R2=R2,
pmesh=True,
abund=abund,
doexp=doexp,
mexp=mexp,
cc=cc,
stellar=stellar)[1]
datap = pm.paint(hdictf['position'][:num], hdictf['mass'][:num])
datapR = ft.smooth(datap, Rsm, 'fingauss')
predicts.append(predictR[...].copy())
datas.append(datapR[...].copy())
for i, sg in enumerate(sgs):
hmass, hpos = dg.scatter_catalog(hdictf['mass'],
hdictf['position'],
sg,
seed=i)
datasg = pm.paint(hpos[:num], hmass[:num])
datasgR = ft.smooth(datasg, Rsm, 'fingauss')
datasgs[i].append(datasgR[...].copy())
#####Setup
#bins = np.linspace(-3, 3, 300)
func = dg.normal
colors = ['r', 'b', 'g', 'y', 'm', 'orange', 'brown', 'k']
if doexp:
mms = [10**8, 10**8.5, 10**9, 10**9.2, 10**9.8, 10**10][::-1]
elif stellar:
mms = [1e6, 1e7, 1e8, 1e9, 1e10][::-1]
def gridhalos(pm,
dpath=None,
pos=None,
mass=None,
R1=None,
R2=None,
rank=None,
abund=True,
sigma=None,
seed=123,
pmesh=True,
z=0,
doexp=False,
mexp=None,
cc=None,
stellar=False,
verbose=True):
'''Read halo file or pos/mas arrays and grid and smooth them after scattering or matching abundance or both
The path should have folder FOF in it.
'''
#Matter
bs, nc = pm.BoxSize[0], pm.Nmesh[0]
#Halos
if dpath is not None:
from nbodykit.lab import CurrentMPIComm
CurrentMPIComm.set(
pm.comm
) #WORKAROUND because BigFileCatalog ignores comm o/w. Should not need after nbodykit v0.3.4
fofcat = BigFileCatalog(dpath + 'FOF', header='Header', comm=pm.comm)
halopos = fofcat['PeakPosition'].compute()[1:]
halomass = (fofcat['Mass'].compute() * 10**10)[1:]
try:
hvel = fofcat['CMVelocity'].compute()[1:]
except Exception as e:
print(e)
print('Cannot read velocity')
hvel = None
if verbose: print('BigFileCatalog read')
elif pos is not None:
halopos = pos
if mass is not None:
halomass = mass
else:
print(
'No halo masses given, so mass=1. Scatter and abundance not valid'
)
halomass = np.ones(halopos.shape[0])
sigma = None
abund = False
else:
print('Need either path of Bigfile, or catalogs')
return None
if abund:
if verbose: print('Match abundance')
halomass = mf.icdf_sampling(hmass=halomass, bs=bs, z=z)
if stellar:
halomass = mf.stellar_mass(halomass)
if sigma is not None:
print('Scatter catalog with sigma = %0.2f' % sigma)
halomass, halopos = dg.scatter_catalog(halomass, halopos, sigma, seed)
if doexp:
if verbose:
print('Doing doexp with mexp = %0.2f, cc=%0.2f' % (mexp, cc))
halomass = mf.fmexp(halomass, mexp, cc)
#print('In gridhalos')
#print(rank, halomass.size)
#print(dpath)
if rank is not None:
if rank > halomass.size:
print('\nCatalog not big enough to be able to match abundance')
print('\nDesired rank = %d, catalog size = %d' %
(rank, halomass.size))
return None
else:
halopos = halopos[:rank + 1]
halomass = halomass[:rank + 1]
hvel = hvel[:rank + 1]
halos = {'position': halopos, 'mass': halomass, 'velocity': hvel}
if pmesh:
halomesh = pm.paint(halopos, mass=halomass)
hposmesh = pm.paint(halopos)
meshdict = {'hposmesh': hposmesh.value, 'halomesh': halomesh.value}
dkh = halomesh.r2c()
if R1 is not None:
hmR1mesh = ft.smooth(dkh, R1, 'fingauss')
meshdict['hmR1mesh'] = hmR1mesh.value
if R2 is not None:
hmR2mesh = ft.smooth(dkh, R2, 'fingauss')
meshdict['hmR2mesh'] = hmR2mesh.value
else:
meshdict = None
return meshdict, halos
predictR.value,
M0=M0,
binfit=bins,
c='k',
axin=ax,
func=func,
mbin=mbinsm,
retfit=True,
lsf='--')[0]
fits = []
sgs = [0.2]
for i, sg in enumerate(sgs):
hmass, hpos = dg.scatter_catalog(hdictf['mass'],
hdictf['position'],
sigma=sg)
datasg = pm.paint(hpos[:num], hmass[:num])
datasgR = ft.smooth(datasg, 3, 'fingauss')
fitt = dg.plot_noise(datasgR.value,
predictR.value,
M0=M0,
binfit=bins,
c='k',
axin=ax,
func=func,
mbin=mbinsm,
retfit=True)[0]
fits.append(fitt)
for i in range(3):
predictR.value,
M0=M0,
binfit=bins,
c='k',
axin=ax,
func=func,
mbin=mbinsm,
retfit=True,
lsf='--')[0]
fits = []
sgs = [0.1, 0.15, 0.20]
for i, sg in enumerate(sgs):
hmass, hpos, hvel = dg.scatter_catalog(hdictf['mass'],
hdictf['position'],
sg,
hvel=hdictf['velocity'])
if rsd:
datasg = pm.paint(
hpos[:num] +
(np.array([0, 0, 1]).reshape(-1, 1) * hvel[:num][:, 2]).T,
mass=hmass[:num])
else:
datasg = pm.paint(hpos[:num], mass=hmass[:num])
datasgR = ft.smooth(datasg, Rsm, 'fingauss')
fitt = dg.plot_noise(datasgR.value,
predictR.value,
M0=M0,
binfit=bins,
c=colors[i],
axin=ax,
# bins = np.zeros((mbinsm.size, 1000 ))
# for i in range(0, bins.shape[0]):
# bins[i] = np.linspace(-3*mbinsm[i]/M0, 2*mbinsm[i]/M0, bins.shape[1])
#
print('mbinsm -- ', mbinsm)
####
fig, ax = plt.subplots(3, 3, figsize = (14, 12))
fit0 = dg.plot_noise(datapR.value, predictR.value, M0=M0, binfit=bins, c='k', axin=ax, func=func, mbin=mbinsm, retfit=True, lsf='--')[0]
fits = []
sgs = [0.1, 0.15, 0.20]
for i, sg in enumerate(sgs):
if truemass:
hmass, hpos, hmasstrue = dg.scatter_catalog(hdictf['mass'], hdictf['position'], sg, truemass=True)
datasg = pm.paint(hpos[:num], mass = hmasstrue[:num])
datasgR = ft.smooth(datasg, Rsm, 'fingauss')
else:
hmass, hpos = dg.scatter_catalog(hdictf['mass'], hdictf['position'], sg)
datasg = pm.paint(hpos[:num], mass = hmass[:num])
datasgR = ft.smooth(datasg, Rsm, 'fingauss')
fitt = dg.plot_noise(datasgR.value, predictR.value, M0=M0, binfit=bins, c=colors[i], axin=ax, func=func, mbin=mbinsm, retfit=True)[0]
fits.append(fitt)
ff = mtpath + 'noisehist_M%02d_na.png'%(10*np.log10(M0))
if truemass: ff = ff[:-4] + 'trueM.png'
if Rsm !=3 : ff = ff[:-4] + '_R%d.png'%Rsm
fig.savefig(ff)
tosave = []