These are my public numerical codes and some online material supporting my publications.

Public repositories are available on github. Some of the pages below contain the same information of the repositories’ README, some others instead have more results.

## From here:

**precession: Dynamics of spinning black-hole binaries with python.**

Public python module to perform post-Newtornian evolution of precessing exploiting multi-timescale methods. The code is described carefully arXiv:1605.01067 and has by now been used in many papers by us and others.

**spinprecession: Black-hole binary inspiral: a precession-averaged approach.**

Some animations and data on black-hole binary spin precession, supporting arXiv:1411.0674, 1506.03492, 1506.09116, 1711.10038, 1811.05979, 2003.02281, and 1302.4442.

**filltex: Automagically fill LaTex bibliography**

Are you tired of copying bibtex records when writing papers? We got you covered. This is a web-scraping tool to automatically download citations records from both ADS and INSPIRE and automagically fill bib files. Usage from terminal is straightforward, and it’s also integrated with TexShop!

**skywalker: Things I like in Python**

This is a python module made mostly for myself, where I collect useful functions and tricks to be imported from everywhere.

**corecollapse: Numerical simulations of stellar collapse in scalar-tensor theories of gravity**

Did you know supernova can be used to test gravity? Animations and data release on core-collapse simulations in scalar-tensor theories of gravity, supporting arXiv:1602.06952.

**surrkick: Black-hole kicks from numerical-relativity surrogate models**

Public python module to extract black hole recoils from waveform approximants by directly integrating the linear momentum flux in gravitational waves. The approach is described in arXiv:1802.04276, here we also provide some animations from that paper.

**gwdet: Detectability of gravitational-wave signals from compact binary coalescences**

Tiny python module to compute the probability that a gravitational-signal will be detected averaging over sky location, detector antenna pattern, etc.

**spops: “S”pinning black-hole binary “Pop”ulation “S”ynthesis**

Database containing population synthesis simulations from arXiv:1808.02491, together with a simple python code to query it.

**pdetclassifier: Gravitational-wave selection effects using neural-network classifiers**

Training samples and pre-trained neural networks to estimate LIGO/Virgo detectability. Supporting arXiv:2007.06585.

**generalized-chip: A generalized precession parameter to interpret gravitational-wave data**

Public python script to compute various definitions of chi_p. Supporting arXiv:2011.11948.

## From there:

**pAGN: The one-stop solution for AGN disc modeling**

Great public code to easily compute 1D models of AGN disks. Supporting arXiv:2304.13063.

**updowninjections: Parameter estimation of binary black holes in the endpoint of the up-down instability**

Bilby posterior samples of binaries that were aligned but are now precessing. Supporting arXiv:2002.10465.

**twoprecessingspins: Characterization of merging black holes with two precessing spins**

Posterior samples of >100 LIGO/Virgo injections with two large, misaligned spins . Supporting arXiv:2207.00030.

**WDsatellites: Milky Way Satellites Shining Bright in Gravitational Waves**

LISA white dwarf posteriors. Supporting arXiv:2002.10465.

**binaryBHexp: The binary black hole explorer**

On-the-fly visualization of precessing binary black holes. Use ours, or do your own with our code. Supporting arXiv:1811.06552.

**surfinBH: SURrogate FINal Black Hole properties for mergers of binary black holes**

Public python module to estimate post-merger masses, spins, and kicks for generic systems (yes, this includes spin precession). Supporting arXiv:1809.09125.

**GWpriors: Impact of bayesian priors on the characterization of binary black hole coalescences**

Full posterior samples of the three LIGO 01 events, obtained under a variety of astrophysically motivated prior assumptions. Data release supporting arXiv:1707.04637.

**gw_catalog_mining: Mining gravitational-wave catalogs**

What are we going to do with thousands of gravitational wave observations? Maybe Gaussain process emulators and hierarchical analyses. Webpage and public code supporting arXiv:1806.08365.

**welovespins: Asymmetries and selection biases in effective-spin measurements**

Estimate your own effective-spin posterior with the recipe presented in arXiv:1805.03046.