Skip to main content

xaig

Light, framework-agnostic tooling for E3SM AI campaigns: what an emulator holds inside, sparse autoencoders trained on it, figures, and a local app over them.

Research tool. xaig is early. What is described here works; expect it to change.

Install

The base install pulls only Click. Anything heavier sits behind an extra named after the subpackage that needs it:

$ uv pip install 'xaig[daig]'     # or: pip install 'xaig[daig]'
Extra Pulls Gets you
daig numpy, xarray, netCDF4 xaig.daig: latent diagnostics on a grid
faig matplotlib, cartopy xaig.faig: maps and series figures (brings daig)
waig streamlit xaig waig: a local web app (brings faig)
taig torch xaig.taig and xaig taig: sparse autoencoders (brings daig)
hf huggingface_hub archives read from a Hugging Face repository, hf://datasets/... (brings daig)

A missing extra says so, with the command that fits how xaig was installed.

Try it, with no model and no data

A toy emulator writes a latent archive with nothing but numpy; everything else reads it like any other:

$ xaig daig latent toy scratch/toy/control
$ xaig daig latent info scratch/toy/control --mask-variable sst
$ xaig daig latent region scratch/toy/control --lat 10 --lon -114 --time 2 --centred --pcs 2
$ xaig daig latent fields scratch/toy/control --field precipitation --top 3
$ xaig taig sae scratch/toy/control --features 64 --k 4 --out scratch/toy/sae.npz   # needs xaig[taig]
$ xaig waig --latents scratch/toy                                                  # needs xaig[waig]

The CLI is a thin client of the Python API; anything it can do, a notebook can:

from xaig.daig.latent import Region, analyse_region, open_source

source = open_source("scratch/toy/control", mask_variable="sst")
result = analyse_region(
    source, time=2, layer=3, region=Region(lat=10, lon=-114, radius_km=1500), centred=True
)
result.ranking.channels  # the channels that respond most strongly there

More

BSD-3-Clause. xaig.daig.latent, xaig.faig and xaig.waig grew out of the latent space visualiser for weather models (Tempest, Beylich & Craig 2026, arXiv:2604.20467, doi:10.1007/978-3-032-29915-4_10); see NOTICE. The sparse autoencoders follow MacMillan & Ouellette (2025, arXiv:2512.24440); the B-spline autoencoder of Cheon (2026, arXiv:2605.17493) is what xaig.taig is heading for and does not implement yet.

Release files for xaig 0.3.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for xaig 0.3.0
File Size Uploaded
xaig-0.3.0.tar.gz 123.1 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for xaig 0.3.0
File Interpreter ABI Platform
xaig-0.3.0-py3-none-any.whl Python 3 none any Details

Total release size: 222.7 kB

Release files / xaig-0.3.0.tar.gz

Download URL xaig-0.3.0.tar.gz
Size 123.1 kB
Tags Source
SHA-256 checksum
How to use checksums
5d571182bc2c1d8c8b64f23f76ebf54dd7e1fd44a77f06c2e48a27d571dbeeb6
BLAKE2b-256 checksum
How to use checksums
e705501cb29554e9bcf2e1851d997164e12f7d4b06e3e56633dc398f2715780f
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 25, 2026.

Transparency log

Release files / xaig-0.3.0-py3-none-any.whl

Download URL xaig-0.3.0-py3-none-any.whl
Size 99.6 kB
Tags Python 3
SHA-256 checksum
How to use checksums
d1b9913bd53541f9cac3cebe6de502a07bfb1ac9c8cae9f97e9d7686c06941e8
BLAKE2b-256 checksum
How to use checksums
73f869d671eb3afd20586ee1531ea0c383b1c16f31a96cacc8237bbbfadfb50f
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 25, 2026.

Transparency log

Release history Release notifications | RSS feed

This release

0.3.0 This release

2 release files

0.2.0

2 release files

0.1.0

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page