{"id":16463881,"url":"https://github.com/weber-s/pypmf","last_synced_at":"2025-03-23T10:34:31.301Z","repository":{"id":62582624,"uuid":"348632564","full_name":"weber-s/pyPMF","owner":"weber-s","description":"Positive Matrix Factorization handler","archived":false,"fork":false,"pushed_at":"2022-01-30T12:05:37.000Z","size":624,"stargazers_count":8,"open_issues_count":0,"forks_count":5,"subscribers_count":1,"default_branch":"master","last_synced_at":"2024-10-12T11:15:55.342Z","etag":null,"topics":["atmospheric-science","epa-pmf5","machine-learning","pmf"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/weber-s.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2021-03-17T08:25:23.000Z","updated_at":"2024-09-11T11:14:04.000Z","dependencies_parsed_at":"2022-11-03T22:01:49.960Z","dependency_job_id":null,"html_url":"https://github.com/weber-s/pyPMF","commit_stats":null,"previous_names":[],"tags_count":5,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/weber-s%2FpyPMF","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/weber-s%2FpyPMF/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/weber-s%2FpyPMF/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/weber-s%2FpyPMF/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/weber-s","download_url":"https://codeload.github.com/weber-s/pyPMF/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":221849599,"owners_count":16891496,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["atmospheric-science","epa-pmf5","machine-learning","pmf"],"created_at":"2024-10-11T11:15:50.658Z","updated_at":"2024-10-28T15:37:47.208Z","avatar_url":"https://github.com/weber-s.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"[![Documentation Status](https://img.shields.io/badge/Documentation-API-green)](https://pypmf.readthedocs.io/)\n[![PyPI version](https://badge.fury.io/py/pyPMF.svg)](https://badge.fury.io/py/pyPMF)\n\n\nPositive Matrix Factorization in python\n=======================================\n\nHandle PMF output from various format in handy pandas DataFrame and do lot of stuf with\nthem.\n\nCurrently, only data from the EPA PMF5 is handle, from `xlsx` or sql database output.\n\nHistory\n-------\n\nThis project started because I needed to run several PMF for my PhD and also needed to run\nsome computation on these results.\nThe raw output of the EPA PMF5 software is a bit messy and hard to understand at a first\nglance, and copy/pasting xlsx file is not my taste... So I ended developping this tools\nfor handling the tasks of maping the xlsx output to nice python objects, on which I can\neasily run some computation.\n\nSince I needed to plot the results afterward, I also added some plot utilities in this\npackage. It then has build in support for ploting :\n\n * chemical profile (both absolute and normalized)\n * species repartition among factor\n * timeserie contribution (*for all species* and profiles)\n * uncertainties plots (Bootstrap and DISP)\n * seasonal contribution\n * contribution of sources to polluted and normal days\n * And a lot more!\n\n\nExamples\n========\n\nThe [documentation](https://pypmf.readthedocs.io) has a lot of examples and figures, but here is a short summary:\n\n```python\nfrom pyPMF.PMF import PMF\n\npmf = PMF(site=\"GRE-fr\", reader=\"xlsx\", BDIR=\"./\")\n\n# Read various output\npmf.read.read_base_profiles()\npmf.read.read_base_contributions()\npmf.read.read_constrained_profiles()\npmf.read.read_constrained_contributions()\n# ... or simply :\npmf.read.read_all()\n\n# The pmf has now different attributes associated\npmf.profiles    # name of the different factors\npmf.species     # name of the different species\npmf.dfcontrib_c # contribution dataframe of factors\npmf.dfprofile_c # chemical profile of factors\n# ... and lot more\n\n# plot the results\npmf.plot.plot_stacked_profiles()\n\n\n# or use some utilities\npmf.to_cubic_meter(specie=\"Cu\") # Contribution timeserie of the different factors to the Cu\npmf.to_relative_mass()\n# ... and lot more\n\n```\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fweber-s%2Fpypmf","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fweber-s%2Fpypmf","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fweber-s%2Fpypmf/lists"}