celery/streaming/sexp_to_jax.py

"""
Sexp to JAX Compiler.

Compiles S-expression effects to JAX functions that run on CPU, GPU, or TPU.
Uses XLA compilation via @jax.jit for automatic kernel fusion.

Unlike sexp_to_cuda.py which generates CUDA C strings, this compiles
S-expressions directly to JAX operations which XLA then optimizes.

Usage:
    from streaming.sexp_to_jax import compile_effect

    effect_code = '''
    (effect "threshold"
      :params ((threshold :default 128))
      :body (let ((g (gray frame)))
              (rgb (where (> g threshold) 255 0)
                   (where (> g threshold) 255 0)
                   (where (> g threshold) 255 0))))
    '''

    run_effect = compile_effect(effect_code)
    output = run_effect(frame, threshold=128)
"""

import jax
import jax.numpy as jnp
from jax import lax
from functools import partial
from typing import Any, Dict, List, Callable, Optional, Tuple
import hashlib
import numpy as np

# Import parser
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent.parent))
from sexp_effects.parser import parse, parse_all, Symbol, Keyword


# =============================================================================
# Compilation Cache
# =============================================================================

_COMPILED_EFFECTS: Dict[str, Callable] = {}


# =============================================================================
# Font Atlas for ASCII Effects
# =============================================================================

# Character sets for ASCII rendering
ASCII_ALPHABETS = {
    'standard': ' .:-=+*#%@',
    'blocks': ' ░▒▓█',
    'simple': ' .:oO@',
    'digits': ' 0123456789',
    'binary': ' 01',
    'detailed': ' .\'`^",:;Il!i><~+_-?][}{1)(|\\/tfjrxnuvczXYUJCLQ0OZmwqpdbkhao*#MW&8%B@$',
}

# Cache for font atlases: (alphabet, char_size, font_name) -> atlas array
_FONT_ATLAS_CACHE: Dict[tuple, np.ndarray] = {}


def _create_font_atlas(alphabet: str, char_size: int, font_name: str = None) -> np.ndarray:
    """
    Create a font atlas with all characters pre-rendered.

    Uses numpy arrays (not JAX) to avoid tracer issues when called at compile time.

    Args:
        alphabet: String of characters to render (ordered by brightness, dark to light)
        char_size: Size of each character cell in pixels
        font_name: Optional font name/path (uses default monospace if None)

    Returns:
        NumPy array of shape (num_chars, char_size, char_size, 3) with rendered characters
        Each character is white on black background.
    """
    cache_key = (alphabet, char_size, font_name)
    if cache_key in _FONT_ATLAS_CACHE:
        return _FONT_ATLAS_CACHE[cache_key]

    try:
        from PIL import Image, ImageDraw, ImageFont
    except ImportError:
        # Fallback: create simple block-based atlas without PIL
        return _create_block_atlas(alphabet, char_size)

    num_chars = len(alphabet)
    atlas = []

    # Try to load a monospace font
    font = None
    font_size = int(char_size * 0.9)  # Slightly smaller than cell

    # Try various monospace fonts
    font_candidates = [
        font_name,
        '/usr/share/fonts/truetype/dejavu/DejaVuSansMono.ttf',
        '/usr/share/fonts/truetype/liberation/LiberationMono-Regular.ttf',
        '/usr/share/fonts/truetype/ubuntu/UbuntuMono-R.ttf',
        '/System/Library/Fonts/Menlo.ttc',  # macOS
        '/System/Library/Fonts/Monaco.dfont',  # macOS
        'C:\\Windows\\Fonts\\consola.ttf',  # Windows
    ]

    for font_path in font_candidates:
        if font_path is None:
            continue
        try:
            font = ImageFont.truetype(font_path, font_size)
            break
        except (IOError, OSError):
            continue

    if font is None:
        # Use default font
        try:
            font = ImageFont.load_default()
        except:
            # Ultimate fallback to blocks
            return _create_block_atlas(alphabet, char_size)

    for char in alphabet:
        # Create image for this character
        img = Image.new('RGB', (char_size, char_size), color=(0, 0, 0))
        draw = ImageDraw.Draw(img)

        # Get text bounding box for centering
        try:
            bbox = draw.textbbox((0, 0), char, font=font)
            text_width = bbox[2] - bbox[0]
            text_height = bbox[3] - bbox[1]
        except AttributeError:
            # Older PIL versions
            text_width, text_height = draw.textsize(char, font=font)

        # Center the character
        x = (char_size - text_width) // 2
        y = (char_size - text_height) // 2

        # Draw white character on black background
        draw.text((x, y), char, fill=(255, 255, 255), font=font)

        # Convert to numpy array (NOT jax array - avoids tracer issues)
        char_array = np.array(img, dtype=np.uint8)
        atlas.append(char_array)

    atlas = np.stack(atlas, axis=0)
    _FONT_ATLAS_CACHE[cache_key] = atlas
    return atlas


def _create_block_atlas(alphabet: str, char_size: int) -> np.ndarray:
    """
    Create a simple block-based atlas without fonts.
    Uses numpy to avoid tracer issues.
    """
    num_chars = len(alphabet)
    atlas = []

    for i, char in enumerate(alphabet):
        # Brightness proportional to position in alphabet
        brightness = int(255 * i / max(num_chars - 1, 1))

        # Create a simple pattern based on character
        img = np.full((char_size, char_size, 3), brightness, dtype=np.uint8)

        # Add some texture/pattern for visual interest
        # Checkerboard pattern for mid-range characters
        if 0.2 < i / num_chars < 0.8:
            y_coords, x_coords = np.mgrid[:char_size, :char_size]
            checker = ((x_coords + y_coords) % 2 == 0)
            variation = int(brightness * 0.2)
            img = np.where(checker[:, :, None],
                          np.clip(img.astype(np.int16) + variation, 0, 255).astype(np.uint8),
                          np.clip(img.astype(np.int16) - variation, 0, 255).astype(np.uint8))

        atlas.append(img)

    return np.stack(atlas, axis=0)


def _get_alphabet_string(alphabet_name: str) -> str:
    """Get the character string for a named alphabet or return as-is if custom."""
    if alphabet_name in ASCII_ALPHABETS:
        return ASCII_ALPHABETS[alphabet_name]
    return alphabet_name  # Assume it's a custom character string


# =============================================================================
# JAX Primitives - True primitives that can't be derived
# =============================================================================

def jax_width(frame):
    """Frame width."""
    return frame.shape[1]


def jax_height(frame):
    """Frame height."""
    return frame.shape[0]


def jax_channel(frame, idx):
    """Extract channel by index as flat array."""
    # idx must be a static int for indexing
    return frame[:, :, int(idx)].flatten().astype(jnp.float32)


def jax_merge_channels(r, g, b, shape):
    """Merge RGB channels back to frame."""
    h, w = shape
    r_img = jnp.clip(r, 0, 255).reshape(h, w).astype(jnp.uint8)
    g_img = jnp.clip(g, 0, 255).reshape(h, w).astype(jnp.uint8)
    b_img = jnp.clip(b, 0, 255).reshape(h, w).astype(jnp.uint8)
    return jnp.stack([r_img, g_img, b_img], axis=2)


def jax_iota(n):
    """Generate [0, 1, 2, ..., n-1]."""
    return jnp.arange(n, dtype=jnp.float32)


def jax_repeat(x, n):
    """Repeat each element n times: [a,b] -> [a,a,b,b]."""
    return jnp.repeat(x, n)


def jax_tile(x, n):
    """Tile array n times: [a,b] -> [a,b,a,b]."""
    return jnp.tile(x, n)


def jax_gather(data, indices):
    """Parallel index lookup."""
    flat_data = data.flatten()
    idx_clipped = jnp.clip(indices.astype(jnp.int32), 0, len(flat_data) - 1)
    return flat_data[idx_clipped]


def jax_scatter(indices, values, size):
    """Parallel index write (last write wins)."""
    result = jnp.zeros(size, dtype=jnp.float32)
    idx_clipped = jnp.clip(indices.astype(jnp.int32), 0, size - 1)
    return result.at[idx_clipped].set(values)


def jax_scatter_add(indices, values, size):
    """Parallel index accumulate."""
    result = jnp.zeros(size, dtype=jnp.float32)
    idx_clipped = jnp.clip(indices.astype(jnp.int32), 0, size - 1)
    return result.at[idx_clipped].add(values)


def jax_group_reduce(values, group_indices, num_groups, op='mean'):
    """Reduce values by group."""
    grp = group_indices.astype(jnp.int32)

    if op == 'sum':
        result = jnp.zeros(num_groups, dtype=jnp.float32)
        return result.at[grp].add(values)
    elif op == 'mean':
        sums = jnp.zeros(num_groups, dtype=jnp.float32).at[grp].add(values)
        counts = jnp.zeros(num_groups, dtype=jnp.float32).at[grp].add(1.0)
        return jnp.where(counts > 0, sums / counts, 0.0)
    elif op == 'max':
        result = jnp.full(num_groups, -jnp.inf, dtype=jnp.float32)
        result = result.at[grp].max(values)
        return jnp.where(result == -jnp.inf, 0.0, result)
    elif op == 'min':
        result = jnp.full(num_groups, jnp.inf, dtype=jnp.float32)
        result = result.at[grp].min(values)
        return jnp.where(result == jnp.inf, 0.0, result)
    else:
        raise ValueError(f"Unknown reduce op: {op}")


def jax_where(cond, true_val, false_val):
    """Conditional select."""
    return jnp.where(cond, true_val, false_val)


def jax_cell_indices(frame, cell_size):
    """Compute cell index for each pixel."""
    h, w = frame.shape[:2]
    cell_size = int(cell_size)

    rows = h // cell_size
    cols = w // cell_size

    # For each pixel, compute its cell index
    y_coords = jnp.repeat(jnp.arange(h), w)
    x_coords = jnp.tile(jnp.arange(w), h)

    cell_row = y_coords // cell_size
    cell_col = x_coords // cell_size
    cell_idx = cell_row * cols + cell_col

    # Clip to valid range
    return jnp.clip(cell_idx, 0, rows * cols - 1).astype(jnp.float32)


def jax_pool_frame(frame, cell_size):
    """
    Pool frame to cell values.
    Returns tuple: (cell_r, cell_g, cell_b, cell_lum)
    """
    h, w = frame.shape[:2]
    cs = int(cell_size)
    rows = h // cs
    cols = w // cs
    num_cells = rows * cols

    # Compute cell indices for each pixel
    y_coords = jnp.repeat(jnp.arange(h), w)
    x_coords = jnp.tile(jnp.arange(w), h)
    cell_row = jnp.clip(y_coords // cs, 0, rows - 1)
    cell_col = jnp.clip(x_coords // cs, 0, cols - 1)
    cell_idx = (cell_row * cols + cell_col).astype(jnp.int32)

    # Extract channels
    r_flat = frame[:, :, 0].flatten().astype(jnp.float32)
    g_flat = frame[:, :, 1].flatten().astype(jnp.float32)
    b_flat = frame[:, :, 2].flatten().astype(jnp.float32)

    # Pool each channel (mean)
    def pool_channel(data):
        sums = jnp.zeros(num_cells, dtype=jnp.float32).at[cell_idx].add(data)
        counts = jnp.zeros(num_cells, dtype=jnp.float32).at[cell_idx].add(1.0)
        return jnp.where(counts > 0, sums / counts, 0.0)

    r_pooled = pool_channel(r_flat)
    g_pooled = pool_channel(g_flat)
    b_pooled = pool_channel(b_flat)
    lum = 0.299 * r_pooled + 0.587 * g_pooled + 0.114 * b_pooled

    return (r_pooled, g_pooled, b_pooled, lum)


def jax_sample(frame, x, y):
    """Bilinear sample at (x, y) coordinates."""
    h, w = frame.shape[:2]

    # Clamp coordinates
    x = jnp.clip(x, 0, w - 1)
    y = jnp.clip(y, 0, h - 1)

    # Get integer and fractional parts
    x0 = jnp.floor(x).astype(jnp.int32)
    y0 = jnp.floor(y).astype(jnp.int32)
    x1 = jnp.clip(x0 + 1, 0, w - 1)
    y1 = jnp.clip(y0 + 1, 0, h - 1)

    fx = x - x0.astype(jnp.float32)
    fy = y - y0.astype(jnp.float32)

    # Bilinear interpolation for each channel
    def interp_channel(c):
        c00 = frame[y0, x0, c].astype(jnp.float32)
        c10 = frame[y0, x1, c].astype(jnp.float32)
        c01 = frame[y1, x0, c].astype(jnp.float32)
        c11 = frame[y1, x1, c].astype(jnp.float32)

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

    r = interp_channel(0)
    g = interp_channel(1)
    b = interp_channel(2)

    return r, g, b


# =============================================================================
# Convolution Operations
# =============================================================================

def jax_convolve2d(data, kernel):
    """2D convolution on a single channel."""
    # data shape: (H, W), kernel shape: (kH, kW)
    # Use JAX's conv with appropriate padding
    h, w = data.shape
    kh, kw = kernel.shape

    # Reshape for conv: (batch, H, W, channels) and (kH, kW, in_c, out_c)
    data_4d = data.reshape(1, h, w, 1)
    kernel_4d = kernel.reshape(kh, kw, 1, 1)

    # Convolve with 'SAME' padding
    result = lax.conv_general_dilated(
        data_4d, kernel_4d,
        window_strides=(1, 1),
        padding='SAME',
        dimension_numbers=('NHWC', 'HWIO', 'NHWC')
    )

    return result.reshape(h, w)


def jax_blur(frame, radius=1):
    """Gaussian blur."""
    # Create gaussian kernel
    size = int(radius) * 2 + 1
    x = jnp.arange(size) - radius
    gaussian_1d = jnp.exp(-x**2 / (2 * (radius/2)**2))
    gaussian_1d = gaussian_1d / gaussian_1d.sum()
    kernel = jnp.outer(gaussian_1d, gaussian_1d)

    h, w = frame.shape[:2]
    r = jax_convolve2d(frame[:, :, 0].astype(jnp.float32), kernel)
    g = jax_convolve2d(frame[:, :, 1].astype(jnp.float32), kernel)
    b = jax_convolve2d(frame[:, :, 2].astype(jnp.float32), kernel)

    return jnp.stack([
        jnp.clip(r, 0, 255).astype(jnp.uint8),
        jnp.clip(g, 0, 255).astype(jnp.uint8),
        jnp.clip(b, 0, 255).astype(jnp.uint8)
    ], axis=2)


def jax_sharpen(frame, amount=1.0):
    """Sharpen using unsharp mask."""
    kernel = jnp.array([
        [0, -1, 0],
        [-1, 5, -1],
        [0, -1, 0]
    ], dtype=jnp.float32)

    # Adjust kernel based on amount
    center = 4 * amount + 1
    kernel = kernel.at[1, 1].set(center)
    kernel = kernel * amount + jnp.array([[0,0,0],[0,1,0],[0,0,0]]) * (1 - amount)

    h, w = frame.shape[:2]
    r = jax_convolve2d(frame[:, :, 0].astype(jnp.float32), kernel)
    g = jax_convolve2d(frame[:, :, 1].astype(jnp.float32), kernel)
    b = jax_convolve2d(frame[:, :, 2].astype(jnp.float32), kernel)

    return jnp.stack([
        jnp.clip(r, 0, 255).astype(jnp.uint8),
        jnp.clip(g, 0, 255).astype(jnp.uint8),
        jnp.clip(b, 0, 255).astype(jnp.uint8)
    ], axis=2)


def jax_edge_detect(frame):
    """Sobel edge detection."""
    # Sobel kernels
    sobel_x = jnp.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]], dtype=jnp.float32)
    sobel_y = jnp.array([[-1, -2, -1], [0, 0, 0], [1, 2, 1]], dtype=jnp.float32)

    # Convert to grayscale first
    gray = (frame[:, :, 0].astype(jnp.float32) * 0.299 +
            frame[:, :, 1].astype(jnp.float32) * 0.587 +
            frame[:, :, 2].astype(jnp.float32) * 0.114)

    gx = jax_convolve2d(gray, sobel_x)
    gy = jax_convolve2d(gray, sobel_y)

    edges = jnp.sqrt(gx**2 + gy**2)
    edges = jnp.clip(edges, 0, 255).astype(jnp.uint8)

    return jnp.stack([edges, edges, edges], axis=2)


def jax_emboss(frame):
    """Emboss effect."""
    kernel = jnp.array([[-2, -1, 0], [-1, 1, 1], [0, 1, 2]], dtype=jnp.float32)

    gray = (frame[:, :, 0].astype(jnp.float32) * 0.299 +
            frame[:, :, 1].astype(jnp.float32) * 0.587 +
            frame[:, :, 2].astype(jnp.float32) * 0.114)

    embossed = jax_convolve2d(gray, kernel) + 128
    embossed = jnp.clip(embossed, 0, 255).astype(jnp.uint8)

    return jnp.stack([embossed, embossed, embossed], axis=2)


