Metadata-Version: 2.4
Name: scope-emd
Version: 1.0.0
Summary: scope is the Python-based package for detecting oscillatory                  signals in observational or experimental time series with the                 EMD technique and assessing their statistical significance vs.                 power-law distributed background noise.
Home-page: https://github.com/Warwick-Solar/scope
Author: Dmitrii Kolotkov, Weijie Gu, Sergey Belov, Valery Nakariakov
Author-email: Sergey.Belov@warwick.ac.uk
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: colorednoise>=2.2.0
Requires-Dist: emd>=0.7.0
Requires-Dist: numpy>=1.3.0
Requires-Dist: lmfit>=1.3.2
Requires-Dist: tqdm>=4.0.0
Provides-Extra: dev
Requires-Dist: pytest; extra == "dev"
Requires-Dist: coverage; extra == "dev"
Requires-Dist: pytest-cov; extra == "dev"
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`scope` is the Python-based package for detecting          oscillatory signals in observational or experimental time series with         the EMD technique and assessing their statistical significance vs.         power-law distributed background noise. Oscillatory processes in real         data sets of various origins are often contaminated by a combination of         white and coloured noise with a power-law spectral dependence, so         that the EMD-revealed intrinsic mode functions need to be rigorously         tested against the periodic components generated by noise. To do so,         we compute the _EMD energy spectrum_ containing the total energy and         dominant period of each EMD-revealed intrinsic mode and the noise         confidence limits for modal energy. This allows us to identify the         significant mode(s) with the energy beyond the confidence limits,         which is expected to be of a non-noise origin and associated with a         quasi-periodic oscillatory process of interest. The developed package         does not assume the physical origin of the input data set, making it         readily applicable for analysing oscillatory processes across various         fields of science and industry.
