{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0a1e1eee",
   "metadata": {},
   "source": [
    "# Package-backed method decision: pancreas and RegVelo Schwann\n",
    "\n",
    "This notebook is the final software reproduction of the methodological decision. It uses package APIs rather than notebook-local solvers. Run it after installing the candidate package. The scientific acceptance criteria are:\n",
    "\n",
    "- numerical conservation and solver convergence;\n",
    "- future-fate affinity stability from effective horizon 64 to 128;\n",
    "- affinity stability across endpoint anchor quantiles;\n",
    "- deterministic/dynamical Schwann agreement;\n",
    "- preservation, not correction, of unusual Delta, Epsilon, and Gut progression.\n",
    "\n",
    "Automatic competing-outcome inference is intentionally absent. Competing outcomes may be added explicitly after biological review.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a81a8751",
   "metadata": {},
   "source": [
    "> **Execution provenance.** The displayed outputs were generated with scCS 0.8.0.dev33. Version 0.8.0.dev34 changes documentation and tutorial explanation only; the scientific calculations are unchanged."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bff89594",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scCS 0.8.0.dev33\n",
      "scVelo 0.3.4\n"
     ]
    }
   ],
   "source": [
    "from __future__ import annotations\n",
    "\n",
    "import warnings\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scanpy as sc\n",
    "import scvelo as scv\n",
    "from scipy.spatial.distance import jensenshannon\n",
    "\n",
    "import scCS\n",
    "\n",
    "warnings.filterwarnings(\n",
    "    \"ignore\",\n",
    "    message=\"Please import `convolve` from the `scipy.ndimage` namespace\",\n",
    "    category=DeprecationWarning,\n",
    ")\n",
    "\n",
    "print(\"scCS\", scCS.__version__)\n",
    "print(\"scVelo\", scv.__version__)\n",
    "\n",
    "# Hide known warnings emitted by optional upstream dependencies. These do not\n",
    "# change the scCS calculation and would otherwise distract from the tutorial.\n",
    "warnings.filterwarnings(\n",
    "    \"ignore\",\n",
    "    message=r\"This process .* is multi-threaded, use of fork\\(\\) may lead to deadlocks.*\",\n",
    "    category=DeprecationWarning,\n",
    ")\n",
    "warnings.filterwarnings(\n",
    "    \"ignore\",\n",
    "    category=DeprecationWarning,\n",
    "    module=r\"cellrank\\..*\",\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d291ffce",
   "metadata": {},
   "source": [
    "## Helpers\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e8aa3a2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def row_js(left, right):\n",
    "    return np.array([jensenshannon(a, b, base=2.0) ** 2 for a, b in zip(left, right)])\n",
    "\n",
    "\n",
    "def align_result_cells(left, right, *, root_only=False):\n",
    "    \"\"\"Return aligned indices for two score results using stable cell IDs.\"\"\"\n",
    "    common_ids = pd.Index(left.cell_ids).intersection(right.cell_ids)\n",
    "    left_index = pd.Index(left.cell_ids).get_indexer(common_ids)\n",
    "    right_index = pd.Index(right.cell_ids).get_indexer(common_ids)\n",
    "    keep = np.ones(len(common_ids), dtype=bool)\n",
    "    if root_only:\n",
    "        keep &= left.root_mask[left_index] & right.root_mask[right_index]\n",
    "    return common_ids, left_index[keep], right_index[keep]\n",
    "\n",
    "\n",
    "def score_source(adata, *, root, branches, obs_key, ordering_key, horizon=64, quantile=0.90):\n",
    "    scorer = scCS.SingleScorer(\n",
    "        adata,\n",
    "        root=root,\n",
    "        branches=branches,\n",
    "        obs_key=obs_key,\n",
    "    )\n",
    "    scorer.build_embedding(\n",
    "        ordering_metric=ordering_key,\n",
    "        write_to_adata=False,\n",
    "        verbose=False,\n",
    "    )\n",
    "    scorer.fit(\n",
    "        scoring_mode=\"future_fate\",\n",
    "        future_fate_options={\n",
    "            \"effective_horizon\": horizon,\n",
    "            \"anchor_quantile\": quantile,\n",
    "            \"min_anchor_cells\": 10,\n",
    "            \"progression_scale\": \"rank\",\n",
    "            \"verbose\": False,\n",
    "        },\n",
    "        verbose=False,\n",
    "    )\n",
    "    return scorer, scorer.score(write_to_adata=False, verbose=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f148480",
   "metadata": {},
   "source": [
