"""
Central configuration for Rabi Oscillation Quality Classifier.
V2: Calibrated to real data distributions.
"""
import torch
import os

# ─── Paths ───────────────────────────────────────────────────────────────────
PROJECT_DIR = os.path.dirname(os.path.abspath(__file__))
DATA_DIR = os.path.join(PROJECT_DIR, "data copy")
UI_TEST_DIR = os.path.join(PROJECT_DIR, "ui_test_samples")
MODEL_PATH = os.path.join(PROJECT_DIR, "best_model.pt")
CORRECTIONS_DIR = os.path.join(PROJECT_DIR, "data_corrections")
FAILED_PRED_DIR = os.path.join(PROJECT_DIR, "failed_predictions")

# ─── Device (MPS > CUDA > CPU) ──────────────────────────────────────────────
if torch.backends.mps.is_available():
    DEVICE = torch.device("mps")
elif torch.cuda.is_available():
    DEVICE = torch.device("cuda")
else:
    DEVICE = torch.device("cpu")

# ─── Signal Preprocessing ───────────────────────────────────────────────────
SEQ_LEN = 256           # Increased from 128 for finer resolution
NUM_FIT_PARAMS = 4      # amplitude, T, phase, offset

# ─── Model ───────────────────────────────────────────────────────────────────
NUM_CLASSES = 3         # Classes per head: 0=Bad, 1=Acceptable, 2=Perfect
IN_CHANNELS = 3         # raw signal + fit curve + residual (signal - fit)
RESNET_DIM = 256        # ResNet backbone output dim
DENSE_DIM = 64          # Dense branch output dim
FUSED_DIM = RESNET_DIM + DENSE_DIM  # 320

# ─── Training ────────────────────────────────────────────────────────────────
BATCH_SIZE = 256
LR = 3e-4               # Lower LR for better convergence
WEIGHT_DECAY = 1e-4
EPOCHS = 80              # More epochs
TRAIN_SIZE = 200_000     # Doubled for better generalization
VAL_SIZE = 40_000
PATIENCE = 15            # More patience
FOCAL_GAMMA = 2.0
CLASS_WEIGHTS = [1.2, 0.9, 0.9]  # Stronger upweight for class 0
IGNORE_INDEX = -1

# ─── Synthetic Generator — Calibrated to Real Data ──────────────────────────
# Real data statistics (from analysis of 2736 JSON files):
#   amplitude: mean=0.090, std=0.083, range=[~0, 0.40], p25=0.0002
#   T:         mean=0.101 but p75=0.062, most values 0.047-0.110
#   phase:     range=[-π, π]
#   offset:    mean=0.185, std=0.166, range=[~0, 0.98]
#   x_range:   mean=0.106, range=[0.006, 3.58]
#   n_points:  mean=145, range=[80, 1000]

# Data Quality SNR thresholds (dB)
SNR_PERFECT_MIN = 15.0       # Loosened from 20 (real data is noisier)
SNR_ACCEPTABLE_MIN = 5.0     # Loosened from 8
SNR_BORDERLINE_MIN = 3.0

# Fit Quality error thresholds (fraction)
FIT_PERFECT_MAX_ERR = 0.03
FIT_ACCEPTABLE_MAX_ERR = 0.20
FIT_BAD_MIN_ERR = 0.20
FIT_BORDERLINE_ERR_RANGE = (0.15, 0.30)

# Rabi oscillation parameter ranges — CALIBRATED TO REAL DATA
AMPLITUDE_RANGE = (0.0001, 0.42)   # Real: [~0, 0.40]
PERIOD_RANGE = (0.025, 0.80)       # Real: [0.025, 0.78] for good fits
PHASE_RANGE = (-3.1416, 3.1416)    # Real: [-π, π]
OFFSET_RANGE = (0.0001, 0.98)      # Real: [~0, 0.98]
DECAY_RANGE = (0.0, 30.0)          # Reduced max decay
X_MAX_RANGE = (0.005, 0.50)        # Real: [0.006, 3.58] but most < 0.2

# Extended ranges for diversity
X_MAX_EXTENDED = (0.50, 3.60)      # For occasional long sweeps
PERIOD_BAD_RANGE = (0.002, 19.0)   # Real bad fits have T up to 19

# Per-sample x-axis variation
N_POINTS_RANGE = (80, 500)         # Real: [80, 1000]

# Proportion of borderline cases in Class 0
BORDERLINE_FRACTION = 0.5          # Increased from 0.4

# Proportion of near-zero amplitude samples (mimics ~25% of real data)
NEAR_ZERO_AMP_FRACTION = 0.20