Jackson is a very heavy library; it supports loads of features that we
don't need, and historically has caused issues due to long class loading
times (a little over 2 seconds to load AprilTagFieldLayout). This often
manifests as a help request in the form of "my robot disables when I do
X, but doesn't disable when doing X in subsequent attempts until code
restart." While SC has brought down Jackson loading times significantly,
with AprilTagFieldLayout loads taking only 330 milliseconds, that's
still a rather long delay, and while libraries should handle any JSON
loading ahead of time to prevent delays in auto/teleop, it would still
be good to make the worst case better to reduce user frustration.
Benchmarks indicate using [Avaje
Jsonb](https://github.com/avaje/avaje-jsonb) to load AprilTagFieldLayout
only takes ~70 ms, a fair chunk of which isn't actually in Avaje Jsonb
(~4 ms is spent on using getResourceAsStream to retrieve the JSON file,
~8 ms is spent on just loading the AprilTag class and its dependencies).
Note that all times listed are end-to-end, meaning nothing else was done
except for the operation being benchmarked, and doing arithmetic on them
can be flawed due to some classes being loaded twice, i.e.,
getResourceAsStream and `new AprilTag()` likely load some of the same
JDK classes and so subtracting both from the Avaje Jsonb load time is
likely slightly incorrect because class loading is being double counted.
For our purposes, it's likely accurate enough and is mostly just for
contextualization.
Benchmarks were run on a Raspberry Pi CM5 with 2 GB of RAM. Source code
for the
[results](https://github.com/user-attachments/files/26471452/benchmark.txt)
can be found in the "Fastjson2" commit
(2456d15ca8ebd17635e607cd40bf8816e77869a1).
Avaje Jsonb uses code generation via annotation processors to generate
the classes needed to do JSON serde and uses service providers to find
them, which will require downstream changes in robot projects, as the
different service providers in each library must be merged together for
Avaje Jsonb to function. We will use the Gradle shadow plugin, as its
already used by the installer and therefore adds zero additional
dependencies.
This hooks up the bazel build to the robotpyExamples. It can use the
(formly pyfrc or whatever) automatic unit tests for an example, as well
as exposing the ability to run the example in simulation, with or
without `halsim_gui` with a command such as `bazel run
//robotpyExamples:AddressableLED-sim`
This required building and using wheels instead of just a normal
`py_library`, so that things like `ENTRY_POINTS` can be used. I took a
bare bones approach to building and naming the wheels (for example the
native ones don't have the OS info or python version in them, so they
wouldn't be suitable publish to pypi, but that can always be updated
later.
Also fixes the google compile-testing library to 0.23.0 (the latest
available at time of writing) instead of a wildcard
Jackson versions were inconsistent across projects; most were on 2.19.2,
but the fields subproject was on 2.15.2. All projects are now on 2.19.2
for consistency
Robotpy dropped support for 3.10 recently, so this updates to the next
lowest version. This will be helpful when the examples get merged into
`mostrobotpy`
There's not really a specific reason why I just jumped to 3.11 instead
of a newer version. If anybody suggests otherwise I will gladly bump it
higher.
Easier then the last one that put everything in a sub namespace. By
prefixing the name less things break, and intellisense will be less
confusing to new users during the transition.
Resync with `mostrobotpy`
This mostly involves the big "ignore almost everything in the HAL
project" and some fixups for the Addressable LED classes.
Required two small hand fixes to get it building over here with bazel,
and with more compiler warnings on.
I also manually zeroed out the `repo_url` field in the toml files to
avoid unnecessary churn whenever it goes from a release build to a
development build. I already did this with `version` field in there, and
will do a follow up PR that updates the copybara script to do it
automatically.
---------
Co-authored-by: Default email <default@default.com>
Looks like a build failure got lost in the landing order of python
commands and the big opmode change. This makes it compile again, based
on the java / C++ changes from the opmode PR.
User code:
- OpModeRobot used as the robot base class
- LinearOpMode and PeriodicOpMode are provided opmode base classes
- In Java, annotations can be used to automatically register opmode classes
Additional user code functionality:
- OpMode (string) is available in addition to the overall
auto/teleop/test robot mode
- OpMode does not indicate enable (enable/disable is still separate)
- The HAL API uses integer UIDs; these are exposed at the user API level
as well for faster checks
- User code creates opmodes on startup (these have name, category,
description, etc).
DS:
- DS will present opmode selection lists for auto and teleop for
match/practice. During a match, the DS will automatically activate the
selected opmode in the corresponding match period.
- For testing, an overall mode is selected (e.g. teleop/auto/test) and a
single opmode is selected
Future work:
- Command framework support/integration
- Python annotation support
- Unit tests (needs race-free DS sim updates)
- Porting of examples
Co-authored-by: Joseph Eng <91924258+KangarooKoala@users.noreply.github.com>
After replacing the remaining include guards with `#pragma once`, I was
able to merge all the wpiformat configs into one file in the repo root.
This should make the config easier to reason about and maintain in the
future.
This uses all the infrastructure we put together earlier to actually build and publish all the artifacts.
We might still want to adjust what is built by default to control CI times.
Signed-off-by: Austin Schuh <austin.linux@gmail.com>
Co-authored-by: PJ Reiniger <pj.reiniger@gmail.com>
Co-authored-by: David Vo <auscompgeek@users.noreply.github.com>
