mailsys_differential_feature_extraction/toysnippet/sandbox.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, sys\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "sys.path.append('../')\n",
    "import modules.coordsi as csi\n",
    "import math\n",
    "import time"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A brief summary of each original coordinate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: ./List_Data_Call/21002290020230321/cam1.csv is not exist.\n",
      "                                    approx_total_frame  x_min   x_max  y_min   \n",
      "./List_Data_Call/21004038220221101            478800.0   -3.0  1900.0   -2.0  \\\n",
      "./List_Data_Call/21001264420230215            219600.0   -4.0  1901.0   -6.0   \n",
      "./List_Data_Call/21001555320230216            349200.0   -5.0  1884.0   -3.0   \n",
      "./List_Data_Call/21004030420221004            666000.0   -4.0  1902.0   -3.0   \n",
      "./List_Data_Call/21004008220221017            219600.0   -8.0  1883.0   -2.0   \n",
      "\n",
      "                                     y_max     z_min     z_max  \n",
      "./List_Data_Call/21004038220221101  1051.0  0.923129  4.137998  \n",
      "./List_Data_Call/21001264420230215  1055.0  0.953854  4.400389  \n",
      "./List_Data_Call/21001555320230216  1057.0  0.840809  3.938679  \n",
      "./List_Data_Call/21004030420221004  1057.0  0.933506  4.356071  \n",
      "./List_Data_Call/21004008220221017  1058.0  0.897373  4.207281  \n"
     ]
    }
   ],
   "source": [
    "# initialize root path\n",
    "root = './List_Data_Call/'\n",
    "\n",
    "summary_1 = csi.summaryOriginCoord(root, camid=1)\n",
    "print(summary_1.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./List_Data_Call/21004038220221101 64.0459258556366\n",
      "./List_Data_Call/21001264420230215 34.23401594161987\n",
      "./List_Data_Call/21001555320230216 55.52943301200867\n",
      "./List_Data_Call/21004030420221004 106.67347645759583\n",
      "./List_Data_Call/21004008220221017 31.72646999359131\n",
      "./List_Data_Call/21004056620230120 19.501684188842773\n",
      "./List_Data_Call/21001057620230126 53.51554536819458\n",
      "./List_Data_Call/21003885420220721 76.24831080436707\n",
      "./List_Data_Call/21004107920230313 97.09525084495544\n",
      "./List_Data_Call/21003975920220723 36.387489318847656\n",
      "./List_Data_Call/21004102020230228 31.510535717010498\n",
      "./List_Data_Call/21004036420221013 63.18059492111206\n",
      "./List_Data_Call/21003962420220712 36.284823179244995\n",
      "./List_Data_Call/21004048220221029 50.87060260772705\n",
      "./List_Data_Call/21003992420230119 49.36880159378052\n",
      "./List_Data_Call/21001418920220921 35.04419279098511\n",
      "./List_Data_Call/21004108620230225 94.45536732673645\n",
      "./List_Data_Call/21004095420230303 7.877222776412964\n",
      "./List_Data_Call/21003655120230207 22.74891757965088\n",
      "./List_Data_Call/21003817020230107 24.885369300842285\n",
      "./List_Data_Call/21003981020220923 36.27069091796875\n",
      "./List_Data_Call/21004033920221007 86.56228399276733\n",
      "./List_Data_Call/21004074720230104 37.52795934677124\n",
      "./List_Data_Call/21004037520230324 30.00572395324707\n",
      "./List_Data_Call/21004040420221103 90.27121639251709\n",
      "./List_Data_Call/21004026620221007 69.01991891860962\n",
      "./List_Data_Call/21001870620230315 39.85080003738403\n",
      "./List_Data_Call/21004068020230102 7.841302871704102\n",
      "./List_Data_Call/21004094420230206 100.37451100349426\n",
      "./List_Data_Call/21004097420230304 84.17479515075684\n",
      "./List_Data_Call/21004047820221028 89.44327425956726\n",
      "./List_Data_Call/21004051720230228 9.743078708648682\n",
      "./List_Data_Call/21004076520230204 76.26534867286682\n",
      "./List_Data_Call/21002591020230218 88.18855333328247\n",
      "./List_Data_Call/21004105820230311 59.186476945877075\n",
      "./List_Data_Call/21004037520230317 54.6233925819397\n",
