Merge branch 'develop'

README.md

@@ -21,6 +21,7 @@ The currently supported dust maps are:
 8. Lenz, Hensley & Doré (2017)
 9. Peek & Graves (2010)
 10. Leike & Enßlin (2019)
+11. Leike, Glatzle & Enßlin (2020)
 
 To request addition of another dust map in this package, [file an issue on
 GitHub](https://github.com/gregreen/dustmaps/issues), or submit a pull request.
@@ -47,8 +48,8 @@ To fetch the data for the SFD dust map, run:
     python setup.py fetch --map-name=sfd
 
 You can download the other dust maps by changing "sfd" to "planck", "bayestar",
-"iphas", "marshall", "chen2014", "lenz2017", "pg2010", "leikeensslin2019"
-or "bh".
+"iphas", "marshall", "chen2014", "lenz2017", "pg2010", "leikeensslin2019",
+"leike2020" or "bh".
 
 Alternatively, if you have used `pip` to install `dustmaps`, then you can
 configure the data directory and download the data by opening up a python
@@ -83,6 +84,9 @@ interpreter and running:
     >>>
     >>> import dustmaps.leike_ensslin_2019
     >>> dustmaps.leike_ensslin_2019.fetch()
+    >>>
+    >>> import dustmaps.leike2020
+    >>> dustmaps.leike2020.fetch()
 
 
 Querying the Maps

docs/installation.rst

@@ -49,6 +49,9 @@ Start up a python interpreter and type:
     
     import dustmaps.leike_ensslin_2019
     dustmaps.leike_ensslin_2019.fetch()
+    
+    import dustmaps.leike2020
+    dustmaps.leike2020.fetch()
 
 All the dust maps should now be in the path you gave to
 :code:`config['data_dir']`. Note that these dust maps can be very large - some
@@ -105,6 +108,7 @@ to only download those you think you'll need:
     python setup.py fetch --map-name=chen2014
     python setup.py fetch --map-name=lenz2017
     python setup.py fetch --map-name=leikeensslin2019
+    python setup.py fetch --map-name=leike2020
 
 That's it!
 

docs/maps.rst

@@ -113,9 +113,9 @@ The units of reddening used by each map are slightly different:
    should use the conversions provided in
    `Table 6 of Schlafly & Finkbeiner (2011) <http://iopscience.iop.org/0004-637X/737/2/103/article#apj398709t6>`_.
 
-* **References**: Green, Schlafly, Finkbeiner et al. (2019),
-  `Green, Schlafly, Finkbeiner et al. (2018) <http://adsabs.harvard.edu/abs/2018arXiv180103555G>`_,
-  `Green, Schlafly, Finkbeiner et al. (2015) <http://adsabs.harvard.edu/abs/2015arXiv150701005G>`_
+* **References**: `Green, Schlafly, Finkbeiner et al. (2019) <https://ui.adsabs.harvard.edu/abs/2019ApJ...887...93G>`_,
+  `Green, Schlafly, Finkbeiner et al. (2018) <https://ui.adsabs.harvard.edu/abs/2018MNRAS.478..651G>`_
+  and `Green, Schlafly, Finkbeiner et al. (2015) <https://ui.adsabs.harvard.edu/abs/2015ApJ...810...25G>`_.
 * **Website**: `argonaut.skymaps.info <http://argonaut.skymaps.info>`_
 
