diff --git a/notebooks/00-jupyter_introduction.ipynb b/notebooks/00-jupyter_introduction.ipynb
index 3df72bd..2d71df1 100644
--- a/notebooks/00-jupyter_introduction.ipynb
+++ b/notebooks/00-jupyter_introduction.ipynb
@@ -935,6 +935,10 @@
   }
  ],
  "metadata": {
+  "jupytext": {
+   "cell_metadata_filter": "-run_control,-deletable,-editable,-jupyter,-slideshow",
+   "notebook_metadata_filter": "-jupytext.cell_metadata_filter,-jupytext.notebook_metadata_filter"
+  },
   "kernelspec": {
    "display_name": "Python 3 (ipykernel)",
    "language": "python",
@@ -950,7 +954,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.7"
+   "version": "3.12.8"
   },
   "nav_menu": {},
   "toc": {
diff --git a/notebooks/00-jupyter_introduction.md b/notebooks/00-jupyter_introduction.md
index 9de572d..098e7cb 100644
--- a/notebooks/00-jupyter_introduction.md
+++ b/notebooks/00-jupyter_introduction.md
@@ -4,7 +4,7 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.16.4
+    jupytext_version: 1.16.5
 kernelspec:
   display_name: Python 3 (ipykernel)
   language: python
diff --git a/notebooks/01-introduction-tabular-data.ipynb b/notebooks/01-introduction-tabular-data.ipynb
index 48abd92..f96e1c9 100644
--- a/notebooks/01-introduction-tabular-data.ipynb
+++ b/notebooks/01-introduction-tabular-data.ipynb
@@ -5473,7 +5473,9 @@
  "metadata": {
   "celltoolbar": "Nbtutor - export exercises",
   "jupytext": {
-   "formats": "ipynb,md:myst"
+   "cell_metadata_filter": "-run_control,-deletable,-editable,-jupyter,-slideshow",
+   "formats": "ipynb,md:myst",
+   "notebook_metadata_filter": "-jupytext.cell_metadata_filter,-jupytext.notebook_metadata_filter"
   },
   "kernelspec": {
    "display_name": "Python 3 (ipykernel)",
@@ -5490,7 +5492,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.7"
+   "version": "3.12.8"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {
diff --git a/notebooks/01-introduction-tabular-data.md b/notebooks/01-introduction-tabular-data.md
index 9549c2f..0ac9bbe 100644
--- a/notebooks/01-introduction-tabular-data.md
+++ b/notebooks/01-introduction-tabular-data.md
@@ -5,7 +5,7 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.16.4
+    jupytext_version: 1.16.5
 kernelspec:
   display_name: Python 3 (ipykernel)
   language: python
diff --git a/notebooks/02-introduction-geospatial-data.ipynb b/notebooks/02-introduction-geospatial-data.ipynb
index 991e49b..e6c1243 100644
--- a/notebooks/02-introduction-geospatial-data.ipynb
+++ b/notebooks/02-introduction-geospatial-data.ipynb
@@ -21,8 +21,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%matplotlib inline\n",
-    "\n",
     "import pandas as pd\n",
     "import geopandas"
    ]
@@ -40,7 +38,7 @@
    "source": [
     "Geospatial data is often available from specific GIS file formats or data stores, like ESRI shapefiles, GeoJSON files, geopackage files, PostGIS (PostgreSQL) database, ...\n",
     "\n",
-    "We can use the GeoPandas library to read many of those GIS file formats (relying on the `fiona` library under the hood, which is an interface to GDAL/OGR), using the `geopandas.read_file` function.\n",
+    "We can use the GeoPandas library to read many of those GIS file formats (relying on the `pyogrio` library under the hood, which is an interface to GDAL/OGR), using the `geopandas.read_file` function.\n",
     "\n",
     "For example, let's start by reading a shapefile with all the countries of the world (adapted from http://www.naturalearthdata.com/downloads/110m-cultural-vectors/110m-admin-0-countries/, zip file is available in the `/data` directory), and inspect the data:"
    ]
@@ -192,7 +190,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2222,6 +2220,10 @@
  ],
  "metadata": {
   "celltoolbar": "Nbtutor - export exercises",
+  "jupytext": {
+   "cell_metadata_filter": "-run_control,-deletable,-editable,-jupyter,-slideshow",
+   "notebook_metadata_filter": "-jupytext.cell_metadata_filter,-jupytext.notebook_metadata_filter"
+  },
   "kernelspec": {
    "display_name": "Python 3 (ipykernel)",
    "language": "python",
@@ -2237,7 +2239,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.7"
+   "version": "3.12.8"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {
diff --git a/notebooks/02-introduction-geospatial-data.md b/notebooks/02-introduction-geospatial-data.md
index 9042491..1835fbc 100644
--- a/notebooks/02-introduction-geospatial-data.md
+++ b/notebooks/02-introduction-geospatial-data.md
@@ -4,7 +4,7 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.16.4
+    jupytext_version: 1.16.5
 kernelspec:
   display_name: Python 3 (ipykernel)
   language: python
@@ -22,8 +22,6 @@ kernelspec:
 ---
 
 ```{code-cell} ipython3
-%matplotlib inline
-
 import pandas as pd
 import geopandas
 ```
@@ -34,7 +32,7 @@ import geopandas
 
 Geospatial data is often available from specific GIS file formats or data stores, like ESRI shapefiles, GeoJSON files, geopackage files, PostGIS (PostgreSQL) database, ...
