{
    "info": {
        "author": "Igor Gotlibovych",
        "author_email": "igor.gotlibovych@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Intended Audience :: Developers",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6"
        ],
        "description": "[![Build Status](https://travis-ci.com/ig248/pyampd.svg?branch=master)](https://travis-ci.com/ig248/pyampd)\n[![Coverage Status](https://codecov.io/gh/ig248/pyampd/branch/master/graph/badge.svg)](https://codecov.io/gh/ig248/pyampdgit)\n\n# AMPD algorithm in Python\nImplements a function `find_peaks` based on the Automatic Multi-scale\nPeak Detection algorithm proposed by Felix Scholkmann et al. in\n\"An Efficient Algorithm for Automatic Peak Detection in\nNoisy Periodic and Quasi-Periodic Signals\", Algorithms 2012,\n 5, 588-603\n\n![Peak finding](https://raw.githubusercontent.com/ig248/pyampd/master/ass_ampd.png)\n\n## Usage\nInstall from PyPI:\n\n```\npip install pyampd\n```\n\nOr install from source:\n\n```\npip install git+https://github.com/ig248/pyampd\n```\n\nImport function:\n\n```python\nfrom pyampd.ampd import find_peaks\n```\n\nSee `notebooks/ampd.ipynb` for usage examples.\n\n### Specifying maximum scale\nTo improve run-time on large time-series, it is possible to specify the maximum scale to consider:\n```python\npeaks = find_peaks(x, scale=100)\n```\nwill only consider windows up to +-100 point either side of peak candidates.\n\n### Adaptive Scale Selection\nIf the characteristic scale of the signal changes over time, a new algorithm called\nAdaptive Scale Selection can track the changes in optimal scales and detect peaks accordingly:\n```python\npeaks = find_peaks_adaptive(x, window=200)\n```\nwill select the optimal scale at each point using a 200-point running window.\n\n\n### Original implementation\n`find_peaks` is not identical to the algorithm proposed in the original paper (especially near start and end of time series).\n A performance-optimized version of the original implementation is provided in `find_peaks_original`.\n\n\n## Tests\nRun\n```bash\npytest\n```\n\n## Other implementations\n- R: https://cran.r-project.org/web/packages/ampd/index.html\n- MATLAB: https://github.com/mathouse/AMPD-algorithm\n- Python: https://github.com/LucaCerina/ampdLib\n\n## Improvements\nThis Python implementation provides significant speed-ups in two areas:\n1. Efficient tracking of local minima without using random numbers\n2. Introduction of maximum window size, reducing algorithm run-time from\nquadratic to linear in the number of samples.\n3. Better handling of peaks near start/end of the series\n4. Addition of new Adaptive Scale Selection \n\n## ToDo\n- It may be possible to avoid repeated comparisons, and reduce worst-case\nruntime from `O(n^2)` to `O(n log(n))`.\n- `find_peaks_adaptive` could benefit from specifying both `window` and `max_scale`\n\n## References\nOriginal paper: https://doi.org/10.1109/ICRERA.2016.7884365\n\n\n",
        "description_content_type": "text/markdown",
        "docs_url": null,
        "download_url": "",
        "downloads": {
            "last_day": -1,
            "last_month": -1,
            "last_week": -1
        },
        "home_page": "https://github.com/ig248/pyampd",
        "keywords": "",
        "license": "MIT",
        "maintainer": "",
        "maintainer_email": "",
        "name": "pyampd",
        "package_url": "https://pypi.org/project/pyampd/",
        "platform": "",
        "project_url": "https://pypi.org/project/pyampd/",
        "project_urls": {
            "Homepage": "https://github.com/ig248/pyampd"
        },
        "release_url": "https://pypi.org/project/pyampd/0.0.1/",
        "requires_dist": [
            "numpy",
            "scipy"
        ],
        "requires_python": "",
        "summary": "Peak detection using AMPD and ASS-AMPD algorithms",
        "version": "0.0.1"
    },
    "last_serial": 4634033,
    "releases": {
        "0.0.1": [
            {
                "comment_text": "",
                "digests": {
                    "md5": "89da3b6ba286573eec3062f713b83c6b",
                    "sha256": "d923a4372308d447b98cd80f935a059e29323777dd26c0ac131945ad84f5f89c"
                },
                "downloads": -1,
                "filename": "pyampd-0.0.1-py2.py3-none-any.whl",
                "has_sig": false,
                "md5_digest": "89da3b6ba286573eec3062f713b83c6b",
                "packagetype": "bdist_wheel",
                "python_version": "py2.py3",
                "requires_python": null,
                "size": 5106,
                "upload_time": "2018-12-25T20:25:49",
                "url": "https://files.pythonhosted.org/packages/53/b5/587d52d43aa803c20743fd23e2bcbb454a06694c5645de741311678c8750/pyampd-0.0.1-py2.py3-none-any.whl"
            },
            {
                "comment_text": "",
                "digests": {
                    "md5": "48da763e6ab7df22347212859589d051",
                    "sha256": "e333d00b61d2844b0ed0383b010394e0a0cb384a036dd83960cdc647cf71a755"
                },
                "downloads": -1,
                "filename": "pyampd-0.0.1.tar.gz",
                "has_sig": false,
                "md5_digest": "48da763e6ab7df22347212859589d051",
                "packagetype": "sdist",
                "python_version": "source",
                "requires_python": null,
                "size": 4612,
                "upload_time": "2018-12-25T20:25:52",
                "url": "https://files.pythonhosted.org/packages/51/97/6a56d7ed7d1ed5cb25c4643cb327a1903438587991fe364ac6042c2b837d/pyampd-0.0.1.tar.gz"
            }
        ]
    },
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "md5": "89da3b6ba286573eec3062f713b83c6b",
                "sha256": "d923a4372308d447b98cd80f935a059e29323777dd26c0ac131945ad84f5f89c"
            },
            "downloads": -1,
            "filename": "pyampd-0.0.1-py2.py3-none-any.whl",
            "has_sig": false,
            "md5_digest": "89da3b6ba286573eec3062f713b83c6b",
            "packagetype": "bdist_wheel",
            "python_version": "py2.py3",
            "requires_python": null,
            "size": 5106,
            "upload_time": "2018-12-25T20:25:49",
            "url": "https://files.pythonhosted.org/packages/53/b5/587d52d43aa803c20743fd23e2bcbb454a06694c5645de741311678c8750/pyampd-0.0.1-py2.py3-none-any.whl"
        },
        {
            "comment_text": "",
            "digests": {
                "md5": "48da763e6ab7df22347212859589d051",
                "sha256": "e333d00b61d2844b0ed0383b010394e0a0cb384a036dd83960cdc647cf71a755"
            },
            "downloads": -1,
            "filename": "pyampd-0.0.1.tar.gz",
            "has_sig": false,
            "md5_digest": "48da763e6ab7df22347212859589d051",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": null,
            "size": 4612,
            "upload_time": "2018-12-25T20:25:52",
            "url": "https://files.pythonhosted.org/packages/51/97/6a56d7ed7d1ed5cb25c4643cb327a1903438587991fe364ac6042c2b837d/pyampd-0.0.1.tar.gz"
        }
    ]
}