def self_evaluate(self,tests=None):
from neuronunit.optimisation import optimization_management as om_
if tests is not None:
self.tests = tests
OM = self.dtc_to_opt_man()
self = om_.dtc_to_rheo(self)
d = OM.format_test(self)
model = self.dtc_to_model()
scores_ = []
for t in self.tests:
if hasattr(t,'allen'):
continue
if 'RheobaseTest' in t.name: t.score_type = sciunit.scores.ZScore
if 'RheobaseTestP' in t.name: t.score_type = sciunit.scores.ZScore
try:
score_gene = t.judge(model)
except:
score_gene = None
lns = 100
if score_gene is not None:
if not isinstance(type(score_gene),sciunit.scores.InsufficientDataScore):
if not isinstance(type(score_gene.log_norm_score),type(None)):
try:
lns = np.abs(score_gene.log_norm_score)
except:
if score_gene.raw is not None:
lns = np.abs(float(score_gene.raw))
else:
lns = 100.0
else:
try:
lns = np.abs(float(score_gene.raw))
except:
lns = 100
else:
try:
lns = np.abs(float(score_gene.raw))
except:
lns = 100
if lns==np.inf or lns==-np.inf:
lns = np.abs(float(score_gene.raw))
scores_.append(lns)
for i,s in enumerate(scores_):
if s==np.inf or s==-np.inf:
scores_[i] = 100 #np.abs(float(score_gene.raw))
#self.SA = ScoreArray(self.tests, scores_)
self.scores_ = scores_
temp = [ t for t in self.tests if not hasattr(t,'allen') ]
self.SA = ScoreArray(temp, self.scores_)
#self = OM.get_agreement(self)
return self
def iap(self, tests=None):
'''
Inject and plot to terminal with ascii
This is useful for debugging new backends, in bash big/fast command line orientated optimization routines.
'''
from neuronunit.optimisation import optimization_management as om_
if tests is not None:
self.tests = tests
OM = self.dtc_to_opt_man()
self = om_.dtc_to_rheo(self)
model = self.dtc_to_model()
pms = uset_t.params
pms['injected_square_current']['amplitude'] = self.rheobase
model.inject_square_current(pms['injected_square_current'])
nspike = model.get_spike_train()
self.nspike = nspike
vm = model.get_membrane_potential()
return vm
objectives = cell_evaluator.evaluate_with_dicts(best_ind_dict)
model = cell_evaluator.cell_model
cell_evaluator.param_dict(best_ind)
model.attrs = {
str(k): float(v)
for k, v in cell_evaluator.param_dict(best_ind).items()
}
opt = model.model_to_dtc()
opt.attrs = {
str(k): float(v)
for k, v in cell_evaluator.param_dict(best_ind).items()
}
from neuronunit.optimisation.optimization_management import dtc_to_rheo, inject_and_plot_model30, check_bin_vm30, check_bin_vm15
opt = dtc_to_rheo(opt)
opt.rheobase
opt.attrs
vm301, vm151, _, _, target = inject_and_plot_model30(target)
vm302, vm152, _, _, opt = inject_and_plot_model30(opt)
# +
from neuronunit.optimisation.optimization_management import dtc_to_rheo, inject_and_plot_model30, check_bin_vm30, check_bin_vm15
check_bin_vm30(target, opt)
check_bin_vm15(target, opt)
# -
def basic_expVar(trace1, trace2):
# https://github.com/AllenInstitute/GLIF_Teeter_et_al_2018/blob/master/query_biophys/query_biophys_expVar.py