    "## Pancreas dynamical velocity\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c9de8cdf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filtered out 20801 genes that are detected 20 counts (shared).\n",
      "Normalized count data: X, spliced, unspliced.\n",
      "computing moments based on connectivities\n",
      "    finished (0:00:02) --> added \n",
      "    'Ms' and 'Mu', moments of un/spliced abundances (adata.layers)\n",
      "recovering dynamics (using 1/24 cores)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d7e63b62a764405d83a975fc4e812d38",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/2722 [00:00<?, ?gene/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/emil/miniforge3/envs/lab-py312/lib/python3.12/multiprocessing/popen_fork.py:66: DeprecationWarning: This process (pid=92938) is multi-threaded, use of fork() may lead to deadlocks in the child.\n",
      "  self.pid = os.fork()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    finished (0:24:58) --> added \n",
      "    'fit_pars', fitted parameters for splicing dynamics (adata.var)\n",
      "computing velocities\n",
      "    finished (0:00:10) --> added \n",
      "    'velocity', velocity vectors for each individual cell (adata.layers)\n",
      "computing velocity graph (using 1/24 cores)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "670f828f3eb34efda62ee29447e130b5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3696 [00:00<?, ?cells/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/emil/miniforge3/envs/lab-py312/lib/python3.12/multiprocessing/popen_fork.py:66: DeprecationWarning: This process (pid=92938) is multi-threaded, use of fork() may lead to deadlocks in the child.\n",
      "  self.pid = os.fork()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    finished (0:00:18) --> added \n",
      "    'velocity_graph', sparse matrix with cosine correlations (adata.uns)\n",
      "computing terminal states\n",
      "    identified 2 regions of root cells and 1 region of end points .\n",
      "    finished (0:00:00) --> added\n",
      "    'root_cells', root cells of Markov diffusion process (adata.obs)\n",
      "    'end_points', end points of Markov diffusion process (adata.obs)\n",
      "computing latent time using root_cells as prior\n",
      "    finished (0:00:02) --> added \n",
      "    'latent_time', shared time (adata.obs)\n"
     ]
    }
   ],
   "source": [
    "pancreas = scv.datasets.pancreas()\n",
    "scv.pp.filter_and_normalize(pancreas, min_shared_counts=20)\n",
    "if \"X_pca\" not in pancreas.obsm:\n",
    "    sc.pp.pca(pancreas, n_comps=30)\n",
    "sc.pp.neighbors(pancreas, n_neighbors=30, n_pcs=30, use_rep=\"X_pca\", random_state=0)\n",
    "scv.pp.moments(pancreas, n_neighbors=None, n_pcs=None)\n",
    "scv.tl.recover_dynamics(pancreas, n_jobs=1)\n",
    "scv.tl.velocity(pancreas, mode=\"dynamical\")\n",
    "scv.tl.velocity_graph(pancreas)\n",
    "scv.tl.latent_time(pancreas)\n",
    "\n",
    "pancreas_scorer, pancreas_64 = score_source(\n",
    "    pancreas,\n",
    "    root=(\"Ngn3 high EP\", \"Pre-endocrine\"),\n",
    "    branches=[\"Alpha\", \"Beta\", \"Delta\", \"Epsilon\"],\n",
    "    obs_key=\"clusters\",\n",
    "    ordering_key=\"latent_time\",\n",
    "    horizon=64,\n",
    ")\n",
    "_, pancreas_128 = score_source(\n",
    "    pancreas,\n",
    "    root=(\"Ngn3 high EP\", \"Pre-endocrine\"),\n",
    "    branches=[\"Alpha\", \"Beta\", \"Delta\", \"Epsilon\"],\n",
    "    obs_key=\"clusters\",\n",
    "    ordering_key=\"latent_time\",\n",
    "    horizon=128,\n",
    ")\n",
    "_, pancreas_85 = score_source(\n",
    "    pancreas,\n",
    "    root=(\"Ngn3 high EP\", \"Pre-endocrine\"),\n",
    "    branches=[\"Alpha\", \"Beta\", \"Delta\", \"Epsilon\"],\n",
    "    obs_key=\"clusters\",\n",
    "    ordering_key=\"latent_time\",\n",
    "    horizon=64,\n",
    "    quantile=0.85,\n",
    ")\n",
    "_, pancreas_95 = score_source(\n",
    "    pancreas,\n",
    "    root=(\"Ngn3 high EP\", \"Pre-endocrine\"),\n",
    "    branches=[\"Alpha\", \"Beta\", \"Delta\", \"Epsilon\"],\n",
    "    obs_key=\"clusters\",\n",
    "    ordering_key=\"latent_time\",\n",
    "    horizon=64,\n",
    "    quantile=0.95,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f69bf20b",
   "metadata": {},
   "source": [
    "## RegVelo Schwann annotations and velocity models\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c9f3ffaa",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/emil/miniforge3/envs/lab-py312/lib/python3.12/site-packages/cellrank/pl/_heatmap.py:19: DeprecationWarning: Please import `convolve` from the `scipy.ndimage` namespace; the `scipy.ndimage.filters` namespace is deprecated and will be removed in SciPy 2.0.0.\n",
