import numpy as np
import time
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')
from projects.generalize_ising_model.tools.utils import to_normalize, save_results, makedir
import os
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
path_input = '/home/brainlab/Desktop/Rudas/Data/Ising/HCP/data/hcp_mean/J_ij.csv'
path_output = '/home/brainlab/Desktop/Rudas/Data/Ising/HCP/data/hcp_mean/results/'
# Ising Parameters
temperature_parameters = (
0.05, 5, 5
) # Temperature parameters (initial tempeture, final tempeture, number of steps)
no_simulations = 150 # Number of simulation after thermalization
thermalize_time = 0.3 #
J = to_normalize(np.loadtxt(path_input, delimiter=','))
ts = np.linspace(temperature_parameters[0],
temperature_parameters[1],
num=temperature_parameters[2])
colors = ['red', 'green', 'black', 'blue', 'purple']
f = plt.figure(figsize=(18, 10)) # plot the calculated values
simulated_fc, critical_temperature, E, M, S, H = generalized_ising(
J,
temperature_parameters=temperature_parameters,
no_simulations=no_simulations,
thermalize_time=thermalize_time)
makedir(path_output)
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)
main_path = '/home/brainlab/Desktop/Popiel/Ising_HCP/'
parcels = ['Aud', 'CinguloOperc', 'CinguloParietal', 'DMN', 'Dorsal', 'FrontoParietal', 'Retrosplenial', 'SMhand',
'SMmouth', 'Ventral', 'Visual']
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)
for dir in natsorted(os.listdir((path_input_aux + '/' + dirs))):
print(dir)
print(''.join('*' * no_temperature))
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)
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=','))
#temperature_parameters = (0.05, 5, no_temperature) # Temperature parameters (initial tempeture, final tempeture, number of steps)
if not os.path.exists(dir_output_name_case_exp + '/' +
'parameters.pkl'):
temperature_parameters = (
0.005, J.shape[-1] * (np.mean(J) + 0.45),
no_temperature
) # Temperature parameters (initial tempeture, final tempeture, number of steps)
#temperature_parameters = (0.005, 8, no_temperature) # Temperature parameters (initial tempeture, final tempeture, number of steps)
output = open(
dir_output_name_case_exp + '/' + 'parameters.pkl',
'wb')
pickle.dump(