semvec/pypi-packages huggingface dataset:
renovexpy 1.1.3
A Python toolbox for the Renovation Explorer project
Requires Python: >=3.10

# Renovexpy

The `renovexpy` package is a Python toolbox developed as part of [Renovation Explorer](https://www.tue.nl/en/research/research-groups/building-physics-and-services/building-performance/renovatieverkenner) project. It aims to improve and facilitate the renovation of the Dutch housing stock, by providing insights into the impact of various renovation packages.
jprcfai 0.2.18
A package that provides advanced coding tools using the new openAI reasoning models.

This tool provides advanced coding capabilities using the new OpenAI reasoning models. By installing this package with `pip install jprcfai`, you will greatly benefit from faster code development performance.

For more advanced premium tools:
- Automatic deployment using CLI tools for AWS and other platforms.
- Parallel reasoning and final checkpoint evaluation for better results.
- Prompt engineering agents! While there are tools to help with your tasks, they are usually not very flexible. To increase flexibility, we need an automation tool that creates inner automation to execute your task. The objective is to move from the old approach of `AItool -> Task` to `AItool -> AItool -> Task`. This way, we can have a more flexible and powerful tool that can be used for many different tasks and is actually faster in most situations.

For more information and pricing, please contact: joaoprcf@gmail.com
HMAP-tool 1.0.9
Hierarchical Manifold Approximation and Projection for Single Cell Data
Requires Python: >=3.10

# HMAP: Hierarchical Manifold Approximation and Projection

HMAP develop a hierarchical deep generative topographic mapping algorithm to realize the recovery of both **global and local** manifolds underlying single-cell data.


<div align="center">
    <img src="./img/figure1.png" alt="" width="60%">
</div>


## Installation
1. Create a virtual environment and activate it
```bash
conda create -n HMAP python=3.10 scipy numpy pandas scikit-learn && conda activate HMAP
```

2. Install [PyTorch](https://pytorch.org/get-started/locally/) following the official instruction. 
```bash
pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cu126
```

3. Install HMAP
```bash
pip3 install HMAP-tool
```

## Reproducibility

See [the Github repository](https://github.com/ZengFLab/HMAP-reproducibility) for materials and tutorials required to reproduce the results.