def showcurve(sn, hml):
source=sncosmo.get_source('s11-2005hm')
model=sncosmo.Model(source=source)
z0 = float(redshifts[sn])
model.set(z=z0)
numofrows = len(hml[:,1])
#adding flux, fluxerr, mag and magerror columns
flux = [str(float(hml[i][1])*10**((-27-21.49)/2.5)) for i in range(numofrows)]
hml=np.column_stack((hml,flux))
fluxerr = [str(float(hml[i][2])*10**((-27-21.49)/2.5)) for i in range(numofrows)]
hml=np.column_stack((hml,fluxerr))
#adding zpsys and filter columns
ab = ['ab' for i in range(numofrows)]
hml=np.column_stack((hml,ab))
band = ['ptf48r' for i in range(numofrows)]
hml=np.column_stack((hml,band))
''''
'counts to flux eq', hml_dat[8][1]*10**((-27-21.49)/2.5)
'flux to mag eq', -2.5 *np.log10(flux)-21.49
'counts to mag eq', -2.5 *np.log10(hml_dat[8][1]) +27
'''
#plt.errorbar([float(i) for i in hml[:,0]], [float(i) for i in hml[:,1]], yerr = [float(i) for i in hml[:,2]], color = 'blue', fmt = 'o')
#plt.scatter(hml[:,0], hml[:,4], color = 'green')
#plt.show()
hml_dat=astropy.table.Table(data=hml, names=('mjd', 'counts', 'dcounts', 'zp', 'flux', 'fluxerr', 'zpsys', 'filter'), dtype=('float','float','float','float','float','float','str', 'str'))
#print hml_dat
mjdlimits = {'09': (55197-365, 55197),'10': (55197, 55562), '11': (55562, 55927), '12': (55927, 56293)}
res, fitted_model=sncosmo.fit_lc(hml_dat, model, ['t0','amplitude'], nburn=10000, nsamples=50000, bounds={'t0':mjdlimits[sn[:2]]})
#res, fitted_model=sncosmo.fit_lc(hml_dat, model, ['t0','amplitude'], nburn=10000, nsamples=50000, bounds={'t0':(55830,55960)})
# sncosmo.plot_lc(hml_dat, model=fitted_model, model_label=source.name, errors=res.errors, color='blue', figtext=str(sn+'\n'+'Model name: s11-2005hm'+ '\n'+'Reduced Chi Squared: ')+ str(res.chisq/res.ndof), xfigsize=18)
# plt.show()
hml2 = []
#print hml
#print res.parameters[1]
j = 0; k= 0
start = float(res.parameters[1])
peaktime = modellimit(z0)
for row in hml:
datestamp = row[0]
if peaktime + (40.0)*(1+z0) > float(datestamp)-start > peaktime - (20.0)*(1+z0) :
hml2.append(row)
if peaktime + (40.0)*(1+z0) > float(datestamp)-start > peaktime:
j+=1
if peaktime > float(datestamp)-start> peaktime-(20.0)*(1+z0):
k+=1
if j > 0 and k > 0:
hml2= np.array(hml2)
## print 'after', len(hml2)
hml_dat2=astropy.table.Table(data=hml2, names=('mjd', 'counts', 'dcounts', 'zp', 'flux', 'fluxerr', 'zpsys', 'filter'), dtype=('float','float','float','float','float','float','str', 'str'))
res2, fitted_model2=sncosmo.fit_lc(hml_dat2, model, ['t0','amplitude'], nburn=10000, nsamples=50000, bounds={'t0':mjdlimits[sn[:2]]})
#plot model
redchisqaured = res2.chisq/res2.ndof
#sncosmo.plot_lc(hml_dat2, model=fitted_model2, model_label=source.name, zp = 27.00, errors=res2.errors, color='blue', figtext=str(sn+'\n'+'Model name: s11-2005hm'+ '\n'+'Reduced Chi Squared: ')+ str(res2.chisq/res2.ndof), xfigsize=18)
# plt.show()
##print 'Done:', hml2[:,0][0], 'z:',float(res2.parameters[0]), 'Reduced chi^2:', res2.chisq/res2.ndof #'Data points:', len(hml[:,1]),' Type'+types[hml[:,0][0]]+'Model name: '+ source.name,'\n' #'Dof:', res.ndof
return redchisqaured, res2.parameters, z0, hml2 #, res.errors['amplitude']]
else:
print sn, 'didn\'t make the cut'
def model_IIP_nugent(self):
"""
"""
sncosmo.get_source('nugent-sn2p', version='1.2')
p, w, f = sncosmo.read_griddata_ascii(
os.path.join(sncosmo.builtins.get_cache_dir(),
'sncosmo/models/nugent/sn2p_flux.v1.2.dat')
)
mask = (p < 130)
return sncosmo.Model(source=sncosmo.TimeSeriesSource(p[mask], w, f[mask]),
effects=[sncosmo.CCM89Dust()],
effect_names=['host'],
effect_frames=['rest'])
def model_Ia_hsiao(self):
"""
"""
sncosmo.get_source('hsiao', version='3.0')
p, w, f = sncosmo.read_griddata_fits(
os.path.join(sncosmo.builtins.get_cache_dir(),
'sncosmo/models/hsiao/Hsiao_SED_V3.fits')
)
return sncosmo.Model(
source=sncosmo.StretchSource(p, w, f, name='hsiao-stretch'),
effects=[sncosmo.CCM89Dust()],
effect_names=['host'],
effect_frames=['rest']
)
def finalfitcurve(x, source_name, hml):
try:
source = sncosmo.get_source(source_name)
model = sncosmo.Model(source=source)
#fit to model
z0 = float(spec_z(x[:-7]))
hml_dat = astropy.table.Table(
data=hml,
names=('ptfname', 'time', 'magnitude', 'mag_err', 'flux',
'flux_err', 'zp_new', 'zp', 'ra', 'dec', 'zpsys', 'filter'),
dtype=('str', 'float', 'float', 'float', 'float', 'float', 'float',
'float', 'float', 'float', 'str', 'str'))
res, fitted_model = sncosmo.fit_lc(hml_dat,
model, ['z', 't0', 'amplitude'],
bounds={'z': (z0, z0 + 0.0001)},
nburn=10000,
nsamples=50000)
#The following excludes data with not enough distribution..
##Ensuring at least 2 data points on either side of peak.
limit = modellimit(source_name, x[:-7], res.parameters[1])
j = 0
k = 0
for i in hml[:, 1]:
if float(i) > float(limit[2]):
j += 1
else:
k += 1
if j >= 2 and k >= 2:
return np.array([
float(res.chisq / res.ndof), hml[:, 0][0], source_name
]), hml #returns reduced chi squared, sn name and model name.
except ValueError:
print 'error'
def load_dm15(name=None, version=None):
# hack to get hsiao template arrays
hsiao = sncosmo.get_source('hsiao')
phase = hsiao._phase
wave = hsiao._wave
flux = hsiao.flux(phase, wave)
return DM15Source(phase, wave, flux, name=name, version=version)
def showcurve(sn, source_name):
try:
hml=np.load('/home/fcm1g13/Documents/Supernova/CorecollapseLC/ccdata/' + sn)
source=sncosmo.get_source(source_name)
model=sncosmo.Model(source=source)
# print len(hml[:,1]), 'data points'
# Adding zpsys and filter columns. zp system used is ab and filter used is ptf48r
ab = np.zeros(len(hml[:, 1]), dtype='|S2')
band = np.zeros(len(hml[:, 1]), dtype='|S6')
for i in range(len(ab)):
ab[i] = 'ab'
band[i] = 'ptf48r'
hml = np.column_stack((hml, ab, band))
hml_dat=astropy.table.Table(data=hml, names=('ptfname', 'time', 'magnitude', 'mag_err', 'flux', 'flux_err', 'zp_new', 'zp', 'ra', 'dec', 'zpsys', 'filter'), dtype=('str','float','float','float','float','float','float','float','float','float','str', 'str'))
###fitting model
res, fitted_model=sncosmo.fit_lc(hml_dat, model, ['z','t0','amplitude'], bounds={'z':(0.005,0.35)}, nburn=10000, nsamples=50000)
#print 'peak', float(res.parameters[1])
print res
sncosmo.plot_lc(hml_dat, model=fitted_model, errors=res.errors, color='blue', figtext=str(hml[:,0][0]+' Type'+types[hml[:,0][0]]+'\n'+'Model name: '+ source.name), xfigsize=10)
#plt.clfig()
plt.show()
#print #'### Parameters ###'
#print 'SN',str(hml[:,0][0]), 'z:',float(res.parameters[0])# float(res.errors['z']), float(res.parameters[1]), float(res.errors['t0']),float(res.parameters[2]), float(res.errors['x0']), float(res.parameters[3]), float(res.errors['x1']), float(res.parameters[4]), float(res.errors['c']), float(hml[:,8][0]), float(hml[:,9][0])
print 'Done:', hml[:,0][0], 'z:',float(res.parameters[0]), 'Reduced chi^2:', res.chisq/res.ndof,#'Data points:', len(hml[:,1]),' Type'+types[hml[:,0][0]]+'Model name: '+ source.name,'\n' #'Dof:', res.ndof
except ValueError:
sn, source_name, 'cannot be plotted'
def test_salt2source_rcov_vs_snfit():
dirname = os.path.join(os.path.dirname(__file__), "data")
# read parameters and times
f = open(os.path.join(dirname, "salt2_rcov_params_times.dat"), 'r')
meta = read_header(f)
times = np.loadtxt(f)
f.close()
# initialize model and set parameters
source = sncosmo.get_source("salt2", version="2.4") # fixed version
model = sncosmo.Model(source)
model.set(z=meta['Redshift'],
t0=meta['DayMax'],
x0=meta['X0'],
x1=meta['X1'],
c=meta['Color'])
# Test separate bands separately, as thats how they're written to files.
# (And cross-band covariance is zero.)
for band in ('SDSSg', 'SDSSr', 'SDSSi'):
fname = os.path.join(dirname, "salt2_rcov_snfit_{}.dat".format(band))
ref = np.loadtxt(fname, skiprows=1)
rcov = model._bandflux_rcov(band, times)
assert_allclose(ref, rcov, rtol=6.e-5)
def test_salt2source_timeseries_vs_snfit():
"""Test timeseries output from SALT2Source vs pregenerated timeseries
from snfit (SALT2 software)."""
