QC-Project / Project_Codes / Circuit Drawer.ipynb
Circuit Drawer.ipynb
Raw 
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2a615c84-e91e-489c-adff-034f873ebab7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy.interpolate import interp1d\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "591383f2-0ce6-463a-addb-d0900a41a4b4",
   "metadata": {
    "tags": []
   },
   "source": [
    "# ancilla to plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0cdf0cb-2f4c-4566-8b51-432ed4a825eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Cw0giJAr9BTwF3ArEAYMref1RYBFwxrFhVeDQIet9mUyx6vr0gR9/NNYps7RokdF0lpPjlrCEl0pMhJtvNv5PPjbnT+LPfsyjc3YSG2scT5S1fx3OW5NOd8ctiZAoZFO1gr1VKy8DFwHRwN2Aa1ejlxohx+rWDX7+GRo3tj7+/fewd697YhLeJzERevaEpUsLO+I33QyB56DprxQUGMd79oRTpyq6k7CHtyadnhK3JEKikE3VCvZWrSQBlvMP/eiYcOwkiZDjXXwxrF8P0dHGvr8/fPUVXHKJe+MS3uPhh23+b7b8EbLqQ8viD0oHD8KDstRdtXlr0ulJcUtn6VLUzs7SK4DvMWqGDmE0k91lx3WrsG5Gawn8z+HRlSYvD+rUse6/cuGCMQpKVN+RI8bitS++CDfcYF0mS3IIS6mpxuLHR47AnJ2T+O38l4Dle4uGzFCocwaw/MVRsOdGfH98HV9fzFv//sYbYZH3V2xnwtIZmPBF4UO7Bp3Z/Erx/A5bthgjHYuuv+wymD69+Ppdu+C117B6RtHm41PyWMuWRo1EkePHYfXq4vKoKKMpuUhKCuzYUVweEJJGUJOj+Jh88DX5kp3pQ3KyDwG+Pvj7GVtA4Vd/Xx8C/X0JCvDH17fy/69uvtn6Z8XIG6DJNki8FJZ8ZT58003wxReVu7czuTru8jpLSyJUCmcmQo5+87D955P7y/3l/nJ/V9//7beNpW4crej+r362kcd/nAALvOsD6mVvG++7qamwfz8Oj/+eTyfx3qYvMSedrx926P2Lfv4f/rCTKUv/j+ML/s+h92dSjOXTykyW773yRhbe8nq1HlVeIiTzCAkhhKiWmJiKz6mOHC8ftuis+obmJyfAmT+hQYLR9Ogkx5LPcZzfHX/jAt/CuC166WQUzUlXAIHn4WwL43U6kfQREkKIGi4pCT780Hn3L+pP5iy5+fnOfYCTOSsR8r3QEj75FnbfZCQN3qYobp9s6+M+2cbx3bfAf7/BJ9W5nT+lRkgIIWq4Q4eMdeWcJToaOnaEv/5yzv3vH9Kdnh2+YOACx9539myjj2FeHuTnw8yZjr1/69DWFOgCjmflcTI/nwzH3p4GDYC8OrDmJTjdycF3d4GiuPs9B/kBxcf902Ht87DzTgAaNnRuGNJHqBS1r7P014Blb9iBwOoq3Oc4xqixH4HJQMfqh1aOUaNgyZLi/XffhXvuceoja70xSyax+NcvsbcjrGXb/i23WHds/yvySbQq/qTf4cRsTBafzT77DPz8is+3vb4y5fd+ZtOXooK4ozNu5MCbr1vd/6KHn6RAF8c7dKj1jNtffwOWy/Pt/Pds6gQUvx7b+CpSf+QkFm+2P2b23AirXgeMxOSwRXeRxESIjLQ4PSAVRvcv3s8LgPd+Md/T19eYZdzHxyguKIC+faFpU6MZLDoaHnig7BnHb74Zlq5IhrE9wJQDKW1h/TToMxPC9kO+P7zzKzddF+ZRHXjdTWvjZ11QYP27nZUFZ84YyVpeHgQGGv+eR44YQ88LCoBe/4KuC0GbQBWA9gGVD3+MhV+mYDLBRx9BIztWQwoPh86di/f3Hz3LD1v3m/ebRzRgWM+25v2kJGO5o7Kuty0/dQruvNP+uBMSoHlzu3+MpZI+QqICVZ1DyNJDwFsW+5fg7ERo/nyYPNkYhnvokDHUUjhX7CnbPgn2t+1//bXNpIz3xoNP8QJnB74pAIsWEMuFYEu7vjLlJftSlB/3kW8nUPAf6/vvz45Hm4rj/fmwdefjA7nWTSB5+dYBlnj9FXi6d+Vi5rfin/Xx48Ybpm/hX/hGjYxpEMzPzw6B3CDwy8BUEEiwiuTyazNo2SyY6Ggj2cnPL06ETCZjSgV7vfkm/HT/O5z1T4PtY2Ddc0bNxbHuxqf/Lh/QcNAC5s2bYv9NawGljJ950c+9SGCgMVLNVnS0sTTO0hXJ0PVdMOWWTDq7vgtbxzH8ujBGjapaXG2aN6RN88vLLI+IgAEDyr6+tPJly+yPu7pJUEUkERI4JhFqZ7P/I/BY1cKxU/36cOmlxiZcw9wnod9z0GGp8WZqWaXtkw1+GUbb/rrn8Glbx33BWqhs3OS5P27f1MrFHORfx9z0kp8PJ08Wf4o2mYzFiQMDjTfP6GiFT/giLm4ZRkyTephMjh1O1qQJjBmbyeZlb/PrTzcUJ6V5dTD99BLdm/Wkx9itJSbuFJXnrUmnJ8UtiZAAhgENMBKig1StJsf248DPQA7gX63IhGcp0SfBzW379qps3J6gsjH3uxouv7wo0TGaJyy9+abtE5zbAfXfQ2fAUDg6w6gNO3vW+H0YNgyaN78B6+Z4UVXemnR6UtxekQgppR4FrsJYBKsJ8LzWenolrh8NTADaANnAdmCG1roSlb012cDCrTraA82BZhhJ0UDAp9wrqkMm9HOPYcNg4sTCZqd6xwAFAeeL2/a1Kjxu1EIMG1Z87WefWTdX/ZU2mwKKD3S8xhfLiglfm79OttdXpnzYMJgwobDpqoK4lTLmxbG9/8x+s8m3eMCVV4KPRWvVhiAosGgaC/S3voFtfBXp0gUee8z+n/X8+dXvR+EMzZvDQw+5O4qazVuTTk+J2ys6Syul9gCpwB/AeCqRCCmlxgFvA/OBZUAQ8CjQHbhCa73N9pra11naUVxXA/TVV3D//UZHwZYt4ZprnDsqRhTz1o6w3hi3N8YshCcqr7O0t8wj1FFr3R14pArXjgE2aa0f0Fqv1lp/BVyP8dpvdWCMwoXNYAcPGiMPNm+GTz6BTZtc9uha7803oeHAd8A/DXbdDv/9BhL6Gf1Z/hwJ/mmFbfvujtSaN8btjTEL4W28omlMa12JCuUS/IFkm2MZQC7ekwg6zYndX7F2yQwiImMIb9aWqLZ9aXLRtQ65t9YFoAtQJsf/mtkuttrC3jViRbV5Utt+ZXhj3N4YsxDexiuaxooopXwxEpjKNI3dAywA7geWAsHANGAUcLnWep/tNbWpaezXLybw3n/eMO93vbQV9889UOX7pSbuYM8v77J3y1r27D7APx6eSqern3FEqFauvx6++654//PPrRdJFK5x9Ghpbfvujqpi3hi3N8YshKeo1fMIaa3fU0av2reAhYWHE4FBpSVBtU3Ssf1W++FNIss40z7fzR/Lup+Kk8g9v61wSiL05Zdw7JhRM3TwIPTo4fBHCDt4a0dYb4zbG2MWwhu4vGlIKTVQKaXt2NY56Hk3APMwOkwPBIYCu4DvlVIXl3ZNUlIScXFx5m3BAgfP6+5BkhOPWe2HR1VvSG27bldb7e/9c3e17lcWX1+IjYX+/WHsWGjWzCmPEUIIUcO5o0ZoI8ZY64pUe1kWZVQFLQC+0FpPtDj+A7AXmAkMt70uIiKC2tI01jP2applNyTpQjLJaWdp2rLs2UPtcdGV96LUi+YZdo+fvEDqqX3Ua9y2/AuFEEIIN3B5IqS1zsBIQlyhMdAI+N0mhhyl1A7sS8hqtDYhXWnT/qLiA5fcVq37BTVsSUzzliQcycWYpLEef2/YRbcRjkuEcnONKejLWudICCGEsFdNfys5izGBolU1h1LKH+iCsUpo7ZWfD+np1sdCQqp923aX3A/EYiRCJvY4uHbtv/+FoCBo3x6GDIH33nPo7YUQQtQiXpEIKaXilFI3AyMKD3VQSt1cuAVZnPeuUsq8vrPWOht4B/iHUuo/SqmrC/sMfYfxTm2zrGItc+GC9X7dug6pZmnfvbvV/p5du3Dk6MSDByE7G/buhe+/h7//dtithRBC1DLeMmrsYWC0xf4thRtACyCh8HsfSq7r8E/gb2AscDeQBfwFXKO1/sFJ8XqFhKOL8Dn/Evk6FJOKICDkChwxHUmr7t3x8/UlN8/ISc+cO0nSwa00alXqyMVKs51DqKVzl0wSQghRg3lFjZDWeozWWpWxJdieZ3Ntntb6Ta11F611iNY6Qmvdr7YnQQAZKfE0r3+S2AZ/EV1/HakBnzvkvn6BgbRqVQfYB/wGbGHvhhIrPlZZUpL1viRCQgghqsorEiHhHKYLCVb7ucGOG4PevosvcAqjixbs+WODw+69YgWcOQNbtxoTKXbt6rBbCyGEqGUkEarFAjJOWe2req0ddu92Paynef57bwK6IK+MsyuvYUMjAbr5ZggLc9hthRBC1DKSCNVim/ZE8O2+UH45Uo9dp4Pwb+C46Zmju9xGUB2ju5avSdG8XkPSjuxw2P2FEEIIR/CWztLCwbTWHD6SQUFOXfOxBxoPdtj9TT7+3DZkFPXPmmjdqCt+PnXgfPV/3dLTQSlj+LwQQghRXVIjVEudP3+Kgpxs877Jz5+GDaMc+oweA6fQPrKnkQQBHK/+tE3vvw/BwRAZCT17wsKFFV8jhBBClEUSoVrq1AnrxVYDQiMoXJzWcZo2td5PTDQmcayGoqHziYmwcSOcOlX++UIIIUR5pGmslso5t5zbLj7NuSxfzmX6ciEy1vEPqV/fmKQxLQ1Ihry9cCoGogZU+ZYyh5AQQghHkkSoljKl7aR1aJZ5f2/j8855UNRO2PcukGzsH/dxaCLUokXVQxNCCCGkaayW8ks/arWfHxLjnAc1i8CcBAEcr/p8QlrDoUPWx6RGSAghRHVIIlRL1clMttr3a9DBOQ+KutF6P+kA5Jyu0q2Skwtb2QoFB0NERNVDE0IIIaRprJZSPd9m76lfKEj9G5+0IzSMHOScBwW1gobN4PxJaNIeonqDrlqH6dL6Bzm6f7cQQojaRRKhWqpZzM0Qc3PFJzrCoGVQ9yLwrVet20j/ICGEEI4miZBwvgbdHHIbGTEmhBDC0aSPkPAa0lFaCCGEo0mNUC30228zWbv2HcLDowgPj6ZDhxtp0+Z2Fzz5MPAjsAa4GRhRqaulaUwIIYSjSSJUCx0/vo2DB49y8OBR4FdMJpMLEqFXgcct9v2obCIUFAT16kFqqrEvNUJCCCGqSxKhWig5+YjVflhYKxc8Nc5mfw2gAfuHfX37rTGX0NmzRu1Q69aOjE8IIURtJIlQLZSUlGi1HxHR0QVP7QEEARmF+yeAv4F2lbqLUhAaamxCCCFEdUkiVAvdf/+3JCVtIylpDykpB2nS5AoXPDUAGACcAwYWfu+KmighhBCibJII1UJhYV0IC+tCu8pVxjjAV1SmKczSkSOQng6xsVCnjkODEkIIUYvJ8HnhQlWfBnrePOjQwegwHRUFCxc6MCwhhBC1liRCwm20LiAvL92ucy2Hzp88CX5+TgpKCCFErSJNY7XOG8BbQMvCbTjQ32VPT009wF9/LWbPnjXs3fsnV111E9deu7jC62RWaSGEEM4gNUK1zh5gL/A98CawzaVP37HjbRYtmsWvv27m/Pl09uzZYNd1zZtDdHTxIqs1czLFk0BfILGiEz2Mt8YthBBSI1QL2VSt4NqqlfbtbwNeMe//73+HyMk5h79/g3Kv+/JL42tOjtFxOirKaSG6zrx5cOIEAOO//56E1P8BF4C2xNZrxfzrris+NyoKHnrILWGWYBG3YSXwBzAEGGx9rifFLYQQpZBEqNaxWbAL11athIfHERZWn5SU8wD4+JhITNxAdPQQu673969BEymeOAExMQAk5GQQ0zStsCCdhJQMcxkAhw+7Pr6yWMQN54Gdhd//CYwE6hef60lxCyFEKSQRqnU2YyRDBws312cV/frdQnb2Bdq1G0aLFjfi6xvk8hg8T5LF9wXAMZvybOA1i/0QYGzxbk4y/D3HojwYuMliPw1YZn+5Xz1o94xFeSrwbuH3vwJHgV7AdxgzhBfF/R3ginXrhBDCMSQRqnUaFm5d3RbB1Ve/U6nz9+yBc+eMDtKNGhX3E/J247//noScHCCP3PyzVmW5+RkMnvsq4EtsWBjze3cGHrU4oznWiVASbHrForwhYNl+eAbLJskKy+uG2iRC52ye3xDoBGwA8guP5QPrgd6F8QkhhOeTztLC4/3nP3DlldCkCdStC++/7+6IHCMhNZWYsDBiwlJp3ci6rHUjiAm7QExYGAkpKe4JsEKWtUFFCoAXgI8wapmEEMKzSSJUq9i+aXkCDed+h9TtZZ5hOXQ+IwMaNHB6UC6UA5wqo+xUYbknKsC6NsiSxug87e/SiIQQoiqkaaw22TIO9n4OIZFQrxm0vReajnRPLMlrYNcrcPx3SD8DHW6AXl+WemrNnkPoSDllurC8IUZSMcmirIH1qX4NodOtFgdCMJquiqQClSgvMYovxOL5G4DTlF/j0wirRChtL6QfgMbXl3ONEEK4nscnQkqptsBDwFUYY70vAL8Dz2itd9h5jxuB54D2GB+z3wH+pbUu7eNszZV6CDLOG9upvRDZ232xZJ6AfauK94//WuapXbtCcDD873+QllZz5hCKrRdEQoqR5eXmY9U8duA0+PloIJHYsGggEKPJqQwBTeCKTyt44jXVKG9IcWftiRiTcpb33+coxoiyQtunwu7lENkRLn0Smt1BdZZcEUIIR/H4RAi4GiMJWoxR394AmAxsVkr10lpvLe9ipdQ1wFKMIS+PApcCL2J8xH3SeWF7oAs2I5FCLnJPHABNhoLJBAUFxv75REjbA3Xblzj108L3d60hJQXq1XNhnE701mAfjvooYqI1g+dal/n5wMqJYLReB4JHjUL/hYqbWTVGH6KekJkAf39jHD75F5y8C8J3QpcHjKy2pvR+F0J4JW9IhJYA87TW5r+8SqmfgASMj6Z3VXD9bOAXrfW4wv21Sqm6wDSl1Gta69ozHW6qTV+Ueh3cEwcYTTmN2kHibsAPaAPH98NFJROhIkpBeLjLInSyk2zas4MP/oIuzSEru6zz8oGNQGfXhVauk8AOyq8NAqu4/3wO8vMsykIhuSWsWQP160OXLtCmJZhkATkhhOt5fCKktU4u5dh5pdQ+oGl51yqlmgNdgHE2RR8CzwPXAjVkDFIF8vIg80WMYdLJoJIh2I01QgDNJkDiPowWTz84XhfcHJKr5GY9zTd/Gd9vPwqnkyAzCxoWzkUYG2Z5dgFGLYwnmIn9ne4L424+DZJ3wrHthccHAT7Gt+fPw89LYMt/4JJ7oP008K1f+u2EEMIJPD4RKo1SKhS4mIqTmI6FX3dZHtRaH1JKZQBurBJxsQsXMN58IoytbgiY3DyqJ+oG4Ovi/ePHS5zy669w5ozRQTomBgIDXReeM8V/uJyzGvOgsKsawMyhEGqZA5ibw/IhqqyRZa62CaIK7GyqK4w7crixJa2G7S/DoV42560yOsxvegW2zYeLH4OOUyAgwAnxCyGENa9MhDCWUFfA6xWcF1r49WwpZWctymu+1FTrfU/oaNOoEfj5QW6usZ+ZaWQ9ocX/LHPnwn//W3zJhx/CHXe4OE5Hy80l+uxtxHb6lISUcwD0vfoKQqdtdG9cdtlmDF2oiohBMGiQMTvm9u1w4AAUJGPMVF0oKw22nIQdn0D79nDJJRAkM48LIZzH5fMIKaUGKqW0Hdu6Mq6fgjGH/8Na6wPOiDEpKYm4uDjztmDBAmc8xqVyzySidUHxgZAQ9wVTxGSCyEiMppZTwM9wfInVKYdslkarEYut7txJmwZdeWrwvxjf52ZaNArl2vvfrfg6GwkJCbz++utkZGQ4IUgnatAA+vWDkSPh4nzw9bEobARcaiTHO3fCJ5/A+gnlzjMlhBDV4Y4aoY0Yw9grUuKvu1JqPMaIr2la6/fsuEdRTVDDUsoaYnSYKSEiIoItW7bYcXvvseybSaxf+xvhdYOIqFuP/uFjaU8fd4cFTY/AkamY/ymOH4BOD5qLbecQ8vqh81lZxhs8oJSJS6MHcemNT0KEPf8lih07doy5c+eSkZHBq6++yqRJkwjxhOS2MurWhSufhq53wp/T4a8lkDMYq89nBYdgzxuw901o1Q+6PA+hbpz2QQhR47i8RkhrnaG13mvHZjXTnFLqTozJS17VWpczoYqVwu6o5r5CRfeKBYKA3dV8OV4j+dRJcvMLOHk+jZ3HT5CmPGT5g6YdsMpHT+6AAmMIVVoanD5dXOTjA829fQmr7d9ArsVs0YGBRvNPJSQlJZmTIDCSopdffpmzZ0trAfYCgdHQ7T24/Qh0fwTq1LEoXGl80RoOrIVfxrslRFFdJ4G+QO0ZpCu8h1f0EVJKDcfoGL1Qa/24vddprY8opXYA/wAWWhTdAeQCKxwaqAdLTrJ+k4yIrtybr9OE9oQ6IZB5wdjPzYbTP0CToSWaxWJiwNcrfmPLkLYX/rodaAEMB1oZQ8f9K9dpPSQkhMaNG5Nq0e/r1KlTvPzyy/zzn/8kIiLCkVG7jn84dA6Hjl1g3z7YsRIu/GF9zqW2U3+lYHT1q6FzEc2bBydO2HduVBQ8VNUOXM42E2Pk40xgnptjqelOAiOBT4Embo7FO3j824pSqg/wX4zJSxYppXpYFGdrrbdZnPsjEKO1bm1xzlTgW6XU24X3uRSYBsytLXMIaV1AUkqm1bHwmB5lnO1qCpp2gwPxQGugPZzwgSY1cGmNPx4vnE9nP/ASBPeHjvdW+jaBgYFMmDCB+fPn89dff5mPp6Sk8PLLLzNp0iSivLkzla8vdOgA7VrDQQ3bXoezxyAsFprfaXPyjUA68BRwE+Zh+TXFiRPGJwB7HPagWTetErg0YAHGdAoLMN526haf69EJnDfy1qTTfQmcxydCQH8gAOiKsciRpcNArMW+DzavSWv9vVLqZowlNsZg9Mp9kXLXK6hZLpw6TW5uD4xKsCwCA/IJDmvj7rCKNX0eDuzAmFgROJYHXUt2lPbq/kHntsDf31sfu6yf0d5XBf7+/jz44IMsXLiQbdvMnwU4f/48r7zyChMnTiTG3jdQT2Xyh9aPQatJcOR98A3BuuZnPcXzK90GtAG2AB4wItIZCgogJ8fYTCZjhlGTqXgrKDA2kwespW2VwH1kUVC0IO9VGD0zfODw3zYX52Ikt742myiVtyadJWo7V2L8bgwBBluf6+S4Pf63S2s9HZhu57n9yji+DFjmsKC8TJL5k6If4Ed4WBRKecAfyyJNu2DVXev0acjN5eBB65mGvbpGaMufoNtjfp31I6Ft9VZ48fX1Zdy4cSxevJjNmzebj6enp/Pvf/+bhx9+mDZtPCjhrSrlAzFjSyn4l81+S2pEEnTunDGnVloaJCTA2bOQnW1s5TlzBhYW9gDw9TUSIl9fY2TmgAGOiy8721jrxscHfBT45IBPvcJ9H/BZA/ozIBOj6TKb4kk484FNWE8Q2gL4j8X+T1i/EV4NWKxLyBqM2r+iBGkA8IlF+QZgvEX5lYDlGjZbgWctyrsCT1uU7wbetChvD9xnUX4Q+MyivAVwg0X5CWCdRXkkcIVF+VmM7qtF5Q0wasOLZALJFuWBGCtCFdFYfSCwSjo/sTlvB8Yg60KeVGtoFfd5YGfh939i1AxZTKrm5Lg9PhES1Zd89KjVfrin9SGpW9eY16ioz0tBASQmcvCgdc9or02EkpPhYC7GijB/A8uh2z8dMqGlyWRizJgxBAYGsm7dOvPxrKws5s6dy4MPPkiHDjVx3tBcjPEOiuI32ak253yL8QnzYUqfMsxD+1KcOgUbCiu/z561WYstEyOJ6AXUKXltkbzCJU1ycipOoMqVjjGA1+JvRtJS+H46xiCHcxhv8pZNlptg+wFj3V1T4aYoHpqjLKbxUJSyvkyezb7t21Q2YDkv2nnr4tMJsGNXca6gFKifLL7/FdT3FuXHQfUtLFPARlD/V1we2gfaWiZCe4EpFvvXYp0IbcfollpkMFbdUdPWwvGbjPsrQPUG3rN4/jpQ91qU9wU+sChfC+ou4wOC8oWsZhjLb57HSAKLlr/JB37GmLV1jEU8f2KsPBWAkWRdDBSP1CX7AFxYBvgXlkdhJJNFLgBHCq/3x6hxKmdKPn9/O+at+47i/8cFhfu3l326g0ki5GqOXmCyeAm2MiUf34/xy2X8JQpv4sI/+tV4vd/aHrgNuNXe5R3cxO7XO6Fwq4Ad/75KKUaOHElAQACrVhV/cs7NzeWHH36gffv2KGctbOqG32eDH/AFxpvSHIylBy2H1WuMT/3bgJeB+2FeGJywHC1Zhap4V7zeunVLHjP7Ezhd+PVy+55R5giDgsJ7ZWHdw2AdxvrUR0Cl2PGAX7B7CZh/QIklWkosW1dRImRbbtO8nJEBVs3q2YDllHPWHwyNRK+wpvb++22DAeIpvzP+irLLNZSIP/mMkZ+YncOoBSuyw+YmZzCPnrQqzze2E5mFSxNZJhOW9trsH8Ncc2QO20HNTm+/jfGznI+5Riu2G1y92uKk34FnMGrW6mHMHWabwG3E+H/pmuV2JBGqBZJPfoKxyoiR4UdEdXdzRMLRlFKMGDGCOnXq8OWXXwIQExPD+PHjnZcEeYR2GANK87F+M/oBIwkCo9/Ev+HEOIjpWnisElXxWhtvrmkumnKizEQoE/wPGv+NOQgFl4OuA/n5RoxF/YbMyVUucAZMtv/+mzDWqj6KkSQMBCzfqEwU/+ycyQ+jxsF25nATxhtkPkbSY1tzWkGipG3LbbsB2GZezu5gX9n4bJMZ2/gKbPYVJWuDLJ0rLC/6/c4qM1LHyMH4vSqq6btgU34S66bOUEq+ZtfWCkkiVAsknU4q/C4HyCG8eWnzS4qa4NprryUgIID169czceJEAmvK4mwVsn2zeNNmfzhgsZJtwTeQWQDBQH4BZH8F2UONZqSsLDhyBL75xkh+0tON5lpXCQ6Gdu2MhOiPP4w24YAACFhq8555COiH8f+6jZG83XcfFGyFgmshP8l4XzR1xbqPSxDWNSRWU7YB0Y5/TaUqwFiA1zbRGUqJ5i4rwzDe3PMKN+u+hOgrMWocCgo32ybEVhg1IEXlzuxXNpwSNXe6HkZ/No3xD9TI5hofjH5D+YXn2MZfWiJUVm1QEcukojpNpfawTfRs/n1KPP8sJWN3ba2QJEK1QHKS9R+ViOiuZZwpaoL+/fvTp08ffL160qXq+hijev7fGANFpwDLC8vOQ9IG+LvoDSUf2Aw0xvymc+ZrOLnO4n5P45yag6L5vLZifsPw9YU+k4A8CEuEBv4Y/Zw2UbL5YCNG/51Zxbc0hYMpyeKv+zGbZ9qOJjyCdQfcplj3vXKgbhbfa4BTcPyKMk4uix/lvjk2bg2D7jZqxoo2KP1722MOV8oYnZBB0DaqnDhagB5aTnlT0EOAfND5UOdT4EtKrw0C4wddlFQA9AA+xEhIShuEUA3h4Ri1fBbqBducZG8i5rpaodr8l9I9nPKfrWy5uZkok7L6uxYa7cI5hCrxer9+dRAH9uyifZc42nUfQcylozD5elmNhvmPVR4sbQlnLPojxHSHazaXfp2DuSwJcvHvs/3qYXQgnYBRDR+HORHS38BR20/VBVj3u0nDukq/8HW+/Xbh/oNYvfGMToUAy5E9/hhNU0Wysa75KCr/0/r+Zrtsrv++lHOKnMW6lqApRtVR0bHTGJ2si2oW6mOMQrpQ+H1M4fdFNSN+GIlhFOhIKp8AnoSnm0NMWW/MRfKB38D/skrevwLBwVWfa8MVv88REcZae46y+V9UnLQWJRU9MfqDxRqHdeXnMavYDRiz02RR8vcejBlxvseotV1F2QmcZa2Qc0kiVMOdzEhi9ZAOBGhNR1MAF6m6+AU2cHdYpRr22OqKT/IWuWehYSvrRKjbK+6Lp9YKxHpEz3lI2VjKSoYao/NmJ0odjRUQACENjeaqunWhrjIGyxRtlZwdvPIsa4Ns5WGdtPliJEPHC79GYzQ1Fb0uhTHfUmPKrlmxsyN2qWZif21SAXZ3tBalOInRedqepHMj0NnpERmJcwjWQ/4tRRVu0+y4l2UC5zySCNVwx1KNavFspfhD56Abe8cke59+akyL0rKlscXGgp9tU7Mn84+AAWuh80/w+2QIaAChvdwdleA78NNG5UfRCGwTxofWAA2B/4OA64y+Q9di/C0PBvzuxPqTrW0HZCd1SI/CmDa2RL8QSyFAAkRZTpPwO0afqLL+xLd1RHRl2ET58VrKp2TTnbCftyadnpXASSJUwx1PPW6137ReUzdFUjlvvQXr1xcQEnKB1NT6rFgBgwdXfJ3HCe8P126BAmeP1BAVSwM2QP0C6ILR8lSAkd8oCnd2A/8w5nFp/ojFtbZ/Krdi/QbkpPKHkjCaBsrrV5GH0TnYclqMxuWc72zbIGoeHK7EGmmiirw16fSsBE4SoRouwPcULRsGc/BsGqBoGuLpiVAy8BWPPrqGzz77iXXr+jFy5KdeM5liXl46Svng42PTt8nkZX2daqRfsPrjW2oNo2VVfHkLE3eq4FmOKn+Qit8w8vG4daU8ZRmHGs9bk85NEFVQWNtZkXyIOuXUaJT22M6O7hMXF6e3bNni7jAc5CZgGVoHkZPflIzc2TSsM8LdQZXjV4xRDYbk5DAaNz5NRoaJgAD3RWWvlSvvZsOGrxg27FHi4qagVA1bBNRr2duBF8APDj8AL8yt+FSnOokxzNqe2sQ6GH2cPGiGbCE8iFJqq9Y6rrQyD1pwSjiHsYS7UhkE+O6nYR13Vpnb4zK0Lu7AGR6ewqhRO70iCcrIOM6qVf/l9OmzLFz4DLNmNeXEibXuDksAnlYVb5+ZVK7ZY6YTYxGi5pKmsRqtaCSMJU9vY/JFqX7AV4X7IXz00UGMTh2ebdWqR8jIKO7LcebMORo0cGanVGE/z6qKt88mjMkS7ZGD0alUCFFZkgjVaBkY80UcxOgoGoh3VJ2PwZj3ZQDGDGze8Wual5eLyaQoKDBqHq6+eiRBQZ7eJ6u22Oaw5ZRcxxXLXAghpI9QKWpWHyEwaoZSMOYVccU8ErXX6dO/8vXXE9i37y9mzjxCQEA5qzILIYRwifL6CHnHR21RTQoIL9w831tvwfnzxuSwLVvCxRdDkO26jB6qUaPujB37K1lZp706Cbpw4QILFy7ktttuI8pjRpoIIYTjSY1QKWpejZB36dIFduwo2tNs27abLl1SgcquSeQquZQxFtsrZWRk8O9//5ujR48SHBzMxIkTiYnxjok4hRCiNDJqrNb6C/gb56827Dhaw6FD0LLl//jwwzs4cSKKLl0uBh5zd2hl+BNoAbyF/R1bPVdOTg5vvPEGR48aS4Okp6fz73//m2PHPGUiNuFtxo8fz+DBg83b+PHj3R2SEFYkEarRJgLtMOYYaY5nDAku35kzkJoKWVmB3HHHx0RGJhaW/Ebxmgie5GmMvlcPYfysv3NvONXk5+dHdHS01bEWLVrQpIk3dLIXnighIYGYmBjzlpCQ4O6QhLBidx8hpVQsxkx3URjvrMkY1Q2btdayfoBHOlT4VWNMrV7WAouew9cXXn8dDh5sytGj7WjefG9hST7wMzDUfcGVsBH4xmL/EBWvnePZlFKMHDmSwMBAVq5cSevWrXnggQdct5q9EEK4WLl/3ZRSDYCxhVsbSl9ZMEcp9TXwltZ6naMDFFWVR8lJU1q4I5BKqV8fJk4s2hsI7MVY9bIvxjLfnuRvjM8EmYX7V+BZiVrVKKUYPnw4jRo1omvXrgR4w2yWwqOMHz/eXPOTm5trVZabm8vgwoUDY2NjmTRpEsnJyfj5+eHr60tsbCxhYWGuDrlGsPy5g/HznT9/vvsCspO74y4zEVJKPQ5MxZjf/XOMNoBtQBLGX/5QjHfWHhirAq5RSq0DHtJa/+3csEXFzmOsl3QIozYoAs9LJCryIHAbcDnWK397iruBwcAsYAHwIk5bhdwNevbs6e4QhJcqag4rTevWra3O27hxI7/++qv52N133+3QRGjNmjUcOHAAX19f/Pz86NWrF61atXLY/T2J7c/dW5oh3R13eTVCtwP3AF9rrUub5/104fYrMFcpFQU8DgwDXnZ0oKKywjCaksDoLO0JM+VWVnt3B2CHSIzFLp/GaDUWwntprcnMzOTMmTPmLSUlxWr/3nvvpW1bx82YnpeXZ7Xv6GbYQ4cOsW1b8eSUHTp0cGgi9N5777F161ZzolVUs2X51fL7AQMGWCWD1ZWbm4tSCh8fWdewqsrsLK217qq1/rKMJKi0809orR/VWksSVA6llNVWlgULFlidN27cuDLPveyyy6zO3bp1q80ZAUA0W7dutTrvsssuK/Oe48aNszp3wYIFLnlNM2bAk09CTExFr8ngntdUPOVE8Wtq6oB/J3e+pmKV+d2LiIiwOvemm27yitfUvn37Mkcyeeq/k+3oqz59+jjkb8S6det44403eP7557n99tsJDg6mefPmdO7cmVtuuYUffviBLVu2cPDgQc6dO0dKSkqlX9OCBQusNkuWTWd79uyhW7duDvq7Zzh48KDVs++6664y71mVf6d7772Xt956i6ysLC5cuMCZM2c4ffo0x48f5/Dhw3zzzTf885//5JFHHmH8+PFMnjy52q/J8nfP39+foKAgoqOj2bVrF8nJyebzcnNzad68ebV+99577z2eeOIJ87Znzx6HvD8V/T5ffPHF7N69m59++sn8u1DUfOqqUYblNY310VrHOz0CISwsWmQMn7eVnPALa3e8Ckpx1d0fuzwus4Ic+PpiiBkEF89yXxweIifHesqAEydOuCmSyklLS/O6JgTb5oNffnHMKNBjx46xa9cuALKzK55q48yZMxWeExsbW2YfIYDDhw+bz+vQoQMhISHk5eWRnp5eicjtY1vjVF7C7ArOeL7JZKJu3bol7t+6detq/5/MyMggNbV4xG5p/55VUfT7nJaWRn5+PllZxWOubJtPna28Osh1SqkE4APgQ631/5wejXCY/21+G5OPP+GxPagbfhFKef5MCXl5cORI6WVvz5pERAg0bBBIvzEF7ns9++bA6f3GtutDONHHPXF4kfT0dE6ePOmSZxW9wdoqqsVwppycHA4fPkxwcDDh4Y6bxT09PZ3k5GQyMzO5cOECAQEB+Ps7rs9cZfvj2POztOzoWtQx2tLKlStLvS4vL4+PP3bsB51evXrxyiuvmPcd+bOrCpPJuX+7nH3/mqi8ROg+4C7gGeAZpdRGYDHwmdbaEyd0ERY+e3MqCUeMT24B/j48OvtdUBe7OaryHT0K+RWMPj97LovTB9bQuM3VrgnKUn46bH29eD/zAqQluD4ON7Mc4WE7M31eXp7ViKD58+eze/du3nvvPZfE9uKLL5Z6fM+ePVb7BQXWLf6WI5mOlJWNVyAxMZEXX3yRuLg47rvvvirdozQDBw7k2LFjFBQUcPr0afN8PPkV/WexU2ho2UvBBAcHM3ToUEJDQwkLCyM0NJSGDRs65Lmu0qxZM6t9Z/Wlee2118jLyyM3N9fq64cffsj69evN5wUHBzv82Za1QO6u8fJGFS6xoZSKwUiI/gG0xRhF9iVGTdEP9vYh8iY1YYmNRwf7k55RXIX5r482EhrtqUtUGH78EQYOLN6/4grYuBFeH9eUPXuLq3dvv/dO+o7+wPUB7vwvbB4LZBj7Pj5w259Q1xs6dTvO4MGD7Vpy4/Dhw6xcuZLff/+dhQsXmo87OlGwvX9Zvv/+e+rWrUtERATt25f9b1YUd1Wf78jXN3jwYIKCgqwSuSZNmpTorFzZmC2dPXuWhIQEQkNDCQ0NpW7dug59M3X30Oiarujnq7UmJyeHNm3amP/9Dhw4gJ+fsfxPVX/u6enpVs2LQUFB5ns6Iu78/HxycnJo2bIlfn5+KKUcErctVZ1FV7XWh4GZwEylVA+MpOhWYCSQqJT6CKPp7M9qRyocIiv1mFUS5OOjaNC07I6cnuLgQev9li2Nr+27dGPP3m8wJoRswJ4/FH1Huzi4nBzYngu8APwA/Agdbqx1SZBwP8u+FI7QsGFDp9bySNLjXLbNkJZJrJ+fX5UT5CLOqMGCknFbNlk6Iu7KqNQ4Ra31ZmCzUmoixtxBd2EsAvVoZe8lnCc75RidmzUlOS2V5LR06jeog8nHE+fhsVZWItTuisdhSTJFgxz/3ptIQX4+JlcOF/3zT8jKAoKAG8Hvarj0Vtc934sVjWgp4sj+M6XdvyyBgYHUrVuXwMBApz7f0a/P19fX3BEWcMincSFEsaomL82BzsAlGDPIXXBYRKLa6qsmPNjvWQC0LiC7cT03R2SfshKh5pdcQVCdYDIyjRmcMzIzObpjBzFdu7omsKws2LnT+tglvSGwds4bZDsiyHKEh22VNkDHjh3p2LGj0+Kx9/5Hjx41x33gwIEK43b086ui6GcdFhZm189a1G6W/zeL9r2Bu+OuzFpjDTCm+b0TYy0BDazB6Ey93BnBFT63LcaKllcBLTGSrt+BZ7TWOyq4th4wCWP634sAH2A38JLW+ktnxex2FkMdlTIRGF7xp2VPYDtsvigRMvn40K5dO/6wmBRt7+bNLkuEvnz9WvIOnWFwx5HUDQyDwEDo1Mklz/ZE5Y0IcnWVdmV4Y9zeGLNwH29thnR33OWOs1NK+SqlblBKfQGcBP4PaAhMAaK11oO11v918qKrV2MkQYsxFnJ6EGO9iM1KqYo6vkQXnv8zcAdGIrcPWK6UeshpEbvbBZsKupAQ98RRSbY1Qi0slkZr1/VSjJVdTgK72bP9FVzhzJHNrP7hZ1bv2cnTXz3DtzvfJefiVuDmIbhCCCEco7wJFd/ESBxCgbPAQmCx1trVw6mWAPO0xfA2pdRPQAIwEaOfUlkOAS211hkWx1YppZoDT2KsjVDzpK4BzgDhQBjU8/ymsfPnwXJ6En9/iLJoeWrfMxreLf7VO3DgDLlZ5/ALbODUuL6dfz95ecavXlZuPr8c2ck1HZzXzONt3F2lXVXeGLc3xiyEN6hoHqEVGDUx32qtHTOdZCVprZNLOXZeKbUPaFrBtWVNU7oFYznzmunC2xg1J4VC1uHpK8/bNovFxhqj04tEtOpHaINAzpwzKh9z8zT/++192vX5p9NiSvx7Oxs3WK8fPHTkP5yefHkTd1dpV5U3xu2NMQvhDcpLhJqWloR4AqVUKHAx8H4Vb9EH2Ou4iDyIzofU09bH6nn+EO/YWPjqK6N57NAhaNDAulwpE+06tmHjhuJZGvb/vsKpidBX/7cIY9qJE8BRGjcK5Ipb33Ta84QQQrhemYmQbRKklAoG7sVIIsKAcVrr/UqpkcB2rbUrE4s3MEarvV7ZC5VS44AeGH2Gap6M/0GBxYyzAUHg38h98dipQQMYNqz8czr1GMzZ/52mfWRr2jXpQvMW/ZwWz+Ft2/jjjz8wutE1A5pww50jMfk6dui1EEII97Jr1Fhhn5p1GO8IezFqY4p64F4FDATG2nmvgcBqO079WWvdr5TrpwC3A/dqrQ/Y80yLa/sB/wE+0FqXuaBNUlIScXHFE1COGzeu3NV1PcqFVIw8L9nYQiq3jpAn63r183Q9VTx8mJSzkJ0NAQEOf9aXNs0Q0c1i6Tr0YYc/RwghhHvZO3z+VSAbY4mN44DlktM/A89V4pkbAXvaajJsDyilxgMvAtO01pVavEgp1Q34GviJCpK2iIgIvHaJjdR6wN3F+/UqXgrBa9SpA6GhULT6tdZw8qTRruZA+9a/we49XwExgJFk3XjvvShZzFAIIWocexOhQRhNYYeVUrbT+R6ngk7LlgpHcFW6GU0pdSfwFvCq1vqFSl7bCVgFbAduclfHb5dItVkPN6SBW8KorFtuAZPJmDuoZUsYNQosJtMt1rRpcSJEARzf7tBESOsClr8zA6NG7TQQSds2/ejQv7/DniGEEMJz2JsI+VP27NH1gbwyyhxCKTUco2P0Qq3145W8tg1GU9xB4HqtdaYTQvQctnMIecHQ+YIC+PprYzmvIreWtXpF0wbwZzywB/gbjtcDbnRYLDtXPs/BhKLucRo4wfD7+8uKzkIIUUPZmwjtBG4CSpvG9Fpgq8MisqGU6gP8F9gBLCpc+LVIttZ6m8W5PwIxWuvWhfuNMJIgf4zmuw42b2jbtNbZzordLS58BaRjzDkZDiGlVat4lhMnrJOg0FCoX7+MkyMbg/rEaBYDOJcO6fsguG0ZF1TOphUfWe13viSWlpff65B7CyGE8Dz2JkIvA18UJhGfFB7roJS6AWMkWQXjfaqlP0ZHja7ABpuyw0Csxb4P1q+pA0ZHD4BvS7l3C4yJGWuO1EXA+eL9ev0xlobzXGWtMVYqv1BofBEkWrSuHl8ObZ90SCzj/v0Xmz57hG/++xHnUrO48YHXHXJfIYQQnsmuREhrvUwp9SAwG7in8PAHGM1lD2utnbbgjdZ6OjDdznP72eyvwxhmXzvkXYAMiyRIKajr+XMIXXop/PyzkRAdPAgRERVcENWrOBHyrwM5Zx0Wi8k3kJ63v8PlI17m7/VvEtXxBofdWwghhOexe9FVrfV8pdSHGAuuNgJSgI1aa1l53lOk7bbeDw4Dk+OHljtaSAj06WNsdmlxB/gGQtOhEN4flN2/xnbzC2zAxYOmOfy+QgghPEul3kEKl6xY46RYRHX5BMPFt8CFI5B6HEIi3R2Rc4T1NTaHSQceAR7HaE0VQghRW5S36Gp5PTVK0FofrPgs4VQhF8OVn7k7Ci/0H4xBiYsx1vCdTnHXMiGEEDVZeTVCBzDGD9vLdn4hIexy5ZVG81jRHEIPPQRBQfZeXQD8iTE7wvAqPP0s8JLFvRYBUUClpqoSQgjhpSpqGrsALAXWU7mkSAi7ZGTApk3F+z4+MGmSPVeeB+4HfsSY/LAeMJRKtvZi9Pk/Z7HfAKOJTAghRG1Q3rvG3RjtBGOAfsBHGGt0/c/5YYmqeGvJAPwDgmkc0ZHYqCtoH3MNvj6e3Vn60CHr/eho8POz58oQipMggFRgC8Y6a5UxAWN6gWkYkzQ+CTSs5D2EEEJ4q/JWn18MLFZKNcNIiO4ApimlNmN0pvhUa32+rOuFaxUU5LPrl5/IzwX4BoBpM/+geaNL3RpXRWwToXLnELJiAgYAn1ocW0PlEyEFjMCYCusT4OZKXi+EEMKbVdiOoLU+hrHQ6YtKqcuBOzE6UMxVSv1Laz3DyTEKOxxL3l6YBBl8/BRNwy9xX0B2GjAAduwonkOoSZNKXY2RCNXDqLTsWI1IfDHyfSGEELVJZYfP/6aUyi+87j6gs1OiEpV24NjPVvt16gdgMnl+//U6deCSS4yt8m4ELgEuo/J9g14C+lD5GiQhhBA1iV3vHkqp5sA/MGqD2gGbgIewbpcQbtQktAPtruhG6rkTpJ87T0hYuLtDcoGIwq2y/gSewuj/fwMwC7jYgXEJIYTwFuXNIxSC0WHiTqAvxppc0mHaQ3WIHUyHMYPdHYaXeJriQZBfYSxZ9we1aTUWIYQQhvJqhBKBXGAZMAOLIfRKKZPtyVrrAmcEKGouraFFC2jc2PjasiU8/7y9o8Ys71PAiRM/sXfvMpKTD3HbbSvKOXsXRZ3Ji81AkiAhhKidlNalTw+klCpKbOyZP0hrrR2/4JObxMXF6S1btrg7jBrv1CnrztEhIXD+vLFWrL1ycs7x9NNNSU3NAIxrX331MMHB0eVc9RMwBfgNY+m8DUgiJIQQNZdSaqvWOq60svKSl+edFI9wiqcw5r9pWbh1pvIdiF3roM2iLC1bVi4JAvD3b0D9+vXMiZDW8PffS+jadXI5V/UHNmM0i0UiSZAQQtRe5c0jJImQ18gBXsZYIqJIBt6YCFVFu3bdOHq0uLlrz55VFSRCYCQ/N1btgUIIIWoMz36nFHY6gnUSFAXUcVMs9nNUItS+/RDi41dy0UUX0a5dbzp2HFXinD3rXqUgP5cO/SdTShc3IYQQtVR5o8ZGaK2XVeZmSqlIIEZrvbnakYlKsMkoqGJG4WK2s0q3aFG1+7RvfzevvTYaH5/AUssL8rL47xvTOZWURtuPX2f4+Bdoefm9VXuYEEKIGqW8j8ZvKKW2K6XGK6VCy7uJUqq3UmoBxor1nj+dcY3TBngNeAQYAvR2bzh2clSNkMnkX2YSBLDx04c5lZQGwL4Dp3h58ljOHd9atYcJIYSoUcprGmuDsQz3DIykaA+wA0gCsinumRsH1AfigUFa641OjViUogUwyd1BVJqjEqHSaUBRkJPBt0s+tiq5smdnGjS9zJEPE0II4aXKrBHSWmcUriPWDGPB1S0YaxncA/wTGAr4AHOBjlrrqyQJEvbKzoZjx4r3lYKYmOrcUcPZTbBrCqy6Aj6NAp2Pae8BHuw5lo5RjQHw9VVc/8A71YpdCCFEzWHPoqs5GEtpyHIawmEOHzaGuhdp2hQCy27dqlhBLnzZH3Kzio+d+hm2HyM6tBMT+ndi36nNnAzLpGGzbtV4kBCiKs6fP09ycjI5OTnuDkXUIP7+/oSHh1O/fv0q30NGjdUE6wZDUGOo1wpC2kLUzaA8+5/WUR2lzUz+ENUZDv9afGztKMh6HKPlFto2603bUSVHlAkhnCsrK4tTp07RrFkz6tSpg6rshGFClEJrTWZmJseOHSMgIIDAKn6a9ux3S1Gx7ETYt6p438cX7rnFffHY6ehR6/1q9w+aNw+2m+A4jN8FCZkAp4Fnia0TwfyLLzamsT5wAKKi4KGHqvlAIYS9kpKSiIiIICgoyN2hiBpEKUVQUBDh4eEkJSXRvHnzKt1HEiFvd+Ev6/2QRqB83BNLJYwdC7fcYtQMHTxovdRGlZw4AR36QGYCCXkXiGmQVliQRUL6BWNBs27dwMfHaJcTQrhMVlYWTar9n1yI0oWEhJCSklLl6yUR8nape6336zV1TxxVUL8+dOlibA4R3BL8BwNLbArOQfR5IwkSQrhcXl4evr7ydiOcw9fXl7y8vKpf78BYhDtcaAWMBpKNLeRyNwfkHuO//56EnBxITyW3INOqLBcY/MWfwN/EhoUxv7d3zLMkRE0i/YKEs1T3d0sSIW+XGgxcWbxf7wq3hWIvrSEjA4KDHXfPhNRUYpo2hTqn4bx1WesoICgdiCShGtWnQgghah67Fl1SSm1USt2plApwdkCiki5csN4PCXFPHJVw9izUrWt027niCqO/kGPkQECK9WLyvhQuu3bKKBdCeL0jR+DNN2HWLOOr7eALZ5s+fTpKKfLy8rjmmmtoWcpojzlz5qCUYvLkkgtAd+vWjbi4OPN9KtoWLVpUqfj69euHUoq77rqrRNnChQtRSpGQkFCpe9rasmUL48aNo127dgQFBREdHc0//vEPDtkOCfYC9tYI5QCLgdeVUouBBVrrvRVcI1whNdV6v14998RRCUUzSp8+bWy2L6HqjhhTfIYAaRhpfgMKEyNtlNPQUQ8TQrhYYiI8/DAsXw4FFutMT5wIw4cbSZGr+2T36dOHH374gWPHjtGsWTPz8fj4eIKCgoiPj7c6Py0tjW3btjFhwgTGjh3L4MGDzWXfffcds2bN4vPPP7e6V6tWraoU28cff8xTTz1Fhw4dqnR9eZYsWcJff/3FhAkT6NixI8ePH2fmzJnExcWxffv2Ko/gcge7EiGtdT+lVDtgHHAXMFEptR74P2CZ1jrXiTGKMhTk5/DmV/8kLKg+4XXDiQhpzKV1R+PpLfHOWFojtl4QCSnGjXPzoXXj4rIDp8HPRwOJxIZFV/9hQgiXS0yEnj1L/v0AIylauhS2bYONG43aZlfp27cvYCQ+t99+e2E8BWzYsIH77ruPt956i4yMDPPUARs2bCA/P58+ffrQrFkzq4Rn716jfqFLly60bt26WnF16dKFEydO8Mwzz7B06dJq3as0Tz75JBEREVbHevbsSYsWLXjnnXeYMWOGw5/pLHY1jQForfdqrR8FmgJjMD57fwIcU0rNVkp5x5LnNcjZY1v463gi8fv/Ztm2DXyy9RuUv+e3XqakgOUAEkckQvOv82flRB9WTgQ/m8Fhfj6wciKsnGhi/h3Vmb5aCOEuDz9cehJk6eBB108RdvnllxMYGGhV87Njxw7Onz/Po48+islkYuPG4tWn4uPjUUrR28mDNoKDg5k6dSrLli1j61bHLzJtmwQBxMTEEBERwfHjxx3+PGeyOxEqorXO1lp/CEwE1gMRwGRgn1Lqc6WUQysmlVJtlVJzlVI7lVJpSqmTSqmvlVKdq3CvlkqpDKWUVkpVL932AMmHf7faDw/3/GYxgAcegMxMYw6hn36C++6r7h1PYqwHnF/BefnARox2MyGEuyll/2Zvpcby5cV9hmzv4Qz+/v50797dKhGKj4+nbdu2REdH061btxJlHTt2JCwszDkBWRg/fjzR0dE8/fTT5Z5XUFBAXl5ehVuBZXtkKfbs2cPp06dp3769I1+G01UqEVJK1VFK3aOU+g34HWiEkRBFAQ9gDF/6uJxbVMXVwFUYfZSGAg9iJF+blVKVXUL8LUqMKfJeycd3W+2HNwp3UySV5+sLsbFw1VVw8cXVvdtMjD5AhtgwOJxSvMVa/b0pAH6p7gOFEB6qoAC+/tq1z+zbty979uwhOTkZMJKdohqf3r17mxOhrKwsfv/9d/r06eOSuAICAnj22WdZtWoV69evL/O8GTNm4OfnV+FWXnNXXl4e48ePJyIignvvvdcZL8dp7OojpJTqBNwP/AMIBr4CntRar7U47R2lVCLwuYNjXALM07p4iU6l1E9AAkYSVrJbfCmUUrcDlwL/Al5zcIxukXy8EdAByASyCG/Sxb0Buc0miCqAwgmj55dW42yeTDofok65KC4hhDucPeva5xUlNvHx8YwYMYL169fz8ssvA9CrVy9ee+01cnJy+PXXX8nOzjb3K3KFMWPG8NJLLzF16tQyk6Fx48Zx/fXXV3ivqKioMssefvhhNm7cyHfffUfDht41KMXeUWM7gBPA6xgjxk6Wcd4BYJMD4jLTWieXcuy8UmofRn+lCimlGgL/Bh7H6NtUIySdzACKqzsimg5zXzB2ys+HU6eMkR2mSjfMlmUbyNJhQohCrn4fvuKKK/Dz8yM+Pp727duTlJRkrhHq2bMnOTk5/Pbbb+aaIVfVCAH4+PgwY8YMRo4cyYoVK0o9p0mTJjRq1KjCe5nK+KP91FNPsWDBAhYvXszVV19drXjdwd63opuBGK318+UkQWit92itr3JMaGVTSoUCFwN77LzkJWBvYd+mGiP59Gmr/XCL0Qee6uhRaNoUgoKgfXu48053RySEcBet7dsOH7b/g5PJBMOGlX5/ZwkKCiIuLo74+Hji4+OJiooyzy1Uv359OnXqZC5r06aNy9ddu/XWW+nSpQvTpk1Dl/KDqE7T2AsvvMCcOXP4z3/+w51e+gfd3uHzy5wdSCW9gTE7zOsVnaiU6o3RfHapk2NyuaL26CLhMTFuisR+RaM+srNh715o0MCt4QghvEB0tDFPkD0dpocPB3dMYdOnTx9efvllvv322xIjwnr16sVPP/3E5s2bGTVqlMtjU0oxa9Ysrr/++lKH0le1aew///kP06ZN44UXXuDhhx92WLyuZm8foWfLKS7A6ID8h9Z6gx33GgistuOxP2ut+5Vy/RTgduBerfWBCp7lD7wNvKa13l3euZaSkpKIi4sz748bN45x48bZe7lLZKclciH9FyAQqIPJFERDL6gRcsYcQkKImu/NN415gsobQt+yJcyb57qYLNe46tOnD3PmzOG7777jjTfesDqvd+/evPXWW2itq9Us1q9fPxISEqo0K/SQIUPo2bMnq1atKlEWFRVVbv+f0ixZsoRJkyYxePBg+vfvz+bNm81l9erVc8okjs5ibx+h6RjDckobgFh0XCulNgFDtNbljczaCNgzti7D9oBSajzwIjBNa/2eHfeYhDGV8H+UUg0KjwUVfg1RSoVorS/YXhQREcGWLVvsuL37JCdswhgGbgwFD2sYhMkLVlfPzDTa74s6M0oiJISwR5MmsGFD6TNLm0xGTdC8ea6ZTDEzMxMfHx98LP7m9urVCx8fH/Lz80vUCPXu3dvcJFWdRCg9Pb1azWovvPAC/fr1q/L1llauXInWmpUrV7Jy5Uqrsr59+7Ju3TqHPMcVVGnthSVOUuoi4GtgPvAFxsJNjYFbMWabHg00w6h9WaK1dnjXVaXUnRhD6P+ttX7czmsWFcZWlh1a6y62B+Pi4rSnJ0I7vn+Gt2bPMu+3bxfFpAXeM4nV2bPGPEKhocYweiFEzbVnzx6Hzi1z9KgxRP7sWeOD1bBhrm0OGzFiBDt37uTAgXIbJRwqPT2dBg0a8PHHH3Prrbe67LneoqLfMaXUVq11XGll9tYIzQPe1VpbDjs/CryqlPIBXtBaDyicXfoRHDyGRyk1HHgfWGhvElRoNrDI5thg4EngDuBvhwToBknHrUOPaOziBXaqqWFD14/sEELUDM2bu34GaTAWGl2/fj3fffcdjz76qEufvXHjRlq3bs3NN9/s0ufWBvYmQldgzL9Tmj+A5wq/34IxyaLDKKX6AP/FGMK/SCnVw6I4W2u9zeLcHzFGt7UGY1kQYK/N/WILv/21oj5GnqzH8JlEt+tF0rG/SD5+gBYXO3e6diGEqO1uvfVWCgoKmDhxIs8//7xLnz1o0CD27LF3oLSoDHsTofPAAODHUsoGUjxbcyDgsLXEC/UHAoCugG1n7MNArMW+D/a/Jq9WN/wi2va+iLbuDqQSsrPhwAFo0cIYPi+EEN7kYEWLnQmvZO88Qu8BTyql3lBK9VVKtS/8+ibGJIXvFp7XHdjlyAC11tO11qqMLdbm3H62x0q536LCa722Nshb7d1rLKcRHAyRkeCGUaRCCCGEFXtrT4qGz0/EWOsLjJFi6RhNZkXl3wGfOiw6UaMcOlT8fWKi66fBF0IIIWzZO6FiATBNKfUK0AmIxFjy+0+t9TmL835zRpDC1kmgH9ASaIExyfaD5V3gEWxrlVu0cE8cQgghRJEKE6HCSQkTgTFa66+BspewFS7yP2Bf4QZG9ynvS4RkDiEhhBDuVmEfIa11DpAHZDk/HGGfQzb73lG1IomQEEIIT2NvZ+kvMRZeFR7BduSCd2QUkggJIYTwNPZ2ll6BsUzFFxhJ0UmMpTXMtNY/OTY0UZbXNilOpV9Du/AAWjSA1qFdaVrP3VGVr6AAbJfHkT5CQggh3M3eGqGlQFNgBPABxqKpa2y+Chf5K+kIaw4m8+Zvx3nsh+McTY11d0gVOnnSmEeoSMOGsvK8EKLqMnMzmfrjVLLy3NdrY9OmTYwcOZJmzZrh7+9PvXr16NatG8888wwnT54scX5ycjJTpkyhY8eOBAcHExQURKdOnXjqqadKPb8iY8aMQSlV6vpla9asQSlV7TW/9u3bx8SJE7nkkkuoW7cukZGRDBs2jB07dlTrvp7E3hqhq5wahaiUExdOWO03DWnqpkjsJ81iQghH+vP0n3z858fc2O5GLm96ucuf/+qrr/LEE09w1VVXMWvWLFq2bElaWhobN25kwYIFbNmyhRUrVpjP3717N1dffTVaayZMmEBcnLHs1bZt23j77bf5+++/Wb58eZViWb9+PStXrmTw4MEOeW2WfvjhB9auXcvo0aPp2rUr586d46WXXqJHjx788ssvXHbZZQ5/pqvZO3z+Z2cHIuyTk5/D6fTT5n2lFE3qev46Y5IICSEcafOxzZzLOsevx391eSK0du1annjiCSZOnMhrr71mVXbdddcxZcoUPv/8c/OxvLw8brrpJgIDA9m4cSONGhWvRDVgwAAmTZpklTRVRmRkJOHh4UybNs0pidDIkSN56KGHUEqZj/Xv35/Y2Fjmzp3LBx984PBnupq9TWMAKKXClVLXK6VGK6VCC48FKqUqdR9RdUnp61l1xw7eu2EvM686xLiu6fj5+Lk7rApJIiSEcKQfD/5I/YD6rPnfGpc/e86cOYSHhzNnzpxSy4ODgxkzZox5f/ny5ezdu5fZs2dbJUFFfH19GTp0aJViMZlMzJw5k61bt7J06dIq3aM84eHhVkkQQP369Wnbti3Hjx93+PPcwa4aIWX8FF7CWFneH6OjdDfgDPAV8Asw00kxCgvns7bRtFEeYUF5XNI4nb+TA9wdkl3CwqBLFyMhSk2VjtJCCIhbEGfXeUfPH+Vc9jmr2m+tNaF1QtmWuI2Y12PMxxWKG9vdyOuDX3d0uIBRu/Pzzz8zYsQI/P397bpm9erV+Pj4cN111zklphtuuIHu3bvz7LPPMnz4cEym0usmtNbk5+dXeD+TyVTmPQDOnDnDrl27uPvuu6scsyextyZnCvAwMANjPTHL9PAb4HoHxyXKkJW/22o/I9fzm8UAJkyAbdvg3DlISZF1xoQQ9msU3IgAnwB8lS9hdcKICIqgUXAjfE2+NApuRERQBGF1wvA1+dI6tDUTuk9wWiwpKSlkZWURHR1doiwvL89qK3L06FEiIiIIcuJq0y+88AK7d+/mo48+KvOcxYsX4+fnV+F2zz33lPusRx55BK01kyZNcvCrcA97O0uPBWZorf+llPKxKTsAtHJsWKIsWlu3MRXomDLO9ExKQWiou6MQQniTAN8AWoe2ZmDLgSzdvZQgvyACfItrw7PzssnIy+CWDrfwXN/nqONXx+UxJiYmEhkZaXUsNzcXX19732arZ8CAAfTv35/p06czqoxPmkOHDuX333+v8F7h4eFllv3rX//ik08+4d1336V169ZVjteT2Psv1BTYXEZZDhDsmHBERT79qyvT16XQtF42TUNy6N9ioLtDEkIIpzMpEy8NeolOjTvx3NrnrBKh9Nx0nr/qee685E6nxxEWFkZgYCBHjhyxOh4eHm5OMhYsWMA777xjLmvevDmrV68mIyPDqbVCL774Ij169GDhwoW0adOmRHloaCj169ev8D5lNYvNnz+fqVOnMmvWrAprjbyJvYnQcYyVPdeWUtaZkms+CCc5mnqSk2kBnEwLYAtwY7uS80d4muRk+PVXo4N0bCzUcf2HNSGEB9oybkulrzl2/hhKKc5nn6dAF+CjfFBKcez8MSdEWJKvry99+vRh9erV5OTkmPsJ+fr6mofEf/vtt1bXDBw4kHfeeYcVK1Zw0003OS227t27M2zYMGbNmsX8+fNLlC9evNiufj2jR49m0aJFVsc+/PBDHnzwQR577DGefvppR4XsEeztI/Q58KxSqqfFMa2Uags8BixxeGSiBK01x1Ote+k3ref5cwj9+itcfz106ABBQXCzLNYihKiC5Ixk3t32Lrn5ucTUj+HdYe/SvH5zcvNzeXfbu6RkpLgkjsmTJ5OcnMyTTz5p1/kjRozgoosu4sknnyQpKalEeV5eHt99951DYps1axaJiYnMmzevRFlR01hF2/Tp062uW758OXfffTdjx47llVdecUicnsTeGqHpwJVAPHC48NjnQHNgIzDb4ZGJEs5nniEj+wKYjG5adfzq0DCwoZujqpjt0HmZUVoIURXvbH2HtJw0xnQZY+4L1L1pd55b9xwf7PiABVsXMKX3FKfHMWDAAGbPns1TTz3Fzp07ueuuu2jRogVZWVns27ePJUuWEBwcbB527uvry7Jlyxg0aBBdunRh4sSJ5tqjHTt2sGDBAtq1a8eQIUMAY8boxYsXo7UuM4aydOrUiZEjR/LJJ5+UKAsLCyMsLKxS94uPj2fUqFF07tyZMWPGsHlzcS+ZgIAALr300krH6GnsnVAxUynVD7gduAajg3QKxpD5j7XWeWVfLRzl3O5vufzTHYSFBlEvNJiI1i1KzO/giWQOISGEI2TmZfL29W9zQ7sbzMfq+NXhpUEv0bN5T7ae3OqyWCZPnkzPnj2ZO3cuU6dOJSkpicDAQC666CJuu+02xo8fj49P8diiDh06sGPHDl555RUWLVrE9OnT0VrTpk0bRowYwcSJE83npqen07hx4yrHNmPGDD777DOrkWtV9dNPP5Gdnc0ff/xBz549rcpiYmJIsF1E0gupqmScNV1cXJzesqXybdfO9vvyx1j42r/N+50vieXBNz2/e9abb8KyZUZCdPQofPwxjBzp7qiEEK6yZ88e2rdv7+4wvEZUVBSTJk1i8uTJ7g7Fa1T0O6aU2qq1LnXiKteM6xMOkXx8n9V+eJPIMs70LA8/bGwAOTkgubcQQpRu//79ZGdn8+CDD7o7lFrD3pml/TEmVRwFRAO20xlrrbUkVU527nSi1X5EVKx7AqkGOydiFUKIWqlNmzakpLim07cw2Ju8vAw8BKwAlgHZTotIlGnU5c9xQ6O/SE47RnLaCZp1uc3dIQkhhBBezd5E6GbgOa31C84MRlTgwgWC/BsSHdqQ6NBO0MLz5xA6ehTi440O0i1aQOPGxuzSQgghhCewdx6husAmZwYiKlBQAGlp1sdCQtwTSyWsXw933AFXXgmRkXCbVGIJIYTwIPYmQt8Anl/9UJOlnQFt0SIZFAQuWsOmOg7ZDGpr3tw9cQghhBClsfed9A3gA6VUAfA9cMb2BG27GqhwrNQfgEeAECAC6vUA7nBvTHawnUOoRQv3xCGEEEKUxt5EqKhZbDrwXBnn2K5KLxzpwv6ib4wtpJU7o7Fbt25w9qyREB08KJMpCiGE8Cz2JkL3ADL7iztd+J/1fkiMe+KopPHjjQ2M+YNkDiEhhBCexN4lNhY5OQ5RkdRzgMKcj9Zr7cZgqkYpGTEmhBDCs9jbWbpMSimTUirUEcGIcqSOAeYBs4BJUO8at4YjhBDudRLoCyRWdKLDTZ8+HaUUeXl5XHPNNbQspc1/zpw5KKVKXSajW7duxMXFme9T0bZo0aJKxdevXz+UUtx1110lyhYuXIhSqtprhB0+fJgbbriBmJgY6tSpQ3h4OH379uX777+v1n3docxESCl1RinV1WJfKaW+VkrZ/ot3A5KcFaAodOECRjesCKA9hLRxc0AV27sX5s2DFSvg778hW6bhFEI4zEzgl8Kv7tOnTx8OHTrEsWPHrI7Hx8cTFBREfHy81fG0tDS2bdtGnz59GDt2LJs2bTJv06ZNA+Dzzz+3Ol60Kn1lffzxx+zevbtqL6wCaWlphIeHM2vWLL7//nveffddQkJCGDJkCMuWLXPKM52lvKaxBjblJuB6jA7TLqOUaosxq/VVQEuM3sK/A89orXfYeY86wJPAPzCWCDlXeI8RWuscJ4TtWDk51lmEj48xfN7DrVtXvMYYwJ13wgcfuC0cIUSNcRJ4Hygo/PoM0MQtkfTt2xcwEp/bb78dgIKCAjZs2MB9993HW2+9RUZGBkGFf7M3bNhAfn4+ffr0oVmzZjRr1sx8r7179wLQpUsXWreuXveHLl26cOLECZ555hmWLl1arXuVpmPHjrz77rtWx4YMGUKLFi14//33GTFihMOf6SzVbhpzgasxkqDFwFDgQYxqkc1Kqcsqulgp5YexNMjdwKvAoMJ7HMNbRrqlngDOY+4fFBLiFZ1tbIfOt/KOgW5CCI83EyMJAsjHnbVCl19+OYGBgVY1Pzt27OD8+fM8+uijmEwmNm7caC6Lj49HKUXv3r2dGldwcDBTp05l2bJlbN261anPKuLr60v9+vXx9YI57ix5QyK0BOistX5Va71Wa70cGAxkARPtuP4xoCvQS2s9X2sdr7VeqrUer7XOdGLcjnPhe2AyxjxC0yHkK/fGYyfbREiGzgshrCmbzZ7yotqgosr8HOCtSlzvWP7+/nTv3t0qEYqPj6dt27ZER0fTrVu3EmUdO3YkLCzMKfFYGj9+PNHR0Tz99NPlnldQUEBeXl6FW0FBQZnXJiYmMmPGDPbt28fDlk0BXsDjEyGtdbLW1oOutdbngX1AUztu8SDwudb6qDPic4nUojmEcoGTUC/LndHYzXZWaZlMUQhRfZa1QZ6hb9++7Nmzh+TkZMBIdopqfHr37m1OhLKysvj999/p08c1CzUEBATw7LPPsmrVKtavX1/meTNmzMDPz6/CbcaMGSWunTx5Mn5+fkRGRvLyyy+zZMkSBgwY4MyX5XAV1V81tegc7WNx7JzFOc1wscJRahdjfCwo77xooDlwUCn1DnAb4A9sAB7TWm93cqiOccEmowjxjoxCaoSEEI5lWxtkKxF39BUqSmzi4+MZMWIE69ev5+WXXwagV69evPbaa+Tk5PDrr7+SnZ1t7lfkCmPGjOGll15i6tSpZSZD48aN4/rrr6/wXlFRUSWOTZo0iZEjR5KYmMgHH3zA7bffzhdffGHX/TxFRYnQF6Uc+9Jm32JyG5d5o/C5r1dwXtG/2pMYnaNHAgHA88A6pdQlWusjzgrScUzgHwg5hTVB9Tx/xNjZs3DuXPF+YCA0cU9fRiFEjVFRbdBMjGlGXOuKK67Az8+P+Ph42rdvT1JSkrlGqGfPnuTk5PDbb7+Za4ZcVSME4OPjw4wZMxg5ciQrVqwo9ZwmTZrQqFGjCu9lMpVsRLLs8H399dfTr18/Hn/8ca9KhMprGrsbY0Zpy628Y3ZRSg1USmk7tnVlXD8FuB14WGt9wM7XlwEM1Vp/X9jHaAhQB2M0WglJSUnExcWZtwULFtj78pyj1zIYkwGjT8DwHyDyBvfGY4fS1hgr5f+QEKJW0zZbeeUnKL82iMJyy3mFKrq/YwQFBREXF0d8fDzx8fFERUWZ5xaqX78+nTp1Mpe1adOGJi7+VHjrrbfSpUsXpk2bhi5lev/qNI3ZiouL48CBit6aPUuZNUJa68VOeuZGoL0d52XYHlBKjQdeBKZprd+z4x4phV83aK3N99NaH1VK7QUuLe2iiIgItmzZYsftXUlBQCRERLo7ELtIs5gQwrHs6RtUNILM9bVCffr04eWXX+bbb78tMSKsV69e/PTTT2zevJlRo0a5PDalFLNmzeL6668vdSh9dZrGLBUUFPDLL7/QysuGCLt8jFthQrK3stcppe7EGBrwqtb6BTsvOwiUNzLMs3rc1SDSUVoI4TgV9Q0qkoMr5xVSFtOY9OnThzlz5vDdd9/xxhtvWJ3Xu3dv3nrrLbTW1WoW69evHwkJCVWaFXrIkCH07NmTVatWlSiLioqqMMmxNX36dM6cOUPPnj1p0qQJiYmJvPvuu/z222988sknlY7PnbyisUIpNRzjt3uh1vpxe6/TWucC3wG9lFLBFveLBtph9BvyaOnpRzh5Mp7c3FR3h1IpUiMkhHCcyowUc/68QpmZmfj4+ODjUzwVXa9evfDx8UFrXaJGqHfv3uYmqeokQunp6dVqVnvhBXvrECrWtWtXdu3axSOPPMLVV1/N5MmTCQwMZP369YwcOdJhz3EFj5/1SCnVB/gvsANYpJTqYVGcrbXeZnHuj0CM1tpySs7ngN+A75RSrwKBhcfOAW86Ofxq27HjbRYvfhGA+vWD6d37eoYOXeLmqComiZAQwnE2UXFtUJEcjB4YzrN//35iY2OtjtWrV4+8vLxSz4+Kiiq1b46tMWPGMGbMmFLL0tPT2b59Ox9//HGF91m3bl2px/v27WtXHPYYNmwYw4YNc8i93M3jEyGgP8ZIr64Yw94tHQZiLfZ9sHlNWuvdSqn+wBzgU4zJeNYCN2qtTzkpZodJStpv/v78+XTy80v/j+ZpRo+Giy82EqJDh6Cas8ULIWq1bRWf4gJbtmxh/fr1fPfddzz66KMuffbGjRtp3bo1N998s0ufWxt4fCKktZ6Oneubaa37lXH8N4xlOrxOcrL16P7wcO/ohPaPfxibEELUFLfeeisFBQVMnDiR559/3qXPHjRoEHv27HHpM2sLj0+Earvg4PqEhdXjzJlUtIbwcHsG3AkhhHC0g7Zt/qJGkETIw40cuYqRIyE/P4szZ3ZSv35bd4ckhBBC1BiSCHkJH59AIiIud3cYdvnxR1i50hgy37IldOoETe1ZFU4IIYRwMUmEhMP99BO88krx/tNPw6xZ7otHCCGEKItXzCNUex3DGDZ6Ctcv51Z1tpMpytB5IYQQnkoSIY/2BXAlxgypdbFz8JzblbbOmBBCCOGJpGnMo1lWrWRgzAXp+Z54Av76y0iIDh6UOYSEEEJ4LkmEPJrtUE3vaGO66SZjE0IIITydNI15tFZAZ4xmMfCWREgIIWqDTZs2MXLkSJo1a4a/vz/16tWjW7duPPPMM5w8ebLE+cnJyUyZMoWOHTsSHBxMUFAQnTp14qmnnir1/IqMGTMGpVSp65etWbMGpVSZy23Y68KFC9x66620bt2a4OBgGjRowOWXX85HH31Urft6EqkR8mivF37VQApQ332hCCGEO82bBydO2HduVBQ89JBTw3n11Vd54oknuOqqq5g1axYtW7YkLS2NjRs3smDBArZs2cKKFSvM5+/evZurr74arTUTJkwgLi4OgG3btvH222/z999/s3z58irFsn79elauXMngwYMd8tos5eTk4Ovry5QpU4iNjSU7O5tPP/2UO++8k6SkJP75z386/JmuJomQV1BAuLuDsMvSpcYcQi1bGtvll0tnaSGEA5w4ATEx9p17+LBTQ1m7di1PPPEEEydO5LXXXrMqu+6665gyZQqff/65+VheXh433XQTgYGBbNy4kUaNGpnLBgwYwKRJk6ySpsqIjIwkPDycadOmOSURCgsL45NPPrE6dt1117Fv3z7ee++9GpEISdOYcKh162DhQpg6FUaOBIu/BUIIUSPMmTOH8PBw5syZU2p5cHCw1Sryy5cvZ+/evcyePdsqCSri6+vL0KFDqxSLyWRi5syZbN26laVLl1bpHlURFhaGr2/NqEupGa+iBjpx5GvSzvxO3dBuhDfpjb9/Q3eHZBfbofMyh5AQokwLFth/7rZtFdf0lNJXxtHy8vL4+eefGTFiBP7+/nZds3r1anx8fLjuuuucEtMNN9xA9+7defbZZxk+fDgmU+l1HFpr8vPzK7yfyWQqcY+ia8+fP8/SpUtZtWoV7777rkPidzepEfJQ5/a8StvfZhG18gb8F4Wye2Vvd4dkF0mEhBA1WUpKCllZWURHR5coy8vLs9qKHD16lIiICIKCgpwW1wsvvMDu3bvL7cS8ePFi/Pz8KtzuueeeEtfOmzcPPz8/wsPDefjhh5k7dy533XWX016PK0mNkIfyTz9ufaBOY/cEUkmvvQb79hXPISSJkBCiNkhMTCQyMtLqWG5ursuajwYMGED//v2ZPn06o0aNKvWcoUOH8vvvv1d4r/Dwkn1Sb7vtNnr06EFycjJff/01jzzyCD4+Ptx///3Vjt3dJBHyUHUyUqz2fRt0dFMklTN4sLEJIURNFBYWRmBgIEeOHLE6Hh4ebk4yFixYwDvvvGMua968OatXryYjI8OptUIvvvgiPXr0YOHChbRp06ZEeWhoKPXrVzz6uLSmtYiICCIiIgAYPHgwGRkZPP7449xzzz34+flVP3g3kkTIQ+WciyIxRxPkl05d/zxCwrq5OyQhhHCscePsP/fwYftHjTmRr68vffr0YfXq1eTk5Jj7Cfn6+pqHxH/77bdW1wwcOJB33nmHFStWcJMTZ5vt3r07w4YNY9asWcyfP79E+eLFi7n77rsrvM/o0aNZtGhRuefExcWxePFiTp06RbNmzaoaskeQRMgT5efTwn8iFPbDyyvIonGTQe6NSQghBACTJ09m0KBBPPnkkyWGz5dmxIgRXHTRRTz55JP06dPHXLNSJC8vj1WrVjFkyJBqxzZr1iy6dOnCvHnzSpRVp2nM1s8//0zdunVLHQXnbSQR8kQXLljt+tYLB98ANwVjv3fegR9+MPoFtWgBV10FF13k7qiEEMKxBgwYwOzZs3nqqafYuXMnd911Fy1atCArK4t9+/axZMkSgoODUUoBRm3RsmXLGDRoEF26dGHixInm2qMdO3awYMEC2rVrZ06ExowZw+LFi9FaVzq2Tp06MXLkyBJz/4DRrBcWFlap+7399tts3ryZgQMH0qxZM1JSUvjss8/44osvmD17tt0j5zyZJEKeyCYRIiTEPXFU0i+/wBdfFO//3/9JIiSEqJkmT55Mz549mTt3LlOnTiUpKYnAwEAuuugibrvtNsaPH4+Pj4/5/A4dOrBjxw5eeeUVFi1axPTp09Fa06ZNG0aMGMHEiRPN56anp9O4cdUHyMyYMYPPPvvMauRaVXXq1ImvvvqKxx9/nDNnzhAeHk779u359ttvHVKD5QkkEfJAh//+hlOHVhFeN5KIkGbUDWmDcndQdpCh80IIp4mKsn/G6Kgo58ZSqGfPnvTs2dPu88PDw5k9ezazZ88u97wNGzbw6KOPVni/svrxtGrVitzcXLvjKs+VV17J999/75B7eSpJhDzQ7799wuoNG8z7N6ohXNvvKjdGZB9JhIQQTuPktcM8xf79+8nOzubBBx90dyi1hiRCHig50XoV4tBIz88oMjOt10M0maCU+caEEEKUo02bNqSkpFR8onAYmVnaAyWdtv5PEBHd2U2R2M+2xrp5c6gBfeiEEELUcFIj5GG01iSnNAR8gCwgi/CYy90cVcVsm8VkxXkhhBDeQBIhD5Nx5gxZ2c3N+/5+foQ08vxZpaV/kBBCCG8kTWMeJikhwWo/PCwMpTz/n0kSISGEEN7I899ha5nko0et9sNtZiD1VJIICSGE8EbSNOZhko79BJwEAoFAIpp4RyJ06JD1vvQREkII4Q0kEfIwySfjgQPm/fCo1u4Lxk5aS42QEEII7ySJkIdJPp1ktR/erJ2bIqmcTZuMZOjgQWMovZe06AkhhKjlPL6PkFKqrVJqrlJqp1IqTSl1Uin1tVLKrsl1lFI+Sql/KqV2KaXSC69frpS6xNmxV0Vy0jmr/fDmXdwSR2UoBRdfDMOGwaRJ8NprxjEhhKjJNm3axMiRI2nWrBn+/v7Uq1ePbt268cwzz3Dy5MkS5ycnJzNlyhQ6duxIcHAwQUFBdOrUiaeeeqrU8ysyZswYlFL06dOnRNmaNWtQSrFu3bqqvLQyLVmyBKUUzZo1c+h93ckbaoSuBq4CFgN/AA2AycBmpVQvrfXWCq6fCTwJ/Av4CQgHngbWKqU6a62POSvwqug54CpOHz9M8qnTJCenEhZzhbtDEkIIjzJ+/HgSLEbYxsbGMn/+fJfG8Oqrr/LEE09w1VVXMWvWLFq2bElaWhobN25kwYIFbNmyhRUrVpjP3717N1dffTVaayZMmGBefX7btm28/fbb/P333yxfvrxKsaxfv56VK1cyePBgh7y2spw7d45JkybRpEkTpz7H1bwhEVoCzNNa66IDSqmfgARgInBXBdePAT7VWk+zuH4nsAcYArzt4Hir5bpHvnV3CEII4dESEhKIiYmx2neltWvX8sQTTzBx4kRee+01q7LrrruOKVOm8Pnnn5uP5eXlcdNNNxEYGMjGjRtp1KiRuWzAgAFMmjTJKmmqjMjISMLDw5k2bZrTE6HJkyfTuXNnIiMjWbNmjVOf5Uoe3zSmtU62TIIKj50H9gFN7biFP5Bqc+xc4VePf/3e4NFHoVs3uO02mDIFdu1yd0RCCOE8c+bMITw8nDlz5pRaHhwczJgxY8z7y5cvZ+/evcyePdsqCSri6+vL0KFDqxSLyWRi5syZbN26laVLl1bpHvbYsGEDH330EfPmzXPaM9zFG2qESlBKhQIXA+/bcfpbwKNKqRXAWoymsVeAY8BnTguyFtm+HbZsMTaAXr2MPkNCCFGe+++/3+5z4+PjzbVAubm5VmW5ubnm2hBnN5Pl5eXx888/M2LECPztXFBx9erV+Pj4cN111zklphtuuIHu3bvz7LPPMnz4cEym0j/ja63Jz8+v8H4mk8nqHrm5uYwbN44nnniC1q09fyRzZXlrjcgbgAJer+hErfWzwGxgGXAe+B/QEeintfawJX6XYXRlWgL8RsmKLM8kQ+eFEM6WlpZGTEwMMTExJd6MW7dubS5zdjNZSkoKWVlZREdHlyjLy8uz2oocPXqUiIgIgoKCnBbXCy+8wO7du/noo4/KPGfx4sX4+flVuN1zzz1W182ZM4fs7GymTJnitPjdyeU1QkqpgcBqO079WWvdr5TrpwC3A/dqrQ+UuKrk+Q9gdI6eRXGN0FPAD0qp3lrrE7bXJCUlmTuyAYwbN45x48bZEXJ1fYp1JdX7GF2cPFduLthMhk1srFtCEUIIt0lMTCQyMtLqWG5uLr6+rnmbHTBgAP3792f69OmMGjWq1HOGDh3K77//XuG9wsPDzd8fOHCAF154geXLlxMYGOiweD2JO5rGNgLt7Tgvw/aAUmo88CIwTWv9XkU3KGxCew14WWv9nMXxos7WTwD/tL0uIiKCLUXtPC5lU7WC51et+Poa8wYVzSF06hTUqePuqIQQwjnCwsIIDAzkyJEjVsfDw8PNScaCBQt45513zGXNmzdn9erVZGRkOLVW6MUXX6RHjx4sXLiQNm3alCgPDQ2lfv36Fd7HsllswoQJ9O/fnx49enDu3DkAcnJy0Fpz7tw5AgICqOPlf/RdnghprTOAvZW9Til1J0Z/n1e11i/YeVlbIACwSoG11meUUv/DvoTMhbwvEVIKmjUztlKmshBCiDK9/bb9g3Yth8zn5uZaNY8dOHAAPz8/wOgj5Ey+vr706dOH1atXk5OTY+4n5Ovra25J+PZb69G/AwcO5J133mHFihXcdNNNToute/fuDBs2jFmzZpXaT2rx4sXcfffdFd5n9OjRLFq0CDCG/R8+fJiGDRuWOK9hw4ZMnDiR119/vbqhu5VXdJZWSg3HaCdaqLV+vBKXJhZ+vRz42uJ+oUBrjHmJPEQB8BxwCCMhOgJEuTUiIYTwFJZv7LbDxP38/Fi5cqXLYpk8eTKDBg3iySefLDF8vjQjRozgoosu4sknn6RPnz5E2Ey9n5eXx6pVqxgyZEi1Y5s1axZdunQpdXRXVZrGlixZQlZWllX57Nmz2bp1K59//nmNmFjR4xMhpVQf4L/ADmCRUqqHRXG21nqbxbk/AjFa69YAWusEpdS3wBNKqQLgZyAMY0LGAOD/XPQy7GACJrg7CCGEEBUYMGAAs2fP5qmnnmLnzp3cddddtGjRgqysLPbt28eSJUsIDg5GFU6x7+vry7Jlyxg0aBBdunRh4sSJ5tqjHTt2sGDBAtq1a2dOhMaMGcPixYuxmTnGLp06dWLkyJF88sknJcrCwsIICwur1P169OhR4tiiRYsICAigX79+lY7PE3l8IgT0x0haugIbbMoOA7EW+z6UfE23AY8Bowq/pmLUBPXSWrujI1CN8o9/wK+/GiPFWraEhx+WofNCCOeKjY0tMbO0q02ePJmePXsyd+5cpk6dSlJSEoGBgVx00UXcdtttjB8/Hh8fH/P5HTp0YMeOHbzyyissWrSI6dOno7WmTZs2jBgxgokTJ5rPTU9Pp3HjxlWObcaMGXz22WdWI9dE2VRVMs6aLi4uTruns7T3iYuDrRaLnMTHQ+/e7otHCOF59uzZQ/v2HtYl04NFRUUxadIkJk+e7O5QvEZFv2NKqa1a67jSyryhRqiWeBdjsuyWhdtlQKhbI7KHzCEkhBCOs3//frKzs3nwwQfdHUqtIYmQx/gCsOzstxQY4aZY7HP+PJw9W7wfEAA202gIIYSohDZt2pCS4mFz/dZwkgh5DO8bOl+/PqSmwqFDRs3QmTNQxszuQgghhEeSRMgj5GPM72iphRviqLyQELjkEmMTQgghvI0kQh6hAHgHo1boIJAEVDz7pxBCCCGqRxIhj+AH3OXuICotNxcKJ3MVQgghvJL06BBVdv31EBEB3bvDqFGwe7e7IxJCCCEqRxIhUWUHD0JyMvz2GyxZAjJ3lxBCCG8jTWMeYNVbN5J08ggRkdGERbXhoivHENKoo7vDKld+vrHqvKUW3tG/WwghhDCTRMgDbP91AwcPJQPGsmn/bBhJOw9PhI4fN/oIFQkPN0aQCSGEEN5EmsY8QHJyqtV+eHRXN0ViP5lRWghRWy1atAilVKlbgwYN3BbX9u3bmT59OmfOnKnyPcaMGYNSij59+pQoW7NmDUop1q1bV40oYd++fUycOJFLLrmEunXrEhkZybBhw9ixY0e17ltVUiPkZjkZyaReyDHvm0zQsNnlbozIPraJkDSLCSFqm88//5xmzZpZHfP1dd/b6vbt23n++ee54447CA2t3hJN69evZ+XKlQwePNhB0RX74YcfWLt2LaNHj6Zr166cO3eOl156iR49evDLL79w2WWXOfyZ5ZFEyM1OnPwfV1zUBl2QR05ODgT44uMX5O6wKiQ1QkKI2q5Lly60bt3a3WE4XGRkJOHh4UybNs0pidDIkSN56KGHUEqZj/Xv35/Y2Fjmzp3LBx984PBnlkeaxtzsVOJhjmdmcyI7n2TtQ34zz+4bVOTQIet9SYSEEKJYQUEB/fr1IzY2lvPnz5uP//nnn9SpU4cnnnjCfGzJkiX079+fiIgI6taty6WXXsrixYtL3DMvL485c+bQoUMHAgMDiYiIYPDgwezdu5dFixZx9913A8Z6ZUVNdQkJCZWO3WQyMXPmTLZu3crSpUsr/+IrEB4ebpUEAdSvX5+2bdty/Phxhz+vIpIIuVnKqQSr/bph3rFqqdQICSGqSynHbpW9f3Xl5+eTl5dntRUUFABGMvHRRx9x4cIF7r//fgAyMzMZOXIkHTt25IUXXjDf5+DBg9x88818/PHHfPnllwwdOpSxY8cyf/58q+eNHDmSp59+muuuu44vv/ySd955hw4dOnDy5EmGDBnCtGnTAKPJbtOmTWzatInIKq6EfcMNN9C9e3eeffZZ82sqjda6xM+gtK28ewCcOXOGXbt20b59+yrFWx3SNOZm55OOWu3Xj2jupkgqR/oICSFqu3bt2pU4NmTIEL799lsAmjVrxsKFCxkxYgTXXHMNmzZt4siRI/zxxx/4+/ubr5k6dar5+6KapJMnT/J///d/jB8/HoCffvqJpUuXMnfuXCZMmGA+/8YbbzR/36pVK8BxTXYvvPACAwcO5KOPPuKuu0pf/WDx4sXmmqjyjB49mkWLFpVZ/sgjj6C1ZtKkSVWMtuokEXKzYU0PkHe5PxdyAjmb6YNvbLS7Q6pQWhqcPl287+MDzb0jfxNCCIdZvnx5ic7StqPGhg8fzv33388DDzxAdnY27733Hm3atLE6Z//+/Tz77LPEx8eTmJhorj0JCAgwn/PDDz+glOK+++5zzospxYABA+jfvz/Tp09n1KhRpZ4zdOhQfv/99wrvFR4eXmbZv/71Lz755BPeffddt/S5kkTIzYIzd0HgMeoHQrN6QKum7g6pQomJEBYGKSnGfkwMuHGghBBCuMXFF19s1xv36NGjefvtt2nUqBG33367VVlaWhqDBg0iKCiI2bNn06pVK/z9/fm///s/3nvvPfN5KSkphIaGUqdOHYe/jvK8+OKL9OjRg4ULF5ZI4ABCQ0OpX7/iRcJNptJ74syfP5+pU6cya9Ys7rnnnmrHWxXSR8itNFw4bX0oxPM7S7dubSytcf48bN8OpfTpE0KICmnt2K2y93eFjIwM7rnnHi6++GLOnz/PU089ZVW+adMmDh8+zIIFC7jzzju58soriYuLI89mzaLw8HDOnDlDZmamawIv1L17d4YNG8asWbNKffbixYvx8/OrcCstyfnwww958MEHeeyxx3j66add8XJKJZ/j3SnzMOQVzyGEfyAERrkvnkqqVw86d3Z3FEII4bkmTpzI8ePH2b59O99++y2TJk1i8ODBXHPNNYCRKAH4+fmZrzl79ixfffWV1X2uvvpqZs+ezcKFC3nkkUdKfVZRU5qjk6VZs2bRpUsX5s2bV6Ksqk1jy5cv5+6772bs2LG88sorDou1KiQRcqdUBTwGJBtbiB/ggKEMQgghnG779u0kJyeXOB4XF4evry9Lly5l4cKFfPjhh7Rs2ZIJEybwww8/MHr0aHbu3EmjRo248sorqVevHg899BDPP/886enpzJo1i/DwcKth91dddRU33XQTjz76KEePHqV///7k5uYSHx/PkCFD6NevHx06dABg3rx5jB49Gj8/Py655BL8/f0ZM2YMixcvRlehKqxTp06MHDmSTz75pERZWFgYYWFhlbpffHw8o0aNonPnzowZM4bNmzebywICArj00ksrHWO1aK1ls9kuu+wy7RL792v99tvF26pVrnluNSUkaJ2a6u4ohBDeYvfu3e4OwaHef/99DZS5JSUl6SNHjuiGDRvqf/zjH1bXnj59Wjdp0kRfe+21uqCgQGut9Y8//qi7dOmiAwMDdcuWLfXcuXP1c889p4236GK5ubl61qxZuk2bNtrPz0+Hh4fra6+9Vu/du9d8zvTp03VUVJQ2mUwa0IcOHdJaa33zzTfrxo0bV/jaRo8erZs2bVri+IEDB7Svr68G9Nq1ayv5E7NW9NpK22JiYqp0z4p+x4Atuoz3fKVd1VDqReLi4vSWLVuc/6A//gDL51xyCfTo4fznVlPHjrB7N0REGPMHffKJzCMkhCjbnj173DI/jCgWFRXFpEmTmDx5srtDcYqKfseUUlu11nGllUnTmDulWi+2Sr167omjErQunkMoKcnY7BgwIIQQwk32799PdnY2Dz74oLtD8UiSCLnTheeB00C4sYW4dqG5qkhMhKys4v169aCaa/sJIYRwojZt2pBSNN+JKEGGz7vThZ3AbiAeWAYh5U9B7gnOnIFWrYrnDWrZ0jFT1QshhBDuIDVC7pKfAWlnrI+FdHBPLJXQsSMcOAB5eXDsGFy44O6IhBBCiKqTRMhd0vZY7weHgk+we2KpAl9fiI11dxRCCCFE9Ugi5C4hHeHmeEjdA6n7pH1JCCGEcANJhNzFFAihvY3Ni+zYAeHhEBkJZSwdI4QQQngNeSsTlTJkCDRrBkFB0K4dHD/u7oiEEEKIqvP4REgpFaKU+kwpdUApla6UOqeU+k0pdUcl7nGfUmqvUipbKfW3Umq8M2OuqbKyihOf7GzYt8+oHRJCCCG8lTc0jfkDecC/gAQgALgN+FApFaG1fq28i5VS9wFvF16/BhgAvKWUUlrr/3Nm4OV57bW2ZGVlEh7ehIiIGAYMeImQEM+envnwYev9pk2hcI0/IYQQwit5fI2Q1jpFa3271vpdrfWPWuvvtdajgc3APeVdq5TyBV4APtRaP621Xqu1ngYsAmYqpfzKu95ZtC4gIeEwCQnH2LJlCytWLEXrfHeEUikZGXDZZdCwobEvy2oIIWqjRYsWoZQqdWvQoIHb4tq+fTvTp0/nzJkzFZ9chjFjxqCUok+fPiXK1qxZg1KKdevWVSPKst199920b9+eevXqUbduXTp37swbb7xBfr5z3x+9oUaoLClAYAXnXAFEAB/ZHP8QuBvoBax1fGjly8g4TlZWjnnfz8+HkJBWrg6j0i69tHhptHPnSq4QIoQQtcnnn39Os2bNrI75+rrvbXX79u08//zz3HHHHYRWc8r/9evXs3LlSgYPHuyg6CqWmZnJI488QqtWrVBKsWrVKiZOnMiBAweYO3eu057rNYmQUkoBPkB94CbgGuDeCi7rWPh1l83xvwq/dsANiVBy8nar/fDwhijl8ZVzVho0MDYhhKitunTpQuvWrd0dhsNFRkYSHh7OtGnTXJoILVmyxGr/6quv5sSJE7z33ntOTYS86d33ISAXSAbeBCZqrT+o4JqilPiszfEzNuUu1bz5YP71rx089tiHjB49lcGDZSE8IYSoSQoKCujXrx+xsbGcP3/efPzPP/+kTp06PPHEE+ZjS5YsoX///kRERFC3bl0uvfRSFi9eXOKeeXl5zJkzhw4dOhAYGEhERASDBw9m7969LFq0iLvvvhsw1hYraqpLSEiodOwmk4mZM2eydetWli5dWvkX70BhYWFOr2VzeY2QUmogsNqOU3/WWvez2P8Uo19QODAMeEMpla+1ftvRMSYlJREXF2feHzduHOPGjXPY/U0mP0JDLyE09BLatnXYbZ1u3TpjlFiLFhDsPZNgCyE8laMnktXasferQH5+Pnl5eVbHTCaTefvoo4/o3Lkz999/P0uWLCEzM5ORI0fSsWNHXnjhBfM1Bw8e5Oabb+app57CZDIRHx/P2LFjyczMZPz44kHOI0eO5Msvv2TSpEkMHDiQrKws4uPjOXnyJEOGDGHatGnMmjXLqskuMjKySq/thhtuoHv37jz77LMMHz4cUyUmjtNa29WvRymFj49PqdempaXx448/snjxYiZPnlzp+CtFa+3SDQgC2tmxRVdwn0XABcCvnHMeADQQaXO8UeHxh0q77rLLLtPCWkGB1iEhWht/abRu3FjrlBR3RyWE8Aa7d+8uvaDoD4qjNhd5//33deF7SIltyJAhVucuW7ZMA/q9997T9913n65bt67et29fmffOz8/Xubm5euzYsfqSSy4xH//xxx81oOfOnVthXPv376/yaxs9erRu2rSp1lrrNWvWaEAvXrxYa6316tWrNaDXrl1b7j3Wrl1b5s/Hcuvbt2+Ja7/55htzuVJKT5kyxa64y/wdKwRs0WXkCi6vEdJaZwB7HXCrLcBooDFwrIxzivoCdQROWhwvWt10twPiqAINeNeSGikp1guspqYWjx4TQojaaPny5SU6S9uOGhs+fDj3338/DzzwANnZ2bz33nu0adPG6pz9+/fz7LPPEh8fT2JiIgUFBQAEWMxP8sMPP6CU4r777nPOiynFgAED6N+/P9OnT2fUqFF2X3fZZZfx+++/V3je/7d35/FRlWfDx39XSMJSdkNYFQVtVUDxIT7gVhEUUAqK4PsiLkGqoKhArVQRi0ECxeextVipQlGgoMUqL6ioiBtgNW0BxRUXBIGy77shy/X+cZ+EyWSSmWxzZpLr+/mcT2buc59zrjkMOXfutUGDBsXSLrvsMlatWsXBgwd59913efzxxxGRIjVolS1uOkuHcDlwBNhVSp4sXJ+im3BzCBW4GddP6MMqi65UHYA8oJ23TcKn7koR27Ch6Pt27Wx5NGNMzdaxY8eIOkunp6czY8YMUlNTGTJkSJF9R44c4aqrrqJevXpMnTqV9u3bk5yczNNPP81zzz1XmG/v3r00bdqUunXrVvrnKM2UKVPo1q0bs2bNKlaAK0n9+vXp3Llz2HwS4iHSqFGjwq4pPXv2JDk5mUmTJjFy5Ehat25dptgjFfOdpUVkhIjMFpGbRORyEbleRBYAg4BMVT0RkHe9iLxb8F5Vc4DfAukikiki3UXkUdz8QxMCj42eXOBbb1sK/Bk3R2Rs27ix6HubQ8gYU2GV3TgWg44dO8awYcPo2LEjBw8e5MEHHyyyPysri02bNjFz5kxuueUWLr74YtLS0or1PUpJSWHfvn0cP348muHTtWtX+vfvT2ZmZsTXXrFiBUlJSWG3nj17hj1XWloa+fn5bAx+CFWieKgR+hy4FngcV22yB1gH/EJVXw/Km4gbYl9IVZ8REQV+DYwFNgP3qOqfqzrw0LbgaoMKNAdiv+dxcI3QGWf4E4cxxsST0aNHs3XrVtauXcuSJUsYM2YMffr0oXfv3oArKAEkJZ2c33f//v288sorRc7Tq1cvpk6dyqxZs7j33ntDXqugKa2yC0uZmZl07tyZ6dOnR5S/Ik1jwVasWIGI0K4K//qO+YKQqn4EXBNh3tNLSJ+BW2YjBgStU0F8VK2EahozxpiabO3atezZs6dYelpaGomJiSxcuJBZs2Yxb9482rVrx6hRo1i2bBnp6el89tlnpKamcvHFF9OwYUPuvvtuJk6cyNGjR8nMzCQlJaXIsPsrrriCgQMHct9997FlyxZ69OhBTk4OK1eupG/fvnTv3p1zz3XdX6dPn056ejpJSUmcd955JCcnM3ToUObOnVswYKhMOnXqxODBg3nhhRciyt+gQYMiI68j8frrrzN79mz69evHaaedxuHDh3nzzTeZOXMmI0aMoFWrVmWOO2Il9aKuyVvVjxo7oKqfqOpCVV1SxdeqHD16FK2DfvVVvyMyxsSLcCN64k1po8YA3b17t27evFmbNGmiN910U5Fjd+3apS1atNCrr75a8/PzVdWNCOvcubPWqVNH27Vrp9OmTdNHHnlECRoJl5OTo5mZmXrWWWdpUlKSpqSk6NVXX61ff/11YZ6MjAxt1aqVJiQkKKAbN25UVdVBgwZp8+bNw362wFFjgdavX6+JiYkRjRorj3Xr1umAAQO0TZs2mpycrKmpqXrJJZfo/PnzNS8vL+zxFRk1Jhqj7ap+SktL09UFa0kYwDWFBc7L9cUX0KFDidmNMabQunXrOOecc/wOo0Zr1aoVY8aMqfo5eXwS7jsmImtUNWQ1Vcx3ljb+y8mBLVuKplkfIWOMiQ/fffcd2dnZjBxpqxiEEvN9hKqdvKNQK/Y7RwfasgUCJwlt3hzq1fMvHmOMMZE766yz2Lt3r99hxCwrCEXb8y3cBDwNWkLD1nDZy5AUf3MIGWOMMdWBFYSi6cQe+PGIe338MOz5Hq5o6G9MEWjQAAYOdAWiDRusIGSMMab6sIJQNB3+suj7Bikgsf9P0LUrvPzyyfcnfJiG0hhjjKkK1lk6mg7/QJFb3qAK50WoQsnJfkdgjDHGVI7Yr46oTg5dADwF7Af2QsP2PgdkjDHG1GxWEIqmw4dxK4CkuK1BV58Diswzz8Cpp7oh82ecAVFe888YY4ypMlYQiqZDh4q+bxj7HaUPHIC77jr5vnZtOHYMEqxR1RhjTDVgj7Mo2rblE/LyA1YUjmDBOb8FL/jbtq0VgowxxlQf9kiLkry8E0x6YTz3/O1uxi4cSeYbv+JY7bzwB/osuCBkM0obY2q6OXPmICIht8aNG/sW19q1a8nIyGDfvn3lPsfQoUMREX7+858X2/fOO+8gIixfvrwCURa3YMECRIQ2bdpU6nkjZU1jUbJxw/vk57vXh47n8WP+Meo1aO5vUBFo1Qp++UtXINqwAc46y++IjDEmNrz00kvFHt6Jif49VteuXcvEiRO5+eabadq0YhP1fvDBByxdupQ+ffpUUnShHThwgDFjxtCiRYsqvU5prCAUJZu+W17kff1G8TEGvVs3txWwNXqNMcbp3LkzZ555pt9hVLqWLVuSkpLCww8/XOUFod/85jecf/75tGzZknfeeadKr1USaxqLkm2bviE3/+T7nzSNz8W6RPyOwBhjYl9+fj7du3fn9NNP5+DBg4Xpn3/+OXXr1mXs2LGFaQsWLKBHjx40a9aM+vXrc8EFFzB37txi58zNzeWxxx7j3HPPpU6dOjRr1ow+ffrw9ddfM2fOHG677TbArS1W0FT3ww8/lDn2hIQEJk2axJo1a1i4cGHZP3yEPvzwQ+bPn8/06dOr7BqRsIJQlHz1bS0+21mfz3b+hK/31CW77vl+h2SMMb4K7l9TkpkzZxbJN3z48BLzdunSpUjeNWvWhMxXUnpZ5OXlkZubW2TL9/pAJCQkMH/+fA4fPsyIESMAOH78OIMHD6ZDhw5Mnjy58DwbNmxg0KBBPP/88yxevJh+/fpx++2388wzzxS53uDBgxk/fjzXXHMNixcv5i9/+Qvnnnsu27dvp2/fvjz88MOAa7LLysoiKyuLli1bluuzXXvttXTt2pUJEyYUfqZQVLXYPQi1BZ8jJyeH4cOHM3bsWN9r1axpLEp2btsKQG6+kJtfi8Ytz/M5ovDy8mDKFNdBul07t/nYjGuMMTHl7LPPLpbWt29flixZAkCbNm2YNWsW119/Pb179yYrK4vNmzfz8ccfkxwwRf9DDz1U+LqgJmn79u08/fTT3HnnnQC89957LFy4kGnTpjFq1KjC/Nddd13h6/bt3SS9ldVkN3nyZK688krmz5/PrbfeGjLP3LlzC2uiSpOens6cOXMK3z/22GNkZ2czbty4CsdZUVYQipId23dDfj7tTznO93vq0qhx8f9AsWbVKpgw4eT7Ro3cvELGGGNg0aJFxTpLB48aGzBgACNGjOCuu+4iOzub5557jrOCRp189913TJgwgZUrV7Jjx47C2pPatWsX5lm2bBkiwh133FE1HyaEnj170qNHDzIyMrjxxhtD5unXrx+rVq0Ke66UlJTC1+vXr2fy5MksWrSIOnXqVFq85WUFoao0fTqHv93Gsrfgm40bOEY+O7ZCrh7jmbv/SJM/bqVXb2hQHzc86+67/Y4YgB074J57YNGioukHD8KgQfDUU1YzZIwxHTt2jKjmJT09nRkzZpCamsqQIUOK7Dty5AhXXXUV9erVY+rUqbRv357k5GSefvppnnvuucJ8e/fupWnTptSN8tT+U6ZMoVu3bsyaNatYAQ6gadOmNGrUKOx5EgImoBs1ahQ9evSgW7duHPD+uj5x4gSqyoEDB6hdu3ZUP6f1EapCh7/dxtQX2vLexgacSMyncR2oXxsa14E9orz1TVumvtCWQ03awrZtfocLuELQJZfAwoUQqll44UK3f+fO6MdmjKleVLXIVpLhw4cXyTdz5swS865Zs6ZI3i5duoTMV1J6ZTt27BjDhg2jY8eOHDx4kAcffLDI/qysLDZt2sTMmTO55ZZbuPjii0lLSyM3N7dIvpSUFPbt28fx48ejEneBrl270r9/fzIzM0Nee+7cuSQlJYXdhg0bVnjMV199xRtvvEGTJk0Kt7/97W9s27aNJk2aRL25zGqEqtCyt2D3Hjil9ZdunVVPnUSo32wv7HD7X3gB7rzavzgD3XOPmy+oNBs2uMqrl1+OTkzGGBOvRo8ezdatW1m7di1LlixhzJgx9OnTh969ewOuoASQlJRUeMz+/ft55ZVXipynV69eTJ06lVmzZnHvvfeGvFZBU1plF5YyMzPp3LlzyNFd5WkaW7BgAT/++GOR/VOnTmXNmjUh52aqcsElctuULl26aEUNe3G0pjVL0jZNE/Wnp4i2qoue3dBt3Zqj3X+Knp6SqG2aJurZrWvrpDPS1M3Sc3J78MGi55wypXL3V2RLSFDdvLnCt8kYUwN89dVXfodQqWbPnq2AvvTSS5qVlVVsy8nJUVXVl19+WQGdN29e4bF9+/bV5s2b686dO1VVddeuXdqwYUPt0qWLLlmyRF988UXt1KmTtm/fXt0j+qSBAwdqYmKijh07Vt9880199dVX9f7779f3339fVVXXrl2rgI4YMUI/+ugjXbVqlWZnZ6uqanp6erHzhZKenq6tW7culj5kyBAFFCi8XmUq6bqRCvcdA1ZrCc98axqrIqduH8WB47WoXyeRto0htS4UDA6tkwRnpkLHllC/TiJHjtSCbRf6GW6Z5efDq6/6HYUxxvjnhhtu4KKLLiq2HThwgC1btnDHHXdw0003cfPNNxceM3v2bESEoUOHoqo0a9aMRYsWkZeXx6BBgxg3bhy33357kWMKLFiwgIyMDBYvXkz//v0ZNmwYX375ZeEQ+fPPP5+MjAxee+01Lr30Ui688EK2ed0ujh49SvPm5V/N4NFHH/V11uyqVD0/VQxIPNwOjrQguU42p52xnTNageZDdjYgkCDQvGEum7c3g4ONgCZ+h1xm+/eHz2OMMdXN0KFDGTp0aNh8odb8atasGdu3by+S1qNHDz755JNieTMyMoq8T0xMZPz48YwfP77Eaz7yyCM88sgjxdI//PBD7rvvvrAxBw5xD9S+fXtycnLCHl9eJV03GqxGqIq4EZQJnNboZE2QJECdulAwWlCA0xq5fPGoSfyV3Ywxpsb57rvvyM7OZuTIkX6HEpNEbfGoYtLS0nT16tUVOsfmzXBj+0785MwvSEiAnDzXHFZg/S5IqgX5+cLO7/+bFSN60ujJycXOEzjZaqh/qoruD465XbvQo8WCJSTADz/AqaeGz2uMqdnWrVvHOeec43cYphoL9x0TkTWqmhZqnzWNVZHTToO/DkzmtMtqkZSUR59pRfcn1YKloyEnJ4FvvqlD4wacrDoqQbh1viq6v21bGDDADZEPZ8AAKwQZY4yJf/HZJhMXttOu3ackJeWVmispKY8OHT4CjkQnrDCeesrVCpWmXTvweY08Y4wxplJYQajKTELkZFvU6afApr0nt9NPOZlTJB/4R/RDDKFFC/jwQxg40DV/BUpIcOkffQQVGHxgjDHGxAxrGqsyWdAqHza5d89cFiLLpoIXedAqdqZqbtHCTZa4ZYsbIr9/v+sY3b+/NYcZY8pHVUtdYd6Y8qpoX+eYLwiJSAPgWeC/gJZADvAt8KSqzg9zbEtgFHAVcKZ37GfARFVdWZVxwycQG0uHldupp8bM8mfGmDiWlJTE8ePHqVevnt+hmGro+PHjRWbmLqt4aBpLBnKB3wH9gSHAOmCeiPwqzLFdgP8LvALcAAwFfgSWi8gvqipgY4wxJ6WmprJ161aOHTtW4b/ejSmgqhw7doytW7eSmpoa/oASxO3weRHJAuqraqdS8jQGjqhqbkBaIvAlsFNVfx7quMoYPm+MMeakQ4cOsWvXriqdlM/UPElJSaSmptKwYcNS81XX4fN7gTqlZVDVAyHSckVkLRDyhhhjjKl8DRs2DPuwMsYPcVMQEtfLrhbQCBgI9AZ+WY7zJAMX4foKGWOMMaYGi5uCEK7r8Z+81znAaFX9aznOkwG0AW6qpLiMMcYYE6ei3llaRK4UEY1gWx506IvAhcDVwCzgTyIyoozXHgI8CExS1Q9Kyrd7927S0tIKt5kzZ5btQxpjjDEmLvgxauwj4JwItlsDD1LV3aq6WlWXqupIYB7wuIhENGZORPoBc4BnVbX40rwBmjVrxurVqwu34cOHl+kDliYeC1XxGDPEZ9zxGDNY3NEUjzGDxR1N8Rizn6JeEFLVY6r6dQTb5jCnWg3UB8LOcSwiPYGXgEVAmWqRKls8fkHjMWaIz7jjMWawuKMpHmMGizua4jFmP8XDPEIluRy3QNeu0jKJyEW4eYTeBW5W1QjWVjfGGGNMTRDz8wh5/YC6Ae8A/wFOAf4PbqLEB1X1sYC864FNqtrTe382rinuECcnUyykqv8s4Zq7CVgAo5KlAHuq6NxVJR5jhviMOx5jBos7muIxZrC4oykeY65qbVW1Wagd8TBq7HPgWuBxoCnuH3cd8AtVfT0obyJuiH2BbkATb3s/xLlDLnxT0s0yxhhjTPUS8zVCxhhjjDFVJZ77CBljjDHGVIgVhKqYiLQRkT+JSJaIHPPmSDrd77hKIyKDRGShiGwSkeMi8o2I/E5EGvgdW2lEpLeIvCciO0QkW0T+IyJ/F5Fz/Y6tLERkqfc9yfQ7lpKISPcS5v864HdskRCRa0RkpYgcEZFDIrJaRHr4HVdJRGR5KXOuLfU7vpKIyCUiskxEdonIYRH5WESG+R1XOCJyhYj8w/v9t09E5olI2BHK0RLpc0VE6ojI/4rIdu+zZIlIyDU2azIrCFW9M3Gdu/cDJU7iGGPuB/KAh4A+wNPAXcDbIhLL35mmwBrgHqAXMA7oAPxTRNr6GVikRORG4Hy/4yiDUbglawq2K/0NJzxvAMYruO/KAOAG3PQa9fyMK4yRFL3PFwH3efte9Suo0ojIebhBLknAHcD1wCrgWRG5y8/YSiMilwHLgAO45ZxGAz8H3hWR2j6GFijS58qzuHs/AfgFsB14S0Q6V3WAcUVVbavCDUgIeH07oMDpfscVJuZmIdJu9WLv4Xd8ZfwsP/Pi/rXfsUQQaxNgB3CjF3Om3zGVEmt3L8Yr/Y6ljHGfDhwHxvgdSyV8lmeBbKCp37GUEN8U4ARQPyg9C8jyO75S4n4HWA8kBqSled/3kX7H58UT9rmC+4NKgdsC0hKBb4BX/f4MsbTF8l/31YLG4bxFqro7RPIq72fraMZSCfZ6P3N9jSIyjwFfqOrf/A6kGhsG5APP+B1IRYhIPVxN1muqus/veEqQjFsX8nhQ+kFiuzWiG/C2qhb+zlDV1bjfJQN8iypAhM+V/rj7/2LAcbnAAqB3DNVu+S6Wv4wmtlzu/VznaxQREJFaIpIsImcBM3C1LDFduBCRS3G1bnf7HUsZPS8ieSKyV0ReEJHT/A4ojEuBr4HBIvK9iOSKyHoRibf7PgBoAMz1O5BSzPF+PikirUSksYjcAfQEnvAvrLDycDVZwbKBjlGOpSI6ABtV9VhQ+pe4QuqZ0Q8pNsXDPELGZyLSGngUeMf7yyjW/Qvo4r1ej2vOK3UGcj+JSDKuwPa4qn7jdzwROgj8HliBm7D0AlyfsiwRuSCG73crb/tfXLzf42pWnhKRRFWd5mdwZXArblb9N/0OpCSq+oWIdMctbTTSS84B7lTVBX7FFYFvcLVChbw+hi1x8ceLprg+RMH2Bew3WEHIhCEi9XEdS3OB23wOJ1K3AA2BdriO32+LyKWq+oOvUZXsN0BdYLLfgURKVT8BPglIWiEiK4F/4zpQP+xLYOEl4GpShqrq//PS3vNG3IwTkSfV60wRq0SkFa5T+rTA5ptY49XILsTVQNyJayK7FnhGRH5U1ef9jK8U04D53qjNJ3EFhpm4JtW46+pgwrOmMVMiEakLvIYrUPRW1f/4HFJEVHWdqv7L62vTE7c474M+hxWS15Q0HvgtUNtrPmjs7S54X6vEE8QQVf0Y+Ba40O9YSlHQZ+ztoPRluAWcW0Y3nHK5Gfe7O5abxcB1ls7BrQKwRFXfVdVRwN+BabE6AtUroGUCvwZ2Al8BW4E3cKOu4sV+3ACMYAU1QbHatyzqYvKLaPwnIknAy7jREteo6uc+h1QuqnoA1zwWq+3h7YA6wHzcL66CDVxt1n6gkz+hlVss16h8GWZ/PPzFnw58qqqf+h1IGJ1wcQY3J/0bt2ZkavRDioyq/ha3Xtd5QEtVvRE4C/iHr4GVzZfAGV7H+kDn4vpArY9+SLHJCkKmGO8vteeBHsB1WsLitPHAmwTtbFxfkFi0FrgixAaucHQFcfILS0TScNMV/NvvWEqxyPvZOyi9D/AfVd0R5XjKxLvH5xL7tUHgBil09vrABeqKWwA7pmskVPWoqn6uqjtFpA/u90g8jTZ8DTeH0w0FCSKSiFuwfJmqZvsVWKyxPkJRICKDvJcFHXivFrfC/W5VXeFTWKWZjvvPMxk4KiKBHQf/E6tNZCKyCPgY+AzXgfenwK9w/Zt+72NoJfJqrJYHp4sIwCZVLbYvFojI88BG3P0+gOssPQ7XhPCkf5GF9QZuAeYZIpICbMB913sRH33gbsV9n2O1f02gp3ATVb4mIn/G9RHqj5sn6wlVDTUyy3cicgFwNe67DW6k4Vjgf1T1I98CCxLuuaKqn4jIi8AfvRr+jbiJcc8Abop+xLHLFl2NAhEp6SavUNXu0YwlEiLyA1DSTMwTVTUjetFETkQewM222h43PHQLrpDxuxjuKB2S952ZrKox2elYRMbhHmhtcTMy78CNYHpEVWO6H4WINAR+BwzC9aH4Gpiqqi/4GlgY3sNsG/BPVe3ndzyREJGrgQdwQ7nr4GpmZwIzVDXPz9hKIiIdcKM4OwK1cVOG/ElVZ/saWJBIniteP8/JwBCgMfAp8ECs/oHlFysIGWOMMabGsj5CxhhjjKmxrCBkjDHGmBrLCkLGGGOMqbGsIGSMMcaYGssKQsYYY4ypsawgZIwxxpgaywpCxsQ5ERkqIioiB0SkSdC+RG9fhk/hBcbyExGZJyK7vJj+6HdMBUSkuxdT94C05SKyvLQ8sSQgvisjyFvkOyEi14nIfVUaoDExymaWNqb6aISbvC4mF5gF7sZNwjgMtzhrLE28+DFwEW6BzZrgIiBwhvjrcCva/8GXaIzxkRWEjKk+lgH3isgTqrrT72BCOAfYpqp/9TuQYKp6CIjbNfXKKp7XDzSmslnTmDHVR6b3M+yyHCLy3yLyjogcEZGjIvKuiPx3eS8sIjeLyKci8qOI7PGawFoG7FdgKHCq1yxTahOTiEwUkY9F5JB3vveC1rwLbArqLyJPefn2iMh8EWkclDdRRB4Qka+8GHeLyFIROTvoXCXGVEKcvUTkDRHZLiLHROQLEfm1iNSK4NhaIpIZcOxyEekQotlqjrfsTfDxRZruAjTyjtnv3b/nReSUoGMLryEic3Ar2rcO+Lf5wdtXX0T+JCKbRSTba9Z8p+C+GVMdWEHImOpjO26hy+EiUtJacYjIecAK3DpbQ3ELeTYEVojI+WW9qIgMB+bh1mS6Htc019s7X30v20XAW7g1yS7yto+Ln61Qa+AJ4Fovxl3AShHpFCLvNEBx6ylNBAZ6aYEW4NZcegPXDHQHrhmsJRXTDngX19zXF7cqfIZ3rXAygIdwC6heh6vRe7WC8QD8EXc/bgTG4xY6fbmU/JNw92U3J/9tBnj7nsCt3zcRuAoYAazFrVtlTLVgTWPGVC+P4R5Wj+AezqFMALKBnqp6AEBE3gZ+8I67PtKLeTUfk4Dlqjo4IP1r4AMvhidV9Z8isgfIjqRZRlVvD7rGUuBL4HZgdFD2lap6r/d6mYj8DLhdRIaqqopID1zhaLSqPhlw3OJIP2cpcT4TEKfgPnMycL+IPKSq+aGO8zq1/wqYqar3B8SeB0ytYFhfqupt3uulIrIPmC8iPVX13RCf4Xtxq5afCPFvcxHwvKo+G5C2qILxGRNTrEbImGpEVfcBvwdu9QoEofwcWFJQCPKOO4Srjbi8jJf8GZCKq9UIjOMfwKZynA8AEblSRN4Xkb1ALpAD/NS7XrDXg95/jls1vLn3vheuhuQv5YklTJwtRWSGiGwCTnhxZuJqTFJLObQT8BPg70HpCyohrOBzvgTk4wo1ZbUKGCoiD4lIWiRNfsbEGysIGVP9PAHsAx4tYX9TQo/Y2oFrLiuLpt7Pks7XNER6qUTkv3BNNUeAXwLdgAuBT4E6IQ7ZF/Q+2/tZkPcUYJ+qHi9rLGHiTMAVHn+BK/z08OIsaBYLFWuBgia54E7tldHJvcg5VPUEsB/X3FhW9wIzcDV7q4BdIvKEiNSrcJTGxAgrCBlTzajqEeB3wA1A5xBZ9gEtQqS3wD0wy6KgEFLS+YILKZEYiKsFul5VF6vqv1R1NWUvpBXYAzQVkbrlPL4k7YE04AFV/YuqfuDFmRfBsQUFx+ZB6cHvAX7ENbcFOyVEWrFziEgy7t5tjSCuIlT1iKqOU9UzgdOBKcA9uCZUY6oFKwgZUz39GffgywyxbwVwjYg0KEjwXvcDlpfxOt/gaiAGByaKyMVA23KcD6AerjChAefrAZxWjnOB64QsuP5FlamgViSnIEFEkoCbIjj2M+AoriNyoMEh8m4CmotIs4DrtCd0MyEhznkD7nd9VinxZAOlFhRVdZOq/h7X9NixtLzGxBPrLG1MNaSq2SLyKDAzxO5JuOacd0XkMVyB4wHcg72wOU1EngXSVbXE3xOqmiciE4AZIjIfmI9rgpkMfAc8V47wlwJjgDkiMhvXN+i3lKNGw4vxfRFZCPxBRE4F3gOScH2lXlfV5eU5L26U3CZgstfJOQfXATqSmA6IyBPAeBE5jCusXYhrCgz2Eu7fbL6I/AFIAcbharpC6eDdtwW4ezcZ15m9WEfpAF/has3uAlYDP6rq5yKShWv++xzXVHk5cD5udJwx1YLVCBlTfc3GFUaKUNXPgO7AIdwDbR7eQ05VPw3IWsvbSqWqM4FbcB2AXwH+B3jbO9/Rsgatqm8Bo4BLgCW4/im3AuvLeq4Ag3HD1a/DPdifAzpQgdmtvb431+H6Qv0VmA6sJPJRXxm4pqZbvJh64Wrlgq+zHhiEK2AuBn4D3IebnTuU0bgasBe98y/B1QqVZhau4DQF+Dfwmpe+ElfD9DyuU/og4FeqGjw9gTFxS1Q1fC5jjDFR4U0+OVFVM/yOxZiawGqEjDHGGFNjWUHIGGOMMTWWNY0ZY4wxpsayGiFjjDHG1FhWEDLGGGNMjWUFIWOMMcbUWFYQMsYYY0yNZQUhY4wxxtRYVhAyxhhjTI31/wF3/8BhNsThMAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 648x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "w_qubits = [1,2,3,4,5,6,7,8,10,11]\n",
    "jwt2 = [-3.141592654,-3.141592654,-1.570796327,-1.570796327,-1.767145868,-1.767145868,\n",
    "        -1.767145868,-1.742602175,-1.748738098,-1.748738098]\n",
    "gc2 = [-3.141592536,-1.570796327,-1.570796327,-1.570796327,-1.767145868,-1.767145868,\n",
    "       -1.767145868,-1.742602175,-1.748738098,-1.748738098]\n",
    "jwt3 = [-3.141592654,-1.570796327,-2.35619449,-1.963495408,-1.963495408,-2.061670179,\n",
    "        -2.061670179,-2.037126486,-2.043262409,-2.049262409]\n",
    "gc3 = [-3.141592654,-1.570796327,-2.35619449,-1.963495408,-1.963495408,-2.061670179,\n",
    "       -2.037126486,-2.043262409,-2.046330371,-2.046330371]\n",
    "gc4 = [-3.141592653589793,-1.5707963267948966,-2.356194490192345,-1.9634954084936207,\n",
    "       -2.1598449493429825,-2.1598449493429825,-2.1598449493429825,-2.1353012567368124,-2.1475731030398975,-2.1475731030398975]\n",
    "jwt2t = [-1.74916]*10 \n",
    "gc2t = [-1.74916151]*10\n",
    "jwt3t = [-2.04567]*10\n",
    "gc3t = [-2.04567204]*10\n",
    "gc4t = [-2.14539042]*10\n",
    "tv = [-2.224,-2.224,-2.224,-2.224,-2.224,-2.224,-2.224,-2.224,-2.224,-2.224]\n",
    "cjwt2 = interp1d(w_qubits, jwt2, kind = \"quadratic\")\n",
    "X_=np.linspace(1, 11, 100)\n",
    "#jwt2=cjwt2(X_)\n",
    "cjwt3 = interp1d(w_qubits, jwt3, kind = \"quadratic\")\n",
    "#jwt3=cjwt3(X_)\n",
    "cgc2 = interp1d(w_qubits, gc2, kind = \"quadratic\")\n",
    "#gc2=cgc2(X_)\n",
    "cgc3 = interp1d(w_qubits, gc3, kind = \"quadratic\")\n",
    "#gc3=cgc3(X_)\n",
    "cgc4 = interp1d(w_qubits, gc4, kind = \"quadratic\")\n",
    "#gc4=cgc4(X_)\n",
    "plot,ax = plt.subplots(figsize = (9,7))\n",
    "#plot.figure(figsize=(7,4))\n",
    "plt.rcParams.update({'font.size': 16})\n",
    "#ax.plot(X_,jwt2,label = 'JWT, N=2')\n",
    "#ax.plot(X_,gc2,label = 'GC, N=2')\n",
    "#ax.plot(X_,jwt3,label = 'JWT, N=3')\n",
    "#ax.plot(X_,gc3,label = 'GC, N=3')\n",
    "#ax.plot(X_,gc4,label = 'GC, N=4')\n",
    "#ax.plot(w_qubits,tv,label = 'True Value, N=$\\infty$')\n",
    "ax.plot(w_qubits,jwt2,label = 'JWT, N=2', marker = 'o',linestyle = lstyle[0],linewidth =4,markersize =10,color = colors[3],alpha = 0.6)\n",
    "ax.plot(w_qubits,gc2,label = 'GC, N=2', marker = '*',linestyle = lstyle[4],linewidth = 4,markersize = 12,color  = colors[1],alpha = 0.8)\n",
    "ax.plot(w_qubits,jwt3,label = 'JWT, N=3', marker = '^',linestyle = lstyle[2],linewidth = 4,markersize = 12,color = 'yellow',alpha = 1)\n",
    "ax.plot(w_qubits,gc3,label = 'GC, N=3', marker = 's',linestyle = lstyle[3],linewidth = 4,markersize = 11,color = colors[2],alpha = 0.4)\n",
    "ax.plot(w_qubits,gc4,label = 'GC, N=4', marker = 'P',linestyle = lstyle[6],linewidth = 4,markersize = 10,color = colors[0],alpha = 0.6)\n",
    "ax.plot(w_qubits,jwt2t,linestyle =lstyle[1],label = 'Exact, N=2',color = 'blue',linewidth = 4)\n",
    "ax.plot(w_qubits,gc3t,linestyle =lstyle[5],color = 'red',label = 'Exact, N =3',linewidth = 4)\n",
    "ax.plot(w_qubits,gc4t,linestyle ='dotted',color = 'black', label = 'Exact, N=4',linewidth = 4)\n",
    "ax.legend(loc = 'best')\n",
    "ax.tick_params(axis=\"both\",direction=\"in\")\n",
    "ax.set_xlabel(\"No. of ancilla qubits\")\n",
    "ax.set_ylabel(\"Energy (MeV)\")\n",
    "ax.set_xticks([1,2,3,4,5,6,7,8,9,10])\n",
    "#ax.grid()\n",
    "plot.savefig('ancillanum.pdf', dpi = 900,bbox_inches = 'tight',pad_inches = 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1ff5d925-1a99-4b02-80e8-9e9ceb7f4345",
   "metadata": {},
   "outputs": [],
   "source": [
    "lstyle = [ls_dict['ddashdot'],ls_dict['ddash'],ls_dict['ddot'],ls_dict['dash'],ls_dict['ldashdot'],ls_dict['ldash'],ls_dict['dashdot']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "807eb7c3-36f9-4915-9211-231c38698cfa",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "# Count to hist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "f78e026c-602c-48b0-a73d-cec4dc518f59",
   "metadata": {},
   "outputs": [],
   "source": [
    "Counter = {'1100000': 6,\n",
    "         '1110011': 22,\n",
    "         '0101100': 7,\n",
    "         '1011010': 6,\n",
    "         '0010100': 31,\n",
    "         '0011000': 13,\n",
    "         '0111000': 2,\n",
    "         '1001010': 3,\n",
    "         '0101101': 12,\n",
    "         '0101110': 7,\n",
    "         '1110101': 9,\n",
    "         '1000001': 3,\n",
    "         '0001001': 22,\n",
    "         '0011110': 27,\n",
    "         '0101010': 18,\n",
    "         '0011010': 18,\n",
    "         '0100111': 56,\n",
    "         '0011011': 14,\n",
    "         '1100010': 6,\n",
    "         '1011111': 7,\n",
    "         '1010001': 4,\n",
    "         '0111100': 3,\n",
    "         '1111011': 5,\n",
    "         '1110111': 4,\n",
    "         '1010101': 3,\n",
    "         '0001010': 26,\n",
    "         '1001110': 1,\n",
    "         '0100110': 131,\n",
    "         '0001100': 46,\n",
    "         '0010000': 1377,\n",
    "         '1100110': 17,\n",
    "         '0010101': 25,\n",
    "         '0101111': 4,\n",
    "         '1101001': 86,\n",
    "         '1111100': 6,\n",
    "         '0101001': 21,\n",
    "         '1101011': 1091,\n",
    "         '0100010': 272,\n",
    "         '0010010': 85,\n",
    "         '0001111': 5096,\n",
    "         '0100100': 5044,\n",
    "         '0000011': 8,\n",
    "         '1101101': 292,\n",
    "         '1100011': 10,\n",
    "         '0101011': 11,\n",
    "         '1101000': 45,\n",
    "         '0000000': 6199,\n",
    "         '0001011': 31,\n",
    "         '0010001': 218,\n",
    "         '1101111': 42,\n",
    "         '0101000': 24,\n",
    "         '0001110': 361,\n",
    "         '1111000': 10,\n",
    "         '1101100': 6043,\n",
    "         '0100000': 50,\n",
    "         '1110001': 27,\n",
    "         '0110001': 7,\n",
    "         '0011100': 28,\n",
    "         '0000001': 9,\n",
    "         '1101010': 184,\n",
    "         '0001101': 112,\n",
    "         '0000101': 11,\n",
    "         '1110110': 7,\n",
    "         '0010011': 57,\n",
    "         '0010110': 14,\n",
    "         '1101110': 99,\n",
    "         '1111101': 5,\n",
    "         '0100011': 1681,\n",
    "         '0010111': 15,\n",
    "         '0100101': 391,\n",
    "         '0100001': 86,\n",
    "         '0011111': 29,\n",
    "         '1100101': 16,\n",
    "         '1110000': 31,\n",
    "         '0001000': 18,\n",
    "         '1110010': 21,\n",
    "         '1011001': 2,\n",
    "         '1110100': 12,\n",
    "         '1111111': 6,\n",
    "         '1111010': 7,\n",
    "         '1111110': 2,\n",
    "         '1000011': 2,\n",
    "         '0111001': 4,\n",
    "         '1100001': 4,\n",
    "         '0011101': 10,\n",
    "         '0000100': 4,\n",
    "         '1100100': 7,\n",
    "         '0110111': 7,\n",
    "         '1010000': 3,\n",
    "         '1100111': 14,\n",
    "         '1010010': 2,\n",
    "         '1000101': 3,\n",
    "         '0110100': 6,\n",
    "         '1111001': 3,\n",
    "         '1000000': 5,\n",
    "         '1010111': 1,\n",
    "         '0110011': 4,\n",
    "         '1011100': 3,\n",
    "         '1000100': 4,\n",
    "         '0000111': 10,\n",
    "         '0011001': 11,\n",
    "         '0000110': 3,\n",
    "         '1010100': 4,\n",
    "         '0110000': 5,\n",
    "         '1001100': 1,\n",
    "         '1011000': 1,\n",
    "         '0000010': 2,\n",
    "         '0111010': 2,\n",
    "         '1011101': 3,\n",
    "         '0111011': 1,\n",
    "         '1011011': 2,\n",
    "         '1011110': 3,\n",
    "         '1000010': 2,\n",
    "         '1001011': 1,\n",
    "         '0110101': 1,\n",
    "         '0110010': 3,\n",
    "         '1001001': 1,\n",
    "         '0110110': 1}\n",
    "ccount={}\n",
    "for key in Counter:\n",
    "    keys = key\n",
    "    if Counter[key]<20:\n",
    "        pass\n",
    "    else:\n",
