📝 Add caption and name to literalinclude directive

Author: veit

docs/data-processing/apis/grpc/example.rst

@@ -13,6 +13,8 @@ information containing a series of name-value pairs called *fields*. Here’s a
 simple example :download:`accounts.proto`:
 
 .. literalinclude:: accounts.proto
+   :caption: accounts.proto
+   :name: accounts.proto
    :language: proto
    :lines: 1-7
 
@@ -36,6 +38,8 @@ gRPC services are also defined in the ``.proto`` files, with RPC method
 parameters and return types specified as protocol buffer messages:
 
 .. literalinclude:: accounts.proto
+   :caption: accounts.proto
+   :name: accounts.proto
    :language: proto
    :lines: 8-
 
@@ -62,6 +66,8 @@ Create server
 For this we write the file :download:`accounts_server.py`:
 
 .. literalinclude:: accounts_server.py
+   :caption: accounts_server.py
+   :name: accounts_server.py
    :language: python
 
 Create client
@@ -70,6 +76,8 @@ Create client
 For this we create :download:`accounts_client.py`:
 
 .. literalinclude:: accounts_client.py
+   :caption: accounts_client.py
+   :name: accounts_client.py
    :language: python
 
 Run client and server

docs/data-processing/apis/grpc/test.rst

@@ -20,6 +20,8 @@ gRPC can be tested automatically with `pytest-grpc
 +. Then we create a :term:`Test Fixture` for our :doc:`example` with:
 
    .. literalinclude:: tests/test_accounts.py
+      :caption: tests/test_accounts.py
+      :name: tests/test_accounts.py
       :language: python
       :lines: 3-27
 

docs/performance/index.rst

@@ -34,6 +34,8 @@ Steps 2 and 3 are repeated until the assignments no longer change.
 A possible implementation with pure Python could look like this:
 
 .. literalinclude:: py_kmeans.py
+   :caption: py_kmeans.py
+   :name: py_kmeans.py
 
 We can create sample data with:
 
@@ -132,7 +134,9 @@ repetitive operations into compiled code to avoid slow loops.
 With NumPy we can do without some loops:
 
 .. literalinclude:: np_kmeans.py
-    :lines: 1-8
+   :caption: np_kmeans.py
+   :name: np_kmeans.py
+   :lines: 1-8
 
 The advantages of NumPy are that the Python overhead only occurs per array and
 not per array element. However, because NumPy uses a specific language for array
@@ -149,7 +153,9 @@ Special data structures
     This way you can also bypass the loop in the ``compute_centers`` method:
 
     .. literalinclude:: pd_kmeans.py
-        :lines: 2-4, 11-15
+       :caption: pd_kmeans.py
+       :name: pd_kmeans.py
+       :lines: 2-4, 11-15
 
 `scipy.spatial <https://docs.scipy.org/doc/scipy/reference/spatial.html>`_
     for spatial queries like distances, nearest neighbours, k-Means :abbr:`etc
@@ -158,7 +164,9 @@ Special data structures
     Our ``find_labels`` method can then be written more specifically:
 
     .. literalinclude:: sp_kmeans.py
-        :lines: 4-10
+       :caption: sp_kmeans.py
+       :name: sp_kmeans.py
+       :lines: 4-10
 
 `scipy.sparse <https://docs.scipy.org/doc/scipy/reference/sparse.html>`_
     `sparse matrices <https://en.wikipedia.org/wiki/Sparse_matrix>`_
@@ -214,7 +222,9 @@ cumbersome.
 Our example could then look like this:
 
 .. literalinclude:: cy_kmeans.pyx
-    :lines: 1-28
+   :caption: cy_kmeans.pyx
+   :name: cy_kmeans.pyx
+   :lines: 1-28
 
 .. seealso::
     * `Cython Tutorials
@@ -227,7 +237,9 @@ Numba
 scientific Python and NumPy code into fast machine code, for example:
 
 .. literalinclude:: nb_kmeans.py
-    :lines: 1-25
+   :caption: nb_kmeans.py
+   :name: nb_kmeans.py
+   :lines: 1-25
 
 However, Numba requires `LLVM <https://en.wikipedia.org/wiki/LLVM>`_ and some
 Python constructs are not supported.
@@ -263,7 +275,9 @@ can distribute tasks in a cluster.  In doing so, they have different focuses:
 Our example could look like this with Dask:
 
 .. literalinclude:: ds_kmeans.py
-    :lines: 1-32
+   :caption: ds_kmeans.py
+   :name: ds_kmeans.py
+   :lines: 1-32
 
 .. toctree::
     :hidden:

docs/productive/git/install-config.rst

@@ -114,6 +114,8 @@ Then we can use :doc:`pandas:reference/api/pandas.DataFrame.to_csv` in
 :file:`exceltocsv.py` to convert the Excel files:
 
 .. literalinclude:: exceltocsv.py
+    :caption: exceltocsv.py
+    :name: exceltocsv.py
     :language: python
 
 Then the following section is added to the global Git configuration

docs/web/dashboards/panel/fastapi.rst

@@ -18,6 +18,8 @@ up some of the basic configuration in :download:`fastAPI/main.py`:
 #. First, we import all the necessary elements:
 
    .. literalinclude:: fastAPI/main.py
+      :caption: fastAPI/main.py
+      :name: fastAPI/main.py
       :linenos:
       :lines: 1-3, 5-7
 
@@ -41,6 +43,8 @@ up some of the basic configuration in :download:`fastAPI/main.py`:
    the following content, for example:
 
    .. literalinclude:: fastAPI/templates/base.html
+      :caption: fastAPI/templates/base.html
+      :name: fastAPI/templates/base.html
       :language: html
       :linenos:
       :lines: 1-
@@ -72,20 +76,26 @@ up some of the basic configuration in :download:`fastAPI/main.py`:
        a parameterised object that represents your existing code:
 
        .. literalinclude:: fastAPI/sliders/sinewave.py
+          :caption: fastAPI/sliders/sinewave.py
+          :name: fastAPI/sliders/sinewave.py
           :linenos:
           :lines: 1-
 
    :download:`fastAPI/sliders/pn_app.py`
        creates an app function from the ``SineWave`` class:
 
        .. literalinclude:: fastAPI/sliders/pn_app.py
+          :caption: fastAPI/sliders/pn_app.py
+          :name: fastAPI/sliders/pn_app.py
           :linenos:
           :lines: 1-
 
 #. Finally, we return to our :download:`fastAPI/main.py` and import the
    ``createApp`` function:
 
    .. literalinclude:: fastAPI/main.py
+      :caption: fastAPI/main.py
+      :name: fastAPI/main.py
       :lineno-start: 4
       :lines: 4