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test_rmsorn.py
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test_rmsorn.py
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import unittest
import pickle
import numpy as np
from rmsorn.utils import Plotter,Statistics,Initializer
# Getting back the pickled matrices:
with open('sample_matrices.pkl','rb') as f:
matrices_dict, Exc_activity, Inh_activity, Rec_activity, num_active_connections = pickle.load(f)
class TestSorn(unittest.TestCase):
# def test_run_rmsorn(self):
# self.assertRaises(Exception, Run_RMSorn(phase='Plasticity', matrices=None,time_steps=5).run_sorn([0.]))
def test_plotter(self):
self.assertRaises(Exception, Plotter.hist_outgoing_conn(weights= matrices_dict['Wee'],bin_size=5, histtype = 'bar',savefig = False))
self.assertRaises(Exception, Plotter.hist_outgoing_conn(weights= matrices_dict['Wee'],bin_size=5, histtype = 'bar',savefig = False))
self.assertRaises(Exception, Plotter.hist_incoming_conn(weights= matrices_dict['Wee'],bin_size=5, histtype = 'bar',savefig = False))
self.assertRaises(Exception, Plotter.network_connection_dynamics(connection_counts= num_active_connections, initial_steps= 10, final_steps = 10,savefig = False))
self.assertRaises(Exception, Plotter.hist_firing_rate_network(spike_train = np.asarray(Exc_activity), bin_size = 5, savefig= False))
self.assertRaises(Exception, Plotter.scatter_plot(spike_train = np.asarray(Exc_activity), savefig=False))
self.assertRaises(Exception, Plotter.raster_plot(spike_train = np.asarray(Exc_activity), savefig=False))
self.assertRaises(Exception, Plotter.isi_exponential_fit(spike_train = np.asarray(Exc_activity), neuron = 10, bin_size = 5, savefig = False))
self.assertRaises(Exception, Plotter.weight_distribution(weights= matrices_dict['Wee'], bin_size = 5, savefig = False))
self.assertRaises(Exception, Plotter.linear_lognormal_fit(weights= matrices_dict['Wee'],num_points = 10, savefig = False))
self.assertRaises(Exception, Plotter.hamming_distance(hamming_dist=[0,0,0,1,1,1,1,1,1], savefig = False))
def test_statistics(self):
self.assertRaises(Exception, Statistics.firing_rate_neuron(spike_train = np.asarray(Exc_activity), neuron = 10, bin_size = 5))
self.assertRaises(Exception, Statistics.firing_rate_network(spike_train = np.asarray(Exc_activity)))
self.assertRaises(Exception, Statistics.scale_dependent_smoothness_measure(firing_rates = [1,1,5,6,3,7]))
self.assertRaises(Exception, Statistics.autocorr(firing_rates = [1,1,5,6,3,7],t= 2))
self.assertRaises(Exception, Statistics.avg_corr_coeff(spike_train =np.asarray(Exc_activity)))
self.assertRaises(Exception, Statistics.spike_times(spike_train =np.asarray(Exc_activity)))
self.assertRaises(Exception, Statistics.hamming_distance(actual_spike_train =np.asarray(Exc_activity), perturbed_spike_train =np.asarray(Exc_activity)))
self.assertRaises(Exception, Statistics.spike_time_intervals(spike_train =np.asarray(Exc_activity)))
self.assertRaises(Exception, Statistics.fanofactor(spike_train= np.asarray(Exc_activity),neuron = 10,window_size = 10))
self.assertRaises(Exception, Statistics.spike_source_entropy(spike_train =np.asarray(Exc_activity), neurons_in_reservoir = 200))
if __name__ == '__main__':
unittest.main()