# Licensed under a 3-clause BSD style license - see LICENSE.rst
import logging
import numpy as np
import astropy.units as u
from astropy.coordinates import SkyCoord
from astropy.io import fits
from regions import PointSkyRegion
import matplotlib.pyplot as plt
from gammapy.utils.units import unit_from_fits_image_hdu
from ..coord import MapCoord
from ..geom import pix_tuple_to_idx
from ..utils import INVALID_INDEX
from .core import HpxMap
from .geom import HpxGeom
from .io import HPX_FITS_CONVENTIONS, HpxConv
from .utils import HpxToWcsMapping, get_pix_size_from_nside, get_superpixels
__all__ = ["HpxNDMap"]
log = logging.getLogger(__name__)
[docs]class HpxNDMap(HpxMap):
"""HEALPix map with any number of non-spatial dimensions.
This class uses a N+1D numpy array to represent the sequence of
HEALPix image planes. Following the convention of WCS-based maps
this class uses a column-wise ordering for the data array with the
spatial dimension being tied to the last index of the array.
Parameters
----------
geom : `~gammapy.maps.HpxGeom`
HEALPIX geometry object.
data : `~numpy.ndarray`
HEALPIX data array.
If none then an empty array will be allocated.
meta : `dict`
Dictionary to store meta data.
unit : str or `~astropy.units.Unit`
The map unit
"""
def __init__(self, geom, data=None, dtype="float32", meta=None, unit=""):
data_shape = geom.data_shape
if data is None:
data = self._make_default_data(geom, data_shape, dtype)
super().__init__(geom, data, meta, unit)
@staticmethod
def _make_default_data(geom, shape_np, dtype):
if geom.npix.size > 1:
data = np.full(shape_np, np.nan, dtype=dtype)
idx = geom.get_idx(local=True)
data[idx[::-1]] = 0
else:
data = np.zeros(shape_np, dtype=dtype)
return data
[docs] @classmethod
def from_wcs_tiles(cls, wcs_tiles, nest=True):
"""Create HEALPix map from WCS tiles.
Parameters
----------
wcs_tiles : list of `WcsNDMap`
Wcs map tiles
nest : bool
Whether to use nested HEALPix scheme
Returns
-------
hpx_map : `HpxNDMap`
HEALPix map
"""
import healpy as hp
geom_wcs = wcs_tiles[0].geom
geom_hpx = HpxGeom.create(
binsz=geom_wcs.pixel_scales[0],
frame=geom_wcs.frame,
nest=nest,
axes=geom_wcs.axes,
)
map_hpx = cls.from_geom(geom=geom_hpx, unit=wcs_tiles[0].unit)
coords = map_hpx.geom.get_coord().skycoord
nside_superpix = hp.npix2nside(len(wcs_tiles))
hpx_ref = HpxGeom(nside=nside_superpix, nest=nest, frame=geom_wcs.frame)
idx = np.arange(map_hpx.geom.to_image().npix)
indices = get_superpixels(idx, map_hpx.geom.nside, nside_superpix, nest=nest)
for wcs_tile in wcs_tiles:
hpx_idx = int(hpx_ref.coord_to_idx(wcs_tile.geom.center_skydir)[0])
mask = indices == hpx_idx
map_hpx.data[mask] = wcs_tile.interp_by_coord(coords[mask])
return map_hpx
[docs] def to_wcs_tiles(
self, nside_tiles=4, margin="0 deg", method="nearest", oversampling_factor=1
):
"""Convert HpxNDMap to a list of WCS tiles
Parameters
----------
nside_tiles : int
Nside for super pixel tiles. Usually nsi
margin : Angle
Width margin of the wcs tile
method : {'nearest', 'linear'}
Interpolation method
oversampling_factor : int
Oversampling factor.
Returns
-------
wcs_tiles : list of `WcsNDMap`
WCS tiles.
"""
wcs_tiles = []
wcs_geoms = self.geom.to_wcs_tiles(nside_tiles=nside_tiles, margin=margin)
for geom in wcs_geoms:
if oversampling_factor > 1:
geom = geom.upsample(oversampling_factor)
wcs_map = self.interp_to_geom(geom=geom, method=method)
wcs_tiles.append(wcs_map)
return wcs_tiles
[docs] @classmethod
def from_hdu(cls, hdu, hdu_bands=None, format=None, colname=None):
"""Make a HpxNDMap object from a FITS HDU.
Parameters
----------
hdu : `~astropy.io.fits.BinTableHDU`
The FITS HDU
hdu_bands : `~astropy.io.fits.BinTableHDU`
The BANDS table HDU
format : str, optional
FITS convention. If None the format is guessed. The following
formats are supported:
- "gadf"
- "fgst-ccube"
- "fgst-ltcube"
- "fgst-bexpcube"
- "fgst-srcmap"
- "fgst-template"
- "fgst-srcmap-sparse"
- "galprop"
- "galprop2"
colname : str, optional
Data column name to be used for the HEALPix map.
Returns
-------
map : `HpxMap`
HEALPix map
"""
if format is None:
format = HpxConv.identify_hpx_format(hdu.header)
geom = HpxGeom.from_header(hdu.header, hdu_bands, format=format)
hpx_conv = HPX_FITS_CONVENTIONS[format]
shape = geom.axes.shape[::-1]
# TODO: Should we support extracting slices?
meta = cls._get_meta_from_header(hdu.header)
unit = unit_from_fits_image_hdu(hdu.header)
map_out = cls(geom, None, meta=meta, unit=unit)
colnames = hdu.columns.names
cnames = []
if hdu.header.get("INDXSCHM", None) == "SPARSE":
pix = hdu.data.field("PIX")
vals = hdu.data.field("VALUE")
if "CHANNEL" in hdu.data.columns.names:
chan = hdu.data.field("CHANNEL")
chan = np.unravel_index(chan, shape)
idx = chan + (pix,)
else:
idx = (pix,)
map_out.set_by_idx(idx[::-1], vals)
else:
if colname is not None:
cnames.append(colname)
else:
for c in colnames:
if c.find(hpx_conv.colstring) == 0:
cnames.append(c)
nbin = len(cnames)
if nbin == 1:
map_out.data = hdu.data.field(cnames[0])
else:
for idx, cname in enumerate(cnames):
idx = np.unravel_index(idx, shape)
map_out.data[idx + (slice(None),)] = hdu.data.field(cname)
return map_out
[docs] def to_wcs(
self,
sum_bands=False,
normalize=True,
proj="AIT",
oversample=2,
width_pix=None,
hpx2wcs=None,
fill_nan=True,
):
from gammapy.maps import WcsNDMap
if sum_bands and self.geom.nside.size > 1:
map_sum = self.sum_over_axes()
return map_sum.to_wcs(
sum_bands=False,
normalize=normalize,
proj=proj,
oversample=oversample,
width_pix=width_pix,
)
# FIXME: Check whether the old mapping is still valid and reuse it
if hpx2wcs is None:
geom_wcs_image = self.geom.to_wcs_geom(
proj=proj, oversample=oversample, width_pix=width_pix
).to_image()
hpx2wcs = HpxToWcsMapping.create(self.geom, geom_wcs_image)
# FIXME: Need a function to extract a valid shape from npix property
if sum_bands:
axes = np.arange(self.data.ndim - 1)
hpx_data = np.apply_over_axes(np.sum, self.data, axes=axes)
hpx_data = np.squeeze(hpx_data)
wcs_shape = tuple([t.flat[0] for t in hpx2wcs.npix])
wcs_data = np.zeros(wcs_shape).T
wcs = hpx2wcs.wcs.to_image()
else:
hpx_data = self.data
wcs_shape = tuple([t.flat[0] for t in hpx2wcs.npix]) + self.geom.shape_axes
wcs_data = np.zeros(wcs_shape).T
wcs = hpx2wcs.wcs.to_cube(self.geom.axes)
# FIXME: Should reimplement instantiating map first and fill data array
hpx2wcs.fill_wcs_map_from_hpx_data(hpx_data, wcs_data, normalize, fill_nan)
return WcsNDMap(wcs, wcs_data, unit=self.unit)
def _pad_spatial(self, pad_width, mode="constant", cval=0):
geom = self.geom._pad_spatial(pad_width=pad_width)
map_out = self._init_copy(geom=geom, data=None)
map_out.coadd(self)
coords = geom.get_coord(flat=True)
m = self.geom.contains(coords)
coords = tuple([c[~m] for c in coords])
if mode == "constant":
map_out.set_by_coord(coords, cval)
elif mode == "interp":
raise ValueError("Method 'interp' not supported for HpxMap")
else:
raise ValueError(f"Unrecognized pad mode: {mode!r}")
return map_out
[docs] def crop(self, crop_width):
geom = self.geom.crop(crop_width)
map_out = self._init_copy(geom=geom, data=None)
map_out.coadd(self)
return map_out
[docs] def upsample(self, factor, order=0, preserve_counts=True, axis_name=None):
if axis_name:
raise NotImplementedError(
"HpxNDMap.upsample does currently not support upsampling of non-spatial axes."
