def gen_dataset(nsne, under_model):
#under_model = pop_model()
z_true, x1_true, c_true, x0_true = under_model.gen_dataset_params(nsne)
t0_true = np.zeros(nsne)
if not os.path.exists("testdata_%s" % (under_model.name(nsne))):
os.mkdir("testdata_%s" % (under_model.name(nsne)))
# Pretend we observe all SNe with the same cadence and bands
time = np.arange(-30., 70.)
band = np.array(25 * ['desg', 'desr', 'desi', 'desz'])
zp = 25. * np.ones_like(time)
zpsys = np.array(100 * ['ab'])
model = sncosmo.Model(source='salt2-extended')
i = 1
for z, t0, x0, x1, c in zip(z_true, t0_true, x0_true, x1_true, c_true):
param_dict = dict(z=z, t0=t0, x0=x0, x1=x1, c=c)
model.set(**param_dict)
flux_true = model.bandflux(band, time, zp=zp, zpsys=zpsys)
fluxerr = 0.1 * np.max(flux_true) * np.ones_like(flux_true)
flux = normal(flux_true, fluxerr)
data = Table((time, band, flux, fluxerr, zp, zpsys),
names=('time', 'band', 'flux', 'fluxerr', 'zp', 'zpsys'),
meta=param_dict)
sncosmo.write_lc(
data, 'testdata_%s/sn{0:02d}_%s.dat'.format(i) %
(under_model.name(nsne), under_model.name(nsne)))
i += 1
return
def make_new_files(filename, table, output, sntype):
with open(filename) as f:
data = f.read().split("\n")
filters = data[5].split()
survey = data[0].split()
stuffRA = data[6].split()
stuffDec = data[7].split()
MWEBV = data[11].split()
if sntype > 3:
if sntype >=20 and sntype < 30:
sntype=2
if sntype >=30:
sntype=3
if sntype==1:
typestring='SN Type = Ia , MODEL = mlcs2k2.SNchallenge'
elif sntype==2:
typestring='SN Type = II , MODEL = SDSS-017564'
elif sntype==3:
typestring='SN Type = Ic , MODEL = SDSS-014475'
else:
typestring='SOMETHING WENT HORRIBLY WRONG'
table.meta = {survey[0][:-1]: survey[1], stuffRA[0][:-1]: stuffRA[1], stuffDec[0][:-1]: stuffDec[1],filters[0][:-1]: filters[1],
MWEBV[0][:-1]: MWEBV[1], 'SNTYPE': -9, 'SIM_COMMENT': typestring }
#table.rename_column('mjd', 'MJD')
#table.rename_column('filter ', 'FLT')
#table.rename_column('flux', 'FLUXCAL')
#table.rename_column('flux_error', 'FLUXCALERR')
sncosmo.write_lc(table, 'new_mocks/%s'%output,pedantic=True, format = 'snana')
def writeLightCurve(self,
fname,
format='ascii',
nightlyCoadd=False,
**kwargs):
lc = self.SNCosmoLC(nightlyCoadd=nightlyCoadd)
sncosmo.write_lc(lc, fname, format=format, **kwargs)
def writeLightCurve(self,
fname,
format='ascii',
nightlyCoadd=False,
**kwargs):
lc = self.SNCosmoLC(nightlyCoadd=nightlyCoadd)
sncosmo.write_lc(lc, fname, format=format, **kwargs)
def test_roundtripping():
for format in ['json', 'ascii', 'salt2']:
f = NamedTemporaryFile(delete=False)
f.close() # close to ensure that we can open it in write_lc()
# raw=True is for the benefit of salt2 writer that modifies column
# and header names by default.
sncosmo.write_lc(lcdata, f.name, format=format, raw=True,
pedantic=False)
data = sncosmo.read_lc(f.name, format=format)
for key in lcdata.colnames:
assert np.all(data[key] == lcdata[key])
for key in lcdata.meta:
assert data.meta[key] == lcdata.meta[key]
os.unlink(f.name)
def test_roundtripping():
for format in ['json', 'ascii', 'salt2']:
f = NamedTemporaryFile(delete=False)
f.close() # close to ensure that we can open it in write_lc()
