semvec/pypi-packages huggingface dataset:
gocardless-fintools 0.5
A CLI tool for managing bank account connections via GoCardless Bank Account Data API
Requires Python: >=3.6

# GoCardless Bank Account Manager

A command-line interface (CLI) tool for managing bank account connections and retrieving financial data using the GoCardless (formerly Nordigen) Bank Account Data API.

## Features

- 🏦 Browse and connect to banks by country
- 💰 Check account balances
- 📊 View and download transactions
- 🔄 Automatic token management
- 📥 Bulk transaction export to CSV
- 📥 Bulk transaction convert to CSV
- 🌍 Multi-country support
- 🔐 Secure credential management

## Prerequisites

- Python 3.6 or higher
- GoCardless Bank Account Data API credentials (Secret ID and Secret Key)
- Internet connection

## Installation

1. Clone this repository:
```bash
git clone https://github.com/thetombrider/gocardlessconnection
cd gocardlessconnection
```

2. Install required dependencies:
```bash
pip install -r requirements.txt
```

3. Create a `.env` file in the project root with your GoCardless credentials:
```plaintext
GOCARDLESS_SECRET_ID=your_secret_id
GOCARDLESS_SECRET_KEY=your_secret_key
```

## Initial Setup

Generate initial access tokens by running:
```bash
python generate_token.py
```

## Usage

### Browse and Connect to Banks
```bash
python connector.py browse-banks --country IT
```

### Check Account Balances
```bash
python connector.py check-balances --bank-id BANK_ID
```

### View Transactions
```bash
python connector.py check-transactions --bank-id BANK_ID
```

### Export Transactions to CSV
```bash
python connector.py export-transactions --bank-id BANK_ID
```

### Convert Transactions to CSV
```bash
python connector.py convert-transactions --bank-id BANK_ID
```
tsfuse 0.2.3
Automated feature construction for multiple time series data
Requires Python: <3.11,>=3.10

# TSFuse

Python package for automatically constructing features from multi-view time series data.

## Installation

TSFuse requires Python 3 and the following packages:

- Cython>=0.28.5
- numpy>=1.16.1

These packages can be installed using pip:

    pip install "cython>=0.28.5" "numpy>=1.16.1"
    
To install the latest unreleased version of TSFuse from GitHub:

    pip install git+https://github.com/arnedb/tsfuse#egg=tsfuse
    
## Documentation

The documentation is available on [https://arnedb.github.io/tsfuse/](https://arnedb.github.io/tsfuse/)

Examples on how to use TSFuse are shown in the [getting started](https://arnedb.github.io/tsfuse/getting-started.html) page and the [synthetic sine waves demo](notebooks/Synthetic%20Sine%20Waves%20Demo.ipynb) notebook.

## Paper

To learn more about TSFuse's feature construction method, read the following paper:

> Arne De Brabandere, Pieter Robberechts, Tim Op De Beéck and Jesse Davis. [Automating Feature Construction for Multi-View Time Series Data](https://www.google.com/url?q=https%3A%2F%2Fupvedues-my.sharepoint.com%2F%3Ab%3A%2Fg%2Fpersonal%2Fjorallo_upv_edu_es%2FETxycG2WhmFBmVN7CNW8yKsBQHwhhlzdyegEx1AnNeRa2w%3Fe%3DbPQR7e&sa=D&sntz=1&usg=AFQjCNH-zTIQtPE2M0m0h_uUPN_25SaGCw). ECML/PKDD Workshop on Automating Data Science 2019.
pcviewer 0.1.2
A simple tool for visualizing point cloud sequences
Requires Python: >=3.7

# PCViewer
        
        PCViewer是一个简单易用的点云数据可视化工具，支持动态点云序列的实时渲染和交互式查看。
        
        ## 安装
        
        ```bash
        pip install pcviewer
        ```
        
        支持Python 3.7及以上版本。如果你使用conda环境，建议先创建新的环境：
        
        ```bash
        conda create -n pcviewer python=3.9
        conda activate pcviewer
        pip install pcviewer
        ```
        
        ## 使用示例
        只需将需要可视化的点云数据传入`start_servers`函数即可启动可视化服务器，然后在浏览器中访问`http://localhost:8080`来交互式查看点云数据。
        
        ```python
        import numpy as np
        from PCViewer import start_servers
        
        # 创建示例点云数据
        n_frames = 30
        n_points = 1000
        
        # 生成旋转的立方体点云
        point_clouds = []
        colors = []
        for i in range(n_frames):
            angle = i * 2 * np.pi / n_frames
            points = np.random.rand(n_points, 3) * 2 - 1
            
            rotation = np.array([
                [np.cos(angle), -np.sin(angle), 0],
                [np.sin(angle), np.cos(angle), 0],
                [0, 0, 1]
            ])
            points = points @ rotation.T
            
            color = np.zeros((n_points, 3))
            color[:, 0] = points[:, 0] / 2 + 0.5
            color[:, 1] = points[:, 1] / 2 + 0.5
            color[:, 2] = points[:, 2] / 2 + 0.5
            
            point_clouds.append(points)
            colors.append(color)
        
