def test_fitzpatrick99_r_v():
"""Test that we can access the `r_v` attribute."""
f = extinction.Fitzpatrick99()
assert f.r_v == 3.1
f = extinction.Fitzpatrick99(2.1)
assert f.r_v == 2.1
def get_F99(this_rest_lambs):
ff = extinction.Fitzpatrick99(3.1)
fprime = extinction.Fitzpatrick99(3.11)
F99_31 = ff(this_rest_lambs, 3.1)
F99_dAdRV = (fprime(this_rest_lambs, 3.11) - F99_31) / 0.01
return F99_31, F99_dAdRV
def get_jacobian(rest_waves, redshift, params, electrons_per_sec, fund_cal_fns,
crnl_fns):
# "PSF_wghtd_e_allbutdark"
if redshift > 0.1:
EBV_step = 0.01 * params["mw_norm"]
else:
EBV_step = 0.05 * params["mw_norm"]
jacobian = []
jacobian_names = []
for fund_cal_fn in fund_cal_fns:
jacobian.append(fund_cal_fn(rest_waves * (1. + redshift)))
jacobian_names.append("Fundamental Calibration")
for crnl_fn in crnl_fns:
jacobian.append(crnl_fn(electrons_per_sec))
jacobian_names.append("Count-Rate Nonlinearity")
ff = extinction.Fitzpatrick99(3.1)
jacobian.append(ff(rest_waves * (1. + redshift), 3.1 * EBV_step))
jacobian_names.append("MW Extinction Normalization")
jacobian.append(
ff(rest_waves * (1. + redshift), 3.1 * params["mw_zeropoint"])
) # Independent zeropoint uncertainty of this in E(B-V)
jacobian_names.append("MW Extinction Zeropoint")
if redshift < 0.1: # R_V uncertainty of 0.2; E(B-V) of 0.01; ignore nearby SNe as these are spread on sky.
jacobian.append([0] * rest_waves)
else:
R_V_prime = 3.1 - params["mw_RV_uncertainty"]
ff_prime = extinction.Fitzpatrick99(R_V_prime)
jacobian.append(
ff_prime(rest_waves * (1. + redshift), 3.1 * EBV_step) -
ff(rest_waves * (1. + redshift), 3.1 * EBV_step))
jacobian_names.append("MW Extinction $R_V$")
jacobian.append(
getIGextinction(redshift, rest_waves) * params["ig_extinction"])
jacobian_names.append("IG Extinction")
jacobian = transpose(array(jacobian))
#save_img(jacobian, "jacobian.fits")
return jacobian, jacobian_names
def test_fitzpatrick99_rv_knots():
"""Test that Fitzpatrick function has the right R_V dependence at
the knots."""
wave = np.array([26500., 12200., 6000., 5470., 4670., 4110.])
# "the IR points at 1/lambda < 1 invum are simply scaled by R/3.1"
# "the optical points are vertically offset by an amount R - 3.1 , with
# slight corrections made to preserve the normalization"
for r in [1., 2., 3., 4., 5., 6.]:
# `expected` gives expected A(lambda) for E(B-V) = 1 (A_V = R)
expected = [
r / 3.1 * 0.265, # Table 3 scaled by R/3.1
r / 3.1 * 0.829, # Table 3 scaled by R/3.1
-0.426 + 1.0044 * r, # Table 4
-0.050 + 1.0016 * r, # Table 4
0.701 + 1.0016 * r, # Table 4
1.208 + 1.0032 * r - 0.00033 * r**2
] # Table 4
result = extinction.Fitzpatrick99(r)(wave, r)
# Note that we don't expect an exact match because, as noted in the
# code, "the optical spline points are not taken from F99 table 4,
# but rather updated versions from E. Fitzpatrick (matching the IDL
# astrolib routine FM_UNRED).
assert_allclose(result, expected, rtol=0.003)
def test_fitzpatrick99_func():
"""Check passing R_V."""
wave = np.array([2000., 30000.])