source = sncosmo.get_source("salt2", version="2.4") # fixed version
model = sncosmo.Model(source)
dirname = os.path.join(os.path.dirname(__file__), "data")
for fname in ['salt2_timeseries_1.dat',
'salt2_timeseries_2.dat',
'salt2_timeseries_3.dat',
'salt2_timeseries_4.dat']:
f = open(os.path.join(dirname, fname), 'r')
meta = read_header(f)
time, wave, fluxref = sncosmo.read_griddata_ascii(f)
f.close()
# The output from snfit's Salt2Model.SpectrumFlux() has a
# different definition than sncosmo's model.flux() by a factor
# of a^2. snfit's definition is the rest-frame flux but at a
# blue-shifted wavelength. (There is no correction for photon
# energy or time dilation. These corrections are made in the
# integration step.)
a = 1. / (1. + meta['Redshift'])
fluxref *= a**2
model.set(z=meta['Redshift'], t0=meta['DayMax'], x0=meta['X0'],
x1=meta['X1'], c=meta['Color'])
flux = model.flux(time, wave)
# super good agreement!
assert_allclose(flux, fluxref, rtol=1e-13)
def fitcurve(x, source_name, hml):
try:
source=sncosmo.get_source(source_name)
model=sncosmo.Model(source=source)
#adding zpsys and filter columns
ab=np.zeros(len(hml[:,1]), dtype='|S2')
for i in range(len(ab)):
ab[i]='ab'
hml=np.column_stack((hml,ab))
band=np.zeros(len(hml[:,1]), dtype='|S6')
for i in range(len(band)):
band[i]='ptf48r'
hml=np.column_stack((hml,band))
#fit to model
z0 = float(spec_z(x[:-7]))
hml_dat=astropy.table.Table(data=hml, names=('ptfname', 'time', 'magnitude', 'mag_err', 'flux', 'flux_err', 'zp_new', 'zp', 'ra', 'dec', 'zpsys', 'filter'), dtype=('str','float','float','float','float','float','float','float','float','float','str', 'str'))
res, fitted_model=sncosmo.fit_lc(hml_dat, model, ['z','t0','amplitude'], bounds={'z':(z0,z0 + 0.0001)}, nburn=10000, nsamples=50000)
#The following excludes data points not in the range of the model and data sets with fewer than 4 data points
limit = modellimit(source_name, x[:-7], res.parameters[1])
hml2 = []
for j in range(len(hml[:,1])):
datapoint = hml [:,1][j]
if (res.parameters[1]- limit[0])< float(datapoint) < (res.parameters[1]+limit[1]):
hml2.append(hml[j])
hml2 = np.array(hml2)
if len(hml2)>3:
return finalfitcurve(x, source_name, hml2)
except ValueError:
print 'error'
def model_for_fit(self):
#print 'there man, to fit'
dust = sncosmo.OD94Dust()
self.fit_model = 'salt2-extended'
self.fit_version = '1.0'
source = sncosmo.get_source(self.fit_model, version=self.fit_version)
self.SN_fit_model = sncosmo.Model(source=source,
effects=[dust, dust],
effect_names=['host', 'mw'],
effect_frames=['rest', 'obs'])
self.SN_fit_model.set(z=self.z)
self.SN_fit_model.set_source_peakabsmag(self.peakAbsMagBesselB,
'bessellB',
'vega',
cosmo=cosmology.WMAP9)
self.lsstmwebv = EBVbase()
self.ebvofMW = self.lsstmwebv.calculateEbv(
equatorialCoordinates=np.array([[np.radians(self.radeg)],
[np.radians(self.decdeg)]]))[0]
self.SN_fit_model.set(mwebv=self.ebvofMW)
self.SN_fit_model.set(mwebv=0.)
def fitting(self, sn_name):
"""Fit the light curve for a SN (data as Astropy table) using the model
SALT2 z and mwebv are setted, it returns the values of the SALT2 parameters
and their errors. We plot the fit on the notebook.
"""
self.read(sn_name)
self.source = sncosmo.get_source('salt2', version='2.4')
dust = sncosmo.CCM89Dust()
self.model = sncosmo.Model(source=self.source,
effects=[dust],
effect_names=['mw'],
effect_frames=['obs'])
self.model.set(mwebv=self.mwebv)
self.model.set(z=self.zhl)
#self.model.set(c=0)
try:
res, fitted_model = sncosmo.fit_lc(self.data,
self.model,
['t0', 'x1', 'c', 'x0'],
phase_range=(-15, 45))
#res, fitted_model = sncosmo.fit_lc(self.data, self.model, ['t0','x1', 'x0'])
#return res, fitted_model
except:
#except RuntimeError:
print('Fail:', sn_name)
res, fitted_model = 'fit fail', np.nan
return res, fitted_model
def test_sugarsource():
"""Test timeseries output from SUGARSource vs pregenerated timeseries
from the original files."""
source = sncosmo.get_source("sugar")
model = sncosmo.Model(source)
q1 = [-1, 0, 1, 2]
q2 = [1, 0, -1, -2]
q3 = [-1, 1, 0, -2]
Av = [-0.1, 0, 0.2, 0.5]
q0 = [10**(-0.4 * 34), 10**(-0.4 * 33), 10**(-0.4 * 38), 10**(-0.4 * 42)]
time = np.linspace(-5, 30, 10)
wave = np.linspace(4000, 8000, 10)
for i in range(len(q1)):
fluxref = sugar_model(q0=q0[i],
q1=q1[i],
q2=q2[i],
q3=q3[i],
Av=Av[i],
phase=time,
wave=wave)
model.set(z=0, t0=0, q0=q0[i], q1=q1[i], q2=q2[i], q3=q3[i], Av=Av[i])
flux = model.flux(time, wave)
assert_allclose(flux, fluxref, rtol=1e-13)
def fitting_z(self, sn_name):
"""SALT2 fit using SNCOSMO for the SN without => z free parameter of the
fit
"""
self.read(sn_name)
self.source = sncosmo.get_source('salt2', version='2.4')
dust = sncosmo.CCM89Dust()
self.model = sncosmo.Model(source=self.source,
effects=[dust],
effect_names=['mw'],
effect_frames=['obs'])
self.model.set(mwebv=self.mwebv)
#self.model.set(c=0)
try:
results, fit_model = sncosmo.fit_lc(self.data, self.model,
['t0', 'x1', 'c', 'x0'])
#res, fitted_model = sncosmo.fit_lc(self.data, self.model, ['t0','x1', 'x0'])
#return res, fitted_model
except:
#except RuntimeError:
print('Fail:', sn_name)
results, fit_model = 'fit fail', np.nan
return results, fit_model
def lightcurve_Ibc(filter, z, hostebv_Ibc):
"""Given a filter, redshift z at given phase, generates the observed
magnitude for SNe Type Ibc"""
zp = zero_point[filter]
zpsys = zero_point['zpsys']
model_Ibc = ['s11-2005hl', 's11-2005hm', 's11-2006fo', 'nugent-sn1bc',
'nugent-hyper', 's11-2006jo', 'snana-2004fe',
'snana-2004gq', 'snana-sdss004012', 'snana-2006fo',
'snana-sdss014475', 'snana-2006lc', 'snana-04d1la',
'snana-04d4jv', 'snana-2004gv', 'snana-2006ep',
'snana-2007y', 'snana-2004ib', 'snana-2005hm',
'snana-2006jo', 'snana-2007nc']
obsflux_Ibc = []
phase_arrays = []
for i in model_Ibc:
model_i = sncosmo.Model(source=sncosmo.get_source(i), effects=[dust],
effect_names=['host'], effect_frames=['rest'])
mabs = -17.56
model_i.set(z=z)
phase_array_i = np.linspace(model_i.mintime(), model_i.maxtime(), 100)
model_i.set_source_peakabsmag(mabs, 'bessellb', 'ab')
p_core_collapse = {'z': z, 't0': t0, 'hostebv': hostebv_Ibc,
'hostr_v': hostr_v}
model_i.set(**p_core_collapse)
phase_arrays.append(phase_array_i)
obsflux_i = model_i.bandflux(filter, phase_array_i, zp, zpsys)
obsflux_Ibc.append(obsflux_i)
keys = model_Ibc
values = []
for i, item in enumerate(model_Ibc):
values.append([obsflux_Ibc[i], phase_arrays[i]])
dict_Ibc = dict(zip(keys, values))
return (dict_Ibc)
def __init__(self,
model='salt2-extended',
version='1.0',
z=0.1,
telescope=None,
Plot=False,
bands='ugrizy'):
self.Plot = Plot
#transmission=telescope.throughputs
self.z = z
#bands=[b[-1:] for b in np.unique(select['band'])]
self.model = model
self.version = version
#print 'hello',bands,telescope.airmass
"""
for filtre in bands:
if telescope.airmass > 0:
band=sncosmo.Bandpass(transmission.atmosphere[filtre].wavelen,transmission.atmosphere[filtre].sb, name='LSST::'+filtre,wave_unit=u.nm)
else:
band=sncosmo.Bandpass(transmission.system[filtre].wavelen,transmission.system[filtre].sb, name='LSST::'+filtre,wave_unit=u.nm)
sncosmo.registry.register(band, force=True)
"""
source = sncosmo.get_source(model, version=version)
dust = sncosmo.OD94Dust()
self.SN_fit_model = sncosmo.Model(source=source)
self.SN_fit_model.set(z=self.z)
def select_model(x):
# Load file containing data set
hml = np.load(os.path.abspath('') + '/ccdata/' + x)
# print len(hml[:,1]), 'data points' #number of rows and therefore data points
# Adding zpsys and filter columns. zp system used is ab and filter used is ptf48r
ab = np.zeros(len(hml[:, 1]), dtype='|S2')
band = np.zeros(len(hml[:, 1]), dtype='|S6')
for i in range(len(ab)):
ab[i] = 'ab'
band[i] = 'ptf48r'
