Getting started

Requirements

ps_eor supports Python 3.10 and later. Its functionality is split between the core package and optional dependencies for ML-GPR inference.

Core installation

Install the data handling, flagging, power-spectrum, spherical-analysis, simulation, and command-line functionality with:

$ pip install ps-eor

This also installs the pstool command. The installation can be checked with:

$ python -c "import ps_eor; print(ps_eor.__version__)"
$ pstool --help

ML-GPR installation

Install the Gaussian-process backend and its standard inference methods, including MCMC, MAP, Dynesty nested sampling, and Pyro NUTS, with:

$ pip install "ps-eor[ml-gpr]"

The high-level ML-GPR interface can then be imported from the package:

$ python -c "from ps_eor.ml_gpr import MLGPRForegroundFitter"

UltraNest is a separate optional dependency:

$ pip install "ps-eor[ml-gpr,ultranest]"

Installing from source

For an editable development installation:

$ git clone https://gitlab.com/flomertens/ps_eor.git
$ cd ps_eor
$ pip install -e ".[ml-gpr,dev]"

Documentation sections

The topic-based user guide covers data handling, power spectra, foreground separation, ML-GPR, simulations, and spherical analysis. The command-line documentation describes every pstool command. Exact classes, functions, parameters, and return values are listed in the API reference.