# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""Utilities for dealing with HEALPix projections and mappings."""
from collections import OrderedDict
import re
import copy
import numpy as np
from astropy.io import fits
from astropy.coordinates import SkyCoord
from astropy.units import Quantity
from .utils import INVALID_INDEX
from .wcs import WcsGeom
from .geom import MapGeom, MapCoord, pix_tuple_to_idx
from .geom import coordsys_to_frame, skycoord_to_lonlat
from .geom import find_and_read_bands, make_axes
# Not sure if we should expose this in the docs or not:
# HPX_FITS_CONVENTIONS, HpxConv
__all__ = ["HpxGeom"]
# Approximation of the size of HEALPIX pixels (in degrees) for a particular order.
# Used to convert from HEALPIX to WCS-based projections.
HPX_ORDER_TO_PIXSIZE = np.array(
[32.0, 16.0, 8.0, 4.0, 2.0, 1.0, 0.50, 0.25, 0.1, 0.05, 0.025, 0.01, 0.005, 0.002]
)
class HpxConv:
"""Data structure to define how a HEALPIX map is stored to FITS."""
def __init__(self, convname, **kwargs):
self.convname = convname
self.colstring = kwargs.get("colstring", "CHANNEL")
self.firstcol = kwargs.get("firstcol", 1)
self.hduname = kwargs.get("hduname", "SKYMAP")
self.bands_hdu = kwargs.get("bands_hdu", "EBOUNDS")
self.quantity_type = kwargs.get("quantity_type", "integral")
self.coordsys = kwargs.get("coordsys", "COORDSYS")
def colname(self, indx):
return "{}{}".format(self.colstring, indx)
@classmethod
def create(cls, convname="gadf"):
return copy.deepcopy(HPX_FITS_CONVENTIONS[convname])
HPX_FITS_CONVENTIONS = {}
"""Various conventions for storing HEALPIX maps in FITS files"""
HPX_FITS_CONVENTIONS[None] = HpxConv("gadf", bands_hdu="BANDS")
HPX_FITS_CONVENTIONS["gadf"] = HpxConv("gadf", bands_hdu="BANDS")
HPX_FITS_CONVENTIONS["fgst-ccube"] = HpxConv("fgst-ccube")
HPX_FITS_CONVENTIONS["fgst-ltcube"] = HpxConv(
"fgst-ltcube", colstring="COSBINS", hduname="EXPOSURE", bands_hdu="CTHETABOUNDS"
)
HPX_FITS_CONVENTIONS["fgst-bexpcube"] = HpxConv(
"fgst-bexpcube", colstring="ENERGY", hduname="HPXEXPOSURES", bands_hdu="ENERGIES"
)
HPX_FITS_CONVENTIONS["fgst-srcmap"] = HpxConv(
"fgst-srcmap", hduname=None, quantity_type="differential"
)
HPX_FITS_CONVENTIONS["fgst-template"] = HpxConv(
"fgst-template", colstring="ENERGY", bands_hdu="ENERGIES"
)
HPX_FITS_CONVENTIONS["fgst-srcmap-sparse"] = HpxConv(
"fgst-srcmap-sparse", colstring=None, hduname=None, quantity_type="differential"
)
HPX_FITS_CONVENTIONS["galprop"] = HpxConv(
"galprop",
colstring="Bin",
hduname="SKYMAP2",
bands_hdu="ENERGIES",
quantity_type="differential",
coordsys="COORDTYPE",
)
HPX_FITS_CONVENTIONS["galprop2"] = HpxConv(
"galprop",
colstring="Bin",
hduname="SKYMAP2",
bands_hdu="ENERGIES",
quantity_type="differential",
)
def unravel_hpx_index(idx, npix):
"""Convert flattened global map index to an index tuple.
Parameters
----------
idx : `~numpy.ndarray`
Flat index.
npix : `~numpy.ndarray`
Number of pixels in each band.
Returns
-------
idx : tuple of `~numpy.ndarray`
Index array for each dimension of the map.
"""
if npix.size == 1:
return tuple([idx])
dpix = np.zeros(npix.size, dtype="i")
dpix[1:] = np.cumsum(npix.flat[:-1])
bidx = np.searchsorted(np.cumsum(npix.flat), idx + 1)
pix = idx - dpix[bidx]
return tuple([pix] + list(np.unravel_index(bidx, npix.shape)))
def ravel_hpx_index(idx, npix):
"""Convert map index tuple to a flattened index.
Parameters
----------
idx : tuple of `~numpy.ndarray`
Returns
-------
idx : `~numpy.ndarray`
"""
if len(idx) == 1:
return idx[0]
# TODO: raise exception for indices that are out of bounds
idx0 = idx[0]
idx1 = np.ravel_multi_index(idx[1:], npix.shape, mode="clip")
npix = np.concatenate((np.array([0]), npix.flat[:-1]))
return idx0 + np.cumsum(npix)[idx1]
def coords_to_vec(lon, lat):
"""Converts longitude and latitude coordinates to a unit 3-vector.
Returns
-------
array(3,n) with v_x[i],v_y[i],v_z[i] = directional cosines
"""
phi = np.radians(lon)
theta = (np.pi / 2) - np.radians(lat)
sin_t = np.sin(theta)
cos_t = np.cos(theta)
x = sin_t * np.cos(phi)
y = sin_t * np.sin(phi)
z = cos_t
# Stack them into the output array
out = np.vstack((x, y, z)).swapaxes(0, 1)
return out
def get_nside_from_pix_size(pixsz):
"""Get the NSIDE that is closest to the given pixel size.
Parameters
----------
pix : `~numpy.ndarray`
Pixel size in degrees.
Returns
-------
nside : `~numpy.ndarray`
NSIDE parameter.
"""
import healpy as hp
pixsz = np.array(pixsz, ndmin=1)
nside = 2 ** np.linspace(1, 14, 14, dtype=int)
nside_pixsz = np.degrees(hp.nside2resol(nside))
return nside[np.argmin(np.abs(nside_pixsz - pixsz[..., None]), axis=-1)]
def get_pix_size_from_nside(nside):
"""Estimate of the pixel size from the HEALPIX nside coordinate.
This just uses a lookup table to provide a nice round number
for each HEALPIX order.
"""
order = nside_to_order(nside)
if np.any(order < 0) or np.any(order > 13):
raise ValueError("HEALPIX order must be 0 to 13. Got: {!r}".format(order))
return HPX_ORDER_TO_PIXSIZE[order]
def hpx_to_axes(h, npix):
"""Generate a sequence of bin edge vectors corresponding to the axes of a HPX object.
"""
x = h.ebins
z = np.arange(npix[-1] + 1)
return x, z
def hpx_to_coords(h, shape):
"""Generate an N x D list of pixel center coordinates.
``N`` is the number of pixels and ``D`` is the dimensionality of the map.
