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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Event-based timeseries\n",
+    "\n",
+    "This notebooks illustrates the use of ModelSkill for assessing the skill of a model over event-based timeseries. This type of data is characterized by limited periods of activity, separated by intervals of no or irrelavant activity.\n",
+    "\n",
+    "The example shown below is obtained from a combined urban drainage system, where wet events (observation or model > 0) alternate with dry periods (observation and model = 0). In this case, ModelSkill can be used to compute metrics on the entire observation period, only the periods of activity, or separately on each event."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import modelskill as ms"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import timeseries data\n",
+    "path = '../tests/testdata/event-based_timeseries.csv'\n",
+    "df = pd.read_csv(path, sep=',', header=0, index_col=0, parse_dates=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
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+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>model</th>\n",
+       "      <th>obs</th>\n",
+       "      <th>event</th>\n",
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+       "      <th>count</th>\n",
+       "      <td>4623.000000</td>\n",
+       "      <td>4623.000000</td>\n",
+       "      <td>4623.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.009067</td>\n",
+       "      <td>0.007625</td>\n",
+       "      <td>1.176725</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.054241</td>\n",
+       "      <td>0.050980</td>\n",
+       "      <td>5.125146</td>\n",
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+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
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+       "      <th>50%</th>\n",
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+       "             model          obs        event\n",
+       "count  4623.000000  4623.000000  4623.000000\n",
+       "mean      0.009067     0.007625     1.176725\n",
+       "std       0.054241     0.050980     5.125146\n",
+       "min       0.000000     0.000000     0.000000\n",
+       "25%       0.000000     0.000000     0.000000\n",
+       "50%       0.000000     0.000000     0.000000\n",
+       "75%       0.000000     0.000000     0.000000\n",
+       "max       0.760000     0.761500    29.000000"
+      ]
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+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The dataset has been preprocessed to define an auxiliary indicator (event). The indicator is set to 0 for dry periods or given an interger value for all recorded/modelled events. The event indicators are assign sequentially in chronological order."
+   ]
+  },
+  {
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+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
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+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:      (time: 4623)\n",
+       "Coordinates:\n",
+       "  * time         (time) datetime64[ns] 2022-08-18T06:00:00 ... 2023-08-18\n",
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+       "    name:           obs\n",
+       "    gtype:          point\n",
+       "    quantity_name:  Discharge</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-a43877ac-a562-4ca9-8bba-0a7e2ac384b8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a43877ac-a562-4ca9-8bba-0a7e2ac384b8' 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>: 4623</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-57631f66-1708-4835-a36b-efe1ff4860d6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-57631f66-1708-4835-a36b-efe1ff4860d6' 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'>2022-08-18T06:00:00 ... 2023-08-18</div><input id='attrs-ba45671a-803b-4b7e-87a9-60608917eee4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ba45671a-803b-4b7e-87a9-60608917eee4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-710831ee-a2f8-467e-af41-7be80247de5f' class='xr-var-data-in' type='checkbox'><label for='data-710831ee-a2f8-467e-af41-7be80247de5f' 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;2022-08-18T06:00:00.000000000&#x27;, &#x27;2022-08-18T08:00:00.000000000&#x27;,\n",