)
if order != 0:
raise ValueError(
"HpxNDMap.upsample currently only supports nearest upsampling"
)
geom = self.geom.upsample(factor)
coords = geom.get_coord()
data = self.get_by_coord(coords)
if preserve_counts:
data /= factor**2
return self._init_copy(geom=geom, data=data)
[docs] def downsample(self, factor, preserve_counts=True, axis_name=None):
if axis_name:
raise NotImplementedError(
"HpxNDMap does currently not support upsampling of non-spatial axes."
)
geom = self.geom.downsample(factor)
coords = self.geom.get_coord()
vals = self.get_by_coord(coords)
map_out = self._init_copy(geom=geom, data=None)
map_out.fill_by_coord(coords, vals)
if not preserve_counts:
map_out.data /= factor**2
return map_out
[docs] def to_nside(self, nside, preserve_counts=True):
"""Upsample or downsample the map to a given nside
Parameters
----------
nside : int
Nside
preserve_counts : bool
Preserve the integral over each bin. This should be true
if the map is an integral quantity (e.g. counts) and false if
the map is a differential quantity (e.g. intensity).
Returns
-------
geom : `~HpxNDMap`
Healpix map with new nside.
"""
factor = nside / self.geom.nside
if factor > 1:
return self.upsample(factor=int(factor), preserve_counts=preserve_counts)
elif factor < 1:
return self.downsample(
factor=int(1 / factor), preserve_counts=preserve_counts
)
else:
return self.copy()
[docs] def interp_by_coord(self, coords, method="linear", fill_value=None):
# inherited docstring
coords = MapCoord.create(coords, frame=self.geom.frame)
if method == "linear":
return self._interp_by_coord(coords)
elif method == "nearest":
return self.get_by_coord(coords)
else:
raise ValueError(f"Invalid interpolation method: {method!r}")
[docs] def interp_by_pix(self, pix, method=None, fill_value=None):
"""Interpolate map values at the given pixel coordinates."""
raise NotImplementedError
[docs] def cutout(self, position, width, *args, **kwargs):
"""Create a cutout around a given position.
Parameters
----------
position : `~astropy.coordinates.SkyCoord`
Center position of the cutout region.
width : `~astropy.coordinates.Angle` or `~astropy.units.Quantity`
Diameter of the circular cutout region.
Returns
-------
cutout : `~gammapy.maps.HpxNDMap`
Cutout map
"""
geom = self.geom.cutout(position=position, width=width)
if self.geom.is_allsky:
idx = geom._ipix
else:
idx = self.geom.to_image().global_to_local((geom._ipix,))
data = self.data[..., idx]
return self.__class__(geom=geom, data=data, unit=self.unit, meta=self.meta)
[docs] def stack(self, other, weights=None, nan_to_num=True):
"""Stack cutout into map.
Parameters
----------
other : `HpxNDMap`
Other map to stack
weights : `HpxNDMap`
Array to be used as weights. The spatial geometry must be equivalent
to `other` and additional axes must be broadcastable.
nan_to_num: bool
Non-finite values are replaced by zero if True (default).
"""
if self.geom == other.geom:
idx = None
elif self.geom.is_aligned(other.geom):
if self.geom.is_allsky:
idx = other.geom._ipix
else:
idx = self.geom.to_image().global_to_local((other.geom._ipix,))[0]
else:
raise ValueError(
"Can only stack equivalent maps or cutout of the same map."
)
data = other.quantity.to_value(self.unit)
if nan_to_num:
data = data.copy()
data[~np.isfinite(data)] = 0
if weights is not None:
if not other.geom.to_image() == weights.geom.to_image():
raise ValueError("Incompatible spatial geoms between map and weights")
data = data * weights.data
if idx is None:
self.data += data
else:
self.data[..., idx] += data
[docs] def smooth(self, width, kernel="gauss"):
"""Smooth the map.
Iterates over 2D image planes, processing one at a time.
Parameters
----------
width : `~astropy.units.Quantity`, str or float
Smoothing width given as quantity or float. If a float is given it
interpreted as smoothing width in pixels. If an (angular) quantity
is given it converted to pixels using ``healpy.nside2resol``.
It corresponds to the standard deviation in case of a Gaussian kernel,
and the radius in case of a disk kernel.
kernel : {'gauss', 'disk'}
Kernel shape
Returns
-------
image : `HpxNDMap`
Smoothed image (a copy, the original object is unchanged).
"""
import healpy as hp
nside = self.geom.nside
lmax = int(3 * nside - 1) # maximum l of the power spectrum
ipix = self.geom._ipix
if not self.geom.is_allsky:
# stack into an all sky map
full_sky_geom = HpxGeom.create(
nside=self.geom.nside,
nest=self.geom.nest,
frame=self.geom.frame,
axes=self.geom.axes,
)
full_sky_map = HpxNDMap.from_geom(full_sky_geom)
for img, idx in self.iter_by_image_data():
full_sky_map.data[idx][ipix] = img
else:
full_sky_map = self
# The smoothing width is expected by healpy in radians
if isinstance(width, (u.Quantity, str)):
width = u.Quantity(width)
width = width.to_value("rad")
else:
binsz = np.degrees(hp.nside2resol(nside))
width = width * binsz
width = np.deg2rad(width)
smoothed_data = np.empty(self.data.shape, dtype=float)
for img, idx in full_sky_map.iter_by_image_data():
img = img.astype(float)
if self.geom.nest:
# reorder to ring to do the smoothing
img = hp.pixelfunc.reorder(img, n2r=True)
if kernel == "gauss":
data = hp.sphtfunc.smoothing(
img, sigma=width, pol=False, verbose=False, lmax=lmax
)
elif kernel == "disk":
# create the step function in angular space
theta = np.linspace(0, width)
beam = np.ones(len(theta))
beam[theta > width] = 0
# convert to the spherical harmonics space
window_beam = hp.sphtfunc.beam2bl(beam, theta, lmax)
# normalize the window beam
window_beam = window_beam / window_beam.max()
data = hp.sphtfunc.smoothing(
img, beam_window=window_beam, pol=False, verbose=False, lmax=lmax
)
else:
raise ValueError(f"Invalid kernel: {kernel!r}")
if self.geom.nest:
# reorder back to nest after the smoothing
data = hp.pixelfunc.reorder(data, r2n=True)
smoothed_data[idx] = data[ipix]
return self._init_copy(data=smoothed_data)
[docs] def convolve(self, kernel, convolution_method="wcs-tan", **kwargs):
"""Convolve map with a WCS kernel.
It projects the map into a WCS geometry, convolves with a WCS kernel and
projects back into the initial Healpix geometry.
If the kernel is two dimensional, it is applied to all image planes likewise.
If the kernel is higher dimensional it must match the map in the number of
dimensions and the corresponding kernel is selected for every image plane.
Parameters
----------
kernel : `~gammapy.irf.PSFKernel`
Convolution kernel. The pixel size must be upsampled by a factor 2 or bigger
with respect to the input map to prevent artifacts in the projection.
convolution_method : str
Supported methods are :
'wcs-tan': project on WCS geometry and convolve with WCS kernel.
See `~gammapy.maps.HpxNDMap.convolve_wcs`.
**kwargs : dict
Keyword arguments passed to `~gammapy.maps.WcsNDMap.convolve`.
Returns
-------
map : `HpxNDMap`
Convolved map.
"""
if convolution_method == "wcs-tan":
return self.convolve_wcs(kernel, **kwargs)
elif convolution_method == "":
return self.convolve_full(kernel)
else:
raise ValueError(
f"Not a valid method for HPX convolution: {convolution_method}"
)
[docs] def convolve_wcs(self, kernel, **kwargs):
"""Convolve map with a WCS kernel.
It projects the map into a WCS geometry, convolves with a WCS kernel and
projects back into the initial Healpix geometry.
