Tutorial¶
This notebook gets you started with using Text-Fabric for coding in the Dead-Sea Scrolls.
Familiarity with the underlying data model is recommended.
Tip¶
If you start computing with this tutorial, first copy its parent directory to somewhere else, outside your repository. If you pull changes from the repository later, your work will not be overwritten. Where you put your tutorial directory is up to you. It will work from any directory.
Data¶
Text-Fabric will fetch the data set for you from GitHub, and check for updates.
The data will be stored in the text-fabric-data in your home directory.
Features¶
The data of the corpus is organized in features. They are columns of data. Think of the corpus as a gigantic spreadsheet, where row 1 corresponds to the first sign, row 2 to the second sign, and so on, for all ~ 1.5 M signs, followed by ~ 500 K word nodes and yet another 200 K nodes of other types.
The information which reading each sign has, constitutes a column in that spreadsheet. The DSS corpus contains > 50 columns.
Instead of putting that information in one big table, the data is organized in separate columns. We call those columns features.
%load_ext autoreload
%autoreload 2
import os
import collections
Incantation¶
The simplest way to get going is by this incantation:
from tf.app import use
A = use("ETCBC/dss", hoist=globals())
Locating corpus resources ...
| 0.73s T otype from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 7.90s T oslots from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 1.20s T fullo from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 0.98s T glex from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 0.37s T lang from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 0.97s T glexo from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 1.09s T lexe from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 0.07s T punce from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 1.13s T lexo from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 0.07s T punco from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 1.81s T after from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 0.07s T punc from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 0.14s T fragment from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 0.11s T line from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 4.05s T glyph from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 0.14s T scroll from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 0.97s T glexe from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 3.94s T glyphe from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 1.04s T morpho from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 1.27s T fulle from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 3.95s T glypho from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 1.19s T lex from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 1.33s T full from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | | 0.22s C __levels__ from otype, oslots, otext | | 8.01s C __order__ from otype, oslots, __levels__ | | 0.39s C __rank__ from otype, __order__ | | 13s C __levUp__ from otype, oslots, __rank__ | | 5.69s C __levDown__ from otype, __levUp__, __rank__ | | 1.26s C __characters__ from otext | | 4.58s C __boundary__ from otype, oslots, __rank__ | | 0.72s C __sections__ from otype, oslots, otext, __levUp__, __levDown__, __levels__, scroll, fragment, line | 0.00s T alt from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 0.47s T biblical from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 0.45s T book from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 3.61s T book_etcbc from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 0.44s T chapter from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 0.68s T cl from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 0.00s T cl2 from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 0.01s T cor from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 0.46s T g_cons from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 0.88s T g_nme_etcbc from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 0.90s T g_prs_etcbc from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 0.44s T gn from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 0.11s T gn2 from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 0.00s T gn3 from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 0.42s T gn_etcbc from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 0.00s T halfverse from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 0.00s T intl from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 3.93s T lang_etcbc from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 1.09s T lex_etcbc from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 0.01s T md from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 0.00s T merr from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 0.00s T nr from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 0.44s T nu from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 0.11s T nu2 from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 0.00s T nu3 from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 1.02s T nu_etcbc from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 0.53s T occ from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 0.12s T ps from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 0.11s T ps2 from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 0.00s T ps3 from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 1.03s T ps_etcbc from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 1.15s T rec from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 0.01s T rem from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 0.01s T script from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 0.12s T sim from ~/text-fabric-data/github/ETCBC/dss/parallels/tf/1.9 | 0.97s T sp from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 0.90s T sp_etcbc from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 0.90s T srcLn from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 0.24s T st from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 4.15s T type from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 0.18s T unc from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 0.85s T uvf_etcbc from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 0.01s T vac from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 0.44s T verse from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 0.16s T vs from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 1.01s T vs_etcbc from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 0.16s T vt from ~/text-fabric-data/github/ETCBC/dss/tf/1.9 | 1.01s T vt_etcbc from ~/text-fabric-data/github/ETCBC/dss/tf/1.9