+       "       &#x27;2022-08-18T10:00:00.000000000&#x27;, ..., &#x27;2023-08-17T20:00:00.000000000&#x27;,\n",
+       "       &#x27;2023-08-17T22:00:00.000000000&#x27;, &#x27;2023-08-18T00:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e4b80480-beaf-447d-acd7-86c472415a0d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e4b80480-beaf-447d-acd7-86c472415a0d' 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>Observation</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-53ca1adc-3e5a-4325-a96e-2229d778be3b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-53ca1adc-3e5a-4325-a96e-2229d778be3b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-49348396-0051-4930-938a-cf6893e8fc98' class='xr-var-data-in' type='checkbox'><label for='data-49348396-0051-4930-938a-cf6893e8fc98' 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>color :</span></dt><dd>black</dd><dt><span>unit :</span></dt><dd>m3/s</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>model</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-ac9adc74-ab61-4815-b100-1c0703ca4fc3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ac9adc74-ab61-4815-b100-1c0703ca4fc3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c09eae26-4f13-4bee-8585-84f7957a73d1' class='xr-var-data-in' type='checkbox'><label for='data-c09eae26-4f13-4bee-8585-84f7957a73d1' 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([0., 0., 0., ..., 0., 0., 0.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>event</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-8a7793b5-e3e5-42ba-be51-d6c2853d6234' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8a7793b5-e3e5-42ba-be51-d6c2853d6234' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbef09b4-be2b-4837-a94d-ac466f620613' class='xr-var-data-in' type='checkbox'><label for='data-dbef09b4-be2b-4837-a94d-ac466f620613' 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>auxiliary</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan</div><input id='attrs-9977365c-0552-449f-80bc-d5652f8891bb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9977365c-0552-449f-80bc-d5652f8891bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-226a9be5-e4a1-4c6f-b831-2faaa88f7e76' class='xr-var-data-in' type='checkbox'><label for='data-226a9be5-e4a1-4c6f-b831-2faaa88f7e76' 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(nan)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan</div><input id='attrs-a0487623-27f2-464b-a5cd-9f9bae2f15a7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a0487623-27f2-464b-a5cd-9f9bae2f15a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc3f044e-a037-4e86-bc06-183107e40268' class='xr-var-data-in' type='checkbox'><label for='data-bc3f044e-a037-4e86-bc06-183107e40268' 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(nan)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e974926b-f335-49b3-9e62-7858fe00b665' class='xr-section-summary-in' type='checkbox'  ><label for='section-e974926b-f335-49b3-9e62-7858fe00b665' 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-258a21ac-4741-4ed4-9242-167227d66e0e' class='xr-index-data-in' type='checkbox'/><label for='index-258a21ac-4741-4ed4-9242-167227d66e0e' 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;2022-08-18 06:00:00&#x27;, &#x27;2022-08-18 08:00:00&#x27;,\n",
+       "               &#x27;2022-08-18 10:00:00&#x27;, &#x27;2022-08-18 12:00:00&#x27;,\n",
+       "               &#x27;2022-08-18 14:00:00&#x27;, &#x27;2022-08-18 16:00:00&#x27;,\n",
