diff --git a/docs/userguide/misc/Extensions.ipynb b/docs/userguide/misc/Extensions.ipynb
index 11f2474..818743f 100644
--- a/docs/userguide/misc/Extensions.ipynb
+++ b/docs/userguide/misc/Extensions.ipynb
@@ -22,13 +22,6 @@
     "tags": []
    },
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failed to import module h5tbx\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
@@ -449,10 +442,10 @@
        "  * y        (y) int32 2 3\n",
        "  * x        (x) int32 1 2 3\n",
        "Data variables:\n",
-       "    xvel     (y, x) float64 0.8223 0.2422 0.1878 0.9232 0.3065 0.4602\n",
-       "    yvel     (y, x) float64 0.09715 0.8928 0.0728 0.875 0.5002 0.1652</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-7663c484-f3a1-44f8-8ea9-b0a20e3da157' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7663c484-f3a1-44f8-8ea9-b0a20e3da157' 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-27eff1cc-f158-46d6-8ffc-2657a7796661' class='xr-section-summary-in' type='checkbox'  checked><label for='section-27eff1cc-f158-46d6-8ffc-2657a7796661' 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-2c68447d-0a9b-49b1-9498-65a795d406b0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2c68447d-0a9b-49b1-9498-65a795d406b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-800216ee-a97e-4752-a577-aa18716e8b9b' class='xr-var-data-in' type='checkbox'><label for='data-800216ee-a97e-4752-a577-aa18716e8b9b' 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-882f6f84-29fb-497e-b046-3b93780100fc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-882f6f84-29fb-497e-b046-3b93780100fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0950bd5f-9072-464e-8561-fc8997786c5f' class='xr-var-data-in' type='checkbox'><label for='data-0950bd5f-9072-464e-8561-fc8997786c5f' 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-9fca13f9-47a5-4fa9-9c16-9dd713880482' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9fca13f9-47a5-4fa9-9c16-9dd713880482' 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'>0.8223 0.2422 ... 0.3065 0.4602</div><input id='attrs-8236c8a7-4631-4ee5-a6c3-1b98b4435d9b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8236c8a7-4631-4ee5-a6c3-1b98b4435d9b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66fee915-8cfe-4890-945d-36809e719502' class='xr-var-data-in' type='checkbox'><label for='data-66fee915-8cfe-4890-945d-36809e719502' 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([[0.82232337, 0.24221677, 0.18778663],\n",
-       "       [0.92318962, 0.30645707, 0.46021954]])</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'>0.09715 0.8928 ... 0.5002 0.1652</div><input id='attrs-580aa888-fd23-46e8-92b0-34ac8e17e376' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-580aa888-fd23-46e8-92b0-34ac8e17e376' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8d214f3-c594-41a1-a2cc-e1738a52f805' class='xr-var-data-in' type='checkbox'><label for='data-e8d214f3-c594-41a1-a2cc-e1738a52f805' 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([[0.09715052, 0.892789  , 0.07280271],\n",
-       "       [0.87501267, 0.50023224, 0.16519806]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-38d91f1e-d2b5-4468-b223-dc1be7b9b59d' class='xr-section-summary-in' type='checkbox'  ><label for='section-38d91f1e-d2b5-4468-b223-dc1be7b9b59d' 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-19084b06-9541-445d-9711-841f7990db09' class='xr-index-data-in' type='checkbox'/><label for='index-19084b06-9541-445d-9711-841f7990db09' 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-c974a61a-d4dc-49e6-b252-cab330b10658' class='xr-index-data-in' type='checkbox'/><label for='index-c974a61a-d4dc-49e6-b252-cab330b10658' 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-5d3458eb-0b3c-42e7-8758-8a93e189e5f5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5d3458eb-0b3c-42e7-8758-8a93e189e5f5' 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 0.03456 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>"
       ],
       "text/plain": [
        "<xarray.Dataset>\n",
@@ -461,8 +454,8 @@
        "  * y        (y) int32 2 3\n",
        "  * x        (x) int32 1 2 3\n",
        "Data variables:\n",
-       "    xvel     (y, x) float64 0.8223 0.2422 0.1878 0.9232 0.3065 0.4602\n",
-       "    yvel     (y, x) float64 0.09715 0.8928 0.0728 0.875 0.5002 0.1652"
+       "    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"
       ]
      },
      "execution_count": 3,
@@ -885,12 +878,12 @@
        "  * y                       (y) int32 2 3\n",
