{"id":13605511,"url":"https://github.com/maruedt/chemometrics","last_synced_at":"2025-04-10T19:42:58.065Z","repository":{"id":37007807,"uuid":"260949013","full_name":"maruedt/chemometrics","owner":"maruedt","description":"Python library for chemometric data analysis","archived":false,"fork":false,"pushed_at":"2022-10-16T12:58:07.000Z","size":39787,"stargazers_count":31,"open_issues_count":4,"forks_count":5,"subscribers_count":4,"default_branch":"main","last_synced_at":"2025-04-06T22:04:12.679Z","etag":null,"topics":["chemometrics","data-analysis","ihm","mcr","mvda","pca","pls","preprocessing","python","scikit-learn","spectroscopy","statistics"],"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/maruedt.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"COPYING","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2020-05-03T14:52:16.000Z","updated_at":"2024-12-22T14:19:43.000Z","dependencies_parsed_at":"2022-08-18T00:10:31.602Z","dependency_job_id":null,"html_url":"https://github.com/maruedt/chemometrics","commit_stats":null,"previous_names":[],"tags_count":4,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/maruedt%2Fchemometrics","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/maruedt%2Fchemometrics/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/maruedt%2Fchemometrics/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/maruedt%2Fchemometrics/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/maruedt","download_url":"https://codeload.github.com/maruedt/chemometrics/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248281420,"owners_count":21077423,"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":["chemometrics","data-analysis","ihm","mcr","mvda","pca","pls","preprocessing","python","scikit-learn","spectroscopy","statistics"],"created_at":"2024-08-01T19:00:59.598Z","updated_at":"2025-04-10T19:42:58.027Z","avatar_url":"https://github.com/maruedt.png","language":"Python","funding_links":[],"categories":["Python"],"sub_categories":[],"readme":"[![codecov](https://codecov.io/gh/maruedt/chemometrics/branch/main/graph/badge.svg?token=PUAZA21N10)](https://codecov.io/gh/maruedt/chemometrics)\n[![Documentation Status](https://readthedocs.org/projects/chemometrics/badge/?version=latest)](https://chemometrics.readthedocs.io/en/latest/?badge=latest)\n\n# chemometrics\n\n**chemometrics** is a free and open source library for visualization, modeling, and prediction of multivariate data.\n\nThe package streamlines chemometric workflows by providing powerful preprocessing algorithms combined with simple\nfunctional calls for generating fully calibrated chemometric models. A variety of analytical visualizations help to\nunderstand the data and build trust into the generated models. Looking for further extending the models? chemometrics is\nfully compatible with scikit-learn. Use advanced machine learning algorithms to get most out of your data.\n\nThe documentation is hosted at https://chemometrics.readthedocs.io\n\n## Installation\nchemometrics is distributed over PyPI. The simplest way to install chemometrics is by running\n\n```\npython -m pip install chemometrics\n```\n\n## Example applications\n\nPlotting with reference coloring:\n```python\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport chemometrics as cm\n\nplt.figure(figsize=[12, 5])\nlines = cm.plot_colored_series(D.T, x=wavenumbers, reference=C[:, 1])\nplt.xlabel(r'Wavenumber / $\\mathrm{cm^{-1}}$')\nplt.ylabel('Intensity / AU')\n```\n\n![spectra](docs/source/examples/colored_spectra_by_reference.png \"colored spectra\")\n\nPreprocessing by smoothing and performing a second derivative:\n```python\nX_deriv = cm.Whittaker(constraint_order=3, deriv=2).fit_transform(X)\ncm.plot_colored_series(X_deriv.T, reference=Y)\nplt.xlabel('Wavenumber / nm')\nplt.ylabel('$d^2A/dl^2$ / $mAU/nm^2$')\n```\n![derived spectra](docs/source/examples/peaches_deriv.png \"Second derivative NIR spectra\")\n\n\nA working PLS model is just one function call away:\n```python\ncm.fit_pls(X_deriv, Y)\n```\n![CV scores](docs/source/examples/pls_cv.png \"Cross-validation scores\")\n![PLS analytics](docs/source/examples/pls_analysis.png \"PLS analytical plots\")\n\nInterested in more? Check out following pages:\n- [Full workflow](https://chemometrics.readthedocs.io/en/stable/examples/basic_pls_example.html)  from loading the data to interpreting the calibrated PLS model\n- A variety of [examples](https://chemometrics.readthedocs.io/en/stable/examples.html)\n- [Overview of the API](https://chemometrics.readthedocs.io/en/stable/api.html)\n\n\n## Requirements\n- Python \u003e= 3.8\n- NumPy \u003e= 1.19.2\n- SciPy \u003e= 1.5.2\n- scikit-learn \u003e= 0.23.2\n- matplotlib \u003e= 3.3.2\n\nEarlier versions of the required libraries may work but have not been tested.\n\n\n## Copyright and license\nchemometrics is released under GPLv3.\n\nCopyright 2021, 2022 Matthias Rüdt\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmaruedt%2Fchemometrics","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmaruedt%2Fchemometrics","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmaruedt%2Fchemometrics/lists"}