{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "from utility import *\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "profile = load_profiles('../messungen/gold_diffr/peaks.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00000000e+00, 1.30194940e-03],\n       [3.30000000e-02, 1.02512983e-03],\n       [6.60000000e-02, 6.93774735e-04],\n       ...,\n       [1.64120000e+01, 1.80837044e-02],\n       [1.64450000e+01, 1.76819364e-02],\n       [1.64780000e+01, 1.72794780e-02]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile[0,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 640x480 with 1 Axes>,\n <matplotlib.axes._subplots.AxesSubplot at 0x7fc81264b730>)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib qt5\n",
    "plot_diffr_profile(profile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": [
       "iVBORw0KGgoAAAANSUhEUgAAAWAAAAEYCAYAAABiECzgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOydeXiURda370onZCHNEgOEsAQCEgwkEEBRGBAJIC6MoKPMKC7v6OCoDAoKgiuKKIKo+Ko4uLwo6qfOoKADg2xurEJYwqJhCQQSDCQGQodsnU59f/RCd6eXJ0kvCan7unLR9Zyq55yuNJWqX5+nSkgpUSgUCkXgCQl2AAqFQtFUUQOwQqFQBAk1ACsUCkWQUAOwQqFQBAk1ACsUCkWQUAOwQqFQBAm/DMBCiDghxHtCiO1u7BFCiDeFEDOFEB8IIXrY2Y4JIb63/Hyi0d9EX8XuCxpSPA0pFlDxeKIhxQINK56GFAv4MB4ppc9/gD8BY4AdbuwzgOmW1ynAT3a2WXXwt8OpPMZdOUA2t/EEITbVN57f/45g+Vd949W/ps9xgPzXqW+8/fhlBiyl/Ddg8FDlBmCLpe5eoI8QooXFNlQIMV0IMVsIMaiOIYzxUA6EzVM8gY7NUywAY6wrFuD9AMTmKZ4G1zcBtnmKp6n1TW3iCYT/uvaNR4RlxPY5QohhwCtSygEubFnAeCnlbks5FxgmpTwshLhCSvmzECIK2AncKKU87OIeE4GJAOHh4f0TExNttoqKCsLDw12WA2ErLS0lKirKLz6qq6uRUmI0GgkLCwNweO1cdr6nq7rl5eUIISgsLKRLly4u63ryURubp3h85UOrzTmeQPtXfePZv9bPcSD817Zvjh49WgJkcYHFUsrFOBHqfCFAnAb0duUWlmtIKX+2/FsqhNgNDAZqDMCWN7MYIDk5WR44cMBmKywsJDY21mU5ELaMjAz69+/vFx+HDx8mKioKg8GAXm/uQvvXzuXs7Gzs/zi5q7t582ZmzZrFmjVrXNb15KM2Nk/x+MqHVptzPIH2r/rGs3+tn+NA+K9t3/Ts2TPL1eTTmYBlQQghYuxkhpXAVZbrKcAeKeU5IUS6EGK0XbPuwJFAxahQKBSBxC8zYCHE1cCdQHshxFPAAsxfvBUBc4GFwCsWW3fgXkvT08AsIUQ/IB5YJqXc6MHPGGBMhw4dKCwstF0vLi52qGdfDoStoqLCbTz19XH+/HlMJhOlpaU2m/1r57LRaMRgMHitW1paSnV1tdu6nnzUxuYpHl/50GpzjifQ/lXfePav9XMcCP916JuWQojFwDdSym9wg18GYCnlD8APTpen29nLgIdctNsL3FILP98A3yQnJ//NfukO4Knsb1tOTo7ffJw9e9amL9sve+xf25fDwsLc2uzLUVFRhISEeKzrC5u3eAJtc44nmLGpvqn759jf/p1fe+sboFhK6TVVLVgasKIBsWXLFpYtW0ZBQQGvv/46999/P5GRkcEOKyiYTCZKSkowmUxUV1djNBoBHF47l31la968OUVFRX71odXmHE8w/NuXA9033upa49HpdNSHRj0AKwnCTH0liN69e9O7d2+mT59OVFQUVVVVGAyGJrnMrqysJDY2llatWpnzNEPMX5NUV1fbXjuXfWWrrKykWbNmfvWh1eYcTzD825cD3Tfe6lZWVhIWFsaZM2c4ffq0q8908CSIQKEkCN9JEIGyNfRlttFoJCYmBiFEjf909q+dy76wCSH87qM2Nud4Au3fvhyMvvFUVwiBTqfjkksuobCwsM4ShNoLookTc+Ajmp3c5nCt2cltRO9+L0gRBR8hRLBDUDQS6vtZadQzYCVBmKmPBBHSvBtd1z7CycEvUqpPJvLUDtpuesJcboIShE6no7q6GsD2r/Nrf9mklG7tgfDvKZ5g+LcvB7pvvNW1j0dKqSQIJUHUTYIwJAzmP8XPcc13MzjZbgwdzq7izMjXCY0fSJRTgnlTkSDcLTsDJUFs376dJ554goqKCubMmcOQIUNc3mfv3r2cPXuWIUOG+DS2q666io0bNyKE4JtvvqFPnz507tyZkJAQPv/8c3799Vf0ej0tWrTgvvvuA7DVsz5J2RQkCPvYVBaEos58mN+VHGM6D+d/gqHfA1TGDwx2SA2CF/97kF9PlQDmWY79ctO+XBtbz3bRPHVDT49+L7/8coYOHYrBYLANvq7Ys2cPOTk5HuvUhc2bNyOEoKqqim+++YZWrVrRuXNnAMaPH297HN5+QLLWs3+UXeEdNQAr6G3MZIJuHZ+F38qtBz6jIn6gGoQbCB9++CFPP/00kyZN4tixY2RlZfHVV19RWlrK119/TXFxMVJKJk6cSFFREa+88grJyckcOnSIxx9/nJYtW3L33Xej0+lISUlh27Zt/PnPf+bee+/l4MGDzJ8/n8suu4z9+/czY8YMDh48yKOPPsqaNWvIy8tjz549AGzdupXHH3+cBx54gPj4eAwGA+3bt2fKlCls377dVu/nn3/m0UcfDWaXNSoa9QCsNGAz9dKAczbxmGEefzdO5lyLNAZdfg3xdpqwFh8XqwY849rufkl1cqerOuuKUkruvPNOli5dSkpKCo899hiPPPIIa9eu5aabbmLMmDHk5OTwxBNPEBISwq233srcuXMZOHAgGzduZPr06Xz22Wc89thjPP300zz//POcOnWKG264gf/5n/9h9erVhIeHM2nSJE6ePElYWBg33HADCxcuREpJ//79SU1NZcKECfzhD3+gurqa0aNHM2bMGKqrq7nqqqv461//6lBv6NChSgNWGrDSgLVqwGHnj/CKfjpbyhJJ1YUQ2v0azka9TqvTeylrN0BpwEFKQxNC2MpCCJKSkggJCaFNmzacP3/edt1aJyQkhH379rFhwwY2btxIRUUFer3eVq9Hjx6EhITQrl07SkpKCAkJ4d5772XBggWMGDGCHj16MHfuXIf72v9YfZw6dYpnn32W6Ohozp07x5kzZ9Dr9Q71fNU39mWlASsuSoqS72Jfbj5QYrtWaZUg7P6qK4KLq3QnnU6HlJKioiJKS0tJTU1l7Nix9OrVC6PRyIoVKzy23759O9OmTWPWrFnMmDGDTz75hClTprj0ceTIEcrKyliwYAFZWVlUV1ezatWqGvUOHz5M27ZtadGihbM7hQsa9QCsJAgz9X0SzmSqAsBkUpvxNIQ0tIyMDH766ScqKyuZM2cOOTk5LFmyhLvuuouffvqJvXv3MnLkSPr378+nn35KTk4ODz30EIsWLWLhwoUkJCSQl5fHn//8Z8rKyvjkk0/Yu3cv27dvZ9++fRQXF7Ns2TKqq6uZNm0aXbp0oaCggPvuu4+VK1eSk5PD4sWLeeaZZxg2bBgffPABJpOJd955h549e3L//fdz6aWXkpeXx5IlS3jqqads9aqrq3n77bfrnb7mXL5YJQi/bcgeSNR+wPXbD3jGmnx25ZWQGhfBO7dd5rJuU9nz1mg00qOH+YjCQD8K7LzpeKD9e4onGP7ty4HuG2917eP59ddfHf7fWvYDzmhQ+wErFAqFwhElQSgJQkkQDUyC8KeP2izB1ZNwKgvCIyoLwjeb8eh05yz/qv2AG0oWRDD8e4snmFkIF2sWhJIgFAqFIkg06hmwkiDMKAlCSRC+tjnHoyQIJUHUQEkQSoKor60+EkTotreQ7dOg86ALy9FjG9Gd3IkcNNltOyVBKAnCdk9vFRRNh8z8cpbvK/ReUQGAbJ9G6Ff3EZJjPjdWHNtI6Ff3Idv3rdd9t2/fzsiRIxk2bBizZ89m8uTJTJkypcYszBszZ85k1KhR9YpF4V8a9QxYSRBmfCVBAKw+UEB6QniNukqCcLE87TwIOXYxYcv/hintHnS7PsQ4djFVnQYRUoelu3VZ279/f4YMGYLBYODJJ58EYOTIkaxevZrRo0driw2YOHEi27dvr9PS3T6e2r4PJUEoCUJJELWUINI6RGMyVaHThbq9r5IgXCxPuw7FlHYPoZtfxTT4Ueg6lBCnpP26Lmut5aqqKn7//Xf0ej1///vf6d69O7m5udx4442MGjWKbdu28dZbb9G3b18yMzN5/vnn6dKli+3x45CQEB5++GFOnjzJ3/72N0pLS1m/fj2dO3dm165dfPrpp17jqc378JXNvnyxShCNegBWKIKNOLYR3a4PMQ1+lJCdS6hO+AN0HuSTe2/dupXZs2fz+++/M336dL799lu6devG9OnTOX/+PH379uWXX36hefPmzJ49m65du7J8+XIWLVrEyy+/bLvPjz/+SHx8PAsXLqS6uprbbruNm266iTvuuINt27Z5iEDhb9QArFDUEavmaxy7GLoOpTrhD2YN2FKuL1deeSVPP/00YF4Cjxs3jksuuYT58+dTXV1Nr169KCoqIjIykkWLFhEbG8vRo0eprKy03ePQoUO8+uqrdO3a1XZt3rx5zJ8/n7fffpvRo0dz5ZVXqnPwgoTfBmAhxAjgZuA0IKWUzznZuwDPAfuBXsCrUso9FtsEIA0wAUeklP9040NpwPg2DQ2qHDRRLT6aqgasO7nTUfO1aMKc3GWeCbtp5+6ervYDto8lJSWFdu3a8dBDD1FdXc1nn31G69atefDBB20z2nXr1vHZZ59RXW0+taJ79+78+9//5oYbbuDLL7/kj3/8I/v37+ett96ioqKCa6+9lhtvvJG0tDSlAV8sGrAQIgp4B+glpawQQiwTQqRLKdfbVXsd+FBK+ZUQIgX4GOgjhOgIPAakSSmlEGK7EGKDlPKQsx+lAfs2DQ2UBlwbDdiaauag+VpmwvXRgDMyMti0aRMVFRWsWLGCcePGAfD4448zc+ZMXn75Zc6ePUtiYiJhYWH85S9/4Z133iEnJ4fc3Fz279/Prl27+Prrrzlx4gTff/89o0eP5oEHHqCwsJATJ06wfft2IiMj6dWrFykpKbY4lAZ8cWjAVwE5UsoKS3kTcANgPwBfChy3vM4GUoUQscC1QIa8sE3bFuA6oMYArFBcjPTv359vv/2Waqcv8/R6PW+++SbguDvXuHHjuOWWW2z1rLb+/fvz7LPPEhISwogRI2ypbNZ2zvdXBB5/DcBtAfvdvM9ZrtmzEbgSyACusFxrobEtQoiJwESAtm3bkpGRYbNVVFSQk5PjshwIW1FRkdt46uvDYDAQGhqK0WgkLCwMwOG1c9lgMJCdne3SZi2XlZn/TpoPkDTa6mv1URubp3h85UOrzTkeo9FIq1atqKiw74/aH7xZV1t1dbXNdzD8e4onGP7ty4HuG2917eMxmUw1PtNArBBiBxdYLKVcjBP+GoBPA/Zz8haWa/Y8CkwVQkwBzgC/A7mWet2d2h52dmB5M4vBvB+wu/13nctqP2DHup/vyOXXolKHNDT7/XGb4n7ArvbAdZ4t+sOm9gNunPsB63S6Gp9poDCY+wFvARKEENYeGwysFELECCGsZ5XEA69IKV+z1F8jpawEvgX6iwt/bq4C/uunOJs832WbFxsjk1oHORKFounhlxmwlLJUCPEA8IYQogDIlFKuF0LMA4qAucAg4HrLND0GmGRpmyuEeAV4TQhhAt5z9QUcqCwIK/XJgjCZqkmNiyA9IZyVe1UWhNqMx3U8KguiEWVBWIJaC6x1ujbd7vUSYImbth9jzorw5kNlQVioaxaEThdiy3ywf+2qrsqC8O+38GozHpUFoVAo7MjNzaWiosJnOmOzZs3o1KlTgKJXNHQa9QCsJAgz9ZUgrLKDehCjpgRRVlZG8+bNfTYAnz9/3u2y3nmZffbsWbp3786aNWvo29dxh7VgSBBLly5lzJgxtGrVyu/+T5w4wbRp0+jduzdPPfWUzyWIgwcPMnv2bJYuXeq27syZM8nIyGDNmjWNT4IIBEqCUBJEfW1aJAjrctM+Dcm+XBub9Zqr187L7C+++IIxY8bwwQcf8OabbwZdgvj444+5+uqriYmJ8bv/hIQEbrjhBo4fP+7wO/CVj549e/LRRx95bPf3v/+diRMn2q4pCULhd3bllbB8XyFje8d6r6zwK4cPH2b+/Pn07duXuXPn8ssvvzBp0iReeeUVkpKSmDRpEn369OHJJ59k+fLlrF+/noSEBDIyMnj33Xe54447OH/+PIsXL+b48eMsWLCAFStWsHTpUj788EM+/vhjtm3bxrfffktiYiI5OTnMnz+f3bt3849//IM+ffoQExPDp59+yquvvkpOTg7/+7//S1JSEp07d2by5MmsWbOGiooKJk2axJ133sldd93FhAkTOH78OKNGjbKlZD7zzDMAzJ8/n19//ZWkpCQ2b95Ms2bNePXVV1myZAlGo5GIiAgqKyt57jnzzgW//PILCxYsIDMzk969ezNt2jSqqqqYOnUq7dq1o7i4mNTUVCZMmMCCBQuYM2cOzz77LDt37qS0tJR3332X6dOn23aQu+GGG7j22mt58803WbhwIYcOmb/fnzNnji21bO/evXz22WcAlJWVMXv2bHbu3ElaWprtffiKRj0AKwnCjK8kiMEdw8nML7ftCawkiGqqq6tty01fSBDWa/b/WrFf1v78888MGjSI2NhYRo4cyWeffcZ9991HamoqUkpiY2MZM2YMOTk5NnngpptuYsKECWzevJmoqCheeuklJk2aRJcuXfjggw84ePAgBQUFtGrVipkzZ6LT6Zg8eTIHDhwgMjKS2bNns3jxYiZNmsSYMWMwGAzMnTuXO+64gx49ethsCQkJVFdX07lzZ6SUdOvWjSFDhtjinz17NiNHjmTmzJlUV1eTlJTEU089xf79+/n000/ZtWsXAPfccw/p6el07NiR999/n5UrV5KcnMyWLVtsfR8WFsaUKVOorKxk4MCBjB49mi1btlBZWcnMmTORUtK3b1/bk37vvPMOw4cP5x//+AcZGRnMmzePxMREHnvsMcrKyujTpw8HDhzgwQcftO0Ot3btWrZt28aXX35JSEgIS5Yssfn/7bffarwP59+VkiCUBOETCWJsCmz9zeRQR0kQwZEgli1bRlRUFPv37yciIoIPPviAiRMnOuwTbP96/vz5zJ8/n0WLFnHttdcyePBgkpOTMRgMnDhxAiEEY8eO5ZtvvuHgwYO89NJLZGRkIITg7bffBuDMmTPo9XrbvXv27ElISAh9+vShurrawZ81XlexhISE2PapqK6uplmzZoSEhJCVlUW3bt1s7RMTE21tPvroI2bNmkVBQQEPPfSQw32s909MTCQrK4t9+/Zx6tQpFixYAEBycjIFBQXExcUhhCA5OZmQkBAuv/xyXnjhBS655BJb3d69e3P27Fnatm1r6/99+/bZ4goJCeGvf/2rzda1a1dCQ0NtfwxcZWUoCUKhuIg4d+6cbZZq/