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# LSST Data Management System 

# Copyright 2016-2019 AURA/LSST. 

# 

# This product includes software developed by the 

# LSST Project (http://www.lsst.org/). 

# 

# 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, 

# but WITHOUT ANY WARRANTY; without even the implied warranty of 

# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 

# GNU General Public License for more details. 

# 

# You should have received a copy of the LSST License Statement and 

# the GNU General Public License along with this program. If not, 

# see <https://www.lsstcorp.org/LegalNotices/>. 

"""Blob classes that reduce a multi-visit dataset and encapsulate data 

for measurement classes, plotting functions, and JSON persistence. 

""" 

 

__all__ = ['build_matched_dataset', 'reduceSources'] 

 

import numpy as np 

import astropy.units as u 

from sqlalchemy.exc import OperationalError 

import sqlite3 

 

import lsst.geom as geom 

import lsst.daf.persistence as dafPersist 

from lsst.afw.table import (SourceCatalog, SchemaMapper, Field, 

MultiMatch, SimpleRecord, GroupView, 

SOURCE_IO_NO_FOOTPRINTS) 

import lsst.afw.table as afwTable 

from lsst.afw.fits import FitsError 

from lsst.verify import Blob, Datum 

 

from .util import (getCcdKeyName, raftSensorToInt, positionRmsFromCat, 

ellipticity_from_cat) 

 

 

def build_matched_dataset(repo, dataIds, matchRadius=None, safeSnr=50., 

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. 

safeSnr : `float`, optional 

Minimum median SNR for a match to be considered "safe". 

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.* 

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.* 

 

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 serialiable attributes of this class. 

reduceSources(blob, blob._matchedCatalog, safeSnr) 

return blob 

 

 

def _loadAndMatchCatalogs(repo, dataIds, matchRadius, 

doApplyExternalPhotoCalib=False, externalPhotoCalibName=None, 

doApplyExternalSkyWcs=False, externalSkyWcsName=None, 

skipTEx=False, skipNonSrd=False): 

"""Load data from specific visits and returned a calibrated catalog matched 

with a reference. 

 

Parameters 

---------- 

repo : `str` or `lsst.daf.persistence.Butler` 

A Butler 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. 

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 "doApplyExternalWcs" 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. 

 

Returns 

------- 

catalog : `lsst.afw.table.SourceCatalog` 

A new calibrated SourceCatalog. 

matches : `lsst.afw.table.GroupView` 

A GroupView of the matched sources. 

 

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.") 

 

# Following 

# https://github.com/lsst/afw/blob/tickets/DM-3896/examples/repeatability.ipynb 

if isinstance(repo, dafPersist.Butler): 

butler = repo 

else: 

butler = dafPersist.Butler(repo) 

dataset = 'src' 

 

# 2016-02-08 MWV: 

# I feel like I could be doing something more efficient with 

# something along the lines of the following: 

# dataRefs = [dafPersist.ButlerDataRef(butler, vId) for vId in dataIds] 

 

ccdKeyName = getCcdKeyName(dataIds[0]) 

 

# Hack to support raft and sensor 0,1 IDs as ints for multimatch 

if ccdKeyName == 'sensor': 

ccdKeyName = 'raft_sensor_int' 

for vId in dataIds: 

vId[ccdKeyName] = raftSensorToInt(vId) 

 

schema = butler.get(dataset + "_schema").schema 

mapper = SchemaMapper(schema) 

mapper.addMinimalSchema(schema) 

mapper.addOutputField(Field[float]('base_PsfFlux_snr', 

'PSF flux SNR')) 

mapper.addOutputField(Field[float]('base_PsfFlux_mag', 

'PSF magnitude')) 

mapper.addOutputField(Field[float]('base_PsfFlux_magErr', 

'PSF magnitude uncertainty')) 

if not skipNonSrd: 

# Needed because addOutputField(... 'slot_ModelFlux_mag') will add a field with that literal name 

aliasMap = schema.getAliasMap() 

