def build_matched_dataset(repo,
                          dataIds,
                          matchRadius=None,
                          safeSnr=50.,
                          useJointCal=False,
                          skipTEx=False):
    blob = Blob('MatchedMultiVisitDataset')

    if not matchRadius:
        matchRadius = afwGeom.Angle(1, afwGeom.arcseconds)

    # Extract single filter
    blob['filterName'] = Datum(quantity=set([dId['filter']
                                             for dId in dataIds]).pop(),
                               description='Filter name')

    # Record important configuration
    blob['useJointCal'] = Datum(
        quantity=useJointCal,
        description='Whether jointcal/meas_mosaic calibrations were used')

    # Match catalogs across visits
    blob._catalog, blob._matchedCatalog = \
        _loadAndMatchCatalogs(repo, dataIds, matchRadius,
                                   useJointCal=useJointCal, skipTEx=False)

    blob.magKey = blob._matchedCatalog.schema.find("base_PsfFlux_mag").key
    # Reduce catalogs into summary statistics.
    # These are the serialiable attributes of this class.
    _reduceStars(blob, blob._matchedCatalog, safeSnr)
    return blob
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def build_matched_dataset(repo,
                          dataIds,
                          matchRadius=None,
                          safeSnr=50.,
                          useJointCal=False,
                          skipTEx=False):
    """Construct a container for matched star catalogs from multple visits, with filtering,
    summary statistics, and modelling.

    `lsst.verify.Blob` instances are serializable to JSON.

    Parameters
    ----------
    repo : `str` or `Butler`
        A Butler instance or a repository URL that can be used to construct
        one.
    dataIds : `list` of `dict`
        List of `butler` data IDs of Image catalogs to compare to reference.
        The `calexp` cpixel image is needed for the photometric calibration.
    matchRadius :  afwGeom.Angle(), optional
        Radius for matching. Default is 1 arcsecond.
    safeSnr : `float`, optional
        Minimum median SNR for a match to be considered "safe".
    useJointCal : `bool`, optional
        Use jointcal/meas_mosaic outputs to calibrate positions and fluxes.
    skipTEx : `bool`, optional
        Skip TEx calculations (useful for older catalogs that don't have
        PsfShape measurements).

    Attributes of returned Blob
    ----------
    filterName : `str`
        Name of filter used for all observations.
    mag : `astropy.units.Quantity`
        Mean PSF magnitudes of stars over multiple visits (magnitudes).
    magerr : `astropy.units.Quantity`
        Median 1-sigma uncertainty of PSF magnitudes over multiple visits
        (magnitudes).
    magrms : `astropy.units.Quantity`
        RMS of PSF magnitudes over multiple visits (magnitudes).
    snr : `astropy.units.Quantity`
        Median signal-to-noise ratio of PSF magnitudes over multiple visits
        (dimensionless).
    dist : `astropy.units.Quantity`
        RMS of sky coordinates of stars over multiple visits (milliarcseconds).

        *Not serialized.*
    goodMatches
        all good matches, as an afw.table.GroupView;
        good matches contain only objects whose detections all have

        1. a PSF Flux measurement with S/N > 1
        2. a finite (non-nan) PSF magnitude. This separate check is largely
           to reject failed zeropoints.
        3. and do not have flags set for bad, cosmic ray, edge or saturated

        *Not serialized.*

    safeMatches
        safe matches, as an afw.table.GroupView. Safe matches
        are good matches that are sufficiently bright and sufficiently
        compact.

        *Not serialized.*
    magKey
        Key for `"base_PsfFlux_mag"` in the `goodMatches` and `safeMatches`
        catalog tables.

        *Not serialized.*
    """
    blob = Blob('MatchedMultiVisitDataset')

    if not matchRadius:
        matchRadius = afwGeom.Angle(1, afwGeom.arcseconds)

    # Extract single filter
    blob['filterName'] = Datum(quantity=set([dId['filter']
                                             for dId in dataIds]).pop(),
                               description='Filter name')

    # Record important configuration
    blob['useJointCal'] = Datum(
        quantity=useJointCal,
        description='Whether jointcal/meas_mosaic calibrations were used')

    # Match catalogs across visits
    blob._catalog, blob._matchedCatalog = \
        _loadAndMatchCatalogs(repo, dataIds, matchRadius,
                              useJointCal=useJointCal, skipTEx=skipTEx)

    blob.magKey = blob._matchedCatalog.schema.find("base_PsfFlux_mag").key
    # Reduce catalogs into summary statistics.
    # These are the serialiable attributes of this class.
    _reduceStars(blob, blob._matchedCatalog, safeSnr)
    return blob
Esempio n. 3
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def build_matched_dataset(repo,
                          dataIds,
                          matchRadius=None,
                          brightSnrMin=None,
                          brightSnrMax=None,
                          faintSnrMin=None,
                          faintSnrMax=None,
                          doApplyExternalPhotoCalib=False,
                          externalPhotoCalibName=None,
                          doApplyExternalSkyWcs=False,
                          externalSkyWcsName=None,
                          skipTEx=False,
                          skipNonSrd=False):
    """Construct a container for matched star catalogs from multple visits, with filtering,
    summary statistics, and modelling.

    `lsst.verify.Blob` instances are serializable to JSON.