      "  from scipy.ndimage.filters import convolve\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing velocities\n",
      "    finished (0:00:00) --> added \n",
      "    'velocity', velocity vectors for each individual cell (adata.layers)\n",
      "computing velocity graph (using 1/24 cores)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4d24c4f083064e18aa547884766d6f7e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/8821 [00:00<?, ?cells/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/emil/miniforge3/envs/lab-py312/lib/python3.12/multiprocessing/popen_fork.py:66: DeprecationWarning: This process (pid=92938) is multi-threaded, use of fork() may lead to deadlocks in the child.\n",
      "  self.pid = os.fork()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    finished (0:00:12) --> added \n",
      "    'velocity_graph', sparse matrix with cosine correlations (adata.uns)\n",
      "recovering dynamics (using 1/24 cores)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6bf81fcc1e0b43a4b4dcf37ed170d93d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1141 [00:00<?, ?gene/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/emil/miniforge3/envs/lab-py312/lib/python3.12/multiprocessing/popen_fork.py:66: DeprecationWarning: This process (pid=92938) is multi-threaded, use of fork() may lead to deadlocks in the child.\n",
      "  self.pid = os.fork()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    finished (0:10:56) --> added \n",
      "    'fit_pars', fitted parameters for splicing dynamics (adata.var)\n",
      "computing velocities\n",
      "    finished (0:00:06) --> added \n",
      "    'velocity', velocity vectors for each individual cell (adata.layers)\n",
      "computing velocity graph (using 1/24 cores)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2b2f63007992475f9a70d5539d2a7467",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/8821 [00:00<?, ?cells/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/emil/miniforge3/envs/lab-py312/lib/python3.12/multiprocessing/popen_fork.py:66: DeprecationWarning: This process (pid=92938) is multi-threaded, use of fork() may lead to deadlocks in the child.\n",
      "  self.pid = os.fork()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    finished (0:00:08) --> added \n",
      "    'velocity_graph', sparse matrix with cosine correlations (adata.uns)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n_cells</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell_type_new</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>6845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ChC</th>\n",
       "      <td>762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Common Progenitor</th>\n",
       "      <td>675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gut neuron</th>\n",
       "      <td>294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gut</th>\n",
       "      <td>245</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   n_cells\n",
       "cell_type_new             \n",
       "Other                 6845\n",
       "ChC                    762\n",
       "Common Progenitor      675\n",
       "Gut neuron             294\n",
       "Gut                    245"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import regvelo as rgv\n",
    "\n",
    "SCHWANN_CLUSTER_ANNOTATION = {\n",
    "    \"15\": \"Common Progenitor\",\n",
    "    \"5\": \"Common Progenitor\",\n",
    "    \"16\": \"Gut\",\n",
    "    \"12\": \"Gut neuron\",\n",
    "    \"8\": \"ChC\",\n",
    "    \"6\": \"ChC\",\n",
    "}\n",
    "\n",
    "\n",
    "def clear_velocity_outputs(adata, *, clear_dynamics=False):\n",
    "    for key in (\"velocity\", \"velocity_u\", \"velocity_variance\"):\n",
    "        adata.layers.pop(key, None)\n",
    "    for key in (\"velocity_graph\", \"velocity_graph_neg\", \"velocity_params\"):\n",
    "        adata.uns.pop(key, None)\n",
    "    for key in (\n",
    "        \"velocity_self_transition\",\n",
    "        \"root_cells\",\n",
    "        \"end_points\",\n",
    "        \"velocity_pseudotime\",\n",
    "        \"latent_time\",\n",
    "    ):\n",
    "        if key in adata.obs:\n",
    "            del adata.obs[key]\n",
    "    if clear_dynamics:\n",
    "        for key in list(adata.var.columns):\n",
    "            if str(key).startswith(\"fit_\"):\n",
    "                del adata.var[key]\n",
    "        for key in list(adata.layers):\n",
    "            if str(key).startswith(\"fit_\"):\n",
    "                del adata.layers[key]\n",
    "        adata.uns.pop(\"recover_dynamics\", None)\n",
    "\n",
    "\n",
    "def prepare_schwann_annotations(adata):\n",
    "    \"\"\"Reproduce the curated furcation and inverse-CytoTRACE ordering.\"\"\"\n",
    "    if \"neighbors\" not in adata.uns or \"connectivities\" not in adata.obsp:\n",
    "        raise RuntimeError(\"The original RegVelo neighbor graph is required.\")\n",
    "\n",
    "    if \"sccs_leiden\" not in adata.obs:\n",
    "        sc.tl.leiden(\n",
    "            adata,\n",
    "            resolution=1.0,\n",
    "            random_state=0,\n",
    "            key_added=\"sccs_leiden\",\n",
    "        )\n",
    "\n",
    "    leiden = adata.obs[\"sccs_leiden\"].astype(str)\n",
    "    adata.obs[\"cell_type_new\"] = (\n",
    "        leiden.map(SCHWANN_CLUSTER_ANNOTATION).fillna(\"Other\").astype(\"category\")\n",
    "    )\n",
    "\n",
    "    required = (\"Common Progenitor\", \"Gut\", \"Gut neuron\", \"ChC\")\n",
    "    counts = (\n",
    "        adata.obs[\"cell_type_new\"]\n",
    "        .astype(str)\n",
    "        .value_counts()\n",
    "        .reindex(required, fill_value=0)\n",
    "    )\n",
    "    if np.any(counts <= 0):\n",
    "        raise ValueError(\n",
    "            f\"The curated furcation was not reproduced. Observed counts: {counts.to_dict()}\"\n",
    "        )\n",
    "\n",
    "    cytotrace = pd.to_numeric(adata.obs[\"CytoTRACE\"], errors=\"coerce\").to_numpy(float)\n",
    "    finite = np.isfinite(cytotrace)\n",
    "    if not finite.any():\n",
    "        raise ValueError(\"CytoTRACE contains no finite values.\")\n",
    "    lower = float(np.nanmin(cytotrace))\n",
    "    upper = float(np.nanmax(cytotrace))\n",
    "    if upper <= lower:\n",
    "        raise ValueError(\"CytoTRACE has no usable dynamic range.\")\n",
    "    inverse = np.full(adata.n_obs, np.nan, dtype=float)\n",
    "    inverse[finite] = 1.0 - (cytotrace[finite] - lower) / (upper - lower)\n",
    "    adata.obs[\"inverse_cytotrace_pseudotime\"] = inverse\n",
    "\n",
    "\n",
    "def fit_schwann_velocity(base, mode):\n",
    "    \"\"\"Fit velocity while preserving the original RegVelo neighbor graph.\n",
    "\n",
    "    Deterministic and dynamical models are therefore compared on identical\n",
    "    biological neighborhoods. Only the velocity model changes.\n",
    "    \"\"\"\n",
    "    adata = base.copy()\n",
    "    clear_velocity_outputs(\n",
    "        adata,\n",
    "        clear_dynamics=(mode == \"dynamical\"),\n",
    "    )\n",
    "    if \"neighbors\" not in adata.uns or \"connectivities\" not in adata.obsp:\n",
    "        raise RuntimeError(\"The original RegVelo neighbor graph is required.\")\n",
    "    if \"X_pca\" not in adata.obsm:\n",
    "        n_comps = min(30, adata.n_obs - 1, adata.n_vars - 1)\n",
    "        sc.pp.pca(adata, n_comps=n_comps)\n",
    "    if not {\"Ms\", \"Mu\"}.issubset(adata.layers):\n",
    "        scv.pp.moments(adata, n_neighbors=None, n_pcs=None)\n",
    "    if mode == \"dynamical\":\n",
    "        scv.tl.recover_dynamics(adata, max_iter=20, n_jobs=1)\n",
    "        scv.tl.velocity(adata, mode=\"dynamical\")\n",
    "    else:\n",
    "        scv.tl.velocity(adata, mode=\"deterministic\", fit_offset=False)\n",
    "    scv.tl.velocity_graph(adata, n_jobs=1)\n",
    "    prepare_schwann_annotations(adata)\n",
    "    return adata\n",
    "\n",
    "\n",
    "schwann_base = rgv.datasets.schwann()\n",
    "schwann_base.var_names_make_unique()\n",
    "prepare_schwann_annotations(schwann_base)\n",
    "\n",
    "schwann_det = fit_schwann_velocity(schwann_base, \"deterministic\")\n",
    "schwann_dyn = fit_schwann_velocity(schwann_base, \"dynamical\")\n",
    "\n",
    "display(\n",
    "    schwann_base.obs[\"cell_type_new\"]\n",
    "    .astype(str)\n",
    "    .value_counts()\n",
    "    .to_frame(\"n_cells\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e9e78942",
   "metadata": {},
   "outputs": [],
   "source": [
    "schwann_results = {}\n",
    "for model, adata in {\"deterministic\": schwann_det, \"dynamical\": schwann_dyn}.items():\n",
    "    scorer64, result64 = score_source(\n",
    "        adata,\n",
    "        root=\"Common Progenitor\",\n",
    "        branches=[\"Gut\", \"Gut neuron\", \"ChC\"],\n",
    "        obs_key=\"cell_type_new\",\n",
    "        ordering_key=\"inverse_cytotrace_pseudotime\",\n",
