I have a signal that is measured at 100Hz and I need to apply the Savitzky-Golay smoothing filter on this signal. However, on closer inspection my signal is not measured at perfectly constant rate, the delta between measurements ranges between 9.7 and 10.3 ms.
Is there a way to use the Savitzky-Golay filter on not equally spaced data? Are there other methods that I could apply?
One method would be to resample your data so that it is equally spaced, then you can do whatever processing you like. Bandlimited resampling using linear filtering isn't going to be a good option since the data isn't uniformly spaced, so you could use some sort of local polynomial interpolation (e.g. cubic splines) to estimate what the underlying signal's values are at "exact" 10-millisecond intervals.
Because of the way the Savitzky-Golay filter is derived (i.e as local least-squares polynomial fits), there's a natural generalization to nonuniform sampling--it's just much more computationally expensive.
Savitzky-Golay Filters in General
For the standard filter, the idea is to fit a polynomial to a local set of samples [using least squares], then replace the center sample with the value of the polynomial at the center index (i.e. at 0). That means the standard SG filter coefficients can be generated by inverting a Vandermonde matrix of sample indicies. For example, to generate a local parabolic fit across five samples $y_0\dots y_4$ (with local indicies -2,-1,0,1,2), the system of design equations $Ac = y$ would be as follows:
$$ \begin{bmatrix}-2^0 & -2^1 & -2^2 \\ -1^0 & -1^1 & -1^2 \\ 0^0 & 0^1 & 0^2 \\ 1^0 & 1^1 & 1^2 \\ 2^0 & 2^1 & 2^2 \end{bmatrix} \begin{bmatrix} c_0 \\ c_1 \\ c_2 \end{bmatrix} = \begin{bmatrix} y_0 \\ y_1 \\ y_2 \\ y_3 \\ y_4 \end{bmatrix}. $$
In the above, the $c_0 \dots c_2$ are the unknown coefficients of the least squares polynomial $c_0 + c_1x + c_2x^2$. Since the value of the polynomial at $x = 0$ is just $c_0$, computing the pseudoinverse of the design matrix (i.e. $c = (A^TA)^{-1}A^T y\space $) will yield the SG filter coefficients in the top row. In this case, they would be
$$ \begin{bmatrix}c_0 \\ c_1 \\ c_2 \end{bmatrix} = \begin{bmatrix} -3 & 12 & 17 & 12 & -3 \\ -7 & -4 & 0 & 4 & 7 \\ 5 & -3 & -5 & -3 & 5 \\ \end{bmatrix} \begin{bmatrix} y_0 \\ y_1 \\ y_2 \\ y_3 \\ y_4 \end{bmatrix}. $$
Note that since the derivative of $c_0 + c_1x + c_2x^2$ is $c_1 + 2c_2x$, the second row of the matrix (which evaluates $c_1$) will be a smoothed derivative filter. The same argument applies for successive rows--they give smoothed higher-order derivatives. Note that I scaled the matrix by 35 so the first row would match the smoothing coefficients given on Wikipedia (above). The derivative filters each differ by other scaling factors.
Nonuniform Sampling
When the samples are evenly spaced, the filter coefficients are translation-invariant, so the result is just an FIR filter. For nonuniform samples, the coefficients will differ based on the local sample spacing, so the design matrix will need to be constructed and inverted at each sample. If the nonuniform sample times are $x_n$, and we construct local coordinates $t_n$ with each center sample time fixed at $0$, i.e.
$$ \begin{align} t_{-2} & = x_{-2} - x_0 \\ t_{-1} & = x_{-1} - x_0 \\ t_0 & = x_0 - x_0 \\ t_1 & = x_1 - x_0 \\ t_2 & = x_2 - x_0 \end{align} $$
then each design matrix will be of the following form:
$$ A = \begin{bmatrix} t_{-2}^0 & t_{-2}^1 & t_{-2}^2 \\ t_{-1}^0 & t_{-1}^1 & t_{-1}^2 \\ t_0^0 & t_0^1 & t_0^2 \\ t_1^0 & t_1^1 & t_1^2 \\ t_2^0 & t_2^1 & t_2^2 \end{bmatrix} = \begin{bmatrix} 1 & t_{-2} & t_{-2}^2 \\ 1 & t_{-1} & t_{-1}^2 \\ 1 & 0 & 0 \\ 1 & t_1 & t_1^2 \\ 1 & t_2 & t_2^2 \end{bmatrix}. $$
The first row of the pseudoinverse of $A$ dotted with the local sample values will yield $c_0$, the smoothed value at that sample.
As techwinder did in C++, I used datageist's algorithm and implemented it in Python. Maybe this will help somebody in the future.
