def extract(self, x, y):
if x is None or y is None:
return 0
if self.similarity:
return 1 - float(hamming_distance(unicode(x), unicode(y))) / max(len(x), len(y))
else:
return hamming_distance(unicode(x), unicode(y))
def build_read_names_given_seq(target,
read_names_by_seq_fpath,
allowed_read_names_set,
is_interesting_seq,
max_ham,
verbose=True):
interesting_reads = defaultdict(set)
for i, line in enumerate(open(read_names_by_seq_fpath)):
if verbose and i % 10000 == 0:
sys.stdout.write('.')
sys.stdout.flush()
words = line.strip().split()
seq = words[0]
if is_interesting_seq(seq):
read_names = set(words[1:]) & allowed_read_names_set
interesting_reads[seq].update(read_names)
last_start = len(seq) - len(target)
if last_start < 0:
continue
min_ham_idx = min(
range(0, last_start + 1),
key=lambda i: hamming_distance(unicode(
target), unicode(seq[i:i + len(target)])))
min_ham = hamming_distance(
unicode(target),
unicode(seq[min_ham_idx:min_ham_idx + len(target)]))
if min_ham <= max_ham:
min_ham_seq = seq[min_ham_idx:min_ham_idx + len(target)]
interesting_reads[min_ham_seq].update(read_names)
return interesting_reads
def distance_filter(df,
c,
thresh=3,
suffix1='_x',
suffix2='_y',
col1=None,
col2=None,
nonull=None):
if df.shape[0] == 0:
df = pd.DataFrame()
else:
if (col1 is not None) and (col2 is not None):
c1 = col1 + suffix1
c2 = col2 + suffix2
else:
c1 = c + suffix1
c2 = c + suffix2
if nonull is not None:
df['distance'] = df.apply(
lambda x: jf.hamming_distance(x[c1], x[c2]), axis=1)
else:
df['distance'] = df.apply(
lambda x: 10
if (pd.isnull(x[c1])
| pd.isnull(x[c2])) else jf.hamming_distance(x[c1], x[c2]),
axis=1)
df = df[df.distance <= thresh]
return df
def output_calculation(id_1, id_2, id_1_column1_name, id_2_column1_name,
id_1_column2_name, id_2_column2_name):
return [
str(id_1),
str(id_2),
str(jellyfish.hamming_distance(
id_1_column1_name,
id_2_column1_name)), ##################Change Algorithm here!!!
str(jellyfish.hamming_distance(id_1_column2_name, id_2_column2_name))
]
def write_distance2csv(column1_name, column2_name):
process = 0
process_sub = 0
total = 100
# distance_info = []
with open(path_1) as f1, open(column1_name + column2_name +
'_similarity.csv',
'w+',
newline='') as csv_file:
headers = [
'id_1', 'id_2', column1_name + '_similarity',
column2_name + '_similarity'
]
writer = csv.writer(csv_file)
writer.writerow(headers)
reader1 = csv.DictReader(f1)
for id_1 in reader1:
if process >= 100:
break
# logger.info(id_1)
process += 1
with open(path_2) as f2:
reader2 = csv.DictReader(f2)
process_sub = 0
for id_2 in reader2:
# logger.info(id_1)
process_sub += 1
logger.info('processing: ' + str(process) + '/' +
str(total) + '__' + str(process_sub) + '/' +
str(total))
logger.info(id_1['EnterpriseID'])
logger.info(len(id_2['EnterpriseID']))
if len(id_2['EnterpriseID']) == 0:
break
elif id_1['EnterpriseID'] >= id_2['EnterpriseID']:
continue
else:
try:
writer.writerow([
str(id_1['EnterpriseID']),
str(id_2['EnterpriseID']),
str(
jellyfish.hamming_distance(
id_1[column1_name],
id_2[column1_name])),
str(
jellyfish.hamming_distance(
id_1[column2_name],
id_2[column2_name]))
])
except Exception as e:
logger.info(Exception, ': ', e)
def get_closest_hamming(needle, haystack):
closest = None
for x in haystack:
if (closest == None):
closest = (x, jellyfish.hamming_distance(needle, x))
else:
temp = (x, jellyfish.hamming_distance(needle, x))
if (temp[1] < closest[1]):
closest = temp
if (closest == None):
return None
return closest[0]
def get_closest_hamming(needle,haystack): closest = None; for x in haystack: if(closest == None): closest = (x,jellyfish.hamming_distance(needle,x)); else: temp = (x,jellyfish.hamming_distance(needle,x)); if(temp[1] < closest[1]): closest = temp; if(closest == None): return None; return closest[0];
def compare_two_texts(self, string_a, string_b, normalize_value=True):
"""
Compare two string and return the value of Hamming algorithm
the value is normalized between 0 and 1 values.
