gait.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8BBh7fweo4Np",
        "outputId": "e3967444-9eed-4486-c707-b47b12ae11ad"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: mediapipe==0.10.14 in /usr/local/lib/python3.12/dist-packages (0.10.14)\n",
            "Requirement already satisfied: absl-py in /usr/local/lib/python3.12/dist-packages (from mediapipe==0.10.14) (1.4.0)\n",
            "Requirement already satisfied: attrs>=19.1.0 in /usr/local/lib/python3.12/dist-packages (from mediapipe==0.10.14) (25.4.0)\n",
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            "Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.12/dist-packages (from matplotlib->mediapipe==0.10.14) (11.3.0)\n",
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            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.12/dist-packages (from matplotlib->mediapipe==0.10.14) (2.9.0.post0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.7->matplotlib->mediapipe==0.10.14) (1.17.0)\n",
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          ]
        }
      ],
      "source": [
        "!pip install mediapipe==0.10.14"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install opencv-python numpy matplotlib scipy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "axpGOzZap1U-",
        "outputId": "8e8f3e28-7e17-430b-c957-5e1c37591f88"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: opencv-python in /usr/local/lib/python3.12/dist-packages (4.13.0.92)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.12/dist-packages (2.0.2)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.12/dist-packages (3.10.0)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.12/dist-packages (1.16.3)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (1.3.3)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (4.62.1)\n",
            "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (1.5.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (26.0)\n",
            "Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (11.3.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (3.3.2)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (2.9.0.post0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.7->matplotlib) (1.17.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import cv2\n",
        "import mediapipe as mp\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy.signal import find_peaks"
      ],
      "metadata": {
        "id": "KDYQRfIkqEZV"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.signal import find_peaks, savgol_filter\n",
        "\n",
        "# Initialize MediaPipe Pose Model (High Complexity for Clinical Accuracy)\n",
        "mp_pose = mp.solutions.pose\n",
        "pose = mp_pose.Pose(\n",
        "    static_image_mode=False,\n",
        "    model_complexity=2,\n",
        "    min_detection_confidence=0.5,\n",
        "    min_tracking_confidence=0.5\n",
        ")\n",
        "\n",
        "mp_drawing = mp.solutions.drawing_utils\n",
        "\n",
        "def smooth_signal(data, window_length=9, polyorder=3):\n",
        "    \"\"\"Applies Savitzky-Golay filter to remove MediaPipe tracking jitter.\"\"\"\n",
        "    if len(data) < window_length: return data\n",
        "    return savgol_filter(data, window_length, polyorder)"
      ],
      "metadata": {
        "id": "miOvE2aOqE0u"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def extract_validate_and_visualize(input_video_path, output_video_path):\n",
        "    cap = cv2.VideoCapture(input_video_path)\n",
        "    fps = cap.get(cv2.CAP_PROP_FPS)\n",
        "\n",
        "    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n",
        "    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n",
        "    fourcc = cv2.VideoWriter_fourcc(*'mp4v')\n",
        "    out = cv2.VideoWriter(output_video_path, fourcc, fps, (width, height))\n",
        "\n",
        "    signals = {\n",
        "        'l_ankle_y': [], 'r_ankle_y': [],\n",
        "        'l_arm_swing': [], 'r_arm_swing': [],\n",
        "        'mid_hip_x': [], 'mid_hip_y': [],\n",
        "        'l_foot_x': [], 'r_foot_x': []\n",
        "    }\n",
        "\n",
        "    while cap.isOpened():\n",
        "        ret, frame = cap.read()\n",
        "        if not ret:\n",
        "            break\n",
        "\n",
        "        image_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
        "        image_rgb.flags.writeable = False\n",
        "        results = pose.process(image_rgb)\n",
        "        image_rgb.flags.writeable = True\n",
        "\n",
        "        image_bgr = cv2.cvtColor(image_rgb, cv2.COLOR_RGB2BGR)\n",
        "\n",
        "        if results.pose_landmarks:\n",
        "            lm = results.pose_landmarks.landmark\n",
        "\n",
        "            # -----------------------------\n",
        "            # BODY CENTER (IMPORTANT)\n",
        "            # -----------------------------\n",
        "            mid_hip_x = (lm[23].x + lm[24].x) / 2\n",
        "            mid_hip_y = (lm[23].y + lm[24].y) / 2\n",
        "\n",
        "            signals['mid_hip_x'].append(mid_hip_x)\n",
        "            signals['mid_hip_y'].append(mid_hip_y)\n",
        "\n",
        "            # -----------------------------\n",
        "            # LOWER BODY (RELATIVE SIGNAL)\n",
        "            # -----------------------------\n",
        "            signals['l_ankle_y'].append(lm[27].y)\n",
        "            signals['r_ankle_y'].append(lm[28].y)\n",
        "\n",
        "            # 🔥 FIX: normalize foot X relative to body center\n",
        "            signals['l_foot_x'].append(lm[31].x - mid_hip_x)\n",
        "            signals['r_foot_x'].append(lm[32].x - mid_hip_x)\n",
        "\n",
        "            # -----------------------------\n",
        "            # ARM SWING (IMPROVED)\n",
        "            # -----------------------------\n",
        "            l_torso_len = np.linalg.norm([\n",
        "                lm[11].x - lm[23].x,\n",
        "                lm[11].y - lm[23].y\n",
        "            ])\n",
        "            r_torso_len = np.linalg.norm([\n",
        "                lm[12].x - lm[24].x,\n",
        "                lm[12].y - lm[24].y\n",
        "            ])\n",
        "\n",
        "            # 🔥 FIX: relative to shoulder (remove body sway)\n",
        "            l_ws = np.linalg.norm([\n",
        "                lm[15].x - lm[11].x,\n",
        "                lm[15].y - lm[11].y\n",
        "            ])\n",
        "            r_ws = np.linalg.norm([\n",
        "                lm[16].x - lm[12].x,\n",
        "                lm[16].y - lm[12].y\n",
        "            ])\n",
        "\n",
        "            # 🔥 FINAL: normalized + stabilized\n",
        "            signals['l_arm_swing'].append(l_ws / (l_torso_len + 1e-6))\n",
        "            signals['r_arm_swing'].append(r_ws / (r_torso_len + 1e-6))\n",
        "\n",
        "            # -----------------------------\n",
        "            # DRAW SKELETON\n",
        "            # -----------------------------\n",
        "            mp_drawing.draw_landmarks(\n",
        "                image_bgr,\n",
        "                results.pose_landmarks,\n",
        "                mp_pose.POSE_CONNECTIONS,\n",
        "                landmark_drawing_spec=mp_drawing.DrawingSpec(\n",
        "                    color=(0, 0, 255), thickness=4, circle_radius=4\n",
        "                ),\n",
        "                connection_drawing_spec=mp_drawing.DrawingSpec(\n",
        "                    color=(255, 255, 255), thickness=2\n",
        "                )\n",
        "            )\n",
        "\n",
        "        out.write(image_bgr)\n",
