{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PolyCAT Dataset — Quickstart\n",
    "\n",
    "This notebook demonstrates how to load and visualize the PolyCAT dataset.\n",
    "\n",
    "**Display setup:** 27\" 4K monitor (3840×2160) at 70 cm viewing distance ≈ 78.5 px/deg."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "from collections import defaultdict\n",
    "from pathlib import Path\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Adjust to your project root\n",
    "PROJECT_ROOT = Path(\"../..\").resolve()\n",
    "\n",
    "# Display constants (27\" 4K at 70 cm)\n",
    "SCREEN_W, SCREEN_H = 3840, 2160\n",
    "PPD = 78.5  # pixels per degree\n",
    "\n",
    "def load_csv(path):\n",
    "    with open(path) as f:\n",
    "        return list(csv.DictReader(f))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Load Metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "participants = load_csv(PROJECT_ROOT / \"data/metadata/participants.csv\")\n",
    "trials = load_csv(PROJECT_ROOT / \"data/metadata/trials.csv\")\n",
    "quality = load_csv(PROJECT_ROOT / \"data/metadata/quality_metrics.csv\")\n",
    "\n",
    "print(f\"Participants: {len(participants)}\")\n",
    "print(f\"Trials: {len(trials)}\")\n",
    "print(f\"\\nQuality metrics columns: {list(quality[0].keys())}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Quality Overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pids = [q[\"participant_id\"] for q in quality]\n",
    "tracking_ratios = [float(q[\"tracking_ratio_mean\"]) for q in quality]\n",
    "fix_per_trial = [float(q[\"fixations_per_trial_mean\"]) for q in quality]\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
    "\n",
    "axes[0].bar(range(len(pids)), tracking_ratios, color=\"steelblue\")\n",
    "axes[0].set_xticks(range(len(pids)))\n",
    "axes[0].set_xticklabels(pids, rotation=45, ha=\"right\", fontsize=7)\n",
    "axes[0].set_ylabel(\"Fixation tracking ratio\")\n",
    "axes[0].set_title(\"Per-eye fixation tracking ratio by participant\")\n",
    "axes[0].axhline(y=0.6, color=\"red\", linestyle=\"--\", alpha=0.5, label=\"Exclusion threshold\")\n",
    "axes[0].legend(fontsize=8)\n",
    "\n",
    "axes[1].bar(range(len(pids)), fix_per_trial, color=\"darkorange\")\n",
    "axes[1].set_xticks(range(len(pids)))\n",
    "axes[1].set_xticklabels(pids, rotation=45, ha=\"right\", fontsize=7)\n",
    "axes[1].set_ylabel(\"Mean fixations per trial (both eyes)\")\n",
    "axes[1].set_title(\"Fixation count by participant\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Load Fixations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fixations = load_csv(PROJECT_ROOT / \"data/fixations/fixations_all.csv\")\n",
    "print(f\"Total fixations: {len(fixations):,}\")\n",
    "\n",
    "# Quick summary\n",
    "durations = [float(f[\"duration_ms\"]) for f in fixations]\n",
    "print(f\"Duration: mean={np.mean(durations):.0f} ms, median={np.median(durations):.0f} ms\")\n",
    "print(f\"Columns: {list(fixations[0].keys())}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Fixation Duration Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 4))\n",
    "ax.hist(durations, bins=np.arange(0, 2000, 25), color=\"steelblue\", edgecolor=\"white\")\n",
    "ax.set_xlabel(\"Fixation duration (ms)\")\n",
    "ax.set_ylabel(\"Count\")\n",
    "ax.set_title(\"Fixation Duration Distribution\")\n",
    "ax.axvline(np.median(durations), color=\"red\", linestyle=\"--\",\n",
    "           label=f\"Median: {np.median(durations):.0f} ms\")\n",
    "ax.legend()\n",
    "ax.set_xlim(0, 1500)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Spatial Distribution of Fixations\n",
    "\n",
    "Fixation positions on the 3840×2160 px display (27\" 4K at 70 cm ≈ 78.5 px/deg)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Right eye fixations only\n",
    "r_fix = [(float(f[\"x_px\"]), float(f[\"y_px\"])) for f in fixations if f[\"eye\"] == \"R\"]\n",
    "xs = np.array([p[0] for p in r_fix])\n",
    "ys = np.array([p[1] for p in r_fix])\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
    "\n",
    "# Heatmap in pixels\n",
    "h, xedges, yedges = np.histogram2d(xs, ys, bins=[96, 54],\n",
    "                                    range=[[0, SCREEN_W], [0, SCREEN_H]])\n",
    "axes[0].imshow(h.T, extent=[0, SCREEN_W, SCREEN_H, 0],\n",
    "               aspect=\"equal\", cmap=\"hot\", interpolation=\"gaussian\")\n",
    "axes[0].set_xlabel(\"X (pixels)\")\n",
    "axes[0].set_ylabel(\"Y (pixels)\")\n",
    "axes[0].set_title(f\"Fixation density — all participants (N={len(r_fix):,})\")\n",
    "\n",
    "# Heatmap in degrees\n",
    "xs_deg = (xs - SCREEN_W / 2) / PPD\n",
    "ys_deg = (ys - SCREEN_H / 2) / PPD\n",
    "h2, _, _ = np.histogram2d(xs_deg, ys_deg, bins=[96, 54],\n",
    "                           range=[[-SCREEN_W/2/PPD, SCREEN_W/2/PPD],\n",
    "                                  [-SCREEN_H/2/PPD, SCREEN_H/2/PPD]])\n",
