top_drives/src/context.rs

//! Analysis context and aggregated car counts.
//!
//! Contains the mutable analysis state (`AnalysisContext`) and the
//! aggregated per-car win counts (`CarCounts`) computed from it.

use std::collections::{HashMap, HashSet};
use std::sync::Arc;

use dashmap::DashMap;

use crate::types::{BestCarRecord, Car, TripletKey};

/// Holds the mutable analysis state for a records analysis run.
///
/// Replaces the former global `static BEST_CARS`. Created in `main()` and
/// passed through the call chain so that no global mutable state is needed.
///
/// Uses `Arc<DashMap>` for concurrent access from spawned tasks.
/// `Arc::clone` only increments the reference count, so all tasks share
/// the same underlying map.
pub struct AnalysisContext {
    /// Best car records for each track/RQ/surface combination.
    /// Wrapped in `Arc` so it can be cheaply shared across async tasks.
    pub best_cars: Arc<DashMap<TripletKey, Vec<BestCarRecord>>>,
}

impl Default for AnalysisContext {
    fn default() -> Self {
        Self::new()
    }
}

impl AnalysisContext {
    /// Create a new, empty analysis context.
    pub fn new() -> Self {
        AnalysisContext { best_cars: Arc::new(DashMap::new()) }
    }
}

/// Aggregated per-car win counts across all track/RQ/condition combinations.
/// Computed once from `AnalysisContext` and shared across all output functions.
///
/// The composite key `(car_rid, normalized_config)` ensures that the same car
/// with different final builds (e.g., "323" vs "332") is tracked separately.
pub struct CarCounts {
    /// Map from (car_rid, normalized_config) → (BestCarRecord, count of tracks where this car+config is best)
    pub by_rid: HashMap<(String, String), (BestCarRecord, usize)>,
    /// Map from guid → set of (car_rid, normalized_config) for O(1) ownership/wishlist lookups.
    /// A guid may map to multiple entries if the same car is best with different configs.
    pub guid_to_rid: HashMap<String, HashSet<(String, String)>>,
    /// Map from guid → Car (for O(1) lookups of car metadata by GUID).
    /// Built once in `compute_car_counts()` and reused by trash/wishlist output.
    pub car_map: HashMap<String, Car>,
    /// Full car list from the database (kept for any callers that need iteration).
    pub car_list: Vec<Car>,
    /// Actual tune config for each owned car (guid → config string from playerDeck).
    /// Used by the improve command to determine which cars are not yet at a final build.
    pub owned_configs: HashMap<String, String>,
}

/// Computes per-car win counts from the analysis context.
///
/// Returns a `CarCounts` struct that can be shared across all output functions.
/// This function cannot fail — it only performs `DashMap` iteration and
/// `HashMap` inserts.
///
/// Groups by `(car_rid, normalized_config)` so that the same car with different
/// final builds is counted as separate entries.
pub fn compute_car_counts(
    ctx: &AnalysisContext,
    car_list: Vec<Car>,
    owned_configs: HashMap<String, String>,
) -> CarCounts {
    let mut by_rid: HashMap<(String, String), (BestCarRecord, usize)> = HashMap::new();
    let mut guid_to_rid: HashMap<String, HashSet<(String, String)>> = HashMap::new();

    for entry in ctx.best_cars.iter() {
        for best_car in entry.value() {
            let key = (best_car.car_rid.clone(), best_car.normalized_config.clone());
            let rid_entry = by_rid.entry(key.clone()).or_insert_with(|| (best_car.clone(), 0usize));
            rid_entry.1 += 1;
            // Keep the record with the best (lowest) time for this car+config.
            if best_car.time < rid_entry.0.time {
                rid_entry.0 = best_car.clone();
            }
            // Track all (rid, config) pairs for each guid.
            guid_to_rid.entry(best_car.guid.clone()).or_default().insert(key);
        }
    }

    // Build guid → Car lookup once, reused by trash/wishlist output functions.
    let car_map: HashMap<String, Car> =
        car_list.iter().map(|c| (c.guid.clone(), c.clone())).collect();

    CarCounts { by_rid, guid_to_rid, car_map, car_list, owned_configs }
}

#[cfg(test)]
mod tests {
    use super::*;

    fn empty_owned_configs() -> HashMap<String, String> {
        HashMap::new()
    }

    #[test]
    fn test_compute_car_counts_basic() {
        let ctx = AnalysisContext::new();
        let record = BestCarRecord {
            car_rid: "Test_Car".to_string(),
            brand: "TestBrand".to_string(),
            only_name: "TestModel".to_string(),
            year: 2020,
            rq: 85,
            config: "111".to_string(),
            normalized_config: "332".to_string(),
            time: 100.0,
            owned: true,
            prize: false,
            guid: "guid_test".to_string(),
        };

        // Manually insert into best_cars
        ctx.best_cars
            .insert(("Laguna Seca".to_string(), 85, "Dry".to_string()), vec![record.clone()]);
        ctx.best_cars
            .insert(("Monaco Streets".to_string(), 85, "Dry".to_string()), vec![record.clone()]);

        let car_list = vec![Car {
            rid: "Test_Car".to_string(),
            brand: "TestBrand".to_string(),
            only_name: "TestModel".to_string(),
            year: 2020,
            rq: 85,
            prize: false,
            guid: "guid_test".to_string(),
        }];

        let my_data = empty_owned_configs();
        let counts = compute_car_counts(&ctx, car_list, my_data);

        // Should have exactly 1 entry in by_rid
        assert_eq!(counts.by_rid.len(), 1);
        let (rec, count) = counts.by_rid.get(&("Test_Car".to_string(), "332".to_string())).unwrap();
        assert_eq!(count, &2); // best on 2 tracks
        assert_eq!(rec.car_rid, "Test_Car");

        // guid_to_rid should map guid → set of (rid, config)
        let guid_entries = counts.guid_to_rid.get("guid_test").unwrap();
        assert_eq!(guid_entries.len(), 1);
        assert!(guid_entries.contains(&("Test_Car".to_string(), "332".to_string())));

        // car_map should have the car
        assert!(counts.car_map.contains_key("guid_test"));
        assert_eq!(counts.car_list.len(), 1);
    }

    #[test]
    fn test_compute_car_counts_keeps_best_time() {
        let ctx = AnalysisContext::new();

        let record_fast = BestCarRecord {
            car_rid: "Car_A".to_string(),
            brand: "Brand".to_string(),
            only_name: "Model".to_string(),
            year: 2020,
            rq: 85,
            config: "111".to_string(),
            normalized_config: "332".to_string(),
            time: 90.0,
            owned: false,
            prize: false,
            guid: "guid_a".to_string(),
        };
        let record_slow = BestCarRecord {
            car_rid: "Car_A".to_string(),
            brand: "Brand".to_string(),
            only_name: "Model".to_string(),
            year: 2020,
            rq: 85,
            config: "233".to_string(),
            normalized_config: "233".to_string(),
            time: 110.0,
            owned: false,
            prize: false,
            guid: "guid_a".to_string(),
        };

        ctx.best_cars.insert(("Track1".to_string(), 85, "Dry".to_string()), vec![record_fast]);
        ctx.best_cars.insert(("Track2".to_string(), 85, "Dry".to_string()), vec![record_slow]);

        let my_data = empty_owned_configs();
        let counts = compute_car_counts(&ctx, vec![], my_data);
        // Different normalized configs → separate entries
        assert_eq!(counts.by_rid.len(), 2);
        let (rec_fast, count_fast) =
            counts.by_rid.get(&("Car_A".to_string(), "332".to_string())).unwrap();
        assert_eq!(*count_fast, 1);
        assert_eq!(rec_fast.time, 90.0);
        let (rec_slow, count_slow) =
            counts.by_rid.get(&("Car_A".to_string(), "233".to_string())).unwrap();
        assert_eq!(*count_slow, 1);
        assert_eq!(rec_slow.time, 110.0);
    }
}