GofChianti

Lightweight Python access to precomputed CHIANTI contribution functions G(T, nₑ) — no IDL, no ChiantiPy at runtime.

GofChianti gives you direct, offline-capable access to CHIANTI contribution functions for selected spectral lines. The heavy computation is done once by the maintainers using the official CHIANTI/SSW IDL routines; the results are stored compactly and shipped as a small dataset. As a user you simply ask for a line, and GofChianti downloads (and caches) the precomputed G(T, nₑ), then lets you interpolate it at any density/temperature — optionally scaled by any elemental abundance set.

Install

pip install gofchianti

Quick start

import astropy.units as u
import gofchianti as gc

# 1. What is available?
df = gc.available_lines()            # pandas DataFrame (ion, λ, version, f, A, ...)
df = gc.available_lines(version="11.0.2")

# 2. Load a line (downloaded + cached on first use, offline afterwards).
cf = gc.get_line("Fe_12", 195.119)

# 3. Evaluate the *bare* contribution function G(nₑ, T).
#    Inputs are astropy Quantities; the result is a Quantity too.
g = cf.get_gofnt(density=1e9 * u.cm**-3, temperature=1.5e6 * u.K)

# 3b. Give any two of (density, temperature, pressure). Pressure may be a
#     reduced pressure nₑ*T (cm^-3 K, CHIANTI-style) or a thermal pressure (Pa).
g = cf.get_gofnt(pressure=1e15 * u.cm**-3 * u.K, temperature=1.5e6 * u.K)
g = cf.get_gofnt(pressure=0.02 * u.Pa, density=1e9 * u.cm**-3)

# 4. Abundance-scaled values: multiply by Fe/H from a CHIANTI abundance set.
cf = gc.get_line("Fe_12", 195.119, abundance="sun_photospheric_2021_asplund")
g = cf.get_gofnt(density=1e9 * u.cm**-3, temperature=1.5e6 * u.K)  # now × Fe/H

Abundances

G is stored without any elemental abundance. To get abundance-scaled values, attach an abundance set:

gc.available_abundances()                       # names shipped with the dataset
cf.set_abundance("sun_coronal_2021_chianti")    # by name (fetched/cached)
cf.set_abundance("/path/to/custom.abund")       # a local .abund file
cf.set_abundance(gc.Abundance.from_dex({"Fe": 8.0, "H": 12.0}))  # custom inline
cf.set_abundance(None)                          # back to bare G

Offline use

gc.download_all()        # fetch the entire dataset into the cache, once

After that everything works with no network access.

Cache & configuration

Downloaded files live in an OS-standard cache directory (~/.cache/gofchianti on Linux) and everything works offline after the first download. Every setting is available both as a function call and as an environment variable. All variables are prefixed GOFCHIANTI_ so they are easy to find in your shell profile:

Setting	Function	Environment variable
Cache directory	gc.set_cache_dir(path)	GOFCHIANTI_CACHE
Local dataset dir (used instead of downloading)	gc.set_dataset_dir(path)	GOFCHIANTI_DATASET_DIR
Download base URL	gc.set_base_url(url)	GOFCHIANTI_BASE_URL

By default the dataset is downloaded from the IAS SPICE data server:

https://spice.osups.universite-paris-saclay.fr/spice-data/contribution_functions/

Point GOFCHIANTI_BASE_URL (or gc.set_base_url(...)) elsewhere to use a mirror or a local copy. Clear the cache with gc.clear_cache().

For maintainers

Regenerating and publishing the dataset is a maintainer-only task. None of this is part of the installed package — end users only ever pip install gofchianti and call the API above.

Prerequisites

• IDL + SSW/CHIANTI to compute the raw G(T, nₑ) tables. The exact IDL routines used to produce the *_gofnt_v-*.dat files are vendored, for reference, under maintainers/idl/ (compute_gofnt.pro, chi_find_transition.pro).

• A local CHIANTI abundance directory, e.g. /usr/local/ssw/packages/chianti/dbase/abundance.

• rsync with SSH access to the web server that hosts the dataset (the default publishing backend).

• Optionally the gh CLI, authenticated (gh auth login), if you also publish a GitHub release (secondary backend).

• The dev/maintainer dependencies:

  pip install -e ".[dev]"     # tests
  pip install -e ".[maintainer]"  # ChiantiPy, only if you regenerate inputs
  

1. Regenerate the dataset

Convert the IDL .dat output into the shippable dataset (per-line .npz, catalog.parquet, the bundled abundance files and a hashed manifest.json):

python maintainers/convert_dat_to_npz.py \
    --dat-dir ../gofnt \
    --abund-src /usr/local/ssw/packages/chianti/dbase/abundance \
    --out-dir ./dataset

The same tool is exposed as a console script after install:

gofchianti-build-dataset --dat-dir ../gofnt --out-dir ./dataset

The build also refreshes the catalogue bundled inside the package (src/gofchianti/data/catalog.parquet) so available_lines() works offline. Pass --package-data-dir "" to skip that bundling. The dataset/ directory itself is git-ignored — it is distributed as release assets, not committed.

2. Publish the dataset

Publishing copies the flat asset set (per-line .npz, the .abund files, catalog.parquet and manifest.json) to wherever end users download it from. rsync over SSH is the default backend; a GitHub release via gh is a secondary option. Add --publish to publish for real, or --dry-run to print and validate the actions without touching the remote.

rsync over SSH (default)

The destination is an rsync/ssh target user@host:/path/, given with --dest or the GOFCHIANTI_UPLOAD_DEST environment variable. Files are sent flat so they line up with the flat download URL, and rsync only transfers what changed (safe to re-run):

# Validate the asset list + command without touching the remote:
python maintainers/convert_dat_to_npz.py --dat-dir ../gofnt --out-dir ./dataset \
    --dry-run --verbose 1

# Publish for real (destination via flag or GOFCHIANTI_UPLOAD_DEST):
python maintainers/convert_dat_to_npz.py --dat-dir ../gofnt --out-dir ./dataset \
    --publish \
    --dest user@host:/var/www/spice-data/contribution_functions/ \
    --verbose 1

Authentication. For security the tool never stores or forwards a password itself — authentication is delegated to ssh:

• SSH key — pass --ssh-key /path/to/key (or set GOFCHIANTI_SSH_KEY). A key already loaded in ssh-agent needs no argument and allows fully unattended runs.
• Interactive — with no key, ssh prompts for the password or key passphrase directly, in real time; the secret is typed into ssh and never passes through this tool.

Password-from-file / password-from-env piping (e.g. sshpass) is intentionally not supported: it would expose the secret in the process list and on disk. Use an SSH key or ssh-agent for automation.

The public download URL matching the example destination above is https://spice.osups.universite-paris-saclay.fr/spice-data/contribution_functions/.

GitHub release (secondary)

python maintainers/convert_dat_to_npz.py --dat-dir ../gofnt --out-dir ./dataset \
    --publish --target github --repo OWNER/NAME --verbose 1

The tag defaults to dataset-v<dataset_version> and the upload is idempotent (re-uploads with --clobber). Use --target both to publish to the web server and a GitHub release in one run. End users only fetch from releases/latest/download/ if you also point GOFCHIANTI_BASE_URL there.

3. Run the tests

pytest

The suite runs fully offline against the freshly built dataset/. See maintainers/convert_dat_to_npz.py for the converter's full docstring and the IDL output quirks it handles.