notebooks/simple_mean_variance.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "simple_mean_variance.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyO8AONm/1lvhbZR3T/bSKx7",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/yanghoonkim/FinML/blob/main/notebooks/simple_mean_variance.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aNxXf04RW78o"
      },
      "source": [
        "## Install\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kRZap8eE6Zer",
        "outputId": "d550f1c5-95e7-4416-e926-511864fe280a"
      },
      "source": [
        "!git clone https://github.com/yanghoonkim/FinML\r\n",
        "!mv /content/FinML/* .\r\n",
        "!rm -rf FinML\r\n",
        "\r\n",
        "!pip3 install -r requirements.txt"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'FinML'...\n",
            "remote: Enumerating objects: 259, done.\u001b[K\n",
            "remote: Counting objects: 100% (259/259), done.\u001b[K\n",
            "remote: Compressing objects: 100% (196/196), done.\u001b[K\n",
            "remote: Total 259 (delta 120), reused 187 (delta 59), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (259/259), 52.50 KiB | 5.25 MiB/s, done.\n",
            "Resolving deltas: 100% (120/120), done.\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 1)) (1.1.5)\n",
            "Requirement already satisfied: pandas-datareader in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 2)) (0.9.0)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 3)) (1.4.1)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 4)) (3.2.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 5)) (4.41.1)\n",
            "Collecting dateparser\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/78/c4/b5ddc3eeac974d85055d88c1e6b62cc492fc1a93dbe3b66a45a756a7b807/dateparser-1.0.0-py2.py3-none-any.whl (279kB)\n",
            "\u001b[K     |████████████████████████████████| 286kB 6.0MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.15.4 in /usr/local/lib/python3.6/dist-packages (from pandas->-r requirements.txt (line 1)) (1.19.4)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas->-r requirements.txt (line 1)) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.6/dist-packages (from pandas->-r requirements.txt (line 1)) (2.8.1)\n",