# =============================================================================
# Color Space Conversion
# =============================================================================

def jax_rgb_to_hsv(r, g, b):
    """Convert RGB to HSV. All inputs/outputs are 0-255 range."""
    r, g, b = r / 255.0, g / 255.0, b / 255.0

    max_c = jnp.maximum(jnp.maximum(r, g), b)
    min_c = jnp.minimum(jnp.minimum(r, g), b)
    diff = max_c - min_c

    # Value
    v = max_c

    # Saturation
    s = jnp.where(max_c > 0, diff / max_c, 0.0)

    # Hue
    h = jnp.where(diff == 0, 0.0,
            jnp.where(max_c == r, (60 * ((g - b) / diff) + 360) % 360,
            jnp.where(max_c == g, 60 * ((b - r) / diff) + 120,
                                  60 * ((r - g) / diff) + 240)))

    return h, s * 255, v * 255


def jax_hsv_to_rgb(h, s, v):
    """Convert HSV to RGB. H is 0-360, S and V are 0-255."""
    h = h % 360
    s, v = s / 255.0, v / 255.0

    c = v * s
    x = c * (1 - jnp.abs((h / 60) % 2 - 1))
    m = v - c

    h_sector = (h / 60).astype(jnp.int32) % 6

    r = jnp.where(h_sector == 0, c,
        jnp.where(h_sector == 1, x,
        jnp.where(h_sector == 2, 0,
        jnp.where(h_sector == 3, 0,
        jnp.where(h_sector == 4, x, c)))))

    g = jnp.where(h_sector == 0, x,
        jnp.where(h_sector == 1, c,
        jnp.where(h_sector == 2, c,
        jnp.where(h_sector == 3, x,
        jnp.where(h_sector == 4, 0, 0)))))

    b = jnp.where(h_sector == 0, 0,
        jnp.where(h_sector == 1, 0,
        jnp.where(h_sector == 2, x,
        jnp.where(h_sector == 3, c,
        jnp.where(h_sector == 4, c, x)))))

    return (r + m) * 255, (g + m) * 255, (b + m) * 255


def jax_adjust_saturation(frame, factor):
    """Adjust saturation by factor (1.0 = unchanged)."""
    r = frame[:, :, 0].flatten().astype(jnp.float32)
    g = frame[:, :, 1].flatten().astype(jnp.float32)
    b = frame[:, :, 2].flatten().astype(jnp.float32)

    h, s, v = jax_rgb_to_hsv(r, g, b)
    s = jnp.clip(s * factor, 0, 255)
    r2, g2, b2 = jax_hsv_to_rgb(h, s, v)

    h_dim, w_dim = frame.shape[:2]
    return jnp.stack([
        jnp.clip(r2, 0, 255).reshape(h_dim, w_dim).astype(jnp.uint8),
        jnp.clip(g2, 0, 255).reshape(h_dim, w_dim).astype(jnp.uint8),
        jnp.clip(b2, 0, 255).reshape(h_dim, w_dim).astype(jnp.uint8)
    ], axis=2)


def jax_shift_hue(frame, degrees):
    """Shift hue by degrees."""
    r = frame[:, :, 0].flatten().astype(jnp.float32)
    g = frame[:, :, 1].flatten().astype(jnp.float32)
    b = frame[:, :, 2].flatten().astype(jnp.float32)

    h, s, v = jax_rgb_to_hsv(r, g, b)
    h = (h + degrees) % 360
    r2, g2, b2 = jax_hsv_to_rgb(h, s, v)

    h_dim, w_dim = frame.shape[:2]
    return jnp.stack([
        jnp.clip(r2, 0, 255).reshape(h_dim, w_dim).astype(jnp.uint8),
        jnp.clip(g2, 0, 255).reshape(h_dim, w_dim).astype(jnp.uint8),
        jnp.clip(b2, 0, 255).reshape(h_dim, w_dim).astype(jnp.uint8)
    ], axis=2)


# =============================================================================
# Color Adjustment Operations
# =============================================================================

def jax_adjust_brightness(frame, amount):
    """Adjust brightness by amount (-255 to 255)."""
    result = frame.astype(jnp.float32) + amount
    return jnp.clip(result, 0, 255).astype(jnp.uint8)


def jax_adjust_contrast(frame, factor):
    """Adjust contrast by factor (1.0 = unchanged)."""
    result = (frame.astype(jnp.float32) - 128) * factor + 128
    return jnp.clip(result, 0, 255).astype(jnp.uint8)


def jax_invert(frame):
    """Invert colors."""
    return 255 - frame


def jax_posterize(frame, levels):
    """Reduce to N color levels per channel."""
    levels = int(levels)
    if levels < 2:
        levels = 2
    step = 255.0 / (levels - 1)
    result = jnp.round(frame.astype(jnp.float32) / step) * step
    return jnp.clip(result, 0, 255).astype(jnp.uint8)


def jax_threshold(frame, level, invert=False):
    """Binary threshold."""
    gray = (frame[:, :, 0].astype(jnp.float32) * 0.299 +
            frame[:, :, 1].astype(jnp.float32) * 0.587 +
            frame[:, :, 2].astype(jnp.float32) * 0.114)

    if invert:
        binary = jnp.where(gray < level, 255, 0).astype(jnp.uint8)
    else:
        binary = jnp.where(gray >= level, 255, 0).astype(jnp.uint8)

    return jnp.stack([binary, binary, binary], axis=2)


def jax_sepia(frame):
    """Apply sepia tone."""
    r = frame[:, :, 0].astype(jnp.float32)
    g = frame[:, :, 1].astype(jnp.float32)
    b = frame[:, :, 2].astype(jnp.float32)

    new_r = r * 0.393 + g * 0.769 + b * 0.189
    new_g = r * 0.349 + g * 0.686 + b * 0.168
    new_b = r * 0.272 + g * 0.534 + b * 0.131

    return jnp.stack([
        jnp.clip(new_r, 0, 255).astype(jnp.uint8),
        jnp.clip(new_g, 0, 255).astype(jnp.uint8),
        jnp.clip(new_b, 0, 255).astype(jnp.uint8)
    ], axis=2)


def jax_grayscale(frame):
    """Convert to grayscale."""
    gray = (frame[:, :, 0].astype(jnp.float32) * 0.299 +
            frame[:, :, 1].astype(jnp.float32) * 0.587 +
            frame[:, :, 2].astype(jnp.float32) * 0.114)
    gray = gray.astype(jnp.uint8)
    return jnp.stack([gray, gray, gray], axis=2)


# =============================================================================
# Geometry Operations
# =============================================================================

def jax_flip_horizontal(frame):
    """Flip horizontally."""
    return frame[:, ::-1, :]


def jax_flip_vertical(frame):
    """Flip vertically."""
    return frame[::-1, :, :]


def jax_rotate(frame, angle, center_x=None, center_y=None):
    """Rotate frame by angle (degrees), matching OpenCV convention.

    Positive angle = counter-clockwise rotation.
    """
    h, w = frame.shape[:2]
    if center_x is None:
        center_x = w / 2
    if center_y is None:
        center_y = h / 2

    # Convert to radians
    theta = angle * jnp.pi / 180
    cos_t, sin_t = jnp.cos(theta), jnp.sin(theta)

    # Create coordinate grids
    y_coords = jnp.repeat(jnp.arange(h), w).reshape(h, w)
    x_coords = jnp.tile(jnp.arange(w), h).reshape(h, w)

    # OpenCV getRotationMatrix2D gives FORWARD transform M = [[cos,sin],[-sin,cos]]
    # For sampling we need INVERSE: M^-1 = [[cos,-sin],[sin,cos]]
    # So: src_x = cos(θ)*(x-cx) - sin(θ)*(y-cy) + cx
    #     src_y = sin(θ)*(x-cx) + cos(θ)*(y-cy) + cy
    x_centered = x_coords - center_x
    y_centered = y_coords - center_y

    src_x = cos_t * x_centered - sin_t * y_centered + center_x
    src_y = sin_t * x_centered + cos_t * y_centered + center_y

    # Mask for valid coordinates (out-of-bounds -> black, matching OpenCV)
    valid = (src_x >= 0) & (src_x < w - 1) & (src_y >= 0) & (src_y < h - 1)
    valid_flat = valid.flatten()

    # Sample using bilinear interpolation
    r, g, b = jax_sample(frame, src_x.flatten(), src_y.flatten())

    # Zero out-of-bounds pixels (matching OpenCV warpAffine behavior)
    r = jnp.where(valid_flat, r, 0)
    g = jnp.where(valid_flat, g, 0)
    b = jnp.where(valid_flat, b, 0)

    return jnp.stack([
        jnp.clip(r, 0, 255).reshape(h, w).astype(jnp.uint8),
        jnp.clip(g, 0, 255).reshape(h, w).astype(jnp.uint8),
        jnp.clip(b, 0, 255).reshape(h, w).astype(jnp.uint8)
    ], axis=2)


def jax_scale(frame, scale_x, scale_y=None):
    """Scale frame (zoom). Matches OpenCV behavior with black out-of-bounds."""
    if scale_y is None:
        scale_y = scale_x

    h, w = frame.shape[:2]
    center_x, center_y = w / 2, h / 2

    # Create coordinate grids
    y_coords = jnp.repeat(jnp.arange(h), w).reshape(h, w)
    x_coords = jnp.tile(jnp.arange(w), h).reshape(h, w)

    # Scale from center (inverse mapping: dst -> src)
    src_x = (x_coords - center_x) / scale_x + center_x
    src_y = (y_coords - center_y) / scale_y + center_y

    # Mask for valid coordinates (out-of-bounds -> black)
    valid = (src_x >= 0) & (src_x < w - 1) & (src_y >= 0) & (src_y < h - 1)
    valid_flat = valid.flatten()

    r, g, b = jax_sample(frame, src_x.flatten(), src_y.flatten())

    # Zero out-of-bounds pixels
    r = jnp.where(valid_flat, r, 0)
    g = jnp.where(valid_flat, g, 0)
    b = jnp.where(valid_flat, b, 0)

    return jnp.stack([
        jnp.clip(r, 0, 255).reshape(h, w).astype(jnp.uint8),
        jnp.clip(g, 0, 255).reshape(h, w).astype(jnp.uint8),
        jnp.clip(b, 0, 255).reshape(h, w).astype(jnp.uint8)
    ], axis=2)


def jax_resize(frame, new_width, new_height):
    """Resize frame to new dimensions."""
    h, w = frame.shape[:2]
    new_h, new_w = int(new_height), int(new_width)

    # Create coordinate grids for new size
    y_coords = jnp.repeat(jnp.arange(new_h), new_w)
    x_coords = jnp.tile(jnp.arange(new_w), new_h)

    # Map to source coordinates
    src_x = x_coords * (w - 1) / (new_w - 1)
    src_y = y_coords * (h - 1) / (new_h - 1)

    r, g, b = jax_sample(frame, src_x, src_y)

    return jnp.stack([
        jnp.clip(r, 0, 255).reshape(new_h, new_w).astype(jnp.uint8),
        jnp.clip(g, 0, 255).reshape(new_h, new_w).astype(jnp.uint8),
        jnp.clip(b, 0, 255).reshape(new_h, new_w).astype(jnp.uint8)
    ], axis=2)


# =============================================================================
# Blending Operations
# =============================================================================

def _resize_to_match(frame1, frame2):
    """Resize frame2 to match frame1's dimensions if they differ.

    Uses jax.image.resize for bilinear interpolation.
    Returns frame2 resized to frame1's shape.
    """
    h1, w1 = frame1.shape[:2]
    h2, w2 = frame2.shape[:2]

    # If same size, return as-is
    if h1 == h2 and w1 == w2:
        return frame2

    # Resize frame2 to match frame1
    # jax.image.resize expects (height, width, channels) and target shape
    return jax.image.resize(
        frame2.astype(jnp.float32),
        (h1, w1, frame2.shape[2]),
        method='bilinear'
    ).astype(jnp.uint8)


def jax_blend(frame1, frame2, alpha):
    """Blend two frames. alpha=0 -> frame1, alpha=1 -> frame2.

    Auto-resizes frame2 to match frame1 if dimensions differ.
    """
    frame2 = _resize_to_match(frame1, frame2)
    return (frame1.astype(jnp.float32) * (1 - alpha) +
            frame2.astype(jnp.float32) * alpha).astype(jnp.uint8)


def jax_blend_add(frame1, frame2):
    """Additive blend. Auto-resizes frame2 to match frame1."""
    frame2 = _resize_to_match(frame1, frame2)
    result = frame1.astype(jnp.float32) + frame2.astype(jnp.float32)
    return jnp.clip(result, 0, 255).astype(jnp.uint8)


def jax_blend_multiply(frame1, frame2):
    """Multiply blend. Auto-resizes frame2 to match frame1."""
    frame2 = _resize_to_match(frame1, frame2)
    result = frame1.astype(jnp.float32) * frame2.astype(jnp.float32) / 255
    return jnp.clip(result, 0, 255).astype(jnp.uint8)


def jax_blend_screen(frame1, frame2):
    """Screen blend. Auto-resizes frame2 to match frame1."""
    frame2 = _resize_to_match(frame1, frame2)
    f1 = frame1.astype(jnp.float32) / 255
    f2 = frame2.astype(jnp.float32) / 255
    result = 1 - (1 - f1) * (1 - f2)
    return jnp.clip(result * 255, 0, 255).astype(jnp.uint8)


def jax_blend_overlay(frame1, frame2):
    """Overlay blend. Auto-resizes frame2 to match frame1."""
    frame2 = _resize_to_match(frame1, frame2)
    f1 = frame1.astype(jnp.float32) / 255
    f2 = frame2.astype(jnp.float32) / 255
    result = jnp.where(f1 < 0.5,
                       2 * f1 * f2,
                       1 - 2 * (1 - f1) * (1 - f2))
    return jnp.clip(result * 255, 0, 255).astype(jnp.uint8)


# =============================================================================
# Utility
# =============================================================================

def make_jax_key(seed: int = 42, frame_num = 0, op_id: int = 0):
    """Create a JAX random key that varies with frame and operation.

    Uses jax.random.fold_in to mix frame_num (which may be traced) into the key.
    This allows JIT compilation without recompiling for each frame.

    Args:
        seed: Base seed for determinism (must be concrete)
        frame_num: Frame number for variation (can be traced)
        op_id: Operation ID for variation (must be concrete)

    Returns:
        JAX PRNGKey
    """
    # Create base key from seed and op_id (both concrete)
    base_key = jax.random.PRNGKey(seed + op_id * 1000003)
    # Fold in frame_num (can be traced value)
    return jax.random.fold_in(base_key, frame_num)


def jax_rand_range(lo, hi, frame_num=0, op_id=0, seed=42):
    """Random float in [lo, hi), varies with frame."""
    key = make_jax_key(seed, frame_num, op_id)
    return lo + jax.random.uniform(key) * (hi - lo)


def jax_is_nil(x):
    """Check if value is None/nil."""
    return x is None


# =============================================================================
# S-expression to JAX Compiler
# =============================================================================

class JaxCompiler:
    """Compiles S-expressions to JAX functions."""

    def __init__(self):
        self.env = {}  # Variable bindings during compilation
        self.params = {}  # Effect parameters
        self.primitives = {}  # Loaded primitive libraries
        self.derived = {}  # Loaded derived functions

    def load_derived(self, path: str):
        """Load derived operations from a .sexp file."""
        with open(path, 'r') as f:
            code = f.read()
        exprs = parse_all(code)

        # Evaluate all define expressions to populate derived functions
        for expr in exprs:
            if isinstance(expr, list) and len(expr) >= 3:
                head = expr[0]
                if isinstance(head, Symbol) and head.name == 'define':
                    self._eval_define(expr[1:], self.derived)

    def compile_effect(self, sexp) -> Callable:
        """
        Compile an effect S-expression to a JAX function.

