Today's task list:
- [x] Review Tarun's and Sachin's PRs
- [x] Work on the website content and structure
-  Work on BMD papers
- [~] Do CITI course
- [x] Work on parsing the walking data
- [x] Submit PyCon proposals
-  Working on wrapping Ton's walking models
-  Book hotel for BMD
-  Post update about BMD copyright
-  Finish reading the van der Kooij paper
-  See if our controller can drive an OpenSim model or Ton's 2D model
-  Wrap the HBM C code
-  Duplicate website backups on a S3 bucket
-  Work on the website theme
-  Make generic settings on the lab website
-  Review the TODO items on the Yeadon paper
-  Do FERPA course, due Sept 20
-  Write up database proposal
-  Try out CSympy with some mechanics problems
-  Email Mounir about teaching
I've been using sys id for a long time now and learnt it on my own for my dissertation work, but I've hit enough walls in just being a user that I really need to start understanding what the hell is going on. I like Ljung's book "System Identification: Theory for the User", but it think the title would better be "System Identification: Theory for the Theorist" because he doesn't show you how to actually implement things computationally, which is what a "user" really needs. So I'm going to work through his book from page one and come up with the computations for the main methods in a series of IPython notebooks. I hope that my knowledge will then be sufficient to design a general system id package in Python and actually be readable (unlike the system id toolbox in Matlab).
The repo is at:
And the computations for Chapter 1 have been started here:
Walking System Identification
Over the weekend I implemented a method to zero out the identification of particular gains in the gain matrix (closing issue https://github.com/moorepants/DynamicistToolKit/issues/5).
The results are here for the example data:
I'm not sure why the sum of the residuals is being returned as zero from numpu.linalg.lstsq. It seems like the A matrix becomes rank deficient once I delete columns which correspond to different feedback loops. It also seems like the gains that I don't zero out have similar profiles to the previous full gain matrix calculations.
I also found some bugs and spent a long time working on it today. Will report more tomorrow.
I submitted a PyDy tutorial and a python for human walking data analysis for PyCon 2014 in Montreal. We'll see what happens...