From bae7c699a826ecaa9de06e17b85f9b0a74a3897a Mon Sep 17 00:00:00 2001
From: hurry <hurry060215@gmail.com>
Date: Sat, 20 Jun 2026 12:11:46 +0800
Subject: [PATCH 01/15] Add SPATA2-style spatial utilities tutorial

---
 docs/Tutorials-space/t_spata2py.ipynb | 163 ++++++++++++++++++++++++++
 1 file changed, 163 insertions(+)
 create mode 100644 docs/Tutorials-space/t_spata2py.ipynb

diff --git a/docs/Tutorials-space/t_spata2py.ipynb b/docs/Tutorials-space/t_spata2py.ipynb
new file mode 100644
index 0000000..a5c9c30
--- /dev/null
+++ b/docs/Tutorials-space/t_spata2py.ipynb
@@ -0,0 +1,163 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# SPATA2-style spatial utilities in OmicVerse\n",
+    "\n",
+    "This notebook demonstrates the AnnData-native `ov.space.spata2_*` utilities: coordinate extraction, variable joins, tissue outlines, spatial outlier filtering, and explicit pixel/unit conversion.\n",
+    "\n",
+    "The API is inspired by SPATA2's object and spatial-data foundation, but it runs fully in Python and does not require R or `rpy2`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from anndata import AnnData\n",
+    "import omicverse as ov\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a small spatial AnnData object\n",
+    "\n",
+    "Real workflows usually start from `scanpy.read_visium`, OmicVerse I/O helpers, Xenium, MERFISH, or other spatial loaders. Here we use a tiny fixture so the example is self-contained.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coords = np.array([\n",
+    "    [0.0, 0.0],\n",
+    "    [0.0, 1.0],\n",
+    "    [1.0, 0.0],\n",
+    "    [1.0, 1.0],\n",
+    "    [0.5, 0.5],\n",
+    "    [12.0, 12.0],  # isolated artifact-like spot\n",
+    "])\n",
+    "counts = np.array([\n",
+    "    [1.0, 0.0, 5.0],\n",
+    "    [2.0, 1.0, 4.0],\n",
+    "    [3.0, 1.0, 3.0],\n",
+    "    [4.0, 2.0, 2.0],\n",
+    "    [5.0, 3.0, 1.0],\n",
+    "    [8.0, 0.0, 0.0],\n",
+    "])\n",
+    "obs = pd.DataFrame(\n",
+    "    {\n",
+    "        \"region\": [\"core\", \"core\", \"edge\", \"edge\", \"core\", \"artifact\"],\n",
+    "        \"total_counts\": counts.sum(axis=1),\n",
+    "    },\n",
+    "    index=[f\"spot_{idx}\" for idx in range(coords.shape[0])],\n",
+    ")\n",
+    "adata = AnnData(X=counts, obs=obs, var=pd.DataFrame(index=[\"GeneA\", \"GeneB\", \"GeneC\"]))\n",
+    "adata.obsm[\"spatial\"] = coords\n",
+    "adata\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Coordinates and molecular variables\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ov.space.spata2_get_coords(adata, include_obs=[\"region\"])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ov.space.spata2_join_variables(adata, [\"region\", \"GeneA\", \"GeneB\"])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Tissue outline and spatial outliers\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "outline = ov.space.spata2_tissue_outline(adata)\n",
+    "outline\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "outliers = ov.space.spata2_identify_outliers(adata, radius=1.6, min_neighbors=2)\n",
+    "outliers\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filtered = ov.space.spata2_remove_outliers(adata)\n",
+    "filtered.obs_names.tolist()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explicit distance conversion\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pixels = np.array([0.0, 10.0, 25.0])\n",
+    "units = ov.space.spata2_pixels_to_unit(pixels, pixels_per_unit=5.0)\n",
+    "ov.space.spata2_unit_to_pixels(units, pixels_per_unit=5.0)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "pygments_lexer": "ipython3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From c73be5f65c8b519642fbbfac0035eeefe0053e02 Mon Sep 17 00:00:00 2001
From: hurry <hurry060215@gmail.com>
Date: Sat, 20 Jun 2026 12:11:47 +0800
Subject: [PATCH 02/15] List SPATA2-style spatial utilities tutorial

---
 docs/Tutorials-space/index.md | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/docs/Tutorials-space/index.md b/docs/Tutorials-space/index.md
index 55b799c..fe04c1d 100644
--- a/docs/Tutorials-space/index.md
+++ b/docs/Tutorials-space/index.md
@@ -2,6 +2,10 @@
 
 This page mirrors the `Space` section in `mkdocs.yml` and provides a markdown entry point for the spatial tutorial notebooks.
 
+## SPATA2-style utilities
+
+- [SPATA2-style AnnData utilities](t_spata2py.ipynb)
+
 ## Preprocess
 
 - [Crop and Rotation of spatial transcriptomic data](t_crop_rotate.ipynb)

From 08b1f7bf84227d6db0b260b58113d55ef269e2cb Mon Sep 17 00:00:00 2001
From: hurry <hurry060215@gmail.com>
Date: Sat, 20 Jun 2026 17:01:42 +0800
Subject: [PATCH 03/15] Expand SPATA2-style utilities tutorial

---
 docs/Tutorials-space/t_spata2py.ipynb | 1569 ++++++++++++++++++++++---
 1 file changed, 1406 insertions(+), 163 deletions(-)

diff --git a/docs/Tutorials-space/t_spata2py.ipynb b/docs/Tutorials-space/t_spata2py.ipynb
index a5c9c30..61e3c79 100644
--- a/docs/Tutorials-space/t_spata2py.ipynb
+++ b/docs/Tutorials-space/t_spata2py.ipynb
@@ -1,163 +1,1406 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# SPATA2-style spatial utilities in OmicVerse\n",
-    "\n",
-    "This notebook demonstrates the AnnData-native `ov.space.spata2_*` utilities: coordinate extraction, variable joins, tissue outlines, spatial outlier filtering, and explicit pixel/unit conversion.\n",
-    "\n",
-    "The API is inspired by SPATA2's object and spatial-data foundation, but it runs fully in Python and does not require R or `rpy2`.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "from anndata import AnnData\n",
-    "import omicverse as ov\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create a small spatial AnnData object\n",
-    "\n",
-    "Real workflows usually start from `scanpy.read_visium`, OmicVerse I/O helpers, Xenium, MERFISH, or other spatial loaders. Here we use a tiny fixture so the example is self-contained.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "coords = np.array([\n",
-    "    [0.0, 0.0],\n",
-    "    [0.0, 1.0],\n",
-    "    [1.0, 0.0],\n",
-    "    [1.0, 1.0],\n",
-    "    [0.5, 0.5],\n",
-    "    [12.0, 12.0],  # isolated artifact-like spot\n",
-    "])\n",
-    "counts = np.array([\n",
-    "    [1.0, 0.0, 5.0],\n",
-    "    [2.0, 1.0, 4.0],\n",
-    "    [3.0, 1.0, 3.0],\n",
-    "    [4.0, 2.0, 2.0],\n",
-    "    [5.0, 3.0, 1.0],\n",
-    "    [8.0, 0.0, 0.0],\n",
-    "])\n",
-    "obs = pd.DataFrame(\n",
-    "    {\n",
-    "        \"region\": [\"core\", \"core\", \"edge\", \"edge\", \"core\", \"artifact\"],\n",
-    "        \"total_counts\": counts.sum(axis=1),\n",
-    "    },\n",
-    "    index=[f\"spot_{idx}\" for idx in range(coords.shape[0])],\n",
-    ")\n",
-    "adata = AnnData(X=counts, obs=obs, var=pd.DataFrame(index=[\"GeneA\", \"GeneB\", \"GeneC\"]))\n",
-    "adata.obsm[\"spatial\"] = coords\n",
-    "adata\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Coordinates and molecular variables\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ov.space.spata2_get_coords(adata, include_obs=[\"region\"])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ov.space.spata2_join_variables(adata, [\"region\", \"GeneA\", \"GeneB\"])\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Tissue outline and spatial outliers\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "outline = ov.space.spata2_tissue_outline(adata)\n",
-    "outline\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "outliers = ov.space.spata2_identify_outliers(adata, radius=1.6, min_neighbors=2)\n",
-    "outliers\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "filtered = ov.space.spata2_remove_outliers(adata)\n",
-    "filtered.obs_names.tolist()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Explicit distance conversion\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pixels = np.array([0.0, 10.0, 25.0])\n",
-    "units = ov.space.spata2_pixels_to_unit(pixels, pixels_per_unit=5.0)\n",
-    "ov.space.spata2_unit_to_pixels(units, pixels_per_unit=5.0)\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "name": "python",
-   "pygments_lexer": "ipython3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1319eed3",
+   "metadata": {},
+   "source": [
+    "# SPATA2-style spatial utilities in OmicVerse\n",
+    "\n",
+    "This tutorial documents the first AnnData-native SPATA2 reconstruction slice exposed through `ov.space`. It accompanies the draft implementation PR and the standalone `py-SPATA2` rewrite repository.\n",
+    "\n",
+    "Related links:\n",
+    "\n",
+    "- API PR: [omicverse/omicverse#847](https://github.com/omicverse/omicverse/pull/847)\n",
+    "- Rewrite repository: [omicverse/py-SPATA2](https://github.com/omicverse/py-SPATA2)\n",
+    "- SPATA2 documentation: [theMILOlab.github.io/SPATA2](https://themilolab.github.io/SPATA2/index.html)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43f96e85",
+   "metadata": {},
+   "source": [
+    "## What this notebook covers\n",
+    "\n",
+    "The current stable slice focuses on the SPATA2 spatial-data foundation:\n",
+    "\n",
+    "| SPATA2 concept | OmicVerse API used here | Output |\n",
+    "|---|---|---|\n",
+    "| Coordinate table | `ov.space.spata2_get_coords` | observation-indexed `DataFrame` |\n",
+    "| Molecular / metadata variables | `ov.space.spata2_join_variables` | coordinates joined with `obs` and gene values |\n",
+    "| Tissue outline | `ov.space.spata2_tissue_outline` | convex outline stored in `adata.uns` |\n",
+    "| Spatial artifacts | `ov.space.spata2_identify_outliers` | Boolean outlier flag in `adata.obs` |\n",
+    "| Cleaned object | `ov.space.spata2_remove_outliers` | filtered `AnnData` |\n",
+    "| Pixel scale | `spata2_pixels_to_unit`, `spata2_unit_to_pixels` | explicit distance conversion |\n",
+    "\n",
+    "It is not yet a full SPATA2 port. The parity section at the end shows the current namespace audit status.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f3ee46b",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "The notebook uses only `omicverse`, `AnnData`, `numpy`, `pandas`, and `matplotlib`. No R runtime, `rpy2`, or SPATA2 R installation is required.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "82bd9f5c",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-20T09:00:16.170918Z",
+     "iopub.status.busy": "2026-06-20T09:00:16.170918Z",
+     "iopub.status.idle": "2026-06-20T09:00:20.013422Z",
+     "shell.execute_reply": "2026-06-20T09:00:20.013422Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from anndata import AnnData\n",
+    "import matplotlib.pyplot as plt\n",
+    "import omicverse as ov\n",
+    "\n",
+    "%config InlineBackend.figure_format = 'png'\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "1ad0c9ce",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-20T09:00:20.015422Z",
+     "iopub.status.busy": "2026-06-20T09:00:20.015422Z",
+     "iopub.status.idle": "2026-06-20T09:00:37.349404Z",
+     "shell.execute_reply": "2026-06-20T09:00:37.349404Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "omicverse 2.1.3rc1\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "D:\\Anaconda3\\envs\\omicverse\\lib\\site-packages\\anndata\\utils.py:434: FutureWarning: Importing read_loom from `anndata` is deprecated. Import anndata.io.read_loom instead.\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SPATA2-style API: spata2_get_coords spata2_identify_outliers\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('omicverse', ov.__version__ if hasattr(ov, '__version__') else 'unknown')\n",
+    "print('SPATA2-style API:', ov.space.spata2_get_coords.__name__, ov.space.spata2_identify_outliers.__name__)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba62a222",
+   "metadata": {},
+   "source": [
+    "## Build a Visium-like tutorial fixture\n",
+    "\n",
+    "The reference PRs in this documentation tree use canonical public datasets when the backend performs a full biological analysis. The current SPATA2 slice only exercises coordinate and spatial-data utilities, so this notebook uses a deterministic fixture with:\n",
+    "\n",
+    "- a 20 x 20 tissue grid;\n",
+    "- three smooth gene-like spatial variables;\n",
+    "- two deliberately isolated artifact spots.\n",
+    "\n",
+    "That makes the expected tissue outline and outlier behavior easy to inspect in a rendered notebook.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c09f59c3",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-20T09:00:37.352404Z",
+     "iopub.status.busy": "2026-06-20T09:00:37.351404Z",
+     "iopub.status.idle": "2026-06-20T09:00:37.366404Z",
+     "shell.execute_reply": "2026-06-20T09:00:37.365405Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.omicverse.anndata+json": {
+       "name": null,
+       "previews": {
+        "layers": {},
+        "obs": {
+         "dtypes": {
+          "region": "object",
+          "total_counts": "float64"
+         },
+         "name": ".obs",
+         "shape": [
+          402,
+          2
+         ],
+         "table": {
+          "columns": [
+           "region",
+           "total_counts"
+          ],
+          "data": [
+           [
+            "left",
+            6.7466547045256275
+           ],
+           [
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+          "index": [
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+           "spot_021",
+           "spot_022",
+           "spot_023"
+          ]
+         },
+         "type": "dataframe"
+        },
+        "obsm": {
+         "spatial": {
+          "dtype": "float64",
+          "name": "obsm[\"spatial\"]",
+          "shape": [
+           402,
+           2
+          ],
+          "table": {
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+           "data": [
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+            ],
+            [
+             6.0,
+             0.0
+            ],
+            [
+             7.0,
+             0.0
+            ],
+            [
+             8.0,
+             0.0
+            ],
+            [
+             9.0,
+             0.0
+            ]
+           ],
+           "index": [
+            "0",
+            "1",
+            "2",
+            "3",
+            "4",
+            "5",
+            "6",
+            "7",
+            "8",
+            "9"
+           ]
+          },
+          "type": "array"
+         }
+        },
+        "uns": {},
+        "var": {
+         "dtypes": {},
+         "name": ".var",
+         "shape": [
+          3,
+          0
+         ],
+         "table": {
+          "columns": [],
+          "data": [
+           [],
+           [],
+           []
+          ],
+          "index": [
+           "GeneA",
+           "GeneB",
+           "GeneC"
+          ]
+         },
+         "type": "dataframe"
+        }
+       },
+       "ref": "obj:22c0d5203a0",
+       "summary": {
+        "embedding_keys": [
+         "spatial"
+        ],
+        "embedding_keys_more": 0,
+        "embedding_keys_total": 1,
+        "layers": [],
+        "layers_more": 0,
+        "layers_total": 0,
+        "obs_columns": [
+         "region",
+         "total_counts"
+        ],
+        "obs_columns_more": 0,
+        "obs_columns_total": 2,
+        "obsm_keys": [
+         "spatial"
+        ],
+        "obsm_keys_more": 0,
+        "obsm_keys_total": 1,
+        "shape": [
+         402,
+         3
+        ],
+        "uns_keys": [],
+        "uns_keys_more": 0,
+        "uns_keys_total": 0,
+        "var_columns": [],
+        "var_columns_more": 0,
+        "var_columns_total": 0
+       },
+       "type": "anndata"
+      },
+      "text/plain": [
+       "AnnData object with n_obs × n_vars = 402 × 3\n",
+       "    obs: 'region', 'total_counts'\n",
+       "    obsm: 'spatial'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n_side = 20\n",
+    "grid_x, grid_y = np.meshgrid(np.arange(n_side), np.arange(n_side))\n",
+    "coords = np.column_stack([grid_x.ravel(), grid_y.ravel()]).astype(float)\n",
+    "outlier_coords = np.array([[55.0, 55.0], [58.0, 53.0]])\n",
+    "coords = np.vstack([coords, outlier_coords])\n",
+    "\n",
+    "rng = np.random.default_rng(7)\n",
+    "gene_a = coords[:, 0] / coords[:, 0].max() * 6 + rng.normal(0, 0.15, coords.shape[0])\n",
+    "gene_b = coords[:, 1] / coords[:, 1].max() * 5 + rng.normal(0, 0.15, coords.shape[0])\n",
+    "gene_c = np.sqrt((coords[:, 0] - 10) ** 2 + (coords[:, 1] - 10) ** 2)\n",
+    "gene_c = 8 - gene_c / gene_c.max() * 6 + rng.normal(0, 0.1, coords.shape[0])\n",
+    "counts = np.clip(np.column_stack([gene_a, gene_b, gene_c]), 0, None)\n",
+    "\n",
+    "obs = pd.DataFrame(\n",
+    "    {\n",
+    "        'region': np.where(coords[:, 0] < 10, 'left', 'right'),\n",
+    "        'total_counts': counts.sum(axis=1),\n",
+    "    },\n",
+    "    index=[f'spot_{idx:03d}' for idx in range(coords.shape[0])],\n",
+    ")\n",
+    "adata = AnnData(X=counts, obs=obs, var=pd.DataFrame(index=['GeneA', 'GeneB', 'GeneC']))\n",
+    "adata.obsm['spatial'] = coords\n",
+    "adata\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26b04623",
+   "metadata": {},
+   "source": [
+    "## Extract coordinates\n",
+    "\n",
+    "`spata2_get_coords` reads `adata.obsm['spatial']` by default and returns a tidy table. Observation metadata can be joined in the same call.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "17684abe",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-20T09:00:37.368404Z",
+     "iopub.status.busy": "2026-06-20T09:00:37.368404Z",
+     "iopub.status.idle": "2026-06-20T09:00:37.381404Z",
+     "shell.execute_reply": "2026-06-20T09:00:37.381404Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.omicverse.dataframe+json": {
+       "dtypes": {
+        "barcode": "object",
+        "region": "object",
+        "total_counts": "float64",
+        "x": "float64",
+        "y": "float64"
+       },
+       "name": null,
+       "ref": "obj:22c0d521ab0",
+       "shape": [
+        5,
+        5
+       ],
+       "table": {
+        "columns": [
+         "barcode",
+         "x",
+         "y",
+         "region",
+         "total_counts"
+        ],
+        "data": [
+         [
+          "spot_000",
+          0.0,
+          0.0,
+          "left",
+          6.7466547045256275
+         ],
+         [
+          "spot_001",
+          1.0,
+          0.0,
+          "left",
+          7.053847043120326
+         ],
+         [
+          "spot_002",
+          2.0,
+          0.0,
+          "left",
+          7.128637064825885
+         ],
+         [
+          "spot_003",
+          3.0,
+          0.0,
+          "left",
+          7.082770087011939
+         ],
+         [
+          "spot_004",
+          4.0,
+          0.0,
+          "left",
+          7.299024494965832
+         ]
+        ],
+        "index": [
+         "spot_000",
+         "spot_001",
+         "spot_002",
+         "spot_003",
+         "spot_004"
+        ]
+       },
+       "type": "dataframe"
+      },
+      "text/plain": [
+       "           barcode    x    y region  total_counts\n",
+       "spot_000  spot_000  0.0  0.0   left      6.746655\n",
+       "spot_001  spot_001  1.0  0.0   left      7.053847\n",
+       "spot_002  spot_002  2.0  0.0   left      7.128637\n",
+       "spot_003  spot_003  3.0  0.0   left      7.082770\n",
+       "spot_004  spot_004  4.0  0.0   left      7.299024"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coords_df = ov.space.spata2_get_coords(adata, include_obs=['region', 'total_counts'])\n",
+    "coords_df.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6868776e",
+   "metadata": {},
+   "source": [
+    "## Join coordinates with variables\n",
+    "\n",
+    "`spata2_join_variables` mirrors the SPATA2 pattern of collecting spatial coordinates together with variables of interest. Names in `adata.obs` are treated as metadata; names in `adata.var_names` are pulled from the expression matrix.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "1f0a1c37",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-20T09:00:37.384404Z",
+     "iopub.status.busy": "2026-06-20T09:00:37.383404Z",
+     "iopub.status.idle": "2026-06-20T09:00:37.397404Z",
+     "shell.execute_reply": "2026-06-20T09:00:37.397404Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.omicverse.dataframe+json": {
+       "dtypes": {
+        "GeneA": "float64",
+        "GeneB": "float64",
+        "GeneC": "float64",
+        "barcode": "object",
+        "region": "object",
+        "x": "float64",
+        "y": "float64"
+       },
+       "name": null,
+       "ref": "obj:22c0d5210c0",
+       "shape": [
+        5,
+        7
+       ],
+       "table": {
+        "columns": [
+         "barcode",
+         "x",
+         "y",
+         "region",
+         "GeneA",
+         "GeneB",
+         "GeneC"
+        ],
+        "data": [
+         [
+          "spot_000",
+          0.0,
+          0.0,
+          "left",
+          0.00018452300362238613,
+          0.0,
+          6.746470181522005
+         ],
+         [
+          "spot_001",
+          1.0,
+          0.0,
+          "left",
+          0.14826010648833945,
+          0.1543768849046773,
+          6.751210051727309
+         ],
+         [
+          "spot_002",
+          2.0,
+          0.0,
+          "left",
+          0.1657758734198053,
+          0.031559240824445535,
+          6.931301950581634
+         ],
+         [
+          "spot_003",
+          3.0,
+          0.0,
+          "left",
+          0.17675605177261577,
+          0.0,
+          6.906014035239323
+         ],
+         [
+          "spot_004",
+          4.0,
+          0.0,
+          "left",
+          0.3455924856725175,
+          0.0,
+          6.953432009293315
+         ]
+        ],
+        "index": [
+         "spot_000",
+         "spot_001",
+         "spot_002",
+         "spot_003",
+         "spot_004"
+        ]
+       },
+       "type": "dataframe"
+      },
+      "text/plain": [
+       "           barcode    x    y region     GeneA     GeneB     GeneC\n",
+       "spot_000  spot_000  0.0  0.0   left  0.000185  0.000000  6.746470\n",
+       "spot_001  spot_001  1.0  0.0   left  0.148260  0.154377  6.751210\n",
+       "spot_002  spot_002  2.0  0.0   left  0.165776  0.031559  6.931302\n",
+       "spot_003  spot_003  3.0  0.0   left  0.176756  0.000000  6.906014\n",
+       "spot_004  spot_004  4.0  0.0   left  0.345592  0.000000  6.953432"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "feature_df = ov.space.spata2_join_variables(adata, ['region', 'GeneA', 'GeneB', 'GeneC'])\n",
+    "feature_df.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "09f29795",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-20T09:00:37.399404Z",
+     "iopub.status.busy": "2026-06-20T09:00:37.399404Z",
+     "iopub.status.idle": "2026-06-20T09:00:37.773690Z",
+     "shell.execute_reply": "2026-06-20T09:00:37.772963Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x300 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(1, 3, figsize=(10, 3), constrained_layout=True)\n",
+    "for ax, gene in zip(axes, ['GeneA', 'GeneB', 'GeneC']):\n",
+    "    sc = ax.scatter(feature_df['x'], feature_df['y'], c=feature_df[gene], s=18, cmap='viridis')\n",
+    "    ax.set_title(gene)\n",
+    "    ax.set_aspect('equal')\n",
+    "    ax.set_xlabel('x')\n",
+    "    ax.set_ylabel('y')\n",
+    "    fig.colorbar(sc, ax=ax, fraction=0.046, pad=0.04)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "68bf8c24",
+   "metadata": {},
+   "source": [
+    "## Identify the tissue outline\n",
+    "\n",
+    "The first implementation uses a deterministic convex hull. Later reconstruction passes should add parity for SPATA2's richer outline and annotation handling.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "97611dfc",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-20T09:00:37.775691Z",
+     "iopub.status.busy": "2026-06-20T09:00:37.775691Z",
+     "iopub.status.idle": "2026-06-20T09:00:37.788691Z",
+     "shell.execute_reply": "2026-06-20T09:00:37.788691Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.omicverse.dataframe+json": {
+       "dtypes": {
+        "x": "float64",
+        "y": "float64"
+       },
+       "name": null,
+       "ref": "obj:22c7ddcd750",
+       "shape": [
+        5,
+        2
+       ],
+       "table": {
+        "columns": [
+         "x",
+         "y"
+        ],
+        "data": [
+         [
+          58.0,
+          53.0
+         ],
+         [
+          55.0,
+          55.0
+         ],
+         [
+          0.0,
+          19.0
+         ],
+         [
+          0.0,
+          0.0
+         ],
+         [
+          19.0,
+          0.0
+         ]
+        ],
+        "index": [
+         "0",
+         "1",
+         "2",
+         "3",
+         "4"
+        ]
+       },
+       "type": "dataframe"
+      },
+      "text/plain": [
+       "           x     y\n",
+       "vertex            \n",
+       "0       58.0  53.0\n",
+       "1       55.0  55.0\n",
+       "2        0.0  19.0\n",
+       "3        0.0   0.0\n",
+       "4       19.0   0.0"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "outline = ov.space.spata2_tissue_outline(adata)\n",
+    "outline\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bfe4b7a1",
+   "metadata": {},
+   "source": [
+    "## Detect spatial outliers\n",
+    "\n",
+    "Here a radius-neighborhood rule marks observations with too few nearby spots. The two isolated coordinates are expected to be flagged.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "b3df29bb",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-20T09:00:37.791238Z",
+     "iopub.status.busy": "2026-06-20T09:00:37.791238Z",
+     "iopub.status.idle": "2026-06-20T09:00:37.804238Z",
+     "shell.execute_reply": "2026-06-20T09:00:37.804238Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "spata2_spatial_outlier\n",
+      "kept       400\n",
+      "outlier      2\n",
+      "Name: count, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.omicverse.dataframe+json": {
+       "dtypes": {
+        "region": "object",
+        "total_counts": "float64"
+       },
+       "name": null,
+       "ref": "obj:22c0d522cb0",
+       "shape": [
+        2,
+        2
+       ],
+       "table": {
+        "columns": [
+         "region",
+         "total_counts"
+        ],
+        "data": [
+         [
+          "right",
+          12.67020073237883
+         ],
+         [
+          "right",
+          12.80379983730633
+         ]
+        ],
+        "index": [
+         "spot_400",
+         "spot_401"
+        ]
+       },
+       "type": "dataframe"
+      },
+      "text/plain": [
+       "         region  total_counts\n",
+       "spot_400  right     12.670201\n",
+       "spot_401  right     12.803800"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "outliers = ov.space.spata2_identify_outliers(adata, radius=2.0, min_neighbors=2)\n",
+    "print(outliers.value_counts().rename(index={False: 'kept', True: 'outlier'}))\n",
+    "adata.obs.loc[outliers, ['region', 'total_counts']]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "0a57c3af",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-20T09:00:37.806238Z",
+     "iopub.status.busy": "2026-06-20T09:00:37.806238Z",
+     "iopub.status.idle": "2026-06-20T09:00:37.882238Z",
+     "shell.execute_reply": "2026-06-20T09:00:37.882238Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(5, 5))\n",
+    "ax.scatter(coords_df['x'], coords_df['y'], s=18, c=np.where(outliers, 'crimson', 'steelblue'), alpha=0.9)\n",
+    "closed = pd.concat([outline, outline.iloc[[0]]], ignore_index=True)\n",
+    "ax.plot(closed['x'], closed['y'], color='black', linewidth=1.5, label='convex tissue outline')\n",
+    "ax.set_aspect('equal')\n",
+    "ax.set_xlabel('x')\n",
+    "ax.set_ylabel('y')\n",
+    "ax.legend(frameon=False)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7caf4f1a",
+   "metadata": {},
+   "source": [
+    "## Remove flagged observations\n",
+    "\n",
+    "`spata2_remove_outliers` returns a filtered copy by default, preserving the original object for comparison.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "8dc25ba5",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-20T09:00:37.886237Z",
+     "iopub.status.busy": "2026-06-20T09:00:37.886237Z",
+     "iopub.status.idle": "2026-06-20T09:00:37.898238Z",
+     "shell.execute_reply": "2026-06-20T09:00:37.898238Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "before: 402 after: 400\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.omicverse.dataframe+json": {
+       "dtypes": {
+        "region": "object",
+        "spata2_spatial_outlier": "bool",
+        "total_counts": "float64"
+       },
+       "name": null,
+       "ref": "obj:22c7de7bb50",
+       "shape": [
+        5,
+        3
+       ],
+       "table": {
+        "columns": [
+         "region",
+         "total_counts",
+         "spata2_spatial_outlier"
+        ],
+        "data": [
+         [
+          "right",
+          10.439553070239775,
+          false
+         ],
+         [
+          "right",
+          10.174393338504789,
+          false
+         ],
+         [
+          "right",
+          10.29893327549868,
+          false
+         ],
+         [
+          "right",
+          10.259728697196483,
+          false
+         ],
+         [
+          "right",
+          10.336551804483957,
+          false
+         ]
+        ],
+        "index": [
+         "spot_395",
+         "spot_396",
+         "spot_397",
+         "spot_398",
+         "spot_399"
+        ]
+       },
+       "type": "dataframe"
+      },
+      "text/plain": [
+       "         region  total_counts  spata2_spatial_outlier\n",
+       "spot_395  right     10.439553                   False\n",
+       "spot_396  right     10.174393                   False\n",
+       "spot_397  right     10.298933                   False\n",
+       "spot_398  right     10.259729                   False\n",
+       "spot_399  right     10.336552                   False"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "filtered = ov.space.spata2_remove_outliers(adata)\n",
+    "print('before:', adata.n_obs, 'after:', filtered.n_obs)\n",
+    "filtered.obs.tail()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1bf0429",
+   "metadata": {},
+   "source": [
+    "## Convert pixel distances to physical units\n",
+    "\n",
+    "SPATA2's R API carries explicit distance units. The first Python slice keeps unit conversion explicit: pass a scale factor and round-trip it when needed.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "2ed3b0d8",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-20T09:00:37.900238Z",
+     "iopub.status.busy": "2026-06-20T09:00:37.900238Z",
+     "iopub.status.idle": "2026-06-20T09:00:37.914238Z",
+     "shell.execute_reply": "2026-06-20T09:00:37.914238Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.omicverse.dataframe+json": {
+       "dtypes": {
+        "micrometers": "float64",
+        "pixels": "int32",
+        "roundtrip_pixels": "float64"
+       },
+       "name": null,
+       "ref": "obj:22c7df28610",
+       "shape": [
+        4,
+        3
+       ],
+       "table": {
+        "columns": [
+         "pixels",
+         "micrometers",
+         "roundtrip_pixels"
+        ],
+        "data": [
+         [
+          0,
+          0.0,
+          0.0
+         ],
+         [
+          55,
+          27.5,
+          55.0
+         ],
+         [
+          110,
+          55.0,
+          110.0
+         ],
+         [
+          220,
+          110.0,
+          220.0
+         ]
+        ],
+        "index": [
+         "0",
+         "1",
+         "2",
+         "3"
+        ]
+       },
+       "type": "dataframe"
+      },
+      "text/plain": [
+       "   pixels  micrometers  roundtrip_pixels\n",
+       "0       0          0.0               0.0\n",
+       "1      55         27.5              55.0\n",
+       "2     110         55.0             110.0\n",
+       "3     220        110.0             220.0"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pixels = np.array([0, 55, 110, 220])\n",
+    "micrometers = ov.space.spata2_pixels_to_unit(pixels, pixels_per_unit=2.0)\n",
+    "roundtrip = ov.space.spata2_unit_to_pixels(micrometers, pixels_per_unit=2.0)\n",
+    "pd.DataFrame({'pixels': pixels, 'micrometers': micrometers, 'roundtrip_pixels': roundtrip})\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d7a30ff",
+   "metadata": {},
+   "source": [
+    "## Notebook benchmark\n",
+    "\n",
+    "This lightweight benchmark records wall-clock time for the exact operations used above on the rendered fixture. The larger `py-SPATA2` benchmark is stored in the rewrite repository.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "121cb3b6",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-20T09:00:37.917238Z",
+     "iopub.status.busy": "2026-06-20T09:00:37.917238Z",
+     "iopub.status.idle": "2026-06-20T09:00:37.930238Z",
+     "shell.execute_reply": "2026-06-20T09:00:37.930238Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.omicverse.dataframe+json": {
+       "dtypes": {
+        "operation": "object",
+        "seconds": "float64",
+        "shape": "object"
+       },
+       "name": null,
+       "ref": "obj:22c7df476d0",
+       "shape": [
+        5,
+        3
+       ],
+       "table": {
+        "columns": [
+         "operation",
+         "seconds",
+         "shape"
+        ],
+        "data": [
+         [
+          "spata2_get_coords",
+          0.00043509999522939324,
+          [
+           402,
+           3
+          ]
+         ],
+         [
+          "spata2_join_variables",
+          0.0008641000022180378,
+          [
+           402,
+           6
+          ]
+         ],
+         [
+          "spata2_tissue_outline",
+          0.00082230000407435,
+          [
+           5,
+           2
+          ]
+         ],
+         [
+          "spata2_identify_outliers",
+          0.0007560999947600067,
+          [
+           402
+          ]
+         ],
+         [
+          "spata2_remove_outliers",
+          0.0008131999929901212,
+          [
+           400,
+           3
+          ]
+         ]
+        ],
+        "index": [
+         "0",
+         "1",
+         "2",
+         "3",
+         "4"
+        ]
+       },
+       "type": "dataframe"
+      },
+      "text/plain": [
+       "                  operation   seconds     shape\n",
+       "0         spata2_get_coords  0.000435  (402, 3)\n",
+       "1     spata2_join_variables  0.000864  (402, 6)\n",
+       "2     spata2_tissue_outline  0.000822    (5, 2)\n",
+       "3  spata2_identify_outliers  0.000756    (402,)\n",
+       "4    spata2_remove_outliers  0.000813  (400, 3)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import time\n",
+    "\n",
+    "bench_rows = []\n",
+    "for label, call in [\n",
+    "    ('spata2_get_coords', lambda: ov.space.spata2_get_coords(adata)),\n",
+    "    ('spata2_join_variables', lambda: ov.space.spata2_join_variables(adata, ['region', 'GeneA', 'GeneB'])),\n",
+    "    ('spata2_tissue_outline', lambda: ov.space.spata2_tissue_outline(adata, write_key=None)),\n",
+    "    ('spata2_identify_outliers', lambda: ov.space.spata2_identify_outliers(adata, radius=2.0, min_neighbors=2, write_key=None)),\n",
+    "    ('spata2_remove_outliers', lambda: ov.space.spata2_remove_outliers(adata)),\n",
+    "]:\n",
+    "    start = time.perf_counter()\n",
+    "    result = call()\n",
+    "    elapsed = time.perf_counter() - start\n",
+    "    bench_rows.append({'operation': label, 'seconds': elapsed, 'shape': getattr(result, 'shape', (len(result),))})\n",
+    "\n",
+    "pd.DataFrame(bench_rows)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3470427d",
+   "metadata": {},
+   "source": [
+    "## Current parity status\n",
+    "\n",
+    "The rewrite repository audits the real SPATA2 3.1.4 `NAMESPACE`, not a hand-picked subset. A symbol counts as implemented only when Python exposes the same R-style export name.\n",
+    "\n",
+    "Full tables:\n",
+    "\n",
+    "- [NAMESPACE_PARITY.md](https://github.com/omicverse/py-SPATA2/blob/master/NAMESPACE_PARITY.md)\n",
+    "- [spata2_namespace_parity.csv](https://github.com/omicverse/py-SPATA2/blob/master/references/spata2_namespace_parity.csv)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "e69c2519",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-20T09:00:37.932238Z",
+     "iopub.status.busy": "2026-06-20T09:00:37.932238Z",
+     "iopub.status.idle": "2026-06-20T09:00:37.946238Z",
+     "shell.execute_reply": "2026-06-20T09:00:37.946238Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.omicverse.dataframe+json": {
+       "dtypes": {
+        "metric": "object",
+        "value": "object"
+       },
+       "name": null,
+       "ref": "obj:22c7df47f70",
+       "shape": [
+        4,
+        2
+       ],
+       "table": {
+        "columns": [
+         "metric",
+         "value"
+        ],
+        "data": [
+         [
+          "R exports audited from SPATA2 3.1.4 NAMESPACE",
+          751
+         ],
+         [
+          "R-compatible Python symbols implemented in py-SPATA2",
+          16
+         ],
+         [
+          "Strict namespace coverage",
+          "2.1%"
+         ],
+         [
+          "ov.space tutorial API functions exercised here",
+          8
+         ]
+        ],
+        "index": [
+         "0",
+         "1",
+         "2",
+         "3"
+        ]
+       },
+       "type": "dataframe"
+      },
+      "text/plain": [
+       "                                              metric value\n",
+       "0      R exports audited from SPATA2 3.1.4 NAMESPACE   751\n",
+       "1  R-compatible Python symbols implemented in py-...    16\n",
+       "2                          Strict namespace coverage  2.1%\n",
+       "3     ov.space tutorial API functions exercised here     8"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "parity = pd.DataFrame(\n",
+    "    [\n",
+    "        ['R exports audited from SPATA2 3.1.4 NAMESPACE', 751],\n",
+    "        ['R-compatible Python symbols implemented in py-SPATA2', 16],\n",
+    "        ['Strict namespace coverage', '2.1%'],\n",
+    "        ['ov.space tutorial API functions exercised here', 8],\n",
+    "    ],\n",
+    "    columns=['metric', 'value'],\n",
+    ")\n",
+    "parity\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a36d83c5",
+   "metadata": {},
+   "source": [
+    "## Interpretation\n",
+    "\n",
+    "Use these helpers today for AnnData coordinate inspection, variable tables, quick tissue outlines, and artifact filtering. Do not treat this notebook as evidence of full SPATA2 parity yet. The next reconstruction passes should follow the audited export surface module by module: object containers, readers/initiation, get/set/add accessors, spatial annotations, trajectories, gradient screening, and plotting.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.10.20"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 521a41c7ba7db247b496d886a3dfecb69b2d85c9 Mon Sep 17 00:00:00 2001
From: hurry <hurry060215@gmail.com>
Date: Sat, 20 Jun 2026 17:01:45 +0800
Subject: [PATCH 04/15] Add Chinese SPATA2-style utilities tutorial

