This is work in progress
This Jupyter Notebook shows a number of cases where the interpretation of the Greek text involves some kind of choice that had to be made. However, it does not necessarily claim the data is 'wrong'.
For this notebook two data sets of Text-Fabric will be loaded. As consequence the option hoist=globals() can not be used and the Advanced API calls require to be called in a differnt manner.
%load_ext autoreload
%autoreload 2
# Loading the Text-Fabric code
# Note: it is assumed Text-Fabric is installed in your environment
from tf.fabric import Fabric
from tf.app import use
# load the N1904GBI app and data
N1904GBI = use ("tonyjurg/Nestle1904GBI", version="0.4")
Locating corpus resources ...
The requested app is not available offline ~/text-fabric-data/github/tonyjurg/Nestle1904GBI/app not found
The requested data is not available offline ~/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf/0.4 not found
| 0.19s T otype from ~/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf/0.4 | 1.95s T oslots from ~/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf/0.4 | 0.59s T word from ~/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf/0.4 | 0.49s T after from ~/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf/0.4 | 0.59s T book from ~/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf/0.4 | 0.51s T chapter from ~/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf/0.4 | 0.51s T verse from ~/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf/0.4 | | 0.05s C __levels__ from otype, oslots, otext | | 1.67s C __order__ from otype, oslots, __levels__ | | 0.07s C __rank__ from otype, __order__ | | 2.21s C __levUp__ from otype, oslots, __rank__ | | 1.41s C __levDown__ from otype, __levUp__, __rank__ | | 0.06s C __characters__ from otext | | 0.92s C __boundary__ from otype, oslots, __rank__ | | 0.04s C __sections__ from otype, oslots, otext, __levUp__, __levels__, book, chapter, verse | | 0.22s C __structure__ from otype, oslots, otext, __rank__, __levUp__, book, chapter, verse | 0.52s T booknum from ~/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf/0.4 | 0.61s T bookshort from ~/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf/0.4 | 0.49s T case from ~/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf/0.4 | 0.51s T clause from ~/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf/0.4 | 0.07s T clauserule from ~/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf/0.4 | 0.02s T clausetype from ~/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf/0.4 | 0.45s T degree from ~/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf/0.4 | 0.56s T formaltag from ~/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf/0.4 | 0.86s T functionaltag from ~/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf/0.4 | 0.98s T gloss from ~/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf/0.4 | 0.48s T gn from ~/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf/0.4 | 0.56s T lemma from ~/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf/0.4 | 0.52s T lex_dom from ~/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf/0.4 | 0.53s T ln from ~/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf/0.4 | 0.45s T monad from ~/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf/0.4 | 0.45s T mood from ~/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf/0.4 | 0.66s T nodeID from ~/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf/0.4 | 0.61s T normalized from ~/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf/0.4 | 0.52s T nu from ~/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf/0.4 | 0.51s T number from ~/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf/0.4 | 0.45s T person from ~/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf/0.4 | 0.74s T phrase from ~/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf/0.4 | 0.27s T phrasefunction from ~/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf/0.4 | 0.28s T phrasefunctionlong from ~/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf/0.4 | 0.27s T phrasetype from ~/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf/0.4 | 0.47s T sentence from ~/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf/0.4 | 0.51s T sp from ~/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf/0.4 | 0.53s T splong from ~/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf/0.4 | 0.54s T strongs from ~/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf/0.4 | 0.49s T subj_ref from ~/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf/0.4 | 0.44s T tense from ~/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf/0.4 | 0.49s T type from ~/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf/0.4 | 0.45s T voice from ~/text-fabric-data/github/tonyjurg/Nestle1904GBI/tf/0.4
| Name | # of nodes | # slots / node | % coverage |
|---|---|---|---|
| book | 27 | 5102.93 | 100 |
| chapter | 260 | 529.92 | 100 |
| sentence | 5720 | 24.09 | 100 |
| verse | 7943 | 17.35 | 100 |
| clause | 16124 | 8.54 | 100 |
| phrase | 72674 | 1.90 | 100 |
| word | 137779 | 1.00 | 100 |
3tonyjurg/Nestle1904GBIC:/Users/tonyj/text-fabric-data/github/tonyjurg/Nestle1904GBI/app
reference]noneunknownNA''text-orig-fulllayout-orig-full}TrueC:/Users/tonyj/text-fabric-data/github/tonyjurg/Nestle1904GBI/_tempNestle 1904 (GBI nodes)tonyjurg/tfNestle1904GBINestle1904GBI0.4https://bibleol.3bmoodle.dk/text/show_text/nestle1904/<1>/<2>/<3>{book}''#{clause}''#{phrase}''lemmastrongsgloss]grc# load the N1904LFT app and data
N1904LFT = use ("tonyjurg/Nestle1904LFT", version="0.6")
Locating corpus resources ...
