from models.LIFACnoise import LifacNoiseModel from stimuli.SinusoidalStepStimulus import SinusoidalStepStimulus from CellData import CellData from Baseline import get_baseline_class from FiCurve import get_fi_curve_class from AdaptionCurrent import Adaption import numpy as np from warnings import warn from scipy.optimize import minimize import time from helperFunctions import plot_errors import matplotlib.pyplot as plt class Fitter: def __init__(self, params=None): if params is None: self.base_model = LifacNoiseModel({"step_size": 0.00005}) else: self.base_model = LifacNoiseModel(params) if "step_size" not in params: self.base_model.set_variable("step_size", 0.00005) self.best_parameters_found = [] self.smallest_error = np.inf # self.fi_contrasts = [] self.eod_freq = 0 self.data_sampling_interval = -1 self.sc_max_lag = 2 # values to be replicated: self.baseline_freq = 0 self.vector_strength = -1 self.serial_correlation = [] self.coefficient_of_variation = 0 self.burstiness = -1 self.f_inf_values = [] self.f_inf_slope = 0 self.f_zero_values = [] # self.f_zero_slopes = [] self.f_zero_slope_at_straight = 0 self.f_zero_straight_contrast = 0 self.f_zero_fit = [] self.f_zero_curve_contrast = 0 self.f_zero_curve_contrast_idx = -1 self.f_zero_curve_freq = np.array([]) self.f_zero_curve_time = np.array([]) self.errors = [] # self.tau_a = 0 # counts how often the cost_function was called self.counter = 0 def set_data_reference_values(self, cell_data: CellData): self.eod_freq = cell_data.get_eod_frequency() self.data_sampling_interval = cell_data.get_sampling_interval() data_baseline = get_baseline_class(cell_data) data_baseline.load_values(cell_data.get_data_path()) self.baseline_freq = data_baseline.get_baseline_frequency() self.vector_strength = data_baseline.get_vector_strength() self.serial_correlation = data_baseline.get_serial_correlation(self.sc_max_lag) self.coefficient_of_variation = data_baseline.get_coefficient_of_variation() self.burstiness = data_baseline.get_burstiness() fi_curve = get_fi_curve_class(cell_data, cell_data.get_fi_contrasts(), save_dir=cell_data.get_data_path()) self.f_inf_slope = fi_curve.get_f_inf_slope() contrasts = np.array(cell_data.get_fi_contrasts()) if self.f_inf_slope < 0: contrasts = contrasts * -1 # print("old contrasts:", cell_data.get_fi_contrasts()) # print("new contrasts:", contrasts) contrasts = sorted(contrasts) fi_curve = get_fi_curve_class(cell_data, contrasts) self.fi_contrasts = fi_curve.stimulus_values self.f_inf_values = fi_curve.f_inf_frequencies self.f_inf_slope = fi_curve.get_f_inf_slope() self.f_zero_values = fi_curve.f_zero_frequencies self.f_zero_fit = fi_curve.f_zero_fit # self.f_zero_slopes = [fi_curve.get_f_zero_fit_slope_at_stimulus_value(c) for c in self.fi_contrasts] self.f_zero_slope_at_straight = fi_curve.get_f_zero_fit_slope_at_straight() self.f_zero_straight_contrast = self.f_zero_fit[3] max_contrast = max(contrasts) test_contrast = 0.5 * max_contrast diff_contrasts = np.abs(contrasts - test_contrast) self.f_zero_curve_contrast_idx = np.argmin(diff_contrasts) self.f_zero_curve_contrast = contrasts[self.f_zero_curve_contrast_idx] times, freqs = fi_curve.get_mean_time_and_freq_traces() self.f_zero_curve_freq = freqs[self.f_zero_curve_contrast_idx] self.f_zero_curve_time = times[self.f_zero_curve_contrast_idx] # around 1/3 of the value at straight # self.f_zero_slope = fi_curve.get_fi_curve_slope_at(fi_curve.get_f_zero_and_f_inf_intersection()) # adaption = Adaption(fi_curve) # self.tau_a = adaption.get_tau_real() def fit_model_to_data(self, data: CellData, start_parameters, fit_routine_func: callable): self.set_data_reference_values(data) return fit_routine_func(start_parameters) def fit_routine_1(self, start_parameters): self.counter = 0 # fit only v_offset, mem_tau, input_scaling, dend_tau x0 = np.array([start_parameters["mem_tau"], start_parameters["noise_strength"], start_parameters["input_scaling"], start_parameters["tau_a"], start_parameters["delta_a"], start_parameters["dend_tau"], start_parameters["refractory_period"]]) initial_simplex = create_init_simples(x0, search_scale=2) # error_list = [error_bf, error_vs, error_sc, error_cv, # error_f_inf, error_f_inf_slope, error_f_zero, error_f_zero_slope] error_weights = (0, 2, 2, 2, 1, 1, 1, 1, 0, 1) fmin = minimize(fun=self.cost_function_all, args=(error_weights,), x0=x0, method="Nelder-Mead", options={"initial_simplex": initial_simplex, "xatol": 0.001, "maxfev": 600, "maxiter": 400}) plot_errors(self.errors) return fmin, self.base_model.get_parameters() def cost_function_all(self, X, error_weights=None): for i in range(len(X)): if X[i] < 0: print("tried impossible value") return 1000 + abs(X[i]) * 10000 self.base_model.set_variable("mem_tau", X[0]) self.base_model.set_variable("noise_strength", X[1]) self.base_model.set_variable("input_scaling", X[2]) self.base_model.set_variable("tau_a", X[3]) self.base_model.set_variable("delta_a", X[4]) self.base_model.set_variable("dend_tau", X[5]) self.base_model.set_variable("refractory_period", X[6]) # TODO add tests for parameters punish impossible values (immediate high error) but also add a slope towards valid points base_stimulus = SinusoidalStepStimulus(self.eod_freq, 0) # find right v-offset test_model = self.base_model.get_model_copy() test_model.set_variable("noise_strength", 0) time1 = time.time() v_offset = test_model.find_v_offset(self.baseline_freq, base_stimulus) self.base_model.set_variable("v_offset", v_offset) time2 = time.time() # print("time taken for finding v_offset: {:.2f}s".format(time2-time1)) # [error_bf, error_vs, error_sc, error_f_inf, error_f_inf_slope, error_f_zero, error_f_zero_slope] error_list = self.calculate_errors(error_weights) # print("sum: {:.2f}, ".format(sum(error_list))) if sum(error_list) < self.smallest_error: self.smallest_error = sum(error_list) self.best_parameters_found = X self.errors.append(error_list) return sum(error_list) def cost_function_without_ref_period(self, X, error_weights=None): self.base_model.set_variable("mem_tau", X[0]) self.base_model.set_variable("noise_strength", X[1]) self.base_model.set_variable("input_scaling", X[2]) self.base_model.set_variable("tau_a", X[3]) self.base_model.set_variable("delta_a", X[4]) self.base_model.set_variable("dend_tau", X[5]) base_stimulus = SinusoidalStepStimulus(self.eod_freq, 0) # find right v-offset test_model = self.base_model.get_model_copy() test_model.set_variable("noise_strength", 0) v_offset = test_model.find_v_offset(self.baseline_freq, base_stimulus) self.base_model.set_variable("v_offset", v_offset) # [error_bf, error_vs, error_sc, error_f_inf, error_f_inf_slope, error_f_zero, error_f_zero_slope] error_list = self.calculate_errors(error_weights) return sum(error_list) def cost_function_all_without_noise(self, X, error_weights=None): self.base_model.set_variable("mem_tau", X[0]) self.base_model.set_variable("input_scaling", X[1]) self.base_model.set_variable("tau_a", X[2]) self.base_model.set_variable("delta_a", X[3]) self.base_model.set_variable("dend_tau", X[4]) self.base_model.set_variable("noise_strength", 0) base_stimulus = SinusoidalStepStimulus(self.eod_freq, 0) # find right v-offset test_model = self.base_model.get_model_copy() test_model.set_variable("noise_strength", 0) v_offset = test_model.find_v_offset(self.baseline_freq, base_stimulus) self.base_model.set_variable("v_offset", v_offset) # [error_bf, error_vs, error_sc, error_f_inf, error_f_inf_slope, error_f_zero, error_f_zero_slope] error_list = self.calculate_errors(error_weights) return sum(error_list) def