lab2.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Paraskevi-KIvroglou/Qiskit-Summer-School-2024-Labs/blob/main/lab2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nkJCphKCKkkh"
      },
      "source": [
        "# Lab 2: Utility-Scale Layer Fidelity Experiment\n",
        "\n",
        "Samantha Barron, Haimeng Zhang"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X8BIA7jyKkkj"
      },
      "source": [
        "In this lab we will show a guided construction of how to calculate the EPLG (Error Per Layered Gate) and LF (Layer Fidelity). These metrics quantify error rates in a circuit, and are particularly useful in understanding the overhead required to run error mitigation at utility-scale workloads.\n",
        "\n",
        "For more details on the background of these metrics, see the [Quantum Research Blog post](https://www.ibm.com/quantum/blog/quantum-metric-layer-fidelity) or the associated technical paper, [Benchmarking Quantum Processor Performance at Scale](https://arxiv.org/abs/2311.05933). You can also find an implementation of the Layer Fidelity experiment [here](https://github.com/qiskit-community/qiskit-device-benchmarking/blob/main/notebooks/layer_fidelity.ipynb) using `qiskit-experiments` module, whereas, in this notebook, we will directly use Qiskit Runtime Primitives for circuit execution.\n",
        "\n",
        "The first few parts of this lab contain graded exercises, whereas the latter portions of this lab are a guided walkthrough of how to use the Qiskit Runtime to do these utility-scale calculations.\n",
        "\n",
        "Table of Contents:\n",
        "- In Section 0 we list the package requirement to run this notebook.\n",
        "- In Sections 1-3 we will set up the layers and associated circuits which define the disjoint sets of qubits on which we will perform EPLG calculations.\n",
        "- In Section 4 we construct the total EPLG circuits.\n",
        "- In Section 5-7 we construct the `BindingsArray`, `ObservablesArray`s, and `EstimatorPub`s for the EPLG characterization.\n",
        "- In Section 8 we submit these to the `EstimatorV2` Qiskit Runtime Primitive.\n",
        "- In Section 9 we perform the data analysis and fittings used to extract the EPLG and LF with the resulting data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h7YZFzTXKkkk"
      },
      "source": [
        "# Part 0: Requirements\n",
        "\n",
        "To run this notebook, you will need to install the following packages:\n",
        "\n",
        "For circuit construction and execution on the IBM Quantum hardware:\n",
        "```\n",
        "qiskit >= 1.0.0\n",
        "qiskit-ibm-runtime >= 0.21.1\n",
        "```\n",
        "For data analysis and display:\n",
        "```\n",
        "lmfit\n",
        "pandas\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install qiskit==1.0.0\n",
        "!pip install qiskit-ibm-runtime\n",
        "!pip install pandas\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UocBjp0vKrj_",
        "outputId": "be0ee8ce-f200-4730-c5cc-9f8747e34b4a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting qiskit==1.0.0\n",
            "  Downloading qiskit-1.0.0-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (5.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.6/5.6 MB\u001b[0m \u001b[31m13.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting rustworkx>=0.14.0 (from qiskit==1.0.0)\n",
            "  Downloading rustworkx-0.15.1-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.0 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m33.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy<2,>=1.17 in /usr/local/lib/python3.10/dist-packages (from qiskit==1.0.0) (1.25.2)\n",
            "Requirement already satisfied: scipy>=1.5 in /usr/local/lib/python3.10/dist-packages (from qiskit==1.0.0) (1.11.4)\n",
            "Requirement already satisfied: sympy>=1.3 in /usr/local/lib/python3.10/dist-packages (from qiskit==1.0.0) (1.13.0)\n",
            "Collecting dill>=0.3 (from qiskit==1.0.0)\n",
            "  Downloading dill-0.3.8-py3-none-any.whl (116 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m7.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: python-dateutil>=2.8.0 in /usr/local/lib/python3.10/dist-packages (from qiskit==1.0.0) (2.8.2)\n",
            "Collecting stevedore>=3.0.0 (from qiskit==1.0.0)\n",
            "  Downloading stevedore-5.2.0-py3-none-any.whl (49 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.7/49.7 kB\u001b[0m \u001b[31m2.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from qiskit==1.0.0) (4.12.2)\n",
            "Collecting symengine>=0.11 (from qiskit==1.0.0)\n",
            "  Downloading symengine-0.11.0-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (39.4 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m39.4/39.4 MB\u001b[0m \u001b[31m10.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.0->qiskit==1.0.0) (1.16.0)\n",
            "Collecting pbr!=2.1.0,>=2.0.0 (from stevedore>=3.0.0->qiskit==1.0.0)\n",
            "  Downloading pbr-6.0.0-py2.py3-none-any.whl (107 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m107.5/107.5 kB\u001b[0m \u001b[31m7.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy>=1.3->qiskit==1.0.0) (1.3.0)\n",
            "Installing collected packages: symengine, rustworkx, pbr, dill, stevedore, qiskit\n",
            "Successfully installed dill-0.3.8 pbr-6.0.0 qiskit-1.0.0 rustworkx-0.15.1 stevedore-5.2.0 symengine-0.11.0\n",
            "Collecting qiskit-ibm-runtime\n",
            "  Downloading qiskit_ibm_runtime-0.25.0-py3-none-any.whl (2.9 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.9/2.9 MB\u001b[0m \u001b[31m25.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: requests>=2.19 in /usr/local/lib/python3.10/dist-packages (from qiskit-ibm-runtime) (2.31.0)\n",
            "Collecting requests-ntlm>=1.1.0 (from qiskit-ibm-runtime)\n",
            "  Downloading requests_ntlm-1.3.0-py3-none-any.whl (6.6 kB)\n",
            "Requirement already satisfied: numpy>=1.13 in /usr/local/lib/python3.10/dist-packages (from qiskit-ibm-runtime) (1.25.2)\n",
            "Requirement already satisfied: urllib3>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from qiskit-ibm-runtime) (2.0.7)\n",
            "Requirement already satisfied: python-dateutil>=2.8.0 in /usr/local/lib/python3.10/dist-packages (from qiskit-ibm-runtime) (2.8.2)\n",
            "Requirement already satisfied: websocket-client>=1.5.1 in /usr/local/lib/python3.10/dist-packages (from qiskit-ibm-runtime) (1.8.0)\n",
            "Collecting ibm-platform-services>=0.22.6 (from qiskit-ibm-runtime)\n",
            "  Downloading ibm-platform-services-0.55.1.tar.gz (323 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m323.1/323.1 kB\u001b[0m \u001b[31m29.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: pydantic>=2.5.0 in /usr/local/lib/python3.10/dist-packages (from qiskit-ibm-runtime) (2.8.2)\n",
            "Collecting qiskit>=1.1.0 (from qiskit-ibm-runtime)\n",
            "  Downloading qiskit-1.1.1-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.3/4.3 MB\u001b[0m \u001b[31m41.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting ibm-cloud-sdk-core<4.0.0,>=3.20.3 (from ibm-platform-services>=0.22.6->qiskit-ibm-runtime)\n",
            "  Downloading ibm-cloud-sdk-core-3.20.3.tar.gz (62 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.7/62.7 kB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from pydantic>=2.5.0->qiskit-ibm-runtime) (0.7.0)\n",
            "Requirement already satisfied: pydantic-core==2.20.1 in /usr/local/lib/python3.10/dist-packages (from pydantic>=2.5.0->qiskit-ibm-runtime) (2.20.1)\n",
            "Requirement already satisfied: typing-extensions>=4.6.1 in /usr/local/lib/python3.10/dist-packages (from pydantic>=2.5.0->qiskit-ibm-runtime) (4.12.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.0->qiskit-ibm-runtime) (1.16.0)\n",
            "Requirement already satisfied: rustworkx>=0.14.0 in /usr/local/lib/python3.10/dist-packages (from qiskit>=1.1.0->qiskit-ibm-runtime) (0.15.1)\n",
            "Requirement already satisfied: scipy>=1.5 in /usr/local/lib/python3.10/dist-packages (from qiskit>=1.1.0->qiskit-ibm-runtime) (1.11.4)\n",
            "Requirement already satisfied: sympy>=1.3 in /usr/local/lib/python3.10/dist-packages (from qiskit>=1.1.0->qiskit-ibm-runtime) (1.13.0)\n",
            "Requirement already satisfied: dill>=0.3 in /usr/local/lib/python3.10/dist-packages (from qiskit>=1.1.0->qiskit-ibm-runtime) (0.3.8)\n",
            "Requirement already satisfied: stevedore>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from qiskit>=1.1.0->qiskit-ibm-runtime) (5.2.0)\n",
            "Requirement already satisfied: symengine>=0.11 in /usr/local/lib/python3.10/dist-packages (from qiskit>=1.1.0->qiskit-ibm-runtime) (0.11.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19->qiskit-ibm-runtime) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19->qiskit-ibm-runtime) (3.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19->qiskit-ibm-runtime) (2024.7.4)\n",
            "Requirement already satisfied: cryptography>=1.3 in /usr/local/lib/python3.10/dist-packages (from requests-ntlm>=1.1.0->qiskit-ibm-runtime) (42.0.8)\n",
            "Collecting pyspnego>=0.4.0 (from requests-ntlm>=1.1.0->qiskit-ibm-runtime)\n",
            "  Downloading pyspnego-0.11.0-py3-none-any.whl (130 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m130.4/130.4 kB\u001b[0m \u001b[31m14.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: cffi>=1.12 in /usr/local/lib/python3.10/dist-packages (from cryptography>=1.3->requests-ntlm>=1.1.0->qiskit-ibm-runtime) (1.16.0)\n",
            "Collecting urllib3>=1.21.1 (from qiskit-ibm-runtime)\n",
            "  Downloading urllib3-2.2.2-py3-none-any.whl (121 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m121.4/121.4 kB\u001b[0m \u001b[31m13.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting PyJWT<3.0.0,>=2.8.0 (from ibm-cloud-sdk-core<4.0.0,>=3.20.3->ibm-platform-services>=0.22.6->qiskit-ibm-runtime)\n",
            "  Downloading PyJWT-2.8.0-py3-none-any.whl (22 kB)\n",
            "Requirement already satisfied: pbr!=2.1.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from stevedore>=3.0.0->qiskit>=1.1.0->qiskit-ibm-runtime) (6.0.0)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy>=1.3->qiskit>=1.1.0->qiskit-ibm-runtime) (1.3.0)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.12->cryptography>=1.3->requests-ntlm>=1.1.0->qiskit-ibm-runtime) (2.22)\n",
            "Building wheels for collected packages: ibm-platform-services, ibm-cloud-sdk-core\n",
            "  Building wheel for ibm-platform-services (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ibm-platform-services: filename=ibm_platform_services-0.55.1-py3-none-any.whl size=339686 sha256=7cc6354181f7289533e51dbe8cf25c2cce9e7fd5b77af21abcb6630e51567a83\n",
            "  Stored in directory: /root/.cache/pip/wheels/85/ac/f7/fcc0f6fb6fd090cb2c4f6f9fc55fd5aa36af0bfe1f44a9af71\n",
            "  Building wheel for ibm-cloud-sdk-core (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ibm-cloud-sdk-core: filename=ibm_cloud_sdk_core-3.20.3-py3-none-any.whl size=102558 sha256=248961b010a398aa34af39868b03fc6d4cd4de733ad9ab3c5fd51bc53d68b4fd\n",
            "  Stored in directory: /root/.cache/pip/wheels/37/52/12/4283874e4e1f9be8c89f05de7971a7619fdffe8abf1ac989cd\n",
            "Successfully built ibm-platform-services ibm-cloud-sdk-core\n",
            "Installing collected packages: urllib3, PyJWT, qiskit, pyspnego, ibm-cloud-sdk-core, requests-ntlm, ibm-platform-services, qiskit-ibm-runtime\n",
            "  Attempting uninstall: urllib3\n",
            "    Found existing installation: urllib3 2.0.7\n",
            "    Uninstalling urllib3-2.0.7:\n",
            "      Successfully uninstalled urllib3-2.0.7\n",
            "  Attempting uninstall: PyJWT\n",
            "    Found existing installation: PyJWT 2.3.0\n",
            "    Uninstalling PyJWT-2.3.0:\n",
            "      Successfully uninstalled PyJWT-2.3.0\n",
            "  Attempting uninstall: qiskit\n",
            "    Found existing installation: qiskit 1.0.0\n",
            "    Uninstalling qiskit-1.0.0:\n",
            "      Successfully uninstalled qiskit-1.0.0\n",
            "Successfully installed PyJWT-2.8.0 ibm-cloud-sdk-core-3.20.3 ibm-platform-services-0.55.1 pyspnego-0.11.0 qiskit-1.1.1 qiskit-ibm-runtime-0.25.0 requests-ntlm-1.3.0 urllib3-2.2.2\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (2.0.3)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2023.4)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2024.1)\n",
            "Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.10/dist-packages (from pandas) (1.25.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install lmfit"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2fYQRKOoLHNP",
        "outputId": "f2c34786-9e6c-4469-93b8-ff268853e483"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting lmfit\n",
            "  Downloading lmfit-1.3.2-py3-none-any.whl (98 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m98.9/98.9 kB\u001b[0m \u001b[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting asteval>=1.0 (from lmfit)\n",
            "  Downloading asteval-1.0.1-py3-none-any.whl (21 kB)\n",
            "Requirement already satisfied: numpy>=1.19 in /usr/local/lib/python3.10/dist-packages (from lmfit) (1.25.2)\n",
            "Requirement already satisfied: scipy>=1.6 in /usr/local/lib/python3.10/dist-packages (from lmfit) (1.11.4)\n",
            "Collecting uncertainties>=3.2.2 (from lmfit)\n",
            "  Downloading uncertainties-3.2.2-py3-none-any.whl (58 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m7.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: dill>=0.3.4 in /usr/local/lib/python3.10/dist-packages (from lmfit) (0.3.8)\n",
            "Installing collected packages: uncertainties, asteval, lmfit\n",
            "Successfully installed asteval-1.0.1 lmfit-1.3.2 uncertainties-3.2.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DBw81NfdKkkk",
        "outputId": "a4beecc8-1b0f-42d5-f57b-4f4fb7d7c33e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "env: QXToken=83929dc16f4bc749feed93049d2f4ef81b63fd6cffbbe2195f68fcb217f324137228edc161984889bf018862ec410f76bf4d35553912629875c5fe380d6d4a7c\n"
          ]
        }
      ],
      "source": [
        "%set_env QXToken=83929dc16f4bc749feed93049d2f4ef81b63fd6cffbbe2195f68fcb217f324137228edc161984889bf018862ec410f76bf4d35553912629875c5fe380d6d4a7c"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install qiskit_aer\n",
        "!pip install matplotlib\n",
        "!pip install pylatexenc\n",
        "!pip install prototype-zne\n",
        "!pip install git+https://github.com/qiskit-community/Quantum-Challenge-Grader.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v9rl-ClTPCpj",
        "outputId": "e4ad62f0-e6db-42a3-ace5-6f71fe2b1c7c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting qiskit_aer\n",
            "  Downloading qiskit_aer-0.14.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.4 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.4/12.4 MB\u001b[0m \u001b[31m19.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: qiskit>=0.45.2 in /usr/local/lib/python3.10/dist-packages (from qiskit_aer) (1.1.1)\n",
            "Requirement already satisfied: numpy>=1.16.3 in /usr/local/lib/python3.10/dist-packages (from qiskit_aer) (1.25.2)\n",
            "Requirement already satisfied: scipy>=1.0 in /usr/local/lib/python3.10/dist-packages (from qiskit_aer) (1.11.4)\n",
            "Requirement already satisfied: psutil>=5 in /usr/local/lib/python3.10/dist-packages (from qiskit_aer) (5.9.5)\n",
            "Requirement already satisfied: rustworkx>=0.14.0 in /usr/local/lib/python3.10/dist-packages (from qiskit>=0.45.2->qiskit_aer) (0.15.1)\n",
            "Requirement already satisfied: sympy>=1.3 in /usr/local/lib/python3.10/dist-packages (from qiskit>=0.45.2->qiskit_aer) (1.13.0)\n",
            "Requirement already satisfied: dill>=0.3 in /usr/local/lib/python3.10/dist-packages (from qiskit>=0.45.2->qiskit_aer) (0.3.8)\n",
            "Requirement already satisfied: python-dateutil>=2.8.0 in /usr/local/lib/python3.10/dist-packages (from qiskit>=0.45.2->qiskit_aer) (2.8.2)\n",
            "Requirement already satisfied: stevedore>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from qiskit>=0.45.2->qiskit_aer) (5.2.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from qiskit>=0.45.2->qiskit_aer) (4.12.2)\n",
            "Requirement already satisfied: symengine>=0.11 in /usr/local/lib/python3.10/dist-packages (from qiskit>=0.45.2->qiskit_aer) (0.11.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.0->qiskit>=0.45.2->qiskit_aer) (1.16.0)\n",
            "Requirement already satisfied: pbr!=2.1.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from stevedore>=3.0.0->qiskit>=0.45.2->qiskit_aer) (6.0.0)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy>=1.3->qiskit>=0.45.2->qiskit_aer) (1.3.0)\n",
            "Installing collected packages: qiskit_aer\n",
            "Successfully installed qiskit_aer-0.14.2\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (3.7.1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.2.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (4.53.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.4.5)\n",
            "Requirement already satisfied: numpy>=1.20 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.25.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (24.1)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (3.1.2)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
            "Collecting pylatexenc\n",
            "  Downloading pylatexenc-2.10.tar.gz (162 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m162.6/162.6 kB\u001b[0m \u001b[31m3.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: pylatexenc\n",
            "  Building wheel for pylatexenc (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pylatexenc: filename=pylatexenc-2.10-py3-none-any.whl size=136816 sha256=75396a8fae8e6e115a18b530c503e19d62b93cf5ebd78f9e9f5f6198e31905aa\n",
            "  Stored in directory: /root/.cache/pip/wheels/d3/31/8b/e09b0386afd80cfc556c00408c9aeea5c35c4d484a9c762fd5\n",
            "Successfully built pylatexenc\n",
            "Installing collected packages: pylatexenc\n",
            "Successfully installed pylatexenc-2.10\n",
            "Collecting prototype-zne\n",
            "  Downloading prototype_zne-1.3.1-py3-none-any.whl (56 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.5/56.5 kB\u001b[0m \u001b[31m1.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: qiskit>=0.45.1 in /usr/local/lib/python3.10/dist-packages (from prototype-zne) (1.1.1)\n",
            "Requirement already satisfied: rustworkx>=0.14.0 in /usr/local/lib/python3.10/dist-packages (from qiskit>=0.45.1->prototype-zne) (0.15.1)\n",
            "Requirement already satisfied: numpy<3,>=1.17 in /usr/local/lib/python3.10/dist-packages (from qiskit>=0.45.1->prototype-zne) (1.25.2)\n",
            "Requirement already satisfied: scipy>=1.5 in /usr/local/lib/python3.10/dist-packages (from qiskit>=0.45.1->prototype-zne) (1.11.4)\n",
            "Requirement already satisfied: sympy>=1.3 in /usr/local/lib/python3.10/dist-packages (from qiskit>=0.45.1->prototype-zne) (1.13.0)\n",
            "Requirement already satisfied: dill>=0.3 in /usr/local/lib/python3.10/dist-packages (from qiskit>=0.45.1->prototype-zne) (0.3.8)\n",
            "Requirement already satisfied: python-dateutil>=2.8.0 in /usr/local/lib/python3.10/dist-packages (from qiskit>=0.45.1->prototype-zne) (2.8.2)\n",
            "Requirement already satisfied: stevedore>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from qiskit>=0.45.1->prototype-zne) (5.2.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from qiskit>=0.45.1->prototype-zne) (4.12.2)\n",
            "Requirement already satisfied: symengine>=0.11 in /usr/local/lib/python3.10/dist-packages (from qiskit>=0.45.1->prototype-zne) (0.11.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.0->qiskit>=0.45.1->prototype-zne) (1.16.0)\n",
            "Requirement already satisfied: pbr!=2.1.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from stevedore>=3.0.0->qiskit>=0.45.1->prototype-zne) (6.0.0)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy>=1.3->qiskit>=0.45.1->prototype-zne) (1.3.0)\n",
            "Installing collected packages: prototype-zne\n",
            "Successfully installed prototype-zne-1.3.1\n",
            "Collecting git+https://github.com/qiskit-community/Quantum-Challenge-Grader.git\n",
            "  Cloning https://github.com/qiskit-community/Quantum-Challenge-Grader.git to /tmp/pip-req-build-tffbp_4b\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/qiskit-community/Quantum-Challenge-Grader.git /tmp/pip-req-build-tffbp_4b\n",
            "  Resolved https://github.com/qiskit-community/Quantum-Challenge-Grader.git to commit bcaff898473fd54f3e2668f162817947787f7268\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting typeguard (from qc-grader==0.19.7)\n",
            "  Downloading typeguard-4.3.0-py3-none-any.whl (35 kB)\n",
            "Collecting jsonpickle==3.0.3 (from qc-grader==0.19.7)\n",
            "  Downloading jsonpickle-3.0.3-py3-none-any.whl (40 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.8/40.8 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting ipycytoscape (from qc-grader==0.19.7)\n",
            "  Downloading ipycytoscape-1.3.3-py2.py3-none-any.whl (3.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.6/3.6 MB\u001b[0m \u001b[31m35.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from qc-grader==0.19.7) (5.15.0)\n",
            "Collecting networkx==3.2.1 (from qc-grader==0.19.7)\n",
            "  Downloading networkx-3.2.1-py3-none-any.whl (1.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m49.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: graphviz in /usr/local/lib/python3.10/dist-packages (from qc-grader==0.19.7) (0.20.3)\n",
            "Requirement already satisfied: ipywidgets>=7.6.0 in /usr/local/lib/python3.10/dist-packages (from ipycytoscape->qc-grader==0.19.7) (7.7.1)\n",
            "Collecting spectate>=1.0.0 (from ipycytoscape->qc-grader==0.19.7)\n",
            "  Downloading spectate-1.0.1-py2.py3-none-any.whl (11 kB)\n",
            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly->qc-grader==0.19.7) (8.5.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from plotly->qc-grader==0.19.7) (24.1)\n",
            "Requirement already satisfied: typing-extensions>=4.10.0 in /usr/local/lib/python3.10/dist-packages (from typeguard->qc-grader==0.19.7) (4.12.2)\n",
            "Requirement already satisfied: ipykernel>=4.5.1 in /usr/local/lib/python3.10/dist-packages (from ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (5.5.6)\n",
            "Requirement already satisfied: ipython-genutils~=0.2.0 in /usr/local/lib/python3.10/dist-packages (from ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (0.2.0)\n",
            "Requirement already satisfied: traitlets>=4.3.1 in /usr/local/lib/python3.10/dist-packages (from ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (5.7.1)\n",
            "Requirement already satisfied: widgetsnbextension~=3.6.0 in /usr/local/lib/python3.10/dist-packages (from ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (3.6.7)\n",
            "Requirement already satisfied: ipython>=4.0.0 in /usr/local/lib/python3.10/dist-packages (from ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (7.34.0)\n",
            "Requirement already satisfied: jupyterlab-widgets>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (3.0.11)\n",
            "Requirement already satisfied: jupyter-client in /usr/local/lib/python3.10/dist-packages (from ipykernel>=4.5.1->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (6.1.12)\n",
            "Requirement already satisfied: tornado>=4.2 in /usr/local/lib/python3.10/dist-packages (from ipykernel>=4.5.1->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (6.3.3)\n",
            "Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.10/dist-packages (from ipython>=4.0.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (67.7.2)\n",
            "Collecting jedi>=0.16 (from ipython>=4.0.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7)\n",
            "  Downloading jedi-0.19.1-py2.py3-none-any.whl (1.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m57.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: decorator in /usr/local/lib/python3.10/dist-packages (from ipython>=4.0.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (4.4.2)\n",
            "Requirement already satisfied: pickleshare in /usr/local/lib/python3.10/dist-packages (from ipython>=4.0.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (0.7.5)\n",
            "Requirement already satisfied: prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from ipython>=4.0.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (3.0.47)\n",
            "Requirement already satisfied: pygments in /usr/local/lib/python3.10/dist-packages (from ipython>=4.0.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (2.16.1)\n",