import numpy as np def non_uniform_savgol(x, y, window, polynom): """ Applies a Savitzky-Golay filter to y with non-uniform spacing as defined in x This is based on https://dsp.stackexchange.com/questions/1676/savitzky-golay-smoothing-filter-for-not-equally-spaced-data The borders are interpolated like scipy.signal.savgol_filter would do Parameters ---------- x : array_like List of floats representing the x values of the data y : array_like List of floats representing the y values. Must have same length as x window : int (odd) Window length of datapoints. Must be odd and smaller than x polynom : int The order of polynom used. Must be smaller than the window size Returns ------- np.array of float The smoothed y values """ if len(x) != len(y): raise ValueError('"x" and "y" must be of the same size') if len(x) < window: raise ValueError('The data size must be larger than the window size') if type(window) is not int: raise TypeError('"window" must be an integer') if window % 2 == 0: raise ValueError('The "window" must be an odd integer') if type(polynom) is not int: raise TypeError('"polynom" must be an integer') if polynom >= window: raise ValueError('"polynom" must be less than "window"') half_window = window // 2 polynom += 1 # Initialize variables A = np.empty((window, polynom)) # Matrix tA = np.empty((polynom, window)) # Transposed matrix t = np.empty(window) # Local x variables y_smoothed = np.full(len(y), np.nan) # Start smoothing for i in range(half_window, len(x) - half_window, 1): # Center a window of x values on x[i] for j in range(0, window, 1): t[j] = x[i + j - half_window] - x[i] # Create the initial matrix A and its transposed form tA for j in range(0, window, 1): r = 1.0 for k in range(0, polynom, 1): A[j, k] = r tA[k, j] = r r *= t[j] # Multiply the two matrices tAA = np.matmul(tA, A) # Invert the product of the matrices tAA = np.linalg.inv(tAA) # Calculate the pseudoinverse of the design matrix coeffs = np.matmul(tAA, tA) # Calculate c0 which is also the y value for y[i] y_smoothed[i] = 0 for j in range(0, window, 1): y_smoothed[i] += coeffs[0, j] * y[i + j - half_window] # If at the end or beginning, store all coefficients for the polynom if i == half_window: first_coeffs = np.zeros(polynom) for j in range(0, window, 1): for k in range(polynom): first_coeffs[k] += coeffs[k, j] * y[j] elif i == len(x) - half_window - 1: last_coeffs = np.zeros(polynom) for j in range(0, window, 1): for k in range(polynom): last_coeffs[k] += coeffs[k, j] * y[len(y) - window + j] # Interpolate the result at the left border for i in range(0, half_window, 1): y_smoothed[i] = 0 x_i = 1 for j in range(0, polynom, 1): y_smoothed[i] += first_coeffs[j] * x_i x_i *= x[i] - x[half_window] # Interpolate the result at the right border for i in range(len(x) - half_window, len(x), 1): y_smoothed[i] = 0 x_i = 1 for j in range(0, polynom, 1): y_smoothed[i] += last_coeffs[j] * x_i x_i *= x[i] - x[-half_window - 1] return y_smoothed 
"As a cheap alternative, one can simply pretend that the data points are equally spaced ...
if the change in $f$ across the full width of the $N$ point window is less than $\sqrt{N/2}$ times the measurement noise on a single point, then the cheap method can be used."
$\qquad -$ Numerical Recipes pp. 771-772
(derivation anyone ?)
("Pretend equally spaced" means:
take the nearest $\pm N/2$ points around each $t$ where you want SavGol($t$),
not snap all $t_i \to i$ . That may be obvious, but got me for a while.)
I found out, that there are two ways to use the savitzky-golay algorithm in Matlab. Once as a filter, and once as a smoothing function, but basically they should do the same.
If it's of any help, I've made a C implementation of the method described by datageist. Free to use at your own risk.
/** * @brief smooth_nonuniform * Implements the method described in https://dsp.stackexchange.com/questions/1676/savitzky-golay-smoothing-filter-for-not-equally-spaced-data * free to use at the user's risk * @param n the half size of the smoothing sample, e.g. n=2 for smoothing over 5 points * @param the degree of the local polynomial fit, e.g. deg=2 for a parabolic fit */ bool smooth_nonuniform(uint deg, uint n, std::vector<double>const &x, std::vector<double> const &y, std::vector<double>&ysm) { if(x.size()!=y.size()) return false; // don't even try if(x.size()<=2*n) return false; // not enough data to start the smoothing process // if(2*n+1<=deg+1) return false; // need at least deg+1 points to make the polynomial int m = 2*n+1; // the size of the filter window int o = deg+1; // the smoothing order std::vector<double> A(m*o); memset(A.data(), 0, m*o*sizeof(double)); std::vector<double> tA(m*o); memset(tA.data(), 0, m*o*sizeof(double)); std::vector<double> tAA(o*o); memset(tAA.data(), 0, o*o*sizeof(double)); std::vector<double> t(m); memset(t.data(), 0, m* sizeof(double)); std::vector<double> c(o); memset(c.data(), 0, o* sizeof(double)); // do not smooth start and end data int sz = y.size(); ysm.resize(sz); memset(ysm.data(), 0,sz*sizeof(double)); for(uint i=0; i<n; i++) { ysm[i]=y[i]; ysm[sz-i-1] = y[sz-i-1]; } // start smoothing for(uint i=n; i<x.size()-n; i++) { // make A and tA for(int j=0; j<m; j++) { t[j] = x[i+j-n] - x[i]; } for(int j=0; j<m; j++) { double r = 1.0; for(int k=0; k<o; k++) { A[j*o+k] = r; tA[k*m+j] = r; r *= t[j]; } } // make tA.A matMult(tA.data(), A.data(), tAA.data(), o, m, o); // make (tA.A)-¹ in place if (o==3) { if(!invert33(tAA.data())) return false; } else if(o==4) { if(!invert44(tAA.data())) return false; } else { if(!inverseMatrixLapack(o, tAA.data())) return false; } // make (tA.A)-¹.tA matMult(tAA.data(), tA.data(), A.data(), o, o, m); // re-uses memory allocated for matrix A // compute the polynomial's value at the center of the sample ysm[i] = 0.0; for(int j=0; j<m; j++) { ysm[i] += A[j]*y[i+j-n]; } } std::cout << " x y y_smoothed" << std::endl; for(uint i=0; i<x.size(); i++) std::cout << " " << x[i] << " " << y[i] << " "<< ysm[i] << std::endl; return true; } 
