def calc_bias(aa, h1mesh, suff):
'''Compute the bias(es) for the HI'''
if rank == 0: print('Calculating bias')
if rank == 0:
print("Processing a={:.4f}...".format(aa))
print('Reading DM mesh...')
dm = BigFileMesh(args['matterfile'] % (aa), 'N1024').paint()
dm /= dm.cmean()
if rank == 0: print('Computing DM P(k)...')
pkmm = FFTPower(dm, mode='1d').power
k, pkmm = pkmm['k'], pkmm['power'] # Ignore shotnoise.
if rank == 0: print('Done.')
#
pkh1h1 = FFTPower(h1mesh, mode='1d').power
kk = pkh1h1.coords['k']
pkh1h1 = pkh1h1['power'] - pkh1h1.attrs['shotnoise']
pkh1mm = FFTPower(h1mesh, second=dm, mode='1d').power['power']
if rank == 0: print('Done.')
# Compute the biases.
b1x = np.abs(pkh1mm / (pkmm + 1e-10))
b1a = np.abs(pkh1h1 / (pkmm + 1e-10))**0.5
if rank == 0: print("Finishing processing a={:.4f}.".format(aa))
#
if rank == 0:
fout = open(outfolder + "HI_bias_{:6.4f}.txt".format(aa), "w")
fout.write("# {:>8s} {:>10s} {:>10s} {:>15s}\n".\
format("k","b1_x","b1_a","Pkmm"))
for i in range(1, kk.size):
fout.write("{:10.5f} {:10.5f} {:10.5f} {:15.5e}\n".\
format(kk[i],b1x[i],b1a[i],pkmm[i].real))
fout.close()
def compute_power(output, Nmesh=1024, species=2, spec2=None):
"""Compute the compensated power spectrum from a catalogue."""
catnu = BigFileCatalog(output, dataset=str(species) + '/', header='Header')
sp = sptostr(species)
sp2 = sptostr(spec2)
outfile = path.join(
output, "../power-" + sp + sp2 + "-%.4f.txt" % catnu.attrs["Time"][0])
if path.isfile(outfile):
return
catnu.to_mesh(Nmesh=Nmesh, window='cic', compensated=True, interlaced=True)
if spec2 is not None:
catcdm = BigFileCatalog(output,
dataset=str(spec2) + '/',
header='Header')
catcdm.to_mesh(Nmesh=Nmesh,
window='cic',
compensated=True,
interlaced=True)
pkcross = FFTPower(catnu, mode='1d', Nmesh=1024, second=catcdm)
power = pkcross.power
else:
pknu = FFTPower(catnu, mode='1d', Nmesh=1024)
power = pknu.power
numpy.savetxt(
outfile,
numpy.array([power['k'], power['power'].real, power['modes']]).T)
return power
def main(ns, ns1, ns2):
if ns.verbose:
setup_logging('info')
print('\n Catalogs are - \n%s\n%s\n' % (ns1, ns2))
cat1 = read_cat(ns1)
mesh1 = cat1.to_mesh(interlaced=True,
compensated=True,
window='tsc',
Nmesh=ns.nmesh).paint(mode='complex')
cat2 = read_cat(ns2)
if ns.unique_k:
dk = 0
else:
dk = None
rm = FFTPower(mesh1, second=mesh1, mode='1d', dk=dk)
nmin = numpy.unique(
numpy.int32(
numpy.logspace(numpy.log10(ns.nmin),
numpy.log10(ns.nmax),
ns.nn,
endpoint=True)))
nmin0 = cat1.comm.allreduce(cat2['Length'].min().compute(), MPI.MIN)
nmax0 = cat1.comm.allreduce(cat2['Length'].max().compute(), MPI.MAX)
nmin = nmin[nmin >= nmin0]
nmin = nmin[nmin < nmax0]
save_bs(ns.output, 'a-matter', rm)
r = []
b = []
a = []
for nmin1 in nmin:
cat2 = read_cat(ns2, nmin1)
mesh2 = cat2.to_mesh(interlaced=True,
compensated=True,
window='tsc',
Nmesh=ns.nmesh)
r.append(FFTPower(mesh1, second=mesh2, mode='1d', dk=dk))
save_bs(ns.output, 'x-nmin-%05d' % nmin1, r[-1])
bias = fit_bias(r[-1], rm)
abundance = r[-1].attrs['N2'] / cat2.attrs['BoxSize'][0]**3
b.append(bias)
a.append(abundance)
if cat1.comm.rank == 0:
print('Bias of N=', nmin1, bias, abundance)
basename = ns.output.rsplit('.', 1)[0]
if cat1.comm.rank == 0:
numpy.savetxt(basename + '-bias.txt', numpy.array([nmin, b, a]).T)
if ns.with_plot:
if cat1.comm.rank == 0:
figure = make_plot(rm, r, nmin)
figure.savefig(basename + '.png')
def fiddlebias(aa, saveb=False, bname='h1bias.txt', ofolder=None):
#Fiddling bias for halos. Deprecated.
print('Read in catalogs')
halocat = readincatalog(aa, matter=False)
hpos = halocat['Position']
hmass = halocat['Mass']
h1mass = dohod.HI_mass(hmass, aa)
dm = BigFileMesh(project + sim + '/fastpm_%0.4f/'%aa + '/dmesh_N%04d'%nc, '1').paint()
if ofolder is None: ofolder = project + '/%s/fastpm_%0.4f/'%(sim, aa)
#measure power
pm = ParticleMesh(BoxSize = bs, Nmesh = [nc, nc, nc])
pkm = FFTPower(dm/dm.cmean(), mode='1d').power
k, pkm = pkm['k'], pkm['power']
##H1
print("H1")
h1mesh = pm.paint(hpos, mass=h1mass)
pkh1 = FFTPower(h1mesh/h1mesh.cmean(), mode='1d').power['power']
pkh1m = FFTPower(h1mesh/h1mesh.cmean(), second=dm/dm.cmean(), mode='1d').power['power']
#Bias
b1h1 = pkh1m/pkm
b1h1sq = pkh1/pkm
if saveb:
np.savetxt(ofolder+bname, np.stack((k, b1h1, b1h1sq**0.5), axis=1),
header='k, pkh1xm/pkm, pkh1/pkm^0.5')
return k, b1h1, b1h1sq
def evaluate1(model, data, norm=True, kmin=None, dk=None):
'''return position order:
px,p1,p2
'''
from nbodykit.lab import FieldMesh, FFTPower, ProjectedFFTPower
if kmin is None: kmin = 0
if dk is None: dk = 2 * numpy.pi / model.BoxSize[0]
if norm:
mod = model / model.cmean()
dat = data / data.cmean()
else:
mod, dat = model, data
px = FFTPower(first=FieldMesh(mod),
second=FieldMesh(dat),
mode='1d',
kmin=kmin,
dk=dk)
p1 = FFTPower(first=FieldMesh(mod), mode='1d', kmin=kmin, dk=dk)
p2 = FFTPower(first=FieldMesh(dat), mode='1d', kmin=kmin, dk=dk)
return px.power, p1.power, p2.power
def calc_bias(aa,mcut,suff):
'''Compute the bias(es) for the HI'''
print('Read in DM mesh')
dm = BigFileMesh(project+sim+'/fastpm_%0.4f/'%aa+\
'/dmesh_N%04d'%nc,'1').paint()
dm /= dm.cmean()
pkmm = FFTPower(dm,mode='1d').power
k,pkmm= pkmm['k'],pkmm['power'] # Ignore shotnoise.
