csv.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8e992383-b0d5-4eba-983e-745861762678",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import datetime\n",
    "import datetime as dt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "20091d52-f6d7-4bd8-b174-4422a60a8491",
   "metadata": {},
   "outputs": [],
   "source": [
    "filea='./csv_25/bres_qtableno_euc_25.csv'\n",
    "fileb='./csv_25/bres_qtabletraceno_euc_25.csv'\n",
    "filec='./csv_25/bres_sarsano_euc_25.csv'\n",
    "filed='./csv_25/bres_sarsatraceno_euc_25.csv'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0b78e3d9-506b-4ab9-8d91-8a4d21d03eef",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./csv_25/bres_qtableno_euc_25.csv\n",
      "===============30%===================\n",
      "1.0\n",
      "53.44\n",
      "0:35:07.465211\n",
      "===============40%===================\n",
      "1.0\n",
      "52.24\n",
      "0:34:44.021210\n",
      "===============Average===================\n",
      "1.0\n",
      "52.84\n",
      "0:34:55.743211\n"
     ]
    }
   ],
   "source": [
    "print(filea)\n",
    "df=pd.read_csv(filea,header=0)\n",
    "df.sort_values([\"NAME\"], axis=0,ascending=[False],inplace=True)\n",
    "\n",
    "l2=[]\n",
    "l3=[]\n",
    "l4=[]\n",
    "l5=[]\n",
    "\n",
    "m2=[]\n",
    "m3=[]\n",
    "m4=[]\n",
    "m5=[]\n",
    "\n",
    "count2=[]\n",
    "count3=[]\n",
    "count4=[]\n",
    "count5=[]\n",
    "\n",
    "\n",
    "for index, row in df.iterrows():\n",
    "    \n",
    "    if row[\"PATH\"][-12:-4]==\"[24, 24]\":\n",
    "        if row[\"OBS\"]==0.3:\n",
    "            l3.append(row[\"TIME\"][7:])\n",
    "            m3.append(row[\"STP_PL\"])\n",
    "        elif row[\"OBS\"]==0.4:\n",
    "            l4.append(row[\"TIME\"][7:])\n",
    "            m4.append(row[\"STP_PL\"])\n",
    "    else:\n",
    "        if row[\"OBS\"]==0.3:\n",
    "            count3.append(row[\"TIME\"][7:])\n",
    "        elif row[\"OBS\"]==0.4:\n",
    "            count4.append(row[\"TIME\"][7:])\n",
    "\n",
    "\n",
    "print(\"===============30%===================\")\n",
    "print((25-len(count3))/25)\n",
    "print(np.average(m3))\n",
    "avg=0\n",
    "tmp=0\n",
    "for i in range(len(l3)):\n",
    "    date_time_obj = datetime.strptime(l3[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "f=avg/len(l3)\n",
    "print(str(dt.timedelta(microseconds=f)))\n",
    "\n",
    "\n",
    "print(\"===============40%===================\")\n",
    "print((25-len(count4))/25)\n",
    "print(np.average(m4))\n",
    "avg=0\n",
    "tmp=0\n",
    "for i in range(len(l4)):\n",
    "    date_time_obj = datetime.strptime(l4[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "f=avg/len(l4)\n",
    "print(str(dt.timedelta(microseconds=f)))\n",
    "\n",
    "print(\"===============Average===================\")\n",
    "print((50-len(count3+count4))/50)\n",
    "print(np.average(m2+m3+m4+m5))\n",
    "avg=0\n",
    "tmp=0\n",
    "for i in range(len(l3)):\n",
    "    date_time_obj = datetime.strptime(l3[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "for i in range(len(l4)):\n",
    "    date_time_obj = datetime.strptime(l4[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "\n",
    "    \n",
    "f=avg/len(l3+l4)\n",
    "print(str(dt.timedelta(microseconds=f)))     \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "91126dc2-f585-4689-ab6d-8701742c9f7a",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./csv_25/bres_qtabletraceno_euc_25.csv\n",
      "===============30%===================\n",
      "0.84\n",
      "55.04761904761905\n",
      "0:25:57.444026\n",
      "===============40%===================\n",
      "0.96\n",
      "54.25\n",
      "0:25:05.910508\n",
      "===============Average===================\n",
      "0.9\n",
      "54.62222222222222\n",
      "0:25:29.959483\n"
     ]
    }
   ],
   "source": [
    "print(fileb)\n",
    "df=pd.read_csv(fileb,header=0)\n",
    "df.sort_values([\"NAME\"], axis=0,ascending=[False],inplace=True)\n",
    "\n",
    "l2=[]\n",
    "l3=[]\n",
    "l4=[]\n",
    "l5=[]\n",
    "\n",
    "m2=[]\n",
    "m3=[]\n",
    "m4=[]\n",
    "m5=[]\n",
    "\n",
    "count2=[]\n",
    "count3=[]\n",
    "count4=[]\n",
    "count5=[]\n",
    "\n",
    "\n",
    "for index, row in df.iterrows():\n",
    "    \n",
    "    if row[\"PATH\"][-12:-4]==\"[24, 24]\":\n",
    "        if row[\"OBS\"]==0.3:\n",
    "            l3.append(row[\"TIME\"][7:])\n",
    "            m3.append(row[\"STP_PL\"])\n",
    "        elif row[\"OBS\"]==0.4:\n",
    "            l4.append(row[\"TIME\"][7:])\n",
    "            m4.append(row[\"STP_PL\"])\n",
    "    else:\n",
    "        if row[\"OBS\"]==0.3:\n",
    "            count3.append(row[\"TIME\"][7:])\n",
    "        elif row[\"OBS\"]==0.4:\n",
    "            count4.append(row[\"TIME\"][7:])\n",
    "\n",
    "\n",
    "print(\"===============30%===================\")\n",
    "print((25-len(count3))/25)\n",
    "print(np.average(m3))\n",
    "avg=0\n",
    "tmp=0\n",
    "for i in range(len(l3)):\n",
    "    date_time_obj = datetime.strptime(l3[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "f=avg/len(l3)\n",
    "print(str(dt.timedelta(microseconds=f)))\n",
    "\n",
    "\n",
    "print(\"===============40%===================\")\n",
    "print((25-len(count4))/25)\n",
    "print(np.average(m4))\n",
    "avg=0\n",
    "tmp=0\n",
    "for i in range(len(l4)):\n",