            "Requirement already satisfied: backcall in /usr/local/lib/python3.10/dist-packages (from ipython>=4.0.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (0.2.0)\n",
            "Requirement already satisfied: matplotlib-inline in /usr/local/lib/python3.10/dist-packages (from ipython>=4.0.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (0.1.7)\n",
            "Requirement already satisfied: pexpect>4.3 in /usr/local/lib/python3.10/dist-packages (from ipython>=4.0.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (4.9.0)\n",
            "Requirement already satisfied: notebook>=4.4.1 in /usr/local/lib/python3.10/dist-packages (from widgetsnbextension~=3.6.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (6.5.5)\n",
            "Requirement already satisfied: parso<0.9.0,>=0.8.3 in /usr/local/lib/python3.10/dist-packages (from jedi>=0.16->ipython>=4.0.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (0.8.4)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (3.1.4)\n",
            "Requirement already satisfied: pyzmq<25,>=17 in /usr/local/lib/python3.10/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (24.0.1)\n",
            "Requirement already satisfied: argon2-cffi in /usr/local/lib/python3.10/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (23.1.0)\n",
            "Requirement already satisfied: jupyter-core>=4.6.1 in /usr/local/lib/python3.10/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (5.7.2)\n",
            "Requirement already satisfied: nbformat in /usr/local/lib/python3.10/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (5.10.4)\n",
            "Requirement already satisfied: nbconvert>=5 in /usr/local/lib/python3.10/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (6.5.4)\n",
            "Requirement already satisfied: nest-asyncio>=1.5 in /usr/local/lib/python3.10/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (1.6.0)\n",
            "Requirement already satisfied: Send2Trash>=1.8.0 in /usr/local/lib/python3.10/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (1.8.3)\n",
            "Requirement already satisfied: terminado>=0.8.3 in /usr/local/lib/python3.10/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (0.18.1)\n",
            "Requirement already satisfied: prometheus-client in /usr/local/lib/python3.10/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (0.20.0)\n",
            "Requirement already satisfied: nbclassic>=0.4.7 in /usr/local/lib/python3.10/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (1.1.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.10/dist-packages (from jupyter-client->ipykernel>=4.5.1->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (2.8.2)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.10/dist-packages (from pexpect>4.3->ipython>=4.0.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (0.7.0)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.10/dist-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython>=4.0.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (0.2.13)\n",
            "Requirement already satisfied: platformdirs>=2.5 in /usr/local/lib/python3.10/dist-packages (from jupyter-core>=4.6.1->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (4.2.2)\n",
            "Requirement already satisfied: notebook-shim>=0.2.3 in /usr/local/lib/python3.10/dist-packages (from nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (0.2.4)\n",
            "Requirement already satisfied: lxml in /usr/local/lib/python3.10/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (4.9.4)\n",
            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (4.12.3)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (6.1.0)\n",
            "Requirement already satisfied: defusedxml in /usr/local/lib/python3.10/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (0.7.1)\n",
            "Requirement already satisfied: entrypoints>=0.2.2 in /usr/local/lib/python3.10/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (0.4)\n",
            "Requirement already satisfied: jupyterlab-pygments in /usr/local/lib/python3.10/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (0.3.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (2.1.5)\n",
            "Requirement already satisfied: mistune<2,>=0.8.1 in /usr/local/lib/python3.10/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (0.8.4)\n",
            "Requirement already satisfied: nbclient>=0.5.0 in /usr/local/lib/python3.10/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (0.10.0)\n",
            "Requirement already satisfied: pandocfilters>=1.4.1 in /usr/local/lib/python3.10/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (1.5.1)\n",
            "Requirement already satisfied: tinycss2 in /usr/local/lib/python3.10/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (1.3.0)\n",
            "Requirement already satisfied: fastjsonschema>=2.15 in /usr/local/lib/python3.10/dist-packages (from nbformat->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (2.20.0)\n",
            "Requirement already satisfied: jsonschema>=2.6 in /usr/local/lib/python3.10/dist-packages (from nbformat->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (4.19.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.1->jupyter-client->ipykernel>=4.5.1->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (1.16.0)\n",
            "Requirement already satisfied: argon2-cffi-bindings in /usr/local/lib/python3.10/dist-packages (from argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (21.2.0)\n",
            "Requirement already satisfied: attrs>=22.2.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (23.2.0)\n",
            "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (2023.12.1)\n",
            "Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (0.35.1)\n",
            "Requirement already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (0.19.0)\n",
            "Requirement already satisfied: jupyter-server<3,>=1.8 in /usr/local/lib/python3.10/dist-packages (from notebook-shim>=0.2.3->nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (1.24.0)\n",
            "Requirement already satisfied: cffi>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (1.16.0)\n",
            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (2.5)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (0.5.1)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.0.1->argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (2.22)\n",
            "Requirement already satisfied: anyio<4,>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from jupyter-server<3,>=1.8->notebook-shim>=0.2.3->nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (3.7.1)\n",
            "Requirement already satisfied: websocket-client in /usr/local/lib/python3.10/dist-packages (from jupyter-server<3,>=1.8->notebook-shim>=0.2.3->nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (1.8.0)\n",
            "Requirement already satisfied: idna>=2.8 in /usr/local/lib/python3.10/dist-packages (from anyio<4,>=3.1.0->jupyter-server<3,>=1.8->notebook-shim>=0.2.3->nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (3.7)\n",
            "Requirement already satisfied: sniffio>=1.1 in /usr/local/lib/python3.10/dist-packages (from anyio<4,>=3.1.0->jupyter-server<3,>=1.8->notebook-shim>=0.2.3->nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (1.3.1)\n",
            "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio<4,>=3.1.0->jupyter-server<3,>=1.8->notebook-shim>=0.2.3->nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (1.2.2)\n",
            "Building wheels for collected packages: qc-grader\n",
            "  Building wheel for qc-grader (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for qc-grader: filename=qc_grader-0.19.7-py3-none-any.whl size=41434 sha256=83264fe644bad62fccda623b980d1698b73fb188ccc4c69ca0cf7174754d5d8a\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-_7qusvks/wheels/7e/67/91/88d6a5b4dd4beb318ed4e79c807e2be98b7d3c2ea66565a116\n",
            "Successfully built qc-grader\n",
            "Installing collected packages: typeguard, spectate, networkx, jsonpickle, jedi, ipycytoscape, qc-grader\n",
            "  Attempting uninstall: networkx\n",
            "    Found existing installation: networkx 3.3\n",
            "    Uninstalling networkx-3.3:\n",
            "      Successfully uninstalled networkx-3.3\n",
            "  Attempting uninstall: jsonpickle\n",
            "    Found existing installation: jsonpickle 3.2.2\n",
            "    Uninstalling jsonpickle-3.2.2:\n",
            "      Successfully uninstalled jsonpickle-3.2.2\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "torch 2.3.1+cu121 requires nvidia-cublas-cu12==12.1.3.1; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.1+cu121 requires nvidia-cuda-cupti-cu12==12.1.105; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.1+cu121 requires nvidia-cuda-nvrtc-cu12==12.1.105; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.1+cu121 requires nvidia-cuda-runtime-cu12==12.1.105; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.1+cu121 requires nvidia-cudnn-cu12==8.9.2.26; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.1+cu121 requires nvidia-cufft-cu12==11.0.2.54; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.1+cu121 requires nvidia-curand-cu12==10.3.2.106; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.1+cu121 requires nvidia-cusolver-cu12==11.4.5.107; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.1+cu121 requires nvidia-cusparse-cu12==12.1.0.106; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.1+cu121 requires nvidia-nccl-cu12==2.20.5; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.1+cu121 requires nvidia-nvtx-cu12==12.1.105; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed ipycytoscape-1.3.3 jedi-0.19.1 jsonpickle-3.0.3 networkx-3.2.1 qc-grader-0.19.7 spectate-1.0.1 typeguard-4.3.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "26xlxQjLKkkl"
      },
      "source": [
        "# Part 1: Define 1-D Chain\n",
        "To start, we will choose a backend. In this lab you can choose whatever backend you want.\n",
        "\n",
        "In order to compute the EPLG and LF, we need to choose a chain of qubits on the device to characterize. The first problem is this, you should provide:\n",
        "1. A chain of qubits on the device which is a simply connected path graph of qubits on the device. I.e., provide a list `PATH_ANSWER: list[int]` whose elements are physical qubits on the device, and adjacent elements of the list are connected on the coupling map by edges. The list should contain at least `3` elements.\n",
        "2. The backend you have chosen: `BACKEND: IBMBackend`.\n",
        "\n",
        "Answers to grade:\n",
        "- `PATH_ANSWER: list[int]`\n",
        "- `BACKEND: IBMBackend`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rP6uFYIyKkkm"
      },
      "outputs": [],
      "source": [
        "from qiskit_ibm_runtime import QiskitRuntimeService\n",
        "import os\n",
        "\n",
        "QiskitRuntimeService.save_account(channel = 'ibm_quantum', token = os.environ['QXToken'], set_as_default= True)\n",
        "\n",
        "service = QiskitRuntimeService()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "backends_available = service.backends()\n",
        "print(backends_available)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1BQVHTk7Ogro",
        "outputId": "8c6a1e5c-7ae1-49a0-8dc0-9f1fb6328252"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[<IBMBackend('ibm_brisbane')>, <IBMBackend('ibm_kyoto')>, <IBMBackend('ibm_osaka')>, <IBMBackend('ibm_sherbrooke')>]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9bDDtYguKkkm"
      },
      "outputs": [],
      "source": [
        "# part 1: pick a backend and define 1-D chain\n",
        "\n",
        "### Your code goes here ###\n",
        "\n",
        "BACKEND = service.backend(\"ibm_sherbrooke\")\n",
        "\n",
        "PATH_ANSWER = list(range(10))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j--SbhF2Kkkn",
        "outputId": "c152c861-a2cb-49f2-d04d-5cad37f7cbaa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Submitting your answer. Please wait...\n",
            "Nice work, all your answers look correct!\n"
          ]
        }
      ],
      "source": [
        "# grade part 1\n",
        "from qc_grader.challenges.qgss_2024 import grade_lab2_ex1\n",
        "grade_lab2_ex1(PATH_ANSWER, BACKEND)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MiR5NoD7Kkkn"
      },
      "source": [
        "# Part 2: Define Disjoint Layers\n",
        "\n",
        "Next, we need to form two sets of edges which correspond to the different layers we will use in benchmarking, say `LAYER_1_ANSWER` and `LAYER_2_ANSWER`, each with type `list[tuple[int, int]]`. These layers should have the following properties:\n",
        "- Each `LAYER_i_ANSWER` contains edges on distinct sets of qubits, e.g. `(0, 1)` and `(1, 2)` cannot both be in `LAYER_i_ANSWER`, since they both contain `1`.\n",
        "- Every qubit in `PATH_ANSWER` must be contained in at least one of the `LAYER_i`s.\n",
        "\n",
        "Answers to grade:\n",
        "- `LAYER_1_ANSWER: list[tuple[int, int]]`\n",
        "- `LAYER_2_ANSWER: list[tuple[int, int]]`\n",
        "- `PATH_ANSWER: list[int]` (same as before)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(list(BACKEND.coupling_map))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xBnueEaXWFG4",
        "outputId": "645f7d12-e5f6-4e42-9d9d-8c0126f67d4f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[(1, 0), (1, 2), (3, 2), (4, 3), (4, 15), (5, 4), (6, 5), (7, 6), (7, 8), (8, 9), (10, 9), (10, 11), (11, 12), (12, 13), (14, 0), (14, 18), (16, 8), (17, 12), (17, 30), (18, 19), (19, 20), (20, 33), (21, 20), (21, 22), (22, 15), (23, 22), (23, 24), (25, 24), (26, 16), (26, 25), (26, 27), (28, 27), (29, 28), (29, 30), (31, 30), (31, 32), (32, 36), (33, 39), (34, 24), (35, 28), (35, 47), (36, 51), (37, 38), (38, 39), (40, 39), (41, 40), (41, 53), (42, 41), (43, 34), (43, 42), (43, 44), (45, 44), (45, 46), (47, 46), (47, 48), (49, 48), (49, 50), (50, 51), (52, 37), (53, 60), (54, 45), (55, 49), (56, 52), (56, 57), (57, 58), (59, 58), (59, 60), (61, 60), (61, 62), (63, 62), (63, 64), (64, 54), (64, 65), (66, 65), (67, 66), (68, 55), (68, 67), (69, 68), (69, 70), (71, 58), (71, 77), (72, 62), (73, 66), (73, 85), (74, 70), (75, 76), (77, 76), (78, 77), (78, 79), (79, 91), (80, 79), (80, 81), (81, 72), (81, 82), (82, 83), (84, 83), (85, 84), (86, 85), (86, 87), (87, 88), (89, 74), (89, 88), (90, 75), (90, 94), (92, 83), (92, 102), (93, 87), (93, 106), (95, 94), (95, 96), (97, 96), (97, 98), (98, 91), (99, 98), (99, 100), (101, 100), (101, 102), (102, 103), (104, 103), (105, 104), (106, 105), (107, 106), (107, 108), (109, 96), (110, 100), (110, 118), (111, 104), (112, 108), (112, 126), (113, 114), (114, 109), (114, 115), (116, 115), (117, 116), (117, 118), (119, 118), (119, 120), (120, 121), (121, 122), (122, 111), (123, 122), (124, 123), (125, 124), (126, 125)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u-XlLSntKkko",
        "outputId": "b7ea4324-4b0c-4064-873a-1259c35cacd7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<generator object make_pairs at 0x7c36ecbf6650>\n",
            "[(1, 0), (3, 2), (5, 4), (7, 6), [8, 9]]\n",
            "[[1, 2], (4, 3), (6, 5), [7, 8]]\n"
          ]
        }
      ],
      "source": [
        "# part 2: define disjoint layers from a 1D chain\n",
        "\n",
        "### Your code goes here ###\n",
        "\n",
        "def make_pairs(path: list):\n",
        "    pairs = []\n",
        "    for i in range(len(path)-1):\n",
        "        yield [path[i], path[i+1]]\n",
        "\n",
        "def make_disjoint_layers(pairs):\n",
        "  layer1 =[]\n",
        "  layer2 =[]\n",
        "  for i, pair in enumerate(pairs):\n",
        "    if(tuple(pair) not in list(BACKEND.coupling_map)):\n",
        "      pair = tuple(reversed(pair))\n",
        "    if i%2 == 0:\n",
        "      layer1.append(pair)\n",
        "    else:\n",
        "      layer2.append(pair)\n",
        "  return layer1, layer2\n",
        "\n",
        "pairs = make_pairs(PATH_ANSWER)\n",
        "print(pairs)\n",
        "LAYER_1_ANSWER , LAYER_2_ANSWER = make_disjoint_layers(pairs)\n",
        "print(LAYER_1_ANSWER)\n",
        "print(LAYER_2_ANSWER)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qw9-ilHsKkko",
        "outputId": "76df2c2c-a79a-4bae-bbb9-7a61b86e178e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Submitting your answer. Please wait...\n",
            "Nice work, all your answers look correct!\n"
          ]
        }
      ],
      "source": [
        "# grade part 2\n",
        "from qc_grader.challenges.qgss_2024 import grade_lab2_ex2\n",
        "grade_lab2_ex2(LAYER_1_ANSWER, LAYER_2_ANSWER, PATH_ANSWER, BACKEND)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HgTQ3hdOKkkp"
      },
      "source": [
        "# Part 3: Construct Clifford Entangling Layers\n",
        "Now we can use the `LAYER_i` from the previous problem to construct the `QuantumCircuit`s for each layer, which we will use for the EPLG experiments. Now it's your job to construct two circuits, `CIRC_1_ANSWER` and `CIRC_2_ANSWER`.\n",
        "- Each of them should contain a two-qubit Clifford gate for every pair in `LAYER_1_ANSWER` and `LAYER_2_ANSWER` from before.\n",
        "- The two-qubit Clifford gate (`GATE_NAME`) must be one provided by the backend you choose, i.e. it is in `backend.basis_gates`, should be one of `'ecr'`, `'cz'` or `'cx'` depending on your backend.\n",
        "\n",
        "Answers to grade:\n",
        "- `CIRCUIT_1_ANSWER: QuantumCircuit`\n",
        "- `CIRCUIT_2_ANSWER: QuantumCircuit`\n",
        "- `LAYER_1_ANSWER: list[tuple[int, int]]` (same as before)\n",
        "- `LAYER_2_ANSWER: list[tuple[int, int]]` (same as before)\n",
        "- `GATE_NAME: str`"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(BACKEND.num_qubits)\n",
        "print(BACKEND.basis_gates)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N71Wbw0cKySV",
        "outputId": "24bacffd-94dd-49b0-e3de-cfe649256f6e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "127\n",
            "['ecr', 'id', 'rz', 'sx', 'x']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hSuP93y4Kkkp"
      },
      "outputs": [],
      "source": [
        "# part 2: define disjoint layers from a 1D chain\n",
        "\n",
        "### Your code goes here ###\n",
        "from qiskit import QuantumCircuit\n",
        "\n",
        "CIRC_1_ANSWER = QuantumCircuit(BACKEND.num_qubits)\n",
        "CIRC_2_ANSWER = QuantumCircuit(BACKEND.num_qubits)\n",
        "\n",
        "for circuit, layer in [(CIRC_1_ANSWER, LAYER_1_ANSWER), (CIRC_2_ANSWER, LAYER_2_ANSWER)]:\n",
        "  for qi, qj in layer:\n",
        "    circuit.ecr(qi,qj) #Transpilling can add more gates, sent it to backend and we added more instructions that we intended too, it will error.\n",
        "\n",
        "GATE_NAME = 'ecr'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xMhcF7XbKkkp",
        "outputId": "3fe1d3a2-c3b6-4318-bd69-22ffe0d5a107"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Submitting your answer. Please wait...\n",
            "Nice work, all your answers look correct!\n",
            "Submitting your answer. Please wait...\n",
            "Nice work, all your answers look correct!\n"
          ]
        }
      ],
      "source": [
        "# grade part 3\n",
        "from qc_grader.challenges.qgss_2024 import grade_lab2_ex3\n",
        "grade_lab2_ex3(CIRC_1_ANSWER, LAYER_1_ANSWER, GATE_NAME, BACKEND)\n",
        "grade_lab2_ex3(CIRC_2_ANSWER, LAYER_2_ANSWER, GATE_NAME, BACKEND)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "CIRC_1_ANSWER.draw(fold =-1, idle_wires=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "id": "ZR22abAdMSIX",
        "outputId": "5305ff99-13fb-4617-a0b2-08295f307d2f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     ┌──────┐\n",
              "q_0: ┤1     ├\n",
              "     │  Ecr │\n",
              "q_1: ┤0     ├\n",
              "     ├──────┤\n",
              "q_2: ┤1     ├\n",
              "     │  Ecr │\n",
              "q_3: ┤0     ├\n",
              "     ├──────┤\n",
              "q_4: ┤1     ├\n",
              "     │  Ecr │\n",
              "q_5: ┤0     ├\n",
              "     ├──────┤\n",
              "q_6: ┤1     ├\n",
              "     │  Ecr │\n",
              "q_7: ┤0     ├\n",
              "     ├──────┤\n",
              "q_8: ┤0     ├\n",
              "     │  Ecr │\n",
              "q_9: ┤1     ├\n",
              "     └──────┘"
            ],
            "text/html": [
              "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">     ┌──────┐\n",
              "q_0: ┤1     ├\n",
              "     │  Ecr │\n",
              "q_1: ┤0     ├\n",
              "     ├──────┤\n",
              "q_2: ┤1     ├\n",
              "     │  Ecr │\n",
              "q_3: ┤0     ├\n",
              "     ├──────┤\n",
              "q_4: ┤1     ├\n",
              "     │  Ecr │\n",
              "q_5: ┤0     ├\n",
              "     ├──────┤\n",
              "q_6: ┤1     ├\n",
              "     │  Ecr │\n",
              "q_7: ┤0     ├\n",
              "     ├──────┤\n",
              "q_8: ┤0     ├\n",
              "     │  Ecr │\n",
              "q_9: ┤1     ├\n",
              "     └──────┘</pre>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "CIRC_2_ANSWER.draw(fold =-1, idle_wires=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "mUfJpKQDMckm",
        "outputId": "1cdb413b-0a58-4c5c-e3b1-7d84f3bd955f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     ┌──────┐\n",
              "q_1: ┤0     ├\n",
              "     │  Ecr │\n",
              "q_2: ┤1     ├\n",
              "     ├──────┤\n",
              "q_3: ┤1     ├\n",
              "     │  Ecr │\n",
              "q_4: ┤0     ├\n",
              "     ├──────┤\n",
              "q_5: ┤1     ├\n",
              "     │  Ecr │\n",
              "q_6: ┤0     ├\n",
              "     ├──────┤\n",
              "q_7: ┤0     ├\n",
              "     │  Ecr │\n",
              "q_8: ┤1     ├\n",
              "     └──────┘"
            ],
            "text/html": [
              "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">     ┌──────┐\n",
              "q_1: ┤0     ├\n",
              "     │  Ecr │\n",
              "q_2: ┤1     ├\n",
              "     ├──────┤\n",
              "q_3: ┤1     ├\n",
              "     │  Ecr │\n",
              "q_4: ┤0     ├\n",
              "     ├──────┤\n",
              "q_5: ┤1     ├\n",
              "     │  Ecr │\n",
              "q_6: ┤0     ├\n",
              "     ├──────┤\n",
              "q_7: ┤0     ├\n",
              "     │  Ecr │\n",
              "q_8: ┤1     ├\n",
              "     └──────┘</pre>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aUk5TgiPKkkq"
      },
      "source": [
        "# Part 4: Construct EPLG Experiment Circuits\n",
        "\n",
        "*No grading for this part*\n",
        "\n",
        "In this part, we will use the circuits you constructed to construct the total EPLG circuits.  These circuits interleave the layers from before between layers of random Clifford gates.\n",
        "\n",
        "A few technical asides:\n",
        "1. There is a slight difference between the circuits constructed here and those defined in the EPLG paper. For a circuit of depth $d$ (for even $d$), we randomly sample 1q Clifford gates for $d/2$ layers, and then we invert the circuit for the last $d/2$ layers. This provides a simple implmentation for our purposes, and the details of this difference are beyond the scope of this lab.\n",
        "2. We perform 1q Clifford sampling by taking advantage of the fact that for backends whose single qubit gates are composed of $R_z(\\theta_1) \\sqrt{X} R_z(\\theta_2) \\sqrt{X} R_z(\\theta_3)$, when $\\theta_i \\in \\{ \\pm \\pi / 2, \\pm \\pi, 0 \\}$, the resulting single qubit gate is a Clifford. This allows us to randomly sample 1q Clifford gates simply by changing parameters."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9kQO5EqzKkkq"
      },
      "outputs": [],
      "source": [
        "from qiskit.circuit import Parameter\n",
        "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n",
        "\n",
        "def eplg_circuit(num_qubits: int, depth: int, layer: QuantumCircuit, qubits: tuple[int, ...]) -> QuantumCircuit:\n",
        "    if depth % 2 != 0:\n",
        "        raise ValueError(f\"The depth must be even, got {depth}\")\n",
        "\n",
        "    def parameters():\n",
        "        _depth = 0\n",
        "        while True:\n",
        "            for zi in range(3):\n",
        "                for q in range(num_qubits):\n",
        "                    yield Parameter(f\"d{_depth}_q{q}_z{zi}\")\n",
        "            _depth += 1\n",
        "    _parameters = parameters()\n",
        "\n",
        "    circ = QuantumCircuit(num_qubits)\n",
        "\n",
        "    def _1q_layer():\n",
        "        for j in range(3):\n",
        "            for k in qubits:\n",
        "                circ.rz(next(_parameters), k)\n",
        "            if j == 2:\n",
        "                continue\n",
        "            circ.sx(qubits)\n",
        "\n",
        "    for _depth in range(depth // 2):\n",
        "        circ.barrier(qubits)\n",
