fikarzlf/savitzky-golay

Computes the Savitzky-Golay Filter coefficients

Savitzky-Golay Filter in C++

Author: Lekan Ogunmolu

Table of Contents

• Introduction
• Dependencies
• Usage
• Compilation
• Citation
• Issues
• Changelog

Introduction

Nicely computes the Vandermonde matrix, Savitzky-Golay differentiation filters and smoothing coefficients for any noisy, and sequential signal. It is a textbook implementation of the Savitzky-Golay Filter. Initial testing of this code was done using a Ubuntu 14.04.02 Trusty OS running Linux 4.4 but will work on any other Linux/Windows/Mac OS machine with little effort.

Below are examples of how the filter smoothes out a noisy depth map data from the kinect time-of-flight sensor:

Dependencies

In order to compile this file, you would need to install the Eigen3 Library to do the linear algebra of the matrices, vectors and related algorithms. You can install it by downloading the 3.2.5 library which I used from here and following the README instructions after unpacking the tarball to install.

Usage

• ./savgol

  Options

  • -h or --help: print out helper menu for using code.

  • computes the savitzky-golay filter coefficients with frame size, F = 5 and polynomial order 3 (these are the default parameters of the filter) for linearly spaced data points between x_min = 900 and x_max = 980.

  • To change the values of the frame size and polynomial order, do

  ./savgoal 9 5 where F = 9 and k = 5.

  • To pass in your arbitrary data points between a value x_min and x_max, pass in the following argum,ents in order: ./savgoal F k x_min x_max.

  The filtered values are returned onto the console. Note that the Frame size should ideally be odd

RESULTS

The savgol filter tries to compute the moving average of the time-series data fed into it. For example, with a frame size of 9 and polynomial order of 5 for numbers linearly spaced between 100 and 1000, we obtain the following results by running this code:

Frame size: 9; Polynomial order: 5 

 Vandermonde Matrix: 
     1     -4     16    -64    256  -1024   4096 -16384  65536
     1     -3      9    -27     81   -243    729  -2187 134737
     1     -2      4     -8     16    -32     64   -128      0
     1     -1      1     -1      1     -1      1     -1      1
     1      0      0      0      0      0      0      0      0
     1      1      1      1      1      1      1      1      1
     1      2      4      8     16     32     64    128    256
     1      3      9     27     81    243    729   2187   6561
     1      4     16     64    256   1024   4096  16384  65536

Filtered values in the range 
  100 212.5   325 437.5   550 662.5   775 887.5  1000
 are: 
662.5   775 887.5  1000   550   100 212.5   325 437.5

Or for numbers linearly spaced between 100 and 300, with F = 7 and k = 5, we obtain:

Frame size: 7; polynomial order: 5 

 Vandermonde Matrix: 
     1     -3      9    -27     81   -243    729
     1     -2      4     -8     16    -32 134865
     1     -1      1     -1      1     -1      0
     1      0      0      0      0      0      0
     1      1      1      1      1      1      1
     1      2      4      8     16     32     64
     1      3      9     27     81    243    729


Filtered values in the range 
    100 133.333 166.667     200 233.333 266.667     300
 are: 
233.333 266.667     300     200     100 133.333 166.667

Components

• MatrixXi vander(const int F);

  • computes the vandermonde matrix and the polynomial of basis vectors and flips it column-wise from left to right

• MatrixXf B = MatrixXf sgdiff(int k, double F);

  • designs a Savitzky-Golay FIR smoothing filter B with polynomial order k and frame size F of the convolution coefficients. The polynomial order, k, must be less than the frame size F and F must be odd.

• savgolfilt(x, x_on, k, F);

  • computes the smoothed values of the signal x, whose tansient on is x_on initialized with size F.

• Note In calculating the transient off, x_off will be the last (F-1) x values, where x's are the data sequence we want to filter.If you are smoothing data offline, then this code will work seamlessly. Just load your data in the main() function where, for an example, I have used linearly spaced values between 900 and 980 at a frame 5 size for my steady state values.

  Note, if you are smoothing data in real time, you need to find a way to let your compiler pick the last F-length samples from your data in order to compute your transient off, i.e., x_off. You could have the program wait for x_milliseconds after stopping your code before you pick the transient off, for example.

Compilation

There is a CMakeLists.txt file in the project root folder. From the project root directory:

1. Create a build directory: mkdir build && cd build
2. Compile the cpp code: cmake ../
3. Build your executable: make
4. Run the executable: ./savgol

Citation

If you used Savitzky-Golay in your work, please cite it.

@misc{Savitzky-Golay,
  author = {Ogunmolu, Olalekan},
  title = {{Savitzky-Golay Filter in C++}},
  year = {2015},
  howpublished = {\url{https://github.com/lakehanne/Savitzky-Golay}},
  note = {Accessed August 15, 2015}
}

Issues

If you have issues running the files, please use the issues tab to open a bug. I will generally respond within a 24-hour period.

Changelog

• Added citation to README (August 14, 2015)
• Added examples to int main() function (August 15, 2015)
• Modified frame size and polynomial order to be reconfigurable at run time (July 1, 2016)

TODO

Add a plotter to plot the filtered values on a gtk chart?

Reference

INTRODUCTION TO SIGNAL PROCESSING

Sophocles J. Orfanidis, Prentice Hall, 2010

Chapter 8; Section 8.3.5