{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1413313/924302625.py:7: RuntimeWarning: invalid value encountered in log\n",
      "  return A * np.log(t + B)\n",
      "/opt/module/anaconda2022-10/envs/py3916/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 26102 (\\N{CJK UNIFIED IDEOGRAPH-65F6}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/module/anaconda2022-10/envs/py3916/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 38388 (\\N{CJK UNIFIED IDEOGRAPH-95F4}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/module/anaconda2022-10/envs/py3916/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 28201 (\\N{CJK UNIFIED IDEOGRAPH-6E29}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/module/anaconda2022-10/envs/py3916/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 24230 (\\N{CJK UNIFIED IDEOGRAPH-5EA6}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/module/anaconda2022-10/envs/py3916/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 21407 (\\N{CJK UNIFIED IDEOGRAPH-539F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/module/anaconda2022-10/envs/py3916/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 22987 (\\N{CJK UNIFIED IDEOGRAPH-59CB}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/module/anaconda2022-10/envs/py3916/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 25968 (\\N{CJK UNIFIED IDEOGRAPH-6570}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/module/anaconda2022-10/envs/py3916/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 25454 (\\N{CJK UNIFIED IDEOGRAPH-636E}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/module/anaconda2022-10/envs/py3916/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 25311 (\\N{CJK UNIFIED IDEOGRAPH-62DF}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/module/anaconda2022-10/envs/py3916/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 21512 (\\N{CJK UNIFIED IDEOGRAPH-5408}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/module/anaconda2022-10/envs/py3916/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 26354 (\\N{CJK UNIFIED IDEOGRAPH-66F2}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/module/anaconda2022-10/envs/py3916/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 32447 (\\N{CJK UNIFIED IDEOGRAPH-7EBF}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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ppyzHaowcOZJhw4alvY+NjaV6UR+dLiLiiSzLDFB96y1YtMg8PgHziOTxx02IucQjB4+XlARr1qQHl02b0r8HYAboNm+e3uPSrBkUL56vJVUuk72hA9k9Ly/YFmQ2bdpEdHQ0jRo1SmtLSUlh9erVTJo0iV27djFp0iS2b9/ODTfcAED9+vX56aefePfdd3n//fczva6vry++vr4Fcg8iIpIPEhPhiy9MgLlw9lG7djB4MERGFs61XyzLPC777jsTXFatMjOxLhQaaoJL27bQsmWBb6HQNLg8VQJKcCQmMaulYAkKMFOxC4ptQaZ169Zs27bNpa1v377UqVOHZ555hjPnf3heF/1l9fb2xnlhIhURkcLhyBGzdcDkyXDsmGnz84PeveGJJ+D66+2tLz9ER5sel9TwcviizYgDA9ODS5s2cNVV9tR5nreXg9GdQhk4c3NWS8EyulNoga4nY1uQKVOmDGFhYS5tpUqVokKFCoSFhZGcnExISAgDBgzgtddeo0KFCixcuJDly5dnOu1YREQ81Nat8OabphcmOdm0Va9uxr7071+4Zh8lJZnF+r77znxs2eJ6vEQJuOUW0/vUti3Uret2+z5FhlVh8n2NMqwjE1SU15HJTPHixVm6dCkjRoygU6dOxMfHExISwowZM7j99tvtLk9ERK6E0wlff20CzA8/pLeHh8OQIdC1qxkD4uksC/74w4SWb7/N/HFRgwYmtLRrZ9Zz8YAlLCLDqtA2NEgr+17sxws38gKuvfZa5s+fb08xbmzVqlUMGDAgwyBpp9NJy5YtWb9+vcsU9FTx8fHs2LGDiRMn8umnn1Lsov9JnD17llGjRtG8eXM6dOiAn59fhmsEBwezYMGCvL0hESk6zpwxu05PnAi7d5s2b2/4z39g6FAzYNXTnTxppkN/+635OHjQ9XhQkAkt7dqZx0WBgfbUeYW8vRwFNsX6UtwqyEj2JCQk0LNnT8aMGePSvm/fPkaMGIHD4WDr1q0ZPq9Vq1ZYlsXJkyeZNGlShvV4pk+fTlxcHMnJyURERDB9+vQM12jevHne3YiIFB1RUWb69OTJcOKEaQsIgIcfNo+Q8mCNE9ukpMDGjSa0LFsG69a5zi7y9YUWLaB9exNe3PBxkSdTkBERkfyzfTu8/rrZA+nsWdN2zTVm9tGDD5q1TzzR4cPmcdGyZWaQbmo4S3X99Sa4tG9vxrxk0sMteUNB5kKWlfHZZUHx81NCF5HCIXX7gNdeM70UqW66CYYNgy5dPG/13bNnzZouy5bBN9/Ab7+5Hg8IMI+JIiNNr4sn9zB5GAWZC505Y9+/DuLjoVQpe762iEheSE6G2bNNgEld/8XLy+w6PWyYWbzNkxw4YELLN9+YYBYfn37M4TA7RUdGml6XZs0Kx+BkD6TvuoiIXJm4OJg61QzgTR3Y6ucH/fqZGUjXXGNnddmXlAQ//ZQeXn7/3fV45comtERGmllGlSrZU6e4UJC5kJ+fa+Iu6