"""
Safe Primitives for S-Expression Effects

These are the building blocks that user-defined effects can use.
All primitives operate only on image data - no filesystem, network, etc.
"""

import numpy as np
import cv2
from typing import Any, Callable, Dict, List, Tuple, Optional
from dataclasses import dataclass
import math


@dataclass
class ZoneContext:
    """Context for a single cell/zone in ASCII art grid."""
    row: int
    col: int
    row_norm: float  # Normalized row position 0-1
    col_norm: float  # Normalized col position 0-1
    luminance: float  # Cell luminance 0-1
    saturation: float  # Cell saturation 0-1
    hue: float  # Cell hue 0-360
    r: float  # Red component 0-1
    g: float  # Green component 0-1
    b: float  # Blue component 0-1


class DeterministicRNG:
    """Seeded RNG for reproducible effects."""

    def __init__(self, seed: int = 42):
        self._rng = np.random.RandomState(seed)

    def random(self, low: float = 0, high: float = 1) -> float:
        return self._rng.uniform(low, high)

    def randint(self, low: int, high: int) -> int:
        return self._rng.randint(low, high + 1)

    def gaussian(self, mean: float = 0, std: float = 1) -> float:
        return self._rng.normal(mean, std)


# Global RNG instance (reset per frame with seed param)
_rng = DeterministicRNG()


def reset_rng(seed: int):
    """Reset the global RNG with a new seed."""
    global _rng
    _rng = DeterministicRNG(seed)


# =============================================================================
# Color Names (FFmpeg/X11 compatible)
# =============================================================================

NAMED_COLORS = {
    # Basic colors
    "black": (0, 0, 0),
    "white": (255, 255, 255),
    "red": (255, 0, 0),
    "green": (0, 128, 0),
    "blue": (0, 0, 255),
    "yellow": (255, 255, 0),
    "cyan": (0, 255, 255),
    "magenta": (255, 0, 255),

    # Grays
    "gray": (128, 128, 128),
    "grey": (128, 128, 128),
    "darkgray": (169, 169, 169),
    "darkgrey": (169, 169, 169),
    "lightgray": (211, 211, 211),
    "lightgrey": (211, 211, 211),
    "dimgray": (105, 105, 105),
    "dimgrey": (105, 105, 105),
    "silver": (192, 192, 192),

    # Reds
    "darkred": (139, 0, 0),
    "firebrick": (178, 34, 34),
    "crimson": (220, 20, 60),
    "indianred": (205, 92, 92),
    "lightcoral": (240, 128, 128),
    "salmon": (250, 128, 114),
    "darksalmon": (233, 150, 122),
    "lightsalmon": (255, 160, 122),
    "tomato": (255, 99, 71),
    "orangered": (255, 69, 0),
    "coral": (255, 127, 80),

    # Oranges
    "orange": (255, 165, 0),
    "darkorange": (255, 140, 0),

    # Yellows
    "gold": (255, 215, 0),
    "lightyellow": (255, 255, 224),
    "lemonchiffon": (255, 250, 205),
    "papayawhip": (255, 239, 213),
    "moccasin": (255, 228, 181),
    "peachpuff": (255, 218, 185),
    "palegoldenrod": (238, 232, 170),
    "khaki": (240, 230, 140),
    "darkkhaki": (189, 183, 107),

    # Greens
    "lime": (0, 255, 0),
    "limegreen": (50, 205, 50),
    "forestgreen": (34, 139, 34),
    "darkgreen": (0, 100, 0),
    "seagreen": (46, 139, 87),
    "mediumseagreen": (60, 179, 113),
    "springgreen": (0, 255, 127),
    "mediumspringgreen": (0, 250, 154),
    "lightgreen": (144, 238, 144),
    "palegreen": (152, 251, 152),
    "darkseagreen": (143, 188, 143),
    "greenyellow": (173, 255, 47),
    "chartreuse": (127, 255, 0),
    "lawngreen": (124, 252, 0),
    "olivedrab": (107, 142, 35),
    "olive": (128, 128, 0),
    "darkolivegreen": (85, 107, 47),
    "yellowgreen": (154, 205, 50),

    # Cyans/Teals
    "aqua": (0, 255, 255),
    "teal": (0, 128, 128),
    "darkcyan": (0, 139, 139),
    "lightcyan": (224, 255, 255),
    "aquamarine": (127, 255, 212),
    "mediumaquamarine": (102, 205, 170),
    "paleturquoise": (175, 238, 238),
    "turquoise": (64, 224, 208),
    "mediumturquoise": (72, 209, 204),
    "darkturquoise": (0, 206, 209),
    "cadetblue": (95, 158, 160),

    # Blues
    "navy": (0, 0, 128),
    "darkblue": (0, 0, 139),
    "mediumblue": (0, 0, 205),
    "royalblue": (65, 105, 225),
    "cornflowerblue": (100, 149, 237),
    "steelblue": (70, 130, 180),
    "dodgerblue": (30, 144, 255),
    "deepskyblue": (0, 191, 255),
    "lightskyblue": (135, 206, 250),
    "skyblue": (135, 206, 235),
    "lightsteelblue": (176, 196, 222),
    "lightblue": (173, 216, 230),
    "powderblue": (176, 224, 230),
    "slateblue": (106, 90, 205),
    "mediumslateblue": (123, 104, 238),
    "darkslateblue": (72, 61, 139),
    "midnightblue": (25, 25, 112),

    # Purples/Violets
    "purple": (128, 0, 128),
    "darkmagenta": (139, 0, 139),
    "darkviolet": (148, 0, 211),
    "blueviolet": (138, 43, 226),
    "darkorchid": (153, 50, 204),
    "mediumorchid": (186, 85, 211),
    "orchid": (218, 112, 214),
    "violet": (238, 130, 238),
    "plum": (221, 160, 221),
    "thistle": (216, 191, 216),
    "lavender": (230, 230, 250),
    "indigo": (75, 0, 130),
    "mediumpurple": (147, 112, 219),
    "fuchsia": (255, 0, 255),
    "hotpink": (255, 105, 180),
    "deeppink": (255, 20, 147),
    "mediumvioletred": (199, 21, 133),
    "palevioletred": (219, 112, 147),

    # Pinks
    "pink": (255, 192, 203),
    "lightpink": (255, 182, 193),
    "mistyrose": (255, 228, 225),

    # Browns
    "brown": (165, 42, 42),
    "maroon": (128, 0, 0),
    "saddlebrown": (139, 69, 19),
    "sienna": (160, 82, 45),
    "chocolate": (210, 105, 30),
    "peru": (205, 133, 63),
    "sandybrown": (244, 164, 96),
    "burlywood": (222, 184, 135),
    "tan": (210, 180, 140),
    "rosybrown": (188, 143, 143),
    "goldenrod": (218, 165, 32),
    "darkgoldenrod": (184, 134, 11),

    # Whites
    "snow": (255, 250, 250),
    "honeydew": (240, 255, 240),
    "mintcream": (245, 255, 250),
    "azure": (240, 255, 255),
    "aliceblue": (240, 248, 255),
    "ghostwhite": (248, 248, 255),
    "whitesmoke": (245, 245, 245),
    "seashell": (255, 245, 238),
    "beige": (245, 245, 220),
    "oldlace": (253, 245, 230),
    "floralwhite": (255, 250, 240),
    "ivory": (255, 255, 240),
    "antiquewhite": (250, 235, 215),
    "linen": (250, 240, 230),
    "lavenderblush": (255, 240, 245),
    "wheat": (245, 222, 179),
    "cornsilk": (255, 248, 220),
    "blanchedalmond": (255, 235, 205),
    "bisque": (255, 228, 196),
    "navajowhite": (255, 222, 173),

    # Special
    "transparent": (0, 0, 0),  # Note: no alpha support, just black
}


def parse_color(color_spec: str) -> Optional[Tuple[int, int, int]]:
    """
    Parse a color specification into RGB tuple.

    Supports:
    - Named colors: "red", "green", "lime", "navy", etc.
    - Hex colors: "#FF0000", "#f00", "0xFF0000"
    - Special modes: "color", "mono", "invert" return None (handled separately)

    Returns:
        RGB tuple (r, g, b) or None for special modes
    """
    if color_spec is None:
        return None

    color_spec = str(color_spec).strip().lower()

    # Special modes handled elsewhere
    if color_spec in ("color", "mono", "invert"):
        return None

    # Check named colors
    if color_spec in NAMED_COLORS:
        return NAMED_COLORS[color_spec]

    # Handle hex colors
    hex_str = None
    if color_spec.startswith("#"):
        hex_str = color_spec[1:]
    elif color_spec.startswith("0x"):
        hex_str = color_spec[2:]
    elif all(c in "0123456789abcdef" for c in color_spec) and len(color_spec) in (3, 6):
        hex_str = color_spec

    if hex_str:
        try:
            if len(hex_str) == 3:
                # Short form: #RGB -> #RRGGBB
                r = int(hex_str[0] * 2, 16)
                g = int(hex_str[1] * 2, 16)
                b = int(hex_str[2] * 2, 16)
                return (r, g, b)
            elif len(hex_str) == 6:
                r = int(hex_str[0:2], 16)
                g = int(hex_str[2:4], 16)
                b = int(hex_str[4:6], 16)
                return (r, g, b)
        except ValueError:
            pass

    # Unknown color - default to None (will use original colors)
    return None


# =============================================================================
# Image Primitives
# =============================================================================

def prim_width(img: np.ndarray) -> int:
    """Get image width."""
    return img.shape[1]


def prim_height(img: np.ndarray) -> int:
    """Get image height."""
    return img.shape[0]


def prim_make_image(w: int, h: int, color: List[int]) -> np.ndarray:
    """Create a new image filled with color."""
    img = np.zeros((int(h), int(w), 3), dtype=np.uint8)
    if color:
        img[:, :] = color[:3]
    return img


def prim_copy(img: np.ndarray) -> np.ndarray:
    """Copy an image."""
    return img.copy()


def prim_pixel(img: np.ndarray, x: int, y: int) -> List[int]:
    """Get pixel at (x, y) as [r, g, b]."""
    h, w = img.shape[:2]
    x, y = int(x), int(y)
    if 0 <= x < w and 0 <= y < h:
        return list(img[y, x])
    return [0, 0, 0]


def prim_set_pixel(img: np.ndarray, x: int, y: int, color: List[int]) -> np.ndarray:
    """Set pixel at (x, y). Returns modified image."""
    h, w = img.shape[:2]
    x, y = int(x), int(y)
    if 0 <= x < w and 0 <= y < h:
        img[y, x] = color[:3]
    return img


def prim_sample(img: np.ndarray, x: float, y: float) -> List[float]:
    """Bilinear sample at float coordinates."""
    h, w = img.shape[:2]
    x = np.clip(x, 0, w - 1)
    y = np.clip(y, 0, h - 1)

    x0, y0 = int(x), int(y)
    x1, y1 = min(x0 + 1, w - 1), min(y0 + 1, h - 1)
    fx, fy = x - x0, y - y0

    c00 = img[y0, x0].astype(float)
    c10 = img[y0, x1].astype(float)
    c01 = img[y1, x0].astype(float)
    c11 = img[y1, x1].astype(float)

    c = (c00 * (1 - fx) * (1 - fy) +
         c10 * fx * (1 - fy) +
         c01 * (1 - fx) * fy +
         c11 * fx * fy)

    return list(c)


def prim_channel(img: np.ndarray, c: int) -> np.ndarray:
    """Extract a single channel as 2D array."""
    return img[:, :, int(c)].copy()


def prim_merge_channels(r: np.ndarray, g: np.ndarray, b: np.ndarray) -> np.ndarray:
    """Merge three channels into RGB image."""
    return np.stack([r, g, b], axis=-1).astype(np.uint8)


def prim_resize(img: np.ndarray, w: int, h: int, mode: str = "linear") -> np.ndarray:
    """Resize image. Mode: linear, nearest, area."""
    w, h = int(w), int(h)
    if w < 1 or h < 1:
        return img
    interp = {
        "linear": cv2.INTER_LINEAR,
        "nearest": cv2.INTER_NEAREST,
        "area": cv2.INTER_AREA,
    }.get(mode, cv2.INTER_LINEAR)
    return cv2.resize(img, (w, h), interpolation=interp)


def prim_crop(img: np.ndarray, x: int, y: int, w: int, h: int) -> np.ndarray:
    """Crop a region from image."""
    ih, iw = img.shape[:2]
    x, y, w, h = int(x), int(y), int(w), int(h)
    x = max(0, min(x, iw))
    y = max(0, min(y, ih))
    w = max(0, min(w, iw - x))
    h = max(0, min(h, ih - y))
    return img[y:y + h, x:x + w].copy()


def prim_paste(dst: np.ndarray, src: np.ndarray, x: int, y: int) -> np.ndarray:
    """Paste src onto dst at position (x, y)."""
    dh, dw = dst.shape[:2]
    sh, sw = src.shape[:2]
    x, y = int(x), int(y)

