<commit_before># aas225-hack-sfr-mstar-morphology
Looking at star forming galaxies in 3DHST to see if scatter in the main sequence of star 
forming galaxies is correlated with morphology.

Using data from Skelton et al. and van der wel et al. 
http://www.mpia-hd.mpg.de/homes/vdwel/3dhstcandels.html
http://3dhst.research.yale.edu/Data.php

This is from an idea from the Stellar Mass and Star Formation Rate session after talking with <NAME> and <NAME>.<commit_msg>added todo list to readme<commit_after># aas225-hack-sfr-mstar-morphology
Looking at star forming galaxies in 3DHST to see if scatter in the main sequence of star 
forming galaxies is correlated with morphology.

Using data from Skelton et al. and van der wel et al. 
http://www.mpia-hd.mpg.de/homes/vdwel/3dhstcandels.html
http://3dhst.research.yale.edu/Data.php

This is from an idea from the Stellar Mass and Star Formation Rate session after talking with <NAME> and <NAME>.

###To Do
* Using [matplotlib colorbar](http://matplotlib.org/examples/pylab_examples/colorbar_tick_labelling_demo.html) to visualize effective radius, sersic, and axis ratio along sfr - stellar mass diagram
* maybe try [voronoi binning](http://www-astro.physics.ox.ac.uk/~mxc/software/#binning) or the [weighted version here](http://www.phy.ohiou.edu/~diehl/WVT/)
* compare to [Wuyts et al. 2011](http://adslabs.org/adsabs/abs/2011ApJ...742...96W/)
* try machine learning or n-d statistics to tease out relationships? PCA doesn't work too well
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