+       "               &#x27;2022-08-18 18:00:00&#x27;, &#x27;2022-08-18 20:00:00&#x27;,\n",
+       "               &#x27;2022-08-18 22:00:00&#x27;, &#x27;2022-08-19 00:00:00&#x27;,\n",
+       "               ...\n",
+       "               &#x27;2023-08-17 06:00:00&#x27;, &#x27;2023-08-17 08:00:00&#x27;,\n",
+       "               &#x27;2023-08-17 10:00:00&#x27;, &#x27;2023-08-17 12:00:00&#x27;,\n",
+       "               &#x27;2023-08-17 14:00:00&#x27;, &#x27;2023-08-17 16:00:00&#x27;,\n",
+       "               &#x27;2023-08-17 18:00:00&#x27;, &#x27;2023-08-17 20:00:00&#x27;,\n",
+       "               &#x27;2023-08-17 22:00:00&#x27;, &#x27;2023-08-18 00:00:00&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=4623, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bb0a57bc-c961-42b3-9c57-195218c792ed' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bb0a57bc-c961-42b3-9c57-195218c792ed' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>obs</dd><dt><span>gtype :</span></dt><dd>point</dd><dt><span>quantity_name :</span></dt><dd>Discharge</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:      (time: 4623)\n",
+       "Coordinates:\n",
+       "  * time         (time) datetime64[ns] 2022-08-18T06:00:00 ... 2023-08-18\n",
+       "Data variables:\n",
+       "    Observation  (time) float64 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0\n",
+       "    model        (time) float64 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0\n",
+       "    event        (time) float64 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0\n",
+       "    x            float64 nan\n",
+       "    y            float64 nan\n",
+       "Attributes:\n",
+       "    name:           obs\n",
+       "    gtype:          point\n",
+       "    quantity_name:  Discharge"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cmp = ms.from_matched(df, obs_item='obs', mod_items=['model'], aux_items=['event'], quantity=ms.Quantity(name='Discharge', unit='m3/s'))\n",
+    "cmp.data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
+        {
+         "line": {
+          "color": "#1f78b4"
+         },
+         "name": "model",
+         "type": "scatter",
+         "x": [
+          "2022-08-18T06:00:00",
+          "2022-08-18T08:00:00",
+          "2022-08-18T10:00:00",
+          "2022-08-18T12:00:00",
+          "2022-08-18T14:00:00",
+          "2022-08-18T16:00:00",
+          "2022-08-18T18:00:00",
+          "2022-08-18T20:00:00",
+          "2022-08-18T22:00:00",
+          "2022-08-19T00:00:00",
+          "2022-08-19T02:00:00",
+          "2022-08-19T04:00:00",
+          "2022-08-19T06:00:00",
+          "2022-08-19T08:00:00",
+          "2022-08-19T10:00:00",
+          "2022-08-19T12:00:00",
+          "2022-08-19T14:00:00",
+          "2022-08-19T16:00:00",
+          "2022-08-19T18:00:00",
+          "2022-08-19T20:00:00",
+          "2022-08-19T22:00:00",
+          "2022-08-20T00:00:00",
+          "2022-08-20T02:00:00",
+          "2022-08-20T04:00:00",
+          "2022-08-20T06:00:00",
+          "2022-08-20T08:00:00",
+          "2022-08-20T10:00:00",
+          "2022-08-20T12:00:00",
+          "2022-08-20T14:00:00",
+          "2022-08-20T16:00:00",
+          "2022-08-20T18:00:00",
+          "2022-08-20T20:00:00",
+          "2022-08-20T22:00:00",
+          "2022-08-21T00:00:00",
+          "2022-08-21T02:00:00",
+          "2022-08-21T04:00:00",
+          "2022-08-21T06:00:00",
+          "2022-08-21T08:00:00",
+          "2022-08-21T10:00:00",
+          "2022-08-21T12:00:00",
+          "2022-08-21T14:00:00",
+          "2022-08-21T16:00:00",
+          "2022-08-21T18:00:00",
+          "2022-08-21T20:00:00",
+          "2022-08-21T22:00:00",
+          "2022-08-22T00:00:00",
+          "2022-08-22T02:00:00",
+          "2022-08-22T04:00:00",
+          "2022-08-22T06:00:00",
+          "2022-08-22T08:00:00",
+          "2022-08-22T10:00:00",
+          "2022-08-22T12:00:00",
