Automated smooth Nth order derivatives of noisy data
This little project came about because I kept running into the same problem: cleanly differentiating sensor data before doing analysis. There are a ton of ways to solve this problem, I've always personally been a fan of using kalman filters for the job as its easy to get the double whammy of resampling/upsampling to a fixed consistent rate and also smoothing/outlier rejection. I wrote a little numpy only bayesian filtering/smoothing library recently (https://github.com/hugohadfield/bayesfilter/) so this felt like a fun and very useful first thing to…
Automated smooth Nth order derivatives of noisy data launched on October 16, 2024 with 146 votes, #333 of 5,185 launches that month and more than 95% of that year's launches. The site was still online in September 2026, 2 years on.
In plain words
This project provides automated computation of higher-order derivatives from noisy sensor data. It uses Kalman filtering to smooth data and remove outliers while simultaneously resampling to a consistent rate, built on a NumPy-based Bayesian filtering library. The tool is designed for engineers and data analysts who need to differentiate sensor measurements before performing further analysis, offering a streamlined alternative to manual filtering approaches.
written from the facts on this page · September 2026
From the sources
Automated, smooth, N'th order derivatives of non-uniformly sampled time series data - hugohadfield/kalmangrad
kalmangrad is a python package that calculates automated smooth N'th order derivatives of non-uniformly sampled time series data. The approach leverages Bayesian filtering techniques to compute derivatives up to any specified order, offering a robust alternative to traditional numerical differentiation methods that are sensitive to noise. This package is built on top of the underlying bayesfilter package. Estimating derivatives from noisy data is a common challenge in fields like signal processing, control systems, and data analysis. Traditional numerical differentiation amplifies noise, leading to inaccurate results. Anyone who has naiivly attempted to differentiate sensor data has run…from github.com
In the maker’s words, at launch
This little project came about because I kept running into the same problem: cleanly differentiating sensor data before doing analysis. There are a ton of ways to solve this problem, I've always personally been a fan of using kalman filters for the job as its easy to get the double whammy of…
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