Colossus is a python toolkit for calculations pertaining to cosmology, the large-scale structure of the universe, and the properties of dark matter halos.Please see the Online Documentation for details (including instructions for Installation). The name is an acronym for COsmology, haLO, and large-Scale StrUcture toolS. Correspondingly, Colossus consists of three top-level modules:
- Cosmology: Implements LCDM cosmologies with curvature, relativistic species, and different dark energy equations of state. Incldues standard calculations such as densities and times, but also more advanced computations such as the power spectrum, variance, and correlation function.
- Large-scale structure: Deals with peaks in Gaussian random fields and the statistical properties of halos such as peak height, peak curvature, halo bias, and the mass function.
- Dark matter halos: Deals with halo masses and radii in arbitrary SO definitions, pseudo-evolution, implements general and specific halo density profiles (Einasto, Hernquist, NFW, DK14), computes models for halo concentration and the splashback radius.
Colossus is developed with the following chief design goals in mind:
- Intuitive use: The fundamental philosophy of Colossus is to make it easy to evaluate complex astrophysical quantities in a single or in a few lines of code. For this purpose, numerous fitting functions have been pre-programmed.
- Stand-alone, pure python: No dependencies beyond numpy and scipy, no C modules to be compiled. You can install Colossus either as a python package using pip or clone the repository.
- Performance: Computationally intensive routines have been optimized for speed, often using interpolation tables. Virtually all functions accept either numbers or numpy arrays as input.
While Colossus has been tested extensively, there is no guarantee that it is free of bugs. Use it at your own risk, and please report any errors, inconveniences and unclear documentation to the developer.
If you use Colossus for a publication, please cite the code paper (Diemer 2017). Many Colossus routines implement the results of other papers. If you use such routines, please take care to cite the relevant papers as well (they will be mentioned in the function and/or module documentation).
If you are planning on using Colossus long-term or for publications, it is recommended that you sign up for email updates. I only send alerts to this list if there is a significant update or bug fix that users absolutely need to know about. Of course, you can unsubscribe at any time.