    # Calculate valid regions
    sx1 = max(0, -x)
    sy1 = max(0, -y)
    sx2 = min(sw, dw - x)
    sy2 = min(sh, dh - y)

    dx1 = max(0, x)
    dy1 = max(0, y)
    dx2 = dx1 + (sx2 - sx1)
    dy2 = dy1 + (sy2 - sy1)

    if dx2 > dx1 and dy2 > dy1:
        dst[dy1:dy2, dx1:dx2] = src[sy1:sy2, sx1:sx2]

    return dst


# =============================================================================
# Color Primitives
# =============================================================================

def prim_rgb(r: float, g: float, b: float) -> List[int]:
    """Create RGB color."""
    return [int(np.clip(r, 0, 255)),
            int(np.clip(g, 0, 255)),
            int(np.clip(b, 0, 255))]


def prim_red(c: List[int]) -> int:
    return c[0] if c else 0


def prim_green(c: List[int]) -> int:
    return c[1] if len(c) > 1 else 0


def prim_blue(c: List[int]) -> int:
    return c[2] if len(c) > 2 else 0


def prim_luminance(c: List[int]) -> float:
    """Calculate luminance (grayscale value)."""
    if not c:
        return 0
    return 0.299 * c[0] + 0.587 * c[1] + 0.114 * c[2]


def prim_rgb_to_hsv(c: List[int]) -> List[float]:
    """Convert RGB to HSV."""
    r, g, b = c[0] / 255, c[1] / 255, c[2] / 255
    mx, mn = max(r, g, b), min(r, g, b)
    diff = mx - mn

    if diff == 0:
        h = 0
    elif mx == r:
        h = (60 * ((g - b) / diff) + 360) % 360
    elif mx == g:
        h = (60 * ((b - r) / diff) + 120) % 360
    else:
        h = (60 * ((r - g) / diff) + 240) % 360

    s = 0 if mx == 0 else diff / mx
    v = mx

    return [h, s * 100, v * 100]


def prim_hsv_to_rgb(hsv: List[float]) -> List[int]:
    """Convert HSV to RGB."""
    h, s, v = hsv[0], hsv[1] / 100, hsv[2] / 100
    c = v * s
    x = c * (1 - abs((h / 60) % 2 - 1))
    m = v - c

    if h < 60:
        r, g, b = c, x, 0
    elif h < 120:
        r, g, b = x, c, 0
    elif h < 180:
        r, g, b = 0, c, x
    elif h < 240:
        r, g, b = 0, x, c
    elif h < 300:
        r, g, b = x, 0, c
    else:
        r, g, b = c, 0, x

    return [int((r + m) * 255), int((g + m) * 255), int((b + m) * 255)]


def prim_blend_color(c1: List[int], c2: List[int], alpha: float) -> List[int]:
    """Blend two colors."""
    alpha = np.clip(alpha, 0, 1)
    return [int(c1[i] * (1 - alpha) + c2[i] * alpha) for i in range(3)]


def prim_average_color(img: np.ndarray) -> List[int]:
    """Get average color of image/region."""
    return [int(x) for x in img.mean(axis=(0, 1))]


# =============================================================================
# Image Operations (Bulk)
# =============================================================================

def prim_map_pixels(img: np.ndarray, fn: Callable) -> np.ndarray:
    """Apply function to each pixel: fn(x, y, [r,g,b]) -> [r,g,b]."""
    result = img.copy()
    h, w = img.shape[:2]
    for y in range(h):
        for x in range(w):
            color = list(img[y, x])
            new_color = fn(x, y, color)
            if new_color is not None:
                result[y, x] = new_color[:3]
    return result


def prim_map_rows(img: np.ndarray, fn: Callable) -> np.ndarray:
    """Apply function to each row: fn(y, row) -> row."""
    result = img.copy()
    h = img.shape[0]
    for y in range(h):
        row = img[y].copy()
        new_row = fn(y, row)
        if new_row is not None:
            result[y] = new_row
    return result


def prim_for_grid(img: np.ndarray, cell_size: int, fn: Callable) -> np.ndarray:
    """Iterate over grid cells: fn(gx, gy, cell_img) for side effects."""
    cell_size = max(1, int(cell_size))
    h, w = img.shape[:2]
    rows = h // cell_size
    cols = w // cell_size

    for gy in range(rows):
        for gx in range(cols):
            y, x = gy * cell_size, gx * cell_size
            cell = img[y:y + cell_size, x:x + cell_size]
            fn(gx, gy, cell)

    return img


def prim_fold_pixels(img: np.ndarray, init: Any, fn: Callable) -> Any:
    """Fold over pixels: fn(acc, x, y, color) -> acc."""
    acc = init
    h, w = img.shape[:2]
    for y in range(h):
        for x in range(w):
            color = list(img[y, x])
            acc = fn(acc, x, y, color)
    return acc


# =============================================================================
# Convolution / Filters
# =============================================================================

def prim_convolve(img: np.ndarray, kernel: List[List[float]]) -> np.ndarray:
    """Apply convolution kernel."""
    k = np.array(kernel, dtype=np.float32)
    return cv2.filter2D(img, -1, k)


def prim_blur(img: np.ndarray, radius: int) -> np.ndarray:
    """Gaussian blur."""
    radius = max(1, int(radius))
    ksize = radius * 2 + 1
    return cv2.GaussianBlur(img, (ksize, ksize), 0)


def prim_box_blur(img: np.ndarray, radius: int) -> np.ndarray:
    """Box blur (faster than Gaussian)."""
    radius = max(1, int(radius))
    ksize = radius * 2 + 1
    return cv2.blur(img, (ksize, ksize))


def prim_edges(img: np.ndarray, low: int = 50, high: int = 150) -> np.ndarray:
    """Canny edge detection, returns grayscale edges."""
    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
    edges = cv2.Canny(gray, int(low), int(high))
    return cv2.cvtColor(edges, cv2.COLOR_GRAY2RGB)


def prim_sobel(img: np.ndarray) -> np.ndarray:
    """Sobel edge detection."""
    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY).astype(np.float32)
    sx = cv2.Sobel(gray, cv2.CV_32F, 1, 0)
    sy = cv2.Sobel(gray, cv2.CV_32F, 0, 1)
    magnitude = np.sqrt(sx ** 2 + sy ** 2)
    magnitude = np.clip(magnitude, 0, 255).astype(np.uint8)
    return cv2.cvtColor(magnitude, cv2.COLOR_GRAY2RGB)


def prim_dilate(img: np.ndarray, size: int = 1) -> np.ndarray:
    """Morphological dilation."""
    kernel = np.ones((size, size), np.uint8)
    return cv2.dilate(img, kernel, iterations=1)


def prim_erode(img: np.ndarray, size: int = 1) -> np.ndarray:
    """Morphological erosion."""
    kernel = np.ones((size, size), np.uint8)
    return cv2.erode(img, kernel, iterations=1)


# =============================================================================
# Geometric Transforms
# =============================================================================

def prim_translate(img: np.ndarray, dx: float, dy: float) -> np.ndarray:
    """Translate image."""
    h, w = img.shape[:2]
    M = np.float32([[1, 0, dx], [0, 1, dy]])
    return cv2.warpAffine(img, M, (w, h), borderMode=cv2.BORDER_REFLECT)


def prim_rotate(img: np.ndarray, angle: float, cx: float = None, cy: float = None) -> np.ndarray:
    """Rotate image around center."""
    h, w = img.shape[:2]
    if cx is None:
        cx = w / 2
    if cy is None:
        cy = h / 2
    M = cv2.getRotationMatrix2D((cx, cy), angle, 1.0)
    return cv2.warpAffine(img, M, (w, h), borderMode=cv2.BORDER_REFLECT)


def prim_scale(img: np.ndarray, sx: float, sy: float, cx: float = None, cy: float = None) -> np.ndarray:
    """Scale image around center."""
    h, w = img.shape[:2]
    if cx is None:
        cx = w / 2
    if cy is None:
        cy = h / 2

    M = np.float32([
        [sx, 0, cx * (1 - sx)],
        [0, sy, cy * (1 - sy)]
    ])
    return cv2.warpAffine(img, M, (w, h), borderMode=cv2.BORDER_REFLECT)


def prim_flip_h(img: np.ndarray) -> np.ndarray:
    """Flip horizontally."""
    return cv2.flip(img, 1)


def prim_flip_v(img: np.ndarray) -> np.ndarray:
    """Flip vertically."""
    return cv2.flip(img, 0)


def prim_remap(img: np.ndarray, map_x: np.ndarray, map_y: np.ndarray) -> np.ndarray:
    """Remap using coordinate maps."""
    return cv2.remap(img, map_x.astype(np.float32), map_y.astype(np.float32),
                     cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT)


def prim_make_coords(w: int, h: int) -> Tuple[np.ndarray, np.ndarray]:
    """Create coordinate grid (map_x, map_y)."""
    map_x = np.tile(np.arange(w, dtype=np.float32), (h, 1))
    map_y = np.tile(np.arange(h, dtype=np.float32).reshape(-1, 1), (1, w))
    return map_x, map_y


# =============================================================================
# Blending
# =============================================================================

def prim_blend_images(a: np.ndarray, b: np.ndarray, alpha: float) -> np.ndarray:
    """Blend two images. Auto-resizes b to match a if sizes differ."""
    alpha = np.clip(alpha, 0, 1)
    # Auto-resize b to match a if different sizes
    if a.shape[:2] != b.shape[:2]:
        b = cv2.resize(b, (a.shape[1], a.shape[0]), interpolation=cv2.INTER_LINEAR)
    return (a.astype(float) * (1 - alpha) + b.astype(float) * alpha).astype(np.uint8)


def prim_blend_mode(a: np.ndarray, b: np.ndarray, mode: str) -> np.ndarray:
    """Blend with various modes: add, multiply, screen, overlay, difference.
    Auto-resizes b to match a if sizes differ."""
    # Auto-resize b to match a if different sizes
    if a.shape[:2] != b.shape[:2]:
        b = cv2.resize(b, (a.shape[1], a.shape[0]), interpolation=cv2.INTER_LINEAR)
    af = a.astype(float) / 255
    bf = b.astype(float) / 255

    if mode == "add":
        result = af + bf
    elif mode == "multiply":
        result = af * bf
    elif mode == "screen":
        result = 1 - (1 - af) * (1 - bf)
    elif mode == "overlay":
        mask = af < 0.5
        result = np.where(mask, 2 * af * bf, 1 - 2 * (1 - af) * (1 - bf))
    elif mode == "difference":
        result = np.abs(af - bf)
    elif mode == "lighten":
        result = np.maximum(af, bf)
    elif mode == "darken":
        result = np.minimum(af, bf)
    else:
        result = af

    return (np.clip(result, 0, 1) * 255).astype(np.uint8)


def prim_mask(img: np.ndarray, mask_img: np.ndarray) -> np.ndarray:
    """Apply grayscale mask to image."""
    if len(mask_img.shape) == 3:
        mask = cv2.cvtColor(mask_img, cv2.COLOR_RGB2GRAY)
    else:
        mask = mask_img
    mask_f = mask.astype(float) / 255
    result = img.astype(float) * mask_f[:, :, np.newaxis]
    return result.astype(np.uint8)


# =============================================================================
# Drawing
# =============================================================================

# Simple font (5x7 bitmap characters)
FONT_5X7 = {
    ' ': [0, 0, 0, 0, 0, 0, 0],
    '.': [0, 0, 0, 0, 0, 0, 4],
    ':': [0, 0, 4, 0, 4, 0, 0],
    '-': [0, 0, 0, 14, 0, 0, 0],
    '=': [0, 0, 14, 0, 14, 0, 0],
    '+': [0, 4, 4, 31, 4, 4, 0],
    '*': [0, 4, 21, 14, 21, 4, 0],
    '#': [10, 31, 10, 10, 31, 10, 0],
    '%': [19, 19, 4, 8, 25, 25, 0],
    '@': [14, 17, 23, 21, 23, 16, 14],
    '0': [14, 17, 19, 21, 25, 17, 14],
    '1': [4, 12, 4, 4, 4, 4, 14],
    '2': [14, 17, 1, 2, 4, 8, 31],
    '3': [31, 2, 4, 2, 1, 17, 14],
    '4': [2, 6, 10, 18, 31, 2, 2],
    '5': [31, 16, 30, 1, 1, 17, 14],
    '6': [6, 8, 16, 30, 17, 17, 14],
    '7': [31, 1, 2, 4, 8, 8, 8],
    '8': [14, 17, 17, 14, 17, 17, 14],
    '9': [14, 17, 17, 15, 1, 2, 12],
}

# Add uppercase letters
for i, c in enumerate('ABCDEFGHIJKLMNOPQRSTUVWXYZ'):
    FONT_5X7[c] = [0] * 7  # Placeholder


def prim_draw_char(img: np.ndarray, char: str, x: int, y: int,
                   size: int, color: List[int]) -> np.ndarray:
    """Draw a character at position."""
    # Use OpenCV's built-in font for simplicity
    font = cv2.FONT_HERSHEY_SIMPLEX
    scale = size / 20.0
    thickness = max(1, int(size / 10))
    cv2.putText(img, char, (int(x), int(y + size)), font, scale, tuple(color[:3]), thickness)
    return img


def prim_draw_text(img: np.ndarray, text: str, x: int, y: int,
                   size: int, color: List[int]) -> np.ndarray:
    """Draw text at position."""
    font = cv2.FONT_HERSHEY_SIMPLEX
    scale = size / 20.0
    thickness = max(1, int(size / 10))
    cv2.putText(img, text, (int(x), int(y + size)), font, scale, tuple(color[:3]), thickness)
    return img


def prim_fill_rect(img: np.ndarray, x: int, y: int, w: int, h: int,
                   color: List[int]) -> np.ndarray:
    """Fill rectangle."""
    x, y, w, h = int(x), int(y), int(w), int(h)
    img[y:y + h, x:x + w] = color[:3]
    return img


def prim_draw_line(img: np.ndarray, x1: int, y1: int, x2: int, y2: int,
                   color: List[int], thickness: int = 1) -> np.ndarray:
    """Draw line."""
    cv2.line(img, (int(x1), int(y1)), (int(x2), int(y2)), tuple(color[:3]), int(thickness))
    return img