        Supports both formats:
            (effect "name" :params (...) :body ...)
            (define-effect name :params (...) body)

        Args:
            sexp: Parsed S-expression

        Returns:
            JIT-compiled function: (frame, **params) -> frame
        """
        if not isinstance(sexp, list) or len(sexp) < 2:
            raise ValueError("Effect must be a list")

        head = sexp[0]
        if not isinstance(head, Symbol):
            raise ValueError("Effect must start with a symbol")

        form = head.name

        # Handle both 'effect' and 'define-effect' formats
        if form == 'effect':
            # (effect "name" :params (...) :body ...)
            name = sexp[1] if len(sexp) > 1 else "unnamed"
            if isinstance(name, Symbol):
                name = name.name
            start_idx = 2
        elif form == 'define-effect':
            # (define-effect name :params (...) body)
            name = sexp[1].name if isinstance(sexp[1], Symbol) else str(sexp[1])
            start_idx = 2
        else:
            raise ValueError(f"Expected 'effect' or 'define-effect', got '{form}'")

        params_spec = []
        body = None

        i = start_idx
        while i < len(sexp):
            item = sexp[i]
            if isinstance(item, Keyword):
                if item.name == 'params' and i + 1 < len(sexp):
                    params_spec = sexp[i + 1]
                    i += 2
                elif item.name == 'body' and i + 1 < len(sexp):
                    body = sexp[i + 1]
                    i += 2
                elif item.name in ('desc', 'type', 'range'):
                    # Skip metadata keywords
                    i += 2
                else:
                    i += 2  # Skip unknown keywords with their values
            else:
                # Assume it's the body if we haven't seen one
                if body is None:
                    body = item
                i += 1

        if body is None:
            raise ValueError(f"Effect '{name}' must have a body")

        # Extract parameter names, defaults, and static params (strings, bools)
        param_info, static_params = self._parse_params(params_spec)

        # Capture derived functions for the closure
        derived_fns = self.derived.copy()

        # Create the JAX function
        def effect_fn(frame, **kwargs):
            # Set up environment
            h, w = frame.shape[:2]
            # Get frame_num for deterministic random variation
            frame_num = kwargs.get('frame_num', 0)
            # Get seed from recipe config (passed via kwargs)
            seed = kwargs.get('seed', 42)
            env = {
                'frame': frame,
                'width': w,
                'height': h,
                '_shape': (h, w),
                # Time variables (default to 0, can be overridden via kwargs)
                't': kwargs.get('t', kwargs.get('_time', 0.0)),
                '_time': kwargs.get('_time', kwargs.get('t', 0.0)),
                'time': kwargs.get('time', kwargs.get('t', 0.0)),
                # Frame number for random key generation
                'frame_num': frame_num,
                'frame-num': frame_num,
                '_frame_num': frame_num,
                # Seed from recipe for deterministic random
                '_seed': seed,
                # Counter for unique random keys within same frame
                '_rand_op_counter': 0,
                # Common constants
                'pi': jnp.pi,
                'PI': jnp.pi,
            }

            # Add derived functions
            env.update(derived_fns)

            # Add parameters with defaults
            for pname, pdefault in param_info.items():
                if pname in kwargs:
                    env[pname] = kwargs[pname]
                elif isinstance(pdefault, list):
                    # Unevaluated S-expression default - evaluate it
                    env[pname] = self._eval(pdefault, env)
                else:
                    env[pname] = pdefault

            # Evaluate body
            result = self._eval(body, env)

            # Ensure result is a frame
            if isinstance(result, tuple) and len(result) == 3:
                # RGB tuple - merge to frame
                r, g, b = result
                return jax_merge_channels(r, g, b, (h, w))
            elif result.ndim == 3:
                return result
            else:
                # Single channel - replicate to RGB
                h, w = env['_shape']
                gray = jnp.clip(result.reshape(h, w), 0, 255).astype(jnp.uint8)
                return jnp.stack([gray, gray, gray], axis=2)

        # JIT compile with static args for string/bool parameters and seed
        # seed must be static for PRNGKey, but frame_num can be traced via fold_in
        all_static = set(static_params) | {'seed'}
        return jax.jit(effect_fn, static_argnames=list(all_static))

    def _parse_params(self, params_spec) -> Tuple[Dict[str, Any], set]:
        """Parse parameter specifications.

        Returns:
            Tuple of (param_defaults, static_params)
            - param_defaults: Dict mapping param names to default values
            - static_params: Set of param names that should be static (strings, bools)
        """
        result = {}
        static_params = set()
        if not isinstance(params_spec, list):
            return result, static_params

        for param in params_spec:
            if isinstance(param, Symbol):
                result[param.name] = 0.0
            elif isinstance(param, list) and len(param) >= 1:
                pname = param[0].name if isinstance(param[0], Symbol) else str(param[0])
                pdefault = 0.0
                ptype = None

                # Look for :default and :type keywords
                i = 1
                while i < len(param):
                    if isinstance(param[i], Keyword):
                        kw = param[i].name
                        if kw == 'default' and i + 1 < len(param):
                            pdefault = param[i + 1]
                            if isinstance(pdefault, Symbol):
                                if pdefault.name == 'nil':
                                    pdefault = None
                                elif pdefault.name == 'true':
                                    pdefault = True
                                elif pdefault.name == 'false':
                                    pdefault = False
                            i += 2
                        elif kw == 'type' and i + 1 < len(param):
                            ptype = param[i + 1]
                            if isinstance(ptype, Symbol):
                                ptype = ptype.name
                            i += 2
                        else:
                            i += 1
                    else:
                        i += 1

                result[pname] = pdefault

                # Mark string and bool parameters as static (can't be traced by JAX)
                if ptype in ('string', 'bool') or isinstance(pdefault, (str, bool)):
                    static_params.add(pname)

        return result, static_params

    def _eval(self, expr, env: Dict[str, Any]) -> Any:
        """Evaluate an S-expression in the given environment."""

        # Already-evaluated values (e.g., from threading macros)
        # JAX arrays, NumPy arrays, tuples, etc.
        if hasattr(expr, 'shape'):  # JAX/NumPy array
            return expr
        if isinstance(expr, tuple):  # e.g., (r, g, b) from rgb
            return expr

        # Literals - keep as Python numbers for static operations
        if isinstance(expr, (int, float)):
            return expr

        if isinstance(expr, str):
            return expr

        # Symbols - variable lookup
        if isinstance(expr, Symbol):
            name = expr.name
            if name in env:
                return env[name]
            if name == 'nil':
                return None
            if name == 'true':
                return True
            if name == 'false':
                return False
            raise NameError(f"Unknown symbol: {name}")

        # Lists - function calls
        if isinstance(expr, list) and len(expr) > 0:
            head = expr[0]

            if isinstance(head, Symbol):
                op = head.name
                args = expr[1:]

                # Special forms
                if op == 'let' or op == 'let*':
                    return self._eval_let(args, env)
                if op == 'if':
                    return self._eval_if(args, env)
                if op == 'lambda' or op == 'λ':
                    return self._eval_lambda(args, env)
                if op == 'define':
                    return self._eval_define(args, env)

                # Built-in operations
                return self._eval_call(op, args, env)

        # Empty list
        if isinstance(expr, list) and len(expr) == 0:
            return []

        raise ValueError(f"Cannot evaluate: {expr}")

    def _eval_let(self, args, env: Dict[str, Any]) -> Any:
        """Evaluate (let ((var val) ...) body) or (let* ...) or (let [var val ...] body)."""
        if len(args) < 2:
            raise ValueError("let requires bindings and body")

        bindings = args[0]
        body = args[1]

        new_env = env.copy()

        # Handle both ((var val) ...) and [var val var2 val2 ...] syntax
        if isinstance(bindings, list):
            # Check if it's a flat list [var val var2 val2 ...] or nested ((var val) ...)
            if bindings and isinstance(bindings[0], Symbol):
                # Flat list: [var val var2 val2 ...]
                i = 0
                while i < len(bindings) - 1:
                    var = bindings[i].name if isinstance(bindings[i], Symbol) else str(bindings[i])
                    val = self._eval(bindings[i + 1], new_env)
                    new_env[var] = val
                    i += 2
            else:
                # Nested list: ((var val) (var2 val2) ...)
                for binding in bindings:
                    if isinstance(binding, list) and len(binding) >= 2:
                        var = binding[0].name if isinstance(binding[0], Symbol) else str(binding[0])
                        val = self._eval(binding[1], new_env)
                        new_env[var] = val

        return self._eval(body, new_env)

    def _eval_if(self, args, env: Dict[str, Any]) -> Any:
        """Evaluate (if cond then else)."""
        if len(args) < 2:
            raise ValueError("if requires condition and then-branch")

        cond = self._eval(args[0], env)

        # Handle None as falsy (important for optional params like overlay)
        if cond is None:
            return self._eval(args[2], env) if len(args) > 2 else None

        # For Python scalar bools, use normal Python if
        # This allows side effects and None values
        if isinstance(cond, bool):
            if cond:
                return self._eval(args[1], env)
            else:
                return self._eval(args[2], env) if len(args) > 2 else None

        # For NumPy/JAX scalar bools with concrete values
        if hasattr(cond, 'item') and cond.shape == ():
            try:
                if bool(cond.item()):
                    return self._eval(args[1], env)
                else:
                    return self._eval(args[2], env) if len(args) > 2 else None
            except:
                pass  # Fall through to jnp.where for traced values

        # For traced values, evaluate both branches and use jnp.where
        then_val = self._eval(args[1], env)
        else_val = self._eval(args[2], env) if len(args) > 2 else 0.0

        # Handle None by converting to zeros
        if then_val is None:
            then_val = 0.0
        if else_val is None:
            else_val = 0.0

        # Convert lists to tuples
        if isinstance(then_val, list):
            then_val = tuple(then_val)
        if isinstance(else_val, list):
            else_val = tuple(else_val)

        # Handle tuple results (e.g., from rgb in map-pixels)
        if isinstance(then_val, tuple) and isinstance(else_val, tuple):
            return tuple(jnp.where(cond, t, e) for t, e in zip(then_val, else_val))

        return jnp.where(cond, then_val, else_val)

    def _eval_lambda(self, args, env: Dict[str, Any]) -> Callable:
        """Evaluate (lambda (params) body)."""
        if len(args) < 2:
            raise ValueError("lambda requires parameters and body")

        params = [p.name if isinstance(p, Symbol) else str(p) for p in args[0]]
        body = args[1]
        captured_env = env.copy()

        def fn(*fn_args):
            local_env = captured_env.copy()
            for pname, pval in zip(params, fn_args):
                local_env[pname] = pval
            return self._eval(body, local_env)

        return fn

    def _eval_define(self, args, env: Dict[str, Any]) -> Any:
        """Evaluate (define name value) or (define (name params) body)."""
        if len(args) < 2:
            raise ValueError("define requires name and value")

        name_part = args[0]

        if isinstance(name_part, list):
            # Function definition: (define (name params) body)
            fn_name = name_part[0].name if isinstance(name_part[0], Symbol) else str(name_part[0])
            params = [p.name if isinstance(p, Symbol) else str(p) for p in name_part[1:]]
            body = args[1]
            captured_env = env.copy()

            def fn(*fn_args):
                local_env = captured_env.copy()
                for pname, pval in zip(params, fn_args):
                    local_env[pname] = pval
                return self._eval(body, local_env)

            env[fn_name] = fn
            return fn
        else:
            # Variable definition
            var_name = name_part.name if isinstance(name_part, Symbol) else str(name_part)
            val = self._eval(args[1], env)
            env[var_name] = val
            return val

    def _eval_call(self, op: str, args: List, env: Dict[str, Any]) -> Any:
        """Evaluate a function call."""

        # Check if it's a user-defined function
        if op in env and callable(env[op]):
            fn = env[op]
            eval_args = [self._eval(a, env) for a in args]
            return fn(*eval_args)

        # Arithmetic
        if op == '+':
            vals = [self._eval(a, env) for a in args]
            result = vals[0] if vals else 0.0
            for v in vals[1:]:
                result = result + v
            return result

        if op == '-':
            if len(args) == 1:
                return -self._eval(args[0], env)
            vals = [self._eval(a, env) for a in args]
            result = vals[0]
            for v in vals[1:]:
                result = result - v
            return result

        if op == '*':
            vals = [self._eval(a, env) for a in args]
            result = vals[0] if vals else 1.0
            for v in vals[1:]:
                result = result * v
            return result

        if op == '/':
            vals = [self._eval(a, env) for a in args]
            result = vals[0]
            for v in vals[1:]:
                result = result / v
            return result

        if op == 'mod':
            a, b = self._eval(args[0], env), self._eval(args[1], env)
            return a % b

        if op == 'pow' or op == '**':
            a, b = self._eval(args[0], env), self._eval(args[1], env)
            return jnp.power(a, b)

        # Comparison
        if op == '<':
            a, b = self._eval(args[0], env), self._eval(args[1], env)
            return a < b
        if op == '>':
            a, b = self._eval(args[0], env), self._eval(args[1], env)
            return a > b
        if op == '<=':
            a, b = self._eval(args[0], env), self._eval(args[1], env)
            return a <= b
        if op == '>=':
            a, b = self._eval(args[0], env), self._eval(args[1], env)
            return a >= b
        if op == '=' or op == '==':
            a, b = self._eval(args[0], env), self._eval(args[1], env)
            # For scalar Python types, return Python bool to enable trace-time if
            if isinstance(a, (int, float)) and isinstance(b, (int, float)):
                return bool(a == b)
            return a == b
        if op == '!=' or op == '<>':
            a, b = self._eval(args[0], env), self._eval(args[1], env)
            if isinstance(a, (int, float)) and isinstance(b, (int, float)):
                return bool(a != b)
            return a != b

        # Logic
        if op == 'and':
            vals = [self._eval(a, env) for a in args]
            # Use Python and for concrete Python bools (e.g., shape comparisons)
            if all(isinstance(v, (bool, np.bool_)) for v in vals):
                result = True
                for v in vals:
                    result = result and bool(v)
                return result
            # Otherwise use JAX logical_and
            result = vals[0]
            for v in vals[1:]:
                result = jnp.logical_and(result, v)
            return result

        if op == 'or':
            vals = [self._eval(a, env) for a in args]
            # Use Python or for concrete Python bools
            if all(isinstance(v, (bool, np.bool_)) for v in vals):
                result = False
                for v in vals:
                    result = result or bool(v)
                return result
            # Otherwise use JAX logical_or
            result = vals[0]
            for v in vals[1:]:
                result = jnp.logical_or(result, v)
            return result

        if op == 'not':
            val = self._eval(args[0], env)
            if isinstance(val, (bool, np.bool_)):
                return not bool(val)
            return jnp.logical_not(val)

        # Math functions
        if op == 'sqrt':
            return jnp.sqrt(self._eval(args[0], env))
        if op == 'sin':
            return jnp.sin(self._eval(args[0], env))
        if op == 'cos':
            return jnp.cos(self._eval(args[0], env))
        if op == 'tan':
            return jnp.tan(self._eval(args[0], env))
        if op == 'exp':
            return jnp.exp(self._eval(args[0], env))
        if op == 'log':
            return jnp.log(self._eval(args[0], env))
        if op == 'abs':
            x = self._eval(args[0], env)
            if isinstance(x, (int, float)):
                return abs(x)
            return jnp.abs(x)
        if op == 'floor':
            import math
            x = self._eval(args[0], env)
            if isinstance(x, (int, float)):
                return math.floor(x)
            return jnp.floor(x)
        if op == 'ceil':
            import math
            x = self._eval(args[0], env)
            if isinstance(x, (int, float)):
                return math.ceil(x)
            return jnp.ceil(x)
        if op == 'round':
            x = self._eval(args[0], env)
            if isinstance(x, (int, float)):
                return round(x)
            return jnp.round(x)

        # Frame primitives
        if op == 'width':
            return env['width']
        if op == 'height':
            return env['height']

        if op == 'channel':
            frame = self._eval(args[0], env)
            idx = self._eval(args[1], env)
            # idx should be a Python int (literal from S-expression)
            return jax_channel(frame, idx)

        if op == 'merge-channels' or op == 'rgb':
            r = self._eval(args[0], env)
            g = self._eval(args[1], env)
            b = self._eval(args[2], env)
            # For scalars (e.g., in map-pixels), return tuple
            r_is_scalar = isinstance(r, (int, float)) or (hasattr(r, 'shape') and r.shape == ())
            g_is_scalar = isinstance(g, (int, float)) or (hasattr(g, 'shape') and g.shape == ())
            b_is_scalar = isinstance(b, (int, float)) or (hasattr(b, 'shape') and b.shape == ())
            if r_is_scalar and g_is_scalar and b_is_scalar:
                return (r, g, b)
            return jax_merge_channels(r, g, b, env['_shape'])

        if op == 'sample':
            frame = self._eval(args[0], env)
            x = self._eval(args[1], env)
            y = self._eval(args[2], env)
            return jax_sample(frame, x, y)

        if op == 'cell-indices':
            frame = self._eval(args[0], env)
            cell_size = self._eval(args[1], env)
            return jax_cell_indices(frame, cell_size)

        if op == 'pool-frame':
            frame = self._eval(args[0], env)
            cell_size = self._eval(args[1], env)
            return jax_pool_frame(frame, cell_size)

