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add test for step size dependence of lifac noise

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a.ott 2020-01-24 13:39:46 +01:00
parent 6fcdf1eee0
commit 867c4461d8
1 changed files with 78 additions and 2 deletions
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tests/ModelTests.py
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@ -1,15 +1,18 @@
import matplotlib.pyplot as plt
import numpy as np
import helperFunctions as hf
from models.FirerateModel import FirerateModel
from models.LIFAC import LIFACModel
from models.LIFACnoise import LifacNoiseModel
from stimuli.StepStimulus import StepStimulus
from stimuli.SinusAmplitudeModulation import SinusAmplitudeModulationStimulus
def main():
# test_firerate_model()
test_stepsize_influence()
# test_stepsize_influence()
test_lifac_noise()
def test_stepsize_influence():
# model = LIFACModel()
@ -44,5 +47,78 @@ def test_firerate_model():
plt.show()
def test_lifac_noise():
model = LifacNoiseModel()
start = 0.2
duration = 30
total_time = duration + 2*start
step_size = model.get_parameters()["step_size"]/1000
time = np.arange(0, total_time, step_size)
stimulus = SinusAmplitudeModulationStimulus(700, 20, 0.2, 10, start, duration)
model.simulate(stimulus, total_time)
fig, axes = plt.subplots(nrows=3, sharex=True)
sparse_time = np.arange(0, total_time, 1/5000)
axes[0].plot(sparse_time, [stimulus.value_at_time_in_s(x) for x in sparse_time], label="given stimulus")
axes[0].plot(sparse_time, [hf.rectify(stimulus.value_at_time_in_s(x)) for x in sparse_time], label="seen stimulus")
axes[0].set_title("Stimulus")
axes[0].set_ylabel("stimulus strength")
axes[0].legend()
axes[1].plot(time, model.get_voltage_trace())
axes[1].set_title("Voltage trace")
axes[1].set_ylabel("voltage")
t, f = hf.calculate_isi_frequency(model.get_spiketimes(), 0, step_size)
axes[2].plot(t, f)
axes[2].set_title("ISI frequency trace")
axes[2].set_ylabel("Frequency")
spiketimes_small_step = model.get_spiketimes()
model.set_variable("step_size", 0.02)
model.simulate(stimulus, total_time)
print(model.get_adaption_trace()[int(0.1/(0.01/1000))])
step_size = model.get_parameters()["step_size"] / 1000
time = np.arange(0, total_time, step_size)
t, f = hf.calculate_isi_frequency(model.get_spiketimes(), 0, step_size)
axes[1].plot(time, model.get_voltage_trace())
axes[2].plot(t, f)
spiketimes_big_step = model.get_spiketimes()
print("CV:")
print("small step:", hf.calculate_coefficient_of_variation(spiketimes_small_step))
print("big step:", hf.calculate_coefficient_of_variation(spiketimes_big_step))
plt.show()
plt.close()
max_lag = 5
x = np.arange(1, max_lag+1, 1)
serial_cor_small = hf.calculate_serial_correlation(spiketimes_small_step, max_lag)
serial_cor_big = hf.calculate_serial_correlation(spiketimes_big_step, max_lag)
print(serial_cor_small)
print(serial_cor_big)
plt.plot(x, serial_cor_small, 'o', label='small step',)
plt.plot(x, serial_cor_big, 'o', label='big step')
plt.ylim(-1, 1)
plt.legend()
plt.show()
plt.close()
bins = np.arange(0, max(np.diff(spiketimes_small_step)), 0.0001)
plt.hist(np.diff(spiketimes_small_step), bins=bins, alpha=0.5)
plt.hist(np.diff(spiketimes_big_step), bins=bins, alpha=0.5)
plt.show()
if __name__ == '__main__':
main()