# Copyright (c) 2017 Weitian LI <weitian@aaronly.me>
# MIT license
"""
Generic simulation sky supporting both sky patch and HEALPix all-sky
maps.
References
----------
* Python - 3. Data Model
https://docs.python.org/3/reference/datamodel.html#special-method-names
"""
import logging
import copy
from datetime import datetime, timezone
import numpy as np
from scipy import ndimage
from astropy.io import fits
import astropy.units as au
from astropy.coordinates import SkyCoord
from regions import PixCoord, RectanglePixelRegion
from reproject import reproject_interp, reproject_to_healpix
import healpy as hp
from .utils.wcs import make_wcs
from .utils.io import (read_fits_healpix,
write_fits_healpix,
write_fits_image)
from .utils.random import spherical_uniform
from .utils.units import UnitConversions as AUC
logger = logging.getLogger(__name__)
class SkyBase:
"""
The base class for both the sky patch and HEALPix all-sky
map classes.
Attributes
----------
type_ : str
The type of the sky image
Values: ``patch`` or ``healpix``
data : `~numpy.ndarray`
The data array read from input sky image, or to be written into
output FITS file.
frequency_ : float
The frequency of the input/output sky image.
Unit: [MHz]
pixelsize_ : float
The pixel size of the sky image.
Unit: [arcsec]
creator_ : str
The creator of the (output) sky image.
Default: ``__name__``
header_ : `~astropy.io.fits.Header`
The FITS header information of the input/output file.
float32_ : bool
Whether to use single/float32 data type to save the sky image?
Default: True
clobber_ : bool, optional
Whether to overwrite the existing output file.
Default: False
checksum_ : bool, optional
Whether to calculate the checksum data for the output
FITS file, which may cost some time.
Default: False
"""
def __init__(self, float32=True, clobber=False, checksum=False):
self.type_ = None
self.data = None
self.frequency_ = None # [MHz]
self.pixelsize_ = None # [arcsec]
self.creator_ = __name__
self.header_ = fits.Header()
self.float32_ = float32
self.clobber_ = clobber
self.checksum_ = checksum
def __add__(self, other):
"""Binary arithmetic operation: ``+``."""
if isinstance(other, self.__class__):
return self.data + other.data
elif isinstance(other, (int, float, np.ndarray)):
return self.data + other
else:
raise NotImplemented
def __sub__(self, other):
"""Binary arithmetic operation: ``-``."""
if isinstance(other, self.__class__):
return self.data - other.data
elif isinstance(other, (int, float, np.ndarray)):
return self.data - other
else:
raise NotImplemented
def __mul__(self, other):
"""Binary arithmetic operation: ``*``."""
if isinstance(other, self.__class__):
return self.data * other.data
elif isinstance(other, (int, float, np.ndarray)):
return self.data * other
else:
raise NotImplemented
def __truediv__(self, other):
"""Binary arithmetic operation: ``/``."""
if isinstance(other, self.__class__):
return self.data / other.data
elif isinstance(other, (int, float, np.ndarray)):
return self.data / other
else:
raise NotImplemented
def __pow__(self, other):
"""Binary arithmetic operation: ``**``."""
if isinstance(other, self.__class__):
return self.data ** other.data
elif isinstance(other, (int, float, np.ndarray)):
return self.data ** other
else:
raise NotImplemented
def __iadd__(self, other):
"""
Augmented arithmetic assignment: ``+=``.
NOTE
----
These augmented arithmetic assignment methods should attempt
to do the operation in-place (modifying ``self``) and return
the result (which could be, but does not have to be, ``self``).
