adapt for new FiCurve class
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a.ott
parent
256931845e
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676a0e4945
@ -10,11 +10,8 @@ import functions as fu
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class Adaption:
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def __init__(self, cell_data: CellData, fi_curve: FICurve = None):
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self.cell_data = cell_data
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if fi_curve is None:
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self.fi_curve = get_fi_curve_class(cell_data, cell_data.get_fi_contrasts())
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else:
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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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@ -25,27 +22,42 @@ class Adaption:
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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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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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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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start_idx = self.__find_start_idx_for_exponential_fit(i)
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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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# 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.cell_data.get_sampling_interval()
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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.cell_data.get_delay() + length_of_fit > self.cell_data.get_stimulus_end():
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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.cell_data.get_stimulus_end() - (start_idx * sampling_interval)
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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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@ -54,6 +66,12 @@ class Adaption:
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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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@ -82,16 +100,17 @@ class Adaption:
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return tau
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def __find_start_idx_for_exponential_fit(self, mean_freq_idx):
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time_axes = self.cell_data.get_time_axes_mean_frequencies()[mean_freq_idx]
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stimulus_start_idx = int((self.cell_data.get_stimulus_start() + time_axes[0]) / self.cell_data.get_sampling_interval())
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if self.fi_curve.f_inf_frequencies[mean_freq_idx] > self.fi_curve.f_baseline_frequencies[mean_freq_idx] * 1.1:
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def __find_start_idx_for_exponential_fit(self, time, frequency, f_base, f_inf, 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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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 self.cell_data.get_mean_isi_frequencies()[mean_freq_idx][stimulus_start_idx + j] == self.fi_curve.f_zero_frequencies[mean_freq_idx]:
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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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@ -99,21 +118,21 @@ class Adaption:
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j += 1
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elif self.fi_curve.f_inf_frequencies[mean_freq_idx] < self.fi_curve.f_baseline_frequencies[mean_freq_idx] * 0.9:
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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.cell_data.get_sampling_interval())
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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 self.cell_data.get_mean_isi_frequencies()[mean_freq_idx][stimulus_start_idx + j] == self.fi_curve.f_zero_frequencies[mean_freq_idx]:
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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 self.cell_data.get_mean_isi_frequencies()[mean_freq_idx][stimulus_start_idx + j + 1] > self.fi_curve.f_zero_frequencies[mean_freq_idx]:
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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.cell_data.get_sampling_interval():
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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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@ -124,28 +143,29 @@ class Adaption:
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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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# --- old way to calculate with the fi slope at middle of the fi curve
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# fi_curve_slope = self.fi_curve.get_fi_curve_slope_of_straight()
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# self.tau_real = np.median(tau_effs) * (fi_curve_slope / f_infinity_slope)
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# print("fi_slope to f_inf slope:", fi_curve_slope/f_infinity_slope)
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# print("fi_slope:", fi_curve_slope, "f_inf slope:", f_infinity_slope)
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# print("current tau: {:.1f}ms".format(np.median(tau_effs) * (fi_curve_slope / f_infinity_slope) * 1000))
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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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# new way to calculate with the fi curve slope at the intersection point of it and the f_inf line
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factor = self.fi_curve.get_f_zero_fit_slope_at_f_inf_fit_intersection() / f_infinity_slope
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self.tau_real = np.median(tau_effs) * factor
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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())
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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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@ -153,26 +173,30 @@ class Adaption:
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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.cell_data.get_fi_contrasts():
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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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for i in range(len(self.cell_data.get_fi_contrasts())):
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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!")
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print("no fit vars for index {}!".format(i))
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continue
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plt.plot(self.cell_data.get_time_axes_mean_frequencies()[i], self.cell_data.get_mean_isi_frequencies()[i])
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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.cell_data.get_sampling_interval())
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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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@ -181,6 +205,6 @@ class Adaption:
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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.cell_data.get_fi_contrasts()[i], 3)) + ".png")
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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()
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