def calc_latency_by_stim(rast, stimparams): stim_psth, _ = Spikes.calc_psth_by_stim(rast, stimparams, bins = np.arange(0, 0.334, 0.001)) stim_psth_smoo = np.apply_along_axis(myconv, 2, stim_psth) stim_peak_times = np.apply_along_axis(np.argmax, 2, stim_psth_smoo) return stim_peak_times
def calc_fake_strf(rast, stimparams): ''' 2-d matrix no.freqs x no.time bins, values are spontaneous firing rate. basically each row is a psth for a particular frequency ''' stim_psth, _ = Spikes.calc_psth_by_stim(rast, stimparams) fake_strf = stim_psth.mean(1) return fake_strf
rast = frr['rast'].value stimparams = frr['stimID'].value frr.close() # if not np.isnan(spktimes[0]): cf = cfs[cfs[:, 0]==unitnum, 1][0] cf_hz = ix2freq[20:][int(cf)] freqs = stimparams[:, 0] rrs = stimparams[:, 1] ufreqs = np.unique(freqs) urrs = np.unique(rrs) nrrs = urrs.size # now we determine which of the frequencies we played is closest to this neuron's CF thisfreq, thisfreq_ix, thisfreq_err = misc.closest(ufreqs, cf_hz, log = True) if np.abs(thisfreq_err) > 0.2: print 'No close frequency found!' thisfreq = ufreqs[thisfreq_ix] # isolate the parts of the raster for this frequency and build a psth for each RR ix = RF.get_trials(stimparams, np.array([thisfreq, np.nan])) thisrast = rast[ix, :1050] thisstims = stimparams[ix, :] psths, ustims = Spikes.calc_psth_by_stim(thisrast, thisstims) rrtf = RR.calc_rrtf_all(thisrast, thisstims, thisfreq, urrs) db.resize(db.size+1) db[-1] = np.array((gen, exp, sess, unitnum, cf_hz, rrtf, urrs), dtype = dtype) np.savez(savepath, db)
# perform analysis if len(rr_path) > 0: rr_file = h5py.File(rr_path, 'r') rr_rast = rr_file['rast'].value rr_stimparams = rr_file['stimID'].value rr_file.close() # rr_rast = rr_rast[1:, :] # rr_stimparams = rr_stimparams[:-1, :] ufreqs = np.unique(rr_stimparams[:, 0]) urrs = np.unique(rr_stimparams[:, 1]) freq_played, freq_ix_played, _ = misc.closest(ufreqs, cf, log = True) rr_bins = np.arange(0, 6, 0.05) rr_psth_stim, usp = Spikes.calc_psth_by_stim(rr_rast, rr_stimparams, bins = rr_bins) cf_psth = rr_psth_stim[freq_ix_played, :, :] noise_psth = rr_psth_stim[0, :, :] fig = plt.figure() nrrs = cf_psth.shape[0] ax = [] for i in range(nrrs): ax.append(fig.add_subplot(nrrs, 1, i+1)) ax[-1].plot(rr_bins[:-1], cf_psth[i, :]) RR.plot_tone_pips(urrs[i], 6, 0.05, 0.025, ax = ax[-1], color = 'r') if i>0: ax[-1].set_xticklabels('') misc.sameyaxis(ax)
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