def test_dmtx0b():
# test design matrix creation when no paradigm is provided
#
tr = 1.0
frametimes = np.linspace(0, 127 * tr, 128)
X, names = dmtx_light(frametimes, drift_model="Polynomial", drift_order=3)
assert_almost_equal(X[:, 0], np.linspace(0, 1.0, 128))
def test_dmtx0():
# test design matrix creation when no paradigm is provided
#
tr = 1.0
frametimes = np.linspace(0, 127 * tr, 128)
X, names = dmtx_light(frametimes, drift_model="Polynomial", drift_order=3)
print names
assert_true(len(names) == 4)
def test_dmtx0d():
# test design matrix creation when regressors are provided manually
#
tr = 1.0
frametimes = np.linspace(0, 127 * tr, 128)
ax = np.random.randn(128, 4)
X, names = dmtx_light(frametimes, drift_model="Polynomial", drift_order=3, add_regs=ax)
assert_true((len(names) == 8) & (X.shape[1] == 8))
def test_dmtx0c():
# test design matrix creation when regressors are provided manually
#
tr = 1.0
frametimes = np.linspace(0, 127 * tr, 128)
ax = np.random.randn(128, 4)
X, names = dmtx_light(frametimes, drift_model="Polynomial", drift_order=3, add_regs=ax)
assert_almost_equal(X[:, 0], ax[:, 0])
def test_dmtx1():
# basic test based on basic_paradigm and canonical hrf
tr = 1.0
frametimes = np.linspace(0, 127 * tr, 128)
paradigm = basic_paradigm()
hrf_model = "Canonical"
X, names = dmtx_light(frametimes, paradigm, hrf_model=hrf_model, drift_model="Polynomial", drift_order=3)
print names
assert_true(len(names) == 7)
def test_dmtx6():
# idem test_dmtx1 with a block paradigm and the hrf derivative
#
tr = 1.0
frametimes = np.linspace(0, 127 * tr, 128)
paradigm = block_paradigm()
hrf_model = "Canonical With Derivative"
X, names = dmtx_light(frametimes, paradigm, hrf_model=hrf_model, drift_model="Polynomial", drift_order=3)
assert_true(len(names) == 10)
def test_dmtx1d():
# idem test_dmtx1, but different test
#
tr = 1.0
frametimes = np.linspace(0, 127 * tr, 128)
paradigm = basic_paradigm()
hrf_model = "Canonical"
X, names = dmtx_light(frametimes, paradigm, hrf_model=hrf_model, drift_model="Polynomial", drift_order=3)
assert_true((np.isnan(X) == 0).all())
def test_dmtx3():
# idem test_dmtx1 with a different drift term
#
tr = 1.0
frametimes = np.linspace(0, 127 * tr, 128)
paradigm = basic_paradigm()
hrf_model = "Canonical"
X, names = dmtx_light(frametimes, paradigm, hrf_model=hrf_model, drift_model="Blank")
print names
assert_true(len(names) == 4)
def test_dmtx12():
# check that the 11th column of a FIR design matrix is indeed OK
tr = 1.0
frametimes = np.linspace(0, 127 * tr, 128)
paradigm = basic_paradigm()
hrf_model = "FIR"
X, names = dmtx_light(
frametimes, paradigm, hrf_model=hrf_model, drift_model="Polynomial", drift_order=3, fir_delays=range(1, 5)
)
assert_true(np.all(X[paradigm[paradigm[:, 0] == 2, 1] + 5, 11] == 1))
def test_dmtx1b():
# idem test_dmtx1, but different test
#
tr = 1.0
frametimes = np.linspace(0, 127 * tr, 128)
paradigm = basic_paradigm()
hrf_model = "Canonical"
X, names = dmtx_light(frametimes, paradigm, hrf_model=hrf_model, drift_model="Polynomial", drift_order=3)
print np.shape(X)
assert_true(X.shape == (128, 7))
def test_dmtx5():
