def get_psd_stats(bird, block, seg, hemi, data_dir='/auto/tdrive/mschachter/data'):
transforms_dir = os.path.join(data_dir, bird, 'transforms')
cf_file = os.path.join(transforms_dir, 'PairwiseCF_%s_%s_%s_%s_raw.h5' % (bird, block, seg, hemi))
cft = PairwiseCFTransform.load(cf_file)
electrodes = cft.df.electrode1.unique()
estats = dict()
for e in electrodes:
i = (cft.df.electrode1 == e) & (cft.df.electrode1 == cft.df.electrode2) & (cft.df.decomp == 'locked')
indices = cft.df['index'][i].values
psds = cft.psds[indices]
log_transform(psds)
estats[e] = (psds.mean(axis=0), psds.std(axis=0, ddof=1))
return estats
def draw_figures(data_dir='/auto/tdrive/mschachter/data'):
bird = 'GreBlu9508M'
block = 'Site4'
seg = 'Call1'
hemi = 'L'
file_ext = '_'.join([bird, block, seg, hemi])
pcf_file = os.path.join(data_dir, bird, 'transforms', 'PairwiseCF_%s_%s_new.h5' % (file_ext, 'raw'))
pcf = PairwiseCFTransform.load(pcf_file)
g = pcf.df.groupby(['stim_id', 'order', 'stim_type'])
plist = list()
i = (pcf.df.decomp == 'full') & (pcf.df.electrode1 == pcf.df.electrode2)
assert i.sum() > 0
xfull_indices = list(pcf.df['index'][i].values)
print 'len(pcf.df)=%d' % len(pcf.df)
print 'pcf.df[index].max()=%d' % pcf.df['index'].max()
print 'pcf.psds.shape=',pcf.psds.shape
print 'xfull_indices.max()=%d' % max(xfull_indices)
print 'len(xfull_indices)=%d' % len(xfull_indices)
Xfull = pcf.psds[xfull_indices, :]
Xfull /= Xfull.max()
pcf.log_transform(Xfull)
Xfull -= Xfull.mean(axis=0)
Xfull /= Xfull.std(axis=0, ddof=1)
# take log transform of power spectra
i = (pcf.df.decomp == 'onewin') & (pcf.df.electrode1 == pcf.df.electrode2)
assert i.sum() > 0
xone_indices = list(pcf.df['index'][i].values)
Xonewin = pcf.psds[xone_indices, :]
Xonewin /= Xonewin.max()
pcf.log_transform(Xonewin)
Xonewin -= Xonewin.mean(axis=0)
Xonewin /= Xonewin.std(axis=0, ddof=1)
for (stim_id,order,stim_type),gdf in g:
electrodes = gdf.electrode1.unique()
stim_dur = gdf.stim_duration.values[0]
if stim_dur < 0.050 or stim_dur > 0.400:
continue
for e in electrodes:
i = (gdf.decomp == 'full') & (gdf.electrode1 == e) & (gdf.electrode1 == gdf.electrode2)
assert i.sum() == 1
xi = gdf['index'][i].values[0]
ii = xfull_indices.index(xi)
full_psd = Xfull[ii, :]
i = (gdf.decomp == 'onewin') & (gdf.electrode1 == e) & (gdf.electrode1 == gdf.electrode2)
assert i.sum() == 1
xi = gdf['index'][i].values[0]
ii = xone_indices.index(xi)
onewin_psd = Xonewin[ii, :]
plist.append({'full_psd':full_psd, 'onewin_psd':onewin_psd, 'stim_id':stim_id, 'stim_order':order,
'stim_type':stim_type, 'electrode':e, 'stim_dur':stim_dur})
np.random.shuffle(plist)
plist.sort(key=operator.itemgetter('stim_dur'))
short_plist = [x for k,x in enumerate(plist) if k % 20 == 0]
print 'len(short_plist)=%d' % len(short_plist)
def _plot_psds(_pdata, _ax):
absmax = max(np.abs(_pdata['full_psd']).max(), np.abs(_pdata['onewin_psd']).max())
plt.axhline(0, c='k')
plt.plot(pcf.freqs, _pdata['full_psd'], 'k-', linewidth=3.0, alpha=0.7)
plt.plot(pcf.freqs, _pdata['onewin_psd'], 'g-', linewidth=3.0, alpha=0.7)
plt.title('e%d: %d_%d (%s) %0.3fs' % (_pdata['electrode'], _pdata['stim_id'], _pdata['stim_order'], _pdata['stim_type'], _pdata['stim_dur']))
plt.axis('tight')
plt.ylim(-absmax, absmax)
multi_plot(short_plist, _plot_psds, nrows=5, ncols=9)
plt.show()
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}
def draw_figures(data_dir='/auto/tdrive/mschachter/data'):
pcf_file = os.path.join(data_dir, 'GreBlu9508M', 'transforms', 'PairwiseCF_GreBlu9508M_Site4_Call1_L_raw.h5')
pcf = PairwiseCFTransform.load(pcf_file)
lags_index = np.abs(pcf.lags) == 0.
i = pcf.df.stim_type != 'mlnoise'
df = pcf.df[i]
# transform psds
psds = deepcopy(pcf.psds)
pcf.log_transform(psds)
# psds -= psds.mean(axis=0)
# psds /= psds.std(axis=0, ddof=1)
# transform pairwise cfs
cross_cfs = pcf.cross_cfs[:, lags_index]
# transform synchrony
syncs = deepcopy(pcf.spike_synchrony)
# sync -= sync.mean(axis=0)
# sync /= sync.std(axis=0, ddof=1)
X = list()
electrodes = pcf.df.electrode1.unique()
g = df.groupby(['stim_id', 'order'])
for (stim_id,syllable_order),gdf in g:
for k,e1 in enumerate(electrodes):
for j in range(k):
e2 = electrodes[j]
i = (gdf.decomp == 'locked') & (gdf.electrode1 == e1) & (gdf.electrode2 == e2)
if i.sum() == 0:
i = (gdf.decomp == 'locked') & (gdf.electrode1 == e2) & (gdf.electrode2 == e1)
assert i.sum() == 1
indices = gdf['index'][i]
cf = cross_cfs[indices, :]
cf_sum = np.abs(cf).sum()
# compute average spike synchrony for this stim and electrode
i = (gdf.decomp == 'spike_sync') & ((gdf.electrode1 == e1) | (gdf.electrode2 == e2))
if i.sum() == 0:
i = (gdf.decomp == 'spike_sync') & ((gdf.electrode1 == e2) | (gdf.electrode2 == e1))
assert i.sum() >= 1
indices = gdf['index'][i]
sync12 = syncs[indices].max()
if cf_sum > 0 and sync12 > 0:
X.append((cf_sum, sync12))
X = np.array(X)
cc = np.corrcoef(X[:, 0], X[:, 1])[0, 1]
plt.figure()
ax = plt.subplot(1, 1, 1)
plt.plot(X[:, 1], X[:, 0], 'go')
plt.xlabel('Spike Synchrony')
plt.ylabel('LFP Synchrony (-20ms to 20ms)')
plt.title('cc=%0.2f' % cc)
plt.axis('tight')
plt.show()
