Work in progress!
This Jupyter Notebook showcases several examples of statistical analysis performed on a Text-Fabric corpus.
%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 N1904 app and data
N1904 = use ("tonyjurg/Nestle1904LFT", version="0.6", hoist=globals())
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 | 2.28s T oslots from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.49s T after from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.61s T unicode from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.62s T normalized from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.59s T wordunacc from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.60s T word from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.59s T wordtranslit from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.46s T verse from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.54s T chapter 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 | | 1.84s C __order__ from otype, oslots, __levels__ | | 0.07s C __rank__ from otype, __order__ | | 3.36s C __levUp__ from otype, oslots, __rank__ | | 2.16s C __levDown__ from otype, __levUp__, __rank__ | | 0.22s C __characters__ from otext | | 0.97s 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 | 0.44s T booknumber from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.50s T bookshort from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.49s 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 | 0.57s T containedclause from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.41s T degree from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.57s 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 | 0.55s 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 | 0.54s T ln from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.41s 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 | 0.41s T markorder from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.44s T monad from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.43s T mood from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.51s T morph from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.52s T nodeID from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.48s 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 | 0.42s T punctuation from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.65s T ref from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.64s T reference from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.48s T roleclausedistance from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.48s T sentence from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.52s T sp from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.51s T sp_full from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.55s 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 | 0.44s 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 | 0.37s 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 | 0.35s 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 | 0.38s 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)
N1904.dh(N1904.getCss())
# Set default view in a way to limit noise as much as possible.
N1904.displaySetup(condensed=True, multiFeatures=False, queryFeatures=False)
Although this is somewhat trivial, this example does serve a purpose. We will print te version by means of calling the Text-Fabric parameter VERSION which is fixed for the whole programm. To access any of these parameters in our notebook, it first needs to be imported from tf.parameters.
from tf.parameters import VERSION
