path_entity = simulation + '/' + entity + '/'
if os.path.isdir(path_entity):
print(entity)
simulated_matrix = np.load(path_entity + 'sim_fc.npy')
J = np.loadtxt(path_entity + 'J_ij.csv', delimiter=',')
critical_temperature = np.loadtxt(path_entity + 'ctem.csv',
delimiter=',')
ctemp_sim.append(critical_temperature)
susceptibility_sim.append(
np.loadtxt(path_entity + 'susc.csv', delimiter=','))
c, r = correlation_function(simulated_matrix, J)
index_ct = find_nearest(ts, critical_temperature)
dimensionality = dim(c, r, index_ct)
if not np.isinf(r[-1]):
dimensionality_sim.append(dimensionality)
dimensionality_exp.append(dimensionality_sim)
fig, ax = plt.subplots(figsize=(10, 7))
colors = ['blue', 'green', 'red', 'black', 'cyan']
parts = plt.violinplot(dimensionality_exp,
positions=np.array(sizes_),
showmeans=True,
showmedians=False)
cont = 0
import matplotlib.pyplot as plt
from generalize_ising_model.ising_utils import corrfun, dim, find_nearest
mat = scipy.io.loadmat(
'/home/brainlab/Desktop/Rudas/Scripts/ising/dimentionality/wd1/full.mat')
corr = mat['Corr_all']
J = mat['J_count_MS_Det']
tc_subs = np.squeeze(mat['tc_subs'])
temp = np.squeeze(mat['temp'])
print('Starting')
corr_fun, r_all = corrfun(corr, J)
print(corr_fun.shape)
sub = corr_fun.shape[0]
d = []
for i in range(sub):
print('Sub ' + str(i + 1))
corr_func = corr_fun[i, :, :]
idx_ct = find_nearest(temp, tc_subs[i])
d.append(dim(corr_func, r_all[i, :], idx_ct))
print(d)
plt.scatter(np.linspace(0, len(d), num=len(d)), d)
plt.show()
print('Hola Mundo')