def kernelplot_Mod(d, results, infodict, ax1, ax2, ax3, legend='lower right'):
"""Plot historykernels"""
M = results['model_w_hist']
bootstrap = results['bootstrap']
C = statistics.Kernel_and_Slope_Collector(d.h, d.hf0, range(1, d.hf0))
K = C(M)
print 'K', K
print d.hf0
print 'd.h.shape[0]', d.h.shape[0]
print 'adding modulation kernels'
hr = K[:d.h.shape[0]]
hz = K[d.h.shape[0]:2 * d.h.shape[0]]
hr_pupil = K[2 * d.h.shape[0]:3 * d.h.shape[0]]
hz_pupil = K[3 * d.h.shape[0]:-2]
hr_pupil *= K[-2]
hz_pupil *= K[-2]
if bootstrap is None:
kernellen = (bootstrap.shape[1] - 2) / 2
print kernellen
al = bootstrap[:, -2]
al.shape = (-1, 1)
bootstrap[:, :-2] *= al # Like that?
print K[-2], pl.prctile(bootstrap[:, -2]), pl.mean(bootstrap[:, -2])
hci = statistics.history_kernel_ci(bootstrap[:, kernellen:-2],
bootstrap[:, :kernellen], hz, hr)
else:
hci = None
kl = graphics.history_kernels(hz,
hr,
hci,
ax1,
"left/right",
ground_truth=d.ground_truth)
kl += graphics.history_kernels(hz,
hr,
hci,
ax2,
"correct/incorrect",
ground_truth=d.ground_truth)
labely, labelh = same_y(ax1, ax2)
graphics.label_axes(title="(D) stimulus and response kernels",
xlabel="lag",
ylabel="equivalent stimulus strength",
legend=legend,
ax=ax1)
graphics.label_axes(title="(E) correct and incorrect kernels",
xlabel="lag",
ylabel="equivalent stimulus strength",
legend=legend,
ax=ax2)
# pl.setp ( (ax1,ax2), ylim=(-6,6) )
return kl
def permutationplot(d, results, infodict, ax, noaic=False):
"""permutation test"""
l_obs = results['model_w_hist'].loglikelihood
Caic = results['model_nohist'].loglikelihood + len(
results['model_w_hist'].w) - len(results['model_nohist'].w)
out = [l_obs]
print "l_obs=", l_obs
print "Caic= ", Caic
if not results['permutation_wh'] is None:
hist, C95 = statistics.historytest(results['permutation_wh'][:, 0])
print "C95= ", C95
out.append(C95)
out.append(Caic)
out.append(np.mean(results['permutation_wh'][:, 0] < l_obs))
else:
hist = None
C95 = None
out.append(None)
out.append(Caic)
out.append(None)
if noaic:
Caic = None
graphics.montecarlo_test(l_obs, hist, C95, Caic, ax, "likelihood")
graphics.label_axes(title="(C) Permutation test",
xlabel="log-likelihood",
ax=ax)
return out
def kernelplot(d, results, infodict, ax1, ax2, legend='lower right'):
"""Plot historykernels"""
M = results['model_w_hist']
bootstrap = results['bootstrap']
# hr = d.gethistorykernel ( M.w[d.hf0:d.hf0+d.hlen], al )
# hz = d.gethistorykernel ( M.w[d.hf0+d.hlen:], al )
C = statistics.Kernel_and_Slope_Collector(d.h, d.hf0, range(1, d.hf0))
print d.hf0
K = C(M)
hr = K[:d.h.shape[0]]
hz = K[d.h.shape[0]:-2]
hz *= K[-2]
hr *= K[-2]
print "h_r[1]", hr[0]
print "h_z[1]", hz[0]
if not bootstrap is None:
kernellen = (bootstrap.shape[1] - 2) / 2
print kernellen
al = bootstrap[:, -2]
al.shape = (-1, 1)
bootstrap[:, :-2] *= al # Like that?
