def test_read_salt2_cov():
fname = join(dirname(__file__), "data", "lc-03D4ag.list")
data = sncosmo.read_lc(fname, format="salt2", read_covmat=True)
assert data["Fluxcov"].shape == (len(data), len(data))
assert_allclose(data["Fluxcov"][0:3, 0:3],
[[0.867712297284, 0.01139998771, 0.01119398747],
[0.01139998771, 2.03512047975, 0.01190299234],
[0.01119398747, 0.01190299234, 1.3663344852]])
def test_read_salt2_cov():
fname = join(dirname(__file__), "data", "lc-03D4ag.list")
data = sncosmo.read_lc(fname, format="salt2", read_covmat=True)
assert data["Fluxcov"].shape == (len(data), len(data))
assert_allclose(data["Fluxcov"][0:3, 0:3],
[[0.867712297284, 0.01139998771, 0.01119398747],
[0.01139998771, 2.03512047975, 0.01190299234],
[0.01119398747, 0.01190299234, 1.3663344852]])
def plot_testdata(nsne, under_model):
if not os.path.exists("lcplots_%s"%(under_model.name(nsne))):
os.mkdir("lcplots_%s"%(under_model.name(nsne)))
fnames = glob("testdata_%s/*"%(under_model.name(nsne)))
for fname in fnames:
plotname = fname.replace("testdata_%s"%(under_model.name(nsne)), "lcplots_%s"%(under_model.name(nsne))).replace("dat", "png")
data = sncosmo.read_lc(fname)
sncosmo.plot_lc(data, fname=plotname)
def buildzdist(dirname):
lclist = glob.glob(dirname + "/*")
z = []
for lcfile in lclist:
lc = sncosmo.read_lc(lcfile)
zval = lc.meta["z"]
z.append(zval)
return z
def test_read_salt2_old():
dname = join(dirname(__file__), "data", "SNLS3-04D3gx")
data = sncosmo.read_lc(dname, format="salt2-old")
# Test length and column names:
assert len(data) == 25 + 37 + 38 + 18 # g + r + i + z lengths
assert data.colnames == ["Date", "Flux", "Fluxerr", "ZP", "Filter",
"MagSys"]
# Test a bit of metadata and data
assert data.meta["NAME"] == "04D3gx"
assert_allclose(data.meta["Redshift"], 0.91)
assert_allclose(data.meta["RA"], 215.056948)
assert np.all(data["MagSys"] == "VEGA")
def test_read_salt2_old():
dname = join(dirname(__file__), "data", "SNLS3-04D3gx")
data = sncosmo.read_lc(dname, format="salt2-old")
# Test length and column names:
assert len(data) == 25 + 37 + 38 + 18 # g + r + i + z lengths
assert data.colnames == ["Date", "Flux", "Fluxerr", "ZP", "Filter",
"MagSys"]
# Test a bit of metadata and data
assert data.meta["NAME"] == "04D3gx"
assert_allclose(data.meta["Redshift"], 0.91)
assert_allclose(data.meta["RA"], 215.056948)
assert np.all(data["MagSys"] == "VEGA")
def test_read_salt2():
fname = join(dirname(__file__), "data", "salt2_example.dat")
data = sncosmo.read_lc(fname, format="salt2")
# Test a few columns
assert_allclose(data["Date"], [52816.54, 52824.59, 52795.59, 52796.59])
assert_allclose(data["ZP"], [27.091335, 27.091335, 25.913054, 25.913054])
assert np.all(data["Filter"] == np.array(
["MEGACAM::g", "MEGACAM::g", "MEGACAM::i", "MEGACAM::i"]))
assert np.all(data["MagSys"] == "VEGA")
# Test a bit of metadata
assert_allclose(data.meta["Z_HELIO"], 0.285)
assert_allclose(data.meta["RA"], 333.690959)
assert data.meta["z_source"] == "H"
def test_read_salt2():
fname = join(dirname(__file__), "data", "salt2_example.dat")
data = sncosmo.read_lc(fname, format="salt2")
# Test a few columns
assert_allclose(data["Date"], [52816.54, 52824.59, 52795.59, 52796.59])
assert_allclose(data["ZP"], [27.091335, 27.091335, 25.913054, 25.913054])
assert np.all(data["Filter"] == np.array(["MEGACAM::g", "MEGACAM::g",
"MEGACAM::i", "MEGACAM::i"]))
assert np.all(data["MagSys"] == "VEGA")
# Test a bit of metadata
assert_allclose(data.meta["Z_HELIO"], 0.285)
assert_allclose(data.meta["RA"], 333.690959)
assert data.meta["z_source"] == "H"
def test_read_salt2():
fname = join(dirname(__file__), "data", "lc-03D4ag.list")
data = sncosmo.read_lc(fname, format="salt2")
# Test a few columns
assert_allclose(data["Date"][0:4], [52816.54, 52824.59, 52851.53, 52873.4])
assert_allclose(data["ZP"][0:4], 27.036167)
assert np.all(data["Filter"][0:4] == "MEGACAMPSF::g")
assert np.all(data["MagSys"] == "AB_B12")
# Test a bit of metadata
assert_allclose(data.meta["Z_HELIO"], 0.285)
assert_allclose(data.meta["RA"], 333.690959)
assert data.meta["z_source"] == "H"
def test_read_salt2():
fname = join(dirname(__file__), "data", "lc-03D4ag.list")
data = sncosmo.read_lc(fname, format="salt2")
# Test a few columns
assert_allclose(data["Date"][0:4],
[52816.54, 52824.59, 52851.53, 52873.4])
assert_allclose(data["ZP"][0:4], 27.036167)
assert np.all(data["Filter"][0:4] == "MEGACAMPSF::g")
assert np.all(data["MagSys"] == "AB_B12")
# Test a bit of metadata
assert_allclose(data.meta["Z_HELIO"], 0.285)
assert_allclose(data.meta["RA"], 333.690959)
assert data.meta["z_source"] == "H"
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 fromSALTFormat(cls, fname):
_lc = sncosmo.read_lc(fname, format='salt2')
lc = _lc.to_pandas()
lc.MagSys = 'ab'
def filtername(x):
if 'megacam' in x.lower():
return 'megacam'
else:
return x[:-3].lower()
banddict = dict((key.lower(), filtername(key) + key[-1])
for key in lc.Filter.unique())
return cls(lc,
bandNameDict=banddict,
ignore_case=True,
propDict=_lc.meta)
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_read_lc():
f = StringIO("""
@id 1
@RA 36.0
@description good
time band flux fluxerr zp zpsys
50000. g 1. 0.1 25. ab
50000.1 r 2. 0.1 25. ab
""")
t = sncosmo.read_lc(f, format='ascii')
assert str(t) == (" time band flux fluxerr zp zpsys\n"
"------- ---- ---- ------- ---- -----\n"
"50000.0 g 1.0 0.1 25.0 ab\n"
"50000.1 r 2.0 0.1 25.0 ab")
assert t.meta['id'] == 1
assert t.meta['RA'] == 36.0