print ('TextFabric version: {}'.format(VERSION))
TextFabric version: 12.1.5
Note that any other parameters can be dumped in similar manner.
N1904.showContext(...)
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 versegrcsentencewgwordbookchapterversesentencewg()sentencewgwordwordwordwordword3tonyjurg/Nestle1904LFTC:/Users/tonyj/text-fabric-data/github/tonyjurg/Nestle1904LFT/appword}set()master02greek.htmlHow TF features represent texthttps://annotation.github.io/text-fabric/tf/writing/greek.htmlchapter}verse}wg}sentence}word}verseNestle 1904 (Low Fat Tree)''orig_orderversebookchapternoneunknownNA''0text-orig-fulltxtu grcwordsentenceversewgchapterbookwordsentenceversewgchapterwgwordsentencewgwordsentenceversewgwordltrhttps://github.com/tonyjurg/Nestle1904LFT/blob/main/docs/.mdabouthttps://github.com/tonyjurg/Nestle1904LFThttps://github.com/tonyjurg/Nestle1904LFT/blob/main/docs//about.mdHow TF features represent texthttps://annotation.github.io/text-fabric/tf/writing/greek.htmlhttps://github.com/tonyjurg/Nestle1904LFT/blob/main/docs/.mdabouthttps://github.com/tonyjurg/Nestle1904LFThttps://github.com/tonyjurg/Nestle1904LFT/blob/main/docs//about.mdhttps://github.com/tonyjurg/Nestle1904LFT/blob/main/docs/features/<feature>.mdhomenotyetset()Matthew 1:1<code>Matthew 1:1</code>orig_orderversebookchapter{} grchttps://github.com/tonyjurg/Nestle1904LFT/blob/main/docs/features/<feature>.mdhome[]{}[]{}[]{}[]{}[]{}lemma]{}[]{}[]{}[]{}[]{}[]{}gloss]{}text-orig-fulltxtntxtntxtu grctxtntxtnset(){}txtntxtu grctxtptxtotxttset()chapterbook00True0TrueTrueTrue00True00True{book}book]{chapter}chapter]#{sentence} (start: {book} {chapter}:{headverse})sentencebookchapterheadverse{book} {chapter}:{verse}bookchapterverse#{wgnum}: {wgtype} {wgclass} {clausetype} {wgrole} {wgrule} {junction}wgnumwgtypewgclassclausetypewgrolewgrulejunctionTrue()greekchildren hor wrapcontnr c4lbl c4children hor wrapcontnr c4lbl c4children hor wrapcontnr c2lbl c2children hor wrapcontnr c3lbl c3children hor wrapcontnr c1lbl c1''contnr c0lbl c0hor4TrueTruehor4TrueTruehor2TrueTruehor3TrueTruehor1TrueTruever000{}set(){}C:/Users/tonyj/text-fabric-data/github/tonyjurg/Nestle1904LFT/_temp()set()noneunknownNA''tonyjurgmasterNestle 1904 (Low Fat Tree)notyet()tonyjurg/tfNestle1904LFT0.6https://learner.bible/text/show_text/nestle1904/Show this on the Bible Online Learner websiteenhttps://learner.bible/text/show_text/nestle1904/<1>/<2>/<3>https://learner.bible/text/show_text/nestle1904//word?version=0.6&id=<lid>/tfNestle1904LFT :0''''''''''True()True()''()''()''()True()text-orig-fullhttps://annotation.github.io/text-fabric/tfTrueTrue{book}''TrueTrue{chapter}''0#{sentence} (start: {book} {chapter}:{headverse})''Truechapter verse{book} {chapter}:{verse}''0#{wgnum}: {wgtype} {wgclass} {clausetype} {wgrole} {wgrule} {junction}''Truelemmaglosschapter verse<github>https://nbviewer.jupyter.org/githubverse}0.6https://learner.bible/text/show_text/nestle1904/Show this on the Bible Online Learner websiteenhttps://learner.bible/text/show_text/nestle1904/<1>/<2>/<3>https://learner.bible/text/show_text/nestle1904//word?version=0.6&id=<lid>grcNestle1904LFT]In this example the header of the loaded Text-Fabric dataset is dumped. This is done by means of an API call to A.header().
Please note that in the example below A is replaced by N1904. This is result of the method of incantation:
N1904 = use (... etc ... )
The use function returns an oject whose attributes and methods constitute the advanced API. In the
N1904.header(allMeta=False)
| 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 versegrcThe following API call footprint provides a nicely formatted overview of memory footprint for each of the features in the Text_fabric corpus.
