semvec/pypi-packages huggingface dataset:
arg-lmm 0.1.1
Efficient complex trait analyses from ARG
Requires Python: >=3.9

# arg-lmm

This repository contains the source code for the ARG-RHE software for scalable variance component analysis using ancestral recombination graphs (ARGs), as well as other ARG-based linear mixed model analyses implemented in the ARG-LMM package. These methods are described in Zhu, Kalantzis et al., Cell Genomics, 2025.

For full usage guide, please refer to the [arg-lmm manual](https://palamaralab.github.io/software/arglmm/manual/). The source code, which relies on the arg-needle-lib library, is available in [this repository](https://github.com/palamaraLab/arg-lmm). Additional scripts to reproduce the ARG-based linear mixed model analyses reported in Zhang et al., *Nature Genetics*, 2023 are available in [this repository](https://github.com/PalamaraLab/ARG-LMM_experiments).

This package can be installed from PyPI, for example:

```bash
pip install arg-lmm
```

## Licenses

The `liu_sf` method is copied from `chiscore` under MIT License instead of using PyPI due to a broken `chi2comb` dependency.

## Citation

> Zhu, Kalantzis, et al. (2025), "Fast variance component analysis using large-scale ancestral recombination graphs"
https://doi.org/10.1101/2024.08.31.610262
deltares-coastal-structures-toolbox 1.0.2
A Python toolbox with (empirical) formulas for the design of Coastal Structures.
Requires Python: >=3.10

# Deltares Coastal Structures Toolbox

The Deltares Coastal Structures Toolbox package implements a large selection of (design) formulas for coastal structures. The intent is to make applying these formulas as easy as possible for the user, so most formulas are implemented in multiple ways to enable the calculation of different variables. When applying the formulas from the toolbox, the validity range of those formulas (where available) is checked and a warning is given when the formula is used outside its validity range (including which variable is out of range). The toolbox contains a significant number of tests to ensure the enduring quality of the implementations.

## How to install the toolbox?

The package is available at pip. To install the package run:

`pip install deltares-coastal-structures-toolbox`

## Where to find information about the Deltares Coastal Structures Toolbox?

See the online manual (https://deltares-coastal-structures-toolbox.readthedocs.io/en) for information about the toolbox. In addition, the examples folder contains scripts with applications of the toolbox and a Jupyter notebook is available in the documentation folder.

## Credits

* Joost den Bieman <joost.denbieman@deltares.nl>
* Ivo van der Werf <ivo.vanderwerf@deltares.nl>
fourteen-prompt-toolkit-action-completer 1.2.2
A fairly simple method for registering callables as prompt-toolkit completions (Fourteen fork)
Requires Python: ~=3.12
Keywords: action, completer, prompt, prompt-toolkit, toolkit, validator

# Action Completer (Fourteen Fork)

[![Supported Versions](https://img.shields.io/pypi/pyversions/fourteen-prompt-toolkit-action-completer.svg)](https://pypi.org/project/fourteen-prompt-toolkit-action-completer/)
[![Test Status](https://github.com/Fourteen-IP/prompt-toolkit-action-completer/workflows/Test%20Package/badge.svg)](https://github.com/Fourteen-IP/prompt-toolkit-action-completer/actions?query=workflow%3A%22Test+Package%22)
[![Code Style: Black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://black.readthedocs.io/en/stable/)

**A fairly simple method for registering callables as prompt-toolkit completions.**

> **Note:** This is a fork of [stephen-bunn/prompt-toolkit-action-completer](https://github.com/stephen-bunn/prompt-toolkit-action-completer) maintained by Fourteen-IP with additional features and improvements.

This package provides the basic features to easily construct a custom completer using
decorators to reduce the amount of boilerplate needed to build basic tooling through
[prompt-toolkit](http://python-prompt-toolkit.readthedocs.io/en/stable).
A quick example is provided below, but if you are interested in the available features
and patterns you should read through [the documentation](https://prompt-toolkit-action-completer.readthedocs.org/).

