{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3c6644e7",
   "metadata": {},
   "source": [
    "# Group-label permutation: false positive rate\n",
    "\n",
    "`permute(what=\"groups\")` tests group-level differences (e.g. `group_comparison()`'s\n",
    "null) by shuffling *subject labels* and recomputing the aggregate effect-size map\n",
    "(`cohen(a,b)`, `hedges(a,b)`, ...) from the real, still-spatially-smooth subject data\n",
    "on every draw. This is structurally different from the *spatial* null methods\n",
    "(`moran`/`cornblath`/...) benchmarked in\n",
    "[bench01](bench01_null_methods_fpr.ipynb), which instead generate spatially\n",
    "constrained surrogate maps to permute against a fixed observed map.\n",
    "\n",
    "Because group-label permutation preserves each subject's full spatial pattern\n",
    "rather than reshuffling parcel values, there's a reasonable prior that it is\n",
    "already well calibrated -- unlike naive parcel-value shuffling (bench01's `random`\n",
    "baseline), it never destroys the spatial autocorrelation (SA) that inflates FPR in\n",
    "the first place. That prior has never been checked empirically. This notebook does\n",
    "that, using the same synthetic GRF (Gaussian Random Field) methodology as bench01\n",
    "(Markello & Misic, 2021).\n",
    "\n",
    "> **Reference:** Markello RD & Misic B (2021). Comparing spatial null models for\n",
    "> brain maps. *NeuroImage*, 236, 118052. https://doi.org/10.1016/j.neuroimage.2021.118052\n",
    "\n",
    "> **Reference:** Dukart et al. (2021). JuSpace: A tool for spatial correlation\n",
    "> analyses of magnetic resonance imaging data with nuclear imaging derived\n",
    "> neurotransmitter maps. *Human Brain Mapping*. https://doi.org/10.1002/hbm.25244\n",
    "\n",
    "> **Note:** this notebook is compute-intensive. It is designed to be pre-run. \n",
    "> For a quick smoke-test, set `N_COHORTS=5, N_X_PER_REP=5, N_PERM=100`.\n",
    "\n",
    "> **Scope:** this notebook stays strictly on false positive rate (type I error).\n",
    "> It does not inject true positives / measure power or FDR control across multiple\n",
    "> candidate maps -- that is deferred to a later, separate benchmark.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1dba1ea",
   "metadata": {},
   "source": [
    "## Benchmark design\n",
    "\n",
    "`permute(what=\"groups\")`'s null-generating unit is a whole subject cohort -- one\n",
    "permutation draw relabels subjects and recomputes *one* aggregate row (e.g.\n",
    "`cohen(a,b)`) from the entire Y matrix. That's different from bench01's per-map\n",
    "null, so the \"one big `(N_PAIRS x N_PAIRS)` matrix, take the diagonal\" trick\n",
    "doesn't directly apply (there's no way to get `N_PAIRS` independent *cohorts*'\n",
    "worth of output from one `permute()` call). Instead, for each condition:\n",
    "\n",
    "1. Draw **`N_COHORTS`** independent cohorts: fresh GRF-map draws with arbitrary\n",
    "   group labels. H0 (no true group difference) holds by construction, since group\n",
    "   membership has nothing to do with which GRF map a subject happens to get.\n",
    "2. For each cohort, draw **`N_X_PER_REP`** independent GRF maps as candidate X\n",
    "   reference maps (disjoint from the cohort's own draw and from each other).\n",
    "3. Run `transform_y()` → `colocalize(\"spearman\")` → `permute(what=\"groups\")` →\n",
    "   `get_p_values()`, which returns one `(1, N_X_PER_REP)` row per cohort (multi-row\n",
    "   transforms collapse to 1 row because `\"groups\"` mode force-pools `pooled_p`).\n",
    "4. Pool all `N_COHORTS * N_X_PER_REP` p-values per condition;\n",
    "   `FPR = fraction(p < P_THRESH)`.\n",
    "\n",
    "Compute cost is dominated by the `n_perm` loop, not by how many X columns are tested per call \n",
    "-- so `N_COHORTS=10 x N_X_PER_REP=50` needs ~5x fewer `permute()` calls than a\n",
    "`N_COHORTS=50 x N_X_PER_REP=10` split, for the same statistical design.\n",
    "\n",
    "### Condition grid\n",
    "\n",
    "| axis | unpaired | paired |\n",
    "|---|---|---|\n",
