simpleAssociation.py

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# This file is part of pipe_tasks.
#
# Developed for the LSST Data Management System.
# This product includes software developed by the LSST Project
# (https://www.lsst.org).
# See the COPYRIGHT file at the top-level directory of this distribution
# for details of code ownership.
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
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"""Simple association algorithm for DRP.
Adapted from http://github.com/LSSTDESC/dia_pipe
"""
__all__ = ["SimpleAssociationConfig", "SimpleAssociationTask"]
 
import numpy as np
import pandas as pd
 
import lsst.afw.table as afwTable
import lsst.geom as geom
import lsst.pex.config as pexConfig
import lsst.pipe.base as pipeBase
from lsst.meas.base import IdGenerator
 
from .associationUtils import query_disc, eq2xyz, toIndex
 
 
class SimpleAssociationConfig(pexConfig.Config):
    """Configuration parameters for the SimpleAssociationTask
    """
    tolerance = pexConfig.Field(
        dtype=float,
        doc='maximum distance to match sources together in arcsec',
        default=0.5
    )
    nside = pexConfig.Field(
        dtype=int,
        doc='Healpix nside value used for indexing',
        default=2**18,
    )
 
 
class SimpleAssociationTask(pipeBase.Task):
    """Construct DiaObjects from a DataFrame of DIASources by spatially
    associating the sources.
 
    Represents a simple, brute force algorithm, 2-way matching of DiaSources
    into. DiaObjects. Algorithm picks the nearest, first match within the
    matching radius of a DiaObject to associate a source to for simplicity.
    """
    ConfigClass = SimpleAssociationConfig
    _DefaultName = "simpleAssociation"
 
    def run(self, diaSources, idGenerator=None):
        """Associate DiaSources into a collection of DiaObjects using a
        brute force matching algorithm.
 
        Reproducible is for the same input data is assured by ordering the
        DiaSource data by ccdVisit ordering.
 
        Parameters
        ----------
        diaSources : `pandas.DataFrame`
            DiaSources grouped by CcdVisitId to spatially associate into
            DiaObjects.
        idGenerator : `lsst.meas.base.IdGenerator`, optional
            Object that generates Object IDs and random number generator seeds.
 
        Returns
        -------
        results : `lsst.pipe.base.Struct`
            Results struct with attributes:
 
            ``assocDiaSources``
                Table of DiaSources with updated values for the DiaObjects
                they are spatially associated to (`pandas.DataFrame`).
            ``diaObjects``
                Table of DiaObjects from matching DiaSources
                (`pandas.DataFrame`).
 
        """
 
        # Expected indexes include diaSourceId or meaningless range index
        # If meaningless range index, drop it, else keep it.
        doDropIndex = diaSources.index.names[0] is None
        diaSources.reset_index(inplace=True, drop=doDropIndex)
 
        # Sort by ccdVisit and diaSourceId to get a reproducible ordering for
        # the association.
        diaSources.set_index(["ccdVisitId", "diaSourceId"], inplace=True)
 
        # Empty lists to store matching and location data.
        diaObjectCat = []
        diaObjectCoords = []
        healPixIndices = []
 
        # Create Id factory and catalog for creating DiaObjectIds.
        if idGenerator is None:
            idGenerator = IdGenerator()
        idCat = idGenerator.make_source_catalog(afwTable.SourceTable.makeMinimalSchema())
 
