octave: Advanced Indexing

 
 8.1.1 Advanced Indexing
 -----------------------
 
 An array with ‘nd’ dimensions can be indexed by a vector IDX which has
 from 1 to ‘nd’ elements.  If any element of IDX is not a scalar then the
 complete set of index tuples will be generated from the Cartesian
 product of the index elements.
 
    For the ordinary and most common case, the number of indices (‘nidx =
 numel (IDX)’) matches the number of dimensions ‘nd’.  In this case, each
 element of IDX corresponds to its respective dimension, i.e., ‘IDX(1)’
 refers to dimension 1, ‘IDX(2)’ refers to dimension 2, etc.  If
 ‘nidx < nd’, and every index is less than the size of the array in the
 i^{th} dimension (‘IDX(i) < size (ARRAY, i)’), then the index expression
 is padded with ‘nd - nidx’ trailing singleton dimensions.  If
 ‘nidx < nd’ but one of the indices ‘IDX(i)’ is outside the size of the
 current array, then the last ‘nd - nidx + 1’ dimensions are folded into
 a single dimension with an extent equal to the product of extents of the
 original dimensions.  This is easiest to understand with an example.
 
      A = reshape (1:8, 2, 2, 2)  # Create 3-D array
      A =
 
      ans(:,:,1) =
 
         1   3
         2   4
 
      ans(:,:,2) =
 
         5   7
         6   8
 
      A(2,1,2);   # Case (nidx == nd): ans = 6
      A(2,1);     # Case (nidx < nd), idx within array:
                  # equivalent to A(2,1,1), ans = 2
      A(2,4);     # Case (nidx < nd), idx outside array:
                  # Dimension 2 & 3 folded into new dimension of size 2x2 = 4
                  # Select 2nd row, 4th element of [2, 4, 6, 8], ans = 8
 
    One advanced use of indexing is to create arrays filled with a single
 value.  This can be done by using an index of ones on a scalar value.
 The result is an object with the dimensions of the index expression and
 every element equal to the original scalar.  For example, the following
 statements
 
      a = 13;
      a(ones (1, 4))
 
 produce a vector whose four elements are all equal to 13.
 
    Similarly, by indexing a scalar with two vectors of ones it is
 possible to create a matrix.  The following statements
 
      a = 13;
      a(ones (1, 2), ones (1, 3))
 
 create a 2x3 matrix with all elements equal to 13.
 
    The last example could also be written as
 
      13(ones (2, 3))
 
    It is more efficient to use indexing rather than the code
 construction ‘scalar * ones (N, M, ...)’ because it avoids the
 unnecessary multiplication operation.  Moreover, multiplication may not
 be defined for the object to be replicated whereas indexing an array is
 always defined.  The following code shows how to create a 2x3 cell array
 from a base unit which is not itself a scalar.
 
      {"Hello"}(ones (2, 3))
 
    It should be, noted that ‘ones (1, n)’ (a row vector of ones) results
 in a range (with zero increment).  A range is stored internally as a
 starting value, increment, end value, and total number of values; hence,
 it is more efficient for storage than a vector or matrix of ones
 whenever the number of elements is greater than 4.  In particular, when
 ‘r’ is a row vector, the expressions
 
        r(ones (1, n), :)
 
        r(ones (n, 1), :)
 
 will produce identical results, but the first one will be significantly
 faster, at least for ‘r’ and ‘n’ large enough.  In the first case the
 index is held in compressed form as a range which allows Octave to
 choose a more efficient algorithm to handle the expression.
 
    A general recommendation, for a user unaware of these subtleties, is
 to use the function ‘repmat’ for replicating smaller arrays into bigger
 ones.
 
    A second use of indexing is to speed up code.  Indexing is a fast
 operation and judicious use of it can reduce the requirement for looping
 over individual array elements which is a slow operation.
 
    Consider the following example which creates a 10-element row vector
 a containing the values a(i) = sqrt (i).
 
      for i = 1:10
        a(i) = sqrt (i);
      endfor
 
 It is quite inefficient to create a vector using a loop like this.  In
 this case, it would have been much more efficient to use the expression
 
      a = sqrt (1:10);
 
 which avoids the loop entirely.
 
    In cases where a loop cannot be avoided, or a number of values must
 be combined to form a larger matrix, it is generally faster to set the
 size of the matrix first (pre-allocate storage), and then insert
 elements using indexing commands.  For example, given a matrix ‘a’,
 
      [nr, nc] = size (a);
      x = zeros (nr, n * nc);
      for i = 1:n
        x(:,(i-1)*nc+1:i*nc) = a;
      endfor
 
 is considerably faster than
 
      x = a;
      for i = 1:n-1
        x = [x, a];
      endfor
 
 because Octave does not have to repeatedly resize the intermediate
 result.
 
  -- : IND = sub2ind (DIMS, I, J)
  -- : IND = sub2ind (DIMS, S1, S2, ..., SN)
      Convert subscripts to linear indices.
 