assert t.meta['description'] == 'good'
def test_read_lc():
from astropy.extern.six import StringIO
f = StringIO("""
@id 1
@RA 36.0
@description good
time band flux fluxerr zp zpsys
50000. g 1. 0.1 25. ab
50000.1 r 2. 0.1 25. ab
""")
t = sncosmo.read_lc(f, format='ascii')
assert str(t) == (" time band flux fluxerr zp zpsys\n"
"------- ---- ---- ------- ---- -----\n"
"50000.0 g 1.0 0.1 25.0 ab\n"
"50000.1 r 2.0 0.1 25.0 ab")
assert t.meta['id'] == 1
assert t.meta['RA'] == 36.0
assert t.meta['description'] == 'good'
def importance_sampling(nsne, under_model):
# sampler parameters
ndim = under_model.dim
nwalkers = 20
nburn = 200
nsamples = 500
# Read all SN redshifts and previously-generated parameter samples
snsamples = []
for fname in sorted(glob("testdata_%s/*" % (under_model.name(nsne)))):
z = sncosmo.read_lc(fname).meta['z'] # load whole file just to get 'z'
sfname = fname.replace(
"testdata_%s" % (under_model.name(nsne)),
"samples_%s" % (under_model.name(nsne))).replace(".dat", ".npy")
samples = np.load(sfname)
snsamples.append((z, samples))
# Create sampler
sampler = emcee.EnsembleSampler(nwalkers,
ndim,
under_model.lnlike,
args=(snsamples, ))
# Starting positions
errors = under_model.initial * 0.01
pos = np.array([
under_model.initial + errors * np.random.randn(ndim)
for i in range(nwalkers)
])
# burn-in
pos, prob, state = sampler.run_mcmc(pos, nburn)
print("Burn in done")
# production run
sampler.reset()
sampler.run_mcmc(pos, nsamples)
print("Avg acceptance fraction:", np.mean(sampler.acceptance_fraction))
results = sampler.flatchain
np.save(
"samples_%s/globalsamples_%s.npy" %
(under_model.name(nsne), under_model.name(nsne)), results)
def test_fit_lc_vs_snfit():
"""Test fit_lc versus snfit result for one SN."""
# purposefully use CCM dust to match snfit
model = sncosmo.Model(source='salt2',
effects=[sncosmo.CCM89Dust()],
effect_names=['mw'],
effect_frames=['obs'])
fname = join(dirname(__file__), "data", "lc-03D4ag.list")
data = sncosmo.read_lc(fname,
format='salt2',
read_covmat=True,
expand_bands=True)
model.set(mwebv=data.meta['MWEBV'], z=data.meta['Z_HELIO'])
result, fitted_model = sncosmo.fit_lc(data,
model, ['t0', 'x0', 'x1', 'c'],
bounds={
'x1': (-3., 3.),
'c': (-0.4, 0.4)
},
modelcov=True,
phase_range=(-15., 45.),
wave_range=(3000., 7000.),
warn=False,
verbose=False)
print(result)
assert result.ndof == 25
assert result.nfit == 3
assert_allclose(fitted_model['t0'], 52830.9313, atol=0.01, rtol=0.)
assert_allclose(fitted_model['x0'], 5.6578663e-05, atol=0., rtol=0.005)
assert_allclose(fitted_model['x1'], 0.937399344, atol=0.005, rtol=0.)
assert_allclose(fitted_model['c'], -0.0851965244, atol=0.001, rtol=0.)
# errors
assert_allclose(result.errors['t0'], 0.0955792638, atol=0., rtol=0.01)
assert_allclose(result.errors['x0'], 1.52745001e-06, atol=0., rtol=0.01)
assert_allclose(result.errors['x1'], 0.104657847, atol=0., rtol=0.01)
assert_allclose(result.errors['c'], 0.0234763446, atol=0., rtol=0.01)
def test_fit_lc_vs_snfit():
"""Test fit_lc versus snfit result for one SN."""
# purposefully use CCM dust to match snfit
model = sncosmo.Model(source='salt2',
effects=[sncosmo.CCM89Dust()],
effect_names=['mw'],
effect_frames=['obs'])
fname = join(dirname(__file__), "data", "lc-03D4ag.list")
data = sncosmo.read_lc(fname, format='salt2', read_covmat=True,
expand_bands=True)
model.set(mwebv=data.meta['MWEBV'], z=data.meta['Z_HELIO'])
result, fitted_model = sncosmo.fit_lc(
data, model, ['t0', 'x0', 'x1', 'c'],
bounds={'x1': (-3., 3.), 'c': (-0.4, 0.4)},
modelcov=True,
phase_range=(-15., 45.),
wave_range=(3000., 7000.),
warn=False,
verbose=False)
print(result)
assert result.ndof == 25
assert result.nfit == 3
assert_allclose(fitted_model['t0'], 52830.9313, atol=0.01, rtol=0.)
assert_allclose(fitted_model['x0'], 5.6578663e-05, atol=0., rtol=0.005)
assert_allclose(fitted_model['x1'], 0.937399344, atol=0.005, rtol=0.)
assert_allclose(fitted_model['c'], -0.0851965244, atol=0.001, rtol=0.)
# errors
assert_allclose(result.errors['t0'], 0.0955792638, atol=0., rtol=0.01)
assert_allclose(result.errors['x0'], 1.52745001e-06, atol=0., rtol=0.01)
assert_allclose(result.errors['x1'], 0.104657847, atol=0., rtol=0.01)
assert_allclose(result.errors['c'], 0.0234763446, atol=0., rtol=0.01)
import matplotlib
matplotlib.use('Agg')
from copy import copy
import sncosmo
import numpy as np
from bolomc import bump
from bolomc import burns
from bolomc.distributions import TruncNorm
lc = sncosmo.read_lc('../data/CSP_Photometry_DR2/SN2005elopt+nir_photo.dat', format='csp')
model = sncosmo.Model(bump.BumpSource(),
effect_names=['host','mw'],
effect_frames=['rest','obs'],
effects=[sncosmo.OD94Dust(), sncosmo.F99Dust()])
model2 = copy(model)
model2.set(UV_bump_amp=1.,#blue_bump_amp=0.2,
blue_bump_amp=-0.2,
i1_bump_amp=0.1,
i2_bump_amp=-0.2,
y1_bump_amp=-0.2,
y2_bump_amp=0.2,
y3_bump_amp=-0.1,
j1_bump_amp=-0.2,
j2_bump_amp=0.2,
h1_bump_amp=-0.2,
h2_bump_amp=0.2,
k1_bump_amp=-0.2,
k2_bump_amp=0.2)
import sncosmo
import os
import corner
import copy
import filters
from scipy.stats import chi2
from scipy.integrate import quad
phot_d = None
###########################################################################
# DATA
sn = '16geu' # ground data
fname = '%s_clean.dat' % (sn) # Cleaned light curve
##############################################################################
phot_d = sncosmo.read_lc(fname) # read the ground data
phot_d['imageid'] = 0 # ground data have imageid = 0!
