Dataset{D, T} <: AbstractDataset{D,T}

A dedicated interface for datasets. It contains equally-sized datapoints of length D, represented by SVector{D, T}. These data are a standard Julia Vector{SVector}, and can be obtained with vec(dataset).

When indexed with 1 index, a dataset is like a vector of datapoints. When indexed with 2 indices it behaves like a matrix that has each of the columns be the timeseries of each of the variables.

Dataset also supports most sensible operations like append!, push!, hcat, eachrow, among others, and when iterated over, it iterates over its contained points.

Description of indexing

In the following let i, j be integers, typeof(data) <: AbstractDataset and v1, v2 be <: AbstractVector{Int} (v1, v2 could also be ranges, and for massive performance benefits make v2 an SVector{X, Int}).

• data[i] == data[i, :] gives the ith datapoint (returns an SVector)
• data[v1] == data[v1, :], returns a Dataset with the points in those indices.
• data[:, j] gives the jth variable timeseries, as Vector
• data[v1, v2], data[:, v2] returns a Dataset with the appropriate entries (first indices being "time"/point index, while second being variables)
• data[i, j] value of the jth variable, at the ith timepoint

Use Matrix(dataset) or Dataset(matrix) to convert. It is assumed that each column of the matrix is one variable. If you have various timeseries vectors x, y, z, ... pass them like Dataset(x, y, z, ...). You can use columns(dataset) to obtain the reverse, i.e. all columns of the dataset in a tuple.