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notebooks/.ipynb_checkpoints/celldega_pre-process-checkpoint.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8ce6b74e-54af-48ba-abf2-7ce2caa573bf",
+   "metadata": {},
+   "source": [
+    "# Xenium Pre-process"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b81ab32e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "%env ANYWIDGET_HMR=1\n",
+    "\n",
+    "import celldega as dega"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b47f611d",
+   "metadata": {},
+   "source": [
+    "## Xenium pre processing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "52602e61-45e7-45a2-83a0-9a1b364d6619",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample = 'Xenium_V1_humanLung_Cancer_FFPE_outs'\n",
+    "data_dir = f'../data/instrument_data'\n",
+    "path_landscape_files=f'../data/processed_data/DegaFiles/{sample}'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "13350680",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tile_size=250\n",
+    "\n",
+    "dega.pre.main(\n",
+    "    sample=sample,\n",
+    "    data_root_dir=data_dir,\n",
+    "    tile_size=tile_size,\n",
+    "    path_landscape_files=path_landscape_files,\n",
+    "    use_int_index=True,\n",
+    "    image_tile_layer=\"all\"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2fd7f196-c6cc-4321-8481-6249ed0b96a7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.12.1"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

notebooks/.ipynb_checkpoints/celldega_viz-checkpoint.ipynb

@@ -0,0 +1,101 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8ce6b74e-54af-48ba-abf2-7ce2caa573bf",
+   "metadata": {},
+   "source": [
+    "# Xenium Viz"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "56843452-1a1c-4ae7-95ad-0a7bd909bc90",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "%env ANYWIDGET_HMR=1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cec165b0-bd8f-40d5-b130-22486970aca8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import celldega as dega"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b66e327c-8c1e-41b7-9fbc-48d3f5c06fe3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample = \"WTA_Preview_FFPE_Cervical_Cancer_outs\"\n",
+    "path_dega_files = f\"../data/processed_data/DegaFiles/{sample}\"\n",
+    "\n",
+    "landscape_ist = dega.viz.Landscape(\n",
+    "    technology=\"Xenium\",\n",
+    "    base_url=f\"http://localhost:{dega.viz.get_local_server()}/{path_dega_files}\",\n",
+    ")\n",
+    "\n",
+    "landscape_ist"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c8cd7207-7604-49bb-9172-d92af31ad74b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.12.1"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

notebooks/.ipynb_checkpoints/tissuumaps_pre-process-checkpoint.ipynb

@@ -0,0 +1,1274 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "07f7dbb1-63c6-40e4-b0d9-6ec104aa0adc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !pip install zarr\n",
+    "\n",
+    "import os\n",
+    "import json\n",
+    "from pathlib import Path\n",
+    "import glob\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import scanpy as sc\n",
+    "import pyvips\n",
+    "import zarr\n",
+    "import geopandas as gpd\n",
+    "from shapely.geometry import Polygon\n",
+    "from scipy.sparse import csc_matrix\n",
+    "\n",
+    "import tissuumaps.jupyter as tj\n",
+    "from tissuumaps import read_h5ad\n",
+    "\n",
+    "\n",
+    "# ----------------------------\n",
+    "# Paths\n",
+    "# ----------------------------\n",
+    "# sample = \"WTA_Preview_FFPE_Cervical_Cancer_outs\"\n",
+    "# sample = \"Xenium_Prime_Human_Lymph_Node_Reactive_FFPE_outs\"\n",
+    "# sample = \"Xenium_Prime_Ovarian_Cancer_FFPE_XRrun_outs\"\n",
+    "# sample = \"Xenium_V1_humanLung_Cancer_FFPE_outs\"\n",
+    "\n",
+    "xenium_dir = os.path.abspath(f\"../data/instrument_data/{sample}\")\n",
+    "basedir = os.path.abspath(f\"../data/processed_data/tissuumaps_h5ad/{sample}\")\n",
+    "os.makedirs(basedir, exist_ok=True)\n",
+    "\n",
+    "out_h5ad_name = f\"{sample}_tmap.h5ad\"\n",
+    "out_h5ad = os.path.join(basedir, out_h5ad_name)\n",
+    "\n",
+    "project_path = os.path.join(basedir, \"_project_h5ad.tmap\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aec44404-259a-4042-9230-5a244ec9df0e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ----------------------------\n",
+    "# Transform helpers\n",
+    "# ----------------------------\n",
+    "def write_xenium_transform(data_dir, path_landscape_files):\n",
+    "    cells_zarr_path = Path(data_dir) / \"cells.zarr.zip\"\n",
+    "    if not cells_zarr_path.exists():\n",
+    "        raise FileNotFoundError(f\"Missing: {cells_zarr_path}\")\n",
+    "\n",
+    "    store = zarr.ZipStore(str(cells_zarr_path), mode=\"r\")\n",
+    "    root = zarr.group(store=store)\n",
+    "\n",
+    "    transform = root[\"masks\"][\"homogeneous_transform\"][:]\n",
+    "\n",
+    "    pd.DataFrame(transform[:3, :3]).to_csv(\n",
+    "        Path(path_landscape_files) / \"micron_to_image_transform.csv\",\n",
+    "        sep=\" \",\n",
+    "        header=False,\n",
+    "        index=False,\n",
+    "    )\n",
+    "\n",
+    "    return transform\n",
+    "\n",
+    "\n",
+    "def apply_homogeneous_transform_xy(x, y, transform):\n",
+    "    transform = np.asarray(transform)[:3, :3]\n",
+    "\n",
+    "    xy1 = np.vstack([\n",
+    "        np.asarray(x, dtype=float),\n",
+    "        np.asarray(y, dtype=float),\n",
+    "        np.ones(len(x)),\n",
+    "    ])\n",
+    "\n",
+    "    out = transform @ xy1\n",
+    "    return out[0] / out[2], out[1] / out[2]\n",
+    "    \n",
+    "# ----------------------------\n",
+    "# 1. Transform\n",
+    "# ----------------------------\n",
+    "transform = write_xenium_transform(xenium_dir, basedir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "16f90ba5-914a-4a3d-b3bc-9960481cef1f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ----------------------------\n",
+    "# Polygon helper\n",
+    "# ----------------------------\n",
+    "def xenium_boundaries_to_geojson(\n",
+    "    boundary_path,\n",
+    "    out_geojson,\n",
+    "    transform,\n",
+    "    id_col=\"cell_id\",\n",
+    "    max_polygons=None,\n",
+    "):\n",
+    "    if not os.path.exists(boundary_path):\n",
+    "        return None\n",
+    "\n",
+    "    if boundary_path.endswith(\".parquet\"):\n",
+    "        df = pd.read_parquet(boundary_path)\n",
+    "    else:\n",
+    "        df = pd.read_csv(boundary_path)\n",
+    "\n",
+    "    x_col = \"vertex_x\" if \"vertex_x\" in df.columns else \"x\"\n",
+    "    y_col = \"vertex_y\" if \"vertex_y\" in df.columns else \"y\"\n",
+    "\n",
+    "    x_new, y_new = apply_homogeneous_transform_xy(\n",
+    "        df[x_col].values,\n",
+    "        df[y_col].values,\n",
+    "        transform,\n",
+    "    )\n",
+    "\n",
+    "    df = df.copy()\n",
+    "    df[\"x_img\"] = x_new\n",
+    "    df[\"y_img\"] = y_new\n",
+    "\n",
+    "    ids = []\n",
+    "    geoms = []\n",
+    "\n",
+    "    for i, (cid, sub) in enumerate(df.groupby(id_col, sort=False)):\n",
+    "        if max_polygons is not None and i >= max_polygons:\n",
+    "            break\n",
+    "\n",
+    "        if len(sub) < 3:\n",
+    "            continue\n",
+    "\n",
+    "        coords = list(zip(sub[\"x_img\"].astype(float), sub[\"y_img\"].astype(float)))\n",
+    "\n",
+    "        if coords[0] != coords[-1]:\n",
+    "            coords.append(coords[0])\n",
+    "\n",
+    "        poly = Polygon(coords)\n",
+    "\n",
+    "        if poly.is_valid and not poly.is_empty:\n",
+    "            ids.append(cid)\n",
+    "            geoms.append(poly)\n",
+    "\n",
+    "    gdf = gpd.GeoDataFrame({id_col: ids}, geometry=geoms, crs=None)\n",
+    "    gdf.to_file(out_geojson, driver=\"GeoJSON\")\n",
+    "\n",
+    "    return os.path.basename(out_geojson)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a97826c2-0864-4a35-a7d0-5a64c117a1e2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def add_xenium_default_clustering(adata, xenium_dir):\n",
+    "    import os\n",
+    "    import glob\n",
+    "    import pandas as pd\n",
+    "\n",
+    "    candidates = sorted(glob.glob(\n",
+    "        os.path.join(xenium_dir, \"analysis\", \"clustering\", \"**\", \"clusters.csv\"),\n",
+    "        recursive=True,\n",
+    "    ))\n",
+    "\n",
+    "    if not candidates:\n",
+    "        print(\"No clusters.csv found.\")\n",
+    "        return adata\n",
+    "\n",
+    "    preferred = None\n",
+    "    for p in candidates:\n",
+    "        if \"gene_expression_graphclust\" in p:\n",
+    "            preferred = p\n",
+    "            break\n",
+    "\n",
+    "    if preferred is None:\n",
+    "        preferred = candidates[0]\n",
+    "\n",
+    "    print(\"Using clustering file:\", preferred)\n",
+    "\n",
+    "    clusters = pd.read_csv(preferred)\n",
+    "\n",
+    "    id_col = \"Barcode\" if \"Barcode\" in clusters.columns else clusters.columns[0]\n",
+    "    cluster_col = \"Cluster\" if \"Cluster\" in clusters.columns else clusters.columns[1]\n",
+    "\n",
+    "    clusters[id_col] = clusters[id_col].astype(str)\n",
+    "    clusters[cluster_col] = clusters[cluster_col].astype(str)\n",
+    "\n",
+    "    s = clusters.set_index(id_col)[cluster_col]\n",
+    "\n",
+    "    # IMPORTANT: reindex allows cells missing from clustering file\n",
+    "    adata.obs[\"xenium_default_cluster\"] = (\n",
+    "        s.reindex(adata.obs_names)\n",
+    "        .fillna(\"unclustered\")\n",
+    "        .astype(str)\n",
+    "        .astype(\"category\")\n",
+    "    )\n",
+    "\n",
+    "    print(adata.obs[\"xenium_default_cluster\"].value_counts())\n",
+    "\n",
+    "    return adata\n",
+    "\n",
+    "\n",
+    "# ----------------------------\n",
+    "# 2. Build h5ad in image pixel space\n",
+    "# ----------------------------\n",
+    "adata = sc.read_10x_h5(os.path.join(xenium_dir, \"cell_feature_matrix.h5\"))\n",
+    "adata.var_names_make_unique()\n",
+    "\n",
+    "cells = pd.read_csv(os.path.join(xenium_dir, \"cells.csv.gz\"), index_col=0)\n",
+    "cells = cells.loc[adata.obs_names]\n",
+    "\n",
+    "x_img, y_img = apply_homogeneous_transform_xy(\n",
+    "    cells[\"x_centroid\"].values,\n",
+    "    cells[\"y_centroid\"].values,\n",
+    "    transform,\n",
+    ")\n",
+    "\n",
+    "adata.obs[\"x\"] = x_img.astype(float)\n",
+    "adata.obs[\"y\"] = y_img.astype(float)\n",
+    "adata.obsm[\"spatial\"] = adata.obs[[\"x\", \"y\"]].to_numpy(dtype=\"float64\")\n",
+    "\n",
+    "adata.obs[\"x_centroid_um\"] = cells[\"x_centroid\"].astype(float).values\n",
+    "adata.obs[\"y_centroid_um\"] = cells[\"y_centroid\"].astype(float).values\n",
+    "\n",
+    "obs_cols = [\n",
+    "    \"transcript_counts\",\n",
+    "    \"control_probe_counts\",\n",
+    "    \"genomic_control_counts\",\n",
+    "    \"control_codeword_counts\",\n",
+    "    \"unassigned_codeword_counts\",\n",
+    "    \"deprecated_codeword_counts\",\n",
+    "    \"total_counts\",\n",
+    "    \"cell_area\",\n",
+    "    \"nucleus_area\",\n",
+    "    \"nucleus_count\",\n",
+    "]\n",
+    "\n",
+    "for col in obs_cols:\n",
+    "    if col in cells.columns:\n",
+    "        adata.obs[col] = cells[col].values\n",
+    "\n",
+    "for col in adata.obs.columns:\n",
+    "    if pd.api.types.is_object_dtype(adata.obs[col]):\n",
+    "        adata.obs[col] = adata.obs[col].astype(\"category\")\n",
+    "\n",
+    "adata = add_xenium_default_clustering(adata, xenium_dir)\n",
+    "\n",
+    "adata.X = csc_matrix(adata.X)\n",
+    "adata.uns.pop(\"tmap_obsgroups\", None)\n",
+    "adata.write_h5ad(out_h5ad)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f50d1a11-11ae-40f2-a35a-d0cd24a69935",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ----------------------------\n",
+    "# 3. Make image pyramid\n",
+    "# ----------------------------\n",
+    "def make_one_xenium_pyramid_tifffile_zarr(\n",
+    "    xenium_dir,\n",
+    "    basedir,\n",
+    "    channel=\"morphology_focus_0000.ome.tif\",\n",
+    "    plane=0,\n",
+    "    low=0,\n",
+    "    high=12000,\n",
+    "    gamma=0.5,\n",
+    "    out_name=None,\n",
+    "    rows_per_chunk=512,\n",
+    "):\n",
+    "    import os\n",
+    "    import numpy as np\n",
+    "    import tifffile\n",
+    "    import zarr\n",
+    "    import pyvips\n",
+    "\n",
+    "    src = os.path.join(xenium_dir, \"morphology_focus\", channel)\n",
+    "\n",
+    "    if out_name is None:\n",
+    "        out_name = channel.replace(\".ome.tif\", f\"_plane{plane}_pyramid.tif\")\n",
+    "\n",
+    "    tmp_name = out_name.replace(\".tif\", \"_uint8_tmp.tif\")\n",
+    "    tmp_path = os.path.join(basedir, tmp_name)\n",
+    "    out_path = os.path.join(basedir, out_name)\n",
+    "\n",
+    "    for p in [tmp_path, out_path]:\n",
+    "        if os.path.exists(p):\n",
+    "            os.remove(p)\n",
+    "\n",
+    "    with tifffile.TiffFile(src) as tf:\n",
+    "        store = tf.aszarr(series=0, level=0)\n",
+    "        z = zarr.open(store, mode=\"r\")\n",
+    "\n",
+    "        print(\"zarr shape:\", z.shape, \"dtype:\", z.dtype)\n",
+    "\n",
+    "        if len(z.shape) == 3:\n",
+    "            arr2d = z[plane]\n",
+    "        elif len(z.shape) == 2:\n",
+    "            arr2d = z\n",
+    "        else:\n",
+    "            raise ValueError(f\"Unexpected image shape: {z.shape}\")\n",
+    "\n",
+    "        height, width = arr2d.shape\n",
+    "        print(\"using plane:\", plane, \"height:\", height, \"width:\", width)\n",
+    "\n",
+    "        # Create one full-size uint8 temp TIFF, memory-mapped on disk\n",
+    "        tmp_mm = tifffile.memmap(\n",
+    "            tmp_path,\n",
+    "            shape=(height, width),\n",
+    "            dtype=\"uint8\",\n",
+    "            photometric=\"minisblack\",\n",
+    "            bigtiff=True,\n",
+    "        )\n",
+    "\n",
+    "        for y0 in range(0, height, rows_per_chunk):\n",
+    "            y1 = min(y0 + rows_per_chunk, height)\n",
+    "\n",
+    "            block = arr2d[y0:y1, :].astype(\"float32\")\n",
+    "            block = (block - low) / (high - low)\n",
+    "            block = np.clip(block, 0, 1)\n",
+    "\n",
+    "            if gamma is not None:\n",
+    "                block = block ** gamma\n",
+    "\n",
+    "            tmp_mm[y0:y1, :] = (block * 255).astype(\"uint8\")\n",
+    "\n",
+    "        tmp_mm.flush()\n",
+    "        del tmp_mm\n",
+    "        store.close()\n",
+    "\n",
+    "    img = pyvips.Image.new_from_file(tmp_path, access=\"sequential\")\n",
+    "\n",
+    "    img.tiffsave(\n",
+    "        out_path,\n",
+    "        tile=True,\n",
+    "        pyramid=True,\n",
+    "        compression=\"jpeg\",\n",
+    "        Q=90,\n",
+    "        tile_width=256,\n",
+    "        tile_height=256,\n",
+    "        bigtiff=True,\n",
+    "    )\n",
+    "\n",
+    "    os.remove(tmp_path)\n",
+    "\n",
+    "    print(\"wrote:\", out_path)\n",
+    "\n",
+    "    return {\n",
+    "        \"name\": out_name,\n",
+    "        \"tileSource\": out_name + \".dzi\",\n",
+    "        \"x\": 0,\n",
+    "        \"y\": 0,\n",
+    "        \"scale\": 1,\n",
+    "        \"rotation\": 0,\n",
+    "        \"flip\": False,\n",
+    "    }\n",
+    "\n",
+    "morph_files = sorted(glob.glob(os.path.join(xenium_dir, \"morphology_focus\", \"*.ome.tif\")))\n",
+    "morph_files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4109411c-f39c-40d8-94f1-72be456f8c4d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # ----------------------------\n",
+    "# # 3. Make image pyramid\n",
+    "# # ----------------------------\n",
+    "# def make_one_xenium_pyramid_tifffile_zarr(\n",
+    "#     xenium_dir,\n",
+    "#     basedir,\n",
+    "#     channel=\"morphology_focus_0000.ome.tif\",\n",
+    "#     plane=0,\n",
+    "#     low=0,\n",
+    "#     high=12000,\n",
+    "#     gamma=0.5,\n",
+    "#     out_name=None,\n",
+    "#     rows_per_chunk=512,\n",
+    "# ):\n",
+    "#     import os\n",
+    "#     import numpy as np\n",
+    "#     import tifffile\n",
+    "#     import zarr\n",
+    "#     import pyvips\n",
+    "\n",
+    "#     src = os.path.join(xenium_dir, channel)\n",
+    "\n",
+    "#     if out_name is None:\n",
+    "#         out_name = channel.replace(\".ome.tif\", f\"_plane{plane}_pyramid.tif\")\n",
+    "\n",
+    "#     tmp_name = out_name.replace(\".tif\", \"_uint8_tmp.tif\")\n",
+    "#     tmp_path = os.path.join(basedir, tmp_name)\n",
+    "#     out_path = os.path.join(basedir, out_name)\n",
+    "\n",
+    "#     for p in [tmp_path, out_path]:\n",
+    "#         if os.path.exists(p):\n",
+    "#             os.remove(p)\n",
+    "\n",
+    "#     with tifffile.TiffFile(src) as tf:\n",
+    "#         store = tf.aszarr(series=0, level=0)\n",
+    "#         z = zarr.open(store, mode=\"r\")\n",
+    "\n",
+    "#         print(\"zarr shape:\", z.shape, \"dtype:\", z.dtype)\n",
+    "\n",
+    "#         if len(z.shape) == 3:\n",
+    "#             arr2d = z[plane]\n",
+    "#         elif len(z.shape) == 2:\n",
+    "#             arr2d = z\n",
+    "#         else:\n",
+    "#             raise ValueError(f\"Unexpected image shape: {z.shape}\")\n",
+    "\n",
+    "#         height, width = arr2d.shape\n",
+    "#         print(\"using plane:\", plane, \"height:\", height, \"width:\", width)\n",
+    "\n",
+    "#         # Create one full-size uint8 temp TIFF, memory-mapped on disk\n",
+    "#         tmp_mm = tifffile.memmap(\n",
+    "#             tmp_path,\n",
+    "#             shape=(height, width),\n",
+    "#             dtype=\"uint8\",\n",
+    "#             photometric=\"minisblack\",\n",
+    "#             bigtiff=True,\n",
+    "#         )\n",
+    "\n",
+    "#         for y0 in range(0, height, rows_per_chunk):\n",
+    "#             y1 = min(y0 + rows_per_chunk, height)\n",
+    "\n",
+    "#             block = arr2d[y0:y1, :].astype(\"float32\")\n",
+    "#             block = (block - low) / (high - low)\n",
+    "#             block = np.clip(block, 0, 1)\n",
+    "\n",
+    "#             if gamma is not None:\n",
+    "#                 block = block ** gamma\n",
+    "\n",
+    "#             tmp_mm[y0:y1, :] = (block * 255).astype(\"uint8\")\n",
+    "\n",
+    "#         tmp_mm.flush()\n",
+    "#         del tmp_mm\n",
+    "#         store.close()\n",
+    "\n",
+    "#     img = pyvips.Image.new_from_file(tmp_path, access=\"sequential\")\n",
+    "\n",
+    "#     img.tiffsave(\n",
+    "#         out_path,\n",
+    "#         tile=True,\n",
+    "#         pyramid=True,\n",
+    "#         compression=\"jpeg\",\n",
+    "#         Q=90,\n",
+    "#         tile_width=256,\n",
+    "#         tile_height=256,\n",
+    "#         bigtiff=True,\n",
+    "#     )\n",
+    "\n",
+    "#     os.remove(tmp_path)\n",
+    "\n",
+    "#     print(\"wrote:\", out_path)\n",
+    "\n",
+    "#     return {\n",
+    "#         \"name\": out_name,\n",
+    "#         \"tileSource\": out_name + \".dzi\",\n",
+    "#         \"x\": 0,\n",
+    "#         \"y\": 0,\n",
+    "#         \"scale\": 1,\n",
+    "#         \"rotation\": 0,\n",
+    "#         \"flip\": False,\n",
+    "#     }\n",
+    "    \n",
+    "# morph_files = sorted(glob.glob(os.path.join(xenium_dir, \"morphology_focus.ome.tif\")))\n",
+    "# morph_files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "622a8749-399c-4bd4-855c-10be911d0198",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "image_layers = []\n",
+    "\n",
+    "# Use the first OME file, but extract each plane as a separate stain/channel\n",
+    "channel_file = os.path.basename(morph_files[0])\n",
+    "\n",
+    "for plane in range(4):\n",
+    "    image_layer = make_one_xenium_pyramid_tifffile_zarr(\n",
+    "        xenium_dir=xenium_dir,\n",
+    "        basedir=basedir,\n",
+    "        channel=channel_file,\n",
+    "        plane=plane,\n",
+    "        low=0,\n",
+    "        high=3000,\n",
+    "        gamma=0.4,\n",
+    "        out_name=f\"morphology_focus_plane{plane}_pyramid.tif\",\n",
+    "    )\n",
+    "\n",
+    "    image_layer[\"name\"] = f\"morphology_focus_plane{plane}\"\n",
+    "    image_layers.append(image_layer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "784a65f6-f500-4b7f-9ca6-5c9a3be4361e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ----------------------------\n",
+    "# 4. Create cell boundary GeoJSON\n",
+    "# ----------------------------\n",
+    "region_files = []\n",
+    "\n",
+    "for candidate in [\n",
+    "    os.path.join(xenium_dir, \"cell_boundaries.parquet\"),\n",
+    "    os.path.join(xenium_dir, \"cell_boundaries.csv.gz\"),\n",
+    "]:\n",
+    "    if os.path.exists(candidate):\n",
+    "        name = xenium_boundaries_to_geojson(\n",
+    "            candidate,\n",
+    "            os.path.join(basedir, \"cell_boundaries_image_space.geojson\"),\n",
+    "            transform,\n",
+    "            id_col=\"cell_id\",\n",
+    "        )\n",
+    "        region_files.append({\n",
+    "            \"path\": name,\n",
+    "            \"title\": \"Load cell boundaries\",\n",
+    "            \"comment\": \"Cell boundaries\",\n",
+    "            \"autoLoad\": False,\n",
+    "        })\n",
+    "        break"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "43159e9d-5279-4b90-a5b8-cc39aa08facd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# def xenium_transcripts_to_csv_streaming(\n",
+    "#     xenium_dir,\n",
+    "#     basedir,\n",
+    "#     transform,\n",
+    "#     out_name=\"transcripts_image_space.csv\",\n",
+    "#     min_qv=20,\n",
+    "#     max_transcripts=2_000_000,\n",
+    "#     rows_per_group=None,\n",
+    "# ):\n",
+    "#     import os\n",
+    "#     import numpy as np\n",
+    "#     import pandas as pd\n",
+    "#     import pyarrow.parquet as pq\n",
+    "\n",
+    "#     transcript_path = os.path.join(xenium_dir, \"transcripts.parquet\")\n",
+    "#     if not os.path.exists(transcript_path):\n",
+    "#         raise FileNotFoundError(transcript_path)\n",
+    "\n",
+    "#     out_path = os.path.join(basedir, out_name)\n",
+    "#     if os.path.exists(out_path):\n",
+    "#         os.remove(out_path)\n",
+    "\n",
+    "#     pf = pq.ParquetFile(transcript_path)\n",
+    "\n",
+    "#     transform3 = np.asarray(transform)[:3, :3]\n",
+    "\n",
+    "#     written = 0\n",
+    "#     wrote_header = False\n",
+    "\n",
+    "#     needed_cols = [\"x_location\", \"y_location\", \"feature_name\"]\n",
+    "#     optional_cols = [\"qv\", \"cell_id\"]\n",
+    "\n",
+    "#     # keep only columns that exist\n",
+    "#     schema_cols = set(pf.schema.names)\n",
+    "#     cols = [c for c in needed_cols + optional_cols if c in schema_cols]\n",
+    "\n",
+    "#     print(\"Transcript columns:\", cols)\n",
+    "#     print(\"Row groups:\", pf.num_row_groups)\n",
+    "\n",
+    "#     for rg in range(pf.num_row_groups):\n",
+    "#         if max_transcripts is not None and written >= max_transcripts:\n",
+    "#             break\n",
+    "\n",
+    "#         table = pf.read_row_group(rg, columns=cols)\n",
+    "#         tx = table.to_pandas()\n",
+    "\n",
+    "#         if \"qv\" in tx.columns:\n",
+    "#             tx = tx[tx[\"qv\"] >= min_qv]\n",
+    "\n",
+    "#         if tx.empty:\n",
+    "#             continue\n",
+    "\n",
+    "#         if max_transcripts is not None:\n",
+    "#             remaining = max_transcripts - written\n",
+    "#             if len(tx) > remaining:\n",
+    "#                 tx = tx.sample(remaining, random_state=rg)\n",
+    "\n",
+    "#         x = tx[\"x_location\"].to_numpy(dtype=float)\n",
+    "#         y = tx[\"y_location\"].to_numpy(dtype=float)\n",
+    "\n",
+    "#         xy1 = np.vstack([x, y, np.ones(len(x))])\n",
+    "#         out = transform3 @ xy1\n",
+    "\n",
+    "#         out_df = pd.DataFrame({\n",
+    "#             \"x\": out[0] / out[2],\n",
+    "#             \"y\": out[1] / out[2],\n",
+    "#             \"gene\": tx[\"feature_name\"].astype(str).to_numpy(),\n",
+    "#         })\n",
+    "\n",
+    "#         if \"qv\" in tx.columns:\n",
+    "#             out_df[\"qv\"] = tx[\"qv\"].to_numpy()\n",
+    "\n",
+    "#         if \"cell_id\" in tx.columns:\n",
