The source data for the conversion are the LowFat XML trees files representing the macula-greek version of the Nestle 1904 Greek New Testment. The most recent source data can be found on github https://github.com/Clear-Bible/macula-greek/tree/main/Nestle1904/lowfat. Attribution: "MACULA Greek Linguistic Datasets, available at https://github.com/Clear-Bible/macula-greek/".
The production of the Text-Fabric files consist of two steps. First the creation of piclke files (part 1). Secondly the actual Text-Fabric creation process (part 2). Both steps are independent allowing to start from Part 2 by using the pickle files as input.
Be advised that this Text-Fabric version is a test version (proof of concept) and requires further finetuning, especialy with regards of nomenclature and presentation of (sub)phrases and clauses.
This script harvests all information from the LowFat tree data (XML nodes), puts it into a Panda DataFrame and stores the result per book in a pickle file. Note: pickling (in Python) is serialising an object into a disk file (or buffer).
In the context of this script, 'Leaf' refers to those node containing the Greek word as data, which happen to be the nodes without any child (hence the analogy with the leaves on the tree). These 'leafs' can also be refered to as 'terminal nodes'. Futher, Parent1 is the leaf's parent, Parent2 is Parent1's parent, etc.
For a full description of the source data see document MACULA Greek Treebank for the Nestle 1904 Greek New Testament.pdf
import pandas as pd
import sys
import os
import time
import pickle
import re #regular expressions
from os import listdir
from os.path import isfile, join
import xml.etree.ElementTree as ET
Change BaseDir, XmlDir and PklDir to match location of the datalocation and the OS used.
BaseDir = 'C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\'
XmlDir = BaseDir+'xml\\'
PklDir = BaseDir+'pkl\\'
XlsxDir = BaseDir+'xlsx\\'
# note: create output directory prior running this part
# key: filename, [0]=book_long, [1]=book_num, [3]=book_short
bo2book = {'01-matthew': ['Matthew', '1', 'Matt'],
'02-mark': ['Mark', '2', 'Mark'],
'03-luke': ['Luke', '3', 'Luke'],
'04-john': ['John', '4', 'John'],
'05-acts': ['Acts', '5', 'Acts'],
'06-romans': ['Romans', '6', 'Rom'],
'07-1corinthians': ['I_Corinthians', '7', '1Cor'],
'08-2corinthians': ['II_Corinthians', '8', '2Cor'],
'09-galatians': ['Galatians', '9', 'Gal'],
'10-ephesians': ['Ephesians', '10', 'Eph'],
'11-philippians': ['Philippians', '11', 'Phil'],
'12-colossians': ['Colossians', '12', 'Col'],
'13-1thessalonians':['I_Thessalonians', '13', '1Thess'],
'14-2thessalonians':['II_Thessalonians','14', '2Thess'],
'15-1timothy': ['I_Timothy', '15', '1Tim'],
'16-2timothy': ['II_Timothy', '16', '2Tim'],
'17-titus': ['Titus', '17', 'Titus'],
'18-philemon': ['Philemon', '18', 'Phlm'],
'19-hebrews': ['Hebrews', '19', 'Heb'],
'20-james': ['James', '20', 'Jas'],
'21-1peter': ['I_Peter', '21', '1Pet'],
'22-2peter': ['II_Peter', '22', '2Pet'],
'23-1john': ['I_John', '23', '1John'],
'24-2john': ['II_John', '24', '2John'],
'25-3john': ['III_John', '25', '3John'],
'26-jude': ['Jude', '26', 'Jude'],
'27-revelation': ['Revelation', '27', 'Rev']}
bo2book = {'01-matthew': ['Matthew', '1', 'Matt']}
In order to traverse from the 'leafs' (terminating nodes) upto the root of the tree, it is required to add information to each node pointing to the parent of each node.
(concept taken from https://stackoverflow.com/questions/2170610/access-elementtree-node-parent-node)
def addParentInfo(et):
for child in et:
child.attrib['parent'] = et
addParentInfo(child)
def getParent(et):
if 'parent' in et.attrib:
return et.attrib['parent']
else:
return None
# set some globals
monad=1
CollectedItems= 0
# process books in order
for bo, bookinfo in bo2book.items():
CollectedItems=0
SentenceNumber=0
WordGroupNumber=0
full_df=pd.DataFrame({})
book_long=bookinfo[0]
booknum=bookinfo[1]
book_short=bookinfo[2]
InputFile = os.path.join(XmlDir, f'{bo}.xml')
OutputFile = os.path.join(PklDir, f'{bo}.pkl')
print(f'Processing {book_long} at {InputFile}')
# send xml document to parsing process
tree = ET.parse(InputFile)
