def logp(prob_tc=prob_tc, prob_gc=prob_gc, prob_dp=prob_dp,
samebias=prob_sb, tau_h=tau_h, eff=eff, tau_f=tau_f,
tau_d=tau_d, a=a, tau_x=tau_x, tau_u=tau_u,
nov_img=nov_img, fam_img=fam_img, off_img=off_img,
guide_buff=guide_buff, look_mod=look_mod, value=observations):
rtf_func = lambda x: x
sac = SalSacc(prob_tc, prob_gc, prob_dp, samebias, tau_h, rtf_func, eff,
tau_f, tau_d, a, tau_x=tau_x, tau_u=tau_u)
init_h = [0., 0., 0.]
rs = [off_img, nov_img, fam_img]
pardict = {'tend':prestime, 'tstep':tstep, 'gbuff':guide_buff}
useguides = random.sample(observations, samp_pres)
if par:
outs = sac.simulate_many_par(samp_pres, rs, look_mod, init_h,
pardict, pool=send_pool,
guides=useguides)
else:
outs = sac.simulate_many(samp_pres, rs, look_mod, init_h,
pardict, guides=useguides)
ts, hs_3d, lps_2d, looks_2d, saccts, fixes, ps = proc_many_outs(outs)
logps = np.log(np.mean(ps))
if np.isnan(logps):
print 'nantrip'
logps = eps
print logps
return logps
def sample_eyetrace(prob_tc=prob_tc, prob_gc=prob_gc, prob_dp=prob_dp,
samebias=prob_sb, tau_h=tau_h, eff=eff, tau_f=tau_f,
tau_d=tau_d, a=a, tau_x=tau_x, tau_u=tau_u,
nov_img=nov_img, fam_img=fam_img, off_img=off_img):
rtf_func = lambda x: x
sac = SalSacc(prob_tc, prob_gc, prob_dp, samebias, tau_h, rtf_func, eff,
tau_f, tau_d, a, tau_x=tau_x, tau_u=tau_u)
init_h = [0., 0., 0.]
rs = [off_img, nov_img, fam_img]
pardict = {'tend':prestime, 'tstep':tstep}
print 'beg sim'
outs = sac.simulate_many_par(samp_pres, rs, look_mod, init_h, pardict,
pool=send_pool)
ts, hs_3d, lps_2d, looks_2d, saccts, fixes = proc_many_outs(outs)
print 'end sim'
hs = hs_3d.mean(2)
plooks = np.zeros(hs.shape)
for i, row in enumerate(plooks):
row[:] = np.mean(looks_2d == i, axis=0)
fixarr = np.zeros(sacclen)
fixarr[:] = np.nan
fixarr[:len(fixes)] = fixes
return plooks, fixarr
