245 lines
9.3 KiB
Python
245 lines
9.3 KiB
Python
import unittest
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import numpy as np
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from my_util import helperFunctions as hF
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import matplotlib.pyplot as plt
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from warnings import warn
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class FrequencyFunctionsTester(unittest.TestCase):
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noise_levels = [0, 0.05, 0.1, 0.2]
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frequencies = [0, 1, 5, 30, 100, 500, 750, 1000]
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def setUp(self):
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pass
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def tearDown(self):
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pass
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def test_calculate_eod_frequency(self):
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start = 0
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end = 5
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step = 0.1 / 1000
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freqs = [0, 1, 10, 500, 700, 1000]
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for freq in freqs:
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time = np.arange(start, end, step)
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eod = np.sin(freq*(2*np.pi) * time)
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self.assertEqual(freq, round(hF.calculate_eod_frequency(time, eod), 2))
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def test_mean_freq_of_spiketimes_after_time_x(self):
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simulation_time = 8
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for freq in self.frequencies:
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for n in self.noise_levels:
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spikes = generate_jittered_spiketimes(freq, n, end=simulation_time)
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sim_freq = hF.mean_freq_of_spiketimes_after_time_x(spikes, simulation_time / 4, time_in_ms=False)
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max_diff = round(n*(10+0.7*np.sqrt(freq)), 2)
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# 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)
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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))
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def test_calculate_isi_frequency(self):
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simulation_time = 1
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sampling_interval = 0.00005
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for freq in self.frequencies:
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for n in self.noise_levels:
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spikes = generate_jittered_spiketimes(freq, n, end=simulation_time)
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sim_freq = hF.calculate_isi_frequency_trace(spikes, sampling_interval, time_in_ms=False)
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isis = np.diff(spikes)
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step_length = isis / sampling_interval
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rounded_step_length = np.around(step_length)
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expected_length = sum(rounded_step_length)
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length = len(sim_freq)
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self.assertEqual(expected_length, length)
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def test_calculate_isi_frequency_trace(self):
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sampling_intervals = [0.00005, 0.001, 0.01, 0.2, 0.5, 1]
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test1 = [0, 1, 2, 3, 4] # 1-1-1-1 only 1s in the result
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test2 = [0, 1, 3, 5, 6] # 1-2-2-1
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test3 = [0, 3, 10, 12, 15] # 3-7-2-3
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pos_tests = [test1, test2, test3]
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test4 = generate_jittered_spiketimes(100, 0.2)
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test5 = generate_jittered_spiketimes(500, 0.2)
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test6 = generate_jittered_spiketimes(1000, 0)
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realistic_tests = [test4, test5, test6]
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test_neg_isi = [0, 3, 4, 2, 5] # should raise error non sorted spiketimes
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test_too_small_sampling_rate = [0.001, 0.0015, 0.002]
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neg_tests = [test_neg_isi, test_too_small_sampling_rate]
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for test in pos_tests:
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for sampling_interval in sampling_intervals:
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calculated_trace = hF.calculate_isi_frequency_trace(test, sampling_interval, time_in_ms=False)
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diffs = np.diff(test)
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j = 0
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count = 0
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value = 1/diffs[j]
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for i in range(len(calculated_trace)):
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if calculated_trace[i] == value:
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count += 1
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else:
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expected_length = round(diffs[j] / sampling_interval)
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# if there are multiple isis of the same length after each other add them together
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while expected_length < count and value == 1/diffs[j+1]:
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j += 1
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expected_length += round(diffs[j] / sampling_interval, 0)
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self.assertEqual(count, expected_length, msg="Length of isi frequency part is not right: expected {:.1f} vs {:.1f}".format(float(count), expected_length))
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j += 1
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value = 1/diffs[j]
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count = 1
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for test in neg_tests:
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self.assertRaises(ValueError, hF.calculate_isi_frequency_trace, test, 0.2, False)
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def test_calculate_time_and_frequency_trace(self):
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# !!! the produced frequency trace is tested in the test function for specifically the freq_Trace function
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sampling_intervals = [0.0001, 0.1, 0.5, 1]
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test1 = [0, 1, 2, 5, 7]
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test2 = [1, 3, 5, 6, 7, 10]
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test3 = [-1, 2, 4, 5, 11]
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pos_tests = [test1, test2, test3]
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for sampling_interval in sampling_intervals:
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for test in pos_tests:
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time, freq = hF.calculate_time_and_frequency_trace(test, sampling_interval, time_in_ms=False)
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self.assertEqual(test[0], time[0])
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self.assertEqual(test[-1], round(time[-1]+sampling_interval))
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def test_calculate_mean_of_frequency_traces(self):
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# TODO expand this test to more than this single test case
