fc_empirical_path = '/home/brainlab/Desktop/Rudas/Scripts/ising/hcp_84_fc.csv'
# Ising Parameters
temperature_parameters = (
0.002, 3, 50
) # Temperature parameters (initial tempeture, final tempeture, number of steps)
no_simulations = 1200 # Number of simulation after thermalization
thermalize_time = 0.3 #
J = to_normalize(np.loadtxt(path_input, delimiter=','))
fc = np.loadtxt(fc_empirical_path, delimiter=',')
a_simulated_fc, a_critical_temperature, a_E, a_M, a_S, a_H = generalized_ising(
J,
temperature_parameters=temperature_parameters,
n_time_points=no_simulations,
thermalize_time=thermalize_time,
phi_variables=False,
type='analogy')
to_save_results(temperature_parameters, J, a_E, a_M, a_S, a_H, a_simulated_fc,
a_critical_temperature, '/home/brainlab/Desktop/new_test/a/')
d_simulated_fc, d_critical_temperature, d_E, d_M, d_S, d_H = generalized_ising(
J,
temperature_parameters=temperature_parameters,
n_time_points=no_simulations,
thermalize_time=thermalize_time,
phi_variables=False,
type='digital')
#fetch the Jij csv file
J = to_normalize(np.loadtxt(input_path + file_name))
# Ising Parameters
temperature_parameters = (
0.002, 3, 50
) # Temperature parameters (initial tempeture, final temperature, number of steps)
no_simulations = 1200 # Number of simulations after thermalization
thermalize_time = 0.3 #
start_time = time.time()
Simulated_FC, Critical_Temperature, E, M, S, H, Mean_Spin, time_course = generalized_ising(
J,
temperature_parameters=temperature_parameters,
n_time_points=no_simulations,
thermalize_time=thermalize_time,
phi_variables=True,
return_tc=True,
type="digital")
final_time = time.time()
delta_time = final_time - start_time
print("It took " + str(delta_time) +
" seconds to calculate the simulated functional connectivity matricies")
#Making the TPM based on the GIM
empirical_tpm_concat_sbys(time_course, output_path + "TPM_list")
dim_list = []
hubs_list = []
for tpm_index in range(totalMatrix.shape[2]):
tpm_ = totalMatrix[i, condition, tpm_index, ...]
hist, bin_edges = np.histogram(tpm_index, number_bins)
hist = hist / np.sum(tpm_)
S_list.append(entropy(hist))
D_list.append(nx.density(nx.Graph(tpm_)))
simulated_fc, critical_temperature, E, M, S, H = generalized_ising(
tpm_,
temperature_parameters=temperature_parameters,
n_time_points=1200,
thermalize_time=0.3,
temperature_distribution='lineal',
phi_variables=False,
type='digital')
c, r = correlation_function(simulated_fc, tpm_)
dimensionality = dim(c, r, find_nearest(ts, critical_temperature))
dim_list.append(dimensionality)
h = nx.hits(nx.Graph(tpm_))[0]
hubs_list.append([value for key, value in h.items()])
S_conditions.append(S_list)
den_conditions.append(D_list)
from projects.generalize_ising_model.core import generalized_ising
from projects.generalize_ising_model.tools.utils import to_save_results,to_generate_random_graph,to_normalize,save_graph
sizes = [250]#[250,500]
main_path = '/home/user/Desktop/Popiel/check_ising/'
temperature_params = [(np.log10(75),150,50)]#[(1.3,200,50),(1.8,600,50)]# Params for 5,10,25,100 (-3,4,50),(-1,8,50),[(0,20,50)] (1,100,50),
no_simulations = 4000
thermalize_time =0.3
for ind, size in enumerate(sizes):
save_path = main_path + str(size) + '/'
J = save_graph(save_path + 'Jij_' + str(size) + '.csv',
to_normalize(to_generate_random_graph(size, isolate=False, weighted=True),netx=True))
# Ising Parameters
temperature_parameters = temperature_params[ind] # Temperature parameters (initial tempeture, final tempeture, number of steps)
start_time = time.time()
print('Fitting Generalized Ising model for a ', size, ' by', size, ' random matrix.')
simulated_fc, critical_temperature, E, M, S, H = generalized_ising(J,
temperature_parameters=temperature_parameters,
n_time_points=no_simulations,
thermalize_time=thermalize_time,
temperature_distribution='log')
to_save_results(temperature_parameters, J, E, M, S, H, simulated_fc, critical_temperature, save_path,
temperature_distribution='log')
print('It took ', time.time() - start_time, 'seconds to fit the generalized ising model')
for parcel in parcels:
print('Running', parcel)
parcel_path = main_path + parcel + '/'
#results_path = sub_path + 'results/'
save_path = parcel_path + '/results/'
makedir(save_path)
if not file_exists(save_path + 'phi.csv'):
Jij = to_normalize(load_matrix(parcel_path + 'Jij_avg.csv'))
start_time = time.time()
simulated_fc, critical_temperature, E, M, S, H, spin_mean, tc = generalized_ising(Jij,
temperature_parameters=temperature_parameters,
n_time_points=n_time_points,
thermalize_time=thermalize_time,
phi_variables=True,
return_tc=True)
print('It took ',time.time()-start_time, 'seconds to fit the generalized Ising model')
makedir(save_path)
to_save_results(temperature_parameters, Jij, E, M, S, H, simulated_fc, critical_temperature, save_path)
save_file(spin_mean,save_path,'spin_mean')
save_file(tc,save_path,'time_course')
ts = np.linspace(temperature_parameters[0], temperature_parameters[1], temperature_parameters[2])
phi_temperature, phi_sum, phi_sus = [], [], []
cont = 0
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 projects.generalize_ising_model.core import generalized_ising
import time
from projects.generalize_ising_model.tools.utils import to_save_results, makedir, to_generate_randon_graph, save_graph
from projects.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!')
temperature_parameters = (0.05, 1, 100) # Temperature parameters (initial tempeture, final tempeture, number of steps)
no_simulations = 250 # Number of simulation after thermalization
thermalize_time = 0.3 #
dir_output_name = path_input + 'simulation_' + simulation_name
for root, dirs, files in walk(path_input + 'data/'):
if not os.path.exists(dir_output_name):
os.mkdir(dir_output_name)
np.save(dir_output_name + '/' + 'parameters',
{'temperature_parameters': temperature_parameters,
'no_simulations': no_simulations,
'thermalize_time': thermalize_time})
for dir in sorted(dirs):
print(dir)
print (''.join('*' * temperature_parameters[2]))
sub_dir_output_name = dir_output_name + '/' + dir + '/'
if not os.path.exists(sub_dir_output_name):
os.mkdir(sub_dir_output_name)
J = to_normalize(np.loadtxt(root + dir + '/' + 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)
to_save_results(temperature_parameters, J, E, M, S, H, simulated_fc, critical_temperature, sub_dir_output_name)
pickle.dump(
{
'temperature_parameters': temperature_parameters,
'no_simulations': no_simulations,
'thermalize_time': thermalize_time
}, output)
output.close()
else:
print('Reading parameters')
pkl_file = open(
dir_output_name_case_exp + '/' + 'parameters.pkl',
'rb')
temperature_parameters = pickle.load(
pkl_file)['temperature_parameters']
pkl_file.close()
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,
type='digital')
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)
#gc.collect()
