A python package for fitting UV-optical QSO spectra. This package is developed based on PyQSOFit (v1.1), with some additional features for the LAMOST quasar survey (Jin et al. 2023) and the survey for quasars behind the Galactic plane (Fu et al. 2022). qsofitmore is now a standalone package with the same GNU license as PyQSOFit. For the latest version of PyQSOFit, please see https://github.com/legolason/PyQSOFit.
- Fit high-order (n_upper=6 to n_upper=50) Balmer emission line series using templates from Storey & Hummer (1995).
- Add FeII template from Verner et al. (2009) within [2000, 12000] AA.
- Reading non-SDSS spectra without plateid, mjd and fiberid.
- Narrow line measurements in the line MC process (which enables estimating uncertainties for narrow line parameters).
- Dust maps and extinction laws other than SFD98 and CCM.
- LaTeX rendering for line names in plots.
Dependencies:
astropy
kapteyn (depends on cython)
dustmaps
PyAstronomy
uncertainties
Assuming you have anaconda installed (astropy included), the following steps demonstrate how to install dependencies above.
Install kapteyn:
pip install cython
pip install https://www.astro.rug.nl/software/kapteyn/kapteyn-3.4.tar.gz
Install dustmaps, uncertainties, and PyAstronomy:
pip install dustmaps uncertainties PyAstronomy
Download the SFD98 dust map:
from dustmaps.config import config
config['data_dir'] = '/path/to/store/maps/in'
import dustmaps.sfd
dustmaps.sfd.fetch()Check https://dustmaps.readthedocs.io/en/latest/installation.html for more dust maps.
After installing the dependencies, download and set up the qsofitmore package.
git clone https://github.com/rudolffu/qsofitmore.git
cd qsofitmore
python -m pip install .
# for development use:
python -m pip install -e .
This tutorial can be run under examples directory of qsofitmore. For basics of PyQSOFit, see https://github.com/legolason/PyQSOFit/blob/master/example/example.ipynb
The following script of generating qsopar.fits is based on https://github.com/legolason/PyQSOFit/blob/master/example/example.ipynb
import numpy as np
from astropy.io import fits
path='./output/'
newdata = np.rec.array([(6564.61,'Ha',6400.,6800.,'Ha_br',3,5e-3,0.004,0.017,0.015,0,0,0,0.05),\
(6564.61,'Ha',6400.,6800.,'Ha_na',1,1e-3,5e-4,0.0017,0.01,1,1,0,0.002),\
(6549.85,'Ha',6400.,6800.,'NII6549',1,1e-3,2.3e-4,0.0017,5e-3,1,1,1,0.001),\
(6585.28,'Ha',6400.,6800.,'NII6585',1,1e-3,2.3e-4,0.0017,5e-3,1,1,1,0.003),\
(6718.29,'Ha',6400.,6800.,'SII6718',1,1e-3,2.3e-4,0.0017,5e-3,1,1,2,0.001),\
(6732.67,'Ha',6400.,6800.,'SII6732',1,1e-3,2.3e-4,0.0017,5e-3,1,1,2,0.001),\
(4862.68,'Hb',4640.,5100.,'Hb_br',3,5e-3,0.004,0.022,0.01,0,0,0,0.01),\
(4862.68,'Hb',4640.,5100.,'Hb_na',1,1e-3,2.3e-4,0.0017,0.01,1,1,0,0.002),\
(4960.30,'Hb',4640.,5100.,'OIII4959',1,1e-3,2.3e-4,0.0017,0.01,1,1,0,0.002),\
(5008.24,'Hb',4640.,5100.,'OIII5007',1,1e-3,2.3e-4,0.0017,0.01,1,1,0,0.004),\
# (4960.30,'Hb',4640.,5100.,'OIII4959w',1,3e-3,2.3e-4,0.002,0.01,1,1,0,0.001),\
# (5008.24,'Hb',4640.,5100.,'OIII5007w',1,3e-3,2.3e-4,0.002,0.01,1,1,0,0.002),\
(2798.75,'MgII',2700.,2900.,'MgII_br',1,5e-3,0.004,0.015,0.0017,0,0,0,0.05),\
(2798.75,'MgII',2700.,2900.,'MgII_na',2,1e-3,5e-4,0.0017,0.01,1,1,0,0.002),\
(1908.73,'CIII',1700.,1970.,'CIII_br',2,5e-3,0.004,0.015,0.015,99,0,0,0.01),\
(1549.06,'CIV',1500.,1700.,'CIV_br',2,5e-3,0.004,0.015,0.015,0,0,0,0.05),\
],\
formats='float32,a20,float32,float32,a20,float32,float32,float32,float32,\
float32,float32,float32,float32,float32',\
names='lambda,compname,minwav,maxwav,linename,ngauss,inisig,minsig,maxsig,voff,vindex,windex,findex,fvalue')
#------header-----------------
hdr = fits.Header()
hdr['lambda'] = 'Vacuum Wavelength in Ang'
hdr['minwav'] = 'Lower complex fitting wavelength range'
hdr['maxwav'] = 'Upper complex fitting wavelength range'
hdr['ngauss'] = 'Number of Gaussians for the line'
hdr['inisig'] = 'Initial guess of linesigma [in lnlambda]'
hdr['minsig'] = 'Lower range of line sigma [lnlambda]'
hdr['maxsig'] = 'Upper range of line sigma [lnlambda]'
hdr['voff '] = 'Limits on velocity offset from the central wavelength [lnlambda]'
hdr['vindex'] = 'Entries w/ same NONZERO vindex constrained to have same velocity'
hdr['windex'] = 'Entries w/ same NONZERO windex constrained to have same width'
hdr['findex'] = 'Entries w/ same NONZERO findex have constrained flux ratios'
hdr['fvalue'] = 'Relative scale factor for entries w/ same findex'
#------save line info-----------
hdu = fits.BinTableHDU(data=newdata,header=hdr,name='data')
hdu.writeto(path+'qsopar.fits',overwrite=True)from qsofitmore import QSOFitNew
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from astropy.table import TableThe output path (path) should contain a line list file (qsopar.fits generated in 1-make_parlist.ipynb). The output files (including fits table and plots) are stored in path.
