{
    "cells": [
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# Track Comparison\n",
                "\n",
                "And 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",
                "set_matplotlib_formats('png')\n",
                "\n",
                "import modelskill as ms"
            ]
        },
        {
            "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.ModelResult(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='meter')"
                        ]
                    },
                    "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": [
                        "/home/jan/src/modelskill/modelskill/utils.py:134: UserWarning: Time axis has duplicate entries. Now adding milliseconds to non-unique entries to make index unique.\n",
                        "  warnings.warn(\n"
                    ]
                },
                {
                    "data": {
                        "text/plain": [
                            "TrackObservation: alti, n=1115"
                        ]
                    },
                    "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": {
                        "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": [
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": [
                        "C:\\Users\\rywm\\Repos\\modelskill\\modelskill\\comparison\\_comparison.py:301: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
                        "  res = df.groupby(by=by).apply(calc_metrics)\n"
                    ]
                },
                {
                    "data": {
                        "text/plain": [
                            "<Comparer>\n",
                            "Quantity: Surface Elevation [meter]\n",
                            "Observation: alti, n_points=544\n",
                            " Model: HD, rmse=0.115"
                        ]
                    },
                    "execution_count": 8,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "cc = ms.compare(o1, mr)[0]\n",
                "cc"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "metadata": {},
            "outputs": [
                {
                    "data": {
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                            "  --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: 544)\n",
                            "Coordinates:\n",
                            "  * time         (time) datetime64[ns] 2017-10-27T10:45:19 ... 2017-10-29T13:...\n",
                            "Data variables:\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",
                            "    HD           (time) float32 0.3699 0.356 0.3559 ... 0.7068 0.7068 0.685\n",
                            "    Observation  (time) float64 0.3778 0.4375 0.4489 ... 0.8562 0.8368 0.8218\n",
                            "Attributes:\n",
                            "    gtype:               track\n",
                            "    name:                alti\n",
                            "    quantity_name:       Surface Elevation\n",
                            "    modelskill_version:  1.0.dev2</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-29b2e35a-c0ba-4fcc-9ae6-aa57272aacd5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-29b2e35a-c0ba-4fcc-9ae6-aa57272aacd5' 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>: 544</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7b9dcb61-aebf-40e7-a3e4-6f12f866e503' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7b9dcb61-aebf-40e7-a3e4-6f12f866e503' class='xr-section-summary' >Coordinates: <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 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-084b4d6a-c239-4367-9095-1bcfdab48f4a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-084b4d6a-c239-4367-9095-1bcfdab48f4a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-839cb119-bcf8-435e-94a9-72693b7b64ac' class='xr-var-data-in' type='checkbox'><label for='data-839cb119-bcf8-435e-94a9-72693b7b64ac' 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></ul></div></li><li class='xr-section-item'><input id='section-f11a6e45-ef91-4bc2-8c77-6123e9e251bd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f11a6e45-ef91-4bc2-8c77-6123e9e251bd' class='xr-section-summary' >Data variables: <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>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-92e255de-5c32-4dca-890c-17fb534e7baf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-92e255de-5c32-4dca-890c-17fb534e7baf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-723731cf-9085-4626-b03c-3d99da57a04f' class='xr-var-data-in' type='checkbox'><label for='data-723731cf-9085-4626-b03c-3d99da57a04f' 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>kind :</span></dt><dd>position</dd></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",
                            "        1.0437800e+00,  1.0128470e+00,  9.8198900e-01,  9.5120700e-01,\n",
                            "        9.2050000e-01,  8.8986800e-01,  8.8986800e-01,  8.5931000e-01,\n",
                            "        8.2882500e-01,  7.9841400e-01,  7.6807700e-01,  7.3781200e-01,\n",
                            "        7.0762000e-01,  6.7750000e-01,  6.4745100e-01,  6.1747500e-01,\n",
                            "        5.8756900e-01,  5.5773400e-01,  5.2796900e-01,  4.9827500e-01,\n",
                            "        4.6865000e-01,  4.3909500e-01,  4.0960900e-01,  3.8019200e-01,\n",
                            "        3.5084300e-01,  3.2156200e-01,  3.2156300e-01,  2.4319076e+00,\n",
                            "        2.4228545e+00,  2.4137889e+00,  2.4047105e+00,  2.3956195e+00,\n",
                            "        2.3865156e+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,  2.2297351e+00,  2.2203907e+00,  2.2110324e+00,\n",
                            "        2.2016601e+00,  2.1922736e+00,  2.1828730e+00,  2.1734582e+00,\n",
                            "        2.1640292e+00,  2.1545858e+00,  2.1451279e+00,  2.1356556e+00,\n",
                            "        2.1261688e+00,  2.1166673e+00,  2.1071511e+00,  2.0976202e+00,\n",
                            "        2.0880744e+00,  2.0785138e+00,  2.0689381e+00,  2.0593475e+00,\n",
                            "        2.0497417e+00,  2.0401207e+00,  2.0304845e+00,  2.0208330e+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-6d427bd1-f7cb-4a8d-b267-7e995baa6c32' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6d427bd1-f7cb-4a8d-b267-7e995baa6c32' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c94d061-52ff-4986-b1ed-6bd8623176b7' class='xr-var-data-in' type='checkbox'><label for='data-1c94d061-52ff-4986-b1ed-6bd8623176b7' 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>kind :</span></dt><dd>position</dd></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 , 54.897274 , 54.839909 ,\n",
                            "       54.782539 , 54.725162 , 54.667778 , 54.610388 , 54.610389 ,\n",
                            "       54.552992 , 54.49559  , 54.438181 , 54.380766 , 54.323345 ,\n",
                            "       54.265917 , 54.208484 , 54.151044 , 54.093598 , 54.036146 ,\n",
                            "       53.978688 , 53.921224 , 53.863753 , 53.806277 , 53.748795 ,\n",
                            "       53.691307 , 53.633813 , 53.576313 , 53.518807 , 53.518807 ,\n",
                            "       51.1964343, 51.2533518, 51.3102683, 51.367184 , 51.4240987,\n",
                            "       51.4810129, 51.537926 , 51.5948382, 51.6517496, 51.7086602,\n",
                            "       51.76557  , 51.8224787, 51.8793867, 51.936294 , 51.9932003,\n",
                            "       52.0501051, 52.1070096, 52.1639132, 52.2208162, 52.2777181,\n",
                            "       52.3346193, 52.3915194, 52.448419 , 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",
                            "       53.472458 , 53.5293406, 53.5862226, 53.6431035, 53.6999837,\n",
                            "       53.7568629, 53.8137413, 53.8706187, 53.9274953, 53.9843709,\n",
                            "       54.0412454, 54.098119 , 54.1549921, 54.2118642, 54.2687355,\n",