Data: ETCBC - dss 1.9, Character table, Feature docs
Node types
| Name | # of nodes | # slots / node | % coverage |
|---|---|---|---|
| scroll | 1001 | 1428.81 | 100 |
| lex | 10450 | 129.14 | 94 |
| fragment | 11182 | 127.91 | 100 |
| line | 52895 | 27.04 | 100 |
| clause | 125 | 12.85 | 0 |
| cluster | 101099 | 6.68 | 47 |
| phrase | 315 | 5.10 | 0 |
| word | 500995 | 2.81 | 99 |
| sign | 1430241 | 1.00 | 100 |
Features:
ETCBC/dss/parallels/tf
Dead Sea Scrolls
specified
- apiVersion:
3 - appName:
ETCBC/dss - appPath:
/Users/me/text-fabric-data/github/ETCBC/dss/app - commit:
gd796845ffd026d7896a29d71f730d471cba06631 css:
.full,.glyph,.punc {
font-family: "Ezra SIL", "SBL Hebrew", sans-serif;
}
.scriptpaleohebrew {
border: 1px dashed navy;
}
.scriptgreekcapital {
border: 1px dashed brown;
}
.langa {
text-decoration: underline;
}
.intl1 {
vertical-align: -0.25em;
}
.intl2 {
vertical-align: -0.5em;
}
.langg {
font-family: serif;
text-decoration: underline;
}
.vac1 {
background-color: #aaaaaa;
border 2pt solid #dd3333;
border-radius: 4pt;
}
.rem1 {
font-weight: bold;
color: red;
text-decoration: line-through;
}
.rem2 {
font-weight: bold;
color: maroon;
text-decoration: line-through;
}
.rec1 {
color: teal;
font-size: 80%;
}
.cor1 {
font-weight: bold;
color: dodgerblue;
text-decoration: overline;
}
.cor2 {
font-weight: bold;
color: navy;
text-decoration: overline;
}
.cor3 {
font-weight: bold;
color: navy;
text-decoration: overline;
vertical-align: super;
}
.alt1 {
text-decoration: overline;
}
/* UNSURE: italic*/
.unc1 {
font-weight: bold;
color: #888888;
}
.unc2 {
font-weight: bold;
color: #bbbbbb;
}
.unc3 {
font-weight: bold;
color: #bbbbbb;
text-shadow: #cccccc 1px 1px;
}
.unc4 {
font-weight: bold;
color: #dddddd;
text-shadow: #eeeeee 2px 2px;
}
.empty {
color: #ff0000;
}dataDisplay:
noneValues:
unknown- no value
- showVerseInTuple:
True textFormats:
- layout-orig-full: {method:
layoutOrig} - layout-source-full: {method:
layoutSource} - layout-trans-full: {method:
layoutTrans}
- layout-orig-full: {method:
docs:
- docPage:
about - featureBase:
{docBase}/transcription.md - featurePage:
transcription
- docPage:
- interfaceDefaults: {lineNumbers:
0} - isCompatible:
True - local:
local - localDir:
/Users/me/text-fabric-data/github/ETCBC/dss/_temp provenanceSpec:
- corpus:
Dead Sea Scrolls - doi:
10.5281/zenodo.2652849 moduleSpecs:
:
- backend: no value
- corpus:
Parallel Passages docUrl:
https://nbviewer.jupyter.org/github/etcbc/dss/blob/master/programs/parallels.ipynb- doi:
10.5281/zenodo.2652849 - org:
ETCBC - relative:
parallels/tf - repo:
dss
- org:
ETCBC - relative:
/tf - repo:
dss - version:
1.9 - webBase:
https://www.deadseascrolls.org.il/explore-the-archive - webHint:
Show this scroll in the Leon Levy library - webUrl:
{webBase}/search#q='<1>'
- corpus:
- release:
v1.9 typeDisplay:
- clause: {label:
{nr}} cluster:
- label:
{type} - stretch:
0
- label:
- fragment: {features:
biblical} lex:
- features:
lex lexe - featuresBare:
lexo - label:
True - lexOcc:
word - template:
True
- features:
- line: {features:
biblical} - phrase: {label:
{nr}} - scroll: {features:
biblical} - sign: {exclude: {type:
term}} word:
- base:
True - features:
lang lex cl ps gn nu st vs vt md - featuresBare:
sp - label:
True - lineNumber:
srcLn - wrap:
0
- base:
- clause: {label:
- writing:
hbo
You can see which features have been loaded, and if you click on a feature name, you find its documentation. If you hover over a name, you see where the feature is located on your system.
API¶
The result of the incantation is that we have a bunch of special variables at our disposal that give us access to the text and data of the corpus.
At this point it is helpful to throw a quick glance at the text-fabric API documentation (see the links under API Members above).
The most essential thing for now is that we can use F to access the data in the features
we've loaded.
But there is more, such as N, which helps us to walk over the text, as we see in a minute.
The API members above show you exactly which new names have been inserted in your namespace. If you click on these names, you go to the API documentation for them.
Search¶
Text-Fabric contains a flexible search engine, that does not only work for the data, of this corpus, but also for other corpora and data that you add to corpora.
Search is the quickest way to come up-to-speed with your data, without too much programming.
Jump to the dedicated search search tutorial first, to whet your appetite.
The real power of search lies in the fact that it is integrated in a programming environment. You can use programming to:
- compose dynamic queries
- process query results
Therefore, the rest of this tutorial is still important when you want to tap that power. If you continue here, you learn all the basics of data-navigation with Text-Fabric.
Counting¶
In order to get acquainted with the data, we start with the simple task of counting.
Count all nodes¶
We use the
N.walk() generator
to walk through the nodes.
We compared the TF data to a gigantic spreadsheet, where the rows correspond to the signs.
In Text-Fabric, we call the rows slots, because they are the textual positions that can be filled with signs.
We also mentioned that there are also other textual objects. They are the clusters, lines, faces and documents. They also correspond to rows in the big spreadsheet.
In Text-Fabric we call all these rows nodes, and the N() generator
carries us through those nodes in the textual order.
Just one extra thing: the info statements generate timed messages.
If you use them instead of print you'll get a sense of the amount of time that
the various processing steps typically need.
A.indent(reset=True)
A.info("Counting nodes ...")
i = 0
for n in N.walk():
i += 1
A.info("{} nodes".format(i))
0.00s Counting nodes ... 0.11s 2108303 nodes
Here you see it: over 2M nodes.
What are those nodes?¶
Every node has a type, like sign, or line, face. But what exactly are they?
Text-Fabric has two special features, otype and oslots, that must occur in every Text-Fabric data set.
otype tells you for each node its type, and you can ask for the number of slots in the text.
Here we go!
F.otype.slotType
'sign'
F.otype.maxSlot
1430241
F.otype.maxNode
2108303
F.otype.all
('scroll',
'lex',
'fragment',
'line',
'clause',
'cluster',
'phrase',
'word',
'sign')
C.levels.data
(('scroll', 1428.8121878121879, 1605868, 1606868),
('lex', 129.1396172248804, 1542523, 1552972),
('fragment', 127.90565194061885, 1531341, 1542522),
('line', 27.03924756593251, 1552973, 1605867),
('clause', 12.848, 2107864, 2107988),
('cluster', 6.678582379647672, 1430242, 1531340),
('phrase', 5.098412698412698, 2107989, 2108303),
('word', 2.814359424744758, 1606869, 2107863),
('sign', 1, 1, 1430241))
This is interesting: above you see all the textual objects, with the average size of their objects, the node where they start, and the node where they end.
