{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import pickle as pkl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "OSError",
     "evalue": "[WinError 123] The filename, directory name, or volume label syntax is incorrect: '.*'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-7-516799d61049>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mjobs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'ff1data1/finished'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'.*'\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      4\u001b[0m \u001b[1;31m# print(jobs)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mOSError\u001b[0m: [WinError 123] The filename, directory name, or volume label syntax is incorrect: '.*'"
     ]
    }
   ],
   "source": [
    "jobs = os.listdir('ff1data1/finished')\n",
    "\n",
    "os.listdir('.*')\n",
    "# print(jobs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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 = pkl.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 = pkl.load(f)\n",
    "    Ctt_err = np.append(Ctt_err, x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ctt mean: -0.47938140165503934\n",
      "Ctt error: 0.21563337602896776\n",
      "95% sensitivity: 0.0007811622480085236\n",
      "95% sensitivity: 0.0010155109224110807 (CLs increase added)\n"
     ]
    }
   ],
   "source": [
    "print('Nr. of toys: {} (ff1)')\n",
    "\n",
    "print(\"Ctt mean: {}\".format(np.mean(Ctt)))\n",
    "print('Ctt error: {}'.format(np.mean(Ctt_err)))\n",
    "\n",
    "err2 = 2*np.mean(Ctt_err)\n",
    "\n",
    "print('95% sensitivity: {}'.format(err2**2*4.2/1000))\n",
    "print('95% sensitivity: {} (CLs increase added)'.format(err2**2*4.2/1000*1.3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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