{"id":15563919,"url":"https://github.com/felixpatzelt/scorr","last_synced_at":"2025-04-09T16:52:31.845Z","repository":{"id":57464562,"uuid":"105209448","full_name":"felixpatzelt/scorr","owner":"felixpatzelt","description":"Fast and flexible two- and three-point correlation analysis for time series using spectral methods.","archived":false,"fork":false,"pushed_at":"2019-01-07T23:30:22.000Z","size":6060,"stargazers_count":10,"open_issues_count":0,"forks_count":4,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-03-23T19:01:39.086Z","etag":null,"topics":["correlation","correlations","python","scientific","spectral-methods","time-series"],"latest_commit_sha":null,"homepage":null,"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/felixpatzelt.png","metadata":{"files":{"readme":"README.rst","changelog":"CHANGELOG.rst","contributing":null,"funding":null,"license":"LICENSE.txt","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2017-09-28T23:42:53.000Z","updated_at":"2022-12-16T08:35:46.000Z","dependencies_parsed_at":"2022-08-31T04:52:00.904Z","dependency_job_id":null,"html_url":"https://github.com/felixpatzelt/scorr","commit_stats":null,"previous_names":[],"tags_count":3,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/felixpatzelt%2Fscorr","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/felixpatzelt%2Fscorr/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/felixpatzelt%2Fscorr/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/felixpatzelt%2Fscorr/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/felixpatzelt","download_url":"https://codeload.github.com/felixpatzelt/scorr/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247666016,"owners_count":20975788,"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":["correlation","correlations","python","scientific","spectral-methods","time-series"],"created_at":"2024-10-02T16:30:09.783Z","updated_at":"2025-04-09T16:52:31.827Z","avatar_url":"https://github.com/felixpatzelt.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"scorr\n=====\n\nFast two- and three-point correlation analysis for time series\nusing spectral methods.\n\nThe calculations are FFT-based for optimal performance and offer many options \nfor normalisation, mean removal, averaging, and zero-padding. In particular, \naveraging over pandas groups of different sizes (e.g. different days) is \nsupported.\n    \n======================  ======================================================\nFunction                Synopsis\n======================  ======================================================\nacorr                   Calculate autocorrelation or autocovariance\nacorr_grouped_df        Calculate acorr for pandas groups and average\ncorr_mat                Convert correlation vector to matrix\nfft2x                   Calculate cross-bispectrum\nfftcrop                 Return cropped fft or correlation\nget_nfft                Find a good FFT segment size for pandas groups of \n                        different sizes \npadded_x3corr_norm      Normalise and debias three-point cross-correlations\npadded_xcorr_norm       Normalise and debias two-point cross-correlations\nx3corr                  Calculate three-point cross-correlation matrix\nx3corr_grouped_df       Calculate x3corr for pandas groups and average\nxcorr                   Calculate two-point cross-correlation or covariance\nxcorr_grouped_df        Calculate xcorr for pandas groups and average\nxcorrshift              Convert xcorr output so lag zero is centered\n======================  ======================================================\n\nThe algorithms to calculate three-point correlations and details of daily\naveraging over high-frequency trading data are described in:\n\t\n    Patzelt, F. and Bouchaud, J-P. (2017):\n    Nonlinear price impact from linear models. \n    Journal of Statistical Mechanics: Theory and Experiment, 12, 123404. \n    Preprint at `arXiv:1708.02411 \u003c//arxiv.org/abs/1708.02411\u003e`_.\n\nMore code from the same publication is released in the `priceprop \n\u003chttps://github.com/felixpatzelt/priceprop\u003e`_ package. \n\nPlease find further \nexplanations in the docstrings and in the examples \ndirectory. \n\n\nInstallation\n------------\n\n\tpip install scorr\n\t\n\nDependencies (automatically installed)\n--------------------------------------\n\n    - Python 2.7 or 3.6\n    - NumPy\n    - SciPy\n    - Pandas    \n   \n    \nOptional Dependencies required only for the examples (pip installable)\n----------------------------------------------------------------------\n\n    - Jupyter\n    - Matplotlib\n    - colorednoise\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffelixpatzelt%2Fscorr","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffelixpatzelt%2Fscorr","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffelixpatzelt%2Fscorr/lists"}