"""
x, z = hpx_to_axes(h, shape)
x = np.sqrt(x[0:-1] * x[1:])
z = z[:-1] + 0.5
x = np.ravel(np.ones(shape) * x[:, np.newaxis])
z = np.ravel(np.ones(shape) * z[np.newaxis, :])
return np.vstack((x, z))
def make_hpx_to_wcs_mapping_centers(hpx, wcs):
"""Make the mapping data needed to from from HPX pixelization to a WCS-based array.
Parameters
----------
hpx : `~gammapy.maps.HpxGeom`
The HEALPIX geometry.
wcs : `~gammapy.maps.WcsGeom`
The WCS geometry.
Returns
-------
ipixs : array(nx,ny)
HEALPIX pixel indices for each WCS pixel
-1 indicates the wcs pixel does not contain the center of a HEALPIX pixel
mult_val : array
(nx,ny) of 1.
npix : tuple
(nx,ny) with the shape of the WCS grid
"""
npix = (int(wcs.wcs.crpix[0] * 2), int(wcs.wcs.crpix[1] * 2))
mult_val = np.ones(npix).T.flatten()
sky_crds = hpx.get_sky_coords()
pix_crds = wcs.wcs_world2pix(sky_crds, 0).astype(int)
ipixs = -1 * np.ones(npix, int).T.flatten()
pix_index = npix[1] * pix_crds[0:, 0] + pix_crds[0:, 1]
if hpx._ipix is None:
for ipix, pix_crd in enumerate(pix_index):
ipixs[pix_crd] = ipix
else:
for pix_crd, ipix in zip(pix_index, hpx._ipix):
ipixs[pix_crd] = ipix
ipixs = ipixs.reshape(npix).T.flatten()
return ipixs, mult_val, npix
def make_hpx_to_wcs_mapping(hpx, wcs):
"""Make the pixel mapping from HPX- to a WCS-based geometry.
Parameters
----------
hpx : `~gammapy.maps.HpxGeom`
The HEALPIX geometry
wcs : `~gammapy.maps.WcsGeom`
The WCS geometry
Returns
-------
ipix : `~numpy.ndarray`
array(nx,ny) of HEALPIX pixel indices for each wcs pixel
mult_val : `~numpy.ndarray`
array(nx,ny) of 1./number of WCS pixels pointing at each HEALPIX pixel
npix : tuple
tuple(nx,ny) with the shape of the WCS grid
"""
import healpy as hp
npix = wcs.npix
# FIXME: Calculation of WCS pixel centers should be moved into a
# method of WcsGeom
pix_crds = np.dstack(np.meshgrid(np.arange(npix[0]), np.arange(npix[1])))
pix_crds = pix_crds.swapaxes(0, 1).reshape((-1, 2))
sky_crds = wcs.wcs.wcs_pix2world(pix_crds, 0)
sky_crds *= np.radians(1.0)
sky_crds[0:, 1] = (np.pi / 2) - sky_crds[0:, 1]
mask = ~np.any(np.isnan(sky_crds), axis=1)
ipix = -1 * np.ones((len(hpx.nside), int(npix[0] * npix[1])), int)
m = mask[None, :] * np.ones_like(ipix, dtype=bool)
ipix[m] = hp.ang2pix(
hpx.nside[..., None],
sky_crds[:, 1][mask][None, ...],
sky_crds[:, 0][mask][None, ...],
hpx.nest,
).flatten()
# Here we are counting the number of HEALPIX pixels each WCS pixel
# points to and getting a multiplicative factor that tells use how
# to split up the counts in each HEALPIX pixel (by dividing the
# corresponding WCS pixels by the number of associated HEALPIX
# pixels).
mult_val = np.ones_like(ipix, dtype=float)
for i, t in enumerate(ipix):
count = np.unique(t, return_counts=True)
idx = np.searchsorted(count[0], t)
mult_val[i, ...] = 1.0 / count[1][idx]
if hpx.nside.size == 1:
ipix = np.squeeze(ipix, axis=0)
mult_val = np.squeeze(mult_val, axis=0)
return ipix, mult_val, npix
def match_hpx_pix(nside, nest, nside_pix, ipix_ring):
"""TODO
"""
import healpy as hp
ipix_in = np.arange(12 * nside * nside)
vecs = hp.pix2vec(nside, ipix_in, nest)
pix_match = hp.vec2pix(nside_pix, vecs[0], vecs[1], vecs[2]) == ipix_ring
return ipix_in[pix_match]
def parse_hpxregion(region):
"""Parse the ``HPX_REG`` header keyword into a list of tokens.
"""
m = re.match(r"([A-Za-z\_]*?)\((.*?)\)", region)
if m is None:
raise ValueError("Failed to parse hpx region string: {!r}".format(region))
if not m.group(1):
return re.split(",", m.group(2))
else:
return [m.group(1)] + re.split(",", m.group(2))
def get_hpxregion_dir(region, coordsys):
"""Get the reference direction for a HEALPIX region string.
Parameters
----------
region : str
A string describing a HEALPIX region
coordsys : {'CEL', 'GAL'}
Coordinate system
"""
import healpy as hp
frame = coordsys_to_frame(coordsys)
if region is None:
return SkyCoord(0.0, 0.0, frame=frame, unit="deg")
tokens = parse_hpxregion(region)
if tokens[0] in ["DISK", "DISK_INC"]:
lon, lat = float(tokens[1]), float(tokens[2])
return SkyCoord(lon, lat, frame=frame, unit="deg")
elif tokens[0] == "HPX_PIXEL":
nside_pix = int(tokens[2])
ipix_pix = int(tokens[3])
if tokens[1] == "NESTED":
nest_pix = True
elif tokens[1] == "RING":
nest_pix = False
else:
raise ValueError("Invalid ordering scheme: {!r}".format(tokens[1]))
theta, phi = hp.pix2ang(nside_pix, ipix_pix, nest_pix)
lat = np.degrees((np.pi / 2) - theta)
lon = np.degrees(phi)
return SkyCoord(lon, lat, frame=frame, unit="deg")
else:
raise ValueError("Invalid region type: {!r}".format(tokens[0]))
def get_hpxregion_size(region):
"""Get the approximate size of region (in degrees) from a HEALPIX region string.
"""
tokens = parse_hpxregion(region)
if tokens[0] in {"DISK", "DISK_INC"}:
return float(tokens[3])
elif tokens[0] == "HPX_PIXEL":
pix_size = get_pix_size_from_nside(int(tokens[2]))
return 2.0 * pix_size
else:
raise ValueError("Invalid region type: {!r}".format(tokens[0]))
def is_power2(n):
"""Check if an integer is a power of 2."""
return (n > 0) & ((n & (n - 1)) == 0)
def nside_to_order(nside):
"""Compute the ORDER given NSIDE.
Returns -1 for NSIDE values that are not a power of 2.
