The sequence of underlying dimension matrices.
The sequence of underlying dimension matrices. The part of each dimension
covered by this matrix is reflected by the corresponding entry in ranges!
In other words, the reductions and transformations associated with the
current (and possibly any number of preceding) matrices are not reflected
in the returned objects! To read the dimensional values with respect to
the actual matrix coordinates, the getDimensionKey method can be used.
Produces a matrix key for the dimension of a given index.
Produces a matrix key for the dimension of a given index. Since a
dimension is 1-dimensional, the key will either have a streaming-index of
zero (when useChannels is false), resulting in a 1-channel reader
with shape(index) frames; or it will have a streaming-index of -1
(when useChannels is true), resulting in an n-channel reader with
one frame, where n == shape(index).
the index of the dimension, from zero until rank
if true produces multi-channel file of one frame,
if false produces monophonic file of several frames.
The key of a matrix is an immutable value that represents its current state, possibly prepared with a transposition to be streamed along one of its dimensions.
The key of a matrix is an immutable value that represents its current state, possibly prepared with a transposition to be streamed along one of its dimensions.
the index of the dimension to stream the matrix data through, or -1
to read the whole matrix in one frame.
A matrix has a name.
A matrix has a name. For example, when coming from a NetCDF data source, the matrix name corresponds to a variable name.
The ranges specify the regions inside the underlying dimension matrices covered by this matrix.
The ranges specify the regions inside the underlying dimension matrices covered by this matrix. For example if a 4 x 5 matrix is reduced in its first dimension using a slice 1 to 2 operator, then the _dimension_ will have a size of four, but the first range will be 1 to 2 (and thus size 2).
The shape is the vector of dimensional sizes.
The shape is the vector of dimensional sizes. This is equivalent
to ranges.map(_.size). Note that the dimensions themselves
may be larger.
The units of the matrix cells, or an empty string to indicate that no units are specified.
The units of the matrix cells, or an empty string to indicate that no units are specified.
The rank is the number of dimensions.
The rank is the number of dimensions.
The size is the number of matrix cells, that is the product of the shape.
The size is the number of matrix cells, that is the product of the shape.