        "  * x                       (x) int32 1 2 3\n",
        "Data variables:\n",
-       "    xvel                    (y, x) float64 0.8223 0.2422 ... 0.3065 0.4602\n",
-       "    yvel                    (y, x) float64 0.09715 0.8928 ... 0.5002 0.1652\n",
-       "    magnitude_of_xvel_yvel  (y, x) float64 0.828 0.9251 0.2014 ... 0.5866 0.489</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-46ce5bf4-ed83-448f-a7a7-79a26612ac17' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-46ce5bf4-ed83-448f-a7a7-79a26612ac17' 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-af3d720d-7642-46a1-a208-af194bda352b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-af3d720d-7642-46a1-a208-af194bda352b' 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-4f3d3c07-ae91-4b39-a707-2e3dbc505df0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4f3d3c07-ae91-4b39-a707-2e3dbc505df0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8de2ac08-6dd9-41d7-afba-5166789cf534' class='xr-var-data-in' type='checkbox'><label for='data-8de2ac08-6dd9-41d7-afba-5166789cf534' 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-2481bb61-2a4a-42fb-82e1-b61342c9b3b2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2481bb61-2a4a-42fb-82e1-b61342c9b3b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7617e7b9-c680-4443-bc66-cde67eac41e3' class='xr-var-data-in' type='checkbox'><label for='data-7617e7b9-c680-4443-bc66-cde67eac41e3' 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-fd32d58c-d986-44d5-ae4c-ab33b6ad9716' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fd32d58c-d986-44d5-ae4c-ab33b6ad9716' 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'>0.8223 0.2422 ... 0.3065 0.4602</div><input id='attrs-2ee9574d-b844-4553-9b69-45b041e0007c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2ee9574d-b844-4553-9b69-45b041e0007c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-56d91efb-d80b-4f16-86f3-ee9460156b4a' class='xr-var-data-in' type='checkbox'><label for='data-56d91efb-d80b-4f16-86f3-ee9460156b4a' 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([[0.82232337, 0.24221677, 0.18778663],\n",
-       "       [0.92318962, 0.30645707, 0.46021954]])</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'>0.09715 0.8928 ... 0.5002 0.1652</div><input id='attrs-d791da18-330f-42ec-8166-d66da9ded340' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d791da18-330f-42ec-8166-d66da9ded340' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69362e1f-2eac-4298-ab93-99f45c7d9e92' class='xr-var-data-in' type='checkbox'><label for='data-69362e1f-2eac-4298-ab93-99f45c7d9e92' 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([[0.09715052, 0.892789  , 0.07280271],\n",
-       "       [0.87501267, 0.50023224, 0.16519806]])</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'>0.828 0.9251 ... 0.5866 0.489</div><input id='attrs-3eda73e3-da89-44c6-ae5f-1301b8b2437b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3eda73e3-da89-44c6-ae5f-1301b8b2437b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e55d1d68-6460-499f-a897-2dc949bd703b' class='xr-var-data-in' type='checkbox'><label for='data-e55d1d68-6460-499f-a897-2dc949bd703b' 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([[0.82804224, 0.92506279, 0.20140519],\n",
-       "       [1.2719773 , 0.58664148, 0.48897078]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-155b7402-4682-4435-800b-703aab23a9a4' class='xr-section-summary-in' type='checkbox'  ><label for='section-155b7402-4682-4435-800b-703aab23a9a4' 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-ab63623c-32e2-4fb2-bf79-c802531e6ffa' class='xr-index-data-in' type='checkbox'/><label for='index-ab63623c-32e2-4fb2-bf79-c802531e6ffa' 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-2ca166ec-91db-494b-80e6-c205fb2b1fed' class='xr-index-data-in' type='checkbox'/><label for='index-2ca166ec-91db-494b-80e6-c205fb2b1fed' 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-3f544190-5985-4d68-be20-f0737b1e23d1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3f544190-5985-4d68-be20-f0737b1e23d1' 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 0.03456 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>"
       ],
       "text/plain": [
        "<xarray.Dataset>\n",
@@ -899,9 +892,9 @@
        "  * y                       (y) int32 2 3\n",
        "  * x                       (x) int32 1 2 3\n",
        "Data variables:\n",
-       "    xvel                    (y, x) float64 0.8223 0.2422 ... 0.3065 0.4602\n",
