test1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "f:\\program\\miniconda\\envs\\gpt\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import pathlib\n",
    "import json\n",
    "import textwrap\n",
    "import os\n",
    "import google.generativeai as genai\n",
    "from IPython.display import display\n",
    "from IPython.display import Markdown\n",
    "os.environ['http_proxy'] = 'http://127.0.0.1:7890'\n",
    "os.environ['https_proxy'] = 'http://127.0.0.1:7890'\n",
    "\n",
    "def to_markdown(text):\n",
    "  text = text.replace('•', '  *')\n",
    "  return Markdown(textwrap.indent(text, '> ', predicate=lambda _: True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_api_key(file_p:str, api_name:str) -> str:\n",
    "\twith open(file_p) as f:\n",
    "\t\tdata = json.load(f)\n",
    "\tapi_key=data['gpt_api'][api_name]\n",
    "\treturn api_key\n",
    "api_key=get_api_key('.access.json', 'gemini')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "genai.configure(api_key=api_key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "models/gemini-pro\n",
      "models/gemini-pro-vision\n"
     ]
    }
   ],
   "source": [
    "for m in genai.list_models():\n",
    "  if 'generateContent' in m.supported_generation_methods:\n",
    "    print(m.name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = genai.GenerativeModel('gemini-pro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 0 ns\n",
      "Wall time: 7.92 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "response = model.generate_content(\"What is the meaning of life?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 0 ns\n",
      "Wall time: 2.03 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "response = model.generate_content(\"What is the meaning of life?\", stream=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The meaning of life is a deep and complex question that has been pondered by philosophers\n",
      "________________________________________________________________________________\n",
      ", religious thinkers, and ordinary people for centuries. There is no one definitive answer, as the meaning of life is a personal and subjective experience that can vary from\n",
      "________________________________________________________________________________\n",
      " person to person.\n",
      "\n",
      "Some common themes that emerge when people discuss the meaning of life include:\n",
      "\n",
      "* **Finding purpose and fulfillment:** Many people find meaning in life by pursuing their passions, goals, and interests. This can involve achieving personal or professional success, making a difference in the world, or simply living a life\n",
      "________________________________________________________________________________\n",
      " that is true to oneself.\n",
      "* **Connecting with others:** Strong relationships with family, friends, and loved ones can provide a sense of belonging and purpose. Helping others and contributing to society can also be fulfilling and meaningful.\n",
      "* **Finding meaning in suffering:** Life is often filled with challenges and setbacks. How we respond to these challenges can shape our understanding of the meaning of life. Some people find meaning by overcoming adversity, learning from their mistakes, or finding ways to help others who are suffering.\n",
      "* **Experiencing awe and wonder:** The vastness and beauty of the universe can inspire a sense of awe and wonder. Some\n",
      "________________________________________________________________________________\n",
      " people find meaning in life by exploring the natural world, appreciating art and music, or simply being present in the moment and savoring the simple pleasures of life.\n",
      "\n",
      "Ultimately, the meaning of life is a question that each individual must answer for themselves. There is no right or wrong answer, and the meaning of life can change over time as we grow and evolve.\n",
      "\n",
      "Here are some additional perspectives on the meaning of life:\n",
      "\n",
      "* **Viktor Frankl, a Holocaust survivor and psychiatrist, believed that the meaning of life is to find meaning in every situation, even the most difficult ones. He wrote, \"Man is not fully human unless he transcending himself, unless he is directed toward something beyond himself.\"**\n",
      "* **The Dalai Lama, a Tibetan Buddhist monk and spiritual leader, believes that the meaning of life is to be happy and to help others be happy. He said, \"The purpose of our lives is to be happy. What else could the purpose be?\"**\n",
      "* **Maya Angelou, an American poet and civil rights activist, believed that the meaning of life is to \"leave a legacy of love and service.\" She said, \"I've learned that people will forget what you said, people will forget what you did, but people will never\n",
      "________________________________________________________________________________\n",
      " forget how you made them feel.\"**\n",
      "\n",
      "These are just a few perspectives on the meaning of life. The most important thing is to find a meaning that is true and meaningful to you.\n",
      "________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "for chunk in response:\n",
    "  print(chunk.text)\n",
    "  print(\"_\"*80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "> There is no one definitive answer to the question of the meaning of life. Different people have different beliefs and perspectives on what makes life meaningful. Some common themes that emerge across cultures and individuals include:\n",
       "> \n",
       "> 1. **Finding Purpose and Fulfillment:** Many people find meaning in life by pursuing their passions and interests, setting goals, and working towards personal growth and achievement. They may feel a sense of purpose when they are able to make a positive impact on the world or contribute to something larger than themselves.\n",
       "> \n",