        "        _1q_layer()\n",
        "        circ.barrier(qubits)\n",
        "        circ.compose(layer, inplace=True)\n",
        "    circ.barrier(qubits)\n",
        "    _1q_layer()\n",
        "    circ.barrier(qubits)\n",
        "\n",
        "    # Assuming mirroring is fine\n",
        "    circ = circ.compose(circ.inverse())\n",
        "\n",
        "    circ.measure_active()\n",
        "\n",
        "    pm = generate_preset_pass_manager(basis_gates=[\"ecr\", \"sx\", \"rz\"], optimization_level=0)\n",
        "    circ = pm.run(circ)\n",
        "\n",
        "    circ.metadata[\"layer_depth\"] = depth\n",
        "    circ.metadata[\"qubits\"] = qubits\n",
        "    circ.metadata[\"num_qubits\"] = num_qubits\n",
        "\n",
        "    return circ"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v8id-ii1Kkkq"
      },
      "source": [
        "Here are examples of the EPLG circuits for depth $d=4$. In a later part we will use this function to construct circuits for varying $d$."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "N_KjLFn0Kkkq"
      },
      "outputs": [],
      "source": [
        "example_circuit_1 = eplg_circuit(\n",
        "    num_qubits=BACKEND.num_qubits,\n",
        "    depth=4,\n",
        "    layer=CIRC_1_ANSWER,\n",
        "    qubits=PATH_ANSWER,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "example_circuit_1.draw(fold =-1, idle_wires=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "id": "vsGRwTSWRIIB",
        "outputId": "5a52f2cd-4351-4734-8452-9ae6c1d95002"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             ░ ┌──────────────┐┌────┐┌───────────────┐┌────┐┌───────────────┐ ░ ┌──────┐ ░ ┌───────────────┐┌────┐┌───────────────┐┌────┐┌───────────────┐ ░ ┌──────┐ ░ ┌───────────────┐┌────┐┌───────────────┐┌────┐┌───────────────┐ ░  ░ ┌────────────────────┐┌─────────┐┌─────────┐┌────┐┌─────────┐┌─────────┐┌────────────────────┐┌─────────┐┌─────────┐┌────┐┌─────────┐┌─────────┐┌────────────────────┐ ░ ┌──────┐ ░ ┌────────────────────┐┌─────────┐┌─────────┐┌────┐┌─────────┐┌─────────┐┌────────────────────┐┌─────────┐┌─────────┐┌────┐┌─────────┐┌─────────┐┌────────────────────┐ ░ ┌──────┐ ░ ┌────────────────────┐┌─────────┐┌─────────┐┌────┐┌─────────┐┌─────────┐┌────────────────────┐┌─────────┐┌─────────┐┌────┐┌─────────┐┌─────────┐┌───────────────────┐ ░  ░ ┌─┐                           \n",
              "       q_0: ─░─┤ Rz(d0_q0_z0) ├┤ √X ├┤ Rz(d0_q10_z0) ├┤ √X ├┤ Rz(d0_q20_z0) ├─░─┤1     ├─░─┤ Rz(d0_q30_z0) ├┤ √X ├┤ Rz(d0_q40_z0) ├┤ √X ├┤ Rz(d0_q50_z0) ├─░─┤1     ├─░─┤ Rz(d0_q60_z0) ├┤ √X ├┤ Rz(d0_q70_z0) ├┤ √X ├┤ Rz(d0_q80_z0) ├─░──░─┤ Rz(-1.0*d0_q80_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q70_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q60_z0) ├─░─┤1     ├─░─┤ Rz(-1.0*d0_q50_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q40_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q30_z0) ├─░─┤1     ├─░─┤ Rz(-1.0*d0_q20_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q10_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q0_z0) ├─░──░─┤M├───────────────────────────\n",
              "             ░ ├──────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░ │  Ecr │ ░ ├───────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░ │  Ecr │ ░ ├───────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░  ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤ ░ │  Ecr │ ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤ ░ │  Ecr │ ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├───────────────────┤ ░  ░ └╥┘┌─┐                        \n",
              "       q_1: ─░─┤ Rz(d0_q1_z0) ├┤ √X ├┤ Rz(d0_q11_z0) ├┤ √X ├┤ Rz(d0_q21_z0) ├─░─┤0     ├─░─┤ Rz(d0_q31_z0) ├┤ √X ├┤ Rz(d0_q41_z0) ├┤ √X ├┤ Rz(d0_q51_z0) ├─░─┤0     ├─░─┤ Rz(d0_q61_z0) ├┤ √X ├┤ Rz(d0_q71_z0) ├┤ √X ├┤ Rz(d0_q81_z0) ├─░──░─┤ Rz(-1.0*d0_q81_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q71_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q61_z0) ├─░─┤0     ├─░─┤ Rz(-1.0*d0_q51_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q41_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q31_z0) ├─░─┤0     ├─░─┤ Rz(-1.0*d0_q21_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q11_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q1_z0) ├─░──░──╫─┤M├────────────────────────\n",
              "             ░ ├──────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░ ├──────┤ ░ ├───────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░ ├──────┤ ░ ├───────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░  ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤ ░ ├──────┤ ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤ ░ ├──────┤ ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├───────────────────┤ ░  ░  ║ └╥┘┌─┐                     \n",
              "       q_2: ─░─┤ Rz(d0_q2_z0) ├┤ √X ├┤ Rz(d0_q12_z0) ├┤ √X ├┤ Rz(d0_q22_z0) ├─░─┤1     ├─░─┤ Rz(d0_q32_z0) ├┤ √X ├┤ Rz(d0_q42_z0) ├┤ √X ├┤ Rz(d0_q52_z0) ├─░─┤1     ├─░─┤ Rz(d0_q62_z0) ├┤ √X ├┤ Rz(d0_q72_z0) ├┤ √X ├┤ Rz(d0_q82_z0) ├─░──░─┤ Rz(-1.0*d0_q82_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q72_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q62_z0) ├─░─┤1     ├─░─┤ Rz(-1.0*d0_q52_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q42_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q32_z0) ├─░─┤1     ├─░─┤ Rz(-1.0*d0_q22_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q12_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q2_z0) ├─░──░──╫──╫─┤M├─────────────────────\n",
              "             ░ ├──────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░ │  Ecr │ ░ ├───────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░ │  Ecr │ ░ ├───────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░  ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤ ░ │  Ecr │ ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤ ░ │  Ecr │ ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├───────────────────┤ ░  ░  ║  ║ └╥┘┌─┐                  \n",
              "       q_3: ─░─┤ Rz(d0_q3_z0) ├┤ √X ├┤ Rz(d0_q13_z0) ├┤ √X ├┤ Rz(d0_q23_z0) ├─░─┤0     ├─░─┤ Rz(d0_q33_z0) ├┤ √X ├┤ Rz(d0_q43_z0) ├┤ √X ├┤ Rz(d0_q53_z0) ├─░─┤0     ├─░─┤ Rz(d0_q63_z0) ├┤ √X ├┤ Rz(d0_q73_z0) ├┤ √X ├┤ Rz(d0_q83_z0) ├─░──░─┤ Rz(-1.0*d0_q83_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q73_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q63_z0) ├─░─┤0     ├─░─┤ Rz(-1.0*d0_q53_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q43_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q33_z0) ├─░─┤0     ├─░─┤ Rz(-1.0*d0_q23_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q13_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q3_z0) ├─░──░──╫──╫──╫─┤M├──────────────────\n",
              "             ░ ├──────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░ ├──────┤ ░ ├───────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░ ├──────┤ ░ ├───────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░  ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤ ░ ├──────┤ ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤ ░ ├──────┤ ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├───────────────────┤ ░  ░  ║  ║  ║ └╥┘┌─┐               \n",
              "       q_4: ─░─┤ Rz(d0_q4_z0) ├┤ √X ├┤ Rz(d0_q14_z0) ├┤ √X ├┤ Rz(d0_q24_z0) ├─░─┤1     ├─░─┤ Rz(d0_q34_z0) ├┤ √X ├┤ Rz(d0_q44_z0) ├┤ √X ├┤ Rz(d0_q54_z0) ├─░─┤1     ├─░─┤ Rz(d0_q64_z0) ├┤ √X ├┤ Rz(d0_q74_z0) ├┤ √X ├┤ Rz(d0_q84_z0) ├─░──░─┤ Rz(-1.0*d0_q84_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q74_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q64_z0) ├─░─┤1     ├─░─┤ Rz(-1.0*d0_q54_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q44_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q34_z0) ├─░─┤1     ├─░─┤ Rz(-1.0*d0_q24_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q14_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q4_z0) ├─░──░──╫──╫──╫──╫─┤M├───────────────\n",
              "             ░ ├──────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░ │  Ecr │ ░ ├───────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░ │  Ecr │ ░ ├───────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░  ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤ ░ │  Ecr │ ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤ ░ │  Ecr │ ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├───────────────────┤ ░  ░  ║  ║  ║  ║ └╥┘┌─┐            \n",
              "       q_5: ─░─┤ Rz(d0_q5_z0) ├┤ √X ├┤ Rz(d0_q15_z0) ├┤ √X ├┤ Rz(d0_q25_z0) ├─░─┤0     ├─░─┤ Rz(d0_q35_z0) ├┤ √X ├┤ Rz(d0_q45_z0) ├┤ √X ├┤ Rz(d0_q55_z0) ├─░─┤0     ├─░─┤ Rz(d0_q65_z0) ├┤ √X ├┤ Rz(d0_q75_z0) ├┤ √X ├┤ Rz(d0_q85_z0) ├─░──░─┤ Rz(-1.0*d0_q85_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q75_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q65_z0) ├─░─┤0     ├─░─┤ Rz(-1.0*d0_q55_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q45_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q35_z0) ├─░─┤0     ├─░─┤ Rz(-1.0*d0_q25_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q15_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q5_z0) ├─░──░──╫──╫──╫──╫──╫─┤M├────────────\n",
              "             ░ ├──────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░ ├──────┤ ░ ├───────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░ ├──────┤ ░ ├───────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░  ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤ ░ ├──────┤ ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤ ░ ├──────┤ ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├───────────────────┤ ░  ░  ║  ║  ║  ║  ║ └╥┘┌─┐         \n",
              "       q_6: ─░─┤ Rz(d0_q6_z0) ├┤ √X ├┤ Rz(d0_q16_z0) ├┤ √X ├┤ Rz(d0_q26_z0) ├─░─┤1     ├─░─┤ Rz(d0_q36_z0) ├┤ √X ├┤ Rz(d0_q46_z0) ├┤ √X ├┤ Rz(d0_q56_z0) ├─░─┤1     ├─░─┤ Rz(d0_q66_z0) ├┤ √X ├┤ Rz(d0_q76_z0) ├┤ √X ├┤ Rz(d0_q86_z0) ├─░──░─┤ Rz(-1.0*d0_q86_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q76_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q66_z0) ├─░─┤1     ├─░─┤ Rz(-1.0*d0_q56_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q46_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q36_z0) ├─░─┤1     ├─░─┤ Rz(-1.0*d0_q26_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q16_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q6_z0) ├─░──░──╫──╫──╫──╫──╫──╫─┤M├─────────\n",
              "             ░ ├──────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░ │  Ecr │ ░ ├───────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░ │  Ecr │ ░ ├───────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░  ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤ ░ │  Ecr │ ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤ ░ │  Ecr │ ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├───────────────────┤ ░  ░  ║  ║  ║  ║  ║  ║ └╥┘┌─┐      \n",
              "       q_7: ─░─┤ Rz(d0_q7_z0) ├┤ √X ├┤ Rz(d0_q17_z0) ├┤ √X ├┤ Rz(d0_q27_z0) ├─░─┤0     ├─░─┤ Rz(d0_q37_z0) ├┤ √X ├┤ Rz(d0_q47_z0) ├┤ √X ├┤ Rz(d0_q57_z0) ├─░─┤0     ├─░─┤ Rz(d0_q67_z0) ├┤ √X ├┤ Rz(d0_q77_z0) ├┤ √X ├┤ Rz(d0_q87_z0) ├─░──░─┤ Rz(-1.0*d0_q87_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q77_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q67_z0) ├─░─┤0     ├─░─┤ Rz(-1.0*d0_q57_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q47_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q37_z0) ├─░─┤0     ├─░─┤ Rz(-1.0*d0_q27_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q17_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q7_z0) ├─░──░──╫──╫──╫──╫──╫──╫──╫─┤M├──────\n",
              "             ░ ├──────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░ ├──────┤ ░ ├───────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░ ├──────┤ ░ ├───────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░  ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤ ░ ├──────┤ ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤ ░ ├──────┤ ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├───────────────────┤ ░  ░  ║  ║  ║  ║  ║  ║  ║ └╥┘┌─┐   \n",
              "       q_8: ─░─┤ Rz(d0_q8_z0) ├┤ √X ├┤ Rz(d0_q18_z0) ├┤ √X ├┤ Rz(d0_q28_z0) ├─░─┤0     ├─░─┤ Rz(d0_q38_z0) ├┤ √X ├┤ Rz(d0_q48_z0) ├┤ √X ├┤ Rz(d0_q58_z0) ├─░─┤0     ├─░─┤ Rz(d0_q68_z0) ├┤ √X ├┤ Rz(d0_q78_z0) ├┤ √X ├┤ Rz(d0_q88_z0) ├─░──░─┤ Rz(-1.0*d0_q88_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q78_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q68_z0) ├─░─┤0     ├─░─┤ Rz(-1.0*d0_q58_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q48_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q38_z0) ├─░─┤0     ├─░─┤ Rz(-1.0*d0_q28_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q18_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q8_z0) ├─░──░──╫──╫──╫──╫──╫──╫──╫──╫─┤M├───\n",
              "             ░ ├──────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░ │  Ecr │ ░ ├───────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░ │  Ecr │ ░ ├───────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░  ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤ ░ │  Ecr │ ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤ ░ │  Ecr │ ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├───────────────────┤ ░  ░  ║  ║  ║  ║  ║  ║  ║  ║ └╥┘┌─┐\n",
              "       q_9: ─░─┤ Rz(d0_q9_z0) ├┤ √X ├┤ Rz(d0_q19_z0) ├┤ √X ├┤ Rz(d0_q29_z0) ├─░─┤1     ├─░─┤ Rz(d0_q39_z0) ├┤ √X ├┤ Rz(d0_q49_z0) ├┤ √X ├┤ Rz(d0_q59_z0) ├─░─┤1     ├─░─┤ Rz(d0_q69_z0) ├┤ √X ├┤ Rz(d0_q79_z0) ├┤ √X ├┤ Rz(d0_q89_z0) ├─░──░─┤ Rz(-1.0*d0_q89_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q79_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q69_z0) ├─░─┤1     ├─░─┤ Rz(-1.0*d0_q59_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q49_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q39_z0) ├─░─┤1     ├─░─┤ Rz(-1.0*d0_q29_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q19_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q9_z0) ├─░──░──╫──╫──╫──╫──╫──╫──╫──╫──╫─┤M├\n",
              "             ░ └──────────────┘└────┘└───────────────┘└────┘└───────────────┘ ░ └──────┘ ░ └───────────────┘└────┘└───────────────┘└────┘└───────────────┘ ░ └──────┘ ░ └───────────────┘└────┘└───────────────┘└────┘└───────────────┘ ░  ░ └────────────────────┘└─────────┘└─────────┘└────┘└─────────┘└─────────┘└────────────────────┘└─────────┘└─────────┘└────┘└─────────┘└─────────┘└────────────────────┘ ░ └──────┘ ░ └────────────────────┘└─────────┘└─────────┘└────┘└─────────┘└─────────┘└────────────────────┘└─────────┘└─────────┘└────┘└─────────┘└─────────┘└────────────────────┘ ░ └──────┘ ░ └────────────────────┘└─────────┘└─────────┘└────┘└─────────┘└─────────┘└────────────────────┘└─────────┘└─────────┘└────┘└─────────┘└─────────┘└───────────────────┘ ░  ░  ║  ║  ║  ║  ║  ║  ║  ║  ║ └╥┘\n",
              "measure: 10/═════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════╩══╩══╩══╩══╩══╩══╩══╩══╩══╩═\n",
              "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 0  1  2  3  4  5  6  7  8  9 "
            ],
            "text/html": [
              "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">             ░ ┌──────────────┐┌────┐┌───────────────┐┌────┐┌───────────────┐ ░ ┌──────┐ ░ ┌───────────────┐┌────┐┌───────────────┐┌────┐┌───────────────┐ ░ ┌──────┐ ░ ┌───────────────┐┌────┐┌───────────────┐┌────┐┌───────────────┐ ░  ░ ┌────────────────────┐┌─────────┐┌─────────┐┌────┐┌─────────┐┌─────────┐┌────────────────────┐┌─────────┐┌─────────┐┌────┐┌─────────┐┌─────────┐┌────────────────────┐ ░ ┌──────┐ ░ ┌────────────────────┐┌─────────┐┌─────────┐┌────┐┌─────────┐┌─────────┐┌────────────────────┐┌─────────┐┌─────────┐┌────┐┌─────────┐┌─────────┐┌────────────────────┐ ░ ┌──────┐ ░ ┌────────────────────┐┌─────────┐┌─────────┐┌────┐┌─────────┐┌─────────┐┌────────────────────┐┌─────────┐┌─────────┐┌────┐┌─────────┐┌─────────┐┌───────────────────┐ ░  ░ ┌─┐                           \n",
              "       q_0: ─░─┤ Rz(d0_q0_z0) ├┤ √X ├┤ Rz(d0_q10_z0) ├┤ √X ├┤ Rz(d0_q20_z0) ├─░─┤1     ├─░─┤ Rz(d0_q30_z0) ├┤ √X ├┤ Rz(d0_q40_z0) ├┤ √X ├┤ Rz(d0_q50_z0) ├─░─┤1     ├─░─┤ Rz(d0_q60_z0) ├┤ √X ├┤ Rz(d0_q70_z0) ├┤ √X ├┤ Rz(d0_q80_z0) ├─░──░─┤ Rz(-1.0*d0_q80_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q70_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q60_z0) ├─░─┤1     ├─░─┤ Rz(-1.0*d0_q50_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q40_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q30_z0) ├─░─┤1     ├─░─┤ Rz(-1.0*d0_q20_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q10_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q0_z0) ├─░──░─┤M├───────────────────────────\n",
              "             ░ ├──────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░ │  Ecr │ ░ ├───────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░ │  Ecr │ ░ ├───────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░  ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤ ░ │  Ecr │ ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤ ░ │  Ecr │ ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├───────────────────┤ ░  ░ └╥┘┌─┐                        \n",
              "       q_1: ─░─┤ Rz(d0_q1_z0) ├┤ √X ├┤ Rz(d0_q11_z0) ├┤ √X ├┤ Rz(d0_q21_z0) ├─░─┤0     ├─░─┤ Rz(d0_q31_z0) ├┤ √X ├┤ Rz(d0_q41_z0) ├┤ √X ├┤ Rz(d0_q51_z0) ├─░─┤0     ├─░─┤ Rz(d0_q61_z0) ├┤ √X ├┤ Rz(d0_q71_z0) ├┤ √X ├┤ Rz(d0_q81_z0) ├─░──░─┤ Rz(-1.0*d0_q81_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q71_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q61_z0) ├─░─┤0     ├─░─┤ Rz(-1.0*d0_q51_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q41_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q31_z0) ├─░─┤0     ├─░─┤ Rz(-1.0*d0_q21_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q11_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q1_z0) ├─░──░──╫─┤M├────────────────────────\n",
              "             ░ ├──────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░ ├──────┤ ░ ├───────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░ ├──────┤ ░ ├───────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░  ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤ ░ ├──────┤ ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤ ░ ├──────┤ ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├───────────────────┤ ░  ░  ║ └╥┘┌─┐                     \n",
              "       q_2: ─░─┤ Rz(d0_q2_z0) ├┤ √X ├┤ Rz(d0_q12_z0) ├┤ √X ├┤ Rz(d0_q22_z0) ├─░─┤1     ├─░─┤ Rz(d0_q32_z0) ├┤ √X ├┤ Rz(d0_q42_z0) ├┤ √X ├┤ Rz(d0_q52_z0) ├─░─┤1     ├─░─┤ Rz(d0_q62_z0) ├┤ √X ├┤ Rz(d0_q72_z0) ├┤ √X ├┤ Rz(d0_q82_z0) ├─░──░─┤ Rz(-1.0*d0_q82_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q72_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q62_z0) ├─░─┤1     ├─░─┤ Rz(-1.0*d0_q52_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q42_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q32_z0) ├─░─┤1     ├─░─┤ Rz(-1.0*d0_q22_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q12_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q2_z0) ├─░──░──╫──╫─┤M├─────────────────────\n",
              "             ░ ├──────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░ │  Ecr │ ░ ├───────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░ │  Ecr │ ░ ├───────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░  ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤ ░ │  Ecr │ ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤ ░ │  Ecr │ ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├───────────────────┤ ░  ░  ║  ║ └╥┘┌─┐                  \n",
              "       q_3: ─░─┤ Rz(d0_q3_z0) ├┤ √X ├┤ Rz(d0_q13_z0) ├┤ √X ├┤ Rz(d0_q23_z0) ├─░─┤0     ├─░─┤ Rz(d0_q33_z0) ├┤ √X ├┤ Rz(d0_q43_z0) ├┤ √X ├┤ Rz(d0_q53_z0) ├─░─┤0     ├─░─┤ Rz(d0_q63_z0) ├┤ √X ├┤ Rz(d0_q73_z0) ├┤ √X ├┤ Rz(d0_q83_z0) ├─░──░─┤ Rz(-1.0*d0_q83_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q73_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q63_z0) ├─░─┤0     ├─░─┤ Rz(-1.0*d0_q53_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q43_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q33_z0) ├─░─┤0     ├─░─┤ Rz(-1.0*d0_q23_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q13_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q3_z0) ├─░──░──╫──╫──╫─┤M├──────────────────\n",
              "             ░ ├──────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░ ├──────┤ ░ ├───────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░ ├──────┤ ░ ├───────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░  ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤ ░ ├──────┤ ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤ ░ ├──────┤ ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├───────────────────┤ ░  ░  ║  ║  ║ └╥┘┌─┐               \n",
              "       q_4: ─░─┤ Rz(d0_q4_z0) ├┤ √X ├┤ Rz(d0_q14_z0) ├┤ √X ├┤ Rz(d0_q24_z0) ├─░─┤1     ├─░─┤ Rz(d0_q34_z0) ├┤ √X ├┤ Rz(d0_q44_z0) ├┤ √X ├┤ Rz(d0_q54_z0) ├─░─┤1     ├─░─┤ Rz(d0_q64_z0) ├┤ √X ├┤ Rz(d0_q74_z0) ├┤ √X ├┤ Rz(d0_q84_z0) ├─░──░─┤ Rz(-1.0*d0_q84_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q74_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q64_z0) ├─░─┤1     ├─░─┤ Rz(-1.0*d0_q54_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q44_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q34_z0) ├─░─┤1     ├─░─┤ Rz(-1.0*d0_q24_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q14_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q4_z0) ├─░──░──╫──╫──╫──╫─┤M├───────────────\n",
              "             ░ ├──────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░ │  Ecr │ ░ ├───────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░ │  Ecr │ ░ ├───────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░  ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤ ░ │  Ecr │ ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤ ░ │  Ecr │ ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├───────────────────┤ ░  ░  ║  ║  ║  ║ └╥┘┌─┐            \n",
              "       q_5: ─░─┤ Rz(d0_q5_z0) ├┤ √X ├┤ Rz(d0_q15_z0) ├┤ √X ├┤ Rz(d0_q25_z0) ├─░─┤0     ├─░─┤ Rz(d0_q35_z0) ├┤ √X ├┤ Rz(d0_q45_z0) ├┤ √X ├┤ Rz(d0_q55_z0) ├─░─┤0     ├─░─┤ Rz(d0_q65_z0) ├┤ √X ├┤ Rz(d0_q75_z0) ├┤ √X ├┤ Rz(d0_q85_z0) ├─░──░─┤ Rz(-1.0*d0_q85_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q75_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q65_z0) ├─░─┤0     ├─░─┤ Rz(-1.0*d0_q55_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q45_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q35_z0) ├─░─┤0     ├─░─┤ Rz(-1.0*d0_q25_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q15_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q5_z0) ├─░──░──╫──╫──╫──╫──╫─┤M├────────────\n",
              "             ░ ├──────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░ ├──────┤ ░ ├───────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░ ├──────┤ ░ ├───────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░  ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤ ░ ├──────┤ ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤ ░ ├──────┤ ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├───────────────────┤ ░  ░  ║  ║  ║  ║  ║ └╥┘┌─┐         \n",
              "       q_6: ─░─┤ Rz(d0_q6_z0) ├┤ √X ├┤ Rz(d0_q16_z0) ├┤ √X ├┤ Rz(d0_q26_z0) ├─░─┤1     ├─░─┤ Rz(d0_q36_z0) ├┤ √X ├┤ Rz(d0_q46_z0) ├┤ √X ├┤ Rz(d0_q56_z0) ├─░─┤1     ├─░─┤ Rz(d0_q66_z0) ├┤ √X ├┤ Rz(d0_q76_z0) ├┤ √X ├┤ Rz(d0_q86_z0) ├─░──░─┤ Rz(-1.0*d0_q86_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q76_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q66_z0) ├─░─┤1     ├─░─┤ Rz(-1.0*d0_q56_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q46_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q36_z0) ├─░─┤1     ├─░─┤ Rz(-1.0*d0_q26_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q16_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q6_z0) ├─░──░──╫──╫──╫──╫──╫──╫─┤M├─────────\n",
              "             ░ ├──────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░ │  Ecr │ ░ ├───────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░ │  Ecr │ ░ ├───────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░  ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤ ░ │  Ecr │ ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤ ░ │  Ecr │ ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├───────────────────┤ ░  ░  ║  ║  ║  ║  ║  ║ └╥┘┌─┐      \n",
              "       q_7: ─░─┤ Rz(d0_q7_z0) ├┤ √X ├┤ Rz(d0_q17_z0) ├┤ √X ├┤ Rz(d0_q27_z0) ├─░─┤0     ├─░─┤ Rz(d0_q37_z0) ├┤ √X ├┤ Rz(d0_q47_z0) ├┤ √X ├┤ Rz(d0_q57_z0) ├─░─┤0     ├─░─┤ Rz(d0_q67_z0) ├┤ √X ├┤ Rz(d0_q77_z0) ├┤ √X ├┤ Rz(d0_q87_z0) ├─░──░─┤ Rz(-1.0*d0_q87_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q77_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q67_z0) ├─░─┤0     ├─░─┤ Rz(-1.0*d0_q57_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q47_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q37_z0) ├─░─┤0     ├─░─┤ Rz(-1.0*d0_q27_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q17_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q7_z0) ├─░──░──╫──╫──╫──╫──╫──╫──╫─┤M├──────\n",
              "             ░ ├──────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░ ├──────┤ ░ ├───────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░ ├──────┤ ░ ├───────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░  ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤ ░ ├──────┤ ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤ ░ ├──────┤ ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├───────────────────┤ ░  ░  ║  ║  ║  ║  ║  ║  ║ └╥┘┌─┐   \n",
