summary_table_memory

beast.fitting.fit.summary_table_memory(obs, noisemodel, sedgrid, keys=None, gridbackend='cache', threshold=-10, save_every_npts=None, lnp_npts=None, resume=False, stats_outname=None, pdf1d_outname=None, pdf2d_outname=None, pdf2d_param_list=None, grid_info_dict=None, lnp_outname=None, use_full_cov_matrix=True, surveyname='PHAT', extraInfo=False, do_not_normalize=False)

Do the fitting in memory

Parameters:

obs : Observation object instance

observation catalog

noisemodel : beast noisemodel instance

noise model data

sedgrid : str or grid.SEDgrid instance

model grid

keys : str or list of str

if str - name of the quantity or expression to evaluate from the grid table if list - list of quantities or expresions

gridbackend : str or grid.GridBackend

backend to use to load the grid if necessary (memory, cache, hdf) (see beast.core.grid)

save_every_npts : integer

set to save the files below (if set) every n stars a requirement for recovering from partially complete runs

resume : bool

set to designate this run is resuming a partially complete run

use_full_cov_matrix : bool

set to use the full covariance matrix if it is present in the noise model file

stats_outname : str

set to output the stats file into a FITS file with extensions

pdf1d_outname : str

set to output the 1D PDFs into a FITS file with extensions

pdf2d_outname : str

set to output the 2D PDFs into a FITS file with extensions

pdf2d_param_list : list of strings or None

set to the parameters for which to make the 2D PDFs

grid_info_dict : dict

Set to override the mins/maxes of the 1dpdfs, and the number of unique values.

lnp_outname : str

set to output the sparse likelihoods into a (usually HDF5) file

threshold : float

value above which to use/save for the lnps (defines the sparse likelihood)

lnp_npts : int

set to a number to output a random sampling of the lnp points above the threshold. otherwise, the full sparse likelihood is output

surveyname : str

name of survey [default = ‘PHAT’]

extraInfo : bool

set to get extra information, such as IAU name, brick, field, etc.

do_not_normalize : bool

Do not normalize the prior weights before applying them. This should have no effect on the final outcome when using only a single grid, but is essential when using the subgridding approach.

Returns:

N/A