#
print('Read in central/satellite catalogs')
cencat = BigFileCatalog(project+sim+'/fastpm_%0.4f/cencat'%aa)
satcat = BigFileCatalog(project+sim+'/fastpm_%0.4f/satcat'%aa+suff)
#
cencat['HImass'] = HI_hod(cencat['Mass'],aa,mcut)
satcat['HImass'] = HI_hod(satcat['Mass'],aa,mcut)
totHImass = cencat['HImass'].sum().compute() +\
satcat['HImass'].sum().compute()
cencat['HImass']/= totHImass/float(nc)**3
satcat['HImass']/= totHImass/float(nc)**3
#
allcat = MultipleSpeciesCatalog(['cen','sat'],cencat,satcat)
#
h1mesh = allcat.to_mesh(BoxSize=bs,Nmesh=[nc,nc,nc],weight='HImass')
pkh1h1 = FFTPower(h1mesh,mode='1d').power
pkh1h1 = pkh1h1['power']-pkh1h1.attrs['shotnoise']
pkh1mm = FFTPower(h1mesh,second=dm,mode='1d').power['power']
# Compute the biases.
b1x = np.abs(pkh1mm/(pkmm+1e-10))
b1a = np.abs(pkh1h1/(pkmm+1e-10))**0.5
return(k,b1x,b1a,np.abs(pkmm))
def calc_bias(aa, h1mesh, suff):
'''Compute the bias(es) for the HI'''
if rank == 0:
print("Processing a={:.4f}...".format(aa))
print('Reading DM mesh...')
if ncsim == 10240:
dm = BigFileMesh(scratchyf+sim+'/fastpm_%0.4f/'%aa+\
'/1-mesh/N%04d'%nc,'').paint()
else:
dm = BigFileMesh(project+sim+'/fastpm_%0.4f/'%aa+\
'/dmesh_N%04d/1/'%nc,'').paint()
dm /= dm.cmean()
if rank == 0: print('Computing DM P(k)...')
pkmm = FFTPower(dm, mode='1d').power
k, pkmm = pkmm['k'], pkmm['power'] # Ignore shotnoise.
if rank == 0: print('Done.')
#
pkh1h1 = FFTPower(h1mesh, mode='1d').power
pkh1h1 = pkh1h1['power'] - pkh1h1.attrs['shotnoise']
pkh1mm = FFTPower(h1mesh, second=dm, mode='1d').power['power']
if rank == 0: print('Done.')
# Compute the biases.
b1x = np.abs(pkh1mm / (pkmm + 1e-10))
b1a = np.abs(pkh1h1 / (pkmm + 1e-10))**0.5
if rank == 0: print("Finishing processing a={:.4f}.".format(aa))
return (k, b1x, b1a, np.abs(pkmm))
def calc_bias(aa,mcut,suff):
'''Compute the bias(es) for the HI'''
if rank==0:
print("Processing a={:.4f}...".format(aa))
print('Reading DM mesh...')
dm = BigFileMesh(scratch1+sim+'/fastpm_%0.4f/'%aa+\
'/1-mesh/N%04d'%nc,'').paint()
dm /= dm.cmean()
if rank==0: print('Computing DM P(k)...')
pkmm = FFTPower(dm,mode='1d').power
k,pkmm= pkmm['k'],pkmm['power'] # Ignore shotnoise.
if rank==0: print('Done.')
#
if rank==0: print('Reading central/satellite catalogs...')
cencat = BigFileCatalog(scratch2+sim+'/fastpm_%0.4f/cencat-16node'%aa)
# satcat = BigFileCatalog(scratch2+sim+'/fastpm_%0.4f/satcat'%aa+suff)
# if rank==0: print('Catalogs read.')
# #
# if rank==0: print('Computing HI masses...')
# cencat['HImass'] = HI_hod(cencat['Mass'],aa,mcut)
# satcat['HImass'] = HI_hod(satcat['Mass'],aa,mcut)
# totHImass = cencat['HImass'].sum().compute() +\
# satcat['HImass'].sum().compute()
# cencat['HImass']/= totHImass/float(nc)**3
# satcat['HImass']/= totHImass/float(nc)**3
# if rank==0: print('HI masses done.')
# #
# if rank==0: print('Combining catalogs and computing P(k)...')
# allcat = MultipleSpeciesCatalog(['cen','sat'],cencat,satcat)
# #h1mesh = allcat.to_mesh(BoxSize=bs,Nmesh=[nc,nc,nc],weight='HImass')
cenmesh = pm.paint(cencat['Position'])
#satmesh = pm.paint(satcat['Position'], mass=satcat['HImass'])
h1mesh = cenmesh
pkh1h1 = FFTPower(h1mesh,mode='1d').power
pkh1h1 = pkh1h1['power']-pkh1h1.attrs['shotnoise']
pkh1mm = FFTPower(h1mesh,second=dm,mode='1d').power['power']
if rank==0: print('Done.')