path = "./output/"We can read an example spectrum in csv format using pandas, and load the data to QSOFitNew manually. The data should contain wavelength (in Å), flux and flux error (both in 10^{-17} erg/s/cm^2/Å). In this example, I have already converted the flux and error to 10^{-17} erg/s/cm^2/Å. The object of interest is UGC 3374, which has z=0.02004, ra=88.72336906, and dec=46.43934051.
df = pd.read_csv("./data/UGC_3374_ccds.csv")df| lam | flux | err | |
|---|---|---|---|
| 0 | 3713.125682 | 575.093933 | 15.692233 |
| 1 | 3716.174602 | 595.614136 | 16.098293 |
| 2 | 3719.223522 | 583.948425 | 15.944775 |
| 3 | 3722.272442 | 596.417175 | 15.941905 |
| 4 | 3725.321363 | 599.870239 | 15.927223 |
| ... | ... | ... | ... |
| 1165 | 7265.117806 | 399.388733 | 7.409341 |
| 1166 | 7268.166726 | 385.701477 | 7.304856 |
| 1167 | 7271.215646 | 372.931519 | 7.231922 |
| 1168 | 7274.264566 | 395.445251 | 7.496907 |
| 1169 | 7277.313487 | 426.522888 | 7.874022 |
1170 rows × 3 columns
q = QSOFitNew(lam=df.lam, flux=df.flux, err=df.err,
z=0.02004, ra=88.72336906, dec=46.43934051,
name='UGC_3374', is_sdss=False, path=path)If you have a spectrum generated by IRAF/PyRAF, in which case the 4 bands of the fits file are:
BANDID1 = 'spectrum - background fit, weights variance, clean no'
BANDID2 = 'raw - background fit, weights none, clean no'
BANDID3 = 'background - background fit'
BANDID4 = 'sigma - background fit, weights variance, clean no'
The first and fourth bands are flux and flux error, respectively, in unit erg/s/cm^2/Å.
You can simply load the data with the classmethod QSOFitNew.fromiraf, which does the unit conversion automatically.
q = QSOFitNew.fromiraf("./data/UGC_3374_ccds.fits",redshift=0.02004,telescope='1.3m',path=path)q.setmapname("sfd")If you want to use planck dust map instead, make sure you have installed dustmaps and downloaded the files of planck dust map (see https://dustmaps.readthedocs.io/en/latest/installation.html). Then you can use q.setmapname("planck") to choose the planck map.
By default, QSOFit.Fit() of PyQSOFit does not output derived quantities of narrow lines, including FWHM, sigma, EW, and integrated flux (area); the q.line_prop() method should be called to calculate these parameters after fitting.
In qsofitmore, after specifying MC = True when calling q.Fit(), narrow line properties above are calculated during fitting and are stored in path + objectname.fits.
q.Fit(name = None, deredden = True, wave_range = None, wave_mask =None,
decomposition_host = True, Mi = None, npca_gal = 5, npca_qso = 20,
Fe_uv_op = True, poly = True, BC = False, MC = True, n_trails = 20,
linefit = True, tie_lambda = True, tie_width = True,
tie_flux_1 = True, tie_flux_2 = True,
save_result = True, plot_fig = True, save_fig = True,
plot_line_name = True, plot_legend = False,
# save_fig_path = figpath,
# save_fits_path = respath,
save_fits_name = None)
Try:
q.all_result_name, q.all_result, q.na_line_result, q.conti_result_name, q.gauss_result_name
The broken power-law model is an optional feature in the continuum fitting process. It is enabled by setting broken_pl = True in q.Fit(). The default is False.
The Verner et al. (2009) FeII template is an alternative to the default ones. It is enabled by setting Fe_verner09 = True in addition to Fe_uv_op = True in q.Fit(). The default value of Fe_verner09 is False.
The Storey & Hummer (1995) Balmer emission line series templates are enabled by setting BC = True in q.Fit(). The default value of BC is False. The default electron density is 10^9 cm^-3, and the default electron temperature is 10^4 K. To change the electron density to 10^10 cm^-3, use q.set_log10_electron_density(ne=10) before calling q.Fit(). Currently, only 10^9 and 10^10 cm^-3 are supported.
- Added the Verner et al. (2009) FeII template within [2000, 12000] AA.
- Added the Storey & Hummer (1995) Balmer emission line series templates from n_upper=6 to n_upper=50.
- Merged the
PyQSOFit.pycode intofitmodule.pyto simplify the installation process.qsofitmoreis now a standalone package with the same GNU license asPyQSOFit.
- Added an optional broken power-law model in the continuum fitting process.
- Enabled line property outputs for all narrow lines, OIII core+wing as a whole, and CIV br+na as a whole.
- Used new criterion to verify narrow/broad components in self._PlotFig() to prevent narrow components from being plotted as red (broad) lines.
- Changed prefix of comp_result from number to the complex name.
- Bug fixes.