{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "objc[78687]: Class CaptureDelegate is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_videoio.3.4.16.dylib (0x10b908860) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x15fece480). One of the two will be used. Which one is undefined.\n",
      "objc[78687]: Class CVWindow is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x1061b0a68) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x15fece4d0). One of the two will be used. Which one is undefined.\n",
      "objc[78687]: Class CVView is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x1061b0a90) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x15fece4f8). One of the two will be used. Which one is undefined.\n",
      "objc[78687]: Class CVSlider is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x1061b0ab8) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x15fece520). One of the two will be used. Which one is undefined.\n"
     ]
    }
   ],
   "source": [
    "import mediapipe as mp\n",
    "import cv2\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Drawing helpers\n",
    "mp_drawing = mp.solutions.drawing_utils\n",
    "mp_pose = mp.solutions.pose"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze the width distance\n",
    "\n",
    "1. Extract keypoints of shoulder, foot and knee from the dataset\n",
    "1. Calculate shoulder width, 2 foot width and 2 knees width\n",
    "1. Calculate the ratio between those widths to figure out the correct feet placement and how much should the knee flare out during the workout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import sqrt\n",
    "\n",
    "\n",
    "def rescale_frame(frame, percent=50):\n",
    "    '''\n",
    "    Rescale a frame to a certain percentage compare to its original frame\n",
    "    '''\n",
    "    width = int(frame.shape[1] * percent/ 100)\n",
    "    height = int(frame.shape[0] * percent/ 100)\n",
    "    dim = (width, height)\n",
    "    return cv2.resize(frame, dim, interpolation =cv2.INTER_AREA)\n",
    "\n",
    "\n",
    "def calculate_distance(pointX, pointY) -> float:\n",
    "    '''\n",
    "    Calculate a distance between 2 points\n",
    "    '''\n",
    "\n",
    "    x1, y1 = pointX\n",
    "    x2, y2 = pointY\n",
    "\n",
    "    return sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>video</th>\n",
       "      <th>stage</th>\n",
       "      <th>shoulder_width</th>\n",
       "      <th>feet_width</th>\n",
       "      <th>knee_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [video, stage, shoulder_width, feet_width, knee_width]\n",
       "Index: []"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(columns=[\"video\", \"stage\", \"shoulder_width\", \"feet_width\", \"knee_width\"])\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Extract data from video"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load machine learning model\n",
    "import pickle\n",
    "\n",
    "with open(\"./model/squat_model.pkl\", \"rb\") as f:\n",
    "    trained_model = pickle.load(f)\n",
    "\n",
    "# Prepare for processing input\n",
    "IMPORTANT_LMS = [\n",
    "    \"NOSE\",\n",
    "    \"LEFT_SHOULDER\",\n",
    "    \"RIGHT_SHOULDER\",\n",
    "    \"LEFT_HIP\",\n",
    "    \"RIGHT_HIP\",\n",
    "    \"LEFT_KNEE\",\n",
    "    \"RIGHT_KNEE\",\n",
    "    \"LEFT_ANKLE\",\n",
    "    \"RIGHT_ANKLE\"\n",
    "]\n",
    "\n",
    "headers = [\"label\"] # Label column\n",
    "\n",
    "for lm in IMPORTANT_LMS:\n",
    "    headers += [f\"{lm.lower()}_x\", f\"{lm.lower()}_y\", f\"{lm.lower()}_z\", f\"{lm.lower()}_v\"]\n",
    "\n",
    "\n",
    "def extract_important_keypoints(results) -> list:\n",
    "    '''\n",
    "    Extract important keypoints from mediapipe pose detection\n",
    "    '''\n",
    "    landmarks = results.pose_landmarks.landmark\n",
    "\n",
    "    data = []\n",
    "    for lm in IMPORTANT_LMS:\n",
    "        keypoint = landmarks[mp_pose.PoseLandmark[lm].value]\n",
    "        data.append([keypoint.x, keypoint.y, keypoint.z, keypoint.visibility])\n",
    "    \n",
    "    return np.array(data).flatten().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "video = \"squat_right_4.mp4\"\n",
    "cap = cv2.VideoCapture(f\"../data/squat/{video}\")\n",
    "\n",
    "current_stage = \"\"\n",
    "prediction_probability_threshold = 0.8\n",
    "\n",
    "with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:\n",
    "    while cap.isOpened():\n",
    "        ret, image = cap.read()\n",
    "\n",
    "        if not ret:\n",
    "            break\n",
    "\n",
    "        # Recolor image from BGR to RGB for mediapipe\n",