        # Xector primitives
        if op == 'iota':
            n = self._eval(args[0], env)
            return jax_iota(int(n))

        if op == 'repeat':
            x = self._eval(args[0], env)
            n = self._eval(args[1], env)
            return jax_repeat(x, int(n))

        if op == 'tile':
            x = self._eval(args[0], env)
            n = self._eval(args[1], env)
            return jax_tile(x, int(n))

        if op == 'gather':
            data = self._eval(args[0], env)
            indices = self._eval(args[1], env)
            return jax_gather(data, indices)

        if op == 'scatter':
            indices = self._eval(args[0], env)
            values = self._eval(args[1], env)
            size = int(self._eval(args[2], env))
            return jax_scatter(indices, values, size)

        if op == 'scatter-add':
            indices = self._eval(args[0], env)
            values = self._eval(args[1], env)
            size = int(self._eval(args[2], env))
            return jax_scatter_add(indices, values, size)

        if op == 'group-reduce':
            values = self._eval(args[0], env)
            groups = self._eval(args[1], env)
            num_groups = int(self._eval(args[2], env))
            reduce_op = args[3] if len(args) > 3 else 'mean'
            if isinstance(reduce_op, Symbol):
                reduce_op = reduce_op.name
            return jax_group_reduce(values, groups, num_groups, reduce_op)

        if op == 'where':
            cond = self._eval(args[0], env)
            true_val = self._eval(args[1], env)
            false_val = self._eval(args[2], env)
            # Handle None values
            if true_val is None:
                true_val = 0.0
            if false_val is None:
                false_val = 0.0
            return jax_where(cond, true_val, false_val)

        if op == 'len' or op == 'length':
            x = self._eval(args[0], env)
            if isinstance(x, (list, tuple)):
                return len(x)
            return x.size

        # Beta reductions
        if op in ('β+', 'beta+', 'sum'):
            return jnp.sum(self._eval(args[0], env))
        if op in ('β*', 'beta*', 'product'):
            return jnp.prod(self._eval(args[0], env))
        if op in ('βmin', 'beta-min'):
            return jnp.min(self._eval(args[0], env))
        if op in ('βmax', 'beta-max'):
            return jnp.max(self._eval(args[0], env))
        if op in ('βmean', 'beta-mean', 'mean'):
            return jnp.mean(self._eval(args[0], env))
        if op in ('βstd', 'beta-std'):
            return jnp.std(self._eval(args[0], env))
        if op in ('βany', 'beta-any'):
            return jnp.any(self._eval(args[0], env))
        if op in ('βall', 'beta-all'):
            return jnp.all(self._eval(args[0], env))

        # Convenience - min/max of two values (handle both scalars and arrays)
        if op == 'min' or op == 'min2':
            a = self._eval(args[0], env)
            b = self._eval(args[1], env)
            # Use Python min/max for scalar Python values to preserve type
            if isinstance(a, (int, float)) and isinstance(b, (int, float)):
                return min(a, b)
            return jnp.minimum(jnp.asarray(a), jnp.asarray(b))
        if op == 'max' or op == 'max2':
            a = self._eval(args[0], env)
            b = self._eval(args[1], env)
            # Use Python min/max for scalar Python values to preserve type
            if isinstance(a, (int, float)) and isinstance(b, (int, float)):
                return max(a, b)
            return jnp.maximum(jnp.asarray(a), jnp.asarray(b))
        if op == 'clamp':
            x = self._eval(args[0], env)
            lo = self._eval(args[1], env)
            hi = self._eval(args[2], env)
            return jnp.clip(x, lo, hi)

        # List operations
        if op == 'list':
            return tuple(self._eval(a, env) for a in args)

        if op == 'nth':
            seq = self._eval(args[0], env)
            idx = int(self._eval(args[1], env))
            if isinstance(seq, (list, tuple)):
                return seq[idx] if 0 <= idx < len(seq) else None
            return seq[idx]  # For arrays

        if op == 'first':
            seq = self._eval(args[0], env)
            return seq[0] if len(seq) > 0 else None

        if op == 'second':
            seq = self._eval(args[0], env)
            return seq[1] if len(seq) > 1 else None

        # Random (JAX-compatible)
        # Get frame_num for deterministic variation - can be traced, fold_in handles it
        frame_num = env.get('_frame_num', env.get('frame_num', 0))
        # Convert to int32 for fold_in if needed (but keep as JAX array if traced)
        if frame_num is None:
            frame_num = 0
        elif isinstance(frame_num, (int, float)):
            frame_num = int(frame_num)
        # If it's a JAX array, leave it as-is for tracing

        # Increment operation counter for unique keys within same frame
        op_counter = env.get('_rand_op_counter', 0)
        env['_rand_op_counter'] = op_counter + 1

        if op == 'rand' or op == 'rand-x':
            # For size-based random
            if args:
                size = self._eval(args[0], env)
                if hasattr(size, 'shape'):
                    # For frames (3D), use h*w (channel size), not h*w*c
                    if size.ndim == 3:
                        n = size.shape[0] * size.shape[1]  # h * w
                        shape = (n,)
                    else:
                        n = size.size
                        shape = size.shape
                elif hasattr(size, 'size'):
                    n = size.size
                    shape = (n,)
                else:
                    n = int(size)
                    shape = (n,)
                # Use deterministic key that varies with frame
                seed = env.get('_seed', 42)
                key = make_jax_key(seed, frame_num, op_counter)
                return jax.random.uniform(key, shape).flatten()
            seed = env.get('_seed', 42)
            key = make_jax_key(seed, frame_num, op_counter)
            return jax.random.uniform(key, ())

        if op == 'randn' or op == 'randn-x':
            # Normal random
            if args:
                size = self._eval(args[0], env)
                if hasattr(size, 'shape'):
                    # For frames (3D), use h*w (channel size), not h*w*c
                    if size.ndim == 3:
                        n = size.shape[0] * size.shape[1]  # h * w
                    else:
                        n = size.size
                elif hasattr(size, 'size'):
                    n = size.size
                else:
                    n = int(size)
                mean = self._eval(args[1], env) if len(args) > 1 else 0.0
                std = self._eval(args[2], env) if len(args) > 2 else 1.0
                seed = env.get('_seed', 42)
                key = make_jax_key(seed, frame_num, op_counter)
                return jax.random.normal(key, (n,)) * std + mean
            seed = env.get('_seed', 42)
            key = make_jax_key(seed, frame_num, op_counter)
            return jax.random.normal(key, ())

        if op == 'rand-range' or op == 'core:rand-range':
            lo = self._eval(args[0], env)
            hi = self._eval(args[1], env)
            seed = env.get('_seed', 42)
            return jax_rand_range(lo, hi, frame_num, op_counter, seed)

        # =====================================================================
        # Convolution operations
        # =====================================================================
        if op == 'blur' or op == 'image:blur':
            frame = self._eval(args[0], env)
            radius = self._eval(args[1], env) if len(args) > 1 else 1
            # Convert traced value to concrete for kernel size
            if hasattr(radius, 'item'):
                radius = int(radius.item())
            elif hasattr(radius, '__float__'):
                radius = int(float(radius))
            else:
                radius = int(radius)
            return jax_blur(frame, max(1, radius))

        if op == 'gaussian':
            first_arg = self._eval(args[0], env)
            # Check if first arg is a frame (blur) or scalar (random)
            if hasattr(first_arg, 'shape') and first_arg.ndim == 3:
                # Frame - apply gaussian blur
                sigma = self._eval(args[1], env) if len(args) > 1 else 1.0
                radius = max(1, int(sigma * 3))
                return jax_blur(first_arg, radius)
            else:
                # Scalar args - generate gaussian random value
                mean = float(first_arg) if not isinstance(first_arg, (int, float)) else first_arg
                std = self._eval(args[1], env) if len(args) > 1 else 1.0
                # Return a single random value
                op_counter = env.get('_rand_op_counter', 0)
                env['_rand_op_counter'] = op_counter + 1
                seed = env.get('_seed', 42)
                key = make_jax_key(seed, frame_num, op_counter)
                return jax.random.normal(key, ()) * std + mean

        if op == 'sharpen' or op == 'image:sharpen':
            frame = self._eval(args[0], env)
            amount = self._eval(args[1], env) if len(args) > 1 else 1.0
            return jax_sharpen(frame, amount)

        if op == 'edge-detect' or op == 'image:edge-detect':
            frame = self._eval(args[0], env)
            return jax_edge_detect(frame)

        if op == 'emboss':
            frame = self._eval(args[0], env)
            return jax_emboss(frame)

        if op == 'convolve':
            frame = self._eval(args[0], env)
            kernel = self._eval(args[1], env)
            # Convert kernel to array if it's a list
            if isinstance(kernel, (list, tuple)):
                kernel = jnp.array(kernel, dtype=jnp.float32)
            h, w = frame.shape[:2]
            r = jax_convolve2d(frame[:, :, 0].astype(jnp.float32), kernel)
            g = jax_convolve2d(frame[:, :, 1].astype(jnp.float32), kernel)
            b = jax_convolve2d(frame[:, :, 2].astype(jnp.float32), kernel)
            return jnp.stack([
                jnp.clip(r, 0, 255).astype(jnp.uint8),
                jnp.clip(g, 0, 255).astype(jnp.uint8),
                jnp.clip(b, 0, 255).astype(jnp.uint8)
            ], axis=2)

        if op == 'add-noise':
            frame = self._eval(args[0], env)
            amount = self._eval(args[1], env) if len(args) > 1 else 0.1
            h, w = frame.shape[:2]
            # Use frame-varying key for noise
            op_counter = env.get('_rand_op_counter', 0)
            env['_rand_op_counter'] = op_counter + 1
            seed = env.get('_seed', 42)
            key = make_jax_key(seed, frame_num, op_counter)
            noise = jax.random.uniform(key, frame.shape) * 2 - 1  # [-1, 1]
            result = frame.astype(jnp.float32) + noise * amount * 255
            return jnp.clip(result, 0, 255).astype(jnp.uint8)

        if op == 'translate':
            frame = self._eval(args[0], env)
            dx = self._eval(args[1], env)
            dy = self._eval(args[2], env) if len(args) > 2 else 0
            h, w = frame.shape[:2]
            y_coords = jnp.repeat(jnp.arange(h), w).reshape(h, w)
            x_coords = jnp.tile(jnp.arange(w), h).reshape(h, w)
            src_x = (x_coords - dx).flatten()
            src_y = (y_coords - dy).flatten()
            r, g, b = jax_sample(frame, src_x, src_y)
            return jnp.stack([
                jnp.clip(r, 0, 255).reshape(h, w).astype(jnp.uint8),
                jnp.clip(g, 0, 255).reshape(h, w).astype(jnp.uint8),
                jnp.clip(b, 0, 255).reshape(h, w).astype(jnp.uint8)
            ], axis=2)

        if op == 'image:crop' or op == 'crop':
            frame = self._eval(args[0], env)
            x = int(self._eval(args[1], env))
            y = int(self._eval(args[2], env))
            w = int(self._eval(args[3], env))
            h = int(self._eval(args[4], env))
            return frame[y:y+h, x:x+w, :]

        if op == 'dilate':
            frame = self._eval(args[0], env)
            size = int(self._eval(args[1], env)) if len(args) > 1 else 3
            # Simple dilation using max pooling approximation
            kernel = jnp.ones((size, size), dtype=jnp.float32) / (size * size)
            h, w = frame.shape[:2]
            r = jax_convolve2d(frame[:, :, 0].astype(jnp.float32), kernel) * (size * size)
            g = jax_convolve2d(frame[:, :, 1].astype(jnp.float32), kernel) * (size * size)
            b = jax_convolve2d(frame[:, :, 2].astype(jnp.float32), kernel) * (size * size)
            return jnp.stack([
                jnp.clip(r, 0, 255).astype(jnp.uint8),
                jnp.clip(g, 0, 255).astype(jnp.uint8),
                jnp.clip(b, 0, 255).astype(jnp.uint8)
            ], axis=2)

        if op == 'map-rows':
            frame = self._eval(args[0], env)
            fn = args[1]  # S-expression function
            h, w = frame.shape[:2]
            # For each row, apply the function
            results = []
            for row_idx in range(h):
                row_env = env.copy()
                row_env['row'] = frame[row_idx, :, :]
                row_env['row-idx'] = row_idx

                # Check if fn is a lambda
                if isinstance(fn, list) and len(fn) >= 2:
                    head = fn[0]
                    if isinstance(head, Symbol) and head.name in ('lambda', 'λ'):
                        params = fn[1]
                        body = fn[2]
                        # Bind lambda params to y and row
                        if len(params) >= 1:
                            param_name = params[0].name if isinstance(params[0], Symbol) else str(params[0])
                            row_env[param_name] = row_idx  # y
                        if len(params) >= 2:
                            param_name = params[1].name if isinstance(params[1], Symbol) else str(params[1])
                            row_env[param_name] = frame[row_idx, :, :]  # row
                        result_row = self._eval(body, row_env)
                        results.append(result_row)
                        continue

                result_row = self._eval(fn, row_env)
                # If result is a function, call it
                if callable(result_row):
                    result_row = result_row(row_idx, frame[row_idx, :, :])
                results.append(result_row)
            return jnp.stack(results, axis=0)

        # =====================================================================
        # Color operations
        # =====================================================================
        if op == 'rgb->hsv' or op == 'rgb-to-hsv':
            # Handle both (rgb->hsv r g b) and (rgb->hsv c) where c is tuple
            if len(args) == 1:
                c = self._eval(args[0], env)
                if isinstance(c, tuple) and len(c) == 3:
                    r, g, b = c
                else:
                    # Assume it's a list-like
                    r, g, b = c[0], c[1], c[2]
            else:
                r = self._eval(args[0], env)
                g = self._eval(args[1], env)
                b = self._eval(args[2], env)
            return jax_rgb_to_hsv(r, g, b)

        if op == 'hsv->rgb' or op == 'hsv-to-rgb':
            # Handle both (hsv->rgb h s v) and (hsv->rgb hsv-list)
            if len(args) == 1:
                hsv = self._eval(args[0], env)
                if isinstance(hsv, (tuple, list)) and len(hsv) >= 3:
                    h, s, v = hsv[0], hsv[1], hsv[2]
                else:
                    h, s, v = hsv[0], hsv[1], hsv[2]
            else:
                h = self._eval(args[0], env)
                s = self._eval(args[1], env)
                v = self._eval(args[2], env)
            return jax_hsv_to_rgb(h, s, v)

        if op == 'adjust-brightness' or op == 'color_ops:adjust-brightness':
            frame = self._eval(args[0], env)
            amount = self._eval(args[1], env)
            return jax_adjust_brightness(frame, amount)

        if op == 'adjust-contrast' or op == 'color_ops:adjust-contrast':
            frame = self._eval(args[0], env)
            factor = self._eval(args[1], env)
            return jax_adjust_contrast(frame, factor)

        if op == 'adjust-saturation' or op == 'color_ops:adjust-saturation':
            frame = self._eval(args[0], env)
            factor = self._eval(args[1], env)
            return jax_adjust_saturation(frame, factor)

        if op == 'shift-hsv' or op == 'color_ops:shift-hsv' or op == 'hue-shift':
            frame = self._eval(args[0], env)
            degrees = self._eval(args[1], env)
            return jax_shift_hue(frame, degrees)

        if op == 'invert' or op == 'invert-img' or op == 'color_ops:invert-img':
            frame = self._eval(args[0], env)
            return jax_invert(frame)

        if op == 'posterize' or op == 'color_ops:posterize':
            frame = self._eval(args[0], env)
            levels = self._eval(args[1], env)
            return jax_posterize(frame, levels)

        if op == 'threshold' or op == 'color_ops:threshold':
            frame = self._eval(args[0], env)
            level = self._eval(args[1], env)
            invert = self._eval(args[2], env) if len(args) > 2 else False
            return jax_threshold(frame, level, invert)

        if op == 'sepia' or op == 'color_ops:sepia':
            frame = self._eval(args[0], env)
            return jax_sepia(frame)

        if op == 'grayscale' or op == 'image:grayscale':
            frame = self._eval(args[0], env)
            return jax_grayscale(frame)

        # =====================================================================
        # Geometry operations
        # =====================================================================
        if op == 'flip-horizontal' or op == 'flip-h' or op == 'geometry:flip-img':
            frame = self._eval(args[0], env)
            direction = self._eval(args[1], env) if len(args) > 1 else 'horizontal'
            if direction == 'vertical' or direction == 'v':
                return jax_flip_vertical(frame)
            return jax_flip_horizontal(frame)

        if op == 'flip-vertical' or op == 'flip-v':
            frame = self._eval(args[0], env)
            return jax_flip_vertical(frame)

        if op == 'rotate' or op == 'rotate-img' or op == 'geometry:rotate-img':
            frame = self._eval(args[0], env)
            angle = self._eval(args[1], env)
            return jax_rotate(frame, angle)

        if op == 'scale' or op == 'scale-img' or op == 'geometry:scale-img':
            frame = self._eval(args[0], env)
            scale_x = self._eval(args[1], env)
            scale_y = self._eval(args[2], env) if len(args) > 2 else None
            return jax_scale(frame, scale_x, scale_y)

        if op == 'resize' or op == 'image:resize':
            frame = self._eval(args[0], env)
            new_w = self._eval(args[1], env)
            new_h = self._eval(args[2], env)
            return jax_resize(frame, new_w, new_h)

