task = sys.argv[5]
append_directory = sys.argv[6]
"""
Set directories
"""
current_path = os.getcwd()
parent_path = os.path.abspath(os.path.join(current_path, os.pardir))
input_dir = parent_path + '/data/'
output_dir = parent_path + '/outputs/'
"""
Load data
"""
data = Data(input_dir, output_dir)
sc, hmap, fc_obj = load_data(data)
fc_obj = fisher_z(subdiag(fc_obj))
rejection_threshold = 1.0 - pearsonr(fc_obj, subdiag(sc))[0]
if model_type == 'homogeneous':
from optimization import Homogeneous
pmc_opt = Homogeneous(input_dir, output_dir + append_directory + '/')
pmc_opt.initialize(sc,
fc=fc_obj,
gradient=None,
n_particles=n_samples,
rejection_threshold=rejection_threshold,
norm_sc=True)
else:
from optimization import Heterogeneous
def distance_function(self, synthetic_data):
fit = pearsonr(self.fc_objective, fisher_z(synthetic_data))[0]
penalty = (self.fc_objective.mean() - synthetic_data.mean())**2
distance = 1.0 - (fit - penalty)
return distance
mpl.rcParams['axes.labelsize'] = 26.0
mpl.rcParams['axes.titlesize'] = 26.0
mpl.rcParams['xtick.labelsize'] = 20.0
mpl.rcParams['ytick.labelsize'] = 20.0
fig, axes = plt.subplots(2, 3, figsize=(30, 15))
#import pdb; pdb.set_trace()
matrix_plot(axes[0][0], fc_obj, 'RdBu_r')
axes[0][0].set_title('Empirical FC')
matrix_plot(axes[0][1], homogeneous.get('corr_bold'), 'RdBu_r')
axes[0][1].set_title('Homogeneous model FC')
matrix_plot(axes[0][2], heterogeneous.get('corr_bold'), 'RdBu_r')
axes[0][2].set_title('Heterogeneous model FC')
reg_plot(axes[1][0], fisher_z(subdiag(homogeneous.get('corr_bold'))),
fisher_z(subdiag(fc_obj)))
axes[1][0].set_title('Homogeneous model FC fit')
reg_plot(axes[1][1], fisher_z(subdiag(heterogeneous.get('corr_bold'))),
fisher_z(subdiag(fc_obj)))
axes[1][1].set_title('Heterogeneous model FC fit')
axes[1][2].bar(np.arange(5), theta_heterogeneous.mean(1)[:5])
axes[1][2].set_title('Optimal model parameters')
axes[1][2].set_xticks(np.arange(5) + 0.5)
axes[1][2].set_xticklabels(
['wEI min', 'wEI scale', 'wEE min', 'wEE scale', 'g'], minor=False)
plt.tight_layout()
plt.show()
plt.savefig('Example_model_fit.png', dpi=100)