semvec/pypi-packages huggingface dataset:
PACMANCharge 0.0.8
Partial Atomic Charges for Porous Materials based on Graph Convolutional Neural Network (PACMAN)
License: MIT

<h1 align="center">PACMAN</h1>

<h4 align="center">

</h4>              

A **P**artial **A**tomic **C**harge Predicter for Porous **Ma**terials based on Graph Convolutional Neural **N**etwork (**PACMAN**)   

[![Requires Python 3.9](https://img.shields.io/badge/Python-3.9-blue.svg?logo=python&logoColor=white)](https://python.org/downloads) [![Zenodo](https://img.shields.io/badge/DOI-10.5281%2Fzenodo.10822403-blue)](https://doi.org/10.5281/zenodo.10822403)  [![MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://github.com/sxm13/PACMAN/LICENSE.txt) [![Gmail](https://img.shields.io/badge/Gmail-D14836?style=for-the-badge&logo=gmail&logoColor=white)](mailto:sxmzhaogb@gmail.com) [![Linux](https://img.shields.io/badge/Linux-FCC624?style=for-the-badge&logo=linux&logoColor=black)]() [![Windows](https://img.shields.io/badge/Windows-0078D6?style=for-the-badge&logo=windows&logoColor=white)]()          


# Usage

```sh      
from PACMANCharge import PACMaN
PACMaN.predict(cif_file="./test/Cu-BTC.cif",model_name="MOF",charge_type="DDEC",digits=10,atom_type=True,neutral=True)
```

* cif_file: cif file (without partial atomic charges) **[absolute path]**                                          
* model_name: what type of structure is yours **[MOF/COF]**                                           
* charge_type: what type of charge do you want to assignment **[DDEC/Bader/CM5]**                             
note: for COF, just can use DDEC charge
* digits: digits of charge (recommond use 6)
note: ML models were trained on 6 digit dataset
* atom_type: keep the same partial atomic charge for the same atom types (based on the similarity of partial atomic charges) **[True/False]**                             
* neutral: keep the net charge is zero. We use "mean" method to neuralize the system. Please refer to the manuscript about the method **[True/False]**                                                  

# Website & Zenodo
PACMAN-APP[link](https://gcn-charge-predicter-mtap.streamlit.app/)       
DOWNLOAD full code and dataset[link](https://zenodo.org/records/10822403) But we will not update new vesion in Zenodo.            

# Reference
If you use PACMAN Charge, please cite [this paper]:
```
@article{,
    title={PACMAN: A Robust Partial Atomic Charge Predicter for Nanoporous Materials using Crystal Graph Convolution Network},
    journal={Journal of Chemical Theory and Computation},
    author={Zhao, Guobin and Chung, Yongchul},
    year={2024},
}
```

# Bugs

If you encounter any problem during using ***PACMAN***, please email ```sxmzhaogb@gmail.com``` or create "issues".                 

 
 
**Group:**   [Molecular Thermodynamics & Advance Processes Laboratory](https://sites.google.com/view/mtap-lab/home?authuser=0)
scenario_assessment 0.1.3
Assessment of scenarios generated using the GOBLIN model
License: MIT
Requires Python: <4.0,>=3.9

# 💻 Scenario Assessment Tool for GOBLIN scenarios
[![license](https://img.shields.io/badge/License-MIT-red)](https://github.com/GOBLIN-Proj/scenario_assessment/blob/0.1.0/LICENSE)
[![python](https://img.shields.io/badge/python-3.9-blue?logo=python&logoColor=white)](https://github.com/GOBLIN-Proj/scenario_assessment)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

 This module was constructed to assess and rank [GOBLIN](https://gmd.copernicus.org/articles/15/2239/2022/) (**G**eneral **O**verview for a **B**ackcasting approach of **L**ivestock **IN**tensification) scenarios.

The latest iterations of GOBLIN systematically produce a range of environmental impacts, as well as livestock ouput data (total protein). Scenario outputs are ranked according to thier overall environmental change, and the change to the baseline livestock outouts. 

Scenarios the meet a specified environmental objective are sorted and ranked. The cost to livestock output is prioritised, with the environmental parameters then factored at varios weights. 

        climate_weight = .4
        eutrophication_weight = .3
        ammonia_weight = .3

These weights can be adjusted by the user.

## Installation

Install from git hub. 

```bash
pip install "scenario_assessment@git+https://github.com/GOBLIN-Proj/scenario_assessment.git@main" 

```

Install from PyPI

```bash
pip install scenario_assessment
```

## Usage
The results of the scenarios are passed, using a dictionary, to the FilterResults class. 

In addition, the target amount is also passed, as a proportion. As well as the selected global warming gas.

The search() method is used to rank results.

```python
import pandas as pd 
import os
from scenario_assessment.filter import FilterResults

def main():

    path = "./data"

    climate = pd.read_csv(os.path.join(path, "total_emissions.csv"), index_col =0)
    eutrophication = pd.read_csv(os.path.join(path, "eutrophication_total.csv"), index_col =0)
    air = pd.read_csv(os.path.join(path, "air_quality_total.csv"), index_col =0)
    products = pd.read_csv(os.path.join(path, "beef_and_milk.csv"), index_col =0)

    emission_dict = {"climate_change": climate,
        "air_quality": air,
        "eutrophication":eutrophication,
        "protein_output": products}

    target = 0.02
    gas = "CH4"

    filter_class = FilterResults(target, gas, emission_dict)

    print(filter_class.total_gwp_gas)

    search_results = filter_class.search()

    print(search_results)


if __name__ == "__main__":
    main()
```

## License
This project is licensed under the terms of the MIT license.