correct f_zero_curve error
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a.ott
parent
5fda783dfd
commit
3803628749
109
Fitter.py
109
Fitter.py
@ -29,6 +29,7 @@ class Fitter:
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#
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self.fi_contrasts = []
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self.recording_times = []
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self.eod_freq = 0
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self.data_sampling_interval = -1
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@ -64,6 +65,7 @@ class Fitter:
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def set_data_reference_values(self, cell_data: CellData):
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self.eod_freq = cell_data.get_eod_frequency()
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self.data_sampling_interval = cell_data.get_sampling_interval()
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self.recording_times = cell_data.get_recording_times()
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data_baseline = get_baseline_class(cell_data)
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data_baseline.load_values(cell_data.get_data_path())
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@ -120,11 +122,11 @@ class Fitter:
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x0 = np.array([start_parameters["mem_tau"], start_parameters["noise_strength"],
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start_parameters["input_scaling"], start_parameters["tau_a"], start_parameters["delta_a"],
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start_parameters["dend_tau"], start_parameters["refractory_period"]])
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initial_simplex = create_init_simples(x0, search_scale=2)
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initial_simplex = create_init_simples(x0, search_scale=3)
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# error_list = [error_bf, error_vs, error_sc, error_cv,
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# error_f_inf, error_f_inf_slope, error_f_zero, error_f_zero_slope]
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error_weights = (0, 2, 2, 2, 1, 1, 1, 1, 0, 1)
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# error_list = [error_bf, error_vs, error_sc, error_cv, error_bursty,
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# error_f_inf, error_f_inf_slope, error_f_zero, error_f_zero_slope_at_straight, error_f0_curve]
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error_weights = (1, 2, 2, 2, 2, 1, 1, 1, 0, 1)
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fmin = minimize(fun=self.cost_function_all,
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args=(error_weights,), x0=x0, method="Nelder-Mead",
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options={"initial_simplex": initial_simplex, "xatol": 0.001, "maxfev": 600, "maxiter": 400})
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@ -163,7 +165,6 @@ class Fitter:
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if sum(error_list) < self.smallest_error:
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self.smallest_error = sum(error_list)
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self.best_parameters_found = X
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self.errors.append(error_list)
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return sum(error_list)
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def cost_function_without_ref_period(self, X, error_weights=None):
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@ -290,7 +291,7 @@ class Fitter:
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model = self.base_model
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time1 = time.time()
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model_baseline = get_baseline_class(model, self.eod_freq, trials=5)
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model_baseline = get_baseline_class(model, self.eod_freq, trials=3)
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baseline_freq = model_baseline.get_baseline_frequency()
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vector_strength = model_baseline.get_vector_strength()
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serial_correlation = model_baseline.get_serial_correlation(self.sc_max_lag)
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@ -301,7 +302,7 @@ class Fitter:
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# print("Time taken for all baseline parameters: {:.2f}".format(time2-time1))
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time1 = time.time()
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fi_curve_model = get_fi_curve_class(model, self.fi_contrasts, self.eod_freq, trials=15)
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fi_curve_model = get_fi_curve_class(model, self.fi_contrasts, self.eod_freq, trials=8)
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f_zeros = fi_curve_model.get_f_zero_frequencies()
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f_infinities = fi_curve_model.get_f_inf_frequencies()
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f_infinities_slope = fi_curve_model.get_f_inf_slope()
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@ -317,15 +318,13 @@ class Fitter:
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error_vs = abs((vector_strength - self.vector_strength) / 0.1)
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error_cv = abs((coefficient_of_variation - self.coefficient_of_variation) / 0.1)
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error_bursty = (abs(burstiness - self.burstiness) / 0.2)
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# print("Burstiness: cell {:.2f}, model: {:.2f}, error: {:.2f}".format(self.burstiness, burstiness, error_bursty))
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error_sc = 0
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for i in range(self.sc_max_lag):
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error_sc += abs((serial_correlation[i] - self.serial_correlation[i]) / 0.1)
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# error_sc = error_sc / self.sc_max_lag
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error_f_inf_slope = abs((f_infinities_slope - self.f_inf_slope) / abs(self.f_inf_slope+1/20))
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error_f_inf = calculate_list_error(f_infinities, self.f_inf_values)
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@ -334,47 +333,12 @@ class Fitter:
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/ abs(self.f_zero_slope_at_straight+1 / 10)
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error_f_zero = calculate_list_error(f_zeros, self.f_zero_values)
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times, freqs = fi_curve_model.get_mean_time_and_freq_traces()
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freq_prediction = np.array(freqs[self.f_zero_curve_contrast_idx])
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time_prediction = np.array(times[self.f_zero_curve_contrast_idx])
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stimulus_start = fi_curve_model.get_stimulus_start() - time_prediction[0]
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length = fi_curve_model.get_stimulus_duration() / 2
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if model.get_sampling_interval() == self.data_sampling_interval:
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start_idx = int(stimulus_start / fi_curve_model.get_sampling_interval())
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end_idx = int((stimulus_start + length) / model.get_sampling_interval())
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start_idx_cell = start_idx
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start_idx_model = start_idx
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end_idx_cell = end_idx
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end_idx_model = end_idx
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step_cell = 1
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step_model = 1
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else:
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start_idx_cell = int(stimulus_start / self.data_sampling_interval)
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start_idx_model = int(stimulus_start / fi_curve_model.get_sampling_interval())
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end_idx_cell = int((stimulus_start + length) / self.data_sampling_interval)
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end_idx_model = int((stimulus_start + length) / model.get_sampling_interval())
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if round(model.get_sampling_interval() % self.data_sampling_interval, 4) == 0:
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step_cell = int(model.get_sampling_interval() / self.data_sampling_interval)
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step_model = 1
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else:
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raise ValueError("Model sampling interval is not a multiple of data sampling interval.")