            "Requirement already satisfied: lxml in /usr/local/lib/python3.6/dist-packages (from pandas-datareader->-r requirements.txt (line 2)) (4.2.6)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.6/dist-packages (from pandas-datareader->-r requirements.txt (line 2)) (2.23.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->-r requirements.txt (line 4)) (2.4.7)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->-r requirements.txt (line 4)) (0.10.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->-r requirements.txt (line 4)) (1.3.1)\n",
            "Requirement already satisfied: regex!=2019.02.19 in /usr/local/lib/python3.6/dist-packages (from dateparser->-r requirements.txt (line 6)) (2019.12.20)\n",
            "Requirement already satisfied: tzlocal in /usr/local/lib/python3.6/dist-packages (from dateparser->-r requirements.txt (line 6)) (1.5.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.7.3->pandas->-r requirements.txt (line 1)) (1.15.0)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.19.0->pandas-datareader->-r requirements.txt (line 2)) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.19.0->pandas-datareader->-r requirements.txt (line 2)) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.19.0->pandas-datareader->-r requirements.txt (line 2)) (2020.12.5)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.19.0->pandas-datareader->-r requirements.txt (line 2)) (3.0.4)\n",
            "Installing collected packages: dateparser\n",
            "Successfully installed dateparser-1.0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sAkK4xveWvLP"
      },
      "source": [
        "# Config\r\n",
        "아래에서 symbol에 원하는 종목의 symbol을, \r\n",
        "start_date와 end_date에 분석할 기간의 시작시점과 끝 시점을 넣어주세요."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-nZpiEWOVgx3"
      },
      "source": [
        "args = {\r\n",
        "  \"symbols\": [\r\n",
        "    \"AAPL\",\r\n",
        "    \"TSLA\",\r\n",
        "  ],\r\n",
        "  \"start_date\": \"2020.01.01\",\r\n",
        "  \"end_date\": \"2020.12.31\"\r\n",
        "}"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-MJaBODAaE-z"
      },
      "source": [
        "import os\r\n",
        "import json\r\n",
        "\r\n",
        "# save config.json\r\n",
        "base_dir = os.getcwd()\r\n",
        "with open(os.path.join(base_dir, \"config.json\"), \"w\") as config:\r\n",