# Compute the biases.
b1x = np.abs(pkh1mm/(pkmm+1e-10))
b1a = np.abs(pkh1h1/(pkmm+1e-10))**0.5
if rank==0: print("Finishing processing a={:.4f}.".format(aa))
if rank==0:
fout = open("../data/L%04d/HI_bias_halo_%0.4f.txt"%(bs, aa),"w")
fout.write("# Mcut={:12.4e}Msun/h.\n".format(mcut))
fout.write("# {:>8s} {:>10s} {:>10s} {:>15s}\n".\
format("k","b1_x","b1_a","Pkmm"))
for i in range(1,kk.size):
fout.write("{:10.5f} {:10.5f} {:10.5f} {:15.5e}\n".\
format(kk[i],b1x[i],b1a[i],pkmm[i]))
fout.close()
bavg = np.mean(b1x[1:10])
flog.write("{:6.2f} {:12.4e} {:6.3f}\n".format(1/aa-1,mcut,bavg))
flog.flush()
if rank==0: flog.close()
#
return(k,b1x,b1a,np.abs(pkmm))
def evaluate(self, model, data):
from nbodykit.lab import FieldMesh, FFTPower, ProjectedFFTPower
xm = FFTPower(first=FieldMesh(model.mapp/model.mapp.cmean()), second=FieldMesh(data.mapp/data.mapp.cmean()), mode='1d')
xd = FFTPower(first=FieldMesh(model.d), second=FieldMesh(data.d), mode='1d')
xs = FFTPower(first=FieldMesh(model.s), second=FieldMesh(data.s), mode='1d')
pm1 = FFTPower(first=FieldMesh(model.mapp/model.mapp.cmean()), mode='1d')
pd1 = FFTPower(first=FieldMesh(model.d), mode='1d')
ps1 = FFTPower(first=FieldMesh(model.s), mode='1d')
pm2 = FFTPower(first=FieldMesh(data.mapp/data.mapp.cmean()), mode='1d')
pd2 = FFTPower(first=FieldMesh(data.d), mode='1d')
ps2 = FFTPower(first=FieldMesh(data.s), mode='1d')
data_preview = dict(s=[], d=[], mapp=[])
model_preview = dict(s=[], d=[], mapp=[])
for axes in [[1, 2], [0, 2], [0, 1]]:
data_preview['d'].append(data.d.preview(axes=axes))
data_preview['s'].append(data.s.preview(axes=axes))
data_preview['mapp'].append(data.mapp.preview(axes=axes))
model_preview['d'].append(model.d.preview(axes=axes))
model_preview['s'].append(model.s.preview(axes=axes))
model_preview['mapp'].append(model.mapp.preview(axes=axes))
#data_preview['mapp'] = data.mapp.preview(axes=(0, 1))
#model_preview['mapp'] = model.mapp.preview(axes=(0, 1))
return xm.power, xs.power, xd.power, pm1.power, pm2.power, ps1.power, ps2.power, pd1.power, pd2.power, data_preview, model_preview
def getps2D(f1, f2=None, p2=False):
p1 = FFTPower(f1, mode='2d').power['power']
if f2 is not None:
px = FFTPower(f1, second=f2, mode='2d').power['power']
if p2:
p2 = FFTPower(f2, mode='2d').power['power']
return p1, p2, px
else:
return p1, px
else:
return p1
def compute_fast_power(output,
ICS,
vthresh=850,
Nmesh=1024,
species=2,
spec2=None):
"""Compute the compensated power spectrum from a catalogue."""
sp = sptostr(species)
sp2 = sptostr(spec2)
catnu = BigFileCatalog(output, dataset=str(species) + '/', header='Header')
outfile = path.join(
output,
"../power-fast-" + sp + sp2 + "-%.4f.txt" % catnu.attrs["Time"][0])
if path.isfile(outfile):
return
catics = BigFileCatalog(ICS, dataset=str(species) + '/', header='Header')
fast = (catics['Velocity']**
2).sum(axis=1) < vthresh**2 / catics.attrs["Time"]**3
fastids = catics["ID"][fast].compute()
fastids.sort()
#Note: map_blocks runs elementwise over blocks.
#So we need to pre-compute fastids: if we do not we will
#end up checking whether elements in a block of catnu["ID"]
#are in the equivalent block in fastids.
select = catnu["ID"].map_blocks(wrapper,
fastids,
dtype=numpy.bool,
chunks=catnu["ID"].chunks)
catnu[select].to_mesh(Nmesh=Nmesh,
window='cic',
compensated=True,
interlaced=True)
if spec2 is not None:
catcdm = BigFileCatalog(output,
dataset=str(spec2) + '/',
header='Header')
catcdm.to_mesh(Nmesh=Nmesh,
window='cic',
compensated=True,
interlaced=True)
pkcross = FFTPower(catnu[select],
mode='1d',
Nmesh=Nmesh,
second=catcdm,
dk=5.0e-6)
power = pkcross.power
else:
pknu = FFTPower(catnu[select], mode='1d', Nmesh=Nmesh, dk=5.0e-6)
power = pknu.power
numpy.savetxt(
outfile,
numpy.array([power['k'], power['power'].real, power['modes']]).T)
return power
def evaluate1(model, data, field, M0=0, stoch=False, kmin=None, dk=None):
'''return position order:
xm.power, xs.power, xd.power,
pm1.power, pm2.power,
ps1.power, ps2.power,
pd1.power, pd2.power,
data_preview, model_preview
'''
from nbodykit.lab import FieldMesh, FFTPower, ProjectedFFTPower
if kmin is None: kmin = 0
if dk is None: dk = 2*numpy.pi/model.s.BoxSize[0]
model.mapp += M0
data.mapp += M0
#print('Means are : ', model.mapp.cmean(), data.mapp.cmean())
if abs(model.mapp.cmean()) > 1e-3: modmappmean = model.mapp.cmean()
else: modmappmean = 1.
if abs(data.mapp.cmean()) > 1e-3: datmappmean = data.mapp.cmean()
else: datmappmean = 1.
modmappmean, datmappmean = 1., 1.
#if abs(model.mapp.cmean()) > 1e-3: modmappmean = model.mapp.cmean()
#else: modmappmean = 1.
#if abs(data.mapp.cmean()) > 1e-3: datmappmean = data.mapp.cmean()
#else: datmappmean = 1.