    "        ccount[keys] = Counter[key]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "52a02f60-df53-474a-8098-aaef8f0f3ea4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-70-2cb507edd8db>:11: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  plot.show()\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot,ax = plt.subplots()\n",
    "ax.tick_params(axis=\"both\",direction=\"in\")\n",
    "#plt.figure(figsize=(8,7))\n",
    "x = np.linspace(0,1,39)\n",
    "#plt.xticks(y_pos,ccount.values())\n",
    "ax.bar(x, ccount.values(),width = 0.007)\n",
    "ax.grid()\n",
    "ax.set_xlabel(\"Phase\")\n",
    "ax.set_ylabel(\"Frequency of Outcome\")\n",
    "plot.savefig('ranjwt.png')\n",
    "plot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "26333bfc-7a5c-4dba-a116-1093c6f978a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "Counter={'0000000': 85,\n",
    "         '0000001': 154,\n",
    "         '0010000': 469,\n",
    "         '0010001': 116,\n",
    "         '0010010': 61,\n",
    "         '0010011': 82,\n",
    "         '0010100': 9478,\n",
    "         '0010101': 66,\n",
    "         '0010110': 18,\n",
    "         '0011001': 9,\n",
    "         '0011010': 14,\n",
    "         '0011011': 12,\n",
    "         '0011110': 2,\n",
    "         '0011111': 7,\n",
    "         '0000010': 434,\n",
    "         '0100000': 9,\n",
    "         '0100010': 8,\n",
    "         '0100011': 8,\n",
    "         '0100101': 13,\n",
    "         '0100110': 12,\n",
    "         '0100111': 10,\n",
    "         '0101000': 20,\n",
    "         '0101001': 15,\n",
    "         '0101010': 8,\n",
    "         '0101011': 21,\n",
    "         '0101100': 28,\n",
    "         '0101101': 31,\n",
    "         '0101110': 39,\n",
    "         '0101111': 69,\n",
    "         '0000011': 10733,\n",
    "         '0110000': 184,\n",
    "         '0110001': 1137,\n",
    "         '0110010': 4730,\n",
    "         '0110100': 118,\n",
    "         '0110101': 61,\n",
    "         '0110110': 43,\n",
    "         '0110111': 34,\n",
    "         '0111000': 47,\n",
    "         '0111001': 62,\n",
    "         '0111010': 91,\n",
    "         '0111011': 135,\n",
    "         '0111100': 331,\n",
    "         '0111101': 2527,\n",
    "         '0111110': 5616,\n",
    "         '0111111': 502,\n",
    "         '0000100': 1327,\n",
    "         '1000000': 170,\n",
    "         '1000001': 79,\n",
    "         '1000010': 49,\n",
    "         '1000011': 37,\n",
    "         '1000100': 21,\n",
    "         '1000101': 27,\n",
    "         '1000110': 20,\n",
    "         '1000111': 14,\n",
    "         '1001000': 18,\n",
    "         '1001001': 22,\n",
    "         '1001010': 19,\n",
    "         '1001011': 21,\n",
    "         '1001100': 14,\n",
    "         '1001101': 23,\n",
    "         '1001110': 11,\n",
    "         '0000101': 263,\n",
    "         '1010000': 23,\n",
    "         '1010001': 27,\n",
    "         '1010010': 12,\n",
    "         '1010011': 40,\n",
    "         '1010100': 55,\n",
    "         '1010101': 79,\n",
    "         '1010110': 130,\n",
    "         '1010111': 243,\n",
    "         '1011000': 621,\n",
    "         '1011001': 4747,\n",
    "         '1011010': 9126,\n",
    "         '1011011': 777,\n",
    "         '1011100': 262,\n",
    "         '1011101': 170,\n",
    "         '1011110': 103,\n",
    "         '1011111': 70,\n",
    "         '0000110': 112,\n",
    "         '1100000': 68,\n",
    "         '1100001': 68,\n",
    "         '1100010': 93,\n",
    "         '1100011': 150,\n",
    "         '1100100': 360,\n",
    "         '1100101': 3192,\n",
    "         '1100110': 3488,\n",
    "         '1100111': 371,\n",
    "         '1101000': 129,\n",
    "         '1101001': 90,\n",
    "         '1101010': 43,\n",
    "         '1101100': 28,\n",
    "         '1101101': 19,\n",
    "         '1101110': 20,\n",
    "         '0000111': 65,\n",
    "         '1110000': 13,\n",
    "         '1110001': 19,\n",
    "         '1110101': 13,\n",
    "         '1110110': 9,\n",
    "         '1111001': 14,\n",
    "         '1111010': 17,\n",
    "         '1111011': 14,\n",
    "         '1111100': 23,\n",
    "         '1111101': 23,\n",
    "         '1111110': 28,\n",
    "         '1111111': 44,\n",
    "         '0001000': 6816,\n",
    "         '0001001': 30,\n",
    "         '0001010': 43,\n",
    "         '0001011': 23,\n",
    "         '0001100': 43,\n",
    "         '0001101': 79,\n",
    "         '0001110': 249,\n",
    "         '0001111': 9372,\n",
    "         '0010111': 21,\n",
    "         '0011000': 8,\n",
    "         '0011101': 8,\n",
    "         '0110011': 321,\n",
    "         '1001111': 15,\n",
    "         '1101011': 30,\n",
    "         '1101111': 19,\n",
    "         '1110010': 8,\n",
    "         '1110011': 12,\n",
    "         '1110100': 10,\n",
    "         '1110111': 11,\n",
    "         '1111000': 6,\n",
    "         '0100001': 6,\n",
    "         '0100100': 5,\n",
    "         '0011100': 3}\n",
    "ccount={}\n",
    "for key in Counter:\n",
    "    keys = key\n",
    "    if Counter[key]<20:\n",
    "        pass\n",
    "    else:\n",
    "        ccount[keys] = Counter[key]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "2df25041-b742-46a8-8cc1-3b38ec45f8a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "88"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ccount)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "0a308829-a426-454d-9d4a-88749e5cde76",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-79-f636974908be>:9: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  plot.show()\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot,ax = plt.subplots()\n",
    "ax.tick_params(axis=\"both\",direction=\"in\")\n",
    "x = np.linspace(0,1,88)\n",
    "ax.bar(x, ccount.values(),width = 0.007)\n",
    "ax.grid()\n",
    "ax.set_xlabel(\"Phase\")\n",
    "ax.set_ylabel(\"Frequency of Outcome\")\n",
    "plot.savefig('ranjwt3.png')\n",
    "plot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "103798d4-4f09-4e3f-9e49-a391e7130ac8",
   "metadata": {},
   "outputs": [],
   "source": [
    "Counter = {'00111': 40,\n",
    "         '00001': 6,\n",
    "         '00000': 7,\n",
    "         '11110': 12,\n",
    "         '11100': 62,\n",
    "         '00010': 6,\n",
    "         '11011': 13786,\n",
    "         '01000': 147,\n",
    "         '00101': 8,\n",
    "         '01001': 15515,\n",
    "         '10101': 5,\n",
    "         '11010': 90,\n",
    "         '11101': 16,\n",
    "         '11001': 15,\n",
    "         '10111': 7,\n",
    "         '11000': 12,\n",
    "         '01010': 131,\n",
    "         '10110': 8,\n",
    "         '11111': 6,\n",
    "         '01100': 15,\n",
    "         '10010': 4,\n",
    "         '01111': 9,\n",
    "         '10001': 5,\n",
    "         '01110': 5,\n",
    "         '00110': 20,\n",
    "         '00011': 6,\n",
    "         '00100': 14,\n",
    "         '10100': 3,\n",
    "         '01101': 6,\n",
    "         '01011': 30,\n",
    "         '10000': 1,\n",
    "         '10011': 3}\n",
    "ccount={}\n",
    "for key in Counter:\n",
    "    keys = key\n",
    "    if Counter[key]<20:\n",
    "        pass\n",
    "    else:\n",
    "        ccount[keys] = Counter[key]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "dc174ba8-d1ad-41a5-a791-22ec21ab6409",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ccount)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "273f25c3-c659-4f56-8611-2b4d0cbf664b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-82-61f3a7ebd56d>:9: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  plot.show()\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot,ax = plt.subplots()\n",
    "ax.tick_params(axis=\"both\",direction=\"in\")\n",
    "x = np.linspace(0,1,9)\n",
    "ax.bar(x, ccount.values(),width = 0.007)\n",
    "ax.grid()\n",
    "ax.set_xlabel(\"Phase\")\n",
    "ax.set_ylabel(\"Frequency of Outcome\")\n",
    "plot.savefig('rangc2.png')\n",
    "plot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6b9e046f-e76f-4ef9-abef-c4dacd4e006a",
   "metadata": {},
   "outputs": [],
   "source": [
    "Counter = {'1110111': 13,\n",
    " '0111110': 22,\n",
    " '0000111': 40,\n",
    " '1001111': 17,\n",
    " '0111011': 38,\n",
    " '0001101': 51,\n",
    " '1100111': 31,\n",
    " '0001010': 71,\n",
    " '0001000': 90,\n",
    " '1111001': 36,\n",
    " '1110101': 43,\n",
    " '1010000': 57,\n",
    " '1101111': 32,\n",
    " '1011111': 33,\n",
    " '1001101': 28,\n",
    " '1001010': 57,\n",
    " '0110110': 52,\n",
    " '0011110': 81,\n",
    " '0110001': 81,\n",
    " '1011001': 44,\n",
    " '1110110': 36,\n",
    " '0011100': 130,\n",
    " '1010011': 39,\n",
    " '1110000': 81,\n",
    " '1111111': 21,\n",
    " '1110100': 80,\n",
    " '1011100': 46,\n",
    " '0111100': 69,\n",
    " '0010100': 127,\n",
    " '1010100': 69,\n",
    " '0001111': 38,\n",
    " '0110100': 132,\n",
    " '1101101': 45,\n",
    " '0101001': 130,\n",
    " '0010001': 49,\n",
    " '0000100': 133,\n",
    " '1111010': 37,\n",
    " '1001001': 36,\n",
    " '0111111': 20,\n",
    " '0011010': 76,\n",
    " '1001011': 27,\n",
    " '1101000': 92,\n",
    " '0100001': 204,\n",
    " '0100101': 227,\n",
    " '0100000': 428,\n",
    " '1110001': 40,\n",
    " '0101101': 77,\n",
    " '0010011': 83,\n",
    " '0000101': 78,\n",
    " '0110101': 56,\n",
    " '0001110': 47,\n",
    " '1100100': 176,\n",
    " '0111010': 42,\n",
    " '0011000': 106,\n",
    " '0111101': 37,\n",
    " '1101001': 51,\n",
    " '1000001': 52,\n",
    " '1000000': 122,\n",
    " '1000111': 26,\n",
    " '0101110': 62,\n",
    " '1100010': 122,\n",
    " '1101010': 61,\n",
    " '0100111': 119,\n",
    " '0101011': 92,\n",
    " '0000000': 123,\n",
    " '1010101': 42,\n",
    " '0100100': 449,\n",
    " '0011011': 84,\n",
    " '0100011': 259,\n",
    " '0010111': 41,\n",
    " '1000101': 77,\n",
    " '1000010': 73,\n",
    " '0011001': 54,\n",
    " '0000001': 77,\n",
    " '0110111': 34,\n",
    " '1101110': 43,\n",
    " '0010000': 103,\n",
    " '0001100': 91,\n",
    " '0001011': 47,\n",
    " '1111110': 21,\n",
    " '1010110': 50,\n",
    " '1001110': 27,\n",
    " '1000011': 65,\n",
    " '1100110': 80,\n",
    " '1000100': 127,\n",
    " '0101100': 143,\n",
    " '1000110': 43,\n",
    " '1100001': 94,\n",
    " '0110000': 134,\n",
    " '1111011': 23,\n",
    " '1111000': 45,\n",
    " '1101100': 78,\n",
    " '1100101': 87,\n",
    " '1001000': 64,\n",
    " '1100011': 88,\n",
    " '0100110': 204,\n",
    " '1011110': 20,\n",
    " '0000011': 73,\n",
    " '1010001': 40,\n",
    " '1111100': 45,\n",
    " '0111000': 74,\n",
    " '0101000': 199,\n",
    " '0011101': 59,\n",
    " '0110011': 57,\n",
    " '0101010': 90,\n",
    " '0000110': 70,\n",
    " '0100010': 324,\n",
    " '0010010': 90,\n",
    " '1011011': 36,\n",
    " '1110011': 51,\n",
    " '1101011': 47,\n",
    " '1001100': 67,\n",
    " '0011111': 57,\n",
    " '0101111': 55,\n",
    " '1100000': 154,\n",
    " '1010010': 50,\n",
    " '0110010': 107,\n",
    " '0010101': 65,\n",
    " '1011101': 34,\n",
    " '0001001': 50,\n",
    " '1110010': 50,\n",
    " '0010110': 58,\n",
    " '1010111': 22,\n",
    " '1011010': 42,\n",
    " '0111001': 47,\n",
    " '1011000': 41,\n",
    " '0000010': 95,\n",
    " '1111101': 23}\n",
    "ccount={}\n",
    "for key in Counter:\n",
    "    keys = key\n",
    "    if Counter[key]<10:\n",
    "        pass\n",
    "    else:\n",
    "        ccount[keys] = Counter[key]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "16362ae5-9d3f-4c4e-9e5b-79a0819e4fb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "128"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ccount)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b47c3375-a843-447f-86b7-8ab7cc2eb6a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-8-bffd8a22c06f>:9: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  plot.show()\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot,ax = plt.subplots()\n",
    "ax.tick_params(axis=\"both\",direction=\"in\")\n",
    "x = np.linspace(0,1,128)\n",
    "ax.bar(x, ccount.values(),width = 0.007)\n",
    "ax.grid()\n",
    "ax.set_xlabel(\"Phase\")\n",
    "ax.set_ylabel(\"Frequency of Outcome\")\n",
    "plot.savefig('rangc2real.png')\n",
    "plot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "39a3625f-6e26-4983-b4cf-24123b539b63",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit import *\n",
    "from qiskit.quantum_info.states.statevector import Statevector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2fafd5c4-b73a-4ad6-9543-3d7e99928326",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 206.852x144.48 with 1 Axes>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ide = [[1,0],[0,1]]\n",
    "qr = QuantumRegister(2,)\n",
    "qc = QuantumCircuit(2)\n",
    "#qc.initialize(Statevector.from_label(\"0\"),0)\n",
    "qc.unitary(ide,0,\"V\")\n",
    "qc.draw('mpl')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34efe0c2-240b-48b0-ba2f-5e4bd3f65214",
   "metadata": {},
   "source": [
    "## Implementation of QPE results on real fake quantum computer gaudlupe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e1143238-8b31-4173-88bd-ca1cf7796516",
   "metadata": {},
   "outputs": [],
   "source": [
    "Counter = {'101111': 54,\n",
    " '100111': 34,\n",
    " '101001': 45,\n",
    " '001000': 131,\n",
    " '011111': 49,\n",
    " '011110': 73,\n",
    " '100000': 117,\n",
    " '101100': 56,\n",
    " '110101': 82,\n",
    " '101101': 51,\n",
    " '111001': 53,\n",
    " '100110': 58,\n",
    " '000110': 102,\n",
    " '111010': 74,\n",
    " '111000': 88,\n",
    " '111101': 27,\n",
    " '011001': 138,\n",
    " '011101': 53,\n",
    " '101011': 48,\n",
    " '000000': 124,\n",
    " '001011': 117,\n",
    " '100101': 50,\n",
    " '100001': 127,\n",
    " '010101': 187,\n",
    " '110000': 216,\n",
    " '111110': 38,\n",
    " '101010': 81,\n",
    " '011010': 124,\n",
    " '101110': 49,\n",
    " '001111': 142,\n",
    " '010010': 685,\n",
    " '110010': 189,\n",
    " '010110': 199,\n",
    " '111111': 23,\n",
    " '100010': 124,\n",
    " '000011': 112,\n",
    " '011100': 75,\n",
    " '110001': 177,\n",
    " '000111': 61,\n",
    " '011000': 164,\n",
    " '010100': 295,\n",
    " '101000': 69,\n",
    " '001110': 161,\n",
    " '000100': 99,\n",
    " '010001': 530,\n",
    " '110011': 116,\n",
    " '111100': 48,\n",
    " '110111': 70,\n",
    " '001001': 114,\n",
    " '000001': 100,\n",
    " '001101': 128,\n",
    " '110110': 80,\n",
    " '011011': 75,\n",
    " '010111': 135,\n",
    " '100011': 92,\n",
    " '110100': 129,\n",
    " '111011': 51,\n",
    " '010000': 567,\n",
    " '001100': 114,\n",
    " '100100': 69,\n",
    " '001010': 164,\n",
    " '000010': 155,\n",
    " '000101': 71,\n",
    " '010011': 363}\n",
    "ccount={}\n",
    "for key in Counter:\n",
    "    keys = key\n",
    "    if Counter[key]<10:\n",
    "        pass\n",
    "    else:\n",
    "        ccount[keys] = Counter[key]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "518867c8-8f20-4fa8-bfe0-44609debb7e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Counter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "40e7500d-3369-40e0-b15f-709a726c1742",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-15-262582db8537>:11: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  plot.show()\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot,ax = plt.subplots()\n",