# raw=True is for the benefit of salt2 writer that modifies column
# and header names by default.
sncosmo.write_lc(lcdata, f.name, format=format, raw=True,
pedantic=False)
data = sncosmo.read_lc(f.name, format=format)
for key in lcdata.colnames:
assert np.all(data[key] == lcdata[key])
for key in lcdata.meta:
assert data.meta[key] == lcdata.meta[key]
os.unlink(f.name)
#!/usr/bin/env python
import numpy as np
import sncosmo
from astropy.utils import OrderedDict as odict
from astropy.table import Table
model = sncosmo.ObsModel(source='salt2')
model.set(z=0.5, c=0.2, t0=55100., x1=0.5)
model.set_source_peakabsmag(-19.5, 'bessellb', 'ab')
times = np.linspace(55070., 55150., 40)
bands = np.array(10 * ['sdssg', 'sdssr', 'sdssi', 'sdssz'])
zp = 25. * np.ones(40)
zpsys = np.array(40 * ['ab'])
flux = model.bandflux(bands, times, zp=zp, zpsys=zpsys)
fluxerr = (0.05 * np.max(flux)) * np.ones(40, dtype=np.float)
flux += fluxerr * np.random.randn(40)
data = Table(odict([('time', times), ('band', bands), ('flux', flux),
('fluxerr', fluxerr), ('zp', zp), ('zpsys', zpsys)]),
meta=dict(zip(model.param_names, model.parameters)))
sncosmo.write_lc(data, 'example_photometric_data.dat')
x = Table(relevantdata[0])
print str(x)
print str(len(relevantdata[0]))
# Write out to a file as this is how we will do things in a larger set by looping through, and then read in the file
model.set(**params[0])
# fig_relevant = sncosmo.plot_lc(relevantdata[0], model=model)
#print "Close Window to continute"
#pl.show()
sncosmo.write_lc(Table(relevantdata[0]), fname='lc.dat', format='ascii')
# sncosmo.write_lc(Table(relevantdata[0]), fname='lc.dat.json', format='json')
# sncosmo.write_lc(Table(relevantdata[0]), fname='lc.dat.fits', format='snana') # fits
lc = sncosmo.read_lc('lc.dat', format='ascii')
fmodel = sncosmo.Model(source='salt2-extended')
for z in zvals:
fmodel.set(z=z)
res, fitmodel = sncosmo.fit_lc(relevantdata[0], fmodel, ['t0', 'x0', 'x1', 'c'])
print res
print params[0]
def test_write_lc_snana():
"""Just check if the snana writer works without error."""
f = NamedTemporaryFile(delete=False)
f.close() # close to ensure that we can open it in write_lc()
sncosmo.write_lc(lcdata, f.name, format='snana', pedantic=False)
os.unlink(f.name)
def test_write_lc_salt2():
"""Extra test to see if column renaming works"""
f = NamedTemporaryFile(delete=False)
f.close() # close to ensure that we can open it in write_lc()
sncosmo.write_lc(lcdata, f.name, format='salt2')
os.unlink(f.name)
print(os.path.basename(f), blue + '-' + red)
else:
num = 0
maxBand = None
maxBand2 = None
for Band in [
x for x in np.unique(lc['Band'])
if x in ['B', 'V', 'R', 'g', 'r']
]:
temp = len(lc[lc['Band'] == Band])
if temp > num:
maxBand = Band
num = temp
blue = maxBand
num = 0
maxBand = None
maxBand2 = None
for Band in [x for x in np.unique(lc['Band']) if x in ['J', 'H', 'K']]:
temp = len(lc[lc['Band'] == Band])
if temp > num:
maxBand2 = Band
num = temp
red = maxBand2
if red and blue:
print(os.path.basename(f), blue + '-' + red)
if not blue or not red:
remove.append(f)
sncosmo.write_lc(lc, f[:-4] + '_clipped.dat')
#print(remove)
def test_write_lc_salt2():
"""Extra test to see if column renaming works"""
f = NamedTemporaryFile(delete=False)
f.close() # close to ensure that we can open it in write_lc()
sncosmo.write_lc(lcdata, f.name, format='salt2')
os.unlink(f.name)
import os
import numpy as np
import sncosmo
lcs = sncosmo.read_lc('../lc.standardsystem.sesn_allphot.dat')