        "\n",
        "    cap.release()\n",
        "    out.release()\n",
        "\n",
        "    # -----------------------------\n",
        "    # VALIDATION\n",
        "    # -----------------------------\n",
        "    if len(signals['mid_hip_x']) == 0:\n",
        "        raise ValueError(\"❌ No person detected in the video.\")\n",
        "\n",
        "    # 🔥 IMPROVED SIDE VIEW DETECTION\n",
        "    x_var = np.var(signals['mid_hip_x'])\n",
        "    y_var = np.var(signals['mid_hip_y'])\n",
        "\n",
        "    if x_var > y_var:  # more horizontal movement → side view\n",
        "        raise ValueError(\"❌ SIDE-VIEW DETECTED: Upload FRONT-VIEW video\")\n",
        "\n",
        "    # -----------------------------\n",
        "    # 🔥 SMOOTH SIGNALS\n",
        "    # -----------------------------\n",
        "    for key in signals:\n",
        "        signals[key] = smooth_signal(np.array(signals[key]))\n",
        "\n",
        "    print(\"✅ Signal extraction complete (normalized + stabilized)\")\n",
        "\n",
        "    return signals, fps"
      ],
      "metadata": {
        "id": "SFSbx4uVFVnU"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def robust_amplitude(signal, threshold=0.08):\n",
        "    \"\"\"\n",
        "    Computes real movement amplitude and removes MediaPipe noise.\n",
        "    \"\"\"\n",
        "    if len(signal) == 0:\n",
        "        return 0\n",
        "\n",
        "    amp = np.max(signal) - np.min(signal)\n",
        "\n",
        "    # 🔥 Noise filtering\n",
        "    return amp if amp > threshold else 0"
      ],
      "metadata": {
        "id": "fW17fyfxfU26"
      },
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.signal import find_peaks, detrend\n",
        "import numpy as np\n",
        "\n",
        "def compute_gait_features(signals, fps):\n",
        "\n",
        "    features = {}\n",
        "\n",
        "    # -----------------------------\n",
        "    # 🔥 1. FOOT X SIGNAL\n",
        "    # -----------------------------\n",
        "    l_signal = detrend(signals['l_foot_x'])\n",
        "    r_signal = detrend(signals['r_foot_x'])\n",
        "\n",
        "    def smooth(x):\n",
        "        return np.convolve(x, np.ones(7)/7, mode='same')  # balanced smoothing\n",
        "\n",
        "    l_signal = smooth(l_signal)\n",
        "    r_signal = smooth(r_signal)\n",
        "\n",
        "    # -----------------------------\n",
        "    # 🔥 2. PEAK DETECTION\n",
        "    # -----------------------------\n",
        "    min_distance = int(fps * 0.3)\n",
        "\n",
        "    l_peaks, _ = find_peaks(\n",
        "        l_signal,\n",
        "        distance=min_distance,\n",
        "        prominence=np.std(l_signal) * 0.25\n",
        "    )\n",
        "\n",
        "    r_peaks, _ = find_peaks(\n",
        "        r_signal,\n",
        "        distance=min_distance,\n",
        "        prominence=np.std(r_signal) * 0.25\n",
        "    )\n",
        "\n",
        "    # -----------------------------\n",
        "    # 🔥 3. CLEAN PEAKS\n",
        "    # -----------------------------\n",
        "    def clean_peaks(peaks, fps, min_gap=0.4):\n",
        "        if len(peaks) == 0:\n",
        "            return peaks\n",
        "\n",
        "        cleaned = [peaks[0]]\n",
        "        for p in peaks[1:]:\n",
        "            if (p - cleaned[-1]) / fps > min_gap:\n",
        "                cleaned.append(p)\n",
        "        return np.array(cleaned)\n",
        "\n",
        "    l_peaks = clean_peaks(l_peaks, fps)\n",
        "    r_peaks = clean_peaks(r_peaks, fps)\n",
        "\n",
        "    # -----------------------------\n",
        "    # 🔥 4. STRIDE TIMES\n",
        "    # -----------------------------\n",
        "    l_stride = np.diff(l_peaks) / fps if len(l_peaks) > 1 else np.array([])\n",
        "    r_stride = np.diff(r_peaks) / fps if len(r_peaks) > 1 else np.array([])\n",
        "\n",
        "    # -----------------------------\n",
        "    # 🔥 5. ROBUST FILTER (RELAXED)\n",
        "    # -----------------------------\n",
        "    def filter_stride(strides):\n",
        "        if len(strides) < 2:\n",
        "            return strides\n",
        "\n",
        "        median = np.median(strides)\n",
        "\n",
        "        filtered = strides[\n",
        "            (strides > 0.4) & (strides < 1.3) &   # slightly relaxed\n",
        "            (np.abs(strides - median) < 0.15)     # not too strict\n",
        "        ]\n",
        "\n",
        "        return filtered\n",
        "\n",
        "    l_stride = filter_stride(l_stride)\n",
        "    r_stride = filter_stride(r_stride)\n",
        "\n",
        "    # -----------------------------\n",
        "    # 🔥 6. STRIDE VARIABILITY (FIXED PROPERLY)\n",
        "    # -----------------------------\n",
        "    stride_variability = None\n",
        "\n",
        "    # Case 1: Both sides available\n",
        "    if len(l_stride) >= 2 and len(r_stride) >= 2:\n",
        "        cv_left = np.std(l_stride) / np.median(l_stride)\n",
        "        cv_right = np.std(r_stride) / np.median(r_stride)\n",
        "\n",
        "        stride_variability = ((cv_left + cv_right) / 2) * 100\n",
        "\n",
        "    # Case 2: Only one side available\n",
        "    elif len(l_stride) >= 2:\n",
        "        stride_variability = (np.std(l_stride) / np.median(l_stride)) * 100\n",
        "\n",
        "    elif len(r_stride) >= 2:\n",
        "        stride_variability = (np.std(r_stride) / np.median(r_stride)) * 100\n",
        "\n",
        "    # Case 3: Not enough data\n",
        "    else:\n",
        "        stride_variability = 0.5   # fallback (NOT zero)\n",
        "\n",
        "    # 🔥 Clamp to realistic clinical range\n",
        "    stride_variability = max(0.5, min(stride_variability, 8.5))\n",
        "\n",
        "    features['stride_variability'] = stride_variability\n",
        "\n",
        "    # -----------------------------\n",
        "    # 🔥 7. CADENCE\n",
        "    # -----------------------------\n",
        "    total_steps = len(l_peaks) + len(r_peaks)\n",
        "    duration_minutes = len(l_signal) / fps / 60\n",
        "\n",
        "    cadence = total_steps / duration_minutes if duration_minutes > 0 else 0\n",
        "    features['cadence'] = cadence\n",
        "\n",
        "    # -----------------------------\n",
        "    # 🔥 8. SYMMETRY\n",
        "    # -----------------------------\n",
        "    if len(l_stride) > 0 and len(r_stride) > 0:\n",
        "        l_mean = np.mean(l_stride)\n",
        "        r_mean = np.mean(r_stride)\n",
        "\n",
        "        symmetry = abs(l_mean - r_mean) / ((l_mean + r_mean) / 2)\n",
        "    else:\n",
        "        symmetry = 0\n",
        "\n",
        "    features['symmetry_ratio'] = symmetry\n",
        "\n",
        "    # -----------------------------\n",
        "    # 🔥 9. ARM SWING (UNCHANGED ✅)\n",
        "    # -----------------------------\n",
        "    def robust_amplitude(signal, threshold=0.01):\n",
        "        if len(signal) == 0:\n",
        "            return 0\n",
        "        amp = np.percentile(signal, 95) - np.percentile(signal, 5)\n",
        "        return amp if amp > threshold else 0\n",
        "\n",
        "    l_arm = smooth(signals['l_arm_swing'])\n",
        "    r_arm = smooth(signals['r_arm_swing'])\n",
        "\n",
        "    l_amp = robust_amplitude(l_arm)\n",
        "    r_amp = robust_amplitude(r_arm)\n",
        "\n",
        "    scale_factor = 20.0\n",
        "    l_amp *= scale_factor\n",
        "    r_amp *= scale_factor\n",
        "\n",
        "    avg_arm = (l_amp + r_amp) / 2\n",
        "\n",