 
-We can use the GeoPandas library to read many of those GIS file formats (relying on the `fiona` library under the hood, which is an interface to GDAL/OGR), using the `geopandas.read_file` function.
+We can use the GeoPandas library to read many of those GIS file formats (relying on the `pyogrio` library under the hood, which is an interface to GDAL/OGR), using the `geopandas.read_file` function.
 
 For example, let's start by reading a shapefile with all the countries of the world (adapted from http://www.naturalearthdata.com/downloads/110m-cultural-vectors/110m-admin-0-countries/, zip file is available in the `/data` directory), and inspect the data:
 
diff --git a/notebooks/03-coordinate-reference-systems.ipynb b/notebooks/03-coordinate-reference-systems.ipynb
index c03d4dc..8455d98 100644
--- a/notebooks/03-coordinate-reference-systems.ipynb
+++ b/notebooks/03-coordinate-reference-systems.ipynb
@@ -21,8 +21,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%matplotlib inline\n",
-    "\n",
     "import pandas as pd\n",
     "import geopandas"
    ]
@@ -992,6 +990,10 @@
  ],
  "metadata": {
   "celltoolbar": "Nbtutor - export exercises",
+  "jupytext": {
+   "cell_metadata_filter": "-run_control,-deletable,-editable,-jupyter,-slideshow",
+   "notebook_metadata_filter": "-jupytext.cell_metadata_filter,-jupytext.notebook_metadata_filter"
+  },
   "kernelspec": {
    "display_name": "Python 3 (ipykernel)",
    "language": "python",
@@ -1007,7 +1009,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.7"
+   "version": "3.12.8"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {
diff --git a/notebooks/03-coordinate-reference-systems.md b/notebooks/03-coordinate-reference-systems.md
index a385a2b..e7c3386 100644
--- a/notebooks/03-coordinate-reference-systems.md
+++ b/notebooks/03-coordinate-reference-systems.md
@@ -4,7 +4,7 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.16.4
+    jupytext_version: 1.16.5
 kernelspec:
   display_name: Python 3 (ipykernel)
   language: python
@@ -22,8 +22,6 @@ kernelspec:
 ---
 
 ```{code-cell} ipython3
-%matplotlib inline
-
 import pandas as pd
 import geopandas
 ```
diff --git a/notebooks/04-spatial-relationships-joins.ipynb b/notebooks/04-spatial-relationships-joins.ipynb
index 343c3ca..9c099e2 100644
--- a/notebooks/04-spatial-relationships-joins.ipynb
+++ b/notebooks/04-spatial-relationships-joins.ipynb
@@ -21,8 +21,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%matplotlib inline\n",
-    "\n",
     "import pandas as pd\n",
     "import geopandas"
    ]
@@ -107,7 +105,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from shapely.geometry import LineString\n",
+    "from shapely import LineString\n",
     "line = LineString([paris, brussels])"
    ]
   },
@@ -608,7 +606,7 @@
    "outputs": [],
    "source": [
     "# Import the Point geometry\n",
-    "from shapely.geometry import Point"
+    "from shapely import Point"
    ]
   },
   {
@@ -2816,6 +2814,10 @@
   }
  ],
  "metadata": {
+  "jupytext": {
+   "cell_metadata_filter": "-run_control,-deletable,-editable,-jupyter,-slideshow",
+   "notebook_metadata_filter": "-jupytext.cell_metadata_filter,-jupytext.notebook_metadata_filter"
+  },
   "kernelspec": {
    "display_name": "Python 3 (ipykernel)",
    "language": "python",
@@ -2831,7 +2833,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.7"
+   "version": "3.12.8"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {
diff --git a/notebooks/04-spatial-relationships-joins.md b/notebooks/04-spatial-relationships-joins.md
index 496e380..33cd136 100644
--- a/notebooks/04-spatial-relationships-joins.md
+++ b/notebooks/04-spatial-relationships-joins.md
@@ -4,7 +4,7 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.16.4
+    jupytext_version: 1.16.5
 kernelspec:
   display_name: Python 3 (ipykernel)
   language: python
@@ -22,8 +22,6 @@ kernelspec:
 ---
 
 ```{code-cell} ipython3
-%matplotlib inline
-
 import pandas as pd
 import geopandas
 ```
@@ -67,7 +65,7 @@ brussels = cities.loc[cities['name'] == 'Brussels', 'geometry'].item()
 And a linestring:
 
 ```{code-cell} ipython3
-from shapely.geometry import LineString
+from shapely import LineString
 line = LineString([paris, brussels])
 ```
 
@@ -204,7 +202,7 @@ The location of the Eiffel Tower is: x of 648237.3 and y of 6862271.9
 
 ```{code-cell} ipython3
 # Import the Point geometry
-from shapely.geometry import Point
+from shapely import Point
 ```
 
 ```{code-cell} ipython3
diff --git a/notebooks/05-spatial-operations-overlays.ipynb b/notebooks/05-spatial-operations-overlays.ipynb
index f3c2817..0fea318 100644
--- a/notebooks/05-spatial-operations-overlays.ipynb
+++ b/notebooks/05-spatial-operations-overlays.ipynb
@@ -253,7 +253,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from shapely.geometry import LineString\n",
+    "from shapely import LineString\n",
     "box = LineString([(-10, 0), (50, 0)]).buffer(10, cap_style=3)"
    ]
   },
@@ -2770,6 +2770,10 @@
  ],
  "metadata": {
   "celltoolbar": "Nbtutor - export exercises",
+  "jupytext": {
+   "cell_metadata_filter": "-run_control,-deletable,-editable,-jupyter,-slideshow",
+   "notebook_metadata_filter": "-jupytext.cell_metadata_filter,-jupytext.notebook_metadata_filter"
