def simulate_sacs_compare(pardict, novpres=0, fampres=10000, n=100, nplot=4,
ft_ref_dist=sf.observed_lendistrib, bins=100):
sacs = get_saccaders(pardict)
for sac in sacs:
plot_inds = np.random.randint(0, n-1, nplot)
ts, sals, looks, sac_ts, lookprob = sac.present_many([novpres,
fampres],
n=n,
prestime=4000,
tstep=1)
f = plt.figure(figsize=(7, 5.2))
pref_ax = f.add_subplot(2, 1, 1)
pts, prefs, xticks = s.plot_avg_preference(looks, ax=pref_ax, show=False)
pref_ax.set_xticklabels(xticks, visible=False)
pref_ax.set_xlabel('')
pref_ax.set_yticks([.2, .4])
sal_ax = f.add_subplot(2, 1, 2, sharex=pref_ax)
sal_ax = s.plot_salience(ts, sals[plot_inds[0]], sal_ts=xticks, ax=sal_ax)
for x in plot_inds:
s.plot_presentation(ts, sals[x], looks[x], sac_ts[x], lookprob[x],
show=False)
fixtimes = np.concatenate(map(np.diff, sac_ts), axis=0)
# f2 = plt.figure()
# ax1 = f2.add_subplot(1, 2, 1)
# ax1.hist(ft_ref_dist, bins=bins, histtype='step', label='observed',
# normed=True)
# ax1.hist(fixtimes, bins=bins, histtype='step', label='model', normed=True)
# ax2 = f2.add_subplot(1, 2, 2)
# ax2.hist(map(len, sac_ts), bins=bins, histtype='step',
# label='observed', normed=True)
lp = sf.compare_fixlens(fixtimes, ft_ref_dist)
f.tight_layout()
f.savefig('model_pref_sal.pdf', bbox_inches='tight')
plt.show(block=False)
return pts, prefs, lp
def sample_eyetrace(lt_novpars=lt_novpars, st_novpars=st_novpars,
sal_tc=sal_tc, prob_tc=prob_tc, off_tnov=off_tnov,
sacc_grow=sacc_grow, samebias=samebias,
prob_sp=prob_saccpar):
print 'getting samp'
sac = NaiveSaccader(lt_novfunc, lt_novpars, st_novfunc, st_novpars, sal_tc,
off_tnov, prob_tc, prob_sp, sacc_grow, samebias)
out = sac.present_many([leftviews, rightviews], samp_pres,
prestime=prestime, tstep=tstep)
ts, sals, looks, saccs, lookprobs = out
if plot_preses:
plot_presentation(ts, sals[0], looks[0], saccs[0], lookprobs[0])
fixes = np.concatenate(map(np.diff, saccs), axis=0)
fixarr = np.zeros(sacclen)
fixarr[:] = np.nan
fixarr[:len(fixes)] = fixes
return fixarr
