Added ability to extract unfiltered data

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, uncenter_kmer=True):
+    def get_pos(self, width, unfiltered=False):
         while True:
             event = random.choice(self.pos_events)
             start_idx = event[0]
@@ -73,13 +73,17 @@ class AbfData:
                 start_idx -= random_offset
                 end_idx += room_left - random_offset
                 assert (end_idx - start_idx) == width
+                if unfiltered:
+                    return self.unfiltered_raw[start_idx: end_idx]
                 return self.raw[start_idx: end_idx]
 
-    def get_neg(self, width):
+    def get_neg(self, width, unfiltered=False):
         while True:
             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)):
+                if unfiltered:
+                    return self.unfiltered_raw[candidate_indices]
                 return self.raw[candidate_indices]