雜.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from 工具 import *\n",
    "import matplotlib.pyplot as plt;import matplotlib.font_manager\n",
    "碼表 = pd.read_excel('碼表.xlsx', '正表')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/media/yyr/DATA/anaconda3/envs/迩原/lib/python3.11/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 12285 (\\u2ffd) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/media/yyr/DATA/anaconda3/envs/迩原/lib/python3.11/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 202118 (\\U00031586) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/media/yyr/DATA/anaconda3/envs/迩原/lib/python3.11/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 201592 (\\U00031378) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/media/yyr/DATA/anaconda3/envs/迩原/lib/python3.11/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 202132 (\\U00031594) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/media/yyr/DATA/anaconda3/envs/迩原/lib/python3.11/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 12284 (\\u2ffc) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
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OjxgxIr/+9a/n7Nmz87TTTstf/OIXec899+Ruu+2WXbp0yZNPPjlPPPHE7Nu3bz7//PP52muv5eDBg7OsrCwfeOCBvPHGG3PMmDH5ve99LzMzr7/++oyIbNu2bXbo0CF33HHHLCkpyS9+8YuZmTl9+vRcsmRJ/uY3v8mOHTtm//79c/fdd8+bbropZ8+enXfeeWdOnDgxDzjggKxXr162b98+Dz/88Pzyl7+cTZo0yZ49e+batWvz3HPPzauvvjqff/75fOSRR3KPPfbIQYMG5Wc+85msV69e3nXXXfkf//EfOXXq1NywYUPhPTjqqKPywQcfzAsvvDBnzZqVmZmLFi3K+fPn5/PPP59r1qzJ73//+1ldXV1Y5owzzshjjjkmd9pppzz55JPzxhtvzCuuuCLPPvvs7NGjR06aNCkvvPDCnDhxYo4ZMyZ/8pOf5Pz58/Mb3/hGHnvssYV+pk6dmvvtt1/+6le/yrfffjvbtm1bOFf95S9/yV133TUPPvjgvPzyy3Po0KG5dOnS3HHHHTMzs1OnTvnaa6/lSy+9lPvvv3/+6U9/ygYNGmT9+vVz9913LxxrLrroovzyl79c2B+mT5+eAwcOzEmTJhXOBxs3bszjjjsuS0tL88tf/nL+7W9/y7Kystx///2ze/fuee211+bNN99cY9sZP358jh49Ojdt2pRPP/10Dhs2LCsrK7Np06Z56qmn5m9+85v83e9+l02aNMnPf/7zucMOO2SHDh2yYcOGueeee+aLL76YCxYsyHHjxuUPfvCD7Ny5c+633365zz775CuvvFLj2LJZjx49MiIyIrJNmza54447ZqtWrbKqqiqvvvrq/O1vf1vjc8p895z3rW99KxcsWJC9evXK559/PjPfPUcfdthhOXLkyLz99ttzyJAhefzxx2dm5gEHHJBNmzbN9u3b5+DBg/M//uM/Cv01bNgw165dmyNGjMgf/OAHOW/evNxnn30+sG9t/vzr16+f119/faGWc889N4866qhcsmRJtmjRIl9++eXCMsOGDcurr746MzPvvPPOwj5y7rnnZq9evbJt27b529/+NhcsWJCPPfZY/vCHP8wdd9wxBwwYkH369MkOHTrkL3/5y0J/Z599dk6aNKnw+/XXX5+tWrXKli1b5hFHHJGvvvpqoe3KK6/MsWPH5kUXXVQ4vw0ZMiT32GOPbNu2bc6YMSM/85nPZGVlZV5zzTWF5X7/+98Xzn2bvfeY+8tf/jLPOOOMnDp1av71r3/N6urqnDFjRg4bNiynTJmSzz//fFZUVOSjjz6at912W+6888554403Zrdu3fLQQw/NTZs25a9+9avs2rVrXnvttTW2iQsvvDCHDh2ae+yxR41zVHV1dVZXV+dnP/vZ/NKXvpTf/e5383/+53+ytLQ0y8rKskuXLnnrrbfm2rVrs2HDhjVqX7p0aTZt2rTGvFdeeSV32GGHPO+88wrzZs+enTNmzMgDDzww+/fvn9/61rfyueeey1atWmVZWVmecMIJ2bhx4ywpKcl69erVuPbbvA3XqVMn69WrlxGRu+66a+E4ecMNN+TOO++cmZlvvPFGHnfccdmnT5/s1q1bTp48OX/961/nQQcdlJ06dco6deoUps19lpSUZN26dQs/v/eaMzMLn3Nm5oIFC3LQoEE11vfss8/Ob3/72/n222/nkiVL8g9/+EM++eSTef/99+dNN92U3bp1y86dO2e7du3yuuuuyxUrVmS/fv3y2GOPzRNPPDFLS0vzxBNPzNtvvz3r1auXu+66a37/+98vbFebP6NXXnklr7322rzrrrtyzZo1efzxx+eTTz6Zn/nMZ/Laa6/NqVOn5he+8IXMzFyxYkU+9NBDecghh2SDBg3y61//eu65556588475/jx4/Nvf/tbjXUYNGhQjh8/Pps3b56Zmb17984ePXoUro8aNGiQZWVlhc+nUaNG+dvf/jYz3z0Hjhw5MmfMmJE//OEPs2PHjrnTTjvl/vvvn2effXZmZv7tb3/LXXfdNdu1a/eh19xdunTJHj165E477ZR9+/bNwYMH5x//+Mfceeeds3nz5rlx48Z8/vnns2nTphkRuddee+X3v//9/M53vpMRkQ0bNszRo0fnSSedlHfccUe2adMmO3XqlLvsskvutdde2bVr1zz99NPzmmuuySOOOOIDNXz961/Pq666KjPfvZaaMmVKbtq0Kb/1rW/l+vXr8/XXX8///M//zCeeeCLvueeeHDx4cPbv3z/POOOMbNWqVbZt2zZ79uyZ+++/f9apUyeffPLJXL58eZ5wwgnZtWvXzMx85plnsry8PB9++OG88cYb85xzzikcGyZMmJA77LBDXnXVVTl27NisW7duXnXVVXnRRRflb37zm+zQoUNefvnlOXXq1GzatGm+8MILefrpp+fixYtz9OjROXXq1Jw4cWJecMEFOW7cuMJ3u8zMjh07ZuvWrXOfffbJn/zkJ3ncccflHnvskUOGDMmNGzcWrqHr16+fEyZMyMx3r+k+//nPZ6dOnbJBgwb5wAMPZGbmj370o2zSpElee+21+fnPfz5vvfXWbNy4cfbq1Su7d++eXbp0yVGjRuXw4cNzxowZOWLEiNx3333zmWeeKXynbd68eS5atChnz56dbdq0Kdzz6Nq1a/7ud7/LzMx+/frlvvvum927d88rrrgiy8vLM/PdY9gNN9yQgwcPzp49e2anTp1y3333zTlz5mR1dXWedNJJOWbMmLziiisKx77p06fn17/+9bzzzjuzrKyssG89+eSTedJJJ2X79u2zbt26heNpmzZtcsyYMfl///d/eccddxTOK5u/H5955pn56KOP5oEHHphPP/104X0eM2ZMjevG8847L0899dQ8/vjjs2/fvnnSSScVPqczzjgjb7jhhpw0aVJecskleeKJJ+aYMWMKy5aWluZll12Wffv2zQMPPLAw/7jjjssOHTrk66+/nv369cvFixfnueeemxdffHHh3k7Xrl2zbt262aBBg/zmN7+Zc+fOzfvuuy+vvPLKPO6443L33XfPUaNG5ciRI/Oyyy7LGTNm5Nq1a3PkyJH5uc99Lk8//fQP7J99+/bN3XbbLdu1a5eHHHJIvv7663nCCSfkTTfdlCUlJYXt+OWXX85+/fpl48aNs1u3brlo0aLs169fHnrooXnQQQfln//85+zQoUM2a9Ysu3btml27ds22bdvmfffdl5nvfn865ZRTcsKECTlmzJg85ZRTCjUMHjy4sO+3bt0677///jz00EOzVatW2bVr16xfv35eeumlGRE5fPjwHDduXJaVleU777yTt99+e86bNy+nTp2aAwYMyPHjx+dDDz2Un/nMZ3LkyJE5fvz47N+/f3bs2DHbt2+fs2bNylmzZuWll16ahx12WN5zzz25fv36wnawdOnSHDJkSHbs2DH33HPPwv5WXV2dp5xySk6cODEjIh9//PFcvHhxZr57TN6wYUO+8cYb+ac//SnPOeecHDBgQI4ZMyYPOuigLCsry9dffz1//vOf5/Dhw/O2227Lrl275jPPPJPDhw/Ps846K7/zne8U6sjM3HHHHbNr1665atWq/MMf/pDPPfdcvvjii9muXbu/+1306KOPzgMPPDCvu+66zHz32unEE0/MzMwXX3wxd9xxx2zQoEHusssuOXz48Pz85z+fd955Z/bs2TN/8IMf5IgRI7JBgwa5ZMmS3G+//bJbt2650047ZevWrXPNmjU5YcKEfOCBB3LOnDnZtWvXLCsry1tvvTV//vOf5/e+973s379/7rDDDvnCCy/k22+/nZmZ1113XZaXl+fbb7+dhx12WOH6+atf/Wr27ds3x40bl3fffXeefvrpuW7dunzmmWfy3HPPzZYtW+bdd9+dO+64Y06YMCFbtGhR2P8vvfTSXLlyZc6aNStHjBiRV199dX71q18tnA86duyY9erVy06dOmXr1q2zTZs2uc8+++S5556bXbt2zeHDh+dxxx2X999/f65duzb/+Mc/5ssvv5xPPfVUjfuOa9asyTp16uTnP//5POigg/LEE0/MM888M7t27ZotW7bMQYMG5R//+MesV69e9u7dO/fcc88cPXp0Hn744XnppZfmV7/61fzzn/9cuIYZM2ZMLl++PM8444w8/PDD89vf/nZWVVVlZubixYuzf//+ecEFF2R5eXnuu++++de//jXnzJlT2M4y3z2f33777Tl48OD8/Oc/X5i/fPny3GmnnfKAAw7IHXbYIffff/+cPn16Tpw4MU8++eR8+eWX880338w+ffoUxs7MPPnkk7NHjx555ZVX1ri3ctttt+VTTz2Vu+22W2G//eIXv1j4rnT66afnXXfdlZWVlTl//vw86aSTcs2aNTl16tRs1qxZnnXWWXnNNdfkbbfdlmPGjMmxY8fmxRdfnFdccUVeeOGF2aVLl2zUqFHecccdedlll+URRxxR2NfWrl2bxx9/fLZr1y532GGHPOmkk/Kkk07Ku+66Kz/zmc9kWVlZNmjQIJs1a5aHHHJIXnbZZXnRRRflpZdemi1btsxzzjknTz/99OzTp082bNgwzznnnCwrK8uf/vSnOWPGjIyIPOCAA7K0tLRwb+m0007LSy+9tHDNl5m5atWqbN++fV588cW5cePGvP766/PJJ5/M8847L6+88sq8++67c8WKFZmZedZZZxWuRXv27JklJSXZv3//vPPOO/Mb3/hG/s///E/WqVMnDzzwwJwyZUq+9tprOWnSpGzRokVOnjw5L7744oyIvOCCC3LatGm5ePHiPPnkk7Nr16553HHH5Q9+8IPCfdbS0tIcO3ZsVldX55w5c7Jbt25Zr169/N73vpddunTJ+vXr57nnnpuZmTvssEO2b9++cD4aMmRIdunSJQ899NB8+umns1GjRoXaTzvttPzhD3+Yo0ePLlybd+rUKX/zm99kRBT+RtGoUaPCuWTdunXZrVu33HvvvbOioqLG8ejiiy8uvO+PPvpoPvfcc9mhQ4ecNm1a/vrXv8569erlzjvvnPPmzcsf/ehHefTRR2dmFr5znnnmmVmvXr28/vrrs0ePHtm4cePs169fof9jjjkmb7rppnzuuedy5syZedddd+VVV12VJ554YrZu3TobNmyYkydPzvXr12d5eXn26NEj/+///i8rKytz6dKl+dBDD+Uee+xRuIbcfO+nuro6Fy5cmN/85jezqqoqx40bl3/+85/zlVdeyRdffDHHjBmThx12WO666645a9aswj3pzHfvUW6+5/3GG29kixYt8qWXXipsV7/97W/zf//3fzMz87/+678K28/dd9+dCxcuzNdeey2bNm2aP/3pTwt9durUKZs1a5bf/OY3c/LkyblgwYLctGlTXn/99fnII4/kbbfdlvfcc0/hu/by5cvz9NNPz3333Te7deuWL774YuE7Xvv27fOSSy7JpUuXZmlpafbt2zczM/fZZ5+8+OKL8wtf+EK+/PLL2b59+1y5cmU+88wzeeihh+bVV1+dzz33XD766KPZpEmTPOiggzIicscdd8x69eplZubkyZOzS5cuWV5enqWlpXnAAQfkKaeckqWlpVm/fv1s1KhRPvnkkzlv3rx84IEHslWrVvmtb30rMzPnzp2bO+64Y06aNCm7dOmSP/3pT3Pt2rU5Y8aM/N73vpfz5s3LSy65JJs1a5ZXX311NmvWLFu2bJmPPfZY3nvvvVldXZ1nnHFGHn300dmvX7/cZ599sl69erlu3bo85phjcocddsh99903W7RokbNnz84jjjgiDz744BwzZky2bds299tvvxwzZkx+6UtfygsvvLCwfb/yyitZWVmZDRo0yPPPPz9HjBiRjz76aF555ZXZtm3bvPDCC3PdunU5adKkLC0tzeOOOy4HDBiQnTt3ztNOOy179epVY5/Yf//9s2/fvjljxow85JBD8owzzsg333wzGzdunP/1X/+Vme/eo69bt27+5S9/yfvuuy9fe+217NmzZ+65557ZoEGDHD9+fN5zzz1Zv379wrXibrvtln/6059y5513zpEjR2ZE5FNPPZXf/e53c7fddsvmzZtn3bp188wzz8w+ffrkWWedlYcddljed999ec455+SNN96YzZo1y5122il79+5d+O7cpk2bnDNnTu6222558MEH5w033JCHHXZYTp8+PcePH5/169fPxo0bZ/PmzQvHqSFDhmRm5iOPPJL/9V//lbfeemsOGjQob7zxxmzRokWeddZZud9+++WJJ56Yo0ePzvPPPz87d+6c48ePz+7duxeOPQ0aNMjS0tLs0aNHdu7cOevWrZtlZWUZEYW/83zta1/LsWPHZo8ePbJNmzaFbblv375Zp06drFu3buHewubpvfPe+7enP//5z9m0adMafx