data_preview, model_preview = [], []
if field == 'mapp':
x = FFTPower(first=FieldMesh(model.mapp/modmappmean), second=FieldMesh(data.mapp/datmappmean), mode='1d', kmin=kmin, dk=dk)
p1 = FFTPower(first=FieldMesh(model.mapp/modmappmean), mode='1d', kmin=kmin, dk=dk)
p2 = FFTPower(first=FieldMesh(data.mapp/datmappmean), mode='1d', kmin=kmin, dk=dk)
for axes in [[1, 2], [0, 2], [0, 1]]:
data_preview.append(data.mapp.preview(axes=axes))
model_preview.append(model.mapp.preview(axes=axes))
elif field == 's':
x = FFTPower(first=FieldMesh(model.s), second=FieldMesh(data.s), mode='1d', kmin=kmin, dk=dk)
p1 = FFTPower(first=FieldMesh(model.s), mode='1d', kmin=kmin, dk=dk)
p2 = FFTPower(first=FieldMesh(data.s), mode='1d', kmin=kmin, dk=dk)
for axes in [[1, 2], [0, 2], [0, 1]]:
data_preview.append(data.s.preview(axes=axes))
model_preview.append(model.s.preview(axes=axes))
elif field == 'd':
x = FFTPower(first=FieldMesh(model.d), second=FieldMesh(data.d), mode='1d', kmin=kmin, dk=dk)
p1 = FFTPower(first=FieldMesh(model.d), mode='1d', kmin=kmin, dk=dk)
p2 = FFTPower(first=FieldMesh(data.d), mode='1d', kmin=kmin, dk=dk)
for axes in [[1, 2], [0, 2], [0, 1]]:
data_preview.append(data.d.preview(axes=axes))
model_preview.append(model.d.preview(axes=axes))
else:
print('Field not recongnized')
return 0
return x.power, p1.power, p2.power, data_preview, model_preview
def eval_bfit(hmesh,
mod,
ofolder,
noise=None,
title=None,
fsize=15,
suff=None,
save=True):
pmod = FFTPower(mod, mode='1d').power
k, pmod = pmod['k'], pmod['power']
ph = FFTPower(hmesh, mode='1d').power['power']
pxmodh = FFTPower(hmesh, second=mod, mode='1d').power['power']
perr = FFTPower(hmesh - mod, mode='1d').power['power']
if save:
fig, ax = plt.subplots(1, 3, figsize=(15, 4))
ax[0].plot(k, pxmodh / (pmod * ph)**0.5)
ax[0].set_ylabel('$r_{cc}$', fontsize=fsize)
ax[1].plot(k, (pmod / ph)**0.5)
ax[1].set_ylabel('$\sqrt{P_{mod}/P_{hh}}$', fontsize=fsize)
ax[2].plot(k, perr)
ax[2].set_yscale('log')
ax[2].set_ylabel('$P_{\delta{mod}-\delta_h}$', fontsize=fsize)
if noise is not None: ax[2].axhline(noise)
if hmesh.pm.comm.rank == 0:
for axis in ax:
axis.set_xscale('log')
axis.grid(which='both')
axis.set_xlabel('$k$ (h/Mpc)', fontsize=fsize)
axis.legend(fontsize=fsize)
if title is not None: plt.suptitle(title, fontsize=fsize)
plt.tight_layout(rect=[0, 0, 1, 0.95])
fname = ofolder + 'evalbfit'
if suff is not None: fname = fname + '%s' % suff
print(fname)
fig.savefig(fname + '.png')
plt.close()
return k[1:], perr.real[1:]
else:
return k, ph, pmod, pxmodh, perr
def calc_pkmu(aa, h1mesh, outfolder, los=[0, 0, 1], Nmu=int(4)):
'''Compute the redshift-space P(k) for the HI in mu bins'''
if rank == 0: print('Calculating pkmu')
pkh1h1 = FFTPower(h1mesh, mode='2d', Nmu=Nmu, los=los).power
# Extract what we want.
kk = pkh1h1.coords['k']
sn = pkh1h1.attrs['shotnoise']
pk = pkh1h1['power']
if rank == 0: print('For mu-bins', pkh1h1.coords['mu'])
# Write the results to a file.
if rank == 0:
fout = open(
outfolder + "HI_pks_mu_{:02d}_{:06.4f}.txt".format(Nmu, aa), "w")
fout.write("# Redshift space power spectrum in mu bins.\n")
fout.write("# Subtracting SN={:15.5e}.\n".format(sn))
ss = "# {:>8s}".format(r'k\mu')
for i in range(pkh1h1.shape[1]):
ss += " {:15.5f}".format(pkh1h1.coords['mu'][i])
fout.write(ss + "\n")
for i in range(1, pk.shape[0]):
ss = "{:10.5f}".format(kk[i])
for j in range(pk.shape[1]):
ss += " {:15.5e}".format(np.abs(pk[i, j] - sn))
fout.write(ss + "\n")
fout.close()
def calc_pk1d(aa, suff):
'''Compute the 1D redshift-space P(k) for the HI'''
if rank == 0: print('Read in central/satellite catalogs')
cencat = BigFileCatalog(scratch2 + sim +
'/fastpm_%0.4f/cencat-16node' % aa)
satcat = BigFileCatalog(scratch2 + sim + '/fastpm_%0.4f/satcat' % aa +
suff)
rsdfac = read_conversions(scratch1 + sim + '/fastpm_%0.4f/' % aa)
# Compute the power spectrum
los = [0, 0, 1]
cencat['RSDpos'] = cencat['Position'] + cencat['Velocity'] * los * rsdfac
satcat['RSDpos'] = satcat['Position'] + satcat['Velocity'] * los * rsdfac
cencat['HImass'] = HI_hod(cencat['Mass'], aa)
satcat['HImass'] = HI_hod(satcat['Mass'], aa)
rankHImass = cencat['HImass'].sum().compute() +\
satcat['HImass'].sum().compute()
totHImass = comm.allreduce(rankHImass)
cencat['HImass'] /= totHImass / float(nc)**3
satcat['HImass'] /= totHImass / float(nc)**3
allcat = MultipleSpeciesCatalog(['cen', 'sat'], cencat, satcat)
h1mesh = allcat.to_mesh(BoxSize=bs,Nmesh=[nc,nc,nc],\
position='RSDpos',weight='HImass')
pkh1h1 = FFTPower(h1mesh, mode='1d', kmin=0.025, dk=0.0125).power
# Extract the quantities we want and write the file.
kk = pkh1h1['k']
sn = pkh1h1.attrs['shotnoise']
pk = np.abs(pkh1h1['power'])
if rank == 0:
fout = open("HI_pks_1d_{:6.4f}.txt".format(aa), "w")
fout.write("# Subtracting SN={:15.5e}.\n".format(sn))
fout.write("# {:>8s} {:>15s}\n".format("k", "Pk_0_HI"))
for i in range(kk.size):
fout.write("{:10.5f} {:15.5e}\n".format(kk[i], pk[i] - sn))
fout.close()
def calc_pkhalo(aa, suff):
if rank ==0 : print('Read in central/satellite catalogs')
#cencat = BigFileCatalog(project+sim+'/fastpm_%0.4f/cencat'%aa)
cencat = BigFileCatalog(scratch2+sim+'/fastpm_%0.4f/cencat-16node'%aa)
cencat = BigFileCatalog(scratch1+sim+'/fastpm_%0.4f/'%aa+\
'LL-0.200','')
rsdfac = read_conversions(scratch1+sim+'/fastpm_%0.4f/'%aa)
# Compute the power spectrum
los = [0,0,1]
cpos = cencat['Position'].compute()
start = time()
cenmesh = pm.paint(cencat['Position'])
h1mesh = cenmesh
end = time()
if rank ==0 : print('Time taken to mesh = ', end - start)
start = time()
#pkh1h1 = FFTPower(h1mesh,mode='1d',kmin=0.025,dk=0.0125).power
pkh1h1 = FFTPower(h1mesh/h1mesh.cmean(),mode='1d').power
end = time()
if rank ==0 : print('Time taken to power = ', end - start)
