/
rectify.py
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rectify.py
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import numpy as np
from astropy import units as u, constants as c
from astropy.io import fits
from importer import *
import utils as ut
from spectrophot import Spec2Phot
import regrid
import manga_tools as m
from manga_elines import get_emline_qty
import spec_tools
from elines import (balmer_low, balmer_high, helium, bright_metal, faint_metal)
from itertools import chain
class MaNGA_deredshift(object):
'''
class to deredshift reduced MaNGA data based on velocity info from DAP
preserves cube information, in general
also builds in a check on velocity coverage, and computes a mask
'''
spaxel_side = 0.5 * u.arcsec
def __init__(self, drp_hdulist, dap_hdulist, drpall_row,
max_vel_unc=500. * u.Unit('km/s'), drp_dlogl=None):
self.drp_hdulist = drp_hdulist
self.dap_hdulist = dap_hdulist
self.drpall_row = drpall_row
self.plateifu = self.drp_hdulist[0].header['PLATEIFU']
self.vel = dap_hdulist['STELLAR_VEL'].data * u.Unit('km/s')
self.vel_ivar = dap_hdulist['STELLAR_VEL_IVAR'].data * u.Unit(
'km-2s2')
self.z = drpall_row['nsa_z']
# mask all the spaxels that have high stellar velocity uncertainty
self.vel_ivar_mask = (1. / np.sqrt(self.vel_ivar)) > max_vel_unc
self.vel_mask = m.mask_from_maskbits(
self.dap_hdulist['STELLAR_VEL_MASK'].data, [30])
self.drp_l = drp_hdulist['WAVE'].data
self.drp_logl = np.log10(self.drp_l)
if drp_dlogl is None:
drp_dlogl = ut.determine_dlogl(self.drp_logl)
self.drp_dlogl = drp_dlogl
flux_hdu, ivar_hdu, l_hdu = (drp_hdulist['FLUX'], drp_hdulist['IVAR'],
drp_hdulist['WAVE'])
self.flux = flux_hdu.data
self.ivar = ivar_hdu.data
self.units = {'l': u.AA, 'flux': u.Unit('1e-17 erg s-1 cm-2 AA-1')}
self.S2P = Spec2Phot(lam=(self.drp_l * self.units['l']),
flam=(self.flux * self.units['flux']))
self.DONOTUSE = m.mask_from_maskbits(drp_hdulist['MASK'].data, [10])
self.ivar *= ~self.DONOTUSE
@classmethod
def from_plateifu(cls, plate, ifu, MPL_v, kind, row=None,
**kwargs):
'''
load a MaNGA galaxy from a plateifu specification
'''
plate, ifu = str(plate), str(ifu)
if row is None:
drpall = m.load_drpall(MPL_v, index='plateifu')
row = drpall.loc['{}-{}'.format(plate, ifu)]
drp_hdulist = m.load_drp_logcube(plate, ifu, MPL_v)
dap_hdulist = m.load_dap_maps(plate, ifu, MPL_v, kind)
return cls(drp_hdulist, dap_hdulist, row, **kwargs)
@classmethod
def from_fakedata(cls, plate, ifu, MPL_v, basedir='fakedata', row=None,
kind='SPX-MILESHC-MILESHC', **kwargs):
'''
load fake data based on a particular already-observed galaxy
'''
plate, ifu = str(plate), str(ifu)
if row is None:
drpall = m.load_drpall(MPL_v, index='plateifu')
row = drpall.loc['{}-{}'.format(plate, ifu)]
drp_hdulist = fits.open(
os.path.join(basedir, '{}-{}_drp.fits'.format(plate, ifu)))
dap_hdulist = fits.open(
os.path.join(basedir, '{}-{}_dap.fits'.format(plate, ifu)))
return cls(drp_hdulist, dap_hdulist, row, **kwargs)
def transform_to_restframe(self, l, f, ivar):
'''
bring cube into rest frame
'''
# shift into restframe
l_rest, f_rest, ivar_rest = ut.redshift(
l=l, f=f, ivar=ivar, z_in=self.z_map, z_out=0.)