errfloor = 0.0
##############################################################################
# Here the error bars of the grond data can be adjusted.
lenserr = 0.00 # set to 0.05 when using gravlens model
#phot_d['fluxerr'] = np.sqrt(phot_d['fluxerr']**2+(phot_d['flux']*lenserr)**2)
phot_d['fluxerr'] = np.sqrt((phot_d['flux'] * errfloor)**2 +
(phot_d['fluxerr'])**2) # scale error bars
errfloor = 0.08
##############################################################################
# Set up the data of the HST to be read in
sumdata = False
lcfname = { # the names of the data files
'uvf625w': 'lc_uvf625w_resolved.csv',
def task(filename, i, j, nrv, nebv, kind='mcmc'):
lc = sncosmo.read_lc(filename, format='csp')
model = sncosmo.Model(bump.BumpSource(),
effect_names=['host','mw'],
effect_frames=['rest','obs'],
effects=[sncosmo.OD94Dust(), sncosmo.F99Dust()])
rv_prior = burns.get_hostrv_prior(lc.meta['name'],
'gmm', sncosmo.OD94Dust)
host_ebv, err = burns.get_hostebv(lc.meta['name'])
ebv_prior = TruncNorm(-np.inf, np.inf, host_ebv, err)
rv_prior, low, high = burns.get_hostrv_prior(lc.meta['name'],
'gmm', sncosmo.OD94Dust,
retlims=True)
host_ebv, err = burns.get_hostebv(lc.meta['name'])
rv = np.linspace(low if low >= 0 else 0, high, nrv)[i]
ebvlo = host_ebv - err
ebvhi = host_ebv + err
ebv = np.linspace(ebvlo if ebvlo >= 0 else 0, ebvhi, nebv)[j]
model.set(z=lc.meta['zcmb'])
model.set(mwebv=burns.get_mwebv(lc.meta['name'])[0])
model.set(hostebv=ebv)
model.set(hostr_v=rv)
model.set(t0=burns.get_t0(lc.meta['name']))
vparams = filter(lambda x: 'bump' in x, model._param_names)
vparams += ['t0', 's']
bounds = {b.name + "_bump_amp":(-1,2) for b in
model.source.bumps}
#bounds['hostr_v'] = (rv_prior.mean - 0.5, rv_prior.mean + 0.5)
#bounds['hostebv'] = (0, 0.2)
bounds['s'] = (0, 3.)
res, model = sncosmo.fit_lc(lc,model,['amplitude']+vparams,
bounds=bounds)
bounds['t0'] = (model.get('t0')-2, model.get('t0')+2)
vparams.append('amplitude')
bounds['amplitude'] = (0.5 * model.get('amplitude'),
2 * model.get('amplitude'))
qualifier = '_ebv_%.2f_rv_%.2f' % (ebv, rv)
if kind != 'fit':
if kind == 'mcmc':
result = sncosmo.mcmc_lc(lc, model, vparams,
bounds=bounds,
nwalkers=500,
nburn=1000,
nsamples=20)
elif kind == 'nest':
result = sncosmo.nest_lc(lc, model, vparams, bounds=bounds,
method='multi', npoints=800)
samples = result[0].samples.reshape(500, 20, -1)
vparams = result[0].vparam_names
plot_arg = np.rollaxis(samples, 2)
plotting.plot_chains(plot_arg, param_names=vparams,
filename='fits/%s_samples%s.pdf' % (lc.meta['name'], qualifier))
dicts = [dict(zip(vparams, samp)) for samp in samples.reshape(500 * 20, -1)]
thinned = samples.reshape(500, 20, -1)[:, [0, -1]].reshape(1000, -1)
pickle.dump(samples, open('fits/%s_samples%s.pkl' % (lc.meta['name'], qualifier), 'wb'))
models = [copy(result[1]) for i in range(len(thinned))]
for d, m in zip(dicts, models):
m.set(**d)
fig = sncosmo.plot_lc(data=lc, model=models, ci=(50-68/2., 50., 50+68/2.),
model_label=lc.meta['name'])
fig.savefig('fits/%s%s.pdf' % (lc.meta['name'], qualifier))
else:
fitres, model = sncosmo.fit_lc(lc, model, vparams, bounds=bounds)
fig = sncosmo.plot_lc(data=lc, model=model)
fig.savefig('fits/%s_fit%s.pdf' % (lc.meta['name'], qualifier))
def nonvparams(snname):
lc = sncosmo.read_lc('../data/CSP_Photometry_DR2/%sopt+nir_photo.dat' % snname,
format='csp')
z = lc.meta['zcmb']
mwebv, _ = burns.get_mwebv(snname)
return {'z':z, 'mwebv':mwebv}
def task(filename, i, j, nrv, nebv, kind='mcmc'):
lc = sncosmo.read_lc(filename, format='csp')
model = sncosmo.Model(bump.BumpSource(),
effect_names=['host', 'mw'],
effect_frames=['rest', 'obs'],
effects=[sncosmo.OD94Dust(),
sncosmo.F99Dust()])
rv_prior = burns.get_hostrv_prior(lc.meta['name'], 'gmm', sncosmo.OD94Dust)
host_ebv, err = burns.get_hostebv(lc.meta['name'])
ebv_prior = TruncNorm(-np.inf, np.inf, host_ebv, err)
rv_prior, low, high = burns.get_hostrv_prior(lc.meta['name'],
'gmm',
sncosmo.OD94Dust,
retlims=True)
host_ebv, err = burns.get_hostebv(lc.meta['name'])
rv = np.linspace(low if low >= 0 else 0, high, nrv)[i]
ebvlo = host_ebv - err
ebvhi = host_ebv + err
ebv = np.linspace(ebvlo if ebvlo >= 0 else 0, ebvhi, nebv)[j]
model.set(z=lc.meta['zcmb'])
model.set(mwebv=burns.get_mwebv(lc.meta['name'])[0])
model.set(hostebv=ebv)
model.set(hostr_v=rv)
model.set(t0=burns.get_t0(lc.meta['name']))
vparams = filter(lambda x: 'bump' in x, model._param_names)
vparams += ['t0', 's']
bounds = {b.name + "_bump_amp": (-1, 2) for b in model.source.bumps}
#bounds['hostr_v'] = (rv_prior.mean - 0.5, rv_prior.mean + 0.5)
#bounds['hostebv'] = (0, 0.2)
bounds['s'] = (0, 3.)
