"""
Publication-quality visualization suite for GliomaSAM3-MoE.
Follows detailed spec for figures.
"""
import argparse
import os
import sys
from typing import Dict, List, Optional, Tuple, Any

import numpy as np
import yaml

import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
from matplotlib.colors import LinearSegmentedColormap
import matplotlib.font_manager as fm

# ============================================================================
# Global Style Configuration (Spec A)
# ============================================================================
STYLE = {
    # Canvas
    "aspect_main": (16, 9),
    "aspect_panel": (4, 3),
    "dpi": 300,
    "bg_color": "white",
    
    # Typography
    "font_family": "sans-serif",
    "font_title": 18,
    "font_subtitle": 12,
    "font_label": 10,
    
    # Lines
    "linewidth_contour": 1.5,
    "linewidth_boundary": 2.0,
    
    # Colors (fixed palette)
    "color_WT": "#00BBD4",  # cyan
    "color_TC": "#D81B60",  # magenta
    "color_ET": "#FBC02D",  # yellow
    "alpha_mask": 0.40,
    
    # Error colormap: blue -> white -> red
    "cmap_error": "RdBu_r",
}

def hex_to_rgb(hex_color: str) -> Tuple[float, float, float]:
    h = hex_color.lstrip("#")
    return tuple(int(h[i:i+2], 16) / 255.0 for i in (0, 2, 4))

COLORS = {
    "WT": hex_to_rgb(STYLE["color_WT"]),
    "TC": hex_to_rgb(STYLE["color_TC"]),
    "ET": hex_to_rgb(STYLE["color_ET"]),
}

# ============================================================================
# Setup matplotlib defaults
# ============================================================================
def setup_mpl_style():
    plt.rcParams.update({
        "font.family": STYLE["font_family"],
        "font.size": STYLE["font_label"],
        "axes.titlesize": STYLE["font_subtitle"],
        "axes.labelsize": STYLE["font_label"],
        "figure.facecolor": STYLE["bg_color"],
        "axes.facecolor": STYLE["bg_color"],
        "savefig.facecolor": STYLE["bg_color"],
        "savefig.dpi": STYLE["dpi"],
        "figure.dpi": 100,
    })

setup_mpl_style()

# ============================================================================
# Imports from project
# ============================================================================
ROOT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
SRC_DIR = os.path.join(ROOT_DIR, "src")
if SRC_DIR not in sys.path:
    sys.path.append(SRC_DIR)

from scipy import ndimage as ndi
from scipy.ndimage import zoom

from gliomasam3_moe.data.brats_dataset import SegMambaNPZDataset
from gliomasam3_moe.models.gliomasam3_moe import GliomaSAM3_MoE

# ============================================================================
# Utility Functions
# ============================================================================
def ensure_dir(path: str) -> None:
    os.makedirs(path, exist_ok=True)

def load_config(path: str) -> Dict:
    with open(path, "r") as f:
        return yaml.safe_load(f)

def normalize_volume(vol: np.ndarray, eps: float = 1e-6) -> np.ndarray:
    x = np.asarray(vol, dtype=np.float32)
    x = np.nan_to_num(x, nan=0.0, posinf=0.0, neginf=0.0)
    flat = x.reshape(-1)
    if flat.size == 0:
        return np.zeros_like(x, dtype=np.float32)
    lo, hi = np.percentile(flat, [1, 99])
    if hi - lo < eps:
        return np.zeros_like(x, dtype=np.float32)
    x = np.clip(x, lo, hi)
    x = (x - lo) / (hi - lo + eps)
    return x

def label_to_regions(label: np.ndarray) -> np.ndarray:
    label = np.asarray(label)
    wt = label > 0
    tc = (label == 1) | (label == 4)
    et = label == 4
    return np.stack([wt, tc, et], axis=0).astype(np.uint8)

def regions_to_label(regions: np.ndarray) -> np.ndarray:
    if regions.ndim != 4 or regions.shape[0] != 3:
        raise ValueError("regions must be [3, D, H, W]")
    wt = regions[0] > 0.5
    tc = regions[1] > 0.5
    et = regions[2] > 0.5
    label = np.zeros_like(wt, dtype=np.int16)
    label[wt] = 2
    label[tc] = 1
    label[et] = 4
    return label

def extract_slice(vol: np.ndarray, plane: str, idx: int) -> np.ndarray:
    if plane == "axial":
        img = vol[idx, :, :]
    elif plane == "coronal":
        img = vol[:, idx, :]
    elif plane == "sagittal":
        img = vol[:, :, idx]
    else:
        raise ValueError(f"Unknown plane: {plane}")
    return np.rot90(img)

def select_slices_from_mask(mask: Optional[np.ndarray]) -> Dict[str, int]:
    if mask is None or mask.sum() == 0:
        return {"axial": None, "coronal": None, "sagittal": None}
    m = mask.astype(np.uint8)
    axial = int(np.argmax(m.sum(axis=(1, 2))))
    coronal = int(np.argmax(m.sum(axis=(0, 2))))
    sagittal = int(np.argmax(m.sum(axis=(0, 1))))
    return {"axial": axial, "coronal": coronal, "sagittal": sagittal}

def fallback_slices(shape: Tuple[int, int, int]) -> Dict[str, int]:
    d, h, w = shape
    return {"axial": d // 2, "coronal": h // 2, "sagittal": w // 2}

def get_slices(mask_ref: Optional[np.ndarray], vol_shape: Tuple[int, int, int]) -> Dict[str, int]:
    idx = select_slices_from_mask(mask_ref)
    if any(v is None for v in idx.values()):
        idx = fallback_slices(vol_shape)
    return idx

def mask_boundary(mask2d: np.ndarray, iterations: int = 1) -> np.ndarray:
    if mask2d.sum() == 0:
        return mask2d.astype(bool)
    eroded = ndi.binary_erosion(mask2d.astype(bool), iterations=iterations)
    return np.logical_xor(mask2d.astype(bool), eroded)

def signed_distance(mask: np.ndarray) -> np.ndarray:
    mask = mask.astype(bool)
    if mask.sum() == 0:
        return np.zeros_like(mask, dtype=np.float32)
    outside = ndi.distance_transform_edt(~mask)
    inside = ndi.distance_transform_edt(mask)
    return (inside - outside).astype(np.float32)

def boundary_error_map(pred: np.ndarray, gt: np.ndarray) -> np.ndarray:
    pred = pred.astype(bool)
    gt = gt.astype(bool)
    dist_gt = signed_distance(gt)
    err = np.zeros_like(dist_gt, dtype=np.float32)
    # False positive: predicted but not GT
    fp = pred & ~gt
    # False negative: GT but not predicted
    fn = ~pred & gt
    err[fp] = np.abs(dist_gt[fp])
    err[fn] = -np.abs(dist_gt[fn])
    return err

def connected_components(mask: np.ndarray) -> Tuple[np.ndarray, int]:
    labeled, num = ndi.label(mask.astype(np.uint8))
    return labeled, int(num)

def fft_amplitude_slice(vol: np.ndarray, plane: str = "axial") -> np.ndarray:
    fft = np.fft.fftn(vol)
    amp = np.abs(fft)
    amp = np.fft.fftshift(amp)
    d, h, w = amp.shape
    if plane == "axial":
        sl = amp[d // 2, :, :]
    elif plane == "coronal":
        sl = amp[:, h // 2, :]
    else:
        sl = amp[:, :, w // 2]
    sl = np.log1p(sl)
    return normalize_volume(sl)

def fourier_amplitude_mix(a: np.ndarray, b: np.ndarray, lam: float) -> np.ndarray:
    if a.shape != b.shape:
        b_resized = np.zeros_like(a)
        for c in range(min(a.shape[0], b.shape[0])):
            zoom_factors = tuple(a.shape[i+1] / b.shape[i+1] for i in range(3))
            b_resized[c] = zoom(b[c], zoom_factors, order=1)
        b = b_resized
    fft_a = np.fft.fftn(a, axes=(1, 2, 3))
    fft_b = np.fft.fftn(b, axes=(1, 2, 3))
    amp_a = np.abs(fft_a)
    amp_b = np.abs(fft_b)
    phase = np.exp(1j * np.angle(fft_a))
    amp_mix = (1.0 - lam) * amp_a + lam * amp_b
    mixed = np.fft.ifftn(amp_mix * phase, axes=(1, 2, 3)).real
    return mixed.astype(np.float32)

# ============================================================================
# Overlay Functions (Spec compliant)
# ============================================================================
def overlay_masks_publication(
    base2d: np.ndarray,
    masks: Dict[str, np.ndarray],
    alpha: float = STYLE["alpha_mask"],
    draw_boundary: bool = True,
    boundary_width: int = 2,
) -> np.ndarray:
    """Overlay masks with publication-quality colors and boundaries."""
    base = np.clip(base2d, 0.0, 1.0)
    rgb = np.stack([base, base, base], axis=-1).astype(np.float32)
    
