semvec/pypi-packages huggingface dataset:
SATELLiTES-SBDD 1.0.11
SATELLiTES - Take your synthon-based ligand discovery to infinity and beyond
Requires Python: >=3.10

<p align="center"><img src="src/SATELLiTES-SBDD/SATELLiTES_header_hd.png" width=60% /></p>

<p align="center">Take your synthon-based ligand discovery to infinity and beyond !</p><br />

SATELLiTES is an open-source software designed to facilitate chemical space exploration and aid in the discovery of new hit compounds, starting from a specific two-reagents or three-components chemical reaction encoded in SMARTS format and a list of commercially available or ready-to-use compatible synthons. SATELLiTES is based on the hierarchical combinatorial approaches of synthon-based drug discovery, that can enable the fast exploration of ultra-large chemical libraries.

You can use SATELLiTES in two different ways:
- By using the graphical interface, downloadable from the Python Package Index (https://pypi.org/project/SATELLiTES-SBDD/)
- By using the KNIME extension, downloadable from the extension manager (WIP)

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## How to use SATELLiTES  

If you want to learn how to use SATELLiTES, please have a look at https://satellites.readthedocs.io/en/latest/index.html
  
This software is under active development: if you find any bug, critical issue, or if you just want to make comments/suggestions to improve the tool, please leave a comment in the "Issues" section. Thanks in advance for your help!

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## Citation

If you find SATELLiTES useful or if you use it, please cite us:

C Bedart, JJ Irwin, M Schapira - Take your synthon-based ligand discovery to infinity and beyond with SATELLiTES, a Synthon-based Approach for the Targeted Enumeration of Ligand Libraries and Expeditious Screening  
https://github.com/cbedart/SATELLiTES/  
2024 - To be published

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This software is under active development: if you find any bug, critical issue, or if you just want to make comments/suggestions to improve the tool, please leave a comment in the "Issues" section. Thanks in advance for your help!
astroglam 0.1.0
A code to leverage ALMA and JWST data to infer the gas density, metallicity, and burstiness of galaxies.
Requires Python: <4,>=3.9

# General purpose and references

GLAM is a Python software that will allow you to derive the gas density, metallicity, and deviations from the Kennicutt-Schmidt relation of a galaxy with known star formation rate surface density (Sigma_SFR), known [CII] surface brightness, and known surface brightness of an ionized gas tracer (e.g. Halpha, [OIII]5007, [OIII]88um, ...).
Details on the rationale, the model implementation, and on the equations are discussed in the following papers:
 
  - <a href="https://ui.adsabs.harvard.edu/abs/2021arXiv210605279V/abstract">Vallini et al. 2021</a>
  - <a href="https://ui.adsabs.harvard.edu/abs/2020MNRAS.495L..22V/abstract">Vallini et al. 2020</a> 
  - <a href="https://ui.adsabs.harvard.edu/abs/2019MNRAS.489....1F/abstract">Ferrara et al. 2019</a> 

# Requirements
The code is tested for Python >3.9.  It **requires** <a href="https://github.com/Morisset/PyNeb_devel">Pyneb</a>, 
<a href='https://emcee.readthedocs.io/en/stable'>emcee</a>,  <a href="https://corner.readthedocs.io/en/latest/index.html">corner</a>.

## Acknowledging this code in Scientific Publications

<div class="row codice">
<pre><code><span>@ARTICLE{Ferrara:2019,
       author = <span>{</span> {Ferrara}, A. and {Vallini}, L. and {Pallottini}, A. and {Gallerani}, S. and {Carniani}, S.
                 and {Kohandel}, M. and {Decataldo}, D. and {Behrens}, C.},
        title = "{A physical model for [C II] line emission from galaxies}",
      journal = {\mnras},
         year = 2019,
        month = oct,
       volume = {489},
       number = {1},
        pages = {1-12},
          doi = {10.1093/mnras/stz2031},
archivePrefix = {arXiv},
       eprint = {1908.07536},
 primaryClass = {astro-ph.GA},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2019MNRAS.489....1F},
   }</span></code>
</pre>
</div>


<div class="row codice">
<pre><code><span>@ARTICLE{Vallini2021,
       author = <span>{</span>{Vallini}, L. and {Ferrara}, A. and {Pallottini}, A. and {Carniani}, S. and {Gallerani}, S.},
        title = "{High [OIII]/[CII] surface brightness ratios trace early starburst galaxies}",
      journal = {arXiv e-prints},
     keywords = {Astrophysics - Astrophysics of Galaxies},
         year = 2021,
        month = jun,
          eid = {arXiv:2106.05279},
        pages = {arXiv:2106.05279},
archivePrefix = {arXiv},
       eprint = {2106.05279},
 primaryClass = {astro-ph.GA},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2021arXiv210605279V},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System},
}</span></code>
</pre>
</div>

## Funding
This work is supported by the ERC Advanced Grant INTERSTELLAR H2020/740120 (PI: Ferrara). 

Part of the work of LV has been supported by funding from the EU Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Grant agreement No. 746119.
scbk-mlops 0.3.4
Local version of MLOps intended for limited capabilities (no DB connection) under network segregation
Requires Python: >=3.10

# scbk_mlops

Local version of MLOps intended for limited capabilities (no DB connection) under network segregation

## Features

- 일반화된 데이터 Ingestion 단계
- 모형 뿐만 아닌 다양한 분석 과제에 활용 가능한 EDA 모듈
- Data 전처리 및 Engineering
- AI/ML 모형 학습
- AI/ML 모형 평가
- AI/ML 모형 모니터링

## Installation

```bash
pip install scbk_mlops