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

.. only:: html

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

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

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

.. _sphx_glr_auto_examples_cross_check_mip_corrdet.py:


Auto- and crosscorrelation functions for spike trains
-----------------------------------------------------

A time bin of size `tbin` is centered around the time difference it
represents. If the correlation function is calculated for `tau` in
`[-tau_max, tau_max]`, the pair events contributing to the left-most
bin are those for which `tau` in `[-tau_max-tbin/2, tau_max+tbin/2)` and
so on.

Correlate two spike trains with each other assumes spike times to be ordered in
time. `tau > 0` means spike2 is later than spike1

* tau_max: maximum time lag in ms correlation function
* tbin:    bin size
* spike1:  first spike train [tspike...]
* spike2:  second spike train [tspike...]

.. GENERATED FROM PYTHON SOURCE LINES 41-122

.. code-block:: default


    import nest
    import numpy as np


    def corr_spikes_sorted(spike1, spike2, tbin, tau_max, resolution):
        tau_max_i = int(tau_max / resolution)
        tbin_i = int(tbin / resolution)

        cross = np.zeros(int(2 * tau_max_i / tbin_i + 1), 'd')

        j0 = 0

        for spki in spike1:
            j = j0
            while j < len(spike2) and spike2[j] - spki < -tau_max_i - tbin_i / 2.0:
                j += 1
            j0 = j

            while j < len(spike2) and spike2[j] - spki < tau_max_i + tbin_i / 2.0:
                cross[int(
                    (spike2[j] - spki + tau_max_i + 0.5 * tbin_i) / tbin_i)] += 1.0
                j += 1

        return cross


    nest.ResetKernel()

    resolution = 0.1    # Computation step size in ms
    T = 100000.0        # Total duration
    delta_tau = 10.0
    tau_max = 100.0     # ms correlation window
    t_bin = 10.0        # ms bin size
    pc = 0.5
    nu = 100.0

    nest.local_num_threads = 1
    nest.resolution = resolution
    nest.overwrite_files = True
    nest.rng_seed = 12345

    # Set up network, connect and simulate
    mg = nest.Create('mip_generator')
    mg.set(rate=nu, p_copy=pc)

    cd = nest.Create('correlation_detector')
    cd.set(tau_max=tau_max, delta_tau=delta_tau)

    sr = nest.Create('spike_recorder', params={'time_in_steps': True})

    pn1 = nest.Create('parrot_neuron')
    pn2 = nest.Create('parrot_neuron')

    nest.Connect(mg, pn1)
    nest.Connect(mg, pn2)
    nest.Connect(pn1, sr)
    nest.Connect(pn2, sr)

    nest.Connect(pn1, cd, syn_spec={'weight': 1.0, 'receptor_type': 0})
    nest.Connect(pn2, cd, syn_spec={'weight': 1.0, 'receptor_type': 1})

    nest.Simulate(T)

    n_events_1, n_events_2 = cd.n_events

    lmbd1 = (n_events_1 / (T - tau_max)) * 1000.0
    lmbd2 = (n_events_2 / (T - tau_max)) * 1000.0

    spikes = sr.get('events', 'senders')

    sp1 = spikes[spikes == 4]
    sp2 = spikes[spikes == 5]

    # Find crosscorrelation
    cross = corr_spikes_sorted(sp1, sp2, t_bin, tau_max, resolution)

    print("Crosscorrelation:")
    print(cross)
    print("Sum of crosscorrelation:")
    print(sum(cross))


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

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


.. _sphx_glr_download_auto_examples_cross_check_mip_corrdet.py:

.. only:: html

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


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

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

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

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


.. only:: html

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

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