[yt-users] SFR analysis on 3200 cube data

[Stephen Skory]

Hi Geoffrey,

> Hi everyone, I recently got a hold of some 3200 cube data and was planning

> This is data at a high redshift with only 230595 particles.  But I don't

If you're trying to pick out all the stars in a dataset with 3200^3 +
230595 particles, it's very easy to run out of memory. For example,
this would probably make it crash because it will try to read in all
eleventy bilion particles (approximately):

from yt.mods import *
from yt.analysis_modules.star_analysis.api import *
pf = load("data0030")
dd = pf.h.all_data()
sfr = StarFormationRate(pf, data_source=dd)

So, you have several options depending on how fancy you want to get.
One option is to parallelize the star formation analysis stuff so it
can handle big datasets like this elegantly.

A simple option, faster to completion but slower in run time, is to
instead build up arrays of the star particle data, one grid at a time,
and then analyze those.

from yt.mods import *
from yt.analysis_modules.star_analysis.api import *
pf = load("data0030")
dd = pf.h.all_data()
sm = []
ct = []
for grid in pf.h.grids:
    this_ct = grid['creation_time']
    this_sm = grid['ParticleMassMsun']
    select = (this_ct > 0)
    ct.extend(this_ct[select])
    sm.extend(this_sm[select])
sfr = StarFormationRate(pf, star_mass=sm, star_creation_time=ct,
volume=dd.volume('mpc'))

Good luck!

-- 
Stephen Skory
s at skory.us
http://stephenskory.com/
510.621.3687 (google voice)