TF.footprint()
| feature | members | size in bytes |
|---|---|---|
| levUp | 259,450 | 41,713,080 |
| ref | 137,779 | 17,322,488 |
| reference | 137,779 | 17,322,488 |
| oslots | 3 | 15,801,460 |
| levDown | 121,671 | 14,355,792 |
| monad | 137,779 | 12,951,416 |
| nodeID | 137,779 | 12,695,390 |
| boundary | 2 | 12,320,324 |
| unicode | 137,779 | 11,836,548 |
| word | 137,779 | 11,208,988 |
| normalized | 137,779 | 11,109,174 |
| wordunacc | 137,779 | 11,064,793 |
| gloss | 137,779 | 10,313,083 |
| wordtranslit | 137,779 | 10,122,641 |
| containedclause | 137,779 | 9,884,154 |
| lemma | 137,779 | 9,669,633 |
| book | 154,020 | 9,557,063 |
| chapter | 153,993 | 9,554,764 |
| ln | 137,779 | 9,532,539 |
| subj_ref | 137,779 | 9,454,578 |
| strongs | 137,779 | 9,382,659 |
| sentence | 145,790 | 9,350,252 |
| order | 259,450 | 9,340,240 |
| verse | 145,722 | 9,323,176 |
| lex_dom | 137,779 | 9,198,777 |
| morph | 137,779 | 9,160,456 |
| wordlevel | 137,779 | 9,108,780 |
| roleclausedistance | 137,779 | 9,108,670 |
| bookshort | 137,806 | 9,102,960 |
| booknumber | 137,806 | 9,101,528 |
| sp_full | 137,779 | 9,101,394 |
| sp | 137,779 | 9,101,351 |
| wordrolelong | 137,779 | 9,101,348 |
| type | 137,779 | 9,101,345 |
| wordrole | 137,779 | 9,101,278 |
| mood | 137,779 | 9,101,174 |
| tense | 137,779 | 9,101,160 |
| case | 137,779 | 9,101,108 |
| markafter | 137,779 | 9,101,102 |
| after | 137,779 | 9,101,054 |
| markbefore | 137,779 | 9,101,052 |
| voice | 137,779 | 9,101,049 |
| punctuation | 137,779 | 9,101,048 |
| markorder | 137,779 | 9,101,021 |
| gn | 137,779 | 9,100,991 |
| person | 137,779 | 9,100,984 |
| degree | 137,779 | 9,100,941 |
| nu | 137,779 | 9,100,933 |
| number | 137,779 | 9,100,933 |
| wgnum | 105,430 | 8,645,128 |
| wgrule | 105,430 | 8,243,720 |
| wglevel | 105,430 | 8,199,368 |
| wgrole | 105,430 | 8,195,670 |
| wgrolelong | 105,430 | 8,195,576 |
| wgclass | 105,430 | 8,195,569 |
| clausetype | 105,430 | 8,195,219 |
| wgtype | 105,430 | 8,195,162 |
| junction | 105,430 | 8,195,108 |
| structure | 6 | 3,095,470 |
| rank | 259,450 | 1,077,884 |
| otype | 4 | 973,860 |
| sections | 2 | 570,276 |
| headverse | 8,011 | 523,269 |
| characters | 5 | 99,055 |
| levels | 6 | 1,300 |
| TOTAL | 7,967,669 | 613,490,794 |
The API call A.isLoaded() will show information about loaded features.
N1904.isLoaded()
__boundary__ computed
__characters__ computed
__levDown__ computed
__levUp__ computed
__levels__ computed
__order__ computed
__rank__ computed
__sections__ computed
__structure__ computed
after node (str) ✅ Characters (eg. punctuations) following the word
book node (str) ✅ Book name (in English language)
booknumber node (int) ✅ NT book number (Matthew=1, Mark=2, ..., Revelation=27)
bookshort node (str) ✅ Book name (abbreviated)
case node (str) ✅ Gramatical case (Nominative, Genitive, Dative, Accusative, Vocative)
chapter node (int) ✅ Chapter number inside book
clausetype node (str) ✅ Clause type details (e.g. Verbless, Minor)
containedclause node (str) 🆗 Contained clause (WG number)
degree node (str) ✅ Degree (e.g. Comparitative, Superlative)
gloss node (str) ✅ English gloss
gn node (str) ✅ Gramatical gender (Masculine, Feminine, Neuter)
headverse node (str) ✅ Start verse number of a sentence
junction node (str) ✅ Junction data related to a wordgroup
lemma node (str) ✅ Lexeme (lemma)
lex_dom node (str) ✅ Lexical domain according to Semantic Dictionary of Biblical Greek, SDBG (not
present everywhere?)
ln node (str) ✅ Lauw-Nida lexical classification (not present everywhere?)
