import lsst.afw.geom as afwGeom
import lsst.afw.display.ds9 as ds9
from astropy.time import Time
import re
from astropy.table import Table
import astropy.coordinates as coord
import astropy.units as u
DATADIR="/sps/lsst/data/fouchez/DC2/testCB2/tst3/output"
print(DATADIR)
exit
butler = dafPersist.Butler(DATADI)
visits=butler.queryMetadata('deepDiff_diaSrc', ['visit'], dataId={'filter': 'r'})
print(visits)
print(butler.datasetExists("deepDiff_diaSrc", {"raftName":"R22","detectorName":"S11","filter":'r', "visit":417009}))
rafts = butler.queryMetadata('deepDiff_diaSrc', ['raftName'], dataId={'filter': 'r'})
detectors = butler.queryMetadata('deepDiff_diaSrc', ['detectorName'], dataId={'filter': 'r'})
rafts = ['R22']
detectors=['S02']
multi_matches = None
import warnings
warnings.filterwarnings("ignore")
import sqlite3
from sqlite3 import Error
import numpy as np
import pandas as pd
import lsst.daf.persistence as dp
import astropy.wcs
data_imsim = '/global/cscratch1/sd/desc/DC2/data/Run1.2i_globus_in2p3_20181217/w_2018_39/rerun/281118' # Path to the processed images
butler = dp.Butler(data_imsim)
datarefs = butler.subset(
'src'
).cache # This contains information about the visits that have been processed
corners = np.array([[-0.5, -0.5], [-0.5, 3999.5], [4071.5, -0.5],
[4071.5, 3999.5]])
# query using butler
query = {
'visit': [],
'filter': [],
'bg_mean': [],
'bg_var': [],
'zp': [],
'zp_err': [],
'llcra': [],
'llcdec': [],
'lrcra': [],
'lrcdec': [],
'ulcra': [],
for tract in skymap:
patches[tract.getId()] = ['%i,%i' % x.getIndex() for x in tract]
return patches
if __name__ == '__main__':
db_info = dict(host='scidb1.nersc.gov', database='DESC_DC1_Level_2')
conn = desc.pserv.DbConnection(**db_info)
dry_run = True
dry_run = False
create_script = 'coadd_schema.sql'
repo = '/global/cscratch1/sd/descdm/DC1/DC1-imsim-dithered'
butler = dp.Butler(repo)
patches = get_patches(butler)
tract = 0
band = 'r'
n = 0
nmax = 1
for patch in patches[tract]:
dataId = dict(patch=patch, tract=tract, filter=band)
try:
catalog = butler.get('deepCoadd_meas', dataId=dataId)
except RuntimeError as eobj:
continue
if not os.path.isfile(create_script):
create_sql_schema(catalog, create_script)
def setUp(self):
self.outputDir = tempfile.mkdtemp(dir=ROOT,
prefix='ButlerProxyTestCase-')
self.butler = dafPersist.Butler(self.inputDir,
outPath=os.path.join(
self.outputDir, "proxyOut"))
def testExistingParents(self):
# parents of inputs should be added to the inputs list
butler = dp.Butler(
outputs=dp.RepositoryArgs(mode='w',
mapper=dpTest.EmptyTestMapper,
root=os.path.join(self.testDir, 'a')))
del butler
butler = dp.Butler(inputs=os.path.join(self.testDir, 'a'),
outputs=os.path.join(self.testDir, 'b'))
del butler
butler = dp.Butler(inputs=os.path.join(self.testDir, 'b'))
self.assertEqual(len(butler._repos.inputs()), 2)
# verify serach order:
self.assertEqual(butler._repos.inputs()[0].cfg.root,
os.path.join(self.testDir, 'b'))
self.assertEqual(butler._repos.inputs()[1].cfg.root,
os.path.join(self.testDir, 'a'))
self.assertEqual(len(butler._repos.outputs()), 0)
butler = dp.Butler(outputs=dp.RepositoryArgs(
cfgRoot=os.path.join(self.testDir, 'b'), mode='rw'))
# verify serach order:
self.assertEqual(butler._repos.inputs()[0].cfg.root,
os.path.join(self.testDir, 'b'))
self.assertEqual(butler._repos.inputs()[1].cfg.root,
os.path.join(self.testDir, 'a'))
self.assertEqual(len(butler._repos.outputs()), 1)
self.assertEqual(butler._repos.outputs()[0].cfg.root,
os.path.join(self.testDir, 'b'))
butler = dp.Butler(inputs=os.path.join(self.testDir, 'a'),
outputs=dp.RepositoryArgs(cfgRoot=os.path.join(
self.testDir, 'b'),
mode='rw'))
self.assertEqual(len(butler._repos.inputs()), 2)
# verify serach order:
self.assertEqual(butler._repos.inputs()[0].cfg.root,
os.path.join(self.testDir, 'b'))
self.assertEqual(butler._repos.inputs()[1].cfg.root,
os.path.join(self.testDir, 'a'))
self.assertEqual(len(butler._repos.outputs()), 1)
self.assertEqual(butler._repos.outputs()[0].cfg.root,
os.path.join(self.testDir, 'b'))
# parents of write-only outputs must be be listed with the inputs
with self.assertRaises(RuntimeError):
butler = dp.Butler(outputs=os.path.join(self.testDir, 'b'))
butler = dp.Butler(inputs=os.path.join(self.testDir, 'a'),
outputs=os.path.join(self.testDir, 'b'))
self.assertEqual(len(butler._repos.inputs()), 1)
self.assertEqual(len(butler._repos.outputs()), 1)
self.assertEqual(butler._repos.outputs()[0].cfg.root,
os.path.join(self.testDir, 'b'))
# add a new parent to an existing output
butler = dp.Butler(
outputs=dp.RepositoryArgs(mode='w',
mapper=dpTest.EmptyTestMapper,
root=os.path.join(self.testDir, 'c')))
butler = dp.Butler(inputs=(os.path.join(self.testDir, 'a'),
os.path.join(self.testDir, 'c')),
outputs=os.path.join(self.testDir, 'b'))
# should raise if the input order gets reversed:
with self.assertRaises(RuntimeError):
butler = dp.Butler(inputs=(os.path.join(self.testDir, 'c'),
os.path.join(self.testDir, 'a')),
outputs=os.path.join(self.testDir, 'b'))
def __init__(self, dataRoot):
self.dataRoot = dataRoot
import lsst.daf.persistence as dafPersist
self.butler = dafPersist.Butler(dataRoot)
def getGalaxy(rootdir, visit, ccd, tol):
"""Get list of sources which agree in position with fake ones with tol
"""
# Call the butler
butler = dafPersist.Butler(rootdir)
dataId = {'visit': visit, 'ccd': ccd}
tol = float(tol)
# Get the source catalog and metadata
sources = butler.get('src', dataId)
cal_md = butler.get('calexp_md', dataId)
# Get the X, Y locations of objects on the CCD
srcX, srcY = sources.getX(), sources.getY()
# Get the zeropoint
zeropoint = (2.5 * np.log10(cal_md.get("FLUXMAG0")))
# Get the parent ID
parentID = sources.get('parent')
# Check the star/galaxy separation
extendClass = sources.get('classification.extendedness')
# Get the nChild
nChild = sources.get('deblend.nchild')
# For Galaxies: Get these parameters
# 1. Get the Kron flux and its error
fluxKron, ferrKron = sources.get('flux.kron'), sources.get('flux.kron.err')
magKron = (zeropoint - 2.5 * np.log10(fluxKron))
merrKron = (2.5 / np.log(10) * (ferrKron / fluxKron))
# X, Y locations of the fake galaxies
fakeList = collections.defaultdict(tuple)
# Regular Expression
# Search for keywords like FAKE12
fakename = re.compile('FAKE([0-9]+)')
# Go through all the keywords
counts = 0
for card in cal_md.names():
# To see if the card matches the pattern
m = fakename.match(card)
if m is not None:
# Get the X,Y location for fake object
x, y = map(float, (cal_md.get(card)).split(','))
# Get the ID or index of the fake object
fakeID = int(m.group(1))
