After much effort (and exhaustion) but failure to get a view into
our `numpy` OHLC struct-array, this instead allocates an in-thread-memory
array which is updated with flattened data every flow update cycle.
I need to report what I think is a bug to `numpy` core about the whole
view thing not working but, more or less this gets the same behaviour
and minimizes work to flatten the sampled data for line-graphics
drawing thus improving refresh latency when drawing large downsampled
curves.
TL;DR:
- add `ShmArray.ustruct()` to return a **copy of** (since a view doesn't
work..) the (field filtered) shm array which is the same index-length
as the source data.
- update the OHLC ds curve with view aware data sliced out from the
pre-allocated and incrementally updated data (we had to add a last
index var `._iflat` to track appends - this should be moved into
a renderer eventually?).
There was a lingering issue where the fsp daemon would sync its shm
array with the source data and we'd set the start/end indices to the
same value. Under some races a reader would then read an empty `.array`
which it wasn't expecting. This fixes that as well as tidies up the
`ShmArray.push()` logic and adds a temporary check in `.array` for zero
length if the array hasn't been written yet.
We can now start removing read array length checks in consumer code
and hopefully no more races will show up.
Add an internal `_Token` to do interchange (un)packing for passing
"references" to shm blocks between actors. Part of the token involves
providing the `numpy.dtype` in a cross-actor format. Add a module
variable for caching "known tokens" per actor. Drop use of context
managers since they tear down shm blocks too soon in debug mode and
there seems to be no reason to unlink/close shm before the process has
terminated; if code needs it torn down explicitly, it can.
Logic in `SharedArray.push()` was totally wrong.
Remove all the `multiprocessing.resource_tracker` crap such that we
aren't loading an extra process at every layer and we don't get tons of
errors when cleaning on in an SC way.
This adds a shared memory "incrementing array" sub-sys interface
for single writer, multi-reader style data passing. The main motivation
is to avoid multiple copies of the same `numpy` array across actors
(plus now we can start being fancy like ray).
There still seems to be some odd issues with the "resource tracker"
complaining at teardown (likely partially to do with SIGINT stuff) so
some further digging in the stdlib code is likely coming.
Pertains to #107 and #98