+          "2022-08-22T14:00:00",
+          "2022-08-22T16:00:00",
+          "2022-08-22T18:00:00",
+          "2022-08-22T20:00:00",
+          "2022-08-22T22:00:00",
+          "2022-08-23T00:00:00",
+          "2022-08-23T02:00:00",
+          "2022-08-23T04:00:00",
+          "2022-08-23T06:00:00",
+          "2022-08-23T08:00:00",
+          "2022-08-23T10:00:00",
+          "2022-08-23T12:00:00",
+          "2022-08-23T14:00:00",
+          "2022-08-23T16:00:00",
+          "2022-08-23T18:00:00",
+          "2022-08-23T20:00:00",
+          "2022-08-23T22:00:00",
+          "2022-08-24T00:00:00",
+          "2022-08-24T02:00:00",
+          "2022-08-24T04:00:00",
+          "2022-08-24T06:00:00",
+          "2022-08-24T08:00:00",
+          "2022-08-24T10:00:00",
+          "2022-08-24T12:00:00",
+          "2022-08-24T14:00:00",
+          "2022-08-24T16:00:00",
+          "2022-08-24T18:00:00",
+          "2022-08-24T20:00:00",
+          "2022-08-24T22:00:00",
+          "2022-08-25T00:00:00",
+          "2022-08-25T02:00:00",
+          "2022-08-25T04:00:00",
+          "2022-08-25T06:00:00",
+          "2022-08-25T08:00:00",
+          "2022-08-25T10:00:00",
+          "2022-08-25T12:00:00",
+          "2022-08-25T14:00:00",
+          "2022-08-25T16:00:00",
+          "2022-08-25T18:00:00",
+          "2022-08-25T20:00:00",
+          "2022-08-25T22:00:00",
+          "2022-08-26T00:00:00",
+          "2022-08-26T02:00:00",
+          "2022-08-26T04:00:00",
+          "2022-08-26T06:00:00",
+          "2022-08-26T08:00:00",
+          "2022-08-26T10:00:00",
+          "2022-08-26T12:00:00",
+          "2022-08-26T14:00:00",
+          "2022-08-26T16:00:00",
+          "2022-08-26T18:00:00",
+          "2022-08-26T20:00:00",
+          "2022-08-26T22:00:00",
+          "2022-08-27T00:00:00",
+          "2022-08-27T02:00:00",
+          "2022-08-27T04:00:00",
+          "2022-08-27T06:00:00",
+          "2022-08-27T08:00:00",
+          "2022-08-27T09:45:00",
+          "2022-08-27T10:00:00",
+          "2022-08-27T10:15:00",
+          "2022-08-27T10:30:00",
+          "2022-08-27T10:45:00",
+          "2022-08-27T11:00:00",
+          "2022-08-27T11:15:00",
+          "2022-08-27T11:30:00",
+          "2022-08-27T11:45:00",
+          "2022-08-27T12:00:00",
+          "2022-08-27T12:15:00",
+          "2022-08-27T14:00:00",
+          "2022-08-27T16:00:00",
+          "2022-08-27T18:00:00",
+          "2022-08-27T20:00:00",
+          "2022-08-27T22:00:00",
+          "2022-08-28T00:00:00",
+          "2022-08-28T02:00:00",
+          "2022-08-28T04:00:00",
+          "2022-08-28T06:00:00",
+          "2022-08-28T08:00:00",
+          "2022-08-28T10:00:00",
+          "2022-08-28T12:00:00",
+          "2022-08-28T14:00:00",
+          "2022-08-28T16:00:00",
+          "2022-08-28T18:00:00",
+          "2022-08-28T20:00:00",
+          "2022-08-28T22:00:00",
+          "2022-08-29T00:00:00",
+          "2022-08-29T02:00:00",
+          "2022-08-29T04:00:00",
+          "2022-08-29T06:00:00",
+          "2022-08-29T08:00:00",
+          "2022-08-29T10:00:00",
+          "2022-08-29T12:00:00",
+          "2022-08-29T14:00:00",
+          "2022-08-29T16:00:00",
+          "2022-08-29T18:00:00",
+          "2022-08-29T20:00:00",
+          "2022-08-29T22:00:00",
+          "2022-08-30T00:00:00",
+          "2022-08-30T02:00:00",
+          "2022-08-30T04:00:00",
+          "2022-08-30T06:00:00",
+          "2022-08-30T08:00:00",
+          "2022-08-30T10:00:00",
+          "2022-08-30T12:00:00",
+          "2022-08-30T14:00:00",
+          "2022-08-30T16:00:00",
+          "2022-08-30T18:00:00",
+          "2022-08-30T20:00:00",
+          "2022-08-30T22:00:00",
+          "2022-08-31T00:00:00",
+          "2022-08-31T02:00:00",
+          "2022-08-31T04:00:00",
+          "2022-08-31T06:00:00",
+          "2022-08-31T08:00:00",
+          "2022-08-31T10:00:00",
