import unittest import numpy as np import helperFunctions as hF import matplotlib.pyplot as plt class HelperFunctionsTester(unittest.TestCase): noise_levels = [0, 0.05, 0.1, 0.2] frequencies = [0, 1, 5, 30, 100, 500, 750, 1000] def setUp(self): pass def tearDown(self): pass def test_calculate_eod_frequency(self): start = 0 end = 5 step = 0.1 / 1000 freqs = [0, 1, 10, 500, 700, 1000] for freq in freqs: time = np.arange(start, end, step) eod = np.sin(freq*(2*np.pi) * time) self.assertEqual(freq, round(hF.calculate_eod_frequency(time, eod), 2)) def test__vector_strength__is_1(self): length = 2000 rel_spike_times = np.full(length, 0.3) eod_durations = np.full(length, 0.14) self.assertEqual(1, round(hF.__vector_strength__(rel_spike_times,eod_durations), 2)) def test__vector_strength__is_0(self): length = 2000 period = 0.14 rel_spike_times = np.arange(0, period, period/length) eod_durations = np.full(length, period) self.assertEqual(0, round(hF.__vector_strength__(rel_spike_times, eod_durations), 5)) def test_mean_freq_of_spiketimes_after_time_x(self): simulation_time = 8 for freq in self.frequencies: for n in self.noise_levels: spikes = generate_jittered_spiketimes(freq, n, end=simulation_time) sim_freq = hF.mean_freq_of_spiketimes_after_time_x(spikes, 0.00005, simulation_time/4, time_in_ms=False) max_diff = round(n*(10+0.7*np.sqrt(freq)), 2) # print("noise: {:.2f}".format(n), "\texpected: {:.2f}".format(freq), "\tgotten: {:.2f}".format(round(sim_freq, 2)), "\tfreq diff: {:.2f}".format(abs(freq-round(sim_freq, 2))), "\tmax_diff:", max_diff) self.assertTrue(abs(freq-round(sim_freq)) <= max_diff, msg="expected freq: {:.2f} vs calculated: {:.2f}. max diff was {:.2f}".format(freq, sim_freq, max_diff)) def test_calculate_isi_frequency(self): simulation_time = 1 sampling_interval = 0.00005 for freq in self.frequencies: for n in self.noise_levels: spikes = generate_jittered_spiketimes(freq, n, end=simulation_time) sim_freq = hF.calculate_isi_frequency(spikes, sampling_interval, time_in_ms=False) isis = np.diff(spikes) step_length = isis / sampling_interval rounded_step_length = np.around(step_length) expected_length = sum(rounded_step_length) length = len(sim_freq) self.assertEqual(expected_length, length) # def test(self): # test_distribution() def generate_jittered_spiketimes(frequency, noise_level, start=0, end=5): if frequency == 0: return [] mean_isi = 1 / frequency if noise_level == 0: return np.arange(start, end, mean_isi) spikes = [start] count = 0 while True: next_isi = np.random.normal(mean_isi, noise_level*mean_isi) if next_isi <= 0: count += 1 continue next_spike = spikes[-1] + next_isi if next_spike > end: break spikes.append(spikes[-1] + next_isi) # print("count: {:} percentage of missed: {:.2f}".format(count, count/len(spikes))) if count > 0.01*len(spikes): print("!!! Danger of lowering actual simulated frequency") pass return spikes def test_distribution(): simulation_time = 5 freqs = [5, 30, 100, 500, 1000] noise_level = [0.05, 0.1, 0.2, 0.3] repetitions = 1000 for freq in freqs: diffs_per_noise = [] for n in noise_level: diffs = [] print("#### - freq:", freq, "noise level:", n ) for reps in range(repetitions): spikes = generate_jittered_spiketimes(freq, n, end=simulation_time) sim_freq = hF.mean_freq_of_spiketimes_after_time_x(spikes, 0.0002, simulation_time / 4, time_in_ms=False) diffs.append(sim_freq-freq) diffs_per_noise.append(diffs) fig, axs = plt.subplots(1, len(noise_level), figsize=(3.5*len(noise_level), 4), sharex='all') for i in range(len(diffs_per_noise)): max_diff = np.max(np.abs(diffs_per_noise[i])) print("Freq: ", freq, "noise: {:.2f}".format(noise_level[i]), "mean: {:.2f}".format(np.mean(diffs_per_noise[i])), "max_diff: {:.4f}".format(max_diff)) bins = np.arange(-max_diff, max_diff, 2*max_diff/100) axs[i].hist(diffs_per_noise[i], bins=bins) axs[i].set_title('Noise level: {:.2f}'.format(noise_level[i])) plt.show() plt.close()