Getting Started

swiftsimio is a toolkit for reading data produced by the SWIFT astrophysics simulation code. It is used to ensure that all data have a symbolic unit attached, and can be used for visualisation. Another key feature is the use of the cell metadata in SWIFT snapshots to enable efficient reading of sub-regions.

The SWIFT astrophysical simulation code is used widely. There exists many ways of reading the data from SWIFT, which outputs HDF5 files. These range from reading directly using h5py to using a complex system such as yt; however these either are unsatisfactory (e.g. a lack of unit information in reading HDF5), or too complex for most use-cases. swiftsimio provides an object-oriented API to dynamically read data from SWIFT outputs, including FOF and SOAP catalogues. An extension module for swiftsimio for using catalogues and snapshots in tandem is available: swiftgalaxy.

Getting set up with swiftsimio is easy; it (by design) has very few requirements. There are a number of optional packages that you can install to make the experience better and these are recommended.

Requirements

Python version is required. Unfortunately it is not possible to support swiftsimio on versions of python lower than this.

Python packages

numpy, required for the core numerical routines.
h5py, required to read data from the SWIFT HDF5 output files.
unyt, required for symbolic unit calculations (depends on sympy``).
astropy, required to represent cosmology information.
numba, highly recommended should you wish to use the in-built visualisation tools.

Optional packages

scipy, required if you wish to generate smoothing lengths for particle types that do not store this variable in the snapshots (e.g. dark matter)
tqdm, required for progress bars for some long-running tasks. If not installed no progress bar will be shown.

Installing

swiftsimio can be installed using the pip python packaging manager, or any other packaging manager that you wish to use:

pip install swiftsimio

Development environment

To set up the code for development, first clone the latest master from GitHub:

git clone https://github.com/SWIFTSIM/swiftsimio.git

and install with pip using the -e (“editable”) flag, and specifying optional dependencies for development and building the documentation:

cd swiftsimio
pip install -e .[dev,docs]

Code contributions are very welcome! A good place to start is the contributing guide and how to set up a development environment.

swiftsimio is licensed under GPL-3.0 and community members are expected to abide by the code of conduct.

Usage example

Example usage is shown below, which plots a density-temperature phase diagram, with density and temperature given in CGS units:

import swiftsimio as sw

# This loads all metadata but explicitly does _not_ read any particle data yet
data = sw.load("/path/to/swift/output")

import matplotlib.pyplot as plt

data.gas.densities.convert_to_cgs()
data.gas.temperatures.convert_to_cgs()

plt.loglog()

plt.scatter(
   data.gas.densities,
   data.gas.temperatures,
   s=1
)

plt.xlabel(fr"Gas density $\left[{data.gas.densities.units.latex_repr}\right]$")
plt.ylabel(fr"Gas temperature $\left[{data.gas.temperatures.units.latex_repr}\right]$")

plt.tight_layout()

plt.savefig("test_plot.png", dpi=300)

In the above:

All metadata is read in when the swiftsimio.load function is called.
Only the densities and temperatures (corresponding to the PartType0/Densities and PartType0/Temperatures) datasets are read in.
That data is only read in once the swiftsimio.objects.cosmo_array.convert_to_cgs method is called.
swiftsimio.objects.cosmo_array.convert_to_cgs converts data in-place; i.e. it returns None.
The data is cached: it is not re-read when plt.scatter is called.