NOTE: This notebook requires significant cleaning and rework.
%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
| 0.19s T otype from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 2.39s T oslots from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.58s T normalized from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.49s T after from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.58s T wordtranslit from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.61s T wordunacc from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.46s T verse from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.60s T word from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.61s T unicode from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.47s T chapter from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.55s T book from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | | 0.06s C __levels__ from otype, oslots, otext | | 1.90s C __order__ from otype, oslots, __levels__ | | 0.07s C __rank__ from otype, __order__ | | 3.27s C __levUp__ from otype, oslots, __rank__ | | 1.88s C __levDown__ from otype, __levUp__, __rank__ | | 0.21s C __characters__ from otext | | 0.96s 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 | 0.51s 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 | 0.54s 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 | 0.57s T lemma from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.55s 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 | 0.43s 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 | 0.52s 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 | 0.50s T nu from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.51s T number from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.44s 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 | 0.65s 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 | 0.46s 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 | 0.43s 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 | 0.40s 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 | 0.35s T wgrole from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.35s 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 | 0.52s T wordrole from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.51s 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)
Each query templace can be inspected by use of S.study(). This is particulary helpfull in case the query is complicated.
ComplicatedQuery = '''
wg
/where/
wg phrasefunction=S
/have/
/without/
word sp#conj
/-/
/-/
wg phrasefunction=O
'''
S.study(ComplicatedQuery)
0.00s Checking search template ...
0 1 clause 2 /where 3 phrase phrasefunction=S 4 /have 5 /without 6 word sp#conj 7 /- 8 /- 9 phrase phrasefunction=O 10 line 1: Unknown object type: "clause" line 9: Unknown object type: "phrase" Valid object types are: book, chapter, verse, sentence, wg, word Missing feature "phrasefunction" in line(s) 9
S.showPlan()
15s Cannot show plan if there is no previous "study()"
for result in S.fetch(limit=10):
TF.info(S.glean(result))
22s Cannot fetch if there is no previous "study()"
--------------------------------------------------------------------------- TypeError Traceback (most recent call last) Cell In[6], line 1 ----> 1 for result in S.fetch(limit=10): 2 TF.info(S.glean(result)) TypeError: 'NoneType' object is not iterable
Using python standard functions, it apears to be easy to verify if the result of two queries are the same or different. However, it is good to have a closer look at the matter since there are a few pitfalls that could lead to false results.
# define query template 1
SomeQuery ='''
phrase phrasefunction=V
word lemma=λέγω
'''
# now create two lists with identical query results and compare result lists
SomeResult1=N1904.search(SomeQuery)
SomeResult2=N1904.search(SomeQuery)
print(f'Same result? {SomeResult1 == SomeResult2}')
0 1 phrase phrasefunction=V 2 word lemma=λέγω 3 line 1: Unknown object type: "phrase" Valid object types are: book, chapter, verse, sentence, wg, word Or choose a custom set from: Missing feature "phrasefunction" in line(s) 1
0.00s Cannot load feature "phrasefunction": not in dataset 0.00s 0 results
0 1 phrase phrasefunction=V 2 word lemma=λέγω 3 line 1: Unknown object type: "phrase" Valid object types are: book, chapter, verse, sentence, wg, word Or choose a custom set from: Missing feature "phrasefunction" in line(s) 1
0.00s Cannot load feature "phrasefunction": not in dataset 0.00s 0 results Same result? True
This is exactly what we would expect. But comparing lists can be tricky. Consider the following two queries.
# define query template 1
Query1 ='''
phrase
a:word sp=prep
b:word sp=adj
c:word sp=noun
'''
# define query template 2
Query2 ='''
phrase
a:word sp=prep
b:word sp=noun
c:word sp=adj
'''
# create and compare result lists
ResultQuery1=N1904.search(Query1)
ResultQuery2=N1904.search(Query2)
print(f'Same result? {ResultQuery1 == ResultQuery2}')
0.34s 3851 results 0.35s 3851 results Same result? False
This method of comparing the lists results identifies a difference between the two lists. However, upon closer examination, that may or may not be the case, depending on what is understood as difference. The 'problem' here is that both ResultQuery1 and ResultQuery2 are lists of ordered tuples. The swapping of the feature conditions 'sp=adj' and 'sp=noun' did not result in a different set of results; only the presentation of rhe result differed.
# create 2 result lists
ResultQuery3=N1904.search(Query1,sort=True)
ResultQuery4=N1904.search(Query1,sort=False)
# compare unsorted lists
print(f'Unsorted lists: Same result ? {ResultQuery3 == ResultQuery4}')
# sort both lists on the first tuple
SortedResultQuery3 = sorted(ResultQuery3, key=lambda x: x[0])
SortedResultQuery4 = sorted(ResultQuery4, key=lambda x: x[0])
# compare sorted lists
print(f'Sorted lists: Same result ? {SortedResultQuery3 == SortedResultQuery4}')
0.34s 3851 results 0.33s 3851 results Unsorted lists: Same result ? False Sorted lists: Same result ? False
Unexpectedly the python list compare still viewed the two lists as different. But why? Python does report that the two lists are different because the comparison of lists (SortedResultQuery3 == SortedResultQuery4) checks for the equality of the list objects, not their contents.
The search() function in Text-Fabric returns a list of nodes or tuples representing search results. Even if the search criteria and the data are the same, the two lists, ResultQuery3 and ResultQuery4, are distinct list objects. Hence, when comparing them directly using the == operator, Python considers them as different objects, resulting in False.
To compare the content of the lists, it is advices to first onvert them to sets and compare those sets. See following example:
# Convert tuples to sets
set1 = set(tuple(item) for item in ResultQuery3)
set2 = set(tuple(item) for item in ResultQuery4)
# Compare the sets
if set1 == set2:
print("Lists ResultQuery3 and ResultQuery4 are equal.")
else:
print("Lists ResultQuery3 and ResultQuery4 are not equal.")
Lists ResultQuery3 and ResultQuery4 are equal.
Now, let's compare ResultQuery1 and ResultQuery2 again by first converting them to sets.
# Convert tuples to sets
set1 = set(tuple(item) for item in ResultQuery1)
set2 = set(tuple(item) for item in ResultQuery2)
# Compare the sets
if set1 == set2:
print("Lists ResultQuery1 and ResultQuery2 are equal.")
else:
print("Lists ResultQuery1 and ResultQuery2 are not equal.")
Lists ResultQuery1 and ResultQuery2 are not equal.
This is indeed the result we expected (see earlier mentioned reasons).
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