correct f_zero_curve error

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_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]
+        #                       error_f_inf, error_f_inf_slope, error_f_zero, error_f_zero_slope_at_straight, error_f0_curve]
-        error_weights = (0, 2, 2, 2, 1, 1, 1, 1, 0, 1)
+        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()
+        error_f0_curve = self.calculate_f0_curve_error(model, fi_curve_model)
-        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_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