throwing_phases.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fa58bfa2",
   "metadata": {},
   "source": [
    "# Install and import dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "28b04eb2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: mediapipe in c:\\users\\alcas\\anaconda3\\lib\\site-packages (0.10.8)\n",
      "Requirement already satisfied: opencv-python in c:\\users\\alcas\\anaconda3\\lib\\site-packages (4.8.1.78)\n",
      "Requirement already satisfied: pandas in c:\\users\\alcas\\anaconda3\\lib\\site-packages (2.0.3)\n",
      "Requirement already satisfied: scikit-learn in c:\\users\\alcas\\anaconda3\\lib\\site-packages (1.3.0)\n",
      "Requirement already satisfied: absl-py in c:\\users\\alcas\\anaconda3\\lib\\site-packages (from mediapipe) (2.0.0)\n",
      "Requirement already satisfied: attrs>=19.1.0 in c:\\users\\alcas\\anaconda3\\lib\\site-packages (from mediapipe) (22.1.0)\n",
      "Requirement already satisfied: flatbuffers>=2.0 in c:\\users\\alcas\\anaconda3\\lib\\site-packages (from mediapipe) (23.5.26)\n",
      "Requirement already satisfied: matplotlib in c:\\users\\alcas\\anaconda3\\lib\\site-packages (from mediapipe) (3.7.2)\n",
      "Requirement already satisfied: numpy in c:\\users\\alcas\\anaconda3\\lib\\site-packages (from mediapipe) (1.24.3)\n",
      "Requirement already satisfied: opencv-contrib-python in c:\\users\\alcas\\anaconda3\\lib\\site-packages (from mediapipe) (4.8.1.78)\n",
      "Requirement already satisfied: protobuf<4,>=3.11 in c:\\users\\alcas\\anaconda3\\lib\\site-packages (from mediapipe) (3.20.3)\n",
      "Requirement already satisfied: sounddevice>=0.4.4 in c:\\users\\alcas\\anaconda3\\lib\\site-packages (from mediapipe) (0.4.6)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\alcas\\anaconda3\\lib\\site-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\alcas\\anaconda3\\lib\\site-packages (from pandas) (2023.3.post1)\n",
      "Requirement already satisfied: tzdata>=2022.1 in c:\\users\\alcas\\anaconda3\\lib\\site-packages (from pandas) (2023.3)\n",
      "Requirement already satisfied: scipy>=1.5.0 in c:\\users\\alcas\\anaconda3\\lib\\site-packages (from scikit-learn) (1.11.1)\n",
      "Requirement already satisfied: joblib>=1.1.1 in c:\\users\\alcas\\anaconda3\\lib\\site-packages (from scikit-learn) (1.2.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\alcas\\anaconda3\\lib\\site-packages (from scikit-learn) (2.2.0)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\alcas\\anaconda3\\lib\\site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n",
      "Requirement already satisfied: CFFI>=1.0 in c:\\users\\alcas\\anaconda3\\lib\\site-packages (from sounddevice>=0.4.4->mediapipe) (1.15.1)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\alcas\\anaconda3\\lib\\site-packages (from matplotlib->mediapipe) (1.0.5)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\alcas\\anaconda3\\lib\\site-packages (from matplotlib->mediapipe) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\alcas\\anaconda3\\lib\\site-packages (from matplotlib->mediapipe) (4.25.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\alcas\\anaconda3\\lib\\site-packages (from matplotlib->mediapipe) (1.4.4)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\alcas\\anaconda3\\lib\\site-packages (from matplotlib->mediapipe) (23.1)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\alcas\\anaconda3\\lib\\site-packages (from matplotlib->mediapipe) (9.4.0)\n",
      "Requirement already satisfied: pyparsing<3.1,>=2.3.1 in c:\\users\\alcas\\anaconda3\\lib\\site-packages (from matplotlib->mediapipe) (3.0.9)\n",
      "Requirement already satisfied: pycparser in c:\\users\\alcas\\anaconda3\\lib\\site-packages (from CFFI>=1.0->sounddevice>=0.4.4->mediapipe) (2.21)\n"
     ]
    }
   ],
   "source": [
    "!pip install mediapipe opencv-python pandas scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5969f4c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import mediapipe as mp\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# Set up media pipe\n",
    "mp_drawing = mp.solutions.drawing_utils\n",
    "# Import pose estimation model\n",
    "mp_pose = mp.solutions.pose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8385266f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Assuming X is your feature matrix\n",
    "scaler = StandardScaler()\n",
    "#X_normalized = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b9a55f3",
   "metadata": {},
   "source": [
    "# Backhand phases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cb845286",
   "metadata": {},
   "outputs": [],
   "source": [
    "EXPORT_PATH = r'data\\5_flick_phases.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3c0b8024",
   "metadata": {},
   "outputs": [],
   "source": [
    "landmarks = ['class']\n",
    "for val in range(1, 33+1):\n",
    "    landmarks += ['x{}'.format(val), 'y{}'.format(val), 'z{}'.format(val), 'v{}'.format(val)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1c9787ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(EXPORT_PATH, mode='w', newline='') as f:\n",
    "    csv_writer = csv.writer(f, delimiter=',', quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n",
    "    csv_writer.writerow(landmarks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b80939c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import numpy as np\n",
    "\n",
    "def export_landmark(results, phase):\n",
    "    try:\n",
    "        keypoints = np.array([[res.x, res.y, res.z, res.visibility] for res in results.pose_landmarks.landmark]).flatten()\n",
    "        keypoints = keypoints.astype(object)\n",
    "        keypoints = np.insert(keypoints, 0, phase)\n",
    "        \n",
    "        with open(EXPORT_PATH, mode='a', newline='') as f:\n",
    "            csv_writer = csv.writer(f, delimiter=',', quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n",
    "            csv_writer.writerow(keypoints)\n",
    "\n",
    "    except Exception as e:\n",
    "        print(f\"An error occurred: {e}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c4aac90d",
   "metadata": {},
   "outputs": [],
   "source": [
    "video_path = r'videos\\5_flick_short_front.mp4'\n",
    "cap = cv2.VideoCapture(video_path)\n",
    "\n",
    "# Get the original video width and height\n",
    "width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n",
    "height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n",
    "target_height = 720\n",
    "\n",
    "# Calculate the aspect ratio\n",
    "aspect_ratio = width / height\n",
    "\n",
    "# Calculate the new width based on the target height and original aspect ratio\n",
    "target_width = int(target_height * aspect_ratio)\n",
    "\n",
    "\n",
    "with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:\n",
    "    # Variables for frame navigation\n",
    "    current_frame = 0\n",
    "    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n",
    "    while cap.isOpened():\n",
    "        ret, frame = cap.read()\n",
    "        \n",
    "        if not ret:\n",
    "            print(\"End of video.\")\n",
    "            break  # Exit the loop when the video is finished.\n",
    "        \n",
    "\n",
    "        frame = cv2.resize(frame, (target_width, target_height))\n",
    "        # Recolor image\n",
    "        # default of opencv is rgb, that's why we recolor\n",
    "        image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
    "        # set the image to not be modified and save memory\n",
    "        image.flags.writeable = False\n",
    "        \n",
    "        # Make detection\n",
    "        results = pose.process(image) # get detection of pose\n",
    "        \n",
    "        # Recolor back to BGR for opencv\n",
    "        image.flags.writeable = True\n",
    "        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
    "        \n",
    "        # Extract landmarks\n",
    "        landmarks_results = results.pose_landmarks.landmark\n",
    "        \n",
    "        # draw_landmarks\n",
    "        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS)\n",
    "        \n",
    "        # draw_landmarks window\n",
    "        landmarks_image = np.zeros_like(image)  # Create a black image\n",
    "        mp_drawing.draw_landmarks(landmarks_image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS)\n",
    "        \n",
    "\n",
    "        cv2.imshow('Video', image)\n",
    "        cv2.imshow('Data', landmarks_image)\n",
    "\n",
    "        k = cv2.waitKey(0)  # Wait for a key press\n",
    "        if k == ord('q'):\n",
    "            break\n",
    "        elif k == ord('n'):  # Move to the next frame\n",
    "            current_frame += 1\n",
    "            if current_frame >= total_frames:\n",
    "                current_frame = total_frames - 1\n",
    "            cap.set(cv2.CAP_PROP_POS_FRAMES, current_frame)\n",
    "        elif k == ord('p'):  # Move to the previous frame\n",
    "            current_frame -= 1\n",
    "            if current_frame < 0:\n",
    "                current_frame = 0\n",
    "            cap.set(cv2.CAP_PROP_POS_FRAMES, current_frame)\n",
    "        elif k == ord('1'):  # Capture the release point\n",
    "            export_landmark(results, 'preparation')\n",
    "            current_frame += 1\n",
    "            if current_frame >= total_frames:\n",
    "                current_frame = total_frames - 1\n",
    "            cap.set(cv2.CAP_PROP_POS_FRAMES, current_frame)\n",
    "        elif k == ord('2'):  # Capture the release point\n",
    "            export_landmark(results, 'loading')\n",
    "            current_frame += 1\n",
    "            if current_frame >= total_frames:\n",
    "                current_frame = total_frames - 1\n",
    "            cap.set(cv2.CAP_PROP_POS_FRAMES, current_frame)\n",
    "        elif k == ord('3'):  # Capture the release point\n",
    "            export_landmark(results, 'release')\n",
    "            current_frame += 1\n",
    "            if current_frame >= total_frames:\n",
