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  • ◈ Easy to use toolkit for signal processing and analysis
  • ◈ More of biomedical signal analysis with visualization
  • ◈ Includes basic machine learning models with visualization

Signal Processing Toolkit

Simple and easy to use for signal analysis and predictive analysis

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0.0.9.7 (latest)
0.0.9.7 (latest) 0.0.9.6 0.0.9.5 0.0.9.4 0.0.9.3 0.0.9.2 0.0.9.1 More
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SpKit 0.0.9.7
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  • User Guide
    • 1. Information Theory
    • 2. Signal Filtering
      • 2.1. Drift Removing
      • 2.2. Smoothing
      • 2.3. Spectral Filtering
      • 2.4. Kernel Filtering
      • 2.5. Filtering Pipeline
      • 2.6. Spatial Filtering (3D)
      • 2.7. Graph Filtering
    • 3. Wavelet Analysis
    • 4. Signal Analysis
    • 5. Biomedical Signal Processing
    • 6. Machine Learning
    • 7. Statistics/Utilities
    • 8. Data Samples





2. Signal Filtering¶

  • 2.1. Drift Removing
    • 2.1.1. IIR Filters of Order 1
    • 2.1.2. Spectral filter - IIR Filters
    • 2.1.3. Savitzky-Golay filter
  • 2.2. Smoothing
    • 2.2.1. Spectral Filters
    • 2.2.2. Savitzky-Golay filter
    • 2.2.3. Gaussian Filter
    • 2.2.4. Mollifier
    • 2.2.5. Wavelet Filtering
  • 2.3. Spectral Filtering
    • 2.3.1. Lowpass filtering
    • 2.3.2. Highpass filtering
    • 2.3.3. Bandpass filtering
    • 2.3.4. Bandreject/bandstop filtering
    • 2.3.5. Custom filtering
  • 2.4. Kernel Filtering
    • 2.4.1. Kernel Filtering
  • 2.5. Filtering Pipeline
    • 2.5.1. Filtering Pipeline
    • 2.5.2. Powerline Interference removal
  • 2.6. Spatial Filtering (3D)
    • 2.6.1. Using Adjacency matrix
    • 2.6.2. Using Proximity (distance)
    • 2.6.3. Using Nearest Neighbours
  • 2.7. Graph Filtering
    • 2.7.1. Using Adjacency matrix
    • 2.7.2. Using Proximity (distance)
    • 2.7.3. Using Nearest Neighbours
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