+          "2022-08-31T12:00:00",
+          "2022-08-31T14:00:00",
+          "2022-08-31T16:00:00",
+          "2022-08-31T18:00:00",
+          "2022-08-31T20:00:00",
+          "2022-08-31T22:00:00",
+          "2022-09-01T00:00:00",
+          "2022-09-01T02:00:00",
+          "2022-09-01T04:00:00",
+          "2022-09-01T06:00:00",
+          "2022-09-01T08:00:00",
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+         "text": "obs"
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+         "title": {
+          "text": "Discharge [m3/s]"
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+   "metadata": {},
+   "source": [
+    "**1. Global skill over observation period**\n",
+    "\n",
+    "This is clearly affected by the long dry periods, where both model and observation show no activity (which corresponds to RMSE = 0)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
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+       "<style type=\"text/css\">\n",
+       "#T_376e6_row0_col1 {\n",
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+       "      <td id=\"T_376e6_row0_col1\" class=\"data row0 col1\" >0.050</td>\n",
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+   "metadata": {},
+   "source": [
+    "**2. Global skill over all events**\n",
+    "\n",
+    "By limiting the skill to periods of activity we can better assess how well the model fits the observed data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "#T_d8836_row0_col1 {\n",
+       "  background-color: #fff7ec;\n",
+       "  color: #000000;\n",
+       "  text-decoration: underline;\n",
+       "  font-style: italic;\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_d8836\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_d8836_level0_col0\" class=\"col_heading level0 col0\" >n</th>\n",
+       "      <th id=\"T_d8836_level0_col1\" class=\"col_heading level0 col1\" >rmse</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th class=\"index_name level0\" >observation</th>\n",
+       "      <th class=\"blank col0\" >&nbsp;</th>\n",
+       "      <th class=\"blank col1\" >&nbsp;</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d8836_level0_row0\" class=\"row_heading level0 row0\" >obs</th>\n",
+       "      <td id=\"T_d8836_row0_col0\" class=\"data row0 col0\" >283</td>\n",
+       "      <td id=\"T_d8836_row0_col1\" class=\"data row0 col1\" >0.220</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x20dbaf1f640>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cmp.where(cmp.data.event > 0).skill(metrics='rmse').round(2).style()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'Linear regression'}, xlabel='Observation, Discharge [m3/s]', ylabel='Model, Discharge [m3/s]'>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cmp.where(cmp.data.event > 0).plot.scatter(skill_table=True, title='Linear regression');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**3. Event-based skill assessment**\n",
+    "\n",
+    "The skill metric can be computed separately for each event by grouping the datapoints according to the auxiliary value. This can help identify specific events where the model is in disagreement with the observations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "#T_fc9b7_row28_col2 {\n",
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+       "</style>\n",
+       "<table id=\"T_fc9b7\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_fc9b7_level0_col0\" class=\"col_heading level0 col0\" >observation</th>\n",
+       "      <th id=\"T_fc9b7_level0_col1\" class=\"col_heading level0 col1\" >n</th>\n",
+       "      <th id=\"T_fc9b7_level0_col2\" class=\"col_heading level0 col2\" >rmse</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th class=\"index_name level0\" >event</th>\n",
+       "      <th class=\"blank col0\" >&nbsp;</th>\n",
+       "      <th class=\"blank col1\" >&nbsp;</th>\n",