"""
if ((isinstance(string_a, unicode) and isinstance(string_b, unicode)) or
(isinstance(string_a, str) and isinstance(string_b, str))):
if normalize_value:
return self.__normalized_value(jellyfish.hamming_distance(string_a, string_b))
else:
return jellyfish.hamming_distance(string_a, string_b)
else:
raise TypeError
def write_distance2csv(column1_name, column2_name):
process = 0
process_sub = 0
total = 100
# distance_info = []
with open(path_1) as f1, open(column1_name + column2_name + '_similarity.csv', 'w+', newline='') as csv_file:
headers = ['id_1', 'id_2', column1_name + '_similarity', column2_name + '_similarity']
writer = csv.writer(csv_file)
writer.writerow(headers)
reader1 = csv.DictReader(f1)
for id_1 in reader1:
if process >= 100:
break
# logger.info(id_1)
process += 1
with open(path_2) as f2:
reader2 = csv.DictReader(f2)
process_sub = 0
for id_2 in reader2:
# logger.info(id_1)
process_sub += 1
logger.info('processing: ' + str(process) + '/' + str(total) + '__' + str(process_sub) + '/' + str(total))
logger.info(id_1['EnterpriseID'])
logger.info(len(id_2['EnterpriseID']))
if len(id_2['EnterpriseID']) == 0:
break
elif id_1['EnterpriseID'] >= id_2['EnterpriseID']:
continue
else:
try:
writer.writerow([str(id_1['EnterpriseID']), str(id_2['EnterpriseID']),
str(jellyfish.hamming_distance(id_1[column1_name], id_2[column1_name])), str(jellyfish.hamming_distance(id_1[column2_name], id_2[column2_name]))])
except Exception as e:
logger.info(Exception, ': ', e)
def get_similar_v_genes():
"""Returns a dictionary of V genes 90% similar to a given V gene.
Parameters
----------
None
Returns
-------
v_to_include : dict
Dictionary where the keys are V genes and the items are V genes
90% similar to key.
"""
v_ref = make_v_ref_dict()
v_ref_genes = list(v_ref.keys())
v_ham_mat = np.zeros(shape=(len(v_ref), len(v_ref)))
for idx1, v1 in enumerate(v_ref):
for idx2, v2 in enumerate(v_ref):
seq1 = v_ref[v1]
seq2 = v_ref[v2]
min_len = np.min([len(seq1), len(seq2)])
# Go backwards from where the CDR3 begins.
v_ham_mat[idx1, idx2] = hamming_distance(seq1[-min_len:], seq2[-min_len:]) / min_len
v_to_include = {}
for idx1, v1 in enumerate(v_ref_genes):
v_to_include[v1] = []
for idx2, v2 in enumerate(v_ref_genes):
if v_ham_mat[idx1, idx2] <= 0.1:
v_to_include[v1].append(v2)
return v_to_include
def simple_example():
# String comparison.
str1, str2 = u'jellyfish', u'smellyfish'
print("jellyfish.levenshtein_distance({}, {}) = {}.".format(
str1, str2, jellyfish.levenshtein_distance(str1, str2)))
print("jellyfish.damerau_levenshtein_distance({}, {}) = {}.".format(
str1, str2, jellyfish.damerau_levenshtein_distance(str1, str2)))
print("jellyfish.hamming_distance({}, {}) = {}.".format(
str1, str2, jellyfish.hamming_distance(str1, str2)))
print("jellyfish.jaro_distance({}, {}) = {}.".format(
str1, str2, jellyfish.jaro_distance(str1, str2)))
print("jellyfish.jaro_similarity({}, {}) = {}.".format(
str1, str2, jellyfish.jaro_similarity(str1, str2)))
print("jellyfish.jaro_winkler({}, {}) = {}.".format(
str1, str2, jellyfish.jaro_winkler(str1, str2)))
print("jellyfish.jaro_winkler_similarity({}, {}) = {}.".format(
str1, str2, jellyfish.jaro_winkler_similarity(str1, str2)))
print("jellyfish.match_rating_comparison({}, {}) = {}.".format(
str1, str2, jellyfish.match_rating_comparison(str1, str2)))
