233b8c51ec2' 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'>time</span>: 532</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-db842c5d-7a04-4254-af2f-1b4205d2f17a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-db842c5d-7a04-4254-af2f-1b4205d2f17a' class='xr-section-summary' >Coordinates: <span>(4)</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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2017-10-27T10:45:19 ... 2017-10-...</div><input id='attrs-79c8c7b6-3d34-4a6b-a8d2-486c0d408587' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-79c8c7b6-3d34-4a6b-a8d2-486c0d408587' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-deaacac6-5b44-47a1-a806-dab62b163ed1' class='xr-var-data-in' type='checkbox'><label for='data-deaacac6-5b44-47a1-a806-dab62b163ed1' 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;2017-10-27T10:45:19.000000000&#x27;, &#x27;2017-10-27T10:45:20.000000000&#x27;,\n",
                            "       &#x27;2017-10-27T10:45:21.000000000&#x27;, ..., &#x27;2017-10-29T13:10:42.000000000&#x27;,\n",
                            "       &#x27;2017-10-29T13:10:43.000000000&#x27;, &#x27;2017-10-29T13:10:44.000000000&#x27;],\n",
                            "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.262 1.231 1.2 ... 6.971 7.034</div><input id='attrs-c836c018-dea3-4750-b121-b80c23a660d8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c836c018-dea3-4750-b121-b80c23a660d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-498603a6-5a74-4495-b8ae-ded1f1ea312b' class='xr-var-data-in' type='checkbox'><label for='data-498603a6-5a74-4495-b8ae-ded1f1ea312b' 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([ 1.2624630e+00,  1.2309900e+00,  1.1995940e+00,  1.1682770e+00,\n",
                            "        1.1370370e+00,  1.1058750e+00,  1.0747890e+00,  1.0437800e+00,\n",
                            "        1.0128470e+00,  9.8198900e-01,  9.5120700e-01,  9.2050000e-01,\n",
                            "        8.8986800e-01,  8.5931000e-01,  8.2882500e-01,  7.9841400e-01,\n",
                            "        7.6807700e-01,  7.3781200e-01,  7.0762000e-01,  6.7750000e-01,\n",
                            "        6.4745100e-01,  6.1747500e-01,  5.8756900e-01,  5.5773400e-01,\n",
                            "        5.2796900e-01,  4.9827500e-01,  4.6865000e-01,  4.3909500e-01,\n",
                            "        4.0960900e-01,  3.8019200e-01,  3.5084300e-01,  3.2156200e-01,\n",
                            "        2.4319076e+00,  2.4228545e+00,  2.4137889e+00,  2.4047105e+00,\n",
                            "        2.3956195e+00,  2.3773988e+00,  2.3682692e+00,  2.3591266e+00,\n",
                            "        2.3499710e+00,  2.3408023e+00,  2.3316204e+00,  2.3224254e+00,\n",
                            "        2.3132170e+00,  2.3039954e+00,  2.2947604e+00,  2.2855120e+00,\n",
                            "        2.2762500e+00,  2.2669744e+00,  2.2576852e+00,  2.2483823e+00,\n",
                            "        2.2390656e+00,  2.2203907e+00,  2.2110324e+00,  2.2016601e+00,\n",
                            "        2.1922736e+00,  2.1828730e+00,  2.1734582e+00,  2.1640292e+00,\n",
                            "        2.1545858e+00,  2.1451279e+00,  2.1356556e+00,  2.1261688e+00,\n",
                            "        2.1166673e+00,  2.1071511e+00,  2.0976202e+00,  2.0880744e+00,\n",
                            "        2.0785138e+00,  2.0689381e+00,  2.0497417e+00,  2.0401207e+00,\n",
                            "        2.0304845e+00,  2.0208330e+00,  2.0111660e+00,  2.0014836e+00,\n",
                            "        1.9917857e+00,  1.9820721e+00,  1.9723428e+00,  1.9625978e+00,\n",
                            "...\n",
                            "        2.3503190e+00,  2.4043610e+00,  2.4585100e+00,  2.5127660e+00,\n",
                            "        2.5671290e+00,  2.6216000e+00,  2.6761780e+00,  2.7308640e+00,\n",
                            "        2.7856590e+00,  2.8405620e+00,  2.8955740e+00,  2.9506950e+00,\n",
                            "        3.0059250e+00,  3.0612650e+00,  3.1167160e+00,  3.1722760e+00,\n",
                            "        3.2279470e+00,  3.2837290e+00,  3.3396210e+00,  3.3956260e+00,\n",
                            "        3.4517420e+00,  3.5079700e+00,  3.5643100e+00,  3.6207620e+00,\n",
                            "        3.6773280e+00,  3.7340060e+00,  3.7907980e+00,  3.8477040e+00,\n",
                            "        3.9047240e+00,  3.9618570e+00,  4.0191060e+00,  4.0764690e+00,\n",
                            "        4.1339470e+00,  4.1915410e+00,  4.2492510e+00,  4.3070760e+00,\n",
                            "        4.3650180e+00,  4.4230760e+00,  4.4812520e+00,  4.5395440e+00,\n",
                            "        4.5979540e+00,  4.6564820e+00,  4.7151270e+00,  4.7738910e+00,\n",
                            "        4.8327740e+00,  4.8917750e+00,  4.9508960e+00,  5.0101360e+00,\n",
                            "        5.0694970e+00,  5.1289770e+00,  5.1885780e+00,  5.2482990e+00,\n",
                            "        5.3081410e+00,  5.3681050e+00,  5.4281900e+00,  5.4883980e+00,\n",
                            "        5.5487270e+00,  5.6091790e+00,  5.6697540e+00,  5.7304520e+00,\n",
                            "        5.7912730e+00,  5.8522180e+00,  5.9132870e+00,  5.9744800e+00,\n",
                            "        6.0357980e+00,  6.0972410e+00,  6.1588090e+00,  6.2823220e+00,\n",
                            "        6.3442670e+00,  6.4063390e+00,  6.4685380e+00,  6.5308630e+00,\n",
                            "        6.5933160e+00,  6.6558970e+00,  6.7186060e+00,  6.7814420e+00,\n",
                            "        6.8444080e+00,  6.9075020e+00,  6.9707260e+00,  7.0340790e+00])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>55.3 55.24 55.18 ... 55.28 55.32</div><input id='attrs-8d618f84-0f8e-4a39-b048-4e9c5084bb8c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8d618f84-0f8e-4a39-b048-4e9c5084bb8c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-161d5191-1a70-4104-a0cd-680fdbc7b437' class='xr-var-data-in' type='checkbox'><label for='data-161d5191-1a70-4104-a0cd-680fdbc7b437' 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([55.298639 , 55.241321 , 55.183996 , 55.126665 , 55.069327 ,\n",
                            "       55.011982 , 54.954631 , 54.897273 , 54.839909 , 54.782539 ,\n",
                            "       54.725162 , 54.667778 , 54.610388 , 54.552992 , 54.49559  ,\n",
