sk_rescale {snapKrig}

Up or down-scale a sk grid by an integer factor

Description

Changes the resolution of a sk grid by a factor of up or down. For down-scaling, this introduces NAs at unobserved grid points (and does no interpolation).

Usage

sk_rescale(g, up = NULL, down = NULL)

Arguments

g

a sk grid or any grid object accepted by sk

up

integer > 0, or vector of two, the up-scaling factors

down

integer > 0, or vector of two, the down-scaling factors

Details

Users should specify a sk grid g to re-scale and an integer scaling factor; either up or down (and not both). This effects the scaling of resolution (g[['gres']]) by up or 1/down.

up (or down) should be a vector of two positive integers, the desired re-scaling factors in the y and x dimensions, in that order, or a single value to be used for both.

When up is supplied, a lower resolution grid is returned comprising every upth grid line of g along each dimension. All other grid lines, and any data values lying on them, are ignored. up should be no greater than dim(g) - 1. Note that if up does not evenly divide this number, the bounding box will shrink slightly.

When down is supplied, the function returns a higher resolution grid (say g_fine) with the same bounding box as g. Along each dimension, every downth grid line of g_fine coincides with a grid line of g. Any non-NA values found in g[] are copied to g_fine, and g can be recovered from g_fine with sk_rescale(g_fine, up=down).

Value

a sk grid of the requested resolution

See Also

sk sk_cmean

Other indexing functions: sk_mat2vec(), sk_sub_find(), sk_sub_idx(), sk_vec2mat()

Other sk constructors: sk_snap(), sk_sub(), sk()

Examples

# example data
gdim = c(50, 53)
pars = utils::modifyList(sk_pars(gdim), list(eps=1e-2))
g = sk_sim(gdim, pars)
plot(g)

# upscale
plot(sk_rescale(g, up=1)) # does nothing
plot(sk_rescale(g, up=2))

# downscale
sk_plot(sk_rescale(g, down=1)) # does nothing
sk_plot(sk_rescale(g, down=2))

# length-2 vectors to rescale differently in x and y directions
plot(sk_rescale(g, up=c(2,3)))
plot(sk_rescale(g, down=c(2,3)))

# invert a down-scaling
g_compare = sk_rescale(sk_rescale(g, down=c(5,3)), up=c(5,3))
all.equal(g, g_compare)

# multi-layer example with about 50% of points missing
idx_miss = sample.int(length(g), round(0.5*length(g)))
g_multi = sk_sim(gdim, pars, n_layer=3)
g_multi[idx_miss,] = NA

# plot third layer, then down-scaled and up-scaled versions
sk_plot(g_multi, layer=3)
sk_plot(sk_rescale(g=g_multi, down=2), layer=3)
sk_plot(sk_rescale(g=g_multi, up=2), layer=3)