Today's task list:
- [x] Work on parsing the walking data
- [x] SymPy dev
- [x] Review Tarun's code
- [x] Meet with Tarun
- [x] Get pydy_viz js tests working with Travis
- [x] Go over walking controller with Sandy
-  Make generic settings on the lab website
-  Work on the website theme
-  Setup a data backup for the website
-  Review the TODO items on the Yeadon paper
-  Run variations in guesses for structural id
- [x] Work on BMD papers
-  Book flight for BMD
-  Book hotel for BMD
-  Post update about BMD copyright
Walking System ID
The sparse methods for linear least squares in scipy are significantly slower than the normal method and only give approximate solutions. The lsqr method took 5 minutes as opposed to 30 seconds for the example data set. My A matrix is very sparse, so I thought the sparse methods would be faster. I probably need to construct the A matrix as a sparse matrix data type:
For the sparse algorithms to work.
I went over the project with Sandy and she's going to start thinking about how to excite the subject with lateral random motions while they are walking so we can obtain richer datasets.
Work has started on the Whipple ID paper:
We've about got tests runing on the NiPy buildbot for slow, no cache, and external dependencies. If we can figure out how to run them on each pull request commit, then we can remove the slow downs from Travis (they have a 50 minute time limit). But the solution may still be to break up the test suite.
Test script for buildbot:
Trying to reduce the time on Travis with matplotlib deps: