def calc_model_chisq(data, result, model):
"""Calculate the chi-squared for a given data table and model
Chi-squared is calculated using parameter values from ``model``. Degrees
of freedom are calculated using the number of varied parameters specified
is the ``result`` object.
Args:
data (Table): An sncosmo input table
model (Model): An sncosmo Model
result (Result): sncosmo fitting result
Returns:
The un-normalized chi-squared
The number of data points used in the calculation
"""
data = deepcopy(data)
# Drop any data that is not withing the model's range
min_band_wave = np.array(
[sncosmo.get_bandpass(b).minwave() for b in data['band']])
max_band_wave = np.array(
[sncosmo.get_bandpass(b).maxwave() for b in data['band']])
data = data[(data['time'] >= model.mintime())
& (data['time'] <= model.maxtime()) &
(min_band_wave >= model.minwave()) &
(max_band_wave <= model.maxwave())]
if len(data) == 0:
raise ValueError('No data within model range')
return sncosmo.chisq(data, model), len(data) - len(result.vparam_names)
def get_log_likelihood(self, data):
r""" Uses SNCosmo to move from supernova parameters to a light curve.
"""
arr = []
for z, t0, x1, c, lc, mag, mu in zip(data["redshift"], data["t0"],
data["x1"], data["c"],
data["olc"], data["abs_mag"], data["mu"]):
self.model.set(z=z)
self.model.source.set_peakmag(mag + mu, "bessellb", "ab")
self.model.parameters = [z, t0, self.model.get("x0"), x1, c]
chi2 = sncosmo.chisq(lc, self.model)
arr.append(-0.5 * chi2)
return np.array(arr)
def get_log_likelihood(self, data):
r""" Uses SNCosmo to move from supernova parameters to a light curve.
"""
H0 = data["H0"]
om = data["omega_m"]
if self.cosmology is None or (self.current_h0 != H0 or self.current_omega_m != om):
self.cosmology = FlatwCDM(H0, om)
self.current_h0 = H0
self.current_omega_m = om
self.model.set(z=data["redshift"])
self.model.set_source_peakabsmag(data["luminosity"], 'bessellb', 'ab', cosmo=self.cosmology)
x0 = self.model.get("x0")
self.model.parameters = [data["redshift"], data["t0"], x0, data["x1"], data["c"]]
chi2 = sncosmo.chisq(data["olc"], self.model)
return -0.5 * chi2
model.set(z=zed[0])
res, fitted_model=sncosmo.mcmc_lc(hml_dat, model, ['t0','x0','x1','c'], bounds={'x1':(-3.5,3.5), 'c':(-0.35,0.45)}, nburn=100, nsamples=5000)
#res, fitted_model=sncosmo.fit_lc(hml_dat, model, ['t0','x0','x1','c'], bounds={'x1':(-3.5,3.5), 'c':(-0.35,0.45)}, verbose=True)
#res, fitted_model=sncosmo.nest_lc(hml_dat, model, ['t0','x0','x1','c'], bounds={'x1':(-3.5,3.5), 'c':(-0.35,0.45)},)
pdate=res.parameters[1]
pass_4cut=Check_Dates(hml[:,1].astype(float), pdate)
print hml[:,0][0], pass_4cut
fig=sncosmo.plot_lc(hml_dat, model=fitted_model, errors=res.errors, color=np.random.choice(flat_cols), figtext=str(hml[:,0][0])+'\n'+str(pass_4cut), xfigsize=10, pulls=False)
plt.axvline(-20., color='black', linestyle='--')
plt.axvline(+50., color='black', linestyle='--')
plt.savefig('LC_Fixed/'+str(hml[:,0][0])+'.png', dpi=150, bbox_inches='tight')
plt.close()
print '### Parameters ###'
print str(hml[:,0][0]), float(zed[0]), float(0), 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 'chi2', sncosmo.chisq(hml_dat, fitted_model)
print 'ndof', len(hml_dat)-4. #len(data)-len(vparam_names)
print 'red_chi2', sncosmo.chisq(hml_dat, fitted_model)/(len(hml_dat)-4.)