    "| alpha | 0.0, 1.0, 2.0, 3.0 | same |\n",
    "| cohort size | `(N_A,N_B)` ∈ {(10,10), (15,15), (20,20), (30,30), (10,15), (10,20), (10,30)} | `N_SUBJ` ∈ {10, 15, 20, 30} |\n",
    "| `groups_strategy` | `\"shuffle\"` (see finding below — `\"proportional\"` was tried first and found anti-conservative) | `\"shuffle\"` |\n",
    "| transform | `zscore(a,b)`, `centile(a,b)`, `cohen(a,b)`, `hedges(a,b)` | `elemdiff(a,b)`, `prc(a,b)`, `pairedcohen(a,b)` |\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4f5c71b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ── Configuration ─────────────────────────────────────────────────────────────\n",
    "PARC   = \"Yan200\"\n",
    "ALPHAS = [0.0, 1.0, 2.0, 3.0]\n",
    "\n",
    "N_COHORTS   = 20    # independent cohorts per condition\n",
    "N_X_PER_REP = 50    # independent GRF X maps tested per cohort (→ pooled n = 500/condition)\n",
    "N_PERM      = 1000  # null permutations per permute() call\n",
    "P_THRESH    = 0.05\n",
    "SEED        = 42\n",
    "N_PROC      = -1\n",
    "\n",
    "# unpaired conditions: (n_a, n_b) cohort sizes × transforms\n",
    "UNPAIRED_COHORT_SIZES = [(10, 10), (15, 15), (20, 20), (30, 30), (10, 15), (10, 20), (10, 30)]\n",
    "UNPAIRED_TRANSFORMS   = [\"zscore(a,b)\", \"centile(a,b)\", \"cohen(a,b)\", \"hedges(a,b)\"]\n",
    "\n",
    "# paired conditions: n_subj (n_a = n_b = n_subj, forced) × transforms\n",
    "PAIRED_SUBJ_SIZES = [10, 15, 20, 30]\n",
    "PAIRED_TRANSFORMS = [\"elemdiff(a,b)\", \"prc(a,b)\", \"pairedcohen(a,b)\"]\n",
    "\n",
    "import time\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sn\n",
    "from nispace import NiSpace\n",
    "from nispace.datasets import fetch_reference\n",
    "\n",
    "\n",
    "def sample_disjoint(grf_alpha, n_needed, rng):\n",
    "    \"\"\"n_needed rows sampled without replacement from grf_alpha.\"\"\"\n",
    "    n = len(grf_alpha)\n",
    "    assert n >= n_needed, f\"Need >={n_needed} maps at this alpha; have {n}\"\n",
    "    idx = rng.choice(n, size=n_needed, replace=False)\n",
    "    return grf_alpha.iloc[idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c772607a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_unpaired(grf_alpha, n_a, n_b, transform, rep_seed):\n",
    "    \"\"\"One (alpha, cohort_size, transform) condition -> pooled p-value array.\"\"\"\n",
    "    p_pool = []\n",
    "    for rep in range(N_COHORTS):\n",
    "        rng = np.random.default_rng(rep_seed + rep)\n",
    "        n_y = n_a + n_b\n",
    "        draw = sample_disjoint(grf_alpha, n_y + N_X_PER_REP, rng)\n",
    "        Y, X = draw.iloc[:n_y], draw.iloc[n_y:]\n",
    "        groups = np.array([\"a\"] * n_a + [\"b\"] * n_b)\n",
    "\n",
    "        nsp = NiSpace(x=X, y=Y, parcellation=PARC, verbose=False, n_proc=N_PROC)\n",
    "        nsp.fit()\n",
    "        nsp.transform_y(transform, groups=groups, store=True, verbose=False)\n",
    "        nsp.colocalize(\"spearman\")\n",
    "        nsp.permute(\n",
    "            what=\"groups\", Y_transform=transform, groups=groups,\n",
    "            # next param, group strategy is fixed for now; but explicit so a future \n",
    "            # sweep over \"shuffle\"/\"draw\" is a one-line hange, not a silent default-dependency\n",
    "            groups_strategy=\"shuffle\",  \n",
    "            n_perm=N_PERM, seed=rep_seed + rep, verbose=False,\n",
    "        )\n",
    "        p_row = nsp.get_p_values().values.flatten()\n",
    "        assert p_row.shape == (N_X_PER_REP,), f\"Expected ({N_X_PER_REP},), got {p_row.shape}\"\n",
    "        p_pool.extend(p_row.tolist())\n",
    "    return np.array(p_pool)\n",
    "\n",
    "\n",
    "def run_paired(grf_alpha, n_subj, transform, rep_seed):\n",
    "    \"\"\"One (alpha, n_subj, transform) paired condition -> pooled p-value array.\"\"\"\n",
    "    p_pool = []\n",
    "    for rep in range(N_COHORTS):\n",
    "        rng = np.random.default_rng(rep_seed + rep)\n",
    "        n_y = 2 * n_subj\n",
    "        draw = sample_disjoint(grf_alpha, n_y + N_X_PER_REP, rng)\n",
    "        Y, X = draw.iloc[:n_y], draw.iloc[n_y:]\n",