                            "       54.438181 , 54.380766 , 54.323345 , 54.265917 , 54.208484 ,\n",
                            "       54.151044 , 54.093598 , 54.036146 , 53.978688 , 53.921224 ,\n",
                            "       53.863753 , 53.806277 , 53.748795 , 53.691307 , 53.633813 ,\n",
                            "       53.576313 , 53.518807 , 51.1964343, 51.2533518, 51.3102683,\n",
                            "       51.367184 , 51.4240987, 51.537926 , 51.5948382, 51.6517496,\n",
                            "       51.7086602, 51.76557  , 51.8224787, 51.8793867, 51.936294 ,\n",
                            "       51.9932003, 52.0501051, 52.1070096, 52.1639132, 52.2208162,\n",
                            "       52.2777181, 52.3346193, 52.3915194, 52.5053174, 52.5622149,\n",
                            "       52.6191117, 52.6760077, 52.7329027, 52.7897968, 52.8466901,\n",
                            "       52.9035824, 52.960474 , 53.0173645, 53.074254 , 53.1311428,\n",
                            "       53.1880309, 53.2449181, 53.3018044, 53.3586897, 53.4155743,\n",
                            "       53.5293406, 53.5862226, 53.6431035, 53.6999837, 53.7568629,\n",
                            "       53.8137413, 53.8706187, 53.9274953, 53.9843709, 54.0412454,\n",
                            "       54.098119 , 54.1549921, 54.2118642, 54.2687355, 54.3256057,\n",
                            "       54.382475 , 54.4962113, 54.553078 , 54.6099438, 54.6668088,\n",
                            "       54.7236728, 54.780536 , 54.8373982, 54.8942595, 54.95112  ,\n",
                            "       55.0079794, 55.0648375, 55.1216952, 55.1785519, 55.2354079,\n",
                            "...\n",
                            "       51.455426 , 51.496692 , 51.53793  , 51.57914  , 51.620322 ,\n",
                            "       51.661477 , 51.702604 , 51.743702 , 51.784773 , 51.825815 ,\n",
                            "       51.86683  , 51.907815 , 51.948773 , 51.989701 , 52.030601 ,\n",
                            "       52.071473 , 52.112315 , 52.153129 , 52.193913 , 52.234668 ,\n",
                            "       52.275395 , 52.316091 , 52.356759 , 52.397397 , 52.438005 ,\n",
                            "       52.478584 , 52.519132 , 52.559651 , 52.60014  , 52.640599 ,\n",
                            "       52.681028 , 52.721426 , 52.761794 , 52.802132 , 52.842439 ,\n",
                            "       52.882715 , 52.922961 , 52.963176 , 53.003359 , 53.043512 ,\n",
                            "       53.083633 , 53.123724 , 53.163782 , 53.20381  , 53.243806 ,\n",
                            "       53.283769 , 53.323702 , 53.363602 , 53.40347  , 53.443307 ,\n",
                            "       53.483111 , 53.522882 , 53.562622 , 53.602329 , 53.642003 ,\n",
                            "       53.681644 , 53.721253 , 53.760828 , 53.800371 , 53.83988  ,\n",
                            "       53.879356 , 53.918799 , 53.958208 , 53.997584 , 54.036926 ,\n",
                            "       54.076234 , 54.115508 , 54.154748 , 54.193954 , 54.233126 ,\n",
                            "       54.272263 , 54.311366 , 54.350434 , 54.389467 , 54.428466 ,\n",
                            "       54.46743  , 54.506358 , 54.545252 , 54.58411  , 54.622932 ,\n",
                            "       54.661719 , 54.700471 , 54.739187 , 54.777867 , 54.855118 ,\n",
                            "       54.89369  , 54.932225 , 54.970723 , 55.009185 , 55.047611 ,\n",
                            "       55.085999 , 55.124351 , 55.162665 , 55.200942 , 55.239182 ,\n",
                            "       55.277385 , 55.31555  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan</div><input id='attrs-f5680389-b922-4391-bb8b-fa1b1463371f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f5680389-b922-4391-bb8b-fa1b1463371f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-703a206b-1fa9-407c-919a-d7f1f819677d' class='xr-var-data-in' type='checkbox'><label for='data-703a206b-1fa9-407c-919a-d7f1f819677d' 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(nan)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-46d2d404-80da-446a-b6eb-b50ced6693b3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-46d2d404-80da-446a-b6eb-b50ced6693b3' class='xr-section-summary' >Data variables: <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>Observation</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.3778 0.4375 ... 0.8368 0.8218</div><input id='attrs-046262f9-f3c3-4794-8253-763e15370705' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-046262f9-f3c3-4794-8253-763e15370705' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b323638-5d82-4c5d-be2b-8b83957fcb67' class='xr-var-data-in' type='checkbox'><label for='data-1b323638-5d82-4c5d-be2b-8b83957fcb67' 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></dd><dt><span>units :</span></dt><dd></dd><dt><span>kind :</span></dt><dd>observation</dd><dt><span>color :</span></dt><dd>#d62728</dd></dl></div><div class='xr-var-data'><pre>array([ 0.3778,  0.4375,  0.4489,  0.4711,  0.4678,  0.5024,  0.5429,\n",
                            "        0.584 ,  0.5625,  0.6037,  0.6133,  0.6338,  0.6482,  0.6385,\n",
                            "        0.6824,  0.6905,  0.6954,  0.7481,  0.7695,  0.7657,  0.7927,\n",
                            "        0.8042,  0.8435,  0.8758,  0.9098,  0.9281,  1.0135,  1.0442,\n",
                            "        1.1025,  1.1779,  1.2357,  1.2884, -0.5083, -0.506 , -0.437 ,\n",
                            "       -0.4558, -0.4174, -0.3721, -0.339 , -0.302 , -0.25  , -0.1807,\n",
                            "       -0.1276, -0.1098, -0.0382,  0.0054,  0.0501,  0.1032,  0.1238,\n",
                            "        0.1738,  0.1864,  0.265 ,  0.3251,  0.3698,  0.3665,  0.3852,\n",
                            "        0.3896,  0.4671,  0.4414,  0.471 ,  0.4546,  0.4844,  0.4338,\n",
                            "        0.487 ,  0.458 ,  0.4572,  0.4592,  0.4119,  0.4301,  0.4158,\n",
                            "        0.3859,  0.4234,  0.3773,  0.3467,  0.3258,  0.2946,  0.3025,\n",
                            "        0.2804,  0.2625,  0.2852,  0.2762,  0.2485,  0.2137,  0.2281,\n",
                            "        0.17  ,  0.1838,  0.205 ,  0.1952,  0.162 ,  0.1314,  0.1402,\n",
                            "        0.1288,  0.0977,  0.1088,  0.0799,  0.021 ,  0.0065, -0.0273,\n",
