# Licensed under a 3-clause BSD style license - see LICENSE.rst
import json
import numpy as np
from astropy.io import fits
from .base import Map
from .wcs import WcsGeom
from .utils import find_hdu, find_bands_hdu
__all__ = ["WcsMap"]
[docs]class WcsMap(Map):
"""Base class for WCS map classes.
Parameters
----------
geom : `~gammapy.maps.WcsGeom`
A WCS geometry object.
data : `~numpy.ndarray`
Data array.
"""
[docs] @classmethod
def create(
cls,
map_type="wcs",
npix=None,
binsz=0.1,
width=None,
proj="CAR",
coordsys="CEL",
refpix=None,
axes=None,
skydir=None,
dtype="float32",
conv="gadf",
meta=None,
unit="",
):
"""Factory method to create an empty WCS map.
Parameters
----------
map_type : {'wcs', 'wcs-sparse'}
Map type. Selects the class that will be used to
instantiate the map.
npix : int or tuple or list
Width of the map in pixels. A tuple will be interpreted as
parameters for longitude and latitude axes. For maps with
non-spatial dimensions, list input can be used to define a
different map width in each image plane. This option
supersedes width.
width : float or tuple or list
Width of the map in degrees. A tuple will be interpreted
as parameters for longitude and latitude axes. For maps
with non-spatial dimensions, list input can be used to
define a different map width in each image plane.
binsz : float or tuple or list
Map pixel size in degrees. A tuple will be interpreted
as parameters for longitude and latitude axes. For maps
with non-spatial dimensions, list input can be used to
define a different bin size in each image plane.
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.
coordsys : {'CEL', 'GAL'}, optional
Coordinate system, either Galactic ('GAL') or Equatorial ('CEL').
axes : list
List of non-spatial axes.
proj : string, optional
Any valid WCS projection type. Default is 'CAR' (cartesian).
refpix : tuple
Reference pixel of the projection. If None then this will
be chosen to be center of the map.
dtype : str, optional
Data type, default is float32
conv : {'fgst-ccube','fgst-template','gadf'}, optional
FITS format convention. Default is 'gadf'.
meta : `~collections.OrderedDict`
Dictionary to store meta data.
unit : str or `~astropy.units.Unit`
The unit of the map
Returns
-------
map : `~WcsMap`
A WCS map object.
"""
from .wcsnd import WcsNDMap
# from .wcssparse import WcsMapSparse
geom = WcsGeom.create(
npix=npix,
binsz=binsz,
width=width,
proj=proj,
skydir=skydir,
coordsys=coordsys,
refpix=refpix,
axes=axes,
conv=conv,
)
if map_type == "wcs":
return WcsNDMap(geom, dtype=dtype, meta=meta, unit=unit)
elif map_type == "wcs-sparse":
raise NotImplementedError
else:
raise ValueError("Invalid map type: {!r}".format(map_type))
[docs] @classmethod
def from_hdulist(cls, hdu_list, hdu=None, hdu_bands=None):
"""Make a WcsMap object from a FITS HDUList.
Parameters
----------
hdu_list : `~astropy.io.fits.HDUList`
HDU list containing HDUs for map data and bands.
hdu : str
Name or index of the HDU with the map data.
hdu_bands : str
Name or index of the HDU with the BANDS table.
Returns
-------
wcs_map : `WcsMap`
Map object
"""
if hdu is None:
hdu = find_hdu(hdu_list)
else:
hdu = hdu_list[hdu]
if hdu_bands is None:
hdu_bands = find_bands_hdu(hdu_list, hdu)
if hdu_bands is not None:
hdu_bands = hdu_list[hdu_bands]
return cls.from_hdu(hdu, hdu_bands)
[docs] def to_hdulist(self, hdu=None, hdu_bands=None, sparse=False, conv=None):
"""Convert to `~astropy.io.fits.HDUList`.
Parameters
----------
hdu : str
Name or index of the HDU with the map data.
hdu_bands : str
Name or index of the HDU with the BANDS table.
sparse : bool
Sparsify the map by only writing pixels with non-zero
amplitude.
conv : {'fgst-ccube','fgst-template','gadf',None}, optional
FITS format convention. If None this will be set to the
default convention of the map.
