from CellData import CellData import numpy as np from scipy.optimize import curve_fit from scipy.stats import linregress import matplotlib.pyplot as plt from warnings import warn import functions as fu class FICurve: def __init__(self, cell_data: CellData, contrast: bool = True): self.cell_data = cell_data self.using_contrast = contrast if contrast: self.stimulus_value = cell_data.get_fi_contrasts() else: self.stimulus_value = cell_data.get_fi_intensities() self.f_zeros = [] self.f_infinities = [] self.f_baselines = [] # f_max, f_min, k, x_zero self.boltzmann_fit_vars = [] # increase, offset self.f_infinity_fit = [] self.all_calculate_frequency_points() self.fit_line() self.fit_boltzmann() def all_calculate_frequency_points(self): mean_frequencies = self.cell_data.get_mean_isi_frequencies() time_axes = self.cell_data.get_time_axes_mean_frequencies() if len(mean_frequencies) == 0: warn("FICurve:all_calculate_frequency_points(): mean_frequencies is empty.\n" "Was all_calculate_mean_isi_frequencies already called?") for i in range(len(mean_frequencies)): if time_axes[i][0] > self.cell_data.get_stimulus_start(): warn("TODO: Deal with to strongly cut frequency traces in cell data! ") self.f_zeros.append(-1) self.f_baselines.append(-1) self.f_infinities.append(-1) continue self.f_zeros.append(self.__calculate_f_zero__(time_axes[i], mean_frequencies[i])) self.f_baselines.append(self.__calculate_f_baseline__(time_axes[i], mean_frequencies[i])) self.f_infinities.append(self.__calculate_f_infinity__(time_axes[i], mean_frequencies[i])) def fit_line(self): popt, pcov = curve_fit(fu.clipped_line, self.stimulus_value, self.f_infinities) self.f_infinity_fit = popt def fit_boltzmann(self): max_f0 = float(max(self.f_zeros)) min_f0 = 0.1 # float(min(self.f_zeros)) mean_int = float(np.mean(self.stimulus_value)) total_increase = max_f0 - min_f0 total_change_int = max(self.stimulus_value) - min(self.stimulus_value) start_k = float((total_increase / total_change_int * 4) / max_f0) popt, pcov = curve_fit(fu.full_boltzmann, self.stimulus_value, self.f_zeros, p0=(max_f0, min_f0, start_k, mean_int), maxfev=10000, bounds=([0, 0, -np.inf, -np.inf], [5000, 1, np.inf, np.inf])) self.boltzmann_fit_vars = popt def __calculate_f_baseline__(self, time, frequency, buffer=0.025): stim_start = self.cell_data.get_stimulus_start() - time[0] sampling_interval = self.cell_data.get_sampling_interval() if stim_start < 0.1: warn("FICurve:__calculate_f_baseline__(): Quite short delay at the start.") start_idx = 0 end_idx = int((stim_start-buffer)/sampling_interval) f_baseline = np.mean(frequency[start_idx:end_idx]) return f_baseline def __calculate_f_zero__(self, time, frequency, peak_buffer_percent=0.05, buffer=0.025): stimulus_start = self.cell_data.get_stimulus_start() - time[0] # time start is generally != 0 and != delay sampling_interval = self.cell_data.get_sampling_interval() freq_before = frequency[0:int((stimulus_start - buffer) / sampling_interval)] min_before = min(freq_before) max_before = max(freq_before) mean_before = np.mean(freq_before) # time where the f-zero is searched in start_idx = int((stimulus_start-0.1*buffer) / sampling_interval) end_idx = int((stimulus_start + buffer) / sampling_interval) min_during_start_of_stim = min(frequency[start_idx:end_idx]) max_during_start_of_stim = max(frequency[start_idx:end_idx]) if abs(mean_before-min_during_start_of_stim) > abs(max_during_start_of_stim-mean_before): f_zero = min_during_start_of_stim else: f_zero = max_during_start_of_stim peak_buffer = (max_before - min_before) * peak_buffer_percent if min_before - peak_buffer <= f_zero <= max_before + peak_buffer: end_idx = start_idx + int((end_idx-start_idx)/2) f_zero = np.mean(frequency[start_idx:end_idx]) return f_zero # start_idx = int(stimulus_start / sampling_interval) # end_idx = int((stimulus_start + buffer*2) / sampling_interval) # # freq_before = frequency[start_idx-(int(length_of_mean/sampling_interval)):start_idx] # fb_mean = np.mean(freq_before) # fb_std = np.std(freq_before) # # peak_frequency = fb_mean # count = 0 # for i in range(start_idx + 1, end_idx): # if fb_mean-3*fb_std <= frequency[i] <= fb_mean+3*fb_std: # continue # # if abs(frequency[i] - fb_mean) > abs(peak_frequency - fb_mean): # peak_frequency = frequency[i] # count += 1 # return peak_frequency def __calculate_f_infinity__(self, time, frequency, length=0.1, buffer=0.025): stimulus_end_time = self.cell_data.get_stimulus_start() + self.cell_data.get_stimulus_duration() - time[0] start_idx = int((stimulus_end_time - length - buffer) / self.cell_data.get_sampling_interval()) end_idx = int((stimulus_end_time - buffer) / self.cell_data.get_sampling_interval()) # TODO