The requested app is not available offline ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/app not found
The requested data is not available offline ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 not found
| 0.21s T otype from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 2.27s T oslots from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.63s T unicode from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.59s T wordtranslit from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.60s T normalized from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.49s T chapter from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.49s T verse from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.59s T word from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.50s T after from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.62s T wordunacc from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.58s T book from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | | 0.06s C __levels__ from otype, oslots, otext | | 1.83s C __order__ from otype, oslots, __levels__ | | 0.07s C __rank__ from otype, __order__ | | 3.35s C __levUp__ from otype, oslots, __rank__ | | 1.94s C __levDown__ from otype, __levUp__, __rank__ | | 0.21s C __characters__ from otext | | 1.00s C __boundary__ from otype, oslots, __rank__ | | 0.04s C __sections__ from otype, oslots, otext, __levUp__, __levels__, book, chapter, verse | | 0.22s C __structure__ from otype, oslots, otext, __rank__, __levUp__, book, chapter, verse | 0.45s T booknumber from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.49s T bookshort from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.47s T case from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.32s T clausetype from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.55s T containedclause from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.42s T degree from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.58s T gloss from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.47s T gn from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.03s T headverse from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.32s T junction from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.56s T lemma from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.51s T lex_dom from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.55s T ln from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.42s T markafter from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.42s T markbefore from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.43s T markorder from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.46s T monad from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.44s T mood from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.52s T morph from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.53s T nodeID from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.49s T nu from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.48s T number from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.43s T person from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.43s T punctuation from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.66s T ref from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.66s T reference from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.50s T roleclausedistance from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.46s T sentence from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.50s T sp from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.50s T sp_full from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.53s T strongs from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.44s T subj_ref from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.44s T tense from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.45s T type from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.44s T voice from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.38s T wgclass from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.33s T wglevel from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.36s T wgnum from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.34s T wgrole from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.34s T wgrolelong from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.39s T wgrule from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.33s T wgtype from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.49s T wordlevel from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.49s T wordrole from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.50s T wordrolelong from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
| 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 | 105430 | 6.85 | 524 |
| word | 137779 | 1.00 | 100 |
3tonyjurg/Nestle1904LFTC:/Users/tonyj/text-fabric-data/github/tonyjurg/Nestle1904LFT/app''orig_orderversebookchapternoneunknownNA''0text-orig-fullhttps://github.com/tonyjurg/Nestle1904LFT/blob/main/docs/abouthttps://github.com/tonyjurg/Nestle1904LFThttps://github.com/tonyjurg/Nestle1904LFT/blob/main/docs/features/<feature>.mdlayout-orig-full}TrueC:/Users/tonyj/text-fabric-data/github/tonyjurg/Nestle1904LFT/_tempNestle 1904 (Low Fat Tree)notyettonyjurg/tfNestle1904LFTNestle1904LFT0.6https://learner.bible/text/show_text/nestle1904/Show this on the Bible Online Learner websiteenhttps://learner.bible/text/show_text/nestle1904/<1>/<2>/<3>{webBase}/word?version={version}&id=<lid>TrueTrue{book}''TrueTrue{chapter}''0#{sentence} (start: {book} {chapter}:{headverse})''Truechapter verse{book} {chapter}:{verse}''0#{wgnum}: {wgtype} {wgclass} {clausetype} {wgrole} {wgrule} {junction}''Truelemmaglosschapter versegrc# The following will push the Text-Fabric stylesheet to this notebook (to facilitate proper display with notebook viewer)