    "    date_time_obj = datetime.strptime(l4[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "f=avg/len(l4)\n",
    "print(str(dt.timedelta(microseconds=f)))\n",
    "\n",
    "print(\"===============Average===================\")\n",
    "print((50-len(count3+count4))/50)\n",
    "print(np.average(m2+m3+m4+m5))\n",
    "avg=0\n",
    "tmp=0\n",
    "for i in range(len(l3)):\n",
    "    date_time_obj = datetime.strptime(l3[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "for i in range(len(l4)):\n",
    "    date_time_obj = datetime.strptime(l4[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "\n",
    "    \n",
    "f=avg/len(l3+l4)\n",
    "print(str(dt.timedelta(microseconds=f)))     \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ac8f9fb4-0953-4469-aa31-1e843fcecdf7",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./csv_25/bres_sarsano_euc_25.csv\n",
      "===============30%===================\n",
      "1.0\n",
      "52.48\n",
      "0:35:26.043030\n",
      "===============40%===================\n",
      "1.0\n",
      "51.6\n",
      "0:33:31.986086\n",
      "===============Average===================\n",
      "1.0\n",
      "52.04\n",
      "0:34:29.014558\n"
     ]
    }
   ],
   "source": [
    "print(filec)\n",
    "df=pd.read_csv(filec,header=0)\n",
    "df.sort_values([\"NAME\"], axis=0,ascending=[False],inplace=True)\n",
    "\n",
    "l2=[]\n",
    "l3=[]\n",
    "l4=[]\n",
    "l5=[]\n",
    "\n",
    "m2=[]\n",
    "m3=[]\n",
    "m4=[]\n",
    "m5=[]\n",
    "\n",
    "count2=[]\n",
    "count3=[]\n",
    "count4=[]\n",
    "count5=[]\n",
    "\n",
    "\n",
    "for index, row in df.iterrows():\n",
    "    \n",
    "    if row[\"PATH\"][-12:-4]==\"[24, 24]\":\n",
    "        if row[\"OBS\"]==0.3:\n",
    "            l3.append(row[\"TIME\"][7:])\n",
    "            m3.append(row[\"STP_PL\"])\n",
    "        elif row[\"OBS\"]==0.4:\n",
    "            l4.append(row[\"TIME\"][7:])\n",
    "            m4.append(row[\"STP_PL\"])\n",
    "    else:\n",
    "        if row[\"OBS\"]==0.3:\n",
    "            count3.append(row[\"TIME\"][7:])\n",
    "        elif row[\"OBS\"]==0.4:\n",
    "            count4.append(row[\"TIME\"][7:])\n",
    "\n",
    "\n",
    "print(\"===============30%===================\")\n",
    "print((25-len(count3))/25)\n",
    "print(np.average(m3))\n",
    "avg=0\n",
    "tmp=0\n",
    "for i in range(len(l3)):\n",
    "    date_time_obj = datetime.strptime(l3[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "f=avg/len(l3)\n",
    "print(str(dt.timedelta(microseconds=f)))\n",
    "\n",
    "\n",
    "print(\"===============40%===================\")\n",
    "print((25-len(count4))/25)\n",
    "print(np.average(m4))\n",
    "avg=0\n",
    "tmp=0\n",
    "for i in range(len(l4)):\n",
    "    date_time_obj = datetime.strptime(l4[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "f=avg/len(l4)\n",
    "print(str(dt.timedelta(microseconds=f)))\n",
    "\n",
    "print(\"===============Average===================\")\n",
    "print((50-len(count3+count4))/50)\n",
    "print(np.average(m2+m3+m4+m5))\n",
    "avg=0\n",
    "tmp=0\n",
    "for i in range(len(l3)):\n",
    "    date_time_obj = datetime.strptime(l3[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "for i in range(len(l4)):\n",
    "    date_time_obj = datetime.strptime(l4[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "\n",
    "    \n",
    "f=avg/len(l3+l4)\n",
    "print(str(dt.timedelta(microseconds=f)))     \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "945fe3db-4982-4de8-a11e-b1f21e3a275f",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./csv_25/bres_sarsatraceno_euc_25.csv\n",
      "===============30%===================\n",
      "0.92\n",
      "54.52173913043478\n",
      "0:29:22.459668\n",
      "===============40%===================\n",
      "0.88\n",
      "53.90909090909091\n",
      "0:28:46.828538\n",
      "===============Average===================\n",
      "0.9\n",
      "54.22222222222222\n",
      "0:29:05.040005\n"
     ]
    }
   ],
   "source": [
    "print(filed)\n",
    "df=pd.read_csv(filed,header=0)\n",
    "df.sort_values([\"NAME\"], axis=0,ascending=[False],inplace=True)\n",
    "\n",
    "l2=[]\n",
    "l3=[]\n",
    "l4=[]\n",
    "l5=[]\n",
    "\n",
    "m2=[]\n",
    "m3=[]\n",
    "m4=[]\n",
    "m5=[]\n",
    "\n",
    "count2=[]\n",
    "count3=[]\n",
    "count4=[]\n",
    "count5=[]\n",
    "\n",
    "\n",
    "for index, row in df.iterrows():\n",
    "    \n",
    "    if row[\"PATH\"][-12:-4]==\"[24, 24]\":\n",
    "        if row[\"OBS\"]==0.3:\n",
    "            l3.append(row[\"TIME\"][7:])\n",
    "            m3.append(row[\"STP_PL\"])\n",
    "        elif row[\"OBS\"]==0.4:\n",
    "            l4.append(row[\"TIME\"][7:])\n",
    "            m4.append(row[\"STP_PL\"])\n",
    "    else:\n",
    "        if row[\"OBS\"]==0.3:\n",
    "            count3.append(row[\"TIME\"][7:])\n",
    "        elif row[\"OBS\"]==0.4:\n",
    "            count4.append(row[\"TIME\"][7:])\n",
    "\n",
    "\n",
    "print(\"===============30%===================\")\n",
    "print((25-len(count3))/25)\n",
    "print(np.average(m3))\n",
    "avg=0\n",
    "tmp=0\n",
    "for i in range(len(l3)):\n",
    "    date_time_obj = datetime.strptime(l3[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "f=avg/len(l3)\n",
    "print(str(dt.timedelta(microseconds=f)))\n",
    "\n",
    "\n",
    "print(\"===============40%===================\")\n",