        "    json.dump(args, config)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wdToN4i2XBii"
      },
      "source": [
        "# 메인 코드 실행"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "IoeuXBop9mjn",
        "outputId": "bbbb66b9-7836-40e1-b310-bd9f8dcca868"
      },
      "source": [
        "import os\r\n",
        "import json\r\n",
        "import dateparser\r\n",
        "\r\n",
        "from finml.data_reader import StockMarket\r\n",
        "from finml.portfolio_optimization import SimpleMeanVariance\r\n",
        "from finml.utils.path_utils import set_path\r\n",
        "\r\n",
        "########################################################################################################################\r\n",
        "# INIT\r\n",
        "########################################################################################################################\r\n",
        "base_dir = os.getcwd()\r\n",
        "\r\n",
        "# read config\r\n",
        "with open(os.path.join(base_dir, \"config.json\"), \"r\") as config:\r\n",
        "    args = json.load(config)\r\n",
        "\r\n",
        "with open(os.path.join(base_dir, \"config_system.json\"), \"r\") as config_system:\r\n",
        "    args_system = json.load(config_system)\r\n",
        "\r\n",
        "# param\r\n",
        "pwd = os.getcwd()\r\n",
        "start_date = dateparser.parse(args['start_date'])\r\n",
        "end_date = dateparser.parse(args['end_date'])\r\n",
        "data_dir = set_path(os.path.join(pwd, args_system['path_dir']['data_dir']))\r\n",
        "log_dir = set_path(os.path.join(pwd, args_system['path_dir']['log_dir']))\r\n",
        "symbols = args['symbols']\r\n",
        "\r\n",
        "########################################################################################################################\r\n",
        "# MAIN\r\n",
        "########################################################################################################################\r\n",
        "# get data\r\n",
        "market = StockMarket(start_date=start_date, end_date=end_date, data_dir=data_dir)\r\n",
        "market.get_stock_price(symbols)\r\n",
        "market_mean, market_covariance, _ = market.get_stock_statistics()\r\n",
        "\r\n",
        "# simple mean-variance plot\r\n",
        "SMV = SimpleMeanVariance(market_mean, market_covariance)\r\n",
