Dust in 3D in the Milky Way

You can define environment variable DUST_DIR before installing the code; this is a directory that will contain the dust data. Otherwise mwdust will use ~/.mwdust by default

Standard python setup.py build/install

Either

sudo python setup.py install

or

python setup.py install --prefix=/some/directory/

The installation automatically downloads the relevant dust data. You might have to define an environment variable SUDO_USER if not installing with sudo and you might have to use the -E option when you are installing with sudo to transfer your environment variables to sudo.

Note that on Windows, you need to have the gzip utility (e.g., by installing 7zip) to use the installation script. Using custom implementations of necessary HEALPIx functions, basic evaluation of extinction is available on all platforms (Linux, Mac OS, Windows) for all dust maps. However, some HEALPIx-based features like plotting require healpy, which is unavailable on Windows. Install on Linux/Mac OS for full functionality.

By default, dust maps are download when you use them for the first time. The code can download all of the necessary data at by running

from mwdust import download_all
download_all()

; to download all maps, use the --all-downloads

The data are put in subdirectories of a directory DUST_DIR or ~/.mwdust, with roughly the following lay-out:

$DUST_DIR/
   combined15/
      dust-map-3d.h5
   combined19/
      combine19.h5
   green15/
      dust-map-3d.h5
   green17/
      bayestar2017.h5
   green19/
      bayestar2019.h5
   maps/
      SFD_dust_4096_ngp.fits
      SFD_dust_4096_sgp.fits
   marshall06/
      ReadMe
      table1.dat
   sale14/
      Amap.dat
      ReadMe

The data for the Drimmel et al. (2003) map is installed in the code directory, because it is not very large.

All of the maps can be initialized similar to

import mwdust
drimmel= mwdust.Drimmel03(filter='2MASS H')
combined= mwdust.Combined15(filter='2MASS H')
combined19= mwdust.Combined19(filter='2MASS H')
sfd= mwdust.SFD(filter='2MASS H')

which sets up the Drimmel et al. (2003) map, the combined Bovy et al. (2016) map, an updated version of the combined map using the Green et al. (2019) Bayestar19 map, and the SFD map for the H-band filter. The maps can be evaluate for a given Galactic longitude l, Galactic latitude b, and an array (or scalar) of distances D

drimmel(60.,0.,3.) # inputs are (l,b,D)
array([ 0.38813341])
combined(30.,3.,numpy.array([1.,2.,3.,10.]))
array([ 0.22304147,  0.55687252,  0.86694602,  1.18779507])
# SFD is just the constant SFD extinction
sfd(30.,3.,numpy.array([1.,2.,3.]))
array([ 1.19977335,  1.19977335,  1.19977335])

and they can be plotted as a function of distance at a given (l,b)

combined.plot(55.,0.5) # inputs are (l,b)

(plot not shown). Maps that are derived from the HierarchicalHealpixMap.py class (currently all Green-type maps and the combined maps) can also be plotted on the sky using a Mollweide projection at a given distance using

combined.plot_mollweide(5.) # input is distance in kpc

Currently only a few filters are supported; if no filter is supplied, E(B-V) is returned on the SFD scale if the object is initialized with sf10=True (which tells the code to use re-scalings from Schlafly & Finkbeiner 2011). sf10=True is the default initialization for every map, so be careful in interpreting the raw E(B-V) that come out of the code. Only use sf10=False when you have an extinction map in true E(B-V), not SFD E(B-V). No map currently included in this package is in this situation, so using sf10=False is never recommended.

To check what bandpasses are supported on the sf10=True scale do (these are all the bandpasses from Table 6 in Schlafly & Finkbeiner 2011)

from mwdust.util import extCurves
extCurves.avebvsf.keys()

which gives

['Stromgren u',
   'Stromgren v',
   'ACS clear',
   'CTIO R',
   'CTIO V',
   'CTIO U',
   'CTIO I',
   ...]

To check the bandpasses that are supported on the old SFD scale (sf10=False), do

numpy.array(extCurves.avebv.keys())[True-numpy.isnan(extCurves.avebv.values())]

which gives

array(['CTIO R', 'CTIO V', 'CTIO U', 'CTIO I', 'CTIO B', 'DSS-II i',
   'DSS-II g', 'WISE-1', 'WISE-2', 'DSS-II r', 'UKIRT H', 'UKIRT J',
   'UKIRT K', 'IRAC-1', 'IRAC-2', 'IRAC-3', 'IRAC-4', '2MASS H',
   'SDSS r', 'SDSS u', 'SDSS z', 'SDSS g', 'SDSS i', '2MASS Ks',
   '2MASS J'], dtype='|S14')

When making use of this code in a publication, please cite Bovy et al. (2015a). Also cite the relevant papers for the dust map that you use:

• mwdust.SFD: Schlegel et al. (1998)
• mwdust.Drimmel03: Drimmel et al. (2003)
• mwdust.Marshall06: Marshall et al. (2006)
• mwdust.Sale14: Sale et al. (2014)
• mwdust.Green15: Green et al. (2015)
• mwdust.Green17: Green et al. (2018) (added by @jan-rybizki)
• mwdust.Green19: Green et al. (2019) (added by @jan-rybizki)
• mwdust.Combined15: Combination of Marshall et al. (2006) (mwdust.Marshall06), Green et al. (2015) (mwdust.Green15), and Drimmel et al. (2003) (mwdust.Green15); see Bovy et al. (2015a)
• mwdust.Combined19: Similar to mwdust.Combined15, but using mwdust.Green19 instead of mwdust.Green15; see Bovy et al. (2015a) for details on the combination (added by @jan-rybizki)
• mwdust.Zero: Bovy et al. (2015b) 😏