{"id":15020265,"url":"https://github.com/janjoch/interplot","last_synced_at":"2025-08-16T23:11:50.164Z","repository":{"id":214748951,"uuid":"737270736","full_name":"janjoch/interplot","owner":"janjoch","description":"Create matplotlib and plotly charts with the same few lines of code.","archived":false,"fork":false,"pushed_at":"2024-10-12T21:57:04.000Z","size":127577,"stargazers_count":6,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-06-16T01:33:05.939Z","etag":null,"topics":["jupyter","jupyter-notebook","matplotlib-pyplot","plotly"],"latest_commit_sha":null,"homepage":"https://interplot.janjo.ch/","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/janjoch.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":"2023-12-30T12:25:42.000Z","updated_at":"2025-04-14T06:53:14.000Z","dependencies_parsed_at":null,"dependency_job_id":"e3d4b802-f99a-4635-8b24-033ee07b28af","html_url":"https://github.com/janjoch/interplot","commit_stats":{"total_commits":110,"total_committers":6,"mean_commits":"18.333333333333332","dds":0.6363636363636364,"last_synced_commit":"e96098b9ba13a7dbd6c6c5a5f48cbcf8621fc57a"},"previous_names":["janjoch/interplot"],"tags_count":4,"template":false,"template_full_name":null,"purl":"pkg:github/janjoch/interplot","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/janjoch%2Finterplot","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/janjoch%2Finterplot/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/janjoch%2Finterplot/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/janjoch%2Finterplot/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/janjoch","download_url":"https://codeload.github.com/janjoch/interplot/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/janjoch%2Finterplot/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":270781393,"owners_count":24643820,"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","status":"online","status_checked_at":"2025-08-16T02:00:11.002Z","response_time":91,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":["jupyter","jupyter-notebook","matplotlib-pyplot","plotly"],"created_at":"2024-09-24T19:54:49.699Z","updated_at":"2025-08-16T23:11:50.149Z","avatar_url":"https://github.com/janjoch.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# interplot\n\n[![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/janjoch/interplot/HEAD) [![NBViewer](https://raw.githubusercontent.com/jupyter/design/master/logos/Badges/nbviewer_badge.svg)](https://nbviewer.org/github/janjoch/interplot/tree/main/demo/)\n\nCreate `matplotlib` and `plotly` charts with the same few lines of code.\n\nIt combines the best of the `matplotlib` and the `plotly` worlds through a unified, flat API.\n\nSwitch between `matplotlib` and `plotly` with the single keyword `interactive`. All the necessary boilerplate code to translate between the packages is contained in this module.\n\nCurrently supported building blocks:\n\n- scatter plots\n    - `line`\n    - `scatter`\n    - `linescatter`\n- bar charts `bar`\n- histogram `hist`\n- boxplot `boxplot`\n- heatmap `heatmap`\n- linear regression `regression`\n- line fill `fill`\n- annotations `text`\n\nSupported\n- 2D subplots\n- automatic color cycling\n- 3 different API modes\n    - One line of code\n        ```python\n        \u003e\u003e\u003e interplot.line([0,4,6,7], [1,2,4,8])\n        [plotly line figure]\n\n        \u003e\u003e\u003e interplot.hist(np.random.normal(40, 8, 1000), interactive=False)\n        [matplotlib hist figure]\n\n        \u003e\u003e\u003e interplot.boxplot(\n        \u003e\u003e\u003e     [\n        \u003e\u003e\u003e         np.random.normal(20, 5, 1000),\n        \u003e\u003e\u003e         np.random.normal(40, 8, 1000),\n        \u003e\u003e\u003e         np.random.normal(60, 5, 1000),\n        \u003e\u003e\u003e     ],\n        \u003e\u003e\u003e )\n        [plotly boxplots]\n        ```\n\n    - Decorator to auto-initialize plots to use in your methods\n        ```python\n        \u003e\u003e\u003e @interplot.magic_plot\n        \u003e\u003e\u003e def plot_my_data(fig=None):\n        \u003e\u003e\u003e     # import and process your data...