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Home Page: https://tkamenoko.github.io/inari/
License: MIT License
Write docstrings in markdown!
Home Page: https://tkamenoko.github.io/inari/
License: MIT License
こちらがサンプルなのですが
Submodules のリンクが,両方とも切れてしまいます
(.md を付加する必要がある (?))
There are two ways to indicate the HTML headers in Markdown, the #
and underlining a word/sentence with something like -------
. In numpy docstrings (example here) we must use those multiple hyphens under lines like Parameters
and Returns
to have valid numpy documentation.
Here is the problem, the parsing library currently keys off those -------
to identify sections like Parameters
and Returns
(so you can't just remove them or you break the parser) however it passes the -------
on into the Markdown file. The result, is that a function entry reads like this:
foo{: #MyClass.foo}
def foo(self, bar: float = None) -> None
Some interesting information about the function foo
foo: float
That big "Parameters" is a level 2 header, because it's underlined with ------
.
For reference, that API docs excerpt reads like this in the raw:
------
[**foo**](#MyClass.foo){: #MyClass.foo}
'''
python
def foo(self, bar: float = None) -> None
'''
Some interesting information about the function foo
Parameters
----------
foo: float
------
Here is what those big headers do the the sidebar navigation when I serve up the website:
Strip the -------
lines out before copying the numpy docstrings into markdown.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.