    "print((25-len(count4))/25)\n",
    "print(np.average(m4))\n",
    "avg=0\n",
    "tmp=0\n",
    "for i in range(len(l4)):\n",
    "    date_time_obj = datetime.strptime(l4[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "f=avg/len(l4)\n",
    "print(str(dt.timedelta(microseconds=f)))\n",
    "\n",
    "print(\"===============Average===================\")\n",
    "print((50-len(count3+count4))/50)\n",
    "print(np.average(m2+m3+m4+m5))\n",
    "avg=0\n",
    "tmp=0\n",
    "for i in range(len(l3)):\n",
    "    date_time_obj = datetime.strptime(l3[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "for i in range(len(l4)):\n",
    "    date_time_obj = datetime.strptime(l4[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "\n",
    "    \n",
    "f=avg/len(l3+l4)\n",
    "print(str(dt.timedelta(microseconds=f)))     \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa9af6c7-a49d-4f3d-9798-f2ffa1f4f0a7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5d12381-cdee-4a41-925d-55ea9f37d69c",
   "metadata": {},
   "outputs": [],
   "source": [
    "##BELOW WORKS FOR 11 FILES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "b0186523-3d4a-4c54-9741-baa540a27589",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df1=pd.read_csv('res_sarsatraceprox2_opt5.csv',header=0)\n",
    "#df2=pd.read_csv('res_sarsatraceprox2_opt11.csv',header=0)\n",
    "#df3=pd.read_csv('res_sarsatraceprox2_opt15.csv',header=0)\n",
    "#df4=pd.read_csv('res_sarsatraceprox2_no21.csv',header=0)\n",
    "#df5=pd.read_csv('res_sarsatraceprox2_no25.csv',header=0)\n",
    "\n",
    "#df = pd.concat([df4,df5], ignore_index=True)\n",
    "df=pd.read_csv('./bres_sarsatraceno_base.csv',header=0)\n",
    "\n",
    "filea='./bres_qtable_che.csv'\n",
    "fileb='./bres_qtabletrace_che.csv'\n",
    "filec='./bres_sarsa_che.csv'\n",
    "filed='./bres_sarsatrace_che.csv'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "68948c82-1567-41f8-9882-6f2f41bd2702",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./bres_qtable_che.csv\n",
      "===============Average===================\n",
      "0.885\n",
      "20.943502824858758\n",
      "0:01:00.644172\n"
     ]
    }
   ],
   "source": [
    "print(filea)\n",
    "df=pd.read_csv(filea,header=0)\n",
    "df.sort_values([\"NAME\"], axis=0,ascending=[False],inplace=True)\n",
    "\n",
    "l2=[]\n",
    "l3=[]\n",
    "l4=[]\n",
    "l5=[]\n",
    "\n",
    "m2=[]\n",
    "m3=[]\n",
    "m4=[]\n",
    "m5=[]\n",
    "\n",
    "count2=[]\n",
    "count3=[]\n",
    "count4=[]\n",
    "count5=[]\n",
    "\n",
    "\n",
    "for index, row in df.iterrows():\n",
    "    \n",
    "    if row[\"PATH\"][-12:-4]==\"[10, 10]\":\n",
    "        if row[\"OBS\"]==0.2:\n",
    "            l2.append(row[\"TIME\"][7:])\n",
    "            m2.append(row[\"STP_PL\"])\n",
    "        elif row[\"OBS\"]==0.3:\n",
    "            l3.append(row[\"TIME\"][7:])\n",
    "            m3.append(row[\"STP_PL\"])\n",
    "        elif row[\"OBS\"]==0.4:\n",
    "            l4.append(row[\"TIME\"][7:])\n",
    "            m4.append(row[\"STP_PL\"])\n",
    "        elif row[\"OBS\"]==0.5:\n",
    "            l5.append(row[\"TIME\"][7:])\n",
    "            m5.append(row[\"STP_PL\"])\n",
    "\n",
    "    else:\n",
    "        if row[\"OBS\"]==0.2:\n",
    "            count2.append(row[\"TIME\"][7:])\n",
    "        elif row[\"OBS\"]==0.3:\n",
    "            count3.append(row[\"TIME\"][7:])\n",
    "        elif row[\"OBS\"]==0.4:\n",
    "            count4.append(row[\"TIME\"][7:])\n",
    "        elif row[\"OBS\"]==0.5:\n",
    "            count5.append(row[\"TIME\"][7:])\n",
    "'''\n",
    "print(\"===============20%===================\")\n",
    "print((100-len(count2))/100)\n",
    "print(np.average(m2))\n",
    "avg=0\n",
    "tmp=0\n",
    "for i in range(len(l2)):\n",
    "    date_time_obj = datetime.strptime(l2[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "f=avg/len(l2)\n",
    "print(str(dt.timedelta(microseconds=f)))\n",
    "\n",
    "print(\"===============30%===================\")\n",
    "print((100-len(count3))/100)\n",
    "print(np.average(m3))\n",
    "avg=0\n",
    "tmp=0\n",
    "for i in range(len(l3)):\n",
    "    date_time_obj = datetime.strptime(l3[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "f=avg/len(l3)\n",
    "print(str(dt.timedelta(microseconds=f)))\n",
    "\n",
    "\n",
    "print(\"===============40%===================\")\n",
    "print((100-len(count4))/100)\n",
    "print(np.average(m4))\n",
    "avg=0\n",
    "tmp=0\n",
    "for i in range(len(l4)):\n",
    "    date_time_obj = datetime.strptime(l4[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "f=avg/len(l4)\n",
    "print(str(dt.timedelta(microseconds=f)))\n",
    "\n",
    "print(\"===============50%===================\")\n",
    "print((100-len(count5))/100)\n",
    "print(np.average(m5))\n",
    "avg=0\n",
    "tmp=0\n",
    "for i in range(len(l5)):\n",
    "    date_time_obj = datetime.strptime(l5[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "f=avg/len(l5)\n",
    "print(str(dt.timedelta(microseconds=f)))     \n",
    "'''\n",
    "\n",
    "print(\"===============Average===================\")\n",
    "print((400-len(count2+count3+count4+count5))/400)\n",
    "print(np.average(m2+m3+m4+m5))\n",
    "avg=0\n",
    "tmp=0\n",
    "for i in range(len(l2)):\n",
    "    date_time_obj = datetime.strptime(l2[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "    \n",
    "for i in range(len(l3)):\n",