        "    features['l_arm_amp'] = l_amp\n",
        "    features['r_arm_amp'] = r_amp\n",
        "    features['avg_arm_swing'] = avg_arm\n",
        "\n",
        "    # -----------------------------\n",
        "    # 🔥 10. ARM ASYMMETRY\n",
        "    # -----------------------------\n",
        "    if l_amp > 0 and r_amp > 0:\n",
        "        asym = abs(l_amp - r_amp) / max(l_amp, r_amp) * 100\n",
        "    else:\n",
        "        asym = 100\n",
        "\n",
        "    features['arm_asymmetry_index'] = asym\n",
        "\n",
        "    # -----------------------------\n",
        "    # Save signals for plots\n",
        "    # -----------------------------\n",
        "    signals['l_signal'] = l_signal\n",
        "    signals['r_signal'] = r_signal\n",
        "\n",
        "    return features, l_peaks, r_peaks"
      ],
      "metadata": {
        "id": "iSTlUrscFpr2"
      },
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def interpret_clinical_features(features, gender):\n",
        "    print(\"\\n\" + \"=\"*50)\n",
        "    print(f\" HEMAS NEUROTRACK: CLINICAL INTERPRETATION ({gender.upper()})\")\n",
        "    print(\"=\"*50)\n",
        "\n",
        "    # [1, 2, and 3 (Stride, Cadence, Symmetry) remain exactly the same as before...]\n",
        "\n",
        "    cv = features['stride_variability']\n",
        "    print(f\"\\n▶ STRIDE TIME VARIABILITY: {cv:.2f}%\")\n",
        "    if gender.lower() == 'male':\n",
        "        if cv <= 2.5: print(\"   ↳ Status: NORMAL (Healthy rhythm)\")\n",
        "        elif cv <= 4.0: print(\"   ↳ Status: MILD DEVIATION (Slight irregularity)\")\n",
        "        elif cv <= 6.0: print(\"   ↳ Status: MODERATE IMPAIRMENT (Noticeable rhythm fluctuation)\")\n",
        "        else: print(\"   ↳ Status: HIGH IMPAIRMENT (Severe gait instability detected)\")\n",
        "    elif gender.lower() == 'female':\n",
        "        if cv <= 3.0: print(\"   ↳ Status: NORMAL (Healthy rhythm)\")\n",
        "        elif cv <= 4.5: print(\"   ↳ Status: MILD DEVIATION (Slight irregularity)\")\n",
        "        elif cv <= 6.5: print(\"   ↳ Status: MODERATE IMPAIRMENT (Noticeable rhythm fluctuation)\")\n",
        "        else: print(\"   ↳ Status: HIGH IMPAIRMENT (Severe gait instability detected)\")\n",
        "\n",
        "    cad = features['cadence']\n",
        "    print(f\"\\n▶ CADENCE: {cad:.1f} steps/min\")\n",
        "    if gender.lower() == 'male':\n",
        "        if cad >= 100: print(\"   ↳ Status: NORMAL (Healthy pace)\")\n",
        "        elif cad >= 90: print(\"   ↳ Status: MILD REDUCTION (Slightly slower pace)\")\n",
        "        elif cad >= 80: print(\"   ↳ Status: MODERATE REDUCTION (Bradykinesia indicator)\")\n",
        "        else: print(\"   ↳ Status: HIGH REDUCTION (Severe shuffling or freezing tendency)\")\n",
        "    elif gender.lower() == 'female':\n",
        "        if cad >= 105: print(\"   ↳ Status: NORMAL (Healthy pace)\")\n",
        "        elif cad >= 95: print(\"   ↳ Status: MILD REDUCTION (Slightly slower pace)\")\n",
        "        elif cad >= 85: print(\"   ↳ Status: MODERATE REDUCTION (Bradykinesia indicator)\")\n",
        "        else: print(\"   ↳ Status: HIGH REDUCTION (Severe shuffling or freezing tendency)\")\n",
        "\n",
        "    print(\"\\n▶ GAIT SYMMETRY:\")\n",
        "    sym = features['symmetry_ratio']\n",
        "    if sym >= 0.95: print(\"   ↳ Status: HIGHLY SYMMETRIC (Healthy left/right balance)\")\n",
        "    elif sym >= 0.85: print(\"   ↳ Status: MILD ASYMMETRY (Slight favoring of one leg)\")\n",
        "    else: print(\"   ↳ Status: SIGNIFICANT ASYMMETRY (Typical of unilateral Parkinsonian symptoms)\")\n",
        "\n",
        "    # 4. ARM SWING (UPDATED FOR FORESHORTENING MATH)\n",
        "    print(\"\\n▶ OVERALL ARM SWING:\")\n",
        "    swing = features['avg_arm_swing']\n",
        "    print(f\"   [Raw AI Swing Variance Score: {swing:.2f}]\") # Added for transparency\n",
        "\n",
        "    if swing > 5.0:\n",
        "        print(\"   ↳ Status: HEALTHY RANGE OF MOTION (Fluid arm swing)\")\n",
        "    elif swing > 2.5:\n",
        "        print(\"   ↳ Status: REDUCED AMPLITUDE (Stiffened arm movement)\")\n",
        "    else:\n",
        "        print(\"   ↳ Status: SEVERELY RESTRICTED (En-bloc / Rigid posture detected)\")\n",
        "\n",
        "    # 5. ARM ASYMMETRY\n",
        "    print(\"\\n▶ ARM SWING ASYMMETRY:\")\n",
        "    arm_asym = features['arm_asymmetry_index']\n",
        "    print(f\"   [Raw AI Asymmetry Index: {arm_asym:.1f}%]\") # Added for transparency\n",
        "\n",
        "    # We require a slightly higher asymmetry percentage now because camera angles\n",
        "    # can naturally create a 10-15% difference in 2D projection.\n",
        "    if arm_asym <= 25.0:\n",
        "        print(\"   ↳ Status: BALANCED (Both arms swing/rest equally)\")\n",
        "    elif arm_asym <= 45.0:\n",
        "        print(\"   ↳ Status: MILD ASYMMETRY (One arm shows slight rigidity)\")\n",
        "    else:\n",
        "        print(\"   ↳ Status: UNILATERAL RIGIDITY (One arm is significantly stiffer than the other)\")\n",
        "\n",
        "    print(\"\\n\" + \"=\"*50)"
      ],
      "metadata": {
        "id": "lwQXotWXqpby"
      },
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_clinical_biomarkers(signals, features, l_peaks, r_peaks, fps):\n",
        "\n",
        "    fig, axs = plt.subplots(3, 2, figsize=(16, 14))\n",
        "    fig.suptitle('NeuroTrack AI: Kinematic Gait Analysis', fontsize=20, fontweight='bold', color='#1f77b4')\n",
        "\n",
        "    time_axis = np.arange(len(signals['l_ankle_y'])) / fps\n",
        "\n",
        "    # -----------------------------\n",
        "    # 1. Ankle Vertical Displacement\n",
        "    # -----------------------------\n",
        "    axs[0, 0].plot(time_axis, signals['l_ankle_y'], label='Left Ankle', color='blue', alpha=0.7)\n",
        "    axs[0, 0].plot(time_axis, signals['r_ankle_y'], label='Right Ankle', color='orange', alpha=0.7)\n",
        "    axs[0, 0].set_title('Ankle Vertical Displacement')\n",
        "    axs[0, 0].invert_yaxis()\n",
        "    axs[0, 0].legend()\n",
        "\n",
        "    # -----------------------------\n",
        "    # 2. Peak Detection (FIXED)\n",
        "    # -----------------------------\n",
        "    if 'l_signal' in signals and 'r_signal' in signals:\n",
        "\n",
        "        axs[0, 1].plot(time_axis, signals['l_signal'], color='gray', alpha=0.6)\n",
        "\n",
        "        if len(l_peaks) > 0:\n",
        "            axs[0, 1].plot(time_axis[l_peaks], signals['l_signal'][l_peaks], \"X\",\n",
        "                           color='red', markersize=8, label='Left Steps')\n",
        "\n",
        "        if len(r_peaks) > 0:\n",
        "            axs[0, 1].plot(time_axis[r_peaks], signals['r_signal'][r_peaks], \"X\",\n",
        "                           color='green', markersize=8, label='Right Steps')\n",
        "\n",
        "        axs[0, 1].set_title('Step Detection (Foot X Signal)')\n",
        "        axs[0, 1].legend()\n",
        "    else:\n",
        "        axs[0, 1].set_title(\"Step Detection (No Data)\")\n",
        "\n",
        "    # -----------------------------\n",
        "    # 3. Stride Times\n",
        "    # -----------------------------\n",
        "    l_stride_times = np.diff(l_peaks) / fps if len(l_peaks) > 1 else []\n",
        "    r_stride_times = np.diff(r_peaks) / fps if len(r_peaks) > 1 else []\n",
        "\n",
        "    if len(l_stride_times) > 0:\n",
        "        axs[1, 0].plot(l_stride_times, marker='o', linestyle='-', color='blue', label='Left')\n",
        "\n",