hml = np.column_stack((hml, ab, band))
# Converting into a table using astropy with titles: ptfname, time,
# magnitude, mag_err, flux, flux_err, zp_new, zp, ra, dec, zpsys and filter
hml_dat = astropy.table.Table(
data=hml,
names=('ptfname', 'time', 'magnitude', 'mag_err', 'flux', 'flux_err',
'zp_new', 'zp', 'ra', 'dec', 'zpsys', 'filter'),
dtype=('str', 'float', 'float', 'float', 'float', 'float', 'float',
'float', 'float', 'float', 'str', 'str'))
#sn types are Ib, Ib/c, Ic, Ic-BL, IIP
sn_type = types[hml[:, 0][0]]
# Selecting only Type II supernovae
if sn_type[1] == 'I':
# Selecting model
source = sncosmo.get_source('s11-2004hx', version='1.0')
fit_curve(hml_dat, source, hml)
# Selecting only Type Ic supernovae
elif sn_type[1] == 'c':
# Selecting model
source = sncosmo.get_source('snana-2004fe', version='1.0')
fit_curve(hml_dat, source, hml)
# Selecting TypeIb sn only
elif len(sn_type) == 2 and (sn_type[1] != 'b'):
# Selecting model
source = sncosmo.get_source('snana-2006ep', version='1.0')
fit_curve(hml_dat, source, hml)
# Selecting TypeIbc sn only
elif len(sn_type) > 2 and (sn_type[1] != 'b'):
# Selecting model
source = sncosmo.get_source('nugent-sn1bc', version='1.1')
fit_curve(hml_dat, source, hml)
def select_model(x):
# Load file containing data set
hml = np.load(os.path.abspath('') + '/ccdata/' + x)
# print len(hml[:,1]), 'data points' #number of rows and therefore data points
# Adding zpsys and filter columns. zp system used is ab and filter used is ptf48r
ab = np.zeros(len(hml[:, 1]), dtype='|S2')
band = np.zeros(len(hml[:, 1]), dtype='|S6')
for i in range(len(ab)):
ab[i] = 'ab'
band[i] = 'ptf48r'
hml = np.column_stack((hml, ab, band))
# Converting into a table using astropy with titles: ptfname, time,
# magnitude, mag_err, flux, flux_err, zp_new, zp, ra, dec, zpsys and filter
hml_dat = astropy.table.Table(data=hml, names=(
'ptfname', 'time', 'magnitude', 'mag_err', 'flux', 'flux_err', 'zp_new', 'zp', 'ra', 'dec', 'zpsys',
'filter'),
dtype=(
'str', 'float', 'float', 'float', 'float', 'float', 'float', 'float', 'float',
'float', 'str', 'str'))
#sn types are Ib, Ib/c, Ic, Ic-BL, IIP
sn_type = types[hml[:, 0][0]]
# Selecting only Type II supernovae
if sn_type[1] == 'I':
# Selecting model
source = sncosmo.get_source('s11-2004hx',version='1.0')
fit_curve(hml_dat, source, hml)
# Selecting only Type Ic supernovae
elif sn_type[1]== 'c':
# Selecting model
source = sncosmo.get_source('snana-2004fe',version='1.0')
fit_curve(hml_dat, source, hml)
# Selecting TypeIb sn only
elif len(sn_type)== 2 and (sn_type[1]!= 'b'):
# Selecting model
source = sncosmo.get_source('snana-2006ep', version='1.0')
fit_curve(hml_dat, source, hml)
# Selecting TypeIbc sn only
elif len(sn_type)> 2 and (sn_type[1]!= 'b'):
# Selecting model
source = sncosmo.get_source('nugent-sn1bc', version='1.1')
fit_curve(hml_dat, source, hml)
def Gen_SN():
source = sncosmo.get_source('salt2', version='2.4')
model = sncosmo.Model(source=source)
timearray = range(100)
mabs = -19.05 # Setting the absolute Magnitude of the Supernova
model.set(z=0.01, t0=30) # Setting redshift
model.set_source_peakabsmag(mabs, 'bessellb', 'ab',
cosmo=FlatLambdaCDM(H0=70, Om0=0.3)) # Fixing my peak absolute magnitude
# model.set(x1=x_1, c=colour)
'''
absmagb =model.source_peakabsmag('bessellb','ab', cosmo=FlatLambdaCDM(H0=70,Om0=0.3))
absmag_r =model.source_peakabsmag('bessellr','ab', cosmo=FlatLambdaCDM(H0=70,Om0=0.3))
band=sncosmo.get_bandpass('bessellr') #Retrieving the ptf48r bandpass
maglc=model.bandmag('bessellb','ab',timearray) #Creating a magnitude array of the lightcurve
fluxlc=model.bandflux('bessellb',timearray) #Creating a flux array of the lightcurve
maglc2=model.bandmag('bessellr','ab',timearray) #Creating a magnitude array of the lightcurve
'''
data = np.loadtxt('/home/fcm1g13/Documents/Supernova/PTF48R filter/PTF48R.dat')
wavelength = np.array([row[0] for row in data])
transmission = np.array([row[1] for row in data])
band = sncosmo.Bandpass(wavelength, transmission, name='PTF48R')
wave = np.arange(5580., 7500., 20)
spec = model.flux([20., 30., 35., 40., 50.], wave)
adjspec = transmission * spec[1]
'''
for point in transmission:
for flux in spec[1]:
adjspec.append(point*flux)
'''
print len(transmission)
print len(spec[1])
print adjspec
plt.plot(wave, spec[1], color='#27ae60', label='Flux')
model.bandflux('PTF48R', 30)
plt.plot(wave, np.array(adjspec), color='#2980b9', label='Observed flux')
plt.plot(wavelength, transmission * max(spec[1]))
# plt.plot(wave, spec[2], color = '#27ae60',label = '5 days after peak')
# plt.plot(wave, spec[3], color = '#c0392b',label = '10 days after peak')
# plt.plot(wave, spec[4], color = '#8e44ad',label = '20 days after peak')
plt.title('Model spectrum of Type Ia supernova at peak')
plt.ylabel('Flux in ergs / s / cm^2 / $\AA$')
plt.xlabel('Wavelength in $\AA$')
plt.minorticks_on()
plt.ylim([0, max(spec[1]) * 1.2])
plt.legend()
plt.draw()
return timearray
def fitcurve(x, source_name):
# Get data set needed
hml = np.load('/home/fcm1g13/Documents/Supernova/CorecollapseLC/ccdata/' +
x)
try:
# Import model
source = sncosmo.get_source(source_name)
model = sncosmo.Model(source=source)
# print len(hml[:,1]), 'data points'
# Adding zpsys and filter columns. zp system used is ab and filter used is ptf48r
ab = np.zeros(len(hml[:, 1]), dtype='|S2')
band = np.zeros(len(hml[:, 1]), dtype='|S6')
for i in range(len(ab)):
ab[i] = 'ab'
band[i] = 'ptf48r'
hml = np.column_stack((hml, ab, band))
# Converting into a table using astropy with titles: ptfname, time,
# magnitude, mag_err, flux, flux_err, zp_new, zp, ra, dec, zpsys and filter
hml_dat = astropy.table.Table(
data=hml,
names=('ptfname', 'time', 'magnitude', 'mag_err', 'flux',
'flux_err', 'zp_new', 'zp', 'ra', 'dec', 'zpsys', 'filter'),
dtype=('str', 'float', 'float', 'float', 'float', 'float', 'float',
'float', 'float', 'float', 'str', 'str'))
# Fitting model: model parameter z bound.
res, fitted_model = sncosmo.fit_lc(hml_dat,
model, ['z', 't0', 'amplitude'],
bounds={'z': (0.005, 0.35)})
#The following excludes data with fewer than 4 data points and not enough distribution..
j = 0
k = 0
for i in hml[:, 1]:
if float(i) > float(res.parameters[1]):
j += 1
else:
k += 1
# print hml[:,0][0],k,j
if j >= 2 and k >= 2:
if len(hml[:, 1]) > 3:
#sncosmo.plot_lc(hml_dat, model=fitted_model, errors=res.errors, color='blue', figtext=str(hml[:,0][0]+' Type'+types[hml[:,0][0]]+'\n'+'Model name: '+ source.name), xfigsize=10)
#plt.close()
return np.array([
float(res.chisq / res.ndof), hml[:, 0][0], source_name
]) #returns reduced chi squared, sn name and model name.
else:
pass
else:
pass
except ValueError:
pass
def Fit(self):
#print self.table_for_fit
t=self.table_for_fit
select=t[np.where(np.logical_and(t['flux']/t['fluxerr']>5.,t['flux']>0.))]
idx=select['band']!='LSST::u'
select=select[idx]
if self.z > 0.35:
idx=select['band']!='LSST::g'
select=select[idx]
for filtre in bands:
band=sncosmo.Bandpass(self.transmission.atmosphere[filtre].wavelen, self.transmission.atmosphere[filtre].sb, name='LSST::'+filtre,wave_unit=u.nm)
sncosmo.registry.register(band, force=True)
source=sncosmo.get_source(self.model,version=self.version)
dust = sncosmo.OD94Dust()
"""
SN_fit_model=sncosmo.Model(source=source,effects=[dust, dust],
effect_names=['host', 'mw'],
effect_frames=['rest', 'obs'])
"""
SN_fit_model=sncosmo.Model(source=source)
SN_fit_model.set(z=self.z)
SN_fit_model.set_source_peakabsmag(self.peakAbsMagBesselB, 'bessellB', 'vega',cosmo=self.astropy_cosmo)
"""
lsstmwebv = EBVbase()
ebvofMW = lsstmwebv.calculateEbv(
equatorialCoordinates=np.array([[np.radians(self.radeg)], [np.radians(self.decdeg)]]))[0]
SN_fit_model.set(mwebv=ebvofMW)
"""
z_sim=self.z
self.sigma_c=0.
