The source data for the conversion are the XML node files representing the macula-greek version of Eberhard Nestle's 1904 Greek New Testment (British Foreign Bible Society 1904). The starting dataset is formatted according to Syntax diagram markup by the Global Bible Initiative (GBI). The most recent source data can be found on github https://github.com/Clear-Bible/macula-greek/tree/main/Nestle1904/nodes. Attribution: "MACULA Greek Linguistic Datasets, available at https://github.com/Clear-Bible/macula-greek/".
The production of the Text-Fabric files consist of two major 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.
Please be advised that this Text-Fabric version is a test version (proof of concept) and may requires further finetuning, especialy with regards of nomenclature and presentation of (sub)phrases and clauses.
This script harvests all information from the GBI tree data, 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
In case you need to build in on your own system, you need to change BaseDir, InputDir and OutputDir to match location of the datalocation and the OS used.
BaseDir = 'C:\\Users\\tonyj\\my_new_Jupyter_folder\\test_of_xml_etree\\'
InputDir = BaseDir+'inputfiles\\'
OutputDir = BaseDir+'outputfiles\\'
# 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']}
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
full_df=pd.DataFrame({})
book_long=bookinfo[0]
booknum=bookinfo[1]
book_short=bookinfo[2]
InputFile = os.path.join(InputDir, f'{bo}.xml')
OutputFile = os.path.join(OutputDir, 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 leaves and harvest the data
for elem in tree.iter():
if not list(elem):
# if no child elements, this is a leaf/terminal node
# 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]))
# following 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{}Cat'.format(index), parentnode.attrib.get('Cat'))
elem.set('Parent{}Start'.format(index), parentnode.attrib.get('Start'))
elem.set('Parent{}End'.format(index), parentnode.attrib.get('End'))
elem.set('Parent{}Rule'.format(index), parentnode.attrib.get('Rule'))
elem.set('Parent{}Head'.format(index), parentnode.attrib.get('Head'))
elem.set('Parent{}NodeId'.format(index),parentnode.attrib.get('nodeId'))
elem.set('Parent{}ClType'.format(index),parentnode.attrib.get('ClType'))
elem.set('Parent{}HasDet'.format(index),parentnode.attrib.get('HasDet'))
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)
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))
The node data is not in word order as in running text. This is to sort the dataframe.
BaseDir = 'C:\\Users\\tonyj\\my_new_Jupyter_folder\\test_of_xml_etree\\'
source_dir = BaseDir+'outputfiles\\' #the input files (with 'wordjumps')
output_dir = BaseDir+'outputfiles_sorted\\' #the output files (words in order of running text)
for bo in bo2book:
'''
load all data into a dataframe
process books in order (bookinfo is a list!)
'''
InputFile = os.path.join(source_dir, f'{bo}.pkl')
OutputFile = os.path.join(output_dir, 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})
ItemsInRow+=1
# sort by id
df.sort_values(by=['nodeId'])
#store the resulting DataFrame per book into a pickle file for further processing
#df = df.convert_dtypes(convert_string=True) DO NOT DO THIS! IT MUTILATES THE DATA...
output = open(r"{}".format(OutputFile), 'wb')
pickle.dump(df, output)
output.close()
loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles\01-matthew.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles\02-mark.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles\03-luke.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles\04-john.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles\05-acts.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles\06-romans.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles\07-1corinthians.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles\08-2corinthians.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles\09-galatians.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles\10-ephesians.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles\11-philippians.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles\12-colossians.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles\13-1thessalonians.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles\14-2thessalonians.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles\15-1timothy.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles\16-2timothy.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles\17-titus.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles\18-philemon.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles\19-hebrews.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles\20-james.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles\21-1peter.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles\22-2peter.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles\23-1john.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles\24-2john.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles\25-3john.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles\26-jude.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles\27-revelation.pkl...
This script creates the TextFabric 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 https://github.com/tonyjurg/Nestle1904GBI/tree/main/resources/picklefiles
Change BaseDir, InputDir and OutputDir to match location of the datalocation and the OS used.
