from models.LIFACnoise import LifacNoiseModel from CellData import CellData, icelldata_of_dir from FiCurve import FICurve from AdaptionCurrent import Adaption from stimuli.SinusAmplitudeModulation import SinusAmplitudeModulationStimulus import helperFunctions as hF import numpy as np from scipy.optimize import curve_fit, minimize import functions as fu import time import matplotlib.pyplot as plt def main(): for celldata in icelldata_of_dir("./data/"): start_time = time.time() fitter = Fitter(celldata) fmin, parameters = fitter.fit_model_to_data() print(fmin) print(parameters) end_time = time.time() print('Fitting of cell took function took {:.3f} s'.format((end_time - start_time))) break pass class Fitter: def __init__(self, data: CellData, step_size=None): if step_size is not None: self.model = LifacNoiseModel({"step_size": step_size}) else: self.model = LifacNoiseModel({"step_size": 0.05}) self.data = data self.fi_contrasts = [] self.eod_freq = 0 self.modulation_frequency = 10 self.sc_max_lag = 1 # expected values the model has to replicate self.baseline_freq = 0 self.vector_strength = -1 self.serial_correlation = [] self.f_infinities = [] self.f_infinities_slope = 0 # fixed values needed to fit model self.a_tau = 0 self.a_delta = 0 self.counter = 0 self.calculate_needed_values_from_data() def calculate_needed_values_from_data(self): self.eod_freq = self.data.get_eod_frequency() self.baseline_freq = self.data.get_base_frequency() self.vector_strength = self.data.get_vector_strength() self.serial_correlation = self.data.get_serial_correlation(self.sc_max_lag) fi_curve = FICurve(self.data, contrast=True) self.fi_contrasts = fi_curve.stimulus_value self.f_infinities = fi_curve.f_infinities self.f_infinities_slope = fi_curve.get_f_infinity_slope() f_zero_slope = fi_curve.get_fi_curve_slope_of_straight() self.a_delta = f_zero_slope / self.f_infinities_slope adaption = Adaption(self.data, fi_curve) self.a_tau = adaption.get_tau_real() # mem_tau, (threshold?), (v_offset), noise_strength, input_scaling def cost_function(self, X, tau_a=10, delta_a=3, error_scaling=()): # set model parameters to the given ones: self.model.set_variable("mem_tau", X[0]) self.model.set_variable("noise_strength", X[1]) self.model.set_variable("input_scaling", X[2]) self.model.set_variable("tau_a", tau_a) self.model.set_variable("delta_a", delta_a) # minimize the difference in baseline_freq first by fitting v_offset v_offset = self.__fit_v_offset_to_baseline_frequency__() self.model.set_variable("v_offset", v_offset) # only eod with amplitude 1 and no modulation base_stimulus = SinusAmplitudeModulationStimulus(self.eod_freq, 0, 0) _, spiketimes = self.model.simulate_fast(base_stimulus, 30) baseline_freq = hF.mean_freq_of_spiketimes_after_time_x(spiketimes, 5) # print("model:", baseline_freq, "data:", self.baseline_freq) relative_spiketimes = np.array([s % (1/self.eod_freq) for s in spiketimes]) eod_durations = np.full((len(spiketimes)), 1/self.eod_freq) vector_strength = hF.__vector_strength__(relative_spiketimes, eod_durations) serial_correlation = hF.calculate_serial_correlation(np.array(spiketimes), self.sc_max_lag) f_infinities = [] for contrast in self.fi_contrasts: stimulus = SinusAmplitudeModulationStimulus(self.eod_freq, contrast, self.modulation_frequency) _, spiketimes = self.model.simulate_fast(stimulus, 0.5) if len(spiketimes) < 2: f_infinities.append(0) else: f_infinity = hF.mean_freq_of_spiketimes_after_time_x(spiketimes, 0.4) f_infinities.append(f_infinity) popt, pcov = curve_fit(fu.line, self.fi_contrasts, f_infinities, maxfev=10000) f_infinities_slope = popt[0] error_bf = abs((baseline_freq - self.baseline_freq) / self.baseline_freq) error_vs = abs((vector_strength - self.vector_strength) / self.vector_strength) error_sc = abs((serial_correlation[0] - self.serial_correlation[0]) / self.serial_correlation[0]) error_f_inf_slope = abs((f_infinities_slope - self.f_infinities_slope) / self.f_infinities_slope) #print("vs:", vector_strength, self.vector_strength) #print("sc", serial_correlation[0], self.serial_correlation[0]) #print("f slope:", f_infinities_slope, self.f_infinities_slope) error_f_inf = 0 for i in range(len(f_infinities)): error_f_inf += abs((f_infinities[i] - self.f_infinities[i]) / f_infinities[i]) error_f_inf = error_f_inf / len(f_infinities) self.counter += 1 # print("mem_tau:", X[0], "noise:", X[0], "input_scaling:", X[2]) print("Cost function run times:", self.counter, "errors:", [error_bf, error_vs, error_sc, error_f_inf_slope, error_f_inf]) return error_bf + error_vs + error_sc + error_f_inf_slope + error_f_inf def __fit_v_offset_to_baseline_frequency__(self): test_model = self.model.get_model_copy() voltage_step_size = 1000 simulation_time = 2 v_offset_start = 0 v_offset_current = v_offset_start test_model.set_variable("v_offset", v_offset_current) base_stimulus = SinusAmplitudeModulationStimulus(self.eod_freq, 0, 0) _, spiketimes = test_model.simulate_fast(base_stimulus, simulation_time) if len(spiketimes) < 5: baseline_freq = 0 else: baseline_freq = hF.mean_freq_of_spiketimes_after_time_x(spiketimes, simulation_time/2) if baseline_freq < self.baseline_freq: upwards = True v_offset_current += voltage_step_size else: upwards = False v_offset_current -= voltage_step_size # search for a value below and above the baseline freq: while True: # print(self.counter, baseline_freq, self.baseline_freq, v_offset_current) # self.counter += 1 test_model.set_variable("v_offset", v_offset_current) base_stimulus = SinusAmplitudeModulationStimulus(self.eod_freq, 0, 0) _, spiketimes = test_model.simulate_fast(base_stimulus, simulation_time) if len(spiketimes) < 2: baseline_freq = 0 else: baseline_freq = hF.mean_freq_of_spiketimes_after_time_x(spiketimes, simulation_time/2) if baseline_freq < self.baseline_freq and upwards: v_offset_current += voltage_step_size elif baseline_freq < self.baseline_freq and not upwards: break elif baseline_freq > self.baseline_freq and upwards: break elif baseline_freq > self.baseline_freq and not upwards: v_offset_current -= voltage_step_size elif baseline_freq == self.baseline_freq: return v_offset_current # found the edges use them to allow binary search: if upwards: lower_bound = v_offset_current - voltage_step_size upper_bound = v_offset_current else: lower_bound = v_offset_current upper_bound = v_offset_current + voltage_step_size while True: middle = lower_bound + (upper_bound - lower_bound)/2 # print(self.counter, "measured_freq:", baseline_freq, "wanted_freq:", self.baseline_freq, "current middle:", middle) # self.counter += 1 test_model.set_variable("v_offset", middle) base_stimulus = SinusAmplitudeModulationStimulus(self.eod_freq, 0, 0) _, spiketimes = test_model.simulate_fast(base_stimulus, simulation_time) if len(spiketimes) < 2: baseline_freq = 0 else: baseline_freq = hF.mean_freq_of_spiketimes_after_time_x(spiketimes, simulation_time/2) if abs(baseline_freq - self.baseline_freq) < 1: # print("close enough:", baseline_freq, self.baseline_freq, abs(baseline_freq - self.baseline_freq)) break elif baseline_freq < self.baseline_freq: lower_bound = middle else: upper_bound = middle return middle def fit_model_to_data(self): x0 = np.array([20, 15, 75]) init_simplex = np.array([np.array([2, 1, 10]), np.array([40, 100, 140]), np.array([20, 50, 70]), np.array([150, 1, 200])]) fmin = minimize(fun=self.cost_function, x0=x0, args=(self.a_tau, self.a_delta), method="Nelder-Mead", options={"initial_simplex": init_simplex}) #fmin = minimize(fun=self.cost_function, x0=x0, args=(self.a_tau, self.a_delta), method="BFGS") return fmin, self.model.get_parameters() if __name__ == '__main__': main()