    "        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
    "        image.flags.writeable = False\n",
    "\n",
    "        results = pose.process(image)\n",
    "        # Calculate and compare the distance of 2 ankle and shoulder width\n",
    "        if not results.pose_landmarks:\n",
    "            continue\n",
    "        \n",
    "        # Recolor image from BGR to RGB for mediapipe\n",
    "        image.flags.writeable = True\n",
    "        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
    "\n",
    "        shoulder_width = feet_width = None\n",
    "\n",
    "        try:\n",
    "            # Extract keypoints from frame for the input\n",
    "            row = extract_important_keypoints(results)\n",
    "            X = pd.DataFrame([row], columns=headers[1:])\n",
    "\n",
    "            # Make prediction and its probability\n",
    "            predicted_class = trained_model.predict(X)[0]\n",
    "            prediction_probability = trained_model.predict_proba(X)[0]\n",
    "\n",
    "            # Evaluate model prediction for the current stage\n",
    "            if prediction_probability[prediction_probability.argmax()] >= prediction_probability_threshold:\n",
    "                if predicted_class == \"down\":\n",
    "                    current_stage = \"down\"\n",
    "                elif current_stage == \"middle\" and predicted_class == \"up\":\n",
    "                    current_stage = \"up\"\n",
    "            else:\n",
    "                current_stage = \"middle\"\n",
    "            \n",
    "            # Calculate and compare the distance of 2 ankle and shoulder width\n",
    "            landmarks = results.pose_landmarks.landmark\n",
    "\n",
    "            # * Calculate shoulder width\n",
    "            left_shoulder = [landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].x, landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].y]\n",
    "            right_shoulder = [landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value].x, landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value].y]\n",
    "\n",
    "            shoulder_width = calculate_distance(left_shoulder, right_shoulder)\n",
    "\n",
    "            # * Calculate 2-foot width\n",
    "            left_foot_index = [landmarks[mp_pose.PoseLandmark.LEFT_FOOT_INDEX.value].x, landmarks[mp_pose.PoseLandmark.LEFT_FOOT_INDEX.value].y]\n",
    "            right_foot_index = [landmarks[mp_pose.PoseLandmark.RIGHT_FOOT_INDEX.value].x, landmarks[mp_pose.PoseLandmark.RIGHT_FOOT_INDEX.value].y]\n",
    "\n",
    "            feet_width = calculate_distance(left_foot_index, right_foot_index)\n",
    "            \n",
    "            # * Calculate 2-knee width\n",
    "            left_knee = [landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].x, landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].y]\n",
    "            right_knee = [landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value].x, landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value].y]\n",
    "\n",
    "            knee_width = calculate_distance(left_knee, right_knee)\n",
    "\n",
    "            # Visualization\n",
    "            # Status box\n",
    "            cv2.rectangle(image, (0, 0), (500, 60), (245, 117, 16), -1)\n",
    "\n",
    "            # Display feet distance\n",
    "            cv2.putText(image, \"FEET\", (15, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
    "            cv2.putText(image, str(round(feet_width, 2)), (10, 40), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)\n",
    "\n",
    "            # Display shoulder distance\n",
    "            cv2.putText(image, \"SHOULDER\", (95, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
    "            cv2.putText(image, str(round(shoulder_width, 2)), (90, 40), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)\n",
    "\n",
    "            # Display knee distance\n",
    "            cv2.putText(image, \"KNEE\", (205, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
    "            cv2.putText(image, str(round(knee_width, 2)), (200, 40), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)\n",
    "\n",
    "            # Display knee distance\n",
    "            cv2.putText(image, \"STAGE\", (305, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
    "            cv2.putText(image, current_stage, (300, 40), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)\n",
    "        except Exception as e:\n",
    "            print(f\"Error: {e}\")\n",
    "\n",
    "        # Draw landmarks and connections\n",