# Possibly not needed since base_GaussianFlux is the default, but this ought to be safe 

modelName = aliasMap['slot_ModelFlux'] if 'slot_ModelFlux' in aliasMap.keys() else 'base_GaussianFlux' 

mapper.addOutputField(Field[float](f'{modelName}_mag', 

'Model magnitude')) 

mapper.addOutputField(Field[float](f'{modelName}_magErr', 

'Model magnitude uncertainty')) 

mapper.addOutputField(Field[float](f'{modelName}_snr', 

'Model flux snr')) 

mapper.addOutputField(Field[float]('e1', 

'Source Ellipticity 1')) 

mapper.addOutputField(Field[float]('e2', 

'Source Ellipticity 1')) 

mapper.addOutputField(Field[float]('psf_e1', 

'PSF Ellipticity 1')) 

mapper.addOutputField(Field[float]('psf_e2', 

'PSF Ellipticity 1')) 

newSchema = mapper.getOutputSchema() 

newSchema.setAliasMap(schema.getAliasMap()) 

 

# Create an object that matches multiple catalogs with same schema 

mmatch = MultiMatch(newSchema, 

dataIdFormat={'visit': np.int32, ccdKeyName: np.int32}, 

radius=matchRadius, 

RecordClass=SimpleRecord) 

 

# create the new extented source catalog 

srcVis = SourceCatalog(newSchema) 

 

for vId in dataIds: 

 

photoCalib = _loadPhotoCalib(butler, vId, 

doApplyExternalPhotoCalib, externalPhotoCalibName) 

if photoCalib is None: 

continue 

 

if doApplyExternalSkyWcs: 

wcs = _loadExternalSkyWcs(butler, vId, externalSkyWcsName) 

if wcs is None: 

continue 

 

# We don't want to put this above the first _loadPhotoCalib call 

# because we need to use the first `butler.get` in there to quickly 

# catch dataIDs with no usable outputs. 

try: 

# HSC supports these flags, which dramatically improve I/O 

# performance; support for other cameras is DM-6927. 

oldSrc = butler.get('src', vId, flags=SOURCE_IO_NO_FOOTPRINTS) 

except (OperationalError, sqlite3.OperationalError): 

oldSrc = butler.get('src', vId) 

 

print(len(oldSrc), "sources in ccd %s visit %s" % 

(vId[ccdKeyName], vId["visit"])) 

 

# create temporary catalog 

tmpCat = SourceCatalog(SourceCatalog(newSchema).table) 

tmpCat.extend(oldSrc, mapper=mapper) 

tmpCat['base_PsfFlux_snr'][:] = tmpCat['base_PsfFlux_instFlux'] \ 

/ tmpCat['base_PsfFlux_instFluxErr'] 

 

if doApplyExternalSkyWcs: 

afwTable.wcsUtils.updateSourceCoords(wcs, tmpCat) 

photoCalib.instFluxToMagnitude(tmpCat, "base_PsfFlux", "base_PsfFlux") 

if not skipNonSrd: 

tmpCat['slot_ModelFlux_snr'][:] = (tmpCat['slot_ModelFlux_instFlux'] / 

tmpCat['slot_ModelFlux_instFluxErr']) 

photoCalib.instFluxToMagnitude(tmpCat, "slot_ModelFlux", "slot_ModelFlux") 

 

if not skipTEx: 

_, psf_e1, psf_e2 = ellipticity_from_cat(oldSrc, slot_shape='slot_PsfShape') 

_, star_e1, star_e2 = ellipticity_from_cat(oldSrc, slot_shape='slot_Shape') 

tmpCat['e1'][:] = star_e1 

tmpCat['e2'][:] = star_e2 

tmpCat['psf_e1'][:] = psf_e1 

tmpCat['psf_e2'][:] = psf_e2 

 

srcVis.extend(tmpCat, False) 

mmatch.add(catalog=tmpCat, dataId=vId) 

 

# Complete the match, returning a catalog that includes 

# all matched sources with object IDs that can be used to group them. 

matchCat = mmatch.finish() 

 

# Create a mapping object that allows the matches to be manipulated 

# as a mapping of object ID to catalog of sources. 

allMatches = GroupView.build(matchCat) 

 

return srcVis, allMatches 

 

 

def reduceSources(blob, allMatches, goodSnr=5.0, safeSnr=50.0, safeExtendedness=1.0, extended=False, 

nameFluxKey=None, goodSnrMax=np.Inf, safeSnrMax=np.Inf): 

"""Calculate summary statistics for each star. These are persisted 

as object attributes. 

