| 1 | class DRSSpikes_2D(object):
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| 2 | """ remove spikes (single or double false readings) from DRS4 data
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| 3 | Strategy:
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| 4 | * filter the data, removing the signal, thus spike(s) are clearly visible
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| 5 | * search single and double spikes
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| 6 | * replace the spike by a value derived from the neighbors
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| 7 |
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| 8 | """
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| 9 |
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| 10 | def __init__(self, threshold=7.,
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| 11 | single_pattern=np.array( [-0.5, 1.0, -0.5]) ,
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| 12 | double_pattern=np.array([-1., 1., 1., -1.]),
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| 13 | user_action=lambda self, data: None,
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| 14 | debug = False):
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| 15 | """ initialize spike filter
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| 16 | template_single: template of a single slice spike
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| 17 | template_double: template of a two slice spike
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| 18 | """
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| 19 |
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| 20 | self.threshold = threshold
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| 21 | self.single_pattern = single_pattern * threshold
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| 22 | self.double_pattern = double_pattern * threshold
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| 23 |
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| 24 | self.remove_signal = fir.RemoveSignal()
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| 25 |
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| 26 | self.user_action = user_action
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| 27 | self.debug = debug
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| 28 |
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| 29 | def __call__(self, data):
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| 30 | # shortcuts
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| 31 | row, col = data.shape
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| 32 | thr = self.threshold
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| 33 |
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| 34 | # these are: lists, which will contain positiones of spikes
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| 35 | # lets see if this is feasible
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| 36 | self.singles = []
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| 37 | self.doubles = []
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| 38 | singles = self.singles
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| 39 | doubles = self.doubles
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| 40 |
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| 41 | # indi means indicator, i.e. a filter output, which indicates, where spikes
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| 42 | # are positioned in the unfiltered data
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| 43 | # indi is delayed w.r.t. data by 1
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| 44 | self.indi = self.remove_signal(data)
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| 45 | indi = self.indi
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| 46 |
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| 47 | # cand (candidates), is a tuple of two equal length np.arrays
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| 48 | # each pair of array elements can be understood as coordinates, pointing out
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| 49 | # where the condition was fullfilled in indi.
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| 50 | # e.g. in pixel = cand[0][0] around slice = cand[1][0]
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| 51 | # there is probably a spike
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| 52 | #
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| 53 | # for double spikes, two neighboring slices fulfill the condition
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| 54 | # which lead to something like:
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| 55 | # cand =( array([ ... 3, 3, ... ]) , array([ ... 102, 103 ...]) )
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| 56 | cand = np.where(indi[:,1:-2] > thr)
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| 57 | self.cand = cand
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| 58 | # in order to verify, that the candidate is really a single or double
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| 59 | # spike, we compare the spike with a 3 or 4 slices pattern.
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| 60 | # therefor we want to slice out 4 slices out of indi, where ever the
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| 61 | # condition was fullfilled
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| 62 | #
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| 63 | # note: since indi was sliced in the np.where statement,
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| 64 | # the resulting cand coordinates are reduced by 1 in the slice coordinate
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| 65 | # this is actually what we want, since a spike has a distinctive low-high-low
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| 66 | # pattern in the indicator. So we *want* the indicator slices to be shifted 1
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| 67 | # to the left
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| 68 | # and in addition, by pure chance, the coordinates in cand[1] point directly
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| 69 | # to the real spike in data, since indi was delayed by one anyway.
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| 70 | cand_slices = np.empty( (len(cand[0]), 4), dtype=np.int )
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| 71 | self.cand_slices = cand_slices
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| 72 | for i in range(4):
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| 73 | cand_slices[i] = indi[ (cand[0], cand[1]+i )
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| 74 |
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| 75 | # search for single spikes
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| 76 | sp = self.single_pattern * np.sign( self.single_pattern )
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| 77 | for i, can in enumerate(cand_slices[:-1]):
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| 78 | can *= np.sign(sp)
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| 79 | if np.all( can > sp):
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| 80 | singles.append( (cand[0][i],can[1][i]) )
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| 81 |
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| 82 | # I guess in principle it is possible, that a candidate looks like a
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| 83 | # single and like a double, ... nut with the current patters
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| 84 | # but in case one changes the patterns ... then it might happen.
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| 85 | # In addition the treatment of double spikes is maybed not smart:
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| 86 | # In case both parts of a double spike fulfill the 1st condition
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| 87 | # only the first candidate will fulfill the 2nd condition
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| 88 | # In case only the first part fulfilled the 1st conition, then
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| 89 | # we are fine
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| 90 | # In case only the second part triggered the first time, then
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| 91 | # we sliced out the wrong piece and it wouldn't fulfull the 2nd anyway.
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| 92 | #
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| 93 | # This means, in case there are neighboring hits in cand,
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| 94 | # The 2nd neighbor will never fulfill the 2nd condition.
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| 95 |
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| 96 | # search for double spikes
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| 97 | dp = self.double_pattern * np.sign( self.double_pattern )
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| 98 | for i, can in enumerate( cand_slices ):
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| 99 | can *= np.sign(dp)
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| 100 | if np.all(can > dp):
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| 101 | doubles.append( (cand[0][i],can[1][i]) )
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| 102 |
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| 103 | self.user_action(self, data)
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| 104 |
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| 105 | data = self.remove_single_spikes(singles, data)
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| 106 | data = self.remove_double_spikes(doubles, data)
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| 107 | return data
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| 108 |
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| 109 | def remove_single_spikes(self, singles, data):
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| 110 | for spike in singles:
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| 111 | data[spike[0],spike[1]] = (data[spike[0],spike[1]-1] + data[spike[0],spike[1]+1]) / 2.
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| 112 | return data
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| 113 |
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| 114 | def remove_double_spikes(self, doubles, data):
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| 115 | for spike in doubles:
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| 116 | data[spike[0],spike[1]:spike[1]+2] = (data[spike[0],spike[1]-1] + data[spike[0],spike[1]+2]) / 2.
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| 117 | return data
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| 118 |
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| 119 |
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| 120 |
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