{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# PID Control Testing for Maglev Pod\n",
        "\n",
        "This notebook provides a feedforward + PID control interface for the `LevPodEnv` simulation environment.\n",
        "\n",
        "## Control Architecture\n",
        "- **Feedforward**: `MaglevPredictor` estimates the equilibrium PWM needed to hover at the current gap height\n",
        "- **Height PID**: Corrects residual gap error → additive PWM correction for all coils\n",
        "- **Roll PID**: Corrects roll angle → differential left/right adjustment\n",
        "- **Pitch PID**: Corrects pitch angle → differential front/back adjustment\n",
        "\n",
        "The outputs combine: `coil_pwm = feedforward + height_correction ± roll_adjustment ± pitch_adjustment`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Target Gap Height: 11.86 mm\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "pybullet build time: Feb 21 2026 12:08:04\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from lev_pod_env import LevPodEnv, TARGET_GAP\n",
        "from pid_controller import PIDController, DEFAULT_GAINS\n",
        "from pid_simulation import run_pid_simulation, feedforward_pwm, build_feedforward_lut\n",
        "\n",
        "# Set plot style for better aesthetics\n",
        "plt.style.use('seaborn-v0_8-whitegrid')\n",
        "plt.rcParams['figure.facecolor'] = 'white'\n",
        "plt.rcParams['axes.facecolor'] = 'white'\n",
        "plt.rcParams['font.size'] = 11\n",
        "plt.rcParams['axes.titlesize'] = 14\n",
        "plt.rcParams['axes.labelsize'] = 12\n",
        "\n",
        "print(f\"Target Gap Height: {TARGET_GAP * 1000:.2f} mm\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "---\n",
        "## Load PID gains (JSON or defaults)\n",
        "\n",
        "Try to load `pid_best_params.json` (saved by Optuna in this folder). If missing, use `DEFAULT_GAINS`. You can override any value in the **PID Gains Configuration** cell below."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loaded PID gains from /Users/adipu/Documents/guadaloop_lev_control/lev_sim/pid_best_params.json\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/Users/adipu/Documents/guadaloop_lev_control/.venv/lib/python3.13/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
            "  from .autonotebook import tqdm as notebook_tqdm\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import json\n",
        "\n",
        "try:\n",
        "    import optuna_pid_tune as _optuna_tune\n",
        "    _best_path = os.path.join(os.path.dirname(os.path.abspath(_optuna_tune.__file__)), \"pid_best_params.json\")\n",
        "except Exception:\n",
        "    _best_path = \"pid_best_params.json\"\n",
        "\n",
        "if os.path.isfile(_best_path):\n",
        "    with open(_best_path) as f:\n",
        "        _best = json.load(f)\n",
        "    HEIGHT_KP = _best[\"height_kp\"]\n",
        "    HEIGHT_KI = _best[\"height_ki\"]\n",
        "    HEIGHT_KD = _best[\"height_kd\"]\n",
        "    ROLL_KP = _best[\"roll_kp\"]\n",
        "    ROLL_KI = _best[\"roll_ki\"]\n",
        "    ROLL_KD = _best[\"roll_kd\"]\n",
        "    PITCH_KP = _best[\"pitch_kp\"]\n",
        "    PITCH_KI = _best[\"pitch_ki\"]\n",
        "    PITCH_KD = _best[\"pitch_kd\"]\n",
        "    gains = dict(_best)\n",
        "    print(f\"Loaded PID gains from {_best_path}\")\n",
        "else:\n",
        "    HEIGHT_KP = DEFAULT_GAINS[\"height_kp\"]\n",
        "    HEIGHT_KI = DEFAULT_GAINS[\"height_ki\"]\n",
        "    HEIGHT_KD = DEFAULT_GAINS[\"height_kd\"]\n",
        "    ROLL_KP = DEFAULT_GAINS[\"roll_kp\"]\n",
        "    ROLL_KI = DEFAULT_GAINS[\"roll_ki\"]\n",
        "    ROLL_KD = DEFAULT_GAINS[\"roll_kd\"]\n",
        "    PITCH_KP = DEFAULT_GAINS[\"pitch_kp\"]\n",
        "    PITCH_KI = DEFAULT_GAINS[\"pitch_ki\"]\n",
        "    PITCH_KD = DEFAULT_GAINS[\"pitch_kd\"]\n",
        "    gains = dict(DEFAULT_GAINS)\n",
        "    print(\"Using DEFAULT_GAINS (no pid_best_params.json found). Run Optuna to save best params.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "---\n",
        "## PID Controller Class"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "# PID controller and default gains live in pid_controller.py (imported above).\n",
        "# Override gains in the next cell; they are passed to run_pid_simulation via the gains dict."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "---\n",
        "## PID Gains Configuration (optional overrides)\n",
        "\n",
        "Constants are already set above from `pid_best_params.json` or defaults. To **override**, uncomment and edit a line in the cell below (e.g. `HEIGHT_KP = 50.0`) and re-run that cell; then run the simulation."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "PID Gains (used by simulation):\n",
            "  Height: Kp=100.96239556037482, Ki=0.0, Kd=8.173809263716658\n",
            "  Roll:   Kp=0.600856607986966, Ki=0.0, Kd=-0.1\n",
            "  Pitch:  Kp=50.011470879925824, Ki=0, Kd=1.8990306608320433\n"
          ]
        }
      ],
      "source": [
        "# Optional manual overrides: set any constant below and re-run this cell to use it.\n",
        "# Otherwise the values from the \"Load PID gains\" cell (JSON or DEFAULT_GAINS) are used.\n",
        "# HEIGHT_KP = 50.0\n",
        "# HEIGHT_KI = 5.0\n",
        "# HEIGHT_KD = 10.0\n",
        "# ROLL_KP = 2.0\n",
        "# ROLL_KI = 0.5\n",
        "# ROLL_KD = 0.5\n",
        "# PITCH_KP = 2.0\n",
        "# PITCH_KI = 0.5\n",
        "# PITCH_KD = 0.5\n",
        "\n",
