227 lines
8.1 KiB
Python
227 lines
8.1 KiB
Python
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import helperFunctions as hf
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from CellData import icelldata_of_dir
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import functions as fu
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import numpy as np
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import time
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import matplotlib.pyplot as plt
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import os
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from scipy.signal import argrelmax
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from thunderfish.eventdetection import detect_peaks
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from stimuli.SinusAmplitudeModulation import SinusAmplitudeModulationStimulus
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from models.LIFACnoise import LifacNoiseModel
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from FiCurve import FICurve
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from AdaptionCurrent import Adaption
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from stimuli.StepStimulus import StepStimulus
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from stimuli.SinusoidalStepStimulus import SinusoidalStepStimulus
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def time_test_function():
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for n in [1000]: # number of calls
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print("calls:", n)
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start = time.time()
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for i in range(n):
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data = np.random.normal(size=10000)
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y = [fu.rectify(x) for x in data]
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end = time.time()
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print("time:", end - start)
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def test_cell_data():
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for cell_data in icelldata_of_dir("../data/"):
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#if "2012-12-20-ad" not in cell_data.get_data_path():
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# continue
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print()
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print(cell_data.get_data_path())
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if len(cell_data.get_base_traces(cell_data.TIME)) != 0:
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# print("works!")
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#print("VS:", cell_data.get_vector_strength())
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#print("SC:", cell_data.get_serial_correlation(5))
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print("Eod freq:", cell_data.get_eod_frequency())
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else:
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pass
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#print("NNNOOOOOOOOOO!")
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#print("spiketimes:", len(cell_data.get_base_spikes()))
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#print("Times:", len(cell_data.get_base_traces(cell_data.TIME)))
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#print("EOD:", len(cell_data.get_base_traces(cell_data.EOD)))
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def test_peak_detection():
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for cell_data in icelldata_of_dir("../data/"):
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print()
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print(cell_data.get_data_path())
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times = cell_data.get_base_traces(cell_data.TIME)
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eod = cell_data.get_base_traces(cell_data.EOD)
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v1 = cell_data.get_base_traces(cell_data.V1)
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for i in range(len(v1)):
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pieces = 20
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v1_trace = v1[i]
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total = len(v1_trace)
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all_peaks = []
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plt.plot(times[i], v1[i])
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for n in range(pieces):
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length = int(total/pieces)
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first_index = n*length
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last_index = (n+1)*length
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std = np.std(v1_trace[first_index:last_index])
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peaks, _ = detect_peaks(v1_trace[first_index:last_index], std * 3)
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peaks = peaks + first_index
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all_peaks.extend(peaks)
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plt.plot(times[i][first_index], v1_trace[first_index], 'o', color="green")
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all_peaks = np.array(all_peaks)
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plt.plot(times[i][all_peaks], v1[i][all_peaks], 'o', color='red')
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plt.show()
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def test_simulation_speed():
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parameters = {'mem_tau': 21.348990483539083, 'delta_a': 20.41809814660199, 'input_scaling': 3.0391541280864196, 'v_offset': 26.25, 'threshold': 1, 'v_base': 0, 'step_size': 0.00005, 'tau_a': 158.0404259501454, 'a_zero': 0, 'v_zero': 0, 'noise_strength': 2.87718460648148}
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model = LifacNoiseModel(parameters)
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repetitions = 1
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seconds = 10
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stimulus = SinusAmplitudeModulationStimulus(750, 1, 10, 1, 8)
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time_start = 0
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t_start = time.time()
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for i in range(repetitions):
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v, spikes = model.simulate_fast(stimulus, seconds, time_start)
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print(len(v))
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print(len(spikes))
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#time_v = np.arange(time_start, seconds, model.get_sampling_interval())
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#plt.plot(time_v, v, '.')
