Plot configuration parameters.

Controls the appearance and layout of generated charts.

Default plot configuration.

Creates a 60×20 character plot with reasonable defaults:

defPlot = Plot
  { widthChars   = 60
  , heightChars  = 20
  , leftMargin   = 6
  , bottomMargin = 2
  , titleMargin  = 1
  , xBounds      = (Nothing, Nothing)
  , yBounds      = (Nothing, Nothing)
  , plotTitle    = ""
  , legendPos    = LegendRight
  , colorPalette = [ BrightBlue, BrightMagenta, BrightCyan, BrightGreen, BrightYellow, BrightRed, BrightWhite, BrightBlack]
  , xFormatter   = \ _ _ v -> show v
  , yFormatter   = \ _ _ v -> show v
  , xNumTicks    = 2
  , yNumTicks    = 2
  }

Position of the legend in the plot.

A 24-bit RGB colour.

Axis-aware, width-limited, tick-label formatter.

Given:

• axis context
• a per-tick width budget (in terminal cells)
• and the raw tick value.

returns the label to render.

What the formatter gets to know about the axis/ticks

Create a named data series for multi-series plots.

let s1 = series "Dataset A" [(1,2), (2,4), (3,6)]
    s2 = series "Dataset B" [(1,3), (2,5), (3,7)]
    chart = scatter [s1, s2] defPlot

Create a bin configuration for histograms.

bins 10 0 100  -- 10 bins from 0 to 100
bins 20 (-5) 5 -- 20 bins from -5 to 5

Defines the binning parameters.

Create a histogram from numerical data.

Data is binned according to the provided Bins configuration.

Example

import System.Random

-- Generate random normal-like distribution
let values = take 1000 $ randomRs (0, 100) gen
    chart = histogram (bins 20 0 100) values defPlot

Create a bar chart from categorical data.

Each bar is colored differently and labeled with its category name.

Example

let fruits = [("Apple", 45.2), ("Banana", 38.1), ("Orange", 52.7)]
    chart = bars fruits defPlot { plotTitle = "Fruit Sales" }

Create a scatter plot from multiple data series.

Each series is rendered with a different color and pattern. Points are plotted using Braille characters for sub-character resolution.

Example

let points1 = [(x, x^2) | x <- [-3, -2.5 .. 3]]
    points2 = [(x, 2*x + 1) | x <- [-3, -2.5 .. 3]]
    chart = scatter [series "y = x²" points1,
                     series "y = 2x + 1" points2] defPlot

Create a pie chart showing proportions.

Values are normalized to sum to 100%. Negative values are treated as zero.

Example

let browsers = [("Chrome", 65), ("Firefox", 20), ("Safari", 10), ("Other", 5)]
    chart = pie browsers defPlot { plotTitle = "Browser Market Share" }

Create a stacked bar chart.

Each category can have multiple stacked components.

Example

let sales = [("Q1", [("Product A", 100), ("Product B", 150)]),
             ("Q2", [("Product A", 120), ("Product B", 180)])]
    chart = stackedBars sales defPlot

Create a heatmap visualization of a 2D matrix.

Values are mapped to a color gradient from blue (low) to red (high).

Example

let matrix = [[x * y | x <- [1..10]] | y <- [1..10]]
    chart = heatmap matrix defPlot { plotTitle = "Multiplication Table" }

Create a line graph connecting data points.

Similar to scatter but connects consecutive points with lines. Points are automatically sorted by x-coordinate before connecting.

Example

let sine = [(x, sin x) | x <- [0, 0.1 .. 2*pi]]
    cosine = [(x, cos x) | x <- [0, 0.1 .. 2*pi]]
    chart = lineGraph [series "sin" sine, series "cos" cosine] defPlot

Create a box plot showing statistical distributions.

Displays quartiles, median, and min/max values for each dataset.

Example

let data1 = [1.2, 2.3, 2.1, 3.4, 2.8, 4.1, 3.9]
    data2 = [5.1, 4.8, 6.2, 5.9, 7.1, 6.5, 5.5]
    chart = boxPlot [("Group A", data1), ("Group B", data2)] defPlot

Filled-area chart with the curve closed down to y=0.

Filled band between (x, ymin, ymax) curves — e.g. CI envelopes.

Gaussian KDE per series (Silverman bandwidth).

Points with vertical error bars: (x, y, ymin, ymax) per row.

Horizontal bars sized by their values.

Polar line chart; theta in radians CCW from +x.

Waterfall chart: rows are (label, start, end).

Histogram + KDE overlay per series.

Gaussian / normal-distribution chart in standard-deviation (z-score) units.

Given a population sample, gauss computes the mean (μ) and standard deviation (σ), draws the kernel-density estimate of the distribution as a stippled bell curve, and annotates named markers at their z-score positions. The marker with the largest z-score is highlighted as the outlier.

This reproduces the "where does X sit on the curve?" style of chart: a smooth density curve filled with scattered dots, an x-axis labelled in σ units, and lollipop annotations dropping from each named value down to the axis.

Example

let -- goals + assists per 90 for every attacker in the league
    population = ...
    stars =
        [ (Lewandowski, 1.05)
        , (Mbappé,      1.02)
        , (Haaland,     1.10)
        , (Ronaldo,     1.12)
        , (Messi,       1.45)
        ]
chart = gauss population stars defPlot{plotTitle = "g + a per 90"}

Render a differential flame graph as a full standalone <svg> document. Rows go top→down by depth; a frame's width is proportional to its churn over the root churn; children are laid left→right sorted by descending churn; frames narrower than foMinPx are dropped and recursion stops past foMaxDepth.