rename notebook

toysnippet/moduleimprot.ipynb

@@ -1,44 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sys\n",
-    "sys.path.append('..')\n",
-    "import modules.coordsi as csi"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.8.0"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

toysnippet/sandbox.ipynb

@@ -1,681 +0,0 @@
-{
- "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": {
-      "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>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": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.8.0"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "df0893f56f349688326838aaeea0de204df53a132722cbd565e54b24a8fec5f6"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}