+  },
   "kernelspec": {
    "display_name": "Python 3 (ipykernel)",
    "language": "python",
@@ -2785,7 +2789,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.7"
+   "version": "3.12.8"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {
diff --git a/notebooks/05-spatial-operations-overlays.md b/notebooks/05-spatial-operations-overlays.md
index a3c0cfc..af1e297 100644
--- a/notebooks/05-spatial-operations-overlays.md
+++ b/notebooks/05-spatial-operations-overlays.md
@@ -4,7 +4,7 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.16.4
+    jupytext_version: 1.16.5
 kernelspec:
   display_name: Python 3 (ipykernel)
   language: python
@@ -107,7 +107,7 @@ africa = countries[countries.continent == 'Africa']
 ```
 
 ```{code-cell} ipython3
-from shapely.geometry import LineString
+from shapely import LineString
 box = LineString([(-10, 0), (50, 0)]).buffer(10, cap_style=3)
 ```
 
diff --git a/notebooks/10-introduction-raster.ipynb b/notebooks/10-introduction-raster.ipynb
index 75c300f..eb3804a 100644
--- a/notebooks/10-introduction-raster.ipynb
+++ b/notebooks/10-introduction-raster.ipynb
@@ -21,8 +21,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%matplotlib inline\n",
-    "\n",
     "import numpy as np"
    ]
   },
@@ -490,9 +488,9 @@
        "    TIFFTAG_XRESOLUTION:     1\n",
        "    TIFFTAG_YRESOLUTION:     1\n",
        "    TIFFTAG_RESOLUTIONUNIT:  1 (unitless)\n",
-       "    AREA_OR_POINT:           Area</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'>'band_data'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>band</span>: 3</li><li><span class='xr-has-index'>y</span>: 227</li><li><span class='xr-has-index'>x</span>: 447</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-58482c13-b438-4f75-870a-7bbdf75fe755' class='xr-array-in' type='checkbox' checked><label for='section-58482c13-b438-4f75-870a-7bbdf75fe755' 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>...</span></div><div class='xr-array-data'><pre>[304407 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-97c130ac-7865-4ad9-8536-237b9a27e0e7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-97c130ac-7865-4ad9-8536-237b9a27e0e7' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3</div><input id='attrs-57ed206b-db39-4fe9-a99b-493b5b6f8954' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-57ed206b-db39-4fe9-a99b-493b5b6f8954' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57269f3e-b6e8-455c-8843-a0f7f2c175de' class='xr-var-data-in' type='checkbox'><label for='data-57269f3e-b6e8-455c-8843-a0f7f2c175de' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1, 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'>float64</div><div class='xr-var-preview xr-preview'>5.935e+05 5.935e+05 ... 6.114e+05</div><input id='attrs-15391b82-c648-4e36-9277-1a5d80c472c9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-15391b82-c648-4e36-9277-1a5d80c472c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7b61450-6bff-4242-98c2-42cd6a7a6c43' class='xr-var-data-in' type='checkbox'><label for='data-e7b61450-6bff-4242-98c2-42cd6a7a6c43' 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([593496.235933, 593536.314073, 593576.392213, ..., 611290.930018,\n",
-       "       611331.008158, 611371.086298])</pre></div></li><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'>6.578e+06 6.577e+06 ... 6.568e+06</div><input id='attrs-72ad140d-745f-4d9f-8b98-4eee797be615' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-72ad140d-745f-4d9f-8b98-4eee797be615' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a64c4184-932b-4400-afff-833a287e171a' class='xr-var-data-in' type='checkbox'><label for='data-a64c4184-932b-4400-afff-833a287e171a' 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([6577529.547066, 6577489.434462, 6577449.321859, ..., 6568544.323882,\n",
-       "       6568504.211278, 6568464.098675])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-eb331216-56c9-47d2-a283-6ab904b4729c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eb331216-56c9-47d2-a283-6ab904b4729c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f1b567f1-1bf9-459c-960a-65a1d43b7848' class='xr-var-data-in' type='checkbox'><label for='data-f1b567f1-1bf9-459c-960a-65a1d43b7848' 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>crs_wkt :</span></dt><dd>PROJCS[&quot;WGS 84 / Pseudo-Mercator&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Mercator_1SP&quot;],PARAMETER[&quot;central_meridian&quot;,0],PARAMETER[&quot;scale_factor&quot;,1],PARAMETER[&quot;false_easting&quot;,0],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],EXTENSION[&quot;PROJ4&quot;,&quot;+proj=merc +a=6378137 +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null +wktext +no_defs&quot;],AUTHORITY[&quot;EPSG&quot;,&quot;3857&quot;]]</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / Pseudo-Mercator&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Mercator_1SP&quot;],PARAMETER[&quot;central_meridian&quot;,0],PARAMETER[&quot;scale_factor&quot;,1],PARAMETER[&quot;false_easting&quot;,0],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],EXTENSION[&quot;PROJ4&quot;,&quot;+proj=merc +a=6378137 +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null +wktext +no_defs&quot;],AUTHORITY[&quot;EPSG&quot;,&quot;3857&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>593476.1968631798 40.07813983073409 0.0 6577549.60336745 0.0 -40.1126034998577</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=int64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-601c8eef-8f6e-4716-8796-2eba7449044a' class='xr-section-summary-in' type='checkbox'  ><label for='section-601c8eef-8f6e-4716-8796-2eba7449044a' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>band</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-b1f241de-1947-496f-8afc-33ac49c37c87' class='xr-index-data-in' type='checkbox'/><label for='index-b1f241de-1947-496f-8afc-33ac49c37c87' 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;int64&#x27;, name=&#x27;band&#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><input type='checkbox' disabled/><label></label><input id='index-39b2e2ee-4b51-45f4-8124-9134de7d6138' class='xr-index-data-in' type='checkbox'/><label for='index-39b2e2ee-4b51-45f4-8124-9134de7d6138' 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([593496.2359330952,  593536.314072926, 593576.3922127567,\n",
+       "    AREA_OR_POINT:           Area</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'>'band_data'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>band</span>: 3</li><li><span class='xr-has-index'>y</span>: 227</li><li><span class='xr-has-index'>x</span>: 447</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-a73128b4-b4c7-4a1e-b690-e47062e4caaf' class='xr-array-in' type='checkbox' checked><label for='section-a73128b4-b4c7-4a1e-b690-e47062e4caaf' 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>...</span></div><div class='xr-array-data'><pre>[304407 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-716d9928-c2cd-4042-978d-a1809c4ba1de' class='xr-section-summary-in' type='checkbox'  checked><label for='section-716d9928-c2cd-4042-978d-a1809c4ba1de' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3</div><input id='attrs-61e0216c-8dc9-489a-a7ff-70703ab9dd37' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-61e0216c-8dc9-489a-a7ff-70703ab9dd37' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f430fe8-e6ee-4779-bbba-df0d68f4c169' class='xr-var-data-in' type='checkbox'><label for='data-9f430fe8-e6ee-4779-bbba-df0d68f4c169' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1, 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'>float64</div><div class='xr-var-preview xr-preview'>5.935e+05 5.935e+05 ... 6.114e+05</div><input id='attrs-111ba10d-1261-4a9a-8897-8f0ebba981f9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-111ba10d-1261-4a9a-8897-8f0ebba981f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69486acd-ad49-44d9-9c55-2f63846ecfbc' class='xr-var-data-in' type='checkbox'><label for='data-69486acd-ad49-44d9-9c55-2f63846ecfbc' 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([593496.235933, 593536.314073, 593576.392213, ..., 611290.930018,\n",
+       "       611331.008158, 611371.086298])</pre></div></li><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'>6.578e+06 6.577e+06 ... 6.568e+06</div><input id='attrs-dbd09ce9-67eb-4619-aa9e-b6efbad0c251' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dbd09ce9-67eb-4619-aa9e-b6efbad0c251' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-41b9efc0-0294-478d-82ed-5c95263b174c' class='xr-var-data-in' type='checkbox'><label for='data-41b9efc0-0294-478d-82ed-5c95263b174c' 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([6577529.547066, 6577489.434462, 6577449.321859, ..., 6568544.323882,\n",
+       "       6568504.211278, 6568464.098675])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-46a29fd3-b0e5-4d41-aa69-cb269793e4df' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-46a29fd3-b0e5-4d41-aa69-cb269793e4df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-128de70c-d2e6-4ce6-9005-904aa1a87502' class='xr-var-data-in' type='checkbox'><label for='data-128de70c-d2e6-4ce6-9005-904aa1a87502' 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>crs_wkt :</span></dt><dd>PROJCS[&quot;WGS 84 / Pseudo-Mercator&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Mercator_1SP&quot;],PARAMETER[&quot;central_meridian&quot;,0],PARAMETER[&quot;scale_factor&quot;,1],PARAMETER[&quot;false_easting&quot;,0],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],EXTENSION[&quot;PROJ4&quot;,&quot;+proj=merc +a=6378137 +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null +wktext +no_defs&quot;],AUTHORITY[&quot;EPSG&quot;,&quot;3857&quot;]]</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / Pseudo-Mercator&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Mercator_1SP&quot;],PARAMETER[&quot;central_meridian&quot;,0],PARAMETER[&quot;scale_factor&quot;,1],PARAMETER[&quot;false_easting&quot;,0],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],EXTENSION[&quot;PROJ4&quot;,&quot;+proj=merc +a=6378137 +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null +wktext +no_defs&quot;],AUTHORITY[&quot;EPSG&quot;,&quot;3857&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>593476.1968631798 40.07813983073409 0.0 