try:
params = numpy.loadtxt(optfolder + '/params.txt')
kerror, perror = numpy.loadtxt(optfolder + '/error_ps.txt', unpack=True)
except Exception as e:
mock_model_setup = map.MockModel(dynamic_model,
rsdpos=rsdpos,
rsdfac=rsdfac)
fpos, linear, linearsq, shear = mock_model_setup.get_code().compute(
['xp', 'linear', 'linearsq', 'shear'], init={'parameters': s_truth})
grid = truth_pm.generate_uniform_particle_grid(shift=0.0, dtype='f4')
params, bmod = getbias(truth_pm, hmesh, [linear, linearsq, shear], fpos,
grid)
title = ['%0.3f' % i for i in params]
kerror, perror = eval_bfit(hmesh,
bmod,
optfolder,
noise=noise,
title=title,
fsize=15)
if rank == 0:
numpy.savetxt(optfolder + '/params.txt', params, header='b1, b2, bsq')
numpy.savetxt(optfolder + '/error_ps.txt',
numpy.array([kerror, perror]).T,
header='kerror, perror')
##mock_model_setup = map.MockModel(dynamic_model, rsdpos=rsdpos, rsdfac=rsdfac)
##fpos, linear, linearsq, shear = mock_model_setup.get_code().compute(['xp', 'linear', 'linearsq', 'shear'], init={'parameters': s_truth})
##grid = truth_pm.generate_uniform_particle_grid(shift=0.0, dtype='f4')
##params, bmod = getbias(truth_pm, hmesh, [linear, linearsq, shear], fpos, grid)
##title = ['%0.3f'%i for i in params]
##kerror, perror = eval_bfit(hmesh, bmod, optfolder, noise=noise, title=title, fsize=15)
ipkerror = interp1d(kerror,
except Exception as e:
mock_model_setup = map.MockModel(dynamic_model,
rsdpos=rsdpos,
rsdfac=rsdfac)
fpos, linear, linearsq, shear = mock_model_setup.get_code().compute(
['xp', 'linear', 'linearsq', 'shear'], init={'parameters': s_truth})
grid = truth_pm.generate_uniform_particle_grid(shift=0.0, dtype='f8')
params, bmod = getbias(truth_pm, hmesh, [linear, linearsq, shear], fpos,
grid)
if rank == 0:
numpy.savetxt(optfolder + '/params.txt', params, header='b1, b2, bsq')
title = ['%0.3f' % i for i in params]
kerror, perror = eval_bfit(hmesh,
bmod,
optfolder,
noise=noise,
title=title,
fsize=15)
if rank == 0:
numpy.savetxt(optfolder + '/error_ps.txt',
numpy.array([kerror, perror]).T,
header='kerror, perror')
if stage2:
ipkmodel = interp1d(kerror,
perror,
bounds_error=False,
fill_value=(perror[0], perror[-1]))
ivarmesh = truth_noise_model.get_ivarmesh(data_p, ipkmodel)
FieldMesh(ivarmesh).save(optfolder + 'ivarmesh',
dataset='ivar',
try: fit_p = map.Observable.load(optfolder+'/fitp_up')
except:
fit_p = mock_model.make_observable(s_truth)
fit_p.save(optfolder+'fitp_up/')
title = None
if stage2 is not None:
try:
kerror, perror = numpy.loadtxt(optfolder + '/error_psnup.txt', unpack=True)
ivarmesh = BigFileMesh(optfolder + 'ivarmesh_up', 'ivar').paint()
ipkerror = interp1d(kerror, perror, bounds_error=False, fill_value=(perror[0], perror[-1]))
except:
kerror, perror = eval_bfit(data_n.mapp, fit_p.mapp, optfolder, noise=noise, title=title, fsize=15, suff='-noiseup')
ipkerror = interp1d(kerror, perror, bounds_error=False, fill_value=(perror[0], perror[-1]))
if rank ==0: numpy.savetxt(optfolder + '/error_psnup.txt', numpy.array([kerror, perror]).T, header='kerror, perror')
kerror, perror = eval_bfit(data_p.mapp, fit_p.mapp, optfolder, noise=noise, title=title, fsize=15, suff='-up')
ipkmodel = interp1d(kerror, perror, bounds_error=False, fill_value=(perror[0], perror[-1]))
ivarmesh = truth_noise_model.get_ivarmesh(data_p, ipkmodel)
FieldMesh(ivarmesh).save(optfolder+'ivarmesh_up', dataset='ivar', mode='real')
else:
ivarmesh = None
try:
kerror, perror = numpy.loadtxt(optfolder + '/error_psup.txt', unpack=True)
ipkerror = interp1d(kerror, perror, bounds_error=False, fill_value=(perror[0], perror[-1]))
except:
pkerror = FFTPower(data_p.mapp, second=-1* fit_p.mapp, mode='1d').power
kerror, perror = pkerror['k'], pkerror['power']