    "        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS, mp_drawing.DrawingSpec(color=(244, 117, 66), thickness=2, circle_radius=4), mp_drawing.DrawingSpec(color=(245, 66, 230), thickness=2, circle_radius=2))\n",
    "\n",
    "        cv2.imshow(\"CV2\", image)\n",
    "\n",
    "        # Pressed key for action\n",
    "        k = cv2.waitKey(1) & 0xFF\n",
    "\n",
    "        # Press C to add to df\n",
    "        if k == ord('c'): \n",
    "            df = df.append({ \"video\": video, \"stage\": current_stage, \"shoulder_width\": shoulder_width, \"feet_width\": feet_width, \"knee_width\": knee_width }, ignore_index=True)\n",
    "\n",
    "        # Press q to stop\n",
    "        elif k == ord(\"q\"):\n",
    "            break\n",
    "        else: continue\n",
    "\n",
    "    cap.release()\n",
    "    cv2.destroyAllWindows()\n",
    "\n",
    "    # (Optional)Fix bugs cannot close windows in MacOS (https://stackoverflow.com/questions/6116564/destroywindow-does-not-close-window-on-mac-using-python-and-opencv)\n",
    "    for i in range (1, 5):\n",
    "        cv2.waitKey(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "      <td>squat_right_1.mp4</td>\n",
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       "      <td>0.103278</td>\n",
       "      <td>0.109837</td>\n",
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       "    <tr>\n",
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       "      <td>0.109250</td>\n",
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       "      <th>850</th>\n",
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       "      <td>0.152665</td>\n",
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       "    <tr>\n",
       "      <th>851</th>\n",
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       "      <td>0.154600</td>\n",
       "      <td>0.097331</td>\n",
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       "    <tr>\n",
       "      <th>852</th>\n",
       "      <td>squat_right_4.mp4</td>\n",
       "      <td>up</td>\n",
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       "      <td>0.158117</td>\n",
       "      <td>0.094716</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>853 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 video stage  shoulder_width  feet_width  knee_width\n",
       "0    squat_right_1.mp4  down        0.079002    0.104884    0.099171\n",
       "1    squat_right_1.mp4  down        0.079100    0.104516    0.102053\n",
       "2    squat_right_1.mp4  down        0.079228    0.103585    0.104874\n",
       "3    squat_right_1.mp4  down        0.079085    0.103278    0.109837\n",
       "4    squat_right_1.mp4  down        0.079438    0.102361    0.110328\n",
       "..                 ...   ...             ...         ...         ...\n",
       "848  squat_right_4.mp4    up        0.087186    0.147464    0.112127\n",
       "849  squat_right_4.mp4    up        0.089429    0.153139    0.109250\n",
       "850  squat_right_4.mp4    up        0.090059    0.152665    0.104126\n",
       "851  squat_right_4.mp4    up        0.089556    0.154600    0.097331\n",
       "852  squat_right_4.mp4    up        0.089544    0.158117    0.094716\n",
       "\n",
       "[853 rows x 5 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Analyze the foot placement relate to the shoulder width"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.079228</td>\n",
       "      <td>0.103585</td>\n",
       "      <td>0.104874</td>\n",
       "      <td>1.307429</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>squat_right_1.mp4</td>\n",
       "      <td>down</td>\n",
       "      <td>0.079085</td>\n",
       "      <td>0.103278</td>\n",
       "      <td>0.109837</td>\n",
       "      <td>1.305912</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>squat_right_1.mp4</td>\n",
       "      <td>down</td>\n",
       "      <td>0.079438</td>\n",
       "      <td>0.102361</td>\n",
       "      <td>0.110328</td>\n",
       "      <td>1.288572</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index              video stage  shoulder_width  feet_width  knee_width  \\\n",
       "0      0  squat_right_1.mp4  down        0.079002    0.104884    0.099171   \n",
       "1      1  squat_right_1.mp4  down        0.079100    0.104516    0.102053   \n",
       "2      2  squat_right_1.mp4  down        0.079228    0.103585    0.104874   \n",
       "3      3  squat_right_1.mp4  down        0.079085    0.103278    0.109837   \n",
       "4      4  squat_right_1.mp4  down        0.079438    0.102361    0.110328   \n",
       "\n",
       "   ratio_feet_shoulder  ratio_knee_feet  \n",
       "0             1.327618              0.9  \n",
       "1             1.321318              1.0  \n",
