I have used the Savitzky-Golay filter provided by cookbook, but the result is different from the one given by matlab function sgolayfilt(x,2,15). I attached the numpy code below: #! /usr/bin/env python # -*- coding:utf-8 -*- import numpy as np def savitzky_golay(y, window_size, order, deriv=0): r"""Smooth (and optionally differentiate) data with a Savitzky-Golay filter. The Savitzky-Golay filter removes high frequency noise from data. It has the advantage of preserving the original shape and features of the signal better than other types of filtering approaches, such as moving averages techhniques. Parameters ---------- y : array_like, shape (N,) the values of the time history of the signal. window_size : int the length of the window. Must be an odd integer number. order : int the order of the polynomial used in the filtering. Must be less then `window_size` - 1. deriv: int the order of the derivative to compute (default = 0 means only smoothing) Returns ------- ys : ndarray, shape (N) the smoothed signal (or it's n-th derivative). Notes ----- The Savitzky-Golay is a type of low-pass filter, particularly suited for smoothing noisy data. The main idea behind this approach is to make for each point a least-square fit with a polynomial of high order over a odd-sized window centered at the point. Examples -------- t = np.linspace(-4, 4, 500) y = np.exp( -t**2 ) + np.random.normal(0, 0.05, t.shape) ysg = savitzky_golay(y, window_size=31, order=4) import matplotlib.pyplot as plt plt.plot(t, y, label='Noisy signal') plt.plot(t, np.exp(-t**2), 'k', lw=1.5, label='Original signal') plt.plot(t, ysg, 'r', label='Filtered signal') plt.legend() plt.show() References ---------- .. [1] A. Savitzky, M. J. E. Golay, Smoothing and Differentiation of Data by Simplified Least Squares Procedures. Analytical Chemistry, 1964, 36 (8), pp 1627-1639. .. [2] Numerical Recipes 3rd Edition: The Art of Scientific Computing W.H. Press, S.A. Teukolsky, W.T. Vetterling, B.P. Flannery Cambridge University Press ISBN-13: 9780521880688 """ try: window_size = np.abs(np.int(window_size)) order = np.abs(np.int(order)) except ValueError, msg: raise ValueError("window_size and order have to be of type int") if window_size % 2 != 1 or window_size < 1: raise TypeError("window_size size must be a positive odd number") if window_size < order + 2: raise TypeError("window_size is too small for the polynomials order") order_range = range(order+1) half_window = (window_size -1) // 2 # precompute coefficients b = np.mat([[k**i for i in order_range] for k in range(-half_window, half_window+1)]) m = np.linalg.pinv(b).A[deriv] # pad the signal at the extremes with # values taken from the signal itself firstvals = y[0] - np.abs( y[1:half_window+1][::-1] - y[0] ) lastvals = y[-1] + np.abs(y[-half_window-1:-1][::-1] - y[-1]) y = np.concatenate((firstvals, y, lastvals)) return np.convolve( m, y, mode='valid') if __name__ == "__main__": rrstype = np.dtype({'names':['wavelenth', 'rrs'], 'formats':['f','f']}) record_list = [] record_file = open("200607NO1.txt") for line in record_file: item_list = line.strip().split('\t') record_list.append((item_list[0], item_list[1])) record_file.close() record_array = np.array(record_list,dtype=rrstype) rrs_array = record_array["rrs"] rrs_array_2 = savitzky_golay(rrs_array, 15, 2) rrs_array_2.tofile("savitzky-golay-result.txt", "\n")