# =============================================================================
# Math Primitives
# =============================================================================

def prim_sin(x: float) -> float:
    return math.sin(x)


def prim_cos(x: float) -> float:
    return math.cos(x)


def prim_tan(x: float) -> float:
    return math.tan(x)


def prim_atan2(y: float, x: float) -> float:
    return math.atan2(y, x)


def prim_sqrt(x: float) -> float:
    return math.sqrt(max(0, x))


def prim_pow(x: float, y: float) -> float:
    return math.pow(x, y)


def prim_abs(x: float) -> float:
    return abs(x)


def prim_floor(x: float) -> int:
    return int(math.floor(x))


def prim_ceil(x: float) -> int:
    return int(math.ceil(x))


def prim_round(x: float) -> int:
    return int(round(x))


def prim_min(*args) -> float:
    return min(args)


def prim_max(*args) -> float:
    return max(args)


def prim_clamp(x: float, lo: float, hi: float) -> float:
    return max(lo, min(hi, x))


def prim_lerp(a: float, b: float, t: float) -> float:
    """Linear interpolation."""
    return a + (b - a) * t


def prim_mod(a: float, b: float) -> float:
    return a % b


def prim_random(lo: float = 0, hi: float = 1) -> float:
    """Random number from global RNG."""
    return _rng.random(lo, hi)


def prim_randint(lo: int, hi: int) -> int:
    """Random integer from global RNG."""
    return _rng.randint(lo, hi)


def prim_gaussian(mean: float = 0, std: float = 1) -> float:
    """Gaussian random from global RNG."""
    return _rng.gaussian(mean, std)


def prim_assert(condition, message: str = "Assertion failed"):
    """Assert that condition is true, raise error with message if false."""
    if not condition:
        raise RuntimeError(f"Assertion error: {message}")
    return True


# =============================================================================
# Array/List Primitives
# =============================================================================

def prim_length(seq) -> int:
    return len(seq)


def prim_nth(seq, i: int):
    i = int(i)
    if 0 <= i < len(seq):
        return seq[i]
    return None


def prim_first(seq):
    return seq[0] if seq else None


def prim_rest(seq):
    return seq[1:] if seq else []


def prim_take(seq, n: int):
    return seq[:int(n)]


def prim_drop(seq, n: int):
    return seq[int(n):]


def prim_cons(x, seq):
    return [x] + list(seq)


def prim_append(*seqs):
    result = []
    for s in seqs:
        result.extend(s)
    return result


def prim_reverse(seq):
    return list(reversed(seq))


def prim_range(start: int, end: int, step: int = 1) -> List[int]:
    return list(range(int(start), int(end), int(step)))


def prim_roll(arr: np.ndarray, shift: int, axis: int = 0) -> np.ndarray:
    """Circular roll of array."""
    return np.roll(arr, int(shift), axis=int(axis))


def prim_list(*args) -> list:
    """Create a list."""
    return list(args)


# =============================================================================
# Primitive Registry
# =============================================================================

def prim_add(*args):
    return sum(args)

def prim_sub(a, b=None):
    if b is None:
        return -a  # Unary negation
    return a - b

def prim_mul(*args):
    result = 1
    for x in args:
        result *= x
    return result

def prim_div(a, b):
    return a / b if b != 0 else 0

def prim_lt(a, b):
    return a < b

def prim_gt(a, b):
    return a > b

def prim_le(a, b):
    return a <= b

def prim_ge(a, b):
    return a >= b

def prim_eq(a, b):
    # Handle None/nil comparisons with numpy arrays
    if a is None:
        return b is None
    if b is None:
        return a is None
    if isinstance(a, np.ndarray) or isinstance(b, np.ndarray):
        if isinstance(a, np.ndarray) and isinstance(b, np.ndarray):
            return np.array_equal(a, b)
        return False  # array vs non-array
    return a == b

def prim_ne(a, b):
    return not prim_eq(a, b)


# =============================================================================
# Vectorized Bulk Operations (true primitives for composing effects)
# =============================================================================

def prim_color_matrix(img: np.ndarray, matrix: List[List[float]]) -> np.ndarray:
    """Apply a 3x3 color transformation matrix to all pixels."""
    m = np.array(matrix, dtype=np.float32)
    result = img.astype(np.float32) @ m.T
    return np.clip(result, 0, 255).astype(np.uint8)


def prim_adjust(img: np.ndarray, brightness: float = 0, contrast: float = 1) -> np.ndarray:
    """Adjust brightness and contrast. Brightness: -255 to 255, Contrast: 0 to 3+."""
    result = (img.astype(np.float32) - 128) * contrast + 128 + brightness
    return np.clip(result, 0, 255).astype(np.uint8)


def prim_mix_gray(img: np.ndarray, amount: float) -> np.ndarray:
    """Mix image with its grayscale version. 0=original, 1=grayscale."""
    gray = 0.299 * img[:, :, 0] + 0.587 * img[:, :, 1] + 0.114 * img[:, :, 2]
    gray_rgb = np.stack([gray, gray, gray], axis=-1)
    result = img.astype(np.float32) * (1 - amount) + gray_rgb * amount
    return np.clip(result, 0, 255).astype(np.uint8)


def prim_invert_img(img: np.ndarray) -> np.ndarray:
    """Invert all pixel values."""
    return (255 - img).astype(np.uint8)


def prim_add_noise(img: np.ndarray, amount: float) -> np.ndarray:
    """Add gaussian noise to image."""
    noise = _rng._rng.normal(0, amount, img.shape)
    result = img.astype(np.float32) + noise
    return np.clip(result, 0, 255).astype(np.uint8)


def prim_quantize(img: np.ndarray, levels: int) -> np.ndarray:
    """Reduce to N color levels per channel."""
    levels = max(2, int(levels))
    factor = 256 / levels
    result = (img // factor) * factor + factor // 2
    return np.clip(result, 0, 255).astype(np.uint8)


def prim_shift_hsv(img: np.ndarray, h: float = 0, s: float = 1, v: float = 1) -> np.ndarray:
    """Shift HSV: h=degrees offset, s/v=multipliers."""
    hsv = cv2.cvtColor(img, cv2.COLOR_RGB2HSV).astype(np.float32)
    hsv[:, :, 0] = (hsv[:, :, 0] + h / 2) % 180
    hsv[:, :, 1] = np.clip(hsv[:, :, 1] * s, 0, 255)
    hsv[:, :, 2] = np.clip(hsv[:, :, 2] * v, 0, 255)
    return cv2.cvtColor(hsv.astype(np.uint8), cv2.COLOR_HSV2RGB)


# =============================================================================
# Array Math Primitives (vectorized operations on coordinate arrays)
# =============================================================================

def prim_arr_add(a: np.ndarray, b) -> np.ndarray:
    """Element-wise addition. b can be array or scalar."""
    return (np.asarray(a) + np.asarray(b)).astype(np.float32)


def prim_arr_sub(a: np.ndarray, b) -> np.ndarray:
    """Element-wise subtraction. b can be array or scalar."""
    return (np.asarray(a) - np.asarray(b)).astype(np.float32)


def prim_arr_mul(a: np.ndarray, b) -> np.ndarray:
    """Element-wise multiplication. b can be array or scalar."""
    return (np.asarray(a) * np.asarray(b)).astype(np.float32)


def prim_arr_div(a: np.ndarray, b) -> np.ndarray:
    """Element-wise division. b can be array or scalar."""
    b = np.asarray(b)
    # Avoid division by zero
    with np.errstate(divide='ignore', invalid='ignore'):
        result = np.asarray(a) / np.where(b == 0, 1e-10, b)
    return result.astype(np.float32)


def prim_arr_mod(a: np.ndarray, b) -> np.ndarray:
    """Element-wise modulo."""
    return (np.asarray(a) % np.asarray(b)).astype(np.float32)


def prim_arr_sin(a: np.ndarray) -> np.ndarray:
    """Element-wise sine."""
    return np.sin(np.asarray(a)).astype(np.float32)


def prim_arr_cos(a: np.ndarray) -> np.ndarray:
    """Element-wise cosine."""
    return np.cos(np.asarray(a)).astype(np.float32)


def prim_arr_tan(a: np.ndarray) -> np.ndarray:
    """Element-wise tangent."""
    return np.tan(np.asarray(a)).astype(np.float32)


def prim_arr_sqrt(a: np.ndarray) -> np.ndarray:
    """Element-wise square root."""
    return np.sqrt(np.maximum(0, np.asarray(a))).astype(np.float32)


def prim_arr_pow(a: np.ndarray, b) -> np.ndarray:
    """Element-wise power."""
    return np.power(np.asarray(a), np.asarray(b)).astype(np.float32)


def prim_arr_abs(a: np.ndarray) -> np.ndarray:
    """Element-wise absolute value."""
    return np.abs(np.asarray(a)).astype(np.float32)


def prim_arr_neg(a: np.ndarray) -> np.ndarray:
    """Element-wise negation."""
    return (-np.asarray(a)).astype(np.float32)


def prim_arr_exp(a: np.ndarray) -> np.ndarray:
    """Element-wise exponential."""
    return np.exp(np.asarray(a)).astype(np.float32)


def prim_arr_atan2(y: np.ndarray, x: np.ndarray) -> np.ndarray:
    """Element-wise atan2(y, x)."""
    return np.arctan2(np.asarray(y), np.asarray(x)).astype(np.float32)


def prim_arr_min(a: np.ndarray, b) -> np.ndarray:
    """Element-wise minimum."""
    return np.minimum(np.asarray(a), np.asarray(b)).astype(np.float32)


def prim_arr_max(a: np.ndarray, b) -> np.ndarray:
    """Element-wise maximum."""
    return np.maximum(np.asarray(a), np.asarray(b)).astype(np.float32)


def prim_arr_clip(a: np.ndarray, lo, hi) -> np.ndarray:
    """Element-wise clip to range."""
    return np.clip(np.asarray(a), lo, hi).astype(np.float32)


def prim_arr_where(cond: np.ndarray, a, b) -> np.ndarray:
    """Element-wise conditional: where cond is true, use a, else b."""
    return np.where(np.asarray(cond), np.asarray(a), np.asarray(b)).astype(np.float32)


def prim_arr_floor(a: np.ndarray) -> np.ndarray:
    """Element-wise floor."""
    return np.floor(np.asarray(a)).astype(np.float32)


def prim_arr_lerp(a: np.ndarray, b: np.ndarray, t) -> np.ndarray:
    """Element-wise linear interpolation."""
    a, b = np.asarray(a), np.asarray(b)
    return (a + (b - a) * t).astype(np.float32)


# =============================================================================
# Coordinate Transformation Primitives
# =============================================================================

def prim_polar_from_center(img_or_w, h_or_cx=None, cx=None, cy=None) -> Tuple[np.ndarray, np.ndarray]:
    """
    Create polar coordinates (r, theta) from image center.

    Usage:
        (polar-from-center img)           ; center of image
        (polar-from-center img cx cy)     ; custom center
        (polar-from-center w h cx cy)     ; explicit dimensions

    Returns: (r, theta) tuple of arrays
    """
    if isinstance(img_or_w, np.ndarray):
        h, w = img_or_w.shape[:2]
        if h_or_cx is None:
            cx, cy = w / 2, h / 2
        else:
            cx, cy = h_or_cx, cx if cx is not None else h / 2
    else:
        w = int(img_or_w)
        h = int(h_or_cx)
        cx = cx if cx is not None else w / 2
        cy = cy if cy is not None else h / 2

    y_coords, x_coords = np.mgrid[0:h, 0:w].astype(np.float32)
    dx = x_coords - cx
    dy = y_coords - cy
    r = np.sqrt(dx**2 + dy**2)
    theta = np.arctan2(dy, dx)

    return (r, theta)


def prim_cart_from_polar(r: np.ndarray, theta: np.ndarray, cx: float, cy: float) -> Tuple[np.ndarray, np.ndarray]:
    """
    Convert polar coordinates back to Cartesian.

    Args:
        r: radius array
        theta: angle array
        cx, cy: center point

    Returns: (x, y) tuple of coordinate arrays
    """
    x = (cx + r * np.cos(theta)).astype(np.float32)
    y = (cy + r * np.sin(theta)).astype(np.float32)
    return (x, y)


def prim_normalize_coords(img_or_w, h_or_cx=None, cx=None, cy=None) -> Tuple[np.ndarray, np.ndarray]:
    """
    Create normalized coordinates (-1 to 1) from center.