    "                current_frame = total_frames - 1\n",
    "            cap.set(cv2.CAP_PROP_POS_FRAMES, current_frame)\n",
    "        elif k == ord('4'):  # Capture the release point\n",
    "            export_landmark(results, 'follow')\n",
    "            current_frame += 1\n",
    "            if current_frame >= total_frames:\n",
    "                current_frame = total_frames - 1\n",
    "            cap.set(cv2.CAP_PROP_POS_FRAMES, current_frame)\n",
    "\n",
    "            \n",
    "\n",
    "    # Release the video capture object and close the window\n",
    "    cap.release()\n",
    "    cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56bcaaae",
   "metadata": {},
   "source": [
    "# Anlysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "af824d95",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "9747df82",
   "metadata": {},
   "outputs": [],
   "source": [
    "csv_results = pd.read_csv('data\\\\5_backhand_phases_1.csv') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "524e6240",
   "metadata": {},
   "outputs": [
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       "      <td>0.999961</td>\n",
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       "      <td>0.359770</td>\n",
       "      <td>-0.200257</td>\n",
       "      <td>0.999989</td>\n",
       "      <td>0.447971</td>\n",
       "      <td>0.353768</td>\n",
       "      <td>-0.184593</td>\n",
       "      <td>0.999964</td>\n",
       "      <td>0.452180</td>\n",
       "      <td>...</td>\n",
       "      <td>0.119376</td>\n",
       "      <td>0.931125</td>\n",
       "      <td>0.526917</td>\n",
       "      <td>0.625877</td>\n",
       "      <td>-0.075133</td>\n",
       "      <td>0.990715</td>\n",
       "      <td>0.357815</td>\n",
       "      <td>0.622463</td>\n",
       "      <td>-0.019111</td>\n",
       "      <td>0.991498</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>preparation</td>\n",
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       "      <td>0.363785</td>\n",
       "      <td>-0.186440</td>\n",
       "      <td>0.999990</td>\n",
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       "      <td>-0.169767</td>\n",
       "      <td>0.999967</td>\n",
       "      <td>0.450855</td>\n",
       "      <td>...</td>\n",
       "      <td>0.121690</td>\n",
       "      <td>0.930449</td>\n",
       "      <td>0.528719</td>\n",
       "      <td>0.629331</td>\n",
       "      <td>-0.065466</td>\n",
       "      <td>0.990828</td>\n",
       "      <td>0.359635</td>\n",
       "      <td>0.626833</td>\n",
       "      <td>-0.017349</td>\n",
       "      <td>0.991442</td>\n",
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       "    <tr>\n",
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       "      <td>0.367473</td>\n",
       "      <td>-0.192398</td>\n",
       "      <td>0.999990</td>\n",
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       "      <td>0.362038</td>\n",
       "      <td>-0.176369</td>\n",
       "      <td>0.999969</td>\n",
       "      <td>0.448283</td>\n",
       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>follow</td>\n",
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       "      <td>0.419880</td>\n",
       "      <td>-0.356368</td>\n",
       "      <td>0.999995</td>\n",
       "      <td>0.656873</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.097224</td>\n",
       "      <td>0.990380</td>\n",
       "      <td>0.506964</td>\n",
       "      <td>0.637056</td>\n",
       "      <td>0.349747</td>\n",
       "      <td>0.970034</td>\n",
       "      <td>0.761273</td>\n",
       "      <td>0.644747</td>\n",
       "      <td>-0.181725</td>\n",
       "      <td>0.996191</td>\n",
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       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>follow</td>\n",
       "      <td>0.646206</td>\n",
       "      <td>0.422247</td>\n",
       "      <td>-0.376863</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>0.646643</td>\n",
       "      <td>0.416096</td>\n",
       "      <td>-0.354296</td>\n",
       "      <td>0.999995</td>\n",
       "      <td>0.648938</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.110813</td>\n",
       "      <td>0.991070</td>\n",
       "      <td>0.505274</td>\n",
       "      <td>0.635532</td>\n",
       "      <td>0.351609</td>\n",
       "      <td>0.971328</td>\n",
       "      <td>0.758332</td>\n",
       "      <td>0.644701</td>\n",
       "      <td>-0.211382</td>\n",
       "      <td>0.996515</td>\n",
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       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>follow</td>\n",
       "      <td>0.635622</td>\n",
       "      <td>0.417403</td>\n",
       "      <td>-0.363946</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>0.636326</td>\n",
       "      <td>0.411609</td>\n",
       "      <td>-0.339511</td>\n",
       "      <td>0.999995</td>\n",
       "      <td>0.638855</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.114441</td>\n",
       "      <td>0.991618</td>\n",
       "      <td>0.506746</td>\n",
       "      <td>0.634240</td>\n",
       "      <td>0.379620</td>\n",
       "      <td>0.972446</td>\n",
       "      <td>0.753761</td>\n",
       "      <td>0.644365</td>\n",
       "      <td>-0.213879</td>\n",
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       "      <th>120</th>\n",
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       "      <td>0.999996</td>\n",
       "      <td>0.630359</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.123823</td>\n",
       "      <td>0.992175</td>\n",
       "      <td>0.507842</td>\n",
       "      <td>0.633426</td>\n",
       "      <td>0.349344</td>\n",
       "      <td>0.974180</td>\n",
       "      <td>0.752799</td>\n",
       "      <td>0.644326</td>\n",
       "      <td>-0.226744</td>\n",
       "      <td>0.997061</td>\n",
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       "           class        x1        y1        z1        v1        x2        y2  \\\n",
       "0    preparation  0.445881  0.348113 -0.206969  0.999988  0.452615  0.342187   \n",
       "1    preparation  0.445171  0.354891 -0.228327  0.999988  0.451267  0.348994   \n",
       "2    preparation  0.441894  0.359770 -0.200257  0.999989  0.447971  0.353768   \n",
       "3    preparation  0.440179  0.363785 -0.186440  0.999990  0.446652  0.357958   \n",
       "4    preparation  0.437917  0.367473 -0.192398  0.999990  0.444231  0.362038   \n",
       "..           ...       ...       ...       ...       ...       ...       ...   \n",
       "116       follow  0.662633  0.429554 -0.347085  0.999998  0.663251  0.423780   \n",
       "117       follow  0.654000  0.425920 -0.377975  0.999998  0.654436  0.419880   \n",
       "118       follow  0.646206  0.422247 -0.376863  0.999998  0.646643  0.416096   \n",
       "119       follow  0.635622  0.417403 -0.363946  0.999998  0.636326  0.411609   \n",
       "120       follow  0.627164  0.413475 -0.275743  0.999998  0.627907  0.407793   \n",
       "\n",
       "           z2        v2        x3  ...       z31       v31       x32  \\\n",
       "0   -0.190722  0.999960  0.456759  ...  0.122712  0.930235  0.524166   \n",
       "1   -0.211235  0.999961  0.455261  ...  0.132494  0.930844  0.525950   \n",
       "2   -0.184593  0.999964  0.452180  ...  0.119376  0.931125  0.526917   \n",
       "3   -0.169767  0.999967  0.450855  ...  0.121690  0.930449  0.528719   \n",
       "4   -0.176369  0.999969  0.448283  ...  0.122956  0.931655  0.528836   \n",
       "..        ...       ...       ...  ...       ...       ...       ...   \n",
       "116 -0.326764  0.999995  0.665483  ... -0.113670  0.989677  0.504831   \n",
       "117 -0.356368  0.999995  0.656873  ... -0.097224  0.990380  0.506964   \n",
       "118 -0.354296  0.999995  0.648938  ... -0.110813  0.991070  0.505274   \n",
       "119 -0.339511  0.999995  0.638855  ... -0.114441  0.991618  0.506746   \n",
       "120 -0.249060  0.999996  0.630359  ... -0.123823  0.992175  0.507842   \n",
       "\n",
       "          y32       z32       v32       x33       y33       z33       v33  \n",
       "0    0.614157 -0.056212  0.990734  0.359526  0.610748 -0.017544  0.991672  \n",
       "1    0.620861 -0.048794  0.990670  0.357826  0.617060 -0.009796  0.991575  \n",
       "2    0.625877 -0.075133  0.990715  0.357815  0.622463 -0.019111  0.991498  \n",
       "3    0.629331 -0.065466  0.990828  0.359635  0.626833 -0.017349  0.991442  \n",
       "4    0.632950 -0.067019  0.991060  0.360013  0.631517 -0.014881  0.991558  \n",
       "..        ...       ...       ...       ...       ...       ...       ...  \n",
       "116  0.638193  0.339552  0.970494  0.762275  0.644931 -0.196304  0.995888  \n",
       "117  0.637056  0.349747  0.970034  0.761273  0.644747 -0.181725  0.996191  \n",
       "118  0.635532  0.351609  0.971328  0.758332  0.644701 -0.211382  0.996515  \n",
       "119  0.634240  0.379620  0.972446  0.753761  0.644365 -0.213879  0.996806  \n",
       "120  0.633426  0.349344  0.974180  0.752799  0.644326 -0.226744  0.997061  \n",
       "\n",
       "[121 rows x 133 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csv_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "e80b45c6",
   "metadata": {},
   "outputs": [
    {
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       "      <td>-0.363946</td>\n",
       "      <td>0.999998</td>\n",
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       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>follow</td>\n",
       "      <td>0.627164</td>\n",
       "      <td>0.413475</td>\n",
       "      <td>-0.275743</td>\n",
       "      <td>0.999998</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>121 rows × 5 columns</p>\n",
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      ],
      "text/plain": [
       "           class        x1        y1        z1        v1\n",
       "0    preparation  0.445881  0.348113 -0.206969  0.999988\n",
