def main():
args = get_args()
# path to the text file of the vocabulary and create a dictionary
vocabulary_path = args['vocabulary']
vocabulary = create_dictionary(vocabulary_path)
# decide which distance to use
if args['distance'] == 'levenshtein':
# Levenshtein distance
from levenshtein import levenshtein_distance
levenshtein_distance(get_misspelling(), 'lexique.json')
elif args['distance'] == 'levenshtein2':
# Levenshtein distance using another library
from levenshtein_v2 import levenshtein_distance2
levenshtein_distance2(get_misspelling(), vocabulary)
elif args['distance'] == 'hamming':
# Hamming distance
from hamming import hamming_distance
hamming_distance(get_misspelling(), vocabulary)
elif args['distance'] == 'jarowinkler':
# Jaro-Winkler
from jarowinkler import jarowinkler_distance
jarowinkler_distance(get_misspelling(), vocabulary)
else:
raise Exception("Unknown distance function : {}".format(
args['distance']))
def test_repulsion(self):
p1 = [1, 2, 3, 4, 5, 6, 7, 8]
p2 = [1, 5, 2, 8, 7, 4, 3, 6]
start_distance = hamming_distance(p1, p2)
res = repulsion(p1, p2)
self.assertEqual(p1, [1, 2, 3, 4, 5, 6, 7, 8])
self.assertEqual(p2, [1, 5, 2, 8, 7, 4, 3, 6])
end_distance = hamming_distance(p1, res)
self.assertTrue(end_distance >= start_distance)
def motifEnumerate(dna,k,d):
seq_first=dna[0]
patterns=[]
l=len(seq_first)
for i in xrange(l-k+1):
patterns.append(seq_first[i:i+k])
patterns=list(set(patterns))#unique patterns in the first string in DNA
d_neighbor=[]
for pattern in patterns:
d_neighbor.extend(neighbors(pattern,d))
d_neighbor=list(set(d_neighbor))#collection of unique d_neighbor for All kmer pattern from first string
#######Checking if d_neighbor pattern has a match in REST of dna collection of sequences##########
motif=[]
for patt in d_neighbor:
s=0
for seq in dna[1:]:
c=0
for i in xrange(l-k+1):
if hamming_distance(patt,seq[i:i+k])<=d:
c=1
s=s+1
break
else:
pass
if c==0:#that is patt never exists as a d-neighbor of patterns of an encountered seq! then NO NEED TO CHECK FOR SUBSEQUENT Sequences!
break
elif s==len(dna)-1:
motif.append(patt)
motif=list(set(motif))
return motif
def test_hamming_dist_timing(self):
list_of_strings = [string_generator() for i in range(10)]
print(list_of_strings)
string_result = []
string_start = time.process_time_ns()
for s1 in list_of_strings:
for s2 in list_of_strings:
string_result.append(naive_hamming_distance(s1, s2))
string_end = time.process_time_ns()
print(f'no of mismatches = {string_result}')
print("string hamming: {:,}".format(string_end - string_start))
binary_result = []
binary_start = time.process_time_ns()
for s1 in list_of_strings:
for s2 in list_of_strings:
binary_result.append(hamming_distance(s1, s2))
binary_end = time.process_time_ns()
print("binary hamming: {:,}".format(binary_end - binary_start))
list_of_preprocessed_binaries = []
for s in list_of_strings:
list_of_preprocessed_binaries.append(string_to_hamming_binary(s))
binary_result_pre = []
binary_start_pre = time.process_time_ns()
for s1 in list_of_preprocessed_binaries:
