def protocol(recarr, design_type, *hrfs):
""" Create an object that can evaluate the FIAC
Subclass of formulae.Formula, but not necessary.
Parameters
----------
recarr : (N,) structured array
with fields 'time' and 'event'
design_type : str
one of ['event', 'block']. Handles how the 'begin' term is
handled. For 'block', the first event of each block is put in
this group. For the 'event', only the first event is put in this
group. The 'begin' events are convolved with hrf.glover.
hrfs: symoblic HRFs
Each event type ('SSt_SSp','SSt_DSp','DSt_SSp','DSt_DSp') is
convolved with each of these HRFs in order.
Returns
-------
f: Formula
Formula for constructing design matrices.
contrasts : dict
Dictionary of the contrasts of the experiment.
"""
event_types = np.unique(recarr['event'])
N = recarr.size
if design_type == 'block':
keep = np.not_equal((np.arange(N)) % 6, 0)
else:
keep = np.greater(np.arange(N), 0)
# This first frame was used to model out a potentially
# 'bad' first frame....
_begin = recarr['time'][~keep]
termdict = {}
termdict['begin'] = utils.define('begin', utils.events(_begin, f=hrf.glover))
drift = formulae.natural_spline(utils.T,
knots=[N_ROWS/2.+1.25],
intercept=True)
for i, t in enumerate(drift.terms):
termdict['drift%d' % i] = t
# After removing the first frame, keep the remaining
# events and times
times = recarr['time'][keep]
events = recarr['event'][keep]
# Now, specify the experimental conditions. This creates expressions named
# SSt_SSp0, SSt_SSp1, etc. with one expression for each (eventtype, hrf)
# pair
for v in event_types:
k = np.array([events[i] == v for i in range(times.shape[0])])
for l, h in enumerate(hrfs):
# Make sure event type is a string (not byte string)
term_name = '%s%d' % (to_str(v), l)
termdict[term_name] = utils.define(term_name,
utils.events(times[k], f=h))
f = formulae.Formula(list(termdict.values()))
Tcontrasts = {}
Tcontrasts['average'] = (termdict['SSt_SSp0'] + termdict['SSt_DSp0'] +
termdict['DSt_SSp0'] + termdict['DSt_DSp0']) / 4.
Tcontrasts['speaker'] = (termdict['SSt_DSp0'] - termdict['SSt_SSp0'] +
termdict['DSt_DSp0'] - termdict['DSt_SSp0']) * 0.5
Tcontrasts['sentence'] = (termdict['DSt_DSp0'] + termdict['DSt_SSp0'] -
termdict['SSt_DSp0'] - termdict['SSt_SSp0']) * 0.5
Tcontrasts['interaction'] = (termdict['SSt_SSp0'] - termdict['SSt_DSp0'] -
termdict['DSt_SSp0'] + termdict['DSt_DSp0'])
# Ftest
Fcontrasts = {}
Fcontrasts['overall1'] = formulae.Formula(list(Tcontrasts.values()))
return f, Tcontrasts, Fcontrasts
def __new__(cls, name, level):
# Names or levels can be byte strings
new = Term.__new__(cls, "%s_%s" % (to_str(name), to_str(level)))
new.level = level
new.factor_name = name
return new
def __new__(cls, name, level):
# Names or levels can be byte strings
new = Term.__new__(cls, "%s_%s" % (to_str(name), to_str(level)))
new.level = level
new.factor_name = name
return new
def protocol(recarr, design_type, *hrfs):
""" Create an object that can evaluate the FIAC
Subclass of formulae.Formula, but not necessary.
Parameters
----------
recarr : (N,) structured array
with fields 'time' and 'event'
design_type : str
one of ['event', 'block']. Handles how the 'begin' term is
handled. For 'block', the first event of each block is put in
this group. For the 'event', only the first event is put in this
group. The 'begin' events are convolved with hrf.glover.
hrfs: symoblic HRFs
Each event type ('SSt_SSp','SSt_DSp','DSt_SSp','DSt_DSp') is
convolved with each of these HRFs in order.
Returns
-------
f: Formula
Formula for constructing design matrices.
contrasts : dict
Dictionary of the contrasts of the experiment.
"""
event_types = np.unique(recarr['event'])
N = recarr.size
if design_type == 'block':
keep = np.not_equal((np.arange(N)) % 6, 0)
else:
keep = np.greater(np.arange(N), 0)
# This first frame was used to model out a potentially
# 'bad' first frame....
_begin = recarr['time'][~keep]
termdict = {}
termdict['begin'] = utils.define('begin', utils.events(_begin,
f=hrf.glover))
drift = formulae.natural_spline(utils.T,
knots=[N_ROWS / 2. + 1.25],
intercept=True)
for i, t in enumerate(drift.terms):
termdict['drift%d' % i] = t
# After removing the first frame, keep the remaining
# events and times
times = recarr['time'][keep]
events = recarr['event'][keep]
# Now, specify the experimental conditions. This creates expressions named
# SSt_SSp0, SSt_SSp1, etc. with one expression for each (eventtype, hrf)
# pair
for v in event_types:
k = np.array([events[i] == v for i in range(times.shape[0])])
for l, h in enumerate(hrfs):
# Make sure event type is a string (not byte string)
term_name = '%s%d' % (to_str(v), l)
termdict[term_name] = utils.define(term_name,
utils.events(times[k], f=h))
f = formulae.Formula(list(termdict.values()))
Tcontrasts = {}
Tcontrasts['average'] = (termdict['SSt_SSp0'] + termdict['SSt_DSp0'] +
termdict['DSt_SSp0'] + termdict['DSt_DSp0']) / 4.
Tcontrasts['speaker'] = (termdict['SSt_DSp0'] - termdict['SSt_SSp0'] +
termdict['DSt_DSp0'] - termdict['DSt_SSp0']) * 0.5
Tcontrasts['sentence'] = (termdict['DSt_DSp0'] + termdict['DSt_SSp0'] -
termdict['SSt_DSp0'] -
termdict['SSt_SSp0']) * 0.5
Tcontrasts['interaction'] = (termdict['SSt_SSp0'] - termdict['SSt_DSp0'] -
termdict['DSt_SSp0'] + termdict['DSt_DSp0'])
# Ftest
Fcontrasts = {}
Fcontrasts['overall1'] = formulae.Formula(list(Tcontrasts.values()))
return f, Tcontrasts, Fcontrasts
