57 karma · joined August 9, 2015
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
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.
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.
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.
FYI it's here: https://github.com/alols/xcape
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).
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.
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.