                            "       54.3256057, 54.382475 , 54.4962113, 54.4393436, 54.553078 ,\n",
                            "       54.6099438, 54.6668088, 54.7236728, 54.780536 , 54.8373982,\n",
                            "...\n",
                            "       50.749779 , 50.7915   , 51.455426 , 51.496692 , 51.53793  ,\n",
                            "       51.57914  , 51.620322 , 51.661477 , 51.702604 , 51.743702 ,\n",
                            "       51.784773 , 51.825815 , 51.86683  , 51.907815 , 51.948773 ,\n",
                            "       51.989701 , 52.030601 , 52.071473 , 52.112315 , 52.153129 ,\n",
                            "       52.193913 , 52.234668 , 52.275395 , 52.316091 , 52.356759 ,\n",
                            "       52.397397 , 52.438005 , 52.478584 , 52.519132 , 52.559651 ,\n",
                            "       52.60014  , 52.640599 , 52.681028 , 52.721426 , 52.761794 ,\n",
                            "       52.802132 , 52.842439 , 52.882715 , 52.922961 , 52.963176 ,\n",
                            "       53.003359 , 53.043512 , 53.083633 , 53.123724 , 53.163782 ,\n",
                            "       53.20381  , 53.243806 , 53.283769 , 53.323702 , 53.363602 ,\n",
                            "       53.40347  , 53.443307 , 53.483111 , 53.522882 , 53.562622 ,\n",
                            "       53.602329 , 53.642003 , 53.681644 , 53.721253 , 53.760828 ,\n",
                            "       53.800371 , 53.83988  , 53.879356 , 53.918799 , 53.958208 ,\n",
                            "       53.997584 , 54.036926 , 54.076234 , 54.115508 , 54.154748 ,\n",
                            "       54.193954 , 54.233126 , 54.272263 , 54.311366 , 54.350434 ,\n",
                            "       54.389467 , 54.428466 , 54.46743  , 54.506358 , 54.545252 ,\n",
                            "       54.58411  , 54.622932 , 54.661719 , 54.700471 , 54.739187 ,\n",
                            "       54.777867 , 54.855118 , 54.89369  , 54.932225 , 54.970723 ,\n",
                            "       55.009185 , 55.047611 , 55.085999 , 55.124351 , 55.162665 ,\n",
                            "       55.200942 , 55.239182 , 55.277385 , 55.31555  ])</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-9f3d7905-c53f-45d9-ab6d-30e90576a47d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9f3d7905-c53f-45d9-ab6d-30e90576a47d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f666f29-de04-45e3-ab82-74e06a140791' class='xr-var-data-in' type='checkbox'><label for='data-7f666f29-de04-45e3-ab82-74e06a140791' 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>kind :</span></dt><dd>model</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,  4.59940046e-01,\n",
                            "        4.59939986e-01,  4.59872961e-01,  4.59805846e-01,  5.35151005e-01,\n",
                            "        5.35086930e-01,  5.53068697e-01,  5.53068638e-01,  5.53002000e-01,\n",
                            "        5.91679752e-01,  5.91613472e-01,  6.14721179e-01,  6.14651024e-01,\n",
                            "        6.92026496e-01,  6.91959083e-01,  7.34060585e-01,  7.33989537e-01,\n",
                            "        7.33918488e-01,  8.07319760e-01,  8.50688815e-01,  8.50617766e-01,\n",
                            "        9.51076686e-01,  9.51010704e-01,  9.50944722e-01,  1.15824819e+00,\n",
                            "        1.15820146e+00,  1.15815461e+00,  1.15815461e+00, -5.56605160e-01,\n",
                            "       -5.56446075e-01, -4.88739014e-01, -4.88595307e-01, -4.88451451e-01,\n",
                            "       -3.98261070e-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",
                            "        2.29255453e-01,  2.29255497e-01,  3.29792768e-01,  3.29814523e-01,\n",
                            "        3.29836279e-01,  3.30011189e-01,  4.00271535e-01,  4.00268555e-01,\n",
                            "        4.00265574e-01,  4.00262564e-01,  4.23327267e-01,  4.23296958e-01,\n",
                            "        4.23266619e-01,  3.92616272e-01,  3.92576545e-01,  3.90274525e-01,\n",
                            "        3.90222490e-01,  3.46917361e-01,  3.46858948e-01,  3.46858889e-01,\n",
                            "        3.03083003e-01,  3.03013802e-01,  2.79559374e-01,  2.79490113e-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>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-87f4da6b-2f07-4735-9eaf-8a0e9a912cd9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-87f4da6b-2f07-4735-9eaf-8a0e9a912cd9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23e01d14-0787-4b4b-b77a-6c056e4ba090' class='xr-var-data-in' type='checkbox'><label for='data-23e01d14-0787-4b4b-b77a-6c056e4ba090' 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>kind :</span></dt><dd>observation</dd><dt><span>unit :</span></dt><dd>meter</dd><dt><span>color :</span></dt><dd>#d62728</dd><dt><span>weight :</span></dt><dd>1.0</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 ,  0.5817,  0.5625,  0.6037,  0.6133,  0.6338,  0.6482,\n",
                            "        0.6437,  0.6385,  0.6824,  0.6905,  0.6954,  0.7481,  0.7695,\n",
                            "        0.7657,  0.7927,  0.8042,  0.8435,  0.8758,  0.9098,  0.9281,\n",
                            "        1.0135,  1.0442,  1.1025,  1.1779,  1.2357,  1.2884,  1.3084,\n",
                            "       -0.5083, -0.506 , -0.437 , -0.4558, -0.4174, -0.4214, -0.3721,\n",
                            "       -0.339 , -0.302 , -0.25  , -0.1807, -0.1276, -0.1098, -0.0382,\n",
                            "        0.0054,  0.0501,  0.1032,  0.1238,  0.1738,  0.1864,  0.265 ,\n",
                            "        0.3251,  0.3295,  0.3698,  0.3665,  0.3852,  0.3896,  0.4671,\n",
                            "        0.4414,  0.471 ,  0.4546,  0.4844,  0.4338,  0.487 ,  0.458 ,\n",
                            "        0.4572,  0.4592,  0.4119,  0.4301,  0.4158,  0.4034,  0.3859,\n",
                            "        0.4234,  0.3773,  0.3467,  0.3258,  0.2946,  0.3025,  0.2804,\n",
                            "        0.2625,  0.2852,  0.2762,  0.2485,  0.2137,  0.2281,  0.17  ,\n",
                            "        0.1838,  0.205 ,  0.175 ,  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",
                            "...\n",
                            "        1.2202,  1.204 ,  1.1605,  1.2002,  1.2039,  1.2307,  1.2151,\n",
                            "        1.1634,  1.0952,  1.1142,  1.0802,  1.0727,  1.0536,  1.0119,\n",
                            "        0.9656,  0.9914,  0.9728,  0.9813,  0.9698,  0.9979,  0.9672,\n",
                            "        0.9242,  0.9098,  0.8882,  0.8759,  0.8368,  0.8731,  0.8064,\n",
                            "        0.8199,  0.8675,  0.8544,  0.8069,  0.7771,  0.7606,  0.753 ,\n",
                            "        0.6954, -0.4108, -0.4066, -0.4572, -0.4903, -0.4522, -0.0665,\n",
                            "       -0.122 , -0.0804, -0.0264, -0.0309,  0.0175,  0.0301,  0.0643,\n",
                            "        0.0861,  0.0877,  0.1356,  0.1878,  0.2797,  0.2345,  0.3212,\n",
                            "        0.3309,  0.3368,  0.3873,  0.4347,  0.4313,  0.4716,  0.473 ,\n",
                            "        0.5719,  0.5185,  0.5664,  0.6304,  0.6318,  0.7146,  0.737 ,\n",
                            "        0.6474,  0.6977,  0.7506,  0.8584,  0.8072,  0.8446,  0.8694,\n",
                            "        0.8546,  0.8971,  0.9012,  0.9497,  0.9861,  1.0477,  0.996 ,\n",
                            "        1.0681,  1.0409,  1.0564,  1.0139,  1.0568,  1.1473,  1.1096,\n",
                            "        1.1158,  1.0753,  1.1388,  1.1439,  1.1677,  1.1982,  1.1331,\n",
                            "        1.2567,  1.1535,  1.2455,  1.1809,  1.2228,  1.2054,  1.1702,\n",
                            "        1.1167,  1.1228,  1.1234,  1.1102,  1.1485,  1.1599,  1.1248,\n",
                            "        1.1867,  1.0446,  1.094 ,  1.0638,  1.0441,  1.1125,  1.1013,\n",
                            "        1.0331,  1.1585,  1.0049,  1.0158,  1.0632,  0.9968,  1.0605,\n",
                            "        1.0035,  1.0703,  1.0284,  1.0866,  0.9857,  0.9519,  0.9803,\n",
