We developed a new quick pseudo-3D photoionization code based on Cloudy (G. Ferland) and IDL (RSI) tools. The code is running the 1D photoionization code Cloudy various times, changing at each run the input parameters (e.g. inner radius, density law) according to an angular law describing the morphology of the object. Then a cube is generated by interpolating the outputs of Cloudy. In each cell of the cube, the physical conditions (electron temperature and density, ionic fractions) and the emissivities of lines are determined. Associated tools (VISNEB and VELNEB_3D) are used to rotate the nebula and to compute surface brightness maps and emission line profiles, given a velocity law and taking into account the effect of the thermal broadening and eventually the turbulence. Integrated emission line profiles are computed, given aperture shapes and positions (seeing and instrumental width effects are included). The main advantage of this tool is the short time needed to compute a model (a few tens minutes).

Cloudy_3D has been superseded by pycloudy (ascl:1304.020).

PyCloudy is a Python library that handles input and output files of the Cloudy photoionization code (Gary Ferland). It can also generate 3D nebula from various runs of the 1D Cloudy code. pyCloudy allows you to:
- define and write input file(s) for Cloudy code. As you can have it in a code, you may generate automatically sets of input files, changing parameters from one to the other.<
- read the Cloudy output files and play with the data: you will be able to plot line emissivity ratio vs. the radius of the nebula, the electron temperature, or any Cloudy output.
- build pseudo-3D models, a la Cloudy_3D, by running a set of models, changing parameters (e.g. inner radius, density) following angular laws, reading the outputs of the set of models and interpolating the results (Te, ne, line emissivities) in a 3D cube.

PyNeb (previously PyNebular) is an update and expansion of the IRAF package NEBULAR; rewritten in Python, it is designed to be more user-friendly and powerful, increasing the speed, easiness of use, and graphic visualization of emission lines analysis. In PyNeb, the atom is represented as an n-level atom. For given density and temperature, PyNeb solves the equilibrium equations and determines the level populations. PyNeb can compute physical conditions from suitable diagnostic line ratios and level populations, critical densities and line emissivities, and can compute and display emissivity grids as a function of Te and Ne. It can also deredden line intensities, read and manage observational data, and plot and compare atomic data from different publications, and compute ionic abundances from line intensities and physical conditions and elemental abundances from ionic abundances and icfs.

BOND determines oxygen and nitrogen abundances in giant H II regions by comparison with a large grid of photoionization models. The grid spans a wide range in O/H, N/O and ionization parameter U, and covers different starburst ages and nebular geometries. Unlike other statistical methods, BOND relies on the [Ar III]/[Ne III] emission line ratio to break the oxygen abundance bimodality. By doing so, it can measure oxygen and nitrogen abundances without assuming any a priori relation between N/O and O/H. BOND takes into account changes in the hardness of the ionizing radiation field, which can come about due to the ageing of H II regions or the stochastically sampling of the IMF. The emission line ratio He I/Hβ, in addition to commonly used strong lines, constrains the hardness of the ionizing radiation field. BOND relies on the emission line ratios [O III]/Hβ, [O II]/Hβ and [N II]/Hβ, [Ar III]/Hβ, [Ne III]/Hβ, He I/Hβ as its input parameters, while its output values are the measurements and uncertainties for O/H and N/O.

pyHIIexplorerV2 extracts the integrated spectra of HII regions from integral field spectroscopy (IFS) datacubes. The detection of HII regions performed by pyHIIexplorer is based on two assumptions: 1) HII regions have strong emission lines that are clearly above the continuum emission and the average ionized gas emission across each galaxy, and 2) the typical size of HII regions is about a few hundreds of parsecs, which corresponds to a usual projected size of a few arcsec at the distance of our galaxies. These assumptions will define clumpy structures with a high Ha emission line contrast in comparison to the continuum. pyHIIexplorerV2 is written in Python; it is based on and is a successor to HIIexplorer (ascl:1603.017).