def _buildTree(self, simDataRa, simDataDec, leafsize=100):
"""Build KD tree on simDataRA/Dec using utility function from mafUtils.
simDataRA, simDataDec = RA and Dec values (in radians).
leafsize = the number of Ra/Dec pointings in each leaf node."""
self.opsimtree = simsUtils._buildTree(simDataRa,
simDataDec,
leafsize)
def _buildTree(self, simDataRa, simDataDec, leafsize=100):
"""Build KD tree on simDataRA/Dec using utility function from mafUtils.
simDataRA, simDataDec = RA and Dec values (in radians).
leafsize = the number of Ra/Dec pointings in each leaf node."""
self.opsimtree = simsUtils._buildTree(simDataRa,
simDataDec,
leafsize)
def _readMap(self):
filename = 'TRIstarDensity_%s_nside_%i.npz' % (self.filtername, self.nside)
starMap = np.load(os.path.join(self.mapDir, filename))
self.starMap = starMap['starDensity'].copy()
self.starMapBins = starMap['bins'].copy()
self.starmapNside = hp.npix2nside(np.size(self.starMap[:, 0]))
# note, the trilegal maps are in galactic coordinates, and nested healpix.
gal_l, gal_b = _hpid2RaDec(self.nside, np.arange(hp.nside2npix(self.nside)), nest=True)
# Convert that to RA,dec. Then do nearest neighbor lookup.
ra, dec = _equatorialFromGalactic(gal_l, gal_b)
self.tree = _buildTree(ra, dec)
def __init__(self, raCol='fieldRA', decCol='fieldDec', degrees=True):
self.colsReq = [raCol, decCol]
self.units = ['#']
self.raCol = raCol
self.decCol = decCol
self.degrees = degrees
fields_db = FieldsDatabase()
# Returned RA/Dec coordinates in degrees
fieldid, ra, dec = fields_db.get_id_ra_dec_arrays("select * from Field;")
asort = np.argsort(fieldid)
self.tree = _buildTree(np.radians(ra[asort]),
np.radians(dec[asort]))
def testKDTreeAPI(self):
"""
Make sure the API provided by scipy to the kdTree algorithm is functional.
"""
_ra = np.linspace(0., 2.*np.pi)
_dec = np.linspace(-np.pi, np.pi)
Ra, Dec = np.meshgrid(_ra, _dec)
tree = utils._buildTree(Ra.flatten(), Dec.flatten())
x, y, z = utils._xyz_from_ra_dec(_ra, _dec)
indx = tree.query_ball_point(list(zip(x, y, z)), utils.xyz_angular_radius())
self.assertEqual(indx.shape, _ra.shape)
def testKDTreeAPI(self):
"""
Make sure the API provided by scipy to the kdTree algorithm is functional.
"""
_ra = np.linspace(0., 2. * np.pi)
_dec = np.linspace(-np.pi, np.pi)
Ra, Dec = np.meshgrid(_ra, _dec)
tree = utils._buildTree(Ra.flatten(), Dec.flatten())
x, y, z = utils._xyz_from_ra_dec(_ra, _dec)
indx = tree.query_ball_point(list(zip(x, y, z)),
utils.xyz_angular_radius())
self.assertEqual(indx.shape, _ra.shape)
def _make_fields(self):
"""
Make tesselation of the sky
"""
# RA and dec in radians
fields = read_fields()
# crop off so we only worry about things that are up
good = np.where(fields['dec'] > (self.alt_limit_rad - self.fov_rad))[0]
self.fields = fields[good]
self.fields_empty = np.zeros(self.fields.size)
# we'll use a single tessellation of alt az
leafsize = 100
self.tree = _buildTree(self.fields['RA'], self.fields['dec'], leafsize, scale=None)
def hp_kd_tree(nside=None, leafsize=100, scale=1e5):
"""
Generate a KD-tree of healpixel locations
Parameters
----------
nside : int
A valid healpix nside
leafsize : int (100)
Leafsize of the kdtree
Returns
-------
tree : scipy kdtree
"""
if nside is None:
nside = set_default_nside()
hpid = np.arange(hp.nside2npix(nside))
ra, dec = _hpid2RaDec(nside, hpid)
return _buildTree(ra, dec, leafsize, scale=scale)