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\\test_of_xml_etree\\'
source_dir = BaseDir+'outputfiles_sorted\\' # the input for the walker is the output of the xml to excel
#output_dir = BaseDir+'outputfilesTF\\' #the TextFabric files
output_dir = 'C:\\text-fabric-data\\github\\tonyjurg\\Nestle1904GBI\\tf'
# 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']}
Text-Fabric API info can be found at https://annotation.github.io/text-fabric/tf/convert/walker.html
Notes regarding the logic of interpreting the data are included in the python code of the director function.
TF = Fabric(locations=output_dir, silent=False)
cv = CV(TF)
version = "0.1 (Initial)"
# the following is required to prevent passing float data to the walker function
def sanitize(input):
if isinstance(input, float): return ''
else: return (input)
def director(cv):
NoneType = type(None) # needed as tool to validate certain data
prev_book = "Matthew" # start at first book
IndexDict = {} # init an empty dictionary
for bo,bookinfo in bo2book.items():
# load all data into a dataframe and process books in order (bookinfo is a list!)
book=bookinfo[0]
booknum=int(bookinfo[1])
book_short=bookinfo[2]
book_loc = os.path.join(source_dir, f'{bo}.pkl')
# Report progress or reading data
print(f'\tloading {book_loc}...')
pkl_file = open(book_loc, 'rb')
df = pickle.load(pkl_file)
pkl_file.close()
# reset/load the following initial variables (we are at the start of a new book)
phrasefunction = prev_phrasefunction = phrasefunction_long = prev_phrasefunction_long='TBD'
this_clausetype = this_clauserule = phrasetype="unknown"
prev_chapter = prev_verse = prev_sentence = prev_clause = prev_phrase = int(1)
sentence_track = clause_track = phrase_track = 1
sentence_done = clause_done = phrase_done = verse_done = chapter_done = book_done = False
wrdnum = 0 # start at 0
# build a dictionary of column names for this book
ItemsInRow=1
for itemname in df.columns.to_list():
IndexDict.update({'i_{}'.format(itemname): ItemsInRow})
ItemsInRow+=1
# Create a set of nodes at the start a new book
book_done = chapter_done = verse_done = phrase_done = clause_done = sentence_done = False
this_book = cv.node('book')
cv.feature(this_book, book=book, booknum=booknum, book_short=book_short)
this_chapter = cv.node('chapter')
cv.feature(this_chapter, chapter=1)
this_verse = cv.node('verse')
cv.feature(this_verse, verse=1)
this_sentence = cv.node('sentence')
cv.feature(this_sentence, sentence=1)
this_clause = cv.node('clause')
this_phrase = cv.node('phrase')
'''
Walks through the texts and triggers
slot and node creation events.
