generateEphemerides.py

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# This file is part of ap_association.
#
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# This product includes software developed by the LSST Project
# (https://www.lsst.org).
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"""Generate per-visit solar system ephemerides using Sorcha.
 
Sorcha is an open-source solar system survey simulation tool, which
efficiently generates ephemerides for solar system objects from input orbits
and observation pointings. It is currently the fastest robust and maintained
ephemeris generator, and can fit our use case well.
 
Sorcha is a command line tool, and is not designed for direct use in python.
This task creates a temporary directory in which to run Sorcha as designed,
providing it with all required input files and reading the Sorcha-generated
ephemerides from a csv. While awkward and un-pipetask-like, it works and takes
advantage of Sorcha's as-designed speed.
 
Eventually, this should be replaced with adam_core's ephemeris generation
tools which propagate forward orbital uncertainty into on-sky ellipses.
Doing so will require re-implementing the healpix binning method described
in https://arxiv.org/abs/2506.02140. Doing so will not only improve this code
but also allow on-sky uncertainties to be used during association. When this is
done, mpsky should be modified to do the same.
"""
 
__all__ = ["GenerateEphemeridesConfig", "GenerateEphemeridesTask"]
 
 
import numpy as np
import glob
from importlib import resources
import os
import pandas as pd
from sqlite3 import connect as sqlite_connect
from subprocess import Popen, PIPE
from tempfile import TemporaryDirectory
from textwrap import dedent
from time import sleep
 
from lsst.pex.config import Field
from lsst.pipe.base import connectionTypes, NoWorkFound, PipelineTask, \
    PipelineTaskConfig, PipelineTaskConnections, Struct
import lsst.pipe.tasks
from lsst.utils.timer import timeMethod
 
 
class GenerateEphemeridesConnections(PipelineTaskConnections,
                                     dimensions=("instrument",)):
 
    visitSummaries = connectionTypes.Input(
        doc="Summary of visit information including ra, dec, and time",
        name="preliminary_visit_summary",
        storageClass="ExposureCatalog",
        dimensions={"instrument", "visit"},
        multiple=True
    )
    mpcorb = connectionTypes.Input(
        doc="Minor Planet Center orbit table used for association",
        name="mpcorb",
        storageClass="DataFrame",
        dimensions={},
    )
 
    # The following 9 prequisite inputs are Sorcha's required auxiliary files.
    de440s = connectionTypes.PrerequisiteInput(
        doc="NAIF DE440 ephemeris file (de440s.bsp)",
        name="de440s",
        storageClass="SSPAuxiliaryFile",
        dimensions={},
    )
    sb441_n16 = connectionTypes.PrerequisiteInput(
        doc="NAIF DE440 ephemeris file (sb441_n16.bsp)",
        name="sb441_n16",
        storageClass="SSPAuxiliaryFile",
        dimensions={},
    )
    obsCodes = connectionTypes.PrerequisiteInput(
        doc="MPC observatory code file (ObsCodes.json)",
        name="obscodes",
        storageClass="SSPAuxiliaryFile",
        dimensions={},
    )
    linux_p1550p2650 = connectionTypes.PrerequisiteInput(
        doc="TODO (linux_p1550p2650.440)",
        name="linux_p1550p2650",
        storageClass="SSPAuxiliaryFile",
        dimensions={},
    )
    pck00010 = connectionTypes.PrerequisiteInput(
        doc="orientation of planets, moons, the Sun, and selected asteroids. (pck00010.pck)",
        name="pck00010",
        storageClass="SSPAuxiliaryFile",
        dimensions={},
    )
    earth_latest_high_prec = connectionTypes.PrerequisiteInput(
        doc="High-precision Earth orientation parameters (EOP) kernel",
        name="earth_latest_high_prec",
        storageClass="SSPAuxiliaryFile",
        dimensions={},
    )
    earth_620120_250826 = connectionTypes.PrerequisiteInput(
        doc="Historical EOP",
        name="earth_620120_250826",
        storageClass="SSPAuxiliaryFile",
        dimensions={},
    )
    earth_2025_250826_2125_predict = connectionTypes.PrerequisiteInput(
        doc="Longterm EOP predictions",
        name="earth_2025_250826_2125_predict",
        storageClass="SSPAuxiliaryFile",
        dimensions={},
    )
    naif0012 = connectionTypes.PrerequisiteInput(
        doc="Leapsecond tls file",
        name="naif0012",
        storageClass="SSPAuxiliaryFile",
        dimensions={},
    )
 
