SED_Tools

Download, process, and standardize stellar atmosphere models for SED_Model and MESA

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

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, SED_Model and other codes.

Key Features

Multi-source downloads — Fetch stellar atmosphere spectra from SVO, MSG, MAST (BOSZ), and NJM mirror
Photometric filters — Download transmission curves from the NJM mirror with SVO Filter Profile Service fallback
Unit standardization — Convert all spectra to consistent units (wavelength in Å, flux in erg/cm²/s/Å)
External integration — Generate binary flux cubes, HDF5 bundles, and lookup tables. This is for SED_Model and MESA's colors module
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

Installation

From PyPI

pip install sed-tools

From Source

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

Development Install

pip install -e .

Requirements

Python ≥ 3.10
numpy, pandas, h5py, astropy, matplotlib
PyTorch (required for ML completer and generator)

See pyproject.toml for the complete dependency list.

Quick Start

Interactive Mode

Launch the interactive menu:

sed-tools

Direct Commands

# Download photometric filter transmission curves
sed-tools filters

# Combine installed filter sets
sed-tools filters-combine Gaia2MASS GAIA/GAIA 2MASS/2MASS

# Download stellar atmosphere spectra
sed-tools spectra

# 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

# Fill coarse Teff gaps in a flux cube to reduce interpolation stair-stepping
sed-tools densify --flux-cube data/stellar_models/tmap2/flux_cube.bin \
                  --output data/stellar_models/tmap2_dense/flux_cube.bin \
                  --teff-spacing 1000

# Ingest an external grid of .txt spectra into the pipeline
sed-tools import --path /path/to/spectra/ --name MyGrid

# Report parameter-space coverage of an installed grid
sed-tools coverage --models Kurucz2003all

# Show or set the data directory
sed-tools config
sed-tools config --set /path/to/data --move

CLI Reference

`sed-tools filters`

Download photometric filter transmission curves interactively. The browser merges the SVO Filter Profile Service catalogue with the NJM mirror: facilities that exist on SVO are listed first, and any facilities available only on the NJM mirror are surfaced alongside them (marked [NJM]). Filters are fetched from the NJM mirror when available (fastest), with automatic fallback to SVO for everything else.

# 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

Downloaded filter sets can be combined without rebuilding spectra or flux cubes, because a filter set is only a directory of *.dat curves plus the MESA index file whose lines list those curves. The helper below copies the selected curves into a new instrument folder and writes the matching index file automatically.

# Create data/filters/Combined/Gaia2MASS/ with a Gaia2MASS index file
sed-tools filters-combine Gaia2MASS GAIA/GAIA 2MASS/2MASS

# Pick custom MESA-facing labels and fail on duplicate band names
sed-tools filters-combine optical_ir Generic/Johnson 2MASS/2MASS \
  --facility Custom --instrument optical_ir --on-conflict error

The same operation is available from Python:

from sed_tools.api import Filters

combined = Filters.combine("Gaia2MASS", "GAIA/GAIA", "2MASS/2MASS")
print(combined)

`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

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

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

What it does:

Queries the selected source for available models
Downloads spectrum files matching your criteria
Standardizes units (wavelength → Å, flux → erg/cm²/s/Å)
Generates lookup_table.csv with stellar parameters
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

`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

Generated files:

File	Description
`flux_cube.bin`	Binary flux cube (required by SED_Model and other codes, such as MESA)
`lookup_table.csv`	Parameter lookup table
`*.h5`	HDF5 bundle with all spectra

`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

`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:

Trains on complete SED libraries with full wavelength coverage
Uses black body radiation as a physics-based baseline
Neural network learns corrections to the black body approximation
Masked training handles heterogeneous wavelength grids
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:

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

`sed-tools densify`

Fill coarse Teff gaps in an existing flux cube to eliminate stair-stepping artefacts in MESA photometric outputs. New Teff nodes are filled with ML predictions (when a generator model is provided and the point is within its training range) or a Planck blackbody scaled to the nearest real SED's bolometric flux. The densifier never loads the full cube into RAM — it uses np.memmap throughout.

# Densify with blackbody fallback (no ML model)
sed-tools densify \
  --flux-cube data/stellar_models/tmap2/flux_cube.bin \
  --output data/stellar_models/tmap2_dense/flux_cube.bin \
  --teff-spacing 1000

# Densify with ML generator for in-range points, blackbody elsewhere
sed-tools densify \
  --flux-cube data/stellar_models/tmap2/flux_cube.bin \
  --output data/stellar_models/tmap2_dense/flux_cube.bin \
  --teff-spacing 1000 \
  --ml-model data/stellar_models/models/sed_generator_Kurucz2003all

See notebook 10 for a worked example including before/after comparison plots.

