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I'm starting to work on documentation of python33 modules. But I'm beginner in this field.
I will be very grateful if you will help me to choose good instrument to do this.
I read a lot of topics in Internet. I've fount that the most popular are sphinx and Epydoc. But which of them is better to use? Almost all problems discussed in the Internet are quite old. I'm sure that situation is changed since 2011 year.. May be somebody is pro in doing docs for python docs. Please help to make first steps.
Cannot imagine more useful and helpful material except:
PEP8 Comments section
PEP8 Documentation Strings section
Documentation thread of The Hitchhiker’s Guide to Python
And, yes:
Epydoc is discontinued. Use Sphinx instead.
Hope that helps.
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I like the matplotlib documentation at this web address,
http://matplotlib.org/index.html
but I cannot access it when my laptop computer is offline (traveling, etc.)
Can anyone recommend a way to download the html docs for matplotlib?
This is possible for the python docs.
Thanks.
Bill
The whole matplotlib.org website is stored in a github repository. You can download the whole thing from:
https://github.com/matplotlib/matplotlib.github.com
(direct zip link: https://github.com/matplotlib/matplotlib.github.com/archive/master.zip)
The result will probably be a pretty big zip file. Alternatively, as others have said, you can build the documentation yourself by following:
http://matplotlib.org/devel/documenting_mpl.html
HTH
The Documentation is available as a PDF for download at:
http://matplotlib.org/Matplotlib.pdf
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I search an awesome Python library date like Moment.js. Do you know some?
I want especially a library that can do conversions like this: 09/25/2012 00:00 - 09/25/2012 13:00 to 09/25/2012 00:00 - 13:00.
Some Python libraries inspired by Moment.js:
arrow
delorean
moment
Arrow and Delorean seem a bit more mature, although I have not really used any of them extensively.
datetime is as good as it gets. See it action here
datetime and it comes with the standard library. And here is Doug Hellman's introduction to the "module of the week".
You can try these 3rd party packages:
dateutil
mxDatetime : its parser is pretty good. Check whether the license works for you, though.
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I'm looking hard but I cannot find any XMPP server in python with the following features:
using epoll, just like http://www.gevent.org/
supporting BOSH
modular design
use little RAM/CPU for up to 1000 users
more important than the previous requirement: the CPU/RAM usage must be predictable
Prosody looks quite good feature-wise, but I don't know how many users it can support simultaneously and how it is performance-wise.
Could someone give me an idea?
For a rough idea of how Prosody is performance-wise, see this post on their ML. https://groups.google.com/d/topic/prosody-users/SlXpfwJfgY4/discussion
xmpp.org uses Prosody, any other questions? :P
btw, if you want to toy with it a little, you can always run prosody using luajit (didn't test that myself, but I'm fairly sure it would work). Expect at least 2-4x faster execution.
Look # ejabberd too.
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I'm recently started to learn Twisted framework and now looking for some cheat sheets/reference cards with basic Twisted stuff. Such as deferreds, callbacks, reactor, protocols, factories, transports, so on.
At the moment found nothing neither on http://refcardz.dzone.com/ nor on http://www.cheat-sheets.org/
Any help appreciated.
The closest thing I know of is Everything You Always Wanted to Know About Twisted
It's not really a "cheat-sheet", but it is a concise introduction to most of the basic concepts.
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An ARC file is a lossless data-compression format.
http://en.wikipedia.org/wiki/ARC_%28file_format%29
I've tried googling some, but the Python ARC readers are 404 errors, or cannot be found.
Anyone know of any library I can use?
If you are able to use SWIG then possibly the ARC source code from FreeBSD could be used. Or you could have a look at the source, and perhaps reimplement it in Python. I remember ARC and it did not last very long as a popular tool so I suspect that it is not overly complex.