diff --git a/README.md b/README.md
index e69de29..f6df6fd 100644
--- a/README.md
+++ b/README.md
@@ -0,0 +1,4 @@
+
+```bash
+jupytext --to py:percent <script>.ipynb
+```
\ No newline at end of file
diff --git a/play.ipynb b/play.ipynb
deleted file mode 100644
index 567f1a1..0000000
--- a/play.ipynb
+++ /dev/null
@@ -1,655 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from copy import deepcopy\n",
-    "from datetime import datetime, timedelta\n",
-    "from pathlib import Path\n",
-    "from typing import (Any, Generator, Optional, Sequence, TypeAlias, TypedDict,\n",
-    "                    cast, overload)\n",
-    "\n",
-    "import awkward as ak\n",
-    "import jax\n",
-    "import jax.numpy as jnp\n",
-    "import numpy as np\n",
-    "import orjson\n",
-    "from beartype import beartype\n",
-    "from cv2 import undistortPoints\n",
-    "from jaxtyping import Array, Float, Num, jaxtyped\n",
-    "from matplotlib import pyplot as plt\n",
-    "from numpy.typing import ArrayLike\n",
-    "from scipy.spatial.transform import Rotation as R\n",
-    "\n",
-    "from app.camera import Camera, CameraParams, Detection\n",
-    "from app.visualize.whole_body import visualize_whole_body\n",
-    "\n",
-    "NDArray: TypeAlias = np.ndarray"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "DATASET_PATH = Path(\"samples\") / \"04_02\" \n",
-    "AK_CAMERA_DATASET: ak.Array = ak.from_parquet(DATASET_PATH / \"camera_params.parquet\")\n",
-    "display(AK_CAMERA_DATASET)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class Resolution(TypedDict):\n",
-    "    width: int\n",
-    "    height: int\n",
-    "\n",
-    "class Intrinsic(TypedDict):\n",
-    "    camera_matrix: Num[Array, \"3 3\"]\n",
-    "    \"\"\"\n",
-    "    K\n",
-    "    \"\"\"\n",
-    "    distortion_coefficients: Num[Array, \"N\"]\n",
-    "    \"\"\"\n",
-    "    distortion coefficients; usually 5\n",
-    "    \"\"\"\n",
-    "\n",
-    "class Extrinsic(TypedDict):\n",
-    "    rvec: Num[NDArray, \"3\"]\n",
-    "    tvec: Num[NDArray, \"3\"]\n",
-    "\n",
-    "class ExternalCameraParams(TypedDict):\n",
-    "    name: str\n",
-    "    port: int\n",
-    "    intrinsic: Intrinsic\n",
-    "    extrinsic: Extrinsic\n",
-    "    resolution: Resolution\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def read_dataset_by_port(port: int) -> ak.Array:\n",
-    "    P = DATASET_PATH / f\"{port}.parquet\"\n",
-    "    return ak.from_parquet(P)\n",
-    "\n",
-    "KEYPOINT_DATASET = {int(p): read_dataset_by_port(p) for p in ak.to_numpy(AK_CAMERA_DATASET[\"port\"])}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "KEYPOINT_DATASET[5601]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class KeypointDataset(TypedDict):\n",
-    "    frame_index: int\n",
-    "    boxes: Num[NDArray, \"N 4\"]\n",
-    "    kps: Num[NDArray, \"N J 2\"]\n",
-    "    kps_scores: Num[NDArray, \"N J\"]\n",
-    "\n",
-    "\n",
-    "@jaxtyped(typechecker=beartype)\n",
-    "def to_transformation_matrix(\n",
-    "    rvec: Num[NDArray, \"3\"], tvec: Num[NDArray, \"3\"]\n",
-    ") -> Num[NDArray, \"4 4\"]:\n",
-    "    res = np.eye(4)\n",
-    "    res[:3, :3] = R.from_rotvec(rvec).as_matrix()\n",
-    "    res[:3, 3] = tvec\n",
-    "    return res\n",
-    "\n",
-    "\n",
-    "@jaxtyped(typechecker=beartype)\n",
-    "def undistort_points(\n",
-    "    points: Num[NDArray, \"M 2\"],\n",
-    "    camera_matrix: Num[NDArray, \"3 3\"],\n",
-    "    dist_coeffs: Num[NDArray, \"N\"],\n",
-    ") -> Num[NDArray, \"M 2\"]:\n",
-    "    K = camera_matrix\n",
-    "    dist = dist_coeffs\n",
-    "    res = undistortPoints(points, K, dist, P=K)  # type: ignore\n",
-    "    return res.reshape(-1, 2)\n",
-    "\n",
-    "\n",
-    "def from_camera_params(camera: ExternalCameraParams) -> Camera:\n",
-    "    rt = jnp.array(\n",
-    "        to_transformation_matrix(\n",
-    "            ak.to_numpy(camera[\"extrinsic\"][\"rvec\"]),\n",
-    "            ak.to_numpy(camera[\"extrinsic\"][\"tvec\"]),\n",
-    "        )\n",
-    "    )\n",
-    "    K = jnp.array(camera[\"intrinsic\"][\"camera_matrix\"]).reshape(3, 3)\n",
-    "    dist_coeffs = jnp.array(camera[\"intrinsic\"][\"distortion_coefficients\"])\n",
-    "    image_size = jnp.array(\n",
-    "        (camera[\"resolution\"][\"width\"], camera[\"resolution\"][\"height\"])\n",
-    "    )\n",
-    "    return Camera(\n",
-    "        id=camera[\"name\"],\n",
-    "        params=CameraParams(\n",
-    "            K=K,\n",
-    "            Rt=rt,\n",
-    "            dist_coeffs=dist_coeffs,\n",
-    "            image_size=image_size,\n",
-    "        ),\n",
-    "    )\n",
-    "\n",
-    "\n",
-    "def preprocess_keypoint_dataset(\n",
-    "    dataset: Sequence[KeypointDataset],\n",
-    "    camera: Camera,\n",
-    "    fps: float,\n",
-    "    start_timestamp: datetime,\n",
-    ") -> Generator[Detection, None, None]:\n",
-    "    frame_interval_s = 1 / fps\n",
-    "    for el in dataset:\n",
-    "        frame_index = el[\"frame_index\"]\n",
-    "        timestamp = start_timestamp + timedelta(seconds=frame_index * frame_interval_s)\n",
-    "        for kp, kp_score in zip(el[\"kps\"], el[\"kps_scores\"]):\n",
-    "            yield Detection(\n",
-    "                keypoints=jnp.array(kp),\n",
-    "                confidences=jnp.array(kp_score),\n",
-    "                camera=camera,\n",
-    "                timestamp=timestamp,\n",
-    "            )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "DetectionGenerator: TypeAlias = Generator[Detection, None, None]\n",
-    "\n",
-    "\n",
-    "def sync_batch_gen(gens: list[DetectionGenerator], diff: timedelta):\n",
-    "    \"\"\"\n",