    ssObjects = connectionTypes.Output(
        doc="Sorcha-provided Solar System objects observable in this detector-visit",
        name="preloaded_ss_object_visit",
        storageClass="DataFrame",
        dimensions=("instrument", "visit"),
        multiple=True,
    )
 
 
class GenerateEphemeridesConfig(
        PipelineTaskConfig,
        pipelineConnections=GenerateEphemeridesConnections):
    observatoryCode = Field(
        dtype=str,
        doc="IAU Minor Planet Center observer code for queries "
            "(default is X05 for Rubin Obs./LSST)",
        default='X05'
    )
    observatoryFOVRadius = Field(
        dtype=float,
        doc="The field of view of the observatory (degrees)",
        default=2.06,
    )
    loggingSleepTime = Field(
        dtype=int,
        doc="Time (seconds) to sleep between checking Sorcha's progress",
        default=60,
    )
 
 
class GenerateEphemeridesTask(PipelineTask):
    """Task to query the Sorcha service and retrieve the solar system objects
    that are observable within the input visit.
    """
    ConfigClass = GenerateEphemeridesConfig
    _DefaultName = "GenerateEphemerides"
 
    def runQuantum(
        self,
        butlerQC,
        inputRefs,
        outputRefs,
    ):
        """Do butler IO and transform to provide in memory
        objects for tasks `~Task.run` method.
 
        Parameters
        ----------
        butlerQC : `QuantumContext`
            A butler which is specialized to operate in the context of a
            `lsst.daf.butler.Quantum`.
        inputRefs : `InputQuantizedConnection`
            Datastructure whose attribute names are the names that identify
            connections defined in corresponding `PipelineTaskConnections`
            class. The values of these attributes are the
            `lsst.daf.butler.DatasetRef` objects associated with the defined
            input/prerequisite connections.
        outputRefs : `OutputQuantizedConnection`
            Datastructure whose attribute names are the names that identify
            connections defined in corresponding `PipelineTaskConnections`
            class. The values of these attributes are the
            `lsst.daf.butler.DatasetRef` objects associated with the defined
            output connections.
        """
        inputs = butlerQC.get(inputRefs)
        outputs = self.run(**inputs)
        for ref in outputRefs.ssObjects:
            dataId = ref.dataId
            ephemeris_visit = outputs.ssObjects[dataId['visit']]
            butlerQC.put(ephemeris_visit, ref)
 
    @timeMethod
    def run(self, visitSummaries, mpcorb, de440s, sb441_n16, obsCodes, linux_p1550p2650, pck00010,
            earth_latest_high_prec, earth_620120_250826, earth_2025_250826_2125_predict, naif0012):
        """Parse the information on the current visit and retrieve the
        observable solar system objects from Sorcha.
 
        Parameters
        ----------
        visitSummary : `lsst.afw.table.ExposureCatalog`
            Has rows with .getVisitInfo, which give the center and time of exposure
 
        mpcorb, de440s, sb441_n16, obsCodes, linux_p1550p2650, pck00010,
            earth_latest_high_prec, earth_620120_250826, earth_2025_250826_2125_predict,
            naif0012 : `lsst.pipe.tasks.sspAuxiliaryFile.SSPAuxiliaryFile`s
            Minor Planet Center orbit table used for association
 
        Returns
        -------
        result : `lsst.pipe.base.Struct`
            Results struct with components:
 
            - ``ssObjects`` : `pandas.DataFrame`
                DataFrame containing Solar System Objects near the detector
                footprint as retrieved by Sorcha. The columns are as follows:
 
                ``Name``
                    object name (`str`)
                ``ra``
                    RA in decimal degrees (`float`)
                ``dec``
                    DEC in decimal degrees (`float`)
 
