params = {
'name': 'CellFitProblem',
'maximize': False,
'normalize': True,
'model_dir': '../../../model/cells/toymodel1.json',
'mechanism_dir': '../../../model/channels/schmidthieber',
'variables': variables,
'data_dir': '../../../data/toymodels/toymodel1/ramp.csv',
'get_var_to_fit': 'get_v',
'fitnessweights': [1.0],
'errfun': 'rms',
'insert_mechanisms': True
}
# create problem
problem = CellFitProblem(**params)
dt_exp = problem.simulation_params['dt']
dts = dt_exp / 2**dt_fac
channel_list = get_channel_list(problem.cell, 'soma')
ion_list = get_ionlist(channel_list)
# initialize errors
error_weights = np.zeros([n_models, len(dts)])
error_traces = np.zeros([n_models, len(dts)])
# create pseudo random number generator
seed = time()
#np.savetxt(save_dir+'/seed_'+str(trial)+'.txt', np.array([seed]))
prng = Random()
prng.seed(seed)
params = {
'name': 'CellFitProblem',
'maximize': False,
'normalize': True,
'model_dir': '../../../model/cells/dapmodelnaka.json',
'mechanism_dir': '../../../model/channels/schmidthieber',
'variables': [],
'data_dir': '../../../data/2015_08_11d/merged/step_dap_zap.csv',
'get_var_to_fit': 'get_v',
'fitnessweights': [1.0],
'errfun': 'rms',
'insert_mechanisms': True
}
# create problem
problem = CellFitProblem(**params)
# save all information
if not os.path.exists(save_dir):
os.makedirs(save_dir)
with open(save_dir+'/problem.json', 'w') as f:
json.dump(params, f, indent=4)
with open(save_dir+'/cell.json', 'w') as f:
json.dump(Cell.from_modeldir(params['model_dir']).get_dict(), f, indent=4)
for trial in range(0, n_trials):
# get current traces
v_exp = problem.data.v.values
t_exp = problem.data.t.values
i_exp = problem.data.i.values