                            "        0.9243,  0.9137,  0.8562,  0.8368,  0.8218])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e6f87575-5e62-4c5e-8cb9-6a448d61fb01' class='xr-section-summary-in' type='checkbox'  ><label for='section-e6f87575-5e62-4c5e-8cb9-6a448d61fb01' 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-9b3164d4-8fb2-46a1-8ba8-3859f20475b6' class='xr-index-data-in' type='checkbox'/><label for='index-9b3164d4-8fb2-46a1-8ba8-3859f20475b6' 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 10:45:26.001000&#x27;,\n",
                            "               &#x27;2017-10-27 10:45:26.002000&#x27;,        &#x27;2017-10-27 10:45:27&#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=544, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-38f14e35-199f-4d16-84e7-3899c3b7e642' class='xr-section-summary-in' type='checkbox'  checked><label for='section-38f14e35-199f-4d16-84e7-3899c3b7e642' 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>name :</span></dt><dd>alti</dd><dt><span>quantity_name :</span></dt><dd>Surface Elevation</dd><dt><span>modelskill_version :</span></dt><dd>1.0.dev2</dd></dl></div></li></ul></div></div>"
                        ],
                        "text/plain": [
                            "<xarray.Dataset>\n",
                            "Dimensions:      (time: 544)\n",
                            "Coordinates:\n",
                            "  * time         (time) datetime64[ns] 2017-10-27T10:45:19 ... 2017-10-29T13:...\n",
                            "Data variables:\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",
                            "    HD           (time) float32 0.3699 0.356 0.3559 ... 0.7068 0.7068 0.685\n",
                            "    Observation  (time) float64 0.3778 0.4375 0.4489 ... 0.8562 0.8368 0.8218\n",
                            "Attributes:\n",
                            "    gtype:               track\n",
                            "    name:                alti\n",
                            "    quantity_name:       Surface Elevation\n",
                            "    modelskill_version:  1.0.dev2"
                        ]
                    },
                    "execution_count": 9,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "cc.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": [
                "cc.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": [
                        "/home/jan/src/modelskill/modelskill/utils.py:134: UserWarning: Time axis has duplicate entries. Now adding milliseconds to non-unique entries to make index unique.\n",
                        "  warnings.warn(\n"
                    ]
                },
                {
                    "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>x</th>\n",
                            "      <th>y</th>\n",
                            "      <th>Model_surface_elevation</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>NaN</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2017-10-26 04:37:54</th>\n",
                            "      <td>8.221631</td>\n",
                            "      <td>54.948460</td>\n",
                            "      <td>NaN</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2017-10-26 04:37:55</th>\n",
                            "      <td>8.189390</td>\n",
                            "      <td>55.008549</td>\n",
                            "      <td>NaN</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2017-10-26 04:37:56</th>\n",
                            "      <td>8.157065</td>\n",
                            "      <td>55.068626</td>\n",
                            "      <td>NaN</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>NaN</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>...</th>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2017-10-30 20:54:43</th>\n",
                            "      <td>3.703790</td>\n",
                            "      <td>55.363529</td>\n",
                            "      <td>NaN</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2017-10-30 20:54:44</th>\n",
                            "      <td>3.672147</td>\n",
                            "      <td>55.420830</td>\n",
                            "      <td>NaN</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2017-10-30 20:54:45</th>\n",
                            "      <td>3.640425</td>\n",
                            "      <td>55.478127</td>\n",
                            "      <td>NaN</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2017-10-30 20:54:46</th>\n",
                            "      <td>3.608623</td>\n",
                            "      <td>55.535419</td>\n",
                            "      <td>NaN</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2017-10-30 20:54:47</th>\n",
                            "      <td>3.576741</td>\n",
                            "      <td>55.592701</td>\n",
                            "      <td>NaN</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "<p>1115 rows × 3 columns</p>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "                            x          y  Model_surface_elevation\n",
                            "2017-10-26 04:37:37  8.757272  53.926136                      NaN\n",
                            "2017-10-26 04:37:54  8.221631  54.948460                      NaN\n",
                            "2017-10-26 04:37:55  8.189390  55.008549                      NaN\n",
                            "2017-10-26 04:37:56  8.157065  55.068626                      NaN\n",
                            "2017-10-26 04:37:58  8.124656  55.128700                      NaN\n",
                            "...                       ...        ...                      ...\n",
                            "2017-10-30 20:54:43  3.703790  55.363529                      NaN\n",
                            "2017-10-30 20:54:44  3.672147  55.420830                      NaN\n",
                            "2017-10-30 20:54:45  3.640425  55.478127                      NaN\n",
                            "2017-10-30 20:54:46  3.608623  55.535419                      NaN\n",
                            "2017-10-30 20:54:47  3.576741  55.592701                      NaN\n",
                            "\n",
                            "[1115 rows x 3 columns]"
                        ]
                    },
                    "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'"
                        ]
                    },
                    "execution_count": 12,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "mr"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 13,
            "metadata": {},
            "outputs": [
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": [
                        "/home/jan/src/modelskill/modelskill/utils.py:134: UserWarning: Time axis has duplicate entries. Now adding milliseconds to non-unique entries to make index unique.\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": [
                {
                    "data": {
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                        ],
                        "text/plain": [
                            "<MULTIPOINT (8.757 53.926, 8.222 54.948, 8.189 55.009, 8.157 55.069, 8.125 5...>"
                        ]
                    },
                    "execution_count": 14,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "o1.geometry"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 15,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
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                            "    .dataframe thead th {\n",
                            "        text-align: right;\n",
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                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr style=\"text-align: right;\">\n",
                            "      <th></th>\n",
                            "      <th>x</th>\n",
                            "      <th>y</th>\n",
                            "      <th>surface_elevation</th>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>date</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",
