How to generate API documentation from docstrings, for functional code - python-3.x

Question

All I want is to generate API docs from function docstrings in my source code, presumably through sphinx's autodoc extension, to comprise my lean API documentation. My code follows the functional programming paradigm, not OOP, as demonstrated below.
I'd probably, as a second step, add one or more documentation pages for the project, hosting things like introductory comments, code examples (leveraging doctest I guess) and of course linking to the API documentation itself.
What might be a straightforward flow to accomplish documentation from docstrings here? Sphinx is a great popular tool, yet I find its getting started pages a bit dense.
What I've tried, from within my source directory:
$ mkdir documentation
$ sphinx-apidoc -f --ext-autodoc -o documentation .
No error messages, yet this doesn't find (or handle) the docstrings in my source files; it just creates an rst file per source, with contents like follows:
tokenizer module
================
.. automodule:: tokenizer
:members:
:undoc-members:
:show-inheritance:
Basically, my source files look like follows, without much module ceremony or object oriented contents in them (I like functional programming, even though it's python this time around). I've truncated the sample source file below of course, it contains more functions not shown below.
tokenizer.py
from hltk.util import clean, safe_get, safe_same_char
"""
Basic tokenization for text
not supported:
+ forms of pseuod elipsis (...)
support for the above should be added only as part of an automata rewrite
"""
always_swallow_separators = u" \t\n\v\f\r\u200e"
always_separators = ",!?()[]{}:;"
def is_one_of(char, chars):
'''
Returns whether the input `char` is any of the characters of the string `chars`
'''
return chars.count(char)
Or would you recommend a different tool and flow for this use case?
Many thanks!

Answer 1

If you find Sphinx too cumbersome and particular to use for simple projects, try pdoc:
$ pdoc --html tokenizer.py