If the kernel is two dimensional, it is applied to all image planes likewise.
If the kernel is higher dimensional should either match the map in the number of
dimensions or the map must be an image (no non-spatial axes). In that case, the
corresponding kernel is selected and applied to every image plane or to the single
input image respectively.
Parameters
----------
kernel : `~gammapy.irf.PSFKernel`
Convolution kernel. The pixel size must be upsampled by a factor 2 or bigger
with respect to the input map to prevent artifacts in the projection.
**kwargs : dict
Keyword arguments passed to `~gammapy.maps.WcsNDMap.convolve`.
Returns
-------
map : `HpxNDMap`
Convolved map.
"""
# TODO: maybe go through `.to_wcs_tiles()` to make this work for allsky maps
if self.geom.is_allsky:
raise ValueError(
"Convolution via WCS projection is not supported for allsky maps."
)
if self.geom.width > 10 * u.deg:
log.warning(
"Convolution via WCS projection is not recommended for large "
"maps (> 10 deg). Perhaps the method `convolve_full()` is more suited for "
"this case."
)
geom_kernel = kernel.psf_kernel_map.geom
wcs_size = np.max(geom_kernel.to_image().pixel_scales.deg)
hpx_size = get_pix_size_from_nside(self.geom.nside[0])
if wcs_size > 0.5 * hpx_size:
raise ValueError(
f"The kernel pixel size of {wcs_size} has to be smaller by at least"
f" a factor 2 than the pixel size of the input map of {hpx_size}"
)
geom_wcs = self.geom.to_wcs_geom(proj="TAN").to_image()
hpx2wcs = HpxToWcsMapping.create(
hpx=self.geom, wcs=geom_wcs.to_binsz(binsz=wcs_size)
)
# Project to WCS and convolve
wcs_map = self.to_wcs(hpx2wcs=hpx2wcs, fill_nan=False)
conv_wcs_map = wcs_map.convolve(kernel=kernel, **kwargs)
if self.geom.is_image and geom_kernel.ndim > 2:
target_geom = self.geom.to_cube(geom_kernel.axes)
else:
target_geom = self.geom
# and back to hpx
data = np.zeros(target_geom.data_shape)
data = hpx2wcs.fill_hpx_map_from_wcs_data(
wcs_data=conv_wcs_map.data, hpx_data=data
)
return HpxNDMap.from_geom(target_geom, data=data)
[docs] def convolve_full(self, kernel):
"""Convolve map with a symmetrical WCS kernel.
It extracts the radial profile of the kernel (assuming radial symmetry) and
convolves via `hp.sphtfunc.smoothing`. Since no projection is applied, this is
suited for full-sky and large maps.
If the kernel is two dimensional, it is applied to all image planes likewise.
If the kernel is higher dimensional it must match the map in the number of
dimensions and the corresponding kernel is selected for every image plane.
Parameters
----------
kernel : `~gammapy.irf.PSFKernel`
Convolution kernel. The pixel size must be upsampled by a factor 2 or bigger
with respect to the input map to prevent artifacts in the projection.
Returns
-------
map : `HpxNDMap`
Convolved map.
