def test_init(self):
factors=[{'sleepdep':['normal','deprived']},
{'stimulus':['left', 'right']}]
responses=['left','right']
design=Design(factors,responses, 'stimulus')
assert design.nresponses()==len(responses)
assert design.nconditions()==4
assert len(design.factors_to_int)==len(design.factors_from_int)
k=['deprived','left']
assert design.condidx( k )==2
def test_init(self):
factors = [{
'sleepdep': ['normal', 'deprived']
}, {
'stimulus': ['left', 'right']
}]
responses = ['left', 'right']
design = Design(factors, responses, 'stimulus')
assert design.nresponses() == len(responses)
assert design.nconditions() == 4
assert len(design.factors_to_int) == len(design.factors_from_int)
k = ['deprived', 'left']
assert design.condidx(k) == 2
def test_init(self):
factors = [{
'sleepdep': ['normal', 'deprived']
}, {
'stimulus': ['left', 'right']
}]
responses = ['left', 'right']
design = Design(factors, responses, 'stimulus')
dat = pd.read_csv('./data/sleep_stop_onesubj_test.csv')
assert dat.shape[0] == 800
dat.columns = [
'sleepdep', 'stimulus', 'SSD', 'response', 'correct', 'RT'
]
ds = StopTaskDataSet(design, dat)
pars = pSSLBA_modelA.paramspec(.2, .15, .2, 1.0, 1.0, 2, 1, 0.5)
mod = pSSLBA_modelA(design, pars)
print mod.parstring(full=True)
print mod
print mod.go_accumulators[0]
nsamples = 100
x = np.linspace(0.01, 10, nsamples)
condition = 0
dens = mod.dens_acc_go(x, condition, 1)
cscore = 0
for i in range(design.nresponses()):
score = scipy.integrate.quad(mod.dens_acc_go,
0.01,
np.infty,
args=(condition, i))[0]
print "Chance of winning Acc %i (condition=%s): %f" % (
i, str(design.condidx(condition)), score)
cscore += score
assert abs(cscore - 1) < 1e-6
print cscore
L = mod.loglikelihood(ds)
assert np.isfinite(L)
print "L=", L