# ini files you want to base each set of runs on
defaults = ['common.ini']
importanceDefaults = ['importance_sampling.ini']
# set up list of groups of parameters and data sets
groups = []
# make first group of runs (all parameter variations with all data combinations)
g = batchJob.jobGroup('main')
g.params = [[], ['mnu'], ['nnu']]
g.datasets = []
# lists of dataset names to combine, with corresponding sets of inis to include
g.datasets.append(batchJob.dataSet(['plikHM', 'TT', 'lowTEB'], ['plik_dx11dr2_HM_v18_TT.ini', 'lowTEB.ini']))
g.datasets.append(batchJob.dataSet(['plikHM', 'TT', 'lowTEB', 'lensing'], ['plik_dx11dr2_HM_v18_TT.ini', 'lowTEB.ini', 'lensing.ini']))
# add importance name tags, and list of specific .ini files to include (in batch1/)
g.importanceRuns = []
g.importanceRuns.append([['BAO'], ['BAO.ini']])
groups.append(g)
# try to match each new run name to exisitng covmat
# e.g. get name without particular data combinations
covWithoutNameOrder = ['lensing', 'BAO']
# or try replacing various names (these are standard for provided planck_covmats)
covNameMappings = {'plikHM':'plik', 'plikLite':'plik'}
# for mapping to nominal mission names try
ini_dir = 'batch2/'
# directory to look for existing covariance matrices
covmat = 'planck_covmats/BKPlanck.covmat'
# ini files you want to base each set of runs on
defaults = ['common.ini']
# set up list of groups of parameters and data sets
groups = []
# make first group of runs (all parameter variations with all data combinations)
g = batchJob.jobGroup('main')
g.params = [['r']]
# skip lensing for now as slow
variants = ['fiducial', 'y1y2', '9bins', 'no217', 'relaxbetad', 'relaxalphad', 'sync000', 'sync100']
g.datasets = []
for i, var in enumerate(variants):
g.datasets.append(batchJob.dataSet(['BKPlanckonly', var], [{'root_dir':''},
'BKPlanck/BKPlanck_0%u_%s.ini' % (i + 1, var)]))
# add importance name tags, and list of specific .ini files to include (in batch1/)
g.importanceRuns = []
groups.append(g)
