{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['2247847', '2247848', '2247849', '2247850', '2247851', '2247852', '2247853', '2247854', '2247855', '2247856', '2247857', '2247858', '2247859', '2247860', '2247861', '2247862', '2247863', '2247864', '2247865', '2247866', '2247867', '2247868', '2247869', '2247870', '2247871', '2247872', '2247873', '2247874', '2247875', '2247876', '2247877', '2247878', '2247879', '2247880', '2247882', '2247884', '2247885']\n"
     ]
    }
   ],
   "source": [
    "jobs = os.listdir('ff1data1/finished')\n",
    "# print(jobs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "Ctt = np.array([])\n",
    "Ctt_err = np.array([])\n",
    "\n",
    "for job in jobs:\n",
    "    with open('ff1data1/finished/{}/data/results/Ctt_list.pkl'.format(job), 'rb') as f:\n",
    "        x = pickle.load(f)\n",
    "    Ctt = np.append(Ctt, x)\n",
    "    \n",
    "    with open('ff1data1/finished/{}/data/results/Ctt_error_list.pkl'.format(job), 'rb') as f:\n",
    "        x = pickle.load(f)"
   ]
  },
  {
   "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": "3.7.3"
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 "nbformat": 4,
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