def test_resid_rename_warnings_ar():
model = ARModel(design=X, rho=0.4)
results_ar = model.fit(Y)
with pytest.warns(FutureWarning, match="'resid'"):
assert_array_equal(results_ar.resid, results_ar.residuals)
with pytest.warns(FutureWarning, match="'wresid"):
assert_array_equal(results_ar.wresid, results_ar.whitened_residuals)
def ts_glm(pupilts,
con_onsets,
incon_onsets,
neut_onsets,
blinks,
sampling_rate=30.):
"""
Currently runs the following contrasts:
Incongruent: [0,1,0,0,0]
Congruent: [0,0,1,0,0]
Neutral: [0,0,0,1,0]
Incon-Neut: [0,1,0,-1,0]
Con-Neut: [0,0,1,-1,0]
Incon-Con: [0,1,-1,0,0]
"""
signal_filt = ts.TimeSeries(pupilts, sampling_rate=sampling_rate)
con_ts = get_event_ts(pupilts, con_onsets)
incon_ts = get_event_ts(pupilts, incon_onsets)
neut_ts = get_event_ts(pupilts, neut_onsets)
kernel_end_sec = 3.
kernel_length = kernel_end_sec / (1 / sampling_rate)
kernel_x = np.linspace(0, kernel_end_sec, int(kernel_length))
con_reg, con_td_reg = pupil_utils.regressor_tempderiv(con_ts,
kernel_x,
s1=1000.,
tmax=1.30)
incon_reg, incon_td_reg = pupil_utils.regressor_tempderiv(incon_ts,
kernel_x,
s1=1000.,
tmax=1.30)
neut_reg, neut_td_reg = pupil_utils.regressor_tempderiv(neut_ts,
kernel_x,
s1=1000.,
tmax=1.30)
#kernel = pupil_utils.pupil_irf(kernel_x, s1=1000., tmax=1.30)
#plot_event(signal_filt, con_ts, incon_ts, neut_ts, kernel, pupil_fname)
intercept = np.ones_like(signal_filt.data)
X = np.array(
np.vstack((intercept, incon_reg, con_reg, neut_reg, blinks.values)).T)
Y = np.atleast_2d(signal_filt).T
model = ARModel(X, rho=1.).fit(Y)
tIncon = float(model.Tcontrast([0, 1, 0, 0, 0]).t)
tCon = float(model.Tcontrast([0, 0, 1, 0, 0]).t)
tNeut = float(model.Tcontrast([0, 0, 0, 1, 0]).t)
tIncon_Neut = float(model.Tcontrast([0, 1, 0, -1, 0]).t)
tCon_Neut = float(model.Tcontrast([0, 0, 1, -1, 0]).t)
tIncon_Con = float(model.Tcontrast([0, 1, -1, 0, 0]).t)
resultdict = {
'Incon_t': tIncon,
'Con_t': tCon,
'Neut_t': tNeut,
'InconNeut_t': tIncon_Neut,
'ConNeut_t': tCon_Neut,
'InconCon_t': tIncon_Con
}
return resultdict
def ts_glm(pupilts, trg_onsets, std_onsets, blinks, sampling_rate=30.):
signal_filt = ts.TimeSeries(pupilts, sampling_rate=sampling_rate)
trg_ts = get_event_ts(pupilts, trg_onsets)
std_ts = get_event_ts(pupilts, std_onsets)
kernel_end_sec = 2.5
kernel_length = kernel_end_sec / (1 / sampling_rate)
kernel_x = np.linspace(0, kernel_end_sec, int(kernel_length))
trg_reg, trg_td_reg = pupil_utils.regressor_tempderiv(trg_ts, kernel_x)
std_reg, std_td_reg = pupil_utils.regressor_tempderiv(std_ts, kernel_x)
#kernel = pupil_irf(kernel_x)
#plot_event(signal_filt, trg_ts, std_ts, kernel, fname)
intercept = np.ones_like(signal_filt.data)
X = np.array(np.vstack((intercept, trg_reg, std_reg, blinks.values)).T)
Y = np.atleast_2d(signal_filt).T
model = ARModel(X, rho=1.).fit(Y)
tTrg = float(model.Tcontrast([0, 1, 0, 0]).t)
tStd = float(model.Tcontrast([0, 0, 1, 0]).t)
tTrgStd = float(model.Tcontrast([0, 1, -1, 0]).t)
resultdict = {
'Target_Beta': tTrg,
'Standard_Beta': tStd,
'ContrastT': tTrgStd
}
return resultdict
def test_AR_degenerate():
Xd = X.copy()
Xd[:, 0] = Xd[:, 1] + Xd[:, 2]
model = ARModel(design=Xd, rho=0.9)
results = model.fit(Y)
assert results.df_residuals == 31
def test_AR():
model = ARModel(design=X, rho=0.4)
results = model.fit(Y)
assert results.df_residuals == 30
assert results.residuals.shape[0] == 40
assert results.predicted.shape[0] == 40
def test_AR_degenerate():
Xd = X.copy()
Xd[:, 0] = Xd[:, 1] + Xd[:, 2]
model = ARModel(design=Xd, rho=0.9)
results = model.fit(Y)
assert_equal(results.df_resid, 31)
def test_AR():
model = ARModel(design=X, rho=0.4)
results = model.fit(Y)
assert_equal(results.df_resid, 30)