def test_sphDist(self):
"""Test great circle angular separation calculation."""
expected = np.fabs(np.radians(np.linspace(-10., 10., 101)))
cat = self.makeDataHorizontal()
ra_mean, dec_mean = (0., 0.)
result = sphDist(ra_mean, dec_mean, cat['coord_ra'], cat['coord_dec'])
self.assertTrue(np.allclose(result, expected, atol=1.e-15))
def matchVisitComputeDistance(visit_obj1, ra_obj1, dec_obj1,
visit_obj2, ra_obj2, dec_obj2):
"""Calculate obj1-obj2 distance for each visit in which both objects are seen.
For each visit shared between visit_obj1 and visit_obj2,
calculate the spherical distance between the obj1 and obj2.
visit_obj1 and visit_obj2 are assumed to be unsorted.
Parameters
----------
visit_obj1 : scalar, list, or numpy.array of int or str
List of visits for object 1.
ra_obj1 : scalar, list, or numpy.array of float
List of RA in each visit for object 1. [radians]
dec_obj1 : scalar, list or numpy.array of float
List of Dec in each visit for object 1. [radians]
visit_obj2 : list or numpy.array of int or str
List of visits for object 2.
ra_obj2 : list or numpy.array of float
List of RA in each visit for object 2. [radians]
dec_obj2 : list or numpy.array of float
List of Dec in each visit for object 2. [radians]
Results
-------
list of float
spherical distances (in radians) for matching visits.
"""
distances = []
visit_obj1_idx = np.argsort(visit_obj1)
visit_obj2_idx = np.argsort(visit_obj2)
j_raw = 0
j = visit_obj2_idx[j_raw]
for i in visit_obj1_idx:
while (visit_obj2[j] < visit_obj1[i]) and (j_raw < len(visit_obj2_idx)-1):
j_raw += 1
j = visit_obj2_idx[j_raw]
if visit_obj2[j] == visit_obj1[i]:
if np.isfinite([ra_obj1[i], dec_obj1[i],
ra_obj2[j], dec_obj2[j]]).all():
distances.append(sphDist(ra_obj1[i], dec_obj1[i],
ra_obj2[j], dec_obj2[j]))
return distances
def calcRmsDistances(groupView, annulus, magRange, verbose=False):
"""Calculate the RMS distance of a set of matched objects over visits.
Parameters
----------
groupView : lsst.afw.table.GroupView
GroupView object of matched observations from MultiMatch.
annulus : length-2 `astropy.units.Quantity`
Distance range (i.e., arcmin) in which to compare objects.
E.g., `annulus=np.array([19, 21]) * u.arcmin` would consider all
objects separated from each other between 19 and 21 arcminutes.
magRange : length-2 `astropy.units.Quantity`
Magnitude range from which to select objects.
verbose : bool, optional
Output additional information on the analysis steps.
Returns
-------
rmsDistances : `astropy.units.Quantity`
RMS angular separations of a set of matched objects over visits.
"""
# First we make a list of the keys that we want the fields for
importantKeys = [groupView.schema.find(name).key for
name in ['id', 'coord_ra', 'coord_dec',
'object', 'visit', 'base_PsfFlux_mag']]
minMag, maxMag = magRange.to(u.mag).value
def magInRange(cat):
mag = cat.get('base_PsfFlux_mag')
w, = np.where(np.isfinite(mag))
medianMag = np.median(mag[w])
return minMag <= medianMag and medianMag < maxMag
groupViewInMagRange = groupView.where(magInRange)
# List of lists of id, importantValue
matchKeyOutput = [obj.get(key)
for key in importantKeys
for obj in groupViewInMagRange.groups]
jump = len(groupViewInMagRange)
ra = matchKeyOutput[1*jump:2*jump]
dec = matchKeyOutput[2*jump:3*jump]
visit = matchKeyOutput[4*jump:5*jump]