cost_function_only_adaption(self, X, error_weights=None): self.base_model.set_variable("mem_tau", X[0]) self.base_model.set_variable("input_scaling", X[1]) self.base_model.set_variable("delta_a", X[2]) self.base_model.set_variable("dend_tau", X[3]) base_stimulus = SinusoidalStepStimulus(self.eod_freq, 0) # find right v-offset test_model = self.base_model.get_model_copy() test_model.set_variable("noise_strength", 0) v_offset = test_model.find_v_offset(self.baseline_freq, base_stimulus) self.base_model.set_variable("v_offset", v_offset) # [error_bf, error_vs, error_sc, error_f_inf, error_f_inf_slope, error_f_zero, error_f_zero_slope] error_list = self.calculate_errors(error_weights) return sum(error_list) def cost_function_with_fixed_adaption_tau(self, X, tau_a, error_weights=None): # set model parameters: model = self.base_model model.set_variable("mem_tau", X[0]) model.set_variable("noise_strength", X[1]) model.set_variable("input_scaling", X[2]) model.set_variable("delta_a", X[3]) model.set_variable("dend_tau", X[4]) model.set_variable("tau_a", tau_a) base_stimulus = SinusoidalStepStimulus(self.eod_freq, 0) # find right v-offset test_model = model.get_model_copy() test_model.set_variable("noise_strength", 0) v_offset = test_model.find_v_offset(self.baseline_freq, base_stimulus) model.set_variable("v_offset", v_offset) error_list = self.calculate_errors(error_weights) return sum(error_list) def cost_function_with_fixed_adaption_with_dend_tau_no_noise(self, X, tau_a, delta_a, error_weights=None): # set model parameters: model = self.base_model model.set_variable("mem_tau", X[0]) model.set_variable("input_scaling", X[1]) model.set_variable("dend_tau", X[2]) model.set_variable("tau_a", tau_a) model.set_variable("delta_a", delta_a) model.set_variable("noise_strength", 0) base_stimulus = SinusoidalStepStimulus(self.eod_freq, 0) # find right v-offset test_model = model.get_model_copy() test_model.set_variable("noise_strength", 0) v_offset = test_model.find_v_offset(self.baseline_freq, base_stimulus) model.set_variable("v_offset", v_offset) error_list = self.calculate_errors(error_weights) return sum(error_list) def cost_function_with_fixed_adaption_with_dend_tau(self, X, tau_a, delta_a, error_weights=None): # set model parameters: model = self.base_model model.set_variable("mem_tau", X[0]) model.set_variable("noise_strength", X[1]) model.set_variable("input_scaling", X[2]) model.set_variable("dend_tau", X[3]) model.set_variable("tau_a", tau_a) model.set_variable("delta_a", delta_a) base_stimulus = SinusoidalStepStimulus(self.eod_freq, 0) # find right v-offset test_model = model.get_model_copy() test_model.set_variable("noise_strength", 0) v_offset = test_model.find_v_offset(self.baseline_freq, base_stimulus) model.set_variable("v_offset", v_offset) error_list = self.calculate_errors(error_weights) return sum(error_list) def calculate_errors(self, error_weights=None, model=None): if model is None: model = self.base_model time1 = time.time() model_baseline = get_baseline_class(model, self.eod_freq, trials=5) baseline_freq = model_baseline.get_baseline_frequency() vector_strength = model_baseline.get_vector_strength() serial_correlation = model_baseline.get_serial_correlation(self.sc_max_lag) coefficient_of_variation = model_baseline.get_coefficient_of_variation() burstiness = model_baseline.get_burstiness() time2 = time.time() # print("Time taken for all baseline parameters: {:.2f}".format(time2-time1)) time1 = time.time() fi_curve_model = get_fi_curve_class(model, self.fi_contrasts, self.eod_freq, trials=15) f_zeros = fi_curve_model.get_f_zero_frequencies() f_infinities = fi_curve_model.get_f_inf_frequencies() f_infinities_slope = fi_curve_model.get_f_inf_slope() # f_zero_slopes = [fi_curve_model.get_f_zero_fit_slope_at_stimulus_value(x) for