MAPPINGS V v5.2.1 Ionization Models File Names File names are meant to be intuitive and indicate the ionization source with aprefix and the contents with a suffix. A full list of prefixes is provided here. Model Prefix AGN Jin+ 2012 agn-jin12 AGN OXAF isobaric bubble agn-oxaf-cpr AGN OXAF isochoric bubble agn-oxaf-cdn BPASS isobaric bubble bpass-cpr-shl BPASS isobaric filled sphere bpass-cpr-sph BPASS isobaric plane bpass-cpr-ppl BPASS isochoric bubble bpass-cdn-shl BPASS isochoric plane bpass-cdn-ppl STARBURST99 isobaric bubble sb99-cpr-shl STARBURST99 isochoric bubble sb99-cdn-shl shocks shck precursor prec shock + precursor shckprec dusty shocks shck-dst dusty precursor prec-dst dusty shock + precursor shckprec-dst Files ending in _fluxes.csv contain the line fluxes relative to Hβ.Files ending in _propts.csv contain nebular properties of the model ionizedregion. Table Formats Formatting is by comma-separated values. The first column will always be themodel identifier, or key. This key is specific to a row in both the flux andproperties tables of the same prefix. Tables are sorted by key identifier, whichcan be used to ensure that fluxes and properties are properly matched. The next few subsequent columns contain the unique assumptions regarding thatparticular model, including ionization parameter, metallicity, velocity,density, magnetic field strength, and ionizing SED. Future work will enable use of keys to join tables via SQL. Presently, werecommend users join tables using pandas as in the following example for theshock+precursor models, eliminating any duplicate column headings. >> import pandas as pd >> fluxes = pd.read_csv('shock-precursor_fluxes.csv') >> propts = pd.read_csv('shock-precursor_propts.csv) >> joiner = [k for k in fluxes.keys() if k in propts.keys()] >> shkprc = fluxes.merge(propts, how='inner', on=joiner) Multiple model sets can also be combined using pandas as in the followingexample for BPASS isobaric and isochoric models: >> import pandas as pd >> bpass_cpr = pd.read_csv('bpass-cpr_fluxes.csv') >> bpass_cdn = pd.read_csv('bpass-cdn_fluxes.csv') >> bpass_tot = pd.concat([bpass_cpr,bpass_cdn]) >> bpass_tot.reset_index(inplace=True) Lines Included We include a large suite of lines spanning FUV through the MIR. Only a subset ofthese lines were considered for our inaugural publication, which focused on thestronger FUV lines. These lines include emission from various species of thefollowing elements:H, He, C, N, O, S, Ne, Ar, Si, Mg, Ca, and Fe. Lines are labeled following PyNeb formatting: EI_WaveU where E is the element (e.g., O for oxygen), I is the ion species as an Arabicnumeral, Wave is the wavelength, and U is the unit of the wavelength. If theline is a recombination line, it will contain the ‘r’ suffix. For wavelengthsless than 1 um, we express Wave in A as a whole number. For wavelengths greaterthan 1 um, we express Wave in um with two decimal places. Someexamples: Emission Line Column C III] 1909 Å C3_1909A He II 4686 Å He2r_4686A [Ar II] 6.98 µm Ar2_0698um [O IV] 24.89 µm O4_2489um The full set of lines in this release is included in the aa_line_list.md filein this repository. If a line of interest is not included, please let us know,and we will add it as soon as we are able. Properties Included Total Hβ flux, metallicity as $\zeta_{\rm O}$, and gas density $\rm n_H$.For photoionization models, ionization parameter and assumed luminosity($Q(\rm H)$ for stellar populations, $L_{tot}$ for AGN)For shock models, shock velocity and magnetic field. Total and dust-depleted abundances as 12+log(X/H) Relative ionic abundances as $\chi_i/\chi$ Ionic temperatures and densities in units of K and cm$^{-3}$,respectively.