"""
if isinstance(other, self.__class__):
self.data += other.data
return self
elif isinstance(other, (int, float, np.ndarray)):
self.data += other
return self
else:
raise NotImplemented
def __isub__(self, other):
"""Augmented arithmetic assignment: ``-=``."""
if isinstance(other, self.__class__):
self.data -= other.data
return self
elif isinstance(other, (int, float, np.ndarray)):
self.data -= other
return self
else:
raise NotImplemented
def __imul__(self, other):
"""Augmented arithmetic assignment: ``*=``."""
if isinstance(other, self.__class__):
self.data *= other.data
return self
elif isinstance(other, (int, float, np.ndarray)):
self.data *= other
return self
else:
raise NotImplemented
def __itruediv__(self, other):
"""Augmented arithmetic assignment: ``/=``."""
if isinstance(other, self.__class__):
self.data /= other.data
return self
elif isinstance(other, (int, float, np.ndarray)):
self.data /= other
return self
else:
raise NotImplemented
def __neg__(self):
"""Unary arithmetic operation: ``-``."""
return -self.data
def __abs__(self):
"""Unary arithmetic operation: ``abs()``."""
return np.abs(self.data)
def add(self, obj, *args, **kwargs):
"""
Add/superimpose an object to the sky image.
"""
raise NotImplementedError
@property
def shape(self):
"""
Numpy array shape of the (current/output) sky data.
"""
return self.data.shape
@property
def frequency(self):
"""
The frequency of the sky image.
Unit: [MHz]
"""
if self.frequency_ is not None:
return self.frequency_
else:
return self.header_.get("FREQ", None)
@frequency.setter
def frequency(self, value):
"""
Set the frequency of the sky image.
Unit: [MHz]
"""
self.frequency_ = value
@property
def pixelsize(self):
"""
Pixel size of the sky image.
Unit: [arcsec]
"""
return self.pixelsize_
@property
def creator(self):
"""
The creator of the sky image.
"""
if self.creator_ is not None:
return self.creator_
else:
return self.header_.get("CREATOR", None)
@creator.setter
def creator(self, value):
"""
Set the creator of the sky image.
"""
self.creator_ = value
@property
def header(self):
"""
The FITS header of the current sky.
"""
hdr = self.header_.copy()
hdr["SkyType"] = (self.type_, "Patch / HEALPix")
hdr["PixSize"] = (self.pixelsize, "Pixel size [arcsec]")
hdr["CREATOR"] = (self.creator, "Sky Creator")
hdr["FREQ"] = (self.frequency, "Sky frequency [MHz]")
hdr["DATE"] = (datetime.now(timezone.utc).astimezone().isoformat(),
"File creation date")
return hdr
def merge_header(self, header, update=False):
"""
Merge the supplied header to the instance's FITS header.
Do not overwrite the original keywords by default (``update=False``).
"""
self.header_.extend(header, update=update)
def add_header(self, key, value, comment=None):
"""
Add/update a key to the FITS header.
"""
if comment is None:
self.header_[key] = value
else:
self.header_[key] = (value, comment)
def add_history(self, history):
"""
Add history to the FITS header.
"""
self.header_.add_history(history)
def copy(self):
"""
Return a (deep) copy of this instance.
"""
return copy.deepcopy(self)
def load(self, infile, frequency=None):
"""
Load the given sky image into this instance.
Parameters
----------
infile : str
The path to the given input sky image.
frequency : float, optional
The frequency of the given sky image if applicable.
Unit: [MHz]
"""
raise NotImplementedError
def open(self, infile, frequency=None):
"""
Open the given input file as a *new* instance.
The current instance is *copied*, load the given sky image,
and then returned.
Returns
-------
sky : a *new* instance with given sky image loaded.
"""
sky = self.copy()
sky.load(infile=infile, frequency=frequency)
return sky
def write(self, outfile, clobber=None):
"""
Write the sky image (with current data) into a FITS file.
Parameters
----------
outfile : str
The path/filename to the output FITS file.
clobber : bool, optional
If not ``None``, then overwrite the default ``self.clobber_``
from the configuration file, to determine whether to overwrite
the existing output file.
"""
raise NotImplementedError
@property
def area(self):
"""
Sky coverage of the sky.
Unit: [deg^2]
"""
raise NotImplementedError
def random_points(self, n=1):
"""
Generate uniformly distributed random points within the
sky image (coverage).
Parameters
----------
n : int, optional
The number of random points required.