for s2 in list_of_preprocessed_binaries:
binary_result_pre.append(binary_hamming_dist_calc(s1, s2))
binary_end_pre = time.process_time_ns()
print("prepro hamming: {:,}".format(binary_end_pre - binary_start_pre))
self.assertEqual(string_result, binary_result)
self.assertEqual(binary_result_pre, binary_result)
def test_hamming_dist_all_different(self):
s1 = 'CAT'
s2 = 'GGG'
expected_dist = 3
actual_dist = naive_hamming_distance(s1, s2)
self.assertEqual(expected_dist, actual_dist)
actual_dist = hamming_distance(s1, s2)
self.assertEqual(expected_dist, actual_dist)
def test_hamming_dist_same(self):
s1 = 'CAT'
s2 = 'CAT'
expected_dist = 0
actual_dist = naive_hamming_distance(s1, s2)
self.assertEqual(expected_dist, actual_dist)
actual_dist = hamming_distance(s1, s2)
self.assertEqual(expected_dist, actual_dist)
def test_haystack_generator(self):
string_length = 6
expected_dist = 3
haystack = string_generator(string_length)
needle = string_mutator(haystack, expected_dist)
actual_dist = naive_hamming_distance(needle, haystack)
self.assertEqual(expected_dist, actual_dist)
actual_dist = hamming_distance(needle, haystack)
self.assertEqual(expected_dist, actual_dist)
def neighbors(pattern, d):
if d == 0:
return pattern
elif len(pattern) == 1:
return ['A', 'C', 'G', 'T']
neighborhood = [] #should contain all d neighborhood k mer patterns
suffixNeighbors = neighbors(suffix(pattern), d)
for pat in suffixNeighbors:
if hamming_distance(suffix(pattern), pat) < d:
for nuc in ['A', 'C', 'G', 'T']:
neighborhood.append(nuc + pat)
else:
neighborhood.append(first_symbol(pattern) + pat)
return neighborhood
def aprxPattern(text,pattern,d=0):
'''text-Nucleotide sequence\
pattern-pattern to be matched
d-default value 0 other wise user entered hamming distance value\
The function returns locations of the aproximate matches of the pattern in the text provided '''
l=len(text)
k=len(pattern)
pos=[]
for i in xrange(l-k+1):
kmer=text[i:i+k]
if hamming_distance(pattern,kmer)<=d:
pos.append(i)
else:
pass
return len(pos)
def imagediff(method, file_name1, file_name2):
if method == "file size":
try:
size1 = os.path.getsize(file_name1)
except os.error:
print >> sys.stderr, "ERROR: Unable to access ", file_name1
sys.exit(-1)
try:
size2 = os.path.getsize(file_name2)
except os.error:
print >> sys.stderr, "ERROR: Unable to access ", file_name2
return float(abs(size1 - size2)) / max(size1, size2)
else:
try:
file1 = open(file_name1, "r")
string1 = file1.read()
except IOError:
print >> sys.stderr, "ERROR: Unable to open ", file_name1
finally:
file1.close()
try:
file2 = open(file_name2, "r")
string2 = file2.read()
except IOError:
print >> sys.stderr, "ERROR: Unable to open ", file_name2
finally:
file2.close()
if method == "levenshtein":
try:
return float(levenshtein_distance(string1, string2)) / max(len(string1), len(string2))
except ZeroDivisionError:
return 1
elif method == "hamming":
try:
return float(hamming_distance(string1, string2)) / min(len(string1), len(string2))
except ZeroDivisionError:
return 1
else:
print >> sys.stderr, "ERROR: Invalid method."
