EMD: Empirical Mode Decomposition and Hilbert-Huang spectral analyses in Python
The Empirical Mode Decomposition (EMD) package contains Python (>=3.5) functions for analysis of non-linear and non-stationary oscillatory time series. EMD implements a family of sifting algorithms, instantaneous frequency transformations, power spectrum construction and single-cycle feature anal...
| Main Authors: | , , , , |
|---|---|
| Format: | Journal article |
| Language: | English |
| Published: |
Open Journals
2021
|
| _version_ | 1826267606413213696 |
|---|---|
| author | Quinn, AJ Lopes Dos Santos, V Dupret, D De Ozorio Nobre, A Woolrich, M |
| author_facet | Quinn, AJ Lopes Dos Santos, V Dupret, D De Ozorio Nobre, A Woolrich, M |
| author_sort | Quinn, AJ |
| collection | OXFORD |
| description | The Empirical Mode Decomposition (EMD) package contains Python (>=3.5) functions for analysis of non-linear and non-stationary oscillatory time series. EMD implements a family of sifting algorithms, instantaneous frequency transformations, power spectrum construction and single-cycle feature analysis. These implementations are supported by online documentation containing a range of practical tutorials. |
| first_indexed | 2024-03-06T20:56:46Z |
| format | Journal article |
| id | oxford-uuid:3986f4b9-1402-4e0e-aa47-4090230a0671 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T20:56:46Z |
| publishDate | 2021 |
| publisher | Open Journals |
| record_format | dspace |
| spelling | oxford-uuid:3986f4b9-1402-4e0e-aa47-4090230a06712022-03-26T13:56:06ZEMD: Empirical Mode Decomposition and Hilbert-Huang spectral analyses in PythonJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:3986f4b9-1402-4e0e-aa47-4090230a0671EnglishSymplectic ElementsOpen Journals2021Quinn, AJLopes Dos Santos, VDupret, DDe Ozorio Nobre, AWoolrich, MThe Empirical Mode Decomposition (EMD) package contains Python (>=3.5) functions for analysis of non-linear and non-stationary oscillatory time series. EMD implements a family of sifting algorithms, instantaneous frequency transformations, power spectrum construction and single-cycle feature analysis. These implementations are supported by online documentation containing a range of practical tutorials. |
| spellingShingle | Quinn, AJ Lopes Dos Santos, V Dupret, D De Ozorio Nobre, A Woolrich, M EMD: Empirical Mode Decomposition and Hilbert-Huang spectral analyses in Python |
| title | EMD: Empirical Mode Decomposition and Hilbert-Huang spectral analyses in Python |
| title_full | EMD: Empirical Mode Decomposition and Hilbert-Huang spectral analyses in Python |
| title_fullStr | EMD: Empirical Mode Decomposition and Hilbert-Huang spectral analyses in Python |
| title_full_unstemmed | EMD: Empirical Mode Decomposition and Hilbert-Huang spectral analyses in Python |
| title_short | EMD: Empirical Mode Decomposition and Hilbert-Huang spectral analyses in Python |
| title_sort | emd empirical mode decomposition and hilbert huang spectral analyses in python |
| work_keys_str_mv | AT quinnaj emdempiricalmodedecompositionandhilberthuangspectralanalysesinpython AT lopesdossantosv emdempiricalmodedecompositionandhilberthuangspectralanalysesinpython AT dupretd emdempiricalmodedecompositionandhilberthuangspectralanalysesinpython AT deozorionobrea emdempiricalmodedecompositionandhilberthuangspectralanalysesinpython AT woolrichm emdempiricalmodedecompositionandhilberthuangspectralanalysesinpython |