---
 docs_zh/Tutorials-space/t_spata2py.ipynb | 1406 ++++++++++++++++++++++
 1 file changed, 1406 insertions(+)
 create mode 100644 docs_zh/Tutorials-space/t_spata2py.ipynb

diff --git a/docs_zh/Tutorials-space/t_spata2py.ipynb b/docs_zh/Tutorials-space/t_spata2py.ipynb
new file mode 100644
index 0000000..7007e58
--- /dev/null
+++ b/docs_zh/Tutorials-space/t_spata2py.ipynb
@@ -0,0 +1,1406 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "5cfcc263",
+   "metadata": {},
+   "source": [
+    "# OmicVerse ?? SPATA2 ??????\n",
+    "\n",
+    "?????????? `ov.space` ??? AnnData-native SPATA2 ?????????? draft ?? PR ???? `py-SPATA2` ?????\n",
+    "\n",
+    "?????\n",
+    "\n",
+    "- API PR: [omicverse/omicverse#847](https://github.com/omicverse/omicverse/pull/847)\n",
+    "- ????: [omicverse/py-SPATA2](https://github.com/omicverse/py-SPATA2)\n",
+    "- SPATA2 ??: [theMILOlab.github.io/SPATA2](https://themilolab.github.io/SPATA2/index.html)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a1ab594",
+   "metadata": {},
+   "source": [
+    "## ???????\n",
+    "\n",
+    "???????? SPATA2 ?????????\n",
+    "\n",
+    "| SPATA2 ?? | ?????? OmicVerse API | ?? |\n",
+    "|---|---|---|\n",
+    "| ??? | `ov.space.spata2_get_coords` | observation-indexed `DataFrame` |\n",
+    "| ???? / ??? | `ov.space.spata2_join_variables` | ?? + `obs` + ??? |\n",
+    "| ???? | `ov.space.spata2_tissue_outline` | convex outline??? `adata.uns` |\n",
+    "| ???? / ??? | `ov.space.spata2_identify_outliers` | `adata.obs` ?????? |\n",
+    "| ?????? | `ov.space.spata2_remove_outliers` | ???? `AnnData` |\n",
+    "| ???? | `spata2_pixels_to_unit`, `spata2_unit_to_pixels` | ?????? |\n",
+    "\n",
+    "?????? SPATA2 ?????? parity ????????? namespace ?????\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7f75abf",
+   "metadata": {},
+   "source": [
+    "## ????\n",
+    "\n",
+    "? notebook ??? `omicverse`?`AnnData`?`numpy`?`pandas` ? `matplotlib`???? R?`rpy2` ? SPATA2 R ??\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d7e6190a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-20T09:00:40.052797Z",
+     "iopub.status.busy": "2026-06-20T09:00:40.052797Z",
+     "iopub.status.idle": "2026-06-20T09:00:41.664646Z",
+     "shell.execute_reply": "2026-06-20T09:00:41.664646Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from anndata import AnnData\n",
+    "import matplotlib.pyplot as plt\n",
+    "import omicverse as ov\n",
+    "\n",
+    "%config InlineBackend.figure_format = 'png'\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c9bba300",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-20T09:00:41.666647Z",
+     "iopub.status.busy": "2026-06-20T09:00:41.666647Z",
+     "iopub.status.idle": "2026-06-20T09:00:47.831182Z",
+     "shell.execute_reply": "2026-06-20T09:00:47.831182Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "omicverse 2.1.3rc1\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "D:\\Anaconda3\\envs\\omicverse\\lib\\site-packages\\anndata\\utils.py:434: FutureWarning: Importing read_loom from `anndata` is deprecated. Import anndata.io.read_loom instead.\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SPATA2-style API: spata2_get_coords spata2_identify_outliers\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('omicverse', ov.__version__ if hasattr(ov, '__version__') else 'unknown')\n",
+    "print('SPATA2-style API:', ov.space.spata2_get_coords.__name__, ov.space.spata2_identify_outliers.__name__)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c4a02bd",
+   "metadata": {},
+   "source": [
+    "## ???? Visium-like ????\n",
+    "\n",
+    "?????????????????????? SPATA2 ??????????????????????????? synthetic fixture?\n",
+    "\n",
+    "- 20 x 20 ??????\n",
+    "- ????????????\n",
+    "- ??????? artifact-like spot?\n",
+    "\n",
+    "??????????????????????\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "d45a3fba",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-20T09:00:47.833183Z",
+     "iopub.status.busy": "2026-06-20T09:00:47.833183Z",
+     "iopub.status.idle": "2026-06-20T09:00:47.847183Z",
+     "shell.execute_reply": "2026-06-20T09:00:47.847183Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.omicverse.anndata+json": {
+       "name": null,
+       "previews": {
+        "layers": {},
+        "obs": {
+         "dtypes": {
+          "region": "object",
+          "total_counts": "float64"
+         },
+         "name": ".obs",
+         "shape": [
+          402,
+          2
+         ],
+         "table": {
+          "columns": [
+           "region",
+           "total_counts"
+          ],
+          "data": [
+           [
+            "left",
+            6.7466547045256275
+           ],
+           [
+            "left",
+            7.053847043120326
+           ],
+           [
+            "left",
+            7.128637064825885
+           ],
+           [
+            "left",
+            7.082770087011939
+           ],
+           [
+            "left",
+            7.299024494965832
+           ],
+           [
+            "left",
+            7.4893627202029265
+           ],
+           [
+            "left",
+            7.55085688051206
+           ],
+           [
+            "left",
+            7.936667992235289
+           ],
+           [
+            "left",
+            7.980497947921937
+           ],
+           [
+            "left",
+            8.012734164286691
+           ],
+           [
+            "right",
+            8.242546928219493
+           ],
+           [
+            "right",
+            8.419187753116988
+           ],
+           [
+            "right",
+            8.31814783629384
+           ],
+           [
+            "right",
+            8.112086974983596
+           ],
+           [
+            "right",
+            8.650773568599867
+           ],
+           [
+            "right",
+            8.586951861027314
+           ],
+           [
+            "right",
+            8.596927916174462
+           ],
+           [
+            "right",
+            8.4816648731433
+           ],
+           [
+            "right",
+            8.418354818423285
+           ],
+           [
+            "right",
+            8.455377180713363
+           ],
+           [
+            "left",
+            6.8360530207904615
+           ],
+           [
+            "left",
+            7.049176988827524
+           ],
+           [
+            "left",
+            6.891803914405125
+           ],
+           [
+            "left",
+            7.639107939473838
+           ]
+          ],
+          "index": [
+           "spot_000",
+           "spot_001",
+           "spot_002",
+           "spot_003",
+           "spot_004",
+           "spot_005",
+           "spot_006",
+           "spot_007",
+           "spot_008",
+           "spot_009",
+           "spot_010",
+           "spot_011",
+           "spot_012",
+           "spot_013",
+           "spot_014",
+           "spot_015",
+           "spot_016",
+           "spot_017",
+           "spot_018",
+           "spot_019",
+           "spot_020",
+           "spot_021",
+           "spot_022",
+           "spot_023"
+          ]
+         },
+         "type": "dataframe"
+        },
+        "obsm": {
+         "spatial": {
+          "dtype": "float64",
+          "name": "obsm[\"spatial\"]",
+          "shape": [
+           402,
+           2
+          ],
+          "table": {
+           "columns": [
+            "0",
+            "1"
+           ],
+           "data": [
+            [
+             0.0,
+             0.0
+            ],
+            [
+             1.0,
+             0.0
+            ],
+            [
+             2.0,
+             0.0
+            ],
+            [
+             3.0,
+             0.0
+            ],
+            [
+             4.0,
+             0.0
+            ],
+            [
+             5.0,
+             0.0
+            ],
+            [
+             6.0,
+             0.0
+            ],
+            [
+             7.0,
+             0.0
+            ],
+            [
+             8.0,
+             0.0
+            ],
+            [
+             9.0,
+             0.0
+            ]
+           ],
+           "index": [
+            "0",
+            "1",
+            "2",
+            "3",
+            "4",
+            "5",
+            "6",
+            "7",
+            "8",
+            "9"
+           ]
+          },
+          "type": "array"
+         }
+        },
+        "uns": {},
+        "var": {
+         "dtypes": {},
+         "name": ".var",
+         "shape": [
+          3,
+          0
+         ],
+         "table": {
+          "columns": [],
+          "data": [
+           [],
+           [],
+           []
+          ],
+          "index": [
+           "GeneA",
+           "GeneB",
+           "GeneC"
+          ]
+         },
+         "type": "dataframe"
+        }
+       },
+       "ref": "obj:24804499000",
+       "summary": {
+        "embedding_keys": [
+         "spatial"
+        ],
+        "embedding_keys_more": 0,
+        "embedding_keys_total": 1,
+        "layers": [],
+        "layers_more": 0,
+        "layers_total": 0,
+        "obs_columns": [
+         "region",
+         "total_counts"
+        ],
+        "obs_columns_more": 0,
+        "obs_columns_total": 2,
+        "obsm_keys": [
+         "spatial"
+        ],
+        "obsm_keys_more": 0,
+        "obsm_keys_total": 1,
+        "shape": [
+         402,
+         3
+        ],
+        "uns_keys": [],
+        "uns_keys_more": 0,
+        "uns_keys_total": 0,
+        "var_columns": [],
+        "var_columns_more": 0,
+        "var_columns_total": 0
+       },
+       "type": "anndata"
+      },
+      "text/plain": [
+       "AnnData object with n_obs × n_vars = 402 × 3\n",
+       "    obs: 'region', 'total_counts'\n",
+       "    obsm: 'spatial'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n_side = 20\n",
+    "grid_x, grid_y = np.meshgrid(np.arange(n_side), np.arange(n_side))\n",
+    "coords = np.column_stack([grid_x.ravel(), grid_y.ravel()]).astype(float)\n",
+    "outlier_coords = np.array([[55.0, 55.0], [58.0, 53.0]])\n",
+    "coords = np.vstack([coords, outlier_coords])\n",
+    "\n",
+    "rng = np.random.default_rng(7)\n",
+    "gene_a = coords[:, 0] / coords[:, 0].max() * 6 + rng.normal(0, 0.15, coords.shape[0])\n",
+    "gene_b = coords[:, 1] / coords[:, 1].max() * 5 + rng.normal(0, 0.15, coords.shape[0])\n",
+    "gene_c = np.sqrt((coords[:, 0] - 10) ** 2 + (coords[:, 1] - 10) ** 2)\n",
+    "gene_c = 8 - gene_c / gene_c.max() * 6 + rng.normal(0, 0.1, coords.shape[0])\n",
+    "counts = np.clip(np.column_stack([gene_a, gene_b, gene_c]), 0, None)\n",
+    "\n",
+    "obs = pd.DataFrame(\n",
+    "    {\n",
+    "        'region': np.where(coords[:, 0] < 10, 'left', 'right'),\n",
+    "        'total_counts': counts.sum(axis=1),\n",
+    "    },\n",
+    "    index=[f'spot_{idx:03d}' for idx in range(coords.shape[0])],\n",
+    ")\n",
+    "adata = AnnData(X=counts, obs=obs, var=pd.DataFrame(index=['GeneA', 'GeneB', 'GeneC']))\n",
+    "adata.obsm['spatial'] = coords\n",
+    "adata\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd82082d",
+   "metadata": {},
+   "source": [
+    "## ????\n",
+    "\n",
+    "`spata2_get_coords` ???? `adata.obsm['spatial']`??? tidy coordinate table???????? observation metadata?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f0ec90db",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-20T09:00:47.849182Z",
+     "iopub.status.busy": "2026-06-20T09:00:47.849182Z",
+     "iopub.status.idle": "2026-06-20T09:00:47.863183Z",
+     "shell.execute_reply": "2026-06-20T09:00:47.863183Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.omicverse.dataframe+json": {
+       "dtypes": {
+        "barcode": "object",
+        "region": "object",
+        "total_counts": "float64",
+        "x": "float64",
+        "y": "float64"
+       },
+       "name": null,
+       "ref": "obj:2480449a200",
+       "shape": [
+        5,
+        5
+       ],
+       "table": {
+        "columns": [
+         "barcode",
+         "x",
+         "y",
+         "region",
+         "total_counts"
+        ],
+        "data": [
+         [
+          "spot_000",
+          0.0,
+          0.0,
+          "left",
+          6.7466547045256275
+         ],
+         [
+          "spot_001",
+          1.0,
+          0.0,
+          "left",
+          7.053847043120326
+         ],
+         [
+          "spot_002",
+          2.0,
+          0.0,
+          "left",
+          7.128637064825885
+         ],
+         [
+          "spot_003",
+          3.0,
+          0.0,
+          "left",
+          7.082770087011939
+         ],
+         [
+          "spot_004",
+          4.0,
+          0.0,
+          "left",
+          7.299024494965832
+         ]
+        ],
+        "index": [
+         "spot_000",
+         "spot_001",
+         "spot_002",
+         "spot_003",
+         "spot_004"
+        ]
+       },
+       "type": "dataframe"
+      },
+      "text/plain": [
+       "           barcode    x    y region  total_counts\n",
+       "spot_000  spot_000  0.0  0.0   left      6.746655\n",
+       "spot_001  spot_001  1.0  0.0   left      7.053847\n",
+       "spot_002  spot_002  2.0  0.0   left      7.128637\n",
+       "spot_003  spot_003  3.0  0.0   left      7.082770\n",
+       "spot_004  spot_004  4.0  0.0   left      7.299024"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coords_df = ov.space.spata2_get_coords(adata, include_obs=['region', 'total_counts'])\n",
+    "coords_df.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6c6ce562",
+   "metadata": {},
+   "source": [
+    "## ???????\n",
+    "\n",
+    "`spata2_join_variables` ?? SPATA2 ???????????????????? workflow?`adata.obs` ????? metadata ???`adata.var_names` ????????????\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "88578e4a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-20T09:00:47.865182Z",
+     "iopub.status.busy": "2026-06-20T09:00:47.865182Z",
+     "iopub.status.idle": "2026-06-20T09:00:47.879182Z",
+     "shell.execute_reply": "2026-06-20T09:00:47.879182Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.omicverse.dataframe+json": {
+       "dtypes": {
+        "GeneA": "float64",
+        "GeneB": "float64",
+        "GeneC": "float64",
+        "barcode": "object",
+        "region": "object",
+        "x": "float64",
+        "y": "float64"
+       },
+       "name": null,
+       "ref": "obj:24804498460",
+       "shape": [
+        5,
+        7
+       ],
+       "table": {
+        "columns": [
+         "barcode",
+         "x",
+         "y",
+         "region",
+         "GeneA",
+         "GeneB",
+         "GeneC"
+        ],
+        "data": [
+         [
+          "spot_000",
+          0.0,
+          0.0,
+          "left",
+          0.00018452300362238613,
+          0.0,
+          6.746470181522005
+         ],
+         [
+          "spot_001",
+          1.0,
+          0.0,
+          "left",
+          0.14826010648833945,
+          0.1543768849046773,
+          6.751210051727309
+         ],
+         [
+          "spot_002",
+          2.0,
+          0.0,
+          "left",
+          0.1657758734198053,
+          0.031559240824445535,
+          6.931301950581634
+         ],
+         [
+          "spot_003",
+          3.0,
+          0.0,
+          "left",
+          0.17675605177261577,
+          0.0,
+          6.906014035239323
+         ],
+         [
+          "spot_004",
+          4.0,
+          0.0,
+          "left",
+          0.3455924856725175,
+          0.0,
+          6.953432009293315
+         ]
+        ],
+        "index": [
+         "spot_000",
+         "spot_001",
+         "spot_002",
+         "spot_003",
+         "spot_004"
+        ]
+       },
+       "type": "dataframe"
+      },
+      "text/plain": [
+       "           barcode    x    y region     GeneA     GeneB     GeneC\n",
+       "spot_000  spot_000  0.0  0.0   left  0.000185  0.000000  6.746470\n",
+       "spot_001  spot_001  1.0  0.0   left  0.148260  0.154377  6.751210\n",
+       "spot_002  spot_002  2.0  0.0   left  0.165776  0.031559  6.931302\n",
+       "spot_003  spot_003  3.0  0.0   left  0.176756  0.000000  6.906014\n",
+       "spot_004  spot_004  4.0  0.0   left  0.345592  0.000000  6.953432"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "feature_df = ov.space.spata2_join_variables(adata, ['region', 'GeneA', 'GeneB', 'GeneC'])\n",
+    "feature_df.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b73f0360",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-20T09:00:47.881182Z",
+     "iopub.status.busy": "2026-06-20T09:00:47.881182Z",
+     "iopub.status.idle": "2026-06-20T09:00:48.224182Z",
+     "shell.execute_reply": "2026-06-20T09:00:48.224182Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x300 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(1, 3, figsize=(10, 3), constrained_layout=True)\n",
+    "for ax, gene in zip(axes, ['GeneA', 'GeneB', 'GeneC']):\n",
+    "    sc = ax.scatter(feature_df['x'], feature_df['y'], c=feature_df[gene], s=18, cmap='viridis')\n",
+    "    ax.set_title(gene)\n",
+    "    ax.set_aspect('equal')\n",
+    "    ax.set_xlabel('x')\n",
+    "    ax.set_ylabel('y')\n",
+    "    fig.colorbar(sc, ax=ax, fraction=0.046, pad=0.04)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bcbb5978",
+   "metadata": {},
+   "source": [
+    "## ??????\n",
+    "\n",
+    "?????????? convex hull???????????? SPATA2 ???? outline ? annotation ???\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "330ac2c2",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-20T09:00:48.226182Z",
+     "iopub.status.busy": "2026-06-20T09:00:48.226182Z",
+     "iopub.status.idle": "2026-06-20T09:00:48.241182Z",
+     "shell.execute_reply": "2026-06-20T09:00:48.240182Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.omicverse.dataframe+json": {
+       "dtypes": {
+        "x": "float64",
+        "y": "float64"
+       },
+       "name": null,
+       "ref": "obj:2480449a590",
+       "shape": [
+        5,
+        2
+       ],
+       "table": {
+        "columns": [
+         "x",
+         "y"
+        ],
+        "data": [
+         [
+          58.0,
+          53.0
+         ],
+         [
+          55.0,
+          55.0
+         ],
+         [
+          0.0,
+          19.0
+         ],
+         [
+          0.0,
+          0.0
+         ],
+         [
+          19.0,
+          0.0
+         ]
+        ],
+        "index": [
+         "0",
+         "1",
+         "2",
+         "3",
+         "4"
+        ]
+       },
+       "type": "dataframe"
+      },
+      "text/plain": [
+       "           x     y\n",
+       "vertex            \n",
+       "0       58.0  53.0\n",
+       "1       55.0  55.0\n",
+       "2        0.0  19.0\n",
+       "3        0.0   0.0\n",
+       "4       19.0   0.0"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "outline = ov.space.spata2_tissue_outline(adata)\n",
+    "outline\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d4c3ce9",
+   "metadata": {},
+   "source": [
+    "## ???????\n",
+    "\n",
+    "???? radius-neighborhood ????????????? observation ????????????????????????\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "93e34bcb",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-20T09:00:48.242182Z",
+     "iopub.status.busy": "2026-06-20T09:00:48.242182Z",
+     "iopub.status.idle": "2026-06-20T09:00:48.257182Z",
+     "shell.execute_reply": "2026-06-20T09:00:48.256183Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "spata2_spatial_outlier\n",
+      "kept       400\n",
+      "outlier      2\n",
+      "Name: count, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.omicverse.dataframe+json": {
+       "dtypes": {
+        "region": "object",
+        "total_counts": "float64"
+       },
+       "name": null,
+       "ref": "obj:2480449b430",
+       "shape": [
+        2,
+        2
+       ],
+       "table": {
+        "columns": [
+         "region",
+         "total_counts"
+        ],
+        "data": [
+         [
+          "right",
+          12.67020073237883
+         ],
+         [
+          "right",
+          12.80379983730633
+         ]
+        ],
+        "index": [
+         "spot_400",
+         "spot_401"
+        ]
+       },
+       "type": "dataframe"
+      },
+      "text/plain": [
+       "         region  total_counts\n",
+       "spot_400  right     12.670201\n",
+       "spot_401  right     12.803800"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "outliers = ov.space.spata2_identify_outliers(adata, radius=2.0, min_neighbors=2)\n",
+    "print(outliers.value_counts().rename(index={False: 'kept', True: 'outlier'}))\n",
+    "adata.obs.loc[outliers, ['region', 'total_counts']]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "cd8b9c27",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-20T09:00:48.259182Z",
+     "iopub.status.busy": "2026-06-20T09:00:48.258182Z",
+     "iopub.status.idle": "2026-06-20T09:00:48.336182Z",
+     "shell.execute_reply": "2026-06-20T09:00:48.336182Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(5, 5))\n",
+    "ax.scatter(coords_df['x'], coords_df['y'], s=18, c=np.where(outliers, 'crimson', 'steelblue'), alpha=0.9)\n",
+    "closed = pd.concat([outline, outline.iloc[[0]]], ignore_index=True)\n",
+    "ax.plot(closed['x'], closed['y'], color='black', linewidth=1.5, label='convex tissue outline')\n",
+    "ax.set_aspect('equal')\n",
+    "ax.set_xlabel('x')\n",
+    "ax.set_ylabel('y')\n",
+    "ax.legend(frameon=False)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb02529d",
+   "metadata": {},
+   "source": [
+    "## ???????\n",
+    "\n",
+    "`spata2_remove_outliers` ???????? copy??????????\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "fc32071b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-20T09:00:48.338182Z",
+     "iopub.status.busy": "2026-06-20T09:00:48.338182Z",
+     "iopub.status.idle": "2026-06-20T09:00:48.351182Z",
+     "shell.execute_reply": "2026-06-20T09:00:48.351182Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "before: 402 after: 400\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.omicverse.dataframe+json": {
+       "dtypes": {
+        "region": "object",
+        "spata2_spatial_outlier": "bool",
+        "total_counts": "float64"
+       },
+       "name": null,
+       "ref": "obj:24804499bd0",
+       "shape": [
+        5,
+        3
+       ],
+       "table": {
+        "columns": [
+         "region",
+         "total_counts",
+         "spata2_spatial_outlier"
+        ],
+        "data": [
+         [
+          "right",
+          10.439553070239775,
+          false
+         ],
+         [
+          "right",
+          10.174393338504789,
+          false
+         ],
+         [
+          "right",
+          10.29893327549868,
+          false
+         ],
+         [
+          "right",
+          10.259728697196483,
+          false
+         ],
+         [
+          "right",
+          10.336551804483957,
+          false
+         ]
+        ],
+        "index": [
+         "spot_395",
+         "spot_396",
+         "spot_397",
+         "spot_398",
+         "spot_399"
+        ]
+       },
+       "type": "dataframe"
+      },
+      "text/plain": [
+       "         region  total_counts  spata2_spatial_outlier\n",
+       "spot_395  right     10.439553                   False\n",
+       "spot_396  right     10.174393                   False\n",
+       "spot_397  right     10.298933                   False\n",
+       "spot_398  right     10.259729                   False\n",
+       "spot_399  right     10.336552                   False"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "filtered = ov.space.spata2_remove_outliers(adata)\n",
+    "print('before:', adata.n_obs, 'after:', filtered.n_obs)\n",
+    "filtered.obs.tail()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "197ba6c0",
+   "metadata": {},
+   "source": [
+    "## ???????????\n",
+    "\n",
+    "SPATA2 R API ????????Python ????????????? scale factor????? round-trip ????\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "825a735d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-20T09:00:48.352182Z",
+     "iopub.status.busy": "2026-06-20T09:00:48.352182Z",
+     "iopub.status.idle": "2026-06-20T09:00:48.368182Z",
+     "shell.execute_reply": "2026-06-20T09:00:48.367182Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.omicverse.dataframe+json": {
+       "dtypes": {
+        "micrometers": "float64",
+        "pixels": "int32",
+        "roundtrip_pixels": "float64"
+       },
+       "name": null,
+       "ref": "obj:24806a1bd90",
+       "shape": [
+        4,
+        3
+       ],
+       "table": {
+        "columns": [
+         "pixels",
+         "micrometers",
+         "roundtrip_pixels"
+        ],
+        "data": [
+         [
+          0,
+          0.0,
+          0.0
+         ],
+         [
+          55,
+          27.5,
+          55.0
+         ],
+         [
+          110,
+          55.0,
+          110.0
+         ],
+         [
+          220,
+          110.0,
+          220.0
+         ]
+        ],
+        "index": [
+         "0",
+         "1",
+         "2",
+         "3"
+        ]
+       },
+       "type": "dataframe"
+      },
+      "text/plain": [
+       "   pixels  micrometers  roundtrip_pixels\n",
+       "0       0          0.0               0.0\n",
+       "1      55         27.5              55.0\n",
+       "2     110         55.0             110.0\n",
+       "3     220        110.0             220.0"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pixels = np.array([0, 55, 110, 220])\n",
+    "micrometers = ov.space.spata2_pixels_to_unit(pixels, pixels_per_unit=2.0)\n",
+    "roundtrip = ov.space.spata2_unit_to_pixels(micrometers, pixels_per_unit=2.0)\n",
+    "pd.DataFrame({'pixels': pixels, 'micrometers': micrometers, 'roundtrip_pixels': roundtrip})\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a913c09",
+   "metadata": {},
+   "source": [
+    "## Notebook benchmark\n",
+    "\n",
+    "???? benchmark ???????????? fixture ?? wall-clock ?????? `py-SPATA2` benchmark ?????????\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "4135e67b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-20T09:00:48.371182Z",
+     "iopub.status.busy": "2026-06-20T09:00:48.370182Z",
+     "iopub.status.idle": "2026-06-20T09:00:48.383182Z",
+     "shell.execute_reply": "2026-06-20T09:00:48.383182Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.omicverse.dataframe+json": {
+       "dtypes": {
+        "operation": "object",
+        "seconds": "float64",
+        "shape": "object"
+       },
+       "name": null,
+       "ref": "obj:248046de4d0",
+       "shape": [
+        5,
+        3
+       ],
+       "table": {
+        "columns": [
+         "operation",
+         "seconds",
+         "shape"
+        ],
+        "data": [
+         [
+          "spata2_get_coords",
+          0.0004924999957438558,
+          [
+           402,
+           3
+          ]
+         ],
+         [
+          "spata2_join_variables",
+          0.0008758000039961189,
+          [
+           402,
+           6
+          ]
+         ],
+         [
+          "spata2_tissue_outline",
+          0.0008729000110179186,
+          [
+           5,
+           2
+          ]
+         ],
+         [
+          "spata2_identify_outliers",
+          0.0008899999957066029,
+          [
+           402
+          ]
+         ],
+         [
+          "spata2_remove_outliers",
+          0.0008275000145658851,
+          [
+           400,
+           3
+          ]
+         ]
+        ],
+        "index": [
+         "0",
+         "1",
+         "2",
+         "3",
+         "4"
+        ]
+       },
+       "type": "dataframe"
+      },
+      "text/plain": [
+       "                  operation   seconds     shape\n",
+       "0         spata2_get_coords  0.000492  (402, 3)\n",
+       "1     spata2_join_variables  0.000876  (402, 6)\n",
+       "2     spata2_tissue_outline  0.000873    (5, 2)\n",
+       "3  spata2_identify_outliers  0.000890    (402,)\n",
+       "4    spata2_remove_outliers  0.000828  (400, 3)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import time\n",
+    "\n",
+    "bench_rows = []\n",
+    "for label, call in [\n",
+    "    ('spata2_get_coords', lambda: ov.space.spata2_get_coords(adata)),\n",
+    "    ('spata2_join_variables', lambda: ov.space.spata2_join_variables(adata, ['region', 'GeneA', 'GeneB'])),\n",
+    "    ('spata2_tissue_outline', lambda: ov.space.spata2_tissue_outline(adata, write_key=None)),\n",
+    "    ('spata2_identify_outliers', lambda: ov.space.spata2_identify_outliers(adata, radius=2.0, min_neighbors=2, write_key=None)),\n",
+    "    ('spata2_remove_outliers', lambda: ov.space.spata2_remove_outliers(adata)),\n",
+    "]:\n",
+    "    start = time.perf_counter()\n",
+    "    result = call()\n",
+    "    elapsed = time.perf_counter() - start\n",
+    "    bench_rows.append({'operation': label, 'seconds': elapsed, 'shape': getattr(result, 'shape', (len(result),))})\n",
+    "\n",
+    "pd.DataFrame(bench_rows)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4bc61d87",
+   "metadata": {},
+   "source": [
+    "## ?? parity ??\n",
+    "\n",
+    "?????????? SPATA2 3.1.4 `NAMESPACE`?????????? Python ????? R-style export ???? implemented?\n",
+    "\n",
+    "?????\n",
+    "\n",
+    "- [NAMESPACE_PARITY.md](https://github.com/omicverse/py-SPATA2/blob/master/NAMESPACE_PARITY.md)\n",
+    "- [spata2_namespace_parity.csv](https://github.com/omicverse/py-SPATA2/blob/master/references/spata2_namespace_parity.csv)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "2fe270ce",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-20T09:00:48.385182Z",
+     "iopub.status.busy": "2026-06-20T09:00:48.385182Z",
+     "iopub.status.idle": "2026-06-20T09:00:48.398182Z",
+     "shell.execute_reply": "2026-06-20T09:00:48.398182Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.omicverse.dataframe+json": {
+       "dtypes": {
+        "metric": "object",
+        "value": "object"
+       },
+       "name": null,
+       "ref": "obj:248044dc1c0",
+       "shape": [
+        4,
+        2
+       ],
+       "table": {
+        "columns": [
+         "metric",
+         "value"
+        ],
+        "data": [
+         [
+          "R exports audited from SPATA2 3.1.4 NAMESPACE",
+          751
+         ],
+         [
+          "R-compatible Python symbols implemented in py-SPATA2",
+          16
+         ],
+         [
+          "Strict namespace coverage",
+          "2.1%"
+         ],
+         [
+          "ov.space tutorial API functions exercised here",
+          8
+         ]
+        ],
+        "index": [
+         "0",
+         "1",
+         "2",
+         "3"
+        ]
+       },
+       "type": "dataframe"
+      },
+      "text/plain": [
+       "                                              metric value\n",
+       "0      R exports audited from SPATA2 3.1.4 NAMESPACE   751\n",
+       "1  R-compatible Python symbols implemented in py-...    16\n",
+       "2                          Strict namespace coverage  2.1%\n",
+       "3     ov.space tutorial API functions exercised here     8"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "parity = pd.DataFrame(\n",
+    "    [\n",
+    "        ['R exports audited from SPATA2 3.1.4 NAMESPACE', 751],\n",
+    "        ['R-compatible Python symbols implemented in py-SPATA2', 16],\n",
+    "        ['Strict namespace coverage', '2.1%'],\n",
+    "        ['ov.space tutorial API functions exercised here', 8],\n",
+    "    ],\n",
+    "    columns=['metric', 'value'],\n",
+    ")\n",
+    "parity\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1495723b",
+   "metadata": {},
+   "source": [
+    "## ????\n",
+    "\n",
+    "??????????? AnnData ?????????????????? artifact ???????? notebook ???? SPATA2 parity ????????????????????????reader/initiation?get/set/add accessors?spatial annotations?trajectories?gradient screening ? plotting?\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.10.20"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 31968736aead8638cb21b364dd64fe996c64cbcb Mon Sep 17 00:00:00 2001
From: hurry <hurry060215@gmail.com>
Date: Sat, 20 Jun 2026 17:01:48 +0800
Subject: [PATCH 05/15] Register expanded SPATA2 tutorial in space index

---
 docs/Tutorials-space/index.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/Tutorials-space/index.md b/docs/Tutorials-space/index.md
index fe04c1d..f36936d 100644
--- a/docs/Tutorials-space/index.md
+++ b/docs/Tutorials-space/index.md
@@ -4,7 +4,7 @@ This page mirrors the `Space` section in `mkdocs.yml` and provides a markdown en
 
 ## SPATA2-style utilities
 
-- [SPATA2-style AnnData utilities](t_spata2py.ipynb)
+- [SPATA2-style AnnData utilities](t_spata2py.ipynb) ? coordinate tables, variable joins, tissue outlines, spatial outlier filtering, unit conversion, benchmark, and namespace-parity status for the `py-SPATA2` rewrite.
 
 ## Preprocess
 

From d70f8e730478cdecc7e7f4c3af7bb8744080b89d Mon Sep 17 00:00:00 2001
From: hurry <hurry060215@gmail.com>
Date: Sat, 20 Jun 2026 17:01:51 +0800
Subject: [PATCH 06/15] Register Chinese SPATA2 tutorial in space index

---
 docs_zh/Tutorials-space/index.md | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/docs_zh/Tutorials-space/index.md b/docs_zh/Tutorials-space/index.md
index 7ea0666..211c1d7 100644
--- a/docs_zh/Tutorials-space/index.md
+++ b/docs_zh/Tutorials-space/index.md
@@ -1,3 +1,7 @@
+## SPATA2-style utilities
+
+- [SPATA2 ?? AnnData ??](t_spata2py.ipynb) ? ???????????????????????????benchmark ? `py-SPATA2` namespace parity ???
+
 # 空间转录组学教程
 
 此页面镜像了 `mkdocs.yml` 中的 `Space` 部分，为空间教程笔记本提供了一个 markdown 入口点。

From 93edf84e843f8716ded4b810d29acdd157b72820 Mon Sep 17 00:00:00 2001
From: hurry <hurry060215@gmail.com>
Date: Sat, 20 Jun 2026 17:01:54 +0800
Subject: [PATCH 07/15] Add SPATA2 spatial tutorial Sphinx index

---
 docs/tutorials/index_spatial_spata2.md | 9 +++++++++
 1 file changed, 9 insertions(+)
 create mode 100644 docs/tutorials/index_spatial_spata2.md

diff --git a/docs/tutorials/index_spatial_spata2.md b/docs/tutorials/index_spatial_spata2.md
new file mode 100644
index 0000000..bd80e00
--- /dev/null
+++ b/docs/tutorials/index_spatial_spata2.md
@@ -0,0 +1,9 @@
+# SPATA2-style utilities
+
+AnnData-native SPATA2 reconstruction tutorials.
+
+```{toctree}
+:maxdepth: 1
+
+../Tutorials-space/t_spata2py
+```

From d9184cf3abf039f84a24d228e9f472e399154594 Mon Sep 17 00:00:00 2001
From: hurry <hurry060215@gmail.com>
Date: Sat, 20 Jun 2026 17:01:55 +0800
Subject: [PATCH 08/15] Add Chinese SPATA2 spatial tutorial Sphinx index

---
 docs_zh/tutorials/index_spatial_spata2.md | 9 +++++++++
 1 file changed, 9 insertions(+)
 create mode 100644 docs_zh/tutorials/index_spatial_spata2.md

diff --git a/docs_zh/tutorials/index_spatial_spata2.md b/docs_zh/tutorials/index_spatial_spata2.md
new file mode 100644
index 0000000..8359563
--- /dev/null
+++ b/docs_zh/tutorials/index_spatial_spata2.md
@@ -0,0 +1,9 @@
+# SPATA2 ??????
+
+AnnData-native SPATA2 ?????
+
+```{toctree}
+:maxdepth: 1
+
+../Tutorials-space/t_spata2py
+```

From 5dd97c7fbd7d7022dcbf087b52fd1241cb8cfd8c Mon Sep 17 00:00:00 2001
From: hurry <hurry060215@gmail.com>
Date: Sat, 20 Jun 2026 17:01:59 +0800
Subject: [PATCH 09/15] Wire SPATA2 tutorial into spatial Sphinx tree

---
 docs/tutorials/index_spatial.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/docs/tutorials/index_spatial.md b/docs/tutorials/index_spatial.md
index 3e5429b..1bde1a1 100644
--- a/docs/tutorials/index_spatial.md
+++ b/docs/tutorials/index_spatial.md
@@ -5,6 +5,7 @@ Tutorials for spatial data preprocessing, clustering, deconvolution, downstream
 ```{toctree}
 :maxdepth: 2
 
+index_spatial_spata2
 index_spatial_preprocessing
 index_spatial_clustering
 index_spatial_deconvolution

From 69151465e2b2c4f7069bfad9470e51e56a705e7e Mon Sep 17 00:00:00 2001
From: hurry <hurry060215@gmail.com>
Date: Sat, 20 Jun 2026 17:02:01 +0800
Subject: [PATCH 10/15] Wire Chinese SPATA2 tutorial into spatial Sphinx tree

---
 docs_zh/tutorials/index_spatial.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/docs_zh/tutorials/index_spatial.md b/docs_zh/tutorials/index_spatial.md
index 542f74a..6b2f65b 100644
--- a/docs_zh/tutorials/index_spatial.md
+++ b/docs_zh/tutorials/index_spatial.md
@@ -38,3 +38,4 @@
 ../Tutorials-space/t_gaston
 ../Tutorials-space/t_slat
 ```
+index_spatial_spata2

From 08d7f4a2ce22954236d119243349fb41dafaf4e1 Mon Sep 17 00:00:00 2001
From: hurry <hurry060215@gmail.com>
Date: Sat, 20 Jun 2026 17:02:06 +0800
Subject: [PATCH 11/15] Register SPATA2 tutorial in main tutorial overview

---
 docs/Tutorial.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/docs/Tutorial.md b/docs/Tutorial.md
index 50b91a4..8064a2c 100644
--- a/docs/Tutorial.md
+++ b/docs/Tutorial.md
@@ -91,6 +91,8 @@ This page is the markdown overview for the tutorial structure defined in `mkdocs
 
 ## Spatial Transcriptomics
 
+- [SPATA2-style AnnData utilities](Tutorials-space/t_spata2py.ipynb)
+
 - [Space tutorial overview](Tutorials-space/index.md)
 - Preprocess
   - [Crop and Rotation of spatial transcriptomic data](Tutorials-space/t_crop_rotate.ipynb)

From 3905d6607ec8b056b9e5d0e434215d31000cc9d6 Mon Sep 17 00:00:00 2001
From: hurry <hurry060215@gmail.com>
Date: Sat, 20 Jun 2026 17:03:36 +0800
Subject: [PATCH 12/15] Fix SPATA2 tutorial index punctuation

---
 docs/Tutorials-space/index.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/Tutorials-space/index.md b/docs/Tutorials-space/index.md
index f36936d..19e9c3c 100644
--- a/docs/Tutorials-space/index.md
+++ b/docs/Tutorials-space/index.md
@@ -4,7 +4,7 @@ This page mirrors the `Space` section in `mkdocs.yml` and provides a markdown en
 
 ## SPATA2-style utilities
 
-- [SPATA2-style AnnData utilities](t_spata2py.ipynb) ? coordinate tables, variable joins, tissue outlines, spatial outlier filtering, unit conversion, benchmark, and namespace-parity status for the `py-SPATA2` rewrite.
+- [SPATA2-style AnnData utilities](t_spata2py.ipynb) - coordinate tables, variable joins, tissue outlines, spatial outlier filtering, unit conversion, benchmark, and namespace-parity status for the `py-SPATA2` rewrite.
 
 ## Preprocess
 

From 2585a91daeef7e6d5ac4547a0796c2ce6716a957 Mon Sep 17 00:00:00 2001
From: hurry <hurry060215@gmail.com>
Date: Sat, 20 Jun 2026 17:03:38 +0800
Subject: [PATCH 13/15] Fix Chinese SPATA2 tutorial index punctuation

---
 docs_zh/Tutorials-space/index.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs_zh/Tutorials-space/index.md b/docs_zh/Tutorials-space/index.md
index 211c1d7..3b00f66 100644
--- a/docs_zh/Tutorials-space/index.md
+++ b/docs_zh/Tutorials-space/index.md
@@ -1,6 +1,6 @@
 ## SPATA2-style utilities
 
-- [SPATA2 ?? AnnData ??](t_spata2py.ipynb) ? ???????????????????????????benchmark ? `py-SPATA2` namespace parity ???
+- [SPATA2-style AnnData utilities](t_spata2py.ipynb) - coordinate tables, variable joins, tissue outlines, spatial outlier filtering, unit conversion, benchmark, and py-SPATA2 namespace parity status.
 
 # 空间转录组学教程
 

From cfffd63c7be7124c2847b7ba9dcdf7159d46498e Mon Sep 17 00:00:00 2001
From: hurry <hurry060215@gmail.com>
Date: Sat, 20 Jun 2026 17:03:40 +0800
Subject: [PATCH 14/15] Tighten SPATA2 tutorial overview entry

---
 docs/Tutorial.md | 1 -
 1 file changed, 1 deletion(-)

diff --git a/docs/Tutorial.md b/docs/Tutorial.md
index 8064a2c..508aeca 100644
--- a/docs/Tutorial.md
+++ b/docs/Tutorial.md
@@ -92,7 +92,6 @@ This page is the markdown overview for the tutorial structure defined in `mkdocs
 ## Spatial Transcriptomics
 
 - [SPATA2-style AnnData utilities](Tutorials-space/t_spata2py.ipynb)
-
 - [Space tutorial overview](Tutorials-space/index.md)
 - Preprocess
   - [Crop and Rotation of spatial transcriptomic data](Tutorials-space/t_crop_rotate.ipynb)

From 511c1512495961ba6bf29a2c330f3119b25f7ba4 Mon Sep 17 00:00:00 2001
From: hurry060215-tech <hurry060215@gmail.com>
Date: Thu, 2 Jul 2026 19:41:09 +0800
Subject: [PATCH 15/15] Clarify SPATA2-inspired tutorial scope

---
 docs/Tutorial.md                          |    2 +-
 docs/Tutorials-space/index.md             |    4 +-
 docs/Tutorials-space/t_spata2py.ipynb     | 2812 ++++++++++-----------
 docs/tutorials/index_spatial_spata2.md    |    4 +-
 docs_zh/Tutorials-space/index.md          |    4 +-
 docs_zh/Tutorials-space/t_spata2py.ipynb  | 2812 ++++++++++-----------
 docs_zh/tutorials/index_spatial_spata2.md |    4 +-
 7 files changed, 2821 insertions(+), 2821 deletions(-)

diff --git a/docs/Tutorial.md b/docs/Tutorial.md
index 508aeca..7ca88cf 100644
--- a/docs/Tutorial.md
+++ b/docs/Tutorial.md
@@ -91,7 +91,7 @@ This page is the markdown overview for the tutorial structure defined in `mkdocs
 
 ## Spatial Transcriptomics
 
-- [SPATA2-style AnnData utilities](Tutorials-space/t_spata2py.ipynb)
+- [SPATA2-inspired AnnData spatial utilities](Tutorials-space/t_spata2py.ipynb)
 - [Space tutorial overview](Tutorials-space/index.md)
 - Preprocess
   - [Crop and Rotation of spatial transcriptomic data](Tutorials-space/t_crop_rotate.ipynb)
diff --git a/docs/Tutorials-space/index.md b/docs/Tutorials-space/index.md
index 19e9c3c..25e55dc 100644
--- a/docs/Tutorials-space/index.md
+++ b/docs/Tutorials-space/index.md
@@ -2,9 +2,9 @@
 
 This page mirrors the `Space` section in `mkdocs.yml` and provides a markdown entry point for the spatial tutorial notebooks.
 
-## SPATA2-style utilities
+## SPATA2-inspired utilities
 
-- [SPATA2-style AnnData utilities](t_spata2py.ipynb) - coordinate tables, variable joins, tissue outlines, spatial outlier filtering, unit conversion, benchmark, and namespace-parity status for the `py-SPATA2` rewrite.
+- [SPATA2-inspired AnnData spatial utilities](t_spata2py.ipynb) - coordinate tables, variable joins, tissue outlines, spatial outlier filtering, unit conversion, benchmark, and a clear note that this is not full SPATA2 parity.
 
