def test_opt_1(self):
specimen_id = self.ids[1]
cellmodel = "ADEXP"
if cellmodel == "IZHI":
model = model_classes.IzhiModel()
if cellmodel == "MAT":
model = model_classes.MATModel()
if cellmodel == "ADEXP":
model = model_classes.ADEXPModel()
target_num_spikes = 8
dtc = DataTC()
dtc.backend = cellmodel
dtc._backend = model._backend
dtc.attrs = model.attrs
dtc.params = {
k: np.mean(v)
for k, v in MODEL_PARAMS[cellmodel].items()
}
dtc = dtc_to_rheo(dtc)
assert dtc.rheobase is not None
self.assertIsNotNone(dtc.rheobase)
vm, plt, dtc = inject_and_plot_model(dtc, plotly=False)
fixed_current = 122 * qt.pA
model, suite, nu_tests, target_current, spk_count = opt_setup(
specimen_id,
cellmodel,
target_num_spikes,
provided_model=model,
fixed_current=False,
cached=True)
model = dtc.dtc_to_model()
model.seeded_current = target_current['value']
model.allen = True
model.seeded_current
model.NU = True
cell_evaluator, simple_cell = opt_setup_two(model,
cellmodel,
suite,
nu_tests,
target_current,
spk_count,
provided_model=model)
NGEN = 15
MU = 12
mapping_funct = dask_map_function
final_pop, hall_of_fame, logs, hist = opt_exec(MU, NGEN, mapping_funct,
cell_evaluator)
opt, target = opt_to_model(hall_of_fame, cell_evaluator, suite,
target_current, spk_count)
best_ind = hall_of_fame[0]
fitnesses = cell_evaluator.evaluate_with_lists(best_ind)
assert np.sum(fitnesses) < 0.7
self.assertGreater(0.7, np.sum(fitnesses))
#obnames = [obj.name for obj in cell_evaluator.objectives]
gen_numbers = logs.select('gen')
min_fitness = logs.select('min')
max_fitness = logs.select('max')
avg_fitness = logs.select('avg')
plt.plot(gen_numbers, max_fitness, label='max fitness')
plt.plot(gen_numbers, avg_fitness, label='avg fitness')
plt.plot(gen_numbers, min_fitness, label='min fitness')
plt.plot(gen_numbers, min_fitness, label='min fitness')
plt.semilogy()
plt.xlabel('generation #')
plt.ylabel('score (# std)')
plt.legend()
plt.xlim(min(gen_numbers) - 1, max(gen_numbers) + 1)
#model = opt.dtc_to_model()
plt.plot(opt.vm15.times, opt.vm15)
plt.plot(opt.vm15.times, opt.vm15)
target.vm15 = suite.traces['vm15']
plt.plot(target.vm15.times, target.vm15)
target.vm15 = suite.traces['vm15']
check_bin_vm15(target, opt)
best_ind = hall_of_fame[0]
fitnesses = cell_evaluator.evaluate_with_lists(best_ind)
assert np.sum(fitnesses) < 0.55
obnames = [obj.name for obj in cell_evaluator.objectives]
gen_numbers = logs.select('gen')
min_fitness = logs.select('min')
max_fitness = logs.select('max')
avg_fitness = logs.select('avg')
plt.plot(gen_numbers, max_fitness, label='max fitness')
plt.plot(gen_numbers, avg_fitness, label='avg fitness')
plt.plot(gen_numbers, min_fitness, label='min fitness')
plt.plot(gen_numbers, min_fitness, label='min fitness')
plt.semilogy()
plt.xlabel('generation #')
plt.ylabel('score (# std)')
plt.legend()
plt.xlim(min(gen_numbers) - 1, max(gen_numbers) + 1)
#plt.show()
model = opt.dtc_to_model()
plt.plot(opt.vm15.times, opt.vm15)
#plt.show()
plt.plot(opt.vm15.times, opt.vm15)
#plt.show()
target.vm15 = suite.traces['vm15']
plt.plot(target.vm15.times, target.vm15)
#plt.show()
target.vm15 = suite.traces['vm15']
check_bin_vm15(target, opt)
opt.attrs
def opt_setup(specimen_id,
cellmodel,
target_num,
provided_model=None,
cached=None,
fixed_current=False):
if cached is not None:
with open(str(specimen_id) + 'later_allen_NU_tests.p', 'rb') as f:
suite = pickle.load(f)
else:
sweep_numbers, data_set, sweeps = make_allen_tests_from_id.allen_id_to_sweeps(
specimen_id)
vmm, stimulus, sn, spike_times = make_allen_tests_from_id.get_model_parts_sweep_from_spk_cnt(
target_num, data_set, sweep_numbers, specimen_id)
suite, specimen_id = make_allen_tests_from_id.make_suite_known_sweep_from_static_models(
vmm, stimulus, specimen_id)
with open(str(specimen_id) + 'later_allen_NU_tests.p', 'wb') as f:
pickle.dump(suite, f)
target = StaticModel(vm=suite.traces['vm15'])
target.vm15 = suite.traces['vm15']
