def test_ar_modeling():
# Compare against standard routines
rng = np.random.RandomState(20110903)
N = 10
Y = rng.normal(size=(N,1)) * 10 + 100
X = np.c_[np.linspace(-1,1,N), np.ones((N,))]
my_model = OLSModel(X)
results = my_model.fit(Y)
# fmristat wrapper
rhos = estimateAR(results.resid, my_model.design, order=2)
assert_equal(rhos.shape, (2,))
assert_true(np.all(np.abs(rhos <= 1)))
# standard routine
rhos2 = ar_bias_correct(results, 2)
assert_array_almost_equal(rhos, rhos2, 8)
# Make 2D and 3D Y
Y = rng.normal(size=(N,4)) * 10 + 100
results = my_model.fit(Y)
rhos = estimateAR(results.resid, my_model.design, order=2)
assert_equal(rhos.shape, (2,4))
assert_true(np.all(np.abs(rhos <= 1)))
rhos2 = ar_bias_correct(results, 2)
assert_array_almost_equal(rhos, rhos2, 8)
# 3D
results.resid = np.reshape(results.resid, (N,2,2))
rhos = estimateAR(results.resid, my_model.design, order=2)
assert_equal(rhos.shape, (2,2,2))
assert_true(np.all(np.abs(rhos <= 1)))
rhos2 = ar_bias_correct(results, 2)
assert_array_almost_equal(rhos, rhos2, 8)
def test_ar_modeling():
# Compare against standard routines
rng = np.random.RandomState(20110903)
N = 10
Y = rng.normal(size=(N, 1)) * 10 + 100
X = np.c_[np.linspace(-1, 1, N), np.ones((N, ))]
my_model = OLSModel(X)
results = my_model.fit(Y)
# fmristat wrapper
rhos = estimateAR(results.resid, my_model.design, order=2)
assert_equal(rhos.shape, (2, ))
assert_true(np.all(np.abs(rhos <= 1)))
# standard routine
rhos2 = ar_bias_correct(results, 2)
assert_array_almost_equal(rhos, rhos2, 8)
# Make 2D and 3D Y
Y = rng.normal(size=(N, 4)) * 10 + 100
results = my_model.fit(Y)
rhos = estimateAR(results.resid, my_model.design, order=2)
assert_equal(rhos.shape, (2, 4))
assert_true(np.all(np.abs(rhos <= 1)))
rhos2 = ar_bias_correct(results, 2)
assert_array_almost_equal(rhos, rhos2, 8)
# 3D
results.resid = np.reshape(results.resid, (N, 2, 2))
rhos = estimateAR(results.resid, my_model.design, order=2)
assert_equal(rhos.shape, (2, 2, 2))
assert_true(np.all(np.abs(rhos <= 1)))
rhos2 = ar_bias_correct(results, 2)
assert_array_almost_equal(rhos, rhos2, 8)
def test_altprotocol():
block, bT, bF = protocol(descriptions['block'], 'block', *delay.spectral)
event, eT, eF = protocol(descriptions['event'], 'event', *delay.spectral)
blocka, baT, baF = altprotocol(altdescr['block'], 'block', *delay.spectral)
eventa, eaT, eaF = altprotocol(altdescr['event'], 'event', *delay.spectral)
for c in bT.keys():
baf = baT[c]
if not isinstance(baf, formulae.Formula):
baf = formulae.Formula([baf])
bf = bT[c]
if not isinstance(bf, formulae.Formula):
bf = formulae.Formula([bf])
X = baf.design(t, return_float=True)
Y = bf.design(t, return_float=True)
if X.ndim == 1:
X.shape = (X.shape[0], 1)
m = OLSModel(X)
r = m.fit(Y)
remaining = (r.resid**2).sum() / (Y**2).sum()
assert_almost_equal(remaining, 0)
for c in bF.keys():
baf = baF[c]
if not isinstance(baf, formulae.Formula):
baf = formulae.Formula([baf])
bf = bF[c]
if not isinstance(bf, formulae.Formula):
bf = formulae.Formula([bf])
X = baf.design(t, return_float=True)
Y = bf.design(t, return_float=True)
if X.ndim == 1:
X.shape = (X.shape[0], 1)
m = OLSModel(X)
r = m.fit(Y)
remaining = (r.resid**2).sum() / (Y**2).sum()
assert_almost_equal(remaining, 0)
def test_altprotocol():
block, bT, bF = protocol(descriptions['block'], 'block', *delay.spectral)
event, eT, eF = protocol(descriptions['event'], 'event', *delay.spectral)
blocka, baT, baF = altprotocol(altdescr['block'], 'block', *delay.spectral)
eventa, eaT, eaF = altprotocol(altdescr['event'], 'event', *delay.spectral)
for c in bT.keys():
baf = baT[c]
if not isinstance(baf, formulae.Formula):
baf = formulae.Formula([baf])
bf = bT[c]
if not isinstance(bf, formulae.Formula):
bf = formulae.Formula([bf])
X = baf.design(t, return_float=True)
Y = bf.design(t, return_float=True)
if X.ndim == 1:
X.shape = (X.shape[0], 1)
m = OLSModel(X)
r = m.fit(Y)
remaining = (r.resid**2).sum() / (Y**2).sum()
assert_almost_equal(remaining, 0)
for c in bF.keys():
baf = baF[c]
if not isinstance(baf, formulae.Formula):
baf = formulae.Formula([baf])
bf = bF[c]
if not isinstance(bf, formulae.Formula):
bf = formulae.Formula([bf])
X = baf.design(t, return_float=True)
Y = bf.design(t, return_float=True)
if X.ndim == 1:
X.shape = (X.shape[0], 1)
m = OLSModel(X)
r = m.fit(Y)
remaining = (r.resid**2).sum() / (Y**2).sum()
assert_almost_equal(remaining, 0)
tempdict[v] = np.zeros(mask_array.sum())
output[contrast_id] = tempdict
########################################
# Perform a GLM analysis
########################################
print 'Fitting a GLM (this takes time)...'
fmri_image = load(data_path)
Y = fmri_image.get_data()[mask_array]
X = design_matrix.matrix
m = OLSModel(X)
# Fit the model, storing an estimate of an AR(1) parameter at each voxel
result = m.fit(Y.T)
ar1 = ((result.resid[1:] * result.resid[:-1]).sum(0) /
(result.resid ** 2).sum(0))
ar1 *= 100
ar1 = ar1.astype(np.int) / 100.
for val in np.unique(ar1):
armask = np.equal(ar1, val)
m = ARModel(X, val)
d = Y[armask]
results = m.fit(d.T)
# Output the results for each contrast
for (contrast_id, contrast_val) in contrasts.items():
resT = results.Tcontrast(contrast_val)
