diff --git a/Fitter.py b/Fitter.py index c0b96c1..b10cc17 100644 --- a/Fitter.py +++ b/Fitter.py @@ -29,6 +29,7 @@ class Fitter: # self.fi_contrasts = [] + self.recording_times = [] self.eod_freq = 0 self.data_sampling_interval = -1 @@ -64,6 +65,7 @@ class Fitter: def set_data_reference_values(self, cell_data: CellData): self.eod_freq = cell_data.get_eod_frequency() self.data_sampling_interval = cell_data.get_sampling_interval() + self.recording_times = cell_data.get_recording_times() data_baseline = get_baseline_class(cell_data) data_baseline.load_values(cell_data.get_data_path()) @@ -120,11 +122,11 @@ class Fitter: x0 = np.array([start_parameters["mem_tau"], start_parameters["noise_strength"], start_parameters["input_scaling"], start_parameters["tau_a"], start_parameters["delta_a"], start_parameters["dend_tau"], start_parameters["refractory_period"]]) - initial_simplex = create_init_simples(x0, search_scale=2) + initial_simplex = create_init_simples(x0, search_scale=3) - # error_list = [error_bf, error_vs, error_sc, error_cv, - # error_f_inf, error_f_inf_slope, error_f_zero, error_f_zero_slope] - error_weights = (0, 2, 2, 2, 1, 1, 1, 1, 0, 1) + # error_list = [error_bf, error_vs, error_sc, error_cv, error_bursty, + # error_f_inf, error_f_inf_slope, error_f_zero, error_f_zero_slope_at_straight, error_f0_curve] + error_weights = (1, 2, 2, 2, 2, 1, 1, 1, 0, 1) fmin = minimize(fun=self.cost_function_all, args=(error_weights,), x0=x0, method="Nelder-Mead", options={"initial_simplex": initial_simplex, "xatol": 0.001, "maxfev": 600, "maxiter": 400}) @@ -163,7 +165,6 @@ class Fitter: if sum(error_list) < self.smallest_error: self.smallest_error = sum(error_list) self.best_parameters_found = X - self.errors.append(error_list) return sum(error_list) def cost_function_without_ref_period(self, X, error_weights=None): @@ -290,7 +291,7 @@ class Fitter: model = self.base_model time1 = time.time() - model_baseline = get_baseline_class(model, self.eod_freq, trials=5) + model_baseline = get_baseline_class(model, self.eod_freq, trials=3) baseline_freq = model_baseline.get_baseline_frequency() vector_strength = model_baseline.get_vector_strength() serial_correlation = model_baseline.get_serial_correlation(self.sc_max_lag) @@ -301,7 +302,7 @@ class Fitter: # print("Time taken for all baseline parameters: {:.2f}".format(time2-time1)) time1 = time.time() - fi_curve_model = get_fi_curve_class(model, self.fi_contrasts, self.eod_freq, trials=15) + fi_curve_model = get_fi_curve_class(model, self.fi_contrasts, self.eod_freq, trials=8) f_zeros = fi_curve_model.get_f_zero_frequencies() f_infinities = fi_curve_model.get_f_inf_frequencies() f_infinities_slope = fi_curve_model.get_f_inf_slope() @@ -317,15 +318,13 @@ class Fitter: error_vs = abs((vector_strength - self.vector_strength) / 0.1) error_cv = abs((coefficient_of_variation - self.coefficient_of_variation) / 0.1) error_bursty = (abs(burstiness - self.burstiness) / 0.2) + # print("Burstiness: cell {:.2f}, model: {:.2f}, error: {:.2f}".format(self.burstiness, burstiness, error_bursty)) error_sc = 0 for i in range(self.sc_max_lag): error_sc += abs((serial_correlation[i] - self.serial_correlation[i]) / 0.1) # error_sc = error_sc / self.sc_max_lag - - - error_f_inf_slope = abs((f_infinities_slope - self.f_inf_slope) / abs(self.f_inf_slope+1/20)) error_f_inf = calculate_list_error(f_infinities, self.f_inf_values) @@ -334,47 +333,12 @@ class Fitter: / abs(self.f_zero_slope_at_straight+1 / 10) error_f_zero = calculate_list_error(f_zeros, self.f_zero_values) - times, freqs = fi_curve_model.get_mean_time_and_freq_traces() - freq_prediction = np.array(freqs[self.f_zero_curve_contrast_idx]) - time_prediction = np.array(times[self.f_zero_curve_contrast_idx]) - stimulus_start = fi_curve_model.get_stimulus_start() - time_prediction[0] - length = fi_curve_model.get_stimulus_duration() / 2 - - if model.get_sampling_interval() == self.data_sampling_interval: - start_idx = int(stimulus_start / fi_curve_model.get_sampling_interval()) - end_idx = int((stimulus_start + length) / model.get_sampling_interval()) - start_idx_cell = start_idx - start_idx_model = start_idx - end_idx_cell = end_idx - end_idx_model = end_idx - step_cell = 1 - step_model = 1 - else: - start_idx_cell = int(stimulus_start / self.data_sampling_interval) - start_idx_model = int(stimulus_start / fi_curve_model.get_sampling_interval()) - end_idx_cell = int((stimulus_start + length) / self.data_sampling_interval) - end_idx_model = int((stimulus_start + length) / model.get_sampling_interval()) - if round(model.get_sampling_interval() % self.data_sampling_interval, 4) == 0: - step_cell = int(model.get_sampling_interval() / self.data_sampling_interval) - step_model = 1 - else: - raise ValueError("Model sampling interval is not a multiple of data sampling interval.") - - if len(time_prediction) == 0 or len(time_prediction) < end_idx_model or time_prediction[0] > fi_curve_model.get_stimulus_start(): - error_f0_curve = 200 - else: - data_curve = self.f_zero_curve_freq[start_idx_cell:end_idx_cell:step_cell] - model_curve = freq_prediction[start_idx_model:end_idx_model:step_model] - if len(data_curve) < len(model_curve): - model_curve = model_curve[:len(data_curve)] - elif len(model_curve) < len(data_curve): - data_curve = data_curve[:len(model_curve)] - - error_f0_curve = np.mean((model_curve - data_curve)**2) / 100 + error_f0_curve = self.calculate_f0_curve_error(model, fi_curve_model) error_list = [error_bf, error_vs, error_sc, error_cv, error_bursty, error_f_inf, error_f_inf_slope, error_f_zero, error_f_zero_slope_at_straight, error_f0_curve] + self.errors.append(error_list) if error_weights is not None and len(error_weights) == len(error_list): for i in range(len(error_weights)): error_list[i] = error_list[i] * error_weights[i] @@ -389,6 +353,55 @@ class Fitter: # raise ValueError("Some error value(s) is/are NaN!") return error_list + def calculate_f0_curve_error(self, model, fi_curve_model): + buffer = 0.05 + test_duration = 0.05 + + + # prepare model frequency curve: + times, freqs = fi_curve_model.get_mean_time_and_freq_traces() + freq_prediction = np.array(freqs[self.f_zero_curve_contrast_idx]) + time_prediction = np.array(times[self.f_zero_curve_contrast_idx]) + stimulus_start = fi_curve_model.get_stimulus_start() - time_prediction[0] + + model_start_idx = int((stimulus_start - buffer) / fi_curve_model.get_sampling_interval()) + model_end_idx = int((stimulus_start - buffer + test_duration) / model.get_sampling_interval()) + + if len(time_prediction) == 0 or len(time_prediction) < model_end_idx \ + or time_prediction[0] > fi_curve_model.get_stimulus_start(): + error_f0_curve = 200 + return error_f0_curve + + model_curve = freq_prediction[model_start_idx:model_end_idx] + + # prepare cell frequency_curve: + + stimulus_start = self.recording_times[1] - self.f_zero_curve_time[0] + cell_start_idx = int((stimulus_start - buffer) / self.data_sampling_interval) + cell_end_idx = int((stimulus_start - buffer + test_duration) / self.data_sampling_interval) + + if round(model.get_sampling_interval() % self.data_sampling_interval, 4) == 0: + step_cell = int(round(model.get_sampling_interval() / self.data_sampling_interval)) + else: + raise ValueError("Model sampling interval is not a multiple of data sampling interval.") + + cell_curve = self.f_zero_curve_freq[cell_start_idx:cell_end_idx:step_cell] + + # plt.close() + # plt.plot(cell_curve) + # plt.plot(model_curve) + # plt.savefig("./figures/f_zero_curve_error_{}.png".format(time.strftime("%H:%M:%S"))) + # plt.close() + + if len(cell_curve) < len(model_curve): + model_curve = model_curve[:len(cell_curve)] + elif len(model_curve) < len(cell_curve): + cell_curve = cell_curve[:len(model_curve)] + + error_f0_curve = np.mean((model_curve - cell_curve) ** 2) / 100 + + return error_f0_curve + def calculate_list_error(fit, reference): error = 0 @@ -399,8 +412,6 @@ def calculate_list_error(fit, reference): return norm_error -def calculate_f0_curve_error(data_ficurve, model_ficurve): - return 0 def normed_quadratic_freq_error(fit, ref, factor=2): return (abs(fit-ref)/factor)**2 / ref