    Returns: (x_norm, y_norm) tuple of arrays where center is (0,0)
    """
    if isinstance(img_or_w, np.ndarray):
        h, w = img_or_w.shape[:2]
        if h_or_cx is None:
            cx, cy = w / 2, h / 2
        else:
            cx, cy = h_or_cx, cx if cx is not None else h / 2
    else:
        w = int(img_or_w)
        h = int(h_or_cx)
        cx = cx if cx is not None else w / 2
        cy = cy if cy is not None else h / 2

    y_coords, x_coords = np.mgrid[0:h, 0:w].astype(np.float32)
    x_norm = (x_coords - cx) / (w / 2)
    y_norm = (y_coords - cy) / (h / 2)

    return (x_norm, y_norm)


def prim_coords_x(coords: Tuple[np.ndarray, np.ndarray]) -> np.ndarray:
    """Get x/first component from coordinate tuple."""
    return coords[0]


def prim_coords_y(coords: Tuple[np.ndarray, np.ndarray]) -> np.ndarray:
    """Get y/second component from coordinate tuple."""
    return coords[1]


def prim_make_coords_centered(w: int, h: int, cx: float = None, cy: float = None) -> Tuple[np.ndarray, np.ndarray]:
    """
    Create coordinate grids centered at (cx, cy).
    Like make-coords but returns coordinates relative to center.
    """
    w, h = int(w), int(h)
    if cx is None:
        cx = w / 2
    if cy is None:
        cy = h / 2

    y_coords, x_coords = np.mgrid[0:h, 0:w].astype(np.float32)
    return (x_coords - cx, y_coords - cy)


# =============================================================================
# Specialized Distortion Primitives
# =============================================================================

def prim_wave_displace(w: int, h: int, axis: str, freq: float, amp: float, phase: float = 0) -> Tuple[np.ndarray, np.ndarray]:
    """
    Create wave displacement maps.

    Args:
        w, h: dimensions
        axis: "x" (horizontal waves) or "y" (vertical waves)
        freq: wave frequency (waves per image width/height)
        amp: wave amplitude in pixels
        phase: phase offset in radians

    Returns: (map_x, map_y) for use with remap
    """
    w, h = int(w), int(h)
    map_x = np.tile(np.arange(w, dtype=np.float32), (h, 1))
    map_y = np.tile(np.arange(h, dtype=np.float32).reshape(-1, 1), (1, w))

    if axis == "x" or axis == "horizontal":
        # Horizontal waves: displace x based on y
        wave = np.sin(2 * np.pi * freq * map_y / h + phase) * amp
        map_x = map_x + wave
    elif axis == "y" or axis == "vertical":
        # Vertical waves: displace y based on x
        wave = np.sin(2 * np.pi * freq * map_x / w + phase) * amp
        map_y = map_y + wave
    elif axis == "both":
        wave_x = np.sin(2 * np.pi * freq * map_y / h + phase) * amp
        wave_y = np.sin(2 * np.pi * freq * map_x / w + phase) * amp
        map_x = map_x + wave_x
        map_y = map_y + wave_y

    return (map_x, map_y)


def prim_swirl_displace(w: int, h: int, strength: float, radius: float = 0.5,
                        cx: float = None, cy: float = None, falloff: str = "quadratic") -> Tuple[np.ndarray, np.ndarray]:
    """
    Create swirl displacement maps.

    Args:
        w, h: dimensions
        strength: swirl strength in radians
        radius: effect radius as fraction of max dimension
        cx, cy: center (defaults to image center)
        falloff: "linear", "quadratic", or "gaussian"

    Returns: (map_x, map_y) for use with remap
    """
    w, h = int(w), int(h)
    if cx is None:
        cx = w / 2
    if cy is None:
        cy = h / 2

    radius_px = max(w, h) * radius

    y_coords, x_coords = np.mgrid[0:h, 0:w].astype(np.float32)
    dx = x_coords - cx
    dy = y_coords - cy
    dist = np.sqrt(dx**2 + dy**2)
    angle = np.arctan2(dy, dx)

    # Normalized distance for falloff
    norm_dist = dist / radius_px

    # Calculate falloff factor
    if falloff == "linear":
        factor = np.maximum(0, 1 - norm_dist)
    elif falloff == "gaussian":
        factor = np.exp(-norm_dist**2 * 2)
    else:  # quadratic
        factor = np.maximum(0, 1 - norm_dist**2)

    # Apply swirl rotation
    new_angle = angle + strength * factor

    # Calculate new coordinates
    map_x = (cx + dist * np.cos(new_angle)).astype(np.float32)
    map_y = (cy + dist * np.sin(new_angle)).astype(np.float32)

    return (map_x, map_y)


def prim_fisheye_displace(w: int, h: int, strength: float, cx: float = None, cy: float = None,
                          zoom_correct: bool = True) -> Tuple[np.ndarray, np.ndarray]:
    """
    Create fisheye/barrel distortion displacement maps.

    Args:
        w, h: dimensions
        strength: distortion strength (-1 to 1, positive=bulge, negative=pinch)
        cx, cy: center (defaults to image center)
        zoom_correct: auto-zoom to hide black edges

    Returns: (map_x, map_y) for use with remap
    """
    w, h = int(w), int(h)
    if cx is None:
        cx = w / 2
    if cy is None:
        cy = h / 2

    y_coords, x_coords = np.mgrid[0:h, 0:w].astype(np.float32)

    # Normalize coordinates
    x_norm = (x_coords - cx) / (w / 2)
    y_norm = (y_coords - cy) / (h / 2)
    r = np.sqrt(x_norm**2 + y_norm**2)

    # Apply barrel/pincushion distortion
    if strength > 0:
        r_distorted = r * (1 + strength * r**2)
    else:
        r_distorted = r / (1 - strength * r**2 + 0.001)

    # Calculate scale factor
    with np.errstate(divide='ignore', invalid='ignore'):
        scale = np.where(r > 0, r_distorted / r, 1)

    # Apply zoom correction
    if zoom_correct and strength > 0:
        zoom = 1 + strength * 0.5
        scale = scale / zoom

    # Calculate new coordinates
    map_x = (x_norm * scale * (w / 2) + cx).astype(np.float32)
    map_y = (y_norm * scale * (h / 2) + cy).astype(np.float32)

    return (map_x, map_y)


def prim_kaleidoscope_displace(w: int, h: int, segments: int, rotation: float = 0,
                                cx: float = None, cy: float = None, zoom: float = 1.0) -> Tuple[np.ndarray, np.ndarray]:
    """
    Create kaleidoscope displacement maps.

    Args:
        w, h: dimensions
        segments: number of symmetry segments (3-16)
        rotation: rotation angle in degrees
        cx, cy: center (defaults to image center)
        zoom: zoom factor

    Returns: (map_x, map_y) for use with remap
    """
    w, h = int(w), int(h)
    segments = max(3, min(int(segments), 16))
    if cx is None:
        cx = w / 2
    if cy is None:
        cy = h / 2

    segment_angle = 2 * np.pi / segments

    y_coords, x_coords = np.mgrid[0:h, 0:w].astype(np.float32)

    # Translate to center
    x_centered = x_coords - cx
    y_centered = y_coords - cy

    # Convert to polar
    r = np.sqrt(x_centered**2 + y_centered**2)
    theta = np.arctan2(y_centered, x_centered)

    # Apply rotation
    theta = theta - np.deg2rad(rotation)

    # Fold angle into first segment and mirror
    theta_normalized = theta % (2 * np.pi)
    segment_idx = (theta_normalized / segment_angle).astype(int)
    theta_in_segment = theta_normalized - segment_idx * segment_angle

    # Mirror alternating segments
    mirror_mask = (segment_idx % 2) == 1
    theta_in_segment = np.where(mirror_mask, segment_angle - theta_in_segment, theta_in_segment)

    # Apply zoom
    r = r / zoom

    # Convert back to Cartesian
    map_x = (r * np.cos(theta_in_segment) + cx).astype(np.float32)
    map_y = (r * np.sin(theta_in_segment) + cy).astype(np.float32)

    return (map_x, map_y)


# =============================================================================
# Character/ASCII Art Primitives
# =============================================================================

# Character sets ordered by visual density (light to dark)
CHAR_ALPHABETS = {
    "standard": " .`'^\",:;Il!i><~+_-?][}{1)(|/tfjrxnuvczXYUJCLQ0OZmwqpdbkhao*#MW&8%B@$",
    "blocks": " ░▒▓█",
    "simple": " .-:=+*#%@",
    "digits": " 0123456789",
}

# Global atlas cache: keyed on (frozenset(chars), cell_size) ->
# (atlas_array, char_to_idx) where atlas_array is (N, cell_size, cell_size) uint8.
_char_atlas_cache = {}
_CHAR_ATLAS_CACHE_MAX = 32


def _get_char_atlas(alphabet: str, cell_size: int) -> dict:
    """Get or create character atlas for alphabet (legacy dict version)."""
    atlas_arr, char_to_idx = _get_render_atlas(alphabet, cell_size)
    # Build legacy dict from array
    idx_to_char = {v: k for k, v in char_to_idx.items()}
    return {idx_to_char[i]: atlas_arr[i] for i in range(len(atlas_arr))}


def _get_render_atlas(unique_chars_or_alphabet, cell_size: int):
    """Get or build a stacked numpy atlas for vectorised rendering.

    Args:
        unique_chars_or_alphabet: Either an alphabet name (str looked up in
            CHAR_ALPHABETS), a literal character string, or a set/frozenset
            of characters.
        cell_size: Pixel size of each cell.

    Returns:
        (atlas_array, char_to_idx) where
            atlas_array: (num_chars, cell_size, cell_size) uint8 masks
            char_to_idx: dict mapping character -> index in atlas_array
    """
    if isinstance(unique_chars_or_alphabet, (set, frozenset)):
        chars_tuple = tuple(sorted(unique_chars_or_alphabet))
    else:
        resolved = CHAR_ALPHABETS.get(unique_chars_or_alphabet, unique_chars_or_alphabet)
        chars_tuple = tuple(resolved)

    cache_key = (chars_tuple, cell_size)
    cached = _char_atlas_cache.get(cache_key)
    if cached is not None:
        return cached

    font = cv2.FONT_HERSHEY_SIMPLEX
    font_scale = cell_size / 20.0
    thickness = max(1, int(cell_size / 10))

    n = len(chars_tuple)
    atlas = np.zeros((n, cell_size, cell_size), dtype=np.uint8)
    char_to_idx = {}

    for i, char in enumerate(chars_tuple):
        char_to_idx[char] = i
        if char and char != ' ':
            try:
                (text_w, text_h), _ = cv2.getTextSize(char, font, font_scale, thickness)
                text_x = max(0, (cell_size - text_w) // 2)
                text_y = (cell_size + text_h) // 2
                cv2.putText(atlas[i], char, (text_x, text_y),
                            font, font_scale, 255, thickness, cv2.LINE_AA)
            except Exception:
                pass

    # Evict oldest entry if cache is full
    if len(_char_atlas_cache) >= _CHAR_ATLAS_CACHE_MAX:
        _char_atlas_cache.pop(next(iter(_char_atlas_cache)))

    _char_atlas_cache[cache_key] = (atlas, char_to_idx)
    return atlas, char_to_idx


def prim_cell_sample(img: np.ndarray, cell_size: int) -> Tuple[np.ndarray, np.ndarray]:
    """
    Sample image into cell grid, returning average colors and luminances.

    Uses cv2.resize with INTER_AREA (pixel-area averaging) which is
    ~25x faster than numpy reshape+mean for block downsampling.

    Args:
        img: source image
        cell_size: size of each cell in pixels

    Returns: (colors, luminances) tuple
        - colors: (rows, cols, 3) array of average RGB per cell
        - luminances: (rows, cols) array of average brightness 0-255
    """
    cell_size = max(1, int(cell_size))
    h, w = img.shape[:2]
    rows = h // cell_size
    cols = w // cell_size

    if rows < 1 or cols < 1:
        return (np.zeros((1, 1, 3), dtype=np.uint8),
                np.zeros((1, 1), dtype=np.float32))

    # Crop to exact grid then block-average via cv2 area interpolation.
    grid_h, grid_w = rows * cell_size, cols * cell_size
    cropped = img[:grid_h, :grid_w]
    colors = cv2.resize(cropped, (cols, rows), interpolation=cv2.INTER_AREA)

    # Compute luminance
    luminances = (0.299 * colors[:, :, 0] +
                  0.587 * colors[:, :, 1] +
                  0.114 * colors[:, :, 2]).astype(np.float32)

    return (colors, luminances)


def cell_sample_extended(img: np.ndarray, cell_size: int) -> Tuple[np.ndarray, np.ndarray, List[List[ZoneContext]]]:
    """
    Sample image into cell grid, returning colors, luminances, and full zone contexts.