6577549.60336745 0.0 -40.1126034998577</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=int64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fb4f3020-8fd5-4b91-871e-f4b99bf10715' class='xr-section-summary-in' type='checkbox'  ><label for='section-fb4f3020-8fd5-4b91-871e-f4b99bf10715' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>band</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-0bd6d94f-24fc-4b4a-acf4-92da5950892c' class='xr-index-data-in' type='checkbox'/><label for='index-0bd6d94f-24fc-4b4a-acf4-92da5950892c' 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;int64&#x27;, name=&#x27;band&#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><input type='checkbox' disabled/><label></label><input id='index-4ae3e099-48a8-4292-a220-d07aa1515db1' class='xr-index-data-in' type='checkbox'/><label for='index-4ae3e099-48a8-4292-a220-d07aa1515db1' 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([593496.2359330952,  593536.314072926, 593576.3922127567,\n",
        "       593616.4703525874, 593656.5484924181, 593696.6266322489,\n",
        "       593736.7047720796, 593776.7829119103,  593816.861051741,\n",
        "       593856.9391915718,\n",
@@ -501,7 +499,7 @@
        "       611130.6174586182,  611170.695598449, 611210.7737382797,\n",
        "       611250.8518781104, 611290.9300179412, 611331.0081577719,\n",
        "       611371.0862976026],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=447))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-dd012419-84c9-4eae-ae78-8c922d51f8ae' class='xr-index-data-in' type='checkbox'/><label for='index-dd012419-84c9-4eae-ae78-8c922d51f8ae' 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([6577529.5470656995,    6577489.4344622,  6577449.321858699,\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=447))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-b01e37b6-eca3-480e-b26a-e181fb7583eb' class='xr-index-data-in' type='checkbox'/><label for='index-b01e37b6-eca3-480e-b26a-e181fb7583eb' 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([6577529.5470656995,    6577489.4344622,  6577449.321858699,\n",
        "          6577409.2092552,    6577369.0966517,    6577328.9840482,\n",
        "          6577288.8714447,  6577248.758841201,    6577208.6462377,\n",
        "        6577168.533634201,\n",
@@ -510,7 +508,7 @@
        "       6568704.7742957305,  6568664.661692231,  6568624.549088731,\n",
        "        6568584.436485231,  6568544.323881731,  6568504.211278232,\n",
        "        6568464.098674731],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=227))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-70c032ea-d8c1-4b32-8198-a1bd8f4e633d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-70c032ea-d8c1-4b32-8198-a1bd8f4e633d' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>TIFFTAG_XRESOLUTION :</span></dt><dd>1</dd><dt><span>TIFFTAG_YRESOLUTION :</span></dt><dd>1</dd><dt><span>TIFFTAG_RESOLUTIONUNIT :</span></dt><dd>1 (unitless)</dd><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd></dl></div></li></ul></div></div>"
+       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=227))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-30a45800-2f27-49d6-9511-45cae6a419e0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-30a45800-2f27-49d6-9511-45cae6a419e0' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>TIFFTAG_XRESOLUTION :</span></dt><dd>1</dd><dt><span>TIFFTAG_YRESOLUTION :</span></dt><dd>1</dd><dt><span>TIFFTAG_RESOLUTIONUNIT :</span></dt><dd>1 (unitless)</dd><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.DataArray 'band_data' (band: 3, y: 227, x: 447)> Size: 1MB\n",
@@ -2461,6 +2459,10 @@
  ],
  "metadata": {
   "celltoolbar": "Nbtutor - export exercises",
+  "jupytext": {
+   "cell_metadata_filter": "-run_control,-deletable,-editable,-jupyter,-slideshow",
+   "notebook_metadata_filter": "-jupytext.cell_metadata_filter,-jupytext.notebook_metadata_filter"
+  },
   "kernelspec": {
    "display_name": "Python 3 (ipykernel)",
    "language": "python",
@@ -2476,7 +2478,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.7"
+   "version": "3.12.8"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/10-introduction-raster.md b/notebooks/10-introduction-raster.md
index 2cdf762..4402b4f 100644
--- a/notebooks/10-introduction-raster.md
+++ b/notebooks/10-introduction-raster.md
@@ -4,7 +4,7 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.16.4
+    jupytext_version: 1.16.5
 kernelspec:
   display_name: Python 3 (ipykernel)
   language: python
@@ -22,8 +22,6 @@ kernelspec:
 ---
 
 ```{code-cell} ipython3
-%matplotlib inline
-
 import numpy as np
 ```
 
diff --git a/notebooks/11-xarray-intro.ipynb b/notebooks/11-xarray-intro.ipynb
index ba79aac..13471aa 100644
--- a/notebooks/11-xarray-intro.ipynb
+++ b/notebooks/11-xarray-intro.ipynb
@@ -11577,6 +11577,10 @@
  ],
  "metadata": {
   "celltoolbar": "Nbtutor - export exercises",
+  "jupytext": {
+   "cell_metadata_filter": "-run_control,-deletable,-editable,-jupyter,-slideshow",
+   "notebook_metadata_filter": "-jupytext.cell_metadata_filter,-jupytext.notebook_metadata_filter"
+  },
   "kernelspec": {
