OneEuroFilter
Simple Python and Julia implementations of the 1€ Filter. The codes can be used as a pseudocode for implementing the algorithm in other languages.
Install / Use
/learn @jaantollander/OneEuroFilterREADME
1€ Filter (One Euro Filter)
A simple Python implementation of the 1€ Filter (1e filter, 1 euro filter, One Euro Filter). The code in one_euro_filter.py can be used as pseudocode for implementing the algorithm in other languages. Detailed pseudocode about the underlying mathematics of the algorithm and sources can be found in a blog post that I wrote about it, Noise Filtering Using 1€ Filter.
import math
def smoothing_factor(t_e, cutoff):
r = 2 * math.pi * cutoff * t_e
return r / (r + 1)
def exponential_smoothing(a, x, x_prev):
return a * x + (1 - a) * x_prev
class OneEuroFilter:
def __init__(self, t0, x0, dx0=0.0, min_cutoff=1.0, beta=0.0,
d_cutoff=1.0):
"""Initialize the one euro filter."""
# The parameters.
self.min_cutoff = float(min_cutoff)
self.beta = float(beta)
self.d_cutoff = float(d_cutoff)
# Previous values.
self.x_prev = float(x0)
self.dx_prev = float(dx0)
self.t_prev = float(t0)
def __call__(self, t, x):
"""Compute the filtered signal."""
t_e = t - self.t_prev
# The filtered derivative of the signal.
a_d = smoothing_factor(t_e, self.d_cutoff)
dx = (x - self.x_prev) / t_e
dx_hat = exponential_smoothing(a_d, dx, self.dx_prev)
# The filtered signal.
cutoff = self.min_cutoff + self.beta * abs(dx_hat)
a = smoothing_factor(t_e, cutoff)
x_hat = exponential_smoothing(a, x, self.x_prev)
# Memorize the previous values.
self.x_prev = x_hat
self.dx_prev = dx_hat
self.t_prev = t
return x_hat
Related Skills
node-connect
339.3kDiagnose OpenClaw node connection and pairing failures for Android, iOS, and macOS companion apps
claude-opus-4-5-migration
83.9kMigrate prompts and code from Claude Sonnet 4.0, Sonnet 4.5, or Opus 4.1 to Opus 4.5
frontend-design
83.9kCreate distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, or applications. Generates creative, polished code that avoids generic AI aesthetics.
model-usage
339.3kUse CodexBar CLI local cost usage to summarize per-model usage for Codex or Claude, including the current (most recent) model or a full model breakdown. Trigger when asked for model-level usage/cost data from codexbar, or when you need a scriptable per-model summary from codexbar cost JSON.
