def test_convolve_functions():
# replicate convolution
# This is a square wave on [0,1]
f1 = (t > 0) * (t < 1)
# ff1 is the numerical implementation of same
lam_f1 = lambdify(t, f1)
ff1 = lambda x : lam_f1(x).astype(np.int)
# The convolution of ``f1`` with itself is a triangular wave on
# [0,2], peaking at 1 with height 1
tri = convolve_functions(f1, f1, [0,2], 1.0e-3, name='conv')
yield assert_equal(str(tri), 'conv(t)')
ftri = lambdify(t, tri)
time, value = numerical_convolve(ff1, ff1, [0, 2], 1.0e-3)
y = ftri(time)
# numerical convolve about the same as ours
yield assert_array_almost_equal(value, y)
# peak is at 1
yield assert_array_almost_equal(time[np.argmax(y)], 1)
# Flip the interval and get the same result
tri = convolve_functions(f1, f1, [2, 0], 1.0e-3)
ftri = lambdify(t, tri)
y = ftri(time)
yield assert_array_almost_equal(value, y)
# offset square wave by 1
f2 = (t > 1) * (t < 2)
tri = convolve_functions(f1, f2, [0,3], 1.0e-3)
ftri = lambdify(t, tri)
y = ftri(time)
yield assert_array_almost_equal(time[np.argmax(y)], 2)
# offset both by 1 and start interval at one
tri = convolve_functions(f2, f2, [1,3], 1.0e-3)
ftri = lambdify(t, tri)
# get numerical version
lam_f2 = lambdify(t, f2)
ff2 = lambda x : lam_f2(x).astype(np.int)
time, value = numerical_convolve(ff2, ff2, [1, 3], 1.0e-3)
# and our version, compare
y = ftri(time)
yield assert_array_almost_equal(y, value)
def convolve_regressors(paradigm, hrf_model, end_time, fir_delays=[0],
fir_duration=1.):
""" Creation of a formula that represents
the convolution of the conditions onset with a certain hrf model
Parameters
----------
paradigm: paradigm instance
hrf_model: string that can be 'Canonical',
'Canonical With Derivative' or 'FIR'
that specifies the hemodynamic reponse function
end_time: float,
end time of the paradigm (needed only for block designs)
fir_delays=[0], optional, array of shape(nb_onsets) or list
in case of FIR design, yields the array of delays
used in the FIR model
fir_duration=1., float, duration of the FIR block
in general it should eb equal to the tr
Returns
-------
f: formula instance,
contains the convolved regressors as functions of time
names: list of strings,
the condition names, that depend on the hrf model used
if 'Canonical' then this is identical to the input names
if 'Canonical With Derivative', then two names are produced for
input name 'name': 'name' and 'name_derivative'
fixme
-----
normalization of the columns of the design matrix ?
"""
listc = []
hnames = []
typep = paradigm.type
for nc in np.unique(paradigm.con_id):
onsets = paradigm.onset[paradigm.con_id == nc]
nos = np.size(onsets)
if paradigm.amplitude is not None:
values = paradigm.amplitude[paradigm.con_id == nc]
else:
values = np.ones(nos)
if nos < 1:
continue
if typep == 'event':
if hrf_model == "Canonical":
c = utils.define(
nc, utils.events(onsets, values, f=hrf.glover))
listc.append(c)
hnames.append(nc)
elif hrf_model == "Canonical With Derivative":
c1 = utils.define(
nc, utils.events(onsets, values, f=hrf.glover))
c2 = utils.define(nc + "_derivative",
utils.events(onsets, values, f=hrf.dglover))
listc.append(c1)
listc.append(c2)
hnames.append(nc)
hnames.append(nc + "_derivative")
elif hrf_model == "FIR":
for i, ft in enumerate(fir_delays):
lnames = nc + "_delay_%d" % i
changes = np.hstack((onsets + ft,
onsets + ft + fir_duration))
ochanges = np.argsort(changes)
lvalues = np.hstack((values, np.zeros(nos)))
changes = changes[ochanges]
lvalues = lvalues[ochanges]
c = utils.define(lnames,
utils.step_function(changes, lvalues))
listc.append(c)
hnames.append(lnames)
else:
raise NotImplementedError('unknown hrf model')
elif typep == 'block':
offsets = onsets + paradigm.duration[paradigm.con_id == nc]
intervals = [[on, off] for (on, off) in zip(onsets, offsets)]
blks = utils.blocks(intervals, values)
changes = np.hstack((onsets, offsets))
cvalues = np.hstack((values, - values))
if hrf_model == "Canonical":
c = utils.convolve_functions(blks, hrf.glover(hrf.T),
[0, end_time], 0.001)
listc.append(c)
hnames.append(nc)
elif hrf_model == "Canonical With Derivative":
c1 = utils.convolve_functions(blks, hrf.glover(hrf.T),
[0, end_time], 0.001)
c2 = utils.events(changes, cvalues, f=hrf.glover)
listc.append(c1)
listc.append(c2)
hnames.append(nc)
hnames.append(nc + "_derivative")
elif hrf_model == "FIR":
raise NotImplementedError(
'block design are not compatible with FIR')
else:
raise NotImplementedError('unknown hrf model')
# create the formula
p = formula.Formula(listc)
return p, hnames