sne = np.unique(lcs['Name'])
for s in sne:
sncosmo.write_lc(lcs[lcs['Name'] == s], 'lc_' + s + '.dat')
def simulate_simlib(simlibfile, snmodelsource, outfile="LC/simulatedlc.dat", restrict=10):
"""
Simulate SN based on the simlibfile using SNCosmo SALT models
Parameters
----------
simlibfile :
snmodelsource:
outfile:
Returns
-------
"""
from astropy.units import Unit
from astropy.coordinates import SkyCoord
# read the simlibfile into obstables
meta, obstables = sncosmo.read_snana_simlib(simlibfilename)
# set the SNCosmo model source
dustmaproot = os.getenv("SIMS_DUSTMAPS_DIR")
map_dir = os.path.join(dustmaproot, "DustMaps")
dust = sncosmo.CCM89Dust()
model = Model(
source=snmodelsource, effects=[dust, dust], effect_frames=["rest", "obs"], effect_names=["host", "mw"]
)
maxSNperField = restrict
# Different fields in SIMLIB are indexed by libids
libids = obstables.keys()
lcs = []
for libid in libids:
# Get the obstable corresponding to each field
obstable = obstables[libid]
manipulateObsTable(obstable)
# Need Area from PixSize
ra = obstable.meta["RA"]
dec = obstable.meta["DECL"]
area = obstable.meta["PIXSIZE"]
maxmjd = obstable["time"].max()
minmjd = obstable["time"].min()
rangemjd = maxmjd - minmjd
skycoords = SkyCoord(ra, dec, unit="deg")
t_mwebv = sncosmo.get_ebv_from_map(skycoords, mapdir=map_dir, interpolate=False)
model.set(mwebv=t_mwebv)
params = []
# col = Table.Column(obstable['SEARCH'].size*['ab'], name='zpsys')
# obstable['FLT'].name = 'band'
# obstable['MJD'].name = 'time'
# obstable['ZPTAVG'].name = 'zp'
# obstable['CCD_GAIN'].name = 'gain'
# obstable['SKYSIG'].name = 'skynoise'
# obstable.add_column(col)
redshifts = list(sncosmo.zdist(0.0, 1.2, ratefunc=cosmoRate, time=rangemjd, area=area))
print "num SN generated ", len(redshifts)
for i, z in enumerate(redshifts):
mabs = normal(-19.3, 0.3)
model.set(z=z)
model.set_source_peakabsmag(mabs, "bessellb", "ab")
x0 = model.get("x0")
# RB: really should not be min, max but done like in catalogs
p = {"z": z, "t0": uniform(minmjd, maxmjd), "x0": x0, "x1": normal(0.0, 1.0), "c": normal(0.0, 0.1)}
params.append(p)
if maxSNperField is not None:
if i == maxSNperField:
break
print "realizing SN"
lcslib = sncosmo.realize_lcs(obstable, model, params, trim_observations=True)
lcs.append(lcslib)
# alllcsintables = vstack(lcslib)
# print alllcsintables[0]
# print alllcsintables[MJD].size
# write light curves to disk
for i, field in enumerate(lcs):
for snid, lc in enumerate(field):
sncosmo.write_lc(lc, fname=outfile + "_" + str(i) + "_" + str(snid) + ".dat", format="ascii")
return lcs
#!/usr/bin/env python
import numpy as np
import sncosmo
from astropy.utils import OrderedDict as odict
from astropy.table import Table
model = sncosmo.Model(source='salt2')
model.set(z=0.5, c=0.2, t0=55100., x1=0.5)
model.set_source_peakabsmag(-19.5, 'bessellb', 'ab')
times = np.linspace(55070., 55150., 40)
bands = np.array(10 * ['sdssg', 'sdssr', 'sdssi', 'sdssz'])
zp = 25. * np.ones(40)
zpsys = np.array(40 * ['ab'])
flux = model.bandflux(bands, times, zp=zp, zpsys=zpsys)
fluxerr = (0.05 * np.max(flux)) * np.ones(40, dtype=np.float)
flux += fluxerr * np.random.randn(40)
data = Table(odict([('time', times), ('band', bands), ('flux', flux),
('fluxerr', fluxerr), ('zp', zp), ('zpsys', zpsys)]),
meta=dict(zip(model.param_names, model.parameters)))
sncosmo.write_lc(data, 'example_photometric_data.dat')
def test_write_lc_snana():
"""Just check if the snana writer works without error."""