Count individual object types¶
This is an intuitive way to count the number of nodes in each type.
Note in passing, how we use the indent in conjunction with info to produce neat timed
and indented progress messages.
A.indent(reset=True)
A.info("counting objects ...")
for otype in F.otype.all:
i = 0
A.indent(level=1, reset=True)
for n in F.otype.s(otype):
i += 1
A.info("{:>7} {}s".format(i, otype))
A.indent(level=0)
A.info("Done")
0.00s counting objects ... | 0.00s 1001 scrolls | 0.00s 10450 lexs | 0.00s 11182 fragments | 0.01s 52895 lines | 0.00s 125 clauses | 0.01s 101099 clusters | 0.00s 315 phrases | 0.06s 500995 words | 0.17s 1430241 signs 0.26s Done
Feature statistics¶
F
gives access to all features.
Every feature has a method
freqList()
to generate a frequency list of its values, higher frequencies first.
Here are the parts of speech:
F.sp.freqList()
(('ptcl', 154464),
('subs', 108562),
('unknown', 80256),
('verb', 58873),
('suff', 45747),
('adjv', 10633),
('numr', 6526),
('pron', 5784))
Signs, words and clusters have types. We can count them separately:
F.type.freqList("cluster")
(('rec', 93733),
('vac', 3522),
('cor3', 1582),
('unc2', 906),
('rem2', 706),
('alt', 333),
('cor2', 147),
('cor', 95),
('rem', 75))
F.type.freqList("word")
(('glyph', 470605), ('punct', 29927), ('numr', 463))
F.type.freqList("sign")
(('cons', 1156780),
('empty', 98407),
('missing', 53864),
('sep', 46453),
('punct', 29927),
('unc', 27168),
('term', 15532),
('numr', 2029),
('add', 65),
('foreign', 16))
for (w, amount) in F.glyph.freqList("word")[0:20]:
print(f"{amount:>5} {w}")
45393 ו 20491 ה 19378 ל 18225 ב 6389 את 5863 מ 4894 אשר 4789 יהוה 4355 א 4236 כול 4185 על 4172 אל 3262 כי 3091 כ 3005 לא 2841 כל 2424 לוא 1938 ארץ 1829 ישראל 1653 יום
hapaxes1 = sorted(lx for (lx, amount) in F.lex.freqList("word") if amount == 1)
len(hapaxes1)
3813
for lx in hapaxes1[0:20]:
print(lx)
# # # # # # # # # # # # # # # # # # # ות # # # # # ל # # # # # # # # ם # # # # ב # # # # ה # # # # ו # # # # # ך # # # # ל # # # # # # תא # # # ד # # # דב # # # דה # # # ה # # # # # הו # # # הם # # # ות # # # ט # # # כת
An other way to find lexemes with only one occurrence is to use the occ edge feature from lexeme nodes to the word nodes of
its occurrences.
hapaxes2 = sorted(F.lex.v(lx) for lx in F.otype.s("lex") if len(E.occ.f(lx)) == 1)
len(hapaxes2)
3813
for lx in hapaxes2[0:20]:
print(lx)
# # # # # # # # # # # # # # # # # # # ות # # # # # ל # # # # # # # # ם # # # # ב # # # # ה # # # # ו # # # # # ך # # # # ל # # # # # # תא # # # ד # # # דב # # # דה # # # ה # # # # # הו # # # הם # # # ות # # # ט # # # כת
The feature lex contains lexemes that may have uncertain characters in it.
The function glex has all those characters stripped.
Let's use glex instead.
hapaxes1g = sorted(lx for (lx, amount) in F.glex.freqList("word") if amount == 1)
len(hapaxes1)
3813
for lx in hapaxes1g[0:20]:
print(lx)
100 115 126 150 300 32 350 50 52 536 54 61 65 66 67 71 83 92 99 ידה
If we are not interested in the numerals:
for lx in [x for x in hapaxes1g if not x.isdigit()][0:20]:
print(lx)
ידה לוט נַחַל שֵׂעָר ֶ אֱגֹוז אֱלִידָד אֱלִיעָם אֱלִישֶׁבַע אֲבִיאֵל אֲבִיטַל אֲבִיעֶזְרִי אֲבִיעֶזֶר אֲבִישׁוּעַ אֲבַטִּיחַ אֲגֹורָה אֲדַמְדַּם אֲדָר אֲדֹנִי אֲדֹנִיָּה
Small occurrence base¶
The occurrence base of a word are the scrolls in which occurs.
We compute the occurrence base of each word, based on lexemes according to the glex feature.
occurrenceBase1 = collections.defaultdict(set)
A.indent(reset=True)
A.info("compiling occurrence base ...")
for w in F.otype.s("word"):
scroll = T.sectionFromNode(w)[0]
occurrenceBase1[F.glex.v(w)].add(scroll)
A.info(f"{len(occurrenceBase1)} entries")
0.00s compiling occurrence base ... 2.83s 8265 entries
Wow, that took long!
We looked up the scroll for each word.