    "        horizon=64,\n",
    "        quantile=0.90,\n",
    "    )\n",
    "    _, result128 = score_source(\n",
    "        adata,\n",
    "        root=\"Common Progenitor\",\n",
    "        branches=[\"Gut\", \"Gut neuron\", \"ChC\"],\n",
    "        obs_key=\"cell_type_new\",\n",
    "        ordering_key=\"inverse_cytotrace_pseudotime\",\n",
    "        horizon=128,\n",
    "        quantile=0.90,\n",
    "    )\n",
    "    _, result85 = score_source(\n",
    "        adata,\n",
    "        root=\"Common Progenitor\",\n",
    "        branches=[\"Gut\", \"Gut neuron\", \"ChC\"],\n",
    "        obs_key=\"cell_type_new\",\n",
    "        ordering_key=\"inverse_cytotrace_pseudotime\",\n",
    "        horizon=64,\n",
    "        quantile=0.85,\n",
    "    )\n",
    "    _, result95 = score_source(\n",
    "        adata,\n",
    "        root=\"Common Progenitor\",\n",
    "        branches=[\"Gut\", \"Gut neuron\", \"ChC\"],\n",
    "        obs_key=\"cell_type_new\",\n",
    "        ordering_key=\"inverse_cytotrace_pseudotime\",\n",
    "        horizon=64,\n",
    "        quantile=0.95,\n",
    "    )\n",
    "    schwann_results[model] = {\n",
    "        \"scorer\": scorer64,\n",
    "        \"h64\": result64,\n",
    "        \"h128\": result128,\n",
    "        \"q85\": result85,\n",
    "        \"q95\": result95,\n",
    "    }\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71886775",
   "metadata": {},
   "source": [
    "## Decision table\n",
    "\n",
    "This final gate compares the same scientific quantities used in the public\n",
    "tutorials. Both Schwann velocity models preserve the original RegVelo neighbor\n",
    "graph; deterministic velocity is a sensitivity analysis, while dynamical\n",
    "velocity remains the primary Schwann model. The gate checks horizon stability,\n",
    "anchor-quantile stability, cell coverage, and cross-model agreement.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d4b22ca3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>criterion</th>\n",
       "      <th>value</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Pancreas dynamical: root CFA stable h64 vs h128</td>\n",
       "      <td>0.000122</td>\n",
       "      <td>PASS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pancreas dynamical: root CFA stable q0.85 vs q...</td>\n",
       "      <td>0.005554</td>\n",
       "      <td>PASS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pancreas dynamical: root CFA coverage</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>PASS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Schwann deterministic: root CFA stable h64 vs ...</td>\n",
       "      <td>0.001622</td>\n",
       "      <td>PASS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Schwann deterministic: root CFA stable q0.85 v...</td>\n",
       "      <td>0.013149</td>\n",
       "      <td>PASS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Schwann deterministic: root CFA coverage</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>PASS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Schwann dynamical: root CFA stable h64 vs h128</td>\n",
       "      <td>0.000612</td>\n",
       "      <td>PASS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Schwann dynamical: root CFA stable q0.85 vs q0.95</td>\n",
       "      <td>0.010306</td>\n",
       "      <td>PASS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Schwann dynamical: root CFA coverage</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>PASS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Schwann deterministic/dynamical CFA agreement</td>\n",
       "      <td>0.015043</td>\n",
       "      <td>PASS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Schwann deterministic/dynamical SOF agreement</td>\n",
       "      <td>0.951158</td>\n",
       "      <td>PASS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Schwann deterministic/dynamical FFS agreement</td>\n",
       "      <td>0.742901</td>\n",
       "      <td>INFO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            criterion     value status\n",
       "0     Pancreas dynamical: root CFA stable h64 vs h128  0.000122   PASS\n",
       "1   Pancreas dynamical: root CFA stable q0.85 vs q...  0.005554   PASS\n",
       "2               Pancreas dynamical: root CFA coverage  1.000000   PASS\n",
       "3   Schwann deterministic: root CFA stable h64 vs ...  0.001622   PASS\n",
       "4   Schwann deterministic: root CFA stable q0.85 v...  0.013149   PASS\n",
