Revert "Redraw wall/bush maze tiles with richer pixel-art detail"

The user asked to go back to the pre-commit assets. The original BMP_{1..4}_*.png tiles have been restored. The generator script introduced in that commit is also removed.
Redraw wall/bush maze tiles with richer pixel-art detail
2026-06-27 20:21:05 +02:00 · 2026-06-27 20:19:41 +02:00 · 2026-06-27 20:18:04 +02:00 · 2026-06-27 19:52:27 +02:00 · 2026-06-27 19:04:37 +02:00 · 2026-06-27 18:56:04 +02:00
208 changed files with 14360 additions and 4225 deletions
@@ -1,76 +0,0 @@
-# Piano di distribuzione ARM con AppImage
-
-Questo repository ora e pronto per essere portato dentro un bundle AppImage senza dipendere dalla directory corrente e senza scrivere nel filesystem montato in sola lettura dell'AppImage.
-
-## Stato attuale
-
- Le risorse di runtime vengono risolte a partire dal root del progetto tramite `MICE_PROJECT_ROOT`.
- I dati persistenti (`scores.txt`, `user_profiles.json`) vengono scritti in una directory utente persistente:
-  - `MICE_DATA_DIR`, se impostata.
-  - altrimenti `${XDG_DATA_HOME}/mice`.
-  - fallback: `~/.local/share/mice`.
- E presente uno scaffold di packaging in `packaging/`.
-
-## Strategia consigliata
-
-1. Costruire l'AppImage su una macchina `aarch64` reale o in una chroot/container ARM.
-2. Creare dentro `AppDir` un ambiente Python copiato localmente con `python -m venv --copies`.
-3. Installare le dipendenze Python da `requirements.txt` dentro quel Python locale.
-4. Copiare il gioco e gli asset in `AppDir/usr/share/mice`.
-5. Bundlare le librerie native richieste da SDL2 e dai wheel Python dentro `AppDir/usr/lib`.
-6. Usare `AppRun` per esportare `LD_LIBRARY_PATH`, `MICE_PROJECT_ROOT` e `MICE_DATA_DIR` prima del lancio di `rats.py`.
-7. Generare il file finale con `appimagetool`.
-
-## Perche costruire nativamente su ARM
-
- Un AppImage deve contenere binari della stessa architettura del target.
- `PySDL2`, `numpy` e `Pillow` portano con se librerie native o dipendenze native.
- Il cross-build da `x86_64` a `aarch64` e possibile, ma aumenta molto il rischio di incompatibilita su `glibc`, `libSDL2` e wheel Python.
-
-## Comando di build
-
-Da una macchina Linux `aarch64` con `python3`, `rsync`, `ldd`, `ldconfig` e `appimagetool` disponibili:
-
-```bash
-./packaging/build_appimage_aarch64.sh
-```
-
-Output previsto:
-
- `dist/AppDir`
- `dist/Mice-aarch64.AppImage`
-
-## Dipendenze host richieste al builder ARM
-
-Serve un sistema di build ARM con almeno:
-
- `python3`
- `python3-venv`
- `rsync`
- `glibc` userland standard
- `appimagetool`
- librerie di sviluppo/runtime installate sul builder, in particolare:
-  - `libSDL2`
-  - `libSDL2_ttf`
-
-## Test minimi da fare sul target ARM
-
-1. Avvio del gioco da shell.
-2. Caricamento font e immagini.
-3. Riproduzione audio WAV.
-4. Salvataggio punteggi in `~/.local/share/mice/scores.txt`.
-5. Creazione e lettura profili in `~/.local/share/mice/user_profiles.json`.
-6. Cambio livello da `assets/Rat/level.dat`.
-
-## Rischi residui
-
- La relocazione di un venv copiato dentro AppImage e pratica, ma va verificata sul target reale.
- Se il target ARM ha un userland molto vecchio, conviene costruire l'AppImage su una distro ARM con `glibc` piu vecchia del target.
- Se emergono problemi di relocazione del Python del venv, il passo successivo corretto e passare a un Python relocatable tipo `python-build-standalone` mantenendo invariato il launcher.
-
-## File introdotti
-
- `runtime_paths.py`
- `packaging/appimage/AppRun`
- `packaging/appimage/mice.desktop`
- `packaging/build_appimage_aarch64.sh`
@@ -1,221 +0,0 @@
-# Ottimizzazione Sistema di Collisioni con NumPy
-
-## Sommario
-
-Il sistema di collisioni del gioco è stato ottimizzato per gestire **oltre 200 unità simultanee** mantenendo performance elevate (50+ FPS).
-
-## Problema Originale
-
-### Analisi del Vecchio Sistema
-
-1. **Metodo Rat.collisions()**: O(n²) nel caso peggiore
-   - Ogni ratto controllava tutte le unità nelle sue celle
-   - Controllo AABB manuale per ogni coppia
-   - Con molti ratti nella stessa cella, diventava O(n²)
-
-2. **Calcoli bbox ridondanti**
-   - bbox calcolata in `draw()` ma usata anche in `collisions()`
-   - Nessun caching
-
-3. **Esplosioni bombe**: Iterazioni multiple sulle stesse posizioni
-   - Loop annidati per ogni direzione dell'esplosione
-   - Controllo manuale di `unit_positions` e `unit_positions_before`
-
-4. **Gas**: Controllo vittime a ogni frame anche quando non necessario
-
-## Soluzione Implementata
-
-### Nuovo Sistema: CollisionSystem (engine/collision_system.py)
-
-#### Caratteristiche Principali
-
-1. **Approccio Ibrido**
-   - < 10 candidati: Metodo semplice senza overhead NumPy
-   - ≥ 10 candidati: Operazioni vettorizzate con NumPy
-   - Ottimale per tutti gli scenari
-
-2. **Spatial Hashing**
-   - Dizionari `spatial_grid` e `spatial_grid_before`
-   - Lookup O(1) per posizioni
-   - Solo candidati nella stessa cella vengono controllati
-
-3. **Pre-allocazione Array NumPy**
-   - Arrays pre-allocati con capacità iniziale di 100
-   - Raddoppio dinamico quando necessario
-   - Riduce overhead di `vstack`/`append`
-
-4. **Collision Layers**
-   - Matrice di collisione 6x6 per filtrare interazioni non necessarie
-   - Layers: RAT, BOMB, GAS, MINE, POINT, EXPLOSION
-   - Controllo O(1) se due layer possono collidere
-
-5. **AABB Vettorizzato**
-   - Controllo collisioni bbox per N unità in una sola operazione
-   - Broadcasting NumPy per calcoli paralleli
-
-### Struttura del Sistema
-
-```python
-class CollisionSystem:
-    - register_unit()      # Registra unità nel frame corrente
-    - get_collisions_for_unit()  # Trova tutte le collisioni per un'unità
-    - get_units_in_area()  # Ottiene unità in più celle (esplosioni)
-    - check_aabb_collision_vectorized()  # AABB vettorizzato
-    - _simple_collision_check()  # Metodo semplice per pochi candidati
-```
-
-### Modifiche alle Unità
-
-#### 1. Unit (units/unit.py)
- Aggiunto attributo `collision_layer`
- Inizializzazione con layer specifico
-
-#### 2. Rat (units/rat.py)
- Usa `CollisionSystem.get_collisions_for_unit()`
- Eliminati loop manuali
- Tolleranza AABB gestita dal sistema
-
-#### 3. Bomb (units/bomb.py)
- Esplosioni usano `get_units_in_area()`
- Raccolta posizioni esplosione → query batch
- Singola operazione per trovare tutte le vittime
-
-#### 4. Gas (units/gas.py)
- Usa `get_units_in_cell()` per trovare vittime
- Separazione tra position e position_before
-
-#### 5. Mine (units/mine.py)
- Controllo trigger con `get_units_in_cell()`
- Layer-based detection
-
-### Integrazione nel Game Loop (rats.py)
-
-```python
-# Inizializzazione
-self.collision_system = CollisionSystem(
-    self.cell_size, self.map.width, self.map.height
-)
-
-# Update loop (3 passaggi)
-1. Move: Tutte le unità si muovono
-2. Register: Registrazione nel collision system + backward compatibility
-3. Collisions + Draw: Controllo collisioni e rendering
-```
-
-## Performance
-
-### Test Results (250 unità su griglia 30x30)
-
-**Stress Test - 100 frames:**
-```
-Total time: 332.41ms
-Average per frame: 3.32ms
-FPS capacity: 300.8 FPS
-Target (50 FPS): ✓ PASS
-```
-
-### Confronto Scenari Reali
-
-| Numero Unità | Frame Time | FPS Capacity |
-|--------------|------------|--------------|
-| 50           | ~0.5ms     | 2000 FPS     |
-| 100          | ~1.3ms     | 769 FPS      |
-| 200          | ~2.5ms     | 400 FPS      |
-| 250          | ~3.3ms     | 300 FPS      |
-| 300          | ~4.0ms     | 250 FPS      |
-
-**Conclusione**: Il sistema mantiene **performance eccellenti** anche con 300+ unità, ben oltre il target di 50 FPS.
-
-### Vantaggi per Scenari Specifici
-
-1. **Molti ratti in poche celle**: 
-   - Vecchio: O(n²) per celle dense
-   - Nuovo: O(n) con spatial hashing
-
-2. **Esplosioni bombe**:
-   - Vecchio: Loop annidati per ogni direzione
-   - Nuovo: Singola query batch per tutte le posizioni
-
-3. **Scalabilità**:
-   - Vecchio: Degrada linearmente con numero unità
-   - Nuovo: Performance costante grazie a spatial hashing
-
-## Compatibilità
-
- **Backward compatible**: Mantiene `unit_positions` e `unit_positions_before`
- **Rimozione futura**: Questi dizionari possono essere rimossi dopo test estesi
- **Nessuna breaking change**: API delle unità invariata
-
-## File Modificati
-
-1. ✅ `requirements.txt` - Aggiunto numpy
-2. ✅ `engine/collision_system.py` - Nuovo sistema (370 righe)
-3. ✅ `units/unit.py` - Aggiunto collision_layer
-4. ✅ `units/rat.py` - Ottimizzato collisions()
-5. ✅ `units/bomb.py` - Esplosioni vettorizzate
-6. ✅ `units/gas.py` - Query ottimizzate
-7. ✅ `units/mine.py` - Detection ottimizzata
-8. ✅ `units/points.py` - Aggiunto collision_layer
-9. ✅ `rats.py` - Integrato CollisionSystem nel game loop
-10. ✅ `test_collision_performance.py` - Benchmark suite
-
-## Prossimi Passi (Opzionali)
-
-1. **Rimozione backward compatibility**: Eliminare `unit_positions`/`unit_positions_before`
-2. **Profiling avanzato**: Identificare ulteriori bottleneck
-3. **Spatial grid gerarchico**: Per mappe molto grandi (>100x100)
-4. **Caching bbox**: Se le unità non si muovono ogni frame
-
-## Installazione
-
-```bash
-cd /home/enne2/Sviluppo/mice
-source .venv/bin/activate
-pip install numpy
-```
-
-## Testing
-
-```bash
-# Benchmark completo
-python test_collision_performance.py
-
-# Gioco normale
-./mice.sh
-```
-
-## Note Tecniche
-
-### Approccio Ibrido Spiegato
-
-Il sistema usa un **threshold di 10 candidati** per decidere quando usare NumPy:
-
- **< 10 candidati**: Loop Python semplice (no overhead numpy)
- **≥ 10 candidati**: Operazioni vettorizzate NumPy
-
-Questo è ottimale perché:
- Con pochi candidati, l'overhead di creare array NumPy supera i benefici
- Con molti candidati, la vettorizzazione compensa l'overhead iniziale
-
-### Memory Layout
-
-```
-Arrays NumPy (pre-allocati):
- bboxes: (capacity, 4) float32  → ~1.6KB per 100 unità
- positions: (capacity, 2) int32  → ~800B per 100 unità
- layers: (capacity,) int8        → ~100B per 100 unità
-
-Total: ~2.5KB per 100 unità (trascurabile)
-```
-
-## Conclusioni
-
-L'ottimizzazione con NumPy è **altamente efficace** per il caso d'uso di Mice! con 200+ unità:
-
-✅ Performance eccellenti (300+ FPS con 250 unità)  
-✅ Scalabilità lineare grazie a spatial hashing  
-✅ Backward compatible  
-✅ Approccio ibrido ottimale per tutti gli scenari  
-✅ Memory footprint minimo  
-
-Il sistema è **pronto per la produzione**.
@@ -1,773 +0,0 @@
-# NumPy Tutorial: Dal Tuo Sistema di Collisioni al Codice Ottimizzato
-
-Questo documento spiega NumPy usando come esempio reale il sistema di collisioni di Mice!, confrontando il tuo approccio originale con la versione ottimizzata.
-
-## Indice
-1. [Introduzione: Il Problema delle Performance](#1-introduzione-il-problema-delle-performance)
-2. [Cos'è NumPy e Perché Serve](#2-cosè-numpy-e-perché-serve)
-3. [Concetti Base di NumPy](#3-concetti-base-di-numpy)
-4. [Dal Tuo Codice a NumPy: Caso Pratico](#4-dal-tuo-codice-a-numpy-caso-pratico)
-5. [Spatial Hashing: L'Algoritmo Intelligente](#5-spatial-hashing-lalgoritmo-intelligente)
-6. [Operazioni Vettoriali in NumPy](#6-operazioni-vettoriali-in-numpy)
-7. [Best Practices e Pitfalls](#7-best-practices-e-pitfalls)
-
---
-
-## 1. Introduzione: Il Problema delle Performance
-
-### Il Tuo Sistema Originale (Funzionava Bene!)
-
-```python
-# rats.py - Il tuo approccio originale
-def update_maze(self):
-    # Popolava dizionari con le posizioni delle unità
-    self.unit_positions = {}
-    self.unit_positions_before = {}
-    
-    for unit in self.units.values():
-        unit.move()
-        # Raggruppa unità per posizione
-        self.unit_positions.setdefault(unit.position, []).append(unit)
-        self.unit_positions_before.setdefault(unit.position_before, []).append(unit)
-    
-    for unit in self.units.values():
-        unit.collisions()  # Ogni unità controlla le proprie collisioni
-```
-
-### Il Problema con 200+ Unità
-
-Con 5-10 topi: **funziona perfetto** ✅  
-Con 200+ topi: **FPS crollano** ❌
-
-**Perché?**
- Ogni topo controlla collisioni con TUTTI gli altri topi
- 200 topi = 200 × 200 = **40,000 controlli per frame!**
- Complessità: **O(n²)** - cresce in modo quadratico
-
---
-
-## 2. Cos'è NumPy e Perché Serve
-
-### NumPy in 3 Parole
-**Array multidimensionali ottimizzati**
-
-### Perché è Veloce?
-
-```python
-# Python puro (lento ❌)
-distances = []
-for i in range(1000):
-    for j in range(1000):
-        dx = x[i] - y[j]
-        dy = x[i] - y[j]
-        distances.append((dx**2 + dy**2)**0.5)
-# Tempo: ~500ms con 1 milione di operazioni
-
-# NumPy (veloce ✅)
-import numpy as np
-distances = np.sqrt((x[:, None] - y[None, :])**2 + (x[:, None] - y[None, :])**2)
-# Tempo: ~5ms - 100 volte più veloce!
-```
-
-### Perché la Differenza?
-
-1. **Codice C Compilato**: NumPy è scritto in C/C++, non Python interpretato
-2. **Operazioni Vettoriali**: Calcola migliaia di valori in parallelo
-3. **Memoria Contigua**: Dati organizzati efficientemente in RAM
-4. **CPU SIMD**: Usa istruzioni speciali della CPU per parallelismo hardware
-
---
-
-## 3. Concetti Base di NumPy
-
-### Array vs Liste Python
-
-```python
-# Lista Python (flessibile ma lenta)
-lista = [1, 2, 3, 4, 5]
-lista.append("sei")  # OK - tipi misti
-lista[0] = "uno"     # OK - cambio tipo
-
-# Array NumPy (veloce ma rigido)
-import numpy as np
-array = np.array([1, 2, 3, 4, 5])
-# array[0] = "uno"  # ERRORE! Tipo fisso: int64
-```
-
-**Regola**: NumPy sacrifica flessibilità per velocità
-
-### Operazioni Elemento per Elemento
-
-```python
-# Python puro
-lista_a = [1, 2, 3]
-lista_b = [4, 5, 6]
-risultato = []
-for a, b in zip(lista_a, lista_b):
-    risultato.append(a + b)
-# risultato = [5, 7, 9]
-
-# NumPy (broadcasting)
-array_a = np.array([1, 2, 3])
-array_b = np.array([4, 5, 6])
-risultato = array_a + array_b  # [5, 7, 9] - automatico!
-```
-
-### Broadcasting: Operazioni su Array di Dimensioni Diverse
-
-```python
-# Esempio reale dal tuo gioco: calcolare distanze
-unit_positions = np.array([[10, 20], [30, 40], [50, 60]])  # 3 unità
-target = np.array([25, 35])  # 1 bersaglio
-
-# Vogliamo: distanza di ogni unità dal bersaglio
-# Senza broadcasting (noioso):
-distances = []
-for pos in unit_positions:
-    dx = pos[0] - target[0]
-    dy = pos[1] - target[1]
-    distances.append(np.sqrt(dx**2 + dy**2))
-
-# Con broadcasting (elegante):
-diff = unit_positions - target  # NumPy espande target automaticamente
-distances = np.sqrt((diff**2).sum(axis=1))
-# Output: [18.03, 7.07, 28.28]
-```
-
-**Come funziona?**
-```
-unit_positions:  [[10, 20],     target: [25, 35]
-                  [30, 40],     
-                  [50, 60]]     Broadcasting lo espande a:
-                                        [[25, 35],
-                                         [25, 35],
-                                         [25, 35]]
-```
-
---
-
-## 4. Dal Tuo Codice a NumPy: Caso Pratico
-
-### Fase 1: Il Tuo Approccio con i Dizionari
-
-```python
-# units/rat.py - Il tuo codice originale
-def collisions(self):
-    # Prende unità nella stessa cella
-    units_here = self.game.unit_positions.get(self.position, [])
-    units_before = self.game.unit_positions_before.get(self.position_before, [])
-    
-    # Controlla ogni unità
-    for other_unit in units_here + units_before:
-        if other_unit.id == self.id:
-            continue
-        
-        # Logica di collisione...
-        if self.sex == other_unit.sex and self.fight:
-            self.die(other_unit)
-        elif self.sex != other_unit.sex:
-            self.fuck(other_unit)
-```
-
-**Pro del Tuo Approccio:**
- ✅ Semplice e leggibile
- ✅ Usa dizionari Python nativi
- ✅ Funziona perfettamente con poche unità
-
-**Problema con 200+ Unità:**
- ❌ Ogni topo itera su liste di Python
- ❌ Controlli ripetuti (topo A controlla B, poi B controlla A)
- ❌ Nessuna ottimizzazione per distanze
-
-### Fase 2: Spatial Hashing (L'Idea Geniale)
-
-Prima di NumPy, serve un algoritmo migliore: **Spatial Hashing**
-
-```python
-# Concetto: dividi il mondo in "celle" (griglia)
-# Ogni cella contiene solo le unità al suo interno
-
-# Mondo di gioco:
-#   0   1   2   3
-# 0 [ ] [ ] [ ] [ ]
-# 1 [ ] [A] [B] [ ]
-# 2 [ ] [ ] [C] [ ]
-# 3 [ ] [ ] [ ] [ ]
-
-# Dizionario spatial hash:
-spatial_grid = {
-    (1, 1): [unit_A],
-    (2, 1): [unit_B],
-    (2, 2): [unit_C]
-}
-
-# Quando unit_A cerca collisioni:
-# Controlla SOLO celle (1,1) e adiacenti (0,0), (0,1), (0,2), (1,0), (1,2), (2,0), (2,1), (2,2)
-# Non controlla unit_C a (2,2) - troppo lontano!
-```
-
-**Vantaggio**: Da O(n²) a O(n)!
- 200 unità: da 40,000 controlli a ~1,800 controlli (celle adiacenti)
-
-### Fase 3: NumPy per Calcoli Massivi
-
-```python
-# engine/collision_system.py - Il nuovo approccio
-
-class CollisionSystem:
-    def __init__(self, cell_size=32):
-        # Pre-allocazione: prepara spazio per array NumPy
-        self.unit_ids = np.zeros(100, dtype=np.int64)  # Array di ID
-        self.bboxes = np.zeros((100, 4), dtype=np.float32)  # Array di bounding box
-        self.positions = np.zeros((100, 2), dtype=np.int32)  # Array di posizioni
-        self.current_size = 0  # Quante unità registrate
-        self.capacity = 100  # Capacità massima prima di resize
-```
-
-**Perché Pre-allocazione?**
-```python
-# Cattivo: crescita lenta ❌
-array = np.array([])
-for i in range(1000):
-    array = np.append(array, i)  # Crea NUOVO array ogni volta!
-# Tempo: ~200ms
-
-# Buono: pre-allocazione ✅
-array = np.zeros(1000)
-for i in range(1000):
-    array[i] = i  # Modifica array esistente
-# Tempo: ~2ms
-```
-
-### Fase 4: Registrazione Unità
-
-```python
-def register_unit(self, unit_id, bbox, position, position_before, collision_layer):
-    """Registra un'unità nel sistema di collisione"""
-    
-    # Se array pieno, raddoppia capacità
-    if self.current_size >= self.capacity:
-        self._resize_arrays(self.capacity * 2)
-    
-    idx = self.current_size
-    
-    # Inserisci dati negli array NumPy
-    self.unit_ids[idx] = unit_id
-    self.bboxes[idx] = bbox  # [x1, y1, x2, y2]
-    self.positions[idx] = position
-    self.position_before[idx] = position_before
-    self.layers[idx] = collision_layer.value
-    
-    # Spatial hashing: aggiungi a griglia
-    cell = (position[0], position[1])
-    self.spatial_grid[cell].append(idx)  # Salva INDICE, non unità
-    
-    self.current_size += 1
-```
-
-**Nota Importante**: Salviamo **indici** negli array, non oggetti Python!
- `spatial_grid[(5, 10)] = [0, 3, 7]` → Unità agli indici 0, 3, 7 degli array NumPy
- Accesso veloce: `self.bboxes[0]`, `self.bboxes[3]`, `self.bboxes[7]`
-
-### Fase 5: Collisioni Vettoriali con NumPy
-
-```python
-def get_collisions_for_unit(self, unit_id, bbox, collision_layer):
-    """Trova tutte le collisioni per un'unità"""
-    
-    # 1. Trova celle da controllare (spatial hashing)
-    x, y = bbox[0] // self.cell_size, bbox[1] // self.cell_size
-    cells_to_check = [
-        (x-1, y-1), (x, y-1), (x+1, y-1),
-        (x-1, y),   (x, y),   (x+1, y),
-        (x-1, y+1), (x, y+1), (x+1, y+1)
-    ]
-    
-    # 2. Raccogli candidati da celle adiacenti
-    candidates = []
-    for cell in cells_to_check:
-        candidates.extend(self.spatial_grid.get(cell, []))
-    
-    if len(candidates) < 10:
-        # POCHI candidati: usa Python normale
-        collisions = []
-        for idx in candidates:
-            if self.unit_ids[idx] == unit_id:
-                continue
-            if self._check_bbox_collision(bbox, self.bboxes[idx]):
-                collisions.append((self.layers[idx], self.unit_ids[idx]))
-        return collisions
-    
-    else:
-        # MOLTI candidati: USA NUMPY! ✨
-        return self._vectorized_collision_check(unit_id, bbox, candidates)
-```
-
-### Fase 6: La Magia di NumPy - Vectorized Collision Check
-
-```python
-def _vectorized_collision_check(self, unit_id, bbox, candidate_indices):
-    """Controlla collisioni usando NumPy per massima velocità"""
-    
-    # Converti candidati in array NumPy
-    candidate_indices = np.array(candidate_indices, dtype=np.int32)
-    
-    # Filtra l'unità stessa (non collidere con se stessi)
-    mask = self.unit_ids[candidate_indices] != unit_id
-    candidate_indices = candidate_indices[mask]
-    
-    if len(candidate_indices) == 0:
-        return []
-    
-    # Estrai bounding box di TUTTI i candidati in un colpo solo
-    candidate_bboxes = self.bboxes[candidate_indices]  # Shape: (N, 4)
-    # candidate_bboxes = [[x1, y1, x2, y2],  # candidato 0
-    #                     [x1, y1, x2, y2],  # candidato 1
-    #                     ...]
-    
-    # Controllo collisione AABB (Axis-Aligned Bounding Box)
-    # Due rettangoli collidono se:
-    # - bbox.x1 < other.x2  AND
-    # - bbox.x2 > other.x1  AND
-    # - bbox.y1 < other.y2  AND
-    # - bbox.y2 > other.y1
-    
-    # NumPy calcola TUTTE le collisioni contemporaneamente! 🚀
-    colliding_mask = (
-        (bbox[0] < candidate_bboxes[:, 2]) &  # bbox.x1 < others.x2
-        (bbox[2] > candidate_bboxes[:, 0]) &  # bbox.x2 > others.x1
-        (bbox[1] < candidate_bboxes[:, 3]) &  # bbox.y1 < others.y2
-        (bbox[3] > candidate_bboxes[:, 1])    # bbox.y2 > others.y1
-    )
-    # colliding_mask = [True, False, True, False, True, ...]
-    
-    # Filtra solo unità che collidono
-    colliding_indices = candidate_indices[colliding_mask]
-    
-    # Restituisci coppie (layer, unit_id)
-    return list(zip(
-        self.layers[colliding_indices],
-        self.unit_ids[colliding_indices]
-    ))
-```
-
-**Spiegazione Dettagliata del Codice NumPy:**
-
-```python
-# Esempio concreto con 3 candidati
-bbox = [10, 20, 30, 40]  # Nostro topo: x1=10, y1=20, x2=30, y2=40
-
-candidate_bboxes = np.array([
-    [5,  15, 25, 35],   # Candidato 0
-    [50, 60, 70, 80],   # Candidato 1 (lontano)
-    [15, 25, 35, 45]    # Candidato 2
-])
-
-# Controllo bbox[0] < candidate_bboxes[:, 2]
-# bbox[0] = 10
-# candidate_bboxes[:, 2] = [25, 70, 35]  # Colonna x2 di tutti i candidati
-# 10 < [25, 70, 35] = [True, True, True]
-
-# Controllo bbox[2] > candidate_bboxes[:, 0]
-# bbox[2] = 30
-# candidate_bboxes[:, 0] = [5, 50, 15]  # Colonna x1
-# 30 > [5, 50, 15] = [True, False, True]
-
-# ... altri controlli ...
-
-# Combinazione finale (AND logico):
-colliding_mask = [True, False, True]  # Solo 0 e 2 collidono!
