semvec/pypi-packages huggingface dataset:
perprof-web 0.0.2
Web interface for perprof-py
Requires Python: >=3.7
Keywords: benchmark,comparison,performance profile,flask

# perprof-web

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Web interface for the [perprof-py](https://github.com/abelsiqueira/perprof-py) for creating performance profiles (as described by [Dolan and Moré](http://arxiv.org/abs/cs/0102001)).

## Reference

When using this software for publications, please cite the paper below, which
describes this project:

> Siqueira, A. S., Costa da Silva, R. G. and Santos, L.-R., (2016). Perprof-web: A Python Package for Performance Profile of Mathematical Optimization Software. Journal of Open Research Software. 4(1), p.e12. DOI: [10.5334/jors.81](http://doi.org/10.5334/jors.81).

## Install

Use `perprof-web` from Docker if you just want to use it.

## How to use

Run perprof-web using Docker with

```bash
docker run -p 5000:5000 abelsiqueira/perprof-web
```

## Getting Help

If you didn't find something at the documentation, want to report a bug, or request a new feature, please open an [issue](https://github.com/abelsiqueira/perprof-web/issues).
flaw 0.0.3
A package that makes python easier to understand

# dpyselfembed
A way for selfbots to send embeds again.  
It uses [Rauf's embed generator](https://embed.rauf.wtf/).

### Install
> ```bash
> $ pip install dpyselfembed
> ```

### Example
> ```python
> import dpyselfembed
> 
> embed = dpyselfembed.Embed("dpyselfembed", 
>   description="A way for selfbots to send embeds again.", 
>   colour="ff0000"
> )
> embed.set_author("Benny")
> 
> url = embed.generate_url(hide_url=True) # You can also convert the embed to a string.
> print(url) # The url will be put in your ctx.send() content.
> ```

### Limitations
> Because the embeds are web embeds there are limitations.  
> - No footers.
> - Max 350 character description.

### Credits
> Used code from [Discord.py Self Embed](https://github.com/bentettmar/discord.py-self_embed)
openfertility 0.1.0
Machine learning and data analysis for fertility

![Open Ferlility logo](docs/static/OpenFertility.svg)

Open Fertility is an open-source project dedicated to advancing fertility analysis and prediction through machine learning. The goal is to foster a __community-driven__ initiative that empowers fertility professionals, researchers, and enthusiasts. 

Inspired by the paper [_An annotated human blastocyst dataset to benchmark deep learning architectures for in vitro fertilization_](https://www.nature.com/articles/s41597-023-02182-3) the aim is to leverage the open data they published to develop initial models.

The primary objectives are to:

- Cultivate an inclusive and collaborative community, bringing together experts and enthusiasts.
- Provide comprehensive tools for training, evaluating, and applying machine learning techniques.
- Effective data visualization, to gain valuable insights and interpret data with clarity.
- Deliver intuitive user interfaces that grant easy access to the models.


Get in touch if you feel interested in participating!

# Installation

During this early develoment stage, a pip install is still not provided.

So, follow these steps:

1. Download or clone the files
2. Navigate to the directory containing the repository
3. Run `pip install .` 


# Early features

## Download the blastocyst dataset

```python
import openfertility as of

blasto2k = of.datasets.blasto2k.Dataset()
blasto2k.download()
```