Loading NWB files using Neo
===========================

This notebook demonstrates how to load electrophysiology data using Neo
for eFEL eFeatures extraction.

.. code:: ipython3

    import efel
    
    import numpy
    
    %matplotlib notebook
    %matplotlib inline
    from matplotlib import pyplot as plt
    plt.rcParams['figure.figsize'] = 10, 10

We will use the voltage trace recordings obtained from a mouse thalamic
cell classified as a bursting accommodating (bAC) etype. The data is
stored in an NWB (Neurodata Without Borders) file, and we will use the
`Python Neo <https://github.com/NeuralEnsemble/python-neo>`__ library to
load it.

.. code:: ipython3

    test_data = "../../tests/testdata/JY180308_A_1.nwb"
    stim_start = 250
    stim_end = 600
    blocks = efel.io.load_neo_file(test_data, stim_start, stim_end)
    traces = blocks[0][0]

Let’s get a trace that includes a burst

.. code:: ipython3

    trace = traces[5]

.. code:: ipython3

    time = trace['T']
    voltage = trace['V']

.. code:: ipython3

    plt.rcParams['figure.figsize'] = 10, 10
    
    fig1, ax1 = plt.subplots(1)
    ax1.plot(time, voltage)
    ax1.set_xlabel('Time (ms)')
    ax1.set_ylabel('Membrane voltage (mV)');



.. image:: load_nwb_files/load_nwb_7_0.png


We can now use the eFEL to extract eFeature values from the trace shown
above.

We will use the ``get_feature_values()`` function, which accepts a list
of trace and the requested eFeature names as input.

Let’s extract some burst-related features

.. code:: ipython3

    efel.set_setting("ignore_first_ISI", False) # Don't ignore the first spike
    efel.set_setting("strict_burst_factor", 4.0) # The burst detection can be fine-tuned by changing the setting strict_burst_factor. Default value is 2.0.
    feature_values = efel.get_feature_values([trace], ['spikes_per_burst', 'strict_burst_number', 'strict_burst_mean_freq', 'peak_time', 'AP_height', 'peak_indices', 'burst_begin_indices', 'burst_end_indices'])[0]
    feature_values




.. parsed-literal::

    {'spikes_per_burst': array([4]),
     'strict_burst_number': array([1]),
     'strict_burst_mean_freq': array([28.96451846]),
     'peak_time': array([330.4, 343.2, 382.7, 468.5]),
     'AP_height': array([11.40000057, 11.71249962, 12.23750019, 12.19999981]),
     'peak_indices': array([3304, 3432, 3827, 4685]),
     'burst_begin_indices': array([0]),
     'burst_end_indices': array([3])}



.. code:: ipython3

    burst_begin_indices = feature_values['burst_begin_indices'][0]
    burst_end_indices = feature_values['burst_end_indices'][0]
    peak_times = feature_values['peak_time']
    ap_heights = feature_values['AP_height']
    burst_mean_freq = feature_values['strict_burst_mean_freq']
    
    time_spike_indices = numpy.where((time > stim_start) & (time < stim_end))
    time_spike = time[time_spike_indices]
    voltage_spike = voltage[time_spike_indices]
    
    plt.figure(figsize=(10, 6))
    plt.plot(time_spike, voltage_spike, label='Voltage Trace')
    
    burst_start = peak_times[burst_begin_indices]
    burst_end = peak_times[burst_end_indices]
    mean_frequency = burst_mean_freq[0]
    
    plt.axvspan(burst_start, burst_end, color='yellow', alpha=0.3, label='Burst Interval')
    
    for spike_time in peak_times[burst_begin_indices:burst_end_indices+1]:
        plt.axvline(x=spike_time, color='red', linestyle='--', label='Spike' if 'Spike' not in plt.gca().get_legend_handles_labels()[1] else "")
    
    plt.text((burst_start + burst_end) / 2, max(voltage_spike), f'Burst Mean Freq: {mean_frequency:.2f} Hz', horizontalalignment='center', color='black')
    
    plt.legend()
    plt.xlabel('Time (ms)')
    plt.ylabel('Voltage (mV)')
    plt.show()



.. image:: load_nwb_files/load_nwb_10_0.png


We can save the feature values obtained from ``get_feature_values`` for
later use. Two functions, ``save_feature_values_to_json`` and
``save_feature_values_to_csv``, are provided for this purpose. They save
the feature values as a JSON file and a CSV file, respectively.

.. code:: ipython3

    efel.io.save_feature_to_json(feature_values, 'output.json')
    efel.io.save_feature_to_csv(feature_values, 'output.csv')