Include elapsed_secs in learning card state (#2862)

* Include elapsed_time in learning card state

* Suggested updates, elapsed_time -> elapsed_secs

* Remove outdated comment

proto/anki/scheduler.proto

@@ -72,6 +72,7 @@ message SchedulingState {
   message Learning {
     uint32 remaining_steps = 1;
     uint32 scheduled_secs = 2;
+    uint32 elapsed_secs = 3;
     optional cards.FsrsMemoryState memory_state = 6;
   }
   message Review {

rslib/src/scheduler/answering/current.rs

@@ -1,7 +1,9 @@
 // Copyright: Ankitects Pty Ltd and contributors
 // License: GNU AGPL, version 3 or later; http://www.gnu.org/licenses/agpl.html
 
+use super::get_fuzz_seed_for_id_and_reps;
 use super::CardStateUpdater;
+use crate::card::CardQueue;
 use crate::card::CardType;
 use crate::decks::DeckKind;
 use crate::scheduler::states::CardState;
@@ -64,15 +66,40 @@ impl CardStateUpdater {
         let ease_factor = self.card.ease_factor();
         let remaining_steps = self.card.remaining_steps();
         let memory_state = self.card.memory_state;
+        let elapsed_secs = |last_ivl: u32| {
+            match self.card.queue {
+                CardQueue::Learn => {
+                    // Decrease reps by 1 to get correct seed for fuzz.
+                    // If the fuzz calculation changes, this will break.
+                    let last_ivl_with_fuzz = self.learning_ivl_with_fuzz(
+                        get_fuzz_seed_for_id_and_reps(self.card.id, self.card.reps - 1),
+                        last_ivl,
+                    );
+                    let last_answered_time = due as i64 - last_ivl_with_fuzz as i64;
+                    (self.now.0 - last_answered_time) as u32
+                }
+                CardQueue::DayLearn => {
+                    let days_since_col_creation = self.timing.days_elapsed as i32;
+                    // Need .max(1) for same day learning cards pushed to the next day.
+                    // 86_400 is the number of seconds in a day.
+                    let last_ivl_as_days = (last_ivl / 86_400).max(1) as i32;
+                    let elapsed_days = days_since_col_creation - due + last_ivl_as_days;
+                    (elapsed_days * 86_400) as u32
+                }
+                _ => 0, // Not used for other card queues.
+            }
+        };
 
         match self.card.ctype {
             CardType::New => NormalState::New(NewState {
                 position: due.max(0) as u32,
             }),
             CardType::Learn => {
+                let last_ivl = self.learn_steps().current_delay_secs(remaining_steps);
                 LearnState {
-                    scheduled_secs: self.learn_steps().current_delay_secs(remaining_steps),
+                    scheduled_secs: last_ivl,
                     remaining_steps,
+                    elapsed_secs: elapsed_secs(last_ivl),
                     memory_state,
                 }
             }
@@ -87,9 +114,12 @@ impl CardStateUpdater {
                 memory_state,
             }
             .into(),
-            CardType::Relearn => RelearnState {
+            CardType::Relearn => {
+                let last_ivl = self.relearn_steps().current_delay_secs(remaining_steps);
+                RelearnState {
                     learning: LearnState {
-                    scheduled_secs: self.relearn_steps().current_delay_secs(remaining_steps),
+                        scheduled_secs: last_ivl,
+                        elapsed_secs: elapsed_secs(last_ivl),
                         remaining_steps,
                         memory_state,
                     },
@@ -102,6 +132,7 @@ impl CardStateUpdater {
                         memory_state,
                     },
                 }
+            }
             .into(),
         }
     }

rslib/src/scheduler/answering/learning.rs

@@ -76,12 +76,12 @@ impl CardStateUpdater {
 
     /// Adds secs + fuzz to current time
     pub(super) fn fuzzed_next_learning_timestamp(&self, secs: u32) -> i32 {
-        TimestampSecs::now().0 as i32 + self.with_learning_fuzz(secs) as i32
+        TimestampSecs::now().0 as i32 + self.learning_ivl_with_fuzz(self.fuzz_seed, secs) as i32
     }
 
     /// Add up to 25% increase to seconds, but no more than 5 minutes.
-    fn with_learning_fuzz(&self, secs: u32) -> u32 {
+    pub(super) fn learning_ivl_with_fuzz(&self, input_seed: Option<u64>, secs: u32) -> u32 {
-        if let Some(seed) = self.fuzz_seed {
+        if let Some(seed) = input_seed {
             let mut rng = StdRng::seed_from_u64(seed);
             let upper_exclusive = secs + ((secs as f32) * 0.25).min(300.0).floor() as u32;
             if secs >= upper_exclusive {

rslib/src/scheduler/answering/mod.rs

@@ -270,6 +270,9 @@ impl Collection {
         let mut updater = self.card_state_updater(card)?;
         answer.cap_answer_secs(updater.config.inner.cap_answer_time_to_secs);
         let current_state = updater.current_card_state();
+        // If the states aren't equal, it's probably because some time has passed.
+        // Try to fix this by setting elapsed_secs equal.
+        self.set_elapsed_secs_equal(&current_state, &mut answer.current_state);
         require!(
             current_state == answer.current_state,
             "card was modified: {current_state:#?} {:#?}",
@@ -417,6 +420,41 @@ impl Collection {
         self.add_tags_to_notes_inner(&[nid], "leech")?;
         Ok(())
     }
+
+    /// Update the elapsed time of the answer state to match the current state.
+    ///
+    /// Since the state calculation takes the current time into account, the
+    /// elapsed_secs will probably be different for the two states. This is fine
+    /// for elapsed_secs, but we set the two values equal to easily compare
+    /// the other values of the two states.
+    fn set_elapsed_secs_equal(&self, current_state: &CardState, answer_state: &mut CardState) {
+        if let (Some(current_state), Some(answer_state)) = (
+            match current_state {
+                CardState::Normal(normal_state) => Some(normal_state),
+                CardState::Filtered(FilteredState::Rescheduling(resched_filter_state)) => {
+                    Some(&resched_filter_state.original_state)
+                }
+                _ => None,
+            },
+            match answer_state {
+                CardState::Normal(normal_state) => Some(normal_state),
+                CardState::Filtered(FilteredState::Rescheduling(resched_filter_state)) => {
+                    Some(&mut resched_filter_state.original_state)
+                }
+                _ => None,
+            },
+        ) {
+            match (current_state, answer_state) {
+                (NormalState::Learning(answer), NormalState::Learning(current)) => {
+                    current.elapsed_secs = answer.elapsed_secs;
+                }
+                (NormalState::Relearning(answer), NormalState::Relearning(current)) => {
+                    current.learning.elapsed_secs = answer.learning.elapsed_secs;
+                }
+                _ => {} // Other states don't use elapsed_secs.
+            }
+        }
+    }
 }
 
 #[cfg(test)]
@@ -476,12 +514,16 @@ impl Card {
 }
 
 /// Return a consistent seed for a given card at a given number of reps.
-/// If in test environment, disable fuzzing.
 fn get_fuzz_seed(card: &Card) -> Option<u64> {
+    get_fuzz_seed_for_id_and_reps(card.id, card.reps)
+}
+
+/// If in test environment, disable fuzzing.
+fn get_fuzz_seed_for_id_and_reps(card_id: CardId, card_reps: u32) -> Option<u64> {
     if *crate::PYTHON_UNIT_TESTS || cfg!(test) {
         None
     } else {
-        Some((card.id.0 as u64).wrapping_add(card.reps as u64))
+        Some((card_id.0 as u64).wrapping_add(card_reps as u64))
     }
 }
 
@@ -670,4 +712,105 @@ mod test {
 
         Ok(())
     }
+
+    #[test]
+    fn elapsed_secs() -> Result<()> {
+        let mut col = Collection::new();
+        let mut conf = col.get_deck_config(DeckConfigId(1), false)?.unwrap();
+        let nt = col.get_notetype_by_name("Basic")?.unwrap();
+        let mut note = nt.new_note();
+        // Need to set col age for interday learning test, arbitrary
+        col.storage
+            .db
+            .execute_batch("update col set crt=1686045847")?;
+        // Fails when near cutoff since it assumes inter- and intraday learning
+        if col.timing_today()?.near_cutoff() {
+            return Ok(());
+        }
+        col.add_note(&mut note, DeckId(1))?;
+        // 5942.7 minutes for just over four days
+        conf.inner.learn_steps = vec![1.0, 10.5, 15.0, 20.0, 5942.7];
+        col.storage.update_deck_conf(&conf)?;
+
+        // Intraday learning, review same day
+        let expected_elapsed_secs = 662;
+        let post_answer = col.answer_good();
+        let card = col.storage.get_card(post_answer.card_id)?.unwrap();
+        let shift_due_time = card.due - expected_elapsed_secs;
+        assert_elapsed_secs_approx_equal(
+            &mut col,
+            shift_due_time,
+            post_answer,
+            expected_elapsed_secs,
+        )?;
+
+        // Intraday learning, learn ahead
+        let expected_elapsed_secs = 212;
+        let post_answer = col.answer_good();
+        let card = col.storage.get_card(post_answer.card_id)?.unwrap();
+        let shift_due_time = card.due - expected_elapsed_secs;
+        assert_elapsed_secs_approx_equal(
+            &mut col,
+            shift_due_time,
+            post_answer,
+            expected_elapsed_secs,
+        )?;
+
+        // Intraday learning, review two (and some) days later
+        let expected_elapsed_secs = 184092;
+        let post_answer = col.answer_good();
+        let card = col.storage.get_card(post_answer.card_id)?.unwrap();
+        let shift_due_time = card.due - expected_elapsed_secs;
+        assert_elapsed_secs_approx_equal(
+            &mut col,
+            shift_due_time,
+            post_answer,
+            expected_elapsed_secs,
+        )?;
+
+        // Interday learning four (and some) days, review three days late
+        let expected_elapsed_secs = 7 * 86_400;
+        let post_answer = col.answer_good();
+        let now = TimestampSecs::now();
+        let timing = col.timing_for_timestamp(now)?;
+        let col_age = timing.days_elapsed as i32;
+        let shift_due_time = col_age - 3; // Three days late
+        assert_elapsed_secs_approx_equal(
+            &mut col,
+            shift_due_time,
+            post_answer,
+            expected_elapsed_secs,
+        )?;
+
+        Ok(())
+    }
+
+    fn assert_elapsed_secs_approx_equal(
+        col: &mut Collection,
+        shift_due_time: i32,
+        post_answer: test_helpers::PostAnswerState,
+        expected_elapsed_secs: i32,
+    ) -> Result<()> {
+        // Change due time to fake card answer_time,
+        // works since answer_time is calculated as due - last_ivl
+        let update_due_string = format!("update cards set due={}", shift_due_time);
+        col.storage.db.execute_batch(&update_due_string)?;
+        col.clear_study_queues();
+        let current_card_state = current_state(col, post_answer.card_id);
+        let state = match current_card_state {
+            CardState::Normal(NormalState::Learning(state)) => state,
+            _ => panic!("State is not Normal: {:?}", current_card_state),
+        };
+        let elapsed_secs = state.elapsed_secs as i32;
+        // Give a 1 second leeway when the test runs on the off chance
+        // that the test runs as a second rolls over.
+        assert!(
+            (elapsed_secs - expected_elapsed_secs).abs() <= 1,
+            "elapsed_secs: {} != expected_elapsed_secs: {}",
+            elapsed_secs,
+            expected_elapsed_secs
+        );
+
+        Ok(())
+    }
 }