#--------------------
# Phonetic encoding.
ss = u'Jellyfish'
print("jellyfish.metaphone({}) = {}.".format(ss, jellyfish.metaphone(ss)))
print("jellyfish.soundex({}) = {}.".format(ss, jellyfish.soundex(ss)))
print("jellyfish.nysiis({}) = {}.".format(ss, jellyfish.nysiis(ss)))
print("jellyfish.match_rating_codex({}) = {}.".format(
ss, jellyfish.match_rating_codex(ss)))
def select_fitness(population, target, population_size):
fitness_size = population_size * 2
res_list = fitness_size * [None]
for index, a_str in enumerate(population):
comp_dist = hamming_distance(a_str, target)
res_list[index] = (index, a_str, comp_dist) # index, string, fitness
return res_list
def alldist(filex, filey):
xread = open(filex, 'r').read()
yread = open(filey, 'r').read()
lvd = jellyfish.levenshtein_distance(xread,yread)
dlvd= jellyfish.damerau_levenshtein_distance(xread,yread)
spsum = spamsum.match(xread,yread)
spsum = 100 - spsum
spsum = float(spsum/100.00)
# print lvd
res = float( lvd / 100.00 )
dres= float(dlvd / 100.00 )
# print res
# print "Levenshtein Distance=",res
jaro = jellyfish.jaro_distance(xread,yread)
## Added jaro-winkler distance by fahim 20111011
jarowink = jellyfish.jaro_winkler(xread,yread)
jaro = 1.0 - jaro
jarowink = 1.0 - jarowink
# print "Jaro Distance = ",jaro
ham = jellyfish.hamming_distance(xread,yread)
ham = float ( ham / 100.00)
print "Hamming Distance = ", ham
# print "KL-divergence between d1 and d2:", kldiv(tokenize(d1), tokenize(d2))
# print "KL-divergence between d2 and d1:", kldiv(tokenize(d2), tokenize(d1))
# print "Spamsum Match score: ", spsum
kl = kldiv(tokenize(xread), tokenize(yread))
return res, dres , jaro, jarowink, ham, kl, spsum
def correct_barcode_map(config_params, barcodes_in_data, barcode_to_gene):
barcode_tolerance = int(config_params['barcode_tolerance'])
orig_barcodes = set(barcode_to_gene.keys())
full_map = {x:x for x in orig_barcodes}
correcting_map = {}
unmatched_barcodes = set(barcodes_in_data).difference(orig_barcodes)
for orig_barcode in barcode_to_gene.keys():
for unmatched_barcode in unmatched_barcodes:
barcode_dist = jf.hamming_distance(orig_barcode, unmatched_barcode)
if barcode_dist <= barcode_tolerance:
if get_verbosity(config_params) >= 3:
print 'bad : corrected --> {0} : {1}'.format(unmatched_barcode, orig_barcode)
if correcting_map.has_key(unmatched_barcode):
correcting_map[unmatched_barcode].append(orig_barcode)
else:
correcting_map[unmatched_barcode] = [orig_barcode]
# Now, filter out any unmatched barcodes that map to multiple original barcodes
for key in correcting_map.keys():
if len(correcting_map[key]) > 1:
correcting_map[key].pop()
# The corrected barcodes are still lists - turn them back into strings!
corrected_barcodes = correcting_map.keys()
for barcode in corrected_barcodes:
correcting_map[barcode] = correcting_map[barcode][0]
# Update the mapping of original barcodes to themselves with the mapping of
# unmatched barcodes to original barcodes
full_map.update(correcting_map)
return full_map
def ham_dist_vectorform(strings: list) -> np.array:
"""Constructs the Hamming distance vector-form for a VJL grouping used for clustering.
This function takes in a set of strings, the observed CDR3s in a VJL grouping,
and computes the upper triangle of the Hamming (normalized by length) square matrix.
This vector-form is what is used as input for the single-linkage clustering
algorithm given by scipy.
Parameters
----------
strings : list of strings
Returns
-------
dists : np.array
Vector-form of normalized Hamming distances with np.float16 precision.
"""
normalization = len(strings[0])
num_seqs = len(strings)
num_entries = int((num_seqs**2 - num_seqs) / 2)
dists = np.zeros(num_entries,dtype=np.float16)
index = 0
for i,s1 in enumerate(strings):
for s2 in strings[i + 1:]:
dists[index] = hamming_distance(s1,s2)
index += 1
return dists / normalization
def inverse_hamming_dist(row):
return 1.0 / (
jellyfish.hamming_distance(
UnicodeDammit(str(row["question1"])).markup.lower(),
UnicodeDammit(str(row["question2"])).markup.lower())
or
1.0
)
def get_max_ham_dists(min_len, max_len):
dists = defaultdict(list)
for _ in xrange(50000):
ref_seq = rand_seq(max_len)
new_seq = rand_seq(max_len)
for i in range(min_len, max_len+1):
dists[i].append(hamming_distance(unicode(ref_seq[:i]), unicode(new_seq[:i])))
max_ham_dists = [min(np.percentile(dists[i], 0.1), int(i/4)) for i in range(min_len, max_len+1)]
return max_ham_dists
def barcode_hamming(observed,barcodes):
"""Compute entropy of probabilistic barcode assignment.
observed -- SeqRecord of the barcode
barcodes -- list of barcode possibilities (python strings)
"""
obs_seq = observed.seq.tostring()
distances = [(barcode,hamming_distance(obs_seq,barcode)) for barcode in barcodes]
closest = min(distances,key=lambda p: p[1])
return closest # tuple of (barcode, distance)
def barcode_hamming(observed,barcodes):
"""Compute entropy of probabilistic barcode assignment.