-    "    given a list of detection generators, return a generator that yields a batch of detections\n",
-    "\n",
-    "    Args:\n",
-    "        gens: list of detection generators\n",
-    "        diff: maximum timestamp difference between detections to consider them part of the same batch\n",
-    "    \"\"\"\n",
-    "    N = len(gens)\n",
-    "    last_batch_timestamp: Optional[datetime] = None\n",
-    "    next_batch_timestamp: Optional[datetime] = None\n",
-    "    current_batch: list[Detection] = []\n",
-    "    next_batch: list[Detection] = []\n",
-    "    paused: list[bool] = [False] * N\n",
-    "    finished: list[bool] = [False] * N\n",
-    "\n",
-    "    def reset_paused():\n",
-    "        \"\"\"\n",
-    "        reset paused list based on finished list\n",
-    "        \"\"\"\n",
-    "        for i in range(N):\n",
-    "            if not finished[i]:\n",
-    "                paused[i] = False\n",
-    "            else:\n",
-    "                paused[i] = True\n",
-    "\n",
-    "    EPS = 1e-6\n",
-    "    # a small epsilon to avoid floating point precision issues\n",
-    "    diff_esp = diff - timedelta(seconds=EPS)\n",
-    "    while True:\n",
-    "        for i, gen in enumerate(gens):\n",
-    "            try:\n",
-    "                if finished[i] or paused[i]:\n",
-    "                    continue\n",
-    "                val = next(gen)\n",
-    "                if last_batch_timestamp is None:\n",
-    "                    last_batch_timestamp = val.timestamp\n",
-    "                    current_batch.append(val)\n",
-    "                else:\n",
-    "                    if abs(val.timestamp - last_batch_timestamp) >= diff_esp:\n",
-    "                        next_batch.append(val)\n",
-    "                        if next_batch_timestamp is None:\n",
-    "                            next_batch_timestamp = val.timestamp\n",
-    "                        paused[i] = True\n",
-    "                        if all(paused):\n",
-    "                            yield current_batch\n",
-    "                            current_batch = next_batch\n",
-    "                            next_batch = []\n",
-    "                            last_batch_timestamp = next_batch_timestamp\n",
-    "                            next_batch_timestamp = None\n",
-    "                            reset_paused()\n",
-    "                    else:\n",
-    "                        current_batch.append(val)\n",
-    "            except StopIteration:\n",
-    "                finished[i] = True\n",
-    "                paused[i] = True\n",
-    "        if all(finished):\n",
-    "            if len(current_batch) > 0:\n",
-    "                # All generators exhausted, flush remaining batch and exit\n",
-    "                yield current_batch\n",
-    "            break"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@overload\n",
-    "def to_projection_matrix(\n",
-    "    transformation_matrix: Num[NDArray, \"4 4\"], camera_matrix: Num[NDArray, \"3 3\"]\n",
-    ") -> Num[NDArray, \"3 4\"]: ...\n",
-    "\n",
-    "\n",
-    "@overload\n",
-    "def to_projection_matrix(\n",
-    "    transformation_matrix: Num[Array, \"4 4\"], camera_matrix: Num[Array, \"3 3\"]\n",
-    ") -> Num[Array, \"3 4\"]: ...\n",
-    "\n",
-    "\n",
-    "@jaxtyped(typechecker=beartype)\n",
-    "def to_projection_matrix(\n",
-    "    transformation_matrix: Num[Any, \"4 4\"],\n",
-    "    camera_matrix: Num[Any, \"3 3\"],\n",
-    ") -> Num[Any, \"3 4\"]:\n",
-    "    return camera_matrix @ transformation_matrix[:3, :]\n",
-    "\n",
-    "to_projection_matrix_jit = jax.jit(to_projection_matrix)\n",
-    "\n",
-    "\n",
-    "@jaxtyped(typechecker=beartype)\n",
-    "def dlt(\n",
-    "    H1: Num[NDArray, \"3 4\"],\n",
-    "    H2: Num[NDArray, \"3 4\"],\n",
-    "    p1: Num[NDArray, \"2\"],\n",
-    "    p2: Num[NDArray, \"2\"],\n",
-    ") -> Num[NDArray, \"3\"]:\n",
-    "    \"\"\"\n",
-    "    Direct Linear Transformation\n",
-    "    \"\"\"\n",
-    "    A = [\n",
-    "        p1[1] * H1[2, :] - H1[1, :],\n",
-    "        H1[0, :] - p1[0] * H1[2, :],\n",
-    "        p2[1] * H2[2, :] - H2[1, :],\n",
-    "        H2[0, :] - p2[0] * H2[2, :],\n",
-    "    ]\n",
-    "    A = np.array(A).reshape((4, 4))\n",
-    "\n",
-    "    B = A.transpose() @ A\n",
-    "    from scipy import linalg\n",
-    "\n",
-    "    U, s, Vh = linalg.svd(B, full_matrices=False)\n",
-    "    return Vh[3, 0:3] / Vh[3, 3]\n",
-    "\n",
-    "\n",
-    "@overload\n",
-    "def homogeneous_to_euclidean(points: Num[NDArray, \"N 4\"]) -> Num[NDArray, \"N 3\"]: ...\n",
-    "\n",
-    "\n",
-    "@overload\n",
-    "def homogeneous_to_euclidean(points: Num[Array, \"N 4\"]) -> Num[Array, \"N 3\"]: ...\n",
-    "\n",
-    "\n",
-    "@jaxtyped(typechecker=beartype)\n",
-    "def homogeneous_to_euclidean(\n",
-    "    points: Num[Any, \"N 4\"],\n",
-    ") -> Num[Any, \"N 3\"]:\n",
-    "    \"\"\"\n",
-    "    将齐次坐标转换为欧几里得坐标\n",
-    "\n",
-    "    Args:\n",
-    "        points: homogeneous coordinates (x, y, z, w) in numpy array or jax array\n",
-    "\n",
-    "    Returns:\n",
-    "        euclidean coordinates (x, y, z) in numpy array or jax array\n",
-    "    \"\"\"\n",
-    "    return points[..., :-1] / points[..., -1:]\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "FPS = 24\n",
-    "image_gen_5600 = preprocess_keypoint_dataset(KEYPOINT_DATASET[5600], from_camera_params(AK_CAMERA_DATASET[AK_CAMERA_DATASET[\"port\"] == 5600][0]), FPS, datetime(2024, 4, 2, 12, 0, 0))  # type: ignore\n",
-    "image_gen_5601 = preprocess_keypoint_dataset(KEYPOINT_DATASET[5601], from_camera_params(AK_CAMERA_DATASET[AK_CAMERA_DATASET[\"port\"] == 5601][0]), FPS, datetime(2024, 4, 2, 12, 0, 0))  # type: ignore\n",