    "        groups = np.array([\"a\"] * n_subj + [\"b\"] * n_subj)\n",
    "        subjects = np.tile(np.arange(n_subj), 2)\n",
    "\n",
    "        nsp = NiSpace(x=X, y=Y, parcellation=PARC, verbose=False, n_proc=N_PROC)\n",
    "        nsp.fit()\n",
    "        nsp.transform_y(transform, groups=groups, subjects=subjects, store=True, verbose=False)\n",
    "        nsp.colocalize(\"spearman\")\n",
    "        nsp.permute(\n",
    "            what=\"groups\", Y_transform=transform, groups=groups, subjects=subjects,\n",
    "            # next param, group strategy is fixed for now; but explicit so a future \n",
    "            # sweep over \"shuffle\"/\"draw\" is a one-line hange, not a silent default-dependency\n",
    "            groups_strategy=\"shuffle\",\n",
    "            n_perm=N_PERM, seed=rep_seed + rep, verbose=False,\n",
    "        )\n",
    "        p_row = nsp.get_p_values().values.flatten()\n",
    "        assert p_row.shape == (N_X_PER_REP,), f\"Expected ({N_X_PER_REP},), got {p_row.shape}\"\n",
    "        p_pool.extend(p_row.tolist())\n",
    "    return np.array(p_pool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1695bebc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95% CI for n_pooled=1000: [0.036, 0.064]\n",
      "\n",
      "============================================================\n",
      "ALPHA: 0.0\n",
      "  OK unpaired  N=(10,10)  zscore(a,b)       FPR=0.051  (54s, total 54s)\n",
      "  OK unpaired  N=(10,10)  centile(a,b)      FPR=0.062  (49s, total 103s)\n",
      "  OK unpaired  N=(10,10)  cohen(a,b)        FPR=0.046  (39s, total 142s)\n",
      "  OK unpaired  N=(10,10)  hedges(a,b)       FPR=0.051  (38s, total 180s)\n",
      "  OK unpaired  N=(15,15)  zscore(a,b)       FPR=0.049  (42s, total 222s)\n",
      "  OK unpaired  N=(15,15)  centile(a,b)      FPR=0.046  (55s, total 277s)\n",
      "  OK unpaired  N=(15,15)  cohen(a,b)        FPR=0.051  (39s, total 316s)\n",
      "  OK unpaired  N=(15,15)  hedges(a,b)       FPR=0.058  (37s, total 353s)\n",
      "  OK unpaired  N=(20,20)  zscore(a,b)       FPR=0.037  (44s, total 397s)\n",
      "  OK unpaired  N=(20,20)  centile(a,b)      FPR=0.046  (61s, total 458s)\n",
      "  OK unpaired  N=(20,20)  cohen(a,b)        FPR=0.050  (37s, total 495s)\n",
      "  OK unpaired  N=(20,20)  hedges(a,b)       FPR=0.038  (37s, total 532s)\n",
      "  OK unpaired  N=(30,30)  zscore(a,b)       FPR=0.054  (52s, total 584s)\n",
      "  OK unpaired  N=(30,30)  centile(a,b)      FPR=0.049  (83s, total 667s)\n",
      "  OK unpaired  N=(30,30)  cohen(a,b)        FPR=0.056  (39s, total 706s)\n",
      "  OK unpaired  N=(30,30)  hedges(a,b)       FPR=0.045  (38s, total 744s)\n",
      "  OK unpaired  N=(10,15)  zscore(a,b)       FPR=0.054  (38s, total 782s)\n",
      "  OK unpaired  N=(10,15)  centile(a,b)      FPR=0.057  (45s, total 828s)\n",
      "  OK unpaired  N=(10,15)  cohen(a,b)        FPR=0.054  (36s, total 864s)\n",
      "  OK unpaired  N=(10,15)  hedges(a,b)       FPR=0.061  (36s, total 900s)\n",
      "  !! unpaired  N=(10,20)  zscore(a,b)       FPR=0.064  (38s, total 938s)\n",
      "  OK unpaired  N=(10,20)  centile(a,b)      FPR=0.041  (47s, total 986s)\n",
      "  OK unpaired  N=(10,20)  cohen(a,b)        FPR=0.054  (37s, total 1022s)\n",
      "  OK unpaired  N=(10,20)  hedges(a,b)       FPR=0.049  (37s, total 1060s)\n",
      "  OK unpaired  N=(10,30)  zscore(a,b)       FPR=0.051  (38s, total 1098s)\n",
      "  OK unpaired  N=(10,30)  centile(a,b)      FPR=0.053  (46s, total 1145s)\n",
      "  OK unpaired  N=(10,30)  cohen(a,b)        FPR=0.037  (37s, total 1182s)\n",
      "  OK unpaired  N=(10,30)  hedges(a,b)       FPR=0.060  (37s, total 1219s)\n",
      "  OK paired    N=10         elemdiff(a,b)     FPR=0.060  (38s, total 1257s)\n",
      "  !! paired    N=10         prc(a,b)          FPR=0.036  (39s, total 1296s)\n",
      "  OK paired    N=10         pairedcohen(a,b)  FPR=0.058  (37s, total 1333s)\n",
      "  OK paired    N=15         elemdiff(a,b)     FPR=0.050  (41s, total 1374s)\n",
      "  OK paired    N=15         prc(a,b)          FPR=0.059  (42s, total 1416s)\n",
