197 lines
7.7 KiB
Python
197 lines
7.7 KiB
Python
from models.LIFACnoise import LifacNoiseModel
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from CellData import CellData, icelldata_of_dir
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from FiCurve import FICurve
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from AdaptionCurrent import Adaption
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from stimuli.SinusAmplitudeModulation import SinusAmplitudeModulationStimulus
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import helperFunctions as hF
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import numpy as np
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from scipy.optimize import curve_fit, minimize
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import functions as fu
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import time
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import matplotlib.pyplot as plt
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def main():
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# run_test_with_fixed_model()
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# quit()
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fitter = Fitter()
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fmin, params = fitter.fit_model_to_values(700, 1400, [-0.3], 1, [0.7, 0.8, 0.9, 1, 1.1, 1.2, 1.3], [1370, 1380, 1390, 1400, 1410, 1420, 1430], 100, 0.02, 0.01)
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print("calculated parameters:")
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print(params)
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def run_with_real_data():
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for celldata in icelldata_of_dir("./data/"):
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start_time = time.time()
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fitter = Fitter(celldata)
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fmin, parameters = fitter.fit_model_to_data()
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print(fmin)
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print(parameters)
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end_time = time.time()
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print('Fitting of cell took function took {:.3f} s'.format((end_time - start_time)))
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pass
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def run_test_with_fixed_model():
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a_tau = 10
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a_delta = 0.08
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parameters = {'mem_tau': 5, 'delta_a': a_delta, 'input_scaling': 100,
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'v_offset': 80, 'threshold': 1, 'v_base': 0, 'step_size': 0.00005, 'tau_a': a_tau,
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'a_zero': 0, 'v_zero': 0, 'noise_strength': 0.5}
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model = LifacNoiseModel(parameters)
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eod_freq = 750
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contrasts = np.arange(0.5, 1.51, 0.1)
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modulation_freq = 10
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baseline_freq, vector_strength, serial_correlation = model.calculate_baseline_markers(eod_freq)
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f_infinities, f_infinities_slope = model.calculate_fi_markers(contrasts, eod_freq, modulation_freq)
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fitter = Fitter()
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fmin, fit_parameters = fitter.fit_model_to_values(eod_freq, baseline_freq, serial_correlation, vector_strength, contrasts, f_infinities, f_infinities_slope, a_delta, a_tau)
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print("calculated parameters:")
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print(fit_parameters)
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print("ref parameters:")
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print(parameters)
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class Fitter:
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def __init__(self, step_size=None):
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if step_size is not None:
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self.model = LifacNoiseModel({"step_size": step_size})
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else:
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self.model = LifacNoiseModel({"step_size": 0.0005})
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# self.data = data
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self.fi_contrasts = []
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self.eod_freq = 0
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self.modulation_frequency = 10
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self.sc_max_lag = 1
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# expected values the model has to replicate
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self.baseline_freq = 0
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self.vector_strength = -1
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self.serial_correlation = []
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self.f_infinities = []
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self.f_infinities_slope = 0
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# fixed values needed to fit model
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self.a_tau = 0
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self.a_delta = 0
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self.counter = 0
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def calculate_needed_values_from_data(self, data: CellData):
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self.eod_freq = data.get_eod_frequency()
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self.baseline_freq = data.get_base_frequency()
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self.vector_strength = data.get_vector_strength()
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self.serial_correlation = data.get_serial_correlation(self.sc_max_lag)
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fi_curve = FICurve(data, contrast=True)
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self.fi_contrasts = fi_curve.stimulus_value
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self.f_infinities = fi_curve.f_infinities
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self.f_infinities_slope = fi_curve.get_f_infinity_slope()
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f_zero_slope = fi_curve.get_fi_curve_slope_of_straight()
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self.a_delta = f_zero_slope / self.f_infinities_slope
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adaption = Adaption(data, fi_curve)
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self.a_tau = adaption.get_tau_real()
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# mem_tau, (threshold?), (v_offset), noise_strength, input_scaling