f = NamedTemporaryFile(delete=False)
f.close() # close to ensure that we can open it in write_lc()
sncosmo.write_lc(lcdata, f.name, format='snana', pedantic=False)
os.unlink(f.name)
def do_stuff(ctr):
p = pb.ProgressBar(maxval=740, widgets = [pb.Percentage(),pb.Bar(),pb.ETA()]).start()
pbctr = 0
for i,l in enumerate(lc):
p.update(pbctr)
pbctr += 1
restcut = (3000,7000)
data = sncosmo.read_lc(l, format='salt2')
try:
z = data.meta['Redshift']
except:
pass
try:
z = data.meta['Z_CMB']
except:
pass
try:
survey = data.meta['SURVEY']
except:
pass
try:
survey = data.meta['SET']
except:
pass
nickname = data.meta['SN']
try:
nickname = str(int(nickname))
except:
pass
mwebv = data.meta['MWEBV']
dust = sncosmo.CCM89Dust()
data = astropy.table.Table(data, masked=True)
#rename columns so that my fitter can handle things
data.rename_column('Filter', 'tmp')
data.rename_column('Date', 'time')
data.rename_column('Flux', 'flux')
data.rename_column('Fluxerr', 'fluxerr')
data.rename_column('MagSys', 'zpsys')
data.rename_column('ZP', 'zp')
if survey == 'SNLS':
sn_nickname = l.split('/')[-1].split('.')[0].split('-')[-1]
band = []
for j, bp in enumerate(data['tmp']):
band.append( '%s-%s' %(sn_nickname, bp) )
band = astropy.table.Column(band, name='band')
data.add_column(band)
data.remove_column('tmp')
else:
data.rename_column('tmp', 'band')
# deal with swope filters
mask = (data['band'] == 'SWOPE2::V')
nswopev = len(mask.nonzero()[0])
if nswopev > 0:
band = []
for j, bp in enumerate(data['band']):
if (bp == 'SWOPE2::V'):
if (data['time'][j] < 53749):
band.append('swope2::v_lc3014')
elif (data['time'][j] < 53760):
band.append('swope2::v_lc3009')
else:
band.append('swope2::v_lc9844')
else:
band.append(bp)
data.remove_column('band')
band = astropy.table.Column(band, name='band')
data.add_column(band)
ind = np.where( (data['band'] == 'SWOPE2::V') & (data['time']>53749.) & ((data['time']<=53760.)) )
data['band'][ind] = 'swope2::v_lc3009'
ind = np.where( (data['band'] == 'SWOPE2::V') & (data['time']>53760.) )
data['band'][ind] = 'swope2::v_lc9844'
# print ind
#deal with filter coverage
#also deal with STANDARD filter zeropoints
unique_bands = np.unique(data['band'])
fit_bands = []
nofit_bands = []
# print unique_bands
tmperr = np.copy(data['fluxerr'])
for ub in unique_bands:
# print ub
bp = sncosmo.get_bandpass(ub)
rest = bp.wave_eff / (1.0+z)
if (rest >= restcut[0]) & (rest <= restcut[1]):
fit_bands.append(ub)
else:
nofit_bands.append(ub)
if 'STANDARD' in ub:
ind = np.where(data['band'] == ub)
data['zp'][ind] = data['zp'][ind] - float(standard_zps[ub])
errcor = 10**(-0.4*standard_zps[ub])
data['fluxerr'][ind] *= errcor
if '4SHOOTER2' in ub:
ind = np.where(data['band'] == ub)
data['zp'][ind] = data['zp'][ind] - float(FourShooter_zps[ub])
