def _rotate_polygon(lon, lat, lon0, lat0):
"""
Given a polygon with vertices defined by (lon, lat), rotate the polygon
such that the North pole of the spherical coordinates is now at (lon0,
lat0). Therefore, to end up with a polygon centered on (lon0, lat0), the
polygon should initially be drawn around the North pole.
"""
# Create a representation object
polygon = UnitSphericalRepresentation(lon=lon, lat=lat)
# Determine rotation matrix to make it so that the circle is centered
# on the correct longitude/latitude.
m1 = rotation_matrix(-(0.5 * np.pi * u.radian - lat0), axis='y')
m2 = rotation_matrix(-lon0, axis='z')
transform_matrix = m2 * m1
# Apply 3D rotation
polygon = polygon.to_cartesian()
try:
polygon = polygon.transform(transform_matrix)
except: # TODO: remove once Astropy 1.1 is no longer supported
polygon = _transform_cartesian(polygon, transform_matrix)
polygon = UnitSphericalRepresentation.from_cartesian(polygon)
return polygon.lon, polygon.lat
def _rotate_polygon(lon, lat, lon0, lat0):
"""
Given a polygon with vertices defined by (lon, lat), rotate the polygon
such that the North pole of the spherical coordinates is now at (lon0,
lat0). Therefore, to end up with a polygon centered on (lon0, lat0), the
polygon should initially be drawn around the North pole.
"""
# Create a representation object
polygon = UnitSphericalRepresentation(lon=lon, lat=lat)
# Determine rotation matrix to make it so that the circle is centered
# on the correct longitude/latitude.
m1 = rotation_matrix(-(0.5 * np.pi * u.radian - lat0), axis='y')
m2 = rotation_matrix(-lon0, axis='z')
transform_matrix = m2 * m1
# Apply 3D rotation
polygon = polygon.to_cartesian()
try:
polygon = polygon.transform(transform_matrix)
except: # TODO: remove once Astropy 1.1 is no longer supported
polygon = _transform_cartesian(polygon, transform_matrix)
polygon = UnitSphericalRepresentation.from_cartesian(polygon)
return polygon.lon, polygon.lat
def observed_to_icrs(observed_coo, icrs_frame):
# if the data are UnitSphericalRepresentation, we can skip the distance calculations
is_unitspherical = (isinstance(observed_coo.data,
UnitSphericalRepresentation)
or observed_coo.cartesian.x.unit == u.one)
usrepr = observed_coo.represent_as(UnitSphericalRepresentation)
lon = usrepr.lon.to_value(u.radian)
lat = usrepr.lat.to_value(u.radian)
if isinstance(observed_coo, AltAz):
# the 'A' indicates zen/az inputs
coord_type = 'A'
lat = PIOVER2 - lat
else:
coord_type = 'H'
# first set up the astrometry context for ICRS<->CIRS at the observed_coo time
astrom = erfa_astrom.get().apco(observed_coo)
# Topocentric CIRS
cirs_ra, cirs_dec = erfa.atoiq(coord_type, lon, lat, astrom) << u.radian
if is_unitspherical:
srepr = SphericalRepresentation(cirs_ra, cirs_dec, 1, copy=False)
else:
srepr = SphericalRepresentation(lon=cirs_ra,
lat=cirs_dec,
distance=observed_coo.distance,
copy=False)
# BCRS (Astrometric) direction to source
bcrs_ra, bcrs_dec = aticq(srepr, astrom) << u.radian
# Correct for parallax to get ICRS representation
if is_unitspherical:
icrs_srepr = UnitSphericalRepresentation(bcrs_ra, bcrs_dec, copy=False)
else:
icrs_srepr = SphericalRepresentation(lon=bcrs_ra,
lat=bcrs_dec,
distance=observed_coo.distance,
copy=False)
observer_icrs = CartesianRepresentation(astrom['eb'],
unit=u.au,
xyz_axis=-1,
copy=False)
newrepr = icrs_srepr.to_cartesian() + observer_icrs
icrs_srepr = newrepr.represent_as(SphericalRepresentation)
return icrs_frame.realize_frame(icrs_srepr)
def boundaries(nside, pix, step=1, nest=False):
"""Drop-in replacement for healpy `~healpy.boundaries`."""
pix = np.asarray(pix)
if pix.ndim > 1:
# For consistency with healpy we only support scalars or 1D arrays
raise ValueError("Array has to be one dimensional")
lon, lat = boundaries_lonlat(pix,
step,
nside,
order='nested' if nest else 'ring')
rep_sph = UnitSphericalRepresentation(lon, lat)
rep_car = rep_sph.to_cartesian().xyz.value.swapaxes(0, 1)
if rep_car.shape[0] == 1:
return rep_car[0]
else:
return rep_car