       "> 2. **Relationships and Connections:** Strong and supportive relationships with family, friends, and loved ones can provide a sense of belonging, purpose, and meaning in life. Many people find joy and fulfillment in nurturing and caring for others, and in building a sense of community and connection with those around them.\n",
       "> \n",
       "> 3. **Experiencing and Appreciating the World:** Some individuals find meaning in life by embracing and appreciating the beauty and wonders of the world around them. This could include enjoying nature, appreciating art and culture, traveling, or simply taking the time to savor moments of joy and awe.\n",
       "> \n",
       "> 4. **Making a Difference:** Many people find meaning in life by contributing to something greater than themselves. This could involve volunteering, working in a meaningful career, or advocating for causes that they are passionate about. They may feel a sense of purpose when they are able to make a positive impact on the lives of others or work towards a larger goal.\n",
       "> \n",
       "> 5. **Learning and Growing:** Lifelong learning and personal growth can also contribute to a sense of meaning and purpose. When individuals challenge themselves to learn new things, explore new perspectives, and grow as individuals, they may feel a sense of accomplishment and fulfillment.\n",
       "> \n",
       "> 6. **Spiritual or Religious Beliefs:** For some people, religion or spirituality provides a framework for understanding the meaning of life. They may find purpose and meaning through their beliefs, rituals, and practices, and through their connection to a higher power or spiritual community.\n",
       "> \n",
       "> Ultimately, the meaning of life is a personal and subjective matter. It is something that each individual must discover and define for themselves. There is no single \"correct\" answer, and what is meaningful to one person may not be meaningful to another."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_markdown(response.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "safety_ratings {\n",
       "  category: HARM_CATEGORY_SEXUALLY_EXPLICIT\n",
       "  probability: NEGLIGIBLE\n",
       "}\n",
       "safety_ratings {\n",
       "  category: HARM_CATEGORY_HATE_SPEECH\n",
       "  probability: NEGLIGIBLE\n",
       "}\n",
       "safety_ratings {\n",
       "  category: HARM_CATEGORY_HARASSMENT\n",
       "  probability: NEGLIGIBLE\n",
       "}\n",
       "safety_ratings {\n",
       "  category: HARM_CATEGORY_DANGEROUS_CONTENT\n",
       "  probability: NEGLIGIBLE\n",
       "}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response.prompt_feedback"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x17e52790430>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import rdkit\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import rdDepictor,MolFromMolBlock,rdDistGeom\n",
    "rdDepictor.SetPreferCoordGen(True)\n",
    "etkdg = rdDistGeom.ETKDGv3()\n",
    "etkdg.randomSeed = 0xa700f\n",
    "etkdg.verbose = False\n",
    "etkdg.numThreads = 0\n",
    "etkdg.optimizerForceTol = 0.0135\n",
    "etkdg.useRandomCoords = True\n",
    "rdkit.__version__\n",
    "\n",
    "mol_block='''211206-ETA-0009\n",
    "\n",
    "Created by GaussView 6.0.16\n",
    " 87 95  0  0  0  0  0  0  0  0999 V2000\n",
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    "  9 10  4  0  0  0  0\n",
    "  9 61  1  0  0  0  0\n",
    " 10 11  4  0  0  0  0\n",
    " 10 62  1  0  0  0  0\n",
    " 11 12  4  0  0  0  0\n",
    " 11 21  1  0  0  0  0\n",
    " 12 13  4  0  0  0  0\n",
    " 12 63  1  0  0  0  0\n",
    " 13 29  1  0  0  0  0\n",
    " 14 15  4  0  0  0  0\n",
    " 15 16  4  0  0  0  0\n",
    " 15 19  1  0  0  0  0\n",
    " 16 17  4  0  0  0  0\n",
    " 17 18  4  0  0  0  0\n",
    " 17 20  1  0  0  0  0\n",
    " 19 40  4  0  0  0  0\n",
    " 19 44  4  0  0  0  0\n",
    " 20 35  4  0  0  0  0\n",
    " 20 39  4  0  0  0  0\n",
    " 21 22  4  0  0  0  0\n",
    " 21 26  4  0  0  0  0\n",
    " 22 23  4  0  0  0  0\n",
    " 23 24  4  0  0  0  0\n",
    " 23 28  1  0  0  0  0\n",
    " 24 25  4  0  0  0  0\n",
    " 25 26  4  0  0  0  0\n",
    " 25 27  1  0  0  0  0\n",
    " 27 45  4  0  0  0  0\n",
    " 27 49  4  0  0  0  0\n",
    " 28 50  4  0  0  0  0\n",
    " 28 54  4  0  0  0  0\n",
    " 29 30  4  0  0  0  0\n",
    " 29 34  4  0  0  0  0\n",
    " 30 31  4  0  0  0  0\n",
    " 30 64  1  0  0  0  0\n",
    " 31 32  4  0  0  0  0\n",
    " 31 65  1  0  0  0  0\n",
    " 32 33  4  0  0  0  0\n",
    " 32 55  4  0  0  0  0\n",
    " 33 34  4  0  0  0  0\n",
    " 33 66  1  0  0  0  0\n",
    " 34 67  1  0  0  0  0\n",
    " 35 36  4  0  0  0  0\n",
    " 35 68  1  0  0  0  0\n",
    " 36 37  4  0  0  0  0\n",
    " 36 69  1  0  0  0  0\n",
    " 37 38  4  0  0  0  0\n",
    " 37 70  1  0  0  0  0\n",
    " 38 39  4  0  0  0  0\n",
    " 38 71  1  0  0  0  0\n",
    " 39 72  1  0  0  0  0\n",
    " 40 41  4  0  0  0  0\n",
    " 40 73  1  0  0  0  0\n",
    " 41 42  4  0  0  0  0\n",
    " 41 74  1  0  0  0  0\n",
    " 42 43  4  0  0  0  0\n",
    " 42 75  1  0  0  0  0\n",
    " 43 44  4  0  0  0  0\n",
    " 43 76  1  0  0  0  0\n",
    " 44 77  1  0  0  0  0\n",
    " 45 46  4  0  0  0  0\n",
    " 45 78  1  0  0  0  0\n",
    " 46 47  4  0  0  0  0\n",
    " 46 79  1  0  0  0  0\n",
    " 47 48  4  0  0  0  0\n",
    " 47 80  1  0  0  0  0\n",
    " 48 49  4  0  0  0  0\n",
    " 48 81  1  0  0  0  0\n",
    " 49 82  1  0  0  0  0\n",
    " 50 51  4  0  0  0  0\n",
    " 50 83  1  0  0  0  0\n",
    " 51 52  4  0  0  0  0\n",
    " 51 84  1  0  0  0  0\n",
    " 52 53  4  0  0  0  0\n",
    " 52 85  1  0  0  0  0\n",
    " 53 54  4  0  0  0  0\n",
    " 53 86  1  0  0  0  0\n",
    " 54 87  1  0  0  0  0\n",
    " 55 56  3  0  0  0  0\n",
    "M  END\n",
    "'''\n",
    "mol= MolFromMolBlock(mol_block)\n",