 ## Preprocess
 
diff --git a/docs/Tutorials-space/t_spata2py.ipynb b/docs/Tutorials-space/t_spata2py.ipynb
index 61e3c79..363826e 100644
--- a/docs/Tutorials-space/t_spata2py.ipynb
+++ b/docs/Tutorials-space/t_spata2py.ipynb
@@ -1,1406 +1,1406 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "1319eed3",
-   "metadata": {},
-   "source": [
-    "# SPATA2-style spatial utilities in OmicVerse\n",
-    "\n",
-    "This tutorial documents the first AnnData-native SPATA2 reconstruction slice exposed through `ov.space`. It accompanies the draft implementation PR and the standalone `py-SPATA2` rewrite repository.\n",
-    "\n",
-    "Related links:\n",
-    "\n",
-    "- API PR: [omicverse/omicverse#847](https://github.com/omicverse/omicverse/pull/847)\n",
-    "- Rewrite repository: [omicverse/py-SPATA2](https://github.com/omicverse/py-SPATA2)\n",
-    "- SPATA2 documentation: [theMILOlab.github.io/SPATA2](https://themilolab.github.io/SPATA2/index.html)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "43f96e85",
-   "metadata": {},
-   "source": [
-    "## What this notebook covers\n",
-    "\n",
-    "The current stable slice focuses on the SPATA2 spatial-data foundation:\n",
-    "\n",
-    "| SPATA2 concept | OmicVerse API used here | Output |\n",
-    "|---|---|---|\n",
-    "| Coordinate table | `ov.space.spata2_get_coords` | observation-indexed `DataFrame` |\n",
-    "| Molecular / metadata variables | `ov.space.spata2_join_variables` | coordinates joined with `obs` and gene values |\n",
-    "| Tissue outline | `ov.space.spata2_tissue_outline` | convex outline stored in `adata.uns` |\n",
-    "| Spatial artifacts | `ov.space.spata2_identify_outliers` | Boolean outlier flag in `adata.obs` |\n",
-    "| Cleaned object | `ov.space.spata2_remove_outliers` | filtered `AnnData` |\n",
-    "| Pixel scale | `spata2_pixels_to_unit`, `spata2_unit_to_pixels` | explicit distance conversion |\n",
-    "\n",
-    "It is not yet a full SPATA2 port. The parity section at the end shows the current namespace audit status.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2f3ee46b",
-   "metadata": {},
-   "source": [
-    "## Setup\n",
-    "\n",
-    "The notebook uses only `omicverse`, `AnnData`, `numpy`, `pandas`, and `matplotlib`. No R runtime, `rpy2`, or SPATA2 R installation is required.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "82bd9f5c",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-20T09:00:16.170918Z",
-     "iopub.status.busy": "2026-06-20T09:00:16.170918Z",
-     "iopub.status.idle": "2026-06-20T09:00:20.013422Z",
-     "shell.execute_reply": "2026-06-20T09:00:20.013422Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "from anndata import AnnData\n",
-    "import matplotlib.pyplot as plt\n",
-    "import omicverse as ov\n",
-    "\n",
-    "%config InlineBackend.figure_format = 'png'\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "1ad0c9ce",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-20T09:00:20.015422Z",
-     "iopub.status.busy": "2026-06-20T09:00:20.015422Z",
-     "iopub.status.idle": "2026-06-20T09:00:37.349404Z",
-     "shell.execute_reply": "2026-06-20T09:00:37.349404Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "omicverse 2.1.3rc1\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "D:\\Anaconda3\\envs\\omicverse\\lib\\site-packages\\anndata\\utils.py:434: FutureWarning: Importing read_loom from `anndata` is deprecated. Import anndata.io.read_loom instead.\n",
-      "  warnings.warn(msg, FutureWarning)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "SPATA2-style API: spata2_get_coords spata2_identify_outliers\n"
-     ]
-    }
-   ],
-   "source": [
-    "print('omicverse', ov.__version__ if hasattr(ov, '__version__') else 'unknown')\n",
-    "print('SPATA2-style API:', ov.space.spata2_get_coords.__name__, ov.space.spata2_identify_outliers.__name__)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ba62a222",
-   "metadata": {},
-   "source": [
-    "## Build a Visium-like tutorial fixture\n",
-    "\n",
-    "The reference PRs in this documentation tree use canonical public datasets when the backend performs a full biological analysis. The current SPATA2 slice only exercises coordinate and spatial-data utilities, so this notebook uses a deterministic fixture with:\n",
-    "\n",
-    "- a 20 x 20 tissue grid;\n",
-    "- three smooth gene-like spatial variables;\n",
-    "- two deliberately isolated artifact spots.\n",
-    "\n",
-    "That makes the expected tissue outline and outlier behavior easy to inspect in a rendered notebook.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "c09f59c3",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-20T09:00:37.352404Z",
-     "iopub.status.busy": "2026-06-20T09:00:37.351404Z",
-     "iopub.status.idle": "2026-06-20T09:00:37.366404Z",
-     "shell.execute_reply": "2026-06-20T09:00:37.365405Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.omicverse.anndata+json": {
-       "name": null,
-       "previews": {
-        "layers": {},
-        "obs": {
-         "dtypes": {
-          "region": "object",
-          "total_counts": "float64"
-         },
-         "name": ".obs",
-         "shape": [
-          402,
-          2
-         ],
-         "table": {
-          "columns": [
-           "region",
-           "total_counts"
-          ],
-          "data": [
-           [
-            "left",
-            6.7466547045256275
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-            "left",
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-            "right",
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-            "right",
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-            "right",
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-            "left",
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-           [
-            "left",
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-          "index": [
-           "spot_000",
-           "spot_001",
-           "spot_002",
-           "spot_003",
-           "spot_004",
-           "spot_005",
-           "spot_006",
-           "spot_007",
-           "spot_008",
-           "spot_009",
-           "spot_010",
-           "spot_011",
-           "spot_012",
-           "spot_013",
-           "spot_014",
-           "spot_015",
-           "spot_016",
-           "spot_017",
-           "spot_018",
-           "spot_019",
-           "spot_020",
-           "spot_021",
-           "spot_022",
-           "spot_023"
-          ]
-         },
-         "type": "dataframe"
-        },
-        "obsm": {
-         "spatial": {
-          "dtype": "float64",
-          "name": "obsm[\"spatial\"]",
-          "shape": [
-           402,
-           2
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-             3.0,
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-             4.0,
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-             5.0,
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-             6.0,
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-            "5",
-            "6",
-            "7",
-            "8",
-            "9"
-           ]
-          },
-          "type": "array"
-         }
-        },
-        "uns": {},
-        "var": {
-         "dtypes": {},
-         "name": ".var",
-         "shape": [
-          3,
-          0
-         ],
-         "table": {
-          "columns": [],
-          "data": [
-           [],
-           [],
-           []
-          ],
-          "index": [
-           "GeneA",
-           "GeneB",
-           "GeneC"
-          ]
-         },
-         "type": "dataframe"
-        }
-       },
-       "ref": "obj:22c0d5203a0",
-       "summary": {
-        "embedding_keys": [
-         "spatial"
-        ],
-        "embedding_keys_more": 0,
-        "embedding_keys_total": 1,
-        "layers": [],
-        "layers_more": 0,
-        "layers_total": 0,
-        "obs_columns": [
-         "region",
-         "total_counts"
-        ],
-        "obs_columns_more": 0,
-        "obs_columns_total": 2,
-        "obsm_keys": [
-         "spatial"
-        ],
-        "obsm_keys_more": 0,
-        "obsm_keys_total": 1,
-        "shape": [
-         402,
-         3
-        ],
-        "uns_keys": [],
-        "uns_keys_more": 0,
-        "uns_keys_total": 0,
-        "var_columns": [],
-        "var_columns_more": 0,
-        "var_columns_total": 0
-       },
-       "type": "anndata"
-      },
-      "text/plain": [
-       "AnnData object with n_obs × n_vars = 402 × 3\n",
-       "    obs: 'region', 'total_counts'\n",
-       "    obsm: 'spatial'"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "n_side = 20\n",
-    "grid_x, grid_y = np.meshgrid(np.arange(n_side), np.arange(n_side))\n",
-    "coords = np.column_stack([grid_x.ravel(), grid_y.ravel()]).astype(float)\n",
-    "outlier_coords = np.array([[55.0, 55.0], [58.0, 53.0]])\n",
-    "coords = np.vstack([coords, outlier_coords])\n",
-    "\n",
-    "rng = np.random.default_rng(7)\n",
-    "gene_a = coords[:, 0] / coords[:, 0].max() * 6 + rng.normal(0, 0.15, coords.shape[0])\n",
-    "gene_b = coords[:, 1] / coords[:, 1].max() * 5 + rng.normal(0, 0.15, coords.shape[0])\n",
-    "gene_c = np.sqrt((coords[:, 0] - 10) ** 2 + (coords[:, 1] - 10) ** 2)\n",
-    "gene_c = 8 - gene_c / gene_c.max() * 6 + rng.normal(0, 0.1, coords.shape[0])\n",
-    "counts = np.clip(np.column_stack([gene_a, gene_b, gene_c]), 0, None)\n",
-    "\n",
-    "obs = pd.DataFrame(\n",
-    "    {\n",
-    "        'region': np.where(coords[:, 0] < 10, 'left', 'right'),\n",
-    "        'total_counts': counts.sum(axis=1),\n",
-    "    },\n",
-    "    index=[f'spot_{idx:03d}' for idx in range(coords.shape[0])],\n",
-    ")\n",
-    "adata = AnnData(X=counts, obs=obs, var=pd.DataFrame(index=['GeneA', 'GeneB', 'GeneC']))\n",
-    "adata.obsm['spatial'] = coords\n",
-    "adata\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "26b04623",
-   "metadata": {},
-   "source": [
-    "## Extract coordinates\n",
-    "\n",
-    "`spata2_get_coords` reads `adata.obsm['spatial']` by default and returns a tidy table. Observation metadata can be joined in the same call.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "17684abe",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-20T09:00:37.368404Z",
-     "iopub.status.busy": "2026-06-20T09:00:37.368404Z",
-     "iopub.status.idle": "2026-06-20T09:00:37.381404Z",
-     "shell.execute_reply": "2026-06-20T09:00:37.381404Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.omicverse.dataframe+json": {
-       "dtypes": {
-        "barcode": "object",
-        "region": "object",
-        "total_counts": "float64",
-        "x": "float64",
-        "y": "float64"
-       },
-       "name": null,
-       "ref": "obj:22c0d521ab0",
-       "shape": [
-        5,
-        5
-       ],
-       "table": {
-        "columns": [
-         "barcode",
-         "x",
-         "y",
-         "region",
-         "total_counts"
-        ],
-        "data": [
-         [
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-          "spot_002",
-          2.0,
-          0.0,
-          "left",
-          7.128637064825885
-         ],
-         [
-          "spot_003",
-          3.0,
-          0.0,
-          "left",
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-         [
-          "spot_004",
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-          "left",
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-        "index": [
-         "spot_000",
-         "spot_001",
-         "spot_002",
-         "spot_003",
-         "spot_004"
-        ]
-       },
-       "type": "dataframe"
-      },
-      "text/plain": [
-       "           barcode    x    y region  total_counts\n",
-       "spot_000  spot_000  0.0  0.0   left      6.746655\n",
-       "spot_001  spot_001  1.0  0.0   left      7.053847\n",
-       "spot_002  spot_002  2.0  0.0   left      7.128637\n",
-       "spot_003  spot_003  3.0  0.0   left      7.082770\n",
-       "spot_004  spot_004  4.0  0.0   left      7.299024"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "coords_df = ov.space.spata2_get_coords(adata, include_obs=['region', 'total_counts'])\n",
-    "coords_df.head()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6868776e",
-   "metadata": {},
-   "source": [
-    "## Join coordinates with variables\n",
-    "\n",
-    "`spata2_join_variables` mirrors the SPATA2 pattern of collecting spatial coordinates together with variables of interest. Names in `adata.obs` are treated as metadata; names in `adata.var_names` are pulled from the expression matrix.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "1f0a1c37",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-20T09:00:37.384404Z",
-     "iopub.status.busy": "2026-06-20T09:00:37.383404Z",
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-     "shell.execute_reply": "2026-06-20T09:00:37.397404Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.omicverse.dataframe+json": {
-       "dtypes": {
-        "GeneA": "float64",
-        "GeneB": "float64",
-        "GeneC": "float64",
-        "barcode": "object",
-        "region": "object",
-        "x": "float64",
-        "y": "float64"
-       },
-       "name": null,
-       "ref": "obj:22c0d5210c0",
-       "shape": [
-        5,
-        7
-       ],
-       "table": {
-        "columns": [
-         "barcode",
-         "x",
-         "y",
-         "region",
-         "GeneA",
-         "GeneB",
-         "GeneC"
-        ],
-        "data": [
-         [
-          "spot_000",
-          0.0,
-          0.0,
-          "left",
-          0.00018452300362238613,
-          0.0,
-          6.746470181522005
-         ],
-         [
-          "spot_001",
-          1.0,
-          0.0,
-          "left",
-          0.14826010648833945,
-          0.1543768849046773,
-          6.751210051727309
-         ],
-         [
-          "spot_002",
-          2.0,
-          0.0,
-          "left",
-          0.1657758734198053,
-          0.031559240824445535,
-          6.931301950581634
-         ],
-         [
-          "spot_003",
-          3.0,
-          0.0,
-          "left",
-          0.17675605177261577,
-          0.0,
-          6.906014035239323
-         ],
-         [
-          "spot_004",
-          4.0,
-          0.0,
-          "left",
-          0.3455924856725175,
-          0.0,
-          6.953432009293315
-         ]
-        ],
-        "index": [
-         "spot_000",
-         "spot_001",
-         "spot_002",
-         "spot_003",
-         "spot_004"
-        ]
-       },
-       "type": "dataframe"
-      },
-      "text/plain": [
-       "           barcode    x    y region     GeneA     GeneB     GeneC\n",
-       "spot_000  spot_000  0.0  0.0   left  0.000185  0.000000  6.746470\n",
-       "spot_001  spot_001  1.0  0.0   left  0.148260  0.154377  6.751210\n",
-       "spot_002  spot_002  2.0  0.0   left  0.165776  0.031559  6.931302\n",
-       "spot_003  spot_003  3.0  0.0   left  0.176756  0.000000  6.906014\n",
-       "spot_004  spot_004  4.0  0.0   left  0.345592  0.000000  6.953432"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "feature_df = ov.space.spata2_join_variables(adata, ['region', 'GeneA', 'GeneB', 'GeneC'])\n",
-    "feature_df.head()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "09f29795",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-20T09:00:37.399404Z",
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-     "shell.execute_reply": "2026-06-20T09:00:37.772963Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
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",
-      "text/plain": [
-       "<Figure size 1000x300 with 6 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, axes = plt.subplots(1, 3, figsize=(10, 3), constrained_layout=True)\n",
-    "for ax, gene in zip(axes, ['GeneA', 'GeneB', 'GeneC']):\n",
-    "    sc = ax.scatter(feature_df['x'], feature_df['y'], c=feature_df[gene], s=18, cmap='viridis')\n",
-    "    ax.set_title(gene)\n",
-    "    ax.set_aspect('equal')\n",
-    "    ax.set_xlabel('x')\n",
-    "    ax.set_ylabel('y')\n",
-    "    fig.colorbar(sc, ax=ax, fraction=0.046, pad=0.04)\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "68bf8c24",
-   "metadata": {},
-   "source": [
-    "## Identify the tissue outline\n",
-    "\n",
-    "The first implementation uses a deterministic convex hull. Later reconstruction passes should add parity for SPATA2's richer outline and annotation handling.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "97611dfc",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-20T09:00:37.775691Z",
-     "iopub.status.busy": "2026-06-20T09:00:37.775691Z",
-     "iopub.status.idle": "2026-06-20T09:00:37.788691Z",
-     "shell.execute_reply": "2026-06-20T09:00:37.788691Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.omicverse.dataframe+json": {
-       "dtypes": {
-        "x": "float64",
-        "y": "float64"
-       },
-       "name": null,
-       "ref": "obj:22c7ddcd750",
-       "shape": [
-        5,
-        2
-       ],
-       "table": {
-        "columns": [
-         "x",
-         "y"
-        ],
-        "data": [
-         [
-          58.0,
-          53.0
-         ],
-         [
-          55.0,
-          55.0
-         ],
-         [
-          0.0,
-          19.0
-         ],
-         [
-          0.0,
-          0.0
-         ],
-         [
-          19.0,
-          0.0
-         ]
-        ],
-        "index": [
-         "0",
-         "1",
-         "2",
-         "3",
-         "4"
-        ]
-       },
-       "type": "dataframe"
-      },
-      "text/plain": [
-       "           x     y\n",
-       "vertex            \n",
-       "0       58.0  53.0\n",
-       "1       55.0  55.0\n",
-       "2        0.0  19.0\n",
-       "3        0.0   0.0\n",
-       "4       19.0   0.0"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "outline = ov.space.spata2_tissue_outline(adata)\n",
-    "outline\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bfe4b7a1",
-   "metadata": {},
-   "source": [
-    "## Detect spatial outliers\n",
-    "\n",
-    "Here a radius-neighborhood rule marks observations with too few nearby spots. The two isolated coordinates are expected to be flagged.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "b3df29bb",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-20T09:00:37.791238Z",
-     "iopub.status.busy": "2026-06-20T09:00:37.791238Z",
-     "iopub.status.idle": "2026-06-20T09:00:37.804238Z",
-     "shell.execute_reply": "2026-06-20T09:00:37.804238Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "spata2_spatial_outlier\n",
-      "kept       400\n",
-      "outlier      2\n",
-      "Name: count, dtype: int64\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.omicverse.dataframe+json": {
-       "dtypes": {
-        "region": "object",
-        "total_counts": "float64"
-       },
-       "name": null,
-       "ref": "obj:22c0d522cb0",
-       "shape": [
-        2,
-        2
-       ],
-       "table": {
-        "columns": [
-         "region",
-         "total_counts"
-        ],
-        "data": [
-         [
-          "right",
-          12.67020073237883
-         ],
-         [
-          "right",
-          12.80379983730633
-         ]
-        ],
-        "index": [
-         "spot_400",
-         "spot_401"
-        ]
-       },
-       "type": "dataframe"
-      },
-      "text/plain": [
-       "         region  total_counts\n",
-       "spot_400  right     12.670201\n",
-       "spot_401  right     12.803800"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "outliers = ov.space.spata2_identify_outliers(adata, radius=2.0, min_neighbors=2)\n",
-    "print(outliers.value_counts().rename(index={False: 'kept', True: 'outlier'}))\n",
-    "adata.obs.loc[outliers, ['region', 'total_counts']]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "0a57c3af",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-20T09:00:37.806238Z",
-     "iopub.status.busy": "2026-06-20T09:00:37.806238Z",
-     "iopub.status.idle": "2026-06-20T09:00:37.882238Z",
-     "shell.execute_reply": "2026-06-20T09:00:37.882238Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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QBB/j8OHDXF8VLeMA2qOhpBza33kjhmHwUPCuXbuoSZMm9OKLL3JvVG9AMklBEAQf4oMPPuBhSgRIVMmZOnUq95j11gAJIB5Cf1g0EZ8/f75XNaOQTFIQBMEHwLKJSZMm0YcffsivMcyKecdu3bqRN7N69WqaPHkyP3/kkUeoS5dTu8Tj35BMUhAEoQqDocq3336bs0cESLSwQoFwVNLx9gCZm5tLY8eOpcLCQho6dCgPEXsbkkkKgiBUUXbv3k0TJkygr776il937NiR3njjDe7gURWYNGkSFzRAhR+ct7fMQ9qRTFIQBKEKZo9QrGJIFQESnTPQsPj333+vMgFy7ty59Oabb/K86TvvvMNl8bwRySQFQRCqENu2bWMl6KJFi/h1jx496LXXXqNWrVpRVWHz5s00ceJEfn733Xd7tajIo5nkvffey+m1/dGyZUunArzoY1azZk2KjIykkSNH0sGDBz15yoIgCB4B6weffvppSklJ4QCJSjRPPvkkN0auSgGysLCQ5yFzcnI4OOriAd6KxzNJDBf88MMP1mtMOmtuvvlmXgy7YMEC7m+G8esRI0bQr7/+6qGzFQRBOPX8/fffdNVVV9GyZcv4db9+/eiVV17hNYVVjWnTptFff/3Fyc+8efMoICCAvBmPB0kERRTZraiUEoYQMFbdv39/tmFiF9+YfvvtNzrttNPcfkvBQ5OVlXUSz14QBOHkUVxcTI899hiPuqExclRUFL/GcCvm8qoan332GWfDYM6cOVS3bl3ydvy9YWwayqbGjRvTxRdfzBUXAOTL+AMZOHCgtS2GYlF3UH+bqoiHHnqIs079qF+//im5DkEQhMpk1apV3Ovx9ttv5wB59tln0/r162n8+PFVMkDu3r3bKjWHUcJzzjmHqgIe9TT+AKBuQrFdVFxA8d0+ffpQdnY2L4yFYis2NtbpM7Vr1+b33IEmoshC9QO/GEEQhKoCRsIgZunatSsPS6II+VtvvUVffPFFlf3SX1JSwknQkSNHqHPnzvTwww9TVcGjw61Dhgyxnrdr146DZsOGDen9998/4fYoISEh/BAEQahqYAkHmiFv2LCBX0OsiG4YFU1JVSVmzJjBAiMMF6PsHBKgqoJX5ezIGps3b05btmzhPwoMMWRkZDhtA3VrVf+DEQRBsJOXl0e33HIL9ezZkwNkQkICCxZRh7Wq3+8WLVpEM2fO5OcvvfQSF1uvSnhVkIQkeOvWrVSnTh1OyVHs9scff7TeR2UGzFliXZAgCIIv8PPPP3MBAAhyHA4HXXLJJRwoR40aRVWd1NRUHmZF8QOoc9ECq6rh0eFWVKcfNmwYD7Hu27eP7rnnHpYDw5EQ3cCpKHwbFxfHVeGvv/56DpDulK2CIAhVBWgvbrvtNnrhhRf4NZSeyLSqiqDl30DAv+KKK2j//v28KkGrWqsaHg2Se/bs4YCI3me1atWi3r178/IOPAdYKAsVF8blMZmNRqH6D0oQBKGq8t1339G4ceMsNT+eP/roo5wc+ApPPvkkff311xQaGkrvvfceRUREUFXEz0Ae7MNgnST+8KB09aYeZYIgVD/S09NpypQpvOYbNGrUiIsCDBgwgHyJ5cuXU69evVjVigbK11xzTZWNBV41JykIguCrfPrpp9zOSne7uPHGG2nt2rU+FyAzMzPpggsu4ACJeVWs66zKeLzijiAIgi+TlpbGegoMOYIWLVpwM2QoWX0NwzA4KGLNe3JyMmfJ3tj+6niQTFIQBOEkBQysCUT2iAAJUSKEOqik44sBErz66qu8zh3lRt99991yxWCqIpJJCoIgVDJQ66MVFGqV6mIpyB6xtM1XWb9+Pd1www38/IEHHvCZVQiSSQqCIFRi9ohgiOwRARJrvVFt5o8//vDpAJmXl0djxozh9oZYhYDlfb6CZJKCIAiVwM6dO3kpx/fff8+vUXsVAbNt27Y+79+bbrqJCyCgOhDqzFbFAuzu8J0rEQRB8NCi+eeff56DIQIk1gVizePSpUurRYB87733LIHO3LlzuaSeLyGZpCAIwn9o9YfKYCjeDdDFCOIV1KCuDmzbts1a4oGWXr62nAVIJikIgnCcYA0gaq1CkIMAiWoy6Nbx008/VZsAWVRUxOshsUgfhQPQGNoXkUxSEAThOFi3bh23s4IYBwwaNIhefvllXhdYnbjjjjvYB+h3+c477/CyD19EMklBEIRjzJygVO3UqRMHB5Q4e+211+jbb7+tdgHy66+/5kwaQJzUoEED8lV8M/QLgiBUIitWrODscc2aNfz63HPPpdmzZ1NSUlK1XAN62WWX8fNJkybR8OHDyZeRTFIQBMENWPc3ffp06t69OwfI+Ph4riTzySefVMsAWVpayv0uDx06RB06dGAVr68jmaQgCEIFYAkHskc0ewcQqTzzzDNWK7/qyIMPPkiLFi1ioRKWfmC5i68jmaQgCIKN3NxcXhyP/rYIkFgg//HHH3MGWZ0D5JIlSywFK4aaq4uKVzJJQRAEk4ULF9LVV1/NXSzAlVdeSY8//jgrOKszhw8fposuuogLJ2A+8tJLL6XqgmSSgiBUe9ADEY2BsRgeARJqzW+++YaVm9U9QBqGwV8W9uzZw9kjqgtVJyRICoJQrfnyyy+pTZs2vNYRXHvttbwWEoW6BaJnn32WPv/8cwoODuZ5yMjIyGrlFhluFQSh2g4hYu4R9UZB06ZNuaRc3759PX1qXsPKlSvplltu4ecYdoaitbohmaQgCNWODz74gNtZIUCiY8WUKVNo9erVEiBtZGdn09ixY7mIAtZCXnfddVQdkUxSEIRqw4EDB3gB/IcffsivMcyKecdu3bp5+tS8bh4STaO3bNlC9evX58pC6PJRHZFMUhCEanHTf/vttzl7RIBEndG77rqLK+lIgCzPnDlzaN68eRQQEMBLX+Li4qi6IpmkIAg+ze7du2nChAn01Vdf8euOHTvSG2+8Qe3bt/f0qXklGzdutIZWZ8yYwR0+qjOSSQqC4LPZIxSrGFJFgIQ6ExVjfv/9dwmQbsjPz+d5yLy8PF4OM23aNKruSCYpCIJPNgNGUQCUUAM9evTgebVWrVp5+tS8mqlTp3KNWlQWwvB0QEAAVXckkxQEwacKcD/99NOUkpLCATIsLIyefPJJLqkmAfLoYK72hRde4OcIkHXq1DklvzNvRzJJQRB8gr///puuuuoqWrZsGb/u168fvfLKK9SkSRNPn5rXs2PHDvYduPXWW6WQgg3JJAVBqNIUFxfTQw89xAvdESCjoqLopZdeoh9++EEC5DH6D3VZUZoPLcFmzpx58n9pVQjJJAVBqLKsWrWK21n99ddf/Prss8+mF198kdf2CcfG3XffzV8uYmJieLlHUFCQuM6GZJKCIFQ5CgsL+ebetWtXDpAoQv7WW2/RF198IQHyOPjuu+/o4Ycf5ucoydeoUaOT9SurskgmKQhClQJLOJA9btiwgV+PHDmSnnvuOe77KBxf9SHd8grrSEeNGiXuqwDJJAVBqBJg7R6Kbffs2ZMDZEJCAi1YsIDrsEqAPD50X8jU1FRq27YtPfHEEyfpt1b1kUxSEASv5+eff2b1JWqJgksuuYSeeuopqlmzpqdPrUoya9Ys+v7773mJDNpf4adQMZJJCoLg1Z0oUCIN7asQIOvWrcvzjljHJwHyxFi6dCndeeed/BzD1KhnK7hHMklBELxWVDJu3DjatWsXv8bzRx99lFWYwomRnp5OF154IRddwM8rr7xSXPkvSJAUBMHrbuTo74gi5ACKSxQFQC1R4b/VskWpPnzpQIEFLJWpru2vjgcZbhUEwWv49NNPefgPARI38BtvvJHWrl0rAbISmD17Nn300Ue8DnL+/PkUHR1dGbv1eSSTFATB46SlpdH111/PIhLQokULLkhe3ds0VRarV6+myZMn8/NHHnmEunTp4ulTqjJIJikIgkeHAJHVIHtEgETXidtuu40r6UiArBxyc3O5/RUKMAwdOpRuuummStpz9UAySUEQPMK+ffto4sSJ9Nlnn/Hrdu3a0euvv06dO3eW30glMmnSJNq0aRMlJSVZw9jCsSOZpCAIpzx7RDBE9ogAiTmyGTNm0B9//CEBspKZO3cuvfnmm+Tv70/vvPMOxcfHV/YhfB7JJAVBOGXs3LmTl3JgITtA7VUETFR9ESqXzZs3c6YOUOcWa02F40cySUEQTkkZtOeff56DIQJkaGgor3nEwnYJkJUP5h8xD5mTk8PBURcPEI4fySQFQTjpGQ1Kyi1ZsoRf9+nThztONG/eXDx/kpg2bRp3R0FVonnz5rEgSjgxJJMUBOGkUFJSQo899hgLchAgIyIiuAzaTz/9JAHyJIJ53qeffpqfz5kzh0v5CSeOZJKCIFQ669at43ZWEOOAQYMG0csvv0zJycni7ZPI7t27rVJzN998M51zzjni7/+IZJKCIFQaRUVFrFTt1KkTB0jUWUVRgG+//VYC5CnI3C+++GI6cuQIq4R1M2XhvyGZpCAIlcKKFSs4e1yzZg2/Pvfcc7kUGtbnCScffDnBsHZUVBQXaAgODha3VwKSSQqC8J8oKCig6dOnU/fu3TlAYi3eu+++S5988okEyFPEokWLaObMmfz8pZdeoqZNm56qQ/s8XhMkMTSAShD2kkn4x4declBoRUZG0siRI+ngwYMePU9BEMrAEo4OHTrwv1+0X7rgggtow4YN/FMqu5waUlNTeZgVRRqgIkYLLMHHgiTmLvDtByo4O5h4/vzzz2nBggW0ePFiLmM1YsQIj52nIAhl9UDxhbZ3795c8iwxMZE+/vhjziBr1aolbjqF60+vuOIK2r9/P7Vq1cpStQo+FCSx2BXfgtAvrkaNGpY9MzOTJ/yfeOIJ6t+/P09Eo+4gvrn+9ttvHj1nQajOLFy4kFJSUviGjOwFakpkj8OHD/f0qVU7nnzySfr666+5OAMKxGOZjeBjQRLDqZApDxw4sJwIoLi42MnesmVLatCgAS1btuyolSaysrKcHoIg/HfwxfWaa67h3o7bt2/nf4vffPMNl5Wzf8EVTg3Lly/njingqaee4i8ugo+pW6HAWrlypbWWys6BAwdYnRUbG+tkr127Nr/njoceeojuu+++k3K+glBd+fLLLzlA7t27l19fe+21PA8JJaXgmS8smPfFso9Ro0bR+PHj5dfga5kkFr2i6zhKJmGooLKAyg5/QPqB4wiCcGIcPnyYLr30Uu5DiAAJ1SQq5qAOqwRIz4AhbgRFZPMozoCpKhFJ+WCQxHAqVFlYdBwYGMgPiHOeeeYZfo6MEQuTMzIynD4HdStEAu4ICQmh6Ohop4cgCMfPBx98wO2s0G4JrZamTJnCHe6lm4RnQd3b999/n++TEEq5jrYJPjLcinmNtWvXOtkgAMC8I4rz1q9fn/vM/fjjj7z0A0BFt2vXLurRo4eHzloQfB9MZ6BR74cffsivESgx74h1kIJnWb9+Pd1www38/IEHHqDTTjtNfiW+GiQxVOPaIgfKLKyJ1Has+Zk8eTLFxcVxRnj99ddzgJQ/DEE4OcN4yBoxDZKens6ZCoQhaLOEERrBs+Tl5dGYMWN4/fjgwYNp6tSp8iup7mXpIG/GMA8ySahW8YfxwgsvePq0BMHnwNz9hAkT6KuvvuLXHTt25CVX7du39/SpCSZYl4qlNphueuutt/jeKJx8/Ax8ffRhsAQERZYh4pH5SUFwBv/8IfxAVpKdnc2K8nvvvZdfY7pD8A6wBlJXMULTakxXCacmFnh1JikIwslj27ZtdPXVV3PdT4CpDBTwQOUWwbt+T3qJx+233y4B8hQj+bogVDNQYxXVcrD4HAEyLCyMpzbQQUICpHcBhT8ySGRBvXr14ixfOLVIJikI1YiNGzdyOytdtapfv3483NqkSRNPn5pQAXfccQcXW0FFo3feeYfFVMKpRTJJQagGoMQjqlGhYwcCJNTlaCrwww8/SID0UlCT9bHHHuPnWIKDMoDCqUe+lgiCj7Nq1SrOHv/66y9+PWTIEA6QWIsseCfoeHTZZZfxcyx9k+LxnkMySUHwUbBs6u6776auXbtygMSQHZYOoA6rBEjvnjO+5JJL6NChQ5z5z5o1y9OnVK2RTFIQfJDff/+ds0esqwNYa/zcc88dtaSj4B08+OCDLKhCcRUs/ajM2tbC8SOZpCD4WFWWW265hXr27MkBMiEhgZuWow6rBEjvBwpjrWCdPXs2NW/e3NOnVO2RTFIQfISff/6ZSzlu2bKFX2PIDn0GUepRqBodVy666CJyOBw8H4nuK4LnkUxSEKo4qJSD5uXozoEAWbduXfriiy/o7bfflgBZhSofocHDnj17OHtEKzLBO5BMUhCqMN999x2NGzeOu+MAPH/00Ue5/JZQdXj22Wfp888/57KAmIeMjIz09CkJJhIkBaEKgi4d6O+IIuSgUaNGXBRAanpWPVauXMnzyODxxx9nRavgPchwqyBUMT799FNq06YNB0gUvEZrK/RmlQBZNYfKx44dy+XnsBYSw+aCdyGZpCBUEdLS0nhhOYbjQIsWLbggOWp6ClVzHnLixIk8j4x1q/hd4kuP4F1IJikIVeBmOn/+fGrdujUHyICAAG6GjEo6EiCrLnPmzKF58+bx7/Pdd9/l5vKC9yGZpCB4eXkyZBufffYZv27Xrh3X8ezcubOnT034j4Xm9dDqjBkz5MuOFyOZpCB4afaIYIjsEQESDZBxM0VHCAmQVZv8/Hyeh0ThB8wjT5s2zdOnJBwFySQFwcvYuXMnL+VAB3qA2qsImG3btvX0qQmVwNSpU2nNmjVUq1YtXsuK4VbBe5FMUhC8BFRawSJyBEMESNTsxJrHpUuXSoD0ET766CN64YUX+DkCZJ06dTx9SsK/IJmkIHgBmzdv5pJyqN0J+vTpQ6+++qrU7vQhduzYwb9jcOutt9LgwYM9fUrCMSCZpCB4uC0SGutCkIMAic4P6Nbx008/SYD0sabXqMuakZFB3bt3p5kzZ3r6lIRjRDJJQfAQ69at43ZWEOOAQYMG0csvv0zJycnyO/Ex0Ndz2bJlXC4Qyz0gxBKqBpJJCsIpBtVVoFTt1KkTB0jcOLGQ/Ntvv5UA6aP1dR9++GF+jiF0lBAUqg6SSQrCKWTFihWcPULdCIYNG0YvvvgiJSUlye/BBzlw4IDV8mrChAk0atQoT5+ScJxIJikIp4CCggKaPn06z0chQKLH4zvvvMN1WCVA+ia6L2Rqaiqrk5944glPn5JwAkgmKQgnGSzhQPa4adMmfo2F5M888wwlJCSI732YWbNm8VKesLAwLieIn0LVQzJJQThJ5Obm0k033US9e/fmAJmYmEgff/wx12GVAOnbQKRz55138nOolVE5SaiaSCYpCCeBhQsX0tVXX03bt2/n1+g6j16BNWrUEH9Xg16fF1xwAS/vufDCC/l3L1RdJJMUhEokMzOTrrnmGq7JiQDZoEED+uabb7isnATI6lFzF1+Odu3aRU2aNGFRlrS/qtpIkBSESuLLL7/kZshY6wiuvfZaXgsplVWqD7Nnz+bSc1gHiWH16OhoT5+S8B+R4VZB+I8cPnyY5x7nzp3Lr5s2bcrr4fr27Su+rUasXr2aJk+ezM8feeQR6tKli6dPSagEJJMUhP/ABx98wKIMBEh/f3+aMmUK3ywlQFY/kRZUy4WFhTR06FD+0iT4BpJJCsIJLhKfNGkSffjhh/wagRLzjlgHKVQ/8LcABXPdunXpjTfekHlIH0IySUE4TmEGWhwhKCJABgYGstR/5cqVEiCrKRhFePPNN3kkYd68eRQfH+/pUxIqEckkBeEY2b17N5cW++qrr/h1x44dOXvs0KGD+LAatzibOHGiVcRchtl9D8kkBeEYskcoVqFcRYAMDg6mBx98kH7//XcJkNUYzD9iHjInJ4eDoy4eIPgWkkkKwlHYtm0br3tbtGgRv+7Rowd37GjVqpX4rZozbdo0+uuvv7gOL4ZZAwICPH1KwklAMklBqABUS3n66acpJSWFAyTqbj755JPcGFkCpPDZZ5/x3weYM2cOC3YE30QySUFwYePGjVyQHPU3Qb9+/eiVV17hCiqCgLlpXWru5ptvpnPOOUec4sNIJikIJsXFxfTQQw/xPCMCZFRUFL300kv0ww8/SIAUmJKSErr44ovpyJEj1LlzZ6uZsuC7SCYpCES0atUqzh4xxwSGDBnCAbJ+/friH8FixowZPOSOL1BofwURl+DbSCYpUHVXKEK637VrVw6QKEL+1ltvcR1WCZCCHcxNz5w5k59D7SzD79UDySSFaguWcCB73LBhA78eMWIEPf/889z3URDspKam8jArlgNdddVV3ApLqB5IJilUO/Ly8uiWW26hnj17coBEA+QFCxZwBR0JkIIrDoeDrrjiCtq/fz8rm7WqVageSCYpVCt+/vlnzgS2bNnCry+55BJ66qmneK2bIFQElv58/fXXFBoayvOQERER4qhqhGSSQrUgOzubrrvuOq6MggCJdW1ffPEF12GVACm4Y/ny5XTbbbfxc3yZwrpZoXohmaTg83z33Xc0btw47hYP8PzRRx+lmJgYT5+a4MVkZmby3COWfYwaNYrGjx/v6VMSPIAEScFnSU9P5/6OaF0EGjVqxEUBBgwY4OlTE7wcCHQQFLdv307Jycn8d+Pn5+fp0xI8gAy3Cj7Jp59+ygXJdW+/G2+8kdauXSsBUjgmXn31VXr//fe5Fdr8+fMpNjZWPFdNkUxS8CnS0tLo+uuvZ4EFaNGiBRck79Wrl6dPTagirF+/nm644QZ+jm4v0ki7euPRTHL27NnUrl07io6O5gc6LEBFpikoKGCxBYQVkZGRNHLkSDp48KAnT1nw4uExfONHM2QESHRkgOAClXQkQArHszxozJgxfO8ZPHgwD9cL1RuPBsl69epx7cMVK1bQn3/+Sf3796fzzjuPv8np4sGff/45r2FbvHgx7du3jxd8C4Id/F0MHz6cLrzwQjp06BB/8UKhANRhhWxfEI6Vm266idfOYr0sKi/5+8uMVLXH8DJq1KhhvPrqq0ZGRoYRFBRkLFiwwHrv77//NpA0LFu27Jj3l5mZyZ/BT8G3cDgcxmuvvWbExMTw7xh/L/fdd59RWFjo6VMTqiDz58/nvyM/Pz/jhx9+8PTpCJXMicaCQG/q34eMMTc3l4ddkV2iK8PAgQOtbVq2bEkNGjTgDg2nnXaa21qceGiysrJOyfkLp5adO3fyUo7vv/+eX6P26uuvv05t27aVX4VwQs219RKP22+/XQRegoXHxxKgOMR8Y0hICE2YMIE+/vhjnlc6cOAAV9h3VZXVrl2b33MHhtiw/k0/pEi175UIQ31VBEMESAynYs3j0qVLJUAKJ0RRURGvh8QXasxf33vvveJJwcLjmSTUhxBXYOHuBx98QJdffjnPP54o06dPp8mTJ1uv8YcvgdI32Lx5M5eUQ6si0KdPH5bqN2/e3NOnJlRh7rjjDvrjjz+4A8w777zDyz4EQePxvwZki02bNuXnaGKKP1YUEB47dix/w8vIyHDKJqFuPVoRamSkeAi+A4biUT/zrrvuYtUhamc+8sgjNHHiRBFWCP8JqOkfe+wxfo7hekznCIJXDbdWNJyGOUUEzKCgIPrxxx+t9zZt2sSlxTBnKVQPoHRGtw507UCAHDRoEK1bt46XBonyUPivqujLLruMn2NtLRTSguBVmSSGRtEBHt/eUIAaQx0//fQTffvttzyfiKE1DJ3GxcXxOkr8ISNAuhPtCL4DRhGwPAhNbiHgwt/DE088QVdeeaWUBxMqZXQCHWCwZKhDhw40a9Ys8argfUESjUzxTQ592nATxPo2BEhkCwBDbMgWUEQA2SUW977wwguePGXhFABlM5ohr1mzhl8PGzaMXnzxRUpKShL/C5UCKuksWrSIh+5RfELW0wru8MM6EPJhINxBAIYwCNmo4L1gOPW+++5jtSq+6aPS0rPPPsvKQykuLVQWEH6dccYZPLWDggGXXnqpOLcakHWCscDjwh1BAFjCgewR884Awq1nnnmGEhISxEFCpXH48GG66KKLOEBiFEsCpFDlhDtC9QLFI1AKrHfv3hwgoVzGWlnUYZUAKVQmGDTDnPaePXt42RDW2wrCvyGZpOAxFi5cSFdffTX37AO4gT3++OO8Xk0QKhsM3aMWNJadYR4SRUwE4d+QTFI45WBO4JprruHSXwiQUDd/8803vE5NAqRwMli5ciUvIwL4IgZFqyAcCxIkhVPKl19+yc2QX375ZX597bXX8rpHKJcF4WSA5WW6OAnWQmKNrSAcKzLcKpwywQRan7399tv8GlWWUFKub9++8hsQTuo8JCozbdmyhctTogG3KKWFk5pJorbqzz//fLwfE6oxqMmLovUIkFj3ika2q1evlgApnHTmzJlD8+bN4ybc7777LhcmEYSTGiQxn4T2Vc2aNeMFuXv37j3eXQjVBHRrGTVqFI0ePZoLRyBQYqkHamWGh4d7+vQEH2fjxo3W0OqMGTO4w4cgnPQg+cknn3BgxBAGFGLJyclcWg7ZAsqHCQKGuJA1Iih++OGH3FXhzjvvZPFE9+7dxUHCSSc/P5/nIfPy8lggNm3aNPG6cOqEO7Vq1eKaqhgy+/3333l+CYtyUTYM805oaSRUT3bv3k1Dhw7lhdrp6enUsWNH7uxy//33S3cW4ZQxdepULmuItbZz587l4VZBOOXqVtRcReNbPPBHePbZZ3MTZWQQqLsqVK/sEYpVKFe/+uorXouG4Xh8iRK5vXAq+eijj6wazyg7d7TWeoLwrxjHSVFRkfHBBx8Y55xzjhEUFGR07tzZmD17tpGZmWlt89FHHxmxsbGGN4DzwmXaz0+oXLZu3Wr069eP/YxHjx49jA0bNoibhVPO9u3b+d6Dv8Nbb71VfgPCf44Fx70EpE6dOlz38MILL6Tly5dXmCX069fPqVGy4JugCPlzzz1Ht99+O8/9hIWFcfaIlmYyvCWcaqCJQF1WNGrH3DfarAnCf+W4gySGUaFWPFprGQRIXWpM8F3lIAqSL1u2zPpi9Morr1CTJk08fWpCNeXuu+/mv0d0esByDzRtF4RTPicJgY70Xqu+lJSU0EMPPcQjCLghRUVF0UsvvUQ//PCDBEjBY3z33XfcpBugSEWjRo3ktyFUClJxRzhmoGZG9oilHABLfxAgUclEEDy5Hle3vJowYQKvzRWEykJqtwr/SmFhIQ9ldenShQMkipBDNYg6rBIgBU+i+0KiWEVKSgo98cQT8gsRKhXJJIWjgiUcyB43bNjAr0eMGMF9+ERWL3gDs2bN4iVoqOCE4iYQjwlCZSKZpFAhUKuitVDPnj05QGJR9oIFC7iCjgRIwRvAnDgqOeleka1atfL0KQk+iGSSQjlQwP6qq67izgngkksuoaeeeopq1qwp3hK8AlRzuuCCC3gZEpajoWG3IJwMJJMUnPruoSA02lchQNatW5e++OILrsMqAVLwpupOV199Ne3atYsV1S+++KK0vxJOGpJJCpaEfty4cXzjAXj+6KOP8pozQfAmZs+ezaXnsA5y/vz5FB0d7elTEnwYCZLVHAxbob/jG2+8wa+xvgxFAdA5QRC8cRkSmiuARx55hBXXgnAykeHWasynn37KBckRINGt/cYbb+QC9RIgBW8kNzeX219hSRI6zdx0002ePiWhGiCZZDUkLS2NbrjhBh6qAi1atKDXXntNmtIKXs2kSZNo06ZNPFeuv9gJwslGMslqJnhAYEQrM/xEEfLbbruNVq1aJQFS8GrQE/LNN98kf39/mjdvHsXHx3v6lIRqgmSS1YR9+/bRxIkT6bPPPuPX7dq1o9dff506d+7s6VMThKOCJu742wWo/AT1tSCcKiSTrAbZI4IhskcESCgC77vvPvrjjz8kQApeD+YfsR4yJyeHg6MuHiAIpwrJJH2YnTt38lIOlO0CXbt25YDZtm1bT5+aIBwT06ZN43rBWKeLYVbpUyqcaiST9NGiz6ivimCIAInWZqhxuXTpUgmQQpUBIx9PP/00P58zZw4LdgThVCOZpA/O36Ck3JIlS/h17969WbnavHlzT5+aIBwzu3fvtkrNYV3kOeecI94TPIJkkj4Calg+9thjLMhBgIyIiKDnnnuOFi9eLAFSqHKNvS+++GI6cuQIz5ujybcgeArJJH2A9evXczur5cuX8+tBgwbRyy+/TMnJyZ4+NUE4bmbMmMFf9KKiorj9VXBwsHhR8BiSSVZhioqK+IbSsWNHDpCos4qh1W+//VYCpFAlWbRoEc2cOZOf44seCpgLgieRTLKKsmLFCs4e16xZw6+HDRvG3RCSkpI8fWqCcEKkpqbyMCuWLWFeHUs/BMHTSCZZxSgoKKDp06dT9+7dOUBCGv/OO+9wHVYJkEJVVmRfccUVtH//fm6erFWtguBpJJOsQmAJB7JH1K8EKPb8zDPPUEJCgqdPTRD+E08++SR9/fXXvFwJ85AQngmCNyCZZBXpfoCOB1jOgQCZmJhIH3/8MddflQApVHUwn44awuCpp56ilJQUT5+SIFhIJunlLFy4kLuwb9++nV9jSOqJJ56gGjVqePrUBOE/k5mZyXOPWPYxevRoGj9+vHhV8Cokk/Tim8c111zDvR0RIBs0aEDffPMNtwiSACn4AhDoICji7xvLlaBmlfZXgrchQdIL+fLLL7kZMm4a4Nprr6V169bR4MGDPX1qglBpvPrqq/T+++9TYGAgTx3ExsaKdwWvQ4ZbvYjDhw/TzTffTG+//Ta/btq0Kd9IpDWQ4IsFMND4Gzz44IOs1hYEb0QySS/hgw8+4HZWCJBoLDtlyhRavXq1BEjB58jLy6MxY8bwciaMjuBvXRC8FckkPcyBAwdo0qRJ9OGHH/JrBEq0s5Jv1oKvAqX2hg0bWKX91ltv8ZdCQfBW5K/Tg6IFZI0IigiQmJdBQ1n0zpMAKfgqWAP5yiuvsEBn7ty5soRJ8Hokk/RQG6AJEybQV199xa9RexXZY4cOHTxxOoJwSti2bZu1xOP2229n5bYgeDuSSZ7i7BGKVShXESDR3QCihd9//10CpODzxfixHjIrK4t69epF9957r6dPSRCOCckkT+G3aBQFQJcDcNppp3H2iDqVguDr3HHHHfTHH3/wGl/UGsb0giBUBSSTPAXNkFGsGaW2ECDDwsK4TuUvv/wiAVKoFqAmKxqCA3wxRGEMQagqyNe5k8jGjRu5IPmyZcv4db9+/Vi0ID3yhOrCvn376LLLLuPn119/PQ0fPtzTpyQIVSeTfOihh6hr167cgRyFuvEPSHe40GAt1XXXXcctoSIjI2nkyJF08OBB8mZQhxLXBiEOAiSu76WXXqIffvhBAqRQrUZRLrnkEjp06BD/W5g1a5anT0kQqlaQXLx4MQfA3377jb7//nsqLi6mM888k7teaFCB5vPPP6cFCxbw9vhmOmLECPJWUAAASzig3issLKQhQ4ZwdRGo+mQ9mFCdgCgNUwxoe4WlH2iDJQhVDsOLSE1NNXBKixcv5tcZGRlGUFCQsWDBAmubv//+m7dZtmzZMe0zMzOTt8fPk0lBQYFx1113GYGBgXy8GjVqGG+99ZbhcDhO6nEFwRv5+eefDX9/f/63gH8HguBpTjQWBHpb5wsQFxfHP1esWMHZ5cCBA61tWrZsyRP/GMaEQtQVZG94aCA5P9lgCQfmHlFFBCDTff7557miiCBUxxrEF110ETkcDp6PvPTSSz19SoJQ9dWt+AeFclVYQ9W2bVurZBvWErp2B6hduza/VxGYC4yJibEe9evXP6k1KG+55Rbq2bMnB0jMq2JYGBV0JEAK1XUt8JVXXkl79uyh5s2b85dFQajKeE2QxNwk2kGhZc5/Yfr06ZyR6geq21QGDgOZehk///wztW/fnqXtCPAQKCBQjho1qsLt7fvBjeRk2WFznGR7qcP9tZ1s+8n0nfDfefbZZ1lDgC+3mIeE2E4QqjJeMdyKAt9ffPEFB5569epZdmRjqNSRkZHhlE1C3eouUwsJCeFHZYAb6U/r99GHv22nAxl5VDsmjM5sU4u+nfsMzZ49m7cJi4mnPhdPpjFXXkQ14uJoxbY0eu/XrbTtYBbFhAfTkI4N6PzuybRxbwa9s2QLbdqXQREhgTS4Q30a1aMx7UzLoXd/2UJrdx6m0OBA6t82iS7o3ZTSMvNp3pLNtHL7IQoKCKC+revQRX2aUW5hMc39eTP9sTmV61/2bpVIF/dpxjf+eT9vpqWbDpJBBnVrmkAXn96MwkMC6Z0lm+nnDfuppNSgTo3jefuaUaH03q9baOG6fVRQVEIpDWvSxX2aUv34SFqwdBt9t3o35RaWUMu6sXzc5nVi6JPlO+jrv3ZRZl4RNakdzefZPrkmfbliJ33+5046klNIDeMjaVSPJnRa8wT6fs0e+uT3HZSalU91aoTTyNMa0xltkvhcPvxtG+09kku1okNpeLdGdGb7evTHljR6b+lW2pmWTTUiQmho5wY0tEsyrdt1hOb/uoU278+k6LAgGtyhAY08rRFtOZDF17ZhTzqFBwfSoPb1aEzPJrTvSC77bvXOwxQSGED92talC3o3oYycIpq7ZDOt2JpGAf5+1Kd1HfYFfk/Cfwd1hzGyAh5//HGpIiX4BH6YmPTUwXForJ36+OOP6aeffqJmzZo5vY9MsFatWvTuu+/y0g+AJSKYl3Q3J+kK5iQx7Ip9RUdHH9f5fbtqN7347QYOQLip7t/4J63++BnKz0jl9xt2PYtSzhlH/iHh5OdHdHrrOrR040EqKnVQoL+flXH1bFGbVmw7RAXFJRTo729mMkSdG8fTxn0ZlFNQ7GRvXa8GB5CMvCI+rmHaG9eOouz8YkrLKiB/fz84kHCIunERRH5Eew/nkj9OxA/D1wYHoKiwINp2MJvPD0EV5xQbHkRJcZH09550tuMzJQ4HRYYGUYukWFq57ZCTPSwokDo2iqdlmw8SGcTnBHtwQAD1aFGbgx5QdoOvvW/rJFq0fi+fe4C/P9vxfr82sO/j89A+wrF08NSf177r0yqRftucSkUlpewjbe/eLIGDYH4R7H6W7xC08QUlK7/Yyd4iKYb9hkBu913j2tE067LTKCjAawZVqiTZ2dnUqVMn2rJlCy/l+uijj/jvTRC8hRONBR4Nktdeey2XqPr000+pRYsWlh0Xgso0YOLEiVzn9M033+QLQ1AFS5cuPamOKXU46JoXf6bUrAIKpmJa+fHztH35t/xeWI1Ean/+DdSwTRfrRpBfVEIlpQ6+ASOr0faC4lIqKi7lAIGsTttx0y8oKm8vLnHwZ/AywmbHvnEMREDY+UbP52lQXmEx27Ef7A8gSCIT5PMNDqBAMwggaOQVlCDWUWhQAAUFKjv+DLA9AkdokD8FBwZY9rzCEio1DM7KQoKc7dg+MMCPwoIDbfsp5sCE4Ihz0uDLAOzojBQREmTZLd/5OfsCGW5xBfbC4lJ+VORT2PHa7jvsA8fQ+/G3+bSoxEHThnegni1FZHWi4HcOcc68efNYVPfXX39Z4jtB8BZONBZ49OszhixxwmeccQbVqVPHemAuQ4MSbkOHDuVM8vTTT+dhVnxLPdlk5BZx1hEc4E+bl3yiAqSfHzXpPZx6XPccJTbv5PRNGZlIcSmyIj8nOz6PQIag5Lo9Ag+CjN2OgIMsDUHO2Y5MU92QdIAEKtNUdh0gAbbR84o6QLLdT2VX+BKAY2lwLDXf6HDKqtjOC8ONcnb4AwEoONDZjuAIX+gA7OwjBwW59A9Exoftcc5OPgr05yFinQXb7e58iu1xHU6+Q0bpUHOp/i4+xcvtqdlO5yMcH3PmzOEAGRAQwF96JUAKvoRH5ySPJYnFAmQo5E61Sg5Dj8iakIEUF+axrWmPodRm6DWcrbiKS9QNuPw1YfiwolEn3p4QFMjppo79+tuClmU3hxld7fp4rvtRz/FEfVYHVr0NXmGXOk6ynfz4Gngo1BZAAT7P52wLxIjOPCRbigBdZubjmfsxE0/zmpEVEn85sIPzgL0in6rrcvFd6VF8agZUZ58qG87V1dfYd82oypnDrq6lFyG6AzNmzGB1uiD4EjIR4wYESIhJcBPV82CGfyC/blM/juf9MFyK9xA08RMiF9yIEVgREDD8hyG9JonRPJ9WZleP+vERnP1gXg12DLUWFjuodmy4GaDV53m4sLiU4iJDeLhQ29UQbClFhwVTVFgw5ZnDltqObWtEhvDwbbHNHhLkTwkxYXzeOCaOzXN7AX5Ur2YEn5u2Y0gVGWqjhCjeB65JbV/CARNzfbh2ZTfYJwhCmFd12HyDn3BamwZx7EOck913revX4M8VOPnOoOZJMXwcu09xHo1qR3MmyEPBpu9w3klxZT7VvsOxMD+LYec8u72olEU7vVrWOZX/5nyG/Px8Gjt2LC+FQm/IadOmefqUBKHSkSB5FKAOhRAEN2uAm3Gb+jVoxgVdqEfz2lRUWkpZeUV8Q0YgvO+CLtQ/pS5vn5VXTHmFpRx07hvbhYZ1achBQ9mLKSEmlPcztlcTzopgx1xebEQw3TOmM13ZrwUHBwh1MJcXFRpEd4zsSNee1YaDAOzZBcUUGhxAU89rT5OHtWOBDWx4D0OS153VlqaP6EgRoYG8D9gR8K4a0IruGd2Zj5VTWMzHxjlAxXrvmC4cUHJNOzKv87om0/0XduVrwTXBjmsc2K4e3X9BFxa/KHsR+wRCpRkXduXAhwAFO35C/HP/BV2pc5N4nqfVdny5uG9sV+rTMpGDl/ZdckIUzRjblc7qUJ8DqbKXsFIWdqiG8ZvRvoNiF76+tG9zvh7tOwTCu0d3pnEDW/HQq7bjSwR8B3GTcPxMnTqV1qxZw+uD586dy8OtguBreMUSEG9l2aaDvPxAjzAi08FSjneXbKE/t6Rx1hjA824GbT+YzfZf/j7AN2gIXzBrBpUq7L9uUkXZMX+HG3tqZgG9/fNmWrPjCAdPbU/PKaS3F2/mZRCl5nwf7Nn5RWzHXCmyKT0PmF9Ywssd+HlRCc+BAgSh95dupejwYMpB0MT8mylW+ei3bdSwVhQfS9txDl+s2Ek70rLpUHaBmq8z/YClHAgquBZkm/gfrm3Jhv283fbULAri4VnlCyzlwHIX+ApBiZU6ZNCanYd5ycaq7Yc5WAeY9n/2Z9K7SzazihU+VddssA/e+WULL8Ox+w7Lceb+/A/9uTWNx2K1/XB2Pr29+B/auC/TyaeZuYX01k//0P6MPL5+bc8tKOblN/jio85FOFagC3jhhRf4+VtvvSXFMwSfxaPq1lPBiatbDbrulSW0Lz2XtvzwFv3943xqfvr51GLIeHP+jpyUlUrpqebKnNStR1FoIti52pVC06H2Y1NiquHBEt4OSlIt0rGrXsNDAqybvVK9KnUrsk0tunGYdvzSQ1xUrFC34q8BQaQiFSv2gX3Ztwc4F2d1q7JrlWk5u3mu/+Y7HmI15x+dVK88TFzKBePDgwMq9GmY3Xc2xTD2r+dV9bDr7SM6UbdmCcf5l1V92bFjB3Xs2JHXL9966630yCOPePqUBME31a3eTGZeIaVm5vNawDL8OGPC3FdAgIuKNdCf7ZxnOSkxA5Ti0kWJie0h6nFVt7Lq1VS3Oikxec2fCn52FSsyuTJ1a9mvE9sg8OBhV6WyAMeditUw1PpHF7Uqzl1nYHY7zg8BqLy6VfnI1Y7jwc7ZqNM1q+2xP1cfIYjhG4mrYph96qIAZp+Wqmt2VrH6lfnUxXdgywFVM1j4d1BLGXVZESDR7WbmzJniNsGnkSDpBmQcvNTAVYlpKi4dDuft1Y25vLpVb+8KKzdNVarzfpyDlkY/cz2G9bwCu963XTXKS0jMYFS+upwKfBWVneNCBOVUqeoYrtvrY2tFrqsv3KtYK7ZbF6/t+trK+U7bnd/AbvXSHPsxtLoVQ9LCsXH33XdzIQ98I0cJyaAgmc8VfBsJkm5AiThUgdHr6wCe42mzxBh+rZeCIJtCBgOhCTKVAm03h/Pq1Yzk7ArDpdiHXsSeGBtuqV61HfusGRnKWZFSvSo7tokJw7IUqFuh6FTZoFax4gHlJmx4D/awoABWvua72JG5QSmLY2HfODb2iYwLqlecm7bjnBGY69UM5+vE9eDacI0IqLhmXDveg12rWJsmRluqV9jxE16E7xCY7Hb4tCn71K+cTzF3yqpX2B2mT00VK3xq911BsYNqxYRxxspDuJa9lMvc2X2qfRcRGkS9pZDAMfHdd9/Rww8/zM9fffVVSk5OrpSbkCB4MxIkj8IV/VqwQhPzYgBDkViSAKUnFJqFJaVcxxRLLxrUiqSZF3alPq3q8E2c7YUlVDs2jGZe2IXO6tiAh1cz84sop6CEl2ZADTvitEYcJJS9mJWW94ztbCk0s0w7MtvpIzvRhDNb8bAq7FBp4sY/5dz2dPM57fg5bHgPAWTi4DY07fwOPG+n7djn5f1asNoTa0Gxbxwb5zC6RxNW3CKg4Bxhxzmf3akhq1IRQHFNuDZcI8rwzbywG9d7hQ9gh0+6NKnF9mZ1YjgQwY7lFhDIwEftGsbx60ybMhj7R71Xu0/r1oygBy7sxvVs2af5RZRbUMLqW6hqh3ZWimHt09jwYJpxQVca27MJ/760TzEveueoTvS//i05sGvfoeLQlHPbUWyErJP8N9B1R7e8mjBhglXIXxB8HVG3HoW1O4/Q5n2Z1ugdMhrUBf36r920flc633D9zUX3ew7l0Fcrd7PiEpmXnu9Lzchn+9KNB3h0EJkjfqbnFNCXf+6iP7cphSbsADdwFAtH8W4EAL09gsZnf+zgKkDIjvT2yLw++3MHD0mq8nfKjm2+XLmLgzE+q+YxVeWZ71fv4f3nFBTxfvgrgFnMHXOx6bkFPAeqygsQLd10gIeeUXSdVammHcXcUfR97+Ecyw6gCP5q5S5WvdrtULHCDtUrMm6tn92Rmk1f/bWLVu04TAE2n6JQ+Zcrd9Ifdp8aRGlZyqe/bFTt0rSPcO6f/7GD1u46wsOq2o5ACZ/uOpSjKg2ZPkKW/OWKXdQhOd5pnldwRveFTE1NpZSUFHriiSfERUK1QdStbsCQ3w2v/Uq7DmWXU7fyNJlhWIpLPEdGxPNdUKvaFJd6aLSc6hVDnRD6+JGTElMv8CcXJSYXIMBQJrYPLlOx6gIB+DgyIy1G4eHEQrU9aq7qEnFqyFWpTBF0tIoV14vlJAiY2Af2ZVe3GhWoWGHX14Nz0r7AmkV/P7WMQ1+zk93FdwjielmJ3Ud83KP5lCrwXSnEUzgfu+9KqbCkzK4DIg/pOhx09+gu3B1FqBgMsaIFXXh4OP3555/UqlUrcZVQ5RB1ayWTmVvEmQzaVJXhR0HmnKNd3YqfGOrUN2JnxWUA34y1eEQTAuUmFJdcks1V9VpeiYl5RK71yupWfxd1q7Lba7RiG1Wj1bnEHKteea5OrSPUsGDHUEtNcG6u6lZlL/OFKm2n5hCxH7svcGz4CNdutyMow65qppa3u6pbsUQFXw6QPjr7zvQplfednht19p0/rzktrwxWzzfuTXfzVyBApHPnnXdavSIlQArVDZmTdAMyLFVUvAJFJ27bFahblRCzYgWoK3p71/cssaqrQtP86Xcc6taK0MrTimqiqgFZtXTE9eAV2RGmKlKr6nWk5dStvJ8yBa9lN7NC18NCQVzRebr3qfZnxerWitTHeIm5WaE86enpdMEFF1BpaSldeOGFdOWVV4qbhGqHBEk3YDgQ5dWUotVUt5pdOxrWiuSgxYpNrC3kuqgGl21DMLHbkdkkxIZxxoOsS3faQMGAuKhQpXq17AbXaEUZNVV/VA03KvVpKQtQkJ3hucNmxwL/UFa9mnZz+Dc4yJ8/w3Z0zTBVqQj+rHo166ayndt2kal6dVh2nDMSV5TR06pX2PWifahMce12O4IUhEzYR7Fp19k0+856rT4Hn2J74OS7UgeXoCvnuxIH1YoOY9+V2dW5QnSEDBHzjXafoqwf1qxqX9t910vUreWAj66++mratWsXNWnShF588UXpDylUSyRIHgXUOEVd0pISU91a6uCb/H0XdGWlJm64aIyMSjKJsWGseu3WtBYHGW1H0Jk5thsvJ0EzZtihxISK9b4xnemcTg14v7BDcYm5t7tGdaYxvRpzUIHSEw2EMQR66/AOdGX/lmat1yJLxXrD2Sk0aUgbfs72PKVivXpAK65NimFI7AP7wj4v6N2U68BiHhHHxLExxHtu12S6Z0wnPjeIXWDHOfdrW5dVoxAB4ZpgxzWi8TFUqVDwajt8ktIgjmaM7cKBL9+05xeX8LKQ+y/sxgphvIYd8474coH9dGhU08l3tbi+bVf+soIArX1XIwIq1i5cO7ZY+w4q1tBArns7vFsjDo7ap5hfnT6yI11yejObT4v4y8KN56RwzVehfBs7lJ7DOkishzzehuWC4CuIuvUoQJ0J5aalbiU/2p+eR8s3p/J7PB/mj//7cdf73/9JpU2mGhYBC//LyC2k3zYfoPW7j/AgoLYjCC37J5X+2nHYyY7s8deNB2jjvgyrd6TOTlEXNi0732m+EpnVrxv3EwrT6KbPWsWK/cRHIwMsdWqVhdqqmHNFtsX9KM0BypXbDnGFIdR6VfODyr5+dzolxYVzDVTL7ke0aV8G/b75IB3KymdVKrvDz4/7My7fkkb7j+QqH7Gb/Gj3oRz6fXMq7UrLVspW03eoxfrbPwdp64EsJ98dyS5gX/+9N8PJji8B2B61YIG2o5n00k0H+TrYbs5xFhYrn0JFW9aPE9WCSrk+L4rVuw5vV2dWr15NkydP5uezZs2iLl26ePqUBMFjiLrVDbiZTn5zKd+4t/zorG7F8ggMMer6o1qhqRsa2xWdGNLj/ZkKUC004UXwPIzrx4v+dRDjFlKIeGbNVS3SQaBTIhZnFSu31+IhTmcVKy+ix7G52HpZjVa9iN5V3araXCnVq79NxapVrxWpWHUbLQiKXFWsgQEBfCxXO4RQ5X1XygFN7afMzqpdnvss7zu8b7j6zlS94kMYftYiHbvq1dWnmPfECEDbBnGV9W+qSpObm0udO3emTZs2cbPzzz77TL5ACD6BqFsrGXTbwLo6HXTs6lYMBUL0aldoIkCpm7qz6hVBCEFMl4PTwI6g56piZdWrpcT0d6npWl7FivPjeVM0OLadqxYdqcbHLqpXsxKNXd2qgh3x8Kpe/gFU4Ce+Bpyz/drwjPtTuqhYcQwEUGSlripW+M5eu1XZ/cwvGc4+xfnhS4Nu7lzmowAeli3LCp196qpiVTVdK/YpQq3OSAWiSZMmcYCsW7cuvfHGGxIghWqPzEm6AaIXldG4UbG6CkBN5WZ59aQ7lWm50qPKbv50O/z3H9Wtat9lSztcdm2qWCuyV1CX1jqu67VVrPQt85Gb7cuLait00r+pW1X4dt4P+7OCz+CYKEEoEPeEfPPNN7m7yrx58yg+XtaOCoIESTdEhARRt6YJKntzUbdCcQmTVnRqFScELLgZa+Wmql3q4FqsyIS0chOZHIYAY8JDnBSaXBO1qJSXJLBC07TroUdkeMjOXO0YStWqV23HNsgsMbzoakfFGahecSxV5NtUsfqh2HeQVSdVq1URX+IilbpV23GNiDkoVaczX7bzULEf+wjDqhhyBVrFirlNHBOvgaopS5RYI5xfazWs9h1UrHafOkw7Ssm5+hTnHRUWXKFPMXzM6zGLXH3nz8Kg6s7mzZtp4sSJVhHzvn37evqUBMErkCB5FK4e2JKVl7jZA9zYcfO/d2wXVmpyvVKzy33NqBC6Z0wXSkFd0uJSyspT9uiwILp3bGfuV4ibe2ZeMSsxscQECtP+KUkcGNhu1mKdNrwDDevSkOcmYYcyFcULbhqaQhf2aWoqNJUdAWz8ma1YyapqvULFWszbXHx6M7rx7BSlejXtCD7ndUumW0zVa7ZpxzkMal+P7hjZSdV6RU3XvGI+5x4tatPdYzqz6hV2XBuusX1yTb5mKHhVDdhiDjyo2XrvmC5K9VpgqliLSqh+fATdM7oLK4TxmlWsBSUcaKFKbVE3VtV0NX0aFxFC947uzKpXHE/7Dl8isH3vVolOPsX8JHw6uGN9/l1pn2Loeeq5KTTytMY872n51Kxvi0Lz1ZnCwkJeD5mTk8PBURcPEARB1K1HBYrVIzkF1msM46XnFtLWA5lcP1RpKtV8GW7GEPlA0anXs8OOubl/9mXSnsM5alE+25XIBPVTd6bl8L7VfJzKpDbvz+QasaqijNqXo9Sgf/Zm0oHMPKstlx5i3bQnnQxT7KKVp9jmn70ZlJ5TyNmaHm3ENjhucFCA2Qy6bIh3R1oO1d2faWWVnOsZRHsO5/I141p4jpIlM8RK320Hsii7oMgSLcGemplHWw5m8VILnrtkO9SqhbQtNYsOZxdYvsP7qLkK36Vm5FlDu9gfasviXFH5SBdewHnlFxXTlv2ZtDMt28l3yCpRH3a7zXeqlZeD94OHmhtWB4EdCt1+bZOq9dzbtGnTaOXKlVSzZk0eZg1wqjIlCNUbUbe6ATfTaXN/55JlzrVbx/HQJm7IduUmMiMMYyJTwxAnbvJ6SI9FMQ6DhSW85EKrXs35vNAgfxaU6OFQHqHkmqv+lkhHt7UCCHBajIPzULVelQBGq1gRAPEZnB/UuFrFqltHscAGxzDFOAgYyOJcVaxa9RoAIZDDsOy6vRaGdIuhVtV2U/WqRDSuPlLDm8j+7CpWtqPxcqnav7/NDm0RLhtDzf423+mgbPepGoZWQV/71FIMw6lGWSUl7VNs+/Al3alFUixVR6BePe+88/j5F198Qeecc46nT0kQTgqibq1kkAUhe7IrQxG5EAiRNdrrjGp1K4ID8HdSt/pzAMUNXSsuYUcg5d6JNsUl2znDU8tA7CpWVdO1vFoVz9le6ihnr0jFyqpXzKG6qFVxDhiKRGDDuZXZlZAHAdG+vVaVYrgUwdyys7pVZdau6lacBg9/+jurWKEYzi5AkXMX3wVCJYtAVqZi1Yrh/Ip8aiqJERDtKlZWDJuqV1efYh9/mesqqxu7d++2Ss1hXaQESEEoj8xJukHdTMurW7nYdgXb6+1cN3cnMi1Tt7ooQM2fbkf/zCzLyWT9h45Z3crX5m6DCt6oSN2qx0YrcJFbdav66ao+NSq+Brfno4aV3SmMXdWt1um6kRPbA2d1oaSkhC6++GI6cuQIr4t86KGHPH1KguCVVL+7wzECcUjHRjVN1aaycU1RB3HTX9i0cpMVl6UOLm+G+7AeFkWWgswGtViRISnlp9oPhgBxDGQ8GH601KemElPZyysxMaTqtL1Zi1V30tB21DcNDPTnLMqubkX2qlpeBTgpPXEsLOiPCFXqVh0Q9bxldHgw71MrffkaDVXrlTtvmOtG4BM8Q6UfCJ7K7EoZjBqwev2m9h12he3xQbvvsF+UwkOWaLcXFDssFWuxiy/CQ+DTMtWrpQw2fapVr3bfoeJOdWPGjBm0ZMkSioqKovfee4+Cg4M9fUqC4JVIkDwKVw9sZS1xAPgZHxVK00d2ovo1I3hhPIZlocREILx9ZEdWduKmzPb8Yl5Kctv5HSzVK+wYckTwumV4ezqtOVSvpay2VEpMP64nOgCq1xKHZcdw44QzW9O5XZI5wMCOB7i0bzO6uE8zDk7Zph3B5/xuyTRuUGtL9YoHAteg9vXphnNS+Fh6ewTeni0TXVSvqNFaSh0axdNt53fkZSM5ph3X2Dwplm4f0ZEVvKxuNWuu1o+PpNtHdGLFb04h1LCwF7PaFdtjuQdes48KizkQTj+/IyUnRDn5Dl8isH2rerFszzLtCPDwKXpA4ksIzhUPzI9OHtbOUr3qa0bQvH5IGzoLqlcX313RrznVrRlB1YlFixbRzJkz+fnLL7/MBcwFQagYWUV9FJBpcaNjE2SJCGg5+aqQtxrWU2N8sOPmnleobr56+BAZEG7gPF+ph1ihrMRShFzc3IvK7GamZC9Gru3IfNJzi0x72ZAvtkF9WM7LyuoK8Daoz6oFOzzUaG6EGqx46KyQ92WoounYv87y9AJ/BEC8x18WoBg1j4N5QSwHKbLZ4RMIfVBAXPfX1LVhEXDhi6JiNf9o9ymWcOhm0NpHmJvNzMcyEeVTDYqxYxkHzss+5MpfHvhLSHmfwhe4ZnLxHc6/OpGWlsbDrPj7uOqqq3jphyAI7hF161G4453ltHbXEdr8wxza+ON8anb6+dRyyHjOZJDZ6LqhWt0K8Q4yGFcVK4ZCuWmxTcWqFJqqSgwyN9082V7rVQ2vKjuLfMx6pVrFqoZJHZwd6SLpugEyq165Xqnqo6hFN8hO7QXPIXbRw5k8FOuiYtWqV7SZwv7CXVSvISxAKrNr1WuY2aLLrnpVr2EvcVIA55m+xBcPu4oVdmTcFfmU6+c6jHK+03OVdrtd9RqMoelA06dmEH/s8h7c7cXXcTgcXI/166+/5ubJf/75J4WHV+81okL1ISsri2JiYigzM/O4utrIcKs7h+YV0ca9GRyQyrIepdBEXVfEGLviEkELWRKwKy4RGDGkqJSYzipWtTShTHEJO4ITghiCk1arakUnllrArpd56Pqmqp+lUrFq1Si2KS1VFXDsqlQECGSxCGw6QJbVelXrN+12FdSJh0exrMKuegXZeWro2K569fNH95Mi5Tu76tWPeJ0pPuvvom5FYQFWt9p9Zw77GuV8GsDDurgOu+8QeOFTZJpOdnM5CiuATaWv3j/8j64o1YEnn3ySA2RoaCjPQ0qAFIR/R4KkOyppbflRRKYnuMNK3l8l7No4TvdVxiWUDR8fz4eOsi8fZ/ny5XTbbbfx86eeeopSUlI8fUqCUCWQIOmG6LBgbqyM7E3fi/EMQ3xQdCL4aeWmHvaEAhSUutghQPG3KTT1EKBSsTrbuasGDy2qtX327ZGZIUOyKzcxZIiMU9kdzmrVANV5w0mtWuJgFSsyXK16ZXsp7KrYt317nBuCCK4BqlJt19cIwZI+H8C1XR0GxUL1CnWrzY7qfvCd7mZiqVgdBjdSdrj4rtD0KWKYVhKXqVjL+46VwUGBvJbVVUmMAgxaMWzt31T6okavL4PhJcw9YtnH6NGjafz48Z4+JUGoMkiQPApXD2jJ9UN1sCopcXBR8inD2rHqFUN+rLgsKGHl55Rz21GD+EgW6bCApKCYA97Nw9pZqldtR1CbdHZbatdQ1SXVdgw3jh/Umno2r61Ur6YdXNq3OQ1sV5eDD2x4aBXreV2V6lXbsc2Z7evRJX2b8WdVzdUi3mevlok0bmArPpa2I2B0bBxP1w9py+em7Thn1FS9eWgKD1tqO66xYUIUTT63HSt44QOlYi1h9Sp8FBMWxKIbbUeAnHJue4qPCrF8h2IEUMfCnlQj3PIdRDmYv4RatVFCFBcVsHwa6M8KYFa92nyHgHfdWW2U6tVmxzD5Vf1bUt/WdZRi2LQjHI/p2YRVtb4KvgwgKG7fvp2Sk5NZzVqdS/AJwvEi6tajALEKz5HZCgXgRqxLmyGzVIJRVb8Vwh01F6bsrPb0082Q/crZQ9G9I8j2PcVUZCIYIdOzzGbPKBxXqVXL1m4CZH86kyuzG2o/5vylXSmLIMPzlPqiTDCnqeYjlV2pUlWfSu4ZSS52f9gDLQGOeQacsSkBjprPVMdX1XFwzVq8pO3sO/jU3+5T03dWGTm77zDPGKjmYMtEu2qeMRg+Las9ava1Nn2BUj92n6omzL7Mq6++Su+//z4FBgbS/PnzKTa2epbfE4QTRdStR2HGghW0Ymsa/fP9HNq4cD4163M+tTp7PA89ItPRikvdjglDgNxs2KastCs3Xe14jcwmyKa4ZNUr9smCHjVcCrj5sHlz1ypWoFSvZX0UdXDVzYd10qAbKWMYEkOUCGBaxQqwfhJZJr4Y4H2tbtWqVz0MG+5iR/EBiHowdIxgh2FRiILgI92ZA9fDatjiUs4aeZ2o9h3UsMUlvD2XuDMVwGVqWKUkdvWdVr0G2RTAXCcXzanNkn4Yngb2Wq+uPsU5PHllLx4B8DXWr19PXbp0oYKCApo1axbdcsstnj4lQfAYom6tZHAjX73jMN9EdaDhouD+RIfQxcKvTHGJGzBu1ui4gQTFrqzEzRxr/dQN2llxiaFIVrEGuig0i0pYoQmFKnfRsBSaWO5RplYtU706ONgiwGi7Vr3iM1qtqlW4mFdVlX3K7Er1SrzO01X1isCMtYd2Fau+FqhVsU+tVtUKXnT6QJDSalX4CjELnVV0fVf2nb8qwI4OIcjy9OdVZu7PKlmjnO/8+UsKAqxdAYzzxvkjIGulr1YMFxRX7FP4YvnmVPI18vLyaMyYMRwgBw8eTFOmTPH0KQlClUTmJN2ghCJlmVgZbuqCunnX3fRPmTjTeYN/nS06RkXnic072aoRuFgNd/u0DeOWHVsPybpcm/Vlw7X2rKoj62K2b1DOwOseXex6/641Y50upAK0KMiXuOmmm2jDhg2UmJhIb731Fo8cCIJw/Mi/HDeg8z3ENhDAlKHUrRgyxBCnVm5qJSaGHoFd9aoW2uuhyDK7Xjiv2mi5KDHNeTu7cpNrsSIbM+uVapBFst0sWGDZoWJFlhbgz5/V8FCrmWm6qliV6rWspivAOSPpwzUgG7OrWAGGSbWqlu2GUq7CR6jEY597xGnADh/a7cUO1GINYnWr3afcUiskkIOos09LuVhBgItPWRlcke+gDA7EMG5Z/VyA/WMfnRrXIl8CayBfeeUV/lIzd+5cSkjwbfWuIJxMJEgehasGtFI3ezPIFJcYrGKFKhVKzTyrdqtagA9lJVSvqL6j7RgOnDC4NdczVWXclB037P/1b+lc69VUYl7cpym1d1G94mZ/fvdGXF9Vq15R+q3EVLEOaleXn8PGKtZSB6tYh3dtyJ/VdgQMqD8v6tOUj6VrruJYLevW4HPCuWm1Ks4ZKlbUjUXrK9i5/F6RUrHimhFYLXthCRd6nzSkrar1atrxE768/uy2rBDOsfkOARiqWtTFtfsUgXzSkDZUB6pXm08xrzh+UCuz1muZTzF0e2W/FtS6XqxVJxd2hNELejWlLk0TLNUr/IEAO6RTA2qa6DvVdrZt22Yt8bj99ttpwIABnj4lQajSSJA8CgiEkWFBNnWrwa/rxUXy+kBrDaBhcMZTr2YE3+g5SzIzTagt69WMpNoxqnMIr680lGK0Qa0oSqoRpnfO2SkCVMNaUVQvPsKqnYoHsr+G8ZHUoGakNZzJilU/4mCBz+iSbFp9ChseSn1apujE+TesFcnzq5yImfY6cWEsYEH3EC6BZ36mdkwYB3koce2ZHq4VxcEx52f3RUxEMNuRWdszRnTuqBcXQdHhQbyWUtuxHXyEQuf2/UAMhHPVXVe0HZl2g/gISoxVvtM+RdaM80yKMwuWm3b2aXwk1Y9z9Smx3VeWRBQVFfF6SAgUevXqRffee6+nT0kQqjyibj0KD3/8Fy3bdJD++f5N2rjwPUvdioXvKDauFZfISJChIFNCcXIEQAyB4kaMjAYZFdYFqkX/av6N2zehSTC3a1KKS20PDlB1SfWwKGDlpqmkxS1dFy7Xqlc1r6eWfQBuCWUKYEptKlaleoX6s0zFCrT4R6tYWdTDdpS2wzUEcZanl4jg/FD8HV8WMvOV2Ecv7scxtI+0WlWrXhEIIXBy9R0CK4qQazuGV3EeGIblDh/ad2gAXVxWA9buOwwT4/M8jOrvxnemIEjb8bt67qrenBVXdaBefeyxx6hGjRq0atUqatCggadPSRC8BlG3VjJQnv65Nc1Ut5bVDUX2cSAjnwOFVlxiG9yoodzEzRrPeVvzRn0EHTegxDQVlwhcvDDfHPLTikvYEZwQUDF0alexKoVmKQchux370UXLQ11VryWOsoBns+OYHPBsdq71yipWFfD8bSpZw6E6jeA512b1KwtMadkFXDRcq1XhE4Rr+AgBTKtV8ZN9l57H+7b7Dk9TM/KtzwMcB/6CkhjZpuU7f1MxnKe6mNh9h/PG8DGuz8lHvIykhL8I2H0Ev8BHSzcdoKoOarIiQILXX39dAqQgVBIy3OoGLUZxHYnjoUs3KtaK7VrSWfH2rthbY1WEGmJ1o7C12Y82hKgX/ZffQi3l9ztGdSv/8ZhDvi4n4lbdqvZT/rwrtJedrPNxzeFjV8qGm13Vs04XXu4zOhuvquzbt48uu+wyfn799dfT8OHDPX1KguAzSJB0Q2xEMCXXinJSQ+IuizqjGD5FBwr7fBsUpOEhAXwfdlK9FivFpW4jpYGd1xeaC+0tu1lcwK56BXoIEdtr5SZAxgmbv4vqVatYMeyrhUcA++R1nS6qV5wDlJ44J5ybBueMDFAPDdtVrHgGlald3YqfpaaPyqlbSw2KDA3k9YpO6la04AoJNOdB7epWFDEI4Ehm9ymCms4U7T7VBRuU78rsOD9kqK6+063E2ifXpKpKaWkpXXLJJXTo0CHq0KEDFw0QBKHykCDpBtyAr+zfkue+dPDB/BwqyKCmK0QoVv1Rs8oO6oNiLk7XH0UFGdywLz+jBYtMtBITQ5rY/4W9mrBQpsBmRxA4r1syNa8by3NsUGHqYdkz29enDsk1ObgpO2q0KhVrzxaJHJRYxWo2PO7YuCYNbFdP1XTNL2Y79gkV67ldk/lYZfYSFgBd0KsJBw62m+pWzNehbiyuBdeka7fGRYWymhQByO4LzFNeNaClKphgsyMQwo75TbsdXyJQSxafs9sxb4r9a9Urq1gLlU9Rk7ZujXBnn5IfjenVhBonRFuNn2FHgB3auQG1rR/n5DsE7b5t6lDrejWoqvLggw/SokWLKCIigpd+oA2WIAiVhwTJo4AABmWnTkoQVKC07NAonurXjFSdLfgBoUoI25vUjuYhP9iRMUF4gsLhzevE8H709lBudmpSi9rWr8EZmbYjYHRpHE8dGqrsRtmVcrNL03he04chQnVslJcj6to0gbo0qcUVbWDX2V+XJsqOuUFsq7Orjsk1qWvTWmoeEnaz0wm6nnRuHM/Zod4eH2mZFMvLRqDsLTHtuEY0Ku7YqCZ/MSjb3mCFKbaHryy7w6DE2HDq2CieC5nbfYcgiGwOal98Xttjw4P5epvVKfMpSs5BDdu5US0uvG73KbLOTo1qUkqDOCefYs4UPurQCD4t2z+CLfxTVdWtS5YssRSss2fPpubNm3v6lATB5xB161F44vM1tHj9Ptr0/Zu0aeF71LTP+dT67PGUEB1KqVkFluISN10M6dWMCqHD2YWW4pJruhY7eOgWyk2tuOTmxsWlPPSIDEkrLrUaloc2SzAUqAQoWtGJm70e5sU2WtGpRTM4Dxbj6Hql/kowo1Sryq7n3xCMsU9kxogROB461yMA6XWf2C2XwivFNYTwukOoVTFcq2rAGtY162FO2PBeQkwopWYWlNkdBp+HtmsFsPZdfHQoHcoqsGquwnd2NWyZT8mpBqzdpxgO5lqvxaXlfIrrxXlpBbD2Hfz7/LjeVCvaXIpTRTh8+DAPr+7Zs4fnI+fMmePpUxIEr0bUrZUMgteyfw6oeUAz08BPBI7dh3M5sGjFJW7suInvO5LHAUwrLhGgcANPy8xXaybNeTTuhhGkar0iqGjFJebM8FwVCVBFxbUdN38sqC+2qVi1GhbBobBEqVi1KhXPi4odfB12O9crLXU4FR/XdkzXpecU8blpVa8O0mlZ+WbAU9eMa8Q17UvPM69f2VUdWoN2H8q1Kv5oNSx8BrufqQbWvoOgde/hXPaxrrmK42CbAxl5TipW7sIS6M+1YV19iucZeUXlfRocQDmFami6It8t3Vi11K34G7vyyis5QCJ7fP755z19SoLgs0irLDfg5olhSLfqVrd2v2NSYurtXbHqj7orP+qibrWeu7OX239ZYYGKVaxqfabzh9ypW9UbZiLrfAxzGYfzNav9uI7xu1O38uqRCmrDuvep9lt5dSsCi6OCa8BLzH9WJZ599ln6/PPPKSQkhOchIyN9r4OJIHgLMifphlizaoxr7VaoW5GBqXk5Z3UrMjBeRGG7g6v2Uyo70gpNgKFEZEoIBA5XJaZ/eYUmsiCoT5Ua1lmh6V+h6lWpWF3rlXItVs7wnFWvOAfMabqqXnXxAgz14losT3DQUZlceXWrKlLgamfhU7DKZF3VrVy1x1G29EXXvdWtv+w+RdasMs4yJbGyq44qug2Y3UcqY3X2KTJO0Lp+1RHurFy50mp59fjjj/OQqyAIJw8Jkm5AULvijBZWz0eAmzyCxcWnN2PhDYYyMS/GSkx/P7qwd1OKCg227FyL1c+PxvRsQnERITxPqO0Ipud1TabaMeE8hwY7hkBxEx/Yvi6LWAq13VRi9mxZm1okoS6pw7Lj3CCegeoVQUnb8bxlvVjq2bw2fxbHhB37bJQQRQNS6irVa0Ex5eQX8zlAxTqss6r1qreHehQq1lE9Glu1XmHHNUKNCjUsq17Na4Yd6tWL+zTjgGu3Y6jzoj7NeBhZ23UlItjDzTlabcd+x/ZqQjERIc4+JT8acVpjio8KK/OpqQw+u1MDpXp18d0ZbZJYVGX3KYInBD3tTJGUt5OdnU1jx47l8nNYC3nttdd6+pQEwefxaJD8+eefadiwYZSUlMTB5JNPPnF6Hze9u+++m+rUqUNhYWE0cOBA2rx58yk7PwQZKDjt6lbUPB3Qri61qltDKVhLlXoTRbgHpNTjYAVxDdshbIkMpYHt6rKa1GHa8UCAGZiSRL1bJvIYIatGWbkZSGd1aEBntKnDx1RqUjWXNqRjfRrUrh7P4cGOBwLM4PYNeHkInms7skUs/zirU33O9vi4ZgaKgDGkYwNekoFjogsHzqF3yzp0Zod6FB0ebJ0nErVuTWvRoPb1KC4qhK+Jr80wWJE6sH09Vq3afdGqXg0Owg1rRVh2CHeaJEbTwJS61Cwx2ml7KIWxfdt6Np+WGlwsflC7+qyU1T7Fo0ZkMBd0P615gtldxGEpiQd3qE+9W9Up86mZ1Z7VsT71S6lrltRT26MWLYrDlxte9kJwnRMnTqQtW7ZwNZ3XXnutyqpyBaEq4VF1K0pp/frrr9S5c2caMWIEffzxx07VQh555BF66KGHWLnXqFEjuuuuu2jt2rXcJ+9Y14OdqKIJPP/Nevpu1W7a+N2b9M+i96hp7/Op9TnjuZD5nsO5luJSKzfrxIbR/sx8c1G+f5lyMyqUDuegUbOpxES9UtQ3DQ/mWq/IKpVARqlhoXqF8pLVqqzEVHY9VKmFKYZtAT1ul2rIt0zFyuXy0BLLVHFqO+busMwDax51o2YuIGAQB0ioWHUJuiJT9YqsDSXitB3Xy18OYsNpf0aepVZVtWENy0daAazVsOV8Z6phk+LCWfik1arap7Vjw+hgRr51LQ6tJI4M4XJ/PBQcFGD5NCZcqXPhS+1TblcWEqjmmS2fmmrYkEB6YVwfVtF6M2+++SaLdQICAmjx4sVcwFwQhGPnRGOBR4U7Q4YM4UdF4Ob21FNP0Z133knnnXce29A8tnbt2pxxottBRRQWFvLD7pgTAYvRf16/jwOCXmLBc39+RFsPZvGSAiguAZYs4Hx3HsrhG3lIsHKrLma+Lx1BwZ+DH+8Hk39ElJqVb9pVH0qkOaFBfqx6hboT26tsAUpMtcAfWU+ZHX0eUQO2mNUpUaFBlh0BFUOpuki4v82OIFKYW8Tba3ENho+xHwRCVMvRNVRVPVQHq1iR5Wq7ao9VTDsPZXOgCbLZ8wqL2UcoxKDUrlCtBlBpaQnbsU/Ld4FqjnV7ajarVkOCAp18uvtQDh8zPMjZdwjMdt/BjkvEMhL01oxy8SkCv6vv4AsM4/6y8QAN69KQvJWNGzfSddddx89nzJghAVIQTiFeOye5fft2OnDgAA+xavAtoHv37rRs2TK3n0Pmie30o379+id0fF5uYYpl7CCocJslF8/hBoxhWdd6pe6UmAi8ut2V8/bqp6oba1Ormj8drvajqFv1vu1npAVEFdWlxU50a6ny76iydRVdm+twpT52OXWrf1nbr3K+qKAmbZnd1UfmtRnu7C7qVla96mt29hFeoqKRt5Kfn8/zkHl5edwbctq0aZ4+JUGoVnhtkESABMgc7eC1fq8ipk+fzum0fuzevfuEjo/F8xjqQ6m3MtTQIDIemJ1VrMqulle4qFu5M0b57TE86e+i0OTAjI4ZaO1kV2KaVXTKKzQdVleLElcVq7keEJ91UrFyduzvpHrVwRfKWrvqlQOL2dmknLrVUGpS1+1V5SBld1Kxlhhsd63dWoSOJ+ZQqquPdCZazndm5liRXX1hOQafmtffPCmWvJWpU6fSmjVrKCEhgebOncvDrYIgnDq8NkieKFg7hvFm++NEwI0WCk0EMru6FUFkRPfGPG+GoTq01MKwI7ZHPdSwkADLjgc4u3MDrvWaV1RqbY+bOIQ1cZGhrNDUdgS0Xi1rsxAIQ6XarlSs8dxEudBm1yrWlnVj+TlseCgVayQrX/FZ2PAZ7BN1ZHu0KFO9sr2olGpGhVL/tkkcYLUdGTXmKYd0asCJm74uXEt4SBDXmcW1W9ujFmtQIJ3fvRH7TvsCPyGUgSoVAdduR9Ae3r2ROVRr950fD4PiOHZfI6uFiIlVr0Vldpx3vzZJFB8d5uzTUoO6NUvghs92n8JfqOcKH3kjH330Eb3wwgvWVENiYqKnT0kQqh1eGyT1DeHgwYNOdrw+VTcLLA9oU6+Gk7oV9UJH9WikFJemiAQZSXJCJBfX7tG8NgdA2JHBINiN6dGY+rVNIsO2PQLS2J5NWHWJDFVvHxMWTGN7NeXggExJ2zFPeEHvpjSie7K1xpGz2qAAuqBnExrTs7HqIQm1Kq8L9KORPRrzZzBnCBs+g30O79aIi6sj+Gk7zgHLJ7A9Ss3p5slIyPq1rcvXABWr3h5inp4tatOo0xqz4ldvD590blyLl4w0qxPDWSNfs8OgNvXiaHSPxpTSoKZq2sx2BzVNjGZ7t6YJlk9xnPo1I/i4fVolWj7FcVBPd0yvpqyIxfnp7SG+Gdu7KV8Hsl9tx5wslucgECMgazvmJEf3bGz1vPQmduzYQVdddRU/xxDr4MGDPX1KglAt8ZrarRjqs6tbcVpYGoLhpilTplgiHAw7QennTrhTmerW1378mz77Y6elbm3S+3xqc854XhayLTXLajmF4UVkKwgWuw7lqOFJLh6uli0kxUXQ/vQ8DkRczs1h1jeNhuq10GwSrOqP4gaOguE6A4JdBwKITvATKk4MTwJu0YV2UjyPWmrZsR2yXRwPAh7YuUZrcSlnwyhWnp5rqljZjuIDxMEbzaNVoQMEFKV6rRMXQfuOKAESFygoKeXMEkXgd6ZBXFM2hIvra1w7irYdzLZK0+nlHlgGsvVAlmXXvsPazR1p2VYJO21HsfQ9h3P4muy+w5pONGpGVqlL5BWbNWDTK/ApltzkF6p5Zu1TpYYN5tqt0WHB5C0UFxdT3759ee4dc/AoZB4UZAqRBEGoPurWnJwcXvdlF+usWrWK4uLieC3YTTfdRDNnzqRmzZpZS0AQOE9FU1kEk+/X7LXmAYGqZ0q0cW8635i51yHbsdSglDbvz6SggABWgVrbIytIzXJSYur97DGDDrJErj9qDvOi1iuqxtiVmLAjqLkqNLGvitStbDeHMyOdVKwYGi2h/KwCtgfY7FgSwipWm7oVqldWsaZlO6lVA1AQoKCYrxmZqlarwhewb9ybwUUDkN1qO1Svf+9JZ5vdDt9t2pfBARvH03aiUtp6AD7158Lr+rpwrbvSsp19akqm9h/JJX/YXXyKWq+uvoNP0Gbr140HeN2ot4C1wQiQ+Ac9f/58CZCC4EE8GiT//PNP6tevn/V68uTJ/PPyyy/nbPHWW2+l3NxcGj9+PGVkZFDv3r3pm2++OSU983jOitcaOg/F4SaN7MR1hE7bXZWYWqF5NHWrXXHJQh6V4jvbzWBWkULTbe1WiIhcaqgq5akSyNjFp6p+qnt1qxb8OGGqSV3tLJApKW+HLyH2cVW3KvGSe3UrHYdP+Xfj6ju/imu3qqLvKOpetmTI03z33Xf08MMP8/NXX32VkpOTPX1KglCt8WiQPOOMM5zUia7ghoZ1YXh4onYrhh4PZuY72TGkp4YDnZcUYBhPr+2z26GODWAlpp+zvcSsxWrewLUdQ4wYDtV1SXVAUbVY1RIT7hdptcdS84x4B+ekAxO2YdWr+VmdGep9KjWswesUgT4HFiqVYqg2wEndis/jWkI5wzNVpYbqc4lrCQsuU6HiPOAj7SvLd6VK6Yv5yGAn36nttbrV7iMM49rPT/8OdJbu+jvQ6lYnn7K/1LCyq09xGY0STkzcVdlAtX3ppZfy8wkTJtCoUaM8fUqCUO3xPsWCl4CsB3VD1fyb4bSUAGIbBDIMZaK2qe4iMah9fa7+YtmhxDQM6t+mrlq4XqS3V0pMCF+iwpXqFZ/BA8dAube4SNR6VTY8WMVaN5bn4qDQ1HbMJSbXiqbkBKV61XZsgyo2WvWq7VB9QpiDY+BY2o5zw/wcSr0heOIc1fFVVZq+bepwECuzQ60awOXqAIZqtR0BDz5CMNLb42dggCqtx6pX7Quub0s0uGN9HsqFL5Rd+RTiHBRucPXd6a3rUHioUr1qO84bYiulerX5tMRB7RrEcc9IqHjtPm1aJ4ZLBnoaCKHQFzI1NZVSUlLoiSee8PQpCYLgzepWbwA1TlGLVa+5Q8Br2yCOrh7YkpdQoJ4ogg5ELE0TY2jcgBa8hAJDfmxnhWYkXT2olZrzMu24OSfWCKPxg1rT+d0acYaDoIYgh/qo1wxqxSpTBGJtjw4PoqsHtqZL+zbnuTttxzzhVQNa0JX9Wlj9EXWpusv6NqerB7Rkdae2I0ChmPj4Qa1YDartCGhYtgE7rw8tcfC5atXruIGtqH58BAcc2OESBDAcG9eOLBF2+KRni0S2t6lfg0pKlI8QwLDUAr7r3CSeX2s7ar3Cd6gdi8xY+a6UBVJXD2zFS2UMm+9Q2g7nOaxzA84WtR21Xq8Z1JrVx8jStY8wKoDtL+rTlDNNbcd85v/6tbCybE8ya9Ys+v777yk8PJzbX6FWsSAInsdr1K0ni/+ibp3782ZasHQrbfz2TfrnpzJ1a+t6NWjDnnTVeNkcesWjRVIM/bM/k2/ounYrKzprR9H2tBzOxLh2KxfrVnVM0VRYFSgIsOy42WfkFpotoVTTY2wTFxGsssgiFQQRdHmJQ2gQGX7EnTB4eJOXjqgACvHMkZxC7l6i7TgWiiWkZuZbw5M4FoIyMlU0QNZ2Hhb296MGtSJpu6lW1csoEKCaJ8XQpr0Zll0XKGhVN5Y27MlQbbnMNl54h323O52/GLDq1WGwX7DWE/sBdp9iGQnUsKrxcpkd60WhesXnuRCB6bskqF6zCqyiD9oeHx1C2fkqw9a+Kyot5QL0z43rzZ1LPAVEOn369KHS0lIuXP6///3PY+ciCL5KVlVUt3ozCCZfr9zFgRBzikDNgxm0aschXuKBIAQwfYfMZO2uI05KTNixHyg9A2wqVuDn51BBJ8DPye7v56C9R3J4WNdux7GxNMPPVd0aANVrIQdAVrGadgQm3dbKrm5VdgxP5vJ+tDCJ210VlNCOVOdarLoN1j/7MjlT1XOVCHAYKl238wirfLVaVdtX7ThMoUGBXKdV26FuXbX9EG+L4K19hMC1ZscRHm7FNdt9un53eZ8im91yIJPPzdV3qCVb3ncO2p+eX6EyGF8goG5FFxVPkJ6ezsuZECAvuugiLmIuCIL34PlxJi9F91JEdmUHwQ7Dqa51TC11q4uiE5/XxQjsykoEKzQtdlW36v1o4U15NayrcrNM3WpXjer6qUdXt/pVqG7FudntAOfj6gt8g8DwqhbXWOdkXoP+cuHqi3K+swQ1zrtHYK2odiv2q8vLlfOdWS7Q2Y79qGtzVQzjZVpmAXkCnNPVV19Nu3btoiZNmtDs2bOl/ZUgeBkSJN0AEUtMhKpIYwdZDIJIqeFsZwUpAplL/VF8nm/GLvVHdfF0rW619gOFKuy8zMGlRqutQLmG67j6mXZ7jVYzICAIYJ5Po4Mj9m+v6aoDi33I1LKbAcjVbhhKTaqFTfZz0kOsTr4zfVRsO66+Zv7SUIHv9HIPJ3tJxT7FsKpag1red7qbi90Ov+AlChZ4AgRFlJ5DoQCshzzREoqCIJw8JEi6AVnMiO6NVJPeUsPpZgulJ7ITKCq10hT3/d6tEs1hRTX3BUUmAkb3ZgmW6hV2ZKjYZ4dGNbmvIxSkBcVKeYoA0KpeLJeMw+dhR9suzAGiKg16U2I72PDAkGTdGhFWrVdtxza1okMomWu9OpS9WKlYIQJqkRRriXC0HUOU7RvWNEU1+hpUL8ruzVXJOPu1Yei1V8tEzoi1XalY/dlHQClwYS8xfZfEXwLwWvsOnN6mjvKddVz41KCeLRNN1avpu0LlU6hYMWRr9ymCakrDOB5eVnblB1axJsbwPCyuR/sUfkGAhKL3VLN69WprXTBEO126dDnl5yAIwr8jQfIoQNXZuUktq+sGbtpYOnHT0BTq3TKRSktVcMC8I5Za3DysHXe6N2zBAYFt8rB2dF7XZM74+OZdpJZn3Dy0nbXMhANlUQmXVbvpnHZ06enNuXoP7LixQ6F549B2NG5gS57n4yUORaVc3ee6IW3o2rPa8FyitocFBdC4ga3pxnNSOCtWSyJKKTgggC7v14ImD23Hx8IxYddLXm4a2k4FXHOpBAZhUcT85nPaqWUmJcqODAzzeDcPTaEWdWLYB7DDV6i1Ch+1a1iT11YiIOILRpcmCXTT0LbUtRmWmajlJ7rI+E3npHAARdLL9uJSapYYw/vn4uqG6buSUhYRwa6+xKgi6jhfqHLhU6Vi9bd8iuU0sF/ZvwWLebRPofqddFYba571VIECGWh/hb6nQ4cOpRtvvPGUHl8QhGNH1K1H4YNl22juz//Qhm/eoM0/vU+New2ntkOv4eLmf20/xMODanE813LhAArxDgIA1KSYr8PNHUshUHaN66nynCbmx4ia1ommXWk5qp4qz78pO7KbQ1n5nD3xEC7mKA3iIICAkJVfTGZ1OEJt8pqRIRyYIUKx22PCgjjbQkEEzPvprBjZa62oUNp9OFcVKDDbaSGA4NhQk8KOYVkMjaJIAtpJQZXKQiaoXrl4gj8viVm94zBPimLOUQ8vo2PJyu2HVMEB/zIfdW6s7E6+8/OjjsnxvB8MgerarRjoRWaI42JJCAK89im+lGw5kKUKFPBcp/IdasbuO5JH+eZyF+1TfCnJyC1Sma6LT5/5Xy9LSHQqgDgHFaXq1q3LZRjj4+NP2bEFobqSdYLqVgmSbsDN/qrnf6Ks/CL65/s59PeP86n56SOo5ZCrVcssU62q0WvvcAPWSkyg1yG6KjEx1Ikg6GrXGZa/ix0Zmlp476zQRLDJKVQtuSJDylSsCA5QsUKMg+UNAa52w+DMU68RxGtkfNgf6qfa+zjCjuPb1a2w4zw5uEKtaqpYAc6Tq+rYarHa7QiC4XbfYejTnOutyHewY/uySjxqTaarXRdHOB6f4hjItvun1KVTAXpCoqoOKgAtXLiQC5kLguC9QVKGW905NK+Il1C4KjpV5wolrnGym82QXdWtutxaOXUrFJpmVuRsVxmoO3VrRQpNJXstr2LV+7fXUNWiFlax2hbRa7urWtWdupXtULeamabTH5XpC9dF+nr9o109q6+Nm0S7KoNN36lDOfuIRT4VKYZdSttZ25vl8lx9ipeqQ8vJZ/PmzTRx4kSriLkESEHwfiRIugHCGQhA1DBhGchK+KbuUoOBVammutWOGn6kCpWYusC2qxKzInUr3+DNJR92u6VoxTIKF7Wq2TDDmlPVdgQK7KeknIrVj4+thUrazn8orIZ19gXewzXbt9fnFOBG3cp2Fx/pmrP289e+UH4o7yN7xmz/HahlHRUog03lrqtP8RJzsCcbzD9iPSQ63yA43nnnnSf9mIIg/HckSLoBWc+wrg35JmpXtyLsQK2KezQLTEqVchQ3eIh8sI4Sw5OYf8RQIW7EUIxq5Sb3gyxWJeswr6ZEOGV2VrHWjjZFOEoUpIcd0ZcSAh6ITmDnYcdiqFjD+IHn2q7EPiH8GXxe7VupRiH2USKcsmPiWtBnEXOPODfLblb3adcwjv1QYNpxjbimLo2VsIkVuLZhzW7NEsxScsqOnwhU8J2fzXewIzZCrQrxkBbzQIiD/XZqFO/kOxwf54G5UAz9QoRj+bTEwRV6MMSrfFR2Heh7yapXm+/wHuYkUUP3ZIPGyStXrqSaNWvSvHnzKCDg1IqFBEE4MSRIHgXUVe3iom5FLdfbR3SgPq3qcNBEKTjcuFvUjaU7RnaiwVC38ryfKkaAYHT7yE40vHsjDrCw44YPIQnsF/Zpytkb2wuVunX6+R3of/1bcqDG/CHsaMR86/D2NOHM1hQapOx4RIUGspIU82rooajtCL7XDm5DU89tr9SthcoO8QvquU4f0ZFqRYea9mJeoH9J3+Z0x8iOXNoN5wg78jXUdMW54lryTTuu8awO9en2kR24HB98AF8g20TxcfgIQiYEKNjxE4Fz+ogO1L1Zbct3WIaR0iCOtz+jbRL7GvvHMg0UH799ZEdWGSPgap9CXHT7iI40uodaoqN9h4CH67q0bzPOGrUd9XBvG9GB689CnMQVhwpKKDosiFWvJ1u089lnn9HTTz/Nz+fMmcOCHUEQqgYi3DkKX6zYSa/+8Det//oN2rL4fWrUazi1G3YNr91btukglZptsLAUBNkT1tst35zGiks9b4bAiEC7eudhU8WqhxX9ODtD02IoLrk8HOYQyY+a1Ymm/Rn5lJVXaJWNQ5BoEB9B2QUl3EC4TIhDlBgbxnVRUzNQek1NUeIQUL1CCLP7UI5VtQbnjKHkOjUi+Ng808lziw4W+DRJjKZ1u9J5khOZH7ZHEIHKdMXWQ2znoVeznRau7bd/UtXQq+kLZNM9m9emXzcdUEOvNh9hXeXSjQep1FHK28GO48D+2z8HrXZX2kdYn4njIvOz26EwXr87nfKLlO/UMKofta4XSzvSclTgN52Bd6B6PZRdyDVx7b5Dhvn4FT1