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def cost_function(self, X, tau_a=10, delta_a=3, error_scaling=()):
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freq_sampling_rate = 0.005
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# set model parameters to the given ones:
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self.model.set_variable("mem_tau", X[0])
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self.model.set_variable("noise_strength", X[1])
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self.model.set_variable("input_scaling", X[2])
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self.model.set_variable("tau_a", tau_a)
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self.model.set_variable("delta_a", delta_a)
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# minimize the difference in baseline_freq first by fitting v_offset
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# v_offset = self.__fit_v_offset_to_baseline_frequency__()
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base_stimulus = SinusAmplitudeModulationStimulus(self.eod_freq, 0, 0)
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v_offset = self.model.find_v_offset(self.baseline_freq, base_stimulus)
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self.model.set_variable("v_offset", v_offset)
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# only eod with amplitude 1 and no modulation
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_, spiketimes = self.model.simulate_fast(base_stimulus, 30)
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baseline_freq = hF.mean_freq_of_spiketimes_after_time_x(spiketimes, freq_sampling_rate, 5)
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# print("model:", baseline_freq, "data:", self.baseline_freq)
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relative_spiketimes = np.array([s % (1/self.eod_freq) for s in spiketimes if s > 0])
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eod_durations = np.full((len(relative_spiketimes)), 1/self.eod_freq)
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vector_strength = hF.__vector_strength__(relative_spiketimes, eod_durations)
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serial_correlation = hF.calculate_serial_correlation(np.array(spiketimes), self.sc_max_lag)
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f_infinities = []
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for contrast in self.fi_contrasts:
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stimulus = SinusAmplitudeModulationStimulus(self.eod_freq, contrast, self.modulation_frequency)
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_, spiketimes = self.model.simulate_fast(stimulus, 1)
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if len(spiketimes) < 2:
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f_infinities.append(0)
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else:
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f_infinity = hF.mean_freq_of_spiketimes_after_time_x(spiketimes, freq_sampling_rate, 0.5)
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f_infinities.append(f_infinity)
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popt, pcov = curve_fit(fu.line, self.fi_contrasts, f_infinities, maxfev=10000)
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f_infinities_slope = popt[0]
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error_bf = abs((baseline_freq - self.baseline_freq) / self.baseline_freq)
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error_vs = abs((vector_strength - self.vector_strength) / self.vector_strength)
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error_sc = abs((serial_correlation[0] - self.serial_correlation[0]) / self.serial_correlation[0])
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error_f_inf_slope = abs((f_infinities_slope - self.f_infinities_slope) / self.f_infinities_slope)
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#print("vs:", vector_strength, self.vector_strength)
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#print("sc", serial_correlation[0], self.serial_correlation[0])
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#print("f slope:", f_infinities_slope, self.f_infinities_slope)
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error_f_inf = 0
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for i in range(len(f_infinities)):
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error_f_inf += abs((f_infinities[i] - self.f_infinities[i]) / f_infinities[i])
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error_f_inf = error_f_inf / len(f_infinities)
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self.counter += 1
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# print("mem_tau:", X[0], "noise:", X[0], "input_scaling:", X[2])
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errors = [error_bf, error_vs, error_sc, error_f_inf_slope, error_f_inf]
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print("Cost function run times:", self.counter, "error sum:", sum(errors), errors)
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return error_bf + error_vs + error_sc + error_f_inf_slope + error_f_inf
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def fit_model_to_data(self, data: CellData):
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self.calculate_needed_values_from_data(data)
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return self.fit_model()
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def fit_model_to_values(self, eod_freq, baseline_freq, sc, vs, fi_contrasts, fi_inf_values, fi_inf_slope, a_delta, a_tau):
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self.eod_freq = eod_freq
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self.baseline_freq = baseline_freq
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self.serial_correlation = sc
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self.vector_strength = vs
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self.fi_contrasts = fi_contrasts
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self.f_infinities = fi_inf_values
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self.f_infinities_slope = fi_inf_slope
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self.a_delta = a_delta
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self.a_tau = a_tau
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return self.fit_model()
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def fit_model(self):
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x0 = np.array([20, 15, 75])
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init_simplex = np.array([np.array([2, 1, 10]), np.array([40, 100, 140]), np.array([20, 50, 70]), np.array([150, 1, 200])])
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fmin = minimize(fun=self.cost_function, x0=x0, args=(self.a_tau, self.a_delta), method="Nelder-Mead", options={"initial_simplex": init_simplex})
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return fmin, self.model.get_parameters()
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if __name__ == '__main__':
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main()
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