markafter node (str) 🆗 Text critical marker after word
markbefore node (str) 🆗 Text critical marker before word
markorder node (str) Order of punctuation and text critical marker
monad node (int) ✅ Monad (smallest token matching word order in the corpus)
mood node (str) ✅ Gramatical mood of the verb (passive, etc)
morph node (str) ✅ Morphological tag (Sandborg-Petersen morphology)
nodeID node (str) ✅ Node ID (as in the XML source data)
normalized node (str) ✅ Surface word with accents normalized and trailing punctuations removed
nu node (str) ✅ Gramatical number (Singular, Plural)
number node (str) ✅ Gramatical number of the verb (e.g. singular, plural)
orig_order NOT LOADED
oslots edge
otext config
otype node (str)
person node (str) ✅ Gramatical person of the verb (first, second, third)
punctuation node (str) ✅ Punctuation after word
ref node (str) ✅ Value of the ref ID (taken from XML sourcedata)
reference node (str) ✅ Reference (to nodeID in XML source data, not yet post-processes)
roleclausedistance node (str) ⚠️ Distance to the wordgroup defining the syntactical role of this word
sentence node (int) ✅ Sentence number (counted per chapter)
sp node (str) ✅ Part of Speech (abbreviated)
sp_full node (str) ✅ Part of Speech (long description)
strongs node (str) ✅ Strongs number
subj_ref node (str) 🆗 Subject reference (to nodeID in XML source data, not yet post-processes)
tense node (str) ✅ Gramatical tense of the verb (e.g. Present, Aorist)
type node (str) ✅ Gramatical type of noun or pronoun (e.g. Common, Personal)
unicode node (str) ✅ Word as it apears in the text in Unicode (incl. punctuations)
verse node (int) ✅ Verse number inside chapter
voice node (str) ✅ Gramatical voice of the verb (e.g. active,passive)
wgclass node (str) ✅ Class of the wordgroup (e.g. cl, np, vp)
wglevel node (int) 🆗 Number of the parent wordgroups for a wordgroup
wgnum node (int) ✅ Wordgroup number (counted per book)
wgrole node (str) ✅ Syntactical role of the wordgroup (abbreviated)
wgrolelong node (str) ✅ Syntactical role of the wordgroup (full)
wgrule node (str) ✅ Wordgroup rule information (e.g. Np-Appos, ClCl2, PrepNp)
wgtype node (str) ✅ Wordgroup type details (e.g. group, apposition)
word node (str) ✅ Word as it appears in the text (excl. punctuations)
wordlevel node (str) 🆗 Number of the parent wordgroups for a word
wordrole node (str) ✅ Syntactical role of the word (abbreviated)
wordrolelong node (str) ✅ Syntactical role of the word (full)
wordtranslit node (str) 🆗 Transliteration of the text (in latin letters, excl. punctuations)
wordunacc node (str) ✅ Word without accents (excl. punctuations)
This example will show various statistics on node types. The call to C.levels.data results in list of ordered tuples which will be nicely displayed using the tabulate function.
# Library to format table
from tabulate import tabulate
headers = ["Node", "Avarage # of slots","first","last"]
ResultList= C.levels.data
print(tabulate(ResultList, headers=headers, tablefmt='fancy_grid'))
╒══════════╤══════════════════════╤═════════╤════════╕ │ Node │ Avarage # of slots │ first │ last │ ╞══════════╪══════════════════════╪═════════╪════════╡ │ book │ 5102.93 │ 137780 │ 137806 │ ├──────────┼──────────────────────┼─────────┼────────┤ │ chapter │ 529.919 │ 137807 │ 138066 │ ├──────────┼──────────────────────┼─────────┼────────┤ │ verse │ 17.346 │ 146078 │ 154020 │ ├──────────┼──────────────────────┼─────────┼────────┤ │ sentence │ 17.1987 │ 138067 │ 146077 │ ├──────────┼──────────────────────┼─────────┼────────┤ │ wg │ 6.85239 │ 154021 │ 259450 │ ├──────────┼──────────────────────┼─────────┼────────┤ │ word │ 1 │ 1 │ 137779 │ ╘══════════╧══════════════════════╧═════════╧════════╛
for NodeType in F.otype.all:
print (NodeType, F.otype.sInterval(NodeType))
book (137780, 137806) chapter (137807, 138066) verse (146078, 154020) sentence (138067, 146077) wg (154021, 259450) word (1, 137779)
Note that the ranges shown as output of this command are (except, possibly with repect to order) the same as found in file otype.tf:
@node
@TextFabric version=11.4.10
...
@valueType=str
@writtenBy=Text-Fabric
@dateWritten=2023-06-19T16:21:20Z
1-137779 word
137780-137806 book
137807-138066 chapter
138067-154190 clause
154191-226864 phrase
226865-232584 sentence
232585-240527 verse