semvec/pypi-packages huggingface dataset:
weltschmerz 1.1.0rc1
local anime file database with deduplication and sorting
Requires Python: >=3.6

# weltschmerz

Maintain a (local) database for anime files.
Contains scripts to hash and sort anime files,
as well as looking up files in AniDB utilizing the `yumemi` Python module.

This project is licensed under the terms of the MIT license. These third-party files are included:

* `pyselect.py`: Copyright 2013 Matthew Behrens, under the terms of the MIT license.
* `sorttable.js`: Copyright 2007 Stuart Langridge, under the terms of the X11/MIT license.

See also [LICENSE.md](./LICENSE.md)

## Requirements

* Python 3 (>=3.6)
* [yumemi](https://pypi.org/project/yumemi/) Python module (optional, for interacting with AniDB)
* SQL database
  * PostgreSQL works, and is recommended
  * SQLite3 works, but does not support the `numeric` collation, order for T/S/P/O episodes might be wrong

## Configuration file

Copy `weltschmerz.cfg.example` to `weltschmerz.cfg` or `$HOME/.config/weltschmerz/weltschmerz.cfg` and adjust as necessary.

## Import AniDB MyList export

It is recommended to import your MyList first, as that will populate the Anime, Episode, and File tables.

1. Request an `xml-plain-full` export of your MyList at [AniDB - MyList Export](https://anidb.net/user/export)
1. Download export file
1. Extract the export file:`tar xf <mylist_export>.tgz`
1. Parse the export and populate the local database: `./parse_mylist_export.py --mylist-xml-files anime/a*.xml`

## Hash local files

`weltschmerz files hash --folder /anime/by-id/00/00/01/ --folder /anime/by-id/00/00/02/`

## Add files to your AniDB MyList

Lookup the local files in AniDB and add them to your MyList, if they are present in AniDB.
`weltschmerz anidb lookup-files --folder /anime/by-id/00 --folder /anime/by-id/01 --debug --online --add-to-mylist --mylist-state 4`

## Deduplicate local files

Interactively deduplicate local files, as well as set the modified time of duplicate files to the oldest one.
`weltschmerz files remove-duplicates --preferred-directory-pattern /anime/by-id`

## Sort local files

Sort/Move files, this will put the files into folders according to their anime ids (padded to 6 digits) from AniDB.
e.g. files for [Seikai no Monshou (anime id 1)](https://anidb.net/anime/1) will be put in `<target_basedir>/by-id/00/00/01`.

`weltschmerz files sort --source-basedir /anime/unsorted --target-basedir /anime`

## Create symlinks for local anime folders

To be able to look for local anime by name, create symlinks to point to the folders created by "Sort local files".
This creates a `by-name` folder under the target basedir.

1. Download anime titles dump (XML) from AniDB, see [AniDB Wiki - API/Anime Titles](https://wiki.anidb.net/API#Anime_Titles) for instructions.
1. Decompress the title dump: `gunzip anime-titles.xml.gz`
1. Run script to create the symlinks: `./animetitle.py anime-titles.xml /anime/`
zhpych 0.1.0
增强版中文Python语法解释器，全面支持中文变量名、函数名、类名
Requires Python: >=3.8
Keywords: chinese python, cnpy plus, 中文编程, 中文变量名, 中文函数名

# cnpy_plus - 增强版中文Python语法解释器
`cnpy_plus` 是一款轻量级中文Python语法解释器，**全面支持中文变量名、中文函数名、中文类名**，可将中文语法代码无缝转换为标准Python代码并直接执行，覆盖Python所有核心语法。

## 核心特性
1.  **中文标识符全面支持**：变量名、函数名、类名均可使用纯中文（如`学生姓名 = "小明"`、`定义 计算总和()`、`类 学生()`）
2.  **全Python语法覆盖**：条件判断、循环、函数、类、异常处理、异步语法等均支持中文关键字
3.  **内置函数中文映射**：默认支持`打印()`、`输入()`、`长度()`等常用内置函数的中文调用
4.  **无缝转换与执行**：一键转换中文代码为Python代码，也可直接执行中文代码并返回执行结果
5.  **友好错误提示**：提供行号、列号的精准语法错误提示，便于调试

## 安装方式
### 方式1：从PyPI安装（推荐）
```bash
pip install zhpych
talenWF 0.1.2
Modern TALEN window finder compatible with MitoEdit. Made as a part of the St. Jude KIDS25 BioHackathon Project. This tool is an effort to revamp and modernize the TAL Effector-Nucleotide Targeter (TALE-NT) 2.0 (Doyle et al., 2012, Nucleic Acids Research)
Requires Python: >=3.9
Keywords: TALEN, genome editing, bioinformatics, mitochondria

# talenWF

A minimal TALEN window finder packaged for CLI and API use, intended to be a drop-in for MitoEdit's TAL finding functionality.

## Features

- 🔍 **TALEN Window Finding**: Efficiently finds TALEN binding sites in DNA sequences
- 🐍 **Python API**: Easy-to-use class-based interface
- 💻 **Command Line Interface**: Ready-to-use CLI tool
- 🔄 **MitoEdit Compatible**: Drop-in replacement for MitoEdit's TAL finding functionality
- 📊 **Pandas Integration**: Returns results as DataFrames for easy analysis
- 🧬 **BioPython Support**: Robust FASTA file handling
- ⚡ **Memory Efficient**: Generator-based processing for large sequences

## Installation

### From PyPI (recommended)

```bash
pip install talenWF
```

### From source (development)

```bash
git clone https://github.com/yourusername/talenWF.git
cd talenWF
pip install -e .
```

### With development dependencies

```bash
pip install talenWF[dev]
```

## CLI

```bash
talenWF-findtal --fasta /path/to/seq.fasta --min 14 --max 18 --arraymin 14 --arraymax 18 --outpath /tmp/talenWF.tsv
```

## API

```python
from talenWF import FindTALTask

# Using the modern class-based API
task = FindTALTask(
    fasta="/path/to/seq.fasta",
    min_spacer=14,
    max_spacer=18,
    array_min=14,
    array_max=18,
    outpath="/tmp/talenWF.tsv"
)
df = task.run()
```

## Citation

If you use talenWF in your research, please cite the original TALE-NT 2.0 tool:

```
Doyle, E. L., Booher, N. J., Standage, D. S., Voytas, D. F., Brendel, V. P., VanDyk, J. K., & Bogdanove, A. J. (2012). 
TAL Effector-Nucleotide Targeter (TALE-NT) 2.0: tools for TAL effector design and target prediction. 
Nucleic Acids Research, 40(W1), W117-W122. https://doi.org/10.1093/nar/gks608
```

### About This Project

talenWF is a modernized implementation of the TALE-NT 2.0 algorithm, developed as part of the St. Jude KIDS25 BioHackathon Project. This tool provides a Python-based, memory-efficient alternative to the original TALE-NT 2.0 web interface, with improved performance and easier integration into bioinformatics workflows.

**Key Improvements:**
- Modern Python implementation with class-based API
- Memory-efficient generator-based processing
- Command-line interface for batch processing
- Pandas integration for data analysis
- BioPython support for robust sequence handling

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.