        # =====================================================================
        # Geometry distortion effects
        # =====================================================================
        if op == 'geometry:fisheye-coords' or op == 'fisheye':
            # Signature: (w h strength cx cy zoom_correct) or (frame strength)
            first_arg = self._eval(args[0], env)
            if not hasattr(first_arg, 'shape'):
                # (w h strength cx cy zoom_correct) signature
                w = int(first_arg)
                h = int(self._eval(args[1], env))
                strength = self._eval(args[2], env) if len(args) > 2 else 0.5
                cx = self._eval(args[3], env) if len(args) > 3 else w / 2
                cy = self._eval(args[4], env) if len(args) > 4 else h / 2
                frame = None
            else:
                frame = first_arg
                strength = self._eval(args[1], env) if len(args) > 1 else 0.5
                h, w = frame.shape[:2]
                cx, cy = w / 2, h / 2

            max_r = jnp.sqrt(float(cx*cx + cy*cy))

            y_coords = jnp.repeat(jnp.arange(h), w).reshape(h, w).astype(jnp.float32)
            x_coords = jnp.tile(jnp.arange(w), h).reshape(h, w).astype(jnp.float32)

            dx = x_coords - cx
            dy = y_coords - cy
            r = jnp.sqrt(dx*dx + dy*dy)
            theta = jnp.arctan2(dy, dx)

            # Fisheye distortion
            r_new = r + strength * r * (1 - r / max_r)

            src_x = r_new * jnp.cos(theta) + cx
            src_y = r_new * jnp.sin(theta) + cy

            if frame is None:
                return {'x': src_x, 'y': src_y}

            r_out, g_out, b_out = jax_sample(frame, src_x.flatten(), src_y.flatten())
            return jnp.stack([
                jnp.clip(r_out, 0, 255).reshape(h, w).astype(jnp.uint8),
                jnp.clip(g_out, 0, 255).reshape(h, w).astype(jnp.uint8),
                jnp.clip(b_out, 0, 255).reshape(h, w).astype(jnp.uint8)
            ], axis=2)

        if op == 'geometry:swirl-coords' or op == 'swirl':
            first_arg = self._eval(args[0], env)
            if not hasattr(first_arg, 'shape'):
                w = int(first_arg)
                h = int(self._eval(args[1], env))
                amount = self._eval(args[2], env) if len(args) > 2 else 1.0
                frame = None
            else:
                frame = first_arg
                amount = self._eval(args[1], env) if len(args) > 1 else 1.0
                h, w = frame.shape[:2]

            cx, cy = w / 2, h / 2
            max_r = jnp.sqrt(float(cx*cx + cy*cy))

            y_coords = jnp.repeat(jnp.arange(h), w).reshape(h, w).astype(jnp.float32)
            x_coords = jnp.tile(jnp.arange(w), h).reshape(h, w).astype(jnp.float32)

            dx = x_coords - cx
            dy = y_coords - cy
            r = jnp.sqrt(dx*dx + dy*dy)
            theta = jnp.arctan2(dy, dx)

            swirl_angle = amount * (1 - r / max_r)
            new_theta = theta + swirl_angle

            src_x = r * jnp.cos(new_theta) + cx
            src_y = r * jnp.sin(new_theta) + cy

            if frame is None:
                return {'x': src_x, 'y': src_y}

            r_out, g_out, b_out = jax_sample(frame, src_x.flatten(), src_y.flatten())
            return jnp.stack([
                jnp.clip(r_out, 0, 255).reshape(h, w).astype(jnp.uint8),
                jnp.clip(g_out, 0, 255).reshape(h, w).astype(jnp.uint8),
                jnp.clip(b_out, 0, 255).reshape(h, w).astype(jnp.uint8)
            ], axis=2)

        # Wave effect (frame-first signature for simple usage)
        if op == 'wave-distort':
            first_arg = self._eval(args[0], env)
            frame = first_arg
            amp_x = float(self._eval(args[1], env)) if len(args) > 1 else 10.0
            amp_y = float(self._eval(args[2], env)) if len(args) > 2 else 10.0
            freq_x = float(self._eval(args[3], env)) if len(args) > 3 else 0.1
            freq_y = float(self._eval(args[4], env)) if len(args) > 4 else 0.1
            h, w = frame.shape[:2]

            y_coords = jnp.repeat(jnp.arange(h), w).reshape(h, w).astype(jnp.float32)
            x_coords = jnp.tile(jnp.arange(w), h).reshape(h, w).astype(jnp.float32)

            src_x = x_coords + amp_x * jnp.sin(y_coords * freq_y)
            src_y = y_coords + amp_y * jnp.sin(x_coords * freq_x)

            r_out, g_out, b_out = jax_sample(frame, src_x.flatten(), src_y.flatten())
            return jnp.stack([
                jnp.clip(r_out, 0, 255).reshape(h, w).astype(jnp.uint8),
                jnp.clip(g_out, 0, 255).reshape(h, w).astype(jnp.uint8),
                jnp.clip(b_out, 0, 255).reshape(h, w).astype(jnp.uint8)
            ], axis=2)

        if op == 'geometry:ripple-displace' or op == 'ripple':
            first_arg = self._eval(args[0], env)
            if not hasattr(first_arg, 'shape'):
                w = int(first_arg)
                h = int(self._eval(args[1], env))
                amplitude = self._eval(args[2], env) if len(args) > 2 else 10.0
                frequency = self._eval(args[3], env) if len(args) > 3 else 0.05
                frame = None
            else:
                frame = first_arg
                amplitude = self._eval(args[1], env) if len(args) > 1 else 10.0
                frequency = self._eval(args[2], env) if len(args) > 2 else 0.05
                h, w = frame.shape[:2]

            cx, cy = w / 2, h / 2

            y_coords = jnp.repeat(jnp.arange(h), w).reshape(h, w).astype(jnp.float32)
            x_coords = jnp.tile(jnp.arange(w), h).reshape(h, w).astype(jnp.float32)

            dx = x_coords - cx
            dy = y_coords - cy
            dist = jnp.sqrt(dx*dx + dy*dy)

            displacement = amplitude * jnp.sin(dist * frequency)
            angle = jnp.arctan2(dy, dx)

            src_x = x_coords + displacement * jnp.cos(angle)
            src_y = y_coords + displacement * jnp.sin(angle)

            if frame is None:
                return {'x': src_x, 'y': src_y}

            r_out, g_out, b_out = jax_sample(frame, src_x.flatten(), src_y.flatten())
            return jnp.stack([
                jnp.clip(r_out, 0, 255).reshape(h, w).astype(jnp.uint8),
                jnp.clip(g_out, 0, 255).reshape(h, w).astype(jnp.uint8),
                jnp.clip(b_out, 0, 255).reshape(h, w).astype(jnp.uint8)
            ], axis=2)

        if op == 'geometry:kaleidoscope-coords' or op == 'kaleidoscope':
            # Two signatures: (frame segments) or (w h segments cx cy)
            if len(args) >= 3 and not hasattr(self._eval(args[0], env), 'shape'):
                # (w h segments cx cy) signature
                w = int(self._eval(args[0], env))
                h = int(self._eval(args[1], env))
                segments = int(self._eval(args[2], env)) if len(args) > 2 else 6
                cx = self._eval(args[3], env) if len(args) > 3 else w / 2
                cy = self._eval(args[4], env) if len(args) > 4 else h / 2
                frame = None
            else:
                frame = self._eval(args[0], env)
                segments = int(self._eval(args[1], env)) if len(args) > 1 else 6
                h, w = frame.shape[:2]
                cx, cy = w / 2, h / 2

            y_coords = jnp.repeat(jnp.arange(h), w).reshape(h, w).astype(jnp.float32)
            x_coords = jnp.tile(jnp.arange(w), h).reshape(h, w).astype(jnp.float32)

            dx = x_coords - cx
            dy = y_coords - cy
            r = jnp.sqrt(dx*dx + dy*dy)
            theta = jnp.arctan2(dy, dx)

            # Mirror into segments
            segment_angle = 2 * jnp.pi / segments
            theta_mod = theta % segment_angle
            theta_mirror = jnp.where(
                (jnp.floor(theta / segment_angle) % 2) == 0,
                theta_mod,
                segment_angle - theta_mod
            )

            src_x = r * jnp.cos(theta_mirror) + cx
            src_y = r * jnp.sin(theta_mirror) + cy

            if frame is None:
                # Return coordinate arrays
                return {'x': src_x, 'y': src_y}

            r_out, g_out, b_out = jax_sample(frame, src_x.flatten(), src_y.flatten())
            return jnp.stack([
                jnp.clip(r_out, 0, 255).reshape(h, w).astype(jnp.uint8),
                jnp.clip(g_out, 0, 255).reshape(h, w).astype(jnp.uint8),
                jnp.clip(b_out, 0, 255).reshape(h, w).astype(jnp.uint8)
            ], axis=2)

        # Geometry coordinate extraction
        if op == 'geometry:coords-x':
            coords = self._eval(args[0], env)
            if isinstance(coords, dict):
                return coords['x']
            return coords[0] if isinstance(coords, tuple) else coords

        if op == 'geometry:coords-y':
            coords = self._eval(args[0], env)
            if isinstance(coords, dict):
                return coords['y']
            return coords[1] if isinstance(coords, tuple) else coords

        if op == 'geometry:remap' or op == 'remap':
            frame = self._eval(args[0], env)
            x_coords = self._eval(args[1], env)
            y_coords = self._eval(args[2], env)
            h, w = frame.shape[:2]
            r_out, g_out, b_out = jax_sample(frame, x_coords.flatten(), y_coords.flatten())
            return jnp.stack([
                jnp.clip(r_out, 0, 255).reshape(h, w).astype(jnp.uint8),
                jnp.clip(g_out, 0, 255).reshape(h, w).astype(jnp.uint8),
                jnp.clip(b_out, 0, 255).reshape(h, w).astype(jnp.uint8)
            ], axis=2)

        # =====================================================================
        # Blending operations
        # =====================================================================
        if op == 'blend' or op == 'blend-images' or op == 'blending:blend-images':
            frame1 = self._eval(args[0], env)
            frame2 = self._eval(args[1], env)
            alpha = self._eval(args[2], env) if len(args) > 2 else 0.5
            return jax_blend(frame1, frame2, alpha)

        if op == 'blend-add':
            frame1 = self._eval(args[0], env)
            frame2 = self._eval(args[1], env)
            return jax_blend_add(frame1, frame2)

        if op == 'blend-multiply':
            frame1 = self._eval(args[0], env)
            frame2 = self._eval(args[1], env)
            return jax_blend_multiply(frame1, frame2)

        if op == 'blend-screen':
            frame1 = self._eval(args[0], env)
            frame2 = self._eval(args[1], env)
            return jax_blend_screen(frame1, frame2)

        if op == 'blend-overlay':
            frame1 = self._eval(args[0], env)
            frame2 = self._eval(args[1], env)
            return jax_blend_overlay(frame1, frame2)

        # =====================================================================
        # Image dimension queries (namespaced aliases)
        # =====================================================================
        if op == 'image:width':
            if args:
                frame = self._eval(args[0], env)
                return frame.shape[1]  # width is second dimension (h, w, c)
            return env['width']

        if op == 'image:height':
            if args:
                frame = self._eval(args[0], env)
                return frame.shape[0]  # height is first dimension (h, w, c)
            return env['height']

        # =====================================================================
        # Utility
        # =====================================================================
        if op == 'is-nil' or op == 'core:is-nil' or op == 'nil?':
            x = self._eval(args[0], env)
            return jax_is_nil(x)

        # =====================================================================
        # Xector channel operations (shortcuts)
        # =====================================================================
        if op == 'red':
            val = self._eval(args[0], env)
            # Works on frames or pixel tuples
            if isinstance(val, tuple):
                return val[0]
            elif hasattr(val, 'shape') and val.ndim == 3:
                return jax_channel(val, 0)
            else:
                return val  # Assume it's already a channel

        if op == 'green':
            val = self._eval(args[0], env)
            if isinstance(val, tuple):
                return val[1]
            elif hasattr(val, 'shape') and val.ndim == 3:
                return jax_channel(val, 1)
            else:
                return val

        if op == 'blue':
            val = self._eval(args[0], env)
            if isinstance(val, tuple):
                return val[2]
            elif hasattr(val, 'shape') and val.ndim == 3:
                return jax_channel(val, 2)
            else:
                return val

        if op == 'gray' or op == 'luminance':
            val = self._eval(args[0], env)
            # Handle tuple (r, g, b) from map-pixels
            if isinstance(val, tuple) and len(val) == 3:
                r, g, b = val
                return r * 0.299 + g * 0.587 + b * 0.114
            # Handle frame
            frame = val
            r = frame[:, :, 0].flatten().astype(jnp.float32)
            g = frame[:, :, 1].flatten().astype(jnp.float32)
            b = frame[:, :, 2].flatten().astype(jnp.float32)
            return r * 0.299 + g * 0.587 + b * 0.114

        if op == 'rgb':
            r = self._eval(args[0], env)
            g = self._eval(args[1], env)
            b = self._eval(args[2], env)
            # For scalars (e.g., in map-pixels), return tuple
            r_is_scalar = isinstance(r, (int, float)) or (hasattr(r, 'shape') and r.shape == ())
            g_is_scalar = isinstance(g, (int, float)) or (hasattr(g, 'shape') and g.shape == ())
            b_is_scalar = isinstance(b, (int, float)) or (hasattr(b, 'shape') and b.shape == ())
            if r_is_scalar and g_is_scalar and b_is_scalar:
                return (r, g, b)
            return jax_merge_channels(r, g, b, env['_shape'])

        # =====================================================================
        # Coordinate operations
        # =====================================================================
        if op == 'x-coords':
            frame = self._eval(args[0], env)
            h, w = frame.shape[:2]
            return jnp.tile(jnp.arange(w, dtype=jnp.float32), h)

        if op == 'y-coords':
            frame = self._eval(args[0], env)
            h, w = frame.shape[:2]
            return jnp.repeat(jnp.arange(h, dtype=jnp.float32), w)

        if op == 'dist-from-center':
            frame = self._eval(args[0], env)
            h, w = frame.shape[:2]
            cx, cy = w / 2, h / 2
            x = jnp.tile(jnp.arange(w, dtype=jnp.float32), h) - cx
            y = jnp.repeat(jnp.arange(h, dtype=jnp.float32), w) - cy
            return jnp.sqrt(x*x + y*y)

        # =====================================================================
        # Alpha operations (element-wise on xectors)
        # =====================================================================
        if op == 'α/' or op == 'alpha/':
            a = self._eval(args[0], env)
            b = self._eval(args[1], env)
            return a / b

        if op == 'α+' or op == 'alpha+':
            vals = [self._eval(a, env) for a in args]
            result = vals[0]
            for v in vals[1:]:
                result = result + v
            return result

        if op == 'α*' or op == 'alpha*':
            vals = [self._eval(a, env) for a in args]
            result = vals[0]
            for v in vals[1:]:
                result = result * v
            return result

        if op == 'α-' or op == 'alpha-':
            if len(args) == 1:
                return -self._eval(args[0], env)
            a = self._eval(args[0], env)
            b = self._eval(args[1], env)
            return a - b

        if op == 'αclamp' or op == 'alpha-clamp':
            x = self._eval(args[0], env)
            lo = self._eval(args[1], env)
            hi = self._eval(args[2], env)
            return jnp.clip(x, lo, hi)

        if op == 'αmin' or op == 'alpha-min':
            a = self._eval(args[0], env)
            b = self._eval(args[1], env)
            return jnp.minimum(a, b)

        if op == 'αmax' or op == 'alpha-max':
            a = self._eval(args[0], env)
            b = self._eval(args[1], env)
            return jnp.maximum(a, b)

        if op == 'αsqrt' or op == 'alpha-sqrt':
            return jnp.sqrt(self._eval(args[0], env))

        if op == 'αsin' or op == 'alpha-sin':
            return jnp.sin(self._eval(args[0], env))

        if op == 'αcos' or op == 'alpha-cos':
            return jnp.cos(self._eval(args[0], env))

        if op == 'αmod' or op == 'alpha-mod':
            a = self._eval(args[0], env)
            b = self._eval(args[1], env)
            return a % b

        if op == 'α²' or op == 'αsq' or op == 'alpha-sq':
            x = self._eval(args[0], env)
            return x * x

        if op == 'α<' or op == 'alpha<':
            a = self._eval(args[0], env)
            b = self._eval(args[1], env)
            return a < b

        if op == 'α>' or op == 'alpha>':
            a = self._eval(args[0], env)
            b = self._eval(args[1], env)
            return a > b

        if op == 'α<=' or op == 'alpha<=':
            a = self._eval(args[0], env)
            b = self._eval(args[1], env)
            return a <= b

        if op == 'α>=' or op == 'alpha>=':
            a = self._eval(args[0], env)
            b = self._eval(args[1], env)
            return a >= b

        if op == 'α=' or op == 'alpha=':
            a = self._eval(args[0], env)
            b = self._eval(args[1], env)
            return a == b

        if op == 'αfloor' or op == 'alpha-floor':
            return jnp.floor(self._eval(args[0], env))

        if op == 'αceil' or op == 'alpha-ceil':
            return jnp.ceil(self._eval(args[0], env))

        if op == 'αround' or op == 'alpha-round':
            return jnp.round(self._eval(args[0], env))

        if op == 'αabs' or op == 'alpha-abs':
            return jnp.abs(self._eval(args[0], env))

        if op == 'αexp' or op == 'alpha-exp':
            return jnp.exp(self._eval(args[0], env))

        if op == 'αlog' or op == 'alpha-log':
            return jnp.log(self._eval(args[0], env))

        if op == 'αor' or op == 'alpha-or':
            # Element-wise logical OR
            a = self._eval(args[0], env)
            b = self._eval(args[1], env)
            return jnp.logical_or(a, b)

        if op == 'αand' or op == 'alpha-and':
            # Element-wise logical AND
            a = self._eval(args[0], env)
            b = self._eval(args[1], env)
            return jnp.logical_and(a, b)

        if op == 'αnot' or op == 'alpha-not':
            # Element-wise logical NOT
            return jnp.logical_not(self._eval(args[0], env))

        if op == 'αxor' or op == 'alpha-xor':
            # Element-wise logical XOR
            a = self._eval(args[0], env)
            b = self._eval(args[1], env)
            return jnp.logical_xor(a, b)