# Extract the quantities we want and write the file.
kk = pkh1h1['k']
sn = pkh1h1.attrs['shotnoise']
pk = np.abs(pkh1h1['power'])
if rank ==0 :
fout = open("../data/L%04d/Halocat_pks_1d_%0.4f.txt"%(bs, aa),"w")
fout.write("# Subtracting SN={:15.5e}.\n".format(sn))
fout.write("# {:>8s} {:>15s}\n".format("k","Pk_0_HI"))
for i in range(kk.size):
fout.write("{:10.5f} {:15.5e}\n".format(kk[i],pk[i]-sn))
fout.close()
def savenoisep2d(Nmu=4):
for ia, aa in enumerate([0.3333, 0.2000, 0.1429]):
zz = 1/aa-1
for iw, wopt in enumerate(['opt', 'pess']):
#for iw, wopt in enumerate(['opt']):
for it, thopt in enumerate(['opt', 'pess', 'reas']):
#for it, thopt in enumerate([ 'reas']):
if rank == 0: print(aa, wopt, thopt)
dpath = '/global/cscratch1/sd/chmodi/m3127/21cm_cleaning/recon/fastpm_%0.4f/wedge_kmin%0.2f_%s/'%(aa, 0.03, wopt)
dpath += 'L%04d-N%04d-R//thermal-%s-hex/ZA/opt_s999_h1massA_fourier_rsdpos/'%(bs, nc, thopt)
ofolder = '../../data/ZArecon-rsd/kmin-003_wedge-%s/thermal-%s-hex/Nmu%d/'%(wopt, thopt, Nmu)
try: os.makedirs(ofolder)
except: pass
fname = ofolder + 'pnoise-L%04d_%0.4f.txt'%(bs, aa)
if args.pp : fname = fname[:-4] + '-up.txt'
try:
rep = np.loadtxt(fname).T
rpfit = [{'k':rep[0], 'power':rep[i+1]} for i in range(3)]
except:
pass
if args.pp:
ivar = BigFileMesh(dpath+'/ivarmesh_up', 'ivar').paint()
else:
ivar = BigFileMesh(dpath+'/ivarmesh', 'ivar').paint()
svar = (ivar.r2c()**-0.5).c2r()
p0 = FFTPower(svar, mode='2d', Nmu=Nmu).power
if rank == 0: np.savetxt(fname, p0['power'].real)
def savestd():
#for ia, aa in enumerate([0.3333, 0.2000, 0.1429]):
for ia, aa in enumerate([0.1429]):
#for ia, aa in enumerate([0.3333, 0.2000, 0.1429]):
zz = 1 / aa - 1
for iw, wopt in enumerate(['opt', 'pess']):
#for iw, wopt in enumerate(['opt']):
for it, thopt in enumerate(['opt', 'pess', 'reas']):
#for it, thopt in enumerate([ 'pess']):
if rank == 0: print(aa, wopt, thopt)
dpath = '/global/cscratch1/sd/chmodi/m3127/21cm_cleaning/recon/fastpm_%0.4f/wedge_kmin%0.2f_%s/' % (
aa, 0.03, wopt)
dpath += 'L%04d-N%04d-R//thermal-%s-hex/ZA/opt_s999_h1massA_fourier_rsdpos/' % (
bs, nc, thopt)
ofolder = '../../data/ZArecon-rsd/kmin-003_wedge-%s/thermal-%s-hex/' % (
wopt, thopt)
fname = ofolder + 'std-L%04d_%0.4f.txt' % (bs, aa)
fxname = ofolder + 'xstd-L%04d_%0.4f.txt' % (bs, aa)
if args.pp: fname = fname[:-4] + '-up.txt'
if args.pp: fxname = fxname[:-4] + '-up.txt'
try:
rep = np.loadtxt(fname + 's').T
except:
try:
if args.pp:
std = BigFileMesh(dpath + '/stdrecon_up-noise',
'std').paint()
ss = BigFileMesh(dpath + '/datap_up', 's').paint()
else:
std = BigFileMesh(dpath + '/stdrecon-noise',
'std').paint()
ss = BigFileMesh(dpath + '/datap', 's').paint()
p0 = FFTPower(std, mode='1d').power
px = FFTPower(std, second=ss, mode='1d').power
if rank == 0:
np.savetxt(fname,
np.stack([p0['k']] +
[p0['power'].real]).T,
header='k, p0')
if rank == 0:
np.savetxt(fxname,
np.stack([px['k']] +
[px['power'].real]).T,
header='k, px')
except Exception as e:
print(e)
def evaluate2d(model, data, M0=0, kmin=None, dk=None, Nmu=5, retmesh=False, los=[0, 0, 1]):
'''return position order:
xm.power, xs.power, xd.power,
pm1.power, pm2.power,
ps1.power, ps2.power,
pd1.power, pd2.power,
data_preview, model_preview
'''
from nbodykit.lab import FieldMesh, FFTPower, ProjectedFFTPower
if kmin is None: kmin = 0
if dk is None: dk = 2*numpy.pi/model.s.BoxSize[0]
model.mapp += M0
data.mapp += M0
if abs(model.mapp.cmean()) > 1e-3: modmappmean = model.mapp.cmean()
else: modmappmean = 1.
if abs(data.mapp.cmean()) > 1e-3: datmappmean = data.mapp.cmean()
else: datmappmean = 1.
modmappmean, datmappmean = 1., 1.
xm = FFTPower(first=FieldMesh(model.mapp/modmappmean),
second=FieldMesh(data.mapp/datmappmean), mode='2d', kmin=kmin, dk=dk, Nmu=Nmu, los=[0, 0, 1])
xd = FFTPower(first=FieldMesh(model.d), second=FieldMesh(data.d), mode='2d', kmin=kmin, dk=dk,
Nmu=Nmu, los=[0, 0, 1])
xs = FFTPower(first=FieldMesh(model.s), second=FieldMesh(data.s), mode='2d', kmin=kmin, dk=dk, Nmu=Nmu,
los=[0, 0, 1])
pm1 = FFTPower(first=FieldMesh(model.mapp/modmappmean), mode='2d', kmin=kmin, dk=dk, Nmu=Nmu, los=[0, 0, 1])
pd1 = FFTPower(first=FieldMesh(model.d), mode='2d', kmin=kmin, dk=dk, Nmu=Nmu, los=[0, 0, 1])
ps1 = FFTPower(first=FieldMesh(model.s), mode='2d', kmin=kmin, dk=dk, Nmu=Nmu, los=[0, 0, 1])
pm2 = FFTPower(first=FieldMesh(data.mapp/datmappmean), mode='2d', kmin=kmin, dk=dk, Nmu=Nmu, los=[0, 0, 1])
pd2 = FFTPower(first=FieldMesh(data.d), mode='2d', kmin=kmin, dk=dk, Nmu=Nmu, los=[0, 0, 1])
ps2 = FFTPower(first=FieldMesh(data.s), mode='2d', kmin=kmin, dk=dk, Nmu=Nmu, los=[0, 0, 1])
if retmesh:
data_preview = dict(s=[], d=[], mapp=[])
model_preview = dict(s=[], d=[], mapp=[])
for axes in [[1, 2], [0, 2], [0, 1]]:
data_preview['d'].append(data.d.preview(axes=axes))
data_preview['s'].append(data.s.preview(axes=axes))
data_preview['mapp'].append(data.mapp.preview(axes=axes))
model_preview['d'].append(model.d.preview(axes=axes))
model_preview['s'].append(model.s.preview(axes=axes))
model_preview['mapp'].append(model.mapp.preview(axes=axes))
return xm.power, xs.power, xd.power, pm1.power, pm2.power, ps1.power, ps2.power, pd1.power, pd2.power,\
data_preview, model_preview
else:
return xm.power, xs.power, xd.power, pm1.power, pm2.power, ps1.power, ps2.power, pd1.power, pd2.power
def make_galaxy_pk(scale_factor,nc,seed,bs=1536,T=40,B=2,simpath=sc_simpath,outpath=sc_outpath,Rsm=0):
aa = scale_factor # since particle IDs are ordered only need to load at one redshift
zz = 1/aa-1
dgrow = cosmo.scale_independent_growth_factor(zz)
fname = get_filename(nc,seed,T=T,B=B)
spath = simpath + fname
opath = outpath + fname
galpath = opath + '/hod/'
try: os.makedirs(opath+'spectra/')
except : pass
zz = 1/aa-1
pm = ParticleMesh(BoxSize=bs, Nmesh=[nc, nc, nc], dtype='f8')
rank = pm.comm.rank
dyn = BigFileCatalog(spath + '/fastpm_%0.4f/1'%aa)
# Load Matter Mesh
fpos = dyn['Position'].compute()
mlay = pm.decompose(fpos)
mmesh = pm.paint(fpos, layout=mlay)
mmesh /= mmesh.cmean()
