Merge branch 'coa_analysis' of git.wur.nl:lanno001/baseless into coa_analysis

 Conflicts:
	coa_detection/AbfData.py

coa_detection/AbfData.py

@@ -15,8 +15,7 @@ class AbfData:
     :param lowpass_freq: Frequency to use for low pass filter in KHz
     :type lowpass_freq: float
     """
-
-    def __init__(self, abf_fn, normalization, lowpass_freq,
+    def __init__(self, abf_fn, normalization=False, lowpass_freq=80,
                  baseline_threshold=0.65):
         self.abf_fn = abf_fn
         self.normalization = normalization
@@ -33,6 +32,7 @@ class AbfData:
     @raw.setter
     def raw(self, _):
         abf = pyabf.ABF(self.abf_fn)
+        self.unfiltered_raw = abf.sweepY[~np.isnan(abf.sweepY)]
         pyabf.filter.gaussian(abf, self.smoothing_sigma)
         abf.setSweep(0)
         # Drop NaNs because they show up at the edges due to smoothing
@@ -61,7 +61,7 @@ class AbfData:
         plt.hist(event_lengths, bins=100)
         plt.show()
 
-    def get_pos(self, width):
+    def get_pos(self, width, unfiltered=False):
         pos_list = []
         for event in self.pos_events:
             start_idx = event[0]
@@ -73,16 +73,22 @@ class AbfData:
                 start_idx -= random_offset
                 end_idx += room_left - random_offset
                 assert (end_idx - start_idx) == width
-                pos_list.append(self.raw[start_idx: end_idx])
+                if unfiltered:
+                    pos_list.append(self.unfiltered_raw[start_idx: end_idx])
+                else:
+                    pos_list.append(self.raw[start_idx: end_idx])
         return pos_list
 
-    def get_neg(self, width, nb_neg):
+    def get_neg(self, width, nb_neg, unfiltered=False):
         neg_list = []
         for i in range(nb_neg * 100): # take 100 attempts for each neg
             random_idx = random.randint(0, len(self.raw)-width)
             candidate_indices = np.arange(random_idx, random_idx+width)
             if not np.any(np.isin(candidate_indices, self.flat_pos_indices)):
-                neg_list.append(self.raw[candidate_indices])
+                if unfiltered:
+                    neg_list.append(self.unfiltered_raw[candidate_indices])
+                else:
+                    neg_list.append(self.raw[candidate_indices])
             if len(neg_list) >= nb_neg:
                 return neg_list