add new getters and plot_mean_freq curves
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a.ott
parent
d086f4b3a1
commit
256931845e
227
FiCurve.py
227
FiCurve.py
@ -87,6 +87,47 @@ class FICurve:
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fit_vars = self.f_zero_fit
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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 get_mean_time_and_freq_traces(self):
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raise NotImplementedError("NOT YET OVERRIDDEN FROM ABSTRACT CLASS")
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def get_time_and_freq_traces(self):
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raise NotImplementedError("NOT YET OVERRIDDEN FROM ABSTRACT CLASS")
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def get_sampling_interval(self):
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raise NotImplementedError("NOT YET OVERRIDDEN FROM ABSTRACT CLASS")
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def get_delay(self):
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raise NotImplementedError("NOT YET OVERRIDDEN FROM ABSTRACT CLASS")
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def get_stimulus_start(self):
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raise NotImplementedError("NOT YET OVERRIDDEN FROM ABSTRACT CLASS")
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def get_stimulus_end(self):
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raise NotImplementedError("NOT YET OVERRIDDEN FROM ABSTRACT CLASS")
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def get_stimulus_duration(self):
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return self.get_stimulus_end() - self.get_stimulus_start()
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def plot_mean_frequency_curves(self, save_path=None):
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time_traces, freq_traces = self.get_time_and_freq_traces()
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mean_times, mean_freqs = self.get_mean_time_and_freq_traces()
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for i, sv in enumerate(self.stimulus_values):
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for j in range(len(time_traces[i])):
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plt.plot(time_traces[i][j], freq_traces[i][j], color="gray", alpha=0.8)
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plt.plot(mean_times[i], mean_freqs[i], color="black")
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plt.xlabel("Time [s]")
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plt.ylabel("Frequency [Hz]")
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plt.title("Mean frequency at contrast {:.2f} ({:} trials)".format(sv, len(time_traces[i])))
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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_frequency_contrast_{:.2f}.png".format(sv))
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plt.close()
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def plot_fi_curve(self, save_path=None):
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min_x = min(self.stimulus_values)
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max_x = max(self.stimulus_values)
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@ -111,7 +152,6 @@ class FICurve:
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if save_path 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(save_path + "fi_curve.png")
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plt.close()
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@ -173,7 +213,6 @@ class FICurve:
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if save_path 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(save_path + "fi_curve_comparision.png")
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plt.close()
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@ -188,8 +227,8 @@ class FICurveCellData(FICurve):
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super().__init__(stimulus_values)
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def calculate_all_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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mean_frequencies = self.cell_data.get_mean_fi_curve_isi_frequencies()
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time_axes = self.cell_data.get_time_axes_fi_curve_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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@ -217,6 +256,39 @@ class FICurveCellData(FICurve):
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stimulus_start, stimulus_duration, sampling_interval)
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self.f_inf_frequencies.append(f_infinity)
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def get_mean_time_and_freq_traces(self):
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return self.cell_data.get_time_axes_fi_curve_mean_frequencies(), self.cell_data.get_mean_fi_curve_isi_frequencies()
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def get_time_and_freq_traces(self):
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spiketimes = self.cell_data.get_fi_spiketimes()
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time_traces = []
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freq_traces = []
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for i in range(len(spiketimes)):
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trial_time_traces = []
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trial_freq_traces = []
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for j in range(len(spiketimes[i])):
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time, isi_freq = hF.calculate_time_and_frequency_trace(spiketimes[i][j], self.cell_data.get_sampling_interval())
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trial_freq_traces.append(isi_freq)
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trial_time_traces.append(time)
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time_traces.append(trial_time_traces)
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freq_traces.append(trial_freq_traces)
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return time_traces, freq_traces
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def get_sampling_interval(self):
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return self.cell_data.get_sampling_interval()
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def get_delay(self):
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return self.cell_data.get_delay()
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def get_stimulus_start(self):
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return self.cell_data.get_stimulus_start()
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def get_stimulus_end(self):
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return self.cell_data.get_stimulus_end()
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def get_f_zero_inverse_at_frequency(self, frequency):
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# UNUSED
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b_vars = self.f_zero_fit
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@ -227,95 +299,41 @@ class FICurveCellData(FICurve):
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infty_vars = self.f_inf_fit
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return fu.clipped_line(stimulus_value, infty_vars[0], infty_vars[1])
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# def get_fi_curve_slope_at(self, stimulus_value):
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# fit_vars = self.f_zero_fit
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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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#
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# def get_fi_curve_slope_of_straight(self):
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# fit_vars = self.f_zero_fit
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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_values), max(self.stimulus_values), 0.0001)
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# fit_vars = self.f_zero_fit
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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_inf_fit[0], self.f_inf_fit[1])
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#
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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_baseline_frequencies)
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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_inf_fit[0], self.f_inf_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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#
