semvec/pypi-packages huggingface dataset:
tablelinker-light 1.0.0.post2
TableLinker: CSV data processing library (light version)
Requires Python: <4.0,>=3.7
Keywords: CSV, tabular, cleansing, batch processing

# tablelinker-light

![Python 3.7](https://github.com/InfoProto/tablelinker-light/actions/workflows/python-3.7.yml/badge.svg)
![Python 3.8](https://github.com/InfoProto/tablelinker-light/actions/workflows/python-3.8.yml/badge.svg)
![Python 3.9](https://github.com/InfoProto/tablelinker-light/actions/workflows/python-3.9.yml/badge.svg)
![Python 3.10](https://github.com/InfoProto/tablelinker-light/actions/workflows/python-3.10.yml/badge.svg)
![Python 3.11](https://github.com/InfoProto/tablelinker-light/actions/workflows/python-3.11.yml/badge.svg)
![Python 3.12](https://github.com/InfoProto/tablelinker-light/actions/workflows/python-3.12.yml/badge.svg)


TableLinker をコマンドライン / プログラム組み込みで利用するための
ライブラリ Tablelinker-lib の軽量版派生バージョンです。

オリジナルの Tablelinker-lib は国立情報学研究所より
https://github.com/NII-CPS-Center/tablelinker-lib
で公開されており、 MIT ライセンスで利用可能です。

## インストール手順

Poetry を利用します。

```
$ poetry install --with group=dev
$ poetry shell
```

## コマンドラインで利用する場合

tablelinker モジュールを実行すると、標準入力から受け取った CSV を
コンバータで変換し、標準出力に送るパイプとして利用できます。

```
$ cat sample/datafiles/yanai_tourism.csv | \
  python -m tablelinker sample/taskfiles/task.json
```

利用するコンバータと、コンバータに渡すパラメータは JSON ファイルに記述し、
パラメータで指定します。

## 組み込んで利用する場合

`sample.py` を参照してください。
OrthoBase 0.1
A module to perform the multiplet decomposition using Young tables and construct the corresponding projectors
Requires Python: >=3.10

# OrthoBase

A Python library for constructing multiplet projectors in the decomposition of
the product of adjoint representations in the SU(N_c) group. The library also
provides numerous methods to work with Young tableaux. The software achieves
high efficiency through a combination of the
[Z3](https://www.microsoft.com/en-us/research/project/z3-3/) satisfiability
modulo theories solver and the symbolic manipulation program
[FORM](https://www.nikhef.nl/~form/).

Key features:
- Construct multiplet projectors for the decomposition of adjoint
representation products in SU(N_c) groups
- Efficient algorithms implemented using the Z3 solver and FORM symbolic
manipulation
- Comprehensive set of methods for working with Young tableaux
- Easy-to-use Python interface for seamless integration into your projects
- Well-documented code and extensive online documentation for quick start and
reference

The library's toolset provides researchers working in fields involving SU(N_c)
groups and Young tableaux with a powerful and efficient means to streamline
their work in these domains.

# Installation

OrthoBase requires Python version 3.10 or newer and can be installed on any
Unix-like operating system.

The latest release can be installed from [PyPI] using [pip]:

	$ python3 -m pip install --user --upgrade OrthoBase

This will install the library with all its python dependencies. There are
additional dependencies described below. They should be installed manually or
using the package manager of your operating system.

[pip]: https://pypi.org/project/pip/
[pypi]: https://pypi.org/project/OrthoBase/

## Additional dependencies

These dependencies are not automatically installed via pip. Therefore, users
must manually install them and ensure that the relevant files are included in
the `$PATH` and `$LD_LIBRARY_PATH` environment variables:

- FORM (http://www.nikhef.nl/~form/)
- An implementation of MPI (Message Passing Interface) should be installed. Some popular implementations include:
  - [Open MPI](https://www.open-mpi.org/)
  - [MPICH](https://www.mpich.org/)
  - [Intel MPI](https://software.intel.com/en-us/mpi-library)
  - [Microsoft MPI](https://docs.microsoft.com/en-us/message-passing-interface/microsoft-mpi)

# Usage

Refer to the [online documentation](orthobase.readthedocs.io) of our package.

You can run the example calculation as follows:

	$ ./demo.py