# idem test_dmtx1 with a block paradigm
#
tr = 1.0
frametimes = np.linspace(0, 127 * tr, 128)
paradigm = block_paradigm()
hrf_model = "Canonical"
X, names = dmtx_light(frametimes, paradigm, hrf_model=hrf_model, drift_model="Polynomial", drift_order=3)
print names
assert_true(len(names) == 7)
def test_dmtx4():
# idem test_dmtx1 with a different hrf model
#
tr = 1.0
frametimes = np.linspace(0, 127 * tr, 128)
paradigm = basic_paradigm()
hrf_model = "Canonical With Derivative"
X, names = dmtx_light(frametimes, paradigm, hrf_model=hrf_model, drift_model="Polynomial", drift_order=3)
print names
assert_true(len(names) == 10)
def test_dmtx14():
# Check that the first column o FIR design matrix is OK after a 1/2
# time shift
tr = 1.0
frametimes = np.linspace(0, 127 * tr, 128) + tr / 2
paradigm = basic_paradigm()
hrf_model = "FIR"
X, names = dmtx_light(
frametimes, paradigm, hrf_model=hrf_model, drift_model="Polynomial", drift_order=3, fir_delays=range(1, 5)
)
assert_true(np.all(X[paradigm[paradigm[:, 0] == 0, 1] + 1, 0] == 1))
def test_dmtx18():
"""
Test the effect of scaling on the blocks
"""
tr = 1.0
frametimes = np.linspace(0, 127*tr,128)
paradigm = modulated_block_paradigm()
hrf_model='Canonical'
X, names= dm.dmtx_light(frametimes, paradigm, hrf_model=hrf_model,
drift_model='Polynomial', drift_order=3)
assert_true((X[paradigm.onset[paradigm.index==0].astype(np.int)+3,0]>0).all())
def test_dmtx1():
"""
basic test based on basic_paradigm and canonical hrf
"""
tr = 1.0
frametimes = np.linspace(0, 127*tr,128)
paradigm = basic_paradigm()
hrf_model='Canonical'
X, names= dm.dmtx_light(frametimes, paradigm, hrf_model=hrf_model,
drift_model='Polynomial', drift_order=3)
assert_true(len(names)==7)
def test_dmtx1c():
"""
idem test_dmtx1, but different test
"""
tr = 1.0
frametimes = np.linspace(0,127*tr,128)
paradigm = basic_paradigm()
hrf_model='Canonical'
X,names= dm.dmtx_light(frametimes, paradigm, hrf_model=hrf_model,
drift_model='Polynomial', drift_order=3)
assert_true((X[:,-1]==1).all())
def test_dmtx9():
"""
basic test based on basic_paradigm and FIR
"""
tr = 1.0
frametimes = np.linspace(0, 127*tr,128)
paradigm = basic_paradigm()
hrf_model='FIR'
X, names= dm.dmtx_light(frametimes, paradigm, hrf_model=hrf_model,
drift_model='Polynomial', drift_order=3, fir_delays=range(1,5))
print names
assert_true(len(names)==16)
def test_dmtx2():
"""
idem test_dmtx1 with a different drift term
"""
tr = 1.0
frametimes = np.linspace(0,127*tr,128)
paradigm = basic_paradigm()
hrf_model='Canonical'
X,names= dm.dmtx_light(frametimes, paradigm, hrf_model=hrf_model,
drift_model='Cosine', hfcut=63)
print names
assert_true(len(names)==8)
def test_dmtx13():
"""
Check that the fir_duration is well taken into account
"""
tr = 1.0
frametimes = np.linspace(0, 127*tr,128)
paradigm = basic_paradigm()
hrf_model='FIR'
X, names= dm.dmtx_light(frametimes, paradigm, hrf_model=hrf_model,
drift_model='Polynomial', drift_order=3,
fir_delays=range(1,5), fir_duration=2*tr)
assert_true(np.all(X[paradigm.onset[paradigm.index==0]+2, 0]==1))
def test_dmtx15():
"""
basic test based on basic_paradigm, plus user supplied regressors