-       "    yvel                    (y, x) float64 0.09715 0.8928 ... 0.5002 0.1652\n",
-       "    magnitude_of_xvel_yvel  (y, x) float64 0.828 0.9251 0.2014 ... 0.5866 0.489"
+       "    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"
       ]
      },
      "execution_count": 5,
@@ -956,7 +949,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x300 with 4 Axes>"
       ]
@@ -998,7 +991,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 600x600 with 8 Axes>"
       ]
@@ -1041,42 +1034,423 @@
    "source": [
     "## 4. Converting units\n",
     "\n",
-    "The accessory \"units\" for xarray datasets allows to convert units. This assumes, that the unit attribute is called \"units\":"
+    "The accessory \"to_units\" will convert the selected data to the desired units. This assumes, that the unit attribute is called \"units\". It is also possible to change the units of \n",
+    "attached dimensions. In the following examples this is done.\n",
+    "\n",
+    "Note, that not the dataset values itself changes in the file, but whatever data is accessed by the following slice operation."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "055b346f-2c42-49a7-9e59-667eaf3606f6",
+   "execution_count": 18,
+   "id": "e614bf4a-556b-44c0-ac00-11a5113e5cb1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from h5rdmtoolbox.extensions import units"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "76cf75b0-3935-42ec-a3b6-0165db7d8264",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[>] orig dataset:\n",
-      " <xarray.DataArray 'x' ()>\n",
-      "5.2\n",
-      "Attributes:\n",
-      "    units:    m\n",
-      "\n",
-      "[>] new dataset:\n",
-      " <xarray.DataArray 'x' ()>\n",
-      "520.0\n",
-      "Attributes:\n",
-      "    units:    cm\n"
-     ]
+     "data": {
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+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
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+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
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+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
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+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\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",
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+       "  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",
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+       "  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;u&#x27; (x: 1)&gt;\n",
+       "-4.5e+03\n",
+       "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>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'u' (x: 1)>\n",
+       "-4.5e+03\n",
+       "Coordinates:\n",
+       "  * x        (x) float64 -4.5e+03\n",
+       "Attributes:\n",
+       "    units:    mm/s"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "from h5rdmtoolbox.extensions import units\n",
-    "import h5rdmtoolbox as h5tbx\n",
-    "\n",
     "with h5tbx.File(mode='w') as h5:\n",
-    "    ds = h5.create_dataset('x', data=5.2, attrs={'units': 'm'})\n",
-    "    print('[>] orig dataset:\\n', ds[()])\n",
-    "    ds_cm = ds[()].to('cm')\n",
-    "    print('\\n[>] new dataset:\\n', ds_cm[()])"
+    "    h5.create_dataset('x', data=[1, -4.5, 5.71], attrs={'units': 'm'},\n",
+    "                          make_scale=True)\n",
+    "    ds = h5.create_dataset('u', data=[1, -4.5, 5.71], attrs={'units': 'm/s'},\n",
+    "                           attach_scale='x')\n",
+    "    u_mm = h5.u.to_units('mm/s', x='mm')[1:2]\n",
+    "u_mm"
    ]
   },
   {
@@ -1090,7 +1464,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "41f5c2c1-7de5-4e1c-b44e-4d350328f7ef",
    "metadata": {},
    "outputs": [],
@@ -1116,19 +1490,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "2af29af0-aa80-4887-bd6f-8c93d719f2e8",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'h5rdmtoolbox.wrapper.core.Dataset'>\n",
-      "my device\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "with h5tbx.File() as h5:\n",
     "    ds = h5.create_dataset('test', shape=(2,))\n",
@@ -1154,7 +1519,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.18"
+   "version": "3.8.19"
   }
  },
  "nbformat": 4,
diff --git a/h5rdmtoolbox/__init__.py b/h5rdmtoolbox/__init__.py
index e7709cb..73c7232 100644
--- a/h5rdmtoolbox/__init__.py
+++ b/h5rdmtoolbox/__init__.py
@@ -1,11 +1,10 @@
 """h5rdtoolbox repository"""
 