       "1    preparation  0.445171  0.354891 -0.228327  0.999988\n",
       "2    preparation  0.441894  0.359770 -0.200257  0.999989\n",
       "3    preparation  0.440179  0.363785 -0.186440  0.999990\n",
       "4    preparation  0.437917  0.367473 -0.192398  0.999990\n",
       "..           ...       ...       ...       ...       ...\n",
       "116       follow  0.662633  0.429554 -0.347085  0.999998\n",
       "117       follow  0.654000  0.425920 -0.377975  0.999998\n",
       "118       follow  0.646206  0.422247 -0.376863  0.999998\n",
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       "120       follow  0.627164  0.413475 -0.275743  0.999998\n",
       "\n",
       "[121 rows x 5 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csv_results[['class','x1', 'y1', 'z1', 'v1']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "b56182ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7245025396347046"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csv_results[csv_results['class'] == 'release']['x11'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "e01c73da",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>106</th>\n",
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       "      <td>0.443387</td>\n",
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       "      <td>0.699810</td>\n",
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       "      <td>0.692859</td>\n",
       "      <td>0.442279</td>\n",
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       "      <th>112</th>\n",
       "      <td>0.685965</td>\n",
       "      <td>0.441814</td>\n",
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       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>0.679095</td>\n",
       "      <td>0.441120</td>\n",
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       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>0.671348</td>\n",
       "      <td>0.440633</td>\n",
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       "      <th>117</th>\n",
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      "text/plain": [
       "          x11       y11\n",
       "60   0.704371  0.415826\n",
       "61   0.705824  0.416912\n",
       "62   0.707004  0.417844\n",
       "63   0.705403  0.418314\n",
       "64   0.704464  0.418123\n",
       "65   0.703711  0.418030\n",
       "66   0.699732  0.418163\n",
       "67   0.693977  0.418024\n",
       "68   0.688460  0.417850\n",
       "69   0.681902  0.416970\n",
       "70   0.677098  0.415355\n",
       "71   0.672586  0.413547\n",
       "106  0.729144  0.440452\n",
       "107  0.718136  0.442446\n",
       "108  0.710842  0.443568\n",
       "109  0.704139  0.443387\n",
       "110  0.699810  0.442586\n",
       "111  0.692859  0.442279\n",
       "112  0.685965  0.441814\n",
       "113  0.679095  0.441120\n",
       "114  0.671348  0.440633\n",
       "115  0.664536  0.439144\n",
       "116  0.656558  0.436200\n",
       "117  0.648695  0.432808\n",
       "118  0.641163  0.429055\n",
       "119  0.631008  0.424684\n",
       "120  0.621516  0.421060"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csv_results[csv_results['class'] == 'follow'][['x11', 'y11']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "2fa10679",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x1</th>\n",
       "      <th>y1</th>\n",
       "      <th>z1</th>\n",
       "      <th>v1</th>\n",
       "      <th>x2</th>\n",
       "      <th>y2</th>\n",
       "      <th>z2</th>\n",
       "      <th>v2</th>\n",
       "      <th>x3</th>\n",
       "      <th>y3</th>\n",
       "      <th>...</th>\n",
       "      <th>z31</th>\n",
       "      <th>v31</th>\n",
       "      <th>x32</th>\n",
       "      <th>y32</th>\n",
       "      <th>z32</th>\n",
       "      <th>v32</th>\n",
       "      <th>x33</th>\n",
       "      <th>y33</th>\n",
       "      <th>z33</th>\n",
       "      <th>v33</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.692127</td>\n",
       "      <td>0.421696</td>\n",
       "      <td>-0.273542</td>\n",
       "      <td>0.999997</td>\n",
       "      <td>0.692291</td>\n",
       "      <td>0.416153</td>\n",
       "      <td>-0.248330</td>\n",
       "      <td>0.999992</td>\n",
       "      <td>0.694493</td>\n",
       "      <td>0.415934</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.013179</td>\n",
       "      <td>0.983891</td>\n",
       "      <td>0.508975</td>\n",
       "      <td>0.635219</td>\n",
       "      <td>0.237127</td>\n",
       "      <td>0.973442</td>\n",
       "      <td>0.756973</td>\n",
       "      <td>0.642269</td>\n",
       "      <td>-0.066039</td>\n",
       "      <td>0.994010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.027891</td>\n",
       "      <td>0.011629</td>\n",
       "      <td>0.052063</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.027458</td>\n",
       "      <td>0.011637</td>\n",
       "      <td>0.053457</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.027391</td>\n",
       "      <td>0.011530</td>\n",
       "      <td>...</td>\n",
       "      <td>0.076125</td>\n",
       "      <td>0.005409</td>\n",
       "      <td>0.018120</td>\n",
       "      <td>0.002724</td>\n",
       "      <td>0.078998</td>\n",
       "      <td>0.002227</td>\n",
       "      <td>0.027957</td>\n",
       "      <td>0.003105</td>\n",
       "      <td>0.100129</td>\n",
       "      <td>0.001778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.627164</td>\n",
       "      <td>0.406808</td>\n",
       "      <td>-0.377975</td>\n",
       "      <td>0.999994</td>\n",
       "      <td>0.627907</td>\n",
       "      <td>0.401091</td>\n",
       "      <td>-0.356368</td>\n",
       "      <td>0.999984</td>\n",
       "      <td>0.630359</td>\n",
       "      <td>0.400877</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.129345</td>\n",
       "      <td>0.972483</td>\n",
       "      <td>0.483020</td>\n",
       "      <td>0.630577</td>\n",
       "      <td>0.125726</td>\n",
       "      <td>0.969325</td>\n",
       "      <td>0.657481</td>\n",
       "      <td>0.637783</td>\n",
       "      <td>-0.226744</td>\n",
       "      <td>0.990605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.678724</td>\n",
       "      <td>0.411391</td>\n",
       "      <td>-0.303360</td>\n",
       "      <td>0.999996</td>\n",
       "      <td>0.679520</td>\n",
       "      <td>0.405931</td>\n",
       "      <td>-0.278183</td>\n",
       "      <td>0.999991</td>\n",
       "      <td>0.681679</td>\n",
       "      <td>0.405957</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.084448</td>\n",
       "      <td>0.980138</td>\n",
       "      <td>0.497281</td>\n",
       "      <td>0.632799</td>\n",
       "      <td>0.171393</td>\n",
       "      <td>0.971851</td>\n",
       "      <td>0.750723</td>\n",
       "      <td>0.638731</td>\n",
       "      <td>-0.168980</td>\n",
       "      <td>0.992799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.699431</td>\n",
       "      <td>0.417403</td>\n",
       "      <td>-0.252942</td>\n",
       "      <td>0.999997</td>\n",
       "      <td>0.698341</td>\n",
       "      <td>0.411609</td>\n",
       "      <td>-0.224436</td>\n",
       "      <td>0.999993</td>\n",
       "      <td>0.700371</td>\n",
       "      <td>0.411068</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.014844</td>\n",
       "      <td>0.984556</td>\n",
       "      <td>0.506964</td>\n",
       "      <td>0.635688</td>\n",
       "      <td>0.209659</td>\n",
       "      <td>0.973772</td>\n",
       "      <td>0.760039</td>\n",
       "      <td>0.644365</td>\n",
       "      <td>-0.060036</td>\n",
       "      <td>0.994163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.712487</td>\n",
       "      <td>0.434327</td>\n",
       "      <td>-0.236811</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>0.712448</td>\n",
       "      <td>0.429119</td>\n",
       "      <td>-0.210992</td>\n",
       "      <td>0.999994</td>\n",
       "      <td>0.714422</td>\n",
       "      <td>0.428801</td>\n",
       "      <td>...</td>\n",
       "      <td>0.038764</td>\n",
       "      <td>0.987492</td>\n",
       "      <td>0.515272</td>\n",
       "      <td>0.637692</td>\n",
       "      <td>0.308790</td>\n",
       "      <td>0.975310</td>\n",
       "      <td>0.765709</td>\n",
       "      <td>0.644925</td>\n",
       "      <td>0.008045</td>\n",
       "      <td>0.995037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.736283</td>\n",
       "      <td>0.436861</td>\n",
       "      <td>-0.186044</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>0.734883</td>\n",
       "      <td>0.431174</td>\n",
       "      <td>-0.164725</td>\n",
       "      <td>0.999996</td>\n",
       "      <td>0.737211</td>\n",
       "      <td>0.430983</td>\n",
       "      <td>...</td>\n",
       "      <td>0.117459</td>\n",
       "      <td>0.992175</td>\n",
       "      <td>0.548238</td>\n",
       "      <td>0.638552</td>\n",
       "      <td>0.379620</td>\n",
       "      <td>0.976852</td>\n",
       "      <td>0.814310</td>\n",
       "      <td>0.645551</td>\n",
       "      <td>0.091234</td>\n",
       "      <td>0.997061</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 132 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              x1         y1         z1         v1         x2         y2  \\\n",
       "count  27.000000  27.000000  27.000000  27.000000  27.000000  27.000000   \n",
       "mean    0.692127   0.421696  -0.273542   0.999997   0.692291   0.416153   \n",
       "std     0.027891   0.011629   0.052063   0.000001   0.027458   0.011637   \n",
       "min     0.627164   0.406808  -0.377975   0.999994   0.627907   0.401091   \n",
       "25%     0.678724   0.411391  -0.303360   0.999996   0.679520   0.405931   \n",
       "50%     0.699431   0.417403  -0.252942   0.999997   0.698341   0.411609   \n",
       "75%     0.712487   0.434327  -0.236811   0.999998   0.712448   0.429119   \n",
       "max     0.736283   0.436861  -0.186044   0.999998   0.734883   0.431174   \n",