Default: 1
Returns
-------
lon, lat : float, or 1D `~numpy.ndarray`
The longitudes and latitudes (in world coordinate)
generated.
Unit: [deg]
"""
raise NotImplementedError
class SkyPatch(SkyBase):
"""
Support reading & writing FITS file of sky patches.
NOTE/XXX
--------
Currently just use ``CAR`` (Cartesian) sky projection, i.e.,
assuming a flat sky!!
NOTE
----
X: FITS width / sky R.A. <-> data array rows
Y: FITS height / sky Dec. <-> data array columns
Parameters
----------
size : (xsize, ysize) tuple
The (pixel) dimensions of the (output) sky patch.
If the input sky has a different size, then it will be *scaled*
to match this output size.
pixelsize : float
The pixel size of the sky patch, will be used to determine
the sky coordinates.
Unit: [arcsec]
center : (ra, dec) tuple, optional
The (R.A., Dec.) coordinate of the sky patch center.
Unit: [deg]
infile : str, optional
The path to the input sky patch
frequency : float, optional
The frequency of the input sky path
Unit: [MHz]
Attributes
----------
type_ : ``patch``
This is a sky patch.
data : 2D `~numpy.ndarray`
The 2D data array of sky image, with shape (self.ysize, self.xsize).
(HEALPix map stores data in an 1D array.)
"""
def __init__(self, size, pixelsize, center=(0.0, 0.0),
infile=None, frequency=None, **kwargs):
super().__init__(**kwargs)
self.type_ = "patch"
self.xsize, self.ysize = size
# Initialize an empty image
self.data = np.zeros(shape=(self.ysize, self.xsize))
self.pixelsize_ = pixelsize
self.xcenter, self.ycenter = center
if infile is not None:
self.load(infile, frequency)
@property
def area(self):
"""
The sky coverage of this patch.
Unit: [deg^2]
XXX/FIXME
---------
Assumed a flat sky, without WCS projection!!
"""
lonsize, latsize = self.size
return (lonsize * latsize)
@property
def size(self):
"""
The sky patch size along X/longitude and Y/latitude axes.
Returns
-------
(lonsize, latsize) : float tuple
Longitudinal and latitudinal sizes
Unit: [deg]
"""
return (self.xsize * self.pixelsize * AUC.arcsec2deg,
self.ysize * self.pixelsize * AUC.arcsec2deg)
@property
def center(self):
return (self.xcenter, self.ycenter)
@property
def lon_limit(self):
"""
The longitudinal (X axis) limits.
Returns
-------
(lon_min, lon_max) : float tuple
The minimum and maximum longitudes (X axis).
Unit: [deg]
"""
lonsize, latsize = self.size
return (self.xcenter - 0.5*lonsize,
self.xcenter + 0.5*lonsize)
@property
def lat_limit(self):
"""
The latitudinal (Y axis) limits.
Returns
-------
(lat_min, lat_max) : float tuple
The minimum and maximum latitudes (Y axis).
Unit: [deg]
"""
lonsize, latsize = self.size
return (self.ycenter - 0.5*latsize,
self.ycenter + 0.5*latsize)
def add(self, obj, center):
"""
Add/superimpose the object image into this sky patch.
XXX/FIXME
----------
Assumed a flat sky!!
Parameters
----------
obj : 2D `~numpy.ndarray`
The object image to be added into the sky.
NOTE: Should have same pixel size as the sky patch.
center : (ra, dec) tuple
The central coordinate (R.A., Dec.) of the given object.
