{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "baf4de14",
   "metadata": {},
   "source": [
    "# Dyadic Time-Lagged Cross-Correlation Pipeline\n",
    "## 28 Observers x 6 Targets = 168 dyadic analyses, 1000 ms sampling\n",
    "\n",
    "This notebook is a working preview of the Milestone 1 + Milestone 2 deliverable\n",
    "structure for the HCI continuous-tracking study:\n",
    "\n",
    "1. Data processing (here: synthetic dyadic tracking data, since the real\n",
    "   human-subject data is not shared pre-contract).\n",
    "2. Baseline zero-lag Pearson correlations per dyad.\n",
    "3. Time-lagged cross-correlation, lag window -5 s to +10 s, stepped at every\n",
    "   1000 ms increment. That grid is exactly 16 lags, asserted numerically below.\n",
    "4. Peak coefficient + peak lag extraction per dyad.\n",
    "5. Group-level inferential model: linear mixed-effects (statsmodels MixedLM),\n",
    "   trials nested within observers, Test vs Control.\n",
    "6. APA-style reporting of the model output.\n",
    "\n",
    "Every number shown in the execution readouts is computed live in this notebook.\n",
    "The mirror production script (`crosscorr_pipeline_export.py`) is auto-exported\n",
    "from this notebook via `jupyter nbconvert --to script`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "14d3c3d8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-11T22:50:24.649788Z",
     "iopub.status.busy": "2026-07-11T22:50:24.649668Z",
     "iopub.status.idle": "2026-07-11T22:50:27.074761Z",
     "shell.execute_reply": "2026-07-11T22:50:27.074436Z"
    }
   },
   "outputs": [],
   "source": [
    "import json\n",
    "from pathlib import Path\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "RNG = np.random.default_rng(42)\n",
    "\n",
    "# Study design constants (from the project description)\n",
    "N_OBSERVERS = 28          # split Test / Control\n",
    "N_TARGETS = 6             # unique target trials per observer\n",
    "SAMPLE_MS = 1000          # continuous sampling interval\n",
    "TRIAL_SECONDS = 120       # trial length used for the synthetic preview\n",
    "LAG_MIN_S, LAG_MAX_S = -5, 10   # uniform lag window, in seconds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c069b16e",
   "metadata": {},
   "source": [
    "### The lag grid: -5 s to +10 s at 1000 ms steps = exactly 16 coefficients\n",
    "\n",
    "The window is inclusive on both ends, so the count is (10 - (-5)) + 1 = 16.\n",
    "We assert it rather than trust it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e5d96c7b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-11T22:50:27.076433Z",
     "iopub.status.busy": "2026-07-11T22:50:27.076292Z",
     "iopub.status.idle": "2026-07-11T22:50:27.078310Z",
     "shell.execute_reply": "2026-07-11T22:50:27.078086Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lag grid (16 lags, seconds): [-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n"
     ]
    }
   ],
   "source": [
    "LAGS_S = np.arange(LAG_MIN_S, LAG_MAX_S + 1)  # seconds; step = 1 sample at 1000 ms\n",
    "assert len(LAGS_S) == 16, f\"Expected exactly 16 lags, got {len(LAGS_S)}\"\n",
    "print(f\"Lag grid ({len(LAGS_S)} lags, seconds): {LAGS_S.tolist()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "803d7411",
   "metadata": {},
   "source": [
    "### Synthetic dyadic tracking data\n",
    "\n",
    "28 observers (14 Test, 14 Control) each track 6 unique continuous target\n",