       "\n",
       "              z2         v2         x3         y3  ...        z31        v31  \\\n",
       "count  27.000000  27.000000  27.000000  27.000000  ...  27.000000  27.000000   \n",
       "mean   -0.248330   0.999992   0.694493   0.415934  ...  -0.013179   0.983891   \n",
       "std     0.053457   0.000003   0.027391   0.011530  ...   0.076125   0.005409   \n",
       "min    -0.356368   0.999984   0.630359   0.400877  ...  -0.129345   0.972483   \n",
       "25%    -0.278183   0.999991   0.681679   0.405957  ...  -0.084448   0.980138   \n",
       "50%    -0.224436   0.999993   0.700371   0.411068  ...  -0.014844   0.984556   \n",
       "75%    -0.210992   0.999994   0.714422   0.428801  ...   0.038764   0.987492   \n",
       "max    -0.164725   0.999996   0.737211   0.430983  ...   0.117459   0.992175   \n",
       "\n",
       "             x32        y32        z32        v32        x33        y33  \\\n",
       "count  27.000000  27.000000  27.000000  27.000000  27.000000  27.000000   \n",
       "mean    0.508975   0.635219   0.237127   0.973442   0.756973   0.642269   \n",
       "std     0.018120   0.002724   0.078998   0.002227   0.027957   0.003105   \n",
       "min     0.483020   0.630577   0.125726   0.969325   0.657481   0.637783   \n",
       "25%     0.497281   0.632799   0.171393   0.971851   0.750723   0.638731   \n",
       "50%     0.506964   0.635688   0.209659   0.973772   0.760039   0.644365   \n",
       "75%     0.515272   0.637692   0.308790   0.975310   0.765709   0.644925   \n",
       "max     0.548238   0.638552   0.379620   0.976852   0.814310   0.645551   \n",
       "\n",
       "             z33        v33  \n",
       "count  27.000000  27.000000  \n",
       "mean   -0.066039   0.994010  \n",
       "std     0.100129   0.001778  \n",
       "min    -0.226744   0.990605  \n",
       "25%    -0.168980   0.992799  \n",
       "50%    -0.060036   0.994163  \n",
       "75%     0.008045   0.995037  \n",
       "max     0.091234   0.997061  \n",
       "\n",
       "[8 rows x 132 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csv_results[csv_results['class'] == 'follow'].describe()#'x28', 'y28']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "a9e805b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Example data\n",
    "x_values = csv_results[csv_results['class'] == 'release']['x11']\n",
    "y_values = csv_results[csv_results['class'] == 'release']['y11']\n",
    "\n",
    "# Plot x and y values\n",
    "plt.plot(x_values, y_values, label='Data Points', marker='o', linestyle='-')\n",
    "\n",
    "# Customize the plot\n",
    "plt.title('X and Y Values Plot')\n",
    "plt.xlabel('X Values')\n",
    "plt.ylabel('Y Values')\n",
    "plt.legend()  # Show legend with label 'Data Points'\n",
    "plt.grid(True)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "faea0631",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "right_shoulder = csv_results[csv_results['class'] == 'release']['y11']#[['x11', 'y11', 'z11', 'v11']]\n",
    "\n",
    "# Calculate the mean and standard deviation\n",
    "mean_value = right_shoulder.mean()\n",
    "std_deviation = right_shoulder.std()\n",
    "\n",
    "# Plot the values and overlay the standard deviation\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Normalize the values using z-score\n",
    "normalized_right_shoulder = (right_shoulder - mean_value) / std_deviation\n",
    "\n",
    "# Plot the values\n",
    "right_shoulder.plot(label='Values', linewidth=2)\n",
    "\n",
    "# Plot the normalized values\n",
    "#normalized_right_shoulder.plot(label='Normalized Values', linewidth=2)\n",
    "\n",
    "# Overlay mean\n",
    "plt.axhline(y=mean_value, color='r', linestyle='--', label='Mean')\n",
    "\n",
    "# Overlay +/- one standard deviation\n",
    "plt.axhline(y=mean_value + std_deviation, color='g', linestyle='--', label='+1 Std Dev')\n",
    "plt.axhline(y=mean_value - std_deviation, color='g', linestyle='--', label='-1 Std Dev')\n",
    "\n",
    "# Customize the plot\n",
    "plt.title('Values vs. Standard Deviation')\n",
    "plt.xlabel('Index')\n",
    "plt.ylabel('Values')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "18ff8d6e",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "('x11', 'y11', 'z11', 'v11')",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3653\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3652\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 3653\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[0;32m   3654\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\_libs\\index.pyx:147\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\_libs\\index.pyx:176\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:7080\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:7088\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: ('x11', 'y11', 'z11', 'v11')",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[122], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m csv_results[csv_results[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mclass\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfollow\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mx11\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124my11\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mz11\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mv11\u001b[39m\u001b[38;5;124m'\u001b[39m]\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\frame.py:3761\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3759\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m   3760\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[1;32m-> 3761\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mget_loc(key)\n\u001b[0;32m   3762\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[0;32m   3763\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3655\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3653\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[0;32m   3654\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m-> 3655\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m   3656\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m   3657\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m   3658\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m   3659\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[0;32m   3660\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[1;31mKeyError\u001b[0m: ('x11', 'y11', 'z11', 'v11')"
     ]
    }
   ],
   "source": [
    "csv_results[csv_results['class'] == 'follow']['x11', 'y11', 'z11', 'v11']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "d99d9e8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Extract right_shoulder data\n",
    "right_shoulder = csv_results[csv_results['class'] == 'release']['y11']\n",
    "\n",
    "# Calculate the mean and standard deviation\n",
    "mean_value = right_shoulder.mean()\n",
    "std_deviation = right_shoulder.std()\n",
    "\n",
    "# Normalize the values using z-score\n",
    "normalized_right_shoulder = (right_shoulder - mean_value) / std_deviation\n",
    "\n",
    "# Plot the normalized values and overlay the standard deviation\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Plot the normalized values\n",
    "normalized_right_shoulder.plot(label='Normalized Values', linewidth=2)\n",
    "\n",
    "# Overlay mean\n",
    "plt.axhline(y=0, color='r', linestyle='--', label='Mean (normalized)')\n",
    "\n",
    "# Overlay +/- one standard deviation\n",
    "plt.axhline(y=1, color='g', linestyle='--', label='+1 Std Dev (normalized)')\n",
    "plt.axhline(y=-1, color='g', linestyle='--', label='-1 Std Dev (normalized)')\n",
    "\n",
    "# Customize the plot\n",
    "plt.title('Normalized Values vs. Standard Deviation')\n",
    "plt.xlabel('Index')\n",
    "plt.ylabel('Normalized Values')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "038fa269",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3TzfffLMqVaqk48eP648//lBgYKBeeeUVmc1mvfrqqxo6dKjuuusuPfTQQzpz5ozGjh1brEsEpdzLBN955x1NmjRJDRs2dLqHq2HDhqpTp46ee+45GYahypUr69tvv1V8fHyR9v3ggw+qX79+Gj58uO655x4dPHhQEydOVJUqVZy2GzFihObNm6cuXbroySefVPPmzWW325WQkKBFixbpqaeeUrt27fTyyy/r8OHDuuGGGxQTE6MzZ87ov//9rywWi7p27Vqs9w0ApYmABQAV2OjRo5Wenq5p06Zp4sSJaty4saZOnaqvvvrK0VzAZrPp/vvvl8lk0qeffiqzOff23euuu07jx4/XM888o27duunOO+8s9vEnTJig7777rkDDjq5du+rXX3/VmDFjNHDgQNntdrVo0ULffPON02WSpSnvw/uECRN07tw5tW7dWvPnz9eLL75Y7H1t3bpVUu7lfxcG37i4OMd8Wc8//7waN26s//73v5ozZ46ysrIUFRWla665RsOGDXO8ZsiQIZJyzwLefffdqlmzpl544QUtWbLEqb3+5bRq1UqtWrXShg0bCjRmsVgs+vbbb/XEE0/o4Ycflre3t2688Ub9/PPPhU6gfaG+ffvq6NGjmjp1qmbMmKGmTZtqypQpeuWVV5y2CwwM1LJly/T666/r/fff1/79++Xv768aNWroxhtvdMwT1q5dO61du1bPPvusTpw4obCwMLVt21a//vqrU6t7AHA3k2G4ebp7AAAAACgn6CIIAAAAAC5CwAIAAAAAFyFgAQAAAICLELAAAAAAwEUIWAAAAADgIgQsAAAAAHCRCjcPlt1u19GjRxUcHHxVJqoEAAAAUDYZhqHU1FRFR0c75nm8UhUuYB09elSxsbHuLgMAAABAGXHo0CHFxMS4ZF8VLmAFBwdLyv0ihoSElMoxrFarFi1apJ49e8pisZTKMeAajJVnYJw8B2PlORgrz8FYeQbGyXPkH6uMjAzFxsY6MoIrVLiAlXdZYEhISKkGrICAAIWEhPADVsYxVp6BcfIcjJXnYKw8B2PlGRgnz1HYWLny1iGaXAAAAACAixCwAAAAAMBFCFgAAAAA4CIV7h6sojAMQzk5ObLZbCV6vdVqlbe3tzIzM0u8D1wd5XGsvLy85O3tzTQEAAAAbkDAukB2drYSExOVnp5e4n0YhqGoqCgdOnSID7llXHkdq4CAAFWrVk0+Pj7uLgUAAKBCIWDlY7fbtX//fnl5eSk6Olo+Pj4l+tBtt9t17tw5BQUFuWzCMpSO8jZWhmEoOztbJ06c0P79+1WvXr1y8b4AAAA8BQErn+zsbNntdsXGxiogIKDE+7Hb7crOzpafnx8fbsu48jhW/v7+slgsOnjwoOO9AQAA4OooH58oXay8fNBGxcX3MAAAgHvwKQwAAAAAXISABQAAAAAuQsCCWy1evFgmk0lnzpyRJM2cOVNhYWGlesyBAwfqzjvvLNVjXI33AQAAgLKHgFVODBw4UCaTSa+//rrT8gULFnhU+/E+ffpo165dbjv+vHnz5OXlpYSEhELXN2zYUI8//vhVrgoAAACegoBVjvj5+emNN97Q6dOnXbrf7Oxsl+7vUvz9/VW1atWrdrwL3XHHHQoPD9esWbMKrFuxYoV27typIUOGuKEyAAAAeAICVjly4403KioqShMmTLjkdvPmzVOTJk3k6+urmjVr6t///rfT+po1a+q1117TwIEDFRoaqoceeshxydt3332nBg0aKCAgQPfee6/S0tI0a9Ys1axZU5UqVdJjjz0mm83m2Nfs2bPVtm1bBQcHKyoqSn379lVSUtJFa7vw0rqaNWvKZDIV+JfnyJEj6tOnjypVqqTw8HD17t1bBw4ccKy32WwaOXKkwsLCFB4erlGjRskwjIse32Kx6MEHH9TMmTMLbDd9+nS1adNGLVq00KRJk9SsWTMFBgYqNjZWw4cP17lz5y6638IuSxwxYoS6devmeG4YhiZOnKjatWvL399fLVq00JdffulYf/r0aT3wwAOqUqWK/P39Va9ePc2YMeOixwQAAMDV59Z5sJYuXap//etfWrdunRITE/XVV19d9t6YJUuWaOTIkdq6dauio6M1atQoDRs2rFTrvP3t5TqRmlWMVxiyG4bMJpOkkl+eVyXYV98+1qnI23t5eWn8+PHq27evHn/8ccXExBTYZt26dbrvvvs0duxY9enTR7///ruGDx+u8PBwDRw40LHdv/71L7300kt68cUXJUnLly9Xenq63nrrLX322WdKTU3V3XffrbvvvlthYWFauHCh9u3bp3vuuUedOnVSnz59JOWe/Xr11VfVoEEDJSUl6cknn9TAgQO1cOHCIr2nNWvWOAKbzWbTvffeK4vFIklKT09X9+7d1blzZy1dulTe3t567bXXdPPNN+vPP/+Uj4+P/v3vf2v69OmaNm2aGjdurH//+9/66quvdP3111/0mEOGDNGkSZO0ZMkSRwBKS0vT559/rokTJ0rKbYP+1ltvqWbNmtq/f7+GDx+uUaNG6d133y3S+yrMiy++qPnz52vKlCmqV6+eli5dqn79+qlKlSrq2rWrXnrpJW3btk0//PCDIiIitGfPHmVkZJT4eAAAAHA9twastLQ0tWjRQoMGDdI999xz2e3379+vW265RQ899JBmz56tFStWaPjw4apSpUqRXl9SJ1KzdCwls9T270p33XWXWrZsqTFjxmjatGkF1k+aNEk33HCDXnrpJUlS/fr1tW3bNv3rX/9yCljXX3+9nn76acfz5cuXy2q1asqUKapTp44k6d5779XHH3+s48ePKygoSI0bN1b37t3122+/OQLW4MGDHfuoXbu23nrrLV177bU6d+6cgoKCLvt+qlSp4nj8xBNPKDExUWvWrJEkffbZZzKbzfrwww8dZ7VmzJihsLAwLV68WD179tTkyZP1/PPPO74/pk6dqp9++umSx2zcuLHatWunGTNmOALW559/LpvNpvvvv19S7tmnPLVq1dKrr76qRx55pMQBKy0tTZMmTdKvv/6q9u3bS8r9ei1fvlzvvfeeunbtqoSEBLVq1Upt27aVlHt2DwAAAGWLWwNWr1691KtXryJvP3XqVNWoUUOTJ0+WJDVq1Ehr167Vm2++WaoBq0qwbzFf4bozWCXxxhtv6Prrr9dTTz1VYN327dvVu3dvp2UdO3bU5MmTZbPZ5OXlJUmOD/H5BQQEOMKVJEVGRqpmzZpOQSkyMtLpEsANGzZo7Nix2rhxo5KTk2W32yVJCQkJaty4cZHf0/vvv69p06ZpxYoVjtC1bt067dmzR8HBwU7bZmZmau/evTp79qwSExMdgUWSvL291bZt20teJijlnsUaMWKE/ve//yk4OFjTp093nK2TpN9++03jx4/Xtm3blJKSopycHGVmZiotLU2BgYFFfl95tm3bpszMTPXo0cNpeXZ2tlq1aiVJeuSRR3TPPfdo/fr16tmzp+6880516NCh2McCAABwF6vNrtvfXq7G1ULUrWFV3dEi2t0luZxbA1ZxrVy5Uj179nRadtNNN2natGmyWq2OS8fyy8rKUlbWX5f3paSkSJKsVqusVqvTtlarVYZhyG63O4KAJH39z+J9iDUMQ6mpqQoODr7iDn7567jcMfNq79Spk3r27Knnn39eAwYMcNpPXrDIv9+8S/Dsdruj3oCAAKdt7Ha7LBZLgXoKW2az2WS325WWlqaePXuqR48e+uijj1SlShUlJCSoV69eyszMdPo65z3O/zzP4sWL9dhjj+mTTz5Rs2bNHOtsNpvatGmjjz/+uMDXo0qVKgX2XdjXKu/rkfc8z3333acnn3xSc+bMUbdu3bR8+XKNHTtWdrtdBw8e1C233KKHH35Yr7zyiipXrqzly5froYceUlZWlvz9/Qu8D5PJVKCOvOYhdrtdOTk5kqRvv/1W1atXd3ovvr6+stvtuummm7R//359//33+uWXX3TDDTdo+PDh+te//lXg/ee9N6vV6gjNnizvZ/XCn1mUPYyV52CsPAdj5RkYp6LZfOSsdhxL1Y5jqcrOsalX4yqXf5GL5R+r0hgvjwpYx44dU2RkpNOyyMhI5eTk6OTJk6pWrVqB10yYMEGvvPJKgeWLFi1SQECA0zJvb29FRUXp3LlzLumcl5qaesX7KCqr1aqcnBxHgBw9erS6dOmiuLg4SX8Fy7p162rJkiV64oknHK9dvHix6tSpo7S0NEm5H84zMzMdr5FyzwoZhuG0LCsrSzabzWlZ/jo2btyokydP6oUXXnDcD7Zs2TJJuZfEpaSkKD09XVLu18psNhc4zr59+3Tvvfdq5MiRuvHGG52O1ahRI82dO1d+fn4KCQkp9OsSFRWlJUuWqGXLlpKknJwcrV27Vi1atHDaV2Fj1bt3b02bNk07duxQzZo11bp1a6WkpGjZsmXKycnRyy+/LLM5t09MXmONi72PkJAQ/fnnn07HXLdunSwWi1JSUhQTEyNfX1/t3LnTccYqv7zX+fr6Ou59a9u2rcaMGeO43DO/7OxsZWRkaOnSpY7wVh7Ex8e7uwQUEWPlORgrz8FYeQbG6dKWJpok5f7x1zf1iBYuPOy2WuLj4x2fRV3JowKWpAJnhPLOQFzsTNHzzz+vkSNHOp6npKQoNjZWPXv2LPChPDMzU4cOHVJQUJD8/PxKXKMrz2AVlcVikbe3t+M9tW/fXn379tUHH3wgSY7lzz77rNq1a6e33npL9913n1auXKkPP/xQ//vf/xzbmM3mAqHFz89PJpPJaZmvr6+8vLycluWvo1GjRvLx8dGsWbP08MMPa8uWLZo0aZIkKTAwUCEhIY6QGxwcrJCQEKfjZGRk6IEHHlCrVq306KOPOv0AREVFaciQIXrnnXc0YMAAjR07VjExMUpISNBXX32lp59+WjExMXriiSc0ceJENW3aVI0aNdJ//vMfpaSkOGq81Fg9/PDD6tq1q3bt2qWnnnpKoaGhkqSmTZsqJydHH330kW677TatWLFCM2fOvOj7kKSbb75Zb7/9thYsWKD27dvrk08+0Y4dO9SqVSuFhIQoJCRETz31lF588UX5+vqqU6dOSklJ0cqVKxUYGKgBAwZozJgxat26tZo0aaKsrCz98ssvatSoUaHhMjMzU/7+/urSpcsVfS+XFVarVfHx8erRo0ehZ6pRdjBWnoOx8hyMlWdgnIrm5y/+lHRMktT3pg5qHhN61WvIP1al0TDMowJWVFSUjh075rQsKSlJ3t7eCg8PL/Q1vr6+8vUteC+TxWIp8M1vs9lkMplkNpsdZyZKIv9lYVeyn+LIa1+e/3ivvfaavvjiC0lyLG/btq0+//xzvfzyy3rttddUrVo1jRs3zqkZRWG15z3OvywvkFy4LO+1kZGRmjlzpl544QW9/fbbat26td58803dcccdjq9x/v1e+PzEiRPasWOHduzYUaAjomEYCgoK0tKlS/Xss8/q3nvvVWpqqqpXr64bbrhBYWFhMpvNevrpp3Xs2DENHjxYZrNZgwcP1l133aWzZ8/KbDZfcqy6dOmiBg0aaPfu3Ro4cKBjfevWrTVp0iRNnDhRL7zwgrp06aIJEyaof//+hb4PKfd+w5deeknPPfecMjMzNXjwYPXv31+bN292bPPaa68pMjJSb7zxhh5++GGFhYWpdevWeuGFF2Q2m+Xr66vRo0frwIED8vf3V+fOnR2NPi5kNptlMpkK/T73ZOXt/ZRnjJXnYKw8B2PlGRinS9t0+PxVOd5mNYutLIu3+2aNslgspXKlj8m43N3+V4nJZLpsm/Znn31W3377rbZt2+ZY9sgjj2jjxo1auXJlkY6TkpKi0NBQnT17ttAzWPv371etWrWu6K/+drtdKSkpCgkJuWoBCyVTXsfKVd/LZYXVatXChQt1yy238D+tMo6x8hyMledgrDwD43R5J89lqe1rP0uSrqlZSV8Mc0+zrvxjlZGRcdFsUFJu/UR57tw5bdy4URs3bpSU24Z948aNSkhIkJR7eV///v0d2w8bNkwHDx7UyJEjtX37dsf8RvnbiQMAAAAoezYknHE8blWjkvsKKWVuvURw7dq16t69u+N53r1SAwYM0MyZM5WYmOgIW1LufEMLFy7Uk08+qXfeeUfR0dF66623SrVFOwAAAIArtz7htONx6xph7iuklLk1YHXr1u2S8xHlNQ7Ir2vXrlq/fn0pVgUAAADA1TbkC1jl+QxW+bnpBAAAAECZlGOza9Ohs5Kk6mH+igzx/HvEL4aAVYgy0vcDKDG+hwEAQFmy83iqMqw2SVLLcnx5oETAcpLX8aU0JhwDrqa872G6GAEAgLJgfb4GF63L8eWBkofNg1XavLy8FBYWpqSkJElSQEBAiSYKttvtys7OVmZmZrlq/V0elbexMgxD6enpSkpKUlhYmLy8vNxdEgAAwAX3X4W5r5CrgIB1gaioKElyhKySMAxDGRkZ8vf3L1FAw9VTXscqLCzM8b0MAADgbnkt2n28zGoS7Zr5psoqAtYFTCaTqlWrpqpVq8pqtZZoH1arVUuXLlWXLl24RKuMK49jZbFYOHMFAADKjNNp2dp/Mk2S1KR6iHy9y/fnFALWRXh5eZX4Q6qXl5dycnLk5+dXbj60l1eMFQAAQOnacCj//Ffl+/4riSYXAAAAAErRhnwNLsr7/VcSAQsAAABAKVqfwBksAAAAALhiNrvhmGA4MsRX1ULL7wTDeQhYAAAAAErF7qRUncvKkZR79qo8dW2+GAIWAAAAgFJR0e6/kghYAAAAAErJ+oMV6/4riYAFAAAAoJRsOHRGkuRtNqlp9VD3FnOVELAAAAAAuNzZdKv2JJ2TJDWJDpGfpXxPMJyHgAUAAADA5TYePuN43KqCXB4oEbAAAAAAlIL8919VlAYXEgELAAAAQCnIu/9KqjgNLiQCFgAAAAAXs9sNbUjIPYMVEeSrmEr+bq7o6iFgAQAAAHCpfSfPKTUzd4LhVjXCKsQEw3kIWAAAAABcav3BM47HFenyQImABQAAAMDFNhyqmA0uJAIWAAAAABfLO4PlZTapeUzFmGA4DwELAAAAgMukZlq1KylVktQwKlgBPt5urujqImABAAAAcJlNh87KMHIfV7T7ryQCFgAAAAAXymvPLlW8+68kAhYAAAAAF1qfL2BxBgsAAAAASsgwDG04dEaSVDnQR3HhAe4tyA0IWAAAAABcYv/JNJ1Jt0qSWsVWrAmG8xCwAAAAALjEhoQzjscV8f4riYAFAAAAwEUq+v1XEgELAAAAgIvkncEym6TmsWFurcVdCFgAAAAArlhaVo52HEuRJNWPDFaQb8WaYDgPAQsAAADAFfvz8FnZz08w3KqCXh4oEbAAAAAAuIDz/Vdh7ivEzQhYAAAAAK6YcwdBzmABAAAAQIkYhqEN589ghfpbVDsi0M0VuQ8BCwAAAMAVOZScoVNp2ZKklrFhMpsr3gTDeQhYAAAAAK4I81/9hYAFAAAA4IpsyBewWlXgBhcSAQsAAADAFVp/vsGFySS1JGABAAAAQMmcTsvWlqNnJUn1qwYrxM/i5orci4AFAAAAoMSW7j4h4/wEw10bVHFvMWUAAQsAAABAiS3ZdcLxuFt9AhYBCwAAAECJ2O2Glp4PWAE+XmpTs2J3EJQIWAAAAABKaOvRFJ08lzv/VYc6EfL19nJzRe5HwAIAAABQIkt2JTkec/9VLgIWAAAAgBJZvJP7ry5EwAIAAABQbGfTrVp/foLhOlUCFVs5wM0VlQ0ELAAAAADFtnzPSdnz2rPXr+reYsoQAhYAAACAYlu886/7r7px/5UDAQsAAABAsRiG4Zj/ys9i1rW1Kru5orKDgAUAAACgWLYnpiopNUuS1L52uPwstGfPQ8ACAAAAUCx5Z68kqVsD7r/Kj4AFAAAAoFjy33/VlfbsTghYAAAAAIosNdOqdQdz27PXDA9QzYhAN1dUthCwAAAAABTZij2nlHO+PzuXBxZEwAIAAABQZEt2cXngpRCwAAAAABSJYRhasjO3wYWPt1nX1Q53c0VlDwELAAAAQJHsTjqno2czJUnX1Q6Xvw/t2S9EwAIAAABQJHQPvDwCFgAAAIAicZ7/ioBVGAIWAAAAgMtKy8rRmv257dljKvmrNu3ZC0XAAgAAAHBZK/eeUrbNLin37JXJZHJzRWUTAQsAAADAZS3O1569W33mv7oYAhYAAACASzIMQ4vz2rN7mdW+Du3ZL4aABQAAAOCS9p1M0+HTGZKka2pVUqCvt5srKrsIWAAAAAAuKe/slcTlgZdDwAIAAABwSfnbs3elPfslEbAAAAAAXFRGtk2r9p2SJEWH+qle1SA3V1S2EbAAAAAAXNSqfaeUnZPbnr0r7dkvi4AFAAAA4KKcLg/k/qvLImABAAAAuKjFO3Pnv/I2m9SxLu3ZL8ftAevdd99VrVq15OfnpzZt2mjZsmWX3P6TTz5RixYtFBAQoGrVqmnQoEE6derUVaoWAAAAqDgOnEzTgVPpkqQ2cZUU7Gdxc0Vln1sD1ty5czVixAiNHj1aGzZsUOfOndWrVy8lJCQUuv3y5cvVv39/DRkyRFu3btUXX3yhNWvWaOjQoVe5cgAAAKD8y395YLcGXB5YFG4NWJMmTdKQIUM0dOhQNWrUSJMnT1ZsbKymTJlS6ParVq1SzZo19fjjj6tWrVrq1KmTHn74Ya1du/YqVw4AAACUf3mXB0pSN9qzF4nbpmDOzs7WunXr9Nxzzzkt79mzp37//fdCX9OhQweNHj1aCxcuVK9evZSUlKQvv/xSt95660WPk5WVpaysLMfzlJQUSZLVapXVanXBOykob7+ltX+4DmPlGRgnz8FYeQ7GynMwVp6hPI5TltWmlefbs0cG+6pOuF+5eH/5x6o03o/JMAzD5XstgqNHj6p69epasWKFOnTo4Fg+fvx4zZo1Szt37iz0dV9++aUGDRqkzMxM5eTk6I477tCXX34pi6Xw60HHjh2rV155pcDyTz/9VAEBAa55MwAAAEA5s+OMSVO2e0mS2lWxq29du5srcr309HT17dtXZ8+eVUhIiEv26bYzWHku7KNvGMZFe+tv27ZNjz/+uF5++WXddNNNSkxM1DPPPKNhw4Zp2rRphb7m+eef18iRIx3PU1JSFBsbq549e7rsi3ghq9Wq+Ph49ejR46LBD2UDY+UZGCfPwVh5DsbKczBWnqE8jtMf326XdEiS9MD1LdWraZR7C3KR/GOVkZHh8v27LWBFRETIy8tLx44dc1qelJSkyMjIQl8zYcIEdezYUc8884wkqXnz5goMDFTnzp312muvqVq1agVe4+vrK19f3wLLLRZLqX/zX41jwDUYK8/AOHkOxspzMFaeg7HyDOVlnDKtNn37Z6Ikyc9iVvdGUeXifeVnsViUk5Pj8v26rcmFj4+P2rRpo/j4eKfl8fHxTpcM5peeni6z2blkL6/c05ZuutIRAAAAKHd+2npMKZm54eOWZtVoz14Mbu0iOHLkSH344YeaPn26tm/frieffFIJCQkaNmyYpNzL+/r37+/Y/vbbb9f8+fM1ZcoU7du3TytWrNDjjz+ua6+9VtHR0e56GwAAAEC58vnaQ47HfdrGurESz+PWe7D69OmjU6dOady4cUpMTFTTpk21cOFCxcXFSZISExOd5sQaOHCgUlNT9b///U9PPfWUwsLCdP311+uNN95w11sAAAAAypVDyelasSe3e2DN8ABdW6uymyvyLG5vcjF8+HANHz680HUzZ84ssOyxxx7TY489VspVAQAAABXTF+sOOx7/rW3sRRvQoXBuvUQQAAAAQNlhsxv68vzlgWaTdG+bGDdX5HkIWAAAAAAkScv3nNTRs5mSpG4NqioyxM/NFXkeAhYAAAAASc7NLe6juUWJELAAAAAA6HRatuK3HpckRQT56IZGVd1ckWciYAEAAADQgo1HlG2zS5LualVdFi+iQknwVQMAAAAqOMMwNHdNvrmvruHywJIiYAEAAAAV3OYjZ7XjWKokqXWNMNWtGuzmijwXAQsAAACo4Ghu4ToELAAAAKACy7Ta9PXGo5Ikf4uXbmsR7eaKPBsBCwAAAKjAftiSqNTMHEnSrc2rKcjX280VeTYCFgAAAFCBfb7msOMxzS2uHAELAAAAqKAOnkrTyn2nJEm1IwLVNq6SmyvyfAQsAAAAoIL6Yu1fZ6/+1jZWJpPJjdWUD1xgCQAAcAUyrTYdSk7XwVPpstrs6t6wqvwsXu4uC7gsm93Ql+tyA5aX2aR72lR3c0XlAwELAADgEgzD0Km0bB08le4IUgnJ6UpITlNCcrqOp2Q5bX9P6xj9+74WbqoWKLqlu0/oWEqmJKl7g6qqGuzn5orKBwIWAACo8LJz7Dp6JkMHk8+Hp1O54SkvVKVl24q8r/kbDuuRbrWZqBVl3udr8s99FePGSsoXAhYAAKgQzmZYlXD+7NPB5DSns1FHz2TIbhR/nxFBPqpROUA1KgcoLdum+G3HZRjS/37do8l/b+X6NwG4yKlzWfp5+3FJUkSQr7o3rOrmisoPAhYAACgXbHZDiWczzp+BygtSf13WdzbDWux9eptNiqnkrxrhgYo7H6RqhAc4QlVgvvmC0rJy1Hnib0pOy9Y3m47q8RvqqXaVIFe+RcBlvtpwRFZb7l8V7mlTXRYvet+5CgELAAB4jPTsHKcAlZDvLNTh0+mOD4zFEeLnrbjwQKfwFFc5QLGVAxQd5i8vc9G6qgX6emto51qa+ONO2Q3pnd/2ci8WyiTDMPT52r8uD/xbG+a+ciUCFgAAKDMMw9CJ1Cyn4JQ/SJ08l3X5nVzAbJKqhfrnBqfw3OAU5whSgQoNsLis/v7ta+r9pft0Jt2qBRuP6PEb6iouPNBl+wdcYdPhs9p1/JwkqW1cJdWtyplWVyJgAQCAqyorx6bDpzP+uozvgq58mVZ7sffpb/H6KzxdcBlfTKUA+Xhfncufgny9NaRjLf07fpdsdkPv/LZHE+/lLBbKlrn5m1tcw9krVyNgAQAAlzIMQ2fSrRfcA5XmuLQvMSVTRgkaSlQN9nW+jM8RogIVEeRTZiZIHdCxpt5ftk+pmTmav/6IHru+nmIrB7i7LECSdPBUmr7ddFSSFOjjpVubVXNzReUPAQsAABRbjs2uxLOZjrNPF3blS83MKfY+fbzMiqnsn6+ZROBfl/VVCpC/j2dM3hviZ9HgjrX03192K8du6N3FezXh7mbuLgvQN5uO6oX5m3UuK/fn87bm0U6NWuAafEUBAEChzmXl6OCptAsm1839d+R0hnJK0Ne8UoAlX3jyV1zlQMc9UVEhfjIXsaFEWTe4Yy1NX75fqVk5+nLdIT16fV1VD/N3d1mooDKybRr33VbN+eOvSwNrRQRqZM/6bqyq/CJgAQBQQdkN6VhKpo6eTSnQTCIhOV3JadnF3qeX2aToMD+n4FQjX3vzED/XNZQoy0IDLBrYsabe/nWPrDZDUxfv1at3NnV3WaiAdh9P1T8/Xe9oaiFJd7WqrlfvbKogzl6VCr6qAACUY5lWmw4lX9hM4vw9USe9ZF21tNj7DPL1doSmC7vyRYf5M5/OeXlnsdKybZq75pCGd6+jaqGcxcLVYRiGvlh7WC9/s8XROMbf4qVxvZvo3jYxZeaexfKIgAUAgAczDEOn0rKdJ9c9db6xRHKajqdcqq35xT9gRYX4Oc0Jlb8rX+XAstNQoiyrFOij/h1qasrivcq22fXekn0ae0cTd5eFCiA106oXF2zR1xuPOpY1jArW//q2Ut2qwW6srGIgYAEAUMZZbXYdOZ1RSFe+DCWcSlNatq3Y+/T1NquSxaZGNaqoZkSQU1e+mEoB8rN4RkOJsm5op1qaueKAMqw2ffpHgoZ3q6OqIX7uLgvl2JYjZ/Xop+t14FS6Y9kD7Wropdsa83N9lRCwAAAoA85mWC9oJpHmOBt19EyGStBPQuGBPqoRHlBoV74wX7N+/PEH3XJLa1ksFeO+KHcID/LVg+3j9P7SfcrOseu9pfv00m2N3V0WyiHDMDTr9wMav3CHsm25lwQG+3rr9Xua69bmtGK/mghYAABcBTa7oWMpmRftyncm3VrsfXqbTYqp5H9BM4lAR0OJS93AbrUW/3gomYc619as3w8oK8euT1Yf1LCudVQl2NfdZcGDWG12JaVm6djZzNx/KZk6djZDx1Kyzv83U8dTspSd89ck3S1iQvX2/a1VI5w52K42AhYAAC6Snp2jQ8kZf02qm/fvVLoOn85w/FW5OIL9vBUXHlBoV75qoX7ypqFEmVcl2