unfortunately, I don't have access to that code anymore, I wrote a number of loaders for different data set types including CSV. The time series were all modeled as forward iterating stream of tuples, so there is no specific table abstraction. There is an implicit assumption that the stream is ordered by the join key, in a time series this being the timestamp, though nothing in the implementation enforced that.
Joins are always n-way merge joins, so you can write something like y = 2x^2 - 3z + c and fold that into a single streaming operation y = f( x, z, c ) where y, x, z and c are time streams.
When rendered to screen they looked very similar to your examples. With plugins in the IDE you could directly plot and array of time series as a chart.
Joins are always n-way merge joins, so you can write something like y = 2x^2 - 3z + c and fold that into a single streaming operation y = f( x, z, c ) where y, x, z and c are time streams.
When rendered to screen they looked very similar to your examples. With plugins in the IDE you could directly plot and array of time series as a chart.
Since the time I wrote NamedTuples the Julia core team folded the functionality into the core of Julia https://docs.julialang.org/en/v1/manual/types/#Named-Tuple-T.... This is the core of https://juliadb.org/ all credit to the Julia core team