    "extent_deg = [-SCREEN_W/2/PPD, SCREEN_W/2/PPD, SCREEN_H/2/PPD, -SCREEN_H/2/PPD]\n",
    "axes[1].imshow(h2.T, extent=extent_deg, aspect=\"equal\",\n",
    "               cmap=\"hot\", interpolation=\"gaussian\")\n",
    "axes[1].set_xlabel(\"Horizontal (deg)\")\n",
    "axes[1].set_ylabel(\"Vertical (deg)\")\n",
    "axes[1].set_title(\"Fixation density in degrees of visual angle\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Single Trial Scanpath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pick a trial from the first participant\n",
    "scanpath_dir = PROJECT_ROOT / \"data/scanpaths\"\n",
    "pid_dirs = sorted(d for d in scanpath_dir.iterdir() if d.is_dir())\n",
    "pid_dir = pid_dirs[0]\n",
    "trial_file = sorted(pid_dir.glob(\"*.csv\"))[0]\n",
    "\n",
    "scanpath = load_csv(trial_file)\n",
    "# Filter to right eye\n",
    "sp_r = [f for f in scanpath if f[\"eye\"] == \"R\"]\n",
    "\n",
    "sp_x = [float(f[\"x_px\"]) for f in sp_r]\n",
    "sp_y = [float(f[\"y_px\"]) for f in sp_r]\n",
    "sp_dur = [float(f[\"duration_ms\"]) for f in sp_r]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 5.625))  # 16:9 aspect\n",
    "ax.set_xlim(0, SCREEN_W)\n",
    "ax.set_ylim(SCREEN_H, 0)  # Invert Y\n",
    "ax.set_aspect(\"equal\")\n",
    "\n",
    "# Draw scanpath\n",
    "ax.plot(sp_x, sp_y, \"b-\", alpha=0.3, linewidth=1)\n",
    "scatter = ax.scatter(sp_x, sp_y, s=[d/5 for d in sp_dur],\n",
    "                     c=range(len(sp_x)), cmap=\"viridis\", alpha=0.7,\n",
    "                     edgecolors=\"black\", linewidths=0.5)\n",
    "\n",
    "# Number the fixations\n",
    "for i, (x, y) in enumerate(zip(sp_x, sp_y)):\n",
    "    ax.annotate(str(i+1), (x, y), fontsize=7, ha=\"center\", va=\"center\",\n",
    "                color=\"white\", fontweight=\"bold\")\n",
    "\n",
    "ax.set_xlabel(\"X (pixels)\")\n",
    "ax.set_ylabel(\"Y (pixels)\")\n",
    "ax.set_title(f\"Scanpath: {trial_file.stem} (right eye, {len(sp_r)} fixations)\\n\"\n",
    "             f\"Display: 3840×2160 px, 27\\\" 4K at 70 cm\")\n",
    "plt.colorbar(scatter, label=\"Fixation order\", ax=ax)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Saliency Map Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "saliency_dir = PROJECT_ROOT / \"data/saliency_maps/by_polygon\"\n",
    "if saliency_dir.exists():\n",
    "    # Find first available saliency map\n",
    "    poly_dirs = sorted(d for d in saliency_dir.iterdir() if d.is_dir())\n",
    "    if poly_dirs:\n",
    "        npy_files = sorted(poly_dirs[0].glob(\"*_fixmap.npy\"))\n",
    "        if npy_files:\n",
    "            smap = np.load(npy_files[0])\n",
    "            fig, ax = plt.subplots(figsize=(10, 5.625))\n",
    "            im = ax.imshow(smap, cmap=\"hot\", aspect=\"equal\")\n",
    "            ax.set_xlabel(\"X (pixels)\")\n",
    "            ax.set_ylabel(\"Y (pixels)\")\n",
    "            ax.set_title(f\"Saliency map: {poly_dirs[0].name}/{npy_files[0].stem}\\n\"\n",
    "                         f\"Resolution: {smap.shape[1]}×{smap.shape[0]} px, \"\n",
    "                         f\"σ = 1.0 deg = {PPD:.1f} px\")\n",
    "            plt.colorbar(im, label=\"Fixation density\", ax=ax)\n",
    "            plt.tight_layout()\n",
    "            plt.show()\n",
    "        else:\n",
    "            print(\"No .npy saliency maps found.\")\n",
    "    else:\n",
    "        print(\"No polygon directories found.\")\n",
    "else:\n",
    "    print(\"Saliency map directory not found. Run generate_saliency_maps.py first.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Category Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fixation stats by stimulus category\n",
    "by_category = defaultdict(list)\n",
    "for f in fixations:\n",
    "    cat = f.get(\"category\", \"\")\n",
    "    if cat and f[\"eye\"] == \"R\":\n",
    "        by_category[cat].append(float(f[\"duration_ms\"]))\n",
    "\n",
    "cats = sorted(by_category.keys())\n",
    "means = [np.mean(by_category[c]) for c in cats]\n",
    "stds = [np.std(by_category[c]) / np.sqrt(len(by_category[c])) for c in cats]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 4))\n",
    "ax.bar(cats, means, yerr=stds, color=\"steelblue\", edgecolor=\"white\", capsize=3)\n",
    "ax.set_ylabel(\"Mean fixation duration (ms)\")\n",
    "ax.set_title(\"Fixation Duration by Stimulus Category (right eye)\")\n",
    "plt.xticks(rotation=30, ha=\"right\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "**Notes:**\n",
    "- All pixel coordinates are on a 3840×2160 display\n",
    "- Conversion to degrees: `x_deg = (x_px - 1920) / 78.5`, `y_deg = (y_px - 1080) / 78.5`\n",
    "- See `docs/data_dictionary.md` for full field descriptions\n",
    "- See `docs/acquisition_protocol.md` for equipment and procedure details"
   ]
  }
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