def load_inference(loglikelihood, working_path, *param_data):
"""
Load previous simulation results.
:param loglikelihood: loglikelihood data of inference
:param working_path: Where to print the results
:param param_data: List containing: [name, range_min, range_max, resolution, mean, sigma, value]
:return: inference object ready to evaluate
"""
p = []
for item in param_data:
p.append(
RandomVariable(name=item[0],
range_min=float(item[1]),
range_max=float(item[2]),
resolution=float(item[3]),
mean=float(item[4]),
sigma=float(item[5]),
value=float(item[6])))
pset = ParameterSet(*p)
res = Analyse(loglikelihood, pset, working_path)
print("Previous inference data result loaded!")
return res
def load_parameter_set(params_data):
"""
Create ParameterSet object from parameter data
:param params_data: Data list of parameters (save_params output) [param1, param2, ...] param1=param_init...
:return: ParameterSet object
"""
from module.probability import RandomVariable, ParameterSet
p = []
for item in params_data:
p.append(
RandomVariable(name=item[0], range_min=float(item[1]), range_max=float(item[2]), resolution=float(item[3]),
mean=float(item[4]), sigma=float(item[5]), value=float(item[6])))
p_set = ParameterSet(*p)
return p_set
print(invcovmat.shape)
# END OF SETTING UP SIMULATION PARAMETERS -----------------------------------------------------------------------------
# Set up parameters using prior information about them (fix the range we are assuming the true parameter)
prior_params = []
for idx, item in enumerate(p_names):
prior_params.append(
RandomVariable(name=item,
range_min=p_range[idx][0],
range_max=p_range[idx][1],
resolution=p_res[idx],
sigma=p_std[idx],
mean=p_mean[idx]))
prior_set = ParameterSet(*prior_params)
prior_set.batch_len = batch_size
if batch_size is not None:
prior_set.isBatch = True
else:
prior_set.isBatch = False
prior_set.create_batch()
# Create fixed params sampled from prior
fixed_params = sampling_from_prior(prior_set, fixed_param_num)
# Save parameter informations
# Create database for data
database = tb.open_file(
"/Users/Dani/TDK/parameter_estim/stim_protocol2/comb_colored_srsoma-rdend_gpas-dens/paramsetup.hdf5",
mode="w")
mean=current_Ra,
sigma=pRa.sigma)
gpas = RandomVariable(name='gpas',
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()
# Set up random variables
Ra = RandomVariable(name='Ra',
range_min=Ra_start,
range_max=Ra_end,
resolution=80,
mean=current_Ra,
sigma=pRa.sigma)
gpas = RandomVariable(name='gpas',
range_min=gpas_start,
range_max=gpas_end,
resolution=80,
mean=current_gpas,
sigma=pgpas.sigma)
Ra_gpas = ParameterSet(Ra, gpas)
inference = DependentInference(
data,
Ra_gpas,
working_path="/Users/Dani/TDK/parameter_estim/stim_protocol/mc")
multi_comp = partial(
stick_and_ball,
stype='broad') # fix chosen stimulus type for simulations
if __name__ == '__main__':
inference.run_sim(multi_comp, inv_covmat)
inference.run_evaluation()
# Do statistics for the current inference
if stat(Ra) is not str:
"/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()
# Set up random seed
np.random.seed(42)
# Set up parameters using prior information about them (fix the range we are assuming the true parameter)
prior_params = []
for idx, item in enumerate(p_names):
prior_params.append(
RandomVariable(name=item,
range_min=p_range[idx][0],
range_max=p_range[idx][1],
resolution=p_res[idx],
sigma=p_std[idx],
mean=p_mean[idx]))
prior_set = ParameterSet(*prior_params)
# Create fixed params sampled from prior
fixed_params = sampling_from_prior(prior_set, fixed_param_num)
for idx, current_params in enumerate(fixed_params):
print(
"\n\n---------------------------------------- %ith FIXED PARAMETER -------------------------------------"
% idx)
for param in prior_set.params:
param.value = current_params[param.name]
for item in hz:
print(
"\n\n---------------------------------------- Running %i Hz zap protocol"
for item in hz:
print("\n\n---------------------------------------- Running %i Hz zap protocol" % item)
# 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
range_max=1.5,
resolution=60,
mean=1.2,
sigma=0.2,
value=1.)
gpas = RandomVariable(name='gpas',
range_min=0.00005,
range_max=0.00015,
resolution=60,
mean=0.00008,
sigma=0.00002,
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)