from FiCurve import FICurve, get_fi_curve_class from CellData import CellData import matplotlib.pyplot as plt from scipy.optimize import curve_fit import os import numpy as np import functions as fu class Adaption: def __init__(self, cell_data: CellData, fi_curve: FICurve = None): self.cell_data = cell_data if fi_curve is None: self.fi_curve = get_fi_curve_class(cell_data, cell_data.get_fi_contrasts()) else: self.fi_curve = fi_curve # [[a, tau_eff, c], [], [a, tau_eff, c], ...] self.exponential_fit_vars = [] self.tau_real = [] self.fit_exponential() self.calculate_tau_from_tau_eff() def fit_exponential(self, length_of_fit=0.1): mean_frequencies = self.cell_data.get_mean_isi_frequencies() time_axes = self.cell_data.get_time_axes_mean_frequencies() for i in range(len(mean_frequencies)): start_idx = self.__find_start_idx_for_exponential_fit(i) if start_idx == -1: print("start index negative") self.exponential_fit_vars.append([]) continue # shorten length of fit to stay in stimulus region if given length is too long sampling_interval = self.cell_data.get_sampling_interval() used_length_of_fit = length_of_fit if (start_idx * sampling_interval) - self.cell_data.get_delay() + length_of_fit > self.cell_data.get_stimulus_end(): print(start_idx * sampling_interval, "start - end", start_idx * sampling_interval + length_of_fit) print("Shortened length of fit to keep it in the stimulus region!") used_length_of_fit = self.cell_data.get_stimulus_end() - (start_idx * sampling_interval) end_idx = start_idx + int(used_length_of_fit/sampling_interval) y_values = mean_frequencies[i][start_idx:end_idx+1] x_values = time_axes[i][start_idx:end_idx+1] tau = self.__approximate_tau_for_exponential_fit(x_values, y_values, i) # start the actual fit: try: p0 = (self.fi_curve.f_zero_frequencies[i], tau, self.fi_curve.f_inf_frequencies[i]) popt, pcov = curve_fit(fu.exponential_function, x_values, y_values, p0=p0, maxfev=10000, bounds=([-np.inf, 0, -np.inf], [np.inf, np.inf, np.inf])) except RuntimeError: print("RuntimeError happened in fit_exponential.") self.exponential_fit_vars.append([]) continue # Obviously a bad fit - time constant, expected in range 3-10ms, has value over 1 second or is negative if abs(popt[1] > 1) or popt[1] < 0: print("detected an obviously bad fit") self.exponential_fit_vars.append([]) else: self.exponential_fit_vars.append(popt) def __approximate_tau_for_exponential_fit(self, x_values, y_values, mean_freq_idx): if self.fi_curve.f_inf_frequencies[mean_freq_idx] < self.fi_curve.f_baseline_frequencies[mean_freq_idx] * 0.95: test_val = [y > 0.65 * self.fi_curve.f_inf_frequencies[mean_freq_idx] for y in y_values] else: test_val = [y < 0.65 * self.fi_curve.f_zero_frequencies[mean_freq_idx] for y in y_values] try: idx = test_val.index(True) if idx == 0: idx = 1 tau = x_values[idx] - x_values[0] except ValueError: tau = x_values[-1] - x_values[0] return tau def __find_start_idx_for_exponential_fit(self, mean_freq_idx): time_axes = self.cell_data.get_time_axes_mean_frequencies()[mean_freq_idx] stimulus_start_idx = int((self.cell_data.get_stimulus_start() + time_axes[0]) / self.cell_data.get_sampling_interval()) if self.fi_curve.f_inf_frequencies[mean_freq_idx] > self.fi_curve.f_baseline_frequencies[mean_freq_idx] * 1.1: # start setting starting variables for the fit # search for the start_index by searching for the max j = 0 while True: try: if self.cell_data.get_mean_isi_frequencies()[mean_freq_idx][stimulus_start_idx + j] == self.fi_curve.f_zero_frequencies[mean_freq_idx]: start_idx = stimulus_start_idx + j break except IndexError as e: return -1 j += 1 elif self.fi_curve.f_inf_frequencies[mean_freq_idx] < self.fi_curve.f_baseline_frequencies[mean_freq_idx] * 0.9: # start setting starting variables for the fit # search for start by finding the end of the minimum found_min = False j = int(0.05 / self.cell_data.get_sampling_interval()) nothing_to_fit = False while True: if not found_min: if self.cell_data.get_mean_isi_frequencies()[mean_freq_idx][stimulus_start_idx + j] == self.fi_curve.f_zero_frequencies[mean_freq_idx]: found_min = True else: if self.cell_data.get_mean_isi_frequencies()[mean_freq_idx][stimulus_start_idx + j + 1] > self.fi_curve.f_zero_frequencies[mean_freq_idx]: start_idx = stimulus_start_idx + j break if j > 0.1 / self.cell_data.get_sampling_interval(): # no rise in freq until to close to the end of the stimulus (to little place to fit) return -1 j += 1 if nothing_to_fit: return -1 else: # there is nothing to fit to: return -1 return start_idx def calculate_tau_from_tau_eff(self): tau_effs = [] for i in range(len(self.exponential_fit_vars)): if len(self.exponential_fit_vars[i]) == 0: continue tau_effs.append(self.exponential_fit_vars[i][1]) f_infinity_slope = self.fi_curve.get_f_inf_slope() # --- old way to calculate with the fi slope at middle of the fi curve # fi_curve_slope = self.fi_curve.get_fi_curve_slope_of_straight() # self.tau_real = np.median(tau_effs) * (fi_curve_slope / f_infinity_slope) # print("fi_slope to f_inf slope:", fi_curve_slope/f_infinity_slope) # print("fi_slope:", fi_curve_slope, "f_inf slope:", f_infinity_slope) # print("current tau: {:.1f}ms".format(np.median(tau_effs) * (fi_curve_slope / f_infinity_slope) * 1000)) # new way to calculate with the fi curve slope at the intersection point of it and the f_inf line factor = self.fi_curve.get_f_zero_fit_slope_at_f_inf_fit_intersection() / f_infinity_slope self.tau_real = np.median(tau_effs) * factor print("###### tau: {:.1f}ms".format(self.tau_real*1000), "other f_0 slope:", self.fi_curve.get_f_zero_fit_slope_at_f_inf_fit_intersection()) def get_tau_real(self): return np.median(self.tau_real) def get_tau_effs(self): return [ex_vars[1] for ex_vars in self.exponential_fit_vars if ex_vars != []] def plot_exponential_fits(self, save_path: str = None, indices: list = None, delete_previous: bool = False): if delete_previous: for val in self.cell_data.get_fi_contrasts(): prev_path = save_path + "mean_freq_exp_fit_contrast:" + str(round(val, 3)) + ".png" if os.path.exists(prev_path): os.remove(prev_path) for i in range(len(self.cell_data.get_fi_contrasts())): if indices is not None and i not in indices: continue if self.exponential_fit_vars[i] == []: print("no fit vars for index!") continue plt.plot(self.cell_data.get_time_axes_mean_frequencies()[i], self.cell_data.get_mean_isi_frequencies()[i]) vars = self.exponential_fit_vars[i] fit_x = np.arange(0, 0.4, self.cell_data.get_sampling_interval()) plt.plot(fit_x, [fu.exponential_function(x, vars[0], vars[1], vars[2]) for x in fit_x]) plt.ylim([0, max(self.fi_curve.f_zero_frequencies[i], self.fi_curve.f_baseline_frequencies[i])*1.1]) plt.xlabel("Time [s]") plt.ylabel("Frequency [Hz]") if save_path is None: plt.show() else: plt.savefig(save_path + "mean_freq_exp_fit_contrast:" + str(round(self.cell_data.get_fi_contrasts()[i], 3)) + ".png") plt.close()