res, model = sncosmo.fit_lc(lc,
model, ['amplitude'] + vparams,
bounds=bounds)
bounds['t0'] = (model.get('t0') - 2, model.get('t0') + 2)
vparams.append('amplitude')
bounds['amplitude'] = (0.5 * model.get('amplitude'),
2 * model.get('amplitude'))
qualifier = '_ebv_%.2f_rv_%.2f' % (ebv, rv)
if kind != 'fit':
if kind == 'mcmc':
result = sncosmo.mcmc_lc(lc,
model,
vparams,
bounds=bounds,
nwalkers=500,
nburn=1000,
nsamples=20)
elif kind == 'nest':
result = sncosmo.nest_lc(lc,
model,
vparams,
bounds=bounds,
method='multi',
npoints=800)
samples = result[0].samples.reshape(500, 20, -1)
vparams = result[0].vparam_names
plot_arg = np.rollaxis(samples, 2)
plotting.plot_chains(plot_arg,
param_names=vparams,
filename='fits/%s_samples%s.pdf' %
(lc.meta['name'], qualifier))
dicts = [
dict(zip(vparams, samp)) for samp in samples.reshape(500 * 20, -1)
]
thinned = samples.reshape(500, 20, -1)[:, [0, -1]].reshape(1000, -1)
pickle.dump(
samples,
open('fits/%s_samples%s.pkl' % (lc.meta['name'], qualifier), 'wb'))
models = [copy(result[1]) for i in range(len(thinned))]
for d, m in zip(dicts, models):
m.set(**d)
fig = sncosmo.plot_lc(data=lc,
model=models,
ci=(50 - 68 / 2., 50., 50 + 68 / 2.),
model_label=lc.meta['name'])
fig.savefig('fits/%s%s.pdf' % (lc.meta['name'], qualifier))
else:
fitres, model = sncosmo.fit_lc(lc, model, vparams, bounds=bounds)
fig = sncosmo.plot_lc(data=lc, model=model)
fig.savefig('fits/%s_fit%s.pdf' % (lc.meta['name'], qualifier))
np.float64, np.float64, np.float64, np.float64, np.float64,
np.float64, np.float64, np.float64, np.float64, np.float64,
np.float64, np.float64, np.float64, np.float64, np.float64,
np.float64, np.float64, np.float64, np.float64, np.float64,
np.float64, np.float64, np.float64, np.float64, np.float64,
np.float64, np.float64, np.float64, np.float64, np.float64,
np.float64, np.float64, np.float64, np.float64, np.float64,
np.float64, np.float64, np.float64, np.float64, np.float64,
np.float64, np.float64, np.float64, np.float64, np.float64,
np.float64, np.float64, np.float64, np.float64, np.float64,
np.float64)
outdata = astropy.table.Table(masked=True, names=names, dtype=dtype)
for i, lc in enumerate(lcfile):
#################################################################################
if args.jla:
data = sncosmo.read_lc(lc, format='salt2')
nickname = data.meta['SN']
if args.cadencesim:
data = sncosmo.read_lc(lc, format='ascii')
nickname = lc.split('.')[0].split('/')[-1]
#################################################################################
try:
nickname = str(int(nickname))
except:
pass
#################################################################################
if args.jla:
fitfile = './fit_results/emcee/JLA/%s/%s.dat' % (nickname, nickname)
if args.cadencesim:
fitfile = './fit_results/emcee/cadencesim/%s/%s.dat' % (nickname,
return (_snFit(args))
except:
return (None)
files = glob.glob('*clipped.dat')
zpMag = sncosmo.get_magsystem('Vega')
sne = []
times = []
#plt.figure()
for f in files:
if f not in ['lc_2009kn_clipped.dat', 'lc_2010bq_clipped.dat']:
continue
print(f)
t0 = 0
lc = sncosmo.read_lc(f)
f = f[:-8]
if len(lc[lc['Band'] == 'U']) == 0 and len(
lc[lc['Band'] == 'u']) == 0 and len(lc[lc['Band'] == 'J']) == 0:
#os.remove(f)
continue
lc = lc[lc['Band'] != 'U']
lc = lc[lc['Band'] != 'u']
lc = lc[lc['Band'] != 'H']
lc = lc[lc['Band'] != 'J']
lc = lc[lc['Band'] != 'K']
lc = lc[lc['Band'] != 'i']
lc = lc[lc['Band'] != 'I']
if not lc:
#os.remove(f)
figname = (fname.replace("testdata", "lcplots")
.replace(".dat", "_lcfit.png"))
model.parameters[1:5] = showparams
sncosmo.plot_lc(data, model, fname=figname)
labels = ["${0}$".format(s) for s in model.param_names_latex[1:5]]
fig = triangle.corner(samples, labels=labels, bins=30)
figname = (fname.replace("testdata", "lcplots")
.replace(".dat", "_corner.png"))
plt.savefig(figname)
"""
# Get data from all SNe
data_list = []
for fname in fnames[:n_obs]:
data = sncosmo.read_lc(fname)
data_list.append(data)
# Do MCMC:
all_samples = sample_all(data_list)
np.savetxt('emcee_samples.dat', all_samples)
all_params = np.average(all_samples, axis=0)
# Make plots of best fit global parameters and histograms of best fit
# parameters for each supernova:
labels = ['Omega', 'x0_0', 'alpha', 'beta']
fig = triangle.corner(all_samples[:, :4], labels=labels, bins=30)
plt.savefig('global_params_%ssne.png' % n_obs)
fig2, axes = plt.subplots(2, 2)
titles = ['t0', 'sigma', 'x1', 'c']
rank = comm.Get_rank()
size = comm.Get_size()
# partition an iterable i into n parts
_split = lambda i,n: [i[:len(i)/n]]+_split(i[len(i)/n:],n-1) if n != 0 else []
# Shorthand for a few variables with long names.
od94 = sncosmo.OD94Dust
f99 = sncosmo.F99Dust
# Load the run configuration file.
config_filename = sys.argv[1]
config = yaml.load(open(config_filename).read())
# Read the LC to fit.
lc = sncosmo.read_lc(config['lc_filename'], format='csp')
name = lc.meta['name']
# Configure the properties of the host galaxy dust.
dust_type = od94 if config['dust_type'] == 'od94' else f99
bintype = config['burns_bintype']
# Get the host galaxy dust data.
host_ebv, err = burns.get_hostebv(name)
_, rv_low, rv_hi = burns.get_hostrv_prior(name, bintype, dust_type,
retlims=True)