      "./List_Data_Call/21003993520220917 95.81168413162231\n",
      "./List_Data_Call/21003967920220714 36.98200988769531\n",
      "./List_Data_Call/21004043620221025 25.73115038871765\n",
      "./List_Data_Call/21004097520230208 93.56520891189575\n",
      "./List_Data_Call/21004092020230127 89.9213137626648\n",
      "./List_Data_Call/21004026220220928 81.38130640983582\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Found array with 0 sample(s) (shape=(0,)) while a minimum of 1 is required.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[3], line 6\u001b[0m\n\u001b[1;32m      4\u001b[0m start_time \u001b[39m=\u001b[39m time\u001b[39m.\u001b[39mtime()\n\u001b[1;32m      5\u001b[0m df \u001b[39m=\u001b[39m csi\u001b[39m.\u001b[39mreadCam(os\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39mjoin(index,\u001b[39m'\u001b[39m\u001b[39mcam1.csv\u001b[39m\u001b[39m'\u001b[39m))\n\u001b[0;32m----> 6\u001b[0m df \u001b[39m=\u001b[39m csi\u001b[39m.\u001b[39;49mconvertToSI(df, \u001b[39m3.5\u001b[39;49m, \u001b[39m1.5\u001b[39;49m, \u001b[39m3.4\u001b[39;49m)\n\u001b[1;32m      7\u001b[0m df \u001b[39m=\u001b[39m csi\u001b[39m.\u001b[39mgetVelocity(df)\n\u001b[1;32m      8\u001b[0m df \u001b[39m=\u001b[39m csi\u001b[39m.\u001b[39mgetAcceleration(df)\n",
      "File \u001b[0;32m~/workspace/equation20/coordsi.py:62\u001b[0m, in \u001b[0;36mconvertToSI\u001b[0;34m(origin, w, l, h)\u001b[0m\n\u001b[1;32m     60\u001b[0m df \u001b[39m=\u001b[39m origin\u001b[39m.\u001b[39mcopy()\n\u001b[1;32m     61\u001b[0m \u001b[39m#normailze dataframe[0] from -1 to 1\u001b[39;00m\n\u001b[0;32m---> 62\u001b[0m df[\u001b[39m'\u001b[39m\u001b[39mx\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m=\u001b[39m preprocessing\u001b[39m.\u001b[39;49mminmax_scale(df[\u001b[39m'\u001b[39;49m\u001b[39mx\u001b[39;49m\u001b[39m'\u001b[39;49m], feature_range\u001b[39m=\u001b[39;49m(\u001b[39m-\u001b[39;49m(w\u001b[39m/\u001b[39;49m\u001b[39m2\u001b[39;49m), (w\u001b[39m/\u001b[39;49m\u001b[39m2\u001b[39;49m)), axis\u001b[39m=\u001b[39;49m\u001b[39m0\u001b[39;49m, copy\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m)\n\u001b[1;32m     63\u001b[0m \u001b[39m#normailze dataframe[1] from -0.75 to 0.75\u001b[39;00m\n\u001b[1;32m     64\u001b[0m df[\u001b[39m'\u001b[39m\u001b[39my\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m=\u001b[39m preprocessing\u001b[39m.\u001b[39mminmax_scale(df[\u001b[39m'\u001b[39m\u001b[39my\u001b[39m\u001b[39m'\u001b[39m], feature_range\u001b[39m=\u001b[39m(\u001b[39m-\u001b[39m(l\u001b[39m/\u001b[39m\u001b[39m2\u001b[39m), (l\u001b[39m/\u001b[39m\u001b[39m2\u001b[39m)), axis\u001b[39m=\u001b[39m\u001b[39m0\u001b[39m, copy\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/sklearn/preprocessing/_data.py:624\u001b[0m, in \u001b[0;36mminmax_scale\u001b[0;34m(X, feature_range, axis, copy)\u001b[0m\n\u001b[1;32m    550\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"Transform features by scaling each feature to a given range.\u001b[39;00m\n\u001b[1;32m    551\u001b[0m \n\u001b[1;32m    552\u001b[0m \u001b[39mThis estimator scales and translates each feature individually such\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    620\u001b[0m \u001b[39m<sphx_glr_auto_examples_preprocessing_plot_all_scaling.py>`.\u001b[39;00m\n\u001b[1;32m    621\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m    622\u001b[0m \u001b[39m# Unlike the scaler object, this function allows 1d input.\u001b[39;00m\n\u001b[1;32m    623\u001b[0m \u001b[39m# If copy is required, it will be done inside the scaler object.\u001b[39;00m\n\u001b[0;32m--> 624\u001b[0m X \u001b[39m=\u001b[39m check_array(\n\u001b[1;32m    625\u001b[0m     X, copy\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m, ensure_2d\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m, dtype\u001b[39m=\u001b[39;49mFLOAT_DTYPES, force_all_finite\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mallow-nan\u001b[39;49m\u001b[39m\"\u001b[39;49m\n\u001b[1;32m    626\u001b[0m )\n\u001b[1;32m    627\u001b[0m original_ndim \u001b[39m=\u001b[39m X\u001b[39m.