observed -- SeqRecord of the barcode
barcodes -- list of barcode possibilities (python strings)
"""
obs_seq = observed.seq.tostring()
distances = [(barcode,hamming_distance(obs_seq,barcode)) for barcode in barcodes]
closest = min(distances,key=lambda p: p[1])
return closest # tuple of (barcode, distance)
def hamming_pred(string, dictionary):
from jellyfish import hamming_distance
distances = []
for item in dictionary:
distances.append(hamming_distance(string, item))
min_distance = min(distances)
min_index = distances.index(min_distance)
return dictionary[min_index], min_distance
def measure_mrn_similarity(ssn1, ssn2, sign):
if ssn1 == "" or ssn2 == "" or ssn1 is None or ssn2 is None:
return 0
r1 = jellyfish.jaro_winkler(ssn1, ssn2)
r2 = 1 - jellyfish.hamming_distance(ssn1, ssn2) / len(ssn1)
if sign == "t":
print("jw-{} vs hd-{}".format(r1, r2))
elif sign == "w":
return max(r1, r2)
def hamming(self):
self.cluster = []
already = []
for i in range(0,len(self.group)):
if self.group[j] in already:
continue
for j in range(i+1, len(self.group)):
if self.radius >= jf.hamming_distance(str(self.group[i]),str(self.group[j])):
self.cluster.append([self.group[i],self.group[j]])
already = flatten(self.cluster)
return com_check(self.cluster)
def test_hamming_distance(self):
cases = [("", "", 0),
("", "abc", 3),
("abc", "abc", 0),
("acc", "abc", 1),
("abcd", "abc", 1),
("abc", "abcd", 1),
("testing", "this is a test", 13),
]
for (s1, s2, value) in cases:
self.assertEqual(jellyfish.hamming_distance(s1, s2), value)
def measure_distance(word1, word2, distance_type):
if distance_type == 'lv':
distance = Levenshtein.eval(word1, word2)
if distance_type == 'dlv':
distance = jellyfish.damerau_levenshtein_distance(word1, word2)
if distance_type == 'jw':
# Jaro–Winkler indicates the similiraty, we take the inverse
distance = -jellyfish.jaro_winkler_similarity(word1, word2)
if distance_type == 'j':
distance = -jellyfish.jaro_similarity(word1, word2)
if distance_type == 'hm':
distance = jellyfish.hamming_distance(word1, word2)
return distance
def getSimilarity(str1, str2):
distance = {}
if distance_metric1 == "JaroWinkler":
distance[distance_metric1] = jellyfish.jaro_winkler(str1, str2)
if distance_metric2 == "Jaro":
distance[distance_metric2] = jellyfish.jaro_distance(str1, str2)
if distance_metric3 == "MatchRating":
distance[distance_metric3] = jellyfish.match_rating_comparison(
str1, str2)
if distance_metric4 == "Levenshtein":
distance[distance_metric4] = jellyfish.levenshtein_distance(str1, str2)
if distance_metric5 == "Hamming":
distance[distance_metric5] = jellyfish.hamming_distance(str1, str2)
return distance
def remove_duplications(self):
print("\nRemoving duplications...")
duplications = []
for ix in range(len(self.data)):
for yx in range(ix, len(self.data)):
if ix != yx:
if (jf.hamming_distance(
str(self.data.summary[ix])[0:200],
str(self.data.summary[yx])[0:200]) <= 20):
duplications.append(yx)
duplications = list(set(duplications))
self.data = self.data[~self.data.index.isin(duplications)].reset_index(
drop=True)
def read_fastq(config_params, species_config_params, folder, out_path, lane_id):
common_primer_start = int(species_config_params['common_primer_start'])
common_primer_end = common_primer_start + int(species_config_params['common_primer_length'])
common_primer_seq = species_config_params['common_primer_sequence']
common_primer_tolerance = int(config_params['common_primer_tolerance'])
index_tag_start = int(species_config_params['index_tag_start'])
index_tag_end = index_tag_start + int(species_config_params['index_tag_length'])
barcode_start = int(species_config_params['genetic_barcode_start'])
barcode_end = barcode_start + int(species_config_params['genetic_barcode_length'])
out_filename = get_barseq_filename(config_params, lane_id)
# print out_filename
# print common_primer_tolerance
of = open(out_filename, 'wt')
fastq_filenames = [os.path.join(folder, x) for x in os.listdir(folder) if is_fastq_filename(x)]
common_primer_count = 0
barcodes = set()
index_tags = set()
for filename in fastq_filenames:
f = cfo.get_compressed_file_handle(filename)
for line_count, line in enumerate(f):
if get_verbosity(config_params) >= 3:
print line_count, line
if line_count % 4 == 1:
string = line.strip()
common_primer = string[common_primer_start:common_primer_end]
common_primer_dist = jf.hamming_distance(common_primer, common_primer_seq)
if get_verbosity(config_params) >= 3:
print common_primer_dist
if common_primer_dist <= common_primer_tolerance:
common_primer_count += 1
index_tag = string[index_tag_start:index_tag_end]
barcode = string[barcode_start:barcode_end]
index_tags.update(set([index_tag]))
barcodes.update(set([barcode]))
# print "index_tag, barcode : {}, {}".format(index_tag, barcode)
of.write('{0}\t{1}\n'.format(index_tag, barcode))
f.close()
total_counts = (line_count + 1)/ 4
of.close()
return total_counts, common_primer_count, barcodes, index_tags
def comparacion_pares(self, texto1, texto2, tipo="levenshtein", norm=None):
"""
Permite hacer comparaciones entre dos textos de entrada, de acuerdo a \
un tipo de distancia o similitud determinado.
:param texto1: Primer texto de interés a comparar.
:type texto1: str
:param texto2: Segundo texto de interés a comparar.
:type texto2: str
:param tipo: Criterio de comparación a utilizar entre los textos. \
Valor por defecto `'levenshtein'`.
:type tipo: {'damerau_levenshtein', 'levenshtein', 'hamming', \
'jaro_winkler', 'jaro'}, opcional
:param norm: Permite normalizar los resultados en función de la \
longitud de los textos. Si `norm = 1` se normaliza en función al \
texto más corto, si `norm = 2` se normaliza en función al texto \
de mayor extensión.
:type norm: {1,2}, opcional
:return: (float) Valor resultado de la comparación entre `texto1` y \
`texto2`.
