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

.. only:: html

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

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

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

.. _sphx_glr_auto_examples_plot_behavior_and_spikes.py:


=========================================
Visualize spike rates for input behavior
=========================================

.. currentmodule:: neural_data_simulator

The goal of this example is to show the spike rates predicted by the encoder for a given behavior input.

By default, this script will download the data to be plotted from AWS S3. If you prefer to use
your own data, you can start the closed loop simulation in one terminal::

    make run-closed-loop

And then record the streams in another terminal::

    recorder --session "test" --lsl "NDS-Behavior,NDS-SpikeRates,NDS-RawData" --recording-time 10

Make sure to change the variable::

    LOCAL_DATA = True

and replace the variables with the paths to your data::

    BEHAVIOR_DATA_PATH = "the_path_to_your_recorded_behavior_data.npz"
    SPIKES_RATES_DATA_PATH = "the_path_to_your_recorded_spikes_rate_data.npz"
    RAW_DATA_PATH = "the_path_to_your_recorded_raw_data.npz"

.. GENERATED FROM PYTHON SOURCE LINES 32-34

Environment setup
------------------

.. GENERATED FROM PYTHON SOURCE LINES 34-37

.. code-block:: default

    WITH_RAW_DATA = False
    LOCAL_DATA = False








.. GENERATED FROM PYTHON SOURCE LINES 38-41

Set data source
----------------
Retrieve the data from AWS S3 or define the path to your local files.

.. GENERATED FROM PYTHON SOURCE LINES 41-66

.. code-block:: default

    from urllib.parse import urljoin

    import pooch

    DOWNLOAD_BASE_URL = "https://neural-data-simulator.s3.amazonaws.com/sample_data/v1/"

    if not LOCAL_DATA:
        BEHAVIOR_DATA_PATH = pooch.retrieve(
            url=urljoin(DOWNLOAD_BASE_URL, "example_NDS-Behavior.npz"),
            known_hash="md5:5c95928f48a71eb3370885c58e14e765",
        )
        SPIKES_RATES_DATA_PATH = pooch.retrieve(
            url=urljoin(DOWNLOAD_BASE_URL, "example_NDS-SpikeRates.npz"),
            known_hash="md5:64fe24f817969afb6d330283a78bca5f",
        )
        if WITH_RAW_DATA:
            RAW_DATA_PATH = pooch.retrieve(
                url=urljoin(DOWNLOAD_BASE_URL, "example_NDS-RawData.npz"),
                known_hash="md5:887d88387674d8a7d27726e11663eee4",
            )
    else:
        BEHAVIOR_DATA_PATH = "the_path_to_your_recorded_behavior_data.npz"
        SPIKES_RATES_DATA_PATH = "the_path_to_your_recorded_spikes_rate_data.npz"
        RAW_DATA_PATH = "the_path_to_your_recorded_raw_data.npz"








.. GENERATED FROM PYTHON SOURCE LINES 67-70

Load data
---------
Load the data to be plotted.

.. GENERATED FROM PYTHON SOURCE LINES 70-91

.. code-block:: default


    from matplotlib.pyplot import figure
    import matplotlib.pyplot as plt
    import numpy as np

    behavior_file = np.load(BEHAVIOR_DATA_PATH)
    spike_rates_file = np.load(SPIKES_RATES_DATA_PATH)

    spike_rates_data = spike_rates_file["data"]
    spike_rates_timestamps = (
        spike_rates_file["timestamps"] - spike_rates_file["timestamps"][0]
    )

    behavior_data = behavior_file["data"]
    behavior_timestamps = behavior_file["timestamps"] - behavior_file["timestamps"][0]

    if WITH_RAW_DATA:
        raw_file = np.load(RAW_DATA_PATH)
        raw_data = raw_file["data"] / 4
        raw_timestamps = raw_file["timestamps"] - raw_file["timestamps"][0]








.. GENERATED FROM PYTHON SOURCE LINES 92-94

Plot data
----------

.. GENERATED FROM PYTHON SOURCE LINES 94-128

.. code-block:: default


    plt.rcParams.update({"font.size": 14})

    figure(figsize=(20, 5), dpi=180)
    plt.plot(behavior_timestamps, behavior_data[:, 0], "blue", label="velocity x")
    plt.plot(behavior_timestamps, behavior_data[:, 1], "red", label="velocity y")

    plt.xlabel("time (s)")
    plt.legend()
    plt.title("velocities")
    plt.show()


    figure(figsize=(20, 5), dpi=180)
    plt.plot(
        spike_rates_timestamps,
        spike_rates_data[:, 20],
        "k",
        label="Spike rates",
        linewidth=1.5,
    )

    plt.ylabel("rate (spike/s)")
    plt.xlabel("time (s)")
    plt.title("sample spike rate (channel 20)")
    plt.show()

    if WITH_RAW_DATA:
        figure(figsize=(20, 5), dpi=180)
        plt.plot(raw_timestamps, raw_data[:, 20], "k", label="Raw data", alpha=0.8)
        plt.ylabel("signal amplitude (uV)")
        plt.xlabel("time (s)")
        plt.title("sample raw output (channel 20)")
        plt.show()



.. rst-class:: sphx-glr-horizontal


    *

      .. image-sg:: /auto_examples/images/sphx_glr_plot_behavior_and_spikes_001.png
         :alt: velocities
         :srcset: /auto_examples/images/sphx_glr_plot_behavior_and_spikes_001.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/images/sphx_glr_plot_behavior_and_spikes_002.png
         :alt: sample spike rate (channel 20)
         :srcset: /auto_examples/images/sphx_glr_plot_behavior_and_spikes_002.png
         :class: sphx-glr-multi-img






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

   **Total running time of the script:** ( 0 minutes  0.609 seconds)


.. _sphx_glr_download_auto_examples_plot_behavior_and_spikes.py:

.. only:: html

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


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

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

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

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


.. only:: html

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

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