       "5            Schwann deterministic: root CFA coverage  1.000000   PASS\n",
       "6      Schwann dynamical: root CFA stable h64 vs h128  0.000612   PASS\n",
       "7   Schwann dynamical: root CFA stable q0.85 vs q0.95  0.010306   PASS\n",
       "8                Schwann dynamical: root CFA coverage  1.000000   PASS\n",
       "9       Schwann deterministic/dynamical CFA agreement  0.015043   PASS\n",
       "10      Schwann deterministic/dynamical SOF agreement  0.951158   PASS\n",
       "11      Schwann deterministic/dynamical FFS agreement  0.742901   INFO"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FINAL: READY_TO_FREEZE\n"
     ]
    }
   ],
   "source": [
    "rows = []\n",
    "comparisons = [\n",
    "    (\"Pancreas dynamical\", pancreas_64, pancreas_128, pancreas_85, pancreas_95),\n",
    "    (\n",
    "        \"Schwann deterministic\",\n",
    "        schwann_results[\"deterministic\"][\"h64\"],\n",
    "        schwann_results[\"deterministic\"][\"h128\"],\n",
    "        schwann_results[\"deterministic\"][\"q85\"],\n",
    "        schwann_results[\"deterministic\"][\"q95\"],\n",
    "    ),\n",
    "    (\n",
    "        \"Schwann dynamical\",\n",
    "        schwann_results[\"dynamical\"][\"h64\"],\n",
    "        schwann_results[\"dynamical\"][\"h128\"],\n",
    "        schwann_results[\"dynamical\"][\"q85\"],\n",
    "        schwann_results[\"dynamical\"][\"q95\"],\n",
    "    ),\n",
    "]\n",
    "for source, r64, r128, q85, q95 in comparisons:\n",
    "    _, left_h, right_h = align_result_cells(r64, r128, root_only=True)\n",
    "    horizon_js = float(\n",
    "        np.median(\n",
    "            row_js(\n",
    "                r64.future_fate_affinity[left_h],\n",
    "                r128.future_fate_affinity[right_h],\n",
    "            )\n",
    "        )\n",
    "    )\n",
    "    _, left_q, right_q = align_result_cells(q85, q95, root_only=True)\n",
    "    anchor_js = float(\n",
    "        np.median(\n",
    "            row_js(\n",
    "                q85.future_fate_affinity[left_q],\n",
    "                q95.future_fate_affinity[right_q],\n",
    "            )\n",
    "        )\n",
    "    )\n",
    "    coverage = float(np.mean(r64.projection.velocity_defined[r64.root_mask]))\n",
    "    rows.extend(\n",
    "        [\n",
    "            {\n",
    "                \"criterion\": f\"{source}: root CFA stable h64 vs h128\",\n",
    "                \"value\": horizon_js,\n",
    "                \"status\": \"PASS\" if horizon_js <= 0.05 else \"REVIEW\",\n",
    "            },\n",
    "            {\n",
    "                \"criterion\": f\"{source}: root CFA stable q0.85 vs q0.95\",\n",
    "                \"value\": anchor_js,\n",
    "                \"status\": \"PASS\" if anchor_js <= 0.05 else \"REVIEW\",\n",
    "            },\n",
    "            {\n",
    "                \"criterion\": f\"{source}: root CFA coverage\",\n",
    "                \"value\": coverage,\n",
    "                \"status\": \"PASS\" if coverage >= 0.8 else \"REVIEW\",\n",
    "            },\n",
    "        ]\n",
    "    )\n",
    "\n",
    "left = schwann_results[\"deterministic\"][\"h64\"]\n",
    "right = schwann_results[\"dynamical\"][\"h64\"]\n",
    "_, left_index, right_index = align_result_cells(left, right, root_only=True)\n",
    "affinity_js = float(\n",
    "    np.mean(\n",
    "        row_js(\n",
    "            left.future_fate_affinity[left_index],\n",
    "            right.future_fate_affinity[right_index],\n",
    "        )\n",
    "    )\n",
    ")\n",
    "progression_rho = float(\n",
    "    pd.Series(left.signed_ordering_flux[left_index]).corr(\n",
    "        pd.Series(right.signed_ordering_flux[right_index]),\n",
    "        method=\"spearman\",\n",
    "    )\n",
    ")\n",
    "specificity_rho = float(\n",
    "    pd.Series(left.future_fate_specificity[left_index]).corr(\n",
    "        pd.Series(right.future_fate_specificity[right_index]),\n",
    "        method=\"spearman\",\n",
    "    )\n",
    ")\n",
    "rows.extend(\n",
    "    [\n",
    "        {\n",
    "            \"criterion\": \"Schwann deterministic/dynamical CFA agreement\",\n",
    "            \"value\": affinity_js,\n",
    "            \"status\": \"PASS\" if affinity_js <= 0.10 else \"REVIEW\",\n",
    "        },\n",
    "        {\n",
    "            \"criterion\": \"Schwann deterministic/dynamical SOF agreement\",\n",
    "            \"value\": progression_rho,\n",
    "            \"status\": \"PASS\" if progression_rho >= 0.8 else \"REVIEW\",\n",
    "        },\n",