    Args:
        img: source image (RGB)
        cell_size: size of each cell in pixels

    Returns: (colors, luminances, zone_contexts) tuple
        - colors: (rows, cols, 3) array of average RGB per cell
        - luminances: (rows, cols) array of average brightness 0-255
        - zone_contexts: 2D list of ZoneContext objects with full cell data
    """
    cell_size = max(1, int(cell_size))
    h, w = img.shape[:2]
    rows = h // cell_size
    cols = w // cell_size

    if rows < 1 or cols < 1:
        return (np.zeros((1, 1, 3), dtype=np.uint8),
                np.zeros((1, 1), dtype=np.float32),
                [[ZoneContext(0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0)]])

    # Crop to grid
    grid_h, grid_w = rows * cell_size, cols * cell_size
    cropped = img[:grid_h, :grid_w]

    # Reshape and average
    reshaped = cropped.reshape(rows, cell_size, cols, cell_size, 3)
    colors = reshaped.mean(axis=(1, 3)).astype(np.uint8)

    # Compute luminance (0-255)
    luminances = (0.299 * colors[:, :, 0] +
                  0.587 * colors[:, :, 1] +
                  0.114 * colors[:, :, 2]).astype(np.float32)

    # Normalize colors to 0-1 for HSV/saturation calculations
    colors_float = colors.astype(np.float32) / 255.0

    # Compute HSV values for each cell
    max_c = colors_float.max(axis=2)
    min_c = colors_float.min(axis=2)
    diff = max_c - min_c

    # Saturation
    saturation = np.where(max_c > 0, diff / max_c, 0)

    # Hue (0-360)
    hue = np.zeros((rows, cols), dtype=np.float32)
    # Avoid division by zero
    mask = diff > 0
    r, g, b = colors_float[:, :, 0], colors_float[:, :, 1], colors_float[:, :, 2]

    # Red is max
    red_max = mask & (max_c == r)
    hue[red_max] = 60 * (((g[red_max] - b[red_max]) / diff[red_max]) % 6)

    # Green is max
    green_max = mask & (max_c == g)
    hue[green_max] = 60 * ((b[green_max] - r[green_max]) / diff[green_max] + 2)

    # Blue is max
    blue_max = mask & (max_c == b)
    hue[blue_max] = 60 * ((r[blue_max] - g[blue_max]) / diff[blue_max] + 4)

    # Ensure hue is in 0-360 range
    hue = hue % 360

    # Build zone contexts
    zone_contexts = []
    for row in range(rows):
        row_contexts = []
        for col in range(cols):
            ctx = ZoneContext(
                row=row,
                col=col,
                row_norm=row / max(1, rows - 1) if rows > 1 else 0.5,
                col_norm=col / max(1, cols - 1) if cols > 1 else 0.5,
                luminance=luminances[row, col] / 255.0,  # Normalize to 0-1
                saturation=float(saturation[row, col]),
                hue=float(hue[row, col]),
                r=float(colors_float[row, col, 0]),
                g=float(colors_float[row, col, 1]),
                b=float(colors_float[row, col, 2]),
            )
            row_contexts.append(ctx)
        zone_contexts.append(row_contexts)

    return (colors, luminances, zone_contexts)


def prim_luminance_to_chars(luminances: np.ndarray, alphabet: str, contrast: float = 1.0) -> List[List[str]]:
    """
    Map luminance values to characters from alphabet.

    Args:
        luminances: (rows, cols) array of brightness values 0-255
        alphabet: character set name or literal string (light to dark)
        contrast: contrast boost factor

    Returns: 2D list of single-character strings
    """
    chars = CHAR_ALPHABETS.get(alphabet, alphabet)
    num_chars = len(chars)

    # Apply contrast
    lum = luminances.astype(np.float32)
    if contrast != 1.0:
        lum = (lum - 128) * contrast + 128
        lum = np.clip(lum, 0, 255)

    # Map to indices
    indices = ((lum / 255) * (num_chars - 1)).astype(np.int32)
    indices = np.clip(indices, 0, num_chars - 1)

    # Vectorised conversion via numpy char array lookup
    chars_arr = np.array(list(chars))
    char_grid = chars_arr[indices.ravel()].reshape(indices.shape)

    return char_grid.tolist()


def prim_render_char_grid(img: np.ndarray, chars: List[List[str]], colors: np.ndarray,
                          cell_size: int, color_mode: str = "color",
                          background_color: str = "black",
                          invert_colors: bool = False) -> np.ndarray:
    """
    Render a grid of characters onto an image.

    Uses vectorised numpy operations instead of per-cell Python loops:
    the character atlas is looked up via fancy indexing and the full
    mask + colour image are assembled in bulk.

    Args:
        img: source image (for dimensions)
        chars: 2D list of single characters
        colors: (rows, cols, 3) array of colors per cell
        cell_size: size of each cell
        color_mode: "color" (original colors), "mono" (white), "invert",
                    or any color name/hex value ("green", "lime", "#00ff00")
        background_color: background color name/hex ("black", "navy", "#001100")
        invert_colors: if True, swap foreground and background colors

    Returns: rendered image
    """
    # Parse color_mode - may be a named color or hex value
    fg_color = parse_color(color_mode)

    # Parse background_color
    if isinstance(background_color, (list, tuple)):
        bg_color = tuple(int(c) for c in background_color[:3])
    else:
        bg_color = parse_color(background_color)
        if bg_color is None:
            bg_color = (0, 0, 0)

    # Handle invert_colors - swap fg and bg
    if invert_colors and fg_color is not None:
        fg_color, bg_color = bg_color, fg_color

    cell_size = max(1, int(cell_size))

    if not chars or not chars[0]:
        return img.copy()

    rows = len(chars)
    cols = len(chars[0])
    h, w = rows * cell_size, cols * cell_size

    bg = list(bg_color)

    # --- Build atlas & index grid ---
    unique_chars = set()
    for row in chars:
        for ch in row:
            unique_chars.add(ch)

    atlas, char_to_idx = _get_render_atlas(unique_chars, cell_size)

    # Convert 2D char list to index array using ordinal lookup table
    # (avoids per-cell Python dict lookup).
    space_idx = char_to_idx.get(' ', 0)
    max_ord = max(ord(ch) for ch in char_to_idx) + 1
    ord_lookup = np.full(max_ord, space_idx, dtype=np.int32)
    for ch, idx in char_to_idx.items():
        if ch:
            ord_lookup[ord(ch)] = idx

    flat = [ch for row in chars for ch in row]
    ords = np.frombuffer(np.array(flat, dtype='U1'), dtype=np.uint32)
    char_indices = ord_lookup[ords].reshape(rows, cols)

    # --- Vectorised mask assembly ---
    # atlas[char_indices] -> (rows, cols, cell_size, cell_size)
    # Transpose to (rows, cell_size, cols, cell_size) then reshape to full image.
    all_masks = atlas[char_indices]
    full_mask = all_masks.transpose(0, 2, 1, 3).reshape(h, w)

    # Expand per-cell colours to per-pixel (only when needed).
    need_color_full = (color_mode in ("color", "invert")
                       or (fg_color is None and color_mode != "mono"))

    if need_color_full:
        color_full = np.repeat(
            np.repeat(colors[:rows, :cols], cell_size, axis=0),
            cell_size, axis=1)

    # --- Vectorised colour composite ---
    # Use element-wise multiply/np.where instead of boolean-indexed scatter
    # for much better memory access patterns.
    mask_u8 = (full_mask > 0).astype(np.uint8)[:, :, np.newaxis]

    if color_mode == "invert":
        # Background is source colour; characters are black.
        # result = color_full * (1 - mask)
        result = color_full * (1 - mask_u8)
    elif fg_color is not None:
        # Fixed foreground colour on background.
        fg = np.array(fg_color, dtype=np.uint8)
        bg_arr = np.array(bg, dtype=np.uint8)
        result = np.where(mask_u8, fg, bg_arr).astype(np.uint8)
    elif color_mode == "mono":
        bg_arr = np.array(bg, dtype=np.uint8)
        result = np.where(mask_u8, np.uint8(255), bg_arr).astype(np.uint8)
    else:
        # "color" mode – each cell uses its source colour on bg.
        if bg == [0, 0, 0]:
            result = color_full * mask_u8
        else:
            bg_arr = np.array(bg, dtype=np.uint8)
            result = np.where(mask_u8, color_full, bg_arr).astype(np.uint8)

    # Resize to match original if needed
    orig_h, orig_w = img.shape[:2]
    if result.shape[0] != orig_h or result.shape[1] != orig_w:
        padded = np.full((orig_h, orig_w, 3), bg, dtype=np.uint8)
        copy_h = min(h, orig_h)
        copy_w = min(w, orig_w)
        padded[:copy_h, :copy_w] = result[:copy_h, :copy_w]
        result = padded

    return result


def prim_render_char_grid_fx(img: np.ndarray, chars: List[List[str]], colors: np.ndarray,
                              luminances: np.ndarray, cell_size: int,
                              color_mode: str = "color",
                              background_color: str = "black",
                              invert_colors: bool = False,
                              char_jitter: float = 0.0,
                              char_scale: float = 1.0,
                              char_rotation: float = 0.0,
                              char_hue_shift: float = 0.0,
                              jitter_source: str = "none",
                              scale_source: str = "none",
                              rotation_source: str = "none",
                              hue_source: str = "none") -> np.ndarray:
    """
    Render a grid of characters with per-character effects.

    Args:
        img: source image (for dimensions)
        chars: 2D list of single characters
        colors: (rows, cols, 3) array of colors per cell
        luminances: (rows, cols) array of luminance values (0-255)
        cell_size: size of each cell
        color_mode: "color", "mono", "invert", or any color name/hex
        background_color: background color name/hex
        invert_colors: if True, swap foreground and background colors
        char_jitter: base jitter amount in pixels
        char_scale: base scale factor (1.0 = normal)
        char_rotation: base rotation in degrees
        char_hue_shift: base hue shift in degrees (0-360)
        jitter_source: source for jitter modulation ("none", "luminance", "position", "random")
        scale_source: source for scale modulation
        rotation_source: source for rotation modulation
        hue_source: source for hue modulation

    Per-character effect sources:
        "none" - use base value only
        "luminance" - modulate by cell luminance (0-1)
        "inv_luminance" - modulate by inverse luminance (dark = high)
        "saturation" - modulate by cell color saturation
        "position_x" - modulate by horizontal position (0-1)
        "position_y" - modulate by vertical position (0-1)
        "position_diag" - modulate by diagonal position
        "random" - random per-cell value (deterministic from position)
        "center_dist" - distance from center (0=center, 1=corner)

    Returns: rendered image
    """
    # Parse colors
    fg_color = parse_color(color_mode)

    if isinstance(background_color, (list, tuple)):
        bg_color = tuple(int(c) for c in background_color[:3])
    else:
        bg_color = parse_color(background_color)
        if bg_color is None:
            bg_color = (0, 0, 0)

    if invert_colors and fg_color is not None:
        fg_color, bg_color = bg_color, fg_color

    cell_size = max(1, int(cell_size))

    if not chars or not chars[0]:
        return img.copy()

    rows = len(chars)
    cols = len(chars[0])
    h, w = rows * cell_size, cols * cell_size

    bg = list(bg_color)
    result = np.full((h, w, 3), bg, dtype=np.uint8)

    # Normalize luminances to 0-1
    lum_normalized = luminances.astype(np.float32) / 255.0

    # Compute saturation from colors
    colors_float = colors.astype(np.float32) / 255.0
    max_c = colors_float.max(axis=2)
    min_c = colors_float.min(axis=2)
    saturation = np.where(max_c > 0, (max_c - min_c) / max_c, 0)

    # Helper to get modulation value for a cell
    def get_mod_value(source: str, r: int, c: int) -> float:
        if source == "none":
            return 1.0
        elif source == "luminance":
            return lum_normalized[r, c]
        elif source == "inv_luminance":
            return 1.0 - lum_normalized[r, c]
        elif source == "saturation":
            return saturation[r, c]
        elif source == "position_x":
            return c / max(1, cols - 1) if cols > 1 else 0.5
        elif source == "position_y":
            return r / max(1, rows - 1) if rows > 1 else 0.5
        elif source == "position_diag":
            px = c / max(1, cols - 1) if cols > 1 else 0.5
            py = r / max(1, rows - 1) if rows > 1 else 0.5
            return (px + py) / 2.0
        elif source == "random":
            # Deterministic random based on position
            seed = (r * 1000 + c) % 10000
            return ((seed * 9301 + 49297) % 233280) / 233280.0
        elif source == "center_dist":
            cx, cy = (cols - 1) / 2.0, (rows - 1) / 2.0
            dx = (c - cx) / max(1, cx) if cx > 0 else 0
            dy = (r - cy) / max(1, cy) if cy > 0 else 0
            return min(1.0, math.sqrt(dx*dx + dy*dy))
        else:
            return 1.0

    # Build character atlas at base size
    font = cv2.FONT_HERSHEY_SIMPLEX
    base_font_scale = cell_size / 20.0
    thickness = max(1, int(cell_size / 10))

    unique_chars = set()
    for row in chars:
        for ch in row:
            unique_chars.add(ch)

    # For rotation/scale, we need to render characters larger then transform
    max_scale = max(1.0, char_scale * 1.5)  # Allow headroom for scaling
    atlas_size = int(cell_size * max_scale * 1.5)

    atlas = {}
    for char in unique_chars:
        if char and char != ' ':
            try:
                char_img = np.zeros((atlas_size, atlas_size), dtype=np.uint8)
                scaled_font = base_font_scale * max_scale
                (text_w, text_h), _ = cv2.getTextSize(char, font, scaled_font, thickness)
                text_x = max(0, (atlas_size - text_w) // 2)
                text_y = (atlas_size + text_h) // 2
                cv2.putText(char_img, char, (text_x, text_y), font, scaled_font, 255, thickness, cv2.LINE_AA)
                atlas[char] = char_img
            except:
                atlas[char] = None
        else:
            atlas[char] = None