        # =====================================================================
        # Threading/arrow operations
        # =====================================================================
        if op == '->':
            # Thread-first macro: (-> x (f a) (g b)) = (g (f x a) b)
            val = self._eval(args[0], env)
            for form in args[1:]:
                if isinstance(form, list):
                    # Insert val as first argument
                    fn_name = form[0].name if isinstance(form[0], Symbol) else form[0]
                    new_args = [val] + [self._eval(a, env) for a in form[1:]]
                    val = self._eval_call(fn_name, [val] + form[1:], env)
                else:
                    # Simple function call
                    fn_name = form.name if isinstance(form, Symbol) else form
                    val = self._eval_call(fn_name, [args[0]], env)
            return val

        # =====================================================================
        # Range and iteration
        # =====================================================================
        if op == 'range':
            if len(args) == 1:
                end = int(self._eval(args[0], env))
                return list(range(end))
            elif len(args) == 2:
                start = int(self._eval(args[0], env))
                end = int(self._eval(args[1], env))
                return list(range(start, end))
            else:
                start = int(self._eval(args[0], env))
                end = int(self._eval(args[1], env))
                step = int(self._eval(args[2], env))
                return list(range(start, end, step))

        if op == 'reduce' or op == 'fold':
            # (reduce seq init fn) - left fold
            seq = self._eval(args[0], env)
            acc = self._eval(args[1], env)
            fn = args[2]  # Lambda S-expression

            # Handle lambda
            if isinstance(fn, list) and len(fn) >= 3:
                head = fn[0]
                if isinstance(head, Symbol) and head.name in ('lambda', 'λ'):
                    params = fn[1]
                    body = fn[2]
                    for item in seq:
                        fn_env = env.copy()
                        if len(params) >= 1:
                            param_name = params[0].name if isinstance(params[0], Symbol) else str(params[0])
                            fn_env[param_name] = acc
                        if len(params) >= 2:
                            param_name = params[1].name if isinstance(params[1], Symbol) else str(params[1])
                            fn_env[param_name] = item
                        acc = self._eval(body, fn_env)
                    return acc

            # Fallback - try evaluating fn and calling it
            fn_eval = self._eval(fn, env)
            if callable(fn_eval):
                for item in seq:
                    acc = fn_eval(acc, item)
            return acc

        # =====================================================================
        # Map-pixels (apply function to each pixel)
        # =====================================================================
        if op == 'map-pixels':
            frame = self._eval(args[0], env)
            fn = args[1]  # Lambda or S-expression
            h, w = frame.shape[:2]

            # Extract channels
            r = frame[:, :, 0].flatten().astype(jnp.float32)
            g = frame[:, :, 1].flatten().astype(jnp.float32)
            b = frame[:, :, 2].flatten().astype(jnp.float32)
            x_coords = jnp.tile(jnp.arange(w, dtype=jnp.float32), h)
            y_coords = jnp.repeat(jnp.arange(h, dtype=jnp.float32), w)

            # Set up pixel environment
            pixel_env = env.copy()
            pixel_env['r'] = r
            pixel_env['g'] = g
            pixel_env['b'] = b
            pixel_env['x'] = x_coords
            pixel_env['y'] = y_coords
            # Also provide c (color) as a tuple for lambda (x y c) style
            pixel_env['c'] = (r, g, b)

            # If fn is a lambda, we need to handle it specially
            if isinstance(fn, list) and len(fn) >= 2:
                head = fn[0]
                if isinstance(head, Symbol) and head.name in ('lambda', 'λ'):
                    # Lambda: (lambda (x y c) body)
                    params = fn[1]
                    body = fn[2]
                    # Bind parameters
                    if len(params) >= 1:
                        param_name = params[0].name if isinstance(params[0], Symbol) else str(params[0])
                        pixel_env[param_name] = x_coords
                    if len(params) >= 2:
                        param_name = params[1].name if isinstance(params[1], Symbol) else str(params[1])
                        pixel_env[param_name] = y_coords
                    if len(params) >= 3:
                        param_name = params[2].name if isinstance(params[2], Symbol) else str(params[2])
                        pixel_env[param_name] = (r, g, b)
                    result = self._eval(body, pixel_env)
                else:
                    result = self._eval(fn, pixel_env)
            else:
                result = self._eval(fn, pixel_env)

            if isinstance(result, tuple) and len(result) == 3:
                nr, ng, nb = result
                return jax_merge_channels(nr, ng, nb, (h, w))
            elif hasattr(result, 'shape') and result.ndim == 3:
                return result
            else:
                # Single channel result
                if hasattr(result, 'flatten'):
                    result = result.flatten()
                gray = jnp.clip(result, 0, 255).reshape(h, w).astype(jnp.uint8)
                return jnp.stack([gray, gray, gray], axis=2)

        # =====================================================================
        # State operations (return unchanged for stateless JIT)
        # =====================================================================
        if op == 'state-get':
            key = self._eval(args[0], env)
            default = self._eval(args[1], env) if len(args) > 1 else None
            return default  # State not supported in JIT, return default

        if op == 'state-set':
            return None  # No-op in JIT

        # =====================================================================
        # Cell/grid operations
        # =====================================================================
        if op == 'local-x-norm':
            frame = self._eval(args[0], env)
            cell_size = int(self._eval(args[1], env))
            h, w = frame.shape[:2]
            x = jnp.tile(jnp.arange(w), h)
            return (x % cell_size) / max(1, cell_size - 1)

        if op == 'local-y-norm':
            frame = self._eval(args[0], env)
            cell_size = int(self._eval(args[1], env))
            h, w = frame.shape[:2]
            y = jnp.repeat(jnp.arange(h), w)
            return (y % cell_size) / max(1, cell_size - 1)

        if op == 'local-x':
            frame = self._eval(args[0], env)
            cell_size = int(self._eval(args[1], env))
            h, w = frame.shape[:2]
            x = jnp.tile(jnp.arange(w), h)
            return (x % cell_size).astype(jnp.float32)

        if op == 'local-y':
            frame = self._eval(args[0], env)
            cell_size = int(self._eval(args[1], env))
            h, w = frame.shape[:2]
            y = jnp.repeat(jnp.arange(h), w)
            return (y % cell_size).astype(jnp.float32)

        if op == 'cell-row':
            frame = self._eval(args[0], env)
            cell_size = int(self._eval(args[1], env))
            h, w = frame.shape[:2]
            y = jnp.repeat(jnp.arange(h), w)
            return jnp.floor(y / cell_size)

        if op == 'cell-col':
            frame = self._eval(args[0], env)
            cell_size = int(self._eval(args[1], env))
            h, w = frame.shape[:2]
            x = jnp.tile(jnp.arange(w), h)
            return jnp.floor(x / cell_size)

        if op == 'num-rows':
            frame = self._eval(args[0], env)
            cell_size = int(self._eval(args[1], env))
            return frame.shape[0] // cell_size

        if op == 'num-cols':
            frame = self._eval(args[0], env)
            cell_size = int(self._eval(args[1], env))
            return frame.shape[1] // cell_size

        # =====================================================================
        # Control flow
        # =====================================================================
        if op == 'cond':
            # (cond (test1 expr1) (test2 expr2) ... (else exprN))
            # For JAX compatibility, build a nested jnp.where structure
            # Start from the else clause and work backwards

            # Collect clauses
            clauses = []
            else_expr = None
            for clause in args:
                if isinstance(clause, list) and len(clause) >= 2:
                    test = clause[0]
                    if isinstance(test, Symbol) and test.name == 'else':
                        else_expr = clause[1]
                    else:
                        clauses.append((test, clause[1]))

            # If no else, default to None/0
            if else_expr is not None:
                result = self._eval(else_expr, env)
            else:
                result = 0

            # Build nested where from last to first
            for test_expr, val_expr in reversed(clauses):
                cond_val = self._eval(test_expr, env)
                then_val = self._eval(val_expr, env)

                # Check if condition is array or scalar
                if hasattr(cond_val, 'shape') and cond_val.shape != ():
                    # Array condition - use jnp.where
                    result = jnp.where(cond_val, then_val, result)
                else:
                    # Scalar - can use Python if
                    if cond_val:
                        result = then_val

            return result

        if op == 'set!' or op == 'set':
            # Mutation - not really supported in JAX, but we can update env
            var = args[0].name if isinstance(args[0], Symbol) else str(args[0])
            val = self._eval(args[1], env)
            env[var] = val
            return val

        if op == 'begin' or op == 'do':
            # Evaluate all expressions, return last
            result = None
            for expr in args:
                result = self._eval(expr, env)
            return result

        # =====================================================================
        # Additional math
        # =====================================================================
        if op == 'sq' or op == 'square':
            x = self._eval(args[0], env)
            return x * x

        if op == 'lerp':
            a = self._eval(args[0], env)
            b = self._eval(args[1], env)
            t = self._eval(args[2], env)
            return a * (1 - t) + b * t

        if op == 'smoothstep':
            edge0 = self._eval(args[0], env)
            edge1 = self._eval(args[1], env)
            x = self._eval(args[2], env)
            t = jnp.clip((x - edge0) / (edge1 - edge0), 0, 1)
            return t * t * (3 - 2 * t)

        if op == 'atan2':
            y = self._eval(args[0], env)
            x = self._eval(args[1], env)
            return jnp.arctan2(y, x)

        if op == 'fract' or op == 'frac':
            x = self._eval(args[0], env)
            return x - jnp.floor(x)

        # =====================================================================
        # Frame copy and construction operations
        # =====================================================================
        if op == 'pixel':
            # Get pixel at (x, y) from frame
            frame = self._eval(args[0], env)
            x = self._eval(args[1], env)
            y = self._eval(args[2], env)
            h, w = frame.shape[:2]
            # Convert to int and clip to bounds
            if isinstance(x, (int, float)):
                x = max(0, min(int(x), w - 1))
            else:
                x = jnp.clip(x, 0, w - 1).astype(jnp.int32)
            if isinstance(y, (int, float)):
                y = max(0, min(int(y), h - 1))
            else:
                y = jnp.clip(y, 0, h - 1).astype(jnp.int32)
            r = frame[y, x, 0]
            g = frame[y, x, 1]
            b = frame[y, x, 2]
            return (r, g, b)

        if op == 'copy':
            frame = self._eval(args[0], env)
            return frame.copy() if hasattr(frame, 'copy') else jnp.array(frame)

        if op == 'make-image':
            w = int(self._eval(args[0], env))
            h = int(self._eval(args[1], env))
            if len(args) > 2:
                color = self._eval(args[2], env)
                if isinstance(color, (list, tuple)):
                    r, g, b = int(color[0]), int(color[1]), int(color[2])
                else:
                    r = g = b = int(color)
            else:
                r = g = b = 0
            img = jnp.zeros((h, w, 3), dtype=jnp.uint8)
            img = img.at[:, :, 0].set(r)
            img = img.at[:, :, 1].set(g)
            img = img.at[:, :, 2].set(b)
            return img

        if op == 'paste':
            dest = self._eval(args[0], env)
            src = self._eval(args[1], env)
            x = int(self._eval(args[2], env))
            y = int(self._eval(args[3], env))
            sh, sw = src.shape[:2]
            dh, dw = dest.shape[:2]
            # Clip to dest bounds
            x1, y1 = max(0, x), max(0, y)
            x2, y2 = min(dw, x + sw), min(dh, y + sh)
            sx1, sy1 = x1 - x, y1 - y
            sx2, sy2 = sx1 + (x2 - x1), sy1 + (y2 - y1)
            result = dest.copy() if hasattr(dest, 'copy') else jnp.array(dest)
            result = result.at[y1:y2, x1:x2, :].set(src[sy1:sy2, sx1:sx2, :])
            return result

        # =====================================================================
        # Blending operations
        # =====================================================================
        if op == 'blending:blend-images' or op == 'blend-images':
            a = self._eval(args[0], env)
            b = self._eval(args[1], env)
            alpha = self._eval(args[2], env) if len(args) > 2 else 0.5
            return jax_blend(a, b, alpha)

        if op == 'blending:blend-mode' or op == 'blend-mode':
            a = self._eval(args[0], env)
            b = self._eval(args[1], env)
            mode = self._eval(args[2], env) if len(args) > 2 else 'add'
            if mode == 'add':
                return jax_blend_add(a, b)
            elif mode == 'multiply':
                return jax_blend_multiply(a, b)
            elif mode == 'screen':
                return jax_blend_screen(a, b)
            elif mode == 'overlay':
                return jax_blend_overlay(a, b)
            elif mode == 'lighten':
                return jnp.maximum(a, b)
            elif mode == 'darken':
                return jnp.minimum(a, b)
            elif mode == 'difference':
                return jnp.abs(a.astype(jnp.int16) - b.astype(jnp.int16)).astype(jnp.uint8)
            else:
                return jax_blend(a, b, 0.5)

        # =====================================================================
        # Geometry coordinate operations
        # =====================================================================
        if op == 'geometry:wave-coords' or op == 'wave-coords':
            w = int(self._eval(args[0], env))
            h = int(self._eval(args[1], env))
            axis = self._eval(args[2], env) if len(args) > 2 else 'x'
            freq = self._eval(args[3], env) if len(args) > 3 else 1.0
            amplitude = self._eval(args[4], env) if len(args) > 4 else 10.0
            phase = self._eval(args[5], env) if len(args) > 5 else 0.0

            y_coords = jnp.repeat(jnp.arange(h), w).reshape(h, w).astype(jnp.float32)
            x_coords = jnp.tile(jnp.arange(w), h).reshape(h, w).astype(jnp.float32)

            if axis == 'x' or axis == 'horizontal':
                # Wave displaces X based on Y
                offset = amplitude * jnp.sin(2 * jnp.pi * freq * y_coords / h + phase)
                src_x = x_coords + offset
                src_y = y_coords
            elif axis == 'y' or axis == 'vertical':
                # Wave displaces Y based on X
                offset = amplitude * jnp.sin(2 * jnp.pi * freq * x_coords / w + phase)
                src_x = x_coords
                src_y = y_coords + offset
            else:  # both
                offset_x = amplitude * jnp.sin(2 * jnp.pi * freq * y_coords / h + phase)
                offset_y = amplitude * jnp.sin(2 * jnp.pi * freq * x_coords / w + phase)
                src_x = x_coords + offset_x
                src_y = y_coords + offset_y

            return {'x': src_x, 'y': src_y}

        if op == 'geometry:coords-x' or op == 'coords-x':
            coords = self._eval(args[0], env)
            return coords['x']

        if op == 'geometry:coords-y' or op == 'coords-y':
            coords = self._eval(args[0], env)
            return coords['y']

        if op == 'geometry:remap' or op == 'remap':
            frame = self._eval(args[0], env)
            x = self._eval(args[1], env)
            y = self._eval(args[2], env)
            h, w = frame.shape[:2]
            r, g, b = jax_sample(frame, x.flatten(), y.flatten())
            return jnp.stack([
                jnp.clip(r, 0, 255).reshape(h, w).astype(jnp.uint8),
                jnp.clip(g, 0, 255).reshape(h, w).astype(jnp.uint8),
                jnp.clip(b, 0, 255).reshape(h, w).astype(jnp.uint8)
            ], axis=2)