    # Draw order: WT -> TC -> ET (ET on top)
    order = ["WT", "TC", "ET"]
    for key in order:
        if key not in masks:
            continue
        m = masks[key].astype(bool)
        # Handle shape mismatch
        if m.shape != base.shape:
            zoom_factors = (base.shape[0] / m.shape[0], base.shape[1] / m.shape[1])
            m = zoom(m.astype(float), zoom_factors, order=0) > 0.5
        if m.sum() == 0:
            continue
        color = np.array(COLORS.get(key, (1.0, 0.0, 0.0)), dtype=np.float32)
        rgb[m] = (1.0 - alpha) * rgb[m] + alpha * color
        
        if draw_boundary:
            b = mask_boundary(m, iterations=boundary_width)
            rgb[b] = color
    
    return np.clip(rgb, 0, 1)

def draw_contour(ax, mask2d: np.ndarray, color: str, linewidth: float = STYLE["linewidth_contour"]):
    """Draw contour line on axis."""
    if mask2d.sum() == 0:
        return
    ax.contour(mask2d.astype(float), levels=[0.5], colors=[color], linewidths=[linewidth])

# ============================================================================
# Data Loading
# ============================================================================
class CaseLoader:
    def __init__(self, cfg: Dict):
        self.data_cfg = cfg.get("data", {})
        self.cache: Dict[Tuple[str, bool], Dict] = {}

    def get_case(self, case_id: str, include_label: bool = True) -> Dict:
        key = (case_id, include_label)
        if key in self.cache:
            return self.cache[key]
        
        data_format = self.data_cfg.get("format", "nifti")
        if data_format == "segmamba_npz":
            npz_dir = self.data_cfg.get("npz_dir") or self.data_cfg.get("root_dir", "")
            if case_id.endswith(".npz"):
                npz_path = case_id
            else:
                npz_path = os.path.join(npz_dir, case_id + ".npz")
            
            npy_path = npz_path[:-3] + "npy"
            seg_path = npz_path[:-4] + "_seg.npy"
            
            if os.path.isfile(npy_path):
                image = np.load(npy_path, mmap_mode="r")
            else:
                data = np.load(npz_path)
                image = data["data"]
            
            image = np.asarray(image, dtype=np.float32)
            if image.ndim == 5 and image.shape[0] == 1:
                image = image[0]
            if image.ndim == 4 and image.shape[0] != 4 and image.shape[-1] == 4:
                image = image.transpose(3, 0, 1, 2)
            
            label = None
            if include_label:
                if os.path.isfile(seg_path):
                    label = np.load(seg_path, mmap_mode="r")
                else:
                    data = np.load(npz_path)
                    label = data["seg"] if "seg" in data else None
                if label is not None:
                    label = np.asarray(label, dtype=np.int16)
                    if label.ndim == 4 and label.shape[0] == 1:
                        label = label[0]
                    # Map ET label 3 -> 4 if needed
                    if label.max() == 3 and (label == 4).sum() == 0:
                        label = label.copy()
                        label[label == 3] = 4
            
            # Create images dict with modality names
            modalities = self.data_cfg.get("modalities", ["t1n", "t1c", "t2f", "t2w"])
            images = {}
            for i, mod in enumerate(modalities):
                if i < image.shape[0]:
                    images[mod] = normalize_volume(image[i])
            
            out = {"images": images, "label": label, "affine": np.eye(4)}
            self.cache[key] = out
            return out
        
        raise NotImplementedError("Only segmamba_npz format is currently supported")


class PredictionLoader:
    def __init__(self, cfg: Dict):
        pred_cfg = cfg.get("predictions", {})
        self.ours = pred_cfg.get("ours", {})
        self.baselines = pred_cfg.get("baselines", [])

    def get_all_methods(self) -> List[Dict]:
        methods = []
        if self.ours:
            methods.append(self.ours)
        methods.extend(self.baselines)
        return methods

    def load_method(self, method_cfg: Dict, case_id: str) -> Dict:
        import nibabel as nib
        
        pred_dir = method_cfg.get("dir", "")
        pred_type = method_cfg.get("type", "auto")
        
        def _find(base: str) -> Optional[str]:
            for ext in [".nii.gz", ".nii"]:
                path = os.path.join(pred_dir, base + ext)
                if os.path.isfile(path):
                    return path
            return None
        
        paths = {
            "regions_prob": _find(f"{case_id}_regions_prob"),
            "regions_bin": _find(f"{case_id}_regions_bin"),
            "label": _find(f"{case_id}_label"),
            "segmamba_3c": _find(f"{case_id}"),
        }
        
        if pred_type == "auto":
            for key in ["regions_prob", "regions_bin", "label", "segmamba_3c"]:
                if paths[key] is not None:
                    pred_type = key
                    break
        
        path = paths.get(pred_type)
        if path is None:
            raise FileNotFoundError(f"No prediction found for {case_id} in {pred_dir}")
        
        img = nib.load(path)
        arr = np.asarray(img.get_fdata())
        
        out: Dict[str, Optional[np.ndarray]] = {"label": None, "regions": None, "prob": None}
        
        if pred_type in {"regions_prob", "regions_bin"}:
            if arr.ndim == 4 and arr.shape[-1] == 3:
                regions = arr.transpose(3, 0, 1, 2)
            else:
                regions = arr
            out["prob"] = regions.astype(np.float32) if pred_type == "regions_prob" else None
            out["regions"] = (regions > 0.5).astype(np.uint8) if pred_type == "regions_prob" else regions.astype(np.uint8)
            out["label"] = regions_to_label(out["regions"])
        elif pred_type == "segmamba_3c":
            if arr.ndim == 4 and arr.shape[-1] == 3:
                regions = arr.transpose(3, 0, 1, 2).astype(np.uint8)
            else:
                regions = arr.astype(np.uint8)
            out["regions"] = regions
            out["label"] = regions_to_label(regions)
        else:
            label = arr.astype(np.int16)
            out["label"] = label
            out["regions"] = label_to_regions(label)
        
        return out


class AuxCache:
    def __init__(self, aux_dir: Optional[str]):
        self.aux_dir = aux_dir

    def path(self, case_id: str) -> Optional[str]:
        if not self.aux_dir:
            return None
        return os.path.join(self.aux_dir, f"{case_id}_aux.npz")

    def load(self, case_id: str) -> Optional[Dict]:
        path = self.path(case_id)
        if path and os.path.isfile(path):
            data = np.load(path)
            return {k: data[k] for k in data.files}
        return None

    def save(self, case_id: str, data: Dict) -> None:
        if not self.aux_dir:
            return
        ensure_dir(self.aux_dir)
        path = self.path(case_id)
        np.savez_compressed(path, **data)


class ModelRunner:
    def __init__(self, vis_cfg: Dict, model_cfg_path: str, ckpt_path: str, device: str):
        import torch
        import torch.nn.functional as F

        self.torch = torch
        self.F = F
        self.vis_cfg = vis_cfg
        self.cfg = load_config(model_cfg_path)
        self.device = torch.device(device if torch.cuda.is_available() else "cpu")
        self.model = GliomaSAM3_MoE(**self.cfg["model"]).to(self.device)
        ckpt = torch.load(ckpt_path, map_location="cpu")
        state_dict = {k: v for k, v in ckpt["model"].items() if "freqs_cis" not in k}
        self.model.load_state_dict(state_dict, strict=False)
        self.model.eval()

    def load_case_tensor(self, case_id: str):
        data_cfg = self.vis_cfg.get("data", {})
        data_dir = data_cfg.get("npz_dir") or data_cfg.get("root_dir", "")
        if case_id.endswith(".npz"):
            npz_path = case_id
        else:
            npz_path = os.path.join(data_dir, case_id + ".npz")
        dataset = SegMambaNPZDataset(data_dir=data_dir, npz_paths=[npz_path], test=True, ensure_npy=True)
        sample = dataset[0]
        image = sample["image"].unsqueeze(0)
        case = sample["case_id"]
        return image, case

    def forward_intermediate(self, image):
        torch = self.torch
        F = self.F
        model = self.model
        with torch.no_grad():
            b, c, d, h, w = image.shape
            orig_h, orig_w = h, w
            pad_h = (model.patch_size - (h % model.patch_size)) % model.patch_size
            pad_w = (model.patch_size - (w % model.patch_size)) % model.patch_size
            ph0, ph1 = pad_h // 2, pad_h - pad_h // 2
            pw0, pw1 = pad_w // 2, pad_w - pad_w // 2
            if pad_h > 0 or pad_w > 0:
                image = F.pad(image, (pw0, pw1, ph0, ph1, 0, 0))
                h, w = image.shape[-2:]

