docs: fix cross references

docs/source/conf.py

@@ -71,26 +71,28 @@
 
 autodoc2_packages = [
     "../../dhlab/api",
-    "../../dhlab/future",
     "../../dhlab/images",
     "../../dhlab/ngram",
     "../../dhlab/metadata",
     "../../dhlab/text",
     "../../dhlab/visualize",
     "../../dhlab/wordbank",
     {
-        "path": "../../dhlab",
+        "path": "../../dhlab/__init__.py",
         "exclude_files": [
             "ngram/ngram.py",
             "graph_networkx_louvain.py",
             "module_update.py",
             "nbpictures.py",
-            "constants.py",
             "nbtext.py",
             "nbtokenizer.py",
+            "text/nbtokenizer.py",
             "token_map.py",
             "__init__.py",
+        ],
+        "exclude_dirs": [
             "legacy",
+            "future",
             "css_style_sheets",
         ],
         "auto_mode": True,

docs/source/docs_example_use.md

@@ -1,3 +1,15 @@
+---
+jupytext:
+  formats: md:myst
+  text_representation:
+    extension: .md
+    format_name: myst
+kernelspec:
+  display_name: Python 3
+  language: python
+  name: python3
+---
+
 # Examples of use
 
 The python package calls the [DHLAB API](https://api.nb.no/dhlab/) to retrieve and present data from the digital texts.
@@ -8,6 +20,7 @@ Analyses can be performed on both a single document, and on a larger corpus.
 
 Here are some of the text mining and automatic analyses you can do with `dhlab`:
 
+(example_corpus)=
 ## Build a corpus
 
 Build a [corpus](#text.corpus.Corpus) from bibliographic metadata about publications, e.g. books published between 1980 and 2005:
@@ -18,6 +31,7 @@ import dhlab as dh
 corpus = dh.Corpus(doctype="digibok", from_year=1980, to_year=2005)
 ```
 
+(example_count)=
 ## Word frequencies
 
 Retrieve word (token) [frequencies](#text.corpus.Corpus.count) from a corpus:
@@ -27,6 +41,7 @@ Retrieve word (token) [frequencies](#text.corpus.Corpus.count) from a corpus:
 corpus.count()
 ```
 
+(example_chunks)=
 ## Bags of words
 
 Fetch [chunks of text](#text.chunking.Chunks) (paragraphs) as bag of words from a specific publication:
@@ -41,6 +56,7 @@ c.chunks[0]  # The first bag-of-words is the title
 c.chunks[1] # Second bag-of-words is a paragraph, with word counts
 ```
 
+(example_concordance)=
 ## Concordance
 
 Extract [concordances](#text.conc_coll.Concordance) from the corpus:
@@ -53,6 +69,7 @@ concs.concordance
 # including links to the concordance's positions in books on nb.no
 ```
 
+(example_collocations)=
 ## Collocations
 
 Compute [collocations](#text.conc_coll.Collocations), a ranking of relevant words to a given word:
@@ -73,6 +90,7 @@ lte         3
 ein         3
 ```
 
+(example_ngram)=
 ## N-grams
 
 Retrieve [n-gram](#ngram.nb_ngram.nb_ngram) frequencies per year in a time period.
@@ -88,6 +106,7 @@ The `plot` method gives us this graph:
 
 Check out our [N-gram app](https://www.nb.no/ngram/#1_1_1__1_1_3_1810%2C2022_2_2_2_12_2) for an online visual graph of all uni-, bi-, and trigrams in the National Library's digitized collection of publications.
 
+(example_ner)=
 ## Named Entity Recognition
 
 Extract occurrences of [named entities](#text.parse.NER), for example place names:
@@ -99,6 +118,7 @@ docid = 'URN:NBN:no-nb_digibok_2007091701028'
 ner = NER(docid)
 ```
 
+(example_dispersion)=
 ## Word dispersions
 
 Plot narrative graphs of word [dispersions](#text.dispersion.Dispersion) in a publication, for instance in "Kristin Lavransdatter":

docs/source/docs_functionality.md

@@ -3,12 +3,12 @@
 <!-- start docs-functionality -->
 Here are some of the text mining and automatic analyses you can do with `dhlab`:
 
-- Build a [corpus](#dhlab.Corpus) from bibliographic metadata about publications.
-- Retrieve word (token) [frequencies](#dhlab.Corpus.count) from a corpus. 
-- Fetch [chunks of text](#dhlab.Chunks) (paragraphs) as bag of words from a specific publication.
-- Extract [concordances](#dhlab.Concordance)
-- [collocations](#dhlab.Collocations)
-- Retrieve [n-gram](#dhlab.ngram.nb_ngram) frequencies per yer in a time period.
-- Extract occurrences of [named entities](#dhlab.NER).
-- Plot narrative graphs of word [dispersions](#dhlab.text.dispersion.Dispersion) in a publication.
+- Build a [corpus](#example_corpus) from bibliographic metadata about publications.
+- Retrieve word (token) [frequencies](#example_count) from a corpus. 
+- Fetch [chunks of text](#example_chunks) (paragraphs) as bag of words from a specific publication.
+- Extract [concordances](#example_concordance)
+- [collocations](#example_collocations)
+- Retrieve [n-gram](#example_ngram) frequencies per yer in a time period.
+- Extract occurrences of [named entities](#example_ner).
+- Plot narrative graphs of word [dispersions](#example_dispersion) in a publication.
 <!-- end docs-functionality -->

docs/source/index_home.md

@@ -24,7 +24,7 @@ pip install -U dhlab
 
 ```{include} ./docs_functionality.md
 :heading-offset: 1
-relative-docs: docs/source/
+:relative-docs: docs/
 ```
 
 Try some of our [examples](./docs_example_use.md) to get started.