9asGBBHnLIIYXvHJMnT84JEybkMccck4ccckgOHDgwDzjggNy4cWOOGzcud9ppp3zppZeypKQkDznkkGzcuHGeeOKJWVJSkvvuu2/OmzcvO3XqlFOmTMldd901jznmmHzyySdrbCOHHHJI4bvJZkOHDs2xY8fmF77whWzTpk1OmjQpR40alXvvvXfWq1cvH3/88ZwzZ06ee+65+fWvfz2///3v5/Tp03Pjxo25zz77ZI8ePXLlypW5cePG3LhxYw4cODDbtWuXZ5xxRv74xz/OzMwpU6ZkVVVVPv300/nss8/m5z73uXzggQeyYcOG+cgjj2T//v1zl112yaeeeip///vf53/+53/m5ZdfnrfcckteeeWV2adPn/zmN7+Zc+bMyZYtW+b8+fOzW7du+dnPfjavvvrqvOGGG/LHP/5x/vznP89Nmzbl2rVrc/r06blhw4YcPXp04T0uKSnJPffcM0866aR88cUXc+XKlTlnzpxC+/r16wvb1OZjx4UXXph77rln4ftfu3btcuDAgTl58uQcPnx4Zr57r3Pz/rjLLrvk5n9ViYicM2dOXnjhhYXvO++9l7dy5crCeWHzPfMLLrgg//u//zsz3z1fvfPOOzlv3rzcY4898tFHH80JEyYU/qZ0zz335KxZs7Jjx445Y8aMzMzctGlTXn755dm2bdvCfdySkpLCeau0tDTr1KmTzZs3z44dO2bHjh2zSZMmudtuu2VJSUlecsklef755+dXvvKVwrk+M/Ppp5/OXXfdNR9//PGcNGlS7rzzztm/f//s1atX1qtXL3fZZZc899xzs1GjRnnmmWdmz5498+yzz/7A/lGnTp285ZZb8je/+U3efvvtecABBxTux2z+O9vMmTOza9eu+dprr2WvXr1yr732ypdeeqlQy0477ZTDhg3LDh06ZOa75+uIyGOPPTYvv/zy3G+//bJ58+bZrFmzwjZ+wgknZMuWLbNPnz5Zr169LC0tLdw7uOaaa7J79+6Fz3zq1Kn5gx/8IDt06JATJ07MIUOG5Oc+97mcN29eduzYMTMz//d//zcjIkePHp0HHXRQdunSJUePHp2tW7fOHXbYIb/yla9kWVlZDhw4MP/3f/83v/KVr2R1dXX+4Ac/yCOOOCI7duyYEydOzGeffTYzM5999tk866yz8ve//32N/XPXXXfN6dOn54knnpgDBw7MRx55JDMz33nnnXzllVfyxhtvzF69euXq1avz1VdfzTvuuCMHDx6c999/f2a+e8172GGH5Zlnnplf//rXC9enrVq1yl/84hcZETl06NCcP39+jh8/vnBPrbKyMr/85S/nxIkTc8cdd8wWLVpkRGTLli2zefPmOXjw4PzP//zPbNKkSZ544omFv0dvvr546KGHsn379tmgQYPMfPc6ZPr06bl27drC/vG3v/0thw8fnpMmTcqZM2fmnDlzsk6dOnn33XcX1v+VV17J/fffPyMiFy1alK1atcqxY8fmhRdemPfee2/OnDkz77///hw4cGCOHz8+S0pK8tZbb83Bgwdn165d87zzzsvy8vLs2LFjjhgxIrt3754777xzNm3atPA/L23bts1GjRplz5498/TTT8+IyHr16mXr1q1zzJgxeeedd2ajRo2yXbt2hXvcFRUVWV1dncccc0x2794927RpUzg/bf58Ro8enSeccEK2b9++cNzfuHFjPvvss3niiSfm2rVrs1evXnnAAQfkiBEjCtcG3/nOd3L//ffPGTNm5F/+8pc866yz8he/+EUefvjh2aZNm8I2MmXKlGzRokVef/31+fTTT+e4ceMKx8AOHTrkfffdl926dcvvfe97efvtt+d5552XK1asyPLy8jzkkEOyf//+2bhx4zz77LPztNNOy3r16uU555xTWIeLL74499prrzzmmGNyl112Kfyd9f7778/27dvn7rvvnjNmzMjf/e53hf9t2Dz94Q9/yAceeCDPO+//tffe8VEV6//42Zpt2d3sbpJN7z0BUiAEAqFIb1KUpoICgl0RFazI1fvxKnq5it4r194+9gaWqyiIWMFKUQFRlI4gTUhIef/+yO8Z55w9W7NJ+NzvvF+v50XYPXvOnJlnZp4+M6HVatHQ0MBsEa+++ipWr16NwsJCdq+srCycccYZ0Ov1bF7SGrpmzRoArfYx0mcIL7zwAr799lvk5uZi7NixuPjii7FixQqceeaZMJlMGD58OJ555hl8/PHH+Nvf/obY2FjMnz8fx48fx9ixY3HmmWdi+fLlKC0txa233oo9e/bg559/RmZmJkaNGoXy8nK8+uqrSE1NxbZt29hzExMTYbFYAAAPP/wwNBoN/vrXv6JLly7QarXsusWLF7N4qxdeeAFPPvkk7rrrLixZsoTFEQwbNgxNTU2YPXs2unfvznyQALBq1Sr069cPjY2NWL58ObPFbt68GSdPnkRZWRkee+wxVFdXo0+fPnj++efZ/gAAXq8XFRUVeOSRR5jvbt++ffB6vbJ1xmazYcuWLQAAnU6HCy+8EP369cOBAwcwbtw4pKWlQafTYejQoZg3bx4MBgMqKytRVlaGCy+8EOPHj4fL5cK8efMAtPpFe/XqxfbWX375Ba+++ipiY2Oxc+dOFh9w5plnorm5Gd999x2amppQXFyMiooKJCUlYeHChcyeN2jQIGYXX716NcaMGYOEhARcdNFFkCQJVVVVbHwuueQSJCcnY+vWrfjuu+/YvJMkCRs2bJCNYUFBAQDg7rvvxoQJEzBy5EhkZmYiMTERN9xwA/r164f8/HxcffXVePHFF3HHHXegqKgIVVVVrG2xsbEwGo1YuXIlHn30UWRnZ2PBggW46KKL4PF48Pzzz0OSJNx2222IjY1FUlISvF4vJEnC8OHDWXvWrFmDoqIitLS04Omnn2Y2dKDVZmCxWJivmUfPnj2h0+kwY8YMLFiwACaTCZdccgnKyspgNptx8cUXw2q1okuXLixebs+ePaxPbDYbevXqhfvvvx9r167FqlWrsHjxYlitVuaPTExMxM8//4zevXsjNTUV5eXl6Nq1K3Q6HcaOHYvhw4dDq9Wie/fuuOqqq7Bjxw44nU54vV7MmTMHWq0WR44cgc1mYzbG7777DhqNBuXl5ZgzZw4uuugiWK1W3H777bL3M5vNkCQJNTU18Hq9eO+999g+fPXVV+Pcc89FRUUFLr/8cpSUlODUqVM4efIk6uvrWYyR0WhEXV0dDh48CK/Xi23btqGurg6vvvoqNm7cCKvViilTpiAmJkb27HfffZfFERL27duH0tJSfP7552wsH3jgAdTX16O+vh4XXngh8vLyAAA33ngj7rzzTmzZsgUGgwHDhw/H1KlTkZaWhoSEBMTExKBbt24YPnw43nnnHaxevRrl5eUYPnw4SkpKMHLkSHz++eeYM2cOBg4ciB07dmDNmjVoaWnBhg0bIEkSrrvuOpx33nmYOHEi/ud//ofF0Fx++eWQJAnjx4+HJEnYuXMn1q9fj27duuHTTz+V+QmGDh3KZIb9+/fjvPPOw5IlS3D06FH89NNPWLduHd566y088cQTuO2229i4eTwerFu3TtZnCxcuhCRJ8Hq9eOihh5i/uKCgAP/6179kPnkAyM7ORmxsrMzuSKivr8f06dOxdetWfPTRR0wnvPTSS1FTUwO9Xo/Vq1cDaPX/OxwONlZXXHEFxo0bB51Ox+JCyGd/6tQpXH/99cjLy0N2djaeeuopPProo3C73TJfWK9evdC1a1fcf//9uOGGG3DgwAFkZ2dj/vz5GDVqFHbu3ImlS5fi888/x7Jly1BbW4uamhpoNBqYTCZIkgS73Y6cnBwYDAbEx8ejtrYWAwcOxNKlS/HUU0/B4/HAbrfj559/hsfjQX5+vqwPhg0bxuS+ZcuWwWQyYcWKFXj99dfZNaWlpRg2bBg+/PBDnz4UEIgGRAKGgABag0w//vhj5thqaWlBfX09Nm3ahO3btzNB4KuvvsKePXtQXV3NkixiY2NRUlICt9sNq9WK2NhYnHXWWRg4cCAuvPBC7Nq1C6NHj0ZeXp7MeNIW0mg0mD59OkpKSljQksfjgdlshs1mg06nw5VXXokrrriCCVQejwcWiyVqbSAqKyvD0qVLMWXKFHTr1g2SJCExMRGjR4+WBVMpjauREm3CgUir1WLbtm0YN24c5s6di4ceegi9evVCVVUVUlJSoNFoEBcXB51OB7vdjtTU1Kj3ixp169YNH374IROAk5OTkZ6eDpPJxIwX0SSdToc777wTQGsihEajYUYcCtzPycmRBcO15VmBxlij0bQL/6k9pyPG8nRoX3JyMjQaDQoLC9GzZ0/ZOFLgpEajgcfjQXp6OgwGA/R6PUwmE4YOHYo+ffqwMcnNzcX+/fvR0tKC6dOnIycnB5deeilL+OqoftVqtaztl112GYYOHYrzzz9f1n98gGhHktVqhdlsZm3UarVsD6C1TpIkGAwGWeCq0Wjs8LbSuMbExDAnAfUvtZGuoYRCvk9NJhMKCwshSa3ruclkQlxcHLRaLWJjY2GxWFgfuFyuoDwfzfEiHuDfS5Ja1yC+39V+F6ytbW2XJLU6HfV6PRwOB/R6PXNG/jeRv7We5sHpvg5HSkpZxmQydci+Fow0Gg3MZjMLkBbkn2g9Jjks3PEP5d7K3xQWFrK9Nz09nQVI0XwpLCyU7dvhjn20+iYuLi7q92xPCtZf9B5tnaM6na5Ne4faukj7ldlsRkxMDKxWKwwGA4BW4+f/tX0jUp4J5Xc6nU4mYwW6LtrvpdfrkZGRgY8//hh2ux12ux3/+7//ywKCIpmzakTylPKetLa3x7uFQ22RY5Vjx+u7vK7i7xmJiYlR6+dIifqfTyLjP+PHS6PRIDY2Nuzx78z360giHnc6nUhLS2NOE+q/xx9/PGJ+NxqN6NWrl2w8UlNT22RjiYmJ8fmMt0UlJyd3ep+ersTPk1Cv7+w2R0JarRZxcXEh7VPK3xUXF6OqqgrPPPMMfvjhB5jNZqSmprIEZ3KOqq0R0doX3G43DAZDSPOEgv/4eWG1Wv2uYeGsbf72ev496bkkM6vxjNFohMPhgNVqRWFhYcB+0uv1PvaEaFK07buhyKL0LjabDSNGjJD1mcFgULWX8O/P6yehUEfPW+I/tTb6s0NQArMkScjIyMBdd93FrqWgqNmzZyMjIwNlZWWQpD95l/rrdFifAtnUlLZIeleNRoNNmzap/ub/JdmjLX2u9nco1ys/5+ee0WhkieMd8R7+xppsukq9k+y9Hal//DfxIxVMCId3/BH/287so2DvoNyrefuLXq+HwWBAaWkp0tLSEB8fH/Jzn3jiCWRnZ0dtXKI5xoH2Sq1WC7vdju7du6O2tlZ1jaYkj//Xifx7oa6x0Vo3o72+mUwmVFRUsIQHo9Eos2905PyNtg9OjcLVe9TayP/fbDYzm4Dyury8PAwdOpQVOvhvplD45HSQSf/bKZAu8X9pDNoj1uiGG25ga4zaOqDRaJCQkOD396mpqcxPRrFEOp1OZseLFplMJrYOd7ZNvT1JzTbd3tQZsS6nG/lbrylmxWKxwGq1Mj3QarWya8xmc0jysL9nKOe2mt06lHvy41hQUOD3NwaDAX/5y1+Qn58v+zxUnqPr6urqkJmZiZ49e0ZtHPh+9Rc3qtSnlP1nNBqxY8cOAK0J53v37u2ECGWB/0aIBAwBAbRWNTh8+DCrmrF69eqQF3mqij9r1izMmTMHH374IYYMGcKyMw8ePIiCggJYLBZceOGF2Lx5M8aNG4fzzjuPBTvZ7XY4HA62IZSWluLee+/F9ddfD0mSMHPmTBw8eBA7d+7EtddeC41Gg/PPPx/FxcWw2WxhK/IzZ87EuHHjorbRRZNcLhfq6uogSRLLfqdAYH9kMpmg0+lYBSSbzYZx48axAIj8/Hwm7KSkpKCkpKRdjB/+DBBtMUyEoyDYbDb06NEDLpeLBQdFS8GIjY2F3W5n1T07m086kpKSkiBJp5+BNFLFqlevXhEFn/5fpvZUQqNxb36eklHRZrMhJiaGVVDuyHc6Xccj0mQxPsiP/9xsNrMgDromNjYWt912G+Lj42WO0piYGGRkZDDFLpTkE55cLhdLAJSk1qBCvV6PuLg4uFwuTJkyBQ899BBiY2Px/fffs+pfofSJ0WjEZZdd5lfhpr2A3iUhIYGtAdEMIg1lDNQoISEBOp0OmZmZbC7wDmqr1cr6nTfKt6XddFqLMmGK78NozbHExESYzWZWldFutyM+Pp4FLfEJjGqnD/w3k9frxUUXXYTbbrsNMTExQQ2HyuDk9myb2+32G3RDQVu8sao9DLrR4AOTySQzpF1//fVMd1AzPKn1LV3nb42hqu98ewcOHIj8/HzceOONLEjVYrFg6NCh7Jlz5sxhc5tOxenovU2tj0/n/ZX2Hr1ez/YgWsemTp2KPn36sP2FkvJtNhvTlSRJQmVlJWpra30SdiVJkskbGo2Gyf06ne60SDgjng70PQVq9OnTp13XUj4QMNIkqrYSndooSa0yDVUhpPYVFBSwdkWjIAJVVFYmFIX6e38BSZHuvYF4gdYZ2mPtdjtKS0sxbtw49jxKTqciFpIkwePxhJ1EES7fhPue/ihUG8PcuXOZrYkoOzvb57NwqaMcmdGaW8EKRoTbps7YK3j7DtkS2/NZHfVeyiQGqp7qdrs7vI87g9oS3BvsN7ROZmRk4NJLL8XQoUPbdM9o0H9TEEQ03uV0ljslSb5H+3vf/yu6ayAds617Ik+ff/45C6wIVX+NiYlh15ItqLOSskMN2uos3uWfS0UoKODFX7uUPEryu5reezrwM8kZFMhL/jHiD0qScDgcuO6665Cens7eneT1zh6njiT+fenvWbNmhcRLLpcrKM+3B08Qv6rJlFT8qS1jSPs/JQQEupYCxUwmE0aNGhWWz2jJkiW46KKLkJSUhMzMTGi1WlgsFrjdbtZuo9HYLgklZIMinY4+k6Q/T7CQJImdNkPP1mq1sv6nPo5Wu0IdL6fTia5du8pkYCrYEur9lIW32tKecKkz1sr2eJdABSGUFKqvJhDx4xWqvhXtvlbrR0rMk6TwixaFWjglGuMaTl+0pz4bapt5/1a0nm232ztUVw/1vfmiJXV1dezEXzpVla5JT09n7Y9mvIVer1ftZ15G4P2/7dlPBoNB5jcOJJsGGkvl78K1j5jNZtYndCJPKGMZLlGwuV6vR1ZWFntmTU0NLBYL81vwSfM6nS6kYrv+2uuv/cpit2qyfqjPjJbdwp8/JdTg+UgSapSFFk43G0xH6Sdtee9AtgO170g3CxTLE65+b7fbYTabmf03XHkgPT39tNUFle/icrlkfi6ivn37ynx8agV6ExMTIUl/JmX4e2d+jyC5R6/X46qrroJOpxMJGAJRg0jAEBAAsGLFCjzyyCPsWM7Vq1ejtLQUJ0+exO+//47jx49j3759ePLJJxEfH4+srCzk5ORAkiTMnj0bW7duDVmJGjx4MM466yyMHj2aVSy2Wq3Q6/Wswnq/fv2wZMkSuFwuGI1GjBo1Ck899RQWLVrEkjKuv/56lJeX+xUMqqur2bFs4dCtt96Kd999F3q9Hnl5eew9ox2wnZeXx44xtlqtTAgtLi7GpEmTOn3zP/PMMzu9DW0hcrZSFTODwQCHwxH1aiEkVEZD6ecFp2uvvRaZmZk488wz4XA4WDWyvLw8VmF+1KhRSEtLY8KVxWLBvffey95fGWTx97//HUajEUlJSSgqKmJK5wMPPIDbbrsNkiT5ZPKGQtOmTZMJixRAQIp8qH3u8XjCylhub/7JzMxkVQv8nTpxOiluNJ7jxo1Deno6YmJicNlll8mCQtuj+kO0iIy4dCLP6aoYRUK8ciRJrWs+HQXp8Xhgs9kQHx+Pr776SvW3gapn2O125ObmsgBfcpzn5uayPgzmbIqUlIFCHdGXTqcTCQkJMBgMsFqtSEhIYNVX/bWtPcfU33e8zEDGN+ojmoftvd5RMLe/oO5oBbd3xLhHQiRXUXILX6k52s4bk8nE+iI+Pt6nKnRHJk7wFCgRVaPRoEuXLjAYDEhKSmJziJzG/vaLtjhmI3l3f3NZabAzmUxwu90sqYqcem010vJJQfy85p3W7T2GHcUvaqR0ULSVQg3q4a9xOp3Iy8uTyaid4WAPxjNKXdhfoLnZbGYJTsrP9Xo9O20iNzeXfd7e7dfr9SxQ7nSUvzrC4d6Z/BMpOZ1O2O12DB8+POhJjP6S6XQ6XVSDvEnm0Gg65vRHf/zCv2+goFKet6KhJ7XHmh2NNcBut6O4uBh5eXksKfnqq6+GJLXqXi6XCxqNBgMGDIAkta79lCibkJAgk3P4cQ7Ec3a7HTqdDsXFxarfS9KfJwi0VZ48HedtcnKyj4PM33tGIk/wv8nOzsbcuXMxZMgQ1h980nxb+1EtOELt5Ag+OZd/d7IN8WtRdXU1+x3ZRUnWorWJ1jW+GrnBYJAFqlCbomnrU+sXXh7k19vq6moWUBFuYFFb15zs7OyIg5mUcvTpOIdofNvThnU6vbfa6RwUmEN/U2APXZOWluZTbT3SZKUzzjgjYP/QaQFJSUkwGo0RBW4FGksKACD7dqT8wrc70gTq04EvOuvk42BE8oKyv2iNl6Q/g9D8vVdH9F24403784wZM5gMo+SFQPsp+TbV+CgmJgYmk8mvXZYvxBEOtZedIFS5gQJ26PqUlBQf+VDNHnM6JPicThQTEyMr9kZ85HQ6mQ5TW1sLu90Oo9GITz/9FAaDASaTCVVVVcxmoOxj3u7C93lsbCzy8vJYkRJebmvPgiD+9NFIyGg0ok+fPpCk1nkQiS+TfhvNd4x0D44GGQwGZGZmQpJa91PiHbLxtNd6Ea3kTb5IVigFG2JiYnDppZfC4/GgqKjI5/tg48DLCyRbUSEy/jp+HkWjmIe/73i5xWq1Mj2PT04Nh4IFXPOykfK7UPTSzrZTh0rTp09HVVUV5s6d2+ltiXTsiOx2u6yIGm/Di4Q3Q12rAvFtJPcMZS8gmUytgMj/FRkiGvKuMh6AxqItp5QryZ9uRs/i9RF+3JR2ocsvv7zD+laj0cj8rqfbetQePKp2mjbZpdSeF0lCvU6nk9mW1Py/nSXjhPNcKsBGp/IF8snTftenTx+ZLTU+Ph7Dhw8/bQqxtXef8ddOnjwZn376aSdHLAv8N0AkYAgI/P84deoUmpubAfyZgLFkyZI2L/RkeK2oqEBmZiaWLVuGqVOnyqpTRrJxX3nllSgsLERqairq6urw1ltvITExEX369GECCQlizc3NmDNnDnMSjh8/HklJSXA6nRgxYgSOHz8OSZLYseRxcXEoKSmB2Wxm96LMYV7wDXT8Y2crAzk5OVi0aFFI1zocjoDOE/5dysrKMHLkyIDHcgXayClYls/47tmzJ4qLi2EwGJCbm8u+a49K3Grk9Xpx3333wWKxYPHixUyBpYQAUjTIeUzCbVFREZYsWaIq5AfK4G8Lb5CTVaPRsCPjSaE766yzMG7cOFn/8lUmQjnZhhcq1fpco2mtdttZlchC5eeOOgowkFNJr9czJ6bJZEJCQgI0Gk3AquI8+VOAeYdkuM5of7xHJyGE+r7U/tjY2HZTctXejee7+Ph45sAinlfjX5PJ5GM83Lx5M/bu3RvSWCqvUVM8w+kDjUbDqnIrx9XpdLJ3qKyshMfjQXV1Nex2O+rq6ti+WVBQAIPBAKPRGBWDzsCBA2U8otYXfPWwQLylVlUllP7hT38IRMp7UVsjNfq3pwM4GE+ZzWbk5OTgoosuCnmPo4DGQEk5nfn+fGJsly5d2N+LFy/GRRddxILs+JM+wpED+RMiAjkMIqVoBPgEM3KFuj/xDiGqbEmySCj8pcY7/ngtnHsZjUafRARJ8q1SGOo7no6BJcHGJNR+o+Rf6icKxnz++efZNcHmoJInqe9JzjMajbBYLMjOzmaBrDzv0OlFtA+aTCZmSE9ISIDT6YyazERyQaBr4uPj2X5y0003wev1on///jAajcy5qKwQGU3SarWoq6uD3W5HQkICCwymdYh0ANrv1eScUNvW2bpgW/pQLQmps9pyOtw/MTGxzVUN/bVZq9UiOzvbJzCbAmvoGklqdQrwjiZaHwK1i3TFlJQUvP766/j000+xcuXKsPqMlw1p76V9mN+XOpo6MzhR7Z1DcYSG2l4+kPH/ZeIr+hLRvFA7xU4toag995RA4xqNecHfo72SPkOVwyM9QcVut7OTqSiBvrP5SkmUhKzT6dqUjNiZxUQo4YmCJ5Uns3UEkewU6uk0SUlJLCmVdB1lEngklVGpknmPHj3C0slC6a9gQYF0D2UCM+3dfCEf/vnKZwfSSf09W6PR+ATH22w22YnjfKXVQPOZ1lGTyYQePXqw+/P3pt8nJSVh5MiR6NevH4xGIzwej2pAcDT4i/6OjY1tV9mjT58+7FTtYLwRLZtse/0u1D6NJpGdlnxsp0vhIn/ryOmm27Ul2Ja3BYTzPL4PaP+gOa/RaJgNgtflyQbPn7rX0f2jPEE4XAolkUwZZNgR78iPKbWT/p+VlcXe3eVyMR+RyWRi40YyMvl6+GA0k8nU7j698847Lyrjy1NHBsHxpwn5C2zkeUEp27Y1eb49bA5tIXrfSJMBqE+mT5/O/k96vNqc4sc6WuvK6a5XR7t9tHafDoUO+WBh5dxQa1+wxBuab2onRwTjT7XviQ+DjQEVDJSk6O4Foeji4ep1ymIF/P3V2s7Hafkbu9OBl3jii6756z/a43mfcWJiYofIc8pn6PX6NiWZ/18n5X56uq/JoZBer2cnkKsRX4CRbB2dTSRftNd8prWUCv8Ea0ekFBsbC7vdDovFgiVLlrA4CbV9xh/xa69ybjqdTvTq1Qtms5nFdNF3fExGIDtwKPFf0R5X5fspPwtUpNVgMODFF1/EzTffjOzsbPz444+dHLEs8H8dIgFDQAD+T8BoaGjAqVOn2HV0pK9yE4skGJ+nkSNHIjU1lRnxrFYr1qxZE1LA+OlCXq9XFlQLQHYCRzhCBfWnsoIHBSUNHTpUdWMvLCxkf7/66qvIy8uDJLVWZVOrUhSIlIoVKTV6vR59+/ZlFeaefvpp9m6//vorjEYjbDYbPB4PMjIy4HA4IlZKeSEoOTmZOfLOOecczJw5kym+AwcOxKRJk3DVVVcxJz21PRwDndFoRE5ODnMwUT/wyoGykp+/+9A733vvvayCn1pCidPpxLBhw7Bv3z7G/3l5eUhMTITD4cDQoUOh0+mYkB4XFxdx8KU/ooqCfKXscCr4dXaAWVsMm8pgp2i8i7JiYyj973Q6ZcktgZK7IqGcnBxYLBZ2oo/RaGT8S+3k+0IpjJMhn/7PK83RMgx37doVkvSncYQcAuEYW/0ZxnkjmSS1ziM+OIev6mq1WtvFYfzfTm05ypMSNdPS0qJaxSMU4h3rnUX+qlV0dBskSZ5oQ/sYb9TszDYSUbtsNhurktmtWze2vi1evBgaTespRjS+PXr0kB3xTPs0VeqlQKWOHJ+YmJh24b1wZAOSDfv06eMTLNW9e3e/8n20ArslqVUOqqqqiqg6ixr52wfob6XhjKfS0lLU1tb68Hqo7xvKeIYri7dl3lHVV35djTTZIjEx0acSTrjv6s+BHS7/aDQaOByOoDI+7eXhvLNSNmprW9t6D0mSIjbUqwXJ0TvyQQJqY8LzckJCAq6++mrGS6RzRpJ05Y+ysrJ8+osCYSnIpK39fLrsYaG+R7QDfPggKj6AlBJ6JUliVbb59ij3R+X3ygCUSMhkMqF///5BddxI51RaWposUYTnBWq71WqF2+0OWIW4IwJ/+Gf7CypT9ndiYqJP22w2GwoLC9lcttls8Hq97Rb8rnQuZWRksNMNO5KCJVsoE4ZOB8ev0Whs07i0JRhKksKrahluu0K5LjExURYIaDAYVOcaz2NqgetqwSu0xpG8T3aatgaRdPZ+Esl4U8VU6iPqC36eVlVVyfou0P0CFW7hq7MGan80dSx+b6qoqFBtA28DC3afziayDdrtdlRUVESt+IIk/anv2+12WR+p+Rn4eRVpZfRhw4ZBp9MhISGB8Vuw4h7tTVarNep2df4+bZUXoqlrKz/nT1WhtZT/DV91W+0+yj3HarUiNjZWlUejnaRLOoHyHek9qOhaOLyqvI4veqPVamV+vUiSG/xRNOT8aK5XakkNPNHJNxaLpdNOG+4Iu22kfUo8pzYP+AIa9G4XXHAB4zE+eTKUZ+3cuVNWNIL+VfporFYrUlNTZfuJJLXaFng/GvmjAj2T3z+D+Qv9BZz17t2bxRUkJSXJ1qRw+5t8VPxpO4Hm1IQJE6J2UsPpSjExMcy+yyc2BNpnqaCLJLX6Dck3fLrIQqGQVqtlewPv17RarUyupfcZMWKE7N0lSc5/tHe3VTbhixTxnwXy9dIaMGrUKFitVplf9nQYj1D75HQ+JZ0njebP4kFerzeqtk3luCqTrgONaVxcHONNtcJXau/RmX3I/7+zfatKqq2tRW1tLTsdvT2eodSlotGP0bhXJPeM5Ded7ctWI4PBcNoUjo1GwkmwIhS8LSwxMdHHJkaf0/8ffPBBSNKfcTcJCQkwGo0+CW0ks3bEGqPT6VTHjIqi0Wm5/68RfwrwsGHDQrJfGo1Gn/0m0F7Cjy+NudPpxObNmzslTlngvwsiAUNAAMDx48dx4sQJHD58GEuXLo3oWD6Xy4WzzjoLCxYsgEajwbhx4zB37lyWhRyJ4PPSSy/BYDDAbDaHXEWLNo1rr70Wo0ePhiSFZ9js1q0bO86VhBO9Xs+ez1fBIGel2+3G+++/D4fDITOmtUcmaVuFHvo9Gdypvd26dZMdT2q320Puc+UmP2TIEDgcDlZVtqSkRJYxH+6YtAcFGpvOdqhGiyoqKtrMM8XFxVGr0BVpQGO0iDeAZWRk4IwzzpBVCjIajcxQlJmZiUGDBqG2thaS1JoApAxu8df+aAWRBqPU1FTMnz+/XZ7VFqOJ3W6XOe0MBgO6du0Ko9Eoc16F6owLxCftuY4E4letVgu3291phq6OOuElmkRKe3u3nZxBbrebPYt4UjleaoaELl26wOv1IiYmhlWuNJvNMJvNp2W/85XpOvrZvIxARuX2esfO7meicNbGSJPHKNg6LS1N5hSi+wWTLfnfkNMmEv7gkzIlqTU5jioXp6SkyE6Ii4+P99n7YmJiZFWUtFot7Ha7anCKv6QJf8ZjNZ7vqL03XF4J5FhWXhMuUXJyuEE+kfBmZ60z/tp/0003scADSqiy2+1wu92y07Ha0r+h9E2kDkal/BIK7xoMBibbRzKWHUFarRYZGRlB2xXKmLRVRmwLqVXTi4QisX/wwS3UBxTwJEmtullKSgqrJq/T6XDmmWfCZrPBYrHAYrFAq9XCZrPJdP2EhARMnToVAwcOjMp85nmW5kFcXBxiYmKQk5ODzMzMqIwRP4+ffvppH77g29GRTl9+f2tvu4aaE035Pf1tNBoxatQoFgDGJ8oE6h9ytFAyazjjH613VHMG8Z/xsiad+BTOWPl7Lv93KDykPMlXo9GwwidkO9BqW0+9CjTX2mP9UvJCJPdQzjF+HQp1jimdwXyilzK4V6PRoK6uDpLUGpBHn8XExLAiCvRser+srCyfQDqLxQKbzcbaGB8fz4rG0Frsr6JxtPbSuLi4oOOqJq8G4tFQ29beAXPtXcU+lLaHu8arzYFIA1KUJ+vw7eFlQf7+cXFxbH+gNZl+k5OTg/Lycp8gvbYE7ms0GpSUlMj+bzQaUVRUJHs2JWa313h2pL7gcrlkBZSiZa9RnmDCJy7x61koAa6hEs/fkVb/DiVBIBJZKZqBy6H0VTAeUkv6Ul6j1m/0Oz5IPtLEokBtU7ufcu0P9MxQku9PB908mrJgKLISrZO035GswF/XliBaPjEp3IDN9tRBNJrWgilKXiJ7Gf9sjab1tAuas3ySFfVVJL7ecEhpR+T3HGpDIFsfn5Siphu4XK6o6erB+LCuro75/k8nW6OSTjdbaDh9HEnshDLxyN+aqdwbzj///JCfwct2Ho+H8Z4ktf2Ek0Cktnf17NlTxvOLFy/u9HH0N55qn9M6xfcpxQulpqbCbDajoKAAgwcPln0X7nPV7Am0Bl544YWyz9syX0IpHNfe62w4gfZKX2x8fDw8Hg88Hk+b9q5oFnHgbVb0Gdk127MPSb+bOHGirI/407E0Gg0yMjLYOuNPLg+lL/nij6G00Z9Pg08iV3suvyfw8qLBYAjp5K9g7VH+HW0+OB1IuTfx/4/k3QP9pi3v3dF7vxoP8IVNJelPOV15rbJ4UyAdl/+O7sOPAenjJpMp4j3ZX+yPmuxJp+SGO14054qLi1XlnfYsRBrMX+lyuXDmmWdCr9ejpqYGTz75JDIzMzs7VFngvwgiAUNAAMCbb74pOwHjzTffRF5eHjZt2oTvvvsO27Ztw6RJkxAfH48ZM2bINh2n04mbb74ZMTExGDt2LIYMGQKr1Ypu3bqhrKxMZti8+OKL8eijj7KNOJghma/M5PF4kJeXxwzhJSUlGDFiBPLy8tC1a1dMmTJFZsBRVmXhK4H069cP1dXVyMvLw+HDh7Fp0yZWLa979+4AgDPPPJMJ3j169EBOTg60Wi2r1kFkt9uDVub0R/xmTe/KG8XaSvTOBQUFcDqdSE5ODtmp3t7kzxnmdrtZlcpI7rt27Vo4nU7odDpmnKBEEEmSMHjwYBgMBvTr148902QyyaqmUL+99tprMmMlOW+pMiY51CjAhBcg/Rnck5KSZCebxMXFYdq0aWw+9e3bFxdeeCFuuOEGGI1GmM1mdly9JEk4fPgwLBaLrBpGTk4OJk2ahGnTpkGS/qzmpeSjkpISOBwOn4Qmqvip1+vRtWtXWK1WZlzq2bMnUwpDderSNfR8pSGsvRQDMoadbhUXlMRXHuL7hJQFnU4Hh8OBCRMmBLyPTqeTKez0ewpOycvLk51KQEdPt/f7BXpGRynSRqMR3bt3R2lpKc477zx2OhDf3x3RDtqH6Phv/rukpCQ4nU5m+CAeoLlJzgatVouUlBRoNBr2Hm63mwVB+OtTnU4nU0D1ej1Gjx6N0tJSn2tHjRoVdN4ok+gCkb97BauGGerYSpKEV155JaTjZjuC+GcHM6hQQJbRaITL5WLGB6Vxui1BLsqKyeH2eVuDMfgTKuizoqIixut8IBRRW+Uefg8neUrN0MAHwyv7Rem85cdMklr5Oi8vTyZnBgpGaCtPdlZg9TfffANJat27e/To0WntiIRvLRaLLNFPkiRZciWNY6TPUP7GYrEwHuEDCA0GA+Lj46HT6eD1etka5fF4UFRUxORGvk3BklZPJyLjHBn7KUEtLi6OBU+GSyRnZ2RkhLQeqFWJjyafUQCT1+tFr1692DPoZDpJak1e52VcpYM30jUgJiYGVqvV5358hUIil8slq1jdlj1WmUBF73DnnXfiuuuukzn56NTHQMcXdxbxx3tHu7pxqGNLz6UTdZR7RSCeDTaGbQ24ot/m5uYiKysLer0eNpsNXbt2ZZWpKamJ7CNJSUlR06/4k6g6S3azWq1ISkqSyQKTJ0+WyeShBuMH4gf6PNREHDXHUqhEa5bT6WS6REJCAnsnvjqfksfI6aPV/nkqQFZWFqqrq2V2AH88qNFoWKC72vuTLsOvX3TyYTQonLE6HUnZl4F4KtL7BvpMmTQXaaCtv/En8re2aTQadmKVXq9XDXQnOSpQv7Ql+MxfEhX/Oc9jJpMpYGIt3ybelqd2f61Wy2y0wZKkosVz/FxUO6W3vQJ7yS7C37stMhzJ4GQTyMzMZJ+F21/KIAE1fbUtdDoFlPijYPal0tJSpsOoBUUQ8adptYX401nsdjvefvttv9Uxo8FP4VCoJ6m0JcgpUB+SX0xZLOHqq6+WJaSESnySG5+4q3xfjUaDgoIC2cncWq0WCQkJAdd4SfpTFvJ3ylEofUE6PV89P5yxCuU3JAuFmixxulB7FIALNBahklp1+Y6iYHJJqPdQniQZDf2XtyMpT6BQk59ITjAYDJg8eTKbAz169MAZZ5zB5ivJHHwCIN2Dr3bMPyMuLg5arRa9evViCSzkV/XXfv4UoXB4tD2S1v0F9KsFY0fKB6E8m2SnSO+vHK9otZEPfFfaHfn29u/fnyW/8Ak/0e6rq6++mrUl1L5S6kfttSZHuo7yVaolSWpz0o5a8Ge4v6fTTKI1dsr1u61jQPa6aNi1iDrTN6h8Nr1fOL54f8G/9J1akQQ+eFdt/aH/R4MXgr0Hr19GKo+Ho4taLJaoJ9FGox969eqF9PT0Ti8+y5NaW/jCP8r3CmX8QpHp1OZEoD6MhpxoMplksVftMe6RJPyr7S80r0k+i6R4bbhzuy36gj+5KtJ+5CnUooNq493W57dlDemoYp15eXk+fU/FZ5Ttp+IbRBRPpPw9H5/rcDgQHx+PmTNndmaYssB/GUQChoAAgJMnT+LIkSNobGwEAKxevRqlpaVYvXo1vv76a6xbt44JtZE4S0lo7tu3Lwv0zsjIYIHekWw6F110EZKSklBaWoo+ffogJiYGmZmZLPAnOzubKbx2u51VuE9LS4PJZILb7ca4ceOwZMkSfPjhh0yAiImJwdNPP42ysjJIUqthu2vXrkEVw1CU4/j4eBw5coRdS++elJTEjvvWarW49dZbodG0Hg9mNpsjEj4tFgtT8oYMGQKdTofY2FiMGDECWq0WmZmZMgEtJSWFbbipqamIjY1FbGwsioqKcPLkSRbcYzKZ0L179zYLDW0NEKLACErYSExMxMmTJ/HUU08xQwMJHEePHg343NGjR8Pj8aCiokLmwA/X4dYeCp9Wq5UdHdZRRnVJkgIaleldc3JycM4550T8jGgaAsKh5ORkuFwumRHbZDKx5JpoKBRut5sFVw8ZMiTk+/HOpXB4KtC1lZWVyMjIgFarxaRJk2AymZjB3mAwoKysDEVFRYy/QlG0lU6p9iR6N96gRU6C9nomr8B0lDJls9lY4IDT6WTGcZvNxniUeIP/nb+KGG19/3CupQql4RIFS1MgntFoZGt4ewTnqRlolddQ8h45iMggYrFYMGjQIJ9+54nar9ZHaslQ0Xi/oqIi9OzZk7WpPYx8oVThi2QcKMiYTiyTJHmgAiUiRvt9ok3K4CWlcVzptFW7h06nQ05OjkyOpe8iCaTgecxfcD+fuEKkrErFtzsuLg4ej4edupCeno4ZM2bIAmrpb34NTUlJgVarZUYfus5oNMJqtbJqou1VJYruq9PpUFBQoNoX/vrPZDLJErwDUbh7ktFoxLhx4/zO2WA6R25uriw4Sfl8p9PJgnIlSV6llT8phe8femeNpjWptW/fvkHfOdR1TKvVsmdpNK0J0DabTXYccrj83VaKZqBWKG2KhnwZajt4pxU912g0sgA7qiwpSXJnPI0N3a+2thb3338/66uOSubnT57q6IQkfk2nE8+qq6tZf/J7YluC6wKNH6+T8LxVUlLC5ERJ+lOHUfIfzStKFqQiAF26dFHlj/bgQ7Ug2LY8K1KHXFuCYSIhf3K51+sN21FJMrFyrHQ6nU8lR/pc7T78fs/LIWRrClQpmoh+x5/62p6k0+lwwQUXIC4uDg6HQ5ZQFU6SMf9uVJyA7H58MYNIiqoon9/RFehOJ/IXLKKUkZX2LKWeHawPbTYbunXr5hPcRLoj/6xQ2ylJkdujOjNRWaNRTxKggkjUD2RLkKTWPYvfw9pih9NoWpOveDlzzJgxLBks2O/1er1sDur1etTW1sp0CH7ehxv0yifR8zZVtT2hLXM3EhstnwAYybPVArFC6Q/eTzJ37lxMnz4dHo8HWq2WyeX+EtcTExMRGxsLvV6PnJwcv3OML7qh0WhU9Vi1/UXZD50duEQJQ/7eUclL/B5L81JtHkRTHgnFxqBGxcXFMt9HMApF1qX3MhgMyM7OltkylH0WSt/zeqlS3wyn7cGeo9aX0ST+xE+i1NRU5mNzuVw+z46Wf4QvgMKPQ7g8yLcvPj6e+SvpMyo4sWzZMh9+CEQ0T+Li4hAbGytbA8aOHcv+NplMEa0H0UyiC0ah9mlaWhokSUJ6ejpMJhPbB/k+Jhs5v75Q4iet1cpx4cdXklplK/508pycHKY3mM1mWfEy0jPC2csC+YWpiEWkfalMSCf/PCXout1ujBgxol39o3yCGa0/BoMhYFEpNR5Q0/vU5rc//uHtAm63G2azOeBcaMvaEcgWm5WVhd69e7OK9GSjVyZzdIRvV6/XIzExkT175MiRsu9p36XiA0VFRbDZbD6+Gj5BOpgcpqzuHW7/RZtCPaGnowPT6Znhrif+iOZeWxIryEdGfhby1dO/Hd0/0SB/CaLRHO9Q+qYj+Yv4KpQ1huxn48ePj8iX1pFkt9sxZsyYoP0dTpX8UNfhjuR/pd7Ky2edkUgfTrsjuT4nJ8dnz5g6dWqb2tKWYiM8hZrkEc35zdtxNJrWZP5AdgT++bwcFe6JvME+V/Jhbm4uNBqNrDBTpEXm+PuqFYGk9xk7dmzEfe1vvijtq7yOzj9Lp9MxPi0tLWV290mTJnVytLLAfwtEAoaAAIC33noL3333nU8CxokTJ7Bt2zakpKSgT58+cDgcqK2txZAhQ9jmN3jwYDgcDrjdbsTHx2PIkCE4ePCgzNiSmZkJi8WCmJgYDBgwAC6XCxUVFTj33HORlZWFyspKWeBvTU0NYmJisHHjRlZ1469//SsmT57MNpCUlBQkJCTAZDLBbrfLHCoUUKLVapGXl4fu3bszQ0lhYSFqa2shSRKmTJmCAwcOYODAgZCkVuHojjvuYIkRbrcbU6dOZRt7RUUFe6fevXuzDa2mpgYLFixg1eLy8vKwcuVKmUFAkloTG9588022kasZe/V6Pc4991ymWCiNKwkJCZg9e3abNv6zzz4bMTExqKysZG3IzMxkxojs7GzU1dUhOTkZ//jHP3DppZfKHF95eXnsXqWlpczw+v3338PpdKJ///5ISUlhQVX85k5GQ6XBqHfv3uz5ZNhqamrCsGHDoNFoWLC4RqPBueeei0WLFmH06NE466yzWGBMNLLelQYCg8GAqVOnsmAVg8GAHj16sOclJCSgqqoKOp0OAGTP79GjBwYPHuwjGAYSFEMJEPEntI0bNy6id+aFMrvdzsaGd3DS+8bGxvqtikjGpnCMLLwAKEnhBz5QX9L6YrVacfbZZzNjD1X2qa2tlQXxqgXUk6PXbDb7TX4gw01MTIzMIZeRkYHU1FRZ1dFAxPezss9IsA836J8PaNbr9cjOzg55PlitVtnzSPi22Wx+lTt/1Xn4Z7rdbqa08MYCCnoxGo24/PLL2ekt9F2k85gyv8kgrNFoIj6hKFyiPufnTW5uLkwmEwv4pYSC7Oxs5vD4/fffWTDQ7bffDgCsKpzT6URlZSX69u2LoqIiFkgxZcoU2dHdkvTnuqBUpEJpezBeC7eaAB80oRY8r6zUR59VVFTA6XRixowZbM/W6/WYOHEiOznmzTffREFBAQ4cOCBzTHXEGEdKyqQINYcJ7Yvp6emy4MhoHunLE+/IidY9aVyJn0iW0mg0iIuLkzn46FploCAF8oQypuSoVHPG87ITz9/Dhw+H2WxWTSAjB6i/43vdbjeKi4tlwQB0elU0AkRo3eoIIyjt43q9HgUFBXj99dfZc7/++msWgB3tClJ0P6quruSBSPqM/iZnIMn4tN663W4sXLiQ8YfL5UJMTAwLMOratatsjTObzXA6nRgxYgRrq9Vqlck+1HY+mYr2HvqN8r1obEk+CTUgeOrUqQH7aOLEiezvaCdGKttH/deWcYrktw899BDmz58vO/J33LhxzLlKsp7JZILL5WIngUWDX5Wf0TMpAJnf83lHPd9ngfZYZWCIMlCN9g+9Xo8uXbrAbDYjLS0takG/NMaBfh/u0e8OhyPivUWtr5TrkD896sorr4QktcoVlCRCQSr0e5JLDQaDamV/5VoeraRGvv9ycnJY8YhI7xdK4AYfvGo2m2Gz2VBaWoq33noLXbp0wcSJEzF69GgkJiYy24LBYEBycjI0Gk1IwbPKZyqDVeiefJIefzKl8j7RdvLQ3/yJczyvabVaVjGW9FxqK70//y7+dHC6X3vIo8SrSn7RaDQsaJb/fOTIkSzoMSsri52m2bdvX1gsFpk8FhMTg4KCArbf9+3bF3l5eUhISIDFYsEll1zC5iS/r6nZcoKRUkY83WV3tUIxvI5fU1MDSfLVkYK9n0ajQXJyMhs3m83m8xyyR0hSqyxNY280GpGSksJsDrxz0GAwsN+QfMHzxuTJk6HRaFiRlqSkJJ+TfaNJfGA5reFUiVU53/kTO9uDbDZb1JO5+fkYDbnL335AciP1Fc1Df7YXWoeUewR/D76vExISVE8caQu159z2t+eE08/Kk1GDjUVbbFOhnPrNv4uSl+x2O3t2WlpaWPZBfsz5E1ZpDZCk1iD8oUOHwmg0+q1s7O+ZfJ8o10GdrvXkL6vVKgs+j4+PR0lJSdC2R7P4iTIBLxS+Cue+fFJ7sPvx9js1uYfk/9TUVJnPhR9L5drOU3FxcdjBsPz11O9qBRrCJTXfjL+5EG3bB+8j0Gg0rLiKss+VAaIdKZcoeZEf1zFjxrAgfdo7qZ0mkwkLFizwuR/fdrvdznx0bZlLkfRHIJ0qEA9E2j6+jaHIpTyv8YFgfBVog8GACy+8UFZ8kC94odPpmL1Jo9Fg+PDhjN/4kyx4e5e/vvJ3Ooua/V2v1yMhIQHJycmYPn06xowZg/j4eHTt2hWDBg1SrWTNJ7bxST+UnEH8QYHq8fHxbSq0QO9bWFiousbxeyJftIV8Q3Sd2snE4fBnNNaUcPg/kL2H1kJah0LlT2WftnXu+LM9hvqeyvWSbyuN5eWXX85krEB+DD45NtT2q9m+orVmq/km/FGo+i+/xvO+WuIT5V5dUlLCfkP2TH4tMZvNyMzMZL8LVx6MRj+p3SeQPUt5PW9PofEn/48k/VmAip9PtAfS3/y6Qvoz8QL5c+gZpCOT7kS2ndmzZ8vuGRMTw+zzSlu3mg8gXL6KpI95n1lHUCjyYqDxTUtLk+1ppPOHeh9+PPwVE4kmpaWlye7t8Xhw++23y/g5OTlZZqvnE7Z4XnC5XDJ9LdoUKt9ZrVYkJiaiZ8+eUZHjIyHS+ULxdbUXfyvtHdHsB94e2r17d6arKXXo2NhYWeExak+oskk4bSadm/+/0p9L8RZ0b1oL1WStUOI/ghVPCdXXGcheKkmthcAsFovMVmU2m5GVlcV8DPycpUKttJcqEznoc97PQUW76T2UMW/8u/J+Db6/1RIi/dnq2pPClX2VNtLS0lI4HA6MHz9eJGAIRA0iAUNAAMC7776Lxx57DD///DOA1gSMoqIivPHGG0hMTGTVTysrK/HVV1+he/fuzHgwa9YsPPXUU5CkVgF21KhR8Hq9smrYtBmR8tOjRw/k5ubi+uuvZ45gSWqtqEQnVFACRmlpKcxmM/Lz82G1WjF69GhotVr07NlTloE4Z84cVumV39iUm09xcTELzLDZbHA6nYiJiUFycjK6deuGSy65hG2OAwYMwPz585nQwm+yvCGrrq4OHo+HGRiGDx+OhoYG9OjRQ/Z8/m96T/qcgqbsdjsTLHQ6HX788UfccsstbKOvra3FHXfcIXun5ORkXHDBBbjgggswaNAgmM1mbNq0CXFxcSgpKWGOT2r3tddei8rKSmg0fwZ3OhwOVaE+KytL9ns+qJHGk4y7hw8fhsvlQv/+/ZGcnAyDwYCUlBRUV1ernnRC2egajYYlzERaLWPWrFls3IxGIxvPr7/+GqdOnYIktRpNeSWeBOeXXnpJNViuV69eMJlMePDBB9mY2Gw2WCwWWK1WZqSk65VCqpqAVVlZiS1btrCqpmVlZZg5cyamT58Oo9GIQYMGobCwEGPHjoXRaGQnt1Dbbr75ZowePRoajQZpaWno378/br/9dmi1WlgsFhaQrBRCqdqH2WzGxIkTUVtbK6saaDQaUVhYyALDDQYDS2qi8Y6Pj4der2enwtB9y8rKEB8fj3nz5qmOMT9n2lPQ5BV1/oh0pcGSrhswYICsnTT3le2PiYmJ6MhQXqnJyspibTKbzT7JV/z/eYO52r2Vio5er0fXrl1ZQFNhYSGsViuqq6uZQYsSmEwmE2bNmsWeV1lZieTkZNV7BuJjnrxeL0tC8tcP/HspSblG0glFweaSVquF1+v1qYSo0+mY8muz2VgwNx80Y7PZZIaUhISEsIxDFJAbqH8CKZwGg4FVonz77beh0+mQmJiIxYsXo6mpyW8/8oHfynsOGTIEkiThnXfegSS1Bvgp5y6/L9OeQ3sCKZpJSUmsSiRd++STTzJeCuT0laTWvaSurs5nfGfNmhWSMqh89qBBg7Bw4UJmCF20aBFGjhyJK664go19YmIi9Ho9unXr5tPn4QRqKK+12+0+AXsFBQU+AbP8nPV4PMxYqgyW0el0mDdvHsaOHQubzcaucTgc6Nq1Kzt5isaHX8ckqTXYy+PxyCr405pM18TGxjJeGTZsGAtEJmM2VYeixMWEhISQgkD5ecwHI1IfxcXFMYcenRJCJ3spKTU1lf2eKnnxz1Gukf5OFaG+o+DatlQht1qtMpmM+pZ42m63IzMzEy+99BLr48GDB2POnDkRP5Nkq/z8fNlYU19T8pjRaGTJV06nk/FfqHuSv77j+3fx4sVs