semvec/pypi-packages huggingface dataset:
python-aitest-writer 0.1.0
Generate pytest test cases for your Python app using Claude AI
Requires Python: >=3.7

# python_aitest_writer

Generate comprehensive pytest test cases for your Python application using Claude AI.

## Features

- Automatically generates pytest-style test cases for your Python codebase
- Uses Claude AI for high-quality, context-aware test generation
- Writes tests to a `tests/` directory in your project
- CLI tool for easy integration into your workflow

## Installation

```bash
pip install python_aitest_writer
```

## Requirements
- Python 3.7+
- [Anthropic Claude API key](https://docs.anthropic.com/claude/docs/quickstart)
- Internet connection (for API calls)

## Usage

1. Set your Anthropic API key:
   ```bash
   export ANTHROPIC_API_KEY=sk-ant-...
   ```
2. Run the tool on your Python app:
   ```bash
   python-aitest-writer /path/to/your/python/app
   ```
   - The tool will scan all `.py` files, send them to Claude, and write generated tests to a `tests/` directory inside your app.
3. Run the generated tests:
   ```bash
   cd /path/to/your/python/app
   pytest
   ```

## Troubleshooting
- **ModuleNotFoundError:** Run `pytest` from your project root. The generated test files include a `sys.path` patch for import safety.
- **API Errors:** Ensure your `ANTHROPIC_API_KEY` is set and valid.
- **No tests generated:** Check that your app directory contains `.py` files and is accessible.

## License
MIT
SRPAstro.FITS 3.0.10
Tools for handling FITS files under SRP
Requires Python: >=3
Keywords: astronomy data analysis

# Swift Reduction Package


The Swift Reduction Package (hereafter SRP) is a packet of command line tools to solve problems (e.g. basic reduction and analysis tasks of optical/NIR astronomical data, quick cosmological computations, units conversions, etc.) often met in astronomical research activities.

SRP was originally developed in the context of the Swift follow-up activities of the Milan GRB team at the INAF/Brera Astronomical Observatory. The package is designed to be an aid to any researcher to drive further observation of a followed-up GRB counterpart and “swift” can therefore be read simply as “rapid”, “agile”, etc.
 
 This subpackage if the SRP suite is devoted to the management of FITS file data.
thorcast_api_client 0.1.4.2
Un client Python pour interagir avec l'API ThorCast
Requires Python: <4.0,>=3.8

# ThorCast API Client V0.1

## 📌 Description (je suis nul en Markdown , j'espère que ce sera lisible)

`thorcast_api_client` est un client Python permettant d'interagir avec l'API **ThorCast** mise en place par Holmium pour MINT.

Il prend en charge (normalement) :
- L'**authentification JWT**
- La récupération des **courbes de charge prévues**
- La récupération des **courbes de charge historiques**
- La gestion des **erreurs API**

## 🚀 Installation

### Via `pip`
```sh
pip install thorcast_api_client
```

### Via `poetry` pour les amateurs de Baudelaire
```sh
poetry add thorcast_api_client
```

## 📜 Utilisation 

### 🔒 Authentification : 

L'authentification passe par la requête d'un token JWS en injectant un MDP/Username :

```sh
from thorcast_api_client.client import ThorCastClient

client = ThorCastClient("mon_user", "mon_mdp")
```
#### Lister les versions disponibles pour une ou plusieurs courbes LT

```sh
versions = client.get_lt_curves_versions(["prm1", "prm2"])
print(versions)
```
#### Récupérer les courbes de charge LT

```sh
courbes = client.get_lt_curves(["prm1", "prm2"], "2023-01-01T00:00:00Z", "2025-01-01T00:00:00Z")
print(courbes)
```

#### Récupérer les versions des courbes de catalogue 🤷 (rien compris à ce que ça remonte)

```sh
version_catalogues = client.get_catalog_versions(["catalogue1", "catalogue2"], "2023-01-01T00:00:00Z", "2025-01-01T00:00:00Z")
print(version_catalogues)
```
#### Récupérer les courbes de charge historiques

```sh
courbes_catalogue = client.get_catalog_curves(["id_catalogue1", "id_catalogue2"])
print(courbes_catalogue)
```
LineageTree 2.0.3
Structure for Lineage Trees
Requires Python: >=3.10

# LineageTree

This library allows to import and work with cell (but not limited to cells) lineage trees.
With LineageTree you can read from:

- TGMM algorithm outputs described in [Amat et al. 2014](https://www.nature.com/articles/nmeth.3036)
- TrackMate files described in [Tinevez et al. 2017](https://doi.org/10.1016/j.ymeth.2016.09.016)
- MaMuT files described in [Wolff et al. 2018](https://doi.org/10.7554/eLife.34410)
- SVF algorithm outputs described in [McDole, Guignard et al. 2018](https://doi.org/10.1016/j.cell.2018.09.031)
- ASTEC algorithm outputs described in [Guignard, Fiuza et al. 2020](https://doi.org/10.1126/science.aar5663)
- Data from the [Digital development Database](http://digital-development.org/index.html) described in [Du et al. 2014](https://www.cell.com/fulltext/S0092-8674(13)01542-0) and [Du et al. 2015](https://www.sciencedirect.com/science/article/pii/S1534580715004876?via%3Dihub)
- and few others

## Basic usage

Once installed the library can be called the following way (as an example):

```python
from LineageTree import lineageTree
```

and one can then load lineage trees the following way:

For `.lT` files:

```python
lT = lineageTree.load('path/to/file.lT')
```

For ASTEC data:

```python
from LineageTree import read_from_ASTEC
lT = read_from_ASTEC('path/to/ASTEC.pkl')
```

For MaMuT or TrackMate:

```python
from LineageTree import read_from_mamut_xml
lT = read_from_mamut_xml('path/to/MaMuT.xml')
```

For TGMM:

```python
from LineageTree import read_from_tgmm_xml
lT = read_from_tgmm_xml('path/to/single_time_file{t:04d}.xml', tb=0, te=500)
```

For Mastodon:

```python
from LineageTree import read_from_mastodon
lT = read_from_mastodon('path/to/Mastodon.mastodon')
```

or, for Mastodon csv file:

```python
from LineageTree import read_from_mastodon_csv
lT = read_from_mastodon_csv(['path/to/nodes.csv', 'path/to/links.csv'])
```

## Quick install

To quickly install the library together with its dependencies one can run:

```shell
pip install LineageTree
```

or, for the latest version if you have cloned the directory:

```shell
pip install .
```

or for the latest version wihtout cloning the directory

```shell
pip install git+https://github.com/leoguignard/LineageTree
```
visitor-design-pattern 1.0.10
Radiative Transfer Model template
Requires Python: >=3.5
Keywords: visitor_design_pattern

======================
Visitor Design Pattern
======================


Flexible and complete pytonic implementation of the visitor design pattern


Features
--------

 - Implement a visitor design pattern for any data structure
 - Convenient decorator API
 - Ready to use accept method for tree-like data structures
 - Compatible with Attrs


Links
-----

 - Access the sphinx Documentation_ and Usage_
 - See the Pypi_ package


Credits
-------

This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.

.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage
.. _Documentation: https://visitor-design-pattern.readthedocs.io/en/latest/index.html
.. _Pypi: https://pypi.org/project/visitor-design-pattern/
.. _Usage: https://visitor-design-pattern.readthedocs.io/en/latest/usage.html


=======
History
=======

0.1.0 (2021-04-14)
------------------

* First version.