"""
nside = np.array(nside, ndmin=1)
order = -1 * np.ones_like(nside)
m = is_power2(nside)
order[m] = np.log2(nside[m]).astype(int)
return order
def upix_to_pix(upix):
"""Get the pixel index and nside from a unique pixel number."""
nside = np.power(2, np.floor(np.log2(upix / 4)) / 2).astype(int)
pix = upix - 4 * np.power(nside, 2)
return pix, nside
def pix_to_upix(pix, nside):
"""Compute the unique pixel number from the pixel number and nside."""
return pix + 4 * np.power(nside, 2)
def get_superpixels(idx, nside_subpix, nside_superpix, nest=True):
"""Compute the indices of superpixels that contain a subpixel.
Parameters
----------
idx : `~numpy.ndarray`
Array of HEALPix pixel indices for subpixels of NSIDE
``nside_subpix``.
nside_subpix : int or `~numpy.ndarray`
NSIDE of subpixel.
nside_superpix : int or `~numpy.ndarray`
NSIDE of superpixel.
nest : bool
If True, assume NESTED pixel ordering, otherwise, RING pixel
ordering.
Returns
-------
idx_super : `~numpy.ndarray`
Indices of HEALpix pixels of nside ``nside_superpix`` that
contain pixel indices ``idx`` of nside ``nside_subpix``.
"""
import healpy as hp
idx = np.array(idx)
nside_superpix = np.asarray(nside_superpix)
nside_subpix = np.asarray(nside_subpix)
if not nest:
idx = hp.ring2nest(nside_subpix, idx)
if np.any(~is_power2(nside_superpix)) or np.any(~is_power2(nside_subpix)):
raise ValueError("NSIDE must be a power of 2.")
ratio = np.array((nside_subpix // nside_superpix) ** 2, ndmin=1)
idx //= ratio
if not nest:
m = idx == INVALID_INDEX.int
idx[m] = 0
idx = hp.nest2ring(nside_superpix, idx)
idx[m] = INVALID_INDEX.int
return idx
def get_subpixels(idx, nside_superpix, nside_subpix, nest=True):
"""Compute the indices of subpixels contained within superpixels.
This function returns an output array with one additional
dimension of size N for subpixel indices where N is the maximum
number of subpixels for any pair of ``nside_superpix`` and
``nside_subpix``. If the number of subpixels is less than N the
remaining subpixel indices will be set to -1.
Parameters
----------
idx : `~numpy.ndarray`
Array of HEALPix pixel indices for superpixels of NSIDE
``nside_superpix``.
nside_superpix : int or `~numpy.ndarray`
NSIDE of superpixel.
nside_subpix : int or `~numpy.ndarray`
NSIDE of subpixel.
nest : bool
If True, assume NESTED pixel ordering, otherwise, RING pixel
ordering.
Returns
-------
idx_sub : `~numpy.ndarray`
Indices of HEALpix pixels of nside ``nside_subpix`` contained
within pixel indices ``idx`` of nside ``nside_superpix``.
"""
import healpy as hp
if not nest:
idx = hp.ring2nest(nside_superpix, idx)
idx = np.asarray(idx)
nside_superpix = np.asarray(nside_superpix)
nside_subpix = np.asarray(nside_subpix)
if np.any(~is_power2(nside_superpix)) or np.any(~is_power2(nside_subpix)):
raise ValueError("NSIDE must be a power of 2.")
# number of subpixels in each superpixel
npix = np.array((nside_subpix // nside_superpix) ** 2, ndmin=1)
x = np.arange(np.max(npix), dtype=int)
idx = idx * npix
if not np.all(npix[0] == npix):
x = np.broadcast_to(x, idx.shape + x.shape)
idx = idx[..., None] + x
idx[x >= np.broadcast_to(npix[..., None], x.shape)] = INVALID_INDEX.int
else:
idx = idx[..., None] + x
if not nest:
m = idx == INVALID_INDEX.int
idx[m] = 0
idx = hp.nest2ring(nside_subpix[..., None], idx)
idx[m] = INVALID_INDEX.int
return idx
[docs]class HpxGeom(MapGeom):
"""Geometry class for HEALPIX maps.
This class performs mapping between partial-sky indices (pixel
number within a HEALPIX region) and all-sky indices (pixel number
within an all-sky HEALPIX map). Multi-band HEALPIX geometries use
a global indexing scheme that assigns a unique pixel number based
on the all-sky index and band index. In the single-band case the
global index is the same as the HEALPIX index.
By default the constructor will return an all-sky map.
Partial-sky maps can be defined with the ``region`` argument.
Parameters
----------
nside : `~numpy.ndarray`
HEALPIX nside parameter, the total number of pixels is
12*nside*nside. For multi-dimensional maps one can pass
either a single nside value or a vector of nside values
defining the pixel size for each image plane. If nside is not
a scalar then its dimensionality should match that of the
non-spatial axes.
nest : bool
True -> 'NESTED', False -> 'RING' indexing scheme
coordsys : str
Coordinate system, 'CEL' | 'GAL'
region : str or tuple
Spatial geometry for partial-sky maps. If none the map will
encompass the whole sky. String input will be parsed
according to HPX_REG header keyword conventions. Tuple
input can be used to define an explicit list of pixels
encompassed by the geometry.
axes : list
Axes for non-spatial dimensions.
conv : str
Convention for FITS serialization format.
sparse : bool
If True defer allocation of partial- to all-sky index mapping
arrays. This option is only compatible with partial-sky maps
with an analytic geometry (e.g. DISK).
"""
is_hpx = True
def __init__(
self,
nside,
nest=True,
coordsys="CEL",
region=None,
axes=None,
conv="gadf",
sparse=False,
):
# FIXME: Figure out what to do when sparse=True
# FIXME: Require NSIDE to be power of two when nest=True
self._nside = np.array(nside, ndmin=1)
self._axes = make_axes(axes, conv)
if self.nside.size > 1 and self.nside.shape != self.shape_axes:
raise ValueError(
"Wrong dimensionality for nside. nside must "
"be a scalar or have a dimensionality consistent "
"with the axes argument."
)
self._order = nside_to_order(self._nside)
self._nest = nest
self._coordsys = coordsys
self._maxpix = 12 * self._nside * self._nside
self._maxpix = self._maxpix * np.ones(self.shape_axes, dtype=int)
self._sparse = sparse
self._ipix = None
self._rmap = None
self._region = region
self._create_lookup(region)
if self._ipix is not None:
self._rmap = {}
for i, ipix in enumerate(self._ipix.flat):
self._rmap[ipix] = i
self._npix = self._npix * np.ones(self.shape_axes, dtype=int)
self._conv = conv
self._center_skydir = self._get_ref_dir()
lon, lat, frame = skycoord_to_lonlat(self._center_skydir)
self._center_coord = tuple(
[lon, lat]
+ [ax.pix_to_coord((float(ax.nbin) - 1.0) / 2.0) for ax in self.axes]
)
self._center_pix = self.coord_to_pix(self._center_coord)
@property
def data_shape(self):
"""Shape of the Numpy data array matching this geometry."""
npix_shape = [np.max(self.npix)]
ax_shape = [ax.nbin for ax in self.axes]
return tuple(npix_shape + ax_shape)[::-1]
def _create_lookup(self, region):
"""Create local-to-global pixel lookup table."""
