def design_matrix(
misc_file, output_file, session, paradigm_file, frametimes,
hrf_model="Canonical", drift_model="Blank", add_regs=None, drift_order=2,
hfcut=128, fir_delays=[0], fir_duration=1., model="default",
add_reg_names=None, verbose=0):
"""
Estimation of the design matrix and update of misc info
Parameters
----------
misc_file: string,
path of misc info file that is updated with info on design matrix
output_file: string,
path of the (.csv) file
where the design matrix shall be written
session: string,
id of the session
paradigm_file: string,
path of (.csv) paradigm-describing file
or None, if no such file exists
concerning the following parameters, please refer to
nipy.neurospin.utils.design_matrix
Returns
-------
dmtx: nipy.neurospin.utils.design_matrix.DesignMatrix
instance
"""
import nipy.neurospin.utils.design_matrix as dm
# get the condition names
misc = ConfigObj(misc_file)
if session.isdigit():
_session = int(session)
else:
_session = misc["sessions"].index(session)
_names = misc["tasks"]
# get the paradigm
if isinstance(paradigm_file, basestring):
_paradigm = dm.load_protocol_from_csv_file(paradigm_file, _session)
else:
_paradigm = None
# compute the design matrix
dmtx = dm.DesignMatrix(frametimes, _paradigm, hrf_model=hrf_model,
drift_model=drift_model, hfcut=hfcut, drift_order=drift_order,
fir_delays=fir_delays, fir_duration=fir_duration, cond_ids=_names,
add_regs=add_regs, add_reg_names=add_reg_names)
dmtx.estimate()
# write the design matrix
dmtx.write_csv(output_file)
# write some info in the misc file
if not misc.has_key(model):
misc[model] = {}
misc[model]["regressors_%s" % session] = dmtx.names
misc[model]["design matrix cond"] = dmtx.design_cond
misc.write()
return dmtx
# confounds
hrf_model = 'Canonical With Derivative'
drift_model = "Cosine"
hfcut = 128
# write directory
swd = tempfile.mkdtemp()
print 'Computation will be performed in temporary directory: %s' % swd
########################################
# Design matrix
########################################
print 'Loading design matrix...'
paradigm = dm.load_protocol_from_csv_file(paradigm_file, session=0)
design_matrix = dm.DesignMatrix( frametimes, paradigm, hrf_model=hrf_model,
drift_model=drift_model, hfcut=hfcut)
design_matrix.show()
pylab.savefig(op.join(swd, 'design_matrix.png'))
# design_matrix.save(...)
########################################
# Mask the data
########################################
print 'Computing a brain mask...'
mask_path = op.join(swd, 'mask.nii')
mask_array = compute_mask_files( data_path, mask_path, False, 0.4, 0.9)
