{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# InterAct Dataset - Quick Start\n",
    "\n",
    "This notebook demonstrates how to load and explore the InterAct dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sqlite3\n",
    "import numpy as np\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Loading the Databases\n",
    "\n",
    "The dataset includes two SQLite databases:\n",
    "- `scenarios.db` - scenario metadata (relationships, emotions, descriptions)\n",
    "- `actors.db` - actor info and recording sessions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Connect to databases\n",
    "scenarios_db = sqlite3.connect('scenarios.db')\n",
    "actors_db = sqlite3.connect('actors.db')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# View available relationships\n",
    "relationships = scenarios_db.execute('SELECT * FROM relationships').fetchall()\n",
    "print(f\"Total relationships: {len(relationships)}\")\n",
    "print(\"Sample relationships:\")\n",
    "for r in relationships[:5]:\n",
    "    print(f\"  {r[0]}: {r[1]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# View available emotions\n",
    "emotions = scenarios_db.execute('SELECT * FROM emotions').fetchall()\n",
    "print(f\"Total emotions: {len(emotions)}\")\n",
    "print(\"Sample emotions:\")\n",
    "for e in emotions[:5]:\n",
    "    print(f\"  {e[0]}: {e[1]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Query scenarios by emotion (e.g., find all \"anger\" scenarios)\n",
    "anger_scenarios = scenarios_db.execute('''\n",
    "    SELECT s.id, r.name, e.name, s.scenario \n",
    "    FROM scenarios s\n",
    "    JOIN relationships r ON s.relationship_id = r.id\n",
    "    JOIN emotions e ON s.primary_emotion_id = e.id\n",
    "    WHERE e.name = 'anger'\n",
    "    LIMIT 3\n",
    "''').fetchall()\n",
    "\n",
    "print(\"Scenarios with 'anger' emotion:\")\n",
    "for s in anger_scenarios:\n",
    "    print(f\"\\nScenario {s[0]} ({s[1]} / {s[2]}):\")\n",
    "    print(f\"  {s[3][:100]}...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Finding Actor Pairs\n",
    "\n",
    "Each recording session has one male and one female actor. The `sessions` table maps dates to actor pairs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# View all sessions\n",
    "sessions = actors_db.execute('SELECT * FROM sessions').fetchall()\n",
    "print(\"Recording sessions:\")\n",
    "print(\"Date       | Male | Female\")\n",
    "print(\"-\" * 28)\n",
    "for s in sessions:\n",
    "    print(f\"{s[0]}  | {s[1]}  | {s[2]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# View all actors\n",
    "actors = actors_db.execute('SELECT * FROM actors').fetchall()\n",
    "print(\"Actors:\")\n",
    "for a in actors:\n",
    "    print(f\"  {a[0]}: {a[1]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_actor_pair(date):\n",
    "    \"\"\"Get the male and female actor IDs for a given recording date.\"\"\"\n",
    "    result = actors_db.execute(\n",
    "        'SELECT male_id, female_id FROM sessions WHERE date = ?', \n",
    "        (date,)\n",
    "    ).fetchone()\n",
    "    return result\n",
    "\n",
    "# Example: get actors for a specific date\n",
    "date = '20231119'\n",
    "male_id, female_id = get_actor_pair(date)\n",
    "print(f\"Session {date}: male={male_id}, female={female_id}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Loading Performance Data\n",
    "\n",
    "Performance files follow the naming convention: `<date>_<actor_id>_<scenario_id>.<ext>`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example performance\n",
    "date = '20231119'\n",
    "actor_id = '001'\n",
