pymcfost is a python interface to the 3D radiative transfer code mcfost. The goal is to provide a simple and light interface to explore and plot a single (or a few) model(s).
pymcfost offers (or will offer) the following functionalities:
- set up continuum and line models,
- read a single model or library of models,
- plot basic quantities, e.g. density structures, temperature maps on the various grids available in mcfost,
- plot observables : SEDs, image (with convolution), polarisation maps and vectors, visibilities, channels maps (with spatial and spectral convolution), moment maps.
- convert units, e.g. W.m-2 to Jy or brightness temperature
- provides an interface to the ALMA CASA simulator
- provides a fast and simplfied version of the ALMA simulator (spatial convolution with Gaussian, spectral convolution and noise), ie ignoring uv sampling,
- consistent interface with the casa_cube python package to compare observations with models
- read and plot dust models, including Mie, DHS and aggregates dust properties calculations
- (TBD) direct interface to the ML chemical predictions
pip install pymcfost
git clone https://github.com/cpinte/pymcfost.git
cd pymcfost
pip install .
To install in developer mode: (i.e. so that code changes here are immediately available without needing to repeat the above step):
pip install -e .
In case you are curious, pymcfost was born as an attempt to port in python the functions that were available in the yorick-mcfost code, which is still available here: https://github.com/cpinte/yomcfost. The fitting routines of the yorick interface are yet to be ported into pymcfost. An alternative python distribution is available at https://github.com/swolff9/mcfost-python . It is more tailored towards handling large grid of models and model fitting.
- python >= 3.8 vs python 2.x
- only parameter file >= 3.0
- handles parameter files with mutiple zones, dust population, molecules, stars, etc. Parameter files are stored in objects rather than dicts, allowing more flexibility.
- does not and will not handle observational data, only models