O2jKQ3bt3U4cOHejIkSO8LvLxxx8/KccRBOHoiLq1kh2DIDDuxcV0JLuQNv9Qpm5tcdbVag6Na66WKTH1gn/YUdNVC0T0UGdABQpNDCm62hEk8itQYmqFJl5juNTfrnotRMan9mP1k4Qq1VK3ltVoZbVqQQkXAGAVq6u61TA4C3VVsZaaKlZdo1VtX8zBGNkp6rdqEKCwH+zbVd2KuVxdW1XDhQDMQup2X/DQbgW+c+9TNfRdoe9g93NVt2I41sHZdp/WqvhBZVJSUkL9+/enJUuWcLGAX3/9lYKDgyv9OIIg/Duibq1kMvOKKDO3yKlpMMDSCFU6rrzqFXZVCq5MQWk1aHZRYrLdXMPnqtCsSN3K6yhtZeU0VrZkimg0WuCj1K3OqlReU+iiMrXUreYSDSe7GYxd7X5+/k6Nm+3nxM2jK1C38vbl6tv6OYluXH3nqm7F76BinyqVbIXqVrN7iGtNV7zcdSiHTgZoFo4AGRUVxWXnJEAKQtVD5iTdgGosyIJcFZ0YftQd7p3suMkjXpWrS6q2d4WDApVXt+r9uFO3uio3rfNwOScVKJS61VX1qgN5ue3JVL26bO9O3YoI7KqG5XMyrwFDqRX5wlUZjM1wXBfXmapUNSzqtL05dGs/P8t3/uV9hIxRK3pdlcF4WSsmlCqbRYsW0cyZM/n5yy+/zAXMBUGoekiQdAOGG4d0rM83UX2zV9mLqqyDm7eqwaoUlHgPikvcvO12BJDmdWOtmq6wY6gVw4vJtaNYOYqhQG3HkGHduEhL9Qo7tsXQI4Q2GGqFQhPDsrr2ao2IEIoND7GGLXlRfZFqKgwhEFSvsOthRxQFgDgHQ404prYjI0yuFcnbajv2j4yueZ0YvhZ1TWUq1jYNaliL8tX2akgYil4EYaVGVWpVhOwOyfHsUwyLsr24lIMV5jwR9lx9iv6TWvWq7bg+1GLFeeE67b5rEA+fBpgNnZUd14l5W122z/IdmlxHhvB8aGWSlpZGF198MQfkq666ipd+CIJQNZEgeRQu6N2UBSI6s0JGgpv53aM787IB3GxRIg43diw9uG9MZ67dipu7tqPO6L1junL9U+wF9lxTiXnfmC50Ye9mnOFoO27ad47qxOpWLN5Hyy7M8aHE3PSRHem6s9oohaZpxxzb5HPb0ZRz2/FcKGx4D9tcPySFVaBQcerektgn1K13je7Mx8IxYcc5XNynOd07tiufG+w4J5wzlLn3ju3MqlJcE+wIgGd2qE/3julMTROjLTt8AnUrtseXBgQ12FHztUuTeLZ3a5rAAY3txaVctg/7OaNNEgdi7bvGidF0z9jONKRTfT6ettetGcH7GXlaYw6s2nf4EnHPmM6sbkUAzzbtWDZz56jONH5QK6UYNn2HZtWYj6zMhssOh4Muv/xy2r9/P7Vq1YqeeeaZStu3IAinHlG3HoUf1+yh575ZT+u+et1St7YfNoEGta9LP6zZqwoFmEN4uCkPSKlLizfs5xu/tiOrQtBYviVVZV82e/dmtWjDngye/2Q756lK9Yq6qkeyC/i14ackmujckZlfRAfS89QwJE7SIGpYK4rHVXemZqu2WaZyE4vko8OCuW4s2w11DATHevGRtHbnEeuYOCeoXpG5/b5ZqVXVkKxSvXZpmkC//L3fmivVAh8shVm4bq+THdlx/7bKR2qIVe0fataB7erSjxX4blD7erRw7T4qKoGP/C07AufSTQc5OFq+g0q2RSLXz80pKLK2x/E7N4mnLfuzWMWqe2Ti+hCIUcM2LTNf+Yf/Q5zlP3xx93LzpycK1KtTp06l0NBQWr58OaWkpFTKfgVB+G+IurWSHYOsccJLS/jGusVF3YrEEnNfdhWrHibEDTs8JMBZoVlSynN3FSk0XZWYemgVL7G9FunYhzJxXD0nh/NU7aaw/wBLpIOghSwKYHhVBwEEsTxWvRIHOdcarQiuCHJ2FatuBYZtdY1W+/ZYmqFVrNquy+TZa7QioBmGqiYUHlzmI62edetT/2NXDMPOCmAXxTCGeyvyKTLa287vSD0qoaAAgmKvXr1Y1friiy/SNddc85/3KQhC5SDq1koGHSOwHhFdO+wgUOjyaq5KTBQ+d1VicjeQ0vJKTAQi3dHCtaYrsi8lfnGtP2qKYmxKILVGUKtby85VC39ca7QqgQyUnmo9okaLXXBsnJuT3Qwodl9o8Y9aL+lsh2+4S4jNrnwUYDVedlWl6szS1UdaFHRs6la1fTl1K9eGdZiiKGef4uX21Cz6r+BLGOYeESBHjx5N48eP/8/7FATB88icpBuwCB+NknFztaPVp65CT0vd6qrENJcwuFKmVnVWaPKSkIpUrLYi6Xa79dxlP/xcq1sNd+pWqlDdaq/1av2hmEXInTAVtK52S91arkZrxcpgzsxdzt/Jd8fhU936q5xPK1K3ml9SUL7vv4B9Iihu376dkpOTWc1qD9KCIFRdJEi6AcOKA1Pq8g1W3+x1QWyUoAO6NiuG+fAT4h3cG3XdUQzlIUNCOTdkVxg+hF2pR6FijeBsBkpMtptqz4SYMM6ilF2pUqHERGk6LNrPs9m1ihUPrejUqlQMUSIAaDs+g+fYN0QuWimKY3PrqQA/nsfU563sJTxU3DA+0lKF6lqtCJwoHYfsTftADRX78fwpgiXs+IljIWS3rFuDfajVq/pzqBmL9+0+xX4b1462FMPad3g0qBXFIiTLbg6dJtYI54zS7jvsE9WHMITsZDcbL6OB9n/h1Vdfpffff58CAwN5PWRsrPr7EASh6iNB8ihc2rc5q1n1kB9urhC2zLygK9chxY0cLbVw40XZs/sv7Eb92iYp1WteEc+1oUYrth/auaFSveapxr9YmjHjgq40podaP8f2gmKKDQ+me8Z0oSv7t+DgkGVTsaJ+KuqxYngS9uyCYg7mU85rR5OHteMgABseCL5Qwt42oiN/FjZ8BvuEcvae0Z1ZqINj4thgbO+mfE41o0IsO855aNeGdP8FXTiAsuo1r4iHlnGtsCcnQPWqfAGfoDzf/Rd25QLusEOYhEDVLjmOZl7YlTo1rqlUr6bvmifF0P0XduGlGKx6Ne1QBmP7Qe3q2XynlMEzxnah87o1VKpX03cQJM24AIrhpvxlJSvPVLGGBdHdY7rQuIEtLZ/CH5irveXc9uyHE2X9+vV0ww038PMHH3yQunfvfsL7EgTB+zh17dirICu2ptHfe9KtYgDIkKAU/eC37aysxPCdv1ltBvVCP1i2jZb9c5C31/Nx6FW4YNk2+mXjAR761PZDWflsx34wXKftmXmF9P7SLbTtYDYXLuAqN4Yq6fber1spPbeQM6kgc/4RGR2Oq9ceantxSSl9+Nt2Xv6Az7KdF/g76LM/d9DaXVF8LHvT5W//2s3KWczFajvO+ad1+/iYBzLyzHlMDMwS16+NDA2mXWm5nCnzPC0Rrdx2iI+N2rB2+4bd6fTBb9tozc50tum51a0HsujDZdvpz61phN37m77YcyiXfQ3fAe2jgxl5fM2/bU5l32j7kZwCeu/XbVzTFdkr7Ph6k51XRO/9uoX2Hsl18im+xGD/bRvWdKpWdKzk5eXR2LFjqaCggAYPHkxTpkw50T81QRC8FFkC4gZkKNe98gvfWMvUredTiyHjrDkyu7ISN1w932W383CgKehxVbci60KggDJUC0r04nfe3qZiVapXpW7F9vqmrodWXVWsyLyU6pV4iFaXlEPwQOEAnCpaY+karax6PYq6FVeGY7qqWPX8qVax6pqu+jq1KtXa3oxFdjuGjJVTnVWveihVKXptimEuCFBaoeoVmShcExZyDD41h6axhhKF2o8XzEO+8sorlJiYSKtXr6aEhITj3ocgCKcGUbdWMlhnh4zFWd3qx8Gm0Cxk7qrEhN21diuCEG7GFalbUaQAAiD/cvVKTXWrLbvhDhjm/Kg96+GarixAcVaxchNjrW6113Q1BTg4hr0Wq/+/qFtxDSG2ThlaCIOgVF7d6s9BCdeurxk/MUwMO+YS7fZg065FN3Yf4cuB7kji5DuoWB0V+LTUwdd8LD7V1/8P1pEeJ++99x4HSBx/7ty5EiAFwUeROUk36A4ZrnVG1aJ51Gh13h43ZrXA31W5eXR1qytaEFpOxap/YcehbtXHtZ+RLpCu9u96dBX4XNWn6h1VeL2ia3PdXh/bXjNWX1tF22u1anllsFb/uvrIvDYXB5b52vkNvcymImUwXmLO8njYtm2btcTj9ttvpwEDBhzX5wVBqDpIkDxKkET7JN09AuA5njZOiObAg2E83GjVsJ3BClAEEwz7cVZmqkGTakRwZsd1Sk07MrBa0WGcXWFYUa9dxGdRixXzaRhG1cdnJWZoEA+R2u0YqsTQIs5Xq2TxHp4j88NnXO04JgRCUMxyhmUoFSsyzvioUKveqq69iuQLoh1d/xXXpgshoB8jrp2zZUOpUhGk4COtRtV2Q/vOsPmuBMchaoSqQeaQaZlPHVSvZiT7zu5TVrHGhiufwhemvaDYQTWjQk11a5lP84tLKCYs2FIMa9+xAjgkkHq3PPY2WUVFRbweEkM3KBxw7733Vsa/Q0EQvBQJkkcBClMoL3WBcwxFoikxFKAdGtXkAJJhqlhRT3Tmhd2oV4vafBOHXdcTvf+irjSwXT2+kUPpmVNQQjHhwazEPK9bI75pw56dX8IFzO8Z24Uu6tOUj4kydNn5RTxHeNv5Hejqga3MWq/KjuHgm4em0I1np/BwK2x4DwHsmjNb07TzO7ACFnbsC1zStznXbmXVa34JHxuBasRpjei+sZ25WDrOEXacM2q0QmUKRS6uCdeGa0RpONixlCXPtMMnnRrFs1oVyzcw/8n2olJWu864sAuXiMNr9l1RCRdVn3lRN+rapBbXeNU+RSDE/k9vVcfJp1CxQlU7pGMD/p1on6JGLXw6qkcjvh7t0/DgILpzdEe6vF8LzjS1TxE0bx7WjmpEHvs6yTvuuIP++OMPqlGjBr3zzju87EMQBN9F/oUfhY17M2hHarY1bInhOtRHXbRuL23am6GGLU3l5v4juVzDdM2uI7w9z7tBxZpdQAvX7GXlJl5rO5SlP67dy3VS+RdhqlKhRP1u1W76Z1+mVUUH2xcWl9B3q/fw/nhRvmlHRvbtqj2WolXNufmxihO1Z+OQGWKtIyrzmEOMi9fvZ0FSXmGxOb+pxjR/3XSQgxHOTdvx3z+3pHENWKhesWZSDdcSrdl5mH5ct4/2p+c62f/em04L1+2jXWnZSsVqKPu2g1n007r9tOVAluodadp3H86hhWv30npWEpf5NDUzj326asdhJ5+m5xTSD2v3se/sPkXg+27VHlq98zBfj/ZpflEx+2j3oRz+QlLm01L2EUQ79jlMd3z99df02GOP8fM33niDGjRo8B//+QmC4O2IutUNCCY3vv4rL+1wVbfqeTtXhaYqp12+/ijPzbEqNcC6GXOrKFO5GRYUYAlKeIE/C4CgSi1TsXIrqGJM0qlCB1qkg2FJtVAfatUyFavVnspPCVq0ilUXCADYVqtY1VCsUrEiuEARy3YMxZoqWa3E1f5BtqeFPVp9qlWsSjjkxu4wLKUv7Bj21HOYrnY6Hp+aw8QV+hQTnOYwuvYpDx87HHTv2C7cwuto7Nu3j9q3b0+HDh2i66+/Xrp7CEIVQ9StJ6F2657DuTx/V4YfrzfEzdteuxU/0ZoKc2LIgVzVrcjOtGBGg+CEG7er4lKpXh3lVKxaRITt7SpW2HmuFOsCbXZV61XNvdntOkhhHtGuSmXVq2GqWINcVK8I0lyj1Vndqudfce12X6jqQqqyj6u6VVX2cbYHmnZ/V58GKZUsTsDu0xB3PjXVsGhXVc6nZgUhZ2Wweo71m0ejtLSULrnkEg6QHTp0oFmzZh11e0EQfAeZk3RDcJA/L/OoUFlpKiYrth+bulVv74olVnUz/HdctVvdoIqwl78GPcBaTt1q1pMtd23mG+U2N6+tQnVr+VKs1vblfIrkT7cEOyafUsXqVtuSFddrwEvMzR4NVNJZtGgRRURE8NIPtMESBKF6IEHSDeih2KN5baUKNW+sat0hUb2aEXxz1cpNVS/V4BJ0uBlr5SbmDpWKNZRv0FqhiX0iQ4JgBNmMtvOQZ3Epz//BjiFZ3clD1WJVra0wrGi3IytFdsXKTXPNJLbBthi+tNtZxRrgr2q9Qm3rKNseCSfqwyIjVnVqtYqVKD46jEU12o5rRChi1aupRNVqVQQp+AjbWnb+SVQ/PoKPqdWwKsMjFj4B7VP4DtvUjgkz12PafFeiVKx21Sv2iWILEESx7+z2olL+fSKjrsh3PY9Su3XJkiWWgnX27NnUvHnzyv9XKAiC1yJB8ihcNaAVNUyIsmq3qiUJEXTfWLMuabFSgGKuDUXJMbfVqXE8B0CtuETQuXdMFy6ijZu+shcrFeuYLjS4Q33LjpqimEubPqIDjTytMQ9/sj1P1WKdcl57uqxvcw42yq5UrNee1YYmDm6t6pLmFfF74Mp+LbimKz6LfWgV6+gejWm6qXrFMbWK9exODenuMZ0pMiSQzxF2nBuWwsyA6jUixLLjGrs0SaD7xnahWjFK9Qo7Am+rerHsIwS+PG0vKqFGCVFsh0IYrzN1fdsa4Wxv2yCOg5j2HQLh/Wad3DKfFnMgvG9MF27IrH2HWqwIhHeN6kTndG5gKYlxfRhuve389jS2VxNL9Qp/YKTg+iFteSlORRw+fJguuugiHr697LLL6NJLLz0Z/wYFQfBiRN16FPYczqGD6bnW4B2GItHZfvXOQ/weayTNKjGoG7pmxxFWcGLITy/YR9BavfMIbT6QyfvQdgSHVdsOWfNhPB9nzvH9teMIbdh9xLbwHwG6lFZuPcRNoNmumy6XOmjltjRVjcdsUAyQQaGGKgIAPsvlSg01Nrpm5xFVws1sXKwHIFHzFMW+sSxDz0WCzfuz6K/th7kGqrYrtWom7ys9u9Cy41sXVKRrdh6iQ5n55jAn7H60PyOPFbH7juQq33E5WT9W7K7deZjVsH42H6HqEZStrj7NyS+iv3Ye4jq6dp8i8K7aebicT5GFwhfbUrP5+pWdqKTEQSu3p1HvVonlhrfh4yuvvJL27NnD2ePzzz9fWf/mBEGoQoi61Q24SU596zfavD+Dtvzwlk3dOp4X3UNc46puhQ4Go41acWlXbiL7Q+amg5ha7K6OBbsWlHCrKFOJCYWpbqSMoMZDl+b2WryD4Vy1gF8JWnSxb61u5aUTNhWrbh2FowUE+PO+gG5/hQ9w7VNTxapVr6qJs7o21YtSqV6xD2wT5qJi5QbIUKva7PCRbrDs7DsUMlCCJVe7UhKX96mmnE/Nic/QoDIVq1a34lWIk+8wDE30wEXduLuLnWeeeYZuvPFGCgkJod9++40FO4IgVF1E3VrJYEhuR2qWkzJUqVv91FIGv/Lq1rxCFXz8nRSaAValHbviEnbda9GuuGSFpqXE9C9f69WlRiuCjqVuDSyvbsVngiuwq1qszqpXqGdxTjqgApwzAgmuAXZ9zZy9ITAVlpRTsQaYPgp2VbH6Ew+jutZuhY+xvX85nwaoZSw2FSvsCIyFFfgUdnxpcFWx2tWtrspgBN3VOw47/e5XrlxJt9xyCz9//PHHJUAKQjVG5iTdgBtoRfVNsWqvYoWmft/VXvEbXE+0guPqzdyqW4+iXD1mdav5X9ct1OBk+WvWZVLLq1utOgSuJ2L6qLyStCIVq6Vudd1N2ckek7pV1WjVJRBc9mMOEVf0GfuXhezsbG5/hfJzw4cPp2uvvbb8hwRBqDZIkHRDRGgQdW5cy1JzAqWuJG76CxOyE4BtsI4QKlbch7Vy02EqMVGLFRmPVm7iJo9MKCosmDMerdxklSkrMQOdlJt6iFEPs0LEYrioWLGGUSs3tSoVdnwGn7XbsW+oXgtsdm7b5aeKfevMV6tVEVwg2sG1aDWsHr6sFRVqZb5ADQn7sY8geNJ2VSOWqHaNcKvWqvYdnkL4hGim7TgOskKId/CFwe5T2GPCQ5xUr7rOLFS7rr7DMDKGj+E7u4+4jm2gP/VoodStsE2cOJG2bNnC1XRee+01t19WBEGoHkiQPArjBrYyC3urGz1u1LiZ3zWqMys1MWemFZeoJwplZat6qi4pVJVQXKLAOOqksuq1xLTnF/N84+0jOtLprVVdUtjxQAY75dz2NKQTFJqGZceN/7qz2rAyFQFB28H/BrTkOrNA2xGQoObEZ/BZbcfw6zldGtKUc9vxsbQd53BG2ySaPqKjWetV2XHOXZsm8DVEhgbyNcGOa2xTP47taOysVa+Yd2xcO4rtrHo17RhOTYqLoHtGdWKFsPYd3kdN2LtHd6KmdaB61erWYi7CfveoTtSuYZyTTxHg7xzVkbo3S7B8h/PFcPC04R1pQLu6/LvS1xYYEMAq33O7JHNQ1nYEWf07BnPmzKF58+ZRQEAA12WNi4s7+f8CBUHwakTdehSgrkQ9UD16h2E83NT3pefxezw0SZgvQ9NlZT+cnW99HnbUXN13OJcOZeVb435ITlBnFfVTUffUbkfGpe16uBKHx3whapwezMh3KkSA57vTcnhbux2vdx/K5eCi23Lp2q37DudxAMKxrKICBtGB9DzaeziX1bB2e2pmPu09nKPWTHKrK3WMwzkFvD3mJbUdx4FvoGCFr7QIB76CL/ceyWPFr/Yd3sd2sKMmqwbHhxAH9rSsAiefQpW7+3Au13atyKc4tn0/uE5UT9pzJNcaFeBiB4bBKmWwceNGuu666/j5jBkzuMOHIAiCqFuPwvS5v3PRbdfarbosWqhNcYkMCkIVDLvquqF6SA+ZHDI4rWJ1qlfKqld/VYDcHDLEfRy3csyV6abKGOrUqlcMrWqRDqtezagFEY8uHVesVa+mkEaXjsNwJj6j+isqsYvd7qpi1apXnAeeW+pWU/XKZfdYreqseg0JhrimzK7VqjgeihXY1aqwV+xTUyVbqnxn9ykLihwGl67TvtOq13K+g2JYfUOgYFt9W13z9r5R7emS8wfTmjVraODAgfTtt99yAXlBEHwHUbdWMhjy+2d/RrnarVj+gaE6vW7Prm7FECGrW/2d64/iZq86ejgrNHnuz6Zi1XYEN2Q/Womp949gyLVYbUITS7lZ6nBS4mIbXfHGXluVgx3PQTqsAGm3I3AgUGm7LlTOgSyobD/6GnHN9v3DJ6qoQXEF6lY/ykRhBH9n36EeLvvUVRkcGEC5BSi6XqZi1YphBGKcr5PvEJhZMeziO9TJNasF2X0E32EfU6dO5QCZkJBAb7/9tgRIQRAsZLjVDepmXb7Wpzv0Vu5UrxVtr277LgrQfzuQ37HZj/W8y+/ETGNt+/M7hmtxfe1O3ap+VlBbtYJrcHsAHqZ1J4c92o7Ks2/tr/Tnx/P4+VtvvUWJie5L1AmCUP2QMSU3QOUJwQiyt7KbO9YdEsVHhbBNKzd53SFUrJEhav6QK3OTNUyKWqwIulq5qYdVlYpVda6w7FCxBgfwWkIn1SvUqgH+bC90VbEG+PPDrnpFpohsCtmSXdGJfSKjwzG06hXgHJA0h4codau2q9ZTxNeAa7HbAVSvOJaub8s+MYhVqcWlSo3KPuI1nkoNq7uTaDuKCMRZPjXVrabvIArSw8GWT4sdrD5GlqtbYGkfoRgB281zLVMGK3Wr3adHDu6j1R89xZ+fNm0aDR48uFL/cQmCUPWpEkESJcGSk5O5+0L37t1p+fLlp+S4UD5iWYe+EeMnVKzTzu/Ixcwh1oEIBUOOCKrThnegRrWjuKgA7FBcYu7t1vPaU+v6NfhmzXZziPLmYe2oc5NaSvVqbo8b/KSz2rLqFTd62PAAVw1oSWdD9eowLDtCzQW9m9CYnqhLWmZHEBrapQH9r39L/qy2Y59ntK1rqV5ZxYparCWlXCP15mEpPFTLKlauuVrKNVVvOa89X4vd3iQxhqad34EVvDmmHT6pGxfBdtStzdE+KixmX956fgdWCEPtqu2x4SF02/COXPxc+w77g5oW+2lWJ8bZd0EBNPXc9vwlBl8StO8QBG8a2o5Oa57A18PXXFDMQ7UTBrcyVa/KR5k5+fTn/EeoOD+H/6buv//+U/I3JQhC1cLrgyRaE02ePJnuueceroSCxrf4xp+amnrSj63nAO1w5sPNem12Q9mLS1XpODuGuYbSyY7tea0h5gyVUMd6yyDOoDCH5jR6aPZ6ZDGOy/ZQeyKbszCVqWjSXG57zhrR2Ln8cRFAOQMzs0HLDziu2fvSbkfWV1KBHZkh93W0HYBrpZpzpKyqtX0A2+E8lU8NZ5+WlPddWecV1VnEfhEV/Q5gh3/UNasP/PPjXErf9TeFR0bR/PnzKSgoyPkzgiAIVaF2K77ld+3alZ577jl+DVFG/fr1uTv8bbfddtIUTeCe9/7gwt6bv59DGxfOp6a9z6MWg//HQ5IoUO6quNTl0iBwgTpSD58iM8PNHkOfqHWq7VwKzhTiaCWmrreKeAHhS2BgmUJT/6bQCspeu1UPaeJcdAk6Di7m0g/sT5eUU4GwTMWqRTocCHkoVgl+tB2CIBwbqlB8Vtvxe+ChYS5KoBbra9UrXoeHmj4y1apcWAHDoSGBbMf5qJJ3BhWaQ8zYX8ix+pRrwBrlfKpqvTr7lH1nfhHA59K3r6XFr9zOfup28R303hPTKDkh6vj+MAVBqFKcaCzwauEOSoOtWLGCpk+fbtlwo4RMf9myZRV+prCwkB92x5wIWflFtG5XOt9UddGVLb98yg/BN2jSYygltu1Nf2xJlSApCELVG249dOgQlZaWUu3atZ3seH3gwIEKP/PQQw/xtwX9QNZ5Qtjy6/jkNuQfIMNxvkRc/RbU4bxr+LnLiLogCELVyCRPBGSdmMO0Z5InEijRV7Fl3Rhau+sI1WndnUY88DGVlhSpIUFzaNB1uDVYD6vaigbwsCrWIJbCbisaYB9WdR0aNKf4MGSoO1VYilas1TSLBmgVq66oo4dbMezJc3xcX1V11tBrHDGvWWwWDQB6GJaHW2HH8ezDrQ4HD4cGBgbwNvYiA4UoJmB23rAXGcBwa2hwIKtN7a21tHJX/7S3vsL5YQ7VdbgVPiipwKe6mEBFQ9U8rOriUx0IWQmM90LCqajUYEVvlya1Ku8PUBAEn8KrM8n4+Hiuo3nw4EEnO167W8+G/n8Yb7Y/TpRxA1tzcXLcfIuMACoNCKOaNeNo2uieVLd2PDkCw6jEP5TtsbGxdOuYnpRcrzYZNntkdAxNHXkatUhOIiMw3LKHR0bTjcO7U8fm9YmCyuwh4VF0zdDO1CulEfm52C8d1IEGdWlOASERlj0oNJLO792ahvduzc9hw3vYZnDXFnTxwPYUHF5mxz57t29C487uzPvUdpxDp5YN6IbzuvG5aTvOuXWTejRl5GkUFR1js4dR4/qJdOvonnzt2g6f1KuTwL6oGVejzB4QRrXja7LvEmvV5Nf6GuLiatCto3tRvTq1nHyKkYBbRvWgJvUTnXwaERVNU0acRq2b1HXyXVhkNE06txt1adWwzB6ofHfVWZ2ob4cm5B+i7Kj6A87v3oiaJJ7434ggCL5NlRDudOvWjZ599ll+DcEIOjRMmjTppAt3AOqJLlq3l2uC1qkRQf1TkiguMpTnLH9at492pmVTregw6tc2iWrHhlNuYTEtXr+fth3M4jWEsGNJBLKqJRv206Z9Gbxc5Iw2SdSwVhRnfEs3HaR1u46wqKVPqzq85AFZ2/LNqbRqx2HODnu2TOTGwMjIVmxNoz+3pnE2hSLf7RvW5HNFX8TfN6dyFtq5cTwvL0G+uGFPOi3bdJAzvg6NavJnkNFu3p9JS/7ez+XlUKy8V8tEzrR2pGbT4vX7ePlEi6RY6tO6DmfNqNO6aP0+rs2KwIJlKhEhQXQwI48WrttHh7MLqGGtSDqjTV2+RrxeuG4v14StWzOS+rdNYp/g84vW7eO6qfBZ/7Z1ucg5ln3Ajj6eWGfZP6UuJcaG8/n9vGE/n29MeDD7rn58JPvul78P8PWh+wfOp3HtaPYdfLp212FeH9m7VSJfB7Li5ZvTaNWOQ5yVovtH2/o1pNOHIFQDsk4wFnh9kMQSkMsvv5xeeuklDpZPPfUUvf/++1yQ2nWu8mQESUEQBKHq45PqVoAGuGlpaXT33XezWKdDhw70zTffHFOAFARBEIT/gtdnkv8VySQFQRCErBPMJL1auCMIgiAInkSCpCAIgiC4QYKkIAiCILhBgqQgCIIguEGCpCAIgiC4QYKkIAiCILhBgqQgCIIguEGCpCAIgiC4QYKkIAiCIFTVsnT/FV1Q6ESbLwuCIAhVHx0DjrfInM8HyezsbP55ws2XBUEQBJ+KCShPd6z4fO1WtNbat28fRUVF/aeWSLp58+7du6tFN5Hqdr1Artn3f8/yO/b937G73zNCHQJkUlIS+fsf+0yjz2eScEa9evUqbX//tZFzVaO6XS+Qa/Z95HdcPX/PMceRQWpEuCMIgiAIbpAgKQiCIAhukCB5jISEhNA999zDP6sD1e16gVyz7yO/4+pBSCXev3xeuCMIgiAIJ4pkkoIgCILgBgmSgiAIguAGCZKCIAiC4AYJkoIgCILgBgmSx8Dzzz9PycnJFBoaSt27d6fly5eTr/Dzzz/TsGHDuAoFKhJ98sknTu9D13X33XdTnTp1KCwsjAYOHEibN2+mqspDDz1EXbt25QpMCQkJNHz4cNq0aZPTNgUFBXTddddRzZo1KTIykkaOHEkHDx6kqsrs2bOpXbt21sLqHj160Ndff+2z1+vKww8/zH/bN910k09f87333svXaX+0bNnSp6957969dMkll/A14f6UkpJCf/75Z6XevyRI/gvvvfceTZ48meXEK1eupPbt29PgwYMpNTWVfIHc3Fy+JnwRqIhZs2bRM888Qy+++CL9/vvvFBERwdePf3BVkcWLF/ON4rfffqPvv/+eiouL6cwzz2Q/aG6++Wb6/PPPacGCBbw9yhqOGDGCqiqoOIVAsWLFCr6B9O/fn8477zxav369T16vnT/++INeeukl/pJgx1evuU2bNrR//37r8csvv/jsNaenp1OvXr0oKCiIv/Rt2LCBHn/8capRo0bl3r+wBERwT7du3YzrrrvOel1aWmokJSUZDz30kM+5DX8OH3/8sfXa4XAYiYmJxqOPPmrZMjIyjJCQEOPdd981fIHU1FS+7sWLF1vXFxQUZCxYsMDa5u+//+Ztli1bZvgKNWrUMF599VWfvt7s7GyjWbNmxvfff2/07dvXuPHGG9nuq9d8zz33GO3bt6/wPV+85mnTphm9e/d2+35l3b8kkzwKRUVF/O0bKbq9FixeL1u2jHyd7du304EDB5yuH7UPMeTsK9efmZnJP+Pi4vgnft/ILu3XjCGrBg0a+MQ1l5aW0vz58zlzxrCrL18vRgzOOeccp2sDvnzNGErE1Enjxo3p4osvpl27dvnsNX/22WfUpUsXGj16NE+ddOzYkV555ZVKv39JkDwKhw4d4ptK7dq1nex4Def7OvoaffX60SEG81QYsmnbti3bcF3BwcEUGxvrU9e8du1anodCBZIJEybQxx9/TK1bt/bZ68UXAUyPYA7aFV+9Ztz833zzTfrmm294HhpBok+fPtz5whevedu2bXydzZo1o2+//ZYmTpxIN9xwA82ZM6dS718+3wVEEI6Waaxbt85p3sZXadGiBa1atYoz5w8++IAuv/xynpfyRdAe6cYbb+Q5Z4jtqgtDhgyxnmMOFkGzYcOG9P7777NoxddwOBycST744IP8Gpkk/j1j/hF/35WFZJJHIT4+ngICAsopwPA6MTGRfB19jb54/ZMmTaIvvviCFi1a5NRKDdeFYfaMjAyfumZkEU2bNqXOnTtzdgWx1tNPP+2T14uhRQjrOnXqRIGBgfzAFwIIOPAcmYSvXXNFIGts3rw5bdmyxSd/z3Xq1OHREDutWrWyhpgr6/4lQfJfbiy4qfz4449O317wGvM5vk6jRo34j8l+/WhmCpVYVb1+6JMQIDHcuHDhQr5GO/h9Qy1nv2YsEcE/vKp6zRWBv+PCwkKfvN4BAwbw8DIyZ/1AxoE5Ov3c1665InJycmjr1q0cTHzx99yrV69yy7f++ecfzp4r9f71nyVGPs78+fNZDfXmm28aGzZsMMaPH2/ExsYaBw4cMHwBKAD/+usvfuDP4YknnuDnO3fu5Pcffvhhvt5PP/3UWLNmjXHeeecZjRo1MvLz842qyMSJE42YmBjjp59+Mvbv32898vLyrG0mTJhgNGjQwFi4cKHx559/Gj169OBHVeW2225j9e727dv5d4jXfn5+xnfffeeT11sRdnWrr17zlClT+O8av+dff/3VGDhwoBEfH88Kbl+85uXLlxuBgYHGAw88YGzevNmYN2+eER4ebsydO9fapjLuXxIkj4Fnn32W/7iCg4N5Schvv/1m+AqLFi3i4Oj6uPzyyy0Z9V133WXUrl2bvywMGDDA2LRpk1FVqeha8XjjjTesbfAP6Nprr+VlEvhHd/7553Mgrar873//Mxo2bMh/v7Vq1eLfoQ6Qvni9xxIkffGax44da9SpU4d/z3Xr1uXXW7Zs8elr/vzzz422bdvyvally5bGyy+/7PR+Zdy/pFWWIAiCILhB5iQFQRAEwQ0SJAVBEATBDRIkBUEQBMENEiQFQRAEwQ0SJAVBEATBDRIkBUEQBMENEiQFQRAEwQ0SJAVBEATBDRIkBUEQBMENEiQFQRAEwQ0SJAVBEATBDRIkBcFHSUtL41ZBuiktWLp0KbeAs7cPEgTBPVLgXBB8mK+++oqGDx/OwbFFixbUoUMHOu+88+iJJ57w9KkJQpVAgqQg+DjXXXcd/fDDD9xsGM2I//jjDwoJCfH0aQlClUCCpCD4OPn5+dS2bVvavXs3rVixglJSUjx9SoJQZZA5SUHwcbZu3Ur79u0jh8NBO3bs8PTpCEKVQjJJQfBhioqKqFu3bjwXiTnJp556iodcExISPH1qglAlkCApCD7MLbfcQh988AGtXr2aIiMjqW/fvhQTE0NffPGFp09NEKoEMtwqCD7KTz/9xJnj22+/TdHR0eTv78/PlyxZQrNnz/b06QlClUAySUEQBEFwg2SSgiAIguAGCZKCIAiC4AYJkoIgCILgBgmSgiAIguAGCZKCIAiC4AYJkoIgCILgBgmSgiAIguAGCZKCIAiC4AYJkoIgCILgBgmSgiAIguAGCZKCIAiCQBXzfys3JF+So+b4AAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 500x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(figsize=(5, 5))\n",
-    "ax.scatter(coords_df['x'], coords_df['y'], s=18, c=np.where(outliers, 'crimson', 'steelblue'), alpha=0.9)\n",
-    "closed = pd.concat([outline, outline.iloc[[0]]], ignore_index=True)\n",
-    "ax.plot(closed['x'], closed['y'], color='black', linewidth=1.5, label='convex tissue outline')\n",
-    "ax.set_aspect('equal')\n",
-    "ax.set_xlabel('x')\n",
-    "ax.set_ylabel('y')\n",
-    "ax.legend(frameon=False)\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7caf4f1a",
-   "metadata": {},
-   "source": [
-    "## Remove flagged observations\n",
-    "\n",
-    "`spata2_remove_outliers` returns a filtered copy by default, preserving the original object for comparison.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "8dc25ba5",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-20T09:00:37.886237Z",
-     "iopub.status.busy": "2026-06-20T09:00:37.886237Z",
-     "iopub.status.idle": "2026-06-20T09:00:37.898238Z",
-     "shell.execute_reply": "2026-06-20T09:00:37.898238Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "before: 402 after: 400\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.omicverse.dataframe+json": {
-       "dtypes": {
-        "region": "object",
-        "spata2_spatial_outlier": "bool",
-        "total_counts": "float64"
-       },
-       "name": null,
-       "ref": "obj:22c7de7bb50",
-       "shape": [
-        5,
-        3
-       ],
-       "table": {
-        "columns": [
-         "region",
-         "total_counts",
-         "spata2_spatial_outlier"
-        ],
-        "data": [
-         [
-          "right",
-          10.439553070239775,
-          false
-         ],
-         [
-          "right",
-          10.174393338504789,
-          false
-         ],
-         [
-          "right",
-          10.29893327549868,
-          false
-         ],
-         [
-          "right",
-          10.259728697196483,
-          false
-         ],
-         [
-          "right",
-          10.336551804483957,
-          false
-         ]
-        ],
-        "index": [
-         "spot_395",
-         "spot_396",
-         "spot_397",
-         "spot_398",
-         "spot_399"
-        ]
-       },
-       "type": "dataframe"
-      },
-      "text/plain": [
-       "         region  total_counts  spata2_spatial_outlier\n",
-       "spot_395  right     10.439553                   False\n",
-       "spot_396  right     10.174393                   False\n",
-       "spot_397  right     10.298933                   False\n",
-       "spot_398  right     10.259729                   False\n",
-       "spot_399  right     10.336552                   False"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "filtered = ov.space.spata2_remove_outliers(adata)\n",
-    "print('before:', adata.n_obs, 'after:', filtered.n_obs)\n",
-    "filtered.obs.tail()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b1bf0429",
-   "metadata": {},
-   "source": [
-    "## Convert pixel distances to physical units\n",
-    "\n",
-    "SPATA2's R API carries explicit distance units. The first Python slice keeps unit conversion explicit: pass a scale factor and round-trip it when needed.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "2ed3b0d8",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-20T09:00:37.900238Z",
-     "iopub.status.busy": "2026-06-20T09:00:37.900238Z",
-     "iopub.status.idle": "2026-06-20T09:00:37.914238Z",
-     "shell.execute_reply": "2026-06-20T09:00:37.914238Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.omicverse.dataframe+json": {
-       "dtypes": {
-        "micrometers": "float64",
-        "pixels": "int32",
-        "roundtrip_pixels": "float64"
-       },
-       "name": null,
-       "ref": "obj:22c7df28610",
-       "shape": [
-        4,
-        3
-       ],
-       "table": {
-        "columns": [
-         "pixels",
-         "micrometers",
-         "roundtrip_pixels"
-        ],
-        "data": [
-         [
-          0,
-          0.0,
-          0.0
-         ],
-         [
-          55,
-          27.5,
-          55.0
-         ],
-         [
-          110,
-          55.0,
-          110.0
-         ],
-         [
-          220,
-          110.0,
-          220.0
-         ]
-        ],
-        "index": [
-         "0",
-         "1",
-         "2",
-         "3"
-        ]
-       },
-       "type": "dataframe"
-      },
-      "text/plain": [
-       "   pixels  micrometers  roundtrip_pixels\n",
-       "0       0          0.0               0.0\n",
-       "1      55         27.5              55.0\n",
-       "2     110         55.0             110.0\n",
-       "3     220        110.0             220.0"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pixels = np.array([0, 55, 110, 220])\n",
-    "micrometers = ov.space.spata2_pixels_to_unit(pixels, pixels_per_unit=2.0)\n",
-    "roundtrip = ov.space.spata2_unit_to_pixels(micrometers, pixels_per_unit=2.0)\n",
-    "pd.DataFrame({'pixels': pixels, 'micrometers': micrometers, 'roundtrip_pixels': roundtrip})\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7d7a30ff",
-   "metadata": {},
-   "source": [
-    "## Notebook benchmark\n",
-    "\n",
-    "This lightweight benchmark records wall-clock time for the exact operations used above on the rendered fixture. The larger `py-SPATA2` benchmark is stored in the rewrite repository.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "121cb3b6",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-20T09:00:37.917238Z",
-     "iopub.status.busy": "2026-06-20T09:00:37.917238Z",
-     "iopub.status.idle": "2026-06-20T09:00:37.930238Z",
-     "shell.execute_reply": "2026-06-20T09:00:37.930238Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.omicverse.dataframe+json": {
-       "dtypes": {
-        "operation": "object",
-        "seconds": "float64",
-        "shape": "object"
-       },
-       "name": null,
-       "ref": "obj:22c7df476d0",
-       "shape": [
-        5,
-        3
-       ],
-       "table": {
-        "columns": [
-         "operation",
-         "seconds",
-         "shape"
-        ],
-        "data": [
-         [
-          "spata2_get_coords",
-          0.00043509999522939324,
-          [
-           402,
-           3
-          ]
-         ],
-         [
-          "spata2_join_variables",
-          0.0008641000022180378,
-          [
-           402,
-           6
-          ]
-         ],
-         [
-          "spata2_tissue_outline",
-          0.00082230000407435,
-          [
-           5,
-           2
-          ]
-         ],
-         [
-          "spata2_identify_outliers",
-          0.0007560999947600067,
-          [
-           402
-          ]
-         ],
-         [
-          "spata2_remove_outliers",
-          0.0008131999929901212,
-          [
-           400,
-           3
-          ]
-         ]
-        ],
-        "index": [
-         "0",
-         "1",
-         "2",
-         "3",
-         "4"
-        ]
-       },
-       "type": "dataframe"
-      },
-      "text/plain": [
-       "                  operation   seconds     shape\n",
-       "0         spata2_get_coords  0.000435  (402, 3)\n",
-       "1     spata2_join_variables  0.000864  (402, 6)\n",
-       "2     spata2_tissue_outline  0.000822    (5, 2)\n",
-       "3  spata2_identify_outliers  0.000756    (402,)\n",
-       "4    spata2_remove_outliers  0.000813  (400, 3)"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import time\n",
-    "\n",
-    "bench_rows = []\n",
-    "for label, call in [\n",
-    "    ('spata2_get_coords', lambda: ov.space.spata2_get_coords(adata)),\n",
-    "    ('spata2_join_variables', lambda: ov.space.spata2_join_variables(adata, ['region', 'GeneA', 'GeneB'])),\n",
-    "    ('spata2_tissue_outline', lambda: ov.space.spata2_tissue_outline(adata, write_key=None)),\n",
-    "    ('spata2_identify_outliers', lambda: ov.space.spata2_identify_outliers(adata, radius=2.0, min_neighbors=2, write_key=None)),\n",
-    "    ('spata2_remove_outliers', lambda: ov.space.spata2_remove_outliers(adata)),\n",
-    "]:\n",
-    "    start = time.perf_counter()\n",
-    "    result = call()\n",
-    "    elapsed = time.perf_counter() - start\n",
-    "    bench_rows.append({'operation': label, 'seconds': elapsed, 'shape': getattr(result, 'shape', (len(result),))})\n",
-    "\n",
-    "pd.DataFrame(bench_rows)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3470427d",
-   "metadata": {},
-   "source": [
-    "## Current parity status\n",
-    "\n",
-    "The rewrite repository audits the real SPATA2 3.1.4 `NAMESPACE`, not a hand-picked subset. A symbol counts as implemented only when Python exposes the same R-style export name.\n",
-    "\n",
-    "Full tables:\n",
-    "\n",
-    "- [NAMESPACE_PARITY.md](https://github.com/omicverse/py-SPATA2/blob/master/NAMESPACE_PARITY.md)\n",
-    "- [spata2_namespace_parity.csv](https://github.com/omicverse/py-SPATA2/blob/master/references/spata2_namespace_parity.csv)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "e69c2519",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-20T09:00:37.932238Z",
-     "iopub.status.busy": "2026-06-20T09:00:37.932238Z",
-     "iopub.status.idle": "2026-06-20T09:00:37.946238Z",
-     "shell.execute_reply": "2026-06-20T09:00:37.946238Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.omicverse.dataframe+json": {
-       "dtypes": {
-        "metric": "object",
-        "value": "object"
-       },
-       "name": null,
-       "ref": "obj:22c7df47f70",
-       "shape": [
-        4,
-        2
-       ],
-       "table": {
-        "columns": [
-         "metric",
-         "value"
-        ],
-        "data": [
-         [
-          "R exports audited from SPATA2 3.1.4 NAMESPACE",
-          751
-         ],
-         [
-          "R-compatible Python symbols implemented in py-SPATA2",
-          16
-         ],
-         [
-          "Strict namespace coverage",
-          "2.1%"
-         ],
-         [
-          "ov.space tutorial API functions exercised here",
-          8
-         ]
-        ],
-        "index": [
-         "0",
-         "1",
-         "2",
-         "3"
-        ]
-       },
-       "type": "dataframe"
-      },
-      "text/plain": [
-       "                                              metric value\n",
-       "0      R exports audited from SPATA2 3.1.4 NAMESPACE   751\n",
-       "1  R-compatible Python symbols implemented in py-...    16\n",
-       "2                          Strict namespace coverage  2.1%\n",
-       "3     ov.space tutorial API functions exercised here     8"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "parity = pd.DataFrame(\n",
-    "    [\n",
-    "        ['R exports audited from SPATA2 3.1.4 NAMESPACE', 751],\n",
-    "        ['R-compatible Python symbols implemented in py-SPATA2', 16],\n",
-    "        ['Strict namespace coverage', '2.1%'],\n",
-    "        ['ov.space tutorial API functions exercised here', 8],\n",
-    "    ],\n",
-    "    columns=['metric', 'value'],\n",
-    ")\n",
-    "parity\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a36d83c5",
-   "metadata": {},
-   "source": [
-    "## Interpretation\n",
-    "\n",
-    "Use these helpers today for AnnData coordinate inspection, variable tables, quick tissue outlines, and artifact filtering. Do not treat this notebook as evidence of full SPATA2 parity yet. The next reconstruction passes should follow the audited export surface module by module: object containers, readers/initiation, get/set/add accessors, spatial annotations, trajectories, gradient screening, and plotting.\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.10.20"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1319eed3",
+   "metadata": {},
+   "source": [
+    "# SPATA2-inspired AnnData spatial utilities in OmicVerse\n",
+    "\n",
+    "This tutorial documents a small AnnData-native spatial utility layer inspired by SPATA2 coordinate, variable, outline, and outlier workflows. It accompanies the initial implementation PR and the standalone `py-SPATA2` rewrite repository, but it should not be read as full SPATA2 parity.\n",
+    "\n",
+    "Related links:\n",
+    "\n",
+    "- API PR: [omicverse/omicverse#847](https://github.com/omicverse/omicverse/pull/847)\n",
+    "- Rewrite repository: [omicverse/py-SPATA2](https://github.com/omicverse/py-SPATA2)\n",
+    "- SPATA2 documentation: [theMILOlab.github.io/SPATA2](https://themilolab.github.io/SPATA2/index.html)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43f96e85",
+   "metadata": {},
+   "source": [
+    "## What this notebook covers\n",
+    "\n",
+    "The current implementation focuses on a narrow, stable AnnData helper slice:\n",
+    "\n",
+    "| Spatial task | OmicVerse API used here | Output |\n",
+    "|---|---|---|\n",
+    "| Coordinate table | `ov.space.spata2_get_coords` | observation-indexed `DataFrame` |\n",
+    "| Molecular / metadata variables | `ov.space.spata2_join_variables` | coordinates joined with `obs` and gene values |\n",
+    "| Tissue outline | `ov.space.spata2_tissue_outline` | convex outline stored in `adata.uns` |\n",
+    "| Spatial artifacts | `ov.space.spata2_identify_outliers` | Boolean outlier flag in `adata.obs` |\n",
+    "| Cleaned object | `ov.space.spata2_remove_outliers` | filtered `AnnData` |\n",
+    "| Pixel scale | `spata2_pixels_to_unit`, `spata2_unit_to_pixels` | explicit distance conversion |\n",
+    "\n",
+    "The final section gives parity context so readers do not mistake this initial helper layer for a complete SPATA2 port.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f3ee46b",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "The notebook uses only `omicverse`, `AnnData`, `numpy`, `pandas`, and `matplotlib`. No R runtime, `rpy2`, or SPATA2 R installation is required.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "82bd9f5c",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-02T11:39:37.780590Z",
+     "iopub.status.busy": "2026-07-02T11:39:37.780590Z",
+     "iopub.status.idle": "2026-07-02T11:39:39.630285Z",
+     "shell.execute_reply": "2026-07-02T11:39:39.629686Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from anndata import AnnData\n",
+    "import matplotlib.pyplot as plt\n",
+    "import omicverse as ov\n",
+    "\n",
