Ideally, a comprehensive documentation set should be created as part of developing a Text-Fabric dataset. However, in practice, this is not always completed during the initial phase or after changes to features. This Jupyter Notebook contains Python code to automatically generate (and thus ensure consistency) a documentation set for any Text-Fabric dataset. It serves as a robust starting point for the development of a brand new documentation set or as validation for an existing one. One major advantage is that the resulting documentation set is fully hyperlinked, a task that can be laborious if done manually.
The main steps in producing the documentation set are:
Your environment should (obviously) include the Python package Text-Fabric. In the current implementation of the script, the Python package markdown2 is also required. If not installed yet, it can be installed using pip. (note: possibly in a future version this dependancy might be removed).
!pip install markdown2
Collecting markdown2 Downloading markdown2-2.4.11-py2.py3-none-any.whl.metadata (2.0 kB) Downloading markdown2-2.4.11-py2.py3-none-any.whl (41 kB) ---------------------------------------- 0.0/41.1 kB ? eta -:--:-- --------- ------------------------------ 10.2/41.1 kB ? eta -:--:-- ------------------- -------------------- 20.5/41.1 kB 217.9 kB/s eta 0:00:01 ---------------------------------------- 41.1/41.1 kB 328.0 kB/s eta 0:00:00 Installing collected packages: markdown2 Successfully installed markdown2-2.4.11
At this stage the Text-Fabric dataset is loaded which will be used to create a documentation set. See documentation for function use for various options regaring storage locations.
%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 ...
| 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/appe68bd68c7c4c862c1464d995d51e27db7691254f''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}TruelocalC:/Users/tonyj/text-fabric-data/github/tonyjurg/Nestle1904LFT/_tempNestle 1904 (Low Fat Tree)10.5281/zenodo.10182594tonyjurg/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>v0.6TrueTrue{book}''TrueTrue{chapter}''0#{sentence} (start: {book} {chapter}:{headverse})''Truechapter verse{book} {chapter}:{verse}''0#{wgnum}: {wgtype} {wgclass} {clausetype} {wgrole} {wgrule} {junction}''Truelemmaglosschapter versegrc# set the title for all pages (indicating the dataset the documentation is describing)
pageTitle="N1904 Greek New Testament Text-Fabric dataset [tonyjurg/Nestle1904LFT - 0.6](https://github.com/tonyjurg/Nestle1904LFT)"
# location to store the resulting files.For now the same location as where the notebook resides (no ending slash)
resultLocation = ""
# Set verbose to True if you want dictionaries printed. Setting to False does mute the output
verbose=True
The following will create a dictionary containing the mapping from node type to node number.
# Initialize an empty dictionary
nodeDict = {}
# Iterate over C.levels.data
for item in C.levels.data:
node,_,start,end = item
# Create empty node list
nodeDict[node] = []
# Append the tuple (start, end) to the node's list
nodeDict[node].append((start, end))
# Print resulting dictionary depending on setting 'verbose'
if verbose: print(nodeDict)
print('finished')
{'book': [(137780, 137806)], 'chapter': [(137807, 138066)], 'verse': [(146078, 154020)], 'sentence': [(138067, 146077)], 'wg': [(154021, 259450)], 'word': [(1, 137779)]}
finished
Or alternative (with identical result)
# Initialize an empty dictionary
nodeDict = {}
# Iterate over node types
for NodeType in F.otype.all:
nodeDict[NodeType] = []
start, end = F.otype.sInterval(NodeType)
# Append the tuple (start, end) to the node's list
nodeDict[NodeType].append((start, end))
# Print resulting dictionary depending on setting 'verbose'
if verbose: print(nodeDict)
print('finished')
{'book': [(137780, 137806)], 'chapter': [(137807, 138066)], 'verse': [(146078, 154020)], 'sentence': [(138067, 146077)], 'wg': [(154021, 259450)], 'word': [(1, 137779)]}
finished
The following will create a feature list with information about the node types that contain values for that specific feature.
