219 lines
8.7 KiB
Python
219 lines
8.7 KiB
Python
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from FiCurve import FICurve
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import matplotlib.pyplot as plt
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from scipy.optimize import curve_fit
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import os
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import numpy as np
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from my_util import functions as fu
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class Adaption:
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def __init__(self, fi_curve: FICurve):
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self.fi_curve = fi_curve
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# [[a, tau_eff, c], [], [a, tau_eff, c], ...]
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self.exponential_fit_vars = []
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self.tau_real = []
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self.fit_exponential()
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self.calculate_tau_from_tau_eff()
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def fit_exponential(self, length_of_fit=0.1):
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time_axes, mean_frequencies = self.fi_curve.get_mean_time_and_freq_traces()
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f_baselines = self.fi_curve.get_f_baseline_frequencies()
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f_infinities = self.fi_curve.get_f_inf_frequencies()
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f_zeros = self.fi_curve.get_f_zero_frequencies()
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for i in range(len(mean_frequencies)):
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if abs(f_zeros[i] - f_infinities[i]) < 20:
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self.exponential_fit_vars.append([])
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continue
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start_idx = self.__find_start_idx_for_exponential_fit(time_axes[i], mean_frequencies[i],
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f_baselines[i], f_infinities[i], f_zeros[i])
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if start_idx == -1:
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# print("start index negative")
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self.exponential_fit_vars.append([])
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continue
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# shorten length of fit to stay in stimulus region if given length is too long
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sampling_interval = self.fi_curve.get_sampling_interval()
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used_length_of_fit = length_of_fit
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if (start_idx * sampling_interval) - self.fi_curve.get_delay() + length_of_fit > self.fi_curve.get_stimulus_end():
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print(start_idx * sampling_interval, "start - end", start_idx * sampling_interval + length_of_fit)
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print("Shortened length of fit to keep it in the stimulus region!")
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used_length_of_fit = self.fi_curve.get_stimulus_end() - (start_idx * sampling_interval)
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end_idx = start_idx + int(used_length_of_fit/sampling_interval)
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y_values = mean_frequencies[i][start_idx:end_idx+1]
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x_values = time_axes[i][start_idx:end_idx+1]
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plt.title("f_zero {:.2f}, f_inf {:.2f}".format(f_zeros[i], f_infinities[i]))
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plt.plot(time_axes[i], mean_frequencies[i])
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plt.plot(x_values, y_values)
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plt.show()
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plt.close()
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tau = self.__approximate_tau_for_exponential_fit(x_values, y_values, i)
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# start the actual fit:
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try:
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p0 = (self.fi_curve.f_zero_frequencies[i], tau, self.fi_curve.f_inf_frequencies[i])
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popt, pcov = curve_fit(fu.exponential_function, x_values, y_values,
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p0=p0, maxfev=10000, bounds=([-np.inf, 0, -np.inf], [np.inf, np.inf, np.inf]))
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# plt.plot(time_axes[i], mean_frequencies[i])
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# plt.plot(x_values, [fu.exponential_function(x, popt[0], popt[1], popt[2]) for x in x_values])
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# plt.show()
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# plt.close()
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except RuntimeError:
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print("RuntimeError happened in fit_exponential.")
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self.exponential_fit_vars.append([])
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continue
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# Obviously a bad fit - time constant, expected in range 3-10ms, has value over 1 second or is negative
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if abs(popt[1] > 1) or popt[1] < 0:
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print("detected an obviously bad fit")
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self.exponential_fit_vars.append([])
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else:
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self.exponential_fit_vars.append(popt)
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def __approximate_tau_for_exponential_fit(self, x_values, y_values, mean_freq_idx):
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if self.fi_curve.f_inf_frequencies[mean_freq_idx] < self.fi_curve.f_baseline_frequencies[mean_freq_idx] * 0.95:
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test_val = [y > 0.65 * self.fi_curve.f_inf_frequencies[mean_freq_idx] for y in y_values]
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else:
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test_val = [y < 0.65 * self.fi_curve.f_zero_frequencies[mean_freq_idx] for y in y_values]
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try:
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idx = test_val.index(True)
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if idx == 0:
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idx = 1
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tau = x_values[idx] - x_values[0]
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except ValueError:
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tau = x_values[-1] - x_values[0]
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return tau
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def __find_start_idx_for_exponential_fit(self, time, frequency, f_base, f_inf, f_zero):
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# plt.plot(time, frequency)
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# plt.plot((time[0], time[-1]), (f_base, f_base), "-.")