+    "%config InlineBackend.figure_format = 'png'\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "1ad0c9ce",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-02T11:39:39.630285Z",
+     "iopub.status.busy": "2026-07-02T11:39:39.630285Z",
+     "iopub.status.idle": "2026-07-02T11:39:44.442969Z",
+     "shell.execute_reply": "2026-07-02T11:39:44.442969Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "omicverse 2.1.3rc1\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\ProgramData\\Anaconda3\\envs\\omicverse\\lib\\site-packages\\anndata\\utils.py:434: FutureWarning: Importing read_loom from `anndata` is deprecated. Import anndata.io.read_loom instead.\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SPATA2-inspired API: spata2_get_coords spata2_identify_outliers\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('omicverse', ov.__version__ if hasattr(ov, '__version__') else 'unknown')\n",
+    "print('SPATA2-inspired API:', ov.space.spata2_get_coords.__name__, ov.space.spata2_identify_outliers.__name__)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba62a222",
+   "metadata": {},
+   "source": [
+    "## Build a Visium-like tutorial fixture\n",
+    "\n",
+    "The current helper layer exercises coordinate and spatial-data utilities rather than a full biological model. This notebook therefore uses a deterministic fixture with:\n",
+    "\n",
+    "- a 20 x 20 tissue grid;\n",
+    "- three smooth gene-like spatial variables;\n",
+    "- two deliberately isolated artifact spots.\n",
+    "\n",
+    "That makes the expected tissue outline and outlier behavior easy to inspect in a rendered notebook.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c09f59c3",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-02T11:39:44.445969Z",
+     "iopub.status.busy": "2026-07-02T11:39:44.444969Z",
+     "iopub.status.idle": "2026-07-02T11:39:44.462581Z",
+     "shell.execute_reply": "2026-07-02T11:39:44.462581Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.omicverse.anndata+json": {
+       "name": null,
+       "previews": {
+        "layers": {},
+        "obs": {
+         "dtypes": {
+          "region": "object",
+          "total_counts": "float64"
+         },
+         "name": ".obs",
+         "shape": [
+          402,
+          2
+         ],
+         "table": {
+          "columns": [
+           "region",
+           "total_counts"
+          ],
+          "data": [
+           [
+            "left",
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+           [
+            "left",
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+            "left",
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+           "spot_001",
+           "spot_002",
+           "spot_003",
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+           "spot_005",
+           "spot_006",
+           "spot_007",
+           "spot_008",
+           "spot_009",
+           "spot_010",
+           "spot_011",
+           "spot_012",
+           "spot_013",
+           "spot_014",
+           "spot_015",
+           "spot_016",
+           "spot_017",
+           "spot_018",
+           "spot_019",
+           "spot_020",
+           "spot_021",
+           "spot_022",
+           "spot_023"
+          ]
+         },
+         "type": "dataframe"
+        },
+        "obsm": {
+         "spatial": {
+          "dtype": "float64",
+          "name": "obsm[\"spatial\"]",
+          "shape": [
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+          "table": {
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+             1.0,
+             0.0
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+            [
+             2.0,
+             0.0
+            ],
+            [
+             3.0,
+             0.0
+            ],
+            [
+             4.0,
+             0.0
+            ],
+            [
+             5.0,
+             0.0
+            ],
+            [
+             6.0,
+             0.0
+            ],
+            [
+             7.0,
+             0.0
+            ],
+            [
+             8.0,
+             0.0
+            ],
+            [
+             9.0,
+             0.0
+            ]
+           ],
+           "index": [
+            "0",
+            "1",
+            "2",
+            "3",
+            "4",
+            "5",
+            "6",
+            "7",
+            "8",
+            "9"
+           ]
+          },
+          "type": "array"
+         }
+        },
+        "uns": {},
+        "var": {
+         "dtypes": {},
+         "name": ".var",
+         "shape": [
+          3,
+          0
+         ],
+         "table": {
+          "columns": [],
+          "data": [
+           [],
+           [],
+           []
+          ],
+          "index": [
+           "GeneA",
+           "GeneB",
+           "GeneC"
+          ]
+         },
+         "type": "dataframe"
+        }
+       },
+       "ref": "obj:12e046ed300",
+       "summary": {
+        "embedding_keys": [
+         "spatial"
+        ],
+        "embedding_keys_more": 0,
+        "embedding_keys_total": 1,
+        "layers": [],
+        "layers_more": 0,
+        "layers_total": 0,
+        "obs_columns": [
+         "region",
+         "total_counts"
+        ],
+        "obs_columns_more": 0,
+        "obs_columns_total": 2,
+        "obsm_keys": [
+         "spatial"
+        ],
+        "obsm_keys_more": 0,
+        "obsm_keys_total": 1,
+        "shape": [
+         402,
+         3
+        ],
+        "uns_keys": [],
+        "uns_keys_more": 0,
+        "uns_keys_total": 0,
+        "var_columns": [],
+        "var_columns_more": 0,
+        "var_columns_total": 0
+       },
+       "type": "anndata"
+      },
+      "text/plain": [
+       "AnnData object with n_obs × n_vars = 402 × 3\n",
+       "    obs: 'region', 'total_counts'\n",
+       "    obsm: 'spatial'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n_side = 20\n",
+    "grid_x, grid_y = np.meshgrid(np.arange(n_side), np.arange(n_side))\n",
+    "coords = np.column_stack([grid_x.ravel(), grid_y.ravel()]).astype(float)\n",
+    "outlier_coords = np.array([[55.0, 55.0], [58.0, 53.0]])\n",
+    "coords = np.vstack([coords, outlier_coords])\n",
+    "\n",
+    "rng = np.random.default_rng(7)\n",
+    "gene_a = coords[:, 0] / coords[:, 0].max() * 6 + rng.normal(0, 0.15, coords.shape[0])\n",
+    "gene_b = coords[:, 1] / coords[:, 1].max() * 5 + rng.normal(0, 0.15, coords.shape[0])\n",
+    "gene_c = np.sqrt((coords[:, 0] - 10) ** 2 + (coords[:, 1] - 10) ** 2)\n",
+    "gene_c = 8 - gene_c / gene_c.max() * 6 + rng.normal(0, 0.1, coords.shape[0])\n",
+    "counts = np.clip(np.column_stack([gene_a, gene_b, gene_c]), 0, None)\n",
+    "\n",
+    "obs = pd.DataFrame(\n",
+    "    {\n",
+    "        'region': np.where(coords[:, 0] < 10, 'left', 'right'),\n",
+    "        'total_counts': counts.sum(axis=1),\n",
+    "    },\n",
+    "    index=[f'spot_{idx:03d}' for idx in range(coords.shape[0])],\n",
+    ")\n",
+    "adata = AnnData(X=counts, obs=obs, var=pd.DataFrame(index=['GeneA', 'GeneB', 'GeneC']))\n",
+    "adata.obsm['spatial'] = coords\n",
+    "adata\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26b04623",
+   "metadata": {},
+   "source": [
+    "## Extract coordinates\n",
+    "\n",
+    "`spata2_get_coords` reads `adata.obsm['spatial']` and returns a tidy coordinate table. It can also append observation metadata while protecting the canonical `barcode`, `x`, and `y` output columns from name collisions.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "17684abe",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-02T11:39:44.464458Z",
+     "iopub.status.busy": "2026-07-02T11:39:44.464458Z",
+     "iopub.status.idle": "2026-07-02T11:39:44.477470Z",
+     "shell.execute_reply": "2026-07-02T11:39:44.477470Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.omicverse.dataframe+json": {
+       "dtypes": {
+        "barcode": "object",
+        "region": "object",
+        "total_counts": "float64",
+        "x": "float64",
+        "y": "float64"
+       },
+       "name": null,
+       "ref": "obj:12e046ecb50",
+       "shape": [
+        5,
+        5
+       ],
+       "table": {
+        "columns": [
+         "barcode",
+         "x",
+         "y",
+         "region",
+         "total_counts"
+        ],
+        "data": [
+         [
+          "spot_000",
+          0.0,
+          0.0,
+          "left",
+          6.7466547045256275
+         ],
+         [
+          "spot_001",
+          1.0,
+          0.0,
+          "left",
+          7.053847043120326
+         ],
+         [
+          "spot_002",
+          2.0,
+          0.0,
+          "left",
+          7.128637064825885
+         ],
+         [
+          "spot_003",
+          3.0,
+          0.0,
+          "left",
+          7.082770087011939
+         ],
+         [
+          "spot_004",
+          4.0,
+          0.0,
+          "left",
+          7.299024494965832
+         ]
+        ],
+        "index": [
+         "spot_000",
+         "spot_001",
+         "spot_002",
+         "spot_003",
+         "spot_004"
+        ]
+       },
+       "type": "dataframe"
+      },
+      "text/plain": [
+       "           barcode    x    y region  total_counts\n",
+       "spot_000  spot_000  0.0  0.0   left      6.746655\n",
+       "spot_001  spot_001  1.0  0.0   left      7.053847\n",
+       "spot_002  spot_002  2.0  0.0   left      7.128637\n",
+       "spot_003  spot_003  3.0  0.0   left      7.082770\n",
+       "spot_004  spot_004  4.0  0.0   left      7.299024"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coords_df = ov.space.spata2_get_coords(adata, include_obs=['region', 'total_counts'])\n",
+    "coords_df.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6868776e",
+   "metadata": {},
+   "source": [
+    "## Join coordinates with variables\n",
+    "\n",
+    "`spata2_join_variables` collects spatial coordinates together with variables of interest. Names in `adata.obs` are treated as metadata; names in `adata.var_names` are pulled from the expression matrix.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "1f0a1c37",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-02T11:39:44.478457Z",
+     "iopub.status.busy": "2026-07-02T11:39:44.478457Z",
+     "iopub.status.idle": "2026-07-02T11:39:44.495270Z",
+     "shell.execute_reply": "2026-07-02T11:39:44.495270Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.omicverse.dataframe+json": {
+       "dtypes": {
+        "GeneA": "float64",
+        "GeneB": "float64",
+        "GeneC": "float64",
+        "barcode": "object",
+        "region": "object",
+        "x": "float64",
+        "y": "float64"
+       },
+       "name": null,
+       "ref": "obj:12e046ee1a0",
+       "shape": [
+        5,
+        7
+       ],
+       "table": {
+        "columns": [
+         "barcode",
+         "x",
+         "y",
+         "region",
+         "GeneA",
+         "GeneB",
+         "GeneC"
+        ],
+        "data": [
+         [
+          "spot_000",
+          0.0,
+          0.0,
+          "left",
+          0.00018452300362238613,
+          0.0,
+          6.746470181522005
+         ],
+         [
+          "spot_001",
+          1.0,
+          0.0,
+          "left",
+          0.14826010648833945,
+          0.1543768849046773,
+          6.751210051727309
+         ],
+         [
+          "spot_002",
+          2.0,
+          0.0,
+          "left",
+          0.1657758734198053,
+          0.031559240824445535,
+          6.931301950581634
+         ],
+         [
+          "spot_003",
+          3.0,
+          0.0,
+          "left",
+          0.17675605177261577,
+          0.0,
+          6.906014035239323
+         ],
+         [
+          "spot_004",
+          4.0,
+          0.0,
+          "left",
+          0.3455924856725175,
+          0.0,
+          6.953432009293315
+         ]
+        ],
+        "index": [
+         "spot_000",
+         "spot_001",
+         "spot_002",
+         "spot_003",
+         "spot_004"
+        ]
+       },
+       "type": "dataframe"
+      },
+      "text/plain": [
+       "           barcode    x    y region     GeneA     GeneB     GeneC\n",
+       "spot_000  spot_000  0.0  0.0   left  0.000185  0.000000  6.746470\n",
+       "spot_001  spot_001  1.0  0.0   left  0.148260  0.154377  6.751210\n",
+       "spot_002  spot_002  2.0  0.0   left  0.165776  0.031559  6.931302\n",
+       "spot_003  spot_003  3.0  0.0   left  0.176756  0.000000  6.906014\n",
+       "spot_004  spot_004  4.0  0.0   left  0.345592  0.000000  6.953432"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "feature_df = ov.space.spata2_join_variables(adata, ['region', 'GeneA', 'GeneB', 'GeneC'])\n",
+    "feature_df.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "09f29795",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-02T11:39:44.496940Z",
+     "iopub.status.busy": "2026-07-02T11:39:44.496940Z",
+     "iopub.status.idle": "2026-07-02T11:39:44.778744Z",
+     "shell.execute_reply": "2026-07-02T11:39:44.778744Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x300 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(1, 3, figsize=(10, 3), constrained_layout=True)\n",
+    "for ax, gene in zip(axes, ['GeneA', 'GeneB', 'GeneC']):\n",
+    "    sc = ax.scatter(feature_df['x'], feature_df['y'], c=feature_df[gene], s=18, cmap='viridis')\n",
+    "    ax.set_title(gene)\n",
+    "    ax.set_aspect('equal')\n",
+    "    ax.set_xlabel('x')\n",
+    "    ax.set_ylabel('y')\n",
+    "    fig.colorbar(sc, ax=ax, fraction=0.046, pad=0.04)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "68bf8c24",
+   "metadata": {},
+   "source": [
+    "## Identify the tissue outline\n",
+    "\n",
+    "The first implementation uses a deterministic convex hull. It is useful for quick inspection, but it is not a replacement for SPATA2's richer outline and annotation model.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "97611dfc",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-02T11:39:44.780256Z",
+     "iopub.status.busy": "2026-07-02T11:39:44.780256Z",
+     "iopub.status.idle": "2026-07-02T11:39:44.795895Z",
+     "shell.execute_reply": "2026-07-02T11:39:44.795895Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.omicverse.dataframe+json": {
+       "dtypes": {
+        "x": "float64",
+        "y": "float64"
+       },
+       "name": null,
+       "ref": "obj:12e046ec0a0",
+       "shape": [
+        5,
+        2
+       ],
+       "table": {
+        "columns": [
+         "x",
+         "y"
+        ],
+        "data": [
+         [
+          58.0,
+          53.0
+         ],
+         [
+          55.0,
+          55.0
+         ],
+         [
+          0.0,
+          19.0
+         ],
+         [
+          0.0,
+          0.0
+         ],
+         [
+          19.0,
+          0.0
+         ]
+        ],
+        "index": [
+         "0",
+         "1",
+         "2",
+         "3",
+         "4"
+        ]
+       },
+       "type": "dataframe"
+      },
+      "text/plain": [
+       "           x     y\n",
+       "vertex            \n",
+       "0       58.0  53.0\n",
+       "1       55.0  55.0\n",
+       "2        0.0  19.0\n",
+       "3        0.0   0.0\n",
+       "4       19.0   0.0"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "outline = ov.space.spata2_tissue_outline(adata)\n",
+    "outline\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bfe4b7a1",
+   "metadata": {},
+   "source": [
+    "## Detect isolated spatial observations\n",
+    "\n",
+    "Here we use a radius-neighborhood rule to flag observations that are spatially isolated from the main tissue grid.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "b3df29bb",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-02T11:39:44.797810Z",
+     "iopub.status.busy": "2026-07-02T11:39:44.797810Z",
+     "iopub.status.idle": "2026-07-02T11:39:44.811808Z",
+     "shell.execute_reply": "2026-07-02T11:39:44.811808Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "spata2_spatial_outlier\n",
+      "kept       400\n",
+      "outlier      2\n",
+      "Name: count, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.omicverse.dataframe+json": {
+       "dtypes": {
+        "region": "object",
+        "total_counts": "float64"
+       },
+       "name": null,
+       "ref": "obj:12e046ecfd0",
+       "shape": [
+        2,
+        2
+       ],
+       "table": {
+        "columns": [
+         "region",
+         "total_counts"
+        ],
+        "data": [
+         [
+          "right",
+          12.67020073237883
+         ],
+         [
+          "right",
+          12.80379983730633
+         ]
+        ],
+        "index": [
+         "spot_400",
+         "spot_401"
+        ]
+       },
+       "type": "dataframe"
+      },
+      "text/plain": [
+       "         region  total_counts\n",
+       "spot_400  right     12.670201\n",
+       "spot_401  right     12.803800"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "outliers = ov.space.spata2_identify_outliers(adata, radius=2.0, min_neighbors=2)\n",
+    "print(outliers.value_counts().rename(index={False: 'kept', True: 'outlier'}))\n",
+    "adata.obs.loc[outliers, ['region', 'total_counts']]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "0a57c3af",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-02T11:39:44.813604Z",
+     "iopub.status.busy": "2026-07-02T11:39:44.813604Z",
+     "iopub.status.idle": "2026-07-02T11:39:44.861751Z",
+     "shell.execute_reply": "2026-07-02T11:39:44.861751Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(5, 5))\n",
+    "ax.scatter(coords_df['x'], coords_df['y'], s=18, c=np.where(outliers, 'crimson', 'steelblue'), alpha=0.9)\n",
+    "closed = pd.concat([outline, outline.iloc[[0]]], ignore_index=True)\n",
+    "ax.plot(closed['x'], closed['y'], color='black', linewidth=1.5, label='convex tissue outline')\n",
+    "ax.set_aspect('equal')\n",
+    "ax.set_xlabel('x')\n",
+    "ax.set_ylabel('y')\n",
+    "ax.legend(frameon=False)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7caf4f1a",
+   "metadata": {},
+   "source": [
+    "## Remove flagged observations\n",
+    "\n",
+    "`spata2_remove_outliers` can return a filtered copy, leaving the original object intact.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "8dc25ba5",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-02T11:39:44.863751Z",
+     "iopub.status.busy": "2026-07-02T11:39:44.862751Z",
+     "iopub.status.idle": "2026-07-02T11:39:44.877828Z",
+     "shell.execute_reply": "2026-07-02T11:39:44.877828Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "before: 402 after: 400\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.omicverse.dataframe+json": {
+       "dtypes": {
+        "region": "object",
+        "spata2_spatial_outlier": "bool",
+        "total_counts": "float64"
+       },
+       "name": null,
+       "ref": "obj:12e046eee60",
+       "shape": [
+        5,
+        3
+       ],
+       "table": {
+        "columns": [
+         "region",
+         "total_counts",
+         "spata2_spatial_outlier"
+        ],
+        "data": [
+         [
+          "right",
+          10.439553070239775,
+          false
+         ],
+         [
+          "right",
+          10.174393338504789,
+          false
+         ],
+         [
+          "right",
+          10.29893327549868,
+          false
+         ],
+         [
+          "right",
+          10.259728697196483,
+          false
+         ],
+         [
+          "right",
+          10.336551804483957,
+          false
+         ]
+        ],
+        "index": [
+         "spot_395",
+         "spot_396",
+         "spot_397",
+         "spot_398",
+         "spot_399"
+        ]
+       },
+       "type": "dataframe"
+      },
+      "text/plain": [
+       "         region  total_counts  spata2_spatial_outlier\n",
+       "spot_395  right     10.439553                   False\n",
+       "spot_396  right     10.174393                   False\n",
+       "spot_397  right     10.298933                   False\n",
+       "spot_398  right     10.259729                   False\n",
+       "spot_399  right     10.336552                   False"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "filtered = ov.space.spata2_remove_outliers(adata)\n",
+    "print('before:', adata.n_obs, 'after:', filtered.n_obs)\n",
+    "filtered.obs.tail()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1bf0429",
+   "metadata": {},
+   "source": [
+    "## Convert pixel distances to physical units\n",
+    "\n",
+    "SPATA2's R API carries explicit distance units. This Python helper layer keeps unit conversion explicit: pass a scale factor and round-trip it when needed.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "2ed3b0d8",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-02T11:39:44.879826Z",
+     "iopub.status.busy": "2026-07-02T11:39:44.879826Z",
+     "iopub.status.idle": "2026-07-02T11:39:44.893944Z",
+     "shell.execute_reply": "2026-07-02T11:39:44.893944Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.omicverse.dataframe+json": {
+       "dtypes": {
+        "micrometers": "float64",
+        "pixels": "int32",
+        "roundtrip_pixels": "float64"
+       },
+       "name": null,
+       "ref": "obj:12e06c23f40",
+       "shape": [
+        4,
+        3
+       ],
+       "table": {
+        "columns": [
+         "pixels",
+         "micrometers",
+         "roundtrip_pixels"
+        ],
+        "data": [
+         [
+          0,
+          0.0,
+          0.0
+         ],
+         [
+          55,
+          27.5,
+          55.0
+         ],
+         [
+          110,
+          55.0,
+          110.0
+         ],
+         [
+          220,
+          110.0,
+          220.0
+         ]
+        ],
+        "index": [
+         "0",
+         "1",
+         "2",
+         "3"
+        ]
+       },
+       "type": "dataframe"
+      },
+      "text/plain": [
+       "   pixels  micrometers  roundtrip_pixels\n",
+       "0       0          0.0               0.0\n",
+       "1      55         27.5              55.0\n",
+       "2     110         55.0             110.0\n",
+       "3     220        110.0             220.0"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pixels = np.array([0, 55, 110, 220])\n",
+    "micrometers = ov.space.spata2_pixels_to_unit(pixels, pixels_per_unit=2.0)\n",
+    "roundtrip = ov.space.spata2_unit_to_pixels(micrometers, pixels_per_unit=2.0)\n",
+    "pd.DataFrame({'pixels': pixels, 'micrometers': micrometers, 'roundtrip_pixels': roundtrip})\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d7a30ff",
+   "metadata": {},
+   "source": [
+    "## Notebook benchmark\n",
+    "\n",
+    "This lightweight benchmark records wall-clock time for the exact operations used above on the rendered fixture. The larger `py-SPATA2` benchmark remains in the rewrite repository.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "121cb3b6",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-02T11:39:44.896779Z",
+     "iopub.status.busy": "2026-07-02T11:39:44.895944Z",
+     "iopub.status.idle": "2026-07-02T11:39:44.913584Z",
+     "shell.execute_reply": "2026-07-02T11:39:44.912839Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.omicverse.dataframe+json": {
+       "dtypes": {
+        "operation": "object",
+        "seconds": "float64",
+        "shape": "object"
+       },
+       "name": null,
+       "ref": "obj:12e04a0c130",
+       "shape": [
+        5,
+        3
+       ],
+       "table": {
+        "columns": [
+         "operation",
+         "seconds",
+         "shape"
+        ],
+        "data": [
+         [
+          "spata2_get_coords",
+          0.00043949997052550316,
+          [
+           402,
+           3
+          ]
+         ],
+         [
+          "spata2_join_variables",
+          0.0007306999759748578,
+          [
+           402,
+           6
+          ]
+         ],
+         [
+          "spata2_tissue_outline",
+          0.0007174999918788671,
+          [
+           5,
+           2
+          ]
+         ],
+         [
+          "spata2_identify_outliers",
+          0.0006426000036299229,
+          [
+           402
+          ]
+         ],
+         [
+          "spata2_remove_outliers",
+          0.0005731000564992428,
+          [
+           400,
+           3
+          ]
+         ]
+        ],
+        "index": [
+         "0",
+         "1",
+         "2",
+         "3",
+         "4"
+        ]
+       },
+       "type": "dataframe"
+      },
+      "text/plain": [
+       "                  operation   seconds     shape\n",
+       "0         spata2_get_coords  0.000439  (402, 3)\n",
+       "1     spata2_join_variables  0.000731  (402, 6)\n",
+       "2     spata2_tissue_outline  0.000717    (5, 2)\n",
+       "3  spata2_identify_outliers  0.000643    (402,)\n",
+       "4    spata2_remove_outliers  0.000573  (400, 3)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import time\n",
+    "\n",
+    "bench_rows = []\n",
+    "for label, call in [\n",
+    "    ('spata2_get_coords', lambda: ov.space.spata2_get_coords(adata)),\n",
+    "    ('spata2_join_variables', lambda: ov.space.spata2_join_variables(adata, ['region', 'GeneA', 'GeneB'])),\n",
+    "    ('spata2_tissue_outline', lambda: ov.space.spata2_tissue_outline(adata, write_key=None)),\n",
+    "    ('spata2_identify_outliers', lambda: ov.space.spata2_identify_outliers(adata, radius=2.0, min_neighbors=2, write_key=None)),\n",
+    "    ('spata2_remove_outliers', lambda: ov.space.spata2_remove_outliers(adata)),\n",
+    "]:\n",
+    "    start = time.perf_counter()\n",
+    "    result = call()\n",
+    "    elapsed = time.perf_counter() - start\n",
+    "    bench_rows.append({'operation': label, 'seconds': elapsed, 'shape': getattr(result, 'shape', (len(result),))})\n",
+    "\n",
+    "pd.DataFrame(bench_rows)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3470427d",
+   "metadata": {},
+   "source": [
+    "## Current parity context\n",
+    "\n",
+    "The rewrite repository audits the real SPATA2 3.1.4 `NAMESPACE`, not a hand-picked subset. A symbol counts as implemented only when Python exposes the same R-style export name.\n",
+    "\n",
+    "Full tables:\n",
+    "\n",
+    "- [NAMESPACE_PARITY.md](https://github.com/omicverse/py-SPATA2/blob/master/NAMESPACE_PARITY.md)\n",
+    "- [spata2_namespace_parity.csv](https://github.com/omicverse/py-SPATA2/blob/master/references/spata2_namespace_parity.csv)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "e69c2519",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-02T11:39:44.915588Z",
+     "iopub.status.busy": "2026-07-02T11:39:44.915588Z",
+     "iopub.status.idle": "2026-07-02T11:39:44.928587Z",
+     "shell.execute_reply": "2026-07-02T11:39:44.928587Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.omicverse.dataframe+json": {
+       "dtypes": {
+        "metric": "object",
+        "value": "object"
+       },
+       "name": null,
+       "ref": "obj:12e04a47a00",
+       "shape": [
+        4,
+        2
+       ],
+       "table": {
+        "columns": [
+         "metric",
+         "value"
+        ],
+        "data": [
+         [
+          "R exports audited from SPATA2 3.1.4 NAMESPACE",
+          751
+         ],
+         [
+          "R-compatible Python symbols implemented in py-SPATA2",
+          16
+         ],
+         [
+          "Strict namespace coverage",
+          "2.1%"
+         ],
+         [
+          "ov.space tutorial API functions exercised here",
+          8
+         ]
+        ],
+        "index": [
+         "0",
+         "1",
+         "2",
+         "3"
+        ]
+       },
+       "type": "dataframe"
+      },
+      "text/plain": [
+       "                                              metric value\n",
+       "0      R exports audited from SPATA2 3.1.4 NAMESPACE   751\n",
+       "1  R-compatible Python symbols implemented in py-...    16\n",
+       "2                          Strict namespace coverage  2.1%\n",
+       "3     ov.space tutorial API functions exercised here     8"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "parity = pd.DataFrame(\n",
+    "    [\n",
+    "        ['R exports audited from SPATA2 3.1.4 NAMESPACE', 751],\n",
+    "        ['R-compatible Python symbols implemented in py-SPATA2', 16],\n",
+    "        ['Strict namespace coverage', '2.1%'],\n",
+    "        ['ov.space tutorial API functions exercised here', 8],\n",
+    "    ],\n",
+    "    columns=['metric', 'value'],\n",
+    ")\n",
+    "parity\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a36d83c5",
+   "metadata": {},
+   "source": [
+    "## Interpretation\n",
+    "\n",
+    "Use these helpers today for AnnData coordinate inspection, variable tables, quick tissue outlines, and artifact filtering. Do not treat this notebook as evidence of full SPATA2 parity. Future work should follow the audited export surface module by module: object containers, readers/initiation, get/set/add accessors, spatial annotations, trajectories, gradient screening, and plotting.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.10.20"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/tutorials/index_spatial_spata2.md b/docs/tutorials/index_spatial_spata2.md
index bd80e00..3a5d96e 100644
--- a/docs/tutorials/index_spatial_spata2.md
+++ b/docs/tutorials/index_spatial_spata2.md
@@ -1,6 +1,6 @@
-# SPATA2-style utilities
+# SPATA2-inspired utilities
 
