README.md

Buff.jl

Buff is a Julia package that wraps best-in-class signal-processing libraries behind a unified, type-stable, multiple-dispatch API. Pass plot=true to any transform to instantly visualise the result with PlotlyJS.

Sub-module	What it uses	Functions
Outliers	StatsBase.jl (zscore, mad, winsor)	detect_outliers, remove_outliers, winsorize
Interpolate	Interpolations.jl (linear); pure-Julia natural cubic spline	interpolate_linear, interpolate_cubic, fill_missing
Filter	DSP.jl (Butterworth, conv, filtfilt); pure-Julia SG / EMA	moving_average, lowpass_filter, highpass_filter, bandpass_filter, bandstop_filter, exponential_smoothing, savitzky_golay
LTTB	pure-Julia LTTB algorithm	lttb
Upsample	Interpolations.jl	upsample_linear, upsample_nearest
Trend	pure-Julia OLS	linear_regression, polynomial_regression, detrend
Plots	PlotlyJS.jl	plot_signal, plot_comparison, plot_regression

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Installation

using Pkg
Pkg.add(url = "https://github.com/viallww/Buff")

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Quick start

using Buff

t = 0.0:0.01:4π
y = sin.(t) .+ 0.3 .* randn(length(t))

Outlier detection & removal

y_noisy = copy(y)
y_noisy[50] = 10.0          # inject a spike

mask    = detect_outliers(y_noisy; method = :zscore, threshold = 3.0)
y_clean = remove_outliers(y_noisy; method = :iqr, threshold = 1.5)

# Winsorise to [5th, 95th] percentile
y_win = winsorize(y_noisy; limits = (0.05, 0.95))

Interpolation & missing-value filling

x = [0.0, 1.0, 2.5, 4.0, 5.0]
z = [0.0, 1.0, 0.6, 0.3, 0.0]

# Linear via Interpolations.jl
z_lin = interpolate_linear(x, z, 0.0:0.1:5.0)

# Natural cubic spline (pure Julia, handles non-uniform knots)
z_cub = interpolate_cubic(x, z, 0.0:0.1:5.0)

# Fill missing values
v = [1.0, missing, missing, 4.0, missing, 6.0]
fill_missing(v; method = :linear)      # → [1, 2, 3, 4, 5, 6]
fill_missing(v; method = :forward)     # LOCF
fill_missing(v; method = :backward)    # NOCB

Filtering (DSP.jl powered)

# Centred moving average (DSP.conv based)
y_ma = moving_average(y, 11)

# Exponential smoothing
y_ema = exponential_smoothing(y, 0.3)

# Savitzky-Golay
y_sg = savitzky_golay(y, 11, 3)

# Butterworth low-pass at 5 Hz (signal sampled at 100 Hz)
y_lp = lowpass_filter(y, 5.0; fs = 100.0, order = 4)

# Band-pass 1–10 Hz
y_bp = bandpass_filter(y, 1.0, 10.0; fs = 100.0, order = 4)

LTTB downsampling (with optional plot)

t_dense = collect(0.0:0.001:10.0)
sig     = sin.(t_dense) .+ 0.1 .* randn(length(t_dense))

# Returns (x_out, y_out) – 200 representative points
x_ds, y_ds = lttb(t_dense, sig, 200)

# Same call + interactive PlotlyJS overlay
x_ds, y_ds = lttb(t_dense, sig, 200; plot = true)

Upsampling (with optional plot)

x_low = [0.0, 1.0, 2.0, 3.0, 4.0]
y_low = [0.0, 1.0, 0.5, 0.8, 0.2]

# 4× linear upsampling via Interpolations.jl
x_hi, y_hi = upsample_linear(x_low, y_low, 4; plot = true)

# Nearest-neighbour
x_hi, y_hi = upsample_nearest(x_low, y_low, 4)

Trend / regression

x = collect(1.0:50.0)
y = 2.5 .* x .+ 1.0 .+ randn(50)

res = linear_regression(x, y; plot = true)
# res.slope, res.intercept, res.r_squared, res.y_fit

res2 = polynomial_regression(x, y .^ 2, 2; plot = true)

detrended = detrend(y; method = :linear)

Direct plot helpers

p = plot_signal(y; title = "Raw signal")
p = plot_comparison(x_low, y_low, x_hi, y_hi; title = "Upsampled")
p = plot_regression(x, y, res.y_fit)
display(p)

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Design principles

• Type stability – all functions are parameterised on element type T<:Real and return concrete Vector{float(T)} or Vector{T} outputs.
• Multiple dispatch – every function has variants for y-only (integer x-index), (x, y) pairs, and explicit output-grid arguments.
• Best-in-class back-ends – linear interpolation and upsampling delegate to Interpolations.jl; DSP filters use DSP.jl's filtfilt for zero-phase response; outlier statistics use StatsBase.jl.
• Zero mandatory plots – plotting is always opt-in via plot=true and requires no display server to use the numerical functions.

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Documentation

Full API docs are generated with Documenter.jl and deployed to GitHub Pages.