semvec/pypi-packages huggingface dataset:
procell 1.7.2
ProCell - cell proliferation framework

# ProCell

ProCell is an [award winning](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8791485) modeling and simulation framework designed to investigate cell proliferation dynamics that, differently from other approaches, takes into account the inherent stochasticity of cell division events.

ProCell manipulates raw data coming from flow cytometry experiments. Specifically, uses as input:
- a histogram of initial cell fluorescences (e.g., GFP signal in the population); 
- the number of different sub-populations, along with their proportions;
- the mean and standard deviation of division time for each population;
- a fluorescence minimum threshold; 
- a maximum simulation time T, expressed in hours.

The output produced by ProCell is a histogram of GFP fluorescence after time T.

## Installing and using ProCell

ProCell can be easily installed with pip:

<code>pip install procell</code>

Once installed, ProCell's new GUI [designed by Luca Zanini](https://www.behance.net/gallery/84353179/-UX-UI-PROCELL) can be launched by typing:

<code>python -m procell.gui</code>

from the console. 

A tutorial about modeling, calibration, simulation and validation will be published soon. 

You can find a list of ProCell's pre-requisites here:

https://docs.google.com/document/d/1OC0DDQQHAKs6sMOi7hWleWcwgU04h9RAsZzOye6dnAU/edit?usp=sharing

## GPU version of ProCell

We are developing a GPU-accelerated version of ProCell, able to strongly reduce the computational effort of simulations. The alpha implementation can be downloaded from: https://github.com/ericniso/cuda-pro-cell

## More info about ProCell

If you need additional information about ProCell please write to: nobile@disco.unimib.it.

## Citing ProCell

Nobile M.S., Vlachou T., Spolaor S., Bossi D., Cazzaniga P., Lanfrancone L., Mauri G., Pelicci P.G., Besozzi D.: Modeling cell proliferation in human acute myeloid leukemia xenografts, Bioinformatics, 35(18):3378â€“3386, 2019

Nobile M.S., Vlachou T., Spolaor S., Cazzaniga P., Mauri G., Pelicci P.G., Besozzi D.: ProCell: Investigating cell proliferation with Swarm Intelligence, Proceedings of the 16th IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2019), Certosa di Pontignano, Siena, Tuscany, Italy, 2019
pesi 0.0.4
Build your Python project to Docker image on remote Docker server
Requires Python: >=3.5

# PESI（[English version README](./README-EN.md)）
从终端打包Python项目到远程Docker服务器

## 使用环境（目前测试）
- Linux
- Python 3.5 / 3.6

## Base on python package
- click == 7.1.1
- docker == 4.1.0(已经集成在lib中)
- requests == 2.23.0
- websocket == 0.2.1

## 安装
```bash
    sudo pip install pesi
```

## 快速上手(example)
> **注意**: 确保你要远程的Docker服务器或者本地Docker已经打开remote api端口,取决于你在setup.cfg里面的配置(
https://success.docker.com/article/how-do-i-enable-the-remote-api-for-dockerd)

```bash
    git clone https://github.com/pesi1874/pesi.git
    cd psei/example
    pesi build
```

## 项目文件结构要求
```bash
|-- project-name/
    |--deploy/
        |-- Dockerfile
        |-- requirements.txt
        |-- run.sh
    |--project-name/
        |--main.py
    |--setup.cfg
```

## 构建项目
```bash
    cd <python project>
    pesi build
```


## Help
```bash
    pesi --help
```