A python package for Empirical Mode Decomposition and related spectral analyses.
Please note that this project is in active development for the moment - the API may change relatively quickly between releases!
Installation
You can install the latest stable release from the PyPI repository
pip install emd
or clone and install the source code.
git clone https://gitlab.com/emd-dev/emd.git
cd emd
pip install .
Requirements are specified in requirements.txt. Main functionality only depends on numpy and scipy for computation and matplotlib for visualisation.
Quick Start
Full documentation can be found at https://emd.readthedocs.org and development/issue tracking at gitlab.com/emd-dev/emd
Import emd
import emd
Define a simulated waveform containing a non-linear wave at 5Hz and a sinusoid at 1Hz.
sample_rate = 1000
seconds = 10
num_samples = sample_rate*seconds
import numpy as np
time_vect = np.linspace(0, seconds, num_samples)
freq = 5
nonlinearity_deg = .25 # change extent of deformation from sinusoidal shape [-1 to 1]
nonlinearity_phi = -np.pi/4 # change left-right skew of deformation [-pi to pi]
x = emd.simulate.abreu2010(freq, nonlinearity_deg, nonlinearity_phi, sample_rate, seconds)
x += np.cos(2*np.pi*1*time_vect)
Estimate IMFs
imf = emd.sift.sift(x)
Compute instantaneous frequency, phase and amplitude using the Normalised Hilbert Transform Method.
IP, IF, IA = emd.spectra.frequency_transform(imf, sample_rate, 'hilbert')
Compute Hilbert-Huang spectrum
freq_range = (0, 10, 100) # 0 to 10Hz in 100 steps
f, hht = emd.spectra.hilberthuang(IF, IA, freq_range, sum_time=False)
Make a summary plot
```python
import matplotlib.pyplot as plt
plt.figure(figsize=(16, 8))
plt.subplot(211, frameon=False)
plt.plot(time_vect, x, 'k')
plt.plot(time_vect, imf[:, 0]-4, 'r')
plt.plot(time_vect, imf[:, 1]-8, 'g')
plt.plot(time_vect, imf[:, 2]-12, 'b')
plt.xlim(time_vect[0], time_vect[-1])
plt.grid(True)
plt.subplot(212)
plt.pcolormesh(time_vect, f, hht, cmap='ocean_r')
plt.ylabel('Frequency (Hz)')
plt.xlabel('Time (secs)')
plt.grid(True)
plt.show()
Metadata
Release files for emd 0.8.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| emd-0.8.1.tar.gz | 9.4 MB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| emd-0.8.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 9.5 MB
Release files / emd-0.8.1.tar.gz
| Download URL | emd-0.8.1.tar.gz |
|---|---|
| Size | 9.4 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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No |
| Uploaded via |
twine/6.1.0 CPython/3.13.2
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Release files / emd-0.8.1-py3-none-any.whl
| Download URL | emd-0.8.1-py3-none-any.whl |
|---|---|
| Size | 99.1 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
bbd8fb9e9a7c02b1d4de206409b859978577ab8c3ec2b163ce1ccc095c271b3f
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.13.2
|
Security for emd 0.8.1
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