      The input DIMS is a dimension vector where each element is the size
      of the array in the respective dimension (Seesize XREFsize.).
      The remaining inputs are scalars or vectors of subscripts to be
      converted.
 
      The output vector IND contains the converted linear indices.
 
      Background: Array elements can be specified either by a linear
      index which starts at 1 and runs through the number of elements in
      the array, or they may be specified with subscripts for the row,
      column, page, etc.  The functions ‘ind2sub’ and ‘sub2ind’
      interconvert between the two forms.
 
      The linear index traverses dimension 1 (rows), then dimension 2
      (columns), then dimension 3 (pages), etc.  until it has numbered
      all of the elements.  Consider the following 3-by-3 matrices:
 
           [(1,1), (1,2), (1,3)]     [1, 4, 7]
           [(2,1), (2,2), (2,3)] ==> [2, 5, 8]
           [(3,1), (3,2), (3,3)]     [3, 6, 9]
 
      The left matrix contains the subscript tuples for each matrix
      element.  The right matrix shows the linear indices for the same
      matrix.
 
      The following example shows how to convert the two-dimensional
      indices ‘(2,1)’ and ‘(2,3)’ of a 3-by-3 matrix to linear indices
      with a single call to ‘sub2ind’.
 
           s1 = [2, 2];
           s2 = [1, 3];
           ind = sub2ind ([3, 3], s1, s2)
               ⇒ ind =  2   8
 
      See also: Seeind2sub XREFind2sub, Seesize XREFsize.
 
  -- : [S1, S2, ..., SN] = ind2sub (DIMS, IND)
      Convert linear indices to subscripts.
 
      The input DIMS is a dimension vector where each element is the size
      of the array in the respective dimension (Seesize XREFsize.).
      The second input IND contains linear indies to be converted.
 
      The outputs S1, ..., SN contain the converted subscripts.
 
      Background: Array elements can be specified either by a linear
      index which starts at 1 and runs through the number of elements in
      the array, or they may be specified with subscripts for the row,
      column, page, etc.  The functions ‘ind2sub’ and ‘sub2ind’
      interconvert between the two forms.
 
      The linear index traverses dimension 1 (rows), then dimension 2
      (columns), then dimension 3 (pages), etc.  until it has numbered
      all of the elements.  Consider the following 3-by-3 matrices:
 
           [1, 4, 7]     [(1,1), (1,2), (1,3)]
           [2, 5, 8] ==> [(2,1), (2,2), (2,3)]
           [3, 6, 9]     [(3,1), (3,2), (3,3)]
 
      The left matrix contains the linear indices for each matrix
      element.  The right matrix shows the subscript tuples for the same
      matrix.
 
      The following example shows how to convert the two-dimensional
      indices ‘(2,1)’ and ‘(2,3)’ of a 3-by-3 matrix to linear indices
      with a single call to ‘sub2ind’.
 
      The following example shows how to convert the linear indices ‘2’
      and ‘8’ in a 3-by-3 matrix into subscripts.
 
           ind = [2, 8];
           [r, c] = ind2sub ([3, 3], ind)
               ⇒ r =  2   2
               ⇒ c =  1   3
 
      If the number of output subscripts exceeds the number of
      dimensions, the exceeded dimensions are set to ‘1’.  On the other
      hand, if fewer subscripts than dimensions are provided, the
      exceeding dimensions are merged into the final requested dimension.
      For clarity, consider the following examples:
 
           ind  = [2, 8];
           dims = [3, 3];
           ## same as dims = [3, 3, 1]
           [r, c, s] = ind2sub (dims, ind)
               ⇒ r =  2   2
               ⇒ c =  1   3
               ⇒ s =  1   1
           ## same as dims = [9]
           r = ind2sub (dims, ind)
               ⇒ r =  2   8
 
      See also: Seeind2sub XREFind2sub, Seesize XREFsize.
 
  -- : isindex (IND)
  -- : isindex (IND, N)
      Return true if IND is a valid index.
 
      Valid indices are either positive integers (although possibly of
      real data type), or logical arrays.
 
      If present, N specifies the maximum extent of the dimension to be
      indexed.  When possible the internal result is cached so that
      subsequent indexing using IND will not perform the check again.
 
      Implementation Note: Strings are first converted to double values
      before the checks for valid indices are made.  Unless a string
      contains the NULL character "\0", it will always be a valid index.
 
  -- : VAL = allow_noninteger_range_as_index ()
  -- : OLD_VAL = allow_noninteger_range_as_index (NEW_VAL)
  -- : allow_noninteger_range_as_index (NEW_VAL, "local")
      Query or set the internal variable that controls whether
      non-integer ranges are allowed as indices.
 
      This might be useful for MATLAB compatibility; however, it is still
      not entirely compatible because MATLAB treats the range expression
      differently in different contexts.
 
      When called from inside a function with the "local" option, the
      variable is changed locally for the function and any subroutines it
      calls.  The original variable value is restored when exiting the
      function.