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.

• Examples
  • Load the PMF
    • From XLSX output
    • From SQL database
    • PMF object
  • Read the data
    • Organization
    • Contribution
    • Chemical profiles
    • Uncertainties
  • Plot utilities
    • Chemical profile (per microgram of total variable)
    • Chemical profile (in percentage of the sum of each species)
    • Contribution time series and uncertainties
    • Profiles plot summary
    • Seasonnal contribution
    • Chemical profile stacked (in percentage of the sum of each species)
    • Stacked contributions
    • Stacked samples contributions
  • Utilities
    • Convert to cubic meter
    • Relative contributions of species to the total mass
    • Relative contribution of the factor for each species
• API
  • pyPMF.PMF module
  • pyPMF.reader module
  • pyPMF.plotter module
  • pyPMF.utils module

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!