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, fi_curve: FICurve):
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):
time_axes, mean_frequencies = self.fi_curve.get_mean_time_and_freq_traces()
f_baselines = self.fi_curve.get_f_baseline_frequencies()
f_infinities = self.fi_curve.get_f_inf_frequencies()
f_zeros = self.fi_curve.get_f_zero_frequencies()
for i in range(len(mean_frequencies)):
if abs(f_zeros[i] - f_infinities[i]) < 20:
self.exponential_fit_vars.append([])
continue
start_idx = self.__find_start_idx_for_exponential_fit(time_axes[i], mean_frequencies[i],
f_baselines[i], f_infinities[i], f_zeros[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.fi_curve.get_sampling_interval()
used_length_of_fit = length_of_fit
if (start_idx * sampling_interval) - self.fi_curve.get_delay() + length_of_fit > self.fi_curve.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.fi_curve.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]
plt.title("f_zero {:.2f}, f_inf {:.2f}".format(f_zeros[i], f_infinities[i]))
plt.plot(time_axes[i], mean_frequencies[i])
plt.plot(x_values, y_values)
plt.show()
plt.close()
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]))
# plt.plot(time_axes[i], mean_frequencies[i])
# plt.plot(x_values, [fu.exponential_function(x, popt[0], popt[1], popt[2]) for x in x_values])
# plt.show()
# plt.close()
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, time, frequency, f_base, f_inf, f_zero):
# plt.plot(time, frequency)
# plt.plot((time[0], time[-1]), (f_base, f_base), "-.")
# plt.plot((time[0], time[-1]), (f_inf, f_inf), "-")
# plt.plot((time[0], time[-1]), (f_zero, f_zero))
stimulus_start_idx = int((self.fi_curve.get_stimulus_start() - time[0]) / self.fi_curve.get_sampling_interval())
# plt.plot((time[stimulus_start_idx], ), (0, ), 'o')
#
# plt.show()
# plt.close()
if f_inf > f_base * 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 frequency[stimulus_start_idx + j] == f_zero:
start_idx = stimulus_start_idx + j
break
except IndexError as e:
return -1
j += 1
elif f_inf < f_base * 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.fi_curve.get_sampling_interval())
nothing_to_fit = False
while True:
if not found_min:
if frequency[stimulus_start_idx + j] == f_zero:
found_min = True
else:
if frequency[stimulus_start_idx + j + 1] > f_zero:
start_idx = stimulus_start_idx + j
break
if j > 0.1 / self.fi_curve.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
# plt.plot(time, frequency)
# plt.plot(time[start_idx], frequency[start_idx], 'o')
# plt.show()
# plt.close()
return start_idx
def calculate_tau_from_tau_eff(self):
tau_effs = []
indices = []
for i in range(len(self.exponential_fit_vars)):
if len(self.exponential_fit_vars[i]) == 0:
continue
indices.append(i)
tau_effs.append(self.exponential_fit_vars[i][1])
f_infinity_slope = self.fi_curve.get_f_inf_slope()
approx_tau_reals = []
for i, idx in enumerate(indices):
factor = self.fi_curve.get_f_zero_fit_slope_at_stimulus_value(self.fi_curve.stimulus_values[idx]) / f_infinity_slope
approx_tau_reals.append(tau_effs[i] * factor)
self.tau_real = np.median(approx_tau_reals)
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 get_delta_a(self):
return self.fi_curve.get_f_zero_fit_slope_at_straight() / self.fi_curve.get_f_inf_slope() / 100
def plot_exponential_fits(self, save_path: str = None, indices: list = None, delete_previous: bool = False):
if delete_previous:
for val in self.fi_curve.stimulus_values():
prev_path = save_path + "mean_freq_exp_fit_contrast:" + str(round(val, 3)) + ".png"
if os.path.exists(prev_path):
os.remove(prev_path)
time_axes, mean_freqs = self.fi_curve.get_mean_time_and_freq_traces()
for i in range(len(self.fi_curve.stimulus_values)):
if indices is not None and i not in indices:
continue
if self.exponential_fit_vars[i] == []:
print("no fit vars for index {}!".format(i))
continue
plt.plot(time_axes[i], mean_freqs[i])
vars = self.exponential_fit_vars[i]
fit_x = np.arange(0, 0.4, self.fi_curve.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.fi_curve.stimulus_values[i], 3)) + ".png")
plt.close()