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

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_examples_generated_data.py>`
        to download the full example code

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

.. _sphx_glr_examples_generated_data.py:


Creating simple plots from various Neo structures
=================================================

It includes a function that generates toy data.

.. GENERATED FROM PYTHON SOURCE LINES 7-130

.. code-block:: Python


    import numpy as np
    import quantities as pq
    from matplotlib import pyplot as plt

    import neo


    def generate_block(n_segments=3, n_channels=4, n_units=3,
                       data_samples=1000, feature_samples=100):
        """
        Generate a block with a single recording channel group and a number of
        segments, recording channels and units with associated analog signals
        and spike trains.
        """
        feature_len = feature_samples / data_samples

        # Create Block to contain all generated data
        block = neo.Block()

        # Create multiple Segments
        block.segments = [neo.Segment(index=i) for i in range(n_segments)]
        # Create multiple ChannelIndexes
        block.channel_indexes = [neo.ChannelIndex(name='C%d' % i, index=i) for i in range(n_channels)]

        # Attach multiple Units to each ChannelIndex
        for channel_idx in block.channel_indexes:
            channel_idx.units = [neo.Unit('U%d' % i) for i in range(n_units)]

        # Create synthetic data
        for seg in block.segments:
            feature_pos = np.random.randint(0, data_samples - feature_samples)

            # Analog signals: Noise with a single sinewave feature
            wave = 3 * np.sin(np.linspace(0, 2 * np.pi, feature_samples))
            for channel_idx in block.channel_indexes:
                sig = np.random.randn(data_samples)
                sig[feature_pos:feature_pos + feature_samples] += wave

                signal = neo.AnalogSignal(sig * pq.mV, sampling_rate=1 * pq.kHz)
                seg.analogsignals.append(signal)
                channel_idx.analogsignals.append(signal)

                # Spike trains: Random spike times with elevated rate in short period
                feature_time = feature_pos / data_samples
                for u in channel_idx.units:
                    random_spikes = np.random.rand(20)
                    feature_spikes = np.random.rand(5) * feature_len + feature_time
                    spikes = np.hstack([random_spikes, feature_spikes])

                    train = neo.SpikeTrain(spikes * pq.s, 1 * pq.s)
                    seg.spiketrains.append(train)
                    u.spiketrains.append(train)

        return block


    block = generate_block()

    # In this example, we treat each segment in turn, averaging over the channels
    # in each:
    for seg in block.segments:
        print("Analysing segment %d" % seg.index)

        siglist = seg.analogsignals
        time_points = siglist[0].times
        avg = np.mean(siglist, axis=0)  # Average over signals of Segment

        plt.figure()
        plt.plot(time_points, avg)
        plt.title("Peak response in segment %d: %f" % (seg.index, avg.max()))

    # The second alternative is spatial traversal of the data (by channel), with
    # averaging over trials. For example, perhaps you wish to see which physical
    # location produces the strongest response, and each stimulus was the same:

    # There are multiple ChannelIndex objects connected to the block, each
    # corresponding to a a physical electrode
    for channel_idx in block.channel_indexes:
        print("Analysing channel %d: %s" % (channel_idx.index, channel_idx.name))

        siglist = channel_idx.analogsignals
        time_points = siglist[0].times
        avg = np.mean(siglist, axis=0)  # Average over signals of RecordingChannel

        plt.figure()
        plt.plot(time_points, avg)
        plt.title("Average response on channel %d" % channel_idx.index)

    # There are three ways to access the spike train data: by Segment,
    # by ChannelIndex or by Unit.

    # By Segment. In this example, each Segment represents data from one trial,
    # and we want a peristimulus time histogram (PSTH) for each trial from all
    # Units combined:
    for seg in block.segments:
        print("Analysing segment %d" % seg.index)
        stlist = [st - st.t_start for st in seg.spiketrains]
        count, bins = np.histogram(np.hstack(stlist))
        plt.figure()
        plt.bar(bins[:-1], count, width=bins[1] - bins[0])
        plt.title("PSTH in segment %d" % seg.index)

    # By Unit. Now we can calculate the PSTH averaged over trials for each Unit:
    for unit in block.list_units:
        stlist = [st - st.t_start for st in unit.spiketrains]
        count, bins = np.histogram(np.hstack(stlist))
        plt.figure()
        plt.bar(bins[:-1], count, width=bins[1] - bins[0])
        plt.title("PSTH of unit %s" % unit.name)

    # By ChannelIndex. Here we calculate a PSTH averaged over trials by
    # channel location, blending all Units:
    for chx in block.channel_indexes:
        stlist = []
        for unit in chx.units:
            stlist.extend([st - st.t_start for st in unit.spiketrains])
        count, bins = np.histogram(np.hstack(stlist))
        plt.figure()
        plt.bar(bins[:-1], count, width=bins[1] - bins[0])
        plt.title("PSTH blend of recording channel group %s" % chx.name)

    plt.show()


.. _sphx_glr_download_examples_generated_data.py:

.. only:: html

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

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

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

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

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


.. only:: html

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

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_