if isinstance(region, str):
ipix = [
self.get_index_list(nside, self._nest, region)
for nside in self._nside.flat
]
self._ibnd = np.concatenate(
[i * np.ones_like(p, dtype="int16") for i, p in enumerate(ipix)]
)
self._ipix = [
ravel_hpx_index((p, i * np.ones_like(p)), np.ravel(self._maxpix))
for i, p in enumerate(ipix)
]
self._region = region
self._indxschm = "EXPLICIT"
self._npix = np.array([len(t) for t in self._ipix])
if self.nside.ndim > 1:
self._npix = self._npix.reshape(self.nside.shape)
self._ipix = np.concatenate(self._ipix)
elif isinstance(region, tuple):
region = [np.asarray(t) for t in region]
m = np.any(np.stack([t >= 0 for t in region]), axis=0)
region = [t[m] for t in region]
self._ipix = ravel_hpx_index(region, self._maxpix)
self._ipix = np.unique(self._ipix)
region = unravel_hpx_index(self._ipix, self._maxpix)
self._region = "explicit"
self._indxschm = "EXPLICIT"
if len(region) == 1:
self._npix = np.array([len(region[0])])
else:
self._npix = np.zeros(self.shape_axes, dtype=int)
idx = np.ravel_multi_index(region[1:], self.shape_axes)
cnt = np.unique(idx, return_counts=True)
self._npix.flat[cnt[0]] = cnt[1]
elif region is None:
self._region = None
self._indxschm = "IMPLICIT"
self._npix = self._maxpix
else:
raise ValueError("Invalid region string: {!r}".format(region))
[docs] def local_to_global(self, idx_local):
"""Compute a local index (partial-sky) from a global (all-sky)
index.
Returns
-------
idx_global : tuple
A tuple of pixel index vectors with global HEALPIX pixel
indices.
"""
if self._ipix is None:
return idx_local
if self.nside.size > 1:
idx = ravel_hpx_index(idx_local, self._npix)
else:
idx_tmp = tuple(
[idx_local[0]] + [np.zeros(t.shape, dtype=int) for t in idx_local[1:]]
)
idx = ravel_hpx_index(idx_tmp, self._npix)
idx_global = unravel_hpx_index(self._ipix[idx], self._maxpix)
return idx_global[:1] + tuple(idx_local[1:])
[docs] def global_to_local(self, idx_global, ravel=False):
"""Compute global (all-sky) index from a local (partial-sky) index.
Parameters
----------
idx_global : tuple
A tuple of pixel indices with global HEALPix pixel indices.
ravel : bool
Return a raveled index.
Returns
-------
idx_local : tuple
A tuple of pixel indices with local HEALPIX pixel indices.
"""
if self.nside.size == 1:
idx = np.array(idx_global[0], ndmin=1)
else:
idx = ravel_hpx_index(idx_global, self._maxpix)
if self._rmap is not None:
retval = np.full(idx.size, -1, "i")
m = np.in1d(idx.flat, self._ipix)
retval[m] = np.searchsorted(self._ipix, idx.flat[m])
retval = retval.reshape(idx.shape)
else:
retval = idx
if self.nside.size == 1:
idx_local = tuple([retval] + list(idx_global[1:]))
else:
idx_local = unravel_hpx_index(retval, self._npix)
m = np.any(np.stack([t == INVALID_INDEX.int for t in idx_local]), axis=0)
for i, t in enumerate(idx_local):
idx_local[i][m] = INVALID_INDEX.int
if not ravel:
return idx_local
else:
return ravel_hpx_index(idx_local, self.npix)
def __getitem__(self, idx_global):
"""Implement global-to-local index lookup.
For all-sky maps it just returns the input array. For
partial-sky maps it returns the local indices corresponding to
the indices in the input array, and -1 for those pixels that
are outside the selected region. For multi-dimensional maps
with a different ``NSIDE`` in each band the global index is an
unrolled index for both HEALPIX pixel number and image slice.
Parameters
----------
idx_global : `~numpy.ndarray`
An array of global (all-sky) pixel indices. If this is a
tuple, list, or array of integers it will be interpreted
as a global (raveled) index. If this argument is a tuple
of lists or arrays it will be interpreted as a list of
unraveled index vectors.
Returns
-------
idx_local : `~numpy.ndarray`
An array of local HEALPIX pixel indices.
"""
# Convert to tuple representation
if (
isinstance(idx_global, int)
or (isinstance(idx_global, tuple) and isinstance(idx_global[0], int))
or isinstance(idx_global, np.ndarray)
):
idx_global = unravel_hpx_index(np.array(idx_global, ndmin=1), self._maxpix)
return self.global_to_local(idx_global, ravel=True)
[docs] def coord_to_pix(self, coords):
import healpy as hp
coords = MapCoord.create(coords, coordsys=self.coordsys)
theta, phi = coords.theta, coords.phi
c = self.coord_to_tuple(coords)
if self.axes:
bins = []
idxs = []
for i, ax in enumerate(self.axes):
bins += [ax.coord_to_pix(c[i + 2])]
idxs += [ax.coord_to_idx(c[i + 2])]
# FIXME: Figure out how to handle coordinates out of
# bounds of non-spatial dimensions
if self.nside.size > 1:
nside = self.nside[tuple(idxs)]
else:
nside = self.nside
m = ~np.isfinite(theta)
theta[m] = 0.0
phi[m] = 0.0
pix = hp.ang2pix(nside, theta, phi, nest=self.nest)
pix = tuple([pix] + bins)
if np.any(m):
for p in pix:
p[m] = INVALID_INDEX.int
else:
pix = (hp.ang2pix(self.nside, theta, phi, nest=self.nest),)
return pix
[docs] def pix_to_coord(self, pix):
import healpy as hp
if self.axes:
bins = []
vals = []
for i, ax in enumerate(self.axes):
bins += [pix[1 + i]]
vals += [ax.pix_to_coord(pix[1 + i])]
idxs = pix_tuple_to_idx(bins)
if self.nside.size > 1:
nside = self.nside[idxs]
else:
nside = self.nside
ipix = np.round(pix[0]).astype(int)
m = ipix == INVALID_INDEX.int
ipix[m] = 0
theta, phi = hp.pix2ang(nside, ipix, nest=self.nest)
coords = [np.degrees(phi), np.degrees(np.pi / 2.0 - theta)]
coords = tuple(coords + vals)
if np.any(m):
for c in coords:
c[m] = INVALID_INDEX.float
else:
ipix = np.round(pix[0]).astype(int)
theta, phi = hp.pix2ang(self.nside, ipix, nest=self.nest)
coords = (np.degrees(phi), np.degrees(np.pi / 2.0 - theta))
return coords
[docs] def pix_to_idx(self, pix, clip=False):