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# return x_values[best_idx]
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#
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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.f_zero_fit
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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_values)
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# max_x = max(self.stimulus_values)
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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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#
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# plt.plot(self.stimulus_values, self.f_baseline_frequencies, color='blue', label='f_base')
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# if comp_f_baselines is not None:
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# plt.plot(self.stimulus_values, comp_f_baselines, 'o', color='skyblue', label='comp_values base')
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#
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# plt.plot(self.stimulus_values, self.f_inf_frequencies, 'o', color='green', label='f_inf')
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# plt.plot(x_values, [fu.clipped_line(x, self.f_inf_fit[0], self.f_inf_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_values, comp_f_infs, 'o', color='lime', label='comp values f_inf')
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#
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# plt.plot(self.stimulus_values, self.f_zero_frequencies, 'o', color='orange', label='f_zero')
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# popt = self.f_zero_fit
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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_values, comp_f_zeros, 'o', color='wheat', label='comp_values f_zero')
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#
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# plt.legend()
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# plt.ylabel("Frequency [Hz]")
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# plt.xlabel("Stimulus value")
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#
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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, save_path=None):
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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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mean_frequencies = np.array(self.cell_data.get_mean_fi_curve_isi_frequencies())
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time_axes = self.cell_data.get_time_axes_fi_curve_mean_frequencies()
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sampling_interval = self.cell_data.get_sampling_interval()
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stim_start = self.cell_data.get_stimulus_start()
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stim_duration = self.cell_data.get_stimulus_duration()
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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_zero_frequencies[i], self.f_zero_frequencies[i]), label="f_zero")
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axes.plot((time_axes[i][0], time_axes[i][-1]), (self.f_inf_frequencies[i], self.f_inf_frequencies[i]), '--', label="f_inf")
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axes.plot((time_axes[i][0], time_axes[i][-1]), (self.f_baseline_frequencies[i], self.f_baseline_frequencies[i]), label="f_base")
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axes.set_title(str(self.stimulus_values[i]))
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plt.legend()
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for i, c in enumerate(self.stimulus_values):
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time = time_axes[i]
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frequency = mean_frequencies[i]
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if save_path is None:
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plt.show()
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if len(time) == 0 or min(time) > stim_start \
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or max(time) < stim_start + stim_duration:
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continue
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fig, ax = plt.subplots(1, 1, figsize=(8, 8))
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ax.plot(time, frequency)
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start_idx, end_idx = hF.time_window_detect_f_baseline(time[0], stim_start, sampling_interval)
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ax.plot((time[start_idx], time[end_idx]), (self.f_baseline_frequencies[i], self.f_baseline_frequencies[i]),
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label="f_base", color="deepskyblue")
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start_idx, end_idx = hF.time_window_detect_f_infinity(time[0], stim_start, stim_duration,
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sampling_interval)
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ax.plot((time[start_idx], time[end_idx]), (self.f_inf_frequencies[i], self.f_inf_frequencies[i]),
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label="f_inf", color="limegreen")
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start_idx, end_idx = hF.time_window_detect_f_zero(time[0], stim_start, sampling_interval)
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ax.plot((time[start_idx], time[end_idx]), (self.f_zero_frequencies[i], self.f_zero_frequencies[i]),
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label="f_zero", color="orange")
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plt.legend()
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if save_path is not None:
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plt.savefig(save_path + "/detections_contrast_{:.2f}.png".format(c))
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else:
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plt.savefig(save_path + "GENERATE_NAMES.png")
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plt.show()
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plt.close()
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@ -374,6 +392,37 @@ class FICurveModel(FICurve):
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f_baseline = hF.detect_f_baseline_in_freq_trace(time, frequency, self.stim_start, sampling_interval)
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self.f_baseline_frequencies.append(f_baseline)
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def get_mean_time_and_freq_traces(self):
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return self.mean_time_traces, self.mean_frequency_traces
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def get_sampling_interval(self):
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return self.model.get_sampling_interval()
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def get_delay(self):
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return 0
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def get_stimulus_start(self):
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return self.stim_start
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def get_stimulus_end(self):
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return self.stim_start + self.stim_duration
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def get_time_and_freq_traces(self):
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time_traces = []
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freq_traces = []
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for v in range(len(self.stimulus_values)):
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times_for_value = []
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freqs_for_value = []
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for s in self.spiketimes_array[v]:
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t, f = hF.calculate_time_and_frequency_trace(s, self.model.get_sampling_interval())
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times_for_value.append(t)
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freqs_for_value.append(f)
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time_traces.append(times_for_value)
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freq_traces.append(freqs_for_value)
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return time_traces, freq_traces
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def plot_f_point_detections(self, save_path=None):
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sampling_interval = self.model.get_sampling_interval()
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