* Pack FSRS data into card.data
* Update FSRS card data when preset or weights change
+ Show FSRS stats in card stats
* Show a warning when there's a limited review history
* Add some translations; tweak UI
* Fix default requested retention
* Add browser columns, fix calculation of R
* Property searches
eg prop:d>0.1
* Integrate FSRS into reviewer
* Warn about long learning steps
* Hide minimum interval when FSRS is on
* Don't apply interval multiplier to FSRS intervals
* Expose memory state to Python
* Don't set memory state on new cards
* Port Jarret's new tests; add some helpers to make tests more compact
https://github.com/open-spaced-repetition/fsrs-rs/pull/64
* Fix learning cards not being given memory state
* Require update to v3 scheduler
* Don't exclude single learning step when calculating memory state
* Use relearning step when learning steps unavailable
* Update docstring
* fix single_card_revlog_to_items (#2656)
* not need check the review_kind for unique_dates
* add email address to CONTRIBUTORS
* fix last first learn & keep early review
* cargo fmt
* cargo clippy --fix
* Add Jarrett to about screen
* Fix fsrs_memory_state being initialized to default in get_card()
* Set initial memory state on graduate
* Update to latest FSRS
* Fix experiment.log being empty
* Fix broken colpkg imports
Introduced by "Update FSRS card data when preset or weights change"
* Update memory state during (re)learning; use FSRS for graduating intervals
* Reset memory state when cards are manually rescheduled as new
* Add difficulty graph; hide eases when FSRS enabled
* Add retrievability graph
* Derive memory_state from revlog when it's missing and shouldn't be
---------
Co-authored-by: Jarrett Ye <jarrett.ye@outlook.com>
* Support searching for deck configs by name
* Integrate FSRS optimizer into Anki
* Hack in a rough implementation of evaluate_weights()
* Interrupt calculation if user closes dialog
* Fix interrupted error check
* log_loss/rmse
* Update to latest fsrs commit; add progress info to weight evaluation
* Fix progress not appearing when pretrain takes a while
* Update to latest commit
The approach in #2542 unfortunately introduced a regression, as whilst
it ensured that duplicate keys are removed when downgrading, it no longer
prevented the duplicates from being removed when converting to a legacy
Schema11 object. This resulted in things like backend.get_notetype_legacy()
returning duplicate keys, and could break syncing:
https://forums.ankiweb.net/t/windows-desktop-sync-error/33128
As syncing and schema11 object usage is quite common compared to downgrading,
the extra Value deserialization seemed a bit expensive, so I've switched
back to explicitly removing the problem keys. To ensure we don't forget to
add new keys in the future, I've added some new tests that should alert us
whenever a newly-added key is missing from the reserved list.
Also fix minilints declaring a stamp it wasn't creating. The same
approach is necessary with archives now too, as it no longer executes
under a standard "runner run".
For now, rustls is hard-coded - we could pass the desired TLS impl in
from the ./ninja script, but the runner is not recompiled frequently
anyway.
Workspace deps were introduced in Rust 1.64. They don't cover all the
cases that Hakari did unfortunately, but they are simpler to maintain,
and they avoid a couple of issues that Hakari had:
- It sometimes made updating dependencies harder due to the locked versions,
so you had to disable Hakari, do the updates, and then re-generate (
e.g. 943dddf28f)
- The current Hakari config was breaking AnkiDroid's build, as it was
stopping a cross-compile from functioning correctly.
- Dropped the protobuf extensions in favor of explicitly listing out
methods in both services if we want to implement both, as it's clearer.
- Move Service/Method wrappers into a separate crate that the various
clients can import, to easily get at the list of backend services and
their correct indices and comments.
I'd been thinking it might be useful for a future API service, but
I think that's better implemented with more codegen, so we have a
statically-typed interface.
* Automatically elide empty inputs and outputs to backend methods
* Refactor service generation
Despite the fact that the majority of our Protobuf service methods require
an open collection, they were not accessible with just a Collection
object. To access the methods (e.g. because we haven't gotten around to
exposing the correct API in Collection yet), you had to wrap the collection
in a Backend object, and pay a mutex-acquisition cost for each call, even
if you have exclusive access to the object.
This commit migrates the majority of service methods to the Collection, so
they can now be used directly, and improves the ergonomics a bit at the
same time.
The approach taken:
- The service generation now happens in rslib instead of anki_proto, which
avoids the need for trait constraints and associated types.
- Service methods are assumed to be collection-based by default. Instead of
implementing the service on Backend, we now implement it on Collection, which
means our methods no longer need to use self.with_col(...).
- We automatically generate methods in Backend which use self.with_col() to
delegate to the Collection method.
- For methods that are only appropriate for the backend, we add a flag in
the .proto file. The codegen uses this flag to write the method into a
BackendFooService instead of FooService, which the backend implements.
- The flag can also allows us to define separate implementations for collection
and backend, so we can e.g. skip the collection mutex in the i18n service
while also providing the service on a collection.
This is of limited usefulness at the moment, as it doesn't help consumers
of the public API.
Also removed detached comments from the included comments.
