{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# POPSICLE — copick demos\n",
    "\n",
    "Minimal load-and-visualize examples for each released POPSICLE\n",
    "sub-benchmark. Every demo:\n",
    "\n",
    "1. Opens the Croissant manifest from this Hugging Face repo.\n",
    "2. Loops over each split and prints per-run annotation summaries.\n",
    "3. Streams one tomogram from the CryoET Data Portal and overlays\n",
    "   that run's picks (Phantom) or segmentation masks (Bacterial,\n",
    "   Yeast) on a midplane slice.\n",
    "\n",
    "All data is read on demand from `s3://cryoet-data-portal-public/`\n",
    "— nothing large lands on disk. The `metadata.json` itself is fetched\n",
    "from this dataset repo on Hugging Face.\n"
   ],
   "id": "47b82559f640e147"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "Install the runtime dependencies. `copick` provides the\n",
    "`from_croissant` reader and the `run.picks`, `run.segmentations`,\n",
    "and `tomo.numpy()` helpers used below; `s3fs` lets `copick`\n",
    "stream from the portal's public bucket anonymously.\n"
   ],
   "id": "366127a2865d9709"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": "%pip install --quiet \"copick==1.24.1\" s3fs matplotlib\n",
   "id": "676fa018721ffe42",
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Phantom — multi-class macromolecular localization\n",
    "\n",
    "Six particle classes (apo-ferritin, beta-amylase, beta-galactosidase,\n",
    "ribosome, thyroglobulin, virus-like-particle) on 492 lysate\n",
    "tomograms split into `train` / `val` / `test`. Picks are restricted\n",
    "to the original ground-truth author (Ariana Peck).\n"
   ],
   "id": "3f59243cd291b5fd"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "import copick\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "PHANTOM_URL = (\n",
    "    \"https://huggingface.co/datasets/biohub/popsicle/resolve/main/\"\n",
    "    \"phantom/Croissant/metadata.json\"\n",
    ")\n",
    "\n",
    "root = copick.from_croissant(\n",
    "    PHANTOM_URL,\n",
    "    overlay_root=\"/tmp/popsicle-overlay\",\n",
    "    static_fs_args={\"anon\": True},          # public portal bucket\n",
    ")\n",
    "\n",
    "for split, run_names in root.splits.items():\n",
    "    print(f\"{split}: {len(run_names)} runs\")\n",
    "    for run in root.get_runs_in_split(split)[:3]:  # head sample\n",
    "        for pick_set in run.picks:\n",
    "            print(\n",
    "                f\"  {run.name}  {pick_set.pickable_object_name}: \"\n",
    "                f\"{len(pick_set.points)} pts\"\n",
    "            )\n"
   ],
   "id": "2adfade6d8aa6d6e",
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Visualize: midplane slice of one Phantom tomogram with picks overlaid.\n",
    "run = root.get_runs_in_split(\"train\")[0]\n",
    "vs = run.voxel_spacings[0]               # 10 Å for Phantom\n",
    "tomo = vs.tomograms[0]                   # any tomo_type at this voxel spacing\n",
    "arr = tomo.numpy()                       # (Z, Y, X) — streams from portal S3\n",
    "z_mid = arr.shape[0] // 2\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "ax.imshow(arr[z_mid], cmap=\"gray\")\n",
    "for pick_set in run.picks:\n",
    "    # CopickPoint locations are in physical units (Å); convert to voxel indices.\n",
    "    pts = np.array(\n",
    "        [(p.location.x, p.location.y, p.location.z) for p in pick_set.points]\n",
    "    ) / vs.voxel_size\n",
    "    near = pts[np.abs(pts[:, 2] - z_mid) < 5]   # within ±5 voxels of the slice\n",
    "    ax.scatter(near[:, 0], near[:, 1], s=24, label=pick_set.pickable_object_name)\n",
