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))
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:
for noise in space.get_range('noise_rate_mu'):
subspace = space.get_nontrivial_subspace(('noise_rate_mu', noise))
rhm = RectangularHeatmapPlotter(subspace)
fig_mi, ax_mi, data_mi = rhm.plot_and_save(heat_dim='point_mi_qe', base_dir='/home/ucbtepi/code/network/figures', file_extension=file_extension, aspect=2)
plt.close(rhm.fig)
np.savetxt('data_mi.csv', data_mi, delimiter=',')
if plot_line_n_trials:
training_size = 30
testing_size = space.get_range('n_trials') - training_size
subspace = space.get_nontrivial_subspace(('training_size', training_size))
mi_qe = subspace._get_attribute_array('point_mi_qe')
mi_pt = subspace._get_attribute_array('point_mi_pt')
mi_nsb = subspace._get_attribute_array('point_mi_nsb')
mi_plugin = subspace._get_attribute_array('point_mi_plugin')
fig, ax = plt.subplots(figsize=(3,1.75))
ax.locator_params(tight=True, nbins=5)
