sdf-xarray

sdf-xarray provides a backend for xarray to read SDF files as created by EPOCH using the SDF-C library. Part of BEAM (Broad EPOCH Analysis Modules).

Installation

Install from PyPI with:

pip install sdf-xarray

Note

For use within jupyter notebooks, run this additional command after installation:

pip install "sdf-xarray[jupyter]"

or from a local checkout:

git clone https://github.com/epochpic/sdf-xarray.git
cd sdf-xarray
pip install .

We recommend switching to uv to manage packages.

Usage

Single file loading

import xarray as xr

df = xr.open_dataset("0010.sdf")

print(df["Electric_Field_Ex"])

# <xarray.DataArray 'Electric_Field_Ex' (X_x_px_deltaf_electron_beam: 16)> Size: 128B
# [16 values with dtype=float64]
# Coordinates:
#   * X_x_px_deltaf_electron_beam  (X_x_px_deltaf_electron_beam) float64 128B 1...
# Attributes:
#     units:    V/m
#     full_name: "Electric Field/Ex"

Multi-file loading

To open a whole simulation at once, pass preprocess=sdf_xarray.SDFPreprocess() to xarray.open_mfdataset:

import xarray as xr
from sdf_xarray import SDFPreprocess

with xr.open_mfdataset("*.sdf", preprocess=SDFPreprocess()) as ds:
    print(ds)

# Dimensions:
# time: 301, X_Grid_mid: 128, ...
# Coordinates: (9) ...
# Data variables: (18) ...
# Indexes: (9) ...
# Attributes: (22) ...

SDFPreprocess checks that all the files are from the same simulation, as ensures there's a time dimension so the files are correctly concatenated.

If your simulation has multiple output blocks so that not all variables are output at every time step, then those variables will have NaN values at the corresponding time points.

For more in depth documentation please visit: https://sdf-xarray.readthedocs.io/

Citing

If sdf-xarray contributes to a project that leads to publication, please acknowledge this by citing sdf-xarray. This can be done by clicking the "cite this repository" button located near the top right of this page.

Contributing

We welcome contributions to the BEAM ecosystem! Whether it's reporting issues, suggesting features, or submitting pull requests, your input helps improve these tools for the community.

How to Contribute

There are many ways to get involved:

• Report bugs: Found something not working as expected? Open an issue with as much detail as possible.
• Request a feature: Got an idea for a new feature or enhancement? Open a feature request on GitHub Issues!
• Improve the documentation: We aim to keep our docs clear and helpful—if something's missing or unclear, feel free to suggest edits.
• Submit code changes: Bug fixes, refactoring, or new features are welcome.

All code is automatically linted, formatted, and tested via GitHub Actions.

To run checks locally before opening a pull request, see CONTRIBUTING.md or readthedocs documentation

Broad EPOCH Analysis Modules (BEAM)

BEAM is a collection of independent yet complementary open-source tools for analysing EPOCH simulations, designed to be modular so researchers can adopt only the components they require without being constrained by a rigid framework. In line with the FAIR principles — Findable, Accessible, Interoperable, and Reusable — each package is openly published with clear documentation and versioning (Findable), distributed via public repositories (Accessible), designed to follow common standards for data structures and interfaces (Interoperable), and includes licensing and metadata to support long-term use and adaptation (Reusable). The packages are as follows:

• sdf-xarray: Reading and processing SDF files and converting them to xarray.
• epydeck: Input deck reader and writer.
• epyscan: Create campaigns over a given parameter space using various sampling methods.

PlasmaFAIR

Originally developed by PlasmaFAIR, EPSRC Grant EP/V051822/1