SED_Tools

Download, process, and standardize stellar atmosphere models for MESA

Installation • Quick Start • CLI Reference • Python API • Data Sources

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Overview

SED_Tools is a Python package for working with stellar spectral energy distributions (SEDs). It provides unified access to multiple stellar atmosphere catalogs, standardizes spectral data to consistent units, and generates output files compatible with MESA's colors module.

Key Features

• Multi-source downloads — Fetch stellar atmosphere spectra from SVO, MSG, MAST (BOSZ), and NJM mirror
• Photometric filters — Download transmission curves from the SVO Filter Profile Service
• Unit standardization — Convert all spectra to consistent units (wavelength in Å, flux in erg/cm²/s/Å)
• MESA integration — Generate binary flux cubes, HDF5 bundles, and lookup tables
• Grid combination — Merge multiple stellar libraries into unified "omni grids"
• ML completion — Extend incomplete SEDs to broader wavelength ranges using neural networks
• ML generation — Create complete SEDs from stellar parameters (Teff, logg, [M/H]) using neural networks

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Installation

From Source

git clone https://github.com/yourusername/SED_Tools.git
cd SED_Tools
pip install .

Development Install

pip install -e .

Requirements

• Python ≥ 3.9
• numpy, pandas, h5py, astropy, matplotlib
• PyTorch (required for ML completer and generator)

See pyproject.toml for the complete dependency list.

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Quick Start

Interactive Mode

Launch the interactive menu:

sed-tools

Direct Commands

# Download stellar atmosphere spectra
sed-tools spectra

# Download photometric filter transmission curves
sed-tools filters

# Build flux cubes and lookup tables from downloaded spectra
sed-tools rebuild

# Combine multiple grids into a unified ensemble
sed-tools combine

# Train or apply the ML SED completer (extends existing spectra)
sed-tools ml_completer

# Train or apply the ML SED generator (creates SEDs from parameters)
sed-tools ml_generator

---

CLI Reference

sed-tools spectra

Download stellar atmosphere spectra from remote catalogs.

# Interactive source and model selection
sed-tools spectra

# Download specific models
sed-tools spectra --models Kurucz2003all PHOENIX

# Force a specific source
sed-tools spectra --source svo --models Kurucz2003all

# Parallel downloads
sed-tools spectra --models Kurucz2003all --workers 8

What it does:

1. Queries the selected source for available models
2. Downloads spectrum files matching your criteria
3. Standardizes units (wavelength → Å, flux → erg/cm²/s/Å)
4. Generates lookup_table.csv with stellar parameters
5. Automatically runs rebuild to create binary files

Sources:

Source	Description
njm	NJM server (default, fastest)
svo	Spanish Virtual Observatory
msg	MSG grids (Townsend)
mast	MAST BOSZ library

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sed-tools filters

Download photometric filter transmission curves from the SVO Filter Profile Service.

# Interactive facility/instrument/filter selection
sed-tools filters

Output structure:

data/filters/Generic/Johnson/
├── B.dat           # Filter transmission curve
├── V.dat
├── R.dat
└── Johnson         # Index file for MESA

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sed-tools rebuild

Build MESA-compatible binary files from downloaded text spectra.

# Rebuild all local models
sed-tools rebuild

# Rebuild specific models
sed-tools rebuild --models Kurucz2003all PHOENIX

Generated files:

File	Description
flux_cube.bin	Binary flux cube (required by MESA)
lookup_table.csv	Parameter lookup table
*.h5	HDF5 bundle with all spectra

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sed-tools combine

Merge multiple stellar atmosphere grids into a unified ensemble.

# Interactive model selection
sed-tools combine

# Combine all available local models
sed-tools combine --non-interactive

Use cases:

• Extend temperature coverage by combining hot and cool star models
• Fill gaps in parameter space using complementary libraries
• Create comprehensive grids for population synthesis

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sed-tools ml_completer

Train and apply neural networks to extend incomplete SEDs to broader wavelength ranges.

Use case: You have spectra with limited wavelength coverage (e.g., optical-only) and need to extend them into UV or IR.

# Interactive mode
sed-tools ml_completer

How it works:

1. Trains on complete SED libraries with full wavelength coverage
2. Uses black body radiation as a physics-based baseline
3. Neural network learns corrections to the black body approximation
4. Masked training handles heterogeneous wavelength grids
5. Blends ML predictions with black body at extrapolation boundaries

---

sed-tools ml_generator

Train and apply neural networks to generate complete SEDs from stellar parameters alone.

Use case: You need SEDs for arbitrary stellar parameters but don't have an input spectrum — just Teff, logg, and [M/H].

# Interactive mode
sed-tools ml_generator

How it works:

1. Trains on flux cubes mapping (Teff, logg, [M/H]) → full SED
2. Network learns the complete spectral shape from 3 parameters
3. Log-scaling and normalization handle flux dynamic range
4. Generates diagnostic plots showing parameter space coverage

---

Python API

The Python API provides full parity with the CLI plus additional capabilities for building data pipelines.