        "# Build gains dict from current constants (used by run_pid_simulation)\n",
        "gains = {\n",
        "    \"height_kp\": HEIGHT_KP, \"height_ki\": HEIGHT_KI, \"height_kd\": HEIGHT_KD,\n",
        "    \"roll_kp\": ROLL_KP, \"roll_ki\": ROLL_KI, \"roll_kd\": ROLL_KD,\n",
        "    \"pitch_kp\": PITCH_KP, \"pitch_ki\": PITCH_KI, \"pitch_kd\": PITCH_KD,\n",
        "}\n",
        "\n",
        "print(\"PID Gains (used by simulation):\")\n",
        "print(f\"  Height: Kp={HEIGHT_KP}, Ki={HEIGHT_KI}, Kd={HEIGHT_KD}\")\n",
        "print(f\"  Roll:   Kp={ROLL_KP}, Ki={ROLL_KI}, Kd={ROLL_KD}\")\n",
        "print(f\"  Pitch:  Kp={PITCH_KP}, Ki={PITCH_KI}, Kd={PITCH_KD}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loading maglev model from /Users/adipu/Documents/guadaloop_lev_control/lev_sim/maglev_model.pkl...\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/Users/adipu/Documents/guadaloop_lev_control/.venv/lib/python3.13/site-packages/sklearn/base.py:463: InconsistentVersionWarning: Trying to unpickle estimator PolynomialFeatures from version 1.7.2 when using version 1.8.0. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
            "https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
            "  warnings.warn(\n",
            "/Users/adipu/Documents/guadaloop_lev_control/.venv/lib/python3.13/site-packages/sklearn/base.py:463: InconsistentVersionWarning: Trying to unpickle estimator LinearRegression from version 1.7.2 when using version 1.8.0. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
            "https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model loaded. Degree: 6\n",
            "Force R2: 1.0000\n",
            "Torque R2: 0.9999\n",
            "Feedforward LUT ready. At target gap (11.86 mm): PWM = -0.0006\n"
          ]
        }
      ],
      "source": [
        "# ============================================================\n",
        "#           FEEDFORWARD USING MAGLEV PREDICTOR\n",
        "# ============================================================\n",
        "# Builds gap → equilibrium PWM LUT in pid_simulation (used by run_pid_simulation).\n",
        "build_feedforward_lut()\n",
        "print(f\"Feedforward LUT ready. At target gap ({TARGET_GAP*1000:.2f} mm): PWM = {feedforward_pwm(TARGET_GAP * 1000):.4f}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "---\n",
        "## Simulation Runner"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [],
      "source": [
        "# run_pid_simulation is imported from pid_simulation (see imports).\n",
        "# It accepts gains=... (dict with height_kp, height_ki, ...), plus initial_gap_mm, max_steps, etc.\n",
        "# The gains dict is built in the \"PID Gains Configuration\" cell above."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "---\n",
        "## Plotting Functions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [],
      "source": [
        "def plot_results(data: dict, title_suffix: str = \"\"):\n",
        "    \"\"\"\n",
        "    Create aesthetic plots of simulation results.\n",
        "    \n",
        "    Args:\n",
        "        data: Dictionary from run_pid_simulation()\n",
        "        title_suffix: Optional suffix for plot titles\n",
        "    \"\"\"\n",
        "    fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n",
        "    fig.suptitle(f'PID Control Performance{title_suffix}', fontsize=16, fontweight='bold', y=1.02)\n",
        "    \n",
        "    target_gap_mm = TARGET_GAP * 1000\n",
        "    time = data['time']\n",
        "    \n",
        "    # Color palette\n",
        "    colors = {\n",
        "        'primary': '#2563eb',    # Blue\n",
        "        'secondary': '#7c3aed',  # Purple\n",
        "        'accent1': '#059669',    # Green\n",
        "        'accent2': '#dc2626',    # Red\n",
        "        'target': '#f59e0b',     # Orange\n",
        "        'grid': '#e5e7eb'\n",
        "    }\n",
        "    \n",
        "    # ========== Gap Height Plot ==========\n",
        "    ax1 = axes[0, 0]\n",
        "    ax1.plot(time, data['gap_avg'], color=colors['primary'], linewidth=2, label='Average Gap')\n",
        "    ax1.plot(time, data['gap_front'], color=colors['secondary'], linewidth=1, alpha=0.6, label='Front Yoke')\n",
        "    ax1.plot(time, data['gap_back'], color=colors['accent1'], linewidth=1, alpha=0.6, label='Back Yoke')\n",
        "    ax1.axhline(y=target_gap_mm, color=colors['target'], linestyle='--', linewidth=2, label=f'Target ({target_gap_mm:.1f}mm)')\n",
        "    \n",
        "    ax1.set_xlabel('Time (s)')\n",
        "    ax1.set_ylabel('Gap Height (mm)')\n",
        "    ax1.set_title('Gap Height Over Time', fontweight='bold')\n",
        "    ax1.legend(loc='best', framealpha=0.9)\n",
        "    ax1.set_ylim([0, 20])\n",
        "    ax1.grid(True, alpha=0.3)\n",
        "    \n",
        "    # Add settling info\n",
        "    final_error = abs(data['gap_avg'][-1] - target_gap_mm)\n",
        "    ax1.text(0.98, 0.02, f'Final error: {final_error:.2f}mm', \n",
        "             transform=ax1.transAxes, ha='right', va='bottom',\n",
        "             fontsize=10, bbox=dict(boxstyle='round', facecolor='white', alpha=0.8))\n",
        "    \n",
        "    # ========== Roll Angle Plot ==========\n",
        "    ax2 = axes[0, 1]\n",
        "    ax2.plot(time, data['roll_deg'], color=colors['primary'], linewidth=2)\n",
        "    ax2.axhline(y=0, color=colors['target'], linestyle='--', linewidth=1.5, alpha=0.7)\n",
        "    ax2.fill_between(time, data['roll_deg'], 0, alpha=0.2, color=colors['primary'])\n",
