diff --git a/docs/practical_examples/tmp.grpah.html b/docs/practical_examples/tmp.grpah.html
index c126064..55ea855 100644
--- a/docs/practical_examples/tmp.grpah.html
+++ b/docs/practical_examples/tmp.grpah.html
@@ -88,8 +88,8 @@ <h1></h1>
                   
 
                   // parsing and collecting nodes and edges from the python
-                  nodes = new vis.DataSet([{"color": "black", "id": 0, "label": "_:N25", "shape": "dot", "size": 20, "title": "_:N25"}, {"color": "#97c2fc", "id": 1, "label": "A hint", "shape": "dot", "title": "A hint"}, {"color": "#97c2fc", "id": 2, "label": "prov:Person", "shape": "dot", "title": "prov:Person"}, {"color": "#97c2fc", "id": 3, "label": "Probst", "shape": "dot", "title": "Probst"}, {"color": "#97c2fc", "id": 4, "label": "Matthias", "shape": "dot", "title": "Matthias"}]);
-                  edges = new vis.DataSet([{"from": 0, "label": "skos:definition", "to": 1}, {"from": 2, "label": "foaf:lastName", "to": 3}, {"from": 2, "label": "foaf:firstName", "to": 4}]);
+                  nodes = new vis.DataSet([{"color": "#97c2fc", "id": 0, "label": "prov:Person", "shape": "dot", "title": "prov:Person"}, {"color": "#97c2fc", "id": 1, "label": "Matthias", "shape": "dot", "title": "Matthias"}, {"color": "#97c2fc", "id": 2, "label": "Probst", "shape": "dot", "title": "Probst"}, {"color": "black", "id": 3, "label": "_:N25", "shape": "dot", "size": 20, "title": "_:N25"}, {"color": "#97c2fc", "id": 4, "label": "A hint", "shape": "dot", "title": "A hint"}]);
+                  edges = new vis.DataSet([{"from": 0, "label": "foaf:firstName", "to": 1}, {"from": 0, "label": "foaf:lastName", "to": 2}, {"from": 3, "label": "skos:definition", "to": 4}]);
 
