I have an array in R, created by a function like this:
A <- array(data=NA, dim=c(2,4,4), dimnames=list(c("x","y"),NULL,NULL))
And I would like to select along one dimension, so for the example above I would have:
A["x",,]
dim(A["x",,]) #[1] 4 4
Is there a way to generalize if I do not know in advance how many dimensions (in addition to the named one I want to select by) my array might have? I would like to write a function that takes input that might formatted as A above, or as:
B <- c(1,2)
names(B) <- c("x", "y")
C <- matrix(1, 2, 2, dimnames=list(c("x","y"),NULL))
The general background is that I am working on an ODE model, so for deSolve's ODE function it must take a single named vector with my current state. For some other functions, like calculating phase-planes/direction fields, it would be more practical to have a higher-dimensional array to apply the differential equation to, and I would like to avoid having many copies of the same function, simply with different numbers of commas after the dimension I want to select.
An ndarray is a (usually fixed-size) multidimensional container of items of the same type and size. The number of dimensions and items in an array is defined by its shape , which is a tuple of N non-negative integers that specify the sizes of each dimension.
Use integer array indexing to select an element by index Use the syntax np. array[i,j] to retrieve an element at row index i and column index j from the array. To retrieve multiple elements, use the syntax np. array[(row_values), (column_values)] where row_values and column_values are a tuple of the same size.
One dimensional array contains elements only in one dimension. In other words, the shape of the NumPy array should contain only one value in the tuple.
Prerequisite: Arrays in C/C++ A multi-dimensional array can be termed as an array of arrays that stores homogeneous data in tabular form. Data in multidimensional arrays are stored in row-major order. The general form of declaring N-dimensional arrays is: data_type array_name[size1][size2]....[sizeN];
I spent quite a lot of time figuring out the fastest way to do this for plyr, and the best I could come up with was manually constructing the call to [
:
index_array <- function(x, dim, value, drop = FALSE) {
# Create list representing arguments supplied to [
# bquote() creates an object corresponding to a missing argument
indices <- rep(list(bquote()), length(dim(x)))
indices[[dim]] <- value
# Generate the call to [
call <- as.call(c(
list(as.name("["), quote(x)),
indices,
list(drop = drop)))
# Print it, just to make it easier to see what's going on
print(call)
# Finally, evaluate it
eval(call)
}
(You can find more information about this technique at https://github.com/hadley/devtools/wiki/Computing-on-the-language)
You can then use it as follows:
A <- array(data=NA, dim=c(2,4,4), dimnames=list(c("x","y"),NULL,NULL))
index_array(A, 2, 2)
index_array(A, 2, 2, drop = TRUE)
index_array(A, 3, 2, drop = TRUE)
It would also generalise in a straightforward way if you want to extract based on more than one dimension, but you'd need to rethink the arguments to the function.
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