    "trajectories. Targets are smooth band-limited signals. Each observer tracks\n",
    "with a group-dependent response lag and coupling strength, plus motor noise:\n",
    "Test observers are simulated as faster, tighter trackers; Control observers\n",
    "as slower, looser ones. Positive lag means the observer trails the target."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "460d4455",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-11T22:50:27.079495Z",
     "iopub.status.busy": "2026-07-11T22:50:27.079421Z",
     "iopub.status.idle": "2026-07-11T22:50:27.086495Z",
     "shell.execute_reply": "2026-07-11T22:50:27.086265Z"
    },
    "lines_to_next_cell": 1
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dyads: 168 (28 observers x 6 targets); Test n=14 observers, Control n=14 observers\n"
     ]
    }
   ],
   "source": [
    "N_SAMPLES = TRIAL_SECONDS  # one sample per second at 1000 ms\n",
    "\n",
    "def make_target(rng: np.random.Generator, n: int) -> np.ndarray:\n",
    "    \"\"\"Smooth pseudo-random target trajectory: sum of low-frequency sinusoids.\"\"\"\n",
    "    t = np.arange(n)\n",
    "    sig = np.zeros(n)\n",
    "    for _ in range(4):\n",
    "        freq = rng.uniform(0.01, 0.06)          # cycles per sample\n",
    "        amp = rng.uniform(0.5, 1.5)\n",
    "        phase = rng.uniform(0, 2 * np.pi)\n",
    "        sig += amp * np.sin(2 * np.pi * freq * t + phase)\n",
    "    return sig\n",
    "\n",
    "def make_observer_trace(rng, target, lag_samples: int, coupling: float,\n",
    "                        noise_sd: float) -> np.ndarray:\n",
    "    \"\"\"Observer output = lagged copy of the target, scaled + noise.\"\"\"\n",
    "    lagged = np.roll(target, lag_samples)\n",
    "    # roll wraps; overwrite the wrapped head with the target's first value + noise\n",
    "    lagged[:lag_samples] = target[0]\n",
    "    return coupling * lagged + rng.normal(0, noise_sd, len(target))\n",
    "\n",
    "GROUPS = {f\"O{i+1:02d}\": (\"Test\" if i < 14 else \"Control\") for i in range(N_OBSERVERS)}\n",
    "TARGETS = {f\"T{j+1}\": make_target(RNG, N_SAMPLES) for j in range(N_TARGETS)}\n",
    "\n",
    "rows, series = [], {}\n",
    "for obs_id, group in GROUPS.items():\n",
    "    # Group-dependent tracking parameters (per observer, jittered per trial)\n",
    "    base_lag = RNG.normal(2.0, 0.6) if group == \"Test\" else RNG.normal(4.0, 0.9)\n",
    "    base_coupling = RNG.normal(0.85, 0.05) if group == \"Test\" else RNG.normal(0.60, 0.08)\n",
    "    for tgt_id, target in TARGETS.items():\n",
    "        lag_s = int(np.clip(round(base_lag + RNG.normal(0, 0.5)), 1, 7))\n",
    "        coupling = float(np.clip(base_coupling + RNG.normal(0, 0.05), 0.2, 0.98))\n",
    "        observer = make_observer_trace(RNG, target, lag_s, coupling, noise_sd=0.55)\n",
    "        rows.append({\"observer\": obs_id, \"group\": group, \"target\": tgt_id,\n",
    "                     \"gen_lag_s\": lag_s})\n",
    "        series[f\"{obs_id}|{tgt_id}\"] = (target, observer)\n",
    "\n",
    "dyads = pd.DataFrame(rows)\n",
    "n_dyads = len(dyads)\n",
    "assert n_dyads == N_OBSERVERS * N_TARGETS == 168, f\"Expected 168 dyads, got {n_dyads}\"\n",
    "print(f\"Dyads: {n_dyads} (28 observers x 6 targets); \"\n",
    "      f\"Test n={sum(g == 'Test' for g in GROUPS.values())} observers, \"\n",
