def construct_partial_suffix_array(word, k):
'''Constructs a suffix array from the given word.'''
# Check that the word ends in the out of alphabet character '$'.
word += ['', '$'][word[-1] != '$']
# Construct the suffix array for the given word.
suffix_array = construct_suffix_array(word)
# Return the position and value of elements that are multiples of k.
return [(i, s) for i, s in enumerate(suffix_array) if s % k == 0]
def construct_partial_suffix_array(word, k):
'''Constructs a suffix array from the given word.'''
# Check that the word ends in the out of alphabet character '$'.
word += ['', '$'][word[-1] != '$']
# Construct the suffix array for the given word.
suffix_array = construct_suffix_array(word)
# Return the position and value of elements that are multiples of k.
return [(i, s) for i, s in enumerate(suffix_array) if s % k == 0]
def get_multi_pattern_count(word, patterns):
'''Precomputes the necessary information and passes each pattern to multiple pattern matching function.'''
# Construct the Burrows-Wheeler Transform and Suffix Array.
bwt = burrows_wheeler_transform(word)
suffix_array = construct_suffix_array(word)
# Create the count dictionary.
symbols = set(bwt)
current_count = {ch:0 for ch in symbols}
count = {0:{ch:current_count[ch] for ch in symbols}}
for i in xrange(len(bwt)):
current_count[bwt[i]] += 1
count[i+1] = {ch:current_count[ch] for ch in symbols}
# Get the index of the first occurrence of each character in the sorted Burrows-Wheeler Transformation.
sorted_bwt = sorted(bwt)
first_occurrence = {ch:sorted_bwt.index(ch) for ch in set(bwt)}
# Pass the information and patters along to the BWMatching algorithm.
matches = []
for pattern in patterns:
matches += multi_pattern_match_bw(bwt, suffix_array, first_occurrence, count, pattern)
return matches
def get_multi_pattern_count(word, patterns):
'''Precomputes the necessary information and passes each pattern to multiple pattern matching function.'''
# Construct the Burrows-Wheeler Transform and Suffix Array.
bwt = burrows_wheeler_transform(word)
suffix_array = construct_suffix_array(word)
# Create the count dictionary.
symbols = set(bwt)
current_count = {ch: 0 for ch in symbols}
count = {0: {ch: current_count[ch] for ch in symbols}}
for i in xrange(len(bwt)):
current_count[bwt[i]] += 1
count[i + 1] = {ch: current_count[ch] for ch in symbols}
# Get the index of the first occurrence of each character in the sorted Burrows-Wheeler Transformation.
sorted_bwt = sorted(bwt)
first_occurrence = {ch: sorted_bwt.index(ch) for ch in set(bwt)}
# Pass the information and patters along to the BWMatching algorithm.
matches = []
for pattern in patterns:
matches += multi_pattern_match_bw(bwt, suffix_array, first_occurrence,
count, pattern)
return matches
def multi_approx_pattern_match(word, patterns, d):
'''Returns the starting indices of all approximate matches to the given list of patterns using the seed method.'''
# Construct the Burrows-Wheeler Transform and Suffix Array.
bwt = burrows_wheeler_transform(word)
suffix_array = construct_suffix_array(word)
# Create the count dictionary.
symbols = set(bwt)
current_count = {ch:0 for ch in symbols}
count = {0:{ch:current_count[ch] for ch in symbols}}
for i in xrange(len(bwt)):
current_count[bwt[i]] += 1
count[i+1] = {ch:current_count[ch] for ch in symbols}
# Get the index of the first occurrence of each character in the sorted Burrows-Wheeler Transformation.
sorted_bwt = sorted(bwt)
first_occurrence = {ch:sorted_bwt.index(ch) for ch in set(bwt)}
# Detect and extend seeds to find the approximate pattern locations.
matches = []
for num, pattern in enumerate(patterns):
seed_locations = seed_detection(bwt, suffix_array, first_occurrence, count, word, pattern, d)
matches += [seed_index for seed_index in seed_locations if seed_extension(word, pattern, seed_index, d) is True]
return matches