                            "        0.0117, -0.0234, -0.0074,  0.3918,  0.3286,  0.3577,  0.3811,\n",
                            "        0.3698,  0.3304,  0.3576,  0.3842,  0.3406,  0.3589,  0.3941,\n",
                            "        0.3512,  0.3411,  0.3555,  0.3418,  0.2989,  0.292 ,  0.3341,\n",
                            "        0.3608,  0.3604,  0.3558,  0.3067,  0.3621,  0.2993,  0.2834,\n",
                            "        0.3294,  0.3093,  0.2776,  0.245 ,  0.2482,  0.2867,  0.1857,\n",
                            "        0.2494,  0.2225,  1.219 ,  1.2172,  1.2268,  1.2307,  1.2273,\n",
                            "...\n",
                            "        0.183 ,  1.2829,  1.3124,  1.2756,  1.2202,  1.204 ,  1.1605,\n",
                            "        1.2002,  1.2039,  1.2307,  1.2151,  1.1634,  1.0952,  1.1142,\n",
                            "        1.0802,  1.0727,  1.0536,  1.0119,  0.9656,  0.9728,  0.9813,\n",
                            "        0.9698,  0.9979,  0.9672,  0.9242,  0.9098,  0.8882,  0.8759,\n",
                            "        0.8368,  0.8731,  0.8064,  0.8199,  0.8675,  0.8544,  0.8069,\n",
                            "        0.7771,  0.753 ,  0.6954, -0.4108, -0.4066, -0.4572, -0.4903,\n",
                            "       -0.4522, -0.0665, -0.122 , -0.0804, -0.0264, -0.0309,  0.0175,\n",
                            "        0.0301,  0.0643,  0.0861,  0.0877,  0.1356,  0.1878,  0.2797,\n",
                            "        0.2345,  0.3212,  0.3309,  0.3368,  0.3873,  0.4347,  0.4313,\n",
                            "        0.4716,  0.473 ,  0.5719,  0.5185,  0.5664,  0.6304,  0.6318,\n",
                            "        0.7146,  0.737 ,  0.6474,  0.6977,  0.7506,  0.8584,  0.8072,\n",
                            "        0.8446,  0.8694,  0.8546,  0.8971,  0.9012,  0.9497,  0.9861,\n",
                            "        1.0477,  0.996 ,  1.0681,  1.0409,  1.0564,  1.0139,  1.0568,\n",
                            "        1.1473,  1.1096,  1.1158,  1.0753,  1.1388,  1.1439,  1.1677,\n",
                            "        1.1982,  1.1331,  1.2567,  1.1535,  1.2455,  1.1809,  1.2228,\n",
                            "        1.2054,  1.1702,  1.1167,  1.1228,  1.1234,  1.1102,  1.1485,\n",
                            "        1.1599,  1.1248,  1.1867,  1.0446,  1.094 ,  1.0638,  1.0441,\n",
                            "        1.1125,  1.1013,  1.0331,  1.1585,  1.0049,  1.0158,  1.0632,\n",
                            "        0.9968,  1.0605,  1.0035,  1.0703,  1.0284,  1.0866,  0.9857,\n",
                            "        0.9519,  0.9803,  0.9243,  0.9137,  0.8562,  0.8368,  0.8218])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HD</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.3699 0.356 ... 0.7068 0.685</div><input id='attrs-ad2d422e-1633-4b6c-ad54-9472fd70b676' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ad2d422e-1633-4b6c-ad54-9472fd70b676' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1202c6ab-4da6-403f-b471-2320038022e2' class='xr-var-data-in' type='checkbox'><label for='data-1202c6ab-4da6-403f-b471-2320038022e2' 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>name :</span></dt><dd>HD</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>eumType :</span></dt><dd>EUMType.Surface_Elevation</dd><dt><span>eumUnit :</span></dt><dd>EUMUnit.meter</dd><dt><span>long_name :</span></dt><dd>Surface Elevation</dd><dt><span>kind :</span></dt><dd>model</dd><dt><span>color :</span></dt><dd>#5ad45a</dd></dl></div><div class='xr-var-data'><pre>array([ 3.69931817e-01,  3.55970353e-01,  3.55910420e-01,  3.85431111e-01,\n",
                            "        4.25780147e-01,  4.25716728e-01,  4.25653338e-01,  4.59940106e-01,\n",
                            "        4.59872961e-01,  4.59805846e-01,  5.35151005e-01,  5.35086930e-01,\n",
                            "        5.53068757e-01,  5.53002000e-01,  5.91679752e-01,  5.91613472e-01,\n",
                            "        6.14721179e-01,  6.14651024e-01,  6.92026496e-01,  6.91959083e-01,\n",
                            "        7.34060585e-01,  7.33989537e-01,  7.33918488e-01,  8.07319760e-01,\n",
                            "        8.50688815e-01,  8.50617766e-01,  9.51076686e-01,  9.51010704e-01,\n",
                            "        9.50944722e-01,  1.15824819e+00,  1.15820146e+00,  1.15815473e+00,\n",
                            "       -5.56605160e-01, -5.56446075e-01, -4.88739014e-01, -4.88595307e-01,\n",
                            "       -4.88451600e-01, -3.41673672e-01, -3.41565728e-01, -3.41457784e-01,\n",
                            "       -2.42190570e-01, -2.42096007e-01, -2.42001429e-01, -1.15995137e-02,\n",
                            "       -1.32777486e-02, -1.32092135e-02, -1.31406784e-02,  6.84411526e-02,\n",
                            "        1.56138435e-01,  1.56195790e-01,  1.56253159e-01,  2.38782272e-01,\n",
                            "        2.29255423e-01,  3.29792768e-01,  3.29814523e-01,  3.29836279e-01,\n",
                            "        3.30011189e-01,  4.00271535e-01,  4.00268555e-01,  4.00265574e-01,\n",
                            "        4.00262564e-01,  4.23327267e-01,  4.23296958e-01,  4.23266619e-01,\n",
                            "        3.92616272e-01,  3.92576545e-01,  3.90274525e-01,  3.90222490e-01,\n",
                            "        3.46917361e-01,  3.46859008e-01,  3.03083003e-01,  3.03013802e-01,\n",
                            "        2.79559374e-01,  2.79490113e-01,  2.37180129e-01,  1.90825284e-01,\n",
                            "        1.90746069e-01,  1.90666869e-01,  1.65146843e-01,  1.52750924e-01,\n",
                            "...\n",
                            "        5.06607115e-01,  5.37447989e-01,  5.37445962e-01,  5.83109856e-01,\n",
                            "        5.83105445e-01,  6.11486018e-01,  6.11477613e-01,  5.91955543e-01,\n",
                            "        6.55335128e-01,  6.55320168e-01,  6.55305266e-01,  6.55290365e-01,\n",
                            "        7.75347829e-01,  7.75327146e-01,  7.75306463e-01,  7.33285964e-01,\n",
                            "        8.11623633e-01,  8.11599314e-01,  8.11574996e-01,  8.44816923e-01,\n",
                            "        8.82582068e-01,  8.82555127e-01,  9.07505155e-01,  9.07477558e-01,\n",
                            "        9.39957857e-01,  9.39929783e-01,  9.62679744e-01,  9.78279293e-01,\n",