    "      f\"Control n={sum(g == 'Control' for g in GROUPS.values())} observers\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f83f207",
   "metadata": {},
   "source": [
    "### Baseline zero-lag Pearson + time-lagged cross-correlation per dyad\n",
    "\n",
    "For each dyad and each lag k in the 16-lag grid, we shift the observer series\n",
    "by k samples relative to the target and compute Pearson's r on the overlap:\n",
    "positive k tests \"observer trails the target by k seconds\". Zero-lag r is the\n",
    "baseline. Peak r and its lag are extracted from the 16-coefficient function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8648d5cd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-11T22:50:27.087625Z",
     "iopub.status.busy": "2026-07-11T22:50:27.087557Z",
     "iopub.status.idle": "2026-07-11T22:50:27.728592Z",
     "shell.execute_reply": "2026-07-11T22:50:27.728328Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computed 2688 lagged coefficients (168 dyads x 16 lags); all dyads have exactly 16: True\n",
      "Peak-lag recovery vs. generating lag: 94.6% of 168 dyads\n"
     ]
    }
   ],
   "source": [
    "def lagged_pearson(x: np.ndarray, y: np.ndarray, lag: int) -> float:\n",
    "    \"\"\"Pearson r between x[t] and y[t + lag], computed on the overlapping window.\"\"\"\n",
    "    if lag > 0:\n",
    "        a, b = x[:-lag], y[lag:]\n",
    "    elif lag < 0:\n",
    "        a, b = x[-lag:], y[:lag]\n",
    "    else:\n",
    "        a, b = x, y\n",
    "    return float(stats.pearsonr(a, b)[0])\n",
    "\n",
    "records = []\n",
    "for row in dyads.itertuples(index=False):\n",
    "    target, observer = series[f\"{row.observer}|{row.target}\"]\n",
    "    ccf = np.array([lagged_pearson(target, observer, int(k)) for k in LAGS_S])\n",
    "    assert len(ccf) == 16, f\"Dyad {row.observer}|{row.target}: {len(ccf)} coefficients\"\n",
    "    peak_idx = int(np.argmax(ccf))\n",
    "    records.append({\n",
    "        \"observer\": row.observer, \"group\": row.group, \"target\": row.target,\n",
    "        \"gen_lag_s\": row.gen_lag_s,\n",
    "        \"r_zero_lag\": ccf[LAGS_S.tolist().index(0)],\n",
    "        \"r_peak\": float(ccf[peak_idx]),\n",
    "        \"peak_lag_s\": int(LAGS_S[peak_idx]),\n",
    "        \"ccf\": ccf.round(4).tolist(),\n",
    "    })\n",
    "\n",
    "results = pd.DataFrame(records)\n",
    "total_coeffs = int(results[\"ccf\"].apply(len).sum())\n",
    "print(f\"Computed {total_coeffs} lagged coefficients \"\n",
    "      f\"({n_dyads} dyads x {len(LAGS_S)} lags); \"\n",
    "      f\"all dyads have exactly 16: {bool((results['ccf'].apply(len) == 16).all())}\")\n",
    "\n",
    "# Sanity check: recovered peak lag should match the generating lag\n",
    "lag_recovery = (results[\"peak_lag_s\"] == results[\"gen_lag_s\"]).mean()\n",
    "print(f\"Peak-lag recovery vs. generating lag: {lag_recovery:.1%} of 168 dyads\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dd49889",
   "metadata": {},
   "source": [
    "### Descriptives: zero-lag baseline vs peak, by group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2e79e688",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-11T22:50:27.729780Z",
     "iopub.status.busy": "2026-07-11T22:50:27.729712Z",
     "iopub.status.idle": "2026-07-11T22:50:27.734297Z",
     "shell.execute_reply": "2026-07-11T22:50:27.734097Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        r_zero_lag        r_peak        peak_lag_s       \n",