                   nodeColors = {};
                   allNodes = nodes.get({ returnType: "Object" });
diff --git a/docs/userguide/misc/Extensions.ipynb b/docs/userguide/misc/Extensions.ipynb
index 818743f..7140752 100644
--- a/docs/userguide/misc/Extensions.ipynb
+++ b/docs/userguide/misc/Extensions.ipynb
@@ -9,14 +9,20 @@
     "\n",
     "The wrapper class functionalities can be extended using composition or inheritance.\n",
     "\n",
-    "## Composition\n",
+    "The concept of composition adds a component to a composite class. The relationship is such, that the composite class does not know the component class but wise versa. This allows to add additional functionality without changing the original code. Adding components to an existing wrapper class will be called \"registering\" here.\n",
     "\n",
-    "The concept of composition adds a component to a composite class. The relationship is such, that the composite class does not know the component class but wise versa. This allows to add additional functionality without changing the original code. Adding components to an existing wrapper class will be called \"registering\" here."
+    "Concretely, four different so-called accessory classes are implemented:\n",
+    "- `Vector`\n",
+    "- `Magnitude`\n",
+    "- `normalize`\n",
+    "- `to_units`\n",
+    "\n",
+    "These accessories interfaces must be activated by an import call. They generally are designed such, that they are called on a dataset or group with respective arguments, which prepares a the action being performed when data is selected. Best is, to learn by example:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 20,
    "id": "a1cbc998-a747-4370-b362-d04c4d6508be",
    "metadata": {
     "tags": []
@@ -28,7 +34,7 @@
        "using(\"h5py\")"
       ]
      },
-     "execution_count": 1,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -43,28 +49,25 @@
    "id": "a3edcad1-6c32-4d07-a581-1aa57f917259",
    "metadata": {},
    "source": [
-    "## Built-in extensions\n",
-    "You may add your own extensions, but the packages comes already with some, that may be useful:\n",
-    "\n",
-    "### 1. Vector\n",
-    "The `Vector` extension returns a `xr.Dataset` including the provided datasets when the `Vector` object is sliced. It just saves some time and makes it a one-liner to create the `xr.Dataset`."
+    "## 1. Vector\n",
+    "The `Vector` extension returns a `xr.Dataset`. First specify which of the datasets from the HDF5 should be contained in the resulting `xr.Dataset`. Then, index the data array using numpy notation or `sel()` or `isel()`. Only then, the data is loaded from the file:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 23,
    "id": "7eecb901-7e7c-473d-848c-409e1f0a0e8c",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "from h5rdmtoolbox.extensions import vector"
+    "from h5rdmtoolbox.extensions import vector  # make `Vector` appear as a property of groups"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 24,
    "id": "aa41f9f9-64d2-4d3d-8cd3-4cbcca88ae88",
    "metadata": {
     "tags": []
@@ -442,10 +445,13 @@
        "  * y        (y) int32 2 3\n",
        "  * x        (x) int32 1 2 3\n",
        "Data variables:\n",
-       "    xvel     (y, x) float64 0.03456 0.9389 0.03886 0.1708 0.5039 0.8131\n",
-       "    yvel     (y, x) float64 0.1247 0.3435 0.5736 0.8872 0.6234 0.06209</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-796a4aad-d5ac-4093-b158-ee7624f25428' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-796a4aad-d5ac-4093-b158-ee7624f25428' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 2</li><li><span class='xr-has-index'>x</span>: 3</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-aec707d4-9f1e-4072-8178-c1715e49a888' class='xr-section-summary-in' type='checkbox'  checked><label for='section-aec707d4-9f1e-4072-8178-c1715e49a888' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>2 3</div><input id='attrs-28863b63-27af-42b6-b2c2-7ed27cb857ce' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-28863b63-27af-42b6-b2c2-7ed27cb857ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3fbd0573-af26-4c00-9aaf-622d4cca51d2' class='xr-var-data-in' type='checkbox'><label for='data-3fbd0573-af26-4c00-9aaf-622d4cca51d2' 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>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([2, 3])</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'>int32</div><div class='xr-var-preview xr-preview'>1 2 3</div><input id='attrs-a4257333-9c06-4211-bb12-c89ab37c6580' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a4257333-9c06-4211-bb12-c89ab37c6580' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a36a06d6-a982-4224-8cfa-54a6c1dff45b' class='xr-var-data-in' type='checkbox'><label for='data-a36a06d6-a982-4224-8cfa-54a6c1dff45b' 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>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([1, 2, 3])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-93455695-de09-48d8-af75-191e5c1791b0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-93455695-de09-48d8-af75-191e5c1791b0' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>xvel</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.03456 0.9389 ... 0.5039 0.8131</div><input id='attrs-242c305b-1f3c-490d-916b-0ba560a0b745' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-242c305b-1f3c-490d-916b-0ba560a0b745' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3439568c-cd6e-4f44-998d-98df71ab7b49' class='xr-var-data-in' type='checkbox'><label for='data-3439568c-cd6e-4f44-998d-98df71ab7b49' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.0345619 , 0.93892353, 0.03885579],\n",
-       "       [0.17084225, 0.5039308 , 0.81305982]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>yvel</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1247 0.3435 ... 0.6234 0.06209</div><input id='attrs-20095bf5-2312-49ca-bb8c-c61b4ee6045e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-20095bf5-2312-49ca-bb8c-c61b4ee6045e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-219ab782-32ca-49d0-b317-0c70f68f8f55' class='xr-var-data-in' type='checkbox'><label for='data-219ab782-32ca-49d0-b317-0c70f68f8f55' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.12466993, 0.34351228, 0.57358334],\n",
-       "       [0.88717918, 0.62339574, 0.06208907]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-815fc5d0-bff4-470e-bdfb-3eefc7244736' class='xr-section-summary-in' type='checkbox'  ><label for='section-815fc5d0-bff4-470e-bdfb-3eefc7244736' 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-576b6e9f-6752-4b62-a3b6-c2cabf946e16' class='xr-index-data-in' type='checkbox'/><label for='index-576b6e9f-6752-4b62-a3b6-c2cabf946e16' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([2, 3], dtype=&#x27;int32&#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-cdba540a-1e94-40f4-8106-1d0d32a2c81a' class='xr-index-data-in' type='checkbox'/><label for='index-cdba540a-1e94-40f4-8106-1d0d32a2c81a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3], dtype=&#x27;int32&#x27;, name=&#x27;x&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-640ad5ab-0745-4203-a931-8e7c407164b9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-640ad5ab-0745-4203-a931-8e7c407164b9' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "    xvel     (y, x) float64 0.9042 0.06279 0.2518 0.7133 0.5988 0.09251\n",
+       "    yvel     (y, x) float64 0.2222 0.5316 0.9987 0.251 0.3073 0.8381\n",
+       "Attributes:\n",
+       "    long_name:  x vel\n",
+       "    units:      m/s</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-cf4566ab-1f51-49d7-857e-f4594a53cb3e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cf4566ab-1f51-49d7-857e-f4594a53cb3e' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 2</li><li><span class='xr-has-index'>x</span>: 3</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-fcf89a8d-1af8-4b84-a38f-8b7ba48f9a7c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fcf89a8d-1af8-4b84-a38f-8b7ba48f9a7c' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>2 3</div><input id='attrs-45db4500-a1f8-4352-92d0-56aad623acf2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-45db4500-a1f8-4352-92d0-56aad623acf2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-46564f0c-47e4-4e8d-a31f-e8dffab64bdf' class='xr-var-data-in' type='checkbox'><label for='data-46564f0c-47e4-4e8d-a31f-e8dffab64bdf' 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>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([2, 3])</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'>int32</div><div class='xr-var-preview xr-preview'>1 2 3</div><input id='attrs-2f33872d-3359-42cb-a0d5-28f6f98e60e7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2f33872d-3359-42cb-a0d5-28f6f98e60e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d28918f-95e6-4ca6-9649-08837809407f' class='xr-var-data-in' type='checkbox'><label for='data-6d28918f-95e6-4ca6-9649-08837809407f' 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>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([1, 2, 3])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b1004d01-9f86-411e-803f-96699f29f03d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b1004d01-9f86-411e-803f-96699f29f03d' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>xvel</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9042 0.06279 ... 0.5988 0.09251</div><input id='attrs-5b5828d9-f24e-4c02-b7fd-d623023bb8ac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5b5828d9-f24e-4c02-b7fd-d623023bb8ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1db55fa1-c1c0-4344-a40f-ad6d952c108e' class='xr-var-data-in' type='checkbox'><label for='data-1db55fa1-c1c0-4344-a40f-ad6d952c108e' 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>x vel</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><pre>array([[0.90424152, 0.06279333, 0.25184731],\n",
+       "       [0.71334915, 0.59882563, 0.09251494]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>yvel</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.2222 0.5316 ... 0.3073 0.8381</div><input id='attrs-71eec31b-8f1c-401b-95d1-3e827ea1f6bc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-71eec31b-8f1c-401b-95d1-3e827ea1f6bc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0443a9be-4d1a-4ec4-96c6-020ebc287c0b' class='xr-var-data-in' type='checkbox'><label for='data-0443a9be-4d1a-4ec4-96c6-020ebc287c0b' 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>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><pre>array([[0.22221867, 0.5316426 , 0.99869606],\n",
+       "       [0.25101917, 0.30728995, 0.83814418]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bf1e15da-f38d-43bc-8a12-e1abfac88531' class='xr-section-summary-in' type='checkbox'  ><label for='section-bf1e15da-f38d-43bc-8a12-e1abfac88531' 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-7367ef96-beed-4ed6-81d8-d33f929c787d' class='xr-index-data-in' type='checkbox'/><label for='index-7367ef96-beed-4ed6-81d8-d33f929c787d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([2, 3], dtype=&#x27;int32&#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-9903e15f-e39e-4f82-8e7a-9d815081b82d' class='xr-index-data-in' type='checkbox'/><label for='index-9903e15f-e39e-4f82-8e7a-9d815081b82d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3], dtype=&#x27;int32&#x27;, name=&#x27;x&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-903f6780-5883-413b-85d1-132c8826d2b3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-903f6780-5883-413b-85d1-132c8826d2b3' 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>x vel</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset>\n",
@@ -454,11 +460,14 @@
        "  * y        (y) int32 2 3\n",
        "  * x        (x) int32 1 2 3\n",
        "Data variables:\n",
-       "    xvel     (y, x) float64 0.03456 0.9389 0.03886 0.1708 0.5039 0.8131\n",
-       "    yvel     (y, x) float64 0.1247 0.3435 0.5736 0.8872 0.6234 0.06209"
+       "    xvel     (y, x) float64 0.9042 0.06279 0.2518 0.7133 0.5988 0.09251\n",
+       "    yvel     (y, x) float64 0.2222 0.5316 0.9987 0.251 0.3073 0.8381\n",
+       "Attributes:\n",
+       "    long_name:  x vel\n",
+       "    units:      m/s"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -470,8 +479,10 @@
     "    \n",
     "    h5.create_dataset('x', data=[1, 2, 3], make_scale=True, attrs={'units': 'm'})\n",
     "    h5.create_dataset('y', data=[1, 2, 3, 4], make_scale=True, attrs={'units': 'm'})\n",
-    "    h5.create_dataset('u', data=np.random.random((4, 3)), attach_scales=('y', 'x'))\n",
-    "    h5.create_dataset('v', data=np.random.random((4, 3)), attach_scales=('y', 'x'))\n",
+    "    h5.create_dataset('u', data=np.random.random((4, 3)), attach_scales=('y', 'x'),\n",
+    "                     attrs=dict(units='m/s', long_name='x vel'))\n",
+    "    h5.create_dataset('v', data=np.random.random((4, 3)), attach_scales=('y', 'x'),\n",
+    "                     attrs=dict(units='m/s'))\n",
     "    \n",
     "    vec = h5.Vector(xvel='u', yvel='v')[1:3]\n",
     "vec"
@@ -482,8 +493,8 @@
    "id": "d3cddc5a-d646-4d20-a2ce-b024fa10e9c2",
    "metadata": {},
    "source": [
-    "### 2. Magnitude\n",
-    "This extension let's you compute the magnitude of given `xr.DataArray` objects, which goes well with the above extension:"
+    "## 2. Magnitude\n",
+    "Similar to `Vector`, the `Magnitude` accessory takes two or more datasets and calculates the magnitude from them. Again, data is only loaded after the involved dtasets are specified. If unit attributes are given, the calculation will check for mismatching units."
    ]
   },
   {
@@ -495,16 +506,90 @@
    },
    "outputs": [],
    "source": [
-    "from h5rdmtoolbox.extensions import magnitude # automatically available when vetor is imported"
+    "from h5rdmtoolbox.extensions import magnitude"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
+   "id": "0700f532-2443-4ed0-8f78-697c5e42385a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with h5tbx.File(h5.hdf_filename) as h5:\n",
+    "    mag = h5.Magnitude('u', 'v', name='mag')[1:3, 0:2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
    "id": "0f017c63-70c1-4007-9a55-6d5de6e3997b",
    "metadata": {
     "tags": []
    },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x20e1264b100>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mag.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fb9894f1-3e62-4199-9e20-0209ce1f5975",
+   "metadata": {},
+   "source": [
+    "## 3 Normalization\n",
+    "\n",
+    "Data is generally stored in one form (and physical unit) in the HDF5 file. However, in some scientific cases, data may be required in a normalized value.\n",
+    "\n",
+    "Consider the following example of a velocity dataset. The accessory `normalize` allows to normalize the data values or the coordinates.\n",
+    "\n",
+    "We will show two scenarios: Either the data values are normalized or the attached coordinates, too (or both)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "8d4ed2f8-3e47-4534-aa11-8e51a09703c1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from h5rdmtoolbox.extensions import normalize"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "008c140e-2127-47de-81fe-c1559484fa47",
+   "metadata": {},
+   "source": [
+    "Normalize the data by various reference values, here `x_ref` and `phi`. Note, that a unit may be provided:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "144da84f-5bd6-44d1-a24e-e55ebc4ecbae",
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -872,128 +957,474 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:                 (y: 2, x: 3)\n",
-       "Coordinates:\n",
-       "  * y                       (y) int32 2 3\n",
-       "  * x                       (x) int32 1 2 3\n",
-       "Data variables:\n",
-       "    xvel                    (y, x) float64 0.03456 0.9389 ... 0.5039 0.8131\n",
-       "    yvel                    (y, x) float64 0.1247 0.3435 ... 0.6234 0.06209\n",
-       "    magnitude_of_xvel_yvel  (y, x) float64 0.1294 0.9998 ... 0.8016 0.8154</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-035404a0-dfc0-4599-b843-a7e012617024' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-035404a0-dfc0-4599-b843-a7e012617024' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 2</li><li><span class='xr-has-index'>x</span>: 3</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d6789292-0221-43a6-95e5-cb283a46833d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d6789292-0221-43a6-95e5-cb283a46833d' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>2 3</div><input id='attrs-3160cfc9-f68d-43b8-9f64-3bb014847ab7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3160cfc9-f68d-43b8-9f64-3bb014847ab7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-023df3dc-9e94-44b7-9b53-1bad4f78ede6' class='xr-var-data-in' type='checkbox'><label for='data-023df3dc-9e94-44b7-9b53-1bad4f78ede6' 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>units :</span></dt><dd>meter</dd></dl></div><div class='xr-var-data'><pre>array([2, 3])</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'>int32</div><div class='xr-var-preview xr-preview'>1 2 3</div><input id='attrs-d6a97275-fa48-4f0f-b6e1-a4c68ac8b348' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d6a97275-fa48-4f0f-b6e1-a4c68ac8b348' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8de0b101-978a-49d9-810f-84849fc53b6a' class='xr-var-data-in' type='checkbox'><label for='data-8de0b101-978a-49d9-810f-84849fc53b6a' 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>units :</span></dt><dd>meter</dd></dl></div><div class='xr-var-data'><pre>array([1, 2, 3])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-416cd7b7-0136-4a58-b197-84a003799a58' class='xr-section-summary-in' type='checkbox'  checked><label for='section-416cd7b7-0136-4a58-b197-84a003799a58' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>xvel</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.03456 0.9389 ... 0.5039 0.8131</div><input id='attrs-fb397cdc-21c1-4849-b53f-3f8e25f591a0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fb397cdc-21c1-4849-b53f-3f8e25f591a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7298e73e-0792-4c7c-ab83-e697193fd165' class='xr-var-data-in' type='checkbox'><label for='data-7298e73e-0792-4c7c-ab83-e697193fd165' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.0345619 , 0.93892353, 0.03885579],\n",
-       "       [0.17084225, 0.5039308 , 0.81305982]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>yvel</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1247 0.3435 ... 0.6234 0.06209</div><input id='attrs-ea471002-d015-4062-a727-436168d7cfb1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ea471002-d015-4062-a727-436168d7cfb1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9bcfe27e-b6cc-4d31-8a62-daf3b56800a7' class='xr-var-data-in' type='checkbox'><label for='data-9bcfe27e-b6cc-4d31-8a62-daf3b56800a7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.12466993, 0.34351228, 0.57358334],\n",
-       "       [0.88717918, 0.62339574, 0.06208907]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>magnitude_of_xvel_yvel</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1294 0.9998 ... 0.8016 0.8154</div><input id='attrs-99a7cbfb-2277-458e-ad40-aab7fd172f60' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-99a7cbfb-2277-458e-ad40-aab7fd172f60' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c0497ac6-7253-429e-bdf7-4a0ce08bec05' class='xr-var-data-in' type='checkbox'><label for='data-c0497ac6-7253-429e-bdf7-4a0ce08bec05' 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>processing_comment :</span></dt><dd>computed from: xvel, yvel</dd></dl></div><div class='xr-var-data'><pre>array([[0.12937201, 0.99978902, 0.57489793],\n",
-       "       [0.90347882, 0.8016037 , 0.81542708]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e10f6400-4c87-4194-a9ff-5a8714e789e8' class='xr-section-summary-in' type='checkbox'  ><label for='section-e10f6400-4c87-4194-a9ff-5a8714e789e8' 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-9aad6f3e-a1d6-4bdf-bf22-280d3ac22323' class='xr-index-data-in' type='checkbox'/><label for='index-9aad6f3e-a1d6-4bdf-bf22-280d3ac22323' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([2, 3], dtype=&#x27;int32&#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-00255bef-9d91-4bfd-bcba-fa9d2d31d45b' class='xr-index-data-in' type='checkbox'/><label for='index-00255bef-9d91-4bfd-bcba-fa9d2d31d45b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3], dtype=&#x27;int32&#x27;, name=&#x27;x&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-36f43d74-8a47-429b-8dff-03aa85bc2f29' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-36f43d74-8a47-429b-8dff-03aa85bc2f29' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;x/x_ref/phi&#x27; ()&gt;\n",
