import sys
sys.path.append("../")
import numpy as np
from scipy.stats import pearsonr
from utils.parameters import PSlice, ParameterSpace, ParameterSpacePoint
space = ParameterSpace(PSlice(300), PSlice(6), PSlice(2.), PSlice(4), PSlice(5.), PSlice(.1, 1.0, .1), PSlice(-30., 5., 5.), PSlice(120), PSlice(30), PSlice(10,80,10), PSlice(10), PSlice(20), PSlice(200), PSlice(40), PSlice(0.), PSlice(0), PSlice(5.), PSlice(2.))
space.load_analysis_results()
mi_arr = space.get_nontrivial_subspace(('training_size', 40))._get_attribute_array('point_mi_qe')
nm_arr = space.get_nontrivial_subspace(('training_size', 40))._get_attribute_array('new_measure')
print(pearsonr(np.delete(mi_arr, 5, axis=0).flat, np.delete(nm_arr, 5, axis=0).flat))
multineuron_metric_mixing = psl(0.)
linkage_method = psl(1) # 0: ward, 1: kmeans
tau = psl(5)
dt = psl(2)
space = ParameterSpace(n_grc_dend,
connectivity_rule,
input_spatial_correlation_scale,
active_mf_fraction,
gaba_scale,
dta,
inh_cond_scaling,
exc_cond_scaling,
modulation_frequency,
stim_rate_mu,
stim_rate_sigma,
noise_rate_mu,
noise_rate_sigma,
n_stim_patterns,
n_trials,
sim_duration,
ana_duration,
training_size,
multineuron_metric_mixing,
linkage_method,
tau,
dt)
space.load_analysis_results()
if plot_mi_heatmap:
