# Create data
if args.behave == 'learn':
trials, acc, p, prng = behave.learn(
n_cond, args.n_trials,
loc=prng.normal(3, .3), prng=prng
)
elif args.behave == 'random':
trials, acc, p, prng = behave.random(
n_cond, args.n_trials, prng=prng
)
else:
raise ValueError('--behave not understood')
df, rlpars = reinforce.rescorla_wagner(trials, acc, p, prng=prng)
# 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)
# 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(
trial, acc, p, prng = behave.learn(
args.n_cond, args.n_trials,
loc=prng.normal(3, .3), prng=prng
)
elif args.behave == 'random':
trial, acc, p, prng = behave.random(
args.n_cond, args.n_trials, prng=prng
)
else:
raise ValueError('--behave not understood')
df, rlpars = reinforce.rescorla_wagner(
trial, acc, p, alpha=args.alpha, prng=prng
)
del df['rand']
l = trial.shape[0]
df['count'] = np.repeat(n, l)
df['index'] = np.arange(l, dtype=np.int)
dfs.append(df)
df = pd.concat(dfs, axis=0)
if args.convolve:
tocon = ['box', 'acc', 'p', 'rpe', 'value']
condf = convolve_hrf(df, dg(), tocon)
for con in tocon:
df[con] = condf[con]
df.to_csv(args.name, index=False, float_format='%.8f')
# 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:
# Create data
if args.behave == 'learn':
trials, acc, p, prng = behave.learn(
n_cond, args.n_trials,
loc=prng.normal(3, .3), prng=prng
)
elif args.behave == 'random':
trials, acc, p, prng = behave.random(
n_cond, args.n_trials, prng=prng
)
else:
raise ValueError('--behave not understood')
df, rlpars = reinforce.rescorla_wagner(trials, acc, p, prng=prng)
# Convolve with HRF
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)