        "    \n",
        "    ax2.set_xlabel('Time (s)')\n",
        "    ax2.set_ylabel('Roll Angle (degrees)')\n",
        "    ax2.set_title('Roll Angle Over Time', fontweight='bold')\n",
        "    ax2.grid(True, alpha=0.3)\n",
        "    \n",
        "    ax2.set_ylim([-4, 4])\n",
        "    \n",
        "    # ========== Pitch Angle Plot ==========\n",
        "    ax3 = axes[1, 0]\n",
        "    ax3.plot(time, data['pitch_deg'], color=colors['secondary'], linewidth=2)\n",
        "    ax3.axhline(y=0, color=colors['target'], linestyle='--', linewidth=1.5, alpha=0.7)\n",
        "    ax3.fill_between(time, data['pitch_deg'], 0, alpha=0.2, color=colors['secondary'])\n",
        "    \n",
        "    ax3.set_xlabel('Time (s)')\n",
        "    ax3.set_ylabel('Pitch Angle (degrees)')\n",
        "    ax3.set_title('Pitch Angle Over Time', fontweight='bold')\n",
        "    ax3.grid(True, alpha=0.3)\n",
        "    \n",
        "    ax3.set_ylim([-6, 6])\n",
        "    \n",
        "    # ========== Current Draw Plot ==========\n",
        "    ax4 = axes[1, 1]\n",
        "    ax4.plot(time, data['current_FL'], linewidth=1.5, label='Front Left', alpha=0.8)\n",
        "    ax4.plot(time, data['current_FR'], linewidth=1.5, label='Front Right', alpha=0.8)\n",
        "    ax4.plot(time, data['current_BL'], linewidth=1.5, label='Back Left', alpha=0.8)\n",
        "    ax4.plot(time, data['current_BR'], linewidth=1.5, label='Back Right', alpha=0.8)\n",
        "    ax4.plot(time, data['current_total'], color='black', linewidth=2, label='Total', linestyle='--')\n",
        "    \n",
        "    ax4.set_xlabel('Time (s)')\n",
        "    ax4.set_ylabel('Current (A)')\n",
        "    ax4.set_title('Coil Current Draw Over Time', fontweight='bold')\n",
        "    ax4.legend(loc='best', framealpha=0.9, ncol=2)\n",
        "    ax4.grid(True, alpha=0.3)\n",
        "    \n",
        "    # Add average power info\n",
        "    avg_total_current = np.mean(data['current_total'])\n",
        "    ax4.text(0.98, 0.98, f'Avg total: {avg_total_current:.2f}A', \n",
        "             transform=ax4.transAxes, ha='right', va='top',\n",
        "             fontsize=10, bbox=dict(boxstyle='round', facecolor='white', alpha=0.8))\n",
        "    \n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "    \n",
        "    return fig\n",
        "\n",
        "\n",
        "def show_current_slice(data: dict, t_start: float, t_end: float):\n",
        "    \"\"\"\n",
        "    Plot coil current draw over a time slice (same as bottom-right graph in plot_results()).\n",
        "\n",
        "    Args:\n",
        "        data: Dictionary from run_pid_simulation()\n",
        "        t_start: Start time in seconds (inclusive)\n",
        "        t_end: End time in seconds (inclusive)\n",
        "    \"\"\"\n",
        "    time = np.asarray(data['time'])\n",
        "    mask = (time >= t_start) & (time <= t_end)\n",
        "    if not np.any(mask):\n",
        "        print(f'No data in range [{t_start}, {t_end}] s.')\n",
        "        return\n",
        "    t = time[mask]\n",
        "    fig, ax = plt.subplots(figsize=(10, 4))\n",
        "    ax.plot(t, np.asarray(data['current_FL'])[mask], linewidth=1.5, label='Front Left', alpha=0.8)\n",
        "    ax.plot(t, np.asarray(data['current_FR'])[mask], linewidth=1.5, label='Front Right', alpha=0.8)\n",
        "    ax.plot(t, np.asarray(data['current_BL'])[mask], linewidth=1.5, label='Back Left', alpha=0.8)\n",
        "    ax.plot(t, np.asarray(data['current_BR'])[mask], linewidth=1.5, label='Back Right', alpha=0.8)\n",
        "    ax.plot(t, np.asarray(data['current_total'])[mask], color='black', linewidth=2, label='Total', linestyle='--')\n",
        "    ax.set_xlabel('Time (s)')\n",
        "    ax.set_ylabel('Current (A)')\n",
        "    ax.set_title(f'Coil Current Draw ({t_start}–{t_end} s)', fontweight='bold')\n",
        "    ax.legend(loc='best', framealpha=0.9, ncol=2)\n",
        "    ax.grid(True, alpha=0.3)\n",
        "    avg_total = np.mean(np.asarray(data['current_total'])[mask])\n",
        "    ax.text(0.98, 0.98, f'Avg total: {avg_total:.2f}A', transform=ax.transAxes, ha='right', va='top',\n",
        "             fontsize=10, bbox=dict(boxstyle='round', facecolor='white', alpha=0.8))\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "    return fig"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [],
      "source": [
        "def plot_control_signals(data: dict):\n",
        "    \"\"\"\n",
        "    Plot the PWM control signals sent to each coil.\n",
        "    \n",
        "    Args:\n",
        "        data: Dictionary from run_pid_simulation()\n",
        "    \"\"\"\n",
        "    fig, ax = plt.subplots(figsize=(12, 5))\n",
        "    \n",
        "    time = data['time']\n",
        "    \n",
        "    ax.plot(time, data['pwm_FL'], label='Front Left', linewidth=1.5, alpha=0.8)\n",
        "    ax.plot(time, data['pwm_FR'], label='Front Right', linewidth=1.5, alpha=0.8)\n",
        "    ax.plot(time, data['pwm_BL'], label='Back Left', linewidth=1.5, alpha=0.8)\n",
        "    ax.plot(time, data['pwm_BR'], label='Back Right', linewidth=1.5, alpha=0.8)\n",
        "    \n",
        "    if 'ff_pwm' in data and len(data.get('ff_pwm', [])) > 0:\n",
        "        ff = data['ff_pwm'] if isinstance(data['ff_pwm'], np.ndarray) else np.array(data['ff_pwm'])\n",
        "        ax.plot(time, ff, label='Feedforward', color='black', linewidth=2, linestyle='-.', alpha=0.7)\n",
        "    \n",