try:
res, fitted_model = sncosmo.fit_lc(select, SN_fit_model,['t0', 'x0', 'x1', 'c'],bounds={'z':(z_sim-0.1, z_sim+0.1)})
self.sigma_c=res['errors']['c']
"""
print z_sim,self.sigma_c
if z_sim>=0.409 and z_sim<0.411:
sncosmo.plot_lc(select, model=fitted_model,color='k',pulls=True,errors=res.errors)
plt.show()
"""
except (RuntimeError, TypeError, NameError):
print 'crashed'
for sel in select:
print sel
print self.z
self.Plot_bands(select)
plt.show()
print 'crashed'
def luminosity(**kwargs):
source = sncosmo.get_source("nugent-sn1bc")
source.set_peakmag(-17.5+kwargs['x0'], 'desg', 'ab') # magnitude at 10pc
dust = sncosmo.CCM89Dust()
model = sncosmo.Model(source=source,effects=[dust], effect_names=['host'], effect_frames=['rest'])
kwargs2 = dict(kwargs)
del kwargs2['x0']
model.set(**kwargs2)
return model
def test_register_source():
phase = np.linspace(0., 100., 10)
wave = np.linspace(800., 20000., 100)
flux = np.ones((len(phase), len(wave)), dtype=float)
source = sncosmo.TimeSeriesSource(phase, wave, flux, name='test_source')
sncosmo.register(source)
# Retrieving a source makes a copy, so just check that the names are the
# same.
assert sncosmo.get_source('test_source').name == 'test_source'
def luminosity(**kwargs):
source = sncosmo.get_source("nugent-sn1bc")
source.set_peakmag(-17.5 + kwargs["x0"], "desg", "ab") # magnitude at 10pc
dust = sncosmo.CCM89Dust()
model = sncosmo.Model(source=source, effects=[dust], effect_names=["host"], effect_frames=["rest"])
kwargs2 = dict(kwargs)
del kwargs2["x0"]
model.set(**kwargs2)
return model
def luminosity(**kwargs):
source = sncosmo.get_source('salt2')
source.set_peakmag(-19.5-SNIa.alpha*kwargs['x1']+SNIa.beta*kwargs['c']+kwargs['x0'], 'desg', 'ab') # magnitude at 10pc
dust = sncosmo.CCM89Dust()
model = sncosmo.Model(source=source,effects=[dust], effect_names=['host'], effect_frames=['rest'])
kwargs2 = dict(kwargs)
del kwargs2['x0']
model.set(**kwargs2)
return model
def luminosity(**kwargs):
source = sncosmo.get_source("salt2")
source.set_peakmag(
-19.5 - SNIa.alpha * kwargs["x1"] + SNIa.beta * kwargs["c"] + kwargs["x0"], "desg", "ab"
) # magnitude at 10pc
dust = sncosmo.CCM89Dust()
model = sncosmo.Model(source=source, effects=[dust], effect_names=["host"], effect_frames=["rest"])
kwargs2 = dict(kwargs)
del kwargs2["x0"]
model.set(**kwargs2)
return model
def model_limits(sou):
source = sncosmo.get_source(sou)
model = sncosmo.Model(source=source)
timearray = range(-20, 300)
fluxlc = model.bandflux('ptf48r',timearray) # Creating a flux array of the light curve
modelbreak = []
for i in range(len(fluxlc)-1):
if fluxlc[i] == fluxlc[i+1]:
modelbreak.append(timearray[i+1])
for i in range(len(modelbreak)-1):
if (modelbreak[i+1]-modelbreak[i]) > 20:
return sou, modelbreak[i], modelbreak[i+1]
def get_snana_filenames(sntype):
"""
"""
if not sntype.startswith('SN '):
sntype = 'SN %s'%sntype
reg = sncosmo.registry._get_registry(sncosmo.Source)
source_tuples = [(v['name'], v['version'])
for v in reg.get_loaders_metadata()
if v['name'].startswith('snana')
and v['type'] == sntype]
filenames = []
for name, version in source_tuples:
filepath = os.path.join(sncosmo.builtins.get_cache_dir(),
'sncosmo',
reg._loaders[(name, version)][1])
if not os.exists(filepath):
sncosmo.get_source(name, version=version)
filenames.append(filepath)
return filenames
def showcurve(sn, source_name):
try:
hml = np.load(
'/home/fcm1g13/Documents/Supernova/CorecollapseLC/ccdata/' + sn)
source = sncosmo.get_source(source_name)
model = sncosmo.Model(source=source)
# print len(hml[:,1]), 'data points'
# Adding zpsys and filter columns. zp system used is ab and filter used is ptf48r
ab = np.zeros(len(hml[:, 1]), dtype='|S2')
band = np.zeros(len(hml[:, 1]), dtype='|S6')
for i in range(len(ab)):
ab[i] = 'ab'
band[i] = 'ptf48r'
hml = np.column_stack((hml, ab, band))
hml_dat = astropy.table.Table(
data=hml,
names=('ptfname', 'time', 'magnitude', 'mag_err', 'flux',
'flux_err', 'zp_new', 'zp', 'ra', 'dec', 'zpsys', 'filter'),
dtype=('str', 'float', 'float', 'float', 'float', 'float', 'float',
'float', 'float', 'float', 'str', 'str'))
###fitting model
res, fitted_model = sncosmo.fit_lc(hml_dat,
model, ['z', 't0', 'amplitude'],
bounds={'z': (0.005, 0.35)},
nburn=10000,
nsamples=50000)
#print 'peak', float(res.parameters[1])
print res
sncosmo.plot_lc(
hml_dat,
model=fitted_model,
errors=res.errors,
color='blue',
figtext=str(hml[:, 0][0] + ' Type' + types[hml[:, 0][0]] + '\n' +
'Model name: ' + source.name),
xfigsize=10)
#plt.clfig()
plt.show()
#print #'### Parameters ###'
#print 'SN',str(hml[:,0][0]), 'z:',float(res.parameters[0])# float(res.errors['z']), float(res.parameters[1]), float(res.errors['t0']),float(res.parameters[2]), float(res.errors['x0']), float(res.parameters[3]), float(res.errors['x1']), float(res.parameters[4]), float(res.errors['c']), float(hml[:,8][0]), float(hml[:,9][0])
print 'Done:', hml[:, 0][0], 'z:', float(
res.parameters[0]
), 'Reduced chi^2:', res.chisq / res.ndof, #'Data points:', len(hml[:,1]),' Type'+types[hml[:,0][0]]+'Model name: '+ source.name,'\n' #'Dof:', res.ndof
except ValueError:
sn, source_name, 'cannot be plotted'
def fitcurve(x, source_name):
# Get data set needed
hml=np.load('/home/fcm1g13/Documents/Supernova/CorecollapseLC/ccdata/' + x)
try:
# Import model
source=sncosmo.get_source(source_name)
model=sncosmo.Model(source=source)
# print len(hml[:,1]), 'data points'
# Adding zpsys and filter columns. zp system used is ab and filter used is ptf48r
ab = np.zeros(len(hml[:, 1]), dtype='|S2')
band = np.zeros(len(hml[:, 1]), dtype='|S6')
for i in range(len(ab)):
ab[i] = 'ab'
band[i] = 'ptf48r'
hml = np.column_stack((hml, ab, band))
# Converting into a table using astropy with titles: ptfname, time,
# magnitude, mag_err, flux, flux_err, zp_new, zp, ra, dec, zpsys and filter
hml_dat = astropy.table.Table(data=hml, names=(
'ptfname', 'time', 'magnitude', 'mag_err', 'flux', 'flux_err', 'zp_new', 'zp', 'ra', 'dec', 'zpsys',
'filter'),
dtype=(
'str', 'float', 'float', 'float', 'float', 'float', 'float', 'float', 'float',
'float', 'str', 'str'))
# Fitting model: model parameter z bound.
res, fitted_model = sncosmo.fit_lc(hml_dat, model, ['z', 't0', 'amplitude'], bounds={'z': (0.005, 0.35)})
#The following excludes data with fewer than 4 data points and not enough distribution..
j=0; k=0
for i in hml[:,1]:
if float(i)>float(res.parameters[1]):
j+=1
else:
k+=1
# print hml[:,0][0],k,j
if j>=2 and k>=2:
if len(hml[:,1])>3:
#sncosmo.plot_lc(hml_dat, model=fitted_model, errors=res.errors, color='blue', figtext=str(hml[:,0][0]+' Type'+types[hml[:,0][0]]+'\n'+'Model name: '+ source.name), xfigsize=10)
#plt.close()
return np.array([float(res.chisq/res.ndof), hml[:,0][0], source_name]) #returns reduced chi squared, sn name and model name.
else:
pass
else:
pass
except ValueError:
pass
def __init__(self):
"""A version of the Hsiao model with an added stretch parameter
This source class is equivalent to that of the default Hsiao model,
except an additional parameter ``x1`` has been included.