+       "0.3333\n",
+       "Attributes:\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'>'x/x_ref/phi'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-00f511c2-72aa-411c-8db3-0163763b363f' class='xr-array-in' type='checkbox' ><label for='section-00f511c2-72aa-411c-8db3-0163763b363f' 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>0.3333</span></div><div class='xr-array-data'><pre>array(0.33333333)</pre></div></div></li><li class='xr-section-item'><input id='section-7d141240-3682-4409-be7a-3176dc4f5855' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7d141240-3682-4409-be7a-3176dc4f5855' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-bf624752-b76b-4a98-be96-069ec286a4b3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bf624752-b76b-4a98-be96-069ec286a4b3' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-0ac0a213-3106-49c2-b394-97a24da82cf0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0ac0a213-3106-49c2-b394-97a24da82cf0' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd></dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.Dataset>\n",
-       "Dimensions:                 (y: 2, x: 3)\n",
-       "Coordinates:\n",
-       "  * y                       (y) int32 2 3\n",
-       "  * x                       (x) int32 1 2 3\n",
-       "Data variables:\n",
-       "    xvel                    (y, x) float64 0.03456 0.9389 ... 0.5039 0.8131\n",
-       "    yvel                    (y, x) float64 0.1247 0.3435 ... 0.6234 0.06209\n",
-       "    magnitude_of_xvel_yvel  (y, x) float64 0.1294 0.9998 ... 0.8016 0.8154"
+       "<xarray.DataArray 'x/x_ref/phi' ()>\n",
+       "0.3333\n",
+       "Attributes:\n",
+       "    units:    "
       ]
      },
-     "execution_count": 5,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "vec.magnitude.compute_from('xvel', 'yvel')"
+    "with h5tbx.File() as h5:\n",
+    "    h5.create_dataset('x', data=[1, 2, 3], make_scale=True, attrs={'units': 'm'})\n",
+    "    h5.create_dataset('y', data=[1, 2, 3, 4], make_scale=True, attrs={'units': 'm'})\n",
+    "    h5.create_dataset('u', data=np.random.random((4, 3)), attach_scales=('y', 'x'))\n",
+    "    h5.create_dataset('v', data=np.random.random((4, 3)), attach_scales=('y', 'x'))\n",
+    "\n",
+    "    x_norm = h5.x.normalize(x_ref='2 m', phi=3)[1]\n",
+    "\n",
+    "x_norm"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "fb9894f1-3e62-4199-9e20-0209ce1f5975",
+   "id": "fc22af39-e75b-4c05-b991-20af9a4616c0",
    "metadata": {},
    "source": [
-    "### 3 Normalization\n",
-    "Oftentimes, scientific plots show dimensionless data or in relation to another variable. The accessor `normalize` allows to normalize the data values or the coordinates."
+    "Now, let's normalize the velocity \"u\" **and its coordinate** x:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "af7129af-1ec4-4bee-9aad-52bea6d43791",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from h5rdmtoolbox.extensions import normalize\n",
-    "\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d71b7238-6c62-499a-a593-f83b15e9e594",
+   "execution_count": 9,
+   "id": "a6b8c83e-9c97-475a-903d-3478ffd92eee",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "### 3.1 Normalize array data\n",
-    "\n",
-    "In the below example, the `xvel` data is normalized by `v_ref=3.5 m/s`. Note, that the data is divided by 3.5 while considering the unit (`m/s`), so the resulting dataset (right plot) is dimensionless. Also note, that the **dataset name is changed** accordingly."
+    "with h5tbx.File(h5.hdf_filename) as h5:\n",
+    "    u_norm = h5.u.normalize(u_ref='2 m/s', name='uu').x(x_ref='2 m', phi=3, name='epsilon').isel(y=0)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "be5f90b8-5bc0-4618-b74c-f6052ac28e1e",
-   "metadata": {
-    "tags": []
-   },
+   "execution_count": 10,
+   "id": "99ac8eff-1069-45c2-b943-e2efd3cec0f5",
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --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",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;uu&#x27; (epsilon: 3)&gt;\n",
+       "0.4201 0.2418 0.02388\n",
+       "Coordinates:\n",
+       "    y        int32 1\n",
+       "  * epsilon  (epsilon) float64 0.1667 0.3333 0.5\n",
+       "Attributes:\n",
+       "    units:    s/m</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'>'uu'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>epsilon</span>: 3</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-dc3d08e7-5c03-4582-8c4c-d9f4cb5c4ffa' class='xr-array-in' type='checkbox' ><label for='section-dc3d08e7-5c03-4582-8c4c-d9f4cb5c4ffa' 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>0.4201 0.2418 0.02388</span></div><div class='xr-array-data'><pre>array([0.42014101, 0.2417668 , 0.0238765 ])</pre></div></div></li><li class='xr-section-item'><input id='section-c8baa89d-86a3-4638-bd9a-bf8aa1ffcb2a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c8baa89d-86a3-4638-bd9a-bf8aa1ffcb2a' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1</div><input id='attrs-0ea4894f-c2c6-4855-bca6-2af0127250b7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0ea4894f-c2c6-4855-bca6-2af0127250b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f261096e-7f58-4e4c-a917-6cae341f9382' class='xr-var-data-in' type='checkbox'><label for='data-f261096e-7f58-4e4c-a917-6cae341f9382' 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>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array(1)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>epsilon</span></div><div class='xr-var-dims'>(epsilon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1667 0.3333 0.5</div><input id='attrs-efa25bdc-b806-4110-a9cd-d750a786a3a4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-efa25bdc-b806-4110-a9cd-d750a786a3a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29c01ce2-0212-4d10-811c-16c91dbeb613' class='xr-var-data-in' type='checkbox'><label for='data-29c01ce2-0212-4d10-811c-16c91dbeb613' 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>units :</span></dt><dd>1/m</dd></dl></div><div class='xr-var-data'><pre>array([0.166667, 0.333333, 0.5     ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-895b9248-de4a-4c23-b3ea-175c05854571' class='xr-section-summary-in' type='checkbox'  ><label for='section-895b9248-de4a-4c23-b3ea-175c05854571' 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>epsilon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6ee43603-48d9-4d65-aa1a-91106e92441e' class='xr-index-data-in' type='checkbox'/><label for='index-6ee43603-48d9-4d65-aa1a-91106e92441e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0.16666666666666666, 0.3333333333333333, 0.5], dtype=&#x27;float64&#x27;, name=&#x27;epsilon&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4b955125-f311-4810-9ead-14938d079bef' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4b955125-f311-4810-9ead-14938d079bef' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>s/m</dd></dl></div></li></ul></div></div>"
+      ],
       "text/plain": [
-       "<Figure size 800x300 with 4 Axes>"
+       "<xarray.DataArray 'uu' (epsilon: 3)>\n",
+       "0.4201 0.2418 0.02388\n",
+       "Coordinates:\n",
+       "    y        int32 1\n",
+       "  * epsilon  (epsilon) float64 0.1667 0.3333 0.5\n",
+       "Attributes:\n",
+       "    units:    s/m"
       ]
      },
+     "execution_count": 10,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "fig, axs = plt.subplots(1, 2, figsize=(8, 3))\n",
-    "\n",
-    "vec.xvel.plot(ax=axs[0])\n",
-    "# normalize ALL coordinates:\n",
-    "norm_vec = vec.xvel.normalize(v_ref='3.5 m/s', rename=True)\n",
-    "norm_vec.plot(ax=axs[1])\n",
-    "\n",
-    "axs[0].set_title('original')\n",
-    "axs[1].set_title('normalized data')\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "767048b8-7070-472d-86ef-6af53f044da7",
-   "metadata": {},
-   "source": [
-    "### 3.1 Normalize coordinates\n",
-    "\n",
-    "Now, let's normalize the coordinates with `L=100 mm`:"
+    "u_norm"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "0adfb995-76c4-4cb5-b346-aa059dcc5a9e",
+   "execution_count": 11,
+   "id": "b3f9c0dc-e6e1-46ad-980f-dd120302b53b",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 600x600 with 8 Axes>"
+       "[<matplotlib.lines.Line2D at 0x20e12704760>]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1001,30 +1432,7 @@
     }
    ],
    "source": [
-    "fig, _axs = plt.subplots(2, 2, figsize=(6, 6),\n",
-    "                        tight_layout=True)\n",
-    "axs = _axs.ravel()\n",
-    "\n",
-    "vec.xvel.plot(ax=axs[0])\n",
-    "\n",
-    "norm_vec = vec.xvel.normalize.coords(L='100 mm', rename=True)  # no units!\n",
-    "norm_vec.plot(ax=axs[1])\n",
-    "\n",
-    "norm_vec = vec.xvel.normalize.coords(x={'L': '100 mm'}, rename=True)\n",
-    "norm_vec.plot(ax=axs[2])\n",
-    "\n",
-    "norm_vec = vec.xvel.normalize.coords(y={'L': '100 mm'}, rename=True)\n",
-    "norm_vec.plot(ax=axs[3])\n",
-    "\n",
-    "axs[0].set_title('original')\n",
-    "axs[1].set_title('normalize all coords')\n",
-    "axs[2].set_title('normalize x-coord')\n",
-    "axs[3].set_title('normalize y-coord')\n",
-    "# for ax in axs:\n",
-    "#     ax.set_aspect(1)\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
+    "u_norm.plot()"
    ]
   },
   {
@@ -1042,7 +1450,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 12,
    "id": "e614bf4a-556b-44c0-ac00-11a5113e5cb1",
    "metadata": {},
    "outputs": [],
@@ -1052,7 +1460,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 13,
    "id": "76cf75b0-3935-42ec-a3b6-0165db7d8264",
    "metadata": {},
    "outputs": [
@@ -1427,7 +1835,7 @@
        "Coordinates:\n",
        "  * x        (x) float64 -4.5e+03\n",
        "Attributes:\n",
-       "    units:    mm/s</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'>'u'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>x</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-2194efb0-0827-49d2-815d-ad30849bdbf7' class='xr-array-in' type='checkbox' ><label for='section-2194efb0-0827-49d2-815d-ad30849bdbf7' 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>-4.5e+03</span></div><div class='xr-array-data'><pre>array([-4500.])</pre></div></div></li><li class='xr-section-item'><input id='section-70838a76-fbf4-4e37-a418-bb92cbdebf61' class='xr-section-summary-in' type='checkbox'  checked><label for='section-70838a76-fbf4-4e37-a418-bb92cbdebf61' 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'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-4.5e+03</div><input id='attrs-41cd674e-83de-493b-98a3-92f8ba76915c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-41cd674e-83de-493b-98a3-92f8ba76915c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-672c7f3f-00a2-451d-9f17-15acabdc26d1' class='xr-var-data-in' type='checkbox'><label for='data-672c7f3f-00a2-451d-9f17-15acabdc26d1' 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>units :</span></dt><dd>mm</dd></dl></div><div class='xr-var-data'><pre>array([-4500.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-908feaf4-ab84-4b22-b5d9-4073a5fd163e' class='xr-section-summary-in' type='checkbox'  ><label for='section-908feaf4-ab84-4b22-b5d9-4073a5fd163e' 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>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a0b2557a-60fc-4077-b958-c5a672e78649' class='xr-index-data-in' type='checkbox'/><label for='index-a0b2557a-60fc-4077-b958-c5a672e78649' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-4500.0], dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9a66d8cd-6815-4fe7-8cfa-65fb2bfbe54c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9a66d8cd-6815-4fe7-8cfa-65fb2bfbe54c' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>mm/s</dd></dl></div></li></ul></div></div>"
+       "    units:    mm/s</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'>'u'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>x</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-ae002787-74e5-4a66-9ab7-ad8bce189afe' class='xr-array-in' type='checkbox' ><label for='section-ae002787-74e5-4a66-9ab7-ad8bce189afe' 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>-4.5e+03</span></div><div class='xr-array-data'><pre>array([-4500.])</pre></div></div></li><li class='xr-section-item'><input id='section-512ba4cd-db50-4471-8ad0-04190bae6c4c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-512ba4cd-db50-4471-8ad0-04190bae6c4c' 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'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-4.5e+03</div><input id='attrs-8e4cc63f-8265-482f-bdc3-e58518becd4c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8e4cc63f-8265-482f-bdc3-e58518becd4c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c89714c1-8266-4422-894b-92fc358d83be' class='xr-var-data-in' type='checkbox'><label for='data-c89714c1-8266-4422-894b-92fc358d83be' 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>units :</span></dt><dd>mm</dd></dl></div><div class='xr-var-data'><pre>array([-4500.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-90873b1a-d25c-4a12-b123-f3ab0c32f960' class='xr-section-summary-in' type='checkbox'  ><label for='section-90873b1a-d25c-4a12-b123-f3ab0c32f960' 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>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-43dac240-a231-40ba-a146-0a297eaefc9f' class='xr-index-data-in' type='checkbox'/><label for='index-43dac240-a231-40ba-a146-0a297eaefc9f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-4500.0], dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ae72e4eb-deb9-48ed-a108-93d02cb38b3f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ae72e4eb-deb9-48ed-a108-93d02cb38b3f' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>mm/s</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.DataArray 'u' (x: 1)>\n",
@@ -1438,7 +1846,7 @@
        "    units:    mm/s"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1458,13 +1866,14 @@
    "id": "b7730172-11e8-47f0-93b1-9bc47aa0e1dd",
    "metadata": {},
    "source": [
-    "### Registration of datasets\n",
-    "We can also \"register\" dataset accessory. In the following example, we add \"device\" as a \"property with methods\". So \"device\" seems to be a property which has a method \"add\". Such an implementation facilitates the interaction with HDF data, too. Note, that this \"property-like\" accessory is available for all `Dataset` objects from now on in this session:"
+    "## Custom accessory\n",
+    "\n",
+    "Besides the already implemented ones, users can register custom accessories, too. In the following example, we add \"device\" as a \"property with methods\". So \"device\" appears as a property which has a method \"add\". Such an implementation facilitates the interaction with HDF data, too. Note, that this \"property-like\" accessory is available for all `Dataset` objects from now on in this session:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "id": "41f5c2c1-7de5-4e1c-b44e-4d350328f7ef",
    "metadata": {},
    "outputs": [],
@@ -1490,10 +1899,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "id": "2af29af0-aa80-4887-bd6f-8c93d719f2e8",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'h5rdmtoolbox.wrapper.core.Dataset'>\n",
+      "my device\n"
+     ]
+    }
+   ],
    "source": [
     "with h5tbx.File() as h5:\n",
     "    ds = h5.create_dataset('test', shape=(2,))\n",
@@ -1501,6 +1919,14 @@
     "    ds.device.add('my device')\n",
     "    print(ds.device.name)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c569c3ff-aa88-4bf9-968f-5a3c210ac6b2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/docs/userguide/wrapper/DatasetSlicing.ipynb b/docs/userguide/wrapper/DatasetSlicing.ipynb
index 55a6754..ea8d9f1 100644
--- a/docs/userguide/wrapper/DatasetSlicing.ipynb
+++ b/docs/userguide/wrapper/DatasetSlicing.ipynb
@@ -20,13 +20,6 @@
    "id": "9fc2b312-16d5-4aa7-8f10-9107a2e53c08",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failed to import module h5tbx\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
@@ -282,32 +275,32 @@
        "\n",
        "              <ul style=\"list-style-type: none;\" class=\"h5grp-sections\">\n",
        "                    <li>\n",
-       "                        <input id=\"group-ds--6374139400\" type=\"checkbox\" checked>\n",
-       "                        <label style=\"font-weight: bold\" for=\"group-ds--6374139400\">\n",
+       "                        <input id=\"group-ds--7714627300\" type=\"checkbox\" checked>\n",
+       "                        <label style=\"font-weight: bold\" for=\"group-ds--7714627300\">\n",
        "                        /<span>(3)</span></label>\n",
        "                  \n",
        "<ul class=\"h5tb-attr-list\">\n",
        "                    </ul>\n",
        "\n",
-       "                <ul id=\"ds-1-/vel-6375523300\" class=\"h5tb-var-list\">\n",
-       "                    <input id=\"ds-2-/vel-6375526200\" class=\"h5tb-varname-in\" type=\"checkbox\" >\n",
-       "                    <label class='h5tb-varname' for=\"ds-2-/vel-6375526200\">vel</label>\n",
+       "                <ul id=\"ds-1-/vel-7717606400\" class=\"h5tb-var-list\">\n",
+       "                    <input id=\"ds-2-/vel-7717615900\" class=\"h5tb-varname-in\" type=\"checkbox\" >\n",
+       "                    <label class='h5tb-varname' for=\"ds-2-/vel-7717615900\">vel</label>\n",
        "                    <span class=\"h5tb-dims\">(y: 11, x: 5) [float64]</span>\n",
        "                <ul class=\"h5tb-attr-list\"><li style=\"list-style-type: none; font-style: italic\">long_name : velocity</li><li style=\"list-style-type: none; font-style: italic\">units : m/s</li>\n",
        "                </ul>\n",
        "                </ul>\n",
        "\n",
-       "                <ul id=\"ds-1-/x-6376183400\" class=\"h5tb-var-list\">\n",
-       "                    <input id=\"ds-2-/x-6376186200\" class=\"h5tb-varname-in\" type=\"checkbox\" >\n",
-       "                    <label class='h5tb-varname' for=\"ds-2-/x-6376186200\">x</label>\n",
+       "                <ul id=\"ds-1-/x-7721433600\" class=\"h5tb-var-list\">\n",
+       "                    <input id=\"ds-2-/x-7721453200\" class=\"h5tb-varname-in\" type=\"checkbox\" >\n",
+       "                    <label class='h5tb-varname' for=\"ds-2-/x-7721453200\">x</label>\n",
        "                    <span class=\"h5tb-dims\">(5) [float64]</span>\n",
        "                <ul class=\"h5tb-attr-list\"><li style=\"list-style-type: none; font-style: italic\">long_name : x</li><li style=\"list-style-type: none; font-style: italic\">units : mm</li>\n",
        "                </ul>\n",
        "                </ul>\n",
        "\n",
-       "                <ul id=\"ds-1-/y-6376593500\" class=\"h5tb-var-list\">\n",
-       "                    <input id=\"ds-2-/y-6376596000\" class=\"h5tb-varname-in\" type=\"checkbox\" >\n",
-       "                    <label class='h5tb-varname' for=\"ds-2-/y-6376596000\">y</label>\n",
+       "                <ul id=\"ds-1-/y-7725879400\" class=\"h5tb-var-list\">\n",
+       "                    <input id=\"ds-2-/y-7725900600\" class=\"h5tb-varname-in\" type=\"checkbox\" >\n",
+       "                    <label class='h5tb-varname' for=\"ds-2-/y-7725900600\">y</label>\n",
        "                    <span class=\"h5tb-dims\">(11) [float64]</span>\n",
        "                <ul class=\"h5tb-attr-list\"><li style=\"list-style-type: none; font-style: italic\">long_name : y</li><li style=\"list-style-type: none; font-style: italic\">units : mm</li>\n",
        "                </ul>\n",
@@ -721,27 +714,27 @@
        "  fill: currentColor;\n",
        "}\n",
        "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;vel&#x27; (y: 11, x: 5)&gt;\n",
-       "0.03267 0.7154 0.7752 0.4488 0.5885 0.4677 ... 0.678 0.1382 0.3756 0.8476 0.4079\n",
+       "0.1955 0.2172 0.5719 0.05115 0.8259 0.652 ... 0.3608 0.4717 0.729 0.9591 0.1419\n",
        "Coordinates:\n",
        "  * y        (y) float64 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0\n",
        "  * x        (x) float64 0.0 2.5 5.0 7.5 10.0\n",
        "Attributes:\n",
        "    long_name:  velocity\n",
-       "    units:      m/s</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'>'vel'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 11</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-b701e435-2395-45bc-92fc-0eec39539f81' class='xr-array-in' type='checkbox' ><label for='section-b701e435-2395-45bc-92fc-0eec39539f81' 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>0.03267 0.7154 0.7752 0.4488 0.5885 ... 0.1382 0.3756 0.8476 0.4079</span></div><div class='xr-array-data'><pre>array([[0.03267225, 0.71537327, 0.77523968, 0.44881555, 0.58847744],\n",
