def createJsonRepresentation(app):
unit = getUnit(app)
root = {}
allWits = []
rdgs = [el for el in app.childNodes if el.nodeType == 1]
for rdg in rdgs:
appLevel = {}
appLevel['id'] = rdg.getAttribute('wit')
tokenList = []
ws = rdg.getElementsByTagName('w')
for ind, w in enumerate(ws):
if not 3 in [child.nodeType for child in w.childNodes]:
continue
currentWord = w
if ind == 0:
previousWord = ''
else:
previousWord = ws[ind-1]
token = {}
token['t'] = currentWord.toxml()[8 + len(w.getAttribute('n')):-4]
token['n'] = Preprocessing.conflate(currentWord)
token['u'] = unit
tokenList.append(token)
appLevel['tokens'] = tokenList
allWits.append(appLevel)
root['witnesses'] = allWits
return json.loads(json.dumps(root))
docLevel['id'] = afile
tokenList = []
if debug:
html.write('<h2>' + afile + '</h2><table border = "1"><th>Original<th>Conflated</th>')
ws = minidom.parse(os.path.join(path, afile)).getElementsByTagName('w')
words = []
for w in range(len(ws)):
if not 3 in [child.nodeType for child in ws[w].childNodes]: #checking presence of text nodes inside the w
continue
currentWord = ws[w]
previousWord = ''
try:
previousWord = ws[w-1]
except IndexError:
pass
token = {}
token['t'] = currentWord.toxml()[8+len(currentWord.getAttribute('n')):-4]
c = Preprocessing.conflate(currentWord)
if c == Preprocessing.conflate(previousWord):
c += '1' # tag '1' to the end of a wod that we suspect is repeated in the manuscript.
token['n'] = c
token['u'] = unit
words.append(c)
tokenList.append(token)
docLevel['tokens'] = tokenList
alldocs.append(docLevel)
root['witnesses'] = alldocs
with open(os.path.join(path, jsonFileName), 'w') as Json:
Json.write(json.dumps(root, ensure_ascii=False).encode('utf-8'))
print 'Took', datetime.datetime.now()-startTimeX2J, 'to execute XMLsToJSON.py'
root = {}
alldocs = []
rdgs = [el for el in minidom.parse(os.path.join(path, afile)).getElementsByTagName('*') if el.localName in ['lem', 'rdg']]
for rdg in rdgs:
docLevel = {}
docLevel['id'] = rdg.getAttribute('wit')
tokenList = []
ws = rdg.getElementsByTagName('w')
words = []
for w in range(len(ws)):
if not 3 in [child.nodeType for child in ws[w].childNodes]: #checking presence of text nodes inside the w
continue
currentWord = ws[w]
previousWord = ''
try:
previousWord = ws[w-1]
except IndexError:
pass
token = {}
token['t'] = currentWord.toxml()[8 + len(ws[w].getAttribute('n')):-4]
c = Preprocessing.conflate(currentWord)
token['n'] = c
token['u'] = unit
words.append(c)
tokenList.append(token)
docLevel['tokens'] = tokenList
alldocs.append(docLevel)
root['witnesses'] = alldocs
with open(os.path.join(path, afile[:-3] + 'json'), 'w') as Json:
Json.write(json.dumps(root, ensure_ascii=False).encode('utf-8'))