Returns
-------
hdu_list : `~astropy.io.fits.HDUList`
"""
if sparse:
hdu = "SKYMAP" if hdu is None else hdu.upper()
else:
hdu = "PRIMARY" if hdu is None else hdu.upper()
if sparse and hdu == "PRIMARY":
raise ValueError("Sparse maps cannot be written to the PRIMARY HDU.")
if self.geom.axes:
hdu_bands_out = self.geom.make_bands_hdu(
hdu=hdu_bands, hdu_skymap=hdu, conv=conv
)
hdu_bands = hdu_bands_out.name
else:
hdu_bands = None
hdu_out = self.make_hdu(hdu=hdu, hdu_bands=hdu_bands, sparse=sparse, conv=conv)
hdu_out.header["META"] = json.dumps(self.meta)
hdu_out.header["BUNIT"] = self.unit.to_string("fits")
if hdu == "PRIMARY":
hdulist = [hdu_out]
else:
hdulist = [fits.PrimaryHDU(), hdu_out]
if self.geom.axes:
hdulist += [hdu_bands_out]
return fits.HDUList(hdulist)
[docs] def make_hdu(self, hdu="SKYMAP", hdu_bands=None, sparse=False, conv=None):
"""Make a FITS HDU from this map.
Parameters
----------
hdu : str
The HDU extension name.
hdu_bands : str
The HDU extension name for BANDS table.
sparse : bool
Set INDXSCHM to SPARSE and sparsify the map by only
writing pixels with non-zero amplitude.
Returns
-------
hdu : `~astropy.io.fits.BinTableHDU` or `~astropy.io.fits.ImageHDU`
HDU containing the map data.
"""
header = self.geom.make_header()
if hdu_bands is not None:
header["BANDSHDU"] = hdu_bands
if sparse:
hdu_out = self._make_hdu_sparse(self.data, self.geom.npix, hdu, header)
elif hdu == "PRIMARY":
hdu_out = fits.PrimaryHDU(self.data, header=header)
else:
hdu_out = fits.ImageHDU(self.data, header=header, name=hdu)
return hdu_out
@staticmethod
def _make_hdu_sparse(data, npix, hdu, header):
shape = data.shape
# We make a copy, because below we modify `data` to handle non-finite entries
# TODO: The code below could probably be simplified to use expressions
# that create new arrays instead of in-place modifications
# But first: do we want / need the non-finite entry handling at all and always cast to 64-bit float?
data = data.copy()
if len(shape) == 2:
data_flat = np.ravel(data)
data_flat[~np.isfinite(data_flat)] = 0
nonzero = np.where(data_flat > 0)
value = data_flat[nonzero].astype(float)
cols = [
fits.Column("PIX", "J", array=nonzero[0]),
fits.Column("VALUE", "E", array=value),
]
elif npix[0].size == 1:
shape_flat = shape[:-2] + (shape[-1] * shape[-2],)
data_flat = np.ravel(data).reshape(shape_flat)
data_flat[~np.isfinite(data_flat)] = 0
nonzero = np.where(data_flat > 0)
channel = np.ravel_multi_index(nonzero[:-1], shape[:-2])
value = data_flat[nonzero].astype(float)
cols = [
fits.Column("PIX", "J", array=nonzero[-1]),
fits.Column("CHANNEL", "I", array=channel),
fits.Column("VALUE", "E", array=value),
]
else:
data_flat = []
channel = []
pix = []
for i, _ in np.ndenumerate(npix[0]):
data_i = np.ravel(data[i[::-1]])
data_i[~np.isfinite(data_i)] = 0
pix_i = np.where(data_i > 0)
data_i = data_i[pix_i]
data_flat += [data_i]
pix += pix_i
channel += [
np.ones(data_i.size, dtype=int)
* np.ravel_multi_index(i[::-1], shape[:-2])
]
pix = np.concatenate(pix)
channel = np.concatenate(channel)
value = np.concatenate(data_flat).astype(float)
cols = [
fits.Column("PIX", "J", array=pix),
fits.Column("CHANNEL", "I", array=channel),
fits.Column("VALUE", "E", array=value),
]
return fits.BinTableHDU.from_columns(cols, header=header, name=hdu)