"""
tipo = tipo.lower()
if "damerau" in tipo:
salida = jellyfish.damerau_levenshtein_distance(texto1, texto2)
elif "levenshtein" in tipo:
salida = jellyfish.levenshtein_distance(texto1, texto2)
elif "hamming" in tipo:
salida = jellyfish.hamming_distance(texto1, texto2)
elif "winkler" in tipo:
salida = jellyfish.jaro_winkler_similarity(texto1, texto2)
elif "jaro" in tipo:
salida = jellyfish.jaro_similarity(texto1, texto2)
else:
print(
(
"Por favor seleccione un criterio válido "
"para comparar los strings."
)
)
return None
if norm in [1, 2] and "jaro" not in tipo:
if norm == 1:
salida /= min(len(texto1), len(texto2))
else:
salida /= max(len(texto1), len(texto2))
return salida
def rmBadSeqs(fname, canonIndex):
# Remove the sequences that have only one coding
# posibility and are more than edit distance 1
# away from all canonical sequences for this target
# that meet the entropy cutoff.
# Get all sequences meeting the entropy threshold
fin = open(fname, 'r')
goodSeqs = [i.strip().split('\t')[0] for i in fin.readlines()\
if eval(i.strip().split('\t')[3]) != 1]
fin.close()
# Convert to canonical set of sequences
canonSeqs = []
for seq in goodSeqs:
canonSeq = ''
for i in canonIndex:
canonSeq += seq[i]
canonSeqs.append(canonSeq)
canonSeqs = set(canonSeqs)
#print canonSeqs
# Write all the good sequences from fname into a temp
# file and then replace fname with the tmp file
fin = open(fname, 'r')
fout = open('tmp.txt', 'w')
for line in fin:
sp_line = line.strip().split('\t')
numPoss = eval(sp_line[3])
if numPoss > 1:
fout.write(line)
else:
seq = sp_line[0]
seq2 = ''
for i in canonIndex:
seq2 += seq[i]
for canonSeq in canonSeqs:
if jellyfish.hamming_distance(seq2, canonSeq) <= 1:
#print "Here"
fout.write(line)
break
fout.close()
normalizeFreq('tmp.txt', 1)
os.system('mv tmp.txt ' + fname)
def calc_distance(string1, string2, method):
if method == "levenshtein":
distance=jellyfish.levenshtein_distance(string1, string2)
elif method == "damerau_levenshtein":
distance= damerau_levenshtein_distance(string1, string2)
elif method == "hamming_distance":
distance= jellyfish.hamming_distance(string1, string2)
elif method == "jaro_winkler":
distance= jellyfish.jaro_winkler(string1, string2)
elif method == "cosine":
vector1 = text_to_vector(string1)
vector2 = text_to_vector(string2)
distance = get_cosine(vector1, vector2)
elif method=="jaccard":
x_set=ngrams(string1, 1)
y_set=ngrams(string2, 1)
distance = jaccard_similarity(x_set, y_set)
return distance
def cal_str_similarity(str_1, str_2, option):
multiset_1 = str_1.split()
multiset_2 = str_2.split()
# Jaccard similarity
if option == 'JACC':
return 1.0 - dist.jaccard(multiset_1, multiset_2)
# Cosine similarity
if option == 'COS':
comm_len = len([word for word in multiset_1 if word in multiset_2])
return comm_len * 1.0 / math.sqrt(len(multiset_1) * len(multiset_2))
# Dice similarity
elif option == 'DICE':
comm_len = len([word for word in multiset_1 if word in multiset_2])
return comm_len * 2.0 / (len(multiset_1) + len(multiset_2))
# Edit similarity
elif option == 'ES':
return 1.0 - jf.levenshtein_distance(str_1, str_2) * 1.0 / max(
len(str_1), len(str_2))
# Hamming similarity
elif option == 'HAMMING':
return 1.0 - jf.hamming_distance(str_1, str_2) * 1.0 / max(
len(str_1), len(str_2))
# Jaro distance
elif option == 'JARO':
return jf.jaro_distance(str_1, str_2)
# Jaro-Winkler distance
elif option == 'JARO-WINKLER':
return jf.jaro_winkler(str_1, str_2)
# Overlap similarity
elif option == 'OVERLAP':
comm_len = len([word for word in multiset_1 if word in multiset_2])
return comm_len * 1.0 / max(len(multiset_1), len(multiset_2))
# entity string start with mention string
elif option == 'START-WITH':
return str_2.startswith(str_1)
elif option == 'END-WITH':
return str_2.endswith(str_1)
elif option == 'SAME':
return str_1 == str_2
def string_comparison(self, text1, text2, choice='levenshtein_distance'):
'''
text1: String Input 1
text2: String Input 2
choice: 'levenshtein_distance' or 'damerau_levenshtein_distance' or 'hamming_distance' or 'jaro_distance' or 'jaro_winkler' or 'match_rating_comparison'
'''
# https://jellyfish.readthedocs.io/en/latest/comparison.html
if choice == 'levenshtein_distance':
return jellyfish.levenshtein_distance(text1, text2)
elif choice == 'damerau_levenshtein_distance':
