Plot Methods

The method key of a layer chooses how that layer is drawn. Most method names map one-to-one to a Matplotlib Axes method of the same name, so the layer's style keys are simply that method's keyword arguments. A few methods (voronoi, jp*, dynesty_runplot) are Jarvis-PLOT additions.

This page explains the rules shared by every method. Each method has its own reference page — pick one from the tables below.

How a method receives its data

You never pass positional arguments. Jarvis-PLOT evaluates each entry in coordinates into a named array (x, y, z, c, u, v, …) and hands them to the method, together with the merged style:

- method: scatter
  coordinates:
    x: {expr: mass}        # → x
    y: {expr: xsec}        # → y
    c: {expr: LogL}        # → c (per-point color)
  style:
    s: 4                   # → Matplotlib Axes.scatter(..., s=4)
    cmap: viridis

So even for Matplotlib methods whose signature is positional (plot, fill, quiver), you still write named coordinates; Jarvis-PLOT forwards them in the right order.

Everything in style that is not consumed by Jarvis-PLOT is passed straight through to the underlying Matplotlib method — so any keyword the Matplotlib method accepts works, even if it is not listed on the method's page here.

Method reference

2D primitives

Method	Matplotlib	Draws
`scatter`	`Axes.scatter`	Scatter points (optional per-point color)
`plot`	`Axes.plot`	Line / marker plot
`step`	`Axes.step`	Staircase line
`errorbar`	`Axes.errorbar`	Points with error bars
`bar`	`Axes.bar`	Vertical bars
`barh`	`Axes.barh`	Horizontal bars
`hist`	`Axes.hist`	Histogram (one or many datasets)
`fill`	`Axes.fill`	Filled polygon
`fill_between`	`Axes.fill_between`	Fill between two y-curves
`fill_betweenx`	`Axes.fill_betweenx`	Fill between two x-curves
`quiver`	`Axes.quiver`	Vector field

Grid / image

Method	Matplotlib	Draws
`pcolormesh`	`Axes.pcolormesh`	Pseudocolor mesh (with built-in gridding)
`pcolor`	`Axes.pcolor`	Pseudocolor (slower)
`imshow`	`Axes.imshow`	Raster image
`contour`	`Axes.contour`	Contour lines (+ HPD / likelihood mode)
`contourf`	`Axes.contourf`	Filled contours

Scatter → interpolate (Jarvis-PLOT)

Method	Based on	Draws
`jpcontour`	`Axes.contour`	Interpolate scattered points → contour lines
`jpcontourf`	`Axes.contourf`	Interpolate scattered points → filled contours
`jpfield`	`Axes.pcolormesh`	Interpolate scattered points → pseudocolor field

Triangulation

Method	Matplotlib	Draws
`tripcolor`	`Axes.tripcolor`	Triangulated pseudocolor
`tripcolor_axes`	`Axes.tripcolor`	`tripcolor` forced to axes space
`tricontour`	`Axes.tricontour`	Triangulated contour lines
`tricontourf`	`Axes.tricontourf`	Triangulated filled contours
`triplot`	`Axes.triplot`	The triangulation mesh

Voronoi & special (Jarvis-PLOT)

Method	Based on	Draws
`voronoi`	custom	Voronoi cells colored by `z` (or single fill)
`voronoif`	custom	Voronoi cell boundaries with hatched fill
`dynesty_runplot`	dynesty	Nested-sampling diagnostic panel

Choosing a method

Raw samples → scatter
Profile-likelihood map → a profile transform, then voronoi (cells) or pcolormesh (smooth)
Posterior density map → a posterior_density transform, then pcolormesh
Contours over scattered points → jpcontour
Overlaid limits / reference curves → plot, fill, fill_between

Colormaps

Color-mapped methods accept any Matplotlib colormap name (e.g. viridis, magma, terrain) — append _r to reverse any of them. Jarvis-PLOT also ships these custom maps:

jarvis_rainbow, jarvis_rainbow2, gambit_cmap, chrisB, qual22, SpectralB, RdBuB

(reversed forms such as jarvis_rainbow2_r are available too). See Matplotlib: Choosing colormaps.