              "       q_8: ─░─┤ Rz(d0_q8_z0) ├┤ √X ├┤ Rz(d0_q18_z0) ├┤ √X ├┤ Rz(d0_q28_z0) ├─░─┤0     ├─░─┤ Rz(d0_q38_z0) ├┤ √X ├┤ Rz(d0_q48_z0) ├┤ √X ├┤ Rz(d0_q58_z0) ├─░─┤0     ├─░─┤ Rz(d0_q68_z0) ├┤ √X ├┤ Rz(d0_q78_z0) ├┤ √X ├┤ Rz(d0_q88_z0) ├─░──░─┤ Rz(-1.0*d0_q88_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q78_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q68_z0) ├─░─┤0     ├─░─┤ Rz(-1.0*d0_q58_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q48_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q38_z0) ├─░─┤0     ├─░─┤ Rz(-1.0*d0_q28_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q18_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q8_z0) ├─░──░──╫──╫──╫──╫──╫──╫──╫──╫─┤M├───\n",
              "             ░ ├──────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░ │  Ecr │ ░ ├───────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░ │  Ecr │ ░ ├───────────────┤├────┤├───────────────┤├────┤├───────────────┤ ░  ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤ ░ │  Ecr │ ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤ ░ │  Ecr │ ░ ├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├────────────────────┤├─────────┤├─────────┤├────┤├─────────┤├─────────┤├───────────────────┤ ░  ░  ║  ║  ║  ║  ║  ║  ║  ║ └╥┘┌─┐\n",
              "       q_9: ─░─┤ Rz(d0_q9_z0) ├┤ √X ├┤ Rz(d0_q19_z0) ├┤ √X ├┤ Rz(d0_q29_z0) ├─░─┤1     ├─░─┤ Rz(d0_q39_z0) ├┤ √X ├┤ Rz(d0_q49_z0) ├┤ √X ├┤ Rz(d0_q59_z0) ├─░─┤1     ├─░─┤ Rz(d0_q69_z0) ├┤ √X ├┤ Rz(d0_q79_z0) ├┤ √X ├┤ Rz(d0_q89_z0) ├─░──░─┤ Rz(-1.0*d0_q89_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q79_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q69_z0) ├─░─┤1     ├─░─┤ Rz(-1.0*d0_q59_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q49_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q39_z0) ├─░─┤1     ├─░─┤ Rz(-1.0*d0_q29_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q19_z0) ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ √X ├┤ Rz(π/2) ├┤ Rz(π/2) ├┤ Rz(-1.0*d0_q9_z0) ├─░──░──╫──╫──╫──╫──╫──╫──╫──╫──╫─┤M├\n",
              "             ░ └──────────────┘└────┘└───────────────┘└────┘└───────────────┘ ░ └──────┘ ░ └───────────────┘└────┘└───────────────┘└────┘└───────────────┘ ░ └──────┘ ░ └───────────────┘└────┘└───────────────┘└────┘└───────────────┘ ░  ░ └────────────────────┘└─────────┘└─────────┘└────┘└─────────┘└─────────┘└────────────────────┘└─────────┘└─────────┘└────┘└─────────┘└─────────┘└────────────────────┘ ░ └──────┘ ░ └────────────────────┘└─────────┘└─────────┘└────┘└─────────┘└─────────┘└────────────────────┘└─────────┘└─────────┘└────┘└─────────┘└─────────┘└────────────────────┘ ░ └──────┘ ░ └────────────────────┘└─────────┘└─────────┘└────┘└─────────┘└─────────┘└────────────────────┘└─────────┘└─────────┘└────┘└─────────┘└─────────┘└───────────────────┘ ░  ░  ║  ║  ║  ║  ║  ║  ║  ║  ║ └╥┘\n",
              "measure: 10/═════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════╩══╩══╩══╩══╩══╩══╩══╩══╩══╩═\n",
              "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 0  1  2  3  4  5  6  7  8  9 </pre>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "55GJSLgCKkkr"
      },
      "outputs": [],
      "source": [
        "example_circuit_2 = eplg_circuit(\n",
        "    num_qubits=BACKEND.num_qubits,\n",
        "    depth=4,\n",
        "    layer=CIRC_2_ANSWER,\n",
        "    qubits=PATH_ANSWER,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tNKrGFNqKkkr"
      },
      "source": [
        "# Part 5: Construct `BindingsArray`s\n",
        "\n",
        "*No grading for this part*\n",
        "\n",
        "In the previous section, the circuits we constructed are parameterized such that if we sample with the appropriate values, they will always produce random single-qubit Clifford gates.\n",
        "\n",
        "For our EPLG calculations, we need to sample lots of sets of parameters. This is expressed conveniently with the `BindingsArray`. The `BindingsArray` can be thought of as an array whose elements correspond to sets of parameters and their bound values.\n",
        "\n",
        "For example, if we have a circuit with $k$ parameters, and we need to sample all of the parameters $m$ times, the `BindingsArray` that accomplishes this would have shape `(m,)`. Similarly, if we want to have a more complicated set of bindings to perform, our `BindingsArray` can have arbitrary shape, and supports `numpy`-like reshaping operations, slicing, etc.\n",
        "\n",
        "In this section, we will construct a function which accepts a `QuantumCircuit`, the number of samples we want to perform, and a `numpy` RNG to perform the single-qubit Clifford sampling. We will use this later on to produce our `EstimatorPub`s."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TxSSXW3RKkkr"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "\n",
        "from qiskit.primitives.containers.bindings_array import BindingsArray\n",
        "\n",
        "def get_clifford_rz_samples(\n",
        "    circ: QuantumCircuit,\n",
        "    num_samples: int,\n",
        "    rng: np.random.Generator,\n",
        ") -> BindingsArray:\n",
        "    \"\"\"Uniformly sample the Rz parameters in a `QuantumCircuit`\n",
        "    from -pi, -pi/2, 0, +pi/2, +pi.\n",
        "    \"\"\"\n",
        "\n",
        "    _allowed_ops = {'rz', 'sx', 'sxdg', 'ecr', 'barrier', 'measure'}\n",
        "    if (_other := set(circ.count_ops().keys()) - _allowed_ops):\n",
        "        raise ValueError(f\"Circuit must only contain ops: {_allowed_ops}, got {_other}\")\n",
        "\n",
        "    sampled_pars = rng.integers(-2, 3, size=(num_samples, len(circ.parameters))) * (np.pi/2)\n",
        "\n",
        "    return BindingsArray({\n",
        "        tuple(circ.parameters): sampled_pars\n",
        "    })"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H0i92GvbKkkr"
      },
      "source": [
        "As an example, we can take each of our circuits and produce bindings"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rt2x602cKkkr"
      },
      "outputs": [],
      "source": [
        "num_samples = 10\n",
        "ARRAY_1_ANSWER = get_clifford_rz_samples(\n",
        "    circ=example_circuit_1,\n",
        "    num_samples=num_samples,\n",
        "    rng=np.random.default_rng(42))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(ARRAY_1_ANSWER.shape)\n",
        "print(ARRAY_1_ANSWER)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HIkdQKnrSQ-u",
        "outputId": "32122906-dd78-4762-ed59-be5e981d0331"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(10,)\n",
            "BindingsArray(<shape=(10,), num_parameters=90, parameters=['d0_q0_z0', 'd0_q10_z0', 'd0_q11_z0', 'd0_q12_z0', 'd0_q13_z0', ...]>)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xAP6Pgn-Kkks",
        "outputId": "35c768ba-80e1-42fa-dcc6-f364e0bb3893"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Submitting your answer. Please wait...\n",
            "Nice work, all your answers look correct! \n",
            " Notice that the shape of the BindingsArray is [10], and there are {num_parameters} parameters.\n"
          ]
        }
      ],
      "source": [
        "# grade part 4\n",
        "from qc_grader.challenges.qgss_2024 import grade_lab2_ex4\n",
        "grade_lab2_ex4(ARRAY_1_ANSWER, example_circuit_1, num_samples)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zLqPU9MlKkks"
      },
      "source": [
        "Notice that despite the large number of parameters, the `BindingsArray` has shape `(10,)`, meaning it has `10` entries. As we discussed before, this is because its elements correspond to sets of parameters and their bound values"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ndW3qnCcKkks"
      },
      "source": [
        "# Part 6: Make `ObservablesArray`s\n",
        "\n",
        "*No grading for this part*\n",
        "\n",
        "The only remaining thing that we need to produce an `EstimatorPub` is an `ObservablesArray`. The `ObservablesArray` can be seen as a array whose elements are observables. In our case, they will be `SparsePauliOp`s.\n",
        "\n",
        "In order to calculate the EPLG and LF, we need to calculate the expectation values $\\text{Tr}\\left( \\rho \\ket{00}_{i,j}\\bra{00}_{i,j}\\right)$ and $\\text{Tr}\\left( \\rho \\ket{0}_{i}\\bra{0}_{i}\\right)$ for qubits $i$, $j$ depending on the layers we chose before.\n",
        "\n",
        "Alternatively we could do this using the `SamplerV2` rather than the `EstimatorV2`. This would come with the tradeoff of having more post-processing that we have to do, but we can have the `EstimatorV2` do this for us by constructing the projectors $\\ket{0}_{i}\\bra{0}_{i}$ and $\\ket{00}_{i,j}\\bra{00}_{i,j}$ and using them as observables.\n",
        "\n",
        "This means that the expectation values we get back from the `EstimatorV2` will be the process fidelities that we're interested in!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3ZmdZYNAKkks"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "from itertools import product\n",
        "from qiskit.quantum_info import SparsePauliOp\n",
        "\n",
        "def process_fidelity_observable(num_qubits: int, qubits: tuple[int, ...]) -> SparsePauliOp:\n",
        "    \"\"\"Produces the all 0 projector on `qubits` tensored with the identity on all other qubits\"\"\"\n",
        "    pauli_strings = []\n",
        "\n",
        "    for p_sub_str in product([\"I\", \"Z\"], repeat=len(qubits)):\n",
        "        p_str = [\"I\"] * num_qubits\n",
        "        for qi, pi in zip(qubits, p_sub_str):\n",
        "            p_str[qi] = pi\n",
        "        pauli_strings.append(\"\".join(p_str[::-1]))\n",
        "\n",
        "    return SparsePauliOp(pauli_strings, np.ones(len(pauli_strings)) / 2**len(qubits))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E4ejIpebKkks"
      },
      "source": [
        "Now we need to produce `ObservablesArray`s which correspond to the projectors we are interested in, i.e. the process fidelities for subsets of our path."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dwsDOR0qKkkt"
      },
      "outputs": [],
      "source": [
        "from qiskit.primitives.containers.observables_array import ObservablesArray\n",
        "\n",
        "LAYER_1_SUBSYSTEMS = LAYER_1_ANSWER + [(q,) for q in set(PATH_ANSWER) - set(item for sublist in LAYER_1_ANSWER for item in sublist)]\n",
        "LAYER_2_SUBSYSTEMS = LAYER_2_ANSWER + [(q,) for q in set(PATH_ANSWER) - set(item for sublist in LAYER_2_ANSWER for item in sublist)]\n",
        "\n",
        "LAYER_1_OBSERVABLES = ObservablesArray([process_fidelity_observable(BACKEND.num_qubits, qubits) for qubits in LAYER_1_SUBSYSTEMS])\n",
        "LAYER_2_OBSERVABLES = ObservablesArray([process_fidelity_observable(BACKEND.num_qubits, qubits) for qubits in LAYER_2_SUBSYSTEMS])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tkx5BeF2Kkkt"
      },
      "source": [
        "Note that the `LAYER_i_OBSERVABLES` have a shape which corresponds to the number of subsystems in each layer.\n",
        "\n",
        "Since all of the observables in our `ObservablesArray`s are diagonal in the computational basis (i.e. they only consist of Paulis which have $I$ and $Z$ terms), the `EstimatorV2` does not need to compute expectation values in additional bases."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3ANRodANKkkt",
        "outputId": "2bda61ac-d0b8-44ff-d7eb-a63282c6c7d3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "LAYER_1_OBSERVABLES.shape == (len(LAYER_1_SUBSYSTEMS),)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ty1-7b9DKkkt",
        "outputId": "97c4a8c5-9ef4-452a-8caa-9441257bd645"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "LAYER_2_OBSERVABLES.shape == (len(LAYER_2_SUBSYSTEMS),)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(LAYER_1_OBSERVABLES)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P4wHGUXFTYb8",
        "outputId": "3e8637cf-1c26-4dc3-951a-745c847a9e00"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ObservablesArray([{'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII': 0.25, 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZ': 0.25, 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZI': 0.25, 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZ': 0.25}\n",
            "                  {'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII': 0.25, 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZII': 0.25, 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIII': 0.25, 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZII': 0.25}\n",
            "                  {'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII': 0.25, 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIII': 0.25, 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIII': 0.25, 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIII': 0.25}\n",
            "                  {'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII': 0.25, 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIII': 0.25, 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIII': 0.25, 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIII': 0.25}\n",
            "                  {'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII': 0.25, 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIII': 0.25, 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIII': 0.25, 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIII': 0.25}], shape=(5,))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(LAYER_2_OBSERVABLES)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TGt4ZFdqTvPI",
        "outputId": "87ff770b-0056-4ba5-e823-670d1045d7d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ObservablesArray([{'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII': 0.25, 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZII': 0.25, 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZI': 0.25, 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZI': 0.25}\n",
            "                  {'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII': 0.25, 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIII': 0.25, 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIII': 0.25, 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIII': 0.25}\n",
            "                  {'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII': 0.25, 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIII': 0.25, 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIII': 0.25, 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIII': 0.25}\n",
            "                  {'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII': 0.25, 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIII': 0.25, 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIII': 0.25, 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIII': 0.25}\n",
            "                  {'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII': 0.5, 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZ': 0.5}\n",
            "                  {'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII': 0.5, 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIII': 0.5}], shape=(6,))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IYQkq9sxKkkt"
      },
      "source": [
        "# Part 7: Make Sequence of `EstimatorPub`s\n",
        "\n",
        "*No grading for this part*\n",
        "\n",
        "Finally, we can construct our `EstimatorPub`s. The [`EstimatorPub`](https://github.com/Qiskit/qiskit/blob/main/qiskit/primitives/containers/estimator_pub.py#L36) is can be seen as a tuple consisting of a `QuantumCircuit`, an `ObservablesArray`, and a `BindingsArray` (and optionally a precision). Given an `EstimatorPub` with a given shape, the `EstimatorV2` will return arrays of expectation values (and their standard deviations) whose shape is the same as the input `EstimatorPub`.\n",
        "\n",
        "In order to construct the `EstimatorPub`s needed to do the EPLG calculations, we need `EstimatorPub`s which vary in\n",
        "- the input circuit (i.e. one for each disjoint set of edges) (in our case `2`)\n",
        "- the depth of the circuit (we have chosen `6` depths here by default).\n",
        "\n",
        "Therefore, we will have $2 \\times 6 = 12$ `EstimatorPub`s total. Within each of these, we have `10` twirling samples, and `k` fidelities (which depends on the path you chose, this should roughly be half of the length of the path). So we have $12$ `EstimatorPub`s, each with $10 k$ elements.\n",
        "\n",
        "Therefore, the total number of expectation values that we *expect* to receive is $120k$. For a length $30$ chain, $k = 15$, meaning we should roughly $1,800$ expectation values back overall (depending on the edges of the chain)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3lnOp2XkKkkt"
      },
      "outputs": [],
      "source": [
        "rng = np.random.default_rng(42)\n",
        "num_samples = 10\n",
        "depths = [2, 4, 8, 16, 32, 64]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YLyL53LjKkku",
        "outputId": "1fa066ee-049b-4f54-a353-fd64403b7cbe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        }
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "ModuleNotFoundError",
          "evalue": "No module named 'qiskit'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-d703efdf7401>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mqiskit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprimitives\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontainers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestimator_pub\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mEstimatorPub\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mestimator_pubs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mdepth\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdepths\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mlayer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobservables\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mCIRC_1_ANSWER\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"circ_1\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mLAYER_1_OBSERVABLES\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mCIRC_2_ANSWER\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"circ_2\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mLAYER_2_OBSERVABLES\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'qiskit'",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ],
          "errorDetails": {
            "actions": [
              {
                "action": "open_url",
                "actionText": "Open Examples",
                "url": "/notebooks/snippets/importing_libraries.ipynb"
              }
            ]
          }
        }
      ],
      "source": [
        "from qiskit.primitives.containers.estimator_pub import EstimatorPub\n",
        "\n",
        "estimator_pubs = []\n",
        "for depth in depths:\n",
        "    for layer, name, observables in [(CIRC_1_ANSWER, \"circ_1\", LAYER_1_OBSERVABLES), (CIRC_2_ANSWER, \"circ_2\", LAYER_2_OBSERVABLES)]:\n",
        "        circ = eplg_circuit(\n",
        "            num_qubits=BACKEND.num_qubits,\n",
        "            depth=depth,\n",
        "            layer=layer,\n",
        "            qubits=PATH_ANSWER,\n",
        "        )\n",
        "        circ.metadata[\"layer_idx\"] = name\n",
        "        bindings_array = get_clifford_rz_samples(\n",
        "            circ=circ,\n",
        "            num_samples=num_samples,\n",
        "            rng=rng,\n",
        "        )\n",
        "\n",
        "        # We need to reshape the `ObservablesArray`s and `BindingsArray`s so they\n",
        "        # can be broadcasted.\n",
        "        # https://numpy.org/doc/stable/user/basics.broadcasting.html#basics-broadcasting\n",
        "        estimator_pubs.append(EstimatorPub(\n",
        "            circuit=circ,\n",
        "            observables=observables.reshape((*observables.shape, 1)),\n",
        "            parameter_values=bindings_array.reshape((1, *bindings_array.shape)),\n",
        "        ))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fsnO67qwKkku"
      },
      "source": [
        "As a sanity check, we can count the number of expectation values we should obtain overall from the shape of the `EstimatorPub`s."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ru6DCnZyKkku",
        "outputId": "b4a6f604-3900-4e9a-e4c6-ab308db213a3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "600"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "num_evs_expected = 0\n",
        "for pub in estimator_pubs:\n",
        "    num_evs_expected += np.prod(estimator_pubs[0].shape)\n",
        "num_evs_expected"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xwb4tUkMKkku"
      },
      "source": [
        "# Part 8: Submit to Runtime\n",
        "\n",
        "*No grading for this part*\n",
        "\n",
        "Finally, we are ready to hand off our `EstimatorPub`s to the `EstimatorV2`.\n",
        "\n",
        "Since the calculation of EPLG is SPAM-robust, we should turn off measurement mitigation, which is enabled by default."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hpOx0T8OKkku"
      },
      "outputs": [],
      "source": [
        "from qiskit_ibm_runtime import EstimatorV2\n",
        "estimator = EstimatorV2(backend=BACKEND)\n",
        "estimator.options.resilience.measure_mitigation = False"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "shlJTyDrKkkv"
      },
      "source": [
        "Now we can run the `EstimatorV2` with our `EstimatorPub`s and wait for the result.\n",
        "\n",
        "With the default settings in this notebook, this should take no more than 10 minutes to complete (not accounting for queue time)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5SLalF3RKkkv"
      },
      "outputs": [],
      "source": [
        "estimator_job = estimator.run(estimator_pubs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PkFHDCA0Kkkv"
      },
      "source": [
        "And we can get the `PrimitiveResult` with"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FIa1s1GBKkkv",
        "outputId": "5864c7c3-d805-4996-b07f-4a0ad0b991f9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PrimitiveResult([PubResult(data=DataBin(evs=np.ndarray(<shape=(5, 10), dtype=float64>), stds=np.ndarray(<shape=(5, 10), dtype=float64>), ensemble_standard_error=np.ndarray(<shape=(5, 10), dtype=float64>), shape=(5, 10)), metadata={'shots': 4096, 'target_precision': 0.015625, 'circuit_metadata': {'layer_depth': 2, 'qubits': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 'num_qubits': 127, 'layer_idx': 'circ_1'}, 'num_randomizations': 32}), PubResult(data=DataBin(evs=np.ndarray(<shape=(6, 10), dtype=float64>), stds=np.ndarray(<shape=(6, 10), dtype=float64>), ensemble_standard_error=np.ndarray(<shape=(6, 10), dtype=float64>), shape=(6, 10)), metadata={'shots': 4096, 'target_precision': 0.015625, 'circuit_metadata': {'layer_depth': 2, 'qubits': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 'num_qubits': 127, 'layer_idx': 'circ_2'}, 'num_randomizations': 32}), PubResult(data=DataBin(evs=np.ndarray(<shape=(5, 10), dtype=float64>), stds=np.ndarray(<shape=(5, 10), dtype=float64>), ensemble_standard_error=np.ndarray(<shape=(5, 10), dtype=float64>), shape=(5, 10)), metadata={'shots': 4096, 'target_precision': 0.015625, 'circuit_metadata': {'layer_depth': 4, 'qubits': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 'num_qubits': 127, 'layer_idx': 'circ_1'}, 'num_randomizations': 32}), PubResult(data=DataBin(evs=np.ndarray(<shape=(6, 10), dtype=float64>), stds=np.ndarray(<shape=(6, 10), dtype=float64>), ensemble_standard_error=np.ndarray(<shape=(6, 10), dtype=float64>), shape=(6, 10)), metadata={'shots': 4096, 'target_precision': 0.015625, 'circuit_metadata': {'layer_depth': 4, 'qubits': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 'num_qubits': 127, 'layer_idx': 'circ_2'}, 'num_randomizations': 32}), PubResult(data=DataBin(evs=np.ndarray(<shape=(5, 10), dtype=float64>), stds=np.ndarray(<shape=(5, 10), dtype=float64>), ensemble_standard_error=np.ndarray(<shape=(5, 10), dtype=float64>), shape=(5, 10)), metadata={'shots': 4096, 'target_precision': 0.015625, 'circuit_metadata': {'layer_depth': 8, 'qubits': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 'num_qubits': 127, 'layer_idx': 'circ_1'}, 'num_randomizations': 32}), PubResult(data=DataBin(evs=np.ndarray(<shape=(6, 10), dtype=float64>), stds=np.ndarray(<shape=(6, 10), dtype=float64>), ensemble_standard_error=np.ndarray(<shape=(6, 10), dtype=float64>), shape=(6, 10)), metadata={'shots': 4096, 'target_precision': 0.015625, 'circuit_metadata': {'layer_depth': 8, 'qubits': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 'num_qubits': 127, 'layer_idx': 'circ_2'}, 'num_randomizations': 32}), PubResult(data=DataBin(evs=np.ndarray(<shape=(5, 10), dtype=float64>), stds=np.ndarray(<shape=(5, 10), dtype=float64>), ensemble_standard_error=np.ndarray(<shape=(5, 10), dtype=float64>), shape=(5, 10)), metadata={'shots': 4096, 'target_precision': 0.015625, 'circuit_metadata': {'layer_depth': 16, 'qubits': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 'num_qubits': 127, 'layer_idx': 'circ_1'}, 'num_randomizations': 32}), PubResult(data=DataBin(evs=np.ndarray(<shape=(6, 10), dtype=float64>), stds=np.ndarray(<shape=(6, 10), dtype=float64>), ensemble_standard_error=np.ndarray(<shape=(6, 10), dtype=float64>), shape=(6, 10)), metadata={'shots': 4096, 'target_precision': 0.015625, 'circuit_metadata': {'layer_depth': 16, 'qubits': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 'num_qubits': 127, 'layer_idx': 'circ_2'}, 'num_randomizations': 32}), PubResult(data=DataBin(evs=np.ndarray(<shape=(5, 10), dtype=float64>), stds=np.ndarray(<shape=(5, 10), dtype=float64>), ensemble_standard_error=np.ndarray(<shape=(5, 10), dtype=float64>), shape=(5, 10)), metadata={'shots': 4096, 'target_precision': 0.015625, 'circuit_metadata': {'layer_depth': 32, 'qubits': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 'num_qubits': 127, 'layer_idx': 'circ_1'}, 'num_randomizations': 32}), PubResult(data=DataBin(evs=np.ndarray(<shape=(6, 10), dtype=float64>), stds=np.ndarray(<shape=(6, 10), dtype=float64>), ensemble_standard_error=np.ndarray(<shape=(6, 10), dtype=float64>), shape=(6, 10)), metadata={'shots': 4096, 'target_precision': 0.015625, 'circuit_metadata': {'layer_depth': 32, 'qubits': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 'num_qubits': 127, 'layer_idx': 'circ_2'}, 'num_randomizations': 32}), PubResult(data=DataBin(evs=np.ndarray(<shape=(5, 10), dtype=float64>), stds=np.ndarray(<shape=(5, 10), dtype=float64>), ensemble_standard_error=np.ndarray(<shape=(5, 10), dtype=float64>), shape=(5, 10)), metadata={'shots': 4096, 'target_precision': 0.015625, 'circuit_metadata': {'layer_depth': 64, 'qubits': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 'num_qubits': 127, 'layer_idx': 'circ_1'}, 'num_randomizations': 32}), PubResult(data=DataBin(evs=np.ndarray(<shape=(6, 10), dtype=float64>), stds=np.ndarray(<shape=(6, 10), dtype=float64>), ensemble_standard_error=np.ndarray(<shape=(6, 10), dtype=float64>), shape=(6, 10)), metadata={'shots': 4096, 'target_precision': 0.015625, 'circuit_metadata': {'layer_depth': 64, 'qubits': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 'num_qubits': 127, 'layer_idx': 'circ_2'}, 'num_randomizations': 32})], metadata={'dynamical_decoupling': {'enable': False, 'sequence_type': 'XX', 'extra_slack_distribution': 'middle', 'scheduling_method': 'alap'}, 'twirling': {'enable_gates': False, 'enable_measure': True, 'num_randomizations': 'auto', 'shots_per_randomization': 'auto', 'interleave_randomizations': True, 'strategy': 'active-accum'}, 'resilience': {'measure_mitigation': False, 'zne_mitigation': False, 'pec_mitigation': False}, 'version': 2})"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "primitive_result = estimator_job.result()\n",