This is a project that I originally created for myself several times when building
personal utilities with prompt-toolkit, and figured it might be useful for other people
to eventually use or extend.
As a side-effect of this being a personal utility, the provided functionality may not
*exactly* fit what you are looking for and the provided tests do not check all edge
cases properly yet.

```python
from pathlib import Path

from action_completer import ActionCompleter
from prompt_toolkit.shortcuts import prompt
from prompt_toolkit.completion import PathCompleter
from prompt_toolkit.validation import Validator

completer = ActionCompleter()


@completer.action("cat")
@completer.param(
  PathCompleter(),
  cast=Path,
  validators=[
      Validator.from_callable(
        lambda p: Path(p).is_file(),
        error_message="Path is not an existing file"
      )
  ]
)
def _cat_action(filepath: Path):
  with filepath.open("r") as file_handle:
      print(file_handle.read())


prompt_result = prompt(
  ">>> ",
  completer=completer,
  validator=completer.get_validator()
)
completer.run_action(prompt_result)
```

![Example Recording](docs/source/_static/assets/recordings/004-cat-path-validation.gif)
aplicacion-ventas-wcantaro 0.1.0
Paquete para gestionar ventas, precios, impuestos y descuentos
Requires Python: >=3.7

# Aplicacion de Ventas

Este Paquete proporciona funcionalidades para gestionar ventas, incluyendo cÃ¡lculos
de precios, impuestos y descuentos

## Instalacion

Puedes instalar el Paquete usando:

```bash
pip install .
"
meyelens 0.2.0
Gaze-tracking and pupillometry with MEYE
Requires Python: >=3.8

# MEYElens





Quick checklist – fresh install of WSL + GPU-enabled TensorFlow (Windows 11 / Windows 10 21H2+)

    All commands are meant for PowerShell (admin) or Ubuntu 22.04 inside WSL as noted.

1. Install WSL and Ubuntu

    Open PowerShell as Administrator

    wsl --install

        Reboot when prompted.

    Launch Ubuntu from the Start menu and create a Linux username and password.

2. Install NVIDIA WSL GPU driver on Windows

    Download “CUDA enabled driver for WSL” from NVIDIA’s site, run the installer, then reboot Windows.

3. Prepare Ubuntu repositories (inside WSL)

# import NVIDIA key
sudo mkdir -p /usr/share/keyrings
curl -fsSL https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-archive-keyring.gpg \
 | sudo tee /usr/share/keyrings/cuda-archive-keyring.gpg >/dev/null

# CUDA repo
echo "deb [signed-by=/usr/share/keyrings/cuda-archive-keyring.gpg] \
https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/ /" \
 | sudo tee /etc/apt/sources.list.d/cuda.list

# cuDNN repo
echo "deb [signed-by=/usr/share/keyrings/cuda-archive-keyring.gpg] \
https://developer.download.nvidia.com/compute/cudnn/repos/ubuntu2204/x86_64/ /" \
 | sudo tee /etc/apt/sources.list.d/cudnn.list

sudo apt update

4. Install minimal CUDA 12.4 runtime and cuDNN 8.9

sudo apt install -y cuda-compat-12-4 libcudnn8 libcudnn8-dev

Add runtime path:

echo 'export LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/lib/wsl/lib:$LD_LIBRARY_PATH' >> ~/.bashrc
source ~/.bashrc
sudo ldconfig

Verify cuDNN appears:

ldconfig -p | grep cudnn | head

5. Install Miniconda and TensorFlow

# Miniconda (run once)
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh
source ~/.bashrc

# create env and install TF built for CUDA 12 + cuDNN 8.9
conda create -n tf python=3.10 -y
conda activate tf
pip install --upgrade pip
pip install --no-deps tensorflow==2.16.2

6. Confirm everything

# driver visible?
nvidia-smi

# TensorFlow sees GPU?
python - <<'PY'
import tensorflow as tf
print("TF:", tf.__version__)
print("GPUs:", tf.config.list_physical_devices('GPU'))
PY
python-st3215 1.1.0
A Python library for interfacing with the ST3215 smart servo.
Requires Python: >=3.8

# Python-ST3215

[![wakatime](https://wakatime.com/badge/user/b67f4ae3-1ee8-40ea-b8d8-196354064008/project/2d953882-4dfb-4c53-8a18-c698dc275f82.svg)](https://wakatime.com/badge/user/b67f4ae3-1ee8-40ea-b8d8-196354064008/project/2d953882-4dfb-4c53-8a18-c698dc275f82)
[![PyPI Version](https://img.shields.io/pypi/v/python-st3215)](https://pypi.org/project/python-st3215/)
![Python Versions](https://img.shields.io/pypi/pyversions/python-st3215)
![License](https://img.shields.io/github/license/alessiodam/python-st3215)
![Downloads](https://img.shields.io/pypi/dm/python-st3215)
[![Issues](https://img.shields.io/github/issues/alessiodam/python-st3215)](https://github.com/alessiodam/python-st3215/issues)

Python-ST3215 is a lightweight and intuitive Python library for communicating with ST3215 Smart Servos over a serial bus.
It provides a high-level interface for reading and writing servo parameters, controlling motion, and working with the servo memory map.


## Features

* Simple API for controlling ST3215 servos
* High-level wrapper for movement and parameter access
* Helpful exceptions for robust applications
* Fully typed and documented through docstrings
* Tested against Waveshare ST3215 hardware


## Installation

Install via pip:

```bash
pip install python-st3215
```


## Quick Start

```python
from python_st3215 import ST3215, ServoNotRespondingError

controller = ST3215("/dev/ttyUSB0")

try:
    servo = controller.wrap_servo(1)
    print("Current location:", servo.sram.read_current_location())
except ServoNotRespondingError:
    print("Servo not responding")
finally:
    controller.close()
```


## Examples

A collection of example scripts is available in the [`examples/`](examples) directory.
These cover tasks such as motion control, serial communication tests, reading/writing parameters, and more.

## Documentation

The library is fully documented through docstrings.
Hover over classes and functions in your editor to see type hints, parameter descriptions, and usage notes.

## Hardware Compatibility

| Brand     | SKU   | Product                        |
|-----------|-------|--------------------------------|
| Waveshare | 22414 | ST3215 Series Serial Bus Servo |

## Memory Table

The complete memory map is documented in [`MEMORY_TABLE.md`](MEMORY_TABLE.md).

## License

This project is licensed under the **GPL-3.0-or-later** license.
See the [`LICENSE`](LICENSE) file for full details.

## Supporting the Project

If this library has been useful to you, consider supporting its development:
**[https://ko-fi.com/alessiodam](https://ko-fi.com/alessiodam)**
python-docs-theme 2025.12
The Sphinx theme for the CPython docs and related projects
Requires Python: >=3.12

# Python Docs Sphinx Theme

This is the theme for the Python documentation.
It requires Python 3.12 or newer and Sphinx 7.3 or newer.

Note that when adopting this theme, you're also borrowing an element of the
trust and credibility established by the CPython core developers over the
years. That's fine, and you're welcome to do so for other Python community
projects if you so choose, but please keep in mind that in doing so you're also
choosing to accept some of the responsibility for maintaining that collective
trust.

To use the theme, install it into your docs build environment via `pip`
(preferably in a virtual environment).


## Configuration options

To use this theme, add the following to `conf.py`:

- `html_theme = 'python_docs_theme'`

- `html_sidebars`, defaults taken from https://www.sphinx-doc.org/en/master/usage/configuration.html#confval-html_sidebars


## Translation

This theme supports localization. For more information see the [Python Developer's
Guide](https://devguide.python.org/documentation/translations/translating/#how-do-i-translate-the-python-docs-sphinx-theme).


## Preview

See a demo of the CPython docs using this theme:

- https://python-docs-theme-previews.readthedocs.io

The kitchen sink is a showcase of every Sphinx feature:

- https://sphinx-themes.org/sample-sites/python-docs-theme/kitchen-sink/
dkarchiver 1.0.1
Den K Simple Installer
Requires Python: >=3.12

<h1 align="center">Den K's Archiver - dkarchiver</h1>



<!-- ABOUT THE PROJECT -->
## About The Project


Den K's Archiver contains a set of scripts to help you use different archive formats in a simple way.
There is also a 'search_in_archive' function that can search for a file using a callable function provided by the user inside an archive and extract only it.


<!-- GETTING STARTED -->
## Getting Started

To get a local copy up and running follow these simple steps.

### Installation

1. Install Python.
   <br>
2. Install the library using pip:
   ```cmd
   pip install dkarchiver
   ```
   <br>


<!-- LICENSE -->
## License

Distributed under the MIT License. See `LICENSE.txt` for more information.



<!-- HISTORY -->
## History

[History.md](https://github.com/BugSec-Official/atomicshop/blob/main/History.md#history)
betalens 1.0.0
量化分析与回测框架
Requires Python: >=3.8

# Betalens

[![Python Version](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

**Betalens** 是一个用于量化分析和回测的 Python 框架，包含因子分析、数据获取、回测、绩效分析、稳健性检验等核心模块，适用于量化研究和策略开发。

## ✨ 特性

- 📊 **因子分析** - 支持单因子/多因子分组、打标签、生成多空权重
- 📈 **数据管理** - PostgreSQL 数据库接口，支持时间序列查询、EDE格式解析
- 🔄 **回测框架** - 多资产多权重回测，自动获取价格数据
- 📋 **绩效分析** - 计算夏普比率、最大回撤、年化收益等指标，生成报告
- 🧪 **稳健性检验** - 因子增量检验、Bootstrap 重采样

## 📦 安装

