Q_all_memory

beast.fitting.fit.Q_all_memory(prev_result, obs, sedgrid, obsmodel, qnames_in, p=[16.0, 50.0, 84.0], gridbackend='cache', max_nbins=50, stats_outname=None, pdf1d_outname=None, pdf2d_outname=None, pdf2d_param_list=None, grid_info_dict=None, lnp_outname=None, lnp_npts=None, save_every_npts=None, threshold=-40, resume=False, use_full_cov_matrix=True, do_not_normalize=False)

Fit each star, calculate various fit statistics, and output them to files. All done in one function for speed and ability to resume partially completed runs.

Parameters:

prev_result : dict

previous results to include in the output summary table usually basic data on each source

obs : Observation object instance

observation catalog

sedgrid : str or grid.SEDgrid instance

model grid

obsmodel : beast noisemodel instance

noise model data

qnames : list

names of quantities

p : array-like

list of percentile values

gridbackend : str or grid.GridBackend

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

max_nbins : int

maxiumum number of bins to use for the 1D likelihood calculations

save_every_npts : int

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 strs 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.

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