{ "info": { "author": "Antonino Ingargiola", "author_email": "tritemio@gmail.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 2 - Pre-Alpha", "Intended Audience :: Developers", "License :: OSI Approved :: GNU General Public License v3 (GPLv3)", "Natural Language :: English", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6" ], "description": "===========\nPycorrelate\n===========\n\n\n.. image:: https://img.shields.io/pypi/v/pycorrelate.svg\n :target: https://pypi.python.org/pypi/pycorrelate\n\n.. image:: https://img.shields.io/travis/tritemio/pycorrelate.svg\n :target: https://travis-ci.org/tritemio/pycorrelate\n\n.. image:: https://ci.appveyor.com/api/projects/status/dcanybpqi2o1ecwi/branch/master?svg=true\n :target: https://ci.appveyor.com/project/tritemio/pycorrelate/branch/master\n\n.. image:: https://readthedocs.org/projects/pycorrelate/badge/?version=latest\n :target: https://pycorrelate.readthedocs.io/en/latest/?badge=latest\n :alt: Documentation Status\n\n\n**Pycorrelate** computes fast and accurate cross-correlation over\narbitrary time lags.\nCross-correlations can be calculated on \"uniformly-sampled\" signals\nor on \"point-processes\", such as photon timestamps.\nPycorrelate allows computing cross-correlation at log-spaced lags covering\nseveral orders of magnitude. This type of cross-correlation is\ncommonly used in physics or biophysics for techniques such as\n*fluorescence correlation spectroscopy* (`FCS `__) or\n*dynamic light scattering* (`DLS `__).\n\nTwo types of correlations are implemented:\n\n- `ucorrelate `__:\n the classical text-book linear cross-correlation between two signals\n defined at **uniformly spaced** intervals.\n Only positive lags are computed and a max lag can be specified.\n Thanks to the limit in the computed lags, this function can be much faster than\n `numpy.correlate `__.\n\n- `pcorrelate `__:\n cross-correlation of discrete events\n in a point-process. In this case input arrays can be timestamps or\n positions of \"events\", for example **photon arrival times**.\n This function implements the algorithm in\n `Laurence et al. Optics Letters (2006) `__.\n This is a generalization of the multi-tau algorithm which retains\n high execution speed while allowing arbitrary time-lag bins.\n\nPycorrelate is implemented in Python 3 and operates on standard numpy arrays.\nExecution speed is optimized using `numba `__.\n\n* Free software: GNU General Public License v3\n* Documentation: https://pycorrelate.readthedocs.io.\n\n\n=======\nHistory\n=======\n\n0.2.1 (2017-11-15)\n------------------\n\n- Added normalization for FCS curves (see `pnormalize `__).\n- Added example notebook showing how to fit a simple FCS curve\n- Renamed `ucorrelate `__ argument from `maxlags` to `maxlag`.\n- Added `theory page `__ in the documentation, showing the exact formula used for CCF calculations.\n\n0.1.0 (2017-07-23)\n------------------\n\n* First release on PyPI.", "description_content_type": null, "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/tritemio/pycorrelate", "keywords": "pycorrelate", "license": "GNU General Public License v3", "maintainer": "", "maintainer_email": "", "name": "pycorrelate", "package_url": "https://pypi.org/project/pycorrelate/", "platform": "", "project_url": "https://pypi.org/project/pycorrelate/", "project_urls": { "Homepage": "https://github.com/tritemio/pycorrelate" }, "release_url": "https://pypi.org/project/pycorrelate/0.3/", "requires_dist": null, "requires_python": "", "summary": "Fast and accurate timestamps correlation in python.", "version": "0.3" }, "last_serial": 3337072, "releases": { "0.1.0": [ { "comment_text": "", "digests": { "md5": "1954e53c0093484ea12c2b289871ae7d", "sha256": "0ca47f02d9e4331fa9d7c95d2af1a9a8d0106b0d145483f68c6f7dd8d4e109e9" }, "downloads": -1, "filename": "pycorrelate-0.1.0.tar.gz", "has_sig": false, "md5_digest": "1954e53c0093484ea12c2b289871ae7d", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 965260, "upload_time": "2017-08-01T19:58:35", "url": "https://files.pythonhosted.org/packages/c7/be/4a396834cae52063f337eb7e00e20e0a856cea781c6fd29fc025faf1692f/pycorrelate-0.1.0.tar.gz" } ], "0.2.1": [ { "comment_text": "", "digests": { "md5": "6c62e08bc20b919e0045ac65c76bf1d2", "sha256": "1a34e368a2b5ed2359f3c02adcb38d82437e7b1aa96f50356dbff6937e2559d8" }, "downloads": -1, "filename": "pycorrelate-0.2.1.tar.gz", "has_sig": false, "md5_digest": "6c62e08bc20b919e0045ac65c76bf1d2", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 2018394, "upload_time": "2017-11-15T08:58:18", "url": "https://files.pythonhosted.org/packages/d2/d2/806d4e7597a9a568c9f5fa33e4f6156c05db1e0ec5faf29d9daa2304003e/pycorrelate-0.2.1.tar.gz" } ], "0.3": [ { "comment_text": "", "digests": { "md5": "ce89f421ba6aacc5d3410635fe17a090", "sha256": "bbd358b0c924c900b8b345eaa9b9ef659b18e6f639c017898362f77dc6a0cce3" }, "downloads": -1, "filename": "pycorrelate-0.3.tar.gz", "has_sig": false, "md5_digest": "ce89f421ba6aacc5d3410635fe17a090", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 206015, "upload_time": "2017-11-16T01:43:03", "url": "https://files.pythonhosted.org/packages/bf/b1/58b7f6001ef5ff8ca23c08f07b72b37c369ddae9f917dce2471e60582822/pycorrelate-0.3.tar.gz" } ] }, "urls": [ { "comment_text": "", "digests": { "md5": "ce89f421ba6aacc5d3410635fe17a090", "sha256": "bbd358b0c924c900b8b345eaa9b9ef659b18e6f639c017898362f77dc6a0cce3" }, "downloads": -1, "filename": "pycorrelate-0.3.tar.gz", "has_sig": false, "md5_digest": "ce89f421ba6aacc5d3410635fe17a090", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 206015, "upload_time": "2017-11-16T01:43:03", "url": "https://files.pythonhosted.org/packages/bf/b1/58b7f6001ef5ff8ca23c08f07b72b37c369ddae9f917dce2471e60582822/pycorrelate-0.3.tar.gz" } ] }