print K[-2], pl.prctile(bootstrap[:, -2]), pl.mean(bootstrap[:, -2])
hci = statistics.history_kernel_ci(bootstrap[:, kernellen:-2],
bootstrap[:, :kernellen], hz, hr)
else:
hci = None
kl = graphics.history_kernels(hz,
hr,
hci,
ax1,
"left/right",
ground_truth=d.ground_truth)
kl += graphics.history_kernels(hz,
hr,
hci,
ax2,
"correct/incorrect",
ground_truth=d.ground_truth)
labely, labelh = same_y(ax1, ax2)
graphics.label_axes(title="(D) stimulus and response kernels",
xlabel="lag",
ylabel="equivalent stimulus strength",
legend=legend,
ax=ax1)
graphics.label_axes(title="(E) correct and incorrect kernels",
xlabel="lag",
ylabel="equivalent stimulus strength",
legend=legend,
ax=ax2)
# pl.setp ( (ax1,ax2), ylim=(-6,6) )
return kl
def pmfplot(d, results, infodict, ax, errors=True):
"""Generate the pmf plot"""
for i, c in enumerate(infodict['conditions']):
c = int(c)
d_ = d.getsummary(c)
x = pl.mgrid[infodict['xmin']:infodict['xmax']:100j]
if len(d.th_features) > 0:
# d_[:,0] = u_v ( d_[:,0], results['model_w_hist'].nu )
# x = u_v ( x, results['model_w_hist'].nu )
d_[:, 0] = u_v(d_[:, 0], results['model_nohist'].nu)
x = u_v(x, results['model_nohist'].nu)
if errors:
graphics.plot_data_summary(d_, ax, infodict['colors'][i],
infodict['labels'][i])
else:
# AEU: change the marker to make datapoints small. What goes wrong in the plot here?
ax.plot(d_[:, 0],
d_[:, 1] / d_[:, 2],
'.',
color=infodict['colors'][i],
label=infodict['labels'][i])
# wfit = results['model_w_hist'].w[infodict['indices'][c]]
wfit = results['model_nohist'].w[infodict['indices'][i]]
w0fit = results['model_w_hist'].w0[infodict['indices'][i]]
pfit = results['model_w_hist'].pi
p0fit = results['model_nohist'].pi
if not d.ground_truth is None:
wgfit = d.ground_truth['w'][infodict['indices'][i]]
pgfit = d.ground_truth['pi']
gt = graphics.plot_pmf(pgfit, wgfit, x, ax,
(np.array([255, 240, 240], 'd') / 255 +
infodict['colors'][i]) / 2.)
pl.setp(gt, linestyle='--')
# graphics.plot_pmf ( pfit, w0fit, x, ax, [.9,.9,.9], alpha=0.1 )
graphics.plot_pmf(p0fit, wfit, x, ax, infodict['colors'][i])
graphics.label_axes(title="(A) psychometric function",
xlabel=r"transduced stimulus $u_\nu(s\tilde{z})$",
ylabel=r"probability for $r=1$",
nxticks=5,
ax=ax)
def slopeplot(d, results, infodict, ax):
"""slope results of the permutation test"""
ax = graphics.prepare_axes(ax, haveon=('bottom', ))
h = np.histogram(results['permutation_wh'][:, 1])
graphics.montecarlo_test(results['model_w_hist'].w[1],
h,
pl.prctile(results['permutation_wh'][:, 1], 95),
ax=ax,
labeling='slope')
# ax.bar ( b[:-1], h, pl.diff(b), edgecolor=graphics.histogram_color, facecolor=graphics.histogram_color )
# yrange = ax.get_ylim ()
# ax.axvline ( results['model_w_hist'].w[1], ymin=yrange[0], ymax=yrange[1] )
# ax.set_ylim ( yrange )
# # ax.set_xlim ( .3,3 )
# # ax.set_xticks ( [.5,1,1.5,2,2.5,3] )
graphics.label_axes(title="(F) slope effects", xlabel=r"slope", ax=ax)
def nonlinearityplot(d, results, infodict, ax):
"""Plot with the nonlinearity"""
xmin, xmax = infodict['xmin'], infodict['xmax']
M = results['model_w_hist']
for c in infodict['conditions']:
d_ = d.getsummary(c)
graphics.plot_nonlinearity_summary(d_,
M.w,
M.pi,
ax,
color=infodict['colors'][c],
label=infodict['labels'][c])
graphics.plot_nonlinearity(M.nu, np.mgrid[xmin:xmax:100j], ax, 'k')
if not d.ground_truth is None:
nl = graphics.plot_nonlinearity(d.ground_truth['nu'],
np.mgrid[xmin:xmax:100j], ax,
np.array([255, 230, 230], 'd') / 255)
pl.setp(nl, linestyle='--')
graphics.label_axes(title="(B) nonlinearity",
xlabel=r"raw stimulus $s\tilde{z}$",
ylabel=r"transduced stimulus $u_\nu(s\tilde{z})$",
nxticks=5,
ax=ax)
