add test to hand fit model parameters and test for FiCurve

generalTests.py

@@ -10,6 +10,8 @@ from scipy.signal import argrelmax
 from thunderfish.eventdetection import detect_peaks
 from stimuli.SinusAmplitudeModulation import SinusAmplitudeModulationStimulus
 from models.LIFACnoise import LifacNoiseModel
+from FiCurve import FICurve
+
 
 def time_test_function():
     for n in [1000]:  # number of calls
@@ -96,10 +98,68 @@ def test_simulation_speed():
     print("took:", round((t_end-t_start)/repetitions, 5), "seconds for " + str(seconds) + "s simulation", "step size:", parameters["step_size"]*1000, "ms")
 
 
+def test_fi_curve_class():
+    for cell_data in icelldata_of_dir("./data/"):
+        fi_curve = FICurve(cell_data)
+        fi_curve.plot_fi_curve()
+        # fi_curve.plot_f_point_detections()
+
+
+    pass
+
+
+def test_parameters():
+    parameters = {'mem_tau': 21., 'delta_a': 0.1, 'input_scaling': 400.,
+                  'v_offset': 85.25, 'threshold': 0.1, 'v_base': 0, 'step_size': 0.00005, 'tau_a': 0.01,
+                  'a_zero': 0, 'v_zero': 0, 'noise_strength': 3}
+    model = LifacNoiseModel(parameters)
+
+    base_stimulus_freq = 350
+    stimulus = SinusAmplitudeModulationStimulus(base_stimulus_freq, 1.2, 5, 5, 20)
+
+    plot_model_during_stimulus(model, stimulus, 30)
+
+    bf, vs, sc = model.calculate_baseline_markers(base_stimulus_freq)
+    contrasts = [0.7, 0.8, 0.9, 1, 1.1, 1.2, 1.3]
+    modulation_frequency = 1
+    f_infs, f_inf_slope = model.calculate_fi_markers(contrasts, base_stimulus_freq, modulation_frequency)
+
+    print("Baseline frequency: {:.2f}".format(bf))
+    print("Vector strength:    {:.2f}".format(vs))
+    print("serial correlation: {:.2f}".format(sc[0]))
+
+    print("f infinity slope:   {:.2f}".format(f_inf_slope))
+    print("f infinities: \n", f_infs)
+
+
+def plot_model_during_stimulus(model: LifacNoiseModel, stimulus:SinusAmplitudeModulationStimulus, total_time):
+    model.simulate_fast(stimulus, total_time)
+
+    time = np.arange(0, total_time, model.get_sampling_interval())
+    fig, axes = plt.subplots(4, 1, figsize=(9, 4*2),  sharex="all")
+
+    stimulus_array = stimulus.as_array(0, total_time, model.get_sampling_interval())
+    axes[0].plot(time, stimulus_array)
+    axes[0].set_title("Stimulus")
+    axes[1].plot(time, rectify_stimulus_array(stimulus_array))
+    axes[1].set_title("rectified Stimulus")
+    axes[2].plot(time, model.get_voltage_trace())
+    axes[2].set_title("Voltage")
+    axes[3].plot(time, model.get_adaption_trace())
+    axes[3].set_title("Adaption")
+    plt.show()
+
+
+def rectify_stimulus_array(stimulus_array: np.ndarray):
+    return np.array([x if x > 0 else 0 for x in stimulus_array])
+
+
 if __name__ == '__main__':
     # time_test_function()
     # test_cell_data()
     # test_peak_detection()
-    test_simulation_speed()
+    # test_simulation_speed()
+    # test_parameters()
+    test_fi_curve_class()
     pass