def approx_pattern_count(text, pattern, d):
fragment_length = len(pattern)
count = 0
for i, fragment in enumerate(["".join(x) for x in window(text, fragment_length)]):
if hamming_distance(fragment, pattern) <= d:
count += 1
return count
def approx_pattern_count(text, pattern, d):
fragment_length = len(pattern)
count = 0
for i, fragment in enumerate(
["".join(x) for x in window(text, fragment_length)]):
if hamming_distance(fragment, pattern) <= d:
count += 1
return count
def enumerate_motifs(dnas, k, d):
kmers = []
for text in dnas:
kmers.extend(["".join(x) for x in window(text, k)])
resulting_set = set()
for kmer in kmers:
for modified_kmer in generate_words_with_mismatches(kmer, d):
will_add = True
for text in dnas:
was_found = False
for fragment in ["".join(x) for x in window(text, k)]:
if hamming_distance(fragment, modified_kmer) <= d:
was_found = True
break
if not was_found:
will_add = False
break
if will_add:
resulting_set.add(modified_kmer)
return list(resulting_set)
def distance(kmer, string, k):
result = k+2
for fragment in ["".join(x) for x in window(string, k)]:
if hamming_distance(kmer, fragment) < result:
result = hamming_distance(kmer, fragment)
return result
def distance(kmer, string, k):
result = k + 2
for fragment in ["".join(x) for x in window(string, k)]:
if hamming_distance(kmer, fragment) < result:
result = hamming_distance(kmer, fragment)
return result
from _01_07_hamming_distance import hamming_distance
from _01_02_frequent_words import window
if __name__ == '__main__':
with open('in.txt', 'r') as f:
pattern = f.readline().strip()
text = f.readline().strip()
d = int(f.readline())
fragment_length = len(pattern)
indexes = list()
for i, fragment in enumerate(
["".join(x) for x in window(text, fragment_length)]):
if hamming_distance(fragment, pattern) <= d:
indexes.append(i)
with open('out.txt', 'w') as f:
f.write(' '.join(map(str, indexes)))
from _01_07_hamming_distance import hamming_distance
from _01_02_frequent_words import window
if __name__ == '__main__':
with open('in.txt', 'r') as f:
pattern = f.readline().strip()
text = f.readline().strip()
d = int(f.readline())
fragment_length = len(pattern)
indexes = list()
for i, fragment in enumerate(["".join(x) for x in window(text, fragment_length)]):
if hamming_distance(fragment, pattern) <= d:
indexes.append(i)
with open('out.txt', 'w') as f:
f.write(' '.join(map(str, indexes)))
