From 867c4461d8cbf750280f063e3a7ffa679a318597 Mon Sep 17 00:00:00 2001
From: "a.ott" <a.ott@student.uni-tuebingen.de>
Date: Fri, 24 Jan 2020 13:39:46 +0100
Subject: [PATCH] add test for step size dependence of lifac noise

---
 tests/ModelTests.py | 80 +++++++++++++++++++++++++++++++++++++++++++--
 1 file changed, 78 insertions(+), 2 deletions(-)

diff --git a/tests/ModelTests.py b/tests/ModelTests.py
index 6ea5d7c..f666f96 100644
--- a/tests/ModelTests.py
+++ b/tests/ModelTests.py
@@ -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()
\ No newline at end of file