[REQ] Support signal events, especially neural spikes

[cboulay]

Currently ezmsg-sigproc has SampleTriggerMessage.

@dataclass(unsafe_hash=True)
class SampleTriggerMessage:
    timestamp: float = field(default_factory=time.time)
    period: Optional[Tuple[float, float]] = None
    value: Any = None

I think we need something different to represent signal events, especially signal spikes like neural firing, heart beats, etc. The reason I don't like SampleTriggerMessage is because it has a pair of floats that are optional, and it has a value payload that can be anything (incl. None), thus it is difficult / slow to convert many instances of this struct to e.g. a sparse array.

I haven't put too much thought into it, but I'm thinking something like the following:

class EventMessage:
    timestamp: float
    ch_idx: int
    sub_idx: int  # e.g., 'unit id'
    value: numeric

When a graph has a source that generates events like this, the next step might be a node to accumulate a bunch of events in a given window and convert it to a sparse matrix (e.g., scipy.sparse.csr) with a given sampling rate. Then, assuming we have nodes compatible with sparse matrices, we can convolve with some kernel to get event rate.

As support for event processing develops, I think there will be some mix of making ezmsg-sigproc nodes compatible with sparse matrices, and making custom nodes.

I guess my first question for the team is where do we want to define EventMessage? Here? ezmsg-sigproc? Or a new package called ezmsg-event that will contain other utilities for dealing with events and sparse matrices?

My only objection to putting the message structure in ezmsg-event is that data source modules that can provide events (e.g., ezmsg-blackrock) shouldn't have scipy as a transient dependency.