"""
super().__init__()
self._parent = sncosmo.get_source('hsiao')
self.name = 'hsiao_x1'
self.version = '3.0.x1'
self._wave = self._parent._wave
self._parameters = np.array([1., 0])
hsiao_phase = self._parent._phase
self._phase = hsiao_phase[(hsiao_phase >= -18) & (hsiao_phase <= 85)]
def Gen_SN():
# Use below if on Iridis
# source = sncosmo.SALT2Source(modeldir="/scratch/cf5g09/Monte_Carlos/salt2-4")
##Use below if not on iridis
source = sncosmo.get_source('salt2', version='2.4')
model = sncosmo.Model(source=source)
timearray = range(100)
mabs = -19.05 # Setting the absolute Magnitude of the Supernova
model.set(z=0.01, t0=30) # Setting redshift
model.set_source_peakabsmag(mabs, 'ptf48r', 'ab',
cosmo=FlatLambdaCDM(H0=70, Om0=0.3)) # Fixing my peak absolute magnitude
# model.set(x1=x_1, c=colour)
absmag_r = model.source_peakabsmag('ptf48r', 'ab', cosmo=FlatLambdaCDM(H0=70, Om0=0.3))
print absmag_r
band = sncosmo.get_bandpass('ptf48r') # Retrieving the ptf48r bandpass
maglc = model.bandmag('ptf48r', 'ab', timearray) # Creating a magnitude array of the lightcurve
fluxlc = model.bandflux('ptf48r', timearray) # Creating a flux array of the lightcurve
model2 = model
model2.set_source_peakabsmag(mabs, 'bessellr', 'ab',
cosmo=FlatLambdaCDM(H0=70, Om0=0.3)) # Fixing my peak absolute magnitude
# model.set(x1=x_1, c=colour)
absmag_r = model2.source_peakabsmag('bessellr', 'ab', cosmo=FlatLambdaCDM(H0=70, Om0=0.3))
maglc2 = model2.bandmag('bessellr', 'ab', timearray) # Creating a magnitude array of the lightcurve
fluxlc2 = model2.bandflux('bessellr', timearray) # Creating a magnitude array of the lightcurve
plt.scatter(timearray, fluxlc, color='blue', label='ptf48r')
plt.scatter(timearray, fluxlc2, color='red', label='bessellr')
model.bandflux('PTF48R', 30)
# plt.gca().invert_yaxis()
plt.title('SNTypeIa peak 30 days')
plt.legend()
plt.show()
# print sn_par
return maglc, fluxlc, timearray
def _create_fit_data_iter(priors_hs, priors_bg, kwargs_hs, kwargs_bg):
"""Create an iterable of data used to run light-curve fits
Args:
priors_hs (dict): Priors to use when fitting hsiao
priors_bg (dict): Priors to use when fitting sn91bg
kwargs_hs (dict): Kwargs to pass ``fit_func`` when fitting salt2
kwargs_bg (dict): Kwargs to pass ``fit_func`` when fitting sn91bg
Returns:
- An iterable of models, vparams, priors, and kwargs for fitting
- A table to store output data
"""
# Set default kwargs and protect against mutation
priors_hs = deepcopy(priors_hs) or dict()
priors_bg = deepcopy(priors_bg) or dict()
kwargs_hs = deepcopy(kwargs_hs) or dict()
kwargs_bg = deepcopy(kwargs_bg) or dict()
# Define models for normal and 91bg SNe with host galaxy dust
dust_kw = dict(effects=[DUST], effect_names=['mw'], effect_frames=['obs'])
bg_source = sncosmo.get_source('sn91bg', version='hsiao_phase')
sn91bg = sncosmo.Model(bg_source, **dust_kw)
hsiao = sncosmo.Model('hsiao_x1', **dust_kw)
# Determine what parameters to vary for each model
# Hsiao does not have a c parameter. We don't vary mwebv
vparams = {'z', 't0', 'amplitude', 'x1', 'c'}
out_data = create_empty_table(vparams.union({'mwebv'}))
if 'z' in priors_bg and 'z' in priors_hs:
vparams -= {'z'}
hsiao_vparams = set(hsiao.param_names).intersection(vparams)
sn91bg_vparams = set(sn91bg.param_names).intersection(vparams)
# Create iterators over the data we need to fit
model_args = zip(
(hsiao, sn91bg), # The models
(hsiao_vparams, sn91bg_vparams), # The parameters to vary
(priors_hs, priors_bg), # The priors
(kwargs_hs, kwargs_bg) # The fitting kwargs
)
return model_args, out_data
def lightcurve_Ia(filter, z, x1, c, x0=None):
"""Given a filter and redshift z, generates the observed
flux for SNe Type Ia"""
alpha = 0.12
beta = 3.
mabs = -19.1 - alpha*x1 + beta*c
zp = zero_point[filter]
zpsys = zero_point['zpsys']
# Checking if bandpass is outside spectral range for SALT2. If yes,
# use salt2-extended.
salt_name = 'salt2'
salt_version = '2.4'
rest_salt_max_wav = 9200
rest_salt_min_wav = 2000
salt_max_wav = (1 + z) * rest_salt_max_wav
salt_min_wav = (1 + z) * rest_salt_min_wav
band = sncosmo.get_bandpass(filter)
if (band.wave[0] < salt_min_wav or band.wave[-1] > salt_max_wav):
salt_name = 'salt2-extended'
salt_version = '1.0'
# Type Ia model
model_Ia = sncosmo.Model(source=sncosmo.get_source(salt_name,
version=salt_version))
if x0 is not None:
p = {'z': z, 't0': t0, 'x0': x0, 'x1': x1,
'c': c}
else:
p = {'z': z, 't0': t0, 'x1': x1, 'c': c}
model_Ia.set(z=z)
model_Ia.set_source_peakabsmag(mabs, 'bessellb', 'vega')
model_Ia.set(**p)
phase_array = np.linspace(model_Ia.mintime(), model_Ia.maxtime(), 100)
obsflux_Ia = model_Ia.bandflux(filter, phase_array, zp=zp, zpsys=zpsys)
keys = ['phase_array', 'obsflux']
values = [phase_array, obsflux_Ia]
dict_Ia = dict(zip(keys, values))
np.savetxt('test.dat', np.c_[dict_Ia['phase_array'], dict_Ia['obsflux']])
x0 = model_Ia.get('x0')
return (dict_Ia, x0, salt_name, salt_version)
def showcurve(sn, source_name, hml):
try:
source=sncosmo.get_source(source_name)
model=sncosmo.Model(source=source)
#fit to model
z0 = float(spec_z(sn[:-7]))
hml_dat=astropy.table.Table(data=hml, names=('ptfname', 'time', 'magnitude', 'mag_err', 'flux', 'flux_err', 'zp_new', 'zp', 'ra', 'dec', 'zpsys', 'filter'), dtype=('str','float','float','float','float','float','float','float','float','float','str', 'str'))
res, fitted_model=sncosmo.fit_lc(hml_dat, model, ['z','t0','amplitude'], bounds={'z':(z0, z0+0.001)}, nburn=10000, nsamples=50000)
#plot model
sncosmo.plot_lc(hml_dat, model=fitted_model, errors=res.errors, color='blue', figtext=str(hml[:,0][0]+' Type'+types[hml[:,0][0]]+'\n'+'Model name: '+ source.name + '\n'+'Reduced Chi Squared: ')+ str(res.chisq/res.ndof), xfigsize=10)
plt.show()
#print 'Parameters:''z:',float(res.parameters[0]), float(res.errors['z']), float(res.parameters[1]), float(res.errors['t0']),float(res.parameters[2]), float(res.errors['x0']), float(res.parameters[3]), float(res.errors['x1']), float(res.parameters[4]), float(res.errors['c']), float(hml[:,8][0]), float(hml[:,9][0])'
print 'Done:', hml[:,0][0], 'z:',float(res.parameters[0]), 'Reduced chi^2:', res.chisq/res.ndof,#'Data points:', len(hml[:,1]),' Type'+types[hml[:,0][0]]+'Model name: '+ source.name,'\n' #'Dof:', res.ndof
except ValueError:
print sn, source_name, 'cannot be plotted'
def fitcurve(x, source_name, hml):
try:
source = sncosmo.get_source(source_name)
model = sncosmo.Model(source=source)
#adding zpsys and filter columns
ab = np.zeros(len(hml[:, 1]), dtype='|S2')
for i in range(len(ab)):
ab[i] = 'ab'
hml = np.column_stack((hml, ab))
band = np.zeros(len(hml[:, 1]), dtype='|S6')
for i in range(len(band)):
band[i] = 'ptf48r'
hml = np.column_stack((hml, band))
#fit to model
z0 = float(spec_z(x[:-7]))
hml_dat = astropy.table.Table(
data=hml,
names=('ptfname', 'time', 'magnitude', 'mag_err', 'flux',
'flux_err', 'zp_new', 'zp', 'ra', 'dec', 'zpsys', 'filter'),
dtype=('str', 'float', 'float', 'float', 'float', 'float', 'float',
'float', 'float', 'float', 'str', 'str'))
res, fitted_model = sncosmo.fit_lc(hml_dat,
model, ['z', 't0', 'amplitude'],
bounds={'z': (z0, z0 + 0.0001)},
nburn=10000,
nsamples=50000)
#The following excludes data points not in the range of the model and data sets with fewer than 4 data points
limit = modellimit(source_name, x[:-7], res.parameters[1])
hml2 = []
for j in range(len(hml[:, 1])):
datapoint = hml[:, 1][j]
if (res.parameters[1] - limit[0]) < float(datapoint) < (
res.parameters[1] + limit[1]):
hml2.append(hml[j])
hml2 = np.array(hml2)
if len(hml2) > 3:
return finalfitcurve(x, source_name, hml2)
except ValueError:
print 'error'
def get_model_cc(z, sn_type, all_model):
"""Given a filter and redshift z, generates the observed
magnitude for SNe Type Ibc or Type II"""
my_model = choice(all_model)
if my_model in ['s11-2004hx', 'nugent-sn2l']:
sn_type = 'IIl'
elif my_model in ['nugent-sn2n', 'snana-2006ez', 'snana-20069ix']:
sn_type = 'IIn'
model = sncosmo.Model(source=sncosmo.get_source(my_model),
effects=[dust], effect_names=['host'],
effect_frames=['rest'])
mabs = all_mabs(sn_type)
model.set(z=z)
model.set_source_peakabsmag(mabs, 'bessellb', 'vega')
p = {'z': z, 't0': t0, 'hostebv': uniform(-0.1, 0.65), 'hostr_v': hostr_v}
model.set(**p)
p['model_name'] = my_model
max_phase = max_cc_phase(my_model)
my_phase = uniform(-2, 3) + max_phase
return model, p, my_phase
def lightcurve_II(filter, z, hostebv_II):
"""Given a filter and redshift z, generates the observed magnitude for
SNe Type II"""
zp = zero_point[filter]
zpsys = zero_point['zpsys']
model_II = ['s11-2005lc', 's11-2005gi', 's11-2006jl', 'nugent-sn2p',
'snana-2004hx', 'snana-2005gi', 'snana-2006gq',
'snana-2006kn', 'snana-2006jl', 'snana-2006iw',
'snana-2006kv', 'snana-2006ns', 'snana-2007iz',
'snana-2007nr', 'snana-2007nr', 'snana-2007kw',
'snana-2007ky', 'snana-2007lj', 'snana-2007lb',
'snana-2007ll', 'snana-2007nw', 'snana-2007ld',
'snana-2007md', 'snana-2007lz', 'snana-2007lx',
'snana-2007og', 'snana-2007ny', 'snana-2007nv',
'snana-2007pg', 's11-2004hx', 'nugent-sn2l', 'nugent-sn2n',
'snana-2006ez', 'snana-2006ix']
obsflux_II = []
phase_arrays = []
for i in model_II:
model_i = sncosmo.Model(source=sncosmo.get_source(i), effects=[dust],
effect_names=['host'],
effect_frames=['rest'])
if i == 's11-2004hx' == 'nugent-sn2l':
mabs = -17.98
else:
mabs = -16.75
model_i.set(z=z)
phase_array_i = np.linspace(model_i.mintime(), model_i.maxtime(), 100)
model_i.set_source_peakabsmag(mabs, 'bessellb', 'ab')
p_core_collapse = {'z': z, 't0': t0, 'hostebv': hostebv_II,
'hostr_v': hostr_v}
model_i.set(**p_core_collapse)
phase_arrays.append(phase_array_i)
obsflux_i = model_i.bandflux(filter, phase_array_i, zp, zpsys)
obsflux_II.append(obsflux_i)
keys = model_II
values = []
for i, item in enumerate(model_II):
values.append([obsflux_II[i], phase_arrays[i]])
dict_II = dict(zip(keys, values))
return (dict_II)
def add_sncosmo_params(self, glsne_merged_df):
source = sncosmo.get_source('salt2-extended')
model = sncosmo.Model(source=source)
# Use cosmoDC2 settings
cosmo = FlatLambdaCDM(H0=71, Om0=0.265, Tcmb0=0, Neff=3.04, m_nu=None, Ob0=0.045)
x0 = []
for sn_row_zs, sn_row_mb in zip(glsne_merged_df['zs'].values, glsne_merged_df['MB'].values):
z = sn_row_zs
MB = sn_row_mb
model.set(z=z)
model.set_source_peakabsmag(MB, 'bessellb', 'ab', cosmo=cosmo)
x0.append(model.get('x0'))
glsne_merged_df['x0'] = x0
glsne_merged_df['x1'] = 1.