            image = image.to(self.device)
            x_plus, _ = model.hfdi(image)
            x_spec, spectral_stats = model.spectral(image)

            x2d = x_plus.permute(0, 2, 1, 3, 4).reshape(b * d, 7, h, w)
            tokens, (gh, gw) = model.encoder2d(x2d)
            n = gh * gw
            tokens = tokens.view(b, d, n, -1)
            tokens = model.slice_adapter(tokens, direction="forward")

            z = tokens.mean(dim=(1, 2))
            pi_et = model.attr_head(z)["pi_et"]
            token_ids = model._select_concept_tokens(pi_et, label=None)
            prompt = model.prompt_encoder(token_ids)
            tokens = model.prompt_film(tokens, prompt)

            u = tokens.view(b, d, gh, gw, -1).permute(0, 4, 1, 2, 3)
            u_msda = model.dual_enhance.msda(u)
            u_lv1 = model.dual_enhance.fa_level(u)
            u_fa = model.dual_enhance.fa_fuse(torch.cat([u, u_lv1], dim=1))
            pool = torch.cat([u_fa, u_msda], dim=1).mean(dim=(2, 3, 4))
            eta = torch.sigmoid(model.dual_enhance.fcf_mlp(pool)).view(b, 1, 1, 1, 1)
            u_fuse = eta * u_fa + (1.0 - eta) * u_msda
            u_spec = model.dual_enhance.spec_stem(x_spec)
            u_out = model.dual_enhance.fuse_conv(torch.cat([u_fuse, u_spec], dim=1))

            logits, gamma = model.moe_decoder(u_out, z, prompt, spectral_stats, target_size=(d, h, w))
            if pad_h > 0 or pad_w > 0:
                logits = logits[:, :, :, ph0 : ph0 + orig_h, pw0 : pw0 + orig_w]

            et_pre = torch.sigmoid(logits[:, 2:3])
            et_post = et_pre * pi_et.view(b, 1, 1, 1, 1)

            u_up = F.interpolate(u_out, size=(d, h, w), mode="trilinear", align_corners=False)
            logits_all = torch.stack([exp(u_up) for exp in model.moe_decoder.experts], dim=1)
            prob_all = torch.sigmoid(logits_all)
            mean_prob = prob_all.mean(dim=(3, 4, 5))
            contrib = gamma.view(b, -1, 1) * mean_prob

            return {
                "pi_et": pi_et,
                "moe_gamma": gamma,
                "spectral_stats": spectral_stats,
                "et_pre": et_pre,
                "et_post": et_post,
                "expert_contrib": contrib,
                "x_spec": x_spec,
                "u_fuse": u_fuse,
                "u_spec": u_spec,
                "logits": logits,
            }


# ============================================================================
# Figure Saving (Spec L)
# ============================================================================
def save_figure(fig, out_dir: str, name: str, close: bool = True):
    """Save figure as PNG and PDF at 300 dpi."""
    ensure_dir(out_dir)
    png_path = os.path.join(out_dir, f"{name}.png")
    pdf_path = os.path.join(out_dir, f"{name}.pdf")
    # Use pad_inches to prevent axis/image overlap
    fig.savefig(png_path, dpi=STYLE["dpi"], bbox_inches="tight", pad_inches=0.1, facecolor=STYLE["bg_color"])
    fig.savefig(pdf_path, dpi=STYLE["dpi"], bbox_inches="tight", pad_inches=0.1, facecolor=STYLE["bg_color"])
    if close:
        plt.close(fig)
    print(f"  Saved: {png_path}")


def finalize_figure(fig, title: str = None):
    """Finalize figure layout to prevent axis overlap."""
    if title:
        fig.suptitle(title, fontsize=STYLE["font_title"], fontweight="bold", y=0.98)
    # Use constrained layout or manual adjustment
    try:
        fig.tight_layout(rect=[0, 0.02, 1, 0.95] if title else [0, 0, 1, 1])
    except Exception:
        pass
    fig.subplots_adjust(wspace=0.3, hspace=0.3)


# ============================================================================
# B) Main Qualitative Comparison (Spec B)
# ============================================================================
def make_qualitative(cfg: Dict, case_loader: CaseLoader, pred_loader: PredictionLoader, out_dir: str) -> None:
    """
    Main qualitative comparison figure.
    Layout: Each row = one case
    Columns: [T1n | T1ce | T2f | FLAIR | GT | Ours | Baseline...]
    Small inset for coronal/sagittal view
    """
    cases = cfg.get("cases", {}).get("qualitative", [])
    if not cases:
        return
    
    methods = pred_loader.get_all_methods()
    modalities = cfg.get("data", {}).get("modalities", ["t1n", "t1c", "t2f", "t2w"])
    mod_labels = {"t1n": "T1", "t1c": "T1ce", "t2f": "FLAIR", "t2w": "T2"}
    
    n_cols = len(modalities) + 1 + len(methods)  # modalities + GT + methods
    n_rows = len(cases)
    
    fig_width = 2.0 * n_cols
    fig_height = 2.0 * n_rows
    fig, axes = plt.subplots(n_rows, n_cols, figsize=(fig_width, fig_height))
    if n_rows == 1:
        axes = axes.reshape(1, -1)
    
    for row_idx, case_id in enumerate(cases):
        case = case_loader.get_case(case_id, include_label=True)
        images = case["images"]
        label = case["label"]
        
        # Use T1ce as reference for slice selection
        ref_mod = "t1c" if "t1c" in images else list(images.keys())[0]
        base = images[ref_mod]
        
        # Find best slice based on tumor
        mask_ref = label_to_regions(label)[2] if label is not None else None
        idx = get_slices(mask_ref, base.shape)
        plane = "axial"
        slice_idx = idx[plane]
        
        col_idx = 0
        
        # Plot modalities
        for mod in modalities:
            ax = axes[row_idx, col_idx]
            if mod in images:
                img2d = extract_slice(images[mod], plane, slice_idx)
                ax.imshow(img2d, cmap="gray", aspect="equal")
            ax.axis("off")
            if row_idx == 0:
                ax.set_title(mod_labels.get(mod, mod.upper()), fontsize=STYLE["font_subtitle"], fontweight="bold")
            col_idx += 1
        
        # Plot GT
        ax = axes[row_idx, col_idx]
        base2d = extract_slice(base, plane, slice_idx)
        if label is not None:
            gt_regions = label_to_regions(label)
            masks = {
                "WT": extract_slice(gt_regions[0], plane, slice_idx) > 0,
                "TC": extract_slice(gt_regions[1], plane, slice_idx) > 0,
                "ET": extract_slice(gt_regions[2], plane, slice_idx) > 0,
            }
            overlay = overlay_masks_publication(base2d, masks)
            ax.imshow(overlay, aspect="equal")
        else:
            ax.imshow(base2d, cmap="gray", aspect="equal")
        ax.axis("off")
        if row_idx == 0:
            ax.set_title("GT", fontsize=STYLE["font_subtitle"], fontweight="bold")
        col_idx += 1
        
        # Plot methods
        for method in methods:
            ax = axes[row_idx, col_idx]
            try:
                pred = pred_loader.load_method(method, case_id)
                pred_regions = pred["regions"]
                masks = {
                    "WT": extract_slice(pred_regions[0], plane, slice_idx) > 0,
                    "TC": extract_slice(pred_regions[1], plane, slice_idx) > 0,
                    "ET": extract_slice(pred_regions[2], plane, slice_idx) > 0,
                }
                overlay = overlay_masks_publication(base2d, masks)
                ax.imshow(overlay, aspect="equal")
            except Exception as e:
                ax.imshow(base2d, cmap="gray", aspect="equal")
                ax.text(0.5, 0.5, "N/A", transform=ax.transAxes, ha="center", va="center", fontsize=STYLE["font_label"])
            ax.axis("off")
            if row_idx == 0:
                ax.set_title(method.get("name", "Method"), fontsize=STYLE["font_subtitle"], fontweight="bold")
            
            # Add small inset for coronal view (top-right corner)
            inset_ax = ax.inset_axes([0.65, 0.65, 0.33, 0.33])
            try:
                cor_idx = idx["coronal"]
                base_cor = extract_slice(base, "coronal", cor_idx)
                if "pred_regions" in dir():
                    masks_cor = {
                        "WT": extract_slice(pred_regions[0], "coronal", cor_idx) > 0,
                        "TC": extract_slice(pred_regions[1], "coronal", cor_idx) > 0,
                        "ET": extract_slice(pred_regions[2], "coronal", cor_idx) > 0,
                    }
                    overlay_cor = overlay_masks_publication(base_cor, masks_cor)
                    inset_ax.imshow(overlay_cor, aspect="equal")
                else:
                    inset_ax.imshow(base_cor, cmap="gray", aspect="equal")
            except:
                pass
            inset_ax.axis("off")
            inset_ax.patch.set_edgecolor("white")
            inset_ax.patch.set_linewidth(1)
            
            col_idx += 1
        
        # Add case ID on the left
        axes[row_idx, 0].text(-0.15, 0.5, case_id.split("-")[-1], transform=axes[row_idx, 0].transAxes,
                              rotation=90, va="center", ha="right", fontsize=STYLE["font_label"])
    
    finalize_figure(fig, "Main Qualitative Comparison")
    save_figure(fig, out_dir, "Fig1_qualitative_comparison")