fakeList[counts] = [fakeID, x, y]
counts += 1
# Match the fake object to the source list
srcIndex = collections.defaultdict(list)
for fid, fcoord in fakeList.items():
separation = np.sqrt(np.abs(srcX-fcoord[1])**2 +
np.abs(srcY-fcoord[2])**2)
matched = (separation <= tol)
matchId = np.where(matched)[0]
matchSp = separation[matchId]
sortId = [matchId for (matchSp, matchId) in
sorted(zip(matchSp, matchId))]
# DEBUG:
# print fid, fcoord, matchId
# print sortId, sorted(matchSp), matchId
# Select the index of all matched object
srcIndex[fid] = sortId
# Return the source list
mapper = SchemaMapper(sources.schema)
mapper.addMinimalSchema(sources.schema)
newSchema = mapper.getOutputSchema()
newSchema.addField('fakeId', type=int,
doc='id of fake source matched to position')
srcList = SourceCatalog(newSchema)
srcList.reserve(sum([len(s) for s in srcIndex.values()]))
# Return a list of interesting parameters
srcParam = []
nFake = 0
for matchIndex in srcIndex.values():
# Check if there is a match
if len(matchIndex) > 0:
# Only select the one with the smallest separation
# TODO: actually get the one with minimum separation
ss = matchIndex[0]
fakeObj = fakeList[nFake]
diffX = srcX[ss] - fakeObj[1]
diffY = srcY[ss] - fakeObj[2]
paramList = (fakeObj[0], fakeObj[1], fakeObj[2],
magKron[ss], merrKron[ss], diffX, diffY,
parentID[ss], nChild[ss], extendClass[ss])
srcParam.append(paramList)
else:
fakeObj = fakeList[nFake]
paramList = (fakeObj[0], fakeObj[1], fakeObj[2],
0, 0, -1, -1, -1, -1, -1)
srcParam.append(paramList)
# Go to another fake object
nFake += 1
# Make a numpy record array
srcParam = np.array(srcParam, dtype=[('fakeID', int),
('fakeX', float),
('fakeY', float),
('magKron', float),
('errKron', float),
('diffX', float),
('diffY', float),
('parentID', int),
('nChild', int),
('extendClass', float)])
return srcIndex, srcParam, srcList, zeropoint
def list_cc_images(
date_filter,
butler_path="/lsstdata/offline/teststand/NCSA_comcam/gen2repo",
is_ccs=False,
from_seqnum=None,
save_file=False):
"""Create catalog of ComCam images.
Functions to parse a Butler instance and write an image information
catalog. The currently collected information is:
Exposure Date, Sequence Number, Object Name, Image Type,
Exposure Time, Filter
This information can be written into a file by using the save_file
parameter. The created file is named images_YYYYMMDD.csv.
One can append to the file by using the from_seqnum parameter and
passing it the last sequence number found previously.
Attributes
----------
date_filter : str
Date to filter on in YYYY-MM-DD format.
butler_path : str
Path to Butler containing the relevant files.
from_seqnum : int, optional
Only list images after this number.
save_file : bool, optional
Create a CSV file with the image catalog information.
"""
butler = dafPersist.Butler(butler_path)
dfilter = dict(dayObs=date_filter, detector=4)
images = butler.queryMetadata('raw',
['dayObs', 'seqnum', 'expId', 'detector'],
dfilter)
print(f"Number of Images: {len(images)}")
if is_ccs:
titles = ["ExpDate", "SeqNum", "ImgType", "TestType",
"ExpTime"] #, "Filter"]
else:
titles = [
"ExpDate", "SeqNum", "Object", "ImgType", "TestType", "ExpTime"
] #, "Filter"]
print("\t".join(titles))
ofile = None
if save_file:
if is_ccs:
head = "ccs"
else:
head = "arc"
ofilename = f"{head}_images_{date_filter.replace('-', '')}.csv"
if from_seqnum is None:
mode = 'w'
else:
mode = 'a'
ofile = open(ofilename, mode, newline='')
writer = csv.writer(ofile)
if from_seqnum is None:
writer.writerow(titles)
with warnings.catch_warnings():
warnings.simplefilter('ignore')
infos = []
for image in images:
seqnum = image[1]
if from_seqnum is not None and seqnum <= from_seqnum:
continue
dataId = dict(**dfilter, seqnum=seqnum)
raw = butler.get('raw', dataId)
header = raw.getInfo().getMetadata().toDict()
if is_ccs:
info = (header['DATE-OBS'], seqnum, header['IMGTYPE'],
header['TESTTYPE'], header['EXPTIME']
) #, header['FILTER']]
else:
info = (header['DATE-OBS'], seqnum, str(header['OBJECT']),
header['IMGTYPE'], header['TESTTYPE'],
header['EXPTIME']) #, header['FILTER']]
#print("\t".join(info))
infos.append(info)
infos.sort(key=operator.itemgetter(1))
if save_file:
for info in infos:
writer.writerow(info)
import lsst.daf.persistence as dafPersistence
from astropy.io import fits
import numpy as np
DATA_DIR = "/local2/ph1jxm/GOTO/processedDataV16/DATA/rerun/outID"
butler = dafPersistence.Butler(DATA_DIR)
diffexp = butler.get("deepDiff_differenceExp",
visit=54480,
ccd=4,
immediate=True)
diasrc = butler.get("deepDiff_diaSrc", visit=54480, ccd=4, immediate=True)
calexp = butler.get("calexp", visit=54480, ccd=4, immediate=True)
diffexpArray = diffexp.getImage().getArray()
xs, ys = diasrc.getX(), diasrc.getY()
xs, ys = xs[~np.isnan(xs)], ys[~np.isnan(ys)]
np.random.seed(1)
real_x, real_y = np.random.rand(500) * calexp.getWidth() - 20, np.random.rand(
500) * calexp.getHeight() + 21
match = 0
i = 1
for x, y in zip(xs, ys):
x0, y0 = int(x) - 10, int(y) - 10
x1, y1 = int(x) + 11, int(y) + 11
subArray = diffexpArray[y0:y1, x0:x1]
hdu = fits.PrimaryHDU(subArray)
dist = np.amin((real_x - x)**2 + (real_y - y)**2)
if dist < 25:
match += 1
def setUp(self):
self.tempRoot = tempfile.mkdtemp()
self.butler = dafPersist.Butler(root=self.tempRoot, mapper=MinMapper)
self.butler.defineAlias(self.localTypeName,
self.localTypeNameIsAliasOf)
default=10,
help='maxNumberOfPeaks',
type=int)
opt, args = parser.parse_args()
if len(args):
parser.print_help()
sys.exit(-1)
if not opt.data:
opt.data = os.path.join(os.environ['SUPRIME_DATA_DIR'], 'rerun',
opt.rerun)
print('Data directory:', opt.data)
butler = dafPersist.Butler(opt.data)
dataId = dict(visit=opt.visit, ccd=opt.ccd)
ps = PlotSequence('deb')
sources = None
if opt.sources:
flags = 0
sources = afwTable.SourceCatalog.readFits(opt.sources, opt.hdu, flags)
print('Read sources from', opt.sources, ':', sources)
makeplots(butler,
dataId,
ps,
sources=sources,
pids=opt.pid,
minsize=opt.minsize,
def setUp(self):
self.butler = dafPersist.Butler(os.path.join(ROOT, 'butlerAlias/data/input'), MinMapper)
self.butler.defineAlias(self.datasetType, 'raw')
"""
import os
import shutil
import subprocess
from astropy.io import fits
import lsst.daf.persistence as dafPersist
import lsst.utils
dataDir = lsst.utils.getPackageDir('testdata_jointcal')
repo = os.path.join(dataDir, 'hsc')
sourcerepo = '/datasets/hsc/repo'
butlerTest = dafPersist.Butler(repo)
butlerSource = dafPersist.Butler(sourcerepo)
subset = butlerTest.subset('src')
for dataRef in subset:
# Confirm that this src catalog actually exists in the repo.
if not butlerTest.datasetExists('src', dataId=dataRef.dataId):
continue
sourceRawUri = butlerSource.getUri('raw', dataId=dataRef.dataId)
testRawUri = butlerTest.getUri('raw', dataId=dataRef.dataId, write=True)
testPath = os.path.dirname(testRawUri)
if not os.path.isdir(testPath):
os.makedirs(testPath)
default='id',
help="id option to put in front "
"of the visit name. Could be 'selectId' or 'id'")
args = parser.parse_args()
if not os.path.exists(args.input):
raise IOError("Input directory does not exists")
if args.idopt not in ['selectId', 'id']:
raise IOError("Option idopt must be 'selectid' or 'id'")
dummy = os.system('mkdir -p %s' % args.output)
# Load the butler for this input directory
datadir = args.input
print("butler call on dir %s" % datadir)
butler = dafPersist.Butler(datadir)
if args.increment:
print("INFO: Checking for dataIds that haven't been processed yet.")
# Only keep visit that haven't been processed yet (no calexp data)
dataids = compare_dataIds(get_dataIds(args.type),
get_dataIds('calexp'))
else:
# Process all visit found in the input directories
dataids = get_dataIds(args.type)
print("INFO %i (new) dataIds to process found" % len(dataids))
# Get the list of available filters
filters = set([dataid['filter'] for dataid in dataids])
print("INFO: Working on %i filters:" % len(filters), filters)
def __init__(self, dataRoot, butler_policy, butler_keys, logger):
"""Instantiate ButlerGet to be passed to ImageGetter."""
self.butler = dafPersist.Butler(dataRoot)
self.butler_policy = butler_policy
self.butler_keys = butler_keys
logger.debug("Instantiate ButlerGet.")
sub = afwImage.MaskedImageF(newExp.getMaskedImage(), lsstGeom.Box2I(lsstGeom.Point2I(_bbox.getMinX()-xmin, _bbox.getMinY()-ymin), _bbox.getDimensions()), afwImage.PARENT)
#sub <<= exposure.getMaskedImage()
sub.assign(exposure.getMaskedImage())
image_fname = '%s_%s.fits' % (specObjID, band)
newExp.setXY0(lsstGeom.Point2I(xmin,ymin))
newExp.setWcs(wcs)
newExp.writeFits(image_fname)
except Exception as e:
print(e.args)
if __name__ == '__main__':
#butler = dafPersist.Butler('/gpfs02/HSC_DR/hsc_ssp/dr2/s18a/data/s18a_wide')
butler = dafPersist.Butler('/gpfs02/HSC_DR/hsc_ssp/dr3/s20a/data/s20a_wide')
skymap = butler.get('deepCoadd_skyMap')
"""
src = butler.get('deepCoadd_meas', {'tract': 9813, 'patch': '4,4', 'filter': 'HSC-I'})
print(src[0].getCoord())
print(type(src[0].getCoord()))
import sys
sys.exit(1)
"""
bands = ['HSC-G', 'HSC-R', 'HSC-I', 'HSC-Z', 'HSC-Y']
f = open('cut_out.txt')
for line in f:
if line[0] == '#':
def coaddImageCutFull(root, ra, dec, size, saveSrc=True, savePsf=True,
filt='HSC-I', prefix='hsc_coadd_cutout', verbose=True,
extraField1=None, extraValue1=None, butler=None,
visual=True, imgOnly=False):
"""Get the cutout around a location."""
coaddData = "deepCoadd_calexp"
pipeNew = True
# Get the SkyMap of the database
if butler is None:
try:
butler = dafPersist.Butler(root)
if verbose:
print SEP
print "## Load in the Butler"
except Exception:
print WAR
print '## Can not load the correct Butler!'
skyMap = butler.get("deepCoadd_skyMap", immediate=True)
# (Ra, Dec) Pair for the center
raDec = afwCoord.Coord(ra*afwGeom.degrees, dec*afwGeom.degrees)
# [Ra, Dec] list
raList, decList = getCircleRaDec(ra, dec, size)
points = map(lambda x, y: afwGeom.Point2D(x, y), raList, decList)
raDecList = map(lambda x: afwCoord.IcrsCoord(x), points)
# Expected size and center position
dimExpect = int(2 * size + 1)
cenExpect = (dimExpect/2.0, dimExpect/2.0)
sizeExpect = int(dimExpect ** 2)
# Get the half size of the image in degree
sizeDegree = size * 0.168 / 3600.0
# Verbose
if verbose:
print SEP
print " Input Ra, Dec: %10.5f, %10.5f" % (ra, dec)
print " Cutout size is expected to be %d x %d" % (dimExpect, dimExpect)
# Create empty arrays
imgEmpty = np.empty((dimExpect, dimExpect), dtype="float")
imgEmpty.fill(np.nan)
if not imgOnly:
mskEmpty = np.empty((dimExpect, dimExpect), dtype="uint8")
varEmpty = np.empty((dimExpect, dimExpect), dtype="float")
detEmpty = np.empty((dimExpect, dimExpect), dtype="float")
mskEmpty.fill(np.nan)
varEmpty.fill(np.nan)
detEmpty.fill(np.nan)
# Figure out the area we want, and read the data.
# For coadds the WCS is the same in all bands,
# but the code handles the general case
# Start by finding the tract and patch
matches = skyMap.findTractPatchList(raDecList)
tractList, patchList = getTractPatchList(matches)
nPatch = len(patchList)
if verbose:
print "### Will deal with %d patches" % nPatch
# Prefix of the output file
outPre = prefix + '_' + filt + '_full'
newX = []
newY = []
boxX = []
boxY = []
boxSize = []
#
trList = []
paList = []
zpList = []
#
imgArr = []
mskArr = []
varArr = []
detArr = []
psfArr = []
#
srcArr = []
refArr = []
forceArr = []
# Go through all these images
for j in range(nPatch):
# Tract, patch
tract, patch = tractList[j], patchList[j]
print SEP
print "### Dealing with %d - %s" % (tract, patch)
print SEP
# Check if the coordinate is available in all three bands.
try:
# Get the coadded exposure
coadd = butler.get(coaddData, tract=tract,
patch=patch, filter=filt,
immediate=True)
except Exception, errMsg:
print WAR
print " No data is available in %d - %s" % (tract, patch)
print "#########################################################"
print WAR
else:
# Get the WCS information
wcs = coadd.getWcs()
# Check if cdMatrix has been assigned
cdExist = 'cdMatrix' in locals()
if not cdExist:
# Get the CD Matrix of the WCS
cdMatrix = wcs.getCDMatrix()
# Get the pixel size in arcsec
pixScale = wcs.pixelScale().asDegrees() * 3600.0
# Convert the central coordinate from Ra,Dec to pixel unit
pixel = wcs.skyToPixel(raDec)
pixel = afwGeom.Point2I(pixel)
# Define the bounding box for the central pixel
bbox = afwGeom.Box2I(pixel, pixel)
# Grow the bounding box to the desired size
bbox.grow(int(size))
xOri, yOri = bbox.getBegin()
# Compare to the coadd image, and clip
bbox.clip(coadd.getBBox(afwImage.PARENT))
# Get the masked image
try:
subImage = afwImage.ExposureF(coadd, bbox,
afwImage.PARENT)
except Exception:
print WAR
print '### SOMETHING IS WRONG WITH THIS BOUNDING BOX !!'