```bash
git clone https://github.com/Janiszzz/betalens.git
cd betalens
pip install -r requirements.txt
```

或使用 pip 安装（开发模式）：

```bash
pip install -e .
```

## 🚀 快速开始

```python
import pandas as pd
from betalens.datafeed import Datafeed, get_absolute_trade_days
from betalens.backtest import BacktestBase
from betalens.analyst import PortfolioAnalyzer, ReportExporter

# 1. 获取数据
data = Datafeed("daily_market_data")
date_ranges = get_absolute_trade_days('2020-01-01', '2024-01-01', 'W')

params = {
    'codes': ['000001.SZ'],
    'datetimes': date_ranges,
    'metric': "收盘价(元)",
    'time_tolerance': 48
}
price = data.query_nearest_before(params)

# 2. 构建权重
weights = pd.DataFrame(...)  # 你的权重逻辑
weights['cash'] = 1 - weights.sum(axis=1)

# 3. 回测
bb = BacktestBase(weight=weights, symbol="", amount=1000000)
bb.nav.plot()

# 4. 绩效分析
analyzer = PortfolioAnalyzer(bb.nav)
exporter = ReportExporter(analyzer)
exporter.generate_annual_report()
```

## 📁 项目结构

```
betalens/
├── betalens/              # 主包
│   ├── analyst/           # 绩效分析模块
│   ├── backtest/          # 回测模块
│   ├── datafeed/          # 数据管理模块
│   ├── factor/            # 因子分析模块
│   └── robust/            # 稳健性检验模块
├── docs/                  # 文档
├── examples/              # 示例代码
├── tests/                 # 测试代码
├── requirements.txt       # 依赖列表
├── setup.py              # 安装脚本
└── README.md             # 本文件
```

## 📖 文档

详细文档请访问 [docs/](docs/) 目录：

- [快速开始](docs/getting-started.md)
- [Datafeed 使用指南](docs/datafeed-guide.md)
- [Backtest 回测指南](docs/backtest-guide.md)
- [Analyst 分析指南](docs/analyst-guide.md)
- [API 参考](docs/api-reference.md)

## 🔧 依赖

- Python >= 3.8
- pandas >= 1.3.0
- numpy >= 1.20.0
- psycopg2 >= 2.9.0
- prettytable >= 3.0.0
- matplotlib >= 3.4.0
- openpyxl >= 3.0.0

完整依赖列表见 [requirements.txt](requirements.txt)

## 🤝 贡献

欢迎贡献代码！请遵循以下步骤：

1. Fork 本仓库
2. 创建特性分支 (`git checkout -b feature/AmazingFeature`)
3. 提交更改 (`git commit -m 'Add some AmazingFeature'`)
4. 推送到分支 (`git push origin feature/AmazingFeature`)
5. 提交 Pull Request

## 📄 许可证

本项目采用 MIT 许可证 - 详见 [LICENSE](LICENSE) 文件

## 📧 联系

- 作者：Janis
- GitHub：[@Janiszzz](https://github.com/Janiszzz)

---

如果这个项目对你有帮助，请给一个 ⭐ Star！
paddle-onnxocr 0.0.13
paddleOCR的onnx实现
Requires Python: >=3.10

# paddle-onnxocr

## 安装