# ============================================================================
# C) ET-absent Case Study (Spec C)
# ============================================================================
def make_et_absent(cfg: Dict, case_loader: CaseLoader, aux: AuxCache, runner: Optional[ModelRunner], out_dir: str) -> None:
    """
    ET-absent case study.
    Three columns: Before gate | After gate | π_ET value with colorbar
    """
    cases = cfg.get("cases", {}).get("et_absent", [])
    if not cases:
        return
    
    for case_idx, case_id in enumerate(cases):
        case = case_loader.get_case(case_id, include_label=False)
        images = case["images"]
        ref_mod = "t1c" if "t1c" in images else list(images.keys())[0]
        base = images[ref_mod]
        
        # Load or compute aux data
        aux_data = aux.load(case_id)
        needed_keys = ["pi_et", "et_pre", "et_post"]
        if aux_data is None or not all(k in aux_data for k in needed_keys):
            if runner is None:
                continue
            image, _ = runner.load_case_tensor(case_id)
            out = runner.forward_intermediate(image)
            new_data = {
                "pi_et": out["pi_et"].detach().cpu().numpy(),
                "et_pre": out["et_pre"].detach().cpu().numpy(),
                "et_post": out["et_post"].detach().cpu().numpy(),
            }
            if aux_data is not None:
                aux_data.update(new_data)
            else:
                aux_data = new_data
            aux.save(case_id, aux_data)
        
        if aux_data is None:
            continue
        
        et_pre = aux_data["et_pre"][0, 0]
        et_post = aux_data["et_post"][0, 0]
        pi_et = float(np.asarray(aux_data["pi_et"]).reshape(-1)[0])
        
        idx = get_slices(et_pre > 0.3, base.shape)
        plane = "axial"
        slice_idx = idx[plane]
        
        fig = plt.figure(figsize=(14, 5))
        gs = fig.add_gridspec(1, 4, width_ratios=[1, 1, 0.6, 0.05], wspace=0.25)
        
        base2d = extract_slice(base, plane, slice_idx)
        pre2d = extract_slice(et_pre, plane, slice_idx)
        post2d = extract_slice(et_post, plane, slice_idx)
        
        # Before gate
        ax0 = fig.add_subplot(gs[0])
        ax0.imshow(base2d, cmap="gray", aspect="equal")
        im = ax0.imshow(pre2d, cmap="YlOrRd", alpha=0.6, vmin=0, vmax=1, aspect="equal")
        draw_contour(ax0, pre2d > 0.5, STYLE["color_ET"], linewidth=STYLE["linewidth_boundary"])
        ax0.set_title("ET Before Gate", fontsize=STYLE["font_subtitle"], fontweight="bold")
        ax0.axis("off")
        
        # After gate
        ax1 = fig.add_subplot(gs[1])
        ax1.imshow(base2d, cmap="gray", aspect="equal")
        ax1.imshow(post2d, cmap="YlOrRd", alpha=0.6, vmin=0, vmax=1, aspect="equal")
        draw_contour(ax1, post2d > 0.5, STYLE["color_ET"], linewidth=STYLE["linewidth_boundary"])
        ax1.set_title("ET After Gate", fontsize=STYLE["font_subtitle"], fontweight="bold")
        ax1.axis("off")
        
        # π_ET value with bar and stats
        ax2 = fig.add_subplot(gs[2])
        ax2.barh(0.5, pi_et, height=0.25, color=STYLE["color_ET"], edgecolor="black", linewidth=1.5)
        ax2.axvline(0.5, color="gray", linestyle="--", linewidth=1.5, label="Threshold")
        ax2.set_xlim(0, 1)
        ax2.set_ylim(0, 1)
        ax2.set_xlabel("π_ET value", fontsize=STYLE["font_label"])
        ax2.set_title(f"π_ET = {pi_et:.3f}", fontsize=STYLE["font_subtitle"], fontweight="bold",
                     color="green" if pi_et > 0.5 else "red")
        ax2.set_yticks([])
        ax2.spines["top"].set_visible(False)
        ax2.spines["right"].set_visible(False)
        ax2.spines["left"].set_visible(False)
        
        # Stats text
        pre_vox = int((pre2d > 0.5).sum())
        post_vox = int((post2d > 0.5).sum())
        diff_pct = (pre_vox - post_vox) / max(pre_vox, 1) * 100
        ax2.text(0.5, 0.2, f"Before: {pre_vox}\nAfter: {post_vox}\nΔ: {diff_pct:+.1f}%",
                transform=ax2.transAxes, fontsize=STYLE["font_label"], ha="center", va="bottom",
                bbox=dict(boxstyle="round,pad=0.3", facecolor="lightyellow", edgecolor="gray", alpha=0.9))
        
        # Colorbar
        ax_cbar = fig.add_subplot(gs[3])
        cbar = fig.colorbar(im, cax=ax_cbar)
        cbar.set_label("ET Prob", fontsize=STYLE["font_label"])
        
        fig.suptitle(f"ET Gate Study: {case_id}", fontsize=STYLE["font_title"], fontweight="bold", y=0.98)
        fig.tight_layout(rect=[0, 0, 1, 0.93])
        save_figure(fig, out_dir, f"Fig2_{chr(ord('a')+case_idx)}_et_absent_{case_id}")


# ============================================================================
# D) Boundary Error Visualization (Spec D)
# ============================================================================
def make_boundary(cfg: Dict, case_loader: CaseLoader, pred_loader: PredictionLoader, out_dir: str) -> None:
    """
    Boundary error visualization.
    Shows GT boundary (white) + Pred boundary (black) + signed error heatmap
    """
    cases = cfg.get("cases", {}).get("boundary", [])
    if not cases:
        return
    
    region_name = cfg.get("visualization", {}).get("boundary_region", "ET")
    region_idx = {"WT": 0, "TC": 1, "ET": 2}[region_name]
    
    for case_idx, case_id in enumerate(cases):
        case = case_loader.get_case(case_id, include_label=True)
        if case["label"] is None:
            continue
        
        images = case["images"]
        ref_mod = "t1c" if "t1c" in images else list(images.keys())[0]
        base = images[ref_mod]
        
        gt_regions = label_to_regions(case["label"])
        pred = pred_loader.load_method(pred_loader.ours, case_id)
        pred_regions = pred["regions"]
        
        mask_ref = gt_regions[region_idx]
        idx = get_slices(mask_ref, base.shape)
        plane = "axial"
        slice_idx = idx[plane]
        
        fig, axes = plt.subplots(1, 3, figsize=(12, 4))
        
        base2d = extract_slice(base, plane, slice_idx)
        gt2d = extract_slice(gt_regions[region_idx], plane, slice_idx) > 0
        pred2d = extract_slice(pred_regions[region_idx], plane, slice_idx) > 0
        
        # Resize pred2d if needed
        if pred2d.shape != gt2d.shape:
            zoom_factors = (gt2d.shape[0] / pred2d.shape[0], gt2d.shape[1] / pred2d.shape[1])
            pred2d = zoom(pred2d.astype(float), zoom_factors, order=0) > 0.5
        
        err2d = boundary_error_map(pred2d, gt2d)
        
        # Panel 1: Base image with boundaries
        ax = axes[0]
        ax.imshow(base2d, cmap="gray", aspect="equal")
        draw_contour(ax, gt2d, "white", linewidth=STYLE["linewidth_boundary"])
        draw_contour(ax, pred2d, "black", linewidth=STYLE["linewidth_contour"])
        ax.set_title("Boundaries: GT (white) vs Pred (black)", fontsize=STYLE["font_subtitle"])
        ax.axis("off")
        
        # Panel 2: Overlay comparison
        ax = axes[1]
        overlay = overlay_masks_publication(base2d, {region_name: gt2d}, alpha=0.3)
        ax.imshow(overlay, aspect="equal")
        draw_contour(ax, pred2d, "black", linewidth=STYLE["linewidth_contour"])
        ax.set_title(f"GT ({region_name}) + Pred Boundary", fontsize=STYLE["font_subtitle"])
        ax.axis("off")
        
