Get started with Graphpro
#########################

To start with the software, we need either a specific protein or a MD simulation, we can download a public simulation of the Adk protein opening.

    .. code-block:: bash

        !gdown 1NwFINNlKYDhYrlpB6o4lPyftl-UMq7vk
        !gdown 1VHXDJu_i6LUNpwYYuKhrdmMB9nKuFImq

GraphPro allow a simple integration with MDAnalisys, allowing scienties that are familiar with the tool to use its power to boost research on geometrical learning.

    .. code-block:: python

        import nglview as nv
        import MDAnalysis as mda

        pdb_file = 'MD05_ADK_protein_fit_tiny.pdb'
        trajectory = 'MD05_ADK_protein_fit_tiny.xtc'

        u = mda.Universe(pdb_file, trajectory)

        view = nv.show_mdanalysis(u)
        view.add_surface(selection="protein", opacity=0.8)
        view.background = 'gray'

        view

Static graph generation
^^^^^^^^^^^^^^^^^^^^^^^
Graphpro uses package to generate from a set of trajectory represenations. md_analisys, cretes a generator, that enable to generate graphs from static files or trajectories.

In this example a contact map with cutoff of 8 angstroms is use to generate the graph representaton per residue.


    .. code-block:: python
        
        import graphpro as gp
        from graphpro.graphgen import ContactMap

        graph_gen = gp.md_analisys(u)
        G = graph_gen.generate(ContactMap(cutoff=8))
        G.plot(figsize=(6,6))

    .. image:: img/static_graph.png
        :width: 400
        :alt: AdK Open Close Trajectory

Trajectory generation
^^^^^^^^^^^^^^^^^^^^^^^
Using the same generator we can generated a set of graphs over the trajectory file that returns us a graph collection with a graph each frame.

    .. code-block:: python

        trajectory_graphs = graph_gen.generate_trajectory(ContactMap(cutoff=6))
        len(trajectory_graphs)

In this simulation we can see how AdK closes the active side pocket, we can see this been relected in the approximate diam change on the graph structure.


    .. code-block:: python

        from networkx.algorithms import approximation

        diameter_in_frame = [approximation.diameter(graph.to_networkx()) for graph in trajectory_graphs]


    .. code-block:: python

        import matplotlib.pyplot as plt

        plt.style.use('ggplot')
        plt.title('Adk trajectory closing ')
        plt.xlabel('frame')
        plt.ylabel('graph diameter')
        plt.plot(diameter_in_frame)


    .. image:: img/adk_open_close.png
        :width: 400
        :alt: AdK Open Close Trajectory

Towards geometrical learning
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
More importantly, the specific collection of anotated graphs can be gransform to a gemetric data structure.

The basic method to_data, allow the transfomation of our internal graph representation to PyG Data.

This holds an homegenus graph representation that holds both node level attributes as well as edge attributes.

    .. code-block:: python

        data = G.to_data()

        # Analyzing the graph structure:
        print(data.num_nodes)
        print(data.is_directed())