def test_create_evoked_physio_signal(self):
import pyhrf.paradigm
phy_params = phy.PHY_PARAMS_FRISTON00
tr = 1.
duration = 20.
ne = np.array([[10., 5.]])
nb_conds, nb_vox = ne.shape
# one single stimulation at the begining
paradigm = pyhrf.paradigm.Paradigm({'c': [np.array([0.])]}, [duration],
{'c': [np.array([1.])]})
s, f, hbr, cbv = phy.create_evoked_physio_signals(phy_params, paradigm,
ne, tr)
# shape of a signal: (nb_vox, nb_scans)
if 0:
import matplotlib.pyplot as plt
t = np.arange(f[0].size) * tr
plt.plot(t, f[0])
plt.title('inflow')
plt.show()
self.assertEqual(s.shape, (paradigm.get_rastered(tr)['c'][0].size,
nb_vox))
# check signal causality:
self.assertEqual(f[0, 0], 1.)
npt.assert_approx_equal(f[-1, 0], 1., significant=2)
# non-regression test:
self.assertEqual(np.argmax(f[:, 0]) * tr, 2)
def rasterize_paradigm(paradigm, dt, condition_defs):
""" Return binary sequences of onsets approximated on temporal grid of
temporal resolution dt, for all conditions. 'paradigm' is expected to be
an instance of 'pyhrf.paradigm.mpar.Paradigm'
"""
rparadigm = paradigm.get_rastered(dt)
return np.vstack([rparadigm[c.name][0] for c in condition_defs])
def rasterize_paradigm(paradigm, dt, condition_defs):
""" Return binary sequences of onsets approximated on temporal grid of
temporal resolution dt, for all conditions. 'paradigm' is expected to be
an instance of 'pyhrf.paradigm.mpar.Paradigm'
"""
rparadigm = paradigm.get_rastered(dt)
return np.vstack([rparadigm[c.name][0] for c in condition_defs])
def test_create_evoked_physio_signal(self):
import pyhrf.paradigm
phy_params = phy.PHY_PARAMS_FRISTON00
tr = 1.
duration = 20.
ne = np.array([[10., 5.]])
nb_conds, nb_vox = ne.shape
# one single stimulation at the begining
paradigm = pyhrf.paradigm.Paradigm({'c': [np.array([0.])]}, [duration],
{'c': [np.array([1.])]})
s, f, hbr, cbv = phy.create_evoked_physio_signals(
phy_params, paradigm, ne, tr)
# shape of a signal: (nb_vox, nb_scans)
if 0:
import matplotlib.pyplot as plt
t = np.arange(f[0].size) * tr
plt.plot(t, f[0])
plt.title('inflow')
plt.show()
self.assertEqual(s.shape,
(paradigm.get_rastered(tr)['c'][0].size, nb_vox))
# check signal causality:
self.assertEqual(f[0, 0], 1.)
npt.assert_approx_equal(f[-1, 0], 1., significant=2)
# non-regression test:
self.assertEqual(np.argmax(f[:, 0]) * tr, 2)