return jellyfish.damerau_levenshtein_distance(text1, text2)
elif choice == 'hamming_distance':
return jellyfish.hamming_distance(text1, text2)
elif choice == 'jaro_distance':
return jellyfish.jaro_distance(text1, text2)
elif choice == 'jaro_winkler':
return jellyfish.jaro_winkler(text1, text2)
elif choice == 'match_rating_comparison':
return jellyfish.match_rating_comparison(text1, text2)
else:
print("Wrong Choice")
def classify_seq(rec1, rec2, min_len, max_len, max_ham_dists, log_p_struct):
bases = set('ACGT')
# Store as strings
seq1 = str(rec1.seq)
seq2_rc = str(rec2.seq.reverse_complement())
loc_max_len = min(max_len, len(seq1), len(seq2_rc))
# Find aligning sequence, indels are not allowed, starts of reads included
sig_lens = [i for i, max_ham in zip(range(min_len, loc_max_len + 1), max_ham_dists)
if hamming_distance(unicode(seq1[:i]), unicode(seq2_rc[-i:])) < max_ham]
if len(sig_lens) != 1:
return None
seq2_len = sig_lens[0]
seq2_match = seq2_rc[-seq2_len:]
seq1_match = seq1[:seq2_len]
# Get corresponding quality scores
quals1 = rec1.letter_annotations['phred_quality'][:seq2_len]
quals2 = rec2.letter_annotations['phred_quality'][::-1][-seq2_len:]
# Build consensus sequence
ML_bases = []
for r1, q1, r2, q2 in zip(seq1_match, quals1, seq2_match, quals2):
if r1 in bases and r1 == r2:
ML_bases.append(r1)
elif set([r1, r2]) <= bases and q1 > 2 and q2 > 2:
r1_score = log_p_struct[r1][r1][q1] + log_p_struct[r1][r2][q2]
r2_score = log_p_struct[r2][r1][q1] + log_p_struct[r2][r2][q2]
if r1_score > r2_score:
ML_bases.append(r1)
else:
ML_bases.append(r2)
elif r1 in bases and q1 > 2:
ML_bases.append(r1)
elif r2 in bases and q2 > 2:
ML_bases.append(r2)
else:
return None
return ''.join(ML_bases)
def processMatchesHammingDistance():
inputQueue = deque(inputList)
row = inputQueue.pop()
while row is not None:
bestMatchScore = -1
bestMatchRow = ''
for rowToCompare in inputQueue:
score = jellyfish.hamming_distance(row, rowToCompare)
if bestMatchScore == -1 or score < bestMatchScore:
bestMatchScore = score
bestMatchRow = rowToCompare
bestMatchPerColumn[row] = {
'match': bestMatchRow,
'score': bestMatchScore
}
if len(inputQueue) > 1:
row = inputQueue.pop()
else:
return
def orderByRel(jobs, kw, algo):
"""Order based on algo type"""
for i in range(len(jobs)):
if algo == 1:
jobs[i][5] = levenshtein_distance(jobs[i][0].strip(), kw)
elif algo == 2:
jobs[i][5] = damerau_levenshtein_distance(jobs[i][0].strip(), kw)
elif algo == 3:
jobs[i][5] = hamming_distance(jobs[i][0].strip(), kw)
elif algo == 4:
jobs[i][5] = 1 - jaro_distance(jobs[i][0].strip(), kw)
elif algo == 5:
jobs[i][5] = 1 - jaro_winkler(jobs[i][0].strip(), kw)
# jobs.sort(jobs, key=lambda job: job[5])
jobs_sorted = sorted(jobs, key=lambda dist: dist[5])
return jobs_sorted
def cluster_singlecell_aa(singlecell_annotation, lineages, thresh):
"""Performs single-linkage clustering (using aa CDR3) on a single cell given 90% V gene similarity.
Parameters
----------
singlecell_annotation : dict
Dictionary containing information about the annotated single cell sequence.
lineages : dict
Unnested dictionary of clustered annotations [(V, J, L, cluster_id)].
threshold : float
Distance threshold for the single-linkage clustering algorithm.
Returns
-------
to_insert : np.array
np.array of keys of lineages into which single cells clustered successfully.
"""
v_gene = singlecell_annotation['v_gene']['gene']
cdr3 = singlecell_annotation['junc_nt']
cdr3_aa = translate(cdr3)
len_cdr3 = len(cdr3)
len_cdr3_aa = len(cdr3_aa)
subset = [key for key in lineages
if key[2] == str(len_cdr3)
and key[0] in v_to_include[v_gene]]
min_distances = np.ones(len(subset))
for idx, vjlc in enumerate(subset):
distances = np.zeros(len(lineages[vjlc]), dtype=np.float16)
for idxa, annotation in enumerate(lineages[vjlc]):
distances[idxa] = hamming_distance(cdr3_aa, translate(annotation['junc_nt']))
distances /= len_cdr3_aa
min_distances[idx] = (np.min(distances))
to_insert = np.array(subset)[min_distances <= thresh]
return to_insert
def comparacion_pares(self, texto1, texto2, tipo='levenshtein', norm=None):
""" Permite hacer comparaciones entre dos textos de entrada, de acuerdo a un tipo de \
distancia o similitud determinado.
:param texto1: (str) Primer texto de interés a comparar.
:param texto2: (str) Segundo texto de interés a comparar.