\u001b[39mndim\n\u001b[1;32m    629\u001b[0m \u001b[39mif\u001b[39;00m original_ndim \u001b[39m==\u001b[39m \u001b[39m1\u001b[39m:\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/sklearn/utils/validation.py:931\u001b[0m, in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[1;32m    929\u001b[0m     n_samples \u001b[39m=\u001b[39m _num_samples(array)\n\u001b[1;32m    930\u001b[0m     \u001b[39mif\u001b[39;00m n_samples \u001b[39m<\u001b[39m ensure_min_samples:\n\u001b[0;32m--> 931\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m    932\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39mFound array with \u001b[39m\u001b[39m%d\u001b[39;00m\u001b[39m sample(s) (shape=\u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m) while a\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    933\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39m minimum of \u001b[39m\u001b[39m%d\u001b[39;00m\u001b[39m is required\u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    934\u001b[0m             \u001b[39m%\u001b[39m (n_samples, array\u001b[39m.\u001b[39mshape, ensure_min_samples, context)\n\u001b[1;32m    935\u001b[0m         )\n\u001b[1;32m    937\u001b[0m \u001b[39mif\u001b[39;00m ensure_min_features \u001b[39m>\u001b[39m \u001b[39m0\u001b[39m \u001b[39mand\u001b[39;00m array\u001b[39m.\u001b[39mndim \u001b[39m==\u001b[39m \u001b[39m2\u001b[39m:\n\u001b[1;32m    938\u001b[0m     n_features \u001b[39m=\u001b[39m array\u001b[39m.\u001b[39mshape[\u001b[39m1\u001b[39m]\n",
      "\u001b[0;31mValueError\u001b[0m: Found array with 0 sample(s) (shape=(0,)) while a minimum of 1 is required."
     ]
    }
   ],
   "source": [
    "#make a loop for each index\n",
    "for index, row in summary_1.iterrows():\n",
    "    timestamp = []\n",
    "    start_time = time.time()\n",
    "    df = csi.readCam(os.path.join(index,'cam1.csv'))\n",
    "    df = csi.convertToSI(df, 3.5, 1.5, 3.4)\n",
    "    df = csi.getVelocity(df)\n",
    "    df = csi.getAcceleration(df)\n",
    "    df = csi.getFullTrace(df,row['approx_total_frame'] )\n",
    "    df = csi.addValidDetection(df, 25.0)\n",
    "    df.to_csv(os.path.join('./handletrace/', index[17:]+'.csv'), index=False)\n",
    "    #save spend time and index of summary_1 in timestamp list    \n",
    "    timestamp.append([index, time.time() - start_time])\n",
    "#save timestamp list to csv file\n",
    "pd.DataFrame(timestamp, columns=['index', 'time']).to_csv('./handletrace/timestamp.csv', index=False)\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "import a origin coord and convert"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr_1 = csi.readCam(os.path.join(summary_1.index[0],'cam1.csv'))\n",
    "si_1 = csi.convertToSI(arr_1, 3.5, 1.5, 3.4)\n",
    "#cartesian coordinate system\n",
    "#구면 좌표계로 변경도 해보자...나중에...\n",
    "#meter/(1/30)s\n",
    "si_1['positionvectorvalue'] = (si_1['x']**2 + si_1['y']**2 + si_1['z']**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "cal_buffer_1, cal_buffer_2 = si_1['positionvectorvalue'].copy(),  si_1['positionvectorvalue'].copy()\n",
    "#print(cal_buffer_1,'\\n',cal_buffer_2)\n",
    "cal_buffer_1, cal_buffer_2= cal_buffer_1.to_frame(), cal_buffer_2.to_frame()\n",
    "cal_buffer_1 = list(cal_buffer_1.loc[:, 'positionvectorvalue'])\n",
    "cal_buffer_2 = list(cal_buffer_2.loc[:, 'positionvectorvalue'])\n",
    "cal_buffer_3 = [0.0]\n",
    "for v in range(0, len(cal_buffer_2)-1):\n",
    "    cal_buffer_3.append(cal_buffer_2[v])\n",
    "buffer_a = np.array(cal_buffer_1)\n",
    "buffer_b = np.array(cal_buffer_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "velocity = (buffer_a - buffer_b)\n",
    "for x in range(0, len(velocity)):\n",
    "    velocity[x] = math.sqrt(abs(velocity[x]))\n",
    "velocity[0]=0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#merge each list as Series\n",
    "si_1['velocity'] = velocity\n",