"""
tr = 1.0
frametimes = np.linspace(0, 127*tr,128)
paradigm = basic_paradigm()
hrf_model='Canonical'
ax = np.random.randn(128,4)
X, names= dm.dmtx_light(frametimes, paradigm, hrf_model=hrf_model,
drift_model='Polynomial', drift_order=3, add_regs=ax)
assert_true((len(names)==11)&(X.shape[1]==11))
def test_dmtx16():
"""
check that additional regressors are put at the reight place
"""
tr = 1.0
frametimes = np.linspace(0, 127*tr,128)
paradigm = basic_paradigm()
hrf_model ='Canonical'
ax = np.random.randn(128,4)
X, names= dm.dmtx_light(frametimes, paradigm, hrf_model=hrf_model,
drift_model='Polynomial', drift_order=3, add_regs=ax)
assert_almost_equal(X[:,3:7],ax)
def test_dmtx12():
"""
check that the 11th column of a FIR design matrix is indeed OK
"""
tr = 1.0
frametimes = np.linspace(0, 127*tr,128)
paradigm = basic_paradigm()
hrf_model='FIR'
X, names= dm.dmtx_light(frametimes, paradigm, hrf_model=hrf_model,
drift_model='Polynomial', drift_order=3,
fir_delays=range(1,5))
assert_true(np.all(X[paradigm.onset[paradigm.index==2]+4, 11]==1))
def test_dmtx17():
"""
Test the effect of scaling on the events
"""
tr = 1.0
frametimes = np.linspace(0, 127*tr,128)
paradigm = modulated_event_paradigm()
hrf_model = 'Canonical'
X, names= dm.dmtx_light(frametimes, paradigm, hrf_model=hrf_model,
drift_model='Polynomial', drift_order=3)
assert_true((X[paradigm.onset[paradigm.con_id=='c0'].astype(np.int)+1,0]\
> 0).all())
def test_dmtx7():
# idem test_dmtx1, but odd paradigm
#
tr = 1.0
frametimes = np.linspace(0, 127 * tr, 128)
conditions = [0, 0, 0, 1, 1, 1, 3, 3, 3]
# no condition '2'
onsets = [30, 70, 100, 10, 30, 90, 30, 40, 60]
paradigm = np.vstack(([conditions, onsets])).T
return paradigm
hrf_model = "Canonical"
X, names = dmtx_light(frametimes, paradigm, hrf_model=hrf_model, drift_model="Polynomial", drift_order=3)
assert_true(len(names) == 7)
def test_dmtx19():
"""
Test the effect of scaling on a FIR model
"""
tr = 1.0
frametimes = np.linspace(0, 127*tr,128)
paradigm = modulated_event_paradigm()
hrf_model='FIR'
X, names= dm.dmtx_light(frametimes, paradigm, hrf_model=hrf_model,
drift_model='Polynomial', drift_order=3,
fir_delays=range(1,5))
idx = paradigm.onset[paradigm.index==0].astype(np.int)
assert_true((X[idx+1,0]==X[idx+2,1]).all())
def test_dmtx11():
"""
check that the second column of the FIR design matrix is OK indeed
"""
tr = 1.0
frametimes = np.linspace(0, 127*tr,128)
paradigm = basic_paradigm()
hrf_model='FIR'
X, names= dm.dmtx_light(frametimes, paradigm, hrf_model=hrf_model,
drift_model='Polynomial', drift_order=3,
fir_delays=range(1,5))
onset = paradigm.onset[paradigm.con_id=='c0'].astype(np.int)
assert_true(np.all(X[onset+3, 2]==1))
def test_dmtx14():
"""
Check that the first column o FIR design matrix is OK after a 1/2
time shift
"""
tr = 1.0
frametimes = np.linspace(0, 127*tr,128)+tr/2
paradigm = basic_paradigm()
hrf_model='FIR'
X, names= dm.dmtx_light(frametimes, paradigm, hrf_model=hrf_model,
drift_model='Polynomial', drift_order=3,
fir_delays=range(1,5))
assert_true(np.all(X[paradigm.onset[paradigm.index==0]+1,0]==1))
def test_dmtx13():
