def add_new_designs(self, weights=None, R=None):
'''
This function generates the population.
:param experiment: The experimental setup of the fMRI experiment.
:type experiment: experiment
:param weights: weights for efficiency calculation.
:type weights: list of floats, summing to 1
:param seed: The seed for ramdom processes.
:type seed: integer or None
'''
# weights
if weights == None:
weights = self.weights
if not R:
R = np.round(np.array(self.R) * self.G).tolist()
print(R)
if self.exp.n_stimuli in [6, 10] and R[2] > 0:
print(
"warning: for this number of conditions/stimuli, there are no msequences possible. Replaced by random designs."
)
R[1] = R[1] + R[2]
R[2] = 0
NDes = 0
self.change_seed()
k = 0
while NDes < np.sum(R):
k = k + 1
print(k)
self.change_seed()
ind = np.sum(NDes >= np.cumsum(R))
ordertype = ['blocked', 'random', 'msequence'][ind]
order = generate.order(self.exp.n_stimuli,
self.exp.n_trials,
self.exp.P,
ordertype=ordertype,
seed=self.seed)
ITI, ITIlam = generate.iti(ntrials=self.exp.n_trials,
model=self.exp.ITImodel,
min=self.exp.ITImin,
max=self.exp.ITImax,
mean=self.exp.ITImean,
lam=self.exp.ITIlam,
seed=self.seed)
if ITIlam:
self.exp.ITIlam = ITIlam
des = design(order=order, ITI=ITI, experiment=self.exp)
fulldes = self.check_develop(des, weights)
if fulldes == False:
continue
else:
self.designs.append(fulldes)
NDes = NDes + 1
return self
ef = Enforce(TNTpseudM)
ef.add_constraint(MaxRep,cols=[TNTpseudM.T_ID],maxrep=3)
ef.add_constraint(MaxRep,cols=[TNTpseudM.NT_ID],maxrep=3)
ef.add_constraint(MaxRep,cols=[TNTpseudM.neu_ID],maxrep=3)
ef.add_constraint(MaxRep,cols=[TNTpseudM.neg_ID],maxrep=3)
ef.add_constraint(MinDist,cols=[TNTpseudM.null_ID],mindist=2)
# Enforce the constraints
TNTpseudM = ef.enforce()
# convert new datamatrix to a list as input for DES1
TNTorder = list(TNTpseudM.order)
TNTdES = neurodesign.classes.design(
order = TNTorder,
ITI = generate.iti(ntrials=124, model="exponential", min=1.4, mean=2, max=2.6,
resolution=0.1, seed=1234)[0],
onsets = [], experiment = TNTfMRI)
TNTdES.designmatrix()
TNTdES.FeCalc();TNTdES.FdCalc();TNTdES.FfCalc();TNTdES.FcCalc()
TNTdES.FCalc(weights=[0.25, 0.25, 0.25, 0.25])
print("estimation efficiency = ", TNTdES.Fe, "\tdetection power = ", TNTdES.Fd, "\tstimulus frequency = ", TNTdES.Ff, "\tdesign predictability = ", TNTdES.Fc)
if TNTdES.Fe > Fe_threshold and TNTdES.Fd > Fd_threshold and TNTdES.Ff > Ff_threshold and TNTdES.Fc > Fc_threshold or time.time() > timeout:
break
print("FINAL: estimation efficiency = ", TNTdES.Fe, "\tdetection power = ", TNTdES.Fd, "\tstimulus frequency = ", TNTdES.Ff, "\tdesign predictability = ", TNTdES.Fc)
#print('TNT Run-1 Order Matrix:')
experiment=EXP)
# expand to design matrix
DES.designmatrix()
DES.FCalc(weights=[0, 0.5, 0.25, 0.25])
DES.FdCalc()
DES.FcCalc()
DES.FfCalc()
DES.FeCalc()
# define second design
DES2 = neurodesign.design(
order=[0, 0, 1, 1, 2, 2, 0, 0, 1, 1, 2, 2, 0, 0, 1, 1, 2, 2, 0, 1],
ITI=generate.iti(20, "exponential", min=1, mean=2, max=4, seed=1234)[0],
experiment=EXP)
DES2.designmatrix()
DES2.FeCalc()
DES2.FdCalc()
DES2.FcCalc()
DES2.FfCalc()
# crossover to obtain design 3 and 4
DES3, DES4 = DES.crossover(DES2, seed=2000)
DES3.order
DES4.order
DES3.designmatrix()
DES3.FeCalc()