Package: papyri
PapyriSee the legendaryVilla of Papyri, who get its name from it's
collection of many papyrus scrolls.WhatA set of tools to build better documentation for Python project.Opinionated therefore can understand more about the structure of your project.Allow automatic cross link (back and forth) between documentation across python packages.Use a documentation IR, to separate building the docs from rendering the docs in many contexts.This should hopefully allow a conda-forge-like model, where project upload their IR to a given repo, and asingle
websitethat contain multiple project documentation (without sub domains) can be build with better cross link between
project andefficientpage rebuild.This should also allow to reader documentation onnon htmlbackend (think terminal), or provide documentation if
IDE (Spyder/Jupyterlab), without having to iframe it.install:Pip install from Pypi:$pipisntallpapyriInstall given package documentation:$papyriinstallpackage_name[package_name[package_name[...]]]Only numpy 1.20.0, scipy_1.5.0 are currently installable and published.
For other packages you will need to build locally which is a much more involved
process.Run terminal IPython with Papyri as an extension:$ ipython --ext papyri.ipythonThis will augment the?operator to show better documentation (when install withpapyri install ...dev installYou may need to get a modified version of numpydoc depending on the stage of development.# clone this repo
# cd this repo
pip install flit
flit install --symlinkSome functionality require tree_sitter_rst, see build_tree_sitter.py, and CI config file on how to build the tree-sitter
shared object locally.Instructions / OverviewIn the end there should be roughly 3 steps:try itIt isslowon full numpy/scipy, use--no-infersee below for a subpar but
faster experience.$ papyri gen numpy
$ papyri gen scipy scipy.stats # list submodules if they can't be automatically foundThis will create intermediate docs files in in~/.papyri/data/<library name>_<library_version>$ papyri ingest ~/.papyri/data/<path to folder generated at previous step>This will crosslink the newly generate folder will the existing ones.$ papyri render  # render all the html pages statically in ~/.papyri/html
$ papyri serve-static # start a http.server with the propoer root to serve above files.
$ papyri serve # start a server that will render the pages on the fly (nice to debug or iterate on theme, rendering)
$ papyri ascii <fully qualified names> # try to render in the terminal.
$ papyri browse <fully qualified name> # urwid documentation browser.Hacking on scrapping librariespapyri gen --no-infer [...]will skip type
inference of examples.--execoption need to be passed to try to execute examples.When run from this repo root, per project configuration are read frompapyri.tomlgeneration (papyri gen module_name),Which collect the documentation of a project into a doc-bundle; a number of
doc-blobs (currently json file), with a defined semantic structure, and
some metadata (version of the project this documentation refers to, and
potentially some other blobs)During the generation a number of normalisation and inference can and should
happen, for exampleusing type inference into theExamplessections of docstrings and storing
those as pairs (token, reference), so that you can later decide that
clicking onnp.arrayin an example brings you to numpy array
documentation; whether or not we are currently in the numpy doc.Parsing "See Also" into a well defined structurerunning Example to generate images for docs with images (not implemented)resolve package local references for example building numpy doc
"zeroes_like" is non ambiguous and shoudl be Normalized to
"numpy.zeroes_like",~.pyplot.histogram, normalized tomatplotlib.pyplot.histogramas thetargetandhistogramas the text
...etc.The Generation step is likely project specific, as there might be import
conventions that are per-project and should not need to be repeated (import pandas as pd, for example,)Ingestion (papyri ingest)The ingestion step take doc-bundle and/or doc-blobs and add them into a graph of
known items; the ingestion is critical to efficiently build the collection graph
metadata and understand which items refers to which; this allow the following:Update the list of backreferences to a docbundleUpdate forward references metadata to know whether links are valid.Currently the ingestion loads all in memory and update all the bundle in place
but this can likely be done more efficiently.A lot more can likely be done at larger scale, like detecting if documentation
have changed in previous version so infer for which versions of a library this
documentation is valid.There is also likely some curating that might need to be done at that point, as
for example, numpy.array have an extremely large number of back-references.Rendering (papyri render)Rendering can be done on on client side, which allows a lot of flexibility and
customisation.on a client IDE; the links can allow to navigate in the doc "Inspector" (for
example spyder) and will/can link only to already existing libraries of current
environment.online experience can allow (back-)links to private doc-bundles to users.tree sitter info.https://tree-sitter.github.io/tree-sitter/creating-parsers