        "\n",
        "primitive_result"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bh26aurvKkkv"
      },
      "source": [
        "# Part 9: Data Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S-Qy_bIoKkkv"
      },
      "source": [
        "The final step is to perform the fittings of the decay curves to perform the EPLG analysis.\n",
        "\n",
        "Let's first grab some of the relevant data from the `PrimitiveResult`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UHnwRArsKkkw"
      },
      "outputs": [],
      "source": [
        "data_out = {\n",
        "    \"evs\": [pub_res.data.evs for pub_res in primitive_result],\n",
        "    \"stds\": [pub_res.data.stds for pub_res in primitive_result],\n",
        "    \"result_metadata\": primitive_result.metadata,\n",
        "    \"pub_metadata\": [pub_res.metadata for pub_res in primitive_result]\n",
        "}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T6hUu6w0Kkkw"
      },
      "source": [
        "Now we can rearrange the expectation values into an array in a convenient form, as well as average over the randomized Cliffords."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "t3cbf7NOKkkw"
      },
      "outputs": [],
      "source": [
        "# evs_at_depth is a\n",
        "evs_at_depth = np.zeros((len(depths), data_out['evs'][0].shape[0]*2))\n",
        "for i in range(len(depths)):\n",
        "    layer1_evs = np.average(data_out['evs'][2*i], axis=-1)\n",
        "    layer2_evs = np.average(data_out['evs'][2*i+1], axis=-1)\n",
        "    evs_at_depth[i,:] = np.array([item for pair in zip(layer1_evs, layer2_evs) for item in pair])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YAF43TBoKkkw"
      },
      "source": [
        "The resulting shape has two axes, one for the depth (axis `0`, with shape `6`) and one for the number of subsystem fidelities (axis `1`, dependent on your path)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QFBp6o_fKkkw",
        "outputId": "5987fe3c-6277-4a33-c09e-a09d4629af1e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "evs_at_depth.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KX7o3vjBKkkw"
      },
      "source": [
        "Now we can plot the decay curves for the different subsystems."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6NWWtwvsKkkx"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 471
        },
        "id": "PKsp3-RaKkkx",
        "outputId": "cc3335a4-4e9a-42c1-860e-423117ef2786"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "ax = plt.gca()\n",
        "for i in range(evs_at_depth.shape[1]):\n",
        "    ax.plot(depths, evs_at_depth[:,i])\n",
        "\n",
        "ax.set_ylabel(\"Ground State Population\")\n",
        "ax.set_xlabel(\"Depth\")\n",
        "ax.grid()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R6cCNI9MKkkx"
      },
      "source": [
        "Now we need to fit these to exponential decays, we can do this with the `lmfit` python package. The exponential decay has the following form:\n",
        "$$\n",
        "A\\alpha^L+B\n",
        "$$\n",
        "where $\\alpha$ is the error rate, $L$ denotes the number of cycles (depth) in the EPLG circuits, parameters $A$ and $B$ are the fitting parameters to capture the SPAM (state preparation and measurement) errors."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DkP2cM3bKkky"
      },
      "outputs": [],
      "source": [
        "import lmfit\n",
        "model= lmfit.models.ExpressionModel(\n",
        "            expr=\"a * alpha ** x + b\",\n",
        "            name=\"rb_decay\")\n",
        "\n",
        "params_dict = {\n",
        "    'a': {'value': 1.0,\n",
        "            'min': -1.0,\n",
        "            'max': 1.0,\n",
        "            },\n",
        "    'b': {'value': 0.0,\n",
        "            'min': -1.0,\n",
        "            'max': 1.0,\n",
        "            },\n",
        "    'alpha': {'value': 0.9,\n",
        "              'min': 0.0,\n",
        "              'max': 1.0\n",
        "              },\n",
        "}\n",
        "p = model.make_params(**params_dict)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aeb0xb5VKkky"
      },
      "outputs": [],
      "source": [
        "alphas = []\n",
        "for i in range(evs_at_depth.shape[-1]):\n",
        "    result = model.fit(evs_at_depth[:,i], p, x=depths)\n",
        "    alphas.append(result.params['alpha'].value)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pUSyJQEyKkk1"
      },
      "source": [
        "Now we can finally use the expressions in the EPLG paper to compute process fidelities.\n",
        "$\\alpha$ is related to the average gate error vis\n",
        "$$\n",
        "\\epsilon = \\frac{D-1}{D} (1-\\alpha)\n",
        "$$\n",
        "where $D$ denotes the the dimension of the Hilbert space, e.g., $D=4$ for two-qubit processes. The average gate fidelity is $F_g = 1-\\epsilon$.\n",
        "The process fidelity is related to the average gate fidelity via\n",
        "$$\n",
        "F_g=\\frac{DF_p+1}{D+1}\n",
        "$$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4KOxjwikKkk1"
      },
      "outputs": [],
      "source": [
        "def cal_process_fid(alpha, D=4):\n",
        "    epsilon = (D-1) * (1 - alpha)/D\n",
        "    Fg = 1 - epsilon\n",
        "    Fp = ((D+1) * Fg -1)/D\n",
        "    return Fp"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e9d_ve8KKkk1"
      },
      "source": [
        "And with a little data-wrangling in `pandas` we can apply this to our data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 943
        },
        "id": "1z3MhtMZKkk1",
        "outputId": "1aed97e2-cd36-4499-ccb6-ed7bba906295"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  qubit pair     alpha  process fidelity\n",
              "0     (1, 0)  0.984847          0.985794\n",
              "1     [1, 2]  0.900000          0.906250\n",
              "2     (3, 2)  0.977216          0.978640\n",
              "3     (4, 3)  0.986107          0.986976\n",
              "4     (5, 4)  0.988473          0.989193\n",
              "5     (6, 5)  0.986671          0.987504\n",
              "6     (7, 6)  0.973845          0.975479\n",
              "7     [7, 8]  0.980908          0.982101"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9ea78ea3-c3ce-4f89-9af1-4741525086bd\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>qubit pair</th>\n",
              "      <th>alpha</th>\n",
              "      <th>process fidelity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>(1, 0)</td>\n",
              "      <td>0.984847</td>\n",
              "      <td>0.985794</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>[1, 2]</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>0.906250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>(3, 2)</td>\n",
              "      <td>0.977216</td>\n",
              "      <td>0.978640</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>(4, 3)</td>\n",
              "      <td>0.986107</td>\n",
              "      <td>0.986976</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>(5, 4)</td>\n",
              "      <td>0.988473</td>\n",
              "      <td>0.989193</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>(6, 5)</td>\n",
              "      <td>0.986671</td>\n",
              "      <td>0.987504</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>(7, 6)</td>\n",
              "      <td>0.973845</td>\n",
              "      <td>0.975479</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>[7, 8]</td>\n",
              "      <td>0.980908</td>\n",
              "      <td>0.982101</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9ea78ea3-c3ce-4f89-9af1-4741525086bd')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9ea78ea3-c3ce-4f89-9af1-4741525086bd button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-9ea78ea3-c3ce-4f89-9af1-4741525086bd');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-49c91969-f2de-4a37-8182-29ae83e04003\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-49c91969-f2de-4a37-8182-29ae83e04003')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-49c91969-f2de-4a37-8182-29ae83e04003 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_9d0e0df8-444e-4026-ade0-4bb7c0d6ceef\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_9d0e0df8-444e-4026-ade0-4bb7c0d6ceef button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"qubit pair\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alpha\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.029626456936817196,\n        \"min\": 0.9,\n        \"max\": 0.9884725780608923,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.9,\n          0.9866713567329777,\n          0.9848468458595392\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"process fidelity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.027774803378266117,\n        \"min\": 0.90625,\n        \"max\": 0.9891930419320865,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.90625,\n          0.9875043969371666,\n          0.985793917993318\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 43
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:root:Quickchart encountered unexpected dtypes in columns: \"(['qubit pair'],)\"\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x7c36de0d7cd0>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">Distributions</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "_df_0['index'].plot(kind='hist', bins=20, title='index')\n",
              "plt.gca().spines[['top', 'right',]].set_visible(False)"
            ],
            "text/html": [
              "      <div class=\"colab-quickchart-chart-with-code\" id=\"chart-0b96fb69-b830-44b9-95d4-c29f6f87ff27\">\n",
              "        <img style=\"width: 180px;\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAioAAAGrCAYAAADuNLxTAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90\n",
              "bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9h\n",
              "AAAPYQGoP6dpAAAe9UlEQVR4nO3dfZCV9X3//9fC2k2iIooQUFgJwqKmAt5AKZhEZdJoqmjFttEQ\n",
              "oZUbbVLr4FRK1ClqIiRp0xg7GVZhaJWp0QqxVJs4NRqjkQio8QargIK7GgRHI5hEUZbz+8PJ/rJf\n",
              "pHXNnj0f5PGYOTN7rv3ste/rJMM8vc6156qrVCqVAAAUqEetBwAA2B2hAgAUS6gAAMUSKgBAsYQK\n",
              "AFAsoQIAFEuoAADFEioAQLGECgBQLKECVM2pp56aq6+++n3//AknnJC5c+d23UDAHqe+1gMAH1zf\n",
              "//73az0CsIdzRgUAKJZQAarmxBNPzOWXX54kqaury3XXXZfx48dnv/32y9FHH50HHnigfe2OHTty\n",
              "6aWXpn///unbt2/mzJmzy/5efPHFnHvuuTn00EPTr1+/nHPOOXn55ZeTJD/60Y+y7777ZtWqVUmS\n",
              "SqWSP/7jP87pp58e916FPZdQAbrNwoUL86//+q957bXXMmHChHz+859v/97Xv/713Hrrrbnnnnvy\n",
              "wgsvpL6+Pg899FD797dv354JEybkkEMOydq1a/Pcc8+lvr4+5557bpJ3omju3Lk5++yz88orr+Tq\n",
              "q6/O008/nZtuuil1dXXdfqxA1xAqQLe55JJLMnTo0NTX12f69OlpaWnJ5s2bkySLFy/OJZdckqOO\n",
              "OioNDQ2ZO3duDjzwwPafvfPOO/P666/nG9/4Rvbdd9/st99+mT9/fu6+++688MILSZK//du/zejR\n",
              "ozNhwoR8/etfz9KlS9O7d+9aHCrQRVxMC3SbQw45pP3rfffdN0ny+uuv56Mf/WheeOGFfOxjH2v/\n",
              "fs+ePdPY2Nj+fN26ddm8eXOHeEmShoaGtLS0ZODAgUmSWbNmZfz48TnnnHMyatSoKh4N0B2EClCE\n",
              "gQMHZuPGje3P29ra0tra2v68f//+Oeyww/Lss8/udh9bt27NlClTcv755+fWW2/Nf/7nf+b000+v\n",
              "5thAlXnrByjClClT8o//+I95+umns3379lx11VV59dVX279/1lln5e23384VV1yRrVu3Jkm2bNmS\n",
              "W265Jck7F89OmTIlw4cPzw033JDm5uacd955ee6552pyPEDXECpAEWbPnp2zzjorn/rUpzJw4MC8\n",
              "9dZb+YM/+IP27++///5ZsWJFWlpacvTRR6dXr14ZN25cfvzjHydJvva1r+Xxxx9vv3j2nHPOyeTJ\n",
              "kzNp0qS88cYbtTos4HdUV/F3ewBAoZxRAQCKJVQAgGIJFQCgWEIFACiWUAEAiiVUAIBiCRUAoFgf\n",
              "iFC59tpraz0CAFAFH4hQef7552s9AgBQBR+IUAEAPpiECgBQLKECABRLqAAAxRIqAECxhAoAUCyh\n",
              "AgAUS6gAAMUSKgBAsYQKAFAsoQIAFEuoAADFqmqoXHTRRRk8eHDq6urys5/9bLfrFi1alGHDhuXw\n",
              "ww/P9OnT8/bbb1dzLABgD1HVUDn77LPzwAMP5LDDDtvtmg0bNuSKK67I/fffn/Xr12fz5s25/vrr\n",
              "qzkWALCHqGqofPKTn8zAgQP/1zW33XZbJk6cmP79+6euri4XXHBBbr755t2u3759e7Zt29bh0dbW\n",
              "1tWjAwAFqK/1AC0tLR3OuAwePDgtLS27XT9v3rxceeWVHbaNHTu2KrMN/rs7q7LfJNk4/4+rst89\n",
              "ceZq8np0VK3Xw2vRkdejI6/H/6+ar8WeOPN7scddTDtnzpxs3bq1w2PMmDG1HgsAqIKan1FpbGzM\n",
              "s88+2/5848aNaWxs3O36hoaGNDQ0dNjWs2fPqs0HANROzc+oTJo0KcuXL89LL72USqWSBQsW5HOf\n",
              "+1ytxwIAClDVUJk5c2YGDhyYF154IZ/5zGcydOjQJMm0adOyfPnyJMmQIUNy5ZVXZvz48Rk6dGj6\n",
              "9u2bmTNnVnMsAGAPUdW3fpqbm991+8KFCzs8nz59eqZPn17NUQCAPVDN3/oBANgdoQIAFEuoAADF\n",
              "EioAQLGECgBQLKECABRLqAAAxRIqAECxhAoAUCyhAgAUS6gAAMUSKgBAsYQKAFAsoQIAFEuoAADF\n",
              "EioAQLGECgBQLKECABRLqAAAxRIqAECxhAoAUCyhAgAUS6gAAMUSKgBAsYQKAFAsoQIAFEuoAADF\n",
              "EioAQLGECgBQLKECABRLqAAAxRIqAECxhAoAUCyhAgAUS6gAAMUSKgBAsYQKAFAsoQIAFEuoAADF\n",
              "EioAQLGECgBQLKECABRLqAAAxRIqAECxhAoAUCyhAgAUS6gAAMUSKgBAsYQKAFAsoQIAFEuoAADF\n",
              "EioAQLGECgBQLKECABRLqAAAxRIqAECxhAoAUCyhAgAUS6gAAMUSKgBAsYQKAFCsqobKunXrMm7c\n",
              "uDQ1NWX06NFZs2bNLmt27tyZWbNm5aijjsqIESNy0kknZf369dUcCwDYQ1Q1VGbOnJkZM2Zk7dq1\n",
              "mT17dqZOnbrLmuXLl+cnP/lJHnvssTz++OOZMGFCvvzlL1dzLABgD1G1UNmyZUtWr16dyZMnJ0km\n",
              "TZqU1tbWXc6W1NXVZfv27XnzzTdTqVSybdu2DBw4sFpjAQB7kPpq7bi1tTUDBgxIff07v6Kuri6N\n",
              "jY1paWnJ0KFD29edfvrpuffee9O/f//sv//+OfTQQ3Pfffftdr/bt2/P9u3bO2xra2urzkEAADVV\n",
              "84tpV69enSeffDIvvvhifv7zn2fChAm54IILdrt+3rx5OeCAAzo8Vq5c2Y0TAwDdpWqhMmjQoGza\n",
              "tCk7duxIklQqlbS0tKSxsbHDuhtvvDEnn3xyevfunR49emTKlCm59957d7vfOXPmZOvWrR0eY8aM\n",
              "qdZhAAA1VLVQ6devX4499tgsWbIkSbJ06dIMHDiww9s+STJkyJDcc889eeutt5Ikd9xxR37/939/\n",
              "t/ttaGhIr169Ojx69uxZrcMAAGqoateoJElzc3OmTp2aa665Jr169crixYuTJNOmTcvEiRMzceLE\n",
              "fPGLX8z//M//ZOTIkdlnn33Sv3//LFiwoJpjAQB7iKqGyvDhw7NixYpdti9cuLD964aGhtxwww3V\n",
              "HAMA2EPV/GJaAIDdESoAQLGECgBQLKECABRLqAAAxRIqAECxhAoAUCyhAgAUS6gAAMUSKgBAsYQK\n",
              "AFAsoQIAFEuoAADFEioAQLGECgBQLKECABRLqAAAxRIqAECxhAoAUCyhAgAUS6gAAMUSKgBAsYQK\n",
              "AFAsoQIAFEuoAADFEioAQLGECgBQLKECABRLqAAAxRIqAECxhAoAUCyhAgAUS6gAAMUSKgBAsYQK\n",
              "AFAsoQIAFEuoAADFEioAQLGECgBQLKECABRLqAAAxRIqAECxhAoAUCyhAgAUS6gAAMUSKgBAsYQK\n",
              "AFAsoQIAFEuoAADFEioAQLGECgBQLKECABRLqAAAxRIqAECxhAoAUCyhAgAUS6gAAMUSKgBAsYQK\n",
              "AFAsoQIAFEuoAADFEioAQLGqGirr1q3LuHHj0tTUlNGjR2fNmjXvuu6JJ57IiSeemCOPPDJHHnlk\n",
              "li1bVs2xAIA9RH01dz5z5szMmDEjU6dOzW233ZapU6dm1apVHdb8+te/zhlnnJEbb7wxJ5xwQtra\n",
              "2vLqq69WcywAYA9RtTMqW7ZsyerVqzN58uQkyaRJk9La2pr169d3WPdv//ZvGTt2bE444YQkSc+e\n",
              "PdO3b9/d7nf79u3Ztm1bh0dbW1u1DgMAqKGqhUpra2sGDBiQ+vp3TtrU1dWlsbExLS0tHdY99dRT\n",
              "aWhoyGmnnZZRo0blvPPOy8svv7zb/c6bNy8HHHBAh8fKlSurdRgAQA11OlTuuuuuLh1gx44dufvu\n",
              "u9Pc3JxHH300hx56aC688MLdrp8zZ062bt3a4TFmzJgunQkAKEOnQ+Wqq67K8OHDc+2112bbtm27\n",
              "XTdo0KBs2rQpO3bsSJJUKpW0tLSksbGxw7rGxsacdNJJOfTQQ1NXV5fJkyfnpz/96W7329DQkF69\n",
              "enV49OzZs7OHAQDsATodKj/5yU/y3e9+N08++WSampryV3/1V3nqqad2WdevX78ce+yxWbJkSZJk\n",
              "6dKlGThwYIYOHdph3Z/92Z9l1apV7dHzX//1Xxk5cuT7ORYA4APmfV2jcswxx+SGG27ID37wg9xx\n",
              "xx0ZMWJEPv3pT+eJJ57osK65uTnNzc1pamrK/Pnzs3jx4iTJtGnTsnz58iTvnFH58pe/nHHjxmXE\n",
              "iBG55557smDBgt/xsACAD4L39efJd999d6677ro88cQT+eIXv5jzzz8/P/rRj/Inf/InHf6qZ/jw\n",
              "4VmxYsUuP79w4cIOz7/whS/kC1/4wvsZBQD4AOt0qBx55JE5+OCDc9FFF+Wss85qvz7k7LPPzqJF\n",
              "i7p8QABg79XpUFmyZEmOO+64d/3e97///d95IACA3+j0NSoPP/xwh0+OfeWVV3LDDTd06VAAAMn7\n",
              "CJXvfOc7Oeigg9qf9+nTJ9/5zne6dCgAgOR9hEqlUtllm4+wBwCqodOhMmDAgNx6663tz2+55ZYM\n",
              "GDCgS4cCAEjex8W03/rWt3LGGWfk0ksvTZJ85CMfyX/8x390+WAAAJ0OlSOOOCJPPfVUnnnmmSTv\n",
              "fFaKj7AHAKrhfX3gW11dXXr37p0dO3bkxRdfTJJd7uEDAPC76nSo/Mu//Esuuuii7LPPPunR451L\n",
              "XOrq6rJly5YuHw4A2Lt1OlSuvvrqrFq1KsOHD6/GPAAA7Tr9Vz8HH3ywSAEAukWnQ+XMM8/Mt771\n",
              "rWzZsiXbtm1rfwAAdLVOv/Vz2WWXJUlmzZqVurq6VCqV1NXV+dA3AKDLdTpUdu7cWY05AAB20em3\n",
              "fpJ3bkx40003JUlee+21bNq0qUuHAgBI3udNCf/yL/8yc+fOTfLO3ZPPPffcrp4LAKDzoXL99dfn\n",
              "pz/9aXr16pUkOfzww/Pyyy93+WAAAJ0OlYaGhnz4wx/usK2+/n19wC0AwP+q06HSt2/frF27NnV1\n",
              "dUne+aRaH58PAFTD+7p78jnnnJOnn346gwYNSq9evXLHHXdUYzYAYC/X6VAZOnRoHnrooTzzzDOp\n",
              "VCrungwAVE2nQ6WlpSVJsu+++yaJuycDAFXT6VA57rjj2j+R9s0338yvf/3r9OnTx92TAYAu1+lQ\n",
              "+X//FHnZsmV57LHHumwgAIDfeF+fTPvbzjrrrNx5551dMQsAQAedPqPy23dKbmtry0MPPeTuyQBA\n",
              "VXQ6VHr37t1+jUrPnj0zbNiwfPvb367GbADAXs7dkwGAYv3O16gAAFRLp8+o9OjRo/3j839bpVJJ\n",
              "XV1d2traumQwAIBOh8pVV12VN954IxdeeGGSZMGCBfnwhz+ciy++uKtnAwD2cp0Ole9973t5+OGH\n",
              "259/5StfyXHHHZfLLrusSwcDAOj0NSqvv/56h0+h3bJlS15//fUuHQoAIHkfZ1QuueSSjBw5Mp/9\n",
              "7GeTJD/4wQ8yd+7crp4LAKDzoTJz5syMHz8+9957b5Jk1qxZ+fjHP97lgwEAdDpUkqRPnz45+uij\n",
              "c+KJJ2bHjh1566238nu/93tdPRsAsJfr9DUqt912W8aOHZu/+Iu/SJKsWbMmZ555ZlfPBQDQ+VCZ\n",
              "N29eHnnkkfTu3TtJMnLkyDz//PNdPRcAQOdDpWfPnunTp0+Hbd72AQCqodOhsv/++2fz5s3tn077\n",
              "wx/+MAcddFCXDwYA0OmLab/2ta/l1FNPzXPPPZcTTjghGzZsyJ133lmN2QCAvVynQmXnzp1pa2vL\n",
              "vffemwcffDCVSiXjxo1rv14FAKArdSpUevTokRkzZuSxxx7LqaeeWq2ZAACSvI9rVIYNG5b169dX\n",
              "YxYAgA46fY3Kq6++mlGjRmXcuHHZb7/92rcvW7asSwcDAHjPoTJjxoxcf/31mTJlSiZOnJgDDzyw\n",
              "mnMBALz3UFm9enWSZMqUKTn22GPzyCOPVG0oAIDkfVyjkiSVSqWr5wAA2MV7PqPyxhtv5Iknnkil\n",
              "Usmbb77Z/vVvjBgxoioDAgB7r06FysSJE9uf//bXdXV1ee6557p2MgBgr/eeQ2Xjxo1VHAMAYFfv\n",
              "6xoVAIDuIFQAgGIJFQCgWEIFACiWUAEAiiVUAIBiCRUAoFhCBQAollABAIpV1VBZt25dxo0bl6am\n",
              "powePTpr1qzZ7dpKpZKTTz45vXv3ruZIAMAepKqhMnPmzMyYMSNr167N7NmzM3Xq1N2u/ad/+qcc\n",
              "fvjh1RwHANjDVC1UtmzZktWrV2fy5MlJkkmTJqW1tTXr16/fZe2aNWty++235+/+7u/+z/1u3749\n",
              "27Zt6/Boa2vr8vkBgNqrWqi0trZmwIABqa9/576HdXV1aWxsTEtLS4d1b7/9dqZPn57m5ub07Nnz\n",
              "/9zvvHnzcsABB3R4rFy5sirHAADUVs0vpr3yyitz1lln5cgjj3xP6+fMmZOtW7d2eIwZM6bKUwIA\n",
              "tVBfrR0PGjQomzZtyo4dO1JfX59KpZKWlpY0NjZ2WHffffelpaUl//zP/5wdO3Zk27ZtGTx4cFat\n",
              "WpW+ffvust+GhoY0NDR02PZezsQAAHueqp1R6devX4499tgsWbIkSbJ06dIMHDgwQ4cO7bDu/vvv\n",
              "z/PPP5+NGzfmgQceSK9evbJx48Z3jRQAYO9S1bd+mpub09zcnKampsyfPz+LFy9OkkybNi3Lly+v\n",
              "5q8GAD4AqvbWT5IMHz48K1as2GX7woUL33X94MGD89prr1VzJABgD1Lzi2kBAHZHqAAAxRIqAECx\n",
              "hAoAUCyhAgAUS6gAAMUSKgBAsYQKAFAsoQIAFEuoAADFEioAQLGECgBQLKECABRLqAAAxRIqAECx\n",
              "hAoAUCyhAgAUS6gAAMUSKgBAsYQKAFAsoQIAFEuoAADFEioAQLGECgBQLKECABRLqAAAxRIqAECx\n",
              "hAoAUCyhAgAUS6gAAMUSKgBAsYQKAFAsoQIAFEuoAADFEioAQLGECgBQLKECABRLqAAAxRIqAECx\n",
              "hAoAUCyhAgAUS6gAAMUSKgBAsYQKAFAsoQIAFEuoAADFEioAQLGECgBQLKECABRLqAAAxRIqAECx\n",
              "hAoAUCyhAgAUS6gAAMUSKgBAsYQKAFAsoQIAFEuoAADFEioAQLGECgBQLKECABRLqAAAxapqqKxb\n",
              "ty7jxo1LU1NTRo8enTVr1uyy5p577smYMWNy1FFH5eMf/3guvfTS7Ny5s5pjAQB7iKqGysyZMzNj\n",
              "xoysXbs2s2fPztSpU3dZc+CBB+a73/1unnrqqTz88MN58MEHc+ONN1ZzLABgD1G1UNmyZUtWr16d\n",
              "yZMnJ0kmTZqU1tbWrF+/vsO6Y445JkOGDEmSfOhDH8qoUaOycePG3e53+/bt2bZtW4dHW1tbtQ4D\n",
              "AKihqoVKa2trBgwYkPr6+iRJXV1dGhsb09LSstufeemll3LbbbfltNNO2+2aefPm5YADDujwWLly\n",
              "ZZfPDwDUXjEX027bti2nn356Lr300hx//PG7XTdnzpxs3bq1w2PMmDHdOCkA0F3qq7XjQYMGZdOm\n",
              "TdmxY0fq6+tTqVTS0tKSxsbGXda+/vrrOeWUU3LGGWdk1qxZ/+t+Gxoa0tDQ0GFbz549u3R2AKAM\n",
              "VTuj0q9fvxx77LFZsmRJkmTp0qUZOHBghg4d2mHdL3/5y5xyyik55ZRTcvnll1drHABgD1TVt36a\n",
              "m5vT3NycpqamzJ8/P4sXL06STJs2LcuXL0+SXHvttVm5cmWWLVuWUaNGZdSoUfnqV79azbEAgD1E\n",
              "1d76SZLhw4dnxYoVu2xfuHBh+9eXXXZZLrvssmqOAQDsoYq5mBYA4P8lVACAYgkVAKBYQgUAKJZQ\n",
              "AQCKJVQAgGIJFQCgWEIFACiWUAEAiiVUAIBiCRUAoFhCBQAollABAIolVACAYgkVAKBYQgUAKJZQ\n",
              "AQCKJVQAgGIJFQCgWEIFACiWUAEAiiVUAIBiCRUAoFhCBQAollABAIolVACAYgkVAKBYQgUAKJZQ\n",
              "AQCKJVQAgGIJFQCgWEIFACiWUAEAiiVUAIBiCRUAoFhCBQAollABAIolVACAYgkVAKBYQgUAKJZQ\n",
              "AQCKJVQAgGIJFQCgWEIFACiWUAEAiiVUAIBiCRUAoFhCBQAollABAIolVACAYgkVAKBYQgUAKJZQ\n",
              "AQCKJVQAgGIJFQCgWEIFACiWUAEAiiVUAIBiCRUAoFhCBQAollABAIpV1VBZt25dxo0bl6ampowe\n",
              "PTpr1qx513WLFi3KsGHDcvjhh2f69Ol5++23qzkWALCHqGqozJw5MzNmzMjatWsze/bsTJ06dZc1\n",
              "GzZsyBVXXJH7778/69evz+bNm3P99ddXcywAYA9RtVDZsmVLVq9encmTJydJJk2alNbW1qxfv77D\n",
              "uttuuy0TJ05M//79U1dXlwsuuCA333zzbve7ffv2bNu2rcOjra2tWocBANRQXaVSqVRjxw8//HDO\n",
              "PffcPPPMM+3bxowZk/nz5+fkk09u3/bXf/3XOeSQQzJnzpwkyVNPPZVTTjklLS0t77rfuXPn5sor\n",
              "r+yw7Ygjjsipp57apfO3tbVl5cqVGTNmTHr27Nml+95T7O2vwd5+/InXwPHv3cefeA2qffyHHXZY\n",
              "/uZv/uZ/XVPf5b+1yubMmZNZs2Z12NbQ0JCGhoYu/T3btm3LAQcckLvuuiu9evXq0n3vKfb212Bv\n",
              "P/7Ea+D49+7jT7wGJRx/1UJl0KBB2bRpU3bs2JH6+vpUKpW0tLSksbGxw7rGxsY8++yz7c83bty4\n",
              "y5rfVo0oAQDKVLVrVPr165djjz02S5YsSZIsXbo0AwcOzNChQzusmzRpUpYvX56XXnoplUolCxYs\n",
              "yOc+97lqjQUA7EGq+lc/zc3NaW5uTlNTU+bPn5/FixcnSaZNm5bly5cnSYYMGZIrr7wy48ePz9Ch\n",
              "Q9O3b9/MnDmzmmMBAHuIql6jMnz48KxYsWKX7QsXLuzwfPr06Zk+fXo1R+m0hoaG/P3f//1e/TbT\n",
              "3v4a7O3Hn3gNHP/effyJ16CE46/aX/0AAPyufIQ+AFAsoQIAFEuoAADFEioAQLGEym681zs/fxBd\n",
              "dNFFGTx4cOrq6vKzn/2s1uPUxJtvvpkzzzwzTU1NGTlyZD796U/vcp+qD7o/+qM/yogRIzJq1Kh8\n",
              "4hOfyKOPPlrrkWpi8eLFqaury+23317rUbrd4MGDM3z48IwaNSqjRo3KLbfcUuuRutX27dvzpS99\n",
              "KcOGDcvRRx/dfu+6vcUrr7zS/r/9qFGj0tTUlPr6+rz66qvdO0iFd3XSSSdVFi9eXKlUKpV///d/\n",
              "rxx//PG1Hagb3XfffZXW1tbKYYcdVnn00UdrPU5NvPHGG5U777yzsnPnzkqlUqlcd911lU996lO1\n",
              "Haqb/eIXv2j/etmyZZURI0bUbpga2bBhQ+UP//APK2PHjq1873vfq/U43W5v/jegUqlULr744sqX\n",
              "vvSl9n8HNm3aVOOJausb3/hG5bTTTuv23+uMyrt4r3d+/qD65Cc/mYEDB9Z6jJr60Ic+lM9+9rOp\n",
              "q6tLkowdOzYbN26s7VDdrHfv3u1fb926tf212Fvs3Lkz06ZNy3XXXbfXfobG3uxXv/pVFi1alK9+\n",
              "9avt/9/v379/jaeqrUWLFuX888/v9t8rVN5Fa2trBgwYkPr6dz4Pr66uLo2Njbu9ozMffNdee23O\n",
              "OOOMWo/R7c4777wMGjQoV1xxRW666aZaj9OtvvnNb2b8+PE57rjjaj1KTZ133nk5+uijc/755+fl\n",
              "l1+u9Tjd5tlnn81BBx2Ua665Jscff3w+8YlP5Ic//GGtx6qZBx98ML/4xS9y2mmndfvvFirwf7jm\n",
              "mmuyfv36zJs3r9ajdLsbb7wxra2t+cpXvpLZs2fXepxu8+STT2bp0qW5/PLLaz1KTf34xz/O448/\n",
              "nkceeSQHH3xwpkyZUuuRus2OHTvy/PPP56ijjsrq1avz7W9/O3/+53+ezZs313q0mli0aFHOO++8\n",
              "9v+A707d/xv3AO/1zs988P3DP/xDli1blrvvvjsf+chHaj1OzUyZMiUXXHBBXnnllfTp06fW41Td\n",
              "/fffn40bN2bYsGFJkpdeeikzZszIpk2bcuGFF9Z4uu7zm3/z9tlnn1x88cVpamqq8UTdp7GxMT16\n",