      "  OK paired    N=15         pairedcohen(a,b)  FPR=0.038  (36s, total 1452s)\n",
      "  OK paired    N=20         elemdiff(a,b)     FPR=0.053  (44s, total 1496s)\n",
      "  OK paired    N=20         prc(a,b)          FPR=0.054  (45s, total 1541s)\n",
      "  OK paired    N=20         pairedcohen(a,b)  FPR=0.045  (36s, total 1577s)\n",
      "  OK paired    N=30         elemdiff(a,b)     FPR=0.054  (50s, total 1627s)\n",
      "  OK paired    N=30         prc(a,b)          FPR=0.053  (51s, total 1678s)\n",
      "  OK paired    N=30         pairedcohen(a,b)  FPR=0.039  (36s, total 1714s)\n",
      "\n",
      "============================================================\n",
      "ALPHA: 1.0\n",
      "  !! unpaired  N=(10,10)  zscore(a,b)       FPR=0.031  (38s, total 1753s)\n",
      "  OK unpaired  N=(10,10)  centile(a,b)      FPR=0.049  (46s, total 1799s)\n",
      "  !! unpaired  N=(10,10)  cohen(a,b)        FPR=0.067  (37s, total 1836s)\n",
      "  OK unpaired  N=(10,10)  hedges(a,b)       FPR=0.042  (37s, total 1873s)\n",
      "  OK unpaired  N=(15,15)  zscore(a,b)       FPR=0.045  (42s, total 1915s)\n",
      "  OK unpaired  N=(15,15)  centile(a,b)      FPR=0.055  (54s, total 1970s)\n",
      "  OK unpaired  N=(15,15)  cohen(a,b)        FPR=0.039  (37s, total 2007s)\n",
      "  OK unpaired  N=(15,15)  hedges(a,b)       FPR=0.045  (37s, total 2044s)\n",
      "  OK unpaired  N=(20,20)  zscore(a,b)       FPR=0.047  (44s, total 2088s)\n",
      "  OK unpaired  N=(20,20)  centile(a,b)      FPR=0.057  (60s, total 2148s)\n",
      "  OK unpaired  N=(20,20)  cohen(a,b)        FPR=0.037  (38s, total 2186s)\n",
      "  OK unpaired  N=(20,20)  hedges(a,b)       FPR=0.049  (37s, total 2222s)\n",
      "  OK unpaired  N=(30,30)  zscore(a,b)       FPR=0.061  (50s, total 2272s)\n",
      "  !! unpaired  N=(30,30)  centile(a,b)      FPR=0.067  (75s, total 2348s)\n",
      "  OK unpaired  N=(30,30)  cohen(a,b)        FPR=0.061  (37s, total 2385s)\n",
      "  OK unpaired  N=(30,30)  hedges(a,b)       FPR=0.047  (39s, total 2424s)\n",
      "  OK unpaired  N=(10,15)  zscore(a,b)       FPR=0.048  (43s, total 2467s)\n",
      "  OK unpaired  N=(10,15)  centile(a,b)      FPR=0.037  (50s, total 2516s)\n",
      "  !! unpaired  N=(10,15)  cohen(a,b)        FPR=0.031  (37s, total 2554s)\n",
      "  OK unpaired  N=(10,15)  hedges(a,b)       FPR=0.052  (37s, total 2591s)\n",
      "  OK unpaired  N=(10,20)  zscore(a,b)       FPR=0.063  (43s, total 2634s)\n",
      "  OK unpaired  N=(10,20)  centile(a,b)      FPR=0.048  (48s, total 2682s)\n",
      "  !! unpaired  N=(10,20)  cohen(a,b)        FPR=0.065  (38s, total 2720s)\n",
      "  OK unpaired  N=(10,20)  hedges(a,b)       FPR=0.037  (37s, total 2757s)\n",
      "  OK unpaired  N=(10,30)  zscore(a,b)       FPR=0.043  (38s, total 2795s)\n",
      "  OK unpaired  N=(10,30)  centile(a,b)      FPR=0.060  (46s, total 2841s)\n",
      "  OK unpaired  N=(10,30)  cohen(a,b)        FPR=0.046  (37s, total 2878s)\n",
      "  OK unpaired  N=(10,30)  hedges(a,b)       FPR=0.048  (37s, total 2915s)\n",
      "  OK paired    N=10         elemdiff(a,b)     FPR=0.052  (38s, total 2953s)\n",
      "  OK paired    N=10         prc(a,b)          FPR=0.061  (40s, total 2993s)\n",
      "  OK paired    N=10         pairedcohen(a,b)  FPR=0.042  (36s, total 3029s)\n",
      "  OK paired    N=15         elemdiff(a,b)     FPR=0.049  (41s, total 3069s)\n",
      "  OK paired    N=15         prc(a,b)          FPR=0.052  (42s, total 3111s)\n",
      "  OK paired    N=15         pairedcohen(a,b)  FPR=0.055  (36s, total 3147s)\n",
      "  !! paired    N=20         elemdiff(a,b)     FPR=0.035  (44s, total 3191s)\n",
      "  OK paired    N=20         prc(a,b)          FPR=0.059  (45s, total 3236s)\n",
      "  OK paired    N=20         pairedcohen(a,b)  FPR=0.049  (36s, total 3272s)\n",
      "  OK paired    N=30         elemdiff(a,b)     FPR=0.042  (50s, total 3321s)\n",
      "  OK paired    N=30         prc(a,b)          FPR=0.056  (51s, total 3372s)\n",
      "  OK paired    N=30         pairedcohen(a,b)  FPR=0.043  (36s, total 3408s)\n",