3TzjjDPY2PCGRNpPKeGRKhHy1c+Uhkf+vdSC0/21S6/Xo6SkhI1JKO+qvNbfc/r06QNJ+tMA2rVrV3ZtVlYW40d+7SVDm0ajYXM3VGeT8jpeH+IDW5UVCOkefGUujebPpEKeZ/n+bg9Dc7B3VOpefLt4I6W/hB7eIUaJaMXFxUz/6tevH4YMGSL7rdqR0oECe/hx4NcX0nNov8zIyGAyMPE2z8fKOcEHXatV2VTyoU6nC1rBORRnRLDx5b/ng+0kqXWOa7ValJWVITc3F5IkT5KlpGXqkwceeAB33XWXajszMzPhdrtZ0DbpDjwP0DP0er3qGkFzUavV4oorrpB9n5KSguzsbKY3qb17KLzOJ1xQ25UOVprryoABf2uo8uQYtd8oq//6mz/Bkt3OPPNMSJLEbCzEQw6HA9nZ2SyRwF+lV9I1MjIykJCQAI/Hg0GDBqGsrEyWCEtFBpRzJZonHqi1MRQnjT85OFznzrnnnstOODObzXjggQf8jqOSh4I9iz/qm4LbtVotO91Oo9HIeIL6lJf/+PWJnHr+nqvU36xWKzvpUJIkVFdXB3035b396UORnlpLCUD0//Hjx/u8vyRJLPGHkoXo++nTp7OEW1r3lfOU7kP6ur8kW9IXSO8K1hfKfuH7oC2V0PjnKKseq41xMB2V9iGz2cyCeyK1wYTqMFUbZ55vSf82Go1Bq7Ty6zvpyPRbXv4hncbhcMhO/vPXf7weRnNCGfRqsVhYQDjZd4m3AiU6hDK24fQ5vUNbqnWbTKaQdCF/xVTCeR9+zvHBmtFwNvuzKYVKanqj2+2GxWJBaWmpbP+IJKmYX19ovJR6a6Rj2JZxCRbEQKTX65l8RycLaLVadOnShc070nnI70K/SUpKgs1mY31gsVhgMplYtUh6htfrxbvvvstsTaHItfyYka40a9Ys2el9JMvHxsbiww8/ZO9z0UUXyea7v2eEGsRE643avYLxI/l0eD1SbQyipafx+xLv6zAYDIiPj5dVuae+laQ/18JQ7UD8KbDK7ywWCwtIpz7gq+Pzso7L5cKwYcPYb41GI/r16+dTAInve7fb7bOPpaeny07xi2QO0TgE4wve9qVGZL8Mtv7xOhDpzmQT9nq9bG2muRgN/pAk332Fn9+h/N7pdMLj8eCaa65h76HVapGYmOh3bg8cODDo6S+B9Dm1vqRCDIH6hvp1xIgRPvdR/pbmKdm+lNdFovfQ6SBGoxEzZsxg84v0Nt6W9+mnnzJ5g+QmOl1+6dKl7J7dunXDnDlzUFJSwpKln3rqKSxfvhz5+fnQ6XRISEhgY0GJXPx6rtPp0KdPH3bfc889F3379mVyg9q7qvkc+DGlYl38/CE7Kd/XycnJzBY+bdo0PPvsszJ7iFarZf5Kus/VV18t4zWSz5RF4kIdF/7evC8/HCJ5M5C9KVBic3uc6ETvouaLDad/lH4Hai8FsfOFFOhdySbJ70H86SC8v0wpB6i1jd/rQ2kzne7CX08yMCVCkb8j2v3Ok789NNI1XOmf0Wj+jNug5H++v4IVGoyU1NqvNjbhJrhHqmeG0laex0pKSpjNpa6uThZwT6fHKmN81OzLBoOB+Q9IBqbgX6vVymw1er2e+eTJFkFrpNfrhdvtxrx585jOzuuYZL8wGAwBC6D4W0P8JRqTHV5ZBCPYONCarLx3XFwcs+sqv+P9OIH4pS1jTPYC5anqWq0WgwcPZjYQ2r+1Wi2cTifrh8zMzID3DyWmR43UfHnhvru/pEM+kUi598THx8t8djS+sbGxPrYXh8PhY2dXi4PyR/78clT8LtT3DHQt7RXB7hFNvgqXeNkllGKJ5NfnZUqr1Sr7bc+ePVVlY51OJ+PJrKysgIWx/MnUvCzIvwfPd0RDhgyRjdGMGTPYPdRs4WR/dLlcspgbsv8FG0/e5hZojNtzbyM5lvQHsqf601dCXReUv/HnL+V5geJTJelP+Vej+TMxkHxGZrNZJvdLku/cov9TXBTZ4HnbaUJCAlJSUljb8vLy4HK54HQ6RQKGQNQgEjAEBABs3LgRH330EQ4ePAigNQEj1I2rLQFvHUW02ej1+rAcTv6IhH4+YMtqtTKl6YYbbsCcOXMwf/78Ds1Yl6Q/q46SgqgkvrJ9oPtQFUhSDDtynGnjdzgcOOOMMyI2flZWVmLnzp148skn2X35oHvqh1dffZUZjQYMGIBHH30UkhT5sbkajUbGZyR0pqSk+CjM1L9qBq9g5PF44HA4MGTIEGi1rae9kFAaCZ9XV1fLAvuor5KSkmA0GuFyuVj76LpwMrjp/WJjY+H1emUOCL4Sp78K5GqkTGqi03aIz2NjY7FgwQIUFBRE5YQUf2QymZCZmYmUlJSQ5jwfcOyvYmqoBlqTyYTq6mpWlYmMEfzvw33vW2+9FQMGDEBpaSkzaIS6lmk0GsyePZvxdkJCQkTO7VCI2qQ0pJBywwf8zp07N6rV/v0ZVvggklCI9o3y8nJceeWVLCBIo9FgwoQJyM/Ph9PpxJw5c1BUVASDwcDe12KxQKfTYdy4cUhMTIRGo8GWLVtk9yeFrby8XGZwISWTdzC/8soriI+PbxfnQDg0depUmbEg2BpDBjU61YlPLqOxSExMhMvlQq9evVigotKZSolllGDqcDhYAiitKXwlNLo3H4SVm5vrE1xL+22PHj0wdepUtk6npqaqVoKWJHXHEPF1KFWpQ+W9tLQ09OrVS9WQwjtB+KDCaBMZU/nPKJhHOc8o4FeSJJasqwzE5INK/D1TGcir0+kwZswYmUGLvlu6dGlU50Qk+xCfFMIbViXpz6qmU6ZMkfEkOc2UyaLBiD8Fy19CBSVu8vctKChAfn6+7KS6PXv2sN+RIclms7FkMD7gzOl0wmq1orCw0MeBOXnyZBk/0p6iNCK21x4fDlEFeeo75XoQHx8vm8P+Kj/TnOBPfaDk2lCN06HwIq2HxEt07+zsbJaw1RbiZWk1WZfGjSrPBAvUDJX4ICSPxyOrOkiBvsRPWq0WU6ZMkVV+pTHQarXo27ev3zbR/KOE5UjbzsssFOjGr4sUHBHM4M7ve53pnPDHC2qBkdT29PR0WVB0VlYWO4mTv543UmdkZPjoPIEC2/jkP74dfLVc5W9oHOikFUlq3b95Z1h1dfVpsf74e2eSBY1GI5KSkjBz5kxZeysqKjB27Fi43W4f51y0idZvWuN5ZzivD9L1o0ePZu9BCQV820OR7UNJLKfkK17Oa8sc4vdLk8kEi8XSpuDqYMQHaNA7FhcX47zzzmN9XlpaGvL9UlNTWSAZjRetdyaTCXfffTdqamrYPKiqqmLPIbk4kP6ndKQtXLgQeXl5sv2Qgq7akx/9UbDge14uJJmd1+95+V6jaU2e9Hq9uOaaa1hxm0D86I9oPlPfpqSkYMiQITK9gZKLlLJFTEwM7rrrLvY5BZLRHKLAEjX+J1sZvx4Hc0aSfJ+Xl8eCOcaMGSNL3pWkVvmRTnZKSkpCZmYmysrKojKOXbp0kSWnKW2aGo1GZu9TntLDO8s1Go2sej+d9qt8ZmxsrE/f63Q6VFZW+vB1sPH3twapzYtwT6vyRzQ2kdixw32+TqfDqFGj2G+dTidcLpdM71HeV43veDmgPRIgNBoNq6rIV7S88847fdb1QP3m7zs6+VOSpJD0fAqkoJNvo/2+auNqtVrhdrsxZswYViTIH//qdDrWL3369MG5554Lo9GI2NhYltBLQWGXXHIJioqKkJOTw3T35ORk6PV69O7dm+mjtIbQ37RGZGVlsUIkNC58P/Ny3b333iuTQ7VarV8/hzJ5hF+z9Ho9SxRLT0/3SXjjbQlqQWwkkymfxbc3UBBXJIEggXib58NQfqOWgBjuibk8kXzNB+/6myuhJMXQGKsFGPEyJ1+4R21M2uLLU85LrVbLguWoHZScS+8byknaFBgkSf59enq9ntkfKKBUyUc6nU4WSBkJKdeqmJgYZrfwN45q/BqpP0zZv2q/V9PrqI+oGrBa35Dd1WAwMP2Av4bve2WAKvkDH3nkETbedE9etiS5hy9wd/nll6u+VzjUq1cvJrPQGjR48GBcd911TMf7+eefERcXB51Ox4rnGY1GvPXWW6y4D18VPtDz+MAy8r1rtVpm++NPu3/33XexbNkySFKrXMXr5YGeQQHPgwYNYr/xV6QnUlJbx/nEsmDEv49SZuF1NJPJhJ9//pn9zQcpKoPp1U7kCneeaDQaPPfcc6q/UybQhEJUjIwSfSi5h4oWhXM/f/OT+EZtjOPi4uByuWSFpSTpT7812Tc72y7Dr4X8Ok+8Fej0JZLxwo1H4PdH4jePxyM7KYL2B1r/qZBpNN89GnEwPK+rrQ/8PqimiwWie++9VxZ3EOj35N91uVzIzc1lciE/lvz4Rvq+ZOcnXdFfX/IJaeE+Q6nfdnS8kr8xliTfoohKG9Drr7/O9l3liZ8WiwWFhYWIiYlBdnY2kpKSUFRUhIqKCr/FcNRIKV+mpKTI2hBovSIinSI7OxuZmZkRxbYQ0VpGvjN/tpZg98rJyfEJAFfyGMVLkfyi1+uZ7YruSbyu3N/UTvTmk3jUyOFw+Mw7fu9Q7iP+EjfC0YHJR0Cn4wX6vb++CjfeQ0ndunVDUVER7r77bkhSq6/Q6XTC6XSyttBpSKR7+9P5Io2VC4dC5blwiRLUSHcMJjeQ7h+JzKIca5rnxNtKmwb54oh/QxlzfhxGjhwZNo+0haf48aDEcj5OwmQyIScnx4fXKRExOTmZFfCNj49HXV2dOAFDIKoQCRgCAgBWrFiBsrIy9O/fH/369UNBQQEcDgfKy8thsViQmZmJyZMnywJDlBu9v03fX3BhuERV1EhxJCcLL9BRNdikpCTY7XafgAyNprUSfN++fVFZWYlevXqxo6z27NmDwsJCvPnmm9i1axcLMFcKqQaDAUuXLmUZnvwzqE/mz5+P+vp6LF26VFYhQilc8s6t6upqjBs3DhqNBhUVFVFx0PObayiZuYE2cZ6ys7Mxc+ZM6PV6XHbZZSxQUu2oWo1Gg8svv5wFd5WWluLZZ5+VVXTMzc3F4MGDodfrMWnSpKCCcCDq3r27alIAGRmUDha9Xo8xY8awcdbpdLKKlbzTTxkwFo6ApNPp8NRTTwGALKhwwoQJAIADBw6wxISxY8dixYoVISl3kZDL5WKVygMRr3xqtVpMmDBBduSiJLUaucrLy1V5Rjl3yEjg8XhUHSdxcXG46KKLWPXzzMzMoA5IPhhR7XsS0DMzM1WrpiYmJvo4OiJVYILxg78M82Ck0Wj8VjzgiQ+a5d+FqnUr18P4+Piw1wW+giQf5BEpr2o0rcHkdrsdycnJyM7O9nF8ZWZmRiVxLi8vT5VXqqursWXLFtUjI6NhkApXOaWgwLq6OpjNZjgcDtx+++2yPgnkyGpPBZwM4HyFNv65ymAJvj2zZ8/2CXAJxtPhEq3TynXDZDL5OLMCUbj7bzhGvVDeISYmBhkZGZg4cSJeeOEFpKWl+VwXzcqJbSUy3hsMBtjtdhw5cgTDhg3z4UVlRU3lfTSa1soO2dnZPtXNIpFh1Mhut8NoNKJnz54+fOLPkON0OqHT6fx+r9VqUV5ejvr6euZ4fvDBB3Hrrbf6jNGwYcMYv7jdbkycOBGJiYksQDPYmAbaP0Kd++np6bI5GhcXJ5vP/vbdsrIyDB8+HIWFhcjLy0NcXBwLlLBarUhNTcV1113Hrucr6NP/+WSC9iAKgA83CIQ3qpFRj4J5EhMTo5q81150zTXX+N0rQ01sDbTXTps2jQWfSVIr/0YrQJJ3sNXW1qKoqAgajYZVVOKDkPr3748JEyZAkuRy6umSgBMu35HuESiQjRKg9Xo95s+f3+bnUr9Rpfm2rq+xsbFISEiQOUaCVQNUe1ev1xvyOkbOT94xn5KS4jd4idqmFhhAcmxcXBxLam9rHysD5/zJFWrPMpvNiI2NZUUInE4nMjIyZEFier0eubm5MBqNsNvt0Ol0sFqtTObng9hoTaQ+UyZoP/DAA1i1ahWys7Nx5ZVX+gTp83IaJXmnp6fjnnvuUR1n4umBAwey/vUX4Ej7O9l51O4XShEInp9mzJiBCy64IOC+HYzMZjPy8vKCrpsul0umP0Qrgcxfm+jeVLU8JydHJiMqHaJFRUU+gYikV2q1WlRVVaF3795snC0Wi6yips1mU02imD17NtPfwrHX5Ofny+wG0SAKkLPZbCGd0kGyaCi6azQDeGJjY2E2m1FZWYnS0lLExcUxfuGfEax6eyAHelspFN0iUIEHm80Gr9fL1pukpCQ2t5XPkaTW9ZiqVdI8J1sR8aDJZGInNBqNRhb0rOT3aFJHFSLg+4UKA+h0OsycORN2u53tW3xCgr++pwrFofIGXasMLqR3Jz7LyMiQ7e28szwQtWeyGVEk46/T6TBy5EhWOTlQn4YzftSeoqIiv0lMakG+oY5VuAm+fPAzJS1oNBp06dIFXbp0QVxcHJKTk1mBD+K9UHjIXzKM2WzG1Vdfzaqx0t4UTVtPoP1Guecrx7dv376YO3euz+/i4uJC2sdC7X++f/j5pHyPtiQsBCN6d2W140C/4RO/1RLI6G+Hw4GqqqqQdWONpvXkbioW1BGkdqIBtSXYqQxq40jXULJ9sCSvtvI8BRq53W50794dBoOB6dy8f4rWZjqdlOQ40hnV+MJfdWp/skVbZGk1Up507Y+CBSiHSuHuEyT38SeL8eN99tlnB72Hv/WkoqICvXr1kiXcBaPS0lJUVlbioYcewoQJE+B0OuFwOFBYWAij0Qiv14vKykqZnE8nigc7cZP6xmq1wul0wuv1Ij093ce3oDz90+v1hnSiQWeQwWCAx+PBa6+9pqoTxMTEYMqUKcznosbfOp0OWVlZEZ/8x1MolcHD7Tt/uonRaMQdd9zBZOu2tDs3N1cWpNpW4vVXtfVRo/nzlF+1+UNzkvZN5R4aaJ7Tqc3B2piQkCC7Z6jrOJ2ALknqNgtqu3IM+f+Hqpfy9yQ7GBUBUbOFhmJ7V65x7UUU1N3ez1HynNrngfhar9dj/PjxsoIcRUVF7ZqsEMm9R48eLeNrm83G7IBtbY8/+Y5/XqDYjlCI4j7Ky8sD2oeDjSd9R4HE5Gvj7Z82mw0VFRVsTySbEe+XVM5JZREzul+XLl18+ioa62RHzMFoEu1D4ciHeXl5PkXk6F7ED/5+q/Yc/nqn0ymLv1MbG4vF4uNziSRQ3p+u1BFyENlqdTodK8LJ96PaOFHbeP+Akrxeb8ATYvj1MFJekSQpJJ+3v3v4+45PflXyBf/7zpxjxDN6vR49evTwkV9MJhPbi9X6h2S6wsJCVtDYZrOhsLCQyQ78nOJlT7PZLIvl4/tKKcdT37ndbkybNq0TI5UF/psgEjAEBPzg/fffR1xcHF566SX2WUtLi+q1dBRteno6zj77bIwePZoFuw4ePBiPPvoofvjhByxYsABerxeJiYlYuHAhzGYzzj33XL9taGhowB133IENGzYgLy8PEydOxP33348NGzbg8ssvx/bt2wEAhw8fRr9+/WAwGJCbm4vs7GzMmTMHO3bswJYtWzBv3jzs