 
@@ -149,15 +149,28 @@ monochromatic extinction.
 Leike & Enßlin (2019)
 ~~~~~~~~~~~~~~~~~~~~~~
 
-A three-dimensional map of Milky Way dust extinction, based on the Gaia DR2 catalog
-parallaxes and G-band extinctions, and incorporating a Gaussian process prior on
-the dust extinction density. The map is calculated on a Cartesian grid, spanning
-a (600 pc)³ box centered on the Sun.
+A three-dimensional map of Milky Way dust extinction, incorporating a Gaussian
+process prior on the dust extinction density. The map is based on the Gaia
+DR2 catalog parallaxes and G-band extinctions, and spans a (600 pc)³ box
+centered on the Sun.
 
 * **Reference**: `Leike & Enßlin (2019) <https://ui.adsabs.harvard.edu/abs/2019arXiv190105971L/abstract>`_
 * **Website**: `Zenodo <https://doi.org/10.5281/zenodo.2542807>`_
 
 
+Leike, Glatzle & Enßlin (2020)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+A three-dimensional map of Milky Way dust extinction, incorporating a Gaussian
+process prior on the dust extinction density, similar to Leike & Enßlin (2019).
+The map is based on data from Gaia, 2MASS, Pan-STARRS 1 and ALLWISE, and is
+calculated on a Cartesian grid spanning a (740 pc)×(740 pc)×(540 pc) box (in
+Galactic *x*, *y* and *z*, respectively) centered on the Sun.
+
+* **References**: `Leike, Glatzle & Enßlin (2020) <https://ui.adsabs.harvard.edu/abs/2020A%26A...639A.138L/abstract>`_
+* **Website**: `Zenodo <https://doi.org/10.5281/zenodo.3993082>`_
+
+
 Marshall et al. (2006)
 ~~~~~~~~~~~~~~~~~~~~~~
 

docs/modules.rst

@@ -33,6 +33,20 @@ iphas (Sale et al. 2014)
     :special-members:
     :show-inheritance:
 
+leike_ensslin_2019 (Leike & Enßlin 2019)
+----------------------------------------
+.. automodule:: dustmaps.leike_ensslin_2019
+    :members:
+    :special-members:
+    :show-inheritance:
+
+leike2020 (Leike, Glatzle & Enßlin 2020)
+----------------------------------------
+.. automodule:: dustmaps.leike2020
+    :members:
+    :special-members:
+    :show-inheritance:
+
 lenz2017 (Lenz, Hensley & Doré 2017)
 ------------------------------------
 .. automodule:: dustmaps.lenz2017

