def rr_make_contactsheets():
'''
loop through all the sessions and plot the rrtfs
'''
fig = plt.figure(figsize = (30, 18));
txt_suptitle = fig.suptitle('')
ax_cfrrtf = fig.add_axes((0.76, 0.76, 0.24, 0.23));
ax_cfvs = ax_cfrrtf.twinx();
ax_cfcircpsthall = fig.add_axes((0.62, (11/14.)-0.02, 0.1, (1/7.)+0.04), polar = True)
ax_cfcircpsthall.set_xticklabels(''); ax_cfcircpsthall.set_yticklabels('');
ax_rf = fig.add_axes((0.67, 0.51, 0.33, 0.23));
ax_rfrast = fig.add_axes((0.67, 0.25, 0.33, 0.24));
ax_rfrast.set_xticklabels('');
ax_rfpsth = fig.add_axes((0.67, 0.01, 0.33, 0.24));
ax_cfrr = [fig.add_axes((0.03, 1-((i+1)/7.), 0.35, 1/7.)) for i in np.arange(nrrs)]
ax_cfalignedpsth = [fig.add_axes((0.38, 1-((i+1)/7.), 0.17, 1/7.)) for i in np.arange(nrrs)]
ax_cfcircpsth = [fig.add_axes((0.53, 1-((i+1)/7.), 0.1, 1/7.), polar = True) for i in np.arange(nrrs)]
# ax_noiserr = [fig.add_subplot(nrrs, 3, i) for i in np.arange(1, 3*nrrs, 3)]
for sessionpath in sessionpaths:
session = os.path.split(sessionpath)[1]
unitinfos = fileconversion.get_session_unitinfo(sessionpath, onlycomplete = ('RF', 'RR', 'VOC'))
for unitkey in unitinfos.keys():
txt_suptitle.set_text('%s %s' % (session, unitkey))
unitinfo = unitinfos[unitkey]
rf_ix = unitinfo['stimtype'].index('RF')
f_rf = h5py.File(unitinfo['fpath'][rf_ix], 'r')
rf_rast = f_rf['rast'].value
rf_stimparams = f_rf['stimID'].value
cf_ix = f_rf['cf'].value
f_rf.close()
cf = ix2freq[20:][int(cf_ix)]
''' calculate and plot RF, psth, and sorted raster'''
rf = RF.calc_rf(rf_rast, rf_stimparams)
rf_psth = Spikes.calc_psth(rf_rast)
RF.plot_rf(rf, cf = cf_ix, axes_on = False, ax = ax_rf) # plot RF
ax_rf.axvline(cf_ix, color = 'r', lw = 1.5)
Spikes.plot_sorted_raster(rf_rast, rf_stimparams, ax = ax_rfrast) # plot raster
ax_rfpsth.plot(t_rf, Spikes.exp_smoo(rf_psth, tau = 0.005)) # plot PSTH
''' calcualte and plot RRTFs for CF and noise stimuli '''
rr_ix = unitinfo['stimtype'].index('RR')
f_rr = h5py.File(unitinfo['fpath'][rr_ix], 'r')
rr_rast = f_rr['rast'].value
rr_stimparams = f_rr['stimID'].value
f_rr.close()
# find the played CF
rr_ufreqs = np.unique(rr_stimparams[:, 0])
urrs = np.unique(rr_stimparams[:, 1])
npips = (urrs*4).astype(int)
rr_freq, rr_ufreq_ix, _ = misc.closest(rr_ufreqs, cf, log = True)
ax_rf.axvline(RF.calc_freq2ix(rr_freq), color = 'g', lw = 1.5)
# calculate the PSTHs for each repetition rate
tmp = Spikes.calc_psth_by_stim(rr_rast, rr_stimparams)
rr_cfpth = tmp[0][rr_ufreq_ix, :, :]
# rrtf_noisepsth = tmp[0][0, :, :]
# plot the aligned psths
RR.aligned_psth_separate_all(rr_rast, rr_stimparams, rr_freq, npips, axs = ax_cfalignedpsth)
[a.set_yticklabels('') for a in ax_cfalignedpsth]
[a.set_xticklabels('') for a in ax_cfalignedpsth[:-1]]
# plot circular psths
r, V, theta = RR.circ_psth_all(rr_rast, rr_stimparams, rr_freq, npips, axs = ax_cfcircpsth)
[a.set_yticklabels('') for a in ax_cfcircpsth]
[a.set_xticklabels('') for a in ax_cfcircpsth]
# plot all circular summed vector strengths
ax_cfcircpsthall.plot(theta, V, '.-')
[ax_cfcircpsthall.plot([0, th], [0, v], color = 'b', alpha = 1-(i/10.)) for i, (th, v) in enumerate(zip(theta, V))]
# plot RRTF
rrtf = RR.calc_rrtf_all(rr_rast, rr_stimparams, rr_freq, urrs, npips)
ax_cfrrtf.plot(rrtf, '.-', ms = 10)
ax_cfvs.plot(V*np.cos(theta), 'g.-', ms = 10)
for tick in ax_cfvs.yaxis.get_major_ticks():
tick.set_pad(-5)
tick.label2.set_horizontalalignment('right')
# plot repetition rate PSTHs
for i in xrange(nrrs):
# RR.plot_rrtf(t_rrtf, rrtf_noisepsth[i, :], urrs[i], int(4*urrs[i]), onset = 0.05, duration = 0.025, ax = ax_noiserr[i])
RR.plot_rrtf(t_rrtf, rr_cfpth[i, :], urrs[i], int(4*urrs[i]), onset = 0.05, duration = 0.025, ax = ax_cfrr[i])
# ax_noiserr[0].set_title('Noise RRTFs')
ax_cfrr[0].set_title('CF RRTFs (%.0f kHz)' % (cf/1000))
# [a.set_xlim(0, 4.5) for a in ax_noiserr]
[a.set_xlim(0, 4.5) for a in ax_cfrr]
misc.sameyaxis(ax_cfrr+ax_cfalignedpsth)
