sum function

sum([X])

sum({X})

Result is the sum of all elements of the array [X] or the list {X}

**Input**: numeric array/list

**Result**: numeric

**Example**:

sum([2,3,4]) --> 9

sum([[1,2],[3,4]]) --> [4,6]

**Comment**:

Note the behaviour with nested arrays. A new array results, consisting of the sum of the first value of each array, the sum of the second value of each array, etc.

sum function

sum([X])

sum({X})

Result is the sum of all elements of the array [X] or the list {X}

**Input**: numeric array/list

**Result**: numeric

**Example**:

sum([2,3,4]) --> 9

sum([[1,2],[3,4]]) --> [4,6]

**Comment**:

Note the behaviour with nested arrays. A new array results, consisting of the sum of the first value of each array, the sum of the second value of each array, etc. The reason it is implemented this way is that the converse (generating an array of the sums of each sub-array in the original, i.e., [3,7] in the above example) is easier to generate explicitly if required. In the case where the 2-D array is a value coming out of nested 1-D submodels, it can be produced by putting the sum() function inside the outer submodel. If the array is only available as a 2-D value, the same effect can be produced using the element() and makearray() functions as follows:

makearray(sum(element([[1,2],[3,4]], place_in(1))),2) --> [3,7]

All other cumulative functions behave the same way regarding selection of elements from multidimensional arrays.

In: Contents >> Working with equations >> Functions >> Built-in functions