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])
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])