+       "      <th class=\"blank col2\" >&nbsp;</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_fc9b7_level0_row0\" class=\"row_heading level0 row0\" >0.000000</th>\n",
+       "      <td id=\"T_fc9b7_row0_col0\" class=\"data row0 col0\" >obs</td>\n",
+       "      <td id=\"T_fc9b7_row0_col1\" class=\"data row0 col1\" >4340</td>\n",
+       "      <td id=\"T_fc9b7_row0_col2\" class=\"data row0 col2\" >0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_fc9b7_level0_row1\" class=\"row_heading level0 row1\" >1.000000</th>\n",
+       "      <td id=\"T_fc9b7_row1_col0\" class=\"data row1 col0\" >obs</td>\n",
+       "      <td id=\"T_fc9b7_row1_col1\" class=\"data row1 col1\" >11</td>\n",
+       "      <td id=\"T_fc9b7_row1_col2\" class=\"data row1 col2\" >0.076</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_fc9b7_level0_row2\" class=\"row_heading level0 row2\" >2.000000</th>\n",
+       "      <td id=\"T_fc9b7_row2_col0\" class=\"data row2 col0\" >obs</td>\n",
+       "      <td id=\"T_fc9b7_row2_col1\" class=\"data row2 col1\" >9</td>\n",
+       "      <td id=\"T_fc9b7_row2_col2\" class=\"data row2 col2\" >0.638</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_fc9b7_level0_row3\" class=\"row_heading level0 row3\" >3.000000</th>\n",
+       "      <td id=\"T_fc9b7_row3_col0\" class=\"data row3 col0\" >obs</td>\n",
+       "      <td id=\"T_fc9b7_row3_col1\" class=\"data row3 col1\" >7</td>\n",
+       "      <td id=\"T_fc9b7_row3_col2\" class=\"data row3 col2\" >0.297</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_fc9b7_level0_row4\" class=\"row_heading level0 row4\" >4.000000</th>\n",
+       "      <td id=\"T_fc9b7_row4_col0\" class=\"data row4 col0\" >obs</td>\n",
+       "      <td id=\"T_fc9b7_row4_col1\" class=\"data row4 col1\" >3</td>\n",
+       "      <td id=\"T_fc9b7_row4_col2\" class=\"data row4 col2\" >0.028</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_fc9b7_level0_row5\" class=\"row_heading level0 row5\" >5.000000</th>\n",
+       "      <td id=\"T_fc9b7_row5_col0\" class=\"data row5 col0\" >obs</td>\n",
+       "      <td id=\"T_fc9b7_row5_col1\" class=\"data row5 col1\" >5</td>\n",
+       "      <td id=\"T_fc9b7_row5_col2\" class=\"data row5 col2\" >0.175</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_fc9b7_level0_row6\" class=\"row_heading level0 row6\" >6.000000</th>\n",
+       "      <td id=\"T_fc9b7_row6_col0\" class=\"data row6 col0\" >obs</td>\n",
+       "      <td id=\"T_fc9b7_row6_col1\" class=\"data row6 col1\" >4</td>\n",
+       "      <td id=\"T_fc9b7_row6_col2\" class=\"data row6 col2\" >0.114</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_fc9b7_level0_row7\" class=\"row_heading level0 row7\" >7.000000</th>\n",
+       "      <td id=\"T_fc9b7_row7_col0\" class=\"data row7 col0\" >obs</td>\n",
+       "      <td id=\"T_fc9b7_row7_col1\" class=\"data row7 col1\" >5</td>\n",
+       "      <td id=\"T_fc9b7_row7_col2\" class=\"data row7 col2\" >0.162</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_fc9b7_level0_row8\" class=\"row_heading level0 row8\" >8.000000</th>\n",
+       "      <td id=\"T_fc9b7_row8_col0\" class=\"data row8 col0\" >obs</td>\n",
+       "      <td id=\"T_fc9b7_row8_col1\" class=\"data row8 col1\" >5</td>\n",
+       "      <td id=\"T_fc9b7_row8_col2\" class=\"data row8 col2\" >0.161</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_fc9b7_level0_row9\" class=\"row_heading level0 row9\" >9.000000</th>\n",
+       "      <td id=\"T_fc9b7_row9_col0\" class=\"data row9 col0\" >obs</td>\n",
+       "      <td id=\"T_fc9b7_row9_col1\" class=\"data row9 col1\" >7</td>\n",
+       "      <td id=\"T_fc9b7_row9_col2\" class=\"data row9 col2\" >0.135</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_fc9b7_level0_row10\" class=\"row_heading level0 row10\" >10.000000</th>\n",
+       "      <td id=\"T_fc9b7_row10_col0\" class=\"data row10 col0\" >obs</td>\n",
+       "      <td id=\"T_fc9b7_row10_col1\" class=\"data row10 col1\" >5</td>\n",
