semvec/pypi-packages huggingface dataset:
vectorlab 1.0.3
A lab for vectors.
Requires Python: >=3.8

<p align="center">
  <img src="https://raw.githubusercontent.com/Kr4t0n/vectorlab/main/static/logo.png"/>
</p>

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[![Python Version](https://img.shields.io/pypi/pyversions/vectorlab)](https://pypi.org/project/vectorlab/)

VectorLab is a library to help solve scientific and engineering problems of mathematics and machine learning. It is built upon various fabulous softwares and tries to fill the gap when playing around with those softwares.

VectorLab consists of many modules including data generation, graph processing, series processing, statistics, optimization, ensemble mechanism and more.

VectorLab is distributed under the 3-Clause BSD license.

## Installation

### Dependencies

VectorLab supports **Python 3.8+** only.

### PyPI Installation

The lastest version of VectorLab could be installed from PyPI.

```
pip install -U vectorlab
```

VectorLab also needs PyTorch and PyG support. It is highly
**recommended** to handle these two package installations **manually**. 
If you only care for the CPU version of PyTorch and PyG, you can
also install vectorlab from PyPI with extensions.

```
pip install -U -e vectorlab[.full]
```

## Development

We welcome contributions from all sources.

### Source code

You can check the latest commit with *git*:

```
git clone https://github.com/Kr4t0n/vectorlab.git
```

### Testing

After the regular installation, you will need *pytest* to test VectorLab. You can install corresponding dependencies with:

```
pip install -U -e vectorlab[.test]
```

You can launch the test suite with:

```
pytest vectorlab
```
maid-sweeper 0.6.96
A maid that sweeps your files
Requires Python: >=3.10

# Maid Sweeper for files

Instead of cleaning the unused files, it calls a maid to label them and sweep them under the rug accordingly.

However, the maid can practice Danshari given permission. For example, she can [sell your unused iPad for money](https://comic-days.com/episode/3269754496647364302).

Like Toki, she has two modes:

`tag`: Label the files/directories automatically, based on their types and names.

- code projects and application directories are labeled, and their children are not scanned
- others are labeled based on the extensions

`sweep`: Carry out actions based on the labels`.

## Feature

* AsyncIO, so all operations are in parallel
* MongoDB for fast indexing
* Save time by not scanning every single file inside code and program directories and not checking the metadata
* Kyoufu

## Installation

1. Have Python 3.10.
2. Install by running `pip install maid-sweeper`.

## Usage

1. Start a MongoDB instance.
2. Run `maid-sweeper tag D:\Study`, then you can find tagged entries in the database. Sweeping works on all directories tagged.
3. Run `maid-sweeper sweep -t video game -x del \q \f {}`, and the maid is going to remove all 'video' or 'game' tagged files and directories.
    * Any other commands is OK as well
    * By default the maid will do a sweep -- remove all those files. Kyoufu!

## Ideas

- Multithreading
- Remove type hints
- Tags based on time
    * How does it affect other tags? If not why bother?
    * Maybe not tag, but just metadata
    * There will be IO cost
- Group similarly named files: 01.jpg, 02.jpg, etc.
- Understand human language so they can toss away garbage
- Optionally clean up the database after sweeping.
- Single line mode: do the tag, sweep, and clean up database entries with a single command.
- Allow users to specify tags