    "ax.tick_params(axis=\"both\",direction=\"in\")\n",
    "x = np.linspace(0,1,64)\n",
    "ax.bar(x, ccount.values(),width = 0.007)\n",
    "ax.grid()\n",
    "ax.set_xlabel(\"Phase\")\n",
    "#ax.plot(label = ['GC,EFT,N=2'])\n",
    "ax.legend(['GC,EFT,N=2'],loc = 'best')\n",
    "ax.set_ylabel(\"Frequency of Outcome\")\n",
    "plot.savefig('rangc2real.png')\n",
    "plot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "69bc764a-0a88-4c42-b61a-5cc8107b41dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "Counter = {'101111': 88,\n",
    " '100100': 167,\n",
    " '000111': 106,\n",
    " '001011': 93,\n",
    " '000011': 129,\n",
    " '011111': 108,\n",
    " '001000': 154,\n",
    " '110101': 126,\n",
    " '011110': 133,\n",
    " '111000': 129,\n",
    " '010001': 138,\n",
    " '000100': 159,\n",
    " '010101': 130,\n",
    " '011101': 104,\n",
    " '011000': 189,\n",
    " '010100': 150,\n",
    " '111010': 116,\n",
    " '000110': 112,\n",
    " '100110': 126,\n",
    " '111001': 88,\n",
    " '101000': 176,\n",
    " '001110': 98,\n",
    " '101101': 98,\n",
    " '011100': 150,\n",
    " '110001': 143,\n",
    " '111111': 56,\n",
    " '100010': 177,\n",
    " '001111': 92,\n",
    " '010010': 148,\n",
    " '011001': 142,\n",
    " '111101': 92,\n",
    " '101001': 145,\n",
    " '100111': 100,\n",
    " '010110': 148,\n",
    " '110010': 134,\n",
    " '010011': 130,\n",
    " '000101': 115,\n",
    " '110111': 77,\n",
    " '001001': 116,\n",
    " '110011': 95,\n",
    " '111100': 98,\n",
    " '000001': 135,\n",
    " '001101': 117,\n",
    " '010111': 98,\n",
    " '100011': 131,\n",
    " '110100': 133,\n",
    " '011011': 123,\n",
    " '110110': 110,\n",
    " '111011': 70,\n",
    " '001100': 141,\n",
    " '010000': 208,\n",
    " '100000': 241,\n",
    " '101110': 101,\n",
    " '101011': 117,\n",
    " '000000': 176,\n",
    " '110000': 161,\n",
    " '000010': 137,\n",
    " '001010': 142,\n",
    " '101100': 134,\n",
    " '011010': 146,\n",
    " '111110': 78,\n",
    " '101010': 135,\n",
    " '100101': 134,\n",
    " '100001': 149}\n",
    "ccount={}\n",
    "for key in Counter:\n",
    "    keys = key\n",
    "    if Counter[key]<10:\n",
    "        pass\n",
    "    else:\n",
    "        ccount[keys] = Counter[key]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8fe6c883-095d-4fb0-81df-d21b24d3dada",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Counter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "23d3d499-6863-47dc-9d08-47155e5dbbf8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-21-6d06062579a5>:11: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  plot.show()\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot,ax = plt.subplots()\n",
    "ax.tick_params(axis=\"both\",direction=\"in\")\n",
    "x = np.linspace(0,1,64)\n",
    "ax.bar(x, ccount.values(),width = 0.007)\n",
    "ax.grid()\n",
    "ax.set_xlabel(\"Phase\")\n",
    "#ax.plot(label = ['GC,EFT,N=2'])\n",
    "ax.legend(['GC,EFT,N=3'],loc = 'best')\n",
    "ax.set_ylabel(\"Frequency of Outcome\")\n",
    "plot.savefig('rangc3real.png')\n",
    "plot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "99dcdd61-f81d-4c42-89da-4e2123672f40",
   "metadata": {},
   "outputs": [],
   "source": [
    "Counter = {'101101': 99,\n",
    " '000011': 164,\n",
    " '011110': 76,\n",
    " '100100': 148,\n",
    " '111010': 135,\n",
    " '110111': 118,\n",
    " '001001': 154,\n",
    " '001101': 107,\n",
    " '001111': 74,\n",
    " '010010': 124,\n",
    " '100111': 77,\n",
    " '110100': 160,\n",
    " '001110': 103,\n",
    " '101000': 144,\n",
    " '111000': 161,\n",
    " '001011': 98,\n",
    " '000110': 170,\n",
    " '110101': 139,\n",
    " '010101': 112,\n",
    " '101111': 86,\n",
    " '110010': 157,\n",
    " '010110': 108,\n",
    " '011111': 77,\n",
    " '111111': 101,\n",
    " '100010': 136,\n",
    " '101100': 116,\n",
    " '111100': 154,\n",
    " '110011': 139,\n",
    " '001000': 193,\n",
    " '101110': 94,\n",
    " '100000': 151,\n",
    " '011000': 130,\n",
    " '010100': 116,\n",
    " '011101': 87,\n",
    " '101011': 85,\n",
    " '000000': 261,\n",
    " '100011': 89,\n",
    " '010111': 91,\n",
    " '111001': 133,\n",
    " '100110': 108,\n",
    " '110000': 205,\n",
    " '011011': 88,\n",
    " '110110': 124,\n",
    " '001010': 133,\n",
    " '000010': 177,\n",
    " '010011': 89,\n",
    " '000101': 129,\n",
    " '000001': 209,\n",
    " '101001': 133,\n",
    " '011100': 137,\n",
    " '110001': 139,\n",
    " '001100': 131,\n",
    " '010000': 188,\n",
    " '111011': 137,\n",
    " '000111': 118,\n",
    " '010001': 128,\n",
    " '000100': 190,\n",
    " '111101': 111,\n",
    " '011001': 110,\n",
    " '100101': 97,\n",
    " '100001': 116,\n",
    " '111110': 116,\n",
    " '011010': 94,\n",
    " '101010': 118}\n",
    "ccount={}\n",
    "for key in Counter:\n",
    "    keys = key\n",
    "    if Counter[key]<10:\n",
    "        pass\n",
    "    else:\n",
    "        ccount[keys] = Counter[key]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3a553b08-bc24-4a69-a6f6-634f217bb0a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Counter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "19e62bc0-e39c-4d0c-8b41-55539c8fc062",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-25-b322469a2808>:11: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  plot.show()\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot,ax = plt.subplots()\n",
    "ax.tick_params(axis=\"both\",direction=\"in\")\n",
    "x = np.linspace(0,1,64)\n",
    "ax.bar(x, ccount.values(),width = 0.007)\n",
    "ax.grid()\n",
    "ax.set_xlabel(\"Phase\")\n",
    "#ax.plot(label = ['GC,EFT,N=2'])\n",
    "ax.legend(['GC,EFT,N=4'],loc = 'best')\n",
    "ax.set_ylabel(\"Frequency of Outcome\")\n",
    "plot.savefig('rangc4real.png')\n",
    "plot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7b91fb7d-385c-40bc-bebf-b8ed6de808e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "Counter = {'0111011': 40,\n",
    " '1001111': 36,\n",
    " '1101111': 23,\n",
    " '1111010': 42,\n",
    " '1111101': 35,\n",
    " '0001111': 49,\n",
    " '1110010': 52,\n",
    " '0001001': 67,\n",
    " '1101100': 47,\n",
    " '0010100': 90,\n",
    " '0001101': 63,\n",
    " '1100111': 32,\n",
    " '0101101': 70,\n",
    " '1110110': 36,\n",
    " '1101101': 42,\n",
    " '1011011': 41,\n",
    " '0010011': 56,\n",
    " '1100101': 47,\n",
    " '1111001': 46,\n",
    " '0001100': 73,\n",
    " '0001000': 83,\n",
    " '1001110': 46,\n",
    " '1000011': 45,\n",
    " '1101011': 42,\n",
    " '1110011': 41,\n",
    " '0010001': 80,\n",
    " '1000000': 98,\n",
    " '0110101': 78,\n",
    " '0001110': 66,\n",
    " '1101001': 44,\n",
    " '0111010': 74,\n",
    " '0011000': 97,\n",
    " '1001101': 44,\n",
    " '1001010': 60,\n",
    " '0010111': 70,\n",
    " '0100011': 110,\n",
    " '1000101': 57,\n",
    " '0010010': 84,\n",
    " '0000010': 76,\n",
    " '0001010': 64,\n",
    " '1010101': 42,\n",
    " '0100100': 124,\n",
    " '0000111': 45,\n",
    " '1000001': 72,\n",
    " '0000011': 58,\n",
    " '1010001': 36,\n",
    " '0111100': 93,\n",
    " '1010000': 77,\n",
    " '1110111': 36,\n",
    " '1101010': 53,\n",
    " '1100010': 49,\n",
    " '0101001': 97,\n",
    " '0100101': 92,\n",
    " '0010000': 111,\n",
    " '1011100': 62,\n",
    " '1111111': 37,\n",
    " '1110100': 59,\n",
    " '1001011': 42,\n",
    " '1011000': 48,\n",
    " '1011010': 52,\n",
    " '0111001': 64,\n",
    " '1111011': 33,\n",
    " '1111000': 54,\n",
    " '1110101': 50,\n",
    " '0000110': 70,\n",
    " '0101010': 90,\n",
    " '0111110': 46,\n",
    " '0000000': 97,\n",
    " '0100001': 96,\n",
    " '1101000': 51,\n",
    " '1100100': 62,\n",
    " '0000101': 73,\n",
    " '0010101': 76,\n",
    " '0110010': 69,\n",
    " '0110111': 52,\n",
    " '1101110': 43,\n",
    " '0100000': 149,\n",
    " '1110001': 50,\n",
    " '0110011': 69,\n",
    " '0101000': 120,\n",
    " '1111100': 49,\n",
    " '0111000': 84,\n",
    " '0011101': 74,\n",
    " '0110000': 125,\n",
    " '1100001': 57,\n",
    " '0101011': 85,\n",
    " '0100111': 81,\n",
    " '0111101': 53,\n",
    " '0100010': 122,\n",
    " '1001001': 53,\n",
    " '0011010': 87,\n",
    " '0111111': 46,\n",
    " '1011111': 29,\n",
    " '0010110': 49,\n",
    " '1010111': 35,\n",
    " '0011011': 66,\n",
    " '0110110': 59,\n",
    " '1010100': 37,\n",
    " '1011001': 44,\n",
    " '1001100': 57,\n",
    " '1000010': 80,\n",
    " '0011001': 87,\n",
    " '0000001': 87,\n",
    " '1100110': 48,\n",
    " '1000100': 69,\n",
    " '0101100': 99,\n",
    " '1000110': 57,\n",
    " '0101110': 70,\n",
    " '1000111': 48,\n",
    " '0011110': 68,\n",
    " '0000100': 90,\n",
    " '0011100': 109,\n",
    " '0110001': 97,\n",
    " '1010110': 43,\n",
    " '1010011': 41,\n",
    " '1110000': 60,\n",
    " '0110100': 86,\n",
    " '0011111': 72,\n",
    " '0101111': 54,\n",
    " '1010010': 47,\n",
    " '1100000': 68,\n",
    " '1100011': 42,\n",
    " '0100110': 82,\n",
    " '1001000': 76,\n",
    " '1011110': 42,\n",
    " '1111110': 40,\n",
    " '0001011': 60,\n",
    " '1011101': 33}\n",
    "ccount={}\n",
    "for key in Counter:\n",
    "    keys = key\n",
    "    if Counter[key]<10:\n",
    "        pass\n",
    "    else:\n",
    "        ccount[keys] = Counter[key]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "744342de-ac93-4cd5-a164-daaeebef760b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "128"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Counter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "381fbb99-557d-4435-9dfc-18c5bb2af21d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-28-bca1c4afb682>:11: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  plot.show()\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot,ax = plt.subplots()\n",
    "ax.tick_params(axis=\"both\",direction=\"in\")\n",
    "x = np.linspace(0,1,128)\n",
    "ax.bar(x, ccount.values(),width = 0.007)\n",
    "ax.grid()\n",
    "ax.set_xlabel(\"Phase\")\n",
    "#ax.plot(label = ['GC,EFT,N=2'])\n",
    "ax.legend(['JWT,Central,N=2'],loc = 'best')\n",
    "ax.set_ylabel(\"Frequency of Outcome\")\n",
    "plot.savefig('cjwt2real.png')\n",
    "plot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d2633f7-bb59-41c6-96d7-2eadaffc7f7b",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Circuit Drawer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cca9a6b2-da45-4f9c-8cdc-5f4ae2fd56d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit import *\n",
    "from qiskit.visualization import circuit_drawer\n",
    "from sympy import Symbol \n",
    "from qiskit.circuit import Parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f0b124f1-f701-4b51-8a1f-ebdbd614a04c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 447.797x264.88 with 1 Axes>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theta1 = Parameter(r'$\\theta_1$')\n",
    "theta2 = Parameter(r'$\\theta_2$')\n",
    "theta3 = Parameter(r'$\\theta_3$')\n",
    "cir = QuantumCircuit(4)\n",
    "cir.x(3)\n",
    "cir.ry(theta1,2)\n",
    "cir.cx(2,3)\n",
    "cir.cry(theta2,2,1)\n",
    "cir.cx(1,2)\n",
    "cir.cry(theta3,1,0)\n",
    "cir.cx(0,1)\n",
    "#cir.z(1)\n",
    "#cir.measure([0],[0])\n",
    "circuit_drawer(cir,filename = 'ansatz_3.pdf',output = 'mpl')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c86ae27-6c2a-4aef-adff-3a8d9fb9eef1",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Convergence Scatter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a5c0d922-bbe6-4c5f-b197-79599f9f1a2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "it = list(range(11))\n",
    "#ordering of data [0] GC, N=2,[1,2,3] JWT , N = 2,3,4 in state_fid_data,std_dev,eig\n",
    "state_fid_data_means = [[0.7811682521129961,0.7805620815128815,0.9392538879158376,0.9935407785459155,0.999869984358725,0.9999790663697364,0.9999583586054452,\n",
    "                        0.9999832658425232,0.9999793239188361,0.9999553459778363,0.9999513499555956],\n",
    "                       [0.7900858193998321,0.7805498068365525,0.9399057240808842,0.9935798352923092,0.9998944576024911,0.9999648296349418,0.9999869893381316,\n",
    "                        0.9999595929223293,0.9999686190822787,0.9999687144029272,0.9999577220984959],\n",
    "                       [0.8030514618458806,0.7687752180019763,0.9124786169959259,0.9765388406380673,0.9961943267795575,0.999563286533341,0.9999037463758806,\n",
    "                        0.9999275838134618,0.9999399539917555,0.9999542780298922,0.9999491793663207],\n",
    "                       [0.8269187247499428,0.7572000595795249,0.9211977180912372,0.9802517249153462,0.9963175559216598,0.9995931239934848,0.9998602068224349,\n",
    "                        0.99991082145308,0.9999259434094107,0.9999084458998109,0.9998808852457215]]\n",
    "std_devs = [[1.1102230246251565e-16,0.004336134543169076,0.003543199882407062,0.0011258436103789983,0.00011188700170552664,2.0533451130853145e-05,\n",
    "           4.572299560288622e-05,2.888747203897957e-05,2.3240984484926247e-05,7.449673459780588e-05,7.073287688566731e-05],\n",
    "           [1.1102230246251565e-16,0.0039002391003512852,0.003207460268065,0.0009807802058940922,0.0001684536345248254,3.2913660918135956e-05,1.1992157854244605e-05,\n",
    "            5.3342550283206824e-05,2.9841339796615095e-05,3.4498964976926335e-05,6.10553579437429e-05],\n",
    "            [0.0,0.00362561137864568,0.004071783385383677,0.0022421806476977716,0.0006698059309518042,0.00026110782190259926,9.108745745306846e-05,\n",
    "             6.736191558137965e-05,4.2113351985687086e-05,4.257713003540622e-05,4.771045793736167e-05],\n",
    "           [0.0,0.005351686379248542,0.004224285406040166,0.0019990070485034267,0.0008427632492870641,0.0002551933523475059,0.00011383519274655713,\n",
    "            7.005425541237612e-05,7.004466513673829e-05,5.98920548639722e-05,6.24328555237925e-05]]\n",
    "ideal = [[-1.74916151]*11,[-2.04567]*11,[-2.144]*11]\n",
    "eigs = [[1.6015335387519762, 1.5691325151137034, -0.858165010344839, -1.665277645406934, -1.7491578257565517, -1.7491230860727898, -1.7490988764398057, -1.7488811663213937, -1.7491483356882438, -1.7491560163623876, -1.7487902352863314],\n",
    "        [-0.8202221201487483,-1.6648684918833092,-1.7489670280887455,-1.748148456910136,-1.7491557224352732,-1.749157536662069,-1.748148456910136,-1.74888816122835,-1.747825366253232,-1.7490120874465567,-1.7490120874465567],\n",
    "        [0.7171144942422691, 0.9678664159818775, -1.14456007753354, -1.7897708826216487, -1.9996908472350106, -2.040783106087494, -2.044818883348187, -2.044510448896607, -2.0446562014378413, -2.044803399427644, -2.045051170819651],\n",
    "        [-0.0376939148123161, 0.701939821095076, -1.084457613638169, -1.8073919660720672, -2.0710764398095023, -2.1195727173686256, -2.1376934151694136, -2.1423271952602474, -2.1421983180967508, -2.141152401268161, -2.1416669508277097]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b56c2c5d-3dfd-4388-a98e-5f95f7c27294",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = ['GC, N=2','JWT, N=2','JWT, N=3','JWT, N=4']\n",
    "new_labels = ['Exact, N=2','Exact, N=3','Exact, N=4']\n",
    "new_colors = ['black','red','blue']\n",
    "colors = ['black','green','red','blue']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "1e9ecb26-f966-4d66-88e1-11c651697e6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import OrderedDict\n",