# Load halo mesh: HOD parameters fixed for now...
mmin = 10**12.5; m1fac = 20
cendir ='cencat-aa-%.04f-Mmin-%.1f-M1f-%.1f-alpha-0p8-subvol'%(aa,np.log10(mmin), m1fac)
satdir ='satcat-aa-%.04f-Mmin-%.1f-M1f-%.1f-alpha-0p8-subvol'%(aa,np.log10(mmin), m1fac)
cencat = BigFileCatalog(galpath + cendir)
satcat = BigFileCatalog(galpath + satdir)
hpos = np.concatenate((cencat['Position'],satcat['Position']))
hlay = pm.decompose(hpos)
hmesh = pm.paint(hpos, layout=hlay)
hmesh /= hmesh.cmean()
phm = FFTPower(mmesh, second=hmesh, mode='1d').power
k, phm = phm['k'], phm['power'].real
phh = FFTPower(hmesh, mode='1d').power
k, phh = phh['k'], phh['power'].real
np.savetxt(opath+'spectra/pks-%04d-%04d-%04d-gal.txt'%(aa*10000, bs, nc), np.vstack([k, phh, phm]).T.real, header='k, phh, phm/ ', fmt='%0.4e')
def calc_bias(aa, mcut, suff):
'''Compute the bias(es) for the HI'''
if rank == 0:
print("Processing a={:.4f}...".format(aa))
print('Reading DM mesh...')
dm = BigFileMesh(scratch1+sim+'/fastpm_%0.4f/'%aa+\
'/1-mesh/N%04d'%nc,'').paint()
dm /= dm.cmean()
if rank == 0: print('Computing DM P(k)...')
pkmm = FFTPower(dm, mode='1d').power
k, pkmm = pkmm['k'], pkmm['power'] # Ignore shotnoise.
if rank == 0: print('Done.')
#
if rank == 0: print('Reading central/satellite catalogs...')
cencat = BigFileCatalog(scratch2 + sim +
'/fastpm_%0.4f/cencat-16node' % aa)
satcat = BigFileCatalog(scratch2 + sim + '/fastpm_%0.4f/satcat' % aa +
suff)
if rank == 0: print('Catalogs read.')
#
if rank == 0: print('Computing HI masses...')
cencat['HImass'] = HI_hod(cencat['Mass'], aa, mcut)
satcat['HImass'] = HI_hod(satcat['Mass'], aa, mcut)
rankHImass = cencat['HImass'].sum().compute() +\
satcat['HImass'].sum().compute()
rankHImass = np.array([rankHImass])
totHImass = np.zeros(1, dtype='float')
comm.Allreduce(rankHImass, totHImass, MPI.SUM)
totHImass = totHImass[0]
cencat['HImass'] /= totHImass / float(nc)**3
satcat['HImass'] /= totHImass / float(nc)**3
if rank == 0: print('HI masses done.')
#
if rank == 0: print('Combining catalogs and computing P(k)...')
allcat = MultipleSpeciesCatalog(['cen', 'sat'], cencat, satcat)
h1mesh = allcat.to_mesh(BoxSize=bs, Nmesh=[nc, nc, nc], weight='HImass')
pkh1h1 = FFTPower(h1mesh, mode='1d').power
pkh1h1 = pkh1h1['power'] - pkh1h1.attrs['shotnoise']
pkh1mm = FFTPower(h1mesh, second=dm, mode='1d').power['power']
if rank == 0: print('Done.')
# Compute the biases.
b1x = np.abs(pkh1mm / (pkmm + 1e-10))
b1a = np.abs(pkh1h1 / (pkmm + 1e-10))**0.5
if rank == 0: print("Finishing processing a={:.4f}.".format(aa))
return (k, b1x, b1a, np.abs(pkmm))
def main(ns, ns1, ns2):
if ns.verbose:
setup_logging('info')
cat1 = read_cat(ns1)
mesh1 = cat1.to_mesh(interlaced=True, compensated=True, window='tsc', Nmesh=ns.nmesh)
if ns1 is not ns2:
cat2 = read_cat(ns2)
mesh2 = cat2.to_mesh(interlaced=True, compensated=True, window='tsc', Nmesh=ns.nmesh)
else:
mesh2 = None
if ns1.with_rsd != ns2.with_rsd:
warnings.warn("Two catalogs have different with-rsd settings, this may not be intended.")
if ns.mode is None:
if ns1.with_rsd or ns2.with_rsd:
ns.mode = '2d'
else:
ns.mode = '1d'
if ns.unique_k:
dk = 0
else:
dk = None
r = FFTPower(mesh1, second=mesh2, mode=ns.mode, dk=dk)
basename = ns.output.rsplit('.', 1)[0]
if ns.output.endswith('.json'):
r.save(ns.output)
elif ns.output.endswith('.txt'):
if cat1.comm.rank == 0:
for var in r.power.data.dtype.names:
numpy.savetxt(basename + '-%s.txt' % var,
r.power[var].real
)
if ns.with_plot:
if cat1.comm.rank == 0:
figure = make_plot(r)
figure.savefig(basename + '.png')
def compute_power(output, Nmesh=4096):
"""Compute the compensated power spectrum from a catalogue."""