        # Panel 3: Signed error heatmap
        ax = axes[2]
        ax.imshow(base2d, cmap="gray", aspect="equal")
        max_err = max(np.abs(err2d).max(), 1.0)
        im = ax.imshow(err2d, cmap=STYLE["cmap_error"], alpha=0.7, vmin=-max_err, vmax=max_err, aspect="equal")
        ax.set_title("Signed Boundary Error", fontsize=STYLE["font_subtitle"])
        ax.axis("off")
        
        # Colorbar
        cbar = fig.colorbar(im, ax=ax, orientation="vertical", fraction=0.046, pad=0.04)
        cbar.set_label("Error (blue=FN, red=FP)", fontsize=STYLE["font_label"])
        
        finalize_figure(fig, f"Boundary Analysis: {case_id} ({region_name})")
        save_figure(fig, out_dir, f"Fig3_{chr(ord('a')+case_idx)}_boundary_{case_id}")


# ============================================================================
# E) Tiny/Fragmented ET Cases (Spec E)
# ============================================================================
def make_tiny_et(cfg: Dict, case_loader: CaseLoader, pred_loader: PredictionLoader, out_dir: str) -> None:
    """
    Tiny/fragmented ET visualization with ROI zoom.
    """
    cases = cfg.get("cases", {}).get("tiny_et", [])
    if not cases:
        return
    
    methods = pred_loader.get_all_methods()
    
    for case_idx, case_id in enumerate(cases):
        case = case_loader.get_case(case_id, include_label=True)
        images = case["images"]
        ref_mod = "t1c" if "t1c" in images else list(images.keys())[0]
        base = images[ref_mod]
        
        gt_regions = label_to_regions(case["label"]) if case["label"] is not None else None
        
        # Find ET centroid for ROI
        if gt_regions is not None:
            et_mask = gt_regions[2]
            if et_mask.sum() > 0:
                coords = np.where(et_mask)
                centroid = [int(np.mean(c)) for c in coords]
            else:
                centroid = [s // 2 for s in base.shape]
        else:
            centroid = [s // 2 for s in base.shape]
        
        plane = "axial"
        slice_idx = centroid[0]
        
        # Create ROI around ET (64x64 region)
        roi_size = 64
        cy, cx = centroid[1], centroid[2]
        y_start = max(0, cy - roi_size // 2)
        y_end = min(base.shape[1], cy + roi_size // 2)
        x_start = max(0, cx - roi_size // 2)
        x_end = min(base.shape[2], cx + roi_size // 2)
        
        n_cols = 1 + len(methods)  # GT + methods
        fig, axes = plt.subplots(1, n_cols, figsize=(3 * n_cols, 3))
        if n_cols == 1:
            axes = [axes]
        
        base2d = extract_slice(base, plane, slice_idx)
        base_roi = base2d[y_start:y_end, x_start:x_end]
        
        col_idx = 0
        
        # GT
        ax = axes[col_idx]
        if gt_regions is not None:
            et2d = extract_slice(gt_regions[2], plane, slice_idx) > 0
            et_roi = et2d[y_start:y_end, x_start:x_end]
            overlay = overlay_masks_publication(base_roi, {"ET": et_roi}, alpha=0.5)
            ax.imshow(overlay, aspect="equal")
        else:
            ax.imshow(base_roi, cmap="gray", aspect="equal")
        ax.set_title("GT", fontsize=STYLE["font_subtitle"], fontweight="bold")
        ax.axis("off")
        col_idx += 1
        
        # Methods
        for method in methods:
            ax = axes[col_idx]
            try:
                pred = pred_loader.load_method(method, case_id)
                pred_et = pred["regions"][2]
                et2d = extract_slice(pred_et, plane, slice_idx) > 0
                # Resize if needed
                if et2d.shape != base2d.shape:
                    zoom_factors = (base2d.shape[0] / et2d.shape[0], base2d.shape[1] / et2d.shape[1])
                    et2d = zoom(et2d.astype(float), zoom_factors, order=0) > 0.5
                et_roi = et2d[y_start:y_end, x_start:x_end]
                overlay = overlay_masks_publication(base_roi, {"ET": et_roi}, alpha=0.5)
                ax.imshow(overlay, aspect="equal")
            except:
                ax.imshow(base_roi, cmap="gray", aspect="equal")
            ax.set_title(method.get("name", "Method"), fontsize=STYLE["font_subtitle"], fontweight="bold")
            ax.axis("off")
            col_idx += 1
        
        finalize_figure(fig, f"Tiny ET ROI: {case_id}")
        save_figure(fig, out_dir, f"Fig4_{chr(ord('a')+case_idx)}_tiny_et_{case_id}")


# ============================================================================
# G) MoE Routing Interpretability (Spec G)
# ============================================================================
def make_moe_routing(cfg: Dict, case_loader: CaseLoader, aux: AuxCache, runner: Optional[ModelRunner], out_dir: str) -> None:
    """
    MoE routing visualization with grouped bar chart.
    Shows expert contributions for WT/TC/ET.
    """
    cases = cfg.get("cases", {}).get("moe", [])
    if not cases:
        return
    
    for case_idx, case_id in enumerate(cases):
        aux_data = aux.load(case_id)
        needed_keys = ["moe_gamma", "expert_contrib"]
        if aux_data is None or not all(k in aux_data for k in needed_keys):
            if runner is None:
                continue
            image, _ = runner.load_case_tensor(case_id)
            out = runner.forward_intermediate(image)
            new_data = {
                "moe_gamma": out["moe_gamma"].detach().cpu().numpy(),
                "expert_contrib": out["expert_contrib"].detach().cpu().numpy(),
            }
            if aux_data is not None:
                aux_data.update(new_data)
            else:
                aux_data = new_data
            aux.save(case_id, aux_data)
        
        if aux_data is None:
            continue
        
        gamma = np.asarray(aux_data["moe_gamma"])[0]
        contrib = np.asarray(aux_data["expert_contrib"])[0]
        
        n_experts = contrib.shape[0]
        x = np.arange(n_experts)
        width = 0.25
        
        # Find active experts (top-k, usually k=2)
        active_experts = np.where(gamma > 0.01)[0]
        top_k = len(active_experts)
        
        fig, ax = plt.subplots(figsize=(10, 5))
        
        # Grouped bars for WT/TC/ET with highlight for active experts
        for i in range(n_experts):
            alpha = 1.0 if i in active_experts else 0.3
            edge_width = 2 if i in active_experts else 0.5
            ax.bar(x[i] - width, contrib[i, 0], width, color=STYLE["color_WT"], 
                   edgecolor="black", linewidth=edge_width, alpha=alpha)
            ax.bar(x[i], contrib[i, 1], width, color=STYLE["color_TC"], 
                   edgecolor="black", linewidth=edge_width, alpha=alpha)
            ax.bar(x[i] + width, contrib[i, 2], width, color=STYLE["color_ET"], 
                   edgecolor="black", linewidth=edge_width, alpha=alpha)
        
        # Legend for regions (place at upper left)
        from matplotlib.patches import Patch
        legend_elements = [
            Patch(facecolor=STYLE["color_WT"], edgecolor="black", label="WT"),
            Patch(facecolor=STYLE["color_TC"], edgecolor="black", label="TC"),
            Patch(facecolor=STYLE["color_ET"], edgecolor="black", label="ET"),
        ]
        
        # Routing weights as line with markers
        ax.plot(x, gamma, "ko-", linewidth=2, markersize=8, label="Routing γ", zorder=10)
        
        # Annotate active experts with their gamma values
        for i in active_experts:
            ax.annotate(f"γ={gamma[i]:.2f}", xy=(x[i], gamma[i]), 
                       xytext=(0, 10), textcoords="offset points",
                       fontsize=STYLE["font_label"], fontweight="bold", ha="center",
                       bbox=dict(boxstyle="round,pad=0.2", facecolor="yellow", alpha=0.8))
        
        ax.set_xlabel("Expert Index", fontsize=STYLE["font_subtitle"])
        ax.set_ylabel("Contribution", fontsize=STYLE["font_subtitle"])
        ax.set_xticks(x)
        ax.set_xticklabels([f"E{i}\n{'(active)' if i in active_experts else ''}" for i in range(n_experts)],
                          fontsize=STYLE["font_label"])
        # Legend at upper right
        ax.legend(handles=legend_elements, loc="upper right", fontsize=STYLE["font_label"],
                 bbox_to_anchor=(0.99, 0.99))
        ax.spines["top"].set_visible(False)
        ax.spines["right"].set_visible(False)
        ax.set_ylim(0, None)
        
        # Add explanation text (upper right, below legend)
        ax.text(0.98, 0.72, f"Top-k = {top_k} (sparse gating)\nActive: {', '.join([f'E{i}' for i in active_experts])}",
               transform=ax.transAxes, fontsize=STYLE["font_label"], va="top", ha="right",
               bbox=dict(boxstyle="round,pad=0.3", facecolor="lightyellow", edgecolor="gray", alpha=0.9))
        
        finalize_figure(fig, f"MoE Expert Routing: {case_id}")
        save_figure(fig, out_dir, f"Fig5_{chr(ord('a')+case_idx)}_moe_routing_{case_id}")