print " %d -- %s -- %s " % (tract, patch, filt)
print " Bounding Box Size: %d" % (bbox.getWidth() *
bbox.getHeight())
else:
# Extract the image array
imgArr.append(subImage.getMaskedImage().getImage().getArray())
if not imgOnly:
# Extract the detect mask array
mskDet = getCoaddMskPlane(subImage, 'DETECTED')
detArr.append(mskDet.getArray())
# Extract the variance array
imgVar = subImage.getMaskedImage().getVariance().getArray()
varArr.append(imgVar)
# Extract the bad mask array
mskBad = getCoaddBadMsk(subImage, pipeNew=pipeNew)
mskArr.append(mskBad.getArray())
# Save the width of the BBox
boxX.append(bbox.getWidth())
# Save the heigth of the BBox
boxY.append(bbox.getHeight())
# Save the size of the BBox in unit of pixels
boxSize.append(bbox.getWidth() * bbox.getHeight())
# New X, Y origin coordinates
newX.append(bbox.getBeginX() - xOri)
newY.append(bbox.getBeginY() - yOri)
# Tract, Patch
trList.append(tract)
paList.append(patch)
# Photometric zeropoint
zpList.append(2.5 * np.log10(
coadd.getCalib().getFluxMag0()[0]))
# If necessary, save the psf images
if savePsf and (not imgOnly):
psfImg = getCoaddPsfImage(coadd, raDec)
psfArr.append(psfImg)
# Get the new (X,Y) coordinate of the galaxy center
newCenExist = 'newCenX' in locals() and 'newCenY' in locals()
if not newCenExist:
subWcs = subImage.getWcs()
newCenX, newCenY = subWcs.skyToPixel(raDec)
newCenX = newCenX - xOri
newCenY = newCenY - yOri
yref.append(point.getY())
return xref, yref, ref_cat
def get_seps(src_cat, calexp, ref_cat, mag_cut=22):
src_mags \
= calexp.getCalib().getMagnitude(src_cat['slot_ModelFlux_instFlux'])
mag_sel = np.where(src_mags < mag_cut)
src = SkyCoord(ra=src_cat['coord_ra'][mag_sel],
dec=src_cat['coord_dec'][mag_sel], unit='rad')
ref = SkyCoord(ra=ref_cat['coord_ra'], dec=ref_cat['coord_dec'], unit='rad')
_, dist, _ = src.match_to_catalog_sky(ref)
return dist.milliarcsecond
if __name__ == '__main__':
import sys
butler = dp.Butler('/global/cscratch1/sd/jchiang8/desc/Run1.2p_analysis/output_2018-10-04/rerun/jchiang/w_2018_39')
ref_cat = RefCat(butler)
visit, raft, sensor = sys.argv[1:4]
dataId = dict(visit=int(visit), raftName=raft, detectorName=sensor)
calexp = butler.get('calexp', dataId=dataId)
src = butler.get('src', dataId=dataId)
xref, yref, my_ref_cat = ref_cat.get_pixel_coords(dataId)
show_mask = False
show_mask = True
colors = default_colors if show_mask else {}
fig = plt.figure(figsize=(18, 7.5))
fig.add_subplot(1, 2, 1)
display_calexp(calexp, colors=colors, percentiles=(0, 99.95))
xvals, yvals, src_cat \
= overlay_sources(src, calexp, ref_pix_coords=(xref, yref))
def returnMatchTable(rootDir,
visit,
ccdList,
outfile=None,
fakeCat=None,
overwrite=False,
filt=None,
tol=1.0,
pixMatch=False,
multiband=False,
reffMatch=False,
pix=0.168,
multijobs=1,
includeMissing=True,
minRad=None,
raCol='RA',
decCol='Dec'):
"""
Driver (main function) for return match to fakes.
INPUT: rootDir = rerun directory
visit = visit id (int) (or tracts)
ccdList = list of ccds to look at (or patches)
outdir = output directory for matched file,
None means no output written
fakeCat = fake catalog to match to,
None means the fake sources are just
extracted from the header of the CCDs based on
position but no matching is done
overwrite = whether to overwrite the existing output file,
default is False
pixMatch = do pixel matching instead of ra/dec matching
even if there is a catalog supplied
multiband = whether match to forced photometry catalogs
from multiband process
reffMatch = whether match fake sources in pixel radius
or using tol x Reff (Only for Ra, Dec match)
OUTPUT: returns an astropy.table.Table with all the entries
from the source catalog for objects which match in pixel
position to the fake sources
"""
butler = dafPersist.Butler(rootDir)
slist = None
if multijobs > 1:
try:
from joblib import Parallel, delayed
mlist = Parallel(n_jobs=multijobs)(
delayed(returnMatchSingle)(butler,
None,
visit,
ccd,
filt=filt,
fakeCat=fakeCat,
includeMissing=includeMissing,
pixMatch=pixMatch,
reffMatch=reffMatch,
tol=tol,
multiband=multiband,
minRad=minRad,
pix=pix,
decCol=decCol,
raCol=raCol) for ccd in ccdList)
for m in mlist:
if m is not None:
if slist is None:
slist = m.copy(True)
else:
slist.extend(m, True)
del m
except ImportError:
print("# Can not import joblib, stop multiprocessing!")
for ccd in ccdList:
slist = returnMatchSingle(butler,
slist,
visit,
ccd,
filt=filt,
fakeCat=fakeCat,
includeMissing=includeMissing,
pixMatch=pixMatch,
reffMatch=reffMatch,
tol=tol,
pix=pix,
multiband=multiband,
minRad=minRad,
raCol=raCol,
decCol=decCol)
else:
for ccd in ccdList:
slist = returnMatchSingle(butler,
slist,
visit,
ccd,
filt=filt,
fakeCat=fakeCat,
includeMissing=includeMissing,
pixMatch=pixMatch,
reffMatch=reffMatch,
tol=tol,
pix=pix,
multiband=multiband,
minRad=minRad,
raCol=raCol,
decCol=decCol)
if slist is None:
print("Returns no match....!")
return None
else:
astroTable = getAstroTable(slist, mags=True)
if fakeCat is not None:
astroTable = matchToFakeCatalog(astroTable, fakeCat)
if outfile is not None:
try:
astroTable.write(outfile + '.fits',
format='fits',
overwrite=overwrite)
except IOError:
print("Try setting the option -w to overwrite the file.")
raise
return astroTable
def tractFindVisits(rerun,
tract,
filter='HSC-I',
patch=None,
dataDir='/lustre/Subaru/SSP/rerun/'):
"""Return the list of input Visits to coadd."""
butler = dafPersist.Butler(os.path.join(dataDir, rerun))
pipeVersion = dafPersist.eupsVersions.EupsVersions().versions['hscPipe']
if StrictVersion(pipeVersion) >= StrictVersion('3.9.0'):
coaddData = "deepCoadd_calexp"
else:
coaddData = "deepCoadd"
if patch is not '':
"""
Only 1 Patch is required
"""
coadd = butler.get(coaddData,
dataId={
"tract": tract,
"patch": patch,
"filter": filter
},
immediate=True)
ccdInputs = coadd.getInfo().getCoaddInputs().ccds
visits = np.unique(ccdInputs.get("visit"))
print "\n# Visits for Tract=%d Filter=%s Patch=%s\n" % (tract, filter,
patch)
else:
"""
Go through all the possible patches
"""
visits = np.empty([0], dtype=int)
for pa in itertools.combinations_with_replacement((np.arange(9)), 2):
patch = str(pa[0]) + ',' + str(pa[1])
try:
coadd = butler.get(coaddData,
dataId={
"tract": tract,
"patch": patch,
"filter": filter
},
immediate=True)
except Exception:
continue
ccdInputs = coadd.getInfo().getCoaddInputs().ccds
vTemp = np.unique(ccdInputs.get("visit"))
visits = np.unique(np.append(visits, vTemp))
for pa in itertools.combinations_with_replacement((np.arange(9)), 2):
patch = str(pa[1]) + ',' + str(pa[0])
try:
coadd = butler.get(coaddData,
dataId={
"tract": tract,
"patch": patch,
"filter": filter
},
immediate=True)
except Exception:
continue
ccdInputs = coadd.getInfo().getCoaddInputs().ccds
vTemp = np.unique(ccdInputs.get("visit"))
visits = np.unique(np.append(visits, vTemp))
print "\n# Input visits for Tract=%d Filter=%s\n" % (tract, filter)
line = ''
print " # Input CCDs includes %d Visits\n" % len(visits)
for vv in visits:
line = line + str(vv) + '^'
print line[:-1] + '\n'
return visits
def testNonexistentValue(self):
butler = dafPersist.Butler(
outputs={'mode': 'rw', 'root': self.tmpRoot, 'mapper': ImgMapper})
ButlerSubsetTestCase.registerAliases(butler)
subset = butler.subset(self.calexpTypeName, skyTile=2349023905239)
self.assertEqual(len(subset), 0)
def sfp_validation_plots(repo,
visit,
outdir='.',
flux_type='base_PsfFlux',
opsim_db=None,
figsize=(12, 10),
max_offset=0.1,
sn_min=150):
"""
Create the single-frame validation plots.