      "\n",
      "============================================================\n",
      "ALPHA: 2.0\n",
      "  !! unpaired  N=(10,10)  zscore(a,b)       FPR=0.093  (38s, total 3447s)\n",
      "  OK unpaired  N=(10,10)  centile(a,b)      FPR=0.057  (46s, total 3492s)\n",
      "  OK unpaired  N=(10,10)  cohen(a,b)        FPR=0.040  (37s, total 3529s)\n",
      "  OK unpaired  N=(10,10)  hedges(a,b)       FPR=0.062  (36s, total 3566s)\n",
      "  OK unpaired  N=(15,15)  zscore(a,b)       FPR=0.057  (41s, total 3606s)\n",
      "  OK unpaired  N=(15,15)  centile(a,b)      FPR=0.055  (52s, total 3659s)\n",
      "  !! unpaired  N=(15,15)  cohen(a,b)        FPR=0.067  (37s, total 3695s)\n",
      "  OK unpaired  N=(15,15)  hedges(a,b)       FPR=0.047  (38s, total 3733s)\n",
      "  OK unpaired  N=(20,20)  zscore(a,b)       FPR=0.047  (44s, total 3777s)\n",
      "  !! unpaired  N=(20,20)  centile(a,b)      FPR=0.068  (65s, total 3843s)\n",
      "  OK unpaired  N=(20,20)  cohen(a,b)        FPR=0.061  (39s, total 3882s)\n",
      "  !! unpaired  N=(20,20)  hedges(a,b)       FPR=0.077  (37s, total 3919s)\n",
      "  OK unpaired  N=(30,30)  zscore(a,b)       FPR=0.040  (50s, total 3969s)\n",
      "  OK unpaired  N=(30,30)  centile(a,b)      FPR=0.039  (75s, total 4043s)\n",
      "  OK unpaired  N=(30,30)  cohen(a,b)        FPR=0.061  (37s, total 4080s)\n",
      "  OK unpaired  N=(30,30)  hedges(a,b)       FPR=0.052  (37s, total 4117s)\n",
      "  OK unpaired  N=(10,15)  zscore(a,b)       FPR=0.057  (38s, total 4155s)\n",
      "  OK unpaired  N=(10,15)  centile(a,b)      FPR=0.039  (49s, total 4205s)\n",
      "  OK unpaired  N=(10,15)  cohen(a,b)        FPR=0.047  (39s, total 4243s)\n",
      "  OK unpaired  N=(10,15)  hedges(a,b)       FPR=0.057  (38s, total 4281s)\n",
      "  !! unpaired  N=(10,20)  zscore(a,b)       FPR=0.036  (39s, total 4321s)\n",
      "  !! unpaired  N=(10,20)  centile(a,b)      FPR=0.029  (49s, total 4370s)\n",
      "  !! unpaired  N=(10,20)  cohen(a,b)        FPR=0.035  (39s, total 4409s)\n",
      "  OK unpaired  N=(10,20)  hedges(a,b)       FPR=0.050  (38s, total 4447s)\n",
      "  OK unpaired  N=(10,30)  zscore(a,b)       FPR=0.043  (39s, total 4486s)\n",
      "  OK unpaired  N=(10,30)  centile(a,b)      FPR=0.042  (47s, total 4533s)\n",
      "  OK unpaired  N=(10,30)  cohen(a,b)        FPR=0.058  (37s, total 4570s)\n",
      "  OK unpaired  N=(10,30)  hedges(a,b)       FPR=0.047  (37s, total 4607s)\n",
      "  OK paired    N=10         elemdiff(a,b)     FPR=0.038  (39s, total 4646s)\n",
      "  OK paired    N=10         prc(a,b)          FPR=0.042  (40s, total 4686s)\n",
      "  OK paired    N=10         pairedcohen(a,b)  FPR=0.051  (36s, total 4722s)\n",
      "  OK paired    N=15         elemdiff(a,b)     FPR=0.041  (42s, total 4764s)\n",
      "  OK paired    N=15         prc(a,b)          FPR=0.057  (43s, total 4807s)\n",
      "  OK paired    N=15         pairedcohen(a,b)  FPR=0.037  (38s, total 4845s)\n",
      "  OK paired    N=20         elemdiff(a,b)     FPR=0.053  (46s, total 4890s)\n",
      "  OK paired    N=20         prc(a,b)          FPR=0.055  (53s, total 4943s)\n",
      "  OK paired    N=20         pairedcohen(a,b)  FPR=0.052  (39s, total 4982s)\n",
      "  !! paired    N=30         elemdiff(a,b)     FPR=0.067  (62s, total 5044s)\n",
      "  !! paired    N=30         prc(a,b)          FPR=0.064  (63s, total 5107s)\n",
      "  OK paired    N=30         pairedcohen(a,b)  FPR=0.060  (42s, total 5149s)\n",
      "\n",
      "============================================================\n",
      "ALPHA: 3.0\n",
      "  OK unpaired  N=(10,10)  zscore(a,b)       FPR=0.053  (45s, total 5195s)\n",
      "  OK unpaired  N=(10,10)  centile(a,b)      FPR=0.044  (57s, total 5251s)\n",
      "  OK unpaired  N=(10,10)  cohen(a,b)        FPR=0.048  (42s, total 5293s)\n",
      "  OK unpaired  N=(10,10)  hedges(a,b)       FPR=0.050  (48s, total 5341s)\n",
      "  !! unpaired  N=(15,15)  zscore(a,b)       FPR=0.029  (47s, total 5387s)\n",
      "  !! unpaired  N=(15,15)  centile(a,b)      FPR=0.084  (64s, total 5452s)\n",
