https://github.com/weber-s/pypmf

Positive Matrix Factorization handler
https://github.com/weber-s/pypmf

atmospheric-science epa-pmf5 machine-learning pmf

Last synced: 9 days ago
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Positive Matrix Factorization handler

• Host: GitHub
• URL: https://github.com/weber-s/pypmf
• Owner: weber-s
• Created: 2021-03-17T08:25:23.000Z (over 3 years ago)
• Default Branch: master
• Last Pushed: 2022-01-30T12:05:37.000Z (almost 3 years ago)
• Last Synced: 2024-10-12T11:15:55.342Z (25 days ago)
• Topics: atmospheric-science, epa-pmf5, machine-learning, pmf
• Language: Python
• Homepage:
• Size: 609 KB
• Stars: 8
• Watchers: 1
• Forks: 5
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
[![Documentation Status](https://img.shields.io/badge/Documentation-API-green)](https://pypmf.readthedocs.io/)

[![PyPI version](https://badge.fury.io/py/pyPMF.svg)](https://badge.fury.io/py/pyPMF)

Positive Matrix Factorization in python

=======================================

Handle PMF output from various format in handy pandas DataFrame and do lot of stuf with

them.

Currently, only data from the EPA PMF5 is handle, from `xlsx` or sql database output.

History

-------

This project started because I needed to run several PMF for my PhD and also needed to run

some computation on these results.

The raw output of the EPA PMF5 software is a bit messy and hard to understand at a first

glance, and copy/pasting xlsx file is not my taste... So I ended developping this tools

for handling the tasks of maping the xlsx output to nice python objects, on which I can

easily run some computation.

Since I needed to plot the results afterward, I also added some plot utilities in this

package. It then has build in support for ploting :

 * chemical profile (both absolute and normalized)

 * species repartition among factor

 * timeserie contribution (*for all species* and profiles)

 * uncertainties plots (Bootstrap and DISP)

 * seasonal contribution

 * contribution of sources to polluted and normal days

 * And a lot more!

Examples

========

The [documentation](https://pypmf.readthedocs.io) has a lot of examples and figures, but here is a short summary:

```python

from pyPMF.PMF import PMF

pmf = PMF(site="GRE-fr", reader="xlsx", BDIR="./")

# Read various output

pmf.read.read_base_profiles()

pmf.read.read_base_contributions()

pmf.read.read_constrained_profiles()

pmf.read.read_constrained_contributions()

# ... or simply :

pmf.read.read_all()

# The pmf has now different attributes associated

pmf.profiles    # name of the different factors

pmf.species     # name of the different species

pmf.dfcontrib_c # contribution dataframe of factors

pmf.dfprofile_c # chemical profile of factors

# ... and lot more

# plot the results

pmf.plot.plot_stacked_profiles()

# or use some utilities

pmf.to_cubic_meter(specie="Cu") # Contribution timeserie of the different factors to the Cu

pmf.to_relative_mass()

# ... and lot more

```