But there is another way:
Start with scrolls, and iterate through their words.
occurrenceBase2 = collections.defaultdict(set)
A.indent(reset=True)
A.info("compiling occurrence base ...")
for s in F.otype.s("scroll"):
scroll = F.scroll.v(s)
for w in L.d(s, otype="word"):
occurrenceBase2[F.glex.v(w)].add(scroll)
A.info("done")
A.info(f"{len(occurrenceBase2)} entries")
0.00s compiling occurrence base ... 0.19s done 0.19s 8265 entries
Much better. Are the results equal?
occurrenceBase1 == occurrenceBase2
True
Yes.
occurrenceBase = occurrenceBase2
An overview of how many words have how big occurrence bases:
occurrenceSize = collections.Counter()
for (w, scrolls) in occurrenceBase.items():
occurrenceSize[len(scrolls)] += 1
occurrenceSize = sorted(
occurrenceSize.items(),
key=lambda x: (-x[1], x[0]),
)
for (size, amount) in occurrenceSize[0:10]:
print(f"base size {size:>4} : {amount:>5} words")
print("...")
for (size, amount) in occurrenceSize[-10:]:
print(f"base size {size:>4} : {amount:>5} words")
base size 1 : 2789 words base size 2 : 1109 words base size 3 : 692 words base size 4 : 462 words base size 5 : 335 words base size 6 : 256 words base size 7 : 219 words base size 8 : 182 words base size 9 : 177 words base size 10 : 122 words ... base size 457 : 1 words base size 459 : 1 words base size 538 : 1 words base size 600 : 1 words base size 605 : 1 words base size 629 : 1 words base size 745 : 1 words base size 761 : 1 words base size 844 : 1 words base size 997 : 1 words
Let's give the predicate private to those words whose occurrence base is a single scroll.
privates = {w for (w, base) in occurrenceBase.items() if len(base) == 1}
len(privates)
2789
Peculiarity of scrolls¶
As a final exercise with scrolls, lets make a list of all scrolls, and show their
- total number of words
- number of private words
- the percentage of private words: a measure of the peculiarity of the scroll
scrollList = []
empty = set()
ordinary = set()
for d in F.otype.s("scroll"):
scroll = T.scrollName(d)
words = {F.glex.v(w) for w in L.d(d, otype="word")}
a = len(words)
if not a:
empty.add(scroll)
continue
o = len({w for w in words if w in privates})
if not o:
ordinary.add(scroll)
continue
p = 100 * o / a
scrollList.append((scroll, a, o, p))
scrollList = sorted(scrollList, key=lambda e: (-e[3], -e[1], e[0]))
print(f"Found {len(empty):>4} empty scrolls")
print(f"Found {len(ordinary):>4} ordinary scrolls (i.e. without private words)")
Found 0 empty scrolls Found 507 ordinary scrolls (i.e. without private words)
print(
"{:<20}{:>5}{:>5}{:>5}\n{}".format(
"scroll",
"#all",
"#own",
"%own",
"-" * 35,
)
)
for x in scrollList[0:20]:
print("{:<20} {:>4} {:>4} {:>4.1f}%".format(*x))
print("...")
for x in scrollList[-20:]:
print("{:<20} {:>4} {:>4} {:>4.1f}%".format(*x))
scroll #all #own %own ----------------------------------- 4Q341 32 21 65.6% 4Q340 15 5 33.3% 11Q26 6 2 33.3% 4Q313a 3 1 33.3% 4Q358 3 1 33.3% 4Q347 10 3 30.0% 4Q124 86 25 29.1% 4Q282d 7 2 28.6% 1Q70bis 11 3 27.3% 1Q70 24 6 25.0% 4Q346a 4 1 25.0% 4Q357 4 1 25.0% 1Q41 9 2 22.2% 3Q15 269 58 21.6% 4Q561 73 15 20.5% 4Q559 129 26 20.2% 4Q360a 20 4 20.0% 1Q58 5 1 20.0% 4Q250b 5 1 20.0% 4Q468bb 5 1 20.0% ... 4Q427 343 2 0.6% 4Q2 174 1 0.6% 4Q366 185 1 0.5% 4Q98 192 1 0.5% 4Q56 963 5 0.5% 4Q394 194 1 0.5% 4Q59 404 2 0.5% 4Q88 208 1 0.5% 11Q20 429 2 0.5% 4Q57 875 4 0.5% 11Q11 222 1 0.5% 4Q58 450 2 0.4% 4Q174 241 1 0.4% 4Q13 257 1 0.4% 4Q524 280 1 0.4% 4Q271 293 1 0.3% 4Q84 350 1 0.3% 4Q33 365 1 0.3% 4Q428 385 1 0.3% 1QpHab 463 1 0.2%
Tip¶
See the lexeme recipe in the cookbook for how you get from a lexeme node to its word occurrence nodes.
Locality API¶
We travel upwards and downwards, forwards and backwards through the nodes.
The Locality-API (L) provides functions: u() for going up, and d() for going down,
n() for going to next nodes and p() for going to previous nodes.
These directions are indirect notions: nodes are just numbers, but by means of the
oslots feature they are linked to slots. One node contains an other node, if the one is linked to a set of slots that contains the set of slots that the other is linked to.
And one if next or previous to an other, if its slots follow or precede the slots of the other one.
L.u(node) Up is going to nodes that embed node.
L.d(node) Down is the opposite direction, to those that are contained in node.
L.n(node) Next are the next adjacent nodes, i.e. nodes whose first slot comes immediately after the last slot of node.
L.p(node) Previous are the previous adjacent nodes, i.e. nodes whose last slot comes immediately before the first slot of node.
All these functions yield nodes of all possible node types. By passing an optional parameter, you can restrict the results to nodes of that type.
The result are ordered according to the order of things in the text.
The functions return always a tuple, even if there is just one node in the result.
Going up¶
We go from the first word to the scroll it contains.
Note the [0] at the end. You expect one scroll, yet L returns a tuple.
To get the only element of that tuple, you need to do that [0].