# Initialize an empty dictionary
featureDict = {}
# Iterate over Fall(), all features
for item in Fall():
# Use a set to store unique values for each feature
featureDict[item] = set()
for node, content in Fs(item).items():
featureDict[item].add(F.otype.v(node))
# Print the resulting dictionary depending on setting 'verbose'
if verbose: print(featureDict)
print('finished')
{'after': {'word'}, 'book': {'chapter', 'sentence', 'word', 'book', 'verse'}, 'booknumber': {'word', 'book'}, 'bookshort': {'word', 'book'}, 'case': {'word'}, 'chapter': {'word', 'sentence', 'verse', 'chapter'}, 'clausetype': {'wg'}, 'containedclause': {'word'}, 'degree': {'word'}, 'gloss': {'word'}, 'gn': {'word'}, 'headverse': {'sentence'}, 'junction': {'wg'}, 'lemma': {'word'}, 'lex_dom': {'word'}, 'ln': {'word'}, 'markafter': {'word'}, 'markbefore': {'word'}, 'markorder': {'word'}, 'monad': {'word'}, 'mood': {'word'}, 'morph': {'word'}, 'nodeID': {'word'}, 'normalized': {'word'}, 'nu': {'word'}, 'number': {'word'}, 'otype': {'wg', 'chapter', 'sentence', 'word', 'book', 'verse'}, 'person': {'word'}, 'punctuation': {'word'}, 'ref': {'word'}, 'reference': {'word'}, 'roleclausedistance': {'word'}, 'sentence': {'word', 'sentence'}, 'sp': {'word'}, 'sp_full': {'word'}, 'strongs': {'word'}, 'subj_ref': {'word'}, 'tense': {'word'}, 'type': {'word'}, 'unicode': {'word'}, 'verse': {'word', 'verse'}, 'voice': {'word'}, 'wgclass': {'wg'}, 'wglevel': {'wg'}, 'wgnum': {'wg'}, 'wgrole': {'wg'}, 'wgrolelong': {'wg'}, 'wgrule': {'wg'}, 'wgtype': {'wg'}, 'word': {'word'}, 'wordlevel': {'word'}, 'wordrole': {'word'}, 'wordrolelong': {'word'}, 'wordtranslit': {'word'}, 'wordunacc': {'word'}}
finished
The following will create a dictionairy with the description per feature (taken from the meta data)
# Initialize an empty dictionary
featureMetaDict = {}
# Iterate over Fall(), all features
for item in Fall():
featureMetaDict[item] = []
featureMetaData=Fs(item).meta
# Check if 'description' key exists in the meta dictionary
if 'description' in featureMetaData:
featureDescription = featureMetaData['description']
else:
featureDescription = "No feature description"
# Check if 'valueType' key exists in the meta dictionary
if 'valueType' in featureMetaData:
featureType = "unknown"
if featureMetaData["valueType"] == 'str': featureType = "string"
if featureMetaData["valueType"] == 'int': featureType = "integer"
else:
featureType = "not found"
if item!='otype':
FeatureFrequenceLists=Fs(item).freqList()
FoundItems=0
FeatureValueSetList = [] # Initialize an empty list to store feature value sets
for value, freq in FeatureFrequenceLists:
FoundItems+=1
FeatureValueSet = value
FeatureFrequencySet = freq
FeatureValueSetList.append((FeatureValueSet,FeatureFrequencySet))
if FoundItems==10: break
featureMetaDict[item].append((featureDescription, featureType, FeatureValueSetList))
# Print resulting dictionary depending on setting 'verbose'
if verbose: print(featureMetaDict)
print('finished')