ebvlo = host_ebv - err
ebvhi = host_ebv + err
# Do ebv / r_v gridding.
nrv = config['nrv']
import matplotlib
matplotlib.use('Agg')
from copy import copy
import sncosmo
import numpy as np
from bolomc import bump
from bolomc import burns
from bolomc.distributions import TruncNorm
lc = sncosmo.read_lc('../data/CSP_Photometry_DR2/SN2005elopt+nir_photo.dat',
format='csp')
model = sncosmo.Model(bump.BumpSource(),
effect_names=['host', 'mw'],
effect_frames=['rest', 'obs'],
effects=[sncosmo.OD94Dust(),
sncosmo.F99Dust()])
model2 = copy(model)
model2.set(
UV_bump_amp=1., #blue_bump_amp=0.2,
blue_bump_amp=-0.2,
i1_bump_amp=0.1,
i2_bump_amp=-0.2,
y1_bump_amp=-0.2,
y2_bump_amp=0.2,
y3_bump_amp=-0.1,
j1_bump_amp=-0.2,
j2_bump_amp=0.2,
h1_bump_amp=-0.2,
def load_example_lcs():
return ([
_standardize(sncosmo.read_lc(f)) for f in glob.glob(
os.path.join(__dir__, 'example_setup', 'lcs', '*.lc'))
])
from astropy.table import Table
import numpy as np
import glob
from IPython import embed
import sys
sne = glob.glob('./lc/*.list')
names = ['SN', 'tmax', 'color', 'x1r', 'x1f', 'mB', 'mwebv']
dtype = [
'S50', np.float64, np.float64, np.float64, np.float64, np.float64,
np.float64
]
t = Table(names=names, dtype=dtype)
for i, sn in enumerate(sne):
data = sncosmo.read_lc(sn, format='salt2')
daymax = data.meta['DAYMAX']
x1r = data.meta['X1R']
x1f = data.meta['X1F']
c = data.meta['C']
mB = data.meta['MB']
name = sn.split('.')[-2].split('/')[-1]
mwebv = data.meta['MWEBV']
t.add_row([name, daymax, c, x1r, x1f, mB, mwebv])
t.write('./cadence_sim_inputs.txt',
format='ascii.fixed_width',
delimiter=' ',
overwrite=True)
def sample_lcs(nsne, under_model):
if not os.path.exists("samples_%s" % (under_model.name(nsne))):
os.mkdir("samples_%s" % (under_model.name(nsne)))
# sampler parameters
ndim = 4
nwalkers = 20
nburn = 100
nsamples = 500
thin = 10
# define likelihood
model = sncosmo.Model(source='salt2-extended')
bounds = {
0: (-20., 40.), # t0
2: (-4., 4.), # x1
3: (-0.4, 0.4)
}
def lnlike(parameters, data):
"""Return log L for array of parameters."""
# If any parameters are out-of-bounds, return 0 probability.
for i, b in bounds.items():
if not b[0] < parameters[i] < b[1]:
return -np.inf
model.parameters[1:5] = parameters # set model t0, x0, x1, c
mflux = model.bandflux(data['band'],
data['time'],
zp=data['zp'],
zpsys=data['zpsys'])
chisq = np.sum(((data['flux'] - mflux) / data['fluxerr'])**2)
return -chisq / 2.
def sample(data):
"""Return MCMC samples for model defined above."""
# fix redshift in the model
model.set(z=data.meta['z'])
# Create sampler
sampler = emcee.EnsembleSampler(nwalkers,
ndim,
lnlike,
args=(data, ),
a=3.50)
# Starting positions of walkers.
# Here we cheat by setting the "guess" equal to the known true parameters!
current = np.array([
data.meta['t0'], data.meta['x0'], data.meta['x1'], data.meta['c']
])
errors = np.array([1., 0.1 * data.meta['x0'], 1., 0.1])
pos = [
current + errors * np.random.randn(ndim) for i in range(nwalkers)
]
# burn-in
pos, prob, state = sampler.run_mcmc(pos, nburn)
sampler.reset()
# production run
sampler.run_mcmc(pos, nsamples, thin=thin)
print("Avg acceptance fraction:", np.mean(sampler.acceptance_fraction))
return sampler.flatchain
fnames = sorted(glob("testdata_%s/*" % (under_model.name(nsne))))
for fname in fnames:
print("processing", fname)
# read data, run sampler on it, save samples
data = sncosmo.read_lc(fname)
samples = sample(data)
sfname = (fname.replace(
"testdata_%s" % (under_model.name(nsne)),
"samples_%s" % (under_model.name(nsne))).replace(".dat", ".npy"))
np.save(sfname, samples)
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 _read_data(filename,**kwargs):
table_done=True
try:
table = sncosmo.read_lc(filename, masked=True,**kwargs)
for col in table.colnames:
if col != _get_default_prop_name(col.lower()):
table.rename_column(col, _get_default_prop_name(col.lower()))
except:
try:
table = ascii.read(filename, masked=True,**kwargs)
for col in table.colnames:
if col != _get_default_prop_name(col.lower()):
table.rename_column(col, _get_default_prop_name(col.lower()))
except:
table = Table(masked=True)
table_done=False
delim = kwargs.get('delim', None)
myCurve=curve()
with anyOpen(filename) as f:
lines=f.readlines()
length=mode([len(l.split()) for l in lines])[0][0]#uses the most common line length as the correct length
for i,line in enumerate(lines):
if np.any([x in line for x in _comment_char]):
continue
line = line.strip(delim)
if len(line)==0:
continue
if np.any([x in line for x in _meta__]):
pos = line.find(' ')
if (lines[i + 1][0] in _meta__ or lines[i + 1][0] in _comment_char or len(
line) != length): # just making sure we're not about to put the col names into meta
if (pos == -1 or not any([_isfloat(x) for x in line.split()])):
if line[-1] not in string.punctuation:
line=line+'.'
else:
myCurve.meta[line[1:pos]] = _cast_str(line[pos:])
continue
line=line.split()
if len(line)!= length:
raise(RuntimeError,"Make sure your data are in a square matrix.")
if table.colnames:
colnames=table.colnames
else:
colnames = odict.fromkeys([_get_default_prop_name(x.lower()) for x in line])
if len(colnames) != len(line):
raise (RuntimeError, "Do you have duplicate column names?")
colnames=odict(zip(colnames,range(len(colnames))))
startLine=i
break
f.close()
if not table_done:
lines = [x.strip().split() for x in lines[startLine + 1:] if not np.any([y in x for y in _comment_char])]
for col in colnames:
table[col]=np.asarray([_cast_str(x[colnames[col]]) for x in lines])
colnames=colnames.keys()
for col in [_get_default_prop_name(x) for x in ['band','zp','zpsys']]:
if col not in colnames:
temp=kwargs.get(col,None)
if temp is not None:
table[col]=temp
else:
print('Column "%s" is not in your file and you did not define it in kwargs.'%col)
sys.exit(1)
table=standardize_table_colnames(table)
bnds = {x for x in table[_get_default_prop_name('band')]}
table=_norm_flux_mag(table)
for band in bnds:
if _isfloat(band[0]):
band='band_'+band
try:
if band[0:5]=='band_':
sncosmo.get_bandpass(band[5:])
else:
sncosmo.get_bandpass(band)
except:
print('Skipping band %s, not in registry.' %band)
table.mask[table[_get_default_prop_name('band')]==band]=True
continue
myCurve.bands.append(band)
myCurve.table=table
myCurve.zpsys=table['zpsys'][0]
return myCurve
def load_example_lc():
return (_standardize(
sncosmo.read_lc(
os.path.join(__dir__, 'example_setup', 'lcs',
'example_2006aj.lc'))))
def curveToColor(lc,
colors,
bandFit=None,
snType='II',
bandDict=_filters,
color_bands=_opticalBands,
zpsys='AB',
model=None,
singleBand=False,
verbose=True,
**kwargs):
"""
Function takes a lightcurve file and creates a color table for it.