If you are like me, you keep forgetting it, and that will lead to weird error messages later on.
firstScroll = L.u(1, otype="scroll")[0]
print(firstScroll)
1605868
And let's see all the containing objects of sign 3:
s = 3
for otype in F.otype.all:
if otype == F.otype.slotType:
continue
up = L.u(s, otype=otype)
upNode = "x" if len(up) == 0 else up[0]
print("sign {} is contained in {} {}".format(s, otype, upNode))
sign 3 is contained in scroll 1605868 sign 3 is contained in lex 1542524 sign 3 is contained in fragment 1531341 sign 3 is contained in line 1552973 sign 3 is contained in clause x sign 3 is contained in cluster x sign 3 is contained in phrase x sign 3 is contained in word 1606870
Going next¶
Let's go to the next nodes of the first scroll.
afterFirstScroll = L.n(firstScroll)
for n in afterFirstScroll:
print(
"{:>7}: {:<13} first slot={:<6}, last slot={:<6}".format(
n,
F.otype.v(n),
E.oslots.s(n)[0],
E.oslots.s(n)[-1],
)
)
secondScroll = L.n(firstScroll, otype="scroll")[0]
17149: sign first slot=17149 , last slot=17149 1612982: word first slot=17149 , last slot=17149 1553387: line first slot=17149 , last slot=17176 1531359: fragment first slot=17149 , last slot=18207 1605869: scroll first slot=17149 , last slot=33885
Going previous¶
And let's see what is right before the second scroll.
for n in L.p(secondScroll):
print(
"{:>7}: {:<13} first slot={:<6}, last slot={:<6}".format(
n,
F.otype.v(n),
E.oslots.s(n)[0],
E.oslots.s(n)[-1],
)
)
1605868: scroll first slot=1 , last slot=17148 1531358: fragment first slot=15658 , last slot=17148 1553386: line first slot=17099 , last slot=17148 1612981: word first slot=17147 , last slot=17148 17148: sign first slot=17148 , last slot=17148
Going down¶
We go to the fragments of the first scroll, and just count them.
fragments = L.d(firstScroll, otype="fragment")
print(len(fragments))
18
The first line¶
We pick two nodes and explore what is above and below them: the first line and the first word.
for n in [
F.otype.s("word")[0],
F.otype.s("line")[0],
]:
A.indent(level=0)
A.info("Node {}".format(n), tm=False)
A.indent(level=1)
A.info("UP", tm=False)
A.indent(level=2)
A.info("\n".join(["{:<15} {}".format(u, F.otype.v(u)) for u in L.u(n)]), tm=False)
A.indent(level=1)
A.info("DOWN", tm=False)
A.indent(level=2)
A.info("\n".join(["{:<15} {}".format(u, F.otype.v(u)) for u in L.d(n)]), tm=False)
A.indent(level=0)
A.info("Done", tm=False)
Node 1606869 | UP | | 1542523 lex | | 1552973 line | | 1531341 fragment | | 1605868 scroll | DOWN | | 2 sign Node 1552973 | UP | | 1531341 fragment | | 1605868 scroll | DOWN | | 1430242 cluster | | 1 sign | | 1606869 word | | 2 sign | | 1606870 word | | 3 sign | | 4 sign | | 5 sign | | 1606871 word | | 6 sign | | 7 sign | | 8 sign | | 9 sign | | 1606872 word | | 10 sign | | 11 sign | | 1606873 word | | 12 sign | | 13 sign | | 14 sign | | 15 sign | | 16 sign | | 1606874 word | | 17 sign | | 18 sign | | 19 sign | | 1606875 word | | 20 sign | | 1606876 word | | 21 sign | | 22 sign | | 23 sign | | 24 sign | | 1606877 word | | 25 sign | | 1606878 word | | 26 sign | | 27 sign | | 28 sign | | 29 sign Done
Text API¶
So far, we have mainly seen nodes and their numbers, and the names of node types. You would almost forget that we are dealing with text. So let's try to see some text.
In the same way as F gives access to feature data,
T gives access to the text.
That is also feature data, but you can tell Text-Fabric which features are specifically
carrying the text, and in return Text-Fabric offers you
a Text API: T.
Formats¶
DSS text can be represented in a number of ways:
orig: unicodetrans: ETCBC transcriptionsource: as in Abegg's data files
All three can be represented in two flavours:
full: all glyphs, but no bracketings and flagsextra: everything
If you wonder where the information about text formats is stored:
not in the program text-fabric, but in the data set.
It has a feature otext, which specifies the formats and which features
must be used to produce them. otext is the third special feature in a TF data set,
next to otype and oslots.
It is an optional feature.
If it is absent, there will be no T API.
Here is a list of all available formats in this data set.
T.formats
{'lex-default': 'word',
'lex-orig-full': 'word',
'lex-source-full': 'word',
'lex-trans-full': 'word',
'morph-source-full': 'word',
'text-orig-extra': 'word',
'text-orig-full': 'sign',
'text-source-extra': 'word',
'text-source-full': 'sign',
'text-trans-extra': 'word',
'text-trans-full': 'sign',
'layout-orig-full': 'sign',
'layout-source-full': 'sign',
'layout-trans-full': 'sign'}
Using the formats¶
The T.text() function is central to get text representations of nodes. Its most basic usage is
T.text(nodes, fmt=fmt)
where nodes is a list or iterable of nodes, usually word nodes, and fmt is the name of a format.
If you leave out fmt, the default text-orig-full is chosen.
The result is the text in that format for all nodes specified:
You see for each format in the list above its intended level of operation: sign or word.
If TF formats a node according to a defined text-format, it will descend to constituent nodes and represent those constituent nodes.
In this case, the formats ending in -extra specify the word level as the descend type.
Because, in this dataset, the features that contain the text-critical brackets are only defined at the word level.
At the sign level, those brackets are no longer visible, but they have left their traces in other features.
If we do not specify a format, the default format is used (text-orig-full).