      "              mean    std   mean    std       mean    std\n",
      "group                                                    \n",
      "Control      0.442  0.169  0.838  0.047      4.060  0.841\n",
      "Test         0.798  0.100  0.908  0.028      1.821  0.809\n"
     ]
    }
   ],
   "source": [
    "desc = results.groupby(\"group\")[[\"r_zero_lag\", \"r_peak\", \"peak_lag_s\"]].agg([\"mean\", \"std\"])\n",
    "print(desc.round(3).to_string())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1e49f82",
   "metadata": {},
   "source": [
    "### Group-level inferential model: linear mixed-effects (MixedLM)\n",
    "\n",
    "Trials are nested within observers (6 repeated dyads per observer), so we fit\n",
    "a linear mixed-effects model with a random intercept per observer. Peak r is\n",
    "Fisher z-transformed before modeling (correlations are not interval-scaled).\n",
    "A second model tests the group difference in peak lag (tracking latency)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dd0b2616",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-11T22:50:27.735359Z",
     "iopub.status.busy": "2026-07-11T22:50:27.735290Z",
     "iopub.status.idle": "2026-07-11T22:50:27.773878Z",
     "shell.execute_reply": "2026-07-11T22:50:27.773666Z"
    },
    "lines_to_next_cell": 1
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Mixed Linear Model Regression Results\n",
      "=======================================================\n",
      "Model:              MixedLM Dependent Variable: z_peak \n",
      "No. Observations:   168     Method:             REML   \n",
      "No. Groups:         28      Scale:              0.0233 \n",
      "Min. group size:    6       Log-Likelihood:     64.2171\n",
      "Max. group size:    6       Converged:          Yes    \n",
      "Mean group size:    6.0                                \n",
      "-------------------------------------------------------\n",
      "              Coef. Std.Err.   z    P>|z| [0.025 0.975]\n",
      "-------------------------------------------------------\n",
      "Intercept     1.237    0.022 55.271 0.000  1.193  1.281\n",
      "group[T.Test] 0.299    0.032  9.461 0.000  0.237  0.361\n",
      "Group Var     0.003    0.014                           \n",
      "=======================================================\n",
      "\n",
      "         Mixed Linear Model Regression Results\n",
      "========================================================\n",
      "Model:            MixedLM Dependent Variable: peak_lag_s\n",
      "No. Observations: 168     Method:             REML      \n",
      "No. Groups:       28      Scale:              0.3095    \n",
      "Min. group size:  6       Log-Likelihood:     -170.7018 \n",
      "Max. group size:  6       Converged:          Yes       \n",
      "Mean group size:  6.0                                   \n",
      "--------------------------------------------------------\n",
      "              Coef.  Std.Err.   z    P>|z| [0.025 0.975]\n",
      "--------------------------------------------------------\n",
      "Intercept      4.060    0.179 22.726 0.000  3.709  4.410\n",
      "group[T.Test] -2.238    0.253 -8.859 0.000 -2.733 -1.743\n",
      "Group Var      0.395    0.243                           \n",