# Now add all the parent info to the nodes in the xtree [important!]
addParentInfo(tree.getroot())
start_time = time.time()
# walk over all the XML data
for elem in tree.iter():
if elem.tag == 'sentence':
# add running number to 'sentence' tags
SentenceNumber+=1
elem.set('SN', SentenceNumber)
if elem.tag == 'wg':
# add running number to 'wg' tags
WordGroupNumber+=1
elem.set('WGN', WordGroupNumber)
if elem.tag == 'w':
# all nodes containing words are tagged with 'w'
# show progress on screen
CollectedItems+=1
if (CollectedItems%100==0): print (".",end='')
#Leafref will contain list with book, chapter verse and wordnumber
Leafref = re.sub(r'[!: ]'," ", elem.attrib.get('ref')).split()
#push value for monad to element tree
elem.set('monad', monad)
monad+=1
# add some important computed data to the leaf
elem.set('LeafName', elem.tag)
elem.set('word', elem.text)
elem.set('book_long', book_long)
elem.set('booknum', int(booknum))
elem.set('book_short', book_short)
elem.set('chapter', int(Leafref[1]))
elem.set('verse', int(Leafref[2]))
# folling code will trace down parents upto the tree and store found attributes
parentnode=getParent(elem)
index=0
while (parentnode):
index+=1
elem.set('Parent{}Name'.format(index), parentnode.tag)
elem.set('Parent{}Type'.format(index), parentnode.attrib.get('type'))
elem.set('Parent{}Appos'.format(index), parentnode.attrib.get('appositioncontainer'))
elem.set('Parent{}Class'.format(index), parentnode.attrib.get('class'))
elem.set('Parent{}Rule'.format(index), parentnode.attrib.get('rule'))
elem.set('Parent{}Role'.format(index), parentnode.attrib.get('role'))
elem.set('Parent{}Cltype'.format(index), parentnode.attrib.get('cltype'))
elem.set('Parent{}Unit'.format(index), parentnode.attrib.get('unit'))
elem.set('Parent{}Junction'.format(index), parentnode.attrib.get('junction'))
elem.set('Parent{}SN'.format(index), parentnode.attrib.get('SN'))
elem.set('Parent{}WGN'.format(index), parentnode.attrib.get('WGN'))
currentnode=parentnode
parentnode=getParent(currentnode)
elem.set('parents', int(index))
#this will push all elements found in the tree into a DataFrame
df=pd.DataFrame(elem.attrib, index={monad})
full_df=pd.concat([full_df,df])
#store the resulting DataFrame per book into a pickle file for further processing
df = df.convert_dtypes(convert_string=True)
# sort by s=id
sortkey='{http://www.w3.org/XML/1998/namespace}id'
full_df.rename(columns={sortkey: 'id'}, inplace=True)
full_df.sort_values(by=['id'])
output = open(r"{}".format(OutputFile), 'wb')
pickle.dump(full_df, output)
output.close()
print("\nFound ",CollectedItems, " items in %s seconds\n" % (time.time() - start_time))
Processing Matthew at C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\xml\01-matthew.xml ...................................................................................................................................................................................... Found 18299 items in 337.3681836128235 seconds Processing Mark at C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\xml\02-mark.xml ................................................................................................................ Found 11277 items in 144.04719877243042 seconds Processing Luke at C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\xml\03-luke.xml .................................................................................................................................................................................................. Found 19456 items in 1501.197922706604 seconds Processing John at C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\xml\04-john.xml ............................................................................................................................................................ Found 15643 items in 237.1071105003357 seconds Processing Acts at C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\xml\05-acts.xml ....................................................................................................................................................................................... Found 18393 items in 384.3644151687622 seconds Processing Romans at C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\xml\06-romans.xml ....................................................................... Found 7100 items in 71.03568935394287 seconds Processing I_Corinthians at C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\xml\07-1corinthians.xml .................................................................... Found 6820 items in 58.47511959075928 seconds Processing II_Corinthians at C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\xml\08-2corinthians.xml ............................................ Found 4469 items in 31.848721027374268 seconds Processing Galatians at C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\xml\09-galatians.xml ...................... Found 2228 items in 13.850211143493652 seconds Processing Ephesians at C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\xml\10-ephesians.xml ........................ Found 2419 items in 17.529520511627197 seconds Processing Philippians at C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\xml\11-philippians.xml ................ Found 1630 items in 9.271572589874268 seconds Processing Colossians at C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\xml\12-colossians.xml ............... Found 1575 items in 10.389309883117676 seconds Processing I_Thessalonians at C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\xml\13-1thessalonians.xml .............. Found 1473 items in 8.413437604904175 seconds Processing II_Thessalonians at C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\xml\14-2thessalonians.xml ........ Found 822 items in 4.284915447235107 seconds Processing I_Timothy at C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\xml\15-1timothy.xml ............... Found 1588 items in 10.419771671295166 seconds Processing II_Timothy at C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\xml\16-2timothy.xml ............ Found 1237 items in 7.126454591751099 seconds Processing Titus at C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\xml\17-titus.xml ...... Found 658 items in 3.1472580432891846 seconds Processing Philemon at C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\xml\18-philemon.xml ... Found 335 items in 1.3175146579742432 seconds Processing Hebrews at C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\xml\19-hebrews.xml ................................................. Found 4955 items in 44.31139326095581 seconds Processing James at C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\xml\20-james.xml ................. Found 1739 items in 8.570415496826172 seconds Processing I_Peter at C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\xml\21-1peter.xml ................ Found 1676 items in 10.489561557769775 seconds Processing II_Peter at C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\xml\22-2peter.xml .......... Found 1098 items in 6.005697250366211 seconds Processing I_John at C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\xml\23-1john.xml ..................... Found 2136 items in 10.843079566955566 seconds Processing II_John at C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\xml\24-2john.xml .. Found 245 items in 0.9535031318664551 seconds Processing III_John at C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\xml\25-3john.xml .. Found 219 items in 1.0913233757019043 seconds Processing Jude at C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\xml\26-jude.xml .... Found 457 items in 1.8929190635681152 seconds Processing Revelation at C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\xml\27-revelation.xml .................................................................................................. Found 9832 items in 125.92533278465271 seconds
# just dump some things to test the result
for bo in bo2book:
'''
load all data into a dataframe
process books in order (bookinfo is a list!)
