{"id":14989259,"url":"https://github.com/zgana/histlite","last_synced_at":"2025-04-12T01:22:08.535Z","repository":{"id":57437371,"uuid":"191667301","full_name":"zgana/histlite","owner":"zgana","description":"A somewhat \"lite\" histogram library","archived":false,"fork":false,"pushed_at":"2024-02-13T16:07:20.000Z","size":3265,"stargazers_count":6,"open_issues_count":2,"forks_count":4,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-03-25T21:05:51.338Z","etag":null,"topics":["histograms","matplotlib","numpy","plotting","scipy","statistics"],"latest_commit_sha":null,"homepage":"https://histlite.readthedocs.io","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/zgana.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,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2019-06-13T01:16:47.000Z","updated_at":"2024-10-16T11:32:17.000Z","dependencies_parsed_at":"2024-09-25T00:34:30.387Z","dependency_job_id":null,"html_url":"https://github.com/zgana/histlite","commit_stats":{"total_commits":38,"total_committers":1,"mean_commits":38.0,"dds":0.0,"last_synced_commit":"36c00638ab795d82ede61db0ea0dd4f5b4a252b4"},"previous_names":[],"tags_count":6,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zgana%2Fhistlite","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zgana%2Fhistlite/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zgana%2Fhistlite/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zgana%2Fhistlite/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/zgana","download_url":"https://codeload.github.com/zgana/histlite/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248501958,"owners_count":21114712,"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":["histograms","matplotlib","numpy","plotting","scipy","statistics"],"created_at":"2024-09-24T14:17:57.290Z","updated_at":"2025-04-12T01:22:08.516Z","avatar_url":"https://github.com/zgana.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# histlite\n\nSee documentation on [ReadTheDocs](https://histlite.readthedocs.io/en/latest/index.html).\n\n**histlite** is a histogram calculation and plotting library that tries to be\n\"lite\" on data structures but rich in statistics and visualization features.\nSo far, development has taken place during my (Mike Richman) time as a graduate\nstudent and post-doctoral researcher in the field of particle astrophysics —\nspecifically, working with the IceCube Neutrino Observatory.  Histlite is\nintended both to facilitate high-paced exploratory data analysis as well as to\nserve as a building block for potentially very complex maximum likelihood data\nanalysis implementations.\n\nThe core design considerations are:\n\n* It must be trivial to work with and interchange between 1D, 2D, or ND histograms.\n* It should be as simple as possible to perform bin-wise arithmetic\n  operations on one or more histograms; to perform sums, integrals, etc. and\n  thus normalizations along one or more axes simultaneously; and to perform\n  spline or user-defined functional fits\n* It should be as simple as possible to achieve publication-quality plots.\n\nThe primary histogramming functionality consists of a thin wrapper around\n`numpy.histogramdd`.  Statistical tools leverage **scipy** but include\ncustom solutions for some use cases.  (Importantly, error propagation is\ncurrently handled manually but may be migrated to the **uncertainties**\npackage in the future.)  Plotting is done using **matplotlib**.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzgana%2Fhistlite","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fzgana%2Fhistlite","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzgana%2Fhistlite/lists"}