semvec/pypi-packages huggingface dataset:
spaik-coding-agents 0.2.2
Pre-built coding agents for AI-assisted software development
Requires Python: >=3.10
Keywords: agents, ai, claude, coding, cursor, developer-tools

# Siili Coding Agents

Pre-built coding agents for AI-assisted software development.

Spaik SDK is an open-source project developed by engineers at Siili Solutions Oyj. This is not an official Siili product.

## Installation

```bash
pip install spaik-coding-agents
```

## Agents

### Claude Code Agent

Wrapper for Claude Code SDK with streaming support.

```python
import anyio
from spaik_coding_agents import ClaudeAgent, ClaudeAgentOptions

async def main():
    agent = ClaudeAgent(
        options=ClaudeAgentOptions(
            working_directory=".",
            yolo=True,  # Bypass permission prompts
        )
    )
    
    async for block in agent.stream_blocks("Create a hello world script"):
        print(block)

anyio.run(main)
```

### Synchronous Usage

```python
from spaik_coding_agents import ClaudeAgent

agent = ClaudeAgent()
agent.run("Fix the bug in main.py")
```

### Cursor Agent

Integration with Cursor AI editor.

```python
from spaik_coding_agents import CursorAgent

agent = CursorAgent()
result = agent.run("Refactor this function")
```

## Requirements

- Python 3.10+
- spaik-sdk >= 0.2.0
- Claude Code SDK (for ClaudeAgent)

## License

MIT - Copyright (c) 2026 Siili Solutions Oyj
swanspawner 1.2.36
SWAN JupyterHub spawner
Requires Python: >=3.8
Keywords: CERN, JupyterHub, SWAN, Spawner

# SWAN Spawner

Spawner for JupyterHub that enables configuring a session with CVMFS/LCG stacks, support for GPU, oAuth tokens, etc. 
If Binder is used to configure the Docker image used, it reverts to the default upstream configuration.
Works with both Docker and Kubernetes.

## Installation

```bash
pip3 install swanspawner
```

## Usage

Add to your JupyterHub config file

```python
c.JupyterHub.spawner_class = 'swanspawner.SwanDockerSpawner'
```

If you deploy with Docker, or

```python
c.JupyterHub.spawner_class = 'swanspawner.SwanKubeSpawner'
```

If you deploy with Kubernetes.

## Jupyter Notebook environment variables set during spawn

| env      |
|  ---     | 
| ROOT_LCG_VIEW_NAME   |
| ROOT_LCG_VIEW_PLATFORM   |
| USER_ENV_SCRIPT   |
| ROOT_LCG_VIEW_PATH   |
| USER  |
| USER_ID  |
| USER_GID  |
| HOME  |
| EOS_PATH_FORMAT  |
| SERVER_HOSTNAME  |
| MAX_MEMORY  |
| JPY_USER  |
| JPY_COOKIE_NAME  |
| JPY_BASE_URL  |
| JPY_HUB_PREFIX  |
| JPY_HUB_API_URL  |
| ACCESS_TOKEN  |
| OAUTH_INSPECTION_ENDPOINT  |

## Spawn Form configuration

To configure custom form, please set path to config file as below

```python
c.SwanSpawner.options_form_config = '<path>'
```

High level objects 

```json
{
 "header": <options header text>,
 "options": <array of options objects>
...
}
```

Options type label

```json
{
 "options": [
    {
      "type": "label",
      "label": {
        "value": <id of label>,
        "text": <displayed text of label>
      }
    },
    ...
]
...
}
```

Options type selection

```json
{
 "options": [
    {
      "type": "selection",
      "lcg": {
        "value": <id of lcg>,
        "text": <displayed text of lcg>
      },
      "platforms": [
        {
            "value": <id of platform>,
            "text": <displayed text of platform>
        }
        ...
      ],
      "cores": [
        {
            "value": <id of cores selection>,
            "text": <displayed text of cores selection>
        }
        ...
      ],
      "memory": [
        {
            "value": <id of memory selection>,
            "text": <displayed text of memory selection>
        }
        ...
      ],
      "clusters": [
        {
            "value": <id of cluster>,
            "text": <displayed text of cluster>
        }
        ...
      ]
    },
    ...
]
...
}
```
An example json file can be seen in [options_form_config.json]()

## Mount options

To mount EOS or CVMFS with SwanDockerSpawner (which requires a mount with propagation "shared"), a new configuration was introduced by upstream:

```python
c.SwanSpawner.mounts = [
    {
        'source': '/eos',
        'target': '/eos',
        'type': 'bind',
        'propagation': 'shared'
    },
    {
        'source': '/cvmfs',
        'target': '/cvmfs',
        'type': 'bind',
        'propagation': 'shared'
    }
]
```
zfx-uu 1.0.0
未开放


    免责声明:
    本模块是“按原样”提供的，没有任何明示或暗示的保证。在任何情况下，作者或版权持有者均不对因使用本模块而产生的任何索赔、损害或其他责任负责，无论是在合同、侵权或其他情况下。