    # Render characters with effects
    for r in range(rows):
        for c in range(cols):
            char = chars[r][c]
            if not char or char == ' ':
                continue

            char_img = atlas.get(char)
            if char_img is None:
                continue

            # Get per-cell modulation values
            jitter_mod = get_mod_value(jitter_source, r, c)
            scale_mod = get_mod_value(scale_source, r, c)
            rot_mod = get_mod_value(rotation_source, r, c)
            hue_mod = get_mod_value(hue_source, r, c)

            # Compute effective values
            eff_jitter = char_jitter * jitter_mod
            eff_scale = char_scale * (0.5 + 0.5 * scale_mod) if scale_source != "none" else char_scale
            eff_rotation = char_rotation * (rot_mod * 2 - 1)  # -1 to 1 range
            eff_hue_shift = char_hue_shift * hue_mod

            # Apply transformations
            transformed = char_img.copy()

            # Rotation
            if abs(eff_rotation) > 0.5:
                center = (atlas_size // 2, atlas_size // 2)
                rot_matrix = cv2.getRotationMatrix2D(center, eff_rotation, 1.0)
                transformed = cv2.warpAffine(transformed, rot_matrix, (atlas_size, atlas_size))

            # Scale - resize to target size
            target_size = max(1, int(cell_size * eff_scale))
            if target_size != atlas_size:
                transformed = cv2.resize(transformed, (target_size, target_size), interpolation=cv2.INTER_LINEAR)

            # Compute position with jitter
            base_y = r * cell_size
            base_x = c * cell_size

            if eff_jitter > 0:
                # Deterministic jitter based on position
                jx = ((r * 7 + c * 13) % 100) / 100.0 - 0.5
                jy = ((r * 11 + c * 17) % 100) / 100.0 - 0.5
                base_x += int(jx * eff_jitter * 2)
                base_y += int(jy * eff_jitter * 2)

            # Center the character in the cell
            offset = (target_size - cell_size) // 2
            y1 = base_y - offset
            x1 = base_x - offset

            # Determine color
            if fg_color is not None:
                color = np.array(fg_color, dtype=np.uint8)
            elif color_mode == "mono":
                color = np.array([255, 255, 255], dtype=np.uint8)
            elif color_mode == "invert":
                # Fill cell with source color first
                cy1 = max(0, r * cell_size)
                cy2 = min(h, (r + 1) * cell_size)
                cx1 = max(0, c * cell_size)
                cx2 = min(w, (c + 1) * cell_size)
                result[cy1:cy2, cx1:cx2] = colors[r, c]
                color = np.array([0, 0, 0], dtype=np.uint8)
            else:  # color mode
                color = colors[r, c].copy()

            # Apply hue shift
            if abs(eff_hue_shift) > 0.5 and color_mode not in ("mono", "invert") and fg_color is None:
                # Convert to HSV, shift hue, convert back
                color_hsv = cv2.cvtColor(color.reshape(1, 1, 3), cv2.COLOR_RGB2HSV)
                # Cast to int to avoid uint8 overflow, then back to uint8
                new_hue = (int(color_hsv[0, 0, 0]) + int(eff_hue_shift * 180 / 360)) % 180
                color_hsv[0, 0, 0] = np.uint8(new_hue)
                color = cv2.cvtColor(color_hsv, cv2.COLOR_HSV2RGB).flatten()

            # Blit character to result
            mask = transformed > 0
            th, tw = transformed.shape[:2]

            for dy in range(th):
                for dx in range(tw):
                    py = y1 + dy
                    px = x1 + dx
                    if 0 <= py < h and 0 <= px < w and mask[dy, dx]:
                        result[py, px] = color

    # Resize to match original if needed
    orig_h, orig_w = img.shape[:2]
    if result.shape[0] != orig_h or result.shape[1] != orig_w:
        padded = np.full((orig_h, orig_w, 3), bg, dtype=np.uint8)
        copy_h = min(h, orig_h)
        copy_w = min(w, orig_w)
        padded[:copy_h, :copy_w] = result[:copy_h, :copy_w]
        result = padded

    return result


def _render_with_cell_effect(
    frame: np.ndarray,
    chars: List[List[str]],
    colors: np.ndarray,
    luminances: np.ndarray,
    zone_contexts: List[List['ZoneContext']],
    cell_size: int,
    bg_color: tuple,
    fg_color: tuple,
    color_mode: str,
    cell_effect,  # Lambda or callable: (cell_image, zone_dict) -> cell_image
    extra_params: dict,
    interp,
    env,
    result: np.ndarray,
) -> np.ndarray:
    """
    Render ASCII art using a cell_effect lambda for arbitrary per-cell transforms.

    Each character is rendered to a cell image, the cell_effect is called with
    (cell_image, zone_dict), and the returned cell is composited into result.

    This allows arbitrary effects (rotate, blur, etc.) to be applied per-character.
    """
    grid_rows = len(chars)
    grid_cols = len(chars[0]) if chars else 0
    out_h, out_w = result.shape[:2]

    # Build character atlas (cell-sized colored characters on transparent bg)
    font = cv2.FONT_HERSHEY_SIMPLEX
    font_scale = cell_size / 20.0
    thickness = max(1, int(cell_size / 10))

    # Helper to render a single character cell
    def render_char_cell(char: str, color: np.ndarray) -> np.ndarray:
        """Render a character onto a cell-sized RGB image."""
        cell = np.full((cell_size, cell_size, 3), bg_color, dtype=np.uint8)
        if not char or char == ' ':
            return cell

        try:
            (text_w, text_h), _ = cv2.getTextSize(char, font, font_scale, thickness)
            text_x = max(0, (cell_size - text_w) // 2)
            text_y = (cell_size + text_h) // 2

            # Render character in white on mask, then apply color
            mask = np.zeros((cell_size, cell_size), dtype=np.uint8)
            cv2.putText(mask, char, (text_x, text_y), font, font_scale, 255, thickness, cv2.LINE_AA)

            # Apply color where mask is set
            for ch in range(3):
                cell[:, :, ch] = np.where(mask > 0, color[ch], bg_color[ch])
        except:
            pass

        return cell

    # Helper to evaluate cell_effect (handles artdag Lambda objects)
    def eval_cell_effect(cell_img: np.ndarray, zone_dict: dict) -> np.ndarray:
        """Call cell_effect with (cell_image, zone_dict), handle Lambda objects."""
        if callable(cell_effect):
            return cell_effect(cell_img, zone_dict)

        # Check if it's an artdag Lambda object
        try:
            from artdag.sexp.parser import Lambda as ArtdagLambda
            from artdag.sexp.evaluator import evaluate as artdag_evaluate
            if isinstance(cell_effect, ArtdagLambda):
                # Build env with closure values
                eval_env = dict(cell_effect.closure) if cell_effect.closure else {}
                # Bind lambda parameters
                if len(cell_effect.params) >= 2:
                    eval_env[cell_effect.params[0]] = cell_img
                    eval_env[cell_effect.params[1]] = zone_dict
                elif len(cell_effect.params) == 1:
                    # Single param gets zone_dict with cell as 'cell' key
                    zone_dict['cell'] = cell_img
                    eval_env[cell_effect.params[0]] = zone_dict

                # Add primitives to eval env
                eval_env.update(PRIMITIVES)

                # Add effect runner - allows calling any loaded sexp effect on a cell
                # Usage: (apply-effect "effect_name" cell {"param" value ...})
                # Or: (apply-effect "effect_name" cell) for defaults
                def apply_effect_fn(effect_name, frame, params=None):
                    """Run a loaded sexp effect on a frame (cell)."""
                    if interp and hasattr(interp, 'run_effect'):
                        if params is None:
                            params = {}
                        result, _ = interp.run_effect(effect_name, frame, params, {})
                        return result
                    return frame
                eval_env['apply-effect'] = apply_effect_fn

                # Also inject loaded effects directly as callable functions
                # These wrappers take positional args in common order for each effect
                # Usage: (blur cell 5) or (rotate cell 45) etc.
                if interp and hasattr(interp, 'effects'):
                    for effect_name in interp.effects:
                        # Create a wrapper that calls run_effect with positional-to-named mapping
                        def make_effect_fn(name):
                            def effect_fn(frame, *args):
                                # Map common positional args to named params
                                params = {}
                                if name == 'blur' and len(args) >= 1:
                                    params['radius'] = args[0]
                                elif name == 'rotate' and len(args) >= 1:
                                    params['angle'] = args[0]
                                elif name == 'brightness' and len(args) >= 1:
                                    params['factor'] = args[0]
                                elif name == 'contrast' and len(args) >= 1:
                                    params['factor'] = args[0]
                                elif name == 'saturation' and len(args) >= 1:
                                    params['factor'] = args[0]
                                elif name == 'hue_shift' and len(args) >= 1:
                                    params['degrees'] = args[0]
                                elif name == 'rgb_split' and len(args) >= 1:
                                    params['offset_x'] = args[0]
                                    if len(args) >= 2:
                                        params['offset_y'] = args[1]
                                elif name == 'pixelate' and len(args) >= 1:
                                    params['block_size'] = args[0]
                                elif name == 'wave' and len(args) >= 1:
                                    params['amplitude'] = args[0]
                                    if len(args) >= 2:
                                        params['frequency'] = args[1]
                                elif name == 'noise' and len(args) >= 1:
                                    params['amount'] = args[0]
                                elif name == 'posterize' and len(args) >= 1:
                                    params['levels'] = args[0]
                                elif name == 'threshold' and len(args) >= 1:
                                    params['level'] = args[0]
                                elif name == 'sharpen' and len(args) >= 1:
                                    params['amount'] = args[0]
                                elif len(args) == 1 and isinstance(args[0], dict):
                                    # Accept dict as single arg
                                    params = args[0]
                                result, _ = interp.run_effect(name, frame, params, {})
                                return result
                            return effect_fn
                        eval_env[effect_name] = make_effect_fn(effect_name)

                result = artdag_evaluate(cell_effect.body, eval_env)
                if isinstance(result, np.ndarray):
                    return result
                return cell_img
        except ImportError:
            pass

        # Fallback: return cell unchanged
        return cell_img

    # Render each cell
    for r in range(grid_rows):
        for c in range(grid_cols):
            char = chars[r][c]
            zone = zone_contexts[r][c]

            # Determine character color
            if fg_color is not None:
                color = np.array(fg_color, dtype=np.uint8)
            elif color_mode == "mono":
                color = np.array([255, 255, 255], dtype=np.uint8)
            elif color_mode == "invert":
                color = np.array([0, 0, 0], dtype=np.uint8)
            else:
                color = colors[r, c].copy()

            # Render character to cell image
            cell_img = render_char_cell(char, color)

            # Build zone dict
            zone_dict = {
                'row': zone.row,
                'col': zone.col,
                'row-norm': zone.row_norm,
                'col-norm': zone.col_norm,
                'lum': zone.luminance,
                'sat': zone.saturation,
                'hue': zone.hue,
                'r': zone.r,
                'g': zone.g,
                'b': zone.b,
                'char': char,
                'color': color.tolist(),
                'cell_size': cell_size,
            }
            # Add extra params (energy, rotation_scale, etc.)
            if extra_params:
                zone_dict.update(extra_params)

            # Call cell_effect
            modified_cell = eval_cell_effect(cell_img, zone_dict)

            # Ensure result is valid
            if modified_cell is None or not isinstance(modified_cell, np.ndarray):
                modified_cell = cell_img
            if modified_cell.shape[:2] != (cell_size, cell_size):
                # Resize if cell size changed
                modified_cell = cv2.resize(modified_cell, (cell_size, cell_size))
            if len(modified_cell.shape) == 2:
                # Convert grayscale to RGB
                modified_cell = cv2.cvtColor(modified_cell, cv2.COLOR_GRAY2RGB)

            # Composite into result
            y1 = r * cell_size
            x1 = c * cell_size
            y2 = min(y1 + cell_size, out_h)
            x2 = min(x1 + cell_size, out_w)
            ch = y2 - y1
            cw = x2 - x1
            result[y1:y2, x1:x2] = modified_cell[:ch, :cw]

    # Resize to match original frame if needed
    orig_h, orig_w = frame.shape[:2]
    if result.shape[0] != orig_h or result.shape[1] != orig_w:
        bg = list(bg_color)
        padded = np.full((orig_h, orig_w, 3), bg, dtype=np.uint8)
        copy_h = min(out_h, orig_h)
        copy_w = min(out_w, orig_w)
        padded[:copy_h, :copy_w] = result[:copy_h, :copy_w]
        result = padded

    return result


def prim_ascii_fx_zone(
    frame: np.ndarray,
    cols: int,
    char_size_override: int,  # If set, overrides cols-based calculation
    alphabet: str,
    color_mode: str,
    background: str,
    contrast: float,
    char_hue_expr,       # Expression, literal, or None
    char_sat_expr,       # Expression, literal, or None
    char_bright_expr,    # Expression, literal, or None
    char_scale_expr,     # Expression, literal, or None
    char_rotation_expr,  # Expression, literal, or None
    char_jitter_expr,    # Expression, literal, or None
    interp,              # Interpreter for expression evaluation
    env,                 # Environment with bound values
    extra_params=None,   # Extra params to include in zone dict for lambdas
    cell_effect=None,    # Lambda (cell_image, zone_dict) -> cell_image for arbitrary cell effects
) -> np.ndarray:
    """
    Render ASCII art with per-zone expression-driven transforms.

