ASCL.net

Astrophysics Source Code Library

Making codes discoverable since 1999

Searching for codes credited to 'Pontzen, Andrew'

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[ascl:1305.002] pynbody: N-Body/SPH analysis for python

Pynbody is a lightweight, portable, format-transparent analysis package for astrophysical N-body and smooth particle hydrodynamic simulations supporting PKDGRAV/Gasoline, Gadget, N-Chilada, and RAMSES AMR outputs. Written in python, the core tools are accompanied by a library of publication-level analysis routines.

[ascl:1912.018] Tangos: Framework and web interface for database-driven analysis of numerical structure formation simulations

Tangos builds databases (along the lines of Eagle or MultiDark) for cosmological and zoom simulations. Its
modular system generates and queries databases. It is designed to store and manage results from a user's own analysis code, provides web and python interfaces, and allows users to construct science-focused queries, including across entire merger trees, without requiring knowledge of SQL. Tangos manages the process of populating the database with science data, including auto-parallelizing the analysis. It can be customized to work with multiple python modules such as pynbody (ascl:1305.002) or yt (ascl:1011.022) to process raw simulation data; it defaults to using SQLite, but allows use of other databases as the underlying store through the use of SQLAlchemy.

[ascl:2006.020] GenetIC: Initial conditions generator for cosmological simulations

GenetIC generates initial conditions for cosmological simulations, especially for zoom simulations of galaxies. It provides support for "genetic modifications" of specific attributes of simulations to allow study of the impact of such modifications on the outcomes; the code can also produce constrained initial conditions.

[ascl:2105.019] RandomQuintessence: Integrate the Klein-Gordon and Friedmann equations with random initial conditions

RandomQuintessence integrates the Klein-Gordon and Friedmann equations for quintessence models with random initial conditions and functional forms for the potential. Quintessence models generically impose non-trivial structure on observables like the equation of state of dark energy. There are three main modules; montecarlo_nompi.py sets initial conditions, loops over a bunch of randomly-initialised models, integrates the equations, and then analyses and saves the resulting solutions for each model. Models are defined in potentials.py; each model corresponds to an object that defines the functional form of the potential, various model parameters, and functions to randomly draw those parameters. All of the equation-solving code and methods to analyze the solution are kept in solve.py under the base class DEModel(). Other files available analyze and plot the data in a variety of ways.