DOC improve code block CSS (scikit-learn#27136)

Author: tuscland

doc/conf.py

@@ -68,6 +68,7 @@
 # Specify how to identify the prompt when copying code snippets
 copybutton_prompt_text = r">>> |\.\.\. "
 copybutton_prompt_is_regexp = True
+copybutton_exclude = "style"
 
 try:
     import jupyterlite_sphinx  # noqa: F401

doc/developers/advanced_installation.rst

@@ -229,10 +229,13 @@ console:
 For 64-bit Python, configure the build environment by running the following
 commands in ``cmd`` or an Anaconda Prompt (if you use Anaconda):
 
-    ::
+.. sphinx-prompt 1.3.0 (used in doc-min-dependencies CI task) does not support `batch` prompt type,
+.. so we work around by using a known prompt type and an explicit prompt text.
+..
+.. prompt:: bash C:\>
 
-      $ SET DISTUTILS_USE_SDK=1
-      $ "C:\Program Files (x86)\Microsoft Visual Studio\2019\BuildTools\VC\Auxiliary\Build\vcvarsall.bat" x64
+    SET DISTUTILS_USE_SDK=1
+    "C:\Program Files (x86)\Microsoft Visual Studio\2019\BuildTools\VC\Auxiliary\Build\vcvarsall.bat" x64
 
 Replace ``x64`` by ``x86`` to build for 32-bit Python.
 

doc/install.rst

@@ -71,32 +71,32 @@ Then run:
 
   <div class="highlight">
     <pre class="sk-expandable" data-packager="pip" data-os="linux" data-venv="no"
-    ><span class="prompt1">pip3 install -U scikit-learn</span></pre>
-    
+    ><span>pip3 install -U scikit-learn</span></pre>
+
     <pre class="sk-expandable" data-packager="pip" data-os="windows" data-venv="no"
-    ><span class="prompt1">pip install -U scikit-learn</span></pre>
-    
+    ><span>pip install -U scikit-learn</span></pre>
+
     <pre class="sk-expandable" data-packager="pip" data-os="mac" data-venv="no"
-    ><span class="prompt1">pip install -U scikit-learn</span></pre>
-    
+    ><span>pip install -U scikit-learn</span></pre>
+
     <pre class="sk-expandable" data-packager="pip" data-os="linux" data-venv=""
-    ><span class="prompt1">python3 -m venv sklearn-venv</span>
-  <span class="prompt1">source sklearn-venv/bin/activate</span>
-  <span class="prompt1">pip3 install -U scikit-learn</span></pre>
-    
+    ><span>python3 -m venv sklearn-venv</span>
+  <span>source sklearn-venv/bin/activate</span>
+  <span>pip3 install -U scikit-learn</span></pre>
+
     <pre class="sk-expandable" data-packager="pip" data-os="windows" data-venv=""
-    ><span class="prompt1">python -m venv sklearn-venv</span>
-  <span class="prompt1">sklearn-venv\Scripts\activate</span>
-  <span class="prompt1">pip install -U scikit-learn</span></pre>
-    
+    ><span>python -m venv sklearn-venv</span>
+  <span>sklearn-venv\Scripts\activate</span>
+  <span>pip install -U scikit-learn</span></pre>
+
     <pre class="sk-expandable" data-packager="pip" data-os="mac" data-venv=""
-    ><span class="prompt1">python -m venv sklearn-venv</span>
-  <span class="prompt1">source sklearn-venv/bin/activate</span>
-  <span class="prompt1">pip install -U scikit-learn</span></pre>
+    ><span>python -m venv sklearn-venv</span>
+  <span>source sklearn-venv/bin/activate</span>
+  <span>pip install -U scikit-learn</span></pre>
 
     <pre class="sk-expandable" data-packager="conda"
-    ><span class="prompt1">conda create -n sklearn-env -c conda-forge scikit-learn</span>
-  <span class="prompt1">conda activate sklearn-env</span></pre>
+    ><span>conda create -n sklearn-env -c conda-forge scikit-learn</span>
+  <span>conda activate sklearn-env</span></pre>
   </div>
 
 In order to check your installation you can use
@@ -105,29 +105,29 @@ In order to check your installation you can use
 
   <div class="highlight">
     <pre class="sk-expandable" data-packager="pip" data-os="linux" data-venv="no"
-    ><span class="prompt1">python3 -m pip show scikit-learn  # to see which version and where scikit-learn is installed</span>
-  <span class="prompt1">python3 -m pip freeze  # to see all packages installed in the active virtualenv</span>
-  <span class="prompt1">python3 -c "import sklearn; sklearn.show_versions()"</span></pre>
+    ><span>python3 -m pip show scikit-learn  # to see which version and where scikit-learn is installed</span>
+  <span>python3 -m pip freeze  # to see all packages installed in the active virtualenv</span>
+  <span>python3 -c "import sklearn; sklearn.show_versions()"</span></pre>
 