-    "image_gen_5602 = preprocess_keypoint_dataset(KEYPOINT_DATASET[5602], from_camera_params(AK_CAMERA_DATASET[AK_CAMERA_DATASET[\"port\"] == 5602][0]), FPS, datetime(2024, 4, 2, 12, 0, 0))  # type: ignore\n",
-    "\n",
-    "display(1/FPS)\n",
-    "sync_gen = sync_batch_gen([image_gen_5600, image_gen_5601, image_gen_5602], timedelta(seconds=1/FPS))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "detections = next(sync_gen)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from app.camera import calculate_affinity_matrix_by_epipolar_constraint\n",
-    "\n",
-    "sorted_detections, affinity_matrix = calculate_affinity_matrix_by_epipolar_constraint(detections, \n",
-    "                                                                                      alpha_2d=2000)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "display(list(map(lambda x: {\"timestamp\": str(x.timestamp), \"camera\": x.camera.id}, sorted_detections)))\n",
-    "with jnp.printoptions(precision=3, suppress=True):\n",
-    "    display(affinity_matrix)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from app.solver._old import GLPKSolver\n",
-    "\n",
-    "def clusters_to_detections(clusters: list[list[int]], sorted_detections: list[Detection]) -> list[list[Detection]]:\n",
-    "    \"\"\"\n",
-    "    given a list of clusters (which is the indices of the detections in the sorted_detections list),\n",
-    "    extract the detections from the sorted_detections list\n",
-    "\n",
-    "    Args:\n",
-    "        clusters: list of clusters, each cluster is a list of indices of the detections in the `sorted_detections` list\n",
-    "        sorted_detections: list of SORTED detections\n",
-    "\n",
-    "    Returns:\n",
-    "        list of clusters, each cluster is a list of detections\n",
-    "    \"\"\"\n",
-    "    return [[sorted_detections[i] for i in cluster] for cluster in clusters]\n",
-    "\n",
-    "solver = GLPKSolver()\n",
-    "aff_np = np.asarray(affinity_matrix).astype(np.float64)\n",
-    "clusters, sol_matrix = solver.solve(aff_np)\n",
-    "display(clusters)\n",
-    "display(sol_matrix)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "WIDTH = 2560\n",
-    "HEIGHT = 1440\n",
-    "\n",
-    "clusters_detections = clusters_to_detections(clusters, sorted_detections)\n",
-    "im = np.zeros((HEIGHT, WIDTH, 3), dtype=np.uint8)\n",
-    "for el in clusters_detections[0]:\n",
-    "    im = visualize_whole_body(np.asarray(el.keypoints), im)\n",
-    "\n",
-    "p = plt.imshow(im)\n",
-    "display(p)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "im_prime = np.zeros((HEIGHT, WIDTH, 3), dtype=np.uint8)\n",
-    "for el in clusters_detections[1]:\n",
-    "    im_prime = visualize_whole_body(np.asarray(el.keypoints), im_prime)\n",
-    "\n",
-    "p_prime = plt.imshow(im_prime)\n",
-    "display(p_prime)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@jaxtyped(typechecker=beartype)\n",
-    "def triangulate_one_point_from_multiple_views_linear(\n",
-    "    proj_matrices: Float[Array, \"N 3 4\"],\n",
-    "    points: Num[Array, \"N 2\"],\n",
-    "    confidences: Optional[Float[Array, \"N\"]] = None,\n",
-    ") -> Float[Array, \"3\"]:\n",
-    "    \"\"\"\n",
-    "    Args:\n",
-    "        proj_matrices: 形状为(N, 3, 4)的投影矩阵序列\n",
-    "        points: 形状为(N, 2)的点坐标序列\n",
-    "        confidences: 形状为(N,)的置信度序列，范围[0.0, 1.0]\n",
-    "\n",
-    "    Returns:\n",
-    "        point_3d: 形状为(3,)的三角测量得到的3D点\n",
-    "    \"\"\"\n",
-    "    assert len(proj_matrices) == len(points)\n",
-    "\n",
-    "    N = len(proj_matrices)\n",
-    "    confi: Float[Array, \"N\"]\n",
-    "    if confidences is None:\n",
-    "        confi = jnp.ones(N, dtype=np.float32)\n",
-    "    else:\n",
-    "        # Use square root of confidences for weighting - more balanced approach\n",
-    "        confi = jnp.sqrt(jnp.clip(confidences, 0, 1))\n",
-    "\n",
-    "    A = jnp.zeros((N * 2, 4), dtype=np.float32)\n",
-    "    for i in range(N):\n",
-    "        x, y = points[i]\n",
-    "        A = A.at[2 * i].set(proj_matrices[i, 2] * x - proj_matrices[i, 0])\n",
-    "        A = A.at[2 * i + 1].set(proj_matrices[i, 2] * y - proj_matrices[i, 1])\n",
-    "        A = A.at[2 * i].mul(confi[i])\n",
-    "        A = A.at[2 * i + 1].mul(confi[i])\n",
-    "\n",
-    "    # https://docs.jax.dev/en/latest/_autosummary/jax.numpy.linalg.svd.html\n",
-    "    _, _, vh = jnp.linalg.svd(A, full_matrices=False)\n",
-    "    point_3d_homo = vh[-1]  # shape (4,)\n",
-    "\n",
-    "    # replace the Python `if` with a jnp.where\n",
-    "    point_3d_homo = jnp.where(\n",
-    "        point_3d_homo[3] < 0,  # predicate (scalar bool tracer)\n",
-    "        -point_3d_homo,  # if True\n",
-    "        point_3d_homo,  # if False\n",
-    "    )\n",
-    "\n",
-    "    point_3d = point_3d_homo[:3] / point_3d_homo[3]\n",
-    "    return point_3d\n",
-    "\n",
-    "\n",
-    "@jaxtyped(typechecker=beartype)\n",
-    "def triangulate_points_from_multiple_views_linear(\n",
-    "    proj_matrices: Float[Array, \"N 3 4\"],\n",
-    "    points: Num[Array, \"N P 2\"],\n",
-    "    confidences: Optional[Float[Array, \"N P\"]] = None,\n",
-    ") -> Float[Array, \"P 3\"]:\n",
-    "    \"\"\"\n",
-    "    Batch-triangulate P points observed by N cameras, linearly via SVD.\n",
-    "\n",
-    "    Args:\n",
-    "        proj_matrices: (N, 3, 4) projection matrices\n",
-    "        points:       (N, P, 2) image-coordinates per view\n",
-    "        confidences:  (N, P, 1) optional per-view confidences in [0,1]\n",
-    "\n",
-    "    Returns:\n",