{'after': [('✅ Characters (eg. punctuations) following the word', 'string', [(' ', 119270), (', ', 9462), ('. ', 5717), ('· ', 2359), ('; ', 971)])], 'book': [('✅ Book name (in English language)', 'string', [('Luke', 21785), ('Matthew', 20529), ('Acts', 20307), ('John', 17582), ('Mark', 12695), ('Revelation', 10726), ('Romans', 8014), ('I_Corinthians', 7798), ('Hebrews', 5513), ('II_Corinthians', 4992)])], 'booknumber': [('✅ NT book number (Matthew=1, Mark=2, ..., Revelation=27)', 'integer', [(3, 19457), (5, 18394), (1, 18300), (4, 15644), (2, 11278), (27, 9833), (6, 7101), (7, 6821), (19, 4956), (8, 4470)])], 'bookshort': [('✅ Book name (abbreviated)', 'string', [('Luke', 19457), ('Acts', 18394), ('Matt', 18300), ('John', 15644), ('Mark', 11278), ('Rev', 9833), ('Rom', 7101), ('1Cor', 6821), ('Heb', 4956), ('2Cor', 4470)])], 'case': [('✅ Gramatical case (Nominative, Genitive, Dative, Accusative, Vocative)', 'string', [('', 58261), ('nominative', 24197), ('accusative', 23031), ('genitive', 19515), ('dative', 12126), ('vocative', 649)])], 'chapter': [('✅ Chapter number inside book', 'integer', [(1, 12922), (2, 10923), (3, 9652), (4, 9631), (5, 8788), (6, 7561), (9, 7157), (7, 7069), (11, 7007), (12, 6970)])], 'clausetype': [('✅ Clause type details (e.g. Verbless, Minor)', 'string', [('', 102662), ('VerbElided', 1009), ('Verbless', 929), ('Minor', 830)])], 'containedclause': [('🆗 Contained clause (WG number)', 'string', [('', 8372), ('2', 148), ('172', 69), ('97', 69), ('389', 68), ('346', 63), ('822', 62), ('1455', 60), ('129', 59), ('1083', 58)])], 'degree': [('✅ Degree (e.g. Comparitative, Superlative)', 'string', [('', 137266), ('comparative', 313), ('superlative', 200)])], 'gloss': [('✅ English gloss', 'string', [('the', 9857), ('and', 6212), ('-', 5496), ('in', 2320), ('And', 2218), ('not', 2042), ('of the', 1551), ('for', 1501), ('that', 1498), ('you', 1226)])], 'gn': [('✅ Gramatical gender (Masculine, Feminine, Neuter)', 'string', [('', 63804), ('masculine', 41486), ('feminine', 18736), ('neuter', 13753)])], 'headverse': [('✅ Start verse number of a sentence', 'string', [('1', 298), ('7', 270), ('12', 267), ('9', 264), ('13', 260), ('11', 256), ('5', 255), ('8', 254), ('10', 252), ('6', 252)])], 'junction': [('✅ Junction data related to a wordgroup', 'string', [('', 103128), ('apposition', 2302)])], 'lemma': [('✅ Lexeme (lemma)', 'string', [('ὁ', 19783), ('καί', 8978), ('αὐτός', 5561), ('σύ', 2892), ('δέ', 2787), ('ἐν', 2743), ('ἐγώ', 2567), ('εἰμί', 2457), ('λέγω', 2255), ('εἰς', 1766)])], 'lex_dom': [('✅ Lexical domain according to Semantic Dictionary of Biblical Greek, SDBG (not present everywhere?)', 'string', [('092004', 26322), ('', 10487), ('089017', 4370), ('093001', 3672), ('033006', 3225), ('012001', 3000), ('089015', 2810), ('092003', 2444), ('069002', 1857), ('092001', 1846)])], 'ln': [('✅ Lauw-Nida lexical classification (not present everywhere?)', 'string', [('92.24', 19781), ('', 10488), ('92.11', 4718), ('89.92', 2903), ('89.87', 2756), ('33.69', 2336), ('69.3', 1736), ('92.1', 1732), ('92.7', 1494), ('12.1', 1247)])], 'markafter': [('🆗 Text critical marker after word', 'string', [('', 137728), ('—', 31), (')', 11), (']]', 7), ('(', 1), (']', 1)])], 'markbefore': [('🆗 Text critical marker before word', 'string', [('', 137745), ('—', 16), ('(', 10), ('[[', 7), ('[', 1)])], 'markorder': [(' Order of punctuation and text critical marker', 'string', [('', 137694), ('0', 34), ('3', 32), ('2', 10), ('1', 9)])], 'monad': [('✅ Monad (smallest token matching word order in the corpus)', 