def realign_from_photodiode(self, debug=False, verbose=True):
if verbose:
print(
'---> Realigning data with respect to photodiode signal [...] '
)
if debug:
from datavyz import ges as ge
success = True
# extract parameters
dt = 1. / self.metadata['NIdaq-acquisition-frequency']
tlim, tnew = [0, self.Screen.photodiode.t[-1]], 0
t0 = self.metadata['time_start'][0]
length = self.metadata['presentation-duration'] + self.metadata[
'presentation-interstim-period']
npulses = int(self.metadata['presentation-duration'])
self.metadata['time_start_realigned'] = []
Nepisodes = np.sum(self.metadata['time_start'] < tlim[1])
for i in range(Nepisodes):
cond = (self.Screen.photodiode.t >=
t0 - .3) & (self.Screen.photodiode.t <= t0 + length)
try:
tnew, integral, threshold = find_onset_time(
self.Screen.photodiode.t[cond] - t0,
self.Screen.photodiode.val[cond], npulses)
if debug and ((i < 3) or (i > Nepisodes - 3)):
ge.plot(self.Screen.photodiode.t[cond],
self.Screen.photodiode.val[cond])
ge.plot(self.Screen.photodiode.t[cond],
Y=[integral, integral * 0 + threshold])
ge.show()
except Exception:
success = False # one exception is enough to make it fail
t0 += tnew
self.metadata['time_start_realigned'].append(t0)
t0 += length
if verbose:
if success:
print('[ok] --> succesfully realigned')
else:
print('[X] --> realignement failed')
if success:
self.metadata['time_start_realigned'] = np.array(
self.metadata['time_start_realigned'])
self.metadata['time_stop_realigned'] = self.metadata['time_start_realigned']+\
self.metadata['presentation-duration']
else:
self.metadata['time_start_realigned'] = np.array([])
self.metadata['time_stop_realigned'] = np.array([])
return success
# ge.title(AX[i], "a=%.2f, k=%.2f, $\gamma$=%.2f" % (residual.x[0], residual.x[1], residual.x[2]), color=getattr(ge, color), size='small')
ge.title(AX[i],
"k=%.2f, $\gamma$=%.2f" % (residual.x[0], residual.x[1]),
color=getattr(ge, color),
size='small')
ge.scatter(lum,
array,
ax=AX[i],
color=getattr(ge, color),
label='data',
ms=3)
ge.plot(lum,
func(lum, residual.x),
ax=AX[i],
lw=3,
alpha=.5,
color=getattr(ge, color),
label='fit')
ge.annotate(AX[i],
'$\lambda$=%s' % LAMBDA[color], (0.5, .1),
color=getattr(ge, color))
ge.set_plot(AX[i],
xlabel='(computer) luminosity',
xticks=[0, 0.5, 1],
yticks=[0, 0.5, 1],
ylabel='measured I (norm.)')
fig.savefig('doc/gamma-correction-%s.png' % correc)
ge.show()
return pca
if __name__=='__main__':
from sklearn.datasets import load_breast_cancer
from datavyz import ges
raw, data = load_breast_cancer(), {}
for i, key in enumerate(raw['feature_names']):
data[key] = np.log10(raw['data'][i])
ges.features_plot(data, features=list(data.keys())[:6])
ges.cross_correl_plot(data, features=list(data.keys())[:6])
pca = PCA(data, n_components = len(data)) # desired_var_explained=0.9)
ges.plot(100.*pca.explained_variance_ratio_,
m='o', ms=4, xlabel='component #', ylabel='% var. expl.')
ges.parallel_plot(pca.components_,
fig_args=dict(figsize=(3,1)))
ges.show()