"""
obj = np.asarray(obj)
nrow, ncol = obj.shape
xc, yc = center
ric, cic = self.world2pix(xc, yc)
# Index ranges (inclusive at both ends) for the supplied object
# image on the sky array
rimin0, rimax0 = ric - nrow//2, ric + (nrow-1)//2
cimin0, cimax0 = cic - ncol//2, cic + (ncol-1)//2
# Index ranges for the supplied object image
rimin1, rimax1 = 0, nrow-1
cimin1, cimax1 = 0, ncol-1
# Check the object boundaries
if ((rimin0 >= self.ysize) or (rimax0 < 0) or
(cimin0 >= self.xsize) or (cimax0 < 0)):
logger.warning("The given object is beyond the sky coverage")
return
if rimin0 < 0:
rimin1 = -rimin0
rimin0 = 0
if rimax0 >= self.ysize:
rimax1 = nrow - (rimax0-self.ysize) - 2
rimax0 = self.ysize-1
if cimin0 < 0:
cimin1 = -cimin0
cimin0 = 0
if cimax0 >= self.xsize:
cimax1 = nrow - (cimax0-self.xsize) - 2
cimax0 = self.xsize-1
self.data[rimin0:(rimax0+1),
cimin0:(cimax0+1)] += obj[rimin1:(rimax1+1),
cimin1:(cimax1+1)]
def world2pix(self, x, y):
"""
Convert the world coordinates (R.A., Dec.) into the pixel
coordinates (indexes) within the sky data array.
Parameters
----------
x, y : float, `~numpy.ndarray`
The R.A., Dec. world coordinates
Unit: [deg]
Returns
-------
ri, ci : int, `~numpy.ndarray`
The row, column indexes within the sky data array.
"""
pixelsize = self.pixelsize * AUC.arcsec2deg # [deg]
x, y = np.asarray(x), np.asarray(y) # [deg]
ri0, ci0 = self.ysize//2, self.xsize//2
ri = np.round((y - self.ycenter) / pixelsize + ri0).astype(np.int)
ci = np.round((x - self.xcenter) / pixelsize + ci0).astype(np.int)
return (ri, ci)
def load(self, infile, frequency=None):
"""
Load input sky image from file into this instance.
Parameters
----------
infile : str
The path to the input sky patch
frequency : float, optional
The frequency of the sky patch;
Unit: [MHz]
"""
self.infile = infile
if frequency is not None:
self.frequency = frequency
with fits.open(infile) as f:
self.data = f[0].data
header = f[0].header.copy(strip=True)
self.header_.extend(header, update=True)
self.ysize_in, self.xsize_in = self.data.shape
logger.info("Loaded sky patch from: %s (%dx%d)" %
(infile, self.xsize_in, self.ysize_in))
if (self.xsize_in != self.xsize) or (self.ysize_in != self.ysize):
logger.warning("Scale input sky patch to size %dx%d" %
(self.xsize, self.ysize))
zoom = ((self.ysize+0.1)/self.ysize_in,
(self.xsize+0.1)/self.xsize_in)
self.data = ndimage.zoom(self.data, zoom=zoom, order=1)
def write(self, outfile, clobber=None):
"""
Write current data to file.
"""
if clobber is None:
clobber = self.clobber_
write_fits_image(outfile, image=self.data, header=self.header,
float32=self.float32_,
clobber=clobber,
checksum=self.checksum_)
@property
def header(self):
"""
FITS header of the sky for storing information in the output file.
"""
hdr = super().header
hdr.extend(self.wcs.to_header(), update=True)
hdr["OBJECT"] = "Sky Patch"
hdr["EXTNAME"] = "IMAGE"
hdr["RA0"] = (self.center[0], "R.A. of patch center [deg]")
hdr["DEC0"] = (self.center[1], "Dec. of patch center [deg]")
return hdr
@property
def wcs(self):
"""
The WCS header with sky projection information, for sky <->
pixel coordinate(s) conversion.
NOTE/XXX
--------
Currently just use the `CAR` (Cartesian) projection,
i.e., assuming a flat sky.
"""
w = make_wcs(center=(self.xcenter, self.ycenter),
size=(self.xsize, self.ysize),
pixelsize=self.pixelsize,
frame="ICRS", projection="CAR")
return w
def contains(self, skycoord):
"""
Check whether the given (list of) sky coordinate(s) falls
inside this sky patch (region).
