Module: run_search#

class kbmod.run_search.SearchRunner(config=None)[source]#

A class to run the KBMOD grid search.

Attributes:

phase_timesdict: A dictionary mapping the search phase to the timing information, a list of [starting time, ending time] in seconds.
phase_memorydict: A dictionary mapping the search phase the memory information, a list of [starting memory, ending memory] in bytes.

Methods

`display_phase_stats`()	Output the statistics for each phase.
`do_core_search`(config, stack, trj_generator)	Performs search on the GPU.
`load_and_filter_results`(search, config[, ...])	This function loads results that are output by the grid search.
`run_search`(config, stack[, trj_generator, ...])	This function serves as the highest-level python interface for starting a KBMOD search given an ImageStack and SearchConfiguration.
`run_search_from_work_unit`(work)	Run a KBMOD search from a WorkUnit object.

display_phase_stats()[source]#: Output the statistics for each phase.

do_core_search(config, stack, trj_generator)[source]#

Performs search on the GPU.

Parameters:

configSearchConfiguration: The configuration parameters
stackImageStackPy: The stack of image data.
trj_generatorTrajectoryGenerator: The object to generate the candidate trajectories for each pixel.

Returns:

keepResults: The results.

load_and_filter_results(search, config, batch_size=100000)[source]#

This function loads results that are output by the grid search. It can then generate psi + phi curves and perform sigma-G filtering (depending on the parameter settings).

Parameters:

searchkbmod.search: The search function object.
configSearchConfiguration: The configuration parameters
batch_sizeint: The number of results to load at once. This is used to limit the memory usage when loading results. Default is 100000.

Returns:

keepResults: A Results object containing values from trajectories.

run_search(config, stack, trj_generator=None, workunit=None, extra_meta=None)[source]#

This function serves as the highest-level python interface for starting a KBMOD search given an ImageStack and SearchConfiguration.

Parameters:

configSearchConfiguration: The configuration parameters
stackImageStackPy: The stack of image data.
trj_generatorTrajectoryGenerator, optional: The object to generate the candidate trajectories for each pixel. If None uses the default EclipticCenteredSearch
workunitWorkUnit, optional: An optional WorkUnit with additional meta-data, including the per-image WCS.
extra_metadict, optional: Any additional metadata to save as part of the results file.

Returns:

keepResults: The results.

run_search_from_work_unit(work)[source]#

Run a KBMOD search from a WorkUnit object.

Parameters:

workWorkUnit: The input data and configuration.

Returns:

keepResults: The results.

kbmod.run_search.append_positions_to_results(workunit, results)[source]#

Append predicted (x, y) and (RA, dec) positions in the original images. If the images were reprojected, also appends the (RA, dec) in the common frame.

Parameters:

workunitWorkUnit: The WorkUnit with all the WCS information.
resultsResults: The current table of results including the per-pixel trajectories. This is modified in-place.

kbmod.run_search.check_gpu_memory(config, stack, trj_generator=None)[source]#

Check whether we can run this search on the GPU.

Parameters:

configSearchConfiguration: The configuration parameters
stackImageStackPy: The stack of image data.
trj_generatorTrajectoryGenerator, optional: The object to generate the candidate trajectories for each pixel.

Returns:

validbool: Returns True if the search will fit on GPU and False otherwise.

kbmod.run_search.configure_kb_search_stack(search, config)[source]#

Configure the kbmod SearchStack object from a search configuration.

Parameters:

searchkb.StackSearch: The SearchStack object.
configSearchConfiguration: The configuration parameters