# FIXME: Look for better method to clip HPX indices
# TODO: copy idx to avoid modifying input pix?
# pix_tuple_to_idx seems to always make a copy!?
idx = pix_tuple_to_idx(pix)
idx_local = self.global_to_local(idx)
for i, _ in enumerate(idx):
if clip:
if i > 0:
np.clip(idx[i], 0, self.axes[i - 1].nbin - 1, out=idx[i])
else:
np.clip(idx[i], 0, None, out=idx[i])
else:
if i > 0:
mask = (idx[i] < 0) | (idx[i] >= self.axes[i - 1].nbin)
np.putmask(idx[i], mask, -1)
else:
mask = (idx_local[i] < 0) | (idx[i] < 0)
np.putmask(idx[i], mask, -1)
return tuple(idx)
[docs] def to_slice(self, slices, drop_axes=True):
if len(slices) == 0 and self.ndim == 2:
return copy.deepcopy(self)
if len(slices) != self.ndim - 2:
raise ValueError()
nside = np.ones(self.shape_axes, dtype=int) * self.nside
nside = np.squeeze(nside[slices])
axes = [ax.slice(s) for ax, s in zip(self.axes, slices)]
if drop_axes:
axes = [ax for ax in axes if ax.nbin > 1]
slice_dims = [0] + [i + 1 for i, ax in enumerate(axes) if ax.nbin > 1]
else:
slice_dims = np.arange(self.ndim)
if self.region == "explicit":
idx = self.get_idx()
slices = (slice(None),) + slices
idx = [p[slices[::-1]] for p in idx]
idx = [p[p != INVALID_INDEX.int] for p in idx]
if drop_axes:
idx = [idx[i] for i in range(len(idx)) if i in slice_dims]
region = tuple(idx)
else:
region = self.region
return self.__class__(nside, self.nest, self.coordsys, region, axes, self.conv)
@property
def axes(self):
"""List of non-spatial axes."""
return self._axes
@property
def shape_axes(self):
"""Shape of non-spatial axes."""
return tuple([ax.nbin for ax in self._axes])
@property
def ndim(self):
"""Number of dimensions (int)."""
return len(self._axes) + 2
@property
def ordering(self):
"""HEALPix ordering ('NESTED' or 'RING')."""
return "NESTED" if self.nest else "RING"
@property
def nside(self):
"""NSIDE in each band."""
return self._nside
@property
def order(self):
"""ORDER in each band (NSIDE = 2**ORDER). Set to -1 for bands with
NSIDE that is not a power of 2.
"""
return self._order
@property
def nest(self):
"""Is HEALPix order nested? (bool)."""
return self._nest
@property
def npix(self):
"""Number of pixels in each band.
For partial-sky geometries this can
be less than the number of pixels for the band NSIDE.
"""
return self._npix
@property
def conv(self):
"""Name of default FITS convention associated with this geometry."""
return self._conv
@property
def hpx_conv(self):
return HPX_FITS_CONVENTIONS[self.conv]
@property
def coordsys(self):
return self._coordsys
@property
def projection(self):
"""Map projection."""
return "HPX"
@property
def region(self):
"""Region string."""
return self._region
@property
def is_allsky(self):
"""Flag for all-sky maps."""
if self._region is None:
return True
else:
return False
@property
def is_regular(self):
"""Flag identifying whether this geometry is regular in non-spatial dimensions.
False for multi-resolution or irregular geometries.
If true all image planes have the same pixel geometry.
"""
if self.nside.size > 1 or self.region == "explicit":
return False
else:
return True
@property
def center_coord(self):
"""Map coordinates of the center of the geometry (tuple)."""
return self._center_coord
@property
def center_pix(self):
"""Pixel coordinates of the center of the geometry (tuple)."""
return self._center_pix
@property
def center_skydir(self):
"""Sky coordinate of the center of the geometry.
Returns
-------
pix : `~astropy.coordinates.SkyCoord`
"""
return self._center_skydir
@property
def ipix(self):
"""HEALPIX pixel and band indices for every pixel in the map."""
return self.get_idx()
[docs] def to_ud_graded(self, order):
"""Upgrade or downgrade the resolution of this geometry to the given
order. This method does not preserve the geometry footprint.
Returns
-------
geom : `~HpxGeom`
A HEALPix geometry object.
"""
if np.any(self.order < 0):
raise ValueError("Upgrade and degrade only implemented for standard maps")
axes = copy.deepcopy(self.axes)
return self.__class__(
2 ** order,
self.nest,
coordsys=self.coordsys,
region=self.region,
axes=axes,
conv=self.conv,
)
[docs] def to_swapped(self):
"""Make a copy of this geometry with a swapped ORDERING. (NEST->RING
or vice versa)
Returns
-------
geom : `~HpxGeom`
A HEALPix geometry object.
"""
axes = copy.deepcopy(self.axes)
return self.__class__(
self.nside,
not self.nest,
coordsys=self.coordsys,
region=self.region,
axes=axes,
conv=self.conv,
)
[docs] def to_image(self):
return self.__class__(
np.max(self.nside),
self.nest,
coordsys=self.coordsys,
region=self.region,
conv=self.conv,
)
[docs] def to_cube(self, axes):
axes = copy.deepcopy(self.axes) + axes
return self.__class__(
np.max(self.nside),
self.nest,
coordsys=self.coordsys,
region=self.region,
conv=self.conv,
axes=axes,
)
def _get_neighbors(self, idx):
import healpy as hp
nside = self._get_nside(idx)
idx_nb = (hp.get_all_neighbours(nside, idx[0], nest=self.nest),)
idx_nb += tuple([t[None, ...] * np.ones_like(idx_nb[0]) for t in idx[1:]])
return idx_nb
[docs] def pad(self, pad_width):
if self.is_allsky:
raise ValueError("Cannot pad an all-sky map.")
idx = self.get_idx(flat=True)
idx_r = ravel_hpx_index(idx, self._maxpix)
# TODO: Pre-filter indices to find those close to the edge
idx_nb = self._get_neighbors(idx)
idx_nb = ravel_hpx_index(idx_nb, self._maxpix)
for _ in range(pad_width):
# Mask of neighbors that are not contained in the geometry
# TODO: change this to numpy.isin when we require Numpy 1.13+
# Here and everywhere in Gamampy -> search for "isin"
# see https://github.com/gammapy/gammapy/pull/1371
mask_edge = np.in1d(idx_nb, idx_r, invert=True).reshape(idx_nb.shape)
idx_edge = idx_nb[mask_edge]
idx_edge = np.unique(idx_edge)
idx_r = np.sort(np.concatenate((idx_r, idx_edge)))
idx_nb = unravel_hpx_index(idx_edge, self._maxpix)
idx_nb = self._get_neighbors(idx_nb)
idx_nb = ravel_hpx_index(idx_nb, self._maxpix)
idx = unravel_hpx_index(idx_r, self._maxpix)
return self.__class__(
self.nside.copy(),
self.nest,
coordsys=self.coordsys,
region=idx,
conv=self.conv,
axes=copy.deepcopy(self.axes),
)
[docs] def crop(self, crop_width):
if self.is_allsky:
raise ValueError("Cannot crop an all-sky map.")