-    "        (P, 3) 3D point for each of the P tracks\n",
-    "    \"\"\"\n",
-    "    N, P, _ = points.shape\n",
-    "    assert proj_matrices.shape[0] == N\n",
-    "    if confidences is None:\n",
-    "        conf = jnp.ones((N, P), dtype=jnp.float32)\n",
-    "    else:\n",
-    "        conf = jnp.sqrt(jnp.clip(confidences, 0.0, 1.0))\n",
-    "\n",
-    "    # vectorize your one‐point routine over P\n",
-    "    vmap_triangulate = jax.vmap(\n",
-    "        triangulate_one_point_from_multiple_views_linear,\n",
-    "        in_axes=(None, 1, 1),  # proj_matrices static, map over points[:,p,:], conf[:,p]\n",
-    "        out_axes=0,\n",
-    "    )\n",
-    "\n",
-    "    # returns (P, 3)\n",
-    "    return vmap_triangulate(proj_matrices, points, conf)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from dataclasses import dataclass\n",
-    "from copy import copy as shallow_copy, deepcopy as deep_copy\n",
-    "\n",
-    "\n",
-    "@jaxtyped(typechecker=beartype)\n",
-    "@dataclass(frozen=True)\n",
-    "class Tracking:\n",
-    "    id: int\n",
-    "    keypoints: Float[Array, \"J 3\"]\n",
-    "    last_active_timestamp: datetime\n",
-    "\n",
-    "    def __repr__(self) -> str:\n",
-    "        return f\"Tracking({self.id}, {self.last_active_timestamp})\"\n",
-    "\n",
-    "\n",
-    "@jaxtyped(typechecker=beartype)\n",
-    "def triangle_from_cluster(\n",
-    "    cluster: list[Detection],\n",
-    ") -> tuple[Float[Array, \"N 3\"], datetime]:\n",
-    "    proj_matrices = jnp.array([el.camera.params.projection_matrix for el in cluster])\n",
-    "    points = jnp.array([el.keypoints_undistorted for el in cluster])\n",
-    "    confidences = jnp.array([el.confidences for el in cluster])\n",
-    "    latest_timestamp = max(el.timestamp for el in cluster)\n",
-    "    return (\n",
-    "        triangulate_points_from_multiple_views_linear(\n",
-    "            proj_matrices, points, confidences=confidences\n",
-    "        ),\n",
-    "        latest_timestamp,\n",
-    "    )\n",
-    "\n",
-    "\n",
-    "# res  = {\n",
-    "#     \"a\": triangle_from_cluster(clusters_detections[0]).tolist(),\n",
-    "#     \"b\": triangle_from_cluster(clusters_detections[1]).tolist(),\n",
-    "# }\n",
-    "# with open(\"samples/res.json\", \"wb\") as f:\n",
-    "#     f.write(orjson.dumps(res))\n",
-    "\n",
-    "\n",
-    "class GlobalTrackingState:\n",
-    "    _last_id: int\n",
-    "    _trackings: dict[int, Tracking]\n",
-    "\n",
-    "    def __init__(self):\n",
-    "        self._last_id = 0\n",
-    "        self._trackings = {}\n",
-    "\n",
-    "    def __repr__(self) -> str:\n",
-    "        return (\n",
-    "            f\"GlobalTrackingState(last_id={self._last_id}, trackings={self._trackings})\"\n",
-    "        )\n",
-    "\n",
-    "    @property\n",
-    "    def trackings(self) -> dict[int, Tracking]:\n",
-    "        return shallow_copy(self._trackings)\n",
-    "\n",
-    "    def add_tracking(self, cluster: list[Detection]) -> Tracking:\n",
-    "        kps_3d, latest_timestamp = triangle_from_cluster(cluster)\n",
-    "        next_id = self._last_id + 1\n",
-    "        tracking = Tracking(\n",
-    "            id=next_id, keypoints=kps_3d, last_active_timestamp=latest_timestamp\n",
-    "        )\n",
-    "        self._trackings[next_id] = tracking\n",
-    "        self._last_id = next_id\n",
-    "        return tracking\n",
-    "\n",
-    "\n",
-    "global_tracking_state = GlobalTrackingState()\n",
-    "for cluster in clusters_detections:\n",
-    "    global_tracking_state.add_tracking(cluster)\n",
-    "display(global_tracking_state)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "next_group = next(sync_gen)\n",
-    "display(next_group)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from app.camera import classify_by_camera\n",
-    "\n",
-    "# let's do cross-view association\n",
-    "trackings = sorted(global_tracking_state.trackings.values(), key=lambda x: x.id)\n",
-    "detections = shallow_copy(next_group)\n",
-    "# cross-view association matrix with shape (T, D), where T is the number of trackings, D is the number of detections\n",
-    "affinity = np.zeros((len(trackings), len(detections)))\n",
-    "detection_by_camera = classify_by_camera(detections)\n",
-    "for i, tracking in enumerate(trackings):\n",
-    "    for c, detections in detection_by_camera.items():\n",
-    "        camera = next(iter(detections)).camera\n",
-    "        # pixel space, unnormalized\n",
-    "        tracking_2d_projection = camera.project(tracking.keypoints)\n",
-    "        \n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/playground.py b/playground.py
new file mode 100644
index 0000000..1226447
--- /dev/null
+++ b/playground.py
@@ -0,0 +1,562 @@
+# ---
+# jupyter:
+#   jupytext:
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.17.0
+#   kernelspec:
+#     display_name: .venv
+#     language: python
+#     name: python3
+# ---
+
+# %%
+from copy import deepcopy
+from datetime import datetime, timedelta
+from pathlib import Path
+from typing import (
+    Any,
+    Generator,
+    Optional,
+    Sequence,
+    TypeAlias,
+    TypedDict,
+    cast,
+    overload,
+)
+
+import awkward as ak
+import jax
+import jax.numpy as jnp
+import numpy as np
+import orjson
+from beartype import beartype
+from cv2 import undistortPoints
+from jaxtyping import Array, Float, Num, jaxtyped
+from matplotlib import pyplot as plt
+from numpy.typing import ArrayLike
+from scipy.spatial.transform import Rotation as R
+
+from app.camera import Camera, CameraParams, Detection
+from app.visualize.whole_body import visualize_whole_body
+from IPython.display import display