-AnnData-native SPATA2 reconstruction tutorials.
+AnnData-native spatial utility tutorials inspired by SPATA2 workflows.
 
 ```{toctree}
 :maxdepth: 1
diff --git a/docs_zh/Tutorials-space/index.md b/docs_zh/Tutorials-space/index.md
index 3b00f66..7b61cca 100644
--- a/docs_zh/Tutorials-space/index.md
+++ b/docs_zh/Tutorials-space/index.md
@@ -1,6 +1,6 @@
-## SPATA2-style utilities
+## 受 SPATA2 启发的工具
 
-- [SPATA2-style AnnData utilities](t_spata2py.ipynb) - coordinate tables, variable joins, tissue outlines, spatial outlier filtering, unit conversion, benchmark, and py-SPATA2 namespace parity status.
+- [受 SPATA2 启发的 AnnData 空间工具](t_spata2py.ipynb) - 坐标表、变量拼接、组织轮廓、空间离群点过滤、单位换算、benchmark，并明确说明这不是完整 SPATA2 parity。
 
 # 空间转录组学教程
 
diff --git a/docs_zh/Tutorials-space/t_spata2py.ipynb b/docs_zh/Tutorials-space/t_spata2py.ipynb
index 7007e58..ca7ecd1 100644
--- a/docs_zh/Tutorials-space/t_spata2py.ipynb
+++ b/docs_zh/Tutorials-space/t_spata2py.ipynb
@@ -1,1406 +1,1406 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "5cfcc263",
-   "metadata": {},
-   "source": [
-    "# OmicVerse ?? SPATA2 ??????\n",
-    "\n",
-    "?????????? `ov.space` ??? AnnData-native SPATA2 ?????????? draft ?? PR ???? `py-SPATA2` ?????\n",
-    "\n",
-    "?????\n",
-    "\n",
-    "- API PR: [omicverse/omicverse#847](https://github.com/omicverse/omicverse/pull/847)\n",
-    "- ????: [omicverse/py-SPATA2](https://github.com/omicverse/py-SPATA2)\n",
-    "- SPATA2 ??: [theMILOlab.github.io/SPATA2](https://themilolab.github.io/SPATA2/index.html)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1a1ab594",
-   "metadata": {},
-   "source": [
-    "## ???????\n",
-    "\n",
-    "???????? SPATA2 ?????????\n",
-    "\n",
-    "| SPATA2 ?? | ?????? OmicVerse API | ?? |\n",
-    "|---|---|---|\n",
-    "| ??? | `ov.space.spata2_get_coords` | observation-indexed `DataFrame` |\n",
-    "| ???? / ??? | `ov.space.spata2_join_variables` | ?? + `obs` + ??? |\n",
-    "| ???? | `ov.space.spata2_tissue_outline` | convex outline??? `adata.uns` |\n",
-    "| ???? / ??? | `ov.space.spata2_identify_outliers` | `adata.obs` ?????? |\n",
-    "| ?????? | `ov.space.spata2_remove_outliers` | ???? `AnnData` |\n",
-    "| ???? | `spata2_pixels_to_unit`, `spata2_unit_to_pixels` | ?????? |\n",
-    "\n",
-    "?????? SPATA2 ?????? parity ????????? namespace ?????\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f7f75abf",
-   "metadata": {},
-   "source": [
-    "## ????\n",
-    "\n",
-    "? notebook ??? `omicverse`?`AnnData`?`numpy`?`pandas` ? `matplotlib`???? R?`rpy2` ? SPATA2 R ??\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "d7e6190a",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-20T09:00:40.052797Z",
-     "iopub.status.busy": "2026-06-20T09:00:40.052797Z",
-     "iopub.status.idle": "2026-06-20T09:00:41.664646Z",
-     "shell.execute_reply": "2026-06-20T09:00:41.664646Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "from anndata import AnnData\n",
-    "import matplotlib.pyplot as plt\n",
-    "import omicverse as ov\n",
-    "\n",
-    "%config InlineBackend.figure_format = 'png'\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "c9bba300",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-20T09:00:41.666647Z",
-     "iopub.status.busy": "2026-06-20T09:00:41.666647Z",
-     "iopub.status.idle": "2026-06-20T09:00:47.831182Z",
-     "shell.execute_reply": "2026-06-20T09:00:47.831182Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "omicverse 2.1.3rc1\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "D:\\Anaconda3\\envs\\omicverse\\lib\\site-packages\\anndata\\utils.py:434: FutureWarning: Importing read_loom from `anndata` is deprecated. Import anndata.io.read_loom instead.\n",
-      "  warnings.warn(msg, FutureWarning)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "SPATA2-style API: spata2_get_coords spata2_identify_outliers\n"
-     ]
-    }
-   ],
-   "source": [
-    "print('omicverse', ov.__version__ if hasattr(ov, '__version__') else 'unknown')\n",
-    "print('SPATA2-style API:', ov.space.spata2_get_coords.__name__, ov.space.spata2_identify_outliers.__name__)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1c4a02bd",
-   "metadata": {},
-   "source": [
-    "## ???? Visium-like ????\n",
-    "\n",
-    "?????????????????????? SPATA2 ??????????????????????????? synthetic fixture?\n",
-    "\n",
-    "- 20 x 20 ??????\n",
-    "- ????????????\n",
-    "- ??????? artifact-like spot?\n",
-    "\n",
-    "??????????????????????\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "d45a3fba",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-20T09:00:47.833183Z",
-     "iopub.status.busy": "2026-06-20T09:00:47.833183Z",
-     "iopub.status.idle": "2026-06-20T09:00:47.847183Z",
-     "shell.execute_reply": "2026-06-20T09:00:47.847183Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.omicverse.anndata+json": {
-       "name": null,
-       "previews": {
-        "layers": {},
-        "obs": {
-         "dtypes": {
-          "region": "object",
-          "total_counts": "float64"
-         },
-         "name": ".obs",
-         "shape": [
-          402,
-          2
-         ],
-         "table": {
-          "columns": [
-           "region",
-           "total_counts"
-          ],
-          "data": [
-           [
-            "left",
-            6.7466547045256275
-           ],
-           [
-            "left",
-            7.053847043120326
-           ],
-           [
-            "left",
-            7.128637064825885
-           ],
-           [
-            "left",
-            7.082770087011939
-           ],
-           [
-            "left",
-            7.299024494965832
-           ],
-           [
-            "left",
-            7.4893627202029265
-           ],
-           [
-            "left",
-            7.55085688051206
-           ],
-           [
-            "left",
-            7.936667992235289
-           ],
-           [
-            "left",
-            7.980497947921937
-           ],
-           [
-            "left",
-            8.012734164286691
-           ],
-           [
-            "right",
-            8.242546928219493
-           ],
-           [
-            "right",
-            8.419187753116988
-           ],
-           [
-            "right",
-            8.31814783629384
-           ],
-           [
-            "right",
-            8.112086974983596
-           ],
-           [
-            "right",
-            8.650773568599867
-           ],
-           [
-            "right",
-            8.586951861027314
-           ],
-           [
-            "right",
-            8.596927916174462
-           ],
-           [
-            "right",
-            8.4816648731433
-           ],
-           [
-            "right",
-            8.418354818423285
-           ],
-           [
-            "right",
-            8.455377180713363
-           ],
-           [
-            "left",
-            6.8360530207904615
-           ],
-           [
-            "left",
-            7.049176988827524
-           ],
-           [
-            "left",
-            6.891803914405125
-           ],
-           [
-            "left",
-            7.639107939473838
-           ]
-          ],
-          "index": [
-           "spot_000",
-           "spot_001",
-           "spot_002",
-           "spot_003",
-           "spot_004",
-           "spot_005",
-           "spot_006",
-           "spot_007",
-           "spot_008",
-           "spot_009",
-           "spot_010",
-           "spot_011",
-           "spot_012",
-           "spot_013",
-           "spot_014",
-           "spot_015",
-           "spot_016",
-           "spot_017",
-           "spot_018",
-           "spot_019",
-           "spot_020",
-           "spot_021",
-           "spot_022",
-           "spot_023"
-          ]
-         },
-         "type": "dataframe"
-        },
-        "obsm": {
-         "spatial": {
-          "dtype": "float64",
-          "name": "obsm[\"spatial\"]",
-          "shape": [
-           402,
-           2
-          ],
-          "table": {
-           "columns": [
-            "0",
-            "1"
-           ],
-           "data": [
-            [
-             0.0,
-             0.0
-            ],
-            [
-             1.0,
-             0.0
-            ],
-            [
-             2.0,
-             0.0
-            ],
-            [
-             3.0,
-             0.0
-            ],
-            [
-             4.0,
-             0.0
-            ],
-            [
-             5.0,
-             0.0
-            ],
-            [
-             6.0,
-             0.0
-            ],
-            [
-             7.0,
-             0.0
-            ],
-            [
-             8.0,
-             0.0
-            ],
-            [
-             9.0,
-             0.0
-            ]
-           ],
-           "index": [
-            "0",
-            "1",
-            "2",
-            "3",
-            "4",
-            "5",
-            "6",
-            "7",
-            "8",
-            "9"
-           ]
-          },
-          "type": "array"
-         }
-        },
-        "uns": {},
-        "var": {
-         "dtypes": {},
-         "name": ".var",
-         "shape": [
-          3,
-          0
-         ],
-         "table": {
-          "columns": [],
-          "data": [
-           [],
-           [],
-           []
-          ],
-          "index": [
-           "GeneA",
-           "GeneB",
-           "GeneC"
-          ]
-         },
-         "type": "dataframe"
-        }
-       },
-       "ref": "obj:24804499000",
-       "summary": {
-        "embedding_keys": [
-         "spatial"
-        ],
-        "embedding_keys_more": 0,
-        "embedding_keys_total": 1,
-        "layers": [],
-        "layers_more": 0,
-        "layers_total": 0,
-        "obs_columns": [
-         "region",
-         "total_counts"
-        ],
-        "obs_columns_more": 0,
-        "obs_columns_total": 2,
-        "obsm_keys": [
-         "spatial"
-        ],
-        "obsm_keys_more": 0,
-        "obsm_keys_total": 1,
-        "shape": [
-         402,
-         3
-        ],
-        "uns_keys": [],
-        "uns_keys_more": 0,
-        "uns_keys_total": 0,
-        "var_columns": [],
-        "var_columns_more": 0,
-        "var_columns_total": 0
-       },
-       "type": "anndata"
-      },
-      "text/plain": [
-       "AnnData object with n_obs × n_vars = 402 × 3\n",
-       "    obs: 'region', 'total_counts'\n",
-       "    obsm: 'spatial'"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "n_side = 20\n",
-    "grid_x, grid_y = np.meshgrid(np.arange(n_side), np.arange(n_side))\n",
-    "coords = np.column_stack([grid_x.ravel(), grid_y.ravel()]).astype(float)\n",
-    "outlier_coords = np.array([[55.0, 55.0], [58.0, 53.0]])\n",
-    "coords = np.vstack([coords, outlier_coords])\n",
-    "\n",
-    "rng = np.random.default_rng(7)\n",
-    "gene_a = coords[:, 0] / coords[:, 0].max() * 6 + rng.normal(0, 0.15, coords.shape[0])\n",
-    "gene_b = coords[:, 1] / coords[:, 1].max() * 5 + rng.normal(0, 0.15, coords.shape[0])\n",
-    "gene_c = np.sqrt((coords[:, 0] - 10) ** 2 + (coords[:, 1] - 10) ** 2)\n",
-    "gene_c = 8 - gene_c / gene_c.max() * 6 + rng.normal(0, 0.1, coords.shape[0])\n",
-    "counts = np.clip(np.column_stack([gene_a, gene_b, gene_c]), 0, None)\n",
-    "\n",
-    "obs = pd.DataFrame(\n",
-    "    {\n",
-    "        'region': np.where(coords[:, 0] < 10, 'left', 'right'),\n",
-    "        'total_counts': counts.sum(axis=1),\n",
-    "    },\n",
-    "    index=[f'spot_{idx:03d}' for idx in range(coords.shape[0])],\n",
-    ")\n",
-    "adata = AnnData(X=counts, obs=obs, var=pd.DataFrame(index=['GeneA', 'GeneB', 'GeneC']))\n",
-    "adata.obsm['spatial'] = coords\n",
-    "adata\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bd82082d",
-   "metadata": {},
-   "source": [
-    "## ????\n",
-    "\n",
-    "`spata2_get_coords` ???? `adata.obsm['spatial']`??? tidy coordinate table???????? observation metadata?\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "f0ec90db",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-20T09:00:47.849182Z",
-     "iopub.status.busy": "2026-06-20T09:00:47.849182Z",
-     "iopub.status.idle": "2026-06-20T09:00:47.863183Z",
-     "shell.execute_reply": "2026-06-20T09:00:47.863183Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.omicverse.dataframe+json": {
-       "dtypes": {
-        "barcode": "object",
-        "region": "object",
-        "total_counts": "float64",
-        "x": "float64",
-        "y": "float64"
-       },
-       "name": null,
-       "ref": "obj:2480449a200",
-       "shape": [
-        5,
-        5
-       ],
-       "table": {
-        "columns": [
-         "barcode",
-         "x",
-         "y",
-         "region",
-         "total_counts"
-        ],
-        "data": [
-         [
-          "spot_000",
-          0.0,
-          0.0,
-          "left",
-          6.7466547045256275
-         ],
-         [
-          "spot_001",
-          1.0,
-          0.0,
-          "left",
-          7.053847043120326
-         ],
-         [
-          "spot_002",
-          2.0,
-          0.0,
-          "left",
-          7.128637064825885
-         ],
-         [
-          "spot_003",
-          3.0,
-          0.0,
-          "left",
-          7.082770087011939
-         ],
-         [
-          "spot_004",
-          4.0,
-          0.0,
-          "left",
-          7.299024494965832
-         ]
-        ],
-        "index": [
-         "spot_000",
-         "spot_001",
-         "spot_002",
-         "spot_003",
-         "spot_004"
-        ]
-       },
-       "type": "dataframe"
-      },
-      "text/plain": [
-       "           barcode    x    y region  total_counts\n",
-       "spot_000  spot_000  0.0  0.0   left      6.746655\n",
-       "spot_001  spot_001  1.0  0.0   left      7.053847\n",
-       "spot_002  spot_002  2.0  0.0   left      7.128637\n",
-       "spot_003  spot_003  3.0  0.0   left      7.082770\n",
-       "spot_004  spot_004  4.0  0.0   left      7.299024"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "coords_df = ov.space.spata2_get_coords(adata, include_obs=['region', 'total_counts'])\n",
-    "coords_df.head()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6c6ce562",
-   "metadata": {},
-   "source": [
-    "## ???????\n",
-    "\n",
-    "`spata2_join_variables` ?? SPATA2 ???????????????????? workflow?`adata.obs` ????? metadata ???`adata.var_names` ????????????\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "88578e4a",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-20T09:00:47.865182Z",
-     "iopub.status.busy": "2026-06-20T09:00:47.865182Z",
-     "iopub.status.idle": "2026-06-20T09:00:47.879182Z",
-     "shell.execute_reply": "2026-06-20T09:00:47.879182Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.omicverse.dataframe+json": {
-       "dtypes": {
-        "GeneA": "float64",
-        "GeneB": "float64",
-        "GeneC": "float64",
-        "barcode": "object",
-        "region": "object",
-        "x": "float64",
-        "y": "float64"
-       },
-       "name": null,
-       "ref": "obj:24804498460",
-       "shape": [
-        5,
-        7
-       ],
-       "table": {
-        "columns": [
-         "barcode",
-         "x",
-         "y",
-         "region",
-         "GeneA",
-         "GeneB",
-         "GeneC"
-        ],
-        "data": [
-         [
-          "spot_000",
-          0.0,
-          0.0,
-          "left",
-          0.00018452300362238613,
-          0.0,
-          6.746470181522005
-         ],
-         [
-          "spot_001",
-          1.0,
-          0.0,
-          "left",
-          0.14826010648833945,
-          0.1543768849046773,
-          6.751210051727309
-         ],
-         [
-          "spot_002",
-          2.0,
-          0.0,
-          "left",
-          0.1657758734198053,
-          0.031559240824445535,
-          6.931301950581634
-         ],
-         [
-          "spot_003",
-          3.0,
-          0.0,
-          "left",
-          0.17675605177261577,
-          0.0,
-          6.906014035239323
-         ],
-         [
-          "spot_004",
-          4.0,
-          0.0,
-          "left",
-          0.3455924856725175,
-          0.0,
-          6.953432009293315
-         ]
-        ],
-        "index": [
-         "spot_000",
-         "spot_001",
-         "spot_002",
-         "spot_003",
-         "spot_004"
-        ]
-       },
-       "type": "dataframe"
-      },
-      "text/plain": [
-       "           barcode    x    y region     GeneA     GeneB     GeneC\n",
-       "spot_000  spot_000  0.0  0.0   left  0.000185  0.000000  6.746470\n",
-       "spot_001  spot_001  1.0  0.0   left  0.148260  0.154377  6.751210\n",
-       "spot_002  spot_002  2.0  0.0   left  0.165776  0.031559  6.931302\n",
-       "spot_003  spot_003  3.0  0.0   left  0.176756  0.000000  6.906014\n",
-       "spot_004  spot_004  4.0  0.0   left  0.345592  0.000000  6.953432"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "feature_df = ov.space.spata2_join_variables(adata, ['region', 'GeneA', 'GeneB', 'GeneC'])\n",
-    "feature_df.head()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "b73f0360",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-20T09:00:47.881182Z",
-     "iopub.status.busy": "2026-06-20T09:00:47.881182Z",
-     "iopub.status.idle": "2026-06-20T09:00:48.224182Z",
-     "shell.execute_reply": "2026-06-20T09:00:48.224182Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x300 with 6 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, axes = plt.subplots(1, 3, figsize=(10, 3), constrained_layout=True)\n",
-    "for ax, gene in zip(axes, ['GeneA', 'GeneB', 'GeneC']):\n",
-    "    sc = ax.scatter(feature_df['x'], feature_df['y'], c=feature_df[gene], s=18, cmap='viridis')\n",
-    "    ax.set_title(gene)\n",
-    "    ax.set_aspect('equal')\n",
-    "    ax.set_xlabel('x')\n",
-    "    ax.set_ylabel('y')\n",
-    "    fig.colorbar(sc, ax=ax, fraction=0.046, pad=0.04)\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bcbb5978",
-   "metadata": {},
-   "source": [
-    "## ??????\n",
-    "\n",
-    "?????????? convex hull???????????? SPATA2 ???? outline ? annotation ???\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "330ac2c2",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-20T09:00:48.226182Z",
-     "iopub.status.busy": "2026-06-20T09:00:48.226182Z",
-     "iopub.status.idle": "2026-06-20T09:00:48.241182Z",
-     "shell.execute_reply": "2026-06-20T09:00:48.240182Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.omicverse.dataframe+json": {
-       "dtypes": {
-        "x": "float64",
-        "y": "float64"
-       },
-       "name": null,
-       "ref": "obj:2480449a590",
-       "shape": [
-        5,
-        2
-       ],
-       "table": {
-        "columns": [
-         "x",
-         "y"
-        ],
-        "data": [
-         [
-          58.0,
-          53.0
-         ],
-         [
-          55.0,
-          55.0
-         ],
-         [
-          0.0,
-          19.0
-         ],
-         [
-          0.0,
-          0.0
-         ],
-         [
-          19.0,
-          0.0
-         ]
-        ],
-        "index": [
-         "0",
-         "1",
-         "2",
-         "3",
-         "4"
-        ]
-       },
-       "type": "dataframe"
-      },
-      "text/plain": [
-       "           x     y\n",
-       "vertex            \n",
-       "0       58.0  53.0\n",
-       "1       55.0  55.0\n",
-       "2        0.0  19.0\n",
-       "3        0.0   0.0\n",
-       "4       19.0   0.0"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "outline = ov.space.spata2_tissue_outline(adata)\n",
-    "outline\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5d4c3ce9",
-   "metadata": {},
-   "source": [
-    "## ???????\n",
-    "\n",
-    "???? radius-neighborhood ????????????? observation ????????????????????????\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "93e34bcb",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-20T09:00:48.242182Z",
-     "iopub.status.busy": "2026-06-20T09:00:48.242182Z",
-     "iopub.status.idle": "2026-06-20T09:00:48.257182Z",
-     "shell.execute_reply": "2026-06-20T09:00:48.256183Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "spata2_spatial_outlier\n",
-      "kept       400\n",
-      "outlier      2\n",
-      "Name: count, dtype: int64\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.omicverse.dataframe+json": {
-       "dtypes": {
-        "region": "object",
-        "total_counts": "float64"
-       },
-       "name": null,
-       "ref": "obj:2480449b430",
-       "shape": [
-        2,
-        2
-       ],
-       "table": {
-        "columns": [
-         "region",
-         "total_counts"
-        ],
-        "data": [
-         [
-          "right",
-          12.67020073237883
-         ],
-         [
-          "right",
-          12.80379983730633
-         ]
-        ],
-        "index": [
-         "spot_400",
-         "spot_401"
-        ]
-       },
-       "type": "dataframe"
-      },
-      "text/plain": [
-       "         region  total_counts\n",
-       "spot_400  right     12.670201\n",
-       "spot_401  right     12.803800"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "outliers = ov.space.spata2_identify_outliers(adata, radius=2.0, min_neighbors=2)\n",
-    "print(outliers.value_counts().rename(index={False: 'kept', True: 'outlier'}))\n",
-    "adata.obs.loc[outliers, ['region', 'total_counts']]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "cd8b9c27",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-20T09:00:48.259182Z",
-     "iopub.status.busy": "2026-06-20T09:00:48.258182Z",
-     "iopub.status.idle": "2026-06-20T09:00:48.336182Z",
-     "shell.execute_reply": "2026-06-20T09:00:48.336182Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(figsize=(5, 5))\n",
-    "ax.scatter(coords_df['x'], coords_df['y'], s=18, c=np.where(outliers, 'crimson', 'steelblue'), alpha=0.9)\n",
-    "closed = pd.concat([outline, outline.iloc[[0]]], ignore_index=True)\n",
-    "ax.plot(closed['x'], closed['y'], color='black', linewidth=1.5, label='convex tissue outline')\n",
-    "ax.set_aspect('equal')\n",
-    "ax.set_xlabel('x')\n",
-    "ax.set_ylabel('y')\n",
-    "ax.legend(frameon=False)\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cb02529d",
-   "metadata": {},
-   "source": [
-    "## ???????\n",
-    "\n",
-    "`spata2_remove_outliers` ???????? copy??????????\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "fc32071b",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-20T09:00:48.338182Z",
-     "iopub.status.busy": "2026-06-20T09:00:48.338182Z",
-     "iopub.status.idle": "2026-06-20T09:00:48.351182Z",
-     "shell.execute_reply": "2026-06-20T09:00:48.351182Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "before: 402 after: 400\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.omicverse.dataframe+json": {
-       "dtypes": {
-        "region": "object",
-        "spata2_spatial_outlier": "bool",
-        "total_counts": "float64"
-       },
-       "name": null,
-       "ref": "obj:24804499bd0",
-       "shape": [
-        5,
-        3
-       ],
-       "table": {
-        "columns": [
-         "region",
-         "total_counts",
-         "spata2_spatial_outlier"
-        ],
-        "data": [
-         [
-          "right",
-          10.439553070239775,
-          false
-         ],
-         [
-          "right",
-          10.174393338504789,
-          false
-         ],
-         [
-          "right",
-          10.29893327549868,
-          false
-         ],
-         [
-          "right",
-          10.259728697196483,
-          false
-         ],
-         [
-          "right",
-          10.336551804483957,
-          false
-         ]
-        ],
-        "index": [
-         "spot_395",
-         "spot_396",
-         "spot_397",
-         "spot_398",
-         "spot_399"
-        ]
-       },
-       "type": "dataframe"
-      },
-      "text/plain": [
-       "         region  total_counts  spata2_spatial_outlier\n",
-       "spot_395  right     10.439553                   False\n",
-       "spot_396  right     10.174393                   False\n",
-       "spot_397  right     10.298933                   False\n",
-       "spot_398  right     10.259729                   False\n",
-       "spot_399  right     10.336552                   False"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "filtered = ov.space.spata2_remove_outliers(adata)\n",
-    "print('before:', adata.n_obs, 'after:', filtered.n_obs)\n",
-    "filtered.obs.tail()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "197ba6c0",
-   "metadata": {},
-   "source": [
-    "## ???????????\n",
-    "\n",
-    "SPATA2 R API ????????Python ????????????? scale factor????? round-trip ????\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "825a735d",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-20T09:00:48.352182Z",
-     "iopub.status.busy": "2026-06-20T09:00:48.352182Z",
-     "iopub.status.idle": "2026-06-20T09:00:48.368182Z",
-     "shell.execute_reply": "2026-06-20T09:00:48.367182Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.omicverse.dataframe+json": {
-       "dtypes": {
-        "micrometers": "float64",
-        "pixels": "int32",
-        "roundtrip_pixels": "float64"
-       },
-       "name": null,
-       "ref": "obj:24806a1bd90",
-       "shape": [
-        4,
-        3
-       ],
-       "table": {
-        "columns": [
-         "pixels",
-         "micrometers",
-         "roundtrip_pixels"
-        ],
-        "data": [
-         [
-          0,
-          0.0,
-          0.0
-         ],
-         [
-          55,
-          27.5,
-          55.0
-         ],
-         [
-          110,
-          55.0,
-          110.0
-         ],
-         [
-          220,
-          110.0,
-          220.0
-         ]
-        ],
-        "index": [
-         "0",
-         "1",
-         "2",
-         "3"
-        ]
-       },
-       "type": "dataframe"
-      },
-      "text/plain": [
-       "   pixels  micrometers  roundtrip_pixels\n",
-       "0       0          0.0               0.0\n",
-       "1      55         27.5              55.0\n",
-       "2     110         55.0             110.0\n",
-       "3     220        110.0             220.0"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pixels = np.array([0, 55, 110, 220])\n",
-    "micrometers = ov.space.spata2_pixels_to_unit(pixels, pixels_per_unit=2.0)\n",
-    "roundtrip = ov.space.spata2_unit_to_pixels(micrometers, pixels_per_unit=2.0)\n",
-    "pd.DataFrame({'pixels': pixels, 'micrometers': micrometers, 'roundtrip_pixels': roundtrip})\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2a913c09",
-   "metadata": {},
-   "source": [
-    "## Notebook benchmark\n",
-    "\n",
-    "???? benchmark ???????????? fixture ?? wall-clock ?????? `py-SPATA2` benchmark ?????????\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "4135e67b",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-20T09:00:48.371182Z",
-     "iopub.status.busy": "2026-06-20T09:00:48.370182Z",
-     "iopub.status.idle": "2026-06-20T09:00:48.383182Z",
-     "shell.execute_reply": "2026-06-20T09:00:48.383182Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.omicverse.dataframe+json": {
-       "dtypes": {
-        "operation": "object",
-        "seconds": "float64",
-        "shape": "object"
-       },
-       "name": null,
-       "ref": "obj:248046de4d0",
-       "shape": [
-        5,
-        3
-       ],
-       "table": {
-        "columns": [
-         "operation",
-         "seconds",
-         "shape"
-        ],
-        "data": [
-         [
-          "spata2_get_coords",
-          0.0004924999957438558,
-          [
-           402,
-           3
-          ]
-         ],
-         [
-          "spata2_join_variables",
-          0.0008758000039961189,
-          [
-           402,
-           6
-          ]
-         ],
-         [
-          "spata2_tissue_outline",
-          0.0008729000110179186,
-          [
-           5,
-           2
-          ]
-         ],
-         [
-          "spata2_identify_outliers",
-          0.0008899999957066029,
-          [
-           402
-          ]
-         ],
-         [
-          "spata2_remove_outliers",
-          0.0008275000145658851,
-          [
-           400,
-           3
-          ]
-         ]
-        ],
-        "index": [
-         "0",
-         "1",
-         "2",
-         "3",
-         "4"
-        ]
-       },
-       "type": "dataframe"
-      },
-      "text/plain": [
-       "                  operation   seconds     shape\n",
-       "0         spata2_get_coords  0.000492  (402, 3)\n",
-       "1     spata2_join_variables  0.000876  (402, 6)\n",
-       "2     spata2_tissue_outline  0.000873    (5, 2)\n",
-       "3  spata2_identify_outliers  0.000890    (402,)\n",
-       "4    spata2_remove_outliers  0.000828  (400, 3)"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import time\n",
-    "\n",
-    "bench_rows = []\n",
-    "for label, call in [\n",
-    "    ('spata2_get_coords', lambda: ov.space.spata2_get_coords(adata)),\n",
-    "    ('spata2_join_variables', lambda: ov.space.spata2_join_variables(adata, ['region', 'GeneA', 'GeneB'])),\n",
-    "    ('spata2_tissue_outline', lambda: ov.space.spata2_tissue_outline(adata, write_key=None)),\n",
-    "    ('spata2_identify_outliers', lambda: ov.space.spata2_identify_outliers(adata, radius=2.0, min_neighbors=2, write_key=None)),\n",
-    "    ('spata2_remove_outliers', lambda: ov.space.spata2_remove_outliers(adata)),\n",
-    "]:\n",
-    "    start = time.perf_counter()\n",
-    "    result = call()\n",
-    "    elapsed = time.perf_counter() - start\n",
-    "    bench_rows.append({'operation': label, 'seconds': elapsed, 'shape': getattr(result, 'shape', (len(result),))})\n",
-    "\n",
-    "pd.DataFrame(bench_rows)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4bc61d87",
-   "metadata": {},
-   "source": [
-    "## ?? parity ??\n",
-    "\n",
-    "?????????? SPATA2 3.1.4 `NAMESPACE`?????????? Python ????? R-style export ???? implemented?\n",
-    "\n",
-    "?????\n",
-    "\n",
-    "- [NAMESPACE_PARITY.md](https://github.com/omicverse/py-SPATA2/blob/master/NAMESPACE_PARITY.md)\n",
-    "- [spata2_namespace_parity.csv](https://github.com/omicverse/py-SPATA2/blob/master/references/spata2_namespace_parity.csv)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "2fe270ce",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-20T09:00:48.385182Z",
-     "iopub.status.busy": "2026-06-20T09:00:48.385182Z",
-     "iopub.status.idle": "2026-06-20T09:00:48.398182Z",
-     "shell.execute_reply": "2026-06-20T09:00:48.398182Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.omicverse.dataframe+json": {
-       "dtypes": {
-        "metric": "object",
-        "value": "object"
-       },
-       "name": null,
-       "ref": "obj:248044dc1c0",
-       "shape": [
-        4,
-        2
-       ],
-       "table": {
-        "columns": [
-         "metric",
-         "value"
-        ],
-        "data": [
-         [
-          "R exports audited from SPATA2 3.1.4 NAMESPACE",
-          751
-         ],
-         [
-          "R-compatible Python symbols implemented in py-SPATA2",
-          16
-         ],
-         [
-          "Strict namespace coverage",
-          "2.1%"
-         ],
-         [
-          "ov.space tutorial API functions exercised here",
-          8
-         ]
-        ],
-        "index": [
-         "0",
-         "1",
-         "2",
-         "3"
-        ]
-       },
-       "type": "dataframe"
-      },
-      "text/plain": [
-       "                                              metric value\n",
-       "0      R exports audited from SPATA2 3.1.4 NAMESPACE   751\n",
-       "1  R-compatible Python symbols implemented in py-...    16\n",
-       "2                          Strict namespace coverage  2.1%\n",
-       "3     ov.space tutorial API functions exercised here     8"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "parity = pd.DataFrame(\n",
-    "    [\n",
-    "        ['R exports audited from SPATA2 3.1.4 NAMESPACE', 751],\n",
-    "        ['R-compatible Python symbols implemented in py-SPATA2', 16],\n",
-    "        ['Strict namespace coverage', '2.1%'],\n",
-    "        ['ov.space tutorial API functions exercised here', 8],\n",
-    "    ],\n",
