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

Positive Matrix Factorization handler

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

Science Score: 10.0%

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    1 of 3 committers (33.3%) from academic institutions
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    Low similarity (10.6%) to scientific vocabulary

Keywords

atmospheric-science epa-pmf5 machine-learning pmf
Last synced: 6 months ago · JSON representation

Repository

Positive Matrix Factorization handler

Basic Info
  • Host: GitHub
  • Owner: weber-s
  • Language: Python
  • Default Branch: master
  • Homepage:
  • Size: 609 KB
Statistics
  • Stars: 11
  • Watchers: 1
  • Forks: 5
  • Open Issues: 0
  • Releases: 0
Topics
atmospheric-science epa-pmf5 machine-learning pmf
Created almost 5 years ago · Last pushed about 4 years ago
Metadata Files
Readme

README.md

Documentation Status PyPI version

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 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.readbaseprofiles() pmf.read.readbasecontributions() pmf.read.readconstrainedprofiles() pmf.read.readconstrainedcontributions()

... 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.dfcontribc # contribution dataframe of factors pmf.dfprofilec # chemical profile of factors

... and lot more

plot the results

pmf.plot.plotstackedprofiles()

or use some utilities

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

... and lot more

```

Owner

  • Name: Samuël Weber/GwendalD
  • Login: weber-s
  • Kind: user
  • Company: Webu

GitHub Events

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  • Watch event: 2

Committers

Last synced: about 2 years ago

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  • Total Commits: 38
  • Total Committers: 3
  • Avg Commits per committer: 12.667
  • Development Distribution Score (DDS): 0.184
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Samuel s****r@u****r 31
Samuel s****r@w****p 4
Samuel s****r@g****g 3
Committer Domains (Top 20 + Academic)
gresille.org: 1 webu.coop: 1 univ-grenoble-alpes.fr: 1

Issues and Pull Requests

Last synced: 8 months ago

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  • Total pull requests: 0
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Past Year
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Dependencies

doc-requirements.txt pypi
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  • urllib3 *
docs/Pipfile pypi
  • Sphinx *
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docs/Pipfile.lock pypi
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  • future ==0.17.1
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  • sphinx-rtd-theme ==0.4.3
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  • sphinxcontrib-htmlhelp ==1.0.2
  • sphinxcontrib-jsmath ==1.0.1
  • sphinxcontrib-qthelp ==1.0.2
  • sphinxcontrib-serializinghtml ==1.1.3
  • urllib3 ==1.25.3
requirements.txt pypi
  • matplotlib *
  • numpy *
  • pandas *
  • seaborn *
  • xlrd <2
setup.py pypi
  • matplotlib *
  • pandas *
  • seaborn *
  • xlrd <2