print 'absolute magnitue', fitted_model.source_peakabsmag('bessellb','ab')
# print 'chi2', res.chisq
# print 'res.ndof', res.ndof
# print 'red_chisq', res.chisq/res.ndof
cur.execute("INSERT INTO sncosmo_fits (ptfname, redshift, redshift_err, t0, t0_err, x0, x0_err, x1, x1_err, c, c_err, ra, dec, pass_cut, redchi2, abs_mag) VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s);",(str(hml[:,0][0]), float(zed[0]), float(0), 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]), pass_4cut,float(sncosmo.chisq(hml_dat, fitted_model)/(len(hml_dat)-4.)), float(fitted_model.source_peakabsmag('bessellb','ab')),))
conn.commit()
print 'Done:', hml[:,0][0]
except ValueError:
print 'Value Error'
model.set(z=zed[0])
res, fitted_model=sncosmo.mcmc_lc(hml_dat, model, ['t0','x0','x1','c'], bounds={'x1':(-3.5,3.5), 'c':(-0.35,0.45)}, nburn=100, nsamples=5000)
#res, fitted_model=sncosmo.fit_lc(hml_dat, model, ['t0','x0','x1','c'], bounds={'x1':(-3.5,3.5), 'c':(-0.35,0.45)}, verbose=True)
#res, fitted_model=sncosmo.nest_lc(hml_dat, model, ['t0','x0','x1','c'], bounds={'x1':(-3.5,3.5), 'c':(-0.35,0.45)},)
pdate=res.parameters[1]
pass_4cut=Check_Dates(hml, pdate)
#print l[i], pass_4cut
fig=sncosmo.plot_lc(hml_dat, model=fitted_model, errors=res.errors, color=np.random.choice(flat_cols), figtext=str(l[i])+'\n'+str(pass_4cut), xfigsize=10, pulls=False)
plt.axvline(-20., color='black', linestyle='--')
plt.axvline(+50., color='black', linestyle='--')
plt.savefig('LC_Fixed/'+str(l[i])+'.png', dpi=150, bbox_inches='tight')
plt.close()
print '### Parameters ###'
print str(l[i]), float(zed[0]), float(0), 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'])
print 'chi2', sncosmo.chisq(hml_dat, fitted_model)
print 'ndof', len(hml_dat)-4. #len(data)-len(vparam_names)
print 'red_chi2', sncosmo.chisq(hml_dat, fitted_model)/(len(hml_dat)-4.)
print 'absolute magnitue', fitted_model.source_peakabsmag('bessellb','ab')
# print 'chi2', res.chisq
# print 'res.ndof', res.ndof
# print 'red_chisq', res.chisq/res.ndof
#cur.execute("INSERT INTO sncosmo_fits (ptfname, redshift, redshift_err, t0, t0_err, x0, x0_err, x1, x1_err, c, c_err, ra, dec, pass_cut, redchi2, abs_mag) VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s);",(str(l[i]), float(zed[0]), float(0), 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]), pass_4cut,float(sncosmo.chisq(hml_dat, fitted_model)/(len(hml_dat)-4.)), float(fitted_model.source_peakabsmag('bessellb','ab')),))
#conn.commit()
print 'Done:', l[i]
except ValueError:
print 'Value Error'
def mcmc_salt_fit(self, nickname):
# do 2 SALT fits to iterate on the data to use for the fit
bounds = {'t0': self.tmax_bounds}
try:
res, fitted_model = sncosmo.mcmc_lc(self.data,
self.SaltModel,
['t0', 'x0', 'x1', 'c'],
minsnr=3)
except:
# embed()
print 'mcmc_salt_fit 1st iteration failed'
self.SaltModel.set(t0=self.tmax_guess, x0=self.x0_start)
res, fitted_model = sncosmo.mcmc_lc(self.data,
self.SaltModel,
['t0', 'x0', 'x1', 'c'],
bounds=bounds,
guess_amplitude=False,
guess_t0=False,
modelcov=True)
tmax = fitted_model.get('t0')
phase = (self.data['time'] - tmax) / (1 + self.z)
phase_mask = np.array(((phase > -15) & (phase < 45)))
self.data = sncosmo.select_data(self.data, phase_mask)
# fit 2
try:
res, fitted_model = sncosmo.mcmc_lc(self.data,
self.SaltModel,
['t0', 'x0', 'x1', 'c'],
minsnr=3)
except:
print 'mcmc_salt_fit 2nd iteration failed'
raise #nothing succeeded!