    "display_name": "Python 3 (ipykernel)",
    "language": "python",
@@ -11592,7 +11596,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.7"
+   "version": "3.12.8"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {
diff --git a/notebooks/11-xarray-intro.md b/notebooks/11-xarray-intro.md
index 3278058..2657221 100644
--- a/notebooks/11-xarray-intro.md
+++ b/notebooks/11-xarray-intro.md
@@ -4,7 +4,7 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.16.4
+    jupytext_version: 1.16.5
 kernelspec:
   display_name: Python 3 (ipykernel)
   language: python
diff --git a/notebooks/12-xarray-advanced.ipynb b/notebooks/12-xarray-advanced.ipynb
index cdcca15..d42e297 100644
--- a/notebooks/12-xarray-advanced.ipynb
+++ b/notebooks/12-xarray-advanced.ipynb
@@ -35,9 +35,7 @@
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
-    "import cmocean\n",
-    "\n",
-    "%matplotlib inline"
+    "import cmocean"
    ]
   },
   {
@@ -13767,6 +13765,10 @@
   }
  ],
  "metadata": {
+  "jupytext": {
+   "cell_metadata_filter": "-run_control,-deletable,-editable,-jupyter,-slideshow",
+   "notebook_metadata_filter": "-jupytext.cell_metadata_filter,-jupytext.notebook_metadata_filter"
+  },
   "kernelspec": {
    "display_name": "Python 3 (ipykernel)",
    "language": "python",
@@ -13782,7 +13784,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.7"
+   "version": "3.12.8"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {
diff --git a/notebooks/12-xarray-advanced.md b/notebooks/12-xarray-advanced.md
index 9d28528..6dbc593 100644
--- a/notebooks/12-xarray-advanced.md
+++ b/notebooks/12-xarray-advanced.md
@@ -4,7 +4,7 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.16.4
+    jupytext_version: 1.16.5
 kernelspec:
   display_name: Python 3 (ipykernel)
   language: python
@@ -31,8 +31,6 @@ import numpy as np
 import matplotlib.pyplot as plt
 
 import cmocean
-
-%matplotlib inline
 ```
 
 ## `xarray.Dataset` for multiple variables
diff --git a/notebooks/13-raster-processing.ipynb b/notebooks/13-raster-processing.ipynb
index fe9d916..9d27ccc 100644
--- a/notebooks/13-raster-processing.ipynb
+++ b/notebooks/13-raster-processing.ipynb
@@ -4323,7 +4323,7 @@
    },
    "outputs": [],
    "source": [
-    "from shapely.geometry import Point"
+    "from shapely import Point"
    ]
   },
   {
@@ -9664,6 +9664,10 @@
   }
  ],
  "metadata": {
+  "jupytext": {
+   "cell_metadata_filter": "-run_control,-deletable,-editable,-jupyter,-slideshow",
+   "notebook_metadata_filter": "-jupytext.cell_metadata_filter,-jupytext.notebook_metadata_filter"
+  },
   "kernelspec": {
    "display_name": "Python 3 (ipykernel)",
    "language": "python",
@@ -9679,7 +9683,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.7"
+   "version": "3.12.8"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {
diff --git a/notebooks/13-raster-processing.md b/notebooks/13-raster-processing.md
index ab380db..70cb59f 100644
--- a/notebooks/13-raster-processing.md
+++ b/notebooks/13-raster-processing.md
@@ -4,7 +4,7 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.16.4
+    jupytext_version: 1.16.5
 kernelspec:
   display_name: Python 3 (ipykernel)
   language: python
@@ -559,7 +559,7 @@ We want to limit our search for locations to the surroundings of the centre of G
 ```{code-cell} ipython3
 :tags: [nbtutor-solution]
 
-from shapely.geometry import Point
+from shapely import Point
 ```
 
 ```{code-cell} ipython3
diff --git a/notebooks/14-combine-data.ipynb b/notebooks/14-combine-data.ipynb
index 16aa2b9..3f60004 100644
--- a/notebooks/14-combine-data.ipynb
+++ b/notebooks/14-combine-data.ipynb
@@ -593,6 +593,10 @@
   }
  ],
  "metadata": {
+  "jupytext": {
+   "cell_metadata_filter": "-run_control,-deletable,-editable,-jupyter,-slideshow",
+   "notebook_metadata_filter": "-jupytext.cell_metadata_filter,-jupytext.notebook_metadata_filter"
+  },
   "kernelspec": {
    "display_name": "Python 3 (ipykernel)",
    "language": "python",
@@ -608,7 +612,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.7"
+   "version": "3.12.8"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {
diff --git a/notebooks/14-combine-data.md b/notebooks/14-combine-data.md
index a8227b8..9e47e1e 100644
--- a/notebooks/14-combine-data.md
+++ b/notebooks/14-combine-data.md
@@ -4,7 +4,7 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.16.4
+    jupytext_version: 1.16.5
 kernelspec:
   display_name: Python 3 (ipykernel)
   language: python
diff --git a/notebooks/15-xarray-dask-big-data.ipynb b/notebooks/15-xarray-dask-big-data.ipynb
index 252f5d2..d69dd6d 100644
--- a/notebooks/15-xarray-dask-big-data.ipynb
+++ b/notebooks/15-xarray-dask-big-data.ipynb
@@ -204,6 +204,10 @@
   }
  ],
  "metadata": {
+  "jupytext": {
+   "cell_metadata_filter": "-run_control,-deletable,-editable,-jupyter,-slideshow",
+   "notebook_metadata_filter": "-jupytext.cell_metadata_filter,-jupytext.notebook_metadata_filter"
+  },
   "kernelspec": {
    "display_name": "Python 3 (ipykernel)",
    "language": "python",
@@ -219,7 +223,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.7"
+   "version": "3.12.8"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {
diff --git a/notebooks/15-xarray-dask-big-data.md b/notebooks/15-xarray-dask-big-data.md
