"/Users/Dani/TDK/parameter_estim/stim_protocol2/zap/best_comb/%i/stim.txt"
% item)
working_path = "/Users/Dani/TDK/parameter_estim/stim_protocol2/zap/best_comb/%i" % item
# Do statistics for each parameter
stat_list = []
for _ in p_names:
stat_list.append(np.empty((n, 6), dtype=np.float))
# Load fixed parameters: list of parameters to be inferred
fixed_params = []
for name in p_names:
fixed_params.append(get_default_param(name))
# Generate deterministic trace and create synthetic data with noise model
t, v = model(stype='custom', custom_stim=stim)
data = white(noise, v)
pset = ParameterSet(*fixed_params)
modell = partial(model, stype='custom', custom_stim=stim)
inf = IndependentInference(model=modell,
noise_std=noise,
target_trace=data,
parameter_set=pset,
working_path=working_path,
speed=speed)
if __name__ == '__main__':
inf.run_sim()
range_min=gpas_start,
range_max=gpas_end,
resolution=40,
mean=current_gpas,
sigma=pgpas.sigma)
cm = RandomVariable(name='cm',
range_min=cm_start,
range_max=cm_end,
resolution=40,
mean=current_cm,
sigma=pcm.sigma)
Ra_cm_gpas = ParameterSet(Ra, cm, gpas)
inference = IndependentInference(
data,
Ra_cm_gpas,
working_path="/Users/Dani/TDK/parameter_estim/stim_protocol2/ramp",
speed='min')
multi_comp = partial(
stick_and_ball, stype='custom',
custom_stim=stim) # fix chosen stimulus type for simulations
if __name__ == '__main__':
inference.run_sim(multi_comp, noise_sigma)
inference.run_evaluation()
# Do statistics for the current inference
Ra_stat[i, 0], Ra_stat[i, 1], Ra_stat[i, 2], Ra_stat[
i, 3], Ra_stat[4], Ra_stat[5] = stat(Ra)
# Stimulus path
stim = np.loadtxt("/Users/Dani/TDK/parameter_estim/stim_protocol2/zap/%i/stim.txt" % item)
working_path = "/Users/Dani/TDK/parameter_estim/stim_protocol2/zaps/%i(%i)" % (item,i)
# Generate deterministic trace and create synthetic data with noise model
_, v = model(stype='custom', custom_stim=stim,
Ra=current_value['Ra'], gpas=current_value['gpas'], cm=current_value['cm'])
# Generate noise_rep synthetic data (noise_rep portion noise realisation)
data = more_w_trace(noise, v, noise_rep)
pset = ParameterSet(*current_params)
modell = partial(model, stype='custom', custom_stim=stim)
inf = IndependentInference(model=modell, noise_std=noise, target_trace=data, parameter_set=pset, working_path=working_path)
if __name__ == '__main__':
inf.run_moretrace_inf()
for item in duration:
print("\n\n---------------------------------------- Running %i ms impulse protocol" % item)
# Stimulus path
stim = np.loadtxt("/Users/Dani/TDK/parameter_estim/stim_protocol2/steps/%i/stim.txt" % item)
working_path = "/Users/Dani/TDK/parameter_estim/stim_protocol2/steps/%i(%i)" % (item, i)
# Generate deterministic trace and create synthetic data with noise model
_, v = model(stype='custom', custom_stim=stim,
Ra=current_value['Ra'], gpas=current_value['gpas'], cm=current_value['cm'])
noise_sigma = 7.
