add error of mean-square with the isi bins
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a.ott
parent
e9c414dab5
commit
eeb43fd7fc
126
Fitter.py
126
Fitter.py
@ -32,6 +32,7 @@ class Fitter:
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self.sc_max_lag = 2
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# values to be replicated:
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self.isi_bins = np.array(0)
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self.baseline_freq = 0
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self.vector_strength = -1
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self.serial_correlation = []
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@ -64,20 +65,26 @@ class Fitter:
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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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self.baseline_freq = data_baseline.get_baseline_frequency()
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self.isi_bins = calculate_histogram_bins(data_baseline.get_interspike_intervals())
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# plt.close()
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# plt.plot(self.isi_bins)
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# plt.show()
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# plt.close()
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self.vector_strength = data_baseline.get_vector_strength()
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self.serial_correlation = data_baseline.get_serial_correlation(self.sc_max_lag)
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self.coefficient_of_variation = data_baseline.get_coefficient_of_variation()
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self.burstiness = data_baseline.get_burstiness()
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fi_curve = get_fi_curve_class(cell_data, cell_data.get_fi_contrasts(), save_dir=cell_data.get_data_path())
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self.f_inf_slope = fi_curve.get_f_inf_slope()
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contrasts = np.array(cell_data.get_fi_contrasts())
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fi_curve = get_fi_curve_class(cell_data, contrasts, save_dir=cell_data.get_data_path())
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self.f_inf_slope = fi_curve.get_f_inf_slope()
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if self.f_inf_slope < 0:
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contrasts = contrasts * -1
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# print("old contrasts:", cell_data.get_fi_contrasts())
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# print("new contrasts:", contrasts)
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contrasts = sorted(contrasts)
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fi_curve = get_fi_curve_class(cell_data, contrasts)
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fi_curve = get_fi_curve_class(cell_data, contrasts, save_dir=cell_data.get_data_path())
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self.fi_contrasts = fi_curve.stimulus_values
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self.f_inf_values = fi_curve.f_inf_frequencies
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@ -121,9 +128,6 @@ class Fitter:
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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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if error_weights is None:
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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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@ -150,13 +154,13 @@ class Fitter:
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# find right v-offset
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test_model = self.base_model.get_model_copy()
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test_model.set_variable("noise_strength", 0)
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time1 = time.time()
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# time1 = time.time()
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v_offset = test_model.find_v_offset(self.baseline_freq, base_stimulus)
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self.base_model.set_variable("v_offset", v_offset)
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time2 = time.time()
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# time2 = time.time()
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# print("time taken for finding v_offset: {:.2f}s".format(time2-time1))
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# [error_bf, error_vs, error_sc, error_f_inf, error_f_inf_slope, error_f_zero, error_f_zero_slope]
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error_list = self.calculate_errors(error_weights)
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# print("sum: {:.2f}, ".format(sum(error_list)))
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if sum(error_list) < self.smallest_error:
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@ -168,18 +172,18 @@ class Fitter:
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if model is None:
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model = self.base_model
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time1 = time.time()
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# time1 = time.time()
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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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coefficient_of_variation = model_baseline.get_coefficient_of_variation()
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burstiness = model_baseline.get_burstiness()
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time2 = time.time()
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# time2 = time.time()
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isi_bins = calculate_histogram_bins(model_baseline.get_interspike_intervals())
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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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# time1 = time.time()
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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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@ -187,15 +191,17 @@ class Fitter:
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# f_zero_slopes = [fi_curve_model.get_f_zero_fit_slope_at_stimulus_value(x) for x in self.fi_contrasts]
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f_zero_slope_at_straight = fi_curve_model.get_f_zero_fit_slope_at_stimulus_value(self.f_zero_straight_contrast)
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time2 = time.time()
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# time2 = time.time()
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# print("Time taken for all fi-curve parameters: {:.2f}".format(time2 - time1))
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# calculate errors with reference values
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error_bf = abs((baseline_freq - self.baseline_freq) / self.baseline_freq)
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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_cv = abs((coefficient_of_variation - self.coefficient_of_variation) / 0.2)
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error_bursty = (abs(burstiness - self.burstiness) / 0.2)
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error_hist = np.mean((isi_bins - self.isi_bins) ** 2) / 200
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# print("error hist: {:.2f}".format(error_hist))
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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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@ -209,11 +215,11 @@ class Fitter:
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# error_f_zero_slopes = calculate_list_error(f_zero_slopes, self.f_zero_slopes)
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error_f_zero_slope_at_straight = abs(self.f_zero_slope_at_straight - f_zero_slope_at_straight) \
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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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error_f_zero = calculate_list_error(f_zeros, self.f_zero_values) / 25
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error_f0_curve = self.calculate_f0_curve_error(model, fi_curve_model)
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error_f0_curve = self.calculate_f0_curve_error(model, fi_curve_model) / 10
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error_list = [error_bf, error_vs, error_sc, error_cv, error_bursty,
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error_list = [error_bf, error_vs, error_sc, error_cv, error_hist, 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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@ -232,18 +238,20 @@ class Fitter:
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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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buffer = 0.00
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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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if len(time_prediction) == 0:
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return 200
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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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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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@ -256,7 +264,7 @@ class Fitter:
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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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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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@ -264,7 +272,6 @@ class Fitter:
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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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@ -280,6 +287,69 @@ class Fitter:
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return error_f0_curve
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def calculate_f0_curve_error_new(self, model, fi_curve_model):
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buffer = 0.05
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test_duration = 0.05
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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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if len(time_prediction) == 0:
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return 200
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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) / 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 = np.array(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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cell_time = self.f_zero_curve_time[cell_start_idx:cell_end_idx:step_cell]
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cell_curve_std = np.std(self.f_zero_curve_freq)
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model_curve_std = np.std(freq_prediction)
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model_limit = self.baseline_freq + model_curve_std
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cell_limit = self.baseline_freq + cell_curve_std
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cell_full_precicion = np.array(self.f_zero_curve_freq[cell_start_idx:cell_end_idx])
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cell_points_above = cell_full_precicion > cell_limit
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cell_area_above = sum(cell_full_precicion[cell_points_above]) * self.data_sampling_interval
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model_points_above = model_curve > model_limit
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model_area_above = sum(model_curve[model_points_above]) * model.get_sampling_interval()
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# plt.close()
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# plt.plot(cell_time, cell_curve, color="blue")
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# plt.plot((cell_time[0], cell_time[-1]), (cell_limit, cell_limit),
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# color="lightblue", label="area: {:.2f}".format(cell_area_above))
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#
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# plt.plot(time_prediction[model_start_idx:model_end_idx], model_curve, color="orange")
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# plt.plot((time_prediction[model_start_idx], time_prediction[model_end_idx]), (model_limit, model_limit),
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# color="red", label="area: {:.2f}".format(model_area_above))
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# plt.legend()
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# plt.title("Error: {:.2f}".format(abs(model_area_above - cell_area_above) / 0.02))
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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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return abs(model_area_above - cell_area_above)
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def calculate_list_error(fit, reference):
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error = 0
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@ -290,6 +360,14 @@ def calculate_list_error(fit, reference):
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return norm_error
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def calculate_histogram_bins(isis):
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isis = np.array(isis) * 1000
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step = 0.1
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bins = np.arange(0, 50, step)
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counts = np.array([np.sum((isis >= b) & (isis < b+0.1)) for b in bins])
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return counts
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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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