duerrp

> just a WM (XMonad), a browser (Firefox+vimperator), Emacs > and a terminal (urxvt, which I only use occasionally) Exactly my setup... now if only Firefox would not peg a CPU core or two in the background and slow down to a grind ever so often... (despite NoScript, uBlock origin etc.)

This... :set gui=none

There's a lot of libraries that do the hard work for you though... Anyway, if an EKF is too much effort, you could also try the "Madgwick" filter:

Madgwick, S. O., Harrison, A. J., & Vaidyanathan, R. (2011). Estimation of imu and marg orientation using a gradient descent algorithm. In Proceedings of the IEEE International Conference on Rehabilitation Robotics

Thanks for the tip, your link is missing an A at the end though...

https://www.youtube.com/watch?v=hEx5DNLWGgA

There's a nice documentary on Zhang (and the progress his work triggered) here:

https://www.youtube.com/watch?v=XIIyKWxGhEA

did you try magit?

Maybe it is of limited use for non-emacs users, but imho it's a fantastic way to use git with a perfect balance between making simple things very easy to do and allowing (almost) anything you can do with the CLI while providing excellent visual feedback.

The Japanese keyboard layout is great for that! I am using Control on the Muhenkan and Henkan buttons left and right of the space bar and Alt on Alt and Katakana/Hiragana/Romaji buttons next to them. I also have Caps lock mapped to Control if pushed and Esc when released without another key (xcape) - works great!

As a side note, if you are already in emacs, you could write your articles in org-mode instead of LaTex.

It's gotten to a point where I am starting to see Latex exclusively as a language to compile to, not something you want to write yourself.

If you haven't used a well-configured org-mode, have a look at this demo for example https://www.youtube.com/watch?v=2t925KRBbFc. There's nothing out there (open source or proprietary) that comes close for scientific work in my opinion.

I was wondering the same thing...

That's a great metaphor...

Not sure if it's the best way or not, but I use offlineimap...

Org mode rocks! And with ob-ipython you basically get same functionality as the notebook...

https://github.com/gregsexton/ob-ipython

It is misleading though...

Also, are they counting all previous versions of the software instead of just the latest?

Moreover, I wonder how the human drivers in the statistics fare if you correct for drunk driving / using cellphone, etc. In my opinion, any autopilot worth it's name should be an order of magnitude better than the average human driver.

While I agree with you that urxvt is a great tool, I disagree with the suggestion that the terminal is "a solved problem" (or that vim is perfection). There is a lot of room for innovation, though not necessarily in the direction that Go Terminal is going.

I don't know why this is down... the article is from 2012.

Sounds very interesting... I'd love to have a look.

That sounds great! I am trying it right now...

FYI it's here: https://github.com/alols/xcape

Ok, I gave the motivating example another shot... not sure if it helps, but please let me know if you have any suggestions.

Thanks for the kind words. Until I left academia I used to work with matlab a lot, and pyexperiment is probably a result of trying to get that experience while making scripts that can easily be shared along with the data needed to run them.

E.g., the issue with irreversible mutation of data is addressed in pyexperiment with rotating state (and log) files. For example if you store the state of your experiment in one run, and then change it in the next, you will get a backup of the old state with a numerical extension (by default up to 5 backups are rotated). Moreover, pyexperiment by default comes with commands to display stored state and configuration options (though they still need to be improved), and both are stored in formats compatible with a host of other software (including matlab).

Btw., along the same lines, I love ipython notebooks, but the way I use them makes them very hard to share, and compared to plain python scripts, version control is a pain (even with the usual hacks to make diffs readable).

Thanks for the feedback. I will try to improve the example and make it more "experimental".

Interesting, thanks. I didn't know yeoman (nor much javascript), but I am looking into it right now.

Thanks for your feedback. I see your point, but at this point I am kind of attached to the name - as a researcher many of my scripts are related to some kind of experiment. This also explains the NumPy and matplotlib dependencies. As for structlog, if I understand the documentation correctly, you could easily add structlog to your project on top of pyexperiment, right?

Thanks for the comments, I just added another motivating example right at the top of the README. Is it clearer now?

Thanks, very interesting. I am reading through your code right now... maybe there's something I can add to pyexperiment.

Thanks, I'll try to rephrase the readme...

Thanks for the suggestion, I'll try to put more examples in the readme.

>> So...it's for configuration management? What do >> "experiments" have to do with this?

Yes and no, pyexperiment handles configuration management for you (by using argparse and configobj etc.), but it's main point is that it saves you from having to set up these components. My main goal was to reduce the overhead of writing the same "framework" code every time, so pyexperiment sets you up with simple one-command solutions for many common tasks.

>> What does this package do that isn't done by click, >> docopt, or argparse?

It will set you up with a framework where e.g., you don't have to write the same boilerplate code to get a multiprocessing cabable logger that logs to a file. You just call log.debug("bla") and that's it.

Pandas and matplotlib are great, I use them all the time. Yet whenever I write a python script for such experiments, beyond some point, I find myself writing code that parses command line arguments, handles simple configuration options, saves the results of my computation in a shareable way, etc.

Pyexperiment collects these bits and pieces into a library where I can just write the relevant stuff - it's mainly solving my own pain-point and I thought I could share it.