stim = np.loadtxt("/Users/Dani/TDK/parameter_estim/stim_protocol2/ramp/stim.txt")
Ra = RandomVariable(name='Ra', range_min=40., range_max=150., resolution=160, mean=100., sigma=20)
gpas = RandomVariable(name='gpas', range_min=0.00005, range_max=0.00015, resolution=160, mean=0.0001, sigma=0.00002)
cm = RandomVariable(name='cm', range_min=0.5, range_max=1.7, resolution=160, mean=1., sigma=0.2)
# Generate deterministic trace and create synthetic data with noise model
# t, v = stick_and_ball(stype='custom', custom_stim=stim)
# data = white(noise_sigma, v)
# np.savetxt("/Users/Dani/TDK/parameter_estim/stim_protocol2/ramp/single/data(9).txt", data)
data = np.loadtxt("/Users/Dani/TDK/parameter_estim/stim_protocol2/ramp/single/data(9).txt")
multi_comp = partial(stick_and_ball, stype='custom', custom_stim=stim) # fix chosen stimulus type for simulations
Ra_cm_gpas = ParameterSet(Ra, cm, gpas)
inference = IndependentInference(model=multi_comp, noise_std=noise_sigma, target_trace=data, parameter_set=Ra_cm_gpas,
working_path="/Users/Dani/TDK/parameter_estim/stim_protocol2/ramp/single", speed="min")
if __name__ == '__main__':
inference.run_sim()
# inference.save_result()
inference.run_evaluation()
print(inference)
fullplot(inference)
plot_joint(inference, Ra, gpas)
plot_joint(inference, Ra, cm)
plot_joint(inference, cm, gpas)
value=0.0001)
# Ra = RandomVariable(name='Ra', range_min=50., range_max=150., resolution=60, mean=100., sigma=20.)
# 2.) Set up parameter set
cm_gpas = ParameterSet(cm, gpas)
# 3.) Sythetic data
t, v = stick_and_ball()
exp_v = white(noise, v)
# 4.) Set up inference
inf = IndependentInference(
model=stick_and_ball,
noise_std=noise,
target_trace=exp_v,
parameter_set=cm_gpas,
working_path=
"/home/terbed/PROJECTS/SPE/parameter-inference/module/examples/output",
speed='max',
save=False)
# 5.) Run inference
if __name__ == "__main__":
inf.run_sim()
# 6.) Run evaluation
inf.run_evaluation()
print("KL divergence test: %f" % inf.KL)
# 7.) Plot solution
for idx, item in enumerate(p_names):
current_params.append(
RandomVariable(item,
range_min=current_minrange[idx],
range_max=current_maxrange[idx],
resolution=res[idx],
mean=current_mean[item],
sigma=fixed_params[idx].sigma))
# Generate deterministic trace and create synthetic data with noise model
t, v = model(stype='custom', custom_stim=stim, **current_mean)
data = white(noise, v)
pset = ParameterSet(*current_params)
inf = IndependentInference(data,
pset,
working_path=working_path,
speed=speed)
modell = partial(model, stype='custom', custom_stim=stim)
if __name__ == '__main__':
inf.run_sim(modell, noise)
inf.run_evaluation()
m = 0
if inf.analyse_result() is None:
print("\nCouldn't fit gauss to data!")
for i, item in enumerate(stat_list):
stat_list[i] = np.delete(stat_list, (i - m), axis=0)
m += 1
else:
cm = RandomVariable(name='cm',
range_min=cm_start,
range_max=cm_end,
resolution=100,
mean=current_cm,
sigma=cm_sig)
gpas = RandomVariable(name='gpas',
range_min=gpas_start,
range_max=gpas_end,
resolution=100,
mean=current_gpas,
sigma=gpas_sig)
cm_gpas = ParameterSet(cm, gpas)
inference = IndependentInference(
data,
cm_gpas,
working_path="/Users/Dani/TDK/parameter_estim/stim_protocol/ow/steps")
one_comp = partial(
one_compartment,
stype='steps') # fix chosen stimulus type for simulations
if __name__ == '__main__':
inference.run_sim(one_comp, noise_sigma)
inference.run_evaluation()
# Do statistics for the current inference
if stat(cm) is not str:
cm_stat[i,
0], cm_stat[i,
1], cm_stat[i,