Parameters
----------
skycoord : `~astropy.coordinate.SkyCoord` or (lon, lat) tuple
The (list of) sky coordinate(s) to check, or the (list of)
longitudes and latitudes of sky coordinates [ deg ]
Returns
-------
inside : bool
(list of) boolean values indicating whether the given
sky coordinate(s) is inside this sky patch.
"""
if not isinstance(skycoord, SkyCoord):
lon, lat = skycoord
skycoord = SkyCoord(lon, lat, unit=au.deg)
wcs = self.wcs
pixcoord = PixCoord.from_sky(skycoord, wcs=wcs)
center = PixCoord(x=self.xcenter, y=self.ycenter)
region = RectanglePixelRegion(center=center,
width=self.xsize, height=self.ysize)
return region.contains(pixcoord)
def reproject_from(self, data, wcs, squeeze=False, eps=1e-5):
"""
Reproject the given image/data together with WCS information
onto the grid of this sky.
Parameters
----------
data : 2D float `~numpy.ndarray`
The input data/image to be reprojected
wcs : `~astropy.wcs.WCS`
The WCS information of the input data/image (naxis=2)
squeeze : bool, optional
Whether to squeeze the reprojected data to only keep
the pixels greater than a small positive value specified
by parameter ``eps``.
Default: False
eps : float, optional
The small positive value to specify the squeeze threshold.
Default: 1e-5
Returns
-------
If ``squeeze=True``, then returns tuple of ``(indexes, values)``,
otherwise, returns the reprojected image/data array.
indexes : 1D int `~numpy.ndarray`
The indexes of the pixels with positive values.
values : 1D float `~numpy.ndarray`
The values of the above pixels.
reprojected : 1D `~numpy.ndarray`
The reprojected data/image with same shape of this sky,
i.e., ``self.data``.
"""
wcs_out = self.wcs
shape_out = (self.ysize, self.xsize)
reprojected, __ = reproject_interp(
input_data=(data, wcs), output_projection=wcs_out,
shape_out=shape_out)
reprojected = reprojected.flatten()
if squeeze:
with np.errstate(invalid="ignore"):
indexes = reprojected > eps
values = reprojected[indexes]
return (indexes, values)
else:
return reprojected
def random_points(self, n=1):
"""
Generate uniformly distributed random points within the sky patch.
Returns
-------
lon : float, or 1D `~numpy.ndarray`
Longitudes (Galactic/equatorial);
Unit: [deg]
lat : float, or 1D `~numpy.ndarray`
Latitudes (Galactic/equatorial);
Unit: [deg]
"""
lon_min, lon_max = self.lon_limit
lat_min, lat_max = self.lat_limit
lon = np.random.uniform(low=lon_min, high=lon_max, size=n)
lat = np.random.uniform(low=lat_min, high=lat_max, size=n)
return (lon, lat)
class SkyHealpix(SkyBase):
"""
Support the HEALPix all-sky map.
Parameters
----------
nside : int
The pixel resolution of HEALPix (must be power of 2)
infile : str, optional
The path to the input sky patch
frequency : float, optional
The frequency of the input sky path
Unit: [MHz]
Attributes
----------
shape : int tuple, (npix,)
The shape (i.e., length) of the HEALPix array
pixelsize : float
The pixel size of the HEALPix map
Unit: [arcsec]
"""
def __init__(self, nside, infile=None, frequency=None, **kwargs):
super().__init__(**kwargs)
self.type_ = "healpix"
self.nside = nside
self.pixelsize_ = (hp.nside2resol(self.nside, arcmin=True) *
AUC.arcmin2arcsec)
self.data = np.zeros(shape=hp.nside2npix(self.nside))
if infile is not None:
self.load(infile, frequency)
@property
def area(self):
"""
The sky coverage of this HEALPix map, i.e., all sky = 4π,
Unit: [deg^2]
"""
return 4*np.pi * AUC.rad2deg**2
def load(self, infile, frequency=None):
"""
Load input HEALPix all-sky map into this instance.