                            "    </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",
                            "    </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",
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                            "    <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",
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                            "    <tr>\n",
                            "      <th>2017-10-26 04:37:58</th>\n",
                            "      <td>8.124656</td>\n",
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                            "      <td>1.0369</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>...</th>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2017-10-30 20:54:43</th>\n",
                            "      <td>3.703790</td>\n",
                            "      <td>55.363528</td>\n",
                            "      <td>0.1111</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2017-10-30 20:54:44</th>\n",
                            "      <td>3.672147</td>\n",
                            "      <td>55.420831</td>\n",
                            "      <td>0.0647</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2017-10-30 20:54:45</th>\n",
                            "      <td>3.640425</td>\n",
                            "      <td>55.478128</td>\n",
                            "      <td>0.0364</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2017-10-30 20:54:46</th>\n",
                            "      <td>3.608623</td>\n",
                            "      <td>55.535418</td>\n",
                            "      <td>0.0283</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2017-10-30 20:54:47</th>\n",
                            "      <td>3.576741</td>\n",
                            "      <td>55.592701</td>\n",
                            "      <td>0.0601</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "<p>1115 rows × 3 columns</p>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "                            x          y  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",
                            "2017-10-30 20:54:43  3.703790  55.363528             0.1111\n",
                            "2017-10-30 20:54:44  3.672147  55.420831             0.0647\n",
                            "2017-10-30 20:54:45  3.640425  55.478128             0.0364\n",
                            "2017-10-30 20:54:46  3.608623  55.535418             0.0283\n",
                            "2017-10-30 20:54:47  3.576741  55.592701             0.0601\n",
                            "\n",
                            "[1115 rows x 3 columns]"
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                    },
                    "execution_count": 15,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "o1.data"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 16,
            "metadata": {},
            "outputs": [],
            "source": [
                "cc = ms.compare(o1, mr)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 17,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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                        "text/plain": [
                            "<Figure size 800x800 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "cc.plot.scatter();"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Spatial skill\n",
                "\n",
                "Load model, load observation, add observation to model and extract."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 18,
            "metadata": {},
            "outputs": [
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": [
                        "/home/jan/src/modelskill/modelskill/utils.py:134: UserWarning: Time axis has duplicate entries. Now adding milliseconds to non-unique entries to make index unique.\n",
                        "  warnings.warn(\n"
                    ]
                },
                {
                    "data": {
                        "text/plain": [
                            "<ComparerCollection>\n",
                            "Comparer: alti"
                        ]
                    },
                    "execution_count": 18,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "fn = '../tests/testdata/NorthSeaHD_and_windspeed.dfsu'\n",
                "mr = ms.ModelResult(fn, name='HD', item=0)\n",
                "fn = '../tests/testdata/altimetry_NorthSea_20171027.csv'\n",
                "df = pd.read_csv(fn, index_col=0, parse_dates=True)\n",
                "o1 = ms.TrackObservation(df, item=2, name='alti')\n",
                "cc = ms.compare(o1, mr)\n",
                "cc"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Get metrics binned by a regular spatial grid, returns xarray Dataset"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 19,
            "metadata": {},
            "outputs": [
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": [
                        "C:\\Users\\rywm\\Repos\\modelskill\\modelskill\\comparison\\_comparison.py:301: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
                        "  res = df.groupby(by=by).apply(calc_metrics)\n"
                    ]
                }
            ],
            "source": [
                "ss = cc.spatial_skill(metrics=['bias'])"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 20,
            "metadata": {},
            "outputs": [
                {
                    "data": {
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                            "</defs>\n",
                            "</svg>\n",
                            "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
                            " *\n",
                            " */\n",
                            "\n",
                            ":root {\n",
                            "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
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                            "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
                            "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
                            "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
                            "  --xr-background-color: var(--jp-layout-color0, white);\n",
                            "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
                            "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
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                            "\n",
                            "html[theme=dark],\n",
                            "body[data-theme=dark],\n",
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                            "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
                            "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
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                            "  --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",
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                            "\n",
                            ".xr-wrap {\n",
                            "  display: block !important;\n",
                            "  min-width: 300px;\n",
                            "  max-width: 700px;\n",
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                            "\n",
                            ".xr-text-repr-fallback {\n",
                            "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
                            "  display: none;\n",
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                            "\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",
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                            "\n",
                            ".xr-obj-type {\n",
                            "  color: var(--xr-font-color2);\n",
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                            "\n",
                            ".xr-sections {\n",
                            "  padding-left: 0 !important;\n",