-```
-
---
-
-## 5. Spatial Hashing: L'Algoritmo Intelligente
-
-### Visualizzazione Pratica
-
-```
-Mondo di gioco 640x480, cell_size=32
-
-Griglia spaziale:
-     0    1    2    3    4    5   ...  19
-  ┌────┬────┬────┬────┬────┬────┬────┬────┐
-0 │    │    │    │    │    │    │    │    │
-  ├────┼────┼────┼────┼────┼────┼────┼────┤
-1 │    │ R1 │ R2 │    │    │    │    │    │  R = Rat
-  ├────┼────┼────┼────┼────┼────┼────┼────┤  B = Bomb
-2 │    │ R3 │ B1 │ R4 │    │    │    │    │  M = Mine
-  ├────┼────┼────┼────┼────┼────┼────┼────┤
-3 │    │    │ M1 │    │    │    │    │    │
-  └────┴────┴────┴────┴────┴────┴────┴────┘
-```
-
-### Come Funziona il Lookup
-
-```python
-# R1 cerca collisioni da cella (1, 1)
-def get_collisions_for_unit(self, unit_id, bbox):
-    x, y = 1, 1  # Posizione R1
-    
-    # Controlla 9 celle (3x3 centrato su R1):
-    cells = [
-        (0,0), (1,0), (2,0),  # Riga sopra
-        (0,1), (1,1), (2,1),  # Riga centrale (include R1)
-        (0,2), (1,2), (2,2)   # Riga sotto
-    ]
-    
-    # spatial_grid è un dizionario:
-    # {
-    #   (1, 1): [idx_R1],
-    #   (2, 1): [idx_R2],
-    #   (1, 2): [idx_R3],
-    #   (2, 2): [idx_B1, idx_R4],
-    #   (2, 3): [idx_M1]
-    # }
-    
-    candidates = []
-    for cell in cells:
-        candidates.extend(self.spatial_grid.get(cell, []))
-    
-    # candidates = [idx_R1, idx_R2, idx_R3, idx_B1, idx_R4]
-    # NON include idx_M1 perché (2,3) è fuori dal range 3x3!
-```
-
-### Benefici Misurabili
-
-```python
-# SENZA spatial hashing (O(n²)):
-# 200 unità → 200 × 200 = 40,000 controlli
-
-# CON spatial hashing (O(n)):
-# 200 unità, distribuite su 20×15=300 celle
-# Media 0.67 unità per cella
-# Ogni unità controlla 9 celle × 0.67 = ~6 candidati
-# 200 unità × 6 candidati = 1,200 controlli
-# 
-# Miglioramento: 40,000 → 1,200 = 33x più veloce! 🚀
-```
-
---
-
-## 6. Operazioni Vettoriali in NumPy
-
-### Broadcasting Avanzato: Esplosioni
-
-```python
-def get_units_in_area(self, positions, layer_filter=None):
-    """Trova unità in un'area (es. esplosione bomba)"""
-    
-    # positions: lista di posizioni esplose
-    # es. [(10, 10), (10, 11), (11, 10), (11, 11)]  # Esplosione 2x2
-    
-    if self.current_size == 0:
-        return []
-    
-    # Converti in array NumPy
-    area_positions = np.array(positions, dtype=np.int32)  # Shape: (4, 2)
-    
-    # Prendi posizioni di TUTTE le unità
-    all_positions = self.positions[:self.current_size]  # Shape: (N, 2)
-    # es. all_positions = [[5, 5], [10, 10], [15, 15], [10, 11], ...]
-    
-    # Broadcasting trick per confrontare OGNI posizione esplosione con OGNI unità
-    # area_positions[:, None, :] → Shape: (4, 1, 2)
-    # all_positions[None, :, :] → Shape: (1, N, 2)
-    # Risultato → Shape: (4, N, 2) - tutte le combinazioni!
-    
-    matches = (area_positions[:, None, :] == all_positions[None, :, :]).all(axis=2)
-    # matches[i, j] = True se esplosione i colpisce unità j
-    
-    # any(axis=0): almeno una posizione esplosione colpisce quella unità?
-    unit_hit_mask = matches.any(axis=0)  # Shape: (N,)
-    
-    # Filtra per layer se richiesto
-    if layer_filter:
-        valid_layers = self.layers[:self.current_size] == layer_filter.value
-        unit_hit_mask = unit_hit_mask & valid_layers
-    
-    # Restituisci ID delle unità colpite
-    hit_indices = np.where(unit_hit_mask)[0]
-    return self.unit_ids[hit_indices].tolist()
-```
-
-**Spiegazione con Esempio Concreto:**
-
-```python
-# Bomba esplode creando 4 celle di fuoco
-area_positions = np.array([[10, 10], [10, 11], [11, 10], [11, 11]])
-
-# Ci sono 3 topi nel gioco
-all_positions = np.array([[5, 5], [10, 10], [10, 11]])
-
-# Broadcasting:
-area_positions[:, None, :].shape  # (4, 1, 2)
-all_positions[None, :, :].shape   # (1, 3, 2)
-
-# Confronto elemento per elemento:
-matches = (area_positions[:, None, :] == all_positions[None, :, :]).all(axis=2)
-# matches = [
-#   [False, False, False],  # Esplosione (10,10) vs [(5,5), (10,10), (10,11)]
-#   [False, True,  False],  # Esplosione (10,11) vs ...
-#   [False, False, True ],  # Esplosione (11,10) vs ...
-#   [False, False, False]   # Esplosione (11,11) vs ...
-# ]
-
-# Collassa su asse 0 (almeno UNA esplosione colpisce?)
-unit_hit_mask = matches.any(axis=0)  # [False, True, True]
-# Topo 0 (5,5): NON colpito
-# Topo 1 (10,10): COLPITO (da esplosione 0)
-# Topo 2 (10,11): COLPITO (da esplosione 1)
-```
-
-### Calcolo Distanze Vettoriale
-
-```python
-# Esempio: trovare tutti i topi entro raggio 50 pixel da una bomba
-
-bomb_position = np.array([100, 100])  # Posizione bomba
-
-# Posizioni di tutti i topi (array NumPy)
-rat_positions = self.positions[:self.current_size]  # Shape: (N, 2)
-
-# Calcolo distanze usando broadcasting
-diff = rat_positions - bomb_position  # Shape: (N, 2)
-# diff[i] = [rat_x - bomb_x, rat_y - bomb_y]
-
-distances = np.sqrt((diff ** 2).sum(axis=1))  # Shape: (N,)
-# distances[i] = sqrt((dx)^2 + (dy)^2)
-
-# Trova topi entro raggio
-within_radius = distances < 50  # Boolean mask
-hit_rat_indices = np.where(within_radius)[0]
-
-# Esempio output:
-# rat_positions = [[90, 90], [110, 110], [200, 200]]
-# diff = [[-10, -10], [10, 10], [100, 100]]
-# distances = [14.14, 14.14, 141.42]
-# within_radius = [True, True, False]
-# hit_rat_indices = [0, 1]  # Primi due topi colpiti!
-```
-
---
-
-## 7. Best Practices e Pitfalls
-
-### ✅ Quando Usare NumPy
-
-```python
-# BUONO: Operazioni su molti dati
-positions = np.array([[...] for _ in range(1000)])
-distances = np.sqrt(((positions - target)**2).sum(axis=1))
-
-# CATTIVO: Operazioni su pochi dati (overhead NumPy!)
-positions = np.array([[10, 20], [30, 40]])  # Solo 2 elementi
-distances = np.sqrt(((positions - target)**2).sum(axis=1))
-# Più lento di un semplice loop Python!
-```
-
-**Regola nel tuo codice:**
-```python
-if len(candidates) < 10:
-    # Usa Python normale
-    for idx in candidates:
-        ...
-else:
-    # Usa NumPy
-    self._vectorized_collision_check(...)
-```
-
-### ✅ Pre-allocazione vs Append
-
-```python
-# CATTIVO ❌ (lento con array grandi)
-array = np.array([])
-for i in range(10000):
-    array = np.append(array, i)  # O(n) ad ogni append!
-
-# BUONO ✅ (veloce)
-array = np.zeros(10000)
-for i in range(10000):
-    array[i] = i  # O(1) ad ogni assegnazione
-
-# MIGLIORE ✅ (senza loop!)
-array = np.arange(10000)  # Operazione vettoriale nativa
-```
-
-### ✅ Memory Layout e Performance
-
-```python
-# Row-major (C order) - default NumPy
-array = np.zeros((1000, 3), order='C')
-# Memoria: [x0, y0, z0, x1, y1, z1, ...]
-# Veloce per accesso righe: array[i, :]
-
-# Column-major (Fortran order)
-array = np.zeros((1000, 3), order='F')
-# Memoria: [x0, x1, ..., y0, y1, ..., z0, z1, ...]
-# Veloce per accesso colonne: array[:, j]
-
-# Nel tuo caso (posizioni):
-self.positions = np.zeros((100, 2))  # Row-major è perfetto
-# Accesso frequente: self.positions[idx] → [x, y] di un'unità
-```
-
-### ⚠️ Pitfall Comuni
-
-#### 1. Copy vs View
-
-```python
-# View (condivide memoria)
-a = np.array([1, 2, 3])
-b = a[:]  # b è una VIEW di a
-b[0] = 999
-print(a)  # [999, 2, 3] - modificato anche a!
-
-# Copy (memoria separata)
-a = np.array([1, 2, 3])
-b = a.copy()
-b[0] = 999
-print(a)  # [1, 2, 3] - a è immutato
-```
-
-**Nel tuo codice:**
-```python
-def get_collisions_for_unit(self, ...):
-    # Filtriamo candidati
-    candidate_indices = candidate_indices[mask]  # Crea VIEW
-    
-    # Se modifichi candidate_indices dopo, potresti modificare l'originale!
-    # Soluzione: .copy() se necessario
-```
-
-#### 2. Broadcasting Inatteso
-
-```python
-a = np.array([1, 2, 3])
-b = np.array([[1], [2], [3]])
-
-# Cosa succede?
-result = a + b
-# Broadcasting espande a:
-# [[1, 2, 3],     [[1], [1], [1]]     [[2, 3, 4],
-#  [1, 2, 3],  +   [2], [2], [2]]  =   [3, 4, 5],
-#  [1, 2, 3]]      [3], [3], [3]]      [4, 5, 6]]
-```
-
-**Verifica sempre le shape:**
-```python
-print(f"Shape: {array.shape}")  # Sempre prima di operazioni complesse!
-```
-
-#### 3. Integer Overflow
-
-```python
-# ATTENZIONE con dtype piccoli!
-a = np.array([250], dtype=np.uint8)  # Max 255
-b = a + 10  # 260, ma uint8 wrap: diventa 4!
-
-# Soluzione: usa dtype appropriati
-a = np.array([250], dtype=np.int32)  # Max ~2 miliardi
-b = a + 10  # 260 ✅
-```
-
---
-
-## Confronto Finale: Prima vs Dopo
-
-### Codice Originale (Il Tuo)
-
-```python
-# rats.py
-def update_maze(self):
-    self.unit_positions = {}
-    self.unit_positions_before = {}
-    
-    for unit in self.units.values():
-        unit.move()
-        self.unit_positions.setdefault(unit.position, []).append(unit)
-        self.unit_positions_before.setdefault(unit.position_before, []).append(unit)
-    
-    for unit in self.units.values():
-        unit.collisions()
-
-# units/rat.py
-def collisions(self):
-    for other_unit in self.game.unit_positions.get(self.position, []):
-        if other_unit.id == self.id:
-            continue
-        # Controlla collisione...
-```
-
-**Complessità**: O(n²) nel caso peggiore  
-**Performance**: ~50ms con 200 unità  
-**Memoria**: Dizionari Python + liste di oggetti
-
-### Codice Ottimizzato (NumPy)
-
-```python
-# rats.py - 4-pass loop
-def update_maze(self):
-    self.collision_system.clear()
-    
-    # Pass 1: Pre-registra posizioni
-    for unit in self.units.values():
-        self.collision_system.register_unit(unit.id, unit.bbox, ...)
-    
-    # Pass 2: Movimento
-    for unit in self.units.values():
-        unit.move()
-    
-    # Pass 3: Ri-registra dopo movimento
-    self.collision_system.clear()
-    for unit in self.units.values():
-        self.collision_system.register_unit(unit.id, unit.bbox, ...)
-    
-    # Pass 4: Collisioni
-    for unit in self.units.values():
-        unit.collisions()
-
-# units/rat.py
-def collisions(self):
-    collisions = self.game.collision_system.get_collisions_for_unit(
-        self.id, self.bbox, self.collision_layer
-    )
-    for _, other_id in collisions:
-        other_unit = self.game.get_unit_by_id(other_id)
-        if isinstance(other_unit, Rat):
-            # Controlla collisione...
-```
-
-**Complessità**: O(n) con spatial hashing + NumPy  
-**Performance**: ~3ms con 250 unità (16x più veloce!)  
-**Memoria**: Array NumPy pre-allocati (più efficiente)
-
---
-
-## Conclusione
-
-### Cosa Hai Imparato
-
-1. **NumPy**: Array veloci per operazioni matematiche massive
-2. **Broadcasting**: Operazioni automatiche su array di dimensioni diverse
-3. **Spatial Hashing**: Ridurre O(n²) a O(n) con partizionamento spaziale
-4. **Vectorization**: Sostituire loop Python con operazioni NumPy parallele
-5. **Pre-allocazione**: Evitare allocazioni ripetute per velocità
-
-### Quando Applicare Queste Tecniche
-
- ✅ **Usa NumPy quando**: Hai 50+ elementi da processare con operazioni matematiche
- ✅ **Usa Spatial Hashing quando**: Controlli collisioni/prossimità in spazio 2D/3D
- ✅ **Usa Vectorization quando**: Stesso calcolo ripetuto su molti dati
- ❌ **Non usare quando**: Pochi elementi (< 10) o logica complessa non matematica
-
-### Risorse per Approfondire
-
- **NumPy Documentation**: https://numpy.org/doc/stable/
- **NumPy Quickstart**: https://numpy.org/doc/stable/user/quickstart.html
- **Broadcasting**: https://numpy.org/doc/stable/user/basics.broadcasting.html
- **Performance Tips**: https://numpy.org/doc/stable/user/c-info.performance.html
-
---
-
-**Il tuo codice originale era ottimo per il caso d'uso iniziale.** L'ottimizzazione con NumPy è stata necessaria solo quando hai scalato a 200+ unità. Questo è un esempio perfetto di "ottimizza quando serve", non prematuramente! 🎯
@@ -15,6 +15,17 @@ Mice! is a strategic game where players must kill rats with bombs before they re
 - **Scoring**: Points system to track player progress.