`sed-tools import`

Ingest an external directory of .txt spectra (with parameter headers) into the SED_Tools pipeline. Files are copied or moved into the configured model root, then processed through the standard clean → lookup → HDF5 → flux-cube pipeline.

# Import spectra, build all outputs
sed-tools import --path /path/to/my_spectra/ --name MyGrid

# Preview parseable headers without importing
sed-tools import --path /path/to/my_spectra/ --name MyGrid --dry-run

# Move files instead of copying; skip HDF5 bundle
sed-tools import --path /path/to/my_spectra/ --name MyGrid --move --no-h5

`sed-tools coverage`

Report parameter-space coverage statistics for one or more installed grids. Prints per-axis ranges, unique grid values, fill fraction, and optionally writes a Teff–logg + 3D scatter plot.

# Report and plot coverage for a single model
sed-tools coverage --models Kurucz2003all

# Report without generating a plot
sed-tools coverage --models Kurucz2003all --no-plot

`sed-tools mesa_prepare`

Export a specific extra-axis sub-variant (e.g. one alpha-enhancement, turbulent velocity, or mixing length combination) from a model that has multiple variants in its fluxcube_library/. Produces a clean, self-contained MESA-ready folder containing only the selected variant's flux cube.

# Interactive model and variant selection
sed-tools mesa_prepare

`sed-tools config`

Show or change the root data directory where SED_Tools stores stellar models and filters.

# Show current data directory
sed-tools config

# Change the data directory (and optionally move existing data)
sed-tools config --set /new/data/path --move

Python API

The Python API exposes the core CLI capabilities together with additional building blocks for 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 SED_Model/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

Synthetic Photometry

EvaluatedSED.photometry(...) accepts individual filter names, filter files, or a filter-set / instrument directory name. For example, with the standard MESA colors-data layout, "GAIA" expands to the files in filters/GAIA/GAIA/.

import os
from pathlib import Path
from sed_tools.api import SED

colors_data = Path(os.path.expandvars("$MESA_DIR")) / "data/colors_data"

sed = SED.local(
    "Kurucz2003all",
    model_root=colors_data / "stellar_models",
    filter_root=colors_data / "filters",
)

spec = sed(teff=6000, logg=2.0, metallicity=-1.0)
phot = spec.photometry("GAIA", system="AB")

mags = {res.filter_name: res.magnitude for res in phot.values()}
bp_rp = mags["Gbp"] - mags["Grp"]
print(bp_rp)

If a filter specification is ambiguous, pass a specific file path or a specific filter directory.

Combining Grids

ensemble = SED.combine(
    catalogs=['Kurucz2003all', '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)}

Grid Coverage

# Report parameter-space coverage and write a diagnostic plot
report = SED.coverage('Kurucz2003all')
# Keys: n_spectra, n_distinct_nodes, fill_fraction, axes (per-axis min/max/unique)

# Skip the plot, or write it to a custom path
report = SED.coverage('Kurucz2003all', plot=False)
report = SED.coverage('Kurucz2003all', out_path='coverage.png')

Importing External Grids

# Ingest a directory of .txt spectra into the pipeline
result = SED.import_grid('/path/to/my_spectra/', name='MyGrid')

# Preview parseable headers without actually importing
result = SED.import_grid('/path/to/my_spectra/', name='MyGrid', dry_run=True)

# Move files instead of copying; skip HDF5
result = SED.import_grid('/path/to/my_spectra/', name='MyGrid', move=True, build_h5=False)

`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 filters`	`Filters.fetch(...)`
`sed-tools filters-combine X A B`	`Filters.combine('X', 'A', 'B')`
`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 densify --flux-cube F --output O`	`sed_tools.grid_densifier.densify_grid(src, dst)`
`sed-tools import --path P --name N`	`SED.import_grid('/path/', name='N')`
`sed-tools coverage --models X`	`SED.coverage('X')`
`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(...)`

MESA Integration

Installing Downloaded Data

Copy or symlink the generated data into your MESA installation:

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

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

MESA Inlist Configuration

&controls
    ! Stellar atmosphere model
    stellar_atm = '/data/colors_data/stellar_models/Kurucz2003all/'
    
    ! Photometric filter set
    instrument = '/data/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"

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

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, including BOSZ 2024 fixed-resolution and original-resolution grids

MAST BOSZ 2024 wavelength grids

The BOSZ 2024 fixed-resolution products (BOSZ-2024-r500 through BOSZ-2024-r50000) are resampled spectra. SED_Tools uses the official STScI wavelength grids distributed with the archive under bosz2024/wavelength_grids/; it does not synthesize or guess wavelength axes from row counts. The original-resolution product (BOSZ-2024-rorig) carries its wavelength column inline.

