API Documentation

PairPlots.pairplot Function

pairplot(inputs...; figure=(;), kwargs...)

Convenience method to generate a new Makie figure with resolution (800,800) and then call pairplot as usual. Returns the figure.

Example:

fig = pairplot(table)

source

pairplot(gridpos::Makie.GridPosition, inputs...; kwargs...)

Create a pair plot at a given grid position of a Makie figure.

Example

fig = Figure()
pairplot(fig[2,3], table)
fig

source

pairplot(gridpos::Makie.GridLayout, inputs...; kwargs...)

Convenience function to create a reasonable pair plot given one or more inputs that aren't full specified. Wraps input tables in PairPlots.Series() with a distinct color specified for each series.

Here are the defaults applied for a single data table:

pairplot(fig[1,1], table) == # approximately the following:
pairplot(
    PairPlots.Series(table, color=Makie.RGBA(0., 0., 0., 0.5)) => (
        PairPlots.HexBin(colormap=Makie.cgrad([:transparent, :black]),),
        PairPlots.Scatter(filtersigma=2),
        PairPlots.Contour(),
        PairPlots.MarginDensity(
            color=:transparent,
            color=:black,
            linewidth=1.5f0
        ),
        PairPlots.MarginConfidenceLimits()
    )
)

Here are the defaults applied for 2 to 5 data tables:

pairplot(fig[1,1], table1, table2) == # approximately the following:
pairplot(
    PairPlots.Series(table1, color=Makie.wong_colors(0.5)[1]) => (
        PairPlots.Scatter(filtersigma=2),
        PairPlots.Contourf(),
        PairPlots.MarginDensity(
            linewidth=2.5f0
        )
    ),
    PairPlots.Series(table2, color=Makie.wong_colors(0.5)[2]) => (
        PairPlots.Scatter(filtersigma=2),
        PairPlots.Contourf(),
        PairPlots.MarginDensity(
            linewidth=2.5f0
        )
    ),
)

For 6 or more tables, the defaults are approximately:

PairPlots.Series(table1, color=Makie.wong_colors(0.5)[series_i]) => (
    PairPlots.Contour(sigmas=[1]),
    PairPlots.MarginDensity(
        linewidth=2.5f0
    )
)

source

pairplot(gridpos::Makie.GridLayout, inputs...; kwargs...)

Main PairPlots function. Create a pair plot by plotting into a grid layout within a Makie figure.

Inputs should be one or more Pair of PairPlots.AbstractSeries => tuple of VizType.

Additional arguments:

• labels: customize the axes labels with a Dict of column name (symbol) to string, Makie rich text, or LaTeX string.

• diagaxis: customize the Makie.Axis of plots along the diagonal with a named tuple of keyword arguments.

• bodyaxis: customize the Makie.Axis of plots under the diagonal with a named tuple of keyword arguments.

• axis: customize the axes by parameter using a Dict of column name (symbol) to named tuple of axis settings. x and y are automatically prepended based on the parameter and subplot. For global properties, see diagaxis and bodyaxis.

• legend: additional keyword arguments to the Legend constructor, used if one or more series are labelled. You can of course also create your own Legend and inset it into the Figure for complete control.

• fullgrid: true or false (default). Show duplicate plots above the diagonal to make a full grid.

Examples

Basic usage:

table = (;a=randn(1000), b=randn(1000), c=randn(1000))
pairplot(table)

Customize labels:

pairplot(table, labels=Dict(:a=>L"\sum{a^2}"))

Use a pseudo log scale for one variable:

pairplot(table, axis=(; a=(;scale=Makie.pseudolog10) ))

source

PairPlots.AbstractSeries Type

AbstractSeries

Represents some kind of series in PairPlots.

source

PairPlots.Series Type

Series(data; label=nothing, kwargs=...)

A data series in PairPlots. Wraps a Tables.jl compatible table. You can optionally pass a label for this series to use in the plot legend. Keyword arguments are forwarded to every plot call for this series.

Examples:

ser = Series(table; label="series 1", color=:red)

source

Series(matrix::AbstractMatrix; label=nothing, kwargs...)

Convenience constructor to build a Series from an abstract matrix. The columns are named accordingly to the axes of the Matrix (usually :1 though N).

source

PairPlots.Truth Type

Truth(truths; labels=nothing, kwargs=...)

Mark a set of "true" values given by truths which should be either a named tuple, Dict, etc that can be indexed by the column name and returns a value or values.

source

PairPlots.VizType Type

A type of PairPlots visualization. VizType is the supertype for all PairPlots visualization types.

source

PairPlots.VizTypeBody Type

VizTypeBody <: VizType

A type of PairPlots visualization that compares two variables.

source

PairPlots.VizTypeDiag Type

VizTypeDiag <: VizType

A type of PairPlots visualization that only shows one variable. Used for the plots along the diagonal.

source

PairPlots.HexBin Type

HexBin(;kwargs...)