       "2             1.307429              1.0  \n",
       "3             1.305912              1.1  \n",
       "4             1.288572              1.1  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"ratio_feet_shoulder\"] = df[\"feet_width\"] / df[\"shoulder_width\"]\n",
    "\n",
    "# Round up the value\n",
    "# df[\"ratio_feet_shoulder\"] = [round(row, 1) for row in df[\"ratio_feet_shoulder\"]]\n",
    "\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.1. Describe data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean value of the ratio between shoulder width and 2 foot: 1.8044653349001174\n",
      "Max value of the ratio between shoulder width and 2 foot: 2.8\n",
      "Min value of the ratio between shoulder width and 2 foot: 1.2\n",
      "\n"
     ]
    }
   ],
   "source": [
    "mean = df[\"ratio_feet_shoulder\"].mean(axis=0)\n",
    "min_val = df[\"ratio_feet_shoulder\"].min()\n",
    "max_val = df[\"ratio_feet_shoulder\"].max()\n",
    "\n",
    "print(\n",
    "f'''\n",
    "Mean value of the ratio between shoulder width and 2 foot: {mean}\n",
    "Max value of the ratio between shoulder width and 2 foot: {max_val}\n",
    "Min value of the ratio between shoulder width and 2 foot: {min_val}\n",
    "'''\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2 Conclusion\n",
    "\n",
    "According to the analyzed data extracted from the dataset:\n",
    "\n",
    "1. Feet distance usually wider compare to shoulder distance\n",
    "1. The mean ratio between the 2 is 1.8\n",
    "1. **Conclusion**:\n",
    "    - Feet distance / shoulder width < 1.2 => **Foot placement is too tight**\n",
    "    - Feet distance / shoulder width > 2.8 => **Foot placement is too wide**\n",
    "    - Else: => **Correct form**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Analyze knee position during the workout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>index</th>\n",
       "      <th>video</th>\n",
       "      <th>stage</th>\n",
       "      <th>shoulder_width</th>\n",
       "      <th>feet_width</th>\n",
       "      <th>knee_width</th>\n",
       "      <th>ratio_feet_shoulder</th>\n",
       "      <th>ratio_knee_feet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>squat_right_1.mp4</td>\n",
       "      <td>down</td>\n",
       "      <td>0.079002</td>\n",
       "      <td>0.104884</td>\n",
       "      <td>0.099171</td>\n",
       "      <td>1.327618</td>\n",
       "      <td>0.945534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>squat_right_1.mp4</td>\n",
       "      <td>down</td>\n",
       "      <td>0.079100</td>\n",
       "      <td>0.104516</td>\n",
       "      <td>0.102053</td>\n",
       "      <td>1.321318</td>\n",
       "      <td>0.976432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>squat_right_1.mp4</td>\n",
       "      <td>down</td>\n",
       "      <td>0.079228</td>\n",
       "      <td>0.103585</td>\n",
       "      <td>0.104874</td>\n",
       "      <td>1.307429</td>\n",
       "      <td>1.012446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>squat_right_1.mp4</td>\n",
       "      <td>down</td>\n",
       "      <td>0.079085</td>\n",
       "      <td>0.103278</td>\n",
       "      <td>0.109837</td>\n",
       "      <td>1.305912</td>\n",
       "      <td>1.063504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>squat_right_1.mp4</td>\n",
       "      <td>down</td>\n",
       "      <td>0.079438</td>\n",
       "      <td>0.102361</td>\n",
       "      <td>0.110328</td>\n",
       "      <td>1.288572</td>\n",
       "      <td>1.077832</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index              video stage  shoulder_width  feet_width  knee_width  \\\n",
       "0      0  squat_right_1.mp4  down        0.079002    0.104884    0.099171   \n",
       "1      1  squat_right_1.mp4  down        0.079100    0.104516    0.102053   \n",
       "2      2  squat_right_1.mp4  down        0.079228    0.103585    0.104874   \n",
       "3      3  squat_right_1.mp4  down        0.079085    0.103278    0.109837   \n",
       "4      4  squat_right_1.mp4  down        0.079438    0.102361    0.110328   \n",
       "\n",
       "   ratio_feet_shoulder  ratio_knee_feet  \n",
       "0             1.327618         0.945534  \n",
       "1             1.321318         0.976432  \n",
       "2             1.307429         1.012446  \n",
       "3             1.305912         1.063504  \n",
       "4             1.288572         1.077832  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"ratio_knee_feet\"] = df[\"knee_width\"] / df[\"feet_width\"]\n",
    "# df[\"ratio_knee_feet\"] = [round(row, 1) for row in df[\"ratio_knee_feet\"]] # round up\n",