    "ls_dict = OrderedDict(\n",
    "            [('solid',     (0, ())),\n",
    "            ('ldot',      (0, (1, 10))),\n",
    "            ('dot',       (0, (1, 5))),\n",
    "            ('ddot',      (0, (1, 1))),\n",
    "            ('ldash',     (0, (5, 10))),\n",
    "            ('dash',      (0, (5, 5))),\n",
    "            ('ddash',     (0, (5, 1))),\n",
    "            ('ldashdot',  (0, (3, 10, 1, 10))),\n",
    "            ('dashdot',   (0, (3, 5, 1, 5))),\n",
    "            ('ddashdot',  (0, (3, 1, 1, 1))),\n",
    "            ('ldashdot',  (0, (3, 10, 1, 10, 1, 10))),\n",
    "            ('dashdot',   (0, (3, 5, 1, 5, 1, 5))),\n",
    "            ('ddashdot',  (0, (3, 1, 1, 1, 1, 1)))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "aa915655-1e4a-47ae-80ae-8e35a4a4ea7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,10))\n",
    "gs = fig.add_gridspec(2, hspace=0)\n",
    "axs = gs.subplots(sharex=True)\n",
    "markers_list = ['D','^','o','*']\n",
    "linestyles = [ls_dict['ddashdot'],ls_dict['ddash'],ls_dict['ddot'],ls_dict['dash']]\n",
    "for i in range(len(state_fid_data_means)):\n",
    "    axs[0].plot(it, state_fid_data_means[i],marker = markers_list[i],color = colors[i],linestyle = linestyles[i],markersize = 11,label = labels[i],linewidth = 3)\n",
    "    axs[1].plot(it,eigs[i],label = labels[i],marker = markers_list[i],color = colors[i],linestyle = linestyles[i],markersize = 11,linewidth = 3)\n",
    "for i in range(len(ideal)):\n",
    "    axs[1].plot(it,ideal[i],linestyle = lstyle[i+4],label = new_labels[i], color = new_colors[i],linewidth = 3)\n",
    "#ax1.set_xlabel('Iteration')\n",
    "plt.rcParams.update({'font.size': 18})\n",
    "axs[1].set_xlabel('Iterations')\n",
    "axs[0].set_ylabel('State Fidelity')\n",
    "axs[1].set_ylabel('Energy (MeV)')\n",
    "axs[1].set_xticks([0,1,2,3,4,5,6,7,8,9,10])\n",
    "axs[0].tick_params(axis=\"both\",direction=\"in\")\n",
    "axs[0].set_xticks([0,1,2,3,4,5,6,7,8,9,10])\n",
    "axs[1].tick_params(axis=\"both\",direction=\"in\")\n",
    "axs[0].legend(loc = 'best')\n",
    "axs[1].legend(loc = 'best')\n",
    "#ax.set_title('Line plot with error bars')\n",
    "plt.savefig('convergence2.pdf', dpi = 900,bbox_inches = 'tight',pad_inches = 0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c9ee0b7f-4bbd-45f8-baf0-6f641138ff8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "font = {'size': 15}\n",
    "# using rc function\n",
    "plt.rc('font', **font)\n",
    "vals = ['GC, N = 2','JWT, N = 2','JWT, N = 3','JWT, N = 4']\n",
    "for i in range(len(state_fid_data)):\n",
    "    plt.scatter(it,state_fid_data[i],label = vals[i],alpha = 0.9)\n",
    "plt.hlines(1.00,-1,10,linestyles = 'dashed',color = 'black')\n",
    "\n",
    "plt.legend(loc = 'best')\n",
    "plt.xlim(-1,10)\n",
    "plt.grid()\n",
    "plt.xlabel(\"Iteration\")\n",
    "plt.ylabel(\"State Fidelity\")\n",
    "plt.savefig('state_fid.png',dpi = 1200)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "caa96de1-e0f4-4d72-95e7-649c1fd2c848",
   "metadata": {},
   "outputs": [],
   "source": [
    "eig = [-1.7453292519943295,\n",
    " -1.5707963267948966,\n",
    " -1.832595714594046,\n",
    " -1.7671458676442586,-1.7671458676442586,-1.7671458676442586,-1.7671458676442586,-1.7671458676442586,-1.7671458676442586,-1.7671458676442586]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6df47bf6-8f94-4b52-aa79-c97788a6291a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(it,eig, label = 'GC, N = 2')\n",
    "plt.plot(it,eig)\n",
    "plt.ylim(-2.1,-1.2)\n",
    "plt.legend(loc = 'best')\n",
    "plt.xlim(-0.2,9.5)\n",
    "plt.grid()\n",
    "plt.xlabel(\"iteration\")\n",
    "plt.ylabel(\"Binding Energy (MeV)\")\n",
    "plt.savefig('BE.png')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "853d5d82-e44d-459e-9022-f78afb28fd62",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-2\n",
      "-1\n",
      "0\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "for i in range(-2,2):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d55a555c-d640-4ee9-a0dc-6812b846c295",
   "metadata": {},
   "source": [
    "# LCU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "bd571c5e-96f9-4b0c-bf68-ff766e2022b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "eigs_eft_gc = [[-1.7452574011132365,-1.7434546053758568,-1.7326288967372632,-1.682813487939633,-1.7578650982135207,-1.718170709381508,-1.8188098180766081,\n",
    "             -1.7794301793271625,-1.7623628241438407,-1.766857913811874,-1.6900796671892842,-1.679177788335581,-1.7686552125632584,-1.7659591065568057,-1.7551651953181926,\n",
    "             -1.7091204038663417,-1.7398477387890505,-1.7344342466139935,-1.754265016240268,-1.7154567460603038],\n",
    "               [-2.0318719318131286,-2.2092844737349644,-2.2760457730477768,-2.130827102400854,-2.1589187913270056,-2.3064968170321354,-2.0261871463436467,\n",
    "                -2.130827102400854,-2.2120751128925757,-2.088539419569189,-2.088539419569189,-2.3340999156964344,-2.2260166176842198,-2.267724903694818,-2.1897280969031865,\n",
    "                -2.153306818137933,-2.200907868080913,-2.2092844737349644,-2.2510624290302754,-2.1897280969031865],\n",
    "               [-2.2193396860099526,-2.1286304149057216,-2.229774162939293,-2.0233405917007303,-2.0936088165502227,-2.037418373552203,-1.799994719279269,\n",
    "                -2.1111289418965278,-2.4885320116366465,-2.368277405065477,-2.2818482221963734,-2.340668824945201,-1.9209119182700967,-1.8889921652444528,-2.051484055885279,-2.357929540789751,-2.4164828845652906,-2.149607645110379,-2.1146307267784863,-2.1705582036613116]]\n",
    "eigs_eft_jwt = [[-1.790189338040907,-1.6564143573724976,-1.7208829894910433,-1.7668571919267286,-1.7910853159658373,-1.7632613312832017,-1.7785321194450407,\n",
    "                 -1.8205943063692738,-1.764160454471483,-1.7830177563876584,-1.7371407528517215,-1.7991444293047412,-1.768654490509034,-1.7335309062499995,-1.7506625213418676,\n",
    "                 -1.757864377174367,-1.8205943063692738,-1.7991444293047412,-1.629923476128857,-1.6764491563338852],\n",
    "                [-1.7268339953373317,-1.7093552805824768,-1.7901262293280933,-1.7529925102406896,-1.6679145604699421,-1.6990119345083503,-1.7522008733398664,\n",
    "                 -1.7434885627513452,-1.8231891309515778,-1.7853937106730555,-1.684671883558842,-1.7276277256539974,-1.766440382307982,-1.708560035323364,\n",
    "                 -1.8231891309515778,-1.7419036574106261,-1.7909147569319508,-1.699807973623674,-1.7948564294465896,-1.8027349532498782],[-1.8425737864995853, -1.8287415287776412, -1.4383533133460347, -1.6273262312626215, -1.424298867197468, -1.7317054044179727, \n",
    "         -1.959847395482683, -1.2550291173491352, -1.5994191697547038, -1.8079791002389847, -2.1723329438210115, -1.7247601035650995, \n",
    "         -1.773337505864582, -1.9185179143384694, -2.0079812775921546, -1.7455903279917493, -1.7664035412017043, -1.6412682555255387, \n",
    "         -1.7455903279917493, -2.349212252779733]]\n",
    "eigs_cen_gc = [[-0.032185167691734407, -0.03453856932707744, -0.042064743689465445, -0.0594416224920975, -0.06928723183014807, -0.047234832417953676,\n",
    "                -0.02841826442529971, -0.031243610482047046, -0.03877292495749085, -0.032185167691734407, -0.08146011905781503, -0.024178346585818744,\n",
    "                -0.007195820546516352, -0.04018387239898935, -0.05381005945924411, -0.04958375537807047, -0.07303472541302991, -0.04488521135009549, \n",
    "                -0.03077278973805253, -0.03030194089458238],\n",
    "              [-1.4289385930582252, -2.0433542650377383, -1.8362541685299831, -1.84160343784208, -1.7987507641554181, -1.5878933733463647, \n",
    "               -1.6151103168091385, -1.707240508148928, -1.6639631175611385, -1.9693666396312306,-1.6042301255013403, -1.7342190815956222, \n",
    "               -1.7987507641554181, -1.8309028141389874, -1.9162756301661528, -1.423423550970055, -2.006409683905737, -1.9162756301661528, \n",
    "               -1.979960459778649, -1.5933411563019604],\n",
    "              [-1.7518360762359855, -1.96948138124257, -1.814209600051214, -1.6454493696722636, -1.8515607448577924, -2.0745327330175023, \n",
    "               -1.7580802945969332, -1.6203523448816917, -1.4881833636302204, -1.6329039602726994, -1.9136918043606599, -1.5449118088417357, \n",
    "               -1.5637930287538708, -1.5763726855233635, -1.3108733565683046, -1.3996846802032863, -1.8079791002389847, -1.4376507773642793, \n",
    "               -1.5449118088417357, -1.5008007029354467]]\n",
    "eigs_cen_jwt = [[-0.040654132207841975, -0.051931980852333304, -0.04441520330880255, -0.06460037228256077, -0.03030194089458238, -0.050992774321168, \n",
    "                 -0.027947275008951777, -0.02464956118088857, -0.03595027355680358, -0.027476257462915932, -0.048174485340082285, -0.041594567848081354,\n",
    "                 -0.003889403001005931, -0.07678044922296756, -0.028889225717000144, -0.0161633843235105, -0.05568769239015037, -0.045825143590006334, \n",
    "                 -0.027947275008951777, -0.05287107588512452],\n",
    "               [0.1278508304476952, -0.2503217817315092, 0.07732547565454531, 0.02381863096249162, 0.01439497227055156, 0.042682764640750076, \n",
    "                -0.048286987507353135, 0.08678667148122102, 0.00811564049262628, -0.038900422682420416, 0.00811564049262628, -0.0888983662495145, \n",
    "                -0.016976891606565875, 0.0836323101143536, 0.042682764640750076, -0.22868589604714437, -0.2750130390234612, 0.01439497227055156, \n",
    "                -0.30890204832238577, 0.061569386879044924],\n",
    "               ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "34f566cb-913a-4fa1-857c-354b5a1e5b47",
   "metadata": {},
   "outputs": [],
   "source": [
    "true_eft = [-1.74916,-2.04567,-2.144]\n",
    "true_cen = [-0.0413,-1.709,-1.739]\n",
    "eft_qpe_gc = [-1.7491560163623876,-2.03712648631213,-2.14757310303989]\n",
    "eft_qpe_jwt = [-1.74873809818963,-2.12302941043372,-2.0816701789183]\n",
    "cen_qpe_jwt = [-0.0429514620607979,-2.1046216409791,-1.6935147898257479]\n",
    "cen_qpe_gc = [-0.0429514620607979,-1.70885459,-1.1351457830353744]\n",
    "#qpe_err_eft_gc = [0.0125512,0.0203612,0.0113645294]\n",
    "#qpe_err_eft_jwt = [0.032912,0.0398718,0.02142305]\n",
    "#qpe_err_cen_gc = [0.013616,0.02064193,0.06032562]\n",
    "#qpe_err_cen_jwt = [0.026426,0.0345692,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "2c5ff2f2-06ff-46d8-b4ef-d633896fe3f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import median_abs_deviation as mad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "287f9fd4-e6a7-4784-95cd-65daf4547d3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3.98363761e-06,  8.54351369e-03, -3.57310304e-03])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(eft_qpe_gc)-np.array(true_eft)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "0dffddb9-cb23-4b08-a713-a6e8595b1095",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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q1b322msr1Y3dBg8enMm3KyIZEoa2pUnaX0FEGqbPdsA9r8GpG6BTp2xHU2HYsGEMGTIEd2fr1q28/fbbPPHEE8yYMYPBgwfz6KOP0qZNm0rH/PznP+fII4/kq6++YvXq1dx9992MHDmSDz74gF//+teV6l5//fV8/etfr/K6nTt3TufbEgkPtS2VZKRtcXdtSWx9+/Z1kVz25ptvpvcFTuzuvneB+9ix6X2dBD377LMO+I033lilbNeuXX7FFVc44KecckrF/unTpzvgjz76aKX6a9eu9ebNm3urVq28rKzM3d0nTJjggC9ZsiTpGBP5TNS2SK5T27JHPrUtwFKvJqfRMLGI1N369bBgHVzZD+67DzZsyHZENWrcuDE333wzxxxzDHPnzmX+/Pk11v/a177GIYccwpYtW9i4cWOGohQRtS3ZoWRQROpu4kQ4ugt0aw0jRgTP88CoUaMAeOqpp2qst3PnTtauXUuTJk2qDPts3ryZ0tLSKtv27dvTFbZIeKhtyUrbomRQROpm/frgG/up+wfPx43Li2/wAN/4xjcAWLlyZaX9W7dupbS0lI8//pglS5Zw7rnnsnHjRs4++2yaN29eqe7gwYNp3759lW3KlCkZex8iDZLalqy1LbqBRETqZuJEGDkS2kQavc6d93yDnzQpu7HVolWrVgBs2bKl0v4LL7yw0vOCggJGjhzJ5MmTq5xjypQp9OrVq8r+ePtEpA7UtmStbVEyKCKJK//mvnw5zB29Z/+4cdC7d/CYQ3f/xSpvqMsb7nLXXHMN3/rWt2jUqBEtW7bkoIMOomXLlnHP0a9fP4qLi9Meq0ioqG3JatuiZFBEElf+zT12qoM8+Qb/2muvAXDggQdW2n/YYYdpnkCRbFLbklVKBkUkMdHf3OPJg2/w06ZNA+C007TuqUjOUNuSdbqBREQSU90393LR3+BzzO7du7nyyiuZP38+Q4YMYeDAgdkOSUTKqW3JOvUMikhiliyBF1+EW2+tvP9Cq/w8y43hsmXLeOCBBwAqrRKwZs0aTjrpJGbOnFmv8z/99NOsWLGiyv69996b7373u/U6t0goqW0Bstu2KBkUkcTETqY6PTIcckHN82pl2qxZs5g1axaNGjWiRYsWdO3alUGDBjFs2DBOOeWUep//mmuuibu/S5cuSgZFkqG2Bchu22Ies7iyJKa4uNiXLl2a7TBEqvXWW29x8MEHp+8FcrTBzmWJfCbFxcWobZFcprYl9yTymZjZS+4e93ZlXTMoIiIiEmIaJhaR5Ohbu4ikg9qWjFPPoIiIiEiIKRkUERERCbG8TwbNrJGZjTWzFWa2w8zeN7ObzWzvBI/3arbP0x27iIiISLY1hGsGbwF+DjwO3AwcHHl+hJkNdvevEjjHC8BdMfvKUhqliIiISA7K62TQzA4FLgUec/ezova/B9wOnAckMgvku+7+QHqiFMked8fMaq8oaadpvKQhUduSO1LRtuT7MPEwwIBbY/bfDWwDzk/0RGa2l5m1SF1oItnVuHFjysrUwZ0rdu3aRZMmef39WwRQ25JrUtG25HsyeCTwFbA4eqe77wBeiZQn4myC5HGrmX1sZneYWetUBiqSaS1btmTLli3ZDkMitm7dSrNmzbIdhki9qW3JAaXvBBupaVvyPRncFyh1951xytYBRWa2Vy3nWAxcS5AQjgT+C4wBXqipp3Djxo0UFxdXbHfdFXvJoUh2tWvXjk8//ZTS0lK+/PJLDVNmibuzbds2SktLad++fa311bZIrlPbkhvq2rbUJN/HLAqBeIkgwI6oOl9WdwJ3Pypm1wwzew34PXBZ5LGK9u3ba8koyWlNmzZlv/32Y9OmTaxevZrdu3dnO6TQatq0KR07dkzo27vaFsl1altywOcfA07TfZok3LbUJN+TwW1Ah2rKmkXVqasbgQnAaVSTDIrkg6ZNm9K5c2c6d+6c7VBEpAFR25Jl068MHlO0Wku+DxN/SDAU3DROWReCIeRqewWr4+5l5eeuZ3wiIiIiOS3fk8ElBO+hX/ROM2sGHA4kNdYSOb4r8FE94xMRERHJafmeDD4MOHB5zP6LCK4VfLB8h5n1MLODoiuZ2T7VnPe3BEPo/0hZpCIiIiI5KK+vGXT3181sCjDGzB4D5rBnBZLnqDzh9H+AbgTzEpa7ysz6A88Ca4EWwBDgeOB/wB1pfxMiIiIiWZTXyWDE5cBq4GKCGz5KCZK4axJYim4ecAjBlDL7ALuBd4DfAJMi8xWKiIiINFh5nwy6+26CNYlvrqVe9zj7/g78PT2RiYiIiOS+fL9mUERERETqQcmgiIiISIgpGRQRERHJJ5/tgEmLYcOGlJxOyaCIiIhIPnn6XXhvM0ycmJLTKRkUERERyRfr18OCdXBlP7jvvpT0DioZFBEREckXEyfC0V2gW2sYMSIlvYNKBkVERETywfr1QW/gqfsHz8eNS0nvoJJBERERkXwwcSKMHAltmgXPO3dOSe+gkkERERGRXFfeKzhuXOX9KegdVDIoIiIikuvKewU7d668PwW9g3m/HJ2IiIhIg1beK7h8efzyceOgd+/gsVOnOp9ePYMiIiIiuay6XsFy9ewdVM+giIiISC5bsgRefBFuvbXy/gut8vOBA5M6vZJBERGRfDT9tODxgqeyG4ek3/z5lZ+n+LPXMLGIiEg+SvH6tBJeSgZFRETyUYrXp5XwUjIoIiKSb9KwPq2El5JBERGRfJOG9WklvJQMioiI5JM0rU8r4aVkUEREJJ+kaX1aCS8lgyIiIvkijevTSnglNc+gmfUCDgU6AA5sBN5w93dSGJuIiIhES2R92kmTshOb5K2Ek0EzOxj4CXA2UL7wXfnU1x6p8xHwCHCnu7+VwjhFRETCLc3r00p41ZoMmlkP4I/Ad4HtwAvAnUAJ8AlBQtgOOADoD4wGLjWzx4D/c/d30xO6iIhIiNRlfVr1DjZsKV51JpGewTeB14EfAY+5+xc1VTazvQl6Dy+LHNusnjGKiIhImtenlfBKJBk8x91nJ3rCSLJ4H3CfmZ2RdGQiIiKyR+z6tCIpUuvdxO4+28yaJnNyd/97MseJiIiISGYkOrXMejObYmZ90xqNiIiIiGRUosngZuCnwGIze8XMLjWzdmmMS0REREQyIKFk0N2/DgwGZgI9gduAdWb2kJmdlMb4RERERCSNEl6BxN3/6+4/JJhj8CfAK8D3gafNbI2ZXWdm3dMSpYiIiIikRZ2Xo3P3re5+l7sPAA4GbgYKgKuBVWb2HzMbnuI4RURERCQN6rU2sbu/7e7jgK7Ad4B/AccDM1IQm4iIiIikWb2SwSj9gKHA0ZHnX6bovCIiIiKSRgmvTRzLzDoCI4ALgAMJlqV7BZgGPJiK4EREREQkveqUDJpZE4IewAuAkyPHfwb8GZjm7i+nOkARERERSZ+EkkEz+wZBAvgDYJ/I7mcJegEfc/ed6QlPRERERNIp0Z7BVyKP7wO/A6a7++p0BCQiIiIimZNoMvhXgl7Af7m7pzEeEREREcmghJJBd/9+ugMRERERkcyr19QyZtbUzLqY2V6pCkhEREREMiepZNDM+pjZf4GtwFrgmMj+DpEVSAanMEYRERERSZM6J4NmdjjwAtCDmJVG3P1joDkwMhXBiYiIiEh6JdMzeD3wIXAo8CuCyaaj/YdgRRIRERERyXHJJIPfAu5298+BeHcWrwX2rVdUIiINwfTTgk1EJIclkww2AzbXUN4qyVhEREREJMOSSQZLgL41lH8beDO5cEREREQknpJNJYydO5aON3Wk8fWN6XhTR8bOHUvJppJ6nTeZZHAm8MOYO4YdwMx+AZwC3F+vqERERESkwtPvPE3/af1pXtCcBRcuYOdVO1lw4QKaFzSn/7T+PP3O00mfO9EVSKLdBJwI/BNYQZAI3mJm7YFOwL+BPyUdkYiIiIhUKNlUwognRjD7vNkM+NqAiv092vXghhNu4Du9vsPQh4ayaNQierTrUefz17ln0N2/JEgGrwS2AzuAXkApMA443d2/qnMkIiIiIlLF5MWTuajPRZUSwWgDvjaA0UeMZsqSKUmdP6lJp919l7vf4u7F7r63uxe6+zfd/WZ335VUJCIiIiJSxcw3ZjLqiFE11hndZzQzX5+Z1PnrtRydiIiIiKRX6bZSurXpVmOd/VrvR+m20qTOn9A1g5Gl5+rC3f2EJOIRERERkShFhUWs+WxNjdcDrt28lqLCoqTOn+gNJMcBZcCXCdaPNxm1SKiUbCph8uLJzHxjJqXbSikqLGJ47+GM6TcmqQt8RUQknIb3Hs60l6dxwwk3VFtn6rKpDD9seFLnT3SYeBfBsnPPAD8AWrt7yxq2jE48bWaNzGysma0