index ca6a6e4..7c5d3e1 100644
--- a/notebooks/15-xarray-dask-big-data.md
+++ b/notebooks/15-xarray-dask-big-data.md
@@ -4,7 +4,7 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.16.4
+    jupytext_version: 1.16.5
 kernelspec:
   display_name: Python 3 (ipykernel)
   language: python
diff --git a/notebooks/90_package_numpy.ipynb b/notebooks/90_package_numpy.ipynb
index dac775c..6354c0c 100644
--- a/notebooks/90_package_numpy.ipynb
+++ b/notebooks/90_package_numpy.ipynb
@@ -2315,6 +2315,10 @@
  ],
  "metadata": {
   "celltoolbar": "Nbtutor - export exercises",
+  "jupytext": {
+   "cell_metadata_filter": "-run_control,-deletable,-editable,-jupyter,-slideshow",
+   "notebook_metadata_filter": "-jupytext.cell_metadata_filter,-jupytext.notebook_metadata_filter"
+  },
   "kernelspec": {
    "display_name": "Python 3 (ipykernel)",
    "language": "python",
@@ -2330,7 +2334,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.7"
+   "version": "3.12.8"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/90_package_numpy.md b/notebooks/90_package_numpy.md
index b6c7e57..04f6161 100644
--- a/notebooks/90_package_numpy.md
+++ b/notebooks/90_package_numpy.md
@@ -4,7 +4,7 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.16.4
+    jupytext_version: 1.16.5
 kernelspec:
   display_name: Python 3 (ipykernel)
   language: python
diff --git a/notebooks/91_package_rasterio.ipynb b/notebooks/91_package_rasterio.ipynb
index e1b4d10..cf1cded 100644
--- a/notebooks/91_package_rasterio.ipynb
+++ b/notebooks/91_package_rasterio.ipynb
@@ -1226,6 +1226,10 @@
  ],
  "metadata": {
   "celltoolbar": "Nbtutor - export exercises",
+  "jupytext": {
+   "cell_metadata_filter": "-run_control,-deletable,-editable,-jupyter,-slideshow",
+   "notebook_metadata_filter": "-jupytext.cell_metadata_filter,-jupytext.notebook_metadata_filter"
+  },
   "kernelspec": {
    "display_name": "Python 3 (ipykernel)",
    "language": "python",
@@ -1241,7 +1245,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.7"
+   "version": "3.12.8"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/91_package_rasterio.md b/notebooks/91_package_rasterio.md
index f08f6ae..b54818f 100644
--- a/notebooks/91_package_rasterio.md
+++ b/notebooks/91_package_rasterio.md
@@ -4,7 +4,7 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.16.4
+    jupytext_version: 1.16.5
 kernelspec:
   display_name: Python 3 (ipykernel)
   language: python
diff --git a/notebooks/case-argo-sea-floats.ipynb b/notebooks/case-argo-sea-floats.ipynb
index 93faa08..8e9e211 100644
--- a/notebooks/case-argo-sea-floats.ipynb
+++ b/notebooks/case-argo-sea-floats.ipynb
@@ -1407,6 +1407,10 @@
  ],
  "metadata": {
   "celltoolbar": "Nbtutor - export exercises",
+  "jupytext": {
+   "cell_metadata_filter": "-run_control,-deletable,-editable,-jupyter,-slideshow",
+   "notebook_metadata_filter": "-jupytext.cell_metadata_filter,-jupytext.notebook_metadata_filter"
+  },
   "kernelspec": {
    "display_name": "Python 3 (ipykernel)",
    "language": "python",
@@ -1422,7 +1426,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.7"
+   "version": "3.12.8"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {
diff --git a/notebooks/case-argo-sea-floats.md b/notebooks/case-argo-sea-floats.md
index bd81dd5..3d50d2a 100644
--- a/notebooks/case-argo-sea-floats.md
+++ b/notebooks/case-argo-sea-floats.md
@@ -4,7 +4,7 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.16.4
+    jupytext_version: 1.16.5
 kernelspec:
   display_name: Python 3 (ipykernel)
   language: python
diff --git a/notebooks/case-curieuzeneuzen-air-quality.ipynb b/notebooks/case-curieuzeneuzen-air-quality.ipynb
index b47f944..0002fae 100644
--- a/notebooks/case-curieuzeneuzen-air-quality.ipynb
+++ b/notebooks/case-curieuzeneuzen-air-quality.ipynb
@@ -5001,6 +5001,10 @@
  ],
  "metadata": {
   "celltoolbar": "Nbtutor - export exercises",
+  "jupytext": {
+   "cell_metadata_filter": "-run_control,-deletable,-editable,-jupyter,-slideshow",
+   "notebook_metadata_filter": "-jupytext.cell_metadata_filter,-jupytext.notebook_metadata_filter"
+  },
   "kernelspec": {
    "display_name": "Python 3 (ipykernel)",
    "language": "python",
@@ -5016,7 +5020,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.7"
+   "version": "3.12.8"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {
diff --git a/notebooks/case-curieuzeneuzen-air-quality.md b/notebooks/case-curieuzeneuzen-air-quality.md
index f9ad03b..b84354a 100644
--- a/notebooks/case-curieuzeneuzen-air-quality.md
+++ b/notebooks/case-curieuzeneuzen-air-quality.md
@@ -4,7 +4,7 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.16.4
+    jupytext_version: 1.16.5
 kernelspec:
   display_name: Python 3 (ipykernel)
   language: python
diff --git a/notebooks/case-sea-surface-temperature.ipynb b/notebooks/case-sea-surface-temperature.ipynb
index d6df608..f7eec62 100644
--- a/notebooks/case-sea-surface-temperature.ipynb
+++ b/notebooks/case-sea-surface-temperature.ipynb
@@ -4715,6 +4715,10 @@
  ],
  "metadata": {
   "celltoolbar": "Nbtutor - export exercises",
+  "jupytext": {
+   "cell_metadata_filter": "-run_control,-deletable,-editable,-jupyter,-slideshow",