glsne_merged_df['c'] = 0.
return glsne_merged_df
def get_model_Ia(z, min_phase, max_phase):
"""Given a filter, redshift z at given phase, generates the observed
magnitude for SNe Type Ia"""
alpha = 0.12
beta = 3.
x1 = normal(0., 1.)
c = normal(0., 0.1)
my_phase = uniform(min_phase, max_phase)
mabs = normal(-19.1 - alpha * x1 + beta * c, scale=0.15)
salt_name, salt_version = which_salt(z)
model_Ia = sncosmo.Model(
source=sncosmo.get_source(salt_name, version=salt_version))
model_Ia.set(z=z)
model_Ia.set_source_peakabsmag(mabs, 'bessellb', 'vega')
p = {'z': z, 't0': t0, 'x1': x1, 'c': c}
p['x0'] = model_Ia.get('x0')
model_Ia.set(**p)
p['salt_name'] = salt_name
p['salt_version'] = salt_version
return model_Ia, p, my_phase
def get_model_Ia(z, min_phase, max_phase):
"""Given a filter, redshift z at given phase, generates the observed
magnitude for SNe Type Ia"""
alpha = 0.12
beta = 3.
x1 = normal(0., 1.)
c = normal(0., 0.1)
my_phase = uniform(min_phase, max_phase)
mabs = normal(-19.1 - alpha*x1 + beta*c, scale=0.15)
salt_name, salt_version = which_salt(z)
model_Ia = sncosmo.Model(source=sncosmo.get_source(salt_name,
version=salt_version))
model_Ia.set(z=z)
model_Ia.set_source_peakabsmag(mabs, 'bessellb', 'vega')
p = {'z': z, 't0': t0, 'x1': x1, 'c': c}
p['x0'] = model_Ia.get('x0')
model_Ia.set(**p)
p['salt_name'] = salt_name
p['salt_version'] = salt_version
return model_Ia, p, my_phase
def get_model_cc(z, sn_type, all_model):
"""Given a filter and redshift z, generates the observed
magnitude for SNe Type Ibc or Type II"""
my_model = choice(all_model)
if my_model in ['s11-2004hx', 'nugent-sn2l']:
sn_type = 'IIl'
elif my_model in ['nugent-sn2n', 'snana-2006ez', 'snana-20069ix']:
sn_type = 'IIn'
model = sncosmo.Model(source=sncosmo.get_source(my_model),
effects=[dust],
effect_names=['host'],
effect_frames=['rest'])
mabs = all_mabs(sn_type)
model.set(z=z)
model.set_source_peakabsmag(mabs, 'bessellb', 'vega')
p = {'z': z, 't0': t0, 'hostebv': uniform(-0.1, 0.65), 'hostr_v': hostr_v}
model.set(**p)
p['model_name'] = my_model
max_phase = max_cc_phase(my_model)
my_phase = uniform(-2, 3) + max_phase
return model, p, my_phase
def fitcurve(x, source_name, hml):
try:
source = sncosmo.get_source(source_name)
model = sncosmo.Model(source=source)
# print len(hml[:,1]), 'data points'
# Adding zpsys and filter columns. zp system used is ab and filter used is ptf48r
ab = np.zeros(len(hml[:, 1]), dtype='|S2')
band = np.zeros(len(hml[:, 1]), dtype='|S6')
for i in range(len(ab)):
ab[i] = 'ab'
band[i] = 'ptf48r'
hml = np.column_stack((hml, ab, band))
# Converting into a table using astropy with titles: ptfname, time,
# magnitude, mag_err, flux, flux_err, zp_new, zp, ra, dec, zpsys and filter
hml_dat = astropy.table.Table(
data=hml,
names=('ptfname', 'time', 'magnitude', 'mag_err', 'flux',
'flux_err', 'zp_new', 'zp', 'ra', 'dec', 'zpsys', 'filter'),
dtype=('str', 'float', 'float', 'float', 'float', 'float', 'float',
'float', 'float', 'float', 'str', 'str'))
# Fitting model: model parameter z bound.
res, fitted_model = sncosmo.fit_lc(hml_dat,
model, ['z', 't0', 'amplitude'],
bounds={'z': (0.005, 0.35)})
#The following excludes data points not in the range of the model and data sets with fewer than 4 data points
limit = modellimit(source_name, res.parameters[0], res.parameters[1])
hml2 = []
for j in range(len(hml[:, 1])):
datapoint = hml[:, 1][j]
if (res.parameters[1] - limit[0]) < float(datapoint) < (
res.parameters[1] + limit[1]):
hml2.append(hml[j])
hml2 = np.array(hml2)
if len(hml2) > 3:
return finalfitcurve(x, source_name, hml2)
except ValueError:
pass
def modellimit(source_name, x, t0):
source=sncosmo.get_source(source_name)
model=sncosmo.Model(source=source)
#Creating a flux array of the model lightcurve
timearray= np.arange(t0-120,t0+500, 0.01)
model.set(z=spec_z(x),t0=t0)
fluxlc=model.bandflux('ptf48r',timearray)
modelbreak = [t0-120.0]
for i in range(len(fluxlc)-1):
if fluxlc[i] == fluxlc[i+1]:
modelbreak.append(timearray[i+1])
#find the limits of the model by defining where the flux does not change.
for i in range(len(modelbreak)-1):
if abs(modelbreak[i+1]-modelbreak[i]) > 20:
minimum = t0 - modelbreak[i]
maximum = modelbreak[i+1] - t0
#return the difference of t0 and the edges of the model and the time of peak flux
##this is done as some models use t0 as peak and others use it for start of model
return minimum, maximum, timearray[list(fluxlc).index(max(fluxlc))]
def modellimit(source_name, x, t0):
source = sncosmo.get_source(source_name)
model = sncosmo.Model(source=source)
#Creating a flux array of the model lightcurve
timearray = np.arange(t0 - 120, t0 + 500, 0.01)
model.set(z=spec_z(x), t0=t0)
fluxlc = model.bandflux('ptf48r', timearray)
modelbreak = [t0 - 120.0]
for i in range(len(fluxlc) - 1):
if fluxlc[i] == fluxlc[i + 1]:
modelbreak.append(timearray[i + 1])
#find the limits of the model by defining where the flux does not change.
for i in range(len(modelbreak) - 1):
if abs(modelbreak[i + 1] - modelbreak[i]) > 20:
minimum = t0 - modelbreak[i]
maximum = modelbreak[i + 1] - t0
#return the difference of t0 and the edges of the model and the time of peak flux
##this is done as some models use t0 as peak and others use it for start of model
return minimum, maximum, timearray[list(fluxlc).index(max(fluxlc))]
def Plot_LC_sncosmo(table, telescope):
print('What will be plotted', table)
prefix = 'LSST::'
print(table.dtype)
for band in 'ugrizy':
name_filter = prefix + band
if telescope.airmass > 0:
bandpass = sncosmo.Bandpass(telescope.atmosphere[band].wavelen,
telescope.atmosphere[band].sb,
name=name_filter,
wave_unit=u.nm)
else:
bandpass = sncosmo.Bandpass(telescope.system[band].wavelen,
telescope.system[band].sb,
name=name_filter,
wave_unit=u.nm)
# print('registering',name_filter)
sncosmo.registry.register(bandpass, force=True)
source = sncosmo.get_source('salt2-extended', version='1.0')
dust = sncosmo.OD94Dust()
model = sncosmo.Model(source=source)
print('metadata', table.meta)
model.set(z=table.meta['z'],
c=table.meta['Color'],
t0=table.meta['DayMax'],
x1=table.meta['X1'])
z = table.meta['z']
tab = table[['flux', 'fluxerr', 'band', 'zp', 'zpsys', 'time']]
res, fitted_model = sncosmo.fit_lc(tab,
model, ['t0', 'x0', 'x1', 'c'],
bounds={'z': (z - 0.001, z + 0.001)})
print(res)
print('jjj', fitted_model)
sncosmo.plot_lc(data=tab, model=fitted_model, errors=res['errors'])
plt.draw()
plt.pause(20.)