    "\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1 Describe and analyze data\n",
    "\n",
    "There are 3 stages of a squat to analyze: UP, MIDDLE, DOWN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean value of the ratio between 2 knee width and 2 foot: 0.7131609870740305\n",
      "Max value of the ratio between 2 knee width and 2 foot: 1.1\n",
      "Min value of the ratio between 2 knee width and 2 foot: 0.5\n",
      "\n"
     ]
    }
   ],
   "source": [
    "mean = df[\"ratio_knee_feet\"].mean(axis=0)\n",
    "min_val = df[\"ratio_knee_feet\"].min()\n",
    "max_val = df[\"ratio_knee_feet\"].max()\n",
    "\n",
    "print(\n",
    "f'''\n",
    "Mean value of the ratio between 2 knee width and 2 foot: {mean}\n",
    "Max value of the ratio between 2 knee width and 2 foot: {max_val}\n",
    "Min value of the ratio between 2 knee width and 2 foot: {min_val}\n",
    "'''\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**UP position**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean value of the ratio between 2 knee width and 2 foot in UP position: 0.6345776031434184\n",
      "Max value of the ratio between 2 knee width and 2 foot in UP position: 1.0\n",
      "Min value of the ratio between 2 knee width and 2 foot in UP position: 0.5\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df_ration_knee_feet = df[ df[\"stage\"] == \"up\" ][\"ratio_knee_feet\"]\n",
    "\n",
    "mean = df_ration_knee_feet.mean(axis=0)\n",
    "min_val = df_ration_knee_feet.min()\n",
    "max_val = df_ration_knee_feet.max()\n",
    "\n",
    "print(\n",
    "f'''\n",
    "Mean value of the ratio between 2 knee width and 2 foot in UP position: {mean}\n",
    "Max value of the ratio between 2 knee width and 2 foot in UP position: {max_val}\n",
    "Min value of the ratio between 2 knee width and 2 foot in UP position: {min_val}\n",
    "'''\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**MIDDLE position**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean value of the ratio between 2 knee width and 2 foot in MIDDLE position: 0.7952830188679246\n",
      "Max value of the ratio between 2 knee width and 2 foot in MIDDLE position: 1.0\n",
      "Min value of the ratio between 2 knee width and 2 foot in MIDDLE position: 0.7\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df_ration_knee_feet = df[ df[\"stage\"] == \"middle\" ][\"ratio_knee_feet\"]\n",
    "\n",
    "mean = df_ration_knee_feet.mean(axis=0)\n",
    "min_val = df_ration_knee_feet.min()\n",
    "max_val = df_ration_knee_feet.max()\n",
    "\n",
    "print(\n",
    "f'''\n",
    "Mean value of the ratio between 2 knee width and 2 foot in MIDDLE position: {mean}\n",
    "Max value of the ratio between 2 knee width and 2 foot in MIDDLE position: {max_val}\n",
    "Min value of the ratio between 2 knee width and 2 foot in MIDDLE position: {min_val}\n",
    "'''\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**DOWN position**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean value of the ratio between 2 knee width and 2 foot in DOWN position: 0.8457627118644067\n",
      "Max value of the ratio between 2 knee width and 2 foot in DOWN position: 1.1\n",
      "Min value of the ratio between 2 knee width and 2 foot in DOWN position: 0.7\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df_ration_knee_feet = df[ df[\"stage\"] == \"down\" ][\"ratio_knee_feet\"]\n",
    "\n",
    "mean = df_ration_knee_feet.mean(axis=0)\n",
    "min_val = df_ration_knee_feet.min()\n",
    "max_val = df_ration_knee_feet.max()\n",
    "\n",
    "print(\n",
    "f'''\n",
    "Mean value of the ratio between 2 knee width and 2 foot in DOWN position: {mean}\n",
    "Max value of the ratio between 2 knee width and 2 foot in DOWN position: {max_val}\n",
    "Min value of the ratio between 2 knee width and 2 foot in DOWN position: {min_val}\n",
    "'''\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>video</th>\n",
       "      <th>stage</th>\n",
       "      <th>shoulder_width</th>\n",
       "      <th>feet_width</th>\n",
       "      <th>knee_width</th>\n",
       "      <th>ratio_feet_shoulder</th>\n",
       "      <th>ratio_knee_feet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>squat_right_2.mp4</td>\n",
       "      <td>down</td>\n",
       "      <td>0.076213</td>\n",
       "      <td>0.150768</td>\n",
       "      <td>0.106627</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>squat_right_2.mp4</td>\n",
       "      <td>down</td>\n",
       "      <td>0.074955</td>\n",
       "      <td>0.150676</td>\n",
       "      <td>0.107060</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>squat_right_2.mp4</td>\n",
       "      <td>down</td>\n",