:param lc: Name of lightcurve file you want to read, or astropy Table containing data
:type lc: str or astropy.Table
:param colors: Colors you want to calculate for the given SN (i.e U-B, r'-J)
:type colors: str or list of strings
:param bandFit: If there is a specific band you would like to fit instead of default
:type bandFit: str,optional
:param snType: Classification of SN
:type snType: str,optional
:param bandDict: sncosmo bandpass for each band used in the fitting/table generation
:type bandDict: dict,optional
:param color_bands: bands making up the known component of chosen colors
:type color_bands: list,optional
:param zpsys: magnitude system (i.e. AB or Vega)
:type zpsys: str,optional
:param model: If there is a specific sncosmo model you would like to fit with, otherwise all models mathcing the SN classification will be tried
:type model: str,optional
:param singleBand: If you would like to only fit the bands in the color
:type singleBand: Boolean,optional
:param verbose: If you would like printing information to be turned on/off
:type verbose: Boolean,optional
:param kwargs: Catches all SNCOSMO fitting parameters here
:returns: colorTable: Astropy Table object containing color information
"""
bands = append([col[0] for col in colors], [col[-1] for col in colors])
for band in _filters:
if band not in bandDict.keys() and band in bands:
bandDict[band] = sncosmo.get_bandpass(_filters[band])
if not isinstance(colors, (tuple, list)):
colors = [colors]
zpMag = sncosmo.get_magsystem(zpsys)
if isinstance(lc, str):
curve = _standardize(sncosmo.read_lc(lc))
else:
try:
curve = _standardize(lc)
except:
raise RuntimeError("Can't understand your lightcurve.")
if _get_default_prop_name('zpsys') not in curve.colnames:
curve[_get_default_prop_name('zpsys')] = zpsys
colorTable = Table(masked=True)
colorTable.add_column(Column(data=[], name=_get_default_prop_name('time')))
for band in bandDict:
if not isinstance(bandDict[band], sncosmo.Bandpass):
bandDict = _bandCheck(bandDict, band)
t0 = None
if verbose:
print('Getting best fit for: ' + ','.join(colors))
args = []
for p in _fittingParams:
args.append(kwargs.get(p, _fittingParams[p]))
if p == 'method':
try:
importlib.import_module(_fittingPackages[args[-1]])
except RuntimeError:
sys.exit()
for color in colors: #start looping through desired colors
if bandDict[color[
0]].wave_eff < _UVrightBound: #then extrapolating into the UV from optical
if not bandFit:
bandFit = color[-1]
if singleBand:
color_bands = [color[-1]]
blue = curve[curve[_get_default_prop_name('band')] ==
color[0]] #curve on the blue side of current color
red = curve[[
x in color_bands for x in curve[_get_default_prop_name('band')]
]] #curve on the red side of current color
else: #must be extrapolating into the IR
if not bandFit:
bandFit = color[0]
if singleBand:
color_bands = [color[0]]
blue = curve[[
x in color_bands for x in curve[_get_default_prop_name('band')]
]]
red = curve[curve[_get_default_prop_name('band')] == color[-1]]
if len(blue) == 0 or len(red) == 0:
if verbose:
print('Asked for color %s but missing necessary band(s)' %
color)
bandFit = None
continue
btemp = [
bandDict[blue[_get_default_prop_name('band')][i]].name
for i in range(len(blue))
]
rtemp = [
bandDict[red[_get_default_prop_name('band')][i]].name
for i in range(len(red))
]
blue.remove_column(_get_default_prop_name('band'))
blue[_get_default_prop_name('band')] = btemp
red.remove_column(_get_default_prop_name('band'))
red[_get_default_prop_name('band')] = rtemp
#now make sure we have zero-points and fluxes for everything
if _get_default_prop_name('zp') not in blue.colnames:
blue[_get_default_prop_name('zp')] = [
zpMag.band_flux_to_mag(1 / sncosmo.constants.HC_ERG_AA,
blue[_get_default_prop_name('band')][i])
for i in range(len(blue))
]
if _get_default_prop_name('zp') not in red.colnames:
red[_get_default_prop_name('zp')] = [
zpMag.band_flux_to_mag(1 / sncosmo.constants.HC_ERG_AA,
red[_get_default_prop_name('band')][i])
for i in range(len(red))
]
if _get_default_prop_name('flux') not in blue.colnames:
blue = mag_to_flux(blue, bandDict, zpsys)
if _get_default_prop_name('flux') not in red.colnames:
red = mag_to_flux(red, bandDict, zpsys)
if not t0: #this just ensures we only run the fitting once
if not model:
if verbose:
print('No model provided, running series of models.')
mod, types = loadtxt(os.path.join(__dir__, 'data', 'sncosmo',
'models.ref'),
dtype='str',
unpack=True)
modDict = {mod[i]: types[i] for i in range(len(mod))}
if snType != 'Ia':
mods = [
x for x in sncosmo.models._SOURCES._loaders.keys()
if x[0] in modDict.keys()
and modDict[x[0]][:len(snType)] == snType
]
elif snType == 'Ia':
mods = [
x for x in sncosmo.models._SOURCES._loaders.keys()
if 'salt2' in x[0]
]
mods = {
x[0] if isinstance(x, (tuple, list)) else x
for x in mods
}
if bandFit == color[0] or len(blue) > len(red):
args[0] = blue
if len(blue) > 60:
fits = []
for mod in mods:
fits.append(_snFit([mod] + args))
else:
fits = pyParz.foreach(mods, _snFit, args)
fitted = blue
notFitted = red
fit = color[0]
elif bandFit == color[-1] or len(blue) < len(red):
args[0] = red
'''
data,temp= sncosmo_fitting.cut_bands(photometric_data(red), sncosmo.Model(tempMod),
z_bounds=all_bounds.get('z', None),
warn=True)
print(data.fluxerr)
cov = diag(data.fluxerr**2) if data.fluxcov is None else data.fluxcov
invcov = linalg.pinv(cov)
args.append(invcov)
sys.exit()
fits=pyParz.foreach(mods,_snFit,args)
args.pop()
'''
if len(red) > 60:
fits = []
for mod in mods:
fits.append(_snFit([mod] + args))
else:
fits = pyParz.foreach(mods, _snFit, args)
fitted = red
notFitted = blue
fit = color[-1]
else:
raise RuntimeError(
'Neither band "%s" nor band "%s" has more points, and you have not specified which to fit.'