                            "  display: grid;\n",
                            "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
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                            "\n",
                            ".xr-section-item {\n",
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                            "\n",
                            ".xr-section-item input {\n",
                            "  display: none;\n",
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                            "\n",
                            ".xr-section-item input + label {\n",
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                            "\n",
                            ".xr-section-item input:enabled + label {\n",
                            "  cursor: pointer;\n",
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                            "\n",
                            ".xr-section-item input:enabled + label:hover {\n",
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                            "\n",
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                            "  color: var(--xr-font-color2);\n",
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                            "\n",
                            ".xr-section-summary > span {\n",
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                            "\n",
                            ".xr-section-summary-in:disabled + label {\n",
                            "  color: var(--xr-font-color2);\n",
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                            "\n",
                            ".xr-section-summary-in + label:before {\n",
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                            "  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",
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                            "\n",
                            ".xr-section-summary-in:checked + label:before {\n",
                            "  content: '▼';\n",
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                            "\n",
                            ".xr-section-summary-in:checked + label > span {\n",
                            "  display: none;\n",
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                            "\n",
                            ".xr-section-summary,\n",
                            ".xr-section-inline-details {\n",
                            "  padding-top: 4px;\n",
                            "  padding-bottom: 4px;\n",
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                            "\n",
                            ".xr-section-inline-details {\n",
                            "  grid-column: 2 / -1;\n",
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                            "\n",
                            ".xr-section-details {\n",
                            "  display: none;\n",
                            "  grid-column: 1 / -1;\n",
                            "  margin-bottom: 5px;\n",
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                            "\n",
                            ".xr-section-summary-in:checked ~ .xr-section-details {\n",
                            "  display: contents;\n",
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                            "\n",
                            ".xr-array-wrap {\n",
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                            "\n",
                            ".xr-array-wrap > label {\n",
                            "  grid-column: 1;\n",
                            "  vertical-align: top;\n",
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                            "\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",
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                            "\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",
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                            "\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",
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                            "\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, 50,  9,  0,  0],\n",
                            "       [ 0, 36, 51,  0,  0],\n",
                            "       [14, 72, 33, 15, 28],\n",
                            "       [37, 83,  0, 20, 76]])\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-429ac91a-803b-4331-b8cb-e6ee36f2114a' class='xr-array-in' type='checkbox' checked><label for='section-429ac91a-803b-4331-b8cb-e6ee36f2114a' 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 50 9 0 0 0 36 51 0 0 14 72 33 15 28 37 83 0 20 76</span></div><div class='xr-array-data'><pre>array([[ 3, 17,  0,  0,  0],\n",
                            "       [ 0, 50,  9,  0,  0],\n",
                            "       [ 0, 36, 51,  0,  0],\n",
                            "       [14, 72, 33, 15, 28],\n",
                            "       [37, 83,  0, 20, 76]])</pre></div></div></li><li class='xr-section-item'><input id='section-1bff012b-b5da-4eff-b6b2-a2035b9ac7c0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1bff012b-b5da-4eff-b6b2-a2035b9ac7c0' 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-b4872643-a666-412f-ac3a-8181b7df27ce' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b4872643-a666-412f-ac3a-8181b7df27ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-498c066b-99ff-4586-828d-fd0173929c60' class='xr-var-data-in' type='checkbox'><label for='data-498c066b-99ff-4586-828d-fd0173929c60' 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-ac117f84-0693-42da-9884-1891dc2ecbf3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ac117f84-0693-42da-9884-1891dc2ecbf3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-596b606f-61a0-41b2-aa0f-5108704c638c' class='xr-var-data-in' type='checkbox'><label for='data-596b606f-61a0-41b2-aa0f-5108704c638c' 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-cab0f76b-2186-4ad6-8f09-37978d84140e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cab0f76b-2186-4ad6-8f09-37978d84140e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b1655b9-d647-4437-97a2-1c7afd27f660' class='xr-var-data-in' type='checkbox'><label for='data-4b1655b9-d647-4437-97a2-1c7afd27f660' 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-938478c9-f27e-4580-a80b-0def52df0adf' class='xr-section-summary-in' type='checkbox'  ><label for='section-938478c9-f27e-4580-a80b-0def52df0adf' 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-ca580441-9047-4e8a-bf3f-0a5719667d50' class='xr-index-data-in' type='checkbox'/><label for='index-ca580441-9047-4e8a-bf3f-0a5719667d50' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CategoricalIndex([50.6045, 51.655, 52.703, 53.751, 54.799], categories=[50.6045, 51.6550, 52.7030, 53.7510, 54.7990], ordered=True, dtype=&#x27;category&#x27;, name=&#x27;y&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b00f0ff6-018c-456d-bbdd-4aa18eda6c5a' class='xr-index-data-in' type='checkbox'/><label for='index-b00f0ff6-018c-456d-bbdd-4aa18eda6c5a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CategoricalIndex([-0.43599999999999994, 1.543, 3.5169999999999995, 5.4915,\n",
                            "                  7.465999999999999],\n",
                            "                 categories=[-0.4360, 1.5430, 3.5170, 5.4915, 7.4660], ordered=True, dtype=&#x27;category&#x27;, name=&#x27;x&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-64650669-7178-49ac-a7f9-c12364988452' class='xr-section-summary-in' type='checkbox'  checked><label for='section-64650669-7178-49ac-a7f9-c12364988452' 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, 50,  9,  0,  0],\n",
                            "       [ 0, 36, 51,  0,  0],\n",
                            "       [14, 72, 33, 15, 28],\n",
                            "       [37, 83,  0, 20, 76]])\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": 20,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "ss.ds['n']"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Plot using xarray - convenient methods coming soon!"