      "========================================================\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mhnd/Documents/upwork/proposal-timeseries-crosscorr/.venv/lib/python3.9/site-packages/statsmodels/regression/mixed_linear_model.py:2237: ConvergenceWarning: The MLE may be on the boundary of the parameter space.\n",
      "  warnings.warn(msg, ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "results[\"z_peak\"] = np.arctanh(results[\"r_peak\"])\n",
    "results[\"group\"] = pd.Categorical(results[\"group\"], categories=[\"Control\", \"Test\"])\n",
    "\n",
    "m_sync = smf.mixedlm(\"z_peak ~ group\", results, groups=results[\"observer\"]).fit(reml=True)\n",
    "print(m_sync.summary())\n",
    "\n",
    "m_lag = smf.mixedlm(\"peak_lag_s ~ group\", results, groups=results[\"observer\"]).fit(reml=True)\n",
    "print(m_lag.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "657a47ce",
   "metadata": {},
   "source": [
    "### APA-style results reporting (7th edition statistical style)\n",
    "\n",
    "The write-up below is generated from the fitted models, in the shape a\n",
    "results section expects: descriptives as M and SD, fixed effects as\n",
    "b, SE, z, p, and 95% CI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f563f9c6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-11T22:50:27.775015Z",
     "iopub.status.busy": "2026-07-11T22:50:27.774947Z",
     "iopub.status.idle": "2026-07-11T22:50:27.779993Z",
     "shell.execute_reply": "2026-07-11T22:50:27.779790Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Across the 168 dyadic trials, peak cross-correlation was higher in the Test group (M = .91, SD = .03) than in the Control group (M = .84, SD = .05). A linear mixed-effects model on Fisher z-transformed peak correlations, with random intercepts for observers, showed a significant group effect, b = .30, SE = .03, z = 9.46, p < .001, 95% CI [.24, .36]. Test observers also tracked with shorter latency (M = 1.82 s, SD = .81) than Control observers (M = 4.06 s, SD = .84), b = -2.24, SE = .25, z = -8.86, p < .001, 95% CI [-2.73, -1.74].\n"
     ]
    }
   ],
   "source": [
    "def apa_p(p: float) -> str:\n",
    "    return \"p < .001\" if p < 0.001 else f\"p = {p:.3f}\".replace(\"0.\", \".\")\n",
    "\n",
    "def apa_stat(v: float, dp: int = 2) -> str:\n",
    "    return f\"{v:.{dp}f}\".replace(\"0.\", \".\", 1) if abs(v) < 1 else f\"{v:.{dp}f}\"\n",
    "\n",
    "def mixedlm_apa(m, term: str) -> dict:\n",
    "    ci = m.conf_int().loc[term]\n",
    "    return {\"b\": float(m.params[term]), \"se\": float(m.bse[term]),\n",
    "            \"z\": float(m.tvalues[term]), \"p\": float(m.pvalues[term]),\n",
    "            \"ci_lo\": float(ci[0]), \"ci_hi\": float(ci[1])}\n",
    "\n",
    "g = results.groupby(\"group\", observed=True)\n",
    "stats_by_group = {\n",
    "    grp: {\"r_peak_m\": float(d[\"r_peak\"].mean()), \"r_peak_sd\": float(d[\"r_peak\"].std()),\n",
    "          \"r0_m\": float(d[\"r_zero_lag\"].mean()), \"r0_sd\": float(d[\"r_zero_lag\"].std()),\n",
    "          \"lag_m\": float(d[\"peak_lag_s\"].mean()), \"lag_sd\": float(d[\"peak_lag_s\"].std())}\n",
    "    for grp, d in g\n",
    "}\n",
    "fx_sync = mixedlm_apa(m_sync, \"group[T.Test]\")\n",
    "fx_lag = mixedlm_apa(m_lag, \"group[T.Test]\")\n",
    "\n",
    "t, c = stats_by_group[\"Test\"], stats_by_group[\"Control\"]\n",
    "apa_paragraph = (\n",
    "    f\"Across the 168 dyadic trials, peak cross-correlation was higher in the Test group \"\n",