"""
import healpy as hp
nside = self.geom.nside
lmax = int(3 * nside - 1) # maximum l of the power spectrum
nest = self.geom.nest
allsky = self.geom.is_allsky
ipix = self.geom._ipix
if not allsky:
# stack into an all sky map
full_sky_geom = HpxGeom.create(
nside=self.geom.nside,
nest=self.geom.nest,
frame=self.geom.frame,
axes=self.geom.axes,
)
full_sky_map = HpxNDMap.from_geom(full_sky_geom)
for img, idx in self.iter_by_image_data():
full_sky_map.data[idx][ipix] = img
else:
full_sky_map = self
# Get radial profile from the kernel
psf_kernel = kernel.psf_kernel_map
center_pix = psf_kernel.geom.center_pix[:2]
center = max(center_pix)
dim = np.argmax(center_pix)
pixels = [0, 0]
pixels[dim] = np.linspace(
0, center, int(center + 1)
) # assuming radially symmetric kernel
pixels[abs(1 - dim)] = center_pix[abs(1 - dim)] * np.ones(int(center + 1))
coords = psf_kernel.geom.pix_to_coord(pixels)
coordinates = SkyCoord(coords[0], coords[1], frame=psf_kernel.geom.frame)
angles = coordinates.separation(psf_kernel.geom.center_skydir).rad
values = psf_kernel.get_by_pix(pixels)
# Do the convolution in each image plane
convolved_data = np.empty(self.data.shape, dtype=float)
for img, idx in full_sky_map.iter_by_image_data():
img = img.astype(float)
if nest:
# reorder to ring to do the convolution
img = hp.pixelfunc.reorder(img, n2r=True)
radial_profile = np.reshape(values[:, idx], (values.shape[0],))
window_beam = hp.sphtfunc.beam2bl(
np.flip(radial_profile), np.flip(angles), lmax
)
window_beam = window_beam / window_beam.max()
data = hp.sphtfunc.smoothing(
img, beam_window=window_beam, pol=False, verbose=False, lmax=lmax
)
if nest:
# reorder back to nest after the convolution
data = hp.pixelfunc.reorder(data, r2n=True)
convolved_data[idx] = data[ipix]
return self._init_copy(data=convolved_data)
[docs] def get_by_idx(self, idx):
# inherited docstring
idx = pix_tuple_to_idx(idx)
idx = self.geom.global_to_local(idx)
return self.data.T[idx]
def _interp_by_coord(self, coords):
"""Linearly interpolate map values."""
pix, wts = self.geom.interp_weights(coords)
if self.geom.is_image:
return np.sum(self.data.T[tuple(pix)] * wts, axis=0)
val = np.zeros(pix[0].shape[1:])
# Loop over function values at corners
for i in range(2 ** len(self.geom.axes)):
pix_i = []
wt = np.ones(pix[0].shape[1:])[np.newaxis, ...]
for j, ax in enumerate(self.geom.axes):
idx = ax.coord_to_idx(coords[ax.name])
idx = np.clip(idx, 0, len(ax.center) - 2)
w = ax.center[idx + 1] - ax.center[idx]
c = u.Quantity(coords[ax.name], ax.center.unit, copy=False).value
if i & (1 << j):
wt *= (c - ax.center[idx].value) / w.value
pix_i += [idx + 1]
else:
wt *= 1.0 - (c - ax.center[idx].value) / w.value
pix_i += [idx]
if not self.geom.is_regular:
pix, wts = self.geom.interp_weights(coords, idxs=pix_i)
wts[pix[0] == INVALID_INDEX.int] = 0
wt[~np.isfinite(wt)] = 0
val += np.nansum(wts * wt * self.data.T[tuple(pix[:1] + pix_i)], axis=0)
return val
def _resample_by_idx(self, idx, weights=None, preserve_counts=False):
idx = pix_tuple_to_idx(idx)
msk = np.all(np.stack([t != INVALID_INDEX.int for t in idx]), axis=0)
if weights is not None:
weights = weights[msk]
idx = [t[msk] for t in idx]
idx_local = list(self.geom.global_to_local(idx))
msk = idx_local[0] >= 0
idx_local = [t[msk] for t in idx_local]
if weights is not None:
if isinstance(weights, u.Quantity):
weights = weights.to_value(self.unit)
weights = weights[msk]
idx_local = np.ravel_multi_index(idx_local, self.data.T.shape)
idx_local, idx_inv = np.unique(idx_local, return_inverse=True)
weights = np.bincount(idx_inv, weights=weights)
if not preserve_counts:
weights /= np.bincount(idx_inv).astype(self.data.dtype)
self.data.T.flat[idx_local] += weights
[docs] def fill_by_idx(self, idx, weights=None):
return self._resample_by_idx(idx, weights=weights, preserve_counts=True)
[docs] def set_by_idx(self, idx, vals):
idx = pix_tuple_to_idx(idx)
idx_local = self.geom.global_to_local(idx)
self.data.T[idx_local] = vals
def _make_cols(self, header, conv):
shape = self.data.shape
cols = []
if header["INDXSCHM"] == "SPARSE":
data = self.data.copy()