                            "        9.93009984e-01,  9.92986023e-01,  1.01252019e+00,  1.01249659e+00,\n",
                            "        1.02222669e+00,  1.02220666e+00,  1.02218652e+00,  9.93419528e-01,\n",
                            "        9.93408680e-01,  1.01949608e+00,  1.01948714e+00,  1.01947832e+00,\n",
                            "        9.99739408e-01,  9.99734640e-01,  1.00588322e+00,  9.79266524e-01,\n",
                            "        9.79267716e-01,  9.73056495e-01,  9.73062456e-01,  9.24553573e-01,\n",
                            "        9.24561977e-01,  8.91650498e-01,  8.91657352e-01,  8.29208255e-01,\n",
                            "        8.36624622e-01,  8.36636007e-01,  7.95329869e-01,  8.00262630e-01,\n",
                            "        8.00277770e-01,  8.00292969e-01,  7.71054685e-01,  7.71070004e-01,\n",
                            "        7.71085262e-01,  7.71100581e-01,  7.48452067e-01,  7.19396591e-01,\n",
                            "        7.19412446e-01,  7.19428241e-01,  7.26109326e-01,  7.26125777e-01,\n",
                            "        7.26142287e-01,  7.46161699e-01,  7.23324955e-01,  7.23343730e-01,\n",
                            "        7.06753433e-01,  7.06772268e-01,  7.06791103e-01,  6.84967458e-01],\n",
                            "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wl category</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;high&#x27; &#x27;high&#x27; ... &#x27;high&#x27; &#x27;high&#x27;</div><input id='attrs-93e3f299-9bf6-49a2-9200-d345df165636' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-93e3f299-9bf6-49a2-9200-d345df165636' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0b47fb5c-bb96-4319-8888-5c232d391da8' class='xr-var-data-in' type='checkbox'><label for='data-0b47fb5c-bb96-4319-8888-5c232d391da8' 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;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;,\n",
                            "       &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;,\n",
                            "       &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;,\n",
                            "       &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;,\n",
                            "       &#x27;low&#x27;, &#x27;low&#x27;, &#x27;low&#x27;, &#x27;low&#x27;, &#x27;low&#x27;, &#x27;low&#x27;, &#x27;low&#x27;, &#x27;low&#x27;, &#x27;low&#x27;,\n",
                            "       &#x27;low&#x27;, &#x27;low&#x27;, &#x27;low&#x27;, &#x27;low&#x27;, &#x27;low&#x27;, &#x27;low&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;,\n",
                            "       &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;,\n",
                            "       &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;,\n",
                            "       &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;,\n",
                            "       &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;,\n",
                            "       &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;,\n",
                            "       &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;low&#x27;, &#x27;low&#x27;, &#x27;low&#x27;, &#x27;low&#x27;,\n",
                            "       &#x27;low&#x27;, &#x27;low&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;,\n",
                            "       &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;,\n",
                            "       &#x27;high&#x27;, &#x27;high&#x27;, &#x27;low&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;,\n",
                            "       &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;,\n",
                            "       &#x27;high&#x27;, &#x27;low&#x27;, &#x27;low&#x27;, &#x27;low&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;,\n",
                            "       &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;,\n",
                            "       &#x27;high&#x27;, &#x27;high&#x27;, &#x27;low&#x27;, &#x27;low&#x27;, &#x27;low&#x27;, &#x27;low&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;,\n",
                            "       &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;,\n",
                            "...\n",
                            "       &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;low&#x27;, &#x27;low&#x27;,\n",
                            "       &#x27;low&#x27;, &#x27;low&#x27;, &#x27;low&#x27;, &#x27;low&#x27;, &#x27;low&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;,\n",
                            "       &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;,\n",
                            "       &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;,\n",
                            "       &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;,\n",
                            "       &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;,\n",
                            "       &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;,\n",
                            "       &#x27;high&#x27;, &#x27;low&#x27;, &#x27;low&#x27;, &#x27;low&#x27;, &#x27;low&#x27;, &#x27;low&#x27;, &#x27;low&#x27;, &#x27;high&#x27;, &#x27;high&#x27;,\n",
                            "       &#x27;low&#x27;, &#x27;low&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;,\n",
                            "       &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;,\n",
                            "       &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;,\n",
                            "       &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;,\n",
                            "       &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;,\n",
                            "       &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;,\n",
                            "       &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;,\n",
                            "       &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;,\n",
                            "       &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;,\n",
                            "       &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;,\n",
                            "       &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;,\n",
