def get_gaussian_fit(z, t0, x0, x1, c, lc, seed, temp_dir, interped, type="iminuit"):
model = sncosmo.Model(source='salt2-extended')
p = {'z': z, 't0': t0, 'x0': x0, 'x1': x1, 'c': c}
model.set(**p)
correct_model = sncosmo.Model(source='salt2-extended')
correct_model.set(**p)
if type == "iminuit":
res, fitted_model = sncosmo.fit_lc(lc, model, ['t0', 'x0', 'x1', 'c'],
guess_amplitude=False, guess_t0=False)
chain = np.random.multivariate_normal(res.parameters[1:], res.covariance, size=int(1e5))
fig = sncosmo.plot_lc(lc, model=[fitted_model, correct_model], errors=res.errors)
fig.savefig(temp_dir + os.sep + "lc_%d.png" % seed, bbox_inches="tight", dpi=300)
elif type == "mcmc":
res, fitted_model = sncosmo.mcmc_lc(lc, model, ['t0', 'x0', 'x1', 'c'], nburn=500, nwalkers=20,
nsamples=1500, guess_amplitude=False, guess_t0=False)
chain = np.random.multivariate_normal(res.parameters[1:], res.covariance, size=int(1e5))
elif type == "nestle":
bounds = {"t0": [980, 1020], "x0": [0.1e-6, 9e-3], "x1": [-10, 10], "c": [-1, 1]}
res, fitted_model = sncosmo.nest_lc(lc, model, ['t0', 'x0', 'x1', 'c'], bounds,
guess_amplitude=False, guess_t0=False)
chain = np.random.multivariate_normal(res.parameters[1:], res.covariance, size=int(1e5))
else:
raise ValueError("Type %s not recognised" % type)
map = {"x0": "$x_0$", "x1": "$x_1$", "c": "$c$", "t0": "$t_0$"}
parameters = [map[a] for a in res.vparam_names]
chain, parameters = add_mu_to_chain(interped, chain, parameters)
return chain, parameters, res.parameters[1:], res.covariance
def test_nest_lc(self):
"""Ensure that nested sampling runs."""
np.random.seed(0) # seed the RNG for reproducible results.
self.model.set(**self.params)
res, fitmodel = sncosmo.nest_lc(
self.data, self.model, ['z', 't0', 'amplitude'],
bounds={'z': (0., 1.0)}, guess_amplitude_bound=True, nobj=50)
assert_allclose(fitmodel.parameters, self.model.parameters, rtol=0.05)
def test_nest_lc(self):
"""Ensure that nested sampling runs.
Pass in parameter names in order different from that stored in
model; tests parameter re-ordering.
"""
np.random.seed(0) # seed the RNG for reproducible results.
self.model.set(**self.params)
res, fitmodel = sncosmo.nest_lc(
self.data, self.model, ['amplitude', 'z', 't0'],
bounds={'z': (0., 1.0)}, guess_amplitude_bound=True, nobj=50)
assert_allclose(fitmodel.parameters, self.model.parameters, rtol=0.05)
def test_nest_lc(self):
"""Ensure that nested sampling runs.
Pass in parameter names in order different from that stored in
model; tests parameter re-ordering.
"""
rstate = RandomState(0)
self.model.set(**self.params)
res, fitmodel = sncosmo.nest_lc(
self.data, self.model, ['amplitude', 'z', 't0'],
bounds={'z': (0., 1.0)}, guess_amplitude_bound=True, npoints=50,
rstate=rstate)
assert_allclose(fitmodel.parameters, self.model.parameters, rtol=0.05)
def test_nest_lc(self):
"""Ensure that nested sampling runs.
Pass in parameter names in order different from that stored in
model; tests parameter re-ordering.
"""
rstate = RandomState(0)
self.model.set(**self.params)
res, fitmodel = sncosmo.nest_lc(
self.data, self.model, ['amplitude', 'z', 't0'],
bounds={'z': (0., 1.0)}, guess_amplitude_bound=True, npoints=50,
rstate=rstate)
assert_allclose(fitmodel.parameters, self.model.parameters, rtol=0.05)
def nest_fit(data, model, vparam_names, **kwargs):
"""Fit light curves using nested sampling
Args:
data (Table): Table of photometric data
model (Model): The model to fit
vparam_names (iterable): Model parameters to vary
Any other parameters for ``sncosmo.nest_lc``
Returns:
- A dictionary like object with fit results
- A fitted ``Model`` instance
"""
data, model, vparam_names, kwargs = \
_copy_data(data, model, vparam_names, kwargs)
return sncosmo.nest_lc(data, model, vparam_names, **kwargs)
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 runNest(self):
nestOut = sncosmo.nest_lc(self.data, self.model, vparam_names=self.vparams,
bounds=self.bounds, guess_amplitude_bound=True,
minsnr=3.0, verbose=True)
return nestOut
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))