    "#print(si_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "cal_buffer_4, cal_buffer_5 = si_1['velocity'].copy(),  si_1['velocity'].copy()\n",
    "#print(cal_buffer_1,'\\n',cal_buffer_2)\n",
    "cal_buffer_4, cal_buffer_5= cal_buffer_4.to_frame(), cal_buffer_5.to_frame()\n",
    "cal_buffer_4 = list(cal_buffer_4.loc[:, 'velocity'])\n",
    "cal_buffer_5 = list(cal_buffer_5.loc[:, 'velocity'])\n",
    "cal_buffer_6 = [0.0]\n",
    "for v in range(0, len(cal_buffer_5)-1):\n",
    "    cal_buffer_6.append(cal_buffer_5[v])\n",
    "buffer_c = np.array(cal_buffer_4)\n",
    "buffer_d = np.array(cal_buffer_6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "acceleration = buffer_c - buffer_d\n",
    "acceleration[1] = 0.0\n",
    "#print(acceleration)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "si_1['acceleration'] = acceleration\n",
    "#print(si_1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate Velocity of all detected objects"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "TODO: approx_total_frame 개수 만큼의 빈 컬럼 생성 (나중에 전체 데이터프레임은 20등분으로 잘릴 예정) -> done\n",
    "실제 값이 존재하는 프레임을 찾고, 그 프레임을 인덱스에 맞추어 x,y,z 집어넣기\n",
    "존재한다면 (x^2 + y^2 + z^2)^(1/3) 을 계산하여 새 컬럼에 저장\n",
    "비 등속 보간 작업을 해야 하는데, 어떤 방식으로 할지 결정하기(piecewise_polynomial?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>frame</th>\n",
       "      <th>positionvectorvalue</th>\n",
       "      <th>velocity</th>\n",
       "      <th>acceleration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>62495.000000</td>\n",
       "      <td>62495.000000</td>\n",
       "      <td>62495.000000</td>\n",
       "      <td>62495.000000</td>\n",
       "      <td>62495.000000</td>\n",
       "      <td>62495.000000</td>\n",
       "      <td>62495.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.202211</td>\n",
       "      <td>-0.207365</td>\n",
       "      <td>1.601385</td>\n",
       "      <td>42805.432355</td>\n",
       "      <td>2.733538</td>\n",
       "      <td>0.138262</td>\n",
       "      <td>0.000002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.162759</td>\n",
       "      <td>0.238811</td>\n",
       "      <td>0.041177</td>\n",
       "      <td>27211.098449</td>\n",
       "      <td>0.162993</td>\n",
       "      <td>0.120752</td>\n",
       "      <td>0.124054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.750000</td>\n",
       "      <td>-1.750000</td>\n",
       "      <td>0.930000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.109630</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.650274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.328348</td>\n",
       "      <td>-0.377956</td>\n",
       "      <td>1.574043</td>\n",
       "      <td>19851.500000</td>\n",
       "      <td>2.637680</td>\n",
       "      <td>0.067702</td>\n",
       "      <td>-0.045567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.183048</td>\n",
       "      <td>-0.240016</td>\n",
       "      <td>1.598190</td>\n",
       "      <td>38725.000000</td>\n",
       "      <td>2.706999</td>\n",
       "      <td>0.111651</td>\n",
       "      <td>0.000205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-0.096154</td>\n",
       "      <td>-0.032186</td>\n",
       "      <td>1.625495</td>\n",
       "      <td>69024.500000</td>\n",
       "      <td>2.793686</td>\n",
       "      <td>0.168573</td>\n",
       "      <td>0.045744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.750000</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>233066.000000</td>\n",
       "      <td>6.039652</td>\n",
       "      <td>1.941675</td>\n",
       "      <td>1.692079</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  x             y             z          frame   \n",
       "count  62495.000000  62495.000000  62495.000000   62495.000000  \\\n",
       "mean      -0.202211     -0.207365      1.601385   42805.432355   \n",
       "std        0.162759      0.238811      0.041177   27211.098449   \n",
