This repository contains the code for the papers "The stellar population structure of the Galactic disk" Bovy, Rix, Schlafly et al. (2015) and "On Galactic density modeling in the presence of dust extinction" Bovy, Rix, Green et al. (2015). This code is provided for illustration purposes only, but it should allow you to perform the same analysis as in these papers if you want. Much of this code runs for quite a long time and no guidance is given here as to how long each command takes (none of it takes ridiculously long though).

This code heavily depends on the apogee and mwdust general-use packages. You need to install these (including downloading all of the necessary dust data) first. This code also makes use of the numpy, scipy, matplotlib stack. Parts of the code require healpy. The code will complain if you do not have other required packages.

This code is mainly concerned with fitting spatial-density profiles to subsets of APOGEE data. Almost all of the commands below require the effective-selection function (see the papers above for a description of this), which you should first calculate as:

python calc_effsel.py ../essf/essf_green15.sav --dmap=green15 -m 32
python calc_effsel.py ../essf/essf_marshall06.sav --dmap=marshall06 -m 32
python calc_effsel.py ../essf/essf_sale14.sav --dmap=sale14 -m 32
python calc_effsel.py ../essf/essf_drimmel03.sav --dmap=drimmel03 -m 32
python calc_effsel.py ../essf/essf_zero.sav --dmap=zero -m 32

The first two of these are the most important, as they are used in most parts of the code. The option -m 32 calculates this in parallel (for different lines of sight) on 32 CPUs. There is some overhead in first computing and saving the overall APOGEE selection function (see apogee.select.apogeeSelect), so you want to run these in series (at least the first two).

The heart of the code are the densprofiles.py and fitDens.py files. These contain all of the different density profiles and the code to fit and MCMC-explore a given density profile to a subset of APOGEE data.

The notebook SpatialFitExample has general explorations of the fit provided by different types of density profiles. It formed the backbone of the exploratory phase of this project.

The following sections list the commands that generate the figures in the paper.

Figure 1:

python plot_dustwapogee.py ./dustwapogee.ps

Figure 2:

python plot_vs_jkz.py -o ./rcmodel_mode_jkz_h_parsec.ps rcmodel_mode_jkz_h_parsec_newlogg.sav -b H -t mode --parsec

(this code is in jobovy/apogee-rc as it was part of an earlier paper).

Figure 3:

python plot_rcdistancecomparison.py ./rcdistcomp.ps

Figure 4:

python plot_afe_spectra.py afe_feh-0.3_residuals.sav ./spectra_ELEM.ps

Figure 5:

python plot_afefeh.py ./overall_afefeh.ps 

Figure 6:

python plot_spatial_broad.py ./spatial_broad_SAMPLE.ps

Checking the abundance scatter of open clusters:

python check-abundanceScatter-clusters.py

Figure 7:

python fitBroadSubsamples.py lowlow ../broadfits/lowlow.sav ./lowlow.txt ./lowlow_LOC.ps
python fitBroadSubsamples.py solar ../broadfits/solar.sav ./solar.txt ./solar_LOC.ps
python fitBroadSubsamples.py highfeh ../broadfits/highfeh.sav ./highfeh.txt ./highfeh_LOC.ps       

Figure 8:

python fitBroadSubsamples.py highalpha ../broadfits/highalpha.sav ./highalpha.txt ./highalpha_LOC.ps 

Figure 9:

python plot_broadsurfdens.py ./broadfits-radial.ps

Fits are done as follows:

python fitMAPs.py tribrokenexp ../mapfits/tribrokenexp.sav
python fitMAPs.py tribrokenexpfixedflare ../mapfits/tribrokenexpfixedflare.sav
python fitMAPs.py tribrokenexpflare ../mapfits/tribrokenexpflare.sav
python fitMAPs.py tribrokenexpinvlinflare ../mapfits/tribrokenexpinvlinflare.sav
python fitMAPs.py tribrokenexplinflare ../mapfits/tribrokenexplinflare.sav
python fitMAPs.py tribrokentwoexp ../mapfits/tribrokentwoexp.sav

that is, fitMAPs.py just fits a given density profile to all MAPs. It also runs MCMC for each MAP and saves all of these results (in a rather large file!).

Figure 10:

python plot_maprmax.py ../mapfits/tribrokenexpflare.sav ./mapfits-rpeak.png

Figure 11:

python plot_mapsurfdens.py ./mapfits-radial.ps
python plot_mapsurfdens_highalpha.py ./mapfits-radial-highalpha.ps 

Figure 12:

python plot_mapflarepdf.py flare_lowalpha.sav ./mapfits-flare-lowalpha.ps
python plot_mapflarepdf.py flare_highalpha.sav ./mapfits-flare-highalpha.ps

Figure 13:

python plot_mapflare.py ./mapfits-radialflare.ps
python plot_mapflare_highalpha.py ./mapfits-radialflare-highalpha.ps 

Figure 14:

python plot_maphz.py ./mapfits-hz.png

Figure 15:

python plot_maptwohz.py ./mapfits-twohz.ps

Many fewer figures in this paper (phew!), but here we go. Figure 1:

python plot_dust_gaia.py 5.0 ./dust_5.0kpc.ps 

Figure 2:

python plot_gaia_rcmag.py ./gaia_mg.ps

(see gaia-rc.py for some code to get the RC's properties in the Gaia passband). Figure 3:

python plot_powspec.py 5.0 ../savs/PowspecDensAndDustAndESSF ./powspec_dens_dust_essf_D5.0.ps
python plot_powspec.py 6.3 ../savs/PowspecDensAndDustAndESSF ./powspec_dens_dust_essf_D6.3.ps

Figure 4:

python plot_distanceintegral.py ../savs/distInt.sav /dev/null

(and similar for subsets of the sky, see options in plot_distanceintegral.py).

Figure 5:

python plot_ah_location.py 4240 ./ah_4240.png

and similar for other locations (like 4240). Figure 6:

python plot_effsel_location.py 4240 ./effsel_4240.ps 

also similar for other locations.