..
    SPDX-FileCopyrightText: 2015-2023 Tanguy Fardet
    SPDX-License-Identifier: CC-BY-SA-4.0
    doc/user/activity-analysis.rst

.. _activ_analysis:

=================
Activity analysis
=================

Principle
=========

The interesting fact about having a link between the graph and the simulation
is that you can easily analyze the activity be taking into account what you
know from the graph structure.


Sorted rasters
==============

Rater plots can be sorted depending on some specific node property, e.g. the
degree or the betweenness:

.. code-block:: python

    import nest

    import nngt
    from nngt.simulation import monitor_nodes, plot_activity

    pop = nngt.NeuralPop.uniform(1000, neuron_model="aeif_psc_alpha")
    net = nngt.generation.gaussian_degree(100, 20, population=pop)

    nodes = net.to_nest()
    recorders, recordables = monitor_nodes(nodes)
    simtime = 1000.
    nest.Simulate(simtime)

    fignums = plot_activity(
        recorders, recordables, network=net, show=True, hist=False,
        limits=(0.,simtime), sort="in-degree")


Activity properties
===================

NNGT can also be used to analyze the general properties of a raster.

Either from a .gdf file containing the raster data

.. code-block:: python

    import nngt
    from nngt.simulation import analyze_raster

    a = analyze_raster("path/to/raster.gdf")
    print(a.phases)
    print(a.properties)

Or from a spike detector gid ``sd``:

.. code-block:: python

    a = analyze_raster(sd)

**Additional information:**

.. toctree::
   :maxdepth: 1

   ../modules/simulation

**Go to other tutorials:**

* :ref:`intro`
* :ref:`graph_gen`
* :ref:`parallelism`
* :ref:`neural_groups`
* :ref:`nest_int`
* :ref:`graph-prop`