 - **Performance**: Optimized collision detection system supporting 200+ simultaneous units using NumPy vectorization.

+## Utilities
+
+### Microphone Visualizer
+
+A small SDL2 microphone visualizer is available in `tools/mic_visualizer.py`.
+
+- List capture devices: `python tools/mic_visualizer.py --list-devices`
+- Open the default microphone: `python tools/mic_visualizer.py`
+- Open a specific input: `python tools/mic_visualizer.py --device-index 1`
+- On muOS, use `mice_mic.sh` as a launcher in `ROMS/Ports` and it will run fullscreen with gamepad quit support.
+
 ## Engine Architecture

 The Mice! game engine is built on a modular architecture designed for flexibility and maintainability. The engine follows a component-based design pattern where different systems handle specific aspects of the game.
@@ -1,308 +0,0 @@
-# Game Profile Manager
-
-A PySDL2-based user profile management system designed for gamepad-only control with virtual keyboard input. This system allows players to create, edit, delete, and select user profiles for games using only gamepad inputs or directional keys, with no need for physical keyboard text input.
-
-## Features
-
- **640x480 Resolution**: Optimized for retro gaming systems and handheld devices
- **Create New Profiles**: Add new user profiles with custom names using virtual keyboard
- **Profile Selection**: Browse and select active profiles 
- **Edit Settings**: Modify profile settings including difficulty, volume levels, and preferences
- **Delete Profiles**: Remove unwanted profiles
- **Gamepad/Directional Navigation**: Full control using only gamepad/joystick inputs or arrow keys
- **Virtual Keyboard**: Text input using directional controls - no physical keyboard typing required
- **JSON Storage**: Profiles stored in human-readable JSON format
- **Persistent Settings**: All changes automatically saved
-
-## Installation
-
-### Requirements
- Python 3.6+
- PySDL2
- SDL2 library
-
-### Setup
-```bash
-# Install required Python packages
-pip install pysdl2
-
-# For Ubuntu/Debian users, you may also need:
-sudo apt-get install libsdl2-dev libsdl2-ttf-dev
-
-# Make launcher executable
-chmod +x launch_profile_manager.sh
-```
-
-## Usage
-
-### Running the Profile Manager
-```bash
-# Method 1: Use the launcher script
-./launch_profile_manager.sh
-
-# Method 2: Run directly with Python
-python3 profile_manager.py
-```
-
-### Gamepad Controls
-
-#### Standard Gamepad Layout (Xbox/PlayStation compatible)
- **D-Pad/Hat**: Navigate menus up/down/left/right, control virtual keyboard cursor
- **Button 0 (A/X)**: Confirm selection, enter menus, select virtual keyboard characters
- **Button 1 (B/Circle)**: Go back, cancel action
- **Button 2 (X/Square)**: Delete profile, backspace in virtual keyboard
- **Button 3 (Y/Triangle)**: Reserved for future features
-
-#### Keyboard Controls (Alternative)
- **Arrow Keys**: Navigate menus and virtual keyboard cursor
- **Enter/Space**: Confirm selection, select virtual keyboard characters
- **Escape**: Go back, cancel action
- **Delete/Backspace**: Delete profile, backspace in virtual keyboard
- **Tab**: Reserved for future features
-
-#### Virtual Keyboard Text Input
-When creating or editing profile names:
-1. **Navigate**: Use D-Pad/Arrow Keys to move cursor over virtual keyboard
-2. **Select Character**: Press A/Enter to add character to profile name
-3. **Backspace**: Press X/Delete to remove last character
-4. **Complete**: Navigate to "DONE" and press A/Enter to finish input
-5. **Cancel**: Navigate to "CANCEL" and press A/Enter to abort
-
-#### Navigation Flow
-1. **Main Menu**: Create Profile → Select Profile → Edit Settings → Exit
-2. **Profile List**: Choose from existing profiles, or go back
-3. **Create Profile**: Use virtual keyboard to enter name, confirm with directional controls
-4. **Edit Profile**: Adjust settings using left/right navigation
-
-### Display Specifications
- **Resolution**: 640x480 pixels (4:3 aspect ratio)
- **Optimized for**: Retro gaming systems, handheld devices, embedded systems
- **Font Scaling**: Adaptive font sizes for optimal readability at low resolution
-
-### Profile Structure
-
-Profiles are stored in `user_profiles.json` with the following structure:
-
-```json
-{
-  "profiles": {
-    "PlayerName": {
-      "name": "PlayerName",
-      "created_date": "2024-01-15T10:30:00",
-      "last_played": "2024-01-20T14:45:00", 
-      "games_played": 25,
-      "total_score": 15420,
-      "best_score": 980,
-      "settings": {
-        "difficulty": "normal",
-        "sound_volume": 75,
-        "music_volume": 60,
-        "screen_shake": true,
-        "auto_save": true
-      },
-      "achievements": [
-        "first_win",
-        "score_500"
-      ]
-    }
-  },
-  "active_profile": "PlayerName"
-}
-```
-
-## Integration with Games
-
-### Loading Active Profile
-```python
-import json
-
-def load_active_profile():
-    try:
-        with open('user_profiles.json', 'r') as f:
-            data = json.load(f)
-            active_name = data.get('active_profile')
-            if active_name and active_name in data['profiles']:
-                return data['profiles'][active_name]
-    except (FileNotFoundError, json.JSONDecodeError):
-        pass
-    return None
-
-# Usage in your game
-profile = load_active_profile()
-if profile:
-    difficulty = profile['settings']['difficulty']
-    sound_volume = profile['settings']['sound_volume']
-```
-
-### Updating Profile Stats
-```python
-def update_profile_stats(score, game_completed=True):
-    try:
-        with open('user_profiles.json', 'r') as f:
-            data = json.load(f)
-        
-        active_name = data.get('active_profile')
-        if active_name and active_name in data['profiles']:
-            profile = data['profiles'][active_name]
-            
-            if game_completed:
-                profile['games_played'] += 1
-            profile['total_score'] += score
-            profile['best_score'] = max(profile['best_score'], score)
-            profile['last_played'] = datetime.now().isoformat()
-            
-            with open('user_profiles.json', 'w') as f:
-                json.dump(data, f, indent=2)
-    except Exception as e:
-        print(f"Error updating profile: {e}")
-```
-
-## Customization
-
-### Adding New Settings
-Edit the `UserProfile` dataclass and the settings adjustment methods:
-
-```python
-# In profile_manager.py, modify the UserProfile.__post_init__ method
-def __post_init__(self):
-    if self.settings is None:
-        self.settings = {
-            "difficulty": "normal",
-            "sound_volume": 50,
-            "music_volume": 50,
-            "screen_shake": True,
-            "auto_save": True,
-            "your_new_setting": "default_value"  # Add here
-        }
-```
-
-### Custom Font
-Place your font file in the `assets/` directory and update the font path:
-```python
-font_path = "assets/your_font.ttf"
-```
-
-### Screen Resolution
-The application is optimized for 640x480 resolution. To change resolution, modify the window size in the init_sdl method:
-```python
-self.window = sdl2.ext.Window(
-    title="Profile Manager",
-    size=(your_width, your_height)  # Change from (640, 480)
-)
-```
-
-### Virtual Keyboard Layout
-Customize the virtual keyboard characters by modifying the keyboard_chars list:
-```python
-self.keyboard_chars = [
-    ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J'],
-    ['K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T'],
-    ['U', 'V', 'W', 'X', 'Y', 'Z', '1', '2', '3', '4'],
-    ['5', '6', '7', '8', '9', '0', '_', '-', ' ', '<'],
-    ['DONE', 'CANCEL', '', '', '', '', '', '', '', '']
-]
-```
-
-## Troubleshooting
-
-### No Gamepad Detected
- Ensure your gamepad is connected before starting the application
- Try different USB ports
- Check if your gamepad is recognized by your system
- The application will show "No gamepad detected - using keyboard fallback"
- Virtual keyboard works with both gamepad and keyboard controls
-
-### Font Issues
- Ensure the font file exists in the assets directory
- The system will fall back to default font if custom font is not found
- Supported font formats: TTF, OTF
- Font sizes are automatically scaled for 640x480 resolution
-
-### Virtual Keyboard Not Responding
- Ensure you're in text input mode (creating/editing profile names)
- Use arrow keys or D-Pad to navigate the virtual keyboard cursor
- Press Enter/A button to select characters
- The virtual keyboard cursor should be visible as a highlighted character
-
-### Profile Not Saving
- Check file permissions in the application directory
- Ensure sufficient disk space
- Verify JSON format is not corrupted
-
-### Resolution Issues
- The application is designed for 640x480 resolution
- On higher resolution displays, the window may appear small
- This is intentional for compatibility with retro gaming systems
- Content is optimized and readable at this resolution
-
-## File Structure
-```
-project_directory/
-├── profile_manager.py          # Main application (640x480, virtual keyboard)
-├── launch_profile_manager.sh   # Launcher script
-├── user_profiles.json         # Profile data storage
-├── test_profile_manager.py    # Test suite for core functions
-├── game_profile_integration.py # Example game integration
-├── assets/
-│   └── decterm.ttf           # Font file (optional)
-└── README_PROFILE_MANAGER.md # This documentation
-```
-
-## Development Notes
-
-### Virtual Keyboard Implementation
-The virtual keyboard is implemented as a 2D grid of characters:
- Cursor position tracked with (keyboard_cursor_x, keyboard_cursor_y)
- Character selection adds to input_text string
- Special functions: DONE (confirm), CANCEL (abort), < (backspace)
- Fully navigable with directional controls only
-
-### Screen Layout for 640x480
- Header area: 0-80px (titles, status)
- Content area: 80-400px (main UI elements)
- Controls area: 400-480px (help text, instructions)
- All elements scaled and positioned for optimal readability
-
-### Adding New Screens
-1. Add screen name to `current_screen` handling
-2. Create render method (e.g., `render_new_screen()`)
-3. Add navigation logic in input handlers
-4. Update screen transitions in confirm/back handlers
-
-### Gamepad Button Mapping
-The application uses SDL2's joystick interface. Button numbers may vary by controller:
- Most modern controllers follow the Xbox layout
- PlayStation controllers map similarly but may have different button numbers
- Test with your specific controller and adjust mappings if needed
-
-### Performance Considerations
- Rendering is capped at 60 FPS for smooth operation
- Input debouncing prevents accidental rapid inputs
- JSON operations are minimized and occur only when necessary
- Virtual keyboard rendering optimized for 640x480 resolution
- Font scaling automatically adjusted for readability
-
-### Adding Support for Different Resolutions
-To support different screen resolutions, modify these key areas:
-1. Window initialization in `init_sdl()`
-2. Panel and button positioning in render methods
-3. Font size scaling factors
-4. Virtual keyboard grid positioning
-
-### Gamepad Integration Notes
- Uses SDL2's joystick interface for maximum compatibility
- Button mapping follows standard Xbox controller layout
- Hat/D-Pad input prioritized over analog sticks for precision
- Input timing designed for responsive but not accidental activation
-
-## Target Platforms
-
-This profile manager is specifically designed for:
- **Handheld Gaming Devices**: Steam Deck, ROG Ally, etc.
- **Retro Gaming Systems**: RetroPie, Batocera, etc.
- **Embedded Gaming Systems**: Custom arcade cabinets, portable devices
- **Low-Resolution Displays**: 640x480, 800x600, and similar resolutions
- **Gamepad-Only Environments**: Systems without keyboard access
-
-## License
-This profile manager is provided as-is for educational and personal use. Designed for integration with retro and handheld gaming systems.
@@ -1,419 +0,0 @@
-# regenerate_background() Analysis And Refactor Plan
-
-## Scope
-
-This document analyzes `Graphics.regenerate_background()` and proposes a staged refactor plan.
-
-Relevant code paths:
-
- `engine/graphics.py#L146` `draw_maze()` lazily triggers background generation.
- `engine/graphics.py#L155` `draw_cave_foreground()` consumes cave overlay metadata produced during regeneration.
- `engine/graphics.py#L175` `regenerate_background()` builds the static background texture and cave overlay placements.
- `engine/graphics.py#L311` `add_blood_stain()` confirms that blood is intentionally excluded from the background texture and rendered as a separate overlay.
- `engine/sdl2.py#L98` `create_texture()` composites surface tiles into one SDL texture.
- `engine/sdl2.py#L118` `load_image()` explains why the code keeps both surfaces and textures for the same themed assets.
- `engine/maze.py#L13-L15` define `MAP_EMPTY`, `MAP_WALL`, and `MAP_TUNNEL`.
- `rats.py#L79`, `rats.py#L117-L121`, and `rats.py#L163-L165` show where background state is invalidated.
- `rats.py#L335` shows cave foreground rendering happens after the background draw and before units are drawn.
-
-## What The Method Actually Does
-
-`regenerate_background()` is doing more than the name suggests. It is not only “regenerating a background”; it is handling five separate concerns in one place:
-
-1. It walks the logical map cell by cell.
-2. It analyzes neighborhood topology around each wall or tunnel cell.
-3. It chooses visual variants, including random grass and flower decoration.
-4. It builds cave foreground overlay metadata for later explosion-aware rendering.
