def test_orthogonalize():
# --
# np
# --
# Two lines:
dm1 = np.vstack([np.arange(10), np.arange(10)]).T
dmo = dm.orthogonalize(dm1, [0,1])
# Col 0 is unchanged
# Orth col 1 is all zeros as all
# variance was assigned to 0.
assert np.allclose(dmo[:,0], dm1[:,0])
assert np.allclose(dmo[:,1], np.zeros_like(dmo[:,1]))
# -----------------
# repeat for pandas
# -----------------
dm1 = pd.DataFrame(dm1, columns=['0', '1'])
dmo = dm.orthogonalize(dm1, ['0','1'])
assert np.allclose(dmo['0'].values, dm1['0'].values)
assert np.allclose(dmo['1'].values, np.zeros_like(dmo['1'].values))
)
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)
smo = smf.ols(model, data=df).fit()
print(smo.summary2())
stato = None
if test == 't':
)
# 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, orth
)[orth[1]]
# Hold bold constant in the predictor loops
df_pred['bold'] = df_bold['bold']
# Finally! Get modeling.
for model_name, model, test, hypoth in zip(*model_configs):
# Regress
smo = smf.ols(model, data=df_pred).fit()
print(smo.summary2())
# Hypoth
stato = None
if test == 't':
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,
orth)[orth[1]]
# Hold bold constant in the predictor loops
df_pred['bold'] = df_bold['bold']
# Finally! Get modeling.
for model_name, model, test, hypoth in zip(*model_configs):
# Regress
smo = smf.ols(model, data=df_pred).fit()
print(smo.summary2())
# Hypoth
stato = None
if test == 't':
stato = smo.t_test(hypoth)
)
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)
# Regress
smo = smf.ols(model, data=df).fit()
print(smo.summary2())