-       "       [0.46766272, 0.78556669, 0.42897773, 0.4533245 , 0.98940192],\n",
-       "       [0.9383029 , 0.69233003, 0.87004673, 0.6586948 , 0.86618609],\n",
-       "       [0.48867332, 0.15560548, 0.95363503, 0.65989826, 0.58677507],\n",
-       "       [0.55833002, 0.1351151 , 0.6607069 , 0.21901041, 0.39669509],\n",
-       "       [0.14356703, 0.65216524, 0.53262494, 0.83345774, 0.54110714],\n",
-       "       [0.33278661, 0.74768159, 0.26418856, 0.71721895, 0.42925456],\n",
-       "       [0.33984233, 0.40688911, 0.27161079, 0.75647065, 0.93444986],\n",
-       "       [0.95428975, 0.91210696, 0.42452528, 0.58024098, 0.80393753],\n",
-       "       [0.82011736, 0.10417137, 0.2794295 , 0.9982119 , 0.90854497],\n",
-       "       [0.67798326, 0.13816222, 0.37561511, 0.84757712, 0.40787561]])</pre></div></div></li><li class='xr-section-item'><input id='section-866f387d-e250-4e6a-aae9-2090c9584b43' class='xr-section-summary-in' type='checkbox'  checked><label for='section-866f387d-e250-4e6a-aae9-2090c9584b43' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span 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'>0.0 0.5 1.0 1.5 ... 3.5 4.0 4.5 5.0</div><input id='attrs-76e9185d-6c6f-4355-9e70-9dede2a6afd6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-76e9185d-6c6f-4355-9e70-9dede2a6afd6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e236754-8c14-4bc8-ab22-56fdb4732e42' class='xr-var-data-in' type='checkbox'><label for='data-4e236754-8c14-4bc8-ab22-56fdb4732e42' 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>y</dd><dt><span>units :</span></dt><dd>mm</dd></dl></div><div class='xr-var-data'><pre>array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. ])</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'>0.0 2.5 5.0 7.5 10.0</div><input id='attrs-d5dc5401-de76-4371-8ee7-efcd9449e4c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d5dc5401-de76-4371-8ee7-efcd9449e4c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ffac235-9f6b-4d78-af2c-04bc57ef6052' class='xr-var-data-in' type='checkbox'><label for='data-9ffac235-9f6b-4d78-af2c-04bc57ef6052' 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>x</dd><dt><span>units :</span></dt><dd>mm</dd></dl></div><div class='xr-var-data'><pre>array([ 0. ,  2.5,  5. ,  7.5, 10. ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-026bb250-3a89-448d-be52-c06af80c4ae5' class='xr-section-summary-in' type='checkbox'  ><label for='section-026bb250-3a89-448d-be52-c06af80c4ae5' 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-45de47e2-7a91-4216-8c52-cdd17c213d2c' class='xr-index-data-in' type='checkbox'/><label for='index-45de47e2-7a91-4216-8c52-cdd17c213d2c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0], dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5a098546-cfd4-41c9-bcff-1473efd95c13' class='xr-index-data-in' type='checkbox'/><label for='index-5a098546-cfd4-41c9-bcff-1473efd95c13' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0.0, 2.5, 5.0, 7.5, 10.0], dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-182480f1-13e6-4d94-8efd-923be247eee6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-182480f1-13e6-4d94-8efd-923be247eee6' 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>velocity</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div></li></ul></div></div>"
+       "    units:      m/s</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'>'vel'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 11</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-ea8443fc-4625-408b-abd1-d7ff1f5df734' class='xr-array-in' type='checkbox' ><label for='section-ea8443fc-4625-408b-abd1-d7ff1f5df734' 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>0.1955 0.2172 0.5719 0.05115 0.8259 ... 0.4717 0.729 0.9591 0.1419</span></div><div class='xr-array-data'><pre>array([[0.19551543, 0.21723572, 0.57192219, 0.05114613, 0.82586645],\n",
+       "       [0.65204411, 0.27035611, 0.64282322, 0.34563818, 0.78382405],\n",
+       "       [0.15354701, 0.13197385, 0.53242705, 0.46598591, 0.3193404 ],\n",
+       "       [0.24765567, 0.21154385, 0.17521748, 0.49393686, 0.71075356],\n",
+       "       [0.77672343, 0.76843807, 0.85598686, 0.52576786, 0.12592183],\n",
+       "       [0.11212213, 0.86014426, 0.22412851, 0.90550408, 0.14689475],\n",
+       "       [0.76329797, 0.88658578, 0.70876641, 0.00989451, 0.00770115],\n",
+       "       [0.48483632, 0.66433282, 0.24615475, 0.78235776, 0.95685209],\n",
+       "       [0.92982355, 0.34482073, 0.51473298, 0.74247707, 0.838179  ],\n",
+       "       [0.39746769, 0.23545768, 0.4388846 , 0.80797417, 0.51061225],\n",
+       "       [0.36082964, 0.4717019 , 0.72903745, 0.95906968, 0.14185989]])</pre></div></div></li><li class='xr-section-item'><input id='section-e661358d-1a54-43e1-bab6-f7597cdade99' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e661358d-1a54-43e1-bab6-f7597cdade99' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span 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'>0.0 0.5 1.0 1.5 ... 3.5 4.0 4.5 5.0</div><input id='attrs-515fb881-80ea-4242-b635-07072cd627cb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-515fb881-80ea-4242-b635-07072cd627cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e547a79-ace0-46e7-b47b-ecae77a22496' class='xr-var-data-in' type='checkbox'><label for='data-1e547a79-ace0-46e7-b47b-ecae77a22496' 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>y</dd><dt><span>units :</span></dt><dd>mm</dd></dl></div><div class='xr-var-data'><pre>array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. ])</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'>0.0 2.5 5.0 7.5 10.0</div><input id='attrs-6c55ae42-c538-4018-9766-f3edeeac26a9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6c55ae42-c538-4018-9766-f3edeeac26a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62fdf419-1401-48ea-8d1c-d8ae329e956f' class='xr-var-data-in' type='checkbox'><label for='data-62fdf419-1401-48ea-8d1c-d8ae329e956f' 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>x</dd><dt><span>units :</span></dt><dd>mm</dd></dl></div><div class='xr-var-data'><pre>array([ 0. ,  2.5,  5. ,  7.5, 10. ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-62602844-bb8f-41ea-9865-71c69acb58be' class='xr-section-summary-in' type='checkbox'  ><label for='section-62602844-bb8f-41ea-9865-71c69acb58be' 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-33f93017-dc09-4c95-85e0-a071778214e5' class='xr-index-data-in' type='checkbox'/><label for='index-33f93017-dc09-4c95-85e0-a071778214e5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0], dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0771cb3a-131b-4043-8897-111a20f17dd5' class='xr-index-data-in' type='checkbox'/><label for='index-0771cb3a-131b-4043-8897-111a20f17dd5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0.0, 2.5, 5.0, 7.5, 10.0], dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2b7318a6-1bf4-438e-a06a-07146040cd0a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2b7318a6-1bf4-438e-a06a-07146040cd0a' 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>velocity</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.DataArray 'vel' (y: 11, x: 5)>\n",
-       "0.03267 0.7154 0.7752 0.4488 0.5885 0.4677 ... 0.678 0.1382 0.3756 0.8476 0.4079\n",
+       "0.1955 0.2172 0.5719 0.05115 0.8259 0.652 ... 0.3608 0.4717 0.729 0.9591 0.1419\n",
        "Coordinates:\n",
        "  * y        (y) float64 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0\n",
        "  * x        (x) float64 0.0 2.5 5.0 7.5 10.0\n",
@@ -1176,17 +1169,17 @@
        "  fill: currentColor;\n",
        "}\n",
        "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;vel&#x27; (x: 5)&gt;\n",
-       "0.5583 0.1351 0.6607 0.219 0.3967\n",
+       "0.7767 0.7684 0.856 0.5258 0.1259\n",
        "Coordinates:\n",
        "    y        float64 2.0\n",
        "  * x        (x) float64 0.0 2.5 5.0 7.5 10.0\n",
        "Attributes:\n",
        "    long_name:  velocity\n",
-       "    units:      m/s</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'>'vel'</div><ul class='xr-dim-list'><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-01675832-88f9-4e4e-93e2-22228781295a' class='xr-array-in' type='checkbox' ><label for='section-01675832-88f9-4e4e-93e2-22228781295a' 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>0.5583 0.1351 0.6607 0.219 0.3967</span></div><div class='xr-array-data'><pre>array([0.55833002, 0.1351151 , 0.6607069 , 0.21901041, 0.39669509])</pre></div></div></li><li class='xr-section-item'><input id='section-8e21a7d5-af6e-4fa5-adee-230365096077' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8e21a7d5-af6e-4fa5-adee-230365096077' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-bd5d3c2e-51dd-4fdd-91b8-31d68d7d1167' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd5d3c2e-51dd-4fdd-91b8-31d68d7d1167' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6adb516b-f45e-43b8-9da5-75a3f7320edb' class='xr-var-data-in' type='checkbox'><label for='data-6adb516b-f45e-43b8-9da5-75a3f7320edb' 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>y</dd><dt><span>units :</span></dt><dd>mm</dd></dl></div><div class='xr-var-data'><pre>array(2.)</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'>0.0 2.5 5.0 7.5 10.0</div><input id='attrs-bfb0bfd7-8392-44ea-8e28-260df0015a02' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bfb0bfd7-8392-44ea-8e28-260df0015a02' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eeca645b-0082-4f00-ad4d-26da308298d5' class='xr-var-data-in' type='checkbox'><label for='data-eeca645b-0082-4f00-ad4d-26da308298d5' 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>x</dd><dt><span>units :</span></dt><dd>mm</dd></dl></div><div class='xr-var-data'><pre>array([ 0. ,  2.5,  5. ,  7.5, 10. ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-925f9793-86df-400b-a8af-1de3f74c45eb' class='xr-section-summary-in' type='checkbox'  ><label for='section-925f9793-86df-400b-a8af-1de3f74c45eb' 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>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4e44726f-6468-4e2e-801a-0d5beed7b4c8' class='xr-index-data-in' type='checkbox'/><label for='index-4e44726f-6468-4e2e-801a-0d5beed7b4c8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0.0, 2.5, 5.0, 7.5, 10.0], dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0b970bda-b912-45e8-a31b-5664aec79bb4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0b970bda-b912-45e8-a31b-5664aec79bb4' 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>velocity</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div></li></ul></div></div>"
+       "    units:      m/s</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'>'vel'</div><ul class='xr-dim-list'><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-23a4568d-58de-48f3-aec9-b59082a6a89a' class='xr-array-in' type='checkbox' ><label for='section-23a4568d-58de-48f3-aec9-b59082a6a89a' 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>0.7767 0.7684 0.856 0.5258 0.1259</span></div><div class='xr-array-data'><pre>array([0.77672343, 0.76843807, 0.85598686, 0.52576786, 0.12592183])</pre></div></div></li><li class='xr-section-item'><input id='section-6e8df0e1-f569-474e-b247-202f35819c92' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6e8df0e1-f569-474e-b247-202f35819c92' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-12979279-1f99-4654-a928-5a8f3452a312' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-12979279-1f99-4654-a928-5a8f3452a312' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-edc2daa1-b495-44dd-aa63-3a949ab0000e' class='xr-var-data-in' type='checkbox'><label for='data-edc2daa1-b495-44dd-aa63-3a949ab0000e' 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>y</dd><dt><span>units :</span></dt><dd>mm</dd></dl></div><div class='xr-var-data'><pre>array(2.)</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'>0.0 2.5 5.0 7.5 10.0</div><input id='attrs-1672db4b-a0d4-4e2b-b149-85ec99e27386' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1672db4b-a0d4-4e2b-b149-85ec99e27386' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0526d411-8197-489a-b595-662ecad0c132' class='xr-var-data-in' type='checkbox'><label for='data-0526d411-8197-489a-b595-662ecad0c132' 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>x</dd><dt><span>units :</span></dt><dd>mm</dd></dl></div><div class='xr-var-data'><pre>array([ 0. ,  2.5,  5. ,  7.5, 10. ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c3e501ab-7d25-4909-86cd-6d02fbf6c583' class='xr-section-summary-in' type='checkbox'  ><label for='section-c3e501ab-7d25-4909-86cd-6d02fbf6c583' 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>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8a82e893-89fa-4cdb-9dbb-f5d4a149268c' class='xr-index-data-in' type='checkbox'/><label for='index-8a82e893-89fa-4cdb-9dbb-f5d4a149268c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0.0, 2.5, 5.0, 7.5, 10.0], dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f5536971-2036-4d15-ad35-973cf9f9c1b5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f5536971-2036-4d15-ad35-973cf9f9c1b5' 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>velocity</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.DataArray 'vel' (x: 5)>\n",
-       "0.5583 0.1351 0.6607 0.219 0.3967\n",
+       "0.7767 0.7684 0.856 0.5258 0.1259\n",
        "Coordinates:\n",
        "    y        float64 2.0\n",
        "  * x        (x) float64 0.0 2.5 5.0 7.5 10.0\n",
@@ -1221,7 +1214,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.contour.QuadContourSet at 0x251daef2bb0>"
+       "<matplotlib.contour.QuadContourSet at 0x1fb4ba36670>"
       ]
      },
      "execution_count": 6,
@@ -1230,7 +1223,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -1706,17 +1699,17 @@
        "  fill: currentColor;\n",
        "}\n",
        "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;vel&#x27; (x: 5)&gt;\n",
-       "0.5583 0.1351 0.6607 0.219 0.3967\n",
+       "0.7767 0.7684 0.856 0.5258 0.1259\n",
        "Coordinates:\n",
        "    y        float64 2.0\n",
        "  * x        (x) float64 0.0 2.5 5.0 7.5 10.0\n",
        "Attributes:\n",
        "    long_name:  velocity\n",
-       "    units:      m/s</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'>'vel'</div><ul class='xr-dim-list'><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-0a2ef583-4bf3-48c0-b4d7-1dfffc06284f' class='xr-array-in' type='checkbox' ><label for='section-0a2ef583-4bf3-48c0-b4d7-1dfffc06284f' 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>0.5583 0.1351 0.6607 0.219 0.3967</span></div><div class='xr-array-data'><pre>array([0.55833002, 0.1351151 , 0.6607069 , 0.21901041, 0.39669509])</pre></div></div></li><li class='xr-section-item'><input id='section-7c1ed23d-1155-4a0a-9a23-1d7af182c11d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7c1ed23d-1155-4a0a-9a23-1d7af182c11d' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-935ec02c-43a5-4ffb-bf65-d1325e084ad8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-935ec02c-43a5-4ffb-bf65-d1325e084ad8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-737d81f0-ba94-479c-bec2-da04bb28e183' class='xr-var-data-in' type='checkbox'><label for='data-737d81f0-ba94-479c-bec2-da04bb28e183' 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>y</dd><dt><span>units :</span></dt><dd>mm</dd></dl></div><div class='xr-var-data'><pre>array(2.)</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'>0.0 2.5 5.0 7.5 10.0</div><input id='attrs-7cb1d1ec-63e3-47e5-a1c0-548148db04b5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7cb1d1ec-63e3-47e5-a1c0-548148db04b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62769cbb-a5d0-45d3-b3c0-04df44576b6f' class='xr-var-data-in' type='checkbox'><label for='data-62769cbb-a5d0-45d3-b3c0-04df44576b6f' 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>x</dd><dt><span>units :</span></dt><dd>mm</dd></dl></div><div class='xr-var-data'><pre>array([ 0. ,  2.5,  5. ,  7.5, 10. ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a35b10b1-ebdd-4daa-ad52-7575691cef1d' class='xr-section-summary-in' type='checkbox'  ><label for='section-a35b10b1-ebdd-4daa-ad52-7575691cef1d' 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>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8de01c05-cabb-4a26-af79-a0bbe4d518e7' class='xr-index-data-in' type='checkbox'/><label for='index-8de01c05-cabb-4a26-af79-a0bbe4d518e7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0.0, 2.5, 5.0, 7.5, 10.0], dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-eed921d7-a7bf-4e25-9f3d-ad362c426d1b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-eed921d7-a7bf-4e25-9f3d-ad362c426d1b' 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>velocity</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div></li></ul></div></div>"
+       "    units:      m/s</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'>'vel'</div><ul class='xr-dim-list'><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-4403713d-5040-43e6-992c-325869fca924' class='xr-array-in' type='checkbox' ><label for='section-4403713d-5040-43e6-992c-325869fca924' 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>0.7767 0.7684 0.856 0.5258 0.1259</span></div><div class='xr-array-data'><pre>array([0.77672343, 0.76843807, 0.85598686, 0.52576786, 0.12592183])</pre></div></div></li><li class='xr-section-item'><input id='section-2cc3c044-daf9-46fd-98d9-e13283c94f8d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2cc3c044-daf9-46fd-98d9-e13283c94f8d' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-950aa6dd-8c69-48d8-8402-6852815888ef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-950aa6dd-8c69-48d8-8402-6852815888ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e30a97e3-be27-45b3-b0b5-faa5c34a1574' class='xr-var-data-in' type='checkbox'><label for='data-e30a97e3-be27-45b3-b0b5-faa5c34a1574' 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>y</dd><dt><span>units :</span></dt><dd>mm</dd></dl></div><div class='xr-var-data'><pre>array(2.)</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'>0.0 2.5 5.0 7.5 10.0</div><input id='attrs-0228acb9-8675-4791-ba60-7006f0b45a90' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0228acb9-8675-4791-ba60-7006f0b45a90' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-111fb2aa-03bd-4902-8fdc-e33483e1e87e' class='xr-var-data-in' type='checkbox'><label for='data-111fb2aa-03bd-4902-8fdc-e33483e1e87e' 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>x</dd><dt><span>units :</span></dt><dd>mm</dd></dl></div><div class='xr-var-data'><pre>array([ 0. ,  2.5,  5. ,  7.5, 10. ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e6e228fa-d182-4d0d-bfd7-79029738cea1' class='xr-section-summary-in' type='checkbox'  ><label for='section-e6e228fa-d182-4d0d-bfd7-79029738cea1' 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>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c46b9bd1-f613-49a1-8501-a44a7101a2c5' class='xr-index-data-in' type='checkbox'/><label for='index-c46b9bd1-f613-49a1-8501-a44a7101a2c5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0.0, 2.5, 5.0, 7.5, 10.0], dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-92789356-3cc8-4bf2-b528-0ac9f0a260b1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-92789356-3cc8-4bf2-b528-0ac9f0a260b1' 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>velocity</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.DataArray 'vel' (x: 5)>\n",
-       "0.5583 0.1351 0.6607 0.219 0.3967\n",
+       "0.7767 0.7684 0.856 0.5258 0.1259\n",
        "Coordinates:\n",
        "    y        float64 2.0\n",
        "  * x        (x) float64 0.0 2.5 5.0 7.5 10.0\n",
@@ -2005,40 +1998,40 @@
        "\n",
        "              <ul style=\"list-style-type: none;\" class=\"h5grp-sections\">\n",
        "                    <li>\n",
-       "                        <input id=\"group-ds--7357506200\" type=\"checkbox\" checked>\n",
-       "                        <label style=\"font-weight: bold\" for=\"group-ds--7357506200\">\n",
+       "                        <input id=\"group-ds--9283968700\" type=\"checkbox\" checked>\n",
+       "                        <label style=\"font-weight: bold\" for=\"group-ds--9283968700\">\n",
        "                        /<span>(4)</span></label>\n",
        "                  \n",
        "<ul class=\"h5tb-attr-list\">\n",
        "                    </ul>\n",
        "\n",
-       "                <ul id=\"ds-1-/uncertainty-7360278300\" class=\"h5tb-var-list\">\n",
-       "                    <input id=\"ds-2-/uncertainty-7360283300\" class=\"h5tb-varname-in\" type=\"checkbox\" >\n",