:param tipo: (str) {'damerau_levenshtein', 'levenshtein', 'hamming', 'jaro_winkler', \
'jaro'} Valor por defecto: 'levenshtein'. Criterio de comparación a utilizar entre los textos.
:param norm: (int) {1, 2} Valor por defecto: None. Permite normalizar \
los resultados en función de la longitud de los textos. \
Si norm=1 se normaliza en función al texto más corto, \
si norm=2 se normaliza en función al texto de mayor extensión.
:return: (float o int) Valor resultado de la comparación.
"""
tipo = tipo.lower()
if 'damerau' in tipo:
salida = jellyfish.damerau_levenshtein_distance(texto1, texto2)
elif 'levenshtein' in tipo:
salida = jellyfish.levenshtein_distance(texto1, texto2)
elif 'hamming' in tipo:
salida = jellyfish.hamming_distance(texto1, texto2)
elif 'winkler' in tipo:
salida = jellyfish.jaro_winkler_similarity(texto1, texto2)
elif 'jaro' in tipo:
salida = jellyfish.jaro_similarity(texto1, texto2)
else:
print(
'Por favor seleccione un criterio válido para comparar los strings.'
)
return None
if norm in [1, 2] and 'jaro' not in tipo:
if norm == 1:
salida /= min(len(texto1), len(texto2))
else:
salida /= max(len(texto1), len(texto2))
return salida
def process_dewlap_dorsal_ventral():
args = get_args()
filenames = getFilenames(args.input_dir)
base_dir_name = os.path.split(args.input_dir)[-1]
# SETUP DATASET
col_headers = []
data_set = []
header_list = []
# Sort files by tissue allowing for missspellings
organized_by_tissue = {'dewlap':[], 'dorsal':[], 'ventral':[]}
for count, filename in enumerate(filenames):
fname = os.path.split(filename)[-1]
for tissue in organized_by_tissue.keys():
distances = sort([jellyfish.hamming_distance(tissue, item) for item in fname.split("_")])
if distances[0] <= 2:
organized_by_tissue[tissue].append(filename)
print organized_by_tissue
def comparison(filename_txt, json_data, debug):
#-----txt---------------------------------------
with open(filename_txt + '.txt', 'r') as myfile:
data_txt = myfile.read().upper().replace("\n", " ")
if (debug):
print(data_txt)
data_txt = ''.join(re.findall("[A-Z0-9]", data_txt))
if (debug):
print(data_txt)
date_txt = find_date(filename_txt + '.txt')
if debug:
print('text date', date_txt)
allout = []
if debug:
print('json_data', json_data)
#-----json--------------------------------------
for text in json_data:
#state # can create a dictionary: state - > state code
date_json = json_data[3] #'DateOfRegistry'
if text == "USA":
text = "UNITEDSTATESOFAMERICA"
#makemodel
if text == json_data[5]:
list_model = [] # list of words descriping the model
# temporal str
textmodel = ""
for place in range(len(text)):
if text[place] != " ":
textmodel += text[place]
else:
list_model.append(textmodel)
textmodel = ""
list_model.append(textmodel)
if debug:
print('list_model1', list_model)
for word in list_model:
if word[0] == "(" and word[-1] == ")":
list_model.remove(word)
temp_word = word[1:-1]
list_model.append(temp_word)
if debug:
print('list_model2', list_model)
#Remove all spaces, commas and other non numerical or ascii characters from the data and the search word
newtext = ''.join(re.findall("[A-Z0-9]", text.upper()))
if (debug):
print('newtext(upper,npspace)', newtext)
arr = []
#Create a window of the size of the seach word and slide it over the text file
#Calculate the levenshtein distance between the window and the search text
for i in range(0, len(data_txt) - len(newtext)):
window = data_txt[i:i + len(newtext)]
d = jellyfish.levenshtein_distance(newtext, window)
d2 = jellyfish.hamming_distance(newtext, window)
d3 = jellyfish.jaro_winkler(newtext, window)
arr.append([window, d, d2, d3])
#Search which window has the smallest distance
p = pd.DataFrame(arr,
columns=["word", "lev", "hamming", "jarowinkler"])
m = p["jarowinkler"].idxmax()
#Output that window and the match is the proportion of the matched window
out = [p.iloc[m, 0], 100 * p.iloc[m, 3]]
if (debug):
print(p)
print(p.xiloc[m])
#date of registry
if text == json_data[3]:
if date_json in date_txt:
out = [date_json, 100]
#yearofbuilt
if text == json_data[-1]:
if out[1] < 100:
out[1] = 0.0
out.insert(0, newtext)
allout.append(out)
#Calculate an overall score with the average of the provided text
out = [allout, pd.DataFrame(allout).loc[:, 2].mean()]
return out
def word_similarity(
word_to_compare='Vignir',
list_of_words=["Heigigr", "Beðurni"],
return_top_n=20,
use_cut_off=False,
cut_off=0.5,
sim_measure='Levenshtein', #SequenceMatcher #Jaro-Winkler #Hamming,