# The stylesheets of N1904GBI and N1904LFT will be identical, so one call will suffice.
N1904GBI.dh(N1904GBI.getCss())
# Set default view in a way to limit noise as much as possible.
# These settings need to be done for both datasets
N1904GBI.displaySetup(condensed=True, multiFeatures=False,queryFeatures=False)
N1904LFT.displaySetup(condensed=True, multiFeatures=False,queryFeatures=False)
WordQuery = '''
word book=John chapter=1 verse=1 word=ἀρχῇ
'''
WordResult = N1904LFT.search(WordQuery)
api=N1904LFT.api
# returns list of ordered tuples, even when only containing one node, hence indexed with [0][0] on next line
print("word:",api.T.text(WordResult[0][0])," Louw-Nida:",api.F.ln.v(WordResult[0][0]))
0.08s 1 result word: ἀρχῇ Louw-Nida: 67.65
However, the ἀρχή of John 1:1, according to Louw-Nida Lexicon can be either:
a:beginning (aspect)=68.1 [^1] b:beginning (time)=67.65 [^2]
Only the second meaning is provided by the feature 'ln'. Although the most appropriate, the meaning of the second one (aspect) might also be implied by the author. Queries depending on 'ln' (or, by consequence, 'lex_dom') may yield incorrect or incomplete results.
[^1] Louw, Johannes P., and Eugene Albert Nida. Greek-English Lexicon of the New Testament: Based on Semantic Domains, Vol. I (New York: United Bible Societies, 1996), 654.
[^2] ibid., 636.
Functional tags are only available on GBI, so the data will be taken from that Text-Fabric dataset. See this Jupyter notebook for an indepth discussion about formal and functional tags.
Consider the two following verses, both containing the same verb ἔρχεται:
Καὶ ἐξῆλθεν ἐκεῖθεν, καὶ ἔρχεται εἰς τὴν πατρίδα αὐτοῦ, .. (He went away from there and came to his hometown, Mark 6:1a, ESV)
Ἰδοὺ ἔρχεται μετὰ τῶν νεφελῶν, καὶ ὄψεται αὐτὸν πᾶς ὀφθαλμὸς ... (Behold, he is coming with the clouds, and every eye will see him, Rev. 1:7a ESV)
The following query pulls the relevant data from the GBI Text-Fabric dataset
ErgetaiQuery = '''
word word=ἔρχεται
/with/
book=Mark chapter=6 verse=1
/or/
book=Revelation chapter=1 verse=7
/-/
'''
ErgetaiResult = N1904GBI.search(ErgetaiQuery)
# returns list of ordered tuples
# We also need to add the app reference (N1904GBI) before we can access the F API functions
api=N1904GBI.api
for tuple in ErgetaiResult:
node=tuple[0]
print(api.T.sectionFromNode(node), " word:",api.T.text(node)," Formal tag:",api.F.formaltag.v(node),
" Functional tag ",api.F.functionaltag.v(node)," gloss:",api.F.gloss.v(node))
0.08s 2 results
('Mark', 6, 1) word: ἔρχεται Formal tag: V-PNI-3S Functional tag V-PNI-3S gloss: came
('Revelation', 1, 7) word: ἔρχεται Formal tag: V-PNI-3S Functional tag V-PNI-3S gloss: He is coming
Both instances are the same praesens indicativus of ἔρχομαι, with identical formal and functional tag, but require to be translated rather different.
Consider the following text from Matthew 1:1:
Βίβλος γενέσεως Ἰησοῦ Χριστοῦ υἱοῦ Δαυεὶδ υἱοῦ Ἀβραάμ.
In this verse there are two appositions to 'Ἰησοῦ Χριστοῦ': 'υἱοῦ Δαυεὶδ' and 'υἱοῦ Ἀβραάμ'. The LFT Text-Fabric data presents 'υἱοῦ Δαυεὶδ' as apposition to 'Ἰησοῦ Χριστοῦ' and 'υἱοῦ Ἀβραάμ' as apposition to 'Ἰησοῦ Χριστοῦ υἱοῦ Δαυεὶδ'. Another choice of apposition can also be argued: both 'υἱοῦ Δαυεὶδ' and 'υἱοῦ Ἀβραάμ' being appositions to 'Ἰησοῦ Χριστοῦ'. This actualy the case in the XML data for the GBI nodes.
