import differentialevolution_par_scinet as df
import numpy as np
version_num = 5
c_mat = np.load("Anat Data/c_mat{}g.npy".format(version_num))
tract_mat = np.load("Anat Data/tract_mat{}g.npy".format(version_num))
w_mat = np.load("Anat Data/w_mat{}g.npy".format(version_num))
ue_array = np.load("Anat Data/ue_array{}g.npy".format(version_num))
#%% TARGET DATA
nodes = 10
num_dim = int((((nodes**2) - nodes) / 2))
skip = 200
targ_data = hf.plot_cor_mat(ue_array, nodes, skip)
np.fill_diagonal(targ_data, 0)
#%% DIFF EVOLUTION PARAMS
bounds = []
lower = 0
upper = 1
for n in range(num_dim):
bounds.append((lower, upper))
evol_params = {
'strategy': 'best2bin',
'maxiter': 400,
'popsize': 15,
'tol': 0.5,
'mut': 0.5,
'init': 'latinhypercube',
'atol': 0,
'mse': 0,
'bound_l': 0,
'bound_u': 4000
}
# SIGNAL PROPERTIES
_Dt = wc_params['dt']
_alpha = 10
_dt = _Dt / _alpha # time step is 1ms: _dt = 0.001
fs = 1 / _dt # Sampling rate, or number of measurements per second
# WEIGHTS AND TRACT
# sick kids
catmatrix = np.load("Anat Data/catmatrix{}.npy".format(file_num))
tract_mat = np.load("Anat Data/tract_mat{}_r.npy".format(file_num))
# TARGET DATA
all_ts = []
for i in range(10):
all_ts.append(catmatrix[i, 3, :])
skip = 200
targ_data = hf.plot_cor_mat(np.array(all_ts), nodes, skip)
np.fill_diagonal(targ_data, 0)
# ARGS FOR RESIDUAL FXN IN DIFF EVOLUTION ALROGITHM
args = (wc_params, targ_data, nodes, tract_mat, skip, wc_seed)
