def _reproject_hpx(self, geom, mode='interp', order=1):
from reproject import reproject_to_healpix
from .hpxnd import HpxNDMap
map_out = HpxNDMap(geom)
coordsys = 'galactic' if geom.coordsys == 'GAL' else 'icrs'
axes_eq = np.all(
[ax0 == ax1 for ax0, ax1 in zip(geom.axes, self.geom.axes)])
for vals, idx in map_out.iter_by_image():
if self.geom.ndim == 2 or axes_eq:
img = self.data[idx[::-1]]
else:
coords = axes_pix_to_coord(geom.axes, idx)
img = self.interp_image(coords, order=order).data
# TODO: For partial-sky HPX we need to map from full- to
# partial-sky indices
if self.geom.projection == 'CAR' and self.geom.is_allsky:
data, footprint = reproject_car_to_hpx((img, self.geom.wcs),
coordsys,
nside=geom.nside,
nested=geom.nest,
order=order)
else:
data, footprint = reproject_to_healpix((img, self.geom.wcs),
coordsys,
nside=geom.nside,
nested=geom.nest,
order=order)
vals[...] = data
return map_out
def _reproject_hpx(self, geom, mode='interp', order=1):
from reproject import reproject_to_healpix
from .hpxnd import HpxNDMap
map_out = HpxNDMap(geom)
coordsys = 'galactic' if geom.coordsys == 'GAL' else 'icrs'
axes_eq = np.all([ax0 == ax1 for ax0, ax1 in
zip(geom.axes, self.geom.axes)])
for vals, idx in map_out.iter_by_image():
if self.geom.ndim == 2 or axes_eq:
img = self.data[idx[::-1]]
else:
coords = axes_pix_to_coord(geom.axes, idx)
img = self.interp_image(coords, order=order).data
# TODO: For partial-sky HPX we need to map from full- to
# partial-sky indices
if self.geom.projection == 'CAR' and self.geom.is_allsky:
data, footprint = reproject_car_to_hpx((img, self.geom.wcs),
coordsys,
nside=geom.nside,
nested=geom.nest,
order=order)
else:
data, footprint = reproject_to_healpix((img, self.geom.wcs),
coordsys,
nside=geom.nside,
nested=geom.nest,
order=order)
vals[...] = data
return map_out
def _reproject_to_hpx(self, geom, mode="interp", order=1):
from reproject import reproject_to_healpix
data = np.empty(geom.data_shape)
coordsys = "galactic" if geom.coordsys == "GAL" else "icrs"
for img, idx in self.iter_by_image():
# TODO: For partial-sky HPX we need to map from full- to
# partial-sky indices
if self.geom.projection == "CAR" and self.geom.is_allsky:
vals, footprint = reproject_car_to_hpx(
(img, self.geom.wcs),
coordsys,
nside=geom.nside,
nested=geom.nest,
order=order,
)
else:
vals, footprint = reproject_to_healpix(
(img, self.geom.wcs),
coordsys,
nside=geom.nside,
nested=geom.nest,
order=order,
)
data[idx] = vals
return self._init_copy(geom=geom, data=data)
def _reproject_to_hpx(self, geom, mode="interp", order=1):
from reproject import reproject_to_healpix
data = np.empty(geom.data_shape)
coordsys = "galactic" if geom.coordsys == "GAL" else "icrs"
for img, idx in self.iter_by_image():
# TODO: For partial-sky HPX we need to map from full- to
# partial-sky indices
if self.geom.projection == "CAR" and self.geom.is_allsky:
vals, footprint = reproject_car_to_hpx(
(img, self.geom.wcs),
coordsys,
nside=geom.nside,
nested=geom.nest,
order=order,
)
else:
vals, footprint = reproject_to_healpix(
(img, self.geom.wcs),
coordsys,
nside=geom.nside,
nested=geom.nest,
order=order,
)
data[idx] = vals
return self._init_copy(geom=geom, data=data)
def f2h(flat, target_header, nside, coord_in='C'):
#project flatsky->healpix
pr, footprint = reproject.reproject_to_healpix((flat, target_header),
coord_system_out='C',
nside=nside,
order='nearest-neighbor',
nested=False)
return pr
def image_reproject_to_healpix_to_file(array, target_image_hdu_header, coordsys='galactic', filepath=None):
"""reproject image array to healpix image and write file
:param array: image data
:param target_image_hdu_header: the HDU object of
:param coordsys: target coordinate system --> {'galactic', 'equatorial'}
:param filepath: the output file path
:return: array, footprint (only if filepath=None)
"""
array, footprint = reproject_to_healpix((array, target_image_hdu_header), coordsys)
if filepath is not None:
# write file
write_map(filepath, array)
else:
# return array & footprint
return array, footprint
def image_reproject_to_healpix_to_file(array,
target_image_hdu_header,
coordsys='galactic',
filepath=None):
"""reproject image array to healpix image and write file
:param array: image data
:param target_image_hdu_header: the HDU object of
:param coordsys: target coordinate system --> {'galactic', 'equatorial'}
:param filepath: the output file path
:return: array, footprint (only if filepath=None)
"""
array, footprint = reproject_to_healpix((array, target_image_hdu_header),
coordsys)
if filepath is not None:
# write file
write_map(filepath, array)
else:
# return array & footprint
return array, footprint
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 f2h(flat, target_header, nside, coord_in='C'):
"""
Returns a HEALPIX map projected and the footprint of a flat one , given a WCS header,
using :py:mod:`reproject` package
**Parameters**
- ``flat`` : {2D array}
flat map
- ``target_header``:
header defined from :func:`set_header`
- ``nside``:{int}
nside of output healpix map
"""
pr, footprint = reproject.reproject_to_healpix((flat, target_header),
coord_system_out='C',
nside=nside,
order='nearest-neighbor',
nested=False)
return pr, footprint
rbins = np.linspace(0, 90, 26)
r = (rbins[1:] + rbins[:-1]) / 2
f = np.zeros(len(rbins) - 1)
R = np.sqrt(X**2 + Y**2)
for i in range(len(rbins) - 1):
inds = (R > rbins[i]) & (R <= rbins[i + 1])
f[i] = Gmap[inds].mean()
plt.figure()
plt.plot(r, f)
plt.xlabel(r'$\theta$')
plt.ylabel(r'$b(\theta)$')
plt.yscale('log')
beam, footprint = reproject_to_healpix((Gmap, wcs.WCS(header)),
'C',
nside=nside)
beam[~np.isfinite(beam)] = 0
beam[beam == hp.UNSEEN] = 0
hp.orthview(np.log10(beam))
#plt.show()
d1 = np.loadtxt('horizon_hwt.txt')
az, el = d1.T
pickup = np.zeros(hp.nside2npix(nside))
lon, lat = hp.pix2ang(nside, np.arange(len(pickup)), lonlat=True)
f = interp1d(az,
el,
fill_value=(el[0], el[-1]),