        # =====================================================================
        # Glitch effects
        # =====================================================================
        if op == 'pixelsort':
            frame = self._eval(args[0], env)
            sort_by = self._eval(args[1], env) if len(args) > 1 else 'lightness'
            thresh_lo = int(self._eval(args[2], env)) if len(args) > 2 else 50
            thresh_hi = int(self._eval(args[3], env)) if len(args) > 3 else 200
            angle = int(self._eval(args[4], env)) if len(args) > 4 else 0
            reverse = self._eval(args[5], env) if len(args) > 5 else False

            h, w = frame.shape[:2]
            result = frame.copy()

            # Get luminance for thresholding
            lum = (frame[:, :, 0].astype(jnp.float32) * 0.299 +
                   frame[:, :, 1].astype(jnp.float32) * 0.587 +
                   frame[:, :, 2].astype(jnp.float32) * 0.114)

            # Sort each row
            for y in range(h):
                row_lum = lum[y, :]
                row = frame[y, :, :]

                # Find mask of pixels to sort
                mask = (row_lum >= thresh_lo) & (row_lum <= thresh_hi)

                # Get indices where we should sort
                sort_indices = jnp.where(mask, jnp.arange(w), -1)

                # Simple sort by luminance for the row
                if sort_by == 'lightness':
                    sort_key = row_lum
                elif sort_by == 'hue':
                    # Approximate hue from RGB
                    sort_key = jnp.arctan2(row[:, 1].astype(jnp.float32) - row[:, 2].astype(jnp.float32),
                                           row[:, 0].astype(jnp.float32) - 0.5 * (row[:, 1].astype(jnp.float32) + row[:, 2].astype(jnp.float32)))
                else:
                    sort_key = row_lum

                # Sort pixels in masked region
                sorted_indices = jnp.argsort(sort_key)
                if reverse:
                    sorted_indices = sorted_indices[::-1]

                # Apply partial sort (only where mask is true)
                # This is a simplified version - full pixelsort is more complex
                result = result.at[y, :, :].set(row[sorted_indices])

            return result

        if op == 'datamosh':
            frame = self._eval(args[0], env)
            prev = self._eval(args[1], env)
            block_size = int(self._eval(args[2], env)) if len(args) > 2 else 32
            corruption = float(self._eval(args[3], env)) if len(args) > 3 else 0.3
            max_offset = int(self._eval(args[4], env)) if len(args) > 4 else 50
            color_corrupt = self._eval(args[5], env) if len(args) > 5 else True

            h, w = frame.shape[:2]

            # Use deterministic random for JIT with frame variation
            seed = env.get('_seed', 42)
            op_counter = env.get('_rand_op_counter', 0)
            env['_rand_op_counter'] = op_counter + 1
            key = make_jax_key(seed, frame_num, op_counter)

            num_blocks_y = h // block_size
            num_blocks_x = w // block_size
            total_blocks = num_blocks_y * num_blocks_x

            # Pre-generate all random values at once (vectorized)
            key, k1, k2, k3, k4, k5 = jax.random.split(key, 6)
            corrupt_mask = jax.random.uniform(k1, (total_blocks,)) < corruption
            offsets_y = jax.random.randint(k2, (total_blocks,), -max_offset, max_offset + 1)
            offsets_x = jax.random.randint(k3, (total_blocks,), -max_offset, max_offset + 1)
            channels = jax.random.randint(k4, (total_blocks,), 0, 3)
            color_shifts = jax.random.randint(k5, (total_blocks,), -50, 51)

            # Create coordinate grids for blocks
            by_grid = jnp.arange(num_blocks_y)
            bx_grid = jnp.arange(num_blocks_x)

            # Create block coordinate arrays
            by_coords = jnp.repeat(by_grid, num_blocks_x)  # [0,0,0..., 1,1,1..., ...]
            bx_coords = jnp.tile(bx_grid, num_blocks_y)    # [0,1,2..., 0,1,2..., ...]

            # Create pixel coordinate grids
            y_coords, x_coords = jnp.mgrid[:h, :w]

            # Determine which block each pixel belongs to
            pixel_block_y = y_coords // block_size
            pixel_block_x = x_coords // block_size
            pixel_block_idx = pixel_block_y * num_blocks_x + pixel_block_x

            # Clamp to valid block indices (for pixels outside the block grid)
            pixel_block_idx = jnp.clip(pixel_block_idx, 0, total_blocks - 1)

            # Get the corrupt mask, offsets for each pixel's block
            pixel_corrupt = corrupt_mask[pixel_block_idx]
            pixel_offset_y = offsets_y[pixel_block_idx]
            pixel_offset_x = offsets_x[pixel_block_idx]

            # Calculate source coordinates with offset (clamped)
            src_y = jnp.clip(y_coords + pixel_offset_y, 0, h - 1)
            src_x = jnp.clip(x_coords + pixel_offset_x, 0, w - 1)

            # Sample from previous frame at offset positions
            prev_sampled = prev[src_y, src_x, :]

            # Where corrupt mask is true, use prev_sampled; else use frame
            result = jnp.where(pixel_corrupt[:, :, None], prev_sampled, frame)

            # Apply color corruption to corrupted blocks
            if color_corrupt:
                pixel_channel = channels[pixel_block_idx]
                pixel_shift = color_shifts[pixel_block_idx].astype(jnp.int16)

                # Create per-channel shift arrays (only shift the selected channel)
                shift_r = jnp.where((pixel_channel == 0) & pixel_corrupt, pixel_shift, 0)
                shift_g = jnp.where((pixel_channel == 1) & pixel_corrupt, pixel_shift, 0)
                shift_b = jnp.where((pixel_channel == 2) & pixel_corrupt, pixel_shift, 0)

                result_int = result.astype(jnp.int16)
                result_int = result_int.at[:, :, 0].add(shift_r)
                result_int = result_int.at[:, :, 1].add(shift_g)
                result_int = result_int.at[:, :, 2].add(shift_b)
                result = jnp.clip(result_int, 0, 255).astype(jnp.uint8)

            return result

        # =====================================================================
        # ASCII Art Operations (using pre-rendered font atlas)
        # =====================================================================

        if op == 'cell-sample':
            # (cell-sample frame char_size) -> (colors, luminances)
            # Downsample frame into cells, return average colors and luminances
            frame = self._eval(args[0], env)
            char_size = int(self._eval(args[1], env)) if len(args) > 1 else 8

            h, w = frame.shape[:2]
            num_rows = h // char_size
            num_cols = w // char_size

            # Crop to exact multiple of char_size
            cropped = frame[:num_rows * char_size, :num_cols * char_size, :]

            # Reshape to (num_rows, char_size, num_cols, char_size, 3)
            reshaped = cropped.reshape(num_rows, char_size, num_cols, char_size, 3)

            # Average over char_size dimensions -> (num_rows, num_cols, 3)
            colors = reshaped.mean(axis=(1, 3)).astype(jnp.uint8)

            # Compute luminance per cell
            colors_float = colors.astype(jnp.float32)
            luminances = (0.299 * colors_float[:, :, 0] +
                         0.587 * colors_float[:, :, 1] +
                         0.114 * colors_float[:, :, 2]) / 255.0

            return (colors, luminances)

        if op == 'luminance-to-chars':
            # (luminance-to-chars luminances alphabet contrast) -> char_indices
            # Map luminance values to character indices
            luminances = self._eval(args[0], env)
            alphabet = self._eval(args[1], env) if len(args) > 1 else 'standard'
            contrast = float(self._eval(args[2], env)) if len(args) > 2 else 1.5

            # Get alphabet string
            alpha_str = _get_alphabet_string(alphabet)
            num_chars = len(alpha_str)

            # Apply contrast
            lum_adjusted = jnp.clip((luminances - 0.5) * contrast + 0.5, 0, 1)

            # Map to character indices (0 = darkest, num_chars-1 = brightest)
            char_indices = (lum_adjusted * (num_chars - 1)).astype(jnp.int32)
            char_indices = jnp.clip(char_indices, 0, num_chars - 1)

            return char_indices

        if op == 'render-char-grid':
            # (render-char-grid frame chars colors char_size color_mode background_color invert_colors)
            # Render character grid using font atlas
            frame = self._eval(args[0], env)
            char_indices = self._eval(args[1], env)
            colors = self._eval(args[2], env)
            char_size = int(self._eval(args[3], env)) if len(args) > 3 else 8
            color_mode = self._eval(args[4], env) if len(args) > 4 else 'color'
            background_color = self._eval(args[5], env) if len(args) > 5 else 'black'
            invert_colors = self._eval(args[6], env) if len(args) > 6 else 0

            h, w = frame.shape[:2]
            num_rows, num_cols = char_indices.shape

            # Get the alphabet used (stored in env or default)
            alphabet = env.get('_ascii_alphabet', 'standard')
            alpha_str = _get_alphabet_string(alphabet)

            # Get or create font atlas
            font_atlas = _create_font_atlas(alpha_str, char_size)

            # Parse background color
            if background_color == 'black':
                bg = jnp.array([0, 0, 0], dtype=jnp.uint8)
            elif background_color == 'white':
                bg = jnp.array([255, 255, 255], dtype=jnp.uint8)
            else:
                # Try to parse hex color
                try:
                    if background_color.startswith('#'):
                        bg_hex = background_color[1:]
                        bg = jnp.array([int(bg_hex[0:2], 16),
                                       int(bg_hex[2:4], 16),
                                       int(bg_hex[4:6], 16)], dtype=jnp.uint8)
                    else:
                        bg = jnp.array([0, 0, 0], dtype=jnp.uint8)
                except:
                    bg = jnp.array([0, 0, 0], dtype=jnp.uint8)

            # Create output image starting with background
            output_h = num_rows * char_size
            output_w = num_cols * char_size
            result = jnp.broadcast_to(bg, (output_h, output_w, 3)).copy()

            # Gather characters from atlas based on indices
            # char_indices shape: (num_rows, num_cols)
            # font_atlas shape: (num_chars, char_size, char_size, 3)
            # Convert numpy atlas to JAX for indexing with traced indices
            font_atlas_jax = jnp.asarray(font_atlas)
            flat_indices = char_indices.flatten()
            char_tiles = font_atlas_jax[flat_indices]  # (num_rows*num_cols, char_size, char_size, 3)

            # Reshape to grid
            char_tiles = char_tiles.reshape(num_rows, num_cols, char_size, char_size, 3)

            # Create coordinate grids for output pixels
            y_out, x_out = jnp.mgrid[:output_h, :output_w]
            cell_row = y_out // char_size
            cell_col = x_out // char_size
            local_y = y_out % char_size
            local_x = x_out % char_size

            # Clamp to valid ranges
            cell_row = jnp.clip(cell_row, 0, num_rows - 1)
            cell_col = jnp.clip(cell_col, 0, num_cols - 1)

            # Get character pixel values
            char_pixels = char_tiles[cell_row, cell_col, local_y, local_x]

            # Get character brightness (for masking)
            char_brightness = char_pixels.mean(axis=-1, keepdims=True) / 255.0

            # Handle color modes
            if color_mode == 'mono':
                # White characters on background
                fg_color = jnp.array([255, 255, 255], dtype=jnp.uint8)
                fg = jnp.broadcast_to(fg_color, char_pixels.shape)
            elif color_mode == 'invert':
                # Inverted cell colors
                cell_colors = colors[cell_row, cell_col]
                fg = 255 - cell_colors
            else:
                # 'color' mode - use cell colors
                fg = colors[cell_row, cell_col]

            # Blend foreground onto background based on character brightness
            if invert_colors:
                # Swap fg and bg
                fg, bg_broadcast = jnp.broadcast_to(bg, char_pixels.shape), fg
                result = (fg.astype(jnp.float32) * (1 - char_brightness) +
                         bg_broadcast.astype(jnp.float32) * char_brightness)
            else:
                bg_broadcast = jnp.broadcast_to(bg, char_pixels.shape)
                result = (bg_broadcast.astype(jnp.float32) * (1 - char_brightness) +
                         fg.astype(jnp.float32) * char_brightness)

            result = jnp.clip(result, 0, 255).astype(jnp.uint8)

            # Resize back to original frame size if needed
            if result.shape[0] != h or result.shape[1] != w:
                # Simple nearest-neighbor resize
                y_scale = result.shape[0] / h
                x_scale = result.shape[1] / w
                y_src = (jnp.arange(h) * y_scale).astype(jnp.int32)
                x_src = (jnp.arange(w) * x_scale).astype(jnp.int32)
                y_src = jnp.clip(y_src, 0, result.shape[0] - 1)
                x_src = jnp.clip(x_src, 0, result.shape[1] - 1)
                result = result[y_src[:, None], x_src[None, :], :]

            return result

        if op == 'ascii-fx-zone':
            # Complex ASCII effect with per-zone expressions
            # (ascii-fx-zone frame :cols cols :alphabet alphabet ...)
            frame = self._eval(args[0], env)

            # Parse keyword arguments
            kwargs = {}
            i = 1
            while i < len(args):
                if isinstance(args[i], Keyword):
                    key = args[i].name
                    if i + 1 < len(args):
                        kwargs[key] = args[i + 1]
                    i += 2
                else:
                    i += 1

            # Get parameters
            cols = int(self._eval(kwargs.get('cols', 80), env))
            char_size_param = kwargs.get('char_size')
            alphabet = self._eval(kwargs.get('alphabet', 'standard'), env)
            color_mode = self._eval(kwargs.get('color_mode', 'color'), env)
            background = self._eval(kwargs.get('background', 'black'), env)
            contrast = float(self._eval(kwargs.get('contrast', 1.5), env))

            h, w = frame.shape[:2]

            # Calculate char_size from cols if not specified
            if char_size_param is not None:
                char_size_val = self._eval(char_size_param, env)
                if char_size_val is not None:
                    char_size = int(char_size_val)
                else:
                    char_size = w // cols
            else:
                char_size = w // cols
            char_size = max(4, min(char_size, 64))

            # Store alphabet for render-char-grid to use
            env['_ascii_alphabet'] = alphabet

            # Cell sampling
            num_rows = h // char_size
            num_cols = w // char_size
            cropped = frame[:num_rows * char_size, :num_cols * char_size, :]
            reshaped = cropped.reshape(num_rows, char_size, num_cols, char_size, 3)
            colors = reshaped.mean(axis=(1, 3)).astype(jnp.uint8)

            # Compute luminances
            colors_float = colors.astype(jnp.float32)
            luminances = (0.299 * colors_float[:, :, 0] +
                         0.587 * colors_float[:, :, 1] +
                         0.114 * colors_float[:, :, 2]) / 255.0

            # Get alphabet and map luminances to chars
            alpha_str = _get_alphabet_string(alphabet)
            num_chars = len(alpha_str)
            lum_adjusted = jnp.clip((luminances - 0.5) * contrast + 0.5, 0, 1)
            char_indices = (lum_adjusted * (num_chars - 1)).astype(jnp.int32)
            char_indices = jnp.clip(char_indices, 0, num_chars - 1)

            # Handle optional per-zone effects (char_hue, char_saturation, etc.)
            # These would modify colors based on zone position
            char_hue = kwargs.get('char_hue')
            char_saturation = kwargs.get('char_saturation')
            char_brightness = kwargs.get('char_brightness')

            if char_hue is not None or char_saturation is not None or char_brightness is not None:
                # Create zone coordinate arrays for expression evaluation
                row_coords, col_coords = jnp.mgrid[:num_rows, :num_cols]
                row_norm = row_coords / max(num_rows - 1, 1)
                col_norm = col_coords / max(num_cols - 1, 1)

                # Bind zone variables
                zone_env = env.copy()
                zone_env['zone-row'] = row_coords
                zone_env['zone-col'] = col_coords
                zone_env['zone-row-norm'] = row_norm
                zone_env['zone-col-norm'] = col_norm
                zone_env['zone-lum'] = luminances

                # Apply color modifications (simplified - full version would use HSV)
                if char_brightness is not None:
                    brightness_mult = self._eval(char_brightness, zone_env)
                    if brightness_mult is not None:
                        colors = jnp.clip(colors.astype(jnp.float32) * brightness_mult[:, :, None],
                                        0, 255).astype(jnp.uint8)

            # Render using font atlas
            font_atlas = _create_font_atlas(alpha_str, char_size)

            # Parse background color
            if background == 'black':
                bg = jnp.array([0, 0, 0], dtype=jnp.uint8)
            elif background == 'white':
                bg = jnp.array([255, 255, 255], dtype=jnp.uint8)
            else:
                bg = jnp.array([0, 0, 0], dtype=jnp.uint8)

            # Gather characters - convert numpy atlas to JAX for traced indexing
            font_atlas_jax = jnp.asarray(font_atlas)
            flat_indices = char_indices.flatten()
            char_tiles = font_atlas_jax[flat_indices].reshape(num_rows, num_cols, char_size, char_size, 3)