    "        {\n",
    "            \"criterion\": \"Schwann deterministic/dynamical FFS agreement\",\n",
    "            \"value\": specificity_rho,\n",
    "            \"status\": \"INFO\",\n",
    "        },\n",
    "    ]\n",
    ")\n",
    "\n",
    "decision = pd.DataFrame(rows)\n",
    "display(decision)\n",
    "blocking = decision[decision[\"status\"].isin([\"REVIEW\", \"FAIL\"])]\n",
    "print(\"FINAL:\", \"READY_TO_FREEZE\" if blocking.empty else \"TARGETED_REVIEW\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cda96cd0",
   "metadata": {},
   "source": [
    "## Visual decision summary\n",
    "\n",
    "The following figures make the acceptance criteria visible rather than relying\n",
    "only on a table. Horizon stability is shown cell by cell, and deterministic\n",
    "versus dynamical Schwann affinity is compared for each fate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c390b822",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x450 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 3, figsize=(18, 10))\n",
    "for column, (source, r64, r128, q85, q95) in enumerate(comparisons):\n",
    "    _, left_h, right_h = align_result_cells(r64, r128, root_only=True)\n",
    "    horizon_divergence = row_js(\n",
    "        r64.future_fate_affinity[left_h],\n",
    "        r128.future_fate_affinity[right_h],\n",
    "    )\n",
    "    axes[0, column].hist(horizon_divergence, bins=35)\n",
    "    axes[0, column].axvline(0.05, linestyle=\"--\", linewidth=1.5)\n",
    "    axes[0, column].set_title(source)\n",
    "    axes[0, column].set_xlabel(\"CFA JS divergence: h64 vs h128\")\n",
    "    axes[0, column].set_ylabel(\"Root cells\")\n",
    "\n",
    "    _, left_q, right_q = align_result_cells(q85, q95, root_only=True)\n",
    "    anchor_divergence = row_js(\n",
    "        q85.future_fate_affinity[left_q],\n",
    "        q95.future_fate_affinity[right_q],\n",
    "    )\n",
    "    axes[1, column].hist(anchor_divergence, bins=35)\n",
    "    axes[1, column].axvline(0.05, linestyle=\"--\", linewidth=1.5)\n",
    "    axes[1, column].set_xlabel(\"CFA JS divergence: q0.85 vs q0.95\")\n",
    "    axes[1, column].set_ylabel(\"Root cells\")\n",
    "fig.suptitle(\"DFFP horizon and endpoint-anchor stability\", y=1.01)\n",
    "fig.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "left = schwann_results[\"deterministic\"][\"h64\"]\n",
    "right = schwann_results[\"dynamical\"][\"h64\"]\n",
    "_, left_index, right_index = align_result_cells(left, right, root_only=True)\n",
    "\n",
    "fig, axes = plt.subplots(1, len(left.fate_names), figsize=(5 * len(left.fate_names), 4.5))\n",
    "for fate_index, (axis, fate) in enumerate(zip(np.atleast_1d(axes), left.fate_names)):\n",
    "    x = left.future_fate_affinity[left_index, fate_index]\n",
    "    y = right.future_fate_affinity[right_index, fate_index]\n",
    "    axis.scatter(x, y, s=10, alpha=0.5)\n",
    "    axis.plot([0, 1], [0, 1], linestyle=\"--\", linewidth=1)\n",
    "    axis.set_xlim(0, 1)\n",
    "    axis.set_ylim(0, 1)\n",
    "    axis.set_title(fate)\n",
    "    axis.set_xlabel(\"Deterministic CFA\")\n",
    "    axis.set_ylabel(\"Dynamical CFA\")\n",
    "fig.suptitle(\"Schwann root future-fate agreement on the same RegVelo graph\", y=1.02)\n",
    "fig.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19fca558",
   "metadata": {},
   "source": [
    "## Biological information, not gates\n",
    "\n",
    "Report Delta, Epsilon, and Gut signed progression descriptively. Negative or mixed values do not fail the method.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c32db6b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pancreas\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>forward_fraction</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>annotation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alpha</th>\n",
       "      <td>481</td>\n",
       "      <td>0.018979</td>\n",
       "      <td>0.498960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beta</th>\n",
       "      <td>591</td>\n",
       "      <td>0.085994</td>\n",
       "      <td>0.739425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delta</th>\n",
       "      <td>70</td>\n",
       "      <td>-0.309134</td>\n",
       "      <td>0.014286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Epsilon</th>\n",
       "      <td>142</td>\n",
       "      <td>0.011887</td>\n",
       "      <td>0.661972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ngn3 high EP</th>\n",
       "      <td>642</td>\n",
