def plot_fake_strf(rast, stimparams): fig = plt.figure(figsize = (15, 10)) ax1 = fig.add_axes([0.05, 0.2, 0.9, 0.75]) ax2 = fig.add_axes([0.05, 0.05, 0.9, 0.15]) fake_strf = calc_fake_strf(rast, stimparams) RF.plot_rf(fake_strf, ax = ax1, axes_on = False) psth = fake_strf.mean(0) psth_smoo = Spikes.hamming_smoo(psth, windlen = 5) ax2.plot(psth_smoo) ax2.set_xlim([0, rast.shape[1]]) plt.draw(); plt.show(); return fig
def add_rf_analysis(frf, funit, stimparams):
rast = frf['rast'].value
# stimparams = frf['stimID'].value
# calculate, smooth, threshold, and cluster RF
rf = calc_rf(rast, stimparams, normed = True, smooth = False)
rf[:, :5] = 0
nan_ix = np.isnan(rf)
if nan_ix.sum() > 0:
print 'NaNs found in rf! Converting NaNs to zeros...'
print nan_ix.sum()
rf[np.isnan(rf)] = 0
rf_smoo = gaussian_filter(rf, 0.5)
rf_thresh = rf_smoo.copy()
rf_mask = rf_thresh<0.25*rf_thresh.max()
rf_thresh[rf_mask] = 0
(rf_clust, max_clust_size) = findmaxcluster(rf_thresh)
if max_clust_size < 10:
print 'F*****G SMALL RF dude'
# baseline_mean : mean firing rate 0-50ms for all trials
baseline_psth = 1000 * rast[:, :50].mean(0)
baseline_mean = baseline_psth.mean()
baseline_std = baseline_psth.std()
fr_thresh = baseline_mean + 2*baseline_std
# evoked and spontaneous psths
ev_psth = calc_evoked_psth(rast, stimparams, rf_clust>0)
ev_psth_smoo = Spikes.hamming_smoo(ev_psth, windlen = 5)
spont_psth = calc_evoked_psth(rast, stimparams, rf_clust==0)
# psth properties
# peak firing rate
resp_max = ev_psth_smoo.max()
peak_time_ix = ev_psth_smoo.argmax()
# first millisecond after the final sub-threshold millisecond
resp_on_ix_ = (ev_psth_smoo[57:peak_time_ix]<fr_thresh).nonzero()[0]
if not resp_on_ix_.any():
resp_on_ix = peak_time_ix
else:
resp_on_ix = resp_on_ix_.max() + 58
resp_off_ix = np.empty(3)
resp_off_ix[0] = (ev_psth_smoo[peak_time_ix:]<resp_max/2).nonzero()[0].min() + peak_time_ix
resp_off_ix[1] = (ev_psth_smoo[peak_time_ix:]<resp_max/4).nonzero()[0].min() + peak_time_ix
resp_off_ix[2] = (ev_psth_smoo[peak_time_ix:]<fr_thresh).nonzero()[0].min() + peak_time_ix
peak_time = peak_time_ix / 1000.
resp_on = resp_on_ix / 1000.
resp_off = resp_off_ix / 1000.
# calculate a decay constant from the peak to the post-peak sub-threshold
y = np.log(ev_psth[peak_time_ix:resp_off_ix[2]])
tmp = linregress(np.arange(y.size), y)
decay_const = tmp[0]
# firing rate statistics on the rf
rf_spont_mean = rf[rf_clust==0].mean()
rf_resp_max = rf[rf_clust>0].max() - rf_spont_mean
rf_resp_median = np.median(rf[rf_clust>0]) - rf_spont_mean
rf_resp_mean = rf[rf_clust>0].mean() - rf_spont_mean
rf_size = (rf_clust>0).sum()
# automated CF measures (center-of-mass and auto)
# automated BW measures
cf_com = calc_rf_com(rf_clust)
cf_auto = calc_cf(rf_clust)
(bw, bw_lr, thresh) = calc_bw_thresh(rf_clust)
# # PSTH for freq/attens in RF
# fig = plt.figure()
# ax = fig.add_subplot(111)
# ax.plot(ev_psth_smoo, 'b.-')
# ax.axhline(baseline_mean+3*baseline_std, color = 'r', ls = '--')
# ax.axvline(resp_on_ix, color = 'r', ls = '--')
# ax.axvline(resp_off_ix[0], color = 'r', ls = '--')
# ax.axvline(resp_off_ix[1], color = 'r', ls = '--')
# ax.axvline(resp_off_ix[2], color = 'r', ls = '--')
rf_shape = rf.shape
d = funit.require_dataset('rf', rf_shape, float)
d.write_direct(rf)
d = funit.require_dataset('rf_clust', rf_shape, float)
d.write_direct(rf_clust)
d = funit.require_dataset('rf_spont_mean', (), float)
d.write_direct(np.array([rf_spont_mean]))
d = funit.require_dataset('rf_resp_median', (), float)
d.write_direct(np.array([rf_resp_median]))
d = funit.require_dataset('rf_resp_mean', (), float)
d.write_direct(np.array([rf_resp_mean]))
d = funit.require_dataset('rf_resp_max', (), float)
d.write_direct(np.array([rf_resp_max]))
d = funit.require_dataset('resp_max', (), float)
d.write_direct(np.array([resp_max]))
d = funit.require_dataset('peak_time', (), float)
d.write_direct(np.array([peak_time]))
d = funit.require_dataset('resp_on', (), float)
d.write_direct(np.array([resp_on]))
d = funit.require_dataset('resp_off', (3,), float)
d.write_direct(np.array([resp_off]))
d = funit.require_dataset('decay_const', (), float)
d.write_direct(np.array([decay_const]))
d = funit.require_dataset('rf_size', (), int)
d.write_direct(np.array([rf_size]))
d = funit.require_dataset('baseline_mean', (), float)
d.write_direct(np.array([baseline_mean]))
d = funit.require_dataset('cf_auto', (), float)
d.write_direct(np.array([cf_auto]))
d = funit.require_dataset('cf_com', (), float)
d.write_direct(np.array([cf_com]))
d = funit.require_dataset('bw', (8,), float)
d.write_direct(bw)
d = funit.require_dataset('bw_lr', (8,2), float)
d.write_direct(bw_lr)
d = funit.require_dataset('thresh', (), int)
d.write_direct(np.array([thresh]))
d = funit.require_dataset('ev_psth', (333,), float)
d.write_direct(ev_psth[:333])
