PyMC2 has some tricky tricks for reducing function evaluations if possible. A question asked and answered on Stack Overflow investigates: http://stackoverflow.com/q/27714635/1935494 and I made a IPython Notebook with more details, too: http://nbviewer.ipython.org/gist/aflaxman/c07c5261bf22f6847098
Here is a little trick for getting around a pesky initialization issue in PyMC2 models, asked and answers on Stack Overflow when thing were quiet around here: http://stackoverflow.com/a/27724637/1935494
This interesting thing crossed my inbox during the quiet time between quarters:
Inspired by Dave and Randy’s presentations earlier in the quarter, our lab happened to publish two preprints today, both with supplemental GitHub repositories.
As mentioned several times, the reproducible part is hard. I would appreciate any feedback on our attempts to provide data and code, and how they might be improved. Of course you are welcome to comment on preprints if you wish.
1) Heare JE, Blake B, Davis JP, Vadopalas B, Roberts SB. (2014) Evidence of Ostrea lurida (Carpenter 1894) population structure in Puget Sound, WA. PeerJ PrePrints 2:e704v1 http://dx.doi.org/10.7287/peerj.preprints.704v1
GitHub Repo (Data and R scripts): https://github.com/jheare/OluridaSurvey2014
2) Indication of family-specific DNA methylation patterns in developing oysters
Claire E. Olson, Steven B. Roberts
GitHub Repo (IPython notebook): https://github.com/che625/olson-ms-nb/tree/1.0
Any feedback on how we might improve our Repositories is certainly welcome.
Very daring. I hope it was ok to share on my blog. I find this level of transparency inspiring.
The discussion that ensued indicates that there is still room for better tools to archive the computational environment where these analyses are being performed. I’ve always dreamed of doing my whole project in a virtual machine and then freezing it for posterity when I’m done. It would be the digital version of keeping a laptop on my shelf for each analysis. Easier said than done, however.
The discussion also resulted in a new wiki listing code products that accompany UW research projects: https://github.com/uwescience/reproducible/wiki/Code-Products
Some notes on them here: http://nbviewer.ipython.org/gist/aflaxman/c93489dd19cee2eabf00
This interesting article crossed my desk recently, A Year of Gun Deaths: What Slate learned from trying, and failing, to record every death by gun in America since Newtown. It is a long piece that touches on many of the things that make population health metrics hard.
It also drew my attention to an IOM report on Priorities for Research to Reduce the Threat of Firearm-Related Violence. Gun deaths are a public health problem.
Did you know I have a fork of PyMC3 that you can run at the same time as PyMC2? I don’t keep it up to date, but people seem to want it every once in a while. Maybe this will help someone find it: https://github.com/aflaxman/pymc
import pymc as pm2 import pymc3 as pm3
Good for head-to-head comparisons…
Playing around with that GIS optimization stuff was a chance for me to revisit some maze-making code I wrote a few years ago: http://healthyalgorithms.com/2011/01/07/piggies-and-mazes/ I wonder what age kids it will be good for.