NeurodataWithoutBorders
method for TimeSeries: get_values
I'd like a function for TimeSeries that gets me the data values that correspond to a specific start and end time. I'd like this to work whether the time is stored as starting_time and rate or as timestamps. Does this already exist?
No, it doesn’t exist yet, but we’ve done a little work on internal infrastructure needed for doing this.
In addition to what Andrew mentioned. As a bit of a side-project, I started experimenting with how this might look in the special case of a TimeSeries on a branch but it's experimental at this point and I think the final solution will likely look different and will use some more of the general infrastructure that Andrew mentioned. I.e., I would not recommend to rely on what I'm describing next as it is experimental and will very likely change. I just wanted to mention it in case you need something quick-and-dirty for some time-critical task.
If you want, you can have a look at the class TimeSeriesHelper on the branch enh/select_timeseries. The class just wraps an existing TimeSeries to provide additional functions. In particular, have a look at the function subset_series which can take a time_range and/or an index_select (e.g., numpy.s_[:,1:10]) to select data from the TimeSeries and creates a new TimeSeries for the corresponding data subset. I have not tested it extensively, but it should in principle work with any TimeSeries, i.e., subset_series should create a new series of the same derived type as the input TimeSeries and all fields should be copied over. Here is a simple test case which shows how it should work. The TimeSeriesHelper class also has a number of other experimental functions to deal with the timestamps issued, e.g., to convert between stamps and index or to convert the timestamps (which are relative to the session_start_time) into absolute timestamps. There are also some functions to convert a TimeSeries to an xarray or pandas series
I don't want to merge this stuff back right now because a) it introduces a bunch of heavy dependencies (xarray, pandas), b) its not well tested, c) the interface is not really how it should look in PyNWB (it's really just me playing with it).
