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

.. only:: html

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

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

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

.. _sphx_glr_auto_examples_microcircuit_rates_microcircuit_rates.py:


Microcircuit firing rates
=========================

Here we calculate the firing rates of the :cite:t:`potjans2014` microcircuit
model.

.. GENERATED FROM PYTHON SOURCE LINES 8-25

.. code-block:: default


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

    # import svgutils for inserting sketch if available
    try:
        import os
        import svgutils.transform as sg
        insert_sketch = True
    except ImportError:
        insert_sketch = False

    # use matplotlib style file
    plt.style.use('frontiers.mplstyle')


.. GENERATED FROM PYTHON SOURCE LINES 26-28

First we create a network model of the microcircuit, passing the parameter
yaml file.

.. GENERATED FROM PYTHON SOURCE LINES 28-31

.. code-block:: default

    microcircuit = nnmt.models.Microcircuit(
        '../../tests/fixtures/integration/config/Bos2016_network_params.yaml')


.. GENERATED FROM PYTHON SOURCE LINES 32-36

Then we simply calculate the firing rates for exponentially shape post
synaptic currents, by calling the respective function and passing the
microcircuit. Here we chose to use the 'taylor' method for calculating the
firing rates.

.. GENERATED FROM PYTHON SOURCE LINES 36-40

.. code-block:: default

    firing_rates = nnmt.lif.exp.firing_rates(microcircuit, method='shift')

    print(f'Mean rates: {firing_rates}')


.. GENERATED FROM PYTHON SOURCE LINES 41-45

Then we compare the rates to the publicated data from :cite:t:`bos2016`. We
load the simulated rates using the data stored as integration test fixtures.
Note that the original data use rates in 1/ms, which we need to convert to
Hz.

.. GENERATED FROM PYTHON SOURCE LINES 45-52

.. code-block:: default

    fix_path = '../../tests/fixtures/integration/data/'
    result = nnmt.input_output.load_h5(
        fix_path + 'Bos2016_publicated_and_converted_data.h5')
    simulated_rates = result['fig_microcircuit']['rates_sim'] * 1000

    print(f'Mean simulated rates: {simulated_rates}')


.. GENERATED FROM PYTHON SOURCE LINES 53-54

Finally, we plot the rates together in one plot.

.. GENERATED FROM PYTHON SOURCE LINES 54-113

.. code-block:: default

    width = 0.03937007874 * 85
    height = width * 1.3

    fig = plt.figure(figsize=(width, height),
                     tight_layout=True)

    sim_colors = ['#4c72b0', '#c44e52']
    thy_color = '#ff8f2fff'

    ax0 = plt.subplot2grid((5, 1), (0, 0), rowspan=3, colspan=1)
    ax0.set_axis_off()
    ax1 = plt.subplot2grid((5, 1), (3, 0), rowspan=2, colspan=1)

    # add labels to panels
    axs = [ax0, ax1]
    label = ['(A)', '(B)']
    y_pos = [1.25, 1.1]
    for n, ax in enumerate(axs):
        ax.text(-0.1, y_pos[n], label[n], transform=ax.transAxes,
                size=11, weight='bold')


    bars = ax1.bar(np.arange(8), simulated_rates,
                   align='center', color=sim_colors[1])

    for i in [0, 2, 4, 6]:
        bars[i].set_color(sim_colors[0])

    nnmt_handle = ax1.scatter(np.arange(8), firing_rates, marker='X',
                              color=thy_color, s=50, zorder=10)
    ax1.set_xticks(np.arange(8))
    ax1.set_xticklabels(['2/3E', '2/3I', '4E', '4I', '5E', '5I', '6E', '6I'])
    ax1.set_yticks([1, 3, 5, 7])
    ax1.set_ylabel(r'rate $\nu\,(1/s)$')

    plt.legend([bars[0], nnmt_handle, bars[1]],
               [None, 'theory', 'simulation'],
               loc='upper left', fontsize=9, ncol=2,
               columnspacing=-2.8, handletextpad=0.2)

    # insert sketch using svgutil, try saving as pdf using inkscape
    if insert_sketch:
        sketch_fn = 'microcircuit_sketch.svg'
        plot_fn = 'microcircuit_rates'
        svg_mpl = sg.from_mpl(fig, savefig_kw=dict(transparent=False))
        w_svg, h_svg = svg_mpl.get_size()
        svg_mpl.set_size((w_svg+'pt', h_svg+'pt'))
        svg_sketch = sg.fromfile(sketch_fn).getroot()
        svg_sketch.moveto(x=30, y=10, scale_x=0.61, scale_y=0.61)
        svg_mpl.append(svg_sketch)
        svg_mpl.save(f'{plot_fn}.svg')
        os_return = os.system(f'inkscape --export-pdf={plot_fn}.pdf {plot_fn}.svg')
        if os_return == 0:
            os.remove(f'{plot_fn}.svg')
        else:
            print('Conversion to pdf using inkscape failed, keeping svg...')

    ax0.annotate('(sketch)', xy=(0.35, 0.6))
    plt.show()


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

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


.. _sphx_glr_download_auto_examples_microcircuit_rates_microcircuit_rates.py:


.. only :: html

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



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

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



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

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


.. only:: html

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

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