This notebook will generate the cfgs to submit the chains for the final version of the chains for Aemulus.
For rmin 0, 0.5, 1.0, 2.0:
For no ab, HSAB, CAB, and CorrAB emu:
HOD
Vpeak sham
UM
Shuffled SHAM?
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import yaml
import copy
from os import path
import numpy as np
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orig_cfg_fname = '/u/ki/swmclau2/Git/pearce/bin/mcmc/nh_gg_sham_hsab_mcmc_config.yaml'
with open(orig_cfg_fname, 'r') as yamlfile:
orig_cfg = yaml.load(yamlfile)
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orig_cfg
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bsub_template="""#BSUB -q medium
#BSUB -W 24:00
#BSUB -J {jobname}
#BSUB -oo /u/ki/swmclau2/Git/pearce/bin/mcmc/config/{jobname}.out
#BSUB -n 8
#BSUB -R "span[ptile=8]"
python /u/ki/swmclau2/Git/pearce/pearce/inference/initialize_mcmc.py {jobname}.yaml
python /u/ki/swmclau2/Git/pearce/pearce/inference/run_mcmc.py {jobname}.yaml
"""
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r_bins = np.logspace(-1, 1.6, 19)
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#emu fnames
#emu_fnames = [#'/nfs/slac/g/ki/ki18/des/swmclau2/xi_gg_zheng07_v4/PearceXiggCosmo.hdf5',\
# '/nfs/slac/g/ki/ki18/des/swmclau2/xi_gg_hsabzheng07_v2/PearceXiggCosmoCorrAB.hdf5']
emu_fnames = [['/u/ki/swmclau2/des/wp_zheng07/PearceWpCosmo.hdf5', '/u/ki/swmclau2/des/ds_zheng07/PearceDsCosmo.hdf5']]
#emu_cov_fnames = [#'/afs/slac.stanford.edu/u/ki/swmclau2/Git/pearce/bin/covmat/xi_gg_nh_emu_cov_v4.npy',
# '/afs/slac.stanford.edu/u/ki/swmclau2/Git/pearce/bin/covmat/xi_gg_nh_emu_hsab_cov_v4.npy']
emu_names = ['HOD']
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meas_cov_fname = '/u/ki/swmclau2/Git/pearce/bin/covmat/wp_ds_full_covmat.npy'
# TODO replace with actual ones onace test boxes are done
emu_cov_fnames = [['/u/ki/swmclau2/Git/pearce/bin/optimization/wp_hod_emu_cov.npy',
'/u/ki/swmclau2/Git/pearce/bin/optimization/ds_hod_emu_cov.npy']]
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#orig_cfg_fname = '/u/ki/swmclau2//Git/pearce/bin/mcmc/nh_gg_sham_hsab_mcmc_config.yaml'
with open(orig_cfg_fname, 'r') as yamlfile:
orig_cfg = yaml.load(yamlfile)
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orig_cfg
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list(r_bins)
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tmp_cfg = copy.deepcopy(orig_cfg)
directory = "/u/ki/swmclau2/Git/pearce/bin/mcmc/config/"
output_dir = "/nfs/slac/g/ki/ki18/des/swmclau2/PearceMCMC/"
#output_dir = "/afs/slac.stanford.edu/u/ki/swmclau2"
jobname_template = "HOD_wp_ds_rmin_{rmin}_{emu_name}"#_fixed_HOD"
for rmin in [None, 0.5, 1.0, 2.0]:
for emu_fname, emu_name, emu_cov in zip(emu_fnames, emu_names, emu_cov_fnames):
tmp_cfg['chain']['nwalkers'] = 200
if rmin is not None:
tmp_cfg['emu']['fixed_params'] = {'z': 0.0, 'rmin':rmin}
tmp_cfg['emu']['training_file'] = emu_fname
tmp_cfg['emu']['emu_type'] = ['NashvilleHot' for i in xrange(len(emu_fname))]
tmp_cfg['emu']['emu_cov_fname'] = emu_cov
tmp_cfg['data']['obs']['obs'] = ['wp','ds']
tmp_cfg['data']['obs']['rbins'] = list(r_bins)
tmp_cfg['data']['cov']['meas_cov_fname'] = meas_cov_fname
tmp_cfg['data']['cov']['emu_cov_fname'] = tmp_cfg['emu']['emu_cov_fname'] # TODO make this not be redundant
jobname = jobname_template.format(rmin=rmin, emu_name=emu_name)
tmp_cfg['fname'] = path.join(output_dir, jobname+'.hdf5')
tmp_cfg['sim']= {'gal_type': 'HOD',
'hod_name': 'zheng07',
'hod_params': {'alpha': 1.083,
'logM0': 13.2,
'logM1': 14.2,
'sigma_logM': 0.2,
'conc_gal_bias': 1.0},
'nd': '5e-4',
'scale_factor': 1.0,
'min_ptcl': 100,
'sim_hps': {'boxno': 1,
'downsample_factor': 1e-2,
'particles': True,
'realization': 0,
'system': 'ki-ls'},
'simname': 'testbox'}
# TODO i shouldnt have to specify this this way
tmp_cfg['data']['sim'] = tmp_cfg['sim']
tmp_cfg['chain']['nsteps'] = 10000
tmp_cfg['chain']['mcmc_type'] = 'normal'
# fix params during MCMC
#tmp_cfg['chain']['fixed_params'].update(tmp_cfg['sim']['hod_params'])
try:
del tmp_cfg['data']['true_data_fname']
del tmp_cfg['data']['true_cov_fname']
except KeyError:
pass
with open(path.join(directory, jobname +'.yaml'), 'w') as f:
yaml.dump(tmp_cfg, f)
with open(path.join(directory, jobname + '.bsub'), 'w') as f:
f.write(bsub_template.format(jobname=jobname))
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