Parameters
----------
infile : str
The path to the input HEALPix all-sky map.
frequency : float, optional
The frequency of the sky patch;
Unit: [MHz]
"""
self.infile = infile
if frequency is not None:
self.frequency = frequency
self.data, header = read_fits_healpix(infile)
self.header_.extend(header, update=True)
self.nside_in = header["NSIDE"]
logger.info("Loaded HEALPix sky map from: {0} (Nside={1})".format(
infile, self.nside_in))
if self.nside_in != self.nside:
self.data = hp.ud_grade(self.data, nside_out=self.nside)
logger.warning("Upgrade/downgrade sky map from Nside " +
"{0} to {1}".format(self.nside_in, self.nside))
def write(self, outfile, clobber=None):
"""
Write current data to file.
"""
if clobber is None:
clobber = self.clobber_
write_fits_healpix(outfile, hpmap=self.data, header=self.header,
float32=self.float32_,
clobber=self.clobber_,
checksum=self.checksum_)
def contains(self, skycoord):
"""
Shim method to be consistent with ``SkyPatch``.
Always returns ``True``, since the HEALPix map covers all sky.
"""
if skycoord.isscalar:
return True
else:
return np.ones(shape=len(skycoord), dtype=np.bool)
def reproject_from(self, data, wcs, squeeze=False):
"""
Reproject the given image/data together with WCS information
onto the grid of this sky.
Parameters
----------
data : 2D float `~numpy.ndarray`
The input data/image to be reprojected
wcs : `~astropy.wcs.WCS`
The WCS information of the input data/image (naxis=2)
squeeze : bool, optional
Whether to squeeze the reprojected data to only keep
the positive pixels.
Returns
-------
If ``squeeze=True``, then returns tuple of ``(indexes, values)``,
otherwise, returns the reprojected image/data array.
indexes : 1D int `~numpy.ndarray`
The indexes of the pixels with positive values.
values : 1D float `~numpy.ndarray`
The values of the above pixels.
reprojected : 1D `~numpy.ndarray`
The reprojected data/image with same shape of this sky,
i.e., ``self.data.shape``.
"""
eps = 1e-5
reprojected, __ = reproject_to_healpix(
input_data=(data, wcs), coord_system_out="galactic",
nested=False, nside=self.nside)
if squeeze:
with np.errstate(invalid="ignore"):
indexes = reprojected > eps
values = reprojected[indexes]
return (indexes, values)
else:
return reprojected
def random_points(self, n=1):
"""
Generate uniformly distributed random points within the sky
(i.e., all sky; on an unit sphere).
Returns
-------
lon : float, or 1D `~numpy.ndarray`
Longitudes (Galactic/equatorial), [0, 360) [deg].
lat : float, or 1D `~numpy.ndarray`
Latitudes (Galactic/equatorial), [-90, 90] [deg].
"""
theta, phi = spherical_uniform(n)
lon = np.degrees(phi)
lat = 90.0 - np.degrees(theta)
return (lon, lat)
##########################################################################
def get_sky(configs):
"""
Sky class factory function to support both the sky patch and
HEALPix all-sky map.
Parameters
----------
configs : ConfigManager object
An `ConfigManager` object contains default and user configurations.
For more details, see the example config specification.
"""
# Parameters for the base sky class
kwargs = {
"float32": configs.getn("output/float32"),
"clobber": configs.getn("output/clobber"),
"checksum": configs.getn("output/checksum"),
}
skytype = configs.getn("sky/type")
if skytype == "patch":
sec = "sky/patch"
xsize = configs.getn(sec+"/xsize")
ysize = configs.getn(sec+"/ysize")
xcenter = configs.getn(sec+"/xcenter")
ycenter = configs.getn(sec+"/ycenter")
pixelsize = configs.getn(sec+"/pixelsize")
return SkyPatch(size=(xsize, ysize), pixelsize=pixelsize,
center=(xcenter, ycenter), **kwargs)
elif skytype == "healpix":
sec = "sky/healpix"
nside = configs.getn(sec+"/nside")
return SkyHealpix(nside=nside, **kwargs)
else:
raise ValueError("unknown sky type: %s" % skytype)