def plot_extmodels(extdata, alax=False):
"""
Plot Milky Way extinction curve models of Cardelli, Clayton, and Mathis (1989, ApJ, 345, 245), only possible for wavelengths between 0.1 and 3.33 micron
Parameters
----------
extdata : ExtData
Extinction data under consideration
alax : boolean [default=False]
Whether or not to plot A(lambda)/A(X) instead of E(lambda-X)
Returns
-------
Overplots extinction curve models
"""
x = np.arange(0.1, 3.33, 0.01) * u.micron
Rvs = [2.0, 3.1, 4.0, 5.0]
style = ["--", "-", ":", "-."]
for i, cRv in enumerate(Rvs):
curve = CCM89(Rv=cRv)
if alax:
if extdata.type_rel_band != "V":
emod = CCM89(cRv)
(indx, ) = np.where(
extdata.type_rel_band == extdata.names["BAND"])
axav = emod(extdata.waves["BAND"][indx[0]])
else:
axav = 1.0
y = curve(x) / axav
else:
# compute A(V)
extdata.calc_AV()
# convert the model curve from A(lambda)/A(V) to E(lambda-V), using the computed A(V) of the data.
y = (curve(x) - 1) * extdata.columns["AV"]
plt.plot(
x.value,
y,
style[i],
color="k",
alpha=0.7,
linewidth=1,
label="R(V) = {:4.1f}".format(cRv),
)
plt.legend(bbox_to_anchor=(0.99, 0.9))
def NewPredictor(spectra, ext1=CCM89(Rv=3.1), ext1bv=0, z=0):
#To notice, the ext1 is the extinction of the host galaxy, while the ext2 is the extinction of milky way.
spectra[:, 0] = spectra[:, 0] / (1 + z)
spectra[:, 1] = spectra[:, 1] / ext1.extinguish(spectra[:, 0] * u.AA,
Ebv=ext1bv)
fw = interp1d(spectra[:, 0], spectra[:, 1], fill_value='extrapolate')
flux = fw(wave2)
spnew = np.array([wave2, flux]).T
spnew = Normalizer(spnew, shortwave=3000, longwave=5000)
flux = spnew[:, 1]
Yout = model.predict(flux.reshape(1, 719, 1))
return Yout
def test_extcurve_call_2(self):
from dust_extinction.parameter_averages import CCM89
Av = 2
wav = np.arange(0.1, 3, 0.001) * u.micron
ext = CCM89(Rv=3.1)
ans = ext.extinguish(wav, Av)
ebv = Av / ext.Rv
redlaw = ReddeningLaw(ext)
extcurve = redlaw.extinction_curve(ebv, wavelengths=wav)
assert_quantity_allclose(extcurve(wav), ans)
assert extcurve.meta['header']['ReddeningLaw'] == '<CCM89(Rv=3.1)>'
def plot_ext_curves():
"""
Plot model extinction curves.
"""
# create the plot
fig, ax = plt.subplots(figsize=(6, 3.5))
# UV-NIR (Cardelli et al. 1989)
x = np.arange(0.12, 3.33, 0.001) * u.micron
curve = CCM89(Rv=3.1)
ax.plot(x.value, curve(x), label="Cardelli+1989")
# finalize and save the plot
ax.set_xscale("log")
plt.rc("axes.formatter", min_exponent=2)
plt.xticks(
[0.1, 0.2, 0.3, 0.4, 0.5, 0.8, 1.0, 2.0, 3.0],
[0.1, 0.2, 0.3, 0.4, 0.5, 0.8, 1.0, 2.0, 3.0],
)
ax.spines["right"].set_visible(False)
ax.spines["top"].set_visible(False)
ax.legend()
plt.savefig("Figures/CCM89_ext_curve.pdf")
# add NIR-MIR (Gordon et al. 2021)
x = np.arange(3, 30.0, 0.1) * u.micron
curve = G21_MWAvg()
ax.plot(x, curve(x), label="Gordon+2021")
# finalize and save the plot
ax.legend(loc="best")
ax.set_yscale("log")
plt.xticks(
[0.1, 0.2, 0.3, 0.5, 0.8, 1, 2, 3, 4, 5, 8, 10, 20, 30],
[0.1, 0.2, 0.3, 0.5, 0.8, 1, 2, 3, 4, 5, 8, 10, 20, 30],
)
plt.tight_layout()
plt.savefig("Figures/model_ext_curve.pdf")
# Gordon et al. 2009
fig, ax = plt.subplots(figsize=(6, 3.5))
curve = GCC09_MWAvg()
x = np.arange(0.116, 0.3, 0.001) * u.micron
ax.plot(x, curve(x), label="Gordon+2009")
ax.spines["right"].set_visible(False)
ax.spines["top"].set_visible(False)
ax.legend()
plt.tight_layout()
plt.savefig("Figures/GCC09_ext_curve.pdf")
def deredden(wavelength, flux, r_v, e_bv, curve="CCM89"):
"""Deredden a spectrum.