        "    ax.axhline(y=0, color='gray', linestyle='-', linewidth=0.5)\n",
        "    ax.axhline(y=1, color='red', linestyle='--', linewidth=1, alpha=0.5, label='Saturation')\n",
        "    ax.axhline(y=-1, color='red', linestyle='--', linewidth=1, alpha=0.5)\n",
        "    \n",
        "    ax.set_xlabel('Time (s)')\n",
        "    ax.set_ylabel('PWM Duty Cycle')\n",
        "    ax.set_title('Control Signals (PWM)', fontsize=14, fontweight='bold')\n",
        "    ax.legend(loc='best', framealpha=0.9)\n",
        "    ax.set_ylim([-1.2, 1.2])\n",
        "    ax.grid(True, alpha=0.3)\n",
        "    \n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "    \n",
        "    return fig"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "---\n",
        "## Run Simulation\n",
        "\n",
        "**Configure the simulation parameters below and run the cell.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loading maglev model from /Users/adipu/Documents/guadaloop_lev_control/lev_sim/maglev_model.pkl...\n",
            "Model loaded. Degree: 6\n",
            "Force R2: 1.0000\n",
            "Torque R2: 0.9999\n",
            "Version = 4.1 Metal - 90.5\n",
            "Vendor = Apple\n",
            "Renderer = Apple M2\n",
            "b3Printf: Selected demo: Physics Server\n",
            "startThreads creating 1 threads.\n",
            "starting thread 0\n",
            "started thread 0 \n",
            "MotionThreadFunc thread started\n",
            "Starting simulation: initial_gap=9.0mm, target=11.86mm\n",
            "  Impulse disturbance: -2N at step 5000\n",
            "  Feedforward: enabled\n",
            "  Applied -2N impulse and 1.00 N·m torque at step 5000\n",
            "numActiveThreads = 0\n",
            "Simulation complete: 10000 steps, 41.66s\n",
            "  Final gap: 10.88mm (target: 11.86mm)\n",
            "  Final roll: 0.000°, pitch: -0.003°\n",
            "stopping threads\n",
            "Thread with taskId 0 exiting\n",
            "Thread TERMINATED\n",
            "destroy semaphore\n",
            "semaphore destroyed\n",
            "destroy main semaphore\n",
            "main semaphore destroyed\n"
          ]
        }
      ],
      "source": [
        "# ============================================================\n",
        "#               SIMULATION PARAMETERS\n",
        "# ============================================================\n",
        "\n",
        "INITIAL_GAP_MM = 9.0     # Starting gap height (mm). Target is ~11.86mm\n",
        "MAX_STEPS = 10000          # Simulation steps (240 steps = 1 second)\n",
        "USE_GUI = True           # PyBullet GUI often hangs in notebooks; keep False. Use recording instead.\n",
        "USE_FEEDFORWARD = True    # Use MaglevPredictor feedforward for base PWM\n",
        "\n",
        "# Recording (headless: no GUI window; files saved to RECORD_DIR)\n",
        "RECORD_VIDEO = False      # Set True to save MP4 of each run (e.g. recordings/sim_YYYYMMDD_HHMMSS.mp4)\n",
        "RECORD_TELEMETRY = False  # Set True to save 4-panel telemetry PNG per run\n",
        "RECORD_DIR = \"recordings\" # Output folder for video and telemetry (created if missing)\n",
        "\n",
        "# Impulse disturbance (one-time force at a specific step)\n",
        "DISTURBANCE_STEP = 5000   # Step at which to apply impulse (e.g., 500). None = disabled\n",
        "DISTURBANCE_FORCE = -2   # Impulse force in Newtons (positive = upward push)\n",
        "\n",
        "# Stochastic disturbance (continuous random noise each step)\n",
        "DISTURBANCE_FORCE_STD = 0  # Std dev of random force noise (Newtons). 0 = disabled\n",
        "\n",
        "# ============================================================\n",
        "\n",
        "# Run simulation (gains from \"PID Gains Configuration\" cell)\n",
        "results = run_pid_simulation(\n",
        "    initial_gap_mm=INITIAL_GAP_MM,\n",
        "    max_steps=MAX_STEPS,\n",
        "    use_gui=USE_GUI,\n",
        "    disturbance_step=DISTURBANCE_STEP,\n",
        "    disturbance_force=DISTURBANCE_FORCE,\n",
        "    disturbance_force_std=DISTURBANCE_FORCE_STD,\n",
        "    use_feedforward=USE_FEEDFORWARD,\n",
        "    record_video=RECORD_VIDEO,\n",
        "    record_telemetry=RECORD_TELEMETRY,\n",
        "    record_dir=RECORD_DIR,\n",
        "    gains=gains,\n",
        "    verbose=True\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'time': array([0.00000000e+00, 4.16666667e-03, 8.33333333e-03, ...,\n",
            "       4.16541667e+01, 4.16583333e+01, 4.16625000e+01], shape=(10000,)), 'gap_front': array([ 9.        ,  9.02630146,  9.07173494, ..., 10.88719188,\n",
            "       10.87327881, 10.88719196], shape=(10000,)), 'gap_back': array([ 9.        ,  9.02630146,  9.07173494, ..., 10.87327859,\n",
            "       10.88719174, 10.87327877], shape=(10000,)), 'gap_avg': array([ 9.        ,  9.02630146,  9.07173494, ..., 10.88023523,\n",
            "       10.88023528, 10.88023537], shape=(10000,)), 'roll_deg': array([-0.00000000e+00, -5.27915552e-06, -2.13414205e-05, ...,\n",
            "        1.30725108e-05,  1.30788079e-05,  1.30724589e-05], shape=(10000,)), 'pitch_deg': array([ 0.        ,  0.        ,  0.        , ..., -0.0031659 ,\n",
            "        0.00316582, -0.00316587], shape=(10000,)), 'current_FL': array([10.2       ,  9.29838848,  8.99919415, ..., -3.97452164,\n",
            "       10.2       , -3.97452164], shape=(10000,)), 'current_FR': array([10.2       ,  9.29830265,  8.9989996 , ..., -3.97452831,\n",