% (color[0], color[-1]))
bestChisq = inf
for f in fits:
if f:
res, mod = f
if res.chisq < bestChisq:
bestChisq = res.chisq
bestFit = mod
bestRes = res
if verbose:
print('Best fit model is "%s", with a Chi-squared of %f' %
(bestFit._source.name, bestChisq))
elif bandFit == color[0] or len(blue) > len(red):
args[0] = blue
bestRes, bestFit = _snFit(append(model, args))
fitted = blue
notFitted = red
fit = color[0]
if verbose:
print(
'The model you chose (%s) completed with a Chi-squared of %f'
% (model, bestRes.chisq))
elif bandFit == color[-1] or len(blue) < len(red):
args[0] = red
bestRes, bestFit = _snFit(append(model, args))
fitted = red
notFitted = blue
fit = color[-1]
if verbose:
print(
'The model you chose (%s) completed with a Chi-squared of %f'
% (model, bestRes.chisq))
else:
raise RuntimeError(
'Neither band "%s" nor band "%s" has more points, and you have not specified which to fit.'
% (color[0], color[-1]))
t0 = _getBandMaxTime(bestFit, fitted, bandDict, 'B',
zpMag.band_flux_to_mag(1, bandDict['B']),
zpsys)
if len(t0) == 1:
t0 = t0[0]
else:
raise RuntimeError('Multiple global maxima in best fit.')
else:
if len(blue) > len(red) or bandFit == color[0]:
fitted = blue
notFitted = red
fit = color[0]
elif len(blue) < len(red) or bandFit == color[-1]:
fitted = red
notFitted = blue
fit = color[-1]
else:
raise RuntimeError(
'Neither band "%s" nor band "%s" has more points, and you have not specified which to fit.'
% (color[0], color[-1]))
#return(bestFit,bestRes,t0,fitted,notFitted)
tGrid, bestMag = _snmodel_to_mag(bestFit, fitted, zpsys, bandDict[fit])
ugrid, UMagErr, lgrid, LMagErr = _getErrorFromModel(
append([bestFit._source.name, fitted], args[1:]), zpsys,
bandDict[fit])
tempTable = Table(
[tGrid - t0, bestMag, bestMag * .1],
names=(_get_default_prop_name('time'),
_get_default_prop_name('mag'),
_get_default_prop_name('magerr')
)) #****RIGHT NOW THE ERROR IS JUST SET TO 10%*****
interpFunc = scint.interp1d(
array(tempTable[_get_default_prop_name('time')]),
array(tempTable[_get_default_prop_name('mag')]))
minterp = interpFunc(
array(notFitted[_get_default_prop_name('time')] - t0))
interpFunc = scint.interp1d(ugrid - t0, UMagErr)
uinterp = interpFunc(
array(notFitted[_get_default_prop_name('time')] - t0))
interpFunc = scint.interp1d(lgrid - t0, LMagErr)
linterp = interpFunc(
array(notFitted[_get_default_prop_name('time')] - t0))
magerr = mean([minterp - uinterp, linterp - minterp], axis=0)
for i in range(len(minterp)):
colorTable.add_row(append(
notFitted[_get_default_prop_name('time')][i] - t0,
[1 for j in range(len(colorTable.colnames) - 1)]),
mask=[
True if j > 0 else False
for j in range(len(colorTable.colnames))
])
if fit == color[0]:
colorTable[color] = MaskedColumn(
append([1 for j in range(len(colorTable) - len(minterp))],
minterp -
array(notFitted[_get_default_prop_name('mag')])),
mask=[
True if j < (len(colorTable) - len(minterp)) else False
for j in range(len(colorTable))
])
else:
colorTable[color] = MaskedColumn(
append([1 for j in range(len(colorTable) - len(minterp))],
array(notFitted[_get_default_prop_name('mag')]) -
minterp),
mask=[
True if j < (len(colorTable) - len(minterp)) else False
for j in range(len(colorTable))
])
colorTable[color[0] + color[-1] + '_err'] = MaskedColumn(
append([1 for j in range(len(colorTable) - len(magerr))], magerr +
array(notFitted[_get_default_prop_name('magerr')])),
mask=[
True if j < (len(colorTable) - len(magerr)) else False
for j in range(len(colorTable))
])
#colorTable['V-r']=MaskedColumn(append([1 for j in range(len(colorTable)-len(magerr))],[bestFit.color('bessellv','sdss::r',zpsys,t0) for i in range(len(linterp))]),mask=[True if j<(len(colorTable)-len(magerr)) else False for j in range(len(colorTable))])
tempVRCorr = 0
for name in bestFit.effect_names:
magCorr = _unredden(
color, bandDict,
bestRes.parameters[bestRes.param_names.index(name + 'ebv')],
bestRes.parameters[bestRes.param_names.index(name + 'r_v')])
colorTable[color] -= magCorr
tempVRCorr += _unredden(
'V-R', bandDict,
bestRes.parameters[bestRes.param_names.index(name + 'ebv')],
bestRes.parameters[bestRes.param_names.index(name + 'r_v')])
corr1 = _ccm_extinction(
sncosmo.get_bandpass('besselli').wave_eff,
bestRes.parameters[bestRes.param_names.index(name + 'ebv')],
r_v=3.1)
corr2 = _ccm_extinction(
sncosmo.get_bandpass('bessellr').wave_eff,
bestRes.parameters[bestRes.param_names.index(name + 'ebv')],
r_v=3.1)
corr3 = _ccm_extinction(
sncosmo.get_bandpass('bessellb').wave_eff,
bestRes.parameters[bestRes.param_names.index(name + 'ebv')],
r_v=3.1)
corr4 = _ccm_extinction(
sncosmo.get_bandpass('bessellv').wave_eff,
bestRes.parameters[bestRes.param_names.index(name + 'ebv')],
r_v=3.1)
vr = [
x - tempVRCorr
for x in bestFit.color('bessellv', 'bessellr', zpsys,
arange(t0 - 20, t0 + 100, 1))
]
#vr=(bestFit.bandmag('bessellr',zpsys,arange(t0-20,t0+100,1))-bestFit.bandmag('besselli',zpsys,arange(t0-20,t0+100,1))-(corr2-corr1))/(bestFit.bandmag('bessellb',zpsys,arange(t0-20,t0+100,1))-bestFit.bandmag('bessellv',zpsys,arange(t0-20,t0+100,1))-(corr3-corr4))
bandFit = None
colorTable.sort(_get_default_prop_name('time'))
return (colorTable, vr)
def grabdata(loc,list):
f = open(list,'r').readlines()
files = []
for file in f:
files.append(os.path.join(loc,file.strip()))
lightcurves = []
hostzs = []
hostz_stds = []
snids = []