catcdm = BigFileCatalog(output, dataset='1/', header='Header')
assert 1 - catcdm.attrs['Time'] < 0.01
catcdm.to_mesh(Nmesh=Nmesh,
resampler='cic',
compensated=True,
interlaced=True)
pkcdm = FFTPower(catcdm, mode='1d', Nmesh=Nmesh)
return pkcdm.power
def write_power(d, path, a):
meshsource = FieldMesh(d)
r = FFTPower(meshsource, mode='1d')
if config.pm.comm.rank == 0:
print('Writing matter power spectrum at %s' % path)
# only root rank saves
numpy.savetxt(path,
numpy.array([
r.power['k'], r.power['power'].real, r.power['modes'],
r.power['power'].real / solver.cosmology.scale_independent_growth_factor(1.0 / a - 1) ** 2,
]).T,
comments='# k p N p/D**2')
def calc_bias(aa,h1mesh, outfolder, fname):
'''Compute the bias(es) for the HI'''
if rank==0: print('Calculating bias')
if rank==0:
print("Processing a={:.4f}...".format(aa))
print('Reading DM mesh...')
if ncsim == 10240:
dm = BigFileMesh(scratchyf+sim+'/fastpm_%0.4f/'%aa+\
'/1-mesh/N%04d'%nc,'').paint()
else:
dm = BigFileMesh(project+sim+'/fastpm_%0.4f/'%aa+\
'/dmesh_N%04d/1/'%nc,'').paint()
dm /= dm.cmean()
if rank==0: print('Computing DM P(k)...')
pkmm = FFTPower(dm,mode='1d').power
k,pkmm= pkmm['k'],pkmm['power'] # Ignore shotnoise.
if rank==0: print('Done.')
#
pkh1h1 = FFTPower(h1mesh,mode='1d').power
kk = pkh1h1.coords['k']
pkh1h1 = pkh1h1['power']-pkh1h1.attrs['shotnoise']
pkh1mm = FFTPower(h1mesh,second=dm,mode='1d').power['power']
if rank==0: print('Done.')
# Compute the biases.
b1x = np.abs(pkh1mm/(pkmm+1e-10))
b1a = np.abs(pkh1h1/(pkmm+1e-10))**0.5
if rank==0: print("Finishing processing a={:.4f}.".format(aa))
#
if rank==0:
fout = open(outfolder + "{}_bias_{:6.4f}.txt".format(fname, aa),"w")
fout.write("# {:>8s} {:>10s} {:>10s} {:>15s}\n".\
format("k","b1_x","b1_a","Pkmm"))
for i in range(1,kk.size):
fout.write("{:10.5f} {:10.5f} {:10.5f} {:15.5e}\n".\
format(kk[i],b1x[i],b1a[i],pkmm[i].real))
fout.close()
def fiddlebiasgal(aa, suff, nc=nc, mcfv=[1.], saveb=False, bname='h1bias', ofolder=None):
'''Fiddle bias for galaxies'''
if ofolder is None: ofolder = project + '/%s/fastpm_%0.4f/'%(sim, aa)
pm = ParticleMesh(BoxSize = bs, Nmesh = [nc, nc, nc])
print('Read in catalogs')
cencat = BigFileCatalog(project + sim + '/fastpm_%0.4f/cencat'%aa)
satcat = BigFileCatalog(project + sim + '/fastpm_%0.4f/satcat'%aa+suff)
cpos, spos = cencat['Position'], satcat['Position']
cmass, smass = cencat['Mass'], satcat['Mass']
pos = np.concatenate((cpos, spos), axis=0)
dm = BigFileMesh(project + sim + '/fastpm_%0.4f/'%aa + '/dmesh_N%04d'%nc, '1').paint()
pkm = FFTPower(dm/dm.cmean(), mode='1d').power
k, pkm = pkm['k'], pkm['power']
b1, b1sq = np.zeros((k.size, len(mcfv))), np.zeros((k.size, len(mcfv)))
for imc, mcf in enumerate(mcfv):
print(mcf)
ch1mass = HI_masscutfiddle(cmass, aa, mcutf=mcf)
sh1mass = HI_masscutfiddle(smass, aa, mcutf=mcf)
h1mass = np.concatenate((ch1mass, sh1mass), axis=0)
#
h1mesh = pm.paint(pos, mass=h1mass)
pkh1 = FFTPower(h1mesh/h1mesh.cmean(), mode='1d').power['power']
pkh1m = FFTPower(h1mesh/h1mesh.cmean(), second=dm/dm.cmean(), mode='1d').power['power']
#Bias
b1[:, imc] = pkh1m/pkm
b1sq[:, imc] = pkh1/pkm
np.savetxt(ofolder+bname+'auto'+suff+'.txt', np.concatenate((k.reshape(-1, 1), b1sq**0.5), axis=1),
header='mcut factors = %s\nk, pkh1xm/pkm, pkh1/pkm^0.5'%mcfv)
np.savetxt(ofolder+bname+'cross'+suff+'.txt', np.concatenate((k.reshape(-1, 1), b1), axis=1),
header='mcut factors = %s\nk, pkh1xm/pkm, pkh1mx/pkm'%mcfv)
return k, b1, b1sq
def evaluate(model, data):
from nbodykit.lab import FieldMesh, FFTPower, ProjectedFFTPower
#print('Means are : ', model.mapp.cmean(), data.mapp.cmean())
if abs(model.mapp.cmean()) > 1e-3: modmappmean = model.mapp.cmean()
else: modmappmean = 1.
if abs(data.mapp.cmean()) > 1e-3: datmappmean = data.mapp.cmean()
else: datmappmean = 1.
modmappmean, datmappmean = 1., 1.
