mwdust 1.7
Dust in the Milky Way
License: New BSD

mwdust
======

**Dust in 3D in the Milky Way**





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.. contents:: **Contents**
    :depth: 3

Installation
-------------

Install the latest released version using ``pip``:

..  code-block::

   pip install mwdust

To install the latest development version, clone the repository and do

..  code-block::

   python setup.py install

or 

..  code-block::

   python setup.py install --user

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.

Dust Data
---------

By default, dust maps are download when you use them for the first time. 
If you define an environment variable ``DUST_DIR``, then all dust data
downloaded by the code will be downloaded to this directory. If you do not
set the ``DUST_DIR`` variable, then ``mwdust`` will download data to ``~/.mwdust``. 

The code can download all of the necessary data at by running

..  code-block:: python

   from mwdust import download_all
   download_all()

Note that some of the maps are very large (multiple GB) and some of the downloads 
are slow, so this may take a while.

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
       zucker25/
      	  decaps_mean.h5
          decaps_mean_and_samples.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.

Usage
------

All of the maps can be initialized similar to

..  code-block:: python

   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*

..  code-block:: python

   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)

..  code-block:: python

   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 be vectorized to evaluate on array inputs of *l*, *b*, *D*

..  code-block:: python

   combined(numpy.array([30.,40.,50.,60.]),numpy.array([3.,4.,3.,6.]),numpy.array([1.,2.,3.,10.]))
   array([0.22304147, 0.3780736 , 0.42528571, 0.22258065])

They can also be plotted on the sky using a Mollweide projection at a given distance using

..  code-block:: python

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

Note that this requires ``healpy`` to be installed, so this does not work on Windows.

Supported bandpasses
---------------------

Currently only a few filters are supported. 
To obtain E(B-V), specify ``filter='E(B-V)'``.
To check what bandpasses are supported on the ``sf10=True`` scale
(these are all the bandpasses from Table 6 in `Schlafly & Finkbeiner
2011 <http://adsabs.harvard.edu/abs/2011ApJ...737..103S>`__), do

..  code-block:: python

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

which gives

..  code-block:: python

   ['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

..  code-block:: python

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

which gives

..  code-block:: python

   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')


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
<http://adsabs.harvard.edu/abs/2011ApJ...737..103S>`__). ``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 when 
not setting ``filter`` or when setting ``filter=None``. *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.

Acknowledging ``mwdust`` and its data
---------------------------------------

When making use of this code in a publication, please cite `Bovy et
al. (2015a) <http://arxiv.org/abs/1509.06751>`__. Also cite the relevant papers for the dust
map that you use:

* **mwdust.SFD**: `Schlegel et al. (1998) <http://adsabs.harvard.edu/abs/1998ApJ...500..525S>`__

* **mwdust.Drimmel03**: `Drimmel et al. (2003) <http://adsabs.harvard.edu/abs/2003A%26A...409..205D>`__

* **mwdust.Marshall06**: `Marshall et al. (2006) <http://adsabs.harvard.edu/abs/2006A%26A...453..635M>`__

* **mwdust.Sale14**: `Sale et al. (2014) <http://adsabs.harvard.edu/abs/2014MNRAS.443.2907S>`__

* **mwdust.Green15**: `Green et al. (2015) <https://ui.adsabs.harvard.edu/abs/2015ApJ...810...25G>`__

* **mwdust.Green17**: `Green et al. (2018) <https://ui.adsabs.harvard.edu/abs/2018MNRAS.478..651G>`__ (added by `@jan-rybizki <https://github.com/jan-rybizki>`__)

* **mwdust.Green19**: `Green et al. (2019) <https://ui.adsabs.harvard.edu/abs/2019arXiv190502734G>`__ (added by `@jan-rybizki <https://github.com/jan-rybizki>`__)

* **mwdust.Zucker25**: `Zucker et al. (2025) <https://ui.adsabs.harvard.edu/abs/2025arXiv250302657Z>`__

* **mwdust.Combined15**: Combination of 
  
  * `Marshall et al. (2006) <http://adsabs.harvard.edu/abs/2006A%26A...453..635M>`__ (**mwdust.Marshall06**),
  * `Green et al. (2015) <http://adsabs.harvard.edu/abs/2015arXiv150701005G>`__ (**mwdust.Green15**), and 
  * `Drimmel et al. (2003) <http://adsabs.harvard.edu/abs/2003A%26A...409..205D>`__ (**mwdust.Drimmel03**); 
  
  see `Bovy et al. (2015a) <http://adsabs.harvard.edu/abs/2015arXiv150906751B>`__.

* **mwdust.Combined19**: Similar to **mwdust.Combined15**, but using **mwdust.Green19** instead of **mwdust.Green15**; see `Bovy et al. (2015a) <http://adsabs.harvard.edu/abs/2015arXiv150906751B>`__ for details on the combination (added by `@jan-rybizki <https://github.com/jan-rybizki>`__)

* **mwdust.Zero**: `Bovy et al. (2015b) <http://adsabs.harvard.edu/abs/2015arXiv150905796B>`__ :smirk: