def get_concat_split_psths(spike_trials_by_stim, psth_lens, bin_size):
"""
Takes an array of arrays of spike times, splits each into even and
odd trials, and computes the PSTH for the even and odd trials.
"""
N = psth_lens.sum()
concat_even_psths = np.zeros([N])
concat_odd_psths = np.zeros([N])
offset = 0
for m,spike_trials in enumerate(spike_trials_by_stim):
even_trials = [ti for k,ti in enumerate(spike_trials) if k % 2]
odd_trials = [ti for k,ti in enumerate(spike_trials) if not k % 2]
duration = psth_lens[m] * bin_size
even_psth = compute_psth(even_trials, duration, bin_size=bin_size)
odd_psth = compute_psth(odd_trials, duration, bin_size=bin_size)
e = offset + psth_lens[m]
concat_even_psths[offset:e] = even_psth
concat_odd_psths[offset:e] = odd_psth
offset = e
return concat_even_psths,concat_odd_psths
def get_concat_split_psths(spike_trials_by_stim, psth_lens, bin_size):
"""
Takes an array of arrays of spike times, splits each into even and
odd trials, and computes the PSTH for the even and odd trials.
"""
N = psth_lens.sum()
concat_even_psths = np.zeros([N])
concat_odd_psths = np.zeros([N])
offset = 0
for m, spike_trials in enumerate(spike_trials_by_stim):
even_trials = [ti for k, ti in enumerate(spike_trials) if k % 2]
odd_trials = [ti for k, ti in enumerate(spike_trials) if not k % 2]
duration = psth_lens[m] * bin_size
even_psth = compute_psth(even_trials, duration, bin_size=bin_size)
odd_psth = compute_psth(odd_trials, duration, bin_size=bin_size)
e = offset + psth_lens[m]
concat_even_psths[offset:e] = even_psth
concat_odd_psths[offset:e] = odd_psth
offset = e
return concat_even_psths, concat_odd_psths
def get_concat_psth(spike_trials_by_stim, psth_lens, bin_size):
"""
Takes a bunch of spike trials, separated by stimulus, creates a PSTH per stimulus,
and concatenates each PSTH into a long array.
"""
N = np.sum(psth_lens)
concat_psths = np.zeros([N])
offset = 0
for k,spike_trials in enumerate(spike_trials_by_stim):
duration = psth_lens[k] * bin_size
psth = compute_psth(spike_trials, duration, bin_size=bin_size)
e = offset + psth_lens[k]
concat_psths[offset:e] = psth
offset = e
return concat_psths
def get_concat_psth(spike_trials_by_stim, psth_lens, bin_size):
"""
Takes a bunch of spike trials, separated by stimulus, creates a PSTH per stimulus,
and concatenates each PSTH into a long array.
"""
N = np.sum(psth_lens)
concat_psths = np.zeros([N])
offset = 0
for k, spike_trials in enumerate(spike_trials_by_stim):
duration = psth_lens[k] * bin_size
psth = compute_psth(spike_trials, duration, bin_size=bin_size)
e = offset + psth_lens[k]
concat_psths[offset:e] = psth
offset = e
return concat_psths
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}