fishbook/frontend/relacs_classes.py

@@ -3,6 +3,7 @@ from .util import BoltzmannFit, unzip_if_needed, gaussian_kernel, zero_crossings
 import numpy as np
 import nixio as nix
 from scipy.stats import circstd
+# from scipy.optimize import curve_fit
 import os
 import subprocess
 from tqdm import tqdm
@@ -371,15 +372,6 @@ class BaselineData:
             l = f.readline()
         return np.asarray(data)
 
-    def __str__(self):
-        str = "Baseline activity\n"
-        str += "Cell %s\t avg. firing rate: %.2f Hz\n" % (self.cell.id, self.baseline_rate)
-        str += "CV: %.2f\t vector strength: %.2f" % (self.coefficient_of_variation, self.vector_strength)
-        return str
-
-    def __repr__(self):
-        return self.__str__()
-
 
 class FIData:
     """Class representing the data recorded with the relacs FI-Curve repro. The instance will load the data upon
@@ -641,14 +633,6 @@ class FIData:
         boltzmann_fit = BoltzmannFit(contrasts, rates)
         return boltzmann_fit
 
-    def __str__(self):
-        str = "FICurve\n"
-        str += "FI Curve recorded for cell %s" % self.__cell.id
-        return str
-    
-    def __repr__(self):
-        return self.__str__()
-
 
 class FileStimulusData:
     """The FileStimulus class provides access to the data recorded and the stimulus presented (if accessible) 
@@ -908,16 +892,12 @@ class FileStimulusData:
         Returns:
             [type]: [description]
         """
-        
         if index == -1:
             time_axes = []
             rates = []
             for i in range(self.size):
                 t = self.time_axis(i)
                 spikes = self.spikes(i)
-                if len(spikes) == 0 or len(t) <= 1:
-                    print("Warning: no spikes or time axis to short for index:", i)
-                    continue
                 r = spike_times_to_rate(spikes, t, kernel_width)
                 time_axes.append(t)
                 rates.append(r)
@@ -925,31 +905,11 @@ class FileStimulusData:
         elif index >= 0 and index < self.size:
             t = self.time_axis(index)
             spikes = self.spikes(index)
-            if len(spikes) == 0 or len(t) <= 1:
-                    print("Warning: no spikes or time axis to short for index:", i)
-                    return None, None
             r = spike_times_to_rate(spikes, t, kernel_width)
             return t, r
         else:
             raise IndexError("FileStimulusData: index %i out of bounds for time_axes of size %i" % (index, self.size))
 
-    def __str__(self):
-        str = "FileStimulusData\n"
-        str += "Data recorded running the FileStimulus RePro on cell %s\n" % self.cell.id
-        contrasts = np.array(self.contrast())
-        stimuli = np.array(self.stimulus_files())
-
-        str += "Stimuli:\n"
-        for s in np.unique(stimuli):
-            str += "%s:\n" % (s.split("/")[-1])
-            s_contrasts = np.unique(contrasts[stimuli == s])
-            for c in s_contrasts:
-                str += "\tcontrast %.2f (%i trials)\n" % (c, len(contrasts[(stimuli == s) & (contrasts == c)])) 
-        return str
-    
-    def __repr__(self):
-        return self.__str__()
-
 
 if __name__ == "__main__":
 

fishbook/frontend/util.py

@@ -216,6 +216,7 @@ class StimSpikesFile:
                     trial_duration = float(trial_duration[:1])
                 else:
                     trial_duration = 0.0
+                print(l, trial_duration)
             if "index:" in l:
                 if len(trial_data) > 0:
                     index_map[(index, trial)] = (trial_duration, trial_data)