            ]
        },
        {
            "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": [
                "fig, axes = plt.subplots(ncols=2, nrows=1, figsize = (10, 5))\n",
                "ss.plot('n', ax=axes[0])\n",
                "ss.plot('bias', ax=axes[1]);"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Minimum number of observations"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 22,
            "metadata": {},
            "outputs": [
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": [
                        "C:\\Users\\rywm\\Repos\\modelskill\\modelskill\\comparison\\_comparison.py:301: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
                        "  res = df.groupby(by=by).apply(calc_metrics)\n"
                    ]
                },
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 1000x500 with 4 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "ss = cc.spatial_skill(metrics=['bias'], n_min=25)\n",
                "fig, axes = plt.subplots(ncols=2, nrows=1, figsize=(10, 5))\n",
                "ss.plot('n', ax=axes[0])\n",
                "ss.plot('bias', ax=axes[1]);"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Multiple bins - spatial 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": 23,
            "metadata": {},
            "outputs": [],
            "source": [
                "all_df = cc.to_dataframe()\n",
                "all_df[\"wl category\"] = 'high'\n",
                "all_df.loc[all_df['mod_val']<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": 24,
            "metadata": {},
            "outputs": [
                {
                    "data": {
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                            "\n",
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                            "\n",
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                            "\n",
                            ".xr-attrs dt,\n",
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                            "\n",
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                            "  font-weight: normal;\n",
                            "  grid-column: 1;\n",
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                            "\n",
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                            "\n",
                            ".xr-icon-database,\n",
                            ".xr-icon-file-text2,\n",
                            ".xr-no-icon {\n",
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                            "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
                            "Dimensions:      (time: 544)\n",
                            "Coordinates:\n",
                            "  * time         (time) datetime64[ns] 2017-10-27T10:45:19 ... 2017-10-29T13:...\n",
                            "Data variables:\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",
                            "    HD           (time) float32 0.3699 0.356 0.3559 ... 0.7068 0.7068 0.685\n",
                            "    Observation  (time) float64 0.3778 0.4375 0.4489 ... 0.8562 0.8368 0.8218\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",
                            "    name:                alti\n",
                            "    quantity_name:       Undefined\n",
                            "    modelskill_version:  1.0.dev2</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-9f5de2a0-5d15-4647-9e36-64d87ef0a69f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9f5de2a0-5d15-4647-9e36-64d87ef0a69f' 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>: 544</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-cb6e3c1b-64e3-4e04-a255-8ba77cb51d27' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cb6e3c1b-64e3-4e04-a255-8ba77cb51d27' class='xr-section-summary' >Coordinates: <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 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-9e19d53e-4c6a-4b60-9201-00c3184c9448' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9e19d53e-4c6a-4b60-9201-00c3184c9448' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a3766fc-4273-40fb-b572-2ec2af30f1d0' class='xr-var-data-in' type='checkbox'><label for='data-4a3766fc-4273-40fb-b572-2ec2af30f1d0' 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></ul></div></li><li class='xr-section-item'><input id='section-520e2033-97b4-4f66-83d8-809d020ff6c4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-520e2033-97b4-4f66-83d8-809d020ff6c4' class='xr-section-summary' >Data variables: <span>(5)</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>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-bd906083-8f53-495d-9d00-322f25dfdefa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd906083-8f53-495d-9d00-322f25dfdefa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae88b348-880f-440c-a5ad-5b8b08ec13b8' class='xr-var-data-in' type='checkbox'><label for='data-ae88b348-880f-440c-a5ad-5b8b08ec13b8' 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>kind :</span></dt><dd>position</dd></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",
                            "        1.0437800e+00,  1.0128470e+00,  9.8198900e-01,  9.5120700e-01,\n",
                            "        9.2050000e-01,  8.8986800e-01,  8.8986800e-01,  8.5931000e-01,\n",
                            "        8.2882500e-01,  7.9841400e-01,  7.6807700e-01,  7.3781200e-01,\n",
                            "        7.0762000e-01,  6.7750000e-01,  6.4745100e-01,  6.1747500e-01,\n",
                            "        5.8756900e-01,  5.5773400e-01,  5.2796900e-01,  4.9827500e-01,\n",
                            "        4.6865000e-01,  4.3909500e-01,  4.0960900e-01,  3.8019200e-01,\n",
                            "        3.5084300e-01,  3.2156200e-01,  3.2156300e-01,  2.4319076e+00,\n",
                            "        2.4228545e+00,  2.4137889e+00,  2.4047105e+00,  2.3956195e+00,\n",
                            "        2.3865156e+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,  2.2297351e+00,  2.2203907e+00,  2.2110324e+00,\n",
                            "        2.2016601e+00,  2.1922736e+00,  2.1828730e+00,  2.1734582e+00,\n",
                            "        2.1640292e+00,  2.1545858e+00,  2.1451279e+00,  2.1356556e+00,\n",
                            "        2.1261688e+00,  2.1166673e+00,  2.1071511e+00,  2.0976202e+00,\n",
                            "        2.0880744e+00,  2.0785138e+00,  2.0689381e+00,  2.0593475e+00,\n",
                            "        2.0497417e+00,  2.0401207e+00,  2.0304845e+00,  2.0208330e+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-3b12dc1e-b8c3-4fe2-b5d4-b719f961cb04' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3b12dc1e-b8c3-4fe2-b5d4-b719f961cb04' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c74f5cc-6417-4126-8738-19e9dda980b1' class='xr-var-data-in' type='checkbox'><label for='data-4c74f5cc-6417-4126-8738-19e9dda980b1' 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>kind :</span></dt><dd>position</dd></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 , 54.897274 , 54.839909 ,\n",
                            "       54.782539 , 54.725162 , 54.667778 , 54.610388 , 54.610389 ,\n",
                            "       54.552992 , 54.49559  , 54.438181 , 54.380766 , 54.323345 ,\n",
                            "       54.265917 , 54.208484 , 54.151044 , 54.093598 , 54.036146 ,\n",
                            "       53.978688 , 53.921224 , 53.863753 , 53.806277 , 53.748795 ,\n",
                            "       53.691307 , 53.633813 , 53.576313 , 53.518807 , 53.518807 ,\n",
                            "       51.1964343, 51.2533518, 51.3102683, 51.367184 , 51.4240987,\n",
                            "       51.4810129, 51.537926 , 51.5948382, 51.6517496, 51.7086602,\n",
                            "       51.76557  , 51.8224787, 51.8793867, 51.936294 , 51.9932003,\n",
                            "       52.0501051, 52.1070096, 52.1639132, 52.2208162, 52.2777181,\n",