K8tIuJJoqLM6rvvvw8xMaYtMNBsHzBwoGes/7J/PyxdaoLLypVmU8pUXl5mOniHDia8NGxYOFcU9nAKMhdyOPR4R0TkcnbtMo+PPvkkfQBv7drw5JNw//3uvQ7K2bNmQbqlS83HhTtog5kaHRlpwkvbtlCunD11SrYpyIiISPasWwcTJsDChWZAL5gBvMOHQ6dO7ttbcfiw6XFZutTMMIqLSz+W2uty++0mvNSv7773IZlSkBERkaxZlpl5NH68WZU2VefO8PTTJsi4m5QUE7qWLjWrB1+8rlblyia03H67el0KAQUZERHJ6Nw5mDvX9MCkzkAqXhx69TIBxt02cDxxwgSur782U6SPH08/5nBA06YmuNx+OzRqpF6XQkRBRkRE0iUmmi0EXnkF/v7btJUqBQMGmC0EqlWzt75UlgU7dpjgsmSJWePlwtV0y5Y1M4xuv92Mealc2bZSJX8pyHiggIAAlixZkuku4O3bt+fUqVM0adIk08/18vKiWrVqPPXUU5kef/bZZylZsiTbt2/P9Bp169a9suJFxC2kOC3XDf8qFsf7gynwxhtw5Ig5qUIFeOIJs4WAO8xASkw0j7cWLzbhZf9+1+M33AAdO5qPiAit61JEOCwrdcRW4RQbG0tAQAAxMTH4+/u7HEtMTGTv3r0EBwdn2IBRckbfSxHPsWx7FGMX7yQqJpGyCbH03biYvpsX4594fvmJatXgqaegf3/7Z3JGRZmxLkuWmIG6F06P9vWF226DO+4wPS9XX21bmZL3LvX7+0KKqyIiRciy7VEMnLmZSnHHGbVhAfduXUap5EQA9pSvxpkhw6j7zCDw8bGnQMsyg3NTe102bHA9XrWqCS533GFCjN1BS2ynICMiUkSkOC2mfLKSF77/nO6/fYdvyjkAtgfW4t3md/PddeFULlaKn4sVp0B3QkpMhB9+gEWLTHj55x/X4zfeaKZ3d+xoFqXTvnRyAQUZEZGiYPdujj87hjkL5lDcmQLAhqtCeTe8Oz9e0zgtHETFJLJ+7wnCa1XI33qio81A3cWLzS7SFz4y8vMzGy/ecYcJL0FB+VuLeDQFGRGRwmzHDnjpJZg9m8rnh0SuvrohkyJ6sL56WKafEh2XmPd1WBb88YfpdVm0CNauTV9UD+Cqq0yvS+fOcOut7r06sLgVBRkRkcJo61YTYObPT2s6cWs7HqzWnq1Va1/yUyuXyaMQce6cmRb91VcmvPz1l+vxRo1McOncGRo00CMjyRUFGRGRwmTjRnjxRRMcUnXrBs89R0C9+hydsBJHTCKZTVd1AEEBJWgafAVTrePjzaOir74yj44uXJjOxwdatzbB5Y473GdNGvFoCjIiIoXB+vUwdqyZqgymd6NnTxg1yqyvAngDozuFMnDmZhzgEmZS+0JGdwrF2yuHPSNHj5qxLgsXwvffQ1JS+rHy5c04ly5dzLiXMmVyd38iWVCQ8UCrVq1iwIABGdZrcTqdtGzZkvXr15N04f9IzouPj2fHjh1MnDiRTz/9lGIXLRZ19uxZRo0aRfPmzenQoQN+fn4ZrhEcHMyCBQvy9oZEJPd+/dUEmGXLzHsvL7ONwKhRZkfqi0SGVWHyfY3S1pFJFRRQgtGdQokMq5K9r/vnnya4LFyYcbzLNdeY4NKli9mLSQvTST7S3y4PlJCQQM+ePRkzZoxL+759+xgxYgQOh4OtF2+SBrRq1QrLsjh58iSTJk2iVatWLsenT59OXFwcycnJREREMH369AzXaN68ed7diIjk3sUBxtsb7r8fnn0Wrr32kp8aGVaFtqFBriv7Bpe/dE+MZcGmTSa4LFgAO3e6Hm/SxASXO+80PUAa7yIFREFGRMSTrFsHY8a4Bpg+fUyAqVUr25fx9nJcfop1cjKsXp3e83Lh+i7FikGrVia4dO4M1avn7D5E8oiCzIUsC86csedr+/npXzAikrUNG2D0aPjmG/M+NcCMGmUe5eSVhAQzWHfBAjNg+OTJ9GOlSpkNGO+6y2wJUK5c3n1dkVxSkLnQmTNQurQ9Xzs+Xktti0hGmzebAJO6Say3N/TuDc89l3cBJibGzDD68ksTlC78B13FiqbH5a67zIyjkiXz5muK5BEFGRERd7RtmwkwqYPrvbzgvvvg+echJOTKr3/smJki/eWXZqZRcnL6serVoWtXE140WFfcnP52XsjPz/SM2PW1RUT++MOMgZkzxzzudjjgnntMqLnuuiu79j//mGA0fz789BM4nenHrr/eBJeuXc1CdXrULR5CQeZCDoce74iIPfbuNbOQPv00PWDcfbcJNaGhub/u33+b4DJ/vhkofKFGjcxieXfdZYKMiAdSkBERsdOhQ2YrgQ8/NEv6gxmTMnasWbY/N3btMsFl3jzYsiW93eGAiIj08HL11VdavYjtFGREROxw7BiMHw/vvpu+Em67dmZ7gaZNc3YtyzLrusybZz62b08/5uVlpkn/5z9mqnSVbC54J+IhFGRERApSbCy8/jq88Ub6mLybb4b//hduuSX717EsMyB43jyYO9eMrUlVrJiZYXT33WaRuooV8/YeRNyIgoyISEFISDC9L+PGwYkTpq1RIxNg2rfP3uBay4LffjMDgefNg92704/5+Jgenf/8xzya0hovUkQoyHiggIAAlixZwpLUdSUu0L59e06dOkWTJk0y/VwvLy+qVavGU089lenxZ599lpIlS7J9+/ZMr1G3bt0rK16kqDl3DqZPN4N2Dx0ybXXqmHExXbtePsBcGF7mzjV7HKXy9TUL1N19t9lNOiAgv+5CxG05LMvKbDf3QiM2NpaAgABiYmLw9/d3OZaYmMjevXsJDg7OsAGj5Iy+lyIXsSwz4Pa558zgW4AaNcwg3vvuu/TaLJZlxrnMnm0CzMXhpUMH6N7d7Cp90f/XRAqLS/3+vpB6ZEREMpHitHK2qeKFVq6EZ56BjRvN+4oVTaB55BETRLKyc2d6eLlwzIuvr9kSILXnpUyZ3N+YSCHjNkFm/PjxjBw5ksGDBzNx4sS09rVr1zJq1CjWrVuHt7c3DRo04Ntvv6VkHi6TXcg7pQqEvodSmCzbHsXYxTuJiklMa6sSUILRnUKJDLvErJ8tW2DECLNXEZgtT558EoYNy7rnZPfu9PBy4WwjHx/T89Kjh8KLyCW4RZDZsGEDU6ZMoV69ei7ta9euJTIykpEjR/LOO+9QrFgx/ve//+Hl5ZUnX7d48eIAnDlzJk+DUVF09uxZALy9vW2uROTKLNsexcCZm7k4mh+JSWTgzM1Mvq9RxjCzd6/pcfn8c/O+eHHT+/Lcc1C5csYvsnevCS6zZ7uu81K8uBn426OHGbCrx0Yil2V7kImPj6dXr15MnTqVl156yeXY0KFDeeKJJxgxYkRaW+3atfPsa3t7e1O2bFmio6MB8PPzw6FluXPM6XRy7Ngx/Pz8KKY9WcSDpTgtxi7emSHEAFiAAxi7eCdtQ4PMY6bjx82g3XffTd+r6N57zVowF2/oeOhQeni5cIXdYsWgTRsTXu68E8qWzZd7EymsbP+tM2jQIDp27EibNm1cgkx0dDTr1q2jV69eREREsGfPHurUqcN///tfbr755iyvl5SURFLq4lKYwUKXEhQUlPb1JPe8vLyoUaOGgqB4tPV7T7g8TrqYBUTFJLLx90M0WzzTTKVO/X9M27YwYQI0bJj+Cf/+a6ZJz5oFq1ebQbyQvkhdz55m5lKFCvl2TyKFna1BZtasWWzevJkNGzZkOPb3338DMGbMGF577TUaNGjAJ598QuvWrdm+fTvXXnttptccN24cY8eOzXYNDoeDKlWqULlyZZIv3P1VcsTHxyfPHvmJ2CU6LusQA+CwnHTd/gP1Wj8ER6NMY/368MorZg0XgLg4WLgQvvgCli9P33YAzE7SPXqYQbvn/xElIlfGtiBz8OBBBg8ezPLlyzOdrus8v2nagAED6Nu3LwANGzZkxYoVTJs2jXHjxmV63ZEjRzJs2LC097GxsVSvXv2y9Xh7e2t8h0gRV7lM1ksH3LRvK8/+MI0bos0/sqhRwzxW6tULzp6FL7804WXJEki8IBA1bGh2r+7eHWrWzOc7ECl6bAsymzZtIjo6mkaNGqW1paSksHr1aiZNmsSu8+suhF606+v111/PgQMHsryur68vvpea3igikoWmweWpElCCIzGJaeNkrj22n2d/nMatf28CIK5EKUqNeR6vQYNg7Vro39+sF3PhY+zatU146dnTvBaRfGNbkGndujXbtm1zaevbty916tThmWee4ZprrqFq1appgSbV7t276dChQ0GWKiJFhLeXg9GdQhk4czOVTp9kyM+f0fN/3+FtOUn28ubThh2p+1BPbvx9HYSEwNGj6Z9crZoJLvfea3at1ngxkQJhW5ApU6YMYWFhLm2lSpWiQoUKae3Dhw9n9OjR1K9fnwYNGjBjxgz++OMP5s2bZ0fJIlIERNYqy7cJP3PVB29R6mwCAKuvbsiRoBr0iNpGqUfuTT+5fHnzyOjee834F40TEylwts9aupQhQ4aQmJjI0KFDOXHiBPXr12f58uXUqlXL7tJEpLCxLDO7aMQIrjv/+DqpajXO+vhyy74tsO/8ei8lS5pp0vfeawb4+vjYV7OIFO29lkREAPj1Vxg61PwJUKIEJCWlT5f29jbTq3v1MiGmdGnbShUpKrTXkojI5Rw8aPZE+uIL1/bUWUdNm5oNHrt3h8DAgq9PRC5LQUZEip4zZ8z+Rx995LrOC0CtWia89OoFWaxXJSLuQ0FGRIqOQ4fg6adh7tz0LQXA7Gl0333mo3lzzTgS8SAKMiJSuJ05Y1bafeed9DEwqZo1M7tV3367Bu2KeCgFGREpfJxO+OknmDHDbNJ45kz6MS8vuOMOmDwZqla1r0YRyRMKMiJSeOzZY8LLp5/Cvn0Zj3fuDO+9B1ddVeCliUj+UJAREc8WG2vGvEyfDj//nN7u5WV6ZgAaN4a334aICFtKFJH8oyAjIp7H6YSVK014+fJLSDAr8OJwQKVKEB1tzqlYEcaNgwcf1Kq7IoWUgoyIeI7UR0czZsCFm8ded53ZWXr1ahNivL3h0Udh7FgoV86+ekUk3ynIiIh7O30a5s2Djz+GVavS2wMCzA7TISFm4O7y5aa9ZUszQ6luXXvqFZECpSAjIu7HssxU6WnTzKyjuDjT7nCYrQL69oX69c3U6fffN8eqVIHXXzc7UGsdGJEiQ0FGRNzH0aPwyScmwPzxR3p7rVomvPTuDZUrw6uvmveJiVCsGAwZAv/3f1CmjG2li4g9FGRExBYpTov1e09w7GQ8123+mdpfz8Wx9Ov0LQP8/OA//4F+/aBFC9PL8t130Lo1/PmnOadVK3j3XQgNte0+RMReCjIiUuCWbY/io+nLafXLEv6zfQWB8SfSDzZvbmYZ9ehhtg4As7XAsGEwZ455HxRkHiPdc48eI4kUcQoyIlJwEhP531sf4f/eB8w98Fta8/GS/iwIu43Z9drx5JA7iQyrYg6cO2d6XJ5/3oyT8fKCxx83s5ECAmy6CRFxJwoyIpL/tm2DqVOxZs6k/smTADhxsOqaRsyu144VIU1J9i6OAxi7eCdtQ4Pw3rgBHnkEtmwx12je3MxOatDAttsQEfejICMi+SM+3sw4mjoV1q0DwAH841+JOfXaMbduG6L8K7l8igXERx/nWO/+BH3+sZm9VK4cjB8P/ftrUTsRyUBBRkTy1pYt8MEH8Nln6dOmixWDzp1Ze9td9Nrvj9PLO+PnWRa37/qFMd9PofJp02vD/ffDa6+ZmUoiIplQkBGRKxcXB7NmmQCzcWN6e0iI6Ul54AEIDIQ9x3FO/TXDp1eLOcqL373HrX9vAiAhuBYlP5oKt95aQDcgIp5KQUZEcm/LFpgyxfS+xMebNh8f6NoVHn7YrLJ7weOgpsHlqRJQgiMxiViAtzOFBzd8xdBfPsMvOYkk72J80upeHlz0PviVtOeeRMSjKMiISM6cPm3Gvrz/PmzYkN5+3XUmvPTubTZuzIS3l4PRnUIZOHMzdY/8xbhl7xB2dA8Av1YPY1T7xxg+uDPeCjEikk0KMiKSPTt3mvDyyScQE2PaiheHbt1MgGnVKltrukQG+7MyajE1Pp2Kt+XkVInS/PfWB/n55k6M7nxD+tRrEZFsUJARkawlJcGXX5oAs3p1evs118CAAWbsS04G4n73HQwYQPC+fQD826krGwc/T9erqzM+uDzeXlrcTkRyRkFGRDLat88M3P3wQzh2zLR5e0PnzibAtG2bs6nQx4/Dk0/CjBnmffXq8P77VLz9diLzvHgRKUoUZETEcDrh22/hvffg66/NGi4AV10FDz1kZh9ddVXOrmlZMHeuWY03Oto8enr8cXjpJW3wKCJ5QkFGpKg7ftzsNj15Muzdm97epg08+ih06mTWgcmpqCjz+QsXmvehoaaHJzw8T8oWEQEFGZGia+NGs4/RrFmQmGjaypaFvn3N1gDXXZe761oWTJ9uNnk8dcqEoGefNR++vnlUvIiIoSAjUpQkJZlHPZMmpW0bAEDDhjBokNlN2s8v99c/cMA8hvruO/O+cWPT21Ov3pXVLSKSBQUZkaLgn3/MzKMPPkgfvOvjA3ffDY89Bs2aZWvqdJYsy1x7+HCzyq+vL7zwgumVyc1jKRGRbNL/YUQKK8syU6YnTYIFCyAlxbRXq2YeHfXvb7YNuFL79plrrVhh3kdEmF6Y2rWv/NoiIpehICNS2Jw5A59/Du+8A7/9lt7eqpXpfenSJW96SSzLbE/w1FNmtd+SJeHll82sJO9MNoUUEckHCjIihcWBA2bq9NSpcOKEaStZ0uwg/dhjULdu3n6tfv3g++/N+5tvNr0w116bd19DRCQbFGREPJllwS+/wFtvuT4+uvpqM3i3Xz8oVy5vv960aTB0qBkLU7IkjBtnemFyskCeiEgeUZAR8URJSWbjxrfegs2b09tvvRUGD4Y77sj7xzuHD5sZSUuXmvcREfDxx7mfpi0ikgcUZEQ8SXS0mX303ntw9KhpK1EC7rsPnngibx8fXWjWLLO43cmTZkbSSy+ZXhmNhRERmynIiHiCbdtg4kT47DPTGwNYVasSfUcbom6pT/Gra1Pn+uvJ81jx77/mEdWcOeZ948Zm9+vQ0Lz+SiIiueI2D7XHjx+Pw+FgyJAhGY5ZlkWHDh1wOBwsTF3uXKSwczrNY5y2bc2CctOmmRBz443se+YRoh/0IbDKQhr8OZYblt/Lvy9dx5ZvZ+Td11+61PTwzJljel7GjIG1axViRMStuEWPzIYNG5gyZQr1slj9c+LEiTiuZLEuEU+SkACffgpvvgl//GHavLygWzcYMoQtsbupv3Zwhk+rZB2n0pon2AI0bN8n91//9Gkzpfr998376683vTBNmuT+miIi+cT2Hpn4+Hh69erF1KlTKZfJ7IqtW7fy+uuvM23aNBuqEylAR4/C//0fVK8OAwaYEOPvb1bH3bMH5swhpWlTqv76AgBeF2X71PdV1o4l5dy53NWwbh00aJAeYoYMgU2bFGJExG3ZHmQGDRpEx44dadOmTYZjZ86c4d577+Xdd98lKCgoW9dLSkoiNjbW5UPEre3caVbGrVkTXnzR7EZ99dWmR+bgQXj9dfMe+GPdtwRyPEOISeXlgCCO88e6b3NWw7lz5tHRTTfBX3+Z1X+//97UULLkldydiEi+svXR0qxZs9i8eTMbNmzI9PjQoUOJiIigS5cu2b7muHHjGDt2bF6VKJI/LAtWrYLXXoOvv05vb9YMnnwS7ror09V3E04eytbls3seYILLffelbyJ5771mV+yyZbN/DRERm9gWZA4ePMjgwYNZvnw5JUqUyHB80aJFrFy5ki1btuTouiNHjmTYsGFp72NjY6levfoV1yuSJ86dg3nzTIDZtMm0ORxw550mwEREXHLzxpLlrsrWl8nWeamL2w0ebMbFBATA5MlmB2wREQ/hsCzLsuMLL1y4kLvuugvvC9ahSElJweFw4OXlxcCBA3n33XfxumC10JSUFLy8vGjRogU//vhjtr5ObGwsAQEBxMTE4O/vn9e3IZI9p0/DRx+ZRzX79pm2kiWhb18zDiWbS/unnDvHvy9dRyUr88dLTguiHRWo9NxuvC+1n9Lx4/Dww/Dll+Z9y5ZmQG+NGjm6LRGR/JLd39+29ci0bt2abdu2ubT17duXOnXq8Mwzz1CxYkUGDBjgcrxu3bq8+eabdOrUqSBLFcm96Giz+/S776bvf1Spktn76NFHoWLFHF3Ou1gxDoePptKaJ3BargN+nef/SRIVPpqgS4WYH34w+y8dOgTFi5txOU89pcXtRMQj2RZkypQpQ1hYmEtbqVKlqFChQlp7ZgN8a9SoQXBwcIHUKJJrf/9tHh99/DEkJpq2kBDz+KhPnysaQNuwfR+2AFXXjiWQ42nt0Y4KRIWPznrqdXKymRU1YYJ5rHTddWaX7MaNc12LiIjd3GIdGZFCY8sWExTmzjUL2gHceCM884wZB5NHvR4N2/chpXUvdqz7loSThyhZ7irqNGufdU/Mnj1m7EvqwPr+/c1KwaVK5Uk9IiJ2sW2MTEHRGBnJd5ZlHteMHw/Ll6e3R0aaANOy5SUH8Oa7zz6DgQPNbtXlysHUqWZxPRERN+b2Y2REPJ7TCV99ZQLM+vWmzdsbevSAp5+G+vXtrS8+3ozFmXF+24IWLUyo0Sw+ESlEFGREcio5Gb74wgSY3383bSVKQL9+ZgyMO4zh2rrVBKrdu832Bv/3fzBqVKZr04iIeDL9X00kuxISzODdV16B/ftNW0CA2R168GCoXNne+sA85nrvPbOtwdmzZoXezz6DW26xuzIRkXyhICNyOXFxZqG4N94w+yGBCS1Dh5op1O4y9urUKTOId/58875TJxO8KlSwtSwRkfykICOSlRMn4J134K234ORJ01azJgwfDg8+6F57EK1fbx4l7dtn1oZ55RXTS6Rd40WkkFOQEblYdLTpfXn3XTNgFsyaKyNHQq9eJii4C8uCt9824So52YzPmT3bTPkWESkCFGREUh0+DK++ClOmmPEwAPXqmUGy3bq538q3p06ZAcap2wx06wYffqjNHkWkSFGQETlwwCxi9+GHZoAsmB6N554z40zc8fHM5s1w991mBeHixeH1181Ua3esVUQkHynISNG1bx+MG2cGxCYnm7abboLnn4d27dwzFFiW6TEaPNiErquvhjlz9ChJRIosBRkpev7+G15+2SwUd+6cabv1VrPWit2r8F7K6dMwYICZTg3QpYsJYeXK2VuXiIiNFGSk6Pj7b/jvf02ASUkxbW3bmgBz88321nY5u3aZMTA7dpixOuPHm8X33DV0iYgUEAUZKfwyCzCRkSbAhIfn/9d3psD+NRB/FEoHQs0I8MrBwOG5c8107/h4CAoys5K0wJ2ICKAgI4XZ3r3w0kuuAaZDBxg9Gpo1K5gadi6CZc9A7OH0Nv+qEDkBQjtf+nPPnTObTr7xhnnfqpXZGiEoKN/KFRHxNF52FyCS5/bvh4cfNmu/TJtmQkyHDvDrr7B0acGGmDm9XUMMQGyUad+5KOvPPXoU2rRJDzFPP2121laIERFxoSAjhcc//5gtA669FqZONT0a7drB2rUFG2DAPE5a9gxgZXLwfNuyEea8i61dC40awapVUKaM2XJgwgRt+CgikgkFGfF8R47AkCEQEmL2REpOhtat4eef4dtvoXnzgq9p/5qMPTEuLIg9ZM5La7Lg/ffNzKnDh+H6683WA1275nu5IiKeSv/EE891/LjZU+idd9JX4m3RAl580YQBO8Ufzdl5iYlmQbuPPjLv777bPBYrXTp/6hMRKSQUZMTzxMaasSNvvGF2pgbz2OjFF824EneYklw6MPvn/fOPmVq9fj14eZlF+oYPd4/7EBFxcwoy4jnOnIFJk8x4kRMnTFuDBibAdOzoXr/4a0aY2UmxUWQ+TsZhjh90wt2NzUaV5crBrFlmXI+IiGSLxsiI+zt71ox9CQkx05FPnIA6dczS/Js2wR13uFeIAbNOTOSE828uru38+9hW0LqNCTH16sHGjQoxIiI5pCAj7islBWbONINeH30UoqLM3kLTp8P27WYciZcb/xUO7QzdPwH/Kq7tflXgj3AYO9nMrOrRA9asgWuusadOEREPpkdL4n4sC5YsgWefNYEFIDDQbOb40EPg42NvfTkR2hnqdExf2TfRB558FX5aZnqRXn7Z9DK5W4+SiIiHUJAR9/LzzzBiBPzyi3lftqxZDO6JJ6BUKVtLyzUvbwhuAVu3wn+6wIED4O8Pn39uxvaIiEiuKciIe9i+HUaOND0xACVLwuDBJsQUht2dv/wS7r/fDFi+9lr46ivzyExERK6IGw8wkCLhwAF44AEz2HXJErOz84AB8NdfZhqyp4cYyzIbVnbrZkJM27awbp1CjIhIHlGPjNjjxAkTVN55B5KSTNt//mN+6V93nb215ZWEBOjXz2z0CObx2Ouva6sBEZE8pP+jSsFKTDTh5eWX4dQp09aypVmht2lTW0vLU0eOQJcuZpG7YsXg3XfNRpYiIpKnFGSkYDidZnDrqFHmcRJAWJhZ3K5Dh8I1a+d//4NOneDgQShf3mz62KqV3VWJiBRKCjKS/1auNEvub95s3l91Fbz0khn86u1tb215bckSuOceiI83j8iWLDGDe0VEJF/kKMgkJydjWZktt545Ly8vimk8QNH1++9m1lHqTKQyZczMpMGDwc/P3trymmXBxInw5JPm9W23wbx5nj9YWUTEzeUoZdxwww1Uq1btsmHG4XBgWRanT59m/fr1V1SgeKDoaBg9GqZONavzFisGAweaBe0qVbK7urx37pwZyDt5snn/0ENmTEzx4vbWJSJSBOQoyJQqVYqVK1dm+/wbb7wxxwWJB0tMNL0SL7+cviv1nXeacTCFZSbSxeLioGdPWLrUjPN55RXTK1OYxvyIiLixHAUZRw7/55zT88VDWRbMnm1W5N2/37Q1aWKmGt9yi7215adDh8yGlVu3mgX8Zs6Erl3trkpEpEjRABa5MuvWwdChsHateV+tmlkf5t573XtDxyv1229w++0mzFSuDIsXF67p4yIiHkJBRnLnn3/MwN2ZM817Pz/TI/Pkk4VnIK8zJX2zx9KBUDPC7Ju0fLlZqTcuzqzQu3Sp2ZVbREQKnNv8k3n8+PE4HA6GDBkCwIkTJ3j88cepXbs2JUuWpEaNGjzxxBPExMTYW2hRd+YMjB1rxrykhpgHHoA//zSDeQtLiNm5CCaGwYw7YH4/8+fEMHh5iOmJiYsza8OsWaMQIyJioxz1yPj4+BAREZHt8ytWrJit8zZs2MCUKVOoV69eWtvhw4c5fPgwr732GqGhoezfv59HHnmEw4cPM2/evJyULXkhdRzM00+bhd4Abr7ZDO5t3NjW0vLczkUwpzdwwew8y4LFe+HHt8z7e+6Bjz8GX19bShQRESNHQaZp06YcO3Ys2+eHhIRc9pz4+Hh69erF1KlTeemll9Law8LCmD9/ftr7WrVq8d///pf77ruPc+fOaX2agrR5s1n75eefzfsaNeDVV+Huuwvf7BxnCix7BpcQ47RgSSJsSTbvb60An8yAYppeLSJitxylgdWrV7No0aJsL4p399138+KLL17ynEGDBtGxY0fatGnjEmQyExMTg7+//yVDTFJSEkmpmxACsbGx2apVMnHsmNlS4MMPTY9EyZJmXMxTT5nXhdH+NRB7OP19sgXzEmD3OXAAHUrAjclw8FcIbmFbmSIiYuR4+nWNGjWyff7lAs+sWbPYvHkzGzZsuOy1/v33X1588UUevszGe+PGjWPs2LHZrlEyce4cvPce/N//QeqYpHvuMevBVK9ub235Lf5o+uszTvgiAf5JMf+ldCsJdYpnPE9ERGyTo8G+ebmOzMGDBxk8eDCfffYZJUqUuOR1YmNj6dixI6GhoYwZM+aS544cOZKYmJi0j4Op4zkke374ARo2NI+SYmKgQQP46Sez4WNhDzFgZicBnHLCtDMmxJQA7vdLDzEXniciIraybaDJpk2biI6OplGjRmltKSkprF69mkmTJpGUlIS3tzdxcXFERkZSpkwZFixYQPHLLPvu6+uLrwZg5tw//5ip03PmmPfly5sVevv3L3wbO15KzQg4XR6m7Yc4C/wd0MsPKqd+DxzgX9WcJyIitrMtyLRu3Zpt27a5tPXt25c6derwzDPP4O3tTWxsLO3bt8fX15dFixZdtudGcuHsWXjzTXjxRTh92ixi98gj5n358nZXV/DW/gofHDMhppIX3OcH/qkdl+d7GCPHm/VkRETEdjkKMgkJCbzwwgvZOvdy42PKlClDWFiYS1upUqWoUKECYWFhxMbG0q5dO86cOcPMmTOJjY1NG7hbqVIlvItSL0F+Wb4cHn8cdu0y72+6CSZNMo+TiqKvvzYzsRISoEEd6JYC5y4YC+Nf1YSY0M721SgiIi5yFGSmTJlCQkJCts9v3759jgtKtXnzZtatWwdknMa9d+9ertYiZLn3zz8wbBjMnWveBwaazQ7vv7/wTafOrpkzzcJ+KSlmwbu5c6GEb+Yr+4qIiNtwWNmdS+2hYmNjCQgISJu6XaQlJ8Nbb8GYMemPkR57DF54AQIC7K7OPm+9BedXlOa++2DaNLjMWCwREclf2f39rVXliorVq+HRR2HHDvM+IsJMsa5f39667GRZJsSlzoQbPBjeeKNwb3YpIlLI6P/Yhd2xY9C3L7RsaUJMxYqmx+GnnxRinnwyPcS88IIZ9KwQIyLiUdQjU1g5nWYvoKefhhMnTNvDD8O4cUVzNtKFUlLM92LaNPP+7bfNoGcREfE4CjKF0Y4dMGAA/PKLeV+vHrz/PoSH21uXOzh7Fnr1gnnzTO/LtGnQp4/dVYmISC6pH70wSUgweyM1aGBCTKlS8PrrsGmTQgyY789dd5kQ4+NjZiYpxIiIeDT1yBQWy5ebhez+/tu879IF3nmnaGwrkB3x8eZ7snKl2fBy4UJo187uqkRE5AopyHi6Y8fMmjAzZ5r3V11lFrW7805by3IrMTFmbZg1a6B0abPw3S232F2ViIjkAT1a8lSWBZ98Atdfb0KMwwFPPAG//64Qc6Hjx6F1axNiypaFFSsUYkREChH1yHiiv/82g3m//968r1cPpk6Fpk3trcvdHDtmQsy2bWba+fLlRXf7BRGRQko9Mp7k3DkzeDcszISYEiXMdOqNGxViLnb0KNx6qwkxQUGwapVCjIhIIaQeGU/x22/Qr58JLWB+SX/wAVy0D5UAUVFw223wxx9QtaoZ4Fu7tt1ViYhIPlCPjLtLSoLnn4fGjU2ICQiADz80Yz0UYjI6sB8impoQU6Uy/KAQIyJSmCnIuLN166BRI3jpJfNYqWtXM5i3X7+iu0v1paz4CBpdC/v+gQAH3H0GltwJOxfZXZmIiOQTBRl3dOYMPPWU2dhx506oXNks4jZ/PlSpYnd17mnlNLj7YTieDGUd8EApKOcFsVEwp7fCjIhIIaUg425SN3N8/XWzX9L995sw062b3ZW5r4MHoPsjcNKZHmLKpv7Vtswfy0aAM8W2EkVEJH8oyLiLM2dgyBCzS/Vff5mF7ZYsMWvFVKhgd3Xu69AhaBGR3hPTpxQEXPzX2oLYQ7B/jS0liohI/tGsJXfw00/Qty/s2WPe9+tnemQCAuyty90dPmxmb+0/lB5iyl4im8cfLbjaRESkQKhHxk5nzsDQoaYXZs8eqFYNvvnGzEpSiLm0o0fNYnd//gnVgi4fYgBKBxZMbSIiUmAUZOzy66/QsCFMnGi2G+jXD7Zvh8hIuytzf//+C23amCnW1avDqp+hRjUgq5lcDvC/CmpGFGSVIiJSABRkClpSEowYATfdBLt3m7Ew6oXJvhMnoG1bE/pSF7u7phZETjh/wsVh5vz7yPHg5V2QlYqISAFQkClIW7dCkyYwYUL6jKRt29QLk10xMdC+vfk+Bga6LgoY2hm6fwL+F01P969q2kM7F3i5IiKS/zTYtyCcOwevvAJjxkByMlSqBFOmwF132V2Z54iPh9tvN6sbV6hg9pqqU8f1nNDOUKejmZ0Uf9SMiakZoZ4YEZFCTEEmv/31F/TuDWvXmvd33QXvv28WuZPsSUyELl1gzRooW9aEmLCwzM/18obgFgVanoiI2EePlvKLZZnAUr++CTH+/jBjhlmdVyEm+5KToXt3MxamdGn49lvtYi0iImnUI5Mfjh41s5C+/tq8v/VW+PhjqFnT3ro8TUqKGUe0eDGUKGEWCGza1O6qRETEjahHJq8tXgx165oQ4+sLb7xhHoUoxOSMZcGAATB7NhQvDl9+adbbERERuYB6ZPLK6dPw5JNmEC+YMPPZZ+ZPyRnLMptmfvQReHnBF19Ahw52VyUiIm5IPTJ5YfNmaNzYhBiHw/wS3rBBISa3xo0zPVkA06Zpw0wREcmSemSuhNNp9kQaNcoMSr3qKrPJ42232V2Z53r/ffP9BHjzTejTx956RETErSnI5NahQ+aX7IoV5n3XrvDBB9qp+krMng2PPmpeP/ec2Q1cRETkEhRkcsOyzJTgNWvAzw/eesvMUnJktdePXNa335oZSpYFjzwCL7xgd0UiIuIBNEYmNxwOePttaN7cjI/p318h5kps2GDGwSQnQ48eMGmSvp8iIpIt6pHJrcaNTY+MfuFemd27zdYDp0+bzSA/+QS8taWAiIhkj3pkroRCzJWJijKbQP77rwmG8+eDj4/dVYmIiAdRj4zYIybGrA2zb5/ZwXrJYvh3K+zTZo8iIpJ9btMjM378eBwOB0MumKmSmJjIoEGDqFChAqVLl6Zbt24cPXrUviIlbyQlmc0z//c/CAyEScPh89tgxh0wv5/5c2IY7Fxkd6UiIuLm3CLIbNiwgSlTplCvXj2X9qFDh7J48WLmzp3LqlWrOHz4MF27drWpSskTTif07Qs//ABlysC7I2Dt0xB72PW82CiY01thRkRELsn2IBMfH0+vXr2YOnUq5cqVS2uPiYnho48+4o033uC2226jcePGfPzxx6xZs4Zff/3Vxorlijz7rNlyoFgxmD8P9k8BrExOPN+2bAQ4UwqyQhER8SC2B5lBgwbRsWNH2rRp49K+adMmkpOTXdrr1KlDjRo1WLt2bZbXS0pKIjY21uVD3MTkyTBhgnn90UcQUjJjT4wLC2IPwf41BVKeiIh4HluDzKxZs9i8eTPjxo3LcOzIkSP4+PhQtmxZl/bAwECOHDmS5TXHjRtHQEBA2kf16tXzumzJjUWL4LHHzOsXX4TevSE+m+OdsnueiIgUObYFmYMHDzJ48GA+++wzSpQokWfXHTlyJDExMWkfBw8ezLNrSy6tXw89e5rxMf37p++lVDowe5+f3fNERKTIsS3IbNq0iejoaBo1akSxYsUoVqwYq1at4u2336ZYsWIEBgZy9uxZTp065fJ5R48eJSgoKMvr+vr64u/v7/IhNtq3Dzp1goQEM9168uT09XdqRoB/VSCr9Xgc4H+VOU9ERCQTtgWZ1q1bs23bNrZu3Zr20aRJE3r16pX2unjx4qxI3ZQR2LVrFwcOHCA8PNyusiUnYmKgY0eIjob69