Getting Started

The SWIFT astrophysical simulation code (http://swift.dur.ac.uk) 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. This (thin) wrapper provides an object-oriented API to read (dynamically) data from SWIFT.

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. All requirements are detailed below.

Requirements

This requires python v3.8.0 or higher. Unfortunately it is not possible to support swiftsimio on versions of python lower than this. It is important that you upgrade if you are still a python2 user.

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).

Optional packages

numba, highly recommended should you wish to use the in-built visualisation tools.
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 python packaging manager, pip, or any other packaging manager that you wish to use:

pip install swiftsimio

Note that this will install any required packages for you.

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 flag,

cd swiftsimio

pip install -e .

Usage

There are many examples of using swiftsimio available in the swiftsimio_examples repository, which also includes examples for reading older (e.g. EAGLE) datasets.

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)

Don’t worry too much about this for now if you can’t understand it, we will get into this much more heavily in the next section.

In the above it’s important to note the following:

All metadata is read in when the swiftsimio.load() function is called.
Only the density 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 and not re-read in when plt.scatter is called.