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test1_f = [0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1]
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test1_t = np.arange(0, 8, 0.5)
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test2_f = [1, 2, 2, 3, 3, 4]
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test2_t = np.arange(0.5, 7.5, 0.5)
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time_traces = [test1_t, test2_t]
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freq_traces = [test1_f, test2_f]
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time, mean = hF.calculate_mean_of_frequency_traces(time_traces, freq_traces, 0.5)
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expected_time = np.arange(0.5, 7, 0.5)
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expected_mean = [0.75, 1.25, 1.25, 2, 2, 2.5]
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time_equal = np.all([time[i] == expected_time[i] for i in range(len(time))])
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mean_equal = np.all([mean[i] == expected_mean[i] for i in range(len(mean))])
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self.assertTrue(time_equal)
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self.assertTrue(mean_equal, msg="expected:\n" + str(expected_mean) + "\n actual: \n" + str(mean))
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self.assertEqual(len(expected_mean), len(mean))
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self.assertEqual(len(expected_time), len(time), msg="expected:\n" + str(expected_time) + "\n actual: \n" + str(time))
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# TODO:
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# all_calculate_mean_isi_frequency_traces(spiketimes, sampling_interval, time_in_ms=False):
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#def test_all_calculate_mean_isi_frequency_traces(self):
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# hF.all_calculate_mean_isi_frequency_traces(,
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def generate_jittered_spiketimes(frequency, noise_level=0., start=0, end=5, method='normal'):
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if method is 'normal':
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return normal_dist_jittered_spikes(frequency, noise_level, start, end)
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elif method is 'poisson':
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if noise_level != 0:
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warn("Poisson jittered spike trains don't support a noise level! ")
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return poisson_jittered_spikes(frequency, start, end)
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def poisson_jittered_spikes(frequency, start, end):
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if frequency == 0:
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return []
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mean_isi = 1 / frequency
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spikes = []
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for part in np.arange(start, end+mean_isi, mean_isi):
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num_spikes_in_part = np.random.poisson(1)
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positions = np.sort(np.random.random(num_spikes_in_part))
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while not __poisson_min_dist_test__(positions):
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positions = np.sort(np.random.random(num_spikes_in_part))
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for pos in positions:
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spikes.append(part+pos*mean_isi)
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while spikes[-1] > end:
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del spikes[-1]
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return spikes
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def __poisson_min_dist_test__(positions):
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if len(positions) > 1:
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diffs = np.diff(positions)
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if len(diffs[diffs < 0.0001]) > 0:
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return False
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return True
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def normal_dist_jittered_spikes(frequency, noise_level, start, end):
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if frequency == 0:
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return []
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mean_isi = 1 / frequency
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if noise_level == 0:
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return np.arange(start, end, mean_isi)
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isis = np.random.normal(mean_isi, noise_level*mean_isi, int((end-start)*1.05/mean_isi))
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spikes = np.cumsum(isis) + start
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spikes = np.sort(spikes)
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if spikes[-1] > end:
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return spikes[spikes < end]
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else:
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additional_spikes = [spikes[-1] + np.random.normal(mean_isi, noise_level*mean_isi)]
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while additional_spikes[-1] < end:
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next_isi = np.random.normal(mean_isi, noise_level*mean_isi)
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additional_spikes.append(additional_spikes[-1] + next_isi)
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additional_spikes = np.sort(np.array(additional_spikes[:-1]))
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spikes = np.concatenate((spikes, additional_spikes))
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return spikes
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def test_distribution():
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simulation_time = 5
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freqs = [5, 30, 100, 500, 1000]
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noise_level = [0.05, 0.1, 0.2, 0.3]
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repetitions = 1000
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for freq in freqs:
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diffs_per_noise = []
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for n in noise_level:
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diffs = []
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print("#### - freq:", freq, "noise level:", n )
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for reps in range(repetitions):
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spikes = generate_jittered_spiketimes(freq, n, end=simulation_time)
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sim_freq = hF.mean_freq_of_spiketimes_after_time_x(spikes, simulation_time / 4, time_in_ms=False)
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diffs.append(sim_freq-freq)
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diffs_per_noise.append(diffs)
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fig, axs = plt.subplots(1, len(noise_level), figsize=(3.5*len(noise_level), 4), sharex='all')
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for i in range(len(diffs_per_noise)):
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max_diff = np.max(np.abs(diffs_per_noise[i]))
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print("Freq: ", freq, "noise: {:.2f}".format(noise_level[i]), "mean: {:.2f}".format(np.mean(diffs_per_noise[i])), "max_diff: {:.4f}".format(max_diff))
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bins = np.arange(-max_diff, max_diff, 2*max_diff/100)
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axs[i].hist(diffs_per_noise[i], bins=bins)
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axs[i].set_title('Noise level: {:.2f}'.format(noise_level[i]))
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plt.show()
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plt.close() |