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import unicodedata
from collections import defaultdict
from itertools import zip_longest
import warnings
def _normalize(s):
return unicodedata.normalize("NFKD", s)
def _check_type(s):
# warn here since each function will call this
warnings.warn(
"The jellyfish._jellyfish module is deprecated and will be removed in jellyfish 1.0.",
DeprecationWarning,
)
if not isinstance(s, str):
raise TypeError("expected str or unicode, got %s" % type(s).__name__)
def levenshtein_distance(s1, s2):
_check_type(s1)
_check_type(s2)
if s1 == s2:
return 0
rows = len(s1) + 1
cols = len(s2) + 1
if not s1:
return cols - 1
if not s2:
return rows - 1
prev = None
cur = range(cols)
for r in range(1, rows):
prev, cur = cur, [r] + [0] * (cols - 1)
for c in range(1, cols):
deletion = prev[c] + 1
insertion = cur[c - 1] + 1
edit = prev[c - 1] + (0 if s1[r - 1] == s2[c - 1] else 1)
cur[c] = min(edit, deletion, insertion)
return cur[-1]
def _jaro_winkler(s1, s2, long_tolerance, winklerize):
_check_type(s1)
_check_type(s2)
s1_len = len(s1)
s2_len = len(s2)
if not s1_len or not s2_len:
return 0.0
min_len = min(s1_len, s2_len)
search_range = max(s1_len, s2_len)
search_range = (search_range // 2) - 1
if search_range < 0:
search_range = 0
s1_flags = [False] * s1_len
s2_flags = [False] * s2_len
# looking only within search range, count & flag matched pairs
common_chars = 0
for i, s1_ch in enumerate(s1):
low = max(0, i - search_range)
hi = min(i + search_range, s2_len - 1)
for j in range(low, hi + 1):
if not s2_flags[j] and s2[j] == s1_ch:
s1_flags[i] = s2_flags[j] = True
common_chars += 1
break
# short circuit if no characters match
if not common_chars:
return 0.0
# count transpositions
k = trans_count = 0
for i, s1_f in enumerate(s1_flags):
if s1_f:
for j in range(k, s2_len):
if s2_flags[j]:
k = j + 1
break
if s1[i] != s2[j]:
trans_count += 1
trans_count //= 2
# adjust for similarities in nonmatched characters
common_chars = float(common_chars)
weight = (
(
common_chars / s1_len
+ common_chars / s2_len
+ (common_chars - trans_count) / common_chars
)
) / 3
# winkler modification: continue to boost if strings are similar
if winklerize and weight > 0.7:
# adjust for up to first 4 chars in common
j = min(min_len, 4)
i = 0
while i < j and s1[i] == s2[i]:
i += 1
if i:
weight += i * 0.1 * (1.0 - weight)
# optionally adjust for long strings
# after agreeing beginning chars, at least two or more must agree and
# agreed characters must be > half of remaining characters
if (
long_tolerance
and min_len > 4
and common_chars > i + 1
and 2 * common_chars >= min_len + i
):
weight += (1.0 - weight) * (
float(common_chars - i - 1) / float(s1_len + s2_len - i * 2 + 2)
)
return weight
def jaro_similarity(s1, s2):
return _jaro_winkler(s1, s2, False, False) # noqa
def jaro_winkler_similarity(s1, s2, long_tolerance=False):
return _jaro_winkler(s1, s2, long_tolerance, True) # noqa
def damerau_levenshtein_distance(s1, s2):
_check_type(s1)
_check_type(s2)
len1 = len(s1)
len2 = len(s2)
infinite = len1 + len2
# character array
da = defaultdict(int)
# distance matrix
score = [[0] * (len2 + 2) for x in range(len1 + 2)]
score[0][0] = infinite
for i in range(0, len1 + 1):