            "       10.2       , -3.97452831], shape=(10000,)), 'current_BL': array([10.2       ,  9.29838848,  8.99919415, ..., 10.2       ,\n",
            "       -3.97451782, 10.2       ], shape=(10000,)), 'current_BR': array([10.2       ,  9.29830265,  8.9989996 , ..., 10.2       ,\n",
            "       -3.97452259, 10.2       ], shape=(10000,)), 'current_total': array([40.8       , 37.19338226, 35.99638748, ..., 28.34904861,\n",
            "       28.34904099, 28.34904861], shape=(10000,)), 'pwm_FL': array([0.95018876, 0.8899194 , 0.83739257, ..., 0.226274  , 0.42596412,\n",
            "       0.226274  ], shape=(10000,), dtype=float32), 'pwm_FR': array([0.95018876, 0.8899151 , 0.83737934, ..., 0.2262737 , 0.42596382,\n",
            "       0.2262737 ], shape=(10000,), dtype=float32), 'pwm_BL': array([0.95018876, 0.8899194 , 0.83739257, ..., 0.4259643 , 0.22627419,\n",
            "       0.4259643 ], shape=(10000,), dtype=float32), 'pwm_BR': array([0.95018876, 0.8899151 , 0.83737934, ..., 0.425964  , 0.2262739 ,\n",
            "       0.425964  ], shape=(10000,), dtype=float32), 'ff_pwm': array([0.66143623, 0.65541619, 0.64500579, ..., 0.22719963, 0.22719963,\n",
            "       0.22719963], shape=(10000,))}\n"
          ]
        }
      ],
      "source": [
        "print(results)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "---\n",
        "## View Results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "data": {
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            "text/plain": [
              "<Figure size 1400x1000 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x400 with 1 Axes>"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Plot main performance metrics\n",
        "plot_results(results)\n",
        "show_current_slice(results, 20, 25)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1200x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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2bZpMnz7d5bKgoCApXry4NG/eXJ544gmpWrVqrrY9YMAA8xwnTZqU6W3WrFkjNptNWrZsma1tAgAAAACA3COMKmQGDx5sTio+Pl7Wr18vTz31lERERMgdd9zhtXFFRUXJ/PnzHd8nJSXJli1b5LnnnpP77rtPvvrqK7FYLDJmzBhJSUnJ18e+/fbbZeLEiYRRAAAAAAB4AGFULmkljS0hQbzFEhZmwpmcKlKkiJQrV87xvVYcrVy5Uj7//HOvhlFaCeU8LlWpUiU5e/asPP7447Jt2zZp0KCBFCtWzGtjBAAAAAAAeUcYlcsg6vCYpyRh2zbxlrAG9aXi88/nKpBKLzw83OX76OhoefHFF+Xnn3+WU6dOmely3bt3N1VJWkGl9u3bZ6a+rVq1SoKDg6VDhw7m+jJlymTY/gsvvCCLFi2SmTNnSpMmTXI0ttDQUMeUQnfT9DZu3GiqmvRr2bJl5eGHHza3ee+996RNmzbmNufPnzdT/b7//nvz2l155ZUybtw4E8zVr1/f3Eav1+eS1XQ+AAAAAACQdzQwz628Z0A+Qafp6RQ47Rtlp2HO5s2bTR+npUuXmqBGw6RPP/3UXB8TE2OqqBITE+X99983wc+///4rjzzySIbt/+9//5PFixeb2+Q0iNJqqDfeeEMuvfRSqVmzZobrjx49KgMHDjQ9obSySwOmV155JcM0vu+++07Kly8vCxYsMONZsmSJvPPOO+a6FStWmK9PPvmkCdMAAAAAAEDBojIqF7QaSauS/HGa3owZM2TWrFmOvkx6atq0qfTu3dtxG61yatWqlaNqqEqVKjJnzhzZvn27+V7DHK02evnll6VEiRLmsueff16+/vprE1DZaTCkIdHs2bPlkksuyXJchw4dkmbNmjm+1+0ULVrUNF0fNWqUWK0Zc1MNx3TanlZeaeVUnTp1TP+r+++/3+V2GoINHz7cnK9WrZp5flpJpexTA3U7TAEEAAAAAKDgEUblkgZBlnTT2/xBv379zApzKjk52Uy309BIK53mzZtnpsVpQ+8ff/xRFi5cKHv37pWdO3fKgQMHpFatWuZ+GkrVqFHDEUQp7eekJ7t//vlH/vrrLyldurRUrFjxouPSyiX71DsNpnS6nE6jGzFihNmGO1q91bhxY8cUPqUhWno6Vmc6budVBQEAAAAAgOcwTa+Q0SCmevXq5lS7dm1TeTR+/HjZunWr/P7775KamipDhw41lU7aC6pnz56mmqp58+aObejlF6NB0scff2z6Uem2Lka3aR9Xu3btTH+p/fv3y7333utSbZW+6bmO92L0dgAAAAAAwDcQRsE09VYa7GzZskV++eUXefXVV2XkyJFy3XXXmalt2hPKfjudDqcVU7rSnd2mTZtMiHTkyBHzfb169cy0Ow26tCfVDz/8kKM9rc3IdfqdVj+99tprbm+jlVh6vU41tFu7di2vKAAAAAAAPowwqpCJjY2V48ePm9OxY8dk9erVMmHCBDNNTsMkDYG0Sumbb74xlUkbNmwwjcn19vYKJe0vpRVW2stJK6q0/9LTTz9tAqioqCiXx+vcubP06tXLhFK6Sl9OXH755SYM0+bnGjqlp9MJtZn62LFjZdeuXaay67nnnjPX5aSfllZx6f1Pnz6do/EBAAAAAICcI4wqZLR5eceOHc1Jw56HHnrIrEanq+JFRERIhQoVTL8m7RmlU/Qefvhhc9mgQYMcTb/1djqNTntOaQ+qu+++21RLTZ061e1j6ip1Wr2Unel66ekqd8WLFzeNydOvklemTBl59913TU+r66+/3qymd9ttt5nrnPtIXczgwYNNg3ZdNRAAAAAAABQsi80+96qQVwvp9LSGDRuaKhl/pS+lVh9p1VJuVtrzNxpC6fNt0aKF47K///7bBFLLly/PVuN0wBcVtmMZCEQcx0Bg4FgG/B/HsW9mK1RGwW9pf6o777xTFi1aZFbH035REydOlNatWxNEAQAAAADgoy6+LBrgo3SqoU4B1NX+tG9UsWLFzOqA2ngdAAAAAAD4JsIo+DVtYq4nAAAAAADgH5imBwAAAAAAAI8hjAIAAAAAAIDHEEYBAAAAAADAYwijAAAAAAAA4DGEUQAAAAAAAPAYwigAAAAAAAB4DGFUITJgwACpX7++29PkyZML9HFHjx7t+P7zzz+Xjh07SpMmTeT7778Xf5L+uQAAAAAAgJwJzuHt4ed69OghY8aMyXB5RESEx8agwVf37t3lwQcflNKlS3vscQEAAAAAgPcRRhUy4eHhUq5cOa+OITo6Wlq2bCmVK1f26jgAAAAAAIDnMU0PDjabTd555x1TtdS0aVO5/vrr5YsvvnDZQ7t27ZJ77rlHmjVrZqbaPfroo3L8+HHH9YmJiTJhwgRp166dtGjRQl588UVJTU011x04cMBMCVRPPvmkdOvWzZw/c+aMPPPMM3L55ZebqXv9+vWTlStXOrY5bdo06d+/vwwfPlyaN2/umG64adMmx20eeOAB83gpKSnme33Mtm3byuLFi8338+bNk969e5vtX3bZZXL77bfLhg0bHPfXsWjFVs+ePaVNmzayatWqLJ8LAAAAAADIHSqj8mDRokXmlFca6Fx66aWO7zUkmTJlijl/ww03mJNdXFyc/N///V+Gy/PDK6+8Il999ZWMGzdOatWqJX/99ZeMHz9ezp49K3fccYccPXrUhDga6mjfJB2LBkW33nqruV+RIkXk+eeflx9//FEmTZoklSpVkrfeektWr14tVatWlYoVK8qKFStMiKVhlG5Hw6PBgwdLUlKSCXt02t4HH3wgQ4YMkY8++siER0rHcuedd5pwyX6f3377TRo1amS+1/Dq/PnzJqDS+6xfv96Mu0uXLqYv1bPPPmvGphVZGp4999xz8tRTTznCKjVnzhyZMWOGFCtWzIRdWT0XAAAAAACQO4RReRAbGysnT56UvNIgJv339u3qY6SvXtLr0l+eXV9++aUsXbrU5TKt+nnttddk9uzZ8vLLL5sAR1WrVk0OHjwoM2fONGHUxx9/LFFRUSbEsZs6daqpQPr222/lqquukgULFsjTTz9tqpyUVhb9+eef5nxQUJBjiqAGPho8/fzzzyZA0nHVq1fPXKdVUhrI6eO++uqrjscaNmyYuZ+9kknDqHvvvdcETyEhIabiSUMpDaOWL19unleJEiWkZMmS8sILL8h1111n7qvTA/v27WsCKmc65vbt25vz586dy/K5AAAAAACA3CGMygOtBCpTpozklQYp6b+3b1cfw5nFYjHXpb88uzTEGTlyZIY+Ujt37pSEhARTpWW1/jd7Mzk52UxXi4+Pl82bN8uOHTvMFD1nej+dvrdnzx4TpDlXeYWFhckll1yS6Xi2b99uAiZ7EGV/jlrBpFVUdvqc7UGU6tq1q3z66admXBpKaSCmIZOGRTqNUEMue+VYq1atzPhef/112b17t+zbt0+2bduWYcpd9erVHedz81wAAAAAAMDFEUblQUFMlVMagGiVkju66l1m12VHZGSkS+hid+TIEUelk07RSy80NNTRh0mrhdLToOjQoUOO6i1nwcGZv83S39b5cuf7aWDmrHXr1mZM2tvpjz/+MP2tNIyaO3euqebasmWLmUKotOpKpxXqtEDtOaU9qTQES18Z5fwYGojl9LkAAAAAAICLo4E5DA2gNGjRQEnDKvtJK4x0upxWS9WtW9dUGGnvJ/v1Og1Op69puFOzZk1TPfT333+7VFZt3bo1072svZm0t5Pe304DoDVr1kidOnUyvZ9Wj2nvqWXLlsm6descTcb18TSE0kqrKlWqmNu+/fbbZlqe9n7S6YZaKbV//37HY7mTm+cCAAAAAAAujjAKjsomrRjSHk3a1FvDmvnz55um4uXLlze30eblGhzpND8NZfSkK9xpfycNf7TqSle90/5T3333nQmutIpKG59nRgOlhg0bmumBWuWk99GKJQ2nBg4ceNEph9rXScenTcW1skmnEOr4dUVAOw3PNFTS3lT//vuvqSzTZuVKpyC6k5vnAgAAAAAALs7v5hzpSm8aIGiVS1YOHDhgVkzTVdi0v5JWxjz00EOmiTbce+KJJ6RUqVImkDp27JgJcbRp+N13322u18BHQxxd6e+2224z+1Knvenqd9qMXGmopBVFGijp6nY9evQwoVFmdBuzZs2SyZMny4MPPmhe28aNG5vASBuSZ0Ubi+tKejp10E4bkGsTc+cwauzYseZ9o+GSTu1r0KCB/O9//3MEadqfyp2cPhcAAAAAAHBxFltm85R8jPYr0n5GM2bMkD59+mQZRmnj6V69ekmNGjVMoKDVMGPGjDFTtDRcSU9XptMeQ1qhk9vG4L5AX8ro6Ggzdc7e8wiA/+FYBvwfxzEQGDiWAf/Hcew5OclW/KIySqdIaZikq6BVqlTpordfunSp6X302WefmWBGp5CdPHnSVMPcd999pjoGAAAAAAAAnucXPaP+/PNPqV27tnz11VeOptRZWb16tTRq1MgEUXY6levcuXMmpQMAAAAAAIB3+EVllE6vy4kjR45IVFSUy2X2JtyHDx+Wpk2bur2f7fhxsYWE/HdBeLhIqVK6jJrI8eMZ71CxYtrXEyd0bqDrdSVLikREiJw/LxIT43qdVmaVKaNzD0XcNcTWsWpvq1OnRBISXK8rVkykaFGRuDiRM2dcxx8UJLbQ0LQV4g4fzrjdsmV1Gbq0++n9nUVGihQvnvZ4+rjOrFaRChXSzut4ddzOtF9UWFja89Tn60z3ge4L3T+6nzLbh7p/dT+724fnzomcPet6nT6ePm5KisixYxm3q+PVcZ88qV3KXa/T56nP180+NPtH95Nytw/LlRMJDhY5fVokPt71On1d9PVxtw/19bzwHnS7D/X9oO8Ld/tQyxs1WHW3D3U6pv297m4f6vtX38fu9qH9/Z3ZPtTt6vbd7UMdj44rNlYkOtr9+1vfh0eOZP7+drcP7e9vvVyvd6b7Xfe/0u2mn2Fsf3/reHRc7t7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            "text/plain": [
              "<Figure size 1200x500 with 1 Axes>"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Plot control signals (optional)\n",
        "plot_control_signals(results)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "---\n",
        "## Compare Multiple Initial Conditions\n",
        "\n",
        "Run this cell to test the controller with different starting heights."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [],
      "source": [
        "def compare_initial_conditions(initial_gaps_mm: list, max_steps: int = 2000):\n",
        "    \"\"\"\n",
        "    Compare PID performance across different initial conditions.\n",
        "    \n",
        "    Args:\n",
        "        initial_gaps_mm: List of starting gap heights to test\n",
        "        max_steps: Maximum steps per simulation\n",
        "    \"\"\"\n",
        "    fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n",
        "    fig.suptitle('PID Performance Comparison: Different Initial Conditions', \n",
        "                 fontsize=16, fontweight='bold', y=1.02)\n",
        "    \n",
        "    target_gap_mm = TARGET_GAP * 1000\n",
        "    colors = plt.cm.viridis(np.linspace(0, 0.8, len(initial_gaps_mm)))\n",
        "    \n",
        "    all_results = []\n",
        "    \n",
        "    for i, gap in enumerate(initial_gaps_mm):\n",
        "        print(f\"Running simulation {i+1}/{len(initial_gaps_mm)}: initial_gap={gap}mm\")\n",
        "        data = run_pid_simulation(initial_gap_mm=gap, max_steps=max_steps, gains=gains, verbose=False)\n",
        "        all_results.append((gap, data))\n",
        "        \n",
        "        label = f'{gap}mm'\n",
        "        \n",
        "        # Gap height\n",
        "        axes[0, 0].plot(data['time'], data['gap_avg'], color=colors[i], \n",
        "                        linewidth=2, label=label)\n",
        "        \n",
        "        # Roll\n",
        "        axes[0, 1].plot(data['time'], data['roll_deg'], color=colors[i], \n",
        "                        linewidth=2, label=label)\n",
        "        \n",
        "        # Pitch\n",
        "        axes[1, 0].plot(data['time'], data['pitch_deg'], color=colors[i], \n",
        "                        linewidth=2, label=label)\n",
        "        \n",
        "        # Total current\n",
        "        axes[1, 1].plot(data['time'], data['current_total'], color=colors[i], \n",
        "                        linewidth=2, label=label)\n",
        "    \n",
        "    # Add target line to gap plot\n",
        "    axes[0, 0].axhline(y=target_gap_mm, color='red', linestyle='--', \n",
        "                       linewidth=2, label=f'Target ({target_gap_mm:.1f}mm)')\n",
        "    \n",
        "    # Configure axes\n",
        "    axes[0, 0].set_xlabel('Time (s)')\n",
        "    axes[0, 0].set_ylabel('Gap Height (mm)')\n",
        "    axes[0, 0].set_title('Average Gap Height', fontweight='bold')\n",
        "    axes[0, 0].legend(loc='best')\n",
        "    axes[0, 0].set_ylim([0, 20])\n",
        "    axes[0, 0].grid(True, alpha=0.3)\n",
        "    \n",
        "    axes[0, 1].axhline(y=0, color='gray', linestyle='--', linewidth=1)\n",
        "    axes[0, 1].set_xlabel('Time (s)')\n",
        "    axes[0, 1].set_ylabel('Roll Angle (degrees)')\n",
        "    axes[0, 1].set_title('Roll Angle', fontweight='bold')\n",
        "    axes[0, 1].legend(loc='best')\n",
        "    axes[0, 1].set_ylim([-4, 4])\n",
        "    axes[0, 1].grid(True, alpha=0.3)\n",
        "    \n",
        "    axes[1, 0].axhline(y=0, color='gray', linestyle='--', linewidth=1)\n",
        "    axes[1, 0].set_xlabel('Time (s)')\n",
        "    axes[1, 0].set_ylabel('Pitch Angle (degrees)')\n",
        "    axes[1, 0].set_title('Pitch Angle', fontweight='bold')\n",
        "    axes[1, 0].legend(loc='best')\n",
        "    axes[1, 0].set_ylim([-6, 6])\n",
        "    axes[1, 0].grid(True, alpha=0.3)\n",
        "    \n",
        "    axes[1, 1].set_xlabel('Time (s)')\n",
        "    axes[1, 1].set_ylabel('Total Current (A)')\n",
        "    axes[1, 1].set_title('Total Current Draw', fontweight='bold')\n",
        "    axes[1, 1].legend(loc='best')\n",