              "9MjnP//5JMkxxxyTj33sY3niiSfy0Y9+tMbTda9f/vKXufXWW7Nq1aqa/H5nVN7Fe73zMx9s3/zm\n",
              "N3PzzTfnv//7vztcr7E3eO211/Lzn/+8/fntt9+ePn365KCDDqrhVN3nwgsvzKZNm7Jx48Zs3Lgx\n",
              "Y8eOzfXXX79XRcqvfvWrvPbaa+3Pb7755hxzzDG1G6ibHXzwwZkwYULuuuuuJMmGDRuyYcOGHHnk\n",
              "kTWerPvdcsstGTlyZI444oia/H5nVHajubk5U6dOzTXXXJNevXq13/l5bzBz5szceeedeemll/KZ\n",
              "z3wm+++//15zIfFvvPDCC7nkkksyZMiQnHTSSUneuTnXQw89VOPJusfWrVvzp3/6p3njjTfSo0eP\n",
              "9O3bN3fcccded0Ht3mzz5s2ZNGlS2traUqlUMmTIkNx44421HqtbLViwIOeff35mz56dHj16pLm5\n",
              "OYceemitx+p2ixYtqumNg92UEAAolrd+AIBiCRUAoFhCBQAollABAIolVACAYgkVAKBYQgUAKJZQ\n",
              "AQCKJVQAgGIJFQCgWP8fcANKoyba3dEAAAAASUVORK5CYII=\n",
              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-0b96fb69-b830-44b9-95d4-c29f6f87ff27\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-0b96fb69-b830-44b9-95d4-c29f6f87ff27\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "_df_1['alpha'].plot(kind='hist', bins=20, title='alpha')\n",
              "plt.gca().spines[['top', 'right',]].set_visible(False)"
            ],
            "text/html": [
              "      <div class=\"colab-quickchart-chart-with-code\" id=\"chart-9d6ba8b4-8607-4aff-89ee-bb328d5d818b\">\n",
              "        <img style=\"width: 180px;\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAioAAAGrCAYAAADuNLxTAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90\n",
              "bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9h\n",
              "AAAPYQGoP6dpAAAifUlEQVR4nO3de3CU5d2H8e+S9F3RJkEQSmhIIuRUCuRASSMHhc4gSMcAQRQc\n",
              "K6eEICqtwVFTiwqi8VQq2mJAKFWYwUYImAmCxRZmorUCDaBCEYKsIRrAqiSgIeRwv38wZtwmYQjZ\n",
              "J3snuT4zz0x29/bOb+ep7dXdJ7suY4wRAACAhbr4ewAAAIDmECoAAMBahAoAALAWoQIAAKxFqAAA\n",
              "AGsRKgAAwFqECgAAsBahAgAArEWoAAAAaxEqAPziscce04gRIy55/c6dO+VyuVRbW+vgVABsQ6gA\n",
              "AABrESoAAMBahAoAx/zpT3/SwIEDFRwcrN69e+tXv/qV/vvf/za5dtSoUbrnnnuUlpamoKAgRUVF\n",
              "6dVXX220bvPmzYqJiVFQUJDGjBmjzz777LJ+H4D2gVAB4JjevXsrPz9fp0+f1vvvv6/Dhw/r3nvv\n",
              "bXb96tWrNXPmTH399dd64YUXlJ6ernfffddrzaZNm7R7926VlZXp22+/1W9/+9vL/n0A7EeoAHDM\n",
              "5MmTFRMToy5duigiIkIPPfSQ/va3vzW7fvz48br55psVGBio8ePHa9KkSfrzn//stSYnJ0chISEK\n",
              "CQnR7bffrl27dl327wNgv0B/DwCg48rPz9dzzz2nkpISnTt3TvX19frmm29UV1fX5Pprr7220e3i\n",
              "4mKv+/r06dPw81VXXaUzZ85c0u8LCAjw4TMD0FZ4RQWAI8rKyjRlyhTde++9Ki0tVWVlpdauXStJ\n",
              "MsY0+c94PJ5Gt8PCwhz7fQDsR6gAcMTZs2dVX1+va665RldccYWOHDminJyci/4zb775prZs2aK6\n",
              "ujpt27ZNmzZt0syZMx37fQDsx1s/ABwRFxennJwc3XnnnTp79qwGDRqkO+64Q7t37272n5k1a5ZW\n",
              "r16tqVOnqlevXsrNzdXIkSMd+30A7OcyvCYKwAKjRo3SiBEjtGTJEn+PAsAivPUDAACsRagAAABr\n",
              "8dYPAACwFq+oAAAAaxEqAADAWoQKAACwFqECAACs1SFCZdmyZf4eAQAAOKBDhMqnn37q7xEAAIAD\n",
              "OkSoAACAjolQAQAA1iJUAACAtQgVAABgLUIFAABYi1ABAADWIlQAAIC1CBUAAGAtQgUAAFiLUAEA\n",
              "ANYiVAAAgLUIFQAAYK02CZU1a9bI5XJp8+bNTT5eWFiouLg4RUdHKy0tTZWVlW0xFgAAsJzjoeLx\n",
              "ePTyyy8rJSWlycfPnj2r2bNna/PmzTpy5Ij69Omjxx9/3OmxAABAO+BoqNTX1ys9PV0vvvii3G53\n",
              "k2u2bt2qxMRExcXFSZLmzZun9evXN7tndXW1KisrvY66ujpH5gcAAP4V6OTmS5cu1fDhwzVkyJBm\n",
              "15SWlioiIqLhdmRkpMrLy1VbW6vAwMbj5eTkaNGiRV73NfdqDQAAnUXkQ1sc2dfz1C8d2fdSOfaK\n",
              "ykcffaSNGzfqd7/7nU/3zc7OVkVFhdeRnJzs098BAADs4NgrKkVFRfJ4PIqOjpYknThxQnPmzFF5\n",
              "ebnuuuuuhnXh4eHavn17w22Px6PQ0NAmX02RJLfb3ehtpICAAAeeAQAA8DfHXlG56667VF5eLo/H\n",
              "I4/Ho5SUFK1cudIrUiRp3LhxKi4u1qFDhyRJy5cv19SpU50aCwAAtCOOXqPSnEceeUR9+vTR3Llz\n",
              "FRQUpFWrVmnixImqra3VwIED9corr/hjLAAAYJk2C5WdO3c2/Lx48WKvx1JTU5WamtpWowAAgHaC\n",
              "T6YFAADWIlQAAIC1CBUAAGAtQgUAAFiLUAEAANYiVAAAgLUIFQAAYC1CBQAAWItQAQAA1iJUAACA\n",
              "tQgVAABgLUIFAABYi1ABAADWIlQAAIC1CBUAAGAtQgUAAFiLUAEAANYiVAAAgLUIFQAAYC1CBQAA\n",
              "WItQAQAA1iJUAACAtQgVAABgLUIFAABYi1ABAADWIlQAAIC1CBUAAGAtQgUAAFiLUAEAANYiVAAA\n",
              "gLUIFQAAYC1CBQAAWItQAQAA1iJUAACAtQgVAABgLUdD5cYbb9TgwYOVkJCgkSNHau/evY3W7Ny5\n",
              "U127dlVCQkLDUVVV5eRYAACgnQh0cvO8vDx169ZNkrRp0ybNmDFD+/fvb7QuNjZW+/btc3IUAADQ\n",
              "DjkaKt9FiiRVVFTI5XK1es/q6mpVV1d73VdXV9fqfQEAgH0cv0blzjvvVN++fbVw4UKtXbu2yTVH\n",
              "jx5VUlKShg4dquXLl190v5ycHIWEhHgdu3btcmJ0AADgZ46Hyquvvqrjx49ryZIlevDBBxs9npSU\n",
              "pLKyMhUXF2vTpk3Kzc1VXl5es/tlZ2eroqLC60hOTnbyKQAAAD9ps7/6mT59unbs2KEvv/zS6/7g\n",
              "4GCFhIRIksLCwjRt2jQVFRU1u4/b7VZwcLDXERAQ4OjsAADAPxwLldOnT+vzzz9vuL1582b16NFD\n",
              "3bt391pXXl6u+vp6SdKZM2dUWFioxMREp8YCAADtiGMX01ZUVGjKlCmqqqpSly5d1LNnTxUWFsrl\n",
              "cik9PV2pqalKTU3Vxo0b9dJLLykwMFC1tbWaMmWKZs6c6dRYAACgHXEZY4y/h2itrKwsLV261N9j\n",
              "AADgN5EPbXFkX89Tv3Rk30vFJ9MCAABrESoAAMBahAoAALAWoQIAAKxFqAAAAGsRKgAAwFqECgAA\n",
              "sBahAgAArEWoAAAAaxEqAADAWoQKAACwFqECAACsRagAAABrESoAAMBahAoAALAWoQIAAKxFqAAA\n",
              "AGsRKgAAwFqECgAAsBahAgAArEWoAAAAaxEqAADAWoQKAACwFqECAACsRagAAABrESoAAMBahAoA\n",
              "ALAWoQIAAKxFqAAAAGsRKgAAwFqECgAAsBahAgAArEWoAAAAaxEqAADAWoQKAACwlqOhcuONN2rw\n",
              "4MFKSEjQyJEjtXfv3ibXrV69WtHR0erfv78yMjJUU1Pj5FgAAKCdcDRU8vLy9MEHH2jfvn3KysrS\n",
              "jBkzGq05duyYFi5cqKKiIpWUlOjkyZNauXKlk2MBAIB2wtFQ6datW8PPFRUVcrlcjdZs2LBBqamp\n",
              "6t27t1wul+bOnav169c3u2d1dbUqKyu9jrq6OifGBwAAfhbo9C+48847tWPHDknSm2++2ejx0tJS\n",
              "RURENNyOjIxUaWlps/vl5ORo0aJFXvelpKT4aFoAAGATxy+mffXVV3X8+HEtWbJEDz74YKv3y87O\n",
              "VkVFhdeRnJzsg0kBAIBt2uyvfqZPn64dO3boyy+/9Lo/PDxcn376acNtj8ej8PDwZvdxu90KDg72\n",
              "OgICAhybGwAA+I9joXL69Gl9/vnnDbc3b96sHj16qHv37l7rJk+erIKCAp04cULGGOXm5mrq1KlO\n",
              "jQUAANoRx65Rqaio0JQpU1RVVaUuXbqoZ8+eKiwslMvlUnp6ulJTU5Wamqp+/fpp0aJFGj58uCRp\n",
              "1KhRyszMdGosAADQjriMMcbfQ7RWVlaWli5d6u8xAADwm8iHtjiyr+epXzqy76Xik2kBAIC1CBUA\n",
              "AGAtQgUAAFiLUAEAANYiVAAAgLUIFQAAYC1CBQAAWItQAQAA1iJUAACAtQgVAABgLUIFAABYi1AB\n",
              "AADWIlQAAIC1CBUAAGAtQgUAAFiLUAEAANYiVAAAgLUIFQAAYC1CBQAAWItQAQAA1iJUAACAtQgV\n",
              "AABgLUIFAABYi1ABAADWIlQAAIC1CBUAAGAtQgUAAFiLUAEAANYiVAAAgLUIFQAAYC1CBQAAWItQ\n",
              "AQAA1iJUAACAtQgVAABgLUIFAABYy7FQOXfunCZOnKiYmBjFx8drzJgxKikpabTO4/EoICBACQkJ\n",
              "DcfRo0edGgsAALQjgU5uPmfOHN10001yuVz64x//qPT0dO3cubPRuqCgIO3bt8/JUQAAQDvk2Csq\n",
              "V1xxhcaPHy+XyyVJSklJkcfjafW+1dXVqqys9Drq6upavS8AALBPm12jsmzZMk2YMKHJx7755hsN\n",
              "HTpUSUlJWrx48UXDIycnRyEhIV7Hrl27nBobAAD4UZuEypNPPqmSkhLl5OQ0eiw0NFSfffaZdu/e\n",
              "rbfffltFRUX6/e9/3+xe2dnZqqio8DqSk5OdHB8AAPiJ46Hy3HPPKT8/X1u3btWVV17Z6HG3261e\n",
              "vXpJkrp3765Zs2apqKio2f3cbreCg4O9joCAAMfmBwAA/uNoqCxdulTr16/X9u3b1a1btybXnDp1\n",
              "SjU1NZIuXH+Sn5+vxMREJ8cCAADthGOhUlZWpgULFuj06dMaPXq0EhIS9POf/1yS9Mgjjyg3N1eS\n",
              "9M477ygxMVHx8fFKSkpS79699fDDDzs1FgAAaEcc+/PksLAwGWOafGzx4sUNP6elpSktLc2pMQAA\n",
              "QDvGJ9MCAABrESoAAMBahAoAALAWoQIAAKzV4lB56623nJgDAACgkRaHyuLFixUbG6tly5apsrLS\n",
              "iZkAAAAkXUaovPvuu3rttdf00UcfKSYmRvPmzdPBgwedmA0AAHRyl3WNSmJiol5++WVt27ZNhYWF\n",
              "Gjx4sMaMGaMPP/zQ1/MBAIBO7LJC5e2339aECROUlpamu+++WydOnFBmZqYmTZrk6/kAAEAn1uJP\n",
              "pv3JT36ia665RvPnz1daWlrDFwLecsstWr16tc8HBAAAnVeLQ2XdunUaMmRIk49t3bq11QMBAAB8\n",
              "p8Vv/fz73//WV1991XD7yy+/1Msvv+zToQAAAKTLCJXly5ere/fuDbd79Oih5cuX+3QoAAAA6TJC\n",
              "palvRK6rq/PJMAAAAN/X4lAJDQ1VXl5ew+2//vWvCg0N9elQAAAA0mVcTPv8889rwoQJeuCBByRJ\n",
              "V155pd544w2fDwYAANDiUImLi9PBgwf18ccfS5JiY2Mb/kQZAADAl1ocKpLkcrnUrVs31dbW6rPP\n",
              "PpMkhYeH+3QwAACAFofKX/7yF82fP18/+MEP1KXLhUtcXC6XTp065fPhAABA59biUHn88ce1e/du\n",
              "xcbGOjEPAABAgxb/1c8111xDpAAAgDbR4lCZOHGinn/+eZ06dUqVlZUNBwAAgK+1+K2fhx9+WJKU\n",
              "lZUll8slY4xcLhcf+gYAAHyuxaFSX1/vxBwAAACNtPitH+nCFxOuXbtWknT69GmVl5f7dCgAAADp\n",
              "Mr+UcNasWXrsscckXfj25Ntvv93XcwEAALQ8VFauXKl//etfCg4OliT1799fX3zxhc8HAwAAaHGo\n",
              "uN1ude3a1eu+wMDL+oBbAACAi2pxqPTs2VOHDx+Wy+WSdOGTavn4fAAA4ITL+vbkadOm6dChQ+rb\n",
              "t6+Cg4NVWFjoxGwAAKCTa3GoREVF6f3339fHH38sYwzfngwAABzT4lApLS2VJF111VWSxLcnAwAA\n",
              "x7Q4VIYMGdLwibTnzp3Tt99+qx49evDtyQAAwOdaHCr/+6fI+fn52r9/v88GAgAA+M5lfTLt96Wl\n",
              "pWnLli2+mAUAAMBLi0Pl+9+Y/PXXX2vbtm1NfnvyuXPnNHHiRMXExCg+Pl5jxoxRSUlJk3sWFhYq\n",
              "Li5O0dHRSktL49uYAQCApMsIlW7duunqq69Wt27d1KtXL2VlZemFF15ocu2cOXP08ccfa//+/Zow\n",
              "YYLS09MbrTl79qxmz56tzZs368iRI+rTp48ef/zxlj8TAADQ4bQ4VOrr61VXV6f6+nrV1NTo4MGD\n",
              "GjduXKN1V1xxhcaPH9/wwXApKSnyeDyN1m3dulWJiYmKi4uTJM2bN0/r169v9vdXV1d7vapTWVmp\n",
              "urq6lj4NAADQDrT6GpVLtWzZMk2YMKHR/aWlpYqIiGi4HRkZqfLyctXW1ja5T05OjkJCQryOXbt2\n",
              "OTY3AADwnxaHSpcuXRQQENDo+O7+pjz55JMqKSlRTk5OqwfOzs5WRUWF15GcnNzqfQEAgH1a/OfJ\n",
              "ixcvVlVVle666y5JUm5urrp27arf/OY3Ta5/7rnnlJ+fr7fffltXXnllo8fDw8O1ffv2htsej0eh\n",
              "oaHNftGh2+2W2+32uo9PxgUAoGNq8SsqmzZt0hNPPKGwsDCFhYVpyZIlys/P11VXXdXwabXfWbp0\n",
              "qdavX6/t27erW7duTe43btw4FRcX69ChQ5Kk5cuXa+rUqS1/JgAAoMNp8SsqZ86c0alTp9SrVy9J\n",
              "0qlTp3TmzJlG68rKyrRgwQL169dPo0ePlnTh1ZD3339fjzzyiPr06aO5c+cqKChIq1at0sSJE1Vb\n",
              "W6uBAwfqlVdeaeXTAgAAHUGLQ2XBggWKj4/X+PHjJUnbtm3TY4891mhdWFiYjDFN7rF48WKv26mp\n",
              "qUpNTW3pKAAAoINrcahkZmZq+PDh2rFjhyQpKytLP/3pT30+GAAAQItDRZJ69OihQYMGadSoUaqt\n",
              "rdX58+f1f//3f76eDQAAdHItvph2w4YNSklJ0cyZMyVJBw4c0MSJE309FwAAQMtDJScnR8XFxQ1/\n",
              "xRMfH69PP/3U13MBAAC0PFQCAgLUo0cPr/t42wcAADihxaESFBSkkydPNnyHz9///nd1797d54MB\n",
              "AAC0+GLap59+WjfddJM++eQTjRgxQseOHdOWLVucmA0AAHRyLQqV7745eceOHfrnP/8pY4yGDRvW\n",
              "7KfOAgAAtEaLQqVLly6aM2eO9u/fr5tuusmpmQAAACRdxjUq0dHRKikpcWIWAAAALy2+RuWrr75S\n",
              "QkKChg0bph/+8IcN9+fn5/t0MAAAgEsOlTlz5mjlypWaPn26UlNTdfXVVzs5FwAAwKWHyp49eyRJ\n",
              "06dPV1JSkoqLix0bCgAAQLqMa1QkNfutyAAAAL50ya+oVFVV6cMPP5QxRufOnWv4+TuDBw92ZEAA\n",
              "ANB5tShUUlNTG25//2eXy6VPPvnEt5MBAIBO75JDxePxODgGAABAY5d1jQoAAEBbIFQAAIC1CBUA\n",
              "AGAtQgUAAFiLUAEAANYiVAAAgLUIFQAAYC1CBQAAWItQAQAA1iJUAACAtQgVAABgLUIFAABYi1AB\n",
              "AADWIlQAAIC1CBUAAGAtQgUAAFiLUAEAANYiVAAAgLUcDZX58+crMjJSLpdL+/bta3LNzp071bVr\n",
              "VyUkJDQcVVVVTo4FAADaiUAnN7/lllv0wAMPaMSIERddFxsb22zIAACAzsvRULn++ut9vmd1dbWq\n",
              "q6u97qurq/P57wEAAP5nxTUqR48eVVJSkoYOHarly5dfdG1OTo5CQkK8jl27drXRpAAAoC35PVSS\n",
              "kpJUVlam4uJibdq0Sbm5ucrLy2t2fXZ2tioqKryO5OTkNpwYAAC0Fb+HSnBwsEJCQiRJYWFhmjZt\n",
              "moqKippd73a7FRwc7HUEBAS01bgAAKAN+T1UysvLVV9fL0k6c+aMCgsLlZiY6OepAACADRwNlczM\n",
              "TIWFhamsrExjx45VVFSUJCk9PV0FBQWSpI0bN2rQoEGKj49XSkqKxowZo5kzZzo5FgAAaCdcxhjj\n",
              "7yFaKysrS0uXLvX3GAAA+E3kQ1sc2dfz1C8d2fdS+f2tHwAAgOYQKgAAwFqECgAAsBahAgAArEWo\n",
              "AAAAaxEqAADAWoQKAACwFqECAACsRagAAABrESoAAMBahAoAALAWoQIAAKxFqAAAAGsRKgAAwFqE\n",
              "CgAAsBahAgAArEWoAAAAaxEqAADAWoQKAACwFqECAACsRagAAABrESoAAMBahAoAALAWoQIAAKxF\n",
              "qAAAAGsRKgAAwFqECgAAsBahAgAArEWoAAAAaxEqAADAWoQKAACwFqECAACsRagAAABrESoAAMBa\n",
              "hAoAALCWo6Eyf/58RUZGyuVyad++fc2uW716taKjo9W/f39lZGSopqbGybEAAEA74Wio3HLLLXrn\n",
              "nXcUERHR7Jpjx45p4cKFKioqUklJiU6ePKmVK1c6ORYAAGgnHA2V66+/XmFhYRdds2HDBqWmpqp3\n",
              "795yuVyaO3eu1q9f3+z66upqVVZWeh11dXW+Hh0AAFgg0N8DlJaWer3iEhkZqdLS0mbX5+TkaNGi\n",
              "RV73paSkODJb5ENbHNlXkjxP/dKxvQGgM3Dyv6Nhj3Z3MW12drYqKiq8juTkZH+PBQAAHOD3V1TC\n",
              "w8N19OjRhtsej0fh4eHNrne73XK73V73BQQEODYfAADwH7+/ojJ58mQVFBToxIkTMsYoNzdXU6dO\n",
              "9fdYAADAAo6GSmZmpsLCwlRWVqaxY8cqKipKkpSenq6CggJJUr9+/bRo0SINHz5cUVFR6tmzpzIz\n",
              "M50cCwAAtBOOvvWzYsWKJu9ftWqV1+2MjAxlZGQ4OQoAAGiH/P7WDwAAQHMIFQAAYC1CBQAAWItQ\n",
              "AQAA1iJUAACAtQgVAABgLUIFAABYi1ABAADWIlQAAIC1CBUAAGAtQgUAAFiLUAEAANYiVAAAgLUI\n",
              "FQAAYC1CBQAAWItQAQAA1iJUAACAtQgVAABgLUIFAABYi1ABAADWIlQAAIC1CBUAAGAtQgUAAFiL\n",
              "UAEAANYiVAAAgLUIFQAAYC1CBQAAWItQAQAA1iJUAACAtQgVAABgLUIFAABYi1ABAADWIlQAAIC1\n",
              "CBUAAGAtQgUAAFjL0VA5cuSIhg0bppiYGA0dOlQHDhxotGbnzp3q2rWrEhISGo6qqionxwIAAO1E\n",
              "oJObZ2Zmas6cOZoxY4Y2bNigGTNmaPfu3Y3WxcbGat++fU6OAgAA2iHHXlE5deqU9uzZozvuuEOS\n",
              "NHnyZB0/flwlJSWt2re6ulqVlZVeR11dnS9GBgAAlnEsVI4fP67Q0FAFBl540cblcik8PFylpaWN\n",
              "1h49elRJSUkaOnSoli9fftF9c3JyFBIS4nXs2rXLkecAAAD8y+8X0yYlJamsrEzFxcXatGmTcnNz\n",
              "lZeX1+z67OxsVVRUeB3JycltODEAAGgrjoVK3759VV5ertraWkmSMUalpaUKDw/3WhccHKyQkBBJ\n",
              "UlhYmKZNm6aioqJm93W73QoODvY6AgICnHoaAADAjxwLlV69eikpKUnr1q2TJG3cuFFhYWGKiory\n",
              "WldeXq76+npJ0pkzZ1RYWKjExESnxgIAAO2Io2/9rFixQitWrFBMTIyeeuoprVmzRpKUnp6ugoIC\n",
              "SRcCZtCgQYqPj1dKSorGjBmjmTNnOjkWAABoJxz98+TY2Fi99957je5ftWpVw8/33HOP7rnnHifH\n",
              "AAAA7ZTfL6YFAABoDqECAACsRagAAABrESoAAMBahAoAALAWoQIAAKxFqAAAAGsRKgAAwFqECgAA\n",
              "sBahAgAArEWoAAAAaxEqAADAWoQKAACwFqECAACsRagAAABrESoAAMBahAoAALAWoQIAAKxFqAAA\n",
              "AGsRKgAAwFqECgAAsBahAgAArEWoAAAAaxEqAADAWoQKAACwFqECAACsRagAAABrESoAAMBahAoA\n",
              "ALAWoQIAAKxFqAAAAGsRKgAAwFqECgAAsBahAgAArEWoAAAAazkaKkeOHNGwYcMUExOjoUOH6sCB\n",
              "A02uW716taKjo9W/f39lZGSopqbGybEAAEA74WioZGZmas6cOTp8+LAefPBBzZgxo9GaY8eOaeHC\n",
              "hSoqKlJJSYlOnjyplStXOjkWAABoJxwLlVOnTmnPnj264447JEmTJ0/W8ePHVVJS4rVuw4YNSk1N\n",
              "Ve/eveVyuTR37lytX7++2X2rq6tVWVnpddTV1Tn1NAAAgB8FOrXx8ePHFRoaqsDAC7/C5XIpPDxc\n",
              "paWlioqKalhXWlqqiIiIhtuRkZEqLS1tdt+cnBwtWrTI6764uDhlZWX5dP66ujqF7tql5ORkBQQE\n",
              "+HRvScrK+rvP9+wM6urqtMvB84LLw3mxU0c/L2n+HuAytbfz4uT/XkVEROjXv/71Rdc4FipOyc7O\n",
              "bhQlbrdbbrfbp7+nsrJSISEheuuttxQcHOzTvXH5OC924rzYifNiJ85LyzgWKn379lV5eblqa2sV\n",
              "GBgoY4xKS0sVHh7utS48PFxHjx5tuO3xeBqt+T4nogQAANjJsWtUevXqpaSkJK1bt06StHHjRoWF\n",
              "hXm97SNduHaloKBAJ06ckDFGubm5mjp1qlNjAQCAdsTRv/pZsWKFVqxYoZiYGD311FNas2aNJCk9\n",
              "PV0FBQWSpH79+mnRokUaPny4oqKi1LNnT2VmZjo5FgAAaCccvUYlNjZW7733XqP7V61a5XU7IyND\n",
              "GRkZTo7SYm63W48++ihvM1mG82InzoudOC924ry0jMsYY/w9BAAAQFP4CH0AAGAtQgUAAFiLUAEA\n",
              "ANYiVAAAgLU6Zahcyrc619fX6/7779fAgQMVFxen2bNn6/z58w2PFxYWKi4uTtHR0UpLS1NlZWVb\n",
              "PoUOqbXn5cMPP9T111+vuLg4DRw4ULNmzVJVVVVbP40Oxxf/vnxnxowZcrlcOn36dBtM3rH54ryU\n",
              "lpbq5ptvVmxsrAYMGKAXX3yxLZ9Ch+OLc/L0009rwIABSkhIUEpKinbt2tWWT8FOphMaPXq0WbNm\n",
              "jTHGmNdff9387Gc/a7Rm5cqVZvTo0aa6utrU19eb9PR088wzzxhjjDlz5ozp1auX+c9//mOMMebu\n",
              "u+82999/f5vN31G19rwcPnzY7N+/3xhjTG1trbn11lvNo48+2lbjd1itPS/f2bhxo0lPTzeSzNdf\n",
              "f90Gk3dsrT0v9fX1JikpyeTl5TWsP3HiRJvM3lG19pzs3bvXhIeHmzNnzhhjjFm7dq0ZOnRom81v\n",
              "q04XKidPnjRBQUGmpqbGGHPhX9Yf/ehH5siRI17r7r77bvPEE0803N64caMZNGiQMcaYvLw8M3bs\n",
              "2IbHDhw4YH784x+3wfQdly/Oy/969tlnzfTp0x2buTPw1Xk5ceKEGTJkiKmsrCRUfMAX52X79u3m\n",
              "uuuua7uhOzhfnJN9+/aZ0NDQhmB88cUXzaRJk9roGdir0731c7Fvdf6+IUOGqKCgQJWVlaqpqVFe\n",
              "Xp48Ho+kpr/x+bvvNcLl8cV5+b5vvvlGq1at0oQJE9pi/A7LV+clIyNDzzzzjIKCgtpy/A7LF+fl\n",
              "4MGD6tmzp6ZOnarExERNmjRJn3zySVs/lQ7DF+ckPj5e9913n6699lqFhYXpD3/4A2/HqZNeo3Ip\n",
              "ZsyYoXHjxumGG27QDTfcoJiYmIb/AMJ/LuW8nD9/XrfddptuvPFGTZo0yU+Tdi4XOy+rVq1SeHi4\n",
              "fvGLX/h5ys7nYueltrZW//jHP7Rw4ULt3btXY8eO1a233urniTu+i52TY8eOKT8/XyUlJSorK9N9\n",
              "992n2267zc8TW8DfL+m0tUt9ee5/rV+/3owYMcIYw1s/TvDFeTHGmPPnz5uJEyea9PR0U19f7+jM\n",
              "nYEvzsvtt99uwsLCTEREhImIiDCSTN++fU1xcbHj83dUvjgvr7/+uhk5cmTDY2fPnjUul8ucP3/e\n",
              "ucE7MF+ck2effdZkZGQ0PHb27FkjyVRXVzs3eDvQ6ULFGGNuuOEGrwuehgwZ0mhNVVWV+eqrr4wx\n",
              "xnzxxRcmPj7eFBQUGGOMqaysND179vS6mHbBggVtM3wH1trzUlNTY9LS0sysWbOIFB9q7Xn5X+Ia\n",
              "FZ9o7Xk5e/asufbaa01ZWZkx5sL/ARswYEDbDN9BtfacbNy40QwYMKDhYtrXXnvNxMTEtM3wFuuU\n",
              "oXLo0CGTkpJioqOjzZAhQ8wHH3xgjDFm9uzZ5o033jDGXLj4Ly4uzgwYMMDExcWZl156yWuPN954\n",
              "w8TGxpr+/fubCRMmmNOnT7f58+hoWnte1q1bZySZwYMHm/j4eBMfH2/mzZvnl+fSkfji35fvI1R8\n",
              "wxfn5a233jLx8fFm8ODBZuTIkQ174PK09pzU19ebhx56yMTGxprBgweb6667zuzZs8cvz8UmfCkh\n",
              "AACwFhfTAgAAaxEqAADAWoQKAACwFqECAACsRagAAABrESoAAMBahAoAALAWoQIAAKxFqAAAAGsR\n",
              "KgAAwFr/D9bgHePEQ8ldAAAAAElFTkSuQmCC\n",
              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-9d6ba8b4-8607-4aff-89ee-bb328d5d818b\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-9d6ba8b4-8607-4aff-89ee-bb328d5d818b\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "_df_2['process fidelity'].plot(kind='hist', bins=20, title='process fidelity')\n",
              "plt.gca().spines[['top', 'right',]].set_visible(False)"
            ],
            "text/html": [
              "      <div class=\"colab-quickchart-chart-with-code\" id=\"chart-c4cb68c7-d733-4b94-bc80-4214c971f4e8\">\n",
              "        <img style=\"width: 180px;\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAioAAAGrCAYAAADuNLxTAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90\n",
              "bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9h\n",
              "AAAPYQGoP6dpAAAnYElEQVR4nO3de3TU9Z3/8deQ1BFKLiSAYEMSJbdSzI1NTLko7hZFrAGiKFhO\n",
              "CRgIoOtusUWzLlaQErRdttTdGGIo9XKKjdyaExQPWthNLYJsICocLomMIRoIB0hCbAi5fH9/cJyf\n",
              "0ySUkHwzn5Dn45zvOZmZT755f/s96rMz35lxWJZlCQAAwED9vD0AAABARwgVAABgLEIFAAAYi1AB\n",
              "AADGIlQAAICxCBUAAGAsQgUAABiLUAEAAMYiVAAAgLEIFQC9ksvl0oQJE+Tv76+xY8eqoqJCAwcO\n",
              "1Geffdbh78yePVvp6elX/TfS09M1e/Zs9+3vfe97evXVV7syNoBO8vX2AABwLbKzszVo0CDV1NSo\n",
              "X7/L/5+rvr7e1r956NAh988ul0u33HKLjh8/roiICFv/LtCX8YwKAElSS0uLWltbvT3GVSsvL1ds\n",
              "bKw7UgBcn/gnHLhOTZw4UY8//rjS0tLk5+eniIgIvfbaa+7Hd+/eLYfDoTfffFNRUVEaMGCAqqur\n",
              "9cUXX+ihhx7STTfdpJtuukkPP/ywvvzyS/fvNTc3a82aNRo1apT8/PwUEhKiF154wf343r17NXHi\n",
              "RAUHByssLEzLli1Tc3OzJOnSpUtavHixhg0bJj8/P4WHh+ull16SJNXU1GjmzJkaPHiw/P39FRUV\n",
              "pU2bNrV7bCNHjtSuXbv04osvauDAgVq1apVcLpccDofKysrc6375y18qNDRUgYGBysjI0KVLlzz2\n",
              "U1NTo0WLFiksLEzBwcGaMmXKFV86Cg8PV35+vqTLLwNJUlxcnAYOHKiFCxdq/fr1GjlypL75Xa+N\n",
              "jY0aPHiwtm3bdsXzBaB9hApwHVu/fr3mzp2r8+fP6ze/+Y0yMjL0wQcfeKz5wx/+oD179qiurk5D\n",
              "hgzRD3/4Q/n4+OjYsWM6evSoLMtSamqqWlpaJEnPPfeccnJy9Lvf/U61tbX6+OOPdccdd0iSjh49\n",
              "qn/6p3/SwoULdfr0af3v//6vCgsL3SHz6quvas+ePfr000914cIFffjhhxo3bpyky1Fx4cIFnThx\n",
              "QrW1tdq5c6dGjRrV7nGVl5drwoQJWrp0qerr6/Vv//Zvbdb8/ve/16pVq/Tmm2/qzJkzSk5O1tat\n",
              "W92PW5al6dOnq66uTgcOHNCXX36p2267TT/84Q/V1NT0d/+3/fploNLSUtXX1ys3N1ezZs3S2bNn\n",
              "9d5777nXbdq0STfeeKPuv//+v7tPAG0RKsB1bMqUKbr//vvl6+urKVOmaPr06frtb3/rsWb16tUK\n",
              "Dg6W0+nUvn37VFpaqpycHAUEBCgwMFAvv/yySkpK9NFHH8myLK1du1arV69WcnKy+vXrp6CgIH3/\n",
              "+9+XJP33f/+37r//fs2cOVO+vr4KCwvT0qVLtWHDBknSDTfcoPr6eh0+fFhNTU0aNmyYEhMT3Y+d\n",
              "PXtWR44ckWVZCgsL6zBUrsaGDRs0b948jR07Vt/61re0YMECxcbGuh8/cOCAPvjgA61bt05BQUFy\n",
              "Op1atWqVTpw4ob17917T3xwwYIDmzJmjvLw8933r1q1TRkaGfHx8rvlYgL6Mi2mB69gtt9zS5nZJ\n",
              "SUmHa06ePKmgoCANGjTIfV9wcLAGDRqkiooKjRw5UvX19YqOjm737x0/fly7du1SYGCg+77W1lb3\n",
              "tS+zZ8/WmTNn9LOf/UxHjhzR2LFjtXLlSo0ZM0Y/+9nP1NLSooyMDFVUVOgHP/iBsrOzr/lC1crK\n",
              "Sk2dOrXDYz1+/Liam5sVEhLS5ndPnjx5TX9TkhYtWqS4uDidPn1a586d01/+8hf9/ve/v+b9AX0d\n",
              "z6gA1zGXy9Xm9t/+h/mbF6OOGDFC58+f1/nz5933nTt3TufPn1doaKgGDx6sgQMH6tixY+3+vWHD\n",
              "humRRx5RTU2Ne6urq3O/G8fHx0c//elPtXfvXn3xxRf67ne/646JAQMGaMWKFSotLVV5ebl8fX01\n",
              "Z86caz72kJCQdo//m7PecMMNOnPmjMe8DQ0NmjVr1t/df0cX8cbExGjcuHHasGGD1q1bp/vuu6/d\n",
              "GAJwdQgV4Dr29ttva/v27WppadGOHTu0detWzZ07t8P1ycnJGj16tB5//HHV1dWptrZWjz32mOLj\n",
              "45WUlCSHw6F//ud/VlZWlvbv3y/LsnTu3Dnt2bNHkrR48WJt2rRJb731li5duqSWlhaVlZVpx44d\n",
              "kqQ//elP2r9/vy5duqQbb7xRAwcOdL8kUlhYqEOHDqm5uVkDBgxQ//795et77U/6zpkzR7/97W/1\n",
              "4Ycfqrm5Wfn5+SotLXU/Pn78eI0ePVqLFi1SdXW1JOn8+fPavHmz/vrXv/7d/Q8ZMkT9+vXT0aNH\n",
              "2zy2ePFi5eXl6bXXXlNmZuY1HwMAQgW4rs2bN0/r169XYGCgHnvsMeXm5mrChAkdrvfx8VFRUZEa\n",
              "GxsVERGhyMhINTc3q7Cw0B0UK1asUEZGhn70ox/Jz89PsbGxKi4uliQlJSVp586deuWVV/Sd73xH\n",
              "wcHBevDBB/X5559Lkqqrq5Wenq6goCANGTJE//M//+N+Z8+JEyc0bdo0BQYG6jvf+Y5Onz6t9evX\n",
              "X/Ox/+hHP9LSpUs1Y8YMDR48WB9++KGmT5/ucaw7d+7UgAEDdPvtt8vPz09xcXHaunWrHA7H391/\n",
              "//79tWrVKmVkZCgwMFCLFy92PzZt2jRdvHhR/v7+mjx58jUfAwDJYX3zfXQArhsTJ07U+PHjtXLl\n",
              "Sm+P0ifdfvvtSk1N1TPPPOPtUYBejYtpAaCbvf322/r000+1fft2b48C9HqECgB0oxEjRqihoUG5\n",
              "ubkaPHiwt8cBej1e+gEAAMbiYloAAGAsQgUAABiLUAEAAMYiVAAAgLGui1BZu3att0cAAAA2uC5C\n",
              "5etPvQQAANeX6yJUAADA9YlQAQAAxiJUAACAsQgVAABgLEIFAAAYi1ABAADGIlQAAICxCBUAAGAs\n",
              "QgUAABiLUAEAAMYiVAAAgLEIFQAAYKweCZUNGzbI4XBo27Zt7T5eVFSkmJgYRUZGKi0tTXV1dT0x\n",
              "FgAAMJztoeJyufTKK68oJSWl3cfr6+v16KOPatu2bTp+/LhuvvlmPf/883aPBQAAegFbQ6W1tVUZ\n",
              "GRl66aWX5HQ6213zzjvvKCEhQTExMZKkxYsXa+PGjR3us7GxUXV1dR5bS0uLLfMDAADv8rVz52vW\n",
              "rNG4ceM0ZsyYDtdUVFQoLCzMfTs8PFxVVVVqbm6Wr2/b8bKzs7V8+XKP+zp6tgYAgL4i/OnttuzX\n",
              "tfo+W/Z7tWx7RuXTTz/V5s2b9e///u/dut+srCzV1tZ6bMnJyd36NwAAgBlse0aluLhYLpdLkZGR\n",
              "kqRTp05pwYIFqqqq0qJFi9zrQkNDtXPnTvdtl8ul4cOHt/tsiiQ5nc42LyP5+PjYcAQAAMDbbHtG\n",
              "ZdGiRaqqqpLL5ZLL5VJKSory8vI8IkWSJk+erJKSEh05ckSSlJOTo5kzZ9o1FgAA6EVsvUalI88+\n",
              "+6xuvvlmLVy4UH5+fsrPz9e0adPU3Nys0aNH69VXX/XGWAAAwDA9Fiq7d+92/7xixQqPx1JTU5Wa\n",
              "mtpTowAAgF6CT6YFAADGIlQAAICxCBUAAGAsQgUAABiLUAEAAMYiVAAAgLEIFQAAYCxCBQAAGItQ\n",
              "AQAAxiJUAACAsQgVAABgLEIFAAAYi1ABAADGIlQAAICxCBUAAGAsQgUAABiLUAEAAMYiVAAAgLEI\n",
              "FQAAYCxCBQAAGItQAQAAxiJUAACAsQgVAABgLEIFAAAYi1ABAADGIlQAAICxCBUAAGAsQgUAABiL\n",
              "UAEAAMYiVAAAgLEIFQAAYCxCBQAAGItQAQAAxiJUAACAsQgVAABgLFtD5e6771ZsbKzi4+M1YcIE\n",
              "HThwoM2a3bt3q3///oqPj3dvDQ0Ndo4FAAB6CV87d15QUKDAwEBJ0tatW5Wenq7S0tI266Kjo3Xw\n",
              "4EE7RwEAAL2QraHydaRIUm1trRwOR5f32djYqMbGRo/7WlpaurxfAABgHtuvUfnxj3+sESNGaNmy\n",
              "ZXr99dfbXVNeXq7ExEQlJSUpJyfnivvLzs5WQECAx7Zv3z47RgcAAF5me6i89tprOnnypFauXKmn\n",
              "nnqqzeOJiYmqrKxUSUmJtm7dqtzcXBUUFHS4v6ysLNXW1npsycnJdh4CAADwkh5718+cOXO0a9cu\n",
              "nT171uN+f39/BQQESJJCQkI0a9YsFRcXd7gfp9Mpf39/j83Hx8fW2QEAgHfYFio1NTX68ssv3be3\n",
              "bdum4OBgBQUFeayrqqpSa2urJOnChQsqKipSQkKCXWMBAIBexLaLaWtrazVjxgw1NDSoX79+GjJk\n",
              "iIqKiuRwOJSRkaHU1FSlpqZq8+bNevnll+Xr66vm5mbNmDFDc+fOtWssAADQizgsy7K8PURXLVmy\n",
              "RGvWrPH2GAAAeE3409tt2a9r9X227Pdq8cm0AADAWIQKAAAwFqECAACMRagAAABjESoAAMBYhAoA\n",
              "ADAWoQIAAIxFqAAAAGMRKgAAwFiECgAAMBahAgAAjEWoAAAAYxEqAADAWIQKAAAwFqECAACMRagA\n",
              "AABjESoAAMBYhAoAADAWoQIAAIxFqAAAAGMRKgAAwFiECgAAMBahAgAAjEWoAAAAYxEqAADAWIQK\n",
              "AAAwFqECAACMRagAAABjESoAAMBYhAoAADAWoQIAAIxFqAAAAGMRKgAAwFiECgAAMBahAgAAjGVr\n",
              "qNx9992KjY1VfHy8JkyYoAMHDrS7bv369YqMjNTIkSM1f/58NTU12TkWAADoJWwNlYKCAn388cc6\n",
              "ePCglixZovT09DZrTpw4oWXLlqm4uFhlZWU6ffq08vLy7BwLAAD0EraGSmBgoPvn2tpaORyONms2\n",
              "bdqk1NRUDRs2TA6HQwsXLtTGjRs73GdjY6Pq6uo8tpaWFjvGBwAAXuZr9x/48Y9/rF27dkmS3n77\n",
              "7TaPV1RUKCwszH07PDxcFRUVHe4vOztby5cv97gvJSWlm6YFAAAmsf1i2tdee00nT57UypUr9dRT\n",
              "T3V5f1lZWaqtrfXYkpOTu2FSAABgmh5718+cOXO0a9cunT171uP+0NBQff755+7bLpdLoaGhHe7H\n",
              "6XTK39/fY/Px8bFtbgAA4D22hUpNTY2+/PJL9+1t27YpODhYQUFBHuseeOABFRYW6tSpU7IsS7m5\n",
              "uZo5c6ZdYwEAgF7EtmtUamtrNWPGDDU0NKhfv34aMmSIioqK5HA4lJGRodTUVKWmpurWW2/V8uXL\n",
              "NW7cOEnSxIkTlZmZaddYAACgF3FYlmV5e4iuWrJkidasWePtMQAA8Jrwp7fbsl/X6vts2e/V4pNp\n",
              "AQCAsQgVAABgLEIFAAAYi1ABAADGIlQAAICxCBUAAGAsQgUAABiLUAEAAMYiVAAAgLEIFQAAYCxC\n",
              "BQAAGItQAQAAxiJUAACAsQgVAABgLEIFAAAYi1ABAADGIlQAAICxCBUAAGAsQgUAABiLUAEAAMYi\n",
              "VAAAgLEIFQAAYCxCBQAAGItQAQAAxiJUAACAsQgVAABgLEIFAAAYi1ABAADGIlQAAICxCBUAAGAs\n",
              "QgUAABiLUAEAAMYiVAAAgLEIFQAAYCxCBQAAGMu2ULl48aKmTZumqKgoxcXFadKkSSorK2uzzuVy\n",
              "ycfHR/Hx8e6tvLzcrrEAAEAv4mvnzhcsWKB7771XDodD//Vf/6WMjAzt3r27zTo/Pz8dPHjQzlEA\n",
              "AEAvZNszKjfeeKOmTJkih8MhSUpJSZHL5eryfhsbG1VXV+extbS0dHm/AADAPD12jcratWs1derU\n",
              "dh/76quvlJSUpMTERK1YseKK4ZGdna2AgACPbd++fXaNDQAAvKhHQmXVqlUqKytTdnZ2m8eGDx+u\n",
              "L774Qh999JHee+89FRcX6z/+4z863FdWVpZqa2s9tuTkZDvHBwAAXmJ7qPzqV7/Sli1b9M4772jA\n",
              "gAFtHnc6nRo6dKgkKSgoSPPmzVNxcXGH+3M6nfL39/fYfHx8bJsfAAB4j62hsmbNGm3cuFE7d+5U\n",
              "YGBgu2uqq6vV1NQk6fL1J1u2bFFCQoKdYwEAgF7CtlCprKzUk08+qZqaGt11112Kj4/X7bffLkl6\n",
              "9tlnlZubK0n685//rISEBMXFxSkxMVHDhg3TM888Y9dYAACgF7Ht7ckhISGyLKvdx1asWOH+OS0t\n",
              "TWlpaXaNAQAAejE+mRYAABiLUAEAAMYiVAAAgLEIFQAAYKxOh8q7775rxxwAAABtdDpUVqxYoejo\n",
              "aK1du1Z1dXV2zAQAACDpGkLlgw8+0JtvvqlPP/1UUVFRWrx4sQ4fPmzHbAAAoI+7pmtUEhIS9Mor\n",
              "r2jHjh0qKipSbGysJk2apE8++aS75wMAAH3YNYXKe++9p6lTpyotLU2PPfaYTp06pczMTE2fPr27\n",
              "5wMAAH1Ypz+Z9rvf/a4GDx6sJ554Qmlpae4vBHzwwQe1fv36bh8QAAD0XZ0OlTfeeENjxoxp97F3\n",
              "3nmnywMBAAB8rdMv/fzf//2fzp0757599uxZvfLKK906FAAAgHQNoZKTk6OgoCD37eDgYOXk5HTr\n",
              "UAAAANI1hEp734jc0tLSLcMAAAB8U6dDZfjw4SooKHDf/sMf/qDhw4d361AAAADSNVxM++tf/1pT\n",