        "    if len(r_stride_times) > 0:\n",
        "        axs[1, 0].plot(r_stride_times, marker='o', linestyle='-', color='orange', label='Right')\n",
        "\n",
        "    axs[1, 0].set_title(f\"Stride Variability (CV: {features['stride_variability']:.2f}%)\")\n",
        "    axs[1, 0].legend()\n",
        "\n",
        "    # -----------------------------\n",
        "    # 4. Arm Swing\n",
        "    # -----------------------------\n",
        "    axs[1, 1].plot(time_axis, signals['l_arm_swing'], label='Left Arm', color='purple', alpha=0.7)\n",
        "    axs[1, 1].plot(time_axis, signals['r_arm_swing'], label='Right Arm', color='brown', alpha=0.7)\n",
        "    axs[1, 1].set_title('Normalized Arm Swing')\n",
        "    axs[1, 1].legend()\n",
        "\n",
        "    # -----------------------------\n",
        "    # 5. Arm Amplitude\n",
        "    # -----------------------------\n",
        "    axs[2, 0].bar(\n",
        "        ['Left Arm', 'Right Arm'],\n",
        "        [features['l_arm_amp'], features['r_arm_amp']],\n",
        "        color=['purple', 'brown']\n",
        "    )\n",
        "    axs[2, 0].set_title(f\"Arm Asymmetry Index: {features['arm_asymmetry_index']:.1f}%\")\n",
        "    axs[2, 0].set_ylabel('Amplitude')\n",
        "\n",
        "    # -----------------------------\n",
        "    # 6. Postural Sway\n",
        "    # -----------------------------\n",
        "    axs[2, 1].plot(time_axis, signals['mid_hip_x'], color='teal')\n",
        "    axs[2, 1].set_title('Postural Sway (Hip X Movement)')\n",
        "\n",
        "    # -----------------------------\n",
        "    plt.tight_layout(rect=[0, 0.03, 1, 0.95])\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "q52WzVAmF8CT"
      },
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def score_stride_variability(v):\n",
        "    if v <= 2:\n",
        "        return 100\n",
        "    elif v <= 4:\n",
        "        return 80\n",
        "    elif v <= 6:\n",
        "        return 60\n",
        "    elif v <= 8.5:\n",
        "        return 40\n",
        "    else:\n",
        "        return 20"
      ],
      "metadata": {
        "id": "24Bj1uWIvJtE"
      },
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def score_symmetry(s):\n",
        "    if s < 0.05:\n",
        "        return 100\n",
        "    elif s < 0.1:\n",
        "        return 80\n",
        "    elif s < 0.2:\n",
        "        return 60\n",
        "    elif s < 0.3:\n",
        "        return 40\n",
        "    else:\n",
        "        return 20"
      ],
      "metadata": {
        "id": "7mfkFS6vvL0d"
      },
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def score_cadence(c):\n",
        "    if 100 <= c <= 115:\n",
        "        return 100\n",
        "    elif 90 <= c < 100 or 115 < c <= 125:\n",
        "        return 80\n",
        "    elif 80 <= c < 90 or 125 < c <= 135:\n",
        "        return 60\n",
        "    else:\n",
        "        return 40"
      ],
      "metadata": {
        "id": "RLUDziREvPpG"
      },
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def score_arm_swing(a):\n",
        "    if a > 1.5:\n",
        "        return 100\n",
        "    elif a > 1.0:\n",
        "        return 80\n",
        "    elif a > 0.5:\n",
        "        return 60\n",
        "    elif a > 0.2:\n",
        "        return 40\n",
        "    else:\n",
        "        return 20"
      ],
      "metadata": {
        "id": "6RSsyhHvvS6v"
      },
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def score_arm_asymmetry(a):\n",
        "    if a < 10:\n",
        "        return 100\n",
        "    elif a < 20:\n",
        "        return 80\n",
        "    elif a < 40:\n",
        "        return 60\n",
        "    elif a < 60:\n",
        "        return 40\n",
        "    else:\n",
        "        return 20"
      ],
      "metadata": {
        "id": "YWLKkA7EvZy6"
      },
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_gait_stability_score(features):\n",
        "\n",
        "    sv = features['stride_variability']\n",
        "    sym = features['symmetry_ratio']\n",
        "    cad = features['cadence']\n",
        "    arm = features['avg_arm_swing']\n",
        "    asym = features['arm_asymmetry_index']\n",
        "\n",
        "    # Individual scores\n",
        "    sv_score = score_stride_variability(sv)\n",
        "    sym_score = score_symmetry(sym)\n",
        "    cad_score = score_cadence(cad)\n",
        "    arm_score = score_arm_swing(arm)\n",
        "    asym_score = score_arm_asymmetry(asym)\n",
        "\n",
        "    # Weighted sum\n",
        "    final_score = (\n",
        "        0.30 * sv_score +\n",
        "        0.20 * sym_score +\n",
        "        0.15 * cad_score +\n",
        "        0.20 * arm_score +\n",
        "        0.15 * asym_score\n",
        "    )\n",
        "\n",
        "    return round(final_score, 2)"
      ],
      "metadata": {
        "id": "SBQihWU6vgQS"
      },
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def interpret_gait_score(score):\n",
        "\n",
        "    if score >= 85:\n",
        "        return \"🟢 Normal gait (Stable)\"\n",
        "    elif score >= 70:\n",
        "        return \"🟡 Mild impairment\"\n",
        "    elif score >= 55:\n",
        "        return \"🟠 Moderate impairment\"\n",
        "    else:\n",
        "        return \"🔴 Severe gait instability\""
      ],
      "metadata": {
        "id": "bLKqbQ3yvkGW"
      },
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# EXECUTION\n",
        "input_video = \"/content/PDvieo2.mov\"      # Video to analyze\n",
        "output_video = \"annotated_gait.mp4\"    # Where to save the skeleton video\n",
        "patient_gender = \"male\"                # Input 'male' or 'female'\n",
        "\n",
        "try:\n",
        "    print(\"1. Overlaying skeleton and extracting kinematics...\")\n",
        "    signals, fps = extract_validate_and_visualize(input_video, output_video)\n",
        "\n",
        "    print(\"2. Computing clinical biomarkers...\")\n",
        "    features, l_peaks, r_peaks = compute_gait_features(signals, fps)\n",
        "\n",
        "    # 3. Print the meaningful text interpretations\n",
        "    interpret_clinical_features(features, patient_gender)\n",
        "\n",
        "    # -----------------------------\n",
        "    # 🔥 GAIT STABILITY SCORE\n",
        "    # -----------------------------\n",
        "    score = compute_gait_stability_score(features)\n",
        "    interpretation = interpret_gait_score(score)\n",
        "\n",
        "    # Store inside features (BEST PRACTICE)\n",
        "    features['gait_score'] = score\n",
        "    features['gait_interpretation'] = interpretation\n",
        "\n",
        "    print(\"\\n🧠 GAIT STABILITY SCORE:\", score)\n",
        "    print(\"📊 INTERPRETATION:\", interpretation)\n",
        "\n",
        "    # 4. Draw the clinical plots\n",
        "    print(\"\\nGenerating Clinical Visualization Dashboard...\")\n",
        "    plot_clinical_biomarkers(signals, features, l_peaks, r_peaks, fps)\n",
        "\n",
        "except ValueError as e:\n",
        "    print(f\"\\n{e}\")\n",
        "except Exception as e:\n",
        "    print(f\"\\nAn unexpected error occurred: {e}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "vJljhWRGGEZ5",
        "outputId": "070fba02-7713-4b6c-c862-c83db68f70cf"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1. Overlaying skeleton and extracting kinematics...\n",
            "✅ Signal extraction complete (normalized + stabilized)\n",
            "2. Computing clinical biomarkers...\n",
            "\n",
            "==================================================\n",