    "    date_time_obj = datetime.strptime(l3[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "for i in range(len(l4)):\n",
    "    date_time_obj = datetime.strptime(l4[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "for i in range(len(l5)):\n",
    "    date_time_obj = datetime.strptime(l5[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "    \n",
    "    \n",
    "f=avg/len(l2+l3+l4+l5)\n",
    "print(str(dt.timedelta(microseconds=f)))     \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "0f9d0a4d-d80d-4f6c-a3c9-2bc365c37b30",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./bres_qtabletrace_che.csv\n",
      "===============Average===================\n",
      "1.0\n",
      "21.92\n",
      "0:00:36.155883\n"
     ]
    }
   ],
   "source": [
    "print(fileb)\n",
    "df=pd.read_csv(fileb,header=0)\n",
    "df.sort_values([\"NAME\"], axis=0,ascending=[False],inplace=True)\n",
    "\n",
    "l2=[]\n",
    "l3=[]\n",
    "l4=[]\n",
    "l5=[]\n",
    "\n",
    "m2=[]\n",
    "m3=[]\n",
    "m4=[]\n",
    "m5=[]\n",
    "\n",
    "count2=[]\n",
    "count3=[]\n",
    "count4=[]\n",
    "count5=[]\n",
    "\n",
    "\n",
    "for index, row in df.iterrows():\n",
    "    \n",
    "    if row[\"PATH\"][-12:-4]==\"[10, 10]\":\n",
    "        if row[\"OBS\"]==0.2:\n",
    "            l2.append(row[\"TIME\"][7:])\n",
    "            m2.append(row[\"STP_PL\"])\n",
    "        elif row[\"OBS\"]==0.3:\n",
    "            l3.append(row[\"TIME\"][7:])\n",
    "            m3.append(row[\"STP_PL\"])\n",
    "        elif row[\"OBS\"]==0.4:\n",
    "            l4.append(row[\"TIME\"][7:])\n",
    "            m4.append(row[\"STP_PL\"])\n",
    "        elif row[\"OBS\"]==0.5:\n",
    "            l5.append(row[\"TIME\"][7:])\n",
    "            m5.append(row[\"STP_PL\"])\n",
    "\n",
    "    else:\n",
    "        if row[\"OBS\"]==0.2:\n",
    "            count2.append(row[\"TIME\"][7:])\n",
    "        elif row[\"OBS\"]==0.3:\n",
    "            count3.append(row[\"TIME\"][7:])\n",
    "        elif row[\"OBS\"]==0.4:\n",
    "            count4.append(row[\"TIME\"][7:])\n",
    "        elif row[\"OBS\"]==0.5:\n",
    "            count5.append(row[\"TIME\"][7:])\n",
    "'''\n",
    "print(\"===============20%===================\")\n",
    "print((100-len(count2))/100)\n",
    "print(np.average(m2))\n",
    "avg=0\n",
    "tmp=0\n",
    "for i in range(len(l2)):\n",
    "    date_time_obj = datetime.strptime(l2[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "f=avg/len(l2)\n",
    "print(str(dt.timedelta(microseconds=f)))\n",
    "\n",
    "\n",
    "print(\"===============30%===================\")\n",
    "print((100-len(count3))/100)\n",
    "print(np.average(m3))\n",
    "avg=0\n",
    "tmp=0\n",
    "for i in range(len(l3)):\n",
    "    date_time_obj = datetime.strptime(l3[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "f=avg/len(l3)\n",
    "print(str(dt.timedelta(microseconds=f)))\n",
    "\n",
    "\n",
    "print(\"===============40%===================\")\n",
    "print((100-len(count4))/100)\n",
    "print(np.average(m4))\n",
    "avg=0\n",
    "tmp=0\n",
    "for i in range(len(l4)):\n",
    "    date_time_obj = datetime.strptime(l4[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "f=avg/len(l4)\n",
    "print(str(dt.timedelta(microseconds=f)))\n",
    "\n",
    "print(\"===============50%===================\")\n",
    "print((100-len(count5))/100)\n",
    "print(np.average(m5))\n",
    "avg=0\n",
    "tmp=0\n",
    "for i in range(len(l5)):\n",
    "    date_time_obj = datetime.strptime(l5[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "f=avg/len(l5)\n",
    "print(str(dt.timedelta(microseconds=f)))\n",
    "'''\n",
    "print(\"===============Average===================\")\n",
    "print((400-len(count2+count3+count4+count5))/400)\n",
    "print(np.average(m2+m3+m4+m5))\n",
    "avg=0\n",
    "tmp=0\n",
    "for i in range(len(l2)):\n",
    "    date_time_obj = datetime.strptime(l2[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "    \n",
    "for i in range(len(l3)):\n",
    "    date_time_obj = datetime.strptime(l3[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "for i in range(len(l4)):\n",
    "    date_time_obj = datetime.strptime(l4[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "for i in range(len(l5)):\n",
    "    date_time_obj = datetime.strptime(l5[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "    \n",
    "    \n",
    "f=avg/len(l2+l3+l4+l5)\n",
    "print(str(dt.timedelta(microseconds=f)))     \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "55acf990-3d4a-414b-a1e0-9891495e111f",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./bres_sarsa_che.csv\n",
      "===============Average===================\n",
      "0.8975\n",
      "20.958217270194986\n",
      "0:01:02.337896\n"
     ]
    }
   ],
   "source": [
    "print(filec)\n",
    "df=pd.read_csv(filec,header=0)\n",
    "df.sort_values([\"NAME\"], axis=0,ascending=[False],inplace=True)\n",
    "\n",
    "l2=[]\n",
    "l3=[]\n",
    "l4=[]\n",
    "l5=[]\n",
    "\n",
    "m2=[]\n",
    "m3=[]\n",
    "m4=[]\n",
    "m5=[]\n",
    "\n",
    "count2=[]\n",
    "count3=[]\n",
    "count4=[]\n",
    "count5=[]\n",
    "\n",
    "\n",
    "for index, row in df.iterrows():\n",
    "    \n",
    "    if row[\"PATH\"][-12:-4]==\"[10, 10]\":\n",
    "        if row[\"OBS\"]==0.2:\n",
    "            l2.append(row[\"TIME\"][7:])\n",
