def __init__(self):
"""Constructor"""
super(GraupnerBrunelEvaluator, self).__init__()
# Graupner-Brunel model parameters and boundaries,
# from (Graupner and Brunel, 2012)
self.graup_params = [('tau_ca', 1e-3, 100e-3), ('C_pre', 0.1, 20.0),
('C_post', 0.1, 50.0), ('gamma_d', 5.0, 5000.0),
('gamma_p', 5.0, 2500.0), ('sigma', 0.35, 70.7),
('tau', 2.5, 2500.0), ('D', 0.0, 50e-3),
('b', 1.0, 100.0)]
self.params = [
bpop.parameters.Parameter(param_name,
bounds=(min_bound, max_bound))
for param_name, min_bound, max_bound in self.graup_params
]
self.param_names = [param.name for param in self.params]
self.protocols, self.sg, self.stdev, self.stderr = \
stdputil.load_neviansakmann()
self.objectives = [
bpop.objectives.Objective(protocol.prot_id)
for protocol in self.protocols
]
def analyse():
"""Generate plot"""
cp = pickle.load(open(cp_filename, "r"))
results = (cp["population"], cp["halloffame"], cp["history"], cp["logbook"])
_, hof, hst, log = results
best_ind = hof[0]
best_ind_dict = evaluator.get_param_dict(best_ind)
print("Best Individual")
for attribute, value in best_ind_dict.items():
print("\t{} : {}".format(attribute, value))
good_solutions = [
evaluator.get_param_dict(ind)
for ind in hst.genealogy_history.itervalues()
if np.all(np.array(ind.fitness.values) < 1)
]
# model_sg = evaluator.compute_synaptic_gain_with_lists(best_ind)
# Load data
protocols, sg, _, stderr = stdputil.load_neviansakmann()
dt = np.array([float(p.prot_id[:3]) for p in protocols])
plt.rcParams["lines.linewidth"] = 2
# plot_epspamp_discrete(dt, model_sg, sg, stderr)
plot_dt_scan(best_ind_dict, good_solutions, dt, sg, stderr)
plot_calcium_transients(protocols, best_ind_dict)
plot_log(log)
plt.show()
def __init__(self):
"""Constructor"""
super(GraupnerBrunelEvaluator, self).__init__()
# Graupner-Brunel model parameters and boundaries,
# from (Graupner and Brunel, 2012)
self.graup_params = [('tau_ca', 1e-3, 100e-3),
('C_pre', 0.1, 20.0),
('C_post', 0.1, 50.0),
('gamma_d', 5.0, 5000.0),
('gamma_p', 5.0, 2500.0),
('sigma', 0.35, 70.7),
('tau', 2.5, 2500.0),
('D', 0.0, 50e-3),
('b', 1.0, 100.0)]
self.params = [bpop.parameters.Parameter
(param_name, bounds=(min_bound, max_bound))
for param_name, min_bound, max_bound in self.
graup_params]
self.param_names = [param.name for param in self.params]
self.protocols, self.sg, self.stdev, self.stderr = \
stdputil.load_neviansakmann()
self.objectives = [bpop.objectives.Objective(protocol.prot_id)
for protocol in self.protocols]
def analyse():
"""Generate plot"""
cp = pickle.load(open(cp_filename, "r"))
results = (cp["population"], cp["halloffame"], cp["history"], cp["logbook"])
_, hof, hst, log = results
best_ind = hof[0]
best_ind_dict = evaluator.get_param_dict(best_ind)
print("Best Individual")
for attribute, value in best_ind_dict.iteritems():
print("\t{} : {}".format(attribute, value))
good_solutions = [
evaluator.get_param_dict(ind)
for ind in hst.genealogy_history.itervalues()
if np.all(np.array(ind.fitness.values) < 1)
]
# model_sg = evaluator.compute_synaptic_gain_with_lists(best_ind)
# Load data
protocols, sg, _, stderr = stdputil.load_neviansakmann()
dt = np.array([float(p.prot_id[:3]) for p in protocols])
plt.rcParams["lines.linewidth"] = 2
# plot_epspamp_discrete(dt, model_sg, sg, stderr)
plot_dt_scan(best_ind_dict, good_solutions, dt, sg, stderr)
plot_calcium_transients(protocols, best_ind_dict)
plot_log(log)
plt.show()