Parameters
----------
repo: str
Data repository containing calexps.
visit: int
Visit number.
outdir: str ['.']
Directory to contain output files.
flux_type: str ['base_PsfFlux']
Flux column to use for selecting well-measured point sources.
opsim_db: str [None]
OpSim db file containing pointing information. This is used
to get the pointing direction for the ref cat selection and
the predicted five sigma depth for the visit. If None, then
the pointing direction will be inferred from the calexps.
figsize: (float, float) [(12, 10)]
Size of the figure in inches.
max_offset: float [0.1]
Maximum offset, in arcsec, for positional matching of point
sources to ref cat stars.
sn_min: float [150]
Mininum signal-to-noise cut on psfFlux/psfFluxErr.
Returns
-------
pandas.DataFrame containg the visit-level metrics:
(median astrometric offset, median delta magitude, median T value,
extrapolated five sigma depth)
"""
butler = dp.Butler(repo)
try:
try:
band = list(butler.subset('src', visit=visit))[0].dataId['filter']
except dp.butlerExceptions.NoResults:
band = list(butler.subset('src', expId=visit))[0].dataId['filter']
except Exception as eobj:
print('visit:', visit)
print(eobj)
raise eobj
center_radec = get_center_radec(butler, visit, opsim_db)
ref_cat = get_ref_cat(butler, visit, center_radec)
os.makedirs(outdir, exist_ok=True)
pickle_file = os.path.join(outdir, f'sfp_validation_v{visit}-{band}.pkl')
if not os.path.isfile(pickle_file):
df = visit_ptsrc_matches(butler,
visit,
center_radec,
max_offset=max_offset)
df.to_pickle(pickle_file)
else:
df = pd.read_pickle(pickle_file)
fig = plt.figure(figsize=figsize)
ax = fig.add_subplot(2, 2, 1)
coord_ra = np.array([_.asRadians() for _ in df['coord_ra']])
coord_dec = np.array([_.asRadians() for _ in df['coord_dec']])
dra = np.degrees(
(df['ref_ra'] - coord_ra) * np.cos(df['ref_dec'])) * 3600 * 1000
ddec = np.degrees((df['ref_dec'] - coord_dec)) * 3600 * 1000
max_offset *= 1e3 * 1.2
xy_range = (-max_offset, max_offset)
plt.hexbin(dra, ddec, mincnt=1)
plt.xlabel('RA offset (mas)')
plt.ylabel('Dec offset (mas)')
plt.xlim(*xy_range)
plt.ylim(*xy_range)
nullfmt = NullFormatter()
ax_ra = ax.twinx()
ax_ra.yaxis.set_major_formatter(nullfmt)
ax_ra.yaxis.set_ticks([])
bins, _, _ = plt.hist(dra,
bins=50,
histtype='step',
range=xy_range,
density=True,
color='red')
ax_ra.set_ylim(0, 2.3 * np.max(bins))
ax_dec = ax.twiny()
ax_dec.xaxis.set_major_formatter(nullfmt)
ax_dec.xaxis.set_ticks([])
bins, _, _ = plt.hist(ddec,
bins=50,
histtype='step',
range=xy_range,
density=True,
color='red',
orientation='horizontal')
ax_dec.set_xlim(0, 2.3 * np.max(bins))
median_offset = np.median(df['offset'])
plt.annotate(f'{median_offset:.1f} mas median offset', (0.5, 0.95),
xycoords='axes fraction',
horizontalalignment='left')
plt.title(f'v{visit}-{band}')
plt.colorbar()
fig.add_subplot(2, 2, 2)
bins = 20
delta_mag = df['src_mag'] - df['ref_mag']
dmag_med = np.nanmedian(delta_mag)
ymin, ymax = dmag_med - 0.5, dmag_med + 0.5
plt.hexbin(df['ref_mag'], delta_mag, mincnt=1)
plot_binned_stats(df['ref_mag'],
delta_mag,
x_range=plt.axis()[:2],
bins=20)
plt.xlabel('ref_mag')
plt.ylabel(f'{flux_type}_mag - ref_mag')
plt.ylim(ymin, ymax)
plt.title(f'v{visit}-{band}')
plt.colorbar()
xmin, xmax = plt.axis()[:2]
fig.add_subplot(2, 2, 3)
T = (df['base_SdssShape_xx'] + df['base_SdssShape_yy']) * 0.2**2
tmed = np.nanmedian(T)
ymin, ymax = tmed - 0.1, tmed + 0.1
plt.hexbin(df['ref_mag'], T, mincnt=1, extent=(xmin, xmax, ymin, ymax))
plot_binned_stats(df['ref_mag'], T, x_range=plt.axis()[:2], bins=20)
plt.xlabel('ref_mag')
plt.ylabel('T (arcsec**2)')
plt.ylim(ymin, ymax)
plt.title(f'v{visit}-{band}')
plt.colorbar()
ax1 = fig.add_subplot(2, 2, 4)
x_range = (12, 26)
plot_detection_efficiency(butler, visit, df, ref_cat, x_range=x_range)
plt.title(f'v{visit}-{band}')
ax2 = ax1.twinx()
ax2.set_ylabel('S/N', color='red')
snr = df['base_PsfFlux_instFlux'] / df['base_PsfFlux_instFluxErr']
ref_mags, SNR_values, _ = plot_binned_stats(df['ref_mag'],
snr,
x_range=x_range,
bins=20,
color='red')
m5, mag_func = extrapolate_nsigma(ref_mags, SNR_values, nsigma=5)
plt.xlim(*x_range)
plt.yscale('log')
ymin, ymax = 1, plt.axis()[-1]
plt.ylim(ymin, ymax)
plt.axhline(5, linestyle=':', color='red')
yvals = np.logspace(np.log10(ymin), np.log10(ymax), 50)
plt.plot(mag_func(np.log10(yvals)), yvals, linestyle=':', color='red')
if opsim_db is not None:
plt.axvline(get_five_sigma_depth(opsim_db, visit),
linestyle='--',
color='red')
plt.tight_layout()
outfile = os.path.join(outdir, f'sfp_validation_v{visit}-{band}.png')
plt.savefig(outfile)
# Make plot of psf_mag - calib_mag distribution.
fig = plt.figure(figsize=(6, 4))
dmag_calib_median = psf_mag_check(repo, visit, sn_min=sn_min)
plt.title(f'v{visit}-{band}')
outfile = os.path.join(outdir, f'delta_mag_calib_v{visit}-{band}.png')
plt.savefig(outfile)
