semvec/pypi-packages huggingface dataset:
joltedmod 0.2.4
JoltEd self-documenting learning module
License: MIT
Requires Python: >=3.9,<4.0


# JoltedMod

JoltedMod is a Python package that utilizes OpenAI's chat models to automatically generate Jupyter Notebook or Markdown-based computer science educational materials. This package can be integrated into a frontend application, such as a CLI or API, to serve educational content.

## Features

- Generate Jupyter Notebook or Markdown content based on a given topic
- Highly configurable using tutorial template JSON structure
- Customizable content creator identity and target audience
- Support for GPT-3.5-turbo and other OpenAI models


## Installation

To install JoltedMod, you can use `pip`:

```
pip install jolted_mod
```

## Usage

Here's an example of how to generate a Jupyter Notebook module for a given topic:

```python
import asyncio
from jolted_mod.main import create_notebook_module

topic = "Intro to for loops in Python"
identity = "professor of computer science"
target_audience = "first year computer science students"
is_code = True
model = "gpt-3.5-turbo"

tutorial_content = asyncio.run(create_notebook_module(topic, identity, target_audience, is_code, model))

print(tutorial_content)
```

## Configuration

You can customize the generated content using a JSON template structure that outlines blocks. An example template file can be found in `tutorial_template.json`.

## Development Setup

JoltedMod uses Poetry for project management. To set up a development environment, follow these steps:

1. Install Poetry if you haven't already:

```
curl -sSL https://install.python-poetry.org | python3 -
```

2. Clone the repository:

```
git clone https://github.com/yourusername/jolted_mod.git
cd jolted_mod
```

3. Install dependencies using Poetry:

```
poetry install
```

4. Activate the virtual environment:

```
poetry shell
```

Now you're ready to start contributing to JoltedMod!

## Contributing

If you'd like to contribute to JoltedMod, feel free to fork the repository and submit a pull request.

## License

JoltedMod is licensed under the [MIT License](LICENSE).
cycIFAAP 5.7.7
Cyclic ImmunoFluoresence (cycIF) Automatic Analysis Pipeline
License: MIT
Requires Python: >=3.8,<3.10
Keywords: cyclic Immunofluorescence,cycif,immunofluorescence,registration,segmentation,features,features extraction,restore,napari,nuclei,nucleus,cells,cell,cell analysis,cell type

# Cyclic ImmunoFluorescence Automatic Analysis Pipeline

This pipeline takes as input a set of cyclic immunofluorescence (cycIF) images and performs the following operations:
 - Registration
 - Nuclei segmentation (using CellPose or own trained Mask R-CNN model)
 - Background subtraction
 - Compute each marker exclusiveness to be used with Restore
 - Use Restore when possible for automatic gating (optionnal)
 - Features extraction
 - Cell type computation
 - Automatic visualization using Napari
 - Quality control (tissue loss and Restore based)


For installation, more information/details and full examples with code and data, visit:
https://www.thibault.biz/Research/cycIFAAP/cycIFAAP.html