+
+NDArray: TypeAlias = np.ndarray
+
+# %%
+DATASET_PATH = Path("samples") / "04_02"
+AK_CAMERA_DATASET: ak.Array = ak.from_parquet(DATASET_PATH / "camera_params.parquet")
+display(AK_CAMERA_DATASET)
+
+
+# %%
+class Resolution(TypedDict):
+    width: int
+    height: int
+
+
+class Intrinsic(TypedDict):
+    camera_matrix: Num[Array, "3 3"]
+    """
+    K
+    """
+    distortion_coefficients: Num[Array, "N"]
+    """
+    distortion coefficients; usually 5
+    """
+
+
+class Extrinsic(TypedDict):
+    rvec: Num[NDArray, "3"]
+    tvec: Num[NDArray, "3"]
+
+
+class ExternalCameraParams(TypedDict):
+    name: str
+    port: int
+    intrinsic: Intrinsic
+    extrinsic: Extrinsic
+    resolution: Resolution
+
+
+# %%
+def read_dataset_by_port(port: int) -> ak.Array:
+    P = DATASET_PATH / f"{port}.parquet"
+    return ak.from_parquet(P)
+
+
+KEYPOINT_DATASET = {
+    int(p): read_dataset_by_port(p) for p in ak.to_numpy(AK_CAMERA_DATASET["port"])
+}
+
+
+# %%
+class KeypointDataset(TypedDict):
+    frame_index: int
+    boxes: Num[NDArray, "N 4"]
+    kps: Num[NDArray, "N J 2"]
+    kps_scores: Num[NDArray, "N J"]
+
+
+@jaxtyped(typechecker=beartype)
+def to_transformation_matrix(
+    rvec: Num[NDArray, "3"], tvec: Num[NDArray, "3"]
+) -> Num[NDArray, "4 4"]:
+    res = np.eye(4)
+    res[:3, :3] = R.from_rotvec(rvec).as_matrix()
+    res[:3, 3] = tvec
+    return res
+
+
+@jaxtyped(typechecker=beartype)
+def undistort_points(
+    points: Num[NDArray, "M 2"],
+    camera_matrix: Num[NDArray, "3 3"],
+    dist_coeffs: Num[NDArray, "N"],
+) -> Num[NDArray, "M 2"]:
+    K = camera_matrix
+    dist = dist_coeffs
+    res = undistortPoints(points, K, dist, P=K)  # type: ignore
+    return res.reshape(-1, 2)
+
+
+def from_camera_params(camera: ExternalCameraParams) -> Camera:
+    rt = jnp.array(
+        to_transformation_matrix(
+            ak.to_numpy(camera["extrinsic"]["rvec"]),
+            ak.to_numpy(camera["extrinsic"]["tvec"]),
+        )
+    )
+    K = jnp.array(camera["intrinsic"]["camera_matrix"]).reshape(3, 3)
+    dist_coeffs = jnp.array(camera["intrinsic"]["distortion_coefficients"])
+    image_size = jnp.array(
+        (camera["resolution"]["width"], camera["resolution"]["height"])
+    )
+    return Camera(
+        id=camera["name"],
+        params=CameraParams(
+            K=K,
+            Rt=rt,
+            dist_coeffs=dist_coeffs,
+            image_size=image_size,
+        ),
+    )
+
+
+def preprocess_keypoint_dataset(
+    dataset: Sequence[KeypointDataset],
+    camera: Camera,
+    fps: float,
+    start_timestamp: datetime,
+) -> Generator[Detection, None, None]:
+    frame_interval_s = 1 / fps
+    for el in dataset:
+        frame_index = el["frame_index"]
+        timestamp = start_timestamp + timedelta(seconds=frame_index * frame_interval_s)
+        for kp, kp_score in zip(el["kps"], el["kps_scores"]):
+            yield Detection(
+                keypoints=jnp.array(kp),
+                confidences=jnp.array(kp_score),
+                camera=camera,
+                timestamp=timestamp,
+            )
+
+
+# %%
+DetectionGenerator: TypeAlias = Generator[Detection, None, None]
+
+
+def sync_batch_gen(gens: list[DetectionGenerator], diff: timedelta):
+    """
+    given a list of detection generators, return a generator that yields a batch of detections
+
+    Args:
+        gens: list of detection generators
+        diff: maximum timestamp difference between detections to consider them part of the same batch
+    """
+    N = len(gens)
+    last_batch_timestamp: Optional[datetime] = None
+    next_batch_timestamp: Optional[datetime] = None
+    current_batch: list[Detection] = []
+    next_batch: list[Detection] = []
+    paused: list[bool] = [False] * N
+    finished: list[bool] = [False] * N
+
+    def reset_paused():
+        """
+        reset paused list based on finished list
+        """
+        for i in range(N):
+            if not finished[i]:
+                paused[i] = False
+            else:
+                paused[i] = True
+
+    EPS = 1e-6
+    # a small epsilon to avoid floating point precision issues
+    diff_esp = diff - timedelta(seconds=EPS)
+    while True:
+        for i, gen in enumerate(gens):
+            try:
+                if finished[i] or paused[i]:
+                    continue
+                val = next(gen)
+                if last_batch_timestamp is None:
+                    last_batch_timestamp = val.timestamp
+                    current_batch.append(val)
+                else:
+                    if abs(val.timestamp - last_batch_timestamp) >= diff_esp:
+                        next_batch.append(val)
+                        if next_batch_timestamp is None:
+                            next_batch_timestamp = val.timestamp
+                        paused[i] = True
+                        if all(paused):
+                            yield current_batch
+                            current_batch = next_batch
+                            next_batch = []
+                            last_batch_timestamp = next_batch_timestamp
+                            next_batch_timestamp = None
+                            reset_paused()
+                    else:
+                        current_batch.append(val)
+            except StopIteration:
+                finished[i] = True
+                paused[i] = True
+        if all(finished):
+            if len(current_batch) > 0:
+                # All generators exhausted, flush remaining batch and exit
+                yield current_batch
+            break
+
+
+# %%
+@overload
+def to_projection_matrix(
+    transformation_matrix: Num[NDArray, "4 4"], camera_matrix: Num[NDArray, "3 3"]
+) -> Num[NDArray, "3 4"]: ...