# Check that the fir_duration is well taken into account
tr = 1.0
frametimes = np.linspace(0, 127 * tr, 128)
paradigm = basic_paradigm()
hrf_model = "FIR"
X, names = dmtx_light(
frametimes,
paradigm,
hrf_model=hrf_model,
drift_model="Polynomial",
drift_order=3,
fir_delays=range(1, 5),
fir_duration=2 * tr,
)
assert_true(np.all(X[paradigm[paradigm[:, 0] == 0, 1] + 3, 0] == 1))
"""
import numpy as np
from nipy.neurospin.utils.design_matrix import dmtx_light
tr = 1.0
frametimes = np.linspace(0,127*tr,128)
conditions = [0,0,0,1,1,1,3,3,3]
onsets=[30,70,100,10,30,90,30,40,60]
hrf_model = 'Canonical'
motion = np.cumsum(np.random.randn(128,6),0)
add_reg_names = ['tx','ty','tz','rx','ry','rz']
#event-related design matrix
paradigm = np.vstack(([conditions, onsets])).T
x1,name1 = dmtx_light(frametimes, paradigm, drift_model='Polynomial',
drift_order=3, add_regs=motion, add_reg_names=add_reg_names)
# block design matrix
duration = 7*np.ones(9)
paradigm = np.vstack(([conditions, onsets, duration])).T
x2,name2 = dmtx_light(frametimes, paradigm, drift_model='Polynomial', drift_order=3)
# FIR model
paradigm = np.vstack(([conditions, onsets])).T
hrf_model = 'FIR'
x3,name3 = dmtx_light(frametimes, paradigm, hrf_model = 'FIR',
drift_model='Polynomial', drift_order=3,
fir_delays = range(1,6))
import matplotlib.pylab as mp
mp.figure()
frametimes = np.linspace(0, (n_scans-1)*tr, n_scans)
conditions = np.arange(20)%2
onsets = np.linspace(5, (n_scans-1)*tr-10, 20) # in seconds
hrf_model = 'Canonical'
motion = np.cumsum(np.random.randn(n_scans, 6),0)
add_reg_names = ['tx','ty','tz','rx','ry','rz']
# write directory
swd = tempfile.mkdtemp()
########################################
# Design matrix
########################################
paradigm = dm.EventRelatedParadigm(conditions, onsets)
X, names = dm.dmtx_light(frametimes, drift_model='Cosine', hfcut=128,
hrf_model=hrf_model, paradigm=paradigm, add_regs=motion, add_reg_names=add_reg_names)
#######################################
# Get the FMRI data
#######################################
fmri_data = surrogate_4d_dataset(shape=shape, n_scans=n_scans)[0]
# if you want to save it as an image
data_file = op.join(swd,'fmri_data.nii')
save(fmri_data, data_file)
########################################
# Perform a GLM analysis
########################################
hrf_model = 'Canonical'
motion = np.cumsum(np.random.randn(n_scans, 6), 0)
add_reg_names = ['tx', 'ty', 'tz', 'rx', 'ry', 'rz']
# write directory
swd = tempfile.mkdtemp()
########################################
# Design matrix
########################################
paradigm = dm.EventRelatedParadigm(conditions, onsets)
X, names = dm.dmtx_light(frametimes,
drift_model='Cosine',
hfcut=128,
hrf_model=hrf_model,
paradigm=paradigm,
add_regs=motion,
add_reg_names=add_reg_names)
#######################################
# Get the FMRI data
#######################################
fmri_data = surrogate_4d_dataset(shape=shape, n_scans=n_scans)[0]
# if you want to save it as an image
data_file = op.join(swd, 'fmri_data.nii')
save(fmri_data, data_file)
########################################
