semvec/pypi-packages huggingface dataset:
django-database-conn-pool 1.1.1
django database backend pooling for connections
License: MIT
Keywords: django connection pooling mysql postgresql cx_Oracle

Push messages with different backends for the Django framework.
===============================================================

[![MIT License](https://img.shields.io/pypi/l/django-database-conn-pool.svg)](https://opensource.org/licenses/MIT)
[![django-database-conn-pool can be installed via wheel](https://img.shields.io/pypi/wheel/django-database-conn-pool.svg)](http://pypi.python.org/pypi/django-database-conn-pool/)
[![Supported Python versions.](https://img.shields.io/pypi/pyversions/django-database-conn-pool.svg)](http://pypi.python.org/pypi/django-database-conn-pool/)

|          |               |   
| ---------|:--------------| 
| Version  |1.1.0           | 
| Web      |               |  
| Download |<http://pypi.python.org/pypi/django-database-conn-pool>  |  
| Source   |<https://github.com/luojidr/django-database-conn-pool>   | 
| Keywords |django, database pool, MySQL, PostgreSQL, Oracle    | 


About
-----

MySQL & Oracle & PostgreSQL connection pool backends of Django, Be based on SQLAlchemy. 
Work fine in multiprocessing and multithreading django project.

Installation
------------

You can install django-database-conn-pool either via the Python Package Index
(PyPI) or from source.

To install using **pip**:

``` {.sh}
$ pip install -U django-database-conn-pool
```

and then add it to your installed apps:

``` {.python}
(1.1): INSTALLED_APPS = [
    ...,
    'database_pool',
    ...,
]

(1.2): DATABASES = {
    'default': {
        'ENGINE': 'django.db.backends.mysql',
        'NAME': 'my_test',
        'HOST': '127.0.0.1',
        'PORT': 3306,
        'USER': 'mysql_test',
        'PASSWORD': '123456',
        'POOL_OPTIONS' : {
            'POOL_SIZE': 10,
            'MAX_OVERFLOW': 15,
            'RECYCLE': 60 * 60
        }
    },
    ......
}

OR

(2): DATABASES = {
    'default': {
        'ENGINE': 'database_pool.backends.mysql',
        'NAME': 'my_test',
        'HOST': '127.0.0.1',
        'PORT': 3306,
        'USER': 'mysql_test',
        'PASSWORD': '123456',
        'POOL_OPTIONS' : {
            'POOL_SIZE': 10,
            'MAX_OVERFLOW': 15,
            'RECYCLE': 60 * 60
        }
    },
    ......
}
```

### Downloading and installing from source

Download the latest version of django-database-conn-pool from
<http://pypi.python.org/pypi/django-database-conn-pool>

You can install it by doing the following,:

    $ tar xvfz django-database-pool-0.0.0.tar.gz
    $ cd django-database-pool-0.0.0
    $ python setup.py build
    # python setup.py install

The last command must be executed as a privileged user if you are not
currently using a virtualenv.
astroslam 1.2022.1228.1
A forward model using SVR to estimate stellar parameters from spectra.
License: MIT

## SLAM [![DOI](https://zenodo.org/badge/161135292.svg)](https://zenodo.org/badge/latestdoi/161135292)

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)](https://ui.adsabs.harvard.edu/abs/2020ApJS..246....9Z/abstract).
Related projects: click [here](https://ui.adsabs.harvard.edu/abs/2020ApJS..246....9Z/citations).

## Author

Bo Zhang (bozhang@nao.cas.cn)

## Home page

- [https://github.com/hypergravity/astroslam](https://github.com/hypergravity/astroslam)
- [https://pypi.org/project/astroslam/](https://pypi.org/project/astroslam/)

## 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](https://zenodo.org/record/3461504)

## Tutorial
[updated on 2020-12-02]
- A new SLAM tutorial can be found [here](https://nbviewer.jupyter.org/github/hypergravity/spectroscopy/blob/main/stellar_parameters/demo_slam/demo_slam.ipynb)
- 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](https://search.datacite.org/works/10.5281/zenodo.3461504).