summary_table_memory

beast.fitting.fit.summary_table_memory(obs, noisemodel, sedgrid, keys=None, gridbackend='memory', threshold=-10, save_every_npts=None, lnp_npts=None, resume=False, max_nbins=200, 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)[source]

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

  • max_nbins (int (default=200)) – maxiumum number of bins to use for the 1D likelihood calculations

  • 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.

Return type:

N/A