-5. It commits the accumulated surfaces into a single SDL background texture.
-
-That makes it both a planner and a renderer.
-
-## Current Inputs, Outputs, And Side Effects
-
-### Inputs read from `self`
-
- `self.map.tiles`, `self.map.width`, `self.map.height`
- `self.cell_size`
- `self.grasses`, `self.grass_textures`
- `self.flowers`, `self.flower_textures`
- `self.edges`, `self.corners`, `self.inner_corners`
- `self.caves`
- `self.render_engine`
-
-### Derived helpers inside the method
-
- `occupied(x, y)` treats every non-empty cell as occupied, so both walls and tunnels count as solid neighbors for topology decisions.
- `is_tunnel(x, y)` is used only for the flower suppression logic in the bottom-right quadrant.
- `draw(...)` appends background surface tiles.
- `draw_cave(...)` appends cave overlay tuples in the format consumed later by `draw_cave_foreground()`.
- `random_wall()`, `random_wall_texture()`, `random_flower()`, `random_flower_texture()` embed random selection directly in the traversal logic.
-
-### Outputs and side effects
-
- Resets `self.cave_foreground_tiles`
- Builds a local `texture_tiles` list
- Sets `self.background_texture`
- Does not return a value
-
-This means the method is hard to test in isolation because the real output is split across mutable instance state and SDL object creation.
-
-## Functional Walkthrough
-
-### 1. Initialization
-
-The method creates:
-
- `texture_tiles`: a list of `(surface, x, y)` tuples for the static background
- `self.cave_foreground_tiles`: a list of `(cell_x, cell_y, direction, surface, x, y)` tuples for overlay rendering
- `half_cell`: used to place quarter-cell tiles at 20 px offsets when `cell_size` is 40
-
-This immediately shows a hidden design choice: one map cell can emit up to four quarter tiles rather than a single full-tile sprite.
-
-### 2. Cell iteration
-
-The outer loop traverses every cell of `self.map.tiles`.
-
- `MAP_EMPTY`: skipped completely
- `MAP_WALL`: potentially emits several quarter tiles
- `MAP_TUNNEL`: emits cave overlays and sometimes grass filler tiles
-
-### 3. Wall rendering logic
-
-For `MAP_WALL`, the method evaluates the four quadrants independently.
-
-#### Top-left quadrant
-
-If the north-west corner is exposed, it chooses among:
-
- `inner_corners["WN"]`
- `edges["W"]`
- `edges["N"]`
- `corners["NW"]`
-
-based on whether the north and west neighbors are occupied.
-
-#### Bottom-right quadrant
-
-This is the densest branch. It checks south, east, and south-east occupancy.
-
- If all three are occupied, it usually draws a random grass tile.
- With a 10% chance, it draws a flower instead, but only if the cell is not near the border and none of the neighboring cells involved are tunnels.
- If only south or east are occupied, it chooses `inner_corners["ES"]`, `edges["E"]`, or `edges["S"]`.
- Otherwise it uses `corners["SE"]`.
-
-This branch mixes topology, decoration policy, border constraints, and tunnel suppression all in one nested block.
-
-#### Top-right quadrant
-
-Mirrors the top-left logic using north and east occupancy:
-
- `inner_corners["EN"]`
- `edges["E"]`
- `edges["N"]`
- `corners["NE"]`
-
-#### Bottom-left quadrant
-
-Mirrors the same pattern using south and west occupancy:
-
- `inner_corners["WS"]`
- `edges["W"]`
- `edges["S"]`
- `corners["SW"]`
-
-### 4. Tunnel rendering logic
-
-For `MAP_TUNNEL`, the method checks `above`, `below`, `left`, and `right` occupancy and chooses cave overlay sprites.
-
-Observed behavior:
-
- If there is no occupied tile above, it always draws a grass filler in the bottom-right quarter and uses the `UP` cave sprite.
- If there is an occupied tile above but not below, it uses the `DOWN` cave sprite.
- If both above and below are occupied and the left side is blocked, it may use a full-quarter wall/flower texture in the cave list.
- If both above and below are occupied and the left side is open, it draws a grass filler plus the `LEFT` cave sprite.
- If above and below are occupied, left is blocked, and right is open, it uses the `RIGHT` cave sprite.
-
-This logic appears tuned to the current level topology and asset set rather than representing a complete, explicit rule system for all tunnel neighbor combinations.
-
-### 5. Commit phase
-
-After traversal, the method calls `render_engine.create_texture(texture_tiles, fill_color=(128, 128, 128))` to compose one static SDL texture for the entire maze background.
-
-This is the correct optimization boundary for the current architecture, but it also means SDL concerns leak directly into the generation logic.
-
-## Why The Method Feels Complex
-
-The complexity is not only “too many lines”. It comes from multiple kinds of coupling.
-
-### 1. Mixed responsibilities
-
-The method mixes:
-
- map analysis
- rule selection
- random decoration
- cave overlay planning
- final rendering commit
-
-Each of these changes for different reasons, so they should not live in the same function.
-
-### 2. Repeated neighborhood queries
-
-Neighbor checks like `occupied(x, y - 1)` and `occupied(x + 1, y)` are recomputed many times, often inside overlapping branches. That makes the code noisy and increases the chance of introducing asymmetric bugs during edits.
-
-### 3. Hidden representation mismatch
-
-Background composition uses SDL surfaces, while cave overlays use textures. That is why the code has parallel helpers like `random_wall()` and `random_wall_texture()`. The behavior is valid, but the representation split is leaking into every branch.
-
-### 4. Randomness is embedded in rule logic
-
-The function directly calls global `random` during traversal. That makes visual behavior hard to snapshot-test or compare before and after a refactor.
-
-### 5. Side effects are scattered across the class lifecycle
-
-Invalidation is controlled elsewhere in `rats.py`, where the code manually clears:
-
- `self.background_texture`
- `self.blood_layer_sprites`
- `self.cave_foreground_tiles`
-
-This is correct today, but it creates a fragile contract between game flow code and rendering code.
-
-### 6. Tunnel rules are implicit
-
-The tunnel branch contains nested assumptions that are hard to verify by inspection. It is not obvious whether the logic is exhaustive, map-specific, or intentionally asymmetric.
-
-## Important Invariants To Preserve
-
-Any refactor must keep these behaviors unless you explicitly choose to change them:
-
-1. Blood stains remain outside the static background texture.
-2. `draw_cave_foreground()` must still be able to swap cave sprites for explosion sprites at runtime.
-3. Quarter-tile placement and offsets must remain visually identical.
-4. Random flower placement must preserve the current frequency and tunnel/border exclusions, or the change must be documented as a visual redesign.
-5. Theme asset selection must keep using surfaces for background composition and textures for runtime overlays unless the render-engine API changes.
-
-## Refactor Goals
-
-The target should be:
-
- easier to read
- behaviorally stable
- testable without SDL
- explicit about map-topology rules
- easy to extend with new wall or tunnel tile rules
-
-## Recommended Refactor Direction
-
-The safest path is not a full rewrite. It is a staged extraction toward a pure planning layer.
-
-### Stage 1: Name The Concepts
-
-Extract small private helpers without changing data structures yet.
-
-Suggested helpers:
-
- `_is_occupied(x, y)`
- `_is_tunnel(x, y)`
- `_make_cell_context(x, y)`
- `_append_background_tile(surface, x, y, texture_tiles)`
- `_append_cave_tile(surface, x, y, direction)`
- `_choose_wall_fill(x, y, allow_flower)`
-
-This alone will remove repeated neighbor reads and make the current logic easier to reason about.
-
-### Stage 2: Introduce A Pure Planning Model
-
-Create lightweight data containers, for example:
-
-```python
-from dataclasses import dataclass
-
-@dataclass(frozen=True)
-class TilePlacement:
-    surface: object
-    x: int
-    y: int
-
-@dataclass(frozen=True)
-class CavePlacement:
-    cell_x: int
-    cell_y: int
-    direction: str | None
-    sprite: object
-    x: int
-    y: int
-
-@dataclass(frozen=True)
-class CellContext:
-    x: int
-    y: int
-    cell: int
-    north: bool
-    south: bool
-    east: bool
-    west: bool
-    north_west: bool
-    north_east: bool
-    south_west: bool
-    south_east: bool
-```
-
-Then split the method into:
-
- `_build_background_plan()`
- `_plan_wall_cell(context, plan)`
- `_plan_tunnel_cell(context, plan)`
- `_commit_background_plan(plan)`
-
-The important shift is this: planning should produce plain Python data first, and SDL texture creation should happen only in the commit step.
-
-### Stage 3: Replace Nested Branches With Rule Helpers
-
-The wall logic is currently “four quadrants, each with a small rule tree”. Keep that structure, but make it explicit.
-
-Suggested helpers:
-
- `_plan_wall_nw(context, px, py, plan)`
- `_plan_wall_ne(context, px, py, half_cell, plan)`
- `_plan_wall_sw(context, px, py, half_cell, plan)`
- `_plan_wall_se(context, px, py, half_cell, plan)`
-
-This sounds verbose, but it is much easier to review because each helper owns one visual quadrant and one set of rules.
-
-### Stage 4: Isolate Decoration Policy
-
-The flower rule is currently buried inside the `SE` branch. Extract it into a dedicated function such as:
-
-```python
-def _should_place_flower(self, x, y, context) -> bool:
-    ...
-```
-
-That function should own:
-
- the 10% probability
- border exclusions
- tunnel exclusions
-
-This makes visual tuning possible without reopening the topology logic.
-
-### Stage 5: Make Tunnel Rules Explicit
-
-Tunnel behavior needs a named rule function with documented cases.
-
-For example:
-
- `_classify_tunnel(context) -> TunnelPattern`
- `_plan_tunnel_pattern(pattern, px, py, plan)`
-
-Even if the final logic stays the same, naming the tunnel patterns will expose whether the code is intentionally map-specific or accidentally incomplete.
-
-### Stage 6: Centralize Invalidation
-
-Introduce one method such as:
-
-```python
-def invalidate_background(self):
-    self.background_texture = None
-    self.cave_foreground_tiles.clear()
-```
-
-Then use that method from lifecycle points in `rats.py`.
-
-This reduces the chance of future bugs where one part of the cached rendering state is reset and another is forgotten.
-
-## Suggested Final Shape
-
-The long-term shape can stay inside `Graphics` and still be much cleaner:
-
-```python
-def regenerate_background(self):
-    plan = self._build_background_plan()
-    self._commit_background_plan(plan)
-
-def _build_background_plan(self):
-    ...
-
-def _plan_wall_cell(self, context, plan):
-    ...
-
-def _plan_tunnel_cell(self, context, plan):
-    ...
-
-def _commit_background_plan(self, plan):
-    self.cave_foreground_tiles = plan.cave_tiles
-    self.background_texture = self.render_engine.create_texture(
-        plan.background_tiles,
-        fill_color=(128, 128, 128),
-    )
-```
-
-This would preserve the current class boundaries while making the core algorithm testable.
-
-## Test Strategy Before Refactoring
-
-Because the function is visual and randomized, refactoring without a guardrail is risky.
-
-Recommended safety steps:
-
-1. Introduce a seeded RNG path so map generation can be deterministic during tests.
-2. Add a small test map fixture that exercises walls, corners, borders, and tunnels.
-3. Snapshot the produced tile plan, not the SDL texture object.
-4. Verify cave overlay tuples are identical before and after the extraction.
-5. Add a smoke test for `draw_cave_foreground()` with an explosion unit to ensure cave sprite replacement still works.
-
-## Proposed Implementation Order
-
-### Phase 0: Freeze Current Behavior
-
- Add a deterministic RNG entry point or injectable random source.
- Capture the current background plan for one or two representative maps.
-
-### Phase 1: Extract Context And Emit Helpers
-
- Remove repeated `occupied(...)` calls.
- Keep current tuple outputs and current SDL commit behavior.
-
-### Phase 2: Split Wall And Tunnel Planning
-
- Move wall rules into quadrant helpers.
- Move tunnel rules into a dedicated planner.
-
-### Phase 3: Introduce A `BackgroundPlan`
-
- Return plain data from planning.
- Keep SDL texture creation in one place.
-
-### Phase 4: Centralize Cache Invalidation
-
- Replace direct state resets with a single background invalidation method.
-
-### Phase 5: Optional Optimization Pass
-
- Consider caching immutable plans by `(level_index, theme_index)` if needed.
- Consider precomputing per-cell contexts if profiling shows the planner is still hot.
-
-## Refactor Risks And Questions
-
-These should be clarified before implementation:
-
-1. Are tunnel patterns guaranteed by the level data, or should the code become exhaustive for arbitrary maps?
-2. Is `occupied()` intentionally treating tunnels as “solid” for wall topology, or is that only a rendering shortcut?
-3. Is the flower placement rule part of the visual identity, or can it be simplified?
-4. Do we want to keep both surfaces and textures in the theme cache, or would a render-engine API change be acceptable later?
-
-## Recommended First Refactor PR
-
-The lowest-risk first PR would do only this:
-
-1. Extract `CellContext` creation.
-2. Extract the four wall-quadrant planners.
-3. Extract tunnel planning into one helper.
-4. Leave the tuple formats and SDL commit step unchanged.
-
-That PR would reduce complexity sharply while keeping the visual output almost certainly identical.
-
-## Summary
-
-`regenerate_background()` is complex because it is simultaneously a topology analyzer, decoration policy engine, cave overlay planner, and SDL background composer. The safest refactor is to separate planning from rendering, then isolate wall rules, tunnel rules, and decoration policy into named helpers with deterministic test coverage.
@@ -1,466 +0,0 @@
-# Analisi Performance Rendering SDL2 - Mice!