      "  OK unpaired  N=(15,15)  cohen(a,b)        FPR=0.046  (42s, total 5493s)\n",
      "  OK unpaired  N=(15,15)  hedges(a,b)       FPR=0.044  (42s, total 5535s)\n",
      "  OK unpaired  N=(20,20)  zscore(a,b)       FPR=0.053  (55s, total 5590s)\n",
      "  !! unpaired  N=(20,20)  centile(a,b)      FPR=0.024  (75s, total 5665s)\n",
      "  OK unpaired  N=(20,20)  cohen(a,b)        FPR=0.045  (42s, total 5708s)\n",
      "  !! unpaired  N=(20,20)  hedges(a,b)       FPR=0.064  (41s, total 5749s)\n",
      "  OK unpaired  N=(30,30)  zscore(a,b)       FPR=0.048  (58s, total 5807s)\n",
      "  OK unpaired  N=(30,30)  centile(a,b)      FPR=0.042  (86s, total 5893s)\n",
      "  OK unpaired  N=(30,30)  cohen(a,b)        FPR=0.056  (42s, total 5935s)\n",
      "  OK unpaired  N=(30,30)  hedges(a,b)       FPR=0.038  (41s, total 5976s)\n",
      "  !! unpaired  N=(10,15)  zscore(a,b)       FPR=0.087  (44s, total 6019s)\n",
      "  !! unpaired  N=(10,15)  centile(a,b)      FPR=0.009  (58s, total 6077s)\n",
      "  OK unpaired  N=(10,15)  cohen(a,b)        FPR=0.054  (43s, total 6120s)\n",
      "  OK unpaired  N=(10,15)  hedges(a,b)       FPR=0.063  (43s, total 6163s)\n",
      "  OK unpaired  N=(10,20)  zscore(a,b)       FPR=0.048  (46s, total 6209s)\n",
      "  !! unpaired  N=(10,20)  centile(a,b)      FPR=0.082  (56s, total 6265s)\n",
      "  OK unpaired  N=(10,20)  cohen(a,b)        FPR=0.063  (42s, total 6307s)\n",
      "  OK unpaired  N=(10,20)  hedges(a,b)       FPR=0.044  (41s, total 6348s)\n",
      "  OK unpaired  N=(10,30)  zscore(a,b)       FPR=0.056  (46s, total 6394s)\n",
      "  OK unpaired  N=(10,30)  centile(a,b)      FPR=0.037  (51s, total 6444s)\n",
      "  OK unpaired  N=(10,30)  cohen(a,b)        FPR=0.055  (39s, total 6484s)\n",
      "  OK unpaired  N=(10,30)  hedges(a,b)       FPR=0.045  (41s, total 6525s)\n",
      "  !! paired    N=10         elemdiff(a,b)     FPR=0.073  (43s, total 6567s)\n",
      "  !! paired    N=10         prc(a,b)          FPR=0.030  (45s, total 6612s)\n",
      "  OK paired    N=10         pairedcohen(a,b)  FPR=0.050  (39s, total 6651s)\n",
      "  OK paired    N=15         elemdiff(a,b)     FPR=0.047  (45s, total 6697s)\n",
      "  OK paired    N=15         prc(a,b)          FPR=0.046  (49s, total 6746s)\n",
      "  OK paired    N=15         pairedcohen(a,b)  FPR=0.038  (39s, total 6785s)\n",
      "  !! paired    N=20         elemdiff(a,b)     FPR=0.019  (52s, total 6837s)\n",
      "  !! paired    N=20         prc(a,b)          FPR=0.072  (53s, total 6890s)\n",
      "  OK paired    N=20         pairedcohen(a,b)  FPR=0.040  (41s, total 6930s)\n",
      "  !! paired    N=30         elemdiff(a,b)     FPR=0.028  (61s, total 6992s)\n",
      "  OK paired    N=30         prc(a,b)          FPR=0.060  (61s, total 7052s)\n",
      "  OK paired    N=30         pairedcohen(a,b)  FPR=0.063  (41s, total 7093s)\n",
      "\n",
      "============================================================\n",
      "160 conditions run in 7093s total.\n"
     ]
    }
   ],
   "source": [
    "n_pooled = N_COHORTS * N_X_PER_REP\n",
    "ci_lo = P_THRESH - 1.96 * np.sqrt(P_THRESH * (1 - P_THRESH) / n_pooled)\n",
    "ci_hi = P_THRESH + 1.96 * np.sqrt(P_THRESH * (1 - P_THRESH) / n_pooled)\n",
    "print(f\"95% CI for n_pooled={n_pooled}: [{ci_lo:.3f}, {ci_hi:.3f}]\")\n",
    "\n",
    "grf = fetch_reference(\"grf\", parcellation=PARC, collection=\"ByAlpha\", verbose=False)\n",
    "\n",
    "records = []\n",
    "cond_id = 0\n",
    "t_start = time.time()\n",
    "\n",
    "for alpha in ALPHAS:\n",
    "    grf_a = grf.loc[f\"alpha-{alpha:.1f}\"]\n",
    "    print(f\"\\n{'='*60}\\nALPHA: {alpha}\")\n",
    "\n",
    "    for (n_a, n_b) in UNPAIRED_COHORT_SIZES:\n",
    "        for transform in UNPAIRED_TRANSFORMS:\n",
    "            cond_id += 1\n",
    "            t0 = time.time()\n",
    "            p_pool = run_unpaired(grf_a, n_a, n_b, transform, SEED + cond_id * 1000)\n",
    "            fpr = (p_pool < P_THRESH).mean()\n",
    "            status = \"OK\" if ci_lo <= fpr <= ci_hi else \"!!\"\n",
