Implemented the initial offline processing implementation, with
dependency layer building and ordered processing
Processing is still single threaded and unoptimized so it can take some
time to complete, the next step is to move from a single threaded design
to a ThreadPoolExecutor that splits up the buffer into a set number of
chunks for processing
API and Protocol docs have been updated (GPT 5.6)
The API methods for fetching a sepcific signal descriptor and fetching
all signal descriptors now work in offline mode, the core_tui.py example
has been updated to fit the new tri mode architecture and loading
recording offload -> processing and then processing -> playback engine
all works
Derived units now use a DLUnit class similar to a DLPak to be easily
stored and loaded, the public API has been updated to either take a
DLUnit object or a Path where it reads the DLUnit from a *.dlunit file
The .dlunit file is a ZIP compressed archive containing two files, a
manifest.json which stores the units uuid, its input signals and its
output signal, and unit.py which is the python code for the unit, the
same rules apply (one python function with a specifically structured
signature) as when a string was passed in the old API
Errors have been added to have a DLUnitError similar to the DLPakError
as the DLUnit class is heavily inspired from the DLPak architecutre
Logging may still be primitive, this will be covered in the next AI
logging sweep to homogenize and clean up the logging aspect of things
All internal and public errors have been revised (GPT 5.6 Sol) to return
more detail without horrendously long error classes, this has reduced
the number of error types but each now carries a reason and a code for
differentiation if needed
PlaybackEngine has been implemented and is functional, core mode changes
what signal descriptors are returned to avoid duplicates, the playback
engine currently discards the derived signals that would be recorded in
the DLPak and derived signals are reprocessed live
Future will require the behavior listed above to be selectable so one of
two options are possible:
A - Derived signals are reprocessed live by the relevant derive units
(current behavior
B - Derived signals are played back directly from the PlaybackEngine,
this requires turning off the routing to the live derive units to avoid
duplicate values
This will also require the option to perform offline processing with
derive units to be able to create derived signals after the fact, this
will likely incur a new 'offline' mode in the core to gate things
correctly, this will likely be useful for heavier processing that cannot
be done live, filters that require a lookahead, or more precise derived
signals using interpolated signals for higher acurracy which also brings
the ability to offline process on a fixed timebase instead of following
either input signal to the derived signal
The initial API documentation is now complete and covers the whole Core
api ready for use
The underlying logic has been updated to correctly return derive unit
signals
h3cx