return l_rest, f_rest, ivar_rest
def correct_and_match(self, template_logl, template_dlogl=None,
method='drizzle', dered_kwargs={}):
'''
gets datacube ready for PCA analysis:
- take out galactic extinction
- compute per-spaxel redshifts
- deredshift observed spectra
- raise alarms where templates don't cover enough l range
- return subarrays of flam, ivar
(this does not perform any fancy interpolation, just "shifting")
(nor are emission line features masked--that must be done in post-)
'''
if template_dlogl is None:
template_dlogl = spec_tools.determine_dlogl(template_logl)
if template_dlogl != self.drp_dlogl:
raise csp.TemplateCoverageError(
'template and input spectra must have same dlogl: ' +
'template\'s is {}; input spectra\'s is {}'.format(
template_dlogl, self.drp_dlogl))
# correct for MW extinction
r_v = 3.1
EBV = self.drp_hdulist[0].header['EBVGAL']
f_mwcorr, ivar_mwcorr = ut.extinction_correct(
l=self.drp_l * u.AA, f=self.flux,
ivar=self.ivar, r_v=r_v, EBV=EBV)
l_rest, f_rest, ivar_rest = self.transform_to_restframe(
self.drp_l, f_mwcorr, ivar_mwcorr)
# and make photometric object to reflect rest-frame spectroscopy
ctr = [i // 2 for i in self.z_map.shape]
# approximate rest wavelength of whole cube as rest wavelength
# of central spaxel
l_rest_ctr = l_rest[:, ctr[0], ctr[1]]
self.S2P_rest = Spec2Phot(lam=(l_rest_ctr * self.units['l']),
flam=(f_rest * self.units['flux']))
self.regrid = regrid.Regridder(
loglgrid=template_logl, loglrest=np.log10(l_rest),
frest=f_rest, ivarfrest=ivar_rest, dlogl=template_dlogl)
# call appropriate regridder method
flux_regr, ivar_regr = getattr(
self.regrid, method)(**dered_kwargs)
spax_mask = np.logical_or.reduce((
self.vel_mask, self.vel_ivar_mask))
self.flux_regr, self.ivar_regr, self.spax_mask = flux_regr, ivar_regr, spax_mask
return flux_regr, ivar_regr, spax_mask
def compute_eline_mask(self, template_logl, template_dlogl=None, ix_eline=7,
half_dv=300. * u.Unit('km/s')):
el_l_air = [balmer_low, balmer_high, helium, bright_metal, faint_metal]
# find mask width for all spaxels
mask_velwidth = determine_eline_mask_dv(
self.dap_hdulist, minimum_value=half_dv.value, n_times_sigma=1.5)
if template_dlogl is None:
template_dlogl = spec_tools.determine_dlogl(template_logl)
EW = self.eline_EW(ix=ix_eline)
add_balmer_low = (EW >= 0. * u.AA)
add_balmer_high = (EW >= 2. * u.AA)
add_helium = (EW >= 10. * u.AA)
add_brightmetal = (EW >= 0. * u.AA)
add_faintmetal = (EW >= 10. * u.AA)
linelistflags = [add_balmer_low, add_balmer_high, add_helium,
add_brightmetal, add_faintmetal]
# full list of mask flags: one corresponds to each line in each eline dict
useflags = list(chain(*map(lambda a: [a[0]] * len(a[1]),
zip(linelistflags, el_l_air))))
temlogl = template_logl
teml = 10.**temlogl
temlogel = np.log(teml)
#full_mask = np.zeros((len(temlogl),) + EW.shape, dtype=bool)
el_lel_vac = np.concatenate(list(map(
lambda d: np.log(spec_tools.air2vac(np.array(list(d.values())),
u.AA).value), el_l_air)))
# iterate through eline types
full_mask = np.logical_or.reduce(
[masked_around_line(
line_logel=lel, dv_map=mask_velwidth, obs_logel=temlogel) * \
flag[None, :, :]
for flag, lel in zip(useflags, el_lel_vac)])
return full_mask
def eline_EW(self, ix):
return self.dap_hdulist['EMLINE_SEW'].data[ix] * u.Unit('AA')
def coadd(self, tem_l, good=None):
'''
return coadded spectrum and ivar
params:
- good: map of good spaxels
'''
if good is None:
good = np.ones_like(self.flux_regr[0, ...])
ivar = self.ivar_regr * good[None, ...]
flux, ivar = ut.coadd(f=self.flux_regr, ivar=ivar)
lam, flux, ivar = (tem_l[:, None, None], flux[:, None, None],
ivar[:, None, None])
return lam, flux, ivar
# =====
# properties
# =====
@property
def z_map(self):
# prepare to de-redshift
# total redshift of each spaxel
z_map = (1. + self.z) * (1. + (self.vel / c.c).to('').value) - 1.
z_map[self.vel_mask] = self.z
return z_map
@property
def SB_map(self):
# RIMG gives nMgy/pix
return self.drp_hdulist['RIMG'].data * \
1.0e-9 * m.Mgy / self.spaxel_side**2.
@property
def Reff(self):
r_ang = self.dap_hdulist['SPX_ELLCOO'].data[0, ...]
Re_ang = self.drpall_row['nsa_elpetro_th50_r']
return r_ang / Re_ang
# =====
# staticmethods
# =====
@staticmethod
def a_map(f, logl, dlogl):
lllims = 10.**(logl - 0.5 * dlogl)
lulims = 10.**(logl + 0.5 * dlogl)
dl = (lulims - lllims)[:, np.newaxis, np.newaxis]
return np.mean(f * dl, axis=0)
def determine_eline_mask_dv(dap_hdulist, minimum_value=300., n_times_sigma=2.5):
'''
determine the velocity mask width for MaNGA cube
'''
sigma = get_emline_qty(dap_hdulist, qty='GSIGMA', key='Ha-6564',
sn_th=3., maskbits=range(32))
# where (sigma < minimum_value) or mask is True, use minimum_value
dv = (n_times_sigma * sigma.data).clip(min=minimum_value)
return dv * (u.km / u.s)
def masked_around_line(line_logel, dv_map, obs_logel):
'''
make cube mask for a single line
'''
dlogel_map = (dv_map / c.c).decompose().value
logel_mask_l = line_logel - dlogel_map[None, :, :]
logel_mask_u = line_logel + dlogel_map[None, :, :]
ismasked = np.logical_and(
(obs_logel[:, None, None] >= logel_mask_l),
(obs_logel[:, None, None] <= logel_mask_u))
return ismasked