    "            print(f\"  {status} unpaired  N=({n_a},{n_b})  {transform:16s}  FPR={fpr:.3f}  \"\n",
    "                  f\"({time.time()-t0:.0f}s, total {time.time()-t_start:.0f}s)\")\n",
    "            records.append({\n",
    "                \"alpha\": alpha, \"pairing\": \"unpaired\", \"cohort_size\": f\"({n_a},{n_b})\",\n",
    "                \"transform\": transform, \"fpr\": fpr, \"n_pooled\": len(p_pool),\n",
    "            })\n",
    "\n",
    "    for n_subj in PAIRED_SUBJ_SIZES:\n",
    "        for transform in PAIRED_TRANSFORMS:\n",
    "            cond_id += 1\n",
    "            t0 = time.time()\n",
    "            p_pool = run_paired(grf_a, n_subj, transform, SEED + cond_id * 1000)\n",
    "            fpr = (p_pool < P_THRESH).mean()\n",
    "            status = \"OK\" if ci_lo <= fpr <= ci_hi else \"!!\"\n",
    "            print(f\"  {status} paired    N={n_subj:<9d}  {transform:16s}  FPR={fpr:.3f}  \"\n",
    "                  f\"({time.time()-t0:.0f}s, total {time.time()-t_start:.0f}s)\")\n",
    "            records.append({\n",
    "                \"alpha\": alpha, \"pairing\": \"paired\", \"cohort_size\": str(n_subj),\n",
    "                \"transform\": transform, \"fpr\": fpr, \"n_pooled\": len(p_pool),\n",
    "            })\n",
    "\n",
    "results = pd.DataFrame(records)\n",
    "print(f\"\\n{'='*60}\\n{len(results)} conditions run in {time.time()-t_start:.0f}s total.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f37d60db",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f70622f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x300 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Heatmap: FPR per (transform × alpha), separately for unpaired cohort sizes and paired N_SUBJ\n",
    "\n",
    "for pairing, cohort_sizes in [(\"unpaired\", UNPAIRED_COHORT_SIZES),\n",
    "                              (\"paired\", PAIRED_SUBJ_SIZES)]:\n",
    "        \n",
    "    ncol = 4\n",
    "    nrow = np.ceil(len(cohort_sizes) / ncol).astype(int)\n",
    "    fig, axes = plt.subplots(nrow, ncol, figsize=(3*ncol, 3*nrow))\n",
    "\n",
    "    if pairing==\"unpaired\":\n",
    "        cohort_sizes = [f\"({a},{b})\" for a, b in UNPAIRED_COHORT_SIZES]\n",
    "\n",
    "    for i, ax in enumerate(axes.ravel()):\n",
    "        if i >= len(cohort_sizes):\n",
    "            ax.set_axis_off()\n",
    "            continue\n",
    "        cohort_size = cohort_sizes[i]\n",
    "        sub = results[(results.pairing == pairing) & (results.cohort_size == str(cohort_size))]\n",
    "        piv = sub.pivot(index=\"transform\", columns=\"alpha\", values=\"fpr\")\n",
    "        sn.heatmap(\n",
    "            piv, ax=ax, annot=True, fmt=\".3f\", annot_kws={\"size\": 9}, cmap=\"RdBu_r\", vmin=0, vmax=0.25,\n",
    "            center=P_THRESH, cbar_kws={\"label\": \"FPR\"}, linewidths=0.5\n",
    "        )\n",
    "        \n",
    "        ax.set_title(f\"{pairing} N={cohort_size}\", fontsize=10)\n",
    "        ax.set_ylabel(\"\")\n",
    "        if not (i / ncol == i // ncol):\n",
    "            ax.set_yticklabels([])\n",
    "        if i >= (nrow * ncol - ncol):\n",
    "            ax.set_xlabel(\"GRF alpha\")\n",
    "        else:\n",
    "            ax.set_xlabel(\"\")\n",
    "        \n",
    "\n",
    "    fig.suptitle(f\"False positive rate, permute(what=\\\"groups\\\")  \"\n",
    "                 f\"(target={P_THRESH}, 95% CI [{ci_lo:.3f}–{ci_hi:.3f}])\", fontsize=11)\n",
    "    plt.tight_layout(pad=2.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5521631c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Line plot: FPR vs alpha, averaged across cohort sizes, one line per transform, split by pairing\n",
    "fig, axes = plt.subplots(1, 2, figsize=(9, 4), sharey=True)\n",
    "\n",
    "for ax, pairing in zip(axes, [\"unpaired\", \"paired\"]):\n",
    "    sub = results[results.pairing == pairing]\n",
    "    for transform, grp in sub.groupby(\"transform\"):\n",
    "        mean_fpr = grp.groupby(\"alpha\")[\"fpr\"].mean()\n",
    "        ax.plot(mean_fpr.index, mean_fpr.values, label=transform, lw=2, marker=\"o\")\n",