+
+
+@overload
+def to_projection_matrix(
+    transformation_matrix: Num[Array, "4 4"], camera_matrix: Num[Array, "3 3"]
+) -> Num[Array, "3 4"]: ...
+
+
+@jaxtyped(typechecker=beartype)
+def to_projection_matrix(
+    transformation_matrix: Num[Any, "4 4"],
+    camera_matrix: Num[Any, "3 3"],
+) -> Num[Any, "3 4"]:
+    return camera_matrix @ transformation_matrix[:3, :]
+
+
+to_projection_matrix_jit = jax.jit(to_projection_matrix)
+
+
+@jaxtyped(typechecker=beartype)
+def dlt(
+    H1: Num[NDArray, "3 4"],
+    H2: Num[NDArray, "3 4"],
+    p1: Num[NDArray, "2"],
+    p2: Num[NDArray, "2"],
+) -> Num[NDArray, "3"]:
+    """
+    Direct Linear Transformation
+    """
+    A = [
+        p1[1] * H1[2, :] - H1[1, :],
+        H1[0, :] - p1[0] * H1[2, :],
+        p2[1] * H2[2, :] - H2[1, :],
+        H2[0, :] - p2[0] * H2[2, :],
+    ]
+    A = np.array(A).reshape((4, 4))
+
+    B = A.transpose() @ A
+    from scipy import linalg
+
+    U, s, Vh = linalg.svd(B, full_matrices=False)
+    return Vh[3, 0:3] / Vh[3, 3]
+
+
+@overload
+def homogeneous_to_euclidean(points: Num[NDArray, "N 4"]) -> Num[NDArray, "N 3"]: ...
+
+
+@overload
+def homogeneous_to_euclidean(points: Num[Array, "N 4"]) -> Num[Array, "N 3"]: ...
+
+
+@jaxtyped(typechecker=beartype)
+def homogeneous_to_euclidean(
+    points: Num[Any, "N 4"],
+) -> Num[Any, "N 3"]:
+    """
+    将齐次坐标转换为欧几里得坐标
+
+    Args:
+        points: homogeneous coordinates (x, y, z, w) in numpy array or jax array
+
+    Returns:
+        euclidean coordinates (x, y, z) in numpy array or jax array
+    """
+    return points[..., :-1] / points[..., -1:]
+
+
+# %%
+FPS = 24
+image_gen_5600 = preprocess_keypoint_dataset(KEYPOINT_DATASET[5600], from_camera_params(AK_CAMERA_DATASET[AK_CAMERA_DATASET["port"] == 5600][0]), FPS, datetime(2024, 4, 2, 12, 0, 0))  # type: ignore
+image_gen_5601 = preprocess_keypoint_dataset(KEYPOINT_DATASET[5601], from_camera_params(AK_CAMERA_DATASET[AK_CAMERA_DATASET["port"] == 5601][0]), FPS, datetime(2024, 4, 2, 12, 0, 0))  # type: ignore
+image_gen_5602 = preprocess_keypoint_dataset(KEYPOINT_DATASET[5602], from_camera_params(AK_CAMERA_DATASET[AK_CAMERA_DATASET["port"] == 5602][0]), FPS, datetime(2024, 4, 2, 12, 0, 0))  # type: ignore
+
+display(1 / FPS)
+sync_gen = sync_batch_gen(
+    [image_gen_5600, image_gen_5601, image_gen_5602], timedelta(seconds=1 / FPS)
+)
+
+# %%
+detections = next(sync_gen)
+
+# %%
+from app.camera import calculate_affinity_matrix_by_epipolar_constraint
+
+sorted_detections, affinity_matrix = calculate_affinity_matrix_by_epipolar_constraint(
+    detections, alpha_2d=2000
+)
+
+# %%
+display(
+    list(
+        map(
+            lambda x: {"timestamp": str(x.timestamp), "camera": x.camera.id},
+            sorted_detections,
+        )
+    )
+)
+with jnp.printoptions(precision=3, suppress=True):
+    display(affinity_matrix)
+
+# %%
+from app.solver._old import GLPKSolver
+
+
+def clusters_to_detections(
+    clusters: list[list[int]], sorted_detections: list[Detection]
+) -> list[list[Detection]]:
+    """
+    given a list of clusters (which is the indices of the detections in the sorted_detections list),
+    extract the detections from the sorted_detections list
+
+    Args:
+        clusters: list of clusters, each cluster is a list of indices of the detections in the `sorted_detections` list
+        sorted_detections: list of SORTED detections
+
+    Returns:
+        list of clusters, each cluster is a list of detections
+    """
+    return [[sorted_detections[i] for i in cluster] for cluster in clusters]
+
+
+solver = GLPKSolver()
+aff_np = np.asarray(affinity_matrix).astype(np.float64)
+clusters, sol_matrix = solver.solve(aff_np)
+display(clusters)
+display(sol_matrix)
+
+# %%
+WIDTH = 2560
+HEIGHT = 1440
+
+clusters_detections = clusters_to_detections(clusters, sorted_detections)
+im = np.zeros((HEIGHT, WIDTH, 3), dtype=np.uint8)
+for el in clusters_detections[0]:
+    im = visualize_whole_body(np.asarray(el.keypoints), im)
+
+p = plt.imshow(im)
+display(p)
+
+# %%
+im_prime = np.zeros((HEIGHT, WIDTH, 3), dtype=np.uint8)
+for el in clusters_detections[1]:
+    im_prime = visualize_whole_body(np.asarray(el.keypoints), im_prime)
+
+p_prime = plt.imshow(im_prime)
+display(p_prime)
+
+
+# %%
+@jaxtyped(typechecker=beartype)
+def triangulate_one_point_from_multiple_views_linear(
+    proj_matrices: Float[Array, "N 3 4"],
+    points: Num[Array, "N 2"],
+    confidences: Optional[Float[Array, "N"]] = None,
+) -> Float[Array, "3"]:
+    """
+    Args:
+        proj_matrices: 形状为(N, 3, 4)的投影矩阵序列
+        points: 形状为(N, 2)的点坐标序列
+        confidences: 形状为(N,)的置信度序列，范围[0.0, 1.0]
+
+    Returns:
+        point_3d: 形状为(3,)的三角测量得到的3D点
+    """
+    assert len(proj_matrices) == len(points)
+
+    N = len(proj_matrices)
+    confi: Float[Array, "N"]
+    if confidences is None:
+        confi = jnp.ones(N, dtype=np.float32)
