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"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"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
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