    "    ax.axhline(P_THRESH, color=\"k\", lw=1, zorder=0)\n",
    "    ax.axhspan(ci_lo, ci_hi, alpha=0.12, color=\"green\", zorder=0, label=\"95% CI\")\n",
    "    ax.set_xlabel(\"GRF alpha (spatial autocorrelation)\")\n",
    "    ax.set_title(pairing)\n",
    "    ax.set_xticks(ALPHAS)\n",
    "    ax.legend(fontsize=8, loc=\"upper left\")\n",
    "    ax.set_ylim(0,0.25)\n",
    "\n",
    "axes[0].set_ylabel(\"False positive rate\")\n",
    "fig.suptitle(\"FPR vs SA level, averaged across cohort sizes\", fontsize=12)\n",
    "plt.tight_layout(w_pad=2.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "778ee629",
   "metadata": {},
   "source": [
    "## Interpretation\n",
    "\n",
    "### `\"shuffle\"` achieves near-nominal FPR across the entire grid\n",
    "\n",
    "Across all 160 conditions (4 alphas x 7 unpaired cohort sizes x 4 unpaired\n",
    "transforms, and 4 alphas x 4 paired `N_subj` x 3 paired transforms):\n",
    "\n",
    "| | mean FPR | std | min | max |\n",
    "|---|---|---|---|---|\n",
    "| unpaired (112 conditions) | 0.051 | 0.012 | 0.009 | 0.093 |\n",
    "| paired (48 conditions) | 0.050 | 0.011 | 0.019 | 0.073 |\n",
    "\n",
    "Both are indistinguishable from the nominal 0.05 target, and critically, **flat\n",
    "across every axis tested** -- no trend by alpha (unpaired 0.049-0.052, paired\n",
    "0.047-0.051 across alpha=0..3), by cohort size (unpaired 0.049-0.053 across all\n",
    "7 size combinations; paired 0.047-0.052 across N_subj=10..30), by transform\n",
    "(unpaired 0.049-0.052 across `zscore`/`centile`/`cohen`/`hedges`; paired\n",
    "0.048-0.054 across `elemdiff`/`prc`/`pairedcohen`), or by balanced vs.\n",
    "unbalanced unpaired cohorts (0.051 vs. 0.050). This directly resolves the\n",
    "question this notebook set out to answer: `permute(what=\"groups\")`'s\n",
    "subject-label-permutation null (with the current `\"shuffle\"` default) is well\n",
    "calibrated across the SA range, cohort sizes, and transforms tested here, for\n",
    "both paired and unpaired designs.\n",
    "\n",
    "### Comparison to bench01\n",
    "\n",
    "Unlike [bench01](bench01_null_methods_fpr.ipynb)'s spatial null methods --\n",
    "where FPR clearly depends on alpha (e.g. `moran` ranges from ~0.02 at alpha=1\n",
    "to ~0.10 at alpha=3, and needs a tuned `n_components`/K to stay within range --\n",
    "`\"groups\"` mode's calibration here shows **no alpha-dependence at all**. This\n",
    "makes sense mechanistically: `permute(what=\"groups\")` never needs a spatial\n",
    "null model in the first place -- it permutes subject labels and recomputes the\n",
    "aggregate map from real subject data each draw, so whatever spatial\n",
    "autocorrelation is present in the data is automatically preserved in every\n",
    "permutation, with no tuning parameter (K, spin procedure, variogram kernel)\n",
    "to get wrong.\n",
    "\n",
    "### Caveat: the displayed 95% CI band is likely too narrow for this design\n",
    "\n",
    "~18% of individual conditions are flagged `!!` (outside the plotted CI band),\n",
    "noticeably more than the ~5% you'd expect if each condition's `n_pooled=1000`\n",
    "p-values behaved like 1000 independent Bernoulli(0.05) draws. They don't\n",
    "quite: the `N_X_PER_REP=50` p-values sharing one cohort all derive from the\n",
    "same `n_perm=1000` permutation null and the same (arbitrary but fixed) Y\n",
    "group-difference map, so they're correlated with each other even though each\n",
    "is individually a valid test. The effective sample size behind each\n",
    "condition's FPR estimate is therefore somewhere between `N_COHORTS=20` and\n",
    "`n_pooled=1000`, not the full 1000 the plotted CI assumes -- so the CI band is\n",
    "an optimistic lower bound on the true sampling uncertainty, and the `!!` rate\n",
    "above should be read as \"somewhat noisier than the band suggests,\" not as\n",
    "evidence of miscalibration (the means, which aggregate across many independent\n",
    "cohorts, are the number to trust).\n"
   ]
  }
 ],
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