data[~np.isfinite(data)] = 0
nonzero = np.where(data > 0)
value = data[nonzero].astype(float)
pix = self.geom.local_to_global(nonzero[::-1])[0]
if len(shape) == 1:
cols.append(fits.Column("PIX", "J", array=pix))
cols.append(fits.Column("VALUE", "E", array=value))
else:
channel = np.ravel_multi_index(nonzero[:-1], shape[:-1])
cols.append(fits.Column("PIX", "J", array=pix))
cols.append(fits.Column("CHANNEL", "I", array=channel))
cols.append(fits.Column("VALUE", "E", array=value))
elif len(shape) == 1:
name = conv.colname(indx=conv.firstcol)
array = self.data.astype(float)
cols.append(fits.Column(name, "E", array=array))
else:
for i, idx in enumerate(np.ndindex(shape[:-1])):
name = conv.colname(indx=i + conv.firstcol)
array = self.data[idx].astype(float)
cols.append(fits.Column(name, "E", array=array))
return cols
[docs] def to_swapped(self):
import healpy as hp
hpx_out = self.geom.to_swapped()
map_out = self._init_copy(geom=hpx_out, data=None)
idx = self.geom.get_idx(flat=True)
vals = self.get_by_idx(idx)
if self.geom.nside.size > 1:
nside = self.geom.nside[idx[1:]]
else:
nside = self.geom.nside
if self.geom.nest:
idx_new = tuple([hp.nest2ring(nside, idx[0])]) + idx[1:]
else:
idx_new = tuple([hp.ring2nest(nside, idx[0])]) + idx[1:]
map_out.set_by_idx(idx_new, vals)
return map_out
[docs] def to_region_nd_map(self, region, func=np.nansum, weights=None, method="nearest"):
"""Get region ND map in a given region.
By default the whole map region is considered.
Parameters
----------
region: `~regions.Region` or `~astropy.coordinates.SkyCoord`
Region.
func : numpy.func
Function to reduce the data. Default is np.nansum.
For boolean Map, use np.any or np.all.
weights : `WcsNDMap`
Array to be used as weights. The geometry must be equivalent.
method : {"nearest", "linear"}
How to interpolate if a position is given.
Returns
-------
spectrum : `~gammapy.maps.RegionNDMap`
Spectrum in the given region.
"""
from gammapy.maps import RegionGeom, RegionNDMap
if isinstance(region, SkyCoord):
region = PointSkyRegion(region)
if weights is not None:
if not self.geom == weights.geom:
raise ValueError("Incompatible spatial geoms between map and weights")
geom = RegionGeom(region=region, axes=self.geom.axes)
if isinstance(region, PointSkyRegion):
coords = geom.get_coord()
data = self.interp_by_coord(coords=coords, method=method)
if weights is not None:
data *= weights.interp_by_coord(coords=coords, method=method)
else:
cutout = self.cutout(position=geom.center_skydir, width=np.max(geom.width))
if weights is not None:
weights_cutout = weights.cutout(
position=geom.center_skydir, width=geom.width
)
cutout.data *= weights_cutout.data
mask = cutout.geom.to_image().region_mask([region]).data
data = func(cutout.data[..., mask], axis=-1)
return RegionNDMap(geom=geom, data=data, unit=self.unit, meta=self.meta.copy())
[docs] def plot(
self,
method="raster",
ax=None,
normalize=False,
proj="AIT",
oversample=2,
width_pix=1000,
**kwargs,
):
"""Quickplot method.
This will generate a visualization of the map by converting to
a rasterized WCS image (method='raster') or drawing polygons
for each pixel (method='poly').
Parameters
----------
method : {'raster','poly'}
Method for mapping HEALPix pixels to a two-dimensional
image. Can be set to 'raster' (rasterization to cartesian
image plane) or 'poly' (explicit polygons for each pixel).
WARNING: The 'poly' method is much slower than 'raster'
and only suitable for maps with less than ~10k pixels.
proj : string, optional
Any valid WCS projection type.
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``.
**kwargs : dict
Keyword arguments passed to `~matplotlib.pyplot.imshow`.
Returns
-------
ax : `~astropy.visualization.wcsaxes.WCSAxes`
WCS axis object
"""
if method == "raster":
m = self.to_wcs(
sum_bands=True,
normalize=normalize,
proj=proj,
oversample=oversample,
width_pix=width_pix,
)
return m.plot(ax, **kwargs)
elif method == "poly":
return self._plot_poly(proj=proj, ax=ax)
else:
raise ValueError(f"Invalid method: {method!r}")
def _plot_poly(self, proj="AIT", step=1, ax=None):
"""Plot the map using a collection of polygons.