    "ax.legend(loc=\"upper right\", fontsize=8)\n",
    "ax.set_title(f\"{run.name}  z={z_mid}\")\n",
    "plt.show()\n"
   ],
   "id": "676c7c5a13c30dea",
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bacterial — multi-class compartment segmentation\n",
    "\n",
    "Five compartment classes (cytosole, flagellum, inclusion,\n",
    "intermembrane-space, membrane) on 80 cellular tomograms across 8\n",
    "bacterial genera, split 68/12 train/test. Annotations sourced from\n",
    "deposition CZCDP-10350 only.\n"
   ],
   "id": "cc6c6d4303b5d9ec"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "import copick\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "BACTERIAL_URL = (\n",
    "    \"https://huggingface.co/datasets/biohub/popsicle/resolve/main/\"\n",
    "    \"bacterial/Croissant/metadata.json\"\n",
    ")\n",
    "\n",
    "root = copick.from_croissant(\n",
    "    BACTERIAL_URL,\n",
    "    overlay_root=\"/tmp/popsicle-overlay\",\n",
    "    static_fs_args={\"anon\": True},          # public portal bucket\n",
    ")\n",
    "\n",
    "for split, run_names in root.splits.items():\n",
    "    print(f\"{split}: {len(run_names)} runs\")\n",
    "    for run in root.get_runs_in_split(split)[:3]:\n",
    "        seg_names = sorted({s.name for s in run.segmentations})\n",
    "        print(f\"  {run.name}: {seg_names}\")\n"
   ],
   "id": "51310d426f43d0d8",
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Visualize: midplane slice of one bacterial tomogram with seg overlays.\n",
    "# Match the tomogram to the segmentation voxel size so slices align.\n",
    "run = root.get_runs_in_split(\"train\")[0]\n",
    "seg_vs = run.segmentations[0].voxel_size\n",
    "vs = run.get_voxel_spacing(seg_vs)\n",
    "tomo = vs.tomograms[0]\n",
    "arr = tomo.numpy()\n",
    "z_mid = arr.shape[0] // 2\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "ax.imshow(arr[z_mid], cmap=\"gray\")\n",
    "cmap = plt.get_cmap(\"tab10\")\n",
    "for i, seg in enumerate(run.segmentations):\n",
    "    mask = seg.numpy()[z_mid]            # (Y, X) — same shape as the tomo slice\n",
    "    layer = np.ma.masked_where(mask == 0, np.full_like(mask, i, dtype=np.int8))\n",
    "    ax.imshow(layer, cmap=cmap, vmin=0, vmax=10, alpha=0.4, interpolation=\"none\")\n",
    "    ax.scatter([], [], color=cmap(i), label=seg.name)\n",
    "ax.legend(loc=\"upper right\", fontsize=8)\n",
    "ax.set_title(f\"{run.name}  z={z_mid}\")\n",
    "plt.show()\n"
   ],
   "id": "28a65dbcedd5d084",
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "## Yeast — multi-class organelle segmentation\n\nSix organelle classes (cytoplasm, nucleus, nuclear-envelope,\nvesicle, membrane-tubule, mitochondrion) on 20 *S. pombe* tomograms\nsplit 16/4 train/test. Low-data, class-imbalanced eukaryotic\ncounterpart to the bacterial benchmark.",
   "id": "aaf332964b36fdb3"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "import copick\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "YEAST_URL = (\n",
    "    \"https://huggingface.co/datasets/biohub/popsicle/resolve/main/\"\n",
    "    \"yeast/Croissant/metadata.json\"\n",
    ")\n",
    "\n",
    "root = copick.from_croissant(\n",
    "    YEAST_URL,\n",
    "    overlay_root=\"/tmp/popsicle-overlay\",\n",
    "    static_fs_args={\"anon\": True},          # public portal bucket\n",
    ")\n",
    "\n",
    "for split, run_names in root.splits.items():\n",
    "    print(f\"{split}: {len(run_names)} runs\")\n",
    "    for run in root.get_runs_in_split(split)[:3]:\n",
    "        seg_names = sorted({s.name for s in run.segmentations})\n",
    "        print(f\"  {run.name}: {seg_names}\")\n"
   ],