     <pre class="sk-expandable" data-packager="pip" data-os="windows" data-venv="no"
-    ><span class="prompt1">python -m pip show scikit-learn  # to see which version and where scikit-learn is installed</span>
-  <span class="prompt1">python -m pip freeze  # to see all packages installed in the active virtualenv</span>
-  <span class="prompt1">python -c "import sklearn; sklearn.show_versions()"</span></pre>
+    ><span>python -m pip show scikit-learn  # to see which version and where scikit-learn is installed</span>
+  <span>python -m pip freeze  # to see all packages installed in the active virtualenv</span>
+  <span>python -c "import sklearn; sklearn.show_versions()"</span></pre>
 
     <pre class="sk-expandable" data-packager="pip" data-os="mac" data-venv="no"
-    ><span class="prompt1">python -m pip show scikit-learn  # to see which version and where scikit-learn is installed</span>
-  <span class="prompt1">python -m pip freeze  # to see all packages installed in the active virtualenv</span>
-  <span class="prompt1">python -c "import sklearn; sklearn.show_versions()"</span></pre>
+    ><span>python -m pip show scikit-learn  # to see which version and where scikit-learn is installed</span>
+  <span>python -m pip freeze  # to see all packages installed in the active virtualenv</span>
+  <span>python -c "import sklearn; sklearn.show_versions()"</span></pre>
 
     <pre class="sk-expandable" data-packager="pip" data-venv=""
-    ><span class="prompt1">python -m pip show scikit-learn  # to see which version and where scikit-learn is installed</span>
-  <span class="prompt1">python -m pip freeze  # to see all packages installed in the active virtualenv</span>
-  <span class="prompt1">python -c "import sklearn; sklearn.show_versions()"</span></pre>
+    ><span>python -m pip show scikit-learn  # to see which version and where scikit-learn is installed</span>
+  <span>python -m pip freeze  # to see all packages installed in the active virtualenv</span>
+  <span>python -c "import sklearn; sklearn.show_versions()"</span></pre>
 
     <pre class="sk-expandable" data-packager="conda"
-    ><span class="prompt1">conda list scikit-learn  # to see which scikit-learn version is installed</span>
-  <span class="prompt1">conda list  # to see all packages installed in the active conda environment</span>
-  <span class="prompt1">python -c "import sklearn; sklearn.show_versions()"</span></pre>
+    ><span>conda list scikit-learn  # to see which scikit-learn version is installed</span>
+  <span>conda list  # to see all packages installed in the active conda environment</span>
+  <span>python -c "import sklearn; sklearn.show_versions()"</span></pre>
   </div>
 
 Note that in order to avoid potential conflicts with other packages it is
@@ -290,7 +290,7 @@ Note that those solvers are not enabled by default, please refer to the
 `scikit-learn-intelex <https://intel.github.io/scikit-learn-intelex/what-is-patching.html>`_
 documentation for more details on usage scenarios. Direct export example:
 
-.. prompt:: bash $
+.. prompt:: python >>>
 
   from sklearnex.neighbors import NearestNeighbors
 
@@ -340,6 +340,6 @@ using the ``regedit`` tool:
 
 #. Reinstall scikit-learn (ignoring the previous broken installation):
 
-.. prompt:: python $
+.. prompt:: bash $
 
     pip install --exists-action=i scikit-learn

doc/modules/array_api.rst

@@ -125,8 +125,8 @@ automatically skipped. Therefore it's important to run the tests with the
 
 .. prompt:: bash $
 
-    $ pip install array-api-compat  # and other libraries as needed
-    $ pytest -k "array_api" -v
+    pip install array-api-compat  # and other libraries as needed
+    pytest -k "array_api" -v
 
 Note on MPS device support
 --------------------------
@@ -143,7 +143,7 @@ To enable the MPS support in PyTorch, set the environment variable
 
 .. prompt:: bash $
 
-    $ PYTORCH_ENABLE_MPS_FALLBACK=1 pytest -k "array_api" -v
+    PYTORCH_ENABLE_MPS_FALLBACK=1 pytest -k "array_api" -v
 
 At the time of writing all scikit-learn tests should pass, however, the
 computational speed is not necessarily better than with the CPU device.

doc/themes/scikit-learn-modern/static/css/theme.css

@@ -1208,6 +1208,10 @@ div.install > input:checked + label {
   display: none;
 }
 
+pre.sk-expandable > span:before {
+  content: "$ ";
+}
+
 /* Show hidden content when the checkbox is checked */
 /* for conda */
 #quickstart-conda:checked  ~* [data-packager="conda"] {