`posterior_2d`

Render weighted posterior samples as a 2D probability-density heatmap, with 1σ/2σ highest-posterior-density (HPD) contours drawn on top. This is the standard Bayesian "corner-style" 2D panel.

It is an encapsulated figure type: set type: posterior_2d on a figure and Jarvis-PLOT builds the layers for you.

What it expands to

Layer 1  pcolormesh      ← posterior_density transform   (the density map, → colorbar)
Layer 2  contour         ← HPD levels of the same density (1σ, 2σ)   [unless hpd: false]
+ extra_layers (yours)

Required keys

Key	Meaning
`data`	DataSet name (or list → concatenated)
`x`, `y`	Axis coordinates `{expr, lim, scale, label}`
`weight`	Posterior weight per sample, e.g. `{expr: "exp(LogL)"}`

Type-specific keys

Key	Default	Purpose
`density`	`{}`	Density-reconstruction settings (see below)
`hpd`	on	HPD contour settings, or `false` to disable (see below)
`colorbar`	see common	Colorbar styling (default label `density`, `vmin` `0`)

Plus the common keys: name, style_card, frame, extra_layers.

`density`

Controls how the smooth density is rebuilt from the weighted samples. These are exactly the options of the posterior_density transform.

density:
  method: voronoi      # voronoi | adaptive | kde | grid   (default: voronoi)
  bins: 120            # support / bin resolution           (default: 64)
  grid: 300            # interpolation grid (voronoi/adaptive only)
  seed: null           # RNG seed for reproducibility

`method`	Behavior	Best for
`voronoi`	Mass-conserving Voronoi aggregation + interpolation	General posteriors (default)
`adaptive`	`voronoi` plus adaptive refinement of high-gradient regions	Multimodal / sharp posteriors
`kde`	Gaussian KDE on a grid (`bins` sets the grid; no `grid`)	Smooth results
`grid`	Plain 2D histogram (fastest)	Quick previews

`hpd`

HPD contours are drawn by default with sensible 1σ/2σ settings. Provide an hpd block to customize them, or hpd: false to turn them off.

hpd:
  masses: [0.6827, 0.9545]            # credible masses (default 1σ, 2σ)
  labels: ["$1\\sigma$", "$2\\sigma$"]
  colors: [black, white]
  linestyles: [solid, solid]
  linewidths: [0.2, 0.2]

hpd: false                            # no contours, density map only

Examples

Minimal

HPD contours and a colorbar appear automatically.

- name: Posterior_XY
  type: posterior_2d
  data: my_samples
  x: {expr: xx, lim: [0, 5], label: "$x$"}
  y: {expr: yy, lim: [0, 5], label: "$y$"}
  weight: {expr: "exp(LogL)"}

Full — adaptive density, custom colorbar, samples overlaid

- name: Posterior_XY
  type: posterior_2d
  data: [df_samples_0, df_samples_1]
  x: {expr: xx, lim: [0, 5], label: "$x$"}
  y: {expr: yy, lim: [0, 5], label: "$y$"}
  weight: {expr: "exp(LogL)"}

  density:
    method: adaptive
    bins: 120
    grid: 300

  colorbar:
    label: "posterior density"
    cmap: jarvis_rainbow2_r
    vmax: 0.8

  hpd:
    masses: [0.6827, 0.9545]
    colors: [black, white]
    linewidths: [0.3, 0.3]

  extra_layers:
    - method: scatter
      coordinates: {x: {expr: xx}, y: {expr: yy}}
      style: {s: 0.5, color: gray, alpha: 0.2, zorder: 5}

Notes

Set colorbar.scale: log for posteriors that span many orders of magnitude.
extra_layers use the standard layer format and inherit the figure's data source if they don't set their own.
To see the exact layers this expands to, run with --debug.