min_characters=2, #Null for no restriction,
filter_non_capital_letters=True):
"""Compare similarity between a word and a list of words
Returns list of similar words/names based on a similarity measure
Args:
word_to_compare (str) -word to compare with each value in list
list_of_words (lst) - list of strings to compare against
return_top_n (int) - return only top n 10 results based on similarity measure
use_cut_off (bool) - whether to use a cut off value based on similarity
cut_off (int) - cut off value
Returns:
Returns two ints; average epoc_loss and epoch_accuracy
"""
word_similarity_list = []
for word in list_of_words:
dict_Words = {}
dict_Words['word_to_compare'] = word_to_compare
dict_Words['word_to_compare_against'] = word
if sim_measure == 'Levenshtein':
##dict_Words['similarity']=Levenshtein.ratio(word_to_compare, word)
dict_Words['similarity'] = jellyfish.levenshtein_distance(
word_to_compare, word) * -1
dict_Words['similarity_measure'] = 'Levenshtein'
elif sim_measure == 'SequenceMatcher':
dict_Words['similarity'] = SequenceMatcher(None, word_to_compare,
word).ratio()
dict_Words['similarity_measure'] = 'SequenceMatcher'
#https://docs.python.org/2.4/lib/sequencematcher-examples.html
elif sim_measure == 'Jaro-Winkler':
dict_Words['similarity'] = jellyfish.jaro_winkler(
word_to_compare, word)
dict_Words['similarity_measure'] = 'Jaro-Winkler'
elif sim_measure == 'Hamming':
dict_Words['similarity'] = jellyfish.hamming_distance(
word_to_compare, word) * -1
dict_Words['similarity_measure'] = 'Hamming'
word_similarity_list.append(dict_Words)
#Convert to frame
df_word_similarity = pd.DataFrame(word_similarity_list)
#Sort
df_word_similarity = df_word_similarity.sort_values(by='similarity',
ascending=False)
#Return top results
if return_top_n > 0:
if len(df_word_similarity) > return_top_n:
df_word_similarity = df_word_similarity[0:return_top_n]
else:
return df_word_similarity[0:0]
#Whether to use cutoff
if use_cut_off:
df_word_similarity = df_word_similarity[
df_word_similarity.similarity > cut_off]
#Filter min characters
if min_characters > 0:
df_word_similarity = df_word_similarity[
df_word_similarity.word_to_compare_against.str.len() >
min_characters]
#Filter out words that does not start with a large character
if filter_non_capital_letters:
df_word_similarity = df_word_similarity[
df_word_similarity.word_to_compare_against.str.istitle()]
return df_word_similarity
def output_calculation(id_1, id_2, id_1_column1_name, id_2_column1_name, id_1_column2_name, id_2_column2_name):
return [str(id_1), str(id_2),
str(jellyfish.hamming_distance(id_1_column1_name, id_2_column1_name)), ##################Change Algorithm here!!!
str(jellyfish.hamming_distance(id_1_column2_name, id_2_column2_name))]
def percent_id(seq1,seq2):
alignment = global_align(seq1,seq2)
return (1. - hamming_distance(alignment[0],alignment[1]) / float(len(alignment[0]))) * 100.
ng=ngram.NGram(pad_len=1,N=2)
a = set(ng.ngrams(ng.pad(a)))
b = set(ng.ngrams(ng.pad(b)))
overlap = len(a & b)
return overlap * 2.0/(len(a) + len(b))
def jaccard(a, b):
ng=ngram.NGram(pad_len=1,N=2)
a = set(ng.ngrams(ng.pad(a)))
b = set(ng.ngrams(ng.pad(b)))
return len(a & b) * 1.0 / len(a | b)
similarity = {
"jaro_winckler": lambda a,b: jellyfish.jaro_winkler(a, b),
"hamming_distance": lambda a,b: 1.0 - float(jellyfish.hamming_distance(a, b)) / max(len(a), len(b)),
"damreau_levenshtein":lambda a,b: 1.0 - float(jellyfish.damerau_levenshtein_distance(a, b)) / max(len(a), len(b)),
"dice":dice,
"jaccard":jaccard
}[args.similarity]
mean = {
"arithmetic_mean":arithmeticMean,
"arithemtic_weighted_mean":arithmeticWeightedMean,
"geometric_mean":geometricMean,
"geometric_weighted_mean": geometricWeightedMean
}[args.mean]
def open_tsv(filename):
return csv.reader(open(filename, "r"), delimiter='\t')
match_tl = match_tl.search(word);
if(len(match_tl)!=0):
m5 = m5 + match_tl[0][1];
m1_tmp = 0;
m2_tmp = 0;
m3_tmp = 0;
word = unicode(word,'utf8');
for txt in product_description:
txt = unicode(txt,'utf8');
a = jf.levenshtein_distance(word,txt);
if(a>=m1_tmp):
m1_tmp = a;
a = jf.damerau_levenshtein_distance(word,txt);
if(a>=m2_tmp):
m2_tmp = a;
a = jf.hamming_distance(word,txt);
if(a>=m3_tmp):
m3_tmp = a;
m1 = m1 + m1_tmp;
m2 = m2 + m2_tmp;
m3 = m3 + m3_tmp;
m6_tmp = 0;
m7_tmp = 0;
m8_tmp = 0;
#word = word.decode('utf-8');
for txt in product_title:
txt = safe_unicode(txt);
a = jf.levenshtein_distance(word,txt);
if(a>=m6_tmp):
m6_tmp = a;
a = jf.damerau_levenshtein_distance(word,txt);