'integer', [(1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1), (8, 1), (9, 1), (10, 1)])], 'mood': [('✅ Gramatical mood of the verb (passive, etc)', 'string', [('', 109422), ('indicative', 15617), ('participle', 6653), ('infinitive', 2285), ('imperative', 1877), ('subjunctive', 1856), ('optative', 69)])], 'morph': [('✅ Morphological tag (Sandborg-Petersen morphology)', 'string', [('CONJ', 16316), ('PREP', 10568), ('ADV', 3808), ('N-NSM', 3475), ('N-GSM', 2935), ('T-NSM', 2905), ('N-ASF', 2870), ('PRT-N', 2701), ('N-ASM', 2456), ('V-PAI-3S', 2271)])], 'nodeID': [('✅ Node ID (as in the XML source data)', 'string', [('', 52046), ('common', 14186), ('personal', 6040), ('proper', 2192), ('relative', 885), ('demonstrative', 755), ('verb', 436), ('noun', 391), ('det', 359), ('interrogative', 341)])], 'normalized': [('✅ Surface word with accents normalized and trailing punctuations removed', 'string', [('καί', 8576), ('ὁ', 2769), ('δέ', 2764), ('ἐν', 2684), ('τοῦ', 2497), ('εἰς', 1755), ('τό', 1664), ('τόν', 1562), ('τήν', 1523), ('αὐτοῦ', 1411)])], 'nu': [('✅ Gramatical number (Singular, Plural)', 'string', [('singular', 69846), ('', 38842), ('plural', 29091)])], 'number': [('✅ Gramatical number of the verb (e.g. singular, plural)', 'string', [('singular', 69846), ('', 38842), ('plural', 29091)])], 'otype': [('No feature description', 'string', [('singular', 69846), ('', 38842), ('plural', 29091)])], 'person': [('✅ Gramatical person of the verb (first, second, third)', 'string', [('', 118360), ('third', 12747), ('second', 3729), ('first', 2943)])], 'punctuation': [('✅ Punctuation after word', 'string', [('', 119270), (',', 9462), ('.', 5717), ('·', 2359), (';', 971)])], 'ref': [('✅ Value of the ref ID (taken from XML sourcedata)', 'string', [('1CO 10:1!1', 1), ('1CO 10:1!10', 1), ('1CO 10:1!11', 1), ('1CO 10:1!12', 1), ('1CO 10:1!13', 1), ('1CO 10:1!14', 1), ('1CO 10:1!15', 1), ('1CO 10:1!16', 1), ('1CO 10:1!17', 1), ('1CO 10:1!18', 1)])], 'reference': [('✅ Reference (to nodeID in XML source data, not yet post-processes)', 'string', [('1CO 10:1!1', 1), ('1CO 10:1!10', 1), ('1CO 10:1!11', 1), ('1CO 10:1!12', 1), ('1CO 10:1!13', 1), ('1CO 10:1!14', 1), ('1CO 10:1!15', 1), ('1CO 10:1!16', 1), ('1CO 10:1!17', 1), ('1CO 10:1!18', 1)])], 'roleclausedistance': [('⚠️ Distance to the wordgroup defining the syntactical role of this word', 'string', [('0', 56129), ('1', 37597), ('2', 22297), ('3', 12084), ('4', 5277), ('5', 2527), ('6', 1041), ('7', 412), ('8', 141), ('9', 64)])], 'sentence': [('✅ Sentence number (counted per chapter)', 'integer', [(3, 1130), (4, 987), (1, 810), (5, 774), (6, 707), (10, 703), (7, 694), (11, 688), (15, 636), (9, 632)])], 'sp': [('✅ Part of Speech (abbreviated)', 'string', [('noun', 28455), ('verb', 28357), ('det', 19786), ('conj', 18227), ('pron', 16177), ('prep', 10914), ('adj', 8452), ('adv', 6147), ('ptcl', 773), ('num', 476)])], 'sp_full': [('✅ Part of Speech (long description)', 'string', [('Noun', 28455), ('Verb', 28357), ('Determiner', 19786), ('Conjunction', 18227), ('Pronoun', 16177), ('Preposition', 10914), ('Adjective', 8452), ('', 6147), ('Particle', 773), ('Numeral', 476)])], 'strongs': [('✅ Strongs number', 'string', [('3588', 19783), ('2532', 8978), ('846', 5561), ('4771', 