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#!/usr/bin/env python
import timeit
import edlib
import editdistance
import Levenshtein
with open('../../test_data/Enterobacteria_Phage_1/mutated_90_perc_oneline.fasta', 'r') as f:
queryFull = f.readline()
print('Read query: ', len(queryFull) ,' characters.')
with open('../../test_data/Enterobacteria_Phage_1/Enterobacteria_phage_1_oneline.fa', 'r') as f:
targetFull = f.readline()
print('Read target: ', len(targetFull) ,' characters.')
for seqLen in [30, 100, 1000, 10000, 50000]:
query = queryFull[:seqLen]
target = targetFull[:seqLen]
numRuns = max(1000000000 // (seqLen**2), 1)
print('Sequence length: ', seqLen)
edlibTime = timeit.timeit(stmt="edlib.align(query, target)",
number=numRuns, globals=globals()) / numRuns
print('Edlib: ', edlibTime)
print(edlib.align(query, target))
editdistanceTime = timeit.timeit(stmt="editdistance.eval(query, target)",
number=numRuns, globals=globals()) / numRuns
print('editdistance: ', editdistanceTime)
levenshteinTime = timeit.timeit(stmt="Levenshtein.distance(query, target)",
number=numRuns, globals=globals()) / numRuns
print('levenshtein: ', levenshteinTime)
print('edlib is %f times faster than editdistance.' % (editdistanceTime / edlibTime))
print('edlib is %f times faster than Levenshtein.' % (levenshteinTime / edlibTime))
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