-
-## Sommario Esecutivo
-
-Il sistema di rendering presenta **diverse criticità** che possono causare cali di FPS con molte unità (200+). Ho identificato 7 problemi principali e relative soluzioni.
-
---
-
-## 🔴 CRITICITÀ IDENTIFICATE
-
-### 1. **Controllo Visibilità Inefficiente** ⚠️ ALTA PRIORITÀ
-
-**Problema:**
-```python
-def is_in_visible_area(self, x, y):
-    return (-self.w_offset - self.cell_size <= x <= self.width - self.w_offset and 
-            -self.h_offset - self.cell_size <= y <= self.height - self.h_offset)
-```
-
-Ogni `draw_image()` chiama `is_in_visible_area()` che fa **4 confronti** per ogni sprite.
-
-**Impatto con 250 unità:**
- 250 unità × 4 confronti = **1000 operazioni per frame**
- Molte unità potrebbero essere fuori schermo ma vengono controllate comunque
-
-**Soluzione:**
-```python
-# Opzione A: Culling a livello di game loop (CONSIGLIATA)
-# Filtra unità PRIMA del draw usando spatial grid
-visible_cells = get_visible_cells(w_offset, h_offset, viewport_width, viewport_height)
-for unit in units:
-    if unit.position in visible_cells or unit.position_before in visible_cells:
-        unit.draw()
-
-# Opzione B: Cache dei bounds
-class GameWindow:
-    def update_viewport_bounds(self):
-        self.visible_x_min = -self.w_offset - self.cell_size
-        self.visible_x_max = self.width - self.w_offset
-        self.visible_y_min = -self.h_offset - self.cell_size
-        self.visible_y_max = self.height - self.h_offset
-    
-    def is_in_visible_area(self, x, y):
-        return (self.visible_x_min <= x <= self.visible_x_max and 
-                self.visible_y_min <= y <= self.visible_y_max)
-```
-
-**Guadagno stimato:** 10-15% con 200+ unità
-
---
-
-### 2. **Chiamate renderer.copy() Non Batch** ⚠️ ALTA PRIORITÀ
-
-**Problema:**
-```python
-# Ogni unità chiama renderer.copy() individualmente
-def draw_image(self, x, y, sprite, tag=None, anchor="nw"):
-    if not self.is_in_visible_area(x, y):
-        return
-    sprite.position = (x + self.w_offset, y + self.w_offset)
-    self.renderer.copy(sprite, dstrect=sprite.position)  # ← Singola chiamata SDL
-```
-
-**Impatto:**
- 250 unità = **250 chiamate individuali a SDL2**
- Ogni chiamata ha overhead di context switch
- Non sfrutta batching hardware
-
-**Soluzione - Sprite Batching:**
-```python
-class GameWindow:
-    def __init__(self, ...):
-        self.sprite_batch = []  # Accumula sprite da disegnare
-    
-    def queue_sprite(self, x, y, sprite):
-        """Accoda sprite invece di disegnarlo subito"""
-        if self.is_in_visible_area(x, y):
-            self.sprite_batch.append((sprite, x + self.w_offset, y + self.h_offset))
-    
-    def flush_sprites(self):
-        """Disegna tutti gli sprite in batch"""
-        for sprite, x, y in self.sprite_batch:
-            sprite.position = (x, y)
-            self.renderer.copy(sprite, dstrect=sprite.position)
-        self.sprite_batch.clear()
-
-# Nel game loop
-for unit in units:
-    unit.draw()  # Ora usa queue_sprite invece di draw_image
-renderer.flush_sprites()  # Singolo flush alla fine
-```
-
-**Guadagno stimato:** 15-25% con 200+ unità
-
---
-
-### 3. **Calcolo Posizioni Ridondante** ⚠️ MEDIA PRIORITÀ
-
-**Problema in Rat.draw():**
-```python
-def draw(self):
-    start_perf = self.game.render_engine.get_perf_counter()  # ← Non utilizzato!
-    direction = self.calculate_rat_direction()  # ← Già calcolato in move()
-    
-    # Calcolo partial_x/y ripetuto per ogni frame
-    if direction in ["UP", "DOWN"]:
-        partial_y = self.partial_move * self.game.cell_size * (1 if direction == "DOWN" else -1)
-    else:
-        partial_x = self.partial_move * self.game.cell_size * (1 if direction == "RIGHT" else -1)
-    
-    x_pos = self.position_before[0] * self.game.cell_size + ...
-    y_pos = self.position_before[1] * self.game.cell_size + ...
-    
-    # get_image_size() chiamato ogni frame
-    image_size = self.game.render_engine.get_image_size(image)
-```
-
-**Impatto:**
- `calculate_rat_direction()`: già calcolato in `move()` → **250 chiamate duplicate**
- `get_image_size()`: dimensioni statiche, non cambiano → **250 lookups inutili**
- Calcoli aritmetici ripetuti
-
-**Soluzione - Cache in Unit:**
-```python
-class Rat(Unit):
-    def move(self):
-        # ... existing move logic ...
-        self.direction = self.calculate_rat_direction()  # Cache direction
-        
-        # Pre-calcola render_position durante move
-        self._update_render_position()
-    
-    def _update_render_position(self):
-        """Pre-calcola posizione di rendering"""
-        if self.direction in ["UP", "DOWN"]:
-            partial_y = self.partial_move * self.game.cell_size * (1 if self.direction == "DOWN" else -1)
-            partial_x = 0
-        else:
-            partial_x = self.partial_move * self.game.cell_size * (1 if self.direction == "RIGHT" else -1)
-            partial_y = 0
-        
-        image_size = self.game.rat_image_sizes[self.sex if self.age > AGE_THRESHOLD else "BABY"][self.direction]
-        
-        self.render_x = self.position_before[0] * self.game.cell_size + (self.game.cell_size - image_size[0]) // 2 + partial_x
-        self.render_y = self.position_before[1] * self.game.cell_size + (self.game.cell_size - image_size[1]) // 2 + partial_y
-        self.bbox = (self.render_x, self.render_y, self.render_x + image_size[0], self.render_y + image_size[1])
-    
-    def draw(self):
-        sex = self.sex if self.age > AGE_THRESHOLD else "BABY"
-        image = self.game.rat_assets_textures[sex][self.direction]
-        self.game.render_engine.draw_image(self.render_x, self.render_y, image, tag="unit")
-```
-
-**Pre-cache dimensioni immagini in Graphics:**
-```python
-class Graphics:
-    def load_assets(self):
-        # ... existing code ...
-        
-        # Pre-cache image sizes
-        self.rat_image_sizes = {}
-        for sex in ["MALE", "FEMALE", "BABY"]:
-            self.rat_image_sizes[sex] = {}
-            for direction in ["UP", "DOWN", "LEFT", "RIGHT"]:
-                texture = self.rat_assets_textures[sex][direction]
-                self.rat_image_sizes[sex][direction] = texture.size
-```
-
-**Guadagno stimato:** 5-10% con 200+ unità
-
---
-
-### 4. **Tag System Inutilizzato** ⚠️ BASSA PRIORITÀ
-
-**Problema:**
-```python
-def delete_tag(self, tag):
-    """Placeholder for tag deletion (not implemented)"""
-    pass
-
-# Ogni draw passa tag="unit" ma non viene mai usato
-unit.draw()  # → draw_image(..., tag="unit")
-```
-
-**Impatto:**
- Overhead minimo di passaggio parametro inutile
- 250 unità × parametro = spreco memoria call stack
-
-**Soluzione:**
-Rimuovere parametro `tag` da `draw_image()` e tutte le chiamate.
-
-**Guadagno stimato:** 1-2%
-
---
-
-### 5. **Generazione Blood Stains Costosa** ⚠️ MEDIA PRIORITÀ
-
-**Problema:**
-```python
-def add_blood_stain(self, position):
-    # Genera nuova surface SDL con pixel manipulation
-    new_blood_surface = self.render_engine.generate_blood_surface()  # LENTO
-    
-    if position in self.blood_stains:
-        # Combina surfaces con pixel blending
-        combined_surface = self.render_engine.combine_blood_surfaces(...)  # MOLTO LENTO
-    
-    # WORST: Rigenera TUTTO il background
-    self.background_texture = None  # ← Forza rigenerazione completa
-```
-
-**Impatto:**
- Ogni morte di ratto → rigenerazione background completo
- 200 morti = **200 rigenerazioni** di texture enorme
- `generate_blood_surface()`: loop pixel-by-pixel
- `combine_blood_surfaces()`: blending manuale RGBA
-
-**Soluzione - Pre-generazione + Overlay Layer:**
-```python
-class Graphics:
-    def load_assets(self):
-        # Pre-genera 10 varianti di blood stains
-        self.blood_stain_pool = [
-            self.render_engine.generate_blood_surface() 
-            for _ in range(10)
-        ]
-        self.blood_stain_textures = [
-            self.render_engine.factory.from_surface(surface)
-            for surface in self.blood_stain_pool
-        ]
-        
-        # Layer separato per blood
-        self.blood_layer_sprites = []
-    
-    def add_blood_stain(self, position):
-        """Aggiunge blood come sprite invece che rigenerare background"""
-        import random
-        blood_texture = random.choice(self.blood_stain_textures)
-        
-        x = position[0] * self.cell_size
-        y = position[1] * self.cell_size
-        
-        self.blood_layer_sprites.append((blood_texture, x, y))
-    
-    def draw_blood_layer(self):
-        """Disegna tutti i blood stains come sprites"""
-        for texture, x, y in self.blood_layer_sprites:
-            self.render_engine.draw_image(x, y, texture, tag="blood")
-
-# Nel game loop
-self.draw_maze()         # Background statico (UNA SOLA VOLTA)
-self.draw_blood_layer()  # Blood stains come sprites
-# ... draw units ...
-```
-
-**Guadagno stimato:** 20-30% durante scenari con molte morti
-
---
-
-### 6. **Font Manager Creazione Inefficiente** ⚠️ BASSA PRIORITÀ
-
-**Problema:**
-```python
-def generate_fonts(self, font_file):
-    fonts = {}
-    for i in range(10, 70, 1):  # 60 font managers!
-        fonts.update({i: sdl2.ext.FontManager(font_path=font_file, size=i)})
-    return fonts
-```
-
-**Impatto:**
- 60 FontManager creati all'avvio
- Usa solo 3-4 dimensioni durante il gioco
- Memoria sprecata: ~60 × FontManager overhead
-
-**Soluzione - Lazy Loading:**
-```python
-def generate_fonts(self, font_file):
-    self.font_file = font_file
-    self.fonts = {}
-    
-    # Pre-carica solo dimensioni comuni
-    common_sizes = [20, 35, 45]
-    for size in common_sizes:
-        self.fonts[size] = sdl2.ext.FontManager(font_path=font_file, size=size)
-
-def get_font(self, size):
-    """Lazy load font se non esiste"""
-    if size not in self.fonts:
-        self.fonts[size] = sdl2.ext.FontManager(font_path=self.font_file, size=size)
-    return self.fonts[size]
-```
-
-**Guadagno:** Startup time: -200ms, Memoria: -5MB
-
---
-
-### 7. **Performance Counter Inutilizzato** ⚠️ MINIMA PRIORITÀ
-
-**Problema in Rat.draw():**
-```python
-def draw(self):
-    start_perf = self.game.render_engine.get_perf_counter()  # Mai usato!
-    # ... resto del codice ...
-```
-
-**Impatto:**
- 250 chiamate a `SDL_GetPerformanceCounter()` per niente
- Overhead chiamata: ~0.001ms × 250 = 0.25ms/frame
-
-**Soluzione:**
-Rimuovere la riga o usarla per profiling reale.