+    "#             out_df[\"cell_id\"] = tx[\"cell_id\"].astype(str).to_numpy()\n",
+    "\n",
+    "#         out_df.to_csv(\n",
+    "#             out_path,\n",
+    "#             mode=\"a\",\n",
+    "#             header=not wrote_header,\n",
+    "#             index=False,\n",
+    "#         )\n",
+    "\n",
+    "#         wrote_header = True\n",
+    "#         written += len(out_df)\n",
+    "\n",
+    "#         print(f\"row group {rg + 1}/{pf.num_row_groups}: wrote {written:,}\")\n",
+    "\n",
+    "#     print(\"wrote:\", out_path, \"n=\", written)\n",
+    "#     return out_name\n",
+    "\n",
+    "# transcript_csv = xenium_transcripts_to_csv_streaming(\n",
+    "#     xenium_dir=xenium_dir,\n",
+    "#     basedir=basedir,\n",
+    "#     transform=transform,\n",
+    "#     min_qv=20,\n",
+    "#     max_transcripts=None,\n",
+    "# )\n",
+    "\n",
+    "# print(\"Done\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b5398cfe-3a3d-4841-b385-f1467f20e45f",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# def xenium_all_transcripts_to_h5ad_empty_X(\n",
+    "#     xenium_dir,\n",
+    "#     basedir,\n",
+    "#     transform,\n",
+    "#     out_name=\"transcripts_all_emptyX_tmap.h5ad\",\n",
+    "#     min_qv=None,\n",
+    "#     include_cell_id=False,\n",
+    "# ):\n",
+    "#     import os\n",
+    "#     import numpy as np\n",
+    "#     import pandas as pd\n",
+    "#     import pyarrow.parquet as pq\n",
+    "#     import anndata as ad\n",
+    "#     from scipy.sparse import csc_matrix\n",
+    "\n",
+    "#     transcript_path = os.path.join(xenium_dir, \"transcripts.parquet\")\n",
+    "#     out_path = os.path.join(basedir, out_name)\n",
+    "\n",
+    "#     pf = pq.ParquetFile(transcript_path)\n",
+    "#     transform3 = np.asarray(transform, dtype=np.float64)[:3, :3]\n",
+    "\n",
+    "#     obs_chunks = []\n",
+    "#     spatial_chunks = []\n",
+    "#     written = 0\n",
+    "\n",
+    "#     schema_cols = set(pf.schema.names)\n",
+    "\n",
+    "#     cols = [\"x_location\", \"y_location\", \"feature_name\"]\n",
+    "#     if min_qv is not None and \"qv\" in schema_cols:\n",
+    "#         cols.append(\"qv\")\n",
+    "#     elif \"qv\" in schema_cols:\n",
+    "#         cols.append(\"qv\")\n",
+    "\n",
+    "#     if include_cell_id and \"cell_id\" in schema_cols:\n",
+    "#         cols.append(\"cell_id\")\n",
+    "\n",
+    "#     for rg in range(pf.num_row_groups):\n",
+    "#         tx = pf.read_row_group(rg, columns=cols).to_pandas()\n",
+    "\n",
+    "#         if min_qv is not None and \"qv\" in tx.columns:\n",
+    "#             tx = tx[tx[\"qv\"] >= min_qv]\n",
+    "\n",
+    "#         if tx.empty:\n",
+    "#             continue\n",
+    "\n",
+    "#         x = tx[\"x_location\"].to_numpy(dtype=np.float64)\n",
+    "#         y = tx[\"y_location\"].to_numpy(dtype=np.float64)\n",
+    "\n",
+    "#         xy1 = np.vstack([\n",
+    "#             x,\n",
+    "#             y,\n",
+    "#             np.ones(len(tx), dtype=np.float64),\n",
+    "#         ])\n",
+    "\n",
+    "#         out = transform3 @ xy1\n",
+    "\n",
+    "#         spatial_chunks.append(\n",
+    "#             np.column_stack([\n",
+    "#                 out[0] / out[2],\n",
+    "#                 out[1] / out[2],\n",
+    "#             ]).astype(np.float32)\n",
+    "#         )\n",
+    "\n",
+    "#         # Compact index: RangeIndex, no huge tx_... strings\n",
+    "#         obs = pd.DataFrame(index=pd.RangeIndex(written, written + len(tx)))\n",
+    "\n",
+    "#         obs[\"gene\"] = tx[\"feature_name\"].astype(\"category\").values\n",
+    "\n",
+    "#         if \"qv\" in tx.columns:\n",
+    "#             obs[\"qv\"] = pd.to_numeric(tx[\"qv\"], downcast=\"integer\")\n",
+    "\n",
+    "#         if include_cell_id and \"cell_id\" in tx.columns:\n",
+    "#             obs[\"cell_id\"] = tx[\"cell_id\"].astype(\"category\").values\n",
+    "\n",
+    "#         obs_chunks.append(obs)\n",
+    "\n",
+    "#         written += len(tx)\n",
+    "#         print(f\"row group {rg + 1}/{pf.num_row_groups}: {written:,}\")\n",
+    "\n",
+    "#     obs = pd.concat(obs_chunks, axis=0)\n",
+    "\n",
+    "#     # Ensure compact categoricals after concat\n",
+    "#     obs[\"gene\"] = obs[\"gene\"].astype(\"category\")\n",
+    "\n",
+    "#     if include_cell_id and \"cell_id\" in obs.columns:\n",
+    "#         obs[\"cell_id\"] = obs[\"cell_id\"].astype(\"category\")\n",
+    "\n",
+    "#     if \"qv\" in obs.columns:\n",
+    "#         obs[\"qv\"] = pd.to_numeric(obs[\"qv\"], downcast=\"integer\")\n",
+    "\n",
+    "#     spatial = np.vstack(spatial_chunks).astype(np.float32)\n",
+    "\n",
+    "#     # Empty X: n_obs x 0 vars\n",
+    "#     X = csc_matrix((len(obs), 0), dtype=np.float32)\n",
+    "#     var = pd.DataFrame(index=pd.Index([], dtype=str))\n",
+    "\n",
+    "#     transcript_adata = ad.AnnData(X=X, obs=obs, var=var)\n",
+    "#     transcript_adata.obsm[\"spatial\"] = spatial\n",
+    "\n",
+    "#     transcript_adata.write_h5ad(out_path)\n",
+    "\n",
+    "#     print(\"wrote:\", out_path)\n",
+    "#     print(\"shape:\", transcript_adata.shape)\n",
+    "#     print(\"obs columns:\", list(transcript_adata.obs.columns))\n",
+    "#     print(\"spatial dtype:\", transcript_adata.obsm[\"spatial\"].dtype)\n",
+    "\n",
+    "#     return out_name\n",
+    "\n",
+    "# transcript_h5ad = xenium_all_transcripts_to_h5ad_empty_X(\n",
+    "#     xenium_dir=xenium_dir,\n",
+    "#     basedir=basedir,\n",
+    "#     transform=transform,\n",
+    "#     min_qv=None,\n",
+    "#     include_cell_id=False,\n",
+    "# )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9423d36b-5e04-484c-8901-b32a494ce7ad",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# def xenium_all_transcripts_to_h5ad_empty_X(\n",
+    "#     xenium_dir,\n",
+    "#     basedir,\n",
+    "#     transform,\n",
+    "#     out_name=\"transcripts_all_emptyX_tmap.h5ad\",\n",
+    "#     min_qv=None,\n",
+    "#     include_cell_id=False,\n",
+    "#     tmp_path=None,\n",
+    "# ):\n",
+    "#     import os\n",
+    "#     import numpy as np\n",
+    "#     import pandas as pd\n",
+    "#     import pyarrow as pa\n",
+    "#     import pyarrow.parquet as pq\n",
+    "#     import anndata as ad\n",
+    "#     from scipy.sparse import csc_matrix\n",
+    "\n",
+    "#     transcript_path = os.path.join(xenium_dir, \"transcripts.parquet\")\n",
+    "#     out_path = os.path.join(basedir, out_name)\n",
+    "#     if tmp_path is None:\n",
+    "#         tmp_path = out_path.replace(\".h5ad\", \"_tmp.parquet\")\n",
+    "\n",
+    "#     pf = pq.ParquetFile(transcript_path)\n",
+    "#     transform3 = np.asarray(transform, dtype=np.float64)[:3, :3]\n",
+    "\n",
+    "#     schema_cols = set(pf.schema.names)\n",
+    "#     cols = [\"x_location\", \"y_location\", \"feature_name\"]\n",
+    "#     if \"qv\" in schema_cols:\n",
+    "#         cols.append(\"qv\")\n",
+    "#     if include_cell_id and \"cell_id\" in schema_cols:\n",
+    "#         cols.append(\"cell_id\")\n",
+    "\n",
+    "#     writer = None\n",
+    "#     written = 0\n",
+    "\n",
+    "#     for rg in range(pf.num_row_groups):\n",
+    "#         tx = pf.read_row_group(rg, columns=cols).to_pandas()\n",
+    "#         if min_qv is not None and \"qv\" in tx.columns:\n",
+    "#             tx = tx[tx[\"qv\"] >= min_qv]\n",
+    "#         if tx.empty:\n",
+    "#             continue\n",
+    "\n",
+    "#         x = tx[\"x_location\"].to_numpy(dtype=np.float64)\n",
+    "#         y = tx[\"y_location\"].to_numpy(dtype=np.float64)\n",
+    "#         xy1 = np.vstack([x, y, np.ones(len(tx), dtype=np.float64)])\n",
+    "#         out = transform3 @ xy1\n",
+    "\n",
+    "#         chunk = pd.DataFrame({\n",
+    "#             \"spatial_x\": (out[0] / out[2]).astype(np.float32),\n",
+    "#             \"spatial_y\": (out[1] / out[2]).astype(np.float32),\n",
+    "#             \"gene\": tx[\"feature_name\"].astype(str).values,\n",
+    "#         })\n",
+    "#         if \"qv\" in tx.columns:\n",
+    "#             chunk[\"qv\"] = pd.to_numeric(tx[\"qv\"], downcast=\"integer\").values\n",
+    "#         if include_cell_id and \"cell_id\" in tx.columns:\n",
+    "#             chunk[\"cell_id\"] = tx[\"cell_id\"].astype(str).values\n",
+    "\n",
+    "#         table = pa.Table.from_pandas(chunk, preserve_index=False)\n",
+    "#         if writer is None:\n",
+    "#             writer = pq.ParquetWriter(tmp_path, table.schema)\n",
+    "#         writer.write_table(table)\n",
+    "\n",
+    "#         written += len(chunk)\n",
+    "#         print(f\"row group {rg + 1}/{pf.num_row_groups}: {written:,}\")\n",
+    "\n",
+    "#     writer.close()\n",
+    "#     print(\"All row groups written to tmp parquet. Building AnnData...\")\n",
+    "\n",
+    "#     df = pd.read_parquet(tmp_path)\n",
+    "#     df[\"gene\"] = df[\"gene\"].astype(\"category\")\n",
+    "#     if include_cell_id and \"cell_id\" in df.columns:\n",
+    "#         df[\"cell_id\"] = df[\"cell_id\"].astype(\"category\")\n",
+    "\n",
+    "#     spatial = df[[\"spatial_x\", \"spatial_y\"]].to_numpy(dtype=np.float32)\n",
+    "#     obs = df.drop(columns=[\"spatial_x\", \"spatial_y\"])\n",
+    "#     obs.index = pd.RangeIndex(len(obs)).astype(str)\n",
+    "\n",
+    "#     X = csc_matrix((len(obs), 0), dtype=np.float32)\n",
+    "#     var = pd.DataFrame(index=pd.Index([], dtype=str))\n",
+    "#     transcript_adata = ad.AnnData(X=X, obs=obs, var=var)\n",
+    "#     transcript_adata.obsm[\"spatial\"] = spatial\n",
+    "#     transcript_adata.write_h5ad(out_path)\n",
+    "\n",
+    "#     os.remove(tmp_path)\n",
+    "#     print(\"wrote:\", out_path)\n",
+    "#     print(\"shape:\", transcript_adata.shape)\n",
+    "#     print(\"obs columns:\", list(transcript_adata.obs.columns))\n",
+    "#     return out_name\n",
+    "\n",
+    "\n",
+    "# transcript_h5ad = xenium_all_transcripts_to_h5ad_empty_X(\n",
+    "#     xenium_dir=xenium_dir,\n",
+    "#     basedir=basedir,\n",
+    "#     transform=transform,\n",
+    "#     min_qv=None,\n",
+    "#     include_cell_id=False,\n",
+    "#     tmp_path=\"/Volumes/T7 Shield/tmp/transcripts_tmp.parquet\",\n",
+    "# )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "02d53e78-c105-413c-9b30-f950b42ed200",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def xenium_all_transcripts_to_h5ad_empty_X(\n",
+    "    xenium_dir,\n",
+    "    basedir,\n",
+    "    transform,\n",
+    "    out_name=\"transcripts_all_emptyX_tmap.h5ad\",\n",
+    "    min_qv=None,\n",
+    "    include_cell_id=False,\n",
+    "    tmp_path=None,\n",
+    "):\n",
+    "    import os\n",
+    "    import numpy as np\n",
+    "    import pandas as pd\n",
+    "    import pyarrow as pa\n",
+    "    import pyarrow.parquet as pq\n",
+    "    import h5py\n",
+    "    from scipy.sparse import csc_matrix\n",
+    "\n",
+    "    transcript_path = os.path.join(xenium_dir, \"transcripts.parquet\")\n",
+    "    out_path = os.path.join(basedir, out_name)\n",
+    "    if tmp_path is None:\n",
+    "        tmp_path = out_path.replace(\".h5ad\", \"_tmp.parquet\")\n",
+    "\n",
+    "    pf = pq.ParquetFile(transcript_path)\n",
+    "    transform3 = np.asarray(transform, dtype=np.float64)[:3, :3]\n",
+    "\n",
+    "    schema_cols = set(pf.schema.names)\n",
+    "    cols = [\"x_location\", \"y_location\", \"feature_name\"]\n",
+    "    if \"qv\" in schema_cols:\n",
+    "        cols.append(\"qv\")\n",
+    "    if include_cell_id and \"cell_id\" in schema_cols:\n",
+    "        cols.append(\"cell_id\")\n",
+    "\n",
+    "    # --- Pass 1: stream row groups → tmp parquet ---\n",
+    "    writer = None\n",
+    "    written = 0\n",
+    "    for rg in range(pf.num_row_groups):\n",
+    "        tx = pf.read_row_group(rg, columns=cols).to_pandas()\n",
+    "        if min_qv is not None and \"qv\" in tx.columns:\n",
+    "            tx = tx[tx[\"qv\"] >= min_qv]\n",
+    "        if tx.empty:\n",
+    "            continue\n",
+    "\n",
+    "        x = tx[\"x_location\"].to_numpy(dtype=np.float64)\n",
+    "        y = tx[\"y_location\"].to_numpy(dtype=np.float64)\n",
+    "        xy1 = np.vstack([x, y, np.ones(len(tx), dtype=np.float64)])\n",
+    "        out = transform3 @ xy1\n",
+    "\n",
+    "        chunk = pd.DataFrame({\n",
+    "            \"spatial_x\": (out[0] / out[2]).astype(np.float32),\n",
+    "            \"spatial_y\": (out[1] / out[2]).astype(np.float32),\n",
+    "            \"gene\": tx[\"feature_name\"].astype(str).values,\n",
+    "        })\n",
+    "        if \"qv\" in tx.columns:\n",
+    "            chunk[\"qv\"] = pd.to_numeric(tx[\"qv\"], downcast=\"integer\").values\n",
+    "        if include_cell_id and \"cell_id\" in tx.columns:\n",
+    "            chunk[\"cell_id\"] = tx[\"cell_id\"].astype(str).values\n",
+    "\n",
+    "        table = pa.Table.from_pandas(chunk, preserve_index=False)\n",
+    "        if writer is None:\n",
+    "            writer = pq.ParquetWriter(tmp_path, table.schema)\n",
+    "        writer.write_table(table)\n",
+    "        written += len(chunk)\n",
+    "        print(f\"row group {rg + 1}/{pf.num_row_groups}: {written:,}\")\n",
+    "\n",
+    "    writer.close()\n",
+    "    total_rows = written\n",
+    "    print(f\"Pass 1 done. {total_rows:,} rows. Writing h5ad...\")\n",
+    "\n",
+    "    # --- Pass 2: stream tmp parquet → h5ad via h5py, one chunk at a time ---\n",
+    "    pf2 = pq.ParquetFile(tmp_path)\n",
+    "    obs_col_names = [c for c in pf2.schema.names if c not in (\"spatial_x\", \"spatial_y\")]\n",
+    "\n",
+    "    with h5py.File(out_path, \"w\") as f:\n",
+    "        # AnnData minimal structure\n",
+    "        f.attrs[\"encoding-type\"] = \"anndata\"\n",
+    "        f.attrs[\"encoding-version\"] = \"0.1.0\"\n",
+    "\n",
+    "        # X group — empty (0 vars)\n",
+    "        xgrp = f.create_group(\"X\")\n",
+    "        xgrp.attrs[\"encoding-type\"] = \"array\"\n",
+    "        xgrp.attrs[\"encoding-version\"] = \"0.2.0\"\n",
+    "        xgrp.create_dataset(\"data\",   data=np.array([], dtype=np.float32))\n",
+    "        xgrp.create_dataset(\"indices\",data=np.array([], dtype=np.int32))\n",
+    "        xgrp.create_dataset(\"indptr\", data=np.zeros(1, dtype=np.int32))\n",
+    "        xgrp.attrs[\"shape\"] = [total_rows, 0]\n",
+    "\n",
+    "        # obsm/spatial — pre-allocate, fill in chunks\n",
+    "        obsm = f.create_group(\"obsm\")\n",
+    "        spatial_ds = obsm.create_dataset(\n",
+    "            \"spatial\", shape=(total_rows, 2), dtype=np.float32\n",
+    "        )\n",
+    "\n",
+    "        # obs — pre-allocate string datasets per column\n",
+    "        obs_grp = f.create_group(\"obs\")\n",
+    "        obs_grp.attrs[\"_index\"] = \"_index\"\n",
+    "        obs_grp.attrs[\"encoding-type\"] = \"dataframe\"\n",
+    "        obs_grp.attrs[\"encoding-version\"] = \"0.2.0\"\n",
+    "        obs_grp.attrs[\"column-order\"] = obs_col_names\n",
+    "\n",
+    "        # pre-allocate index\n",
+    "        idx_ds = obs_grp.create_dataset(\n",
+    "            \"_index\", shape=(total_rows,), dtype=h5py.string_dtype()\n",
+    "        )\n",
+    "        col_datasets = {}\n",
+    "        for col in obs_col_names:\n",
+    "            col_datasets[col] = obs_grp.create_dataset(\n",
+    "                col, shape=(total_rows,), dtype=h5py.string_dtype()\n",
+    "            )\n",
+    "\n",
+    "        # var — empty\n",
+    "        var_grp = f.create_group(\"var\")\n",
+    "        var_grp.attrs[\"_index\"] = \"_index\"\n",
+    "        var_grp.attrs[\"encoding-type\"] = \"dataframe\"\n",
+    "        var_grp.attrs[\"encoding-version\"] = \"0.2.0\"\n",
+    "        var_grp.attrs[\"column-order\"] = []\n",
+    "        var_grp.create_dataset(\"_index\", data=np.array([], dtype=h5py.string_dtype()))\n",
+    "\n",
+    "        # stream fill\n",
+    "        cursor = 0\n",
+    "        for rg in range(pf2.num_row_groups):\n",
+    "            chunk = pf2.read_row_group(rg).to_pandas()\n",
+    "            n = len(chunk)\n",
+    "            sl = slice(cursor, cursor + n)\n",
+    "\n",
+    "            spatial_ds[sl] = chunk[[\"spatial_x\", \"spatial_y\"]].to_numpy(dtype=np.float32)\n",
+    "            idx_ds[sl] = np.arange(cursor, cursor + n).astype(str)\n",
+    "            for col in obs_col_names:\n",
+    "                col_datasets[col][sl] = chunk[col].astype(str).values\n",
+    "\n",
+    "            cursor += n\n",
+    "            if rg % 50 == 0:\n",
+    "                print(f\"  h5ad pass {rg + 1}/{pf2.num_row_groups}: {cursor:,}\")\n",
+    "\n",
+    "    os.remove(tmp_path)\n",
+    "    print(\"wrote:\", out_path)\n",
+    "    print(\"shape:\", (total_rows, 0))\n",
+    "    print(\"obs columns:\", obs_col_names)\n",
+    "    return out_name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cc5c8636-ed10-47e9-bf9c-08d5608095f8",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "transcript_h5ad = xenium_all_transcripts_to_h5ad_empty_X(\n",
+    "    xenium_dir=xenium_dir,\n",
+    "    basedir=basedir,\n",
+    "    transform=transform,\n",
+    "    min_qv=None,\n",
+    "    include_cell_id=False,\n",
+    "    tmp_path=\"/Volumes/T7 Shield/tmp/transcripts_tmp.parquet\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a53c9e37-da32-4583-a74c-604d60df358b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# basedir = os.path.abspath(f\"tissuumaps/{sample}\")\n",
+    "# transcript_csv = f\"transcripts_image_space.csv\"\n",
+    "\n",
+    "# print(basedir)\n",
+    "\n",
+    "# region_files = []\n",
+    "\n",
+    "# for geojson_path in glob.glob(os.path.join(basedir, \"*boundaries*.geojson\")):\n",
+    "#     geojson_name = os.path.basename(geojson_path)\n",
+    "\n",
+    "#     region_files.append({\n",
+    "#         \"path\": geojson_name,          # relative path only\n",
+    "#         \"title\": geojson_name,\n",
+    "#         \"comment\": geojson_name,\n",
+    "#         \"autoLoad\": True,\n",
+    "#     })\n",
+    "\n",
+    "# print(region_files)\n",
+    "\n",
+    "# image_layers = []\n",
+    "\n",
+    "# for tif_path in sorted(\n",
+    "#     glob.glob(os.path.join(basedir, \"morphology_focus_plane*_pyramid.tif\"))\n",
+    "# ):\n",
+    "#     name = os.path.basename(tif_path)\n",
+    "\n",
+    "#     image_layers.append({\n",
+    "#         \"name\": name.replace(\".tif\", \"\"),\n",
+    "#         \"tileSource\": name + \".dzi\",\n",
+    "#         \"x\": 0,\n",
+    "#         \"y\": 0,\n",
+    "#         \"scale\": 1,\n",
+    "#         \"rotation\": 0,\n",
+    "#         \"flip\": False,\n",
+    "#     })\n",
+    "\n",
+    "# print(f\"Found {len(image_layers)} image layers\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1ee1ac2d-6de4-4fe2-b31a-90725d67669c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # ----------------------------\n",
+    "# # 5. Generate TissUUmaps project\n",
+    "# # ----------------------------\n",
+    "# project = read_h5ad.h5ad_to_tmap(basedir, out_h5ad_name)\n",
+    "\n",
+    "# # Images: all stacked and visible\n",
+    "# project[\"layers\"] = image_layers\n",
+    "# project[\"collectionMode\"] = False\n",
+    "# project[\"compositeMode\"] = \"lighter\"\n",
+    "# project[\"backgroundColor\"] = \"#000000\"\n",
+    "\n",
+    "# project[\"filters\"] = []\n",
+    "# project[\"layerFilters\"] = {}\n",
+    "# project[\"layerOpacities\"] = {str(i): 1 for i in range(len(image_layers))}\n",
+    "# project[\"layerVisibilities\"] = {str(i): True for i in range(len(image_layers))}\n",
+    "\n",
+    "# # Polygons: autoload\n",
+    "# project[\"regionFiles\"] = []\n",
+    "# for rf in region_files:\n",
+    "#     rf = dict(rf)\n",
+    "#     rf[\"autoLoad\"] = True\n",
+    "#     project[\"regionFiles\"].append(rf)\n",
+    "\n",
+    "# # Keep h5ad/cell-expression dropdowns available, but not autoloaded\n",
+    "# for mf in project.get(\"markerFiles\", []):\n",
+    "#     mf[\"autoLoad\"] = False\n",
+    "#     mf.setdefault(\"expectedHeader\", {})\n",
+    "#     mf[\"expectedHeader\"][\"shape_fixed\"] = \"disc\"\n",
+    "#     mf[\"expectedHeader\"][\"scale_factor\"] = 1\n",
+    "\n",
+    "#     mf.setdefault(\"expectedRadios\", {})\n",
+    "#     mf[\"expectedRadios\"][\"shape_fixed\"] = True\n",
+    "#     mf[\"expectedRadios\"][\"shape_gr\"] = False\n",
+    "#     mf[\"expectedRadios\"][\"shape_gr_rand\"] = False\n",
+    "#     mf[\"expectedRadios\"][\"sortby_check\"] = False\n",
+    "\n",
+    "# # Transcripts: autoload default marker layer\n",
+    "# if transcript_csv is not None:\n",
+    "#     project[\"markerFiles\"].insert(\n",
+    "#         0,\n",
+    "#         {\n",
+    "#             \"path\": transcript_csv,\n",
+    "#             \"title\": \"Load transcripts\",\n",
+    "#             \"comment\": \"Transcript molecules\",\n",
+    "#             \"name\": \"Transcripts\",\n",
+    "#             \"uid\": \"transcripts\",\n",
+    "#             \"autoLoad\": True,\n",
+    "#             \"hideSettings\": True,\n",
+    "#             \"expectedHeader\": {\n",
+    "#                 \"X\": \"x\",\n",
+    "#                 \"Y\": \"y\",\n",
+    "#                 \"gb_col\": \"gene\",\n",
+    "#                 \"gb_name\": \"\",\n",
+    "#                 \"cb_col\": \"\",\n",
+    "#                 \"cb_cmap\": \"\",\n",
+    "#                 \"scale_factor\": 0.15,\n",
+    "#                 \"shape_fixed\": \"disc\",\n",
+    "#                 \"opacity\": 0.7,\n",
+    "#             },\n",
+    "#             \"expectedRadios\": {\n",
+    "#                 \"cb_col\": False,\n",
+    "#                 \"cb_gr\": True,\n",
+    "#                 \"cb_gr_rand\": True,\n",
+    "#                 \"cb_gr_dict\": False,\n",
+    "#                 \"cb_gr_key\": False,\n",
+    "#                 \"pie_check\": False,\n",
+    "#                 \"scale_check\": False,\n",
+    "#                 \"shape_col\": False,\n",
+    "#                 \"shape_fixed\": True,\n",
+    "#                 \"shape_gr\": False,\n",
+    "#                 \"shape_gr_rand\": False,\n",
+    "#                 \"shape_gr_dict\": False,\n",
+    "#                 \"sortby_check\": False,\n",
+    "#             },\n",
+    "#         },\n",
+    "#     )\n",
+    "\n",
+    "# # project[\"markerFiles\"].insert(\n",
+    "# #     0,\n",
+    "# #     {\n",
+    "# #         \"path\": transcript_h5ad,\n",
+    "# #         \"title\": \"Load transcript AnnData\",\n",
+    "# #         \"comment\": \"All transcript molecules\",\n",
+    "# #         \"name\": \"Transcript AnnData\",\n",
+    "# #         \"uid\": \"transcript_h5ad\",\n",
+    "# #         \"autoLoad\": True,\n",
+    "# #         \"hideSettings\": True,\n",
+    "# #         \"expectedHeader\": {\n",
+    "# #             \"X\": \"/obsm/spatial;0\",\n",
+    "# #             \"Y\": \"/obsm/spatial;1\",\n",
+    "# #             \"gb_col\": \"/obs/gene\",\n",
+    "# #             \"gb_name\": \"\",\n",
+    "# #             \"cb_col\": \"\",\n",
+    "# #             \"cb_cmap\": \"\",\n",
+    "# #             \"scale_factor\": 0.15,\n",
+    "# #             \"shape_fixed\": \"disc\",\n",
+    "# #             \"opacity\": 0.7,\n",
+    "# #         },\n",
+    "# #         \"expectedRadios\": {\n",
+    "# #             \"cb_col\": False,\n",
+    "# #             \"cb_gr\": True,\n",
+    "# #             \"cb_gr_rand\": True,\n",
+    "# #             \"shape_fixed\": True,\n",
+    "# #             \"shape_gr\": False,\n",
+    "# #             \"scale_check\": False,\n",
+    "# #             \"sortby_check\": False,\n",
+    "# #         },\n",
+    "# #     },\n",
+    "# # )\n",
+    "\n",
+    "# with open(project_path, \"w\") as f:\n",
+    "#     json.dump(project, f, indent=2)\n",
+    "\n",
+    "# viewer = tj.opentmap(project_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "68db233e-7105-4862-9635-c68875497962",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "696e478b-61c6-4d35-8b90-813569c4a0ed",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python (tissuumaps_env)",
+   "language": "python",
+   "name": "tissuumaps_env"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.23"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

