hmesh -= 1.
if rank == 0: print('RSD factor is : ', rsdfac)
##
##rankweight = sum(masswt.compute())
##totweight = comm.allreduce(rankweight)
##rankweight = sum((masswt**2).compute())
##totweight2 = comm.allreduce(rankweight)
#noise = bs**3 / (hmesh.csum()**2 / (hmesh**2).csum())
noise = None
if rank == 0: print('Noise : ', noise)
#########################################
#dynamics
stages = numpy.linspace(0.01, aa, nsteps, endpoint=True)
if pmdisp: dynamic_model = NBodyModel(cosmo, truth_pm, B=B, steps=stages)
else: dynamic_model = ZAModel(cosmo, truth_pm, B=B, steps=stages)
if rank == 0: print(dynamic_model)
#noise
if stage2 is not None:
truth_noise_model = mapnoise.ThermalNoise(truth_pm,
seed=100,
aa=aa,
att=stage2,
spread=spread,
hex=hex,
limk=2,
Ns=Ndish)
else:
truth_noise_model = mapnoise.ThermalNoise(truth_pm,
seed=None,
def solve(Nmesh, x0, rtol, run, Nsm):
pm = truth_pm.resize(Nmesh=(Nmesh, Nmesh, Nmesh))
atol = pm.Nmesh.prod() * rtol
x0 = pm.upsample(x0, keep_mean=True)
#data = data_n.downsample(pm)
#IDEAL no noise limit
data = data_p.downsample(pm)
# smooth the data. This breaks the noise model but we don't need it
# for lower resolution anyways.
sml = pm.BoxSize[0] / Nmesh * Nsm
dynamic_model = ZAModel(cosmo, truth_pm, B=B, steps=stages)
mock_model = map.MockModel(dynamic_model)
# an approximate noise model, due to smoothing this is correct only at large scale.
noise_model = truth_noise_model.downsample(pm)
obj = map.SmoothedObjective(mock_model, noise_model, data, prior_ps=pk, sml=sml)#, noised=noised)
prior, chi2 = obj.get_code().compute(['prior', 'chi2'], init={'parameters': data.s})
if pm.comm.rank == 0:
print(prior, chi2) # for 2d chi2 is close to total pixels.
fit_p = mock_model.make_observable(data.s)
r = obj.evaluate(fit_p, data)
try:
os.makedirs(optfolder + '%s' % run)
except:
pass
try:
os.makedirs(optfolder + '%s/2pt' % run)
except:
pass
obj.save_report(r, optfolder + "%s/truth.png" % run)
dg.save_2ptreport(r, optfolder + "%s/2pt/truth.png" % run, pm)
optimizer = LBFGS(m=10, diag_update=scalar_diag)
prob = obj.get_problem(atol=atol, precond=UseComplexSpaceOptimizer)
def monitor(state):
if pm.comm.rank == 0:
print(state)
if state.nit % 5 == 0:
fit_p = mock_model.make_observable(state['x'])
if state.nit % 20 == 0:
fit_p.save(optfolder + '%s/%04d/fit_p' % (run, state['nit']))
r = obj.evaluate(fit_p, data)
#obj.save_report(r, optfolder + "%s/%s%02d-%04d.png"% (run, prefix, int(Nsm*10), state['nit']))
dg.save_report(r, optfolder + "%s/%s_N%02d-%04d.png"% (run, prefix, int(Nsm*10), state['nit']), pm)
dg.save_2ptreport(r, optfolder + "%s/2pt/%s_N%02d-%04d.png"% (run, prefix, int(Nsm*10), state['nit']), pm)
if pm.comm.rank == 0:
print('saved')
state = optimizer.minimize(prob, x0=x0, monitor=monitor)
fit_p = mock_model.make_observable(state['x'])
fit_p.save(optfolder + '%s/best-fit' % run)
r = obj.evaluate(fit_p, data)
obj.save_report(r, optfolder + "%s/%s%02d-best-fit.png" % (run, prefix, int(Nsm*10)))
dg.save_2ptreport(r, optfolder + "%s/2pt/%s_N%02d-best-fit.png" % (run, prefix, int(Nsm*10)), pm)
return state.x