self.SaltModel.set(t0=self.tmax_guess, x0=self.x0_start)
res, fitted_model = sncosmo.mcmc_lc(self.data,
self.SaltModel,
['t0', 'x0', 'x1', 'c'],
bounds=bounds,
guess_amplitude=False,
guess_t0=False,
modelcov=True)
tmax = fitted_model.get('t0')
phase = (self.data['time'] - tmax) / (1 + self.z)
phase_mask = np.array(((phase > -15) & (phase < 45)))
self.data = sncosmo.select_data(self.data, phase_mask)
# pull out the cov
self.whocares, self.SaltCov = fitted_model.bandfluxcov(
self.data['band'], self.data['time'], self.data['zp'],
self.data['zpsys'])
# plot
sncosmo.plot_lc(self.data,
model=fitted_model,
fname='%s/plots/emcee/cadencesim/salt/%s.pdf' %
(scratch, nickname),
color='black')
# pull out the cov and some other parameters
self.invcov = np.linalg.inv(self.SaltCov + self.data['fluxcov'])
self.x0_salt = fitted_model.get('x0')
self.tmax_salt = fitted_model.get('t0')
self.c_salt = fitted_model.get('c')
chisq = sncosmo.chisq(self.data, fitted_model)
res.chisq = chisq
res.ndof = len(self.data) - 4
return self.SaltCov, res
results = pd.DataFrame(columns=columns)
for i in range(imin, imax):
sourceId = sourceIds[i]
print i, sourceId
sys.stdout.flush()
# Get the catsim light curve and set the salt2 model with the
# input truth parameters.
ref_lc = SN_reflc_server.get_SNLightCurve_object(sourceId)
model = Salt2Model(ref_lc.params)
# Compute the chisq stats.
mask = ((ref_lc.lightcurve['bandpass']!='lsstu')
& (ref_lc.lightcurve['fluxerr']==ref_lc.lightcurve['fluxerr']))
data = ref_lc.lightcurve[mask]
ndof = len(data) - 5
chisq_catsim = sncosmo.chisq(data, model)
ndof_catsim = ndof
# Find the nearest object from the Object table within +/-2
# arcsec of the catsim source coordinates.
try:
obj = get_coadd_object(l2_service, model.ra, model.dec, box_size=2)
except ValueError:
# No objects found so skip this one.
continue
objectId = obj.objectId.iloc[0]
# Get the ForcedSource light curves and select the data based
# on nominal time range of the model (and exclude the u band
# data).
lc = lc_factory.create(objectId)
data_mask = ((lc.data['bandpass'] != 'lsstu')
xfigsize=10,
pulls=False)
plt.axvline(-20., color='black', linestyle='--')
plt.axvline(+50., color='black', linestyle='--')
plt.savefig('LC_Fixed/' + str(hml[:, 0][0]) + '.png',
dpi=150,
bbox_inches='tight')
plt.close()
print '### Parameters ###'
print str(hml[:, 0][0]), float(zed[0]), float(0), 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 'chi2', sncosmo.chisq(hml_dat, fitted_model)
print 'ndof', len(hml_dat) - 4. #len(data)-len(vparam_names)
print 'red_chi2', sncosmo.chisq(hml_dat,
fitted_model) / (len(hml_dat) - 4.)