idx = self.get_idx(flat=True)
idx_r = ravel_hpx_index(idx, self._maxpix)
# TODO: Pre-filter indices to find those close to the edge
idx_nb = self._get_neighbors(idx)
idx_nb = ravel_hpx_index(idx_nb, self._maxpix)
for _ in range(crop_width):
# Mask of pixels that have at least one neighbor not
# contained in the geometry
mask_edge = np.in1d(idx_nb, idx_r, invert=True)
mask_edge = np.any(mask_edge, axis=0)
idx_r = idx_r[~mask_edge]
idx_nb = idx_nb[:, ~mask_edge]
idx = unravel_hpx_index(idx_r, self._maxpix)
return self.__class__(
self.nside.copy(),
self.nest,
coordsys=self.coordsys,
region=idx,
conv=self.conv,
axes=copy.deepcopy(self.axes),
)
[docs] def upsample(self, factor):
if not is_power2(factor):
raise ValueError("Upsample factor must be a power of 2.")
if self.is_allsky:
return self.__class__(
self.nside * factor,
self.nest,
coordsys=self.coordsys,
region=self.region,
conv=self.conv,
axes=copy.deepcopy(self.axes),
)
idx = list(self.get_idx(flat=True))
nside = self._get_nside(idx)
idx_new = get_subpixels(idx[0], nside, nside * factor, nest=self.nest)
for i in range(1, len(idx)):
idx[i] = idx[i][..., None] * np.ones(idx_new.shape, dtype=int)
idx[0] = idx_new
return self.__class__(
self.nside * factor,
self.nest,
coordsys=self.coordsys,
region=tuple(idx),
conv=self.conv,
axes=copy.deepcopy(self.axes),
)
[docs] def downsample(self, factor):
if not is_power2(factor):
raise ValueError("Downsample factor must be a power of 2.")
if self.is_allsky:
return self.__class__(
self.nside // factor,
self.nest,
coordsys=self.coordsys,
region=self.region,
conv=self.conv,
axes=copy.deepcopy(self.axes),
)
idx = list(self.get_idx(flat=True))
nside = self._get_nside(idx)
idx_new = get_superpixels(idx[0], nside, nside // factor, nest=self.nest)
idx[0] = idx_new
return self.__class__(
self.nside // factor,
self.nest,
coordsys=self.coordsys,
region=tuple(idx),
conv=self.conv,
axes=copy.deepcopy(self.axes),
)
[docs] @classmethod
def create(
cls,
nside=None,
binsz=None,
nest=True,
coordsys="CEL",
region=None,
axes=None,
conv="gadf",
skydir=None,
width=None,
):
"""Create an HpxGeom object.
Parameters
----------
nside : int or `~numpy.ndarray`
HEALPix NSIDE parameter. This parameter sets the size of
the spatial pixels in the map.
binsz : float or `~numpy.ndarray`
Approximate pixel size in degrees. An NSIDE will be
chosen that correponds to a pixel size closest to this
value. This option is superseded by nside.
nest : bool
True for HEALPIX "NESTED" indexing scheme, False for "RING" scheme
coordsys : {'CEL', 'GAL'}, optional
Coordinate system, either Galactic ('GAL') or Equatorial ('CEL').
skydir : tuple or `~astropy.coordinates.SkyCoord`
Sky position of map center. Can be either a SkyCoord
object or a tuple of longitude and latitude in deg in the
coordinate system of the map.
region : str
HPX region string. Allows for partial-sky maps.
width : float
Diameter of the map in degrees. If set the map will
encompass all pixels within a circular region centered on
``skydir``.
axes : list
List of axes for non-spatial dimensions.
conv : str
Convention for FITS file format.
Returns
-------
geom : `~HpxGeom`
A HEALPix geometry object.
Examples
--------
>>> from gammapy.maps import HpxGeom, MapAxis
>>> axis = MapAxis.from_bounds(0,1,2)
>>> geom = HpxGeom.create(nside=16)
>>> geom = HpxGeom.create(binsz=0.1, width=10.0)
>>> geom = HpxGeom.create(nside=64, width=10.0, axes=[axis])
>>> geom = HpxGeom.create(nside=[32,64], width=10.0, axes=[axis])
"""
if nside is None and binsz is None:
raise ValueError("Either nside or binsz must be defined.")
if nside is None and binsz is not None:
nside = get_nside_from_pix_size(binsz)
if skydir is None:
lon, lat = (0.0, 0.0)
elif isinstance(skydir, tuple):
lon, lat = skydir
elif isinstance(skydir, SkyCoord):
lon, lat, frame = skycoord_to_lonlat(skydir, coordsys=coordsys)
else:
raise ValueError("Invalid type for skydir: {!r}".format(type(skydir)))
if region is None and width is not None:
region = "DISK({:f},{:f},{:f})".format(lon, lat, width / 2.0)
return cls(
nside, nest=nest, coordsys=coordsys, region=region, axes=axes, conv=conv
)
[docs] @staticmethod
def identify_hpx_convention(header):
"""Identify the convention used to write this file."""
# Hopefully the file contains the HPX_CONV keyword specifying
# the convention used
if "HPX_CONV" in header:
return header["HPX_CONV"].lower()
# Try based on the EXTNAME keyword
hduname = header.get("EXTNAME", None)
if hduname == "HPXEXPOSURES":
return "fgst-bexpcube"
elif hduname == "SKYMAP2":
if "COORDTYPE" in header.keys():
return "galprop"
else:
return "galprop2"
# Check the name of the first column
colname = header["TTYPE1"]
if colname == "PIX":
colname = header["TTYPE2"]
if colname == "KEY":
return "fgst-srcmap-sparse"
elif colname == "ENERGY1":
return "fgst-template"
elif colname == "COSBINS":
return "fgst-ltcube"
elif colname == "Bin0":
return "galprop"
elif colname == "CHANNEL1" or colname == "CHANNEL0":
if hduname == "SKYMAP":
return "fgst-ccube"
else:
return "fgst-srcmap"
else:
raise ValueError("Could not identify HEALPIX convention")
[docs] @classmethod
def from_hdu(cls, hdu, hdu_bands=None):
"""Create an HPX object from a BinTable HDU.
Parameters
----------
hdu : `~astropy.io.fits.BinTableHDU`
The FITS HDU
hdu_bands : `~astropy.io.fits.BinTableHDU`
The BANDS table HDU
Returns
-------
hpx : `~HpxGeom`
HEALPix geometry.
"""
# FIXME: Need correct handling of IMPLICIT and EXPLICIT maps
# if HPX region is not defined then geometry is defined by
# the set of all pixels in the table
if "HPX_REG" not in hdu.header:
pix = (hdu.data.field("PIX"), hdu.data.field("CHANNEL"))
else:
pix = None
return cls.from_header(hdu.header, hdu_bands=hdu_bands, pix=pix)
def _make_bands_cols(self):
cols = []
if self.nside.size > 1:
cols += [fits.Column("NSIDE", "I", array=np.ravel(self.nside))]
return cols
[docs] @staticmethod
def get_index_list(nside, nest, region):
"""Get list of pixels indices for all the pixels in a region.
