stuff

experiments/FiCurve.py

@@ -46,8 +46,8 @@ class FICurve:
         self.f_inf_fit = hF.fit_clipped_line(self.stimulus_values, self.f_inf_frequencies)
         self.f_zero_fit = hF.fit_boltzmann(self.stimulus_values, self.f_zero_frequencies)
 
-    def __calculate_time_constant_internal__(self, contrast, mean_frequency, baseline_freq, sampling_interval, pre_duration, plot=False):
-        time_constant_fit_length = 0.05
+    def __calculate_time_constant_internal__(self, contrast, mean_frequency, baseline_freq, sampling_interval, pre_duration, plot=False, plot_data=False):
+        time_constant_fit_length = 0.05 # change to 25 - 30 ms
 
         if contrast > 0:
             maximum_idx = np.argmax(mean_frequency)
@@ -88,6 +88,9 @@ class FICurve:
                          fu.exponential_function(x_values, popt[0], popt[1], popt[2]), color="orange")
                 plt.show()
                 plt.close()
+
+            if plot_data:
+                return popt, np.arange(start_fit_idx, end_fit_idx, 1) * sampling_interval - pre_duration
             return popt, pcov
         except RuntimeError:
             print("RuntimeError happened in fit_exponential.")
@@ -375,7 +378,7 @@ class FICurveCellData(FICurve):
         popt, pcov = super().__calculate_time_constant_internal__(self.stimulus_values[contrast_idx], mean_frequency,
                                                                   baseline_freq, sampling_interval, pre_duration, plot=plot)
 
-        return popt[1]
+        return popt
 
     def calculate_all_frequency_points(self):
         mean_frequencies = self.cell_data.get_mean_fi_curve_isi_frequencies()
@@ -498,7 +501,7 @@ class FICurveModel(FICurve):
     stim_start = 0.5
     total_simulation_time = stim_duration + 2 * stim_start
 
-    def __init__(self, model, stimulus_values, eod_frequency, trials=5):
+    def __init__(self, model, stimulus_values, eod_frequency, trials=5, save_dir=None, recalculate=False):
         self.eod_frequency = eod_frequency
         self.model = model
         self.trials = trials
@@ -506,7 +509,7 @@ class FICurveModel(FICurve):
         self.mean_frequency_traces = []
         self.mean_time_traces = []
         self.set_model_adaption_to_baseline()
-        super().__init__(stimulus_values)
+        super().__init__(stimulus_values, save_dir=None, recalculate=False)
 
     def set_model_adaption_to_baseline(self):
         stimulus = SinusoidalStepStimulus(self.eod_frequency, 0, 0, 0)
@@ -540,7 +543,7 @@ class FICurveModel(FICurve):
 
             if len(time) == 0 or min(time) > self.stim_start \
                     or max(time) < self.stim_start + self.stim_duration:
-                # print("Too few spikes to calculate f_inf, f_0 and f_base")
+
                 self.f_inf_frequencies.append(0)
                 self.f_zero_frequencies.append(0)
                 self.f_baseline_frequencies.append(0)
@@ -569,10 +572,7 @@ class FICurveModel(FICurve):
 
         popt, pcov = super().__calculate_time_constant_internal__(self.stimulus_values[contrast_idx], mean_frequency,
                                                                   baseline_freq, sampling_interval, pre_duration, plot=plot)
-        if len(popt) > 0:
-            return popt[1]
-        else:
-            return -1
+        return popt
 
     def get_mean_time_and_freq_traces(self):
         return self.mean_time_traces, self.mean_frequency_traces
@@ -644,6 +644,6 @@ def get_fi_curve_class(data, stimulus_values, eod_freq=None, trials=5, save_dir=
     if isinstance(data, LifacNoiseModel):
         if eod_freq is None:
             raise ValueError("The FiCurveModel needs the eod variable to work")
-        return FICurveModel(data, stimulus_values, eod_freq, trials=trials)
+        return FICurveModel(data, stimulus_values, eod_freq, trials=trials, save_dir=None, recalculate=False)
 
     raise ValueError("Unknown type: Cannot find corresponding Baseline class. Data was type:" + str(type(data)))