Metadata-Version: 2.4
Name: PMF_toolkits
Version: 0.2.2
Summary: EPA PMF5 output analysis tools in Python
Home-page: https://github.com/DinhNgocThuyVy/PMF_toolkits
Author: Dinh Ngoc Thuy Vy
Author-email: Dinh Ngoc Thuy Vy <dinhvy2101@gmail.com>
License: MIT
Project-URL: Homepage, https://github.com/DinhNgocThuyVy/PMF_toolkits
Project-URL: Repository, https://github.com/DinhNgocThuyVy/PMF_toolkits.git
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE.md
Requires-Dist: pandas>=1.3.0
Requires-Dist: numpy>=1.20.0
Requires-Dist: matplotlib>=3.4.0
Requires-Dist: seaborn>=0.11.0
Requires-Dist: scipy>=1.7.0
Requires-Dist: scikit-learn>=1.0.0
Requires-Dist: openpyxl>=3.0.0
Dynamic: author
Dynamic: home-page
Dynamic: license-file
Dynamic: requires-python

# PMF_toolkits

Python tools for handling, analyzing and visualizing EPA PMF5.0 outputs from receptor modeling studies.

## Installation

Install from PyPI (available since Feb 2027):

```bash
pip install PMF_toolkits
```

Or install the development version from GitHub:

```bash
pip install git+https://github.com/DinhNgocThuyVy/PMF_toolkits.git
```

## Quick Start

```python
from PMF_toolkits import PMF

# Initialize PMF with Excel outputs
pmf = PMF(site="urban_site", reader="xlsx", BDIR="pmf_outputs/")

# Read all data
pmf.read.read_all()

# Plot factor profiles
pmf.visualization.plot_factor_profiles()
```

## Citation

If you use PMF_toolkits in your research, please cite:

```
Dinh, N.T.V. (2025). PMF_toolkits: Python tools for analysis of Positive Matrix Factorization results.
GitHub repository: https://github.com/DinhNgocThuyVy/PMF_toolkits
```