# Make plot of psf_mag - ref_mag distribution.
my_df = df.query('base_PsfFlux_instFlux/base_PsfFlux_instFluxErr'
f' > {sn_min}')
fig = plt.figure(figsize=(6, 4))
dmag_ref_median = plot_dmags(my_df['src_mag'],
my_df['ref_mag'],
sn_min=sn_min)
plt.title(f'v{visit}-{band}')
outfile = os.path.join(outdir, f'delta_mag_ref_v{visit}-{band}.png')
plt.savefig(outfile)
# Make psf whisker plot.
psf_whisker_plot(butler, visit)
outfile = os.path.join(outdir, f'psf_whisker_plot_v{visit}-{band}.png')
plt.savefig(outfile)
df = pd.DataFrame(data=dict(visit=[visit],
ast_offset=[median_offset],
dmag_ref_median=[dmag_ref_median],
dmag_calib_median=[dmag_calib_median],
T_median=[tmed],
m5=[m5],
band=[band]))
metrics_file = os.path.join(outdir, f'sfp_metrics_v{visit}-{band}.pkl')
df.to_pickle(metrics_file)
return df
def reportPatchesWithImages(butler, visits=None, ccdkey='sensor'):
# create a butler object associated to the output directory
butler = dafPersist.Butler(butler)
# Get the skymap
skyMap = butler.get("deepCoadd_skyMap")
# Get the calexp metadata
keys = sorted(butler.getKeys("calexp").keys())
metadata = butler.queryMetadata("calexp", format=keys)
# Create a list of available dataids
dataids = [
dict(zip(keys,
list(v) if not isinstance(v, list) else v)) for v in metadata
]
# Organize the dataids by visit
vdataids = organize_by_visit(dataids, visits=visits)
if visits is None or len(visits) != 1:
# Get the ccds that will be used to compute the visit corner coordinates
# this depend on the instrument, so cannot be hardcoded
ccds = get_visit_corners(butler,
vdataids[list(vdataids)[0]],
getccds=True,
ccdkey=ccdkey)
# Get the corners coordinates for all visits
allcoords = []
for ii, vdataid in enumerate(vdataids):
print("Running on visit %03d / %i" % (ii + 1, len(vdataids)))
allcoords.append(
get_visit_corners(butler,
vdataids[vdataid],
ccds=ccds,
ccdkey=ccdkey))
# Get the tract/patch list in which the visits are
alltps = []
for vdataid, vcoords in zip(vdataids, allcoords):
alltps.extend(
get_tps(skyMap, vcoords, vdataids[vdataid][0]['filter']))
else:
# Only one visit given, so run the code on all sensor/ccd
# Get the corners coordinates for all visits
visit = int(visits[0])
print("%i dataIds loaded for visit" % len(vdataids[visit]), visit)
allcoords = get_dataid_corners(butler, vdataids[visit], ccdkey=ccdkey)
# Get the tract/patch list in which the sensor are
alltps = []
for coords in allcoords:
alltps.extend(get_tps(skyMap, coords,
vdataids[visit][0]['filter']))
# Re-organize the tract and patch list into a dictionnary
tps = {}
for tp in alltps:
if tp[0] not in tps:
tps[tp[0]] = []
if tp[1] not in tps[tp[0]]:
tps[tp[0]].append(tp[1])
return tps
def test():
butler = dafPersist.Butler(inputs='DATA/rerun/coaddForcedPhot')
coadd = butler.get("deepCoadd_calexp",
tract=0,
patch='1,1',
filter='HSC-I')
rSources = butler.get('deepCoadd_forced_src', {
'filter': 'HSC-R',
'tract': 0,
'patch': '1,1'
})
iSources = butler.get('deepCoadd_forced_src', {
'filter': 'HSC-I',
'tract': 0,
'patch': '1,1'
})
rCoaddCalexp = butler.get('deepCoadd_calexp', {
'filter': 'HSC-R',
'tract': 0,
'patch': '1,1'
})
rCoaddCalib = rCoaddCalexp.getCalib()
iCoaddCalexp = butler.get('deepCoadd_calexp', {
'filter': 'HSC-I',
'tract': 0,
'patch': '1,1'
})
iCoaddCalib = iCoaddCalexp.getCalib()
##############################################
# unique_id = str(uuid.uuid4())
# iCoaddCalib.writeFits(unique_id+".fits")
# file_name = "patch_1_1"+".fits"
# coadd.writeFits(file_name)
# new_link = post_image(file_name)
# os.remove(file_name)
display = afwDisplay.getDisplay()
display.mtv(coadd)
display.setMaskTransparency(100)
display.scale("asinh", -1, 30)
###################################################
rCoaddCalib.setThrowOnNegativeFlux(False)
iCoaddCalib.setThrowOnNegativeFlux(False)
# print(iSources.getSchema())
# rMags = rCoaddCalib.getMagnitude(rSources['base_PsfFlux_instFlux'])
iMags = iCoaddCalib.getMagnitude(iSources['base_PsfFlux_flux'])
maxMag = max(iMags)
isDeblended = rSources['deblend_nChild'] == 0
refTable = butler.get('deepCoadd_ref', {
'filter': 'HSC-R^HSC-I',
'tract': 0,
'patch': '1,1'
})
inInnerRegions = refTable['detect_isPatchInner'] & refTable[
'detect_isTractInner']
isSkyObject = refTable['merge_peak_sky']
isPrimary = refTable['detect_isPrimary']
#rMags[isPrimary]
#iMags[isPrimary]
isStellar = iSources['base_ClassificationExtendedness_value'] < 1.
isNotStellar = iSources['base_ClassificationExtendedness_value'] >= 1.
isGoodFlux = ~iSources['base_PsfFlux_flag']
selected = isPrimary & isStellar & isGoodFlux
for src in iSources[selected]:
# iMag = iCoaddCalib.getMagnitude(src['base_PsfFlux_flux'])
# print(iMag)
# if src.getX() > 5900 and src.getX() < 6000 and src.getY() > 6000 and src.getY() < 6100:
# print(str(src.getX())+ ", "+str(src.getY()))
# display.dot("o", src.getX(), src.getY(), size=10, ctype='orange')
#
# if iMag < maxMag/2.75:
# print(str(src.getX())+ ", "+str(src.getY()))
display.dot("o", src.getX(), src.getY(), size=20, ctype='green')
def setUpClass(cls):
# For lsstSim specific reasons, we need to specify the raft and sensor
dataId = dict(visit=840, raft='2,2', sensor='1,1')
butler = dafPersistence.Butler(InputDir)
cls.exposure = butler.get('eimage', dataId=dataId)
cls.visit_info = cls.exposure.getInfo().getVisitInfo()
def main():
butler = dafPersist.Butler(inputs='DATA/rerun/coaddForcedPhot')
patches = ['0,1', '1,0', '1,1', '1,2', '2,0', '2,1', '2,2']
id = 0
for patch in patches:
coadd = butler.get("deepCoadd_calexp",
tract=0,
patch=patch,
filter='HSC-I')
rSources = butler.get('deepCoadd_forced_src', {
'filter': 'HSC-R',
'tract': 0,
'patch': patch
})
iSources = butler.get('deepCoadd_forced_src', {
'filter': 'HSC-I',
'tract': 0,
'patch': patch
})
rCoaddCalexp = butler.get('deepCoadd_calexp', {
'filter': 'HSC-R',
'tract': 0,
'patch': patch
})
rCoaddCalib = rCoaddCalexp.getCalib()
iCoaddCalexp = butler.get('deepCoadd_calexp', {
'filter': 'HSC-I',
'tract': 0,
'patch': patch
})
iCoaddCalib = iCoaddCalexp.getCalib()
###############################################
result = [x.strip() for x in patch.split(',')]
file_name = "patch_" + result[0] + "_" + result[1] + ".fits"
coadd.writeFits(file_name)
# send new unique file to s3 and delete it from local storage
new_link = post_image(file_name)
# os.remove(file_name)
# post_image_database(unique_id)
##################################################
rCoaddCalib.setThrowOnNegativeFlux(False)
iCoaddCalib.setThrowOnNegativeFlux(False)
iMags = iCoaddCalib.getMagnitude(iSources['base_PsfFlux_flux'])
maxMag = max(iMags)
isDeblended = rSources['deblend_nChild'] == 0
refTable = butler.get('deepCoadd_ref', {
'filter': 'HSC-R^HSC-I',
'tract': 0,
'patch': patch
})
inInnerRegions = refTable['detect_isPatchInner'] & refTable[
'detect_isTractInner']
isSkyObject = refTable['merge_peak_sky']
isPrimary = refTable['detect_isPrimary']
isStellar = iSources['base_ClassificationExtendedness_value'] < 1.