score[i + 1][0] = infinite
score[i + 1][1] = i
for i in range(0, len2 + 1):
score[0][i + 1] = infinite
score[1][i + 1] = i
for i in range(1, len1 + 1):
db = 0
for j in range(1, len2 + 1):
i1 = da[s2[j - 1]]
j1 = db
cost = 1
if s1[i - 1] == s2[j - 1]:
cost = 0
db = j
score[i + 1][j + 1] = min(
score[i][j] + cost,
score[i + 1][j] + 1,
score[i][j + 1] + 1,
score[i1][j1] + (i - i1 - 1) + 1 + (j - j1 - 1),
)
da[s1[i - 1]] = i
return score[len1 + 1][len2 + 1]
def soundex(s):
_check_type(s)
if not s:
return ""
s = _normalize(s)
s = s.upper()
replacements = (
("BFPV", "1"),
("CGJKQSXZ", "2"),
("DT", "3"),
("L", "4"),
("MN", "5"),
("R", "6"),
)
result = [s[0]]
count = 1
# find would-be replacement for first character
for lset, sub in replacements:
if s[0] in lset:
last = sub
break
else:
last = None
for letter in s[1:]:
for lset, sub in replacements:
if letter in lset:
if sub != last:
result.append(sub)
count += 1
last = sub
break
else:
if letter != "H" and letter != "W":
# leave last alone if middle letter is H or W
last = None
if count == 4:
break
result += "0" * (4 - count)
return "".join(result)
def hamming_distance(s1, s2):
_check_type(s1)
_check_type(s2)
# ensure length of s1 >= s2
if len(s2) > len(s1):
s1, s2 = s2, s1
# distance is difference in length + differing chars
distance = len(s1) - len(s2)
for i, c in enumerate(s2):
if c != s1[i]:
distance += 1
return distance
def nysiis(s):
_check_type(s)
if not s:
return ""
s = s.upper()
key = []
# step 1 - prefixes
if s.startswith("MAC"):
s = "MCC" + s[3:]
elif s.startswith("KN"):
s = s[1:]
elif s.startswith("K"):
s = "C" + s[1:]
elif s.startswith(("PH", "PF")):
s = "FF" + s[2:]
elif s.startswith("SCH"):
s = "SSS" + s[3:]
# step 2 - suffixes
if s.endswith(("IE", "EE")):
s = s[:-2] + "Y"
elif s.endswith(("DT", "RT", "RD", "NT", "ND")):
s = s[:-2] + "D"
# step 3 - first character of key comes from name
key.append(s[0])
# step 4 - translate remaining chars
i = 1
len_s = len(s)
while i < len_s:
ch = s[i]
if ch == "E" and i + 1 < len_s and s[i + 1] == "V":
ch = "AF"
i += 1
elif ch in "AEIOU":
ch = "A"
elif ch == "Q":
ch = "G"
elif ch == "Z":
ch = "S"
elif ch == "M":
ch = "N"
elif ch == "K":
if i + 1 < len(s) and s[i + 1] == "N":
ch = "N"
else:
ch = "C"
elif ch == "S" and s[i + 1 : i + 3] == "CH":
ch = "SS"
i += 2
elif ch == "P" and i + 1 < len(s) and s[i + 1] == "H":
ch = "F"
i += 1
elif ch == "H" and (
s[i - 1] not in "AEIOU"
or (i + 1 < len(s) and s[i + 1] not in "AEIOU")
or (i + 1 == len(s))
):
if s[i - 1] in "AEIOU":
ch = "A"
else:
ch = s[i - 1]
elif ch == "W" and s[i - 1] in "AEIOU":
ch = s[i - 1]
if ch[-1] != key[-1][-1]:
key.append(ch)
i += 1
key = "".join(key)
# step 5 - remove trailing S
if key.endswith("S") and key != "S":
key = key[:-1]
# step 6 - replace AY w/ Y
if key.endswith("AY"):
key = key[:-2] + "Y"
# step 7 - remove trailing A
if key.endswith("A") and key != "A":
key = key[:-1]
# step 8 was already done
return key
def match_rating_codex(s):
_check_type(s)
# we ignore spaces
s = s.upper().replace(" ", "")
# any remaining non-alphabetic characters are invalid
if not s.isalpha():
raise ValueError("string must be alphabetic")
codex = []
prev = None
first = True
for c in s:
