semvec/pypi-packages huggingface dataset:
astroabc 1.5.0
A Python implementation of an Approximate Bayesian Computation Sequential Monte Carlo (ABC SMC) sampler for parameter estimation.
License: MIT


Approximate Bayesian computation (ABC) and so 
called "likelihood free" Markov chain Monte Carlo 
techniques are popular methods for tackling parameter 
inference in scenarios where the likelihood is intractable or unknown. 
These methods are called likelihood free as they are free from 
the usual assumptions about the form of the likelihood e.g. Gaussian, 
as ABC aims to simulate samples from the parameter posterior distribution directly.
``astroABC`` is a python package that implements  
an Approximate Bayesian Computation Sequential Monte Carlo (ABC SMC) sampler 
as a python class. It is extremely flexible and applicable to a large suite of problems. 
``astroABC`` requires ``NumPy``,``SciPy`` and ``sklearn``. ``mpi4py`` and ``multiprocessing`` are optional.
tdtypes 0.5.1
Database Types for Teradata
License: MIT
Requires Python: >=3.7
Keywords: teradata

# tdtypes

[![PyPi](https://img.shields.io/pypi/v/tdtypes.svg)](https://pypi.python.org/pypi/tdtypes) [![MIT License](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT) ![Python3.7+](https://img.shields.io/pypi/pyversions/tdtypes.svg)

[tdtypes](https://bitbucket.org/padhia/tdtypes) is a Python library provides an abstraction layer for Teradata Database objects and utilities. By default It uses [teradatasql](https://pypi.org/project/teradatasql/), but generally, it should be possible to use any [DB API](https://www.python.org/dev/peps/pep-0249/) compliant library.

*NOTES:*

-   This started as a personal project, but it is being made available as an open-source library in the hope that someone else might find it useful. This library does not come with any kind of warranty.
-   *Python2* series is no longer supported.
-   Although no longer the default, but older [teradata](https://pypi.python.org/pypi/teradata/) and [pyodbc](https://github.com/mkleehammer/pyodbc) modules can still be used.
-   This library is not endorsed by [Teradata Inc](http://www.teradata.com/).

## Installation

Use Python's `pip` utility to install `tdtypes`.

    $ python -m pip install -U tdtypes

## Customizations

It is possible to customize how **tdtypes** library obtains a raw database connection. This might be helpful, for example, to use DB API modules other than `teradatasql`, or get authentication information in ways other than the default implementation. This can be done by creating `tdconn_site.py` module and place it in your `PYTHONPATH`. Please consult `tdconn_default.py` module for guidance on creating your own custom module.

The default implementation allows applications to either pass connection information as a parameter or get connection information from `TDCONN` environment variable at run-time. Either way, connection information consists of a valid *JSON* string as expected by [teradatasql](https://pypi.org/project/teradatasql/) module.

Another customization allows how **tdtypes** library searches for and obtains Table and View XML definitions. The default implementation uses *DBC.TablesV* for searching, and `SHOW ... IN XML` SQL statement to get XML definition.  Placing *tdfinder_site.py* in `PYTHONPATH` will override the default search and instantiation behaviors. To create a custom *tdfinder_site.py* module, please consult *tdfinder_default.py*.

## Support

Report bugs using [issue tracker](https://bitbucket.org/padhia/tdtypes/issues?status=new&status=open). I'll try to provide a fix as soon as I can. If you have already developed a fix, send me a pull request.

## Contributions

Feel free to fork this repository and enhance it in any way you see fit. If you think your changes will benefit more people, send me a pull request.