    Args:
        frame: Source image (H, W, 3) RGB uint8
        cols: Number of character columns
        char_size_override: If set, use this cell size instead of cols-based
        alphabet: Character set name or literal string
        color_mode: "color", "mono", "invert", or color name/hex
        background: Background color name or hex
        contrast: Contrast boost for character selection
        char_hue_expr: Expression for hue shift (evaluated per zone)
        char_sat_expr: Expression for saturation adjustment (evaluated per zone)
        char_bright_expr: Expression for brightness adjustment (evaluated per zone)
        char_scale_expr: Expression for scale factor (evaluated per zone)
        char_rotation_expr: Expression for rotation degrees (evaluated per zone)
        char_jitter_expr: Expression for position jitter (evaluated per zone)
        interp: Interpreter instance for expression evaluation
        env: Environment with bound variables
        cell_effect: Optional lambda that receives (cell_image, zone_dict) and returns
                     a modified cell_image. When provided, each character is rendered
                     to a cell image, passed to this lambda, and the result composited.
                     This allows arbitrary effects to be applied per-character.

    Zone variables available in expressions:
        zone-row, zone-col: Grid position (integers)
        zone-row-norm, zone-col-norm: Normalized position (0-1)
        zone-lum: Cell luminance (0-1)
        zone-sat: Cell saturation (0-1)
        zone-hue: Cell hue (0-360)
        zone-r, zone-g, zone-b: RGB components (0-1)

    Returns: Rendered image
    """
    h, w = frame.shape[:2]
    # Use char_size if provided, otherwise calculate from cols
    if char_size_override is not None:
        cell_size = max(4, int(char_size_override))
    else:
        cell_size = max(4, w // cols)

    # Get zone data using extended sampling
    colors, luminances, zone_contexts = cell_sample_extended(frame, cell_size)

    # Convert luminances to characters
    chars = prim_luminance_to_chars(luminances, alphabet, contrast)

    grid_rows = len(chars)
    grid_cols = len(chars[0]) if chars else 0

    # Parse colors
    fg_color = parse_color(color_mode)
    if isinstance(background, (list, tuple)):
        bg_color = tuple(int(c) for c in background[:3])
    else:
        bg_color = parse_color(background)
        if bg_color is None:
            bg_color = (0, 0, 0)

    # Arrays for per-zone transform values
    hue_shifts = np.zeros((grid_rows, grid_cols), dtype=np.float32)
    saturations = np.ones((grid_rows, grid_cols), dtype=np.float32)
    brightness = np.ones((grid_rows, grid_cols), dtype=np.float32)
    scales = np.ones((grid_rows, grid_cols), dtype=np.float32)
    rotations = np.zeros((grid_rows, grid_cols), dtype=np.float32)
    jitters = np.zeros((grid_rows, grid_cols), dtype=np.float32)

    # Helper to evaluate expression or return literal value
    def eval_expr(expr, zone, char):
        if expr is None:
            return None
        if isinstance(expr, (int, float)):
            return expr

        # Build zone dict for lambda calls
        zone_dict = {
            'row': zone.row,
            'col': zone.col,
            'row-norm': zone.row_norm,
            'col-norm': zone.col_norm,
            'lum': zone.luminance,
            'sat': zone.saturation,
            'hue': zone.hue,
            'r': zone.r,
            'g': zone.g,
            'b': zone.b,
            'char': char,
        }
        # Add extra params (energy, rotation_scale, etc.) for lambdas to access
        if extra_params:
            zone_dict.update(extra_params)

        # Check if it's a Python callable
        if callable(expr):
            return expr(zone_dict)

        # Check if it's an artdag Lambda object
        try:
            from artdag.sexp.parser import Lambda as ArtdagLambda
            from artdag.sexp.evaluator import evaluate as artdag_evaluate
            if isinstance(expr, ArtdagLambda):
                # Build env with zone dict and any closure values
                eval_env = dict(expr.closure) if expr.closure else {}
                # Bind the lambda parameter to zone_dict
                if expr.params:
                    eval_env[expr.params[0]] = zone_dict
                return artdag_evaluate(expr.body, eval_env)
        except ImportError:
            pass

        # It's an expression - evaluate with zone context (sexp_effects style)
        return interp.eval_with_zone(expr, env, zone)

    # Evaluate expressions for each zone
    for r in range(grid_rows):
        for c in range(grid_cols):
            zone = zone_contexts[r][c]
            char = chars[r][c]

            val = eval_expr(char_hue_expr, zone, char)
            if val is not None:
                hue_shifts[r, c] = float(val)

            val = eval_expr(char_sat_expr, zone, char)
            if val is not None:
                saturations[r, c] = float(val)

            val = eval_expr(char_bright_expr, zone, char)
            if val is not None:
                brightness[r, c] = float(val)

            val = eval_expr(char_scale_expr, zone, char)
            if val is not None:
                scales[r, c] = float(val)

            val = eval_expr(char_rotation_expr, zone, char)
            if val is not None:
                rotations[r, c] = float(val)

            val = eval_expr(char_jitter_expr, zone, char)
            if val is not None:
                jitters[r, c] = float(val)

    # Now render with computed transform arrays
    out_h, out_w = grid_rows * cell_size, grid_cols * cell_size
    bg = list(bg_color)
    result = np.full((out_h, out_w, 3), bg, dtype=np.uint8)

    # If cell_effect is provided, use the cell-mapper rendering path
    if cell_effect is not None:
        return _render_with_cell_effect(
            frame, chars, colors, luminances, zone_contexts,
            cell_size, bg_color, fg_color, color_mode,
            cell_effect, extra_params, interp, env, result
        )

    # Build character atlas
    font = cv2.FONT_HERSHEY_SIMPLEX
    base_font_scale = cell_size / 20.0
    thickness = max(1, int(cell_size / 10))

    unique_chars = set()
    for row in chars:
        for ch in row:
            unique_chars.add(ch)

    # For rotation/scale, render characters larger then transform
    max_scale = max(1.0, np.max(scales) * 1.5)
    atlas_size = int(cell_size * max_scale * 1.5)

    atlas = {}
    for char in unique_chars:
        if char and char != ' ':
            try:
                char_img = np.zeros((atlas_size, atlas_size), dtype=np.uint8)
                scaled_font = base_font_scale * max_scale
                (text_w, text_h), _ = cv2.getTextSize(char, font, scaled_font, thickness)
                text_x = max(0, (atlas_size - text_w) // 2)
                text_y = (atlas_size + text_h) // 2
                cv2.putText(char_img, char, (text_x, text_y), font, scaled_font, 255, thickness, cv2.LINE_AA)
                atlas[char] = char_img
            except:
                atlas[char] = None
        else:
            atlas[char] = None

    # Render characters with per-zone effects
    for r in range(grid_rows):
        for c in range(grid_cols):
            char = chars[r][c]
            if not char or char == ' ':
                continue

            char_img = atlas.get(char)
            if char_img is None:
                continue

            # Get per-cell values
            eff_scale = scales[r, c]
            eff_rotation = rotations[r, c]
            eff_jitter = jitters[r, c]
            eff_hue_shift = hue_shifts[r, c]
            eff_brightness = brightness[r, c]
            eff_saturation = saturations[r, c]

            # Apply transformations to character
            transformed = char_img.copy()

            # Rotation
            if abs(eff_rotation) > 0.5:
                center = (atlas_size // 2, atlas_size // 2)
                rot_matrix = cv2.getRotationMatrix2D(center, eff_rotation, 1.0)
                transformed = cv2.warpAffine(transformed, rot_matrix, (atlas_size, atlas_size))

            # Scale - resize to target size
            target_size = max(1, int(cell_size * eff_scale))
            if target_size != atlas_size:
                transformed = cv2.resize(transformed, (target_size, target_size), interpolation=cv2.INTER_LINEAR)

            # Compute position with jitter
            base_y = r * cell_size
            base_x = c * cell_size

            if eff_jitter > 0:
                # Deterministic jitter based on position
                jx = ((r * 7 + c * 13) % 100) / 100.0 - 0.5
                jy = ((r * 11 + c * 17) % 100) / 100.0 - 0.5
                base_x += int(jx * eff_jitter * 2)
                base_y += int(jy * eff_jitter * 2)

            # Center the character in the cell
            offset = (target_size - cell_size) // 2
            y1 = base_y - offset
            x1 = base_x - offset

            # Determine color
            if fg_color is not None:
                color = np.array(fg_color, dtype=np.uint8)
            elif color_mode == "mono":
                color = np.array([255, 255, 255], dtype=np.uint8)
            elif color_mode == "invert":
                cy1 = max(0, r * cell_size)
                cy2 = min(out_h, (r + 1) * cell_size)
                cx1 = max(0, c * cell_size)
                cx2 = min(out_w, (c + 1) * cell_size)
                result[cy1:cy2, cx1:cx2] = colors[r, c]
                color = np.array([0, 0, 0], dtype=np.uint8)
            else:  # color mode - use source colors
                color = colors[r, c].copy()

            # Apply hue shift
            if abs(eff_hue_shift) > 0.5 and color_mode not in ("mono", "invert") and fg_color is None:
                color_hsv = cv2.cvtColor(color.reshape(1, 1, 3), cv2.COLOR_RGB2HSV)
                new_hue = (int(color_hsv[0, 0, 0]) + int(eff_hue_shift * 180 / 360)) % 180
                color_hsv[0, 0, 0] = np.uint8(new_hue)
                color = cv2.cvtColor(color_hsv, cv2.COLOR_HSV2RGB).flatten()

            # Apply saturation adjustment
            if abs(eff_saturation - 1.0) > 0.01 and color_mode not in ("mono", "invert") and fg_color is None:
                color_hsv = cv2.cvtColor(color.reshape(1, 1, 3), cv2.COLOR_RGB2HSV)
                new_sat = np.clip(int(color_hsv[0, 0, 1] * eff_saturation), 0, 255)
                color_hsv[0, 0, 1] = np.uint8(new_sat)
                color = cv2.cvtColor(color_hsv, cv2.COLOR_HSV2RGB).flatten()

            # Apply brightness adjustment
            if abs(eff_brightness - 1.0) > 0.01:
                color = np.clip(color.astype(np.float32) * eff_brightness, 0, 255).astype(np.uint8)

            # Blit character to result
            mask = transformed > 0
            th, tw = transformed.shape[:2]

            for dy in range(th):
                for dx in range(tw):
                    py = y1 + dy
                    px = x1 + dx
                    if 0 <= py < out_h and 0 <= px < out_w and mask[dy, dx]:
                        result[py, px] = color

    # Resize to match original if needed
    orig_h, orig_w = frame.shape[:2]
    if result.shape[0] != orig_h or result.shape[1] != orig_w:
        padded = np.full((orig_h, orig_w, 3), bg, dtype=np.uint8)
        copy_h = min(out_h, orig_h)
        copy_w = min(out_w, orig_w)
        padded[:copy_h, :copy_w] = result[:copy_h, :copy_w]
        result = padded

    return result


def prim_make_char_grid(rows: int, cols: int, fill_char: str = " ") -> List[List[str]]:
    """Create a character grid filled with a character."""
    return [[fill_char for _ in range(cols)] for _ in range(rows)]


def prim_set_char(chars: List[List[str]], row: int, col: int, char: str) -> List[List[str]]:
    """Set a character at position (returns modified copy)."""
    result = [r[:] for r in chars]  # shallow copy rows
    if 0 <= row < len(result) and 0 <= col < len(result[0]):
        result[row][col] = char
    return result


def prim_get_char(chars: List[List[str]], row: int, col: int) -> str:
    """Get character at position."""
    if 0 <= row < len(chars) and 0 <= col < len(chars[0]):
        return chars[row][col]
    return " "


def prim_char_grid_dimensions(chars: List[List[str]]) -> Tuple[int, int]:
    """Get (rows, cols) of character grid."""
    if not chars:
        return (0, 0)
    return (len(chars), len(chars[0]) if chars[0] else 0)


def prim_alphabet_char(alphabet: str, index: int) -> str:
    """Get character at index from alphabet (wraps around)."""
    chars = CHAR_ALPHABETS.get(alphabet, alphabet)
    if not chars:
        return " "
    return chars[int(index) % len(chars)]


def prim_alphabet_length(alphabet: str) -> int:
    """Get length of alphabet."""
    chars = CHAR_ALPHABETS.get(alphabet, alphabet)
    return len(chars)


def prim_map_char_grid(chars: List[List[str]], luminances: np.ndarray, fn: Callable) -> List[List[str]]:
    """
    Map a function over character grid.

    fn receives (row, col, char, luminance) and returns new character.
    This allows per-cell character selection based on position, brightness, etc.