                            "       &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-279ddfab-81a0-44fc-ac7a-c6b55df2dde4' class='xr-section-summary-in' type='checkbox'  ><label for='section-279ddfab-81a0-44fc-ac7a-c6b55df2dde4' class='xr-section-summary' >Indexes: <span>(1)</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>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-26dd395f-9e82-4ad7-94d9-96a2a406789c' class='xr-index-data-in' type='checkbox'/><label for='index-26dd395f-9e82-4ad7-94d9-96a2a406789c' 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(DatetimeIndex([&#x27;2017-10-27 10:45:19&#x27;, &#x27;2017-10-27 10:45:20&#x27;,\n",
                            "               &#x27;2017-10-27 10:45:21&#x27;, &#x27;2017-10-27 10:45:22&#x27;,\n",
                            "               &#x27;2017-10-27 10:45:23&#x27;, &#x27;2017-10-27 10:45:24&#x27;,\n",
                            "               &#x27;2017-10-27 10:45:25&#x27;, &#x27;2017-10-27 10:45:26&#x27;,\n",
                            "               &#x27;2017-10-27 10:45:27&#x27;, &#x27;2017-10-27 10:45:28&#x27;,\n",
                            "               ...\n",
                            "               &#x27;2017-10-29 13:10:35&#x27;, &#x27;2017-10-29 13:10:36&#x27;,\n",
                            "               &#x27;2017-10-29 13:10:37&#x27;, &#x27;2017-10-29 13:10:38&#x27;,\n",
                            "               &#x27;2017-10-29 13:10:39&#x27;, &#x27;2017-10-29 13:10:40&#x27;,\n",
                            "               &#x27;2017-10-29 13:10:41&#x27;, &#x27;2017-10-29 13:10:42&#x27;,\n",
                            "               &#x27;2017-10-29 13:10:43&#x27;, &#x27;2017-10-29 13:10:44&#x27;],\n",
                            "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=532, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1bb9eab3-d64d-46f3-b203-1f7ff3dddb67' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1bb9eab3-d64d-46f3-b203-1f7ff3dddb67' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>gtype :</span></dt><dd>track</dd><dt><span>modelskill_version :</span></dt><dd>1.0.dev23</dd><dt><span>weight :</span></dt><dd>1.0</dd><dt><span>name :</span></dt><dd>alti</dd></dl></div></li></ul></div></div>"
                        ],
                        "text/plain": [
                            "<xarray.Dataset>\n",
                            "Dimensions:      (time: 532)\n",
                            "Coordinates:\n",
                            "  * time         (time) datetime64[ns] 2017-10-27T10:45:19 ... 2017-10-29T13:...\n",
                            "    x            (time) float64 1.262 1.231 1.2 1.168 ... 6.908 6.971 7.034\n",
                            "    y            (time) float64 55.3 55.24 55.18 55.13 ... 55.24 55.28 55.32\n",
                            "    z            float64 nan\n",
                            "Data variables:\n",
                            "    Observation  (time) float64 0.3778 0.4375 0.4489 ... 0.8562 0.8368 0.8218\n",
                            "    HD           (time) float32 0.3699 0.356 0.3559 ... 0.7068 0.7068 0.685\n",
                            "    wl category  (time) object 'high' 'high' 'high' ... 'high' 'high' 'high'\n",
                            "Attributes:\n",
                            "    gtype:               track\n",
                            "    modelskill_version:  1.0.dev23\n",
                            "    weight:              1.0\n",
                            "    name:                alti"
                        ]
                    },
                    "execution_count": 23,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "cmp.data[\"wl category\"] = dftmp[\"wl category\"]\n",
                "cmp.data"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Now aggregate the data by the new column (and x and y):"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 24,
            "metadata": {},
            "outputs": [
                {
                    "data": {
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                            "  display: none;\n",
                            "}\n",
                            "\n",
                            ".xr-section-summary,\n",
                            ".xr-section-inline-details {\n",
                            "  padding-top: 4px;\n",
                            "  padding-bottom: 4px;\n",
                            "}\n",
                            "\n",
                            ".xr-section-inline-details {\n",
                            "  grid-column: 2 / -1;\n",
                            "}\n",
                            "\n",
                            ".xr-section-details {\n",
                            "  display: none;\n",
                            "  grid-column: 1 / -1;\n",
                            "  margin-bottom: 5px;\n",
                            "}\n",
                            "\n",
                            ".xr-section-summary-in:checked ~ .xr-section-details {\n",
                            "  display: contents;\n",
                            "}\n",
                            "\n",
                            ".xr-array-wrap {\n",
                            "  grid-column: 1 / -1;\n",
                            "  display: grid;\n",
                            "  grid-template-columns: 20px auto;\n",
                            "}\n",
                            "\n",
                            ".xr-array-wrap > label {\n",
                            "  grid-column: 1;\n",
                            "  vertical-align: top;\n",
                            "}\n",
                            "\n",
                            ".xr-preview {\n",
                            "  color: var(--xr-font-color3);\n",
                            "}\n",
                            "\n",
                            ".xr-array-preview,\n",
                            ".xr-array-data {\n",
                            "  padding: 0 5px !important;\n",
                            "  grid-column: 2;\n",
                            "}\n",
                            "\n",
                            ".xr-array-data,\n",
                            ".xr-array-in:checked ~ .xr-array-preview {\n",
                            "  display: none;\n",
                            "}\n",
                            "\n",
                            ".xr-array-in:checked ~ .xr-array-data,\n",
                            ".xr-array-preview {\n",
                            "  display: inline-block;\n",
                            "}\n",
                            "\n",
                            ".xr-dim-list {\n",
