def __init__(self,sample,sky_fraction, az, pol, redshift, stellarmass, spacing, load_pairs=False, pairs_src=None):
self.sample = sample
self.sky_fraction = sky_fraction
self.az, self.pol, self.redshift, self.stellarmass = map( self.sample.data.get, (az,pol,redshift,stellarmass) )
self.R_comoving = cosmo.comoving_distance( self.redshift )
self.x, self.y, self.z = mf.sphere2cart( self.az, self.pol)
self.grid = tp.spherical_grid( points = np.array([ self.pol, self.az, 3e5*self.redshift ]).T,
spacing = spacing )
if load_pairs:
if pairs_src is None:
raise Exception('need pairs_src to load pairs')
self.pairs = pair_list(pairs = load(pairs_src+'pairs'),
separations = load(pairs_src+'separations') )
self.pairs.dR_comoving = np.abs(np.diff( self.R_comoving[self.pairs.pairs], axis=1 )).reshape(self.pairs.count,)
self.pairs.parent = self.sample
else:
P = []
for chunk in self.grid.iter_pairs(chunk=True):
pair_chunk = pair_list( np.array(chunk) )
pair_chunk.parent = self.sample
pair_chunk.separations = pair_chunk.compute_rp(
az=az,pol=pol,redshift=redshift,
angular_units='radians',
output_units='physical'
)
cdz = 3e5*np.abs(pair_chunk.parent.data[redshift][pair_chunk.first()] -
pair_chunk.parent.data[redshift][pair_chunk.second()])
separation_limit = cosmo.projected_separation(angular_separation = np.array([ spacing[0] ]),
redshift = np.array([ min(self.redshift) ]),
angular_units = 'radians',
output_units = 'physical')[0]
pair_chunk = pair_chunk.select( np.all([ pair_chunk.first()!=pair_chunk.second(),
pair_chunk.separations<separation_limit, cdz<spacing[1] ], axis=0) )
P.append(pair_chunk.pairs)
P = np.array(list(chain(*P)))
pairs = pair_list(P)
pairs.dR_comoving = np.abs(np.diff( self.R_comoving[pairs.pairs], axis=1 )).reshape(pairs.count,)
pairs.parent = self.sample
pairs.separations = pairs.compute_rp(az=az,pol=pol,redshift=redshift)
self.pairs = pairs
def density_5nn(sample,
spacing=(4 * 0.116, 500),
az='azimuthal_angle',
pol='polar_angle',
redshift='redshift',
mass='StellarMass',
tracerlim=10.4):
c = 299792.458
az, pol, redshift, mass = map(sample.data.get, (az, pol, redshift, mass))
g = tp.spherical_grid(points=np.array([pol, az, c * redshift]).T,
spacing=spacing)
rp5 = np.zeros(sample.count, )
_, _, radial = zip(*g.points)
radial = np.array(radial)
separation_limit = cosmo.projected_separation(
angular_separation=np.array([g.angular_spacing]),
redshift=np.array([min(redshift)]),
angular_units='radians',
output_units='physical')[0]
for pair_chunk in g.iter_pairs(chunk=True):
pair_chunk = pair_list(np.array(pair_chunk), parent=sample)
pair_chunk.separations = pair_chunk.compute_rp(az='azimuthal_angle',
pol='polar_angle',
redshift='redshift',
angular_units='radians',
output_units='physical')
cdz = np.abs(np.diff(radial[pair_chunk.pairs], axis=1)).reshape(-1)
pair_chunk = pair_chunk.select(
np.all([
mass[pair_chunk.second()] > tracerlim,
pair_chunk.first() != pair_chunk.second(),
pair_chunk.separations < separation_limit,
cdz < g.radial_spacing
],
axis=0))
for gal in np.unique(pair_chunk.first()):
nn = 4 if mass[gal] > tracerlim else 5
try:
rp5[gal] = sorted(
pair_chunk.separations[pair_chunk.first() == gal])[nn - 1]
except:
rp5[gal] = -1
assert sum(rp5 == 0) == 0
if any(rp5 == -1):
print('Replacing rp5\'s larger than spacing with max')