+import appdirs
 import logging
 import pathlib
 from logging.handlers import RotatingFileHandler
 
-import appdirs
-
 _logdir = pathlib.Path(appdirs.user_log_dir('h5rdmtoolbox'))
 _logdir.mkdir(parents=True, exist_ok=True)
 
@@ -161,10 +160,12 @@ def get_filesize(hdf_filename: Union[str, pathlib.Path]) -> int:
     """Get the size of the HDF5 file in bytes"""
     return utils.get_filesize(hdf_filename)
 
+
 def get_checksum(hdf_filename: Union[str, pathlib.Path]) -> str:
     """Get the checksum of the HDF5 file"""
     return utils.get_checksum(hdf_filename)
 
+
 def register_dataset_decoder(decoder: Callable, decoder_name: str = None, overwrite: bool = False):
     """A decoder function takes a xarray.DataArray and a dataset as input and returns a xarray.DataArray
     It is called after the dataset is loaded into memory and before being returned to the user. Be careful:
@@ -184,6 +185,15 @@ def register_dataset_decoder(decoder: Callable, decoder_name: str = None, overwr
 atexit_verbose = False
 
 
+def set_loglevel(level: Union[int, str]):
+    """Set the logging level of the h5rdmtoolbox logger"""
+    import logging
+    _logger = logging.getLogger('h5rdmtoolbox')
+    _logger.setLevel(level)
+    for h in _logger.handlers:
+        h.setLevel(level)
+
+
 @atexit.register
 def clean_temp_data(full: bool = False):
     """cleaning up the tmp directory"""
diff --git a/h5rdmtoolbox/extensions/units.py b/h5rdmtoolbox/extensions/units.py
index 45a8212..a3c94ce 100644
--- a/h5rdmtoolbox/extensions/units.py
+++ b/h5rdmtoolbox/extensions/units.py
@@ -1,24 +1,45 @@
 import xarray as xr
+from typing import Optional
+
 from h5rdmtoolbox import get_ureg
+from h5rdmtoolbox.protocols import H5TbxDataset
+from ..wrapper.accessory import Accessory, register_special_dataset
+
+
+class ToUnitsInterface:
+    def __init__(self,
+                 dataset: H5TbxDataset,
+                 dataset_unit: Optional[str] = None,
+                 **coord_units):
+        self.dataset = dataset
+        self.dataset_unit = dataset_unit
+        self.coord_units = coord_units
+
+    def _convert_units(self, data: xr.DataArray):
+        assert isinstance(data, xr.DataArray)
+        assert 'units' in data.attrs, 'No units attribute found in the dataset'
+        for c, cn in self.coord_units.items():
+            assert 'units' in data.coords[c].attrs, f'No units attribute found in the coordinate {c}'
+            data.coords[c] = data.coords[c].pint.quantify(unit_registry=get_ureg()).pint.to(
+                self.coord_units[c]).pint.dequantify()
+        # convert units
+        if self.dataset_unit is None:
+            return data
+        return data.pint.quantify(unit_registry=get_ureg()).pint.to(self.dataset_unit).pint.dequantify()
+
+    def sel(self, method=None, **coords) -> xr.DataArray:
+        return self._convert_units(self.dataset.sel(method=method, **coords))
+
+    def isel(self, **indexers) -> xr.DataArray:
+        return self._convert_units(self.dataset.isel(**indexers))
+
+    def __getitem__(self, *args, **kwargs):
+        return self._convert_units(self.dataset.__getitem__(*args, **kwargs))
+
 
+@register_special_dataset("to_units", "Dataset")
+class ToUnitsAccessory(Accessory):
+    """Accessor to await selected data to be converted to a new units"""
 