                            "  display: inline-block !important;\n",
                            "  list-style: none;\n",
                            "  padding: 0 !important;\n",
                            "  margin: 0;\n",
                            "}\n",
                            "\n",
                            ".xr-dim-list li {\n",
                            "  display: inline-block;\n",
                            "  padding: 0;\n",
                            "  margin: 0;\n",
                            "}\n",
                            "\n",
                            ".xr-dim-list:before {\n",
                            "  content: '(';\n",
                            "}\n",
                            "\n",
                            ".xr-dim-list:after {\n",
                            "  content: ')';\n",
                            "}\n",
                            "\n",
                            ".xr-dim-list li:not(:last-child):after {\n",
                            "  content: ',';\n",
                            "  padding-right: 5px;\n",
                            "}\n",
                            "\n",
                            ".xr-has-index {\n",
                            "  font-weight: bold;\n",
                            "}\n",
                            "\n",
                            ".xr-var-list,\n",
                            ".xr-var-item {\n",
                            "  display: contents;\n",
                            "}\n",
                            "\n",
                            ".xr-var-item > div,\n",
                            ".xr-var-item label,\n",
                            ".xr-var-item > .xr-var-name span {\n",
                            "  background-color: var(--xr-background-color-row-even);\n",
                            "  margin-bottom: 0;\n",
                            "}\n",
                            "\n",
                            ".xr-var-item > .xr-var-name:hover span {\n",
                            "  padding-right: 5px;\n",
                            "}\n",
                            "\n",
                            ".xr-var-list > li:nth-child(odd) > div,\n",
                            ".xr-var-list > li:nth-child(odd) > label,\n",
                            ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
                            "  background-color: var(--xr-background-color-row-odd);\n",
                            "}\n",
                            "\n",
                            ".xr-var-name {\n",
                            "  grid-column: 1;\n",
                            "}\n",
                            "\n",
                            ".xr-var-dims {\n",
                            "  grid-column: 2;\n",
                            "}\n",
                            "\n",
                            ".xr-var-dtype {\n",
                            "  grid-column: 3;\n",
                            "  text-align: right;\n",
                            "  color: var(--xr-font-color2);\n",
                            "}\n",
                            "\n",
                            ".xr-var-preview {\n",
                            "  grid-column: 4;\n",
                            "}\n",
                            "\n",
                            ".xr-index-preview {\n",
                            "  grid-column: 2 / 5;\n",
                            "  color: var(--xr-font-color2);\n",
                            "}\n",
                            "\n",
                            ".xr-var-name,\n",
                            ".xr-var-dims,\n",
                            ".xr-var-dtype,\n",
                            ".xr-preview,\n",
                            ".xr-attrs dt {\n",
                            "  white-space: nowrap;\n",
                            "  overflow: hidden;\n",
                            "  text-overflow: ellipsis;\n",
                            "  padding-right: 10px;\n",
                            "}\n",
                            "\n",
                            ".xr-var-name:hover,\n",
                            ".xr-var-dims:hover,\n",
                            ".xr-var-dtype:hover,\n",
                            ".xr-attrs dt:hover {\n",
                            "  overflow: visible;\n",
                            "  width: auto;\n",
                            "  z-index: 1;\n",
                            "}\n",
                            "\n",
                            ".xr-var-attrs,\n",
                            ".xr-var-data,\n",
                            ".xr-index-data {\n",
                            "  display: none;\n",
                            "  background-color: var(--xr-background-color) !important;\n",
                            "  padding-bottom: 5px !important;\n",
                            "}\n",
                            "\n",
                            ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
                            ".xr-var-data-in:checked ~ .xr-var-data,\n",
                            ".xr-index-data-in:checked ~ .xr-index-data {\n",
                            "  display: block;\n",
                            "}\n",
                            "\n",
                            ".xr-var-data > table {\n",
                            "  float: right;\n",
                            "}\n",
                            "\n",
                            ".xr-var-name span,\n",
                            ".xr-var-data,\n",
                            ".xr-index-name div,\n",
                            ".xr-index-data,\n",
                            ".xr-attrs {\n",
                            "  padding-left: 25px !important;\n",
                            "}\n",
                            "\n",
                            ".xr-attrs,\n",
                            ".xr-var-attrs,\n",
                            ".xr-var-data,\n",
                            ".xr-index-data {\n",
                            "  grid-column: 1 / -1;\n",
                            "}\n",
                            "\n",
                            "dl.xr-attrs {\n",
                            "  padding: 0;\n",
                            "  margin: 0;\n",
                            "  display: grid;\n",
                            "  grid-template-columns: 125px auto;\n",
                            "}\n",
                            "\n",
                            ".xr-attrs dt,\n",
                            ".xr-attrs dd {\n",
