too much sorry future me -.-

tests/ModelTests.py

@@ -3,10 +3,10 @@ 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
+import functions as fu
 
 
 def main():
@@ -14,6 +14,7 @@ def main():
     # test_stepsize_influence()
     test_lifac_noise()
 
+
 def test_stepsize_influence():
     # model = LIFACModel()
     model = FirerateModel()
@@ -60,10 +61,10 @@ def test_lifac_noise():
 
     model.simulate(stimulus, total_time)
 
-    fig, axes = plt.subplots(nrows=3, sharex=True)
+    fig, axes = plt.subplots(nrows=3, sharex="col")
     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].plot(sparse_time, [fu.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()
@@ -79,7 +80,6 @@ def test_lifac_noise():
 
     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))])
@@ -119,6 +119,5 @@ def test_lifac_noise():
     plt.show()
 
 
-
 if __name__ == '__main__':
     main()

tests/stimuli/TestStepStimulus.py

@@ -0,0 +1,55 @@
+import unittest
+from stimuli.StepStimulus import StepStimulus
+import numpy as np
+
+
+class TestStepStimulus(unittest.TestCase):
+
+    def test_time_getters(self):
+        starts = [2, -5, 0, 10]
+        durations = [2, 1000, 0.5]
+        value = 10
+
+        for start in starts:
+            for duration in durations:
+                stimulus = StepStimulus(start, duration, value)
+
+                self.assertEqual(start, stimulus.get_stimulus_start_s(), "reported start (s) was wrong")
+                self.assertEqual(duration, stimulus.get_stimulus_duration_s(), "reported duration (s) was wrong")
+                self.assertEqual(start + duration, stimulus.get_stimulus_end_s(), "reported end (s) was wrong")
+
+                self.assertEqual(start * 1000, stimulus.get_stimulus_start_ms(), "reported start (ms) was wrong")
+                self.assertEqual(duration * 1000, stimulus.get_stimulus_duration_ms(), "reported duration (ms) was wrong")
+                self.assertEqual((start + duration)*1000, stimulus.get_stimulus_end_ms(), "reported end (ms) was wrong")
+
+    def test_duration_must_be_positive(self):
+        self.assertRaises(ValueError, StepStimulus, 1, -1, 3)
+
+    def test_value_at(self):
+        start = 0
+        duration = 2
+        value = 5
+        base_value = -1
+        stimulus = StepStimulus(start, duration, value, base_value)
+
+        for i in np.arange(start-1, start+duration+1, 0.1):
+            if i < start or i > start+duration:
+                self.assertEqual(stimulus.value_at_time_in_s(i), base_value)
+                self.assertEqual(stimulus.value_at_time_in_ms(i*1000), base_value)
+            else:
+                self.assertEqual(stimulus.value_at_time_in_s(i), value)
+                self.assertEqual(stimulus.value_at_time_in_ms(i * 1000), value)
+
+    def test_amplitude(self):
+        stim_values = [-10, -5, 0, 15, 20]
+        base_values = [-10, -5, -2, 0, 1, 15]
+
+        for s_value in stim_values:
+            for b_value in base_values:
+                stimulus = StepStimulus(0, 1, s_value, b_value)
+
+                self.assertEqual(stimulus.get_amplitude(), s_value-b_value)
+
+
+if __name__ == '__main__':
+    unittest.main()