This Example is discussed in detail in a separate Jupiter NoteBook
To what is the κατὰ in 1 Cor. 16:2 the adverbial? The Text-Farbic data suggest to 'μίαν σαββάτου'.
# create the query template
VerseQuery = '''
verse verse=2 chapter=16 book=I_Corinthians
'''
# execute the query template
VerseResult = N1904LFT.search(VerseQuery)
0.00s 1 result
N1904LFT.show(VerseResult, condensed=True, multiFeatures=False)
verse 1
Query for following syntactical construction:
# create the query template
SyntacticQuery1 = '''
verse
wg wgrule=PrepNp
word lemma=κατά sp=prep
word lemma=εἷς case=accusative
'''
SyntacticResult1 = N1904LFT.search(SyntacticQuery1)
N1904LFT.table(SyntacticResult1, condensed=True, multiFeatures=False)
0.21s 6 results
| n | p | verse | word (+1) | word (+1) | wg (+1) |
|---|---|---|---|---|---|
| 1 | John 21:25 | καθ’ ἕν, | καθ’ | ἕν, | |
| 2 | Acts 21:19 | καθ’ | ἓν | καθ’ ἓν | |
| 3 | I_Corinthians 14:31 | καθ’ | ἕνα | καθ’ ἕνα | |
| 4 | I_Corinthians 16:2 | κατὰ | κατὰ μίαν σαββάτου | μίαν | |
| 5 | Ephesians 5:33 | καθ’ | καθ’ ἕνα | ἕνα | |
| 6 | Revelation 4:8 | καθ’ | ἓν | καθ’ ἓν αὐτῶν |
Query for next syntactical construction: {note below: in the recent dataset the function of feature wgtype has changed - this query needs to be reconsidered}
# create the query template
SyntacticQuery2 = '''
verse
wg wgrule=PrepNp wgrole=adv
word sp=prep lemma=κατά
word sp=adj case=accusative
wg wgtype=modifier-scope
word sp=noun
'''
SyntacticResult2 = N1904LFT.search(SyntacticQuery2)
N1904LFT.table(SyntacticResult2, condensed=True, multiFeatures=False)
0.35s 0 results
# create the query template
SyntacticQuery3 = '''
verse
wg
word sp=prep lex_dom=067002
word lemma=εἷς case=accusative
'''
SyntacticResult3 = N1904LFT.search(SyntacticQuery3)
N1904LFT.table(SyntacticResult3, condensed=True, multiFeatures=False)
0.18s 10 results
| n | p | verse | wg (+1) | word (+1) | wg (+1) | wg (+1) | wg |
|---|---|---|---|---|---|---|---|
| 1 | Matthew 28:1 | μίαν | Ὀψὲ δὲ σαββάτων, τῇ ἐπιφωσκούσῃ εἰς μίαν σαββάτων, ἦλθεν Μαριὰμ ἡ Μαγδαληνὴ καὶ ἡ ἄλλη Μαρία θεωρῆσαι τὸν τάφον. | Ὀψὲ σαββάτων, τῇ ἐπιφωσκούσῃ εἰς μίαν σαββάτων, ἦλθεν Μαριὰμ ἡ Μαγδαληνὴ καὶ ἡ ἄλλη Μαρία θεωρῆσαι τὸν τάφον. | Ὀψὲ | ||
| 2 | Mark 15:6 | Κατὰ ἑορτὴν ἀπέλυεν αὐτοῖς ἕνα δέσμιον ὃν παρῃτοῦντο. | ἕνα | Κατὰ | Κατὰ δὲ ἑορτὴν ἀπέλυεν αὐτοῖς ἕνα δέσμιον ὃν παρῃτοῦντο. | ||
| 3 | Acts 28:13 | μετὰ | μίαν | μετὰ μίαν ἡμέραν ἐπιγενομένου νότου δευτεραῖοι ἤλθομεν εἰς Ποτιόλους, | μετὰ μίαν ἡμέραν ἐπιγενομένου νότου | μετὰ μίαν ἡμέραν | |
| 4 | I_Corinthians 16:2 | κατὰ μίαν σαββάτου | κατὰ | κατὰ μίαν σαββάτου ἕκαστος ὑμῶν παρ’ ἑαυτῷ τιθέτω θησαυρίζων ὅ τι ἐὰν εὐοδῶται, ἵνα μὴ ὅταν ἔλθω τότε λογίαι γίνωνται. | μίαν | ||
