Open adam2392 opened 3 years ago
Not sure I understand what you mean. Do you mean for specifying e.g. which channels or source points should be considered out of the full list a data set might have? That indeed would raise interesting questions for the output and for plotting.
There are methods such as spectral_connectivity
, where one can pass in indices = ([1, 2], [0])
for example where they only compute connectivity between node 1/0 and 2/0.
This raises issues for downstream storage in a container and plotting. The way I solve it is by keeping track of indices and keeping the rest as nans. This runs into "storage" concerns if the arrays are big enough. In general though I was thinking we should probably keep track of "what can take in this extra parameter" vs not.
The way I solve it is by keeping track of indices and keeping the rest as nans. This runs into "storage" concerns if the arrays are big enough.
sounds like a use case for scipy.sparse
if memory turns out to be constrained
envelope_correlation
seems like a function we can also add "indices" for.
Because some "connectivity" functions are defined as bivariate functions between two sets of signals, we can allow "indices" argument in various functions.
This should probably be well-documented as showing up as "nans" in a connectivity container?