        """
        if len(visitSummaries) == 0:
            raise NoWorkFound("No visits input!")
        visitInfos = [vs[0].getVisitInfo() for vs in visitSummaries]
        fieldRA = np.array([vi.getBoresightRaDec().getRa().asDegrees() for vi in visitInfos])
        fieldDec = np.array([vi.getBoresightRaDec().getDec().asDegrees() for vi in visitInfos])
        epochMJD = np.array([vi.date.toAstropy().tai.mjd for vi in visitInfos])
        visits = [vi.id for vi in visitInfos]
 
        # Confused about seconds/days units here
        n = len(epochMJD)
        visitTime = np.ones(n) * 30.0  # seconds
        inputVisits = pd.DataFrame({
            "observationMidpointMJD": epochMJD,
            "fieldRA": fieldRA,
            "fieldDec": fieldDec,
            "observationId": visits,
            "visitTime": np.ones(n) * visitTime,
            "observationStartMJD": epochMJD - (visitTime / 2) / 86400.0,
            "visitExposureTime": visitTime,
 
            # The following columns are required by Socha but only used after ephemeris generation
            "filter": ["r"] * n,  # Let's call V-band "r-band" to play nicely with Sorcha...
            "seeingFwhmGeom": [0.01] * n,
            "seeingFwhmEff": [0.01] * n,
            "fiveSigmaDepth": [9999] * n,
            "rotSkyPos": [0] * n,
        })
 
        desig_dict = {un: pack for pack, un in mpcorb[['packed_primary_provisional_designation',
                                                       'unpacked_primary_provisional_designation']].values}
        inputOrbits = mpcorb[
            ['unpacked_primary_provisional_designation', 'q', 'e', 'i',
             'node', 'argperi', 'peri_time', 'epoch_mjd']
        ].rename(columns={'unpacked_primary_provisional_designation': 'ObjID', 'epoch_mjd': 'epochMJD_TDB',
                          'i': 'inc', 'argperi': 'argPeri', 'peri_time': 't_p_MJD_TDB'})
        inputOrbits['FORMAT'] = 'COM'
        # Colors exactly like jake's prep_input_colors
        inputColors = inputOrbits[["ObjID"]].copy()
        inputColors["H_r"] = mpcorb['h']
        inputColors["GS"] = 0.15  # Traditional
 
        eph_str = resources.files(lsst.pipe.tasks).parents[3].joinpath("data/eph.ini").read_text()
        eph_str = eph_str.replace("{OBSCODE}", self.config.observatoryCode)
        eph_str = eph_str.replace("{FOV}", str(self.config.observatoryFOVRadius))
 
        with TemporaryDirectory() as tmpdirname:
            self.log.info(f'temp dir: {tmpdirname}')
 
            # Observations SQLite
            conn = sqlite_connect(f'{tmpdirname}/pointings.db')
            inputVisits.to_sql('observations', conn, if_exists='replace', index=False)
            conn.close()
 
            with open(f'{tmpdirname}/eph.ini', 'w') as ephFile:
                ephFile.write(eph_str)
 
            cache = f'{tmpdirname}/sorcha_cache/'
            self.log.info('making cache')
            os.mkdir(cache)
            for filename, fileref in [
                ('de440s.bsp', de440s),
                ('sb441-n16.bsp', sb441_n16),
                ('ObsCodes.json', obsCodes),
                ('linux_p1550p2650.440', linux_p1550p2650),
                ('pck00010.pck', pck00010),
                ('earth_latest_high_prec.bpc', earth_latest_high_prec),
                ('earth_620120_250826.bpc', earth_620120_250826),
                ('earth_2025_250826_2125_predict.bpc', earth_2025_250826_2125_predict),
                ('naif0012.tls', naif0012),
            ]:
                self.log.info(f'writing {filename}')
                with open(cache + filename, 'wb') as file:
                    file.write(fileref.fileContents.read())
 
            abspath = f'{tmpdirname}/sorcha_cache/'
            split = 79
            n_iter = int(len(str(abspath)) / split)  # Number of splits required
            for n in range(n_iter, 0, -1):
                abspath = abspath[:split * n] + "+' '" + abspath[split * n:]
 
            meta_kernel_text = dedent(f"""\
                                      \\begindata
 