| 5 | Titus 3:10 | μετὰ μίαν καὶ δευτέραν νουθεσίαν | μετὰ | μίαν |
# create the query template
SyntacticQuery4 = '''
verse
wg
a:word sp=adj lemma=εἷς
b:word sp=conj
c:word sp=adj
a <: b
b <: c
'''
SyntacticResult4 = N1904LFT.search(SyntacticQuery4)
N1904LFT.table(SyntacticResult4, condensed=True, multiFeatures=False)
0.25s 8 results
| n | p | verse | word (+1) | word | word | wg (+1) | wg | wg | wg |
|---|---|---|---|---|---|---|---|---|---|
| 1 | Matthew 5:18 | ἓν | ἢ | μία | ἀμὴν γὰρ λέγω ὑμῖν, ἕως ἂν παρέλθῃ ὁ οὐρανὸς καὶ ἡ γῆ, ἰῶτα ἓν ἢ μία κεραία οὐ μὴ παρέλθῃ ἀπὸ τοῦ νόμου, ἕως ἂν πάντα γένηται. | ἀμὴν λέγω ὑμῖν, ἕως ἂν παρέλθῃ ὁ οὐρανὸς καὶ ἡ γῆ, ἰῶτα ἓν ἢ μία κεραία οὐ μὴ παρέλθῃ ἀπὸ τοῦ νόμου, ἕως ἂν πάντα γένηται. | ἕως ἂν παρέλθῃ ὁ οὐρανὸς καὶ ἡ γῆ, ἰῶτα ἓν ἢ μία κεραία οὐ μὴ παρέλθῃ ἀπὸ τοῦ νόμου, ἕως ἂν πάντα γένηται. | ἰῶτα ἓν ἢ μία κεραία | |
| 2 | Ephesians 4:7 | Ἑνὶ δὲ ἑκάστῳ ἡμῶν ἐδόθη ἡ χάρις κατὰ τὸ μέτρον τῆς δωρεᾶς τοῦ Χριστοῦ. | Ἑνὶ | δὲ | ἑκάστῳ | ||||
| 3 | Titus 3:10 | μίαν | καὶ | δευτέραν | μετὰ μίαν καὶ δευτέραν νουθεσίαν | μίαν καὶ δευτέραν νουθεσίαν | μίαν καὶ δευτέραν |
WordQuery = '''
word book=Titus chapter=3 verse=10 word
'''
VerseResult = N1904LFT.search(WordQuery)
N1904LFT.show(VerseResult, condensed=True, multiFeatures=False)
0.07s 8 results
verse 1
api.S.relationsLegend()
= left equal to right (as node)
# left unequal to right (as node)
< left before right (in canonical node ordering)
> left after right (in canonical node ordering)
== left occupies same slots as right
&& left has overlapping slots with right
## left and right do not have the same slot set
|| left and right do not have common slots
[[ left embeds right
]] left embedded in right
<< left completely before right
>> left completely after right
=: left and right start at the same slot
:= left and right end at the same slot
:: left and right start and end at the same slot
<: left immediately before right
:> left immediately after right
=k: left and right start at k-nearly the same slot
:k= left and right end at k-nearly the same slot
:k: left and right start and end at k-near slots
<k: left k-nearly before right
:k> left k-nearly after right
.f. left.f = right.f
.f=g. left.f = right.g
.f~r~g. left.f matches right.g
.f#g. left.f # right.g
.f>g. left.f > right.g
.f<g. left.f < right.g
The warp feature "oslots" and omap features cannot be used in searches.
One of the above relations on nodes and / or slots will suit you better.
The scripts in this notebook require (beside text-fabric) the following Python libraries to be installed in the environment:
{none}
You can install any missing library from within Jupyter Notebook using eitherpip or pip3.