-       "                    <label class='h5tb-varname' for=\"ds-2-/uncertainty-7360283300\">uncertainty</label>\n",
+       "                <ul id=\"ds-1-/uncertainty-9287165200\" class=\"h5tb-var-list\">\n",
+       "                    <input id=\"ds-2-/uncertainty-9287170300\" class=\"h5tb-varname-in\" type=\"checkbox\" >\n",
+       "                    <label class='h5tb-varname' for=\"ds-2-/uncertainty-9287170300\">uncertainty</label>\n",
        "                    <span class=\"h5tb-dims\">(y: 11, x: 5) [float64]</span>\n",
        "                <ul class=\"h5tb-attr-list\"><li style=\"list-style-type: none; font-style: italic\">units : </li>\n",
        "                </ul>\n",
        "                </ul>\n",
        "\n",
-       "                <ul id=\"ds-1-/vel-7361070300\" class=\"h5tb-var-list\">\n",
-       "                    <input id=\"ds-2-/vel-7361073700\" class=\"h5tb-varname-in\" type=\"checkbox\" >\n",
-       "                    <label class='h5tb-varname' for=\"ds-2-/vel-7361073700\">vel</label>\n",
+       "                <ul id=\"ds-1-/vel-9288769900\" class=\"h5tb-var-list\">\n",
+       "                    <input id=\"ds-2-/vel-9288774200\" class=\"h5tb-varname-in\" type=\"checkbox\" >\n",
+       "                    <label class='h5tb-varname' for=\"ds-2-/vel-9288774200\">vel</label>\n",
        "                    <span class=\"h5tb-dims\">(y: 11, x: 5) [float64]</span>\n",
        "                <ul class=\"h5tb-attr-list\"><li style=\"list-style-type: none; font-style: italic\">long_name : velocity</li><li style=\"list-style-type: none; font-style: italic\">units : m/s</li>\n",
        "                </ul>\n",
        "                </ul>\n",
        "\n",
-       "                <ul id=\"ds-1-/x-7361581900\" class=\"h5tb-var-list\">\n",
-       "                    <input id=\"ds-2-/x-7361584900\" class=\"h5tb-varname-in\" type=\"checkbox\" >\n",
-       "                    <label class='h5tb-varname' for=\"ds-2-/x-7361584900\">x</label>\n",
+       "                <ul id=\"ds-1-/x-9289443300\" class=\"h5tb-var-list\">\n",
+       "                    <input id=\"ds-2-/x-9289447500\" class=\"h5tb-varname-in\" type=\"checkbox\" >\n",
+       "                    <label class='h5tb-varname' for=\"ds-2-/x-9289447500\">x</label>\n",
        "                    <span class=\"h5tb-dims\">(5) [float64]</span>\n",
        "                <ul class=\"h5tb-attr-list\"><li style=\"list-style-type: none; font-style: italic\">long_name : x</li><li style=\"list-style-type: none; font-style: italic\">units : mm</li>\n",
        "                </ul>\n",
        "                </ul>\n",
        "\n",
-       "                <ul id=\"ds-1-/y-7361946100\" class=\"h5tb-var-list\">\n",
-       "                    <input id=\"ds-2-/y-7361948900\" class=\"h5tb-varname-in\" type=\"checkbox\" >\n",
-       "                    <label class='h5tb-varname' for=\"ds-2-/y-7361948900\">y</label>\n",
+       "                <ul id=\"ds-1-/y-9289918400\" class=\"h5tb-var-list\">\n",
+       "                    <input id=\"ds-2-/y-9289922100\" class=\"h5tb-varname-in\" type=\"checkbox\" >\n",
+       "                    <label class='h5tb-varname' for=\"ds-2-/y-9289922100\">y</label>\n",
        "                    <span class=\"h5tb-dims\">(11) [float64]</span>\n",
        "                <ul class=\"h5tb-attr-list\"><li style=\"list-style-type: none; font-style: italic\">long_name : y</li><li style=\"list-style-type: none; font-style: italic\">units : mm</li>\n",
        "                </ul>\n",
@@ -2097,7 +2090,7 @@
        "Coordinates:\n",
        "    y            float64 3.0\n",
        "  * x            (x) float64 0.0 2.5 5.0 7.5 10.0\n",
-       "    uncertainty  (x) float64 0.02455 0.02678 0.0261 0.0252 0.02531"
+       "    uncertainty  (x) float64 0.02472 0.02396 0.02501 0.02571 0.02597"
       ]
      },
      "execution_count": 14,
@@ -2121,12 +2114,24 @@
     "\n",
     "It is reasonable to provide physical units to the datasets. This is probably one of the most important meta information about a dataset. You might have noticed its usage in the examples above.\n",
     "\n",
-    "If the dataset is sliced as is, the returned `xarray.DataArray` will have the respective unit. In some cases, it might be necessary to convert the data (and/or its coordinates) into another unit. The `h5rdmtoolbox` provides an interface for this purpose. Address the desired dataset and call `to_units(...)` on it, which will call the interface class. The latter implements all major methods, like `sel`, `isel` and `__getitem__`:"
+    "If the dataset is sliced as is, the returned `xarray.DataArray` will have the respective unit. In some cases, it might be necessary to convert the data (and/or its coordinates) into another unit. The `h5rdmtoolbox` provides an interface for this purpose. Address the desired dataset and call `to_units(...)` on it, which will call the interface class. The latter implements all major methods, like `sel`, `isel` and `__getitem__`.\n",
+    "\n",
+    "Note, that this feature must be \"enabled\" by importing the `units` modue from `extensions`. The implementation principle is discussed [here](../misc/Extensions.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "42e207df-426a-431d-ab1b-de953eda1144",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from h5rdmtoolbox.extensions import units"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "id": "8535f777-4725-4d0b-b0f5-1dcca5d1026b",
    "metadata": {},
    "outputs": [
@@ -2497,61 +2502,61 @@
        "  fill: currentColor;\n",
        "}\n",
        "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;vel&#x27; (y: 11, x: 5)&gt;\n",
-       "32.67 715.4 775.2 448.8 588.5 467.7 ... 908.5 678.0 138.2 375.6 847.6 407.9\n",
+       "195.5 217.2 571.9 51.15 825.9 652.0 ... 510.6 360.8 471.7 729.0 959.1 141.9\n",
        "Coordinates:\n",
        "  * y            (y) float64 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0\n",
        "  * x            (x) float64 0.0 0.0025 0.005 0.0075 0.01\n",
-       "    uncertainty  (y, x) float64 0.02603 0.02506 0.02437 ... 0.02625 0.02349\n",
+       "    uncertainty  (y, x) float64 0.02692 0.02421 0.0255 ... 0.02483 0.02337\n",
        "Attributes:\n",
        "    ANCILLARY_DATASETS:  [&#x27;uncertainty&#x27;]\n",
        "    long_name:           velocity\n",
-       "    units:               mm/s</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'>'vel'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 11</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-ed54a081-137e-4d2b-a9e1-b02622417ebb' class='xr-array-in' type='checkbox' ><label for='section-ed54a081-137e-4d2b-a9e1-b02622417ebb' 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>32.67 715.4 775.2 448.8 588.5 467.7 ... 678.0 138.2 375.6 847.6 407.9</span></div><div class='xr-array-data'><pre>array([[ 32.67224599, 715.37326635, 775.23968155, 448.81554742,\n",
-       "        588.4774405 ],\n",
-       "       [467.66272012, 785.56668965, 428.97773352, 453.32450351,\n",
-       "        989.40192493],\n",
-       "       [938.30290367, 692.33003445, 870.04673032, 658.69479644,\n",
-       "        866.18609458],\n",
-       "       [488.6733198 , 155.60547692, 953.63502661, 659.8982558 ,\n",
-       "        586.77506913],\n",
-       "       [558.33002014, 135.1150979 , 660.70689793, 219.01040934,\n",
-       "        396.695092  ],\n",
-       "       [143.56703394, 652.1652383 , 532.62494488, 833.4577403 ,\n",
-       "        541.10713939],\n",
-       "       [332.78660912, 747.68159201, 264.1885598 , 717.21894777,\n",
-       "        429.25455671],\n",
-       "       [339.84232986, 406.88911387, 271.61078925, 756.47064514,\n",
-       "        934.44986362],\n",
-       "       [954.28974639, 912.10696305, 424.52528111, 580.24097796,\n",
-       "        803.93752716],\n",
-       "       [820.11736437, 104.17137363, 279.42949996, 998.21190247,\n",
-       "        908.54496741],\n",
-       "       [677.98326385, 138.16221647, 375.61510912, 847.57711569,\n",
-       "        407.87561278]])</pre></div></div></li><li class='xr-section-item'><input id='section-893c8b6e-10e7-4ac5-8bd9-03f1fb03472f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-893c8b6e-10e7-4ac5-8bd9-03f1fb03472f' 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'>0.0 0.5 1.0 1.5 ... 3.5 4.0 4.5 5.0</div><input id='attrs-3fb17ad3-0522-4ca3-8f4e-d6fd47195b72' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3fb17ad3-0522-4ca3-8f4e-d6fd47195b72' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a0856954-9da6-48d5-91f9-4ef35aafb741' class='xr-var-data-in' type='checkbox'><label for='data-a0856954-9da6-48d5-91f9-4ef35aafb741' 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>y</dd><dt><span>units :</span></dt><dd>mm</dd></dl></div><div class='xr-var-data'><pre>array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. ])</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'>0.0 0.0025 0.005 0.0075 0.01</div><input id='attrs-b3c713d7-454c-462f-93aa-eacd586e3834' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b3c713d7-454c-462f-93aa-eacd586e3834' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3cfd9ea2-e177-4599-81c4-81192adbbb2a' class='xr-var-data-in' type='checkbox'><label for='data-3cfd9ea2-e177-4599-81c4-81192adbbb2a' 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>x</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([0.    , 0.0025, 0.005 , 0.0075, 0.01  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>uncertainty</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.02603 0.02506 ... 0.02625 0.02349</div><input id='attrs-067f3716-4baa-43ef-a4a6-64bbb2668df7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-067f3716-4baa-43ef-a4a6-64bbb2668df7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2c97b27-46bf-42b8-9105-357025d4552d' class='xr-var-data-in' type='checkbox'><label for='data-c2c97b27-46bf-42b8-9105-357025d4552d' 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>units :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>array([[0.0260284 , 0.02505589, 0.02437319, 0.02790513, 0.02461584],\n",
-       "       [0.02334605, 0.02590457, 0.02579555, 0.02695076, 0.02721009],\n",
-       "       [0.02428165, 0.02539607, 0.02650105, 0.02428948, 0.02633238],\n",
-       "       [0.02546272, 0.02359217, 0.02404606, 0.0257451 , 0.0254047 ],\n",
-       "       [0.02456509, 0.02493577, 0.02556162, 0.02237729, 0.0237952 ],\n",
-       "       [0.02509288, 0.02423386, 0.02622235, 0.02548977, 0.02483051],\n",
-       "       [0.02455363, 0.02678091, 0.02609562, 0.02520017, 0.02531052],\n",
-       "       [0.02529076, 0.02364318, 0.02525206, 0.02496583, 0.02584852],\n",
-       "       [0.02512008, 0.02741506, 0.02423181, 0.02449394, 0.02449132],\n",
-       "       [0.02557398, 0.02353025, 0.02689942, 0.02587392, 0.02598113],\n",
-       "       [0.02480528, 0.02413891, 0.0246641 , 0.02624574, 0.02349087]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fb46ccde-3b71-4093-9a6c-ca3480dd5145' class='xr-section-summary-in' type='checkbox'  ><label for='section-fb46ccde-3b71-4093-9a6c-ca3480dd5145' 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-3802fa11-3698-4132-a819-f70d4ad666d0' class='xr-index-data-in' type='checkbox'/><label for='index-3802fa11-3698-4132-a819-f70d4ad666d0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0], dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b73b8194-bf12-4b03-a0d8-c01df60e9d8b' class='xr-index-data-in' type='checkbox'/><label for='index-b73b8194-bf12-4b03-a0d8-c01df60e9d8b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0.0, 0.0025, 0.005, 0.0075, 0.01], dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ed8ad7cc-0ad5-4477-a68a-968e36e3f891' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ed8ad7cc-0ad5-4477-a68a-968e36e3f891' 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>ANCILLARY_DATASETS :</span></dt><dd>[&#x27;uncertainty&#x27;]</dd><dt><span>long_name :</span></dt><dd>velocity</dd><dt><span>units :</span></dt><dd>mm/s</dd></dl></div></li></ul></div></div>"
+       "    units:               mm/s</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'>'vel'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 11</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-a5a3ad37-df86-4a93-87c5-e8635cbc67e1' class='xr-array-in' type='checkbox' ><label for='section-a5a3ad37-df86-4a93-87c5-e8635cbc67e1' 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>195.5 217.2 571.9 51.15 825.9 652.0 ... 360.8 471.7 729.0 959.1 141.9</span></div><div class='xr-array-data'><pre>array([[195.5154266 , 217.23571869, 571.92218711,  51.14613304,\n",
+       "        825.86645309],\n",
+       "       [652.0441054 , 270.35611091, 642.82322468, 345.63818003,\n",
+       "        783.82404511],\n",
+       "       [153.54700693, 131.97384782, 532.42705174, 465.98591321,\n",
+       "        319.34040101],\n",
+       "       [247.65566546, 211.54385375, 175.21748302, 493.93685917,\n",
+       "        710.75356324],\n",
+       "       [776.72342542, 768.43807102, 855.98686432, 525.76786252,\n",
+       "        125.92182895],\n",
+       "       [112.12212559, 860.14426183, 224.12850547, 905.50408389,\n",
+       "        146.89475046],\n",
+       "       [763.2979654 , 886.58577594, 708.76640973,   9.89451327,\n",
+       "          7.70115424],\n",
+       "       [484.83632482, 664.33281831, 246.15474544, 782.35775592,\n",
+       "        956.8520906 ],\n",
+       "       [929.82355037, 344.82073225, 514.73297864, 742.47707301,\n",
+       "        838.17900363],\n",
+       "       [397.46769075, 235.45768452, 438.8846014 , 807.97416993,\n",
+       "        510.61224856],\n",
+       "       [360.82964154, 471.7018972 , 729.0374545 , 959.06968319,\n",
+       "        141.85988911]])</pre></div></div></li><li class='xr-section-item'><input id='section-a8b7a1b0-ac58-4e3b-9636-d79126bd61d2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a8b7a1b0-ac58-4e3b-9636-d79126bd61d2' 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'>0.0 0.5 1.0 1.5 ... 3.5 4.0 4.5 5.0</div><input id='attrs-10991eaa-4f59-4b74-bf90-1ddc1a18c3e7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-10991eaa-4f59-4b74-bf90-1ddc1a18c3e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4df82b7e-0cfa-4393-ad7c-4da27cc8c1a2' class='xr-var-data-in' type='checkbox'><label for='data-4df82b7e-0cfa-4393-ad7c-4da27cc8c1a2' 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>y</dd><dt><span>units :</span></dt><dd>mm</dd></dl></div><div class='xr-var-data'><pre>array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. ])</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'>0.0 0.0025 0.005 0.0075 0.01</div><input id='attrs-8de106a4-3212-4b1c-87b9-9fe6994f7303' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8de106a4-3212-4b1c-87b9-9fe6994f7303' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3175bdcf-2b33-4c18-912d-13a4ef69f817' class='xr-var-data-in' type='checkbox'><label for='data-3175bdcf-2b33-4c18-912d-13a4ef69f817' 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>x</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([0.    , 0.0025, 0.005 , 0.0075, 0.01  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>uncertainty</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.02692 0.02421 ... 0.02483 0.02337</div><input id='attrs-28afdcf6-678e-4161-b2a4-00b8c3e49340' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-28afdcf6-678e-4161-b2a4-00b8c3e49340' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ef1c64f-afcd-4a80-9c2b-4c2037056921' class='xr-var-data-in' type='checkbox'><label for='data-4ef1c64f-afcd-4a80-9c2b-4c2037056921' 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>units :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>array([[0.02692454, 0.02421182, 0.02550414, 0.02659833, 0.02768016],\n",
+       "       [0.02519791, 0.0245016 , 0.02611983, 0.02398224, 0.02403255],\n",
+       "       [0.02305058, 0.02565204, 0.02434012, 0.02677683, 0.02677303],\n",
+       "       [0.02516467, 0.02452704, 0.02460512, 0.02432051, 0.02454304],\n",
+       "       [0.02433897, 0.02512237, 0.02500057, 0.0246456 , 0.02437865],\n",
+       "       [0.02487184, 0.0259183 , 0.02606799, 0.02483192, 0.0227955 ],\n",
+       "       [0.02472482, 0.02395922, 0.02500573, 0.025707  , 0.02597037],\n",
+       "       [0.02503823, 0.02464805, 0.02497333, 0.02487147, 0.02619704],\n",
+       "       [0.0254272 , 0.02463076, 0.02490241, 0.02617084, 0.02587303],\n",
+       "       [0.02641167, 0.02545709, 0.02383426, 0.02525792, 0.02512138],\n",
+       "       [0.02424729, 0.02556102, 0.02507331, 0.02483157, 0.02336596]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-65ea0856-290b-40c9-af8c-0e45317f8ca1' class='xr-section-summary-in' type='checkbox'  ><label for='section-65ea0856-290b-40c9-af8c-0e45317f8ca1' 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-1c3d73fb-fef6-4455-ab3e-ce23aa48763a' class='xr-index-data-in' type='checkbox'/><label for='index-1c3d73fb-fef6-4455-ab3e-ce23aa48763a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0], dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-76148ffe-88c3-49bc-bdbd-377582f81f56' class='xr-index-data-in' type='checkbox'/><label for='index-76148ffe-88c3-49bc-bdbd-377582f81f56' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0.0, 0.0025, 0.005, 0.0075, 0.01], dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-34b80010-4582-44ca-8988-d7f12996f476' class='xr-section-summary-in' type='checkbox'  checked><label for='section-34b80010-4582-44ca-8988-d7f12996f476' 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>ANCILLARY_DATASETS :</span></dt><dd>[&#x27;uncertainty&#x27;]</dd><dt><span>long_name :</span></dt><dd>velocity</dd><dt><span>units :</span></dt><dd>mm/s</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.DataArray 'vel' (y: 11, x: 5)>\n",
-       "32.67 715.4 775.2 448.8 588.5 467.7 ... 908.5 678.0 138.2 375.6 847.6 407.9\n",
+       "195.5 217.2 571.9 51.15 825.9 652.0 ... 510.6 360.8 471.7 729.0 959.1 141.9\n",
        "Coordinates:\n",
        "  * y            (y) float64 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0\n",
        "  * x            (x) float64 0.0 0.0025 0.005 0.0075 0.01\n",
-       "    uncertainty  (y, x) float64 0.02603 0.02506 0.02437 ... 0.02625 0.02349\n",
+       "    uncertainty  (y, x) float64 0.02692 0.02421 0.0255 ... 0.02483 0.02337\n",
        "Attributes:\n",
        "    ANCILLARY_DATASETS:  ['uncertainty']\n",
        "    long_name:           velocity\n",
        "    units:               mm/s"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2574,7 +2579,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 18,
    "id": "dff826e5-35e4-45bc-8bbd-490e15d6deff",
    "metadata": {},
    "outputs": [
@@ -2584,7 +2589,7 @@
        "'mm/s'"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/h5rdmtoolbox/convention/_h5tbx.py b/h5rdmtoolbox/convention/_h5tbx.py
deleted file mode 100644
index fb46a79..0000000
--- a/h5rdmtoolbox/convention/_h5tbx.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import importlib
-import pathlib
-import sys
-
-from . import generate
-from .._user import UserDir
-from ..errors import ImportConventionError
-
-__this_dir__ = pathlib.Path(__file__)
-convention_name = 'h5tbx'
-convention_user_dir = UserDir['convention'] / convention_name
-
-
-def build_convention():
-    """Build the toolbox convention from the yaml file"""
-    h5tbx_convention_yaml = __this_dir__.parent / f'../data/{convention_name}.yaml'
-    convention_user_dir.mkdir(parents=True, exist_ok=True)
-    generate.write_convention_module_from_yaml(h5tbx_convention_yaml)
-
-
-if not (convention_user_dir / f'{convention_user_dir.name}.py').exists():
-    build_convention()
-
-sys.path.insert(0, str(UserDir['convention'] / convention_name))
-
-try:
-    imported_module = importlib.import_module(convention_name)
-    # Now, you can use the imported module as needed
-    # For example:
-    imported_module.cv
-except NameError:
-    raise ImportConventionError(
-        f'The convention "{convention_name}" could not be imported. Please check the convention file content '
-        f'(location: {UserDir["convention"] / convention_name}).')
-except ImportError as e:
-    print(f"Failed to import module {convention_name} (location: {UserDir['convention'] / convention_name}). err: {e}.")
diff --git a/h5rdmtoolbox/extensions/magnitude.py b/h5rdmtoolbox/extensions/magnitude.py
index 0750429..11721e0 100644
--- a/h5rdmtoolbox/extensions/magnitude.py
+++ b/h5rdmtoolbox/extensions/magnitude.py
@@ -1,77 +1,67 @@
 """Extension to compute magnitude of xarray datasets"""
+import h5py
 import numpy as np
 import xarray as xr
-from typing import Union, Dict
+from typing import Dict, Optional
 