+    else:
+        # Use square root of confidences for weighting - more balanced approach
+        confi = jnp.sqrt(jnp.clip(confidences, 0, 1))
+
+    A = jnp.zeros((N * 2, 4), dtype=np.float32)
+    for i in range(N):
+        x, y = points[i]
+        A = A.at[2 * i].set(proj_matrices[i, 2] * x - proj_matrices[i, 0])
+        A = A.at[2 * i + 1].set(proj_matrices[i, 2] * y - proj_matrices[i, 1])
+        A = A.at[2 * i].mul(confi[i])
+        A = A.at[2 * i + 1].mul(confi[i])
+
+    # https://docs.jax.dev/en/latest/_autosummary/jax.numpy.linalg.svd.html
+    _, _, vh = jnp.linalg.svd(A, full_matrices=False)
+    point_3d_homo = vh[-1]  # shape (4,)
+
+    # replace the Python `if` with a jnp.where
+    point_3d_homo = jnp.where(
+        point_3d_homo[3] < 0,  # predicate (scalar bool tracer)
+        -point_3d_homo,  # if True
+        point_3d_homo,  # if False
+    )
+
+    point_3d = point_3d_homo[:3] / point_3d_homo[3]
+    return point_3d
+
+
+@jaxtyped(typechecker=beartype)
+def triangulate_points_from_multiple_views_linear(
+    proj_matrices: Float[Array, "N 3 4"],
+    points: Num[Array, "N P 2"],
+    confidences: Optional[Float[Array, "N P"]] = None,
+) -> Float[Array, "P 3"]:
+    """
+    Batch-triangulate P points observed by N cameras, linearly via SVD.
+
+    Args:
+        proj_matrices: (N, 3, 4) projection matrices
+        points:       (N, P, 2) image-coordinates per view
+        confidences:  (N, P, 1) optional per-view confidences in [0,1]
+
+    Returns:
+        (P, 3) 3D point for each of the P tracks
+    """
+    N, P, _ = points.shape
+    assert proj_matrices.shape[0] == N
+    if confidences is None:
+        conf = jnp.ones((N, P), dtype=jnp.float32)
+    else:
+        conf = jnp.sqrt(jnp.clip(confidences, 0.0, 1.0))
+
+    # vectorize your one‐point routine over P
+    vmap_triangulate = jax.vmap(
+        triangulate_one_point_from_multiple_views_linear,
+        in_axes=(None, 1, 1),  # proj_matrices static, map over points[:,p,:], conf[:,p]
+        out_axes=0,
+    )
+
+    # returns (P, 3)
+    return vmap_triangulate(proj_matrices, points, conf)
+
+
+# %%
+from dataclasses import dataclass
+from copy import copy as shallow_copy, deepcopy as deep_copy
+
+
+@jaxtyped(typechecker=beartype)
+@dataclass(frozen=True)
+class Tracking:
+    id: int
+    keypoints: Float[Array, "J 3"]
+    last_active_timestamp: datetime
+
+    def __repr__(self) -> str:
+        return f"Tracking({self.id}, {self.last_active_timestamp})"
+
+
+@jaxtyped(typechecker=beartype)
+def triangle_from_cluster(
+    cluster: list[Detection],
+) -> tuple[Float[Array, "N 3"], datetime]:
+    proj_matrices = jnp.array([el.camera.params.projection_matrix for el in cluster])
+    points = jnp.array([el.keypoints_undistorted for el in cluster])
+    confidences = jnp.array([el.confidences for el in cluster])
+    latest_timestamp = max(el.timestamp for el in cluster)
+    return (
+        triangulate_points_from_multiple_views_linear(
+            proj_matrices, points, confidences=confidences
+        ),
+        latest_timestamp,
+    )
+
+
+# res  = {
+#     "a": triangle_from_cluster(clusters_detections[0]).tolist(),
+#     "b": triangle_from_cluster(clusters_detections[1]).tolist(),
+# }
+# with open("samples/res.json", "wb") as f:
+#     f.write(orjson.dumps(res))
+
+
+class GlobalTrackingState:
+    _last_id: int
+    _trackings: dict[int, Tracking]
+
+    def __init__(self):
+        self._last_id = 0
+        self._trackings = {}
+
+    def __repr__(self) -> str:
+        return (
+            f"GlobalTrackingState(last_id={self._last_id}, trackings={self._trackings})"
+        )
+
+    @property
+    def trackings(self) -> dict[int, Tracking]:
+        return shallow_copy(self._trackings)
+
+    def add_tracking(self, cluster: list[Detection]) -> Tracking:
+        kps_3d, latest_timestamp = triangle_from_cluster(cluster)
+        next_id = self._last_id + 1
+        tracking = Tracking(
+            id=next_id, keypoints=kps_3d, last_active_timestamp=latest_timestamp
+        )
+        self._trackings[next_id] = tracking
+        self._last_id = next_id
+        return tracking
+
+
+global_tracking_state = GlobalTrackingState()
+for cluster in clusters_detections:
+    global_tracking_state.add_tracking(cluster)
+display(global_tracking_state)
+
+# %%
+next_group = next(sync_gen)
+display(next_group)
+
+# %%
+from app.camera import classify_by_camera
+
+# let's do cross-view association
+trackings = sorted(global_tracking_state.trackings.values(), key=lambda x: x.id)
+detections = shallow_copy(next_group)
+# cross-view association matrix with shape (T, D), where T is the number of trackings, D is the number of detections
+affinity = np.zeros((len(trackings), len(detections)))
+detection_by_camera = classify_by_camera(detections)
+for i, tracking in enumerate(trackings):