                            "       52.3346193, 52.3915194, 52.448419 , 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",
                            "       53.472458 , 53.5293406, 53.5862226, 53.6431035, 53.6999837,\n",
                            "       53.7568629, 53.8137413, 53.8706187, 53.9274953, 53.9843709,\n",
                            "       54.0412454, 54.098119 , 54.1549921, 54.2118642, 54.2687355,\n",
                            "       54.3256057, 54.382475 , 54.4962113, 54.4393436, 54.553078 ,\n",
                            "       54.6099438, 54.6668088, 54.7236728, 54.780536 , 54.8373982,\n",
                            "...\n",
                            "       50.749779 , 50.7915   , 51.455426 , 51.496692 , 51.53793  ,\n",
                            "       51.57914  , 51.620322 , 51.661477 , 51.702604 , 51.743702 ,\n",
                            "       51.784773 , 51.825815 , 51.86683  , 51.907815 , 51.948773 ,\n",
                            "       51.989701 , 52.030601 , 52.071473 , 52.112315 , 52.153129 ,\n",
                            "       52.193913 , 52.234668 , 52.275395 , 52.316091 , 52.356759 ,\n",
                            "       52.397397 , 52.438005 , 52.478584 , 52.519132 , 52.559651 ,\n",
                            "       52.60014  , 52.640599 , 52.681028 , 52.721426 , 52.761794 ,\n",
                            "       52.802132 , 52.842439 , 52.882715 , 52.922961 , 52.963176 ,\n",
                            "       53.003359 , 53.043512 , 53.083633 , 53.123724 , 53.163782 ,\n",
                            "       53.20381  , 53.243806 , 53.283769 , 53.323702 , 53.363602 ,\n",
                            "       53.40347  , 53.443307 , 53.483111 , 53.522882 , 53.562622 ,\n",
                            "       53.602329 , 53.642003 , 53.681644 , 53.721253 , 53.760828 ,\n",
                            "       53.800371 , 53.83988  , 53.879356 , 53.918799 , 53.958208 ,\n",
                            "       53.997584 , 54.036926 , 54.076234 , 54.115508 , 54.154748 ,\n",
                            "       54.193954 , 54.233126 , 54.272263 , 54.311366 , 54.350434 ,\n",
                            "       54.389467 , 54.428466 , 54.46743  , 54.506358 , 54.545252 ,\n",
                            "       54.58411  , 54.622932 , 54.661719 , 54.700471 , 54.739187 ,\n",
                            "       54.777867 , 54.855118 , 54.89369  , 54.932225 , 54.970723 ,\n",
                            "       55.009185 , 55.047611 , 55.085999 , 55.124351 , 55.162665 ,\n",
                            "       55.200942 , 55.239182 , 55.277385 , 55.31555  ])</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-a7f2a97e-4f99-44d5-990f-00f4669ffba7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a7f2a97e-4f99-44d5-990f-00f4669ffba7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b278c333-6865-40d7-b2d4-834a69e8c857' class='xr-var-data-in' type='checkbox'><label for='data-b278c333-6865-40d7-b2d4-834a69e8c857' 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>kind :</span></dt><dd>model</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,  4.59940046e-01,\n",
                            "        4.59939986e-01,  4.59872961e-01,  4.59805846e-01,  5.35151005e-01,\n",
                            "        5.35086930e-01,  5.53068697e-01,  5.53068638e-01,  5.53002000e-01,\n",
                            "        5.91679752e-01,  5.91613472e-01,  6.14721179e-01,  6.14651024e-01,\n",
                            "        6.92026496e-01,  6.91959083e-01,  7.34060585e-01,  7.33989537e-01,\n",
                            "        7.33918488e-01,  8.07319760e-01,  8.50688815e-01,  8.50617766e-01,\n",
                            "        9.51076686e-01,  9.51010704e-01,  9.50944722e-01,  1.15824819e+00,\n",
                            "        1.15820146e+00,  1.15815461e+00,  1.15815461e+00, -5.56605160e-01,\n",
                            "       -5.56446075e-01, -4.88739014e-01, -4.88595307e-01, -4.88451451e-01,\n",
                            "       -3.98261070e-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",
                            "        2.29255453e-01,  2.29255497e-01,  3.29792768e-01,  3.29814523e-01,\n",
                            "        3.29836279e-01,  3.30011189e-01,  4.00271535e-01,  4.00268555e-01,\n",
                            "        4.00265574e-01,  4.00262564e-01,  4.23327267e-01,  4.23296958e-01,\n",
                            "        4.23266619e-01,  3.92616272e-01,  3.92576545e-01,  3.90274525e-01,\n",
                            "        3.90222490e-01,  3.46917361e-01,  3.46858948e-01,  3.46858889e-01,\n",
                            "        3.03083003e-01,  3.03013802e-01,  2.79559374e-01,  2.79490113e-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>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-2f36ff3f-7005-4fa5-8b69-2dec9b1c4d9b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2f36ff3f-7005-4fa5-8b69-2dec9b1c4d9b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d66acc1-aeca-438f-bf17-c91bc67e2fe2' class='xr-var-data-in' type='checkbox'><label for='data-0d66acc1-aeca-438f-bf17-c91bc67e2fe2' 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>kind :</span></dt><dd>observation</dd><dt><span>unit :</span></dt><dd>Undefined</dd><dt><span>color :</span></dt><dd>#d62728</dd><dt><span>weight :</span></dt><dd>1.0</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 ,  0.5817,  0.5625,  0.6037,  0.6133,  0.6338,  0.6482,\n",
                            "        0.6437,  0.6385,  0.6824,  0.6905,  0.6954,  0.7481,  0.7695,\n",
                            "        0.7657,  0.7927,  0.8042,  0.8435,  0.8758,  0.9098,  0.9281,\n",
                            "        1.0135,  1.0442,  1.1025,  1.1779,  1.2357,  1.2884,  1.3084,\n",
                            "       -0.5083, -0.506 , -0.437 , -0.4558, -0.4174, -0.4214, -0.3721,\n",
                            "       -0.339 , -0.302 , -0.25  , -0.1807, -0.1276, -0.1098, -0.0382,\n",
                            "        0.0054,  0.0501,  0.1032,  0.1238,  0.1738,  0.1864,  0.265 ,\n",
                            "        0.3251,  0.3295,  0.3698,  0.3665,  0.3852,  0.3896,  0.4671,\n",
                            "        0.4414,  0.471 ,  0.4546,  0.4844,  0.4338,  0.487 ,  0.458 ,\n",
                            "        0.4572,  0.4592,  0.4119,  0.4301,  0.4158,  0.4034,  0.3859,\n",
                            "        0.4234,  0.3773,  0.3467,  0.3258,  0.2946,  0.3025,  0.2804,\n",
                            "        0.2625,  0.2852,  0.2762,  0.2485,  0.2137,  0.2281,  0.17  ,\n",
                            "        0.1838,  0.205 ,  0.175 ,  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",
                            "...\n",
                            "        1.2202,  1.204 ,  1.1605,  1.2002,  1.2039,  1.2307,  1.2151,\n",
                            "        1.1634,  1.0952,  1.1142,  1.0802,  1.0727,  1.0536,  1.0119,\n",
                            "        0.9656,  0.9914,  0.9728,  0.9813,  0.9698,  0.9979,  0.9672,\n",
                            "        0.9242,  0.9098,  0.8882,  0.8759,  0.8368,  0.8731,  0.8064,\n",
                            "        0.8199,  0.8675,  0.8544,  0.8069,  0.7771,  0.7606,  0.753 ,\n",
                            "        0.6954, -0.4108, -0.4066, -0.4572, -0.4903, -0.4522, -0.0665,\n",
                            "       -0.122 , -0.0804, -0.0264, -0.0309,  0.0175,  0.0301,  0.0643,\n",
                            "        0.0861,  0.0877,  0.1356,  0.1878,  0.2797,  0.2345,  0.3212,\n",
                            "        0.3309,  0.3368,  0.3873,  0.4347,  0.4313,  0.4716,  0.473 ,\n",
                            "        0.5719,  0.5185,  0.5664,  0.6304,  0.6318,  0.7146,  0.737 ,\n",
                            "        0.6474,  0.6977,  0.7506,  0.8584,  0.8072,  0.8446,  0.8694,\n",
                            "        0.8546,  0.8971,  0.9012,  0.9497,  0.9861,  1.0477,  0.996 ,\n",
