semvec/pypi-packages huggingface dataset:
jupyter-ai-debugger 0.1.0
AI-assisted error debugger with OpenRouter model fallback for Jupyter.

# jupyter_ai_debugger

AI-assisted error debugger for Jupyter notebooks using OpenRouter with automatic model fallback.

## Features
- Hooks into Jupyter/IPython exceptions
- Sends error + code to OpenRouter AI
- Returns explanation and corrected code
- Auto-suggests corrected code into next cell

## Installation
```bash
pip install jupyter-ai-debugger
```

## Usage
Set your API key as an environment variable:
```bash
export OPENROUTER_API_KEY="your_key_here"
```

Or pass it directly in Python:
```python
from jupyter_ai_debugger import AIDebugger
AIDebugger(api_key="your_key").activate()
```

Now any error in Jupyter will trigger AI debugging suggestions!

## License
MIT
libmdbx 0.3.1
The python bindings to libmdbx.
Requires Python: <4.0,>=3.10
Keywords: mdbx, libmdbx, kvdb, bindings

# Python bindings for libmdbx

Python bindings for libmdbx.

Try it with

```bash
pip install libmdbx
```

Contributions and feedbacks are highly welcome.

## Usage

A quick sample to read all values from the default database:

```python
from mdbx import Env

with Env(...) as env:
    with env.ro_transaction() as txn:
        with txn.cursor() as cur:
            for k, v in cur.iter():
                ...
```

## Manual build

Clone the repo

```bash
git clone https://github.com/wtdcode/mdbx-py
git submodule update --init --recursive
```

Install via poetry

```bash
poetry install
```

That's it!

## Credits

Forked from [libmdbx](https://github.com/erthink/libmdbx/tree/python-bindings)
lineagetree 3.0.4
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
```