Clean up "running NESTML" section in the docs

doc/running/index.rst

@@ -172,38 +172,6 @@ The following default values are used, corresponding to the command line default
 
 For a detailed description of all the arguments of ``generate_target()``, see :func:`pynestml.frontend.pynestml_frontend.generate_target`.
 
-A typical script for the NEST Simulator target could look like the following. First, import the function:
-
-.. code-block:: python
-
-   from pynestml.frontend.pynestml_frontend import generate_target
-
-   generate_target(input_path="/home/nest/work/pynestml/models",
-                   target_platform="NEST",
-                   target_path="/tmp/nestml_target")
-
-We can also use a shorthand function for each supported target platform (here, NEST):
-
-.. code-block:: python
-
-   from pynestml.frontend.pynestml_frontend import generate_nest_target
-
-   generate_nest_target(input_path="/home/nest/work/pynestml/models",
-                        target_path="/tmp/nestml_target")
-
-To dynamically load a module with ``module_name`` equal to ``nestmlmodule`` (the default) in PyNEST can be done as follows:
-
-.. code-block:: python
-
-   nest.Install("nestmlmodule")
-
-The NESTML models are then available for instantiation, for example as:
-
-.. code-block:: python
-
-   pre, post = nest.Create("neuron_nestml", 2)
-   nest.Connect(pre, post, "one_to_one", syn_spec={"synapse_model": "synapse_nestml"})
-
 
 Running NESTML from the command line
 ------------------------------------
@@ -245,17 +213,3 @@ This will generate, compile, build, and install the code for a set of specified
    * - ``--codegen_opts``
      - (Optional) Path to a JSON file containing additional options for the target platform code generator. A list of available options can be found under the section "Code generation options" for your intended target platform on the page :ref:`Running NESTML`.
 
-NEST Desktop target
-~~~~~~~~~~~~~~~~~~~
-
-The aim of the NEST Desktop as target is to generate ``json`` files for the neuron models. The resulting file contains details about the state variables, parameters and their initial values defined in their respective ``.nestml`` files. The ``json`` files are used to load them in the NEST Desktop user interface.
-
-For example, for the neuron model ``iaf_psc_exp``, the ``json`` file will be generated by running the ``generate_target`` function with ``target_platform`` option set to ``NEST_DESKTOP``.
-
-.. code-block:: python
-
-   from pynestml.frontend.pynestml_frontend import generate_target
-
-   generate_target(input_path="/home/nest/work/pynestml/models/neurons/iaf_psc_exp.nestml",
-                   target_platform="NEST_DESKTOP",
-                   target_path="/tmp/nestml_target")

doc/running/running_nest.rst

@@ -12,6 +12,42 @@ After NESTML completes, the NEST extension module (by default called ``"nestmlmo
    Several code generator options are available; for an overview see :class:`pynestml.codegeneration.nest_code_generator.NESTCodeGenerator`.
 
 
+NEST workflow example
+---------------------
+
+A typical script for the NEST Simulator target could look like the following. First, import the function:
+
+.. code-block:: python
+
+   from pynestml.frontend.pynestml_frontend import generate_target
+
+   generate_target(input_path="/home/nest/work/pynestml/models",
+                   target_platform="NEST",
+                   target_path="/tmp/nestml_target")
+
+We can also use a shorthand function for each supported target platform (here, NEST):
+
+.. code-block:: python
+
+   from pynestml.frontend.pynestml_frontend import generate_nest_target
+
+   generate_nest_target(input_path="/home/nest/work/pynestml/models",
+                        target_path="/tmp/nestml_target")
+
+To dynamically load a module with ``module_name`` equal to ``nestmlmodule`` (the default) in PyNEST can be done as follows:
+
+.. code-block:: python
+
+   nest.Install("nestmlmodule")
+
+The NESTML models are then available for instantiation, for example as:
+
+.. code-block:: python
+
+   pre, post = nest.Create("neuron_nestml", 2)
+   nest.Connect(pre, post, "one_to_one", syn_spec={"synapse_model": "synapse_nestml"})
+
+
 Simulation loop
 ---------------