figsavepath = os.path.join(studydir, 'Sheets', 'RRTFs', '%s_%s_RRTF.png' % (session, unitkey))
print figsavepath
fig.savefig(figsavepath)
[a.cla() for a in fig.get_axes()] # clear all axes
def calc_coherence_all(lb = 0, ub = 100, prefix = 'VOC'):
# dtype = np.dtype([('gen', 'S2'), ('exp', 'S3'), ('sess', 'S20'), ('unit', 'i4'), ('stim', 'i4'), ('Psignal', '1600f4'), ('Pnoise', '1600f4'), ('snr', '1600f4'), ('coh', '1600f4'), ('F', '1600f4'), ('info_snr', 'f4')])
# coh_info = np.empty(0, dtype = dtype)
npsth = 16000
Gens = []
Exps = []
Sessions = []
Unitnums = []
Stims = []
Info_snrs = []
for session in sessions:
print session
_, gen, exp, _ = session.split('_')
unitinfos = get_session_unitinfo(session, onlycomplete = ('RF', 'RR', 'VOC'))
# fpaths = glob.glob(os.path.join(studydir, 'Sessions', session, 'fileconversion', '%s*.h5' % prefix))
nunits = len(unitinfos)
# loop through penetrations
for j, unitkey in enumerate(unitinfos.keys()):
unitinfo = unitinfos[unitkey]
fpath = unitinfo['fpath'][unitinfo['stimtype'].index('VOC')]
unitnum = unitinfo['unitnum']
print '%i\t(%i of %i)' % (unitnum, j+1, nunits)
f = h5py.File(fpath, 'r')
rast = f['rast'].value
stimparams = f['stimID'].value
stimparams = stimparams[:, 0][..., np.newaxis]
ustim = np.unique(stimparams[:, 0])
nstim = ustim.size
stim = []; info_snr = []
# loop through stimuli
for i in range(nstim):
ix = stimparams[:, 0]==ustim[i]
rast_ = rast[ix, :npsth]
t1 = nt.TimeSeries(rast_, sampling_interval = 0.001)
snr1 = SNRAnalyzer(t1)
F = snr1.mt_frequencies
freq_ix = F<ub
F = F[freq_ix]
info = snr1.mt_information[freq_ix]
pnoise = snr1.mt_noise_psd[freq_ix]
psignal = snr1.mt_signal_psd[freq_ix]
psnr = snr1.mt_snr[freq_ix]
coh = snr1.mt_coherence[freq_ix]
fstep = np.diff(F[:2])[0]
stim.append(ustim[i])
info_snr.append((-np.log2(1-coh)).sum()*fstep)
Gens.append(gen); Exps.append(exp); Sessions.append(session); Unitnums.append(unitnum)
Stims.append(stim); Info_snrs.append(info_snr);
# coh_info.resize(coh_info.size+1)
# coh_info[-1] = np.array((gen, exp, session, unitnum, ustim[i], psignal, pnoise, psnr, coh, F, info_snr), dtype = dtype)
df = pd.DataFrame(dict(gen = Gens, exp = Exps, sess = Sessions, unit = Unitnums, stim = Stims, info_snr = Info_snrs))
d = pd.HDFStore(os.path.join(studydir, 'Analysis', 'coh_info_%s.h5' % session))
d['df'] = df
d.close()
# np.savez(os.path.join(studydir, 'Analysis', 'tmp_coh_info.npz'))
# return coh_info
return df
def analyze():
gens = []
exps = []
sesss = []
unitnums = []
cfs = []
noise_rrtfs = []
cf_rrtfs = []
for session in sessions:
_, gen, exp, _ = session.split("_")
unitinfos = fileconversion.get_session_unitinfo(session, onlycomplete=("RF", "RR", "VOC"))
for unitkey in unitinfos.iterkeys():
unitinfo = unitinfos[unitkey]
print unitinfo["session"], unitinfo["unitnum"]
rr_ix = unitinfo["stimtype"].index("RR")
f = h5py.File(unitinfo["fpath"][rr_ix], "r")
rast = f["rast"].value
stimparams = f["stimID"].value
cf_ix = f["cf"].value
f.close()
ufreqs = np.unique(stimparams[:, 0])
nfreqs = ufreqs.size
urrs = np.unique(stimparams[:, 1])
cf = RF.ix2freq[20:][np.round(cf_ix).astype(int)]
_, ix, err = misc.closest(ufreqs, cf, log=True)
if np.abs(err) > 0.5:
print "Repetition rate stimuli not present for this unit (nearest tone %2.2f octaves away)" % err
continue
ufreq_played = ufreqs[ix]
npips = urrs * 4
noise_rrtf = RR.calc_rrtf_all(rast, stimparams, 0.0, urrs, npips=npips)
cf_rrtf = RR.calc_rrtf_all(rast, stimparams, ufreq_played, urrs, npips=npips)
gens.append(gen)
exps.append(exp)
sesss.append(session)
unitnums.append(unitinfo["unitnum"])
cfs.append(cf_ix)
noise_rrtfs.append(noise_rrtf)
cf_rrtfs.append(cf_rrtf)
df = pd.DataFrame(
dict(gen=gens, exp=exps, sess=sesss, unit=unitnums, cf=cfs, noise_rrtf=noise_rrtfs, cf_rrtf=cf_rrtfs)
)
d = pd.HDFStore(os.path.join(studydir, "Analysis", "rrtf_data.h5"))
d["df"] = df
d.close()
return df