-@xr.register_dataarray_accessor("to")
-class UnitConversionAccessor:
-    """Accessor to convert units of data array. It is
-    also possible to convert its coordinates"""
-
-    def __init__(self, xarray_obj):
-        self._obj = xarray_obj
-
-    def __call__(self, *args, **kwargs):
-        new_obj = self._obj.copy()
-        if len(args) > 0:
-            for arg in args:
-                if isinstance(arg, str):
-                    new_obj = new_obj.pint.quantify(unit_registry=get_ureg()).pint.quantify(unit_registry=get_ureg()).pint.to(arg).pint.dequantify()
-                elif isinstance(arg, dict):
-                    for k, v in arg.items():
-                        new_obj.coords[k] = self._obj.coords[k].pint.quantify(unit_registry=get_ureg()).pint.to(v).pint.dequantify()
-        for k, v in kwargs.items():
-            new_obj.coords[k] = self._obj.coords[k].pint.quantify(unit_registry=get_ureg()).pint.to(v).pint.dequantify()
-        return new_obj
+    def __call__(self, dataset_unit: Optional[str] = None, **coord_units) -> ToUnitsInterface:
+        return ToUnitsInterface(self._obj, dataset_unit=dataset_unit, **coord_units)
diff --git a/h5rdmtoolbox/extensions/vector.py b/h5rdmtoolbox/extensions/vector.py
index 87e36fc..a12ee66 100644
--- a/h5rdmtoolbox/extensions/vector.py
+++ b/h5rdmtoolbox/extensions/vector.py
@@ -1,9 +1,10 @@
 import h5py
 import xarray as xr
+from typing import List, Tuple
 
 # noinspection PyUnresolvedReferences
 from . import magnitude  # automatically make magnitude available
-from ..wrapper.accessory import Accessor, register_special_dataset
+from ..wrapper.accessory import Accessory, register_special_dataset
 from ..wrapper.core import Group, File
 
 
@@ -19,32 +20,29 @@ def __init__(self, **datasets):
         self._data_vars = list(self._datasets.keys())
         self._shape = self._datasets[self._data_vars[0]].shape
 
-    def __getitem__(self, item) -> xr.DataArray:
+    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):
+    def data_vars(self) -> List[str]:
         """List of data variables in the dataset"""
         return self._data_vars
 
     @property
-    def shape(self):
+    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(Accessor):
+class VectorDataset(Accessory):
     """A special dataset for vector data.
      The vector components are stored in the group as datasets."""
 
-    def __init__(self, h5grp: h5py.Group):
-        self._grp = h5grp
-
     def __call__(self, *args, **kwargs) -> HDFXrDataset:
         """Returns a xarray dataset with the vector components as data variables.
 
@@ -68,7 +66,7 @@ def __call__(self, *args, **kwargs) -> HDFXrDataset:
         hdf_datasets = {}
         for arg in args:
             if isinstance(arg, str):
-                ds = self._grp[arg]
+                ds = self._obj[arg]
             elif isinstance(arg, h5py.Dataset):
                 ds = arg
             else:
@@ -77,7 +75,7 @@ def __call__(self, *args, **kwargs) -> HDFXrDataset:
 
         for name, ds in kwargs.items():
             if isinstance(ds, str):
-                ds = self._grp[ds]
+                ds = self._obj[ds]
             elif not isinstance(ds, h5py.Dataset):
                 raise TypeError(f'Invalid type: {type(ds)}')
             hdf_datasets[name.strip('/')] = ds
diff --git a/h5rdmtoolbox/protocols.py b/h5rdmtoolbox/protocols.py
index 4881ce2..dad7319 100644
--- a/h5rdmtoolbox/protocols.py
+++ b/h5rdmtoolbox/protocols.py
@@ -6,11 +6,12 @@
 must have the same method signatures.
 """
 
-import pathlib
-from typing import Protocol, Optional, Union, Dict, List, Any, Tuple
-
 import h5py
+import numpy as np
+import pathlib
 import rdflib
+import xarray as xr
+from typing import Protocol, Optional, Union, Dict, List, Any, Tuple
 
 
 class NamedObject(Protocol):
@@ -154,7 +155,13 @@ def attrs(self) -> H5TbxAttributeManager:
         ...
 
     def __delitem__(self, key): ...
-class H5TbxGroup(H5TbxHLObject):
+
+
+class H5TbxFile(H5TbxHLObject):
+    """Protocol for the h5tbx.File class."""
+
+
+class H5TbxGroup(H5TbxFile):
     """Protocol for the h5tbx.Group class."""
 
     def __getitem__(self, name: str):
@@ -172,6 +179,22 @@ def coords(self):
     def hdf_filename(self) -> pathlib.Path:
         """Return the filename as a pathlib.Path object."""
 