                                      PATH_VALUES = ('{abspath}')
 
                                      PATH_SYMBOLS = ('A')
 
                                      KERNELS_TO_LOAD=(
                                          '$A/naif0012.tls',
                                          '$A/earth_620120_250826.bpc',
                                          '$A/earth_2025_250826_2125_predict.bpc',
                                          '$A/pck00010.pck',
                                          '$A/de440s.bsp',
                                          '$A/earth_latest_high_prec.bpc',
                                      )
 
                                      \\begintext
                                      """)
            with open(f'{tmpdirname}/sorcha_cache/meta_kernel.txt', 'w') as meta_kernel_file:
                meta_kernel_file.write(meta_kernel_text)
            self.log.info('Sorcha process begun')
 
            # Orbits and colors
            inputOrbits.to_csv(f'{tmpdirname}/orbits.csv', index=False)
            inputColors.to_csv(f'{tmpdirname}/colors.csv', index=False)
 
            # FIXME: this is a workaround for breakage in sbpy 0.5.0 caused
            # by astropy 7.2.0. DM-53467 once sbpy is updated.
            try:
                import sbpy.photometry  # noqa: F401
                sorcha_run = ["sorcha", "run"]
            except ImportError:
                import textwrap
                self.log.info("WARNING: Running sorcha-run patched for sbpy issues.")
                sorcha_run = ["python", "-c", textwrap.dedent("""
                    import re, sys
                    print("WARNING: Running sorcha-run patched for sbpy issues.", file=sys.stderr)
 
                    import astropy.units.utils
                    import astropy.units.docgen
                    astropy.units.utils.generate_unit_summary = \
                        astropy.units.docgen.generate_unit_summary
 
                    from sorcha_cmdline.run import main
                    sys.exit(main())
                    """)]
 
            proc = Popen(
                sorcha_run
                + [
                    "-c", f"{tmpdirname}/eph.ini",
                    "-o", f"{tmpdirname}/",
                    "--ob", f"{tmpdirname}/orbits.csv",
                    "-p", f"{tmpdirname}/colors.csv",
                    "--pd", f"{tmpdirname}/pointings.db",
                    "--ew", f"{tmpdirname}/ephemeris",
                    "--ar", f"{tmpdirname}/sorcha_cache/",
                    "-t", "sorcha_output",
                ],
                stdout=PIPE,
                stderr=PIPE,
                text=True
            )
            # As long as Sorcha is running, print its output or 'still running' messages
            outfile = None
            n_iter = 0
            while proc.poll() is None:
                sleep(self.config.loggingSleepTime)
                n_iter += 1
                if outfile is None:
                    outfile = open(glob.glob(f'{tmpdirname}/sorcha_output*.log')[0], 'r')
                new_text = outfile.read()
                if len(new_text) > 0:
                    self.log.info(new_text)
                else:
                    seconds_elapsed = n_iter * self.config.loggingSleepTime
                    self.log.info(f"Sorcha is still running ({seconds_elapsed} seconds)")
 
            stdout, stderr = proc.communicate()
            self.log.info(f"Sorcha STDOUT:\n {stdout}")
            self.log.info(f"Sorcha STDERR:\n {stderr}")
 
            eph_path = f'{tmpdirname}/sorcha_output.csv'
            if not os.path.exists(eph_path):
                raise FileNotFoundError(
                    " Sorcha did not create sorcha_output. Check STDOUT/STDERR above. "
                    f"Directory contents:\n{os.listdir(tmpdirname)}"
                )
 
            # Return Sorcha output directly
            ephemeris = pd.read_csv(eph_path)
        ephemeris['packed_desig'] = ephemeris['ObjID'].map(desig_dict)
        perVisitSsObjects = {FieldID: group for FieldID, group in ephemeris.groupby("FieldID")}
        for v in visits:
            if v not in perVisitSsObjects:
                perVisitSsObjects[v] = ephemeris.iloc[:0]
        return Struct(ssObjects=perVisitSsObjects)