PessimisticPatternMatcherB

class lsst.meas.astrom.PessimisticPatternMatcherB(reference_array, log)

Bases: object

Class implementing a pessimistic version of Optimistic Pattern Matcher B (OPMb) from Tabur (2007), as described in DMTN-031

Parameters:

reference_array : numpy.ndarray, (N, 3)

spherical points x, y, z of to use as reference objects for pattern matching.

log : lsst.log.Log or logging.Logger

Logger for outputting debug info.

Notes

The class loads and stores the reference object in a convenient data structure for matching any set of source objects that are assumed to contain each other. The pessimistic nature of the algorithm comes from requiring that it discovers at least two patterns that agree on the correct shift and rotation for matching before exiting. The original behavior of OPMb can be recovered simply. Patterns matched between the input datasets are n-spoked pinwheels created from n+1 points. Refer to DMTN-031 for more details.

Methods Summary

match(source_array, n_check, n_match, …[, …])

Match a given source catalog into the loaded reference catalog.

Methods Documentation

match(source_array, n_check, n_match, n_agree, max_n_patterns, max_shift, max_rotation, max_dist, min_matches, pattern_skip_array=None)

Match a given source catalog into the loaded reference catalog.

Given array of points on the unit sphere and tolerances, we attempt to match a pinwheel like pattern between these input sources and the reference objects this class was created with. This pattern informs of the shift and rotation needed to align the input source objects into the frame of the references.

Parameters:

source_array : numpy.ndarray, (N, 3)

An array of spherical x,y,z coordinates and a magnitude in units of objects having a lower value for sorting. The array should be of shape (N, 4).

n_check : int

Number of sources to create a pattern from. Not all objects may be checked if n_match criteria is before looping through all n_check objects.

n_match : int

Number of objects to use in constructing a pattern to match.

n_agree : int

Number of found patterns that must agree on their shift and rotation before exiting. Set this value to 1 to recover the expected behavior of Optimistic Pattern Matcher B.

max_n_patters : int

Number of patterns to create from the input source objects to attempt to match into the reference objects.

max_shift : float

Maximum allowed shift to match patterns in arcseconds.

max_rotation : float

Maximum allowed rotation between patterns in degrees.

max_dist : float

Maximum distance in arcseconds allowed between candidate spokes in the source and reference objects. Also sets that maximum distance in the intermediate verify, pattern shift/rotation agreement, and final verify steps.

pattern_skip_array : int

Patterns we would like to skip. This could be due to the pattern being matched on a previous iteration that we now consider invalid. This assumes the ordering of the source objects is the same between different runs of the matcher which, assuming no object has been inserted or the magnitudes have changed, it should be.

Returns:

output_struct : lsst.pipe.base.Struct

Result struct with components

• matches : (N, 2) array of matched ids for pairs. Empty list if no match found (numpy.ndarray, (N, 2) or list)
• distances_rad : Radian distances between the matched objects. Empty list if no match found (numpy.ndarray, (N,))
• pattern_idx: Index of matched pattern. None if no match found (int).
• shift : Magnitude for the shift between the source and reference objects in arcseconds. None if no match found (float).