    "    f\"(M = {apa_stat(t['r_peak_m'])}, SD = {apa_stat(t['r_peak_sd'])}) than in the Control group \"\n",
    "    f\"(M = {apa_stat(c['r_peak_m'])}, SD = {apa_stat(c['r_peak_sd'])}). A linear mixed-effects model \"\n",
    "    f\"on Fisher z-transformed peak correlations, with random intercepts for observers, showed a \"\n",
    "    f\"significant group effect, b = {apa_stat(fx_sync['b'])}, SE = {apa_stat(fx_sync['se'])}, \"\n",
    "    f\"z = {fx_sync['z']:.2f}, {apa_p(fx_sync['p'])}, 95% CI [{apa_stat(fx_sync['ci_lo'])}, \"\n",
    "    f\"{apa_stat(fx_sync['ci_hi'])}]. Test observers also tracked with shorter latency \"\n",
    "    f\"(M = {t['lag_m']:.2f} s, SD = {apa_stat(t['lag_sd'])}) than Control observers \"\n",
    "    f\"(M = {c['lag_m']:.2f} s, SD = {apa_stat(c['lag_sd'])}), b = {fx_lag['b']:.2f}, \"\n",
    "    f\"SE = {apa_stat(fx_lag['se'])}, z = {fx_lag['z']:.2f}, {apa_p(fx_lag['p'])}, \"\n",
    "    f\"95% CI [{fx_lag['ci_lo']:.2f}, {fx_lag['ci_hi']:.2f}].\"\n",
    ")\n",
    "print(apa_paragraph)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06b8e080",
   "metadata": {},
   "source": [
    "### Figures\n",
    "\n",
    "Four views of the same computed objects: one raw dyad, its 16-lag\n",
    "cross-correlation function, the group-mean CCFs, and the peak-r distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "27521046",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-11T22:50:27.781069Z",
     "iopub.status.busy": "2026-07-11T22:50:27.781014Z",
     "iopub.status.idle": "2026-07-11T22:50:28.282747Z",
     "shell.execute_reply": "2026-07-11T22:50:28.282458Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1100x700 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "try:\n",
    "    get_ipython()  # noqa: F821 (defined inside Jupyter, where figures render inline)\n",
    "except NameError:\n",
    "    matplotlib.use(\"Agg\")  # headless backend for plain-script runs\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "mean_ccf_plot = {grp: np.vstack(d[\"ccf\"].to_list()).mean(axis=0) for grp, d in g}\n",
    "ex_key = \"O03|T2\"  # example Test dyad\n",
    "ex_row = results[(results.observer == \"O03\") & (results.target == \"T2\")].iloc[0]\n",
    "tgt, obs = series[ex_key]\n",
    "\n",
    "fig, axes = plt.subplots(2, 2, figsize=(11, 7))\n",
    "ax = axes[0, 0]\n",
    "ax.plot(tgt, lw=1.4, label=\"Target\", color=\"#1a6fb0\")\n",
    "ax.plot(obs, lw=1.0, label=\"Observer O03\", color=\"#d1495b\", alpha=0.85)\n",
    "ax.set_title(f\"Dyad {ex_key}: raw traces (1000 ms sampling)\")\n",
    "ax.set_xlabel(\"Time (s)\"); ax.legend(frameon=False)\n",
    "\n",
    "ax = axes[0, 1]\n",
    "ccf = np.array(ex_row[\"ccf\"])\n",
    "ax.plot(LAGS_S, ccf, \"o-\", color=\"#1a6fb0\", lw=1.4)\n",
    "ax.axvline(0, color=\"#888\", ls=\":\", lw=1)\n",
    "ax.plot(ex_row[\"peak_lag_s\"], ex_row[\"r_peak\"], \"o\", ms=11, mfc=\"none\",\n",
    "        mec=\"#d1495b\", mew=2)\n",
    "ax.set_title(f\"CCF, 16 lags; peak r = {ex_row['r_peak']:.3f} at +{ex_row['peak_lag_s']} s\")\n",
    "ax.set_xlabel(\"Lag (s), + = observer trails\"); ax.set_ylabel(\"Pearson r\")\n",
    "\n",
    "ax = axes[1, 0]\n",
    "for grp, color in [(\"Test\", \"#1a6fb0\"), (\"Control\", \"#d1495b\")]:\n",
    "    ax.plot(LAGS_S, mean_ccf_plot[grp], \"o-\", lw=1.4, color=color, label=grp)\n",
    "ax.axvline(0, color=\"#888\", ls=\":\", lw=1)\n",