errcor = 10**(-0.4*FourShooter_zps[ub])
data['fluxerr'][ind] *= errcor
if 'KEPLERCAM' in ub:
ind = np.where(data['band'] == ub)
data['zp'][ind] = data['zp'][ind] - float(keplercam_zps[ub])
errcor = 10**(-0.4*keplercam_zps[ub])
data['fluxerr'][ind] *= errcor
# print ub
# print data['zp'][ind]
if 'swope' in ub.lower():
ind = np.where(data['band'] == ub)
data['zp'][ind] = data['zp'][ind] - float(swope_zps[ub])
errcor = 10**(-0.4*swope_zps[ub])
data['fluxerr'][ind] *= errcor
if 'sdss' in ub.lower():
ind = np.where(data['band'] == ub)
data['zp'][ind] = data['zp'][ind] - float(sdss_zps[ub])
errcor = 10**(-0.4*sdss_zps[ub])
data['fluxerr'][ind] *= errcor
for nfb in nofit_bands:
mask = data['band'] == nfb
for c in data.colnames:
data[c].mask = (data[c].mask | mask)
mwebv = data.meta['MWEBV']
mask = data['band'].mask.nonzero()[0]
data.remove_rows(mask)
ind = np.where(lcfits['SN'] == nickname)
t0 = lcfits['DayMax'][ind][0]
x1r, x1f, c = np.random.multivariate_normal(mean,cov,size=1)[0]
mu = cosmo.distmod(z).value
absmag = MB - ar*x1r - af*x1f + beta*c + np.random.normal(scale=sigint, size=1)[0]
mB = mu + absmag
source = Salt2XSource(version='2.4', modeldir=modeldir)
model = sncosmo.Model(source=source, effects=[dust], effect_names=['mw'], effect_frames=['obs'])
model.set(z=z, x1=x1f, s=x1r, c=c, t0=t0, mwebv=mwebv)
model.set_source_peakabsmag(absmag, 'bessellb', 'ab')
flux = model.bandflux(data['band'], data['time'], zp=data['zp'], zpsys=data['zpsys'])
whocares, saltcov = model.bandfluxcov(data['band'], data['time'], data['zp'],data['zpsys'])
# handle model cov blowups
saltcov = np.copy(saltcov)
diag = np.copy(saltcov.diagonal())
model_snr = whocares/np.sqrt(diag)
ind = np.where((np.abs(model_snr) < 1) & ~np.isnan(model_snr))
diag[ind] = diag[ind] * np.abs(model_snr[ind])**2
np.fill_diagonal(saltcov, diag)
diagerr = np.diag(data['fluxerr']**2)
fullcov = saltcov + diagerr
try:
np.linalg.cholesky(fullcov)
except:
print 'Cholesky failed... exiting'
sys.exit()
noise = np.random.multivariate_normal(np.zeros(len(diagerr)), fullcov, size=1)[0]
#lower zp, lower flux
data['flux'] = flux + noise
data.meta['x1r'] = x1r
data.meta['x1f'] = x1f
data.meta['c'] = c
data.meta['alpha_r'] = ar
data.meta['alpha_f'] = af
data.meta['beta'] = beta
data.meta['MB'] = MB
data.meta['mB'] = mu+MB
data.meta['DayMax'] = t0
data.meta['cosmology'] = 'Planck15'
data.rename_column('band', 'Filter')
data.rename_column('time', 'Date')
data.rename_column('flux', 'Flux')
data.rename_column('fluxerr', 'Fluxerr')
data.rename_column('zpsys', 'MagSys')
data.rename_column('zp', 'ZP')
if survey == 'SNLS':
for row in data:
tmp = row['Filter']
ind = tmp.find('MEGACAM')
row['Filter'] = row['Filter'][ind:]
sncosmo.write_lc(data,'./cadence_sim/lc/%s_%s.list' %(nickname, ctr), format='salt2')
p.finish()