+       "      <td id=\"T_fc9b7_row10_col2\" class=\"data row10 col2\" >0.067</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_fc9b7_level0_row11\" class=\"row_heading level0 row11\" >11.000000</th>\n",
+       "      <td id=\"T_fc9b7_row11_col0\" class=\"data row11 col0\" >obs</td>\n",
+       "      <td id=\"T_fc9b7_row11_col1\" class=\"data row11 col1\" >9</td>\n",
+       "      <td id=\"T_fc9b7_row11_col2\" class=\"data row11 col2\" >0.075</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_fc9b7_level0_row12\" class=\"row_heading level0 row12\" >12.000000</th>\n",
+       "      <td id=\"T_fc9b7_row12_col0\" class=\"data row12 col0\" >obs</td>\n",
+       "      <td id=\"T_fc9b7_row12_col1\" class=\"data row12 col1\" >12</td>\n",
+       "      <td id=\"T_fc9b7_row12_col2\" class=\"data row12 col2\" >0.327</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_fc9b7_level0_row13\" class=\"row_heading level0 row13\" >13.000000</th>\n",
+       "      <td id=\"T_fc9b7_row13_col0\" class=\"data row13 col0\" >obs</td>\n",
+       "      <td id=\"T_fc9b7_row13_col1\" class=\"data row13 col1\" >2</td>\n",
+       "      <td id=\"T_fc9b7_row13_col2\" class=\"data row13 col2\" >0.047</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_fc9b7_level0_row14\" class=\"row_heading level0 row14\" >14.000000</th>\n",
+       "      <td id=\"T_fc9b7_row14_col0\" class=\"data row14 col0\" >obs</td>\n",
+       "      <td id=\"T_fc9b7_row14_col1\" class=\"data row14 col1\" >6</td>\n",
+       "      <td id=\"T_fc9b7_row14_col2\" class=\"data row14 col2\" >0.122</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_fc9b7_level0_row15\" class=\"row_heading level0 row15\" >15.000000</th>\n",
+       "      <td id=\"T_fc9b7_row15_col0\" class=\"data row15 col0\" >obs</td>\n",
+       "      <td id=\"T_fc9b7_row15_col1\" class=\"data row15 col1\" >9</td>\n",
+       "      <td id=\"T_fc9b7_row15_col2\" class=\"data row15 col2\" >0.063</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_fc9b7_level0_row16\" class=\"row_heading level0 row16\" >16.000000</th>\n",
+       "      <td id=\"T_fc9b7_row16_col0\" class=\"data row16 col0\" >obs</td>\n",
+       "      <td id=\"T_fc9b7_row16_col1\" class=\"data row16 col1\" >2</td>\n",
+       "      <td id=\"T_fc9b7_row16_col2\" class=\"data row16 col2\" >0.033</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_fc9b7_level0_row17\" class=\"row_heading level0 row17\" >17.000000</th>\n",
+       "      <td id=\"T_fc9b7_row17_col0\" class=\"data row17 col0\" >obs</td>\n",
+       "      <td id=\"T_fc9b7_row17_col1\" class=\"data row17 col1\" >6</td>\n",
+       "      <td id=\"T_fc9b7_row17_col2\" class=\"data row17 col2\" >0.115</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_fc9b7_level0_row18\" class=\"row_heading level0 row18\" >18.000000</th>\n",
+       "      <td id=\"T_fc9b7_row18_col0\" class=\"data row18 col0\" >obs</td>\n",
+       "      <td id=\"T_fc9b7_row18_col1\" class=\"data row18 col1\" >4</td>\n",
+       "      <td id=\"T_fc9b7_row18_col2\" class=\"data row18 col2\" >0.107</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_fc9b7_level0_row19\" class=\"row_heading level0 row19\" >19.000000</th>\n",
+       "      <td id=\"T_fc9b7_row19_col0\" class=\"data row19 col0\" >obs</td>\n",
+       "      <td id=\"T_fc9b7_row19_col1\" class=\"data row19 col1\" >4</td>\n",
+       "      <td id=\"T_fc9b7_row19_col2\" class=\"data row19 col2\" >0.130</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_fc9b7_level0_row20\" class=\"row_heading level0 row20\" >20.000000</th>\n",
+       "      <td id=\"T_fc9b7_row20_col0\" class=\"data row20 col0\" >obs</td>\n",
+       "      <td id=\"T_fc9b7_row20_col1\" class=\"data row20 col1\" >4</td>\n",
+       "      <td id=\"T_fc9b7_row20_col2\" class=\"data row20 col2\" >0.092</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_fc9b7_level0_row21\" class=\"row_heading level0 row21\" >21.000000</th>\n",
