# Convolve with HRF
df = convolve_hrf(df, dg(), asbold)
# Orth select regressors
to_orth = [['box', bold] for bold in asbold if bold != 'box']
for orth in to_orth:
df[orth[1]+'_o'] = orthogonalize(df, orth)[orth[1]]
# Do the regressions
n_results = {}
for model_name, model, test, hypoth in zip(*model_configs):
for bold_name in asbold:
l = df.shape[0]
noi, prng = white(l, prng=prng)
df['bold'] = create_bold([df[bold_name].values], None, noi)
smo = smf.ols(model, data=df).fit()
print(smo.summary2())
stato = None
if test == 't':
stato = smo.t_test(hypoth)
elif test == 'F':
stato = smo.f_test(hypoth)
elif test is not None:
raise ValueError("Unknown test")
savedf = None
if args.save_behave:
savedf = df
# creates the BOLD. The second creates
# the predictors.
for alpha_bold in alphas:
# Create all bold options
df_bold, _ = reinforce.rescorla_wagner(trials,
acc,
p,
alpha=alpha_bold,
prng=prng)
# Iter options
for bold_name in asbold:
l = df_bold.shape[0]
noi, prng = white(l, prng=prng)
df_bold['bold'] = create_bold([df_bold[bold_name].values], dg(),
noi)
# Create predictor
for alpha_pred in alphas:
df_pred, _ = reinforce.rescorla_wagner(trials,
acc,
p,
alpha=alpha_pred,
prng=prng)
df_pred = convolve_hrf(df_pred, dg(), asbold)
# Orth select predictors
to_orth = [['box', too] for too in asbold if too != 'box']
for orth in to_orth:
df_pred[orth[1] + '_o'] = orthogonalize(df_pred,
# creates the BOLD. The second creates
# the predictors.
for alpha_bold in alphas:
# Create all bold options
df_bold, _ = reinforce.rescorla_wagner(
trials, acc, p,
alpha=alpha_bold, prng=prng
)
# Iter options
for bold_name in asbold:
l = df_bold.shape[0]
noi, prng = white(l, prng=prng)
df_bold['bold'] = create_bold(
[df_bold[bold_name].values], dg(), noi
)
# Create predictor
for alpha_pred in alphas:
df_pred, _ = reinforce.rescorla_wagner(
trials, acc, p,
alpha=alpha_pred, prng=prng
)
df_pred = convolve_hrf(df_pred, dg(), asbold)
# Orth select predictors
to_orth = [['box', too] for too in asbold if too != 'box']
for orth in to_orth:
df_pred[orth[1]+'_o'] = orthogonalize(
df = convolve_hrf(df, dg(), asbold)
# Orth select regressors
to_orth = [['box', too] for too in asbold if too != 'box']
for orth in to_orth:
df[orth[1]+'_o'] = orthogonalize(df, orth)[orth[1]]
# Do the regressions
n_results = {}
for model_name, model, test, hypoth in zip(*model_configs):
for bold_name in asbold:
l = df.shape[0]
noi, prng = white(l, prng=prng)
# Make bold
df['bold'] = create_bold([df[bold_name].values], None, noi)
# Regress
smo = smf.ols(model, data=df).fit()
print(smo.summary2())
# Hypoth test
stato = None
if test == 't':
stato = smo.t_test(hypoth)
elif test == 'F':
stato = smo.f_test(hypoth)
elif test is not None:
raise ValueError("Unknown test")
# Reformat