for r_v in (3.1, 4.0):
assert np.all(
extinction.Fitzpatrick99(r_v)(wave, 1.0) ==
extinction.fitzpatrick99(wave, 1.0, r_v))
def test_fitzpatrick99_idl():
"""Test that result matches implementation in IDL procedure FM_UNRED"""
for r_v in (2.3, 3.1, 4.0, 5.3):
fname = os.path.join('testdata', 'fm_unred_{:3.1f}.dat'.format(r_v))
wave, a_lambda_ref = np.loadtxt(fname, unpack=True)
a_lambda = extinction.Fitzpatrick99(r_v)(wave, 1.0)
assert_allclose(a_lambda, a_lambda_ref, rtol=0.00005)
import extinction
import astropy.io.ascii as ascii
import numpy as np
f=extinction.Fitzpatrick99(1.766)
A_lamda=f(np.array([4400,5400,6500,8100]),1.06)
mwdust=[0.123, 0.095, 0.076, 0.055]
bandlist = ['B', 'V', 'R', 'I']#,'g','r','i']#, 'J', 'H', 'K']
incat1=incat[incat['DET']=='Y'] # use data detected only
Rcat=incat1[incat1['FILTER']=='R']
Vcat=incat1[incat1['FILTER']=='V']
Bcat=incat1[incat1['FILTER']=='B']
Icat=incat1[incat1['FILTER']=='I']
B1cat=Bcat[(Bcat['MJD']>Tmax-10) & (Bcat['MJD']<Tmax+20)]
x_data = B1cat['MJD']
y_data = B1cat['MAG']
yerr_data = B1cat['MAGERR']
Rv=1.766
def EarlyFit1(incat_name='hgDcSN2019ein.LCfinal.txt', fitcat_name='SN2019ein-PolyFitRes.dat'):
"""
multi band, calculate T0
Minimize the sum of the square of the residual.
"""
case = 1
# Polynomial or Spline fitting result
fitcat = ascii.read(fitcat_name)
def __init__(self, r_v=3.1):
self._param_names = ['ebv']
self.param_names_latex = ['E(B-V)']
self._parameters = np.array([0.])
self._r_v = r_v
self._f = extinction.Fitzpatrick99(r_v=r_v)
def fitExtBlackbod(waves,
fluxes,
fluxerrs=None,
EBV=None,
Rv=3.1,
plot=False,
ptitle=""):
import extinction as extn
from scipy.optimize import curve_fit
import astropy.units as u
if EBV is None:
#blackbody+extinction(Rv=3.1) flux function
#exBBflux = lambda x, T, r, EBV: extn.apply(extn.fm07(x*u.AA, EBV*3.1), planck(x,T)*r)
exBBflux = lambda x, T, r, EBV: extn.apply(
extn.Fitzpatrick99(Rv)(x * u.AA, Rv * EBV),
planck(x, T) * r)
#estimate temperature
est = [10000.0, 1e20, 10]
else:
#exBBflux = lambda x, T, r: extn.apply(extn.fm07(x*u.AA, EBV*3.1), planck(x,T)*r)
exBBflux = lambda x, T, r: extn.apply(
extn.Fitzpatrick99(Rv)(x * u.AA, Rv * EBV),
planck(x, T) * r)
est = [10000.0, 1e25]
if EBV < 0:
est = [1000.0, 1e18]
else:
est = [10000.0, 1e20]
#fit blackbody temperature
if fluxerrs is not None:
popt, pcov = curve_fit(exBBflux,
waves,
fluxes,
sigma=fluxerrs,
p0=est,
absolute_sigma=True,
maxfev=1000000)
else:
popt, pcov = curve_fit(exBBflux,
waves,
fluxes,
p0=est,
maxfev=10000000)
perr = np.sqrt(np.diag(pcov))
T, Terr = popt[0], perr[0] #K
r, rerr = popt[1], perr[1] #dimensionless
if EBV is None:
EBV, EBVerr = popt[2], perr[2] #mag
print "E(B-V) for Rv=" + str(Rv) + ":", EBV, EBVerr
print "T=", T, Terr
else:
EBVerr = 0
#plot fit if given
if plot:
import matplotlib.pyplot as plt
print "Temperature [K]:", T, Terr
print "Received/Emitted:", r, rerr
if fluxerrs is not None:
plt.errorbar(waves, fluxes, yerr=fluxerrs, fmt='g+')
else:
plt.plot(waves, fluxes, color='g')
w = np.linspace(min(waves), max(waves), 100)
plt.plot(
w,
exBBflux(w, *popt),
c='r',
label=
"T = {:.0f} ({:.0f}) K\nr = {:.3f} ({:.3f})\nEBV = {:.3f} ({:.3f})"
.format(T, Terr, r, rerr, EBV, EBVerr))
#plt.plot(w, extn.remove(extn.fm07(w*u.AA, EBV*3.1), exBBflux(w, *popt)), c='b',
# label="dereddened")
plt.xlabel("Wavelength [A]")
plt.ylabel("Flux")
plt.title(ptitle)
plt.legend(loc='lower right')
plt.tight_layout()
plt.show()
w = np.linspace(min(waves), max(waves), 100)
#return blackbody temperature
return T, Terr, r, rerr, EBV, EBVerr, w, exBBflux(w, *popt)