plt.close()
def showcurve(sn, source_name, hml):
try:
source = sncosmo.get_source(source_name)
model = sncosmo.Model(source=source)
#fit to model
z0 = float(spec_z(sn[:-7]))
hml_dat = astropy.table.Table(
data=hml,
names=('ptfname', 'time', 'magnitude', 'mag_err', 'flux',
'flux_err', 'zp_new', 'zp', 'ra', 'dec', 'zpsys', 'filter'),
dtype=('str', 'float', 'float', 'float', 'float', 'float', 'float',
'float', 'float', 'float', 'str', 'str'))
res, fitted_model = sncosmo.fit_lc(hml_dat,
model, ['z', 't0', 'amplitude'],
bounds={'z': (z0, z0 + 0.001)},
nburn=10000,
nsamples=50000)
#plot model
sncosmo.plot_lc(
hml_dat,
model=fitted_model,
errors=res.errors,
color='blue',
figtext=str(hml[:, 0][0] + ' Type' + types[hml[:, 0][0]] + '\n' +
'Model name: ' + source.name + '\n' +
'Reduced Chi Squared: ') + str(res.chisq / res.ndof),
xfigsize=10)
plt.show()
#print 'Parameters:''z:',float(res.parameters[0]), float(res.errors['z']), float(res.parameters[1]), float(res.errors['t0']),float(res.parameters[2]), float(res.errors['x0']), float(res.parameters[3]), float(res.errors['x1']), float(res.parameters[4]), float(res.errors['c']), float(hml[:,8][0]), float(hml[:,9][0])'
print 'Done:', hml[:, 0][0], 'z:', float(
res.parameters[0]
), 'Reduced chi^2:', res.chisq / res.ndof, #'Data points:', len(hml[:,1]),' Type'+types[hml[:,0][0]]+'Model name: '+ source.name,'\n' #'Dof:', res.ndof
except ValueError:
print sn, source_name, 'cannot be plotted'
def finalfitcurve(x, source_name, hml):
try:
source=sncosmo.get_source(source_name)
model=sncosmo.Model(source=source)
#fit to model
z0 = float(spec_z(x[:-7]))
hml_dat=astropy.table.Table(data=hml, names=('ptfname', 'time', 'magnitude', 'mag_err', 'flux', 'flux_err', 'zp_new', 'zp', 'ra', 'dec', 'zpsys', 'filter'), dtype=('str','float','float','float','float','float','float','float','float','float','str', 'str'))
res, fitted_model=sncosmo.fit_lc(hml_dat, model, ['z','t0','amplitude'], bounds={'z':(z0, z0+0.0001)}, nburn=10000, nsamples=50000)
#The following excludes data with not enough distribution..
##Ensuring at least 2 data points on either side of peak.
limit = modellimit(source_name, x[:-7], res.parameters[1])
j=0; k=0
for i in hml[:,1]:
if float(i)>float(limit[2]):
j+=1
else:
k+=1
if j>=2 and k>=2:
return np.array([float(res.chisq/res.ndof), hml[:,0][0], source_name]), hml #returns reduced chi squared, sn name and model name.
except ValueError:
print 'error'
def test_salt2source_rcov_vs_snfit():
dirname = os.path.join(os.path.dirname(__file__), "data")
# read parameters and times
f = open(os.path.join(dirname, "salt2_rcov_params_times.dat"), 'r')
meta = read_header(f)
times = np.loadtxt(f)
f.close()
# initialize model and set parameters
source = sncosmo.get_source("salt2", version="2.4") # fixed version
model = sncosmo.Model(source)
model.set(z=meta['Redshift'], t0=meta['DayMax'], x0=meta['X0'],
x1=meta['X1'], c=meta['Color'])
# Test separate bands separately, as thats how they're written to files.
# (And cross-band covariance is zero.)
for band in ('SDSSg', 'SDSSr', 'SDSSi'):
fname = os.path.join(dirname, "salt2_rcov_snfit_{}.dat".format(band))
ref = np.loadtxt(fname, skiprows=1)
rcov = model._bandflux_rcov(band, times)
assert_allclose(ref, rcov, rtol=5.e-5)
def __init__(self,
model='salt2-extended',
version=1.0,
telescope=None,
display=False,
bands='ugrizy'):
self.display = display
self.bands = bands
# get the bands for sncosmo registration - with and without airmass
for band in bands:
if telescope.airmass > 0:
band = sncosmo.Bandpass(telescope.atmosphere[band].wavelen,
telescope.atmosphere[band].sb,
name='LSST::' + band,
wave_unit=u.nm)
else:
band = sncosmo.Bandpass(telescope.system[band].wavelen,
telescope.system[band].sb,
name='LSST::' + band,
wave_unit=u.nm)
sncosmo.registry.register(band, force=True)
# get the source
source = sncosmo.get_source(model, version=str(version))
# get the dust
dust = sncosmo.OD94Dust()
# sn_fit_model instance
self.SN_fit_model = sncosmo.Model(source=source)
# name parameters
self.parNames = dict(
zip(['z', 't0', 'x0', 'x1', 'c'],
['z', 't0', 'x0', 'x1', 'color']))
def fitcurve(x, source_name, hml):
try:
source=sncosmo.get_source(source_name)
model=sncosmo.Model(source=source)
# print len(hml[:,1]), 'data points'
# Adding zpsys and filter columns. zp system used is ab and filter used is ptf48r
ab = np.zeros(len(hml[:, 1]), dtype='|S2')
band = np.zeros(len(hml[:, 1]), dtype='|S6')
for i in range(len(ab)):
ab[i] = 'ab'
band[i] = 'ptf48r'
hml = np.column_stack((hml, ab, band))
# Converting into a table using astropy with titles: ptfname, time,
# magnitude, mag_err, flux, flux_err, zp_new, zp, ra, dec, zpsys and filter
hml_dat = astropy.table.Table(data=hml, names=(
'ptfname', 'time', 'magnitude', 'mag_err', 'flux', 'flux_err', 'zp_new', 'zp', 'ra', 'dec', 'zpsys',
'filter'),
dtype=(
'str', 'float', 'float', 'float', 'float', 'float', 'float', 'float', 'float',
'float', 'str', 'str'))
# Fitting model: model parameter z bound.
res, fitted_model = sncosmo.fit_lc(hml_dat, model, ['z', 't0', 'amplitude'], bounds={'z': (0.005, 0.35)})
#The following excludes data points not in the range of the model and data sets with fewer than 4 data points
limit = modellimit(source_name, res.parameters[0], res.parameters[1])
hml2 = []
for j in range(len(hml[:,1])):
datapoint = hml [:,1][j]
if (res.parameters[1]- limit[0])< float(datapoint) < (res.parameters[1]+limit[1]):
hml2.append(hml[j])
hml2 = np.array(hml2)
if len(hml2)>3:
return finalfitcurve(x, source_name, hml2)
except ValueError:
pass
def __init__(self, name=None, version=None):
self.name = name
self.version = version
hsiao = sncosmo.get_source("hsiao", version='3.0')
self._phase = hsiao._phase
self._wave = hsiao._wave
self._passed_flux = hsiao._passed_flux
self._param_names = ['amplitude', 's']
self.param_names_latex = ['A', 's']
self._parameters = np.array([1., 1.])
self._model_flux = Spline2d(self._phase, self._wave,
self._passed_flux, kx=2, ky=2)
self.bumps = copy(self.BUMPS)
for bump in self.bumps:
bump._fit_kernel(hsiao)
self._parameters = np.concatenate((self._parameters, [0.]))
self._param_names.append(bump.name + '_bump_amp')
self.param_names_latex.append(bump.name + '_A')
self._parameters = np.concatenate((self.parameters, [0.]))
self._param_names += ['wave_slope']
self.param_names_latex.append('m_\lambda')
def sugar_model(q0=0,
q1=0,
q2=0,
q3=0,
Av=0,
phase=np.linspace(-5, 30, 10),
wave=np.linspace(4000, 8000, 10)):
"""
Give a spectral time series of SUGAR model
for a given set of parameters.
"""
source = sncosmo.get_source('sugar')
mag_sugar = source._model['M0'](phase, wave)
keys = ['ALPHA1', 'ALPHA2', 'ALPHA3', 'CCM']
parameters = [q1, q2, q3, Av]
for i in range(4):
comp = source._model[keys[i]](phase, wave) * parameters[i]
mag_sugar += comp
# Mag AB used in the training of SUGAR.
mag_sugar += 48.59
wave_factor = (wave**2 / 299792458. * 1.e-10)
return (q0 * 10.**(-0.4 * mag_sugar) / wave_factor)
def test_salt2source_timeseries_vs_snfit():
"""Test timeseries output from SALT2Source vs pregenerated timeseries
from snfit (SALT2 software)."""
