def vel_moment(spec, restlam, zabs, vellim=[-50, 50]):
wave = spec.wavelength.value
flux = spec.flux.value
sig = spec.sig.value
cont = spec.co
conterr = np.zeros_like(sig) # Continuum errors will be garbage
EWpix, sigEWf, sigEWc, Npix, sigNf, sigNc = EW_ACD_array(wave,
flux,
sig,
cont,
conterr,
restlam,
zabs,
vellim=vellim)
### Transform to velocity space
vel = joebgoodies.veltrans(zabs, wave, restlam)
velidx = np.where((vel >= vellim[0]) & (vel <= vellim[1]))[0]
### Calculate moments
moment0 = np.sum(Npix)
moment1 = np.sum(vel[velidx] * Npix)
### Mean velocity
meanv = moment1 / moment0
return meanv
def vel_moment_fitcont(wave,
flux,
sig,
restlam,
zabs,
continuumregions,
vellim=[-50, 50],
**kwargs):
### Fit the continuum in the line-free regions
wave, cont, conterr = contFitLegendreAboutLine(wave, flux, sig, restlam,
zabs, continuumregions,
**kwargs)
EWpix, sigEWf, sigEWc, Npix, sigNf, sigNc = EW_ACD_array(wave,
flux,
sig,
cont,
conterr,
restlam,
zabs,
vellim=vellim)
### Transform to velocity space
vel = joebgoodies.veltrans(zabs, wave, restlam)
velidx = np.where((vel >= vellim[0]) & (vel <= vellim[1]))[0]
velup = vel[velidx + 1]
veldown = vel[velidx]
### Calculate moments
moment0 = np.sum(Npix)
moment1 = np.sum(vel[velidx] * Npix)
### Mean velocity
meanv = moment1 / moment0
return meanv
def fitpix(wave,pararr): # define bad pixels cfg.bad_pixels = update_bad_pixels() # this variable stores the indices of bad pixels ll=pararr[0] lz=pararr[3] lv1=pararr[5] lv2=pararr[6] relpix=[] for i in range(len(ll)): vels=jbg.veltrans(lz[i],wave,ll[i]) #w1=ll[i]*(1.+lz[i]+lv1[i]/c) #w2=ll[i]*(1.+lz[i]+lv2[i]/c) #p1=jbg.closest(wave,w1) #p2=jbg.closest(wave,w2) p1=jbg.closest(vels,lv1[i]) p2=jbg.closest(vels,lv2[i]) if ((p1>=10) & (p2<=(len(wave)-1-10))): relpix.extend(range(p1-10,p2+10)) elif (p1<10): relpix.extend(range(0, p2 + 10)) else: relpix.extend(range(p1 - 10, len(wave)-1)) rp = np.unique(np.array(relpix)) clean_rp = np.array([i for i in rp if i not in cfg.bad_pixels]) return clean_rp
def fitpix(wave,pararr,find_bad_pixels=True): if find_bad_pixels: # define bad pixels cfg.bad_pixels = update_bad_pixels() # this variable stores the indices of bad pixels else: cfg.bad_pixels = [] ll=pararr[0] lz=pararr[3] lv1=pararr[5] lv2=pararr[6] relpix=[] for i in range(len(ll)): vels=jbg.veltrans(lz[i],wave,ll[i]) p1=jbg.closest(vels,lv1[i]) p2=jbg.closest(vels,lv2[i]) if ((p1>=10) & (p2<=(len(wave)-1-10))): relpix.extend(range(p1-10,p2+10)) elif (p1<10): relpix.extend(range(0, p2 + 10)) else: relpix.extend(range(p1 - 10, len(wave)-1)) rp = np.unique(np.array(relpix)) clean_rp = np.array([i for i in rp if i not in cfg.bad_pixels]) return clean_rp
def fitpix(wave, pararr, find_bad_pixels=True):
if find_bad_pixels:
# define bad pixels
cfg.bad_pixels = update_bad_pixels(
) # this variable stores the indices of bad pixels
else:
cfg.bad_pixels = []
ll = pararr[0]
lz = pararr[3]
lv1 = pararr[5]
lv2 = pararr[6]
relpix = []
for i in range(len(ll)):
vels = jbg.veltrans(lz[i], wave, ll[i])
p1 = jbg.closest(vels, lv1[i])
p2 = jbg.closest(vels, lv2[i])
if ((p1 >= 10) & (p2 <= (len(wave) - 1 - 10))):
relpix.extend(range(p1 - 10, p2 + 10))
elif (p1 < 10):
relpix.extend(range(0, p2 + 10))
else:
relpix.extend(range(p1 - 10, len(wave) - 1))
rp = np.unique(np.array(relpix))
clean_rp = np.array([i for i in rp if i not in cfg.bad_pixels])
if len(clean_rp) > 1:
# Matt and Kirill trimming off tiny pixel patches
max_index_jump = 4 # this is called buf in makevoigt
min_group_size = 5
last_idx = clean_rp[0]
group_idxs = [last_idx]
really_clean_rp = []
for idx in clean_rp[1:]:
jump = idx - last_idx
if jump <= max_index_jump:
group_idxs.append(idx)
else:
if len(group_idxs) >= min_group_size:
really_clean_rp.extend(group_idxs)
group_idxs = [idx] # start a new group
last_idx = idx
if len(group_idxs) >= min_group_size:
really_clean_rp.extend(group_idxs)
return np.array(really_clean_rp, dtype=int)
else:
return clean_rp
def contFitLegendreAboutLine(wave,
flux,
err,
restlam,
z,
velfitregions,
uniform=True,
**kwargs):
'''
Fits a continuum over a spectral region using the formalism of Sembach
& Savage 1992, estimating the uncertainty at each point of the continuum.