+   "notebook_metadata_filter": "-jupytext.cell_metadata_filter,-jupytext.notebook_metadata_filter"
+  },
   "kernelspec": {
    "display_name": "Python 3 (ipykernel)",
    "language": "python",
@@ -4730,7 +4734,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.7"
+   "version": "3.12.8"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/case-sea-surface-temperature.md b/notebooks/case-sea-surface-temperature.md
index b2083ee..3d3cc4e 100644
--- a/notebooks/case-sea-surface-temperature.md
+++ b/notebooks/case-sea-surface-temperature.md
@@ -4,7 +4,7 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.16.4
+    jupytext_version: 1.16.5
 kernelspec:
   display_name: Python 3 (ipykernel)
   language: python
diff --git a/notebooks/visualization-01-matplotlib.ipynb b/notebooks/visualization-01-matplotlib.ipynb
index bf1cdbf..b436190 100644
--- a/notebooks/visualization-01-matplotlib.ipynb
+++ b/notebooks/visualization-01-matplotlib.ipynb
@@ -664,6 +664,10 @@
   }
  ],
  "metadata": {
+  "jupytext": {
+   "cell_metadata_filter": "-run_control,-deletable,-editable,-jupyter,-slideshow",
+   "notebook_metadata_filter": "-jupytext.cell_metadata_filter,-jupytext.notebook_metadata_filter"
+  },
   "kernelspec": {
    "display_name": "Python 3 (ipykernel)",
    "language": "python",
@@ -679,7 +683,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.7"
+   "version": "3.12.8"
   },
   "nav_menu": {},
   "toc": {
diff --git a/notebooks/visualization-01-matplotlib.md b/notebooks/visualization-01-matplotlib.md
index 27f8645..cdea263 100644
--- a/notebooks/visualization-01-matplotlib.md
+++ b/notebooks/visualization-01-matplotlib.md
@@ -4,7 +4,7 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.16.4
+    jupytext_version: 1.16.5
 kernelspec:
   display_name: Python 3 (ipykernel)
   language: python
diff --git a/notebooks/visualization-02-geopandas.ipynb b/notebooks/visualization-02-geopandas.ipynb
index 17e8bfa..9e9f4a8 100644
--- a/notebooks/visualization-02-geopandas.ipynb
+++ b/notebooks/visualization-02-geopandas.ipynb
@@ -21,8 +21,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%matplotlib inline\n",
-    "\n",
     "import pandas as pd\n",
     "import geopandas\n",
     "\n",
@@ -426,6 +424,10 @@
   }
  ],
  "metadata": {
+  "jupytext": {
+   "cell_metadata_filter": "-run_control,-deletable,-editable,-jupyter,-slideshow",
+   "notebook_metadata_filter": "-jupytext.cell_metadata_filter,-jupytext.notebook_metadata_filter"
+  },
   "kernelspec": {
    "display_name": "Python 3 (ipykernel)",
    "language": "python",
@@ -441,7 +443,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.7"
+   "version": "3.12.8"
   },
   "toc-autonumbering": false,
   "toc-showcode": false,
diff --git a/notebooks/visualization-02-geopandas.md b/notebooks/visualization-02-geopandas.md
index fa11fc6..d5b2629 100644
--- a/notebooks/visualization-02-geopandas.md
+++ b/notebooks/visualization-02-geopandas.md
@@ -4,7 +4,7 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.16.4
+    jupytext_version: 1.16.5
 kernelspec:
   display_name: Python 3 (ipykernel)
   language: python
@@ -22,8 +22,6 @@ kernelspec:
 ---
 
 ```{code-cell} ipython3
-%matplotlib inline
-
 import pandas as pd
 import geopandas
 
diff --git a/notebooks/visualization-03-cartopy.ipynb b/notebooks/visualization-03-cartopy.ipynb
index 6365aee..0fdff94 100644
--- a/notebooks/visualization-03-cartopy.ipynb
+++ b/notebooks/visualization-03-cartopy.ipynb
@@ -785,7 +785,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.7"
+   "version": "3.12.8"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {
diff --git a/notebooks/visualization-03-cartopy.md b/notebooks/visualization-03-cartopy.md
index 2921ee6..d797975 100644
--- a/notebooks/visualization-03-cartopy.md
+++ b/notebooks/visualization-03-cartopy.md
@@ -4,7 +4,7 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.16.4
+    jupytext_version: 1.16.5
 kernelspec:
   display_name: Python 3 (ipykernel)
   language: python
diff --git a/notebooks/visualization-04-interactive.ipynb b/notebooks/visualization-04-interactive.ipynb
index ecaa623..7ba0684 100644
--- a/notebooks/visualization-04-interactive.ipynb
+++ b/notebooks/visualization-04-interactive.ipynb
@@ -39,8 +39,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%matplotlib inline\n",
-    "\n",
     "import pandas as pd\n",
     "import geopandas\n",
     "\n",
@@ -351,7 +349,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.7"
+   "version": "3.12.8"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/visualization-04-interactive.md b/notebooks/visualization-04-interactive.md
index 411278e..945d365 100644
--- a/notebooks/visualization-04-interactive.md
+++ b/notebooks/visualization-04-interactive.md
@@ -4,7 +4,7 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.16.4
+    jupytext_version: 1.16.5
 kernelspec:
   display_name: Python 3 (ipykernel)
   language: python
@@ -37,8 +37,6 @@ There are, however, a bunch of alternatives to matplotlib, mainly focusing on pr
 Altair and Plotly are mostly useful for vector data. Using bokeh through holoviews with raster data is shown below.
 
 ```{code-cell} ipython3
-%matplotlib inline
-
 import pandas as pd
 import geopandas