                            "  padding: 0;\n",
                            "  margin: 0;\n",
                            "  float: left;\n",
                            "  padding-right: 10px;\n",
                            "  width: auto;\n",
                            "}\n",
                            "\n",
                            ".xr-attrs dt {\n",
                            "  font-weight: normal;\n",
                            "  grid-column: 1;\n",
                            "}\n",
                            "\n",
                            ".xr-attrs dt:hover span {\n",
                            "  display: inline-block;\n",
                            "  background: var(--xr-background-color);\n",
                            "  padding-right: 10px;\n",
                            "}\n",
                            "\n",
                            ".xr-attrs dd {\n",
                            "  grid-column: 2;\n",
                            "  white-space: pre-wrap;\n",
                            "  word-break: break-all;\n",
                            "}\n",
                            "\n",
                            ".xr-icon-database,\n",
                            ".xr-icon-file-text2,\n",
                            ".xr-no-icon {\n",
                            "  display: inline-block;\n",
                            "  vertical-align: middle;\n",
                            "  width: 1em;\n",
                            "  height: 1.5em !important;\n",
                            "  stroke-width: 0;\n",
                            "  stroke: currentColor;\n",
                            "  fill: currentColor;\n",
                            "}\n",
                            "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
                            "Dimensions:      (y: 5, x: 5, wl category: 2)\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",
                            "  * wl category  (wl category) object &#x27;high&#x27; &#x27;low&#x27;\n",
                            "Data variables:\n",
                            "    n            (y, x, wl category) int32 0 3 14 3 0 0 0 0 ... 6 0 0 20 0 71 4\n",
                            "    bias         (y, x, wl category) float64 nan nan -0.2252 ... nan -0.1077 nan</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-3b9de8bd-6f90-4028-ac91-7bec59f34a25' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3b9de8bd-6f90-4028-ac91-7bec59f34a25' 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><li><span class='xr-has-index'>wl category</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ce643ea8-a420-4527-8753-7c00d53c339a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ce643ea8-a420-4527-8753-7c00d53c339a' 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-5bde2c1b-28e2-46ed-b813-428af09b2529' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5bde2c1b-28e2-46ed-b813-428af09b2529' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-94445e44-8a55-4449-979f-c675b8ad3c05' class='xr-var-data-in' type='checkbox'><label for='data-94445e44-8a55-4449-979f-c675b8ad3c05' 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-70fe6372-2024-4433-b90f-302604812479' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-70fe6372-2024-4433-b90f-302604812479' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fcf71bd3-110c-475d-b63b-19210a049824' class='xr-var-data-in' type='checkbox'><label for='data-fcf71bd3-110c-475d-b63b-19210a049824' 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 class='xr-has-index'>wl category</span></div><div class='xr-var-dims'>(wl category)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;high&#x27; &#x27;low&#x27;</div><input id='attrs-e5222d11-3774-4d72-9e64-26f05019f789' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e5222d11-3774-4d72-9e64-26f05019f789' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e1094ca-3f64-473e-a1fd-f926575f1e3e' class='xr-var-data-in' type='checkbox'><label for='data-1e1094ca-3f64-473e-a1fd-f926575f1e3e' 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;high&#x27;, &#x27;low&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f27d2ad1-fd69-4c8c-8f6c-01556e8867b8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f27d2ad1-fd69-4c8c-8f6c-01556e8867b8' class='xr-section-summary' >Data variables: <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-var-name'><span>n</span></div><div class='xr-var-dims'>(y, x, wl category)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 3 14 3 0 0 0 ... 6 0 0 20 0 71 4</div><input id='attrs-9755df8b-75f7-4571-88dc-b259ee7163e0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9755df8b-75f7-4571-88dc-b259ee7163e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-41a904d8-1cd8-477a-8336-4d3e20b524a8' class='xr-var-data-in' type='checkbox'><label for='data-41a904d8-1cd8-477a-8336-4d3e20b524a8' 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([[[ 0,  3],\n",
                            "        [14,  3],\n",
                            "        [ 0,  0],\n",
                            "        [ 0,  0],\n",
                            "        [ 0,  0]],\n",
                            "\n",
                            "       [[ 0,  0],\n",
                            "        [25, 24],\n",
                            "        [ 9,  0],\n",
                            "        [ 0,  0],\n",
                            "        [ 0,  0]],\n",
                            "\n",
                            "       [[ 0,  0],\n",
                            "        [34,  0],\n",
                            "        [51,  0],\n",
                            "        [ 0,  0],\n",
                            "        [ 0,  0]],\n",
                            "\n",
                            "       [[13,  0],\n",
                            "        [70,  0],\n",
                            "        [33,  0],\n",
                            "        [15,  0],\n",
                            "        [27,  0]],\n",
                            "\n",
                            "       [[33,  4],\n",
                            "        [73,  6],\n",
                            "        [ 0,  0],\n",
                            "        [20,  0],\n",
