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)