SED — Main Entry Point

Discovery

from sed_tools.api import SED

# List all available catalogs
catalogs = SED.query()

# Filter by source
catalogs = SED.query(source='svo')

# Filter by parameter coverage
catalogs = SED.query(
    teff_min=5000,
    teff_max=7000,
    logg_min=3.5,
    metallicity_min=-1.0,
)

# Local catalogs only
catalogs = SED.query(include_remote=False)

Downloading

# Basic fetch (tries NJM mirror first, falls back to other sources)
sed = SED.fetch('Kurucz2003all')

# Force specific source
sed = SED.fetch('Kurucz2003all', source='svo')

# Fetch with parameter filtering and parallel downloads
sed = SED.fetch(
    'Kurucz2003all',
    teff_min=4000,
    teff_max=8000,
    logg_min=3.0,
    logg_max=5.0,
    metallicity_min=-1.0,
    metallicity_max=0.5,
    workers=8,
)

# Save to disk (generates all MESA-compatible files)
sed.cat.write()
sed.cat.write('/custom/output/path')

Loading Local Data

# Load an installed catalog
sed = SED.local('Kurucz2003all')

# Check parameter coverage
ranges = sed.parameter_ranges()
# {'teff': (3500.0, 50000.0), 'logg': (0.0, 5.0), 'metallicity': (-5.0, 1.0)}

Interpolation

sed = SED.local('Kurucz2003all')

# Interpolate a spectrum at specific stellar parameters
spectrum = sed(teff=5777, logg=4.44, metallicity=0.0)

print(spectrum.wavelength)  # Array in Angstroms
print(spectrum.flux)        # Array in erg/cm²/s/Å

Combining Grids

ensemble = SED.combine(
    catalogs=['Kurucz2003all', 'PHOENIX', 'NextGen'],
    output='my_combined_grid',
)

ML Completion

completer = SED.ml_completer()

# Train on a complete grid
completer.train(grid='combined_grid', epochs=200)

# Extend an incomplete model
extended = completer.extend(
    'sparse_model',
    wavelength_range=(100, 100000),
)
extended.write()

ML Generation

generator = SED.ml_generator()

# Train on a stellar atmosphere library
generator.train(grid='Kurucz2003all', epochs=200)

# Generate a single SED
wl, flux = generator.generate(teff=5777, logg=4.44, metallicity=0.0)

# Generate with diagnostic plots
wl, flux = generator.generate_with_outputs(
    teff=5777, 
    logg=4.44, 
    metallicity=0.0,
    output_dir='output/sun_sed',
)

# Or load a pre-trained model
generator = SED.ml_generator()
generator.load('sed_generator_Kurucz2003all')
wl, flux = generator.generate(teff=6000, logg=4.0, metallicity=-0.5)

# Check parameter ranges
ranges = generator.parameter_ranges()
# {'teff': (3500.0, 50000.0), 'logg': (0.0, 5.0), 'metallicity': (-5.0, 1.0)}

---

Catalog — Spectrum Container

catalog = sed.cat

# Properties
len(catalog)              # Number of spectra
catalog.teff_grid         # Unique Teff values
catalog.logg_grid         # Unique logg values
catalog.parameters        # DataFrame of all parameters

# Iteration
for spec in catalog:
    print(spec.teff, spec.logg, spec.metallicity)

# Filtering
cool_stars = catalog.filter(teff_max=5000)

# Persistence
catalog.write()
catalog.write('/custom/path')

---

Spectrum — Individual SED

spec = catalog[0]

# Data arrays
spec.wavelength    # np.ndarray (Angstroms)
spec.flux          # np.ndarray (erg/cm²/s/Å)
spec.wl            # Alias for wavelength
spec.fl            # Alias for flux

# Stellar parameters
spec.teff          # Effective temperature (K)
spec.logg          # Surface gravity (log g)
spec.metallicity   # [M/H]

# Metadata
spec.filename      # Original filename

# Save individual spectrum
spec.save('/path/to/spectrum.txt')

---

Filters — Photometric Filters

from sed_tools.api import Filters

# Query available filters
all_filters = Filters.query()
hst_filters = Filters.query(facility='HST')

# Download a filter set
path = Filters.fetch(facility='Generic', instrument='Johnson')

---

CatalogInfo — Catalog Metadata

info = SED.query()[0]

info.name              # 'Kurucz2003all'
info.source            # 'svo', 'njm', 'local', etc.
info.teff_range        # (3500.0, 50000.0)
info.logg_range        # (0.0, 5.0)
info.metallicity_range # (-5.0, 1.0)
info.n_spectra         # Number of spectra
info.is_local          # True if already installed

# Coverage checks
info.covers(teff=5777, logg=4.44)
info.covers_range(teff_min=5000, teff_max=6000)

---

CLI to API Mapping

CLI Command	Python API Equivalent
sed-tools spectra (list)	SED.query()
sed-tools spectra --models X	SED.fetch('X')
sed-tools rebuild --models X	sed.cat.write()
sed-tools combine --models A B	SED.combine(['A', 'B'], output='...')
sed-tools ml_completer train	SED.ml_completer().train(...)
sed-tools ml_generator train	SED.ml_generator().train(...)
sed-tools ml_generator generate	SED.ml_generator().generate(...)
sed-tools filters	Filters.fetch(...)