# Calculate the mean position of each object from its constituent visits
# `aggregate` calulates a quantity for each object in the groupView.
meanRa = groupViewInMagRange.aggregate(averageRaFromCat)
meanDec = groupViewInMagRange.aggregate(averageDecFromCat)
annulusRadians = arcminToRadians(annulus.to(u.arcmin).value)
rmsDistances = list()
for obj1, (ra1, dec1, visit1) in enumerate(zip(meanRa, meanDec, visit)):
dist = sphDist(ra1, dec1, meanRa[obj1+1:], meanDec[obj1+1:])
objectsInAnnulus, = np.where((annulusRadians[0] <= dist)
& (dist < annulusRadians[1]))
for obj2 in objectsInAnnulus:
distances = matchVisitComputeDistance(
visit[obj1], ra[obj1], dec[obj1],
visit[obj2], ra[obj2], dec[obj2])
if not distances:
if verbose:
print("No matching visits found for objs: %d and %d" %
(obj1, obj2))
continue
finiteEntries, = np.where(np.isfinite(distances))
# Need at least 2 distances to get a finite sample stdev
if len(finiteEntries) > 1:
# ddof=1 to get sample standard deviation (e.g., 1/(n-1))
rmsDist = np.std(np.array(distances)[finiteEntries], ddof=1)
rmsDistances.append(rmsDist)
# return quantity
rmsDistances = np.array(rmsDistances) * u.radian
return rmsDistances
def calcRmsDistancesVsRef(groupView, refVisit, magRange, band, verbose=False):
"""Calculate the RMS distance of a set of matched objects over visits.
Parameters
----------
groupView : lsst.afw.table.GroupView
GroupView object of matched observations from MultiMatch.
magRange : length-2 `astropy.units.Quantity`
Magnitude range from which to select objects.
verbose : bool, optional
Output additional information on the analysis steps.
Returns
-------
rmsDistances : `astropy.units.Quantity`
RMS angular separations of a set of matched objects over visits.
separations : `astropy.units.Quantity`
Angular separations of the set a matched objects.
"""
minMag, maxMag = magRange.to(u.mag).value
def magInRange(cat):
mag = cat.get('base_PsfFlux_mag')
w, = np.where(np.isfinite(mag))
medianMag = np.median(mag[w])
return minMag <= medianMag and medianMag < maxMag
groupViewInMagRange = groupView.where(magInRange)
# Get lists of the unique objects and visits:
uniqObj = groupViewInMagRange.ids
uniqVisits = set()
for id in uniqObj:
for v, f in zip(groupViewInMagRange[id].get('visit'),
groupViewInMagRange[id].get('filt')):
if f == band:
uniqVisits.add(v)
uniqVisits = list(uniqVisits)
if not isinstance(refVisit, int):
refVisit = int(refVisit)
if refVisit in uniqVisits:
# Remove the reference visit from the set of visits:
uniqVisits.remove(refVisit)
rmsDistances = list()
# Loop over visits, calculating the RMS for each:
for vis in uniqVisits:
distancesVisit = list()
for obj in uniqObj:
visMatch = np.where(groupViewInMagRange[obj].get('visit') == vis)
refMatch = np.where(groupViewInMagRange[obj].get('visit') == refVisit)
raObj = groupViewInMagRange[obj].get('coord_ra')
decObj = groupViewInMagRange[obj].get('coord_dec')
# Require it to have a match in both the reference and visit image:
if np.size(visMatch[0]) > 0 and np.size(refMatch[0]) > 0:
distances = sphDist(raObj[refMatch], decObj[refMatch],
raObj[visMatch], decObj[visMatch])
distancesVisit.append(distances)
finiteEntries = np.where(np.isfinite(distancesVisit))[0]
# Need at least 2 distances to get a finite sample stdev
if len(finiteEntries) > 1:
# Calculate the RMS of these offsets:
# ddof=1 to get sample standard deviation (e.g., 1/(n-1))
pos_rms_rad = np.std(np.array(distancesVisit)[finiteEntries], ddof=1)
pos_rms_mas = geom.radToMas(pos_rms_rad) # milliarcsec
rmsDistances.append(pos_rms_mas)
else:
rmsDistances.append(np.nan)
rmsDistances = np.array(rmsDistances) * u.marcsec
return rmsDistances