                            "        1.0681,  1.0409,  1.0564,  1.0139,  1.0568,  1.1473,  1.1096,\n",
                            "        1.1158,  1.0753,  1.1388,  1.1439,  1.1677,  1.1982,  1.1331,\n",
                            "        1.2567,  1.1535,  1.2455,  1.1809,  1.2228,  1.2054,  1.1702,\n",
                            "        1.1167,  1.1228,  1.1234,  1.1102,  1.1485,  1.1599,  1.1248,\n",
                            "        1.1867,  1.0446,  1.094 ,  1.0638,  1.0441,  1.1125,  1.1013,\n",
                            "        1.0331,  1.1585,  1.0049,  1.0158,  1.0632,  0.9968,  1.0605,\n",
                            "        1.0035,  1.0703,  1.0284,  1.0866,  0.9857,  0.9519,  0.9803,\n",
                            "        0.9243,  0.9137,  0.8562,  0.8368,  0.8218])</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-c23e1b73-c722-4da9-b7d1-bbc0a2d8d465' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c23e1b73-c722-4da9-b7d1-bbc0a2d8d465' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0aad1293-d9b2-4cdb-ac75-04c309ff5ea2' class='xr-var-data-in' type='checkbox'><label for='data-0aad1293-d9b2-4cdb-ac75-04c309ff5ea2' 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;high&#x27;, &#x27;high&#x27;, &#x27;high&#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;low&#x27;, &#x27;low&#x27;, &#x27;low&#x27;,\n",
                            "       &#x27;low&#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;low&#x27;, &#x27;low&#x27;, &#x27;low&#x27;, &#x27;low&#x27;, &#x27;low&#x27;,\n",
                            "       &#x27;low&#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;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;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;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;, &#x27;high&#x27;,\n",
                            "...\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;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;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;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b27be9b3-6011-480f-b654-d94b28b17c6b' class='xr-section-summary-in' type='checkbox'  ><label for='section-b27be9b3-6011-480f-b654-d94b28b17c6b' 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-311f9acc-84a9-48c3-bf53-08ba5258cd80' class='xr-index-data-in' type='checkbox'/><label for='index-311f9acc-84a9-48c3-bf53-08ba5258cd80' 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 10:45:26.001000&#x27;,\n",
                            "               &#x27;2017-10-27 10:45:26.002000&#x27;,        &#x27;2017-10-27 10:45:27&#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=544, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-05b3171b-fd51-4c24-be0e-302946ca7bec' class='xr-section-summary-in' type='checkbox'  checked><label for='section-05b3171b-fd51-4c24-be0e-302946ca7bec' 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>name :</span></dt><dd>alti</dd><dt><span>quantity_name :</span></dt><dd>Undefined</dd><dt><span>modelskill_version :</span></dt><dd>1.0.dev2</dd></dl></div></li></ul></div></div>"
                        ],
                        "text/plain": [
                            "<xarray.Dataset>\n",
                            "Dimensions:      (time: 544)\n",
                            "Coordinates:\n",
                            "  * time         (time) datetime64[ns] 2017-10-27T10:45:19 ... 2017-10-29T13:...\n",
                            "Data variables:\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",
                            "    HD           (time) float32 0.3699 0.356 0.3559 ... 0.7068 0.7068 0.685\n",
                            "    Observation  (time) float64 0.3778 0.4375 0.4489 ... 0.8562 0.8368 0.8218\n",
                            "    wl category  (time) object 'high' 'high' 'high' ... 'high' 'high' 'high'\n",
                            "Attributes:\n",
                            "    gtype:               track\n",
                            "    name:                alti\n",
                            "    quantity_name:       Undefined\n",
                            "    modelskill_version:  1.0.dev2"
                        ]
                    },
                    "execution_count": 24,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "cmp = cc[0]\n",
                "cmp.data[\"wl category\"] = all_df[\"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": 25,
            "metadata": {},
            "outputs": [
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": [
                        "C:\\Users\\rywm\\Repos\\modelskill\\modelskill\\comparison\\_comparison.py:301: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
                        "  res = df.groupby(by=by).apply(calc_metrics)\n"
                    ]
                },
                {
                    "data": {
                        "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) int64 0 3 14 3 0 0 0 0 ... 6 0 0 20 0 72 4\n",
                            "    bias         (y, x, wl category) float64 nan nan -0.2252 ... nan -0.1065 nan"
                        ]
                    },
                    "execution_count": 25,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "ss = cmp.spatial_skill(by=['wl category'], metrics=['bias'], n_min=5)\n",
                "ss"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 26,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 700x300 with 3 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "ss.plot('n');"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 27,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 700x300 with 3 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "ss.plot('bias');"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Multiple observations\n",
                "\n",
                "Add fake 2nd observation to model"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 28,
            "metadata": {},
            "outputs": [
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": [
                        "/home/jan/src/modelskill/modelskill/utils.py:134: UserWarning: Time axis has duplicate entries. Now adding milliseconds to non-unique entries to make index unique.\n",
                        "  warnings.warn(\n"
                    ]
                }
            ],
            "source": [
                "df2 = df.copy()\n",
                "df2['surface_elevation'] = df2['surface_elevation'] - 0.2\n",
                "o2 = ms.TrackObservation(df2, item=2, name='alti2')\n",
                "cmp2 = ms.compare(o2, mr)[0]"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Extract, spatial skill, add attrs, plot."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 29,
            "metadata": {},
            "outputs": [
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": [
                        "C:\\Users\\rywm\\Repos\\modelskill\\modelskill\\comparison\\_comparison.py:301: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
                        "  res = df.groupby(by=by).apply(calc_metrics)\n"
                    ]
                },
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 1000x500 with 3 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "cc = cc + cmp2\n",
                "ss = cc.spatial_skill(metrics=['bias'], n_min=20)\n",
                "ss.ds['bias'].attrs = dict(long_name=\"Bias of surface elevation\", units=\"m\")\n",
                "ss.plot('bias', figsize=(10,5));"
            ]
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "base",
            "language": "python",
            "name": "python3"
        },
        "language_info": {
            "codemirror_mode": {
                "name": "ipython",
                "version": 3
            },
            "file_extension": ".py",
            "mimetype": "text/x-python",
            "name": "python",
            "nbconvert_exporter": "python",
            "pygments_lexer": "ipython3",
            "version": "3.11.1"
        },
        "orig_nbformat": 2,
        "vscode": {
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