'''
# iterate through words and construct objects
for row in df.itertuples():
wrdnum += 1
# get number of parent nodes for this word
parents = row[IndexDict.get("i_parents")]
# get chapter and verse for this word from the data
chapter = row[IndexDict.get("i_chapter")]
verse = row[IndexDict.get("i_verse")]
# get clause rule and type info of parent clause
for i in range(1,parents-1):
item = IndexDict.get("i_Parent{}Cat".format(i))
if row[item]=="CL":
clauseparent=i
this_clauserule=row[IndexDict.get("i_Parent{}Rule".format(i))]
this_clausetype=row[IndexDict.get("i_Parent{}ClType".format(i))]
break
cv.feature(this_clause, clause=clause_track, clauserule=this_clauserule, clausetype=this_clausetype)
# get phrase type info
prev_phrasetype=phrasetype
for i in range(1,parents-1):
item = IndexDict.get("i_Parent{}Cat".format(i))
if row[item]=="np":
_item ="i_Parent{}Rule".format(i)
phrasetype=row[IndexDict.get(_item)]
break
functionaltag=row[IndexDict.get('i_FunctionalTag')]
# determine syntactic categories of clause parts. 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)
phrase_done = False
for i in range(1,clauseparent):
phrasefunction = row[IndexDict.get("i_Parent{}Cat".format(i))]
if phrasefunction=="ADV":
phrasefunction_long='Adverbial function'
if prev_phrasefunction!=phrasefunction: phrase_done = True
break
elif phrasefunction=="IO":
phrasefunction_long='Indirect Object function'
if prev_phrasefunction!=phrasefunction: phrase_done = True
break
elif phrasefunction=="O":
phrasefunction_long='Object function'
if prev_phrasefunction!=phrasefunction: phrase_done = True
break
elif phrasefunction=="O2":
phrasefunction_long='Second Object function'
if prev_phrasefunction!=phrasefunction: phrase_done = True
break
elif phrasefunction=="S":
phrasefunction_long='Subject function'
if prev_phrasefunction!=phrasefunction: phrase_done = True
break
elif phrasefunction=='P':
phrasefunction_long='Predicate function'
if prev_phrasefunction!=phrasefunction: phrase_done = True
break
elif phrasefunction=="V":
phrasefunction_long='Verbal function'
if prev_phrasefunction!=phrasefunction: phrase_done = True
break
elif phrasefunction=="VC":
phrasefunction_long='Verbal Copula function'
if prev_phrasefunction!=phrasefunction: phrase_done = True
break
# determine syntactic categories at word level. 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)
sp=sanitize(row[IndexDict.get("i_Cat")])
if sp=='adj':
sp_full='adjective'
elif sp=='adj':
sp_full='adjective'
elif sp=='conj':
sp_full='conjunction'
elif sp=='det':
sp_full='determiner'
elif sp=='intj':
sp_full='interjection'
elif sp=='noun':
sp_full='noun'
elif sp=='num':
sp_full='numeral'
elif sp=='prep':
sp_full='preposition'
elif sp=='ptcl':
sp_full='particle'
elif sp=='pron':
sp_full='pronoun'
elif sp=='verb':
sp_full='verb'
'''
determine if conditions are met to trigger some action
action will be executed after next word
'''
# detect chapter boundary
if prev_chapter != chapter:
chapter_done = True
verse_done=True
sentence_done = True
clause_done = True
phrase_done = True
# detect verse boundary
if prev_verse != verse:
verse_done=True
'''
-- handle TF events --
Determine what actions need to be done if proper condition is met.
'''
# act upon end of phrase (close)
if phrase_done or clause_done:
cv.feature(this_phrase, phrase=phrase_track, phrasetype=prev_phrasetype, phrasefunction=prev_phrasefunction, phrasefunction_long=prev_phrasefunction_long)
cv.terminate(this_phrase)
prev_phrasefunction=phrasefunction
prev_phrasefunction_long=phrasefunction_long
# act upon end of clause (close)
if clause_done:
cv.terminate(this_clause)
# act upon end of sentence (close)
if sentence_done:
cv.terminate(this_sentence)
# act upon end of verse (close)
if verse_done:
cv.terminate(this_verse)
prev_verse = verse
# act upon end of chapter (close)
if chapter_done:
cv.terminate(this_chapter)
prev_chapter = chapter
# start of chapter (create new)
if chapter_done:
this_chapter = cv.node('chapter')
cv.feature(this_chapter, chapter=chapter)
chapter_done = False
# start of verse (create new)
if verse_done:
this_verse = cv.node('verse')
cv.feature(this_verse, verse=verse)
verse_done = False
# start of sentence (create new)
if sentence_done:
this_sentence= cv.node('sentence')
cv.feature(this_sentence, sentence=sentence_track)
sentence_track += 1
sentence_done = False
# start of clause (create new)
if clause_done:
this_clause = cv.node('clause')
cv.feature(this_clause, clause=clause_track, clauserule=this_clauserule,clausetype=this_clausetype)
clause_track += 1
clause_done = False
phrase_done = True
# start of phrase (create new)
if phrase_done:
this_phrase = cv.node('phrase')
prev_phrase = phrase_track
prev_phrasefunction=phrasefunction
prev_phrasefunction_long=phrasefunction_long
phrase_track += 1
phrase_done = False
# Detect boundaries of sentences, clauses and phrases
text=row[IndexDict.get("i_Unicode")]
if text[-1:] == "." :
sentence_done = True
clause_done = True
phrase_done = True
if text[-1:] == ";" or text[-1:] == ",":
clause_done = True
phrase_done = True
'''
-- create word nodes --
'''
# 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")])
# make word object
this_word = cv.slot()
cv.feature(this_word,
word=row[IndexDict.get("i_Unicode")],
monad=row[IndexDict.get("i_monad")],
orig_order=row[IndexDict.get("i_monad")],
book_long=row[IndexDict.get("i_book_long")],
booknum=booknum,
book_short=row[IndexDict.get("i_book_short")],
chapter=chapter,
sp=sp,
sp_full=sp_full,
verse=verse,
sentence=sentence_track,
clause=clause_track,
phrase=phrase_track,
normalized=sanitize(row[IndexDict.get("i_NormalizedForm")]),
formaltag=sanitize(row[IndexDict.get("i_FormalTag")]),
functionaltag=functionaltag,
strongs=sanitize(row[IndexDict.get("i_StrongNumber")]),
lex_dom=sanitize(row[IndexDict.get("i_LexDomain")]),
ln=sanitize(row[IndexDict.get("i_LN")]),
gloss_EN=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_UnicodeLemma")]),
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")]), # the capital R is critical here!
subj_ref=subjref,
nodeID=row[1] #this is a fixed position.
)
cv.terminate(this_word)
'''
-- wrap up the book --
'''
# close all nodes (phrase, clause, sentence, verse, chapter and book)
cv.feature(this_phrase, phrase=phrase_track, phrasetype=prev_phrasetype,phrasefunction=prev_phrasefunction,phrasefunction_long=prev_phrasefunction_long)
cv.terminate(this_phrase)
cv.feature(this_clause, clause=clause_track, clauserule=this_clauserule, clausetype=this_clausetype)
cv.terminate(this_clause)
cv.feature(this_sentence, sentence=prev_sentence)
cv.terminate(this_sentence)
cv.feature(this_verse, verse=prev_verse)
cv.terminate(this_verse)
cv.feature(this_chapter, chapter=prev_chapter)
cv.terminate(this_chapter)
cv.feature(this_book, book=prev_book)
cv.terminate(this_book)
# clear dataframe for this book
del df
# clear the index dictionary
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}',
'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 (Nestle 1904) based upon GBI tree node data',
'Version': '1904',
'Editors': 'Eberhart Nestle',
'Data source': 'MACULA Greek Linguistic Datasets, available at https://github.com/Clear-Bible/macula-greek/tree/main/Nestle1904/nodes',
'Availability': 'Creative Commons Attribution 4.0 International (CC BY 4.0)',
'Converter_author': 'Tony Jurg, ReMa student Vrije Universiteit Amsterdam, Netherlands',
'Converter_execution': 'Tony Jurg, ReMa student Vrije Universiteit Amsterdam, Netherlands',
'Convertor_source': 'https://github.com/tonyjurg/NA1904/tree/main/resources/converter',
'Converter_version': '{}'.format(version),
'TextFabric version': '{}'.format(VERSION) #imported from tf.parameters
}
intFeatures = { # set of integer valued feature names
'booknum',
'chapter',
'verse',
'sentence',
'clause',
'phrase',
'orig_order',
'monad'
}
featureMeta = { # per feature dicts with metadata
'book': {'description': 'Book'},
'book_long': {'description': 'Book name (fully spelled out)'},
'booknum': {'description': 'NT book number (Matthew=1, Mark=2, ..., Revelation=27)'},
'book_short': {'description': 'Book name (abbreviated)'},
'chapter': {'description': 'Chapter number inside book'},
'verse': {'description': 'Verse number inside chapter'},
'sentence': {'description': 'Sentence number (counted per chapter)'},
'clause': {'description': 'Clause number (counted per chapter)'},
'clauserule': {'description': 'Clause rule'},
'clausetype': {'description': 'Clause type'},
'phrase' : {'description': 'Phrase number (counted per chapter)'},
'phrasetype' : {'description': 'Phrase type information'},
'phrasefunction' : {'description': 'Phrase function (abbreviated)'},
'phrasefunction_long' : {'description': 'Phrase function (long description)'},
'orig_order': {'description': 'Word order within corpus'},
'monad':{'description': 'Monad'},
'word': {'description': 'Word as it appears in the text'},
'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)'},
'formaltag': {'description': 'Formal tag (Sandborg-Petersen morphology)'},
'functionaltag': {'description': 'Functional 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_EN': {'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)'}
}
'''
-- the main function --
'''