Parameters
----------
nside : int
HEALPIX nside parameter
nest : bool
True for 'NESTED', False = 'RING'
region : str
HEALPIX region string
Returns
-------
ilist : `~numpy.ndarray`
List of pixel indices.
"""
import healpy as hp
# TODO: this should return something more friendly than a tuple
# e.g. a namedtuple or a dict
tokens = parse_hpxregion(region)
reg_type = tokens[0]
if reg_type == "DISK":
lon, lat = float(tokens[1]), float(tokens[2])
radius = np.radians(float(tokens[3]))
vec = coords_to_vec(lon, lat)[0]
ilist = hp.query_disc(nside, vec, radius, inclusive=False, nest=nest)
elif reg_type == "DISK_INC":
lon, lat = float(tokens[1]), float(tokens[2])
radius = np.radians(float(tokens[3]))
vec = coords_to_vec(lon, lat)[0]
fact = int(tokens[4])
ilist = hp.query_disc(
nside, vec, radius, inclusive=True, nest=nest, fact=fact
)
elif reg_type == "HPX_PIXEL":
nside_pix = int(tokens[2])
if tokens[1] == "NESTED":
ipix_ring = hp.nest2ring(nside_pix, int(tokens[3]))
elif tokens[1] == "RING":
ipix_ring = int(tokens[3])
else:
raise ValueError("Invalid ordering scheme: {!r}".format(tokens[1]))
ilist = match_hpx_pix(nside, nest, nside_pix, ipix_ring)
else:
raise ValueError("Invalid region type: {!r}".format(reg_type))
return ilist
def _get_ref_dir(self):
"""Compute the reference direction for this geometry."""
import healpy as hp
frame = coordsys_to_frame(self.coordsys)
if self.region == "explicit":
idx = unravel_hpx_index(self._ipix, self._maxpix)
nside = self._get_nside(idx)
vec = hp.pix2vec(nside, idx[0], nest=self.nest)
vec = np.array([np.mean(t) for t in vec])
lonlat = hp.vec2ang(vec, lonlat=True)
return SkyCoord(lonlat[0], lonlat[1], frame=frame, unit="deg")
return get_hpxregion_dir(self.region, self.coordsys)
def _get_region_size(self):
import healpy as hp
if self.region is None:
return 180.0
if self.region == "explicit":
idx = unravel_hpx_index(self._ipix, self._maxpix)
nside = self._get_nside(idx)
ang = hp.pix2ang(nside, idx[0], nest=self.nest, lonlat=True)
frame = coordsys_to_frame(self.coordsys)
dirs = SkyCoord(ang[0], ang[1], unit="deg", frame=frame)
return np.max(dirs.separation(self.center_skydir).deg)
return get_hpxregion_size(self.region)
def _get_nside(self, idx):
if self.nside.size > 1:
return self.nside[tuple(idx[1:])]
else:
return self.nside
[docs] def make_wcs(self, proj="AIT", oversample=2, drop_axes=True, width_pix=None):
"""Make a WCS projection appropriate for this HPX pixelization.
Parameters
----------
drop_axes : bool
Drop non-spatial axes from the
HEALPIX geometry. If False then all dimensions of the
HEALPIX geometry will be copied to the WCS geometry.
proj : str
Projection type of WCS geometry.
oversample : float
Oversampling factor for WCS map. This will be the
approximate ratio of the width of a HPX pixel to a WCS
pixel. If this parameter is None then the width will be
set from ``width_pix``.
width_pix : int
Width of the WCS geometry in pixels. The pixel size will
be set to the number of pixels satisfying ``oversample``
or ``width_pix`` whichever is smaller. If this parameter
is None then the width will be set from ``oversample``.
Returns
-------
wcs : `~gammapy.maps.WcsGeom`
WCS geometry
"""
pix_size = get_pix_size_from_nside(self.nside)
binsz = np.min(pix_size) / oversample
width = 2.0 * self._get_region_size() + np.max(pix_size)
if width_pix is not None and int(width / binsz) > width_pix:
binsz = width / width_pix
if width > 90.0:
width = min(360.0, width), min(180.0, width)
if drop_axes:
axes = None
else:
axes = copy.deepcopy(self.axes)
return WcsGeom.create(
width=width,
binsz=binsz,
coordsys=self.coordsys,
axes=axes,
skydir=self.center_skydir,
proj=proj,
)
[docs] def get_idx(self, idx=None, local=False, flat=False):
if idx is not None and np.any(np.array(idx) >= np.array(self.shape_axes)):
raise ValueError("Image index out of range: {!r}".format(idx))
# Regular all- and partial-sky maps
if self.is_regular:
pix = [np.arange(np.max(self._npix))]
if idx is None:
pix += [np.arange(ax.nbin, dtype=int) for ax in self.axes]
else:
pix += [t for t in idx]
pix = np.meshgrid(*pix[::-1], indexing="ij", sparse=False)[::-1]
pix = self.local_to_global(pix)
# Non-regular all-sky
elif self.is_allsky and not self.is_regular:
shape = (np.max(self.npix),)
if idx is None:
shape = shape + self.shape_axes
else:
shape = shape + (1,) * len(self.axes)
pix = [np.full(shape, -1, dtype=int) for i in range(1 + len(self.axes))]
for idx_img in np.ndindex(self.shape_axes):
if idx is not None and idx_img != idx:
continue
npix = self._npix[idx_img]
if idx is None:
s_img = (slice(0, npix),) + idx_img
else:
s_img = (slice(0, npix),) + (0,) * len(self.axes)
pix[0][s_img] = np.arange(self._npix[idx_img])
for j in range(len(self.axes)):
pix[j + 1][s_img] = idx_img[j]
pix = [p.T for p in pix]
# Explicit pixel indices
else:
if idx is not None:
npix_sum = np.concatenate(([0], np.cumsum(self._npix)))
idx_ravel = np.ravel_multi_index(idx, self.shape_axes)
s = slice(npix_sum[idx_ravel], npix_sum[idx_ravel + 1])
else:
s = slice(None)
pix_flat = unravel_hpx_index(self._ipix[s], self._maxpix)
shape = (np.max(self.npix),)
if idx is None:
shape = shape + self.shape_axes
else:
shape = shape + (1,) * len(self.axes)
pix = [np.full(shape, -1, dtype=int) for _ in range(1 + len(self.axes))]
for idx_img in np.ndindex(self.shape_axes):
if idx is not None and idx_img != idx:
continue
npix = int(self._npix[idx_img])
if idx is None:
s_img = (slice(0, npix),) + idx_img
else:
s_img = (slice(0, npix),) + (0,) * len(self.axes)
if self.axes:
m = np.all(
np.stack([pix_flat[i + 1] == t for i, t in enumerate(idx_img)]),
axis=0,
)
pix[0][s_img] = pix_flat[0][m]
else:
pix[0][s_img] = pix_flat[0]
for j in range(len(self.axes)):
pix[j + 1][s_img] = idx_img[j]
pix = [p.T for p in pix]
if local:
pix = self.global_to_local(pix)
if flat:
pix = tuple([p[p != INVALID_INDEX.int] for p in pix])
return pix
[docs] def get_coord(self, idx=None, flat=False):
pix = self.get_idx(idx=idx, flat=flat)
coords = self.pix_to_coord(pix)
cdict = OrderedDict([("lon", coords[0]), ("lat", coords[1])])
for i, axis in enumerate(self.axes):
cdict[axis.name] = coords[i + 2]
return MapCoord.create(cdict, coordsys=self.coordsys)
[docs] def contains(self, coords):
idx = self.coord_to_idx(coords)
return np.all(np.stack([t != INVALID_INDEX.int for t in idx]), axis=0)
[docs] def get_skydirs(self):
"""Get the sky coordinates of all the pixels in this geometry."""