        "SMV.plot(show=True, save_path=log_dir)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XaNy4MZ06dSIrK4tXXnmFdevW0bt3b4WGVEg64hApQFZWFtOmTWPo0KFs2LCBiy66iLlz59KuXTuNISUVno44RPLYv38/48ePp2HDhtx9993Url2btLQ0li9fTocOHRQaIig4RIBIYIwdO5Zzzz2XPn36cMYZZ/D222+zZMkS2rRpoxv3RPLQqSqp0Pbv38/kyZN55pln2Lx5M1deeSVTpkyhZcuWCguRQ1BwSIWUlZXFlClTGDJkyL8C4+WXX+baa69VYIgchk5VSYWSnZ3N5MmTOe+88+jTpw/16tXj3Xff5ZNPPtFYUiJFpOCQCiE3N5cZM2bQqFEj7rrrLn7zm9/w9ttv89lnn3HdddcpMESKIarBYWatzWyNmWWa2aAC5lc1sznB/CVmVj/PvEeD6WvM7Po8008ys1QzW21m35nZ76O5DVK2HThwgNTUVH7729/SvXt3atSoQVpaGl988QWtW7dWYIgcgagFh5nFAWOAG4B4oIuZxedr1hPY5e7nAiOAYcGy8UBnoDHQGhgbrA9gFLDQ3S8ALgK+i9Y2SNnl7ixcuJBLL72Ujh07YmbMnTuXZcuW6SopkaMUzSOOZkCmu6939yxgNpCYr00i8ErwOhVoYZF/0YnAbHff7+4bgEygmZmdCFwNTAZw9yx3/zmK2yBl0Keffso111zDDTfcwK5du5g2bRorVqygQ4cOHHOMzs6KHK1o/is6A9iU5/3mYFqBbdw9B9gN1Cpk2QbAdmCqmS03s0lmVr2gDzezXmaWbmbp27dvL4ntkRj3zTff0KZNG6666irWrVvHmDFjWL16Nd27d9eNeyIlqKz9+VUJuAQY5+5NgV+B/+g7AXD3Ce6e4O4Jp5xySmnWKKVs48aN3H777Vx00UV88sknPPPMM2RmZtK3b1+qVKkSdnki5U407+PYAtTL875uMK2gNpvNrBJwIrCzkGU3A5vdfUkwPZVDBIeUfzt37mTIkCGMGTOGY445hocffphHHnmEmjVrhl2aSLkWzSOOpUBDM2tgZlWIdHan5WuTBtwRvO4AfOjuHkzvHFx11QBoCHzp7n8HNpnZ+cEyLYBVUdwGiUF79+7l2Wef5ZxzzmHUqFF069aNdevWMWzYMIWGSCmI2hGHu+eYWX/gHSAOmOLuK83sSSDd3dOIdHJPN7NM4Cci4ULQLoVIKOQA/dw9N1j1AGBmEEbrgR7R2gaJLQcOHGDGjBk8/vjjbNq0iZtuuolnn32Wxo0bh12aSIVikT/wy7eEhARPT08Puww5Ch988AEPPfQQGRkZJCQk8Pzzz3PNNdeEXZZIuWVmy9w9oaB5Za1zXCqY1atX06ZNG1q2bMmuXbtITk5myZIlCg2RECk4JCbt2LGDAQMGcOGFF7Jo0SKGDRvG6tWr6dKli+7FEAmZRseVmJKdnc3YsWMZPHgwe/bsoVevXgwePJhTTz017NJEJKDgkJjx9ttvM3DgQNasWUOrVq144YUX1PEtEoN0zC+hW7duHTfddBM33ngj7s6bb77JwoULFRoiMarIRxxmdgVQP+8y7j4tCjVJBbFnzx6efvppRowYwbHHHsvzzz/PgAEDdLe3SIwrUnCY2XTgHCADOHg/hQMKDik2d2fWrFk89NBDbN26lTvvvJNnnnmGOnXqhF2aiBRBUY84EoB4rwg3fUhUffPNN/Tv359Fixbxu9/9jvnz53PZZZeFXZaIFENR+zi+BfTnoByxPXv28OCDD9K0aVNWrlzJhAkTWLJkiUJDpAwq6hFHbWCVmX0J7D840d1vjkpVUm64O3PnzmXgwIFs3bqVu+++m6FDh1KrVq2wSxORI1TU4BgczSKkfPr+++/p168f77zzDpdccgnz58+nWbNmYZclIkepSMHh7v8b7UKk/MjKyuK5557j6aefpkqVKowePZq+ffvqYUoi5URRr6q6HPgb0AioQmS021/d/YQo1iZl0Keffso999zDqlWr6NixIyNHjuT0008PuywRKUFF7Rx/EegCrAOqAXcBY6JVlJQ9u3fvpnfv3lx11VX8+uuvvPXWW6SkpCg0RMqhIt857u6ZQJy757r7VKB19MqSsuS1116jUaNGTJw4kQceeICVK1dy4403hl2WiERJUTvH/xk8OCnDzJ4DtqLhSiq8bdu20b9/f1JTU2nSpAlpaWkkJBQ4fL+IlCNF/eXfPWjbH/iVyPPAb4lWURLb3J0ZM2YQHx/PG2+8wZAhQ0hPT1doiFQQRb2q6gczqwac5u7