params <-
list(family = "red", preset = "homage")

## ----setup, echo = FALSE, message = FALSE-------------------------------------
if (requireNamespace("ggplot2", quietly = TRUE) && requireNamespace("albersdown", quietly = TRUE)) ggplot2::theme_set(albersdown::theme_albers(family = params$family, preset = params$preset))
knitr::opts_chunk$set(collapse = TRUE, comment = "#>", message = FALSE, warning = FALSE)
suppressPackageStartupMessages(library(neuroim2))

## -----------------------------------------------------------------------------
file_name <- system.file("extdata", "global_mask2.nii.gz", package = "neuroim2")
vol <- read_vol(file_name)

## -----------------------------------------------------------------------------
class(vol)
is.array(vol)
dim(vol)
vol[1, 1, 1]
vol[64, 64, 24]

## -----------------------------------------------------------------------------
sp <- space(vol)
sp
dim(vol)
spacing(vol)
origin(vol)

## -----------------------------------------------------------------------------
idx <- 1:5
g <- index_to_grid(vol, idx)
w <- index_to_coord(vol, idx)
idx2 <- coord_to_index(vol, w)
all.equal(idx, idx2)

## -----------------------------------------------------------------------------
vol2 <- as.logical(vol)
class(vol2)
print(vol2[1, 1, 1])

## -----------------------------------------------------------------------------
mask1 <- as.mask(vol > 0.5)
mask1

idx_hi <- which(vol > 0.8)
mask2 <- as.mask(vol, idx_hi)
sum(mask2) == length(idx_hi)

mean_in_mask <- mean(vol[mask1@.Data])
mean_in_mask

## -----------------------------------------------------------------------------
    x <- array(0, c(64,64,64))

## -----------------------------------------------------------------------------
    bspace <- NeuroSpace(dim=c(64,64,64), spacing=c(1,1,1))
    vol <- NeuroVol(x, bspace)
    vol

## -----------------------------------------------------------------------------
    vol2 <- NeuroVol((vol+1)*25, space(vol))
    max(vol2)
    space(vol2)

## ----plot_montage, fig.alt='3x3 montage of axial slices.', fig.cap='Default plot() montage', fig.width=7, fig.height=7----
plot(vol)

## ----slice_mid, fig.alt='Mid-slice of example volume (grayscale image).', fig.cap='Mid-slice of example volume'----
    z <- ceiling(dim(vol)[3] / 2)
    image(vol[,,z], main = paste("Slice z=", z))

## -----------------------------------------------------------------------------
    # Reorient the space (LPI -> RAS) and compare coordinate mappings
    sp_lpi <- space(vol)
    sp_ras <- reorient(sp_lpi, c("R","A","S"))
    g     <- t(matrix(c(10, 10, 10)))
    world_lpi <- grid_to_coord(sp_lpi, g)
    world_ras <- grid_to_coord(sp_ras, g)
    # world_lpi and world_ras differ due to axis remapping

## ----eval=FALSE---------------------------------------------------------------
#     # Create a target space with 2x finer resolution
#     sp  <- space(vol)
#     sp2 <- NeuroSpace(sp@dim * c(2,2,2), sp@spacing/2, origin=sp@origin, trans=trans(vol))
# 
#     # Resample (trilinear)
#     vol_resamp <- resample_to(vol, sp2, method = "linear")
#     dim(vol_resamp)

## -----------------------------------------------------------------------------
    # Downsample by target spacing
    vol_ds1 <- downsample(vol, spacing = spacing(vol)[1:3] * 2)
    dim(vol_ds1)

    # Or by factor
    vol_ds2 <- downsample(vol, factor = 0.5)
    dim(vol_ds2)

## ----eval=FALSE---------------------------------------------------------------
#     write_vol(vol2, "output.nii")
# 
#     ## adding a '.gz' extension results ina gzipped file.
#     write_vol(vol2, "output.nii.gz")

## -----------------------------------------------------------------------------
    tmp <- tempfile(fileext = ".nii.gz")
    write_vol(vol2, tmp)
    file.exists(tmp)
    unlink(tmp)