dir_output_name_case = dir_output_name+ '/' + dirs + '/'
if not os.path.exists(dir_output_name_case):
os.mkdir(dir_output_name_case)
for dir in natsorted(os.listdir((path_input_aux + '/' + dirs))):
print(dir)
print (''.join('*' * temperature_parameters[2]))
dir_output_name_case_exp = dir_output_name_case + '/' + dir
if not os.path.exists(dir_output_name_case_exp):
os.mkdir(dir_output_name_case_exp)
output = open(dir_output_name_case_exp + '/' + 'parameters.pkl', 'wb')
pickle.dump({'temperature_parameters': temperature_parameters, 'no_simulations': no_simulations,
'thermalize_time': thermalize_time}, output)
output.close()
for entity in natsorted(os.listdir((path_input_aux + dirs + '/' + dir))):
sub_dir_output_name = dir_output_name_case_exp + '/' + entity + '/'
if not os.path.exists(sub_dir_output_name):
J = to_normalize(np.loadtxt(path_input_aux + dirs + '/' + dir + '/' + entity + '/' + default_Jij_name, delimiter=','))
start_time = time.time()
simulated_fc, critical_temperature, E, M, S, H = generalized_ising(J,
temperature_parameters=temperature_parameters,
no_simulations=no_simulations,
thermalize_time=thermalize_time)
print(time.time() - start_time)
os.mkdir(sub_dir_output_name)
to_save_results(temperature_parameters, J, E, M, S, H, simulated_fc, critical_temperature, sub_dir_output_name)
def phi_sim_save(size, output_directory,count,temperature_parameters=(-1,5,50), no_simulations=500, thermalize_time=0.3):
import numpy as np
from generalize_ising_model.core import generalized_ising
import time
from generalize_ising_model.ising_utils import to_save_results, makedir, \
to_generate_randon_graph, save_graph
from generalize_ising_model.phi_project.utils import to_estimate_tpm_from_ising_model, to_calculate_mean_phi, \
to_save_phi
makedir(output_directory + '/phi/')
makedir(output_directory + '/ising/')
size = int(size)
output_path_phi = output_directory + '/' + 'phi/' + str(count) + '/'
output_path_ising = output_directory + '/' + 'ising/' + str(count) + '/'
if makedir(output_path_ising) and makedir(output_path_phi):
J = save_graph(output_path_phi + 'Jij_' + str(count) + '.csv',
to_generate_randon_graph(size, isolate=False, weighted=True))
# Ising Parameters
temperature_parameters = temperature_parameters # Temperature parameters (initial tempeture, final tempeture, number of steps)
no_simulations = no_simulations # Number of simulation after thermalization
thermalize_time = thermalize_time #
start_time = time.time()
print('Fitting Generalized Ising model for a ', size, ' by', size, ' random matrix.')
simulated_fc, critical_temperature, E, M, S, H, spin_mean = generalized_ising(J,
temperature_parameters=temperature_parameters,
no_simulations=no_simulations,
thermalize_time=thermalize_time,
temperature_distribution='log')
to_save_results(temperature_parameters, J, E, M, S, H, simulated_fc, critical_temperature, output_path_ising,
temperature_distribution='log')
print('It took ', time.time() - start_time, 'seconds to fit the generalized ising model')
print('Computing Phi for random ' + str(size) + ' by ' + str(size) + ' matrix')
ts = np.logspace(temperature_parameters[0], np.log10(temperature_parameters[1]), temperature_parameters[2])
phi_temperature, phi_sum, phi_sus = [], [], []
cont = 0
start_time = time.time()
for t in ts:
# print('Temperature: ' + str(t))
tpm, fpm = to_estimate_tpm_from_ising_model(J, t)
phi_, phiSum, phiSus = to_calculate_mean_phi(fpm, spin_mean[:, cont], t)
phi_temperature.append(phi_)
phi_sum.append(phiSum)
phi_sus.append(phiSus)
cont += 1
to_save_phi(ts, phi_temperature, phi_sum, phi_sus, S, critical_temperature, size, output_path_phi)
print('It takes ', time.time() - start_time, 'seconds to compute phi for a ', size, 'by', size,
' random matrix.')
else:
print(str(count) + ' is already done!')
i_l.append(v - 1)
j_l.append(w - 1)
J = 1. / D[i_l, j_l]
J[J == np.inf] = 0
J[J == -np.inf] = 0
J = J / np.max(J)
J = np.reshape(J, (len(value), len(value)))
J = to_normalize(J)
start_time = time.time()
print('Fitting Generalized ising model')
simulated_fc, critical_temperature, E, M, S, H, spin_mean = generalized_ising(
J,
temperature_parameters=temperature_parameters,
no_simulations=no_simulations,
thermalize_time=thermalize_time,
temperature_distribution='log')
sub_dir_output_name = dir_output_name + '/' + key + '/'
makedir(dir_output_name + '/' + key)
to_save_results(temperature_parameters, J, E, M, H, S, simulated_fc,
critical_temperature, sub_dir_output_name)
print(time.time() - start_time)
start_time = time.time()
print('Computing Phi for: ' + key)
#ts = np.linspace(temperature_parameters[0], temperature_parameters[1], temperature_parameters[2])
ts = np.logspace(temperature_parameters[0],
np.log10(temperature_parameters[1]),
dir_output_name = path_input + 'simulation'
if not os.path.exists(dir_output_name):
os.mkdir(dir_output_name)
path_input_aux = path_input + 'data/'
for dirs in natsorted(os.listdir(path_input_aux)):
dir_output_name_case = dir_output_name + '/' + dirs + '/'
print(dirs)
J = to_normalize(
np.loadtxt(path_input_aux + dirs + '/' + default_Jij_name,
delimiter=','))
temperature_parameters = (
0.002, 3, no_temperature
) # Temperature parameters (initial tempeture, final tempeture, number of steps)
start_time = time.time()
simulated_fc, critical_temperature, E, M, S, H = generalized_ising(
J,
temperature_parameters=temperature_parameters,
n_time_points=no_simulations,
thermalize_time=thermalize_time,
phi_variables=False)
print(time.time() - start_time)
os.mkdir(dir_output_name_case)
save_results(temperature_parameters, J, E, M, S, H, simulated_fc,
critical_temperature, dir_output_name_case)
gc.collect()