# ============================================================================
# H) Concept Token Interpretability (Spec H)
# ============================================================================
def make_concept_tokens(cfg: Dict, case_loader: CaseLoader, pred_loader: PredictionLoader, out_dir: str) -> None:
    """
    Concept token visualization - split into 3 clear figures per case:
    1. ET Overview: T1ce + ET mask
    2. Fragmentation Analysis: Components → FRAG_BIN
    3. Scale Analysis: Size → SCALE_BIN
    """
    cases = cfg.get("cases", {}).get("concept_tokens", [])
    if not cases:
        return
    
    frag_bins = cfg.get("visualization", {}).get("frag_bins", [1, 3, 5])
    scale_bins = cfg.get("visualization", {}).get("scale_bins", [50, 200, 500])
    frag_labels = ["None", "Low", "Medium", "High"]
    scale_labels = ["Tiny", "Small", "Medium", "Large"]
    
    def bin_value(value, thresholds):
        for i, t in enumerate(thresholds):
            if value <= t:
                return i
        return len(thresholds)
    
    def analyze_et_morphology(et_mask):
        """Analyze ET morphology: size, fragmentation, component sizes."""
        et = et_mask > 0
        et_count = int(et.sum())
        labeled, n_comp = connected_components(et)
        
        comp_sizes = []
        for i in range(1, n_comp + 1):
            comp_sizes.append(int((labeled == i).sum()))
        comp_sizes = sorted(comp_sizes, reverse=True)
        
        frag_bin = bin_value(n_comp, frag_bins)
        scale_bin = bin_value(et_count, scale_bins)
        
        return {
            "total_voxels": et_count,
            "n_components": n_comp,
            "comp_sizes": comp_sizes,
            "frag_bin": frag_bin,
            "frag_label": frag_labels[min(frag_bin, len(frag_labels)-1)],
            "scale_bin": scale_bin,
            "scale_label": scale_labels[min(scale_bin, len(scale_labels)-1)],
            "labeled": labeled,
        }
    
    for case_idx, case_id in enumerate(cases):
        case = case_loader.get_case(case_id, include_label=True)
        pred = pred_loader.load_method(pred_loader.ours, case_id)
        
        images = case["images"]
        ref_mod = "t1c" if "t1c" in images else list(images.keys())[0]
        base = images[ref_mod]
        
        # Analyze morphology
        pred_morph = analyze_et_morphology(pred["regions"][2])
        gt_morph = analyze_et_morphology(label_to_regions(case["label"])[2]) if case["label"] is not None else None
        
        # Find best slice
        mask_ref = pred["regions"][2] if pred_morph["total_voxels"] > 0 else None
        idx = get_slices(mask_ref, base.shape)
        plane = "axial"
        slice_idx = idx[plane]
        base2d = extract_slice(base, plane, slice_idx)
        
        n_comp = pred_morph["n_components"]
        letter = chr(ord('a') + case_idx)
        
        # =====================================================================
        # Figure 1: ET Overview (T1ce + ET mask)
        # =====================================================================
        fig1, axes1 = plt.subplots(1, 2, figsize=(10, 5))
        
        # Left: T1ce
        axes1[0].imshow(base2d, cmap="gray", aspect="equal")
        axes1[0].set_title("T1ce Input", fontsize=STYLE["font_subtitle"], fontweight="bold")
        axes1[0].axis("off")
        
        # Right: ET overlay
        et2d = extract_slice(pred["regions"][2], plane, slice_idx)
        if et2d.shape != base2d.shape:
            zoom_factors = (base2d.shape[0] / et2d.shape[0], base2d.shape[1] / et2d.shape[1])
            et2d = zoom(et2d.astype(float), zoom_factors, order=0)
        overlay = overlay_masks_publication(base2d, {"ET": et2d > 0}, alpha=0.5)
        axes1[1].imshow(overlay, aspect="equal")
        axes1[1].set_title(f"Predicted ET\n({pred_morph['total_voxels']} voxels total)", 
                          fontsize=STYLE["font_subtitle"], fontweight="bold")
        axes1[1].axis("off")
        
        fig1.suptitle(f"ET Prediction Overview: {case_id}", fontsize=STYLE["font_title"], fontweight="bold")
        fig1.tight_layout(rect=[0, 0, 1, 0.92])
        save_figure(fig1, out_dir, f"Fig6_{letter}1_et_overview_{case_id}")
        
        # =====================================================================
        # Figure 2: Fragmentation Analysis (Components → FRAG_BIN)
        # =====================================================================
        fig2, axes2 = plt.subplots(1, 3, figsize=(12, 4.5), gridspec_kw={"width_ratios": [1, 1, 0.8]})
        
        # Left: Connected components visualization
        labeled2d = extract_slice(pred_morph["labeled"], plane, slice_idx)
        if labeled2d.shape != base2d.shape:
            zoom_factors = (base2d.shape[0] / labeled2d.shape[0], base2d.shape[1] / labeled2d.shape[1])
            labeled2d = zoom(labeled2d.astype(float), zoom_factors, order=0)
        
        comp_rgb = np.zeros((*base2d.shape, 3), dtype=np.float32)
        comp_rgb[:] = base2d[:, :, np.newaxis] * 0.3
        
        if n_comp > 0:
            colors = plt.cm.Set1(np.linspace(0, 1, max(n_comp, 3)))[:n_comp]
            for i in range(1, n_comp + 1):
                mask = labeled2d == i
                if mask.sum() > 0:
                    comp_rgb[mask] = colors[i-1][:3]
        
        axes2[0].imshow(comp_rgb, aspect="equal")
        axes2[0].set_title(f"Connected Components\n(n = {n_comp})", 
                          fontsize=STYLE["font_subtitle"], fontweight="bold")
        axes2[0].axis("off")
        
        # Middle: Component sizes bar chart
        if pred_morph["comp_sizes"]:
            comp_sizes = pred_morph["comp_sizes"][:8]
            colors = plt.cm.Set1(np.linspace(0, 1, max(len(comp_sizes), 3)))[:len(comp_sizes)]
            bars = axes2[1].barh(range(len(comp_sizes)), comp_sizes, color=colors, edgecolor="black", linewidth=0.5)
            axes2[1].set_yticks(range(len(comp_sizes)))
            axes2[1].set_yticklabels([f"C{i+1}" for i in range(len(comp_sizes))])
            axes2[1].set_xlabel("Voxels", fontsize=STYLE["font_label"])
            axes2[1].invert_yaxis()
            axes2[1].spines["top"].set_visible(False)
            axes2[1].spines["right"].set_visible(False)
        else:
            axes2[1].text(0.5, 0.5, "No ET", ha="center", va="center", 
                         transform=axes2[1].transAxes, fontsize=STYLE["font_subtitle"])
            axes2[1].axis("off")
        axes2[1].set_title("Component Sizes", fontsize=STYLE["font_subtitle"], fontweight="bold")
        
        # Right: FRAG_BIN selection
        frag_colors = ["#CCCCCC", "#90EE90", "#FFD700", "#FF6347"]
        frag_idx = pred_morph["frag_bin"]
        bar_colors = [frag_colors[i] if i != frag_idx else "#FF0000" for i in range(4)]
        bars = axes2[2].bar(range(4), [1]*4, color=bar_colors, edgecolor="black", linewidth=1.5)
        axes2[2].set_xticks(range(4))
        axes2[2].set_xticklabels(frag_labels, fontsize=STYLE["font_label"], rotation=45, ha="right")
        axes2[2].set_ylim(0, 1.3)
        axes2[2].set_yticks([])
        axes2[2].spines["top"].set_visible(False)
        axes2[2].spines["right"].set_visible(False)
        axes2[2].spines["left"].set_visible(False)
        
        # Arrow pointing to selected bin
        axes2[2].annotate(f"n={n_comp}", xy=(frag_idx, 1.05), xytext=(frag_idx, 1.2),
                         fontsize=STYLE["font_label"], fontweight="bold", ha="center",
                         arrowprops=dict(arrowstyle="->", color="red", lw=2))
        axes2[2].set_title(f"FRAG_BIN = {pred_morph['frag_label']}", 
                          fontsize=STYLE["font_subtitle"], fontweight="bold", color="red")
        
