Пример #1
0
        plt.xlim(0, stim_end+0.005)
        fname = os.path.join(get_this_dir(), 'stim_spec_%d.svg' % stim_id)
        # plt.savefig(fname, facecolor='w', edgecolor='none')


if __name__ == '__main__':

    spec_colormap()
    set_font()

    bird = 'GreBlu9508M'
    block = 'Site4'
    segment = 'Call1'
    hemi = 'L'
    e1 = 6
    e2 = 4
    stim_ids = [43, 53, 190]
    syllable_indices = [2, 1, 0]

    file_ext = '%s_%s_%s_%s' % (bird, block, segment, hemi)
    data_dir = '/auto/tdrive/mschachter/data/%s' % bird
    output_dir = os.path.join(data_dir, 'transforms')

    sefile = os.path.join(output_dir, 'StimEvent_%s.h5' % file_ext)
    se = StimEventTransform.load(sefile, rep_types_to_load=['raw'])
    se.segment_stims()
    se.preprocess('raw')

    draw_figures(se, stim_ids, syllable_indices)
    plt.show()
Пример #2
0
def get_full_data(bird, block, segment, hemi, stim_id, data_dir='/auto/tdrive/mschachter/data'):

    bdir = os.path.join(data_dir, bird)
    tdir = os.path.join(bdir, 'transforms')

    aprops = USED_ACOUSTIC_PROPS

    # load the BioSound
    bs_file = os.path.join(tdir, 'BiosoundTransform_%s.h5' % bird)
    bs = BiosoundTransform.load(bs_file)

    # load the StimEvent transform
    se_file = os.path.join(tdir, 'StimEvent_%s_%s_%s_%s.h5' % (bird,block,segment,hemi))
    print 'Loading %s...' % se_file
    se = StimEventTransform.load(se_file, rep_types_to_load=['raw'])
    se.zscore('raw')
    se.segment_stims_from_biosound(bs_file)

    # load the pairwise CF transform
    pcf_file = os.path.join(tdir, 'PairwiseCF_%s_%s_%s_%s_raw.h5' % (bird,block,segment,hemi))
    print 'Loading %s...' % pcf_file
    pcf = PairwiseCFTransform.load(pcf_file)

    def log_transform(x, dbnoise=100.):
        x /= x.max()
        zi = x > 0
        x[zi] = 20*np.log10(x[zi]) + dbnoise
        x[x < 0] = 0
        x /= x.max()

    all_lfp_psds = deepcopy(pcf.psds)
    log_transform(all_lfp_psds)
    all_lfp_psds -= all_lfp_psds.mean(axis=0)
    all_lfp_psds /= all_lfp_psds.std(axis=0, ddof=1)

    # get overall biosound stats
    bs_stats = dict()
    for aprop in aprops:
        amean = bs.stim_df[aprop].mean()
        astd = bs.stim_df[aprop].std(ddof=1)
        bs_stats[aprop] = (amean, astd)

    for (stim_id2,stim_type2),gdf in se.segment_df.groupby(['stim_id', 'stim_type']):
        print '%d: %s' % (stim_id2, stim_type2)

    # get the spectrogram
    i = se.segment_df.stim_id == stim_id
    last_end_time = se.segment_df.end_time[i].max()

    spec_freq = se.spec_freq
    stim_spec = se.spec_by_stim[stim_id]
    spec_t = np.arange(stim_spec.shape[1]) / se.lfp_sample_rate
    speci = np.min(np.where(spec_t > last_end_time)[0])
    spec_t = spec_t[:speci]
    stim_spec = stim_spec[:, :speci]
    stim_dur = spec_t.max() - spec_t.min()

    # get the raw LFP
    si = int(se.pre_stim_time*se.lfp_sample_rate)
    ei = int(stim_dur*se.lfp_sample_rate) + si
    lfp = se.lfp_reps_by_stim['raw'][stim_id][:, :, si:ei]
    ntrials,nelectrodes,nt = lfp.shape

    # get the raw spikes, spike_mat is ragged array of shape (num_trials, num_cells, num_spikes)
    spike_mat = se.spikes_by_stim[stim_id]
    assert ntrials == len(spike_mat)

    ncells = len(se.cell_df)
    print 'ncells=%d' % ncells
    ntrials = len(spike_mat)

    # compute the PSTH
    psth = list()
    for n in range(ncells):
        # get the spikes across all trials for neuron n
        spikes = [spike_mat[k][n] for k in range(ntrials)]
        # make a PSTH
        _psth_t,_psth = compute_psth(spikes, stim_dur, bin_size=1.0/se.lfp_sample_rate)
        psth.append(_psth)
    psth = np.array(psth)

    if hemi == 'L':
        electrode_order = ROSTRAL_CAUDAL_ELECTRODES_LEFT
    else:
        electrode_order = ROSTRAL_CAUDAL_ELECTRODES_RIGHT

    # get acoustic props and LFP/spike power spectra for each syllable
    syllable_props = list()

    i = bs.stim_df.stim_id == stim_id
    orders = sorted(bs.stim_df.order[i].values)
    cell_index2electrode = None
    for o in orders:
        i = (bs.stim_df.stim_id == stim_id) & (bs.stim_df.order == o)
        assert i.sum() == 1

        d = dict()
        d['start_time'] = bs.stim_df.start_time[i].values[0]
        d['end_time'] = bs.stim_df.end_time[i].values[0]
        d['order'] = o

        for aprop in aprops:
            amean,astd = bs_stats[aprop]
            d[aprop] = (bs.stim_df[aprop][i].values[0] - amean) / astd

        # get the LFP power spectra
        lfp_psd = list()
        for k,e in enumerate(electrode_order):
            i = (pcf.df.stim_id == stim_id) & (pcf.df.order == o) & (pcf.df.decomp == 'full') & \
                (pcf.df.electrode1 == e) & (pcf.df.electrode2 == e)

            assert i.sum() == 1, "i.sum()=%d" % i.sum()

            index = pcf.df[i]['index'].values[0]
            lfp_psd.append(all_lfp_psds[index, :])
        d['lfp_psd'] = np.array(lfp_psd)

        syllable_props.append(d)

    return {'stim_id':stim_id, 'spec_t':spec_t, 'spec_freq':spec_freq, 'spec':stim_spec,
            'lfp':lfp, 'spikes':spike_mat, 'lfp_sample_rate':se.lfp_sample_rate, 'psth':psth,
            'syllable_props':syllable_props, 'electrode_order':electrode_order, 'psd_freq':pcf.freqs,
            'cell_index2electrode':cell_index2electrode, 'aprops':aprops}