    "            m2.append(row[\"STP_PL\"])\n",
    "        elif row[\"OBS\"]==0.3:\n",
    "            l3.append(row[\"TIME\"][7:])\n",
    "            m3.append(row[\"STP_PL\"])\n",
    "        elif row[\"OBS\"]==0.4:\n",
    "            l4.append(row[\"TIME\"][7:])\n",
    "            m4.append(row[\"STP_PL\"])\n",
    "        elif row[\"OBS\"]==0.5:\n",
    "            l5.append(row[\"TIME\"][7:])\n",
    "            m5.append(row[\"STP_PL\"])\n",
    "\n",
    "    else:\n",
    "        if row[\"OBS\"]==0.2:\n",
    "            count2.append(row[\"TIME\"][7:])\n",
    "        elif row[\"OBS\"]==0.3:\n",
    "            count3.append(row[\"TIME\"][7:])\n",
    "        elif row[\"OBS\"]==0.4:\n",
    "            count4.append(row[\"TIME\"][7:])\n",
    "        elif row[\"OBS\"]==0.5:\n",
    "            count5.append(row[\"TIME\"][7:])\n",
    "'''\n",
    "print(\"===============20%===================\")\n",
    "print((100-len(count2))/100)\n",
    "print(np.average(m2))\n",
    "avg=0\n",
    "tmp=0\n",
    "for i in range(len(l2)):\n",
    "    date_time_obj = datetime.strptime(l2[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "f=avg/len(l2)\n",
    "print(str(dt.timedelta(microseconds=f)))\n",
    "\n",
    "\n",
    "print(\"===============30%===================\")\n",
    "print((100-len(count3))/100)\n",
    "print(np.average(m3))\n",
    "avg=0\n",
    "tmp=0\n",
    "for i in range(len(l3)):\n",
    "    date_time_obj = datetime.strptime(l3[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "f=avg/len(l3)\n",
    "print(str(dt.timedelta(microseconds=f)))\n",
    "\n",
    "\n",
    "print(\"===============40%===================\")\n",
    "print((100-len(count4))/100)\n",
    "print(np.average(m4))\n",
    "avg=0\n",
    "tmp=0\n",
    "for i in range(len(l4)):\n",
    "    date_time_obj = datetime.strptime(l4[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "f=avg/len(l4)\n",
    "print(str(dt.timedelta(microseconds=f)))\n",
    "\n",
    "print(\"===============50%===================\")\n",
    "print((100-len(count5))/100)\n",
    "print(np.average(m5))\n",
    "avg=0\n",
    "tmp=0\n",
    "for i in range(len(l5)):\n",
    "    date_time_obj = datetime.strptime(l5[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "f=avg/len(l5)\n",
    "print(str(dt.timedelta(microseconds=f)))\n",
    "'''\n",
    "print(\"===============Average===================\")\n",
    "print((400-len(count2+count3+count4+count5))/400)\n",
    "print(np.average(m2+m3+m4+m5))\n",
    "avg=0\n",
    "tmp=0\n",
    "for i in range(len(l2)):\n",
    "    date_time_obj = datetime.strptime(l2[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "    \n",
    "for i in range(len(l3)):\n",
    "    date_time_obj = datetime.strptime(l3[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "for i in range(len(l4)):\n",
    "    date_time_obj = datetime.strptime(l4[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "for i in range(len(l5)):\n",
    "    date_time_obj = datetime.strptime(l5[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "    \n",
    "    \n",
    "f=avg/len(l2+l3+l4+l5)\n",
    "print(str(dt.timedelta(microseconds=f)))     \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "a446ae15-829c-4b19-81a6-21af546da419",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./bres_sarsatrace_che.csv\n",
      "===============Average===================\n",
      "1.0\n",
      "22.19\n",
      "0:00:37.035562\n"
     ]
    }
   ],
   "source": [
    "print(filed)\n",
    "df=pd.read_csv(filed,header=0)\n",
    "df.sort_values([\"NAME\"], axis=0,ascending=[False],inplace=True)\n",
    "\n",
    "l2=[]\n",
    "l3=[]\n",
    "l4=[]\n",
    "l5=[]\n",
    "\n",
    "m2=[]\n",
    "m3=[]\n",
    "m4=[]\n",
    "m5=[]\n",
    "\n",
    "count2=[]\n",
    "count3=[]\n",
    "count4=[]\n",
    "count5=[]\n",
    "\n",
    "\n",
    "for index, row in df.iterrows():\n",
    "    \n",
    "    if row[\"PATH\"][-12:-4]==\"[10, 10]\":\n",
    "        if row[\"OBS\"]==0.2:\n",
    "            l2.append(row[\"TIME\"][7:])\n",
    "            m2.append(row[\"STP_PL\"])\n",
    "        elif row[\"OBS\"]==0.3:\n",
    "            l3.append(row[\"TIME\"][7:])\n",
    "            m3.append(row[\"STP_PL\"])\n",
    "        elif row[\"OBS\"]==0.4:\n",
    "            l4.append(row[\"TIME\"][7:])\n",
    "            m4.append(row[\"STP_PL\"])\n",
    "        elif row[\"OBS\"]==0.5:\n",
    "            l5.append(row[\"TIME\"][7:])\n",
    "            m5.append(row[\"STP_PL\"])\n",
    "\n",
    "    else:\n",
    "        if row[\"OBS\"]==0.2:\n",
    "            count2.append(row[\"TIME\"][7:])\n",
    "        elif row[\"OBS\"]==0.3:\n",
    "            count3.append(row[\"TIME\"][7:])\n",
    "        elif row[\"OBS\"]==0.4:\n",
    "            count4.append(row[\"TIME\"][7:])\n",
    "        elif row[\"OBS\"]==0.5:\n",
    "            count5.append(row[\"TIME\"][7:])\n",
    "\n",
    "'''            \n",
    "print(\"===============20%===================\")\n",
    "print((100-len(count2))/100)\n",
    "print(np.average(m2))\n",
    "avg=0\n",
    "tmp=0\n",
    "for i in range(len(l2)):\n",
    "    date_time_obj = datetime.strptime(l2[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "f=avg/len(l2)\n",
    "print(str(dt.timedelta(microseconds=f)))\n",
    "\n",
    "\n",
    "print(\"===============30%===================\")\n",
    "print((100-len(count3))/100)\n",
    "print(np.average(m3))\n",
    "avg=0\n",