        "    axes[1, 1].grid(True, alpha=0.3)\n",
        "    \n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "    \n",
        "    return all_results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "============================================================\n",
            "Data structure: results (from run_pid_simulation)\n",
            "============================================================\n",
            "Number of time steps: 10000\n",
            "\n",
            "Key                Shape          Dtype        Min        Max       \n",
            "------------------------------------------------------------\n",
            "current_BL         (10000,)       float64      -9.416     10.2      \n",
            "current_BR         (10000,)       float64      -9.326     10.2      \n",
            "current_FL         (10000,)       float64      -9.667     10.2      \n",
            "current_FR         (10000,)       float64      -9.618     10.2      \n",
            "current_total      (10000,)       float64      0.0007697  40.8      \n",
            "ff_pwm             (10000,)       float64      -0.003738  0.6614    \n",
            "gap_avg            (10000,)       float64      9          11.87     \n",
            "gap_back           (10000,)       float64      9          11.86     \n",
            "gap_front          (10000,)       float64      9          11.89     \n",
            "pitch_deg          (10000,)       float64      -0.007633  0.003622  \n",
            "pwm_BL             (10000,)       float32      -0.06703   0.9502    \n",
            "pwm_BR             (10000,)       float32      -0.06227   0.9502    \n",
            "pwm_FL             (10000,)       float32      -0.0651    0.9502    \n",
            "pwm_FR             (10000,)       float32      -0.06026   0.9502    \n",
            "roll_deg           (10000,)       float64      -0.05699   0.03849   \n",
            "time               (10000,)       float64      0          41.66     \n",
            "------------------------------------------------------------\n",
            "\n",
            "Sample (first and last time steps):\n",
            "\n",
            "  Step 0: t = 0.0000 s\n",
            "    gap_avg=9.000 mm  roll=-0.000°  pitch=0.000°\n",
            "    pwm: FL=0.950 FR=0.950 BL=0.950 BR=0.950\n",
            "    ff_pwm=0.661  current_total=40.800 A\n",
            "\n",
            "  Step 5000: t = 20.8333 s\n",
            "    gap_avg=11.860 mm  roll=0.002°  pitch=0.000°\n",
            "    pwm: FL=-0.001 FR=-0.001 BL=-0.001 BR=-0.001\n",
            "    ff_pwm=-0.001  current_total=0.019 A\n",
            "\n",
            "  Step 9999: t = 41.6625 s\n",
            "    gap_avg=10.880 mm  roll=0.000°  pitch=-0.003°\n",
            "    pwm: FL=0.226 FR=0.226 BL=0.426 BR=0.426\n",
            "    ff_pwm=0.227  current_total=28.349 A\n"
          ]
        }
      ],
      "source": [
        "# Show simulation results data structure in a readable format\n",
        "def show_data_structure(data: dict, name: str = \"results\", sample_rows: int = 3):\n",
        "    \"\"\"Print keys, shapes, dtypes, and a small sample of the simulation data dict.\"\"\"\n",
        "    print(\"=\" * 60)\n",
        "    print(f\"Data structure: {name} (from run_pid_simulation)\")\n",
        "    print(\"=\" * 60)\n",
        "    n = len(data[\"time\"]) if \"time\" in data else 0\n",
        "    print(f\"Number of time steps: {n}\\n\")\n",
        "    print(f\"{'Key':<18} {'Shape':<14} {'Dtype':<12} {'Min':<10} {'Max':<10}\")\n",
        "    print(\"-\" * 60)\n",
        "    for key in sorted(data.keys()):\n",
        "        arr = data[key]\n",
        "        if isinstance(arr, np.ndarray):\n",
        "            sh = str(arr.shape)\n",
        "            dt = str(arr.dtype)\n",
        "            mn = f\"{np.min(arr):.4g}\" if arr.size else \"—\"\n",
        "            mx = f\"{np.max(arr):.4g}\" if arr.size else \"—\"\n",
        "        else:\n",
        "            sh, dt, mn, mx = \"—\", \"—\", \"—\", \"—\"\n",
        "        print(f\"{key:<18} {sh:<14} {dt:<12} {mn:<10} {mx:<10}\")\n",
        "    print(\"-\" * 60)\n",
        "    print(\"\\nSample (first and last time steps):\")\n",
        "    if n >= 1:\n",
        "        sample_idx = [0, n // 2, n - 1] if n >= 3 else list(range(n))\n",
        "        for i in sample_idx:\n",
        "            t = data[\"time\"][i]\n",
        "            print(f\"\\n  Step {i}: t = {t:.4f} s\")\n",
        "            print(f\"    gap_avg={data['gap_avg'][i]:.3f} mm  roll={data['roll_deg'][i]:.3f}°  pitch={data['pitch_deg'][i]:.3f}°\")\n",
        "            print(f\"    pwm: FL={data['pwm_FL'][i]:.3f} FR={data['pwm_FR'][i]:.3f} BL={data['pwm_BL'][i]:.3f} BR={data['pwm_BR'][i]:.3f}\")\n",
        "            if \"ff_pwm\" in data:\n",
        "                print(f\"    ff_pwm={data['ff_pwm'][i]:.3f}  current_total={data['current_total'][i]:.3f} A\")\n",
        "\n",
        "show_data_structure(results, \"results\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Compare different starting heights\n",
        "# Uncomment and run to test multiple initial conditions\n",
        "\n",
        "# comparison_results = compare_initial_conditions(\n",
        "#     initial_gaps_mm=[10.0, 14.0, 18.0, 22.0],\n",
        "#     max_steps=2000\n",
        "# )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "---\n",
        "## PID Tuning with Optuna\n",
        "\n",
        "Bayesian-style optimization (Optuna TPE sampler) tunes all **nine** gains (height, roll, pitch × Kp, Ki, Kd) **jointly** so loop coupling is respected.\n",
