# -*- coding: utf-8 -*-
#Created on Thu Nov 14 11:41:53 2013
"""
Trajectory smoothing routines. These are routines that are out of the
Trajectory object because they precompute values that are dependent only on the
smoothing method, and not on the trajectory itself, so they may be shared for
processing a whole list of trajectories.
"""
from flowtracks.trajectory import Trajectory
import numpy as np
[docs]def savitzky_golay(trajs, fps, window_size, order, deriv=0, rate=1):
"""
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 techniques.
Parameters:
trajs - a list of Trajectory objects
window_size - int,
the length of the window. Must be an odd integer number.
fps - frames per second, used for calculating velocity and acceleration.
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:
new_trajs - a list of Trajectory objects representing the smoothed
trajectories. Trajectories shorter than the window size are discarded.
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.
References:
.. [#] A. Savitzky, M. J. E. Golay, Smoothing and Differentiation of \
Data by Simplified Least Squares Procedures. Analytical \
Chemistry, 1964, 36 (8), pp 1627-1639.
.. [#] 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
.. [#] http://wiki.scipy.org/Cookbook/SavitzkyGolay
"""
from math import factorial
try:
window_size = np.abs(np.int(window_size))
order = np.abs(np.int(order))
except ValueError:
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] * rate**deriv * factorial(deriv)
new_trajs = []
for traj in trajs:
if len(traj) < window_size:
continue
newpos = []
for y in traj.pos().T:
# 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))
newpos.append(np.convolve( m[::-1], y, mode='valid'))
newpos = np.r_[newpos].T
newvel = np.vstack(( np.diff(newpos, axis=0)*fps, np.zeros((1,3)) ))
newacc = np.vstack(( np.diff(newvel[:-1], axis=0)*fps, np.zeros((2,3)) ))
new_trajs.append(Trajectory(newpos, newvel, traj.time(), traj.trajid(),
accel=newacc))
return new_trajs