Usage
=====

The toolbox is designed in a modular way, which means the individual modules can be used in combination with others, but also by themselves.
Users also have the option to choose between a normal Python scripting API and a graphical user interface (GUI). 

.. tip::

  The GUI provides convenient tools for data handling, (dynamic) connectivity estimation, graph analysis, and multiverse exploration, offering a quick visual overview. 
  For greater flexibility, the scripting API is recommended.


GUI
---

After installation, graphical user interface can be accessed through the terminal by typing:

.. code-block:: bash

    comet-gui

On a fresh installation, the first startup may take a little longer due to the initialisation of the Matplotlib backend. A brief introduction for how to use the GUI is provided in the :doc:`tutorials`.


Scripting
---------

Dynamic functional connectivity can be estimated through the `connectivity` module. An example for sliding window correlation:

.. code-block:: python

    from comet import connectivity, utils

    ts = utils.load_example()

    sw = connectivity.SlidingWindow(ts, windowsize=30, shape="gaussian")
    dfc = sw.estimate()


Graph measures can be calculated through the graph module. An example for the clustering coefficient derived from sliding window estimates:

.. code-block:: python

    from comet import connectivity, graph, utils

    ts = utils.load_example()

    sw = connectivity.SlidingWindow(ts, windowsize=30, shape="gaussian")
    dFC = sw.estimate()

    adj = graph.threshold(dFC, type="density", threshold=0.2)
    clustering_coef = graph.clustering_coef(adj)


Multiverse analysis can be conducted through the multiverse module.
This exaple will create and run a multiverse analysis with two decisions (6 possible combinations):

.. code-block:: python

    from comet.multiverse import Multiverse

    forking_paths = {
        "decision1": [1, 2, 3],
        "decision2": ["Hello", "World"]
        }

    def analysis_template():
        print("Decision1:", {{decision1}})
        print("Decision2", {{decision2}})

    mverse = Multiverse(name="example_multiverse")
    mverse.create(analysis_template, forking_paths)
    mverse.summary()
    mverse.run()