        "\n",
        "- **Run optimizer** (script or cell below): saves `pid_best_params.json` in this folder.\n",
        "- **Use new params**: re-run the **Load PID gains** cell at the top, then the **PID Gains** cell and the simulation."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Option A: Run Optuna optimization (slow). Saves pid_best_params.json next to optuna_pid_tune.py.\n",
        "# from optuna_pid_tune import run_optimization\n",
        "# study = run_optimization(n_trials=50, timeout=1800)\n",
        "# print(\"Best:\", study.best_params)\n",
        "\n",
        "# After running Optuna: re-run the \"Load PID gains\" cell at the top of the notebook\n",
        "# to pick up the new pid_best_params.json, then re-run the PID Gains cell and the simulation."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "---\n",
        "## Test Disturbance Rejection\n",
        "\n",
        "Apply a sudden force disturbance and observe recovery."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Test disturbance rejection\n",
        "# Uncomment and run to test disturbance response\n",
        "\n",
        "# Example 1: Impulse disturbance (one-time force)\n",
        "# disturbance_results = run_pid_simulation(\n",
        "#     initial_gap_mm=11.86,         # Start near target\n",
        "#     max_steps=3000,\n",
        "#     use_gui=False,\n",
        "#     disturbance_step=720,        # Apply at 3 seconds\n",
        "#     disturbance_force=-20.0,     # 20N downward push\n",
        "#     verbose=True\n",
        "# )\n",
        "# plot_results(disturbance_results, title_suffix=' (with 20N impulse at t=3s)')\n",
        "\n",
        "# Example 2: Continuous stochastic noise\n",
        "# noisy_results = run_pid_simulation(\n",
        "#     initial_gap_mm=11.86,\n",
        "#     max_steps=3000,\n",
        "#     use_gui=False,\n",
        "#     disturbance_force_std=2.0,   # 2N standard deviation continuous noise\n",
        "#     verbose=True\n",
        "# )\n",
        "# plot_results(noisy_results, title_suffix=' (with 2N std continuous noise)')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "---\n",
        "## Summary Statistics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "==================================================\n",
            "SIMULATION SUMMARY\n",
            "==================================================\n",
            "Duration: 41.66 seconds (10000 steps)\n",
            "Target gap: 11.86 mm\n",
            "\n",
            "Gap Height:\n",
            "  Initial:  9.00 mm\n",
            "  Final:    10.88 mm\n",
            "  Error:    0.980 mm\n",
            "  Max:      11.87 mm\n",
            "  Min:      9.00 mm\n",
            "  Settling time (2%): 0.179 s\n",
            "\n",
            "Orientation (final):\n",
            "  Roll:  +0.000 degrees\n",
            "  Pitch: -0.003 degrees\n",
            "\n",
            "Current Draw:\n",
            "  Average total: 14.23 A\n",
            "  Peak total:    40.80 A\n",
            "==================================================\n"
          ]
        }
      ],
      "source": [
        "def print_summary(data: dict):\n",
        "    \"\"\"Print summary statistics for a simulation run.\"\"\"\n",
        "    target_gap_mm = TARGET_GAP * 1000\n",
        "    \n",
        "    # Calculate settling time (within 2% of target)\n",
        "    tolerance = 0.02 * target_gap_mm\n",
        "    settled_idx = None\n",
        "    for i in range(len(data['gap_avg'])):\n",
        "        if abs(data['gap_avg'][i] - target_gap_mm) < tolerance:\n",
        "            # Check if it stays settled\n",
        "            if all(abs(data['gap_avg'][j] - target_gap_mm) < tolerance \n",
        "                   for j in range(i, min(i+100, len(data['gap_avg'])))):\n",
        "                settled_idx = i\n",
        "                break\n",
        "    \n",
        "    print(\"=\" * 50)\n",
        "    print(\"SIMULATION SUMMARY\")\n",
        "    print(\"=\" * 50)\n",
        "    print(f\"Duration: {data['time'][-1]:.2f} seconds ({len(data['time'])} steps)\")\n",
        "    print(f\"Target gap: {target_gap_mm:.2f} mm\")\n",
        "    print()\n",
        "    print(\"Gap Height:\")\n",
        "    print(f\"  Initial:  {data['gap_avg'][0]:.2f} mm\")\n",
        "    print(f\"  Final:    {data['gap_avg'][-1]:.2f} mm\")\n",
        "    print(f\"  Error:    {abs(data['gap_avg'][-1] - target_gap_mm):.3f} mm\")\n",
        "    print(f\"  Max:      {max(data['gap_avg']):.2f} mm\")\n",
        "    print(f\"  Min:      {min(data['gap_avg']):.2f} mm\")\n",
        "    if settled_idx:\n",
        "        print(f\"  Settling time (2%): {data['time'][settled_idx]:.3f} s\")\n",
        "    else:\n",
        "        print(f\"  Settling time: Not settled within tolerance\")\n",
        "    print()\n",
        "    print(\"Orientation (final):\")\n",
        "    print(f\"  Roll:  {data['roll_deg'][-1]:+.3f} degrees\")\n",
        "    print(f\"  Pitch: {data['pitch_deg'][-1]:+.3f} degrees\")\n",
        "    print()\n",
        "    print(\"Current Draw:\")\n",
        "    print(f\"  Average total: {np.mean(data['current_total']):.2f} A\")\n",
        "    print(f\"  Peak total:    {max(data['current_total']):.2f} A\")\n",
        "    print(\"=\" * 50)\n",
        "\n",
        "# Print summary for last simulation\n",
        "print_summary(results)"
      ]
    }
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