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#plt.show()
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#freq = hf.mean_freq_of_spiketimes_after_time_x(spikes, parameters["step_size"], 0)
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#print(freq)
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t_end = time.time()
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#print("baseline markers:", model.calculate_baseline_markers(750, 3))
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print("took:", round((t_end-t_start)/repetitions, 5), "seconds for " + str(seconds) + "s simulation", "step size:", parameters["step_size"]*1000, "ms")
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def test_fi_curve_class():
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for cell_data in icelldata_of_dir("../data/"):
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fi_curve = FICurve(cell_data)
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fi_curve.get_f_zero_and_f_inf_intersection()
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fi_curve.plot_fi_curve()
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# fi_curve.plot_f_point_detections()
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pass
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def test_adaption_class():
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for cell_data in icelldata_of_dir("../data/"):
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print()
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print(cell_data.get_data_path())
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fi_curve = FICurve(cell_data)
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adaption = Adaption(cell_data, fi_curve)
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adaption.plot_exponential_fits()
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print("tau_effs:", adaption.get_tau_effs())
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print("tau_real:", adaption.get_tau_real())
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fi_curve.plot_fi_curve()
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def test_parameters():
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parameters = {'mem_tau': 21., 'delta_a': 0.1, 'input_scaling': 400.,
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'v_offset': 85.25, 'threshold': 0.1, 'v_base': 0, 'step_size': 0.00005, 'tau_a': 0.01,
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'a_zero': 0, 'v_zero': 0, 'noise_strength': 3}
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model = LifacNoiseModel(parameters)
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base_stimulus_freq = 350
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stimulus = SinusAmplitudeModulationStimulus(base_stimulus_freq, 1.2, 5, 5, 20)
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plot_model_during_stimulus(model, stimulus, 30)
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bf, vs, sc = model.calculate_baseline_markers(base_stimulus_freq)
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contrasts = [0.7, 0.8, 0.9, 1, 1.1, 1.2, 1.3]
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modulation_frequency = 1
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f_infs, f_inf_slope = model.calculate_fi_markers(contrasts, base_stimulus_freq, modulation_frequency)
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print("Baseline frequency: {:.2f}".format(bf))
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print("Vector strength: {:.2f}".format(vs))
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print("serial correlation: {:.2f}".format(sc[0]))
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print("f infinity slope: {:.2f}".format(f_inf_slope))
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print("f infinities: \n", f_infs)
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def test_vector_strength_calculation():
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model = LifacNoiseModel({"noise_strength": 0})
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bf, vs1, sc = model.calculate_baseline_markers(600)
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base_stim = SinusAmplitudeModulationStimulus(600, 0, 0)
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_, spiketimes = model.simulate_fast(base_stim, 30)
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stimulus_trace = base_stim.as_array(0, 30, model.get_sampling_interval())
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time_trace = np.arange(0, 30, model.get_sampling_interval())
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vs2 = hf.calculate_vector_strength_from_spiketimes(time_trace, stimulus_trace, spiketimes, model.get_sampling_interval())
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print("with assumed eod durations vs: {:.3f}".format(vs1))
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print("with detected eod durations vs: {:.3f}".format(vs2))
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def plot_model_during_stimulus(model: LifacNoiseModel, stimulus:SinusAmplitudeModulationStimulus, total_time):
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_, spiketimes = model.simulate_fast(stimulus, total_time)
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time = np.arange(0, total_time, model.get_sampling_interval())
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fig, axes = plt.subplots(5, 1, figsize=(9, 4*2), sharex="all")
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stimulus_array = stimulus.as_array(0, total_time, model.get_sampling_interval())
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axes[0].plot(time, stimulus_array)
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axes[0].set_title("Stimulus")
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axes[1].plot(time, rectify_stimulus_array(stimulus_array))
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axes[1].set_title("rectified Stimulus")
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axes[2].plot(time, model.get_voltage_trace())
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axes[2].set_title("Voltage")
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axes[3].plot(time, model.get_adaption_trace())
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axes[3].set_title("Adaption")
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f_time, f = hf.calculate_time_and_frequency_trace(spiketimes, model.get_sampling_interval())
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axes[4].plot(f_time, f)
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axes[4].set_title("Frequency")
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plt.show()
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def rectify_stimulus_array(stimulus_array: np.ndarray):
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return np.array([x if x > 0 else 0 for x in stimulus_array])
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if __name__ == '__main__':
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# X = [0.05, 0.02, 50, 0.1, 0.03]
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model = LifacNoiseModel()
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# model.set_variable("mem_tau", X[0])
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# model.set_variable("noise_strength", X[1])
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# model.set_variable("input_scaling", X[2])
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# model.set_variable("tau_a", X[3])
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# model.set_variable("delta_a", X[4])
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stim = SinusoidalStepStimulus(700, 0.2, start_time=1, duration=1)
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bf, vs, sc = model.calculate_baseline_markers(700)
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print("baseline freq:{:.2f}\nVector strength: {:.3f}\nSerial cor:".format(bf, vs), sc)
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contrasts = np.arange(-0.3, 0.31, 0.05)
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model.calculate_fi_curve(contrasts, 700)
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f_infinities, slope = model.calculate_fi_markers(contrasts, 700)
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print("FI-Curve\nSlope: {:.2f}\nValues:".format(slope), f_infinities)
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plot_model_during_stimulus(model, stim, 3)
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quit()
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# time_test_function()
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# test_cell_data()
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# test_peak_detection()
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# test_simulation_speed()
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# test_parameters()
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# test_fi_curve_class()
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# test_adaption_class()
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test_vector_strength_calculation()
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pass
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