              "p07V0qVLJUkDBgzQH//4x24fDAAAoNOhEhMTo8OHD+vo0aOSpOjoaPdblAEAALpTp0NFkhwOhwID\n",
              "A9Xc3KwvvvhCkhQaGtqtgwEAAHQ6VH73u9/piSee0Le+9S3163f5EheHw6Hq6upuHw4AAPRtnQ6V\n",
              "559/Xh999JGio6PtmAcAAMCt0+/6GTx4MJECAAB6RKdDZdq0afr1r3+t6upq1dXVuTcAAIDu1umX\n",
              "fp555hlJ0pIlS+RwOGRZlhwOBx/6BgAAul2nQ6W1tdWOOQAAANro9Es/0uUvJnz99dclSTU1Naqq\n",
              "qurWoQAAAKRr/FLCefPm6bnnnpN0+duTH3nkke6eCwAAoPOhkpeXpw8//FD+/v6SpJEjR+rMmTPd\n",
              "PhgAAECnQ8XpdKp///4e9/n6XtMH3AIAAFxRp0NlyJAhOnbsmBwOh6TLn1TLx+cDAAA7XNO3J8+a\n",
              "NUtHjhzRiBEj5O/vr6KiIjtmAwAAfVynQyUiIkJ79+7V0aNHZVkW354MAABs0+lQqaiokCR9+9vf\n",
              "liS+PRkAANim06EyZswY9yfSXrx4UX/9618VHBzMtycDAIBu1+lQ+du3Im/ZskWlpaXdNhAAAMDX\n",
              "rumTab8pLS1N27dv745ZAAAAPHQ6VL75jcnnz5/Xjh072v325IsXL2ratGmKiopSXFycJk2apLKy\n",
              "snb3WVRUpJiYGEVGRiotLY1vYwYAAJKuIVQCAwM1aNAgBQYGaujQoVqyZIl+85vftLt2wYIFOnr0\n",
              "qEpLSzV16lRlZGS0WVNfX69HH31U27Zt0/Hjx3XzzTfr+eef7/yRAACA606nQ6W1tVUtLS1qbW1V\n",
              "U1OTDh8+rMmTJ7dZd+ONN2rKlCnuD4ZLSUmRy+Vqs+6dd95RQkKCYmJiJEmLFy/Wxo0bO/z7jY2N\n",
              "Hs/q1NXVqaWlpbOHAQAAeoEuX6NytdauXaupU6e2ub+iokJhYWHu2+Hh4aqqqlJzc3O7+8nOzlZA\n",
              "QIDHtm/fPtvmBgAA3tPpUOnXr598fHzabF/f355Vq1aprKxM2dnZXR44KytLtbW1HltycnKX9wsA\n",
              "AMzT6bcnr1ixQg0NDVq0aJEkKTc3V/3799e//uu/trv+V7/6lbZs2aL33ntPAwYMaPN4aGiodu7c\n",
              "6b7tcrk0fPjwDr/o0Ol0yul0etzHJ+MCAHB96vQzKlu3btUvfvELhYSEKCQkRCtXrtSWLVv07W9/\n",
              "2/1ptV9bs2aNNm7cqJ07dyowMLDd/U2ePFklJSU6cuSIJCknJ0czZ87s/JEAAIDrTqefUblw4YKq\n",
              "q6s1dOhQSVJ1dbUuXLjQZl1lZaWefPJJ3XrrrbrrrrskXX42ZO/evXr22Wd18803a+HChfLz81N+\n",
              "fr6mTZum5uZmjR49Wq+++moXDwsAAFwPOh0qTz75pOLi4jRlyhRJ0o4dO/Tcc8+1WRcSEiLLstrd\n",
              "x4oVKzxup6amKjU1tbOjAACA61ynQyUzM1Pjxo3Trl27JElLlizR9773vW4fDAAAoNOhIknBwcG6\n",
              "7bbbNHHiRDU3N+vSpUu64YYbuns2AADQx3X6YtpNmzYpJSVFc+fOlSQdOnRI06ZN6+65AAAAOh8q\n",
              "2dnZKikpcb+LJy4uTp9//nl3zwUAAND5UPHx8VFwcLDHfbzsAwAA7NDpUPHz89Pp06fd3+Hz/vvv\n",
              "KygoqNsHAwAA6PTFtC+88ILuvfdeffbZZxo/frxOnDih7du32zEbAADo4zoVKl9/c/KuXbv0l7/8\n",
              "RZZlaezYsR1+6iwAAEBXdCpU+vXrpwULFqi0tFT33nuvXTMBAABIuoZrVCIjI1VWVmbHLAAAAB46\n",
              "fY3KuXPnFB8fr7Fjx2rgwIHu+7ds2dKtgwEAAFx1qCxYsEB5eXmaM2eOUlNTNWjQIDvnAgAAuPpQ\n",
              "2b9/vyRpzpw5SkxMVElJiW1DAQAASNdwjYqkDr8VGQAAoDtd9TMqDQ0N+uSTT2RZli5evOj++Wux\n",
              "sbG2DAgAAPquToVKamqq+/Y3f3Y4HPrss8+6dzIAANDnXXWouFwuG8cAAABo65quUQEAAOgJhAoA\n",
              "ADAWoQIAAIxFqAAAAGMRKgAAwFiECgAAMBahAgAAjEWoAAAAYxEqAADAWIQKAAAwFqECAACMRagA\n",
              "AABjESoAAMBYhAoAADAWoQIAAIxFqAAAAGMRKgAAwFiECgAAMJatofLEE08oPDxcDodDBw8ebHfN\n",
              "7t271b9/f8XHx7u3hoYGO8cCAAC9hK+dO3/wwQe1dOlSjR8//orroqOjOwwZAADQd9kaKnfccUe3\n",
              "77OxsVGNjY0e97W0tHT73wEAAN5nxDUq5eXlSkxMVFJSknJycq64Njs7WwEBAR7bvn37emhSAADQ\n",
              "k7weKomJiaqsrFRJSYm2bt2q3NxcFRQUdLg+KytLtbW1HltycnIPTgwAAHqK10PF399fAQEBkqSQ\n",
              "kBDNmjVLxcXFHa53Op3y9/f32Hx8fHpqXAAA0IO8HipVVVVqbW2VJF24cEFFRUVKSEjw8lQAAMAE\n",
              "toZKZmamQkJCVFlZqXvuuUcRERGSpIyMDBUWFkqSNm/erNtuu01xcXFKSUnRpEmTNHfuXDvHAgAA\n",
              "vYTDsizL20N01ZIlS7RmzRpvjwEAgNeEP73dlv26Vt9ny36vltdf+gEAAOgIoQIAAIxFqAAAAGMR\n",
              "KgAAwFiECgAAMBahAgAAjEWoAAAAYxEqAADAWIQKAAAwFqECAACMRagAAABjESoAAMBYhAoAADAW\n",
              "oQIAAIxFqAAAAGMRKgAAwFiECgAAMBahAgAAjEWoAAAAYxEqAADAWIQKAAAwFqECAACMRagAAABj\n",
              "ESoAAMBYhAoAADAWoQIAAIxFqAAAAGMRKgAAwFiECgAAMBahAgAAjEWoAAAAYxEqAADAWIQKAAAw\n",
              "FqECAACMRagAAABj2RoqTzzxhMLDw+VwOHTw4MEO161fv16RkZEaOXKk5s+fr6amJjvHAgAAvYSt\n",
              "ofLggw/qz3/+s8LCwjpcc+LECS1btkzFxcUqKyvT6dOnlZeXZ+dYAACgl7A1VO644w6FhIRccc2m\n",
              "TZuUmpqqYcOGyeFwaOHChdq4cWOH6xsbG1VXV+extbS0dPfoAADAAL7eHqCiosLjGZfw8HBVVFR0\n",
              "uD47O1vLly/3uC8lJcWW2cKf3m7LfiXJtfo+2/YNAH2Bnf+Ohjl63cW0WVlZqq2t9diSk5O9PRYA\n",
              "ALCB159RCQ0NVXl5ufu2y+VSaGhoh+udTqecTqfHfT4+PrbNBwAAvMfrz6g88MADKiws1KlTp2RZ\n",
              "lnJzczVz5kxvjwUAAAxga6hkZmYqJCRElZWVuueeexQRESFJysjIUGFhoSTp1ltv1fLlyzVu3DhF\n",
              "RERoyJAhyszMtHMsAADQS9j60s+6devavT8/P9/j9vz58zV//nw7RwEAAL2Q11/6AQAA6AihAgAA\n",
              "jEWoAAAAYxEqAADAWIQKAAAwFqECAACMRagAAABjESoAAMBYhAoAADAWoQIAAIxFqAAAAGMRKgAA\n",
              "wFiECgAAMBahAgAAjEWoAAAAYxEqAADAWIQKAAAwFqECAACMRagAAABjESoAAMBYhAoAADAWoQIA\n",
              "AIxFqAAAAGMRKgAAwFiECgAAMBahAgAAjEWoAAAAYxEqAADAWIQKAAAwFqECAACMRagAAABjESoA\n",
              "AMBYhAoAADAWoQIAAIxFqAAAAGPZGirHjx/X2LFjFRUVpaSkJB06dKjNmt27d6t///6Kj493bw0N\n",
              "DXaOBQAAeglfO3eemZmpBQsWKD09XZs2bVJ6ero++uijNuuio6N18OBBO0cBAAC9kG3PqFRXV2v/\n",
              "/v2aPXu2JOmBBx7QyZMnVVZW1qX9NjY2qq6uzmNraWnpjpEBAIBhbAuVkydPavjw4fL1vfykjcPh\n",
              "UGhoqCoqKtqsLS8vV2JiopKSkpSTk3PF/WZnZysgIMBj27dvny3HAAAAvMvrF9MmJiaqsrJSJSUl\n",
              "2rp1q3Jzc1VQUNDh+qysLNXW1npsycnJPTgxAADoKbaFyogRI1RVVaXm5mZJkmVZqqioUGhoqMc6\n",
              "f39/BQQESJJCQkI0a9YsFRcXd7hfp9Mpf39/j83Hx8euwwAAAF5kW6gMHTpUiYmJeuONNyRJmzdv\n",
              "VkhIiCIiIjzWVVVVqbW1VZJ04cIFFRUVKSEhwa6xAABAL2LrSz/r1q3TunXrFBUVpdWrV2vDhg2S\n",
              "pIyMDBUWFkq6HDC33Xab4uLilJKSokmTJmnu3Ll2jgUAAHoJW9+eHB0drT179rS5Pz8/3/3z448/\n",
              "rscff9zOMQAAQC/l9YtpAQAAOkKoAAAAYxEqAADAWIQKAAAwFqECAACMRagAAABjESoAAMBYhAoA\n",
              "ADAWoQIAAIxFqAAAAGMRKgAAwFiECgAAMBahAgAAjEWoAAAAYxEqAADAWIQKAAAwFqECAACMRagA\n",
              "AABjESoAAMBYhAoAADAWoQIAAIxFqAAAAGMRKgAAwFiECgAAMBahAgAAjEWoAAAAYxEqAADAWIQK\n",
              "AAAwFqECAACMRagAAABjESoAAMBYhAoAADAWoQIAAIxFqAAAAGMRKgAAwFi2hsrx48c1duxYRUVF\n",
              "KSkpSYcOHWp33fr16xUZGamRI0dq/vz5ampqsnMsAADQS9gaKpmZmVqwYIGOHTump556Sunp6W3W\n",
              "nDhxQsuWLVNxcbHKysp0+vRp5eXl2TkWAADoJWwLlerqau3fv1+zZ8+WJD3wwAM6efKkysrKPNZt\n",
              "2rRJqampGjZsmBwOhxYuXKiNGzd2uN/GxkbV1dV5bC0tLXYdBgAA8CJfu3Z88uRJDR8+XL6+l/+E\n",
              "w+FQaGioKioqFBER4V5XUVGhsLAw9+3w8HBVVFR0uN/s7GwtX77c476YmBgtWbKkm49ASuv2Pf5/\n",
              "S5a8b+Pee1ZLS4v27dun5ORk+fj4eHscXAXOWe/DOWvLzn9Hd9X1dL7s/O9VWFiY/uVf/uWKa2wL\n",
              "FbtkZWW1iRKn0ymn0+mliVBXV6eAgAC9++678vf39/Y4uAqcs96Hc9a7cL66j22hMmLECFVVVam5\n",
              "uVm+vr6yLEsVFRUKDQ31WBcaGqry8nL3bZfL1WbNNxElAAD0HbZdozJ06FAlJibqjTfekCRt3rxZ\n",
              "ISEhHi/7SJevXSksLNSpU6dkWZZyc3M1c+ZMu8YCAAC9iK3v+lm3bp3WrVunqKgorV69Whs2bJAk\n",
              "ZWRkqLCwUJJ06623avny5Ro3bpwiIiI0ZMgQZWZm2jkWAADoJWy9RiU6Olp79uxpc39+fr7H7fnz\n",
              "52v+/Pl2jgIbOZ1O/fznP+cluV6Ec9b7cM56F85X93FYlmV5ewgAAID28BH6AADAWIQKAAAwFqEC\n",
              "AACMRagAAABjESro0NV8+3Vra6t++tOfavTo0YqJidGjjz6qS5cuSZI++eQT3XHHHYqJidHo0aM1\n",
              "b948NTQ09PRh9CldPWfflJ6eLofDoZqamh6YvO/qjnNWUVGh+++/X9HR0Ro1apReeumlnjyEPqc7\n",
              "ztkLL7ygUaNGKT4+XikpKdq3b19PHkLvYgEduOuuu6wNGzZYlmVZb731lvUP//APbdbk5eVZd911\n",
              "l9XY2Gi1trZaGRkZ1osvvmhZlmUdO3bMKi0ttSzLspqbm62HHnrI+vnPf95T4/dJXT1nX9u8ebOV\n",
              "kZFhSbLOnz/fA5P3XV09Z62trVZiYqJVUFDgXn/q1Kkemb2v6uo5O3DggBUaGmpduHDBsizLev31\n",
              "162kpKQem7+34RkVtOtqv/26tLRUP/jBD3TDDTfI4XDo3nvv1euvvy5JioyMVGxsrCTJx8dHSUlJ\n",
              "crlcPXocfUl3nDNJOn36tFatWqU1a9b06Px9UXecs/fff19Op1MzZsxwr7/pppt67iD6mO44Zw6H\n",
              "Q01NTfrqq68kSTU1NQoJCenZA+lFCBW060rffv1NY8aMUWFhoerq6tTU1KSCgoJ2Y+Srr75Sfn6+\n",
              "pk6d2hPj90nddc7mz5+vF198UX5+fj05fp/UHefs8OHDGjJkiGbOnKmEhARNnz5dn332WU8fSp/R\n",
              "HecsLi5OP/nJT3TLLbcoJCRE//mf/8nLdVdAqKBL0tPTNXnyZN1555268847FRUV5f4H+GuXLl3S\n",
              "ww8/rLvvvlvTp0/30qT42pXOWX5+vkJDQ/WP//iPXp4S33Slc9bc3Kw//elPWrZsmQ4cOKB77rlH\n",
              "Dz30kJcnxpXO2YkTJ7RlyxaVlZWpsrJSP/nJT/Twww97eWKDefu1J5jp9OnTlp+fn9XU1GRZ1uXX\n",
              "wW+66Sbr+PHjV/y9jRs3WuPHj3ffvnTpkjVt2jQrIyPDam1ttXXmvq47ztkjjzxihYSEWGFhYVZY\n",
              "WJglyRoxYoRVUlJi+/x9UXecs7feesuaMGGC+7H6+nrL4XBYly5dsm/wPqw7ztkvf/lLa/78+e7H\n",
              "6uvrLUlWY2OjfYP3YoQKOnTnnXd6XDA2ZsyYNmsaGhqsc+fOWZZlWWfOnLHi4uKswsJCy7Isq6mp\n",
              "yUpLS7PmzZtHpPSQrp6zvyUuprVdV89ZfX29dcstt1iVlZWWZVlWQUGBNWrUqJ4Zvo/q6jnbvHmz\n",
              "NWrUKPfFtG+++aYVFRXVM8P3QoQKOnTkyBErJSXFioyMtMaMGWN9/PHHlmVZ1qOPPmr98Y9/tCzr\n",
              "8rsLYmJirFGjRlkxMTHWyy+/7P79N954w5JkxcbGWnFxcVZcXJy1ePFirxxLX9HVc/a3CBX7dcc5\n",
              "e/fdd624uDgrNjbWmjBhgnsfsEdXz1lra6v19NNPW9HR0VZsbKz1/e9/39q/f79XjqU34EsJAQCA\n",
              "sbiYFgAAGItQAQAAxiJUAACAsQgVAABgLEIFAAAYi1ABAADGIlQAAICxCBUAAGAsQgUAABiLUAEA\n",
              "AMb6fy1QBxMXvpaqAAAAAElFTkSuQmCC\n",
              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-c4cb68c7-d733-4b94-bc80-4214c971f4e8\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-c4cb68c7-d733-4b94-bc80-4214c971f4e8\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x7c36ddf12770>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">2-d distributions</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "_df_3.plot(kind='scatter', x='index', y='alpha', s=32, alpha=.8)\n",
              "plt.gca().spines[['top', 'right',]].set_visible(False)"
            ],
            "text/html": [
              "      <div class=\"colab-quickchart-chart-with-code\" id=\"chart-549af2f9-f7a7-4ccf-a0b7-8033531bb7f6\">\n",
              "        <img style=\"width: 180px;\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAjEAAAGkCAYAAADALHSZAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90\n",
              "bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9h\n",
              "AAAPYQGoP6dpAAAjaUlEQVR4nO3dfXBU5d3/8c/Z3WSDhCAIKDYkgTssESISEBSsBCtqUiWCqFgb\n",
              "dafhoXaoKDJSZrTlVhuwOrEUcYQxg0oUpQ2lDFY71VofiopMAkpEIZWQVZEQHhKSSTbZh98fjLm7\n",
              "P0ATmt2Ti32/Zs4MuFey3xMR33v2nD1WOBwOCwAAwDAOuwcAAAA4E0QMAAAwEhEDAACMRMQAAAAj\n",
              "ETEAAMBIRAwAADASEQMAAIxExAAAACOd9RGzYsUKu0cAAABRcNZHzP79++0eAQAARMFZHzEAAODs\n",
              "RMQAAAAjETEAAMBIRAwAADASEQMAAIxExAAAACMRMQAAwEhEDAAAMBIRAwAAjETEAAAAIxExAHAG\n",
              "WtuDqm/yq7U9aPcoQNxy2T0AAJjk0HG/yj6o0eu7DqqlPaheCU7lZ1+gwonpGpDstns8IK5wJAYA\n",
              "OunQcb9+ub5SL35Yq2Z/QC6HpWZ/QGUf7tf8lypV3+S3e0QgrhAxANBJZR/UqLruuAYmu9Wvd6J6\n",
              "u13q1ztRA5Pdqq47rrL399s9IhBXiBgA6ITW9qBe33VQSS6nXM7IvzpdTofcLqdeq/qGc2SAGCJi\n",
              "AKATmvwBtbQHleg69V+bbpdDLW1BNfkDMZ4MiF9EDAB0QrLbpV4JTrUFQqd83B8IqVeiU8lurpcA\n",
              "YoWIAYBOSEpwKi/7fLUGggoEI0MmEAzJHwgqf9QFSkpw2jRh7HGZOezGSwYA6KQ7Jmbow31HVV13\n",
              "XG6XU26XQ/7AiYAZPqiPCiem2z1iTHCZOXoKjsQAOCPx+Cp8QLJbT92eo8LL0pWc5FIgFFZykkuF\n",
              "l6Vr5e05cfE/cC4zR0/CkRgAXRLvr8IHJLt17zUe/XzK/6jJH1Cy2xVXbyH952Xm316l1dt94i21\n",
              "by8zv/caj81TIl5wJAZAp/Eq/P8kJTg1INkdVwHDZeboaYgYAJ3Gh73FNy4zR09DxADoFF6Fg8vM\n",
              "0dMQMWcoHk9qRHzjVTi4zBw9DbncRfF+UiPi17evwpv9AfU+xR91fyCk5CQXr8LPclxmjp6EIzFd\n",
              "wEmNiGe8CofEZeboWXjJ1AVcWoh4x6twSFxmjp6DIzGdxEmNAK/CESkeLzNHz8KRmE7qykmN/AeN\n",
              "sxmvwgH0FERMJ3FSIxApKcFJvACwFW8ndRInNQIA0LNw2KALOKkRAICegyMxXcBJjQAA9Bwcieki\n",
              "TmoEAKBnIGLOECc1AgBgL95OAgAARiJiAACAkYgYAABgJCIGAAB0WWt7UPVNfltvt8OJvQAAoNMO\n",
              "Hfer7IMavb7roFrag+qV4FR+9gUqnJge848a4UgMAADolEPH/frl+kq9+GGtmv0BuRyWmv0BlX24\n",
              "X/NfqlR9kz+m8xAxAACgU8o+qFF13XENTHarX+9E9Xa71K93ogYmu1Vdd1xl7++P6TxEDAAA+F6t\n",
              "7UG9vuugklxOuZyR+eByOuR2OfVa1TcxPUcmqhGzd+9eTZo0SR6PR+PHj1dVVdVJa0KhkBYtWqTs\n",
              "7GxlZWWpqKhIbW1tHY8/9thjGjlypMaMGaPLL79c27Zti+bIAADgFJr8AbW0B5XoOnU6uF0OtbQF\n",
              "1eQPxGymqEbMvHnzNHfuXO3Zs0eLFy+W1+s9aU1paakqKipUUVGh3bt3y+FwaMWKFZKkHTt26Omn\n",
              "n9a2bdu0Y8cOzZ8/X/Pnz4/myAAA4BSS3S71SnCqLRA65eP+QEi9Ep1KdsfumqGoRUxdXZ22b9+u\n",
              "wsJCSdLMmTPl8/lUXV0dsW7nzp2aOnWqEhMTZVmW8vPztW7dOkmSZVlqb29Xc3OzJOnYsWNKTU09\n",
              "7XP6/X41NjZGbMGgfZd+AQBwtkhKcCov+3y1BoIKBCNDJhAMyR8IKn/UBTG9JU/UIsbn82nw4MFy\n",
              "uU4UmWVZSktLU21tbcS6cePGafPmzWpsbFR7e7s2bNigmpoaSdIll1yi++67T0OHDlVqaqqefPJJ\n",
              "rVy58rTPuWzZMvXt2zdi4+0nAAC6xx0TM5Q5qI8ONfl1pLlNzf6AjjS36VCTX8MH9VHhxPSYzmP7\n",
              "ib1er1d5eXnKzc1Vbm6uPB5PR/js27dPGzduVHV1tb788kvdd999mjVr1mm/15IlS9TQ0BCxTZgw\n",
              "IVa7AgDAWW1AsltP3Z6jwsvSlZzkUiAUVnKSS4WXpWvl7Tkx/5wYKxwOh6Pxjevq6pSZmakjR47I\n",
              "5XIpHA5r8ODBeu+995SZmXnar3v55Ze1atUqvfvuu3riiSe0Z88erVmzRpLU3Nys5ORk+f1+JSYm\n",
              "dmqOhQsXqqSkpFv2CQAAnNDafuIk3mS3K6ZvIf2nqB2JGTRokMaOHauysjJJUnl5uVJTU08KmNbW\n",
              "Vh09elSSVF9fr+XLl+uBBx6QJA0bNkz/+te/1NTUJEnasmWLPB5PpwMGAABER1KCUwOS3bYFjBTl\n",
              "2w6sXr1aXq9XxcXFSklJ0dq1ayVJs2fPVkFBgQoKCtTQ0KApU6bI4XAoFAppwYIFmjZtmiRpxowZ\n",
              "+uijj3TppZfK7Xard+/eeumll6I5MgAAMETU3k7qKXg7CQCAs5PtJ/YCJuoJd28FgHjHXayBLuhJ\n",
              "d28FgHjHkRigk3ra3VsBIN4RMUAn9bS7twJAvCNigE7oiXdvBYB4R8QAndAT794KAPGOiAE6oSfe\n",
              "vRUA4h0RA3RCT7x7KwDEO142Ap10x8QMfbjvqKrrjsvtcsrtcsgfOBEwdty9FQDiHUdigE7qaXdv\n",
              "BYB4x5EYoAsGJLt17zUe/XzK/9h+91YAiHdEDHAGkhKcxAsA2Iy3kwAAgJGIGAAAzgA3grUfbycB\n",
              "ANAF3Ai25+BIDAAAncSNYHsWIgYAgE7iRrA9CxEDAEAncCPYnoeIAQCgE7gRbM9DxAAA0AncCLbn\n",
              "IWIAAOgEbgTb85CLAAB0EjeC7Vk4EgMAQCdxI9iehSMxAAB0ATeC7TmIGAAAzgA3grUfbycBAAAj\n",
              "ETEAAMBIRAwAADASEQMAAIxExAAAACMRMQAAwEhEDAAAMBIRAwAAjETEAAAAIxExAADASEQMAAAw\n",
              "EhEDAACMRMQAAAAjETEAAMBIRAwAADASEQMAAIxExAAAACMRMQAAwEhEDAAAMBIRAwAAjETEAAAA\n",
              "IxExAADASEQMAAAwEhEDAACMRMQAAAAjETEAAMBIRAwAADASEQMAAIxExAAAACMRMQAAwEhEDAAA\n",
              "MBIRAwAAjETEAAAAIxExAADASEQMAAAwEhEDAACMRMQAAAAjETEAAMBIRAwAADASEQMAAIxExAAA\n",
              "ACMRMQAAwEhRjZi9e/dq0qRJ8ng8Gj9+vKqqqk5aEwqFtGjRImVnZysrK0tFRUVqa2vreLy2tlbT\n",
              "pk3TiBEjNHLkSK1cuTKaIwMAAENENWLmzZunuXPnas+ePVq8eLG8Xu9Ja0pLS1VRUaGKigrt3r1b\n",
              "DodDK1askCSFw2HNmDFDd955pz7//HN9+umnuvXWW6M5MgAAMETUIqaurk7bt29XYWGhJGnmzJny\n",
              "+Xyqrq6OWLdz505NnTpViYmJsixL+fn5WrdunSTpzTfflNvt1i233NKx/vzzzz/tc/r9fjU2NkZs\n",
              "wWAwCnsHAADsFrWI8fl8Gjx4sFwulyTJsiylpaWptrY2Yt24ceO0efNmNTY2qr29XRs2bFBNTY0k\n",
              "6dNPP9XAgQN12223KScnRzNmzNAXX3xx2udctmyZ+vbtG7Ft27YtWrsIAABsZPuJvV6vV3l5ecrN\n",
              "zVVubq48Hk9H+AQCAf3jH//QQw89pMrKSl133XXf+XbSkiVL1NDQELFNmDAhVrsCAABiKGoRM2TI\n",
              "EB04cECBQEDSifNbamtrlZaWFrHOsiwtXbpUlZWV2rp1q0aOHKlRo0ZJktLS0pSTk9Px+zvuuEMV\n",
              "FRVqb28/5XO63W6lpKREbE6nM1q7CAAAbBS1iBk0aJDGjh2rsrIySVJ5eblSU1OVmZkZsa61tVVH\n",
              "jx6VJNXX12v58uV64IEHJEn5+fn68ssv9dVXX0mS/vrXv+qiiy5SQkJCtMYGAACGcEXzm69evVpe\n",
              "r1fFxcVKSUnR2rVrJUmzZ89WQUGBCgoK1NDQoClTpsjhcCgUCmnBggWaNm2aJKl379565plndP31\n",
              "1yscDqtv3756+eWXozkyAAAwhBUOh8N2DxFNCxcuVElJid1jAACAbmb7ib0AAABngogBAABGImIA\n",
              "AICRiBgAAGAkIgYAABiJiAEAAEYiYgAAgJGIGAAAYCQiBgAAGImIAQAARiJiAACAkYgYAABgJCIG\n",
              "AAAYiYgBAABGImIAAICRiBgAAGAkIgYAABiJiAEAAEYiYgAAgJGIGAAAYCQiBgAAGImIAQAARiJi\n",
              "AACAkYgYAABgJCIGAAAYiYgBAABGImIAAICRiBgAAGAkIgYAABiJiAEAAEYiYgAAgJGIGAAAYCQi\n",
              "BgAAGImIAQAARiJiAACAkYgYAABgJCIGAAAYiYgBAABGImIAAICRiBgAAGAkIgYAABiJiAEAAEbq\n",
              "csTs3btX+fn5uvDCC9W/f/+ODQAAIJa6HDFz5syR1+tVv3799Pbbb+vmm2/WokWLojEbAADAaXU5\n",
              "YhobGzVr1iw5HA5dfPHFWr16tTZt2hSF0QAAAE6vyxGTkJAgSerTp49qamrk9/tVX1/f7YMBAAB8\n",
              "F1dXv2Dy5Mk6fPiw5s+fr3HjxikxMVG33XZbNGYDAAA4LSscDofP9It9Pp8aGhqUnZ3dnTN1q4UL\n",
              "F6qkpMTuMQAAQDc7o0usQ6GQvv76a4XDYaWkpKi2tra75wIAAPhOXX476bnnntM999yjhIQEORwn\n",
              "GsiyLNXV1XX7cAAAAKfT5Yh55JFH9NFHH2nEiBHRmAcAAKBTuvx20oABAwgYAABgu05HTGNjoxob\n",
              "GzV9+nT9/ve/V11dXcc/a2xsjOaMAAAAJ+n020nnnnuuLMvStxczLVy4sOMxy7IUDAa7fzoAAIDT\n",
              "6HTEhEKhaM4BAADQJV0+sVeSamtr9c4778iyLE2ePFlDhgzp7rkAAAC+U5dP7H3ppZeUk5Oj8vJy\n",
              "/elPf9LYsWP18ssvR2M2AACA0+rykZiHH35Y27dv19ChQyVJNTU1ysvL49YDAAAgprp8JOacc87p\n",
              "CBhJysjI0DnnnNOtQwEAAHyfLkfM9ddfr6VLl+rLL7+Uz+fTww8/rGnTpnGpNQAAiKku3wDy21sN\n",
              "nPKb9cBLrbkBJAAAZ6cunxPDpdYAAKAnOKO7WAMAANit00di+vXrJ8uyOn4fDoc7PsHXsiwdOXIk\n",
              "KgMCAACcSqcjZseOHVEcAwAAoGs6HTHp6emSpLq6Oi1dulQ7duxQa2trx+MVFRXdPx0AAMBpdPmc\n",
              "mKKiIqWnp6u+vl7/+7//qwsvvFDXX399NGYDAAA4rS5HjM/n0+LFi+V2uzVt2jRt3LhRb7zxxinX\n",
              "7t27V5MmTZLH49H48eNVVVV10ppQKKRFixYpOztbWVlZKioqUltb20nrvF6vLMvSsWPHujoyAAA4\n",
              "C3U5YhITEyVJSUlJOnz4sFwul+rr60+5dt68eZo7d6727NmjxYsXy+v1nrSmtLRUFRUVqqio0O7d\n",
              "u+VwOLRixYqINRs3blRCQkJXRwUAAGexLkeMx+PR4cOHVVhYqMsuu0yXXnqpxo0bd9K6uro6bd++\n",
              "XYWFhZKkmTNnyufzqbq6OmLdzp07NXXqVCUmJsqyLOXn52vdunUdjx88eFDFxcWd+sA6v9/f8cnB\n",
              "32497cP3AABA9+hyxJSVlem8887TggUL9Pzzz+vhhx9WWVnZSet8Pp8GDx4sl+vEucOWZSktLU21\n",
              "tbUR68aNG6fNmzersbFR7e3t2rBhg2pqajoenzNnjn73u9+pT58+3zvbsmXL1Ldv34ht27ZtXd1F\n",
              "AABggP/qw+6uuOIK3XDDDR2hcia8Xq/y8vKUm5ur3NxceTyeju/37LPPKi0tTT/60Y869b2WLFmi\n",
              "hoaGiG3ChAlnPBsAAOi5ovaJvUOGDNGBAwcUCAQknfhwvNraWqWlpUWssyxLS5cuVWVlpbZu3aqR\n",
              "I0dq1KhRkqS33npLf/nLX5SRkaGMjAxJ0ujRo1VZWXnK53S73UpJSYnYnE5ntHYRAADY6MwPoXyP\n",
              "QYMGaezYsSorK5PX61V5eblSU1OVmZkZsa61tVUtLS3q16+f6uvrtXz5cj3yyCOSpBdffDFirWVZ\n",
              "+vjjj3XuuedGa2wAAGCIqEWMJK1evVper1fFxcVKSUnR2rVrJUmzZ89WQUGBCgoK1NDQoClTpsjh\n",
              "cCgUCmnBggWaNm1aNMcCAABnASscDoftHiKaFi5c2KkrmwAAgFm4izUAADASEQMAAIxExAAAACMR\n",
              "MQAAwEhEDAAAMBIRAwAAjETEAAAAIxExAADASEQMAAAwEhEDAACMRMQAAAAjETEAAMBIRAwAADAS\n",
              "EQMAAIxExAAAACMRMQAAwEhEDAAAMBIRAwAAjETEAAAAIxExAADASEQMAAAwEhEDAACMRMQAAAAj\n",
              "ETEAAMBIRAwAADASEQMAAIxExAAAACMRMQAAwEhEDAAAMBIRAwAAjETEAAAAIxExAADASEQMAAAw\n",
              "EhEDAACMRMQAAAAjETEAAMBIRAwAADASEQMAAIxExAAAACMRMQAAwEhEDAAAMBIRAwAAjETEAAAA\n",
              "IxExAADASEQMAAAwEhEDAACMRMQAAAAjETEAAMBIRAwAADASEQMAAIxExAAAACMRMQAAwEhEDAAA\n",
              "MBIRAwAAjETEAAAAIxExAADASEQMAAAwEhEDAACMRMQAAAAjETEAAMBIRAwAADASEQMAAIxExAAA\n",
              "ACMRMQAAwEhEDAAAMBIRAwAAjETEAAAAI0U1Yvbu3atJkybJ4/Fo/PjxqqqqOmlNKBTSokWLlJ2d\n",
              "raysLBUVFamtrU2S9Mknn2jy5MnKyspSdna2fvazn6mlpSWaIwMAAENENWLmzZunuXPnas+ePVq8\n",
              "eLG8Xu9Ja0pLS1VRUaGKigrt3r1bDodDK1askCQlJSXpqaee0meffaadO3equblZjz32WDRHBgAA\n",
              "hohaxNTV1Wn79u0qLCyUJM2cOVM+n0/V1dUR63bu3KmpU6cqMTFRlmUpPz9f69atkyQNHz5co0eP\n",
              "liQ5nU6NHz9eNTU1p31Ov9+vxsbGiC0YDEZnBwEAgK2iFjE+n0+DBw+Wy+WSJFmWpbS0NNXW1kas\n",
              "GzdunDZv3qzGxka1t7drw4YNpwyV5uZmPfvss7rxxhtP+5zLli1T3759I7Zt27Z1634BAICewfYT\n",
              "e71er/Ly8pSbm6vc3Fx5PJ6O8PlWW1ubZs2apWuvvVYzZsw47fdasmSJGhoaIrYJEyZEexcAAIAN\n",
              "ohYxQ4YM0YEDBxQIBCRJ4XBYtbW1SktLi1hnWZaWLl2qyspKbd26VSNHjtSoUaM6Hm9vb9esWbM0\n",
              "ePDgjnNlTsftdislJSViczqd3b9zAADAdlGLmEGDBmns2LEqKyuTJJWXlys1NVWZmZkR61pbW3X0\n",
              "6FFJUn19vZYvX64HHnhAkhQIBHTbbbepf//+WrNmjSzLita4AADAMK7vX3LmVq9eLa/Xq+LiYqWk\n",
              "pGjt2rWSpNmzZ6ugoEAFBQVqaGjQlClT5HA4FAqFtGDBAk2bNk2S9Morr2jjxo0aPXq0cnJyJElX\n",
              "XHGFVq1aFc2xAQCAAaxwOBy2e4hoWrhwoUpKSuweAwAAdDPbT+wFAAA4E0QMAAAwEhEDAACMRMQA\n",
              "AAAjETEAAMBIRAwAADASEQMAAIxExAAAACMRMQAAwEhEDAAAMBIRAwAAjETEAAAAIxExAADASEQM\n",
              "AAAwEhEDAACMRMQAAAAjETEAAMBIRAwAADASEQMAAIxExAAAACMRMQAAwEhEDAAAMBIRAwAAjETE\n",
              "AAAAIxExAADASEQMAAAwEhEDAACMRMQAAAAjETEAAMBIRAwAADASEQMAAIxExAAAACMRMQAAwEhE\n",
              "DAAAMBIRAwAAjETEAAAAIxExAADASEQMAAAwEhEDAACMRMQAAAAjETEAAMBIRAwAADASEQMAAIxE\n",
              "xAAAACMRMQAAwEhEDAAAMBIRAwAAjETEAAAAIxExAADASEQMAAAwEhEDAACMRMQAAAAjETEAAMBI\n",
              "RAwAADASEQMAAIxExAAAACMRMQAAwEhEDAAAMBIRAwAAjETEAAAAIxExAADASEQMAAAwEhEDAACM\n",
              "RMQAAAAjETEAAMBIRAzOSGt7UPVNfrW2B+0eBQAQp1x2DwCzHDruV9kHNXp910G1tAfVK8Gp/OwL\n",
              "VDgxXQOS3XaPBwCII1E9ErN3715NmjRJHo9H48ePV1VV1UlrQqGQFi1apOzsbGVlZamoqEhtbW0d\n",
              "j2/ZskVZWVkaPny4brrpJjU2NkZzZHyHQ8f9+uX6Sr34Ya2a/QG5HJaa/QGVfbhf81+qVH2T3+4R\n",
              "AQBxJKoRM2/ePM2dO1d79uzR4sWL5fV6T1pTWlqqiooKVVRUaPfu3XI4HFqxYoUkqampSUVFRdq0\n",
              "aZP27t2rCy+8UI888kg0R8Z3KPugRtV1xzUw2a1+vRPV2+1Sv96JGpjsVnXdcZW9v9/uEQEAcSRq\n",
              "EVNXV6ft27ersLBQkjRz5kz5fD5VV1dHrNu5c6emTp2qxMREWZal/Px8rVu3TpL02muvKScnR1lZ\n",
              "WZKkX/ziF1q/fv1pn9Pv96uxsTFiCwY5Z6M7tLYH9fqug0pyOeVyRv6xcTkdcruceq3qG86RAQDE\n",
              "TNQixufzafDgwXK5Tpx2Y1mW0tLSVFtbG7Fu3Lhx2rx5sxobG9Xe3q4NGzaopqZGklRbW6v09PSO\n",
              "tRkZGTpw4IACgcApn3PZsmXq27dvxLZt27bo7GCcafIH1NIeVKLr1H9k3C6HWtqCavKf+t8NAADd\n",
              "zfark7xer/Ly8pSbm6vc3Fx5PJ6O8OmqJUuWqKGhIWKbMGFCN08cn5LdLvVKcKotEDrl4/5ASL0S\n",
              "nUp2c644ACA2ohYxQ4YMiThqEg6HVVtbq7S0tIh1lmVp6dKlqqys1NatWzVy5EiNGjVKkpSWlqb9\n",
              "+//vPIuampqIozv/P7fbrZSUlIjN6XRGaQ/jS1KCU3nZ56s1EFQgGBkygWBI/kBQ+aMuUFICP28A\n",
              "QGxELWIGDRqksWPHqqysTJJUXl6u1NRUZWZmRqxrbW3V0aNHJUn19fVavny5HnjgAUlSXl6eKioq\n",
              "9Nlnn0mSnn76ad12223RGhnf446JGcoc1EeHmvw60tymZn9AR5rbdKjJr+GD+qhwYvr3fxMAALpJ\n",
              "VI/9r169Wl6vV8XFxUpJSdHatWslSbNnz1ZBQYEKCgrU0NCgKVOmyOFwKBQKacGCBZo2bZokqU+f\n",
              "Pnr22Wc1ffp0BQIBZWdn6/nnn4/myPgOA5Ldeur2HJW9v1+vVX2jlragkpNcumVUKp8TAwCIOSsc\n",
              "DoftHiKaFi5cqJKSErvHOOu0tp84iTfZ7eItJACALTgLE2ckKcFJvAAAbGX71UkAAABngogBAABG\n",
              "ImIAAICRiBgAAGAkIgYAABiJiAEAAEYiYgAAgJGIGAAAYCQiBgAAGImIAQAARjrr75100003KSMj\n",
              "o9u/bzAY1LZt2zRhwgQ5nfH38fvsf3zvv8TPIN73X+JnwP5Hd//T09O1YMGC71xz1kdMtDQ2Nqpv\n",
              "375qaGhQSkqK3ePEHPsf3/sv8TOI9/2X+Bmw//bvP28nAQAAIxExAADASEQMAAAwEhFzhtxut37z\n",
              "m9/I7XbbPYot2P/43n+Jn0G877/Ez4D9t3//ObEXAAAYiSMxAADASEQMAAAwEhEDAACMRMScgb17\n",
              "92rSpEnyeDwaP368qqqq7B4ppu655x5lZGTIsizt2LHD7nFirrW1VdOnT5fH49Ell1yia665RtXV\n",
              "1XaPFVPXXnutRo8erTFjxujKK69UZWWl3SPZYu3atbIsS5s2bbJ7lJjLyMjQiBEjNGbMGI0ZM0av\n",
              "vPKK3SPFlN/v1/z58zV8+HBdfPHFKiwstHukmDp8+HDHv/sxY8bI4/HI5XLpyJEjsR0kjC676qqr\n",
              "wmvXrg2Hw+HwH//4x/Cll15q70Ax9vbbb4d9Pl84PT09XFlZafc4MdfS0hJ+9dVXw6FQKBwOh8Mr\n",
              "V64M5+bm2jtUjB09erTj1xs3bgyPHj3avmFssm/fvvDEiRPDl19+efjPf/6z3ePEXLz+9/+te++9\n",
              "Nzx//vyOvwcOHDhg80T2evzxx8M33HBDzJ+XIzFdVFdXp+3bt3dU98yZM+Xz+eLqlfjkyZOVmppq\n",
              "9xi2SUpK0o9//GNZliVJuvzyy1VTU2PvUDF27rnndvy6oaGh42cRL0KhkGbPnq2VK1fG7eW18ay5\n",
              "uVmlpaX67W9/2/Fn/4ILLrB5KnuVlpaqqKgo5s9LxHSRz+fT4MGD5XK5JEmWZSktLU21tbU2Twa7\n",
              "rFixQjfeeKPdY8TcnXfeqSFDhuihhx7SunXr7B4npkpKSnTFFVdo3Lhxdo9iqzvvvFMXX3yxioqK\n",
              "dOjQIbvHiZl///vf6t+/v4qLi3XppZfqyiuv1Jtvvmn3WLbZunWrjh49qhtuuCHmz03EAP+F4uJi\n",
              "VVdXa9myZXaPEnMvvPCCfD6fHn30US1evNjucWJm165dKi8v14MPPmj3KLZ655139PHHH6uiokID\n",
              "BgzQXXfdZfdIMRMIBLR//36NHDlS27dv1x/+8AfNmjVLBw8etHs0W5SWlurOO+/seHEfS7F/RsMN\n",
              "GTJEBw4cUCAQkMvlUjgcVm1trdLS0uweDTH2xBNPaOPGjXrjjTd0zjnn2D2Obe666y79/Oc/1+HD\n",
              "h3XeeefZPU7Uvfvuu6qpqdHw4cMlSd98843mzp2rAwcO6O6777Z5utj59u+8hIQE3XvvvfJ4PDZP\n",
              "FDtpaWlyOBz66U9/KknKycnR0KFD9cknn+j888+3ebrYampq0oYNG/TRRx/Z8vwciemiQYMGaezY\n",
              "sSorK5MklZeXKzU1VZmZmTZPhlgqKSnR+vXr9fe//z3i/JB4cOzYMX399dcdv9+0aZPOO+889e/f\n",
              "38apYufuu+/WgQMHVFNTo5qaGl1++eVas2ZNXAVMc3Ozjh071vH79evXKycnx76BYmzAgAG6+uqr\n",
              "9be//U2StG/fPu3bt08XXXSRzZPF3iuvvKJLLrlEWVlZtjw/R2LOwOrVq+X1elVcXKyUlBStXbvW\n",
              "7pFiat68eXr11Vf1zTff6LrrrlOfPn3i6sTmL7/8Uvfff7+GDRumq666StKJe4h8+OGHNk8WGw0N\n",
              "DbrlllvU0tIih8OhgQMHasuWLXF3cm88O3jwoGbOnKlgMKhwOKxhw4bphRdesHusmHrmmWdUVFSk\n",
              "xYsXy+FwaPXq1frBD35g91gxV1paqjlz5tj2/Nw7CQAAGIm3kwAAgJGIGAAAYCQiBgAAGImIAWC7\n",
              "MWPG6Pjx413+uptvvlnPPfdc9w8EwAhcnQTAdvF4I1EA/z2OxACwnWVZHZ87kpGRoV//+teaOHGi\n",
              "hg4dqkcffbRj3WeffaZJkyZp1KhRmj59uhobGzseO378uObMmaMJEyZo9OjRmjt3rtra2vT5558r\n",
              "NTVVX3zxhaQTH1KYl5enUCgU030E0P2IGAA9zrFjx/T+++/ro48+0uOPP66vvvpKknTHHXeoqKhI\n",
              "VVVVeuSRR/T22293fM3999+vK6+8Utu2bdPOnTsVCoW0YsUKjRgxQo8//rhuvfVW/fOf/9SqVau0\n",
              "bt06ORz89QeYjreTAPQ4t99+u6QTn4w6bNgw7du3T3369NGOHTvk9XolSRdffLF++MMfdnzNpk2b\n",
              "9P7776ukpESS1NLSIqfTKUn6yU9+orfeekvXXXed3nzzTQ0cODC2OwQgKogYAD1OUlJSx6+dTqcC\n",
              "gcAp1/3npwSHw2GVl5ef8h4+gUBAu3btUv/+/TuO6gAwH8dTARghJSVFOTk5HR9vX1VVpffee6/j\n",
              "8enTp+uxxx7rCJ6jR4923A7jV7/6lUaMGKF3331XixYtiqvbZABnMyIGgDFeeOEFrVmzRtnZ2Xrw\n",
              "wQc1efLkjseefPJJ9erVS2PGjNHo0aN19dVXq6amRlu2bNHrr7+uVatWKTMzUyUlJbr11lvV2tpq\n",
              "454A6A7cOwkAABiJIzEAAMBIRAwAADASEQMAAIxExAAAACMRMQAAwEhEDAAAMBIRAwAAjETEAAAA\n",
              "IxExAADASEQMAAAw0v8DtDQ5j+qshSYAAAAASUVORK5CYII=\n",
              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-549af2f9-f7a7-4ccf-a0b7-8033531bb7f6\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-549af2f9-f7a7-4ccf-a0b7-8033531bb7f6\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "_df_4.plot(kind='scatter', x='alpha', y='process fidelity', s=32, alpha=.8)\n",
              "plt.gca().spines[['top', 'right',]].set_visible(False)"
            ],
            "text/html": [
              "      <div class=\"colab-quickchart-chart-with-code\" id=\"chart-d2bc45ba-fbdc-450b-b098-6e6a36433f96\">\n",