notebooks/.ipynb_checkpoints/tissuumaps_viz-checkpoint.ipynb

@@ -0,0 +1,295 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "07f7dbb1-63c6-40e4-b0d9-6ec104aa0adc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !pip install zarr\n",
+    "\n",
+    "import os\n",
+    "import json\n",
+    "from pathlib import Path\n",
+    "import glob\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import scanpy as sc\n",
+    "import pyvips\n",
+    "import zarr\n",
+    "import geopandas as gpd\n",
+    "from shapely.geometry import Polygon\n",
+    "from scipy.sparse import csc_matrix\n",
+    "\n",
+    "import tissuumaps.jupyter as tj\n",
+    "from tissuumaps import read_h5ad\n",
+    "\n",
+    "\n",
+    "# ----------------------------\n",
+    "# Paths\n",
+    "# ----------------------------\n",
+    "sample = \"WTA_Preview_FFPE_Cervical_Cancer_outs\"\n",
+    "# sample = \"Xenium_Prime_Human_Lymph_Node_Reactive_FFPE_outs\"\n",
+    "# sample = \"Xenium_Prime_Ovarian_Cancer_FFPE_XRrun_outs\"\n",
+    "# sample = \"Xenium_V1_humanLung_Cancer_FFPE_outs\"\n",
+    "\n",
+    "\n",
+    "xenium_dir = os.path.abspath(f\"../data/instrument_data/{sample}\")\n",
+    "basedir = os.path.abspath(f\"../data/processed_data/tissuumaps_h5ad/{sample}\")\n",
+    "os.makedirs(basedir, exist_ok=True)\n",
+    "\n",
+    "out_h5ad_name = f\"{sample}_tmap.h5ad\"\n",
+    "out_h5ad = os.path.join(basedir, out_h5ad_name)\n",
+    "\n",
+    "project_path = os.path.join(basedir, \"_project_h5ad.tmap\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a53c9e37-da32-4583-a74c-604d60df358b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "basedir = os.path.abspath(f\"../data/processed_data/tissuumaps_h5ad/{sample}\")\n",
+    "# transcript_csv = f\"transcripts_image_space.csv\"\n",
+    "transcript_h5ad = f\"transcripts_all_emptyX_tmap.h5ad\"\n",
+    "\n",
+    "print(basedir)\n",
+    "\n",
+    "region_files = []\n",
+    "\n",
+    "for geojson_path in glob.glob(os.path.join(basedir, \"*boundaries*.geojson\")):\n",
+    "    geojson_name = os.path.basename(geojson_path)\n",
+    "\n",
+    "    region_files.append({\n",
+    "        \"path\": geojson_name,          # relative path only\n",
+    "        \"title\": geojson_name,\n",
+    "        \"comment\": geojson_name,\n",
+    "        \"autoLoad\": True,\n",
+    "    })\n",
+    "\n",
+    "print(region_files)\n",
+    "\n",
+    "image_layers = []\n",
+    "\n",
+    "for tif_path in sorted(\n",
+    "    glob.glob(os.path.join(basedir, \"morphology_focus_plane*_pyramid.tif\"))\n",
+    "):\n",
+    "    name = os.path.basename(tif_path)\n",
+    "\n",
+    "    image_layers.append({\n",
+    "        \"name\": name.replace(\".tif\", \"\"),\n",
+    "        \"tileSource\": name + \".dzi\",\n",
+    "        \"x\": 0,\n",
+    "        \"y\": 0,\n",
+    "        \"scale\": 1,\n",
+    "        \"rotation\": 0,\n",
+    "        \"flip\": False,\n",
+    "    })\n",
+    "\n",
+    "print(f\"Found {len(image_layers)} image layers\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bbcabbbd-3f76-48dd-b589-c80e47fc8cbc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ----------------------------\n",
+    "# 5. Generate TissUUmaps project\n",
+    "# ----------------------------\n",
+    "project = read_h5ad.h5ad_to_tmap(basedir, out_h5ad_name)\n",
+    "\n",
+    "# Images: all stacked and visible\n",
+    "project[\"layers\"] = image_layers\n",
+    "\n",
+    "project[\"collectionMode\"] = False\n",
+    "project[\"compositeMode\"] = \"lighter\"\n",
+    "project[\"backgroundColor\"] = \"#000000\"\n",
+    "\n",
+    "project[\"filters\"] = []\n",
+    "project[\"layerFilters\"] = {}\n",
+    "project[\"layerOpacities\"] = {str(i): 1 for i in range(len(image_layers))}\n",
+    "project[\"layerVisibilities\"] = {str(i): True for i in range(len(image_layers))}\n",
+    "\n",
+    "# Polygons: autoload\n",
+    "project[\"regionFiles\"] = []\n",
+    "for rf in region_files:\n",
+    "    rf = dict(rf)\n",
+    "    rf[\"autoLoad\"] = True\n",
+    "    project[\"regionFiles\"].append(rf)\n",
+    "\n",
+    "for i, mf in enumerate(project.get(\"markerFiles\", [])):\n",
+    "    mf.setdefault(\"expectedHeader\", {})\n",
+    "    mf[\"expectedHeader\"][\"shape_fixed\"] = \"disc\"\n",
+    "    mf[\"expectedHeader\"][\"scale_factor\"] = 1\n",
+    "    mf.setdefault(\"expectedRadios\", {})\n",
+    "    mf[\"expectedRadios\"][\"shape_fixed\"] = True\n",
+    "    mf[\"expectedRadios\"][\"shape_gr\"] = False\n",
+    "    mf[\"expectedRadios\"][\"shape_gr_rand\"] = False\n",
+    "    mf[\"expectedRadios\"][\"sortby_check\"] = False\n",
+    "\n",
+    "# Transcripts: autoload default marker layer\n",
+    "# if transcript_csv is not None:\n",
+    "#     project[\"markerFiles\"].insert(\n",
+    "#         0,\n",
+    "#         {\n",
+    "#             \"path\": transcript_csv,\n",
+    "#             \"title\": \"Load transcripts\",\n",
+    "#             \"comment\": \"Transcript molecules\",\n",
+    "#             \"name\": \"Transcripts\",\n",
+    "#             \"uid\": \"transcripts\",\n",
+    "#             \"autoLoad\": True,\n",
+    "#             \"hideSettings\": True,\n",
+    "#             \"expectedHeader\": {\n",
+    "#                 \"X\": \"x\",\n",
+    "#                 \"Y\": \"y\",\n",
+    "#                 \"gb_col\": \"gene\",\n",
+    "#                 \"gb_name\": \"\",\n",
+    "#                 \"cb_col\": \"\",\n",
+    "#                 \"cb_cmap\": \"\",\n",
+    "#                 \"scale_factor\": 0.15,\n",
+    "#                 \"shape_fixed\": \"disc\",\n",
+    "#                 \"opacity\": 0.7,\n",
+    "#             },\n",
+    "#             \"expectedRadios\": {\n",
+    "#                 \"cb_col\": False,\n",
+    "#                 \"cb_gr\": True,\n",
+    "#                 \"cb_gr_rand\": True,\n",
+    "#                 \"cb_gr_dict\": False,\n",
+    "#                 \"cb_gr_key\": False,\n",
+    "#                 \"pie_check\": False,\n",
+    "#                 \"scale_check\": False,\n",
+    "#                 \"shape_col\": False,\n",
+    "#                 \"shape_fixed\": True,\n",
+    "#                 \"shape_gr\": False,\n",
+    "#                 \"shape_gr_rand\": False,\n",
+    "#                 \"shape_gr_dict\": False,\n",
+    "#                 \"sortby_check\": False,\n",
+    "#             },\n",
+    "#         },\n",
+    "#     )\n",
+    "\n",
+    "project[\"markerFiles\"].insert(\n",
+    "    0,\n",
+    "    {\n",
+    "        \"path\": transcript_h5ad,\n",
+    "        \"title\": \"Load transcript AnnData\",\n",
+    "        \"comment\": \"All transcript molecules\",\n",
+    "        \"name\": \"Transcript AnnData\",\n",
+    "        \"uid\": \"transcript_h5ad\",\n",
+    "        \"autoLoad\": True,\n",
+    "        \"hideSettings\": True,\n",
+    "        \"expectedHeader\": {\n",
+    "            \"X\": \"/obsm/spatial;0\",\n",
+    "            \"Y\": \"/obsm/spatial;1\",\n",
+    "            \"gb_col\": \"/obs/gene\",\n",
+    "            \"gb_name\": \"\",\n",
+    "            \"cb_col\": \"\",\n",
+    "            \"cb_cmap\": \"\",\n",
+    "            \"scale_factor\": 0.15,\n",
+    "            \"shape_fixed\": \"disc\",\n",
+    "            \"opacity\": 0.7,\n",
+    "        },\n",
+    "        \"expectedRadios\": {\n",
+    "            \"cb_col\": False,\n",
+    "            \"cb_gr\": True,\n",
+    "            \"cb_gr_rand\": True,\n",
+    "            \"shape_fixed\": True,\n",
+    "            \"shape_gr\": False,\n",
+    "            \"scale_check\": False,\n",
+    "            \"sortby_check\": False,\n",
+    "        },\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "# Set autoLoad explicitly by name after insert\n",
+    "for mf in project[\"markerFiles\"]:\n",
+    "    if mf.get(\"name\") in (\"Transcript AnnData\", \"Categorical observations\"):\n",
+    "        mf[\"autoLoad\"] = True\n",
+    "    else:\n",
+    "        mf[\"autoLoad\"] = False\n",
+    "\n",
+    "with open(project_path, \"w\") as f:\n",
+    "    json.dump(project, f, indent=2)\n",
+    "\n",
+    "print(\"done\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1ee1ac2d-6de4-4fe2-b31a-90725d67669c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "viewer = tj.opentmap(project_path)\n",
+    "viewer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "db4873e9-ba2e-4814-80a9-e32f92a5677a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7394d91a-54ad-49b1-834f-bda9f24f4944",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "68db233e-7105-4862-9635-c68875497962",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4c8a97c2-3285-4b9d-8a6c-7bdb6a89c4a8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python (tissuumaps_env)",
+   "language": "python",
+   "name": "tissuumaps_env"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.23"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

notebooks/.ipynb_checkpoints/vitessce_pre-process-checkpoint.ipynb

@@ -0,0 +1,444 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "nbsphinx": "hidden"
+   },
+   "source": [
+    "# Vitessce Widget Tutorial"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Visualization of a SpatialData object"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import dependencies\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from os.path import join, isfile, isdir\n",
+    "from urllib.request import urlretrieve\n",
+    "import zipfile\n",
+    "import shutil\n",
+    "\n",
+    "from vitessce import (\n",
+    "    VitessceConfig,\n",
+    "    ViewType as vt,\n",
+    "    CoordinationType as ct,\n",
+    "    CoordinationLevel as CL,\n",
+    "    SpatialDataWrapper,\n",
+    "    get_initial_coordination_scope_prefix\n",
+    ")\n",
+    "\n",
+    "from vitessce.data_utils import (\n",
+    "    sdata_morton_sort_points,\n",
+    "    sdata_points_process_columns,\n",
+    "    sdata_points_write_bounding_box_attrs,\n",
+    "    sdata_points_modify_row_group_size,\n",
+    "    sdata_morton_query_rect,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from spatialdata import read_zarr\n",
+    "\n",
+    "import anndata as ad\n",
+    "\n",
+    "ad.settings.zarr_write_format = 3\n",
+    "print(ad.settings.zarr_write_format)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import dask\n",
+    "import tempfile\n",
+    "\n",
+    "# Point Dask temp dir to your external drive\n",
+    "dask.config.set({'temporary_directory': '/Volumes/T7 Shield/tmp'})\n",
+    "\n",
+    "# Create the dir if it doesn't exist\n",
+    "import os\n",
+    "os.makedirs('/Volumes/T7 Shield/tmp', exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "from spatialdata_io import xenium\n",
+    "import spatialdata as sd\n",
+    "import pandas as pd\n",
+    "\n",
+    "xenium_dir = Path(\"../data/instrument_data/Xenium_V1_hPancreas_Cancer_Add_on_FFPE_outs/\")   # folder containing experiment.xenium\n",
+    "out_zarr = Path(\"../data/processed_data/vitessce/Xenium_V1_hPancreas_Cancer_Add_on_FFPE_outs.zarr\")\n",
+    "\n",
+    "sdata = xenium(\n",
+    "    xenium_dir,\n",
+    "    cells_boundaries=True,\n",
+    "    nucleus_boundaries=True,\n",
+    "    cells_labels=True,\n",
+    "    nucleus_labels=True,\n",
+    "    transcripts=True,\n",
+    "    morphology_focus=True,\n",
+    "    aligned_images=True,\n",
+    "    cells_table=True,\n",
+    "    gex_only=True,\n",
+    ")\n",
+    "\n",
+    "print(sdata)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add cluster labels from Xenium instrument output\n",
+    "clusters = pd.read_csv(\n",
+    "    xenium_dir / \"analysis/clustering/gene_expression_graphclust/clusters.csv\"\n",
+    ")\n",
+    "sdata.tables[\"table\"].obs = sdata.tables[\"table\"].obs.merge(\n",
+    "    clusters.set_index(\"Barcode\")[[\"Cluster\"]].rename(columns={\"Cluster\": \"leiden\"}),\n",
+    "    left_index=True,\n",
+    "    right_index=True,\n",
+    "    how=\"left\",\n",
+    ")\n",
+    "sdata.tables[\"table\"].obs[\"leiden\"] = sdata.tables[\"table\"].obs[\"leiden\"].astype(str)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save as a SpatialData Zarr store\n",
+    "sdata.write(out_zarr)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sdata[\"transcripts\"].shape[0].compute()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sdata.tables[\"table\"].X = sdata.tables[\"table\"].X.toarray()\n",
+    "sdata.tables[\"dense_table\"] = sdata.tables[\"table\"]\n",
+    "sdata.write_element(\"dense_table\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: store the two separate images as a single image with two channels.\n",
+    "# Similar to https://github.com/EricMoerthVis/tissue-map-tools/pull/12"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sdata.tables['table'].obs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sdata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sdata.points['transcripts'].head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Sorting Points and creating a new Points element in the SpatialData object"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 1. Sort rows with `sdata_morton_sort_points`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import importlib.metadata\n",
+    "print(importlib.metadata.version(\"vitessce\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sdata = sdata_morton_sort_points(sdata, \"transcripts\")\n",
+    "from vitessce.data_utils.spatialdata_points_zorder import norm_ddf_to_uint, morton_interleave\n",
+    "\n",
+    "element = \"transcripts\"\n",
+    "ddf = sdata.points[element]\n",
+    "attrs = ddf.attrs.copy()\n",
+    "\n",
+    "ddf = norm_ddf_to_uint(ddf)\n",
+    "ddf[\"morton_code_2d\"] = morton_interleave(ddf)\n",
+    "sorted_ddf = ddf.sort_values(by=\"morton_code_2d\", ascending=True)\n",
+    "sorted_ddf.attrs.update(attrs)\n",
+    "sdata.points[element] = sorted_ddf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 2. Clean up columns with `sdata_points_process_columns`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add feature_index column to dataframe, and reorder columns so that feature_name (dict column) is the rightmost column.\n",
+    "ddf = sdata_points_process_columns(sdata, \"transcripts\", var_name_col=\"feature_name\", table_name=\"table\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ddf.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 3. Save sorted dataframe to new Points element"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sdata[\"transcripts_with_morton_codes\"] = ddf\n",
+    "# sdata.write_element(\"transcripts_with_morton_codes\")\n",
+    "\n",
+    "from spatialdata.models import PointsModel\n",
+    "\n",
+    "transformations = sdata[\"transcripts\"].attrs[\"transform\"]\n",
+    "del ddf.attrs[\"transform\"]\n",
+    "\n",
+    "sdata[\"transcripts_with_morton_codes\"] = PointsModel.parse(\n",
+    "    ddf, feature_key=\"feature_name\", instance_key=\"cell_id\", transformations=transformations\n",
+    ")\n",
+    "sdata.write_element(\"transcripts_with_morton_codes\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 4. Write bounding box metadata with `sdata_points_write_bounding_box_attrs`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import shutil\n",
+    "import os\n",
+    "\n",
+    "tmp_dir = '/Volumes/T7 Shield/tmp'\n",
+    "shutil.rmtree(tmp_dir)\n",
+    "os.makedirs(tmp_dir)\n",
+    "print(\"Done\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sdata_points_write_bounding_box_attrs(sdata, \"transcripts_with_morton_codes\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 5. Modify the row group sizes of the Parquet files with `sdata_points_modify_row_group_size`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import shutil\n",
+    "import os\n",
+    "\n",
+    "tmp_dir = '/Volumes/T7 Shield/tmp'\n",
+    "shutil.rmtree(tmp_dir)\n",
+    "os.makedirs(tmp_dir)\n",
+    "print(\"Done\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sdata_points_modify_row_group_size(sdata, \"transcripts_with_morton_codes\", row_group_size=25_000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Done"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Optionally, check the number of row groups in one of the parquet file parts.\n",
+    "import pyarrow.parquet as pq\n",
+    "from os.path import join\n",
+    "\n",
+    "parquet_file = pq.ParquetFile(join(sdata.path, \"points\", \"transcripts_with_morton_codes\", \"points.parquet\", \"part.0.parquet\"))\n",
+    "\n",
+    "# Get the number of row groups in this part-0 file.\n",
+    "num_groups = parquet_file.num_row_groups\n",
+    "num_groups"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.12.1"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {
+     "undefined": {
+      "model_module": "anywidget",
+      "model_module_version": "2.0.0",
+      "model_name": "AnyModel",
+      "state": {
+       "_view_name": "ErrorWidgetView",
+       "error": {},
+       "msg": "Model class 'AnyModel' from module 'anywidget' is loaded but can not be instantiated"
+      }
+     }
+    },
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

notebooks/.ipynb_checkpoints/vitessce_viz-checkpoint.ipynb

@@ -0,0 +1,1133 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "nbsphinx": "hidden"
+   },
+   "source": [
+    "# Vitessce Widget Tutorial"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Visualization of a SpatialData object"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import dependencies\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from os.path import join, isfile, isdir\n",
+    "from urllib.request import urlretrieve\n",
+    "import zipfile\n",
+    "import shutil\n",
+    "\n",
+    "from vitessce import (\n",
+    "    VitessceConfig,\n",
+    "    ViewType as vt,\n",
+    "    CoordinationType as ct,\n",
+    "    CoordinationLevel as CL,\n",
+    "    SpatialDataWrapper,\n",
+    "    get_initial_coordination_scope_prefix\n",
+    ")\n",
+    "\n",
+    "from vitessce.data_utils import (\n",
+    "    sdata_morton_sort_points,\n",
+    "    sdata_points_process_columns,\n",
+    "    sdata_points_write_bounding_box_attrs,\n",
+    "    sdata_points_modify_row_group_size,\n",
+    "    sdata_morton_query_rect,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "from spatialdata_io import xenium\n",
+    "import spatialdata as sd\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from spatialdata import read_zarr\n",
+    "\n",
+    "import anndata as ad\n",
+    "\n",
+    "ad.settings.zarr_write_format = 3\n",
+    "print(ad.settings.zarr_write_format)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ls"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configure Vitessce\n",
+    "\n",
+    "Vitessce needs to know which pieces of data we are interested in visualizing, the visualization types we would like to use, and how we want to coordinate (or link) the views."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# out_zarr = \"../data/processed_data/vitessce/WTA_Preview_FFPE_Cervical_Cancer_outs.zarr\"\n",
+    "# out_zarr = \"../data/processed_data/vitessce/Xenium_Prime_Human_Lymph_Node_Reactive_FFPE_outs.zarr\"\n",
+    "# out_zarr = \"../data/processed_data/vitessce/Xenium_Prime_Ovarian_Cancer_FFPE_XRrun_outs.zarr\"\n",
+    "out_zarr = \"../data/processed_data/vitessce/Xenium_V1_humanLung_Cancer_FFPE_outs.zarr\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vc = VitessceConfig(\n",
+    "    schema_version=\"1.0.18\",\n",
+    "    name='Xenium SpatialData Demo',\n",
+    ")\n",
+    "\n",
+    "# Cell segmentations + gene expression\n",
+    "wrapper = SpatialDataWrapper(\n",
+    "    sdata_path=out_zarr,\n",
+    "    image_path=\"images/morphology_focus\",\n",
+    "    table_path=\"tables/table\",\n",
+    "    obs_feature_matrix_path=\"tables/table/X\",\n",
+    "    obs_segmentations_path=\"shapes/cell_boundaries\",\n",
+    "    # obs_set_paths=[\"tables/table/obs/leiden\"],\n",
+    "    # obs_set_names=[\"Cluster\"],\n",
+    "    coordinate_system=\"global\",\n",
+    "    coordination_values={\n",
+    "        \"obsType\": \"cell\",\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "# Transcripts\n",
+    "points_wrapper = SpatialDataWrapper(\n",
+    "    sdata_path=out_zarr,\n",
+    "    obs_points_path=\"points/transcripts_with_morton_codes\",\n",
+    "    obs_feature_matrix_path=\"tables/dense_table/X\",\n",
+    "    coordinate_system=\"global\",\n",
+    "    coordination_values={\n",
+    "        \"obsType\": \"point\",\n",
+    "        \"featureType\": \"gene\",\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "dataset = vc.add_dataset(name='Xenium').add_object(wrapper).add_object(points_wrapper)\n",
+    "\n",
+    "spatial = vc.add_view(\"spatialBeta\", dataset=dataset)\n",
+    "feature_list = vc.add_view(\"featureList\", dataset=dataset)\n",
+    "layer_controller = vc.add_view(\"layerControllerBeta\", dataset=dataset)\n",
+    "obs_sets = vc.add_view(\"obsSets\", dataset=dataset)\n",
+    "\n",
+    "vc.link_views_by_dict([spatial, layer_controller], {\n",
+    "    'segmentationLayer': CL([{\n",
+    "        'segmentationChannel': CL([{\n",
+    "            'obsType': 'cell',\n",
+    "            # 'obsColorEncoding': 'cellSetSelection',  # <-- this makes it default to cluster colors\n",
+    "        }]),\n",
+    "    }]),\n",
+    "}, scope_prefix=get_initial_coordination_scope_prefix(\"A\", \"obsSegmentations\"))\n",
+    "\n",
+    "vc.link_views_by_dict([spatial, layer_controller], {\n",
+    "    'pointLayer': CL([{\n",
+    "        'obsType': 'point',\n",
+    "    }]),\n",
+    "}, scope_prefix=get_initial_coordination_scope_prefix(\"A\", \"obsPoints\"))\n",
+    "\n",
+    "vc.link_views([spatial, layer_controller, feature_list, obs_sets], ['obsType'], [wrapper.obs_type_label])\n",
+    "\n",
+    "# vc.layout(spatial | (feature_list / layer_controller / obs_sets))\n",
+    "vc.layout(spatial)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Render the widget"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vw = vc.widget()\n",
+    "vw"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.12.1"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {
+     "8fa17251e5d6449a84f5cbc270c6674d": {
+      "model_module": "anywidget",
+      "model_module_version": "2.0.0",
+      "model_name": "AnyModel",
+      "state": {
+       "_anywidget_id": "vitessce.widget.VitessceWidget",
+       "_config": {
+        "coordinationSpace": {
+         "additionalObsSets": {
+          "A": null
+         },
+         "dataset": {
+          "A": "A",
+          "init_A_image_0": "init_A_image_0",
+          "init_A_obsSegmentations_0": "init_A_obsSegmentations_0"
+         },
+         "featureAggregationStrategy": {
+          "A": null,
+          "B": null
+         },
+         "featureColor": {
+          "A": null
+         },
+         "featureFilter": {
+          "A": null
+         },
+         "featureFilterMode": {
+          "A": null
+         },
+         "featureHighlight": {
+          "A": null
+         },
+         "featureSelection": {
+          "A": null
+         },
+         "featureType": {
+          "A": "gene"
+         },
+         "featureValueColormap": {
+          "A": "plasma",
+          "init_A_obsSegmentations_0": "plasma"
+         },
+         "featureValueColormapRange": {
+          "A": [
+           0,
+           1
+          ]
+         },
+         "featureValueType": {
+          "A": "expression"
+         },
+         "fileUid": {
+          "A": null,
+          "init_A_image_0": null,
+          "init_A_obsSegmentations_0": null
+         },
+         "imageChannel": {
+          "A": null,
+          "init_A_image_0": "__dummy__",
+          "init_A_image_1": "__dummy__",
+          "init_A_image_2": "__dummy__",
+          "init_A_image_3": "__dummy__"
+         },
+         "imageLayer": {
+          "A": null,
+          "init_A_image_0": "__dummy__"
+         },
+         "legendVisible": {
+          "A": true
+         },
+         "metaCoordinationScopes": {
+          "init_A_image_0": {
+           "imageLayer": [
+            "init_A_image_0"
+           ],
+           "spatialImageLayer": "init_A_image_0",
+           "spatialTargetT": "init_A_image_0",
+           "spatialTargetZ": "init_A_image_0"
+          },
+          "init_A_obsPoints_0": {
+           "pointLayer": [
+            "init_A_obsPoints_0"
+           ]
+          },
+          "init_A_obsSegmentations_0": {
+           "segmentationLayer": [
+            "init_A_obsSegmentations_0"
+           ]
+          }
+         },
+         "metaCoordinationScopesBy": {
+          "init_A_image_0": {
+           "imageChannel": {
+            "spatialChannelColor": {
+             "init_A_image_0": "init_A_image_0",
+             "init_A_image_1": "init_A_image_1",
+             "init_A_image_2": "init_A_image_2",
+             "init_A_image_3": "init_A_image_3"
+            },
+            "spatialChannelOpacity": {
+             "init_A_image_0": "init_A_image_0",
+             "init_A_image_1": "init_A_image_1",
+             "init_A_image_2": "init_A_image_2",
+             "init_A_image_3": "init_A_image_3"
+            },
+            "spatialChannelVisible": {
+             "init_A_image_0": "init_A_image_0",
+             "init_A_image_1": "init_A_image_1",
+             "init_A_image_2": "init_A_image_2",
+             "init_A_image_3": "init_A_image_3"
+            },
+            "spatialChannelWindow": {
+             "init_A_image_0": "init_A_image_0",
+             "init_A_image_1": "init_A_image_1",
+             "init_A_image_2": "init_A_image_2",
+             "init_A_image_3": "init_A_image_3"
+            },
+            "spatialTargetC": {
+             "init_A_image_0": "init_A_image_0",
+             "init_A_image_1": "init_A_image_1",
+             "init_A_image_2": "init_A_image_2",
+             "init_A_image_3": "init_A_image_3"
+            }
+           },
+           "imageLayer": {
+            "fileUid": {
+             "init_A_image_0": "init_A_image_0"
+            },
+            "imageChannel": {
+             "init_A_image_0": [
+              "init_A_image_0",
+              "init_A_image_1",
+              "init_A_image_2",
+              "init_A_image_3"
+             ]
+            },
+            "photometricInterpretation": {
+             "init_A_image_0": "init_A_image_0"
+            },
+            "spatialLayerOpacity": {
+             "init_A_image_0": "init_A_image_0"
+            },
+            "spatialLayerVisible": {
+             "init_A_image_0": "init_A_image_0"
+            },
+            "spatialTargetResolution": {
+             "init_A_image_0": "init_A_image_0"
+            },
+            "volumetricRenderingAlgorithm": {
+             "init_A_image_0": "init_A_image_0"
+            }
+           }
+          },
+          "init_A_obsPoints_0": {
+           "pointLayer": {
+            "obsType": {
+             "init_A_obsPoints_0": "init_A_obsPoints_0"
+            }
+           }
+          },
+          "init_A_obsSegmentations_0": {
+           "segmentationChannel": {
+            "featureValueColormap": {
+             "init_A_obsSegmentations_0": "init_A_obsSegmentations_0"
+            },
+            "obsColorEncoding": {
+             "init_A_obsSegmentations_0": "init_A_obsSegmentations_0"
+            },
+            "obsHighlight": {
+             "init_A_obsSegmentations_0": "init_A_obsSegmentations_0"
+            },
+            "obsType": {
+             "init_A_obsSegmentations_0": "init_A_obsSegmentations_0"
+            },
+            "spatialChannelColor": {
+             "init_A_obsSegmentations_0": "init_A_obsSegmentations_0"
+            },
+            "spatialChannelOpacity": {
+             "init_A_obsSegmentations_0": "init_A_obsSegmentations_0"
+            },
+            "spatialChannelVisible": {
+             "init_A_obsSegmentations_0": "init_A_obsSegmentations_0"
+            },
+            "spatialChannelWindow": {
+             "init_A_obsSegmentations_0": "init_A_obsSegmentations_0"
+            },
+            "spatialSegmentationFilled": {
+             "init_A_obsSegmentations_0": "init_A_obsSegmentations_0"
+            },
+            "spatialSegmentationStrokeWidth": {
+             "init_A_obsSegmentations_0": "init_A_obsSegmentations_0"
+            }
+           },
+           "segmentationLayer": {
+            "fileUid": {
+             "init_A_obsSegmentations_0": "init_A_obsSegmentations_0"
+            },
+            "segmentationChannel": {
+             "init_A_obsSegmentations_0": [
+              "init_A_obsSegmentations_0"
+             ]
+            },
+            "spatialLayerOpacity": {
+             "init_A_obsSegmentations_0": "init_A_obsSegmentations_0"
+            },
+            "spatialLayerVisible": {
+             "init_A_obsSegmentations_0": "init_A_obsSegmentations_0"
+            }
+           }
+          }
+         },
+         "moleculeHighlight": {
+          "A": null
+         },
+         "obsColorEncoding": {
+          "A": "cellSetSelection",
+          "init_A_obsSegmentations_0": "spatialChannelColor"
+         },
+         "obsFilter": {
+          "A": null
+         },
+         "obsHighlight": {
+          "A": null,
+          "init_A_obsSegmentations_0": null
+         },
+         "obsLabelsType": {
+          "A": null
+         },
+         "obsSetColor": {
+          "A": null
+         },
+         "obsSetExpansion": {
+          "A": null
+         },
+         "obsSetFilter": {
+          "A": null
+         },
+         "obsSetHighlight": {
+          "A": null
+         },
+         "obsSetSelection": {
+          "A": null
+         },
+         "obsType": {
+          "A": "cell",
+          "init_A_obsPoints_0": "point",
+          "init_A_obsSegmentations_0": "cell"
+         },
+         "photometricInterpretation": {
+          "A": null,
+          "init_A_image_0": "BlackIsZero"
+         },
+         "pixelHighlight": {
+          "A": null
+         },
+         "pointLayer": {
+          "A": null,
+          "init_A_obsPoints_0": "__dummy__"
+         },
+         "segmentationChannel": {
+          "A": null,
+          "init_A_obsSegmentations_0": "__dummy__"
+         },
+         "segmentationLayer": {
+          "A": null,
+          "init_A_obsSegmentations_0": "__dummy__"
+         },
+         "spatialAxisFixed": {
+          "A": false
+         },
+         "spatialChannelColor": {
+          "A": [
+           255,
+           255,
+           255
+          ],
+          "init_A_image_0": [
+           0,
+           0,
+           255
+          ],
+          "init_A_image_1": [
+           0,
+           255,
+           0
+          ],
+          "init_A_image_2": [
+           255,
+           0,
+           255
+          ],
+          "init_A_image_3": [
+           255,
+           255,
+           0
+          ],
+          "init_A_obsSegmentations_0": [
+           255,
+           255,
+           255
+          ]
+         },
+         "spatialChannelLabelSize": {
+          "A": 14
+         },
+         "spatialChannelLabelsOrientation": {
+          "A": "vertical"
+         },
+         "spatialChannelLabelsVisible": {
+          "A": true
+         },
+         "spatialChannelOpacity": {
+          "A": 1,
+          "init_A_image_0": 1,
+          "init_A_image_1": 1,
+          "init_A_image_2": 1,
+          "init_A_image_3": 1,
+          "init_A_obsSegmentations_0": 1
+         },
+         "spatialChannelVisible": {
+          "A": true,
+          "init_A_image_0": true,
+          "init_A_image_1": true,
+          "init_A_image_2": true,
+          "init_A_image_3": true,
+          "init_A_obsSegmentations_0": true
+         },
+         "spatialChannelWindow": {
+          "A": null,
+          "init_A_image_0": [
+           0,
+           8339
+          ],
+          "init_A_image_1": [
+           0,
+           8617
+          ],
+          "init_A_image_2": [
+           0,
+           10931
+          ],
+          "init_A_image_3": [
+           0,
+           10716
+          ],
+          "init_A_obsSegmentations_0": null
+         },
+         "spatialImageLayer": {
+          "init_A_image_0": [
+           {
+            "channels": [
+             {
+              "color": [
+               255,
+               255,
+               255
+              ],
+              "selection": {
+               "c": 0
+              },
+              "slider": [
+               0,
+               255
+              ],
+              "visible": true
+             },
+             {
+              "color": [
+               255,
+               255,
+               255
+              ],
+              "selection": {
+               "c": 1
+              },
+              "slider": [
+               0,
+               255
+              ],
+              "visible": true
+             },
+             {
+              "color": [
+               255,
+               255,
+               255
+              ],
+              "selection": {
+               "c": 2
+              },
+              "slider": [
+               0,
+               255
+              ],
+              "visible": true
+             },
+             {
+              "color": [
+               255,
+               255,
+               255
+              ],
+              "selection": {
+               "c": 3
+              },
+              "slider": [
+               0,
+               255
+              ],
+              "visible": true
+             }
+            ],
+            "colormap": null,
+            "domainType": "Min/Max",
+            "index": 0,
+            "modelMatrix": [
+             1,
+             0,
+             0,
+             0,
+             0,
+             1,
+             0,
+             0,
+             0,
+             0,
+             1,
+             0,
+             0,
+             0,
+             0,
+             1
+            ],
+            "opacity": 1,
+            "renderingMode": "Additive",
+            "transparentColor": null,
+            "type": "raster",
+            "use3d": false,
+            "visible": true
+           }
+          ]
+         },
+         "spatialLayerColor": {
+          "A": null
+         },
+         "spatialLayerColormap": {
+          "A": null
+         },
+         "spatialLayerModelMatrix": {
+          "A": null
+         },
+         "spatialLayerOpacity": {
+          "A": 1,
+          "init_A_image_0": 1,
+          "init_A_obsSegmentations_0": 1
+         },
+         "spatialLayerTransparentColor": {
+          "A": null
+         },
+         "spatialLayerVisible": {
+          "A": true,
+          "init_A_image_0": true,
+          "init_A_obsSegmentations_0": true
+         },
+         "spatialMaxResolution": {
+          "A": null
+         },
+         "spatialNeighborhoodLayer": {
+          "A": null
+         },
+         "spatialOrbitAxis": {
+          "A": "Y",
+          "B": "Y"
+         },
+         "spatialPointLayer": {
+          "A": null
+         },
+         "spatialPointStrokeWidth": {
+          "A": 0
+         },
+         "spatialRenderingMode": {
+          "A": "2D"
+         },
+         "spatialRotation": {
+          "A": 0
+         },
+         "spatialRotationOrbit": {
+          "A": 0,
+          "B": 0
+         },
+         "spatialRotationX": {
+          "A": 0,
+          "B": 0
+         },
+         "spatialRotationY": {
+          "A": 0,
+          "B": 0
+         },
+         "spatialRotationZ": {
+          "A": 0,
+          "B": 0
+         },
+         "spatialSegmentationFilled": {
+          "A": true,
+          "init_A_obsSegmentations_0": true
+         },
+         "spatialSegmentationStrokeWidth": {
+          "A": 1,
+          "init_A_obsSegmentations_0": 1
+         },
+         "spatialSliceX": {
+          "A": null
+         },
+         "spatialSliceY": {
+          "A": null
+         },
+         "spatialSliceZ": {
+          "A": null
+         },
+         "spatialSpotFilled": {
+          "A": true
+         },
+         "spatialSpotRadius": {
+          "A": 25
+         },
+         "spatialSpotStrokeWidth": {
+          "A": 1
+         },
+         "spatialTargetC": {
+          "A": null,
+          "init_A_image_0": 0,
+          "init_A_image_1": 1,
+          "init_A_image_2": 2,
+          "init_A_image_3": 3
+         },
+         "spatialTargetResolution": {
+          "A": 0,
+          "init_A_image_0": null
+         },
+         "spatialTargetT": {
+          "A": null,
+          "init_A_image_0": 0
+         },
+         "spatialTargetX": {
+          "A": null,
+          "B": null
+         },
+         "spatialTargetY": {
+          "A": null,
+          "B": null
+         },
+         "spatialTargetZ": {
+          "A": null,
+          "B": null,
+          "init_A_image_0": null
+         },
+         "spatialZoom": {
+          "A": null,
+          "B": null
+         },
+         "spotLayer": {
+          "A": null
+         },
+         "tooltipCrosshairsVisible": {
+          "A": true
+         },
+         "tooltipsVisible": {
+          "A": true
+         },
+         "volumetricRenderingAlgorithm": {
+          "A": "additive",
+          "init_A_image_0": "maximumIntensityProjection"
+         }
+        },
+        "datasets": [
+         {
+          "files": [
+           {
+            "coordinationValues": {
+             "featureType": "gene",
+             "featureValueType": "expression",
+             "obsType": "cell"
+            },
+            "fileType": "obsFeatureMatrix.spatialdata.zarr",
+            "options": {
+             "path": "tables/table/X"
+            },
+            "url": "http://localhost:8000/A/0/f595faf2-239a-47f2-a3c7-27314f281a5a.sdata.zarr"
+           },
+           {
+            "coordinationValues": {
+             "featureType": "gene"
+            },
+            "fileType": "image.spatialdata.zarr",
+            "options": {
+             "coordinateSystem": "global",
+             "path": "images/morphology_focus"
+            },
+            "url": "http://localhost:8000/A/0/f595faf2-239a-47f2-a3c7-27314f281a5a.sdata.zarr"
+           },
+           {
+            "coordinationValues": {
+             "obsType": "cell"
+            },
+            "fileType": "shapes.spatialdata.zarr",
+            "options": {
+             "coordinateSystem": "global",
+             "path": "shapes/cell_boundaries",
+             "tablePath": "tables/table"
+            },
+            "url": "http://localhost:8000/A/0/f595faf2-239a-47f2-a3c7-27314f281a5a.sdata.zarr"
+           },
+           {
+            "coordinationValues": {
+             "featureType": "gene",
+             "featureValueType": "expression",
+             "obsType": "point"
+            },
+            "fileType": "obsFeatureMatrix.spatialdata.zarr",
+            "options": {
+             "path": "tables/dense_table/X"
+            },
+            "url": "http://localhost:8000/A/1/afd11240-c386-4fe7-8bfd-f77a5138e9bc.sdata.zarr"
+           },
+           {
+            "coordinationValues": {
+             "obsType": "point"
+            },
+            "fileType": "obsPoints.spatialdata.zarr",
+            "options": {
+             "coordinateSystem": "global",
+             "path": "points/transcripts_with_morton_codes",
+             "tablePath": "tables/table"
+            },
+            "url": "http://localhost:8000/A/1/afd11240-c386-4fe7-8bfd-f77a5138e9bc.sdata.zarr"
+           }
+          ],
+          "name": "Xenium",
+          "uid": "A"
+         }
+        ],
+        "description": "",
+        "initStrategy": "auto",
+        "layout": [
+         {
+          "component": "spatialBeta",
+          "coordinationScopes": {
+           "additionalObsSets": "A",
+           "dataset": "A",
+           "featureAggregationStrategy": "A",
+           "featureColor": "A",
+           "featureFilterMode": "A",
+           "featureHighlight": "A",
+           "featureSelection": "A",
+           "featureType": "A",
+           "featureValueColormap": "A",
+           "featureValueColormapRange": "A",
+           "featureValueType": "A",
+           "fileUid": "A",
+           "imageChannel": "A",
+           "imageLayer": "A",
+           "legendVisible": "A",
+           "metaCoordinationScopes": [
+            "init_A_image_0",
+            "init_A_obsSegmentations_0",
+            "init_A_obsSegmentations_0",
+            "init_A_obsPoints_0"
+           ],
+           "metaCoordinationScopesBy": [
+            "init_A_image_0",
+            "init_A_obsSegmentations_0",
+            "init_A_obsSegmentations_0",
+            "init_A_obsPoints_0"
+           ],
+           "moleculeHighlight": "A",
+           "obsColorEncoding": "A",
+           "obsFilter": "A",
+           "obsHighlight": "A",
+           "obsLabelsType": "A",
+           "obsSetColor": "A",
+           "obsSetFilter": "A",
+           "obsSetHighlight": "A",
+           "obsSetSelection": "A",
+           "obsType": "A",
+           "photometricInterpretation": "A",
+           "pixelHighlight": "A",
+           "pointLayer": "A",
+           "segmentationChannel": "A",
+           "segmentationLayer": "A",
+           "spatialAxisFixed": "A",
+           "spatialChannelColor": "A",
+           "spatialChannelLabelSize": "A",
+           "spatialChannelLabelsOrientation": "A",
+           "spatialChannelLabelsVisible": "A",
+           "spatialChannelOpacity": "A",
+           "spatialChannelVisible": "A",
+           "spatialChannelWindow": "A",
+           "spatialLayerColor": "A",
+           "spatialLayerColormap": "A",
+           "spatialLayerModelMatrix": "A",
+           "spatialLayerOpacity": "A",
+           "spatialLayerTransparentColor": "A",
+           "spatialLayerVisible": "A",
+           "spatialMaxResolution": "A",
+           "spatialNeighborhoodLayer": "A",
+           "spatialOrbitAxis": "A",
+           "spatialPointLayer": "A",
+           "spatialRenderingMode": "A",
+           "spatialRotation": "A",
+           "spatialRotationOrbit": "A",
+           "spatialRotationX": "A",
+           "spatialRotationY": "A",
+           "spatialRotationZ": "A",
+           "spatialSegmentationFilled": "A",
+           "spatialSegmentationStrokeWidth": "A",
+           "spatialSliceX": "A",
+           "spatialSliceY": "A",
+           "spatialSliceZ": "A",
+           "spatialSpotFilled": "A",
+           "spatialSpotRadius": "A",
+           "spatialSpotStrokeWidth": "A",
+           "spatialTargetC": "A",
+           "spatialTargetResolution": "A",
+           "spatialTargetT": "A",
+           "spatialTargetX": "A",
+           "spatialTargetY": "A",
+           "spatialTargetZ": "A",
+           "spatialZoom": "A",
+           "spotLayer": "A",
+           "tooltipCrosshairsVisible": "A",
+           "tooltipsVisible": "A",
+           "volumetricRenderingAlgorithm": "A"
+          },
+          "h": 12,
+          "uid": "A",
+          "w": 12,
+          "x": 0,
+          "y": 0
+         },
+         {
+          "component": "featureList",
+          "coordinationScopes": {
+           "dataset": "A",
+           "featureFilter": "A",
+           "featureHighlight": "A",
+           "featureSelection": "A",
+           "featureType": "A",
+           "featureValueType": "A",
+           "obsColorEncoding": "A",
+           "obsSetSelection": "A",
+           "obsType": "A"
+          },
+          "h": 1,
+          "uid": "B",
+          "w": 1,
+          "x": 0,
+          "y": 0
+         },
+         {
+          "component": "layerControllerBeta",
+          "coordinationScopes": {
+           "dataset": "A",
+           "featureAggregationStrategy": "B",
+           "featureColor": "A",
+           "featureFilterMode": "A",
+           "featureSelection": "A",
+           "featureType": "A",
+           "featureValueColormap": "A",
+           "featureValueColormapRange": "A",
+           "featureValueType": "A",
+           "fileUid": "A",
+           "imageChannel": "A",
+           "imageLayer": "A",
+           "legendVisible": "A",
+           "metaCoordinationScopes": [
+            "init_A_image_0",
+            "init_A_obsSegmentations_0",
+            "init_A_obsSegmentations_0",
+            "init_A_obsPoints_0"
+           ],
+           "metaCoordinationScopesBy": [
+            "init_A_image_0",
+            "init_A_obsSegmentations_0",
+            "init_A_obsSegmentations_0",
+            "init_A_obsPoints_0"
+           ],
+           "obsColorEncoding": "A",
+           "obsType": "A",
+           "photometricInterpretation": "A",
+           "pointLayer": "A",
+           "segmentationChannel": "A",
+           "segmentationLayer": "A",
+           "spatialChannelColor": "A",
+           "spatialChannelLabelSize": "A",
+           "spatialChannelLabelsOrientation": "A",
+           "spatialChannelLabelsVisible": "A",
+           "spatialChannelOpacity": "A",
+           "spatialChannelVisible": "A",
+           "spatialChannelWindow": "A",
+           "spatialLayerColor": "A",
+           "spatialLayerColormap": "A",
+           "spatialLayerModelMatrix": "A",
+           "spatialLayerOpacity": "A",
+           "spatialLayerTransparentColor": "A",
+           "spatialLayerVisible": "A",
+           "spatialMaxResolution": "A",
+           "spatialNeighborhoodLayer": "A",
+           "spatialOrbitAxis": "B",
+           "spatialPointLayer": "A",
+           "spatialPointStrokeWidth": "A",
+           "spatialRenderingMode": "A",
+           "spatialRotationOrbit": "B",
+           "spatialRotationX": "B",
+           "spatialRotationY": "B",
+           "spatialRotationZ": "B",
+           "spatialSegmentationFilled": "A",
+           "spatialSegmentationStrokeWidth": "A",
+           "spatialSliceX": "A",
+           "spatialSliceY": "A",
+           "spatialSliceZ": "A",
+           "spatialSpotFilled": "A",
+           "spatialSpotRadius": "A",
+           "spatialSpotStrokeWidth": "A",
+           "spatialTargetC": "A",
+           "spatialTargetResolution": "A",
+           "spatialTargetT": "A",
+           "spatialTargetX": "B",
+           "spatialTargetY": "B",
+           "spatialTargetZ": "B",
+           "spatialZoom": "B",
+           "spotLayer": "A",
+           "tooltipCrosshairsVisible": "A",
+           "tooltipsVisible": "A",
+           "volumetricRenderingAlgorithm": "A"
+          },
+          "h": 1,
+          "uid": "C",
+          "w": 1,
+          "x": 0,
+          "y": 0
+         },
+         {
+          "component": "obsSets",
+          "coordinationScopes": {
+           "additionalObsSets": "A",
+           "dataset": "A",
+           "featureSelection": "A",
+           "obsColorEncoding": "A",
+           "obsSetColor": "A",
+           "obsSetExpansion": "A",
+           "obsSetFilter": "A",
+           "obsSetHighlight": "A",
+           "obsSetSelection": "A",
+           "obsType": "A"
+          },
+          "h": 1,
+          "uid": "D",
+          "w": 1,
+          "x": 0,
+          "y": 0
+         }
+        ],
+        "name": "Xenium SpatialData Demo",
+        "uid": "A",
+        "version": "1.0.18"
+       },
+       "_esm": "\nlet importWithMap;\ntry {\n    importWithMap = (await import('https://unpkg.com/dynamic-importmap@0.1.0')).importWithMap;\n} catch(e) {\n    console.warn(\"Import of dynamic-importmap failed, trying fallback.\");\n    importWithMap = (await import('https://cdn.vitessce.io/dynamic-importmap@0.1.0/dist/index.js')).importWithMap;\n}\n\nconst successfulImportMap = {\n    imports: {\n\n    },\n};\nconst importMap = {\n  imports: {\n    \"react\": \"https://esm.sh/react@18.2.0?dev\",\n    \"react-dom\": \"https://esm.sh/react-dom@18.2.0?dev\",\n    \"react-dom/client\": \"https://esm.sh/react-dom@18.2.0/client?dev\",\n  },\n};\nconst fallbackImportMap = {\n    imports: {\n        \"react\": \"https://cdn.vitessce.io/react@18.2.0/index.js\",\n        \"react-dom\": \"https://cdn.vitessce.io/react-dom@18.2.0/index.js\",\n        \"react-dom/client\": \"https://cdn.vitessce.io/react-dom@18.2.0/es2022/client.mjs\",\n        // Replaced with version-specific URL below.\n        \"vitessce\": \"https://cdn.vitessce.io/vitessce@VERSION/dist/index.min.js\",\n    },\n};\n/*\nconst fallbackDevImportMap = {\n    imports: {\n        \"react\": \"https://cdn.vitessce.io/react@18.2.0/index_dev.js\",\n        \"react-dom\": \"https://cdn.vitessce.io/react-dom@18.2.0/index_dev.js\",\n        \"react-dom/client\": \"https://cdn.vitessce.io/react-dom@18.2.0/es2022/client.development.mjs\",\n        // Replaced with version-specific URL below.\n        \"vitessce\": \"https://cdn.vitessce.io/@vitessce/dev@VERSION/dist/index.js\",\n    },\n};\n*/\n\nasync function importWithMapAndFallback(moduleName, importMap, fallbackMap) {\n    let result = null;\n    if (!fallbackMap) {\n        // fallbackMap is null, user may have provided custom JS URL.\n        result = await importWithMap(moduleName, {\n            imports: {\n                ...importMap.imports,\n                ...successfulImportMap.imports,\n            },\n        });\n        successfulImportMap.imports[moduleName] = importMap.imports[moduleName];\n    } else {\n        try {\n            result = await importWithMap(moduleName, {\n                imports: {\n                    ...importMap.imports,\n                    ...successfulImportMap.imports,\n                },\n            });\n            successfulImportMap.imports[moduleName] = importMap.imports[moduleName];\n        } catch (e) {\n            console.warn(`Importing ${moduleName} failed with importMap`, importMap, \"trying fallback\", fallbackMap, successfulImportMap);\n            result = await importWithMap(moduleName, {\n                imports: {\n                    ...fallbackMap.imports,\n                    ...successfulImportMap.imports,\n                },\n            });\n            successfulImportMap.imports[moduleName] = fallbackMap.imports[moduleName];\n        }\n    }\n    return result;\n}\n\n\nconst React = await importWithMapAndFallback(\"react\", importMap, fallbackImportMap);\nconst { createRoot } = await importWithMapAndFallback(\"react-dom/client\", importMap, fallbackImportMap);\n\nconst e = React.createElement;\n\nfunction isAbsoluteUrl(s) {\n    return s?.startsWith('http://') || s?.startsWith('https://');\n}\nconst WORKSPACES_URL_KEYWORD = 'https://workspaces-pt';\nconst OPTIONS_URL_KEYS = ['offsetsUrl', 'refSpecUrl'];\nconst prefersDark = window.matchMedia && window.matchMedia('(prefers-color-scheme: dark)').matches;\n// The jupyter server may be running through a proxy,\n// which means that the client needs to prepend the part of the URL before /proxy/8000 such as\n// https://hub.gke2.mybinder.org/user/vitessce-vitessce-python-swi31vcv/proxy/8000/A/0/cells\n// For workspaces: https://workspaces-pt.hubmapconsortium.org/passthrough/HOSTNAME/PORT/ADDITIONAL_PATH_INFO?QUERY_PARAMS=HELLO_WORLD\nfunction prependBaseUrl(config, proxy, hasHostName) {\n    if (!proxy || hasHostName) {\n        return config;\n    }\n    const { origin, pathname } = new URL(window.location.href);\n    const isInWorkspaces = origin.startsWith(WORKSPACES_URL_KEYWORD);\n    const jupyterLabConfigEl = document.getElementById('jupyter-config-data');\n\n    let baseUrl;\n    if (isInWorkspaces) {\n        const pathSegments = pathname.split('/');\n        const passthroughIndex = pathSegments.indexOf('passthrough');\n        if (passthroughIndex !== -1) {\n            baseUrl = pathSegments.slice(0, passthroughIndex + 3).join('/');\n            baseUrl += '/';\n        }\n    } else if (jupyterLabConfigEl) {\n        // This is jupyter lab\n        baseUrl = JSON.parse(jupyterLabConfigEl.textContent || '').baseUrl;\n    } else {\n        // This is jupyter notebook\n        baseUrl = document.getElementsByTagName('body')[0].getAttribute('data-base-url');\n    }\n    return {\n        ...config,\n        datasets: config.datasets.map(d => ({\n            ...d,\n            files: d.files.map(f => {\n                const updatedFileDef = { ...f };\n                if (f.url && !isAbsoluteUrl(f.url) ) {\n                    // Update the main file URL if necessary.\n                    updatedFileDef.url = `${origin}${baseUrl}${f.url}`;\n                }\n                if (f.options) {\n                    // Update any urls within the options object\n                    const updatedOptions = { ...f.options };\n                    OPTIONS_URL_KEYS.forEach(key => {\n                        const optionValue = updatedOptions[key];\n                        if (optionValue && !isAbsoluteUrl(optionValue)) {\n                            updatedOptions[key] = `${origin}${baseUrl}${optionValue}`;\n                        }\n                    });\n\n                    // Update image URLs if they exist\n                    if ('images' in f.options && Array.isArray(f.options.images)) {\n                        const updatedImages = f.options.images.map(image => {\n                            const updatedImage = { ...image };\n\n                            if (image.url && !isAbsoluteUrl(image.url)) {\n                                updatedImage.url = `${origin}${baseUrl}${image.url}`;\n                            }\n\n                            const metadata = { ...image.metadata };\n                            if (metadata?.omeTiffOffsetsUrl && !isAbsoluteUrl(metadata.omeTiffOffsetsUrl)) {\n                                metadata.omeTiffOffsetsUrl = `${origin}${baseUrl}${metadata.omeTiffOffsetsUrl}`;\n                            }\n\n                            updatedImage.metadata = metadata;\n\n                            return updatedImage;\n                        });\n\n                        updatedOptions.images = updatedImages;\n                    }\n                    updatedFileDef.options = updatedOptions;\n                }\n                return updatedFileDef;\n            }),\n        })),\n    };\n}\n\nasync function render(view) {\n    const cssUid = view.model.get('uid');\n    const jsDevMode = view.model.get('js_dev_mode');\n    const jsPackageVersion = view.model.get('js_package_version');\n    const customJsUrl = view.model.get('custom_js_url');\n    const pluginEsmArr = view.model.get('plugin_esm');\n    const remountOnUidChange = view.model.get('remount_on_uid_change');\n    const storeUrls = view.model.get('store_urls');\n    const invokeTimeout = view.model.get('invoke_timeout');\n    const invokeBatched = view.model.get('invoke_batched');\n    const preventScroll = view.model.get('prevent_scroll');\n\n    const pageMode = view.model.get('page_mode');\n    const pageEsm = view.model.get('page_esm');\n\n    const pkgName = (jsDevMode ? \"@vitessce/dev\" : \"vitessce\");\n\n    const hasCustomJsUrl = customJsUrl.length > 0;\n\n    importMap.imports[\"vitessce\"] = (hasCustomJsUrl\n        ? customJsUrl\n        : `https://unpkg.com/${pkgName}@${jsPackageVersion}`\n    );\n    let fallbackImportMapToUse = null;\n    if (!hasCustomJsUrl) {\n        fallbackImportMapToUse = fallbackImportMap;\n        if (jsDevMode) {\n            fallbackImportMapToUse.imports[\"vitessce\"] = `https://cdn.vitessce.io/vitessce@${jsPackageVersion}/dist/index.min.js`;\n        } else {\n            fallbackImportMapToUse.imports[\"vitessce\"] = `https://cdn.vitessce.io/@vitessce/dev@${jsPackageVersion}/dist/index.js`;\n        }\n    }\n\n    const {\n        Vitessce,\n        PluginFileType,\n        PluginViewType,\n        PluginCoordinationType,\n        PluginJointFileType,\n        PluginAsyncFunction,\n        z,\n        useCoordination,\n        usePageModeView,\n        useGridItemSize,\n        // TODO: names and function signatures are subject to change for the following functions\n        // Reference: https://github.com/keller-mark/use-coordination/issues/37#issuecomment-1946226827\n        useComplexCoordination,\n        useMultiCoordinationScopesNonNull,\n        useMultiCoordinationScopesSecondaryNonNull,\n        useComplexCoordinationSecondary,\n        useCoordinationScopes,\n        useCoordinationScopesBy,\n    } = await importWithMapAndFallback(\"vitessce\", importMap, fallbackImportMapToUse);\n\n    let pluginViewTypes = [];\n    let pluginCoordinationTypes = [];\n    let pluginFileTypes = [];\n    let pluginJointFileTypes = [];\n    let pluginAsyncFunctions = [];\n\n    let pending = [];\n    let batchId = 0;\n\n    async function processBatch(prevPendingArr) {\n        const [dataArr, buffersArr] = await view.experimental.invoke(\"_zarr_get_multi\", prevPendingArr.map(d => d.params), {\n            signal: AbortSignal.timeout(invokeTimeout),\n        });\n        prevPendingArr.forEach((prevPendingItem, i) => {\n            const data = dataArr[i];\n            const bufferData = buffersArr[i];\n            const { params, resolve, reject } = prevPendingItem;\n            const [storeUrl, key] = params;\n\n            if (!data.success) {\n                resolve(undefined);\n                return;\n            }\n\n            if (ArrayBuffer.isView(bufferData)) {\n                resolve(new Uint8Array(bufferData.buffer, bufferData.byteOffset, bufferData.byteLength));\n                return;\n            }\n            resolve(new Uint8Array(bufferData.buffer));\n            return;\n        });\n    }\n\n    function run() {\n        processBatch(pending);\n        pending = [];\n        batchId = 0;\n    }\n\n    function enqueue(params) {\n        batchId = batchId || requestAnimationFrame(() => run());\n        let { promise, resolve, reject } = Promise.withResolvers();\n        pending.push({ params, resolve, reject });\n        return promise;\n    }\n\n\n    const stores = Object.fromEntries(\n        storeUrls.map(storeUrl => ([\n            storeUrl,\n            {\n                async get(key) {\n                    if (invokeBatched) {\n                        return enqueue([storeUrl, key]);\n                    } else {\n                        // Do not submit zarr gets in batches. Instead, submit individually.\n                        const [data, buffers] = await view.experimental.invoke(\"_zarr_get\", [storeUrl, key], {\n                            signal: AbortSignal.timeout(invokeTimeout),\n                        });\n                        if (!data.success) return undefined;\n\n                        if (ArrayBuffer.isView(buffers[0])) {\n                            return new Uint8Array(buffers[0].buffer, buffers[0].byteOffset, buffers[0].byteLength);\n                        }\n                        return new Uint8Array(buffers[0].buffer);\n                    }\n                },\n                async getRange(key, rangeQuery) {\n                    if (invokeBatched) {\n                        return enqueue([storeUrl, key, rangeQuery]);\n                    } else {\n                        // Do not submit zarr gets in batches. Instead, submit individually.\n                        const [data, buffers] = await view.experimental.invoke(\"_zarr_get_range\", [storeUrl, key, rangeQuery], {\n                            signal: AbortSignal.timeout(invokeTimeout),\n                        });\n                        if (!data.success) return undefined;\n\n                        if (ArrayBuffer.isView(buffers[0])) {\n                            return new Uint8Array(buffers[0].buffer, buffers[0].byteOffset, buffers[0].byteLength);\n                        }\n                        return new Uint8Array(buffers[0].buffer);\n                    }\n                },\n            }\n        ])),\n    );\n\n    function invokePluginCommand(commandName, commandParams, commandBuffers) {\n        return view.experimental.invoke(\"_plugin_command\", [commandName, commandParams], {\n            signal: AbortSignal.timeout(invokeTimeout),\n            ...(commandBuffers ? { buffers: commandBuffers } : {}),\n        });\n    }\n\n    for (const pluginEsm of pluginEsmArr) {\n        try {\n            const pluginEsmUrl = URL.createObjectURL(new Blob([pluginEsm], { type: \"text/javascript\" }));\n            const pluginModule = (await import(pluginEsmUrl)).default;\n            URL.revokeObjectURL(pluginEsmUrl);\n\n            const pluginDeps = {\n                React,\n                PluginFileType,\n                PluginViewType,\n                PluginCoordinationType,\n                PluginJointFileType,\n                PluginAsyncFunction,\n                z,\n                invokeCommand: invokePluginCommand,\n                useCoordination,\n                useGridItemSize,\n                useComplexCoordination,\n                useMultiCoordinationScopesNonNull,\n                useMultiCoordinationScopesSecondaryNonNull,\n                useComplexCoordinationSecondary,\n                useCoordinationScopes,\n                useCoordinationScopesBy,\n            };\n            const pluginsObj = await pluginModule.createPlugins(pluginDeps);\n            if(Array.isArray(pluginsObj.pluginViewTypes)) {\n                pluginViewTypes = [...pluginViewTypes, ...pluginsObj.pluginViewTypes];\n            }\n            if(Array.isArray(pluginsObj.pluginCoordinationTypes)) {\n                pluginCoordinationTypes = [...pluginCoordinationTypes, ...pluginsObj.pluginCoordinationTypes];\n            }\n            if(Array.isArray(pluginsObj.pluginFileTypes)) {\n                pluginFileTypes = [...pluginFileTypes, ...pluginsObj.pluginFileTypes];\n            }\n            if(Array.isArray(pluginsObj.pluginJointFileTypes)) {\n                pluginJointFileTypes = [...pluginJointFileTypes, ...pluginsObj.pluginJointFileTypes];\n            }\n            if(Array.isArray(pluginsObj.pluginAsyncFunctions)) {\n                pluginAsyncFunctions = [...pluginAsyncFunctions, ...pluginsObj.pluginAsyncFunctions];\n            }\n        } catch(e) {\n            console.error(\"Error loading plugin ESM or executing createPlugins function.\");\n            console.error(e);\n        }\n    }\n\n    let PageComponent;\n    if(pageMode && pageEsm.length > 0) {\n        try {\n            const pageEsmUrl = URL.createObjectURL(new Blob([pageEsm], { type: \"text/javascript\" }));\n            const pageModule = (await import(pageEsmUrl)).default;\n            URL.revokeObjectURL(pageEsmUrl);\n\n            const pageDeps = {\n                React,\n                usePageModeView,\n            };\n            PageComponent = await pageModule.createPage(pageDeps);\n        } catch(e) {\n            console.error(\"Error loading page ESM or executing createPage function.\")\n            console.error(e);\n        }\n    }\n\n    function VitessceWidget(props) {\n        const { model, styleContainer } = props;\n\n        const [config, setConfig] = React.useState(prependBaseUrl(model.get('_config'), model.get('proxy'), model.get('has_host_name')));\n        const [validateConfig, setValidateConfig] = React.useState(true);\n        const height = model.get('height');\n        const theme = model.get('theme') === 'auto' ? (prefersDark ? 'dark' : 'light') : model.get('theme');\n\n        const divRef = React.useRef();\n\n        React.useEffect(() => {\n            if(!divRef.current || !preventScroll) {\n                return () => {};\n            }\n\n            function handleMouseEnter() {\n                const jpn = divRef.current.closest('.jp-Notebook');\n                if(jpn) {\n                    jpn.style.overflow = \"hidden\";\n                }\n            }\n            function handleMouseLeave(event) {\n                if(event.relatedTarget === null || (event.relatedTarget && event.relatedTarget.closest('.jp-Notebook')?.length)) return;\n                const jpn = divRef.current.closest('.jp-Notebook');\n                if(jpn) {\n                    jpn.style.overflow = \"auto\";\n                }\n            }\n            divRef.current.addEventListener(\"mouseenter\", handleMouseEnter);\n            divRef.current.addEventListener(\"mouseleave\", handleMouseLeave);\n\n            return () => {\n                if(divRef.current) {\n                    divRef.current.removeEventListener(\"mouseenter\", handleMouseEnter);\n                    divRef.current.removeEventListener(\"mouseleave\", handleMouseLeave);\n                }\n            };\n        }, [divRef, preventScroll]);\n\n        // Config changed on JS side (from within <Vitessce/>),\n        // send updated config to Python side.\n        const onConfigChange = React.useCallback((config) => {\n            model.set('_config', config);\n            setValidateConfig(false);\n            model.save_changes();\n        }, [model]);\n\n        // Config changed on Python side,\n        // pass to <Vitessce/> component to it is updated on JS side.\n        React.useEffect(() => {\n            model.on('change:_config', () => {\n                const newConfig = prependBaseUrl(model.get('_config'), model.get('proxy'), model.get('has_host_name'));\n\n                // Force a re-render and re-validation by setting a new config.uid value.\n                // TODO: make this conditional on a parameter from Python.\n                //newConfig.uid = `random-${Math.random()}`;\n                //console.log('newConfig', newConfig);\n                setConfig(newConfig);\n            });\n        }, []);\n\n        const vitessceProps = {\n            height, theme, config, onConfigChange, validateConfig,\n            pluginViewTypes, pluginCoordinationTypes,\n            pluginFileTypes,pluginJointFileTypes, pluginAsyncFunctions,\n            remountOnUidChange, stores, pageMode, styleContainer,\n        };\n\n        return e('div', { ref: divRef, style: { height: height + 'px' } },\n            e(React.Suspense, { fallback: e('div', {}, 'Loading...') },\n                e(React.StrictMode, {},\n                    e(Vitessce, vitessceProps,\n                        (pageMode ? e(PageComponent, {}) : null)\n                    ),\n                ),\n            ),\n        );\n    }\n\n    const root = createRoot(view.el);\n    // Marimo puts AnyWidgets in a Shadow Root, so we need to tell Emotion to\n    // insert styles within the Shadow DOM.\n    const rootNode = view.el.getRootNode();\n    const styleContainer = rootNode === document ? undefined : rootNode;\n    root.render(e(VitessceWidget, { model: view.model, styleContainer }));\n\n    return () => {\n        // Re-enable scrolling.\n        const jpn = view.el.closest('.jp-Notebook');\n        if(jpn) {\n            jpn.style.overflow = \"auto\";\n        }\n\n        // Clean up React and DOM state.\n        root.unmount();\n        if(view._isFromDisplay) {\n            view.el.remove();\n        }\n    };\n}\nexport default { render };\n",
+       "_model_module": "anywidget",
+       "_model_name": "AnyModel",
+       "_view_name": "ErrorWidgetView",
+       "custom_js_url": "",
+       "error": {},
+       "has_host_name": false,
+       "height": 600,
+       "invoke_batched": true,
+       "invoke_timeout": 300000,
+       "js_dev_mode": false,
+       "js_package_version": "3.9.9",
+       "layout": "IPY_MODEL_6adbd17d7ac6415890bb669e3aaf670f",
+       "msg": "Failed to load model class 'AnyModel' from module 'anywidget'",
+       "page_esm": "",
+       "page_mode": false,
+       "plugin_esm": [],
+       "prevent_scroll": true,
+       "proxy": false,
+       "remount_on_uid_change": true,
+       "store_urls": [],
+       "theme": "auto",
+       "uid": "e7a1"
+      }
+     },
+     "undefined": {
+      "model_module": "anywidget",
+      "model_module_version": "2.0.0",
+      "model_name": "AnyModel",
+      "state": {
+       "_view_name": "ErrorWidgetView",
+       "error": {},
+       "msg": "Failed to load model class 'AnyModel' from module 'anywidget'"
+      }
+     }
+    },
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