       "      <td>0.059569</td>\n",
       "      <td>0.970405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pre-endocrine</th>\n",
       "      <td>592</td>\n",
       "      <td>-0.004796</td>\n",
       "      <td>0.439189</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               count      mean  forward_fraction\n",
       "annotation                                      \n",
       "Alpha            481  0.018979          0.498960\n",
       "Beta             591  0.085994          0.739425\n",
       "Delta             70 -0.309134          0.014286\n",
       "Epsilon          142  0.011887          0.661972\n",
       "Ngn3 high EP     642  0.059569          0.970405\n",
       "Pre-endocrine    592 -0.004796          0.439189"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Schwann deterministic\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>forward_fraction</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>annotation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ChC</th>\n",
       "      <td>762</td>\n",
       "      <td>0.048211</td>\n",
       "      <td>0.692913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Common Progenitor</th>\n",
       "      <td>675</td>\n",
       "      <td>0.051989</td>\n",
       "      <td>0.669630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gut</th>\n",
       "      <td>245</td>\n",
       "      <td>-0.187450</td>\n",
       "      <td>0.073469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gut neuron</th>\n",
       "      <td>294</td>\n",
       "      <td>0.023196</td>\n",
       "      <td>0.595238</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count      mean  forward_fraction\n",
       "annotation                                          \n",
       "ChC                  762  0.048211          0.692913\n",
       "Common Progenitor    675  0.051989          0.669630\n",
       "Gut                  245 -0.187450          0.073469\n",
       "Gut neuron           294  0.023196          0.595238"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Schwann dynamical\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>forward_fraction</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>annotation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ChC</th>\n",
       "      <td>762</td>\n",
       "      <td>0.039342</td>\n",
       "      <td>0.674541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Common Progenitor</th>\n",
       "      <td>675</td>\n",
       "      <td>0.020214</td>\n",
       "      <td>0.629630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gut</th>\n",
       "      <td>245</td>\n",
       "      <td>-0.155898</td>\n",
       "      <td>0.106122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gut neuron</th>\n",
       "      <td>294</td>\n",
       "      <td>0.029544</td>\n",
       "      <td>0.612245</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count      mean  forward_fraction\n",
       "annotation                                          \n",
       "ChC                  762  0.039342          0.674541\n",
       "Common Progenitor    675  0.020214          0.629630\n",
       "Gut                  245 -0.155898          0.106122\n",
       "Gut neuron           294  0.029544          0.612245"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sources = {\n",
    "    \"Pancreas\": pancreas,\n",
    "    \"Schwann deterministic\": schwann_det,\n",
    "    \"Schwann dynamical\": schwann_dyn,\n",
    "}\n",
    "results_to_summarize = [\n",
    "    (\"Pancreas\", pancreas_64, \"clusters\"),\n",
    "    (\"Schwann deterministic\", schwann_results[\"deterministic\"][\"h64\"], \"cell_type_new\"),\n",
    "    (\"Schwann dynamical\", schwann_results[\"dynamical\"][\"h64\"], \"cell_type_new\"),\n",
    "]\n",
    "for label, result, annotation_key in results_to_summarize:\n",
    "    source = sources[label]\n",
    "    annotations = source.obs.loc[result.cell_ids, annotation_key].astype(str).to_numpy()\n",
    "    table = pd.DataFrame(\n",
    "        {\n",
    "            \"annotation\": annotations,\n",
    "            \"progression\": result.signed_ordering_flux,\n",
    "        }\n",
    "    )\n",
    "    summary = table.groupby(\"annotation\").progression.agg(\n",
    "        count=\"count\",\n",
    "        mean=\"mean\",\n",
    "        forward_fraction=lambda values: float(np.mean(values > 0)),\n",
    "    )\n",
    "    print(label)\n",
    "    display(summary)"
   ]
  }
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