    "mol\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wAMvvsw+P/3yYlGcQKqO6B+eYFy5cIIQcOnQIHudgaQhWQlqyZAl7JCEhQSaTBQcHs7GoAKCvr89mPcG/l1ATEhIkEgmbf6mvrx8aGhobG8uurrYZtHxFIhG64G1sbAYNGlRSUrJp0yYAQBF0dXXFxteuXcMONAyYr6urQ/cUavHMmTMJIStWrMD29fxUDVm+fDm73GFiYoJ7lL7xxhtPODoKhVAZ1TmYMm9ubo7R5h9++CEArFixolU3QX1RKBQNT7GxU87OzqyAMgyjHZpqaWkpEomio6Pz8/Ojo6NDQkJYtXVwcJBIJCkpKa0dl1qtPn36tFgsxhrMaAgPHDgQo5S8vb2Tk5MxVBYbpKWlEUL++usvbLx///56N1y/fj0AODs7ozm5Y8eO999/n5XFZubmZ8+eDQkJYVtKJJKioiK0T7/55pvWjotCaQiVUR2D3mo2mWf16tVOTk5//vlny+/AeqgeG8TDxk6xyU7wKNsHEQgEuIR65coVmUyGq4oI5piWlZU9tj8JCQlSqdTNzY291tPTUyqVpqenE0Ju376N4aIuLi6TJk0CgOeeew4eZYXiVncAcObMGe173r59GyPq9+/fj+KLezeh3OOeow2Ji4trKKCEEI1Gg062NjweKJSGUBnVMbg4OHjw4A0bNuzbt+/atWvl5eWtyuDct28fNIj+aZ6qqiqMnWKjOOvBLqH+/PPPYrGYDVltJse0tLR09erVuOKJuLq6Llu2jHWRsxQWFg4ZMgQeVSfBuTY+SGJiYvDamzdval+CFqhIJDp+/Dga7wBgZGSkUChSU1PrNSaExMXFBQcHawuo9lozVmu1t7dv+Tf21NBoNJmZmadPn46MjJw1a1b//v3Nzc2//vrr9vb+UZ4EKqM65sqVKxhwro1QKPTx8QkNDZVIJHK5XKFQpKenq9Xq6urq2NjYt956Szus57333gOA1atXt60DGRkZaKJqe/a1cXV1nTdv3ooVK0aMGMG6qrp37y6RSDIyMtj7bN68GQ1bMzMzkUhUT2qrqqr27t3LHqmpqZk8eTIAsDfErNBdu3bh29zcXPZarElqYmIiEoksLCzQAjUzMzt37lzD4cTFxWG4KABYWFhIpdL79+8fOXLkt99+Y9t8/fXXAPD666+37Rv7r6ipqUlPT1coFHK5XCKRhIaGDhgwAFOw6mFgYGBgYPDdd9/ptsOUpqAyqmNycnIwENLIyGjo0KEeHh5YBK8hPB6PnYCbmJgcOnQoJSWltrYW60LVS6lsAxg7JZVKfXx8tLfq1Bb3MWPGTJkyhV1m5XA4AQEBcrn88uXLbDMOhzNt2jS0ENVqdVxcnFgsxnpU2lN1lUo1Z84cvAQXas+dO4f1SgAAXVvXrl1bvny59jIui6enp7aIE0Li4uKCgoJYARWJRBKJhHWy9ejRg22JUatyufwJv7GWk5WVFRcXFxUVJZVKRSKRv79/vWoAjWJtbR0UFLRs2bK33noLj4hEIgwupnQoqIzqnvLycpy0CoVCVMOioqL4+Pjo6OjPPvvMycnpsfWYORzOpEmTJBJJVFRUfHx8q4KlGiUvLw9jp7Qrn7Lo6en5+vr6+/uzvm+U/uHDh0skErSnOBxOr169tCvYDx48uOGa75o1a9gGn3zyiUwmQ1UNDw9ni90BAMMw/fv3xyOWlpb4wtTUdNeuXYSQuLg4TIdFAXVwcNA28Pl8vr+//+LFi1GaNRoNSlh71CGtra2tZ2D6+PjUy+LV/qsJBALtPy7DMB4eHu+++250dHS9BLMdO3bgijYtsNIBoTLaIaipqQkNDQUAQ0NDNpseCQwM5HK5AQEBa9euPXnypEKhiIiIEIvFwcHBrq6uTSlso8sCLSw9p41arY6Pj8fYKe1gKRZbW1ttvfPw8Fi+fDkufSKmpqbvv/8+OuIbRXsR08nJSfvmqML6+vpxcXEYGuXk5JSSklJaWooWJWiVa7G0tJRKpSUlJb169eLxeFhpMDY2tqSkRPvj/quFUfZRJ5PJ8M/h5ubW1J+Dy+U2XLpBBAKBm5ubWCyOioq6f/9+M5+YnJyMS88mJib4/KB0EKiMdhRUKtU777yDP7nIyEj2+M2bN+sVMMYVUrFYjO5mR0dHa2vr9evXs+YPzqAboqen5+bmFhwcLBaLZTJZdHR0fHx8yws8l5eX4+d269atKUVgVUNPT8/Z2Rnn43w+XyQSNVWZPykpqd4aAp/Pl0qlcXFxrq6uALBy5Uqs5ezq6spO5OPi4tDjjx83b948Vi6Tk5MbBi3U1taePXt27dq1Xl5eDMP4+vq2cNSNGphNfcPNY2lp2b9//7Fjx86fP//zzz8PCwuDxiqu1mPr1q1RUVH4uqqqatasWXg3sVhcW1vbwlFQ2hUqox0LnNUyDLN+/fp6p4qKin7++eeJEydqZ80/99xzmLPk5uamrVON2kqNrnhCE6Zr8/3E2KlXXnlFO7wf0V7DNTMz8/DwQEMMxZS9s3aiFEbUAwCuumJZFqz/5OnpiaVYPDw8MJNVoVCwG42Ym5tj2KmJiUnDCnh1dXVoSoeEhGgLH14ybdq0eo+Qhl+aq6trU1ZkU+CzKiAgAL/PiIgIfFzVM4oJIRjv1XzsampqKj6KwsLCtAus4MqJj49PvQViik6gMtrh2LJlC8pQeHi4RqPJy8uLiooKCQnRFiyMxMRYoqKiIn9/fwCwtbW9fPlyM3eu5xqePHla375jDQwMAGwA9OvJAU42Q0JCml8WqKqqwvl1w408tVVbKBTioPT09MRi8f379/39/R0dHa9du0YePTwAAO3cQYMGXbx4ESfCmPw+YMCA/Px8bQG1srKSSqWlpaVlZWVvvPEGHhSJRCUlJewqRD2vNzt3lsvlqKq2trazZs2aNGmSl5eXdixtS8BnT0hICGvax8XFYUBFS/7KKpUK1Rx18MaNG/Hx8Y1eK5fL8cHp7u6+Y8eOgwcP7t69e+nSpXi5kZFRw5AyylOGIYS06l8P5Smwa9eumTNnKpVKExMT9MwCAIfD6dOnT0BAwIgRI1xdXc3NzY2MjIyNjY2NjSsrK6dMmXL48GFjY+O9e/dipuNjycuDQYPgwgX48ccqF5ebpaVXMh6RlpZWWlra8BI+n9+9e3e3fzNr1qwbN24cP37c1dX1+PHjMTExJ0+erKmpaXg5h8PBf3Z6enp1dXU8Hq+oqMjY2HjNmjWffPIJ3r+urm7UqFH5+flXr17F3aRfeOGFJUuWyGSyixcvAoC1tfXChQvnz5+P4kIISUlJ2bBhQ1RUlEql0tfXZ6vtcTgcT0/Pvn37duvWzcDA4N69eykpKXfu3MnNzdVoNC35ivT19R0cHHr06GFvb+/g4ODm5qZS9Ver7d56y+LQIeNHy7OPp7q6uri4uKamBl8UFxcnJCQsXbrUysrqo48+qq6u/vXXX2/dujVgwAAHBwdsgI3z8/NVKlUzd+bxeHv27Hn11Vdb2hVKO0BltINy8uTJefPmoQH42MYCgcDAwKC2traqqophmL59+zo7OwsfYWBgIBAI6r02MxNWVRlGRpoplYybG4weDY/qzz2kuLg4owF37txpVIAYhvH29u7RowcKq7OzM65F7tmz586dO011m8PhhIWFrVy5ctq0aWfOnGGPe3t7X716lcvlqtVqLy8vHo935coV0BLQkpKSy484f/58YWEhe223bt0qKysNDAzUanVpaWmjat4QHo/n7u7ep08fVi7d3Nzs7e3t7e3RplYqlZWVlcXFxXv38vbuNVu06Pr339tPmfJXZWVleXl5aWlpZWVlZWVlWVlZWVkZvi4tLS0vL6+srGS3jG4bxsbGRkZGBgYGpaWlxcXFDMOMGjXKycnJyMjo8uXLcXFx77//fkRExJN8BOUJoTLacVGpVF9++WVdXZ1SqayoqMCfMb6oqKgoKSmpqKioqKh4gl+pDcPMNjQsJ+QVe/tNQmGRubm5iYkJGrlmZmYmJib4GzYzMzM1NTUyMtLT06uuri4oKHjw4EFmZmZGRsalS5eaEkp9fX1HR8du3brxeLzMzMw7d+7U1dU1bKanp6dSqTgczrBhwxwdHTkcztixY1etWnX9+nVzc/OSkhIAsLKyGjt2LC4C/P333/n5+dp34HA4LbEueTwehkNhiWscVGJi4r59+5RKpZ2d3UsvvaSvr88ajNrGo5YcTwUoBwgG4AAsaOEXjc857YfZ1atXCwsLx4wZ07dv39LS0u3bt1tYWGzfvr3eA8/a2lo7bHbz5s25ubnr1q3Dt0OGDDl37lxsbOzYsWNb2BNKe0BltCugPWf85ptvvvrqK0LImDFjRo4cyR5PS0u7dYtkZtYpldUAxQDFpqYm5eWTCJEBxAHMAEhv7ecKBAKGYaqrq21sbOzt7Xk8Hiq+SqVCxW/0qkZVj8vlajQaNQADUADwvUwmkUgAQE9Pj8vlVldXt7BLDMNgnIBAIMDVZEKIWq0uKytTKpWtHeC/EejpCc3NJxsZ5XG53sXFY4KCVuvrCywsmjT58bWpqWk9P1VlZaWlpaVKpcrPzxcKhV988cWiRYtmzZqFBRZaSHl5uaWlJSGkoKCgXvVuylOmkUhASqcDkwXx9aZNm3x9fWfPnn3o0CEbGxupVHrgwIEff/zx2rVrgYFX0tO9PT0hNBRmzgRnZ6iqUiuVi/Lzyx88iJk1q9LDo2zGjNKqqofzU5yrVlRU4LwVDeHy8vKSkpLKysra2lrWRsvLy8vLy2thbxu1HAkhagCGEACwAuj7zTd4vLXaRwhRqVQqlaqhkW5gYIBbBqBljVY2Wt88Hu/48eP//PMPwzBDhw7t1avX9evXr1+/rlQqnZ1z7t4V2tjov/QSjB0Ljo5gYwNubrByJYSFRQcFwRdfQGtXJk+fPl1bWzt48GChUAgAKpXKysqKjZ9tIWfOnKmrq/Pz86MaqnOojHZBRCKRubn51KlTf/jhhx9//BEnHHZ2dsOGXY+I8Naq0QxGRlwjI6FQKLx3D+7fh/R0cHWFTZugicio/+f+/fuHDx/GUJ66urrjx4+r1eri4uIffvhh//79DMMEBQX5+vqyk2LtOXJeXp5ara53Qzs7OyY7m33bt6SkmU9nGIbH4/H5fB6Ph+JoYmJiYGCgr69vYWFhbm5uZmZmZ2dnZWVlampqZWXVo0cPW1tb3O2uKQghGzdulEgkN27cOH36NABwuVw/P79p00r8/e0GDIB6kfWrV8PKlZCeDpMmwdq1sHjx4780pKKiAq1OtoD04sWLP/7444bfSfPgbrJsAQGKLnnqsQGUp0RkZKSenp6JiUlISEi93Zaqq8mvv5IxY4h2hdLDh4lAQADI8uX3myoxdfv27S+++MLPz48NZkIruE+fPnfv3sU2bI2SWbNmNVr1OSUl5dNPP/3999/T09N/++03AGAYJjg4mACw//0zYYKenh6fz28q1rW1mJiYODg4PPfcc76+vsHBwRMnTpwxY8Z7772HMQDbtm375ZdfYmNj/fz8AKBv377ffPNNdnZ289+wRkM++4xwOASALFyYxG541RCs+iqVSoODg1HNLS0thw4dym5C1TxZWVk7dux4++23T5w4wR708vICgFbVVKS0E1RGuywYQjR37lz2iEajOXPmjETyk5nZQ72aPv1flxw9Svz9cwUC87CwMO0Yxtu3b0dERAQEBGirZ0hISFRUVHJyMu5BYm9vf+XKFWy/f/9+lNdx48Y1VQW1rq7u888/Zy1EhmGcGUbDMIRhkhhmyZIlAGBsbBwZGVlZWZmenh4XFxcdHR0REbFw4cLJkyf7+fm5ubk1la6uDYfDeRItFggE9vb2np6eAQEBwcHBISEhIpEoKCg8PFwqk8kiIiI++CDqued28vmG7u7ux44dy8rKwodHWVnZ4cOHJRLJ4MGDtfNoeTxev379MEy1R48eTYX64u7Q4eHh2pViPvjgAzybm5vLMIyhoWFNTU3b/4lQ/iOojHZZvL29AeDIkSP4dtu2bZhbyePp29qqPYBhyrUAABrDSURBVD2JTEa0ytE95NSpU/gLnz17dmpqKqonKwGGhoaontr5P2VlZRipamxszH7c2bNnLSwsuFyutgGFKJXKLVu2sFXxWW7cuMG20T47ZMiQZopxVFVVZWVlxcfHx8bGyuVyqVQqFotDQkJ8fHzs7e0fW9WFz+cLhUJ7e3tnZ+du3brjEWdnZ2tr6+YXAZqn3ucyDGNtbd2/f//x48cvWbIkIiJi48aNWBWbz+cvXrw4KyuLTVIijzbXYjE1NQ0JCdmwYQP7PWBFwVGjRrX2XwWlPaCe+q5JdnZ2t27dDA0NCwoKMNV93bp1y5cvd3JyEolEb775vru7dVPXnjhxYty4cfirxiNCoXDcuHGTJk0aNWpUo+KiVCrfeuutnTt38ni8bdu2YbZ4cnJyfHz89OnT2WZ1dXXffffdsmXL2I2UWby9vS9evIhWW0VFha2trbaPiM/nf/jhhytXrmyqimBTKJXKgoKC4uLinJyc7Ozsev/PzMxsNAaLhcfjOTo6WllZGRoampubGxsbGxoacjj6J05wMzIYR8dbdnYFLi4upaWlhYWFt27dwrjdNv+mBAKBySMSExOFQqGjo6Ozs7O7uzvGopmamuL/N23adPDgwfXr13/88cdt+yzKfwiV0a5JZGTFDz/E9O//YMuWhXgkJyfn1q1bQ4cOfayBBgCffvrpqlWreDzepEmTQkNDX3rppYa58/UghKxatWrVqlUMw3zyyScrV67UPqtUKn/66SfcxqPhtUOGDPnzzz+1Z75nz54NDAys59Pv0aPHtm3btLf2fHKKi4tZYV20qDgvL2fUqGyNJictLa2ZxAFtbG1tX3jhBaFQaGVlhbuTTps2LSgo6MUXX+Tz+Y062bSP3L9/PzU1Va1Wt1Z/zc3NFQqFr69vG0dO+e+gMto1CQmBQ4fg++/hUcHfx1BWVrZs2bIXX3wR93pbunTpZ599tnTpUjbSu4V89dVXCxYs0Gg0s2fP3r59O0aSbt++fdmyZU2FkQYGBv7xxx8Njdz169cvXryYfYt5ogAQGhq6detWa+smrem2kZQEnp5gaQkPHgCPBzJZRkzMvoCAsnnzZtYzY1nbFkuCNnNPXC5wcHBg/y8UCvGFk5MTu7Cbmpo6ZcqUa9euCQSC5cuXT506tbS0FJOgcnNzr169mpCQkJqampeXx34ch8M5c+aMv7//f+WCozwRulpNoLQfVVXE0JBwOCQnp6WX4F5ygwYNwrdYcun3339vw6ez/qWQkJAFCxY01EftX/6wYcO01wTrUS9VHHOBAEAoFMrl8lbtWPVY0tPJ3Llk0aKHb93dCQA5fbq5S6qqqi5cuIA5r5s3b8bAz+7du3t5edna2j5W4CwsLDw9PUeMGDF9+vQFCxa8+OKLeHz06NHR0dEffPDBgAED6m04GBgYKJVKT5061cyXRnn6UBntgsTEEAAyeHArLlm9ejUALFiwgBBSV1dnbGzMMAyaP20gLi7OwsJCW0dYd7m2qvr7+ze/JUZ1dXWPR6n+eDmfz2e34XvxxRcbbmbXBtRqsm8fIYSkpBAccVERef55YmlJGovXapK3384dNGjfL7/8zR4pKipKSEhQKBRRUVEymSw8PDw0NDQgIMDNza2ZRRLt742tP61QKJoJqKLoFhp+3wURCmH8eBg6tBWXPHgw0tdXGRAwGAASEki/fjdsbC61eeLM4/F69er1999/AwCmRebk5HA4HAMDA9Zx5Ofn98cff+C2yU0hEAiOHj3q5eVVXV3NMAyHw6mrq7ty5cqMGTOOHDkSFxfXr1+/Dz/8cNWqVU/iVVerYdEisLaGO3egd2+wtgahEG7cgKIiaKzef5MoFDZ37kzo1+//j2A+aJ8+fRptj6lfWVlZubm5uESQk5OTnp7O5XJNTEyGDBkSGBjo5+fXWq8aRQfoWscp/yUlJQTDsS9fJtrTvua3QdNoiIUFASD37hFCyJYtBIC8+WZbOpCYmBgaGor2lKWlJVvX2cHBQVsOBg0aVFpa2sJ7xsbG4lVYJwVfT58+ff78+Tjn7dmzp0I7kaCVKJVk0SISFka+/55cuvT49uXlmj//JJ9+SsaPJ+yyR24uASAmJqT1G7VQOj1URrsUt26R3r3JvXtk9WrC7jySkUHs7cnWrU1elZREAIiDw8O3b7xBAMi2ba376Lt374rFYqzBYWRkNHv2bAxcxSx17TW+fv36FRYWturmixYtwmudnZ1tbGzw9ciRIxUKBdp6DMOIRCLtivotR6kkEgm5coX4+ZFLl0hBAWlYPbmqqioyMlIsFnt5efn7b2TzrRYseNggKYkMH07Gj2/D51M6PVRGuxS3bpHPPyfvvvsvGd248eFv/pNPKhutr37hAhk4kLz22sO3bm4EgFy71tIPzc/Pl0gkaGzy+fywsLBly5bhLNvFxYWNG0UT9fnnn2+D2KlUKnbzZD8/P9zZDe3QjIwMmUyGn25hYdEG11NdHdm8mRBCPvmE/PMP8fMjw4eTzMx/tamtrWXXDbp393vhBTJ/Ptmxg9y5QwghiYkE1y2Tk1s7MkpXgMpol+LWLbJtG9m1i7z4ItHeBy8ykgiFGnf30FdeeaX52bRSSSZOJL16tci7Ul5eLpPJcEMODocTGhp68uRJ9DgzDDN79uxx48ah9KA16u7u/thE9abIzc1lNwEViUTaOyrHx8enpaWx8aSBgYFJSUlt+5SkJGJrSwCIhQXZu/dfp5YtW7Zx48Zz58419JLPnUs+/ZQQQt5/v20fS+ncUBntUmRkkF9+IYSQSZNIcTFZsYI8SnMnp09fwwxLf3//puy1zEyCu6M/1qqqra2Vy+Xs/Do4OPjq1atyuRzXLp2cnHbv3t3vkbcFCw/37NkT96RrM+fPn2drGK9bt27GjBn4Wl9fPyYmhhASHR2NbjGBQCCVStuWb56bS0JCCACxtc2eN+/9pmIJVCryzz/ku+9IaipZuJDIZOTmTSqjzyhURrssu3YRAGJsTA4ceHgkLS3Nw8Nj586dTV2ydevDqX0zcqBWq6OjozE9H0X59OnTGRkZgYGBeCQ0NPTs2bP29vb4FiM9nZycbt++/eSD2rhxI7tEsHPnTjY7gGEYmUxGCCkqKhKLxbiA0KtXr4YZ/S1BoyFbthA/v3FoQV+8eBGPP3jwICYm5vPPzwcFEROTh0slmzeThQtJdTWZM4fK6DMKldEuS00NmTGDABAOh2zc+NB/3NTO5vfukTt3yNatZOtWsmtXk3KgUChwy3gA6Nu3b3R0tEajkcvlWM3Ezs4uJibmyJEj7BbQWJa4W7duj92xueWwi60CgeD8+fORkZGs/2r27NlYD/DMmTMeHh5P6HpKTExEg5rP5/v5+bm4uDxykc1HAXV1Ja+9Ro4cIQsXEkLI0aPk7bf/q1FSOhNURrs4ERGExyODBn02a9asehqqUpH4eCKVEh8fwjDkvffI1q0kLY3Mn09mz65/H+30bRcXF7lcrlar79y5w3p+QkNDCwoKIiIiWFHDpUwbG5v/JEiepby83NPTEz/CysoqLS3twIEDbDR7UFBQcXExIUSpVMpkMvQL2draRkVFteGzlErlihUr9PT0cLHC1NR0xIgRUqksNvbh6geFQqiMPgv8/nsSmofDhg0rLCzMz8+Pjo4WiUR9+gxgA3dMTP5fRu/dI0OG/P/lFy9eZEusW1tby2SympoaNEIxK9zGxmbfvn0qleq9996DR2AVOGtr62Zq3LWZ5ORk9GsBgKenZ3Fx8YULF8zNzfFI79692QWE1NRUdnOOYcOGJbfelX7u3DkA6N69+z///NPCPegpzxpURp8JLl265ODggOabdgjn8OGpCxeS48dJo3P9pKQkNpZeKBTKZDLMR8zOzg4JCWGN0Pz8/LKysjFjxrDLlBiQZG5u3lRN4ifn119/hUdBVC+99FJdXd3NmzcdHR3ZxYQzZ85gS41GExUVZWVlhQu1Uqm0qZWNRlmzZg0AzJs3r33GQekKUBl9VsjKyrK2tu7WrZtAIAgODpbJZM1EBWVmZorFYqxcZ2hoiAXu8FR0dDR6/IVC4c8//0wIycjIwIVIAOByuRjwZGZmxnpm2onw8HD8RAAICwsjhGRnZ+PWGgCgp6e3Y8cOtnFhYSHrenr++ef/+uuvFn4KVmnZWy/6iULRgsros0JtbS0WHMnIyGimWWFhoUQiQfc6n88Xi8VslNKDBw/Gjx+PIjVmzBg8fv78eTbsydDQcPTo0QBgZGTEGoPth1KpRMlGJf3f//5HCCkrK2MDSBmGkUql2tFdp06d6t27N+t6KtCOrW2M6upqgUDA4XDa5qSiPCNQGX1WwN0u+/bt21SDiooKmUyGK4wMw4SGhqakpLBno6OjLS0tcaoul8vZg2ymvIWFxaRJk1BMn9o+azk5ORhZhYVLDhw4QAipra2dNm0au3AxdepU7dpI1dXVUqkUXU92dnbNu56wfqCPj0+7j4TSmaEy+qyAO9y931gok1KplMvlbI5QcHCw9ppmbm7uxIkT8dTLL798//59QohGo5HJZGxJNxcXl7fffhun0ocPH356oyLk5MmTPB4Pe2JoaPj3339j99g0fADw8/PL/fe2UykpKWyMwejRo5uKacW60YvYKqQUSmNQGX1W8Pf3B4DY2FjtgxhLz9b0HDx48MmTJ7UbsHlBpqamrBFaU1MjEolYkXr++edxmVJPT69tlZ6fkM8++wweVTK1t7e/h4WqCNGOvnJzc6sXd9US19MLL7wAAEePHn1KI6F0TqiMPhOUlZXx+Xwej6edUK9QKHBvZADw9PTEWHr2bHFxsVgsxrMjR45ktamgoGDo0KGslzwoKGjp0qW4QLl79+6nPTBCCCEajQbtZcwC8Pb2Zjcu/fnnn9n8UaFQ2DCp6cGDB+wjwcvL6/z58+ypkpISLperp6fXfG1pCoXK6DPB77//DgD+/v749q+//hr6qKqzk5OTXC5X/btM5qFDhzBAytDQMCIigpXXlJQUd3d3VkOnT5++YcMG1NBmckyfAmVlZeg7MjMzw3k6O6ITJ06wQaY8Hm9rYxUD//zzz+eeew7HJRaL8WFz4MABAHjxxRef6kgonRAqo88ECxYsAIAVK1YkJCSEhoaiplhZWclksnr1ikpKSlgjNCAgIDU1lT2lUCjYEHcACA8P37RpEwBwOByMfNItN27cwCwDTAr44IMPtE+xIaXY84aB9FVVVVKpFLOh7O3tN2zYgEkHK1aseLrjoHQ+qIw+E2A8/NixYzE2yNjYWCKRNKyYd/To0W7duuFaoUwm09aab7/9lp0dMwyzfv36yMhIhmEYhtm+ffvTHU2T7NixA5doUQ23bNnCnsrIyGDtTQCYMGFCZWVlvcuzsrK++OILW1tbVnCFQqGHh8d/UlSF0oWhMtr1SUxMZHVBT09PLBY/aJAQXlpaykan+/n53bp1iz2l0WikUqn2HXbu3Pnjjz/iLnXaUtURQFPaysqKYRgul6vt8iosLEQ/Gw6zX79+9+7dy8jI+P777998883u3buDFuibwpY2NjZtqxRFeUagMtqVKSsrYyvDYyhoo7H3CoXCyckJAAQCgUwm014nraiowJB7lBVjY+OjR4/u2bMHE5ywNl2HoqamBt3rmNRvYmJy/fp19mxFRQUmCOBw0DZnsba2njRp0ldffZWQkJCdna0djcDhcKRSKc2ppzQKldGuSWVlpUwmwzp1yIcffthoM4lEgpoyaNCgeumhWVlZuJsxiqadnd2VK1f27duHb9euXfu0RtM67t69i2FMWObO0dERY10RlUo1ffp09Onj06Vfv34ymSw+Pr6hSh46dIitjwcAr7zyCpaPolC0oTLa1cBYevSzA0CfPn3Q5aIdyoOcO3cOTTY+ny+RSLBSJ8u1a9dwnotLoj169EhNTT1y5AiGZ0ql0qc3pNajUCi4XC6Hw8GSej4+PtpBS1FRUQDg5eU1duxYtEm1zeoHDx78+uuvc+fOxdwndD3xHm21bG9vf+PGDR0MidKBoTLaddBoNNHR0aiMAGBhYYEKiImSQUFBZWVl2LKqqoo1Qr28vK6wO408Yt++fej1xju88MILeXl5x44dw/UBbSd4hwXXc4VCobOzMwBMmjSJNTbfeustAPjyyy+JVoj+qFGjFi1a5OPjw6Zmaa+HXr9+HTc6Rdn94osvdDMqSoeEymgXQaFQsHsfsULA5XKDgoKkUikapz4+Pnl5eefPn0efNY/Hk0gkDVN3WGVBDR05cmRZWVlcXByWLp4/f75OBtha1Gr1K6+8gs8JXNxYvHgxnsJ5+tWrV/EtW28FMTY2fvnll2Uy2R3c9vMRGo1my5YtbLjCwIEDtVP1Kc8yVEY7PadPn2Y39mD9IQEBAREREew2nLdv30Yr1dLSEv0qffv2jY+Pr3erurq6uXPnsgYsALz55ptKpfKvv/7ClYFZs2a1dvtiHVJUVIR7Rk2cOBFDoLZv356WlobfA2ucfvfddwKBABs4OjomJiY2c8/79+9j6hfDMJi/T6FQGe3caHuTGYYJCAj46quvGt3E+MSJE1j+DgDefvvthrtmFhUVYbUOPp+P9mx4eLhGo7ly5QpaczNmzOh0ruqrV6/iqGfPno1D+/DDDwFg8uTJbJvq6mqlUpmZmYn6aGlp+dgqf6+++ioAzJkzp527T+kcUBntxMTGxmK9ZCsrK6lUqu2P1ubw4cOffvopzkZxxdPCwqKexyk9PR1vhQ24XC4mTV679nBb5smTJ9e1ZOv6jsf27dsBwMjICJdE8Xv4+uuvG7YsLy/Hqv76+vr18rJu3Lixfv16dkOUCRMmAMB33333NAZA6fBQGe3EvPvuu491+Kxdu5ad6YvF4vz8fAycNDY2PnbsGLY5d+4cll7GhHR9fX0sMpKUlIQpPRMmTOikGoqggPbq1Yut+Hfu3LlGW6pUqvnz56NpL5FI2BUMdFiFh4djG3y0NF8Am/LsQGW0E4PFOJpShLq6OplMxrpEvL29Mfuzrq5u5syZKJd79uz59ddf0f+O0mBubn769GlCyK1bt7Ai8ssvv9xwBaBzUV1dPWDAAAAIDAzEbyMgIACLCZw9e7bhpntyuRwjnKZMmYJ+pCFDhgBATEwMIeTSpUsA4Obm9vQHQumYUBntrGRlZQGAiYkJxnsePnz42LFjrO84ISEB90PG5CX01Pv6+ubl5RFC1Go1WrJsOU4sKmpvb4/+69TUVLxk5MiR9WqXdFJSU1PZuioYcjBlypSsrCxbW1sjIyPtXZuQo0ePYl0oPz+/27dv6+npcblcjL2XyWS4vqyLcVA6IlRGOysYQx4SEoJvMarx+PHjKpWK3Z/dxcUFyzCznvrevXuzlUM//vhjU1NTExMT9CB5eHjcvXuXEHLv3j0MCRoyZEhXKrV54MABTLQfO3YsLl8sW7YsLCwMtVUsFteL/bpx4wbGnOK+AIMGDcLjuNfTr7/+qotBUDoiVEY7K2+++SY82setoKCAw+EIBIKEhATc5Q3rZrLVi8mjXTO5XC5uWEQI2bZtGzwqGj9o0CDcte3+/ftYDN/Pz0/78q7Bzp07cZVj+PDhuPXITz/9FBUVhd58Hx+fesud2dnZmKHP5/PfeecdQkhtba2RkRHDMA3Lu1CeWaiMdlawoh0mJu7ZswctTZyuOjk5KRSKhpcUFRXt37+ffYvljgBg3LhxuBqQm5uL/npvb292R+Uuxv79+9EURRtTT0/v5MmT8fHxGGFqZWVVb6m0urr6tddeg0cln//8808A8PLy0lX/KR0QKqOdkuTkZACwsbFBV/Ibb7zBRo+Ghoa2RAEjIyOxPRvJlJeXh2VJ+/XrV1hY2O5j0B23bt3CkWLIvaWlZUpKSmlp6YQJEwYPHtwwrUu7VCAap50iHZby1KAy2inZunUrAEybNk2j0cjlcvQUWVpaoiv5saCTBCOc8EhxcTEWc3r++eefhT3Zq6qqMAoKcXd3Lyoq0mg0zTyB1q1bxxYoOXjw4NPsLaWDQ2W0U4LxjzKZjN0lmM/n19tDuFFUKtWcOXNYH71YLCaElJaWopHl7u7eaAZUV0Uul6NBCgCDBw9uGNeVnp4ul8tFIhHWY0U+++wztsgLhUKojHZG2PBvXAk1Nzc3MjIaP378Yy+sqamZPHkyupW8vLwAYPfu3ZWVlbi9Xc+ePbOysp5C/zsUly5dYuvejxo1Sq1WX7t2bdOmTRMnTsQgMBZbW9vQ0NCvvvqqa694UNoAldHOB4Z/I1OnTs3Pz1epVI+diRcVFWEMOe4zbGBgwOFw7t69O3z4cADo3r37M7vjUF5e3sCBA/H7ZMsOII6Ojq+//vr27dvr7XFPoWhDZbTzkZ+f7+vryzDM8uXLW9I+Ozt73bp1GBzq7OyclJR0/PhxdMdjKblu3bqlpaW1d7c7MhqN5s0338T6gfb29qGhoXK5PCEhoROVs6LoECqjnQ+1Wo1zcwMDg9jY2Kaa5efnR0VFhYSEsDsO9ezZMzMzkxCyZMkSAMD4UBsbG2pqIRqNBhMQKJRWQWW0U8J6irhc7rffflvv7G+//RYQEMAmemLVOw8PD3bpE/NEAcDa2rphRjmFQmkVHKB0Qrhc7rZt23ArebFYvHLlSu2z2dnZ586d09PT8/X15fP5hJAJEyZcvnwZ0+STkpIuX74MAEKhUKFQ9OnTRydDoFC6DrrWccoTsWXLFrQ6w8PD2ZrKOTk5u3fv/vLLL/HUe++9p1arMzMzIyIi0Ep1dHQUCAQXL17UbecplK4BT9cyTnki3n33XQcHh9dff33z5s1FRUXff/89n8+3tbW9efPmqlWrGIZZsGCBk5PTkCFDLly4QAgBAAMDg6CgoM2bN7MVjygUypPA4E+L0qk5efLkq6++isXbf/nll48++ujbb7/lcDhubm7p6emseo4YMSI0NHTChAm4sRKFQvlPoDLaRbh48eKYMWMKCgqMjIwqKyvZ40ZGRmPGjAkNDR09ejRuEEKhUP5bqIx2HZKTk1966aWampq8vDx9ff2RI0eGhoZOnDjR2NhY112jULoyVEa7FLm5ueXl5WfOnHnttdfqJeRQKJR2gsoohUKhPBE0bpRCoVCeCCqjFAqF8kRQGaVQKJQngsoohUKhPBFURikUCuWJ+D8hCuW0txpVnQAABJZ6VFh0cmRraXRQS0wgcmRraXQgMjAyMy4wOS4yAAB4nO2RfUzTRxzGr9f2By0FWiilUKA/oECh9IV3p/C7q4wBCoqvRNzcqYMcjjg1MZKZTc0mqHG+xBeYEGZYYIPMl7m4ibG9QwHd5lvU+bKh24Im8wVlzGQ6HKzOP7Zsf8xkyZJle5Inn+fu8v3e2z3vnmvAp2Dwm8p9ftHn1TKBUB/lit8H66/haSb+tPBXJX5PKH/a8R/5d+uf6qj/5iA+vg1U+D2hXAYeE8j+awwAMgqBksgggXIKoZ8oVxCFkkKFIAr+FAoa4hdIoZ+O+KuISk2hOoCo9WKAhkJNlBgYRIKCKQwKEYO1RKujUBsqhiRRGGIjoXEUhiYSfRiF+ggSZiCGcAoNJjHcSIwRFBojxchUCiNdxJRGoSmLREVTGBVLomNIjJlCs0jM2aIYS2FcPIm3UGhJIAmJFCZZiTWZwuQUkmKjMNVO7A4KHU7idFGYlk7SMyjMyCSZWRRm51jlYByQPQPk44FyAvDPBao8ECCBaARiMBDdIHYiiMsH8c8CSwFIeA4kFoKkImAtBsmTQMpkYCsBqaXAPgU4pgJnGXBNA2nTQfoMkDETZM4CWbOBXuV7PwEoFXIoE/xV6gCNIAQGBWt1fkKYIdwYoReiY8xibJQQF29JSAwVkqzJKbYQIdXucLoihbT0jMwsU3iMwvcH4IlBed24bNzXb8ejlcHYM1CLLQ0/Iv3er5HueBU2OUsR3FWLGkt0uODykHT+SAY60diEHFt2o72XG9A7p46iwRYj/rBsxFdT6G2ZNwehvhrU3rQTzxxI8OREfiPlrNexIPuwt6isGa3w3mM0robdGK5HxY4cPvd8rmf0ghGZAkV+a0cr+mHTNmnZwwaWqbuG7AeuSnLVRl+ft9gdZ5N38q0P8InG22xRaT6b5bqIlb3H2b7tSnb2o6v4Usiwb+9qafN1L977catU0j4erRppxVuDJ/KTL3zO9lQr3Vt2P/CcqVKifrqON/cAllp5Rap9dStH5QPs+4W56H7NYX5jvoqrHmrRG9OucDXtZBu7bue9hk5x/7wmT/kdp3eDuY1HV/RIRWWPjqx33+WWrueR+X4qO5jTxyeeWcD7+ivRhV1VzNJVhHXHx+eeOqmRjp2fix/MWMjm1B2S1uxowM0977Mvam9698u78Gh+BVP2MvbT4D7cWpCdV1fxFVugrcPD5walgkK1e8np1aiqqh1NWml2H/6yExUu+xRdCnG5V42o0dDa9Si6wua+elDPRi80Si/bDO5db1ezO9MO5QZ5l+Bzr5TzskdDbGnePLy9fjNvzkjhS/O24NMRHdx+oJhPP3QYV3a28Jcm5PCj4T24zbOca7YpeVyNpnvD/m+l3tHpTPrO1P3ZzA40NaadXZxt6B5sseLl286yFXZZt/ZmPs7UeVnulAE+0BaAb8xfw0T3ezxCsYk3jO1HOyoPcMfYJ3zJ6cvo5/p+fvJuLzcmHkOlb47wxe92cMOitcj6+hDfmb+Ka+AE5Eqp9q7UX8djsW2sV3R5bPVyd+Picwz8r39chl8AA2a4L2pCb0IAAAZRelRYdE1PTCByZGtpdCAyMDIzLjA5LjIAAHicjZhRjt02DEX/ZxVvA2OIFCVSn0kmaIsiE6Cddg/57/7RS9JPllKg8CQT+OldS4e6JGWHibj0168fn15LKePl4T9/vP3+45/H/KlvLxgv//N3jPH4u2KCl28Pv3h8/vrLb++PLx+fPj9Hvnz/6/3jz4fpYzTcgz+79tPH92/PEXp8efBholwKLpR6jwuW4hfzToauHmY2MErHqFLjQkXbqquh66W5rhxWKL4+hhKtOol1WUuNr0WLxIUW3nQNOjqYWo/luInEBVmvq66HrjGNwK+IKEF7lVWn0L2WgwawAlAGJSDLWIUGoQCnOz/uAF9GIlV1FY6csXU7laJuj8My8aqk4lI6LAWvvihnOKXvYZP78gpjOvNz/Z6gbWjZpJxSrG65LPYwlGy2r19zfQoAN69mzIip7nPK4x3RV401/ZbyjK6WupO6Q+2QUntKLTMHUu28bSl1zNqPPsISSGGqphTG2SZ1nzr2x/i5qWwZFOs+qWHSBp9bJlAlTaHy2EFHgJZRY8p6lJp7Cw7k9ZbsbpQdtVrN1YkpZwfnNimHT3JwDzyPpORWHsKyVxADFNIm/VxW9bRUa9ti4jCqHcYtZuWDJOrS02zPe3anXvuhg85Za6WsKaK2s7pTkIrHkjml7aQm3fef3SoQUC+JiK14FkItuzRKSg+RlgVy5WzXtm+BJQGN6B7woEk9G8XYbeWzrEZmdT2oZuBHIdoq2hueR1ujEVXkspxBhaurkkKJRPUJUNw1Ksn3jGzvYpyri/OjCR1qdGaqNd5iqmdRyYhURSWMMwELdmRTSiqVfF2sTzbOltJ/UrbIvy7P7lB6Fqyn1ib0tjeQ/JrtsWbsyGitm0nVTSKfSc48Es5OXlT27XSPyBvxOA1HjqRHPH7apRGrV2tnwSMHM6DCvCmlxLGAfO/pe+3PIkCP2tqpUEhR77G8HGKtZhH0NjZU4ejRrYTzyFa2iAXzI/V3gBrFH0kcs45m56xxkKxSidZD2Wcwl9WhKS2m+yEWFWVeyNkesPwz91i23ivu1OvAeRnjsIrDAOwAnwkzpZrSOnoeyjjCNKVoqHtYlgBFo/uip3U6O7bYfp7JyDKl4r0es5Lna0hR4htAK9l+mLueHlTLI60a71JKadNYDVe4yMRCm9+lnMVvI5MAW2wt87v91CpbTVZr45y1FsptK8Jb/2uSUvh9Wj8ssxTlXbbNai2LGieIh6VucXtaUPanjmh/XsKRG4Z7yintIlu6NH386hl/VuZ/L6bQ7grHTWEvd4V0V8h3hfWuUO4K211hvyu860y/60y/64zedUbvOqN3ndG7zuhdZ/SuM3rXGb3rjN51Ru86Y3edsbvO2F1n7K4zdtcZu+uM3XXG7jrz9f1te4/MN8vP39/frjdLwS9fL5D+sV/viYKher0Oig9db32Ej3a93Pn37XqHw4drKoO4PfR6HxMoxvLS5Z9pLkUOhnvmYsQ5MJcjhxu4c3kdEp+IaHnrEZ+JZnx4uSEf4UvjnJh8WdpRMTtfdE6L6WkGx86L6WnGh3cMipF5F8dmYsEZNDsyFryCYN9ujPD6/C+x4PqUnyNzL9iZ2Z9Al2f2HLlWd2ZA8Yw9DPSRy17fZGDyjL06MzB5roUnZ4qRyVx9nwHOc/XqzABfZvZ99pEZew1m/HPdZefIZK7BbHi0WR5Jc2TySDAPRLM8YObIpfF9jviWx0XxcOvcDXFmhFuv7HZmjLRJKM6MDVhmdmaEW2ek4swIt85ImzMj3Dp3rDkzwr3cac7swU3C5swe3CRszuzBTcLmzB7cVYJRdBiePM2ZEYpcPM7soVw8zuyhTJ7uzM1Jl6cRcfA2ebozA7xNnu7MAL/c6d4ggNkmT/cugWkmTo/UANXyBBBrXTBegB1My4lOKPU+4dQbFznSckR7nQNpOYvpEUjLoUueBP3qUV5+MLhPXI2mJoBazktyy/sEVgeG5X0SqxPDcl2POnLLdTKbMyPDdTKbM4tzLacUeRLoZDZnRhLoZDZnRhLo1VqdGRmuk9mcGWmhk9likzu45ogzIy3sOlacGWlhk3nEATHANUfCTOeaI86MRLHJPJwZiWKTeTgzEsUm83BmZLhNZj+r1pPJPz//vxTXL/8Cg7T1kgfGnVEAAANYelRYdFNNSUxFUyByZGtpdCAyMDIzLjA5LjIAAHicjVS7jhxHDPwVA04kYG7AN9ma0ImcGM4FBcZFB1gP2Ar18S727Ox5WzDgZBZ8NFksFvfD+4/P/Pzmw/uPb6/v07N87q++OmzN8JkRj+5/faO//yvHL9N+aPPLz7/dgz+81nsH/c8OZ45cJuP70/c3tNF2MO9EFhvtbjI2WGl2jH1EBixVK/x4ah0zv/Ywsc6j0E643Kql2xPtLGxdTZiP2IeUtNdSCl6RHIejuHeOcrYzZfBhe1J4p5KRw2uaeege5ANWEbdzJKrKLknaKUldJkn4RMG7sDdscWuMXKFHYxr9uGxw1xAb7fQoP6H1HCIsZ5En3ivnKEPOSeOq/yR7hciJMrqYjzyebJfAeF3zxCUW7XUL5PKeOXNTHY19L3Eky84Wk/dokAGQhq6KVzonHjmzRSI3FDPtRWjJbREIedrY8Iuf3lyMa0moVoNj66rlPYM3pDOWe/mY75RYej4TvceckQ8Uo8onZvJ7zRw851HlHpPZud2GhTcj6ZMDTq6uZOaT9RthkQ5SqsnopZnRzC6xi/eBQawBqUCJ6CMj6x7TATJlD7XsWMi4gBVaa2yKRTF3zLDp+0BMqnjHNaET0yt/HNR7gXBP4Sli2IsTNdUQmbRkq16FwTXFkmpjSnSqa3S+7qzRrBCzHWBB0+G0vp8es25t0S6gWshd5xyKBd3KoyDEi3UW53zjEu3F0eSGQQQVJyvEx1SgboUgTW/ganv0gTkdP/OyCDu4Y09AQl+ueQwWV6jFwQMUaYFaxnGqnRGoHXeLSDKUirMzuolIe6DW3FCbhFjeqtmurfbmis77UlLGzRthR31bKNLuxAbhpqHSeiOVuSBvphTRlq+CrPOEsIS8WgOTz4so176j8GFXc6cmCVdTNmUMid6A4e50Phte81n/N8xINLWjs0tHdogq+YrhqmwCC5pbAVmF/0ctn2PgJJpOErno7D+kMSf3opaJjNuK325/fPvy6fe/vnx9R/vL379++vrny/PLt53f6aPpjybLYsdi16Mty3vJxR5Ld17sBY4u9W1Bbws+s8Ve8NrS39fxF/yx9IulXyz9YukXC/5c+uXSLxe+csFbC1+19K/H/t//AfhU/NY99dFPAAAAAElFTkSuQmCC",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x15a72cf3060>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mol= Chem.AddHs(mol)\n",
    "mol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<rdkit.rdBase._vectint at 0x15a7293a4c0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rdDistGeom.EmbedMultipleConfs(mol, numConfs = 3, params=etkdg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5.68872557, -1.88594468,  2.02764409],\n",
       "       [ 5.64261651, -0.51235153,  1.50349208],\n",
       "       [ 4.57481011, -0.07050656,  0.74060838],\n",
       "       [ 3.76015133, -1.11676106,  0.63401302],\n",
       "       [ 3.56308094, -2.35490883,  0.94994825],\n",
       "       [ 4.65523041, -2.81673681,  1.74887807],\n",
       "       [ 2.30373923, -3.02523226,  0.70250953],\n",
       "       [ 4.21126719,  1.26807099,  0.32235019],\n",
       "       [ 1.40236953, -3.0851158 ,  1.71895258],\n",
       "       [ 0.0820194 , -3.32552205,  1.36507485],\n",
       "       [-0.41431234, -3.50094793,  0.22034591],\n",
       "       [ 0.46939721, -3.50438403, -0.94545192],\n",
       "       [ 1.8500549 , -3.24413265, -0.61265372],\n",
       "       [ 3.09993875,  1.2890843 , -0.66454058],\n",
       "       [ 2.57658767,  2.50230564, -0.79521309],\n",
       "       [ 2.87382536,  3.57619689, -0.18790885],\n",
       "       [ 3.91290567,  3.59965466,  0.78810092],\n",
       "       [ 4.44379322,  2.38012725,  0.8777203 ],\n",
       "       [ 1.37980018,  2.54848555, -1.7665855 ],\n",
       "       [ 4.01885795,  4.64124787,  1.72470658],\n",
       "       [-1.87743129, -3.40215639, -0.08498981],\n",
       "       [-1.85804455, -1.67617438,  0.46798169],\n",
       "       [-3.00830833, -1.1506351 ,  0.35973902],\n",
       "       [-4.14153259, -1.87419885, -0.12803851],\n",
       "       [-4.13503379, -3.11858346, -0.49694206],\n",
       "       [-2.86586604, -3.26792888, -0.26228078],\n",
       "       [-5.32133903, -3.83138022, -0.85625473],\n",
       "       [-3.39034188,  0.27640849,  0.41882431],\n",
       "       [ 2.83924894, -2.97850685, -1.66044999],\n",
       "       [ 4.17494917, -3.36818577, -1.58159519],\n",
       "       [ 5.11423923, -2.8888775 , -2.53283346],\n",
       "       [ 4.65937379, -2.09082644, -3.45693757],\n",
       "       [ 3.32709271, -1.65327167, -3.65890382],\n",
       "       [ 2.48718623, -2.16366827, -2.68834014],\n",
       "       [ 4.95930884,  4.93468749,  2.68263529],\n",
       "       [ 4.88825811,  5.86137781,  3.73944168],\n",
       "       [ 3.74741158,  6.48715691,  3.74502634],\n",
       "       [ 2.67950613,  6.44108247,  3.01227811],\n",
       "       [ 2.79847981,  5.47993053,  1.93601476],\n",
       "       [ 1.0629011 ,  3.77110243, -2.24197688],\n",
       "       [-0.04638625,  3.94381678, -3.16457823],\n",
       "       [-0.74804314,  2.72677473, -3.53575317],\n",
       "       [-0.25838784,  1.58846215, -2.96990972],\n",
       "       [ 0.75674864,  1.45547903, -2.11105124],\n",
       "       [-5.54418431, -5.05237935, -0.42977415],\n",
       "       [-6.86475012, -5.71633441, -0.27442947],\n",
       "       [-7.80151147, -4.74188048, -0.68865764],\n",
       "       [-7.73412229, -3.64233209, -1.07972199],\n",
       "       [-6.3961042 , -2.94573381, -1.24882246],\n",
       "       [-2.65847337,  1.14197684, -0.43287039],\n",
       "       [-3.20503213,  2.48642721, -0.69829836],\n",
       "       [-4.35409031,  2.80088904, -0.09931296],\n",
       "       [-5.11333386,  1.98808828,  0.75868724],\n",
       "       [-4.54879675,  0.82244651,  0.91206945],\n",
       "       [ 5.54765337, -1.25866429, -4.29883884],\n",
       "       [ 6.28104699, -0.57266068, -5.01332895],\n",
       "       [ 6.5193986 , -2.16822562,  2.64635795],\n",
       "       [ 6.43465366,  0.15362609,  1.69189039],\n",
       "       [ 2.83398437, -0.74006347, -0.13534597],\n",
       "       [ 4.59619201, -3.80435235,  2.07146239],\n",
       "       [ 1.71647965, -2.93864815,  2.76611285],\n",
       "       [-0.6033698 , -3.35493828,  2.2955473 ],\n",
       "       [ 0.06218793, -3.66961285, -1.89365429],\n",
       "       [ 4.5031811 , -4.05444304, -0.80294372],\n",