FcPtIvT9BX7lWm168Nl+/T8LY3cXRaukrw54w6dTtfh5AwdPp2uQ6czlJyWrRy7XdYcQ1a7XTk2Q1abXVabXTl2w/E822bX2QxrsSbnfqhzLT1zU0P5ePP7wR0IWAAAFJFhGDpxLquQZhK5z0+kXqqhROFMJik61F+x5+eEcjSWOP/fsACfUngnuNqGda2t2asPKjvHro9WHtQ/utRWeBBnscoLm93QzmOpWnvCpH2/7VViSpYOJWfo0Ol0JZ7NlK0k1/gWQ1iARVEhfoqp5K9+18WpW4OqpXo8XBoBCwCAfLJybDpyOiNfM4m/glRCcroyrMVvKOFv8XJctufU2rxygKpX8pevNzeel3dVQ/zU99oamvl7bsOLD5fv17M3N3R3WSgBwzCUkJyuTYfP6s9DZ/Tn4bPafOTs+d8NXtKevSXar5fZJG+zST5eZnl7meTtZXY8DgvwUVSIr6qF+isyxE/VQv2c/uvvw++QsoSABQCoUAzD0NkMq/M9UKdyW5ofSs7Q0bMZxboUJ0+VYN98zSScg1SVIF/amkMPd62tT1cnKNtm10e/H9A/OtdWpUDOUJZ1J1KztPHQGW06dEabDp/R5iNni3zPZIift2IrByi2UoDjfsnYyv6KqRSgqsG+spwPUBazWWYzvyPKCwIWAKDcybvnIeEiXflSM3OKvU+Ll0mxlZzng8oNUYGKreyvAB/+l4pLqxbqr/uuidHsVQlKy7Zp+or9eqpnA3eXhQsYhqHdSee0aOsxLdp2XH8ePnvZ11QP81ez6iGypB7VzR1bq2aVYMVUClCoPx06KyL+bwAA8EjnsnIc90LlD08Jyek6cjpDOSW45yEswKK4ygEFu/KFBygqxE9e/IUZV+iRbnU1d80hWW2GZq44oKGdais0gA/h7mazG1qfcFqLth5T/LbjTnNIXSg80EctYsPUPCZULWLC1CwmVBFBvrJarVq48Ih6No5k6oMKjoAFACiT7HZDSalZ54NTmlMziYRT6TqVll3sfZpNUvVK/k7tzPOCVGxl/tqM0lc9zF/3tonRnD8OKTUrR7NWHtDjN9Rzd1kVUqbVphV7TmrR1uP6efvxi/5OaRIdok71ItQiJjdUVQ/z55JfXBIBCwDgNplWmw6f/uvMU/6ufIeS05WVU/y25oE+Xucn1PU/f/neXxPtVq/kLwttzeFmw7vV1edrD8tmNzTz9wN6qHNtmhSUsrSsHO1JOqedx1O1+3iqdh4/p7UHkpWeXbBpjZfZpGtrVlbPJpHq0ThSMZWYRwrFQ8ACAJQawzCUnJZ9QTOJdMelfcdSMku036gQv4t25asc6MNfl1GmxVYO0G3Nq+nrjUeVnJatuWsSNLBjLXeXVS5kZNu098Q57Tqeej5M5T4+fDrjkq/zt3ipa/0q6tkkUtc3rMr0CLgiBCwAwBWx2uw6eiZDB0+la/+JVC05YNb3czbq0OlMHUpO17ms4jeU8PE25wanC+6HigsPUEylAPlZ+Gs/PNuwrnX09cajkqQPlu3XA9fFcXb1Cqw7eFrv/rZHv+1MUlFvvwwP9NENjaqqZ+ModaoXwe8VuAwBCwBwWSmZVqfJdfM3ljh65sJJNM1SYtJl9xke6HNBM4m/uvJVDfalZTHKtUbVQtS9QRX9tvOEjpzJ0Lebjuru1jHuLsujGIahFXtO6X+/7daqfckX3S7I11t1qwapQWSw6kUGqX5ksOpHBisyhOkTUDoIWAAA2e2GjqVk5rsHKk0JyRlKOJWmg8npRZ7zJT9vsylfQwnnrnyxlf0V7EdDCVRsw7vX1W87T0iSpi7ZqztbVucPC0Vgtxv6eftxvbN4rzYdOuO0rlqonzrUiVD9yCDVj8oNUtGhfgQpXFUELACoIDKybY57oS7sync4OUPZtuI3lAj283YKTtVDfZW4Z7PuvbmraoQHy5tLnoCLuqZmZbWNq6S1B09r1/Fz+nVHkm5sHOnussqsHJtd329O1Lu/7dXO46lO62pFBOqRbnV0Z8vq8vHm9w7ci4AFAOWEYRg6cS4rNzid+qupRML5IHUiNavY+zSZpGohfvmaSTh35QsLsDj9ZdhqtWrhiT8VWymAcAUUwSPd6mjIrLWSpHcX79ENjapytuUCWTk2zV9/RFOX7NXBC+analQtRP/sXke9mlZjnjqUGQQsAPAg2Tl2HT6dftGufBnWgi2HL8fPYlZc5UDn+6HO/zemkr98vbnxGygt3RtUVYPIYO08nqr1CWe05sBpXVursrvLcruz6Vb9vveklu4+qV+2H1fSBX8gal0jTI9eX1fdGxBIUfYQsACgjDmTnu3cTOLUX4Hq6NkMGUXskJVfRJBvIc0kcoNUlSBu9AbcxWw2aVi32npy7iZJ0pTFe3RtrWvdXNXVl51j14aE01q+JzdUbT58ptBugJ3qRmh49zpqXzuc31soswhYAHCV5djsSjybme9+qHyNJU6lKyWz+G3NLV4mxVYKKNiV7/zjAB9+3QNl1W3No/XmT7t05EyGftt5QtsTU9SoWoi7yypVhmFo74k0Ldt9Qst3n9TKfacKnfRXkny8zOrWoIoe6VZHrWpUusqVAsXH/3EBoBSkZeUUDE/nu/IdPp2hnKJO1JJPWIClwBmo2PP3RUWF+HH/AeChLF5m/aNLbY35ZqskacrivXrr/lZurqr0/LE/WS8u2Kxdx89ddJsGkcHqVC9CnepFqF2tyvyRCB6F71YAKAG7PbehxF+X8aU5mkkcSk7XyXPZxd6n2SRFh/k7h6fKgY7Hof60NQfKq/vaxuqtX3brVFq2vvvzqJ7u2UA1wgPcXZZLncvK0Rs/7NDHqw4WWBcR5KtOdcPVuV4VdaoXocgQPzdUCLgGAQsALiLTanM0lCjsfqisnOK3NQ/w8XKeEyo8MPd55QBFh/nTXhiooPx9vDSoY029uWiX7Ib0/rK9eu3OZu4uy2UW70zS6K+26MiZDMeyZtVDdXuLaupcr4oaRgVzTxXKDQIWgArLMAydTrfq4PmzT/lbmh9KTtexlMwSNZSIDPG9aFe+8EAfPkQAKNSD19XUlMV7lZZt0+drD+uJG+qrSrCvu8u6ImfSszXuu22av/6IY5m/xUvP3NRAAzrU5NJmlEsELAAV0ubDZ/Xwx2t19GxmsV/r421WbCV/xZ0/+3ThPVF+FtqaAyi+0ACLHrguTu8v3afsHLtmrNivUTc3dHdZJbZwc6Je/nqL0yXTHeuG6/W7myu2cvm6/BHIj4AFoEJ669fdlwxXlQN9CjaTOH8mKjLYT2b+6gqgFAzpVEszVxxQts2uj1ce1LBudeTvYX+zSUrJ1Mtfb9WPW485lgX7eevFWxvpvraxnMVHuUfAAlDhpGXlaOmuE5Jy/6d/e4toxeULUjUqByjYj4YSAK6+yBA/3d26uj5bc0ipWTn6dHWChnSo4bZ6MrJtmr/hsDKtdvlbvOTvY5a/xVv+Pl65zy1euY/PP/9l+3G9+t02p+kmbmwUqf+7qymNK1BhELAAVDhLdp1wNKi4o0W0/u+u8nMjOQDP948utTV37SEZhjRt+X71u6a6W+rItNo0YMYf+mN/coleHx7oo7F3NNFtzatx1goVCu2qAFQ4P2z567KVm5tGubESACiodpUg9Tr/u+lEapa+2ph41Wuw2uwa/sn6EoerO1tGK35kV93eIppwhQqHM1gAKpRMq02/bj8uSQr1t+i62uFurggACnqka10t3Jz7x6APlu/Xk/Wv3rHtdkNPf7FJv+5IkiQF+nhp9K2N5W02KcNqU3q2TRlWmzKtNqVn5ygj264Ma44ysm3y8TarzzWxur5h5NUrGChjih2wZs2apYiICN16662SpFGjRun9999X48aNNWfOHMXFxbm8SABwlRV7Tiot2yYp974Aixcn8gGUPc1iQtW5XoSW7T6phOQMbTpl0m1X4biGYejlb7bo641HJeV2Tf1gQFt1qBNxFY4OlA/F/mQxfvx4+fv7S5JWrlyp//3vf5o4caIiIiL05JNPurxAAHClH/NdHtiLywMBlGGPdK3jePzVAbMm/bxb6w6els1eggn6iujNRTs1e1WCJMnLbNK7fVsTroBiKvYZrEOHDqlu3bqSpAULFujee+/VP/7xD3Xs2FHdunVzdX0A4DJWm13x5y8PDPTxUqd6fGgAUHa1rxOuFjGh2nT4rM5aTZqyZL+mLNmvyoE+6lq/iro3rKqu9aooNMA1XU/fW7JX7/y21/H8zb81142NudQPKK5in8EKCgrSqVOnJEmLFi3SjTfeKEny8/NTRkaGa6sDABf6Y3+yzqRbJUndG1ZlQmAAZZrJZNL4u5upeUyI0/LktGx9teGIHp+zQa1fi9d9763U1CV7tet4qgyjZGe35vyRoAk/7HA8H9e7ie5qFXNF9QMVVbHPYPXo0UNDhw5Vq1attGvXLse9WFu3blXNmjVdXR8AuMyPdA8E4GGaRIdq3sPXac6ChfKObaGlu09p2e4TjntJbXZDf+xP1h/7k/X6DzsUU8lftzavpjtbVlejaiGX2Xuu7/48qhe+2ux4/lSP+urfvmZpvB2gQih2wHrnnXf04osv6tChQ5o3b57Cw3M7cK1bt07333+/ywsEAFew2w39tDU3YPl4m9WtQVU3VwQARRfqI93Sprr6XldT2Tl2rTmQrF93JOm3HUnadzLNsd3h0xl6b8k+vbdknxpEBuvOVtV1R8toVQ/zL3S/i3cm6cm5G5V34uuhzrX06PV1r8ZbAsqtYgessLAw/e9//yuw/JVXXnFJQQBQGjYcOq2k1CxJUpd6VRTkyywVADyTj7dZHetGqGPdCL10W2MdOJmmX3ck6dcdSVq175RyzjfB2Hk8VW/8uENv/LhD19aqrLtaVdctTas57tlacyBZw2avk9WWu32ftrF64ZZGzFsFXKESfcJYtmyZ3nvvPe3bt09ffPGFqlevro8//li1atVSp06dXF0jAFyxHzZzeSCA8qlmRKAGd6qlwZ1qKTktW9//eVQLNh7VuoOnHdvkXUY45uut6tagijrXr6KJP+5QptUuSbqlWZTG392McAW4QLGbXMybN0833XST/P39tX79emVl5f5FODU1VePHj3d5gQBwpQzD0I/nLw/0Npt0YyMuDwRQPlUO9NGD7Wtq3iMdtPSZ7nqqR33VrhLoWJ9ts2vRtuN6acEWpWbmSJI614vQf/q0lJeZcAW4QrED1muvvaapU6fqgw8+kMXyV1vQDh06aP369S4tDgBcYevRFB0+ndvltH2dcIUF+Li5IgAofTXCA/TYDfX0y8iu+vbRThrSqZaqBPs6bdMmrpLee7CNfL3pqgq4SrEvEdy5c6e6dOlSYHlISIjOnDnjipoAwKXoHgigIjOZTGoWE6pmMaF64ZZG+n3vSS3cnChvs1lP39RAAT7ckwq4UrF/oqpVq6Y9e/YUaMm+fPly1a5d21V1AYDL5F0eaDJJPZg0E0AF5mU2qXO9Kupcr4q7SwHKrWJfIvjwww/riSee0OrVq2UymXT06FF98sknevrppzV8+PDSqBEASmxPUqr2JJ2TJLWNq6SqwX5urggAAJRnxT6DNWrUKJ09e1bdu3dXZmamunTpIl9fXz399NN69NFHS6NGACgx58sDq7mxEgAAUBGU6KLb//u//9Po0aO1bds22e12NW7cWEFBQa6uDQCu2A/5AtZNTbg8EAAAlK4S39UYEBCgtm3burIWAHCpQ8np2no0RZLUPCZUMZUC3FwRAAAo74p9D1b37t11/fXXX/Rfcb377ruqVauW/Pz81KZNGy1btuyS22dlZWn06NGKi4uTr6+v6tSpo+nTpxf7uADKvx+dzl7RPRAAAJS+Yp/BatmypdNzq9WqjRs3asuWLRowYECx9jV37lyNGDFC7777rjp27Kj33ntPvXr10rZt21SjRo1CX3Pffffp+PHjmjZtmurWraukpCTl5OQU920AqADyugdKUi/aswMAgKug2AHrP//5T6HLx44dq3PnzhVrX5MmTdKQIUM0dOhQSdLkyZP1008/acqUKZowYUKB7X/88UctWbJE+/btU+XKlSWpQLt4AJCkpJRMrTt4WpJUPzJItatwnygAACh9LptZrl+/frr22mv15ptvFmn77OxsrVu3Ts8995zT8p49e+r3338v9DXffPON2rZtq4kTJ+rjjz9WYGCg7rjjDr366qvy9/cv9DVZWVnKyspyPE9Jyb0fw2q1ymq1FqnW4srbb2ntH67DWHmGkozTwj+POB73bFSVMb5K+JnyHIyV52CsPAPj5Dnyj1VpjJfLAtbKlSvl51f0+WVOnjwpm82myEjnrl6RkZE6duxYoa/Zt2+fli9fLj8/P3311Vc6efKkhg8fruTk5IvehzVhwgS98sorBZYvWrRIAQGle8N7fHx8qe4frsNYeYbijNOn28zKu800IHmXFi7cVUpVoTD8THkOxspzMFaegXHyHPHx8UpPT3f5fosdsO6++26n54ZhKDExUWvXrtVLL71U7AJMJlOB/V24LI/dbpfJZNInn3yi0NBQSbmXGd5777165513Cj2L9fzzz2vkyJGO5ykpKYqNjVXPnj0VEhJS7HqLwmq1Kj4+Xj169JDFYimVY8A1GCvPUNxxOp2erZGrl0gyFFvJX0Pv7XTR3ytwLX6mPAdj5TkYK8/AOHmO/GOVkZHh8v0XO2DlBZs8ZrNZDRo00Lhx49SzZ88i7yciIkJeXl4FzlYlJSUVOKuVp1q1aqpevbpTDY0aNZJhGDp8+LDq1atX4DW+vr7y9fUtsNxisZT6N//VOAZcg7HyDEUdp8W7j8lmNyRJtzSrJh8fn9IuDRfgZ8pzMFaeg7HyDIyT57BYLKXSLK/YAWvGjBkuObCPj4/atGmj+Ph43XXXXY7l8fHx6t27d6Gv6dixo7744gudO3fOMbHxrl27ZDabFRMT45K6AHg+p/bsdA8EAABXUbHnwXKlkSNH6sMPP9T06dO1fft2Pfnkk0pISNCwYcMk5V7e179/f8f2ffv2VXh4uAYNGqRt27Zp6dKleuaZZzR48OCLNrkAULGkZlq1fPdJSVJUiJ9axoS5tyAAAFChFOkMVqVKlYp8/0JycnKRD96nTx+dOnVK48aNU2Jiopo2baqFCxcqLi5OkpSYmKiEhATH9kFBQYqPj9djjz2mtm3bKjw8XPfdd59ee+21Ih8TQPn2284TyrbZJUk3NYmU2cy9VwAA4OopUsCaPHlyqRUwfPhwDR8+vNB1M2fOLLCsYcOGdGcBcFE/bkl0PL65aTU3VgIAACqiIgWsAQMGlHYdAHDFMq02/bbjhCSpcqCPrqlZyc0VAQCAiuaK5sHKyMgoMDlXabU+B4DLWb0/WRlWmySpR6NIeXu59TZTAABQARX700daWpoeffRRVa1aVUFBQapUqZLTPwBwl02Hzjged6gb7r5CAABAhVXsgDVq1Cj9+uuvevfdd+Xr66sPP/xQr7zyiqKjo/XRRx+VRo0AUCT5A1bL2DC31QEAACquYl8i+O233+qjjz5St27dNHjwYHXu3Fl169ZVXFycPvnkEz3wwAOlUScAXJJhGNp0+IwkKSzAohqVA9xbEAAAqJCKfQYrOTlZtWrVkpR7v1VeW/ZOnTpp6dKlrq0OAIroyJkMnTyXLUlqERNW5KklAAAAXKnYAat27do6cOCAJKlx48b6/PPPJeWe2QoLC3NlbQBQZJsOnXU8bhET6sZKAABARVbsgDVo0CBt2rRJkvT888877sV68skn9cwzz7i8QAAoirzLAyWpBfdfAQAANynyPVgjRozQ0KFD9eSTTzqWde/eXTt27NDatWtVp04dtWjRolSKBIDL2ZivwUXzmDC31QEAACq2Ip/B+vHHH9WiRQtde+21ev/995WSkiJJqlGjhu6++27CFQC3ybHZtflw7iWC1cP8VSXY180VAQCAiqrIAWvHjh1aunSpmjVrpqefflrR0dHq378/jS0AuN2eE+ccEwzTnh0AALhTse7B6tixo6ZNm6Zjx47p7bff1oEDB9StWzfVq1dPr7/+uo4ePVpadQLAReWf/6pFLA0uAACA+xS7yYUkBQQEaNCgQVq6dKl2796t++67TxMnTlTNmjVdXB4AXN5Gpw6CYe4rBAAAVHglClh50tLStGTJEi1ZskRnzpxRnTp1XFUXABRZ3hkss0lqWp0zWAAAwH1KFLCWLl2qQYMGKSoqSk888YTq16+vZcuWafv27a6uDwAuKSPbpp3HUyVJ9SODFehb5OaoAAAALlfkTyKHDx/WrFmzNHPmTO3du1ft2rXTf/7zH/39739XUFBQadYIABe19ehZ2eyGJC4PBAAA7lfkgFWzZk2Fh4frwQcf1JAhQ9SoUaPSrAsAimSjU4OLMLfVAQAAIBUjYH3++ee644475O3N5TcAyo5Nh/M1uKCDIAAAcLMip6W77767NOsAgBLJa3DhZzGrfmSwe4sBAAAV3hV1EQQAd0pOy1ZCcrokqWl0qCxe/EoDAADuxacRAB5r0+EzjsfcfwUAAMoCAhYAj7WJBhcAAKCMIWAB8Fj5A1ZLWrQDAIAyoEhNLorT4GL+/PklLgYAisowDEcHwUoBFsVW9ndzRQAAAEU8gxUaGur4FxISol9++UVr1651rF+3bp1++eUXhYbSIhnA1XH4dIaS07Il5V4eaDKZ3FwRAABAEc9gzZgxw/H42Wef1X333aepU6fKy8tLkmSz2TR8+HCFhISUTpUAcAGnCYa5PBAAAJQRxb4Ha/r06Xr66acd4UqSvLy8NHLkSE2fPt2lxQHAxfyZr4NgSxpcAACAMqLYASsnJ0fbt28vsHz79u2y2+0uKQoALmfTobOOx81juDwZAACUDUW6RDC/QYMGafDgwdqzZ4+uu+46SdKqVav0+uuva9CgQS4vEAAulGOza/OR3IAVW9lf4UG+bq4IAAAgV7ED1ptvvqmoqCj95z//UWJioiSpWrVqGjVqlJ566imXFwgAF9qddE4ZVpskqTn3XwEAgDKk2AHLbDZr1KhRGjVqlFJSUiSJ5hYArirmvwIAAGVViSYazsnJ0c8//6w5c+Y4WiMfPXpU586dc2lxAFCYTfkaXLSgwQUAAChDin0G6+DBg7r55puVkJCgrKws9ejRQ8HBwZo4caIyMzM1derU0qgTABw2nm9wYTZJTatzBh0AAJQdxT6D9cQTT6ht27Y6ffq0/P39Hcvvuusu/fLLLy4tDgAulJ6do13HUyVJ9SODFeBT7L8TAQAAlJpifzJZvny5VqxYIR8fH6flcXFxOnLkiMsKA4DCbD2aIpvdkMT8VwAAoOwp9hksu90um81WYPnhw4cVHBzskqIA4GLyN7jg/isAAFDWFDtg9ejRQ5MnT3Y8N5lMOnfunMaMGaNbbrnFlbUBQAEb8wcsOggCAIAyptiXCP7nP/9R9+7d1bhxY2VmZqpv377avXu3IiIiNGfOnNKoEQAc8joI+lnMqh8Z5N5iAAAALlDsgBUdHa2NGzdqzpw5Wr9+vex2u4YMGaIHHnjAqekFALjaqbRsHUrOkCQ1qx4qb68SzTQBAABQakrUfsvf31+DBw/W4MGDXV0PAFzU5iNnHY+5PBAAAJRFxf7zr5eXl7p3767k5GSn5cePH5eXl5fLCgOAC/15OF/AosEFAAAog4odsAzDUFZWltq2bastW7YUWAcApeXPwymOx7RoBwAAZVGxA5bJZNK8efN0++23q0OHDvr666+d1gFAaTAM6c/zlwhWDvRRTCXu+QQAAGVPic5geXl56b///a/efPNN9enTR6+99hpnrwCUqlNZ0ul0qySpRUwof9ABAABlUomaXOT5xz/+ofr16+vee+/VkiVLXFUTABSQcO6vQMX9VwAAoKwq9hmsuLg4p2YW3bp106pVq3T48GGXFgYA+R0kYAEAAA9Q7DNY+/fvL7Csbt262rBhg44fP+6SogDgQk4BixbtAACgjHLZLJ1+fn6Ki4tz1e4AwMFqs+twWu7jGpUDVDnQx70FAQAAXESRzmBVrlxZu3btUkREhCpVqnTJm8svnB8LAK7U7qRzstpzf+9weSAAACjLihSw/vOf/yg4OFiSNHny5NKsBwAKyD//VYuYUDdWAgAAcGlFClgDBgwo9DEAXA2bz89/JTHBMAAAKNuKFLBSUlIuv9F5ISEhJS4GAAqz6XBuwPIym9QkmjNYAACg7CpSwAoLC7vspJ6GYchkMslms7mkMACQpL0nzmnn8XOSpEZRwfL38brMKwAAANynSAHrt99+K+06AKBQn6895Hh8e/MoN1YCAABweUUKWF27di3tOgCgAKvNrnnrjkiSzCZDvVtUc3NFAAAAl1bsiYbzpKenKyEhQdnZ2U7LmzdvfsVFAYAkLdl5QifPZUmSmlYyFB7k6+aKAAAALq3YAevEiRMaNGiQfvjhh0LXcw8WAFeZm+/ywOuqGm6sBAAAoGjMxX3BiBEjdPr0aa1atUr+/v768ccfNWvWLNWrV0/ffPNNadQIoAJKSs3UrzuSJEmRwb5qGEbAAgAAZV+xz2D9+uuv+vrrr3XNNdfIbDYrLi5OPXr0UEhIiCZMmKBbb721NOoEUMF8tf6IbPbcUHVXq2h5WXe7uSIAAIDLK/YZrLS0NFWtWlWSVLlyZZ04cUKS1KxZM61fv9611QGokAzDcOoeeG/r6m6sBgAAoOiKHbAaNGignTt3SpJatmyp9957T0eOHNHUqVNVrRodvgBcufUJp7X3RJok6dpalRUXHuDmigAAAIqm2JcIjhgxQomJiZKkMWPG6KabbtInn3wiHx8fzZw509X1AaiAPl9z2PG4T9tYN1YCAABQPMUOWA888IDjcatWrXTgwAHt2LFDNWrUUEREhEuLA1DxpGXl6Ls/j0qSgny91atZlCQaXAAAAM9Q4nmw8gQEBKh169auqAUA9P3mRKVl5073cHuLaAX4eMtqtbq5KgAAgKIpdsAyDENffvmlfvvtNyUlJclutzutnz9/vsuKA1DxfL7mr+YW97WNcWMlAAAAxVfsgPXEE0/o/fffV/fu3RUZGSmTyVQadQGogPaeOKe1B09LkupHBqllbJh7CwIAACimYges2bNna/78+brllltKox4AFVj+1uz3tY3lDzgAAMDjFLtNe2hoqGrXrl0atQCowKw2u+atOyJJ8jabdFcr5r4CAACep9gBa+zYsXrllVeUkZFRGvUAqKAW7zyhk+eyJEk3NopUeJCvmysCAAAovmJfIvi3v/1Nc+bMUdWqVVWzZk1ZLBan9evXr3dZcQAqjvyXB/a5hrmvAACAZyp2wBo4cKDWrVunfv360eQCgEskpWbq1x1JkqTIEF91rsecegAAwDMVO2B9//33+umnn9SpU6fSqAdABfTV+iOy2XMnE76ndYy8vYp99TIAAECZUOxPMbGxsQoJCSmNWgBUQIZhaO4F3QMBAAA8VbED1r///W+NGjVKBw4ccEkB7777rmrVqiU/Pz+1adNGy5YtK9LrVqxYIW9vb7Vs2dIldQBwj/UJp7XvRJok6dpalVUzItDNFQEAAJRcsS8R7Nevn9LT01WnTh0FBAQUaHKRnJxc5H3NnTtXI0aM0LvvvquOHTvqvffeU69evbRt2zbVqFHjoq87e/as+vfvrxtuuEHHjx8v7lsAUIbMXZOvuQVnrwAAgIcrdsCaPHmyyw4+adIkDRkyREOHDnXs+6efftKUKVM0YcKEi77u4YcfVt++feXl5aUFCxa4rB4AV1daVo6++zNRkhTk661ezaLcXBEAAMCVKVbAslqtWrx4sV566aUrnmw4Oztb69at03PPPee0vGfPnvr9998v+roZM2Zo7969mj17tl577bXLHicrK0tZWVmO5ykpKZJy34vVai1h9ZeWt9/S2j9ch7Fyr683HFF6tk2SdGuzKFlMRqFjwTh5DsbKczBWnoOx8gyMk+fIP1alMV7FClgWi0VfffWVXnrppSs+8MmTJ2Wz2RQZGem0PDIyUseOHSv0Nbt379Zzzz2nZcuWydu7aKVPmDBBr7zySoHlixYtUkBAQPELL4b4+PhS3T9ch7Fyjw+2eEnKneohOvOAFi48cMntGSfPwVh5DsbKczBWnoFx8hzx8fFKT093+X6LfYngXXfdpQULFmjkyJEuKeDCebQMwyh0bi2bzaa+ffvqlVdeUf369Yu8/+eff96p1pSUFMXGxqpnz56l1g3RarUqPj5ePXr0KHCPGsoWxsp99p1I0/6VKyRJ9aoG6pH7Olx0Xj3GyXMwVp6DsfIcjJVnYJw8R/6xysjIcPn+ix2w6tatq1dffVW///672rRpo8BA545fjz/+eJH2ExERIS8vrwJnq5KSkgqc1ZKk1NRUrV27Vhs2bNCjjz4qSbLb7TIMQ97e3lq0aJGuv/76Aq/z9fWVr69vgeUWi6XUv/mvxjHgGozV1WUYht5evM/xvM81NeTj43PZ1zFOnoOx8hyMledgrDwD4+Q5LBaLcnJyXL7fYgesDz/8UGFhYVq3bp3WrVvntM5kMhU5YPn4+KhNmzaKj4/XXXfd5VgeHx+v3r17F9g+JCREmzdvdlr27rvv6tdff9WXX36pWrVqFfetAHCTdxfvdTS3CPTx0l2tqru5IgAAANcodsDav3+/yw4+cuRIPfjgg2rbtq3at2+v999/XwkJCRo2bJik3Mv7jhw5oo8++khms1lNmzZ1en3VqlXl5+dXYDmAsmvR1mP61087Hc//fV9LhQcVPMsMAADgiYodsPIzDENSwfuoiqpPnz46deqUxo0bp8TERDVt2lQLFy5UXFycJCkxMVEJCQlXUiKAMmTb0RSNmLvR8fzpnvV1c1NaswMAgPLDXJIXffTRR2rWrJn8/f3l7++v5s2b6+OPPy5RAcOHD9eBAweUlZWldevWqUuXLo51M2fO1OLFiy/62rFjx2rjxo0lOi6Aq+vkuSw99NFaR1v2O1pE65/d67q5KgAAANcq9hmsSZMm6aWXXtKjjz6qjh07yjAMrVixQsOGDdPJkyf15JNPlkadADxYVo5Nwz5epyNncjv1tIgJ1cR7m5f47DcAAEBZVeyA9fbbb2vKlCnq37+/Y1nv3r3VpEkTjR07loAFwIlhGBr91RatPXhakhQZ4qv3+7eVn8XLzZUBAAC4XrEvEUxMTFSHDh0KLO/QoYMSExNdUhSA8uPDZfv15brDkiQ/i1kf9G+ryBA/N1cFAABQOoodsOrWravPP/+8wPK5c+eqXr16LikKQPnw244kjf9hu+P5m39roeYxYe4rCAAAoJQV+xLBV155RX369NHSpUvVsWNHmUwmLV++XL/88kuhwQtAxbTreKoem7NB55uN6vEb6um25tHuLQoAAKCUFfsM1j333KPVq1crIiJCCxYs0Pz58xUREaE//vjDacJgABVXclq2hs5aq3NZubOj92oapRE3cIYbAACUfyWaB6tNmzaaPXu2q2sBUA5k59j1yOx1SkhOlyQ1iQ7Rv+9rIbOZjoEAAKD8K9E8WABQGMMwNOabrVq9P1mSFBHkqw/6t1WAzxXNaQ4AAOAxivypx2w2X3bOGpPJpJycnCsuCoBnmhS/S3P+SJAk+Xib9X7/NooO83dzVQAAAFdPkQPWV199ddF1v//+u95++20ZeXezA6hw/vvzbr396x7H89fvbqbWNSq5sSIAAICrr8gBq3fv3gWW7dixQ88//7y+/fZbPfDAA3r11VddWhwAz/DOb3v0n593OZ6/ckcT3d06xo0VAQAAuEeJ7sE6evSoHnroITVv3lw5OTnauHGjZs2apRo1ari6PgBl3AdL9+lfP+10PH/x1kYa0KGm+woCAABwo2IFrLNnz+rZZ59V3bp1tXXrVv3yyy/69ttv1bRp09KqD0AZNnPFfv3fwr8mEn725oYa2rm2GysCAABwryJfIjhx4kS98cYbioqK0pw5cwq9ZBBAxTF71UGN/Xab4/nIHvX1SLc6bqwIAADA/YocsJ577jn5+/urbt26mjVrlmbNmlXodvPnz3dZcQDKprlrEvTigi2O549dX1ePM5EwAABA0QNW//79L9umHUD5N2/dYT03f7Pj+bCudTSyR303VgQAAFB2FDlgzZw5sxTLAOAJvt54RM98uUl5MzIM6VRLz97cgD++AAAAnFeiLoIAKp7v/0zUyM83yX4+XA1oH6cXb21EuAIAAMiHgAXgshZtPaYnPtsg2/l01bddDY29ownhCgAA4AIELACXtO1oih7/bINyzoerv7WJ0Wu9mxKuAAAACkHAAnBRZ9OtGjZ7nTKtdknSHS2i9fo9zWU2E64AAAAKQ8ACUCi73dCIuRuUkJwuSWpWPVQT720uL8IVAADARRGwABTqrV9367edJyRJlQIsmtKvtfwsXm6uCgAAoGwjYAEo4LcdSfrvL7slSWaT9Nb9rRRTKcDNVQEAAJR9BCwAThJOpeuJzzY45rp6qmcDda5Xxb1FAQAAeAgCFgCHjGybHp69TimZOZKkno0jNbxbHTdXBQAA4DkIWAAkSYZhaPRXm7U9MUWSVDsiUP++rwXt2AEAAIqBgAVAkvTxqoOav+GIJCnAx0vvPdhGwX4WN1cFAADgWQhYALTuYLLGfbvN8fxf97ZQvchgN1YEAADgmQhYQAWXlJqpR2avV449t6vFQ51r6dbm1dxcFQAAgGciYAEVmNVm16OfbFBSapYk6bralfXszQ3dXBUAAIDnImABFdiEhTv0x4FkSVJUiJ/+17e1vL34tQAAAFBSfJICKqj56w9r+or9kiSLl0nv9mutiCBfN1cFAADg2QhYQAX0284kjfryT8fzMbc3UesaldxYEQAAQPlAwAIqmHUHk/XI7HWOphYPXhenB9rVcHNVAAAA5QMBC6hAdhxL0aAZa5RptUuSbmteTWPvaMJkwgAAAC5CwAIqiEPJ6eo/7Q+lZOZIkjrXi9Ck+1rKy0y4AgAAcBUCFlABnEjN0oPTVjvasbeIDdPUfm3k482vAAAAAFfi0xVQzqVkWjVwxh86cCpdklSnSqBmDLxGgb7ebq4MAACg/CFgAeVYptWmh2at1dajKZKk6FA/fTyknSoH+ri5MgAAgPKJgAWUUzk2ux6fs0Gr9+dOJFwpwKKPhrRTdJi/mysDAAAovwhYQDlkGIZe+GqzFm07LkkK8PHSjEHXqm7VIDdXBgAAUL4RsIBy6PUfd+jztYclSRYvk95/sK1axoa5tygAAIAKgLvcgXLkdFq2Zq08oPeW7JMkmUzS5D6t1KlehJsrAwAAqBgIWIAHS0rN1B/7k7V6X7L+2J+sncdTnda/2rupbm1ezU3VAQAAVDwELMCDHDmTodX7TumP/bmBat/JtItuO7JHffW7Lu4qVgcAAAACFuAGdruhL9Yd0rTl+3Um3SqzySQvs0kmkwo8Np//b0qGVUfPZl50n2aT1LR6qK6tWVk3NIpU+zrhV/EdAQAAQCJgAVfdnqRUvTB/i/44kHxF+7F4mdQ8JkztalXWtbUqq01cJQX7WVxUJQAAAEqCgIUKx2qza83+ZC3adlzrDibLnGGWOe64ejSpJj+LV6kdN9Nq07uL92rK4j2y2gzH8mqhfjIMyW4Yshu5LdZthiG73XAstxmGvM1mNaseqmtrVVa72pXVKraS/H1Kr14AAAAUHwELFcK5rBwt3XVC8duO69cdSTqbYc231qzHPtukYN+turlplHq3rK72dcLlZTa57Pgr957S6K82O90zVaNygF67s6m61K/isuMAAADAvQhYKLeSUjL18/YkLdp2TL/vOaVsm/2S26dm5eiLdYf1xbrDqhLsq9ubR+vOVtFqVj1UJlPJwtbptGyNX7hdX6w77FjmbTbpoS619fj19TgDBQAAUM4QsFCuGIahz9ce0pw/DmnjoTOFbhPs661uDauqR+NIta8ZpukLftYxv1jFb0vSuawcSdKJ1CxNX7Ff01fsV+2IQN3RMlq3NY9WzfAAeXtdfn5uwzC0YOMRvfbddp1Ky3Ysb1UjTBPubqaGUSEueb8AAAAoWwhYKFc+XnVQL3+9tcDyqBA/9WgcqR6NI3Vd7XD5eOeGJKvVqoZhhkbe0lS2u836ZXuSvt54RL/tTHLcJ7XvZJom/7xbk3/eLUkKC7AoPNBH4UG+5//ro/BAX8d/g/y89eGyfVq2+6Tj+MG+3hp1cwP1bRfn0ksPAQAAULYQsFBuHEpO1+s/7HA8bxgVrB6NI9WzcZSaVg+57GV+fhYv3dq8mm5tXk1n061auCVRX288otX7k2X81ZNCZ9KtOpNu1d4TF5+DKr9eTaM09o4migzxK9H7AgAAgOcgYKFcMAxDL3y1WenZNklS33Y1NP6uZiXeX2iARfdfW0P3X1tDiWcz9O2mo/pj/2mdPJel5LRsnTqXpbTzx7qY6FA/jevdVDc2jixxHQAAAPAsBCyUC1+sO+y4JK9aqJ+e79XQZfuuFuqvf3Spo390cV6ekW3TqbS8wJXtCF8nz2UpKtRff78mVoG+/IgBAABUJHz6g8dLSsnUa99tczwff1ezqzLhrr+Pl2J8AhRTKaDUjwUAAADPcPl2aEAZZhiGXlywRSmZud3/7mpVXd0bVnVzVQAAAKioCFjwaN9vTtSibcclSRFBPnr5tsZurggAAAAVGQELHis5LVtj8rVkf+WOpqoU6OPGigAAAFDREbDgsV79bptjEt+bmkTqlmZRbq4IAAAAFR0BCx7ptx1J+mrDEUlSiJ+3Xu3d9LLzXAEAAACljYAFj5OaadULX212PH/ptsaqyiS+AAAAKAMIWPA4E37YocSzmZKkLvWr6N42MW6uCAAAAMhFwIJHWbn3lD5dnSBJCvTx0vi7uDQQAAAAZQcBCx4jI9um5+b/6Xj+bK+GTPILAACAMoWABY/x70U7dfBUuiTpmpqV1K9dnJsrAgAAAJwRsOARNiSc1vQV+yVJPt5mvX5Pc5nNXBoIAACAsoWAhTIvI9umUV/+KbuR+/zJG+urTpUg9xYFAAAAFIKAhTJv3HdbtTvpnCSpWfVQPdS5lpsrAgAAAApHwEKZ9s2mo5rzxyFJkr/FS//p00LeXnzbAgAAoGzikyrKrAMn0/TC/L8mFB7Xu4nqVg12Y0UAAADApRGwUCZl5dj06Jz1OpeVI0m6u1V1JhQGAABAmef2gPXuu++qVq1a8vPzU5s2bbRs2bKLbjt//nz16NFDVapUUUhIiNq3b6+ffvrpKlaLq+X1H3Zoy5EUSVLtiEC9eicTCgMAAKDsc2vAmjt3rkaMGKHRo0drw4YN6ty5s3r16qWEhIRCt1+6dKl69OihhQsXat26derevbtuv/12bdiw4SpXjtK0aOsxzVhxQFJuS/a3+7ZSoK+3e4sCAAAAisCtAWvSpEkaMmSIhg4dqkaNGmny5MmKjY3VlClTCt1+8uTJGjVqlK655hrVq1dP48ePV7169fTtt99e5cpRWo6cydAzX/7peP7SrY3UJDrUjRUBAAAARee20wLZ2dlat26dnnvuOaflPXv21O+//16kfdjtdqWmpqpy5coX3SYrK0tZWVmO5ykpuZedWa1WWa3WElR+eXn7La39l1dWm12PfbpeZzNyv243Na6qPm2iS/XryFh5BsbJczBWnoOx8hyMlWdgnDxH/rEqjfEyGYZhuHyvRXD06FFVr15dK1asUIcOHRzLx48fr1mzZmnnzp2X3ce//vUvvf7669q+fbuqVq1a6DZjx47VK6+8UmD5p59+qoCAgJK/Abjctwlm/Xwk96RqZV9DzzS3KYArAwEAAFBK0tPT1bdvX509e1YhISEu2afbP75e2LjAMIwiNTOYM2eOxo4dq6+//vqi4UqSnn/+eY0cOdLxPCUlRbGxserZs6fLvogXslqtio+PV48ePWSxWErlGOXNsj0n9cuq9ZIkb7NJ7w24Vi1jw0r9uIyVZ2CcPAdj5TkYK8/BWHkGxslz5B+rjIwMl+/fbQErIiJCXl5eOnbsmNPypKQkRUZGXvK1c+fO1ZAhQ/TFF1/oxhtvvOS2vr6+8vX1LbDcYrGU+jf/1ThGeZCUmqlR87Yo71zqqJsb6JraVa5qDYyVZ2CcPAdj5TkYK8/BWHkGxslzWCwW5eTkuHy/bmty4ePjozZt2ig+Pt5peXx8vNMlgxeaM2eOBg4cqE8//VS33npraZeJUmazGxrx2UadPJctSerWoIqGdqrt5qoAAACAknHrJYIjR47Ugw8+qLZt26p9+/Z6//33lZCQoGHDhknKvbzvyJEj+uijjyTlhqv+/fvrv//9r6677jrH2S9/f3+FhtJpzhO9+9se/b73lCQpMsRX//5bC5nNzHcFAAAAz+TWgNWnTx+dOnVK48aNU2Jiopo2baqFCxcqLi5OkpSYmOg0J9Z7772nnJwc/fOf/9Q///lPx/IBAwZo5syZV7t8XKFV+07pPz/vkiSZTdJ//95K4UEFL+cEAAAAPIXbm1wMHz5cw4cPL3TdhaFp8eLFpV8QrooNCac1dNZa2c/fd/X4DfV0Xe1w9xYFAAAAXCG3TjSMiunPw2fUf/ofOpeVe1Nhl/pV9Nj19dxcFQAAAHDlCFi4qrYcOat+H65WamZuuOpQJ1zv9WsjL+67AgAAQDlAwMJVs+1oivpNW62U8+GqXa3K+nBAW/n7eLm5MgAAAMA1CFi4KnYeS1W/aat1Jt0qSbqmZiVNH3iNAnzcfhsgAAAA4DIELJS63cdT1feDVUpOy53rqnWNMM0YdK0CfQlXAAAAKF8IWChVe5LO6f4PVuvU+XDVIjZMMwdfqyDCFQAAAMohAhZKzf6Taer7wSqdPJclSWpWPVQfDb5WIX4WN1cGAAAAlA4CFkrFwVNpuv/9VUpKzQ1XTaJD9PGQaxXqT7gCAABA+UXAgssdSk7X/e+v0rGUTElSw6hgzR7STmEBPm6uDAAAAChdBCy41MlzWfr7+6t09GxuuGoQGaxPhrZTpUDCFQAAAMo/AhZc6t+LdurImQxJUr2qQfrkoXYKD/J1c1UAAADA1UHAgsvsPp6quWsOSZKCfb01e2g7RRCuAAAAUIEQsOAyb/y4Q3Yj9/GwbnUUGeLn3oIAAACAq4yABZdYte+Uft6eJEmKCvHT4I613FwRAAAAcPURsHDF7HZDExZudzwf2bO+/H283FgRAAAA4B4ELFyx7zcnatPhs5JyW7Lf0zrGzRUBAAAA7kHAwhXJyrFp4k87HM+f69VQXmaTGysCAAAA3IeAhSvyyaoEHUrObcvesW64utav4uaKAAAAAPchYKHEzmZY9favux3Pn+/VSCYTZ68AAABQcRGwUGJTFu/V6XSrJOnOltFqWj3UzRUBAAAA7kXAQokcOZOh6Sv2S5J8vMx6+qYGbq4IAAAAcD8CFkpk0qJdys6xS5IGdqypmEoBbq4IAAAAcD8CFopt29EUzd9wWJIU6m/RP7vVdXNFAAAAQNlAwEKxvf7jDhlG7uNHu9dVaIDFvQUBAAAAZQQBC8WybPcJLd11QpIUU8lf/TvEubkiAAAAoOwgYKHI7HZD4xf+NanwMzc1kK+3lxsrAgAAAMoWAhaKbMHGI9qemCJJalo9RLc3j3ZzRQAAAEDZQsBCkWRabXrzp52O5y/0aiSzmUmFAQAAgPwIWCiSmb8f0NGzmZKkbg2qqEPdCDdXBAAAAJQ9BCxc1rqDyXrrl92SJLNJer5XIzdXBAAAAJRNBCxc0oaE0xowfY3Ss22SpD7XxKpBVLCbqwIAAADKJgIWLurPw2fUf/ofOpeVI0nqVDdCY25v4uaqAAAAgLKLgIVCbTlyVv0+XK3UzNxw1b52uD7o31Z+FtqyAwAAABdDwEIB246mqN+01Uo5H66urVVZ0wa2lb8P4QoAAAC4FAIWnOw8lqp+01brTLpVktQ2rpJmDLxGAT7ebq4MAAAAKPsIWHDYfTxVfT9YpeS0bElSqxphmjn4WgX6Eq4AAACAoiBgQZK0J+mc7v9gtU6dD1ctYkI1a/C1CiJcAQAAAEVGwIL2n0xT3w9W6eS5LElSs+qh+mhIO4X4WdxcGQAAAOBZCFgV3MFTabr//VVKSs0NV42rhejjIdcq1J9wBQAAABQXAasCO5Scrr4frNaxlExJUsOoYH0ytJ3CAnzcXBkAAADgmQhYFdTptGwNmP6HjpzJkCTVjwzSJ0PbqVIg4QoAAAAoKQJWBZRptemhj9Zq38k0SVKdKoH6ZOh1Cg/ydXNlAAAAgGcjYFUwdruhpz7fpLUHT0uSqgT7atbga1UlmHAFAAAAXCkCVgXz+o879P3mRElSgI+XZgy8RjGVAtxcFQAAAFA+ELAqkI9WHtD7S/dJkswm6Z