xm = FFTPower(first=FieldMesh(model.mapp / modmappmean),
second=FieldMesh(data.mapp / datmappmean),
mode='1d')
xd = FFTPower(first=FieldMesh(model.d),
second=FieldMesh(data.d),
mode='1d')
xs = FFTPower(first=FieldMesh(model.s),
second=FieldMesh(data.s),
mode='1d')
pm1 = FFTPower(first=FieldMesh(model.mapp / modmappmean), mode='1d')
pd1 = FFTPower(first=FieldMesh(model.d), mode='1d')
ps1 = FFTPower(first=FieldMesh(model.s), mode='1d')
pm2 = FFTPower(first=FieldMesh(data.mapp / datmappmean), mode='1d')
pd2 = FFTPower(first=FieldMesh(data.d), mode='1d')
ps2 = FFTPower(first=FieldMesh(data.s), mode='1d')
data_preview = dict(s=[], d=[], mapp=[])
model_preview = dict(s=[], d=[], mapp=[])
for axes in [[1, 2], [0, 2], [0, 1]]:
data_preview['d'].append(data.d.preview(axes=axes))
data_preview['s'].append(data.s.preview(axes=axes))
data_preview['mapp'].append(data.mapp.preview(axes=axes))
model_preview['d'].append(model.d.preview(axes=axes))
model_preview['s'].append(model.s.preview(axes=axes))
model_preview['mapp'].append(model.mapp.preview(axes=axes))
#data_preview['mapp'] = data.mapp.preview(axes=(0, 1))
#model_preview['mapp'] = model.mapp.preview(axes=(0, 1))
return xm.power, xs.power, xd.power, pm1.power, pm2.power, ps1.power, ps2.power, pd1.power, pd2.power, data_preview, model_preview
def evaluate2d1(model, data, field, M0=0, kmin=None, dk=None, Nmu=5, retmesh=False, los=[0, 0, 1]):
'''return position order:
xm.power, xs.power, xd.power,
pm1.power, pm2.power,
ps1.power, ps2.power,
pd1.power, pd2.power,
data_preview, model_preview
'''
from nbodykit.lab import FieldMesh, FFTPower, ProjectedFFTPower
if kmin is None: kmin = 0
if dk is None: dk = 2*numpy.pi/model.s.BoxSize[0]
model.mapp += M0
data.mapp += M0
if abs(model.mapp.cmean()) > 1e-3: modmappmean = model.mapp.cmean()
else: modmappmean = 1.
if abs(data.mapp.cmean()) > 1e-3: datmappmean = data.mapp.cmean()
else: datmappmean = 1.
modmappmean, datmappmean = 1., 1.
if field == 'mapp':
x = FFTPower(first=FieldMesh(model.mapp/modmappmean),
second=FieldMesh(data.mapp/datmappmean), mode='2d', kmin=kmin, dk=dk, Nmu=Nmu, los=[0, 0, 1])
p1 = FFTPower(first=FieldMesh(model.mapp/modmappmean), mode='2d', kmin=kmin, dk=dk, Nmu=Nmu, los=[0, 0, 1])
p2 = FFTPower(first=FieldMesh(data.mapp/datmappmean), mode='2d', kmin=kmin, dk=dk, Nmu=Nmu, los=[0, 0, 1])
elif field == 's':
x = FFTPower(first=FieldMesh(model.s), second=FieldMesh(data.s), mode='2d', kmin=kmin, dk=dk, Nmu=Nmu, los=[0, 0, 1])
p1 = FFTPower(first=FieldMesh(model.s), mode='2d', kmin=kmin, dk=dk, Nmu=Nmu, los=[0, 0, 1])
p2 = FFTPower(first=FieldMesh(data.s), mode='2d', kmin=kmin, dk=dk, Nmu=Nmu, los=[0, 0, 1])
elif field == 'd':
x = FFTPower(first=FieldMesh(model.d), second=FieldMesh(data.d), mode='2d', kmin=kmin, dk=dk, Nmu=Nmu, los=[0, 0, 1])
p1 = FFTPower(first=FieldMesh(model.d), mode='2d', kmin=kmin, dk=dk, Nmu=Nmu, los=[0, 0, 1])
p2 = FFTPower(first=FieldMesh(data.d), mode='2d', kmin=kmin, dk=dk, Nmu=Nmu, los=[0, 0, 1])
else:
print('Field not recongnized')
return 0
return x.power, p1.power, p2.power
def calc_pk1d(aa, h1mesh, outfolder, fname):
'''Compute the 1D redshift-space P(k) for the HI'''
if rank==0: print('Calculating pk1d')
pkh1h1 = FFTPower(h1mesh,mode='1d',kmin=0.025,dk=0.0125).power
# Extract the quantities we want and write the file.
kk = pkh1h1['k']
sn = pkh1h1.attrs['shotnoise']
pk = np.abs(pkh1h1['power'])
if rank==0:
fout = open(outfolder + fname + "1d_{:6.4f}.txt".format(aa),"w")
fout.write("# Subtracting SN={:15.5e}.\n".format(sn))
fout.write("# {:>8s} {:>15s}\n".format("k","Pk_0_HI"))
for i in range(kk.size):
fout.write("{:10.5f} {:15.5e}\n".format(kk[i],pk[i]-sn))
fout.close()
def check_ic_power_spectra(genicfileout, camb_zstr, outdir=".", accuracy=0.07, m_nu=0):
"""Generate the power spectrum for each particle type from the generated simulation files
and check that it matches the input. This is a consistency test on each simulation output."""
#Generate power spectra
output = os.path.join(outdir, genicfileout)
#Now check that they match what we put into the simulation, from CAMB
#Reload the CAMB files from disc, just in case something went wrong writing them.
matterpow = os.path.join(outdir,"camb_linear/ics_matterpow_"+camb_zstr+".dat")
transfer = os.path.join(outdir, "camb_linear/ics_transfer_"+camb_zstr+".dat")
#Load DM catalog and try for a baryon catalog
cats = {1: BigFileCatalog(output, dataset='1/', header='Header')}
try:
cats[0] = BigFileCatalog(output, dataset='0/', header='Header')
except:
pass
omegab = cats[1].attrs['OmegaBaryon']
omega0 = cats[1].attrs['Omega0']
hubble = cats[1].attrs['HubbleParam']
npart = int(np.round(np.cbrt(cats[1].attrs['TotNumPart'][1])))
assert npart > 0
cambpow = CLASSPowerSpectrum(matterpow, transfer,omega0=omega0, omegab=omegab, omeganu=m_nu/93.14/hubble**2)
for sp in cats.keys():
#GenPK output is at PK-[nu,by,DM]-basename(genicfileout)
cats[sp].to_mesh(Nmesh=npart*2, window='cic', compensated=True, interlaced=True)
pk = FFTPower(cats[sp], mode='1d', Nmesh=npart*2, dk=5.0e-6)
#GenPK output is at PK-[nu,by,DM]-basename(genicfileout)
#Load the power spectra
#Convert units from kpc/h to Mpc/h
kk_ic = pk.power['k'][1:]*1e3
Pk_ic = pk.power['power'][1:].real/1e9
modes_ic = pk.power['modes'][1:]
ii = np.isfinite(kk_ic)
kk_ic = kk_ic[ii]
Pk_ic = Pk_ic[ii]
#Load the power spectrum. Note that DM may be total.
ccsp = sp
if len(cats) == 1:
ccsp = -1
if m_nu > 0:
ccsp = 3
Pk_camb = cambpow.get_class_power(species=ccsp)
(kk_ic, Pk_ic) = modecount_rebin(kk_ic, Pk_ic, modes_ic[ii], Pk_camb, ndesired=npart//2)
error = plot_ic_power(kk_ic, Pk_ic, Pk_camb(kk_ic), sp=sp, npart=npart, outdir=outdir)
#Don't worry too much about one failing mode.
if np.size(np.where(error > accuracy)) > 3:
raise RuntimeError("Pk accuracy check failed for "+str(sp)+". Max error: "+str(np.max(error)))
