def save_problem_specification(save_dir, n_trials, pop_size, max_iterations, problem_dicts, variables_all, generator=None):
save_dir += 'specification/'
# create save directory
if os.path.exists(save_dir):
print 'Saving directory already exists!'
return False
os.makedirs(save_dir)
# save number of trials, population size, maximal number of iterations
np.savetxt(save_dir+'/n_trials.txt', np.array([n_trials]))
np.savetxt(save_dir+'/pop_size.txt', np.array([pop_size]))
np.savetxt(save_dir+'/max_iterations.txt', np.array([max_iterations]))
with open(save_dir+'/variables_all.json', 'w') as f:
json.dump(variables_all, f)
# load all different mechanism directories (needed for saving cell)
mechanism_dirs = set([p['mechanism_dir'] for p in problem_dicts])
for m_dir in mechanism_dirs:
load_mechanism_dir(m_dir)
for trial in range(n_trials):
# create directory for each trial
save_dir_trial = save_dir + 'trial'+str(trial)+'/'
os.makedirs(save_dir_trial)
# save seeds
seed = time()
np.savetxt(save_dir_trial+'seed.txt', np.array([seed]))
# save problem dict
with open(save_dir_trial+'problem.json', 'w') as f:
json.dump(problem_dicts[trial], f, indent=4)
# save cell
with open(save_dir_trial+'cell.json', 'w') as f:
json.dump(Cell.from_modeldir(problem_dicts[trial]['model_dir']).get_dict(), f, indent=4)
# save initial population
if generator is not None:
random = Random()
random.seed(seed)
initial_pop = list()
lower_bound, upper_bound, path_variables = get_lowerbound_upperbound_path(problem_dicts[trial]['variables'])
for i in range(pop_size):
initial_pop.append(generator(random, lower_bound, upper_bound))
with open(save_dir_trial+'initial_pop.json', 'w') as f:
json.dump(initial_pop, f, indent=4)
return True
import matplotlib.pyplot as pl
from nrn_wrapper import Cell, complete_mechanismdir
from optimization.simulate import iclamp, extract_simulation_params
__author__ = 'caro'
data_real_dir = '../2015_08_11d/ramp/ramp.csv'
data_new_dir = './ramp.csv'
model_dir = '../../model/cells/dapmodel0.json'
mechanism_dir = '../../model/channels/schmidthieber'
data_real = pd.read_csv(data_real_dir)
# create cell and run simulation
cell = Cell.from_modeldir(model_dir, complete_mechanismdir(mechanism_dir))
simulation_params = extract_simulation_params(data_real)
v, t = iclamp(cell, **simulation_params)
i = data_real.i.values
sec = data_real.sec.values
data = pd.DataFrame({'v': v, 't': t, 'i': i, 'sec': sec})
data.to_csv(data_new_dir, index=None)
pl.figure()
pl.plot(t, v)
pl.show()
pl.figure()
pl.plot(t, i)
pl.show()
"""
dt = 0.01
tstop = 100
t = np.arange(0, tstop+dt, dt)
i_inj = np.zeros(len(t))
i_inj[np.logical_and(20 <= t, t < 60)] = 1
v_init = -60
sec = ['soma', None]
start_idx = int(np.round(20.0/dt)) #np.where(np.abs(np.diff(20 <= t)))[0] + 1
end_idx = int(np.round(60.0/dt)) #np.where(np.abs(np.diff(t <= 60)))[0]
discontinuities=[start_idx, end_idx]
"""
# create cell
cell = Cell.from_modeldir('../demo/test_cell.json', '../demo/channels')
v_rec, t_rec, i_rec = iclamp_adaptive(cell, sec, i_inj, v_init, tstop, dt, celsius=35, pos_i=0.5, pos_v=0.5, atol=1e-8,
continuous=True, discontinuities=discontinuities, interpolate=True)
print tstop
print t_rec[-1]
print np.diff(t_rec)[1:10]
pl.figure()
pl.plot(t_rec, v_rec)
pl.show()
pl.figure()
pl.plot(t, i_inj, 'x')
def get_cell(self):
cell = Cell.from_modeldir(self.model_dir)
insert_mechanisms(cell, self.variable_keys)
return cell
def get_optimal_candidate(self, model_dir, variables):
cell = Cell.from_modeldir(model_dir)
optimal_candidate = [cell.get_attr(var[2][0]) for var in variables]
return optimal_candidate
prng.seed(seed)
# initialize data
t = np.arange(0, problem.simulation_params['tstop']+dt, dt)
i_inj = problem.simulation_params['i_amp']
data = np.zeros((n_data, len(t)/subsample, 2)) # inputs are the trace of v and i_inj
labels = np.zeros(n_data)
for i in range(n_data):
# modify parameter
candidate = problem.generator(prng, None)
# run simulation
problem.update_cell(candidate)
v, t = iclamp(problem.cell, **problem.simulation_params)
data[i, :, 0] = v[:-1:subsample]
data[i, :, 1] = i_inj[:-1:subsample]
labels[i] = candidate[0]
# store data
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)
np.savetxt(save_dir+'/seed.txt', np.array([seed]))
with open(save_dir+'data.npy', 'w') as f:
np.save(f, data)
with open(save_dir+'labels.npy', 'w') as f:
np.save(f, labels)
print dt
"""
dt = 0.01
tstop = 100
t = np.arange(0, tstop+dt, dt)
i_inj = np.zeros(len(t))
i_inj[np.logical_and(20 <= t, t < 60)] = 1
v_init = -60
sec = ['soma', None]
start_idx = int(np.round(20.0/dt)) #np.where(np.abs(np.diff(20 <= t)))[0] + 1
end_idx = int(np.round(60.0/dt)) #np.where(np.abs(np.diff(t <= 60)))[0]
discontinuities=[start_idx, end_idx]
"""
# create cell
cell = Cell.from_modeldir('../demo/test_cell.json', '../demo/channels')
v_rec, t_rec, i_rec = iclamp_adaptive(cell,
sec,
i_inj,
v_init,
tstop,
dt,
celsius=35,
pos_i=0.5,
pos_v=0.5,
atol=1e-8,
continuous=True,
discontinuities=discontinuities,
interpolate=True)