rp5[rp5 == -1] = separation_limit
l = cosmo.comoving_distance(redshift +
(500 / c)) - cosmo.comoving_distance(redshift -
(500 / c))
return np.log10(5 / (np.pi * rp5**2 * l))
def smooth2d(x, y, z, func, smoothness, normalize=False):
f = np.empty(z.shape)
f.fill(np.nan)
if normalize:
normed = lambda a: np.copy(a) / np.std(a)
x, y = normed(x), normed(y)
#if smoothBy=='length':
# xbins = np.arange(min(x)-smoothness, max(x)+smoothness,smoothness)
# ybins = np.arange(min(y)-smoothness, max(y)+smoothness,smoothness)
g = tp.grid(list(zip(x, y)), spacing=smoothness)
for pair_chunk in g.iter_pairs(chunk=True):
pair_chunk = pair_list(np.array(pair_chunk))
pair_chunk.separations = np.linalg.norm(g.points[pair_chunk.first()] -
g.points[pair_chunk.second()],
axis=1)
pair_chunk = pair_chunk.select(
np.logical_and(pair_chunk.first() != pair_chunk.second(),
pair_chunk.separations < smoothness))
pair_chunk.sort(by='first')
try:
bin_counts = np.histogram(pair_chunk.first(),
bins=np.arange(
np.min(pair_chunk.first()),
np.max(pair_chunk.first()) + 2,
1))[0]
except:
if pair_chunk.count == 0:
continue
else:
raise
#print(np.split(pair_chunk.pairs,np.cumsum(bin_counts)[:-1]))
for sub_chunk in np.split(pair_chunk.pairs,
np.cumsum(bin_counts)[:-1]):
if sub_chunk.shape[0] != 0:
assert len(set(sub_chunk[:, 0])) == 1
f[sub_chunk[0, 0]] = func(z[sub_chunk[:, 1]])
IsNan = np.isnan(f)
if any(IsNan):
print(
'smooth2d returned {} values without estimates, imputing with their own value'
.format(np.sum(IsNan)))
f[IsNan] = z[IsNan]
return f
sample = sys.argv[1]
if sample=='sdss':
data_sources = ['../data/SDSS/']
elif sample=='mock':
data_sources = ['../data/LGalaxies/{}/'.format(i) for i in range(8)]
for data_src in data_sources:
data = load(data_src+'data')
pairs = load(data_src+'pairs')
separations = load(data_src+'separations')
part = galaxy_sample(data)
part.define_pairs( pair_list(pairs,separations,parent=part) )
part.define_corrfunc('full_sample',var='Delta_md')
obs_same_halo = np.equal(*(part.pair_list.get('ObsGrNr').T))
obs_diff_halo = np.logical_not(obs_same_halo)
if sample=='sdss':
selections = { 'obs_same_halo': obs_same_halo,
'obs_diff_halo': obs_diff_halo}
elif sample=='mock':
true_same_halo = np.equal(*(part.pair_list.get('FOFCentralGal').T))
true_diff_halo = np.logical_not(true_same_halo)
selections = {'true_same_halo':true_same_halo,
'true_diff_halo':true_diff_halo,
'obs_same_halo': obs_same_halo,
c, h = 299792.458, 0.677
AM_redshift_bins = np.arange(0.02, 0.09, 0.01)
AM_src = '../data/LGalaxies/'
N_iter = int(sys.argv[1])
#print(N_iter)
data_sources = ['../data/LGalaxies/{}/'.format(i) for i in range(8)]
training_src = data_sources[0]
data = load(training_src + 'data')
part = galaxy_sample(data)
part.define_pairs(
pair_list(load(training_src + 'pairs'),
load(training_src + 'separations'),
parent=part))
#initialize group finder
try:
AM = halo_mass_model.RedshiftDependentAbundanceMatching(
src=AM_src, SHAM=False, redshift_bins=AM_redshift_bins)
except:
# easiest way to handle this is to have run derive_quantities.py once with a dummy set of group finder params
raise Exception(
'Need to have halo mass function and subhalo mass function defined already under '
+ AM_src)
gf = tinker_group_finder(part,
sky_fraction=None,
az='azimuthal_angle',