+    def sel(self, method=None, **coords) -> xr.DataArray:
+        """Return the Dataset selected by the coordinates"""
+        ...
+
+    def isel(self, **indexers) -> xr.DataArray:
+        """Return the Dataset indexed by the indexers"""
+        ...
+
+    def __getitem__(self,
+                    args,
+                    new_dtype=None,
+                    nparray=False,
+                    links_as_strings: bool = False) -> Union[xr.DataArray, np.ndarray]:
+        """Return the data array by the item name"""
+        ...
+
 
 class StandardAttribute(Protocol):
 
diff --git a/h5rdmtoolbox/wrapper/accessory.py b/h5rdmtoolbox/wrapper/accessory.py
index c9a9c73..46034ef 100644
--- a/h5rdmtoolbox/wrapper/accessory.py
+++ b/h5rdmtoolbox/wrapper/accessory.py
@@ -1,8 +1,10 @@
 """Module to register attributes of wrapper classes without touching the implementation"""
-import h5py
-from typing import Union
+import logging
+from typing import Union, Type
 
-from .core import Group
+from ..protocols import H5TbxHLObject
+
+logger = logging.getLogger('h5rdmtoolbox')
 
 
 class SpecialDatasetRegistrationWarning(Warning):
@@ -53,13 +55,27 @@ def _register_special_dataset(name, cls, special_dataset, overwrite):
         if not overwrite:
             raise RuntimeError(f'Cannot register the accessor {special_dataset!r} under name {name!r} '
                                f'because it already exists and overwrite is set to {overwrite}')
+    logger.debug(f'Registering special dataset {name!r} for class {cls!r}')
     setattr(cls, name, _CachedHDFAccessor(name, special_dataset))
     return special_dataset
 
 
-def register_special_dataset(name, cls: Union["Dataset", "Group"], overwrite=False):
+def register_special_dataset(name, cls: Union[str, Type[H5TbxHLObject]], overwrite=False):
     """registers a special dataset to a wrapper class"""
 
+    if isinstance(cls, str):
+        if cls.lower() == 'dataset':
+            from .core import Dataset
+            cls = Dataset
+        elif cls.lower() == 'group':
+            from .core import Group
+            cls = Group
+        elif cls.lower() == 'file':
+            from .core import File
+            cls = File
+        else:
+            raise ValueError(f'Invalid class type {cls!r}')
+
     def decorator(accessor):
         """decorator"""
         return _register_special_dataset(name, cls, accessor, overwrite)
@@ -67,8 +83,15 @@ def decorator(accessor):
     return decorator
 
 
-class Accessor:
+class Accessory:
     """Base class for all special datasets"""
 
-    def __init__(self, h5grp: h5py.Group):
-        self._grp = h5grp
+    def __init__(self, obj: H5TbxHLObject):
+        """Initialize the accessor with the object to be accessed
+
+        Parameters
+        ----------
+        obj : H5TbxHLObject
+            The object to which the accessor is attached
+        """
+        self._obj = obj
diff --git a/h5rdmtoolbox/wrapper/core.py b/h5rdmtoolbox/wrapper/core.py
index a60fe99..b4b5379 100644
--- a/h5rdmtoolbox/wrapper/core.py
+++ b/h5rdmtoolbox/wrapper/core.py
@@ -1302,32 +1302,6 @@ def wrapper(*args):
     return obj
 