'''
InputFile = os.path.join(PklDir, f'{bo}.pkl')
print(f'\tloading {InputFile}...')
pkl_file = open(InputFile, 'rb')
df = pickle.load(pkl_file)
pkl_file.close()
# not sure if this is needed
# fill dictionary of column names for this book
IndexDict = {} # init an empty dictionary
ItemsInRow=1
for itemname in df.columns.to_list():
IndexDict.update({'i_{}'.format(itemname): ItemsInRow})
print (itemname)
ItemsInRow+=1
This script creates the Text-Fabric files by recursive calling the TF walker function. API info: https://annotation.github.io/text-fabric/tf/convert/walker.html
The pickle files created by step 1 are stored on Github location T.B.D.
import pandas as pd
import os
import re
import gc
from tf.fabric import Fabric
from tf.convert.walker import CV
from tf.parameters import VERSION
from datetime import date
import pickle
BaseDir = 'C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\'
XmlDir = BaseDir+'xml\\'
PklDir = BaseDir+'pkl\\'
XlsxDir = BaseDir+'xlsx\\'
# key: filename, [0]=book_long, [1]=book_num, [3]=book_short
bo2book = {'01-matthew': ['Matthew', '1', 'Matt'],
'02-mark': ['Mark', '2', 'Mark'],
'03-luke': ['Luke', '3', 'Luke'],
'04-john': ['John', '4', 'John'],
'05-acts': ['Acts', '5', 'Acts'],
'06-romans': ['Romans', '6', 'Rom'],
'07-1corinthians': ['I_Corinthians', '7', '1Cor'],
'08-2corinthians': ['II_Corinthians', '8', '2Cor'],
'09-galatians': ['Galatians', '9', 'Gal'],
'10-ephesians': ['Ephesians', '10', 'Eph'],
'11-philippians': ['Philippians', '11', 'Phil'],
'12-colossians': ['Colossians', '12', 'Col'],
'13-1thessalonians':['I_Thessalonians', '13', '1Thess'],
'14-2thessalonians':['II_Thessalonians','14', '2Thess'],
'15-1timothy': ['I_Timothy', '15', '1Tim'],
'16-2timothy': ['II_Timothy', '16', '2Tim'],
'17-titus': ['Titus', '17', 'Titus'],
'18-philemon': ['Philemon', '18', 'Phlm'],
'19-hebrews': ['Hebrews', '19', 'Heb'],
'20-james': ['James', '20', 'Jas'],
'21-1peter': ['I_Peter', '21', '1Pet'],
'22-2peter': ['II_Peter', '22', '2Pet'],
'23-1john': ['I_John', '23', '1John'],
'24-2john': ['II_John', '24', '2John'],
'25-3john': ['III_John', '25', '3John'],
'26-jude': ['Jude', '26', 'Jude'],
'27-revelation': ['Revelation', '27', 'Rev']}
bo2book_ = {'26-jude': ['Jude', '26', 'Jude']}
# test: sorting the data
import openpyxl
import pickle
#if True:
for bo in bo2book:
'''
load all data into a dataframe
process books in order (bookinfo is a list!)
'''
InputFile = os.path.join(PklDir, f'{bo}.pkl')
#InputFile = os.path.join(PklDir, '01-matthew.pkl')
print(f'\tloading {InputFile}...')
pkl_file = open(InputFile, 'rb')
df = pickle.load(pkl_file)
pkl_file.close()
# not sure if this is needed
# fill dictionary of column names for this book
IndexDict = {} # init an empty dictionary
ItemsInRow=1
for itemname in df.columns.to_list():
IndexDict.update({'i_{}'.format(itemname): ItemsInRow})
ItemsInRow+=1
#print(itemname)
# sort by id
#print(df)
df_sorted=df.sort_values(by=['id'])
df_sorted.to_excel(os.path.join(XlsxDir, f'{bo}.xlsx'), index=False)
API info: https://annotation.github.io/text-fabric/tf/convert/walker.html
The logic of interpreting the data is included in the director function.
TF = Fabric(locations=BaseDir, silent=False)
cv = CV(TF)
version = "0.1.6 (moved all phrases and claused to wordgroup nodes)"
def sanitize(input):
if isinstance(input, float): return ''
if isinstance(input, type(None)): return ''
else: return (input)
def ExpandRole(input):
if input=="adv": return 'Adverbial'
if input=="io": return 'Indirect Object'
if input=="o": return 'Object'
if input=="o2": return 'Second Object'
if input=="s": return 'Subject'
if input=="p": return 'Predicate'
if input=="v": return 'Verbal'
if input=="vc": return 'Verbal Copula'
return ''
# Expantion of part of speach labels. See also the description in
# "MACULA Greek Treebank for the Nestle 1904 Greek New Testament.pdf" page 6&7
# (2.2. Syntactic Categories at Word Level: Part of Speech Labels)
def ExpandSP(input):
if input=='adj': return 'adjective'
if input=='conj': return 'conjunction'
if input=='det': return 'determiner'
if input=='intj': return 'interjection'
if input=='noun': return 'noun'
if input=='num': return 'numeral'
if input=='prep': return 'preposition'
if input=='ptcl': return 'particle'
if input=='pron': return 'pronoun'
if input=='verb': return 'verb'
return input
def director(cv):
NoneType = type(None) # needed as tool to validate certain data
IndexDict = {} # init an empty dictionary
Arrays2Dump=200
DumpedArrays=0
WordGroupDict={} # init a dummy dictionary
PrevWordGroupSet = WordGroupSet = []
PrevWordGroupList = WordGroupList = []
RootWordGroup = 0
WordNumber=FoundWords=WordGroupTrack=0
DummyWGN=200000 # this number is arbitrary but should be high enough not to clash with 'real' WG numbers
'''
process books in order (bookinfo is a list!)
'''
for bo,bookinfo in bo2book.items():
Book = bookinfo[0]
BookNumber = int(bookinfo[1])
BookShort = bookinfo[2]
BookLoc = os.path.join(PklDir, f'{bo}.pkl')
'''
load data for this book into a dataframe. Make sure wordorder is correct
'''
print(f'\tWe are loading {BookLoc}...')
pkl_file = open(BookLoc, 'rb')
df_unsorted = pickle.load(pkl_file)
pkl_file.close()
df=df_unsorted.sort_values(by=['id'])
'''
set up nodes for new book
'''
ThisBookPointer = cv.node('book')
cv.feature(ThisBookPointer, book=Book, booknumber=BookNumber, bookshort=BookShort)
ThisChapterPointer = cv.node('chapter')
cv.feature(ThisChapterPointer, chapter=1)
PreviousChapter=1
ThisVersePointer = cv.node('verse')
cv.feature(ThisVersePointer, verse=1)
PreviousVerse=1
ThisSentencePointer = cv.node('sentence')
cv.feature(ThisSentencePointer, verse=1)
PreviousSentence=1
'''
fill dictionary of column names for this book
sort to ensure proper wordorder
'''
ItemsInRow=1
for itemname in df.columns.to_list():
IndexDict.update({'i_{}'.format(itemname): ItemsInRow})
ItemsInRow+=1
df.sort_values(by=['id'])
'''
Walks through the texts and trigger slot and node creation events.
iterate through words and construct objects
'''
for row in df.itertuples():
WordNumber += 1
FoundWords +=1
'''
First act upon changes in sentences, verse and chapter
'''
NumberOfParents = row[IndexDict.get("i_parents")]
ThisSentence=int(row[IndexDict.get("i_Parent{}SN".format(NumberOfParents-1))])
ThisVerse = sanitize(row[IndexDict.get("i_verse")])
ThisChapter = sanitize(row[IndexDict.get("i_chapter")])
if (ThisSentence!=PreviousSentence):
#cv.feature(ThisSentencePointer, statdata?)
cv.terminate(ThisSentencePointer)
if (ThisVerse!=PreviousVerse):
#cv.feature(ThisVersePointer, statdata?)
cv.terminate(ThisVersePointer)
if (ThisChapter!=PreviousChapter):
#cv.feature(ThisChapterPointer, statdata?)
cv.terminate(ThisChapterPointer)
PreviousChapter = ThisChapter
ThisChapterPointer = cv.node('chapter')
cv.feature(ThisChapterPointer, chapter=ThisChapter)
if (ThisVerse!=PreviousVerse):
PreviousVerse = ThisVerse
ThisVersePointer = cv.node('verse')
cv.feature(ThisVersePointer, verse=ThisVerse, chapter=ThisChapter)
if (ThisSentence!=PreviousSentence):
PreviousSentence=ThisSentence
ThisSentencePointer = cv.node('sentence')
cv.feature(ThisSentencePointer, verse=ThisVerse, chapter=ThisChapter)
# get number of parent nodes (this differs per word)
# decoding the WordGroup data
PrevWordGroupList=WordGroupList
WordGroupList=[] # stores current active WordGroup numbers
for i in range(NumberOfParents-2,0,-1): # reversed itteration
_WGN=row[IndexDict.get("i_Parent{}WGN".format(i))]
if isinstance(_WGN, type(None)):
# handling conditions where XML data has <error role="err_clause-complex-met-no-conditionsClCl2"> e.g. Acts 26:12
# to recover, we need to create a dummy WG with a sufficient high WGN so it can never match any real WGN.
WGN=DummyWGN
else:
WGN=int(_WGN)
if WGN!='':
WordGroupList.append(WGN)
WordGroupDict[(WGN,0)]=WGN
WordGroupDict[(WGN,1)]=sanitize(row[IndexDict.get("i_Parent{}Rule".format(i))])
WordGroupDict[(WGN,2)]=sanitize(row[IndexDict.get("i_Parent{}Cltype".format(i))])
WordGroupDict[(WGN,3)]=sanitize(row[IndexDict.get("i_Parent{}Junction".format(i))])
WordGroupDict[(WGN,6)]=sanitize(row[IndexDict.get("i_Parent{}Class".format(i))])
WordGroupDict[(WGN,7)]=sanitize(row[IndexDict.get("i_Parent{}Role".format(i))])
WordGroupDict[(WGN,8)]=sanitize(row[IndexDict.get("i_Parent{}Type".format(i))])
WordGroupDict[(WGN,9)]=sanitize(row[IndexDict.get("i_Parent{}Appos".format(i))])
if not PrevWordGroupList==WordGroupList:
if RootWordGroup != WordGroupList[0]:
RootWordGroup = WordGroupList[0]
SuspendableWordGoupList = []
