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modelgraph
==========

A tool for visualizing dependencies between different components of your
model.

Motivation
----------

Assume you have some model, represented as a system of ODEs with
potentially several intermediation expression. For example in the
classical Hodgkin Huxley model of the squid axon from 1952 we have the
following expressions

.. code::

   dV_dt = -(-i_Stim + i_Na + i_K + i_L)/Cm
   i_Na = g_Na*m**3*h*(V - E_Na)

From this we can deduce that ``i_Na`` depends on the parameter ``g_Na``,
and since ``dV_dt`` depends on ``i_Na`` it also depends (indirectly) on
the parameter ``g_Na``.

In ``modelgraph`` we can visualize this dependency using the following
code snippet

.. code:: python

   from modelgraph import DependencyGraph
   import gotranx

   # Load ode using gotran
   ode = gotranx.load_ode("hodgkin_huxley_squid_axon_model_1952_original.ode")
   # Build dependency graph
   graph = DependencyGraph(ode)
   # Get the components that depends on g_Na (we call this inverse dependents)
   G = graph.inv_dependency_graph("g_Na")
   # Visualize using matplotlib (python -m pip install matplotlib)
   import matplotlib.pyplot as plt
   nx.draw(G, with_labels=True, font_size=10, node_size=2000)
   plt.savefig("g_Na_mpl.png")

.. figure:: _static/g_Na_mpl.png


.. code:: python

   # Or using pydot (python -m pip install pydot)
   P = nx.nx_pydot.to_pydot(G)
   P.write_png("g_Na_pydot.png")

.. figure:: _static/g_Na_pydot.png


It is also possible to go the other way around, i.e if you want to look
at e.g ``dV_dt`` and see that it depends on

.. code:: python

   # Visualize what dV_dt depdens on
   G_dV_dt = graph.dependency_graph("dV_dt")

   nx.draw(G_dV_dt, with_labels=True, font_size=10, node_size=2000)
   plt.savefig("dV_dt_mpl.png")

   P_dV_dt = nx.nx_pydot.to_pydot(G_dV_dt)
   P_dV_dt.write_png("dV_dt_pydot.png")

Here we only display the graph ``pydot`` since the ``matplotlib``
version seems to be a bit messy if the graph becomes too large

.. figure:: _static/dV_dt_pydot.png


Installation
------------

Install with pip

::

   python3 -m pip install modelgraph

GUI
---

There is also a graphical user interface. To use this you need to
install ``streamlit``

::

   python -m pip install streamlit

You can run the the gui by passing in the ``.ode`` file as a command
line argument e.g

::

   python -m modelgraph demo/hodgkin_huxley_squid_axon_model_1952_original.ode

This will start a ``streamlit`` server, and the GUI is now hosted on
http://localhost:8501

.. figure:: _static/gui.gif


Documentation
-------------

Documentation is hosted at
http://computationalphysiology.github.io/modelgraph.

Automated test
--------------

Tests are provided in the folder
`tests <https://github.com/ComputationalPhysiology/modelgraph/tree/main/tests>`__.
You can run the tests with pytest

::

   python3 -m pytest tests -vv

Contributing
------------

See `the contributing
section <https://computationalphysiology.github.io/simcardems/CONTRIBUTING.html>`__

Authors
-------

-  Henrik Finsberg (henriknf@simula.no)

License
-------

MIT

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.. toctree::
   :maxdepth: 2
   :caption: Contents:

   hodgkin_huxley
   hodgkin_huxley_gotranx
   modules



Indices and tables
==================

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`