        # Add mapping explanation
        fig2.text(0.5, 0.02, 
                  f"Mapping: {n_comp} components → FRAG_BIN = {pred_morph['frag_label']}  "
                  f"(thresholds: ≤{frag_bins[0]}=None, ≤{frag_bins[1]}=Low, ≤{frag_bins[2]}=Med, >{frag_bins[2]}=High)",
                  ha="center", fontsize=STYLE["font_label"], style="italic")
        
        fig2.suptitle(f"Fragmentation Analysis: {case_id}", fontsize=STYLE["font_title"], fontweight="bold")
        fig2.tight_layout(rect=[0, 0.06, 1, 0.92])
        save_figure(fig2, out_dir, f"Fig6_{letter}2_fragmentation_{case_id}")
        
        # =====================================================================
        # Figure 3: Scale Analysis (Size → SCALE_BIN)
        # =====================================================================
        fig3, axes3 = plt.subplots(1, 2, figsize=(10, 5))
        
        # Left: Size visualization (ET with size info)
        axes3[0].imshow(overlay, aspect="equal")
        total_voxels = pred_morph["total_voxels"]
        axes3[0].set_title(f"ET Region\nTotal: {total_voxels} voxels", 
                          fontsize=STYLE["font_subtitle"], fontweight="bold")
        axes3[0].axis("off")
        
        # Right: SCALE_BIN selection with size ruler
        ax_scale = axes3[1]
        scale_colors = ["#E0E0E0", "#87CEEB", "#4169E1", "#1E3A8A"]
        scale_idx = pred_morph["scale_bin"]
        
        # Create size ranges for visualization
        scale_ranges = [f"≤{scale_bins[0]}", f"{scale_bins[0]+1}-{scale_bins[1]}", 
                       f"{scale_bins[1]+1}-{scale_bins[2]}", f">{scale_bins[2]}"]
        
        bar_colors = [scale_colors[i] if i != scale_idx else "#FF0000" for i in range(4)]
        y_pos = np.arange(4)
        bars = ax_scale.barh(y_pos, [scale_bins[0], scale_bins[1]-scale_bins[0], 
                                     scale_bins[2]-scale_bins[1], scale_bins[2]], 
                            color=scale_colors, edgecolor="black", linewidth=1.5, left=[0, scale_bins[0], scale_bins[1], scale_bins[2]])
        
        # Highlight selected bin
        ax_scale.barh(scale_idx, bars[scale_idx].get_width(), 
                     left=bars[scale_idx].get_x(), color="#FF0000", edgecolor="black", linewidth=2)
        
        ax_scale.set_yticks(y_pos)
        ax_scale.set_yticklabels([f"{scale_labels[i]}\n({scale_ranges[i]})" for i in range(4)], 
                                fontsize=STYLE["font_label"])
        ax_scale.set_xlabel("Voxels", fontsize=STYLE["font_label"])
        ax_scale.spines["top"].set_visible(False)
        ax_scale.spines["right"].set_visible(False)
        
        # Mark current value
        ax_scale.axvline(x=total_voxels, color="red", linestyle="--", linewidth=2, label=f"Current: {total_voxels}")
        ax_scale.legend(loc="upper right", fontsize=STYLE["font_label"])
        ax_scale.set_title(f"SCALE_BIN = {pred_morph['scale_label']}", 
                          fontsize=STYLE["font_subtitle"], fontweight="bold", color="red")
        
        # Add mapping explanation
        fig3.text(0.5, 0.02, 
                  f"Mapping: {total_voxels} voxels → SCALE_BIN = {pred_morph['scale_label']}  "
                  f"(thresholds: ≤{scale_bins[0]}=Tiny, ≤{scale_bins[1]}=Small, ≤{scale_bins[2]}=Med, >{scale_bins[2]}=Large)",
                  ha="center", fontsize=STYLE["font_label"], style="italic")
        
        fig3.suptitle(f"Scale Analysis: {case_id}", fontsize=STYLE["font_title"], fontweight="bold")
        fig3.tight_layout(rect=[0, 0.06, 1, 0.92])
        save_figure(fig3, out_dir, f"Fig6_{letter}3_scale_{case_id}")


# ============================================================================
# I) Dual-domain Enhancement (Spec I)
# ============================================================================
def make_dual_domain(cfg: Dict, case_loader: CaseLoader, aux: AuxCache, runner: Optional[ModelRunner], out_dir: str) -> None:
    """
    Dual-domain enhancement visualization.
    Left: Original amplitude spectrum, Right: Enhanced spectrum (same scale)
    """
    cases = cfg.get("cases", {}).get("dual_domain", [])
    if not cases:
        return
    
    for case_idx, case_id in enumerate(cases):
        case = case_loader.get_case(case_id, include_label=False)
        images = case["images"]
        ref_mod = "t1c" if "t1c" in images else list(images.keys())[0]
        base = images[ref_mod]
        
        aux_data = aux.load(case_id)
        needed_keys = ["x_spec"]
        if aux_data is None or not all(k in aux_data for k in needed_keys):
            if runner is None:
                continue
            image, _ = runner.load_case_tensor(case_id)
            out = runner.forward_intermediate(image)
            new_data = {
                "x_spec": out["x_spec"].detach().cpu().numpy(),
            }
            if aux_data is not None:
                aux_data.update(new_data)
            else:
                aux_data = new_data
            aux.save(case_id, aux_data)
        
        if aux_data is None:
            continue
        
        x_spec = aux_data["x_spec"][0]
        
        # Compute amplitude spectra
        amp_orig = fft_amplitude_slice(base, plane="axial")
        amp_spec = fft_amplitude_slice(x_spec[0] if x_spec.ndim == 4 else x_spec, plane="axial")
        
        # Use same scale for both
        vmin = min(amp_orig.min(), amp_spec.min())
        vmax = max(amp_orig.max(), amp_spec.max())
        
        fig = plt.figure(figsize=(12, 5))
        gs = fig.add_gridspec(1, 3, width_ratios=[1, 1, 0.05], wspace=0.15)
        
        ax0 = fig.add_subplot(gs[0])
        im = ax0.imshow(amp_orig, cmap="inferno", vmin=vmin, vmax=vmax, aspect="equal")
        ax0.set_title("Original Amplitude Spectrum", fontsize=STYLE["font_subtitle"], fontweight="bold")
        ax0.axis("off")
        
        ax1 = fig.add_subplot(gs[1])
        ax1.imshow(amp_spec, cmap="inferno", vmin=vmin, vmax=vmax, aspect="equal")
        ax1.set_title("Enhanced Amplitude Spectrum", fontsize=STYLE["font_subtitle"], fontweight="bold")
        ax1.axis("off")
        
        # Colorbar in dedicated subplot
        ax_cbar = fig.add_subplot(gs[2])
        cbar = fig.colorbar(im, cax=ax_cbar)
        cbar.set_label("Log Amplitude", fontsize=STYLE["font_label"])
        
        fig.suptitle(f"Dual-domain Enhancement: {case_id}", fontsize=STYLE["font_title"], fontweight="bold", y=0.98)
        fig.tight_layout(rect=[0, 0, 1, 0.93])
        save_figure(fig, out_dir, f"Fig7_{chr(ord('a')+case_idx)}_dual_domain_{case_id}")


# ============================================================================
# J) AmpMix Augmentation Robustness (Spec J)
# ============================================================================
def make_ampmix(cfg: Dict, case_loader: CaseLoader, runner: Optional[ModelRunner], out_dir: str) -> None:
    """
    AmpMix augmentation robustness visualization.
    Three columns: Original | AmpMix | Prediction comparison
    """
    pairs = cfg.get("cases", {}).get("ampmix", [])
    if not pairs or runner is None:
        return
    
    for pair_idx, pair in enumerate(pairs):
        case_a = pair.get("base")
        case_b = pair.get("mix")
        lam = float(pair.get("lam", 0.5))
        
        if not case_a or not case_b:
            continue
        
        img_a, _ = runner.load_case_tensor(case_a)
        img_b, _ = runner.load_case_tensor(case_b)
        
        mixed = fourier_amplitude_mix(img_a[0].cpu().numpy(), img_b[0].cpu().numpy(), lam)
        mixed_t = runner.torch.from_numpy(mixed).unsqueeze(0).to(runner.device)
        
        # Get predictions
        with runner.torch.no_grad():
            logits_a, _ = runner.model(img_a.to(runner.device))
            logits_m, _ = runner.model(mixed_t)
        
        pred_a = (logits_a.sigmoid() > 0.5).detach().cpu().numpy()[0]
        pred_m = (logits_m.sigmoid() > 0.5).detach().cpu().numpy()[0]
        
        case = case_loader.get_case(case_a, include_label=False)
        images = case["images"]
        ref_mod = "t1c" if "t1c" in images else list(images.keys())[0]
        base = images[ref_mod]
        
        idx = get_slices(pred_a[2] > 0, base.shape)
        plane = "axial"
        slice_idx = idx[plane]
        
        fig, axes = plt.subplots(1, 3, figsize=(12, 4))
        
        base2d = extract_slice(base, plane, slice_idx)
        mix2d = extract_slice(normalize_volume(mixed[0]), plane, slice_idx)
        