/E+WaJIb9+OOP9O7dmzfeeIPLL7+cKVOm0KhRo7DLEpFSVKQjDjNrQ2ScqoXB+4vNLC2ahUlsOXiU0bhxY95//31eeOEFPv30U4WGSAVU1FNVg4FmwM8A7p4BNIhSTRJjtm3bRrt27ejevTvx8fFkZGQwcOBA3ZchUkEVNTiy3X13vmka8LACePXVV7nwwgtZuHAhzz//PIsWLeK8884LuywRCVFRg2OlmXUF4sysoZn9Dfg8inVJyHbv3s3tt99Ohw4dOOuss/jqq6948MEHdZQhIkUOjgFAYyIDHCYDu4H7olWUhOt///d/adKkCcnJyfz3f/83ixcvJj4+PuyyRCRGFDU44oOfSsCxQCKw9HALmVlrM1tjZplmNqiA+VXNbE4wf4mZ1c8z79Fg+hozuz7P9I1m9o2ZZZhZehHrlyLIyspi0KBBNG/enCpVqvDZZ58xePBgKleuHHZpIhJDinoD4EzgISLP5ThQlAXMLI7IsCTXAZuBpWaW5u6r8jTrCexy93PNrDMwDOhkZvFAZyJHOacD75vZee5+8OmDzd19RxFrlyJYu3YtXbt2ZdmyZdx999288MILHH/88WGXJSIxqKhHHNvd/Q133+DuPxz8OcwyzYBMd1/v7lnAbCJHKnklAq8Er1OBFmZmwfTZ7r7f3TcAmcH6pIS5Oy+//DKXXHIJGzZsYN68eUyYMEGhISKHVNQjjv82s0nAB/z7g5zmFbLMGcCmPO83A/kf9/avNu6eY2a7gVrB9C/yLXvGwY8F3jUzB15y9wlF3AbJZ8+ePfTp04eZM2dyzTXXMGPGDOrWrRt2WSIS44oaHD2AC4DK/P9TVQ4UFhzR8gd332JmpwLvmdlqd1+Uv5GZ9QJ6AZx55pmlXWPMW758Obfeeivr16/nySef5LHHHtMVUyJSJEUNjkvd/fxirnsLkTGtDqobTCuozWYzqwScCOwsbFl3P/jff5jZfCKnsP4jOIIjkQkACQkJuuck4O689NJL3H///dSuXZuPP/6Yq666KuyyRKQMKWofx+dBh3VxLAUamlmDYGTdzkD+YUrSgDuC1x2AD93dg+mdg6uuGgANgS/NrLqZ1QAws+pAKyId9lIEe/bsoWvXrvTp04fmzZuTkZGh0BCRYivqEcflRIZU30Ckj8MAd/cmh1og6LPoD7xD5ImBU9x9pZk9CaS7exowGZhuZpnAT0TChaBdCrAKyAH6uXuumf0GmB/pP6cSkOzuC4u/2RXPqlWruOWWW1i7di1Dhw7lkUce4ZhjNDK+iBSfRf7AP0wjs7MKml6EK6tiQkJCgqenV9xbPubMmUPPnj2pXr06s2fPpnnz5mGXJCIxzsyWuXuBz0oo8rDqJVuSlIacnBwGDRpEUlISV155pR7lKiIloqinqqSM2bFjB7feeisfffQR/fv3JykpiSpVqoRdloiUAwqOcujrr7+mbdu2bN26lZdffpk77rjj8AuJiBSRekfLmXnz5nHFFVeQnZ3NJ598otAQkRKn4Cgn3J2nn36aW265hSZNmrB06VIuvfTSsMsSkXJIp6rKgX379tGzZ0+Sk5Pp1q0bEydO5Nhjjw27LBEpp3TEUcZt376dli1bkpyczNChQ5k2bZpCQ0SiSkccZdjatWu58cYb2bJlCykpKXTs2DHskkSkAlBwlFGff/45N998M2bGRx99xOWXXx52SSJSQehUVRn0+uuv06JFC04++WS++OILhYaIlCoFRxkzceJE2rdvT5MmTfj8888555xzwi5JRCoYBUcZ4e4MHTqUXr160apVKz788ENOOeWUsMsSkQpIwVEGuDt//vOf+ctf/sJtt91GWloa1atXD7ssEamgFBwxLjc3l3vuuYfnn3+efv36MW3aNCpXrhx2WSJSgSk4YlhOTg533HEHEydO5LHHHuNvf/ubnqEhIqHT5bgxKjs7m9tuu425c+cyZMgQHnvssbBLEhEBFBwxKTs7m86dOzNv3jySkpJ44IEHwi5JRORfFBwxJm9ojBgxgvvvvz/skkRE/o2CI4bk5OTQvXt35s2bx8iRI7nvvvvCLklE5D+opzVGHDhwgJ49ezJnzhyGDx+u0BCRmKXgiAHuzr333su0adN48skneeihh8IuSUTkkBQcMeCvf/0rY8aM4eGHH+bxxx8PuxwRkUIpOEI2evRonn76aXr27MmwYcMws7BLEhEplIIjRCkpKdx33320bduW8ePHKzREpEyIanCYWWszW2NmmWY2qID5Vc1sTjB/iZnVzzPv0WD6GjO7Pt9ycWa23MzejGb90bRo0SK6d+/OlVdeSXJyMpUq6QI3ESkbohYcZhYHjAFuAOKBLmYWn69ZT2CXu58LjACGBcvGA52BxkBrYGywvoPuA76LVu3RtnbtWtq2bcvZZ59NWloa1apVC7skEZEii+YRRzMg093Xu3sWMBtIzNcmEXgleJ0KtLDI+ZpEYLa773f3DUBmsD7MrC7wJ2BSFGuPmp07d/KnP/2JSpUqsWDBAmrWrBl2SSIixRLN4DgD2JTn/eZgWoFt3D0H2A3UOsyyI4E/AwcK+3Az62Vm6WaWvn379iPdhhKVnZ1Nx44d2bRpE6+99hoNGjQIuyQRkWIrU53jZnYT8A93X3a4tu4+wd0T3D0hVh54NHDgQD766CMmTpzIFVdcEXY5IiJHJJrBsQWol+d93WBagW3MrBJwIrCzkGWvBG42s41ETn1da2YzolF8SZsyZQpjxozhoYceonv37mGXIyJyxKIZHEuBhmbWwMyqEOnsTsvXJg24I3jdAfjQ3T2Y3jm46qoB0BD40t0fdfe67l4/WN+H7t4tittQItLT0+nbty8tW7bk2WefDbscEZGjErVrQN09x8z6A+8AccAUd19pZk8C6e6eBkwGpptZJvATkTAgaJcCrAJygH7unhutWqNp165ddOjQgVNPPZVZs2YRFxd3+IVERGKYRf7AL98SEhI8PT291D/X3WnXrh0LFizgk08+4bLLLiv1GkREjoSZLXP3hILm6a6zKHrxxRd5/fXXGTFihEJDRMqNMnVVVVmyYsUKHn74YW666SYNkS4i5YqCIwr27dvHbbfdxsknn8yUKVM0BpWIlCs6VRUFTzzxBN9++y0LFiwgVu4hEREpKTriKGGff/45SUlJ9OrVixtuuCHsckRESpyCowTt3buX//qv/6JevXo8//zzYZcjIhIVOlVVgoYMGcKaNWt45513qFGjRtjliIhEhY44SsiqVat47rnn6N69O61atQq7HBGRqFFwlAB3p1+/ftSoUYOkpKSwyxERiSqdqioBKSkpfPzxx4wbN05XUYlIuachR47S3r17Of/886lVqxbp6ekai0pEygUNORJFI0aMYNOmTUybNk2hISIVgvo4jsKOHTt49tlnufnmm/njH/8YdjkiIqVCwXEUnnnmGX799VeeeeaZsEsRESk1Co4jtHXrVsaOHUv37t2Jj48PuxwRkVKj4DhCw4cPJzs7myeeeCLsUkRESpWC4wjs2LGD8ePHc9ttt3HOOeeEXY6ISKlScByBF198kb179zJo0KCwSxERKXUKjmLat28fY8aMoU2bNjRq1CjsckRESp2Co5hmzZrFjh07uP/++8MuRUQkFAqOYho7dizx8fE0b9487FJEREKh4CiG5cuXk56eTu/evfU4WBGpsBQcxTBlyhSqVq1Kt27dwi5FRCQ0Co4iysrKYtasWSQmJnLyySeHXY6ISGiiGhxm1trM1phZppn9x7WrZlbVzOYE85eYWf088x4Npq8xs+uDacea2Zdm9rWZrTSz/4lm/Xm999577Ny5U0cbIlLhRS04zCwOGAPcAMQDXcws/9gcPYFd7n4uMIkGXw4AAAkXSURBVAIYFiwbD3QGGgOtgbHB+vYD17r7RcDFQGszuzxa25BXSkoKJ554Itdff31pfJyISMyK5hFHMyDT3de7exYwG0jM1yYReCV4nQq0sEivcyIw2933u/sGIBNo5hG/BO0rBz9Rf6BIdnY2aWlpJCYmUqVKlWh/nIhITItmcJwBbMrzfnMwrcA27p4D7AZqFbasmcWZWQbwD+A9d19S0IebWS8zSzez9O3btx/Vhnz22Wf8/PPPJCbmzz0RkYqnzHWOu3uuu18M1AWamdmFh2g3wd0T3D3haB/n+vbbb1O5cmWuu+66o1qPiEh5EM3g2ALUy/O+bjCtwDZmVgk4EdhZlGXd/WfgIyJ9IFH13nvvccUVV1CjRo1of5SISMyLZnAsBRqaWQMzq0KkszstX5s04I7gdQfgQ488BD0N6BxcddUAaAh8aWanmNlJAGZWDbgOWB3FbeCnn35i+fLltGjRIpofIyJSZkTtmePunmNm/YF3gDhgiruvNLMngXR3TwMmA9PNLBP4iUi4ELRLAVYBOUA/d881s9OAV4IrrI4BUtz9zWhtA0T6NwCuvvrqaH6MiEiZEbXgAHD3BcCCfNP+muf1PqDjIZYdAgzJN20F0LTkKz20xYsXExcXR7NmzUrzY0VEYlaZ6xwvbUuXLqVJkyZUq1Yt7FJERGKCgqMQ7k5GRgaXXHJJ2KWIiMQMBUchtm3bxo4dO/jtb38bdikiIjFDwVGI1asjF2zFx+cfKUVEpOJScBRi3bp1ADRs2DDkSkREYoeCoxAbN24kLi6OevXqHb6xiEgFoeAoxKZNmzj99NOJi4sLuxQRkZih4CjEtm3bqFOnTthliIjEFAVHIXbs2MHRDpAoIlLeKDgK8fPPP3PSSSeFXYaISExRcBTil19+0Yi4IiL5KDgKsW/fPqpWrRp2GSIiMUXBUYjc3FwqVYrqOJAiImWOgqMQkcefi4hIXgqOQlSqVIns7OywyxARiSkKjkJUq1aNf/7zn2GXISISUxQchahRowa//PJL2GWIiMQU9fwW4qabbuK0004LuwwRkZii4ChEUlJS2CWIiMQcnaoSEZFiUXCIiEixKDhERKRYFBwiIlIsUQ0OM2ttZmvMLNPMBhUwv6qZzQnmLzGz+nnmPRpMX2Nm1wfT6pnZR2a2ysxWmtl90axfRET+U9SCw8zigDHADUA80MXM4vM16wnscvdzgRHAsGDZeKAz0BhoDYwN1pcDPOju8cDlQL8C1ikiIlEUzSOOZkCmu6939yxgNpCYr00i8ErwOhVoYZEBohKB2e6+3903AJlAM3ff6u5fAbj7HuA74IwoboOIiOQTzeA4A9iU5/1m/vOX/L/auHsOsBuoVZRlg9NaTYElBX24mfUys3QzS9++ffsRb4SIiPy7MnkDoJkdD7wK3O/u/7egNu4+AZgQtN9uZj+UYon51QZ2hPj5sUL7QfvgIO2H2N8HZx1qRjSDYwtQL8/7usG0gtpsNrNKwInAzsKWNbPKREJjprvPK0oh7h7qg8PNLN3dE8KsIRZoP2gfHKT9ULb3QTRPVS0FGppZAzOrQqSzOy1fmzTgjuB1B+BDd/dgeufgqqsGQEPgy6D/YzLwnbu/EMXaRUTkEKJ2xOHuOWbWH3gHiAOmuPtKM3sSSHf3NCIhMN3MMoGfiIQLQbsUYBWRK6n6uXuumf0B6A58Y2YZwUc95u4LorUdIiLy7yzyB75Ek5n1CvpcKjTtB+2Dg7QfyvY+UHCIiEixaMgREREpFgWHiIgUi4KjiEp63K1g+hQz+4eZfZtvXTXN7D0zWxf89+RobltRlfI+GGxmW8wsI/i5MZrbVhylOQZbRfkuHGYfVKTvwrFm9qWZfR3sh//J075BsI7MYJ1VSmMbC+Tu+jnMD5Grwr4HzgaqAF8D8fna9AXGB687A3OC1/FB+6pAg2A9ccG8q4FLgG/zres5YFDwehAwrALug8HAQ2Fvd2nsB+A04JKgTQ1g7cF1VpTvwmH2QUX6LhhwfNCmMpGRMS4P3qcAnYPX44E+YW27jjiKpsTH3QJw90VELkPOL++6XgHaluTGHKHS3gexqrTHYKsQ34XD7INYFY394O7+S9C+cvDjwTLXBuuAkL8LCo6iieq4WwX4jbtvDV7/HfjNkZVdokp7HwD0N7MVwemsmDhFQ+mPwVbhvgsF7AOoQN8FM4uzyH1q/wDec/clwTI/B+s41GeVGgVHjPPIcWlFvGZ6HHAOcDGwFUgKt5zos8OMwVYRvguH2AcV6rvg7rnufjGRoZaamdmFYdeUn4KjaIoz7hZWxHG3CrHNzE4L1nUakb88wlaq+8DdtwX/gA4AEwlObcWAqOwHO/QYbBXmu3CofVDRvgsHufvPwEdEnkm0EzgpWMehPqvUKDiKpsTH3TrM5+Vd1x3A6yWwDUerVPfBwV+WgXbAt4dqW8pKewy2CvFdKGwfVLDvwilmdhKAmVUDrgNWB8t8FKwDwv4uhNUrX9Z+gBuJXOnxPfCXYNqTwM3B62OBuUQ6ub4Ezs6z7F+C5dYAN+SZPovIoXc2kXOWPYPptYAPgHXA+0DNsLc/hH0wHfgGWEHkH9lpYW9/tPYD8Acip6BWABnBz40V6btwmH1Qkb4LTYDlwbZ+C/w1T/uzg3VkBuusGtZ2a8gREREpFp2qEhGRYlFwiIhIsSg4RESkWBQcIiJSLAoOEREpFgWHSAjM7H4zO+4Q8+40sxdLuyaRolJwiITjfqDA4BCJdZUO30REjoaZVScyJHZdIkNnzwVOBz4ysx3u3tzMegCPAj8TGW57f1j1ihyOgkMk+loDP7r7nwDM7ESgB9Dc3XcEQ2r8D/A7IqOnfkTk7mGRmKRTVSLR9w1wnZkNM7Or3H13vvmXAR+7+3aPPNdhTumXKFJ0OuIQiTJ3X2tmlxAZ1+hpM/sg7JpEjoaOOESizMxOB/7p7jOA4UQelbuHyCNSIfLAomvMrFYwtHjHcCoVKRodcYhE32+B4WZ2gMgowH2A3wMLzezHoHN8MLCYSOd4RmiVihSBRscVEZFi0akqEREpFgWHiIgUi4JDRESKRcEhIiLFouAQEZFiUXCIiEixKDhERKRY/h9tWKDy7f3+dQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UY-7LSZvbeTC"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}