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