                            "        [71,  4]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bias</span></div><div class='xr-var-dims'>(y, x, wl category)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan -0.2252 ... nan -0.1077 nan</div><input id='attrs-6529aef9-5c30-4d17-a16e-f4db2b6d6e47' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6529aef9-5c30-4d17-a16e-f4db2b6d6e47' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0fdb1650-1321-411b-bc2e-21d8f809ec66' class='xr-var-data-in' type='checkbox'><label for='data-0fdb1650-1321-411b-bc2e-21d8f809ec66' 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([[[            nan,             nan],\n",
                            "        [-2.25224493e-01,             nan],\n",
                            "        [            nan,             nan],\n",
                            "        [            nan,             nan],\n",
                            "        [            nan,             nan]],\n",
                            "\n",
                            "       [[            nan,             nan],\n",
                            "        [ 2.16653079e-04, -1.53287844e-02],\n",
                            "        [ 3.85089565e-02,             nan],\n",
                            "        [            nan,             nan],\n",
                            "        [            nan,             nan]],\n",
                            "\n",
                            "       [[            nan,             nan],\n",
                            "        [-2.57227292e-02,             nan],\n",
                            "        [-3.13038143e-02,             nan],\n",
                            "        [            nan,             nan],\n",
                            "        [            nan,             nan]],\n",
                            "\n",
                            "       [[-3.49104723e-02,             nan],\n",
                            "        [-6.60847299e-02,             nan],\n",
                            "        [-9.43395039e-02,             nan],\n",
                            "        [-1.85222249e-01,             nan],\n",
                            "        [ 6.94456386e-02,             nan]],\n",
                            "\n",
                            "       [[-1.61167687e-02,             nan],\n",
                            "        [-6.96253560e-02, -3.24485935e-02],\n",
                            "        [            nan,             nan],\n",
                            "        [-2.47456596e-01,             nan],\n",
                            "        [-1.07691332e-01,             nan]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-76f116d0-9772-416e-9a7f-c873d6107d8e' class='xr-section-summary-in' type='checkbox'  ><label for='section-76f116d0-9772-416e-9a7f-c873d6107d8e' class='xr-section-summary' >Indexes: <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-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fef608d1-e43f-4745-98ef-e793e28099d5' class='xr-index-data-in' type='checkbox'/><label for='index-fef608d1-e43f-4745-98ef-e793e28099d5' 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(Index([50.6045, 51.655, 52.703, 53.751, 54.799], dtype=&#x27;float64&#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-12ce45fd-d91a-495c-b6d4-df7d81287aaf' class='xr-index-data-in' type='checkbox'/><label for='index-12ce45fd-d91a-495c-b6d4-df7d81287aaf' 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(Index([-0.43599999999999994, 1.543, 3.5169999999999995, 5.4915,\n",
                            "       7.465999999999999],\n",
                            "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>wl category</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ea49e573-8e75-4d2d-a611-3891a290acdb' class='xr-index-data-in' type='checkbox'/><label for='index-ea49e573-8e75-4d2d-a611-3891a290acdb' 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(Index([&#x27;high&#x27;, &#x27;low&#x27;], dtype=&#x27;object&#x27;, name=&#x27;wl category&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ebc68d75-5550-454a-8b15-b708c64d46ac' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ebc68d75-5550-454a-8b15-b708c64d46ac' 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, wl category: 2)\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",
                            "  * wl category  (wl category) object 'high' 'low'\n",
                            "Data variables:\n",
                            "    n            (y, x, wl category) int32 0 3 14 3 0 0 0 0 ... 6 0 0 20 0 71 4\n",
                            "    bias         (y, x, wl category) float64 nan nan -0.2252 ... nan -0.1077 nan"
                        ]
                    },
                    "execution_count": 24,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "gs = cmp.gridded_skill(by=['wl category'], metrics=['bias'], n_min=5)\n",
                "gs"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 25,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 700x300 with 3 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "gs.bias.plot();"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Multiple observations\n",
                "\n",
                "Add fake 2nd observation to model"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 26,
            "metadata": {},
            "outputs": [
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": [
                        "C:\\Users\\jem\\Source\\modelskill\\modelskill\\timeseries\\_track.py:135: UserWarning: Removed 22 duplicate timestamps with keep=first\n",
                        "  warnings.warn(\n"
                    ]
                }
            ],
            "source": [
                "import warnings\n",
                "\n",
                "df2 = df.copy()\n",
                "df2['surface_elevation'] = df2['surface_elevation'] - 0.2\n",
                "o2 = ms.TrackObservation(df2, item=2, name='alti2')\n",
                "\n",
                "warnings.filterwarnings('ignore', message=\"duplicate\")\n",
                "cmp2 = ms.match(o2, mr)"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Extract, gridded skill, add attrs, plot."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 27,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 1000x500 with 3 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "cmp = cmp + cmp2\n",
                "gs = cmp.gridded_skill(metrics=['bias'], n_min=20)\n",
                "gs.bias.data.attrs = dict(long_name=\"Bias of surface elevation\", units=\"m\")\n",
                "gs.bias.plot(figsize=(10,5));"
            ]
        },
        {
            "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",
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