        for ccdVisit in diaSources.index.levels[0]:
            # For the first ccdVisit, just copy the DiaSource info into the
            # diaObject data to create the first set of Objects.
            ccdVisitSources = diaSources.loc[ccdVisit]
            if len(diaObjectCat) == 0:
                for diaSourceId, diaSrc in ccdVisitSources.iterrows():
                    self.addNewDiaObject(diaSrc,
                                         diaSources,
                                         ccdVisit,
                                         diaSourceId,
                                         diaObjectCat,
                                         idCat,
                                         diaObjectCoords,
                                         healPixIndices)
                continue
            # Temp list to store DiaObjects already used for this ccdVisit.
            usedMatchIndicies = []
            # Run over subsequent data.
            for diaSourceId, diaSrc in ccdVisitSources.iterrows():
                # Find matches.
                matchResult = self.findMatches(diaSrc["ra"],
                                               diaSrc["dec"],
                                               2*self.config.tolerance,
                                               healPixIndices,
                                               diaObjectCat)
                dists = matchResult.dists
                matches = matchResult.matches
                # Create a new DiaObject if no match found.
                if dists is None:
                    self.addNewDiaObject(diaSrc,
                                         diaSources,
                                         ccdVisit,
                                         diaSourceId,
                                         diaObjectCat,
                                         idCat,
                                         diaObjectCoords,
                                         healPixIndices)
                    continue
                # If matched, update catalogs and arrays.
                if np.min(dists) < np.deg2rad(self.config.tolerance/3600):
                    matchDistArg = np.argmin(dists)
                    matchIndex = matches[matchDistArg]
                    # Test to see if the DiaObject has been used.
                    if np.isin([matchIndex], usedMatchIndicies).sum() < 1:
                        self.updateCatalogs(matchIndex,
                                            diaSrc,
                                            diaSources,
                                            ccdVisit,
                                            diaSourceId,
                                            diaObjectCat,
                                            diaObjectCoords,
                                            healPixIndices)
                        usedMatchIndicies.append(matchIndex)
                    # If the matched DiaObject has already been used, create a
                    # new DiaObject for this DiaSource.
                    else:
                        self.addNewDiaObject(diaSrc,
                                             diaSources,
                                             ccdVisit,
                                             diaSourceId,
                                             diaObjectCat,
                                             idCat,
                                             diaObjectCoords,
                                             healPixIndices)
                # Create new DiaObject if no match found within the matching
                # tolerance.
                else:
                    self.addNewDiaObject(diaSrc,
                                         diaSources,
                                         ccdVisit,
                                         diaSourceId,
                                         diaObjectCat,
                                         idCat,
                                         diaObjectCoords,
                                         healPixIndices)
 
        # Drop indices before returning associated diaSource catalog.
        diaSources.reset_index(inplace=True)
        diaSources.set_index("diaSourceId", inplace=True, verify_integrity=True)
 
        objs = diaObjectCat if diaObjectCat else np.array([], dtype=[('diaObjectId', 'int64'),
                                                                     ('ra', 'float64'),
                                                                     ('dec', 'float64'),
                                                                     ('nDiaSources', 'int64')])
        diaObjects = pd.DataFrame(data=objs)
 
        if "diaObjectId" in diaObjects.columns:
            diaObjects.set_index("diaObjectId", inplace=True, verify_integrity=True)
 
        return pipeBase.Struct(
            assocDiaSources=diaSources,
            diaObjects=diaObjects)
 
    def addNewDiaObject(self,
                        diaSrc,
                        diaSources,
                        ccdVisit,
                        diaSourceId,
                        diaObjCat,
                        idCat,
                        diaObjCoords,
                        healPixIndices):
        """Create a new DiaObject and append its data.
 
        Parameters
        ----------
        diaSrc : `pandas.Series`
            Full unassociated DiaSource to create a DiaObject from.
        diaSources : `pandas.DataFrame`
            DiaSource catalog to update information in. The catalog is
            modified in place.
        ccdVisit : `int`
            Unique identifier of the ccdVisit where ``diaSrc`` was observed.
        diaSourceId : `int`
            Unique identifier of the DiaSource.
        diaObjectCat : `list` of `dict`s
            Catalog of diaObjects to append the new object o.
        idCat : `lsst.afw.table.SourceCatalog`
            Catalog with the IdFactory used to generate unique DiaObject
            identifiers.
        diaObjectCoords : `list` of `list`s of `lsst.geom.SpherePoint`s
            Set of coordinates of DiaSource locations that make up the
            DiaObject average coordinate.
        healPixIndices : `list` of `int`s
            HealPix indices representing the locations of each currently
            existing DiaObject.
        """
        hpIndex = toIndex(self.config.nside,
                          diaSrc["ra"],
                          diaSrc["dec"])
        healPixIndices.append(hpIndex)
 
        sphPoint = geom.SpherePoint(diaSrc["ra"],
                                    diaSrc["dec"],
                                    geom.degrees)
        diaObjCoords.append([sphPoint])
 
        diaObjId = idCat.addNew().get("id")
        diaObjCat.append(self.createDiaObject(diaObjId,
                                              diaSrc["ra"],
                                              diaSrc["dec"]))
        diaSources.loc[(ccdVisit, diaSourceId), "diaObjectId"] = diaObjId
 
    def updateCatalogs(self,
                       matchIndex,
                       diaSrc,
                       diaSources,
                       ccdVisit,
                       diaSourceId,
                       diaObjCat,
                       diaObjCoords,
                       healPixIndices):
        """Update DiaObject and DiaSource values after an association.
 