        # Original
        ax = axes[0]
        masks_a = {
            "WT": extract_slice(pred_a[0], plane, slice_idx) > 0,
            "TC": extract_slice(pred_a[1], plane, slice_idx) > 0,
            "ET": extract_slice(pred_a[2], plane, slice_idx) > 0,
        }
        overlay_a = overlay_masks_publication(base2d, masks_a)
        ax.imshow(overlay_a, aspect="equal")
        ax.set_title("Original + Prediction", fontsize=STYLE["font_subtitle"], fontweight="bold")
        ax.axis("off")
        
        # AmpMix image
        ax = axes[1]
        ax.imshow(mix2d, cmap="gray", aspect="equal")
        ax.set_title(f"AmpMix (λ={lam})", fontsize=STYLE["font_subtitle"], fontweight="bold")
        ax.axis("off")
        
        # AmpMix prediction
        ax = axes[2]
        masks_m = {
            "WT": extract_slice(pred_m[0], plane, slice_idx) > 0,
            "TC": extract_slice(pred_m[1], plane, slice_idx) > 0,
            "ET": extract_slice(pred_m[2], plane, slice_idx) > 0,
        }
        overlay_m = overlay_masks_publication(mix2d, masks_m)
        ax.imshow(overlay_m, aspect="equal")
        ax.set_title("AmpMix + Prediction", fontsize=STYLE["font_subtitle"], fontweight="bold")
        ax.axis("off")
        
        finalize_figure(fig, f"Augmentation Robustness: {case_a}")
        save_figure(fig, out_dir, f"Fig8_{chr(ord('a')+pair_idx)}_ampmix_{case_a}")


# ============================================================================
# K) Failure Cases (Spec K)
# ============================================================================
def make_failure(cfg: Dict, case_loader: CaseLoader, pred_loader: PredictionLoader, out_dir: str) -> None:
    """
    Failure case visualization with red boxes highlighting problem areas.
    """
    cases = cfg.get("cases", {}).get("failure", [])
    notes = cfg.get("cases", {}).get("failure_notes", {})
    if not cases:
        return
    
    for case_idx, case_id in enumerate(cases):
        case = case_loader.get_case(case_id, include_label=True)
        images = case["images"]
        ref_mod = "t1c" if "t1c" in images else list(images.keys())[0]
        base = images[ref_mod]
        
        gt_regions = label_to_regions(case["label"]) if case["label"] is not None else None
        pred = pred_loader.load_method(pred_loader.ours, case_id)
        pred_regions = pred["regions"]
        
        mask_ref = gt_regions[2] if gt_regions is not None else pred_regions[2]
        idx = get_slices(mask_ref, base.shape)
        plane = "axial"
        slice_idx = idx[plane]
        
        fig, axes = plt.subplots(1, 3 if gt_regions is not None else 2, figsize=(10, 4))
        
        base2d = extract_slice(base, plane, slice_idx)
        
        col = 0
        # Base image
        ax = axes[col]
        ax.imshow(base2d, cmap="gray", aspect="equal")
        ax.set_title("Input", fontsize=STYLE["font_subtitle"], fontweight="bold")
        ax.axis("off")
        col += 1
        
        # GT
        if gt_regions is not None:
            ax = axes[col]
            gt_masks = {
                "WT": extract_slice(gt_regions[0], plane, slice_idx) > 0,
                "TC": extract_slice(gt_regions[1], plane, slice_idx) > 0,
                "ET": extract_slice(gt_regions[2], plane, slice_idx) > 0,
            }
            overlay_gt = overlay_masks_publication(base2d, gt_masks)
            ax.imshow(overlay_gt, aspect="equal")
            ax.set_title("Ground Truth", fontsize=STYLE["font_subtitle"], fontweight="bold")
            ax.axis("off")
            col += 1
        
        # Prediction with failure region highlighted
        ax = axes[col]
        pred_masks = {
            "WT": extract_slice(pred_regions[0], plane, slice_idx) > 0,
            "TC": extract_slice(pred_regions[1], plane, slice_idx) > 0,
            "ET": extract_slice(pred_regions[2], plane, slice_idx) > 0,
        }
        # Resize if needed
        for key in pred_masks:
            if pred_masks[key].shape != base2d.shape:
                zoom_factors = (base2d.shape[0] / pred_masks[key].shape[0], base2d.shape[1] / pred_masks[key].shape[1])
                pred_masks[key] = zoom(pred_masks[key].astype(float), zoom_factors, order=0) > 0.5
        
        overlay_pred = overlay_masks_publication(base2d, pred_masks)
        ax.imshow(overlay_pred, aspect="equal")
        
        # Find error region and add red box
        if gt_regions is not None:
            gt_et = extract_slice(gt_regions[2], plane, slice_idx) > 0
            pred_et = pred_masks["ET"]
            error_region = np.logical_xor(gt_et, pred_et)
            if error_region.sum() > 0:
                coords = np.where(error_region)
                y_min, y_max = coords[0].min(), coords[0].max()
                x_min, x_max = coords[1].min(), coords[1].max()
                margin = 10
                rect = Rectangle((x_min - margin, y_min - margin), 
                                  x_max - x_min + 2*margin, y_max - y_min + 2*margin,
                                  linewidth=2, edgecolor='red', facecolor='none')
                ax.add_patch(rect)
        
        ax.set_title("Prediction", fontsize=STYLE["font_subtitle"], fontweight="bold")
        ax.axis("off")
        
        # Add note
        note = notes.get(case_id, "")
        title = f"Failure Case: {case_id}" + (f"\n{note}" if note else "")
        finalize_figure(fig, title)
        save_figure(fig, out_dir, f"Fig9_{chr(ord('a')+case_idx)}_failure_{case_id}")


# ============================================================================
# Main
# ============================================================================
def main():
    parser = argparse.ArgumentParser(description="Publication-quality visualization suite")
    parser.add_argument("--config", required=True, help="Visualization config yaml")
    parser.add_argument("--model-config", default=os.path.join(ROOT_DIR, "configs/train.yaml"), help="Model config yaml")
    parser.add_argument("--checkpoint", default="", help="Model checkpoint for model-based visualizations")
    parser.add_argument("--device", default="cuda")
    parser.add_argument("--run", default="all", help="Comma-separated list or 'all'")
    args = parser.parse_args()
    
    cfg = load_config(args.config)
    out_dir = cfg.get("visualization", {}).get("output_dir", os.path.join(ROOT_DIR, "vis_res"))
    ensure_dir(out_dir)
    
    case_loader = CaseLoader(cfg)
    pred_loader = PredictionLoader(cfg)
    aux_cache = AuxCache(cfg.get("predictions", {}).get("aux_dir"))
    runner = ModelRunner(cfg, args.model_config, args.checkpoint, args.device) if args.checkpoint else None
    
    run_set = set([s.strip() for s in args.run.split(",")]) if args.run != "all" else None
    
    def should_run(name: str) -> bool:
        return run_set is None or name in run_set
    
    print("=" * 60)
    print("Publication-Quality Visualization Suite")
    print("=" * 60)
    
    if should_run("qualitative"):
        print("\n[B] Generating qualitative comparison...")
        make_qualitative(cfg, case_loader, pred_loader, out_dir)
    
    if should_run("et_absent"):
        print("\n[C] Generating ET-absent case study...")
        make_et_absent(cfg, case_loader, aux_cache, runner, out_dir)
    
    if should_run("boundary"):
        print("\n[D] Generating boundary error visualization...")
        make_boundary(cfg, case_loader, pred_loader, out_dir)
    
    if should_run("tiny_et"):
        print("\n[E] Generating tiny/fragmented ET visualization...")
        make_tiny_et(cfg, case_loader, pred_loader, out_dir)
    
    if should_run("moe"):
        print("\n[G] Generating MoE routing visualization...")
        make_moe_routing(cfg, case_loader, aux_cache, runner, out_dir)
    
    if should_run("concept_tokens"):
        print("\n[H] Generating concept token visualization...")
        make_concept_tokens(cfg, case_loader, pred_loader, out_dir)
    
    if should_run("dual_domain"):
        print("\n[I] Generating dual-domain enhancement visualization...")
        make_dual_domain(cfg, case_loader, aux_cache, runner, out_dir)
    
    if should_run("ampmix"):
        print("\n[J] Generating AmpMix robustness visualization...")
        make_ampmix(cfg, case_loader, runner, out_dir)
    
    if should_run("failure"):
        print("\n[K] Generating failure case visualization...")
        make_failure(cfg, case_loader, pred_loader, out_dir)
    
    print("\n" + "=" * 60)
    print(f"All visualizations saved to: {out_dir}")
    print("=" * 60)


if __name__ == "__main__":
    main()