def some_learn(N, k, N_learn, loc, event=True, rand_learn=True, prng=None):
"""
Creates 'uneven' acc, and p value distributions for k trials in
N conditions in the returned trialset where N_learn is the number
of conditions that show learning (via sim_acc_learn()).
N minus N_learn condtions simulated data will be governed instead
by sim_acc_rand. If event is True an event-related trialset is
created.
"""
from copy import deepcopy
prng = process_prng(prng)
if N == N_learn:
raise ValueError('N_learn must be less than N.')
if N_learn <= 0:
raise ValueError('N_learn must be 1 or greater.')
if event:
trialset, prng = event_randomtrial(N, k, mult=1, prng=prng)
else:
trialset, prng = randomtrial(N, k, prng)
N_c = deepcopy(N)
if event:
N_c += 1
acc = [0, ] * (N_c*k)
p = [0, ] * (N_c*k)
names = list(set(trialset))
for ii,n in enumerate(names):
## Skip null trials
if (n == 0) | (n == '0'):
continue
## How many trials/condition?
acc_n = []
p_n = []
## How many trials/condition?
if ii <= N_learn:
print('Learning in iteration {0}.'.format(ii))
if rand_learn:
acc_n, p_n, prng = random_learn(k, 1./N, loc, prng)
else:
acc_n, p_n, prng = learn(k, loc, prng)
else:
acc_n, p_n, prng = randomacc(k,1./N)
for jj,t in enumerate(trialset):
if t == n:
acc[jj] = acc_n.pop(0)
p[jj] = p_n.pop(0)
return trialset, acc, p, prng
def learn(N, k, loc, event=True, prng=None):
"""
Returns a trialset matching N,k along with matching accuracy and
probability lists. If event is True event_random is used in place of
random to create the trialset.
"""
from copy import deepcopy
prng = process_prng(prng)
## For each condition name (n),
## create acc_n,p_n then map those
## into acc,p for each matching trial (t)
## in the trialset
if event:
trialset, prng = event_randomtrial(N, k, 1, prng)
else:
trialset, prng = randomtrial(N, k, prng)
N_c = deepcopy(N)
if event:
N_c += 1
acc = [0, ] * (N_c*k)
p = [0, ] * (N_c*k)
names = list(set(trialset))
for n in names:
## Skip null trials
if (n is 0) | (n is '0'):
continue
## How many trials/condition?
acc_n = []
p_n = []
acc_n, p_n, prng = random_learn(k, 1/N, loc, prng)
for ii, t in enumerate(trialset):
if t == n:
acc[ii] = acc_n.pop(0)
p[ii] = p_n.pop(0)
return trialset, acc, p, prng