BLField has gotten a huge facelift, to make it practical to wrangle
properties without the sharp edges.
- All the "special" UI-exposed property types can now be directly
constructed in a BLField marked with 'prop_ui'.
- The most appropriate internal representation will be chosen to
represent the attribute based on its type annotation, including sized
vector-like `bool`, `int`, `float` for `tuple[...]`.
- Static EnumProperties can now be derived from a special `StrEnum`, to
which a `to_name` and `to_icon` method is attached.
- Dynamic `EnumProperty` can now be used safely, with builtin
workarounds to the real-world reference-loss-crash (realized
in the Tidy3D Cloud Task node) and jankiness like empty enum.
- The update method is now fully managed, negating all bugs related to
improper update callback naming.
- Python-side getter caching is preserved for ui-exposed
properties, with the help of node/socket base class support for
passing a `Signal.InvalidateCache` to BLFields that are altered in the
UI.
The cost to all this niceness is rather low, and arguably, positive:
- Dynamic Enum/String searchers no longer "magically" invoke all the
time, since the values seen by Blender are cached by the BLField.
- To regenerate the searcher output, an `@on_value_changed` should be
made by the user to pass `Signal.ResetEnumItems` or
`Signal.ResetStrSearch` to the `BLField`.
- Since searching is no longer eager, there is no danger of
out-of-reference strings (which crash Blender from EnumProperty), but
also a greatly reduced performance problems associated with
the hot-loop regeneration of EnumProperty strings.
- The base classes are now involved with BLField invalidation, to ensure
that the getter caches are cleared up when the UI changes. For the
price of that small indirection (done cheaply with set lookup),
all attribute lookups are generally done in a single lookup, completely
avoiding Blender until needed.
- This does represent another increase in confidence wrt. the event
system's integrity, but so far, that has been a very productive
direction.
**NOTE**: The entire feature set of BLField is not well tested, and will
likely need adjustments as the codebase is converted to use them.
Enormously important changes to the data flow semantics and invalidation
rules. Especially significant is the way in which the node graph
produces a deeply composed function, compiles it to optimized machine
code with `jax`, and uses a seperately cached data flow to insert values
into the function from anywhere along the node graph without recompiling
the function.
A critical portion of the math system, namely the unit-aware dimensional
representation, is also finished. The `Data` node socket type now
dynamically reports the dimensional properties of the object flowing
through it, courtesy the use of a seperate data flow for information.
This allows for very high-peformance unit-aware nearest-value indexing built on binary
search.
Also, dependency management is completely ironed out. The `pip install`
process now runs concurrently, and the installation log is parsed in the
background to update a progress bar. This is the foundational work for a
similar concurrent process wrt. Tidy3D progress reporting.
The serialization routines are fast and effective.
Overall, the node graph feels snappy, and everything updates smoothly.
Logging on the action chain suggests that there aren't extraneous calls,
and that existing calls (ex. no-op previews) are fast.
There will likely be edge cases, and we'll see how it scales - but
for now, let's go with it!
This especially involved fixing the invalidation logic in
`trigger_action`.
It should now be far more accurate, concise, and performant.
The invalidation check ought still be optimized.
The reason this isn't trivial is because of the loose sockets:
To use our new `@keyed_cache` on a function like `_should_recompute_output_socket`, the loose
socket would also need to do an appropriate invalidation.
Such caching without accounting for invalidation on loose-socket change
would be incorrect.
For now, it seems as though performance is quite good, although it is
unknown whether this will scale to large graphs.
We've also left `kind`-specific invalidation alone for now (maybe
forever).
The crash: When a linked loose socket was deleted, the link remained in the
NodeLinkCache, and caused a crash when trying to ask the already-deleted
socket for removal consent. We fixed this by reporting all socket
removals to the node tree, so that links could be correctly removed
independently of link-change calculation.
The bug: The collection getter was cached improperly; Blender ID types
can't just be saved like that. We need to search every time. Performance
seems unaffected at first glance.
This is essential for:
- Representing ranges as bounds
- Arbitrary symbolic/numeric representation of spectral distributions
- Parametric representation and JIT of critical-path procedures.
Unfortunately this broke a lot of nodes in small ways.
Next step is to finish the low-hanging fruit nodes + fix the ones we
have.
It's only good for dispersive media; specifically, a text file with
three floats per line: 'wl n k'.
A custom script was used to convert Maxim's data.
It's very fast, and has a ton of options.
Only the most important are exposed in the node for now.
A bug in MPL plotting aspect ratio declaration on MPL axis object was
fixed by manually running `set_aspect('auto')` after the fact.
It shouldn't do anything, and it doesn't, other than fix the bug :)
Also brought the plotting function of the viewer to parity with
the 3D preview, so the "Auto Plot" button works as expected.
We have a far more sane approach to nodeps now, which
allows us to essentially have two loggers - one that is
very useful, pretty, and clear, but requires a 'rich'
dependency, and one that is simple.
In this spirit, we factored out services/ too.
We can also set the initial console log level now when
packing the .zip.
There's still work to do with the actual flow for deps
installing / uninstalling.
But it should be far more robust now.
Finally, we have a barebones working `quartodoc`-based docs site.
It's super clever; see <https://github.com/machow/quartodoc>.
As it's "just" a quarto project with some python autodiscovery,
fleshing it out with ex. math, images, diagrams, and so forth
should be exceptionally easy.
As we develop, various linter-guided fixes are being realized.
This will be a long process, best done as we spiff everything up
in preparation for general release.
The big news is that GeoNodes Structure is now implemented,
under the new and vastly more robust chaining system.
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