   "id": "ae83c4a6d735fff9",
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Visualize: midplane slice of one yeast tomogram with organelle masks overlaid.\n",
    "run = root.get_runs_in_split(\"train\")[0]\n",
    "vs = run.voxel_spacings[0]\n",
    "tomo = vs.tomograms[0]\n",
    "arr = tomo.numpy()\n",
    "z_mid = arr.shape[0] // 2\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "ax.imshow(arr[z_mid], cmap=\"gray\")\n",
    "cmap = plt.get_cmap(\"tab10\")\n",
    "for i, seg in enumerate(run.segmentations):\n",
    "    mask = seg.numpy()[z_mid]\n",
    "    layer = np.ma.masked_where(mask == 0, np.full_like(mask, i, dtype=np.int8))\n",
    "    ax.imshow(layer, cmap=cmap, vmin=0, vmax=10, alpha=0.4, interpolation=\"none\")\n",
    "    ax.scatter([], [], color=cmap(i), label=seg.name)\n",
    "ax.legend(loc=\"upper right\", fontsize=8)\n",
    "ax.set_title(f\"{run.name}  z={z_mid}\")\n",
    "plt.show()\n"
   ],
   "id": "e768c0631ca7b5ca",
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MotorBench — single-class flagellar-motor localization\n",
    "\n",
    "One class (`flagellar-motor`) on ~2,400 cellular tomograms split\n",
    "into `train` (1,559 runs / picks from the BYU Kaggle community,\n",
    "deposition CZCDP-10332) and `test` (843 *V. cholerae* runs / 275\n",
    "picks from the held-out evaluation deposition CZCDP-10347).\n",
    "Picks are scoped per-deposition so the train/test boundary from\n",
    "the original challenge is preserved.\n"
   ],
   "id": "31296bb7983a5a50"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "import copick\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "MOTORBENCH_URL = (\n",
    "    \"https://huggingface.co/datasets/biohub/popsicle/resolve/main/\"\n",
    "    \"motorbench/Croissant/metadata.json\"\n",
    ")\n",
    "\n",
    "root = copick.from_croissant(\n",
    "    MOTORBENCH_URL,\n",
    "    overlay_root=\"/tmp/popsicle-overlay\",\n",
    "    static_fs_args={\"anon\": True},          # public portal bucket\n",
    ")\n",
    "\n",
    "for split, run_names in root.splits.items():\n",
    "    print(f\"{split}: {len(run_names)} runs\")\n",
    "    for run in root.get_runs_in_split(split)[:3]:\n",
    "        for pick_set in run.picks:\n",
    "            print(\n",
    "                f\"  {run.name}  {pick_set.pickable_object_name}: \"\n",
    "                f\"{len(pick_set.points)} pts\"\n",
    "            )\n"
   ],
   "id": "4aef5638574e4020",
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Visualize: midplane slice of one MotorBench tomogram with motor picks overlaid.\n",
    "run = root.get_run(\"33914\")\n",
    "vs = run.voxel_spacings[0]\n",
    "tomo = vs.tomograms[0]\n",
    "arr = tomo.numpy()                       # (Z, Y, X) — streams from portal S3\n",
    "z_mid = arr.shape[0] // 2\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "ax.imshow(arr[z_mid], cmap=\"gray\")\n",
    "for pick_set in run.picks:\n",
    "    pts = np.array(\n",
    "        [(p.location.x, p.location.y, p.location.z) for p in pick_set.points]\n",
    "    ) / vs.voxel_size\n",
    "    near = pts[np.abs(pts[:, 2] - z_mid) < 20]   # motors are large; ±20 vox\n",
    "    ax.scatter(\n",
    "        near[:, 0], near[:, 1],\n",
    "        s=100, facecolors=\"none\", edgecolors=\"yellow\", linewidths=2,\n",
    "        label=pick_set.pickable_object_name,\n",
    "    )\n",
    "ax.legend(loc=\"upper right\", fontsize=8)\n",
    "ax.set_title(f\"{run.name}  z={z_mid}\")\n",
    "plt.show()\n"
   ],
   "id": "530a7ad0849a3323",
   "outputs": [],
   "execution_count": null
  }
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