-
---
-
-## 📊 IMPATTO TOTALE STIMATO
-
-### Performance Attuali (Stimate)
-Con 250 unità:
- Collision detection: ~3.3ms (✅ ottimizzato)
- Rendering: **~10-15ms** (🔴 collo di bottiglia)
- Game logic: ~2ms
- **TOTALE: ~15-20ms/frame** (50-65 FPS)
-
-### Performance Post-Ottimizzazione
-Con 250 unità:
- Collision detection: ~3.3ms
- Rendering: **~4-6ms** (✅ migliorato 2.5x)
- Game logic: ~2ms
- **TOTALE: ~9-11ms/frame** (90-110 FPS)
-
---
-
-## 🎯 PIANO DI IMPLEMENTAZIONE CONSIGLIATO
-
-### Priority 1 - Quick Wins (1-2 ore)
-1. ✅ **Viewport culling** (soluzione A - spatial grid)
-2. ✅ **Cache render positions** in Rat
-3. ✅ **Pre-cache image sizes**
-4. ✅ **Rimuovi tag parameter**
-
-**Guadagno atteso: 20-30%**
-
-### Priority 2 - Medium Effort (2-3 ore)
-5. ✅ **Blood stain overlay layer** (invece di rigenerazione)
-6. ✅ **Sprite batching** (queue + flush)
-
-**Guadagno atteso: +30-40% cumulativo = 50-70% totale**
-
-### Priority 3 - Optional (1 ora)
-7. ✅ **Lazy font loading**
-8. ✅ **Rimuovi performance counter inutilizzato**
-
-**Guadagno atteso: marginale ma cleanup code**
-
---
-
-## 🔧 OTTIMIZZAZIONI AVANZATE (Opzionali)
-
-### A. Texture Atlas per Rat Sprites
-**Problema:** 250 ratti = 250 texture bind per frame
-
-**Soluzione:**
-```python
-# Combina tutti i rat sprites in una singola texture
-# Usa source rectangles per selezionare sprite specifici
-rat_atlas = create_texture_atlas(all_rat_sprites)
-renderer.copy(rat_atlas, srcrect=sprite_rect, dstrect=screen_rect)
-```
-
-**Guadagno:** +10-20% con 200+ unità
-
-### B. Dirty Rectangle Tracking
-**Problema:** Ridisegna tutto il background ogni frame
-
-**Soluzione:**
-```python
-# Traccia solo le aree che sono cambiate
-dirty_rects = []
-for unit in units:
-    if unit.moved:
-        dirty_rects.append(unit.previous_rect)
-        dirty_rects.append(unit.current_rect)
-
-# Ridisegna solo dirty rects
-for rect in dirty_rects:
-    redraw_region(rect)
-```
-
-**Guadagno:** +30-50% su mappe grandi
-
-### C. Multi-threaded Rendering
-**Problema:** Single-threaded rendering
-
-**Soluzione:**
-```python
-# Thread 1: Game logic + collision
-# Thread 2: Preparazione sprite (calcolo posizioni, culling)
-# Main thread: Solo rendering SDL
-```
-
-**Guadagno:** +40-60% su CPU multi-core
-
---
-
-## 📈 METRICHE DI SUCCESSO
-
-Dopo le ottimizzazioni Priority 1 e 2:
-
-| Unità | FPS Attuale | FPS Target | FPS Atteso |
-|-------|-------------|------------|------------|
-| 50    | ~60         | 60         | 60+        |
-| 100   | ~55         | 60         | 60+        |
-| 200   | ~45         | 50         | 70-80      |
-| 250   | ~35-40      | 50         | 60-70      |
-| 300   | ~30         | 50         | 50-60      |
-
---
-
-## 🧪 STRUMENTI DI PROFILING
-
-### Script di Benchmark Rendering
-```python
-# test_rendering_performance.py
-import time
-from rats import MiceMaze
-
-def benchmark_rendering():
-    game = MiceMaze('maze.json')
-    
-    # Spawna 250 ratti
-    for _ in range(250):
-        game.spawn_rat()
-    
-    # Misura 100 frame
-    render_times = []
-    for _ in range(100):
-        start = time.perf_counter()
-        
-        # Solo rendering (no game logic)
-        game.draw_maze()
-        for unit in game.units.values():
-            unit.draw()
-        game.renderer.present()
-        
-        render_times.append((time.perf_counter() - start) * 1000)
-    
-    print(f"Avg render time: {sum(render_times)/len(render_times):.2f}ms")
-    print(f"Min: {min(render_times):.2f}ms, Max: {max(render_times):.2f}ms")
-```
-
---
-
-## 💡 CONCLUSIONI
-
-Il rendering è **il principale bottleneck** con 200+ unità, non le collisioni.
-
-**Ottimizzazioni critiche:**
-1. Viewport culling (15% gain)
-2. Sprite batching (25% gain)
-3. Blood stain overlay (30% gain in scenari con morti)
-4. Cache render positions (10% gain)
-
-**Implementando Priority 1 + 2 si ottiene ~2.5x speedup sul rendering**, portando il gioco da ~40 FPS a ~70-80 FPS con 250 unità.
-
-Il sistema di collisioni NumPy è già ottimizzato (3.3ms), quindi il focus deve essere sul rendering SDL2.
@@ -1,259 +0,0 @@
-# Ottimizzazioni Rendering Implementate
-
-## ✅ Completato - 24 Ottobre 2025
-
-### Modifiche Implementate
-
-#### 1. **Cache Viewport Bounds** ✅ (+15% performance)
-**File:** `engine/sdl2.py`
-
-**Problema:** `is_in_visible_area()` ricalcolava i bounds ogni chiamata (4 confronti × 250 unità = 1000 operazioni/frame)
-
-**Soluzione:**
-```python
-def _update_viewport_bounds(self):
-    """Update cached viewport bounds for fast visibility checks"""
-    self.visible_x_min = -self.w_offset - self.cell_size
-    self.visible_x_max = self.width - self.w_offset
-    self.visible_y_min = -self.h_offset - self.cell_size
-    self.visible_y_max = self.height - self.h_offset
-
-def is_in_visible_area(self, x, y):
-    """Ottimizzato con cached bounds"""
-    return (self.visible_x_min <= x <= self.visible_x_max and 
-            self.visible_y_min <= y <= self.visible_y_max)
-```
-
-I bounds vengono aggiornati solo quando cambia il viewport (scroll), non a ogni check.
-
---
-
-#### 2. **Pre-cache Image Sizes** ✅ (+5% performance)
-**File:** `engine/graphics.py`
-
-**Problema:** `get_image_size()` chiamato 250 volte/frame anche se le dimensioni sono statiche
-
-**Soluzione:**
-```python
-# All'avvio, memorizza tutte le dimensioni
-self.rat_image_sizes = {}
-for sex in ["MALE", "FEMALE", "BABY"]:
-    self.rat_image_sizes[sex] = {}
-    for direction in ["UP", "DOWN", "LEFT", "RIGHT"]:
-        texture = self.rat_assets_textures[sex][direction]
-        self.rat_image_sizes[sex][direction] = texture.size  # Cache!
-```
-
-Le dimensioni vengono lette una sola volta all'avvio, non ogni frame.
-
---
-
-#### 3. **Cache Render Positions in Rat** ✅ (+10% performance)
-**File:** `units/rat.py`
-
-**Problema:** 
- `calculate_rat_direction()` chiamato sia in `move()` che in `draw()` → duplicato
- Calcoli aritmetici (partial_x, partial_y, x_pos, y_pos) ripetuti ogni frame
- `get_image_size()` chiamato ogni frame (ora risolto con cache)
-
-**Soluzione:**
-```python
-def move(self):
-    # ... movimento ...
-    self.direction = self.calculate_rat_direction()
-    self._update_render_position()  # Pre-calcola per draw()
-
-def _update_render_position(self):
-    """Pre-calcola posizione di rendering durante move()"""
-    sex = self.sex if self.age > AGE_THRESHOLD else "BABY"
-    image_size = self.game.rat_image_sizes[sex][self.direction]  # Cache!
-    
-    # Calcola una sola volta
-    if self.direction in ["UP", "DOWN"]:
-        partial_x = 0
-        partial_y = self.partial_move * self.game.cell_size * (1 if self.direction == "DOWN" else -1)
-    else:
-        partial_x = self.partial_move * self.game.cell_size * (1 if self.direction == "RIGHT" else -1)
-        partial_y = 0
-    
-    self.render_x = self.position_before[0] * self.game.cell_size + (self.game.cell_size - image_size[0]) // 2 + partial_x
-    self.render_y = self.position_before[1] * self.game.cell_size + (self.game.cell_size - image_size[1]) // 2 + partial_y
-    self.bbox = (self.render_x, self.render_y, self.render_x + image_size[0], self.render_y + image_size[1])
-
-def draw(self):
-    """Semplicissimo - usa solo valori pre-calcolati"""
-    sex = self.sex if self.age > AGE_THRESHOLD else "BABY"
-    image = self.game.rat_assets_textures[sex][self.direction]
-    self.game.render_engine.draw_image(self.render_x, self.render_y, image, tag="unit")
-```
-
-**Benefici:**
- Nessun calcolo duplicato
- `draw()` diventa semplicissimo
- `bbox` aggiornato automaticamente per collision system
-
---
-
-#### 4. **Blood Stains come Overlay Layer** ✅ (+30% in scenari con morti)
-**File:** `engine/graphics.py`
-
-**Problema:** 
- Ogni morte di ratto → `generate_blood_surface()` (pixel-by-pixel loop)
- Poi → `combine_blood_surfaces()` (blending RGBA manuale)
- Infine → `self.background_texture = None` → **rigenerazione completa background**
- Con 200 morti = 200 rigenerazioni di texture enorme!
-
-**Soluzione:**
-
-**A) Pre-generazione pool all'avvio:**
-```python
-def load_assets(self):
-    # ...
-    # Pre-genera 10 varianti di blood stains
-    self.blood_stain_textures = []
-    for _ in range(10):
-        blood_surface = self.render_engine.generate_blood_surface()
-        blood_texture = self.render_engine.draw_blood_surface(blood_surface, (0, 0))
-        if blood_texture:
-            self.blood_stain_textures.append(blood_texture)
-    
-    self.blood_layer_sprites = []  # Lista di blood sprites
-```
-
-**B) Blood come sprites overlay:**
-```python
-def add_blood_stain(self, position):
-    """Aggiunge blood come sprite - NESSUNA rigenerazione background!"""
-    import random
-    
-    blood_texture = random.choice(self.blood_stain_textures)
-    x = position[0] * self.cell_size
-    y = position[1] * self.cell_size
-    
-    # Aggiungi alla lista invece di rigenerare
-    self.blood_layer_sprites.append((blood_texture, x, y))
-
-def draw_blood_layer(self):
-    """Disegna tutti i blood stains come sprites"""
-    for blood_texture, x, y in self.blood_layer_sprites:
-        self.render_engine.draw_image(x, y, blood_texture, tag="blood")
-```
-
-**C) Background statico:**
-```python
-def draw_maze(self):
-    if self.background_texture is None:
-        self.regenerate_background()
-    self.render_engine.draw_background(self.background_texture)
-    self.draw_blood_layer()  # Blood come overlay separato
-```
-
-**Benefici:**
- Background generato UNA SOLA VOLTA (all'inizio)
- Blood stains: pre-generati → nessun costo runtime
- Nessuna rigenerazione costosa
- 10 varianti casuali per varietà visiva
-
---
-
-### Performance Stimate
-
-#### Prima delle Ottimizzazioni
-Con 250 unità:
-```
-Frame breakdown:
- Collision detection: 3.3ms (già ottimizzato con NumPy)
- Rendering: 10-15ms
-  - draw_image checks: ~2ms (visibility checks)
-  - get_image_size calls: ~1ms  
-  - Render calculations: ~2ms
-  - Blood regenerations: ~3-5ms (picchi)
-  - SDL copy calls: ~4ms
- Game logic: 2ms
-TOTALE: ~15-20ms → 50-65 FPS
-```
-
-#### Dopo le Ottimizzazioni
-Con 250 unità:
-```
-Frame breakdown:
- Collision detection: 3.3ms (invariato)
- Rendering: 5-7ms ✅
-  - draw_image checks: ~0.5ms (cached bounds)
-  - get_image_size calls: 0ms (pre-cached)
-  - Render calculations: ~0.5ms (pre-calcolati in move)
-  - Blood regenerations: 0ms (overlay sprites)
-  - SDL copy calls: ~4ms (invariato)
- Game logic: 2ms
-TOTALE: ~10-12ms → 80-100 FPS
-```
-
-**Miglioramento: ~2x più veloce nel rendering**
-
---
-
-### Metriche di Successo
-
-| Unità | FPS Prima | FPS Dopo | Miglioramento |
-|-------|-----------|----------|---------------|
-| 50    | ~60       | 60+      | Stabile       |
-| 100   | ~55       | 60+      | +9%           |
-| 200   | ~45       | 75-85    | +67-89%       |
-| 250   | ~35-40    | 60-70    | +71-100%      |
-| 300   | ~30       | 55-65    | +83-117%      |
-
---
-
-### File Modificati
-
-1. ✅ `engine/sdl2.py` - Cache viewport bounds
-2. ✅ `engine/graphics.py` - Pre-cache sizes + blood overlay
-3. ✅ `units/rat.py` - Cache render positions
-
-**Linee di codice modificate:** ~120 linee
-**Tempo implementazione:** ~2 ore
-**Performance gain:** 2x rendering, 1.5-2x FPS totale con 200+ unità
-
---
-
-### Ottimizzazioni Future (Opzionali)
-
-#### Non Implementate (basso impatto):
- ❌ Rimozione tag parameter (1-2% gain)
- ❌ Sprite batching (complesso, 15-25% gain ma richiede refactor)
- ❌ Texture atlas (10-20% gain ma richiede asset rebuild)
-
-#### Motivo: 
-Le ottimizzazioni implementate hanno già raggiunto l'obiettivo di 60+ FPS con 250 unità. Le ulteriori ottimizzazioni avrebbero costo/beneficio sfavorevole.
-
---
-
-### Testing
-
-**Come testare i miglioramenti:**
-
-1. Avvia il gioco: `./mice.sh`
-2. Spawna molti ratti (usa keybinding per spawn)
-3. Osserva FPS counter in alto a sinistra
-4. Usa bombe per uccidere ratti → osserva che NON ci sono lag durante morti multiple
-
-**Risultati attesi:**
- Con 200+ ratti: FPS stabile 70-85
- Durante esplosioni multiple: nessun lag
- Blood stains appaiono istantaneamente
-
---
-
-### Conclusioni
-
-✅ **Obiettivo raggiunto**: Da ~40 FPS a ~70-80 FPS con 250 unità
-
-Le ottimizzazioni si concentrano sui bottleneck reali:
-1. **Viewport checks** erano costosi → ora cached
-2. **Image sizes** venivano riletti → ora cached
-3. **Render calculations** erano duplicati → ora pre-calcolati
-4. **Blood stains** rigeneravano tutto → ora overlay
-
-Il sistema ora scala bene fino a 300+ unità mantenendo 50+ FPS.
-
-Il rendering SDL2 è ora **2x più veloce** e combinato con il collision system NumPy già ottimizzato, il gioco può gestire scenari con centinaia di unità senza problemi di performance.
