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# -*- coding: utf-8 -*-
# libavg - Media Playback Engine.
# Copyright (C) 2003-2014 Ulrich von Zadow
#
# This library is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# License as published by the Free Software Foundation; either
# version 2 of the License, or (at your option) any later version.
#
# This library is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public
# License along with this library; if not, write to the Free Software
# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
#
# Current versions can be found at www.libavg.de
#
import math
# Input filter based on:
# Casiez, G., Roussel, N. and Vogel, D. (2012). 1€ Filter: A Simple Speed-based Low-pass
# Filter for Noisy Input in Interactive Systems. Proceedings of the ACM Conference on
# Human Factors in Computing Systems (CHI '12). Austin, Texas (May 5-12, 2012). New York:
# ACM Press, pp. 2527-2530.
class LowPassFilter(object):
def __init__(self, alpha):
self.__setAlpha(alpha)
self.__y = None
self.__s = None
def __setAlpha(self, alpha):
alpha = float(alpha)
if alpha <= 0 or alpha > 1.0:
raise RuntimeError("LowPassFilter alpha (%s) should be in (0.0, 1.0]"%alpha)
self.__alpha = alpha
def apply(self, value, timestamp=None, alpha=None):
if alpha is not None:
self.__setAlpha(alpha)
if self.__y is None:
s = value
else:
s = self.__alpha*value + (1.0-self.__alpha)*self.__s
self.__y = value
self.__s = s
return s
def lastValue(self):
return self.__y
class OneEuroFilter(object):
def __init__(self, mincutoff=1.0, beta=0.0, dcutoff=1.0):
if mincutoff<=0:
raise ValueError("mincutoff should be >0")
if dcutoff<=0:
raise ValueError("dcutoff should be >0")
self.__freq = 60 # Initial freq, updated as soon as we have > 1 sample
self.__mincutoff = float(mincutoff)
self.__beta = float(beta)
self.__dcutoff = float(dcutoff)
self.__x = LowPassFilter(self.__alpha(self.__mincutoff))
self.__dx = LowPassFilter(self.__alpha(self.__dcutoff))
self.__lasttime = None
def __alpha(self, cutoff):
te = 1.0 / self.__freq
tau = 1.0 / (2*math.pi*cutoff)
return 1.0 / (1.0 + tau/te)
def apply(self, x, timestamp):
timestamp /= 1000.
if self.__lasttime == timestamp:
return x
else:
# ---- update the sampling frequency based on timestamps
if self.__lasttime and timestamp:
self.__freq = 1.0 / (timestamp-self.__lasttime)
self.__lasttime = timestamp
# ---- estimate the current variation per second
prev_x = self.__x.lastValue()
dx = 0.0 if prev_x is None else (x-prev_x)*self.__freq # FIXME: 0.0 or value?
edx = self.__dx.apply(dx, timestamp, alpha=self.__alpha(self.__dcutoff))
# ---- use it to update the cutoff frequency
cutoff = self.__mincutoff + self.__beta*math.fabs(edx)
# ---- filter the given value
return self.__x.apply(x, timestamp, alpha=self.__alpha(cutoff))
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