Convert filtering multiple data variables tip to section

docs/source/conf.py

@@ -47,6 +47,7 @@
     "sphinx_design",
     "sphinx_gallery.gen_gallery",
     "sphinx_sitemap",
+    "sphinx.ext.autosectionlabel",
 ]
 
 # Configure the myst parser to enable cool markdown features
@@ -76,6 +77,9 @@
 autosummary_generate = True
 autodoc_default_flags = ["members", "inherited-members"]
 
+# Prefix section labels with the document name
+autosectionlabel_prefix_document = True
+
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
 # This pattern also affects html_static_path and html_extra_path.
@@ -104,7 +108,7 @@
         "binderhub_url": "https://mybinder.org",
         "dependencies": ["environment.yml"],
     },
-    'remove_config_comments': True, 
+    "remove_config_comments": True,
     # do not render config params set as # sphinx_gallery_config [= value]
 }
 

examples/filter_and_interpolate.py

@@ -173,33 +173,31 @@
     print(log_entry)
 
 # %%
-# .. tip::
-#    All :py:mod:`movement.filtering` functions are available via the
-#    ``move`` accessor. These ``move`` accessor methods operate on the
-#    ``position`` data variable in the dataset ``ds`` by default.
-#    There is also an additional argument ``data_vars`` that allows us to
-#    specify which data variables in ``ds`` to filter.
-#    When multiple data variable names are specified in ``data_vars``,
-#    the method will return a dictionary with the data variable names as keys
-#    and the filtered DataArrays as values, otherwise it will return a single
-#    DataArray that is the filtered data.
-#    This is useful when we want to apply the same filtering operation to
-#    multiple data variables in ``ds`` at the same time.
+# .. _target-example-filtering-multiple-data-variables:
 #
-#    .. dropdown:: Example
-#
-#       To filter both ``position`` and ``velocity`` data variables
-#       in ``ds``, based on the confidence scores, we can specify
-#       ``data_vars=["position", "velocity"]`` in the method call.
-#       As the filtered data variables are returned as a dictionary, we can
-#       use :py:meth:`xarray.Dataset.update` to update ``ds`` in-place
-#       with the filtered data variables.
-#
-#       .. code-block:: python
-#
-#           ds["velocity"] = ds.move.compute_velocity()
-#           filtered_data_dict = ds.move.filter_by_confidence(
-#              data_vars=["position", "velocity"]
-#           )
-#           ds.update(filtered_data_dict)
+# Filtering multiple data variables
+# ---------------------------------
+# All :py:mod:`movement.filtering` functions are available via the
+# ``move`` accessor. These ``move`` accessor methods operate on the
+# ``position`` data variable in the dataset ``ds`` by default.
+# There is also an additional argument ``data_vars`` that allows us to
+# specify which data variables in ``ds`` to filter.
+# When multiple data variable names are specified in ``data_vars``,
+# the method will return a dictionary with the data variable names as keys
+# and the filtered DataArrays as values, otherwise it will return a single
+# DataArray that is the filtered data.
+# This is useful when we want to apply the same filtering operation to
+# multiple data variables in ``ds`` at the same time.
 #
+# For instance, to filter both ``position`` and ``velocity`` data variables
+# in ``ds``, based on the confidence scores, we can specify
+# ``data_vars=["position", "velocity"]`` in the method call.
+# As the filtered data variables are returned as a dictionary, we can
+# use :py:meth:`xarray.Dataset.update` to update ``ds`` in-place
+# with the filtered data variables.
+
+ds["velocity"] = ds.move.compute_velocity()
+filtered_data_dict = ds.move.filter_by_confidence(
+    data_vars=["position", "velocity"]
+)
+ds.update(filtered_data_dict)

examples/smooth.py

@@ -360,3 +360,9 @@ def plot_raw_and_smooth_timeseries_and_psd(
 # %%
 # Feel free to play around with the parameters of the applied filters and to
 # also look at other keypoints and time ranges.
+
+# %%
+# .. seealso::
+#   :ref:`examples/filter_and_interpolate:Filtering multiple data variables`
+#   in the
+#   :ref:`sphx_glr_examples_filter_and_interpolate.py` example.