```#!/usr/bin/env python
#
# plot_bayestar.py
# An example of how to query the "Bayestar" dust map of
# Green, Schlafly, Finkbeiner et al. (2015).
#
# Copyright (C) 2016  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

import numpy as np
import os.path

try:
import PIL.Image
except ImportError as error:
print('This example requires Pillow or PIL.\n'
raise error

from astropy.coordinates import SkyCoord
import astropy.units as u

from dustmaps.bayestar import BayestarQuery

def numpy2pil(a, vmin, vmax):
a = np.clip((a - vmin) / (vmax - vmin), 0., 1.)
a = (254.99 * a).astype('u1')
return PIL.Image.fromarray(a)

def main():
w,h = (2056,1024)
l_0 = 130.

# Set up Bayestar query object
bayestar = BayestarQuery(max_samples=1)

# Create a grid of coordinates
print('Creating grid of coordinates...')
l = np.linspace(-180.+l_0, 180.+l_0, 2*w)
b = np.linspace(-90., 90., 2*h+2)
b = b[1:-1]
l,b = np.meshgrid(l, b)

l += (np.random.random(l.shape) - 0.5) * 360./(2.*w)
b += (np.random.random(l.shape) - 0.5) * 180./(2.*h)

ebv = np.empty(l.shape+(3,), dtype='f8')

for k,d in enumerate([0.5, 1.5, 5.]):
# d = 5.    # We'll query integrated reddening to a distance of 5 kpc
coords = SkyCoord(l*u.deg, b*u.deg, d*u.kpc, frame='galactic')

# Get the dust median reddening at each coordinate
print('Querying map...')
ebv[:,:,k] = bayestar.query(coords, mode='median')

ebv[:,:,2] -= ebv[:,:,1]
ebv[:,:,1] -= ebv[:,:,0]

# Convert the output array to a PIL image and save
print('Saving image...')
img = numpy2pil(ebv[::-1,::-1,:], 0., 1.5)
img = img.resize((w,h), resample=PIL.Image.LANCZOS)
fname = 'bayestar.png'
img.save(fname)

return 0

if __name__ == '__main__':
main()
```