 

Parameters 

---------- 

blob : `lsst.verify.blob.Blob` 

A verification blob to store Datums in. 

allMatches : `lsst.afw.table.GroupView` 

GroupView object with matches. 

goodSnr : float, optional 

Minimum median SNR for a match to be considered "good"; default 3. 

safeSnr : float, optional 

Minimum median SNR for a match to be considered "safe"; default 50. 

safeExtendedness: float, optional 

Maximum (exclusive) extendedness for sources or minimum (inclusive) if extended==True. 

extended: bool, optional 

Whether to select extended sources, i.e. galaxies. 

goodSnrMax : float, optional 

Maximum median SNR for a match to be considered "good"; default np.Inf. 

safeSnrMax : float, optional 

Maximum median SNR for a match to be considered "safe"; default np.Inf. 

""" 

if nameFluxKey is None: 

nameFluxKey = "slot_ModelFlux" if extended else "base_PsfFlux" 

# Filter down to matches with at least 2 sources and good flags 

flagKeys = [allMatches.schema.find("base_PixelFlags_flag_%s" % flag).key 

for flag in ("saturated", "cr", "bad", "edge")] 

nMatchesRequired = 2 

 

snrKey = allMatches.schema.find(f"{nameFluxKey}_snr").key 

magKey = allMatches.schema.find(f"{nameFluxKey}_mag").key 

magErrKey = allMatches.schema.find(f"{nameFluxKey}_magErr").key 

extendedKey = allMatches.schema.find("base_ClassificationExtendedness_value").key 

 

snrMin, snrMax = goodSnr, goodSnrMax 

 

def extendedFilter(cat): 

if len(cat) < nMatchesRequired: 

return False 

for flagKey in flagKeys: 

if cat.get(flagKey).any(): 

return False 

if not np.isfinite(cat.get(magKey)).all(): 

return False 

extendedness = cat.get(extendedKey) 

return np.min(extendedness) >= safeExtendedness if extended else \ 

np.max(extendedness) < safeExtendedness 

 

def snrFilter(cat): 

# Note that this also implicitly checks for psfSnr being non-nan. 

snr = np.median(cat.get(snrKey)) 

return snrMax >= snr >= snrMin 

 

def fullFilter(cat): 

return extendedFilter(cat) and snrFilter(cat) 

 

# If safeSnr range is a subset of goodSnr, it's safe to only filter on snr again 

# Otherwise, filter on flags/extendedness first, then snr 

isSafeSubset = goodSnrMax >= safeSnrMax and goodSnr <= safeSnr 

goodMatches = allMatches.where(fullFilter) if isSafeSubset else allMatches.where(extendedFilter) 

snrMin, snrMax = safeSnr, safeSnrMax 

safeMatches = goodMatches.where(snrFilter) 

if not isSafeSubset: 

snrMin, snrMax = goodSnr, goodSnrMax 

goodMatches = goodMatches.where(snrFilter) 

 