 
-class UnitConversionInterface:
-    def __init__(self, dataset, dataset_unit, **coord_units):
-        self.dataset = dataset
-        self.dataset_unit = dataset_unit
-        self.coord_units = coord_units
-
-    def _convert_units(self, data: xr.DataArray):
-        assert isinstance(data, xr.DataArray)
-        assert 'units' in data.attrs, 'No units attribute found in the dataset'
-        for c, cn in self.coord_units.items():
-            assert 'units' in data.coords[c].attrs, f'No units attribute found in the coordinate {c}'
-            data.coords[c] = data.coords[c].pint.quantify(unit_registry=get_ureg()).pint.to(
-                self.coord_units[c]).pint.dequantify()
-        # convert units
-        return data.pint.quantify(unit_registry=get_ureg()).pint.to(self.dataset_unit).pint.dequantify()
-
-    def sel(self, method=None, **coords) -> xr.DataArray:
-        return self._convert_units(self.dataset.sel(method=method, **coords))
-
-    def isel(self, **indexers) -> xr.DataArray:
-        return self._convert_units(self.dataset.isel(**indexers))
-
-    def __getitem__(self, *args, **kwargs):
-        return self._convert_units(self.dataset.__getitem__(*args, **kwargs))
-
-
 class Dataset(h5py.Dataset):
     """Wrapper around the h5py.Dataset. Some useful methods are added on top of
     the underlying *h5py* package.
@@ -1349,8 +1323,6 @@ class Dataset(h5py.Dataset):
     * dumps(): string representation of group
     * isel(): Select data by named dimension and index, mimics xarray.isel.
     * sel(): Select data by named dimension and values, mimics xarray.sel.
-    * to_units(): Convert the dataset to a new unit.
-    * write_iso_timestamp(): Write an ISO 8601 timestamp to the current dataset attribute.
 
     The following properties are added to the h5py.Dataset object:
 
@@ -1929,26 +1901,6 @@ def __init__(self, _id):
         super().__init__(_id)
         self._hdf_filename = Path(self.file.filename)
 
-    def to_units(self, dataset_unit, **coord_units) -> UnitConversionInterface:
-        """Return interface, which allows to convert the dataset and/or its dimension scales
-        (coordinates) to a new unit. On the return object, the methods isel() and sel() can be
-        used to select data based on named dimension and index or values - just in the new
-        units.
-
-        Parameters
-        ----------
-        dataset_unit : str
-            The new unit for the dataset.
-        coord_units : Dict
-            The new units for the coordinates.
-
-        Examples
-        --------
-        >>> with h5tbx.File('test.h5', 'r') as h5:
-        >>>     h5.vel.to_units('m/s', time='s', z='m')
-        """
-        return UnitConversionInterface(self, dataset_unit, **coord_units)
-
     def set_primary_scale(self, axis, iscale: int):
         """Set the primary scale for a specific axis.
 
diff --git a/tests/test_extensions.py b/tests/test_extensions.py
index 085157c..0a7ce2f 100644
--- a/tests/test_extensions.py
+++ b/tests/test_extensions.py
@@ -220,16 +220,16 @@ def test_units_to(self):
         with h5tbx.File(mode='w') as h5:
             ds = h5.create_dataset('x', data=[1, 2, 3], make_scale=True, attrs={'units': 'm'})
             y = h5.create_dataset('y', data=[1, 0, 1], attach_scale='x', attrs={'units': 'mm'})
-            ds_cm = ds[()].to('cm')
+            ds_cm = ds.to_units('cm')[()]
             self.assertEqual('cm', ds_cm.attrs['units'])
 
-            y_cm = y[()].to('cm')
+            y_cm = y.to_units('cm')[()]
             self.assertEqual('cm', y_cm.attrs['units'])
 
-            y_xcm = y[()].to({'x': 'cm'})
+            y_xcm = y.to_units({'x': 'cm'})[()]
             self.assertEqual('mm', y_xcm.attrs['units'])
             self.assertEqual('cm', y_xcm.x.attrs['units'])
 
-            y_xcm = y[()].to(x='cm')
+            y_xcm = y.to_units(x='cm')[()]
             self.assertEqual('mm', y_xcm.attrs['units'])
             self.assertEqual('cm', y_xcm.x.attrs['units'])
diff --git a/tests/wrapper/test_core.py b/tests/wrapper/test_core.py
index 6f75902..fef45b2 100644
--- a/tests/wrapper/test_core.py
+++ b/tests/wrapper/test_core.py
@@ -15,8 +15,8 @@
 from h5rdmtoolbox.wrapper import h5yaml
 from h5rdmtoolbox.wrapper.h5attr import AttributeString
 
-logger = h5tbx.logger
-# logger.setLevel('ERROR')
+logger = h5tbx.set_loglevel('ERROR')
+
 __this_dir__ = pathlib.Path(__file__).parent