Parameters
----------
wave
flux
err
restlam
z
velfitregions
uniform
Returns
-------
'''
vels = joebgoodies.veltrans(z, wave, restlam)
### Find indices corresponding to velocities defined in velfitregions
fitidxs = []
for reg in velfitregions:
thesevels = np.where((vels >= reg[0]) & (vels <= reg[1]))[0]
fitidxs.extend(thesevels)
if uniform != True:
fitsol, err, parsol, errmtx, scalefac = fitLegendre(vels[fitidxs],
flux[fitidxs],
sig=err[fitidxs],
**kwargs)
else:
fitsol, err, parsol, errmtx, scalefac = fitLegendre(
vels[fitidxs], flux[fitidxs], **kwargs)
### Normalize x so that domain of fit is -1 to +1
vscale = vels / scalefac # must scale to fitted domain
### Evaluate continuum using all points, not just those in fit regions
continuum = L.legval(vels / scalefac, parsol)
err = errorsLegendre(vscale, errmtx)
return wave, continuum, err
def EW_ACD_array(wave,
flux,
ferr,
cont,
conterr,
restlam,
zabs,
vellim=[-50, 50],
**kwargs):
'''
Returns arrays of equivalent width and apparent column density per pixel as
well as their associated uncertainties due to continuum placement and flux errors.
Parameters
----------
wave: 1D float array
flux: 1D float array
ferr: 1D float array
Error in flux
cont: 1D float array
Continuum fitted to data such as that from contFitLegendreAboutLine()
conterr: 1D float array
Uncertainty in continuum placement at each pixel
restlam: float
Rest-frame wavelength of transition to measure
zabs: float
Redshift of the the absorption system
vellim: 2-element list
Lower and upper bounds over which to compute arrays
Returns
-------
EWpix: 1D float array
Equivalent width in each pixel
sigEWf: 1D float array
EW uncertainty in each pixel due to flux errors
sigEWc: 1D float array
EW uncertainty in each pixel due to continuum placement errors
Npix: 1D float array
Apparent column density * dv (N) in each pixel
sigNf: 1D float array
Uncertainty in N due to flux errors
sigNc: 1D float array
Uncertainty in N due to continuum placement errors
'''
### Set atomic data and constants
c = 299792.458
restlam = ad.closestlam([restlam])
osc = ad.lam2osc([restlam])
### Transform to velocity space
vel = joebgoodies.veltrans(zabs, wave, restlam)
velidx = np.where((vel >= vellim[0]) & (vel <= vellim[1]))[0]
velup = vel[velidx + 1]
veldown = vel[velidx]
dv = np.abs(velup - veldown)
### Identify pixels where flux level is below noise level & replace w/noise
effflux = flux
belowerr = np.where(flux < ferr)[0]
effflux[belowerr] = ferr[belowerr]
### Calculate EW and errors due to flux and continuum placement
print(dv, effflux[velidx], cont[velidx], restlam, c)
EWpix = dv * (1. - effflux[velidx] / cont[velidx]) * restlam / c
sigEWf = dv / cont[velidx] * ferr[velidx] * restlam / c
sigEWc = dv * restlam / c * effflux[velidx] / cont[velidx]**2 * conterr[
velidx] # Will not be added in quad.
### Calculate optical depth and uncertainty due to flux and continuum
tauv = np.log(cont[velidx] / (effflux[velidx]))
tauverr_f = ferr[velidx] / effflux[velidx]
tauverr_c = conterr[velidx] / cont[velidx]
Npix = 1. / 2.654e-15 / restlam / osc * dv * tauv
sigNf = 1. / 2.654e-15 / restlam / osc * dv * tauverr_f
sigNc = 1. / 2.654e-15 / restlam / osc * dv * tauverr_c
return EWpix, sigEWf, sigEWc, Npix, sigNf, sigNc