Plot a NeuroSlice

Display axial slices of a NeuroVol as a faceted montage.

Usage

# S4 method for class 'NeuroSlice,ANY'
plot(
  x,
  cmap = gray(seq(0, 1, length.out = 255)),
  irange = range(x, na.rm = TRUE),
  legend = TRUE
)

# S4 method for class 'NeuroVol,missing'
plot(
  x,
  y,
  cmap = "grays",
  zlevels = unique(round(seq(1, dim(x)[3], length.out = 9))),
  irange = range(x, na.rm = TRUE),
  thresh = c(0, 0),
  alpha = 1,
  legend = TRUE
)

# S4 method for class 'NeuroVol,NeuroVol'
plot(
  x,
  y,
  cmap = "grays",
  zlevels = unique(round(seq(1, dim(x)[3], length.out = 9))),
  ov_cmap = "inferno",
  ov_alpha = 0.5,
  ov_thresh = 0
)

Arguments

x

the background volume to display.

cmap

palette name or hex-color vector for the background (default "grays"). See resolve_cmap.

irange

numeric length-2 intensity range for the color scale.

legend

logical; show the colour bar?

y

optional overlay volume (same dimensions as x). When supplied, the plot is rendered as a background + overlay composite.

zlevels

integer slice indices to display. Default: 9 evenly-spaced slices (3 \(\times\) 3 grid).

thresh

a 2-element vector indicating the lower and upper transparency thresholds.

alpha

opacity for the background layer (0–1).

ov_cmap

overlay palette name (default "inferno").

ov_alpha

overlay opacity (default 0.5).

ov_thresh

overlay threshold; values with \(|v| < \) ov_thresh become transparent (default 0).

Value

a ggplot2 object

Details

The plot method uses ggplot2 to create a raster visualization of the slice data. The intensity values are mapped to colors using the specified colormap and range.

when `x` is a NeuroSlice object, the plot method returns a ggplot2 object containing the raster visualization of the slice data. The plot can be further customized using standard ggplot2 functions.

When a second volume y is supplied it is treated as an overlay (e.g.\ a statistical map) composited on top of x with adjustable transparency. This delegates to plot_overlay.

Examples

# Create example slice
slice_space <- NeuroSpace(c(100, 100))
slice_data <- matrix(rnorm(100*100), 100, 100)
slice <- NeuroSlice(slice_data, slice_space)
# \donttest{
# Basic plot
plot(slice)

# }


dat <- matrix(rnorm(100*100), 100, 100)
slice <- NeuroSlice(dat, NeuroSpace(c(100,100)))
# \donttest{
plot(slice)

# }