    "scenario_id = '051'\n",
    "\n",
    "# File paths\n",
    "bvh_path = f'bvhs/{date}_{actor_id}_{scenario_id}.bvh'\n",
    "face_ict_path = f'face_ict/{date}_{actor_id}_{scenario_id}.npy'\n",
    "face_arkit_path = f'face_arkit/{date}_{actor_id}_{scenario_id}.npy'\n",
    "wav_path = f'wav/{date}_{actor_id}_{scenario_id}.wav'\n",
    "\n",
    "print(f\"BVH: {bvh_path}\")\n",
    "print(f\"Face ICT: {face_ict_path}\")\n",
    "print(f\"Face ARKit: {face_arkit_path}\")\n",
    "print(f\"Audio: {wav_path}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load face blendshape parameters\n",
    "face_ict = np.load(face_ict_path)\n",
    "face_arkit = np.load(face_arkit_path)\n",
    "\n",
    "print(f\"Face ICT shape: {face_ict.shape}\")    # (N, 55) - N frames, 55 ICT blendshapes\n",
    "print(f\"Face ARKit shape: {face_arkit.shape}\")  # (N, 51) - N frames, 51 ARKit blendshapes\n",
    "\n",
    "n_frames = face_ict.shape[0]\n",
    "duration_sec = n_frames / 30  # 30 fps\n",
    "print(f\"\\nFrames: {n_frames}\")\n",
    "print(f\"Duration: {duration_sec:.1f} seconds\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "## 4. Loading Both Actors in an Interaction\n\nFor two-person interaction research, load data from both actors in a scene."
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": "def load_interaction(date, scenario_id):\n    \"\"\"Load face and audio data for both actors in an interaction.\"\"\"\n    male_id, female_id = get_actor_pair(date)\n    \n    data = {}\n    for actor_id, role in [(male_id, 'male'), (female_id, 'female')]:\n        prefix = f'{date}_{actor_id}_{scenario_id}'\n        data[role] = {\n            'actor_id': actor_id,\n            'face_ict': np.load(f'face_ict/{prefix}.npy'),\n            'face_arkit': np.load(f'face_arkit/{prefix}.npy'),\n            'wav_path': f'wav/{prefix}.wav',\n            'bvh_path': f'bvhs/{prefix}.bvh',\n        }\n    \n    return data\n\n# Load an interaction\ninteraction = load_interaction('20231119', '051')\n\nprint(\"Male actor:\", interaction['male']['actor_id'])\nprint(f\"  Face shape: {interaction['male']['face_ict'].shape}\")\nprint(\"\\nFemale actor:\", interaction['female']['actor_id'])\nprint(f\"  Face shape: {interaction['female']['face_ict'].shape}\")"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "## 5. Basic Visualization\n\nPlot face blendshape values over time."
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": "import matplotlib.pyplot as plt\n\n# Plot jawOpen blendshape over time\ntime = np.arange(n_frames) / 30  # Convert to seconds\n\nfig, ax = plt.subplots(figsize=(12, 3))\n\n# ARKit blendshape index (see body_to_render.blend for full list)\nax.plot(time, face_arkit[:, 24])  # 24 = jawOpen\nax.set_ylabel('jawOpen')\nax.set_ylim(0, 1)\nax.set_xlabel('Time (seconds)')\nax.set_title(f'Face Blendshape: {date}_{actor_id}_{scenario_id}')\n\nplt.tight_layout()\nplt.show()"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": "# Compare jaw movement between both actors\nfig, ax = plt.subplots(figsize=(12, 4))\n\nn_frames = interaction['male']['face_arkit'].shape[0]\ntime = np.arange(n_frames) / 30\n\nax.plot(time, interaction['male']['face_arkit'][:, 24], label='Male jawOpen', alpha=0.7)\nax.plot(time, interaction['female']['face_arkit'][:, 24], label='Female jawOpen', alpha=0.7)\n\nax.set_xlabel('Time (seconds)')\nax.set_ylabel('jawOpen')\nax.legend()\nax.set_title('Jaw Movement Comparison - Two-Person Interaction')\nplt.tight_layout()\nplt.show()"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clean up\n",
    "scenarios_db.close()\n",
    "actors_db.close()"
   ]
  }
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