c2mkMUuWLrIyxsiz4+ZyRBmzr+PHK/1ZEREJEu2LYhXpkwZwsLCXNpKlSpFhQoV0tr79evHsGHDKF++PP7+/jz++OOEh4fTvHlzO0qWnEhOhrvvhp07oWpVWLLETLe+WGhn6P4JLHvGdeCvf1UTYkI7F1zNIiLicdx6Zd8333wTLy8vunXrRlJSEu3bt+e9996zuyy5HMsyA3uXLze7gy9eDNWqZX1+aGeo09HMTorXyr4iIpJ9DsuyMlvEo9CIjY0lICCAmJgYjZcpKK+9BsOHm7EwCxdCZ/WqiIhIzmT397ft68hIIbNoETz9tHn9xhsKMSIikq8UZCTvbNsGvXqZR0uPPAKDB9tdkYiIFHIKMpI3oqPNNOv4eLjtNnj77fRp1iIiIvlEQUauXFISdO0K+/dDSAjMnQvFi9tdlYiIFAEKMnJlLAsGDIBffoGAADNDqXx5u6sSEZEiQkFGrsybb8KMGeDtDXPmQJ06dlckIiJFiIKM5N7y5WaaNcDrr0O7dvbWIyIiRY6CjOTOnj3Qo4fZCPKBB+CJJ+yuSEREiiAFGcm5uDjo0gVOnoRmzVw3ghQRESlACjKSM04n9OkDO3ZAlSrw5ZdQooTdVYmISBGlICM589//woIF4ONjQkzVqnZXJCIiRZhbbxopbmbpUhg92ryePBlSdyF3pmjDRxERsYWCjGTPnj2u2w88+KBp37kIlj0DsYfTz/WvCpETzK7WIiIi+UiPluTyzpwxK/eeOmV6YSZONO07F8Gc3q4hBiA2yrTvXFTQlYqISBGjICOXZlnw8MPw229QuTLMmwe+vuZx0rJnACuzTzJ/LBthzhMREcknCjJyaZMmwWefpa/ce9VVpn3/mow9MS4siD1kzhMREcknCjKStTVrYNgw8/qVV6Bly/Rj8Uezd43sniciIpILCjKSuWPHoHt3OHfO/Dl0qOvx0oHZu052zxMREckFBRnJKCUF7rsPDh2C2rXhww8zrtxbM8LMTiKrFX0d4H+VOU9ERCSfKMhIRv/9L3z3HZQsaQb3limT8RwvbzPFGsgYZs6/jxyv9WRERCRfKciIq++/hzFjzOv334ewsKzPDe0M3T8B/yqu7f5VTbvWkRERkXymBfEk3aFDcO+9Zsp1//7Qu/flPye0M9TpqJV9RUTEFgoyYpw7B/fcYwb5NmgAb7+d/c/18obgFvlWmoiISFb0aEmMF16An34y42HmzjXjY0RERNycgozAypXw0kvm9QcfQEiIvfWIiIhkk4JMURcdnb4ZZP/+0LOn3RWJiIhkm4JMUeZ0wv33w5EjcMMN8NZbdlckIiKSIwoyRdmrr6avFzN7Nvj52V2RiIhIjijIFFXr1sGoUeb1O++YHhkREREPoyBTFMXFmfViUlKgRw948EG7KxIREckVBZmi6PHH4e+/oUYNs3rvxfsoiYiIeAgFmaJm1iyYMQO8vOCzz6BsWbsrEhERyTUFmaJk/3545BHzetQouPlme+sRERG5QgoyRcW5c3DffRATA+Hh8H//Z3dFIiIiV0x7LRUVEybAzz+bLQg++wyKXfCjd6Zo00cREfFItvbITJ48mXr16uHv74+/vz/h4eF88803acePHDnC/fffT1BQEKVKlaJRo0bMnz/fxoo91KZNMGaMeT1pEgQHpx/buQgmhsGMO2B+P/PnxDDTLiIi4uZsDTLVqlVj/PjxbNq0iY0bN3LbbbfRpUsXduzYAUDv3r3ZtWsXixYtYtu2bXTt2pXu3buzZcsWO8v2LAkJZvXec+fgP/8xr1PtXARzekPsYdfPiY0y7QozIiLi5hyWZVl2F3Gh8uXL8+qrr9KvXz9Kly7N5MmTuf+CX74VKlRgwoQJ9O/fP1vXi42NJSAggJiYGPz9/fOrbPc1dChMnAhBQbB9O1SoYNqdKabn5eIQk8YB/lVhyDY9ZhIRkQKX3d/fbjPYNyUlhVmzZnH69GnCw8MBiIiIYPbs2Zw4cQKn08msWbNITEykVatWWV4nKSmJ2NhYl48ia8UKE2IApk1LDzFgxsRkGWIALIg9ZM4TERFxU7YP9t22bRvh4eEkJiZSunRpFixYQGhoKABz5syhR48eVKhQgWLFiuHn58eCBQsICQnJ8nrjxo1j7NixBVW++zp1Ch54wLweMAA6dHA9Hn80e9fJ7nkiIiI2sL1Hpnbt2mzdupV169YxcOBA+vTpw86dOwF4/vnnOXXqFN9//z0bN25k2LBhdO/enW3btmV5vZEjRxITE5P2cfDgwYK6FffyxBPwzz8QEgKvvZbxeOnA7F0nu+eJiIjYwO3GyLRp04ZatWrx9NNPExISwvbt27nhgg0N27RpQ0hICO+//362rlckx8h89RXceadZvffnn826MRdLGyMTBWT2V0BjZERExD4eN0YmldPpJCkpiTNnzgDg5eVaore3N06n047SPMPx4+ZREsDw4ZmHGDDhJHLC+TcX77V0/n3keIUYERFxa7YGmZEjR7J69Wr27dvHtm3bGDlyJD/++CO9evWiTp06hISEMGDAANavX8+ePXt4/fXXWb58OXfeeaedZbu3J56Ao0chNDR97ZishHaG7p+AfxXXdv+qpj20c76VKSIikhdsHewbHR1N7969iYqKIiAggHr16vHtt9/Stm1bAJYuXcqIESPo1KkT8fHxhISEMGPGDG6//XY7y3ZfCxfC55+bR0offwwlSlz+c0I7Q52OWtlXREQ8ktuNkclrRWaMzL//wg03QHQ0jBgB48bZXZGIiEiueewYGcmlxx83IeaGGy7/SElERKSQUJApDBYuhFmzwNsbpk8HX1+7KxIRESkQCjKe7tQpePRR83r4cGjSxNZyRERECpKCjKcbPhyiouC662D0aLurERERKVAKMp5s5Ur48EPz+sMPszdLSUREpBBRkPFUZ87AQw+Z1wMHQosW9tYjIiJiAwUZTzV6NPz9N1SrBuPH212NiIiILRRkPNHGjfDGG+b1++9DYV4fR0RE5BIUZDzNuXPmkZLTCffeCx072l2RiIiIbRRkPM3bb8PWrVC+PLz5pt3ViIiI2EpBxpMcOADPP29ev/IKVK5sbz0iIiI2U5DxJI8/bmYr3Xwz9O1rdzUiIiK2s3X3a8kGZ4rZmXrRYli0CIoXhylTzA7XIiIiRZyCjDvbuQiWPQPHDsG78abtFn/gLyDUzspERETcgv5Z7652LoI5vSH2MPyQBHEWlHNAs2TTvnOR3RWKiIjYTkHGHTlTTE8MFhxJgfVnTXvHklD8/DnLRpjzREREijAFGXe0f43pibEsWJoIFnBDMaiV+iTQgthD5jwREZEiTEHGHcUfNX/+LxkOpphemHaZbAiZep6IiEgRpSDjjkoHQqIFy5PM+5a+4J/Jj6p0YMHWJSIi4mY0a8kd1YyAX4rBGQsqekFzn4tOcIB/VXOeiIhIEaYeGXf02zZYE2NedygJ3o4LDp5/HTkevLwLvDQRERF3oiDjbpxOeOwx82f7m6BBddfj/lWh+ycQ2tme+kRERNyIHi25m5kz4ZdfoFQp+HAWVK1iZifFHzVjYmpGqCdGRETkPAUZdxIXByNGmNfPPQfVqpnXwS3sq0lERMSN6dGSOxk3DqKioFYtGDrU7mpERETcnoKMu9i7F954w7x+7TXw9bW3HhEREQ+gIOMuhg+HpCRo3Rq6dLG7GhEREY+gIOMOfvwR5s8HLy94801wOC77KSIiIqIgY7+UFBg82Lx+5BGoW9feekRERDyIgozdPvwQfvsNypaFsWPtrkZERMSjKMjYKSYGnn/evB47FipWtLceERERD6MgY6cJE+DYMahdGwYOtLsaERERj6MgY5eDB83AXjCBpnhxe+sRERHxQAoydnnuOUhMhBYtoLP2TRIREckNBRk7bN0Kn35