2892), ('1161', 2787), ('1722', 2743), ('1473', 2567), ('1510', 2457), ('3004', 2255), ('1519', 1766)])], 'subj_ref': [('🆗 Subject reference (to nodeID in XML source data, not yet post-processes)', 'string', [('', 121204), ('n46003022002', 172), ('n66001009002', 131), ('n45001001001', 104), ('n47010001004', 104), ('n41006030007', 95), ('n50001001001', 92), ('n40005001015', 78), ('n49003001013', 73), ('n51001002007', 71)])], 'tense': [('✅ Gramatical tense of the verb (e.g. Present, Aorist)', 'string', [('', 109422), ('aorist', 11803), ('present', 11579), ('imperfect', 1689), ('future', 1626), ('perfect', 1572), ('pluperfect', 88)])], 'type': [('✅ Gramatical type of noun or pronoun (e.g. Common, Personal)', 'string', [('', 93321), ('common', 23644), ('personal', 11521), ('proper', 4639), ('demonstrative', 1722), ('relative', 1674), ('interrogative', 633), ('indefinite', 552), ('possessive', 70), ('adverbial', 3)])], 'unicode': [('✅ Word as it apears in the text in Unicode (incl. punctuations)', 'string', [('καὶ', 8541), ('ὁ', 2768), ('ἐν', 2683), ('δὲ', 2619), ('τοῦ', 2497), ('εἰς', 1755), ('τὸ', 1657), ('τὸν', 1556), ('τὴν', 1518), ('τῆς', 1300)])], 'verse': [('✅ Verse number inside chapter', 'integer', [(10, 4928), (12, 4910), (4, 4800), (9, 4800), (1, 4793), (3, 4768), (5, 4757), (11, 4734), (8, 4727), (2, 4723)])], 'voice': [('✅ Gramatical voice of the verb (e.g. active,passive)', 'string', [('', 109422), ('active', 20742), ('passive', 3493), ('middle', 2408), ('middlepassive', 1714)])], 'wgclass': [('✅ Class of the wordgroup (e.g. cl, np, vp)', 'string', [('np', 33710), ('cl', 30857), ('cl*', 16378), ('', 12760), ('pp', 11169), ('vp', 207), ('adjp', 168), ('advp', 166), ('adv', 7), ('nump', 7)])], 'wglevel': [('🆗 Number of the parent wordgroups for a wordgroup', 'integer', [(5, 16862), (4, 16527), (6, 15520), (7, 12162), (3, 10442), (8, 9027), (2, 8011), (9, 6135), (10, 4005), (11, 2483)])], 'wgnum': [('✅ Wordgroup number (counted per book)', 'integer', [(2, 27), (3, 27), (4, 27), (5, 27), (6, 27), (7, 27), (8, 27), (11, 27), (12, 27), (13, 27)])], 'wgrole': [('✅ Syntactical role of the wordgroup (abbreviated)', 'string', [('', 69235), ('adv', 16710), ('o', 9329), ('s', 6710), ('p', 1770), ('io', 702), ('v', 405), ('aux', 360), ('o2', 171), ('topic', 25)])], 'wgrolelong': [('✅ Syntactical role of the wordgroup (full)', 'string', [('', 69263), ('Adverbial', 16710), ('Object', 9329), ('Subject', 6710), ('Predicate', 1770), ('Indirect Object', 702), ('Verbal', 405), ('Auxiliar', 360), ('Second Object', 171), ('Verbal Copula', 10)])], 'wgrule': [('✅ Wordgroup rule information (e.g. Np-Appos, ClCl2, PrepNp)', 'string', [('DetNP', 15696), ('', 14701), ('PrepNp', 11044), ('NPofNP', 6819), ('Conj-CL', 5571), ('CLaCL', 3668), ('sub-CL', 3114), ('V2CL', 2753), ('V-O', 2660), ('DetCL', 2011)])], 'wgtype': [('✅ Wordgroup type details (e.g. group, apposition)', 'string', [('', 92932), ('group', 9699), ('apposition', 2799)])], 'word': [('✅ Word as it appears in the text (excl. punctuations)', 'string', [('καὶ', 8545), ('ὁ', 2769), ('ἐν', 2684), ('δὲ', 2620), ('τοῦ', 2497), ('εἰς', 1755), ('τὸ', 1658), ('τὸν', 1556), ('τὴν', 1518), ('αὐτοῦ', 1411)])], 'wordlevel': [('🆗 Number of the parent wordgroups for a word', 