def test_cluster():
numpy.random.seed(1234)
dec = numpy.arcsin(numpy.random.uniform(-1, 1, size=100000)) / numpy.pi * 180
ra = numpy.random.uniform(0, 2 * numpy.pi, size=100000) / numpy.pi * 180
# testing bootstrap
for area, rand, in sphere.bootstrap(4, (ra, dec), 41252.96 / len(dec)):
pass
dataset = sphere.points(ra, dec)
r = cluster.fof(dataset, 0.00001, np=None)
assert r.N == len(dataset)
binning = sphere.AngularBinning(numpy.linspace(0, 1.0, 10))
binningR = correlate.RBinning(binning.edges)
r = correlate.paircount(dataset, dataset, binning=binning, usefast=True)
r1 = correlate.paircount(dataset, dataset, binning=binning, usefast=False)
r2 = correlate.paircount(dataset, dataset, binning=binningR, usefast=True)
assert_equal(r1.sum1, r2.sum1)
assert_equal(r1.sum1, r.sum1)
assert_allclose(
r.sum1,
numpy.diff(2 * numpy.pi * (1 - numpy.cos(numpy.radians(binning.angular_edges)))) / ( 4 * numpy.pi) * len(ra) ** 2, rtol=10e-2)
def corr():
datafile = h5py.File(ns.catalogue, 'r')
randfile = h5py.File(ns.random, 'r')
datamask = datafile['COMPLETENESS'][:] >= 1
for vetoname in ns.use_tycho_veto:
datamask &= ~datafile['TYCHO_VETO'][vetoname][:]
dataRA = datafile['RA'][:][datamask]
dataDEC = datafile['DEC'][:][datamask]
randmask = randfile['COMPLETENESS'][:] >= 1
for vetoname in ns.use_tycho_veto:
randmask &= ~randfile['TYCHO_VETO'][vetoname][:]
randRA = randfile['RA'][:][randmask]
randDEC = randfile['DEC'][:][randmask]
data = sphere.points(dataRA, dataDEC)
rand = sphere.points(randRA, randDEC)
abin = sphere.AngularBinning(np.logspace(-3, 0, 16, endpoint=True))
DD = correlate.paircount(data, data, abin, np=ns.np)
DR = correlate.paircount(data, rand, abin, np=ns.np)
RR = correlate.paircount(rand, rand, abin, np=ns.np)
r = 1. * len(data) / len(rand)
dd = 1.0 * DD.sum1
dr = 1.0 * DR.sum1 * r
rr = 1.0 * RR.sum1 * (r * r)
return abin.angular_centers, (dd - 2 * dr + rr) / rr
def test_field():
numpy.random.seed(1234)
dec = numpy.arcsin(numpy.random.uniform(-1, 1, size=100000)) / numpy.pi * 180
ra = numpy.random.uniform(0, 2 * numpy.pi, size=100000) / numpy.pi * 180
dataset = sphere.field(ra, dec, value=numpy.ones_like(dec) * 0.5)
binning = sphere.AngularBinning(numpy.linspace(0, 1.0, 10))
r = correlate.paircount(dataset, dataset, binning=binning)
def reference_2pcf_angular(thetaedges,position1,weight1,position2=None,weight2=None):
"""Reference pair counting via kdcount"""
tree1 = sphere.points(position1[:,0],position1[:,1],weights=weight1)
factor = 1.
if position2 is None:
tree2 = tree1
factor = 1./2.