```
pip install --no-cache-dir paddle-onnxocr
```

## 使用示例

单次推理

```python
from paddleONNXOCR import PredictSystem
from paddleONNXOCR.predict.ocr_dataclass import OCRResult


async def main():
    """
    推理在线图片
    :return:
    """
    async with PredictSystem() as predictor_system:
        ocr_result: OCRResult = await predictor_system.predict(
            "https://wx2.sinaimg.cn/mw690/005AKOR6ly1hvv14x3e1rj30j615hwfl.jpg"
        )
        print(ocr_result.text)


if __name__ == '__main__':
    import asyncio

    asyncio.run(main())
```

批量推理

```python
import cv2
from PIL import Image
from paddleONNXOCR import PredictSystem


async def main():
    """
    推理在线图片
    :return:
    """
    async with PredictSystem() as predictor_system:
        ocr_result =await predictor_system.predict_batch([
            "https://wx2.sinaimg.cn/mw690/005AKOR6ly1hvv14x3e1rj30j615hwfl.jpg",
            cv2.imread("test.png"),
            Image.open("test.png")
        ])
        print(ocr_result)



if __name__ == '__main__':
    import asyncio

    asyncio.run(main())
```
单例调用
```python
from paddleONNXOCR import PredictSystem

async def main():
    predictor_system = PredictSystem()
    await predictor_system.__aenter__()
    return predictor_system
# 外部拿到实力调用推理，参考api/__init__.py中的lifespan
```

默认情况下，会自动从modelscope下载以下模型:

```
PP-LCNet_x0_25_text_line_ori_infer.onnx-->文本行方向检测模型
PP-LCNet_x1_0_doc_ori.onnx->文档方向分类
PP-OCRv5_mobile_det_infer.onnx->文本检测mobile模型
PP-OCRv5_mobile_rec_infer.onnx->文本识别mobile模型
```

### 更改模型

```python
from paddleONNXOCR import PredictSystem
from paddleONNXOCR.models_enum import DetModels, RecModels

# 切换成server版本
PredictSystem(det_model_name=DetModels.SERVER, rec_model_name=RecModels.SERVER)
```

### 传递本地模型路径

```python
from paddleONNXOCR import PredictSystem

PredictSystem(det_model_path="testDir/xxx.onnx")
```

### 模型启用

```python
from paddleONNXOCR import PredictSystem

PredictSystem()
# use_angle_cls: 启用文本放方向检测，默认True
# use_deskew: 启用倾斜图像旋转矫正，默认False
# use_uvdoc: 启用图像矫正，默认False
# use_doc_cls: 启用图像方向分类，默认True
```

PS:具体参数请点到每一个方法内，有完整解释。

# api接口服务
提供了dokcer构建启动的方式.
执行bash命令，自动构建docker服务启动.
windows下请使用wsl子系统.
```bash
bash run.sh
```

### 依赖项目

```
opencv-python-headless
shapely
pyclipper
onnxruntime
pillow
validators
aiohttp
deskew
modelscope
filetype
pdfplumber
aiofiles
```
splinekit 0.0.5
A set of functions and classes to handle periodic 1D polynomial splines
Requires Python: >=3.11

<!-- splinekit/README.md -->

# splinekit: Spline Operations
`splinekit` is a Python-based open-source software library aimed at the
manipulation of one-dimensional periodic splines.

## Installation
You need at least `Python 3.11` to install `splinekit`.

Create and activate your Python virtual environment

(on Unix)
```shell
python -m venv splinekit-env
source splinekit-env/bin/activate
```

(on macOS)
```shell
python3 -m venv splinekit-env
source splinekit-env/bin/activate
```

(on Windows)

```shell
python -m venv splinekit-env
.splinekit-env/Scripts/Activate
```

To deactivate the environment use