    Example:
        (map-char-grid chars luminances
          (lambda (r c ch lum)
            (if (> lum 128)
                (alphabet-char "blocks" (floor (/ lum 50)))
                ch)))
    """
    if not chars or not chars[0]:
        return chars

    rows = len(chars)
    cols = len(chars[0])
    result = []

    for r in range(rows):
        row = []
        for c in range(cols):
            ch = chars[r][c]
            lum = float(luminances[r, c]) if r < luminances.shape[0] and c < luminances.shape[1] else 0
            new_ch = fn(r, c, ch, lum)
            row.append(str(new_ch) if new_ch else " ")
        result.append(row)

    return result


def prim_map_colors(colors: np.ndarray, fn: Callable) -> np.ndarray:
    """
    Map a function over color grid.

    fn receives (row, col, color) and returns new [r, g, b].
    Color is a list [r, g, b].
    """
    if colors.size == 0:
        return colors

    rows, cols = colors.shape[:2]
    result = colors.copy()

    for r in range(rows):
        for c in range(cols):
            color = list(colors[r, c])
            new_color = fn(r, c, color)
            if new_color is not None:
                result[r, c] = new_color[:3]

    return result


# =============================================================================
# Glitch Art Primitives
# =============================================================================

def prim_pixelsort(img: np.ndarray, sort_by: str = "lightness",
                   threshold_low: float = 50, threshold_high: float = 200,
                   angle: float = 0, reverse: bool = False) -> np.ndarray:
    """
    Pixel sorting glitch effect.

    Args:
        img: source image
        sort_by: "lightness", "hue", "saturation", "red", "green", "blue"
        threshold_low: pixels below this aren't sorted
        threshold_high: pixels above this aren't sorted
        angle: 0 = horizontal, 90 = vertical
        reverse: reverse sort order
    """
    h, w = img.shape[:2]

    # Rotate for vertical sorting
    if 45 <= (angle % 180) <= 135:
        frame = np.transpose(img, (1, 0, 2))
        h, w = frame.shape[:2]
        rotated = True
    else:
        frame = img
        rotated = False

    result = frame.copy()

    # Get sort values
    if sort_by == "lightness":
        sort_values = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY).astype(np.float32)
    elif sort_by == "hue":
        hsv = cv2.cvtColor(frame, cv2.COLOR_RGB2HSV)
        sort_values = hsv[:, :, 0].astype(np.float32)
    elif sort_by == "saturation":
        hsv = cv2.cvtColor(frame, cv2.COLOR_RGB2HSV)
        sort_values = hsv[:, :, 1].astype(np.float32)
    elif sort_by == "red":
        sort_values = frame[:, :, 0].astype(np.float32)
    elif sort_by == "green":
        sort_values = frame[:, :, 1].astype(np.float32)
    elif sort_by == "blue":
        sort_values = frame[:, :, 2].astype(np.float32)
    else:
        sort_values = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY).astype(np.float32)

    # Create mask
    mask = (sort_values >= threshold_low) & (sort_values <= threshold_high)

    # Sort each row
    for y in range(h):
        row = result[y].copy()
        row_mask = mask[y]
        row_values = sort_values[y]

        # Find contiguous segments
        segments = []
        start = None
        for i, val in enumerate(row_mask):
            if val and start is None:
                start = i
            elif not val and start is not None:
                segments.append((start, i))
                start = None
        if start is not None:
            segments.append((start, len(row_mask)))

        # Sort each segment
        for seg_start, seg_end in segments:
            if seg_end - seg_start > 1:
                segment_values = row_values[seg_start:seg_end]
                sort_indices = np.argsort(segment_values)
                if reverse:
                    sort_indices = sort_indices[::-1]
                row[seg_start:seg_end] = row[seg_start:seg_end][sort_indices]

        result[y] = row

    # Rotate back
    if rotated:
        result = np.transpose(result, (1, 0, 2))

    return np.ascontiguousarray(result)


def prim_datamosh(img: np.ndarray, prev_frame: np.ndarray,
                  block_size: int = 32, corruption: float = 0.3,
                  max_offset: int = 50, color_corrupt: bool = True) -> np.ndarray:
    """
    Datamosh/glitch block corruption effect.

    Args:
        img: current frame
        prev_frame: previous frame (or None)
        block_size: size of corruption blocks
        corruption: probability 0-1 of corrupting each block
        max_offset: maximum pixel shift
        color_corrupt: also apply color channel shifts
    """
    if corruption <= 0:
        return img.copy()

    block_size = max(8, min(int(block_size), 128))
    h, w = img.shape[:2]
    result = img.copy()

    for by in range(0, h, block_size):
        for bx in range(0, w, block_size):
            bh = min(block_size, h - by)
            bw = min(block_size, w - bx)

            if _rng.random() < corruption:
                corruption_type = _rng.randint(0, 3)

                if corruption_type == 0 and max_offset > 0:
                    # Shift
                    ox = _rng.randint(-max_offset, max_offset)
                    oy = _rng.randint(-max_offset, max_offset)
                    src_x = max(0, min(bx + ox, w - bw))
                    src_y = max(0, min(by + oy, h - bh))
                    result[by:by+bh, bx:bx+bw] = img[src_y:src_y+bh, src_x:src_x+bw]

                elif corruption_type == 1 and prev_frame is not None:
                    # Duplicate from previous frame
                    if prev_frame.shape == img.shape:
                        result[by:by+bh, bx:bx+bw] = prev_frame[by:by+bh, bx:bx+bw]

                elif corruption_type == 2 and color_corrupt:
                    # Color channel shift
                    block = result[by:by+bh, bx:bx+bw].copy()
                    shift = _rng.randint(1, 3)
                    channel = _rng.randint(0, 2)
                    block[:, :, channel] = np.roll(block[:, :, channel], shift, axis=0)
                    result[by:by+bh, bx:bx+bw] = block

                else:
                    # Swap with another block
                    other_bx = _rng.randint(0, max(0, w - bw))
                    other_by = _rng.randint(0, max(0, h - bh))
                    temp = result[by:by+bh, bx:bx+bw].copy()
                    result[by:by+bh, bx:bx+bw] = img[other_by:other_by+bh, other_bx:other_bx+bw]
                    result[other_by:other_by+bh, other_bx:other_bx+bw] = temp

    return result


def prim_ripple_displace(w: int, h: int, freq: float, amp: float, cx: float = None, cy: float = None,
                         decay: float = 0, phase: float = 0) -> Tuple[np.ndarray, np.ndarray]:
    """
    Create radial ripple displacement maps.

    Args:
        w, h: dimensions
        freq: ripple frequency
        amp: ripple amplitude in pixels
        cx, cy: center
        decay: how fast ripples decay with distance (0 = no decay)
        phase: phase offset

    Returns: (map_x, map_y) for use with remap
    """
    w, h = int(w), int(h)
    if cx is None:
        cx = w / 2
    if cy is None:
        cy = h / 2

    y_coords, x_coords = np.mgrid[0:h, 0:w].astype(np.float32)
    dx = x_coords - cx
    dy = y_coords - cy
    dist = np.sqrt(dx**2 + dy**2)

    # Calculate ripple displacement (radial)
    ripple = np.sin(2 * np.pi * freq * dist / max(w, h) + phase) * amp

    # Apply decay
    if decay > 0:
        ripple = ripple * np.exp(-dist * decay / max(w, h))

    # Displace along radial direction
    with np.errstate(divide='ignore', invalid='ignore'):
        norm_dx = np.where(dist > 0, dx / dist, 0)
        norm_dy = np.where(dist > 0, dy / dist, 0)

    map_x = (x_coords + ripple * norm_dx).astype(np.float32)
    map_y = (y_coords + ripple * norm_dy).astype(np.float32)

    return (map_x, map_y)


PRIMITIVES = {
    # Arithmetic
    '+': prim_add,
    '-': prim_sub,
    '*': prim_mul,
    '/': prim_div,

    # Comparison
    '<': prim_lt,
    '>': prim_gt,
    '<=': prim_le,
    '>=': prim_ge,
    '=': prim_eq,
    '!=': prim_ne,

    # Image
    'width': prim_width,
    'height': prim_height,
    'make-image': prim_make_image,
    'copy': prim_copy,
    'pixel': prim_pixel,
    'set-pixel': prim_set_pixel,
    'sample': prim_sample,
    'channel': prim_channel,
    'merge-channels': prim_merge_channels,
    'resize': prim_resize,
    'crop': prim_crop,
    'paste': prim_paste,

    # Color
    'rgb': prim_rgb,
    'red': prim_red,
    'green': prim_green,
    'blue': prim_blue,
    'luminance': prim_luminance,
    'rgb->hsv': prim_rgb_to_hsv,
    'hsv->rgb': prim_hsv_to_rgb,
    'blend-color': prim_blend_color,
    'average-color': prim_average_color,

    # Vectorized bulk operations
    'color-matrix': prim_color_matrix,
    'adjust': prim_adjust,
    'mix-gray': prim_mix_gray,
    'invert-img': prim_invert_img,
    'add-noise': prim_add_noise,
    'quantize': prim_quantize,
    'shift-hsv': prim_shift_hsv,

    # Bulk operations
    'map-pixels': prim_map_pixels,
    'map-rows': prim_map_rows,
    'for-grid': prim_for_grid,
    'fold-pixels': prim_fold_pixels,

    # Filters
    'convolve': prim_convolve,
    'blur': prim_blur,
    'box-blur': prim_box_blur,
    'edges': prim_edges,
    'sobel': prim_sobel,
    'dilate': prim_dilate,
    'erode': prim_erode,

    # Geometry
    'translate': prim_translate,
    'rotate-img': prim_rotate,
    'scale-img': prim_scale,
    'flip-h': prim_flip_h,
    'flip-v': prim_flip_v,
    'remap': prim_remap,
    'make-coords': prim_make_coords,

    # Blending
    'blend-images': prim_blend_images,
    'blend-mode': prim_blend_mode,
    'mask': prim_mask,

    # Drawing
    'draw-char': prim_draw_char,
    'draw-text': prim_draw_text,
    'fill-rect': prim_fill_rect,
    'draw-line': prim_draw_line,

    # Math
    'sin': prim_sin,
    'cos': prim_cos,
    'tan': prim_tan,
    'atan2': prim_atan2,
    'sqrt': prim_sqrt,
    'pow': prim_pow,
    'abs': prim_abs,
    'floor': prim_floor,
    'ceil': prim_ceil,
    'round': prim_round,
    'min': prim_min,
    'max': prim_max,
    'clamp': prim_clamp,
    'lerp': prim_lerp,
    'mod': prim_mod,
    'random': prim_random,
    'randint': prim_randint,
    'gaussian': prim_gaussian,
    'assert': prim_assert,
    'pi': math.pi,
    'tau': math.tau,

    # Array
    'length': prim_length,
    'len': prim_length,  # alias
    'nth': prim_nth,
    'first': prim_first,
    'rest': prim_rest,
    'take': prim_take,
    'drop': prim_drop,
    'cons': prim_cons,
    'append': prim_append,
    'reverse': prim_reverse,
    'range': prim_range,
    'roll': prim_roll,
    'list': prim_list,

    # Array math (vectorized operations on coordinate arrays)
    'arr+': prim_arr_add,
    'arr-': prim_arr_sub,
    'arr*': prim_arr_mul,
    'arr/': prim_arr_div,
    'arr-mod': prim_arr_mod,
    'arr-sin': prim_arr_sin,
    'arr-cos': prim_arr_cos,
    'arr-tan': prim_arr_tan,
    'arr-sqrt': prim_arr_sqrt,
    'arr-pow': prim_arr_pow,
    'arr-abs': prim_arr_abs,
    'arr-neg': prim_arr_neg,
    'arr-exp': prim_arr_exp,
    'arr-atan2': prim_arr_atan2,
    'arr-min': prim_arr_min,
    'arr-max': prim_arr_max,
    'arr-clip': prim_arr_clip,
    'arr-where': prim_arr_where,
    'arr-floor': prim_arr_floor,
    'arr-lerp': prim_arr_lerp,

    # Coordinate transformations
    'polar-from-center': prim_polar_from_center,
    'cart-from-polar': prim_cart_from_polar,
    'normalize-coords': prim_normalize_coords,
    'coords-x': prim_coords_x,
    'coords-y': prim_coords_y,
    'make-coords-centered': prim_make_coords_centered,

    # Specialized distortion maps
    'wave-displace': prim_wave_displace,
    'swirl-displace': prim_swirl_displace,
    'fisheye-displace': prim_fisheye_displace,
    'kaleidoscope-displace': prim_kaleidoscope_displace,
    'ripple-displace': prim_ripple_displace,

    # Character/ASCII art
    'cell-sample': prim_cell_sample,
    'cell-sample-extended': cell_sample_extended,
    'luminance-to-chars': prim_luminance_to_chars,
    'render-char-grid': prim_render_char_grid,
    'render-char-grid-fx': prim_render_char_grid_fx,
    'ascii-fx-zone': prim_ascii_fx_zone,
    'make-char-grid': prim_make_char_grid,
    'set-char': prim_set_char,
    'get-char': prim_get_char,
    'char-grid-dimensions': prim_char_grid_dimensions,
    'alphabet-char': prim_alphabet_char,
    'alphabet-length': prim_alphabet_length,
    'map-char-grid': prim_map_char_grid,
    'map-colors': prim_map_colors,

    # Glitch art
    'pixelsort': prim_pixelsort,
    'datamosh': prim_datamosh,

}