    Parameters
    ----------
    repo : `str` or `lsst.daf.persistence.Butler`
        A Butler instance or a repository URL that can be used to construct
        one.
    dataIds : `list` of `dict`
        List of `butler` data IDs of Image catalogs to compare to reference.
        The `calexp` cpixel image is needed for the photometric calibration.
    matchRadius :  `lsst.geom.Angle`, optional
        Radius for matching. Default is 1 arcsecond.
    brightSnrMin : `float`, optional
        Minimum median SNR for a source to be considered bright; passed to `filterSources`.
    brightSnrMax : `float`, optional
        Maximum median SNR for a source to be considered bright; passed to `filterSources`.
    faintSnrMin : `float`, optional
        Minimum median SNR for a source to be considered faint; passed to `filterSources`.
    faintSnrMax : `float`, optional
        Maximum median SNR for a source to be considered faint; passed to `filterSources`.
    doApplyExternalPhotoCalib : bool, optional
        Apply external photoCalib to calibrate fluxes.
    externalPhotoCalibName : str, optional
        Type of external `PhotoCalib` to apply.  Currently supported are jointcal,
        fgcm, and fgcm_tract.  Must be set if "doApplyExternalPhotoCalib" is True.
    doApplyExternalSkyWcs : bool, optional
        Apply external wcs to calibrate positions.
    externalSkyWcsName : str, optional:
        Type of external `wcs` to apply.  Currently supported is jointcal.
        Must be set if "doApplyExternalSkyWcs" is True.
    skipTEx : `bool`, optional
        Skip TEx calculations (useful for older catalogs that don't have
        PsfShape measurements).
    skipNonSrd : `bool`, optional
        Skip any metrics not defined in the LSST SRD; default False.

    Attributes of returned Blob
    ----------
    filterName : `str`
        Name of filter used for all observations.
    mag : `astropy.units.Quantity`
        Mean PSF magnitudes of stars over multiple visits (magnitudes).
    magerr : `astropy.units.Quantity`
        Median 1-sigma uncertainty of PSF magnitudes over multiple visits
        (magnitudes).
    magrms : `astropy.units.Quantity`
        RMS of PSF magnitudes over multiple visits (magnitudes).
    snr : `astropy.units.Quantity`
        Median signal-to-noise ratio of PSF magnitudes over multiple visits
        (dimensionless).
    dist : `astropy.units.Quantity`
        RMS of sky coordinates of stars over multiple visits (milliarcseconds).

        *Not serialized.*
    matchesFaint : `afw.table.GroupView`
        Faint matches containing only objects that have:

        1. A PSF Flux measurement with sufficient S/N.
        2. A finite (non-nan) PSF magnitude. This separate check is largely
           to reject failed zeropoints.
        3. No flags set for bad, cosmic ray, edge or saturated.
        4. Extendedness consistent with a point source.

        *Not serialized.*
    matchesBright : `afw.table.GroupView`
        Bright matches matching a higher S/N threshold than matchesFaint.

        *Not serialized.*
    magKey
        Key for `"base_PsfFlux_mag"` in the `matchesFaint` and `matchesBright`
        catalog tables.

        *Not serialized.*

    Raises
    ------
    RuntimeError:
        Raised if "doApplyExternalPhotoCalib" is True and "externalPhotoCalibName"
        is None, or if "doApplyExternalSkyWcs" is True and "externalSkyWcsName" is
        None.
    """
    if doApplyExternalPhotoCalib and externalPhotoCalibName is None:
        raise RuntimeError(
            "Must set externalPhotoCalibName if doApplyExternalPhotoCalib is True."
        )
    if doApplyExternalSkyWcs and externalSkyWcsName is None:
        raise RuntimeError(
            "Must set externalSkyWcsName if doApplyExternalSkyWcs is True.")

    blob = Blob('MatchedMultiVisitDataset')

    if not matchRadius:
        matchRadius = geom.Angle(1, geom.arcseconds)

    # Extract single filter
    blob['filterName'] = Datum(quantity=set([dId['filter']
                                             for dId in dataIds]).pop(),
                               description='Filter name')

    # Record important configuration
    blob['doApplyExternalPhotoCalib'] = Datum(
        quantity=doApplyExternalPhotoCalib,
        description=('Whether external photometric '
                     'calibrations were used.'))
    blob['externalPhotoCalibName'] = Datum(
        quantity=externalPhotoCalibName,
        description='Name of external PhotoCalib dataset used.')
    blob['doApplyExternalSkyWcs'] = Datum(
        quantity=doApplyExternalSkyWcs,
        description='Whether external wcs calibrations were used.')
    blob['externalSkyWcsName'] = Datum(
        quantity=externalSkyWcsName,
        description='Name of external wcs dataset used.')

    # Match catalogs across visits
    blob._catalog, blob._matchedCatalog = \
        _loadAndMatchCatalogs(repo, dataIds, matchRadius,
                              doApplyExternalPhotoCalib=doApplyExternalPhotoCalib,
                              externalPhotoCalibName=externalPhotoCalibName,
                              doApplyExternalSkyWcs=doApplyExternalSkyWcs,
                              externalSkyWcsName=externalSkyWcsName,
                              skipTEx=skipTEx, skipNonSrd=skipNonSrd)

    blob.magKey = blob._matchedCatalog.schema.find("base_PsfFlux_mag").key
    # Reduce catalogs into summary statistics.
    # These are the serializable attributes of this class.
    filterResult = filterSources(
        blob._matchedCatalog,
        brightSnrMin=brightSnrMin,
        brightSnrMax=brightSnrMax,
        faintSnrMin=faintSnrMin,
        faintSnrMax=faintSnrMax,
    )
    blob['brightSnrMin'] = Datum(
        quantity=filterResult.brightSnrMin * u.Unit(''),
        label='Bright SNR Min',
        description='Minimum median SNR for a source to be considered bright')
    blob['brightSnrMax'] = Datum(
        quantity=filterResult.brightSnrMax * u.Unit(''),
        label='Bright SNR Max',
        description='Maximum median SNR for a source to be considered bright')
    summarizeSources(blob, filterResult)
    return blob