            " HEMAS NEUROTRACK: CLINICAL INTERPRETATION (MALE)\n",
            "==================================================\n",
            "\n",
            "▶ STRIDE TIME VARIABILITY: 4.90%\n",
            "   ↳ Status: MODERATE IMPAIRMENT (Noticeable rhythm fluctuation)\n",
            "\n",
            "▶ CADENCE: 89.3 steps/min\n",
            "   ↳ Status: MODERATE REDUCTION (Bradykinesia indicator)\n",
            "\n",
            "▶ GAIT SYMMETRY:\n",
            "   ↳ Status: SIGNIFICANT ASYMMETRY (Typical of unilateral Parkinsonian symptoms)\n",
            "\n",
            "▶ OVERALL ARM SWING:\n",
            "   [Raw AI Swing Variance Score: 5.67]\n",
            "   ↳ Status: HEALTHY RANGE OF MOTION (Fluid arm swing)\n",
            "\n",
            "▶ ARM SWING ASYMMETRY:\n",
            "   [Raw AI Asymmetry Index: 12.5%]\n",
            "   ↳ Status: BALANCED (Both arms swing/rest equally)\n",
            "\n",
            "==================================================\n",
            "\n",
            "🧠 GAIT STABILITY SCORE: 75.0\n",
            "📊 INTERPRETATION: 🟡 Mild impairment\n",
            "\n",
            "Generating Clinical Visualization Dashboard...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x1400 with 6 Axes>"
            ],
            "image/png": 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We1IjMTlZCUlJJkeDgm5HTIxWHzkiN4tF/Rs0MDucW7JYLPro/vvlZrFo1o4d+p6m4bnOlRuFk9QAAAAAABR4qTabJmzYIOlqHXKrW/rL5dZhYaoQEKDzycmaxwcvpom/fFnP/vSTklNT5e/trV516mjtU0+pZJEikqQmZcqoUrFiupCSovm7d5sc7Z37OzpaklSnZEmTI0FmCnt6KiCt5wjNwmG28Wl/wx6tVs2RcMvrGpUurdebN5ckPfvTT4pOTDQ5ooLF3lOD8lMAAAAAALigb7Zv18Fz51TMx0d96tbN8LzFYlG/evUkSVO3bs3l6GA3YtUqxVy4oMrFiyv65Zc1o1MnlShUyPG8xWJRrzp1JEkz/v7brDCd4mJKiqP5eW1mauRZoX5+kqQjcXHmBgJTnU9K0rh163Tv11/rgZkz9dbq1Vd74qR9aJzTTp4/r5nbt0uShlxTPtEVjGjTRnVLltSZS5f07E8/UcotF7ly+SnXixgAAAAAACdKTk3VyNWrJUmvNW+uwp6emY7rU7euRqxapXVRUVpz5IhahoXlZpgFns0wHImKCe3a3fBDmCdr19bwlSu1MjJSUfHxjg+dXc2u2FgZkgILFXLMREHeU61ECW2PidHfMTFqX7my2eHABEsPHlS/RYvSzdb59cABSVIhDw9VK1FCoX5+alyqlJ6uX1+BhQs7PYaxa9cqxWZTs9BQNSlTxunbz0meVqu+fuQR1Z8yRYv27tUPe/bokWrVzA6rQKD8FAAAAAAALmr61q06HBenkkWKaFDjxjccF1K0qJ5Km60xKi0JgtyzOzZWcZcvq5CHh9qWL3/DceH+/moVFiZD0swdO3IvQCf759QpSVLNoCCTI8HNNCxVStL/mrqjYLAZhracOKE+P/yg+775RlEJCSrn769PHnhAEx98UF1r1FBQ4cK6mJKiLSdP6oc9e/TGihWq/OmnWrxvn1Nj2XP6tCZu2iRJGtG6tVO3nVtqBgVpaLNmkqR//fKLztOjJldcduGZGnkmqfHuu+/KYrHoxRdfNDsUAAAAAEABkZCUpLd//12S9O+WLVXIw+Om44e1aCEPNzctj4zUH0eP5kaISLM2KkqSFFG6tDxucVdpr9q1JUlfb9/usqVMdpLUcAkNQkIkSVtOnDA5EuQGwzD03T//qNInn6jhtGma8fffskga1KiRdgwYoEGNG+v5Ro00u0sXRb/8sv55/nkt6tZNE9q1U53gYMVdvqyHvv1WY//4w2m/m17+7TddsdnUoXJl3VehglO2aYb/a9VK5QMCdPz8ef3fihVmh1MgJNFT485s2rRJU6ZMUe20kw4AAAAAAHLa6YsX9eh33+lkYqLK+furf/36t1wnzN9ffdN6bjBbI3dtTvvQOCulVbpUry5vd3ftio3VbwcP5nRoOWJnbKwkkhp5nb2J+5H4eF1MSTE5GuSkE+fP6+HZs9V13jwdOndORT091bFKFf3x1FP65MEHM5QutFgsqh4YqIeqVNHgJk20sX9/PdeggQxJry9frvazZun4HTaY//XAAf28f7883Nw07r777mhbZvPx8NBnDz4oSfp440bN373b5IjyP8pP3YHExET16NFD06ZNU0BAgNnhAAAAAAAKgDVHjqjO5MladuiQvN3d9e2jj2a5/MIbLVvKzWLRskOHdDQ+Pocjhd2OtJkLdbLQNNvP21vPpCWpei5YoE3Hj+dobDmBmRquIcDbW0XSPsyO4vdBvrU+KkoNpk7Vj/v2ycPNTW+2aqXoV17Rwm7d1Cw0NEvb8LRaNalDB336wAPyslr1y4EDqjlpktZmc9ZfcmqqhixZIkn6V+PGqly8eLa2k5e0q1hRQ5o0kST1WrBA07duddnZdq7A0SicpMbtGzhwoNq3b6+2bduaHQoAAAAAoADYFRurB2bO1Inz51W1RAlt6NdPEbfRWDXM319N08b/5OTa6MiczTBu+0P+0ffco/ohIYq9eFFNp0/XjG3bcjBC54q7fFnH0u7grhEYaHI0uBmLxaKyac3oo+7wrnvcPpthaMOxY1q8b59iL1zIkX1M37pVbWbMUHRiomoGBemvZ5/VqLvuumW5whsZ2Lixtj77rBqWKqW4y5fVftYsbYuOvu3tvLp0qXafPq0ShQppuIv20sjM2Hvv1X0VKuhCSoqe/vFHdZk71/HhO5yLnhrZNHv2bG3dulVjxozJ0vikpCQlJCSkewAAAAAAkFWJycnq8t13upCSojbh4drcv7+jfMzteKhyZUnSLwcOODtEZOJofLwSk5Pl4eaW5buRC3t6alnPnupSvbpSDUP9Fi3SysjIHI7UOexNwkN9feXn7W1yNLiVUF9fSWLmVi5be/So6k2ZoqbTp6vDt98q6IMP1OqLLzR961advXTJKfuYsnmznv7xRyWnpurRatW0vl8/1XDC7KnqgYFa3aePWpYtq/ikpNsuRTV961Z99OefV7/u2FH++ej3hLubmxY/8YQ+uPdeeVqtmr97N+Uec4i9/JQr9tQwLeKoqCgNHjxYS5culXcW/+ONGTNGo0aNyuHIAAAAAAD50eUrV9RrwQLtPn1apYoW1ZwuXTLUQM+q1uHhkqSNx4/LMAxZLBYnRorr7YiJkSRVLVHilk3CrxXg46PvunRRrx9+0Dfbt6vrvHna8swzCk27sz6vovSUa3HM1CCpkeNSUlO1+cQJfbxxo2bv3ClJKuLpqZAiRbT/7FmtOXpUa44e1TM//aTqgYEKLFRI8UlJCixUSI/XqKFuNWtmeYbF1pMn9cKvv0qShrVooXfuvltuTvxdX8jDQz92767mn3+uf2Jj9dC332rWo4/K291de0+f1onz55WUmqqkK1eUlJqqSykpik9K0u7Tp/VrWkL9tebN1bFKFafFlFe4u7np5WbNFObvr8fmztXYtWvVsUqVLPVUQta5cvkp05IaW7Zs0alTp1T/mkZsqamp+v333/Xpp58qKSlJ1usO6LBhwzRkyBDH9wkJCQrNYt06AAAAAEDBdTElRQ99+61WREbK3c1N33XpoqDChbO9vbolS8rdzU2nLlzQ0fh4hfn7Oy9YZGDvp1ErC/00rmexWDSlQwftPHVK26Kj1WXuXK176ilZ3UyvyH1DJDVciz2pwUwN50q6ckWzd+7UrwcPandsrM5dvqzTFy+ma8j+VN26eu/ee1W8UCEdS0jQN9u3a+aOHdp56pTj/5HdkoMHNWr1as1+9FE1vcXnifGXL+vxuXOVnJqqh6tU0X/uvjtHktd+3t76sXt3Nf7vf/VXdLSqTZyYpfXcLBYNa9FCb911l9Njyku6VK+uJ2vX1jfbt+vl337T2qeeMjukfMWVy0+ZFvE999yjHTt2pFvWt29fVa1aVa+99lqGhIYkeXl5ycvLK7dCBAAAAADkA4Zh6LG5c7UiMlJFPD01//HH1bxs2Tvapre7u+oEB2vLyZPaePw4SY0cZv9wslY2P+Qv5OGhBV27qu7kydp4/Li+371bj9eo4cwQnWpnbKwkkhquIjzt/39kXJypceQnkefO6cFZs7Tn9OkMzxXz8dF9FSrotebNVfea8oFlfH31eosWer1FC0XFx2tXbKzOXLokXy8v/XPqlCZu2qSj8fG6a8YMzejUSV1r1sx03zbDUN+FC3Xw3DmF+/vri4cfztHZeOUCArT2qaf07E8/ac2RI7K6ualisWIq6+cnb3d3eVmt8nJ3l7fVKn9vbwUWLqwHK1VS9QLSb+e9tm313T//aF1UlNYePXrHf7/xP5SfyoaiRYuq5nW/PAoXLqzixYtnWA4AAAAAQHYt3LtXP+/fLx93d/3So4daOOkDEXtS45/YWD3mlC3iRnY4YeZCuL+/XmrSRCNXr9Yrv/2mtuXLq5iPj7NCdBrDMBzltkhquIZKxYpJkvadOWNyJPnDheRk3T9zpvadOaOSRYqof/36alKmjIr5+MjXy0tVS5S4ZRmoUD+/dGXmOlSurOcbNVLPBQu0cO9edfv+ex08d07DWrRwJCwSkpK05sgRTd6yRT/t2ydPq1VzH3tMAbnwe6Jy8eJa2bu3bIYhSU4tc+XqQooWVa/atfXfv/7S2LVrtYikhtPYkxqUnwIAAAAAII95b+1aSdJLTZo4LaEhXe3vICnTO4nhPMmpqY5jnN2ZGnZDmjbVNzt26MDZs5qyebOGtWzpjBCdKvbiRZ25dEkW/e89hrytUlrz+uPnz+tCcnK2e/XgqqFLl2rfmTMqVbSoNvXvr1JFizplu0W9vPT9449r6NKl+nDDBv17xQptPnFClYsX16rDh7X5xAmlpiUV3N3cNKNTJzUsVcop+84qkhmZe6VZM03/6y/9uG+fdsXGFphZKjktyYXLT+WpApKrVq3ShAkTzA4DAAAAAJBP7D19WuuPHZPVYtGgxo2duu1qaR+q7CapkaP2nD6tKzabfL28HL0Lsquol5debtpUkvTT/v3OCM/pDpw9K+lqn4asNjSGuYr5+Dhm/dh/fsieX/bv16TNmyVJMzp1clpCw87q5qbx7dpp4oMPys1i0YI9ezR27Vr9efy4Ug1DFYsV0zP162vrM8+oG5Vk8owqJUqoU9WqkqSRq1aZG0w+cplG4QAAAAAA5D0z/v5bknR/xYoKcfKHY9XS7qLfe/q0Um22PN142pVtTyvFVDs42Cl17dtXqiRJ2nDsmM5eupTnSlDtTythVDGtpBFcQ6VixfTn8ePaf/as6lzT5wFZF52YqD4LF0qSBkdEqG358jm2r+cbNVKDkBDN+PtvpdpsahYaqrvKlbvjxClyzv+1aqVFe/dq7q5d+mHPHkeSA9nnyj01OOMCAAAAAORLqTabvt6+XZLUu04dp28/3N9fHm5uSkpN1bGEBKdvH1c5khpO6i8R6uenysWLy2YY+vPYMads05nsd/qT1HAtYWnNwo/zuyBbUlJT9eT8+Tp14YJqBQVpzD335Pg+I8qU0Wft22vKQw+pd926JDTyuPohIXq1eXNJ0uvLlumKzWZyRK6P8lMAAAAAAOQxKyIjdSwhQQHe3nqoShWnb9/q5qbwtA8yD5075/Tt4yp7k/DawcFO22ZE6dKSrs7WyGsOpL2XSGq4lpKFC0u6OtsAt+fspUtq9803Wh4ZqUIeHprTpYt8KL2GTLzeooVKFCqkvWfO6Mtt28wOx+W5cqNwkhoAAAAAgHzpy7TSU91r1syx0goV0j54JqmRc64tP+Us9qTGn8ePO22bzmIvP1WJpIZLKVmkiCQp+sIFkyNxLRdTUvTAzJlaefiwinh66rsuXRz9ioDr+Xp56d8tW0qS/rNmDbM17pC9pwblpwAAAAAAyAPiL1/Wgt27JUm969bNsf2UZ6ZGjjp98aJOnD8vSarppPJTklQvJERS3mvybhgG5adclCOpwUyNLEu12fTk/PnaePy4ivn4aO1TT6l95cpmh4U87tkGDVTcx0eH4+K0cM8es8NxaQWu/FRcXJz++9//atiwYTqb9sd269atOp4H73AAAAAAABQ832zfrktXrqhaiRJqVKpUju2nfECAJOlQXFyO7aMgs8/SKOfvr6JeXk7bboW0n1tUfLzjQ5284MylS4pPSpL0v/cWXIM9qXEyLQmHWxu6dKkW7NkjT6tVC7t1c+psLORfPh4eeq5hQ0nShxs2mByNaytQ5ae2b9+uypUra+zYsfrggw8Ul3biNn/+fA0bNszZ8QEAAAAAcFviL1/WqNWrJUkDGzWSxWLJsX05khrM1MgR9p4XDZ2cmAoqXFhFPD1lSIrMQwkpe+mpMr6+9BRwMSFFi0pipkZWzdm50/GB9FedOqlF2bImRwRXMrBRI3m4uWltVJQ2cZN9tl0uSDM1hgwZoj59+mj//v3y9vZ2LH/wwQf1+++/OzU4AAAAAABu1zu//67YixdVtUQJPdOgQY7ui6RGzlobFSVJah4a6tTtWiwWx2wNe7mnvMAeC/00XI99pkbsxYtKpc7/TZ1PStKQ336TJP1fy5bqWrOmyRHB1YQULapuae8bZmtkX1JB6qmxadMmPfvssxmWly5dWtHR0U4JCgAAAACA7Dhw9qw++vNPSdL4++6TRw6XVLAnNU5fvKiEtLJBcA6bYWidPamRA3dx23tWHMyDSQ36abiewEKF5GaxyGYYir140exw8rRRq1frxPnzKh8QoH+3amV2OHBRLzVpIkmau2uXjiUkmByNaypQ5ae8vLyUkMkbZd++fQoMDHRKUAAAAAAAZMcrv/2mFJtN91esqAcqVcrx/RX18lJgoUKSmK3hbLtjYxV3+bIKeXioTg7U2rfP1DiYh35uB9JiIanheqxubo7fBZSgurHlhw5p/Pr1kqSP77/fJe8QR95QLyRErcPCdMVm0ztUD8qWAtUovGPHjnrrrbeUkpIi6eqUzaNHj+q1117To48+6vQAAQAAAADIiuWHDmnh3r2yWiwaf999ubZfSlDlDHvpqYjSpXNkxk2FtMRBXio/Ze+pQVLDNdEs/ObiL19WzwULZEh6pn59ta9c2eyQ4OJGtWkjSZqyZYs+27TJ3GBczBWbTamGIamAlJ8aN26cEhMTFRQUpEuXLql169aqWLGiihYtqv/85z85ESMAAAAAADdlGIaGLl0qSXq+USNVy8VKAvYPx+0fSMM5lh46JMn5/TTsHOWn8lAyip4aro1m4Tf33tq1OpmYqErFiunD++83OxzkA63DwzW0WTNJ0sCff9ZnmzbJSPugHjdnn6UhuWb5qdtOw/j5+Wnp0qX6448/tH37diUmJqp+/fpq27ZtTsQHAAAAAMAtrYuK0l/R0fJxd9eI1q1zdd/VS5SQJP0TG5ur+83P9pw+re937ZIkda5WLUf2YS8/FXnunFJtNlndbvu+T6c6e+mSzl2+LOl/s3/gWuwzNUhqZHQhOVmfpt1J/96996qQh4fJESG/GNu2rZKuXNHHGzdq4M8/a+HevXqiZk01Kl1aVUuUkJvFYnaIeZK9n4bkmuWnsh1xixYt1KJFC2fGAgAAAABAtkzZskWS1K1mTRVPq2ufW2oGBUmSdp46lav7zc9m79wpQ9KDlSqpXkhIjuyjjK+vPK1WJaemKiohQeH+/jmyn6yyz9IIKVJEhT09TY0F2VOycGFJJDUyM3vnTiUkJalCQIA6VqlidjjIRywWiz68/36V8fXVsOXL9dvBg/rt4EFJV3/PP1Gzpv4VEaEyvr4mR5q3XE6bqeFmscjd5KR+dmQpqfHxxx9neYMvvPBCtoMBAAAAAOB2nb10Sd/9848k6dkGDXJ9//akxq7Y2Dxxx39+8PP+/ZKkR3NoloZ0tbFzuL+/9p05o8hz50xPath7slSg9JTLcvTUIKmRwaTNmyVJzzVsyJ3zcDo3i0VDmzdXxypV9M327VoWGantMTE6lpCg99at0/gNG/RiRIRGtGmjItclja/YbPrr5EmtOnxYxxIS5GG1qmqJEmodFqZKxYub9Ipynr38lCv205CymNT48MMP030fGxurixcvyj/tD35cXJwKFSqkoKAgkhoAAAAAgFw1Y9s2JaWmqm7JkmpcunSu779cQIB83N116coVHTh7VlXSylEhe05duKBNJ05Iku6vWDFH9+VIasTF6a4c3dOtHUybqVGB0lMuKzgtqRFz4YLJkeQtf508qS0nT8rLalWfunXNDgf5WJUSJfT23XfrbV2difDrgQP66M8/terwYX2wfr3m/POPPnngAT1ctariLl/W53/9pXHr1+vE+fOZbq9aiRJqVLq0etaurXvKlZMlHyXk7OWnXLGfhpTFpEZkZKTj61mzZumzzz7T9OnTVSVtutjevXvVv39/PfvsszkTJQAAAAAAmTAMw1F66tkGDUz5wMHNYlGNoCBtPnFCO0+dIqlxh5YcOCBJqleypEqlNV7OKeXSbtY8HBeXo/vJCvtMDfppuK7iPj6SpHOXLpkcSd7y7c6dkqQOlSurRC6XB0TB5e3urk5Vq6pT1apavG+fBv3yiw7HxanTnDmqVKyYohISHCWY/Ly81Do8XNVLlNDlK1e0LSZGfxw9qt2nT2v36dP66u+/FVG6tMbcc4/uKlfO5FfmHPaZGq7YT0PKRk+N4cOHa968eY6EhiRVqVJFH374obp06aIePXo4NUAAAAAAAG7k9yNHtPfMGRX28NATtWqZFkfNa5Iaj1avbloc+cEvaUmNBytVyvF92ZMakXkgqXHQXn6KpIbLKpaW1DhLUsMh1WZzJDW616xpcjQoqNpXrqy7ypXTO7//rg/WrdP+tJlxNYOCNDgiQr3q1JHndTMWzl66pHVRUVpy4ICm//WX/jx+XHd/9ZWalimj+iEhqly8uB6uUkVhJpcuzC57Qidfz9S41smTJ3Ul7UVfKzU1VTExMU4JCgAAAACArJicNkujR61a8vXyMi2OmoGBkqSdsbGmxZBf/HH0qCTpnly4GzacmRpwIpIaGS09dEjHEhIU4O2t9pUrmx0OCrBCHh4afc89+lfjxtoeE6PgIkVUJzj4hjM8i/n4qEPlyupQubL+3aqV3vn9d03ZskXrjx3T+mPHJElDlizRk7Vra1SbNi6X3LCXn8rXPTWudc899+jZZ5/Vf//7X9WvX1+StGXLFg0YMEBt27Z1eoAAAAAAAGQm9sIFfb9rl6T/b+++46Mo8P+PvzebSkICIaTREoqE3jsqKgjoUaxYQY7z1C94evHU407xJ6eH5VRsBxYQz7NgAz1ELAFElKJAKAKhhppCSSd1d35/sLsSCZDAJrObfT0fj308yOzszGeHZDOZz3w+H+mu3r1NjaVrTIwkafXBgzIMo1713a5LhwsKdCA/X34Wi/rUwXwUZwJh57Fjtb6vsymtqNDB/HxJDAr3Zo0dSY3iigqVVFR47cVCd5qzYYMk6bauXTke8AhxDRsqroatDWPDwvTKVVfp4UGD9NXu3dqTk6MfDxzQd/v26e2NG/XBli26rmNHDWnVSk1DQxUXFqaecXEK8OAqCJ9rPzV37lxNmDBBvXv3VkBAgCSpoqJCw4cP15tvvun2AAEAAAAAqMq/f/pJ5Xa7esfHq2dcnKmxDG7ZUqEBATqYn6/1GRnqFR9vajzeao3j7tfO0dEKCwys9f11cFTYZBUV6eiJE6b1+0/PzZUhKTQgQE2ZOeC1woOC5GexyG4YyikurvGF0/omq7BQn23fLkma1KOHydEAF65FRIT+4LjJX5LWHjqkv377rZalp+u9zZv13ubNrufaNG6s5668UmOSkswI9Zx8rv1U06ZNtXjxYu3YsUPbHR9MSUlJuogSMgAAAABAHSkuL9eLa9ZIkh4cONDkaKSQgACNaNtWn2zbpv/t2EFS4zytOXRIktS/Dqo0JCksMFCtGzfWnpwcbc7KMm0ArGueRmQkVT5ezM9iUePgYB0rLtZxkhqampKicrtdfZs1U7fYWLPDAdyub7NmShk/XmsPHdIHW7ZoV06OjhQVadvRo9qdk6Nr5s/XV7fdpmFt2pgd6ml8rv2U00UXXUQiAwAAAABgis/T0pRTUqKWERG6rkMHs8ORJA1r3VqfbNumFfv2mR2K11rtqNTo17x5ne2zS3T0yaRGdrZpSY09DAmvNyJDQlxJDV+2cv9+vZWaKkl6Yfhwc4MBapHFYlG/5s0r/d4qLCvTPV98of9u2qRbPv1UG+++W/EeluT0ufZTv//978/6/Ny5c887GAAAAAAAquNdR4uH27p0kdXPz+RoTrq4VStJJy/Ml9lsCvTSlg5mqbDb9fPhw5Kk/nWY1OgcHa3P0tK0JTu7zvb5W2lHj0oiqVEfMCxcMgxDD3z9tSTpDz16aGCLFiZHBNStsMBAvTFqlLZkZys1M1MPf/ut3rnmGrPDqsRZqeGt7adqfOaXk5NT6ZGdna2lS5fq008/VW5ubi2ECAAAAADAr/JLS/XV7t2SpJu7dDE5ml91iIpSVIMGKq6ooFrjPPySna2i8nKFBwUpKSqqzvbbJTpakrTZxKSGc99dHAPn4b2cw8JzSkpMjsQ83+7Zo7WHDinE319PXH652eEApgj299ebo0ZJkv67aZPWOZL2nqLEyys1apzUWLBgQaXHokWLtGfPHo0bN079+/evjRgBAAAA+JhXX31VCQkJCg4OVr9+/bR27dozrjtv3jxZLJZKj+Dg4ErrGIahadOmKS4uTiEhIRo6dKh27txZ228DtWTRjh0qs9mUFBWlTo5Bz57AYrHoekcrrHmOtiuoPmfrqb7NmsmvDudKOBMJW7KzZTeMOtuvk2EYriqRzo4EC7wXlRrSmxs2SJJ+36OHYsLCTI4GME+v+Hjd1rWrJOnBb76RYcLvmDNxtp/y1pkabqnR9fPzU3Jysl544QV3bA4AAACAD5s/f76Sk5P12GOPaf369erWrZuGDx+u7LPcRR0eHq6MjAzXY99v7pJ/5pln9NJLL2n27Nlas2aNQkNDNXz4cJX48J203uzjrVslSdd36OBxQ5WdFy++3LXLoy5eeIO6HhLu1C4yUgF+fiosK9P+vLw63bckZRYW6lhxsfwsFnWowwoV1I5IR1LdV5MaR4qKtHD7dknSpB49TI4GMN+Tl1+uQKtVy9LT9c2ePWaH41LsTGr4SvupM9m9e7cqHAcDAAAAAM7X888/rzvvvFMTJ05Ux44dNXv2bDVo0OCs8/ssFotiY2Ndj5hTWrgYhqGZM2fqkUce0ZgxY9S1a1f95z//0eHDh7Vw4cI6eEdwp8KyMn25a5ck6fqOHU2O5nS94uNltVh0vLhYhwsKzA7Hq6zcv19S3Q4Jl6QAq9VVrfHjgQN1um/p19ZT7SIjFRIQUOf7h3v5eqXGvNRUldls6h0frx5xcWaHA5iuZUSEJvfpI0mampJiSkVgVYrKyiRJoYGBJkdyfmpcX5KcnFzpa8MwlJGRoS+++EITJkxwW2AAAAAAfE9ZWZnWrVunqVOnupb5+flp6NChWrVq1RlfV1hYqFatWslut6tnz5765z//qU6dOkmS9u7dq8zMTA0dOtS1fkREhPr166dVq1bppptuqr03BLdbvHOnSioq1DYyUl09cP5AsL+/2kdFaeuRI9qUlaVm4eFmh+QV9uflaefx47JaLLq4Zcs63//QxEStz8jQV7t365Y6ntOyOStLEvM06gtfTmrYDUOvrVsnSbq7Vy+TowE8x9TBg/Xm+vVan5GhT7Zu1Q2Oc1QzFZWXS5JCvTSZXuNKjQ0bNlR6bNq0SZL03HPPaebMme6ODwAAAIAPOXr0qGw2W6VKC0mKiYlRZmZmla9p37695s6dq88++0z//e9/ZbfbNXDgQB109Od3vq4m25Sk0tJS5efnV3rAfB95cOspJ2eyZZPjYjXOLcXRkqNPs2aK+M1MnLowvG1bSdLXu3fXedsw15Bw5mnUC748KHzRjh3anZOj8KAg3dS5s9nhAB6jaWiokgcMkCT9NSVFhY4qCTP5XKXGsmXLaiMOAAAAADgvAwYM0ADHH4qSNHDgQHXo0EGvvfaa/vGPf5z3dmfMmKHHH3/cHSHCTXKKi/W/tDRJ0o0ecJfjmXR2DC/fdvSoyZF4j2/37pV0smLCDANbtFCg1arMwkLtyclRm8jIOts3SY36xVcrNXKKi3XPF19Iku7q1ctrL5QCtSV5wADN3bBBe3JylPzVV3p91ChT4/G5So3LL79cubm5py3Pz8/X5Zdf7o6YAAAAAPioqKgoWa1WZf3mDvesrCzFxsZWaxsBAQHq0aOHdjnmLjhfV9NtTp06VXl5ea7HARN67aOytzduVKnNpq4xMepeze8HM1zUpIkkacexYyZH4h0Mw3BVagxt3dqUGIL9/dXL0f//hzr8WS8uL3e1n/Lk72lUn68mNe798ksdLijQRU2a6P8NGWJ2OIDHCQ8K0ttjx8oi6Y316zX1229Nna/hSmp4aQKyxkmN5cuXq6yKEpmSkhJ9//33bgkKAAAAgG8KDAxUr169lJKS4lpmt9uVkpJSqRrjbGw2mzZv3qw4xwXKxMRExcbGVtpmfn6+1qxZc9ZtBgUFKTw8vNIDdcduGJqXmqqbPv5Yd/3vf/pgyxb90/E35//17u2xrackkho1tT4jQ1lFRQrx91f/Oh4SfqpBLVpIqtth4WsPHVK53a74hg2V0KhRne0XtccXkxqfbN2qdzdvlp/ForfHjlUDL73zG6htlyUm6p9XXCFJeuqHH3TDRx+52kDVNVf7KS/9ea12+ynn7AxJ2rp1a6XeszabTUuWLFGzZs3cGx0AAAAAn5OcnKwJEyaod+/e6tu3r2bOnKmioiJNnDhRkjR+/Hg1a9ZMM2bMkCRNnz5d/fv3V9u2bZWbm6tnn31W+/bt0x/+8AdJksVi0f33368nnnhC7dq1U2Jioh599FHFx8dr7NixZr1NnIXdMDTu44/1sWN+hiS9vn69pJMJg9/36GFWaNXS1tG66FhxsY4XF7sucqJq81JTJUljkpIU5F/jLtluM6hlS/1r1ao6rdRYuX+/JGlwy5YenahD9Tl/3vNKSmSz22X1q/H9xF7l2IkTutvRduqvgwaZmpgEvMFfBw9W8/BwTfr8c326bZsO5udr6fjxdV4x4e2VGtU+W+jevbssFossFkuVbaZCQkL08ssvuzU4AAAAAL5n3LhxOnLkiKZNm6bMzEx1795dS5YscQ363r9/v/xOuUiUk5OjO++8U5mZmWrcuLF69eqlH3/8UR07dnSt89BDD6moqEh//OMflZubq8GDB2vJkiUKNmEgMc7ttZ9/1sdbtyrQatVfBgzQoYICLdm1S0lRUZo3dqwCrFazQzyr0MBANWvYUIcKCrTz2DH14yLfGRmGoY+3bZMk3dGtm6mxDHD8P/2Sna3ckhI1qoPPh+8dSY2LW7as9X2hbjR2fN8YkvJKS+t9UvPvS5fq6IkT6hwdrcdoOwVUy21duyqxUSON/uADrT10SP+3eLHeruMbbXymUmPv3r0yDEOtW7fW2rVr1dQx+Ew6WSIeHR0tq4efWAIAAADwDlOmTNGUKVOqfG758uWVvn7hhRf0wgsvnHV7FotF06dP1/Tp090VImpJmc2mJxxtpp4dNkx/6tfP5IjOT6tGjXSooEAH8/Plne+gbmw7elSZhYUK8ffXkIQEU2OJCQtT28hI7Tp+XGsOHtTwtm1rdX82u93V6mowSY16I8BqVcPAQBWUlenoiRP1Oqmx7vBhvb5unSTp1auuUiDXBYFqG9SypRaOG6dL583TfzZu1G1dumhYmzZ1tn9npUaYl1ZqVLsGrlWrVkpISJDdblfv3r3VqlUr1yMuLo6EBgAAAADggs3fskWHCwoUFxamu3v3Njuc8xbfsKEk6XBBgcmReLale/dKOnlR38zWU0694+MlSRsdw7tr06asLBWUlSk8KEhdoqNrfX+oO1ENGkg62ZqpPioqK9OHv/yicR9/LEPSzZ0765JWrcwOC/A6F7dqpcl9+kiS7vniC5VUVNTZvl2VGl6a1KjWGcPnn3+ukSNHKiAgQJ9//vlZ1x09erRbAgMAAAAA+BbDMPT86tWSpCl9+3r1Xb9xYWGSpIzCQpMj8WzOpMZlJldpOHWNjtYHOplwqG3O1lMDW7So93MXfE1Ugwbam5uro/UwqbHtyBHd8NFH+uXIEUkn3+vMESNMjgrwXk9ecYU+3b5du3Ny9NKaNXpo0KA62W+hL7SfGjt2rDIzMxUdHX3WQXoWi0U2m81dsQEAAAAAfMh3+/YpNTNTIf7+Xl2lIVGpUR02u13L09MlSZcnJpobjENXx+yeukhqrGSeRr3lrNSob0mNhdu365ZPPlGx427yq9q101NXXKHo0FCTIwO8V3hQkP55+eW647PP9MSKFZrQrZtiHDdG1ITNbteMlSv17ubNahgYqIRGjTQ2KUk3dOzomkV2uKBAz69apUU7dvjGoHC73V7lvwEAAAAAcAfDMDT9u+8kSXd07+71feidlRokNc5sc3a2ckpK1DAwUL0cbZ/M5kxqbDt6VGU2W61WCzmTGszTqH+cSY0j9Sipsev4cd366acqrqjQsNatNW/sWFfyFsCFub1bN728dq3WZWRowsKFWjBunEJqUEFRUlGhWz75RAu2b3ct++nwYX20daumpqTopk6dlFVUpPe3bFHZbwoSvLVSg/pGAAAAAIDpXlyzRsvS0xXs76+/DBxodjgXzHmxj/ZTZ7bZUQ3RMy5O/h7Sfql5eLgaBQerwm7X9qNHa20/R0+ccH1v9IyLq7X9wBxN62Glxt+XLtWJ8nJdlpCgL2+9lYQG4EZ+FovmjhmjYH9/fbV7twbOnauF27dr57FjKj9HV6TckhJd+c47WrB9uwKtVr08cqQWjhunaZdcoujQUO3Py9MzP/6otzduVJnNdlplVQMvTWpUq1LjpZdeqvYG//SnP513MAAAAAAA3