'string', [('6', 21857), ('7', 20984), ('5', 20538), ('8', 16755), ('9', 12772), ('4', 12110), ('10', 8756), ('3', 8537), ('11', 5797), ('12', 3571)])], 'wordrole': [('✅ Syntactical role of the word (abbreviated)', 'string', [('adv', 41598), ('v', 25817), ('s', 22908), ('o', 21929), ('', 9347), ('p', 7474), ('io', 3948), ('vc', 2603), ('aux', 1654), ('o2', 501)])], 'wordrolelong': [('✅ Syntactical role of the word (full)', 'string', [('Adverbial', 41598), ('Verbal', 25817), ('Subject', 22908), ('Object', 21929), ('', 9347), ('Predicate', 7474), ('Indirect Object', 3948), ('Verbal Copula', 2603), ('Auxiliar', 1654), ('Second Object', 501)])], 'wordtranslit': [('🆗 Transliteration of the text (in latin letters, excl. punctuations)', 'string', [('kai', 8576), ('en', 3152), ('o', 3149), ('to', 2885), ('de', 2769), ('ton', 2763), ('tou', 2497), ('eis', 1851), ('ten', 1523), ('auton', 1514)])], 'wordunacc': [('✅ Word without accents (excl. punctuations)', 'string', [('και', 8576), ('ο', 3019), ('δε', 2764), ('εν', 2752), ('του', 2497), ('εις', 1851), ('το', 1664), ('τον', 1562), ('την', 1523), ('αυτου', 1411)])]}
finished
import markdown2
import os
filesCreated=0
for feature in featureDict:
# prepare the data
featureName = feature
nodeList = ''
featureValues=''
for node in featureDict[feature]:
nodeList += f' <A HREF=\"featurebynodetype.md#{node}\">`{node}`</A>'
featureDescription, featureType, valueFreq = featureMetaDict[feature][0]
featureValues="Value|Frequency|\n---|---|\n"
for value, freq in valueFreq:
if value=='':
featureValues+=f"empty |{freq}|\n"
else:
featureValues+=f"`{value}` | {freq} |\n"
featureValues+="Note: only the first 10 items are shown"
# define the template for the feature description pages
FeaturePageTemplate = f"{pageTitle}\n#Feature: {featureName}\nData type|Available for node types|\n---|---|\n`{featureType}` |{nodeList}|\n## Description\n{featureDescription}\n## Values\n{featureValues}\n"
# create the feature file
FeaturePageContent = FeaturePageTemplate.format(featureName=feature, featureType=featureType, nodeList=nodeList)
# Convert the plain text to Markdown
markdown_content = markdown2.markdown(FeaturePageContent, extras=['tables'])
# set up path to location to store the resulting file
fileName = os.path.join(resultLocation, f"{feature}.md")
try:
with open(fileName, "w", encoding="utf-8") as file:
file.write(markdown_content)
filesCreated+=1
# Write the Markdown content to a file
if verbose: print(f"Markdown content written to {fileName}")
except Exception as e:
print(f"Error writing to file {fileName} (please create directory \'{resultLocation}\' first)")
break
if filesCreated!=0: print(f'finished (writing {filesCreated} files)')
Markdown content written to after.md Markdown content written to book.md Markdown content written to booknumber.md Markdown content written to bookshort.md Markdown content written to case.md Markdown content written to chapter.md Markdown content written to clausetype.md Markdown content written to containedclause.md Markdown content written to degree.md Markdown content written to gloss.md Markdown content written to gn.md Markdown content written to headverse.md Markdown content written to junction.md Markdown content written to lemma.md Markdown content written to