Remove the interstellar/dust reddening from a spectrum, given the colour
excess (E(B-V)) and selective extinction (Rv).
This function assumes that the flux is in units of erg s^-1 cm^-2 AA^-1
Parameters
----------
wavelength: np.ndarray
The wavelength bins of the spectrum.
flux: np.ndarray
The flux bins of the spectrum.
r_v: float
The selective extinction coefficient.
e_bv: float
The color excess.
curve: str
The name of the extinction curve to use, either CCM89 or F99.
Returns
-------
flux: np.ndarray
The corrected flux.
"""
wavelength *= u.angstrom
flux *= u.erg / u.s / u.cm / u.cm / u.AA
if curve == "CCM89":
curve = CCM89(Rv=r_v)
elif curve == "F99":
curve = F99(Rv=r_v)
else:
raise ValueError(
"Unknown extinction curve {curve}: CCM89 and F99 are available.")
flux /= curve.extinguish(wavelength, Ebv=e_bv)
return flux
import numpy as np
import matplotlib.pyplot as plt
import astropy.units as u
from dust_extinction.parameter_averages import O94, CCM89
from astrotools.constants import single_column, two_column
extinction_model = CCM89(Rv=3.1)
def extinction(EBV, EBV_err, wavelength, plot=False):
'''Calculate the extinction for a given EBV and wavelength with errors
flux_corr = flux / ext
'''
EBV = np.atleast_1d(EBV)
sample_size = 100000
ext = extinction_model.extinguish(wavelength, Ebv=EBV)
EBV_rand = np.random.normal(loc=EBV,
scale=EBV_err,
size=(sample_size, len(EBV)))
ext_arr = extinction_model.extinguish(wavelength, Ebv=EBV_rand)
ext_err = np.std(ext_arr, axis=0)
ext_mean = np.mean(ext_arr, axis=0)
np.save(Youtposition, Yout)
HSTlist = pd.read_csv('HSTplotout/EbvChi2HST/HSTlist_Ebv.csv')
def NewPredictor(spectra, ext1=CCM89(Rv=3.1), ext1bv=0, z=0):
#To notice, the ext1 is the extinction of the host galaxy, while the ext2 is the extinction of milky way.
spectra[:, 0] = spectra[:, 0] / (1 + z)
spectra[:, 1] = spectra[:, 1] / ext1.extinguish(spectra[:, 0] * u.AA,
Ebv=ext1bv)
fw = interp1d(spectra[:, 0], spectra[:, 1], fill_value='extrapolate')
flux = fw(wave2)
spnew = np.array([wave2, flux]).T
spnew = Normalizer(spnew, shortwave=3000, longwave=5000)
flux = spnew[:, 1]
Yout = model.predict(flux.reshape(1, 719, 1))
return Yout
ObserveYoutData = []
for i in range(len(HSTlist)):
name = glob.glob('ObserveSpectra/HST_UV_raw/' + HSTlist['Name'][i] +
'*')[0]
spr = np.genfromtxt(name)[:, 0:2]
ObserveYoutData.append(
NewPredictor(spr, CCM89(Rv=3.1), HSTlist['Select'][i],
HSTlist['Redshift'][i]))
ObserveYoutData = np.array(ObserveYoutData)
np.save(observeoutposition, ObserveYoutData)
def old_vs_new_linemaps():
with fits.open(data_raw / name / 'ngc628_ha.fits') as hdul:
HA6562_old = hdul[0].data
HA6562_old_header = hdul[0].header
with fits.open(data_raw / name / 'ngc628_ha_err.fits') as hdul:
HA6562_old_err = hdul[0].data
galaxy.lines.append('HA6562_old')
x, y = sources['SkyCoord'].to_pixel(WCS(HA6562_old_header))
sources['x_big'] = x
sources['y_big'] = y
peak_tbl = sources
from pnlf.photometry import light_in_moffat, correct_PSF
from photutils import CircularAnnulus, CircularAperture, aperture_photometry
alpha = galaxy.alpha
aperture_size = galaxy.aperturesize
wavelength = 6562
PSF_correction = correct_PSF(wavelength)