rslib/src/scheduler/service/states/learning.rs

@@ -8,6 +8,7 @@ impl From<anki_proto::scheduler::scheduling_state::Learning> for LearnState {
         LearnState {
             remaining_steps: state.remaining_steps,
             scheduled_secs: state.scheduled_secs,
+            elapsed_secs: state.elapsed_secs,
             memory_state: state.memory_state.map(Into::into),
         }
     }
@@ -18,6 +19,7 @@ impl From<LearnState> for anki_proto::scheduler::scheduling_state::Learning {
         anki_proto::scheduler::scheduling_state::Learning {
             remaining_steps: state.remaining_steps,
             scheduled_secs: state.scheduled_secs,
+            elapsed_secs: state.elapsed_secs,
             memory_state: state.memory_state.map(Into::into),
         }
     }

rslib/src/scheduler/states/learning.rs

@@ -13,6 +13,7 @@ use crate::revlog::RevlogReviewKind;
 pub struct LearnState {
     pub remaining_steps: u32,
     pub scheduled_secs: u32,
+    pub elapsed_secs: u32,
     pub memory_state: Option<FsrsMemoryState>,
 }
 
@@ -39,6 +40,7 @@ impl LearnState {
         LearnState {
             remaining_steps: ctx.steps.remaining_for_failed(),
             scheduled_secs: ctx.steps.again_delay_secs_learn(),
+            elapsed_secs: 0,
             memory_state: ctx.fsrs_next_states.as_ref().map(|s| s.again.memory.into()),
         }
     }
@@ -50,6 +52,7 @@ impl LearnState {
                 .hard_delay_secs(self.remaining_steps)
                 // user has 0 learning steps, which the UI doesn't allow
                 .unwrap_or(60),
+            elapsed_secs: 0,
             memory_state: ctx.fsrs_next_states.as_ref().map(|s| s.hard.memory.into()),
             ..self
         }
@@ -61,6 +64,7 @@ impl LearnState {
             LearnState {
                 remaining_steps: ctx.steps.remaining_for_good(self.remaining_steps),
                 scheduled_secs: good_delay,
+                elapsed_secs: 0,
                 memory_state,
             }
             .into()

rslib/src/scheduler/states/normal.rs

@@ -44,6 +44,7 @@ impl NormalState {
                 let next_states = LearnState {
                     remaining_steps: ctx.steps.remaining_for_failed(),
                     scheduled_secs: 0,
+                    elapsed_secs: 0,
                     memory_state: None,
                 }
                 .next_states(ctx);

rslib/src/scheduler/states/relearning.rs

@@ -41,6 +41,7 @@ impl RelearnState {
                 learning: LearnState {
                     remaining_steps: ctx.relearn_steps.remaining_for_failed(),
                     scheduled_secs: again_delay,
+                    elapsed_secs: 0,
                     memory_state,
                 },
                 review: ReviewState {
@@ -92,6 +93,7 @@ impl RelearnState {
                     remaining_steps: ctx
                         .relearn_steps
                         .remaining_for_good(self.learning.remaining_steps),
+                    elapsed_secs: 0,
                     memory_state,
                 },
                 review: ReviewState {

rslib/src/scheduler/states/review.rs

@@ -104,6 +104,7 @@ impl ReviewState {
                 learning: LearnState {
                     remaining_steps: ctx.relearn_steps.remaining_for_failed(),
                     scheduled_secs: again_delay,
+                    elapsed_secs: 0,
                     memory_state,
                 },
                 review: again_review,