       "min       -0.750000     -1.750000      0.930000       1.000000   \n",
       "25%       -0.328348     -0.377956      1.574043   19851.500000   \n",
       "50%       -0.183048     -0.240016      1.598190   38725.000000   \n",
       "75%       -0.096154     -0.032186      1.625495   69024.500000   \n",
       "max        0.750000      1.750000      1.800000  233066.000000   \n",
       "\n",
       "       positionvectorvalue      velocity  acceleration  \n",
       "count         62495.000000  62495.000000  62495.000000  \n",
       "mean              2.733538      0.138262      0.000002  \n",
       "std               0.162993      0.120752      0.124054  \n",
       "min               1.109630      0.000000     -1.650274  \n",
       "25%               2.637680      0.067702     -0.045567  \n",
       "50%               2.706999      0.111651      0.000205  \n",
       "75%               2.793686      0.168573      0.045744  \n",
       "max               6.039652      1.941675      1.692079  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "si_1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          x    y    z  frame  positionvectorvalue  velocity  acceleration   \n",
      "478795  0.0  0.0  0.0    0.0                  0.0       0.0           0.0  \\\n",
      "478796  0.0  0.0  0.0    0.0                  0.0       0.0           0.0   \n",
      "478797  0.0  0.0  0.0    0.0                  0.0       0.0           0.0   \n",
      "478798  0.0  0.0  0.0    0.0                  0.0       0.0           0.0   \n",
      "478799  0.0  0.0  0.0    0.0                  0.0       0.0           0.0   \n",
      "\n",
      "        isValid  \n",
      "478795      0.0  \n",
      "478796      0.0  \n",
      "478797      0.0  \n",
      "478798      0.0  \n",
      "478799      0.0  \n"
     ]
    }
   ],
   "source": [
    "# create dataframe with 6 columns and create 0 value rows as many as approx_total_frame value in summary_1\n",
    "j = 0\n",
    "handletrace = pd.DataFrame(columns=['x', 'y', 'z', 'frame', 'positionvectorvalue', 'velocity', 'acceleration', 'isValid'])\n",
    "handletrace = pd.concat([handletrace, pd.DataFrame(np.zeros((int(summary_1['approx_total_frame'][j]),8)), columns=['x', 'y', 'z', 'frame', 'positionvectorvalue','velocity', 'acceleration', 'isValid'])], axis=0)\n",
    "print(handletrace.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(int(si_1['frame'][2]))\n",
    "#print(si_1.loc[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# frame의 값에 해당하는 것으로 변경\n",
    "for x in range(0, len(si_1)):\n",
    "    handletrace.loc[int(si_1['frame'][x])] = si_1.loc[x]\n",
    "handletrace['isValid'] = handletrace['isValid'].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               x         y         z  frame  positionvectorvalue  velocity   \n",
      "0       0.000000  0.000000  0.000000    0.0             0.000000  0.000000  \\\n",
      "1      -0.400997 -0.319101  1.632182    1.0             2.926644  0.000000   \n",
      "2      -0.410969 -0.333815  1.629004    2.0             2.933981  0.085661   \n",
      "3      -0.412393 -0.352207  1.634730    3.0             2.966459  0.180214   \n",
      "4      -0.413818 -0.357725  1.638935    4.0             2.985322  0.137342   \n",
      "...          ...       ...       ...    ...                  ...       ...   \n",
      "478795  0.000000  0.000000  0.000000    0.0             0.000000  0.000000   \n",
      "478796  0.000000  0.000000  0.000000    0.0             0.000000  0.000000   \n",
      "478797  0.000000  0.000000  0.000000    0.0             0.000000  0.000000   \n",
      "478798  0.000000  0.000000  0.000000    0.0             0.000000  0.000000   \n",
      "478799  0.000000  0.000000  0.000000    0.0             0.000000  0.000000   \n",
      "\n",
      "        acceleration  isValid  \n",
      "0           0.000000      0.0  \n",
      "1           0.000000      0.0  \n",
      "2           0.000000      0.0  \n",
      "3           0.094553      0.0  \n",
