# Signal Processing toolkit
### Links: **[Homepage](https://spkit.github.io)** | **[Documentation](https://spkit.readthedocs.io/)** | **[Github](https://github.com/Nikeshbajaj/spkit)** | **[PyPi - project](https://pypi.org/project/spkit/)** | _ **Installation:** [pip install spkit](https://pypi.org/project/spkit/)
-----
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[](https://pypi.python.org/pypi/spkit/)
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[](mailto:n.bajaj@qmul.ac.uk)
[](https://doi.org/10.5281/zenodo.4710694)
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-----
## Installation
**Requirement**: numpy, matplotlib, scipy.stats, scikit-learn, seaborn
### with pip
```
pip install spkit
```
### update with pip
```
pip install spkit --upgrade
```
# New in 0.0.9.7:
## MEA Processing Toolkit
# New in 0.0.9.5:
## MEA Processing Toolkit
* sp.mea
## Geometrical Functions
* sp.gemetry
## More on signal processing
* sp.core
## Statistics
* sp.stats
# For updated list of contents and documentation check [github](https://GitHub.com/nikeshbajaj/spkit) or [Documentation](https://spkit.readthedocs.io/)
# List of all functions
# Signal Processing Techniques
## **Information Theory 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
* Plot histogram with optimal bin size
* Computation of optimal bin size for histogram using FD-rule
* Compute bin_width with various statistical measures
* Plot Venn Diagram- joint distribuation and normalized entropy values
## **Dispersion Entropy** --**for time series (physiological signals)**
* **Dispersion Entropy** (Advanced) - for time series signal
* Dispersion Entropy
* Dispersion Entropy - multiscale
* Dispersion Entropy - multiscale - refined
## **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
## **Basic Filtering**
* Removing DC/ Smoothing for multi-channel signals
* Bandpass/Lowpass/Highpass/Bandreject filtering for multi-channel signals
## Biomedical Signal Processing
### MEA Processing Toolkit
**Artifact Removal Algorithm**
* **ATAR Algorithm** [Automatic and Tunable Artifact Removal Algorithm for EEG from artical](https://www.sciencedirect.com/science/article/pii/S1746809419302058)
* **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
* DeepNet (to be updated)
## **Linear Feedback Shift Register**
* pylfsr
# Cite As
```
@software{nikesh_bajaj_2021_4710694,
author = {Nikesh Bajaj},
title = {Nikeshbajaj/spkit: 0.0.9.4},
month = apr,
year = 2022,
publisher = {Zenodo},
version = {0.0.9.4},
doi = {10.5281/zenodo.4710694},
url = {https://doi.org/10.5281/zenodo.4710694}
}
```
# Contacts:
* **Nikesh Bajaj**
* http://nikeshbajaj.in
* n.bajaj[AT]qmul.ac.uk, n.bajaj[AT]imperial[dot]ac[dot]uk
### Imperial College London
______________________________________
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