qXr/2mqZbi4iI5JL2WsprzpRLb/JoWfDUU+bPnj2haVP7ahUREfFwtvbITJ48mXr16uHv74+/vz/h4eF88803LuesXbuW2267jVKlSuHv788tt9xCQkKCTRVfxs5FMDEMZtwB8/uZPyeGmfZUy5bBihXg4wMvv2xfrSIiIoWArUGmWrVqjB8/nk2bNrFx40Zuu+02unTpwo4dOwATYiIjI2nXrh3r169nw4YNPPbYY3h5ueETsZ2LYE5viD3s2h4bZdp3LjKL3z39tGl//HEIDi74OkVERAoRh2VZlt1FXKh8+fK8+uqr9OvXj+bNm9O2bVtefPHFXF8vNjaWgIAAYmJi8Pf3z8NKL+BMMT0vF4eYNA7wrwoBT0L//lCuHOzZY/4UERGRDLL7+9ttujZSUlKYNWsWp0+fJjw8nOjoaNatW0flypWJiIggMDCQli1b8vPPP1/yOklJScTGxrp85Lv9ay4RYgAsOPEPPP+sefvsswoxIiIiecD2ILNt2zZKly6Nr68vjzzyCAsWLCA0NJS///4bgDFjxvDQQw+xbNkyGjVqROvWrfnzzz+zvN64ceMICAhI+6hevXr+30T80cufs/EsREXDVVfBoEH5X5OIiEgRYHuQqV27Nlu3bmXdunUMHDiQPn36sHPnTpxOJwADBgygb9++NGzYkDfffJPatWszbdq0LK83cuRIYmJi0j4OHjyY/zdROvDSx5Ms+OmseT1mDJQsme8liYiIFAW2T7/28fEhJCQEgMaNG7NhwwbeeustRowYAUBoaKjL+ddffz0HDhzI8nq+vr74+vrmX8GZqRlhxsDERgGZDDlaexbOWHDddfDAAwVbm4iISCFme4/MxZxOJ0lJSVx99dVUrVqVXbt2uRzfvXs3NWvWtKm6LHh5Q+SE828uWtzutAVrk8zrl16CYrZnRxERkULD1t+qI0eOpEOHDtSoUYO4uDg+//xzfvzxR7799lscDgfDhw9n9OjR1K9fnwYNGjBjxgz++OMP5s2bZ2fZmQvtDN0/gWXPuA78XV8czgKNGkG3braVJyIiUhjZGmSio6Pp3bs3UVFRBAQEUK9ePb799lvatm0LwJAhQ0hMTGTo0KGcOHGC+vXrs3z5cmrVqmVn2VkL7Qx1Oqav7BsDjOtljo0bB+64/o2IiIgHc7t1ZPJagawjk5V+/WDaNGjVClau1J5KIiIi2eRx68gUOn/9BTNmmNcvv6wQIyIikg8UZPLLiy+aLQk6dIDwcLurERERKZQUZPLD7t0wc6Z5PXasvbWIiIgUYgoy+eGFF8DphE6d4MYb7a5GRESk0FKQyWu//w5ffGFejxljaykiIiKFnYJMXkvtjbnzTrN2jIiIiOQbBZm8tGMHzJ5tXqs3RkREJN8pyOSlsWPBsswKvvXr212NiIhIoacgk1e2b4e5c816MeqNERERKRAKMnnl5ZfNn926QViYvbWIiIgUEQoyeeHPP9PHxowaZW8tIiIiRYitm0Z6LGdK+saQpQNh3MdmptIdd0CDBnZXJyIiUmQoyOTUzkWw7BmIPWzexzhhRrx5rd4YERGRAqUgkxM7F8Gc3sAFG4b/kgROINgb/KPtqkxERKRI0hiZ7HKmmJ6YC0NMvBM2J5vXLUrAshHmPBERESkQCjLZtX9N+uOkVGvOQgpQzRuu9oLYQ+Y8ERERKRAKMtkVf9T1/RknbDxrXt/iY9aPyew8ERERyTcKMtlVOtD1/bqzkAwEeUFIsazPExERkXyjIJNdNSPAvypwvuclCfPda+F7vjfGAf5XmfNERESkQCjIZJeXN0ROOP/GAZEl4PHScH0x0sJN5HhznoiIiBQIBZmcCO0M3T8B/yrmfVkv0xvjX9W0h3a2tz4REZEiRuvI5FRoZ6jT0XVl35oR6okRERGxgYJMbnh5Q3ALu6sQEREp8vRoSURERDyWgoyIiIh4LAUZERER8VgKMiIiIuKxFGRERETEYynIiIiIiMdSkBERERGPpSAjIiIiHktBRkRERDyWgoyIiIh4LAUZERER8ViFfq8ly7IAiI2NtbkSERERya7U39upv8ezUuiDTFxcHADVq1e3uRIRERHJqbi4OAICArI87rAuF3U8nNPp5PDhw5QpUwaHw5Fn142NjaV69eocPHgQf3//PLuuJyiq915U7xuK7r0X1fuGonvvRfW+wf3u3bIs4uLiqFq1Kl5eWY+EKfQ9Ml5eXlSrVi3fru/v7+8WP3A7FNV7L6r3DUX33ovqfUPRvfeiet/gXvd+qZ6YVBrsKyIiIh5LQUZEREQ8loJMLvn6+jJ69Gh8fX3tLqXAFdV7L6r3DUX33ovqfUPRvfeiet/gufde6Af7ioiISOGlHhkRERHxWAoyIiIi4rEUZERERMRjKciIiIiIx1KQyaV3332Xq6++mhIlStCsWTPWr19vd0l5asyYMTgcDpePOnXqpB1PTExk0KBBVKhQgdKlS9OtWzeOHj1qY8W5t3r1ajp16kTVqlVxOBwsXLjQ5bhlWfzf//0fVapUoWTJkrRp04Y///zT5ZwTJ07Qq1cv/P39KVu2LP369SM+Pr4A7yLnLnffDzzwQIa/A5GRkS7neOJ9jxs3jhtvvJEyZcpQuXJl7rzzTnbt2uVyTnb+fh84cICOHTvi5+dH5cqVGT58OOfOnSvIW8mx7Nx7q1atMvzcH3nkEZdzPO3eJ0+eTL169dIWegsPD+ebb75JO15Yf95w+XsvFD9vS3Js1qxZlo+PjzVt2jRrx44d1kMPPWSVLVvWOnr0qN2l5ZnRo0dbN9xwgxUVFZX2cezYsbTjjzzyiFW9enVrxYoV1saNG63mzZtbERERNlace0uXLrVGjRplffnllxZgLViwwOX4+PHjrYCAAGvhwoXW//73P6tz585WcHCwlZCQkHZOZGSkVb9+fevXX3+1fvrpJyskJMS65557CvhOcuZy992nTx8rMjLS5e/AiRMnXM7xxPtu37699fHHH1vbt2+3tm7dat1+++1WjRo1rPj4+LRzLvf3+9y5c1ZYWJjVpk0ba8uWLdbSpUutihUrWiNHjrTjlrItO/fesmVL66GHHnL5ucfExKQd98R7X7RokfX1119bu3fvtnbt2mU9++yzVvHixa3t27dbllV4f96Wdfl7Lww/bwWZXGjatKk1aNCgtPcpKSlW1apVrXHjxtlYVd4aPXq0Vb9+/UyPnTp1yipevLg1d+7ctLbff//dAqy1a9cWUIX54+Jf6E6n0woKCrJeffXVtLZTp05Zvr6+1hdffGFZlmXt3LnTAqwNGzaknfPNN99YDofDOnToUIHVfiWyCjJdunTJ8nMKw31blmVFR0dbgLVq1SrLsrL393vp0qWWl5eXdeTIkbRzJk+ebPn7+1tJSUkFewNX4OJ7tyzzi23w4MFZfk5hufdy5cpZH374YZH6eadKvXfLKhw/bz1ayqGzZ8+yadMm2rRpk9bm5eVFmzZtWLt2rY2V5b0///yTqlWrcs0119CrVy8OHDgAwKZNm0hOTnb5HtSpU4caNWoUuu/B3r17OXLkiMu9BgQE0KxZs7R7Xbt2LWXLlqVJkyZp57Rp0wYvLy/WrVtX4DXnpR9//JHKlStTu3ZtBg4cyPHjx9OOFZb7jomJAaB8+fJA9v5+r127lrp16xIYGJh2Tvv27YmNjWXHjh0FWP2VufjeU3322WdUrFiRsLAwRo4cyZkzZ9KOefq9p6SkMGvWLE6fPk14eHiR+nlffO+pPP3nXeg3jcxr//77LykpKS4/VIDAwED++OMPm6rKe82aNWP69OnUrl2bqKgoxo4dS4sWLdi+fTtHjhzBx8eHsmXLunxOYGAgR44csafgfJJ6P5n9vFOPHTlyhMqVK7scL1asGOXLl/fo70dkZCRdu3YlODiYPXv28Oyzz9KhQwfWrl2Lt7d3obhvp9PJkCFDuOmmmwgLCwPI1t/vI0eOZPp3IvWYJ8js3gHuvfdeatasSdWqVfntt9945pln2LVrF19++SXgufe+bds2wsPDSUxMpHTp0ixYsIDQ0FC2bt1a6H/eWd07FI6ft4KMZKpDhw5pr+vVq0ezZs2oWbMmc+bMoWTJkjZWJgWlZ8+eaa/r1q1LvXr1qFWrFj/++COtW7e2sbK8M2jQILZv387PP/9sdykFLqt7f/jhh9Ne161blypVqtC6dWv27NlDrVq1CrrMPFO7dm22bt1KTEwM8+bNo0+fPqxatcrusgpEVvceGhpaKH7eerSUQxUrVsTb2zvDiPajR48SFBRkU1X5r2zZslx33XX89ddfBAUFcfbsWU6dOuVyTmH8HqTez6V+3kFBQURHR7scP3fuHCdOnChU349rrrmGihUr8tdffwGef9+PPfYYS5Ys4YcffqBatWpp7dn5+x0UFJTp34nUY+4uq3vPTLNmzQBcfu6eeO8+Pj6EhITQuHFjxo0bR/369XnrrbeKxM87q3vPjCf+vBVkcsjHx4fGjRuzYsWKtDan08mKFStcnjkWNvHx8ezZs4cqVarQuHFjihcv7vI92LVrFwcOHCh034Pg4GCCgoJc7jU2NpZ169al3Wt4eDinTp1i06ZNaeesXLkSp9OZ9j+FwuCff/7h+PHjVKlSBfDc+7Ysi8cee4wFCxawcuVKgoODXY5n5+93eHg427Ztcwlyy5cvx9/fP63L3h1d7t4zs3XrVgCXn7sn3vvFnE4nSUlJhfrnnZXUe8+MR/687R5t7IlmzZpl+fr6WtOnT7d27txpPfzww1bZsmVdRnV7uieffNL68ccfrb1791q//PKL1aZNG6tixYpWdHS0ZVlmumKNGjWslStXWhs3brTCw8Ot8PBwm6vOnbi4OGvLli3Wli1bLMB64403rC1btlj79++3LMtMvy5btqz11VdfWb/99pvVpUuXTKdfN2zY0Fq3bp31888/W9dee63bT0O+1H3HxcVZTz31lLV27Vpr79691vfff281atTIuvbaa63ExMS0a3jifQ8cONAKCAiwfvzxR5cpp2fOnEk753J/v1OnpLZr187aunWrtWzZMqtSpUpuNSU1M5e797/++st64YUXrI0bN1p79+61vvrqK+uaa66xbrnllrRreOK9jxgxwlq1apW1d+9e67fffrNGjBhhORwO67vvvrMsq/D+vC3r0vdeWH7eCjK59M4771g1atSwfHx8rKZNm1q//vqr3SXlqR49elhVqlSxfHx8rKuuusrq0aOH9ddff6UdT0hIsB599FGrXLlylp+fn3XXXXdZUVFRNlacez/88IMFZPjo06ePZVlmCvbzzz9vBQYGWr6+vlbr1q2tXbt2uVzj+PHj1j333GOVLl3a8vf3t/r27WvFxcXZcDfZd6n7PnPmjNWuXTurUqVKVvHixa2aNWtaDz30UIaw7on3ndk9A9bHH3+cdk52/n7v27fP6tChg1WyZEmrYsWK1pNPPmklJycX8N3kzOXu/cCBA9Ytt9xilS9f3vL19bVCQkKs4cOHu6wrYlmed+8PPvigVbNmTcvHx8eqVKmS1bp167QQY1mF9+dtWZe+98Ly83ZYlmUVXP+PiIiISN7RGBkRERHxWAoyIiIi4rEUZERERMRjKciIiIiIx1KQEREREY+lICMiIiIeS0FGREREPJaCjIiIiHgs7X4tIm5t1apVDBgwgBIlSri0O51OWrZsyfr16zPdNyY+Pp4dO3bg6+tbUKWKiA0UZETErSUkJNCzZ0/GjBnj0r5v3z5GjBiBw+FI2+juQq1atUILl4sUfnq0JCIiIh5LQUZEREQ8loKMiIiIeCwFGREREfFYCjIiIiLisRRkRERExGMpyIiIiIjHUpARERERj6UgIyIiIh5LQUZEREQ8lrYoEBG3FhAQwJIlS1iyZEmGY+3bt+fUqVM0adIk08/18tK/1UQKO4elzUhERETEQ+mfKyIiIuKxFGRERETEYynIiIiIiMdSkBERERGPpSAjIiIiHktBRkRERDyWgoyIiIh4LAUZERER8VgKMiIiIuKx/h/36uOAFJaaJQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A 拟合值：7.646479129855515\n",
      "B 拟合值：196.38730455353556\n",
      "A1 拟合值：8.18103069999436\n",
      "B1 拟合值：71.32139482929121\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy.optimize import curve_fit\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 定义要拟合的对数函数形式\n",
    "def logarithmic_function(t, A, B):\n",
    "    return A * np.log(t + B)\n",
    "\n",
    "# 生成一些示例数据\n",
    "t_data = np.array([129+0, 129+39, 129+71, 129+116, 129+232])\n",
    "T_data = np.array([44, 45, 46, 47, 48])\n",
    "t_data1 = np.array([0,5,21,41,54,68,82,97,112,129])\n",
    "T_data1 = np.array([35,36,37,38,39,40,41,42,43,44])\n",
    "\n",
    "# 进行拟合\n",
    "params, covariance = curve_fit(logarithmic_function, t_data, T_data)\n",
    "params1, covariance1 = curve_fit(logarithmic_function, t_data1, T_data1)\n",
    "# 拟合得到的参数\n",
    "A_fit, B_fit = params\n",
    "A_fit1, B_fit1 = params1\n",
    "# 生成拟合曲线\n",
    "t_fit = np.linspace(0, 350, 100)\n",
    "T_fit = logarithmic_function(t_fit, A_fit, B_fit)\n",
    "t_fit1 = np.linspace(0, 350, 100)\n",
    "T_fit1 = logarithmic_function(t_fit1, A_fit1, B_fit1)\n",
    "# 绘制原始数据和拟合曲线\n",
    "plt.scatter(t_data, T_data, label='原始数据')\n",
    "plt.scatter(t_data1, T_data1, label='原始数据')\n",
    "plt.plot(t_fit, T_fit, label='拟合曲线', color='red')\n",
    "plt.plot(t_fit1, T_fit1, label='拟合曲线', color='red')\n",
    "plt.xlabel('时间')\n",
    "plt.ylabel('温度')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "print(f'A 拟合值：{A_fit}')\n",
    "print(f'B 拟合值：{B_fit}')\n",
    "print(f'A1 拟合值：{A_fit1}')\n",
    "print(f'B1 拟合值：{B_fit1}')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py3916",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}