      "4          -0.042873      0.0  \n",
      "...              ...      ...  \n",
      "478795      0.000000      0.0  \n",
      "478796      0.000000      0.0  \n",
      "478797      0.000000      0.0  \n",
      "478798      0.000000      0.0  \n",
      "478799      0.000000      0.0  \n",
      "\n",
      "[478800 rows x 8 columns]\n"
     ]
    }
   ],
   "source": [
    "print(handletrace)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''import plotly.graph_objects as go\n",
    "\n",
    "#Create Figure\n",
    "fig = go.Figure()\n",
    "fig.add_trace(go.Scatter( x=list(handletrace.index), y=list(handletrace['velocity'] * 30)))\n",
    "\n",
    "#Set Title\n",
    "fig.update_layout(title_text='Velocity per second by frame')\n",
    "\n",
    "#Add range slider\n",
    "fig.update_layout(\n",
    "    xaxis=dict(\n",
    "        rangeslider=dict(\n",
    "            visible=True\n",
    "        )\n",
    "    )\n",
    ")'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''import plotly.graph_objects as go\n",
    "\n",
    "#Create Figure\n",
    "fig = go.Figure()\n",
    "fig.add_trace(go.Scatter( x=list(handletrace.index), y=list(handletrace['acceleration']*900)))\n",
    "\n",
    "#Set Title\n",
    "fig.update_layout(title_text='acceleration per second^2 by frame')\n",
    "\n",
    "#Add range slider\n",
    "fig.update_layout(\n",
    "    xaxis=dict(\n",
    "        rangeslider=dict(\n",
    "            visible=True\n",
    "        )\n",
    "    )\n",
    ")'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#comapre the value of handletrace['acceleration'] and if it is between -25 and 25, then set handletrace['isValid'] to 1\n",
    "for x in range(0, len(handletrace)):\n",
    "    if (-25/900) < handletrace['acceleration'][x] < (25/900):\n",
    "        handletrace['isValid'][x] = 1\n",
    "    else:\n",
    "        handletrace['isValid'][x] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1. 0.]\n",
      "isValid\n",
      "1.0    437730\n",
      "0.0     41070\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(handletrace['isValid'].unique())\n",
    "print(handletrace['isValid'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               x         y         z  frame  positionvectorvalue  velocity   \n",
      "0       0.000000  0.000000  0.000000    0.0             0.000000  0.000000  \\\n",
      "1      -0.400997 -0.319101  1.632182    1.0             2.926644  0.000000   \n",
      "2      -0.410969 -0.333815  1.629004    2.0             2.933981  0.085661   \n",
      "3      -0.412393 -0.352207  1.634730    3.0             2.966459  0.180214   \n",
      "4      -0.413818 -0.357725  1.638935    4.0             2.985322  0.137342   \n",
      "...          ...       ...       ...    ...                  ...       ...   \n",
      "478795  0.000000  0.000000  0.000000    0.0             0.000000  0.000000   \n",
      "478796  0.000000  0.000000  0.000000    0.0             0.000000  0.000000   \n",
      "478797  0.000000  0.000000  0.000000    0.0             0.000000  0.000000   \n",
      "478798  0.000000  0.000000  0.000000    0.0             0.000000  0.000000   \n",
      "478799  0.000000  0.000000  0.000000    0.0             0.000000  0.000000   \n",
      "\n",
      "        acceleration  isValid  \n",
      "0           0.000000      1.0  \n",
      "1           0.000000      1.0  \n",
      "2           0.000000      1.0  \n",
      "3           0.094553      0.0  \n",
      "4          -0.042873      0.0  \n",
      "...              ...      ...  \n",
      "478795      0.000000      1.0  \n",
      "478796      0.000000      1.0  \n",
      "478797      0.000000      1.0  \n",
      "478798      0.000000      1.0  \n",
      "478799      0.000000      1.0  \n",
      "\n",
      "[478800 rows x 8 columns]\n"
     ]
    }
   ],
   "source": [
    "print(handletrace)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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