# we have a new sentence. rebuild suspendable wordgroup list
# some cleaning of data may be added here to save on memmory...
#for k in range(6): del WordGroupDict[item,k]
for item in reversed(PrevWordGroupList):
if (item not in WordGroupList):
# CLOSE/SUSPEND CASE
SuspendableWordGoupList.append(item)
cv.terminate(WordGroupDict[item,4])
for item in WordGroupList:
if (item not in PrevWordGroupList):
if (item in SuspendableWordGoupList):
# RESUME CASE
#print ('\n resume: '+str(item),end=' ')
cv.resume(WordGroupDict[(item,4)])
else:
# CREATE CASE
#print ('\n create: '+str(item),end=' ')
WordGroupDict[(item,4)]=cv.node('wg')
WordGroupDict[(item,5)]=WordGroupTrack
WordGroupTrack += 1
cv.feature(WordGroupDict[(item,4)], wordgroup=WordGroupDict[(item,0)], junction=WordGroupDict[(item,3)],
clausetype=WordGroupDict[(item,2)], rule=WordGroupDict[(item,1)], wgclass=WordGroupDict[(item,6)],
wgrole=WordGroupDict[(item,7)],wgrolelong=ExpandRole(WordGroupDict[(item,7)]),
wgtype=WordGroupDict[(item,8)],appos=WordGroupDict[(item,8)])
# determine syntactic categories of words or wordgroup. See also the description in
# "MACULA Greek Treebank for the Nestle 1904 Greek New Testament.pdf" page 5&6
# (section 2.4 Syntactic Categories at Clause Level)
# word level roles:
Role=row[IndexDict.get("i_role")]
ValidRoles=["adv","io","o","o2","s","p","v","vc"]
if isinstance (Role,str) and Role in ValidRoles:
WordRole=Role
WordRoleLong=ExpandRole(WordRole)
else:
WordRole=WordRoleLong=''
'''
-- create word nodes --
'''
# determine syntactic categories at word level.
PartOfSpeech=sanitize(row[IndexDict.get("i_class")])
PartOfSpeechFull=ExpandSP(PartOfSpeech)
# some attributes are not present inside some (small) books. The following is to prevent exceptions.
degree=''
if 'i_degree' in IndexDict:
degree=sanitize(row[IndexDict.get("i_degree")])
subjref=''
if 'i_subjref' in IndexDict:
subjref=sanitize(row[IndexDict.get("i_subjref")])
# create the word slots
this_word = cv.slot()
cv.feature(this_word,
after= sanitize(row[IndexDict.get("i_after")]),
id= sanitize(row[IndexDict.get("i_id")]),
unicode= sanitize(row[IndexDict.get("i_unicode")]),
word= sanitize(row[IndexDict.get("i_word")]),
monad= sanitize(row[IndexDict.get("i_monad")]),
orig_order= FoundWords,
book_long= sanitize(row[IndexDict.get("i_book_long")]),
booknumber= BookNumber,
bookshort= sanitize(row[IndexDict.get("i_book_short")]),
chapter= ThisChapter,
ref= sanitize(row[IndexDict.get("i_ref")]),
sp= PartOfSpeech,
sp_full= PartOfSpeechFull,
verse= ThisVerse,
sentence= ThisSentence,
normalized= sanitize(row[IndexDict.get("i_normalized")]),
morph= sanitize(row[IndexDict.get("i_morph")]),
strongs= sanitize(row[IndexDict.get("i_strong")]),
lex_dom= sanitize(row[IndexDict.get("i_domain")]),
ln= sanitize(row[IndexDict.get("i_ln")]),
gloss= sanitize(row[IndexDict.get("i_gloss")]),
gn= sanitize(row[IndexDict.get("i_gender")]),
nu= sanitize(row[IndexDict.get("i_number")]),
case= sanitize(row[IndexDict.get("i_case")]),
lemma= sanitize(row[IndexDict.get("i_lemma")]),
person= sanitize(row[IndexDict.get("i_person")]),
mood= sanitize(row[IndexDict.get("i_mood")]),
tense= sanitize(row[IndexDict.get("i_tense")]),
number= sanitize(row[IndexDict.get("i_number")]),
voice= sanitize(row[IndexDict.get("i_voice")]),
degree= degree,
type= sanitize(row[IndexDict.get("i_type")]),
reference= sanitize(row[IndexDict.get("i_ref")]),
subj_ref= subjref,
nodeID= sanitize(row[1]), #this is a fixed position in dataframe
wordrole= WordRole,
wordrolelong= WordRoleLong
)
cv.terminate(this_word)
'''
wrap up the book. At the end of the book we need to close all nodes in proper order.
'''
for item in WordGroupList:
#cv.feature(WordGroupDict[(item,4)], add some stats?)
cv.terminate(WordGroupDict[item,4])
#cv.feature(ThisSentencePointer, statdata?)
cv.terminate(ThisSentencePointer)
#cv.feature(ThisVersePointer, statdata?)
cv.terminate(ThisVersePointer)
#cv.feature(ThisChapterPonter, statdata?)
cv.terminate(ThisChapterPointer)
#cv.feature(ThisBookPointer, statdata?)