```shell
deactivate
```

Minimal requirement

```shell
pip install numpy scipy sympy matplotlib
```

The interactive part of the documentation is deployed on Jupyter Lab

```shell
pip install jupyterlab ipywidgets
```

Install the `splinekit` library itself

```shell
pip install splinekit
```

## Development Environment
Install `splinekit` development environment in editable mode

```shell
pip install -e .[dev]
```
pgscatalog-utils 3.0.0a1
Utilities for working with PGS Catalog API and scoring files
Requires Python: >=3.10

# `pgscatalog-utils`

This convenience package bundles every PGS Catalog application, including:

| Application                  | Description                                                            | Link                                                                                |
|------------------------------|------------------------------------------------------------------------|-------------------------------------------------------------------------------------|
| `pgscatalog-download`        | Download scoring files from the PGS Catalog in specific genome builds  | [README](https://pygscatalog.readthedocs.io/en/latest/how-to/guides/download.html)  |
| `pgscatalog-combine`         | Combine multiple scoring files into a consistent structure             | [README](https://pygscatalog.readthedocs.io/en/latest/how-to/guides/combine.html)   |
| `pgscatalog-relabel`         | Relabel values in a column based on values in a column in another file | [README](pgscatalog.core/README.md)                                                 |
| `pgscatalog-match`           | Match structured scoring file to variants in target genomes            | [README](https://pygscatalog.readthedocs.io/en/latest/how-to/guides/match.html)     |
| `pgscatalog-matchmerge`      | Merge variant match results, useful on larger datasets                 | [README](https://pygscatalog.readthedocs.io/en/latest/how-to/guides/match.html)     |
| `pgscatalog-intersect`       | Match variants across two different variant information files (e.g. reference & target genomes) | [README](https://pygscatalog.readthedocs.io/en/latest/how-to/guides/intersect.html) |
| `pgscatalog-aggregate`       | Aggregate calculated PGS split across multiple files                   | [README](https://pygscatalog.readthedocs.io/en/latest/how-to/guides/aggregate.html) |
| `pgscatalog-ancestry-adjust` | Adjust calculated PGS in the context of genetic ancestry               | [README](https://pygscatalog.readthedocs.io/en/latest/how-to/guides/ancestry.html)  |

Please note `v1.0.0` contains breaking changes: CLI applications have been renamed and the package has been significantly refactored.

See https://github.com/PGScatalog/pygscatalog for more details.

## Installation 

[![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/pgscatalog-utils/README.html)

or [install via `pip`](https://pypi.org/project/pgscatalog-utils/):

```
$ pipx install pgscatalog-utils
```

## Documentation 

Documentation is available at https://pygscatalog.readthedocs.io/.

## Developer instructions

You'll need [`nox`](https://nox.thea.codes/en/stable/index.html) and [`uv`](https://github.com/astral-sh/uv) installed. 

To get set up with a development environment run:

```
$ nox -s dev
$ source .venv/bin/activate
```

This will create a virtual environment in the current directory.

```
$ pgscatalog-download --help
```
doris 2025.12.10.1
DORIS
Requires Python: >=3.13

################################################################
DORIS
################################################################

:Author: `Olivier Friard <http://penelope.unito.it/friard>`_


DORIS is an easy-to-use interactive object detection and tracking software with a graphical user interface.

DORIS is a free and open-source software available for GNU/Linux, Windows and Mac OS.

This project is at an early stage of development. Please report any bugs and suggestions using the GitHub issues tracker.

DORIS web page: `http://www.boris.unito.it/doris <http://www.boris.unito.it/doris>`_

This project was inspired by the **Tracktor project**: https://github.com/vivekhsridhar/tracktor https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13166

Requirements
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* matplotlib
* numpy
* opencv-python-headless (>=4.0.0)
* pandas
* PyQt5
* scikit-learn
* scipy



User guide
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https://doris.readthedocs.io/en/latest/

A video tutorial is available at https://www.youtube.com/watch?v=toSXiqsWRzY (no audio)


Download binaries (executable versions for Linux, Windows and Mac OS)
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See the `Releases page <https://github.com/olivierfriard/DORIS/releases>`_