        # 启动可视化服务器
        start_servers(np.array(point_clouds), np.array(colors))
        ```
        
        ### 参数说明
        
        `start_servers(point_clouds, colors=None, show_axes=True, show_rings=True)`
        
        - `point_clouds`: numpy数组或torch张量，形状为(n_frames, n_points, 3)，表示点云序列数据
        - `colors`: numpy数组，形状为(n_frames, n_points, 3)，表示点云颜色，可选参数。各颜色通道值范围为[0, 1]，使用RGB格式
        - `show_axes`: 布尔值，是否显示坐标轴，默认为True
        - `show_rings`: 布尔值，是否显示辅助环，默认为True
        
        ## 运行效果
        
        ![PCViewer Demo](demo.gif)
slt_datasets 0.0.2rc45.post1
Multilanguage datasets for Sign Language Translation.
Requires Python: >=3.9

# SLT Datasets Downloader

## Overview

SLT Datasets Downloader is a Python library that allows users to download and process multiple sign language translation (SLT) datasets from different languages. It is designed to facilitate the training of machine learning models for SLT tasks.

## Features
- Supports multiple sign language datasets.
- Provides tools for downloading, preprocessing, and tokenizing datasets.
- Compatible with PyTorch for model training.
- Handles different input types (video, pose) and output types (text, gloss).

## Supported Datasets
The following datasets are supported:

| Name | Input Language | Target Language | Status | # Samples | Hs | Video | Pose | Transcription | Gloss | Other data | # Words | # Singletons | # Signers | Source | BLEU (4) |
|------|---------------|----------------|--------|-----------|----|-------|------|--------------|-------|------------|---------|--------------|----------|--------|----------|
| RWTH-PHOENIX Weather 2014 T | GSL | German | Downloaded | 7,096 | 10 | Yes | No | Yes | Yes | - | 2,887 | 1,077 | 9 | TV | 28.95 |
| LSA-T | LSA | Spanish | Downloaded | 14,880 | 22 | Yes | Yes (MP) | Yes | No | - | 14,239 | 7,150 | 103 | Web | 0.93 (WER) |
| How2Sign | ASL | English | Downloaded | 35,000 | 80 | Yes | Yes | Yes | Yes | Multiple angles, 3D pose, Speech | 16,000 | - | 11 | - | 8.03 |
| LSFB-CONT | French-Belgian SL | French-Belgian Glosses | Downloaded | 85,000 | 25 | Yes | Yes (MP) | No | Yes | - | 6,883 | - | 100 | Laboratory | - |
| ISLTranslate | Indian SL | English | Downloaded | 31,222 | - | Yes | Yes (MP) | Yes | No | - | 11,655 | - | - | - | 6.09 |
| GSL | Greek SL (GSL) | Greek | Downloaded | 40,826 | 10 | Yes | No | No | Yes | Depth | 310 | 0 | 7 | Laboratory | 20.62 (WER) |                           |

## Installation
To install the library and its dependencies, run:
```bash
pip install -r requirements.txt
```

## Usage
### Loading a Dataset
```python
from slt_datasets.SLTDataset import SLTDataset

dataset = SLTDataset(
    data_dir="/path/to/dataset",
    input_mode="video",
    output_mode="text",
    split="train"
)
```
### Accessing Samples
```python
sample_input, sample_output = dataset[0]
```
### Visualizing Pose Data
```python
dataset.visualize_pose(0)
```

## Project Structure
```
 |-src
 | |-slt_datasets
 | | |-SLTDataset.py  # Main dataset loader
 | | |-WordLevelTokenizer.py  # Tokenizer for text processing
 | | |-dataset_comparison.ipynb  # Notebook for dataset comparison
 |-tests
 | |-test_methods.py  # Unit tests for the dataset loader
 |-docs  # Documentation files
 |-requirements.txt  # Dependency list
 |-pyproject.toml  # Project configuration
 |-README.md  # This file
```

## Contributing
Contributions are welcome! Please follow the guidelines in `CONTRIBUTING.md` and ensure your code passes all tests before submitting a pull request.

## License
This project is licensed under the terms of the `LICENSE` file.

## Support
For any issues or questions, please refer to `SUPPORT.md` or open an issue in the repository.