add way to plot detected f_zero, f_inf, f_base. With detection of remaining slope? # x = np.arange(start_idx, end_idx, 1) # time[start_idx:end_idx] # slope, intercept, r_value, p_value, std_err = linregress(x, frequency[start_idx:end_idx]) # if p_value < 0.0001: # plt.title("significant slope: {:.2f}, p: {:.5f}, r: {:.5f}".format(slope, p_value, r_value)) # plt.plot(x, [i*slope + intercept for i in x], color="black") # # # plt.plot((start_idx, end_idx), (np.mean(frequency[start_idx:end_idx]), np.mean(frequency[start_idx:end_idx])), label="f_inf") # plt.legend() # plt.show() # plt.close() return np.mean(frequency[start_idx:end_idx]) def get_f_zero_inverse_at_frequency(self, frequency): b_vars = self.boltzmann_fit_vars return fu.inverse_full_boltzmann(frequency, b_vars[0], b_vars[1], b_vars[2], b_vars[3]) def get_f_infinity_frequency_at_stimulus_value(self, stimulus_value): infty_vars = self.f_infinity_fit return fu.clipped_line(stimulus_value, infty_vars[0], infty_vars[1]) def get_f_infinity_slope(self): return self.f_infinity_fit[0] def get_fi_curve_slope_at(self, stimulus_value): fit_vars = self.boltzmann_fit_vars return fu.derivative_full_boltzmann(stimulus_value, fit_vars[0], fit_vars[1], fit_vars[2], fit_vars[3]) def get_fi_curve_slope_of_straight(self): fit_vars = self.boltzmann_fit_vars return fu.full_boltzmann_straight_slope(fit_vars[0], fit_vars[1], fit_vars[2], fit_vars[3]) def get_f_zero_and_f_inf_intersection(self): x_values = np.arange(min(self.stimulus_value), max(self.stimulus_value), 0.0001) fit_vars = self.boltzmann_fit_vars f_zero = fu.full_boltzmann(x_values, fit_vars[0], fit_vars[1], fit_vars[2], fit_vars[3]) f_inf = fu.clipped_line(x_values, self.f_infinity_fit[0], self.f_infinity_fit[1]) intersection_indicies = np.argwhere(np.diff(np.sign(f_zero - f_inf))).flatten() # print("fi-curve calc intersection:", intersection_indicies, x_values[intersection_indicies]) if len(intersection_indicies) > 1: f_baseline = np.median(self.f_baselines) best_dist = np.inf best_idx = -1 for idx in intersection_indicies: dist = abs(fu.clipped_line(x_values[idx], self.f_infinity_fit[0], self.f_infinity_fit[1]) - f_baseline) if dist < best_dist: best_dist = dist best_idx = idx return x_values[best_idx] elif len(intersection_indicies) == 0: raise ValueError("No intersection found!") else: return x_values[intersection_indicies[0]] def get_fi_curve_slope_at_f_zero_intersection(self): x = self.get_f_zero_and_f_inf_intersection() fit_vars = self.boltzmann_fit_vars return fu.derivative_full_boltzmann(x, fit_vars[0], fit_vars[1], fit_vars[2], fit_vars[3]) def plot_fi_curve(self, savepath: str = None, comp_f_baselines=None, comp_f_zeros=None, comp_f_infs=None): min_x = min(self.stimulus_value) max_x = max(self.stimulus_value) step = (max_x - min_x) / 5000 x_values = np.arange(min_x, max_x, step) plt.plot(self.stimulus_value, self.f_baselines, color='blue', label='f_base') if comp_f_baselines is not None: plt.plot(self.stimulus_value, comp_f_baselines, 'o', color='skyblue', label='comp_values base') plt.plot(self.stimulus_value, self.f_infinities, 'o', color='green', label='f_inf') plt.plot(x_values, [fu.clipped_line(x, self.f_infinity_fit[0], self.f_infinity_fit[1]) for x in x_values], color='darkgreen', label='f_inf_fit') if comp_f_infs is not None: plt.plot(self.stimulus_value, comp_f_infs, 'o', color='lime', label='comp values f_inf') plt.plot(self.stimulus_value, self.f_zeros, 'o', color='orange', label='f_zero') popt = self.boltzmann_fit_vars plt.plot(x_values, [fu.full_boltzmann(x, popt[0], popt[1], popt[2], popt[3]) for x in x_values], color='red', label='f_0_fit') if comp_f_zeros is not None: plt.plot(self.stimulus_value, comp_f_zeros, 'o', color='wheat', label='comp_values f_zero') plt.legend() plt.ylabel("Frequency [Hz]") if self.using_contrast: plt.xlabel("Stimulus contrast") else: plt.xlabel("Stimulus intensity [mv]") if savepath is None: plt.show() else: plt.savefig(savepath + "fi_curve.png") plt.close() def plot_f_point_detections(self): mean_frequencies = np.array(self.cell_data.get_mean_isi_frequencies()) time_axes = self.cell_data.get_time_axes_mean_frequencies() for i in range(len(mean_frequencies)): fig, axes = plt.subplots(1, 1, sharex="all") axes.plot(time_axes[i], mean_frequencies[i], label="voltage") axes.plot((time_axes[i][0],time_axes[i][-1]), (self.f_zeros[i], self.f_zeros[i]), label="f_zero") axes.plot((time_axes[i][0],time_axes[i][-1]), (self.f_infinities[i], self.f_infinities[i]), '--', label="f_inf") axes.plot((time_axes[i][0],time_axes[i][-1]), (self.f_baselines[i], self.f_baselines[i]), label="f_base") axes.set_title(str(self.stimulus_value[i])) plt.legend() plt.show()