cv.terminate(ThisBookPointer)
# clear dataframe for this book, clear the index dictionary
del df
IndexDict.clear()
gc.collect()
'''
-- output definitions --
'''
slotType = 'word' # or whatever you choose
otext = { # dictionary of config data for sections and text formats
'fmt:text-orig-full':'{word}{after}',
'sectionTypes':'book,chapter,verse',
'sectionFeatures':'book,chapter,verse',
'structureFeatures': 'book,chapter,verse',
'structureTypes': 'book,chapter,verse',
}
# configure metadata
generic = { # dictionary of metadata meant for all features
'Name': 'Greek New Testament (NA1904)',
'Version': '1904',
'Editors': 'Nestle',
'Data source': 'MACULA Greek Linguistic Datasets, available at https://github.com/Clear-Bible/macula-greek/tree/main/Nestle1904/lowfat',
'Availability': 'Creative Commons Attribution 4.0 International (CC BY 4.0)',
'Converter_author': 'Tony Jurg, Vrije Universiteit Amsterdam, Netherlands',
'Converter_execution': 'Tony Jurg, Vrije Universiteit Amsterdam, Netherlands',
'Convertor_source': 'https://github.com/tonyjurg/n1904_lft',
'Converter_version': '{}'.format(version),
'TextFabric version': '{}'.format(VERSION) #imported from tf.parameters
}
intFeatures = { # set of integer valued feature names
'booknumber',
'chapter',
'verse',
'sentence',
'wordgroup',
'orig_order',
'monad'
}
featureMeta = { # per feature dicts with metadata
'after': {'description': 'Characters (eg. punctuations) following the word'},
'id': {'description': 'id of the word'},
'book': {'description': 'Book'},
'book_long': {'description': 'Book name (fully spelled out)'},
'booknumber': {'description': 'NT book number (Matthew=1, Mark=2, ..., Revelation=27)'},
'bookshort': {'description': 'Book name (abbreviated)'},
'chapter': {'description': 'Chapter number inside book'},
'verse': {'description': 'Verse number inside chapter'},
'sentence': {'description': 'Sentence number (counted per chapter)'},
'type' : {'description': 'Wordgroup type information (verb, verbless, elided, minor, etc.)'},
'rule' : {'description': 'Wordgroup rule information '},
'orig_order': {'description': 'Word order within corpus (per book)'},
'monad':{'description': 'Monad (currently: order of words in XML tree file!)'},
'word': {'description': 'Word as it appears in the text (excl. punctuations)'},
'unicode': {'description': 'Word as it arears in the text in Unicode (incl. punctuations)'},
'ref': {'description': 'ref Id'},
'sp': {'description': 'Part of Speech (abbreviated)'},
'sp_full': {'description': 'Part of Speech (long description)'},
'normalized': {'description': 'Surface word stripped of punctations'},
'lemma': {'description': 'Lexeme (lemma)'},
'morph': {'description': 'Morphological tag (Sandborg-Petersen morphology)'},
# see also discussion on relation between lex_dom and ln @ https://github.com/Clear-Bible/macula-greek/issues/29
'lex_dom': {'description': 'Lexical domain according to Semantic Dictionary of Biblical Greek, SDBG (not present everywhere?)'},
'ln': {'description': 'Lauw-Nida lexical classification (not present everywhere?)'},
'strongs': {'description': 'Strongs number'},
'gloss': {'description': 'English gloss'},
'gn': {'description': 'Gramatical gender (Masculine, Feminine, Neuter)'},
'nu': {'description': 'Gramatical number (Singular, Plural)'},
'case': {'description': 'Gramatical case (Nominative, Genitive, Dative, Accusative, Vocative)'},
'person': {'description': 'Gramatical person of the verb (first, second, third)'},
'mood': {'description': 'Gramatical mood of the verb (passive, etc)'},
'tense': {'description': 'Gramatical tense of the verb (e.g. Present, Aorist)'},
'number': {'description': 'Gramatical number of the verb'},
'voice': {'description': 'Gramatical voice of the verb'},
'degree': {'description': 'Degree (e.g. Comparitative, Superlative)'},
'type': {'description': 'Gramatical type of noun or pronoun (e.g. Common, Personal)'},
'reference': {'description': 'Reference (to nodeID in XML source data, not yet post-processes)'},
'subj_ref': {'description': 'Subject reference (to nodeID in XML source data, not yet post-processes)'},
'nodeID': {'description': 'Node ID (as in the XML source data, not yet post-processes)'},
'junction': {'description': 'Junction data related to a wordgroup'},
'wordgroup' : {'description': 'Wordgroup number (counted per book)'},
'wgclass' : {'description': 'Class of the wordgroup ()'},
'wgrole' : {'description': 'Role of the wordgroup (abbreviated)'},
'wgrolelong' : {'description': 'Role of the wordgroup (abbreviated)'},
'wordrole' : {'description': 'Role of the word (abbreviated)'},
'wordrolelong': {'description': 'Role of the word (full)'},
'wgtype': {'description': 'Wordgroup type details'},
'clausetype': {'description': 'Clause type details'},
'appos': {'description': 'Apposition details'}
}
'''
-- the main function --
'''
good = cv.walk(
director,
slotType,
otext=otext,
generic=generic,
intFeatures=intFeatures,
featureMeta=featureMeta,
warn=True,
force=True
)
if good:
print ("done")
This is Text-Fabric 11.2.3 51 features found and 0 ignored 0.00s Importing data from walking through the source ... | 0.00s Preparing metadata... | SECTION TYPES: book, chapter, verse | SECTION FEATURES: book, chapter, verse | STRUCTURE TYPES: book, chapter, verse | STRUCTURE FEATURES: book, chapter, verse | TEXT FEATURES: | | text-orig-full after, word | 0.00s OK | 0.00s Following director... We are loading C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\pkl\01-matthew.pkl... We are loading C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\pkl\02-mark.pkl... We are loading C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\pkl\03-luke.pkl... We are loading C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\pkl\04-john.pkl... We are loading C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\pkl\05-acts.pkl... We are loading C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\pkl\06-romans.pkl... We are loading C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\pkl\07-1corinthians.pkl... We are loading C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\pkl\08-2corinthians.pkl... We are loading C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\pkl\09-galatians.pkl... We are loading C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\pkl\10-ephesians.pkl... We