# starting character
# or consonant not preceded by same consonant
if first or (c not in "AEIOU" and c != prev):
codex.append(c)
prev = c
first = False
# just use first/last 3
if len(codex) > 6:
return "".join(codex[:3] + codex[-3:])
else:
return "".join(codex)
def match_rating_comparison(s1, s2):
codex1 = match_rating_codex(s1)
codex2 = match_rating_codex(s2)
len1 = len(codex1)
len2 = len(codex2)
res1 = []
res2 = []
# length differs by 3 or more, no result
if abs(len1 - len2) >= 3:
return None
# get minimum rating based on sums of codexes
lensum = len1 + len2
if lensum <= 4:
min_rating = 5
elif lensum <= 7:
min_rating = 4
elif lensum <= 11:
min_rating = 3
else:
min_rating = 2
# strip off common prefixes
for c1, c2 in zip_longest(codex1, codex2):
if c1 != c2:
if c1:
res1.append(c1)
if c2:
res2.append(c2)
unmatched_count1 = unmatched_count2 = 0
for c1, c2 in zip_longest(reversed(res1), reversed(res2)):
if c1 != c2:
if c1:
unmatched_count1 += 1
if c2:
unmatched_count2 += 1
return (6 - max(unmatched_count1, unmatched_count2)) >= min_rating
def metaphone(s):
_check_type(s)
result = []
s = _normalize(s.lower())
# skip first character if s starts with these
if s.startswith(("kn", "gn", "pn", "wr", "ae")):
s = s[1:]
i = 0
while i < len(s):
c = s[i]
next = s[i + 1] if i < len(s) - 1 else "*****"
nextnext = s[i + 2] if i < len(s) - 2 else "*****"
# skip doubles except for cc
if c == next and c != "c":
i += 1
continue
if c in "aeiou":
if i == 0 or s[i - 1] == " ":
result.append(c)
elif c == "b":
if (not (i != 0 and s[i - 1] == "m")) or next:
result.append("b")
elif c == "c":
if next == "i" and nextnext == "a" or next == "h":
result.append("x")
i += 1
elif next in "iey":
result.append("s")
i += 1
else:
result.append("k")
elif c == "d":
if next == "g" and nextnext in "iey":
result.append("j")
i += 2
else:
result.append("t")
elif c in "fjlmnr":
result.append(c)
elif c == "g":
if next in "iey":
result.append("j")
elif next == "h" and nextnext and nextnext not in "aeiou":
i += 1
elif next == "n" and not nextnext:
i += 1
else:
result.append("k")
elif c == "h":
if i == 0 or next in "aeiou" or s[i - 1] not in "aeiou":
result.append("h")
elif c == "k":
if i == 0 or s[i - 1] != "c":
result.append("k")
elif c == "p":
if next == "h":
result.append("f")
i += 1
else:
result.append("p")
elif c == "q":
result.append("k")
elif c == "s":
if next == "h":
result.append("x")
i += 1
elif next == "i" and nextnext in "oa":
result.append("x")
i += 2
else:
result.append("s")
elif c == "t":
if next == "i" and nextnext in "oa":
result.append("x")
elif next == "h":
result.append("0")
i += 1
elif next != "c" or nextnext != "h":
result.append("t")
elif c == "v":
result.append("f")
elif c == "w":
if i == 0 and next == "h":
i += 1
result.append("w")
elif next in "aeiou":
result.append("w")
elif c == "x":
if i == 0:
if next == "h" or (next == "i" and nextnext in "oa"):
result.append("x")
else:
result.append("s")
else:
result.append("k")
result.append("s")
elif c == "y":
if next in "aeiou":
result.append("y")
elif c == "z":
result.append("s")
elif c == " ":
if len(result) > 0 and result[-1] != " ":
result.append(" ")
i += 1
return "".join(result).upper()
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