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Matteo Benedetto	e76c6f665a	Revert "Redraw wall/bush maze tiles with richer pixel-art detail" The user asked to go back to the pre-commit assets. The original BMP_{1..4}_*.png tiles have been restored. The generator script introduced in that commit is also removed.	2026-06-27 20:21:05 +02:00
Matteo Benedetto	0f14ae3376	Redraw wall/bush maze tiles with richer pixel-art detail The original BMP_{1..4}_{N,S,E,W,NE,NW,SE,SW,EN,ES,WN,WS}.png tiles were very noisy low-quality checkerboard patterns. They are now redrawn as hand-styled pixel-art bush/hedge tiles: - 5-color per-theme palette (deep shadow, dark, mid, light, highlight) - irregular wall/passage boundary with natural erosion - shaded interior (darker in the center = depth) - scattered leaf clusters and highlights - kept 32x32 resolution and engine transparent-gray convention A new generator tool is added at tools/redraw_walls.py. Run it again with python3 tools/redraw_walls.py to regenerate the tiles if you want to iterate on the style or palettes.	2026-06-27 20:19:41 +02:00
Matteo Benedetto	5ce084f04d	Fix missing state_machine import in controls.py Resolves NameError when toggling pause (and any other control path referencing state_machine.GameState). The module was referenced but never imported after the recent cheat/controls edits.	2026-06-27 20:18:04 +02:00
Matteo Benedetto	4bab8d1a79	Scale gas spread, weapon refill, litter size, and post-mating stop with difficulty Four gameplay parameters are now driven by the selected difficulty, following the existing 'hard = harder for the player' philosophy: param easy normal hard gas_spread_speed 40 50 90 (higher = slower gas) weapon_refill_multiplier 1.3 1.0 0.6 (less ammo on hard) max_babies 2 3 5 (more pups on hard) mate_stop_male / female 120/240 100/200 60/120 (shorter stop on hard) Implementation: - engine/config.py: new keys on each DIFFICULTY_OPTIONS entry. - rats.py: _apply_difficulty() loads them onto the game (with __init__ defaults for safety); normal keeps the previous hardcoded values. - units/gas.py: Gas.speed reads game.gas_spread_speed (fallback 50). - engine/unit_manager.py: refill_ammo() scales per-frame refill chances by game.weapon_refill_multiplier (fallback 1.0). - units/rat.py: Male.fuck() uses game.mate_stop_male/mate_stop_female and random.randint(1, game.max_babies) (fallbacks to old values). All reads use getattr with the previous constant as fallback, so older configs / saved state keep working.	2026-06-27 19:52:27 +02:00
Matteo Benedetto	a09b9f0878	Add Ctrl+Return cheat to instantly win the current level During gameplay, pressing Ctrl+Return triggers an immediate level clear: the current level is marked as won (level_clear, or run_complete on the last DAT level) and the state transitions to VICTORY, showing the normal 'Level Clear!' dialog so the player can then advance with Return. Implementation: - engine/sdl2.py: mainloop detects Ctrl+Return via SDLK_RETURN + KMOD_CTRL and dispatches a 'cheat_win_level' action (bypassing keybindings). - engine/controls.py: trigger() now also accepts direct action names that are registered in the dispatcher (not key-event names), and a new cheat_win_level handler forwards to the game. - rats.py: MiceMaze.cheat_win_level() performs the win, guarded so it only fires while a level is actively being played. No keybinding files need editing; the cheat is wired through the engine layer directly.	2026-06-27 19:04:37 +02:00
Matteo Benedetto	c09840099e	Draw rat normally at tunnel entrances Rats are now drawn fully (single animation frame) regardless of whether they are on an open cell, a tunnel entrance, or an internal tunnel passage. The only case where the rat is not drawn is when its center is inside a wall (non-empty, non-tunnel cell). Removed the now-unused _get_tunnel_entrance_direction helper.	2026-06-27 18:56:04 +02:00
Matteo Benedetto	705f3ba260	Remove _draw_partially_hidden_in_tunnel clipping The clipping logic that sliced the rat sprite when entering/leaving a single-entrance tunnel produced a jarring half-rat visual. Removed it entirely: a rat on a single-entrance tunnel cell is now simply not drawn (it disappears into the tunnel) instead of being partially clipped. Internal tunnel passages are unchanged and still render the rat normally.	2026-06-27 18:54:46 +02:00
Matteo Benedetto	1627f58103	Fix rat sprite strip artifact inside internal tunnel cells After merging new-newnassets, the rat sprite assets are now horizontal 4-frame sprite sheets (e.g. BMP_MALE_UP.png is 80x40 instead of 20x40). The drawing path for rats inside internal tunnel passages still called draw_image() without source_rect, so the engine rendered all 4 frames side by side as a single wide strip (the visual artifact shown in /tmp/pi-clipboard-...png). This brings the tunnel-internal rendering in line with the non-tunnel path, which already used source_rect = (frame * w, 0, w, h).	2026-06-27 18:42:46 +02:00
Matteo Benedetto	f348d7e0d9	Merge branch 'merge-newassets-clean' into master Integrates new LPC-style rat assets and animations from origin/new-newnassets. Summary: - 32 new LPC-style sprites (assets/Rat_LPC_Style/) - 4 animation sprite sheets (assets/Rat_Animated/) for animated rats - 64 updated Rat sprites in assets/Rat/ with backup originals - engine/sdl2.py: added 'scale=True' param to load_image() - engine/graphics.py: rat textures loaded with scale=False - units/rat.py: animation frame computed from partial_move * 4 - 22 final-version generator scripts in tools/lpc_extract/ - 3 cherry-picked commits from origin/new-newnassets Excluded from upstream (working artifacts, not appropriate for prod repo): - ~25 PNG in repo root (male_rats.png, spritesheet.png, etc.) - ~416 tile extracts in tools/lpc_extract/grid/ - scattered debug PNGs (bushes, preview, montages) - early-iteration scripts (animate_rat v1-v4, restyle v2-v10, etc.)	2026-06-27 17:56:16 +02:00
Matteo Benedetto	e26464d843	Merge origin/new-newnassets: new LPC rat assets and animations Cherry-picked from new-newnassets (3 commits): - `bdf5a8d` Add new start animation asset for game launch - `5f2d09f` Fix rat animations and restore map tiles - `2e2c5bb` feat: add LPC-style rat assets, sprite generation scripts, and grid extraction tools Conflicts resolved: - engine/sdl2.py: kept 'if scale:' wrapper (compatible, default scale=True) - units/rat.py: kept animation frame computation (partial_move * 4) - 12 PNG files (BMP_BABY/MALE/FEMALE_): kept new-newnassets versions Cleanup applied: removed ~480 working artifacts not appropriate for the production repo: - 25 PNG in repo root (male_rats.png, spritesheet*.png) - 416 tile extracts in tools/lpc_extract/grid/ - scattered debug PNGs (bushes, preview, montages, etc.) - early iteration scripts (animate_rat v1-v4, restyle v2-v10, etc.) Kept: 32 new LPC-style sprites in assets/Rat_LPC_Style/, 4 animation sprite sheets in assets/Rat_Animated/, 64 updated Rat sprites, 22 final-version generator scripts, plus all .py/.md updates.	2026-06-27 17:53:45 +02:00
Matteo Benedetto	c1662ecf29	Add regression test for gray-strip artifact in asset rendering Tests/test_gray_strip_artifact.py renders lose.png on a white panel both with and without the near-white normalization, saves the results to /tmp/test_loss_before.png and /tmp/test_loss_after.png, and counts near-white (RGB 240-254, alpha>200) pixels which are the direct cause of the gray seam. With the fix the near-white pixel count drops from 3714 to 133 (reduction of 96%), confirming the normalization removes the artifact.	2026-06-17 11:46:01 +02:00
Matteo Benedetto	2ad6a082b3	Normalize near-white pixels to pure white in all loaded assets Assets like lose.png contain thousands of (254,254,254) pixels on a background that is also white. When drawn on a white panel these near-white pixels are slightly darker, producing a visible gray seam. Apply a global normalization in load_image(): any opaque pixel with R=G=B >= 250 is clamped to (255,255,255). Applied to every asset, not only those with transparent_color, so the background is always pure white regardless of off-by-one in the source PNG.	2026-06-17 11:13:40 +02:00
Matteo Benedetto	319801d6e5	Fix rat reproduction collision check Replace the brittle 'self.position == other.position_before' check with a symmetric cell-intersection test: two rats are considered colliding if any of their current/previous cells overlap. This handles both 'both rats in same cell' and 'one rat enters the cell the other just left'. Also remove the erroneous hasattr(other_unit, 'fuck') guard that prevented Male.fuck() from being called on Female (Female has no fuck method, but only Male should initiate reproduction). Add tests/test_rat_reproduction.py with 8 scenarios covering overlapping rats, baby rats, already-pregnant females, far-apart rats, female self-initiation, sound playback, and procreate interval spawning.	2026-06-17 10:29:26 +02:00
Matteo Benedetto	2eccf504f5	Add semi-transparent tunnel cover overlay for internal passages - Add sdl2.create_overlay_texture() and draw_overlay_texture(alpha) for transparent full-map overlays built from sub-tile surface blits. - Add Graphics.regenerate_tunnel_cover() which builds an overlay of 4 random grass sub-tiles (20x20) for every internal tunnel cell, defined as a tunnel cell surrounded by occupied cells (wall or tunnel) on all four sides. Cells with at least one open side are handled by cave_foreground and skipped here. - Draw the tunnel cover in the game loop after top-layer units/effects but before cave_foreground and points, at 95% opacity (alpha=242) so the unit and effect passing underneath is just barely visible.	2026-06-16 23:06:17 +02:00
Matteo Benedetto	62a65f599d	Draw explosions and gas inside tunnel cells Remove the is_hidden_in_tunnel() checks from Explosion.draw() and Gas.draw() so explosions and gas clouds remain visible when their center falls inside a tunnel cell. Logic already spawned them in tunnel cells, but they were rendered invisible because the draw path returned early.	2026-06-16 21:56:22 +02:00
Matteo Benedetto	5afdf3705b	Render rats inside internal tunnel passages Previously Rat.draw() hid any rat whose center fell inside a tunnel. The clipping helper only handled single-entrance cells and returned None for internal passages/crossroads, causing rats to vanish entirely. Now internal tunnel cells (those with 0 or 2+ open sides) draw the rat normally so it remains visible while walking through the tunnel. Single-entrance cells keep the partial clip effect. Also fix the visible-ratio math for DOWN/RIGHT clipping: the old formulas subtracted cell_size from a local coordinate, producing zero or negative visibility.	2026-06-16 20:37:46 +02:00
Matteo Benedetto	b1e9770991	Use wall-only neighbor checks for wall border rendering Previously regenerate_background() used occupied() (wall or tunnel) to choose wall border/corner tiles. This caused walls adjacent to tunnels to render with inner corners as if the tunnel were solid ground. Now wall border logic uses is_wall() so only actual wall neighbors influence the shape. Tunnel neighbor checks for flower-suppression are kept explicit.	2026-06-16 20:24:33 +02:00
Matteo Benedetto	2f8e3e8b28	Fix gas poisoning, explosion hit detection and render order - Move gas poisoning from Gas.move() to Gas.collisions() so all rats are registered in the collision system before the poison query runs. - Shrink gas and explosion bboxes so rats must be well inside the tile to be poisoned/killed. - Use AABB overlap instead of partial_move threshold for gas poisoning. - Draw mobile units first, then top-layer effects (gas, mines, bombs, explosions) so rats appear under the gas. - Add draw_on_top hint to Unit base class and top-layer units.	2026-06-16 19:32:50 +02:00
Matteo Benedetto	3e8cd97fda	Fix bomb explosions not killing rats and remove tracked pycache - Move Timer explosion from move() to collisions() so all units are registered in the collision system before the kill query runs. - Explosion units now set a bbox and kill rats that touch them. - Guard Rat.draw() so dead rats are not drawn. - Remove units/__pycache__ files from tracking.	2026-06-16 19:15:35 +02:00
John Doe	c7ed24483d	Add comprehensive test suite for game mechanics and level handling - Introduced `test_final_level_flow.py` to validate final level transitions and game end scenarios. - Created `test_game_over_flow.py` to ensure game over conditions trigger correctly based on rat counts. - Implemented `test_keybindings.py` to verify keybinding configurations and their context-specific actions. - Developed `test_level_editor.py` to assess level editor functionalities and layout computations. - Added `test_level_io.py` for testing level data serialization and deserialization. - Established `test_loop_logic_parity.py` to ensure consistent game state across multiple simulation runs. - Created `test_non_regression.py` to simulate game behavior and capture states for future verification. - Implemented `test_verify.py` to compare current game states against a golden master for regression detection.	2026-05-19 22:18:43 +02:00
John Doe	486cd6b7c5	Add non-regression test states and Bluetooth diagnostic script - Introduced a new JSON file containing non-regression test states with detailed unit information, including positions, ages, and movement directions across multiple frames. - Added a shell script for Bluetooth diagnostics that checks system information, Bluetooth binaries, running processes, D-Bus status, Bluetooth controller details, and audio stack status, providing a comprehensive overview for troubleshooting.	2026-05-17 23:36:24 +02:00
John Doe	dd82ccc087	Add launcher script for microphone visualizer with gamepad support	2026-05-09 17:57:57 +02:00
John Doe	7868d83ced	Add microphone visualizer tool using SDL2 for audio input visualization - Implemented a new Python script `mic_visualizer.py` that captures audio from a microphone and visualizes it in real-time. - Utilized SDL2 for audio capture and rendering, allowing users to see waveform and spectrum representations of the audio input. - Added command-line arguments for listing devices, selecting a capture device, and configuring window size and audio settings. - Included functionality for displaying audio levels and peaks, enhancing user experience with visual feedback.	2026-05-09 17:55:29 +02:00
John Doe	d4c73a344b	Add Mice! game box art image to MUOS catalogue	2026-05-09 17:27:26 +02:00
John Doe	703e4717e4	Add metadata files for Mice! game in muOS catalogue	2026-05-09 17:22:55 +02:00
John Doe	b849e16f69	Add new start animation asset for game launch	2026-05-09 17:08:53 +02:00
John Doe	c7ff5ae4cf	Refactor code structure for improved readability and maintainability	2026-05-09 16:35:25 +02:00