R: Apply arithmetic expressions over all pixels of a data cube

apply_pixel.cube {gdalcubes} | R Documentation

## Apply arithmetic expressions over all pixels of a data cube

### Description

Create a proxy data cube, which applies arithmetic expressions over all pixels of a data cube. Expressions may access band values by name.

### Usage

    ## S3 method for class 'cube'
    apply_pixel(x, expr, names = NULL, keep_bands = FALSE, ..., FUN)

### Arguments

`x` | source data cube
`expr` | character vector with one or more arithmetic expressions (see Details)
`names` | optional character vector with the same length as expr to specify band names for the output cube
`keep_bands` | logical; keep bands of input data cube, defaults to FALSE, i.e. original bands will be dropped
`...` | not used
`FUN` | user-defined R function that is applied on all pixels (see Details)

### Details

The function can either apply simple arithmetic C expressions given as a character vector (expr argument), or apply a custom R reducer function if FUN is provided.

In the former case, gdalcubes uses the tinyexpr library to evaluate expressions in C / C++, you can look at the library documentation to see what kind of expressions you can execute. Pixel band values can be accessed by name.

FUN receives values of the bands from one pixel as a (named) vector and should return a numeric vector with identical length for all pixels. Elements of the result vectors will be interpreted as bands in the result data cube.

### Value

a proxy data cube object

### Note

This function returns a proxy object, i.e., it will not start any computations besides deriving the shape of the result.

### Examples

    # create image collection from example Landsat data only
    # if not already done in other examples
    if (!file.exists(file.path(tempdir(), "L8.db"))) {
      L8_files <- list.files(system.file("L8NY18", package = "gdalcubes"),
                             ".TIF", recursive = TRUE, full.names = TRUE)
      create_image_collection(L8_files, "L8_L1TP", file.path(tempdir(), "L8.db"))
    }

    # 1. Apply a C expression
    L8.col = image_collection(file.path(tempdir(), "L8.db"))
    v = cube_view(extent=list(left=388941.2, right=766552.4,
                  bottom=4345299, top=4744931, t0="2018-04", t1="2018-06"),
                  srs="EPSG:32618", nx = 497, ny=526, dt="P1M")
    L8.cube = raster_cube(L8.col, v)
    L8.cube = select_bands(L8.cube, c("B04", "B05"))
    L8.ndvi = apply_pixel(L8.cube, "(B05-B04)/(B05+B04)", "NDVI")
    L8.ndvi

    plot(L8.ndvi)

    # 2. Apply a user defined R function
    L8.ndvi.noisy = apply_pixel(L8.cube, names="NDVI_noisy",
       FUN=function(x) {
           rnorm(1, 0, 0.1) + (x["B05"]-x["B04"])/(x["B05"]+x["B04"])
       })
    L8.ndvi.noisy

    plot(L8.ndvi.noisy)

[Package gdalcubes version 0.3.1 Index]