https://github.com/rcv911/emd

Empirical Mode Decomposition
https://github.com/rcv911/emd

data-analysis emd empirical-mode-decomposition

Last synced: 11 days ago
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Empirical Mode Decomposition

• Host: GitHub
• URL: https://github.com/rcv911/emd
• Owner: rcv911
• Created: 2018-01-06T14:05:18.000Z (about 8 years ago)
• Default Branch: master
• Last Pushed: 2018-01-06T15:32:32.000Z (about 8 years ago)
• Last Synced: 2023-11-29T20:43:06.811Z (about 2 years ago)
• Topics: data-analysis, emd, empirical-mode-decomposition
• Language: Python
• Size: 1.11 MB
• Stars: 3
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# EMD = Empirical Mode Decomposition 

## Description

[EMD](http://rspa.royalsocietypublishing.org/content/royprsa/454/1971/903.full.pdf) is a method for analysing non-stationary and nonlinear data. 

I'm going to tell you main things about EMD:

- Method is locally adaptive, data-driven, multiscale, high efficient. 

- The user specifies the number of mod. 

- Fast oscillations superimposed to slow oscillations 

(First mode = fast oscillations = high frequency. Last mode = slow oscillations = low frequency).  

- Many applications to speech analysis (biological data, astronomical data, nonlinear physics data, earthquake, climate, etc.).

## Motivation

## Test Data

We are going to use noise sinus:

```python

	noise = random.uniform(-0.05,0.05,10000)

	signal = sin(2*pi*f*t) + noise

```

![](images/test_signal.png)

![](images/test_signal+.png)

## Results

If number of mod = 2

![](images/modes_k2.png)

![](images/result_k2.png)

![](images/result+_k2.png)

If number of mod = 4

![](images/modes_k4.png)

![](images/modes+_k4.png)

![](images/result_k4.png)

![](images/result+_k4.png)

Result EMD for Van der Pol oscillator. The number of mod = 4.

![](images/emd_van_der_pol_oscillator.png)

## Learn more

- [Wiki EMD](https://en.wikipedia.org/wiki/Hilbert%E2%80%93Huang_transform#Empirical_mode_decomposition_(EMD))

- [scipy.interpolate.splrep](https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.splrep.html)

- [scipy.interpolate.splev](https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.splev.html)

< I'm going to add some useful links lately...

## Installation

You can use [Python](https://www.python.org/) with data package: [Anaconda](https://www.anaconda.com/) or [Miniconda](https://conda.io/miniconda).

There's another way - use [Portable Python](http://portablepython.com/). Also you can use whatever IDE for Python.

## License

Free