We examine a portion of biblical material at the start 1Q1.
fragmentNode = T.nodeFromSection(("1Q1", "f1"))
fragmentNode
1540222
signs = L.d(fragmentNode, otype="sign")
words = L.d(fragmentNode, otype="word")
lines = L.d(fragmentNode, otype="line")
print(
f"""
Fragment {T.sectionFromNode(fragmentNode)} with
{len(signs):>3} signs
{len(words):>3} words
{len(lines):>3} lines
"""
)
Fragment ('1Q1', 'f1') with
157 signs
57 words
3 lines
T.text(signs[0:100])
'וירא אלהים כי טוב ׃ ויהי ערב ויהי בקר יום רביעי ׃ ויאמר ╱ אלהים ישרוצו המים שרץ נפש חיה ועוף יעופף על הארץ על פני רקיע השמים '
T.text(words[0:20])
'וירא אלהים כי טוב ׃ ויהי ערב ויהי בקר יום רביעי ׃ ויאמר אלהים ישרוצו ה'
T.text(lines[0:2])
'וירא אלהים כי טוב ׃ ויהי ערב ויהי בקר יום רביעי ׃ ויאמר ╱ אלהים ישרוצו המים שרץ נפש חיה ועוף יעופף על הארץ על פני רקיע השמים ׃ ╱ '
The -extra formats¶
In order to use non-default formats, we have to specify them in the fmt parameter.
T.text(signs[0:100], fmt="text-orig-extra")
''
We do not get much, let's ask why.
T.text(signs[0:2], fmt="text-orig-extra", explain=True)
EXPLANATION: T.text() called with parameters: nodes : iterable of 2 nodes fmt : text-orig-extra targeted at word descend: implicit func : no custom format implementation NODE: sign 770999 TARGET LEVEL: word (descend=None) (format target type) EXPANSION: 0 words FORMATTING: explicit text-orig-extra does <function Text._compileFormat.<locals>.g at 0x3539e9080> MATERIAL: NODE: sign 771000 TARGET LEVEL: word (descend=None) (format target type) EXPANSION: 0 words FORMATTING: explicit text-orig-extra does <function Text._compileFormat.<locals>.g at 0x3539e9080> MATERIAL:
''
The reason can be found in TARGET LEVEL: word and EXPANSION 0 words.
We are applying the word targeted format text-orig-extra to a sign, which does not contain words.
T.text(words[0:20], fmt="text-orig-extra")
'[ וירא אל ]הים כי [ טוב ׃ ויהי ערב ויהי בקר יום רביעי ׃ ויאמר ] [ אלהים יש ]רוצו ה'
T.text(lines[0:2], fmt="text-orig-extra")
'[ וירא אל ]הים כי [ טוב ׃ ויהי ערב ויהי בקר יום רביעי ׃ ויאמר ] [ אלהים יש ]רוצו המים שר#[ ץ נפש חיה ועוף יעופף על הארץ על פני רקיע השמים ׃ '
Note that the direction of the brackets look wrong, because they have not been adapted to the right-to-left writing direction.
We can view them in ETCBC transcription as well:
T.text(words[0:20], fmt="text-trans-extra")
'[ WJR> >L ]HJm KJ [ VWB 00 WJHJ <RB WJHJ BQR JWm RBJ<J 00 WJ>MR ] [ >LHJm J# ]RWYW H'
T.text(lines[0:2], fmt="text-trans-extra")
'[ WJR> >L ]HJm KJ [ VWB 00 WJHJ <RB WJHJ BQR JWm RBJ<J 00 WJ>MR ] [ >LHJm J# ]RWYW HMJm #R#[ y NP# XJH W<Wp J<WPp <L H>Ry <L PNJ RQJ< H#MJm 00 '
Or in Abegg's source encoding:
T.text(words[0:20], fmt="text-source-extra")
']wyra al[hyM ky ]fwb . wyhy orb wyhy bqr ywM rbyoy . wyamr[ ]alhyM yC[rwxw h'
T.text(lines[0:2], fmt="text-source-extra")
']wyra al[hyM ky ]fwb . wyhy orb wyhy bqr ywM rbyoy . wyamr[ ]alhyM yC[rwxw hmyM Cr«]X npC jyh wowP yowpP ol harX ol pny rqyo hCmyM . '
The function T.text() works with nodes of many types.
We compose a set of example nodes and run T.text on them:
exampleNodes = [
F.otype.s("sign")[1],
F.otype.s("word")[1],
F.otype.s("cluster")[0],
F.otype.s("line")[0],
F.otype.s("fragment")[0],
F.otype.s("scroll")[0],
F.otype.s("lex")[1],
]
exampleNodes
[2, 1606870, 1430242, 1552973, 1531341, 1605868, 1542524]
for n in exampleNodes:
print(f"This is {F.otype.v(n)} {n}:")
text = T.text(n)
if len(text) > 200:
text = text[0:200] + f"\nand {len(text) - 200} characters more"
print(text)
print("")
This is sign 2: ו This is word 1606870: עתה This is cluster 1430242: This is line 1552973: ועתה שמעו כל יודעי צדק ובינו במעשי This is fragment 1531341: ועתה שמעו כל יודעי צדק ובינו במעשי אל ׃ כי ריב ל׳ו עם כל בשר ומשפט יעשה בכל מנאצי׳ו ׃ כי במועל׳ם אשר עזבו׳הו הסתיר פני׳ו מישראל וממקדש׳ו ויתנ׳ם לחרב ׃ ובזכר׳ו ברית ראשנים השאיר שאירית לישראל ולא נתנ and 827 characters more This is scroll 1605868: ועתה שמעו כל יודעי צדק ובינו במעשי אל ׃ כי ריב ל׳ו עם כל בשר ומשפט יעשה בכל מנאצי׳ו ׃ כי במועל׳ם אשר עזבו׳הו הסתיר פני׳ו מישראל וממקדש׳ו ויתנ׳ם לחרב ׃ ובזכר׳ו ברית ראשנים השאיר שאירית לישראל ולא נתנ and 21145 characters more This is lex 1542524: h-עַתָּה-<AT.@H-oAt;Dh
Look at the last case, the lexeme node: obviously, the text-format that has been invoked provides
the language (h) of the lexeme, plus its representations in UNICODE, ETCBC, and Abegg transcription.