       "       [ 6.14120415, -3.18158137, -2.46753087],\n",
       "       [ 3.03120447, -1.05181444, -4.46371608],\n",
       "       [ 1.41343494, -1.87311456, -2.81653953],\n",
       "       [ 5.88053818,  4.32491009,  2.58058288],\n",
       "       [ 5.73240271,  5.90534893,  4.36110379],\n",
       "       [ 3.70615071,  7.25548409,  4.62563563],\n",
       "       [ 1.79960048,  6.93210155,  3.05533971],\n",
       "       [ 2.04885093,  5.33331734,  1.20078453],\n",
       "       [ 1.60735108,  4.6059954 , -1.96328492],\n",
       "       [-0.34494561,  4.8391707 , -3.53630901],\n",
       "       [-1.59039466,  2.75002396, -4.18790779],\n",
       "       [-0.79221552,  0.63405457, -3.24665543],\n",
       "       [ 1.04165374,  0.43159402, -1.72232053],\n",
       "       [-4.67441141, -5.70973393, -0.20890163],\n",
       "       [-7.04785997, -6.62111374,  0.09728745],\n",
       "       [-8.88252486, -5.27029621, -0.53358627],\n",
       "       [-8.47585013, -2.87750546, -1.39112899],\n",
       "       [-6.25900072, -2.04550182, -1.62897401],\n",
       "       [-1.80313618,  0.82870736, -0.82676063],\n",
       "       [-2.64553182,  3.1265482 , -1.36576177],\n",
       "       [-4.75509513,  3.82320165, -0.26914184],\n",
       "       [-6.02901267,  2.31490042,  1.20877518],\n",
       "       [-5.06821555,  0.09535386,  1.62552134]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mol=mol.GetConformer(1)\n",
    "mol.GetPositions()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No atoms\n"
     ]
    }
   ],
   "source": [
    "from typing import Optional\n",
    "from pydantic import BaseModel\n",
    "class dataUnimol(BaseModel):\n",
    "    models: Optional[list[str]] = []\n",
    "    names : Optional[list[str]] = []\n",
    "    smiles: Optional[list[str]] = []\n",
    "    molBlocks: Optional[list[str]] = []\n",
    "    atoms : Optional[list[str]] = [] \n",
    "mol=dataUnimol()\n",
    "if mol.atoms:\n",
    "    print(mol.atoms)\n",
    "else:\n",
    "    print(\"No atoms\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 14 15  0  0  0  0  0  0  0  0999 V2000\n"
     ]
    }
   ],
   "source": [
    "\n",
    "lines=mol_block.split('\\n')\n",
    "lines[3] = lines[3].rstrip()[:-9] + '0999 V2000'\n",
    "print(lines[3])\n",
    "mol_block = '\\n'.join(lines)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "allopurinol\n",
      "\n",
      "Created by GaussView 6.0.16\n",
      " 14 15  0  0  0  0  0  0  0  0999 V2000\n",
      "    0.2261    0.5854   -0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.6364   -0.7554    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -0.1457   -1.8754    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -1.4155   -1.5914    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -1.9374   -0.3278    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -1.1838    0.8888   -0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -1.7554    1.9660   -0.0000 O   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    1.4306    1.3327    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    2.4920    0.5365   -0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    1.9896   -0.7296   -0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -2.1417   -2.4006    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -2.9436   -0.2012    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    1.5578    2.4065    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    2.6186   -1.5196    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "  1  2  4  0  0  0  0\n",
      "  1  6  4  0  0  0  0\n",
      "  1  8  4  0  0  0  0\n",
      "  2  3  4  0  0  0  0\n",
      "  2 10  4  0  0  0  0\n",
      "  3  4  2  0  0  0  0\n",
      "  4  5  4  0  0  0  0\n",
      "  4 11  1  0  0  0  0\n",
      "  5  6  1  0  0  0  0\n",
      "  5 12  1  0  0  0  0\n",
      "  6  7  2  0  0  0  0\n",
      "  8  9  4  0  0  0  0\n",
      "  8 13  1  0  0  0  0\n",
      "  9 10  1  0  0  0  0\n",
      " 10 14  1  0  0  0  0\n",
      "M  END\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(mol_block)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[08:54:38] Can't kekulize mol.  Unkekulized atoms: 0 1 3 4 7 8 9\n"
     ]
    }
   ],
   "source": [
    "mol= Chem.MolFromMolBlock(mol_block)\n",
    "mol\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "ename": "ArgumentError",
     "evalue": "Python argument types in\n    rdkit.Chem.rdmolops.AddHs(NoneType)\ndid not match C++ signature:\n    AddHs(class RDKit::ROMol mol, bool explicitOnly=False, bool addCoords=False, class boost::python::api::object onlyOnAtoms=None, bool addResidueInfo=False)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mArgumentError\u001b[0m                             Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[133], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m mol\u001b[39m=\u001b[39m Chem\u001b[39m.\u001b[39;49mAddHs(mol)\n\u001b[0;32m      2\u001b[0m mol\n",
      "\u001b[1;31mArgumentError\u001b[0m: Python argument types in\n    rdkit.Chem.rdmolops.AddHs(NoneType)\ndid not match C++ signature:\n    AddHs(class RDKit::ROMol mol, bool explicitOnly=False, bool addCoords=False, class boost::python::api::object onlyOnAtoms=None, bool addResidueInfo=False)"
     ]
    }
   ],
   "source": [
    "mol= Chem.AddHs(mol)\n",
    "mol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[16:46:53] CTAB version string invalid at line 4\n",
      "error: fail to get 3D-info from molBlock 3-nitrophthalonitrile\n",
      "\n",
      "Created by GaussView 6.0.16\n",
      " 16 16  0  0  0  0  0  0  0  0  0    0\n",
