Skip to main content

Implementation of Empirical Mode Decomposition (EMD) and its variations

Project description

[![codecov](https://codecov.io/gh/laszukdawid/PyEMD/branch/master/graph/badge.svg)](https://codecov.io/gh/laszukdawid/PyEMD)
[![BuildStatus](https://travis-ci.org/laszukdawid/PyEMD.png?branch=master)](https://travis-ci.org/laszukdawid/PyEMD)
[![DocStatus](https://readthedocs.org/projects/pyemd/badge/?version=latest)](https://pyemd.readthedocs.io/)
![Codacy](https://api.codacy.com/project/badge/Grade/5385d5ddc8e84908bd4e38f325443a21%0A%20:alt:%20Codacy%20Badge%0A%20:target:%20https://www.codacy.com/app/laszukdawid/PyEMD?utm_source=github.com&utm_medium=referral&utm_content=laszukdawid/PyEMD&utm_campaign=badger)

# PyEMD

## Links

- HTML documentation: <https://pyemd.readthedocs.org>
- Issue tracker: <https://github.com/laszukdawid/pyemd/issues>
- Source code repository: <https://github.com/laszukdawid/pyemd>

## Introduction

This is yet another Python implementation of Empirical Mode
Decomposition (EMD). The package contains many EMD variations and
intends to deliver more in time.

### EMD variations:
* Ensemble EMD (EEMD),
* Image decomposition (EMD2D),
* "Complete Ensemble EMD" (CEEMDAN)
* different settings and configurations of vanilla EMD.

*PyEMD* allows to use different splines for envelopes, stopping criteria
and extrema interpolation.

### Available splines:
* Natural cubic [default]
* Pointwise cubic
* Akima
* Linear

### Available stopping criteria:
* Cauchy convergence [default]
* Fixed number of iterations
* Number of consecutive proto-imfs

### Extrema detection:
* Discrete extrema [default]
* Parabolic interpolation

## Installation

### Recommended

Simply download this directory either directly from GitHub, or using
command line:

> \$ git clone <https://github.com/laszukdawid/PyEMD>

Then go into the downloaded project and run from command line:

> \$ python setup.py install

### PyPi

Packaged obtained from PyPi is/will be slightly behind this project, so
some features might not be the same. However, it seems to be the
easiest/nicest way of installing any Python packages, so why not this
one?

> \$ pip install EMD-signal

## Example

More detailed examples are included in the
[documentation](https://pyemd.readthedocs.io/en/latest/examples.html) or
in the
[PyEMD/examples](https://github.com/laszukdawid/PyEMD/tree/master/example).

### EMD

In most cases default settings are enough. Simply import `EMD` and pass
your signal to instance or to `emd()` method.

```python
from PyEMD import EMD
import numpy as np

s = np.random.random(100)
emd = EMD()
IMFs = emd(s)
```

The Figure below was produced with input:
$S(t) = cos(22 \pi t^2) + 6t^2$

![simpleExample](https://github.com/laszukdawid/PyEMD/raw/master/example/simple_example.png?raw=true)

### EEMD

Simplest case of using Ensemble EMD (EEMD) is by importing `EEMD` and
passing your signal to the instance or `eemd()` method.

```python
from PyEMD import EEMD
import numpy as np

s = np.random.random(100)
eemd = EEMD()
eIMFs = eemd(s)
```

### CEEMDAN

As with previous methods, there is also simple way to use `CEEMDAN`.

```python
from PyEMD import CEEMDAN
import numpy as np

s = np.random.random(100)
ceemdan = CEEMDAN()
cIMFs = ceemdan(s)
```

### EMD2D

Simplest case is to pass image as monochromatic numpy 2D array. As with
other modules one can use default setting of instance or more explicitly
use `emd2d()` method.

```python
from PyEMD import EMD2D
import numpy as np

x, y = np.arange(128), np.arange(128).reshape((-1,1))
img = np.sin(0.1*x)*np.cos(0.2*y)
emd2d = EMD2D()
IMFs_2D = emd2d(img)
```

## Contact

Feel free to contact me with any questions, requests or simply saying
*hi*. It's always nice to know that I might have contributed to saving
someone's time or that I might improve my skills/projects.

Contact me either through gmail (laszukdawid @ gmail) or search me
favourite web search.

### Citation

If you found this package useful and would like to cite it in your work
please use following structure:

Dawid Laszuk (2017-), **Python implementation of Empirical Mode
Decomposition algorithm**. <http://www.laszukdawid.com/codes>.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for EMD-signal, version 0.2.5
Filename, size File type Python version Upload date Hashes
Filename, size EMD_signal-0.2.5-py2.py3-none-any.whl (33.2 kB) File type Wheel Python version py2.py3 Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page