def _is_change_significant(self, original_word, input_word):
# still using normalization on the original text. With enough modification of the original text,
# this won't be necessary
original_word_normalised = textprocess.normalize(original_word)
input_word_normalised = textprocess.normalize(input_word)
differ = difflib.Differ()
diffs = list(differ.compare([original_word_normalised], [input_word_normalised]))
for i, diff in enumerate(diffs):
if diff.startswith('? '):
for j, c in enumerate(diff[2:]):
if c == '+' or c == '?' or (c == '-' and unicodedata.category(diffs[i-1][j]) != 'Mn'):
return True
return False
def _is_change_significant(self, original_word, input_word):
# still using normalization on the original text. With enough modification of the original text,
# this won't be necessary
original_word_normalised = textprocess.normalize(original_word)
input_word_normalised = textprocess.normalize(input_word)
differ = difflib.Differ()
diffs = list(
differ.compare([original_word_normalised],
[input_word_normalised]))
for i, diff in enumerate(diffs):
if diff.startswith('? '):
for j, c in enumerate(diff[2:]):
if c == '+' or c == '?' or (
c == '-'
and unicodedata.category(diffs[i - 1][j]) != 'Mn'):
return True
return False
def test_normalize_function():
import sqlite3
import qurantextdiff.helpers.textprocess as textprocess
connection = sqlite3.connect('db.sqlite3')
cursor = connection.cursor()
cursor.execute('SELECT * FROM quran_diacritic')
diacritic_verses = []
surah_no = []
verse_no = []
for row in cursor.fetchall():
surah_no.append(row[1])
verse_no.append(row[2])
diacritic_verses.append(row[3])
cursor.execute('SELECT * FROM quran_non_diacritic')
non_diacritic_verses = [row[3] for row in cursor.fetchall()]
diacritic_verses_normalized = textprocess.normalize(diacritic_verses)
mismatches = []
mismatch_chars = []
for s_no, v_no, diacNormal, diac in zip(surah_no, verse_no,
diacritic_verses_normalized,
diacritic_verses):
try:
assert diacNormal == diac
except AssertionError:
for c1, c2 in zip(diacNormal, diac):
if c1 != c2:
if c1 not in mismatch_chars:
mismatch_chars.append(c1)
if c2 not in mismatch_chars:
mismatch_chars.append(c2)
mismatches.append((s_no, v_no, diacNormal, diac))
file = open('mismatches.txt', 'w', encoding='utf-8')
for s, v, d, n in mismatches:
print('{}:{}\n{}\n{}\n'.format(s, v, d, n), file=file)
import unicodedata as ud
print(len(mismatch_chars))
print('\n'.join(mismatch_chars))
for mc in mismatch_chars:
print(ud.name(mc))
def test_normalize_function():
import sqlite3
import qurantextdiff.helpers.textprocess as textprocess
connection = sqlite3.connect("db.sqlite3")
cursor = connection.cursor()
cursor.execute("SELECT * FROM quran_diacritic")
diacritic_verses = []
surah_no = []
verse_no = []
for row in cursor.fetchall():
surah_no.append(row[1])
verse_no.append(row[2])
diacritic_verses.append(row[3])
cursor.execute("SELECT * FROM quran_non_diacritic")
non_diacritic_verses = [row[3] for row in cursor.fetchall()]
diacritic_verses_normalized = textprocess.normalize(diacritic_verses)
mismatches = []
mismatch_chars = []
for s_no, v_no, diacNormal, diac in zip(surah_no, verse_no, diacritic_verses_normalized, diacritic_verses):
try:
assert diacNormal == diac
except AssertionError:
for c1, c2 in zip(diacNormal, diac):
if c1 != c2:
if c1 not in mismatch_chars:
mismatch_chars.append(c1)
if c2 not in mismatch_chars:
mismatch_chars.append(c2)
mismatches.append((s_no, v_no, diacNormal, diac))
file = open("mismatches.txt", "w", encoding="utf-8")
for s, v, d, n in mismatches:
print("{}:{}\n{}\n{}\n".format(s, v, d, n), file=file)
import unicodedata as ud
print(len(mismatch_chars))
print("\n".join(mismatch_chars))
for mc in mismatch_chars:
print(ud.name(mc))
def _create_tooltip(self, s1, s2):
# still using normalisation on the original text. With enough modification of the original text,
# this won't be necessary
# inp_word in already normalised
tooltip = []
inserts, deletes, replaces, displacements = [], [], [], []
sm = difflib.SequenceMatcher(None, textprocess.normalize(s1), s2)
for tag, i1, i2, j1, j2 in sm.get_opcodes():
if tag == 'insert':
inserts.append([s2[j1:j2], i1, False])
elif tag == 'delete':
deletes.append([s1[i1:i2], i1, False])
elif tag == 'replace':
replaces.append([s1[i1:i2], s2[j1:j2], i1])
for d in deletes:
for i in inserts:
if d[0] == i[0] and d[2] is False and i[2] is False:
displacements.append((d[0], d[1], i[1]))
d[2] = True
i[2] = True
break
for chrs, f, t in displacements:
tooltip.append('Displaced {} from {} to {}'.format(
chrs, f + 1, t + 1))
for d in deletes:
if d[2] is False:
tooltip.append('Deleted {} at {}'.format(d[0], d[1]))
for i in inserts:
if i[2] is False:
tooltip.append('Inserted {} at {}'.format(i[0], i[1]))
for r in replaces:
tooltip.append('Replaced {} with {} at {}'.format(
r[0], r[1], r[2]))
return '\n'.join(tooltip)
def _create_tooltip(self, s1, s2):
# still using normalisation on the original text. With enough modification of the original text,
# this won't be necessary
# inp_word in already normalised
tooltip = []
inserts, deletes, replaces, displacements = [], [], [], []
sm = difflib.SequenceMatcher(None, textprocess.normalize(s1), s2)
for tag, i1, i2, j1, j2 in sm.get_opcodes():
if tag == 'insert':
inserts.append([s2[j1:j2], i1, False])
elif tag == 'delete':
deletes.append([s1[i1:i2], i1, False])
elif tag == 'replace':
replaces.append([s1[i1:i2], s2[j1:j2], i1])
for d in deletes:
for i in inserts:
if d[0] == i[0] and d[2] is False and i[2] is False:
displacements.append((d[0], d[1], i[1]))
d[2] = True
i[2] = True
break
for chrs, f, t in displacements:
tooltip.append('Displaced {} from {} to {}'.format(chrs, f + 1, t + 1))
for d in deletes:
if d[2] is False:
tooltip.append('Deleted {} at {}'.format(d[0], d[1]))
for i in inserts:
if i[2] is False:
tooltip.append('Inserted {} at {}'.format(i[0], i[1]))
for r in replaces:
tooltip.append('Replaced {} with {} at {}'.format(r[0], r[1], r[2]))
return '\n'.join(tooltip)
def __init__(self, original_lines, input_lines):
assert len(original_lines) == len(input_lines)
self.original_lines = original_lines
self.input_lines = input_lines
self.input_lines_normalized = textprocess.normalize(input_lines)
def __init__(self, original_lines, input_lines):
assert len(original_lines) == len(input_lines)
self.original_lines = original_lines
self.input_lines = input_lines
self.input_lines_normalized = textprocess.normalize(input_lines)