def multipleApproximatePatternMatching(text,patterns,d): lastC = BWT.BWT(text) firstOccur = BWTMatch.firstOccurrence(lastC) partialsuffixarray = partialSuffixArray.partialSuffixArray(text, K) checkpointarray = mpm.checkPointArray(lastC, C) suffixarray,suffixes = mpm.suffixArray(text) positions = [] for pattern in patterns: '''seed preparation:divide pattern into d + 1 substring, with the length floor(n/(d+1))''' n = len(pattern) k = n / (d+1) #print "pattern",pattern,"n",n,"k",k,"d",d # for each pattern, if we hits same position in the text multiple times, we only keep one record. posstarts = [] for i in range(d+1): if i == d: pat = pattern[i*k:] else: pat = pattern[i*k:(i+1)*k] '''seed detection: for each seed (pat), do exact pattern matching to find which seeds match Text exactly''' position = mpm.PatternMatching(suffixarray,firstOccur,lastC,checkpointarray,partialsuffixarray,pat) if position is not None: for pos in position: '''seed extension: extend seeds in both directions to verify whether Pattern occurs in Text with at most d mismatches.''' posstart = pos-k*i dist = hamming_distance(text[posstart:posstart+n],pattern) if dist is not None and dist <= d: posstarts.append(posstart) positions.extend(list(set(posstarts))) return positions
def multiplePatternMatching(text, patterns): """Input that I can use: LastColumn, FirstOccurrence, CheckPointArray, PartialSuffixArray""" lastC = BWT.BWT(text) firstOccur = BWTMatch.firstOccurrence(lastC) partialsuffixarray = partialSuffixArray.partialSuffixArray(text, K) checkpointarray = checkPointArray(lastC, C) suffixarray,suffixes = suffixArray(text) positions = [] for pattern in patterns: position = PatternMatching(suffixarray,firstOccur,lastC,checkpointarray,partialsuffixarray,pattern) if position is not None: for pos in position: positions.append(pos) return positions
def multiplePatternMatching(text, patterns):
"""Input that I can use: LastColumn, FirstOccurrence, CheckPointArray, PartialSuffixArray"""
lastC = BWT.BWT(text)
firstOccur = BWTMatch.firstOccurrence(lastC)
partialsuffixarray = partialSuffixArray.partialSuffixArray(text, K)
checkpointarray = checkPointArray(lastC, C)
suffixarray, suffixes = suffixArray(text)
positions = []
for pattern in patterns:
position = PatternMatching(suffixarray, firstOccur, lastC,
checkpointarray, partialsuffixarray,
pattern)
if position is not None:
for pos in position:
positions.append(pos)
return positions
def multipleApproximatePatternMatching(text, patterns, d):
lastC = BWT.BWT(text)
firstOccur = BWTMatch.firstOccurrence(lastC)
partialsuffixarray = partialSuffixArray.partialSuffixArray(text, K)
checkpointarray = mpm.checkPointArray(lastC, C)
suffixarray, suffixes = mpm.suffixArray(text)
positions = []
for pattern in patterns:
'''seed preparation:divide pattern into d + 1 substring, with the length floor(n/(d+1))'''
n = len(pattern)
k = n / (d + 1)
#print "pattern",pattern,"n",n,"k",k,"d",d
# for each pattern, if we hits same position in the text multiple times, we only keep one record.
posstarts = []
for i in range(d + 1):
if i == d:
pat = pattern[i * k:]
else:
pat = pattern[i * k:(i + 1) * k]
'''seed detection: for each seed (pat), do exact pattern matching to find which seeds match Text exactly'''
position = mpm.PatternMatching(suffixarray, firstOccur, lastC,
checkpointarray, partialsuffixarray,
pat)
if position is not None:
for pos in position:
'''seed extension: extend seeds in both directions to verify whether Pattern occurs in Text with at most d mismatches.'''
posstart = pos - k * i
dist = hamming_distance(text[posstart:posstart + n],
pattern)
if dist is not None and dist <= d:
posstarts.append(posstart)
positions.extend(list(set(posstarts)))
return positions