+from h5rdmtoolbox.protocols import H5TbxDataset
+from h5rdmtoolbox.wrapper.accessory import Accessory, register_special_dataset
 
-@xr.register_dataset_accessor("magnitude")
-class MagnitudeAccessor:
-    """Accessor to convert units of data array. It is
-    also possible to convert its coordinates"""
 
-    def __init__(self, xarray_obj):
-        """Initialize the accessor"""
-        self._obj = xarray_obj
+class MagnitudeInterface:
+    def __init__(self,
+                 datasets: Dict[str, H5TbxDataset],
+                 name: Optional[str] = None,
+                 keep_attrs: bool = False):
+        self.datasets = datasets
+        self.name = name
+        self.keep_attrs = keep_attrs
+
+    def _compute_magnitude(self, datasets):
+        assert len(datasets) > 1, 'At least two datasets are required to compute magnitude'
+        keys = list(datasets.keys())
+        mag2 = datasets[keys[0]].pint.quantify() ** 2
+        with xr.set_options(keep_attrs=self.keep_attrs):
+            for key in keys[1:]:
+                mag2 += datasets[key].pint.quantify() ** 2
 
-    def compute_from(self,
-                     *data_vars,
-                     name: Union[str, None] = None,
-                     inplace: bool = True,
-                     attrs: Union[Dict, None] = None,
-                     overwrite: bool = False):
-        """compute magnitude from data variable names
-        Parameters
-        ----------
-        data_vars: str
-            Names of data variables to compute magnitude from.
-        name: str
-            Name of the magnitude variable to be used in the dataset.
-            If None, the name is automatically generated.
-            Example: if data_vars = ['u', 'v', 'w'], then name is 'magnitude_of_u_v_w'
-        inplace: bool
-            If True, the magnitude variable is added to the dataset.
-            Otherwise, a new dataset is returned.
-        attrs: dict
-            Attributes to be added to the magnitude variable
-        overwrite: bool
-            If True, the magnitude variable is overwritten if it already exists in the dataset.
-        """
-        mag2 = self._obj[data_vars[0]].pint.quantify() ** 2
-        from .. import consts
-        # anc_ds = []
-        # anc_ds.append(self._obj[data_vars[0]].attrs.get(consts.ANCILLARY_DATASET, ()))
-        for data_var in data_vars[1:]:
-            mag2 += self._obj[data_var].pint.quantify() ** 2
-            # anc_ds.append(self._obj[data_var].attrs.get(consts.ANCILLARY_DATASET, ()))
-        # with xr.set_options(keep_attrs=True):
         mag = np.sqrt(mag2).pint.dequantify()
+        if self.name is None:
+            mag.name = 'magnitude_of_' + '_and_'.join(k.replace(' ', '_') for k in keys)
+        else:
+            mag.name = self.name
+        return mag
 
-        # drop ancillary dataset information:
-        mag.attrs.pop(consts.ANCILLARY_DATASET, None)
+    def __getitem__(self, *args, **kwargs):
+        return self._compute_magnitude(
+            {k: v.__getitem__(*args, **kwargs) for k, v in self.datasets.items()}
+        )
 
-        # gather ancillary dataset information from vector components:
-        _anc = [self._obj[da].attrs.get(consts.ANCILLARY_DATASET, None) for da in data_vars]
+    def isel(self, **indexers):
+        return self._compute_magnitude(
+            {k: v.isel(**indexers) for k, v in self.datasets.items()}
+        )
 
-        _anc = [a for a in _anc if a is not None]
-        if _anc:
-            mag.attrs[consts.ANCILLARY_DATASET] = list(set([item for sublist in _anc for item in sublist]))
+    def sel(self, method=None, **coords):
+        return self._compute_magnitude(
+            {k: v.sel(method=method, **coords) for k, v in self.datasets.items()}
+        )
 
-        joined_names = '_'.join(data_vars)
-        if name is None:
-            name = f'magnitude_of_{joined_names}'
-        if name in self._obj:
-            if not overwrite:
-                raise KeyError(f'The name of variable "{name}" is already exists in the dataset.')
-            del self._obj[name]
-        mag.name = name
-        processing_comment = 'processing_comment'
-        while processing_comment in mag.attrs:
-            processing_comment = f'_{processing_comment}'
-        mag.attrs['processing_comment'] = f'computed from: {joined_names.replace("_", ", ")}'
-        if attrs:
-            mag.attrs.update(attrs)
 
-        if inplace:
-            self._obj[name] = mag
-            return self._obj
-        return mag
+@register_special_dataset("Magnitude", "Group")
+@register_special_dataset("Magnitude", "File")
+class Magnitude(Accessory):
+    def __call__(self, *datasets, name: Optional[str] = None, keep_attrs: bool = False) -> MagnitudeInterface:
+        if len(datasets) < 2:
+            raise ValueError('Please provide at least two datasets to compute magnitude')
+        hdf_datasets = {}
+        for dataset in datasets:
+            if isinstance(dataset, str):
+                ds = self._obj[dataset]
+            elif isinstance(dataset, h5py.Dataset):
+                ds = dataset
+            else:
+                raise TypeError(f'Invalid type: {type(dataset)}')
+            hdf_datasets[ds.name.strip('/')] = ds
+
+        return MagnitudeInterface(hdf_datasets, name=name, keep_attrs=keep_attrs)
diff --git a/h5rdmtoolbox/extensions/normalize.py b/h5rdmtoolbox/extensions/normalize.py
index 6a3cd38..381d12f 100644
--- a/h5rdmtoolbox/extensions/normalize.py
+++ b/h5rdmtoolbox/extensions/normalize.py
@@ -1,170 +1,103 @@
 """Extension to compute normalized xarray datasets and arrays"""
-# import pint_xarray  # noqa: F401
 import xarray as xr
-from numpy.core._exceptions import _UFuncBinaryResolutionError
 from typing import Union, Dict
 
-import h5rdmtoolbox as h5tbx
+from h5rdmtoolbox import get_ureg
+from h5rdmtoolbox.protocols import H5TbxDataset
+from h5rdmtoolbox.wrapper.accessory import Accessory, register_special_dataset
 
 NORM_DELIMITER = '/'
 
 
-# def to_base_units(da: xr.DataArray) -> xr.DataArray:
-#     """Turns the units of an xarray to the base units, e.g. m/mm turns to dimensionless
-#     because pint_xarray has no method `to_base_units()`.
-#     """
-#     final_units = pint.Quantity(da.attrs['units']).to_base_units()
-#     return da.pint.quantify(unit_registry=h5tbx.get_ureg()).pint.to(final_units.units).pint.dequantify()
-
-
-@xr.register_dataarray_accessor("normalize")
-class NormalizeAccessor:
-    """Accessor to normalize a data array.
-    This is helpful if data is to be plotting in dimensionless coordinates.
-    """
-
-    def __init__(self, xarray_obj):
-        """Initialize the accessor"""
-        self._obj = xarray_obj
-
-    def __call__(self, data: Dict = None, rename=False, **data_kwargs):
-        """normalizes the data, not the coords unless the coords are specified.
-
-        Parameters
-        ----------
-        data : Dict
-            The data to normalize. The key is used to have a variable name for the normalized data.s
-        rename : bool, optional
-            If True, the data is renamed to the normalized name, by default False
-        """
-        if not isinstance(rename, bool):
-            raise TypeError(f'rename must be a boolean, not {type(rename)}.')
-
-        if data is None:
-            data = {}
-        data.update(data_kwargs)
-
-        norm_obj = self._obj
-        for k, v in data.items():
-            norm_obj = NormalizeAccessor._normalize(norm_obj, k, v, rename)
-
-        return norm_obj
-
-    @staticmethod
-    def _normalize(obj, name, value, rename):
-        if isinstance(value, str):
-            qobj = xr.DataArray(obj).pint.quantify(unit_registry=h5tbx.get_ureg())
-            q = h5tbx.get_ureg().Quantity(value)
-            norm_obj = (qobj / q).pint.dequantify()
-        elif isinstance(value, (int, float)):
-            # user indicates a float or int, which is interpreted as dimensionless
-            with xr.set_options(keep_attrs=True):
-                try:
-                    norm_obj = obj / value
-                except _UFuncBinaryResolutionError:
-                    raise RuntimeError(f'A error occurred while normalizing {obj.name} by {name}={value}. '
-                                       'This is likely due to a mismatch of data types. '
-                                       'you might specify the dimension to be normalized by '
-                                       'because not every dimension might of of a numeric type. '
-                                       'Orig. error: {e}')
-        else:
-            raise TypeError(f'Normalization must be either a string or a number, not {type(value)}.')
+class NormalizationInterface:
+
+    def __init__(self,
+                 dataset: H5TbxDataset,
+                 norm_data: Dict[str, Union[float, str]],
+                 name: bool = False):
+        self.dataset = dataset
+        self._coord_names = list(dataset.coords())
+        self.norm_data: Dict = norm_data
+        self.cnorm_data: Dict = {}
+        self.cnorm_name: Dict = {}
+        self.name = name
+
+    def __getattr__(self, item: str):
+        if item in self._coord_names:
+            return CoordNormalizationInterface(item, self)
+        return super().__getattribute__(item)
+
+    def _normalize(self, data: xr.DataArray) -> xr.DataArray:
+        assert isinstance(data, xr.DataArray)
+        quantified_data = data.pint.quantify(unit_registry=get_ureg())
+        new_name = self.dataset.basename
+        for k, v in self.norm_data.items():
+            new_name += f'{NORM_DELIMITER}{k}'
+            if isinstance(v, str):
+                q = get_ureg().Quantity(v)
+                quantified_data = quantified_data / q
+            elif isinstance(v, (int, float)):
+                quantified_data = quantified_data / v
+            else:
+                raise TypeError(f'Normalization must be either a string or a number, not {type(v)}.')
 
-        if rename:
-            norm_obj.name = f'{obj.name}{NORM_DELIMITER}{name}'
+        ret = quantified_data.pint.dequantify()
+        if self.name:
+            ret.name = self.name
         else:
-            norm_obj.name = obj.name
-        comment = norm_obj.attrs.get('comment', '')
-        if comment != '':
-            comment += ' '
-        comment += f'Normalized by {name}={value}'
-        norm_obj.attrs['comment'] = comment
-        return norm_obj
-
-    def coords(self, value_or_coord_dict: Dict[str, Union[str, int, float]] = None, rename: bool = False, **coords):
-        """Normalize coordinate data
-
-        Parameters
-        ----------
-        value_or_coord_dict : Dict[str, Union[str, int, float]]
-            The coordinates to normalize. The key is the either a dictionary of key-value paris
-            where the value is a string or a float. In this case the value is interpreted as the
-            normalization quantity. Alternatively, the value of the key-value pair can be a dictionary
-            itself, then the key is interpreted as the name of the normalized coordinate and the value
-            is interpreted as the normalization quantity.
-        rename : bool, optional
-            If True, the coordinate names are renamed to the normalized names, by default False
-
-        Examples
-        --------
-        >>> import h5rdmtoolbox as h5tbx
-        >>> with h5tbx.File() as h5:
-        ...     h5.create_dataset('x', data=[1, 2, 3], make_scale=True)
-        ...     h5.create_dataset('y', data=[1, 2, 3, 4], make_scale=True)
-        ...     h5.create_dataset('u', data=np.random.random((4, 3)), attach_scales=('y', 'x'))
-        ...     u_xnorm = h5['u'][:].normalize.coords(L='3m', rename=True)
-        >>> u_xnorm.plot.contourf()
-        >>> plt.show()
-        """
-        if isinstance(value_or_coord_dict, (int, float, str)):
-            value_or_coord_dict = {k: value_or_coord_dict for k in self._obj.coords}
-
-        if value_or_coord_dict is None:
-            value_or_coord_dict = {}
-        value_or_coord_dict.update(coords)
-
-        def _key_is_coord_name(_key):
-            return _key in self._obj.coords
-
-        if not all(_key_is_coord_name(k) for k in value_or_coord_dict):
-            # if none coord name is specified
-            value_or_coord_dict = {cname: value_or_coord_dict for cname in self._obj.coords}
-        else:
-            for k in value_or_coord_dict:
-                if k not in self._obj.coords:
-                    raise KeyError(f'{k} is not an existing coordinate. These are: {self._obj.coords}')
-
-        # _coord_dict = value_or_coord_dict.copy()
-        # for k, v in value_or_coord_dict.items():
-        #     if isinstance(v, (int, float, str)):
-        #         _coord_dict.pop(k)
-        #         for coord_name in self._obj.coords.keys():
-        #             _coord_dict[coord_name] = {k: v}
-
-        if all(isinstance(v, dict) for v in value_or_coord_dict.values()):
-            # normalize only specific coordinates
-            ret_obj = self._obj
-            for coord_name, norm_data in value_or_coord_dict.items():
-                norm_coord = ret_obj[coord_name].normalize(norm_data, rename)
-                ret_obj = ret_obj.assign_coords({coord_name: norm_coord})
-                if rename:
-                    ret_obj = ret_obj.rename({coord_name: norm_coord.name})
-            return ret_obj
-
-        _update_coords = {}
-
-        for coord_name, v in value_or_coord_dict.items():
-            obj_norm = self._obj[coord_name]
-            if isinstance(v, dict):
-                for ak, av in v.items():
-                    obj_norm = NormalizeAccessor._normalize(xr.DataArray(obj_norm), ak, av, rename)
-            else:
-                # no renaming, because
-                obj_norm = NormalizeAccessor._normalize(xr.DataArray(obj_norm), coord_name, v, False)
-            _update_coords[coord_name] = obj_norm
-
-        # for coord_name in self._obj.coords:
-        #     obj_norm = self._obj[coord_name]
-        #     for k, v in value_or_coord_dict.items():
-        #         if isinstance(v, dict):
-        #             for ak, av in v.items():
-        #                 obj_norm = NormalizeAccessor._normalize(xr.DataArray(obj_norm), ak, av, rename)
-        #         else:
-        #             obj_norm = NormalizeAccessor._normalize(xr.DataArray(obj_norm), k, v, rename)
-        #     _update_coords[coord_name] = obj_norm
-        ret_obj = self._obj.assign_coords(**_update_coords)
-        if rename:
-            for k, v in _update_coords.items():
-                if k != v.name:
-                    ret_obj = ret_obj.rename({k: v.name})
-        return ret_obj
+            ret.name = new_name
+
+        # normalize coordinates:
+        for coord_name, norm_dict in self.cnorm_data.items():
+            new_cname = coord_name
+            quantified_coord_data = quantified_data.coords[coord_name].pint.quantify(unit_registry=get_ureg())
+            for k, v in norm_dict.items():
+                new_cname += f'{NORM_DELIMITER}{k}'
+                if isinstance(v, str):
+                    q = get_ureg().Quantity(v)
+                    quantified_coord_data = quantified_coord_data / q
+                elif isinstance(v, (int, float)):
+                    quantified_coord_data = quantified_coord_data / v
+                else:
+                    raise TypeError(f'Normalization must be either a string or a number, not {type(v)}.')
+
+            ret.coords[coord_name] = quantified_coord_data.pint.dequantify()
+            ret = ret.rename({coord_name: self.cnorm_name.get(coord_name, new_cname)})
+
+        return ret
+
+    def sel(self, method=None, **coords) -> xr.DataArray:
+        return self._normalize(self.dataset.sel(method=method, **coords))
+
+    def isel(self, **indexers) -> xr.DataArray:
+        return self._normalize(self.dataset.isel(**indexers))
+
+    def __getitem__(self, *args, **kwargs):
+        return self._normalize(self.dataset.__getitem__(*args, **kwargs))
+
+
+class CoordNormalizationInterface:
+    def __init__(self, name: str, ni: NormalizationInterface):
+        self._ni = ni
+        self._name = name
+
+    def __call__(self,
+                 name=None,
+                 **norm_data: Dict[str, Union[float, str]]) -> NormalizationInterface:
+        self._ni.cnorm_data[self._name] = norm_data
+        if name is not None:
+            self._ni.cnorm_name[self._name] = name
+        return self._ni
+
+
+@register_special_dataset("normalize", "Dataset")
+class ToUnitsAccessory(Accessory):
+    """Accessor to await selected data to be converted to a new units"""
+
+    def __call__(self,
+                 name=None,
+                 **norm_data: Dict[str, Union[float, str]]) -> NormalizationInterface:
+        assert len(norm_data) > 0, "No normalization data given!"
+        return NormalizationInterface(self._obj,
+                                      norm_data=norm_data,
+                                      name=name)
diff --git a/h5rdmtoolbox/extensions/units.py b/h5rdmtoolbox/extensions/units.py
index a3c94ce..2751bef 100644
--- a/h5rdmtoolbox/extensions/units.py
+++ b/h5rdmtoolbox/extensions/units.py
@@ -3,7 +3,7 @@
 