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# plt.plot((time[0], time[-1]), (f_inf, f_inf), "-")
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# plt.plot((time[0], time[-1]), (f_zero, f_zero))
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stimulus_start_idx = int((self.fi_curve.get_stimulus_start() - time[0]) / self.fi_curve.get_sampling_interval())
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# plt.plot((time[stimulus_start_idx], ), (0, ), 'o')
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#
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# plt.show()
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# plt.close()
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if f_inf > f_base * 1.1:
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# start setting starting variables for the fit
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# search for the start_index by searching for the max
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j = 0
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while True:
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try:
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if frequency[stimulus_start_idx + j] == f_zero:
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start_idx = stimulus_start_idx + j
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break
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except IndexError as e:
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return -1
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j += 1
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elif f_inf < f_base * 0.9:
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# start setting starting variables for the fit
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# search for start by finding the end of the minimum
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found_min = False
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j = int(0.05 / self.fi_curve.get_sampling_interval())
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nothing_to_fit = False
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while True:
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if not found_min:
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if frequency[stimulus_start_idx + j] == f_zero:
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found_min = True
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else:
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if frequency[stimulus_start_idx + j + 1] > f_zero:
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start_idx = stimulus_start_idx + j
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break
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if j > 0.1 / self.fi_curve.get_sampling_interval():
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# no rise in freq until to close to the end of the stimulus (to little place to fit)
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return -1
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j += 1
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if nothing_to_fit:
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return -1
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else:
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# there is nothing to fit to:
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return -1
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# plt.plot(time, frequency)
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# plt.plot(time[start_idx], frequency[start_idx], 'o')
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# plt.show()
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# plt.close()
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return start_idx
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def calculate_tau_from_tau_eff(self):
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tau_effs = []
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indices = []
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for i in range(len(self.exponential_fit_vars)):
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if len(self.exponential_fit_vars[i]) == 0:
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continue
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indices.append(i)
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tau_effs.append(self.exponential_fit_vars[i][1])
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f_infinity_slope = self.fi_curve.get_f_inf_slope()
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approx_tau_reals = []
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for i, idx in enumerate(indices):
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factor = self.fi_curve.get_f_zero_fit_slope_at_stimulus_value(self.fi_curve.stimulus_values[idx]) / f_infinity_slope
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approx_tau_reals.append(tau_effs[i] * factor)
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self.tau_real = np.median(approx_tau_reals)
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def get_tau_real(self):
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return np.median(self.tau_real)
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def get_tau_effs(self):
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return [ex_vars[1] for ex_vars in self.exponential_fit_vars if ex_vars != []]
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def get_delta_a(self):
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return self.fi_curve.get_f_zero_fit_slope_at_straight() / self.fi_curve.get_f_inf_slope() / 100
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def plot_exponential_fits(self, save_path: str = None, indices: list = None, delete_previous: bool = False):
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if delete_previous:
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for val in self.fi_curve.stimulus_values():
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prev_path = save_path + "mean_freq_exp_fit_contrast:" + str(round(val, 3)) + ".png"
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if os.path.exists(prev_path):
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os.remove(prev_path)
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time_axes, mean_freqs = self.fi_curve.get_mean_time_and_freq_traces()
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for i in range(len(self.fi_curve.stimulus_values)):
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if indices is not None and i not in indices:
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continue
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if self.exponential_fit_vars[i] == []:
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print("no fit vars for index {}!".format(i))
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continue
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plt.plot(time_axes[i], mean_freqs[i])
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vars = self.exponential_fit_vars[i]
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fit_x = np.arange(0, 0.4, self.fi_curve.get_sampling_interval())
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plt.plot(fit_x, [fu.exponential_function(x, vars[0], vars[1], vars[2]) for x in fit_x])
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plt.ylim([0, max(self.fi_curve.f_zero_frequencies[i], self.fi_curve.f_baseline_frequencies[i])*1.1])
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plt.xlabel("Time [s]")
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plt.ylabel("Frequency [Hz]")
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if save_path is None:
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plt.show()
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else:
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plt.savefig(save_path + "mean_freq_exp_fit_contrast:" + str(round(self.fi_curve.stimulus_values[i], 3)) + ".png")
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plt.close() |