else: tree2 = sphere.points(position2[:,0],position2[:,1],weights=weight2)
bins = sphere.AngularBinning(np.asarray(thetaedges))
pc = correlate.paircount(tree1,tree2,bins)
return factor*pc.sum1
def ac_yu(self):
from kdcount import correlate
from kdcount import sphere
abin = sphere.AngularBinning(np.logspace(-4, -2.6, 10))
D = sphere.points(self.gal_ra, self.gal_dec)
R = sphere.points(self.ran_ra, self.ran_dec) #weights=wt_array
DD = correlate.paircount(D, D, abin, np=self.ncores)
DR = correlate.paircount(D, R, abin, np=self.ncores)
RR = correlate.paircount(R, R, abin, np=self.ncores)
r = D.norm / R.norm
w = (DD.sum1 - 2 * r * DR.sum1 + r**2 * RR.sum1) / (r**2 * RR.sum1)
return abin.angular_centers, w
def reference_paircount(pos1, w1, edges, pos2=None, w2=None):
"""Reference pair counting via kdcount"""
# set up the trees
tree1 = sphere.points(*pos1, boxsize=None, weights=w1)
if pos2 is None:
tree2 = tree1
else:
tree2 = sphere.points(*pos2, boxsize=None, weights=w2)
# run the pair count
bins = sphere.AngularBinning(edges)
pc = correlate.paircount(tree1, tree2, bins, np=0, compute_mean_coords=True)
return numpy.nan_to_num(pc.pair_counts), numpy.nan_to_num(pc.mean_centers), pc.sum1
def test_cluster():
numpy.random.seed(1234)
dec = numpy.arcsin(numpy.random.uniform(-1, 1,
size=100000)) / numpy.pi * 180
ra = numpy.random.uniform(0, 2 * numpy.pi, size=100000) / numpy.pi * 180
# testing bootstrap
for area, rand, in sphere.bootstrap(4, (ra, dec), 41252.96 / len(dec)):
pass
dataset = sphere.points(ra, dec)
r = cluster.fof(dataset, 0.00001, np=None)
assert r.N == len(dataset)
binning = sphere.FastAngularBinning(numpy.linspace(0, 1.0, 10))
binning1 = sphere.AngularBinning(numpy.linspace(0, 1.0, 10))
binningR = correlate.RBinning(binning.edges)
r = correlate.paircount(dataset, dataset, binning=binning)
r1 = correlate.paircount(dataset,
dataset,
binning=binning1,
compute_mean_coords=True)
r2 = correlate.paircount(dataset, dataset, binning=binningR)
# make sure mean_centers compute angular centers
for i, val in enumerate(r1.mean_centers):
assert binning.angular_edges[i] < val < binning.angular_edges[i + 1]
assert_equal(r1.sum1, r2.sum1)
assert_equal(r1.sum1, r.sum1)
assert_allclose(
r.sum1,
numpy.diff(2 * numpy.pi *
(1 - numpy.cos(numpy.radians(binning.angular_edges)))) /
(4 * numpy.pi) * len(ra)**2,
rtol=10e-2)
def corr():
data1file = h5py.File(ns.catalogue1, 'r')
data2file = h5py.File(ns.catalogue2, 'r')
rand1file = h5py.File(ns.random1, 'r')
data1mask = data1file['COMPLETENESS'][:] >= 1
for vetoname in ns.use_tycho_veto:
data1mask &= ~data1file['TYCHO_VETO'][vetoname][:]
data1RA = data1file['RA'][:][data1mask]
data1DEC = data1file['DEC'][:][data1mask]
rand1mask = rand1file['COMPLETENESS'][:] >= 1
for vetoname in ns.use_tycho_veto:
rand1mask &= ~rand1file['TYCHO_VETO'][vetoname][:]
rand1RA = rand1file['RA'][:][rand1mask]
rand1DEC = rand1file['DEC'][:][rand1mask]
# data2mask = data2file['COMPLETENESS'][:] >= 1
# for vetoname in ns.use_tycho_veto:
# data2mask &= ~data2file['TYCHO_VETO'][vetoname][:]
data2mask = Ellipsis
data2RA = data2file['RA'][:][data2mask]
data2DEC = data2file['DEC'][:][data2mask]
data1 = sphere.points(data1RA, data1DEC)
data2 = sphere.points(data2RA, data2DEC)
rand1 = sphere.points(rand1RA, rand1DEC)
abin = sphere.AngularBinning(np.logspace(-3, 0, 16, endpoint=True))
DD = correlate.paircount(data1, data2, abin, np=ns.np)
DR = correlate.paircount(rand1, data2, abin, np=ns.np)
r = 1. * len(data1) / len(rand1)
dd = 1.0 * DD.sum1
dr = 1.0 * DR.sum1 * r
return abin.angular_centers, (dd - dr) / dr