good = cv.walk(
director,
slotType,
otext=otext,
generic=generic,
intFeatures=intFeatures,
featureMeta=featureMeta,
warn=True,
force=False
)
if good:
print ("done")
This is Text-Fabric 11.4.10 43 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 word | 0.00s OK | 0.00s Following director... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles_sorted\01-matthew.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles_sorted\02-mark.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles_sorted\03-luke.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles_sorted\04-john.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles_sorted\05-acts.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles_sorted\06-romans.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles_sorted\07-1corinthians.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles_sorted\08-2corinthians.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles_sorted\09-galatians.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles_sorted\10-ephesians.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles_sorted\11-philippians.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles_sorted\12-colossians.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles_sorted\13-1thessalonians.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles_sorted\14-2thessalonians.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles_sorted\15-1timothy.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles_sorted\16-2timothy.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles_sorted\17-titus.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles_sorted\18-philemon.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles_sorted\19-hebrews.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles_sorted\20-james.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles_sorted\21-1peter.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles_sorted\22-2peter.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles_sorted\23-1john.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles_sorted\24-2john.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles_sorted\25-3john.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles_sorted\26-jude.pkl... loading C:\Users\tonyj\my_new_Jupyter_folder\test_of_xml_etree\outputfiles_sorted\27-revelation.pkl... | 43s "edge" actions: 0 | 43s "feature" actions: 379313 | 43s "node" actions: 103647 | 43s "resume" actions: 0 | 43s "slot" actions: 137779 | 43s "terminate" actions: 241426 | 27 x "book" node | 260 x "chapter" node | 16124 x "clause" node | 73572 x "phrase" node | 5720 x "sentence" node | 7944 x "verse" node | 137779 x "word" node = slot type | 241426 nodes of all types | 44s OK | 0.03s Removing unlinked nodes ... | | 0.00s 25 unlinked "phrase" nodes: [1, 10018, 27166, 46044, 49656] ... | | 0.00s 25 unlinked nodes | | 0.00s Leaving 241401 nodes | 0.00s checking for nodes and edges ... | 0.00s OK | 0.00s checking (section) features ... | 0.25s OK | 0.00s reordering nodes ... | 0.03s Sorting 27 nodes of type "book" | 0.04s Sorting 260 nodes of type "chapter" | 0.05s Sorting 16124 nodes of type "clause" | 0.09s Sorting 73547 nodes of type "phrase" | 0.19s Sorting 5720 nodes of type "sentence" | 0.21s Sorting 7944 nodes of type "verse" | 0.23s Max node = 241401 | 0.23s OK | 0.00s reassigning feature values ... | | 0.55s node feature "book" with 27 nodes | | 0.55s node feature "book_long" with 137779 nodes | | 0.60s node feature "book_short" with 137806 nodes | | 0.64s node feature "booknum" with 137806 nodes | | 0.69s node feature "case" with 137779 nodes | | 0.74s node feature "chapter" with 138039 nodes | | 0.78s node feature "clause" with 153903 nodes | | 0.83s node feature "clauserule" with 16124 nodes | | 0.84s node feature "clausetype" with 3603 nodes | | 0.84s node feature "degree" with 137779 nodes | | 0.91s node feature "formaltag" with 137779 nodes | | 0.95s node feature "functionaltag" with 137779 nodes | | 0.99s node feature "gloss_EN" with 137779 nodes | | 1.04s node feature "gn" with 137779 nodes | | 1.08s node feature "lemma" with 137779 nodes | | 1.13s node feature "lex_dom" with 137779 nodes | | 1.18s node feature "ln" with 137779 nodes | | 1.22s node feature "monad" with 137779 nodes | | 1.26s node feature "mood" with 137779 nodes | | 1.31s node feature "nodeID" with 137779 nodes | | 1.35s node feature "normalized" with 137779 nodes | | 1.39s node feature "nu" with 137779 nodes | | 1.44s node feature "number" with 137779 nodes | | 1.49s node feature "orig_order" with 137779 nodes | | 1.53s