From 6ab71b2fe927a740cb73611035ed5b1750c2ba9e Mon Sep 17 00:00:00 2001
From: Jan Grewe <jan.grewe@g-node.org>
Date: Tue, 18 Aug 2020 11:32:15 +0200
Subject: [PATCH] [relacs] add __str__ and __repr__ methods to classes

---
 fishbook/frontend/relacs_classes.py | 42 ++++++++++++++++++++++++++++-
 1 file changed, 41 insertions(+), 1 deletion(-)

diff --git a/fishbook/frontend/relacs_classes.py b/fishbook/frontend/relacs_classes.py
index 8f6b813..accab0b 100644
--- a/fishbook/frontend/relacs_classes.py
+++ b/fishbook/frontend/relacs_classes.py
@@ -3,7 +3,6 @@ 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
@@ -372,6 +371,15 @@ 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
@@ -633,6 +641,14 @@ 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) 
@@ -892,12 +908,16 @@ 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)
@@ -905,11 +925,31 @@ 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__":