notebooks/celldega_pre-process.ipynb

@@ -0,0 +1,114 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8ce6b74e-54af-48ba-abf2-7ce2caa573bf",
+   "metadata": {},
+   "source": [
+    "# Xenium Pre-process"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b81ab32e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "%env ANYWIDGET_HMR=1\n",
+    "\n",
+    "import celldega as dega"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b47f611d",
+   "metadata": {},
+   "source": [
+    "## Xenium pre processing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "52602e61-45e7-45a2-83a0-9a1b364d6619",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample = 'Xenium_V1_humanLung_Cancer_FFPE_outs'\n",
+    "data_dir = f'../data/instrument_data'\n",
+    "path_landscape_files=f'../data/processed_data/DegaFiles/{sample}'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "13350680",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tile_size=250\n",
+    "\n",
+    "dega.pre.main(\n",
+    "    sample=sample,\n",
+    "    data_root_dir=data_dir,\n",
+    "    tile_size=tile_size,\n",
+    "    path_landscape_files=path_landscape_files,\n",
+    "    use_int_index=True,\n",
+    "    image_tile_layer=\"all\"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2fd7f196-c6cc-4321-8481-6249ed0b96a7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.12.1"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

notebooks/celldega_viz.ipynb

@@ -0,0 +1,101 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8ce6b74e-54af-48ba-abf2-7ce2caa573bf",
+   "metadata": {},
+   "source": [
+    "# Xenium Viz"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "56843452-1a1c-4ae7-95ad-0a7bd909bc90",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "%env ANYWIDGET_HMR=1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cec165b0-bd8f-40d5-b130-22486970aca8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import celldega as dega"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b66e327c-8c1e-41b7-9fbc-48d3f5c06fe3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample = \"WTA_Preview_FFPE_Cervical_Cancer_outs\"\n",
+    "path_dega_files = f\"../data/processed_data/DegaFiles/{sample}\"\n",
+    "\n",
+    "landscape_ist = dega.viz.Landscape(\n",
+    "    technology=\"Xenium\",\n",
+    "    base_url=f\"http://localhost:{dega.viz.get_local_server()}/{path_dega_files}\",\n",
+    ")\n",
+    "\n",
+    "landscape_ist"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c8cd7207-7604-49bb-9172-d92af31ad74b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.12.1"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

