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🎨 dinglehopper: Reformat using black

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Gerber, Mike 2020-11-10 12:29:55 +01:00
parent 31c63f9e4c
commit 14421c8e53
25 changed files with 774 additions and 466 deletions
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25
qurator/dinglehopper/edit_distance.py
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@ -48,9 +48,10 @@ def _levenshtein_matrix(seq1: Tuple, seq2: Tuple):
for i in tqdm(from_to(1, m), disable=not Config.progress):
for j in from_to(1, n):
D[i, j] = min(
D[i - 1, j - 1] + 1 * (seq1[i - 1] != seq2[j - 1]), # Same or Substitution
D[i - 1, j - 1]
+ 1 * (seq1[i - 1] != seq2[j - 1]), # Same or Substitution
D[i, j - 1] + 1, # Insertion
D[i - 1, j] + 1 # Deletion
D[i - 1, j] + 1, # Deletion
)
return D
@ -81,8 +82,8 @@ def distance(s1: str, s2: str):
Note that this is different from levenshtein() as this function knows about Unicode normalization and grapheme
clusters. This should be the correct way to compare two Unicode strings.
"""
seq1 = list(grapheme_clusters(unicodedata.normalize('NFC', s1)))
seq2 = list(grapheme_clusters(unicodedata.normalize('NFC', s2)))
seq1 = list(grapheme_clusters(unicodedata.normalize("NFC", s1)))
seq2 = list(grapheme_clusters(unicodedata.normalize("NFC", s2)))
return levenshtein(seq1, seq2)
@ -106,11 +107,17 @@ def seq_editops(seq1, seq2):
def _tail_backtrace(i, j, accumulator):
if i > 0 and D[i - 1, j] + 1 == D[i, j]:
return partial(_tail_backtrace, i - 1, j, [('delete', i-1, j)] + accumulator)
return partial(
_tail_backtrace, i - 1, j, [("delete", i - 1, j)] + accumulator
)
if j > 0 and D[i, j - 1] + 1 == D[i, j]:
return partial(_tail_backtrace, i, j - 1, [('insert', i, j-1)] + accumulator)
return partial(
_tail_backtrace, i, j - 1, [("insert", i, j - 1)] + accumulator
)
if i > 0 and j > 0 and D[i - 1, j - 1] + 1 == D[i, j]:
return partial(_tail_backtrace, i - 1, j - 1, [('replace', i-1, j-1)] + accumulator)
return partial(
_tail_backtrace, i - 1, j - 1, [("replace", i - 1, j - 1)] + accumulator
)
if i > 0 and j > 0 and D[i - 1, j - 1] == D[i, j]:
return partial(_tail_backtrace, i - 1, j - 1, accumulator) # NOP
return accumulator
@ -132,6 +139,6 @@ def editops(word1, word2):
Note that this returns indices to the _grapheme clusters_, not characters!
"""
word1 = list(grapheme_clusters(unicodedata.normalize('NFC', word1)))
word2 = list(grapheme_clusters(unicodedata.normalize('NFC', word2)))
word1 = list(grapheme_clusters(unicodedata.normalize("NFC", word1)))
word2 = list(grapheme_clusters(unicodedata.normalize("NFC", word2)))
return seq_editops(word1, word2)