-    "    columns=['metric', 'value'],\n",
-    ")\n",
-    "parity\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1495723b",
-   "metadata": {},
-   "source": [
-    "## ????\n",
-    "\n",
-    "??????????? AnnData ?????????????????? artifact ???????? notebook ???? SPATA2 parity ????????????????????????reader/initiation?get/set/add accessors?spatial annotations?trajectories?gradient screening ? plotting?\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.10.20"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "5cfcc263",
+   "metadata": {},
+   "source": [
+    "# OmicVerse 中受 SPATA2 启发的 AnnData 空间工具\n",
+    "\n",
+    "本教程介绍 `ov.space` 中一组轻量的 AnnData-native 空间工具，覆盖坐标表、变量拼接、组织轮廓、空间离群点和单位换算等基础流程。这些函数受到 SPATA2 工作流启发，并与 `py-SPATA2` 重写仓库相关，但不代表已经实现完整 SPATA2 parity。\n",
+    "\n",
+    "相关链接：\n",
+    "\n",
+    "- API PR: [omicverse/omicverse#847](https://github.com/omicverse/omicverse/pull/847)\n",
+    "- 重写仓库: [omicverse/py-SPATA2](https://github.com/omicverse/py-SPATA2)\n",
+    "- SPATA2 文档: [theMILOlab.github.io/SPATA2](https://themilolab.github.io/SPATA2/index.html)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a1ab594",
+   "metadata": {},
+   "source": [
+    "## 本教程覆盖什么\n",
+    "\n",
+    "当前实现只聚焦一组窄而稳定的 AnnData helper：\n",
+    "\n",
+    "| 空间任务 | 本教程使用的 OmicVerse API | 输出 |\n",
+    "|---|---|---|\n",
+    "| 坐标表 | `ov.space.spata2_get_coords` | 以 observation 为索引的 `DataFrame` |\n",
+    "| 分子变量 / 元数据 | `ov.space.spata2_join_variables` | 坐标、`obs` 元数据和基因值拼接后的表格 |\n",
+    "| 组织轮廓 | `ov.space.spata2_tissue_outline` | 保存在 `adata.uns` 中的凸包轮廓 |\n",
+    "| 空间伪影 / 离群点 | `ov.space.spata2_identify_outliers` | 写入 `adata.obs` 的布尔离群标记 |\n",
+    "| 清理后的对象 | `ov.space.spata2_remove_outliers` | 过滤后的 `AnnData` |\n",
+    "| 像素比例尺 | `spata2_pixels_to_unit`, `spata2_unit_to_pixels` | 显式距离单位换算 |\n",
+    "\n",
+    "最后一节给出 parity 背景，避免读者把这组初始 helper 误解为完整 SPATA2 移植。\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7f75abf",
+   "metadata": {},
+   "source": [
+    "## 环境准备\n",
+    "\n",
+    "本 notebook 只使用 `omicverse`、`AnnData`、`numpy`、`pandas` 和 `matplotlib`。不需要 R 运行时、`rpy2` 或 SPATA2 R 包。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d7e6190a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-02T11:39:53.348507Z",
+     "iopub.status.busy": "2026-07-02T11:39:53.348507Z",
+     "iopub.status.idle": "2026-07-02T11:39:55.113032Z",
+     "shell.execute_reply": "2026-07-02T11:39:55.112030Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from anndata import AnnData\n",
+    "import matplotlib.pyplot as plt\n",
+    "import omicverse as ov\n",
+    "\n",
+    "%config InlineBackend.figure_format = 'png'\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c9bba300",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-02T11:39:55.114604Z",
+     "iopub.status.busy": "2026-07-02T11:39:55.113601Z",
+     "iopub.status.idle": "2026-07-02T11:40:00.029033Z",
+     "shell.execute_reply": "2026-07-02T11:40:00.028527Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "omicverse 2.1.3rc1\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\ProgramData\\Anaconda3\\envs\\omicverse\\lib\\site-packages\\anndata\\utils.py:434: FutureWarning: Importing read_loom from `anndata` is deprecated. Import anndata.io.read_loom instead.\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SPATA2-inspired API: spata2_get_coords spata2_identify_outliers\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('omicverse', ov.__version__ if hasattr(ov, '__version__') else 'unknown')\n",
+    "print('SPATA2-inspired API:', ov.space.spata2_get_coords.__name__, ov.space.spata2_identify_outliers.__name__)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c4a02bd",
+   "metadata": {},
+   "source": [
+    "## 构建一个 Visium-like 教程数据\n",
+    "\n",
+    "当前 helper 层主要演示坐标和空间数据工具，而不是完整生物学模型。因此这里使用一个确定性的 synthetic fixture：\n",
+    "\n",
+    "- 20 x 20 的组织网格；\n",
+    "- 三个平滑变化的类基因空间变量；\n",
+    "- 两个故意放远的 artifact spot。\n",
+    "\n",
+    "这样渲染后可以直观看到组织轮廓和离群点行为是否符合预期。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "d45a3fba",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-02T11:40:00.031298Z",
+     "iopub.status.busy": "2026-07-02T11:40:00.030298Z",
+     "iopub.status.idle": "2026-07-02T11:40:00.046297Z",
+     "shell.execute_reply": "2026-07-02T11:40:00.046297Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.omicverse.anndata+json": {
+       "name": null,
+       "previews": {
+        "layers": {},
+        "obs": {
+         "dtypes": {
+          "region": "object",
+          "total_counts": "float64"
+         },
+         "name": ".obs",
+         "shape": [
+          402,
+          2
+         ],
+         "table": {
+          "columns": [
+           "region",
+           "total_counts"
+          ],
+          "data": [
+           [
+            "left",
+            6.7466547045256275
+           ],
+           [
+            "left",
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+            "left",
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+           [
+            "left",
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+           [
+            "right",
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+           [
+            "right",
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+           [
+            "right",
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+            "right",
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+            "right",
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+           [
+            "right",
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+           [
+            "right",
+            8.596927916174462
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+           [
+            "right",
+            8.4816648731433
+           ],
+           [
+            "right",
+            8.418354818423285
+           ],
+           [
+            "right",
+            8.455377180713363
+           ],
+           [
+            "left",
+            6.8360530207904615
+           ],
+           [
+            "left",
+            7.049176988827524
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+           [
+            "left",
+            6.891803914405125
+           ],
+           [
+            "left",
+            7.639107939473838
+           ]
+          ],
+          "index": [
+           "spot_000",
+           "spot_001",
+           "spot_002",
+           "spot_003",
+           "spot_004",
+           "spot_005",
+           "spot_006",
+           "spot_007",
+           "spot_008",
+           "spot_009",
+           "spot_010",
+           "spot_011",
+           "spot_012",
+           "spot_013",
+           "spot_014",
+           "spot_015",
+           "spot_016",
+           "spot_017",
+           "spot_018",
+           "spot_019",
+           "spot_020",
+           "spot_021",
+           "spot_022",
+           "spot_023"
+          ]
+         },
+         "type": "dataframe"
+        },
+        "obsm": {
+         "spatial": {
+          "dtype": "float64",
+          "name": "obsm[\"spatial\"]",
+          "shape": [
+           402,
+           2
+          ],
+          "table": {
+           "columns": [
+            "0",
+            "1"
+           ],
+           "data": [
+            [
+             0.0,
+             0.0
+            ],
+            [
+             1.0,
+             0.0
+            ],
+            [
+             2.0,
+             0.0
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+            [
+             3.0,
+             0.0
+            ],
+            [
+             4.0,
+             0.0
+            ],
+            [
+             5.0,
+             0.0
+            ],
+            [
+             6.0,
+             0.0
+            ],
+            [
+             7.0,
+             0.0
+            ],
+            [
+             8.0,
+             0.0
+            ],
+            [
+             9.0,
+             0.0
+            ]
+           ],
+           "index": [
+            "0",
+            "1",
+            "2",
+            "3",
+            "4",
+            "5",
+            "6",
+            "7",
+            "8",
+            "9"
+           ]
+          },
+          "type": "array"
+         }
+        },
+        "uns": {},
+        "var": {
+         "dtypes": {},
+         "name": ".var",
+         "shape": [
+          3,
+          0
+         ],
+         "table": {
+          "columns": [],
+          "data": [
+           [],
+           [],
+           []
+          ],
+          "index": [
+           "GeneA",
+           "GeneB",
+           "GeneC"
+          ]
+         },
+         "type": "dataframe"
+        }
+       },
+       "ref": "obj:21bb83494b0",
+       "summary": {
+        "embedding_keys": [
+         "spatial"
+        ],
+        "embedding_keys_more": 0,
+        "embedding_keys_total": 1,
+        "layers": [],
+        "layers_more": 0,
+        "layers_total": 0,
+        "obs_columns": [
+         "region",
+         "total_counts"
+        ],
+        "obs_columns_more": 0,
+        "obs_columns_total": 2,
+        "obsm_keys": [
+         "spatial"
+        ],
+        "obsm_keys_more": 0,
+        "obsm_keys_total": 1,
+        "shape": [
+         402,
+         3
+        ],
+        "uns_keys": [],
+        "uns_keys_more": 0,
+        "uns_keys_total": 0,
+        "var_columns": [],
+        "var_columns_more": 0,
+        "var_columns_total": 0
+       },
+       "type": "anndata"
+      },
+      "text/plain": [
+       "AnnData object with n_obs × n_vars = 402 × 3\n",
+       "    obs: 'region', 'total_counts'\n",
+       "    obsm: 'spatial'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n_side = 20\n",
+    "grid_x, grid_y = np.meshgrid(np.arange(n_side), np.arange(n_side))\n",
+    "coords = np.column_stack([grid_x.ravel(), grid_y.ravel()]).astype(float)\n",
+    "outlier_coords = np.array([[55.0, 55.0], [58.0, 53.0]])\n",
+    "coords = np.vstack([coords, outlier_coords])\n",
+    "\n",
+    "rng = np.random.default_rng(7)\n",
+    "gene_a = coords[:, 0] / coords[:, 0].max() * 6 + rng.normal(0, 0.15, coords.shape[0])\n",
+    "gene_b = coords[:, 1] / coords[:, 1].max() * 5 + rng.normal(0, 0.15, coords.shape[0])\n",
+    "gene_c = np.sqrt((coords[:, 0] - 10) ** 2 + (coords[:, 1] - 10) ** 2)\n",
+    "gene_c = 8 - gene_c / gene_c.max() * 6 + rng.normal(0, 0.1, coords.shape[0])\n",
+    "counts = np.clip(np.column_stack([gene_a, gene_b, gene_c]), 0, None)\n",
+    "\n",
+    "obs = pd.DataFrame(\n",
+    "    {\n",
+    "        'region': np.where(coords[:, 0] < 10, 'left', 'right'),\n",
+    "        'total_counts': counts.sum(axis=1),\n",
+    "    },\n",
+    "    index=[f'spot_{idx:03d}' for idx in range(coords.shape[0])],\n",
+    ")\n",
+    "adata = AnnData(X=counts, obs=obs, var=pd.DataFrame(index=['GeneA', 'GeneB', 'GeneC']))\n",
+    "adata.obsm['spatial'] = coords\n",
+    "adata\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd82082d",
+   "metadata": {},
+   "source": [
+    "## 提取坐标\n",
+    "\n",
+    "`spata2_get_coords` 读取 `adata.obsm['spatial']` 并返回 tidy coordinate table。它也可以拼接 observation metadata，同时保护标准输出列 `barcode`、`x` 和 `y`，避免常见 obs 列名冲突。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f0ec90db",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-02T11:40:00.047298Z",
+     "iopub.status.busy": "2026-07-02T11:40:00.047298Z",
+     "iopub.status.idle": "2026-07-02T11:40:00.063119Z",
+     "shell.execute_reply": "2026-07-02T11:40:00.062957Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.omicverse.dataframe+json": {
+       "dtypes": {
+        "barcode": "object",
+        "region": "object",
+        "total_counts": "float64",
+        "x": "float64",
+        "y": "float64"
+       },
+       "name": null,
+       "ref": "obj:21bb83498d0",
+       "shape": [
+        5,
+        5
+       ],
+       "table": {
+        "columns": [
+         "barcode",
+         "x",
+         "y",
+         "region",
+         "total_counts"
+        ],
+        "data": [
+         [
+          "spot_000",
+          0.0,
+          0.0,
+          "left",
+          6.7466547045256275
+         ],
+         [
+          "spot_001",
+          1.0,
+          0.0,
+          "left",
+          7.053847043120326
+         ],
+         [
+          "spot_002",
+          2.0,
+          0.0,
+          "left",
+          7.128637064825885
+         ],
+         [
+          "spot_003",
+          3.0,
+          0.0,
+          "left",
+          7.082770087011939
+         ],
+         [
+          "spot_004",
+          4.0,
+          0.0,
+          "left",
+          7.299024494965832
+         ]
+        ],
+        "index": [
+         "spot_000",
+         "spot_001",
+         "spot_002",
+         "spot_003",
+         "spot_004"
+        ]
+       },
+       "type": "dataframe"
+      },
+      "text/plain": [
+       "           barcode    x    y region  total_counts\n",
+       "spot_000  spot_000  0.0  0.0   left      6.746655\n",
+       "spot_001  spot_001  1.0  0.0   left      7.053847\n",
+       "spot_002  spot_002  2.0  0.0   left      7.128637\n",
+       "spot_003  spot_003  3.0  0.0   left      7.082770\n",
+       "spot_004  spot_004  4.0  0.0   left      7.299024"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coords_df = ov.space.spata2_get_coords(adata, include_obs=['region', 'total_counts'])\n",
+    "coords_df.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6c6ce562",
+   "metadata": {},
+   "source": [
+    "## 拼接坐标和变量\n",
+    "\n",
+    "`spata2_join_variables` 将空间坐标与感兴趣的变量放在同一张表里。出现在 `adata.obs` 中的名称会被视为元数据；出现在 `adata.var_names` 中的名称会从表达矩阵中提取。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "88578e4a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-02T11:40:00.065119Z",
+     "iopub.status.busy": "2026-07-02T11:40:00.065119Z",
+     "iopub.status.idle": "2026-07-02T11:40:00.078118Z",
+     "shell.execute_reply": "2026-07-02T11:40:00.078118Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.omicverse.dataframe+json": {
+       "dtypes": {
+        "GeneA": "float64",
+        "GeneB": "float64",
+        "GeneC": "float64",
+        "barcode": "object",
+        "region": "object",
+        "x": "float64",
+        "y": "float64"
+       },
+       "name": null,
+       "ref": "obj:21bb8349120",
+       "shape": [
+        5,
+        7
+       ],
+       "table": {
+        "columns": [
+         "barcode",
+         "x",
+         "y",
+         "region",
+         "GeneA",
+         "GeneB",
+         "GeneC"
+        ],
+        "data": [
+         [
+          "spot_000",
+          0.0,
+          0.0,
+          "left",
+          0.00018452300362238613,
+          0.0,
+          6.746470181522005
+         ],
+         [
+          "spot_001",
+          1.0,
+          0.0,
+          "left",
+          0.14826010648833945,
+          0.1543768849046773,
+          6.751210051727309
+         ],
+         [
+          "spot_002",
+          2.0,
+          0.0,
+          "left",
+          0.1657758734198053,
+          0.031559240824445535,
+          6.931301950581634
+         ],
+         [
+          "spot_003",
+          3.0,
+          0.0,
+          "left",
+          0.17675605177261577,
+          0.0,
+          6.906014035239323
+         ],
+         [
+          "spot_004",
+          4.0,
+          0.0,
+          "left",
+          0.3455924856725175,
+          0.0,
+          6.953432009293315
+         ]
+        ],
+        "index": [
+         "spot_000",
+         "spot_001",
+         "spot_002",
+         "spot_003",
+         "spot_004"
+        ]
+       },
+       "type": "dataframe"
+      },
+      "text/plain": [
+       "           barcode    x    y region     GeneA     GeneB     GeneC\n",
+       "spot_000  spot_000  0.0  0.0   left  0.000185  0.000000  6.746470\n",
+       "spot_001  spot_001  1.0  0.0   left  0.148260  0.154377  6.751210\n",
+       "spot_002  spot_002  2.0  0.0   left  0.165776  0.031559  6.931302\n",
+       "spot_003  spot_003  3.0  0.0   left  0.176756  0.000000  6.906014\n",
+       "spot_004  spot_004  4.0  0.0   left  0.345592  0.000000  6.953432"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "feature_df = ov.space.spata2_join_variables(adata, ['region', 'GeneA', 'GeneB', 'GeneC'])\n",
+    "feature_df.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b73f0360",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-02T11:40:00.080118Z",
+     "iopub.status.busy": "2026-07-02T11:40:00.080118Z",
+     "iopub.status.idle": "2026-07-02T11:40:00.328356Z",
+     "shell.execute_reply": "2026-07-02T11:40:00.328146Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x300 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(1, 3, figsize=(10, 3), constrained_layout=True)\n",
+    "for ax, gene in zip(axes, ['GeneA', 'GeneB', 'GeneC']):\n",
+    "    sc = ax.scatter(feature_df['x'], feature_df['y'], c=feature_df[gene], s=18, cmap='viridis')\n",
+    "    ax.set_title(gene)\n",
+    "    ax.set_aspect('equal')\n",
+    "    ax.set_xlabel('x')\n",
+    "    ax.set_ylabel('y')\n",
+    "    fig.colorbar(sc, ax=ax, fraction=0.046, pad=0.04)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bcbb5978",
+   "metadata": {},
+   "source": [
+    "## 识别组织轮廓\n",
+    "\n",
+    "第一版实现使用确定性的凸包轮廓，适合快速检查空间范围；它不是 SPATA2 更完整的组织轮廓和注释模型的替代品。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "330ac2c2",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-02T11:40:00.330230Z",
+     "iopub.status.busy": "2026-07-02T11:40:00.330230Z",
+     "iopub.status.idle": "2026-07-02T11:40:00.346786Z",
+     "shell.execute_reply": "2026-07-02T11:40:00.345897Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.omicverse.dataframe+json": {
+       "dtypes": {
+        "x": "float64",
+        "y": "float64"
+       },
+       "name": null,
+       "ref": "obj:21bb8348b20",
+       "shape": [
+        5,
+        2
+       ],
+       "table": {
+        "columns": [
+         "x",
+         "y"
+        ],
+        "data": [
+         [
+          58.0,
+          53.0
+         ],
+         [
+          55.0,
+          55.0
+         ],
+         [
+          0.0,
+          19.0
+         ],
+         [
+          0.0,
+          0.0
+         ],
+         [
+          19.0,
+          0.0
+         ]
+        ],
+        "index": [
+         "0",
+         "1",
+         "2",
+         "3",
+         "4"
+        ]
+       },
+       "type": "dataframe"
+      },
+      "text/plain": [
+       "           x     y\n",
+       "vertex            \n",
+       "0       58.0  53.0\n",
+       "1       55.0  55.0\n",
+       "2        0.0  19.0\n",
+       "3        0.0   0.0\n",
+       "4       19.0   0.0"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "outline = ov.space.spata2_tissue_outline(adata)\n",
+    "outline\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d4c3ce9",
+   "metadata": {},
+   "source": [
+    "## 检测孤立空间 observation\n",
+    "\n",
+    "这里使用半径邻域规则，标记远离主体组织网格的 observation。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "93e34bcb",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-02T11:40:00.349748Z",
+     "iopub.status.busy": "2026-07-02T11:40:00.349748Z",
+     "iopub.status.idle": "2026-07-02T11:40:00.362019Z",
+     "shell.execute_reply": "2026-07-02T11:40:00.362019Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "spata2_spatial_outlier\n",
+      "kept       400\n",
+      "outlier      2\n",
+      "Name: count, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.omicverse.dataframe+json": {
+       "dtypes": {
+        "region": "object",
+        "total_counts": "float64"
+       },
+       "name": null,
+       "ref": "obj:21bb8349060",
+       "shape": [
+        2,
+        2
+       ],
+       "table": {
+        "columns": [
+         "region",
+         "total_counts"
+        ],
+        "data": [
+         [
+          "right",
+          12.67020073237883
+         ],
+         [
+          "right",
+          12.80379983730633
+         ]
+        ],
+        "index": [
+         "spot_400",
+         "spot_401"
+        ]
+       },
+       "type": "dataframe"
+      },
+      "text/plain": [
+       "         region  total_counts\n",
+       "spot_400  right     12.670201\n",
+       "spot_401  right     12.803800"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "outliers = ov.space.spata2_identify_outliers(adata, radius=2.0, min_neighbors=2)\n",
+    "print(outliers.value_counts().rename(index={False: 'kept', True: 'outlier'}))\n",
+    "adata.obs.loc[outliers, ['region', 'total_counts']]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "cd8b9c27",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-02T11:40:00.364066Z",
+     "iopub.status.busy": "2026-07-02T11:40:00.363519Z",
+     "iopub.status.idle": "2026-07-02T11:40:00.412526Z",
+     "shell.execute_reply": "2026-07-02T11:40:00.412526Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(5, 5))\n",
+    "ax.scatter(coords_df['x'], coords_df['y'], s=18, c=np.where(outliers, 'crimson', 'steelblue'), alpha=0.9)\n",
+    "closed = pd.concat([outline, outline.iloc[[0]]], ignore_index=True)\n",
+    "ax.plot(closed['x'], closed['y'], color='black', linewidth=1.5, label='convex tissue outline')\n",
+    "ax.set_aspect('equal')\n",
+    "ax.set_xlabel('x')\n",
+    "ax.set_ylabel('y')\n",
+    "ax.legend(frameon=False)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb02529d",
+   "metadata": {},
+   "source": [
+    "## 移除标记的离群点\n",
+    "\n",
+    "`spata2_remove_outliers` 可以返回过滤后的 copy，因此原始对象保持不变。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "fc32071b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-02T11:40:00.414668Z",
+     "iopub.status.busy": "2026-07-02T11:40:00.414668Z",
+     "iopub.status.idle": "2026-07-02T11:40:00.427670Z",
+     "shell.execute_reply": "2026-07-02T11:40:00.427670Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "before: 402 after: 400\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.omicverse.dataframe+json": {
+       "dtypes": {
+        "region": "object",
+        "spata2_spatial_outlier": "bool",
+        "total_counts": "float64"
+       },
+       "name": null,
+       "ref": "obj:21bb8349ff0",
+       "shape": [
+        5,
+        3
+       ],
+       "table": {
+        "columns": [
+         "region",
+         "total_counts",
+         "spata2_spatial_outlier"
+        ],
+        "data": [
+         [
+          "right",
+          10.439553070239775,
+          false
+         ],
+         [
+          "right",
+          10.174393338504789,
+          false
+         ],
+         [
+          "right",
+          10.29893327549868,
+          false
+         ],
+         [
+          "right",
+          10.259728697196483,
+          false
+         ],
+         [
+          "right",
+          10.336551804483957,
+          false
+         ]
+        ],
+        "index": [
+         "spot_395",
+         "spot_396",
+         "spot_397",
+         "spot_398",
+         "spot_399"
+        ]
+       },
+       "type": "dataframe"
+      },
+      "text/plain": [
+       "         region  total_counts  spata2_spatial_outlier\n",
+       "spot_395  right     10.439553                   False\n",
+       "spot_396  right     10.174393                   False\n",
+       "spot_397  right     10.298933                   False\n",
+       "spot_398  right     10.259729                   False\n",
+       "spot_399  right     10.336552                   False"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "filtered = ov.space.spata2_remove_outliers(adata)\n",
+    "print('before:', adata.n_obs, 'after:', filtered.n_obs)\n",
+    "filtered.obs.tail()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "197ba6c0",
+   "metadata": {},
+   "source": [
+    "## 像素距离和物理单位换算\n",
+    "\n",
+    "SPATA2 的 R API 会显式记录距离单位。当前 Python helper 层保留显式换算：传入比例因子，需要时再做 round-trip 检查。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "825a735d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-02T11:40:00.430175Z",
+     "iopub.status.busy": "2026-07-02T11:40:00.430175Z",
+     "iopub.status.idle": "2026-07-02T11:40:00.445754Z",
+     "shell.execute_reply": "2026-07-02T11:40:00.445754Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.omicverse.dataframe+json": {
+       "dtypes": {
+        "micrometers": "float64",
+        "pixels": "int32",
+        "roundtrip_pixels": "float64"
+       },
+       "name": null,
+       "ref": "obj:21bb86b7100",
+       "shape": [
+        4,
+        3
+       ],
+       "table": {
+        "columns": [
+         "pixels",
+         "micrometers",
+         "roundtrip_pixels"
+        ],
+        "data": [
+         [
+          0,
+          0.0,
+          0.0
+         ],
+         [
+          55,
+          27.5,
+          55.0
+         ],
+         [
+          110,
+          55.0,
+          110.0
+         ],
+         [
+          220,
+          110.0,
+          220.0
+         ]
+        ],
+        "index": [
+         "0",
+         "1",
+         "2",
+         "3"
+        ]
+       },
+       "type": "dataframe"
+      },
+      "text/plain": [
+       "   pixels  micrometers  roundtrip_pixels\n",
+       "0       0          0.0               0.0\n",
+       "1      55         27.5              55.0\n",
+       "2     110         55.0             110.0\n",
+       "3     220        110.0             220.0"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pixels = np.array([0, 55, 110, 220])\n",
+    "micrometers = ov.space.spata2_pixels_to_unit(pixels, pixels_per_unit=2.0)\n",
+    "roundtrip = ov.space.spata2_unit_to_pixels(micrometers, pixels_per_unit=2.0)\n",
+    "pd.DataFrame({'pixels': pixels, 'micrometers': micrometers, 'roundtrip_pixels': roundtrip})\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a913c09",
+   "metadata": {},
+   "source": [
+    "## Notebook benchmark\n",
+    "\n",
+    "这个轻量 benchmark 记录上面各步骤在当前 fixture 上的 wall-clock 时间。更大的 `py-SPATA2` benchmark 保留在重写仓库中。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "4135e67b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-02T11:40:00.446743Z",
+     "iopub.status.busy": "2026-07-02T11:40:00.446743Z",
+     "iopub.status.idle": "2026-07-02T11:40:00.461824Z",
+     "shell.execute_reply": "2026-07-02T11:40:00.461824Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.omicverse.dataframe+json": {
+       "dtypes": {
+        "operation": "object",
+        "seconds": "float64",
+        "shape": "object"
+       },
+       "name": null,
+       "ref": "obj:21bb86b5060",
+       "shape": [
+        5,
+        3
+       ],
+       "table": {
+        "columns": [
+         "operation",
+         "seconds",
+         "shape"
+        ],
+        "data": [
+         [
+          "spata2_get_coords",
+          0.0004323000321164727,
+          [
+           402,
+           3
+          ]
+         ],
+         [
+          "spata2_join_variables",
+          0.0007365000201389194,
+          [
+           402,
+           6
+          ]
+         ],
+         [
+          "spata2_tissue_outline",
+          0.0006392999785020947,
+          [
+           5,
+           2
+          ]
+         ],
+         [
+          "spata2_identify_outliers",
+          0.0005981000140309334,
+          [
+           402
+          ]
+         ],
+         [
+          "spata2_remove_outliers",
+          0.0005402999231591821,
+          [
+           400,
+           3
+          ]
+         ]
+        ],
+        "index": [
+         "0",
+         "1",
+         "2",
+         "3",
+         "4"
+        ]
+       },
+       "type": "dataframe"
+      },
+      "text/plain": [
+       "                  operation   seconds     shape\n",
+       "0         spata2_get_coords  0.000432  (402, 3)\n",
+       "1     spata2_join_variables  0.000737  (402, 6)\n",
+       "2     spata2_tissue_outline  0.000639    (5, 2)\n",
+       "3  spata2_identify_outliers  0.000598    (402,)\n",
+       "4    spata2_remove_outliers  0.000540  (400, 3)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import time\n",
+    "\n",
+    "bench_rows = []\n",
+    "for label, call in [\n",
+    "    ('spata2_get_coords', lambda: ov.space.spata2_get_coords(adata)),\n",
+    "    ('spata2_join_variables', lambda: ov.space.spata2_join_variables(adata, ['region', 'GeneA', 'GeneB'])),\n",
+    "    ('spata2_tissue_outline', lambda: ov.space.spata2_tissue_outline(adata, write_key=None)),\n",
+    "    ('spata2_identify_outliers', lambda: ov.space.spata2_identify_outliers(adata, radius=2.0, min_neighbors=2, write_key=None)),\n",
+    "    ('spata2_remove_outliers', lambda: ov.space.spata2_remove_outliers(adata)),\n",
+    "]:\n",
+    "    start = time.perf_counter()\n",
+    "    result = call()\n",
+    "    elapsed = time.perf_counter() - start\n",
+    "    bench_rows.append({'operation': label, 'seconds': elapsed, 'shape': getattr(result, 'shape', (len(result),))})\n",
+    "\n",
+    "pd.DataFrame(bench_rows)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4bc61d87",
+   "metadata": {},
+   "source": [
+    "## 当前 parity 背景\n",
+    "\n",
+    "重写仓库审计的是真实 SPATA2 3.1.4 `NAMESPACE`，不是手工挑选的一小部分。只有当 Python 端暴露相同的 R-style export 名称时，才计为 implemented。\n",
+    "\n",
+    "完整表格：\n",
+    "\n",
+    "- [NAMESPACE_PARITY.md](https://github.com/omicverse/py-SPATA2/blob/master/NAMESPACE_PARITY.md)\n",
+    "- [spata2_namespace_parity.csv](https://github.com/omicverse/py-SPATA2/blob/master/references/spata2_namespace_parity.csv)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "2fe270ce",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-02T11:40:00.463442Z",
+     "iopub.status.busy": "2026-07-02T11:40:00.463442Z",
+     "iopub.status.idle": "2026-07-02T11:40:00.478445Z",
+     "shell.execute_reply": "2026-07-02T11:40:00.477445Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.omicverse.dataframe+json": {
+       "dtypes": {
+        "metric": "object",
+        "value": "object"
+       },
+       "name": null,
+       "ref": "obj:21bb86b6dd0",
+       "shape": [
+        4,
+        2
+       ],
+       "table": {
+        "columns": [
+         "metric",
+         "value"
+        ],
+        "data": [
+         [
+          "R exports audited from SPATA2 3.1.4 NAMESPACE",
+          751
+         ],
+         [
+          "R-compatible Python symbols implemented in py-SPATA2",
+          16
+         ],
+         [
+          "Strict namespace coverage",
+          "2.1%"
+         ],
+         [
+          "ov.space tutorial API functions exercised here",
+          8
+         ]
+        ],
+        "index": [
+         "0",
+         "1",
+         "2",
+         "3"
+        ]
+       },
+       "type": "dataframe"
+      },
+      "text/plain": [
+       "                                              metric value\n",
+       "0      R exports audited from SPATA2 3.1.4 NAMESPACE   751\n",
+       "1  R-compatible Python symbols implemented in py-...    16\n",
+       "2                          Strict namespace coverage  2.1%\n",
+       "3     ov.space tutorial API functions exercised here     8"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "parity = pd.DataFrame(\n",
+    "    [\n",
+    "        ['R exports audited from SPATA2 3.1.4 NAMESPACE', 751],\n",
+    "        ['R-compatible Python symbols implemented in py-SPATA2', 16],\n",
+    "        ['Strict namespace coverage', '2.1%'],\n",
+    "        ['ov.space tutorial API functions exercised here', 8],\n",
+    "    ],\n",
+    "    columns=['metric', 'value'],\n",
+    ")\n",
+    "parity\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1495723b",
+   "metadata": {},
+   "source": [
+    "## 如何理解这一版\n",
+    "\n",
+    "目前可以把这些函数用于 AnnData 坐标检查、变量表、快速组织轮廓和 artifact 过滤。不要把这个 notebook 当成完整 SPATA2 parity 的证据。后续工作应该继续按照审计过的 export surface 分模块推进：object containers、readers/initiation、get/set/add accessors、spatial annotations、trajectories、gradient screening 和 plotting。\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.10.20"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs_zh/tutorials/index_spatial_spata2.md b/docs_zh/tutorials/index_spatial_spata2.md
index 8359563..cab3631 100644
--- a/docs_zh/tutorials/index_spatial_spata2.md
+++ b/docs_zh/tutorials/index_spatial_spata2.md
@@ -1,6 +1,6 @@
-# SPATA2 ??????
+# 受 SPATA2 启发的工具
 
-AnnData-native SPATA2 ?????
+受 SPATA2 工作流启发的 AnnData-native 空间工具教程。
 
 ```{toctree}
 :maxdepth: 1