source = sncosmo.get_source("salt2", version="2.4") # fixed version
model = sncosmo.Model(source)
dirname = os.path.join(os.path.dirname(__file__), "data")
for fname in [
'salt2_timeseries_1.dat', 'salt2_timeseries_2.dat',
'salt2_timeseries_3.dat', 'salt2_timeseries_4.dat'
]:
f = open(os.path.join(dirname, fname), 'r')
meta = read_header(f)
time, wave, fluxref = sncosmo.read_griddata_ascii(f)
f.close()
# The output from snfit's Salt2Model.SpectrumFlux() has a
# different definition than sncosmo's model.flux() by a factor
# of a^2. snfit's definition is the rest-frame flux but at a
# blue-shifted wavelength. (There is no correction for photon
# energy or time dilation. These corrections are made in the
# integration step.)
a = 1. / (1. + meta['Redshift'])
fluxref *= a**2
model.set(z=meta['Redshift'],
t0=meta['DayMax'],
x0=meta['X0'],
x1=meta['X1'],
c=meta['Color'])
flux = model.flux(time, wave)
# super good agreement!
assert_allclose(flux, fluxref, rtol=1e-13)
snclist = ['09dh', '09q', '09sk', '09ut', '10aavz', '10acbu', '10bhu', '10bip', '10bzf', '10ciw', '10fmx', '10gvb', '10hfe', '10hie', '10iue', '10lbo', '10osn', '10qqd', '10qts', '10svt', '10tqi', '10tqv', '10xem', '10xik', '10ysd', '10zcn', '11bli', '11bov', '11cmh', '11gcj', '11hyg', '11img', '11ixk', '11jgj', '11klg', '11lbm', '11mnb', '11mwk', '11rka', '12as', '12cde', '12cjy', '12dcp', '12dtf', '12gdy', '12gzk', '12hni', '12jxd', '12ktu', '12lpo']
datadir = '/home/fcm1g13/Documents/Supernova/Ibcsn_pftdata_c/Sullivan_pipeline/data_files/PTF'
tempcompletesnlist = ['09dfk', '09dh', '09dha','09fsr','09q', '09sk', '09ut','10aavz', '10acbu', '10acff', '10acgq', '10bhu', '10bip', '10bzf', '10ciw', '10eqi', '10fbv', '10feq', '10fmx', '10gvb', '10hfe', '10hie', '10inj', '10iue', '10kui', '10lbo', '10osn', '10qif', '10qqd', '10qts', '10svt', '10tqi', '10tqv', '10vnv', '10xem', '10xik', '10xjr', '10ysd', '10zcn','11bli', '11bov', '11cmh', '11gcj', '11hyg', '11ilr', '11img', '11ixk', '11izq', '11jgj', '11kaa', '11klg', '11lbm', '11mnb', '11mwk', '11qcj', '11qiq', '11rka', '12as', '12cde', '12cjy', '12dcp', '12dtf', '12eaw', '12gdy', '12gzk', '12hni', '12jxd', '12ktu', '12lpo', '12mfx']
#tempcompletesnlistwithoutbadones
#snthatdontwork = #REMOVEDSN: '10fia','10wal','11rfh', '12bwq', #'12elh', '12fgw','12gty', '12lvt','11awe','11lmn', '11pnq', '12fes' ,'12grr','12gvr', '12hvv','12ldy'
#completesnlist = tempcompletesnlistwithoutbadones
completesnlist = tempcompletesnlist
completesnlist = snblist
#completesnlist = ['12hvv']
source=sncosmo.get_source('s11-2005hm')
model=sncosmo.Model(source=source)
ib = 0; ic = 0; jo = 1
redchib = []; redchic = []
redshiftvb = []; redshiftvc = []
totalred = []
peakabsmagvalueb = []; peakabsmagvaluec = []
for sn in completesnlist[:]:
x = np.loadtxt(datadir + sn + '.extra_out_PTF48R', dtype= str,skiprows=16, usecols = (0,1,2,3))# comments='#', delimiter=' ')
dataset = []
for i in x:
if float(i[1]) > 0:
dataset.append(i)
hml = np.array(dataset)
hml = averagedata(hml)
####
for sn in completesnlist[:]:
redchilist = []
try:
types[sn].index("c")
if sntype[4] == "b":
source_name_list = b_source_names + c_source_names
else:
source_name_list = c_source_names
except:
source_name_list = b_source_names
for source_name in source_name_list:
source = sncosmo.get_source(source_name)
model = sncosmo.Model(source=source)
x = np.loadtxt(
datadir + sn + ".extra_out_PTF48R", dtype=str, skiprows=16, usecols=(0, 1, 2, 3)
) # comments='#', delimiter=' ')
dataset = []
for i in x:
if float(i[1]) > 0:
dataset.append(i)
hml = np.array(dataset)
hml = averagedata(hml)
try:
y = showcurve(sn, hml, source_name)
# print y
snclist = ['09dh', '09q', '09sk', '09ut', '10aavz', '10acbu', '10bhu', '10bip', '10bzf', '10ciw', '10fmx', '10gvb', '10hfe', '10hie', '10iue', '10lbo', '10osn', '10qqd', '10qts', '10svt', '10tqi', '10tqv', '10xem', '10xik', '10ysd', '10zcn', '11bli', '11bov', '11cmh', '11gcj', '11hyg', '11img', '11ixk', '11jgj', '11klg', '11lbm', '11mnb', '11mwk', '11rka', '12as', '12cde', '12cjy', '12dcp', '12dtf', '12gdy', '12gzk', '12hni', '12jxd', '12ktu', '12lpo']
datadir = '/home/fcm1g13/Documents/Supernova/Ibcsn_pftdata_c/Sullivan_pipeline/data_files/PTF'
tempcompletesnlist = ['09dfk', '09dh', '09dha','09fsr','09q', '09sk', '09ut','10aavz', '10acbu', '10acff', '10acgq', '10bhu', '10bip', '10bzf', '10ciw', '10eqi', '10fbv', '10feq', '10fmx', '10gvb', '10hfe', '10hie', '10inj', '10iue', '10kui', '10lbo', '10osn', '10qif', '10qqd', '10qts', '10svt', '10tqi', '10tqv', '10vnv', '10xem', '10xik', '10xjr', '10ysd', '10zcn','11bli', '11bov', '11cmh', '11gcj', '11hyg', '11ilr', '11img', '11ixk', '11izq', '11jgj', '11kaa', '11klg', '11lbm', '11mnb', '11mwk', '11qcj', '11qiq', '11rka', '12as', '12cde', '12cjy', '12dcp', '12dtf', '12eaw', '12gdy', '12gzk', '12hni', '12jxd', '12ktu', '12lpo', '12mfx']
#tempcompletesnlistwithoutbadones
#snthatdontwork = #REMOVEDSN: '10fia','10wal','11rfh', '12bwq', #'12elh', '12fgw','12gty', '12lvt','11awe','11lmn', '11pnq', '12fes' ,'12grr','12gvr', '12hvv','12ldy'
#completesnlist = tempcompletesnlistwithoutbadones
completesnlist = tempcompletesnlist
completesnlist = snclist
#completesnlist = ['12hvv']
source=sncosmo.get_source('s11-2006fo')
model=sncosmo.Model(source=source)
ib = 0; ic = 0; jo = 1
redchib = []; redchic = []
redshiftvb = []; redshiftvc = []
totalred = []
peakabsmagvalueb = []; peakabsmagvaluec = []
for sn in completesnlist[:]:
x = np.loadtxt(datadir + sn + '.extra_out_PTF48R', dtype= str,skiprows=16, usecols = (0,1,2,3))# comments='#', delimiter=' ')
dataset = []
for i in x:
if float(i[1]) > 0:
dataset.append(i)
hml = np.array(dataset)
hml = averagedata(hml)
def showcurve(sn, hml, source_name):
try:
source = sncosmo.get_source(source_name)
model = sncosmo.Model(source=source)
z0 = specs[sn]
if z0 == 'no data':
#pass
print sn[:-7], 'cannot be plotted'
else:
z0 = float(z0)
model.set(z=z0)
#print z0
#adding zpsys and filter columns
ab = np.zeros(len(hml[:, 1]), dtype='|S2')
for i in range(len(ab)):
ab[i] = 'ab'
hml = np.column_stack((hml, ab))
band = np.zeros(len(hml[:, 1]), dtype='|S6')
for i in range(len(band)):
band[i] = 'ptf48r'
hml = np.column_stack((hml, band))
hml_dat = astropy.table.Table(
data=hml,
names=('ptfname', 'time', 'magnitude', 'mag_err', 'flux',
'flux_err', 'zp_new', 'zp', 'ra', 'dec', 'zpsys',
'filter'),
dtype=('str', 'float', 'float', 'float', 'float', 'float',
'float', 'float', 'float', 'float', 'str', 'str'))
res, fitted_model = sncosmo.fit_lc(hml_dat,
model, ['t0', 'amplitude'],
nburn=10000,
nsamples=50000)
##sncosmo.plot_lc(hml_dat, model=fitted_model, model_label=source.name, errors=res.errors, color='blue', figtext=str(hml[:,0][0]+'\n'+'Model name: nugent-sn1bc'+ '\n'+'Reduced Chi Squared: ')+ str(res.chisq/res.ndof), xfigsize=18)
##plt.show()
zp_new = [hml_dat[i][7] for i in range(len(hml))]
hml2 = []
#print hml
#print res.parameters[1]
j = 0
k = 0
for row in hml:
datestamp = row[1]
peaktime = float(res.parameters[1])
limits = modellimit(z0)
if peaktime + (40.0) * (1 + z0) > float(
datestamp) > peaktime - (15.0) * (1 + z0):
hml2.append(row)
if peaktime + (40.0) * (1 + z0) > float(datestamp) > peaktime:
j += 1
if peaktime > float(datestamp) > peaktime - (15.0) * (1 + z0):
k += 1
if j > 1 and k > 1:
hml2 = np.array(hml2)
##print 'after', len(hml2)
hml_dat2 = astropy.table.Table(
data=hml2,
names=('ptfname', 'time', 'magnitude', 'mag_err', 'flux',
'flux_err', 'zp_new', 'zp', 'ra', 'dec', 'zpsys',
'filter'),
dtype=('str', 'float', 'float', 'float', 'float', 'float',
'float', 'float', 'float', 'float', 'str', 'str'))
res2, fitted_model2 = sncosmo.fit_lc(hml_dat2,
model,
['t0', 'amplitude'],
nburn=10000,
nsamples=50000)
#plot model
redchisqaured = res2.chisq / res2.ndof
if redchisqaured < 1:
pass
#sncosmo.plot_lc(hml_dat2, model=fitted_model2, model_label=source.name, zp = np.mean(zp_new), errors=res2.errors, color='blue', figtext=str(hml[:,0][0]+'\n'+'Model name: nugent-sn1bc'+ '\n'+'Reduced Chi Squared: ')+ str(res2.chisq/res2.ndof), xfigsize=18)
#plt.show()
##print 'Done:', hml2[:,0][0], 'z:',float(res2.parameters[0]), 'Reduced chi^2:', res2.chisq/res2.ndof #'Data points:', len(hml[:,1]),' Type'+types[hml[:,0][0]]+'Model name: '+ source.name,'\n' #'Dof:', res.ndof
return res2.parameters, np.mean(
zp_new
), redchisqaured, z0, hml2 #, res.errors['amplitude']]
else:
pass
#return res.parameters, np.mean(zp_new), res.chisq/res.ndof, z0, hml
except ValueError:
#pass
print sn, 'cannot be plotted'