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.

Supported ANSI colo(u)rs.

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 data = [(Apple, 45.2), (Banana, 38.1), (Orange, 52.7)]
    chart = bars data 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 data = [(Chrome, 65), (Firefox, 20), (Safari, 10), (Other, 5)]
    chart = pie data defPlot { plotTitle = "Browser Market Share" }

Create a stacked bar chart.

Each category can have multiple stacked components.

Example

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