print 'absolute magnitue', fitted_model.source_peakabsmag(
'bessellb', 'ab')
# print 'chi2', res.chisq
# print 'res.ndof', res.ndof
# print 'red_chisq', res.chisq/res.ndof
cur.execute(
"INSERT INTO sncosmo_fits (ptfname, redshift, redshift_err, t0, t0_err, x0, x0_err, x1, x1_err, c, c_err, ra, dec, pass_cut, redchi2, abs_mag) VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s);",
(
str(hml[:, 0][0]),
float(zed[0]),
float(0),
float(res.parameters[1]),
m=np.array(m)
hml_dat=astropy.table.Table(data=m, names=('time', 'band', 'flux', 'fluxerr', 'zp', 'zpsys'), dtype=('float','str','float','float','float','str'))
#print 'Doing:', lcs[i]
res, fitted_model=sncosmo.mcmc_lc(hml_dat, model, ['z','t0','x0','x1','c'], bounds={'z':(0,0.15),'x1':(-3.5,3.5), 'c':(-0.35,0.45)}, nburn=100, nsamples=5000)
#res, fitted_model=sncosmo.fit_lc(hml_dat, model, ['t0','x0','x1','c'], bounds={'x1':(-3.5,3.5), 'c':(-0.35,0.45)}, verbose=True)
#res, fitted_model=sncosmo.nest_lc(hml_dat, model, ['t0','x0','x1','c'], bounds={'x1':(-3.5,3.5), 'c':(-0.35,0.45)},)
#fig=sncosmo.plot_lc(hml_dat, model=fitted_model, errors=res.errors, color=np.random.choice(flat_cols), xfigsize=10)
#print res.parameters
#plt.savefig('Fitted_LCs/'+str(lcs[i])+'.png', dpi=150, bbox_inches='tight')
#plt.close()
# print '### Parameters ###'
# print str(hml[:,0][0]), float(zed[0]), float(0), 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 'chi2', sncosmo.chisq(hml_dat, fitted_model)
# print 'ndof', len(hml_dat)-4. #len(data)-len(vparam_names)
redchi2=sncosmo.chisq(hml_dat, fitted_model)/(len(hml_dat)-4.)
#print 'absolute magnitue', fitted_model.source_peakabsmag('bessellb','ab')
# print 'chi2', res.chisq
# print 'res.ndof', res.ndof
#print 'red_chisq', res.chisq/res.ndof
cur.execute("INSERT INTO inout_fit (snid, redshift, redshift_err, t0, t0_err, x0, x0_err, x1, x1_err, c, c_err, redchi2, abs_mag) VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s);",(int(float(lcs[i])),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(redchi2), float(fitted_model.source_peakabsmag('bessellb','ab')),))
conn.commit()
#print 'Done:', hml[:,0][0]
except ValueError:
print 'Value Error', lcs[i]
vparam_names_used,
bounds=bounds,
**kwargs)
outarr[vparam_names][ind] = tuple(res.parameters)
outarr['ztrue'][ind] = z_true
outarr['zfix'][ind] = zfix
outarr['t_exp_true'][ind] = t0_true
outarr['model'][ind] = model_name
outarr['ID'][ind] = ID
outarr['nobs'][ind] = len(lcs)
for j in vparam_names_used:
outarr[j + '_e'][ind] = res.errors[j]
tmp_chisq = sncosmo.chisq(lcs, fitted_model) / res.ndof
outarr['red_chi2'][ind] = tmp_chisq
t_exp_fitted = get_explosion_time(model_name) * \
(1 +
res.parameters[np.array(model.param_names) == 'z']) \
+ res.parameters[np.array(model.param_names) == 't0']
outarr['t_exp_fit'][ind] = t_exp_fitted
outarr['t_exp_dif'][ind] = t0_true - t_exp_fitted
except (ValueError, RuntimeError, KeyError, RuntimeWarning,
sncosmo.fitting.DataQualityError) as err:
print(err, ' for model ', model_name, ' and lightcurve ', str(ID),
' zfix is ', str(zfix))
outarr['error'][ind] = True
def get_log_likelihood(self, data):
self.model.set(x0=data["x0"], x1=data["x1"], t0=data["t0"], c=data["c"])
chi2 = sncosmo.chisq(data["lc"][0], self.model)
if np.isnan(chi2):
return [-np.inf]
return [-0.5 * chi2]
def get_log_likelihood(self, data):
self.model.set(x0=data["x0"], x1=data["x1"], t0=data["t0"], c=data["c"], z=data["z"][0])
chi2 = sncosmo.chisq(data["lc"][0], self.model)
return [-0.5 * chi2]