wsx1m9r6Z3Wxme2fi+JqUzwl02DVF/LOn0fuaopTMCSS5LZ1TAEge+WwHTFoMGzak/NTpmm9MRHLPmH5juHvZ3Sx8f2Hc8oXvL2Tqy1O55MhLkjp/oslgF2A8cADwOLDOzP5oZgcm9aqpdwswiWCy60uBR4GfA/8ws0TeY32Pjys6IXhh43c46ZO2zC8dqoSggYueAuCGE26gR7seNGnUpGIKgNnnzWbEEyP0R7sBK0/U7nx0HptWlfKX7++f0kRNXzZEwqVHux7MOHMGQx8ayvhnxlOyqYSy3WWUbCph/DPjGfrQUGacOSP5USd3r9MG9AP+AnwK7AYWAqOBFnU9Vyo24FDgK+BvMfsvJRiuHp6O4/v27es1WfXJKi+aWOQL1i5w//BD970L3CcMdG/Xzn39el+wdoEXTSzyVZ+sqvE8kn8uf/pyH//M+Brr/Orfv/Kxc8dmKCLJpDkr53jRxCK/4eFLfVdhEy+75mjf1baN//6RS71oYpHPWTmnxuPr1LbEobZFpOFa9ckqHzt3rHe8saM3vq6xd7yxo4+dOzah33dgqVeTC1lQXndm1gw4C7gAOB7YBvzU3R9I6oRJMrPfAb8BjnX3F2Li+wR4zt2HpPr44uJiX7p0abVxjZ07luYFzYPx/bFjYfkTMOwQeK0XmMGkSYx/Zjw7d+9k0smT6v7GJWd1vKkjCy5cUOM3tJJNJQy8ZyAbrkz98KFkT8mmEvpP6x9MDDvpkSq/9wvHnlPrxLDFxcUk3LYArF8PF1wA994LnToBqG0RkSrM7CV3L45bWF2WmOgGDAT+RdC7dk19z5fE6/+ToIeyaZyyF4GN6Ti+tm/vHW7sEGTqH37o3rat+6Rv+4ShB3gn8FLwG3/xC1/1ySrveGPHimOuuOIKJ+iN9JtuuqnKOS+66KKK8jvvvLNK+bBhwyrKH3zwwSrlp59+ekX57Nmzq5QPGjSoovzZZ5+tUt6nT5+K8qVLl1Yp79mzZ0X522+/XaW8c+fOFeXr1q2rUt6iRYuK8i1btlQpLy8Lfmwr27JlS0VZixYtqpSvW7euorxz585Vyt9+++2K8p49e1YpX7p0aUV5nz59qpQ/++yze+KbgJftLqtUPnv27Iry008/3b/c9aU3vq5xRfmDDz5YUT5s2LAq57/zzjsryi+66KIq5TfddFNF+RVXXFGlfMKECRXlEyZMqFKun73U/OwVnF4Q9ApH/d77PUOC55FRgdp6hRNuW8qd2N23FTTym6M+X7UtlTWUtmXQoEFVymPbllhqWxruz15dUUPPYFLXw5lZZzP7lZmtAJ4HDgb+AExP5nz1tC9Q6u4745StA4rMbK9UH79x40aKi4srtrvuuqtSecWcQBMnwsiR0KYZABuAGcDAF1+s15xAkrsKygpY89maGuvUZwoAyV27Dt4VTAwb83tP584wYgRMnFjrxLAJty0Q9AouWMd9R3dhJNAxUkdti4jURcJrE5tZAXAGwbDwSQS9abOBscA/PXtL0BUC8RI5CJbKK69T3bQ4SR3fvn37GodyigqL+GDFErrfdx8sXw5zR1eUTQRKXn2VdStfUkLQAHX4qENapwCQ3OXNnW7b94I4v/eMGwe9e7PfL8bWmKgl0rZUzDc2cSIc3YUNzZswg2A90C3oy4aI1E1C1wya2e3AcKAt8DpwD/CAu29Kb3i1M7PXgQ7u3jFO2SPAOQRDwHGTwWSPT+SawbPufpFj9hsIt9wC008LCi54KlJhLPPff5HHLjpG1/U0MJWuG4uzjuTC9xfWa0FxyV0db+rI2yVDaNOsTbW/95/t3MxB+8+p9nrRhK8ZPORSOPRQuPqIoAfylKnQuzcsX874N27TNYMiUklN1wwm2jM4BtgOzAKWRY77kZlVV9/d/Za6BpqkD4FDzKxpnKHeLgRDwDVNll3f4+O6bL/v0/rp21j6/NUUw54/BhFLf/BtDhl0G11/e3tdTy05LnoKgNFHjGZ0n9Hs13o/1m5ey9RlU5n68tT6TQEgOesn+57BXtffD2+/G7/CuHHsdeD+/PjOEUm/xph+Y+g/rT9jZr3LviNHQpuVQUFkKPrDqy9naq//sGjUoqRfQ0TCpS7XDDYn6B28KcEtU5YQvI9+0TsjdwMfDlT/FTs1x8fV/e5H+OzcMzj12Qvjzgl06rMXsvn7Z9D97keSOb3kuFN7nsqiUYvYuXsnA+8ZSPPfN2fgPQPZuXsni0Yt4tSep2Y7REmDsS+UMeObsHDX6rjlC3etZsY3g3rJ6tGuBw8ffSvNZz7CDf13UVL2BWX+FSWbSrih/y6azXyEhwbeqi8bIpKwRIeJB9X1xO7+XFIR1ZGZHQa8Cjzu7mdF7b8UuB34oUemuzGzHkCBu69I5vhotQ0Tc8wx8OKLtb+BgQNh/vza64lI7kvB731tw8RAMNy84zOuP7MtMxdPofSrMor27sDww4ZzzROf0qZ5W5ikIWIR2aOmYeKk5xnMJWZ2B8FQ9uPAHIK7m39OMDXMt8tvbjGz1UA3d7dkjo9WazIoIqFVsqmEKUumVEnULjnyklp77GpNBtevD64VXL48GBqOVx65drB83kFpWLTuuSSjpmQw6aXWcszlwJUEq4lMAc4D7gBOT/Au5/oeLyJSoUe7Hkw6eRIbug5m136nsuHKDUw6eVJq/lCXT1sTLxGEStPYSMOjpQglHWrtGTSzE9z9P0md3Gywuz+TVGQ5Tj2DIlKr2LuJE1Brz6AuQQktzVQg9VHfnsG5ZvZfMzvdzBon8GIFZvZdM3uOYMhVRERSZf58cK99UyLY4ExePJmL+lwUNxEEGPC1AYw+YjRTlkzJcGSS7xKZWuYIYBLBBNMbzewZYDFQAmwCDGgH9AT6AycAbQiWqDs85RGLiIiE0Mw3ZrLgwgU11hndZzQD7xmoOSalTmpNBt39DeAkMxsA/IxgFZJhBOvlRTOCye8fA/7s7ktSHKuIiEhoVVqKsBpailCSkfBydO6+EFgYGSruCxwCtCdICjcCbwAv64YLERGR1Ku0FGE1tBShJCPhZLCcu+8mGCZenPpwRBqgJG4iEBGJNbz3cK17LmnRUKaWERERadDG9BvD3cvuZuH7C+OWL3x/IVNfnsolR16S4cgk39W5Z1BERBKk3mBJIa17LuminkGRdPtsB0xaDBs2ZDsSEclzWvdc0kE9gyLp9vS78N7mYEUIrRcrIvVUvsKNpo+RVFHPoEg6rV8PC9bBlf3gvvvUOygiIjlHyaBIOk2cCEd3gW6ttV6siIjkpJQng2ZWaGb7p/q8Inln/fqgN/DUyK/DuHHqHRQRkZyTUDJoZl+a2XlRz1ua2WwzOyxO9e8C76QqQJG8NXEijBwJbZoFzzt3Vu+giIjknER7BpvE1N0LOJ1gBRIRiVXeKzhuXOX96h0UEZEco2sGRdKhvFewc+fK+9U7KCIiOUZTy4ikWnmv4PLl8cvHjYPevYPHTp0yG5uIiEgM9QyKpFp1vYLl1DsoIiI5RD2DIqm2ZAm8+CLcemvl/Rda5ecDB2YsJBERkerUJRkcYmblY1qFgAPnmNnhMfX6piIwkbw1f37l59NPCx61Tq2IiOSguiSDwyNbtB9XU9eTC0dEREREMinRZPD4tEYh0pCpR1BERHJYQsmguz9Xl5OaWdPkwhERERGRTErp3cRm1tfM/gR8mMrzioiIiEh61PtuYjNrB5wPXAgcBhi6ZlBEREQkLySdDJrZyQQJ4FCgKbAVuDfy72GpCE5ERERE0qtOw8Rm1t3MrjezNcAc4HvAPOAHQEd3HwW8nvIoRURERCQtEuoZNLMfEPQCDiJIIN8AbgcedPcN6QtPRERERNIp0WHi+4EdwG3A/e7+StoiEhEREZGMSTQZ3Ak0I7g+8DMz+9Td16QvLBERERHJhESvGewM/JzgJpHrgHfNbJ6ZXWhmLdMWnYiIiIikVULJoLt/5u6T3b0PUAz8BfgGMBXYYGYzzewUM0vpvIUiIiIikl51Tt7cfZm7X0LQW3g+sAg4F3gK+AD4fkojFBEREZG0Sbonz913uvtMdz8B6AH8HigDjkhVcCIiIiKSXikZ1nX31e5+DdAdGAL8LRXnFREREZH0qvdydNHc3YG5kU1EREREcpxu+BAREREJMSWDIiIiIiGmZFBEREQkxJQMioiIiISYkkERERGREFMyKCIiIhJiSgZFREREQkzJoIiIiEiIKRkUERERCTElgyIiIiIhpmRQREREJMSUDIqIiIiEmJJBERERkRBTMigiIiISYkoGRUREREJMyaCIiIhIiCkZFBEREQkxJYMiIiIiIaZkUERERCTEGkQyaGYjzOxlM9tuZh+Z2VQza1+H41ebmVezFaUzdhEREZFsapLtAOrLzMYCk4DngMuArsAVwAAz6+fuXyR4qhXA7+Ps35qSQEVERERyUF4ng5Feu98BS4AT3H13ZP8SYDZBcnhDgqf7yN0fSEugIiIiIjkq34eJzwQKgTvKE0EAd/8H8C5wfl1OZmZNzKxVSiMUERERyWH5ngweGXlcGKdsEXCQmbVI8FxHAduAzWb2mZndZ2b7piJIERERkVyV18PEQHmyti5O2TrAInVW1nKe5cBU4C2gADgOGA2cELnu8MPYAzZu3EhxcXHF84svvpiLL764rvGLiFSitkVEMi0nkkEzawNcXodDbnf3TQRDxAA749TZEXksjFNWibufFrPrITN7HngQuA64KPaY9u3bs3Tp0sQjFhFJgNoWEcm0nEgGgTbAhDrUfwDYRDCsC9AU2B5Tp1nkcRtJcPeZZvZ7IDZRFBEREWkwciIZdPfVBEO6dVU+fNsFWBVT1gXwqDrJWA0MrMfxIiIiIjkt328gWRJ5HBCnrD/wtrt/Xo/zHwB8VI/jRURERHJavieDfycYHh5jZo3Ld5rZd4D9Ca75I2r/fmZ2kJkVRO1rF+/EZnYJwQTW/0hH4CIiIiK5ICeGiZPl7hvN7GrgJuAZM5tFMDz8C4IVRW6NOWQGMAj4OsEQMMAIMxsFzI3sa0JwN/GZQAl1u5ZRREREJK/kdTII4O43m9knwFjgdmAL8AjwqwSHiJcA3wbOBdoTXLv4HvBH4P+5+2fpiFtEREQkF+R9Mgjg7vcC9yZQ77g4+14EhqY8KBEREZE8kO/XDIqIiIhIPSgZFBEREQkxJYMiIiIiIaZkUERERCTElAyKiIiIhJiSQREREZEQUzIoIiIiEmJKBkVERERCTMmgiIiISIgpGRQREREJMSWDIiIiIiGmZFBEREQkxJQMioiIiISYkkERERGREFMyKCIiIhJiSgZFREREQkzJoIiIiEiIKRkUERERCTElgyIiIiIhpmRQREREJMSUDIqIiIiEmJJBERERkRBTMigiIiISYkoGRUREREJMyaCIiIhIiCkZFBEREQkxJYMiIiIiIaZkUERERCTElAyKiIiIhJiSQREREZEQUzIoIiIiEmJKBkVERERCTMmgiIiISIgpGRQREREJMSWDIiIiIiGmZFBEREQkxJQMioiIiISYkkERERGREFMyKCIiIhJiSgZFREREQkzJoIiIiEiIKRkUERERCTElgyIiIiIhpmRQREREJMSUDIqIiIiEmJJBERERkRBTMigiIiISYkoGRUREREJMyaCIiIhIiCkZFBEREQkxJYMiIiIiIaZkUERERCTElAyKiIiIhJiSQREREZEQy/tk0Mx+bGYPmtkKM9ttZp7keYaY2QIz+8LMNpnZo2b29VTHKyIiIpJL8j4ZBMYDQ4GPgQ+TOYGZfQ94EmgO/BK4ETgWeNHM9k1RnCIiIiI5pyEkg8cBrd39WODVuh5sZgXAHcD7wLfc/U/u/gfgZKAjcG2qAr3rrrtSdSrJM/rswysTn71+vsJLn314pfKzz/tk0N1Xu/tX9TjFIGBfYKq7fx513leAecC5kYSx3vRLG1767MNLyaCkkz778FIymFpHRh4XxilbBLQCemUuHBEREZHMaZLtAHJA+TWB6+KUle/rAiyPLnjppZc+N7PoZHojUFrLaxWZWW11pGHSZx9edf3s+5jZ9qjnalukJvrsw6uun3236gpyIhk0szbA5XU45HZ335Sily+MPO6MU7Yjpk4Fd2+ZotcXERERyZqcSAaBNsCEOtR/AEhVMrgt8tg0TlmzmDoiIiIiDUpOJIPuvhqwLL18+XQ0XYC3Ysq6RB7jDSGLiIiI5D3dQAJLIo8D4pT1B7YAKzMXjoiIiEjmhCoZNLPOZnaQmUVfA/gcsB4YbWYtoup+k2AOw0fdvSzJ1+tlZteb2SIz22hmW83sFTP7jZntXZ/3IrnLzA6MrIrzlpltNrNtkRVyJplZ52zHJ5llZoVm9q6ZuZlNTtE51baEkNoWiZbKtiUnhonrw8y+A3wz8vSAyL6rIs8/c/fo/6A/ACOB4wnmEMTdy8zsMuBh4AUzu5tgOpmxBHfx1eVaxlgXApcAs4EHgbLIa/8O+L6Z9Xf37TUcL/mpK9AZeBz4ANgFHAZcDJxnZoe7+8dZjE8y63qgfYrPqbYlnNS2SLTUtS3untcbcC/g1Wyrq6l7XJzznE4wr+A24FPgr0CPesZWTLA6Suz+30XiGJPt/z9tmduAcyKf+7hsx6ItY595H4I/2FdEPvvJKTqv2hZt0Z+72paQbaluW/J+mNjdf+TuVs3WvZq68+Kc50l37+/uhe7e1t3PdveSesa21N03xyl6OPLYuz7nl7yzJvLYNqtRSEaYWWPgbmAu8Fgqz622RWKobQmRdLQteT9MnKe6Rh4/ymoUklZm1gxoQTBF0SHAHyNFc7IWlGTSWOAg4KwMvqbalhBQ2xJ6KW9b8r5nMN9EMvqrCbp3Z2Y5HEmv0QTXnb4P/JNgPs3z3f2FbAYl6WdmXweuA673YOqsTLym2pbwUNsSUulqW9QzmHm3Ekxj82t3fzvLsUh6PQGsIPgGfwQwFCjKZkCSMX8B3gUmZfA1b0VtS1g8gdqWsEpL26JkMIPM7LfAGOAud/9DtuOR9HL3Dwju+AN4wsz+Biwxs0J9/g2XmZ0PnAgc60lOS5XEa6ptCRG1LeGUzrZFw8QZYmbXAlcB04GfZDcayQZ3fw14GfhZtmOR9DCzpgTf2OcAG8zsADM7gD0LxLeO7GuTwte8FrUtoaa2peFLd9tikVuUJY0ijfUE4D7gQnf/KrsRSbaY2avAAe6uiYEboEhD/GkCVX/p7jel4PWuRW2LoLaloUt326Jh4jQzs2sIGuv7UWMdCmbWyd03xNl/PMGUH/MyHpRkyhcEc77Fag/8iWAqiGnAa/V9IbUt4aO2JdTS2raoZzCNzOwSYDKwluAuv9jG+iN3/3fGA5O0MrPHCVYJ+C/B/F/NgL7AeQSTmh/n7q9kLUDJODPrDrwHTHH3MSk4n9qWEFLbIrFS1baoZzC9jow87kcwjBPrOUANdsMzCxgB/JDgW5sTNNx3Aje6+9osxiYNg9qWcFLbImmhnkERERGRENPdxCIiIiIhpmRQREREJMSUDIqIiIiEmJJBERERkRBTMigiIiISYkoGRUREREJMyaCIiIhIiCkZlFAys2vNzCOzt2fj9Veb2bxsvHZUDD81sy1mtk89z9PJzLaZ2chUxSbSEISpnTGz3ma2y8xOrOd5zMyWmdn0VMUmtVMyKBlnZsdFGsjobYeZvWtm083s4GzHmAwz29/M7jKzFZHk6FMze8vM7ousHZozzKw1cB1wi7t/ErW//I/XLjM7KM5x5Z/dleX7Imul/gX4vZkVZiJ+kdqoncm4ScCL0csgxnwGF8U7KFL2ZPlzD1bCuBYYYWaHpzlmidBydJJNs4A5kX83B74BjAbOMrPD3H1NGl/7d8D/A3am4mRmVkywBFgZMANYTvCeegInAVuBZ6MOOZBgKals+RnQhmB923gaA38Avpvg+W4HLgcuAKbUMzaRVFI7k2ZmNgA4ETizhmrXmtkD7r69tvO5+2wzWw38BjgnJUFKjZQMSjYtc/cHoneY2TvAbcD3gFvS9cLuvgvYlcJTTgAKgcPd/dXYQjPrFPP6KfnjkAwzawT8GHja3TdWU20pcKaZDXD3hbWd091Xm9kLkfMqGZRconYm/X4GlLIn6Y61FCgm+ML4hwTP+QAw3sw6RUYfJI00TCy55sPI45fRO83sZ2b2LzNbZ2Zfmtl6M3sg3rU4ZnaamT1nZqVmtt3M1prZY2bWK6pOlWt5zKydmd1iZiWR4aRPzOwlM/tlAnH3BD6J10BDxVBqdIyVruWJiqe6LTrO1mb2RzNbZWY7zWyjmc0ys/0TiBOgH9CN6htuCIaQtwETEzwnwNPAYfGGl0VyjNqZFLUzZtaEoEfwGXcvqybuR4CXgP+zxK9RfhoooObeRkkR9QxKNhWaWVHk382B3sDvCb5h/i2m7pXAIoLhyE2RuqOBb0eGej4BMLNBwGzgDYJvoJ8B+wKDgQOAlTXE8yhwLMH1b69FYjoYOA64sZb3UgIcaGbfc/fHaqkbz2PAqph9zYCbCX5Pt0LFtX4LgP2AewiGiToTfDP/n5kVJzDsNSjyuLiGOhsIekx+Y2ZD3X12Au+hvAfxOGBFAvVFMkHtzB7paGf6Ai2ouT1x4FfAvwmGfq9IINZlBMPrxxH8X0k6ubs2bRndCH65vZptOXBQnGP2jrPvhMgx46L2TYrs61BLDNdG6nWPPG8def6nJN/TAIJeBif4Q3AP8FPg4Grqrwbm1XA+Ax4CvgK+G7X/NmA78M2Y+t2ALcC9CcR6XyTOVjX8vxQDrYCNBH/wGsd8dlfGObZrpOyObP+MadOmdiYz7QzBdcIODK3hM7gy8vxfwA6gW1QdB56sJr5VwOvZ/lkKw6ZhYsmmuwguOj4R+A7wf0ARMMfMukVXdPcvILjeLTJ8UQS8CmwGjoqqujnyeFZk+CJR2wm+hR4Vb0ioNh5cV9eXINFqTdBA/gl408yer8MQbrnfAucCv3L3xyGYcgH4AfA8sM7Miso34AuCHo2TEjh3e2CXu2+p5T1tIbgA/lAgkWljyu9K7pBAXZFMUTtTvVS0M+0jj5sSeL3/A/aKvG4iPkHtSWZkOxvVFr6NmnuXjoqUPRSz/9vAPILGNPZb/n+j6u1DMLzgBN9g5wA/B9rHnO9aor6xR/b9hKChLu85uAM4Icn32A0YQdCgOvA6sFdU+Wqq+cZOkHg5MDVmf4c47z12251AbE8BZdWUlf+/FEee7wW8C7xPMJxU02dXGO+z06YtG5vamcy0M8AvI/u+lchnADwI7Aa+EXleU8/gYmBDtn+WwrCpZ1Byirv/j+Bb97fL95nZkQTDC50Irjs5g+Cb6YkE3xwbRR3/CXAkcDxBI9uS4Nq3lRZMf1DTa/8F6A5cRNDQnw08Y2YPJfE+1rj7DILr814kuPaoX23HmdlxwN3AfwmGfyoVRx6fYU9PR+x2cgLhbQSaRK4Lqu19fAlcTTAEfFkt1dtFnV8kZ6mdseNIXTtT/vvejsRcRXCH9R8TqNsOtScZoRtIJBc1AZpGPR9OMO/dqe7+XvlOM9sbaBt7sLvvJvh2Py9S7xsEd7JdBZxW0wu7+3pgKjDVzBoD9wPDzOxmd19S1zfi7m5m/wMGAl1qqmtmBxJc4P0ucLZXvTNvI8GF6q3c/Zm6xhLljchjT4IpH2ozE/gFwR/IC2uod0DM+UVymdqZ1LQz0e1JIrG+Z2Z/Bi6LJKXVxdkU+FokVkkz9QxKTrFgKaO9CRrVcrvLi2Oq/5qYn+GouwajrSAY9qn2m6uZFVrM6hmRxv61yNMav/Wa2Ynxrh0ys+bsub7mzRqO34dg+PYr4DR3/zS2jrt/RTDE0s/Mzq7mPIlcXzMv8tg/gbp4MF7zK4JJqsfXULX8fM8lcl6RbFE7k9J25mWCofKE2pOI30WOqWnqqiMILlNRe5IB6hmUbOpjZudH/t2U4EaFiwlm178qqt7jwFiCC77vIrib7kSClQRKY855t5l1JRjuWUMwbcO5BMM4M2qIpRfwnJk9TvBN91OC6R5+CrwHvFDLe7kF2MfMZhNct7ON4Fvt8Mi5Z7j76zUc/yegB8EUCgPiDDU97sHF7b8h+Pb/iJk9QnAx95cE1w4NIfjj9qNaYn2JoFdgCNWvQFKJu//LzP5DcGdldYYQ3PmnaWUkl6id2SPl7Yy77zazxwgmqW/qCUx07e6lZnYjNd9IMoTgM3qitvNJCmT7okVt4duIP+XDbuBjgiGBI+MccyZBA/QFQcP8EMEcWKuJukCaYEWB2cAHBBdpbyT4ZnlWzPmupfKUD/sQNLSvEAyRbCeY1uBWoHMC7+kkgpU3Xo3Et4vgOqNnCYZWG8XUj417Xpz/k+ite1TdQoLr+F6PxLkVeIvgGqCjEvwMxkVi7FjN/0txnGP6EvQoVLkon+AaqK+AS7L986VNm7vamUj9jLQzBNcpepz3X/4ZVHfD2YdUcwMJwRfWR7P9cxSWzSL/6SISImbWCngHuNvdr6qtfgLnu4VgDdFe7r6tvucTkfxiZnMJ5mn8VgrOdQZBwt7X3V+p7/mkdkoGRULKzH5CcM3O1z2yskKS5+lM8C3+J+5+X6riE5H8YWaHEvRYDnH3f9XjPEZwl/Ur7n5BquKTmikZFBEREQkx3U0sIiIiEmJKBkVERERCTMmgiIiISIgpGRQREREJMSWDIiIiIiGmZFBEREQkxJQMioiIiISYkkERERGREPv/5yydiiVwlZUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_E1 = []\n",
    "std_E1 = []\n",
    "med_E1 = []\n",
    "mad_E1 = []\n",
    "mean_E2 = []\n",
    "std_E2 = []\n",
    "med_E2 = []\n",
    "mad_E2 = []\n",
    "mean_E3 = []\n",
    "std_E3 = []\n",
    "med_E3 = []\n",
    "mad_E3 = []\n",
    "mean_E4 = []\n",
    "std_E4 = []\n",
    "med_E4 = []\n",
    "mad_E4 = []\n",
    "x = [2,3,4]\n",
    "x_ = [2,3]\n",
    "for i in range(len(eigs_eft_gc)):\n",
    "    #mean_E1.append(np.mean(np.array(eigs_eft_gc[i])-true_eft[i]))\n",
    "    #std_E1.append(np.std(eigs_eft_gc[i]))\n",
    "    med_E1.append(np.median(np.array(eigs_eft_gc[i])-true_eft[i]))\n",
    "    mad_E1.append(mad(eigs_eft_gc[i]))\n",
    "    #mean_E2.append(np.mean(eigs_eft_jwt[i]))\n",
    "    #std_E2.append(np.std(eigs_eft_jwt[i]))\n",
    "    med_E2.append(np.median(np.array(eigs_eft_jwt[i])-true_eft[i]))\n",
    "    mad_E2.append(mad(eigs_eft_jwt[i]))\n",
    "    med_E3.append(np.median(np.array(eigs_cen_gc[i])-true_cen[i]))\n",
    "    mad_E3.append(mad(eigs_cen_gc[i]))\n",
    "    \n",
    "for i in range(len(eigs_cen_jwt)):\n",
    "    #mean_E3.append(np.mean(eigs_cen_gc[i]))\n",
    "    #std_E3.append(np.std(eigs_cen_gc[i]))\n",
    "    med_E4.append(np.median(np.array(eigs_cen_jwt[i])-true_cen[i]))\n",
    "    mad_E4.append(mad(eigs_cen_jwt[i]))\n",
    "    #mean_E4.append(np.mean(eigs_cen_jwt[i]))\n",
    "    #std_E4.append(np.std(eigs_cen_jwt[i]))\n",
    "    \n",
    "fig = plt.figure(figsize = (10,5))\n",
    "gs = fig.add_gridspec(1,2)\n",
    "ax = gs.subplots(sharey=True)\n",
    "plt.rcParams.update({'font.size': 18})\n",
    "ax[0].errorbar(x,np.array(eft_qpe_gc)-np.array(true_eft),label = 'QPE',marker = 'o',fmt = ' ',ms = 10,mfc = 'None',mec = 'green')\n",
    "ax[0].errorbar(x,med_E1,yerr = mad_E1,label = 'DPE',marker = '^',fmt = ' ',ms = 10,mfc = 'None',mec = 'red')\n",
    "ax[0].plot(x,[0,0,0],linestyle = 'dotted',c = 'black',linewidth = 3)\n",
    "ax[0].set_xlabel('Basis Size (N)')\n",
    "ax[0].set_ylabel(r'$\\Delta$E (MeV)')\n",
    "ax[0].set_xticks([2,3,4])\n",
    "ax[0].set_title('GC')\n",
    "ax[0].legend(loc = 'best')\n",
    "ax[0].set_ylim([-1,1])\n",
    "ax[1].errorbar(x,np.array(eft_qpe_jwt)-np.array(true_eft),label = 'QPE',marker = 'o',fmt = ' ',ms = 10,mfc = 'None',mec = 'green')\n",
    "ax[1].errorbar(x,med_E2,yerr = mad_E2,label = 'DPE',marker = '^',fmt = ' ',ms = 10,mfc = 'None',mec = 'red')\n",
    "ax[1].plot(x,[0,0,0],linestyle = 'dotted',c = 'black',linewidth = 3)\n",
    "ax[1].set_xlabel('Basis Size(N)')\n",
    "ax[1].set_xticks([2,3,4])\n",
    "ax[0].tick_params(axis=\"both\",direction=\"in\")\n",
    "ax[1].tick_params(axis=\"both\",direction=\"in\")\n",
    "ax[1].set_title('JWT')\n",
    "ax[1].legend(loc = 'best')\n",
    "plt.subplots_adjust(wspace = -.0)\n",
    "plt.savefig('compare_eft.pdf',dpi = 1200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "0284f27a-2fdf-4e83-b85f-bf6967c9fd4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,5))\n",
    "gs = fig.add_gridspec(1,2)\n",
    "ax = gs.subplots(sharey=True)\n",
    "plt.rcParams.update({'font.size': 18})\n",
    "ax[0].errorbar(x,abs(np.array(cen_qpe_gc)-np.array(true_cen)),label = 'QPE',marker = 'o',fmt = ' ',ms = 10,mfc = 'None',mec = 'green')\n",
    "ax[0].errorbar(x,med_E3,yerr = mad_E3,label = 'DPE',marker = '^',fmt = ' ',ms = 10,mfc = 'None',mec = 'red')\n",
    "ax[0].plot(x,[0,0,0],linestyle = 'dotted',c = 'black',linewidth = 3)\n",
    "ax[0].set_xlabel('Basis Size (N)')\n",
    "ax[0].set_ylabel(r'$\\Delta$E (MeV)')\n",
    "ax[0].set_xticks([2,3,4])\n",
    "ax[0].set_title('GC')\n",
    "ax[0].legend(loc = 'best')\n",
    "ax[0].set_ylim([-1,1.8])\n",
    "ax[1].errorbar(x,abs(np.array(cen_qpe_jwt)-np.array(true_cen[0:3])),label = 'QPE',marker = 'o',fmt = ' ',ms = 10,mfc = 'None',mec = 'green')\n",
    "ax[1].errorbar(x_,med_E4,yerr = mad_E4,label = 'DPE',marker = '^',fmt = ' ',ms = 10,mfc = 'None',mec = 'red')\n",
    "ax[1].plot(x,[0,0,0],linestyle = 'dotted',c = 'black',linewidth = 3)\n",
    "ax[1].set_xlabel('Basis Size(N)')\n",
    "ax[1].set_xticks([2,3,4])\n",
    "ax[0].tick_params(axis=\"both\",direction=\"in\")\n",
    "ax[1].tick_params(axis=\"both\",direction=\"in\")\n",
    "ax[1].set_title('JWT')\n",
    "ax[1].legend(loc = 'best')\n",
    "plt.subplots_adjust(wspace = -.0)\n",
    "plt.savefig('compare_cen.pdf',dpi = 1200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54cee526-ee6d-442c-89d8-60ea1abe2305",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "qcomp",
   "language": "python",
   "name": "qcomp"
  },
  "language_info": {
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    "name": "ipython",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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}
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