Changeset 13631

Timestamp:

05/10/12 11:33:36 (13 years ago)

Author:

neise

Message:

removed new class DRSSpikes_2D ... not needed

File:

• fact/tools/pyscripts/pyfact/drs_spikes.py (modified) (1 diff)

fact/tools/pyscripts/pyfact/drs_spikes.py

@@ -89 +89 @@
         return data.reshape(self.row, self.col)
 
-class DRSSpikes_2D(object):
-    """ remove spikes (single or double false readings) from DRS4 data
-    Strategy:
-    * filter the data, removing the signal, thus spike(s) are clearly visible
-    * search single and double spikes
-    * replace the spike by a value derived from the neighbors
-    
-    """
-    
-    def __init__(self, threshold=7., 
-                 single_pattern=np.array( [-0.5, 1.0, -0.5]) ,
-                 double_pattern=np.array([-1., 1., 1., -1.]), 
-                 user_action=lambda self, data: None,
-                 debug = False):
-        """ initialize spike filter 
-        template_single: template of a single slice spike
-        template_double: template of a two slice spike
-        """
-
-        self.threshold = threshold
-        self.single_pattern = single_pattern * threshold
-        self.double_pattern = double_pattern * threshold
-        
-        self.remove_signal = fir.RemoveSignal()
-
-        self.user_action = user_action
-        self.debug = debug
-
-    def __call__(self, data):
-        # shortcuts
-        row, col = data.shape
-        thr = self.threshold
-        
-        # these are: lists, which will contain positiones of spikes
-        # lets see if this is feasible
-        self.singles = []
-        self.doubles = []
-        singles = self.singles
-        doubles = self.doubles
-
-        # indi means indicator, i.e. a filter output, which indicates, where spikes
-        # are positioned in the unfiltered data
-        # indi is delayed w.r.t. data by 1
-        self.indi = self.remove_signal(data)
-        indi = self.indi
-
-        # cand (candidates), is a tuple of two equal length np.arrays
-        # each pair of array elements can be understood as coordinates, pointing out
-        # where the condition was fullfilled in indi. 
-        # e.g. in pixel = cand[0][0] around slice = cand[1][0]
-        # there is probably a spike
-        #
-        # for double spikes, two neighboring slices fulfill the condition
-        # which lead to something like: 
-        # cand =( array([ ... 3, 3, ... ]) , array([ ... 102, 103 ...]) )
-        cand = np.where(indi[:,1:-2] > thr)
-        self.cand = cand
-        # in order to verify, that the candidate is really a single or double
-        # spike, we compare the spike with a 3 or 4 slices pattern.
-        # therefor we want to slice out 4 slices out of indi, where ever the 
-        # condition was fullfilled
-        #
-        # note: since indi was sliced in the np.where statement, 
-        # the resulting cand coordinates are reduced by 1 in the slice coordinate
-        # this is actually what we want, since a spike has a distinctive low-high-low 
-        # pattern in the indicator. So we *want* the indicator slices to be shifted 1
-        # to the left
-        # and in addition, by pure chance, the coordinates in cand[1] point directly 
-        # to the real spike in data, since indi was delayed by one anyway.
-        cand_slices = np.empty( (len(cand[0]), 4), dtype=np.int )
-        self.cand_slices = cand_slices
-        for i in range(4):
-            cand_slices[i] = indi[ (cand[0], cand[1]+i )
-            
-        # search for single spikes
-        sp = self.single_pattern * np.sign( self.single_pattern )
-        for i, can in enumerate(cand_slices[:-1]):
-            can *= np.sign(sp)
-            if np.all( can > sp):
-                singles.append( (cand[0][i],can[1][i]) )
-
-        # I guess in principle it is possible, that a candidate looks like a 
-        # single and like a double, ... nut with the current patters
-        # but in case one changes the patterns ... then it might happen.
-        # In addition the treatment of double spikes is maybed not smart:
-        #   In case both parts of a double spike fulfill the 1st condition
-        #       only the first candidate will fulfill the 2nd condition
-        #   In case only the first part fulfilled the 1st conition, then
-        #       we are fine
-        #   In case only the second part triggered the first time, then
-        #       we sliced out the wrong piece and it wouldn't fulfull the 2nd anyway.
-        # 
-        #   This means, in case there are neighboring hits in cand,
-        #   The 2nd neighbor will never fulfill the 2nd condition.
-
-        # search for double spikes
-        dp = self.double_pattern * np.sign( self.double_pattern )
-        for i, can in enumerate( cand_slices ):
-            can *= np.sign(dp)
-            if np.all(can > dp):
-                doubles.append( (cand[0][i],can[1][i]) )
-
-        self.user_action(self, data)
-
-        data = self.remove_single_spikes(singles, data)
-        data = self.remove_double_spikes(doubles, data)
-        return data
-
-    def remove_single_spikes(self, singles, data):
-        for spike in singles:
-            data[spike[0],spike[1]] = (data[spike[0],spike[1]-1] + data[spike[0],spike[1]+1]) / 2.
-        return data
-    
-    def remove_double_spikes(self, doubles, data):
-        for spike in doubles:
-            data[spike[0],spike[1]:spike[1]+2] = (data[spike[0],spike[1]-1] + data[spike[0],spike[1]+2]) / 2.
-        return data
-
-
-
 def _test():