def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.normalization = self.compute_normalization()
self.model_1d = models.Sersic1D(self.amplitude, self.r_eff, self.n)
astmodels.Gaussian2D(amplitude=10.,
x_mean=5.,
y_mean=5.,
x_stddev=3.,
y_stddev=3.),
astmodels.KingProjectedAnalytic1D(amplitude=10., r_core=5., r_tide=2.),
astmodels.Logarithmic1D(amplitude=10., tau=3.5),
astmodels.Lorentz1D(amplitude=10., x_0=0.5, fwhm=2.5),
astmodels.Moffat1D(amplitude=10., x_0=0.5, gamma=1.2, alpha=2.5),
astmodels.Moffat2D(amplitude=10., x_0=0.5, y_0=1.5, gamma=1.2, alpha=2.5),
astmodels.Planar2D(slope_x=0.5, slope_y=1.2, intercept=2.5),
astmodels.RedshiftScaleFactor(z=2.5),
astmodels.RickerWavelet1D(amplitude=10., x_0=0.5, sigma=1.2),
astmodels.RickerWavelet2D(amplitude=10., x_0=0.5, y_0=1.5, sigma=1.2),
astmodels.Ring2D(amplitude=10., x_0=0.5, y_0=1.5, r_in=5., width=10.),
astmodels.Sersic1D(amplitude=10., r_eff=1., n=4.),
astmodels.Sersic2D(amplitude=10.,
r_eff=1.,
n=4.,
x_0=0.5,
y_0=1.5,
ellip=0.0,
theta=0.0),
astmodels.Sine1D(amplitude=10., frequency=0.5, phase=1.),
astmodels.Cosine1D(amplitude=10., frequency=0.5, phase=1.),
astmodels.Tangent1D(amplitude=10., frequency=0.5, phase=1.),
astmodels.ArcSine1D(amplitude=10., frequency=0.5, phase=1.),
astmodels.ArcCosine1D(amplitude=10., frequency=0.5, phase=1.),
astmodels.ArcTangent1D(amplitude=10., frequency=0.5, phase=1.),
astmodels.Trapezoid1D(amplitude=10., x_0=0.5, width=5., slope=1.),
astmodels.TrapezoidDisk2D(amplitude=10.,
def findHalfLightSersicdf(pgcs,df1,df2):
#df1 is galbasedf with including r25- get pgc names from this file
#df2 is pickle file
halflights = []
amps = []
ns = []
mses = []
#print('0')
for i in np.arange(len(pgcs)):
print(len(pgcs)-i)
galmask = df2.PGC.isin([pgcs[i]])
rp = df2.loc[galmask]
#print(rp)
pgc=pgcs[i]
if np.isnan(rp.r_arcsec).all()==True:
halflights.append(np.nan)
amps.append(np.nan)
ns.append(np.nan)
mses.append(np.nan)
#print('1')
elif rp.r_arcsec.min()>200:
halflights.append(np.nan)
amps.append(np.nan)
ns.append(np.nan)
mses.append(np.nan)
#print('2')
else:
try:
#print('3')
rp.r_arcsec/=3600.
r25 = df1.loc[i].R25_DEG
#print(r25)`=-0
#r25= r25.tolist()[0]
#print(r25)
mask = rp.r_arcsec<2*r25
#print(mask)
rp = rp[mask]
#mask = rp.I>0
#rp = rp[mask]
ind = np.where(rp.r_arcsec<.5*r25)[0][-1]
sersic = models.Sersic1D(bounds = {'n':(0,14)})
outlier_fit = fitting.FittingWithOutlierRemoval(fitting.LevMarLSQFitter(),sigma_clip, niter=3, sigma=2.5)
fitted_model,filtered_data = outlier_fit(sersic,rp.r_arcsec,rp.I)#,weights=0.1*rp.I)
filtered_data[:ind]=False
fit = fitting.LevMarLSQFitter()
fitted_model = fit(sersic,rp.r_arcsec[~filtered_data],rp.I[~filtered_data])#,weights=(0.1*rp.I[~filtered_data]))
mse = np.nanmean((rp.I[~filtered_data] - fitted_model(rp.I[~filtered_data]))**2)
print('')
print(pgc)
#print(np.round(mse,decimals=2))
"""
if mse>3.:
ns_mse = []
for n in nrange:
sersic = models.Sersic1D(bounds = {'n':(n,n+1)})
outlier_fit = fitting.FittingWithOutlierRemoval(fitting.LevMarLSQFitter(),sigma_clip, niter=3, sigma=3)
fitted_model,filtered_data_chisq = outlier_fit(sersic,rp.r_arcsec[~filtered_data],rp.I[~filtered_data])#,weights=(0.1*rp.I[~filtered_data]))
m = np.nanmean((rp.I[~filtered_data] - fitted_model(rp.I[~filtered_data]))**2)
if m<mse:
mse=m
ns_mse.append(n)
print(mse,n)
else:
pass
if len(ns_mse)==0:
print(ns_mse)
print('none better')
pass
else:
sersic = models.Sersic1D(bounds = {'n':(ns_mse[-1]-1,ns_mse[-1]+1)})
fitted_model = fit(sersic,rp.r_arcsec[~filtered_data],rp.I[~filtered_data])
"""
re = np.round(fitted_model.r_eff.value*3600*0.9,decimals=3)
#re*=0.9
#re = np.round(re,decimals=3)
n = np.round(fitted_model.n.value,decimals=3)
#print(re,saloratio,mm15ratio)
#print(n,mmres.n[i],np.round(mmres['T'][i],decimals=2))
print(re)
if re>250.:
halflights.append(np.nan)
amps.append('fit fail')
ns.append('fit fail')
mses.append('fit fail')
elif re<5.:
halflights.append(np.nan)
amps.append('fit fail')
ns.append('fit fail')
mses.append('fit fail')
else:
halflights.append(re)
amps.append(fitted_model.amplitude.value)
ns.append(n)
mses.append(mse)
except:
halflights.append(np.nan)
amps.append('fit fail')
ns.append('fit fail')
mses.append('fit fail')
redf = pd.DataFrame({'PGC':pgcs,'re':halflights,'amp': amps,'n':ns})
#pgcs = np.asarray(pgcs)
#halflights = np.asarray(halflights,dtype='float')
#amps = np.asarray(amps)
#ns = np.asarray(ns)
#return(pgcs,halflights,amps,ns,datamasks,nummasked)
return(redf)