Plot two variables against eachother using a Makie Hex Bin series. kwargs are forwarded to the plot function and can be used to control the number of bins and the appearance.

source

PairPlots.Hist Type

Hist(;kwargs...)
Hist(histprep_function; kwargs...)

Plot two variables against eachother using a 2D histogram heatmap. kwargs are forwarded to the plot function and can be used to control the number of bins and the appearance.

Note

You can optionally pass a function to override how the histogram is calculated. It should have the signature: prepare_hist(xs, ys, nbins) and return a vector of horizontal bin centers, vertical bin centers, and a matrix of weights.

Tip

Your prepare_hist function it does not have to obey nbins

:::

source

PairPlots.Calculation Type

Calculation(function; digits=3, position=Makie.Point2f(0.01, 1.0), kwargs...)

Apply a function to a pair of variables and add the result as a text label in the appropriate body subplot. Control the position of the label by passing a different value for position. Change the number of digits to round the result by passing digits=N. The first and only positional argument is the function to use to calculate the correlation.

source

PairPlots.Correlation Function

Correlation(;digits=3, position=Makie.Point2f(0.01, 1.0), kwargs...)

Calculate the correlation between two variables and add it as a text label to the plot. This is an alias for Calculation(StatsBase.cor). You can adjust the number of digits, the location of the text, etc. See Calculation for more details.

source

PairPlots.Contour Type

Contour(;sigmas=0.5:0.5:2, bandwidth=1.0, kwargs...)

Plot two variables against eachother using a contour plot. The contours cover the area under a Gaussian given by sigmas, which must be <: AbstractVector. kwargs are forwarded to the plot function and can be used to control the appearance.

KernelDensity.jl is used to produce smoother contours. The bandwidth of the KDE is chosen automatically by that package, but you can scale the bandwidth used up or down by a constant factor by passing bandwidth when creating the series. For example, bandwidth=2.0 will double the KDE bandwidth and result in smoother contours.

Note

Contours are calculated using Contour.jl and plotted as a Makie line series.

See also: Contourf

source

PairPlots.Contourf Type

Contourf(;sigmas=0.5:0.5:2, bandwidth=1.0, kwargs...)

Plot two variables against eachother using a filled contour plot. The contours cover the area under a Gaussian given by sigmas, which must be <: AbstractVector. kwargs are forwarded to the plot function and can be used to control the appearance.

KernelDensity.jl is used to produce smoother contours. The bandwidth of the KDE is chosen automatically by that package, but you can scale the bandwidth used up or down by a constant factor by passing bandwidth when creating the series. For example, bandwidth=2.0 will double the KDE bandwidth and result in smoother contours.

KernelDensity.jl is used to produce smoother contours.

See also: Contour

source

PairPlots.Scatter Type

Scatter(;kwargs...)

Plot two variables against eachother using a scatter plot.kwargs are forwarded to the plot function and can be used to control the appearance.

source

PairPlots.MarginConfidenceLimits Type

MarginConfidenceLimits(format_string::AbstractString, (0.16,0.5,0.84); kwargs...)
MarginConfidenceLimits(formatter::Base.Callable=margin_confidence_default_formatter, (0.16,0.5,0.84); kwargs...)

source

PairPlots.MarginHist Type

MarginHist(;kwargs...)
MarginHist(histprep_function; kwargs...)

Plot a marginal filled histogram of a single variable along the diagonal of the grid. kwargs are forwarded to the plot function and can be used to control the number of bins and the appearance.

Tip

You can optionally pass a function to override how the histogram is calculated. It should have the signature: prepare_hist(xs, nbins) and return a vector of bin centers and a vector of weights.

Note

Your prepare_hist function it does not have to obey nbins

:::

source

PairPlots.MarginStepHist Type

MarginStepHist(;kwargs...)
MarginStepHist(histprep_function; kwargs...)

Plot a marginal histogram without filled columns of a single variable along the diagonal of the grid. kwargs are forwarded to the plot function and can be used to control the number of bins and the appearance.

Tip

You can optionally pass a function to override how the histogram is calculated. It should have the signature: prepare_hist(xs, nbins) and return a vector of bin centers and a vector of weights.

Note

Your prepare_hist function it does not have to obey nbins

:::

source

PairPlots.MarginDensity Type

MarginDensity(;kwargs...)

Plot the smoothed marginal density of a variable along the diagonal of the grid, using Makie's density function. kwargs are forwarded to the plot function and can be used to control the appearance.

source