Parameters
----------
proj : string, optional
Any valid WCS projection type.
step : int
Set the number vertices that will be computed for each
pixel in multiples of 4.
"""
# FIXME: At the moment this only works for all-sky maps if the
# projection is centered at (0,0)
# FIXME: Figure out how to force a square aspect-ratio like imshow
import healpy as hp
from matplotlib.collections import PatchCollection
from matplotlib.patches import Polygon
wcs = self.geom.to_wcs_geom(proj=proj, oversample=1)
if ax is None:
fig = plt.gcf()
ax = fig.add_subplot(111, projection=wcs.wcs, aspect="equal")
wcs_lonlat = wcs.center_coord[:2]
idx = self.geom.get_idx()
vtx = hp.boundaries(self.geom.nside, idx[0], nest=self.geom.nest, step=step)
theta, phi = hp.vec2ang(np.rollaxis(vtx, 2))
theta = theta.reshape((4 * step, -1)).T
phi = phi.reshape((4 * step, -1)).T
patches = []
data = []
def get_angle(x, t):
return 180.0 - (180.0 - x + t) % 360.0
for i, (x, y) in enumerate(zip(phi, theta)):
lon, lat = np.degrees(x), np.degrees(np.pi / 2.0 - y)
# Add a small ofset to avoid vertices wrapping to the
# other size of the projection
if get_angle(np.median(lon), wcs_lonlat[0].to_value("deg")) > 0:
idx = wcs.coord_to_pix((lon - 1e-4, lat))
else:
idx = wcs.coord_to_pix((lon + 1e-4, lat))
dist = np.max(np.abs(idx[0][0] - idx[0]))
# Split pixels that wrap around the edges of the projection
if dist > wcs.npix[0] / 1.5:
lon, lat = np.degrees(x), np.degrees(np.pi / 2.0 - y)
lon0 = lon - 1e-4
lon1 = lon + 1e-4
pix0 = wcs.coord_to_pix((lon0, lat))
pix1 = wcs.coord_to_pix((lon1, lat))
idx0 = np.argsort(pix0[0])
idx1 = np.argsort(pix1[0])
pix0 = (pix0[0][idx0][:3], pix0[1][idx0][:3])
pix1 = (pix1[0][idx1][1:], pix1[1][idx1][1:])
patches.append(Polygon(np.vstack((pix0[0], pix0[1])).T, True))
patches.append(Polygon(np.vstack((pix1[0], pix1[1])).T, True))
data.append(self.data[i])
data.append(self.data[i])
else:
polygon = Polygon(np.vstack((idx[0], idx[1])).T, True)
patches.append(polygon)
data.append(self.data[i])
p = PatchCollection(patches, linewidths=0, edgecolors="None")
p.set_array(np.array(data))
ax.add_collection(p)
ax.autoscale_view()
ax.coords.grid(color="w", linestyle=":", linewidth=0.5)
return ax
[docs] def plot_mask(
self,
method="raster",
ax=None,
proj="AIT",
oversample=2,
width_pix=1000,
**kwargs,
):
"""Plot the mask as a shaded area
Parameters
----------
method : {'raster','poly'}
Method for mapping HEALPix pixels to a two-dimensional
image. Can be set to 'raster' (rasterization to cartesian
image plane) or 'poly' (explicit polygons for each pixel).
WARNING: The 'poly' method is much slower than 'raster'
and only suitable for maps with less than ~10k pixels.
proj : string, optional
Any valid WCS projection type.
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``.
**kwargs : dict
Keyword arguments passed to `~matplotlib.pyplot.imshow`.
Returns
-------
ax : `~astropy.visualization.wcsaxes.WCSAxes`
WCS axis object
"""
if not self.is_mask:
raise ValueError(
"`.plot_mask()` only supports maps containing boolean values."
)
if method == "raster":
m = self.to_wcs(
sum_bands=True,
normalize=False,
proj=proj,
oversample=oversample,
width_pix=width_pix,
)
m.data = np.nan_to_num(m.data).astype(bool)
return m.plot_mask(ax=ax, **kwargs)
else:
raise ValueError(f"Invalid method: {method!r}")