3LkTS5YsgcvlwgUXXICWlhb89ttv+Pvf/y57JiktSrJarczYSsduu91uAEC/fv2watUq9OzZE4WFhVi9ejUAYNWqVTIh0Wq1YtGiRXjkkUfgcrmQnJyMHj16YMqUKXA4HPjb3/6Go0ePAgD27duHbt26we12IzExEStWrMCmTZuwfft2bNiwAf/617/w9ddfo7i4GOeccw4GDx6Mw4cPY9SoUWyzNpvNmDZtGo4fP47Vq1fjhhtuYO+5Z88eLFiwAGlpaTCbzdi8ebPqGOzatQtutxvnn38+tmzZIvvu0ksvlZ1YYDAY0K9fP5mSTwYWXtjjs07NZjOrGN6rVy9cd911mDZtmuyoxKqqKpw6dYo9d/369fB4PMjIyMBtt92Gffv2oampSda2l19+WSZAfP3116yyy6BBg6DX63HnnXciPT2dBbps3LgRNTU12LFjB+bNm8fGrqysDAUFBVi4cCEAYM2aNTCZTMyQo8zAJUMIfa/RaNCvXz9YLBaUlZXhl19+wdVXX40bb7wR//znP5nxLzY2FlOnTmUn0dTX17M519zcjA0bNqCxsRFr165lGdJKIiGMz6J2Op346quv/M6z+fPnIyMjAwMGDMBHH30EADjrrLPgcDgQFxeH2tpazJkzB0uXLsU333yD5uZmrF27FoMHD4bZbEbfvn3x008/oaWlBWvXroXb7cbx48fR3NzM2n733XezSji8UPzEE08AgGz8ZsyYIROYzzjjDFl7Gxsbcdddd+HHH3+UfU7PO3DgAEaOHAm9Xo9u3bph6tSpmDZtGv7+979jx44d+OCDD+ByubB+/Xr27GXLlqF79+6wWq3M+UjHMWo0GpSVlcHpdOK9997z24/ffvst7HY77rvvPtnYAcCxY8cAgFVp0Whaq6Xt2bMHAHDVVVfJglXJgNa9e3c4nU707NkT9913H+655x7U1NRAp9PhwQcfRFlZGZt/er0e1dXVaGxsxJQpU1hfJyYmYtu2bT79pIadO3eykxiGDRuGpUuXyu7Pk1IpoDZ//vnnsnvW19fjuuuug81mQ8+ePfHNN9+oPru5uRk//vgjHnjgAUyePBkFBQW47rrrZNe8+OKLeO2113DzzTfj3XffxcqVK5GdnY0xY8bg4MGDePHFF6HVatGrVy/ExcWhvLwcN910Ez799FMUFBSgsLAQU6ZMYfxVVVWFhoYGNkY//PADNm7ciMcffxz79+9HYmIiFixYgOPHj7M2fPbZZz4Gba1Wiy5dumDdunVYsWKFqoNx+vTpaGpqwubNmzF16lSYzWZ4vV5kZGT4GLhWr16NnJwcPPvss2hubsaOHTuQlJSE5uZmVFZWwmKxYMSIEZgxYwarPESJccOGDYPL5cKVV16J+vp6TJw4UVYJ3OFwYMGCBdi5cycefPBB9O3bl/GeVquFw+HAU089hezsbNx0000yPp49e7ZPIMFXX32FoUOHIjY2FmeffTZsNhtGjhyJDz74AG+//TYGDhwIo9GICRMmsHu99957GDx4MGJiYrBmzRqcOnUK27dvR1ZWFlJSUvDKK6+o8seJEyewd+9e9O/fHy6XC5s2bQIA/PLLL8jNzUVsbCy6du2KGTNm4OWXX8ahQ4dw4sQJLF++HOeffz5KSkpgNBqRmpqKESNG4PHHH4fdbsdnn32GxsZGNDc3Y+zYsT4V2GgfSUpKwoABAzBz5kzMnDkTTqcTU6ZMwaBBg1i1s0mTJuHQoUPYuXMntm7diuHDh2PWrFk+gamXXXaZ7P22b9+O8ePHY9euXQDgs6cBrWvf559/jsLCQhQWFiI/Px8FBQXIzs5mirjBYEBaWhrcbjfbm958800AgCRJmD9/Po4dO4bnn38eM2fOZHurP/muvr4es2fPRnx8PCZOnOizlzQ1NWHDhg2YOXMmkpOT8be//U32Pb93K/HYY49hwYIFmDVrFgoLC9GrVy+cffbZmDt3Lt555x1MnToV+fn56Nq1K1wuF3JycrB79260tLQwfhg8eDAyMjJw7bXX4ueff8YLL7yAG2+8Eb1790Z1dTVqamqQn5/P1icag/z8fJaYxgeIdOnSBY899hh+/vlnbN26FTk5OUhOTkZhYSEOHDiAL774Al988QWuueYatpYeOXIEQKtsUFxcjD/++AMA8OWXX6KsrAw1NTXo0qWLqsP/k08+AQDMmzcPAwYMwAMPPACHw8HW0ZMnTwJonTN2ux3jxo3D1q1bZePV0tKCmpoamEwmViHF4/Hgyy+/xLFjx5Camoq7774b27dvx5NPPsn4rXv37izA3WazsbX9ggsuwKRJk1BUVIQZM2awgLrKykrU1NSgZ8+eyM7OZkF1lHxF+0JbDY803/R6PYYNG4bevXvL1shQqzXRffggpKVLl2LkyJFoaGhAXl4e8vPzWTAZfww5Bag+9NBDaGpqQmNjo4zn+f8DwG233YZ//vOfPnu/Evfddx/bjw8fPszGF2id33v27METTzyBrVu3wuVyoWvXrnj55ZfR2NiIyy+/HJs3b8bDDz+Mvn374vXXX5etK3QCHi+bqvXJq6++iptuugnnnHMOjh49ilOnTuG3335Deno6EhISsGjRIsyYMQNutxsmkwlXXHEFDh06BADYtGkTMjIyMGXKFLjdbmRlZaGkpAQ9evTAeeedh9WrV6O+vh719fV49NFHceaZZ2LIkCGYPXs2SktLWXWVGTNmIDY2Ftu3b2f9tXv3bvzlL39BSkoKzj77bNTX18t43B9++eUX1n+Ar4yzZ88e5OTk4N1332WfbdiwAUVFRWhsbERjYyNuuukmtv9kZmZi3bp1aGxsxLZt25j8xqO+vh5NTU1YtWoVevXqhccffxyJiYnwer3wer1sDioTws1mM2655RakpKQgNjaWBc9T4AM/p6iqsdFoRE5ODubNm4f58+ejb9+++Oyzz3DkyBEsWLAAiYmJyMvLw8iRI/H9998DANauXYuMjAx4PB7MnTsXO3bsYG2n9eqTTz7BeeedB6vViry8PHz55Zc4evQompubcdddd2HNmjV45pln8PPPP7PfTpgwAb1792YJWEajEYmJiairq4Pb7WbJg4MHD0ZFRQVyc3OxcOFCHD16FF9//TWGDBkCl8uFHj16IC4uDm+//TYA4J133sFf/vIXbN26Ff369cPDDz8s0yUrKytx8uRJNDc344knnsCQIUNw+PBhnDhxgrVt69atsrGnudXU1ISWlhbs378fycnJcLvdGDZsGBYtWoSff/5Zda/dv38/47nGxkYf/qPnfv755xgyZAjjvfr6enTp0gUDBw7EiBEj8Oyzz+LYsWMYOnQonE4nq6I6adIkeL1evPzyy7jhhhtgs9ngdruRnJzM1joKPKL1NCMjA1arFS6XCyaTCa+++iprD9kdgNZ9JyUlBQsXLsTSpUvZHF6zZg2ampqwcOFCtk5Rf/34449YuHAhbDYbu9fLL7+Mfv36YezYsbj88stx991346WXXsK//vUvzJ8/Hy+99BJGjBgBnU6HgoIC/OMf/8BZZ52FMWPGYPLkyXA6nUhISEBhYSEMBgOKiopQXFyMvLw8FBQUICsriwVpKp0h8+bNw4QJE+DxeHDPPfewd7vggguQkZGBhIQEJCcno6mpCenp6YzXU1NTkZeXh6KiIpSXl6Ompga9e/dGz549cfPNN2PEiBE+Y0n/nzNnDp599lmZzNLc3OxzvZLPeNB6UFlZiffff599fvDgQUycOBF2ux3x8fFIT09HdnY2UlJSkJGRgddeew0VFRWwWCzIy8vDoEGDMHPmTPz222/o2rUrdDod5s2bx+63dOlSeDwe3H///XC73SguLmYOF7fbjY0bN2Lt2rUyXUWr1aKmpgZNTU04dOgQ0tLSkJyczE65SEtLQ1paGvLz83HGGWfg7LPPRmVlJaZMmYIZM2YgLi4Oubm5LLgvMzOT2cb8HUev1bZWvyVd9/jx43j77bfx17/+Feeffz769OmDLVu24NixY1iwYAFKSkrQp08fbNq0CcePH8f111+PO++8kxVN+eCDD7Bw4ULY7XaUlZVhy5Ytsj1UKRcp/z516hSbqx999BFyc3NllJ2djZycHBnRd5IkyfaQn376CTfeeCOsViteeOEFNDU1oampCW+//TarSv/MM8/gjz/+QEtLCz766CP07duXVXgn26Jer4fL5UJpaSn+/e9/M/47ceIEGhoa8MYbb2Ds2LFwuVy4+OKLsXPnThw+fNhnDhPWrVuH2267ja17e/fulX2/a9cu1h8tLS2s/xoaGmRyzb///W88/vjj+Ne//oURI0bgxRdfRH5+PuLi4jB37lwMHDgQbrcb1dXVuOyyyxAfH493330Xc+fORVFREaqrq3H22WfjqquuwpdffolXXnkFzzzzDI4fP47vv/8eS5cuZfr38ePH0djYiM8++wx9+/ZFSUkJampqkJeXh5qaGiYnzJw5E6+99hoGDx6MlpYWHDlyBDt27GD7FtC6Xq9fvx5vvfUWRo0ahVOnTmH58uUYP348Bg8eDKPRiLKyMtxxxx1YsWIF1q5di5UrVyIuLg4jR45k9juj0YiioiK8//77+OWXX3Dw4EEcO3ZM1kfNzc04fvw4tm/fjhkzZuC3335j+8n333/PCijU19fjxx9/RNeuXVmiUEJCArxeL0ues9vt2L9/PwDgueeeQ2pqKj755BOMHTuWtcfj8aCoqIjpE06nE2azGb1790ZcXBzKyspQXl6Or7/+mrWR2vv444/DZDKhsrISe/bswZEjR5CQkIC+ffuitLSUBV57vV5cdtll2L9/P+bMmYMPPvgAAwYMQHx8PNNX1q5di+zsbPzyyy/4xz/+AUn6M+AxJycH5eXlGDBgAMaPH4+6ujqMHTtWNveUWLp0KbxeL0aPHo2//OUvePjhh2XrVlxcHPLz8/HCCy8AaJUXDAYDrFYrPvroIzb233zzDZYsWYJ77rmH9WVLSwsbk2+++QYTJ06ExWLBmDFjZHIRf+3u3buxa9cuXHrppWwPefbZZzF37lwMGDAAs2bNwoABA5hcvHz5cjgcDmzduhUffvihX1+CP/r4449ZG/j17LfffkP//v0RHx+P8ePH491338WiRYuYXpObmwuv1wuz2Qy73c5OPuTlB9IJiPi91mg0sgIDM2bMwH333YfevXszWQsAXn/9dRiNRnz77bf4n//5H5SUlOCqq65CVVUVbDYbfvjhB2zevBmzZs3CwYMHsWTJEuzcuRPffPMN3nvvPTz22GMYN24cVq1axU4VpYqX1DbeXu1wOLB27VoAwNGjR3HOOedg2bJl2LlzJ06ePInffvuNyd+E77//Hvfddx/zc/C28+LiYmzevBm33HILpk2bhr/+9a+w2+24+OKL8fvvvwNolZ9qamrgdrt9bJj8PGpsbGTPpX+bm5uxb98+vPHGGzh69CguueQSplsnJiYiJycH8fHx8Hq96NatG8rLy9mJB2SXqKqqwrhx45jsRTrHzz//jOLiYnz99deYPHky08mKiopQVVWFqqoqVFZWwmazwWAwoKKign2ek5ODQYMGITc3Fw6Hw0dn/v7779Hc3Izff/8dSUlJuPfee9l7NjU14Z///Cc+/fRTAMCOHTuQk5ODpKQkZGVl4aeffgLQume0tLTgwIEDOHnyJJqamnD48GHZ3ktr/JIlS2R+gebmZrZn/vHHH9i9ezeWL18e0Ebd0tKCU6dO4cCBA3juuefw73//Gy+//DI2bdqkKscDrf7M3Nxctn9ec801GD9+PABg+vTpyMjIwIIFC1BZWQm73Q6Px8NOqaTk1bq6OlRVVeHpp5/G8ePH0dLSgvXr16NLly5wOBy44oor0NzcjFdeeQVdu3ZlRddKSkpQVlbGEp7Ky8tx8OBBnDx5Eg0NDazN06ZNQ1VVFW644Qa89dZb+P33333eh18XgFZb9NChQ3HttdfitddeY3od3/f79u3DsmXLMHPmTAwaNIj5l2gdPnbsGFpaWvD555/j8ssvx8GDB7F161bs3r0bv/76K4YMGYLly5fLnrt7925Mnz4dL7/8MgC5ryo9PR0VFRVYuXJlSLae1NRU7N27F2vXrsWKFStQV1eH+vp6LFmyBIMGDUJLSwvWrFmD5ORkXHXVVaxP/v3vf8Nut2Pnzp2ytgXjnfr6erZmU5t5HZJ4Vdnne/bsQVZWFvr37y/Tta688kr2++nTpyM3NxdabeuJotdddx2ef/55XHnlldi7dy8aGhpkQdk6nQ5lZWUYMWIELr30UgCta8oTTzyBmpoatjbQek6+KYPBgPj4eGRkZCA5ORkejwculwsulwvDhw+H2+3GY489hpKSEni9XtaWtWvX4uWXX0Z5eTkuvvhi/Oc//8Hy5cvx3HPP4fbbb8dFF13E7EGB7C183wGtcgX5a5V+IrIN8lXi9Xo93nvvPb+yOo0DrYH8/k3f8T6ZhQsXIj8/H1lZWRg6dCjuv/9+7Nq1C//85z9RUVEBr9eLQYMGIS8vD4WFhbjxxhvx/fff45577kFSUhLGjRvH9vy77roLOTk56N+/Py6++GIArfYSKvoxa9YsVFdXIzk5GS6XC263m9lo6AQLKoLQu3dvTJ8+HdOmTWOn1ZCv158Pl+itt95C//79UVVVBY1Gg+7du6Nr164oLy9H9+7d0aVLF2RkZMButyMhIYHp0L169ZLpKCtXrmS+zLq6OsYjd955Jz777DMsXrwYV155Jfr27ctiHLp37w6Px4NevXphwoQJLAnlH//4h6ruPGfOHNhsNpSXl0On08HpdOKOO+7A+vXrWRDnLbfcwn5z5MgRXHjhhUhKSsK6devQ3NyMAwcO4NSpU2zf53li8+bN+Pbbb3Hy5EksWrQIu3btwsqVK7F06VL06dMH9957L7O5lZaW4sMPP8Qbb7yBr7/+Gvfeey/zI9OJe2azGRdccAEeeeQRTJ8+Ha+99ho2bdrE9jIe9fX12LJlC9544w08/PDDzCbZ1NTE7JzHjh1DfX0948mffvoJR48eldmd1GyIH3zwASoqKnD33XdDp9MhIyMDlZWVbE/3er1+k2H9BTobjUY88sgjuOmmm1jQeGJiIsrKyjBu3Djs2bMHV111FZMP3W436urqMGnSJFx88cW488478cQTT+Ddd99FaWkpEhMTmQ3v2LFj+P3337F7927s3r0bBw4cwHfffYfx48ezkyfWrVsHALjkkktwwQUX4JZbbsHy5cuxb98+Nqb/+7//i8GDB8PpdGLlypVYvHgxrrnmGtx4443o27cvqqur8e233+L222/HTTfdhBtvvJHN++uvvx6vvfYa+vXrhx07dqChoQFvvvkmDh8+zMZl/fr1WL9+vUxGbGlpYbRhwwbcc889OO+882TryF/+8hdUVVUhPT0dl1xyCbNZECj24YwzzkBMTAzKy8vx008/obm52UfHbm5uxrJlyzBr1iy8+eabyM3NxVdffYWdO3fiqquuQmJiIhITE33kXpq7N998M2pqanDPPffg119/xfz581kRz2PHjuHWW29Fz5494XK5/PpCnE4ns40TPvnkE3g8Hmbrv+GGG9ieuX37dkydOhXFxcWw2+0499xz8cEHHyA1NRXnnHMOKisrkZeXx9Y8eo7NZkNKSgrjN6vVytrn8Xig1WqRnZ2NX3/9FQ0NDWhoaMCpU6d89mp/9n6SVfztSfv375eteUrs3r0bV155Jb744gu0tLTg+++/Z3tmcnIycnJymL74yiuvICUlRfW0IT7RiT89mebooEGDsHPnTowePRopKSmyOZufn4/hw4fD4XCwkyTopHSyofnzYel0OuZX8Hq9WLNmDYBWmXzx4sUAgG3btsFms2HQoEEwGo3YuHEjNm7c6BN8Hsg3p9W2nq5B6w2dnqrX63HzzTf79KvaeLz33ns444wzUFhYiOeeew4NDQ0YMGAApk2bhnHjxiE9PR0fffQRTpw4webnkSNH8OSTT2Ly5Mk+MR9qMj3vA09JSYHZbGbxHbm5uSgsLITb7Wb9VlhYiB07duCBBx5ARkYGvvvuO/zyyy/MTwu06plK2wTx59tvv81iTEaOHIlFixahtLQUixYtQl1dHQoLC5m9pbCwEBqNBueccw47SZbmRWpqKg4dOoTm5mYYjUYmN9bU1ODVV19FfX093nvvPdhsNpxzzjnQ6/UYMmQIVq1ahcbGRuh0Ojz77LOYPHky4uPjUV1djTfeeAP33nsvtNrWU2MTEhKQnZ2N0tJS9OvXD8XFxUhPT2dyQn5+PjweD1atWsXe8T//+Q8GDBiA5uZmvPTSS1i3bh2mT58Ot9sNi8WCxMRE5nMgXqF5TX2dkZEBo9HI9ExezlXynMPhkMWkKGNKMjIymM31nnvuQW1tLb788ksArT7TxMREZi/529/+xuzPvF947ty5SE9Ph9VqxZ133onU1FQ4nU5UVVUx+7HFYoHX68XMmTMRExOD1NRUPP3003jmmWeQkJCAoqIiv8lnFAtK9rT6+no0Nzejvr4eR48exU8//YRp06bhoYcewvbt29HQ0IDXX38dt912Gy644AIAwB133MFiJPV6PYtrTEpKQmpqKjIzM5Gens5sbsSP06dPR01NDdNjlPOvf//+mDRpEuLj4zFq1ChccsklWLlyJYqKipCcnIwZM2awAlXp6ekYPHgwZsyYgdTUVJw4cSKgDiUgEA2IBAwBgQAgY14wtLS0YPny5UwYe+ONN/DJJ5/IjFoEcrABwK+//sqCRdqKTz75hClggbB3714WeN0WNDU1MeXsl19+wYkTJ3zed9++fZg9eza+/PJLZvQkfPfdd7L/v/XWW6qCZnNzM3744Qe/7VAmRRw4cAB33HEHc1Lx91FDS0uLzFGnBjXDgdo1H3/8MU6ePMmclErh/LPPPmPK+c6dO9HY2Ihly5bJHGOUABAILS0teO+994IKCU8//TROnDghC4IAWvti06ZNOHr0KG655RbU1taioqICtbW16N27N2pra1XpkUceUX0OOXA2btyoqpAQX5w4cULVsfPHH3/ghRdekBkCOhLkiOXx7bffYvfu3UF/e+jQIRbcC7T2RSBFmLB69eqgBt62gA9yVIPSWBIMX3zxBbZu3Rr0ugMHDgS9Rm0+7dy5E9u2bVPtk99++y2ke3QG/vGPf+Dee++VGU784dixY3j22Wf9OuB5fPbZZyHx0alTp2SOa6DViHTo0CGmtBCam5vx9ttv+3XSqUFtD6yvr8cjjzyCDz/8MCAPnzp1Ch999BFuuOEGnz1n48aNPsY9CuYk8IazlpYWmeNMuVasWrWKOc6U+8369etx8803MwepmoOzpaUFP/74I9544w3ceuutOHHihI9ji579yiuvMGMqtfHbb79lPKk2t3bt2qWaBKbcF1966SVVuUENfF8BYIbQYDh16hR+/fVX9jc5EXm8/vrrTJ6g+afGN5s2bWLv1dzczPa3zgRvVIoWtmzZoqrsK7Fjxw6WQBIqQh1vf/DXLvqcklrV3oGf39u2bfMxVkcCmhsEXtZrbm6WyQA7duyQBYqQLKb2Ts3NzYxvg0EpCweSjUl+IVBicyjw118tLS0+a9QXX3yB119/3WfOK3+3bNkyNo8bGhqwfft2/POf/8Rbb72F77//ngWDNzU1YevWrdi7dy9OnjyJjRs34rXXXmNJnWqgNY3miD/eUSZGhIJNmzZFfe5t2LBB9fOjR4/i22+/Zf8/ePBgUDk+HBw+fBhPPPEEvvrqK9U1lYIUGhoacPfdd+Ovf/1rwH2V9rbHH38czc3NePXVV7F+/XqmCypRX1/PkuuiBTX5mtaGtiKQPPnDDz+w+cfzlHKdOHbsmM/ca2xsxJtvvulzrRLNzc3MScz/9pVXXvEJKFHD8ePH8dJLL7Gx/uWXX/z2zaFDh3D8+HHcfvvtqK2tRWVlJbp3747evXujuLgYNTU1QXWnw4cP47333vNZezoCwfoyEijniJrMTuDXHF6OV+6bp06dUl1PqN+7d+/uV1fl+/2nn37Cnj17VOU5NYSigwO+gUqEkydPsj2dT2QIdW08ceIEC+SjAFo1ULJaqNiwYQPWr1/vk7jP48svvwxpvgTCgQMHZEEkPI4ePcoCX/bt26e6tv7+++947733ZEFvhw4dUt2r+IAPNTQ2NmLDhg0hycW//vor7rvvPr9rMgCWqPzggw/inXfeCXrPcHH8+HFVGfrLL7/0KRLS2NiIlStXtqsdIRTU19dj48aNPp9v3rw55P1l+fLlPjZBNWzZsgUrV64Mu41txcmTJ3HkyBE0NDRg27Zt2Lt3L1asWIEffvhBNseV+9eJEyewadMmlmTHI9L9/dSpU1i4cCGef/75iH4P+LfTBLIZRQplgP9zzz3nI4+vXLlS1d62bt06Jls0Njb6zLnGxkY888wzbM368ssvcfLkSbzyyiuqehUVueDB74cbN24MKMdt2rRJNt/I7nPq1KmA8vKxY8dkxZzUQIlAzc3NPvNm48aN+Oqrr3zm+pYtW/Dtt9+yz/fv3y+TiwPh8OHDIdmiPvroIx+dhQKulH0Vis00EmzevFnW1o8//hhPPvkkW//37duHPXv24I8//sA333zDAuT4BE0l3n77bb8+nmB74Lp165i8EqrdOpDe196g9/njjz9YcQx/cmA09I59+/Zh586d+OGHH5i87K8QxcMPPxy0D2menThxAj/88INfm0lb9JnO8j90BJqbm/Hyyy+zNaqlpUUmj/JyeENDAz766CM0NTUxvvHX3w0NDfj0009V59GGDRvaxRZ3umPXrl04dOgQWlpa8OGHHwbcTxoaGvDFF1+w/588eZLpKcePH5ft02r+l3BsVdFAMB2K14nV9OBQ/Ik89u7dy9ZZtbntb039+OOP0dLSgsbGRixdutRH3qA9Ytu2bXj77bdDsu2ezjhw4IBq//z000/47rvvmHyg9p4nTpzwsWM2Nzf79bHt2bMHn3/+OTZv3oxffvnFb6LUO++84+OfB4Cvv/46pLW2vr4ev/76K/bt24fff//db2DcPffcgy+//BLff/+93/v+/vvv+Oyzz7B37158++23TKZsaWnBrbfeivvuuw8rVqxg7wO07pWkfx84cACffvqpj8+FsH79etW+PXz4MFatWoWPP/5Y1Ue4atWqkOI2/OH333/H119/jZaWFjzzzDOq/Q2AvYcy6ed0AM1vf34T5R7C+wLr6+v9yhWbNm3C+++/j19//dXnnQ8fPoxDhw5h8+bNLKjfX9tC9WmdOnXKb+KqP4SyPx4+fLjD7IRNTU0sSD8Yjh8/HtBWQqDCAG3hc7pPqLapYOv5nj17WCEEtXm5Zs0aVRvmrl27sHPnTuzfvx+HDx/GCy+8INOZ33nnHezduxfHjh3Df/7zH/b5unXrAtryOgM//PADXnzxxaA+v4aGBqxfv17V3rB9+/aABeZ4HDx4EA8++KBqAUoKygda58R7772HpUuXhr1W0XjystLu3buZ7sjvD1u2bMG6devw008/BY2J+P3330OOmTl06BDeffddv23/+OOPo6oLUjEZJVauXKmq461duzZsuXzPnj2MTw4fPsxs05RMprzf+++/L5vvSr8v0KrT8/4WXse4+uqr8eKLL/rE/vHr4IkTJ/DBBx/gueeeYzKMss8fffRR7Nmzx8c288knn/jYLP3JDs3NzThy5AiOHTsmW38aGxtltiSySzY3NzNb7d69e7FmzRrs2bMHmzZtwsmTJ5kP2996qLZOKOUeZWyPEsTPx44dw44dO9i84O0zv/32m+y++/fvl/F4e+nDDQ0NjF9pDw4XpGeEQgsXLmTvefLkSR9bLL9+KW2VAgLtBQ0ASAICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgJ+oe3sBggICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICJzuEAkYAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICQSASMAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBIJAJGAICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgEgUjAEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQCAKRgCEgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIBAEIgFDQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEAgCEQChoCAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAQBCIBAwBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAYEg+P8AyOeMBf00pdsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 4000x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 統計\n",
    "plt.rcParams[\"font.family\"] = \"Noto Sans CJK JP\", \"KaiXinSongB\"\n",
    "# 式 = '⿳⿳⿰甲乙丙丁戊己';檢構字式(式)\n",
    "碼表.fillna('',inplace=True)\n",
    "所有構件 = 碼表['迩原正解'].str.cat(sep='') + 碼表['迩原或解'].astype(str).str.cat(sep='') + 碼表['迩原別解'].astype(str).str.cat(sep='') + 碼表['迩原又解'].astype(str).str.cat(sep='')\n",
    "計 = Counter(所有構件)\n",
    "plt.figure(dpi=100).set_figwidth(40)\n",
    "plt.bar(*zip(*計.most_common()), width=1)\n",
    "plt.show()\n",
    "# plt.figure().get_dpi()\n",
    "# 系統字型列 = matplotlib.font_manager.findSystemFonts(fontpaths=None)# https://matplotlib.org/stable/api/font_manager_api.html\n",
    "# 字型列 = matplotlib.font_manager.get_font_names()\n",
    "# matplotlib.font_manager.FontProperties('YeMing').get_name()\n",
    "# print(系統字型列)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>碼位</th>\n",
       "      <th>漢字</th>\n",
       "      <th>迩原正解</th>\n",
       "      <th>迩原或解</th>\n",
       "      <th>迩原別解</th>\n",
       "      <th>迩原又解</th>\n",
       "      <th>字通解字</th>\n",
       "      <th>六書</th>\n",
       "      <th>說文整理番號</th>\n",
       "      <th>備註</th>\n",
       "      <th>表頭意義參見：http://kanji-database.sourceforge.net/ids/ids-analysis.html</th>\n",
       "      <th>无名: 12</th>\n",
       "      <th>无名: 13</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>U+229AB</td>\n",
       "      <td>𢦫</td>\n",
       "      <td>𢦫</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>U+58EC</td>\n",
       "      <td>壬</td>\n",
       "      <td>壬</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>壬</td>\n",
       "      <td>象形</td>\n",
       "      <td>5230010</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>U+58F0</td>\n",
       "      <td>声</td>\n",
       "      <td>声</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>声</td>\n",
       "      <td>象形</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>U+58F4</td>\n",
       "      <td>壴</td>\n",
       "      <td>壴</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>→𧯛</td>\n",
       "      <td></td>\n",
       "      <td>1600010</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>U+58FA</td>\n",
       "      <td>壺</td>\n",
       "      <td>壺</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>壺</td>\n",
       "      <td>象形</td>\n",
       "      <td>3950010</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74614</th>\n",
       "      <td>U+2B81A</td>\n",
       "      <td>𫠚</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74615</th>\n",