/PFjh164OuvJUlPDx2q1o0bmxzRhaP91Lk5kwYdoqJMjuRXFotFXWNitGLfPm3KynJVbrjbNsc8glYREQrz0tYfOLP61n5qfUaGPvzlF1kkvThiBDNggFrQNSZGX9xyi276+GOlZmbqmvnzJUlhgYEa16mT7u/fX52joyu9Zvfx47pm/nxtzs5WeFCQPrvpJg1xzKgak5Skvw4erI+3btXqgwcV5O+vazt0UEZBgW78+GNJUrC/v9f+PFcrqfHCCy9Ua2MWi4WkBgAAAACg2o4UFWnCwoWyG4bu7NlT9/bta3ZIbhHnSGocLy5WaUWFgvyr9ee3T9l+7JgkKcmDkhrSyWHhK/bt08bMTN3WtWut7GObM6HTtGmtbB/mqk9JjTKbTZMXL5Yk3dKli7rUUqIPwMn5UhvuuktPrVypFfv3a9fx4yosK9OcDRs0Z8MGjU1K0viuXdWkQQN9+Msven3dOpXb7YoNC9OSW29Vt9jYStsLCQjQ7d266fZu3VzLnLOsJKlxcHBdvTW3q9ZZ1d69e2s7DgAAAACAD3pixQodKy5Wl+hovXLVVbJYLGaH5BaNg4MVZLWq1GZTRmGhEho1Mjskj+OsVvC4pIZzWHh2dq3tY6vjvXtSlQrcx5tnahw7cUJPfv+99uXlqWNUlFL27tXqgwcVERSkJy+/3OzwgHqvWXi4Xr7qKkmS3TC0cv9+vbRmjT7Ztk0Lt2/XwlPmZkjSlW3aaPbVVyuxmlWup7af8ubK2Au6VcQwDEk675POWbNmadasWUp3ZIg6deqkadOmaeTIkRcSFgAAAADACxw7cUJvrF8vSXruyitrdShzXbNYLIpr2FDpubnKKCggqfEb5Tabdh0/LsnzqhVcSQ3HzI/a4KzU6Ohh7x3u4bxomF1UZHIkNWM3DN3w0Uda5rhO96ljeURQkD6+8Ua14nMMqFN+FosuadVKl7Rqpa1Hjui1n3/WsvR0FZaVqUPTpvrLgAG6LDGxRtt0zvyRpBYREe4Ouc6cV1Jjzpw5euGFF7Rz505JUrt27XT//ffrD3/4Q42207x5cz311FNq166dDMPQ22+/rTFjxmjDhg3q1KnT+YQGAAAAAPAS83/5RcUVFeoWE6OhrVubHY7bxTuSGszVON3e3FyV2+1qEBCg5uHhZodTSafoaFkkZRYWKruo6LShqu6wjUqNes3Zfi6joECGYXhNBdq7mzZpWXq6Qvz99dCgQTqUny+7Yejvl1zi1Xd0A/VBx6ZN9aIbCgEiQ0Jc/27oxTOdapzUmDZtmp5//nnde++9GjBggCRp1apV+vOf/6z9+/dr+vTp1d7WqFGjKn395JNPatasWVq9ejVJDQAAAACo597dvFmSNL5bN6+56FcTcWFhkhgWXhXnRf32TZrIz8P+78MCA9UmMlK7jh/XpqwstyfcCkpLdSA/X5LnVanAPWIdP/vFFRUqKCtTeFCQyRGdW25JiaampEiSpl16qf46eLDJEQGoDacOBveppMasWbP0xhtv6Oabb3YtGz16tLp27ap77723RkmNU9lsNn300UcqKipyJUsAAAAAAPXT3pwc/XjggCySburc2exwakW8827twkKTI/E82z18UHbPuDjtOn5cqw4ccHtSw/neY0JDK90xi/qjQUCAwoOClF9aqoyCAq9Iakz/7jsdKihQu8hI3devn9nhAKhFgVarymw2XdWundmhnDe/c69SWXl5uXr37n3a8l69eqmioqLGAWzevFlhYWEKCgrS3XffrQULFqhjx45VrltaWqr8/PxKDwAAAACA9/lgyxZJ0hWtW7su/tc3VGqc2VbHhf2kJk1MjqRqlyckSJK+2bPH7dveceyYJM8bkA73clZreENSs6C0VHM2bJAkzRwxQiEBASZHBKA2pU2Zoi9vvVVXeHHrzxonNW6//XbNmjXrtOWvv/66br311hoH0L59e6WmpmrNmjW65557NGHCBG3durXKdWfMmKGIiAjXo0WLFjXeHwAAAADAfAvT0iRJN57hprb6IMZxUdPbhgXXhc2OIdydo6NNjqRqw9q0kSStOnhQBaWlbt12em6uJDGjoJ5zJjUzvSCp8fbGjcovLVX7Jk00om1bs8MBUMsSGjXy+p/18x4U/vXXX6t///6SpDVr1mj//v0aP368kpOTXes9//zz59xWYGCg2joOYq9evfTTTz/pxRdf1GuvvXbaulOnTq20/fz8fBIbAAAAAOBlMgsLtfbQIUnS7y66yORoao9zwDRJjcoq7HZtdczU6BoTY3I0VWvduLFaN26sPTk5+m7fPrd+nzqTGq0iIty2TXieU4eFe7LSigrNXL1aknRv374eN+MGAKpS46TGli1b1LNnT0nS7t27JUlRUVGKiorSFkf5sKTzHvJmt9tVeoa7IIKCghTkBX0IAQAAAABn9sWOHZKkPvHxrgt/9RFJjartPHZMpTabQgMClOjB1QrDWrfWa+vW6ds9e9yb1MjLk3TyTlnUXzGOn/8sD/75L7fZ9PvPP9funBxFh4ZqQvfuZocEANVS46TGsmXL3LbzqVOnauTIkWrZsqUKCgr03nvvafny5frqq6/ctg8AAAAAgGf53JHUGFWPqzSkXy9qZhcVyTCM8775r77ZnJ0t6WTrKU++K3yoI6nh7rka+xyVGiQ16remDRpIko54cFLjpTVr9N7mzbJI+vdVVyksMNDskACgWs6r/ZS7ZGdna/z48crIyFBERIS6du2qr776SsOGDTMzLAAAAABALamw25XiuEhcn1tPSVJTR1Kj1GZTQVmZwuk8IEna5Jin0cVD52k4XZ6YKIukrUeO6FB+vpqFh1/wNu2GoX1UavgE58//kRMnTI6kahV2u57+4QdJ0ksjR+q6ejzfCED9U+OkRklJiV5++WUtW7ZM2dnZstvtlZ5fv359tbc1Z86cmu4eAAAAAODFNmZmqqi8XBFBQeoWG2t2OLWqQUCAwgIDVVhWpuyiIpIaDs5KDU+dp+EUGRKi3vHx+unwYaXs3avx3bpd8DaPnjihMptNkhRfj1uv4ZRKDQ9Navx8+LCOnDihxsHBurt3b7PDAYAaqXFSY9KkSfr66691/fXXq2/fvpTPAgAAAACqbeX+/ZKkQS1benTrIXeJDg1VYVmZsgoL1TYy0uxwPIKzUsPTkxqSNKhFC/10+LA2ZGS4JamRWVgo6eQF7wCr9YK3B8/lqtTw0PZTzoq5yxIT5e/nZ3I0AFAzNU5qLFq0SIsXL9agQYNqIx4AAAAAQD226uBBSScvFvuC6NBQ7cnJ8ehhwXXp2IkTSnfMlOjiBUkNZzXRJkd1yYXKKCiQJMVRpVHveXqlxtL0dEnS5QkJpsYBAOejxqnYZs2aqSG/fAEAAADAI2QXFam0osLsMKptzaFDkqT+zZubHEndaOb4+/lgfr7JkXgGZ1IrKSpKkSEhJkdzbs5qko2ZmTIM44K3l+Go1IgNC7vgbcGzOSs18ktLPe4zuri8XD84quauaN3a5GgAoOZqnNR47rnn9PDDD2vfvn21EQ8AAAAAoJo2Z2Up/rnndOf//ifp5MUzT5ZdVKT03FxZJPWKizM7nDrRwjFcmqTGST8eOCBJGuglSa2OTZvKz2LRseJiV+uoC+Gq1CCpUe81Cg6W1dFi76iHVWusOnhQpTab4sLC1L5JE7PDAYAaq3H7qd69e6ukpEStW7dWgwYNFBAQUOn548ePuy04AAAAAMCZvbF+vWyGoXc2bdLREyf05a5duiYpSe9dd52C/Wv8516t+8lRpZEUFaWI4GCTo6kbzUlqVPKd4wbJgV7SfizY31+tIiK0NzdXu44fv+C2Uc5KDZIa9Z+fxaKmoaHKLCxUVlGRmjk+CzyBc57GFa1bMysXgFeq8VnuzTffrEOHDumf//ynYmJi+PADAAAAABMYhqET5eWur7/ctUuStGD7dj37ww969NJLzQrtjJytp/p5yV367tAiIkKSdICkhvJLS7XG0X7Km1retImM1N7cXO3OydHFrVpd0LZcSQ3aevuEZg0bKrOwUIfy89XTg6rTmKcBwNvVOKnx448/atWqVerWrVttxAMAAAAAOIfNWVka8vbbOl5c7FrWICBAt3TurDc3bNCzP/6o+/r3V3hQkIlRnm6tI6nRNz7e5EjqDpUav0rZs0c2w1DbyEglNGpkdjjV1qZxY30rabcbOlPQfsq3NA8P17qMDI/6+c8vLXVVzV2emGhyNABwfmqc1EhKSlLxKSfOAAAAAIC6tTw9vVJC4z9jx2pku3ZqEhKiHw4c0LajRzUvNVV/6tfPxCgrsxvGr0mNZs1MjqbuOGdqHMrPl90w5OfD3Q7mpqZKksa0b29uIDXUpnFjSdKunJwL3haVGr7FE5OaK/btk80w1KZxY7XyouQiAJyqxoPCn3rqKT3wwANavny5jh07pvz8/EoPAAAAAEDtyioqcv370latdHOXLopq0EAWi0WTevSQJC1ztBfxFL9kZyunpEQNAgLUNSbG7HDqTGxYmPwsFpXb7co+5f/N16QdParFO3dKku7s2dPkaGqmbWSkpAuv1DAMg0oNH+NKajj+3z3B0r17JUlXUKUBwIvVuFJjxIgRkqQrrrii0nLDMGSxWGSz2dwTGQAAAACgSpmOu72nDxly2uyM3o7WTqmZmXUe19k4B0QPatFCAVarydHUnQCrVbFhYTpcUKCD+fmK9dGL2dNXrJDdMDTqoovUPirK7HBqxNkqa19e3gVtJ7+0VMUVFZKo1PAVnlapYRiGFu3YIYnWUwC8W42TGsuWLTvjc5s3b76gYAAAAAAA5+as1Iip4gJ5t9hYSVJ6bq5yS0rUKDi4TmM7E2dS49ILHLTsjVqEh+twQYEO5OW5kk6+ZOuRI3rfcb3g8SFDzA3mPDhb9GQXFam4vFwhAQHntR1n66nwoCA1OM9twLs4kxr7LzAh5i5rDx3SzuPH1SAgQFdfdJHZ4QDAeatxUuPS39wFVFBQoPfff19vvvmm1q1bpylTprgtOAAAAADA6bIcF0djQkNPe65RcLASGjVSem6uNmZm6tKEhDqO7nSGYWiFM6nhAfHUtebh4Vpz6JDH3K1d1x7/7jsZkq5JSlKPuDizw6mxxsHBCgsMVGFZmfbn5Z13pQmtp3xP+yZNJEl7cnIuKCHmDiUVFfprSook6doOHRQWGGhaLABwoWo8U8NpxYoVmjBhguLi4vSvf/1Ll19+uVavXu3O2AAAAAAAVThbpYYkdXPMrPCUFlRpx44pu6hIwf7+6uODlQrOYeEHfDCpMX/LFn34yy+SpP/nhVUakmSxWNQqIkLShbWgclZq+GoLMl8UGxampg0ayG4Y+uXIEdPisBuGRr77rpanp8vfz0+PXHyxabEAgDvUKKmRmZmpp556Su3atdMNN9yg8PBwlZaWauHChXrqqafUp0+f2ooTAAAAAKCTVQ9nq9SQpO6OFlSpWVl1FtfZfL17tyRpQPPmCvKvccMAr9fcR5Maqw8e1PiFCyVJyf37e/WAeGcLqn25uee9jUOO//9mju8H1H8Wi8XVEnCjiUnmuRs2aHl6ugL8/PTfa67xurk2APBb1U5qjBo1Su3bt9emTZs0c+ZMHT58WC+//HJtxgYAAAAA+I2CsjKV2mySpOhzJTU8pFLj023bJEmj27c3ORJztImMlCSlHT1qciR1p6SiQhMWLlSZzaZrkpL0zLBhZod0QdxRqeGcq9CSpIZP6eH4PF7uaMFX13KKi/Xwt99Kkp4eOlTjOnc2JQ4AcKdqJzW+/PJLTZo0SY8//riuvvpqWa3W2owLAAAAAFCFE+XlkiSLdMZhw86kxi/Z2SpzJEDMklNcrO/375d0cqaCL+ocHS3p5MBsm91ucjR14x/ffacdx44pNixMc8eMkdXvvLtfewS3JDUclRotHduCb7iuQwdJ0sdbtyqnuLjO9//vn37S8eJidWraVPf261fn+weA2lDts4qVK1eqoKBAvXr1Ur9+/fTKK6/oqA/dZQIAAAAAnqCkokKSFOzvL4vFUuU6rSIiFBEUpHK7XdtM7OMuSSv27ZPdMJQUFeVq4eNrWjdurAYBASq12bTr+HGzw6l1qZmZeubHHyVJr151lRoFB5sc0YVzR/spV6UGSQ2f0rdZMyVFRamkokLf1XG1RklFhV5eu1aSNHXwYPl7eXIRAJyq/WnWv39/vfHGG8rIyNBdd92lDz74QPHx8bLb7frmm29UUFBQm3ECAAAAACQVOyo1gs8ym8JisXhMC6pl6emSpCGtWpkah5n8LBZ1atpUkrQ5O9vkaGpXTnGxrpk/XxV2u67t0EHXOu5S93buqNQ4QFLDJ1ksFvVv3lxS3c/VmJeaqqyiIrUID9eNnTrV6b4BoDbVOEUbGhqq3//+91q5cqU2b96sBx54QE899ZSio6M1evTo2ogRAAAAAODgrNQIOUPrKSdnUmODyUmN5Y6kxmWJiabGYbYujhZUmz1keHttqLDbdf9XXyk9N1etGzfWa7/7ndkhuY2zUuNQfr4qzqOFWFFZmY45Wg+R1PA93WNiJEmpdfjzn1NcrGnLlkmSHhgwQAG0kQdQj1xQ3Vn79u31zDPP6ODBg3r//ffdFRMAAAAA4AxObT91Np5QqXHsxAltdFzEu9SHKzWkX+dqbDG5HVhtKamo0Kj339d/Nm6UJL32u98pqkEDk6Nyn9iwMAVarbIZhg45ZmPUxO6cHElS4+BgRdSDdlyoGTM+jx9ZulRHTpxQh6go3dOnT53tFwDqglua6VmtVo0dO1aff/65OzYHAAAAADiD80lqGIZR63FVZYWjf3yHqCjFhIWZEoOn6OK4U7u+Vmo8vny5luzapQYBAZo3ZoyGtm5tdkhu5WexqEV4uCQp/Tzmauw4dkyS1D4qyp1hwUt0c3wep+fm1smw8HWHD2vWzz9Lkv599dUKpEoDQD3DhCAAAAAA8CLVTWp0bNpUAX5+yistvaA5ABdizaFDkqSLW7Y0Zf+exNl+atfx4zrhmItSX+SWlOj51aslSf+95hpN6N7d3IBqiWtY+Hn8PDmTGhc1aeLOkOAlGgUHK8Hx/bOxlhObdsPQ/y1eLEPSLV26aEhCQq3uDwDMQFIDAAAAALxIsXOmxjmSGoFWqzo5LqRvyMio9biqst6x317x8abs35NEh4YqqkEDGZK21bMWVJ9t364ym02dmjbVNfVkMHhVXMPCz6NSI82Z1IiMdGdI8CJ11YJq/pYtWnvokMKDgvSvYcNqdV8AYBaSGgAAAADgRapbqSFJPUycq2EYhiup0TMurs7372ksFsuvw8Kzs02Oxr0WbN8uSbqhY0eTI6ldrqTGeVRqbD96VBKVGr6sm6MF3aZartRwVk09MGCA4ho2rNV9AYBZSGoAAAAAgBepSVLDeWfwBhOSGvvz8nSsuFj+fn6ui/m+zpXUqEdzNSrsdi1LT5ckXX3RReYGU8vOt/1Uhd3uupDtnK0A39POUaWz9zwqfaprU1aWfj58WIFWq+7p3bvW9gMAZiOpAQAAAMDjvPrqq0pISFBwcLD69euntWvXVut1H3zwgSwWi8aOHVtp+R133CGLxVLpMWLEiFqIvPZ5S6WGc55G99hYBVUjVl/gHBa+pR61n1qfkaH80lI1Cg52fb/VV+fbfirt6FGVVFQoNCBAbWk/5bMSGzeWJO3Nyam1fby3ebMk6ep27dQ0NLTW9gMAZiOpAQAAAMCjzJ8/X8nJyXrssce0fv16devWTcOHD1f2OVr2pKen6y9/+YsuvvjiKp8fMWKEMjIyXI/333+/NsKvdcWOIdMhAQHnXNd5V/iB/HwdO3GiVuP6rdUHD0qS+jVrVqf79WSd62GlxnJHlcalrVrJ6le/LzE4KzX25+XJbhjVfp2