73 lines
2.0 KiB
Python
73 lines
2.0 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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import helperFunctions as hF
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import functions as fu
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import time
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from stimuli.SinusoidalStepStimulus import SinusoidalStepStimulus
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def main():
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x_values = np.arange(-1, 1, 0.01)
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popt = [0, 0, 0, 0]
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y_values = [fu.full_boltzmann(x, popt[0], popt[1], popt[2], popt[3]) for x in x_values]
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plt.plot(x_values, y_values)
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plt.show()
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quit()
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for freq in [700, 50, 100, 500, 1000]:
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reps = 1000
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start = time.time()
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for i in range(reps):
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mean_isi = 1 / freq
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n = 0.7
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phase_locking_strength = 0.7
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size = 100000
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final_isis = np.array([])
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while len(final_isis) < size:
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isis = np.random.normal(mean_isi, mean_isi*n, size)
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isi_phase = (isis % mean_isi) / mean_isi
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diff = abs_phase_diff(isi_phase, 0.5)
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chance = np.random.random(size)
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isis_phase_cleaned = []
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for i in range(len(diff)):
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if 1-diff[i]**0.05 > chance[i]:
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isis_phase_cleaned.append(isis[i])
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final_isis = np.concatenate((final_isis, isis_phase_cleaned))
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spikes = np.cumsum(final_isis)
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spikes = np.sort(spikes[spikes > 0])
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clean_isis = np.diff(spikes)
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bins = np.arange(-0.01, 0.01, 0.0001)
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plt.hist(clean_isis, alpha=0.5, bins=bins)
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plt.hist(isis, alpha=0.5, bins=bins)
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plt.show()
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quit()
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end = time.time()
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print("It took {:.2f} s to simulate 10s of spikes at {} Hz".format(end-start, freq))
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def abs_phase_diff(rel_phases:list, ref_phase:float):
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"""
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:param rel_phases: relative phases as a list of values between 0 and 1
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:param ref_phase: reference phase to which the difference is calculated (between 0 and 1)
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:return: list of absolute differences
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"""
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diff = [abs(min(x-ref_phase, x-ref_phase+1)) for x in rel_phases]
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return diff
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if __name__ == '__main__':
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main()
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