astroslam

SLAM

Stellar LAbel Machine (SLAM) is a forward model to estimate stellar labels (e.g., Teff, logg and chemical abundances). It is based on Support Vector Regression (SVR) which is a non-parametric regression method.

For details of SLAM, see Deriving the stellar labels of LAMOST spectra with Stellar LAbel Machine (SLAM). Related projects: click here.

Author

Bo Zhang (bozhang@nao.cas.cn)

Home page

Install

for the latest stable version:
- pip install -U astroslam
for the latest github version:
- pip install -U git+git://github.com/hypergravity/astroslam
for Zenodo version
- https://zenodo.org/record/3461504

Tutorial

[updated on 2020-12-02]

A new SLAM tutorial can be found here
If you are interested in SLAM or have any related questions, do not hesitate to contact me.

Requirements

numpy
scipy
matplotlib
astropy
scikit-learn
joblib
pandas
emcee

How to cite

Paper:

@ARTICLE{2020ApJS..246....9Z,
       author = {{Zhang}, Bo and {Liu}, Chao and {Deng}, Li-Cai},
        title = "{Deriving the Stellar Labels of LAMOST Spectra with the Stellar LAbel Machine (SLAM)}",
      journal = {\apjs},
     keywords = {Astronomical methods, Astronomy data analysis, Bayesian statistics, Stellar abundances, Chemical abundances, Fundamental parameters of stars, Catalogs, Surveys, Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Astrophysics of Galaxies, Astrophysics - Instrumentation and Methods for Astrophysics},
         year = 2020,
        month = jan,
       volume = {246},
       number = {1},
          eid = {9},
        pages = {9},
          doi = {10.3847/1538-4365/ab55ef},
archivePrefix = {arXiv},
       eprint = {1908.08677},
 primaryClass = {astro-ph.SR},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2020ApJS..246....9Z},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

Code:

@misc{https://doi.org/10.5281/zenodo.3461504,
    author = {Zhang, Bo},
    title = {hypergravity/astroslam: Stellar LAbel Machine},
    doi = {10.5281/zenodo.3461504},
    url = {https://zenodo.org/record/3461504},
    publisher = {Zenodo},
    year = {2019}
}

For other formats, please go to https://search.datacite.org/works/10.5281/zenodo.3461504.

Metadata

Release files for astroslam 1.2022.1228.1

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

Source distribution (sdist)

Source distribution for astroslam 1.2022.1228.1
File	Size	Uploaded
astroslam-1.2022.1228.1.tar.gz	481.4 kB	Dec 28, 2022	Details

Built distribution (wheel)

Table of built distributions (wheels) for astroslam 1.2022.1228.1
File	Interpreter	ABI	Platform	Reset
astroslam-1.2022.1228.1-py3-none-any.whl 116.9 kB Dec 28, 2022	Python 3	none	any	Details

Total release size: 598.3 kB

Release files / astroslam-1.2022.1228.1.tar.gz

Download URL	astroslam-1.2022.1228.1.tar.gz
Size	481.4 kB
Tags	Source
SHA-256 checksum How to use checksums	`dd2dd9607f08d6d139c36c0f1b85682f1a8ec1a30e53f421cc3052aaadc2ad57`
BLAKE2b-256 checksum How to use checksums	`ea8301344209dbe2757eb1d8bc038920dc433a3c150f6b7e85f304138ad2c080`
Upload date	Dec 28, 2022
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/4.0.2 CPython/3.9.13`

Release files / astroslam-1.2022.1228.1-py3-none-any.whl

Download URL	astroslam-1.2022.1228.1-py3-none-any.whl
Size	116.9 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`cc0b65be334054aeb0655bf7508633ada893041f9c5be770bec7ea60a27ec718`
BLAKE2b-256 checksum How to use checksums	`0280b49dfc8383eed24bdb4bbb4b930c8a35df69580e3e6b6c087e7c7de0c3dd`
Upload date	Dec 28, 2022
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/4.0.2 CPython/3.9.13`