x in self.fi_contrasts] f_zero_slope_at_straight = fi_curve_model.get_f_zero_fit_slope_at_stimulus_value(self.f_zero_straight_contrast) time2 = time.time() # print("Time taken for all fi-curve parameters: {:.2f}".format(time2 - time1)) # calculate errors with reference values error_bf = abs((baseline_freq - self.baseline_freq) / self.baseline_freq) error_vs = abs((vector_strength - self.vector_strength) / 0.1) error_cv = abs((coefficient_of_variation - self.coefficient_of_variation) / 0.1) error_bursty = (abs(burstiness - self.burstiness) / 0.2) error_sc = 0 for i in range(self.sc_max_lag): error_sc += abs((serial_correlation[i] - self.serial_correlation[i]) / 0.1) # error_sc = error_sc / self.sc_max_lag error_f_inf_slope = abs((f_infinities_slope - self.f_inf_slope) / abs(self.f_inf_slope+1/20)) error_f_inf = calculate_list_error(f_infinities, self.f_inf_values) # error_f_zero_slopes = calculate_list_error(f_zero_slopes, self.f_zero_slopes) error_f_zero_slope_at_straight = abs(self.f_zero_slope_at_straight - f_zero_slope_at_straight) \ / abs(self.f_zero_slope_at_straight+1 / 10) error_f_zero = calculate_list_error(f_zeros, self.f_zero_values) # TODO if model.get_sampling_interval() != self.data_sampling_interval: raise ValueError("Sampling intervals not the same!") times, freqs = fi_curve_model.get_mean_time_and_freq_traces() freq_prediction = np.array(freqs[self.f_zero_curve_contrast_idx]) time_prediction = np.array(times[self.f_zero_curve_contrast_idx]) stimulus_start = fi_curve_model.get_stimulus_start() - time_prediction[0] length = fi_curve_model.get_stimulus_duration() / 2 start_idx = int(stimulus_start / fi_curve_model.get_sampling_interval()) end_idx = int((stimulus_start + length) / model.get_sampling_interval()) if len(time_prediction) == 0 or len(time_prediction) < end_idx or time_prediction[0] > fi_curve_model.get_stimulus_start(): error_f0_curve = 200 else: error_f0_curve = np.mean((self.f_zero_curve_freq[start_idx:end_idx] - freq_prediction[start_idx:end_idx])**2) / 100 # plt.plot(self.f_zero_curve_freq[start_idx:end_idx]) # plt.plot(freq_prediction[start_idx:end_idx]) # plt.plot((self.f_zero_curve_freq[start_idx:end_idx] - freq_prediction[start_idx:end_idx])**2) # plt.show() # plt.close() error_list = [error_bf, error_vs, error_sc, error_cv, error_bursty, error_f_inf, error_f_inf_slope, error_f_zero, error_f_zero_slope_at_straight, error_f0_curve] if error_weights is not None and len(error_weights) == len(error_list): for i in range(len(error_weights)): error_list[i] = error_list[i] * error_weights[i] elif error_weights is not None: warn("Error: weights had different length than errors and were ignored!") if sum(error_list) < 0: print("Error negative: ", error_list) if np.isnan(sum(error_list)): print("--------SOME ERROR VALUE(S) IS/ARE NaN:") print(error_list) return [50 for e in error_list] # raise ValueError("Some error value(s) is/are NaN!") return error_list def calculate_list_error(fit, reference): error = 0 for i in range(len(reference)): # error += abs_freq_error(fit[i] - reference[i]) error += normed_quadratic_freq_error(fit[i], reference[i]) norm_error = error / len(reference) return norm_error def calculate_f0_curve_error(data_ficurve, model_ficurve): return 0 def normed_quadratic_freq_error(fit, ref, factor=2): return (abs(fit-ref)/factor)**2 / ref def abs_freq_error(diff, factor=10): return abs(diff) / factor def create_init_simples(x0, search_scale=3.): dim = len(x0) simplex = [[x0[0]/search_scale], [x0[0]*search_scale]] for i in range(1, dim, 1): for vertex in simplex: vertex.append(x0[i]*search_scale) new_vertex = list(x0[:i]) new_vertex.append(x0[i]/search_scale) simplex.append(new_vertex) return simplex if __name__ == '__main__': print("use run_fitter.py to run the Fitter.")