Status	Documentation	License	Build

OITOOLS is a Julia package for optical interferometry data analysis, developed by Prof. Fabien Baron (Georgia State University) and collaborators. It covers the full workflow for data from arrays such as CHARA, VLTI, and NPOI: reading, plotting, model fitting, image reconstruction, and observation simulation.

Note: Despite having the same name as JMMC's oitools, the two packages are completely unrelated and were developed independently.

using Pkg
pkg"registry add General"
pkg"registry add https://github.com/emmt/EmmtRegistry"
Pkg.add(url="https://github.com/fabienbaron/OITOOLS.jl.git")
using OITOOLS

See the installation guide for Python dependencies (UltraNest, Astroquery).

using OITOOLS
data = readoifits("mydata.oifits")
uvplot(data)
plot_v2(data)
plot_t3phi(data)

Both image-based and parametric-model-based workflows follow the same pattern:

• OIFITS v1 and v2 with automatic quality filtering
• Spectral and temporal binning, polychromatic (per-channel) mode
• OI_CORR correlation matrices (e.g. GRAVITY)
• Float32 mode for large datasets (readoifits(file; T=Float32))

All plot functions accept color="baseline" (default), color="wav", or color="mjd" to colour-code by baseline, wavelength, or timestamp.

plot_v2(data; logplot=true, color="wav")        # single observable
plot_obs(data)                                   # multi-panel: V², T3φ, T3amp
plot_obs(data; obs=["V2","T3PHI","VISAMP"], color="mjd")

# After reconstruction or model fitting:
obs = image_to_obs(x, ft, data)                 # or model_to_obs(model, x, data)
plot_residuals(data, obs)                        # data vs model + residuals
plot_residuals(x, ft, data)                      # convenience shortcut

imdisp(image; pixsize=0.1)                       # display a reconstructed image

UV coverage	V²

Parametric model fitting with a flat-dictionary interface compatible with PMOIRED:

• Component types: uniform disk, Gaussian, limb-darkened (linear, quadratic, power-law), ring, Gaussian ring, crescent, point source, resolved background, and arbitrary radial profiles via Hankel transform
• Chromatic and time-variable models using expression strings with $WL, $MJD, and inter-parameter $-references
• Azimuthal modulations with harmonic coefficients for disk asymmetries
• Three fitting backends:
  • NLopt (gradient-based and gradient-free optimizers)
  • LsqFit (Levenberg-Marquardt with covariance and 1-sigma errors)
  • UltraNest (Bayesian nested sampling with log-evidence)
• Bootstrap error estimation by baseline, time, or wavelength
• PMOIRED conversion: pmoired_to_julia() converts Python dict literals to Julia

• Gradient-based reconstruction with VMLMB (OptimPackNextGen)
• NFFT for speed or exact DFT
• Polychromatic and time-variable imaging
• SPARCO framework for chromatic environments
• Maximum Entropy (BSMEM) reconstruction
• Multiple regularizations: total variation, L1-L2, compactness, entropy, support constraints, and more

The ROTIR package uses OITOOLS for stellar surface imaging (light curve inversion, Doppler imaging, interferometric imaging):

• Simulate synthetic OIFITS from images or parametric models
• Build observations from TOML-based array/combiner/wavelength configs
• Pre-built configs for CHARA, VLTI (UT and AT), and their combiners (MIRC-X, MYSTIC, GRAVITY, MATISSE, SPICA)
• Observation planning with Gantt charts, delay-line feasibility, and SIMBAD target queries

ASPRO-like Gantt chart	chara_plan-like plots

Full documentation is available at fabienbaron.github.io/OITOOLS.jl, including:

• Reading OIFITS files
• Model fitting guide
• Image reconstruction
• Simulation and planning
• Demo scripts
• API reference: OIFITS, Plotting, Model Fitting, Imaging, Observation Planning

using Pkg
pkg"registry add General"
pkg"registry add https://github.com/emmt/EmmtRegistry"
Pkg.develop(url="https://github.com/fabienbaron/OITOOLS.jl.git")
# Python dependencies (optional, for UltraNest and Astroquery)
ENV["PYTHON"] = ""
Pkg.build("PyCall")
using Conda
Conda.add("ultranest", channel="conda-forge")
Conda.add("astroquery", channel="astropy")