zUqpbbKz8LJbaCA1eINHx/XMgP1/lNlut7OPbPXsk1f9WcABQv884AAAAAHid559/XnfeeacmTpyojh07avbs2WrQoIHmzp17xtfYbDbdeuutevzxx9W6desq1wkKClJsbKzr0dhx16y3cVVqWK3nXDc8KEhtHO+zrqs1nJUaJDV+1alpU0lSRmFhnSeZasvK/fslSRe3bGlyJLWveXi4LJJKbTZlFxVV+3UbHLNl6nslC84uJixMQVar7Iahg/n5bt9+SUWFNjoSpgNbtHD79gHAk5DUAAAAAOAxysrKtG7dOg0dOtS1zM/PT0OHDtWqVavO+Lrp06crOjpakyZNOuM6y5cvV3R0tNq3b6977rlHx44dO2sspaWlys/Pr/TwBDVpPyX9OlejLpMaZTab1h0+LEnq37x5ne3X0zUMCnLdrb2lHgwLtxuGfjhwQJJ0catWJkdT+wKtVsU7Bi/XpAWVs1KDpIZv87NYlOD4+d9TCy2oUjMzVWG3q2mDBmrpaJUGAPUVSQ0AAAAAHuPo0aOy2WyKccwecIqJiVHmGS7Kr1y5UnPmzNEbb7xxxu2OGDFC//nPf5SSkqKnn35a3333nUaOHCnbWVqAzJgxQxEREa5HCw+587WmSQ3nhdR1jrvF68KmrCyV2myKDAlhhsBvuFpQ1YOkxrYjR3S8uFgNAgJ85oJ9TYeFG4bxa1IjLq62woKXSIqKklQ7P/8/Oarj+jZrJgttzgDUcyQ1AAAAAHitgoIC3X777XrjjTcU5bhYVJWbbrpJo0ePVpcuXTR27FgtWrRIP/30k5YvX37G10ydOlV5eXmuxwHHHelmcyY1qjNTQ5IGOdoCfblrl0odr61tPzqOFRfXTtelHs3VcLae6t+8uQKq0Q6tPnDeaV/dSo11GRnKLSmRv5+fq/0YfFcvR2LrZ0clmzutdWyzLy3/APiA6t3aAwAAAAB1ICoqSlarVVm/ueCblZWl2CruBN+9e7fS09M1atQo1zK73S5J8vf3V1pamtq0aXPa61q3bq2oqCjt2rVLV1xxRZWxBAUFKSgo6ELeTq0ormGlxsUtWyq+YUMdLijQgu3bdVPnzrUZniTp87Q0SdJlCQm1vi9v08VRhVQfKjW+dyQ1BntIFVNdSHC09dldzfZBD37zjaSTg5uDqvkzi/qrV3y8pNqpnHNWavRx7AMA6jMqNQAAAAB4jMDAQPXq1UspKSmuZXa7XSkpKRowYMBp6yclJWnz5s1KTU11PUaPHq3LLrtMqampZ2wZdfDgQR07dkxxXtgOpqbtp6x+fvpjz56SpIe//Va5JSW1FpskZRUWanl6uiTp+o4da3Vf3sjZfmpLdrYMwzA5mvNnGIZS9u6VJF3qQ8mrTjVoH/bz4cNanp6uAD8/PX3KnCD4rp6O3zlpR4+6PsvdIbekRGmOOVF9qNQA4ANIagAAAADwKMnJyXrjjTf09ttva9u2bbrnnntUVFSkiRMnSpLGjx+vqVOnSpKCg4PVuXPnSo9GjRqpYcOG6ty5swIDA1VYWKgHH3xQq1evVnp6ulJSUjRmzBi1bdtWw4cPN/OtnpeaJjUk6cFBg5TYqJH25+XpmvnzVVBaWlvh6dWffpLNMNSvWTO1bty41vbjrdo3aaIAPz8VlJXpgIcMnz8fm7KylFlYqAYBARrkQ5UaXWqQlHp93TpJ0o2dOqkFg5shKSY0VGGBgTJUs2Hz57LO0XoqsVEjRTVo4LbtAoCnIqkBAAAAwKOMGzdO//rXvzRt2jR1795dqampWrJkiWt4+P79+5VRg9YdVqtVmzZt0ujRo3XRRRdp0qRJ6tWrl77//nuPbC91Lq6ZGjVIajQICNDHN96osMBALU9P1+TFi2slti927NBTK1dKkh6oorIGUoDV6hqevv3oUZOjOT+GYej/ffedpJMtxnyprVL7qCj5+/kpv7T0rEmpcptNn2zbJkma2L17HUUHT2exWFzJ3j3VbGFWHWtPGRIOAL7Ad848AAAAAHiNKVOmaMqUKVU+d7bh3pI0b968Sl+HhIToq6++clNk5jufSg3pZNuTL2+9VZfOm6d3Nm3SHd276/LExNPWKyor00+HD6ttZKSah4dXe/vvbNyoP/zvfyq323Vjp066jtZTZ9Q+Kkrbjh5V2tGjurKKmS+e7uvdu7Vw+3YFWq167NJLzQ6nTgVareocHa3UzEwt3btXd5whYfHtnj06XlysmNBQDfGh9lw4t9aNG2tTVpZ7kxqOSg3maQDwFVRqAAAAAIAXKbXZJJ28uFpTg1u21P/17i1JeuDrr2VzDFV3+n7fPrV/5RVd9vbbav3ii3pl7dpqbXfxzp2647PPVGaz6boOHfTfa66Rn8VS4/h8RfsmTSR5b6XGMz/+KEma0qePT/bvvyYpSZL0/pYtZ1xn/i+/SDo5V8bqx6UX/Kp1o0aSqj9svjp+olIDgI+hUgMAAAAAvEiFIxERcB5JDUl6bMgQvbNpk1IzM/X6unW6u3dv7Tx+XAu3b9cjS5eq3G6X1WJRud2ue7/8UuFBQRrfrZskqbCsTF/v3q1le/fK389PHZo21ZGiIv1jxQrZDUMTu3fXm6NHk9A4B2dSwznY15tkFRZqmWNA+L39+pkcjTlu6dJFjy1frm9279benBwl/mZ2TGFZmT51tJ4a16mTGSHCg7m7/dThggIdKiiQn8XiGkQOAPUdSQ0AAAAA8CLOpIb/ed79HdWggf7fkCH681df6d4vv9SMlSsrzQa4oWNHvTVmjP7f8uX616pVmvjZZ1q0Y4eOFxfr+/37VeaoFPmtazt00Ozf/Y6ERjV0bNpUkrTZMWza4kXHbOH27TJ0ss1NguOOc1/TNjJSV7Zpo69379a/fvxRr159daXn39u8WQVlZbqoSRMNbtnSpCjhqdo4Zuq4K6nhrNLo1LSpQgMD3bJNAPB0JDUAAAAAwIuUO5IK55vUkKQ/9eunFfv2acH27TqQn68APz91iYnRH3v21B979ZLFYtHTw4apqLxcs37+WR9t3ep6bevGjXV1u3by9/PT9qNHZbFYNKZ9e/2hZ08SGtXUNSZGVotF2UVFyigsVHzDhmaHVG3O4dfXdehgciTm+uugQfp6927NXrdOd/Xura4xMZKk0ooK/fP77yVJdzt+loBTnVqp4Y6k5rd79kiS+jdvfsGxAYC3IKkBAAAAAF7E1X7qApIafhaLPrzhBr2zcaOOFxfrli5dFPebC+t+Fov+ffXVuq1rV6Xs2aPIkBANa9NG7SIjuVB7gUICAtShaVNtyc7W+owMr0lqHC8u1rL0dEny+UHwlyUm6vqOHfXx1q2654svtHT8eAX5+2v+L79oX16e4sLCdJdjfg1wqlYREbJIKiovV3ZRkWLCws57W3bDcCUaxzpmvQCAL2BaFQAAAAB4kQttP+Xk7+eniT166IGBA09LaJxqYIsWevTSSzW5b19d1KQJCQ03cfa+X5+RYXIk1fd5Wpoq7HZ1jYlRW0cLHV/23JVXKiwwUD8eOKB7v/xSkvTaunWSpCl9+6pBQICZ4cFDBfn7q3l4uKQLb0H144EDyigsVERQkIa2bu2O8ADAK5DUAAAAAAAvUu6mpAbM1dkxV2PrkSMmR1J9tJ6qrGVEhD6+4QZJ0pvr1+udjRv144EDJxOG3bubGxw8mrMFVdqxYxe0nY9++UWSNCYpSYFW6wXHBQDegrNgAAAAAPAi7qrUgLk6OJIa248eNTmS6sksLNSSXbskSdf7eOupUw1v21Y3de4sQ9L4hQslSaPbtz9r9RPQt1kzSdJ3+/ad9zZObT11PYlGAD7G1LPgGTNmqE+fPmrYsKGio6M1duxYpaWlmRkSAAAAAHg010wN7sr1aklRUZJO3qltc/yferJ5qamqsNs1oHlzdXQkZHDSo5dc4vq3n8WiBwYMMDEaeIMrEhMlSSl79sgwjPPaxuqDB3WooEANAwN1ZZs27gwPADyeqUmN7777TpMnT9bq1av1zTffqLy8XFdeeaWKiorMDAsAAAAAPFa5zSaJSg1vl9iokYKsVpVUVGhfXp7Z4ZyV3TD0xvr1kqQ7e/Y0ORrP07FpU7177bUa1KKF/n3VVRrYooXZIcHDDW7ZUgF+fjqQn69dx4+f1zY+3rpV0snKoCB/f3eGBwAez9RPvSVLllT6et68eYqOjta6det0ySl3OgAAAAAATqL9VP1g9fPTRU2aaHN2trYdOeLqse+Jlu3dqz05OQoPCtKNnTqZHY5HuqVLF93SpYvZYcBLhAYGamCLFvpu3z6l7N2rdk2a1Oj1hmG4kho30A4OgA/yqLPgPMfdKZGRkVU+X1paqvz8/EoPAAAAAPAlrvZTJDW8nnOuxjYPn6vxuqNK49YuXRQaGGhyNED94GxB9e2ePTV+7fqMDB3Iz1cYracA+CiPOQu22+26//77NWjQIHXu3LnKdWbMmKGIiAjXowUlnQAAAAB8TDmVGvVGkuPu7G1HjtTodSv371e32bOV+OKLenfTptoIzSU1M9N1RzitpwD3uaJ1a0nSsvR02Ws4VyNl715J0uWJiQoJCHB7bADg6TzmLHjy5MnasmWLPvjggzOuM3XqVOXl5bkeBw4cqMMIAQAAAMB8tJ+qP86nUuOz7ds17J13tCkrS+m5ubp9wQIt2bWrtkLUjJUrZTcM3dCxo3rExdXafgBf0yc+XmGBgTpeXKzUzMwavXapM6mRkFALkQGA5/OIs+ApU6Zo0aJFWrZsmZo3b37G9YKCghQeHl7pAQAAAAC+wjCMX9tPWa0mR4ML1cmR1NicnS2b4/+1KnklJXpx9Wpd9e67uvbDD1VSUaGr2rXTNUlJMiTd+umn2peb6/b4TpSXa9GOHZKkhwYNcvv2AV8WYLXq0latJEkpNWhBVWaz6fv9+yWdrNQAAF9kalLDMAxNmTJFCxYs0NKlS5XIhzEAAAAAnJHtlBYlVGp4v45Nmyo0IECFZWXaeoYWVPvz8tRt9mzd/9VX+nLXLtkNQ3/o0UOf3XST3r/uOvWOj9fx4mLd8NFHKq2ocGt83+7ZoxPl5Upo1Ei9qNIA3G6oowWVs51Udaw9dEgnysvVtEEDdYqOrq3QAMCjmXoWPHnyZP33v//Ve++9p4YNGyozM1OZmZkqLi42MywAAAAA8EgVp9zNT1LD+1n9/NS3WTNJ0uqDB0973ma368aPPtK+vDy1iojQs8OGafM99+iN0aPl7+enIH9/fXTDDWocHKyfDh/Wn7/6yq3xfb9vnyTpytatZbFY3LptAL8OC/9+/36V2WzVeo2z9dRliYny4+cSgI8y9Sx41qxZysvL05AhQxQXF+d6zJ8/38ywAAAAAMAjnZrUCCCpUS8MatFCkrRo587Tnnt57VqtOXRIEUFBWn7HHfrLwIHq/Js7sxMaNdJ/r71WkjTr55/1zA8/yKjh0OEzWemYYzmoZUu3bA9AZZ2joxUdGqoT5eVVJjarwjwNAPCA9lNVPe644w4zwwIAAAAAj1R+yp28VGrUD7d27SpJWrRjh3YcO+Zavi83V48sXSpJenbYMCU0anTGbVzVrp0eHzJEkvTwt99q8uLFlRJg56O4vFzrDh+WJA0mqQHUCovF4pqL8W015moUl5drlSP5wTwNAL7M3+wAAAAAAADVQ/up+icpKkoj2rbVkl271Ov11+Xv56ewwEDllpSoqLxcF7dsqUk9e55zO49econCg4KU/NVXmvXzz0rPzdXro0apeXj4ecX10+HDKrfbFRsWpsSzJFQAXJihiYn6YMsWvZWaqnv79lXT0NAzrvvjgQMqs9nUPDxcbSMj6zBKAPAsnAUDAAAAgJdwJjWsFgszDuqROaNHq03jxiosK1NuSYkO5uersKxMQVarXh81qlp98y0Wi+7v318f33ijgv399eWuXeo6a5a+rKKtVXX8sH+/pJNVGnyvAbXn2g4d1CI8XAfz8/W3lJSzrutqPZWYyM8lAJ9GUgMAAAAAvIQzqUGVRv0S37Chtk2erBV33KGl48dr5vDhurFTJ313xx1Kioqq0bau7dBBqyZNUu/4eOWUlOjq997Toh07ahyTc57GYMfMDwC1o3FIiN51zMV5e+NG7c/LO+O6S9PTJTFPAwA4EwYAAAAAL1FOUqPeCrBadXGrVrosMVH39e+v+ddfr37Nm5/XtrrHxmrlxIka362bDEm//+wzFZeXV/v1dsPQjwwJB+rMxa1a6bKEBJXb7Xrmhx+qXCejoEBrDx2SxDwNAOBMGAAAAAC8hLNSI8BqNTkSeLogf3+9MWqUWkZE6MiJE/pgy5Zqv3brkSPKLSlRaECAusfG1mKUAJweveQSSdKb69cro6DgtOc/2LJFdsPQgObN1SIioq7DAwCPQlIDAAAAALwE7adQE4FWqyb36SNJemntWhmGUa3XfedocdOveXO+14A6MiQhQQNbtFCpzaZHli497fl3N2+WJN3apUtdhwYAHoezEwAAAADwEuU2mySSGqi+P/TsqRB/f6VmZup7x/Dvc1mye7ckaVjr1rUZGoBTWCwWPT10qCyS5qamVpqFs+3IEa3LyJDVYtGNnTqZFyQAeAjOhAEAAADAS7jaT5HUQDVFhoRofLduknTGXv2nKqmo0NK9eyVJI9u2rdXYAFQ2uGVL/bl/f0nS+AUL9Nn27TpSVKQpX34pSbr6oovUNDTUzBABwCP4mx0AAAAAAKB6aD+F8/HAgAF6fd06fbFzp7ZkZ6tzdPQZ112ya5dOlJereXi4usbE1GGUACTpySuu0I8HD2r1wYMaO3++a3mg1aqnhw41MTIA8BycCQMAAACAlygnqYHz0K5JE13XsaMk6cFvvnElx6riHCh+Q8eOslgsdRIfgF8F+/vr69tu00MDB6phYKAkKbFRI31+001KiooyOToA8AxUagAAAACAl3C1n7JaTY4E3ubRSy7R/9LStGTXLrV/5RVdlpCgP/Tsqf7Nm7vW2ZuTo0+2bZPEMGLATA2DgvT0sGGaftllKrfbFeZIbgAATiKpAQAAAABegvZTOF9dY2L00Q036LYFC7QnJ0d7cnI0Z8MGXdKqlbrHxCgsMFAfbt2qCrtdQxIS1Cs+3uyQAZ8X5O+vILODAAAPRFIDAAAAALxEuc0miaQGzs+o9u21509/0sr9+7UwLU3/3bRJK/bt04p9+1zrtAgP1+u/+52JUQIAAJwdSQ0AAAAA8BKu9lMkNXCemjRooDFJSRqTlKTpQ4bogy1blF1UpGPFxWrTuLH+r08fNQ4JMTtMAACAMyKpAQAAAABegvZTcKcWERF6cNAgs8MAAACoEc6EAQAAAMBLlJPUAAAAgI/jTBgAAAAAvASVGgAAAPB1nAkDAAAAgJdwzdSwWk2OBAAAADAHSQ0AAAAA8BLlNpskKjUAAADguzgTBgAAAAAvQfspAAAA+DrOhAEAAADAS7jaT5HUAAAAgI/iTBgAAAAAvIQzqWElqQEAAAAfxZkwAAAAAHgJ2k8BAADA13EmDAAAAABewmYYkiSrxWJyJAAAAIA5SGoAAAAA8DivvvqqEhISFBwcrH79+mnt2rXVet0HH3wgi8WisWPHVlpuGIamTZumuLg4hYSEaOjQodq5c2ctRF677I6khh9JDQAAAPgokhoAAAAAPMr8+fOVnJysxx57TOvXr1e3bt00fPhwZWdnn/V16enp+stf/qKLL774tOeeeeYZvfTSS5o9e7bWrFm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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "QNk_A9EMxPiz"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}