notebooks/tissuumaps_pre-process.ipynb

@@ -0,0 +1,1274 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "07f7dbb1-63c6-40e4-b0d9-6ec104aa0adc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !pip install zarr\n",
+    "\n",
+    "import os\n",
+    "import json\n",
+    "from pathlib import Path\n",
+    "import glob\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import scanpy as sc\n",
+    "import pyvips\n",
+    "import zarr\n",
+    "import geopandas as gpd\n",
+    "from shapely.geometry import Polygon\n",
+    "from scipy.sparse import csc_matrix\n",
+    "\n",
+    "import tissuumaps.jupyter as tj\n",
+    "from tissuumaps import read_h5ad\n",
+    "\n",
+    "\n",
+    "# ----------------------------\n",
+    "# Paths\n",
+    "# ----------------------------\n",
+    "# sample = \"WTA_Preview_FFPE_Cervical_Cancer_outs\"\n",
+    "# sample = \"Xenium_Prime_Human_Lymph_Node_Reactive_FFPE_outs\"\n",
+    "# sample = \"Xenium_Prime_Ovarian_Cancer_FFPE_XRrun_outs\"\n",
+    "# sample = \"Xenium_V1_humanLung_Cancer_FFPE_outs\"\n",
+    "\n",
+    "xenium_dir = os.path.abspath(f\"../data/instrument_data/{sample}\")\n",
+    "basedir = os.path.abspath(f\"../data/processed_data/tissuumaps_h5ad/{sample}\")\n",
+    "os.makedirs(basedir, exist_ok=True)\n",
+    "\n",
+    "out_h5ad_name = f\"{sample}_tmap.h5ad\"\n",
+    "out_h5ad = os.path.join(basedir, out_h5ad_name)\n",
+    "\n",
+    "project_path = os.path.join(basedir, \"_project_h5ad.tmap\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aec44404-259a-4042-9230-5a244ec9df0e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ----------------------------\n",
+    "# Transform helpers\n",
+    "# ----------------------------\n",
+    "def write_xenium_transform(data_dir, path_landscape_files):\n",
+    "    cells_zarr_path = Path(data_dir) / \"cells.zarr.zip\"\n",
+    "    if not cells_zarr_path.exists():\n",
+    "        raise FileNotFoundError(f\"Missing: {cells_zarr_path}\")\n",
+    "\n",
+    "    store = zarr.ZipStore(str(cells_zarr_path), mode=\"r\")\n",
+    "    root = zarr.group(store=store)\n",
+    "\n",
+    "    transform = root[\"masks\"][\"homogeneous_transform\"][:]\n",
+    "\n",
+    "    pd.DataFrame(transform[:3, :3]).to_csv(\n",
+    "        Path(path_landscape_files) / \"micron_to_image_transform.csv\",\n",
+    "        sep=\" \",\n",
+    "        header=False,\n",
+    "        index=False,\n",
+    "    )\n",
+    "\n",
+    "    return transform\n",
+    "\n",
+    "\n",
+    "def apply_homogeneous_transform_xy(x, y, transform):\n",
+    "    transform = np.asarray(transform)[:3, :3]\n",
+    "\n",
+    "    xy1 = np.vstack([\n",
+    "        np.asarray(x, dtype=float),\n",
+    "        np.asarray(y, dtype=float),\n",
+    "        np.ones(len(x)),\n",
+    "    ])\n",
+    "\n",
+    "    out = transform @ xy1\n",
+    "    return out[0] / out[2], out[1] / out[2]\n",
+    "    \n",
+    "# ----------------------------\n",
+    "# 1. Transform\n",
+    "# ----------------------------\n",
+    "transform = write_xenium_transform(xenium_dir, basedir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "16f90ba5-914a-4a3d-b3bc-9960481cef1f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ----------------------------\n",
+    "# Polygon helper\n",
+    "# ----------------------------\n",
+    "def xenium_boundaries_to_geojson(\n",
+    "    boundary_path,\n",
+    "    out_geojson,\n",
+    "    transform,\n",
+    "    id_col=\"cell_id\",\n",
+    "    max_polygons=None,\n",
+    "):\n",
+    "    if not os.path.exists(boundary_path):\n",
+    "        return None\n",
+    "\n",
+    "    if boundary_path.endswith(\".parquet\"):\n",
+    "        df = pd.read_parquet(boundary_path)\n",
+    "    else:\n",
+    "        df = pd.read_csv(boundary_path)\n",
+    "\n",
+    "    x_col = \"vertex_x\" if \"vertex_x\" in df.columns else \"x\"\n",
+    "    y_col = \"vertex_y\" if \"vertex_y\" in df.columns else \"y\"\n",
+    "\n",
+    "    x_new, y_new = apply_homogeneous_transform_xy(\n",
+    "        df[x_col].values,\n",
+    "        df[y_col].values,\n",
+    "        transform,\n",
+    "    )\n",
+    "\n",
+    "    df = df.copy()\n",
+    "    df[\"x_img\"] = x_new\n",
+    "    df[\"y_img\"] = y_new\n",
+    "\n",
+    "    ids = []\n",
+    "    geoms = []\n",
+    "\n",
+    "    for i, (cid, sub) in enumerate(df.groupby(id_col, sort=False)):\n",
+    "        if max_polygons is not None and i >= max_polygons:\n",
+    "            break\n",
+    "\n",
+    "        if len(sub) < 3:\n",
+    "            continue\n",
+    "\n",
+    "        coords = list(zip(sub[\"x_img\"].astype(float), sub[\"y_img\"].astype(float)))\n",
+    "\n",
+    "        if coords[0] != coords[-1]:\n",
+    "            coords.append(coords[0])\n",
+    "\n",
+    "        poly = Polygon(coords)\n",
+    "\n",
+    "        if poly.is_valid and not poly.is_empty:\n",
+    "            ids.append(cid)\n",
+    "            geoms.append(poly)\n",
+    "\n",
+    "    gdf = gpd.GeoDataFrame({id_col: ids}, geometry=geoms, crs=None)\n",
+    "    gdf.to_file(out_geojson, driver=\"GeoJSON\")\n",
+    "\n",
+    "    return os.path.basename(out_geojson)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a97826c2-0864-4a35-a7d0-5a64c117a1e2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def add_xenium_default_clustering(adata, xenium_dir):\n",
+    "    import os\n",
+    "    import glob\n",
+    "    import pandas as pd\n",
+    "\n",
+    "    candidates = sorted(glob.glob(\n",
+    "        os.path.join(xenium_dir, \"analysis\", \"clustering\", \"**\", \"clusters.csv\"),\n",
+    "        recursive=True,\n",
+    "    ))\n",
+    "\n",
+    "    if not candidates:\n",
+    "        print(\"No clusters.csv found.\")\n",
+    "        return adata\n",
+    "\n",
+    "    preferred = None\n",
+    "    for p in candidates:\n",
+    "        if \"gene_expression_graphclust\" in p:\n",
+    "            preferred = p\n",
+    "            break\n",
+    "\n",
+    "    if preferred is None:\n",
+    "        preferred = candidates[0]\n",
+    "\n",
+    "    print(\"Using clustering file:\", preferred)\n",
+    "\n",
+    "    clusters = pd.read_csv(preferred)\n",
+    "\n",
+    "    id_col = \"Barcode\" if \"Barcode\" in clusters.columns else clusters.columns[0]\n",
+    "    cluster_col = \"Cluster\" if \"Cluster\" in clusters.columns else clusters.columns[1]\n",
+    "\n",
+    "    clusters[id_col] = clusters[id_col].astype(str)\n",
+    "    clusters[cluster_col] = clusters[cluster_col].astype(str)\n",
+    "\n",
+    "    s = clusters.set_index(id_col)[cluster_col]\n",
+    "\n",
+    "    # IMPORTANT: reindex allows cells missing from clustering file\n",
+    "    adata.obs[\"xenium_default_cluster\"] = (\n",
+    "        s.reindex(adata.obs_names)\n",
+    "        .fillna(\"unclustered\")\n",
+    "        .astype(str)\n",
+    "        .astype(\"category\")\n",
+    "    )\n",
+    "\n",
+    "    print(adata.obs[\"xenium_default_cluster\"].value_counts())\n",
+    "\n",
+    "    return adata\n",
+    "\n",
+    "\n",
+    "# ----------------------------\n",
+    "# 2. Build h5ad in image pixel space\n",
+    "# ----------------------------\n",
+    "adata = sc.read_10x_h5(os.path.join(xenium_dir, \"cell_feature_matrix.h5\"))\n",
+    "adata.var_names_make_unique()\n",
+    "\n",
+    "cells = pd.read_csv(os.path.join(xenium_dir, \"cells.csv.gz\"), index_col=0)\n",
+    "cells = cells.loc[adata.obs_names]\n",
+    "\n",
+    "x_img, y_img = apply_homogeneous_transform_xy(\n",
+    "    cells[\"x_centroid\"].values,\n",
+    "    cells[\"y_centroid\"].values,\n",
+    "    transform,\n",
+    ")\n",
+    "\n",
+    "adata.obs[\"x\"] = x_img.astype(float)\n",
+    "adata.obs[\"y\"] = y_img.astype(float)\n",
+    "adata.obsm[\"spatial\"] = adata.obs[[\"x\", \"y\"]].to_numpy(dtype=\"float64\")\n",
+    "\n",
+    "adata.obs[\"x_centroid_um\"] = cells[\"x_centroid\"].astype(float).values\n",
+    "adata.obs[\"y_centroid_um\"] = cells[\"y_centroid\"].astype(float).values\n",
+    "\n",
+    "obs_cols = [\n",
+    "    \"transcript_counts\",\n",
+    "    \"control_probe_counts\",\n",
+    "    \"genomic_control_counts\",\n",
+    "    \"control_codeword_counts\",\n",
+    "    \"unassigned_codeword_counts\",\n",
+    "    \"deprecated_codeword_counts\",\n",
+    "    \"total_counts\",\n",
+    "    \"cell_area\",\n",
+    "    \"nucleus_area\",\n",
+    "    \"nucleus_count\",\n",
+    "]\n",
+    "\n",
+    "for col in obs_cols:\n",
+    "    if col in cells.columns:\n",
+    "        adata.obs[col] = cells[col].values\n",
+    "\n",
+    "for col in adata.obs.columns:\n",
+    "    if pd.api.types.is_object_dtype(adata.obs[col]):\n",
+    "        adata.obs[col] = adata.obs[col].astype(\"category\")\n",
+    "\n",
+    "adata = add_xenium_default_clustering(adata, xenium_dir)\n",
+    "\n",
+    "adata.X = csc_matrix(adata.X)\n",
+    "adata.uns.pop(\"tmap_obsgroups\", None)\n",
+    "adata.write_h5ad(out_h5ad)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f50d1a11-11ae-40f2-a35a-d0cd24a69935",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ----------------------------\n",
+    "# 3. Make image pyramid\n",
+    "# ----------------------------\n",
+    "def make_one_xenium_pyramid_tifffile_zarr(\n",
+    "    xenium_dir,\n",
+    "    basedir,\n",
+    "    channel=\"morphology_focus_0000.ome.tif\",\n",
+    "    plane=0,\n",
+    "    low=0,\n",
+    "    high=12000,\n",
+    "    gamma=0.5,\n",
+    "    out_name=None,\n",
+    "    rows_per_chunk=512,\n",
+    "):\n",
+    "    import os\n",
+    "    import numpy as np\n",
+    "    import tifffile\n",
+    "    import zarr\n",
+    "    import pyvips\n",
+    "\n",
+    "    src = os.path.join(xenium_dir, \"morphology_focus\", channel)\n",
+    "\n",
+    "    if out_name is None:\n",
+    "        out_name = channel.replace(\".ome.tif\", f\"_plane{plane}_pyramid.tif\")\n",
+    "\n",
+    "    tmp_name = out_name.replace(\".tif\", \"_uint8_tmp.tif\")\n",
+    "    tmp_path = os.path.join(basedir, tmp_name)\n",
+    "    out_path = os.path.join(basedir, out_name)\n",
+    "\n",
+    "    for p in [tmp_path, out_path]:\n",
+    "        if os.path.exists(p):\n",
+    "            os.remove(p)\n",
+    "\n",
+    "    with tifffile.TiffFile(src) as tf:\n",
+    "        store = tf.aszarr(series=0, level=0)\n",
+    "        z = zarr.open(store, mode=\"r\")\n",
+    "\n",
+    "        print(\"zarr shape:\", z.shape, \"dtype:\", z.dtype)\n",
+    "\n",
+    "        if len(z.shape) == 3:\n",
+    "            arr2d = z[plane]\n",
+    "        elif len(z.shape) == 2:\n",
+    "            arr2d = z\n",
+    "        else:\n",
+    "            raise ValueError(f\"Unexpected image shape: {z.shape}\")\n",
+    "\n",
+    "        height, width = arr2d.shape\n",
+    "        print(\"using plane:\", plane, \"height:\", height, \"width:\", width)\n",
+    "\n",
+    "        # Create one full-size uint8 temp TIFF, memory-mapped on disk\n",
+    "        tmp_mm = tifffile.memmap(\n",
+    "            tmp_path,\n",
+    "            shape=(height, width),\n",
+    "            dtype=\"uint8\",\n",
+    "            photometric=\"minisblack\",\n",
+    "            bigtiff=True,\n",
+    "        )\n",
+    "\n",
+    "        for y0 in range(0, height, rows_per_chunk):\n",
+    "            y1 = min(y0 + rows_per_chunk, height)\n",
+    "\n",
+    "            block = arr2d[y0:y1, :].astype(\"float32\")\n",
+    "            block = (block - low) / (high - low)\n",
+    "            block = np.clip(block, 0, 1)\n",
+    "\n",
+    "            if gamma is not None:\n",
+    "                block = block ** gamma\n",
+    "\n",
+    "            tmp_mm[y0:y1, :] = (block * 255).astype(\"uint8\")\n",
+    "\n",
+    "        tmp_mm.flush()\n",
+    "        del tmp_mm\n",
+    "        store.close()\n",
+    "\n",
+    "    img = pyvips.Image.new_from_file(tmp_path, access=\"sequential\")\n",
+    "\n",
+    "    img.tiffsave(\n",
+    "        out_path,\n",
+    "        tile=True,\n",
+    "        pyramid=True,\n",
+    "        compression=\"jpeg\",\n",
+    "        Q=90,\n",
+    "        tile_width=256,\n",
+    "        tile_height=256,\n",
+    "        bigtiff=True,\n",
+    "    )\n",
+    "\n",
+    "    os.remove(tmp_path)\n",
+    "\n",
+    "    print(\"wrote:\", out_path)\n",
+    "\n",
+    "    return {\n",
+    "        \"name\": out_name,\n",
+    "        \"tileSource\": out_name + \".dzi\",\n",
+    "        \"x\": 0,\n",
+    "        \"y\": 0,\n",
+    "        \"scale\": 1,\n",
+    "        \"rotation\": 0,\n",
+    "        \"flip\": False,\n",
+    "    }\n",
+    "\n",
+    "morph_files = sorted(glob.glob(os.path.join(xenium_dir, \"morphology_focus\", \"*.ome.tif\")))\n",
+    "morph_files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4109411c-f39c-40d8-94f1-72be456f8c4d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # ----------------------------\n",
+    "# # 3. Make image pyramid\n",
+    "# # ----------------------------\n",
+    "# def make_one_xenium_pyramid_tifffile_zarr(\n",
+    "#     xenium_dir,\n",
+    "#     basedir,\n",
+    "#     channel=\"morphology_focus_0000.ome.tif\",\n",
+    "#     plane=0,\n",
+    "#     low=0,\n",
+    "#     high=12000,\n",
+    "#     gamma=0.5,\n",
+    "#     out_name=None,\n",
+    "#     rows_per_chunk=512,\n",
+    "# ):\n",
+    "#     import os\n",
+    "#     import numpy as np\n",
+    "#     import tifffile\n",
+    "#     import zarr\n",
+    "#     import pyvips\n",
+    "\n",
+    "#     src = os.path.join(xenium_dir, channel)\n",
+    "\n",
+    "#     if out_name is None:\n",
+    "#         out_name = channel.replace(\".ome.tif\", f\"_plane{plane}_pyramid.tif\")\n",
+    "\n",
+    "#     tmp_name = out_name.replace(\".tif\", \"_uint8_tmp.tif\")\n",
+    "#     tmp_path = os.path.join(basedir, tmp_name)\n",
+    "#     out_path = os.path.join(basedir, out_name)\n",
+    "\n",
+    "#     for p in [tmp_path, out_path]:\n",
+    "#         if os.path.exists(p):\n",
+    "#             os.remove(p)\n",
+    "\n",
+    "#     with tifffile.TiffFile(src) as tf:\n",
+    "#         store = tf.aszarr(series=0, level=0)\n",
+    "#         z = zarr.open(store, mode=\"r\")\n",
+    "\n",
+    "#         print(\"zarr shape:\", z.shape, \"dtype:\", z.dtype)\n",
+    "\n",
+    "#         if len(z.shape) == 3:\n",
+    "#             arr2d = z[plane]\n",
+    "#         elif len(z.shape) == 2:\n",
+    "#             arr2d = z\n",
+    "#         else:\n",
+    "#             raise ValueError(f\"Unexpected image shape: {z.shape}\")\n",
+    "\n",
+    "#         height, width = arr2d.shape\n",
+    "#         print(\"using plane:\", plane, \"height:\", height, \"width:\", width)\n",
+    "\n",
+    "#         # Create one full-size uint8 temp TIFF, memory-mapped on disk\n",
+    "#         tmp_mm = tifffile.memmap(\n",
+    "#             tmp_path,\n",
+    "#             shape=(height, width),\n",
+    "#             dtype=\"uint8\",\n",
+    "#             photometric=\"minisblack\",\n",
+    "#             bigtiff=True,\n",
+    "#         )\n",
+    "\n",
+    "#         for y0 in range(0, height, rows_per_chunk):\n",
+    "#             y1 = min(y0 + rows_per_chunk, height)\n",
+    "\n",
+    "#             block = arr2d[y0:y1, :].astype(\"float32\")\n",
+    "#             block = (block - low) / (high - low)\n",
+    "#             block = np.clip(block, 0, 1)\n",
+    "\n",
+    "#             if gamma is not None:\n",
+    "#                 block = block ** gamma\n",
+    "\n",
+    "#             tmp_mm[y0:y1, :] = (block * 255).astype(\"uint8\")\n",
+    "\n",
+    "#         tmp_mm.flush()\n",
+    "#         del tmp_mm\n",
+    "#         store.close()\n",
+    "\n",
+    "#     img = pyvips.Image.new_from_file(tmp_path, access=\"sequential\")\n",
+    "\n",
+    "#     img.tiffsave(\n",
+    "#         out_path,\n",
+    "#         tile=True,\n",
+    "#         pyramid=True,\n",
+    "#         compression=\"jpeg\",\n",
+    "#         Q=90,\n",
+    "#         tile_width=256,\n",
+    "#         tile_height=256,\n",
+    "#         bigtiff=True,\n",
+    "#     )\n",
+    "\n",
+    "#     os.remove(tmp_path)\n",
+    "\n",
+    "#     print(\"wrote:\", out_path)\n",
+    "\n",
+    "#     return {\n",
+    "#         \"name\": out_name,\n",
+    "#         \"tileSource\": out_name + \".dzi\",\n",
+    "#         \"x\": 0,\n",
+    "#         \"y\": 0,\n",
+    "#         \"scale\": 1,\n",
+    "#         \"rotation\": 0,\n",
+    "#         \"flip\": False,\n",
+    "#     }\n",
+    "    \n",
+    "# morph_files = sorted(glob.glob(os.path.join(xenium_dir, \"morphology_focus.ome.tif\")))\n",
+    "# morph_files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "622a8749-399c-4bd4-855c-10be911d0198",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "image_layers = []\n",
+    "\n",
+    "# Use the first OME file, but extract each plane as a separate stain/channel\n",
+    "channel_file = os.path.basename(morph_files[0])\n",
+    "\n",
+    "for plane in range(4):\n",
+    "    image_layer = make_one_xenium_pyramid_tifffile_zarr(\n",
+    "        xenium_dir=xenium_dir,\n",
+    "        basedir=basedir,\n",
+    "        channel=channel_file,\n",
+    "        plane=plane,\n",
+    "        low=0,\n",
+    "        high=3000,\n",
+    "        gamma=0.4,\n",
+    "        out_name=f\"morphology_focus_plane{plane}_pyramid.tif\",\n",
+    "    )\n",
+    "\n",
+    "    image_layer[\"name\"] = f\"morphology_focus_plane{plane}\"\n",
+    "    image_layers.append(image_layer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "784a65f6-f500-4b7f-9ca6-5c9a3be4361e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ----------------------------\n",
+    "# 4. Create cell boundary GeoJSON\n",
+    "# ----------------------------\n",
+    "region_files = []\n",
+    "\n",
+    "for candidate in [\n",
+    "    os.path.join(xenium_dir, \"cell_boundaries.parquet\"),\n",
+    "    os.path.join(xenium_dir, \"cell_boundaries.csv.gz\"),\n",
+    "]:\n",
+    "    if os.path.exists(candidate):\n",
+    "        name = xenium_boundaries_to_geojson(\n",
+    "            candidate,\n",
+    "            os.path.join(basedir, \"cell_boundaries_image_space.geojson\"),\n",
+    "            transform,\n",
+    "            id_col=\"cell_id\",\n",
+    "        )\n",
+    "        region_files.append({\n",
+    "            \"path\": name,\n",
+    "            \"title\": \"Load cell boundaries\",\n",
+    "            \"comment\": \"Cell boundaries\",\n",
+    "            \"autoLoad\": False,\n",
+    "        })\n",
+    "        break"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "43159e9d-5279-4b90-a5b8-cc39aa08facd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# def xenium_transcripts_to_csv_streaming(\n",
+    "#     xenium_dir,\n",
+    "#     basedir,\n",
+    "#     transform,\n",
+    "#     out_name=\"transcripts_image_space.csv\",\n",
+    "#     min_qv=20,\n",
+    "#     max_transcripts=2_000_000,\n",
+    "#     rows_per_group=None,\n",
+    "# ):\n",
+    "#     import os\n",
+    "#     import numpy as np\n",
+    "#     import pandas as pd\n",
+    "#     import pyarrow.parquet as pq\n",
+    "\n",
+    "#     transcript_path = os.path.join(xenium_dir, \"transcripts.parquet\")\n",
+    "#     if not os.path.exists(transcript_path):\n",
+    "#         raise FileNotFoundError(transcript_path)\n",
+    "\n",
+    "#     out_path = os.path.join(basedir, out_name)\n",
+    "#     if os.path.exists(out_path):\n",
+    "#         os.remove(out_path)\n",
+    "\n",
+    "#     pf = pq.ParquetFile(transcript_path)\n",
+    "\n",
+    "#     transform3 = np.asarray(transform)[:3, :3]\n",
+    "\n",
+    "#     written = 0\n",
+    "#     wrote_header = False\n",
+    "\n",
+    "#     needed_cols = [\"x_location\", \"y_location\", \"feature_name\"]\n",
+    "#     optional_cols = [\"qv\", \"cell_id\"]\n",
+    "\n",
+    "#     # keep only columns that exist\n",
+    "#     schema_cols = set(pf.schema.names)\n",
+    "#     cols = [c for c in needed_cols + optional_cols if c in schema_cols]\n",
+    "\n",
+    "#     print(\"Transcript columns:\", cols)\n",
+    "#     print(\"Row groups:\", pf.num_row_groups)\n",
+    "\n",
+    "#     for rg in range(pf.num_row_groups):\n",
+    "#         if max_transcripts is not None and written >= max_transcripts:\n",
+    "#             break\n",
+    "\n",
+    "#         table = pf.read_row_group(rg, columns=cols)\n",
+    "#         tx = table.to_pandas()\n",
+    "\n",
+    "#         if \"qv\" in tx.columns:\n",
+    "#             tx = tx[tx[\"qv\"] >= min_qv]\n",
+    "\n",
+    "#         if tx.empty:\n",
+    "#             continue\n",
+    "\n",
+    "#         if max_transcripts is not None:\n",
+    "#             remaining = max_transcripts - written\n",
+    "#             if len(tx) > remaining:\n",
+    "#                 tx = tx.sample(remaining, random_state=rg)\n",
+    "\n",
+    "#         x = tx[\"x_location\"].to_numpy(dtype=float)\n",
+    "#         y = tx[\"y_location\"].to_numpy(dtype=float)\n",
+    "\n",
+    "#         xy1 = np.vstack([x, y, np.ones(len(x))])\n",
+    "#         out = transform3 @ xy1\n",
+    "\n",
+    "#         out_df = pd.DataFrame({\n",
+    "#             \"x\": out[0] / out[2],\n",
+    "#             \"y\": out[1] / out[2],\n",
+    "#             \"gene\": tx[\"feature_name\"].astype(str).to_numpy(),\n",
+    "#         })\n",
+    "\n",
+    "#         if \"qv\" in tx.columns:\n",
+    "#             out_df[\"qv\"] = tx[\"qv\"].to_numpy()\n",
+    "\n",
+    "#         if \"cell_id\" in tx.columns:\n",
+    "#             out_df[\"cell_id\"] = tx[\"cell_id\"].astype(str).to_numpy()\n",
+    "\n",
+    "#         out_df.to_csv(\n",
+    "#             out_path,\n",
+    "#             mode=\"a\",\n",
+    "#             header=not wrote_header,\n",
+    "#             index=False,\n",
+    "#         )\n",
+    "\n",
+    "#         wrote_header = True\n",
+    "#         written += len(out_df)\n",
+    "\n",
+    "#         print(f\"row group {rg + 1}/{pf.num_row_groups}: wrote {written:,}\")\n",
+    "\n",
+    "#     print(\"wrote:\", out_path, \"n=\", written)\n",
+    "#     return out_name\n",
+    "\n",
+    "# transcript_csv = xenium_transcripts_to_csv_streaming(\n",
+    "#     xenium_dir=xenium_dir,\n",
+    "#     basedir=basedir,\n",
+    "#     transform=transform,\n",
+    "#     min_qv=20,\n",
+    "#     max_transcripts=None,\n",
+    "# )\n",
+    "\n",
+    "# print(\"Done\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b5398cfe-3a3d-4841-b385-f1467f20e45f",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# def xenium_all_transcripts_to_h5ad_empty_X(\n",
+    "#     xenium_dir,\n",
+    "#     basedir,\n",
+    "#     transform,\n",
+    "#     out_name=\"transcripts_all_emptyX_tmap.h5ad\",\n",
+    "#     min_qv=None,\n",
+    "#     include_cell_id=False,\n",
+    "# ):\n",
+    "#     import os\n",
+    "#     import numpy as np\n",
+    "#     import pandas as pd\n",
+    "#     import pyarrow.parquet as pq\n",
+    "#     import anndata as ad\n",
+    "#     from scipy.sparse import csc_matrix\n",
+    "\n",
+    "#     transcript_path = os.path.join(xenium_dir, \"transcripts.parquet\")\n",
+    "#     out_path = os.path.join(basedir, out_name)\n",
+    "\n",
+    "#     pf = pq.ParquetFile(transcript_path)\n",
+    "#     transform3 = np.asarray(transform, dtype=np.float64)[:3, :3]\n",
+    "\n",
+    "#     obs_chunks = []\n",
+    "#     spatial_chunks = []\n",
+    "#     written = 0\n",
+    "\n",
+    "#     schema_cols = set(pf.schema.names)\n",
+    "\n",
+    "#     cols = [\"x_location\", \"y_location\", \"feature_name\"]\n",
+    "#     if min_qv is not None and \"qv\" in schema_cols:\n",
+    "#         cols.append(\"qv\")\n",
+    "#     elif \"qv\" in schema_cols:\n",
+    "#         cols.append(\"qv\")\n",
+    "\n",
+    "#     if include_cell_id and \"cell_id\" in schema_cols:\n",
+    "#         cols.append(\"cell_id\")\n",
+    "\n",
+    "#     for rg in range(pf.num_row_groups):\n",
+    "#         tx = pf.read_row_group(rg, columns=cols).to_pandas()\n",
+    "\n",
+    "#         if min_qv is not None and \"qv\" in tx.columns:\n",
+    "#             tx = tx[tx[\"qv\"] >= min_qv]\n",
+    "\n",
+    "#         if tx.empty:\n",
+    "#             continue\n",
+    "\n",
+    "#         x = tx[\"x_location\"].to_numpy(dtype=np.float64)\n",
+    "#         y = tx[\"y_location\"].to_numpy(dtype=np.float64)\n",
+    "\n",
+    "#         xy1 = np.vstack([\n",
+    "#             x,\n",
+    "#             y,\n",
+    "#             np.ones(len(tx), dtype=np.float64),\n",
+    "#         ])\n",
+    "\n",
+    "#         out = transform3 @ xy1\n",
+    "\n",
+    "#         spatial_chunks.append(\n",
+    "#             np.column_stack([\n",
+    "#                 out[0] / out[2],\n",
+    "#                 out[1] / out[2],\n",
+    "#             ]).astype(np.float32)\n",
+    "#         )\n",
+    "\n",
+    "#         # Compact index: RangeIndex, no huge tx_... strings\n",
+    "#         obs = pd.DataFrame(index=pd.RangeIndex(written, written + len(tx)))\n",
+    "\n",
+    "#         obs[\"gene\"] = tx[\"feature_name\"].astype(\"category\").values\n",
+    "\n",
+    "#         if \"qv\" in tx.columns:\n",
+    "#             obs[\"qv\"] = pd.to_numeric(tx[\"qv\"], downcast=\"integer\")\n",
+    "\n",
+    "#         if include_cell_id and \"cell_id\" in tx.columns:\n",
+    "#             obs[\"cell_id\"] = tx[\"cell_id\"].astype(\"category\").values\n",
+    "\n",
+    "#         obs_chunks.append(obs)\n",
+    "\n",
+    "#         written += len(tx)\n",
+    "#         print(f\"row group {rg + 1}/{pf.num_row_groups}: {written:,}\")\n",
+    "\n",
+    "#     obs = pd.concat(obs_chunks, axis=0)\n",
+    "\n",
+    "#     # Ensure compact categoricals after concat\n",
+    "#     obs[\"gene\"] = obs[\"gene\"].astype(\"category\")\n",
+    "\n",
+    "#     if include_cell_id and \"cell_id\" in obs.columns:\n",
+    "#         obs[\"cell_id\"] = obs[\"cell_id\"].astype(\"category\")\n",
+    "\n",
+    "#     if \"qv\" in obs.columns:\n",
+    "#         obs[\"qv\"] = pd.to_numeric(obs[\"qv\"], downcast=\"integer\")\n",
+    "\n",
+    "#     spatial = np.vstack(spatial_chunks).astype(np.float32)\n",
+    "\n",
+    "#     # Empty X: n_obs x 0 vars\n",
+    "#     X = csc_matrix((len(obs), 0), dtype=np.float32)\n",
+    "#     var = pd.DataFrame(index=pd.Index([], dtype=str))\n",
+    "\n",
+    "#     transcript_adata = ad.AnnData(X=X, obs=obs, var=var)\n",
+    "#     transcript_adata.obsm[\"spatial\"] = spatial\n",
+    "\n",
+    "#     transcript_adata.write_h5ad(out_path)\n",
+    "\n",
+    "#     print(\"wrote:\", out_path)\n",
+    "#     print(\"shape:\", transcript_adata.shape)\n",
+    "#     print(\"obs columns:\", list(transcript_adata.obs.columns))\n",
+    "#     print(\"spatial dtype:\", transcript_adata.obsm[\"spatial\"].dtype)\n",
+    "\n",
+    "#     return out_name\n",
+    "\n",
+    "# transcript_h5ad = xenium_all_transcripts_to_h5ad_empty_X(\n",
+    "#     xenium_dir=xenium_dir,\n",
+    "#     basedir=basedir,\n",
+    "#     transform=transform,\n",
+    "#     min_qv=None,\n",
+    "#     include_cell_id=False,\n",
+    "# )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9423d36b-5e04-484c-8901-b32a494ce7ad",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# def xenium_all_transcripts_to_h5ad_empty_X(\n",
+    "#     xenium_dir,\n",
+    "#     basedir,\n",
+    "#     transform,\n",
+    "#     out_name=\"transcripts_all_emptyX_tmap.h5ad\",\n",
+    "#     min_qv=None,\n",
+    "#     include_cell_id=False,\n",
+    "#     tmp_path=None,\n",
+    "# ):\n",
+    "#     import os\n",
+    "#     import numpy as np\n",
+    "#     import pandas as pd\n",
+    "#     import pyarrow as pa\n",
+    "#     import pyarrow.parquet as pq\n",
+    "#     import anndata as ad\n",
+    "#     from scipy.sparse import csc_matrix\n",
+    "\n",
+    "#     transcript_path = os.path.join(xenium_dir, \"transcripts.parquet\")\n",
+    "#     out_path = os.path.join(basedir, out_name)\n",
+    "#     if tmp_path is None:\n",
+    "#         tmp_path = out_path.replace(\".h5ad\", \"_tmp.parquet\")\n",
+    "\n",
+    "#     pf = pq.ParquetFile(transcript_path)\n",
+    "#     transform3 = np.asarray(transform, dtype=np.float64)[:3, :3]\n",
+    "\n",
+    "#     schema_cols = set(pf.schema.names)\n",
+    "#     cols = [\"x_location\", \"y_location\", \"feature_name\"]\n",
+    "#     if \"qv\" in schema_cols:\n",
+    "#         cols.append(\"qv\")\n",
+    "#     if include_cell_id and \"cell_id\" in schema_cols:\n",
+    "#         cols.append(\"cell_id\")\n",
+    "\n",
+    "#     writer = None\n",
+    "#     written = 0\n",
+    "\n",
+    "#     for rg in range(pf.num_row_groups):\n",
+    "#         tx = pf.read_row_group(rg, columns=cols).to_pandas()\n",
+    "#         if min_qv is not None and \"qv\" in tx.columns:\n",
+    "#             tx = tx[tx[\"qv\"] >= min_qv]\n",
+    "#         if tx.empty:\n",
+    "#             continue\n",
+    "\n",
+    "#         x = tx[\"x_location\"].to_numpy(dtype=np.float64)\n",
+    "#         y = tx[\"y_location\"].to_numpy(dtype=np.float64)\n",
+    "#         xy1 = np.vstack([x, y, np.ones(len(tx), dtype=np.float64)])\n",
+    "#         out = transform3 @ xy1\n",
+    "\n",
+    "#         chunk = pd.DataFrame({\n",
+    "#             \"spatial_x\": (out[0] / out[2]).astype(np.float32),\n",
+    "#             \"spatial_y\": (out[1] / out[2]).astype(np.float32),\n",
+    "#             \"gene\": tx[\"feature_name\"].astype(str).values,\n",
+    "#         })\n",
+    "#         if \"qv\" in tx.columns:\n",
+    "#             chunk[\"qv\"] = pd.to_numeric(tx[\"qv\"], downcast=\"integer\").values\n",
+    "#         if include_cell_id and \"cell_id\" in tx.columns:\n",
+    "#             chunk[\"cell_id\"] = tx[\"cell_id\"].astype(str).values\n",
+    "\n",
+    "#         table = pa.Table.from_pandas(chunk, preserve_index=False)\n",
+    "#         if writer is None:\n",
+    "#             writer = pq.ParquetWriter(tmp_path, table.schema)\n",
+    "#         writer.write_table(table)\n",
+    "\n",
+    "#         written += len(chunk)\n",
+    "#         print(f\"row group {rg + 1}/{pf.num_row_groups}: {written:,}\")\n",
+    "\n",
+    "#     writer.close()\n",
+    "#     print(\"All row groups written to tmp parquet. Building AnnData...\")\n",
+    "\n",
+    "#     df = pd.read_parquet(tmp_path)\n",
+    "#     df[\"gene\"] = df[\"gene\"].astype(\"category\")\n",
+    "#     if include_cell_id and \"cell_id\" in df.columns:\n",
+    "#         df[\"cell_id\"] = df[\"cell_id\"].astype(\"category\")\n",
+    "\n",
+    "#     spatial = df[[\"spatial_x\", \"spatial_y\"]].to_numpy(dtype=np.float32)\n",
+    "#     obs = df.drop(columns=[\"spatial_x\", \"spatial_y\"])\n",
+    "#     obs.index = pd.RangeIndex(len(obs)).astype(str)\n",
+    "\n",
+    "#     X = csc_matrix((len(obs), 0), dtype=np.float32)\n",
+    "#     var = pd.DataFrame(index=pd.Index([], dtype=str))\n",
+    "#     transcript_adata = ad.AnnData(X=X, obs=obs, var=var)\n",
+    "#     transcript_adata.obsm[\"spatial\"] = spatial\n",
+    "#     transcript_adata.write_h5ad(out_path)\n",
+    "\n",
+    "#     os.remove(tmp_path)\n",
+    "#     print(\"wrote:\", out_path)\n",
+    "#     print(\"shape:\", transcript_adata.shape)\n",
+    "#     print(\"obs columns:\", list(transcript_adata.obs.columns))\n",
+    "#     return out_name\n",
+    "\n",
+    "\n",
+    "# transcript_h5ad = xenium_all_transcripts_to_h5ad_empty_X(\n",
+    "#     xenium_dir=xenium_dir,\n",
+    "#     basedir=basedir,\n",
+    "#     transform=transform,\n",
+    "#     min_qv=None,\n",
+    "#     include_cell_id=False,\n",
+    "#     tmp_path=\"/Volumes/T7 Shield/tmp/transcripts_tmp.parquet\",\n",
+    "# )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "02d53e78-c105-413c-9b30-f950b42ed200",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def xenium_all_transcripts_to_h5ad_empty_X(\n",
+    "    xenium_dir,\n",
+    "    basedir,\n",
+    "    transform,\n",
+    "    out_name=\"transcripts_all_emptyX_tmap.h5ad\",\n",
+    "    min_qv=None,\n",
+    "    include_cell_id=False,\n",
+    "    tmp_path=None,\n",
+    "):\n",
+    "    import os\n",
+    "    import numpy as np\n",
+    "    import pandas as pd\n",
+    "    import pyarrow as pa\n",
+    "    import pyarrow.parquet as pq\n",
+    "    import h5py\n",
+    "    from scipy.sparse import csc_matrix\n",
+    "\n",
+    "    transcript_path = os.path.join(xenium_dir, \"transcripts.parquet\")\n",
+    "    out_path = os.path.join(basedir, out_name)\n",
+    "    if tmp_path is None:\n",
+    "        tmp_path = out_path.replace(\".h5ad\", \"_tmp.parquet\")\n",
+    "\n",
+    "    pf = pq.ParquetFile(transcript_path)\n",
+    "    transform3 = np.asarray(transform, dtype=np.float64)[:3, :3]\n",
+    "\n",
+    "    schema_cols = set(pf.schema.names)\n",
+    "    cols = [\"x_location\", \"y_location\", \"feature_name\"]\n",
+    "    if \"qv\" in schema_cols:\n",
+    "        cols.append(\"qv\")\n",
+    "    if include_cell_id and \"cell_id\" in schema_cols:\n",
+    "        cols.append(\"cell_id\")\n",
+    "\n",
+    "    # --- Pass 1: stream row groups → tmp parquet ---\n",
+    "    writer = None\n",
+    "    written = 0\n",
+    "    for rg in range(pf.num_row_groups):\n",
+    "        tx = pf.read_row_group(rg, columns=cols).to_pandas()\n",
+    "        if min_qv is not None and \"qv\" in tx.columns:\n",
+    "            tx = tx[tx[\"qv\"] >= min_qv]\n",
+    "        if tx.empty:\n",
+    "            continue\n",
+    "\n",
+    "        x = tx[\"x_location\"].to_numpy(dtype=np.float64)\n",
+    "        y = tx[\"y_location\"].to_numpy(dtype=np.float64)\n",
+    "        xy1 = np.vstack([x, y, np.ones(len(tx), dtype=np.float64)])\n",
+    "        out = transform3 @ xy1\n",
+    "\n",
+    "        chunk = pd.DataFrame({\n",
+    "            \"spatial_x\": (out[0] / out[2]).astype(np.float32),\n",
+    "            \"spatial_y\": (out[1] / out[2]).astype(np.float32),\n",
+    "            \"gene\": tx[\"feature_name\"].astype(str).values,\n",
+    "        })\n",
+    "        if \"qv\" in tx.columns:\n",
+    "            chunk[\"qv\"] = pd.to_numeric(tx[\"qv\"], downcast=\"integer\").values\n",
+    "        if include_cell_id and \"cell_id\" in tx.columns:\n",
+    "            chunk[\"cell_id\"] = tx[\"cell_id\"].astype(str).values\n",
+    "\n",
+    "        table = pa.Table.from_pandas(chunk, preserve_index=False)\n",
+    "        if writer is None:\n",
+    "            writer = pq.ParquetWriter(tmp_path, table.schema)\n",
+    "        writer.write_table(table)\n",
+    "        written += len(chunk)\n",
+    "        print(f\"row group {rg + 1}/{pf.num_row_groups}: {written:,}\")\n",
+    "\n",
+    "    writer.close()\n",
+    "    total_rows = written\n",
+    "    print(f\"Pass 1 done. {total_rows:,} rows. Writing h5ad...\")\n",
+    "\n",
+    "    # --- Pass 2: stream tmp parquet → h5ad via h5py, one chunk at a time ---\n",
+    "    pf2 = pq.ParquetFile(tmp_path)\n",
+    "    obs_col_names = [c for c in pf2.schema.names if c not in (\"spatial_x\", \"spatial_y\")]\n",
+    "\n",
+    "    with h5py.File(out_path, \"w\") as f:\n",
+    "        # AnnData minimal structure\n",
+    "        f.attrs[\"encoding-type\"] = \"anndata\"\n",
+    "        f.attrs[\"encoding-version\"] = \"0.1.0\"\n",
+    "\n",
+    "        # X group — empty (0 vars)\n",
+    "        xgrp = f.create_group(\"X\")\n",
+    "        xgrp.attrs[\"encoding-type\"] = \"array\"\n",
+    "        xgrp.attrs[\"encoding-version\"] = \"0.2.0\"\n",
+    "        xgrp.create_dataset(\"data\",   data=np.array([], dtype=np.float32))\n",
+    "        xgrp.create_dataset(\"indices\",data=np.array([], dtype=np.int32))\n",
+    "        xgrp.create_dataset(\"indptr\", data=np.zeros(1, dtype=np.int32))\n",
+    "        xgrp.attrs[\"shape\"] = [total_rows, 0]\n",
+    "\n",
+    "        # obsm/spatial — pre-allocate, fill in chunks\n",
+    "        obsm = f.create_group(\"obsm\")\n",
+    "        spatial_ds = obsm.create_dataset(\n",
+    "            \"spatial\", shape=(total_rows, 2), dtype=np.float32\n",
+    "        )\n",
+    "\n",
+    "        # obs — pre-allocate string datasets per column\n",
+    "        obs_grp = f.create_group(\"obs\")\n",
+    "        obs_grp.attrs[\"_index\"] = \"_index\"\n",
+    "        obs_grp.attrs[\"encoding-type\"] = \"dataframe\"\n",
+    "        obs_grp.attrs[\"encoding-version\"] = \"0.2.0\"\n",
+    "        obs_grp.attrs[\"column-order\"] = obs_col_names\n",
+    "\n",
+    "        # pre-allocate index\n",
+    "        idx_ds = obs_grp.create_dataset(\n",
+    "            \"_index\", shape=(total_rows,), dtype=h5py.string_dtype()\n",
+    "        )\n",
+    "        col_datasets = {}\n",
+    "        for col in obs_col_names:\n",
+    "            col_datasets[col] = obs_grp.create_dataset(\n",
+    "                col, shape=(total_rows,), dtype=h5py.string_dtype()\n",
+    "            )\n",
+    "\n",
+    "        # var — empty\n",
+    "        var_grp = f.create_group(\"var\")\n",
+    "        var_grp.attrs[\"_index\"] = \"_index\"\n",
+    "        var_grp.attrs[\"encoding-type\"] = \"dataframe\"\n",
+    "        var_grp.attrs[\"encoding-version\"] = \"0.2.0\"\n",
+    "        var_grp.attrs[\"column-order\"] = []\n",
+    "        var_grp.create_dataset(\"_index\", data=np.array([], dtype=h5py.string_dtype()))\n",
+    "\n",
+    "        # stream fill\n",
+    "        cursor = 0\n",
+    "        for rg in range(pf2.num_row_groups):\n",
+    "            chunk = pf2.read_row_group(rg).to_pandas()\n",
+    "            n = len(chunk)\n",
+    "            sl = slice(cursor, cursor + n)\n",
+    "\n",
+    "            spatial_ds[sl] = chunk[[\"spatial_x\", \"spatial_y\"]].to_numpy(dtype=np.float32)\n",
+    "            idx_ds[sl] = np.arange(cursor, cursor + n).astype(str)\n",
+    "            for col in obs_col_names:\n",
+    "                col_datasets[col][sl] = chunk[col].astype(str).values\n",
+    "\n",
+    "            cursor += n\n",
+    "            if rg % 50 == 0:\n",
+    "                print(f\"  h5ad pass {rg + 1}/{pf2.num_row_groups}: {cursor:,}\")\n",
+    "\n",
+    "    os.remove(tmp_path)\n",
+    "    print(\"wrote:\", out_path)\n",
+    "    print(\"shape:\", (total_rows, 0))\n",
+    "    print(\"obs columns:\", obs_col_names)\n",
+    "    return out_name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cc5c8636-ed10-47e9-bf9c-08d5608095f8",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "transcript_h5ad = xenium_all_transcripts_to_h5ad_empty_X(\n",
+    "    xenium_dir=xenium_dir,\n",
+    "    basedir=basedir,\n",
+    "    transform=transform,\n",
+    "    min_qv=None,\n",
+    "    include_cell_id=False,\n",
+    "    tmp_path=\"/Volumes/T7 Shield/tmp/transcripts_tmp.parquet\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a53c9e37-da32-4583-a74c-604d60df358b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# basedir = os.path.abspath(f\"tissuumaps/{sample}\")\n",
+    "# transcript_csv = f\"transcripts_image_space.csv\"\n",
+    "\n",
+    "# print(basedir)\n",
+    "\n",
+    "# region_files = []\n",
+    "\n",
+    "# for geojson_path in glob.glob(os.path.join(basedir, \"*boundaries*.geojson\")):\n",
+    "#     geojson_name = os.path.basename(geojson_path)\n",
+    "\n",
+    "#     region_files.append({\n",
+    "#         \"path\": geojson_name,          # relative path only\n",
+    "#         \"title\": geojson_name,\n",
+    "#         \"comment\": geojson_name,\n",
+    "#         \"autoLoad\": True,\n",
+    "#     })\n",
+    "\n",
+    "# print(region_files)\n",
+    "\n",
+    "# image_layers = []\n",
+    "\n",
+    "# for tif_path in sorted(\n",
+    "#     glob.glob(os.path.join(basedir, \"morphology_focus_plane*_pyramid.tif\"))\n",
+    "# ):\n",
+    "#     name = os.path.basename(tif_path)\n",
+    "\n",
+    "#     image_layers.append({\n",
+    "#         \"name\": name.replace(\".tif\", \"\"),\n",
+    "#         \"tileSource\": name + \".dzi\",\n",
+    "#         \"x\": 0,\n",
+    "#         \"y\": 0,\n",
+    "#         \"scale\": 1,\n",
+    "#         \"rotation\": 0,\n",
+    "#         \"flip\": False,\n",
+    "#     })\n",
+    "\n",
+    "# print(f\"Found {len(image_layers)} image layers\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1ee1ac2d-6de4-4fe2-b31a-90725d67669c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # ----------------------------\n",
+    "# # 5. Generate TissUUmaps project\n",
+    "# # ----------------------------\n",
+    "# project = read_h5ad.h5ad_to_tmap(basedir, out_h5ad_name)\n",
+    "\n",
+    "# # Images: all stacked and visible\n",
+    "# project[\"layers\"] = image_layers\n",
+    "# project[\"collectionMode\"] = False\n",
+    "# project[\"compositeMode\"] = \"lighter\"\n",
+    "# project[\"backgroundColor\"] = \"#000000\"\n",
+    "\n",
+    "# project[\"filters\"] = []\n",
+    "# project[\"layerFilters\"] = {}\n",
+    "# project[\"layerOpacities\"] = {str(i): 1 for i in range(len(image_layers))}\n",
+    "# project[\"layerVisibilities\"] = {str(i): True for i in range(len(image_layers))}\n",
+    "\n",
+    "# # Polygons: autoload\n",
+    "# project[\"regionFiles\"] = []\n",
+    "# for rf in region_files:\n",
+    "#     rf = dict(rf)\n",
+    "#     rf[\"autoLoad\"] = True\n",
+    "#     project[\"regionFiles\"].append(rf)\n",
+    "\n",
+    "# # Keep h5ad/cell-expression dropdowns available, but not autoloaded\n",
+    "# for mf in project.get(\"markerFiles\", []):\n",
+    "#     mf[\"autoLoad\"] = False\n",
+    "#     mf.setdefault(\"expectedHeader\", {})\n",
+    "#     mf[\"expectedHeader\"][\"shape_fixed\"] = \"disc\"\n",
+    "#     mf[\"expectedHeader\"][\"scale_factor\"] = 1\n",
+    "\n",
+    "#     mf.setdefault(\"expectedRadios\", {})\n",
+    "#     mf[\"expectedRadios\"][\"shape_fixed\"] = True\n",
+    "#     mf[\"expectedRadios\"][\"shape_gr\"] = False\n",
+    "#     mf[\"expectedRadios\"][\"shape_gr_rand\"] = False\n",
+    "#     mf[\"expectedRadios\"][\"sortby_check\"] = False\n",
+    "\n",
+    "# # Transcripts: autoload default marker layer\n",
+    "# if transcript_csv is not None:\n",
+    "#     project[\"markerFiles\"].insert(\n",
+    "#         0,\n",
+    "#         {\n",
+    "#             \"path\": transcript_csv,\n",
+    "#             \"title\": \"Load transcripts\",\n",
+    "#             \"comment\": \"Transcript molecules\",\n",
+    "#             \"name\": \"Transcripts\",\n",
+    "#             \"uid\": \"transcripts\",\n",
+    "#             \"autoLoad\": True,\n",
+    "#             \"hideSettings\": True,\n",
+    "#             \"expectedHeader\": {\n",
+    "#                 \"X\": \"x\",\n",
+    "#                 \"Y\": \"y\",\n",
+    "#                 \"gb_col\": \"gene\",\n",
+    "#                 \"gb_name\": \"\",\n",
+    "#                 \"cb_col\": \"\",\n",
+    "#                 \"cb_cmap\": \"\",\n",
+    "#                 \"scale_factor\": 0.15,\n",
+    "#                 \"shape_fixed\": \"disc\",\n",
+    "#                 \"opacity\": 0.7,\n",
+    "#             },\n",
+    "#             \"expectedRadios\": {\n",
+    "#                 \"cb_col\": False,\n",
+    "#                 \"cb_gr\": True,\n",
+    "#                 \"cb_gr_rand\": True,\n",
+    "#                 \"cb_gr_dict\": False,\n",
+    "#                 \"cb_gr_key\": False,\n",
+    "#                 \"pie_check\": False,\n",
+    "#                 \"scale_check\": False,\n",
+    "#                 \"shape_col\": False,\n",
+    "#                 \"shape_fixed\": True,\n",
+    "#                 \"shape_gr\": False,\n",
+    "#                 \"shape_gr_rand\": False,\n",
+    "#                 \"shape_gr_dict\": False,\n",
+    "#                 \"sortby_check\": False,\n",
+    "#             },\n",
+    "#         },\n",
+    "#     )\n",
+    "\n",
+    "# # project[\"markerFiles\"].insert(\n",
+    "# #     0,\n",
+    "# #     {\n",
+    "# #         \"path\": transcript_h5ad,\n",
+    "# #         \"title\": \"Load transcript AnnData\",\n",
+    "# #         \"comment\": \"All transcript molecules\",\n",
+    "# #         \"name\": \"Transcript AnnData\",\n",
+    "# #         \"uid\": \"transcript_h5ad\",\n",
+    "# #         \"autoLoad\": True,\n",
+    "# #         \"hideSettings\": True,\n",
+    "# #         \"expectedHeader\": {\n",
+    "# #             \"X\": \"/obsm/spatial;0\",\n",
+    "# #             \"Y\": \"/obsm/spatial;1\",\n",
+    "# #             \"gb_col\": \"/obs/gene\",\n",
+    "# #             \"gb_name\": \"\",\n",
+    "# #             \"cb_col\": \"\",\n",
+    "# #             \"cb_cmap\": \"\",\n",
+    "# #             \"scale_factor\": 0.15,\n",
+    "# #             \"shape_fixed\": \"disc\",\n",
+    "# #             \"opacity\": 0.7,\n",
+    "# #         },\n",
+    "# #         \"expectedRadios\": {\n",
+    "# #             \"cb_col\": False,\n",
+    "# #             \"cb_gr\": True,\n",
+    "# #             \"cb_gr_rand\": True,\n",
+    "# #             \"shape_fixed\": True,\n",
+    "# #             \"shape_gr\": False,\n",
+    "# #             \"scale_check\": False,\n",
+    "# #             \"sortby_check\": False,\n",
+    "# #         },\n",
+    "# #     },\n",
+    "# # )\n",
+    "\n",
+    "# with open(project_path, \"w\") as f:\n",
+    "#     json.dump(project, f, indent=2)\n",
+    "\n",
+    "# viewer = tj.opentmap(project_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "68db233e-7105-4862-9635-c68875497962",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "696e478b-61c6-4d35-8b90-813569c4a0ed",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python (tissuumaps_env)",
+   "language": "python",
+   "name": "tissuumaps_env"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.23"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

