import unittest
import numpy as np
import helperFunctions as hF
import matplotlib.pyplot as plt
from warnings import warn
class FrequencyFunctionsTester(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_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, 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_trace(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_calculate_isi_frequency_trace(self):
sampling_intervals = [0.00005, 0.001, 0.01, 0.2, 0.5, 1]
test1 = [0, 1, 2, 3, 4] # 1-1-1-1 only 1s in the result
test2 = [0, 1, 3, 5, 6] # 1-2-2-1
test3 = [0, 3, 10, 12, 15] # 3-7-2-3
pos_tests = [test1, test2, test3]
test4 = generate_jittered_spiketimes(100, 0.2)
test5 = generate_jittered_spiketimes(500, 0.2)
test6 = generate_jittered_spiketimes(1000, 0)
realistic_tests = [test4, test5, test6]
test_neg_isi = [0, 3, 4, 2, 5] # should raise error non sorted spiketimes
test_too_small_sampling_rate = [0.001, 0.0015, 0.002]
neg_tests = [test_neg_isi, test_too_small_sampling_rate]
for test in pos_tests:
for sampling_interval in sampling_intervals:
calculated_trace = hF.calculate_isi_frequency_trace(test, sampling_interval, time_in_ms=False)
diffs = np.diff(test)
j = 0
count = 0
value = 1/diffs[j]
for i in range(len(calculated_trace)):
if calculated_trace[i] == value:
count += 1
else:
expected_length = round(diffs[j] / sampling_interval)
# if there are multiple isis of the same length after each other add them together
while expected_length < count and value == 1/diffs[j+1]:
j += 1
expected_length += round(diffs[j] / sampling_interval, 0)
self.assertEqual(count, expected_length, msg="Length of isi frequency part is not right: expected {:.1f} vs {:.1f}".format(float(count), expected_length))
j += 1
value = 1/diffs[j]
count = 1
for test in neg_tests:
self.assertRaises(ValueError, hF.calculate_isi_frequency_trace, test, 0.2, False)
def test_calculate_time_and_frequency_trace(self):
# !!! the produced frequency trace is tested in the test function for specifically the freq_Trace function
sampling_intervals = [0.0001, 0.1, 0.5, 1]
test1 = [0, 1, 2, 5, 7]
test2 = [1, 3, 5, 6, 7, 10]
test3 = [-1, 2, 4, 5, 11]
pos_tests = [test1, test2, test3]
for sampling_interval in sampling_intervals:
for test in pos_tests:
time, freq = hF.calculate_time_and_frequency_trace(test, sampling_interval, time_in_ms=False)
self.assertEqual(test[0], time[0])
self.assertEqual(test[-1], round(time[-1]+sampling_interval))
def test_calculate_mean_of_frequency_traces(self):
# TODO expand this test to more than this single test case
test1_f = [0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1]
test1_t = np.arange(0, 8, 0.5)
test2_f = [1, 2, 2, 3, 3, 4]
test2_t = np.arange(0.5, 7.5, 0.5)
time_traces = [test1_t, test2_t]
freq_traces = [test1_f, test2_f]
time, mean = hF.calculate_mean_of_frequency_traces(time_traces, freq_traces, 0.5)
expected_time = np.arange(0.5, 7, 0.5)
expected_mean = [0.75, 1.25, 1.25, 2, 2, 2.5]
time_equal = np.all([time[i] == expected_time[i] for i in range(len(time))])
mean_equal = np.all([mean[i] == expected_mean[i] for i in range(len(mean))])
self.assertTrue(time_equal)
self.assertTrue(mean_equal, msg="expected:\n" + str(expected_mean) + "\n actual: \n" + str(mean))
self.assertEqual(len(expected_mean), len(mean))
self.assertEqual(len(expected_time), len(time), msg="expected:\n" + str(expected_time) + "\n actual: \n" + str(time))
# TODO:
# all_calculate_mean_isi_frequency_traces(spiketimes, sampling_interval, time_in_ms=False):
#def test_all_calculate_mean_isi_frequency_traces(self):
# hF.all_calculate_mean_isi_frequency_traces(,
def generate_jittered_spiketimes(frequency, noise_level=0., start=0, end=5, method='normal'):
if method is 'normal':
return normal_dist_jittered_spikes(frequency, noise_level, start, end)
elif method is 'poisson':
if noise_level != 0:
warn("Poisson jittered spike trains don't support a noise level! ")
return poisson_jittered_spikes(frequency, start, end)
def poisson_jittered_spikes(frequency, start, end):
if frequency == 0:
return []
mean_isi = 1 / frequency
spikes = []
for part in np.arange(start, end+mean_isi, mean_isi):
num_spikes_in_part = np.random.poisson(1)
positions = np.sort(np.random.random(num_spikes_in_part))
while not __poisson_min_dist_test__(positions):
positions = np.sort(np.random.random(num_spikes_in_part))
for pos in positions:
spikes.append(part+pos*mean_isi)
while spikes[-1] > end:
del spikes[-1]
return spikes
def __poisson_min_dist_test__(positions):
if len(positions) > 1:
diffs = np.diff(positions)
if len(diffs[diffs < 0.0001]) > 0:
return False
return True
def normal_dist_jittered_spikes(frequency, noise_level, start, end):
if frequency == 0:
return []
mean_isi = 1 / frequency
if noise_level == 0:
return np.arange(start, end, mean_isi)
isis = np.random.normal(mean_isi, noise_level*mean_isi, int((end-start)*1.05/mean_isi))
spikes = np.cumsum(isis) + start
spikes = np.sort(spikes)
if spikes[-1] > end:
return spikes[spikes < end]
else:
additional_spikes = [spikes[-1] + np.random.normal(mean_isi, noise_level*mean_isi)]
while additional_spikes[-1] < end:
next_isi = np.random.normal(mean_isi, noise_level*mean_isi)
additional_spikes.append(additional_spikes[-1] + next_isi)
additional_spikes = np.sort(np.array(additional_spikes[:-1]))
spikes = np.concatenate((spikes, additional_spikes))
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, 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()