from models.FirerateModel import FirerateModel
from models.LIFAC import LIFACModel
from stimuli.StepStimulus import StepStimulus
import helperFunctions as hf
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
import functions as fu


def main():
    #boltzmann_vars = find_fitting_boltzmann()
    #test_firerate_model(boltzmann_vars)
    find_relation()

def find_fitting_boltzmann():
    lifac = LIFACModel({"delta_a": 0})

    frequencies = []
    stim_strengths = np.arange(20, 32, 0.5)
    duration = 0.5
    for stim_strength in stim_strengths:
        lifac.simulate(StepStimulus(0, duration, stim_strength), duration)

        spiketimes = lifac.get_spiketimes()
        if len(spiketimes) == 0:
            frequencies.append(0)
            continue
        time, freq = hf.calculate_isi_frequency(spiketimes, 0, lifac.get_sampling_interval()/1000)

        frequencies.append(freq[-1])

    popt, pcov = curve_fit(fu.line, stim_strengths, frequencies)
    popt2, pcov = curve_fit(fu.full_boltzmann, stim_strengths, frequencies, p0=[700, 0, 5, 25], bounds=([0, 0, -np.inf, -np.inf], [3000, 0.001, np.inf, np.inf]))
    print("line:", popt)
    print("boltzmann:", popt2)
    # plt.plot(stim_strengths, frequencies)
    # plt.plot(stim_strengths, [fu.line(x, popt[0], popt[1]) for x in stim_strengths], '.')
    # plt.plot(stim_strengths, [fu.full_boltzmann(x, popt2[0], popt2[1], popt2[2], popt2[3]) for x in stim_strengths], 'o')
    # plt.show()
    return popt2


def test_firerate_model(boltzmann_vars):
    fr_model = FirerateModel(params={"function_params": boltzmann_vars, "adaptation_factor": 0})

    frequencies = []
    stim_strengths = np.arange(0, 50, 0.5)
    duration = 0.5
    for stim_strength in stim_strengths:
        fr_model.simulate(StepStimulus(0, duration, stim_strength), duration)

        frequencies.append(fr_model.get_frequency()[-1])

    plt.plot(stim_strengths, frequencies)
    plt.plot(np.arange(20, 32, 1), [fu.line(x, 5.10369, -29.7977481) for x in np.arange(20, 32, 1)], 'o')
    plt.show()


def find_relation():
    stimulus_step_size = 1
    stimulus_range = np.arange(20, 32, stimulus_step_size)
    boltzmann_vars = [2.00728705e+02, 1.09905953e-12, 1.03639686e-01, 2.55002788e+01]
    line_vars = [5.10369405, -29.79774806]
    duration = 0.5

    lifac_adaption_strength_range = np.arange(0, 3, 0.2)
    firerate_adaption_variables = []
    for lifac_adaption_strength in lifac_adaption_strength_range:
        lifac = LIFACModel({"delta_a": lifac_adaption_strength, "tau_a": 20}, "")

        adapted_frequencies = []
        for stim in stimulus_range:
            stimulus = StepStimulus(0, duration, stim)
            lifac.simulate(stimulus, duration)
            spiketimes = lifac.get_spiketimes()
            time, freq = hf.calculate_isi_frequency(spiketimes, 0, lifac.get_sampling_interval()/1000)

            adapted_frequencies.append(freq[-1])

        firerate_adaption_strengths = []
        for i in range(len(adapted_frequencies)):
            goal_adapted_freq = adapted_frequencies[i]

            # stimulus_strength_after_adaption = fu.inverse_full_boltzmann(goal_adapted_freq,
            #                                                              boltzmann_vars[0],
            #                                                              boltzmann_vars[1],
            #                                                              boltzmann_vars[2],
            #                                                              boltzmann_vars[3],)

            # assume fitted line as basis of the fire-rate model:
            stimulus_strength_after_adaption = fu.inverse_line(goal_adapted_freq, line_vars[0], line_vars[1])

            adaption_strength = stimulus_range[i] - stimulus_strength_after_adaption

            firerate_adaption = adaption_strength / goal_adapted_freq
            firerate_adaption_strengths.append(firerate_adaption)

        firerate_adaption_variables.append(firerate_adaption_strengths)

        # plt.plot(stimulus_range, firerate_adaption_strength, label=str(lifac_adaption_strength))
        # plt.show()

    for i in range(len(lifac_adaption_strength_range)):
        plt.plot([lifac_adaption_strength_range[i]+p*0.01 for p in range(len(stimulus_range))], firerate_adaption_variables[i])

    mean_firerate_adaption_value = [np.mean(strengths) for strengths in firerate_adaption_variables]

    plt.plot(lifac_adaption_strength_range, mean_firerate_adaption_value)
    plt.title("Relation of adaption strength variables:\n Small 'subplots' value for different stimulus strength")
    plt.xlabel("lifac adaption strength: delta_a")
    plt.ylabel("firerate adaption strength: alpha")
    plt.savefig("figures/adaption_relation_stimulus_strength.png")
    plt.close()
    popt, pcov = curve_fit(fu.line, lifac_adaption_strength_range, mean_firerate_adaption_value)

    print(popt)


if __name__ == '__main__':
    main()