apply_time.array

Apply a function over pixel time series in a four-dimensional (band, time, y, x) array

Description

Apply a function over pixel time series in a four-dimensional (band, time, y, x) array

Usage

apply_time.array(x, FUN, ...)

Arguments

Argument	Description
x	four-dimensional input array with dimensions band, time, y, x (in this order)
FUN	function that receives a vector of band values in a one-dimensional array
…	further arguments passed to FUN

Details

FUN is expected to produce a matrix (or vector if result has only one band) where rows are interpreted as new bands and columns represent time.

Note

This is a helper function that uses the same dimension ordering as gdalcubes. It can be used to simplify the application of R functions e.g. over time series in a data cube.

Examples

d <- c(4,16,32,32)
x <- array(rnorm(prod(d)), d)
z <- apply_time(x, function(v) {
  y = matrix(NA, ncol=ncol(v), nrow=2)
  y[1,] = (v[1,] + v[2,]) / 2
  y[2,] = (v[3,] + v[4,]) / 2
  y
})
dim(z)

[1]  2 16 32 32

# apply_time.array

Apply a function over pixel time series in a four-dimensional (band, time, y, x) array



```{r include=FALSE}
library(gdalcubes)
```

## Description

Apply a function over pixel time series in a four-dimensional (band, time, y, x) array


## Usage


```r
apply_time.array(x, FUN, ...)

```


## Arguments

| Argument    | Description                       |
|:------------|:----------------------------------|
| x | four-dimensional input array with dimensions band, time, y, x (in this order) |
| FUN | function that receives a vector of band values in a one-dimensional array |
| ... | further arguments passed to FUN |


## Details

FUN is expected to produce a matrix (or vector if result has only one band) where rows are interpreted as new bands and columns represent time.


## Note

This is a helper function that uses the same dimension ordering as gdalcubes. It can be used to simplify 
the application of R functions e.g. over time series in a data cube.


## Examples


```{r}
d <- c(4,16,32,32)
x <- array(rnorm(prod(d)), d)
z <- apply_time(x, function(v) {
  y = matrix(NA, ncol=ncol(v), nrow=2)
  y[1,] = (v[1,] + v[2,]) / 2
  y[2,] = (v[3,] + v[4,]) / 2
  y
})
dim(z)
```