      "    1.0272    1.7573    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -0.1401    2.5164    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -1.3798    1.8844    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -1.4608    0.4845    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -0.2840   -0.3082    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.9490    0.3678    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    2.2264   -0.3801    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    2.1689   -1.6042    0.0001 O   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    3.2612    0.2825   -0.0001 O   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -0.4361   -1.7326   -0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -0.7099   -2.8626   -0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -2.7563   -0.1295   -0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -3.8284   -0.5783   -0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    2.0067    2.2182    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -0.0810    3.5997    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -2.2976    2.4624    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "  1  2  4  0  0  0  0\n",
      "  1  6  4  0  0  0  0\n",
      "  1 14  1  0  0  0  0\n",
      "  2  3  4  0  0  0  0\n",
      "  2 15  1  0  0  0  0\n",
      "  3  4  4  0  0  0  0\n",
      "  3 16  1  0  0  0  0\n",
      "  4  5  4  0  0  0  0\n",
      "  4 12  4  0  0  0  0\n",
      "  5  6  4  0  0  0  0\n",
      "  5 10  4  0  0  0  0\n",
      "  6  7  1  0  0  0  0\n",
      "  7  8  4  0  0  0  0\n",
      "  7  9  4  0  0  0  0\n",
      " 10 11  3  0  0  0  0\n",
      " 12 13  3  0  0  0  0\n",
      "M  END\n",
      ";Python argument types in\n",
      "    rdkit.Chem.rdmolops.AddHs(NoneType)\n",
      "did not match C++ signature:\n",
      "    AddHs(class RDKit::ROMol mol, bool explicitOnly=False, bool addCoords=False, class boost::python::api::object onlyOnAtoms=None, bool addResidueInfo=False)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "是平面 True\n"
     ]
    }
   ],
   "source": [
    "s=gen3D(molBlocks=[mol_block], smiles=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dataUnimol(models=[], names=[], smiles=[], molBlocks=[], atoms=[[]], coordinates=[[]], results={})"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"id\": \"chatcmpl-8g69fd9rYVntxPtmygdoLcK6vf49e\",\n",
      "  \"object\": \"chat.completion\",\n",
      "  \"created\": 1705044127,\n",
      "  \"model\": \"gpt-3.5-turbo-0613\",\n",
      "  \"choices\": [\n",
      "    {\n",
      "      \"index\": 0,\n",
      "      \"message\": {\n",
      "        \"role\": \"assistant\",\n",
      "        \"content\": \"Hello! How can I assist you today?\"\n",
      "      },\n",
      "      \"logprobs\": null,\n",
      "      \"finish_reason\": \"stop\"\n",
      "    }\n",
      "  ],\n",
      "  \"usage\": {\n",
      "    \"prompt_tokens\": 9,\n",
      "    \"completion_tokens\": 9,\n",
      "    \"total_tokens\": 18\n",
      "  },\n",
      "  \"system_fingerprint\": null\n",
      "}\n",
      "\n",
      "Request was successful.\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "import json\n",
    "\n",
    "# 替换为你的API密钥\n",
    "api_key = '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'\n",
    "\n",
    "# 设置请求头部\n",
    "headers = {\n",
    "    'Authorization': f'Bearer {api_key}',\n",
    "    'Content-Type': 'application/json',\n",
    "}\n",
    "\n",
    "# 设置请求数据\n",
    "data = {\n",
    "    \"model\": \"gpt-3.5-turbo\",\n",
    "    \"messages\": [\n",
    "        {\n",
    "            \"role\": \"user\",\n",
    "            \"content\": \"hi!\"\n",
    "        }\n",
    "    ]\n",
    "}\n",
    "\n",
    "# 发送POST请求\n",
    "response = requests.post(\n",
    "    'https://openai.sunera.tech/v1/chat/completions',\n",
    "    headers=headers,\n",
    "    data=json.dumps(data)\n",
    ")\n",
    "\n",
    "# 打印响应内容\n",
    "print(response.text)\n",
    "\n",
    "# 检查响应是否成功\n",
    "if response.status_code == 200:\n",
    "    print(\"Request was successful.\")\n",
    "else:\n",
    "    print(f\"Request failed with status code: {response.status_code}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Email sent successfully!\n"
     ]
    }
   ],
   "source": [
    "import smtplib\n",
    "\n",
    "def send_test_email():\n",
    "    sender_email = \"admin@sunera-cn.com\"\n",
    "    receiver_email = \"chensq@sunera-cn.com\"\n",
    "    smtp_server = \"192.168.0.254\"\n",
    "    smtp_port = 25\n",
    "\n",
    "    message = \"\"\"\\\n",
    "    Subject: Test Email\n",
    "    To: {receiver}\n",
    "    From: {sender}\n",
    "\n",
    "    This is a test email.\n",
    "    \"\"\".format(receiver=receiver_email, sender=sender_email)\n",
    "\n",
    "    try:\n",
    "        smtp = smtplib.SMTP(smtp_server, smtp_port)\n",
    "        smtp.sendmail(sender_email, receiver_email, message)\n",
    "        smtp.quit()\n",
    "        print(\"Email sent successfully!\")\n",
    "    except Exception as e:\n",
    "        print(\"Failed to send email:\", e)\n",
    "\n",
    "# 调用发送邮件函数\n",
    "send_test_email()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "邮件发送成功\n"
     ]
    }
   ],
   "source": [
    "import smtplib\n",
    "from email.mime.text import MIMEText\n",
    "from email.header import Header\n",
    "\n",
    "# 邮件服务器地址和端口\n",
    "smtp_server = '192.168.0.254'\n",
    "smtp_port = 25\n",
    "\n",
    "# 发件人和收件人信息\n",
    "sender = 'admin@sunera-cn.com'\n",
    "receivers = ['chensq@sunera-cn.com']\n",
    "cc = ['cc@example.com']\n",
    "all_receivers = receivers + cc  # 合并收件人和抄送人\n",
    "\n",
    "# 邮件内容\n",
    "subject = 'test'\n",
    "message = 'infotest'\n",
    "\n",
    "# 创建邮件对象\n",
    "msg = MIMEText(message, 'plain', 'utf-8')\n",
    "msg['From'] = Header(sender, 'utf-8')\n",
    "msg['To'] = Header(''.join(receivers), 'utf-8')\n",
    "msg['Subject'] = Header(subject, 'utf-8')\n",
    "\n",
    "try:\n",
    "    smtp_obj = smtplib.SMTP(smtp_server, smtp_port)\n",
    "    smtp_obj.sendmail(sender, all_receivers, msg.as_string())\n",
    "    print('邮件发送成功')\n",
    "except smtplib.SMTPException as e:\n",
    "    print('邮件发送失败:', str(e))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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