{"id":19142142,"url":"https://github.com/carlosqsilva/pyspc","last_synced_at":"2025-04-04T07:05:12.473Z","repository":{"id":37484297,"uuid":"61959843","full_name":"carlosqsilva/pyspc","owner":"carlosqsilva","description":"Statistical Process Control Charts Library for Humans","archived":false,"fork":false,"pushed_at":"2023-01-12T02:48:30.000Z","size":344,"stargazers_count":214,"open_issues_count":6,"forks_count":75,"subscribers_count":18,"default_branch":"master","last_synced_at":"2025-03-28T06:05:17.106Z","etag":null,"topics":["control-chart","python","spc"],"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/carlosqsilva.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":"2016-06-25T20:11:56.000Z","updated_at":"2025-03-22T18:52:38.000Z","dependencies_parsed_at":"2023-02-09T09:46:41.610Z","dependency_job_id":null,"html_url":"https://github.com/carlosqsilva/pyspc","commit_stats":null,"previous_names":[],"tags_count":2,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/carlosqsilva%2Fpyspc","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/carlosqsilva%2Fpyspc/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/carlosqsilva%2Fpyspc/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/carlosqsilva%2Fpyspc/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/carlosqsilva","download_url":"https://codeload.github.com/carlosqsilva/pyspc/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247135141,"owners_count":20889420,"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":["control-chart","python","spc"],"created_at":"2024-11-09T07:26:05.812Z","updated_at":"2025-04-04T07:05:12.446Z","avatar_url":"https://github.com/carlosqsilva.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# PySpc\n[![PyPI version](https://badge.fury.io/py/pyspc.svg)](https://badge.fury.io/py/pyspc)\n\nStatistical Process Control Charts Library for Humans\n\nPySpc is a Python library aimed to make Statistical Process Control Charts as easy as possible.\n\nTake a look at my other project [cchart-online](https://carlosqsilva.github.io/ccharts-online/).\n\n## Features\n\nControl Charts by Variables\n* Mean and Amplitude\n* Mean and Standard Deviation\n* Individual Values and Moving Range\n* Individual values with subgroups\n* Exponentially Weighted Moving Average (EWMA)\n* Cumulative Sum (CUSUM)\n\nControl Charts by Attributes\n* P Chart\n* NP Chart\n* C Chart\n* U Chart\n\nMultivariate Control Charts\n* T Square Hotelling\n* T Square Hotelling with SubGroup\n* Multivariate Exponentially Weighted Moving Average (MEWMA)\n\n##Installation\n```bash\n$ pip install pyspc\n```\n\n## Usage\n```python\nfrom pyspc import *\n\na = spc(pistonrings) + ewma()\nprint(a)\n```\n\u003cimg src=\"https://github.com/carlosqsilva/pyspc/blob/master/screenshots/1-screen.png\" align=\"center\" height=\"400\" width=\"450\"\u003e\n\nadding rules highlighting...\n```python\na + rules()\n```\n\n\u003cimg src=\"https://github.com/carlosqsilva/pyspc/blob/master/screenshots/2-screen.png\" align=\"center\" height=\"400\" width=\"450\"\u003e\n\nadding more control charts to the mix...\n```python\na + cusum() + xbar_sbar() + sbar()\n``` \n\n\u003cimg src=\"https://github.com/carlosqsilva/pyspc/blob/master/screenshots/3-screen.png\" align=\"center\" height=\"500\" width=\"450\"\u003e\n\nit comes with 18 sample datasets to play with, available in **./pyspc/sampledata**, you can use your own data (of course). Your data can be nested lists, numpy array or pandas DataFrame.\n```python\nimport numpy\nfrom pyspc import *\nfake_data = numpy.random.randn(30, 5) + 100\na = spc(fake_data) + xbar_rbar() + rbar() + rules()\nprint(a)\n```\n\n\u003cimg src=\"https://github.com/carlosqsilva/pyspc/blob/master/screenshots/5-screen.png\" align=\"center\" height=\"400\" width=\"450\"\u003e\n\n## Gtk Gui\nIts also available a python gui application for those who do not like to mess with code.\n\n```bash\n$ python3 pyspc_gui.py\n```\n![alt text](screenshots/4-screen.png \"Gui Application\")\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcarlosqsilva%2Fpyspc","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcarlosqsilva%2Fpyspc","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcarlosqsilva%2Fpyspc/lists"}