{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import re\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "Ctt = []\n",
    "Ctt_err = []\n",
    "\n",
    "for filename in os.listdir('prelim_results'):\n",
    "    if filename.endswith(\".out\"):\n",
    "        with open('./prelim_results/' + filename) as file:  \n",
    "            data = file.read() \n",
    "            _ = data.partition('value = ')[-1]\n",
    "#             print(_)\n",
    "            _ = _.partition('Mean Ctt error = ')\n",
    "#             print(_[-1])\n",
    "            err = _[0][:-2]\n",
    "            Ctt.append(float(err))\n",
    "#             print(err)\n",
    "            _ = _[-1].partition('\\nSimulation ended\\n')[0]\n",
    "#             print(_)\n",
    "            Ctt_err.append(float(_))\n",
    "            \n",
    "Ctt_mean = np.mean(Ctt)\n",
    "Ctt_err_mean = np.mean(Ctt_err)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ctt_mean =  -0.09987017193178732\n",
      "Ctt_err_mean =  0.14186649892786868\n"
     ]
    }
   ],
   "source": [
    "print('Ctt_mean = ', Ctt_mean)\n",
    "print('Ctt_err_mean = ', Ctt_err_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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