But what format exactly has been invoked? Let's ask.
T.text(exampleNodes[-1], explain=True)
EXPLANATION: T.text() called with parameters: nodes : single node fmt : implicit descend: implicit func : no custom format implementation NODE: lex 1542524 TARGET LEVEL: lex (no expansion needed) (descend=None) (format target type) EXPANSION: 1 lex 1542524 FORMATTING: implicit lex-default does <function Text._compileFormat.<locals>.g at 0x3539e82c0> MATERIAL: lex 1542524 ADDS "h-עַתָּה-<AT.@H-oAt;Dh "
'h-עַתָּה-<AT.@H-oAt;Dh '
The clue is in FORMATTING: implicit lex-default.
Remember that we saw the format lex-default in T.formats.
The Text-API has matched the type of the lexeme node we provided with this default format and applies it, thereby skipping the expansion of the lexeme node to its occurrences.
But we can force the expansion:
T.text(exampleNodes[-1], fmt="lex-default", descend=True)
'h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh h-עַתָּה-<AT.@H-oAt;Dh '
usefulFormats = [
fmt
for fmt in sorted(T.formats)
if not fmt.startswith("layout-") and not fmt == "lex-default"
]
len(usefulFormats)
10
firstLine = T.nodeFromSection(("1Q1", "f1", "1"))
for fmt in usefulFormats:
if not fmt.startswith("layout-"):
print(
"{}:\n\t{}\n".format(
fmt,
T.text(firstLine, fmt=fmt),
)
)
lex-orig-full: h-וְh-ראה h-אֱלֹהִים h-כִּי h-טֹוב h-׃ h-וְh-היה h-עֶרֶב h-וְh-היה h-בֹּקֶר h-יֹום h-רְבִיעִי h-׃ h-וְh-אמר lex-source-full: h-w◊h-rah h-aTløhIyM h-k;Iy h-føwb h-. h-w◊h-hyh h-oRr®b h-w◊h-hyh h-b;Oq®r h-yøwM h-r√bIyoIy h-. h-w◊h-amr lex-trans-full: h-W:h-R>H h->:ELOHIJm h-K.IJ h-VOWB h-00 h-W:h-HJH h-<EREB h-W:h-HJH h-B.OQER h-JOWm h-R:BIJ<IJ h-00 h-W:h->MR morph-source-full: Pcvqw3msj ncmp Pc ams . Pcvqw3msj ncms Pcvqw3msj ncms ncms uomsa . Pcvqw3ms text-orig-extra: [ וירא אל ]הים כי [ טוב ׃ ויהי ערב ויהי בקר יום רביעי ׃ ויאמר ] text-orig-full: וירא אלהים כי טוב ׃ ויהי ערב ויהי בקר יום רביעי ׃ ויאמר ╱ text-source-extra: ]wyra al[hyM ky ]fwb . wyhy orb wyhy bqr ywM rbyoy . wyamr[ text-source-full: wyra alhyM ky fwb . wyhy orb wyhy bqr ywM rbyoy . wyamr ╱ text-trans-extra: [ WJR> >L ]HJm KJ [ VWB 00 WJHJ <RB WJHJ BQR JWm RBJ<J 00 WJ>MR ] text-trans-full: WJR> >LHJm KJ VWB 00 WJHJ <RB WJHJ BQR JWm RBJ<J 00 WJ>MR ╱
Whole text in all formats in a few seconds¶
Part of the pleasure of working with computers is that they can crunch massive amounts of data. The text of the Dead Sea Scrolls is a piece of cake.
It takes just a few seconds to have that cake and eat it. In all useful formats.