notebooks/tissuumaps_viz.ipynb

@@ -0,0 +1,295 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "07f7dbb1-63c6-40e4-b0d9-6ec104aa0adc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !pip install zarr\n",
+    "\n",
+    "import os\n",
+    "import json\n",
+    "from pathlib import Path\n",
+    "import glob\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import scanpy as sc\n",
+    "import pyvips\n",
+    "import zarr\n",
+    "import geopandas as gpd\n",
+    "from shapely.geometry import Polygon\n",
+    "from scipy.sparse import csc_matrix\n",
+    "\n",
+    "import tissuumaps.jupyter as tj\n",
+    "from tissuumaps import read_h5ad\n",
+    "\n",
+    "\n",
+    "# ----------------------------\n",
+    "# Paths\n",
+    "# ----------------------------\n",
+    "sample = \"WTA_Preview_FFPE_Cervical_Cancer_outs\"\n",
+    "# sample = \"Xenium_Prime_Human_Lymph_Node_Reactive_FFPE_outs\"\n",
+    "# sample = \"Xenium_Prime_Ovarian_Cancer_FFPE_XRrun_outs\"\n",
+    "# sample = \"Xenium_V1_humanLung_Cancer_FFPE_outs\"\n",
+    "\n",
+    "\n",
+    "xenium_dir = os.path.abspath(f\"../data/instrument_data/{sample}\")\n",
+    "basedir = os.path.abspath(f\"../data/processed_data/tissuumaps_h5ad/{sample}\")\n",
+    "os.makedirs(basedir, exist_ok=True)\n",
+    "\n",
+    "out_h5ad_name = f\"{sample}_tmap.h5ad\"\n",
+    "out_h5ad = os.path.join(basedir, out_h5ad_name)\n",
+    "\n",
+    "project_path = os.path.join(basedir, \"_project_h5ad.tmap\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a53c9e37-da32-4583-a74c-604d60df358b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "basedir = os.path.abspath(f\"../data/processed_data/tissuumaps_h5ad/{sample}\")\n",
+    "# transcript_csv = f\"transcripts_image_space.csv\"\n",
+    "transcript_h5ad = f\"transcripts_all_emptyX_tmap.h5ad\"\n",
+    "\n",
+    "print(basedir)\n",
+    "\n",
+    "region_files = []\n",
+    "\n",
+    "for geojson_path in glob.glob(os.path.join(basedir, \"*boundaries*.geojson\")):\n",
+    "    geojson_name = os.path.basename(geojson_path)\n",
+    "\n",
+    "    region_files.append({\n",
+    "        \"path\": geojson_name,          # relative path only\n",
+    "        \"title\": geojson_name,\n",
+    "        \"comment\": geojson_name,\n",
+    "        \"autoLoad\": True,\n",
+    "    })\n",
+    "\n",
+    "print(region_files)\n",
+    "\n",
+    "image_layers = []\n",
+    "\n",
+    "for tif_path in sorted(\n",
+    "    glob.glob(os.path.join(basedir, \"morphology_focus_plane*_pyramid.tif\"))\n",
+    "):\n",
+    "    name = os.path.basename(tif_path)\n",
+    "\n",
+    "    image_layers.append({\n",
+    "        \"name\": name.replace(\".tif\", \"\"),\n",
+    "        \"tileSource\": name + \".dzi\",\n",
+    "        \"x\": 0,\n",
+    "        \"y\": 0,\n",
+    "        \"scale\": 1,\n",
+    "        \"rotation\": 0,\n",
+    "        \"flip\": False,\n",
+    "    })\n",
+    "\n",
+    "print(f\"Found {len(image_layers)} image layers\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bbcabbbd-3f76-48dd-b589-c80e47fc8cbc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ----------------------------\n",
+    "# 5. Generate TissUUmaps project\n",
+    "# ----------------------------\n",
+    "project = read_h5ad.h5ad_to_tmap(basedir, out_h5ad_name)\n",
+    "\n",
+    "# Images: all stacked and visible\n",
+    "project[\"layers\"] = image_layers\n",
+    "\n",
+    "project[\"collectionMode\"] = False\n",
+    "project[\"compositeMode\"] = \"lighter\"\n",
+    "project[\"backgroundColor\"] = \"#000000\"\n",
+    "\n",
+    "project[\"filters\"] = []\n",
+    "project[\"layerFilters\"] = {}\n",
+    "project[\"layerOpacities\"] = {str(i): 1 for i in range(len(image_layers))}\n",
+    "project[\"layerVisibilities\"] = {str(i): True for i in range(len(image_layers))}\n",
+    "\n",
+    "# Polygons: autoload\n",
+    "project[\"regionFiles\"] = []\n",
+    "for rf in region_files:\n",
+    "    rf = dict(rf)\n",
+    "    rf[\"autoLoad\"] = True\n",
+    "    project[\"regionFiles\"].append(rf)\n",
+    "\n",
+    "for i, mf in enumerate(project.get(\"markerFiles\", [])):\n",
+    "    mf.setdefault(\"expectedHeader\", {})\n",
+    "    mf[\"expectedHeader\"][\"shape_fixed\"] = \"disc\"\n",
+    "    mf[\"expectedHeader\"][\"scale_factor\"] = 1\n",
+    "    mf.setdefault(\"expectedRadios\", {})\n",
+    "    mf[\"expectedRadios\"][\"shape_fixed\"] = True\n",
+    "    mf[\"expectedRadios\"][\"shape_gr\"] = False\n",
+    "    mf[\"expectedRadios\"][\"shape_gr_rand\"] = False\n",
+    "    mf[\"expectedRadios\"][\"sortby_check\"] = False\n",
+    "\n",
+    "# Transcripts: autoload default marker layer\n",
+    "# if transcript_csv is not None:\n",
+    "#     project[\"markerFiles\"].insert(\n",
+    "#         0,\n",
+    "#         {\n",
+    "#             \"path\": transcript_csv,\n",
+    "#             \"title\": \"Load transcripts\",\n",
+    "#             \"comment\": \"Transcript molecules\",\n",
+    "#             \"name\": \"Transcripts\",\n",
+    "#             \"uid\": \"transcripts\",\n",
+    "#             \"autoLoad\": True,\n",
+    "#             \"hideSettings\": True,\n",
+    "#             \"expectedHeader\": {\n",
+    "#                 \"X\": \"x\",\n",
+    "#                 \"Y\": \"y\",\n",
+    "#                 \"gb_col\": \"gene\",\n",
+    "#                 \"gb_name\": \"\",\n",
+    "#                 \"cb_col\": \"\",\n",
+    "#                 \"cb_cmap\": \"\",\n",
+    "#                 \"scale_factor\": 0.15,\n",
+    "#                 \"shape_fixed\": \"disc\",\n",
+    "#                 \"opacity\": 0.7,\n",
+    "#             },\n",
+    "#             \"expectedRadios\": {\n",
+    "#                 \"cb_col\": False,\n",
+    "#                 \"cb_gr\": True,\n",
+    "#                 \"cb_gr_rand\": True,\n",
+    "#                 \"cb_gr_dict\": False,\n",
+    "#                 \"cb_gr_key\": False,\n",
+    "#                 \"pie_check\": False,\n",
+    "#                 \"scale_check\": False,\n",
+    "#                 \"shape_col\": False,\n",
+    "#                 \"shape_fixed\": True,\n",
+    "#                 \"shape_gr\": False,\n",
+    "#                 \"shape_gr_rand\": False,\n",
+    "#                 \"shape_gr_dict\": False,\n",
+    "#                 \"sortby_check\": False,\n",
+    "#             },\n",
+    "#         },\n",
+    "#     )\n",
+    "\n",
+    "project[\"markerFiles\"].insert(\n",
+    "    0,\n",
+    "    {\n",
+    "        \"path\": transcript_h5ad,\n",
+    "        \"title\": \"Load transcript AnnData\",\n",
+    "        \"comment\": \"All transcript molecules\",\n",
+    "        \"name\": \"Transcript AnnData\",\n",
+    "        \"uid\": \"transcript_h5ad\",\n",
+    "        \"autoLoad\": True,\n",
+    "        \"hideSettings\": True,\n",
+    "        \"expectedHeader\": {\n",
+    "            \"X\": \"/obsm/spatial;0\",\n",
+    "            \"Y\": \"/obsm/spatial;1\",\n",
+    "            \"gb_col\": \"/obs/gene\",\n",
+    "            \"gb_name\": \"\",\n",
+    "            \"cb_col\": \"\",\n",
+    "            \"cb_cmap\": \"\",\n",
+    "            \"scale_factor\": 0.15,\n",
+    "            \"shape_fixed\": \"disc\",\n",
+    "            \"opacity\": 0.7,\n",
+    "        },\n",
+    "        \"expectedRadios\": {\n",
+    "            \"cb_col\": False,\n",
+    "            \"cb_gr\": True,\n",
+    "            \"cb_gr_rand\": True,\n",
+    "            \"shape_fixed\": True,\n",
+    "            \"shape_gr\": False,\n",
+    "            \"scale_check\": False,\n",
+    "            \"sortby_check\": False,\n",
+    "        },\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "# Set autoLoad explicitly by name after insert\n",
+    "for mf in project[\"markerFiles\"]:\n",
+    "    if mf.get(\"name\") in (\"Transcript AnnData\", \"Categorical observations\"):\n",
+    "        mf[\"autoLoad\"] = True\n",
+    "    else:\n",
+    "        mf[\"autoLoad\"] = False\n",
+    "\n",
+    "with open(project_path, \"w\") as f:\n",
+    "    json.dump(project, f, indent=2)\n",
+    "\n",
+    "print(\"done\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1ee1ac2d-6de4-4fe2-b31a-90725d67669c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "viewer = tj.opentmap(project_path)\n",
+    "viewer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "db4873e9-ba2e-4814-80a9-e32f92a5677a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7394d91a-54ad-49b1-834f-bda9f24f4944",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "68db233e-7105-4862-9635-c68875497962",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4c8a97c2-3285-4b9d-8a6c-7bdb6a89c4a8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python (tissuumaps_env)",
+   "language": "python",
+   "name": "tissuumaps_env"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.23"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

notebooks/vitessce_pre-process.ipynb

@@ -0,0 +1,444 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "nbsphinx": "hidden"
+   },
+   "source": [
+    "# Vitessce Widget Tutorial"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Visualization of a SpatialData object"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import dependencies\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from os.path import join, isfile, isdir\n",
+    "from urllib.request import urlretrieve\n",
+    "import zipfile\n",
+    "import shutil\n",
+    "\n",
+    "from vitessce import (\n",
+    "    VitessceConfig,\n",
+    "    ViewType as vt,\n",
+    "    CoordinationType as ct,\n",
+    "    CoordinationLevel as CL,\n",
+    "    SpatialDataWrapper,\n",
+    "    get_initial_coordination_scope_prefix\n",
+    ")\n",
+    "\n",
+    "from vitessce.data_utils import (\n",
+    "    sdata_morton_sort_points,\n",
+    "    sdata_points_process_columns,\n",
+    "    sdata_points_write_bounding_box_attrs,\n",
+    "    sdata_points_modify_row_group_size,\n",
+    "    sdata_morton_query_rect,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from spatialdata import read_zarr\n",
+    "\n",
+    "import anndata as ad\n",
+    "\n",
+    "ad.settings.zarr_write_format = 3\n",
+    "print(ad.settings.zarr_write_format)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import dask\n",
+    "import tempfile\n",
+    "\n",
+    "# Point Dask temp dir to your external drive\n",
+    "dask.config.set({'temporary_directory': '/Volumes/T7 Shield/tmp'})\n",
+    "\n",
+    "# Create the dir if it doesn't exist\n",
+    "import os\n",
+    "os.makedirs('/Volumes/T7 Shield/tmp', exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "from spatialdata_io import xenium\n",
+    "import spatialdata as sd\n",
+    "import pandas as pd\n",
+    "\n",
+    "xenium_dir = Path(\"../data/instrument_data/Xenium_V1_hPancreas_Cancer_Add_on_FFPE_outs/\")   # folder containing experiment.xenium\n",
+    "out_zarr = Path(\"../data/processed_data/vitessce/Xenium_V1_hPancreas_Cancer_Add_on_FFPE_outs.zarr\")\n",
+    "\n",
+    "sdata = xenium(\n",
+    "    xenium_dir,\n",
+    "    cells_boundaries=True,\n",
+    "    nucleus_boundaries=True,\n",
+    "    cells_labels=True,\n",
+    "    nucleus_labels=True,\n",
+    "    transcripts=True,\n",
+    "    morphology_focus=True,\n",
+    "    aligned_images=True,\n",
+    "    cells_table=True,\n",
+    "    gex_only=True,\n",
+    ")\n",
+    "\n",
+    "print(sdata)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add cluster labels from Xenium instrument output\n",
+    "clusters = pd.read_csv(\n",
+    "    xenium_dir / \"analysis/clustering/gene_expression_graphclust/clusters.csv\"\n",
+    ")\n",
+    "sdata.tables[\"table\"].obs = sdata.tables[\"table\"].obs.merge(\n",
+    "    clusters.set_index(\"Barcode\")[[\"Cluster\"]].rename(columns={\"Cluster\": \"leiden\"}),\n",
+    "    left_index=True,\n",
+    "    right_index=True,\n",
+    "    how=\"left\",\n",
+    ")\n",
+    "sdata.tables[\"table\"].obs[\"leiden\"] = sdata.tables[\"table\"].obs[\"leiden\"].astype(str)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save as a SpatialData Zarr store\n",
+    "sdata.write(out_zarr)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sdata[\"transcripts\"].shape[0].compute()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sdata.tables[\"table\"].X = sdata.tables[\"table\"].X.toarray()\n",
+    "sdata.tables[\"dense_table\"] = sdata.tables[\"table\"]\n",
+    "sdata.write_element(\"dense_table\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: store the two separate images as a single image with two channels.\n",
+    "# Similar to https://github.com/EricMoerthVis/tissue-map-tools/pull/12"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sdata.tables['table'].obs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sdata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sdata.points['transcripts'].head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Sorting Points and creating a new Points element in the SpatialData object"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 1. Sort rows with `sdata_morton_sort_points`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import importlib.metadata\n",
+    "print(importlib.metadata.version(\"vitessce\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sdata = sdata_morton_sort_points(sdata, \"transcripts\")\n",
+    "from vitessce.data_utils.spatialdata_points_zorder import norm_ddf_to_uint, morton_interleave\n",
+    "\n",
+    "element = \"transcripts\"\n",
+    "ddf = sdata.points[element]\n",
+    "attrs = ddf.attrs.copy()\n",
+    "\n",
+    "ddf = norm_ddf_to_uint(ddf)\n",
+    "ddf[\"morton_code_2d\"] = morton_interleave(ddf)\n",
+    "sorted_ddf = ddf.sort_values(by=\"morton_code_2d\", ascending=True)\n",
+    "sorted_ddf.attrs.update(attrs)\n",
+    "sdata.points[element] = sorted_ddf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 2. Clean up columns with `sdata_points_process_columns`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add feature_index column to dataframe, and reorder columns so that feature_name (dict column) is the rightmost column.\n",
+    "ddf = sdata_points_process_columns(sdata, \"transcripts\", var_name_col=\"feature_name\", table_name=\"table\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ddf.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 3. Save sorted dataframe to new Points element"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sdata[\"transcripts_with_morton_codes\"] = ddf\n",
+    "# sdata.write_element(\"transcripts_with_morton_codes\")\n",
+    "\n",
+    "from spatialdata.models import PointsModel\n",
+    "\n",
+    "transformations = sdata[\"transcripts\"].attrs[\"transform\"]\n",
+    "del ddf.attrs[\"transform\"]\n",
+    "\n",
+    "sdata[\"transcripts_with_morton_codes\"] = PointsModel.parse(\n",
+    "    ddf, feature_key=\"feature_name\", instance_key=\"cell_id\", transformations=transformations\n",
+    ")\n",
+    "sdata.write_element(\"transcripts_with_morton_codes\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 4. Write bounding box metadata with `sdata_points_write_bounding_box_attrs`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import shutil\n",
+    "import os\n",
+    "\n",
+    "tmp_dir = '/Volumes/T7 Shield/tmp'\n",
+    "shutil.rmtree(tmp_dir)\n",
+    "os.makedirs(tmp_dir)\n",
+    "print(\"Done\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sdata_points_write_bounding_box_attrs(sdata, \"transcripts_with_morton_codes\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 5. Modify the row group sizes of the Parquet files with `sdata_points_modify_row_group_size`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import shutil\n",
+    "import os\n",
+    "\n",
+    "tmp_dir = '/Volumes/T7 Shield/tmp'\n",
+    "shutil.rmtree(tmp_dir)\n",
+    "os.makedirs(tmp_dir)\n",
+    "print(\"Done\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sdata_points_modify_row_group_size(sdata, \"transcripts_with_morton_codes\", row_group_size=25_000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Done"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Optionally, check the number of row groups in one of the parquet file parts.\n",
+    "import pyarrow.parquet as pq\n",
+    "from os.path import join\n",
+    "\n",
+    "parquet_file = pq.ParquetFile(join(sdata.path, \"points\", \"transcripts_with_morton_codes\", \"points.parquet\", \"part.0.parquet\"))\n",
+    "\n",
+    "# Get the number of row groups in this part-0 file.\n",
+    "num_groups = parquet_file.num_row_groups\n",
+    "num_groups"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.12.1"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {
+     "undefined": {
+      "model_module": "anywidget",
+      "model_module_version": "2.0.0",
+      "model_name": "AnyModel",
+      "state": {
+       "_view_name": "ErrorWidgetView",
+       "error": {},
+       "msg": "Model class 'AnyModel' from module 'anywidget' is loaded but can not be instantiated"
+      }
+     }
+    },
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

notebooks/vitessce_viz.ipynb

@@ -0,0 +1,1133 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "nbsphinx": "hidden"
+   },
+   "source": [
+    "# Vitessce Widget Tutorial"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Visualization of a SpatialData object"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import dependencies\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from os.path import join, isfile, isdir\n",
+    "from urllib.request import urlretrieve\n",
+    "import zipfile\n",
+    "import shutil\n",
+    "\n",
+    "from vitessce import (\n",
+    "    VitessceConfig,\n",
+    "    ViewType as vt,\n",
+    "    CoordinationType as ct,\n",
+    "    CoordinationLevel as CL,\n",
+    "    SpatialDataWrapper,\n",
+    "    get_initial_coordination_scope_prefix\n",
+    ")\n",
+    "\n",
+    "from vitessce.data_utils import (\n",
+    "    sdata_morton_sort_points,\n",
+    "    sdata_points_process_columns,\n",
+    "    sdata_points_write_bounding_box_attrs,\n",
+    "    sdata_points_modify_row_group_size,\n",
+    "    sdata_morton_query_rect,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "from spatialdata_io import xenium\n",
+    "import spatialdata as sd\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from spatialdata import read_zarr\n",
+    "\n",
+    "import anndata as ad\n",
+    "\n",
+    "ad.settings.zarr_write_format = 3\n",
+    "print(ad.settings.zarr_write_format)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ls"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configure Vitessce\n",
+    "\n",
+    "Vitessce needs to know which pieces of data we are interested in visualizing, the visualization types we would like to use, and how we want to coordinate (or link) the views."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# out_zarr = \"../data/processed_data/vitessce/WTA_Preview_FFPE_Cervical_Cancer_outs.zarr\"\n",
+    "# out_zarr = \"../data/processed_data/vitessce/Xenium_Prime_Human_Lymph_Node_Reactive_FFPE_outs.zarr\"\n",
+    "# out_zarr = \"../data/processed_data/vitessce/Xenium_Prime_Ovarian_Cancer_FFPE_XRrun_outs.zarr\"\n",
+    "out_zarr = \"../data/processed_data/vitessce/Xenium_V1_humanLung_Cancer_FFPE_outs.zarr\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vc = VitessceConfig(\n",
+    "    schema_version=\"1.0.18\",\n",
+    "    name='Xenium SpatialData Demo',\n",
+    ")\n",
+    "\n",
+    "# Cell segmentations + gene expression\n",
+    "wrapper = SpatialDataWrapper(\n",
+    "    sdata_path=out_zarr,\n",
+    "    image_path=\"images/morphology_focus\",\n",
+    "    table_path=\"tables/table\",\n",
+    "    obs_feature_matrix_path=\"tables/table/X\",\n",
+    "    obs_segmentations_path=\"shapes/cell_boundaries\",\n",
+    "    # obs_set_paths=[\"tables/table/obs/leiden\"],\n",
+    "    # obs_set_names=[\"Cluster\"],\n",
+    "    coordinate_system=\"global\",\n",
+    "    coordination_values={\n",
+    "        \"obsType\": \"cell\",\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "# Transcripts\n",
+    "points_wrapper = SpatialDataWrapper(\n",
+    "    sdata_path=out_zarr,\n",
+    "    obs_points_path=\"points/transcripts_with_morton_codes\",\n",
+    "    obs_feature_matrix_path=\"tables/dense_table/X\",\n",
+    "    coordinate_system=\"global\",\n",
+    "    coordination_values={\n",
+    "        \"obsType\": \"point\",\n",
+    "        \"featureType\": \"gene\",\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "dataset = vc.add_dataset(name='Xenium').add_object(wrapper).add_object(points_wrapper)\n",
+    "\n",
+    "spatial = vc.add_view(\"spatialBeta\", dataset=dataset)\n",
+    "feature_list = vc.add_view(\"featureList\", dataset=dataset)\n",
+    "layer_controller = vc.add_view(\"layerControllerBeta\", dataset=dataset)\n",
+    "obs_sets = vc.add_view(\"obsSets\", dataset=dataset)\n",
+    "\n",
+    "vc.link_views_by_dict([spatial, layer_controller], {\n",
+    "    'segmentationLayer': CL([{\n",
+    "        'segmentationChannel': CL([{\n",
+    "            'obsType': 'cell',\n",
+    "            # 'obsColorEncoding': 'cellSetSelection',  # <-- this makes it default to cluster colors\n",
+    "        }]),\n",
+    "    }]),\n",
+    "}, scope_prefix=get_initial_coordination_scope_prefix(\"A\", \"obsSegmentations\"))\n",
+    "\n",
+    "vc.link_views_by_dict([spatial, layer_controller], {\n",
+    "    'pointLayer': CL([{\n",
+    "        'obsType': 'point',\n",
+    "    }]),\n",
+    "}, scope_prefix=get_initial_coordination_scope_prefix(\"A\", \"obsPoints\"))\n",
+    "\n",
+    "vc.link_views([spatial, layer_controller, feature_list, obs_sets], ['obsType'], [wrapper.obs_type_label])\n",
+    "\n",
+    "# vc.layout(spatial | (feature_list / layer_controller / obs_sets))\n",
+    "vc.layout(spatial)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Render the widget"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vw = vc.widget()\n",
+    "vw"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.12.1"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {
+     "8fa17251e5d6449a84f5cbc270c6674d": {
+      "model_module": "anywidget",
+      "model_module_version": "2.0.0",
+      "model_name": "AnyModel",
+      "state": {
+       "_anywidget_id": "vitessce.widget.VitessceWidget",
+       "_config": {
+        "coordinationSpace": {
+         "additionalObsSets": {
+          "A": null
+         },
+         "dataset": {
+          "A": "A",
+          "init_A_image_0": "init_A_image_0",
+          "init_A_obsSegmentations_0": "init_A_obsSegmentations_0"
+         },
+         "featureAggregationStrategy": {
+          "A": null,
+          "B": null
+         },
+         "featureColor": {
+          "A": null
+         },
+         "featureFilter": {
+          "A": null
+         },
+         "featureFilterMode": {
+          "A": null
+         },
+         "featureHighlight": {
+          "A": null
+         },
+         "featureSelection": {
+          "A": null
+         },
+         "featureType": {
+          "A": "gene"
+         },
+         "featureValueColormap": {
+          "A": "plasma",
+          "init_A_obsSegmentations_0": "plasma"
+         },
+         "featureValueColormapRange": {
+          "A": [
+           0,
+           1
+          ]
+         },
+         "featureValueType": {
+          "A": "expression"
+         },
+         "fileUid": {
+          "A": null,
+          "init_A_image_0": null,
+          "init_A_obsSegmentations_0": null
+         },
+         "imageChannel": {
+          "A": null,
+          "init_A_image_0": "__dummy__",
+          "init_A_image_1": "__dummy__",
+          "init_A_image_2": "__dummy__",
+          "init_A_image_3": "__dummy__"
+         },
+         "imageLayer": {
+          "A": null,
+          "init_A_image_0": "__dummy__"
+         },
+         "legendVisible": {
+          "A": true
+         },
+         "metaCoordinationScopes": {
+          "init_A_image_0": {
+           "imageLayer": [
+            "init_A_image_0"
+           ],
+           "spatialImageLayer": "init_A_image_0",
+           "spatialTargetT": "init_A_image_0",
+           "spatialTargetZ": "init_A_image_0"
+          },
+          "init_A_obsPoints_0": {
+           "pointLayer": [
+            "init_A_obsPoints_0"
+           ]
+          },
+          "init_A_obsSegmentations_0": {
+           "segmentationLayer": [
+            "init_A_obsSegmentations_0"
+           ]
+          }
+         },
+         "metaCoordinationScopesBy": {
+          "init_A_image_0": {
+           "imageChannel": {
+            "spatialChannelColor": {
+             "init_A_image_0": "init_A_image_0",
+             "init_A_image_1": "init_A_image_1",
+             "init_A_image_2": "init_A_image_2",
+             "init_A_image_3": "init_A_image_3"
+            },
+            "spatialChannelOpacity": {
+             "init_A_image_0": "init_A_image_0",
+             "init_A_image_1": "init_A_image_1",
+             "init_A_image_2": "init_A_image_2",
+             "init_A_image_3": "init_A_image_3"
+            },
+            "spatialChannelVisible": {
+             "init_A_image_0": "init_A_image_0",
+             "init_A_image_1": "init_A_image_1",
+             "init_A_image_2": "init_A_image_2",
+             "init_A_image_3": "init_A_image_3"
+            },
+            "spatialChannelWindow": {
+             "init_A_image_0": "init_A_image_0",
+             "init_A_image_1": "init_A_image_1",
+             "init_A_image_2": "init_A_image_2",
+             "init_A_image_3": "init_A_image_3"
+            },
+            "spatialTargetC": {
+             "init_A_image_0": "init_A_image_0",
+             "init_A_image_1": "init_A_image_1",
+             "init_A_image_2": "init_A_image_2",
+             "init_A_image_3": "init_A_image_3"
+            }
+           },
+           "imageLayer": {
+            "fileUid": {
+             "init_A_image_0": "init_A_image_0"
+            },
+            "imageChannel": {
+             "init_A_image_0": [
+              "init_A_image_0",
+              "init_A_image_1",
+              "init_A_image_2",
+              "init_A_image_3"
+             ]
+            },
+            "photometricInterpretation": {
+             "init_A_image_0": "init_A_image_0"
+            },
+            "spatialLayerOpacity": {
+             "init_A_image_0": "init_A_image_0"
+            },
+            "spatialLayerVisible": {
+             "init_A_image_0": "init_A_image_0"
+            },
+            "spatialTargetResolution": {
+             "init_A_image_0": "init_A_image_0"
+            },
+            "volumetricRenderingAlgorithm": {
+             "init_A_image_0": "init_A_image_0"
+            }
+           }
+          },
+          "init_A_obsPoints_0": {
+           "pointLayer": {
+            "obsType": {
+             "init_A_obsPoints_0": "init_A_obsPoints_0"
+            }
+           }
+          },
+          "init_A_obsSegmentations_0": {
+           "segmentationChannel": {
+            "featureValueColormap": {
+             "init_A_obsSegmentations_0": "init_A_obsSegmentations_0"
+            },
+            "obsColorEncoding": {
+             "init_A_obsSegmentations_0": "init_A_obsSegmentations_0"
+            },
+            "obsHighlight": {
+             "init_A_obsSegmentations_0": "init_A_obsSegmentations_0"
+            },
+            "obsType": {
+             "init_A_obsSegmentations_0": "init_A_obsSegmentations_0"
+            },
+            "spatialChannelColor": {
+             "init_A_obsSegmentations_0": "init_A_obsSegmentations_0"
+            },
+            "spatialChannelOpacity": {
+             "init_A_obsSegmentations_0": "init_A_obsSegmentations_0"
+            },
+            "spatialChannelVisible": {
+             "init_A_obsSegmentations_0": "init_A_obsSegmentations_0"
+            },
+            "spatialChannelWindow": {
+             "init_A_obsSegmentations_0": "init_A_obsSegmentations_0"
+            },
+            "spatialSegmentationFilled": {
+             "init_A_obsSegmentations_0": "init_A_obsSegmentations_0"
+            },
+            "spatialSegmentationStrokeWidth": {
+             "init_A_obsSegmentations_0": "init_A_obsSegmentations_0"
+            }
+           },
+           "segmentationLayer": {
+            "fileUid": {
+             "init_A_obsSegmentations_0": "init_A_obsSegmentations_0"
+            },
+            "segmentationChannel": {
+             "init_A_obsSegmentations_0": [
+              "init_A_obsSegmentations_0"
+             ]
+            },
+            "spatialLayerOpacity": {
+             "init_A_obsSegmentations_0": "init_A_obsSegmentations_0"
+            },
+            "spatialLayerVisible": {
+             "init_A_obsSegmentations_0": "init_A_obsSegmentations_0"
+            }
+           }
+          }
+         },
+         "moleculeHighlight": {
+          "A": null
+         },
+         "obsColorEncoding": {
+          "A": "cellSetSelection",
+          "init_A_obsSegmentations_0": "spatialChannelColor"
+         },
+         "obsFilter": {
+          "A": null
+         },
+         "obsHighlight": {
+          "A": null,
+          "init_A_obsSegmentations_0": null
+         },
+         "obsLabelsType": {
+          "A": null
+         },
+         "obsSetColor": {
+          "A": null
+         },
+         "obsSetExpansion": {
+          "A": null
+         },
+         "obsSetFilter": {
+          "A": null
+         },
+         "obsSetHighlight": {
+          "A": null
+         },
+         "obsSetSelection": {
+          "A": null
+         },
+         "obsType": {
+          "A": "cell",
+          "init_A_obsPoints_0": "point",
+          "init_A_obsSegmentations_0": "cell"
+         },
+         "photometricInterpretation": {
+          "A": null,
+          "init_A_image_0": "BlackIsZero"
+         },
+         "pixelHighlight": {
+          "A": null
+         },
+         "pointLayer": {
+          "A": null,
+          "init_A_obsPoints_0": "__dummy__"
+         },
+         "segmentationChannel": {
+          "A": null,
+          "init_A_obsSegmentations_0": "__dummy__"
+         },
+         "segmentationLayer": {
+          "A": null,
+          "init_A_obsSegmentations_0": "__dummy__"
+         },
+         "spatialAxisFixed": {
+          "A": false
+         },
+         "spatialChannelColor": {
+          "A": [
+           255,
+           255,
+           255
+          ],
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+            "options": {
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+            "url": "http://localhost:8000/A/0/f595faf2-239a-47f2-a3c7-27314f281a5a.sdata.zarr"
+           },
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+            "options": {
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+           },
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+            "options": {
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+             "path": "points/transcripts_with_morton_codes",
+             "tablePath": "tables/table"
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+            "url": "http://localhost:8000/A/1/afd11240-c386-4fe7-8bfd-f77a5138e9bc.sdata.zarr"
+           }
+          ],
+          "name": "Xenium",
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+        "initStrategy": "auto",
+        "layout": [
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+           "metaCoordinationScopes": [
+            "init_A_image_0",
+            "init_A_obsSegmentations_0",
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+            "init_A_obsPoints_0"
+           ],
+           "metaCoordinationScopesBy": [
+            "init_A_image_0",
+            "init_A_obsSegmentations_0",
+            "init_A_obsSegmentations_0",
+            "init_A_obsPoints_0"
+           ],
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+           "photometricInterpretation": "A",
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+           "segmentationLayer": "A",
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+           "spatialChannelVisible": "A",
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+           "spatialLayerColormap": "A",
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+           "obsSetSelection": "A",
+           "obsType": "A"
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+          "x": 0,
+          "y": 0
+         },
+         {
+          "component": "layerControllerBeta",
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+           "dataset": "A",
+           "featureAggregationStrategy": "B",
+           "featureColor": "A",
+           "featureFilterMode": "A",
+           "featureSelection": "A",
+           "featureType": "A",
+           "featureValueColormap": "A",
+           "featureValueColormapRange": "A",
+           "featureValueType": "A",
+           "fileUid": "A",
+           "imageChannel": "A",
+           "imageLayer": "A",
+           "legendVisible": "A",
+           "metaCoordinationScopes": [
+            "init_A_image_0",
+            "init_A_obsSegmentations_0",
+            "init_A_obsSegmentations_0",
+            "init_A_obsPoints_0"
+           ],
+           "metaCoordinationScopesBy": [
+            "init_A_image_0",
+            "init_A_obsSegmentations_0",
+            "init_A_obsSegmentations_0",
+            "init_A_obsPoints_0"
+           ],
+           "obsColorEncoding": "A",
+           "obsType": "A",
+           "photometricInterpretation": "A",
+           "pointLayer": "A",
+           "segmentationChannel": "A",
+           "segmentationLayer": "A",
+           "spatialChannelColor": "A",
+           "spatialChannelLabelSize": "A",
+           "spatialChannelLabelsOrientation": "A",
+           "spatialChannelLabelsVisible": "A",
+           "spatialChannelOpacity": "A",
+           "spatialChannelVisible": "A",
+           "spatialChannelWindow": "A",
+           "spatialLayerColor": "A",
+           "spatialLayerColormap": "A",
+           "spatialLayerModelMatrix": "A",
+           "spatialLayerOpacity": "A",
+           "spatialLayerTransparentColor": "A",
+           "spatialLayerVisible": "A",
+           "spatialMaxResolution": "A",
+           "spatialNeighborhoodLayer": "A",
+           "spatialOrbitAxis": "B",
+           "spatialPointLayer": "A",
+           "spatialPointStrokeWidth": "A",
+           "spatialRenderingMode": "A",
+           "spatialRotationOrbit": "B",
+           "spatialRotationX": "B",
+           "spatialRotationY": "B",
+           "spatialRotationZ": "B",
+           "spatialSegmentationFilled": "A",
+           "spatialSegmentationStrokeWidth": "A",
+           "spatialSliceX": "A",
+           "spatialSliceY": "A",
+           "spatialSliceZ": "A",
+           "spatialSpotFilled": "A",
+           "spatialSpotRadius": "A",
+           "spatialSpotStrokeWidth": "A",
+           "spatialTargetC": "A",
+           "spatialTargetResolution": "A",
+           "spatialTargetT": "A",
+           "spatialTargetX": "B",
+           "spatialTargetY": "B",
+           "spatialTargetZ": "B",
+           "spatialZoom": "B",
+           "spotLayer": "A",
+           "tooltipCrosshairsVisible": "A",
+           "tooltipsVisible": "A",
+           "volumetricRenderingAlgorithm": "A"
+          },
+          "h": 1,
+          "uid": "C",
+          "w": 1,
+          "x": 0,
+          "y": 0
+         },
+         {
+          "component": "obsSets",
+          "coordinationScopes": {
+           "additionalObsSets": "A",
+           "dataset": "A",
+           "featureSelection": "A",
+           "obsColorEncoding": "A",
+           "obsSetColor": "A",
+           "obsSetExpansion": "A",
+           "obsSetFilter": "A",
+           "obsSetHighlight": "A",
+           "obsSetSelection": "A",
+           "obsType": "A"
+          },
+          "h": 1,
+          "uid": "D",
+          "w": 1,
+          "x": 0,
+          "y": 0
+         }
+        ],
+        "name": "Xenium SpatialData Demo",
+        "uid": "A",
+        "version": "1.0.18"
+       },
+       "_esm": "\nlet importWithMap;\ntry {\n    importWithMap = (await import('https://unpkg.com/dynamic-importmap@0.1.0')).importWithMap;\n} catch(e) {\n    console.warn(\"Import of dynamic-importmap failed, trying fallback.\");\n    importWithMap = (await import('https://cdn.vitessce.io/dynamic-importmap@0.1.0/dist/index.js')).importWithMap;\n}\n\nconst successfulImportMap = {\n    imports: {\n\n    },\n};\nconst importMap = {\n  imports: {\n    \"react\": \"https://esm.sh/react@18.2.0?dev\",\n    \"react-dom\": \"https://esm.sh/react-dom@18.2.0?dev\",\n    \"react-dom/client\": \"https://esm.sh/react-dom@18.2.0/client?dev\",\n  },\n};\nconst fallbackImportMap = {\n    imports: {\n        \"react\": \"https://cdn.vitessce.io/react@18.2.0/index.js\",\n        \"react-dom\": \"https://cdn.vitessce.io/react-dom@18.2.0/index.js\",\n        \"react-dom/client\": \"https://cdn.vitessce.io/react-dom@18.2.0/es2022/client.mjs\",\n        // Replaced with version-specific URL below.\n        \"vitessce\": \"https://cdn.vitessce.io/vitessce@VERSION/dist/index.min.js\",\n    },\n};\n/*\nconst fallbackDevImportMap = {\n    imports: {\n        \"react\": \"https://cdn.vitessce.io/react@18.2.0/index_dev.js\",\n        \"react-dom\": \"https://cdn.vitessce.io/react-dom@18.2.0/index_dev.js\",\n        \"react-dom/client\": \"https://cdn.vitessce.io/react-dom@18.2.0/es2022/client.development.mjs\",\n        // Replaced with version-specific URL below.\n        \"vitessce\": \"https://cdn.vitessce.io/@vitessce/dev@VERSION/dist/index.js\",\n    },\n};\n*/\n\nasync function importWithMapAndFallback(moduleName, importMap, fallbackMap) {\n    let result = null;\n    if (!fallbackMap) {\n        // fallbackMap is null, user may have provided custom JS URL.\n        result = await importWithMap(moduleName, {\n            imports: {\n                ...importMap.imports,\n                ...successfulImportMap.imports,\n            },\n        });\n        successfulImportMap.imports[moduleName] = importMap.imports[moduleName];\n    } else {\n        try {\n            result = await importWithMap(moduleName, {\n                imports: {\n                    ...importMap.imports,\n                    ...successfulImportMap.imports,\n                },\n            });\n            successfulImportMap.imports[moduleName] = importMap.imports[moduleName];\n        } catch (e) {\n            console.warn(`Importing ${moduleName} failed with importMap`, importMap, \"trying fallback\", fallbackMap, successfulImportMap);\n            result = await importWithMap(moduleName, {\n                imports: {\n                    ...fallbackMap.imports,\n                    ...successfulImportMap.imports,\n                },\n            });\n            successfulImportMap.imports[moduleName] = fallbackMap.imports[moduleName];\n        }\n    }\n    return result;\n}\n\n\nconst React = await importWithMapAndFallback(\"react\", importMap, fallbackImportMap);\nconst { createRoot } = await importWithMapAndFallback(\"react-dom/client\", importMap, fallbackImportMap);\n\nconst e = React.createElement;\n\nfunction isAbsoluteUrl(s) {\n    return s?.startsWith('http://') || s?.startsWith('https://');\n}\nconst WORKSPACES_URL_KEYWORD = 'https://workspaces-pt';\nconst OPTIONS_URL_KEYS = ['offsetsUrl', 'refSpecUrl'];\nconst prefersDark = window.matchMedia && window.matchMedia('(prefers-color-scheme: dark)').matches;\n// The jupyter server may be running through a proxy,\n// which means that the client needs to prepend the part of the URL before /proxy/8000 such as\n// https://hub.gke2.mybinder.org/user/vitessce-vitessce-python-swi31vcv/proxy/8000/A/0/cells\n// For workspaces: https://workspaces-pt.hubmapconsortium.org/passthrough/HOSTNAME/PORT/ADDITIONAL_PATH_INFO?QUERY_PARAMS=HELLO_WORLD\nfunction prependBaseUrl(config, proxy, hasHostName) {\n    if (!proxy || hasHostName) {\n        return config;\n    }\n    const { origin, 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...f };\n                if (f.url && !isAbsoluteUrl(f.url) ) {\n                    // Update the main file URL if necessary.\n                    updatedFileDef.url = `${origin}${baseUrl}${f.url}`;\n                }\n                if (f.options) {\n                    // Update any urls within the options object\n                    const updatedOptions = { ...f.options };\n                    OPTIONS_URL_KEYS.forEach(key => {\n                        const optionValue = updatedOptions[key];\n                        if (optionValue && !isAbsoluteUrl(optionValue)) {\n                            updatedOptions[key] = `${origin}${baseUrl}${optionValue}`;\n                        }\n                    });\n\n                    // Update image URLs if they exist\n                    if ('images' in f.options && Array.isArray(f.options.images)) {\n                        const updatedImages = f.options.images.map(image => {\n                            const updatedImage = { ...image };\n\n                            if (image.url && !isAbsoluteUrl(image.url)) {\n                                updatedImage.url = `${origin}${baseUrl}${image.url}`;\n                            }\n\n                            const metadata = { ...image.metadata };\n                            if (metadata?.omeTiffOffsetsUrl && !isAbsoluteUrl(metadata.omeTiffOffsetsUrl)) {\n                                metadata.omeTiffOffsetsUrl = `${origin}${baseUrl}${metadata.omeTiffOffsetsUrl}`;\n                            }\n\n                            updatedImage.metadata = metadata;\n\n                            return updatedImage;\n                        });\n\n                        updatedOptions.images = updatedImages;\n                    }\n                    updatedFileDef.options = updatedOptions;\n                }\n                return updatedFileDef;\n            }),\n        })),\n    };\n}\n\nasync function render(view) {\n    const cssUid = view.model.get('uid');\n    const jsDevMode = view.model.get('js_dev_mode');\n    const jsPackageVersion = view.model.get('js_package_version');\n    const customJsUrl = view.model.get('custom_js_url');\n    const pluginEsmArr = view.model.get('plugin_esm');\n    const remountOnUidChange = view.model.get('remount_on_uid_change');\n    const storeUrls = view.model.get('store_urls');\n    const invokeTimeout = view.model.get('invoke_timeout');\n    const invokeBatched = view.model.get('invoke_batched');\n    const preventScroll = view.model.get('prevent_scroll');\n\n    const pageMode = view.model.get('page_mode');\n    const pageEsm = view.model.get('page_esm');\n\n    const pkgName = (jsDevMode ? \"@vitessce/dev\" : \"vitessce\");\n\n    const hasCustomJsUrl = customJsUrl.length > 0;\n\n    importMap.imports[\"vitessce\"] = (hasCustomJsUrl\n        ? customJsUrl\n        : `https://unpkg.com/${pkgName}@${jsPackageVersion}`\n    );\n    let fallbackImportMapToUse = null;\n    if (!hasCustomJsUrl) {\n        fallbackImportMapToUse = fallbackImportMap;\n        if (jsDevMode) {\n            fallbackImportMapToUse.imports[\"vitessce\"] = `https://cdn.vitessce.io/vitessce@${jsPackageVersion}/dist/index.min.js`;\n        } else {\n            fallbackImportMapToUse.imports[\"vitessce\"] = `https://cdn.vitessce.io/@vitessce/dev@${jsPackageVersion}/dist/index.js`;\n        }\n    }\n\n    const {\n        Vitessce,\n        PluginFileType,\n        PluginViewType,\n        PluginCoordinationType,\n        PluginJointFileType,\n        PluginAsyncFunction,\n        z,\n        useCoordination,\n        usePageModeView,\n        useGridItemSize,\n        // TODO: names and function signatures are subject to change for the following functions\n        // Reference: https://github.com/keller-mark/use-coordination/issues/37#issuecomment-1946226827\n        useComplexCoordination,\n        useMultiCoordinationScopesNonNull,\n        useMultiCoordinationScopesSecondaryNonNull,\n        useComplexCoordinationSecondary,\n        useCoordinationScopes,\n        useCoordinationScopesBy,\n    } = await importWithMapAndFallback(\"vitessce\", importMap, fallbackImportMapToUse);\n\n    let pluginViewTypes = [];\n    let pluginCoordinationTypes = [];\n    let pluginFileTypes = [];\n    let pluginJointFileTypes = [];\n    let pluginAsyncFunctions = [];\n\n    let pending = [];\n    let batchId = 0;\n\n    async function processBatch(prevPendingArr) {\n        const [dataArr, buffersArr] = await view.experimental.invoke(\"_zarr_get_multi\", prevPendingArr.map(d => d.params), {\n            signal: AbortSignal.timeout(invokeTimeout),\n        });\n        prevPendingArr.forEach((prevPendingItem, i) => {\n            const data = dataArr[i];\n            const bufferData = buffersArr[i];\n            const { params, resolve, reject } = prevPendingItem;\n            const [storeUrl, key] = params;\n\n            if (!data.success) {\n                resolve(undefined);\n                return;\n            }\n\n            if (ArrayBuffer.isView(bufferData)) {\n                resolve(new Uint8Array(bufferData.buffer, bufferData.byteOffset, bufferData.byteLength));\n                return;\n            }\n            resolve(new Uint8Array(bufferData.buffer));\n            return;\n        });\n    }\n\n    function run() {\n        processBatch(pending);\n        pending = [];\n        batchId = 0;\n    }\n\n    function enqueue(params) {\n        batchId = batchId || requestAnimationFrame(() => run());\n        let { promise, resolve, reject } = Promise.withResolvers();\n        pending.push({ params, resolve, reject });\n        return promise;\n    }\n\n\n    const stores = Object.fromEntries(\n        storeUrls.map(storeUrl => ([\n            storeUrl,\n            {\n                async get(key) {\n                    if (invokeBatched) {\n                        return enqueue([storeUrl, key]);\n                    } else {\n                        // Do not submit zarr gets in batches. Instead, submit individually.\n                        const [data, buffers] = await view.experimental.invoke(\"_zarr_get\", [storeUrl, key], {\n                            signal: AbortSignal.timeout(invokeTimeout),\n                        });\n                        if (!data.success) return undefined;\n\n                        if (ArrayBuffer.isView(buffers[0])) {\n                            return new Uint8Array(buffers[0].buffer, buffers[0].byteOffset, buffers[0].byteLength);\n                        }\n                        return new Uint8Array(buffers[0].buffer);\n                    }\n                },\n                async getRange(key, rangeQuery) {\n                    if (invokeBatched) {\n                        return enqueue([storeUrl, key, rangeQuery]);\n                    } else {\n                        // Do not submit zarr gets in batches. Instead, submit individually.\n                        const [data, buffers] = await view.experimental.invoke(\"_zarr_get_range\", [storeUrl, key, rangeQuery], {\n                            signal: AbortSignal.timeout(invokeTimeout),\n                        });\n                        if (!data.success) return undefined;\n\n                        if (ArrayBuffer.isView(buffers[0])) {\n                            return new Uint8Array(buffers[0].buffer, buffers[0].byteOffset, buffers[0].byteLength);\n                        }\n                        return new Uint8Array(buffers[0].buffer);\n                    }\n                },\n            }\n        ])),\n    );\n\n    function invokePluginCommand(commandName, commandParams, commandBuffers) {\n        return view.experimental.invoke(\"_plugin_command\", [commandName, commandParams], {\n            signal: AbortSignal.timeout(invokeTimeout),\n            ...(commandBuffers ? { buffers: commandBuffers } : {}),\n        });\n    }\n\n    for (const pluginEsm of pluginEsmArr) {\n        try {\n            const pluginEsmUrl = URL.createObjectURL(new Blob([pluginEsm], { type: \"text/javascript\" }));\n            const pluginModule = (await import(pluginEsmUrl)).default;\n            URL.revokeObjectURL(pluginEsmUrl);\n\n            const pluginDeps = {\n                React,\n                PluginFileType,\n                PluginViewType,\n                PluginCoordinationType,\n                PluginJointFileType,\n                PluginAsyncFunction,\n                z,\n                invokeCommand: invokePluginCommand,\n                useCoordination,\n                useGridItemSize,\n                useComplexCoordination,\n                useMultiCoordinationScopesNonNull,\n                useMultiCoordinationScopesSecondaryNonNull,\n                useComplexCoordinationSecondary,\n                useCoordinationScopes,\n                useCoordinationScopesBy,\n            };\n            const pluginsObj = await pluginModule.createPlugins(pluginDeps);\n            if(Array.isArray(pluginsObj.pluginViewTypes)) {\n                pluginViewTypes = [...pluginViewTypes, ...pluginsObj.pluginViewTypes];\n            }\n            if(Array.isArray(pluginsObj.pluginCoordinationTypes)) {\n                pluginCoordinationTypes = [...pluginCoordinationTypes, ...pluginsObj.pluginCoordinationTypes];\n            }\n            if(Array.isArray(pluginsObj.pluginFileTypes)) {\n                pluginFileTypes = [...pluginFileTypes, ...pluginsObj.pluginFileTypes];\n            }\n            if(Array.isArray(pluginsObj.pluginJointFileTypes)) {\n                pluginJointFileTypes = [...pluginJointFileTypes, ...pluginsObj.pluginJointFileTypes];\n            }\n            if(Array.isArray(pluginsObj.pluginAsyncFunctions)) {\n                pluginAsyncFunctions = [...pluginAsyncFunctions, ...pluginsObj.pluginAsyncFunctions];\n            }\n        } catch(e) {\n            console.error(\"Error loading plugin ESM or executing createPlugins function.\");\n            console.error(e);\n        }\n    }\n\n    let PageComponent;\n    if(pageMode && pageEsm.length > 0) {\n        try {\n            const pageEsmUrl = URL.createObjectURL(new Blob([pageEsm], { type: \"text/javascript\" }));\n            const pageModule = (await import(pageEsmUrl)).default;\n            URL.revokeObjectURL(pageEsmUrl);\n\n            const pageDeps = {\n                React,\n                usePageModeView,\n            };\n            PageComponent = await pageModule.createPage(pageDeps);\n        } catch(e) {\n            console.error(\"Error loading page ESM or executing createPage function.\")\n            console.error(e);\n        }\n    }\n\n    function VitessceWidget(props) {\n        const { model, styleContainer } = props;\n\n        const [config, setConfig] = React.useState(prependBaseUrl(model.get('_config'), model.get('proxy'), model.get('has_host_name')));\n        const [validateConfig, setValidateConfig] = React.useState(true);\n        const height = model.get('height');\n        const theme = model.get('theme') === 'auto' ? (prefersDark ? 'dark' : 'light') : model.get('theme');\n\n        const divRef = React.useRef();\n\n        React.useEffect(() => {\n            if(!divRef.current || !preventScroll) {\n                return () => {};\n            }\n\n            function handleMouseEnter() {\n                const jpn = divRef.current.closest('.jp-Notebook');\n                if(jpn) {\n                    jpn.style.overflow = \"hidden\";\n                }\n            }\n            function handleMouseLeave(event) {\n                if(event.relatedTarget === null || (event.relatedTarget && event.relatedTarget.closest('.jp-Notebook')?.length)) return;\n                const jpn = divRef.current.closest('.jp-Notebook');\n                if(jpn) {\n                    jpn.style.overflow = \"auto\";\n                }\n            }\n            divRef.current.addEventListener(\"mouseenter\", handleMouseEnter);\n            divRef.current.addEventListener(\"mouseleave\", handleMouseLeave);\n\n            return () => {\n                if(divRef.current) {\n                    divRef.current.removeEventListener(\"mouseenter\", handleMouseEnter);\n                    divRef.current.removeEventListener(\"mouseleave\", handleMouseLeave);\n                }\n            };\n        }, [divRef, preventScroll]);\n\n        // Config changed on JS side (from within <Vitessce/>),\n        // send updated config to Python side.\n        const onConfigChange = React.useCallback((config) => {\n            model.set('_config', config);\n            setValidateConfig(false);\n            model.save_changes();\n        }, [model]);\n\n        // Config changed on Python side,\n        // pass to <Vitessce/> component to it is updated on JS side.\n        React.useEffect(() => {\n            model.on('change:_config', () => {\n                const newConfig = prependBaseUrl(model.get('_config'), model.get('proxy'), model.get('has_host_name'));\n\n                // Force a re-render and re-validation by setting a new config.uid value.\n                // TODO: make this conditional on a parameter from Python.\n                //newConfig.uid = `random-${Math.random()}`;\n                //console.log('newConfig', newConfig);\n                setConfig(newConfig);\n            });\n        }, []);\n\n        const vitessceProps = {\n            height, theme, config, onConfigChange, validateConfig,\n            pluginViewTypes, pluginCoordinationTypes,\n            pluginFileTypes,pluginJointFileTypes, pluginAsyncFunctions,\n            remountOnUidChange, stores, pageMode, styleContainer,\n        };\n\n        return e('div', { ref: divRef, style: { height: height + 'px' } },\n            e(React.Suspense, { fallback: e('div', {}, 'Loading...') },\n                e(React.StrictMode, {},\n                    e(Vitessce, vitessceProps,\n                        (pageMode ? e(PageComponent, {}) : null)\n                    ),\n                ),\n            ),\n        );\n    }\n\n    const root = createRoot(view.el);\n    // Marimo puts AnyWidgets in a Shadow Root, so we need to tell Emotion to\n    // insert styles within the Shadow DOM.\n    const rootNode = view.el.getRootNode();\n    const styleContainer = rootNode === document ? undefined : rootNode;\n    root.render(e(VitessceWidget, { model: view.model, styleContainer }));\n\n    return () => {\n        // Re-enable scrolling.\n        const jpn = view.el.closest('.jp-Notebook');\n        if(jpn) {\n            jpn.style.overflow = \"auto\";\n        }\n\n        // Clean up React and DOM state.\n        root.unmount();\n        if(view._isFromDisplay) {\n            view.el.remove();\n        }\n    };\n}\nexport default { render };\n",
+       "_model_module": "anywidget",
+       "_model_name": "AnyModel",
+       "_view_name": "ErrorWidgetView",
+       "custom_js_url": "",
+       "error": {},
+       "has_host_name": false,
+       "height": 600,
+       "invoke_batched": true,
+       "invoke_timeout": 300000,
+       "js_dev_mode": false,
+       "js_package_version": "3.9.9",
+       "layout": "IPY_MODEL_6adbd17d7ac6415890bb669e3aaf670f",
+       "msg": "Failed to load model class 'AnyModel' from module 'anywidget'",
+       "page_esm": "",
+       "page_mode": false,
+       "plugin_esm": [],
+       "prevent_scroll": true,
+       "proxy": false,
+       "remount_on_uid_change": true,
+       "store_urls": [],
+       "theme": "auto",
+       "uid": "e7a1"
+      }
+     },
+     "undefined": {
+      "model_module": "anywidget",
+      "model_module_version": "2.0.0",
+      "model_name": "AnyModel",
+      "state": {
+       "_view_name": "ErrorWidgetView",
+       "error": {},
+       "msg": "Failed to load model class 'AnyModel' from module 'anywidget'"
+      }
+     }
+    },
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}