.. currentmodule:: brian

.. index::
   pair: example usage; Sound
   pair: example usage; tone
   pair: example usage; Network
   pair: example usage; sequence
   pair: example usage; get_samplerate
   pair: example usage; TanCarney
   pair: example usage; SpikeMonitor
   pair: example usage; ZhangSynapse
   pair: example usage; reinit_default_clock
   pair: example usage; set_default_samplerate
   pair: example usage; MiddleEar
   pair: example usage; run
   pair: example usage; set_global_preferences
   pair: example usage; click
   pair: example usage; silence
   pair: example usage; StateMonitor

.. _example-hears-tan_carney_2003_tan_carney_simple_test:

Example: tan_carney_simple_test (hears/tan_carney_2003)
=======================================================

Fig. 1 and 3 (spking output without spiking/refractory period) should
reproduce the output of the AN3_test_tone.m and AN3_test_click.m
scripts, available in the code accompanying the paper Tan & Carney (2003).
This matlab code is available from
http://www.urmc.rochester.edu/labs/Carney-Lab/publications/auditory-models.cfm

Tan, Q., and L. H. Carney.
"A Phenomenological Model for the Responses of Auditory-nerve Fibers.
II. Nonlinear Tuning with a Frequency Glide".
The Journal of the Acoustical Society of America 114 (2003): 2007.

::

    
    import numpy as np
    import matplotlib.pyplot as plt
    
    from brian.stdunits import kHz, Hz, ms
    from brian.network import Network
    from brian.monitor import StateMonitor, SpikeMonitor
    from brian.globalprefs import set_global_preferences
    
    #set_global_preferences(useweave=True)
    from brian.hears import (Sound, get_samplerate, set_default_samplerate, tone,
                             click, silence, dB, TanCarney, MiddleEar, ZhangSynapse)
    from brian.clock import reinit_default_clock
    
    
    set_default_samplerate(50*kHz)
    sample_length = 1 / get_samplerate(None)
    cf = 1000 * Hz
    
    print 'Testing click response'
    duration = 25*ms    
    levels = [40, 60, 80, 100, 120]
    # a click of two samples
    tones = Sound([Sound.sequence([click(sample_length*2, peak=level*dB),
                                   silence(duration=duration - sample_length)])
               for level in levels])
    ihc = TanCarney(MiddleEar(tones), [cf] * len(levels), update_interval=1)
    syn = ZhangSynapse(ihc, cf)
    s_mon = StateMonitor(syn, 's', record=True, clock=syn.clock)
    R_mon = StateMonitor(syn, 'R', record=True, clock=syn.clock)
    spike_mon = SpikeMonitor(syn)
    net = Network(syn, s_mon, R_mon, spike_mon)
    net.run(duration * 1.5)
    for idx, level in enumerate(levels):
        plt.figure(1)
        plt.subplot(len(levels), 1, idx + 1)
        plt.plot(s_mon.times / ms, s_mon[idx])
        plt.xlim(0, 25)
        plt.xlabel('Time (msec)')
        plt.ylabel('Sp/sec')
        plt.text(15, np.nanmax(s_mon[idx])/2., 'Peak SPL=%s SPL' % str(level*dB));
        ymin, ymax = plt.ylim()
        if idx == 0:
            plt.title('Click responses')
    
        plt.figure(2)
        plt.subplot(len(levels), 1, idx + 1)
        plt.plot(R_mon.times / ms, R_mon[idx])
        plt.xlabel('Time (msec)')
        plt.xlabel('Time (msec)')
        plt.text(15, np.nanmax(s_mon[idx])/2., 'Peak SPL=%s SPL' % str(level*dB));
        plt.ylim(ymin, ymax)
        if idx == 0:
            plt.title('Click responses (with spikes and refractoriness)')
        plt.plot(spike_mon.spiketimes[idx] / ms,
             np.ones(len(spike_mon.spiketimes[idx])) * np.nanmax(R_mon[idx]), 'rx')
    
    print 'Testing tone response'
    reinit_default_clock()
    duration = 60*ms    
    levels = [0, 20, 40, 60, 80]
    tones = Sound([Sound.sequence([tone(cf, duration).atlevel(level*dB).ramp(when='both',
                                                                             duration=10*ms,
                                                                             inplace=False),
                                   silence(duration=duration/2)])
                   for level in levels])
    ihc = TanCarney(MiddleEar(tones), [cf] * len(levels), update_interval=1)
    syn = ZhangSynapse(ihc, cf)
    s_mon = StateMonitor(syn, 's', record=True, clock=syn.clock)
    R_mon = StateMonitor(syn, 'R', record=True, clock=syn.clock)
    spike_mon = SpikeMonitor(syn)
    net = Network(syn, s_mon, R_mon, spike_mon)
    net.run(duration * 1.5)
    for idx, level in enumerate(levels):
        plt.figure(3)
        plt.subplot(len(levels), 1, idx + 1)
        plt.plot(s_mon.times / ms, s_mon[idx])
        plt.xlim(0, 120)
        plt.xlabel('Time (msec)')
        plt.ylabel('Sp/sec')
        plt.text(1.25 * duration/ms, np.nanmax(s_mon[idx])/2., '%s SPL' % str(level*dB));
        ymin, ymax = plt.ylim()
        if idx == 0:
            plt.title('CF=%.0f Hz - Response to Tone at CF' % cf)
    
        plt.figure(4)
        plt.subplot(len(levels), 1, idx + 1)
        plt.plot(R_mon.times / ms, R_mon[idx])
        plt.xlabel('Time (msec)')
        plt.xlabel('Time (msec)')
        plt.text(1.25 * duration/ms, np.nanmax(R_mon[idx])/2., '%s SPL' % str(level*dB));
        plt.ylim(ymin, ymax)
        if idx == 0:
            plt.title('CF=%.0f Hz - Response to Tone at CF (with spikes and refractoriness)' % cf)
        plt.plot(spike_mon.spiketimes[idx] / ms,
             np.ones(len(spike_mon.spiketimes[idx])) * np.nanmax(R_mon[idx]), 'rx')
    
    plt.show()