\n        \u003e\u003e\u003e     data = np.random.normal(2, 3, 1000)\n        \u003e\u003e\u003e     # draw with the fig instance obtained from the decorator function\n        \u003e\u003e\u003e     fig.add_line(data, label=\"my data\")\n        \u003e\u003e\u003e     fig.add_fill((0, 999), (-1, -1), (5, 5), label=\"sigma\")\n\n        \u003e\u003e\u003e plot_my_data(title=\"My Recording\")\n        [plotly figure \"My Recording\"]\n\n        \u003e\u003e\u003e @interplot.magic_plot_preset(interactive=False, title=\"Preset Title\")\n        \u003e\u003e\u003e def plot_my_data_preconfigured(fig=None):\n        \u003e\u003e\u003e     # import and process your data...\n        \u003e\u003e\u003e     data = np.random.normal(2, 3, 1000)\n        \u003e\u003e\u003e     # draw with the fig instance obtained from the decorator function\n        \u003e\u003e\u003e     fig.add_line(data, label=\"my data\")\n        \u003e\u003e\u003e     fig.add_fill((0, 999), (-1, -1), (5, 5), label=\"sigma\")\n\n        \u003e\u003e\u003e plot_my_data_preconfigured()\n        [matplotlib figure \"Preset Title\"]\n        ```\n\n    - The ```interplot.Plot``` class for full control\n        ```python\n        \u003e\u003e\u003e fig = interplot.Plot(\n        \u003e\u003e\u003e     interactive=True,\n        \u003e\u003e\u003e     title=\"Everything Under Control\",\n        \u003e\u003e\u003e     fig_size=(800, 500),\n        \u003e\u003e\u003e     rows=1,\n        \u003e\u003e\u003e     cols=2,\n        \u003e\u003e\u003e     shared_yaxes=True,\n        \u003e\u003e\u003e     # ...\n        \u003e\u003e\u003e )\n        \u003e\u003e\u003e fig.add_hist(np.random.normal(1, 0.5, 1000), row=0, col=0)\n        \u003e\u003e\u003e fig.add_boxplot(\n        \u003e\u003e\u003e     [\n        \u003e\u003e\u003e         np.random.normal(20, 5, 1000),\n        \u003e\u003e\u003e         np.random.normal(40, 8, 1000),\n        \u003e\u003e\u003e         np.random.normal(60, 5, 1000),\n        \u003e\u003e\u003e     ],\n        \u003e\u003e\u003e     row=0,\n        \u003e\u003e\u003e     col=1,\n        \u003e\u003e\u003e )\n        \u003e\u003e\u003e # ...\n        \u003e\u003e\u003e fig.post_process()\n        \u003e\u003e\u003e fig.show()\n        [plotly figure \"Everything Under Control\"]\n\n        \u003e\u003e\u003e fig.save(\"export/path/file.html\")\n        saved figure at export/path/file.html\n        ```\n\n\n## Resources\n\n- **Documentation:** https://interplot.janjo.ch\n- **Demo Notebooks:** https://nbviewer.org/github/janjoch/interplot/tree/main/demo/\n- **Source Code:** https://github.com/janjoch/interplot\n- **PyPI:** https://pypi.org/project/interplot/\n\n\n## Licence\n[![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)\n\n\n## Demo\n\nView on `NBViewer`:\n[![NBViewer](https://raw.githubusercontent.com/jupyter/design/master/logos/Badges/nbviewer_badge.svg)](https://nbviewer.org/github/janjoch/interplot/tree/main/demo/)\n\n\nTry on `Binder`:\n[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/janjoch/interplot/HEAD)\n\n\n## Install\n```pip install interplot```\n\n\n### dev installation\n1. ```git clone https://github.com/janjoch/interplot```\n2. ```cd interplot```\n2. ```pip install -e .```\n\n\n## Contribute\n\nIdeas, bug reports/fixes, feature requests and code submissions are very welcome! Please write to [janjo@duck.com](mailto:janjo@duck.com) or directly into a pull request.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjanjoch%2Finterplot","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjanjoch%2Finterplot","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjanjoch%2Finterplot/lists"}