# Pass field=psfMagKey so np.mean just gets that as its input 

typeMag = "model" if extended else "PSF" 

filter_name = blob['filterName'] 

source_type = f'{"extended" if extended else "point"} sources"' 

blob['snr'] = Datum(quantity=goodMatches.aggregate(np.median, field=snrKey) * u.Unit(''), 

label='SNR({band})'.format(band=filter_name), 

description=f'Median signal-to-noise ratio of {typeMag} magnitudes for {source_type}' 

f' over multiple visits') 

blob['mag'] = Datum(quantity=goodMatches.aggregate(np.mean, field=magKey) * u.mag, 

label='{band}'.format(band=filter_name), 

description=f'Mean of {typeMag} magnitudes for {source_type} over multiple visits') 

blob['magrms'] = Datum(quantity=goodMatches.aggregate(np.std, field=magKey) * u.mag, 

label='RMS({band})'.format(band=filter_name), 

description=f'RMS of {typeMag} magnitudes for {source_type} over multiple visits') 

blob['magerr'] = Datum(quantity=goodMatches.aggregate(np.median, field=magErrKey) * u.mag, 

label='sigma({band})'.format(band=filter_name), 

description=f'Median 1-sigma uncertainty of {typeMag} magnitudes for {source_type}' 

f' over multiple visits') 

# positionRmsFromCat knows how to query a group 

# so we give it the whole thing by going with the default `field=None`. 

blob['dist'] = Datum(quantity=goodMatches.aggregate(positionRmsFromCat) * u.milliarcsecond, 

label='d', 

description=f'RMS of sky coordinates of {source_type} over multiple visits') 

 

# These attributes are not serialized 

blob.goodMatches = goodMatches 

blob.safeMatches = safeMatches 

 

 

def _loadPhotoCalib(butler, dataId, doApplyExternalPhotoCalib, externalPhotoCalibName): 

""" 

Load a photoCalib object. 

 

Parameters 

---------- 

butler: `lsst.daf.persistence.Butler` 

dataId: Butler dataId `dict` 

doApplyExternalPhotoCalib: `bool` 

Apply external photoCalib to calibrate fluxes. 

externalPhotoCalibName: `str` 

Type of external `PhotoCalib` to apply. Currently supported are jointcal, 

fgcm, and fgcm_tract. Must be set if "doApplyExternalPhotoCalib" is True. 

 

Returns 

------- 

photoCalib: `lsst.afw.image.PhotoCalib` or None 

photoCalib to apply. None if a suitable one was not found. 

""" 

 

photoCalib = None 

 

if doApplyExternalPhotoCalib: 

try: 

photoCalib = butler.get(f"{externalPhotoCalibName}_photoCalib", dataId) 

except (FitsError, dafPersist.NoResults) as e: 

print(e) 

print("Could not open external photometric calibration for ", dataId) 

print("Skipping this dataId.") 

photoCalib = None 

else: 

try: 

photoCalib = butler.get('calexp_photoCalib', dataId) 

except (FitsError, dafPersist.NoResults) as e: 

print(e) 

print("Could not open calibrated image file for ", dataId) 

print("Skipping this dataId.") 

except TypeError as te: 

# DECam images that haven't been properly reformatted 

# can trigger a TypeError because of a residual FITS header 

# LTV2 which is a float instead of the expected integer. 

# This generates an error of the form: 

# 

# lsst::pex::exceptions::TypeError: 'LTV2 has mismatched type' 

# 

# See, e.g., DM-2957 for details. 

print(te) 

print("Calibration image header information malformed.") 

print("Skipping this dataId.") 

photoCalib = None 

 

return photoCalib 

 

 

def _loadExternalSkyWcs(butler, dataId, externalSkyWcsName): 

""" 

Load a SkyWcs object. 

 

Parameters 

---------- 

butler: `lsst.daf.persistence.Butler` 

dataId: Butler dataId `dict` 

externalSkyWcsName: `str` 

Type of external `SkyWcs` to apply. Currently supported is jointcal. 

Must be not None if "doApplyExternalSkyWcs" is True. 

 

Returns 

------- 

SkyWcs: `lsst.afw.geom.SkyWcs` or None 

SkyWcs to apply. None if a suitable one was not found. 

""" 

 

try: 

wcs = butler.get(f"{externalSkyWcsName}_wcs", dataId) 

except (FitsError, dafPersist.NoResults) as e: 

print(e) 

print("Could not open external WCS for ", dataId) 

print("Skipping this dataId.") 

wcs = None 

 

return wcs