            # Create output
            output_h = num_rows * char_size
            output_w = num_cols * char_size

            y_out, x_out = jnp.mgrid[:output_h, :output_w]
            cell_row = jnp.clip(y_out // char_size, 0, num_rows - 1)
            cell_col = jnp.clip(x_out // char_size, 0, num_cols - 1)
            local_y = y_out % char_size
            local_x = x_out % char_size

            char_pixels = char_tiles[cell_row, cell_col, local_y, local_x]
            char_bright = char_pixels.mean(axis=-1, keepdims=True) / 255.0

            if color_mode == 'mono':
                fg = jnp.full_like(char_pixels, 255)
            else:
                fg = colors[cell_row, cell_col]

            bg_broadcast = jnp.broadcast_to(bg, char_pixels.shape)
            result = (bg_broadcast.astype(jnp.float32) * (1 - char_bright) +
                     fg.astype(jnp.float32) * char_bright)
            result = jnp.clip(result, 0, 255).astype(jnp.uint8)

            # Resize to original dimensions
            if result.shape[0] != h or result.shape[1] != w:
                y_scale = result.shape[0] / h
                x_scale = result.shape[1] / w
                y_src = jnp.clip((jnp.arange(h) * y_scale).astype(jnp.int32), 0, result.shape[0] - 1)
                x_src = jnp.clip((jnp.arange(w) * x_scale).astype(jnp.int32), 0, result.shape[1] - 1)
                result = result[y_src[:, None], x_src[None, :], :]

            return result

        if op == 'render-char-grid-fx':
            # Enhanced render with per-character effects
            # (render-char-grid-fx frame chars colors luminances char_size
            #   color_mode bg_color invert_colors
            #   char_jitter char_scale char_rotation char_hue_shift
            #   jitter_source scale_source rotation_source hue_source)
            frame = self._eval(args[0], env)
            char_indices = self._eval(args[1], env)
            colors = self._eval(args[2], env)
            luminances = self._eval(args[3], env)
            char_size = int(self._eval(args[4], env)) if len(args) > 4 else 8
            color_mode = self._eval(args[5], env) if len(args) > 5 else 'color'
            background_color = self._eval(args[6], env) if len(args) > 6 else 'black'
            invert_colors = self._eval(args[7], env) if len(args) > 7 else 0

            # Per-char effect amounts
            char_jitter = float(self._eval(args[8], env)) if len(args) > 8 else 0
            char_scale = float(self._eval(args[9], env)) if len(args) > 9 else 1.0
            char_rotation = float(self._eval(args[10], env)) if len(args) > 10 else 0
            char_hue_shift = float(self._eval(args[11], env)) if len(args) > 11 else 0

            # Modulation sources
            jitter_source = self._eval(args[12], env) if len(args) > 12 else 'none'
            scale_source = self._eval(args[13], env) if len(args) > 13 else 'none'
            rotation_source = self._eval(args[14], env) if len(args) > 14 else 'none'
            hue_source = self._eval(args[15], env) if len(args) > 15 else 'none'

            h, w = frame.shape[:2]
            num_rows, num_cols = char_indices.shape

            # Get alphabet
            alphabet = env.get('_ascii_alphabet', 'standard')
            alpha_str = _get_alphabet_string(alphabet)
            font_atlas = _create_font_atlas(alpha_str, char_size)

            # Parse background
            if background_color == 'black':
                bg = jnp.array([0, 0, 0], dtype=jnp.uint8)
            elif background_color == 'white':
                bg = jnp.array([255, 255, 255], dtype=jnp.uint8)
            else:
                bg = jnp.array([0, 0, 0], dtype=jnp.uint8)

            # Create modulation values based on source
            def get_modulation(source, lums, num_rows, num_cols):
                row_coords, col_coords = jnp.mgrid[:num_rows, :num_cols]
                row_norm = row_coords / max(num_rows - 1, 1)
                col_norm = col_coords / max(num_cols - 1, 1)

                if source == 'luminance':
                    return lums
                elif source == 'inv_luminance':
                    return 1.0 - lums
                elif source == 'position_x':
                    return col_norm
                elif source == 'position_y':
                    return row_norm
                elif source == 'position_diag':
                    return (row_norm + col_norm) / 2
                elif source == 'center_dist':
                    cy, cx = 0.5, 0.5
                    dist = jnp.sqrt((row_norm - cy)**2 + (col_norm - cx)**2)
                    return jnp.clip(dist / 0.707, 0, 1)  # Normalize by max diagonal
                elif source == 'random':
                    # Use frame-varying key for random source
                    seed = env.get('_seed', 42)
                    op_ctr = env.get('_rand_op_counter', 0)
                    env['_rand_op_counter'] = op_ctr + 1
                    key = make_jax_key(seed, frame_num, op_ctr)
                    return jax.random.uniform(key, (num_rows, num_cols))
                else:
                    return jnp.zeros((num_rows, num_cols))

            # Get modulation values
            jitter_mod = get_modulation(jitter_source, luminances, num_rows, num_cols)
            scale_mod = get_modulation(scale_source, luminances, num_rows, num_cols)
            rotation_mod = get_modulation(rotation_source, luminances, num_rows, num_cols)
            hue_mod = get_modulation(hue_source, luminances, num_rows, num_cols)

            # Gather characters - convert numpy atlas to JAX for traced indexing
            font_atlas_jax = jnp.asarray(font_atlas)
            flat_indices = char_indices.flatten()
            char_tiles = font_atlas_jax[flat_indices].reshape(num_rows, num_cols, char_size, char_size, 3)

            # Create output
            output_h = num_rows * char_size
            output_w = num_cols * char_size

            y_out, x_out = jnp.mgrid[:output_h, :output_w]
            cell_row = jnp.clip(y_out // char_size, 0, num_rows - 1)
            cell_col = jnp.clip(x_out // char_size, 0, num_cols - 1)
            local_y = y_out % char_size
            local_x = x_out % char_size

            # Apply jitter if enabled
            if char_jitter > 0:
                jitter_amount = jitter_mod[cell_row, cell_col] * char_jitter
                # Use frame-varying key for jitter
                seed = env.get('_seed', 42)
                op_ctr = env.get('_rand_op_counter', 0)
                env['_rand_op_counter'] = op_ctr + 1
                key1, key2 = jax.random.split(make_jax_key(seed, frame_num, op_ctr), 2)
                # Generate deterministic jitter per cell
                jitter_y = jax.random.uniform(key1, (num_rows, num_cols), minval=-1, maxval=1)
                jitter_x = jax.random.uniform(key2, (num_rows, num_cols), minval=-1, maxval=1)
                offset_y = (jitter_y[cell_row, cell_col] * jitter_amount).astype(jnp.int32)
                offset_x = (jitter_x[cell_row, cell_col] * jitter_amount).astype(jnp.int32)
                local_y = jnp.clip(local_y + offset_y, 0, char_size - 1)
                local_x = jnp.clip(local_x + offset_x, 0, char_size - 1)

            char_pixels = char_tiles[cell_row, cell_col, local_y, local_x]
            char_bright = char_pixels.mean(axis=-1, keepdims=True) / 255.0

            # Get foreground colors
            if color_mode == 'mono':
                fg = jnp.full_like(char_pixels, 255)
            else:
                fg = colors[cell_row, cell_col]

            # Apply hue shift if enabled
            if char_hue_shift > 0 and color_mode == 'color':
                hue_shift_amount = hue_mod[cell_row, cell_col] * char_hue_shift
                # Simple hue rotation via channel cycling
                fg_float = fg.astype(jnp.float32)
                shift_frac = (hue_shift_amount / 120.0) % 3  # Cycle through RGB
                # Simplified: blend channels based on shift
                r, g, b = fg_float[:,:,0], fg_float[:,:,1], fg_float[:,:,2]
                shift_frac_2d = shift_frac[:, :, None] if shift_frac.ndim == 2 else shift_frac
                # Just do a simple tint for now
                fg = jnp.clip(fg_float + hue_shift_amount[:, :, None] * 0.5, 0, 255).astype(jnp.uint8)

            # Blend
            bg_broadcast = jnp.broadcast_to(bg, char_pixels.shape)
            if invert_colors:
                result = (fg.astype(jnp.float32) * (1 - char_bright) +
                         bg_broadcast.astype(jnp.float32) * char_bright)
            else:
                result = (bg_broadcast.astype(jnp.float32) * (1 - char_bright) +
                         fg.astype(jnp.float32) * char_bright)

            result = jnp.clip(result, 0, 255).astype(jnp.uint8)

            # Resize to original
            if result.shape[0] != h or result.shape[1] != w:
                y_scale = result.shape[0] / h
                x_scale = result.shape[1] / w
                y_src = jnp.clip((jnp.arange(h) * y_scale).astype(jnp.int32), 0, result.shape[0] - 1)
                x_src = jnp.clip((jnp.arange(w) * x_scale).astype(jnp.int32), 0, result.shape[1] - 1)
                result = result[y_src[:, None], x_src[None, :], :]

            return result

        if op == 'alphabet-char':
            # (alphabet-char alphabet-name index) -> char_index in that alphabet
            alphabet = self._eval(args[0], env)
            index = self._eval(args[1], env)

            alpha_str = _get_alphabet_string(alphabet)
            num_chars = len(alpha_str)

            # Handle both scalar and array indices
            if hasattr(index, 'shape'):
                index = jnp.clip(index.astype(jnp.int32), 0, num_chars - 1)
            else:
                index = max(0, min(int(index), num_chars - 1))

            return index

        if op == 'map-char-grid':
            # (map-char-grid base-chars luminances (lambda (r c ch lum) ...))
            # Map over character grid, allowing per-cell character selection
            base_chars = self._eval(args[0], env)
            luminances = self._eval(args[1], env)
            fn = args[2]  # Lambda expression

            num_rows, num_cols = base_chars.shape

            # For JAX compatibility, we can't use Python loops with traced values
            # Instead, we'll evaluate the lambda for the whole grid at once
            if isinstance(fn, list) and len(fn) >= 3:
                head = fn[0]
                if isinstance(head, Symbol) and head.name in ('lambda', 'λ'):
                    params = fn[1]
                    body = fn[2]

                    # Create grid coordinates
                    row_coords, col_coords = jnp.mgrid[:num_rows, :num_cols]

                    # Bind parameters for whole-grid evaluation
                    fn_env = env.copy()

                    # Params: (r c ch lum)
                    if len(params) >= 1:
                        fn_env[params[0].name if isinstance(params[0], Symbol) else params[0]] = row_coords
                    if len(params) >= 2:
                        fn_env[params[1].name if isinstance(params[1], Symbol) else params[1]] = col_coords
                    if len(params) >= 3:
                        fn_env[params[2].name if isinstance(params[2], Symbol) else params[2]] = base_chars
                    if len(params) >= 4:
                        # Luminances scaled to 0-255 range
                        fn_env[params[3].name if isinstance(params[3], Symbol) else params[3]] = (luminances * 255).astype(jnp.int32)

                    # Evaluate body - should return new character indices
                    result = self._eval(body, fn_env)
                    if hasattr(result, 'shape'):
                        return result.astype(jnp.int32)
                    return base_chars

            return base_chars

        # =====================================================================
        # List operations
        # =====================================================================
        if op == 'take':
            seq = self._eval(args[0], env)
            n = int(self._eval(args[1], env))
            if isinstance(seq, (list, tuple)):
                return seq[:n]
            return seq[:n]  # Works for arrays too

        if op == 'cons':
            item = self._eval(args[0], env)
            seq = self._eval(args[1], env)
            if isinstance(seq, list):
                return [item] + seq
            elif isinstance(seq, tuple):
                return (item,) + seq
            return jnp.concatenate([jnp.array([item]), seq])

        if op == 'roll':
            arr = self._eval(args[0], env)
            shift = self._eval(args[1], env)
            axis = self._eval(args[2], env) if len(args) > 2 else 0
            # Convert to int for concrete values, keep as-is for JAX traced values
            if isinstance(shift, (int, float)):
                shift = int(shift)
            elif hasattr(shift, 'astype'):
                shift = shift.astype(jnp.int32)
            if isinstance(axis, (int, float)):
                axis = int(axis)
            return jnp.roll(arr, shift, axis=axis)

        # =====================================================================
        # Pi constant
        # =====================================================================
        if op == 'pi':
            return jnp.pi

        raise ValueError(f"Unknown operation: {op}")


# =============================================================================
# Public API
# =============================================================================

def compile_effect(code: str) -> Callable:
    """
    Compile an S-expression effect to a JAX function.

    Args:
        code: S-expression effect code

    Returns:
        JIT-compiled function: (frame, **params) -> frame
    """
    # Check cache
    cache_key = hashlib.md5(code.encode()).hexdigest()
    if cache_key in _COMPILED_EFFECTS:
        return _COMPILED_EFFECTS[cache_key]

    # Parse and compile
    sexp = parse(code)
    compiler = JaxCompiler()
    fn = compiler.compile_effect(sexp)

    _COMPILED_EFFECTS[cache_key] = fn
    return fn


def compile_effect_file(path: str, derived_paths: List[str] = None) -> Callable:
    """
    Compile an effect from a .sexp file.

    Args:
        path: Path to the .sexp effect file
        derived_paths: Optional list of paths to derived.sexp files to load

    Returns:
        JIT-compiled function: (frame, **params) -> frame
    """
    with open(path, 'r') as f:
        code = f.read()

    # Parse all expressions in file
    exprs = parse_all(code)

    # Create compiler
    compiler = JaxCompiler()

    # Load derived files if specified
    if derived_paths:
        for dp in derived_paths:
            compiler.load_derived(dp)

    # Process expressions - find require statements and the effect
    effect_sexp = None
    effect_dir = Path(path).parent

    for expr in exprs:
        if not isinstance(expr, list) or len(expr) < 2:
            continue

        head = expr[0]
        if not isinstance(head, Symbol):
            continue

        if head.name == 'require':
            # (require "derived") or (require "path/to/file")
            req_path = expr[1]
            if isinstance(req_path, str):
                # Resolve relative to effect file
                if not req_path.endswith('.sexp'):
                    req_path = req_path + '.sexp'
                full_path = effect_dir / req_path
                if not full_path.exists():
                    # Try sexp_effects directory
                    full_path = Path(__file__).parent.parent / 'sexp_effects' / req_path
                if full_path.exists():
                    compiler.load_derived(str(full_path))

        elif head.name == 'require-primitives':
            # (require-primitives "lib") - currently ignored for JAX
            # JAX has all primitives built-in
            pass

        elif head.name in ('effect', 'define-effect'):
            effect_sexp = expr

    if effect_sexp is None:
        raise ValueError(f"No effect definition found in {path}")

    return compiler.compile_effect(effect_sexp)


def load_derived(derived_path: str = None) -> Dict[str, Callable]:
    """
    Load derived operations from derived.sexp.

    Returns dict of compiled functions that can be used in effects.
    """
    if derived_path is None:
        derived_path = Path(__file__).parent.parent / 'sexp_effects' / 'derived.sexp'

    with open(derived_path, 'r') as f:
        code = f.read()

    exprs = parse_all(code)
    compiler = JaxCompiler()
    env = {}

    for expr in exprs:
        if isinstance(expr, list) and len(expr) >= 3:
            head = expr[0]
            if isinstance(head, Symbol) and head.name == 'define':
                compiler._eval_define(expr[1:], env)

    return env


# =============================================================================
# Test / Demo
# =============================================================================

if __name__ == '__main__':
    import numpy as np

    # Test effect
    test_effect = '''
    (effect "threshold-test"
      :params ((threshold :default 128))
      :body (let* ((r (channel frame 0))
                   (g (channel frame 1))
                   (b (channel frame 2))
                   (gray (+ (* r 0.299) (* g 0.587) (* b 0.114)))
                   (mask (where (> gray threshold) 255 0)))
              (merge-channels mask mask mask)))
    '''

    print("Compiling effect...")
    run_effect = compile_effect(test_effect)

    # Create test frame
    print("Creating test frame...")
    frame = np.random.randint(0, 256, (480, 640, 3), dtype=np.uint8)

    # Run effect
    print("Running effect (first run includes JIT compilation)...")
    import time

    t0 = time.time()
    result = run_effect(frame, threshold=128)
    t1 = time.time()
    print(f"First run (with JIT): {(t1-t0)*1000:.2f}ms")

    # Second run should be faster
    t0 = time.time()
    result = run_effect(frame, threshold=128)
    t1 = time.time()
    print(f"Second run (cached): {(t1-t0)*1000:.2f}ms")

    # Multiple runs
    t0 = time.time()
    for _ in range(100):
        result = run_effect(frame, threshold=128)
    t1 = time.time()
    print(f"100 runs: {(t1-t0)*1000:.2f}ms total, {(t1-t0)*10:.2f}ms avg")

    print(f"\nResult shape: {result.shape}, dtype: {result.dtype}")
    print("Done!")