{"id":24528915,"url":"https://github.com/saezlab/plugy","last_synced_at":"2025-07-05T19:37:09.124Z","repository":{"id":48192303,"uuid":"171736704","full_name":"saezlab/plugy","owner":"saezlab","description":"Flexible processing and analysis of plug microfluidics data","archived":false,"fork":false,"pushed_at":"2022-10-07T15:08:33.000Z","size":5037,"stargazers_count":0,"open_issues_count":6,"forks_count":0,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-02-23T21:26:12.989Z","etag":null,"topics":["biological-data-analysis","cell-based-assay","drug-screening","microfluidics"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/saezlab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2019-02-20T19:34:01.000Z","updated_at":"2021-12-10T22:16:27.000Z","dependencies_parsed_at":"2022-09-12T15:32:19.974Z","dependency_job_id":null,"html_url":"https://github.com/saezlab/plugy","commit_stats":null,"previous_names":[],"tags_count":10,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/saezlab%2Fplugy","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/saezlab%2Fplugy/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/saezlab%2Fplugy/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/saezlab%2Fplugy/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/saezlab","download_url":"https://codeload.github.com/saezlab/plugy/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243769947,"owners_count":20345215,"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":["biological-data-analysis","cell-based-assay","drug-screening","microfluidics"],"created_at":"2025-01-22T07:33:58.691Z","updated_at":"2025-03-15T17:44:34.449Z","avatar_url":"https://github.com/saezlab.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Plugy: Python module for plug microfluidics data analysis\n[![pipeline status](https://github.com/saezlab/plugy/actions/workflows/python-package-conda.yml/badge.svg)](https://git.embl.de/grp-merten/plugy/commits/master)\n\n## Issues\n\nFeedback, questions, bug reports are welcome:\nhttps://github.com/saezlab/plugy/issues\n\n## Installation\n\nImports \u0026 Setup\n\nYou can now install `plugy` as a package using `pip` in your `conda`\nenvironment. To install `pip` in your `conda` environment run the following\nlines on your `bash` or `conda` prompt.\n\n```bash\n# Activate your conda environment replacing 'YOUR_ENVIRONMENT' with the name of your environment\nconda activate YOUR_ENVIRONMENT\n\n# Install pip git support, such that plugy can be directly installed from gitlab\nconda install pip git\n\n# Install plugy into your environment\npip install git+https://github.com/saezlab/plugy@master\n\n# If you want to use the latest development version use this instead\npip install --force-reinstall git+https://github.com/saezlab/plugy@dev\n```\n\n## Quick start\n\nThis notebook will show you how to run a plugy based analysis of a drug\ncombination Braille display microfluidics experiment.\n\nFirst, make sure your Python shell is running in the working directory where\n(or in its subdirectories) you have the data and where you want to save the\nresults.\n\nThe simplest workflow, which is sufficient most of the times, looks like this:\n\n```python\nimport plugy\nexp = plugy.PlugExperiment()\nexp.main()\n```\n\nFurther settings, parameters can be passed to `PlugExperiment`:\n\n```python\nimport plugy\nexp = plugy.PlugExperiment(\n    peak_min_threshold = 0.02,\n    barcoding_param = {\n        'times': (.2, 4.0),\n    },\n    heatmap_second_scale = 'pos-ctrl',\n)\nexp.main()\n```\n\nIf you want to interact with the data use the contents of the `exp` object.\nIt contains all the plug, pmt, channel and sequence data that was used in the\nanalysis. For example, a `pandas.DataFrame` containing the statistics for each\nsample:\n\n```python\nexp.sample_statistics\n```\n\n## Tutorial\n\nYou can find more examples in the plugy guide:\nhttps://github.com/saezlab/plugy/blob/master/notebooks/plugy_guide.ipynb\n\n## Development history\n\nhttps://github.com/saezlab/plugy/blob/master/NEWS.md\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsaezlab%2Fplugy","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsaezlab%2Fplugy","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsaezlab%2Fplugy/lists"}