A step function plot where values remain constant until the next change, creating horizontal-then-vertical transitions. This visualization emphasizes discrete changes rather than interpolated values.

# anyplot.ai
# line-stepwise: Step Line Plot
# Library: makie 0.21.9 | Julia 1.11.9
# Quality: 92/100 | Created: 2026-09-05
using CairoMakie
using Colors
using Random
Random.seed!(42)
# --- Theme tokens -----------------------------------------------------------
const THEME = get(ENV, "ANYPLOT_THEME", "light")
const PAGE_BG = THEME == "light" ? colorant"#FAF8F1" : colorant"#1A1A17"
const INK = THEME == "light" ? colorant"#1A1A17" : colorant"#F0EFE8"
const INK_SOFT = THEME == "light" ? colorant"#4A4A44" : colorant"#B8B7B0"
# Imprint categorical palette — first series is always brand green
const IMPRINT_PALETTE = [
colorant"#009E73", colorant"#C475FD", colorant"#4467A3", colorant"#BD8233",
colorant"#AE3030", colorant"#2ABCCD", colorant"#954477", colorant"#99B314",
]
const BRAND = IMPRINT_PALETTE[1]
# --- Data ---------------------------------------------------------------
# Warehouse pallet inventory over a 24h operating day: each delivery or
# outgoing shipment nudges the stock level, which then holds constant until
# the next event — a natural fit for a post-aligned step plot. Gaps between
# events are floored so clusters of near-simultaneous jumps don't muddy the
# step shape.
n_events = 46
min_gap = 0.18
gaps = min_gap .+ rand(n_events) .* (24 / n_events - min_gap) * 1.8
event_hours = cumsum(gaps)
event_hours = event_hours[event_hours .< 24]
hour = vcat(0.0, event_hours, 24.0)
stock = zeros(Int, length(hour))
stock[1] = 950
for i in 2:length(stock)-1
jump = rand() < 0.55 ? rand(20:80) : -rand(20:80)
stock[i] = clamp(stock[i-1] + jump, 400, 1600)
end
stock[end] = stock[end-1]
peak_idx = argmax(stock)
peak_hour, peak_stock = hour[peak_idx], stock[peak_idx]
# --- Plot -----------------------------------------------------------------
fig = Figure(
resolution = (1600, 900),
fontsize = 14,
backgroundcolor = PAGE_BG,
)
ax = Axis(
fig[1, 1];
title = "line-stepwise · julia · makie · anyplot.ai",
titlesize = 25,
titlecolor = INK,
xlabel = "Hour of Day",
ylabel = "Pallets in Stock",
xlabelsize = 14,
ylabelsize = 14,
xlabelcolor = INK,
ylabelcolor = INK,
xticklabelsize = 12,
yticklabelsize = 12,
xticklabelcolor = INK_SOFT,
yticklabelcolor = INK_SOFT,
xtickcolor = INK_SOFT,
ytickcolor = INK_SOFT,
xticks = 0:6:24,
backgroundcolor = PAGE_BG,
topspinevisible = false,
rightspinevisible = false,
leftspinecolor = INK_SOFT,
bottomspinecolor = INK_SOFT,
xgridvisible = false,
ygridcolor = RGBAf(INK.r, INK.g, INK.b, 0.15),
yminorgridvisible = false,
)
stairs!(ax, hour, stock; step = :post, color = BRAND, linewidth = 3.0)
# Focal point: call out the peak stock level reached during the day
scatter!(ax, [peak_hour], [peak_stock]; color = BRAND, markersize = 12, strokewidth = 2, strokecolor = PAGE_BG)
text!(
ax, peak_hour, peak_stock;
text = "Peak: $(peak_stock) pallets",
color = INK,
fontsize = 13,
align = (peak_hour > 20 ? :right : :left, :bottom),
offset = (peak_hour > 20 ? -10 : 10, 8),
)
# --- Save -------------------------------------------------------------------
save("plot-$(THEME).png", fig; px_per_unit = 2)
Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/line-stepwise/makie/code. Any spec id and library id listed in llms-full.txt fit the same URL shape; every URL below is complete and callable.
{
"spec_id": "line-stepwise",
"language": "julia",
"library": "makie",
"page": "https://anyplot.ai/line-stepwise/julia/makie",
"hub": "https://anyplot.ai/line-stepwise",
"code_json": "https://api.anyplot.ai/specs/line-stepwise/makie/code",
"spec_json": "https://api.anyplot.ai/specs/line-stepwise",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/line-stepwise/julia/makie/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/line-stepwise/julia/makie/plot-dark.png",
"quality_score": 92.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Step Line Plot on anyplot.ai.