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_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_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))

"""
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
########################################