    "tmp=0\n",
    "for i in range(len(l3)):\n",
    "    date_time_obj = datetime.strptime(l3[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "f=avg/len(l3)\n",
    "print(str(dt.timedelta(microseconds=f)))\n",
    "\n",
    "\n",
    "print(\"===============40%===================\")\n",
    "print((100-len(count4))/100)\n",
    "print(np.average(m4))\n",
    "avg=0\n",
    "tmp=0\n",
    "for i in range(len(l4)):\n",
    "    date_time_obj = datetime.strptime(l4[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "f=avg/len(l4)\n",
    "print(str(dt.timedelta(microseconds=f)))\n",
    "\n",
    "print(\"===============50%===================\")\n",
    "print((100-len(count5))/100)\n",
    "print(np.average(m5))\n",
    "avg=0\n",
    "tmp=0\n",
    "for i in range(len(l5)):\n",
    "    date_time_obj = datetime.strptime(l5[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "f=avg/len(l5)\n",
    "print(str(dt.timedelta(microseconds=f)))\n",
    "'''\n",
    "print(\"===============Average===================\")\n",
    "print((400-len(count2+count3+count4+count5))/400)\n",
    "print(np.average(m2+m3+m4+m5))\n",
    "avg=0\n",
    "tmp=0\n",
    "for i in range(len(l2)):\n",
    "    date_time_obj = datetime.strptime(l2[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "    \n",
    "for i in range(len(l3)):\n",
    "    date_time_obj = datetime.strptime(l3[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "for i in range(len(l4)):\n",
    "    date_time_obj = datetime.strptime(l4[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "for i in range(len(l5)):\n",
    "    date_time_obj = datetime.strptime(l5[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "    \n",
    "    \n",
    "f=avg/len(l2+l3+l4+l5)\n",
    "print(str(dt.timedelta(microseconds=f)))     \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f39fb863-95b2-4ee2-8bfd-c1c118adf30b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "961745d9-e9e8-4203-92f6-22ce75c4e48c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d8dddd1d-9882-49cb-a9e7-2caf3d473786",
   "metadata": {},
   "outputs": [],
   "source": [
    "l5=[]\n",
    "l11=[]\n",
    "l15=[]\n",
    "l21=[]\n",
    "l25=[]\n",
    "\n",
    "m5=[]\n",
    "m11=[]\n",
    "m15=[]\n",
    "m21=[]\n",
    "m25=[]\n",
    "\n",
    "count5=[]\n",
    "count11=[]\n",
    "count15=[]\n",
    "count21=[]\n",
    "count25=[]\n",
    "\n",
    "for index, row in df.iterrows():\n",
    "    if row[\"STP_PL\"]<100:\n",
    "        if row[\"NAME\"][:7]==\"Maze_5_\":\n",
    "            l5.append(row[\"TIME\"][7:])\n",
    "            m5.append(row[\"STP_PL\"])\n",
    "        elif row[\"NAME\"][:7]==\"Maze_11\":\n",
    "            l11.append(row[\"TIME\"][7:])\n",
    "            m11.append(row[\"STP_PL\"])\n",
    "        elif row[\"NAME\"][:7]==\"Maze_15\":\n",
    "            l15.append(row[\"TIME\"][7:])\n",
    "            m15.append(row[\"STP_PL\"])\n",
    "        elif row[\"NAME\"][:7]==\"Maze_21\":\n",
    "            l21.append(row[\"TIME\"][7:])\n",
    "            m21.append(row[\"STP_PL\"])\n",
    "        elif row[\"NAME\"][:7]==\"Maze_25\":\n",
    "            l25.append(row[\"TIME\"][7:])\n",
    "            m25.append(row[\"STP_PL\"])\n",
    "    else:\n",
    "        if row[\"NAME\"][:7]==\"Maze_5_\":\n",
    "            count5.append(row[\"TIME\"][7:])\n",
    "        elif row[\"NAME\"][:7]==\"Maze_11\":\n",
    "            count11.append(row[\"TIME\"][7:])\n",
    "        elif row[\"NAME\"][:7]==\"Maze_15\":\n",
    "            count15.append(row[\"TIME\"][7:])\n",
    "        elif row[\"NAME\"][:7]==\"Maze_21\":\n",
    "            count21.append(row[\"TIME\"][7:])\n",
    "        elif row[\"NAME\"][:7]==\"Maze_25\":\n",
    "            count25.append(row[\"TIME\"][7:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "465cf3ae-f37f-4201-9ae2-6afd2ca30562",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\marcb\\anaconda3\\envs\\myenv\\lib\\site-packages\\numpy\\lib\\function_base.py:380: RuntimeWarning: Mean of empty slice.\n",
      "  avg = a.mean(axis)\n",
      "C:\\Users\\marcb\\anaconda3\\envs\\myenv\\lib\\site-packages\\numpy\\core\\_methods.py:189: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    }
   ],
   "source": [
    "print((50-len(count25))/50)\n",
    "print(np.average(m25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "e6b15273-2321-43d4-8087-15b39e5fa11d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:27:00.935672\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "import datetime as dt \n",
    "avg=0\n",
    "tmp=0\n",
    "for i in range(len(l25)):\n",
    "    date_time_obj = datetime.strptime(l25[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "f=avg/len(l25)\n",
    "print(str(dt.timedelta(microseconds=f)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39e1cfcc-0ef4-4364-88e2-5f62fdbbcacc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b56ea72d-1491-4c66-b2cd-6fb4e3c0ef3b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2fb0c30d-3826-4a79-a9da-a771f8a015e3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c966e24-933a-4d7a-b384-23e490013bc7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0f5a6c3-9c5e-4e0f-bae0-2afdab16579b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ff5e9b4-c592-460a-8a6c-20341dc520c2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5fedac3e-2857-40c5-850a-74bdab78a250",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:00:37.035562\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "import datetime as dt \n",
