print('Random - N: ' + str(N))
output = open(dir_output_subname + '/' + 'parameters.pkl', 'wb')
pickle.dump(
{
'temperature_parameters': temperature_parameters,
'no_simulations': no_simulations,
'thermalize_time': thermalize_time
}, output)
output.close()
for entity in range(no_entities):
print()
print('Entity: ' + str(entity + 1))
print(''.join('*' * temperature_parameters[2]))
dir_output_subname_entity = dir_output_subname + '/' + 'entity_' + str(
entity + 1) + '/'
if not os.path.exists(dir_output_subname_entity):
os.mkdir(dir_output_subname_entity)
J = np.random.rand(N, N)
simulated_fc, critical_temperature, E, M, S, H = generalized_ising(
J,
temperature_parameters=temperature_parameters,
no_simulations=no_simulations,
thermalize_time=thermalize_time)
to_save_results(temperature_parameters, J, E, M, S, H,
simulated_fc, critical_temperature,
dir_output_subname_entity)
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!')