 from h5rdmtoolbox import get_ureg
 from h5rdmtoolbox.protocols import H5TbxDataset
-from ..wrapper.accessory import Accessory, register_special_dataset
+from h5rdmtoolbox.wrapper.accessory import Accessory, register_special_dataset
 
 
 class ToUnitsInterface:
diff --git a/h5rdmtoolbox/extensions/vector.py b/h5rdmtoolbox/extensions/vector.py
index a12ee66..ba1a26a 100644
--- a/h5rdmtoolbox/extensions/vector.py
+++ b/h5rdmtoolbox/extensions/vector.py
@@ -1,66 +1,36 @@
+# noinspection PyUnresolvedReferences
 import h5py
 import xarray as xr
-from typing import List, Tuple
+from typing import Dict, Optional
 
-# noinspection PyUnresolvedReferences
+from h5rdmtoolbox.protocols import H5TbxDataset
+from h5rdmtoolbox.wrapper.accessory import Accessory, register_special_dataset
 from . import magnitude  # automatically make magnitude available
-from ..wrapper.accessory import Accessory, register_special_dataset
-from ..wrapper.core import Group, File
-
 
-class HDFXrDataset:
-    """HDF interface to a Xr.Dataset which is returned on demand when sliced"""
 
-    def __init__(self, **datasets):
-        self._datasets = dict(datasets)
-        shapes = [d.shape for d in self._datasets.values()]
-        if not all([shapes[0] == s for s in shapes[1:]]):
-            raise ValueError('Datasets must have equal shapes!')
-
-        self._data_vars = list(self._datasets.keys())
-        self._shape = self._datasets[self._data_vars[0]].shape
+class VectorInterface:
+    def __init__(self,
+                 datasets: Dict[str, H5TbxDataset],
+                 name: Optional[str] = None,
+                 combine_attrs: str = "override"):
+        self.datasets = datasets
+        self.name = name
+        self.combine_attrs = combine_attrs
 
     def __getitem__(self, item) -> xr.Dataset:
-        return xr.merge([da.__getitem__(item).rename(k) for k, da in self._datasets.items()])
-
-    def __repr__(self):
-        return f'<HDF-XrDataset (shape {self.shape} data_vars: {self.data_vars})>'
-
-    @property
-    def data_vars(self) -> List[str]:
-        """List of data variables in the dataset"""
-        return self._data_vars
-
-    @property
-    def shape(self) -> Tuple[int]:
-        """Shape of the dataset (taken from the first dataset)"""
-        return self._shape
-
-
-@register_special_dataset("Vector", Group)
-@register_special_dataset("Vector", File)
-class VectorDataset(Accessory):
-    """A special dataset for vector data.
-     The vector components are stored in the group as datasets."""
-
-    def __call__(self, *args, **kwargs) -> HDFXrDataset:
-        """Returns a xarray dataset with the vector components as data variables.
+        return xr.merge(
+            [da.__getitem__(item).rename(k) for k, da in self.datasets.items()],
+            combine_attrs=self.combine_attrs)
 
-        Either dataset names or datasets can be provided as arguments or keyword arguments.
-        For the latter, the keys are used for the dataset names in the resulting xarray-dataset.
 
-        Examples
-        --------
-        >>> import h5rdmtoolbox as h5tbx
-        >>> import numpy as np
-        >>> from h5rdmtoolbox.extensions import vector
+@register_special_dataset("Vector", "Group")
+@register_special_dataset("Vector", "File")
+class Vector(Accessory):
+    def __call__(self,
+                 *args,
+                 **kwargs) -> VectorInterface:
+        combine_attrs = kwargs.get('kwargs', "override")
 
-        >>> with h5tbx.File() as h5:
-        ...     h5.create_dataset('u', data=np.random.rand(21, 10)))
-        ...     h5.create_dataset('v', data=np.random.rand(21, 10))
-        ...     h5.create_dataset('w', data=np.random.rand(21, 10))
-        ...     h5.Vector(uvel='u', vvel='v', wvel='w')
-        """
         if len(args) and len(kwargs):
             raise ValueError('Either args or kwargs must be provided but not both')
         hdf_datasets = {}
@@ -80,4 +50,79 @@ def __call__(self, *args, **kwargs) -> HDFXrDataset:
                 raise TypeError(f'Invalid type: {type(ds)}')
             hdf_datasets[name.strip('/')] = ds
 
-        return HDFXrDataset(**hdf_datasets)
+        return VectorInterface(
+            hdf_datasets,
+            combine_attrs=combine_attrs
+        )
+
+# class HDFXrDataset:
+#     """HDF interface to a Xr.Dataset which is returned on demand when sliced"""
+#
+#     def __init__(self, **datasets):
+#         self._datasets = dict(datasets)
+#         shapes = [d.shape for d in self._datasets.values()]
+#         if not all([shapes[0] == s for s in shapes[1:]]):
+#             raise ValueError('Datasets must have equal shapes!')
+#
+#         self._data_vars = list(self._datasets.keys())
+#         self._shape = self._datasets[self._data_vars[0]].shape
+#
+#     def __getitem__(self, item) -> xr.Dataset:
+#         return xr.merge([da.__getitem__(item).rename(k) for k, da in self._datasets.items()])
+#
+#     def __repr__(self):
+#         return f'<HDF-XrDataset (shape {self.shape} data_vars: {self.data_vars})>'
+#
+#     @property
+#     def data_vars(self) -> List[str]:
+#         """List of data variables in the dataset"""
+#         return self._data_vars
+#
+#     @property
+#     def shape(self) -> Tuple[int]:
+#         """Shape of the dataset (taken from the first dataset)"""
+#         return self._shape
+#
+#
+# @register_special_dataset("Vector", Group)
+# @register_special_dataset("Vector", File)
+# class VectorDataset(Accessory):
+#     """A special dataset for vector data.
+#      The vector components are stored in the group as datasets."""
+#
+#     def __call__(self, *args, **kwargs) -> HDFXrDataset:
+#         """Returns a xarray dataset with the vector components as data variables.
+#
+#         Either dataset names or datasets can be provided as arguments or keyword arguments.
+#         For the latter, the keys are used for the dataset names in the resulting xarray-dataset.
+#
+#         Examples
+#         --------
+#         >>> from h5rdmtoolbox.extensions import vector        >>>         >>> import h5rdmtoolbox as h5tbx
+#         >>> import numpy as np
+#         >>> with h5tbx.File() as h5:
+#         ...     h5.create_dataset('u', data=np.random.rand(21, 10)))
+#         ...     h5.create_dataset('v', data=np.random.rand(21, 10))
+#         ...     h5.create_dataset('w', data=np.random.rand(21, 10))
+#         ...     h5.Vector(uvel='u', vvel='v', wvel='w')
+#         """
+#         if len(args) and len(kwargs):
+#             raise ValueError('Either args or kwargs must be provided but not both')
+#         hdf_datasets = {}
+#         for arg in args:
+#             if isinstance(arg, str):
+#                 ds = self._obj[arg]
+#             elif isinstance(arg, h5py.Dataset):
+#                 ds = arg
+#             else:
+#                 raise TypeError(f'Invalid type: {type(arg)}')
+#             hdf_datasets[ds.name.strip('/')] = ds
+#
+#         for name, ds in kwargs.items():
+#             if isinstance(ds, str):
+#                 ds = self._obj[ds]
+#             elif not isinstance(ds, h5py.Dataset):
+#                 raise TypeError(f'Invalid type: {type(ds)}')
+#             hdf_datasets[name.strip('/')] = ds
+#
+#         return HDFXrDataset(**hdf_datasets)
diff --git a/h5rdmtoolbox/protocols.py b/h5rdmtoolbox/protocols.py
index dad7319..4d1bf64 100644
--- a/h5rdmtoolbox/protocols.py
+++ b/h5rdmtoolbox/protocols.py
@@ -156,6 +156,8 @@ def attrs(self) -> H5TbxAttributeManager:
 
     def __delitem__(self, key): ...
 
+    def __getitem__(self, *args, **kwargs):
+        ...
 
 class H5TbxFile(H5TbxHLObject):
     """Protocol for the h5tbx.File class."""
@@ -167,6 +169,11 @@ class H5TbxGroup(H5TbxFile):
     def __getitem__(self, name: str):
         ...
 
+    @property
+    def basename(self) -> str:
+        """Return the basename, which is the last part
+        of the HDF5 object path."""
+
 
 class H5TbxDataset(H5TbxHLObject):
     """Protocol for the h5tbx.Dataset class."""
@@ -179,6 +186,11 @@ def coords(self):
     def hdf_filename(self) -> pathlib.Path:
         """Return the filename as a pathlib.Path object."""
 
+    @property
+    def basename(self) -> str:
+        """Return the basename, which is the last part
+        of the HDF5 object path."""
+
     def sel(self, method=None, **coords) -> xr.DataArray:
         """Return the Dataset selected by the coordinates"""
         ...
diff --git a/h5rdmtoolbox/wrapper/core.py b/h5rdmtoolbox/wrapper/core.py
index b4b5379..938b9ae 100644
--- a/h5rdmtoolbox/wrapper/core.py
+++ b/h5rdmtoolbox/wrapper/core.py
@@ -1578,10 +1578,16 @@ def get_data_offset(self):
             return self.rootparent[self.attrs['DATA_OFFSET'].name]
         return None
 
-    def coords(self):
+    def coords(self) -> Dict:
         """Return a dictionary of the dimension scales of the dataset.
         Corresponds to the xarray coordinates."""
-        return {d[0].name.rsplit('/')[-1]: d[0] for d in self.dims if len(d) > 0}
+        coords = {}
+        for dim in self.dims:
+            if len(dim) > 0:
+                for i, d in enumerate(dim):
+                    coords[dim[i].name.rsplit('/')[-1]] = dim[i]
+        return coords
+        # return {d[0].name.rsplit('/')[-1]: d[0] for d in self.dims if len(d) > 0}
 
     def isel(self, **indexers) -> xr.DataArray:
         """Index selection by providing the coordinate name.
@@ -1608,8 +1614,9 @@ def isel(self, **indexers) -> xr.DataArray:
             for cname in indexers.keys():
                 if cname not in ds_coords:
                     raise KeyError(f'Coordinate {cname} not in {list(ds_coords.keys())}')
-            sl = {cname: slice(None) for cname in ds_coords.keys()}
-            for cname, item in indexers.items():
+
+            sl = {cname: slice(None) for cname, _ in zip(ds_coords.keys(), range(self.ndim))}
+            for (cname, item), _ in zip(indexers.items(), range(self.ndim)):
                 sl[cname] = item
         else:
             # no indexers available. User must provide dim_<i> then!
@@ -1617,8 +1624,8 @@ def isel(self, **indexers) -> xr.DataArray:
                 raise KeyError(f'No coordinates available. Provide dim_<i> as key!')
             dim_dict = {f'dim_{i}': slice(None) for i in range(len(self.shape))}
             # indices = [int(cname.split('_')[1]) for cname in indexers.keys()]
-            sl = {cname: slice(None) for cname in dim_dict.keys()}
-            for cname, item in indexers.items():
+            sl = {cname: slice(None) for cname, _ in zip(dim_dict.keys(), range(self.ndim))}
+            for (cname, item), _ in zip(indexers.items(), range(self.ndim)):
                 sl[cname] = item
 
         def _make_ascending(_data):
diff --git a/tests/conventions/test_conventions.py b/tests/conventions/test_conventions.py
index feb63f0..95f5fb2 100644
--- a/tests/conventions/test_conventions.py
+++ b/tests/conventions/test_conventions.py
@@ -38,6 +38,8 @@ def setUp(self) -> None:
             self.connected = False
             warnings.warn('No internet connection', UserWarning)
 
+
+
     def test_list_of_validators(self):
         lov = convention.get_list_of_validators()
         self.assertIsInstance(lov, dict)
@@ -157,7 +159,7 @@ def validate_f1(a, b, c=3, d=2):
     def test_h5tbx(self):
         f = h5tbx.UserDir['convention'] / 'h5tbx' / 'h5tbx.py'
         f.unlink(missing_ok=True)
-        from h5rdmtoolbox.convention._h5tbx import build_convention
+        from h5rdmtoolbox.convention import build_convention
         build_convention()
 
         h5tbx.use('h5tbx')
diff --git a/tests/test_extensions.py b/tests/test_extensions.py
index 0a7ce2f..682016f 100644
--- a/tests/test_extensions.py
+++ b/tests/test_extensions.py
@@ -13,17 +13,28 @@ class TestExtension(unittest.TestCase):
     def setUp(self) -> None:
         h5tbx.use(None)
 