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 BT-Settl
cool = SED.fetch('bt-settl', teff_min=2500, teff_max=7000)
cool.cat.write()

# Combine into unified grid
combined = SED.combine(
    ['Kurucz2003all', 'bt-settl'],
    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]))

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/data/colors_data/stellar_models/Kurucz2003all/
├── flux_cube.bin       # Must exist
└── lookup_table.csv    # Must exist

Filter set cannot be resolved

For installed filter sets, pass either an individual filter stem ("Gbp"), a full file path, or a filter-set name/directory such as "GAIA" when the data are arranged as:

filters/GAIA/GAIA/G.dat
filters/GAIA/GAIA/Gbp.dat
filters/GAIA/GAIA/Grp.dat

If a short filter name matches more than one file, use a full path or a specific filter directory.

MAST BOSZ fixed-resolution download finds no wavelength grid

BOSZ 2024 fixed-resolution spectra require the official wavelength files in the archive's wavelength_grids/ directory. If those files cannot be reached, SED_Tools should fail clearly rather than invent a wavelength axis. You can also try BOSZ-2024-rorig, whose files include wavelength, flux, and continuum columns directly.

Citation

If you use SED_Tools in your research, please cite the archived software release via its Zenodo DOI:

A version-specific Zenodo DOI for Version 0.2.0 will be added following archival of this release.

Machine-readable citation metadata is available in CITATION.cff.

SED_Tools is also the designated data-preparation pipeline for the MESA colors module — see colors/README.rst in the MESA repository.

Contributing

Bug reports, feature requests, and pull requests are welcome — see CONTRIBUTING.md. Please also review our Code of Conduct and Security Policy (for reporting vulnerabilities).

License

MIT License

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

Community Contributions

Thanks to everyone who has filed issues, suggested features, or reported bugs:

@andysantarelli — feature suggestions (grid coverage view, custom atmosphere grids)
@tianzhijia — bug report (BOSZ-2024 download issue)
@rhdtownsend — issue report (Jupyter notebook results)

Name		Name	Last commit message	Last commit date
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docs		docs
jupyter_notebooks		jupyter_notebooks
paper		paper
sed_tools		sed_tools
tests		tests
.gitattributes		.gitattributes
.gitignore		.gitignore
CITATION.cff		CITATION.cff
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
README.md		README.md
SECURITY.md		SECURITY.md
api_usage.py		api_usage.py
pyproject.toml		pyproject.toml
sed_tools.defaults		sed_tools.defaults

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SED_Tools

Overview

Key Features

Installation

From PyPI

From Source

Development Install

Requirements

Quick Start

Interactive Mode

Direct Commands

CLI Reference

sed-tools filters

sed-tools spectra

sed-tools rebuild

sed-tools combine

sed-tools ml_completer

sed-tools ml_generator

sed-tools densify

sed-tools import

sed-tools coverage

sed-tools mesa_prepare

sed-tools config

Python API

SED — Main Entry Point

Discovery

Downloading

Loading Local Data

Interpolation

Synthetic Photometry

Combining Grids

ML Completion

ML Generation

Grid Coverage

Importing External Grids

Catalog — Spectrum Container

Spectrum — Individual SED

Filters — Photometric Filters

CatalogInfo — Catalog Metadata

CLI to API Mapping

MESA Integration

Installing Downloaded Data

MESA Inlist Configuration

Filter Specifications

Directory Structure

Data Sources

MAST BOSZ 2024 wavelength grids

Examples

Batch Processing a Star Catalog

Building a Custom Temperature Grid

Extending UV Coverage with ML

Generating SEDs for Arbitrary Parameters

Troubleshooting

Common Issues

Citation

Contributing

License

Acknowledgments

Community Contributions

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License

Code of conduct

Contributing

Security policy

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`sed-tools filters`

`sed-tools spectra`

`sed-tools rebuild`

`sed-tools combine`

`sed-tools ml_completer`

`sed-tools ml_generator`

`sed-tools densify`

`sed-tools import`

`sed-tools coverage`

`sed-tools mesa_prepare`

`sed-tools config`

`SED` — Main Entry Point

`Catalog` — Spectrum Container

`Spectrum` — Individual SED

`Filters` — Photometric Filters

`CatalogInfo` — Catalog Metadata

Packages