sntypes = []
params = []
max = 20000
cntr = 0
for file in files:
cntr += 1
if cntr > max:
continue
sn = open(file,'r').readlines()
keepgoing = True
i = 0
sntype = -999
snid = -999
hostz = -999
hostz_std = -999
while keepgoing:
if 'SNID:' in sn[i]:
snid = sn[i].split()[1]
if 'SNTYPE:' in sn[i]:
sntype = sn[i].split()[1]
if 'HOST_GALAXY_PHOTO-Z:' in sn[i]:
hostz = float(sn[i].split()[1])
hostz_std = float(sn[i].split()[3])
keepgoing = False
i += 1
hostzs.append(hostz)
hostz_stds.append(hostz_std)
snids.append(snid)
sntypes.append(sntype)
sndata = dt.read(file,23,24)
flux = sndata['FLUXCAL']
fluxerr = sndata['FLUXCALERR']
filt = sndata['FLT']
mjd = sndata['MJD']
badrow = -9
for i,f,fe,flt,m in zip(range(len(flux)),flux,fluxerr,filt,mjd):
if 'g+r+i+z' in str(f):
badrow = i
elif 'g+r+i+z' in str(fe):
badrow = i
elif 'g+r+i+z' in str(flt):
badrow = i
elif 'g+r+i+z' in str(m):
badrow = i
if badrow != -9:
flux = np.delete(flux,badrow)
fluxerr = np.delete(fluxerr,badrow)
filt = np.delete(filt, badrow)
mjd = np.delete(mjd, badrow)
cosmofile = file.split('.')[0]+'.sncosmo'
out = open(cosmofile,'w')
out.write('time band flux fluxerr zp zpsys\n')
for i,f,fe,flt,m in zip(range(len(flux)),flux,fluxerr,filt,mjd):
out.write(str(m)+' des'+str(flt)+' '+str(f)+' '+str(fe)+' 27.5 ab\n')
out.close()
lc = sncosmo.read_lc(cosmofile)
lightcurves.append(lc)
params.append({'snid':snid,'type':sntype,'hostz':hostz,'hostz_std':hostz_std})
print(snid,sntype,hostz,hostz_std)
hostzs = np.array(hostzs)
hostz_stds = np.array(hostz_std)
snids = np.array(snids)
sntypes = np.array(sntypes,dtype='float')
print('unique types',np.unique(sntypes))
dsntypes = np.zeros(len(sntypes))
dsntypes[sntypes > 1] = np.ones(len(sntypes[sntypes > 1]))
dsntypes[sntypes == 1] = np.zeros(len(sntypes[sntypes == 1]))
dsntypes[sntypes == -9] = np.zeros(len(sntypes[sntypes == -9]))-9
print('nonia',len(dsntypes[dsntypes == 1]))
print('ia',len(dsntypes[dsntypes == 0]))
print('none',len(dsntypes[dsntypes == -9]))
'FEED ONLY LCs WITH NON -9 TYPES THROUGH ALL THE LIGHCURVE FITS AND SPLIT THAT INTO TEST AND TRAIN'
knownlightcurves = []
knownparams = []
knowntypes = []
blindlightcurves = []
blindparams = []
for lc,t,p in zip(lightcurves,dsntypes,params):
if t == -9:
blindlightcurves.append(lc)
blindparams.append(p)
else:
knownlightcurves.append(lc)
knownparams.append(p)
knowntypes.append(t)
ndim = 1 + 1 + 1 + len(sources) + 2
fitparams = np.zeros((len(knownlightcurves), ndim)) #THIS NEEDS TO CUT OUT -9S
fitparams, truetypes = lcfit(knownlightcurves, knownparams, fitparams, knowntypes, sources)
print('fitparams.shape', fitparams.shape)
print('truetypes.shape', truetypes.shape)
np.savez('sncc_known2.npz', big_data_array=fitparams, truetypes=truetypes, params=knownparams)
print('saved sncc_known2.npz')
return
x0=5.52396533233e-05,
x1=-1.62106970624,
c=0.079535505334)
times = np.array([
54346.219, 54356.262, 54358.207, 54359.172, 54365.238, 54373.313,
54382.246, 54386.25, 54388.254, 54393.238, 54403.168, 54406.16, 54412.16,
54416.156, 54420.184, 54421.164, 54423.156, 54425.156, 54431.164, 54433.1
])
model_rcov = model._bandflux_rcov('sdssg', times)
relerr2 = np.diag(model_rcov) - model_rcov[0, 1]
for i, t in enumerate(times):
print(i, t, relerr2[i])
data = sncosmo.read_lc('jla_light_curves/lc-SDSS19230.list',
format='salt2',
read_covmat=True)
# print(data)
data = sncosmo.photdata.photometric_data(data)
mask = sncosmo.fitting._data_mask(data, model.get('t0'), model.get('z'),
(-15., 45.), (3000., 7000.))
# print(len(data[mask]))
# print(sncosmo.chisq(data[mask], model, modelcov=True))
# print(data[mask])
result, fitted_model = sncosmo.fit_lc(data,
model, ['t0', 'x0', 'x1', 'c'],
guess_amplitude=False,
guess_t0=False,
phase_range=(-15., 45.),
wave_range=(3000., 7000.),
def snname_from_fname(fname):
return re.match(REGEX, fname).groups()[0]
if __name__ == "__main__":
model = sncosmo.Model(source='salt2',
effects=[sncosmo.F99Dust()],
effect_names=['mw'],
effect_frames=['obs'])
fnames = glob.glob("jla_light_curves/lc-SDSS19230.list")
for fname in fnames[0:1]:
snname = snname_from_fname(fname)
data = sncosmo.read_lc(fname, format='salt2', read_covmat=True)
model.set(mwebv=data.meta['MWEBV'], z=data.meta['Z_HELIO'])
t0 = time.time()
result, m = sncosmo.fit_lc(data, model, ['t0', 'x0', 'x1', 'c'],
modelcov=True, phase_range=(-15., 45.),
wave_range=(3000., 7000.), verbose=True)
print("time:", time.time() - t0, 's')
print(result)
snfit_result = snfitio.read_snfit_result('results_snfit/result-{}.dat'
.format(snname))
print(snfit_result)
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')
from mpi4py import MPI
from bolomc import bump, burns
__whatami__ = "Construct a bolometric light curve from CSP data."
__author__ = "Danny Goldstein <*****@*****.**>"
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
size = comm.Get_size()
# Shorthand for a few variables with long names.
od94 = sncosmo.OD94Dust
f99 = sncosmo.F99Dust
# Read the LC to fit.
lc = sncosmo.read_lc('scripts/sn2011fe.lc', format='ascii')
name = lc.meta['name']
# Configure the properties of the host galaxy dust.
dust_type = od94
bintype = 'gmm'
# rv, ebv
my_jobs = [(3.1, 0.)]
if len(my_jobs) == 0:
result = None # this processor will be idle
param_names = None
else:
result = []
param_names = []
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()