        Parameters
        ----------
        matchIndex : `int`
            Array index location of the DiaObject that ``diaSrc`` was
            associated to.
        diaSrc : `pandas.Series`
            Full unassociated DiaSource to create a DiaObject from.
        diaSources : `pandas.DataFrame`
            DiaSource catalog to update information in. The catalog is
            modified in place.
        ccdVisit : `int`
            Unique identifier of the ccdVisit where ``diaSrc`` was observed.
        diaSourceId : `int`
            Unique identifier of the DiaSource.
        diaObjectCat : `list` of `dict`s
            Catalog of diaObjects to append the new object o.
        diaObjectCoords : `list` of `list`s of `lsst.geom.SpherePoint`s
            Set of coordinates of DiaSource locations that make up the
            DiaObject average coordinate.
        healPixIndices : `list` of `int`s
            HealPix indices representing the locations of each currently
            existing DiaObject.
        """
        # Update location and healPix index.
        sphPoint = geom.SpherePoint(diaSrc["ra"],
                                    diaSrc["dec"],
                                    geom.degrees)
        diaObjCoords[matchIndex].append(sphPoint)
        aveCoord = geom.averageSpherePoint(diaObjCoords[matchIndex])
        diaObjCat[matchIndex]["ra"] = aveCoord.getRa().asDegrees()
        diaObjCat[matchIndex]["dec"] = aveCoord.getDec().asDegrees()
        nSources = diaObjCat[matchIndex]["nDiaSources"]
        diaObjCat[matchIndex]["nDiaSources"] = nSources + 1
        healPixIndices[matchIndex] = toIndex(self.config.nside,
                                             diaObjCat[matchIndex]["ra"],
                                             diaObjCat[matchIndex]["dec"])
        # Update DiaObject Id that this source is now associated to.
        diaSources.loc[(ccdVisit, diaSourceId), "diaObjectId"] = \
            diaObjCat[matchIndex]["diaObjectId"]
 
    def findMatches(self, src_ra, src_dec, tol, hpIndices, diaObjs):
        """Search healPixels around DiaSource locations for DiaObjects.
 
        Parameters
        ----------
        src_ra : `float`
            DiaSource RA location.
        src_dec : `float`
            DiaSource Dec location.
        tol : `float`
            Size of annulus to convert to covering healPixels and search for
            DiaObjects.
        hpIndices : `list` of `int`s
            List of heal pix indices containing the DiaObjects in ``diaObjs``.
        diaObjs : `list` of `dict`s
            Catalog diaObjects to with full location information for comparing
            to DiaSources.
 
        Returns
        -------
        results : `lsst.pipe.base.Struct`
            Results struct containing
 
            ``dists``
                Array of distances between the current DiaSource diaObjects.
                (`numpy.ndarray` or `None`)
            ``matches``
                Array of array indices of diaObjects this DiaSource matches to.
                (`numpy.ndarray` or `None`)
        """
        match_indices = query_disc(self.config.nside,
                                   src_ra,
                                   src_dec,
                                   np.deg2rad(tol/3600.))
        matchIndices = np.argwhere(np.isin(hpIndices, match_indices)).flatten()
 
        if len(matchIndices) < 1:
            return pipeBase.Struct(dists=None, matches=None)
 
        dists = np.array(
            [np.sqrt(np.sum((eq2xyz(src_ra, src_dec)
                             - eq2xyz(diaObjs[match]["ra"],
                                      diaObjs[match]["dec"]))**2))
             for match in matchIndices])
        return pipeBase.Struct(
            dists=dists,
            matches=matchIndices)
 
    def createDiaObject(self, objId, ra, dec):
        """Create a simple empty DiaObject with location and id information.
 
        Parameters
        ----------
        objId : `int`
            Unique ID for this new DiaObject.
        ra : `float`
            RA location of this DiaObject.
        dec : `float`
            Dec location of this DiaObject
 
        Returns
        -------
        DiaObject : `dict`
            Dictionary of values representing a DiaObject.
        """
        new_dia_object = {"diaObjectId": objId,
                          "ra": ra,
                          "dec": dec,
                          "nDiaSources": 1}
        return new_dia_object