       "      <td>U+2B81B</td>\n",
       "      <td>𫠛</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74616</th>\n",
       "      <td>U+2B81C</td>\n",
       "      <td>𫠜</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>→齯</td>\n",
       "      <td>共產</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74617</th>\n",
       "      <td>U+2B81D</td>\n",
       "      <td>𫠝</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>534</th>\n",
       "      <td>U+31594</td>\n",
       "      <td>𱖔</td>\n",
       "      <td>⿱屶土</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>徵刀</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>74618 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            碼位 漢字 迩原正解 迩原或解 迩原別解 迩原又解 字通解字  六書   說文整理番號  備註  \\\n",
       "342    U+229AB  𢦫    𢦫                                        \n",
       "5       U+58EC  壬    壬                   壬  象形  5230010       \n",
       "6       U+58F0  声    声                   声  象形                \n",
       "7       U+58F4  壴    壴                  →𧯛      1600010       \n",
       "8       U+58FA  壺    壺                   壺  象形  3950010       \n",
       "...        ... ..  ...  ...  ...  ...  ...  ..      ...  ..   \n",
       "74614  U+2B81A  𫠚                                             \n",
       "74615  U+2B81B  𫠛                                             \n",
       "74616  U+2B81C  𫠜                       →齯  共產                \n",
       "74617  U+2B81D  𫠝                                             \n",
       "534    U+31594  𱖔  ⿱屶土                                   徵刀   \n",
       "\n",
       "      表頭意義參見：http://kanji-database.sourceforge.net/ids/ids-analysis.html  \\\n",
       "342                                                                        \n",
       "5                                                                          \n",
       "6                                                                          \n",
       "7                                                                          \n",
       "8                                                                          \n",
       "...                                                  ...                   \n",
       "74614                                                                      \n",
       "74615                                                                      \n",
       "74616                                                                      \n",
       "74617                                                                      \n",
       "534                                                                        \n",
       "\n",
       "      无名: 12 无名: 13  \n",
       "342                  \n",
       "5                    \n",
       "6                    \n",
       "7                    \n",
       "8                    \n",
       "...      ...    ...  \n",
       "74614                \n",
       "74615                \n",
       "74616                \n",
       "74617                \n",
       "534                  \n",
       "\n",
       "[74618 rows x 13 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "碼表排序 = 碼表.sort_values(by='漢字', axis=0)\n",
    "碼表排序[\"文或字\"] = 碼表排序[\"漢字\"].apply(lambda z: ord(z))\n",
    "碼表排序.loc[碼表排序[\"漢字\"]==碼表排序[\"迩原正解\"], \"文或字\"] = 0\n",
    "碼表排序 = 碼表排序.sort_values(by=['漢字'], axis=0, key=攏)\n",
    "# 碼表排序 = 碼表排序.sort_values(by=['迩原正解'], axis=0, key=依構件排序)\n",
    "碼表排序 = 碼表排序.sort_values(by='文或字', axis=0)\n",
    "# 碼表排序 = 碼表排序.sort_values(by=['備註'], axis=0, key=依備註排序)\n",
    "碼表排序.drop(columns=['Unnamed: 0','文或字'], inplace=True)\n",
    "碼表排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 碼表排序.to_excel('碼表.xlsx', '正表', freeze_panes=(1,0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "此 23 字无正解：㕚、除、虔、越、窖、翰、預、穿、顯、窒、虹、阞、衍、軒、辥、過、芮、翌、蟲、賢、靜、粵、㱅。\n",
      "請補齊上述 23 字後再運行。\n",
      "攺了 6333 條：\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>字幹式</th>\n",
       "      <th>文式</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21038</th>\n",
       "      <td>喜</td>\n",
       "      <td>⿱壴口</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21135</th>\n",
       "      <td>俁</td>\n",
       "      <td>⿰人⿱口夨</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21279</th>\n",
       "      <td>⿸厂⿰炎刀</td>\n",
       "      <td>⿸厂⿰⿱火火刀</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21381</th>\n",
       "      <td>⿰⿱歺谷又</td>\n",
       "      <td>⿰⿱歺⿱𠔁口又</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21407</th>\n",
       "      <td>咅</td>\n",
       "      <td>⿳一不口</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62399</th>\n",
       "      <td>⿱合酉</td>\n",
       "      <td>⿱⿱亼口酉</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62689</th>\n",
       "      <td>⿱父金</td>\n",
       "      <td>⿱⿽丿又金</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64505</th>\n",
       "      <td>⿰⿲人匕𡿨雚</td>\n",
       "      <td>⿰⿲人匕𡿨T⿱𦫳隹口口</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65369</th>\n",
       "      <td>⿰韋⿰⿱一⿸丆⿱一一殳</td>\n",
       "      <td>⿰⿳夂囗㐄⿰⿱一⿸丆⿱一一殳</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69803</th>\n",
       "      <td>⿱鼓⿲人匕𡿨</td>\n",
       "      <td>⿱⿰壴⿱亇又⿲人匕𡿨</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6333 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               字幹式              文式\n",
       "21038            喜             ⿱壴口\n",
       "21135            俁           ⿰人⿱口夨\n",
       "21279        ⿸厂⿰炎刀         ⿸厂⿰⿱火火刀\n",
       "21381        ⿰⿱歺谷又         ⿰⿱歺⿱𠔁口又\n",
       "21407            咅            ⿳一不口\n",
       "...            ...             ...\n",
       "62399          ⿱合酉           ⿱⿱亼口酉\n",
       "62689          ⿱父金           ⿱⿽丿又金\n",
       "64505       ⿰⿲人匕𡿨雚     ⿰⿲人匕𡿨T⿱𦫳隹口口\n",
       "65369  ⿰韋⿰⿱一⿸丆⿱一一殳  ⿰⿳夂囗㐄⿰⿱一⿸丆⿱一一殳\n",
       "69803       ⿱鼓⿲人匕𡿨      ⿱⿰壴⿱亇又⿲人匕𡿨\n",
       "\n",
       "[6333 rows x 2 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "未列字 = [];有字无解 = []\n",
    "def 窮解(構字式:str, 全表:pd.DataFrame):\n",
    "\t窮式 = ''\n",
    "\tfor 字幹 in 構字式:\n",
    "\t\tif ord(字幹) < 0x3000 or (ord(字幹) >= 0xff00 and ord(字幹) < 0xfff0):\n",
    "\t\t\t窮式 += 字幹\n",
    "\t\t\tcontinue\n",
    "\t\t窮字幹 = 全表.loc[全表[\"漢字\"]==字幹, \"迩原正解\"]\n",
    "\t\tif len(窮字幹) < 1:\n",
    "\t\t\t未列字.append(字幹)\n",
    "\t\t\t窮式 += 字幹;continue\n",
    "\t\t窮字幹 = 窮字幹.item()\n",
    "\t\tif 窮字幹 == '':\n",
    "\t\t\t有字无解.append(字幹)\n",
    "\t\t\t窮式 += 字幹\n",
    "\t\telse:\n",
    "\t\t\t窮式 += 窮字幹\n",
    "\treturn 窮式\n",
    "# 碼表排序[碼表排序.duplicated('漢字', False)]\n",
    "窮一 = 碼表排序[\"迩原正解\"]\n",
    "碼表排序[\"迩原正解窮\"] = 碼表排序[\"迩原正解\"].apply(lambda x: 窮解(x, 碼表排序))\n",
    "計數 = 0\n",
    "while (碼表排序[\"迩原正解窮\"] != 窮一).any() and 計數 < 10:\n",
    "\t異條 = 碼表排序[\"迩原正解窮\"] != 窮一\n",
    "\t窮一 = 碼表排序[\"迩原正解窮\"]\n",
    "\t碼表排序.loc[異條, \"迩原正解窮\"] = 碼表排序.loc[異條, \"迩原正解窮\"].apply(lambda x: 窮解(x, 碼表排序))\n",
    "\t計數 += 1\n",
    "未列字 = list(set(未列字));有字无解 = list(set(有字无解))\n",
    "未列字數 = len(未列字);无解字數 = len(有字无解)\n",
    "if 未列字數 > 0:\n",
    "    print(f'此 {未列字數} 字未列：{\"、\".join(未列字)}。')\n",
    "if 无解字數 > 0:\n",
    "    print(f'此 {无解字數} 字无正解：{\"、\".join(有字无解)}。')\n",
    "if 未列字數 > 0 or 无解字數 > 0:\n",
    "    print(f'請補齊上述 {未列字數+无解字數} 字後再運行。')\n",
    "else:\n",
    "    print('蕆。')\n",
    "差異 = 碼表排序[\"迩原正解\"].compare(碼表排序[\"迩原正解窮\"], result_names=('字幹式','文式'))\n",
    "print('攺了',str(len(差異)),'條：')\n",
    "差異"
   ]
  }
 ],
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