    "avg=0\n",
    "tmp=0\n",
    "for i in range(len(l11)):\n",
    "    date_time_obj = datetime.strptime(l11[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "f=avg/len(l11)\n",
    "print(str(dt.timedelta(microseconds=f)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69cbbef1-26fd-4f4a-9fc4-b448fcc45b04",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d92206ca-6221-4c4c-824d-f201ac0eb07f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "351"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(m11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a4cbe68f-f0e0-410d-9010-29512313566b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "nan\n",
      "1.0\n",
      "nan\n",
      "1.0\n",
      "nan\n",
      "1.0\n",
      "nan\n",
      "0.3466666666666667\n",
      "53.69230769230769\n"
     ]
    }
   ],
   "source": [
    "print((100-len(count5))/100)\n",
    "print(np.average(m5))\n",
    "\n",
    "print((400-len(count11))/400)\n",
    "print(np.average(m11))\n",
    "\n",
    "print((100-len(count15))/100)\n",
    "print(np.average(m15))\n",
    "\n",
    "print((100-len(count21))/100)\n",
    "print(np.average(m21))\n",
    "\n",
    "print((75-len(count25))/75)\n",
    "print(np.average(m25))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "01f62b70-9d86-4e26-a311-76aa1b1786e8",
   "metadata": {},
   "outputs": [
    {
     "ename": "ZeroDivisionError",
     "evalue": "division by zero",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_41928\\1666070062.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      9\u001b[0m     \u001b[0mavg\u001b[0m\u001b[1;33m+=\u001b[0m\u001b[0mdate_time_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mminute\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m60\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m1000000\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m     \u001b[0mavg\u001b[0m\u001b[1;33m+=\u001b[0m\u001b[0mdate_time_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhour\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m60\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m60\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m1000000\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m \u001b[0mf\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mavg\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ml11\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     12\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtimedelta\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmicroseconds\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mZeroDivisionError\u001b[0m: division by zero"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "import datetime as dt \n",
    "avg=0\n",
    "tmp=0\n",
    "for i in range(len(l11)):\n",
    "    date_time_obj = datetime.strptime(l11[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "f=avg/len(l11)\n",
    "print(str(dt.timedelta(microseconds=f)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57a6d29f-4d46-42ba-8f20-83d1b5c2ebd2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4fcd13d0-96fe-4d51-8c53-70d9683c2516",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ab5e2aa2-a8a9-409b-8d90-2ee5795fd697",
   "metadata": {},
   "outputs": [
    {
     "ename": "ZeroDivisionError",
     "evalue": "division by zero",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_41928\\3080994250.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      9\u001b[0m     \u001b[0mavg\u001b[0m\u001b[1;33m+=\u001b[0m\u001b[0mdate_time_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mminute\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m60\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m1000000\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m     \u001b[0mavg\u001b[0m\u001b[1;33m+=\u001b[0m\u001b[0mdate_time_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhour\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m60\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m60\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m1000000\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m \u001b[0mf\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mavg\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ml5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     12\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtimedelta\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmicroseconds\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mZeroDivisionError\u001b[0m: division by zero"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "import datetime as dt \n",
    "avg=0\n",
    "tmp=0\n",
    "for i in range(len(l5)):\n",
    "    date_time_obj = datetime.strptime(l5[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "f=avg/len(l5)\n",
    "print(str(dt.timedelta(microseconds=f)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "91002945-bb99-48bc-b934-00ae73be7b3b",
   "metadata": {},
   "outputs": [
    {
     "ename": "ZeroDivisionError",