    "ax.set_title(\"Group-mean CCF across 168 dyads\")\n",
    "ax.set_xlabel(\"Lag (s)\"); ax.set_ylabel(\"Mean r\"); ax.legend(frameon=False)\n",
    "\n",
    "ax = axes[1, 1]\n",
    "data = [results.loc[results.group == grp, \"r_peak\"] for grp in (\"Test\", \"Control\")]\n",
    "ax.boxplot(data, tick_labels=[\"Test\", \"Control\"], widths=0.5)\n",
    "for i, d in enumerate(data, start=1):\n",
    "    ax.plot(np.full(len(d), i) + RNG.normal(0, 0.05, len(d)), d, \".\",\n",
    "            color=\"#1a6fb0\" if i == 1 else \"#d1495b\", alpha=0.4)\n",
    "ax.set_title(\"Peak r by group (168 dyads)\"); ax.set_ylabel(\"Peak Pearson r\")\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e101e3f9",
   "metadata": {},
   "source": [
    "### Export machine-readable results\n",
    "\n",
    "Everything the demo page shows is written here, from this execution: the lag\n",
    "grid, all 168 cross-correlation functions, per-dyad peaks, group descriptives,\n",
    "both MixedLM fixed effects, and the APA paragraph. Nothing is hand-typed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8ff526fe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-11T22:50:28.283972Z",
     "iopub.status.busy": "2026-07-11T22:50:28.283866Z",
     "iopub.status.idle": "2026-07-11T22:50:28.297729Z",
     "shell.execute_reply": "2026-07-11T22:50:28.297504Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wrote /Users/mhnd/Documents/upwork/proposal-timeseries-crosscorr/analysis/results/results.json and series.json (168 dyads, 2688 coefficients)\n"
     ]
    }
   ],
   "source": [
    "out_dir = Path(__file__).resolve().parent / \"results\" if \"__file__\" in globals() \\\n",
    "    else Path.cwd() / \"results\"\n",
    "out_dir.mkdir(exist_ok=True)\n",
    "\n",
    "mean_ccf = {grp: np.vstack(d[\"ccf\"].to_list()).mean(axis=0).round(4).tolist()\n",
    "            for grp, d in g}\n",
    "\n",
    "payload = {\n",
    "    \"design\": {\"observers\": N_OBSERVERS, \"targets\": N_TARGETS, \"dyads\": n_dyads,\n",
    "               \"sample_ms\": SAMPLE_MS, \"trial_seconds\": TRIAL_SECONDS,\n",
    "               \"lag_window_s\": [LAG_MIN_S, LAG_MAX_S], \"n_lags\": int(len(LAGS_S)),\n",
    "               \"total_coefficients\": total_coeffs,\n",
    "               \"lag_recovery_rate\": round(float(lag_recovery), 4)},\n",
    "    \"lags_s\": LAGS_S.tolist(),\n",
    "    \"group_stats\": stats_by_group,\n",
    "    \"mean_ccf_by_group\": mean_ccf,\n",
    "    \"mixedlm_sync\": fx_sync,\n",
    "    \"mixedlm_lag\": fx_lag,\n",
    "    \"apa_paragraph\": apa_paragraph,\n",
    "    \"dyads\": results.drop(columns=[\"z_peak\"]).to_dict(orient=\"records\"),\n",
    "}\n",
    "(out_dir / \"results.json\").write_text(json.dumps(payload, indent=1))\n",
    "\n",
    "series_payload = {key: {\"target\": np.round(tgt, 2).tolist(),\n",
    "                        \"observer\": np.round(obs, 2).tolist()}\n",
    "                  for key, (tgt, obs) in series.items()}\n",
    "(out_dir / \"series.json\").write_text(json.dumps(series_payload))\n",
    "print(f\"Wrote {out_dir / 'results.json'} and series.json \"\n",
    "      f\"({n_dyads} dyads, {total_coeffs} coefficients)\")"
   ]
  }
 ],
 "metadata": {
  "jupytext": {
   "cell_metadata_filter": "-all",
   "main_language": "python",
   "notebook_metadata_filter": "-all"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