node feature "person" with 137779 nodes | | 1.57s node feature "phrase" with 211326 nodes | | 1.64s node feature "phrasefunction" with 73547 nodes | | 1.67s node feature "phrasefunction_long" with 73547 nodes | | 1.70s node feature "phrasetype" with 73547 nodes | | 1.73s node feature "reference" with 137779 nodes | | 1.78s node feature "sentence" with 143499 nodes | | 1.83s node feature "sp" with 137779 nodes | | 1.87s node feature "sp_full" with 137779 nodes | | 1.91s node feature "strongs" with 137779 nodes | | 1.96s node feature "subj_ref" with 137779 nodes | | 2.01s node feature "tense" with 137779 nodes | | 2.05s node feature "type" with 137779 nodes | | 2.09s node feature "verse" with 145723 nodes | | 2.14s node feature "voice" with 137779 nodes | | 2.19s node feature "word" with 137779 nodes | 1.75s OK 0.00s Exporting 41 node and 1 edge and 1 config features to C:/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf: 0.00s VALIDATING oslots feature 0.02s VALIDATING oslots feature 0.02s maxSlot= 137779 0.02s maxNode= 241401 0.03s OK: oslots is valid | 0.01s T book to C:/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf | 0.13s T book_long to C:/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf | 0.14s T book_short to C:/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf | 0.13s T booknum to C:/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf | 0.15s T case to C:/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf | 0.13s T chapter to C:/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf | 0.15s T clause to C:/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf | 0.03s T clauserule to C:/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf | 0.01s T clausetype to C:/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf | 0.14s T degree to C:/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf | 0.14s T formaltag to C:/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf | 0.14s T functionaltag to C:/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf | 0.15s T gloss_EN to C:/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf | 0.14s T gn to C:/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf | 0.16s T lemma to C:/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf | 0.14s T lex_dom to C:/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf | 0.14s T ln to C:/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf | 0.14s T monad to C:/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf | 0.14s T mood to C:/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf | 0.15s T nodeID to C:/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf | 0.16s T normalized to C:/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf | 0.14s T nu to C:/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf | 0.14s T number to C:/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf | 0.14s T orig_order to C:/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf | 0.06s T otype to C:/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf | 0.14s T person to C:/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf | 0.22s T phrase to C:/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf | 0.08s T phrasefunction to C:/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf | 0.08s T phrasefunction_long to C:/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf | 0.08s T phrasetype to C:/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf | 0.13s T reference to C:/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf | 0.14s T sentence to C:/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf | 0.14s T sp to C:/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf | 0.14s T sp_full to C:/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf | 0.15s T strongs to C:/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf | 0.14s T subj_ref to C:/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf | 0.14s T tense to C:/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf | 0.13s T type to C:/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf | 0.14s T verse to C:/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf | 0.14s T voice to C:/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf | 0.16s T word to C:/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf | 0.40s T oslots to C:/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf | 0.01s M otext to C:/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf 5.69s Exported 41 node features and 1 edge features and 1 config features to C:/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf done