Signal Processing toolkit
Links: Homepage | Documentation | Github | PyPi - project |
Installation
Requirement: numpy, matplotlib, scipy.stats, scikit-learn, seaborn
with pip
pip install spkit
update with pip
pip install spkit --upgrade
For updated list of contents and documentation check github or Documentation
List of functions [check updated list on homepage]
Information Theory and Signal Processing functions
for real valued signals
-
Entropy
- Shannon entropy
- Rényi entropy of order α, Collision entropy,
- Joint entropy
- Conditional entropy
- Mutual Information
- Cross entropy
- Kullback–Leibler divergence
- Spectral Entropy
- Approximate Entropy
- Sample Entropy
- Permutation Entropy
- SVD Entropy
-
Dispersion Entropy (Advanced) - for time series signal
- Dispersion Entropy
- Dispersion Entropy - multiscale
- Dispersion Entropy - multiscale - refined
-
Differential Entropy (Advanced) - for time series signal
- Differential Entropy
- Mutual Information, Conditional, Joint, Entropy
- Transfer Entropy
Matrix Decomposition
- SVD
- ICA using InfoMax, Extended-InfoMax, FastICA & Picard
Continuase Wavelet Transform
- Gauss wavelet
- Morlet wavelet
- Gabor wavelet
- Poisson wavelet
- Maxican wavelet
- Shannon wavelet
Discrete Wavelet Transform
- Wavelet filtering
- Wavelet Packet Analysis and Filtering
Signal Filtering
- Removing DC/ Smoothing for multi-channel signals
- Bandpass/Lowpass/Highpass/Bandreject filtering for multi-channel signals
Biomedical Signal Processing
- EEG Signal Processing
- MEA Processing Toolkit
Artifact Removal Algorithm
- ATAR Algorithm Automatic and Tunable Artifact Removal Algorithm for EEG from artical
- ICA based Algorith
Analysis and Synthesis Models
- DFT Analysis & Synthesis
- STFT Analysis & Synthesis
- Sinasodal Model - Analysis & Synthesis
- to decompose a signal into sinasodal wave tracks
- f0 detection
Ramanajum Methods for period estimation
- Period estimation for a short length sequence using Ramanujam Filters Banks (RFB)
- Minizing sparsity of periods
Fractional Fourier Transform
- Fractional Fourier Transform
- Fast Fractional Fourier Transform
Machine Learning models - with visualizations
- Logistic Regression
- Naive Bayes
- Decision Trees
and many more ...
Cite As
@software{nikesh_bajaj_2021_4710694,
author = {Nikesh Bajaj},
title = {Nikeshbajaj/spkit: 0.0.9.4},
month = apr,
year = 2021,
publisher = {Zenodo},
version = {0.0.9.4},
doi = {10.5281/zenodo.4710694},
url = {https://doi.org/10.5281/zenodo.4710694}
}
Contacts:
- Nikesh Bajaj
- https://nikeshbajaj.in
- n.bajaj[AT]qmul.ac.uk, n.bajaj[AT]imperial[dot]ac[dot]uk
Metadata
Release files for spkit 0.0.9.7
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 | Actions |
|---|---|---|---|---|
| spkit-0.0.9.7.tar.gz | 4.2 MB | Without provenance | Details for spkit-0.0.9.7.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| spkit-0.0.9.7-py3-none-any.whl | Python 3 | none | any | Details for spkit-0.0.9.7-py3-none-any.whl |
Total release size: 8.5 MB
Release files / spkit-0.0.9.7.tar.gz
| Download URL | spkit-0.0.9.7.tar.gz |
|---|---|
| Size | 4.2 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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|
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BLAKE2b-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.1.1 CPython/3.11.5
|
Release files / spkit-0.0.9.7-py3-none-any.whl
| Download URL | spkit-0.0.9.7-py3-none-any.whl |
|---|---|
| Size | 4.3 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
cf3b3542b72e3ae87c2a45b83223a5442f4fb253a2f7886b62b9cecd9b8cc316
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BLAKE2b-256 checksum How to use checksums |
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|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.1.1 CPython/3.11.5
|
Security for spkit 0.0.9.7
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