sys.exit(-1)
def d(pattern, dna):
'''function takes in pattern and collection of strings dna and computes thelowest distance between the pattern and collection '''
k = len(pattern)
#distance=k#maximum possible hamming distance between pattern and region of same length
total_hd = 0
for region in dna:
distance = k + 1
#print region
for i in xrange(
len(region) - k + 1
): #for every region we compute the hamm distance between pattern and substrings of region and selct the substring with least distance
hd = hamming.hamming_distance(pattern, region[i:i + k])
if distance > hd:
distance = hd #we only keep the lowest hamming distance
#print distance
total_hd += distance #sum of the least distances are computed for ALL REGIONS in dna for the PATTERN provided
#print 'total_hd',total_hd,pattern
return total_hd
def h_dis(pattern, seq):
k = len(pattern)
kmer_distance = dict() #kmer and HD
for i in xrange(len(seq) - k + 1):
kmer = seq[i:i + k]
kmer_distance[kmer] = hamming_distance(
pattern,
kmer) #for all kmers in seq compute HD with pattern provided
kmer_dist = kmer_distance.items()
kmer_dist = sorted(kmer_dist, key=lambda x: x[1])
smallest_hd = kmer_dist[0][1]
kmer_dist_leastHD = [kmer_dist[0]]
for k, v in kmer_dist[1:]:
if v == smallest_hd:
kmer_dist_leastHD.append((k, v))
else:
break
return kmer_dist_leastHD # return type is a tuple of form (KMER,leastHD)
def binary_inc_proccessing_time():
SETUP_CODE = '''
from hamming import hamming_distance
from random import choice
from __main__ import string_generator
list_of_strings1 = [string_generator() for i in range(10)]
list_of_strings2 = [string_generator() for i in range(10)]'''
TEST_CODE = '''
s1 = choice(list_of_strings1)
s2 = choice(list_of_strings2)
hamming_distance(s1, s2)
'''
# timeit.repeat statement
times = timeit.repeat(setup=SETUP_CODE,
stmt=TEST_CODE,
repeat=3,
number=10000)
# printing minimum exec. time
print('Binary hamming string search time (including preprocessing): {}'.
format(min(times)))
def print_possible_key_sizes(encoded_content):
assert len(encoded_content) >= MAX_KEY_LENGTH * 4
size2score = {}
for key_size in range(MIN_KEY_LENGTH, MAX_KEY_LENGTH):
# let's try to average as many samples as we can
dist = 0
for i in range(int(len(encoded_content) / (2 * key_size))):
dist += hamming.hamming_distance(
encoded_content[i * 2 * key_size:(i * 2 + 1) * key_size],
encoded_content[(i * 2 + 1) * key_size:(i * 2 + 2) * key_size])
size2score[key_size] = dist / float(
int(len(encoded_content) / (2 * key_size)) * key_size)
# dist1 = hamming.hamming_distance(encoded_content[:key_size],
# encoded_content[key_size: 2*key_size])
# dist2 = hamming.hamming_distance(encoded_content[2*key_size: 3*key_size],
# encoded_content[3*key_size: 4*key_size])
# # average and normalize by dividing by key length
# size2score[key_size] = (dist1 + dist2) / float((2 * key_size))
for key_size, score in sorted(size2score.items(), key=lambda x: x[1]):
print("%d: %.02f" % (key_size, score))
cur_ctext = line
print("Key = %s" % hex(cur_key))
print("Score = %f" % largest)
print("Ciphertext = %s" % cur_ctext)
print("Plaintext = %s" % cur_ptext)
#Challenge 5
print("\n\nChallenge 5: Repeating Key XOR Encryption")
ptext = "Burning 'em, if you ain't quick and nimble\nI go crazy when I hear a cymbal"
key = "ICE"
print("Encrypting %s with key %s" % (ptext, key))
print(xor_encrypt(ptext, key))
#Challenge 6
print("\n\nChallenge 6: Break Repeating Key XOR Encrpytion")
distance = hamming_distance("this is a test", "wokka wokka!!!")
if distance != 37:
print("Distance = %d" % distance)
raise ValueError
ciphertext = base64.b64decode(open("./6.txt", "r").read())
smallest_distance = 1000
cur_keysize = 0
for keysize in range(2, 41):
block1 = ciphertext[0:keysize]
block2 = ciphertext[keysize:keysize * 2]
block3 = ciphertext[keysize * 2:keysize * 3]
block4 = ciphertext[keysize * 3:keysize * 4]