are loading C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\pkl\11-philippians.pkl... We are loading C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\pkl\12-colossians.pkl... We are loading C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\pkl\13-1thessalonians.pkl... We are loading C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\pkl\14-2thessalonians.pkl... We are loading C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\pkl\15-1timothy.pkl... We are loading C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\pkl\16-2timothy.pkl... We are loading C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\pkl\17-titus.pkl... We are loading C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\pkl\18-philemon.pkl... We are loading C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\pkl\19-hebrews.pkl... We are loading C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\pkl\20-james.pkl... We are loading C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\pkl\21-1peter.pkl... We are loading C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\pkl\22-2peter.pkl... We are loading C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\pkl\23-1john.pkl... We are loading C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\pkl\24-2john.pkl... We are loading C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\pkl\25-3john.pkl... We are loading C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\pkl\26-jude.pkl... We are loading C:\Users\tonyj\my_new_Jupyter_folder\Read_from_lowfat\data\pkl\27-revelation.pkl... | 52s "edge" actions: 0 | 52s "feature" actions: 267471 | 52s "node" actions: 129692 | 52s "resume" actions: 9629 | 52s "slot" actions: 137779 | 52s "terminate" actions: 277227 | 27 x "book" node | 260 x "chapter" node | 8011 x "sentence" node | 7943 x "verse" node | 113451 x "wg" node | 137779 x "word" node = slot type | 267471 nodes of all types | 52s OK | 0.00s checking for nodes and edges ... | 0.00s OK | 0.00s checking (section) features ... | 0.20s OK | 0.00s reordering nodes ... | 0.04s Sorting 27 nodes of type "book" | 0.05s Sorting 260 nodes of type "chapter" | 0.06s Sorting 8011 nodes of type "sentence" | 0.09s Sorting 7943 nodes of type "verse" | 0.11s Sorting 113451 nodes of type "wg" | 0.24s Max node = 267471 | 0.24s OK | 0.00s reassigning feature values ... | | 0.00s node feature "after" with 137779 nodes | | 0.04s node feature "appos" with 113451 nodes | | 0.09s node feature "book" with 27 nodes | | 0.09s node feature "book_long" with 137779 nodes | | 0.14s node feature "booknumber" with 137806 nodes | | 0.19s node feature "bookshort" with 137806 nodes | | 0.24s node feature "case" with 137779 nodes | | 0.29s node feature "chapter" with 153939 nodes | | 0.35s node feature "clausetype" with 113451 nodes | | 0.40s node feature "degree" with 137779 nodes | | 0.45s node feature "gloss" with 137779 nodes | | 0.50s node feature "gn" with 137779 nodes | | 0.56s node feature "id" with 137779 nodes | | 0.61s node feature "junction" with 113451 nodes | | 0.65s node feature "lemma" with 137779 nodes | | 0.70s node feature "lex_dom" with 137779 nodes | | 0.76s node feature "ln" with 137779 nodes | | 0.81s node feature "monad" with 137779 nodes | | 0.86s node feature "mood" with 137779 nodes | | 0.91s node feature "morph" with 137779 nodes | | 0.96s node feature "nodeID" with 137779 nodes | | 1.02s node feature "normalized" with 137779 nodes | | 1.07s node feature "nu" with 137779 nodes | | 1.13s node feature "number" with 137779 nodes | | 1.18s node feature "orig_order" with 137779 nodes | | 1.23s node feature "person" with 137779 nodes | | 1.28s node feature "ref" with 137779 nodes | | 1.34s node feature "reference" with 137779 nodes | | 1.39s node feature "rule" with 113451 nodes | | 1.45s node feature "sentence" with 137779 nodes | | 1.50s node feature "sp" with 137779 nodes | | 1.56s node feature "sp_full" with 137779 nodes | | 1.61s node feature "strongs" with 137779 nodes | | 1.66s node feature "subj_ref" with 137779 nodes | | 1.71s node feature "tense" with 137779 nodes | | 1.76s node feature "type" with 137779 nodes | | 1.81s node feature "unicode" with 137779 nodes | | 1.87s node feature "verse" with 153733 nodes | | 1.93s node feature "voice" with 137779 nodes | | 1.98s node feature "wgclass" with 113451 nodes | | 2.03s node feature "wgrole" with 113451 nodes | | 2.08s node feature "wgrolelong" with 113451 nodes | | 2.12s node feature "wgtype" with 113451 nodes | | 2.16s node feature "word" with 137779 nodes | | 2.22s node feature "wordgroup" with 113451 nodes | | 2.26s node feature "wordrole" with 137779 nodes | | 2.31s node feature "wordrolelong" with 137779 nodes | 2.45s OK 0.00s Exporting 48 node and 1 edge and 1 config features to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data/: 0.00s VALIDATING oslots feature 0.02s VALIDATING oslots feature 0.02s maxSlot= 137779 0.02s maxNode= 267471 0.04s OK: oslots is valid | 0.14s T after to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data | 0.12s T appos to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data | 0.01s T book to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data | 0.14s T book_long to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data | 0.13s T booknumber to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data | 0.14s T bookshort to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data | 0.16s T case to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data | 0.15s T chapter to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data | 0.11s T clausetype to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data | 0.14s T degree to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data | 0.15s T gloss to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data | 0.15s T gn to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data | 0.14s T id to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data | 0.11s T junction to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data | 0.17s T lemma to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data | 0.15s T lex_dom to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data | 0.14s T ln to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data | 0.14s T monad to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data | 0.14s T mood to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data | 0.15s T morph to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data | 0.15s T nodeID to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data | 0.17s T normalized to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data | 0.15s T nu to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data | 0.15s T number to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data | 0.15s T orig_order to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data | 0.07s T otype to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data | 0.15s T person to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data | 0.15s T ref to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data | 0.15s T reference to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data | 0.13s T rule to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data | 0.16s T sentence to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data | 0.14s T sp to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data | 0.14s T sp_full to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data | 0.15s T strongs to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data | 0.14s T subj_ref to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data | 0.14s T tense to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data | 0.14s T type to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data | 0.17s T unicode to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data | 0.15s T verse to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data | 0.14s T voice to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data | 0.13s T wgclass to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data | 0.12s T wgrole to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data | 0.11s T wgrolelong to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data | 0.11s T wgtype to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data | 0.17s T word to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data | 0.11s T wordgroup to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data | 0.14s T wordrole to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data | 0.17s T wordrolelong to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data | 0.36s T oslots to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data | 0.00s M otext to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data 7.06s Exported 48 node features and 1 edge features and 1 config features to C:/Users/tonyj/my_new_Jupyter_folder/Read_from_lowfat/data/ done
The TF will be loaded from github repository https://github.com/tonyjurg/n1904_lft
%load_ext autoreload
%autoreload 2
# First, I have to laod different modules that I use for analyzing the data and for plotting:
import sys, os, collections
import pandas as pd
import numpy as np
import re
from tf.fabric import Fabric
from tf.app import use
The following cell loads the TextFabric files from github repository.
# Loading-the-New-Testament-Text-Fabric (add a specific version, eg. 0.1.2)
NA = use ("tonyjurg/n1904_lft", version="0.1.6", hoist=globals())
Locating corpus resources ...
| Name | # of nodes | # slots/node | % coverage |
|---|---|---|---|
| book | 27 | 5102.93 | 100 |
| chapter | 260 | 529.92 | 100 |
| verse | 7943 | 17.35 | 100 |
| sentence | 8011 | 17.20 | 100 |
| wg | 113451 | 7.58 | 624 |
| word | 137779 | 1.00 | 100 |
N.otypeRank
{'word': 0, 'wg': 1, 'sentence': 2, 'verse': 3, 'chapter': 4, 'book': 5}
T.formats
{'text-orig-full': 'word'}
note: the implementation with regards how phrases need to be displayed (esp. with regards to conjunctions) is still to be done.
Search0 = '''
book book=Matthew
chapter chapter=1
verse
'''
Search0 = NA.search(Search0)
NA.show(Search0, start=1, end=2, condensed=True, extraFeatures={'sp','gloss','wordrolelong'}, suppress={'chapter'}, withNodes=False)
0.01s 25 results
verse 1
verse 2
T.structureInfo()
A heading is a tuple of pairs (node type, feature value) of node types and features that have been configured as structural elements These 3 structural elements have been configured node type book with heading feature book node type chapter with heading feature chapter node type verse with heading feature verse You can get them as a tuple with T.headings. Structure API: T.structure(node=None) gives the structure below node, or everything if node is None T.structurePretty(node=None) prints the structure below node, or everything if node is None T.top() gives all top-level nodes T.up(node) gives the (immediate) parent node T.down(node) gives the (immediate) children nodes T.headingFromNode(node) gives the heading of a node T.nodeFromHeading(heading) gives the node of a heading T.ndFromHd complete mapping from headings to nodes T.hdFromNd complete mapping from nodes to headings T.hdMult are all headings with their nodes that occur multiple times There are 8230 structural elements in the dataset.
TF.features['otext'].metaData
{'Availability': 'Creative Commons Attribution 4.0 International (CC BY 4.0)',
'Converter_author': 'Tony Jurg, Vrije Universiteit Amsterdam, Netherlands',
'Converter_execution': 'Tony Jurg, Vrije Universiteit Amsterdam, Netherlands',
'Converter_version': '0.1.6 (moved all phrases and claused to wordgroup nodes)',
'Convertor_source': 'https://github.com/tonyjurg/n1904_lft',
'Data source': 'MACULA Greek Linguistic Datasets, available at https://github.com/Clear-Bible/macula-greek/tree/main/Nestle1904/lowfat',
'Editors': 'Nestle',
'Name': 'Greek New Testament (NA1904)',
'TextFabric version': '11.2.3',
'Version': '1904',
'fmt:text-orig-full': '{word}{after}',
'sectionFeatures': 'book,chapter,verse',
'sectionTypes': 'book,chapter,verse',
'structureFeatures': 'book,chapter,verse',
'structureTypes': 'book,chapter,verse',
'writtenBy': 'Text-Fabric',
'dateWritten': '2023-05-02T15:20:37Z'}
!text-fabric app
^C
!text-fabric app -k
tf.core.nodes.Nodes.otypeRank
--------------------------------------------------------------------------- NameError Traceback (most recent call last) Input In [135], in <cell line: 1>() ----> 1 tf.core.nodes.Nodes.otypeRank NameError: name 'tf' is not defined