     "evalue": "division by zero",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_41928\\1666070062.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      9\u001b[0m     \u001b[0mavg\u001b[0m\u001b[1;33m+=\u001b[0m\u001b[0mdate_time_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mminute\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m60\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m1000000\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m     \u001b[0mavg\u001b[0m\u001b[1;33m+=\u001b[0m\u001b[0mdate_time_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhour\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m60\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m60\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m1000000\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m \u001b[0mf\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mavg\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ml11\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     12\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtimedelta\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmicroseconds\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mZeroDivisionError\u001b[0m: division by zero"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "import datetime as dt \n",
    "avg=0\n",
    "tmp=0\n",
    "for i in range(len(l11)):\n",
    "    date_time_obj = datetime.strptime(l11[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "f=avg/len(l11)\n",
    "print(str(dt.timedelta(microseconds=f)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2a7a5efc-f2f2-4ebc-a6b0-a65e34f17be6",
   "metadata": {},
   "outputs": [
    {
     "ename": "ZeroDivisionError",
     "evalue": "division by zero",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_41928\\3761778340.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      9\u001b[0m     \u001b[0mavg\u001b[0m\u001b[1;33m+=\u001b[0m\u001b[0mdate_time_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mminute\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m60\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m1000000\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m     \u001b[0mavg\u001b[0m\u001b[1;33m+=\u001b[0m\u001b[0mdate_time_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhour\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m60\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m60\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m1000000\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m \u001b[0mf\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mavg\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ml15\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     12\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtimedelta\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmicroseconds\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mZeroDivisionError\u001b[0m: division by zero"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "import datetime as dt \n",
    "avg=0\n",
    "tmp=0\n",
    "for i in range(len(l15)):\n",
    "    date_time_obj = datetime.strptime(l15[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "f=avg/len(l15)\n",
    "print(str(dt.timedelta(microseconds=f)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5a3325d3-c98c-445f-a944-6d1cf3aa3bc0",
   "metadata": {},
   "outputs": [
    {
     "ename": "ZeroDivisionError",
     "evalue": "division by zero",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_41928\\1684011094.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      9\u001b[0m     \u001b[0mavg\u001b[0m\u001b[1;33m+=\u001b[0m\u001b[0mdate_time_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mminute\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m60\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m1000000\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m     \u001b[0mavg\u001b[0m\u001b[1;33m+=\u001b[0m\u001b[0mdate_time_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhour\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m60\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m60\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m1000000\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m \u001b[0mf\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mavg\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ml21\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     12\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtimedelta\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmicroseconds\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mZeroDivisionError\u001b[0m: division by zero"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "import datetime as dt \n",
    "avg=0\n",
    "tmp=0\n",
    "for i in range(len(l21)):\n",
    "    date_time_obj = datetime.strptime(l21[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "f=avg/len(l21)\n",
    "print(str(dt.timedelta(microseconds=f)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8bec53d0-1a0f-485e-a1c4-acd2c522939c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:36:25.327900\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "import datetime as dt \n",
    "avg=0\n",
    "tmp=0\n",
    "for i in range(len(l25)):\n",
    "    date_time_obj = datetime.strptime(l25[i],'%H:%M:%S.%f')\n",
    "    avg+=date_time_obj.microsecond\n",
    "    avg+=date_time_obj.second*1000000\n",
    "    avg+=date_time_obj.minute*60*1000000\n",
    "    avg+=date_time_obj.hour*60*60*1000000\n",
    "f=avg/len(l25)\n",
    "print(str(dt.timedelta(microseconds=f)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f621ef25-e8d3-45ef-a8dd-76b2dd677d9b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "469f710f-d855-460b-b1a6-0fe6e56f8e4d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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