+       "      <td id=\"T_fc9b7_row21_col0\" class=\"data row21 col0\" >obs</td>\n",
+       "      <td id=\"T_fc9b7_row21_col1\" class=\"data row21 col1\" >5</td>\n",
+       "      <td id=\"T_fc9b7_row21_col2\" class=\"data row21 col2\" >0.147</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_fc9b7_level0_row22\" class=\"row_heading level0 row22\" >22.000000</th>\n",
+       "      <td id=\"T_fc9b7_row22_col0\" class=\"data row22 col0\" >obs</td>\n",
+       "      <td id=\"T_fc9b7_row22_col1\" class=\"data row22 col1\" >6</td>\n",
+       "      <td id=\"T_fc9b7_row22_col2\" class=\"data row22 col2\" >0.154</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_fc9b7_level0_row23\" class=\"row_heading level0 row23\" >23.000000</th>\n",
+       "      <td id=\"T_fc9b7_row23_col0\" class=\"data row23 col0\" >obs</td>\n",
+       "      <td id=\"T_fc9b7_row23_col1\" class=\"data row23 col1\" >12</td>\n",
+       "      <td id=\"T_fc9b7_row23_col2\" class=\"data row23 col2\" >0.261</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_fc9b7_level0_row24\" class=\"row_heading level0 row24\" >24.000000</th>\n",
+       "      <td id=\"T_fc9b7_row24_col0\" class=\"data row24 col0\" >obs</td>\n",
+       "      <td id=\"T_fc9b7_row24_col1\" class=\"data row24 col1\" >8</td>\n",
+       "      <td id=\"T_fc9b7_row24_col2\" class=\"data row24 col2\" >0.181</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_fc9b7_level0_row25\" class=\"row_heading level0 row25\" >25.000000</th>\n",
+       "      <td id=\"T_fc9b7_row25_col0\" class=\"data row25 col0\" >obs</td>\n",
+       "      <td id=\"T_fc9b7_row25_col1\" class=\"data row25 col1\" >6</td>\n",
+       "      <td id=\"T_fc9b7_row25_col2\" class=\"data row25 col2\" >0.251</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_fc9b7_level0_row26\" class=\"row_heading level0 row26\" >26.000000</th>\n",
+       "      <td id=\"T_fc9b7_row26_col0\" class=\"data row26 col0\" >obs</td>\n",
+       "      <td id=\"T_fc9b7_row26_col1\" class=\"data row26 col1\" >7</td>\n",
+       "      <td id=\"T_fc9b7_row26_col2\" class=\"data row26 col2\" >0.312</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_fc9b7_level0_row27\" class=\"row_heading level0 row27\" >27.000000</th>\n",
+       "      <td id=\"T_fc9b7_row27_col0\" class=\"data row27 col0\" >obs</td>\n",
+       "      <td id=\"T_fc9b7_row27_col1\" class=\"data row27 col1\" >111</td>\n",
+       "      <td id=\"T_fc9b7_row27_col2\" class=\"data row27 col2\" >0.193</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_fc9b7_level0_row28\" class=\"row_heading level0 row28\" >28.000000</th>\n",
+       "      <td id=\"T_fc9b7_row28_col0\" class=\"data row28 col0\" >obs</td>\n",
+       "      <td id=\"T_fc9b7_row28_col1\" class=\"data row28 col1\" >4</td>\n",
+       "      <td id=\"T_fc9b7_row28_col2\" class=\"data row28 col2\" >0.129</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_fc9b7_level0_row29\" class=\"row_heading level0 row29\" >29.000000</th>\n",
+       "      <td id=\"T_fc9b7_row29_col0\" class=\"data row29 col0\" >obs</td>\n",
+       "      <td id=\"T_fc9b7_row29_col1\" class=\"data row29 col1\" >5</td>\n",
+       "      <td id=\"T_fc9b7_row29_col2\" class=\"data row29 col2\" >0.146</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x11a6c08ead0>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ss = cmp.skill(by=\"event\", metrics='rmse')\n",
+    "ss.style()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "keras",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tests/testdata/event-based_timeseries.csv b/tests/testdata/event-based_timeseries.csv
new file mode 100644
index 000000000..24227c12f
--- /dev/null
+++ b/tests/testdata/event-based_timeseries.csv
@@ -0,0 +1,4624 @@
+,model,obs,event
+2022-08-18 06:00:00,0.0,0.0,0
+2022-08-18 08:00:00,0.0,0.0,0
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