nu_tests = suite.tests
check_bin_vm15(target, target)
attrs = {k: np.mean(v) for k, v in MODEL_PARAMS[cellmodel].items()}
dtc = DataTC(backend=cellmodel, attrs=attrs)
for t in nu_tests:
if t.name == 'Spikecount_1.5x':
spk_count = float(t.observation['mean'])
break
observation_range = {}
observation_range['value'] = spk_count
#if provided_model is not None:
# model = provided_model
#else:
# model = dtc.dtc_to_model()
if provided_model is None:
print('depricated in favor of jithub model')
assert 1 == 2
provided_model = ephys.models.ReducedCellModel(
name='simple_cell',
params=BPO_PARAMS[cellmodel],
backend=cellmodel)
provided_model.params = {
k: np.mean(v)
for k, v in model.params.items()
}
provided_model.backend = cellmodel
provided_model.allen = None
provided_model.allen = True
model = provided_model
#print(model.params)
if fixed_current:
uc = {
'amplitude': fixed_current,
'duration': ALLEN_DURATION,
'delay': ALLEN_DELAY
}
target_current = None
else:
scs = SpikeCountSearch(observation_range)
target_current = scs.generate_prediction(provided_model)
ALLEN_DELAY = 1000.0 * qt.s
ALLEN_DURATION = 2000.0 * qt.s
uc = {
'amplitude': target_current['value'],
'duration': ALLEN_DURATION,
'delay': ALLEN_DELAY
}
#tg = target_current['value']
model = dtc.dtc_to_model()
model._backend.inject_square_current(**uc)
return model, suite, nu_tests, target_current, spk_count
def test_opt(self):
ids = [
324257146, 325479788, 476053392, 623893177, 623960880, 482493761,
471819401
]
specimen_id = ids[1]
cellmodel = "MAT"
if cellmodel == "IZHI":
model = model_classes.IzhiModel()
if cellmodel == "MAT":
model = model_classes.MATModel()
if cellmodel == "ADEXP":
model = model_classes.ADEXPModel()
specific_filter_list = [
'ISI_log_slope_1.5x', 'mean_frequency_1.5x',
'adaptation_index2_1.5x', 'first_isi_1.5x', 'ISI_CV_1.5x',
'median_isi_1.5x', 'Spikecount_1.5x', 'all_ISI_values',
'ISI_values', 'time_to_first_spike', 'time_to_last_spike',
'time_to_second_spike', 'spike_times'
]
simple_yes_list = specific_filter_list
target_num_spikes = 8
dtc = DataTC()
dtc.backend = cellmodel
dtc._backend = model._backend
dtc.attrs = model.attrs
dtc.params = {
k: np.mean(v)
for k, v in MODEL_PARAMS[cellmodel].items()
}
dtc = dtc_to_rheo(dtc)
vm, plt, dtc = inject_and_plot_model(dtc, plotly=False)
fixed_current = 122 * qt.pA
model, suite, nu_tests, target_current, spk_count = opt_setup(
specimen_id,
cellmodel,
target_num_spikes,
provided_model=model,
fixed_current=False)
model = dtc.dtc_to_model()
model.seeded_current = target_current['value']
model.allen = True
model.seeded_current
model.NU = True
cell_evaluator, simple_cell = opt_setup_two(model,
cellmodel,
suite,
nu_tests,
target_current,
spk_count,
provided_model=model)
NGEN = 15
MU = 12
mapping_funct = dask_map_function
final_pop, hall_of_fame, logs, hist = opt_exec(MU, NGEN, mapping_funct,
cell_evaluator)
opt, target = opt_to_model(hall_of_fame, cell_evaluator, suite,
target_current, spk_count)
best_ind = hall_of_fame[0]
fitnesses = cell_evaluator.evaluate_with_lists(best_ind)
assert np.sum(fitnesses) < 0.7
self.assertGreater(0.7, np.sum(fitnesses))
#obnames = [obj.name for obj in cell_evaluator.objectives]
gen_numbers = logs.select('gen')
min_fitness = logs.select('min')
max_fitness = logs.select('max')
avg_fitness = logs.select('avg')
plt.plot(gen_numbers, max_fitness, label='max fitness')
plt.plot(gen_numbers, avg_fitness, label='avg fitness')
plt.plot(gen_numbers, min_fitness, label='min fitness')
plt.plot(gen_numbers, min_fitness, label='min fitness')
plt.semilogy()
plt.xlabel('generation #')
plt.ylabel('score (# std)')
plt.legend()
plt.xlim(min(gen_numbers) - 1, max(gen_numbers) + 1)
#model = opt.dtc_to_model()
plt.plot(opt.vm15.times, opt.vm15)
plt.plot(opt.vm15.times, opt.vm15)
target.vm15 = suite.traces['vm15']
plt.plot(target.vm15.times, target.vm15)
target.vm15 = suite.traces['vm15']
check_bin_vm15(target, opt)