dustmaps/leike2020.py

@@ -0,0 +1,154 @@
+#!/usr/bin/env python
+#
+# leike2020.py
+# 
+# The Leike, Glatzle & Ensslin (2020) dust map.
+#
+# Copyright (C) 2020  Gregory M. Green
+#
+# This program is free software; you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation; either version 2 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along
+# with this program; if not, write to the Free Software Foundation, Inc.,
+# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
+#
+
+from __future__ import print_function, division
+
+import numpy as np
+import h5py
+import astropy.coordinates as coordinates
+import astropy.units as units
+from astropy.coordinates import Longitude
+
+from .map_base import DustMap, ensure_flat_coords
+from .std_paths import *
+from . import fetch_utils
+
+
+class Leike2020Query(DustMap):
+    """
+    A class for querying the Leike, Glatzle & Ensslin (2020) dust map.
+
+    For details on how to use this map, see the original paper:
+    https://ui.adsabs.harvard.edu/abs/2020A%26A...639A.138L/abstract.
+
+    The data is deposited at Zenodo: https://doi.org/10.5281/zenodo.3993082.
+    """
+
+    def __init__(self, map_fname=None):
+        """
+        Args:
+            map_fname (Optional[str]): Filename of the map. Defaults
+                to :obj:`None`, meaning that the default location
+                is used.
+        """
+
+        if map_fname is None:
+            map_fname = os.path.join(
+                data_dir(),
+                'leike_2020',
+                'mean_std.h5'
+            )
+
+        self._data = {}
+
+        with h5py.File(map_fname) as f:
+            self._data['mean'] = f['mean'][:]
+            self._data['std'] = f['std'][:]
+
+        self._xyz0 = (-370., -370., -270.) # Lower edge of map, in pc
+        self._shape = self._data['mean'].shape
+
+    def _coords2idx(self, coords):
+        c = coords.transform_to('galactic').represent_as('cartesian')
+
+        idx = np.empty((3,) + c.shape, dtype='i4')
+        mask = np.zeros(c.shape, dtype=np.bool)
+
+        for i,x in enumerate((c.x, c.y, c.z)):
+            idx[i,...] = np.floor(x.to('pc').value - self._xyz0[i])
+            mask |= (idx[i] < 0) | (idx[i] >= self._shape[i])
+
+        for i in range(3):
+            idx[i, mask] = -1
+
+        return idx, mask
+
+    @ensure_flat_coords
+    def query(self, coords, component='mean'):
+        """
+        Returns the extinction density (in e-foldings / kpc, in Gaia G-band)
+        at the given coordinates.
+
+        Args:
+            coords (:obj:`astropy.coordinates.SkyCoord`): Coordinates at which
+                to query the extinction. Must be 3D (i.e., include distance
+                information).
+            component (str): Which component to return. Allowable values are
+                'mean' (for the mean extinction density) and 'std' (for the
+                standard deviation of extinction density). Defaults to 'mean'.
+
+        Returns:
+            The extinction density, in units of e-foldings / pc, as either a
+            numpy array or float, with the same shape as the input
+            :obj:`coords`.
+        """
+        idx,mask = self._coords2idx(coords)
+
+        v = self._data[component][idx[0], idx[1], idx[2]]
+
+        if np.any(mask):
+            # Set extinction to NaN for out-of-bounds (x, y, z)
+            v[mask] = np.nan
+
+        return v
+
+
+def fetch(clobber=False, fetch_samples=False):
+    """
+    Downloads the 3D dust map of Leike & Ensslin (2020).
+
+    Args:
+        clobber (Optional[bool]): If ``True``, any existing file will be
+            overwritten, even if it appears to match. If ``False`` (the
+            default), ``fetch()`` will attempt to determine if the dataset
+            already exists. This determination is not 100\% robust against data
+            corruption.
+        fetch_samples (Optional[bool]): If ``True``, the samples will also be
+            downloaded. If ``False`` (the default), only the mean and standard
+            deviation will be downloaded. The samples take up 14 GB, which is
+            why the default is not to download them.
+    """
+    dest_dir = fname_pattern = os.path.join(data_dir(), 'leike_2020')
+
+    file_spec = [
+        ('mean_std.h5', '1ea998fdaef58f53da639356362223ba')
+    ]
+    if fetch_samples:
+        file_spec += [
+            ('samples.h5', '581f9ebc4775d37fd431fc6c0984dcf6')
+        ]
+
+    for fn,md5sum in file_spec:
+        fname = os.path.join(dest_dir, fn)
+
+        # Check if the file already exists
+        if (not clobber) and fetch_utils.check_md5sum(fname, md5sum):
+            print('File "{}" appears to exist already. Call '.format(fn)+
+                  '`fetch(clobber=True)` to force overwriting of existing '+
+                  'file.')
+            continue
+
+        # Download from the server
+        url = 'https://zenodo.org/record/3993082/files/{}?download=1'.format(fn)
+        fetch_utils.download_and_verify(url, md5sum, fname)
+

setup.py

@@ -85,6 +85,10 @@ def fetch_leikeensslin2019():
     import dustmaps.leike_ensslin_2019
     dustmaps.leike_ensslin_2019.fetch()
 
+def fetch_leike2020():
+    import dustmaps.leike2020
+    dustmaps.leike2020.fetch()
+
 def fetch_lenz2017():
     import dustmaps.lenz2017
     dustmaps.lenz2017.fetch()
@@ -116,7 +120,8 @@ class FetchCommand(distutils.cmd.Command):
         'chen2014': fetch_chen2014,
         'lenz2017': fetch_lenz2017,
         'pg2010': fetch_pg2010,
-        'leikeensslin2019': fetch_leikeensslin2019
+        'leikeensslin2019': fetch_leikeensslin2019,
+        'leike2020': fetch_leike2020
     }
 
     def initialize_options(self):
@@ -143,11 +148,11 @@ def readme():
 
 setup(
     name='dustmaps',
-    version='1.0.4',
+    version='1.0.5',
     description='Uniform interface for multiple dust reddening maps.',
     long_description=readme(),
     url='https://github.com/gregreen/dustmaps',
-    download_url='https://github.com/gregreen/dustmaps/archive/v1.0.4.tar.gz',
+    download_url='https://github.com/gregreen/dustmaps/archive/v1.0.5.tar.gz',
     author='Gregory M. Green',
     author_email='[email protected]',
     license='GPLv2',