def __call__(self, band, t, extrap=False):
self.template.mktemplate(self.parameters[self.stype])
t = t - self.Tmax
rband = self.parent.restbands[band]
# Now build the lc model
temp, etemp, mask = self.template.eval(rband,
t,
self.parent.z,
gen=self.gen,
extrap=extrap)
K, mask2 = self.kcorr(band, t)
temp = temp + K
# Apply reddening correction:
# Figure out the reddening law
if self.do_Robs:
self.Robs[band] = kcorr.R_obs(band,
self.parent.z,
t,
self.EBVhost,
self.parent.EBVgal,
self.Rv_host[self.calibration],
self.parent.Rv_gal,
self.parent.k_version,
redlaw=self.parent.redlaw)
temp = temp + self.Robs[band] * (self.EBVhost + self.parent.EBVgal)
else:
# Apply Robs*EBVgal:
R = self.MWR(band, t)
temp = temp + self.Robs[
band] * self.EBVhost + R * self.parent.EBVgal
temp = temp + self.DM + self.MMax(rband, self.calibration)
return temp, etemp, mask * mask2
def systematics(self, calibration=1, include_Ho=False):
'''Returns the systematic errors in the paramters as a dictionary.
If no estimate is available, return None for that paramter.'''
systs = dict.fromkeys(self.parameters.keys())
# DM contains systematics for Ho, plus average of calibration
# uncertainties
syst_DM = []
weights = []
for band in self._fbands:
rb = self.parent.restbands[band]
# make a call to get the weights
mod, err, mask = self.__call__(band, self.parent.data[band].MJD)
weights.append(sum(where(mask, power(err, -2), 0)))
if self.stype in ['st']:
dst = self.st - 1.0
else:
dst = self.dm15 - 1.1
Robs = kcorr.R_obs(band,
self.parent.z,
0,
self.EBVhost,
self.parent.EBVgal,
self.Rv_host[calibration],
self.parent.Rv_gal,
self.parent.k_version,
redlaw=self.parent.redlaw)
dRobs = Robs * self.eRv_host[calibration] / self.Rv_host[
calibration]
syst_DM.append(power(dst*self.eb[calibration][rb],2)+\
power(self.EBVhost*dRobs, 2)+\
power(self.ea[calibration][rb], 2)+\
#power(2.17*velerr/(3e5*self.parent.z),2) +\
power(self.sigSN[calibration][rb],2))
syst_DM = array(syst_DM)
weights = array(weights)
systs['DM'] = sum(weights * syst_DM) / sum(weights)
if include_Ho:
systs['DM'] += power(2.17 * 0.1, 2) # assume 10% error in Ho
systs['DM'] = sqrt(systs['DM'])
systs['EBVhost'] = 0.06
if self.stype == 'dm15':
systs['dm15'] = 0.06
else:
systs['st'] = 0.03
systs['Tmax'] = 0.34
return (systs)
def setup(self):
# check to see if we have more than one filter when solving for EBV
if 'EBVhost' not in self.args:
if len(self._fbands) < 2:
raise RuntimeError, "Error: to solve for EBVhost, you need to fit more than one filter"
self.calibration = self.args.get('calibration', 0)
self.gen = 2
for band in self._fbands:
self.Robs[band] = kcorr.R_obs(band,
self.parent.z,
0,
0.01,
0,
self.Rv_host[self.calibration],
self.parent.Rv_gal,
self.parent.k_version,
redlaw=self.parent.redlaw)
def setup(self):
if 'EBVhost' not in self.args:
if len(self._fbands) < 2:
raise RuntimeError, "Error: to solve for EBVhost, you need to fit more than one filter"
self.calibration = self.args.get('calibration', 6)
self.gen = self.args.get('gen', 2)
for band in self._fbands:
#cal = self.args.get('cal',6)
cal = self.calibration
self.Robs[band] = kcorr.R_obs(band,
self.parent.z,
0,
0.01,
0,
self.Rv_host[cal],
self.parent.Rv_gal,
self.parent.k_version,
redlaw=self.parent.redlaw)
def get_max(self, bands, restframe=0, deredden=0):
Tmaxs = []
Mmaxs = []
eMmaxs = []
rbands = []
self.template.mktemplate(self.parameters[self.stype])
for band in bands:
rband = self.parent.restbands[band]
# find where the template truly peaks:
x0 = brent(lambda x: self.template.eval(rband, x, gen=self.gen)[0],
brack=(0., 5.))
Tmaxs.append(x0 + self.Tmax)
mmax = self.DM + self.MMax(rband, self.calibration)
if not restframe and band in self.parent.ks_tck:
# add the K-correction
mmax = mmax + scipy.interpolate.splev(Tmaxs[-1],
self.parent.ks_tck[band])
if not deredden:
if self.do_Robs:
Robs = kcorr.R_obs(band,
self.parent.z,
x0,
self.EBVhost,
self.parent.EBVgal,
self.Rv_host[self.calibration],
self.parent.Rv_gal,
'H3',
redlaw=self.parent.redlaw)
mmax = mmax + Robs * (self.EBVhost + self.parent.EBVgal)
else:
# Apply Robs*EBVgal:
if band in self.parent.Robs:
if type(self.parent.Robs[band]) is type(()):
R = scipy.interpolate.splev(
Tmaxs[-1], self.parent.Robs[band])
else:
R = self.parent.Robs[band]
else:
EBV = max(self.parent.EBVgal, 0.01)
R = kcorr.R_obs(band,
self.parent.z,
0,
0,
EBV,
self.Rv_host[self.calibration],
self.parent.Rv_gal,
self.parent.k_version,
redlaw=self.parent.redlaw)
EBV = max(self.EBVhost, 0.01)
Robs = kcorr.R_obs(band,
self.parent.z,
0,
EBV,
0,
self.Rv_host[self.calibration],
self.parent.Rv_gal,
self.parent.k_version,
redlaw=self.parent.redlaw)
mmax = mmax + Robs * self.EBVhost + R * self.parent.EBVgal
Mmaxs.append(mmax)
eMmaxs.append(self.errors['DM'])
rbands.append(rband)
return (Tmaxs, Mmaxs, eMmaxs, rbands)