2+rdW0eqibqwIAAADKDwJWBRG/7bjGfrPV8fy1O5upe8OqbqwIAAAAKH8IWBXApkNn9Nic9bIbuc+Hd6ujvu1quLcoAAAAoBwiYJVzCafSNWTWGmVa7ZKk3i2j9XTPBm6uCgAAACifCFjl2Om0bA2c+YdOnsuWJLWrVVkT720us9nk5soAAACA8omAVU5lWm36x8drte9E7lxXdasG6f0H28rX28vNlQEAAADlFwGrHLLbDT31xSatOZA711VEkK9mDLxGoQEWN1cGAAAAlG8ErHLojZ926Ps/c+e68rfkznUVW5m5rgAAAIDSRsAqZ+avP6z3luSb6+qBVmoWw1xXAAAAwNVAwCpH9iSd04sLtjiej+vdVNc3jHRjRQAAAEDFQsAqJzKtNj366XqlZ9skSX3axqrfdXFurgoAAACoWAhY5cS477Zpx7FUSVK9qkEae0cTN1cEAAAAVDwErHLg201H9enqBEmSn8Wsdx5oLX8f2rEDAAAAVxsBy8MdPJWm5+dvdjwfd0dT1Y8MdmNFAAAAQMVFwPJgWTk2/fPT9TqXlSNJuqtVdf2tbYybqwIAAAAqLgKWB5uwcIe2HEmRJNWOCNSrdzaVyWRyc1UAAABAxUXA8lA/bT2mmb8fkCT5eJv1dt9WCvL1dm9RAAAAQAVHwPJAh0+n65kvNjmev3RbYzWJZjJhAAAAwN0IWB7GarPrsTkblJKZe9/VLc2i1K9dDTdXBQAAAEAiYHmcNxft1IaEM5Kk2Mr+mnB3c+67AgAAAMoIApYH+W1Hkt5bsk+SZPEy6X/3t1aov8XNVQEAAADIQ8DyEIlnMzTy842O58/e3FAtYsPcVg8AAACAgghYHiAl06p/frJep9OtkqQbGlbVkE613FwVAAAAgAvR17uMO3omQ4NmrNHO46mSpGqhfnrzby247woAAAAogwhYZdjWo2c1eOYaHU/JkiRVCrDovQfbqFKgj5srAwAAAFAYAlYZtWTXCQ2fvU5p2TZJUlx4gGYOula1IgLdXBkAAACAiyFglUFz1yToha+2yGY3JEmtaoTpw/5tFR7k6+bKAAAAAFwKAasMMQxD/4nfpbd+3eNYdlOTSP33763kZ/FyY2UAAAAAioKAVUZk59j13Lw/NX/DEceyQR1r6sVbG8vLTEMLAAAAwBO4vU37u+++q1q1asnPz09t2rTRsmXLLrn9kiVL1KZNG/n5+al27dqaOnXqVaq09JzNsGrgjD8c4cpkkl66rbHG3N6EcAUAAAB4ELcGrLlz52rEiBEaPXq0NmzYoM6dO6tXr15KSEgodPv9+/frlltuUefOnbVhwwa98MILevzxxzVv3ryrXLnrHDmTob9N/V2/7z0lSfL1NmvKA62Z5woAAADwQG4NWJMmTdKQIUM0dOhQNWrUSJMnT1ZsbKymTJlS6PZTp05VjRo1NHnyZDVq1EhDhw7V4MGD9eabb17lyl1jy5GzuuudFdp1/JwkqXKgjz596Drd3LSamysDAAAAUBJuuwcrOztb69at03PPPee0vGfPnvr9998Lfc3KlSvVs2dPp2U33XSTpk2bJqvVKovFUuA1WVlZysrKcjxPSUmRJFmtVlmt1it9G4XK2++l9p94NlN93lv5Vxv2ygGa1r+14sIDSq0uFFSUsYL7MU6eg7HyHIyV52CsPAPj5Dnyj1VpjJfbAtbJkydls9kUGRnptDwyMlLHjh0r9DXHjh0rdPucnBydPHlS1aoVPPMzYcIEvfLKKwWWL1q0SAEBAVfwDi4vPj7+kuuvizDrl6Nm1Qwy9FDtFG1dvVhbS7UiXMzlxgplA+PkORgrz8FYeQ7GyjMwTp4jPj5e6enpLt+v27sImkzOTRwMwyiw7HLbF7Y8z/PPP6+RI0c6nqekpCg2NlY9e/ZUSEhIScu+JKvVqvj4ePXo0aPQs2p5brYb+mh1gv7eNoY27G5S1LGCezFOnoOx8hyMledgrDwD4+Q58o9VRkaGy/fvtoAVEREhLy+vAmerkpKSCpylyhMVFVXo9t7e3goPDy/0Nb6+vvL1LThBr8ViKfVv/qIc46EudUu1BhTN1fh+wJVjnDwHY+U5GCvPwVh5BsbJc1gsFuXk5Lh8v25rcuHj46M2bdoUOI0aHx+vDh06FPqa9u3bF9h+0aJFatu2Ld/IAAAAANzOrV0ER44cqQ8//FDTp0/X9u3b9eSTTyohIUHDhg2TlHt5X//+/R3bDxs2TAcPHtTIkSO1fft2TZ8+XdOmTdPTTz/trrcAAAAAAA5uvQerT58+OnXqlMaNG6fExEQ1bdpUCxcuVFxcnCQpMTHRaU6sWrVqaeHChXryySf1zjvvKDo6Wm+99Zbuueced70FAAAAAHBwe5OL4cOHa/jw4YWumzlzZoFlXbt21fr160u5KgAAAAAoPrdeIggAAAAA5QkBCwAAAABchIAFAAAAAC5CwAIAAAAAFyFgAQAAAICLELAAAAAAwEUIWAAAAADgIgQsAAAAAHARAhYAAAAAuAgBCwAAAABchIAFAAAAAC5CwAIAAAAAFyFgAQAAAICLeLu7gKvNMAxJUkpKSqkdw2q1Kj09XSkpKbJYLKV2HFw5xsozME6eg7HyHIyV52CsPAPj5Dnyj1VGRoakvzKCK1S4gJWamipJio2NdXMlAAAAAMqC1NRUhYaGumRfJsOVcc0D2O12HT16VMHBwTKZTKVyjJSUFMXGxurQoUMKCQkplWPANRgrz8A4eQ7GynMwVp6DsfIMjJPnyD9WwcHBSk1NVXR0tMxm19w9VeHOYJnNZsXExFyVY4WEhPAD5iEYK8/AOHkOxspzMFaeg7HyDIyT58gbK1educpDkwsAAAAAcBECFgAAAAC4CAGrFPj6+mrMmDHy9fV1dym4DMbKMzBOnoOx8hyMledgrDwD4+Q5SnusKlyTCwAAAAAoLZzBAgAAAAAXIWABAAAAgIsQsAAAAADARQhYAAAAAOAiBKwSmjJlipo3b+6YoKx9+/b64YcfHOsNw9DYsWMVHR0tf39/devWTVu3bnVjxcgzYcIEmUwmjRgxwrGM8Sobxo4dK5PJ5PQvKirKsZ5xKjuOHDmifv36KTw8XAEBAWrZsqXWrVvnWM9YlQ01a9Ys8DNlMpn0z3/+UxLjVJbk5OToxRdfVK1ateTv76/atWtr3Lhxstvtjm0Yr7IhNTVVI0aMUFxcnPz9/dWhQwetWbPGsZ5xco+lS5fq9ttvV3R0tEwmkxYsWOC0vijjkpWVpccee0wREREKDAzUHXfcocOHDxe/GAMl8s033xjff/+9sXPnTmPnzp3GCy+8YFgsFmPLli2GYRjG66+/bgQHBxvz5s0zNm/ebPTp08eoVq2akZKS4ubKK7Y//vjDqFmzptG8eXPjiSeecCxnvMqGMWPGGE2aNDESExMd/5KSkhzrGaeyITk52YiLizMGDhxorF692ti/f7/x888/G3v27HFsw1iVDUlJSU4/T/Hx8YYk47fffjMMg3EqS1577TUjPDzc+O6774z9+/cbX3zxhREUFGRMnjzZsQ3jVTbcd999RuPGjY0lS5YYu3fvNsaMGWOEhIQYhw8fNgyDcXKXhQsXGqNHjzbmzZtnSDK++uorp/VFGZdhw4YZ1atXN+Lj443169cb3bt3N1q0aGHk5OQUqxYClgtVqlTJ+PDDDw273W5ERUUZr7/+umNdZmamERoaakydOtWNFVZsqampRr169Yz4+Hija9eujoDFeJUdY8aMMVq0aFHoOsap7Hj22WeNTp06XXQ9Y1V2PfHEE0adOnUMu93OOJUxt956qzF48GCnZXfffbfRr18/wzD4uSor0tPTDS8vL+O7775zWt6iRQtj9OjRjFMZcWHAKsq4nDlzxrBYLMZnn33m2ObIkSOG2Ww2fvzxx2Idn0sEXcBms+mzzz5TWlqa2rdvr/379+vYsWPq2bOnYxtfX1917dpVv//+uxsrrdj++c9/6tZbb9WNN97otJzxKlt2796t6Oho1apVS3//+9+1b98+SYxTWfLNN9+obdu2+tvf/qaqVauqVatW+uCDDxzrGauyKTs7W7Nnz9bgwYNlMpkYpzKmU6dO+uWXX7Rr1y5J0qZNm7R8+XLdcsstkvi5KitycnJks9nk5+fntNzf31/Lly9nnMqooozLunXrZLVanbaJjo5W06ZNiz12BKwrsHnzZgUFBcnX11fDhg3TV199pcaNG+vYsWOSpMjISKftIyMjHetwdX322Wdav369JkyYUGAd41V2tGvXTh999JF++uknffDBBzp27Jg6dOigU6dOMU5lyL59+zRlyhTVq1dPP/30k4YNG6bHH39cH330kSR+psqqBQsW6MyZMxo4cKAkxqmsefbZZ3X//ferYcOGslgsatWqlUaMGKH7779fEuNVVgQHB6t9+/Z69dVXdfToUdlsNs2ePVurV69WYmIi41RGFWVcjh07Jh8fH1WqVOmi2xSV9xXUWuE1aNBAGzdu1JkzZzRv3jwNGDBAS5Yscaw3mUxO2xuGUWAZSt+hQ4f0xBNPaNGiRQX+4pQf4+V+vXr1cjxu1qyZ2rdvrzp16mjWrFm67rrrJDFOZYHdblfbtm01fvx4SVKrVq20detWTZkyRf3793dsx1iVLdOmTVOvXr0UHR3ttJxxKhvmzp2r2bNn69NPP1WTJk20ceNGjRgxQtHR0RowYIBjO8bL/T7++GMNHjxY1atXl5eXl1q3bq2+fftq/fr1jm0Yp7KpJONSkrHjDNYV8PHxUd26ddW2bVtNmDBBLVq00H//+19H17ML025SUlKB5IzSt27dOiUlJalNmzby9vaWt7e3lixZorfeekve3t6OMWG8yp7AwEA1a9ZMu3fv5ueqDKlWrZoaN27stKxRo0ZKSEiQJMaqDDp48KB+/vlnDR061LGMcSpbnnnmGT333HP6+9//rmbNmunBBx/Uk08+6bjygvEqO+rUqaMlS5bo3LlzOnTokP744w9ZrVbVqlWLcSqjijIuUVFRys7O1unTpy+6TVERsFzIMAxlZWU5fsDi4+Md67Kzs7VkyRJ16NDBjRVWTDfccIM2b96sjRs3Ov61bdtWDzzwgDZu3KjatWszXmVUVlaWtm/frmrVqvFzVYZ07NhRO3fudFq2a9cuxcXFSRJjVQbNmDFDVatW1a233upYxjiVLenp6TKbnT+WeXl5Odq0M15lT2BgoKpVq6bTp0/rp59+Uu/evRmnMqoo49KmTRtZLBanbRITE7Vly5bij11JOnPAMJ5//nlj6dKlxv79+40///zTeOGFFwyz2WwsWrTIMIzcVpChoaHG/Pnzjc2bNxv3338/LTrLkPxdBA2D8SornnrqKWPx4sXGvn37jFWrVhm33XabERwcbBw4cMAwDMaprPjjjz8Mb29v4//+7/+M3bt3G5988okREBBgzJ4927ENY1V22Gw2o0aNGsazzz5bYB3jVHYMGDDAqF69uqNN+/z5842IiAhj1KhRjm0Yr7Lhxx9/NH744Qdj3759xqJFi4wWLVoY1157rZGdnW0YBuPkLqmpqcaGDRuMDRs2GJKMSZMmGRs2bDAOHjxoGEbRxmXYsGFGTEyM8fPPPxvr1683rr/+etq0X02DBw824uLiDB8fH6NKlSrGDTfc4AhXhpHbDnLMmDFGVFSU4evra3Tp0sXYvHmzGytGfhcGLMarbMibk8JisRjR0dHG3XffbWzdutWxnnEqO7799lujadOmhq+vr9GwYUPj/fffd1rPWJUdP/30kyHJ2LlzZ4F1jFPZkZKSYjzxxBNGjRo1DD8/P6N27drG6NGjjaysLMc2jFfZMHfuXKN27dqGj4+PERUVZfzzn/80zpw541jPOLnHb7/9Zkgq8G/AgAGGYRRtXDIyMoxHH33UqFy5suHv72/cdtttRkJCQrFrMRmGYVzZSTcAAAAAgMQ9WAAAAADgMgQsAAAAAHARAhYAAAAAuAgBCwAAAABchIAFAAAAAC5CwAIAAAAAFyFgAQAAAICLELAAAAAAwEUIWACACslkMmnBggXuLgMAUM4QsAAAHmfgwIG688473V0GAAAFELAAAAAAwEUIWAAAj9atWzc9/vjjGjVqlCpXrqyoqCiNHTvWaZvdu3erS5cu8vPzU+PGjRUfH19gP0eOHFGfPn1UqVIlhYeHq3fv3jpw4IAkaceOHQoICNCnn37q2H7+/Pny8/PT5s2bS/PtAQA8DAELAODxZs2apcDAQK1evVoTJ07UuHHjHCHKbrfr7rvvlpeXl1atWqWpU6fq2WefdXp9enq6unfvrqCgIC1dulTLly9XUFCQbr75ZmVnZ6thw4Z68803NXz4cB08eFBHjx7VQw89pNdff13NmjVzx1sGAJRRJsMwDHcXAQBAcQwcOFBnzpzRggUL1K1bN9lsNi1btsyx/tprr9X111+v119/XYsWLdItt9yiAwcOKCYmRpL0448/qlevXvrqq6905513avr06Zo4caK2b98uk8kkScrOzlZYWJgWLFignj17SpJuu+02paSkyMfHR2azWT/99JNjewAAJMnb3QUAAHClmjdv7vS8WrVqSkpKkiRt375dNWrUcIQrSWrfvr3T9uvWrdOePXsUHBzstDwzM1N79+51PJ8+fbrq168vs9msLVu2EK4AAAUQsAAAHs9isTg9N5lMstvtkqTCLtS4MBjZ7Xa1adNGn3zySYFtq1Sp4ni8adMmpaWlyWw269ixY4qOjnZF+QCAcoSABQAo1xo3bqyEhAQdPXrUEYhWrlzptE3r1q01d+5cVa1aVSEhIYXuJzk5WQMHDtTo0aN17NgxPfDAA1q/fr38/f1L/T0AADwHTS4AAOXajTfeqAYNGqh///7atGmTli1bptGjRztt88ADDygiIkK9e/fWsmXLtH//fi1ZskRPPPGEDh8+LEkaNmyY/r+dO7RZGA7COPyGVP0FG1DfBRikIxBGQGJrOwEKgq7rACxFUHz685dAyPPoMyd/ueR2u13O53Pmec77/c7pdPrESgB8MYEFwE/bbDZZliWv1yv7/T7H4zHTNP2baa3l8Xik7/uM45hhGHI4HPJ8PrPdbnO9XrOua263W7quS2st9/s9l8sl67p+aDMAvpEvggAAAEVcsAAAAIoILAAAgCICCwAAoIjAAgAAKCKwAAAAiggsAACAIgILAACgiMACAAAoIrAAAACKCCwAAIAiAgsAAKDIH/IXs/06nBbzAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extract right_shoulder data\n",
    "right_shoulder = csv_results[csv_results['class'] == 'loading']['y11']\n",
    "\n",
    "# Perform Min-Max normalization\n",
    "min_value = right_shoulder.min()\n",
    "max_value = right_shoulder.max()\n",
    "normalized_right_shoulder = (right_shoulder - min_value) / (max_value - min_value)\n",
    "\n",
    "# Plot the normalized values\n",
    "plt.figure(figsize=(10, 6))\n",
    "normalized_right_shoulder.plot(label='Normalized Values', linewidth=2)\n",
    "\n",
    "# Customize the plot\n",
    "plt.title('Min-Max Normalized Values')\n",
    "plt.xlabel('Index')\n",
    "plt.ylabel('Normalized Values')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "547fc749",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['preparation', 'preparation', 'preparation', 'preparation',\n",
       "       'preparation', 'preparation', 'preparation', 'preparation',\n",
       "       'preparation', 'preparation', 'preparation', 'preparation',\n",
       "       'preparation', 'preparation', 'preparation', 'preparation',\n",
       "       'preparation', 'preparation', 'preparation', 'preparation',\n",
       "       'preparation', 'preparation', 'preparation', 'preparation',\n",
       "       'preparation', 'preparation', 'preparation', 'preparation',\n",
       "       'preparation', 'preparation', 'preparation', 'loading', 'loading',\n",
       "       'loading', 'loading', 'loading', 'loading', 'loading', 'loading',\n",
       "       'loading', 'loading', 'loading', 'loading', 'loading', 'loading',\n",
       "       'loading', 'loading', 'loading', 'loading', 'loading', 'loading',\n",
       "       'loading', 'loading', 'release', 'release', 'release', 'release',\n",
       "       'release', 'release', 'release', 'follow', 'follow', 'follow',\n",
       "       'follow', 'follow', 'follow', 'follow', 'follow', 'follow',\n",
       "       'follow', 'follow', 'follow', 'preparation', 'preparation',\n",
       "       'preparation', 'preparation', 'preparation', 'preparation',\n",
       "       'loading', 'loading', 'loading', 'loading', 'loading', 'loading',\n",
       "       'loading', 'loading', 'loading', 'loading', 'loading', 'loading',\n",
       "       'loading', 'loading', 'loading', 'loading', 'loading', 'loading',\n",
       "       'loading', 'loading', 'release', 'release', 'release', 'release',\n",
       "       'release', 'release', 'release', 'release', 'follow', 'follow',\n",
       "       'follow', 'follow', 'follow', 'follow', 'follow', 'follow',\n",
       "       'follow', 'follow', 'follow', 'follow', 'follow', 'follow',\n",
       "       'follow'], dtype=object)"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csv_results['class'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df3f06cc",
   "metadata": {},
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