    使用本模块即表示接受此免责声明。如果您不同意此免责声明的任何部分，请勿使用本模块。

    本模块仅供参考和学习用途，用户需自行承担使用本模块的风险。作者对因使用本模块而造成的任何直接或间接损害不承担任何责任。

    作者保留随时更新本免责声明的权利，恕不另行通知。最新版本的免责声明将在模块的最新版本中提供。
Preciso 3.12
A package for modelling precipitation in alloys.
Requires Python: >=3.6



.. image:: https://img.shields.io/badge/Documentation-PreciSo-blue?style=for-the-badge&logo=python
  :target: https://arnall.gitlab.io/preciso/
  
.. placeholder-for-doc-index


About
-----

**PreciSo** is a software written in `C++` and developped by `Michel Perez <https://michel.perez.net.free.fr>`_ and his co-workers.
It does classical nucleation and growth theories calculations for `precipitation <https://en.wikipedia.org/wiki/Classical_nucleation_theory>`_ in metallurgical systems. 

Getting Started
---------------

- Source code : `Gitlab Repository <https://gitlab.com/Arnall/preciso/>`_
- To install **PreciSo**, check out the  `getting started <https://arnall.gitlab.io/preciso/quickstart.html>`_ page.
- To try **PreciSo** with no installation, go here :

.. image:: https://mybinder.org/badge_logo.svg
 :target: https://mybinder.org/v2/gh/https%3A%2F%2Fframagit.org%2Farnall%2Fpreciso/19aa7ce7a2ecb19339c33bb7e8d499e7e23fb820?filepath=notebooks%2F01-Overview.ipynb


Authors
-------

* **Michel Perez** - `MichelPerezNet <http://michel.perez.net.free.fr/>`_
* **Thibaut Chaise**
* **Arnaud Allera** (Python API)
* And many others !


Contribution and support
------------------------

Contributions and feedback are welcome. The `issue tracker <https://gitlab.com/Arnall/preciso/-/issues>`_ is a great tool to submit questions, support requests and report bugs.


To cite this project
--------------------

Perez (M.), Dumont (M.) et Acevedo-Reyes (D.), **Implementation of the classical nucleation theory for precipitation**, Acta Mater., vol. 56, 2008, p. 2119–2132 `(PDF) <http://michel.perez.net.free.fr/Perez08.pdf>`_

License
-------

This project is licensed under the CECILL licence - see the `LICENSE.md <LICENSE.md>`_ file for details
jale 0.2.9
Package allowing users to run Activation Likelihood Estimation Meta-Analysis
Requires Python: <3.13,>=3.9

# JALE

![Python Versions](https://img.shields.io/badge/python-3.9%20|%203.10%20|%203.11%20|%203.12-blue)
![Ruff](https://img.shields.io/badge/code%20style-Ruff-blueviolet)
![PyPI Version](https://img.shields.io/pypi/v/jale.svg)
![License](https://img.shields.io/github/license/LenFrahm/JALE.svg)

A Python package for conducting ALE (Activation Likelihood Estimation) meta-analyses, supporting a range of analysis workflows: standard ALE, probabilistic or cross-validated ALE, standard ALE contrast, and balanced ALE contrast.

For more detailed information regarding installation, usage and features please visit the [Github Wiki](https://github.com/LenFrahm/JALE/wiki).

## Table of Contents
- [Installation](#installation)
- [Usage](#usage)
- [Features](#features)
- [Background and References](#background-and-references)
- [License](#license)

## Installation

To install the ALE Meta-Analysis Package, run:

```bash
pip install jale
```
## Usage

Here’s how to use the project:

JALE requires a project folder that contains 3 files:
1. Experiment Data (Author, Subjects, Coordinates, Tags)
2. Analysis Data (Type of ALE, Tags to be included)
3. Yaml config file (specifying project folder path, filenames and ALE parameters)

The format of these files is very important and you should check the wiki for instructions.

For example files click [here](https://raw.githubusercontent.com/LenFrahm/JALE/master/JALE/assets/examples.zip).

Running an ALE can be done in two ways:

1. via CLI: 

```bash
python -m jale /path/to/yaml/file
```

2. in Python:

```python
from jale.ale import run_ale

run_ale(yaml_path='/path/to/yaml/file')
```

## Features

- Standard ("Main Effect") ALE
- Probabilistic ("Cross-Validated") ALE
- Standard ALE Contrast
- Balanced ALE Contrast

## Background and References

This project is based on research by 
- [Eickhoff et al., 2012](https://doi.org/10.1016/j.neuroimage.2011.09.017).
- [Eickhoff et al., 2016](https://doi.org/10.1016/j.neuroimage.2016.04.072).
- [Frahm et al., 2022](https://doi.org/10.1002/hbm.25898).
- [Frahm et al., 2023](https://doi.org/10.1016/j.neuroimage.2023.120383).