isNotStellar = iSources['base_ClassificationExtendedness_value'] >= 1.
isGoodFlux = ~iSources['base_PsfFlux_flag']
selected = isPrimary & isNotStellar & isGoodFlux
for src in iSources[selected]:
iMag = iCoaddCalib.getMagnitude(src['base_PsfFlux_flux'])
# print(str(src.getX())+ ", "+str(src.getY()))
if iMag < maxMag / 2.75:
post_top_src_database(str(id), file_name, str(src.getX()),
str(src.getY()), 'EXT')
id += 1
selected2 = isPrimary & isStellar & isGoodFlux
for src in iSources[selected2]:
iMag = iCoaddCalib.getMagnitude(src['base_PsfFlux_flux'])
# print(str(src.getX())+ ", "+str(src.getY()))
if iMag < maxMag / 2.75:
post_top_src_database(str(id), file_name, str(src.getX()),
str(src.getY()), 'STAR')
id += 1
print(id)
def mosaicDIASources(repo_dir,
visitid,
ccdnum=10,
cutout_size=30,
template_catalog=None,
xnear=None,
ynear=None,
sourceIds=None,
gridSpec=[7, 4],
dipoleFlag='ip_diffim_ClassificationDipole_value'):
import matplotlib.pyplot as plt
import matplotlib
matplotlib.style.use('ggplot')
import matplotlib.gridspec as gridspec
import lsst.daf.persistence as dafPersist
#
# This matches up which exposures were differenced against which templates,
# and is purely specific to this particular set of data.
if template_catalog is None:
template_catalog = {
197790: [197802, 198372, 198376, 198380, 198384],
197662: [198668, 199009, 199021, 199033],
197408: [197400, 197404, 197412],
197384: [197388, 197392],
197371: [197367, 197375, 197379]
}
# Need to invert this to template_visit_catalog[exposure] = template
template_visit_catalog = {}
for templateid, visits in template_catalog.iteritems():
for visit in visits:
template_visit_catalog[visit] = templateid
def make_cutout(img, x, y, cutout_size=20):
return img[(x - cutout_size // 2):(x + cutout_size // 2),
(y - cutout_size // 2):(y + cutout_size // 2)]
def group_items(items, group_length):
for n in xrange(0, len(items), group_length):
yield items[n:(n + group_length)]
b = dafPersist.Butler(repo_dir)
template_visit = template_visit_catalog[visitid]
templateExposure = b.get("calexp",
visit=template_visit,
ccdnum=ccdnum,
immediate=True)
template_img, _, _ = templateExposure.getMaskedImage().getArrays()
template_wcs = templateExposure.getWcs()
sourceExposure = b.get("calexp",
visit=visitid,
ccdnum=ccdnum,
immediate=True)
source_img, _, _ = sourceExposure.getMaskedImage().getArrays()
subtractedExposure = b.get("deepDiff_differenceExp",
visit=visitid,
ccdnum=ccdnum,
immediate=True)
subtracted_img, _, _ = subtractedExposure.getMaskedImage().getArrays()
subtracted_wcs = subtractedExposure.getWcs()
diaSources = b.get("deepDiff_diaSrc",
visit=visitid,
ccdnum=ccdnum,
immediate=True)
masked_img = subtractedExposure.getMaskedImage()
img_arr, mask_arr, var_arr = masked_img.getArrays()
z1, z2 = zscale_image(img_arr)
top_level_grid = gridspec.GridSpec(gridSpec[0], gridSpec[1])
source_ind = 0
for source_n, source in enumerate(diaSources):
source_id = source.getId()
if sourceIds is not None and not np.in1d(source_id, sourceIds)[0]:
continue
source_x = source.get("ip_diffim_NaiveDipoleCentroid_x")
source_y = source.get("ip_diffim_NaiveDipoleCentroid_y")
if xnear is not None and not np.any(
np.abs(source_x - xnear) <= cutout_size):
continue
if ynear is not None and not np.any(
np.abs(source_y - ynear) <= cutout_size):
continue
#is_dipole = source.get("ip_diffim_ClassificationDipole_value") == 1
dipoleLabel = ''
if source.get(dipoleFlag) == 1:
dipoleLabel = 'Dipole'
if source.get("ip_diffim_DipoleFit_flag_classificationAttempted") == 1:
dipoleLabel += ' *'
template_xycoord = template_wcs.skyToPixel(
subtracted_wcs.pixelToSky(source_x, source_y))
cutouts = [
make_cutout(template_img,
template_xycoord.getY(),
template_xycoord.getX(),
cutout_size=cutout_size),
make_cutout(source_img,
source_y,
source_x,
cutout_size=cutout_size),
make_cutout(subtracted_img,
source_y,
source_x,
cutout_size=cutout_size)
]
try:
subgrid = gridspec.GridSpecFromSubplotSpec(
1, 3, subplot_spec=top_level_grid[source_ind], wspace=0)
except:
continue
for cutout_n, cutout in enumerate(cutouts):
plt.subplot(subgrid[0, cutout_n])
plt.imshow(cutout, vmin=z1, vmax=z2, cmap=plt.cm.gray)
plt.gca().xaxis.set_ticklabels([])
plt.gca().yaxis.set_ticklabels([])
plt.subplot(subgrid[0, 0])
source_ind += 1
#if is_dipole:
#print(source_n, source_id)
plt.ylabel(str(source_n) + dipoleLabel)
ids = args.id
verbose = args.verbose
visit = args.visit
if ids is not None:
mat = re.search(r"visit=(\d+)", ids)
if not mat:
print("Please specify a visit", file=sys.stderr)
sys.exit(1)
visit = int(mat.group(1))
if args.visit is not None and visit != args.visit:
print("Please specify either --id or --visit (or be consistent)", file=sys.stderr)
sys.exit(1)
butler = dafPersist.Butler(args.input)
#
# Lookup the amp names
#
camera = butler.get("camera")
det = list(camera)[0]
ampNames = [a.getName() for a in det]
if visit is None:
visit = butler.queryMetadata("raw", ["visit"])[0]
dataId = dict(visit=visit)
#
# Due to butler stupidity it can't/won't lookup things when you also
# specify a channel. Sigh
#
def coaddImageCutout(root,
ra,
dec,
size,
saveMsk=True,
saveSrc=True,
filt='HSC-I',
prefix='hsc_coadd_cutout',
circleMatch=True,
verbose=True,
extraField1=None,
extraValue1=None,
butler=None,
no_bright_object=False):
"""Cutout coadd image around a RA, DEC."""
# No longer support hscPipe < 4
coaddData = "deepCoadd_calexp"
# Get the SkyMap of the database
if butler is None:
try:
butler = dafPersist.Butler(root)
if verbose:
print(SEP)
print("## Load in the Butler ...")
except Exception:
raise Exception("### Can not load the Butler")
skyMap = butler.get("deepCoadd_skyMap", immediate=True)
# Get the expected cutout size
dimExpect = (2 * size + 1)
sizeExpect = dimExpect**2
# Cutout size in unit of degree
sizeDeg = size * 0.168 / 3600.0
# Verbose
if verbose:
print(SEP)
print(" Input Ra, Dec: %10.5f, %10.5f" % (ra, dec))
print(" Cutout size is expected to be %d x %d" %
(dimExpect, dimExpect))
# First, search for the central (Ra, Dec)
# Define the Ra, Dec pair
coord = afwCoord.IcrsCoord(ra * afwGeom.degrees, dec * afwGeom.degrees)
# Search for overlapped tract, patch pairs
matches = skyMap.findClosestTractPatchList([coord])
# Number of matched tracts
nTract = len(matches)
# Number of matched (patches)
nPatch = 0
for tt in range(nTract):
nPatch += len(matches[tt][1])
if verbose:
print("## Find %d possible matches !" % nPatch)
matchCen = []
for tract, patch in matches:
# Get the (tract, patch) ID
tractId = tract.getId()
patchId = "%d,%d" % patch[0].getIndex()
if verbose:
print("## Choose (Tract, Patch) for center: %d, %s !" %
(tractId, patchId))
matchCen.append((tractId, patchId))
# Get the coadd images
# Try to load the coadd Exposure; the skymap covers larger area than
# available data, which will cause Butler to fail sometime
try:
coadd = butler.get(coaddData,
tract=tractId,
patch=patchId,
filter=filt,
immediate=True)
except Exception, errMsg:
print(WAR)
print(" The desired coordinate is not available !!! ")
print(WAR)
print(errMsg)
""" TODO """
coaddFound = False
noData = True
partialCut = True
continue
else:
"""
It's still possible that the matched location actually has no
useful data (e.g. have been interpolated, or in the NO_DATA
part of the patch)
For this situation, no psf image can be generated !!
"""
coaddFound = True
# Get the Coadded PSF image
psfImg = getCoaddPsfImage(coadd, coord, label=prefix)
if psfImg is None:
noData = True
partialCut = True
continue
else:
noData = False
# Get the WCS information
wcs = coadd.getWcs()
# Convert the central coordinate from Ra,Dec to pixel unit
pixel = wcs.skyToPixel(coord)
pixel = afwGeom.Point2I(pixel)
# Define the bounding box for the central pixel
bbox = afwGeom.Box2I(pixel, pixel)
# Grow the bounding box to the desired size
bbox.grow(int(size))
# Compare to the coadd image, and clip
bbox.clip(coadd.getBBox(afwImage.PARENT))
if bbox.isEmpty():
noData = True
partialCut = True
continue
else:
if bbox.getArea() < sizeExpect:
partialCut = True
if verbose:
print("## Cut out image dimension " + "is : %d x %d " %
(bbox.getWidth(), bbox.getHeight()))
else:
partialCut = False
# Make a new ExposureF object for the cutout region
subImage = afwImage.ExposureF(coadd, bbox, afwImage.PARENT)
# Get the WCS
subWcs = subImage.getWcs()
# Get the central pixel coordinates on new subImage WCS
newX, newY = subWcs.skyToPixel(coord)
# Get the new origin pixel
newOriX, newOriY = subImage.getImage().getXY0()
newX = newX - newOriX
newY = newY - newOriY
# Get the header of the new subimage
subHead = subImage.getMetadata()
subHead.set('RA_CUT', ra)
subHead.set('DEC_CUT', dec)
subHead.set('NEW_X', newX)
subHead.set('NEW_Y', newY)
if partialCut:
subHead.set('PARTIAL', 1)
else:
subHead.set('PARTIAL', 0)
if (extraField1 is not None) and (extraValue1 is not None):
subHead.set(extraField1, extraValue1)
# To see if data are available for all the cut-out region
if partialCut:
warnings.warn("## Only part of the region is available" +
" : %d, %s" % (tractId, patchId))
outPre = (prefix + '_' + str(tractId) + '_' + patchId + '_' +
filt + '_cent')
else:
outPre = (prefix + '_' + str(tractId) + '_' + patchId + '_' +
filt + '_full')
# Define the output file name
outImg = outPre + '.fits'
outPsf = outPre + '_psf.fits'
# Save the cutout image to a new FITS file
subImage.writeFits(outImg)
# Save the PSF image
psfImg.writeFits(outPsf)
if saveMsk is True:
# Get the "Bad" mask plane
mskBad = getCoaddBadMsk(subImage,
no_bright_object=no_bright_object)
mskBad.writeFits(outPre + '_bad.fits')
if saveSrc is True:
# Get the forced photometry source catalog
""" Sometimes the forced photometry catalog is missing """
try:
# TODO: Maybe the measurement catalog is better
srcCat = butler.get('deepCoadd_meas',
tract=tractId,
patch=patchId,
filter=filt,
immediate=True,
flags=afwTable.SOURCE_IO_NO_FOOTPRINTS)
# Get the pixel coordinates for all objects
srcRa = np.array(
map(lambda x: x.get('coord').getRa().asDegrees(),
srcCat))
srcDec = np.array(
map(lambda x: x.get('coord').getDec().asDegrees(),
srcCat))
# Simple Box match
indMatch = ((srcRa > (ra - sizeDeg)) & (srcRa <
(ra + sizeDeg)) &
(srcDec > (dec - sizeDeg)) & (srcDec <
(dec + sizeDeg)))
# Extract the matched subset
srcMatch = srcCat.subset(indMatch)
# Save the src catalog to a FITS file
outSrc = outPre + '_src.fits'
srcMatch.writeFits(outSrc)
except Exception:
print("### Tract: %d Patch: %s" % (tractId, patchId))
warnings.warn("### Can not find the *force catalog !")
noSrcFile = prefix + '_nosrc_' + filt + '.lis'
if not os.path.isfile(noSrcFile):
os.system('touch ' + noSrcFile)
with open(noSrcFile, "a") as noSrc:
try:
noSrc.write("%d %s \n" % (tractId, patchId))
fcntl.flock(noSrc, fcntl.LOCK_UN)
except IOError:
pass
def setUp(self):
self.testData = tempfile.mkdtemp(dir=ROOT,
prefix='TestCompositeTestCase-')
self.firstRepoPath = os.path.join(self.testData, 'repo1')
self.objA = dpTest.TestObject("abc")
self.objB = dpTest.TestObject("def")
self.policy = dafPersist.Policy({
'camera': 'lsst.afw.cameraGeom.Camera',
'datasets': {
'basicObject1': {
'python': 'lsst.daf.persistence.test.TestObject',
'template': 'basic/id%(id)s.pickle',
'storage': 'PickleStorage'
},
'basicObject2': {
'python': 'lsst.daf.persistence.test.TestObject',
'template': 'basic/name%(name)s.pickle',
'storage': 'PickleStorage'
},
'basicPair': {
'python':
'lsst.daf.persistence.test.TestObjectPair',
'composite': {
'a': {
'datasetType': 'basicObject1'
},
'b': {
'datasetType': 'basicObject2'
}
},
'assembler':
'lsst.daf.persistence.test.TestObjectPair.assembler',
'disassembler':
'lsst.daf.persistence.test.TestObjectPair.disassembler'
},
'stdTestType': {
'python': 'lsst.daf.persistence.test.TestObjectPair',
'composite': {
'a': {
'datasetType': 'basicObject1'
},
'b': {
'datasetType': 'basicObject2'
}
}
},
'bypassTestType': {
'python': 'lsst.daf.persistence.test.TestObjectPair',
'composite': {
'a': {
'datasetType': 'basicObject1'
},
'b': {
'datasetType': 'basicObject2'
}
}
}
}
})
repoArgs = dafPersist.RepositoryArgs(
root=self.firstRepoPath,
policy=self.policy,
mapper='lsst.obs.base.test.CompositeMapper')
butler = dafPersist.Butler(outputs=repoArgs)
butler.put(self.objA, 'basicObject1', dataId={'id': 'foo'})
butler.put(self.objB, 'basicObject2', dataId={'name': 'bar'})
del butler
del repoArgs