       "      <td>0.074705</td>\n",
       "      <td>0.149546</td>\n",
       "      <td>0.108448</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>squat_right_2.mp4</td>\n",
       "      <td>down</td>\n",
       "      <td>0.075373</td>\n",
       "      <td>0.150687</td>\n",
       "      <td>0.109482</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>squat_right_2.mp4</td>\n",
       "      <td>down</td>\n",
       "      <td>0.075764</td>\n",
       "      <td>0.151288</td>\n",
       "      <td>0.109265</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>643</th>\n",
       "      <td>squat_right_3.mp4</td>\n",
       "      <td>down</td>\n",
       "      <td>0.082185</td>\n",
       "      <td>0.219941</td>\n",
       "      <td>0.155542</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>squat_right_3.mp4</td>\n",
       "      <td>down</td>\n",
       "      <td>0.081883</td>\n",
       "      <td>0.220649</td>\n",
       "      <td>0.153821</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645</th>\n",
       "      <td>squat_right_3.mp4</td>\n",
       "      <td>down</td>\n",
       "      <td>0.081753</td>\n",
       "      <td>0.219347</td>\n",
       "      <td>0.152637</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>646</th>\n",
       "      <td>squat_right_3.mp4</td>\n",
       "      <td>down</td>\n",
       "      <td>0.080813</td>\n",
       "      <td>0.214849</td>\n",
       "      <td>0.151832</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>647</th>\n",
       "      <td>squat_right_3.mp4</td>\n",
       "      <td>down</td>\n",
       "      <td>0.079627</td>\n",
       "      <td>0.214114</td>\n",
       "      <td>0.151840</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>84 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 video stage  shoulder_width  feet_width  knee_width  \\\n",
       "180  squat_right_2.mp4  down        0.076213    0.150768    0.106627   \n",
       "181  squat_right_2.mp4  down        0.074955    0.150676    0.107060   \n",
       "182  squat_right_2.mp4  down        0.074705    0.149546    0.108448   \n",
       "183  squat_right_2.mp4  down        0.075373    0.150687    0.109482   \n",
       "184  squat_right_2.mp4  down        0.075764    0.151288    0.109265   \n",
       "..                 ...   ...             ...         ...         ...   \n",
       "643  squat_right_3.mp4  down        0.082185    0.219941    0.155542   \n",
       "644  squat_right_3.mp4  down        0.081883    0.220649    0.153821   \n",
       "645  squat_right_3.mp4  down        0.081753    0.219347    0.152637   \n",
       "646  squat_right_3.mp4  down        0.080813    0.214849    0.151832   \n",
       "647  squat_right_3.mp4  down        0.079627    0.214114    0.151840   \n",
       "\n",
       "     ratio_feet_shoulder  ratio_knee_feet  \n",
       "180                  2.0              0.7  \n",
       "181                  2.0              0.7  \n",
       "182                  2.0              0.7  \n",
       "183                  2.0              0.7  \n",
       "184                  2.0              0.7  \n",
       "..                   ...              ...  \n",
       "643                  2.7              0.7  \n",
       "644                  2.7              0.7  \n",
       "645                  2.7              0.7  \n",
       "646                  2.7              0.7  \n",
       "647                  2.7              0.7  \n",
       "\n",
       "[84 rows x 7 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = df[ (df[\"ratio_knee_feet\"] == 0.7) & (df[\"stage\"] == \"down\") ]\n",
    "test\n",
    "\n",
    "# df = df[ (df[\"ratio_knee_feet\"] != 0.4) ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.2 Conclusion\n",
    "\n",
    "According to the analyzed data extracted from the dataset:\n",
    "\n",
    "- In **UP** position:\n",
    "    1. Knee distance usually tighter compare to foot distance\n",
    "    1. The mean ratio between knee/foot is 0.63\n",
    "    1. **Conclusion**:\n",
    "        - Knee distance / foot width < 0.5 => **2 Knees are too tight**\n",
    "        - Knee distance / foot width > 1.0 => **2 Knees are too wide**\n",
    "        - Else: => **Correct form**\n",
    "\n",
    "- In **MIDDLE** position:\n",
    "    1. Knee distance usually equals compare to foot distance\n",
    "    1. The mean ratio between knee/foot is 0.8\n",
    "    1. **Conclusion**:\n",
    "        - Knee distance / foot width < 0.7 => **2 Knees are too tight**\n",
    "        - Knee distance / foot width > 1.0 => **2 Knees are too wide**\n",
    "        - Else: => **Correct form**\n",
    "\n",
