def lc(self, idx, maxObsHistID=None):
"""
Parameters
----------
"""
if maxObsHistID is None:
maxObsHistID = self.maxObsHistID
# obtain the model parameters from the population
paramDict = self.population.modelparams(idx)
self.model.setModelParameters(**paramDict)
myra = paramDict['ra']
mydec = paramDict['dec']
timeRange = (self.model.minMjd, self.model.maxMjd)
if None not in timeRange:
queryTime = 'expMJD < {1} and expMJD > {0}'.format(timeRange[0], timeRange[1])
df = self.pointings.copy().query(queryTime)
else:
df = self.pointings.copy()
if self.pruneWithRadius:
raise ValueError('Not implemented')
numObs = len(self.pointings)
modelFlux = np.zeros(numObs)
fluxerr = np.zeros(numObs)
sn = SNObject(myra, mydec)
for i, rowtuple in enumerate(df.iterrows()):
row = rowtuple[1]
# print(row['expMJD'], row['filter'], row['fiveSigmaDepth'])
bp = self.bandPasses[row['filter']]
modelFlux[i] = self.model.modelFlux(row['expMJD'],
bandpassobj=bp)
fluxerr[i] = sn.catsimBandFluxError(time=row['expMJD'],
bandpassobject=bp,
fluxinMaggies=modelFlux[i],
m5=row['fiveSigmaDepth'])
rng = self.randomState
df.reset_index(inplace=True)
df['objid'] = np.ones(numObs)*np.int(idx)
df['objid'] = df.objid.astype(np.int)
df['fluxerr'] = fluxerr
deviations = rng.normal(size=len(df))
df['deviations'] = deviations
df['zp'] = 0.
df['ModelFlux'] = modelFlux
df['flux'] = df['ModelFlux'] + df['deviations'] * df['fluxerr']
df['zpsys']= 'ab'
df['pid'] = self.pair_method(df.objid, df.obsHistID, self.maxObsHistID)
df['pid'] = df.pid.astype(np.int)
lc = df[['pid', 'obsHistID', 'objid', 'expMJD', 'filter', 'ModelFlux', 'fieldID', 'flux',
'fluxerr', 'deviations', 'zp', 'zpsys']]
lc.set_index('pid', inplace=True)
return lc
def get_snbrightness(self):
"""
getters for brightness related parameters of sn
"""
if self._sn_object_cache is None or len(
self._sn_object_cache) > 1000000:
self._sn_object_cache = {}
c, x1, x0, t0, _z, ra, dec = self.column_by_name('c'),\
self.column_by_name('x1'),\
self.column_by_name('x0'),\
self.column_by_name('t0'),\
self.column_by_name('redshift'),\
self.column_by_name('raJ2000'),\
self.column_by_name('decJ2000')
raDeg = np.degrees(ra)
decDeg = np.degrees(dec)
ebv = self.column_by_name('EBV')
id_list = self.column_by_name('snid')
bandname = self.obs_metadata.bandpass
if isinstance(bandname, list):
raise ValueError('bandname expected to be string, but is list\n')
bandpass = self.lsstBandpassDict[bandname]
# Initialize return array so that it contains the values you would get
# if you passed through a t0=self.badvalues supernova
vals = np.array([[0.0] * len(t0), [np.inf] * len(t0),
[np.nan] * len(t0), [np.inf] * len(t0),
[0.0] * len(t0)]).transpose()
for i in np.where(
np.logical_and(
np.isfinite(t0),
np.abs(self.mjdobs - t0) < self.maxTimeSNVisible))[0]:
if id_list[i] in self._sn_object_cache:
SNobject = self._sn_object_cache[id_list[i]]
else:
SNobject = SNObject()
SNobject.set(z=_z[i], c=c[i], x1=x1[i], t0=t0[i], x0=x0[i])
SNobject.setCoords(ra=raDeg[i], dec=decDeg[i])
SNobject.set_MWebv(ebv[i])
self._sn_object_cache[id_list[i]] = SNobject
if self.mjdobs <= SNobject.maxtime(
) and self.mjdobs >= SNobject.mintime():
# Calculate fluxes
fluxinMaggies = SNobject.catsimBandFlux(
time=self.mjdobs, bandpassobject=bandpass)
mag = SNobject.catsimBandMag(time=self.mjdobs,
fluxinMaggies=fluxinMaggies,
bandpassobject=bandpass)
vals[i, 0] = fluxinMaggies
vals[i, 1] = mag
flux_err = SNobject.catsimBandFluxError(
time=self.mjdobs,
bandpassobject=bandpass,
m5=self.obs_metadata.m5[bandname],
photParams=self.photometricparameters,
fluxinMaggies=fluxinMaggies,
magnitude=mag)
mag_err = SNobject.catsimBandMagError(
time=self.mjdobs,
bandpassobject=bandpass,
m5=self.obs_metadata.m5[bandname],
photParams=self.photometricparameters,
magnitude=mag)
sed = SNobject.SNObjectSED(time=self.mjdobs,
bandpass=self.lsstBandpassDict,
applyExtinction=True)
adu = sed.calcADU(bandpass,
photParams=self.photometricparameters)
vals[i, 2] = flux_err
vals[i, 3] = mag_err
vals[i, 4] = adu
return (vals[:, 0], vals[:, 1], vals[:, 2], vals[:, 3], vals[:, 4])
class SNObject_tests(unittest.TestCase):
@classmethod
def tearDownClass(cls):
sims_clean_up()
def setUp(self):
"""
Setup tests
SN_blank: A SNObject with no MW extinction
"""
mydir = get_config_dir()
print('===============================')
print('===============================')
print (mydir)
print('===============================')
print('===============================')
# A range of wavelengths in Ang
self.wave = np.arange(3000., 12000., 50.)
# Equivalent wavelenths in nm
self.wavenm = self.wave / 10.
# Time to be used as Peak
self.mjdobs = 571190
# Check that we can set up a SED
# with no extinction
self.SN_blank = SNObject()
self.SN_blank.setCoords(ra=30., dec=-60.)
self.SN_blank.set(z=0.96, t0=571181, x1=2.66, c=0.353, x0=1.796e-6)
self.SN_blank.set_MWebv(0.)
self.SN_extincted = SNObject(ra=30., dec=-60.)
self.SN_extincted.set(z=0.96, t0=571181, x1=2.66, c=0.353,
x0=1.796112e-06)
self.SNCosmoModel = self.SN_extincted.equivalentSNCosmoModel()
self.rectify_photParams = PhotometricParameters()
self.lsstBandPass = BandpassDict.loadTotalBandpassesFromFiles()
self.SNCosmoBP = sncosmo.Bandpass(wave=self.lsstBandPass['r'].wavelen,
trans=self.lsstBandPass['r'].sb,
wave_unit=astropy.units.Unit('nm'),
name='lsst_r')
def tearDown(self):
del self.SNCosmoBP
del self.SN_blank
del self.SN_extincted
def test_SNstatenotEmpty(self):
"""
Check that the state of SNObject, stored in self.SNstate has valid
entries for all keys and does not contain keys with None type Values.
"""
myDict = self.SN_extincted.SNstate
for key in myDict:
assert myDict[key] is not None
def test_attributeDefaults(self):
"""
Check the defaults and the setter properties for rectifySED and
modelOutSideRange
"""
snobj = SNObject(ra=30., dec=-60., source='salt2')
self.assertEqual(snobj.rectifySED, True)
self.assertEqual(snobj.modelOutSideTemporalRange, 'zero')
snobj.rectifySED = False
self.assertFalse(snobj.rectifySED, False)
self.assertEqual(snobj.modelOutSideTemporalRange, 'zero')
def test_raisingerror_forunimplementedmodelOutSideRange(self):
"""
check that correct error is raised if the user tries to assign an
un-implemented model value to
`sims.catUtils.supernovae.SNObject.modelOutSideTemporalRange`
"""
snobj = SNObject(ra=30., dec=-60., source='salt2')
assert snobj.modelOutSideTemporalRange == 'zero'
with self.assertRaises(ValueError) as context:
snobj.modelOutSideTemporalRange = 'False'
self.assertEqual('Model not implemented, defaulting to zero method\n',
context.exception.args[0])
def test_rectifiedSED(self):
"""
Check for an extreme case that the SN seds are being rectified. This is
done by setting up an extreme case where there will be negative seds, and
checking that this is indeed the case, and checking that they are not
negative if rectified.
"""
snobj = SNObject(ra=30., dec=-60., source='salt2')
snobj.set(z=0.96, t0=self.mjdobs, x1=-3., x0=1.8e-6)
snobj.rectifySED = False
times = np.arange(self.mjdobs - 50., self.mjdobs + 150., 1.)
badTimes = []
for time in times:
sed = snobj.SNObjectSED(time=time,
bandpass=self.lsstBandPass['r'])
if any(sed.flambda < 0.):
badTimes.append(time)
# Check that there are negative SEDs
assert(len(badTimes) > 0)
snobj.rectifySED = True
for time in badTimes:
sed = snobj.SNObjectSED(time=time,
bandpass=self.lsstBandPass['r'])
self.assertGreaterEqual(sed.calcADU(bandpass=self.lsstBandPass['r'],
photParams=self.rectify_photParams), 0.)
self.assertFalse(any(sed.flambda < 0.))
def test_ComparebandFluxes2photUtils(self):
"""
The SNObject.catsimBandFlux computation uses the sims.photUtils.sed
band flux computation under the hood. This test makes sure that these
definitions are in sync
"""
snobject_r = self.SN_extincted.catsimBandFlux(
bandpassobject=self.lsstBandPass['r'],
time=self.mjdobs)
# `sims.photUtils.Sed`
sed = self.SN_extincted.SNObjectSED(time=self.mjdobs,
bandpass=self.lsstBandPass['r'])
sedflux = sed.calcFlux(bandpass=self.lsstBandPass['r'])
np.testing.assert_allclose(snobject_r, sedflux / 3631.0)
def test_CompareBandFluxes2SNCosmo(self):
"""
Compare the r band flux at a particular time computed in SNObject and
SNCosmo for MW-extincted SEDs. While the underlying sed is obtained
from SNCosmo the integration with the bandpass is an independent
calculation in SNCosmo and catsim
"""
times = self.mjdobs
catsim_r = self.SN_extincted.catsimBandFlux(
bandpassobject=self.lsstBandPass['r'],
time=times)
sncosmo_r = self.SNCosmoModel.bandflux(band=self.SNCosmoBP,
time=times, zpsys='ab',
zp=0.)
np.testing.assert_allclose(sncosmo_r, catsim_r)
def test_CompareBandMags2SNCosmo(self):
"""
Compare the r band flux at a particular time computed in SNObject and
SNCosmo for MW-extincted SEDs. Should work whenever the flux comparison
above works.
"""
times = self.mjdobs
catsim_r = self.SN_extincted.catsimBandMag(
bandpassobject=self.lsstBandPass['r'],
time=times)
sncosmo_r = self.SNCosmoModel.bandmag(band=self.SNCosmoBP,
time=times, magsys='ab')
np.testing.assert_allclose(sncosmo_r, catsim_r)
def test_CompareExtinctedSED2SNCosmo(self):
"""
Compare the extincted SEDS in SNCosmo and SNObject. Slightly more
non-trivial than comparing unextincted SEDS, as the extinction in
SNObject uses different code from SNCosmo. However, this is still
using the same values of MWEBV, rather than reading it off a map.
"""
SNObjectSED = self.SN_extincted.SNObjectSED(time=self.mjdobs,
wavelen=self.wavenm)
SNCosmoSED = self.SNCosmoModel.flux(time=self.mjdobs, wave=self.wave) \
* 10.
np.testing.assert_allclose(SNObjectSED.flambda, SNCosmoSED,
rtol=1.0e-7)
def test_CompareUnextinctedSED2SNCosmo(self):
"""
Compares the unextincted flux Densities in SNCosmo and SNObject. This
is mereley a sanity check as SNObject uses SNCosmo under the hood.
"""
SNCosmoFluxDensity = self.SN_blank.flux(wave=self.wave,
time=self.mjdobs) * 10.
unextincted_sed = self.SN_blank.SNObjectSED(time=self.mjdobs,
wavelen=self.wavenm)
SNObjectFluxDensity = unextincted_sed.flambda
np.testing.assert_allclose(SNCosmoFluxDensity, SNObjectFluxDensity,
rtol=1.0e-7)
def test_redshift(self):
"""
test that the redshift method works as expected by checking that
if we redshift a SN from its original redshift orig_z to new_z where
new_z is smaller (larger) than orig_z:
- 1. x0 increases (decreases)
- 2. source peak absolute magnitude in BesselB band stays the same
"""
from astropy.cosmology import FlatLambdaCDM
cosmo = FlatLambdaCDM(H0=70., Om0=0.3)
orig_z = self.SN_extincted.get('z')
orig_x0 = self.SN_extincted.get('x0')
peakabsMag = self.SN_extincted.source_peakabsmag('BessellB', 'AB', cosmo=cosmo)
lowz = orig_z * 0.5
highz = orig_z * 2.0
# Test Case for lower redshift
self.SN_extincted.redshift(z=lowz, cosmo=cosmo)
low_x0 = self.SN_extincted.get('x0')
lowPeakAbsMag = self.SN_extincted.source_peakabsmag('BessellB', 'AB', cosmo=cosmo)
# Test 1.
self.assertGreater(low_x0, orig_x0)
# Test 2.
self.assertAlmostEqual(peakabsMag, lowPeakAbsMag, places=14)
# Test Case for higher redshift
self.SN_extincted.redshift(z=highz, cosmo=cosmo)
high_x0 = self.SN_extincted.get('x0')
HiPeakAbsMag = self.SN_extincted.source_peakabsmag('BessellB', 'AB', cosmo=cosmo)
# Test 1.
self.assertLess(high_x0, orig_x0)
# Test 2.
self.assertAlmostEqual(peakabsMag, HiPeakAbsMag, places=14)
def test_bandFluxErrorWorks(self):
"""
test that bandflux errors work even if the flux is negative
"""
times = self.mjdobs
e = self.SN_extincted.catsimBandFluxError(times,
self.lsstBandPass['r'],
m5=24.5, fluxinMaggies=-1.0)
assert isinstance(e, np.float)
print(e)
assert not(np.isinf(e) or np.isnan(e))
def main(ramax=58, ramin=56, decmin=-32, decmax=-31, t0=59215, tm=61406):
query = query_tmpl.format(ramin, ramax, decmin, decmax)
sntab = pd.read_sql_query(query, conn)
#sntab.to_csv('./catalogs+tables/sn_cat_rectangle.csv')
#if os.path.isfile('./catalogs+tables/full_t_visits_from_minion.csv'):
# visitab = pd.read_csv('./catalogs+tables/full_t_visits_from_minion.csv')
#else:
res = ObsMetaData.getObservationMetaData(boundLength=2,
boundType='circle',
fieldRA=(ramin - 3, ramax + 3),
fieldDec=(decmin - 3, decmax + 3),
expMJD=(t0, tm))
parsed = [Odict(obsmd.summary['OpsimMetaData']) for obsmd in res]
for obsmd, summ in zip(res, parsed):
ditherRa = np.rad2deg(summ['descDitheredRA'])
ditherDec = np.rad2deg(summ['descDitheredDec'])
ditherRot = np.rad2deg(summ['descDitheredRotTelPos'])
summ['descDitheredRotSkyPos'] = getRotSkyPos(ditherRa, ditherDec,
obsmd, ditherRot)
df = pd.DataFrame(parsed)
df = df[df['filter'].isin(('g', 'r', 'i', 'z'))]
X = df[[
'obsHistID', 'filter', 'FWHMeff', 'descDitheredRA', 'descDitheredDec',
'descDitheredRotTelPos', 'airmass', 'fiveSigmaDepth', 'expMJD',
'descDitheredRotSkyPos', 'fieldRA', 'fieldDec', 'rotSkyPos',
'rotTelPos'
]].copy()
X.descDitheredRA = np.rad2deg(X.descDitheredRA)
X.descDitheredDec = np.rad2deg(X.descDitheredDec)
X.descDitheredRotTelPos = np.rad2deg(X.descDitheredRotTelPos)
#X.descDitheredRotSkyPos = np.rad2deg(X.descDitheredRotSkyPos) already in deg
X.fieldRA = np.rad2deg(X.fieldRA)
X.fieldDec = np.rad2deg(X.fieldDec)
X.rotTelPos = np.rad2deg(X.rotTelPos)
X.rotSkyPos = np.rad2deg(X.rotSkyPos)
X['d1'] = angularSeparation(ramin, decmax, X.descDitheredRA.values,
X.descDitheredDec.values)
X['d2'] = angularSeparation(ramin, decmin, X.descDitheredRA.values,
X.descDitheredDec.values)
X['d3'] = angularSeparation(ramax, decmax, X.descDitheredRA.values,
X.descDitheredDec.values)
X['d4'] = angularSeparation(ramax, decmin, X.descDitheredRA.values,
X.descDitheredDec.values)
visitab = X.query('d1 < 1.75 | d2 < 1.75 | d3 < 1.75 |d4 < 1.75')
del (X)
del (df)
visitab.to_csv('./catalogs+tables/full_t_visits_from_minion.csv')
# setting the observation telescope status
boresight = []
orientation = []
wcs_list = []
for avisit in visitab.itertuples():
bsight = geom.SpherePoint(avisit.descDitheredRA * geom.degrees,
avisit.descDitheredDec * geom.degrees)
orient = (90 - avisit.descDitheredRotSkyPos) * geom.degrees
wcs_list.append([
makeSkyWcs(t,
orient,
flipX=False,
boresight=bsight,
projection='TAN') for t in trans
])
orientation.append(orient)
boresight.append(bsight)
times = visitab['expMJD']
bands = visitab['filter']
depths = visitab['fiveSigmaDepth']
#colnames = ['mjd', 'filter']
data_cols = {'mjd': times, 'filter': bands, 'visitn': visitab['obsHistID']}
n_observ = []
n_trueobserv = []
for asn in sntab.itertuples():
sn_mod = SNObject(ra=asn.snra_in, dec=asn.sndec_in)
sn_mod.set(z=asn.z_in,
t0=asn.t0_in,
x1=asn.x1_in,
c=asn.c_in,
x0=asn.x0_in)
sn_skyp = afwGeom.SpherePoint(asn.snra_in, asn.sndec_in,
afwGeom.degrees)
size = len(times)
sn_flxs = np.zeros(size)
sn_mags = np.zeros(size)
sn_flxe = np.zeros(size)
sn_mage = np.zeros(size)
sn_obsrvd = []
sn_observable = []
ii = 0
for mjd, filt, wcsl, m5 in zip(times, bands, wcs_list, depths):
flux = sn_mod.catsimBandFlux(mjd, LSST_BPass[filt])
mag = sn_mod.catsimBandMag(LSST_BPass[filt], mjd, flux)
flux_er = sn_mod.catsimBandFluxError(mjd, LSST_BPass[filt], m5,
flux)
mag_er = sn_mod.catsimBandMagError(mjd,
LSST_BPass[filt],
m5,
magnitude=mag)
# checking sensors containing this object
contain = [box.contains(afwGeom.Point2I(wcs.skyToPixel(sn_skyp))) \
for box, wcs in zip(boxes, wcsl)]
observed = np.sum(contain) > 0
observable = observed & (flux > 0.0
) #(mag + mag_er < 27.0) & (mag_er < 0.5)
# if observed:
# print('Overlaps ccd', names[np.where(contain)[0][0]])
sn_observable.append(observable)
sn_obsrvd.append(observed)
sn_flxs[ii] = flux # done
sn_mags[ii] = mag
sn_flxe[ii] = flux_er
sn_mage[ii] = mag_er
ii += 1
data_cols[asn.snid_in + '_observable'] = sn_observable
data_cols[asn.snid_in + '_observed'] = sn_obsrvd
data_cols[asn.snid_in + '_flux'] = sn_flxs
data_cols[asn.snid_in + '_fluxErr'] = sn_flxe
data_cols[asn.snid_in + '_mag'] = sn_mags
data_cols[asn.snid_in + '_magErr'] = sn_mage
n_observ.append(np.sum(sn_obsrvd))
n_trueobserv.append(np.sum(sn_observable))
sntab['Nobserv'] = n_observ
sntab['N_trueobserv'] = n_trueobserv
lightcurves = pd.DataFrame(data_cols)
dest_lc = './lightcurves/lightcurves_cat_rect_{}_{}_{}_{}.csv'
lightcurves.to_csv(dest_lc.format(ramax, ramin, decmax, decmin))
dest_snfile = './catalogs+tables/supernovae_cat_rect_{}_{}_{}_{}.csv'
sntab.to_csv(dest_snfile.format(ramax, ramin, decmax, decmin))
print("""Stored the lightcurves in {},
the SN catalog in {}""".format(
dest_lc.format(ramax, ramin, decmax, decmin),
dest_snfile.format(ramax, ramin, decmax, decmin)))
return
def get_snbrightness(self):
"""
getters for brightness related parameters of sn
"""
if self._sn_object_cache is None or len(self._sn_object_cache) > 1000000:
self._sn_object_cache = {}
c, x1, x0, t0, _z, ra, dec = self.column_by_name('c'),\
self.column_by_name('x1'),\
self.column_by_name('x0'),\
self.column_by_name('t0'),\
self.column_by_name('redshift'),\
self.column_by_name('raJ2000'),\
self.column_by_name('decJ2000')
raDeg = np.degrees(ra)
decDeg = np.degrees(dec)
ebv = self.column_by_name('EBV')
id_list = self.column_by_name('snid')
bandname = self.obs_metadata.bandpass
if isinstance(bandname, list):
raise ValueError('bandname expected to be string, but is list\n')
bandpass = self.lsstBandpassDict[bandname]
# Initialize return array so that it contains the values you would get
# if you passed through a t0=self.badvalues supernova
vals = np.array([[0.0]*len(t0), [np.inf]*len(t0),
[np.nan]*len(t0), [np.inf]*len(t0),
[0.0]*len(t0)]).transpose()
for i in np.where(np.logical_and(np.isfinite(t0),
np.abs(self.mjdobs - t0) < self.maxTimeSNVisible))[0]:
if id_list[i] in self._sn_object_cache:
SNobject = self._sn_object_cache[id_list[i]]
else:
SNobject = SNObject()
SNobject.set(z=_z[i], c=c[i], x1=x1[i], t0=t0[i], x0=x0[i])
SNobject.setCoords(ra=raDeg[i], dec=decDeg[i])
SNobject.set_MWebv(ebv[i])
self._sn_object_cache[id_list[i]] = SNobject
if self.mjdobs <= SNobject.maxtime() and self.mjdobs >= SNobject.mintime():
# Calculate fluxes
fluxinMaggies = SNobject.catsimBandFlux(time=self.mjdobs,
bandpassobject=bandpass)
mag = SNobject.catsimBandMag(time=self.mjdobs,
fluxinMaggies=fluxinMaggies,
bandpassobject=bandpass)
vals[i, 0] = fluxinMaggies
vals[i, 1] = mag
flux_err = SNobject.catsimBandFluxError(time=self.mjdobs,
bandpassobject=bandpass,
m5=self.obs_metadata.m5[
bandname],
photParams=self.photometricparameters,
fluxinMaggies=fluxinMaggies,
magnitude=mag)
mag_err = SNobject.catsimBandMagError(time=self.mjdobs,
bandpassobject=bandpass,
m5=self.obs_metadata.m5[
bandname],
photParams=self.photometricparameters,
magnitude=mag)
sed = SNobject.SNObjectSED(time=self.mjdobs,
bandpass=self.lsstBandpassDict,
applyExtinction=True)
adu = sed.calcADU(bandpass, photParams=self.photometricparameters)
vals[i, 2] = flux_err
vals[i, 3] = mag_err
vals[i, 4] = adu
return (vals[:, 0], vals[:, 1], vals[:, 2], vals[:, 3], vals[:, 4])