del flux_HA
for fwhm in np.unique(peak_tbl['fwhm']):
source_part = peak_tbl[peak_tbl['fwhm'] == fwhm]
positions = np.transpose((source_part['x_big'], source_part['y_big']))
gamma = (fwhm - PSF_correction) / (2 * np.sqrt(2**(1 / alpha) - 1))
r = aperture_size * (fwhm - PSF_correction) / 2
aperture = CircularAperture(positions, r=r)
# measure the flux for each source
phot = aperture_photometry(
HA6562_old,
aperture,
error=HA6562_old_err,
)
r_in = 5 * (fwhm - PSF_correction) / 2
r_out = 1. * np.sqrt(3 * r**2 + r_in**2)
annulus_aperture = CircularAnnulus(positions, r_in=r_in, r_out=r_out)
annulus_masks = annulus_aperture.to_mask(method='center')
bkg_median = []
for mask in annulus_masks:
# select the pixels inside the annulus and calulate sigma clipped median
annulus_data = mask.multiply(HA6562_old)
annulus_data_1d = annulus_data[mask.data > 0]
_, median_sigclip, _ = sigma_clipped_stats(
annulus_data_1d[~np.isnan(annulus_data_1d)],
sigma=5,
maxiters=None)
bkg_median.append(median_sigclip)
# save bkg_median in case we need it again
phot['bkg_median'] = np.array(bkg_median)
# multiply background with size of the aperture
phot['aperture_bkg'] = phot['bkg_median'] * aperture.area
phot['flux'] = phot['aperture_sum'] - phot['aperture_bkg']
# correct for flux that is lost outside of the aperture
phot['flux'] /= light_in_moffat(r, alpha, gamma)
# save fwhm in an additional column
phot['fwhm'] = fwhm
# concatenate new sources with output table
if 'flux_HA' in locals():
phot['id'] += np.amax(flux_HA['id'], initial=0)
flux_HA = vstack([flux_HA, phot])
else:
flux_HA = phot
from dust_extinction.parameter_averages import CCM89
# initialize extinction model
extinction_model = CCM89(Rv=Rv)
k = lambda lam: ext_model.evaluate(wavelength * u.angstrom, Rv) * Rv
extinction_mw = extinction_model.extinguish(wavelength * u.angstrom,
Ebv=Ebv)
flux_HA['flux'] /= extinction_mw
flux['HA6562_old'] = flux_HA['flux']
fig, ax = plt.subplots(figsize=(4, 4))
plt.scatter(flux[tbl['type'] == 'PN']['HA6562'],
flux[tbl['type'] == 'PN']['HA6562_old'],
label='PN')
plt.scatter(flux[tbl['type'] == 'SNR']['HA6562'],
flux[tbl['type'] == 'SNR']['HA6562_old'],
label='SNR')
plt.legend()
plt.plot([-1e5, 3e4], [-1e5, 3e4])
plt.xlim([-15000, 20000])
plt.ylim([-15000, 20000])
plt.xlabel(r'H$\alpha$ old / (erg/s$^2$ / cm$^2$ / \AA)')
plt.ylabel(r'H$\alpha$ DR1 / (erg/s$^2$ / cm$^2$ / \AA)')
plt.show()
ax.set_xlabel('$\lambda$ [$\mu m$]', fontsize=1.3 * fontsize)
if args.alav:
ytype = 'alav'
else:
ytype = 'elv'
ax.set_ylabel(extdata._get_ext_ytitle(ytype), fontsize=1.3 * fontsize)
ax.tick_params('both', length=10, width=2, which='major')
ax.tick_params('both', length=5, width=1, which='minor')
ax.set_title(args.extfile)
# plot extinctionm models
if args.extmodels:
x = np.arange(0.12, 3.0, 0.01) * u.micron
Rvs = [2.0, 3.1, 4.0, 5.0]
for cRv in Rvs:
t = CCM89(Rv=cRv)
ax.plot(x,
t(x),
'k--',
linewidth=2,
label='R(V) = {:4.2f}'.format(cRv))
# plot NIR power law model
if args.powerlaw:
ftype = 'BAND'
gbool = np.all([(extdata.npts[ftype] > 0),
(extdata.waves[ftype] > 1.0),
(extdata.waves[ftype] < 5.0)],
axis=0)
xdata = extdata.waves[ftype][gbool]
ydata = extdata.exts[ftype][gbool]