[](https://codecov.io/gh/laszukdawid/PyEMD)
[](https://travis-ci.org/laszukdawid/PyEMD)
[](https://pyemd.readthedocs.io/)
# 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:
$S(t) = cos(22 \pi t^2) + 6t^2$
### 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>.
[](https://travis-ci.org/laszukdawid/PyEMD)
[](https://pyemd.readthedocs.io/)
# 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:
$S(t) = cos(22 \pi t^2) + 6t^2$
### 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>.
1 maintainer
Metadata
1 maintainer
Release files for EMD-signal 0.2.5
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| EMD_signal-0.2.5-py2.py3-none-any.whl | Python 2, Python 3 | none | any | Details |
Release files / EMD_signal-0.2.5-py2.py3-none-any.whl
| Download URL | EMD_signal-0.2.5-py2.py3-none-any.whl |
|---|---|
| Size | 33.2 kB |
| Tags | Python 2 Python 3 |
|
SHA-256 checksum How to use checksums |
ab2ac775eeaff4458f19fa07bf9b8fb689649eecfe05d6f1cc52d57c60dba19c
|
|
BLAKE2b-256 checksum How to use checksums |
02c101529164755df43a96e7bb08e06129aea27846efab4cc825f2bc8fa01f0c
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
Security for EMD-signal 0.2.5
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.