lex_dom.md Markdown content written to ln.md Markdown content written to markafter.md Markdown content written to markbefore.md Markdown content written to markorder.md Markdown content written to monad.md Markdown content written to mood.md Markdown content written to morph.md Markdown content written to nodeID.md Markdown content written to normalized.md Markdown content written to nu.md Markdown content written to number.md Markdown content written to otype.md Markdown content written to person.md Markdown content written to punctuation.md Markdown content written to ref.md Markdown content written to reference.md Markdown content written to roleclausedistance.md Markdown content written to sentence.md Markdown content written to sp.md Markdown content written to sp_full.md Markdown content written to strongs.md Markdown content written to subj_ref.md Markdown content written to tense.md Markdown content written to type.md Markdown content written to unicode.md Markdown content written to verse.md Markdown content written to voice.md Markdown content written to wgclass.md Markdown content written to wglevel.md Markdown content written to wgnum.md Markdown content written to wgrole.md Markdown content written to wgrolelong.md Markdown content written to wgrule.md Markdown content written to wgtype.md Markdown content written to word.md Markdown content written to wordlevel.md Markdown content written to wordrole.md Markdown content written to wordrolelong.md Markdown content written to wordtranslit.md Markdown content written to wordunacc.md finished (writing 55 files)
overviewPage = f"{pageTitle}\n#Features per node type\n"
# Iterate over node types
for NodeType in F.otype.all:
# Initialize an empty list to store keys
FeaturesWithNodeType = []
# Check each set in featureDict for the presence of this nodetype
for feature, value_set in featureDict.items():
if NodeType in value_set:
FeaturesWithNodeType.append(feature)
NodeItemText=f"##{NodeType}\nFeature|Datatype|Description|Examples\n|---|---|---|---|\n"
for item in FeaturesWithNodeType:
featureDescription =featureMetaDict[item][0][0]
DataType="`"+featureMetaDict[item][0][1]+"` "
#Get some example values
FoundItems=0
valueExamples=''
for value, freq in featureMetaDict[item][0][2]:
FoundItems+=1
valueExamples+='`'+str(value)+'` '
if FoundItems==2: break
NodeItemText+=f'<A HREF=\"{item}.md#readme\">{item}</A>| {DataType} | {featureDescription} | {valueExamples} \n'
overviewPage+=NodeItemText
# create the feature overview file
# Convert the plain text to Markdown
markdown_content = markdown2.markdown(overviewPage, extras=['tables'])
# set up path to location to store the resulting file
fileName = os.path.join(resultLocation, "featurebynodetype.md")
try:
with open(fileName, "w", encoding="utf-8") as file:
file.write(markdown_content)
filesCreated+=1
# Write the Markdown content to a file
if verbose: print(f"Markdown content written to {fileName}")
print('Overview page created successfully')
except Exception as e:
print(f"Error writing to file {fileName} (please create directory \'{resultLocation}\' first)")
Markdown content written to featurebynodetype.md Overview page created successfully
Licenced under Creative Commons Attribution 4.0 International (CC BY 4.0)