    "- In **DOWN** position:\n",
    "    1. Knee distance usually equals or a little bit wider compare to foot distance\n",
    "    1. The mean ratio between knee/foot is 0.8\n",
    "    1. **Conclusion**:\n",
    "        - Knee distance / foot width < 0.7 => **2 Knees are too tight**\n",
    "        - Knee distance / foot width > 1.1 => **2 Knees are too wide**\n",
    "        - Else: => **Correct form**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Save Analysis CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"./analyze_pose.csv\", sep=',', encoding='utf-8', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"./analyze_pose.csv\")\n",
    "df = df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>index</th>\n",
       "      <th>shoulder_width</th>\n",
       "      <th>feet_width</th>\n",
       "      <th>knee_width</th>\n",
       "      <th>ratio_feet_shoulder</th>\n",
       "      <th>ratio_knee_feet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>851.000000</td>\n",
       "      <td>851.000000</td>\n",
       "      <td>851.000000</td>\n",
       "      <td>851.000000</td>\n",
       "      <td>851.000000</td>\n",
       "      <td>851.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>425.000000</td>\n",
       "      <td>0.082446</td>\n",
       "      <td>0.148388</td>\n",
       "      <td>0.103947</td>\n",
       "      <td>1.804465</td>\n",
       "      <td>0.713161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>245.806835</td>\n",
       "      <td>0.007008</td>\n",
       "      <td>0.026802</td>\n",
       "      <td>0.021360</td>\n",
       "      <td>0.322933</td>\n",
       "      <td>0.148453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.073848</td>\n",
       "      <td>0.098033</td>\n",
       "      <td>0.063072</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>212.500000</td>\n",
       "      <td>0.077047</td>\n",
       "      <td>0.147486</td>\n",
       "      <td>0.087311</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>425.000000</td>\n",
       "      <td>0.079676</td>\n",
       "      <td>0.151806</td>\n",
       "      <td>0.101141</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>0.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>637.500000</td>\n",
       "      <td>0.089282</td>\n",
       "      <td>0.155863</td>\n",
       "      <td>0.115419</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>850.000000</td>\n",
       "      <td>0.099635</td>\n",
       "      <td>0.220649</td>\n",
       "      <td>0.158517</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.100000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            index  shoulder_width  feet_width  knee_width  \\\n",
       "count  851.000000      851.000000  851.000000  851.000000   \n",
       "mean   425.000000        0.082446    0.148388    0.103947   \n",
       "std    245.806835        0.007008    0.026802    0.021360   \n",
       "min      0.000000        0.073848    0.098033    0.063072   \n",
       "25%    212.500000        0.077047    0.147486    0.087311   \n",
       "50%    425.000000        0.079676    0.151806    0.101141   \n",
       "75%    637.500000        0.089282    0.155863    0.115419   \n",
       "max    850.000000        0.099635    0.220649    0.158517   \n",
       "\n",
       "       ratio_feet_shoulder  ratio_knee_feet  \n",
       "count           851.000000       851.000000  \n",
       "mean              1.804465         0.713161  \n",
       "std               0.322933         0.148453  \n",
       "min               1.200000         0.500000  \n",
       "25%               1.600000         0.600000  \n",
       "50%               1.900000         0.700000  \n",
       "75%               2.000000         0.800000  \n",
       "max               2.800000         1.100000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='ratio_feet_shoulder'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set Theme:\n",
    "sns.set_style('whitegrid')\n",
    "\n",
    "# Creating Strip plot for day-wise revenue:\n",
    "sns.swarmplot(y=\"ratio_feet_shoulder\", data=df, palette=\"viridis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='stage', ylabel='ratio_knee_feet'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set Theme:\n",
    "sns.set_style('whitegrid')\n",
    "\n",
    "# Creating Strip plot for day-wise revenue:\n",
    "sns.swarmplot(x='stage', y=\"ratio_knee_feet\", data=df, palette=\"viridis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.13 (conda)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "9260f401923fb5c4108c543a7d176de9733d378b3752e49535ad7c43c2271b65"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}