{"id":19084778,"url":"https://github.com/ryu577/graphing","last_synced_at":"2025-04-30T09:26:13.503Z","repository":{"id":48935046,"uuid":"322251377","full_name":"ryu577/graphing","owner":"ryu577","description":null,"archived":false,"fork":false,"pushed_at":"2025-01-30T02:48:49.000Z","size":147,"stargazers_count":7,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-23T14:52:34.342Z","etag":null,"topics":["graph","network"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ryu577.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,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2020-12-17T09:55:16.000Z","updated_at":"2025-01-30T02:48:50.000Z","dependencies_parsed_at":"2024-11-09T03:03:09.503Z","dependency_job_id":null,"html_url":"https://github.com/ryu577/graphing","commit_stats":{"total_commits":113,"total_committers":5,"mean_commits":22.6,"dds":0.584070796460177,"last_synced_commit":"0e9b7d8b222a6c679fc321ae13c9e9f7fe2d59b7"},"previous_names":["ryu577/graph"],"tags_count":0,"template":true,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ryu577%2Fgraphing","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ryu577%2Fgraphing/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ryu577%2Fgraphing/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ryu577%2Fgraphing/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ryu577","download_url":"https://codeload.github.com/ryu577/graphing/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":251675790,"owners_count":21625890,"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":["graph","network"],"created_at":"2024-11-09T02:52:36.423Z","updated_at":"2025-04-30T09:26:13.470Z","avatar_url":"https://github.com/ryu577.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# graphing\n\nThis Python library provides several graphing-related utilities that can be used to apply graph theory concepts and graph algorithms to a variety of problems.\n\n## Getting Started\nThis library is available for use on PyPI here: [https://pypi.org/project/graphing/](https://pypi.org/project/graphing/)\n\nFor local development, do the following. \n- Clone this repository.\n- Set up and activate a Python3 virtual environment using `conda`. More info here: [https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#creating-an-environment-with-commands](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#creating-an-environment-with-commands)\n- Navigate to the `graphing` repo.\n- Run the command: `python3 setup.py install` to install the package in the conda virtual environment. \n- As development progresses, run the above command to update the build in the conda virtual environment.\n\n## Sample Code\n\nTry to run the following sample code:\n\n\u003e from graphing.special_graphs.neural_trigraph.path_cover import min_cover_trigraph\n\u003e \n\u003e from graphing.special_graphs.neural_trigraph.rand_graph import *\n\u003e ## Generate a random neural trigraph. Here, it is two sets of edges between layers 1 and 2 (edges1) and layers 2 and 3 (edges2)\n\u003e edges1, edges2 = neur_trig_edges(7, 3, 7, shuffle_p=.05)\n\u003e ## Find the full-path cover for this neural trigraph.\n\u003e paths1 = min_cover_trigraph(edges1, edges2)\n\u003e \n\u003e print(paths1)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fryu577%2Fgraphing","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fryu577%2Fgraphing","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fryu577%2Fgraphing/lists"}