#
# Copyright (C) 2017-2020 Leo Singer
#
# 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 3 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, see <http://www.gnu.org/licenses/>.
#
"""Backdrops for astronomical plots."""
from importlib.resources import files
import json
import warnings
from astropy.io import fits
from astropy.time import Time
from astropy.utils.data import download_file
from astropy.wcs import WCS
from matplotlib.image import imread
import numpy as np
from PIL.Image import DecompressionBombWarning
from reproject import reproject_interp
__all__ = ('bluemarble', 'blackmarble', 'coastlines', 'mellinger',
'reproject_interp_rgb')
def big_imread(*args, **kwargs):
"""Wrapper for imread() that suppresses warnings when loading very large
images (usually tiffs). Most of the all-sky images that we use in this
module are large enough to trigger this warning:
DecompressionBombWarning: Image size (91125000 pixels) exceeds limit of
89478485 pixels, could be decompression bomb DOS attack.
"""
with warnings.catch_warnings():
warnings.simplefilter('ignore', DecompressionBombWarning)
img = imread(*args, **kwargs)
return img
[docs]
def mellinger():
"""Get the Mellinger Milky Way panorama.
Retrieve, cache, and return the Mellinger Milky Way panorama. See
http://www.milkywaysky.com.
Returns
-------
`astropy.io.fits.ImageHDU`
A FITS WCS image in ICRS coordinates.
Examples
--------
.. plot::
:context: reset
:include-source:
:align: center
from astropy.visualization import (ImageNormalize,
AsymmetricPercentileInterval)
from astropy.wcs import WCS
from matplotlib import pyplot as plt
from ligo.skymap.plot import mellinger
from reproject import reproject_interp
ax = plt.axes(projection='astro hours aitoff')
backdrop = mellinger()
backdrop_wcs = WCS(backdrop.header).dropaxis(-1)
interval = AsymmetricPercentileInterval(45, 98)
norm = ImageNormalize(backdrop.data, interval)
backdrop_reprojected = np.asarray([
reproject_interp((layer, backdrop_wcs), ax.header)[0]
for layer in norm(backdrop.data)])
backdrop_reprojected = np.rollaxis(backdrop_reprojected, 0, 3)
ax.imshow(backdrop_reprojected)
"""
url = 'http://galaxy.phy.cmich.edu/~axel/mwpan2/mwpan2_RGB_3600.fits'
hdu, = fits.open(url, cache=True)
return hdu
[docs]
def bluemarble(t, resolution='low'):
"""Get the "Blue Marble" image.
Retrieve, cache, and return the NASA/NOAO/NPP "Blue Marble" image showing
landforms and oceans.
See https://visibleearth.nasa.gov/view.php?id=74117.
Parameters
----------
t : `astropy.time.Time`
Time to embed in the WCS header.
resolution : {'low', 'high'}
Specify which version to use: the "low" resolution version (5400x2700
pixels, the default) or the "high" resolution version (21600x10800
pixels).
Returns
-------
`astropy.io.fits.ImageHDU`
A FITS WCS image in ICRS coordinates.
Examples
--------
.. plot::
:context: reset
:include-source:
:align: center
from matplotlib import pyplot as plt
from ligo.skymap.plot import bluemarble, reproject_interp_rgb
obstime = '2017-08-17 12:41:04'
ax = plt.axes(projection='geo degrees aitoff', obstime=obstime)
ax.imshow(reproject_interp_rgb(bluemarble(obstime), ax.header))
"""
variants = {
'low': '5400x2700',
'high': '21600x10800'
}
url = ('https://eoimages.gsfc.nasa.gov/images/imagerecords/74000/74117/'
'world.200408.3x{}.png'.format(variants[resolution]))
img = big_imread(download_file(url, cache=True))
height, width, ndim = img.shape
gmst_deg = Time(t).sidereal_time('mean', 'greenwich').deg
header = fits.Header(dict(
NAXIS=3,
NAXIS1=ndim, NAXIS2=width, NAXIS3=height,
CRPIX2=width / 2, CRPIX3=height / 2,
CRVAL2=gmst_deg % 360, CRVAL3=0,
CDELT2=360 / width,
CDELT3=-180 / height,
CTYPE2='RA---CAR',
CTYPE3='DEC--CAR',
RADESYSa='ICRS').items())
return fits.ImageHDU(img[:, :, :], header)
[docs]
def blackmarble(t, resolution='low'):
"""Get the "Black Marble" image.
Get the NASA/NOAO/NPP image showing city lights, at the sidereal time given
by t. See https://visibleearth.nasa.gov/view.php?id=79765.
Parameters
----------
t : `astropy.time.Time`
Time to embed in the WCS header.
resolution : {'low', 'mid', 'high'}
Specify which version to use: the "low" resolution version (3600x1800
pixels, the default), the "mid" resolution version (13500x6750 pixels),
or the "high" resolution version (54000x27000 pixels).
Returns
-------
`astropy.io.fits.ImageHDU`
A FITS WCS image in ICRS coordinates.
Examples
--------
.. plot::
:context: reset
:include-source:
:align: center
from matplotlib import pyplot as plt
from ligo.skymap.plot import blackmarble, reproject_interp_rgb
obstime = '2017-08-17 12:41:04'
ax = plt.axes(projection='geo degrees aitoff', obstime=obstime)
ax.imshow(reproject_interp_rgb(blackmarble(obstime), ax.header))
"""
variants = {
'low': '3600x1800',
'high': '13500x6750',
'mid': '54000x27000'
}
url = ('http://eoimages.gsfc.nasa.gov/images/imagerecords/79000/79765/'
'dnb_land_ocean_ice.2012.{}_geo.tif'.format(variants[resolution]))
img = big_imread(download_file(url, cache=True))
height, width, ndim = img.shape
gmst_deg = Time(t).sidereal_time('mean', 'greenwich').deg
header = fits.Header(dict(
NAXIS=3,
NAXIS1=ndim, NAXIS2=width, NAXIS3=height,
CRPIX2=width / 2, CRPIX3=height / 2,
CRVAL2=gmst_deg % 360, CRVAL3=0,
CDELT2=360 / width,
CDELT3=-180 / height,
CTYPE2='RA---CAR',
CTYPE3='DEC--CAR',
RADESYSa='ICRS').items())
return fits.ImageHDU(img[:, :, :], header)
def reproject_interp_rgb(input_data, *args, **kwargs):
data = input_data.data
wcs = WCS(input_data.header).celestial
return np.moveaxis(np.stack([
reproject_interp((data[:, :, i], wcs),
*args, **kwargs)[0].astype(data.dtype)
for i in range(3)]), 0, -1)
def coastlines():
with files(__package__).joinpath(
'ne_simplified_coastline.json').open() as f:
geoms = json.load(f)['geometries']
return [coord for geom in geoms for coord in zip(*geom['coordinates'])]