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MESA Integration

Installing Downloaded Data

Copy or symlink the generated data into your MESA installation:

# Copy
cp -r data/stellar_models/Kurucz2003all $MESA_DIR/colors/data/stellar_models/

# Or symlink (recommended for development)
ln -s $(pwd)/data/stellar_models/Kurucz2003all $MESA_DIR/colors/data/stellar_models/

MESA Inlist Configuration

&controls
    ! Stellar atmosphere model
    stellar_atm = '/colors/data/stellar_models/Kurucz2003all/'
    
    ! Photometric filter set
    instrument = '/colors/data/filters/Generic/Johnson'
/

Filter Specifications

When referencing filters in MESA, use the filename stem only:

• File: data/filters/GAIA/GAIA/G.dat
• Reference: "G"

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Directory Structure

SED_Tools/
├── sed_tools/              # Package source
│   ├── __init__.py
│   ├── api.py              # Python API
│   ├── cli.py              # CLI entry point
│   └── ...
├── data/                   # Downloaded data (created at runtime)
│   ├── stellar_models/
│   │   └── Kurucz2003all/
│   │       ├── flux_cube.bin
│   │       ├── lookup_table.csv
│   │       ├── spectra.h5
│   │       └── *.txt
│   └── filters/
│       └── Generic/
│           └── Johnson/
│               ├── B.dat
│               ├── V.dat
│               └── Johnson
├── docs/
├── tests/
├── pyproject.toml
└── README.md

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Data Sources

Source	URL	Description
NJM Server	nialljmiller.com/SED_Tools/	Pre-processed data host (fastest)
SVO	svo2.cab.inta-csic.es	Spanish Virtual Observatory
MSG	astro.wisc.edu/~townsend	MSG Stellar Atmosphere Grids
MAST	archive.stsci.edu/prepds/bosz	BOSZ Spectral Library

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Examples

Batch Processing a Star Catalog

from sed_tools.api import SED

sed = SED.local('Kurucz2003all')

stars = [
    {'name': 'Sun',         'teff': 5777, 'logg': 4.44, 'met':  0.0},
    {'name': 'Vega',        'teff': 9940, 'logg': 4.30, 'met':  0.0},
    {'name': 'Proxima Cen', 'teff': 3050, 'logg': 5.20, 'met': -0.1},
]

for star in stars:
    spectrum = sed(star['teff'], star['logg'], star['met'])
    spectrum.save(f"output/{star['name']}.txt")

Building a Custom Temperature Grid

from sed_tools.api import SED

# Hot stars from Kurucz
hot = SED.fetch('Kurucz2003all', teff_min=7000, teff_max=50000)
hot.cat.write()

# Cool stars from PHOENIX
cool = SED.fetch('PHOENIX', teff_min=2500, teff_max=7000)
cool.cat.write()

# Combine into unified grid
combined = SED.combine(
    ['Kurucz2003all', 'PHOENIX'],
    output='full_temperature_grid'
)

Extending UV Coverage with ML

from sed_tools.api import SED

# Train on a grid with complete wavelength coverage
completer = SED.ml_completer()
completer.train('BOSZ', epochs=200)

# Extend a model with limited UV coverage
extended = completer.extend(
    'optical_only_model',
    wavelength_range=(912, 100000),  # Extend into UV
)
extended.write()

Generating SEDs for Arbitrary Parameters

from sed_tools.api import SED

# Train a generator on a comprehensive grid
generator = SED.ml_generator()
generator.train('Kurucz2003all', epochs=200)

# Generate SEDs for a list of stars
stars = [
    {'name': 'Sun',    'teff': 5777, 'logg': 4.44, 'met':  0.0},
    {'name': 'Vega',   'teff': 9940, 'logg': 4.30, 'met':  0.0},
    {'name': 'Sirius', 'teff': 9940, 'logg': 4.30, 'met':  0.5},
]

for star in stars:
    wl, flux = generator.generate(
        teff=star['teff'],
        logg=star['logg'],
        metallicity=star['met'],
    )
    # Save or process the SED
    import numpy as np
    np.savetxt(f"output/{star['name']}.txt", np.column_stack([wl, flux]))

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Troubleshooting

Common Issues

Downloads fail or timeout

# Use the NJM mirror (faster, more reliable)
sed-tools spectra --source njm --models Kurucz2003all

# Or reduce parallel workers
sed-tools spectra --models Kurucz2003all --workers 2

Missing PyTorch for ML tools

pip install torch

MESA cannot find flux cube

Verify the directory structure matches MESA expectations:

$MESA_DIR/colors/data/stellar_models/Kurucz2003all/
├── flux_cube.bin       # Must exist
└── lookup_table.csv    # Must exist

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License

MIT License

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Acknowledgments

• MESA — Modules for Experiments in Stellar Astrophysics
• SVO Filter Profile Service — Filter transmission curves
• MAST — Mikulski Archive for Space Telescopes
• MSG Grids — Rich Townsend's stellar atmosphere grids