+    for c, detections in detection_by_camera.items():
+        camera = next(iter(detections)).camera
+        # pixel space, unnormalized
+        tracking_2d_projection = camera.project(tracking.keypoints)
diff --git a/pyproject.toml b/pyproject.toml
index ce4ed63..ab4e5a4 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -11,6 +11,7 @@ dependencies = [
     "cvxopt>=1.3.2",
     "jax[cuda12]>=0.5.1",
     "jaxtyping>=0.2.38",
+    "jupytext>=1.17.0",
     "matplotlib>=3.10.1",
     "opencv-python-headless>=4.11.0.86",
     "orjson>=3.10.15",
diff --git a/uv.lock b/uv.lock
index c6744d0..25b98f8 100644
--- a/uv.lock
+++ b/uv.lock
@@ -450,6 +450,7 @@ dependencies = [
     { name = "cvxopt" },
     { name = "jax", extra = ["cuda12"] },
     { name = "jaxtyping" },
+    { name = "jupytext" },
     { name = "matplotlib" },
     { name = "opencv-python-headless" },
     { name = "orjson" },
@@ -475,6 +476,7 @@ requires-dist = [
     { name = "cvxopt", specifier = ">=1.3.2" },
     { name = "jax", extras = ["cuda12"], specifier = ">=0.5.1" },
     { name = "jaxtyping", specifier = ">=0.2.38" },
+    { name = "jupytext", specifier = ">=1.17.0" },
     { name = "matplotlib", specifier = ">=3.10.1" },
     { name = "opencv-python-headless", specifier = ">=4.11.0.86" },
     { name = "orjson", specifier = ">=3.10.15" },
@@ -1245,6 +1247,23 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/a9/93/858e87edc634d628e5d752ba944c2833133a28fa87bb093e6832ced36a3e/jupyterlab_widgets-3.0.13-py3-none-any.whl", hash = "sha256:e3cda2c233ce144192f1e29914ad522b2f4c40e77214b0cc97377ca3d323db54", size = 214392 },
 ]
 
+[[package]]
+name = "jupytext"
+version = "1.17.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "markdown-it-py" },
+    { name = "mdit-py-plugins" },
+    { name = "nbformat" },
+    { name = "packaging" },
+    { name = "pyyaml" },
+    { name = "tomli", marker = "python_full_version < '3.11'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/17/b5/b04008ae2f3b37f0a955d9232c729589bcf40819732cd08a54bce08feb83/jupytext-1.17.0.tar.gz", hash = "sha256:c74adf6d205a778f481189e986b6066af79bdeb69099f4138d933cc15c398fb6", size = 3746613 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/dc/46/c2fb92e01eb0423bae7fe91c3bf2ca994069f299a6455919f4a9a12960ed/jupytext-1.17.0-py3-none-any.whl", hash = "sha256:d75b7cd198b3640a12f9cdf4d610bb80c9f27a8c3318b00372f90d21466d40e1", size = 164106 },
+]
+
 [[package]]
 name = "kiwisolver"
 version = "1.4.8"
@@ -1332,6 +1351,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/3a/1d/50ad811d1c5dae091e4cf046beba925bcae0a610e79ae4c538f996f63ed5/kiwisolver-1.4.8-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:65ea09a5a3faadd59c2ce96dc7bf0f364986a315949dc6374f04396b0d60e09b", size = 71762 },
 ]
 
+[[package]]
+name = "markdown-it-py"
+version = "3.0.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "mdurl" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/38/71/3b932df36c1a044d397a1f92d1cf91ee0a503d91e470cbd670aa66b07ed0/markdown-it-py-3.0.0.tar.gz", hash = "sha256:e3f60a94fa066dc52ec76661e37c851cb232d92f9886b15cb560aaada2df8feb", size = 74596 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/42/d7/1ec15b46af6af88f19b8e5ffea08fa375d433c998b8a7639e76935c14f1f/markdown_it_py-3.0.0-py3-none-any.whl", hash = "sha256:355216845c60bd96232cd8d8c40e8f9765cc86f46880e43a8fd22dc1a1a8cab1", size = 87528 },
+]
+
 [[package]]
 name = "markupsafe"
 version = "3.0.2"
@@ -1454,6 +1485,27 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/8f/8e/9ad090d3553c280a8060fbf6e24dc1c0c29704ee7d1c372f0c174aa59285/matplotlib_inline-0.1.7-py3-none-any.whl", hash = "sha256:df192d39a4ff8f21b1895d72e6a13f5fcc5099f00fa84384e0ea28c2cc0653ca", size = 9899 },
 ]
 
+[[package]]
+name = "mdit-py-plugins"
+version = "0.4.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "markdown-it-py" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/19/03/a2ecab526543b152300717cf232bb4bb8605b6edb946c845016fa9c9c9fd/mdit_py_plugins-0.4.2.tar.gz", hash = "sha256:5f2cd1fdb606ddf152d37ec30e46101a60512bc0e5fa1a7002c36647b09e26b5", size = 43542 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a7/f7/7782a043553ee469c1ff49cfa1cdace2d6bf99a1f333cf38676b3ddf30da/mdit_py_plugins-0.4.2-py3-none-any.whl", hash = "sha256:0c673c3f889399a33b95e88d2f0d111b4447bdfea7f237dab2d488f459835636", size = 55316 },
+]
+
+[[package]]
+name = "mdurl"
+version = "0.1.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/d6/54/cfe61301667036ec958cb99bd3efefba235e65cdeb9c84d24a8293ba1d90/mdurl-0.1.2.tar.gz", hash = "sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba", size = 8729 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/b3/38/89ba8ad64ae25be8de66a6d463314cf1eb366222074cfda9ee839c56a4b4/mdurl-0.1.2-py3-none-any.whl", hash = "sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8", size = 9979 },
+]
+
 [[package]]
 name = "mistune"
 version = "3.1.2"