-    def test_HDFXrDataset(self):
+    def test_Magnitude(self):
         with h5tbx.File() as h5:
-            u = h5.create_dataset('u', data=np.arange(10, 20))
-            v = h5.create_dataset('v', data=np.arange(10, 100))
-            v2 = h5.create_dataset('v2', data=np.arange(10, 20))
-            with self.assertRaises(ValueError):
-                vector.HDFXrDataset(u=u, v=v)
-            vel = vector.HDFXrDataset(u=u, v=v2)
-            self.assertEqual(vel.data_vars, ['u', 'v'])
-            self.assertEqual(vel.shape, (10,))
-            self.assertIsInstance(vel[:], xr.Dataset)
+            h5.create_dataset('x', data=[1, 2, 3], make_scale=True, attrs={'units': 'm'})
+            h5.create_dataset('y', data=[1, 2, 3, 4], make_scale=True, attrs={'units': 'm'})
+            h5.create_dataset('u', data=np.random.random((4, 3)), attach_scales=('y', 'x'))
+            h5.create_dataset('v', data=np.random.random((4, 3)), attach_scales=('y', 'x'))
+            mag_interface = h5.Magnitude('u', 'v')
+            self.assertIsInstance(mag_interface.datasets, dict)
+            mag = mag_interface[()]
+            self.assertEqual(mag.name, 'magnitude_of_u_and_v')
+            self.assertEqual(float(mag[0, 0]),
+                             float(np.sqrt(h5['u'][0, 0] ** 2 + h5['v'][0, 0] ** 2)))
+            self.assertEqual(float(mag[1, 0]),
+                             float(np.sqrt(h5['u'][1, 0] ** 2 + h5['v'][1, 0] ** 2)))
+            self.assertEqual(float(mag[0, 1]),
+                             float(np.sqrt(h5['u'][0, 1] ** 2 + h5['v'][0, 1] ** 2)))
+            self.assertEqual(float(mag[3, 2]),
+                             float(np.sqrt(h5['u'][3, 2] ** 2 + h5['v'][3, 2] ** 2)))
+
+            mag_interface = h5.Magnitude('u', 'v', name='uv')
+            mag = mag_interface[()]
+            self.assertEqual(mag.name, 'uv')
 
     def test_Vector(self):
         """Test the Vector special dataset"""
@@ -46,22 +57,11 @@ def test_Vector(self):
                 h5.Vector('u', 6.5)
             with self.assertRaises(TypeError):
                 h5.Vector(u='u', v=6.5)
-            vec.magnitude.compute_from('uu', 'vv', inplace=True)
-            self.assertTrue('magnitude_of_uu_vv' in vec.data_vars)
-            for i in range(3):
-                self.assertEqual(float(vec.magnitude_of_uu_vv[i].values),
-                                 np.sqrt(float(vec.uu[i].values) ** 2 + float(vec.vv[i].values) ** 2))
-
-            vec.magnitude.compute_from('uu', 'vv', name='speed', inplace=True)
-            self.assertTrue('speed' in vec.data_vars)
-            with self.assertRaises(KeyError):
-                vec.magnitude.compute_from('uu', 'vv', name='speed', inplace=True)
-            vec.magnitude.compute_from('uu', 'vv', name='speed2', inplace=True, overwrite=True, attrs={'test': 1})
-            self.assertEqual(vec.speed2.attrs['test'], 1)
-            vec2 = vec.magnitude.compute_from('uu', 'vv', name='speed2', inplace=False, overwrite=True,
-                                              attrs={'test': 1})
-            self.assertEqual(vec2.attrs['test'], 1)
-            self.assertIsInstance(vec2, xr.DataArray)
+
+            vec = h5.Vector(uu='u', vv='v')[:]
+            self.assertListEqual(list(vec.data_vars), ['uu', 'vv'])
+            self.assertEqual(vec['uu'][0], 1)
+            self.assertEqual(vec['vv'][0], 2)
 
     def test_normalize_issue_with_time_vector(self):
         with h5tbx.File() as h5:
@@ -70,151 +70,41 @@ def test_normalize_issue_with_time_vector(self):
             h5.create_time_dataset('t',
                                    data=[now, now + datetime.timedelta(seconds=1), now + datetime.timedelta(seconds=2)],
                                    make_scale=True)
-            u = h5.create_dataset('u', data=[-4, 10, 0], attach_scales=[('t', 'x'), ])
+            u = h5.create_dataset('u', data=[-4, 10, 0], attach_scales=[('x', 't'), ],
+                                  attrs=dict(units='m/s'))
 
-            with self.assertRaises(RuntimeError):
-                u[()].normalize.coords(rename=True, L=5)
-            unorm = u[()].normalize.coords(rename=True, x=dict(L=5))
-            np.testing.assert_array_equal(unorm['x/L'].values, u[()].x.values / 5)
-            np.testing.assert_array_equal(unorm.values, u[()].values)
-
-    def test_norm_one_for_all_without_unit(self):
-        with h5tbx.File() as h5:
-            h5.create_dataset('x', data=[1, 2, 3], make_scale=True)
-            h5.create_dataset('y', data=[1, 2, 3, 4], make_scale=True)
-            h5.create_dataset('u', data=np.random.random((4, 3)), attach_scales=('y', 'x'))
-            h5.create_dataset('v', data=np.random.random((4, 3)), attach_scales=('y', 'x'))
+            with self.assertRaises(AssertionError):
+                u.normalize(10)
 
             with self.assertRaises(TypeError):
-                h5['u'][:].normalize(dict(L='3m'), rename=2)
+                u.normalize(ref={'t': 10})[()]
+
+            unorm = u.normalize(ref=10)[()]
+            np.testing.assert_almost_equal(unorm.values, u.values[()] / 10)
+            self.assertEqual(unorm.units, 'm/s')
+            self.assertEqual(unorm.name, 'u/ref')
 
             with self.assertRaises(TypeError):
-                h5['u'][:].normalize(dict(L={'a': 1}), rename=2)
-
-            unorm = h5['u'][:].normalize()
-            self.assertEqual('u', unorm.name)
-            np.testing.assert_array_equal(h5['u'].values[()], unorm.values[()])
-
-            unorm = h5['u'][:].normalize({"L": '3m'}, rename=True)
-            self.assertEqual(f'u{normalize.NORM_DELIMITER}L', unorm.name)
-            self.assertEqual('1/m', unorm.attrs['units'])
-            np.testing.assert_array_equal(unorm.values, h5['u'][()].values / 3)
-            self.assertEqual(unorm.attrs['comment'], 'Normalized by L=3m')
-
-            unorm = h5['u'][:].normalize(dict(L='3m', o=4.5), rename=True)
-            self.assertEqual(f'u{normalize.NORM_DELIMITER}L{normalize.NORM_DELIMITER}o', unorm.name)
-            self.assertEqual('1/m', unorm.attrs['units'])
-            np.testing.assert_array_equal(unorm.values, h5['u'][()].values / 3 / 4.5)
-
-            unorm = h5['u'][:].normalize(dict(L='3m', o=4.5), rename=False)
-            self.assertEqual('u', unorm.name)
-            self.assertEqual('1/m', unorm.attrs['units'])
-            np.testing.assert_array_equal(unorm.values, h5['u'][()].values / 3 / 4.5)
-
-            unorm = h5['v'][:].normalize(dict(L='3m', o=4.5), rename=True)
-            self.assertEqual(f'v{normalize.NORM_DELIMITER}L{normalize.NORM_DELIMITER}o', unorm.name)
-            self.assertEqual('1/m', unorm.attrs['units'])
-            np.testing.assert_array_equal(unorm.values, h5['v'][()].values / 3 / 4.5)
-
-            u_xnorm = h5['u'][:].normalize.coords(dict(L='3m'), rename=True)
-            self.assertEqual('u', u_xnorm.name)
-            for coord in u_xnorm.coords:
-                self.assertEqual(f'{normalize.NORM_DELIMITER}L', coord[-2:])
-            self.assertEqual('', u_xnorm.attrs.get('units', ''))
-            self.assertEqual('1/m', u_xnorm[f'x{normalize.NORM_DELIMITER}L'].attrs['units'])
-            self.assertEqual('1/m', u_xnorm[f'y{normalize.NORM_DELIMITER}L'].attrs['units'])
-
-            u_xnorm = h5['u'][:].normalize.coords(dict(L='3m'), rename=False)
-            self.assertEqual('u', u_xnorm.name)
-            self.assertEqual('', u_xnorm.attrs.get('units', ''))
-            self.assertEqual('1/m', u_xnorm['x'].attrs['units'])
-            self.assertEqual('1/m', u_xnorm['y'].attrs['units'])
-
-            u_xnorm = h5['u'][:].normalize.coords(dict(x={'L': '3m'}), rename=False)
-            self.assertEqual('u', u_xnorm.name)
-            self.assertEqual('', u_xnorm.attrs.get('units', ''))
-            self.assertEqual('1/m', u_xnorm['x'].attrs['units'])
-            self.assertEqual('', u_xnorm['y'].attrs.get('units', ''))
-
-    def test_norm_one_for_all_with_unit(self):
-        with h5tbx.File() as h5:
-            h5.create_dataset('x', data=[1, 2, 3], make_scale=True, attrs={'units': 'm'})
-            h5.create_dataset('y', data=[1, 2, 3, 4], make_scale=True, attrs={'units': 'mm'})
-            h5.create_dataset('u', data=np.random.random((4, 3)), attach_scales=('y', 'x'), attrs={'units': 'm/s'})
-            h5.create_dataset('v', data=np.random.random((4, 3)), attach_scales=('y', 'x'), attrs={'units': 'Pa'})
-
-            unorm = h5['u'][:].normalize(dict(L='3m'), rename=True)
-            self.assertEqual(f'u{normalize.NORM_DELIMITER}L', unorm.name)
-            self.assertEqual('1/s', unorm.attrs['units'])
-            np.testing.assert_array_equal(unorm.values, h5['u'][()].values / 3)
-
-            unorm = h5['u'][:].normalize(dict(L='3m', o=4.5), rename=True)
-            self.assertEqual(f'u{normalize.NORM_DELIMITER}L{normalize.NORM_DELIMITER}o', unorm.name)
-            self.assertEqual('1/s', unorm.attrs['units'])
-            np.testing.assert_array_equal(unorm.values, h5['u'][()].values / 3 / 4.5)
-
-            unorm = h5['u'][:].normalize(dict(L='3m', o=4.5), rename=False)
-            self.assertEqual('u', unorm.name)
-            self.assertEqual('1/s', unorm.attrs['units'])
-            np.testing.assert_array_equal(unorm.values, h5['u'][()].values / 3 / 4.5)
-
-            unorm = h5['v'][:].normalize(dict(L='3m', o=4.5), rename=True)
-            self.assertEqual(f'v{normalize.NORM_DELIMITER}L{normalize.NORM_DELIMITER}o', unorm.name)
-            self.assertEqual('Pa/m', unorm.attrs['units'])
-            np.testing.assert_array_equal(unorm.values, h5['v'][()].values / 3 / 4.5)
-
-            u_xnorm = h5['u'][:].normalize.coords(dict(L='3m'), rename=True)
-            self.assertEqual('u', u_xnorm.name)
-            self.assertEqual('m/s', u_xnorm.attrs.get('units', ''))
-            for coord in u_xnorm.coords:
-                self.assertEqual(f'{normalize.NORM_DELIMITER}L', coord[-2:])
-            self.assertEqual('', u_xnorm[f'x{normalize.NORM_DELIMITER}L'].attrs['units'])
-            self.assertEqual('mm/m', u_xnorm[f'y{normalize.NORM_DELIMITER}L'].attrs['units'])
-
-            u_xnorm = h5['u'][:].normalize.coords(L='3m', rename=True)
-            self.assertEqual('u', u_xnorm.name)
-            self.assertEqual('m/s', u_xnorm.attrs.get('units', ''))
-            for coord in u_xnorm.coords:
-                self.assertEqual(f'{normalize.NORM_DELIMITER}L', coord[-2:])
-            self.assertEqual('', u_xnorm[f'x{normalize.NORM_DELIMITER}L'].attrs['units'])
-            self.assertEqual('mm/m', u_xnorm[f'y{normalize.NORM_DELIMITER}L'].attrs['units'])
-
-            u_xnorm = h5['u'][:].normalize.coords(dict(L='3m'), rename=False)
-            self.assertEqual('u', u_xnorm.name)
-            self.assertEqual('m/s', u_xnorm.attrs.get('units', ''))
-            self.assertEqual('', u_xnorm['x'].attrs['units'])
-            self.assertEqual('mm/m', u_xnorm['y'].attrs['units'])
-            np.testing.assert_array_equal(u_xnorm['x'].values, h5['x'][()].values / 3)
-            np.testing.assert_array_equal(u_xnorm['y'].values, h5['y'][()].values / 3)
-
-            u_xnorm = h5['u'][:].normalize.coords({'x': {"L": '3m'}}, rename=False)
-            self.assertEqual('u', u_xnorm.name)
-            self.assertEqual('m/s', u_xnorm.attrs.get('units', ''))
-            self.assertEqual('', u_xnorm['x'].attrs['units'])
-            self.assertEqual('mm', u_xnorm['y'].attrs.get('units', ''))
-
-            u_xnorm = h5['u'][:].normalize.coords({'x': {"L": '3m'}}, rename=True)
-            self.assertEqual('u', u_xnorm.name)
-            self.assertEqual(f'x{normalize.NORM_DELIMITER}L', u_xnorm["x/L"].name)
-            self.assertEqual('m/s', u_xnorm.attrs.get('units', ''))
-            self.assertEqual('', u_xnorm[f'x{normalize.NORM_DELIMITER}L'].attrs['units'])
-            self.assertEqual('mm', u_xnorm['y'].attrs.get('units', ''))
-
-            u_xnorm = h5['u'][:].normalize.coords(x={"L": '3m'}, rename=True)
-            self.assertEqual('u', u_xnorm.name)
-            self.assertEqual(f'x{normalize.NORM_DELIMITER}L', u_xnorm["x/L"].name)
-            self.assertEqual('m/s', u_xnorm.attrs.get('units', ''))
-            self.assertEqual('', u_xnorm[f'x{normalize.NORM_DELIMITER}L'].attrs['units'])
-            self.assertEqual('mm', u_xnorm['y'].attrs.get('units', ''))
-
-            u_xynorm = h5['u'][:].normalize.coords(x={"L": '3m'}, y=10, rename=True)
-            self.assertEqual('u', u_xynorm.name)
-            self.assertEqual(f'x{normalize.NORM_DELIMITER}L', u_xynorm["x/L"].name)
-            self.assertEqual('y', u_xynorm.y.name)
-            self.assertEqual('m/s', u_xynorm.attrs.get('units', ''))
-            self.assertEqual('', u_xynorm[f'x{normalize.NORM_DELIMITER}L'].attrs['units'])
-            self.assertEqual('mm', u_xynorm['y'].attrs.get('units', ''))
-            self.assertEqual(u_xynorm.y[0], h5['u'][:].y[0] / 10)
+                uxnorm = u.normalize(ref=10, name='uxnorm').x(xref={'t': 10})[()]
+            uxnorm = u.normalize(ref=10, name='uxnorm').x(xref=10)[()]
+            self.assertEqual(uxnorm.name, 'uxnorm')
+            np.testing.assert_almost_equal(uxnorm.values, u.values[()] / 10)
+            np.testing.assert_almost_equal(uxnorm['x/xref'].values, h5.x.values[()] / 10)
+            self.assertEqual(uxnorm.units, 'm/s')
+
+            uxnorm = u.normalize(ref=10, name='uxnorm').x(xref=10).isel(x=slice(None, None, None))
+            np.testing.assert_almost_equal(uxnorm.values, u.values[()] / 10)
+            np.testing.assert_almost_equal(uxnorm['x/xref'].values, h5.x.values[()] / 10)
+
+            uxnorm = u.normalize(ref=10, name='uxnorm').x(xref=10).sel(x=[1.1, 2.1, 3.1], method='nearest')
+            np.testing.assert_almost_equal(uxnorm.values, u.values[()] / 10)
+            np.testing.assert_almost_equal(uxnorm['x/xref'].values, h5.x.values[()] / 10)
+
+            uxnorm = u.normalize(ref='10 m/s').x(xref='10 m', name='xnorm')[()]
+            np.testing.assert_almost_equal(uxnorm.values, u.values[()] / 10)
+            np.testing.assert_almost_equal(uxnorm['xnorm'].values, h5.x.values[()] / 10)
+            self.assertEqual(uxnorm['xnorm'].units, '1/m')
+            self.assertEqual(uxnorm.units, '')
 
     def test_units_to(self):
         with h5tbx.File(mode='w') as h5:
diff --git a/tests/wrapper/test_core.py b/tests/wrapper/test_core.py
index fef45b2..77aba49 100644
--- a/tests/wrapper/test_core.py
+++ b/tests/wrapper/test_core.py
@@ -674,6 +674,14 @@ def test_coords(self):
             self.assertEqual({'time': h5['time']}, h5['vel'].coords())
             self.assertEqual({}, h5['vel_no_scale'].coords())
 
+        # multiple dims:
+        with h5tbx.File() as h5:
+            h5.create_dataset('x1', data=[1, 2, 3], make_scale=True)
+            h5.create_dataset('x2', data=[10, 20, 30], make_scale=True)
+            h5.create_dataset('data', data=[-1, 0, 1], attach_scales=('x1',))
+            h5['data'].dims[0].attach_scale(h5['x2'])
+            self.assertEqual({'x1': h5['x1'], 'x2': h5['x2']}, h5['data'].coords())
+
     def test_isel_sel(self):
         with h5tbx.File() as h5:
             h5.create_dataset('time', data=[1, 2, 3], make_scale=True)
@@ -736,6 +744,9 @@ def test_multi_isel_and_sel(self):
                 method='nearest')
             np.testing.assert_array_almost_equal(d.x.data, [5, 6.666667, 8.333333, 10])
 
+            d = h5['data'].isel(x=[0, 1])
+            self.assertEqual(float(d.x[0]), float(h5.x[0]))
+
     def test_create_dataset_with_ancillary_ds(self):
         with h5tbx.File() as h5:
             h5.create_dataset('flag', data=[1, 0, 1], dtype='int8')