coords = self.get_coord()
frame = "galactic" if self.coordsys == "GAL" else "icrs"
return SkyCoord(coords[0], coords[1], unit="deg", frame=frame)
[docs] def solid_angle(self):
"""Solid angle array (`~astropy.units.Quantity` in ``sr``).
The array has the same dimensionality as ``map.nside``
since all pixels have the same solid angle.
"""
import healpy as hp
return Quantity(hp.nside2pixarea(self.nside), "sr")
def __repr__(self):
str_ = self.__class__.__name__
str_ += "\n\n"
axes = ["skycoord"] + [_.name for _ in self.axes]
str_ += "\taxes : {}\n".format(", ".join(axes))
str_ += "\tshape : {}\n".format(self.data_shape[::-1])
str_ += "\tndim : {}\n".format(self.ndim)
str_ += "\tnside : {nside[0]}\n".format(nside=self.nside)
str_ += "\tnested : {}\n".format(self.nest)
str_ += "\tcoordsys : {}\n".format(self.coordsys)
str_ += "\tprojection : {}\n".format(self.projection)
lon, lat = self.center_skydir.data.lon.deg, self.center_skydir.data.lat.deg
str_ += "\tcenter : {lon:.1f} deg, {lat:.1f} deg\n".format(lon=lon, lat=lat)
return str_
def __eq__(self, other):
"""Test equality between two `~gammapy.maps.HpxGeom`"""
if not isinstance(other, self.__class__):
return NotImplemented
if self._sparse or other._sparse:
return NotImplemented
if self.is_allsky and other.is_allsky is False:
return NotImplemented
# check overall shape and axes compatibility
if self.data_shape != other.data_shape:
return False
for axis, otheraxis in zip(self.axes, other.axes):
if axis != otheraxis:
return False
return (
self.nside == other.nside
and self.coordsys == other.coordsys
and self.order == other.order
and self.nest == other.nest
)
def __ne__(self, other):
return not self.__eq__(other)
class HpxToWcsMapping:
"""Stores the indices need to convert from HEALPIX to WCS.
Parameters
----------
hpx : `~HpxGeom`
HEALPix geometry object.
wcs : `~gammapy.maps.WcsGeom`
WCS geometry object.
"""
def __init__(self, hpx, wcs, ipix, mult_val, npix):
self._hpx = hpx
self._wcs = wcs
self._ipix = ipix
self._mult_val = mult_val
self._npix = npix
self._lmap = self._hpx[self._ipix]
self._valid = self._lmap >= 0
@property
def hpx(self):
"""The HEALPIX projection"""
return self._hpx
@property
def wcs(self):
"""The WCS projection"""
return self._wcs
@property
def ipix(self):
"""An array(nx,ny) of the global HEALPIX pixel indices for each WCS pixel"""
return self._ipix
@property
def mult_val(self):
"""An array(nx,ny) of 1/number of WCS pixels pointing at each HEALPIX pixel"""
return self._mult_val
@property
def npix(self):
"""A tuple(nx,ny) of the shape of the WCS grid"""
return self._npix
@property
def lmap(self):
"""An array(nx,ny) giving the mapping of the local HEALPIX pixel
indices for each WCS pixel.
"""
return self._lmap
@property
def valid(self):
"""Array(nx,ny) of bools giving if each WCS pixel in inside the
HEALPIX region.
"""
return self._valid
@classmethod
def create(cls, hpx, wcs):
"""Create an object that maps pixels from HEALPix geometry ``hpx`` to
WCS geometry ``wcs``.
Parameters
----------
hpx : `~HpxGeom`
HEALPix geometry object.
wcs : `~gammapy.maps.WcsGeom`
WCS geometry object.
Returns
-------
hpx2wcs : `~HpxToWcsMapping`
"""
ipix, mult_val, npix = make_hpx_to_wcs_mapping(hpx, wcs)
return cls(hpx, wcs, ipix, mult_val, npix)
def fill_wcs_map_from_hpx_data(
self, hpx_data, wcs_data, normalize=True, fill_nan=True
):
"""Fill the WCS map from the hpx data using the pre-calculated mappings.
Parameters
----------
hpx_data : `~numpy.ndarray`
The input HEALPIX data
wcs_data : `~numpy.ndarray`
The data array being filled
normalize : bool
True -> preserve integral by splitting HEALPIX values between bins
fill_nan : bool
Fill pixels outside the HPX geometry with NaN.
"""
# FIXME: Do we want to flatten mapping arrays?
shape = tuple([t.flat[0] for t in self._npix])
if self._valid.ndim != 1:
shape = hpx_data.shape[:-1] + shape
valid = np.where(self._valid.reshape(shape))
lmap = self._lmap[self._valid]
mult_val = self._mult_val[self._valid]
wcs_slice = [slice(None) for _ in range(wcs_data.ndim - 2)]
wcs_slice = tuple(wcs_slice + list(valid)[::-1][:2])
hpx_slice = [slice(None) for _ in range(wcs_data.ndim - 2)]
hpx_slice = tuple(hpx_slice + [lmap])
if normalize:
wcs_data[wcs_slice] = mult_val * hpx_data[hpx_slice]
else:
wcs_data[wcs_slice] = hpx_data[hpx_slice]
if fill_nan:
valid = np.swapaxes(self._valid.reshape(shape), -1, -2)
valid = valid * np.ones_like(wcs_data, dtype=bool)
wcs_data[~valid] = np.nan
return wcs_data
def make_wcs_data_from_hpx_data(self, hpx_data, wcs, normalize=True):
"""Create and fill a WCS map from the HEALPIX data using the pre-calculated mappings.
Parameters
----------
hpx_data : TODO
The input HEALPIX data
wcs : TODO
The WCS object
normalize : bool
True -> preserve integral by splitting HEALPIX values between bins
"""
wcs_data = np.zeros(wcs.npix)
self.fill_wcs_map_from_hpx_data(hpx_data, wcs_data, normalize)
return wcs_data