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diff --git a/.nojekyll b/.nojekyll
@@ -1 +1 @@
-d2f04eb7
+41194c4c
diff --git a/reference/Lifemap.html b/reference/Lifemap.html
@@ -219,7 +219,7 @@ <h2 id="toc-title">On this page</h2>
 
 <section id="pylifemap.Lifemap" class="level1">
 <h1>Lifemap</h1>
-<p><code>Lifemap(self, data, *, locate=True, taxid_col='taxid', x_col='pylifemap_x', y_col='pylifemap_y', zoom=5, legend_width=None, width=DEFAULT_WIDTH, height=DEFAULT_HEIGHT)</code></p>
+<p><code>Lifemap(self, data, *, taxid_col='taxid', zoom=5, legend_width=None, width=DEFAULT_WIDTH, height=DEFAULT_HEIGHT)</code></p>
 
 
 </section>

diff --git a/search.json b/search.json
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-    "text": "aggregate_count(d, *, result_col='n', taxid_col='taxid')\nNodes count aggregation along branches.\nAggregates nodes count in a DataFrame with taxonomy ids along the branches of the lifemap tree.\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nd\npd.DataFrame | pl.DataFrame\nDataFrame to aggregate data from.\nrequired\n\n\nresult_col\nstr\nName of the column created to store the counts, by default “n”.\n'n'\n\n\ntaxid_col\nstr\nName of the d column containing taxonomy ids, by default “taxid”.\n'taxid'\n\n\n\n\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\npl.DataFrame\nAggregated DataFrame.\n\n\n\n\n\n\naggregate_num : aggregation of a numeric variable.\naggregate_freq : aggregation of the values counts of a categorical variable.\n\n\n\n&gt;&gt;&gt; from pylifemap import aggregate_count\n&gt;&gt;&gt; import polars as pl\n&gt;&gt;&gt; d = pl.DataFrame({\"taxid\": [33154, 33090, 2]})\n&gt;&gt;&gt; aggregate_count(d)\nshape: (5, 2)\n┌───────┬─────┐\n│ taxid ┆ n   │\n│ ---   ┆ --- │\n│ i32   ┆ u32 │\n╞═══════╪═════╡\n│ 0     ┆ 3   │\n│ 2     ┆ 1   │\n│ 2759  ┆ 2   │\n│ 33090 ┆ 1   │\n│ 33154 ┆ 1   │\n└───────┴─────┘",
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-    "text": "Widget JavaScript modification and bundling\nEach time the widget JavaScript or CSS code is modified in the src/js/ or packages/lifemapjs directories, it must be bundled with one of the following commands in pylifemap:\n# Bundle only once\nnpm run bundle\n# Or, to bundle and watch for code change\nnpm run dev\nThe result is put into src/pylifemap/static."
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-    "text": "Credits\n\nThe iucn data files in the data/ directory have been generated from The IUCN Red List of Threatened Species dataset (DOI 10.15468/0qnb58).\nVisualizations are powered by Open Layers and deck.gl.\nThe widget is developed thanks to the anywidget framework.\nThis documentation website is generated by Quarto and quartodoc."
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+    "section": "Credits",
+    "text": "Credits\n\nThe iucn data files in the data/ directory have been generated from The IUCN Red List of Threatened Species dataset (DOI 10.15468/0qnb58).\nVisualizations are powered by Open Layers and deck.gl.\nThe widget is developed thanks to the anywidget framework.\nThis documentation website is generated by Quarto and quartodoc."
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+    "href": "development.html#install-from-git",
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+    "text": "Widget JavaScript modification and bundling\nEach time the widget JavaScript or CSS code is modified in the src/js/ or packages/lifemapjs directories, it must be bundled with one of the following commands in pylifemap:\n# Bundle only once\nnpm run bundle\n# Or, to bundle and watch for code change\nnpm run dev\nThe result is put into src/pylifemap/static."
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+    "text": "aggregate_count(d, *, result_col='n', taxid_col='taxid')\nNodes count aggregation along branches.\nAggregates nodes count in a DataFrame with taxonomy ids along the branches of the lifemap tree.\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nd\npd.DataFrame | pl.DataFrame\nDataFrame to aggregate data from.\nrequired\n\n\nresult_col\nstr\nName of the column created to store the counts, by default “n”.\n'n'\n\n\ntaxid_col\nstr\nName of the d column containing taxonomy ids, by default “taxid”.\n'taxid'\n\n\n\n\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\npl.DataFrame\nAggregated DataFrame.\n\n\n\n\n\n\naggregate_num : aggregation of a numeric variable.\naggregate_freq : aggregation of the values counts of a categorical variable.\n\n\n\n&gt;&gt;&gt; from pylifemap import aggregate_count\n&gt;&gt;&gt; import polars as pl\n&gt;&gt;&gt; d = pl.DataFrame({\"taxid\": [33154, 33090, 2]})\n&gt;&gt;&gt; aggregate_count(d)\nshape: (5, 2)\n┌───────┬─────┐\n│ taxid ┆ n   │\n│ ---   ┆ --- │\n│ i32   ┆ u32 │\n╞═══════╪═════╡\n│ 0     ┆ 3   │\n│ 2     ┆ 1   │\n│ 2759  ┆ 2   │\n│ 33090 ┆ 1   │\n│ 33154 ┆ 1   │\n└───────┴─────┘",
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+    "text": "Name\nType\nDescription\nDefault\n\n\n\n\nd\npd.DataFrame | pl.DataFrame\nDataFrame to aggregate data from.\nrequired\n\n\nresult_col\nstr\nName of the column created to store the counts, by default “n”.\n'n'\n\n\ntaxid_col\nstr\nName of the d column containing taxonomy ids, by default “taxid”.\n'taxid'",
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+    "text": "aggregate_num : aggregation of a numeric variable.\naggregate_freq : aggregation of the values counts of a categorical variable.",
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diff --git a/site_libs/bootstrap/bootstrap.min.css b/site_libs/bootstrap/bootstrap.min.css