.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/intermediate_usage_example/intermediate_usage_example.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_auto_examples_intermediate_usage_example_intermediate_usage_example.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_intermediate_usage_example_intermediate_usage_example.py:


Intermediate usage example
==========================

Here we show how to use NNMT to calculate the power spectra of the
:cite:t:`potjans2014` microcircuit model.

.. GENERATED FROM PYTHON SOURCE LINES 8-14

.. code-block:: default


    import numpy as np
    import matplotlib.pyplot as plt
    import nnmt



.. GENERATED FROM PYTHON SOURCE LINES 15-18

First we try loading previous results, which makes running this script for
the second time much faster. If there are no stored results, we instantiate
a microcircuit model.

.. GENERATED FROM PYTHON SOURCE LINES 18-24

.. code-block:: default

    try:
        network = nnmt.models.Network(file='microcircuit.h5')
    except IOError:
        network = nnmt.models.Microcircuit('network_params.yaml',
                                           'analysis_params.yaml')


.. GENERATED FROM PYTHON SOURCE LINES 25-27

We compute the working point, which only requires the network parameters
defined in ``network_params.yaml`` (see minimal example as well).

.. GENERATED FROM PYTHON SOURCE LINES 27-30

.. code-block:: default

    print('Compute working point')
    nnmt.lif.exp.working_point(network)


.. GENERATED FROM PYTHON SOURCE LINES 31-35

Then we continue by computing the transfer function, the delay distribution
matrix, the effective connectivity, and finally the power spectrum. This
requires the definition of the analysis frequencies in
``analysis_params.yaml``.

.. GENERATED FROM PYTHON SOURCE LINES 35-44

.. code-block:: default

    print('Compute transfer function')
    nnmt.lif.exp.transfer_function(network)
    print('Compute delay_dist_matrix')
    nnmt.network_properties.delay_dist_matrix(network)
    print('Compute effective connectivity')
    nnmt.lif.exp.effective_connectivity(network)
    print('Compute power spectra')
    nnmt.lif.exp.power_spectra(network)


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We retrieve the analysis frequencies and power spectra and plot the results

.. GENERATED FROM PYTHON SOURCE LINES 46-59

.. code-block:: default

    freqs = network.analysis_params['omegas'] / 2 / np.pi
    spectra = network.results['lif.exp.power_spectra'].T

    for i in range(8):
        plt.plot(freqs, spectra[i], label=network.network_params['populations'][i])
    plt.yscale('log')
    plt.xlabel('frequency (1/s)')
    plt.ylabel('power')
    plt.legend()
    plt.show()

    # save results to h5 file
    network.save('microcircuit.h5')


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  0.000 seconds)


.. _sphx_glr_download_auto_examples_intermediate_usage_example_intermediate_usage_example.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example


    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: intermediate_usage_example.py <intermediate_usage_example.py>`

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: intermediate_usage_example.ipynb <intermediate_usage_example.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

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