A.indent(reset=True)
A.info("writing plain text of all scrolls in all text formats")
text = collections.defaultdict(list)
for ln in F.otype.s("line"):
for fmt in usefulFormats:
if fmt.startswith("text-"):
text[fmt].append(T.text(ln, fmt=fmt, descend=True))
A.info("done {} formats".format(len(text)))
for fmt in sorted(text):
print("{}\n{}\n".format(fmt, "\n".join(text[fmt][0:5])))
0.00s writing plain text of all scrolls in all text formats 4.31s done 6 formats text-orig-extra ועתה שמעו כל יודעי צדק ובינו במעשי אל ׃ כי ריב ל׳ו עם כל בשר ומשפט יעשה בכל מנאצי׳ו ׃ כי במועל׳ם אשר עזבו׳הו הסתיר פני׳ו מישראל וממקדש׳ו ו?יתנ׳ם לחרב ׃ ובזכר׳ו ברית ראשנים השאיר שאירית לישראל ולא נתנ׳ם לכלה ׃ ובקץ חרון שנים שלוש מאות text-orig-full ועתה שמעו כל יודעי צדק ובינו במעשי אל ׃ כי ריב ל׳ו עם כל בשר ומשפט יעשה בכל מנאצי׳ו ׃ כי במועל׳ם אשר עזבו׳הו הסתיר פני׳ו מישראל וממקדש׳ו ויתנ׳ם לחרב ׃ ובזכר׳ו ברית ראשנים השאיר שאירית לישראל ולא נתנ׳ם לכלה ׃ ובקץ חרון שנים שלוש מאות text-source-extra woth Cmow kl ywdoy xdq wbynw bmoCy al . ky ryb l/w oM kl bCr wmCpf yoCh bkl mnaxy/w . ky bmwol/M aCr ozbw/hw hstyr pny/w myCral wmmqdC/w wØytn/M ljrb . wbzkr/w bryt raCnyM hCayr Cayryt lyCral wla ntn/M lklh . wbqX jrwN CnyM ClwC mawt text-source-full □ woth Cmow kl ywdoy xdq wbynw bmoCy al . ky ryb l/w oM kl bCr wmCpf yoCh bkl mnaxy/w . ky bmwol/M aCr ozbw/hw hstyr pny/w myCral wmmqdC/w wytn/M ljrb . wbzkr/w bryt raCnyM hCayr Cayryt lyCral wla ntn/M lklh . wbqX jrwN CnyM ClwC mawt text-trans-extra W<TH #M<W KL JWD<J YDQ WBJNW BM<#J >L 00 KJ RJB L'W <m KL B#R WM#PV J<#H BKL MN>YJ'W 00 KJ BMW<L'm >#R <ZBW'HW HSTJR PNJ'W MJ#R>L WMMQD#'W W?JTN'm LXRB 00 WBZKR'W BRJT R>#NJm H#>JR #>JRJT LJ#R>L WL> NTN'm LKLH 00 WBQy XRWn #NJm #LW# M>WT text-trans-full W<TH #M<W KL JWD<J YDQ WBJNW BM<#J >L 00 KJ RJB L'W <m KL B#R WM#PV J<#H BKL MN>YJ'W 00 KJ BMW<L'm >#R <ZBW'HW HSTJR PNJ'W MJ#R>L WMMQD#'W WJTN'm LXRB 00 WBZKR'W BRJT R>#NJm H#>JR #>JRJT LJ#R>L WL> NTN'm LKLH 00 WBQy XRWn #NJm #LW# M>WT
The full plain text¶
We write all formats to file, in your Downloads folder.
for fmt in T.formats:
if fmt.startswith("text-"):
with open(
os.path.expanduser(f"~/Downloads/{fmt}.txt"),
"w",
# encoding='utf8',
) as f:
f.write("\n".join(text[fmt]))
(if this errors, uncomment the line with encoding)
Sections¶
A section in the DSS is a scroll, a fragment or a line.
Knowledge of sections is not baked into Text-Fabric.
The config feature otext.tf may specify three section levels, and tell
what the corresponding node types and features are.
From that knowledge it can construct mappings from nodes to sections, e.g. from line nodes to tuples of the form:
(scroll acronym, fragment label, line number)
You can get the section of a node as a tuple of relevant scroll, fragment, and line nodes. Or you can get it as a passage label, a string.
You can ask for the passage corresponding to the first slot of a node, or the one corresponding to the last slot.
If you are dealing with scroll and fragment nodes, you can ask to fill out the line and fragment parts as well.
Here are examples of getting the section that corresponds to a node and vice versa.
NB: sectionFromNode always delivers a line specification, either from the
first slot belonging to that node, or, if lastSlot, from the last slot
belonging to that node.
someNodes = (
F.otype.s("sign")[100000],
F.otype.s("word")[10000],
F.otype.s("cluster")[5000],
F.otype.s("line")[15000],
F.otype.s("fragment")[1000],
F.otype.s("scroll")[500],
)
for n in someNodes:
nType = F.otype.v(n)
d = f"{n:>7} {nType}"
first = A.sectionStrFromNode(n)
last = A.sectionStrFromNode(n, lastSlot=True, fillup=True)
tup = (
T.sectionTuple(n),
T.sectionTuple(n, lastSlot=True, fillup=True),
)
print(f"{d:<16} - {first:<18} {last:<18} {tup}")
100001 sign - 1QHa 25:31 1QHa 25:31 ((1605874, 1531445, 1555227), (1605874, 1531445, 1555227)) 1616869 word - 1QS 8:10 1QS 8:10 ((1605869, 1531366, 1553578), (1605869, 1531366, 1553578)) 1435242 cluster - 1Q29 f2:3 1Q29 f2:3 ((1605890, 1531685, 1556400), (1605890, 1531685, 1556400)) 1567973 line - 4Q368 f3:4 4Q368 f3:4 ((1606221, 1534207, 1567973), (1606221, 1534207, 1567973)) 1532341 fragment - 4Q186 f2ii 4Q186 f2ii:3 ((1605991, 1532341), (1605991, 1532341, 1559220)) 1606368 scroll - 4Q471b 4Q471b f1a_d:10 ((1606368,), (1606368, 1536089, 1575660))
Clean caches¶
Text-Fabric pre-computes data for you, so that it can be loaded faster. If the original data is updated, Text-Fabric detects it, and will recompute that data.
But there are cases, when the algorithms of Text-Fabric have changed, without any changes in the data, that you might want to clear the cache of precomputed results.
There are two ways to do that:
- Locate the
.tfdirectory of your dataset, and remove all.tfxfiles in it. This might be a bit awkward to do, because the.tfdirectory is hidden on Unix-like systems. - Call
TF.clearCache(), which does exactly the same.
It is not handy to execute the following cell all the time, that's why I have commented it out. So if you really want to clear the cache, remove the comment sign below.
# TF.clearCache()
Next steps¶
By now you have an impression how to compute around in the corpus. While this is still the beginning, I hope you already sense the power of unlimited programmatic access to all the bits and bytes in the data set.
Here are a few directions for unleashing that power.
- display become an expert in creating pretty displays of your text structures
- search turbo charge your hand-coding with search templates
- exportExcel make tailor-made spreadsheets out of your results
- share draw in other people's data and let them use yours
- similarLines spot the similarities between lines
See the cookbook for recipes for small, concrete tasks.
CC-BY Dirk Roorda