add tests for sinusoidalStep, add FiCurve plot possibilities (temp), add initial simplex generation

unittests/testSinusoidalStepStimulus.py

@@ -0,0 +1,117 @@
+from stimuli.SinusoidalStepStimulus import SinusoidalStepStimulus
+import unittest
+import numpy as np
+import helperFunctions as hF
+import matplotlib.pyplot as plt
+from warnings import warn
+
+
+class SinusoidalStepStimulusTester(unittest.TestCase):
+
+    base_frequencies = [0, 10, 100, 1000]
+    contrasts = [0, 0.5, 1, 1.5]
+    modulation_frequencies = [0, 5, 10, 100]
+    step_sizes = [1, 0.5, 0.00005]
+    time_starts = [0, 2, -2]
+    durations = [2]
+
+    def setUp(self):
+        pass
+
+    def tearDown(self):
+        pass
+
+    def test_consistency_base_frequency(self):
+        contrast = 0.1
+        mod_freq = 5
+        time_start = -1
+        duration = 10
+        step_size = 0.00005
+        for base_freq in self.base_frequencies:
+            stimulus = SinusoidalStepStimulus(base_freq, contrast, 0, 8)
+            self.assertTrue(array_and_time_points_equal(stimulus, time_start, duration, step_size),
+                            msg="Stimulus values inconsistent with base freq: {:.2f}".format(base_freq))
+
+    def test_consistency_contrast(self):
+        base_freq = 700
+        mod_freq = 5
+        time_start = -1
+        duration = 10
+        step_size = 0.00005
+        for contrast in self.contrasts:
+            stimulus = SinusoidalStepStimulus(base_freq, contrast, 0, 8)
+            self.assertTrue(array_and_time_points_equal(stimulus, time_start, duration, step_size),
+                            msg="Stimulus values inconsistent with contrast: {:.2f}".format(contrast))
+
+    def test_consistency_modulation_frequency(self):
+        contrast = 0.1
+        base_freq = 700
+        time_start = -1
+        duration = 10
+        step_size = 0.00005
+        for mod_freq in self.modulation_frequencies:
+            stimulus = SinusoidalStepStimulus(base_freq, contrast, 0, 1)
+            self.assertTrue(array_and_time_points_equal(stimulus, time_start, duration, step_size),
+                            msg="Stimulus values inconsistent with mod freq: {:.2f}".format(mod_freq))
+
+    def test_consistency_step_size(self):
+        contrast = 0.1
+        base_freq = 700
+        time_start = -1
+        duration = 10
+        mod_freq = 10
+        for step_size in self.step_sizes:
+            stimulus = SinusoidalStepStimulus(base_freq, contrast, 0, 8)
+            self.assertTrue(array_and_time_points_equal(stimulus, time_start, duration, step_size),
+                            msg="Stimulus values inconsistent with step_size: {:.3f}ms".format(step_size)*1000)
+
+    def test_consistency_time_start(self):
+        contrast = 0.1
+        base_freq = 700
+        mod_freq = 10
+        duration = 10
+        step_size = 0.00005
+        for time_start in self.time_starts:
+            stimulus = SinusoidalStepStimulus(base_freq, contrast, 0, 8)
+            self.assertTrue(array_and_time_points_equal(stimulus, time_start, duration, step_size),
+                            msg="Stimulus values inconsistent when the time starts at: {:.2f}s".format(time_start))
+
+
+def array_and_time_points_equal(stimulus, start, duration, step_size):
+    precision = 5
+    array = np.around(stimulus.as_array(start, duration, step_size), precision)
+    time = np.arange(start, start+duration, step_size)
+    for i, time_point in enumerate(time):
+        value = stimulus.value_at_time_in_s(time_point)
+        if array[i] != np.round(value, precision):
+            stim_per_point = []
+            for t in time:
+                stim_per_point.append(stimulus.value_at_time_in_s(t))
+
+            stim_per_point = np.around(np.array(stim_per_point), precision)
+            fig, axes = plt.subplots(2, 1, sharex="all")
+            axes[0].plot(time, array, label="array")
+            axes[0].plot(time, stim_per_point, label="individual")
+            axes[0].set_title("stimulus values")
+            axes[0].legend()
+
+            axes[1].plot(time, np.array(stim_per_point)-array)
+            axes[1].set_title("difference")
+            plt.show()
+
+            return False
+
+    # stim_per_point = []
+    # for t in time:
+    #     stim_per_point.append(stimulus.value_at_time_in_s(t))
+    #
+    # stim_per_point = np.around(np.array(stim_per_point), precision)
+    # fig, axes = plt.subplots(1, 1, sharex="all")
+    # axes.plot(time, array, label="array")
+    # axes.plot(time, stim_per_point, label="individual")
+    # axes.set_title("stimulus values")
+    # axes.legend()
+    #
+    # plt.show()
+
+    return True