def start(self):
super(Experiment_sequence, self).start()
c = self.params.c
# Create paper-specific sources
self.test_words = c.source.test_words
if not c.source.control:
source = CountingSource(['ABCD'], np.array([[1.]]), c.N_u_e,
c.N_u_i, c.source.avoid)
else:
from itertools import permutations
source = CountingSource.init_simple(
24,
4, [4, 4],
1,
c.N_u_e,
c.N_u_i,
c.source.avoid,
words=[''.join(x) for x in (permutations('ABCD'))])
# Already add letters for later
source.alphabet = unique("".join(source.words) + 'E_')
source.N_a = len(source.alphabet)
source.lookup = dict(zip(source.alphabet,\
range(source.N_a)))
source = TrialSource(source, c.wait_min_plastic, c.wait_var_plastic,
zeros(source.N_a), 'reset')
self.source_archived = copy.deepcopy(source)
inputtrainsteps = c.steps_plastic + c.steps_noplastic_train
stats_single = [
ActivityStat(),
InputIndexStat(),
SpikesStat(),
ISIsStat(interval=[0, c.steps_plastic]),
ConnectionFractionStat(),
]
stats_all = [
ParamTrackerStat(),
EndWeightStat(),
InputUnitsStat(),
MeanActivityStat(start=inputtrainsteps,
stop=c.N_steps,
N_indices=len(''.join((self.test_words))) + 1,
LFP=False),
MeanPatternStat(start=c.steps_plastic,
stop=c.N_steps,
N_indices=len(''.join((self.test_words))) + 1)
]
return (source, stats_single + stats_all, stats_all)
def start(self):
super(Experiment_sequence,self).start()
c = self.params.c
# Create paper-specific sources
self.test_words = c.source.test_words
if not c.source.control:
source = CountingSource(['ABCD'],np.array([[1.]]),
c.N_u_e,c.N_u_i,c.source.avoid)
else:
from itertools import permutations
source = CountingSource.init_simple(24,4,[4,4],1,
c.N_u_e,c.N_u_i,c.source.avoid, words =
[''.join(x) for x in (permutations('ABCD'))])
# Already add letters for later
source.alphabet = unique("".join(source.words)+'E_')
source.N_a = len(source.alphabet)
source.lookup = dict(zip(source.alphabet,\
range(source.N_a)))
source = TrialSource(source, c.wait_min_plastic,
c.wait_var_plastic,zeros(source.N_a),'reset')
self.source_archived = copy.deepcopy(source)
inputtrainsteps = c.steps_plastic + c.steps_noplastic_train
stats_single = [
ActivityStat(),
InputIndexStat(),
SpikesStat(),
ISIsStat(interval=[0, c.steps_plastic]),
ConnectionFractionStat(),
]
stats_all = [
ParamTrackerStat(),
EndWeightStat(),
InputUnitsStat(),
MeanActivityStat(start=inputtrainsteps,
stop=c.N_steps,
N_indices=len(''.join((self.test_words)))+1,
LFP=False),
MeanPatternStat(start=c.steps_plastic,
stop=c.N_steps,
N_indices=len(''.join((self.test_words)))+1)
]
return (source,stats_single+stats_all,stats_all)