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if len(time_prediction) == 0 or len(time_prediction) < end_idx_model or time_prediction[0] > fi_curve_model.get_stimulus_start():
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error_f0_curve = 200
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else:
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data_curve = self.f_zero_curve_freq[start_idx_cell:end_idx_cell:step_cell]
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model_curve = freq_prediction[start_idx_model:end_idx_model:step_model]
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if len(data_curve) < len(model_curve):
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model_curve = model_curve[:len(data_curve)]
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elif len(model_curve) < len(data_curve):
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data_curve = data_curve[:len(model_curve)]
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error_f0_curve = np.mean((model_curve - data_curve)**2) / 100
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error_f0_curve = self.calculate_f0_curve_error(model, fi_curve_model)
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error_list = [error_bf, error_vs, error_sc, error_cv, error_bursty,
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error_f_inf, error_f_inf_slope, error_f_zero, error_f_zero_slope_at_straight, error_f0_curve]
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self.errors.append(error_list)
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if error_weights is not None and len(error_weights) == len(error_list):
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for i in range(len(error_weights)):
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error_list[i] = error_list[i] * error_weights[i]
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@ -389,6 +353,55 @@ class Fitter:
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# raise ValueError("Some error value(s) is/are NaN!")
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return error_list
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def calculate_f0_curve_error(self, model, fi_curve_model):
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buffer = 0.05
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test_duration = 0.05
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# prepare model frequency curve:
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times, freqs = fi_curve_model.get_mean_time_and_freq_traces()
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freq_prediction = np.array(freqs[self.f_zero_curve_contrast_idx])
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time_prediction = np.array(times[self.f_zero_curve_contrast_idx])
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stimulus_start = fi_curve_model.get_stimulus_start() - time_prediction[0]
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model_start_idx = int((stimulus_start - buffer) / fi_curve_model.get_sampling_interval())
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model_end_idx = int((stimulus_start - buffer + test_duration) / model.get_sampling_interval())
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if len(time_prediction) == 0 or len(time_prediction) < model_end_idx \
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or time_prediction[0] > fi_curve_model.get_stimulus_start():
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error_f0_curve = 200
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return error_f0_curve
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model_curve = freq_prediction[model_start_idx:model_end_idx]
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# prepare cell frequency_curve:
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stimulus_start = self.recording_times[1] - self.f_zero_curve_time[0]
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cell_start_idx = int((stimulus_start - buffer) / self.data_sampling_interval)
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cell_end_idx = int((stimulus_start - buffer + test_duration) / self.data_sampling_interval)
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if round(model.get_sampling_interval() % self.data_sampling_interval, 4) == 0:
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step_cell = int(round(model.get_sampling_interval() / self.data_sampling_interval))
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else:
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raise ValueError("Model sampling interval is not a multiple of data sampling interval.")
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cell_curve = self.f_zero_curve_freq[cell_start_idx:cell_end_idx:step_cell]
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# plt.close()
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# plt.plot(cell_curve)
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# plt.plot(model_curve)
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# plt.savefig("./figures/f_zero_curve_error_{}.png".format(time.strftime("%H:%M:%S")))
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# plt.close()
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if len(cell_curve) < len(model_curve):
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model_curve = model_curve[:len(cell_curve)]
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elif len(model_curve) < len(cell_curve):
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cell_curve = cell_curve[:len(model_curve)]
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error_f0_curve = np.mean((model_curve - cell_curve) ** 2) / 100
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return error_f0_curve
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def calculate_list_error(fit, reference):
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error = 0
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@ -399,8 +412,6 @@ def calculate_list_error(fit, reference):
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return norm_error
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def calculate_f0_curve_error(data_ficurve, model_ficurve):
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return 0
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def normed_quadratic_freq_error(fit, ref, factor=2):
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return (abs(fit-ref)/factor)**2 / ref
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