def __init__(self, freq, mincutoff=1.0, beta=0.0, dcutoff=1.0):
if float(freq) <= 0:
raise ValueError("freq should be >0")
if float(mincutoff) <= 0:
raise ValueError("mincutoff should be >0")
if float(dcutoff) <= 0:
raise ValueError("dcutoff should be >0")
self.__freq = float(freq)
self.__mincutoff = float(mincutoff)
self.__beta = float(beta)
self.__dcutoff = float(dcutoff)
self.__x = SingleExponentialFilter(self.__alpha(self.__mincutoff), variant="lowpass")
self.__dx = SingleExponentialFilter(self.__alpha(self.__dcutoff), variant="lowpass")
self.__lasttime = None
class OneEuroFilter:
# FIXME: find a better name for beta and dcutoff?
def __init__(self, freq, mincutoff=1.0, beta=0.0, dcutoff=1.0):
if float(freq) <= 0:
raise ValueError("freq should be >0")
if float(mincutoff) <= 0:
raise ValueError("mincutoff should be >0")
if float(dcutoff) <= 0:
raise ValueError("dcutoff should be >0")
self.__freq = float(freq)
self.__mincutoff = float(mincutoff)
self.__beta = float(beta)
self.__dcutoff = float(dcutoff)
self.__x = SingleExponentialFilter(self.__alpha(self.__mincutoff), variant="lowpass")
self.__dx = SingleExponentialFilter(self.__alpha(self.__dcutoff), variant="lowpass")
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 __call__(self, x, timestamp=None):
# ---- update the sampling frequency based on timestamps
if self.__lasttime and timestamp:
self.__freq = 1.0 / (timestamp - self.__lasttime)
# print "OneEuroFilter: updating frequency, now %s Hz"%self.__freq
self.__lasttime = timestamp
# ---- estimate the current variation per second
prev_x = self.__x.lastValue()
dx = 0 if prev_x is None else (x - prev_x) * self.__freq
edx = self.__dx(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
# print "cutoff:", cutoff, "alpha:", self.__alpha(cutoff)
return self.__x(x, timestamp, alpha=self.__alpha(cutoff))
def getURL(self):
return "fltr:/oneeuro?freq=%g&mincutoff=%g&beta=%g&dcutoff=%g" % (
self.__freq,
self.__mincutoff,
self.__beta,
self.__dcutoff,
)
def __str__(self):
return self.getURL()
@staticmethod
def generateConfigurations(freq, cutoffstep, maxcutoff, betastep, maxbeta, dcutoffs=[1.0]):
nbcutoffsteps = int(math.floor((maxcutoff - cutoffstep) / cutoffstep))
cutoffs = [(i + 1) * cutoffstep for i in range(nbcutoffsteps + 1)]
betas = [i * betastep for i in range(int(math.floor(maxbeta / betastep) + 1))]
if dcutoffs is None:
dcutoffs = cutoffs
configs = [(freq, mincutoff, beta, dcutoff) for mincutoff in cutoffs for beta in betas for dcutoff in dcutoffs]
return ["fltr:/oneeuro?freq=%g&mincutoff=%g&beta=%g&dcutoff=%g" % args for args in configs]
@staticmethod
def randomConfiguration(freq, **params):
# FIXME: we're in dire need of heuristics and fancy
# probability distributions...
maxcutoff = params.setdefault("maxcutoff", 20) # FIXME: this is an arbitrary choice
if "mincutoff" not in params:
# will be in (0.0, maxcutoff]
params["mincutoff"] = maxcutoff - maxcutoff * numpy.random.random_sample()
if "beta" not in params:
maxbeta = params.get("maxbeta", 100.0)
# will be in [0.0, maxbeta)
params["beta"] = maxbeta * numpy.random.random_sample()
if "dcutoff" not in params:
# will be in (0.0, freq]
params["dcutoff"] = freq - freq * numpy.random.random_sample()
return "fltr:/oneeuro?freq=%g&mincutoff=%g&beta=%g&dcutoff=%g" % (
freq,
params["mincutoff"],
params["beta"],
params["dcutoff"],
)
