Dust in 3D in the Milky Way
Please define an environment variable DUST_DIR
before installing
the code; this is a directory that will contain the dust data.
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.
The code can automatically download all of the necessary data (use the
installation option --no-downloads
to turn this off). These data
are put in subdirectories of a directory DUST_DIR
, with roughly
the following lay-out:
$DUST_DIR/ combined15/ dust-map-3d.h5 green15/ dust-map-3d.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') sfd= mwdust.SFD(filter='2MASS H')
which sets up a Drimmel et al. (2003) 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([ 0.42794197]) drimmel(30.,3.,numpy.array([1.,2.,3.,10.])) array([ 0.24911393, 0.53050198, 0.78045575, 1.14657304]) # 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:
drimmel.plot(55.,0.5) # inputs are (l,b)
(plot not shown).
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.Combined15: Combination of Marshall et al. (2006), Green et al. (2015), and Drimmel et al. (2003); see Bovy et al. (2015a)
- mwdust.Zero: Bovy et al. (2015b) ๐