U/es2dPdu7cSXR0NOfOmTdPOnHihK3NgQMHWLRoEUajkfT0dG666SbS0tK4+eabeeihh5g6dSotW7Zk6tSpDB06FJ1OR7du3QgPD2fKlCmEhoZy+PBh8vPzbfd09YdDSsm+fftISkoiOjraNvM7ceIECQkJbtta38Phwxceaj169KjDU4hffPEFhYWFXH755dx22222OlaOHDlCz549OXv2LM2aNWPatGkAfPXVV259p6SkEBcXx6RJkwBo3749l1xyiUNcqamp/Pjjj7bykiVLuP32292+D1+iBmCFwgPh4WYpxlcSREREhEd/Vj21WbNmtmu//vorrVu3ZubMmTz11FO8+uqrHDt2jPz8fA4ePMgvv/zCtm3b+Pnnn4mKiqJXr1706tULgD/96U8sWrSIq6++Gp1OR0FBAddddx0ArVu3Zv78+TY9NTc3l2effZaDBw+yceNGWrRoQZ8+fWyP1Y8aNYr58+dTUVHBu+++y7333sucOXMYMmQI58+fZ+XKlaSnp/N///d/5OTksGHDBoqLiykuLmbJkiXcc889/OUvf+HOO+8kJSWFiooK2wC3dOlS9u7dS3l5OQ8++CAnTpxg5cqVlJeX88ILL3DgwAFuv/12evfuzWWXXcaMGTOYPXs2lZWVREREMG7cOJYtW0ZxcTFz585l2rRphIWFMW3aNJ588kleeukliouL6dKlCzqdjnfeeYdz587x+eefM378eH7++WeefvppIiMjiYmJoVmzZh7fh68QFzYda3zYacB/2717t+16cXExLVu2dFkOhO3gwYP06NHDLz7y8vKIiIigtLTUJkXYv3Yunzx5kvj4eJc2gKn/OY5OF8L86zpSWlrKsz8UAdjKWnzUxuYpHl/50GpzjseqAVt/d/7eb8DZVllZ6TDwBtq/p3h85WPz5s0MGmQ+MWTixIncfffdDB482Ot9At033urax5OVleXwh7W0tJR+/fodBr5DacA1y0oDdq0Bm8vnHOooDThwT4I1hSfhFi1axE8//YTJZCI+Pp4hQ4a4bWdfVk/CKRQKRT1ZunSpw6yyqdOoB2CVhmbGV2lo9scTWctafFxsaWgmkwkhhN+f9vL2JFyg/Xt7Ei7Q/u3LDXUzHuvRUSoNTUkQDu2UBFE3CaK6upri4mJat25tS0uyoiSI4EkADVGCEEJw9uxZQkJClAShUPiC6Ohozpw5Q2FhYcC/BHM+rSPYX8LZxxMM//blQPeNt7rWeHQ6HaGhdR9GG/UArCQIM0qC8J0EAdhmvqWlpbZTD+xfO5d9ZSsoKKiRIRJI/57iCYZ/+3Kg+8ZbXee+sR+clQSBkiCUBFE3CSKYsam+qfvn2N/+nV976xs0ShAh3iooFAqFwj806hmwkiDM1FWCWJVVTGZ+OalxEUqCCIJ/1Tee/Wv9HAfCfx36RkkQ7spKgjCXf8wxP/c/OrmNkiCC5F/1jZIgFE2Y1LgItfevQhEkGvUMWEkQZuq6dDOZqjCZqh2WUkqCCJx/1TdKgmjUA7CSIOonQeh0oUCVg11JEEqCcBePkiCUBKFQKBQXDWoAVigUiiDhNwlCCDECuBk4DUgp5XNO9veBbnaXUoF+UspjQohjwDHL9Twp5R1ufCgNGKUBN1adU/WN0oD9MgALIaKAd4BeUsoKIcQyIUS6lHK9XbU1UsrPLfVbAEuklMcstiVSylne/CgNWGnA9bUFW+dUfaM0YH9wFZAjpaywlDcBN9hXsA6+Fu4FPrArDxVCTBdCzBZCDPJTjAqFQhFU/HIkkRDiL8B4KeVYS/k+YJiUcoKLuiHAKuA6aQlGCHGFlPJny0x6J3CjlPKwU7uJwESAtm3b9l+1apXNVlFR4bBxhn05ELaioiJiYmL84sNgMBAaGuqwO5TzTlH2ZYPB4PDX2d72wuYSpJQ8PVhvs7283fw386lB0Zp91MbmKR5f+dBqc44n0P5V33j2r/VzHAj/te2b4cOH5wAXdEhYLKVcjBP+0oBPA/Zz8haWa664CfiPtPtLIKX82fJvqRBiNzAYcBiALW9mMUBycrK0HhwIUFhY6LB0ty8HwpaRkYG7eOrr4/Dhw0RFRTl8AJw/nPbl7Oxs28mzzrbIXYcwmaocTqaNjDQ/HZeYmKjZR21snuLxlQ+tNud4Au1f9Y1n/1o/x4HwX9u+AQqllAPwgr8kiC1AghDCOp0bDKwUQsRY9F577gGWWAtCiHQhxGg7e3fgiJ/iVCgUiqDhlxmwZeb6APCGEKIAyJRSrhdCzAOKgLkAQoi+wEEpZYld89PALCFEPyAeWCal3OjKj8qCMKOyIBrnN/2qb1QWhN/S0KSUa4G1TtemO5V3A7udru0FbtHoQ2VBWFBZEI3zm37VN9qyEFQWhEKhUCh8SqPeC0JJEGaUBNE4l9mqb5QE0agHYCVBKAmivrZgL7NV3ygJQqFQKBRBoNYzYCHENVLK7/wRTG1REoQZJUE0zmW26hslQXgdgIUQqcBTQCwggM44bqITNJQE4R8JYldeCetzKkhPiFLLbCVBBM2/fbkpSxCPAi8BP2N+9HeZhjaKRsrIpNYArM06E+RIFIqLHy0SxF4p5S4hxGgp5SEhRIX3JoFBSRBmfClBpCdEsTouApOpSi2zlQShJAg3tkBmQQwWQqwDWgkhngKGamgTEJQE4XsJQq/XW65DVJSSIJQEoSQIV68DKUFMBQqABUAM8ISGNgqFQqHwgtcZsJTyqF1xqh9jUSgUiiaF2wFYCPEdIIHPpZT/DFxI2lEasBlfasDW6851lc6pNOBA+2/SGrCU8hp3toaC0oCVBlxfW7B1TtU3SgN2iRDiOyHEBiHE/d5uolAoFIraU6sZsOWIoGopZblfo9KIkiDMKAmicS6zVd8oCULLk3DTpZTzLMX2wPOAy2PiA42SIJQEUV9bsJfZqm+atgTh6Uu4zkAXoKcQwpr7G4L5izmFQqFQ1BNPM+A0YCzQF/MeEAAm4D/+DkorSoIwoySIxrnMVn2jJAhPGvAKYIUQ4nIp5XZ39YKJkiCUBFFfW7CX2apvmrYE4SkLwjrrPSWE6Gz386K3myoUCoXCO54kiG3AFcAPwFEuyBCdaSCPIysJwoySIBrnMlv1jZIgPEkQV1heTra/gRDiendtAo2SIJQEUV9bsJfZqm+UBOGNX4QQ7YQQMUKIR4D9GtooFAqFwgtatqN8GngWeBk4bXn9V38GpRUlQZhREkTjXGarvlEShJYBeB+QB/SUUt4jhJiuoQ1CiBHAzZgHbSmlfM7JLoB/WIpdgFZSyr9abBMwp8GZgCPuNgNSEoSSIOprC/YyW/VN05YgtAzAvYA3gDVCiEgg0VsDyyPL7wC9pJQVQohlQoh0KeV6u2oTgLNSyo8sbVIt/3YEHgPSpJRSCLFdCLFBSnlIQ6wKhULRaNCiAb8I/ArMBa4E1mpocxWQI6W0Hl+0CbjBqc4dQIwQYrIlta3Ecv1aIENKaX3ibgtwnQafilqwfF8hu/JKvFdUKBR+Q8uG7AeBg5bid0KIcRru2xYw2JXPWa7ZkwC0kFI+L4ToAawWQlymsS1CiImYDwmlbdu2ZGRk2GwVFRXk5OS4LAfCVlRU5Dae+vowGAyEhoZiNBoJCwsDcHjtXDYYDGRnZ9ewfb3bPPimxVTb7FZbWVkZACdPntTkozY2d/H40odWm3M8gfav+sazfy2f40D5r23fALFCiB1cYLGUcjFOaNmM5z5gMhCNORe4BfCVlxmy1TwAACAASURBVGanAXtRpIXlmj3nMOcaI6U8KIRoAXSy1Ovu1PawswPLm1kMkJycLPv372+zFRYWOmin9uVA2DIyMnAXT319HD58mKioKAwGg013sn/tXM7OziYxMbGGLXLXIdI6wO0D42rcJ3KXWe2Jj69pc+WjNjZ38fjSh1abczyB9q/6xrN/LZ/jQPmvbd8AhVLKAXhBiwRxOzBcSpkopeyKtmOJtgAJQohwS3kwsNKSytbCcm09Fj3Zck0H5APfAv3tnsS7CvivBp8KhULRqNDyJdxWKWWhXXmvtwZSylIhxAPAG0KIAiBTSrleCDEPKMKsJ78MzBNCPAF0A+627DOcK4R4BXhNCGEC3nP3BZxKQzNTl/QdT6lmKg0tOLGpvlFpaK5IFUJs4oIOnAJ4nVpLKdfi9IWdlHK63etiwOVpG1LKj4GPNfhQaWgWapu+4ynVTKcLZVdeCd+fMDJ+gEq1CpRN9U3TS0PTIkHoMO/9sMTys1tDG0UjZmRSawC+yzZ4qalQKOqDlhnwX6SUZ60FIcQWP8ZTK5QEYcbXEkR6Qjir4yJcPiXnyodaZqu+8Yd/JUGYaSWE+ABzathKIBfYrKGd31EShH8kCHD/lJwrH7WxqWW26hslQVxAiwTxJLAQ85aUyzE/waZQKBSKeqJlBvyrlPIHIcRAKWWlECLX71FpREkQZnwtQVjtSoJQEoSSIFzbAp0FcSUQIYToDXTV0CYgKAlCSRD1tQV7ma36pmlLEFoG4JeB94BUYBRwr4Y2CoVCofCClgG4UEo5CEAI0QmI8W9I2lEShBklQTTOZbbqGyVBaBmA/w48b3ldAsyigcyClQShJIj62oK9zFZ9oyQIlwghrgaGAVdf2JaBEKCDt5sqFAqFwjueZsBngWNAX8C6R6IJ+NTPMSkaKMeOHWPevHmkpKSQk5NDu3btmDJlSrDDUigaLZ5ORd4D7BFCrJJSFlivCyEuCUhkGlAasJlAacAnT55k1KhRpKenU1payq233sqVV17psE2gp3t6isfb+7gYdU7VN0oD1qIBl1k2YbeKHGOAWzW08ztKAw6sBjxo0CCcadOmTY17eIpN6Zyqb5QGfAEtA/BKzFtQWmfBDSYLQhE81q5dy7Bhw+jevbvDzEShUGhHywD8i5RykrUghGgwD2IoCcKMvySIzPxyPt+Ry/VJLR1sW7duZdOmTTzzzDMYDAa1zFYShF/8KwnCzBEhxEjM+wFL4G7MqWhBR0kQ/pMgRie3ITP/BD/mlDF+QEebbd26dWzfvp1nn32W0tJScnNzSUpKUstsJUH43GZfbsoSxFTMpyJb6UwDGYAV/mNs71hWHyhwuJaZmckDDzxAnz59uOuuu6isrOTuu+8mKSkpSFEqFI0bLQPwTCnlEmtBCDHCf+HUDiVBmPGHBGGuUw1U2WSGrl27snPnTltd60xcLbOVBOEP/01aghBC/EFKudF+8LXe312bQKMkCP9JEAA6XQg6XSh6vZ6YAx8hOg2gMn6gzX6J4QDNTu/lt27j1TJbSRA+t9mXm6IEMVcI4Wrj9SuBod5urLi4KLskmQ7rpnBmxGtUxg8k8tQOWm9+kjMjXgt2aApFo8XTAGwEzru53iBQEoSZ2i7dVmUVsyuvhNS4CK8SRGZ+CZ/vyGVYp2QYNIfY1Q+zTjeK6yq/5cvuT5OqT1bLbCVB+MV/k5YggOlSyu3OF4UQ/T20CShKgqibBPFjTj5gznSIigp3e58Rl7Zkf0EBP+aUcX1SS/5TkIKxfDgPh/6LRfJm1hQlMdhSXy2zlQTha5t9+WKVINweSeRq8LVcz/B2U0XDJ61DNGN7x3qsc31SS9I6RNvKv+37gQm6dWzveDe3h6yjtzHT32EqFBc1WrIgGixKgjBT26WbNQPC+SEKV/ex1t25ZR2PFL/Mq9GPcdeQ65i7vCszDPMoPhxDqT7ZrX+1zFYSRF39N3UJol5Y0tVuBk4DUkr5nJt6dwAfA3opZYnl2jHMO7EB5Ekp73DVVkkQdZMgdLpzDnU83UenO8ehgjKOFexlkpzMkD7D0ev17A9P5ZWw6UwrOUJZuwFqma0kCJ/b7MsXqwShaQAWQsQCccAx6yDppX4U8A7QS0pZIYRYJoRIl1Kud6p3GZDs4hZLpJSztMSm8C8jk1oDsMV0K6OT2zjIFktPJ9K+dyfSgxWcQtHIEVJKzxWEmID5aPq9mPcC7iWlnOOlTTrwhJQy3VKeCnSUUk61qxMFvAXcD1TgOAPeAKwG9MB/pZQ10uGEEBOBiQBt27btv2rVKputoqKC8PBwl+VA2IqKioiJian3fVzZDAYDoaGhGI1GwsLCABxeO5cNBoPDX2ej0cjL2ysAeGpQtOb7ONvWZpfx4YFKesboePzycM3tXMVTF/++sDnHE2j/qm88+/f2OQ6k/9r2zfDhw3OACzokLJZSLsYJLTPgPlLKy4QQj0splwshLtfQpi1gv0XWOcs1e+YAsy1H3Tu3nyGl/NkySO8UQtwopTxsX8HyZhYDJCcny/79LyRnFBYWOizd7cuBsGVkZOAunvr6OHz4MFFRUQ4fAOcPp305OzvbYb9eg8FAZKQ5CyIxMVHzfZxtt7cxkFlsvk98fJzmdq7iqYt/X9ic4wm0f9U3nv17+xwH0n9t+wbzWZoD8IKWAdj6TZB1qlyhoc1pQG9XbmG5BtgO92wN3GY3+E61bP6+Q0r5M4CUslQIsRsYDDgMwAqFQtHY0TIAtxNCvAO0F0K8woWB2BNbgAQhRLiUsgLzAPq2ECIGqJJSngDusVYWQrwEvCqlLLHIF2FSytUWc3fgiPa3pFAoFI0DLQPwI5hPQU4FsoD3vTWwzFwfAN4QQhQAmVLK9UKIeUARMBdACNEGswYMMF0I8U/MM+VZQoh+QDywTEq50ZUflYZmxp9paJ5srjb08dZOpVqpvtHqX6WhmZktpXxCQz0HpJRrgbVO16Y7lQuAFyw/VvKAWzT6UGloFvyVhuYtRQ1qbujjqZ1KtVJ9o9LQLuD2STg7egsh3hRC/EMIUcOLQqFQKOqGlhnweCllmRAiCVgghKi0P6IomCgJwoySIBrnMlv1jZIgtAzA6UKIPGAyMARYoqFNQFASRPAliF15JXx/wsj4AWqZXV+b6hslQbjiY+B14L9ATynlC17qKxow1q0ofYH1KbnvstWpyApFXdAyA35aSvm/fo+kDigJwkxtlm7rDpnvMzQhst4SRHpCFKvjIjCZqjUvD9UyW/WNVv9NWoIQQghpfk55hRCis53p73XJivAHSoKovQSh04WQ1iHadtKx1vu4s5mPNqpSy2wlQfjcZl++WCUITzPgbcAVwA/AUcD6yFpnoEEMwAqFQtGYcTsASymvsLycbD+FFkJc7/eoNKIkCDNal26rsorJzC8nNS7C5fKpLss1k6lKSRBKgvCL/yYtQdgRaX0hhOgLjABWaWjnd5QEUTsJwv4oIndtlQShJAh38SgJwvcShJYsiJ7WF1LK3YBJQxtFAyU1LsLrUUQKhSIwePoS7mHM+0C0EkLcg1kDrgJWBiY07ygJwozWpVtt5QKtEkRmfjmf78jl+qSWXtupZbbqG63+m7QEIaVcCCwUQtwqpfyXu3rBREkQtZMgaisXaLGNTm5DZv4Jfswp05RZoZbZqm+UBHEBrxKE8+ArhBjnrY2i6TC2dyypcRHBDkOhaJR4HYCFEOlCiJ+FENlCiKPAewGIS+Fjlu8r9NkTcAqFwjdoyYL4C3At5vPXFgCP+TWiWqA0YDNatLPVBwoAGNwx3OdamslUDVRpSm1TOqfqG63+m7QGbEeWlPKMECJUSlklhGitoU1AUBqwdg1YpwslrUM0Y1Pa+Fwv0+lCyMwvZ31OhS3DQumcSgOur82+3GQ1YOBqIcRwIEII8R5wpYY2igaEv+WHaxLNH761WWf85kOhuBjRtB8wUA1sBe4D5vs1olqgJAgznpZO5uPjL2zA44/l2rBOYXwXF4HJVOV1gx+1zFZ9o9W/kiAAKeV5u+IbQojxmM+GCzpKgvC+lNqWb35uZvrwToztHVvjCG2t9/Fm07rHsFpmq75REsQFPD2IcZSaJyALzEfMf+7txorgs3xfIb8Wmczar3r6TaFocHiaAc+TUi5yvmg57bhBoCQIM66WQKuyilm42Zz5YN37111dT/fRatN6zJFaZqu+0eq/SUsQ9oOvECIWiAOOuRqUg4WSINwvgawb7/w1JdLhCTVXdZUEoSQILfEoCcL3EoSWBzEmAD8BzwAjhBBPemujaBikdYhmeEKzYIehUCjcoCULoo+U8jIhxONSyuVCiMv9HpVGlARhxnkJtHxvAbvySkiNi8BodL/5jnNZSRBKgnAXj5IggpQFAVhHAesXchUa2iCEGAHcDJwGpJTyOSf7eOAmYDdwOfCRNVDLrDsN89aXR6SU/3TloylIEGuOlLJidx7p3VsyMV2bBLEpNxcw7/sbFnauzsus2tisJySvz6kgPSFKLbOVBFFvm335YpUgtAzA7YQQ7wDthRCvUDMzogZCiCjgHaCXlLJCCLFMCJEupVxvVy0SmCGlPC6ESAO+AL4RQnTE/LhzmpRSCiG2CyE2SCkPaYj1ouLLPad5ce1RwPwXd2K69rbWzIfs7HN+is6RkUmt2ZVXwtqsM6QnxAXEpzuqq6u588476devHyUlJfz2228sWLCAyMhI740VigCiZQB+BLgXSMWc//u+hjZXATlSSutseRNwA2AbgKWUS+zqdwcOWF5fC2RYDgQF2AJcBzgMwEKIiZj3p6Bt27ZkZGTYbBUVFeTk5LgsB8JWVFTkNh5391mTXcqPx0oZ2iWKUYnmGe5XOy/IGGVlZWRkZGAwGAgNDcVoNBIWFgbg8HpDTiWZ+eX0jNGRnZ2NwWAgOzvbdh/7us7l+thSo8LoGaOjrKyMkydPum3nKR5fxVZRUUFiYiI33XQTRqORZ555hg8//JBRo0Z5jMdffaPVFoi+0WoLdt84lwPdN97qeusbIFYIsYMLLJZSLsYJLQPwJuBBV4090BYw2JXPWa45IISIBGYBw4A7atPWEs9igOTkZNm/f3+brbCw0GEpb18OhC0jIwN38bi7z7wdW/jl9ypatAhl5q3mtpFbfmRgV/MvdtvRIrKq2nBFx5ZERUU5PFBh/3rBLvPfqT/2jScxMZbs7GwSExNt/pwfxHB3n7rYIi2+4+Pj3LbzFI8vY3vuObPidebMGYqLi7nqqqtITEz0GI8/+0aLLVB9o8UW7L5xLge6b7zV9dY3QKGUcgBe0DIAZ0opd1oLQohLpJS/e2lzGrAXRVpYrjkgpSwDHhdCdAe+E0IkWup1d2p7WEOcFwXbjhbx6bbjAOzMNTCwaww39e3AtqNFrNidxxXpbbzeQx07ZOb7779n0aJFjBgxgj59+vjsvjfeeCOhoaGEhoYSEhLC++9rWRQqFDXRshnPCSHEaCFEghCiM/C4hjZbgAQhRLilPBhYKYSIEUK0ABBCPCaEsB51nwvEYtaFvwX629muAv6r8f1cFKzYnceK3XkA3NS3A7cP7Ey/jjVEfoUXhg0bxnvvvcfx48dZsmSJz+57zTXXsHTpUv7973/zxRdf+Oy+iqaHlhnwg8CvduXOwHRPDaSUpZYn5t4QQhRgnkWvF0LMA4qAuUA48JYQ4jhwGfCwlPIccM7yZd9rQggT8J67L+AupjQ0o9FIn/aR6HSh7D9pvtanfSSjukVRWFhoS/PylobmfO6bp/Qd53J9bdYYg51qtW/fPgoLCxk2bBilpaW0a9eOw4cP10iRc45Hq4+9e/fy9ttvYzKZSElJ4YorrvBJ3CoNTaWhuWKm/RdmlvQyr0gp1wJrna5Nt3s9x0Pbj4GPNfi4aNLQrCL+ny7vYpv9pndvaaur04WyM9fAdyeiuK2fWYZwToNZvq+QzPxyUuMiNKfvuLpPXW06XSi78kr4/oSR8QOCl2rVokUL3n//fY4cOcL58+c5fvw4s2fPttWpb6rVww8/TPfu3YmKiuLmm2+mefPmDB8+vN5xqzQ0lYZWA6dsBaSU67y1UdSOT7cdZ9vRIvp11HP7wM7cPrAzgMMsevRlsezMNfD6xlM0a9aM9ITwGvex7sdr3Z830FhT0b7LNjDe69cP/qNz586895755CxXu7/Vl7S0NAwGAzqdjoEDB7Jt27YaA7BCoQUtM+AGy8UgQXyyNYfXNpr3bbi6S3O37dK7hMPIrry49ijzNpxgZZtmjLi0JdcntXSQAFLjIhjWKSwoEkR6Qjir4yIcJJBAL7NjDnxESPNuGBIG22yRp3YQ+fsBipLvqvcyOzs7m507d3L99dcDcPDgQa6++mqfvCclQSgJolFxMUgQPxwzpz+/OC6FUd2iPLabmB5LaVkpPxw9z8GCUttR8KuyivkxJ5/sIiOXtokkKipK89LNuVxfm04XSmZ+idvjify9lBSdBtB17SOcjXmdyviBRJ7aQYfNT3JmxGs+kSDatWvHxo0bOX36NJWVlXTu3Jmbb77ZJ+9JSRBKglAEgYFdY7h9YGeH2a87buzZitv6xfP3L37hUEEZDy07ZDtuKK1DNCOTgntkn/0TccFIhauMH8hXnZ9k1MrJHO0wjrSCFZwZaR6MfUFcXBzvv/9+jRxRhaIuNOoBuLFLEF/uOc3OXAP9OuopLCys1WY8gzuaNWCTqYpednIEaF+6OZd9YUtPCGdlm2YujyfyFo+vYluS35WTxnQezv2Q33rcxTl9MmiQRLz5iDnwEWWXJFPWboDNFnlqB9H5ezD0ubfecSsJQkkQjYrGLEF8uu24bZ+HP13exWbXuh/w2BS4c9CFGVhdl27OZV/YdLoQdLpQTUt+X/tfvq+QFr/vYkLYOr6IHM/NOV8T1mO4wwy4rsts0WkAHdZN4cyI10CfzCWGA7Te/CR5g+YoCUJJEDihJIiGjDXV7ImRXW1ZD4r689u+H3gz7A0mGSdTHjWQK/tcbRs06ytDVMYP5Jtus7hm5WTOtBtD97OrODPiNcr0ydT476dQaKBRD8CNVYL4cs9pW9pZepdwW11/nIocaAkCwGSqJjO/hM935DKsU5iDzd9LyW6VWbwS9Sjnw1PJzCvh/7VP4C+D5hB5YgcGfXK9l9kf5HUix5jOw/mfUNjrXn53cU8lQSgJAiVBNEwJwn6LyT9d3oWWLaNq5aO2pyIHQ4IYcWlL9hcU8GNOGdcntQzoMntF81sxmaoYndyGzPwTbMqt4M5B12Dsfo1tllrXZfb6nAqan85gQtg6FlaNY2LWv6HrENAnKwlCSRA44ZsjiRS+ZfUv5tnui+NSLlrp4fqklqR1iA5qDGN7x5LWIZrM/HKW7/OeXaIFq7zxcvNpvFZ1Ky9GTaP1uilEntrhvbFC4YJGPQNujBKEyVRFv4562x4P/joV2UowJIjS0lKX+0J4i8cX/u33whiaEMmuvBJWHyiwPTlYn2V2t8osXo1+jHv/OJr9/znOnpBk8gbNQZe/B0O7AW7baX1PSoJQEkSjorFJEJ9uO86e38oY2DXGbduLQYKAC6ck2z8UoiWe+tp0ulCgCr1ez/gBer7LNjhkZDi307rMXpVVzMKz15HWIRq9Xm/L9Ajtfg2GdgOUBKEkCJxQEkRDw36LyabArrwSVmUVe6/oI5bvK7Q9lOJrvss2z3aC/aCL4uJCDcABxrrhzsWOdaCyDlyBwN+bEVnP2bOyK6/EZ/qyomnSqCWIxqYBG41GTKaqeh1Z31g04PSEqBob83iLp77+XW1GZDJVA1WaNgfy5MP5PoM7hpOZX87qAwVc1SamXnG7iqU+91EasNKAA0Jj0oDXHCm1PXZcHx+NRQM266Q1N+bxl5a3PqeCzPxy0jpEO+jOzk/lObfTqnM632dsCmz9zeRy/+O6vielASsNWOEnrPrv6MuazlltVhli3oYTfl+qW+UHVxqtv6SCYMgsiouLRj0DbiwSxCdbc1w++VYXH41FggDzxjyGAS1ZtKPYlgrmr6WkVX5ITwh3sNlLBc420LbMXpVVbDtpxL6uO5lFSRBKgkBJEA1HgrDu+evqybfa+mhMEgRcWKpbr/lrKWlNe7Nec+ffuZ2WZfaPOeYN80cnt6lR15XMUtf3pCQIJUEo/IR1z9+mSmPPGEiNi3C5v7FVhrBKIApFbWjUM+DGIEHUZc9fT7bGJEFYX9s/kXZZnxC/SRBAjT2IPdlA2zLb+aRpe5t1/+NdeebNh+yPiKrte1IShJIgGhWNQYJYf/gQULs9fz3ZGpsEATB+gJ4fc8rYlVfCT20iuS8pcBKEN5uWZbb903Wu/NtvPjR+QMc6vyclQSgJQuEHmsrDF56wLtU/2FvmcyliVVax1yfgtEogZWVljBgxgueff16zf+vmQ41dZlEEnkY9A27oEoR1398+7SNrteevJ1tjlCDAvFSvGNSGhZsLmLfhBBXl5fVartuX1x0y983QhEiXEoS9BOL80ITzUvKNN96gR48eVFZWUlpaahvce7Vp5nEJ7M6HkiCUBBEUCUIIMQK4GTgNSCnlcy7q3Aa8BDwspfyP3fVjwDFLMU9KeYcrHw1ZglhzpNS27+8NveN85qMxShDW1+MH6DEYSvhgb1m9l+v2ZZ0uhLQO0bb7Ode1SiDgeXOgFStWMHjwYH755RfOnz9PVFSULQNixKUtPcbmyYeSIJQE4Q6/SBBCiCjgHWCKlHIWkCqESHeq0xUoAE64uMUSKeUwy4/LwbehY33w4sVxKdzcp22Qo2k4DE9oFrTluqfNgQ4ePMiRI0e4/vrrbdess9+0DtG2A0/r40OhcEZIKX1/U/Ng+4SUMt1Sngp0lFJOdVH3e+AVpxnwBmA1oAf+K6Xc7KLdRGAiQNu2bfuvWrXKZquoqCA8PNxlORC25XsLWPqriV5twnh+2CU+9WEwGAgNDcVoNBIWZj7ux/61c9n5wE5PdQNhMxgMbC8K54O95tliUusQBncMZ3hCszr52JBTyQd7y+gZo+OpQdFu21nrJbUO4enBF/rD2j8fffQRRqOR8PBwduzYQVVVFeWdBlLe64/8NSWSIfHCa2yufNS2b9z9rgL9e3OOJxj+tX6OA+G/tn0zfPjwHMB+hrFYSrkYJ/wlQbQF7J/PPGe5ppUZUsqfLTPpnUKIG6WUh+0rWN7MYoDk5GTZv39/m62wsNBh6W5fDoTtme/XACbu+ENP+vfv7FMfhw8fJioqyuED4PzhtC9nZ2eTmJjo0uZcDoQtOzub+/okEtumkLVZZ9iVV0LWmTJi28SSnhBeax8LdpmzTP7YN57ExFi37RITYVfRIUymKof+sPbPrFmzLtxzwQLOnz/PyZ63otOFct81l2p6j6581LZv3P2uAv17c44nGP61fo4D4b+2fQMUSikv7NLvBn9lQZwGh4NiW1iuaUJK+bPl31JgNzDYp9EFgKb+4IU3xvaO5a1bLuXhQW0A834RtV26W/f/dfeQhCu8HVG0cuVKtm7dytqN29iy4dtaxaPVR0Pj8OHDLFiwgHfffZc777yTXbt2BTukJoO/ZsBbgAQhRLiUsgLzAPq2ECIGqJJSnnPX0CJfhEkpV1sudQeOuKnb4LIgvtxzmv0FRvqFG11mPjTVLAh38QzrFAaW7IiFmwsAbHqrp/ss31vAoh3mPhncMVzTN+Sujihy/jZ76NChDB06lGn/zSUzv9xlZoUWH9ZMD+dToRtaFoTJZOKZZ55h0aJFlJeXc9NNNxEaGorBYFBZEI01C0JKWSqEeAB4QwhRAGRKKdcLIeYBRcBcIYQAngQSgPFCCKOU8lvMM+VZQoh+QDywTEq50Y2fBpcF4erBC1/6aMxZEO7iGT9AT3hEBPM2nGDh5gJbNsHQhEiHbR7t223KzQVg+vBONaQLd/6tRxRl5pfb9m6wxhO9+z3ORncjtN01LN9XSGZ+OXdccoh7QzMo0d+n+T1a47W+D+dToWvbN/7+3Rw8eBCdTse//vUvzp49S1xcHHfccQdCCJUF4aGur7Ig/JaGJqVcC6x1ujbd7rUEXrD82NfZC9zir7gCQa82YUp+qCVje8dSUV5uG3x35ZWwK6/EVi4pNxIdcWE2eaSo0nZChf3MyBvXJOrJzC9n3gZz8k2q+W8ZlW1TiF/7CGejXmdtVgxXheznyYo3KW27sNbv5fqklra4Gzq5ublkZGTw1ltvIYTgiSeeICwsjPHjxwc7tCZBo34Qo6Hx6bbjbDtaRK82Yd4rK2pwfVJLWy7v8n2FrD5gliQuPOVWYTvuvltMszqdz3Z9UkvbbHvehhP0jNERuesQZcaW9A+dwvSVkxlVPZLbw9dSOHQuofED6/x+rClpzpu1NyT0ej3dunWjRYsWGAwGLr/8crZs2aIG4ADRqAfghqYB/3v7MQCubB9Wrz1/PdkuNg3YXd30hHCuahNDVFQUq7KKWXeomBGXtnTQh6Oiwmulz1rL6QlRVAxqw3fZBioqKm0D/K9cSkrLa3mg4l9sbXcH4fpkouqogVq14HWHih1yiBuaBnzppZfy+++/c/bsWSoqKsjOzqZDhw5KA/ZSt0FrwIGiIWnA1iOHBnaN4fqkZn7zfzFqwN7uM36AvlZaqtZ7jh9gTifKLG3B2qwz3B6Txc0n1mHo9wCXH/iMPMNgQttdUycf9hsQad0rOBg6Z7t27Xj66ad55ZVXiI6OpqSkhMcff5zIyEilAXuo2+A14KaG45HzBcENRlErxvaO5baYI7RaO4czI1+nMn4gFfEDbZpwZR1liJFJrW0ZEVY/DZHrrruO6667rkY+q8L/NOoBuCFJEEajkX4d9YzqFsXBg65PRfaF/6YiQQTCZh9PzIkdHO3/LNX6ZDAYQJ/Mmf7P0urEDgz65Dr5sD+SyVVaWrD7JnrP+1TF9aGs3QCbLfLUDiJ/P0BR8l1KglAShGcakgQRFnbads3VqchKijARGAAAEkJJREFUgkBTPIG2WeMxDnyIaucnmhIGY9SPtj1RVBcfY1NA36KFLcWOQW3cptYFum+q4vrQYfOTnBnxGpXxA4k8tcNW1utrHh+l1Udubi7jxo0jPj6eqqoqysrKuOyyy3j99ddrFbd9WUkQCoWiTlilB/s855FJrW0PgwSLsnYDODPiNZqvfpgNYaO50biaM6MX1llysdK8eXPmzp3L0KFDMRgM/POf/2To0KE+ivriolEPwA1FgrA/9biwsLDGqchKgrhAQ5Ug/O3fuh/yukPFthxnw4CWjE1xHYuv/duXV2UV8122AZOpGp2uBenlw3m46nMWVo2j+mQHrtcbasRTGx/h4eGkpaVhMBg4e/YsO3fu5P7773e5V7OV999/n2PHjtGmTRtycnKYM2cO1dXVAe8bgCVLlnDq1ClCQ83D49SpUykrc8zrVhIEDUeCsD/1WEkQnm0NVYIIhH9rNsf6nArmbTjBoh3F6Fu0sM2QA9E3q7KKbY98p8ZF0Kf6APeEreeTsFuZIFczaWsy4RGjHZ4SrI//ZcuWccstt7itq9frOX36NO+++y6bN2+mZcuW/M///A8//fQTI0eODLgEkZOTw9dff83atWsxGAxMnTqVTZs28Yc//MEvEoQ6kshHqM13FFoZ2zuW6cM7AWZZ4qFlhwKyec/yfYW2wXf68E682S+fWZULMN7wBtfc8zwbejzDm2Fv8Nu+H3zm89tvv+WPf/yjxzrWlLeSEnM+9vnz5+nRo4fPYqgNR48eJT4+3lbu3LkzGze63AnBJzTqGXBDkSCsp+5aY1AShJIgvNmsGRKbcitsksTdyc24vY1/+sZ+5vvwoDakJ4Sj27OHvEFzKLNkfnRPvpxXTz7GpZVZPnkQY+vWrfTu3Zvy8nLKy8s9tnvssceYPHkycXHm02NiY2ODkgXRvXt3Dh48SGFhIVVVVezcuZP27durLAhXNBQJQqcLJSwszFZWEoSSILRmSNw5SM/yfYXM23CCDw9UkllsPgLJ/CVd3Y42cmWzHq308KA2tse9DX3uRa/XO+wbeyAijU/yLmV6TgWpUfXrm6+++ooZM2Z4bbdv3z6WLFnCv/71L1q3bs1zzz3He++9x8MPPxxwCaJdu3bMnz+f9957j+joaJKTk2nRooXHo6xc3QeVBaFQNA6sGvDXu08CFzYiWh0XgU5n/i9qMlWh04UyMql1rR/osO6brOVoJevDI2uzzpCapqvDu7nA22+/rWmjpPz8fFq1amX70qtdu3bk5eXVy3d9aNWqFTNmzMBgMPDEE09wzz33+M1Xox6AG4IE8eWe0+zMNdgyIEBJEEqCqL0tPSGcy8LCiY+Pc8hSALO8ZTJVk5lfYtvP2GSqtu2N4cmH/b7JQxMivcaWnhDF6rgI8yDc8oIkovV9xBz4iJDm3TAkDLbZ7B/ucNVuwIABdO3alRdeeIGYmBgOHTrEzJkzgyJBADz55JP0798fIQRDhw4lLi5OSRCuaAgShKv9f5UEoSSI+vSNdZ8K5yNx1udUsDbrDACZ+SXsLzDnFJtnx+ccZsfWutaNhqYP72TbutNbbKOT25CZb5ZE2sVd2MdCy/sQnQbQde0jnI153eXDHe7azZ8/3+ORQIH83KxYsaKGf+e6SoJoQPTrqFcZEAq/M7Z3rG0w/HxHrsOew1bZYm3WGUymKjLzzV96pcZFMDq5Ta1kC/sHR9ZmnalV28r4gXzV+UlGrZzMhojrND3cEb37PSrbpoA+2Xat2cltxJzYgXHgQ5p915eYAx8hOg1wiLXZyW00O70XQzf/bM+p0tDqgXX/X4Ui0Fyf1JK3brmUt265lPnXdWT68E62vZIB0jpEM314J+Zf17FOmwCN7R1LzxgdhwrKeGjZIc3n9S3fV8i0vR1YYkzntrLPebd8OH/bEuMxza6ybQqt100h8tQOwDzotV43hbJLkt228QdllyTTet0Ump3cBkDkqR20XjfF/MfBTzTqGXCwNeB/bzd/UXB1l+YO/pUGrDTgutjq0zfpCVGkJ8TZylaZqj59MzBOR7OiENvs+rtsA9ck6mvozsv3FtiOiMrML+eqkP38LWIDW2Pv4J5T37D1t2Tm5fVi9YECBncMtz39Z7uHPpnvOj3BqO9m8N9N13Jd5bd82f1putvtxxyQz40+mW2dnmDUysmsa3YhjlQXmzEpDZjga8BhYacZ2DWGO65McLApDVhpwBdD34xMjOT+EYm200ky88vJzC930J3hwoklaR2iubNtNjNL36R09EI6xw+k+PBgPvhxJi9GTWNpfiKZ+eVs/c0E4HSPDpwMTedh+S8WVo3jtb0dSC0oYnSyzuVTef54/6uyilm4t2YcaUX5Nc4nVBqwQqEICGN7x5KeEO7wJaA99jpz9O4fKIx+yXaUU1m7AZwfvZBpp/fSvncn2zFTztzZNpu/lW7AkPoAEzM/pTCqv2XANuvQaTEmEhP99x6tTwleFbKfiRHmOP5mjSMv0eF8wpFJrW1nCdaXRj0AB1uCMBqNLm1KglASRF1sDb1vPMkc1uOhDN3Gm8v2fapPBn0y6WA7Zsr+HpGndhC/6RWOXjGL6oTBRLZK4dlNT5Dcczr/72xPiwQCOwp+scXmUspweh/Wo6x0ugtfdZk3IAqp8doqnbwf9SYFQ1+irN0AQiJ68GzGc6SmPMmS/K62Lzd35ZWQ1DqE0UW5ntIAlQThruwLmzX/d2DXGFq2bKkkiEa6zFYSRPD7JvrIEc6OfJ1qfbLZpr+Gs1GvM+LEDq4eNY7l+wr5evdJ20Mpu/JK3EoZ9mV7aeQCVbb72L9O6xDNg+GnKO23kND4gegx7wd9NuZ1rj29l9QBV6PXm59atKb3ZW0uqCHHWP2jJAj/8em247y49ihgPYJIoVDUlZK+95lf2M3+K+MHYtAno8csgaRGnSPRokHYn5jtibQO0RbttqPtmnNutWPe8UQqnf5YVMYPNKelWWKzpgK+992v7Cqq35OC4McBWAgxArgZOA1IKeVzTvYI4BUgD7gUmCulPGixTQDSABNwREr5Tzc+Ai5BfLnntG3wfWJkV0Z1i1ISxEW0zA6kf9U3nv27+xzbn5htrWd97VwuLS2tU596qzskXjB+QE05xlr+JpgShBAiCngH6CWlrBBCLBNCpEsp19tVewQ4LqWcJ4RIAd4HhgghOgKPAWlSSimE2C6E2CClPOTsx58SxP9v7+yDrarKOPz8+AxyAG8ofjTyJTneGmAkM8cUicSMYRqGHKmJYjARpyjKSECmofKroDDLMoY/GMVMsxqmITO1RkeGIe8tq8mM4BYlWUBXuxeBG8rbH2ufy77Hc8859+Psdfb1fWbuzFl7rb3X77xs3rP2u9d61/d3/Z2Hn9nfudXQ8ePHGTr0QOe83zVXTGTp7MaS1/AQhD9mu236Xpcu+5ZEPeNiYJ+ZdSTlHcBcIO2A5wJrAMzsD5KmSRoFXAk0m5kl7XYCVwGvc8AF9re/yjXf29lZDs5yaMlytXUFR3vRxIYufV00sYEPTj+bOZP76TWo4zhvWGrlgE8H0mmQ2pJj1bSp5lwkLQWWAowcN5HDh0+ecuKE0dFxrGS52rq3nzaUd585lA+cNwyAjg5j+PBhyVkH2b27g3379iV1Jz8DtLa20tzc3FlO1xe37Wlde3s7Q4YMqfpHpb29nZaWlpJ1xeUs6srpyVpbsR63Tf3YprictW0qta1kG2CspCZOssnMNlFErRzwAeiSZnRUcqyaNgeAc4uO7ynuIPkymwAaGxtt+41zOusOHTrU5dE9Xc6irrm5mRkzZtSkjz179jBy5MgKLxNOlltaWjpfXlRqm0VdOT1ZayvW47apH9sUl7O2TaW2lWwDHDKzd1KBWuWC2AmMl1TY9vUSYLukhiTMALCdEKogiQH/zszagEeBGZKUtLsYeKRGOh3HcaJRkxGwmR2RdANwl6SDwO/N7AlJXwNagTuAbwIbJK0ljHivTc59QdIGYKOk14DNpV7AQfyFGN3V+SwIf9Pfmzq3Te/u4yz674Vt4i7EMLPHgMeKjn0h9fkoUDLXnJltBbZW0Uf0fMClPvssCH/T77bxWRC+K7LjOE4d4w7YcRwnErleiuwx4IDHgPMZ53TbeAw41w7YY8AeA+5rXew4p9vGY8CO4zhOBHI9AvYQRMBDEPl8zHbbeAhCJ1Mu5BdJ7cCfU4dGA//tppxF3VjgUJVta11XTksMbfVkm2I9bpv6sU1xOWvbVGpbyTZjzOw0KmFmuf8DmorKm7orZ1TXrZ4I2tw25b9/U6z+3TYV+6/qPs6o/17ZptLfQI0BFw/5f5pxXTk9WWsrpyWGtnJ63DZx+68n2/RETxb999Y2ZRkoIYgmqyLxRVbUk5560gKupxz1pAXqS089aYH+0zNQRsCvS/MWmXrSU09awPWUo560QH3pqSct0E96BsQI2HEcJ48MlBGw4zhO7nAH7DiOE4lcL8SAyrsvZ6hjMnAL8BvgrcB/zOzLMbSkkTQC2AX8wsw+H1HHecCHgaPATGCdmf06op6VwATCXM4pwLUWUqRm1f8ZhPtlmpldmBxrIOTKbkk0rTGzf0fSshE4AhwGpgErzOxftdbSnZ5U3dpEy9iSJ2ekR9Iw4EbgFaCR8P/95p5eO9cOuMrdl7OiAfiBmW1LtD0nabuZNUfQkuYW4LcxBUgaDHwDmGdmJyTdC7waUc8ZwGpgbKJnG+FH/P4MZbwH2AZMTx27DXjczB5KVnluABZF0vKKma0FkHQTcDOwPAMt3elB0uXAqRlpqKTnJuBJM3sKQNLU3lw47yGI7nZfzhwze6bgfBMGEX4doyFpEcEmf42pA7gQELBc0mpgHl1XNWXNEeB/hP0GAU4B/pilADN7mK6bz0K4dwvbe2d2L5fSUnC+CYMII+FMKKVH0jhgIfCtrHSU0wN8BJgoaYWkrwC9ejrIuwM+nSp2UM4aSfOBR83s+YgaGoHzzezHsTSkGE/4sdxiZrcDlwEfjyXGwt6DK4EHJW0BXqDExq8RSN/PbcCpkqI+pUoaA8wB1kfUMIjwdLA6loYSTCCEPO8EngQe6s1F8u6Aq9l9OVMkzQJmAZ+NqQOYDxyTtIrwCPUuSSsiaWkDnjezwnr5p4HLI2lB0nSCA55rZosJo/EvxtKTIn0/jwJeMrOYoZrRwHeAJWbWGksHcAFwHLgeuAEYIWmVpCkRNbUR3q1AuJ8vTUJtPSLXMWBSuy8nYYhLCDdMFCTNBS4FPgOcKWm8me2scFpNMLNbU7reBJyS/FrHYBfwFkmDzew1woh4dyQtAGcDrSnn9iJwTkQ9BQo7hf+DZCfxWEIkjQXuBFaa2X5JC8zsRzG0mFkT0JTomkB4YXpHDC0pngAmEZKAjQf2Jvd2j8j9QgxJVwAfAg4CxyPOgphBeBRpSg69GbjbzLbE0FNA0gLC5qfDEj0PRNIxH3gv4d/pHGB5lrMOirQMBu4CjgEvA+8gvFl/MUMNM4GPAe8Hvgt8HRgBfBXYB0wGVmU0C6KUlh2EAVph5NtuZvNqraU7PWZ2VNK5wDLCKPh2YKOZ1fw9Szf2aQC+BOwFzge+3ZtZPbl3wI7jOHkl7zFgx3Gc3OIO2HEcJxLugB3HcSLhDthxHCcS7oAdx3Ei4Q7YGdBIWizpl5KWpY69T9Kzkj5Vov3dyZTCvva7LOl3cV+v5Qxc8r4Qw3Gq4SkzuwdA0ihgDPBscaNkfvDU/kigZGb3JEl/HKdbfATs5BZJqyUdlTRT0uck/UzS28qdY2ZtSXKVUswGfiVpiqSnJW2VtF7SjmSVI5JWSjos6XpJP5R0r6Qlku6TtL03y1GdNy7ugJ3ckiT2uQ34KGHl2AIz68sS54XAA2b2F2AzMNjMVgIrCCuwMLP1hNwRj5vZ1cBUQka+RYQUm9NLXtlxSuAO2Mk7txLybzzXl6XNSYLtSWb2p9ThgjM/SNekT5jZ3uTjy4TlqAAvFbdznHK4A3byznzCLhKflDSpD9e5Cvh50TFfp+/UFHfATm6RtARYBTwCNAM/SRKnVDrv04TQwZWSrkkOLwQeTOrHEZLGX5YkgFlMyLo3W9LVwOhkdsUsQiasxZIuSK65SNLw/vyezsDFk/E4A5pkGtgEM1tXod0nzGxzP/e9Dvhb7Ix4Tv3iI2BnoPNP4CxJ15VrVAPnex1wVtK/45TER8CO4ziR8BGw4zhOJNwBO47jRMIdsOM4TiTcATuO40TCHbDjOE4k/g+WAuHwRu3JFwAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "candidates, d_candidates, sigma_candidates = analyze_diffr_profile(profile, limits=[100, -1], distance=15, prominence=.001, save=('profile', 'gold_diffr'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([ 4,  4,  8, 11, 12, 24, 27, 32, 40]), array([[0.4365859 , 0.00314502, 0.01437761],\n",
      "       [0.38654813, 0.00246542, 0.00556833],\n",
      "       [0.37642096, 0.00165317, 0.00774769],\n",
      "       [0.39935277, 0.00158683, 0.00070146],\n",
      "       [0.39369265, 0.00147652, 0.00841213],\n",
      "       [0.41640285, 0.00116798, 0.0086763 ],\n",
      "       [0.40118533, 0.00102217, 0.00451444],\n",
      "       [0.4146038 , 0.00100278, 0.0033909 ],\n",
      "       [0.40573231, 0.00085894, 0.00504213]]), array([0.0287859 , 0.02125187, 0.03137904, 0.00844723, 0.01410735,\n",
      "       0.00860285, 0.00661467, 0.0068038 , 0.00206769]))\n"
     ]
    }
   ],
   "source": [
    "hypothesis = evaluate_hypothesis(np.array([candidates, d_candidates, sigma_candidates]).T, maximum=80)\n",
    "print(hypothesis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 & \\(\\sqrt{4}\\) & 0.4366 & 0.0031 & 0.0144 & 0.029 \\\\\n",
      "2 & \\(\\sqrt{4}\\) & 0.3865 & 0.0025 & 0.0056 & 0.021 \\\\\n",
      "3 & \\(\\sqrt{8}\\) & 0.3764 & 0.0017 & 0.0077 & 0.031 \\\\\n",
      "4 & \\(\\sqrt{11}\\) & 0.3994 & 0.0016 & 0.0007 & 0.008 \\\\\n",
      "5 & \\(\\sqrt{12}\\) & 0.3937 & 0.0015 & 0.0084 & 0.014 \\\\\n",
      "6 & \\(\\sqrt{24}\\) & 0.4164 & 0.0012 & 0.0087 & 0.009 \\\\\n",
      "7 & \\(\\sqrt{27}\\) & 0.4012 & 0.001 & 0.0045 & 0.007 \\\\\n",
      "8 & \\(\\sqrt{32}\\) & 0.4146 & 0.001 & 0.0034 & 0.007 \\\\\n",
      "9 & \\(\\sqrt{40}\\) & 0.4057 & 0.0009 & 0.005 & 0.002 \\\\\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(generate_hypethsesis_table(*hypothesis))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "a, d_a, sigma_a = determine_lattice_constant(hypothesis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.4016, 0.0017, 0.0089)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scientific_round(a, d_a, sigma_a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\(\\sqrt{4}\\) & \\(\\mqty(2 & 0 & 0)\\) \\\\\n",
      "\\(\\sqrt{8}\\) & \\(\\mqty(2 & 2 & 0)\\) \\\\\n",
      "\\(\\sqrt{11}\\) & \\(\\mqty(3 & 1 & 1)\\) \\\\\n",
      "\\(\\sqrt{12}\\) & \\(\\mqty(2 & 2 & 2)\\) \\\\\n",
      "\\(\\sqrt{24}\\) & \\(\\mqty(4 & 2 & 2)\\) \\\\\n",
      "\\(\\sqrt{27}\\) & \\(\\mqty(3 & 3 & 3)\\), \\(\\mqty(5 & 1 & 1)\\) \\\\\n",
      "\\(\\sqrt{32}\\) & \\(\\mqty(4 & 4 & 0)\\) \\\\\n",
      "\\(\\sqrt{40}\\) & \\(\\mqty(6 & 2 & 0)\\) \\\\\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(generate_miller_table(hypothesis[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4033916337519268"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hypothesis[1][:,0].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.027602905569007182"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(.4016-.413)/.413"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "argv": [
    "/usr/bin/python3",
    "-m",
    "ipykernel_launcher",
    "-f",
    "{connection_file}"
   ],
   "display_name": "Python 3",
   "env": null,
   "interrupt_mode": "signal",
   "language": "python",
   "metadata": null,
   "name": "python3"
  },
  "name": "gold_diffr.ipynb"
 },
 "nbformat": 4,
 "nbformat_minor": 2
}