              "        <img style=\"width: 180px;\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAjEAAAGkCAYAAADALHSZAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90\n",
              "bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9h\n",
              "AAAPYQGoP6dpAAAnEUlEQVR4nO3df3TV9X3H8df33pvcQEKCyg+zhgSsJFmIQIjBqoWAhyLMAUVW\n",
              "QYc1FRRnUQrjwDgbLetZAe2G8mMoFocdEREH58BcxbrpLGqVxgRUQCFKSLBAAENCkNzcH5/9QcmM\n",
              "EMiV3HvzSZ6Pc+45wP168/76Lcdnvz/uxzHGGAEAAFjGFesBAAAAvgkiBgAAWImIAQAAViJiAACA\n",
              "lYgYAABgJSIGAABYiYgBAABWImIAAICVOnzELF++PNYjAACACOjwEXPo0KFYjwAAACKgw0cMAADo\n",
              "mIgYAABgJSIGAABYiYgBAABWImIAAICViBgAAGAlIgYAAFiJiAEAAFYiYgAAgJWIGAAAYCUiBgAA\n",
              "hK3BH9SJep8a/MGYzeCJ2U8GAADWOX7ap+J3K7T9o2M66w+qS5xbY3Ov1dSbM9QjyRvVWTgTAwAA\n",
              "WuX4aZ8eeaFMz79XqTO+gDwuR2d8ARW/d0gzN5TpRL0vqvMQMQAAoFWK361QefVp9Uzy6qrEeCV6\n",
              "PboqMV49k7wqrz6t4t8fiuo8RAwAALisBn9Q2z86pgSPWx5383zwuF3yetx6Zc/RqN4jQ8QAAIDL\n",
              "qvcFdNYfVLzn4ung9bh0tjGoel8gajMRMQAA4LKSvB51iXOrMRC66Pu+QEhd4t1K8kbvmSEiBgAA\n",
              "NHOxx6cT4twak9tbDYGgAsHmIRMIhuQLBDV2wLVKiHNHbU4esQYAAJIu//j0vTf31XsHa1RefVpe\n",
              "j1tej0u+wLmA6d+rm6benBHVeYkYAACgwzVfatbGXao4cUZd4tyK97iaHp9+9+AXWnVPnnokebXq\n",
              "njwV//6QXtlzVGcbg0pK8OgHA9Ji8j0xRAwAAJ3Y+bMv639/SKfO+uVxOXK7HHXr4lGi16NAMNT0\n",
              "+PRPvpepHkle/eR7mXpoxLdV7wsoyeuJ6iWkr+KeGAAAOqnzX15X/O4h1TUE5DiOjKQvzjSq8uSX\n",
              "CoRCLT4+nRDnVo8kb8wCRiJiAADotM5/ed3VXePlchy5HUcel0selyNfIKST9Y2SYvP4dGsQMQAA\n",
              "dEJf/fK6+Di3HEcyMpIkx3HkOFLtWb9CxsTk8enWIGIAAOhEzj8+fbLe1/TldS7HUXKCRyEjGfP/\n",
              "IWOM1BgIxuTx6dZoX0kFAAAi4uuPT3v/9PRRvMelRK9HPZK8+rIxqIZASC7HKGSMHDn64ku/MmPw\n",
              "+HRrEDEAAHRw52/gLa8+fe7y0Z/ucfmyMaC6BqMkr1sJcR6lX91VJ880qvasX8GQUfeucbr3poyY\n",
              "PD7dGkQMAAAd3FdXnz6/eGOiV+oS59LBk1/q81MNuqprvLwel+LcLiV53erXI0XL7x6sb3XvGuPp\n",
              "W0bEAADQgV1q9emE+HOXkfzBkBK9bjX4Q+e+vC4/Nl9eFy4iBgCADuxyq093jXcrEHJp3Y8K5PW4\n",
              "Y/rldeEiYgAA6MDOrz59xhdQ4kVOrPgC586+XJMY2y+u+yZ4xBoAgA6sPa4+3VY4EwMAQAfX3laf\n",
              "biuciQEAoIM7v/r01JsylJTgUSBklJTg0dSbMrTyT6tT24gzMQAAdALtafXptkLEAADQiSTEua2P\n",
              "l/O4nAQAAKxExAAAACsRMQAAwEpEDAAA7ViDP6gT9T41+IOxHqXd4cZeAADaoeOnfSp+t0LbPzqm\n",
              "s/6gusS5NTb3WivWNIoWzsQAANDOHD/t0yMvlOn59yp1xheQx+XojC+g4vcOaeaGMp2o98V6xHaB\n",
              "iAEAoJ0pfrdC5dWn1TPJq6sS45Xo9eiqxHj1TPKqvPq0in9/KNYjtgtEDAAA7UiDP6jtHx1Tgsct\n",
              "j7v5f6Y9bpe8Hrde2XOUe2RExAAA0K7U+wI66w8q3nPx/0R7PS6dbQyq3heI8mTtDxEDAEA7kuT1\n",
              "qEucW42B0EXf9wVC6hLvVpKXZ3OIGAAA2pGEOLfG5PZWQyCoQLB5yASC51aeHjvg2g6zdMCVIOMA\n",
              "AGhn7r25r947WKPy6tPyetzyelzyBc4FTP9e3TT15oxYj9gucCYGAIB2pkeSV6vuydPUmzKUlOBR\n",
              "IGSUlODR1JsytPKePL4n5k8ieibmwIEDuu+++3TixAmlpKToueee04ABA5ptEwqFNG/ePG3fvl2B\n",
              "QEC33nqrnnrqKcXHx0uSHnvsMf36179WfHy8EhIStGLFCg0dOjSSYwMAEHM9krz6yfcy9dCIb6ve\n",
              "F1CS18MlpK+J6JmYGTNm6MEHH9T+/fs1f/58FRUVXbDNs88+q9LSUpWWlmrfvn1yuVxavny5JGnX\n",
              "rl1avXq1du7cqV27dmnmzJmaOXNmJEcGAKBdSYhzq0eSl4C5iIhFTHV1tUpKSjR16lRJ0qRJk1RV\n",
              "VaXy8vJm2+3evVujRo1SfHy8HMfR2LFjtX79ekmS4zjy+/06c+aMJOnUqVNKS0tr8Wf6fD7V1dU1\n",
              "ewWDPEcPAEBHFLGIqaqqUmpqqjyec1esHMdRenq6Kisrm22Xn5+vbdu2qa6uTn6/X5s2bVJFRYUk\n",
              "adCgQZo9e7b69euntLQ0PfHEE1q5cmWLP3PJkiVKSUlp9tq5c2ekdhEAAMRQzG/sLSoq0pgxY1RY\n",
              "WKjCwkJlZmY2hc/Bgwe1ZcsWlZeX6/Dhw5o9e7YmT57c4mctWLBAtbW1zV7cPwMAiDZWno6OiN3Y\n",
              "26dPHx05ckSBQEAej0fGGFVWVio9Pb3Zdo7jaNGiRVq0aJEkaePGjU03/27evFk33HCD/uzP/kyS\n",
              "9KMf/UiPPPKIGhsbm278/Sqv1yuvt/kd22431xABANHBytPRFbEzMb169dKQIUNUXFws6VyQpKWl\n",
              "6frrr2+2XUNDg2pqaiRJJ06c0NKlSzVv3jxJ0nXXXae3335b9fX1kqSXX35ZmZmZFw0YAABiiZWn\n",
              "oy+ij1ivWbNGRUVFWrx4sZKTk7Vu3TpJ0vTp0zV+/HiNHz9etbW1GjFihFwul0KhkGbNmqVx48ZJ\n",
              "kiZOnKg//OEPuvHGG+X1epWYmKgNGzZEcmQAAL6Rr648fX7hxkTvuW/ZPb/y9E++lxnjKTsWxxhj\n",
              "Yj1EJM2ZM0fLli2L9RgAgA6swR/UhFVv64wvoKsSL7xa8MWZRiUleLT1x7fyqHQbivmNvQAA2I6V\n",
              "p2ODiAEA4Aqx8nRsEDEAAFwhVp6ODZIQAIA2wMrT0ceZGAAA2gArT0cfZ2IAAGgjrDwdXUQMAABt\n",
              "LCHOTbxEAZeTAACAlYgYAAC+gsUb7cHlJAAAxOKNNuJMDACg02PxRjsRMQCATu+rizdelRivRK9H\n",
              "VyXGq2eSt2nxRrQ/RAwAoFNr8Ae1/aNjSvC4m1afPs/jdsnrceuVPUe5R6YdImIAAJ0aizfai4gB\n",
              "AHRqLN5oLyIGANCpsXijvchKAECnx+KNduJMDACg02PxRjtxJgYAALF4o42IGAAAvoLFG+3B5SQA\n",
              "AGAlIgYAAFiJiAEAAFYiYgAAgJWIGAAAYCUiBgAAWImIAQAAViJiAACAlYgYAABgJSIGAABYiYgB\n",
              "AABWImIAAICViBgAAGAlIgYAAFiJiAEAAFYiYgAAgJWIGAAAYCUiBgAAWImIAQAAViJiAACAlYgY\n",
              "AABgJSIGAABYiYgBAABWImIAAICViBgAAGAlIgYAAFiJiAEAAFYiYgAAgJWIGAAAYCUiBgAAWImI\n",
              "AQAAViJiAACAlYgYAABgJSIGAABYiYgBAABWImIAAICViBgAAGAlIgYAAFiJiAEAAFYiYgAAgJWI\n",
              "GAAAYCUiBgAAWImIAQAAViJiAACAlYgYAABgJSIGAABYiYgBAABWImIAAICViBgAAGAlIgYAAFiJ\n",
              "iAEAAFYiYgAAgJUiGjEHDhzQLbfcoszMTBUUFGjPnj0XbBMKhTR37lzl5uYqOztb06ZNU2NjY9P7\n",
              "lZWVGjdunLKyspSTk6OVK1dGcmQAAGCJiEbMjBkz9OCDD2r//v2aP3++ioqKLtjm2WefVWlpqUpL\n",
              "S7Vv3z65XC4tX75ckmSM0cSJE/XDH/5Qn3zyifbu3au77rorkiMDAABLRCxiqqurVVJSoqlTp0qS\n",
              "Jk2apKqqKpWXlzfbbvfu3Ro1apTi4+PlOI7Gjh2r9evXS5L+53/+R16vVz/4wQ+atu/du3eLP9Pn\n",
              "86murq7ZKxgMRmDvAABArEUsYqqqqpSamiqPxyNJchxH6enpqqysbLZdfn6+tm3bprq6Ovn9fm3a\n",
              "tEkVFRWSpL1796pnz56aMmWK8vLyNHHiRH322Wct/swlS5YoJSWl2Wvnzp2R2kUAABBDMb+xt6io\n",
              "SGPGjFFhYaEKCwuVmZnZFD6BQECvv/66Fi5cqLKyMt1+++2XvJy0YMEC1dbWNnsNHTo0WrsCAACi\n",
              "KGIR06dPHx05ckSBQEDSuftbKisrlZ6e3mw7x3G0aNEilZWV6Z133lFOTo4GDBggSUpPT1deXl7T\n",
              "7++9916VlpbK7/df9Gd6vV4lJyc3e7nd7kjtIgAAiKGIRUyvXr00ZMgQFRcXS5I2b96stLQ0XX/9\n",
              "9c22a2hoUE1NjSTpxIkTWrp0qebNmydJGjt2rA4fPqzPP/9ckvSb3/xGf/7nf664uLhIjQ0AACzh\n",
              "ieSHr1mzRkVFRVq8eLGSk5O1bt06SdL06dM1fvx4jR8/XrW1tRoxYoRcLpdCoZBmzZqlcePGSZIS\n",
              "ExP19NNP64477pAxRikpKdq4cWMkRwYAAJZwjDEm1kNE0pw5c7Rs2bJYjwEAANpYzG/sBQAA+CaI\n",
              "GAAAYCUiBgAAWCmsiAmFQpGaAwAAICxhRUzfvn21ePFiHT9+PFLzAAAAtEpYEfPaa6+purpaubm5\n",
              "uvfee/Xee+9Fai4AAIBLCitisrKy9OSTT+rgwYMaNmyY7rrrLhUUFGjDhg3q4E9qAwCAdibsG3uN\n",
              "Mfrtb3+rF198UUlJSbr77rv1wgsv6M4774zEfAAAABcV1jf2LlmyRM8884wGDBig+fPna/To0ZLO\n",
              "faFc//79IzIgAADAxYQVMX/84x/16quvKjMz84L3WA4AAABEU1iXk3r37n1BwPzTP/2TJCk/P7/t\n",
              "pgIAALiMsCJmy5YtrfozAACASGvV5aRXX31V27dv1+eff645c+Y0/XltbW3EBgMAALiUVkVMQkKC\n",
              "unfvLpfLpZSUlKY/79OnjxYuXBix4QAAAFrSqogpLCxUYWGhvv/972vQoEGRngkAAOCyWhUxL7zw\n",
              "gu6++269+eabevPNNy94/9FHH23zwQAAAC6lVRHz8ccfS5LKysoueM9xnLadCAAAoBVaFTH/+I//\n",
              "KElat25dRIcBAABorVZFzLZt2y75/vjx49tkGAAAgNZqVcQ88cQTLb7nOA4RAwAAoq5VEfPGG29E\n",
              "eg4AAICwhPWNvYFAQP/yL/+ihx9+WJL06aef6vXXX4/IYAAAAJcS1gKQM2fOVDAY1FtvvSVJuuaa\n",
              "azR58mSVlJREZDgAAICWhBUx7777rnbt2qW8vDxJUvfu3eX3+yMyGAAAwKWEdTkpISGh2e+DwaBC\n",
              "oVCbDgQAANAaYUXMwIEDVVxcrFAopPLycj300EMaMWJEhEYDAABoWVgRs2zZMu3YsUNHjx7Vrbfe\n",
              "KpfLpaVLl0ZqNgAAgBaFdU9MUlKS1qxZozVr1kRqHgAAgFZpVcT8+7//+yXf/+EPf9gmwwAAALRW\n",
              "qyLmP//zPyVJdXV1evPNN/Xd735XjuPorbfeUmFhIREDAACirlUR89JLL0mSJk6cqJKSEuXm5kqS\n",
              "9uzZo5/+9KeRmw4AAKAFYd3YW15e3hQwkjRgwAAdOHCgzYcCAAC4nLAiJjk5Wc8995yMMTLG6Lnn\n",
              "nlNSUlKkZgMAAGhRWBHzb//2b1q9erW8Xq8SEhL09NNPa926dZGaDQAAoEVhPWKdlZWlnTt36vTp\n",
              "05Kkbt26RWQoAACAy2lVxBw4cED9+/fXBx98cNH3Bw4c2KZDAQAAXE6rImb27Nl6+eWXNWHChAve\n",
              "cxxHn332WZsPBgAAcCmtiphHH31UkrRr1y6lpKREdCAAAIDWaNWNvQsWLJAkjRw5MqLDAAAAtFar\n",
              "zsT4/X499thjqq6u1ooVKy54//yZGgAAgGhpVcT86le/0q9//WudPXtWZWVlzd5zHCcigwEAAFxK\n",
              "qyLmpptu0k033aSMjAzNnz8/0jMBAABcVlhfdkfAAACA9iKsiAEAAGgviBgAAGClbxwxxpim5QcA\n",
              "AACiLayImTZtmk6dOqXGxkYNHjxYvXv31urVqyM1GwAAQIvCipj3339f3bt31/bt25WXl6ejR4/q\n",
              "6aefjtRsAAAALQorYowxkqQdO3boL//yL5WcnCy32x2RwQAAAC4lrIi59tpr9Td/8zd66aWXNGrU\n",
              "KPn9fgWDwUjNBgAA0KKwIub5559XVlaWNm7cqO7du+vzzz/XnDlzIjUbAABAi1r1jb3nJScna9as\n",
              "WXIcR5999pn27t2re++9N1KzAQAAtCisMzG33nqr6uvrdfLkSQ0bNkxLlizRzJkzIzUbAABAi8KK\n",
              "mEAgoG7duum//uu/dN999+ntt9/Wjh07IjUbAABAi8KKmMbGRknS//7v/+q2226TJHk8YV2RAgAA\n",
              "aBNhFcjIkSOVk5OjYDCoNWvWqKamhogBAAAxEVaBrFy5Urt379Z1112nuLg4BYNB/epXv4rUbAAA\n",
              "AC0K63KS4zj69NNPtWrVKkmSz+fjTAwAAIiJsCLmpz/9qdauXavnnnvu3D/scmnGjBmRmAsAAOCS\n",
              "woqYrVu36uWXX1ZiYqIkKTU1VfX19REZDAAA4FLCipguXbpcsFbS+fWUAAAAoimsG1oyMjK0Y8cO\n",
              "OY4jv9+vxYsXa/DgwREaDQAAoGVhRcyKFSt033336cMPP1RiYqJGjhyp4uLiSM0GAADQorAipnfv\n",
              "3tq+fbu+/PJLGWOa7o0BAACItrDuiXnmmWf0xRdfqGvXrkpMTNTJkyf5nhgAABATYUXM6tWrdfXV\n",
              "Vzf9/pprrtHq1avbfCgAAIDLCStiLvYkUjAYbLNhAAAAWiusiElNTdWmTZuafv/iiy8qNTW1zYcC\n",
              "AAC4nLBu7H3yySc1YcIEzZs3T5LUtWtXbd26NSKDAQAAXEpYEZOdna29e/fqk08+kSRlZWVd8OV3\n",
              "AAAA0RD26o3vv/++/vu//1uSNHr0aN14441tPhQAAMDlhP2I9V/91V+purpax48f16RJk7R27dpI\n",
              "zQYAANCisCJm1apVev/99/Xkk0/qiSeeUElJiVasWNHi9gcOHNAtt9yizMxMFRQUaM+ePRdsEwqF\n",
              "NHfuXOXm5io7O1vTpk1TY2PjBdsVFRXJcRydOnUqnJEBAEAHFVbESFLPnj0v+uuLmTFjhh588EHt\n",
              "379f8+fPV1FR0QXbPPvssyotLVVpaan27dsnl8ul5cuXN9tmy5YtiouLC3dUAADQgYUVMf3799ff\n",
              "//3fq7KyUpWVlVq4cKH69+9/0W2rq6tVUlKiqVOnSpImTZqkqqoqlZeXN9tu9+7dGjVqlOLj4+U4\n",
              "jsaOHav169c3vX/s2DEtXrxYy5Ytu+x8Pp9PdXV1zV58jw0AAB1TWBHz9NNP69NPP9WQIUOUn5+v\n",
              "8vJyPfXUUxfdtqqqSqmpqfJ4zt077DiO0tPTVVlZ2Wy7/Px8bdu2TXV1dfL7/dq0aZMqKiqa3n/g\n",
              "gQf0+OOPq1u3bpedb8mSJUpJSWn22rlzZzi7CAAALNHqp5OCwaD++Z//WRs3bmzTAYqKinTo0CEV\n",
              "FhaqS5cuGjVqlH77299KktauXav09HTddtttrfqsBQsWaM6cOc3+bOHChW06LwAAaB9afSbG7Xbr\n",
              "jTfeaPUH9+nTR0eOHFEgEJB0bsmCyspKpaenN9vOcRwtWrRIZWVleuedd5STk6MBAwZIkt544w1t\n",
              "3bpVffv2Vd++fSVJAwcOVFlZ2UV/ptfrVXJycrMX32MDAEDHFNblpL/4i7/QL37xC/3xj39sdt/J\n",
              "xfTq1UtDhgxRcXGxJGnz5s1KS0vT9ddf32y7hoYG1dTUSJJOnDihpUuXNn0j8PPPP6+qqipVVFQ0\n",
              "XWL64IMPlJeXF9ZOAgCAjscxF1vVsQUu14XN4zhOizfPfvLJJyoqKtLJkyeVnJysdevW6YYbbtD0\n",
              "6dM1fvx4jR8/XseOHdOIESPkcrkUCoU0a9YsPfTQQxcf1nFUU1Oj7t27t3ZkzZkzp1U3BQMAALuE\n",
              "FTE2ImIAAOiYwl52oLKyUr/73e/kOI6GDx+uPn36RGIuAACASwrrnpgNGzYoLy9Pmzdv1n/8x39o\n",
              "yJAhbf60EgAAQGuEdSbm5z//uUpKStSvXz9JUkVFhcaMGaMpU6ZEZDgAAICWhHUmpmvXrk0BI0l9\n",
              "+/ZV165d23woAACAywkrYu644w4tWrRIhw8fVlVVlX7+859r3Lhxl3zUGgAAIBKu+BHrpg+6xKPW\n",
              "scTTSQAAdExh3RMTCoUiNQcAAEBYwrqcBAAA0F4QMQAAwEpEDAAAsBIRAwAArETEAAAAKxExAADA\n",
              "SkQMAACwEhEDAACsRMQAAAArETEAAMBKRAwAALASEQMAAKxExAAAACsRMQAAwEpEDAAAsBIRAwAA\n",
              "rETEAAAAKxExAADASkQMAACwEhEDAACsRMQAAAArETEAAMBKRAwAALASEQMAAKxExAAAACsRMQAA\n",
              "wEpEDAAAsBIRAwAArETEAAAAKxExAADASkQMAACwEhEDAACsRMQAAAArETEAAMBKRAwAALASEQMA\n",
              "AKxExAAAACsRMQAAwEpEDAAAsBIRAwAArETEAAAAKxExAADASkQMAACwEhEDAACsRMQAAAArETEA\n",
              "AMBKRAwAALASEQMAAKxExAAAACsRMQAAwEpEDAAAsBIRAwAArETEAAAAKxExAADASkQMAACwEhED\n",
              "AACsRMQAAAArETEAAMBKRAwAALASEQMAAKxExAAAACsRMQAAwEpEDAAAsBIRAwAArETEAAAAK0U0\n",
              "Yg4cOKBbbrlFmZmZKigo0J49ey7YJhQKae7cucrNzVV2dramTZumxsZGSdKHH36o4cOHKzs7W7m5\n",
              "ubr//vt19uzZSI4MAAAsEdGImTFjhh588EHt379f8+fPV1FR0QXbPPvssyotLVVpaan27dsnl8ul\n",
              "5cuXS5ISEhK0atUqffzxx9q9e7fOnDmjxx57LJIjAwAAS0QsYqqrq1VSUqKpU6dKkiZNmqSqqiqV\n",
              "l5c322737t0aNWqU4uPj5TiOxo4dq/Xr10uS+vfvr4EDB0qS3G63CgoKVFFR0eLP9Pl8qqura/YK\n",
              "BoOR2UEAABBTEYuYqqoqpaamyuPxSJIcx1F6eroqKyubbZefn69t27aprq5Ofr9fmzZtumionDlz\n",
              "RmvXrtWECRNa/JlLlixRSkpKs9fOnTvbdL8AAED7EPMbe4uKijRmzBgVFhaqsLBQmZmZTeFzXmNj\n",
              "oyZPnqzRo0dr4sSJLX7WggULVFtb2+w1dOjQSO8CAACIgYhFTJ8+fXTkyBEFAgFJkjFGlZWVSk9P\n",
              "b7ad4zhatGiRysrK9M477ygnJ0cDBgxoet/v92vy5MlKTU1tulemJV6vV8nJyc1ebre77XcOAADE\n",
              "XMQiplevXhoyZIiKi4slSZs3b1ZaWpquv/76Zts1NDSopqZGknTixAktXbpU8+bNkyQFAgFNmTJF\n",
              "V199tZ555hk5jhOpcQEAgGU8l9/km1uzZo2Kioq0ePFiJScna926dZKk6dOna/z48Ro/frxqa2s1\n",
              "YsQIuVwuhUIhzZo1S+PGjZMkvfjii9qyZYsGDhyovLw8SdKtt96qf/3Xf43k2AAAwAKOMcbEeohI\n",
              "mjNnjpYtWxbrMQAAQBuL+Y29AAAA3wQRAwAArETEAAAAKxExAADASkQMAACwEhEDAACsRMQAAAAr\n",
              "ETEAAMBKRAwAALASEQMAAKxExAAAACsRMQAAwEpEDAAAsBIRAwAArETEAAAAKxExAADASkQMAACw\n",
              "EhEDAACsRMQAAAArETEAAMBKRAwAALASEQMAAKxExAAAACsRMQAAwEpEDAAAsBIRAwAArETEAAAA\n",
              "KxExAADASkQMAACwEhEDAACsRMQAAAArETEAAMBKRAwAALASEQMAAKxExAAAACsRMQAAwEpEDAAA\n",
              "sBIRAwAArETEAAAAKxExAADASkQMAACwEhEDAACsRMQAAAArETEAAMBKRAwAALASEfMNNfiDOlHv\n",
              "U4M/GOtRAADolDyxHsA2x0/7VPxuhbZ/dExn/UF1iXNrbO61mnpzhnokeWM9HgAAnQZnYsJw/LRP\n",
              "j7xQpuffq9QZX0Ael6MzvoCK3zukmRvKdKLeF+sRAQDoNIiYMBS/W6Hy6tPqmeTVVYnxSvR6dFVi\n",
              "vHomeVVefVrFvz8U6xEBAOg0iJhWavAHtf2jY0rwuOVxN//X5nG75PW49cqeo9wjAwBAlBAxrVTv\n",
              "C+isP6h4z8X/lXk9Lp1tDKreF4jyZAAAdE5ETCsleT3qEudWYyB00fd9gZC6xLuV5OVeaQAAooGI\n",
              "aaWEOLfG5PZWQyCoQLB5yASCIfkCQY0dcK0S4twxmhAAgM6F0wZhuPfmvnrvYI3Kq0/L63HL63HJ\n",
              "FzgXMP17ddPUmzNiPSIAAJ0GZ2LC0CPJq1X35GnqTRlKSvAoEDJKSvBo6k0ZWnlPHt8TAwBAFHEm\n",
              "Jkw9krz6yfcy9dCIb6veF1CS18MlJAAAYoCI+YYS4tzECwAAMcTlJAAAYCUiBgAAWImIAQAAViJi\n",
              "AACAlYgYAABgJSIGAABYiYgBAABWImIAAICViBgAAGAlIgYAAFjJMcaYWA8RSXfeeaf69u3b5p8b\n",
              "DAa1c+dODR06VG43yw+0BxyT9onj0j5xXNonjsv/y8jI0KxZsy65TYePmEipq6tTSkqKamtrlZyc\n",
              "HOtxII5Je8VxaZ84Lu0TxyU8XE4CAABWImIAAICViBgAAGAlIuYb8nq9+tnPfiav1xvrUfAnHJP2\n",
              "iePSPnFc2ieOS3i4sRcAAFiJMzEAAMBKRAwAALASEQMAAKxExHzNgQMHdMsttygzM1MFBQXas2fP\n",
              "BduEQiHNnTtXubm5ys7O1rRp09TY2Nj0/ssvv6zs7Gz1799fd955p+rq6qK5Cx3SlR6XDz/8UMOH\n",
              "D1d2drZyc3N1//336+zZs9HejQ6nLf6+nFdUVCTHcXTq1KkoTN6xtcVxqays1Lhx45SVlaWcnByt\n",
              "XLkymrvQIbXFcXnssceUk5OjwYMH6zvf+Y527twZzV1ofwyaGTlypFm3bp0xxpiXXnrJ3HjjjRds\n",
              "88wzz5iRI0can89nQqGQmT59unn88ceNMcacPn3a9OrVy+zbt88YY8yPf/xjM3fu3KjN31Fd6XHZ\n",
              "v3+/2b17tzHGmEAgYO666y7zs5/9LFrjd1hXelzO27x5s5k+fbqRZGpqaqIwecd2pcclFAqZIUOG\n",
              "mE2bNjVtf/To0ajM3pFd6XEpKysz6enp5vTp08YYY9avX28KCgqiNn97RMR8xbFjx0y3bt2M3+83\n",
              "xpz7i9y7d29z4MCBZtv9+Mc/Nr/4xS+afr9582Zzww03GGOM2bRpk7n99tub3tuzZ4/51re+FYXp\n",
              "O662OC5f98tf/tLcd999EZu5M2ir43L06FGTn59v6urqiJg20BbH5bXXXjM333xz9IbuBNriuOza\n",
              "tcukpqY2BeXKlSvNxIkTo7QH7ROXk76iqqpKqamp8ng8kiTHcZSenq7Kyspm2+Xn52vbtm2qq6uT\n",
              "3+/Xpk2bVFFRIencKdiMjIymbfv27asjR44oEAhEbT86mrY4Ll915swZrV27VhMmTIjG+B1WWx2X\n",
              "Bx54QI8//ri6desWzfE7rLY4Lnv37lXPnj01ZcoU5eXlaeLEifrss8+ivSsdSlscl0GDBmn27Nnq\n",
              "16+f0tLS9MQTT3T6y3xEzDdQVFSkMWPGqLCwUIWFhcrMzGz6HyZipzXHpbGxUZMnT9bo0aM1ceLE\n",
              "GE3auVzquKxdu1bp6em67bbbYjxl53Op4xIIBPT6669r4cKFKisr0+2336677rorxhN3Dpc6LgcP\n",
              "HtSWLVtUXl6uw4cPa/bs2Zo8eXKMJ46xWJ8Kak9ae7rv61544QXz3e9+1xjD5aRIaIvjYowxjY2N\n",
              "5vvf/76ZPn26CYVCEZ25M2iL43LPPfeYtLQ0k5GRYTIyMowk06dPH1NaWhrx+TuqtjguL730khk2\n",
              "bFjTe/X19cZxHNPY2Bi5wTu4tjguv/zlL80DDzzQ9F59fb2RZHw+X+QGb+eImK8pLCxsduNVfn7+\n",
              "BducPXvWfPHFF8YYY44fP24GDRpktm3bZowxpq6uzvTs2bPZjb1/+7d/G53hO7ArPS5+v9/ceeed\n",
              "5v777ydg2tCVHpevE/fEtIkrPS719fWmX79+5vDhw8aYc//nLCcnJzrDd2BXelw2b95scnJymm7s\n",
              "3bhxo8nMzIzO8O0UEfM1H3/8sfnOd75j+vfvb/Lz880HH3xgjDFm2rRpZuvWrcaYczciZmdnm5yc\n",
              "HJOdnW2eeuqpZp+xdetWk5WVZb797W+bCRMmmFOnTkV9PzqaKz0uxcXFRpIZOHCgGTRokBk0aJB5\n",
              "+OGHY7IvHUlb/H35KiKmbbTFcXn11VfNoEGDzMCBA82wYcOaPgPf3JUel1AoZP7u7/7OZGVlmYED\n",
              "B5qbb77ZlJSUxGRf2gvWTgIAAFbixl4AAGAlIgYAAFiJiAEAAFYiYgC0SxUVFerevXurtmXNJaBz\n",
              "ImIAAICViBgAMffXf/3XuvHGGzVw4EDdcccdOnr06AXbOI6jf/iHf1BeXp4yMzP1/PPPN3t/9erV\n",
              "Gjp0qPr166d169Y1/fncuXNVUFCgwYMHa/jw4frkk08ivj8AooOIARBzTz75pEpKSvTBBx9o2LBh\n",
              "WrRo0UW3cxxHZWVl2r59ux555JFmazB5vV7t3LlTr7zyih599NGm9crmz5+vP/zhD9q1a5cefvhh\n",
              "zZo1Kwp7BCAaWPAHQMxt2LBB69evV0NDgxoaGtSjR4+Lbjd9+nRJ0nXXXafhw4frd7/7nfr27Svp\n",
              "3NkcScrOzpbH49HRo0eVlpam1157TStXrtTp06cVCoX0xRdfRGWfAEQeZ2IAxNRbb72lFStW6De/\n",
              "+Y0++ugjLVu2TA0NDa36Zx3Hafp1QkJC06/dbrcCgYAqKys1c+ZMFRcX66OPPtLGjRtb/dkA2j8i\n",
              "BkBM1dTUqFu3brrmmmvU2NioNWvWtLjt+XtdKioqtGPHDg0bNuySn11bW6u4uDilpqbKGKNVq1a1\n",
              "6ewAYouIARBTY8aMUVZWlrKysjRs2DANHjy4xW2DwaDy8vI0evRorVixoulSUktuuOEGTZkyRQMG\n",
              "DFBBQYHS09PbdngAMcXaSQCs4DiOampqWv3dMQA6Ps7EAAAAK/F0EgArcNIYwNdxJgYAAFiJiAEA\n",
              "AFYiYgAAgJWIGAAAYCUiBgAAWImIAQAAViJiAACAlYgYAABgpf8Darc00pyXKv8AAAAASUVORK5C\n",
              "YII=\n",
              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-d2bc45ba-fbdc-450b-b098-6e6a36433f96\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-d2bc45ba-fbdc-450b-b098-6e6a36433f96\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x7c36de0d5db0>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">Time series</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "import seaborn as sns\n",
              "def _plot_series(series, series_name, series_index=0):\n",
              "  palette = list(sns.palettes.mpl_palette('Dark2'))\n",
              "  xs = series['index']\n",
              "  ys = series['alpha']\n",
              "  \n",
              "  plt.plot(xs, ys, label=series_name, color=palette[series_index % len(palette)])\n",
              "\n",
              "fig, ax = plt.subplots(figsize=(10, 5.2), layout='constrained')\n",
              "df_sorted = _df_5.sort_values('index', ascending=True)\n",
              "_plot_series(df_sorted, '')\n",
              "sns.despine(fig=fig, ax=ax)\n",
              "plt.xlabel('index')\n",
              "_ = plt.ylabel('alpha')"
            ],
            "text/html": [
              "      <div class=\"colab-quickchart-chart-with-code\" id=\"chart-f620d9ad-7972-42ab-8166-3319277e8bd9\">\n",
              "        <img style=\"width: 180px;\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA/MAAAITCAYAAABLz0yVAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90\n",
              "bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9h\n",
              "AAAPYQGoP6dpAABqiUlEQVR4nO3deXzV5Z3G/et3su8hZCHJyUKAJOyETRRZgwi0WtG2dlrb5qlW\n",
              "pzPOOLVWp88znXGmM7adOnacrrY6ttUuOhWdjgW3sLshkiACWSBk30P2/SzPH0mORFETyMnvLJ/3\n",
              "65WXkHPIudIozZX7vr+34XQ6nQIAAAAAAF7DYnYAAAAAAAAwOZR5AAAAAAC8DGUeAAAAAAAvQ5kH\n",
              "AAAAAMDLUOYBAAAAAPAylHkAAAAAALwMZR4AAAAAAC9DmQcAAAAAwMv4fJl/+OGHzY4AAAAAAMCU\n",
              "8vkyX1VVZXYEAAAAAACmlM+XeQAAAAAAfA1lHgAAAAAAL0OZBwAAAADAy1DmAQAAAADwMpR5AAAA\n",
              "AAC8DGUeAAAAAAAvQ5kHAAAAAMDLUOYBAAAAAPAylHkAAAAAALwMZR4AAAAAAC9DmQcAAAAAwMtQ\n",
              "5gEAAAAA8DKUeQAAAAAAvAxlHgAAAAAAL0OZBwAAAADAy1DmAQAAAADwMpR5AAAAAAC8DGUeAAAA\n",
              "AAAvQ5kHAAAAAMDLBJodAAAAwBP024bU1Netlv5uNfd3q/nCX/d3q6W/R73Dg4oJCdOMkHDFhoSP\n",
              "/jNMM0IiLvj1e49FBoXIMAyzPzUAgA+izAMAAJ9ldzh0frBXLf3dFxT1HjX3damlv+eCot6tnuHB\n",
              "KX/9QMOi2IuU/A/+MODC34crLDCIHwIAAD4SZR4AAHidvuEhNfVfUMgvLOejpb2lv1utA72yOx0T\n",
              "/rihAUFKCo9SQliUEsPG/hmpxPBoJYZFKTwoWJ2D/Wof7FPHYN/oP/vVPtg7+s/33j9ot8nmdKh1\n",
              "oEetAz2T+vxCAgIVGxw2+oOA0R8AhIYrNnj0nxf5AcCMkHCFBPCtHQD4C/7GBwAAHsHucKhtoPd9\n",
              "W9vfW1G/8Ne9tqEJf1xDhmaGRigx/IJyHhathLBIJYZFKTH8veI+ldvi+23D48r92K9Hft+vjov8\n",
              "AKB9oE82p0ODdpua+rvV1N89qdcMCwya1A6AGSHhigkJU5AlYEo+ZwDA9KHMAwAAt+odHvyQM+jd\n",
              "au7rUfPoCnvrQI8cTueEP25YYJASw6JdK+euch723sp6YniUZoZGKNCEshoWGKSwwBilRMRM+M84\n",
              "nU712oZGyv1A3/t2AFy4E2D87zuG+uRwOtVvG1a/rVP1vZ2TyhoVFPKBHwBc7HjAe88JU3RwmAIs\n",
              "zFIGALNQ5gEAwKTZHSPbxz/qDHpz38iv+ya5ih4fFvFeGR8r5q7V85FV9cTwKEUEBvvcuXLDMBQZ\n",
              "FKLIoBBZI2dM+M85nA51Dw2O2wHwsT8AGOxV59CAJKl7eFDdw4Oq6WmfeFYZig4O/eARgOCw9x0J\n",
              "GD8oMIqhgAAwJSjzAADApWd48ENW0Mf/vm2gd1Kr6OGBwa6V8vFFfew8eqQSwsxbRfd2FsOimJAw\n",
              "xYSEKVMzJ/zn7A6HOofeX/Q//AcAY7MBeoYH5ZRTnUP96hzqV2V324RfM8CwXGTF/8N2ALz3a4YC\n",
              "AsB4lHkAAHyczWFX20Cvq5C//7q15r4uNfePrLJPZhXdYhiKD41UwmgRHxsclxAWpaT3rahHBIW4\n",
              "8TPEpQqwWBQXGqG40IhJ/bkhu831Q4D2gY/6AcD49w/Yh2V3jsxGaBvondRrBlsCJnYE4H27BEID\n",
              "gyb1OgDgLSjzAAB4obGz1SNFfKSUN41udW8ZHZw2tqLeNtArpya+ih4RGPzBcn6Roj4zNIIz034q\n",
              "OCDQ9e/DZPTbhiewA2Dsrd91bGDYYdeQw37JQwHHHwGI+MBRgPfvEogNCWcoIACPR5kHAMCD2Bx2\n",
              "tQ70qqXvwye6j72/3zY84Y87tor+3hn0Cya6j167NjZAjlV0uMvYUMDkSQ4F7LMNubb5T2QHwNgP\n",
              "A+xOh2soYEPf5IYCzotJ1N8s3aRPzV7KD60AeCTKPAAAbuZ0OkfOoo+V876Ln0dv6e+Z9Cp6ZFDI\n",
              "B69bC//gRPe4EFbR4Z0Mw1BEUIgigkKUGhk74T/ncDrUMzzkOuf/wSMBF98Z0DU0IKecKu9s1t8e\n",
              "fEo/fmefvpF3jbZnLJTF4L8hAJ6DMg8AwGWwOewqaW9UU1+3mvq7Rov6e9etja2qD9gnt4qeEBrp\n",
              "OnN+4db2hPALzqOHRSk8KNiNnx3gvSyGRdHBoYoODlXGJE4D2B0OtQ/26Q/lb+lnJw6orKNZd+z7\n",
              "rRbFpeiby7dqszWHQXwAPILhdE5iFK0Xuvvuu/XQQw+ZHQMA4EOcTqeKW2u162yR/u/cO2od6JnQ\n",
              "n4sMCvnAirlrVf2Cie6sogOeoXOwX788dVi/fPeQekeHQy5PSNe9y7fq6pS5JqcD4O8o8wAATNC5\n",
              "rlY9e7ZYz1YU61xXq+v9McGhSo+aOe7ceWJ49AW/jlJCKKvogLc6P9Crn504qMdPv+baZXPlrCzd\n",
              "u3yrViVlmhsOgN+izAMA8BHaBnr0p4p3tKuiSEUtNa73hwYE6dqMBboxK0/rU+cx+RrwA019Xfrx\n",
              "O/v129I3NeSwS5I2pmbrm8u3amm81eR0APwNZR4AgPfpGx7Si9Wn9GxFkQ7UlcvudEgaOcu+LmWe\n",
              "dmYt07aMhYpk6jvgl+p6OvTw8b16uvyobKN/P1ybvkD35G3V/LhZJqcD4C8o8wAAaGSQ3eGGs9p1\n",
              "tkgvVJ1U3+j5WElaGm/Vzqxlun72UiWGT+5ebQC+q7KrTT8sfkW7zhbLKacMGbp+9hLdnbdFc2IS\n",
              "zI4HwMdR5j2E0+lU20Cv4sMizY4CAH7D6XTqeGutnq0o1p/OHVdL/3uD7DKi4nRD1jLdOCePb8oB\n",
              "fKSyjiY9VPSKnq88IWlkF89n5i7X3y3NV1pUnMnpAPgqrqbzAOUdzfrSy4/L5nDoyGf/nutOAMDN\n",
              "Krva9GxFkZ49W6yKCwbZzQgJ1/Wzl+rGOcu0PCGdv48BTEh2bJJ+vukLOtlWrweLXtbLNaf1VPnb\n",
              "2nW2WH+RvUp/s2STkiNizI4JwMdQ5j2ANXKGWvp7NGAf1un2Ri2ISzY7EgD4nLaBHv3fuRPadbZI\n",
              "x1qqXe8PDQjStekLtHPOMm1IzWaQHYBLtnBmih7f8mUda6nWg8de1sH6cv2m5A09VX5UX8pdo79e\n",
              "vJFdmACmDGXeA4QFBmlt8hwV1pZob20JZR4Apki/bXSQ3dliHagrcw2qshiGrk6eqxvnLNO2jEUM\n",
              "sgMwpZYnpOt3196q1xsr9OCxl/RmU6V+efKwnix9U7cuWKs7Fq3XjJBws2MC8HKUeQ+xJS1XhbUl\n",
              "Kqwp0Z1LNpkdBwC8ls1h16sXDLLrvWCQ3ZKZqdo5Z2SQXVJ4tIkpAfiDK2dl6Y/b79DB+nL9+7GX\n",
              "dLy1Vj9+Z79+ffp13b5onW5bcLWigkPNjgnAS1HmPcRma64k6e2WarUP9GpGaITJiQDAezidTr3T\n",
              "Vqdnzxbpf983yC4tcoZunJOnnVnLNDc20cSUAPyRYRjakJqt9Snz9HLNaf3g2Es63d6o/yh6RY+d\n",
              "ek1/tXiDCnKvVHhQsNlRAXgZyryHSI2MVe6MWSppb9S+ujLdOCfP7EgA4PGqutv03Nli7aoo1tnO\n",
              "Ftf7Z4SE67rZS3RjVp5WJDLIDoD5DMPQ1vQF2pKWqz9XvqsHi17W2c4WPXB0j3558pDuXLJJX8he\n",
              "rdDAILOjAvASlHkPkm/NVUl7owprSyjzAPAhzg/06v/OvaNnK4p1tLnK9f6QgMCRQXZZI4PsggP4\n",
              "vzgAnsdiWHTd7CXanrFQz1UU66GiQlX3nNc/vfl/+vmJg7pr2WbdPG8lwzgBfCy+0/Eg+Wm5+smJ\n",
              "/dpfWyabw65A/hIHAEkjg+xerj6tXRVFI39HXjDIbm3yHN2YladtGQs5ewrAawRaAvTpuSv0qaxl\n",
              "eqr8qB4u3quGvk79/WvP6mcnDujry/K1MytPARaL2VEBeCjKvAdZnpCmmOAwdQ7161hLjVYnZZod\n",
              "CQBMY3c49GrDWT1bUaTdle+OG2S3eGaqdmYt0/VZSzWLQXYAvFiQJUC35FyhT89Zrt+WHdGP39mn\n",
              "qu7z+rtD/6Mfv7Nf38i7Rp/IXCSLQakHMB5l3oMEWgK00Zqt/604rsKaEso8AL/jdDr1blu9dlUU\n",
              "6U8Vx9XU3+16LC1yhnZmLdPOOXmaxyA7AD4mNDBIty5Yq7+Yt0q/KnldPz1xQGc6W/S1/b/T/Bmz\n",
              "9M3lW3VN2nxmgABwocx7mHxr7kiZry3Rt1ZuMzsOAEyL6u7zeq6iWLvOFunMBYPsYkPCdV3mYt04\n",
              "J08rEzP4JhaAzwsPCtZfLd6gL+ZcoUdPHdYv3j2k0+2N+krhb7Q03qp7l2/V+pR5/H0IgDLvaTal\n",
              "ZstiGCppb1RdT4dSI2PNjgQAbtE+0KvnK09o19kivfW+QXbXpM3XjXPytJFBdgD8VFRwqL6+bIsK\n",
              "5l+lR949qMdOvarjrbX6wkv/rSuSMvXN5Vu1ZlaW2TEBmIjvkDzMjNAILU9I19HmKu2tLdEXc9eY\n",
              "HQkApky/bViv1JzWs2eLtK+uTMMOuyTJ0Mggu51zlml7xiJFM8gOACSNXLX59yu26dYFa/XTEwf0\n",
              "m5I39GZTpT695xdanzJP9yy/RssT0s2OCcAElHkPlG/N1dHmKhVS5gH4ALvDodcbK7TrbJF2V72r\n",
              "nuFB12ML45J145w8XT97qZIjYkxMCQCeLSEsSv+0+pO6feE6/eidffpd6REdrC/XwfpyXZM2X/fk\n",
              "XaOFM1PMjglgGlHmPVB+Wo6+f+xFHa4/q37bsMICg8yOBACT4nQ6dfJ8vZ49W6znzh1XU1+X6zFr\n",
              "ZKxuyFqmnVl5ypmRZGJKAPA+yRExeuDKG/SXi9brP4sL9cezx/RyzWm9XHNan8xcrG/kXcOQUMBP\n",
              "UOY90PwZyUoOj1FDX6deb6zQZmuO2ZEAYEJqus/ruYrjeraiSGUdza73xwSH6brZS7Qza5lWJWVw\n",
              "xRIAXKb0qDg9tO4z+uslG/VQ8Sv6U8U7er7yhHZXvaudWcv09WVblBk90+yYANyIMu+BDMPQZmuO\n",
              "flt2RIU1JZR5AB6tfbBPfz53QrsqinSkqdL1/pCAQG1Jm68bs5ZpozVHIQyyA4ApNycmQT/Z8Be6\n",
              "c/EmPVj0kl6sPqVnzhbpuYrjunneSt21dDMDlQEfxXdWHio/LVe/LTuivbUlcjqv5/oRAB6l3zas\n",
              "wprTeraiWHtrS8cNsrsqOUs7s5ZpR+ZiBtkBwDSZHzdLj+V/Scdba/WDYy9pf12Zfld2RH8887a+\n",
              "kHOF7lyyUUnh0WbHBDCFKPMe6urkuQoJCFRNT7vKO5uVHcu5UgDmsjsceqOxQrsqirS78l11XzDI\n",
              "bkFcsm7MytP1WUuVwiA7ADDN0nirntz6FR1pqtS/H3tRbzSe0+OnX9Pvy95Swfwr9VeLNyguNMLs\n",
              "mACmAGXeQ4UHBWvNrCwdqCtTYU0JZR6AKZxOp06db9CuimI9V1E8bpBdasToILs5y5Q7Y5aJKQEA\n",
              "77c6KVP/s+12vdpwVv9+7CUda6nWz989qCdK3tBtC6/W7QvXKSYkzOyYgNvZHQ4Vt9Zob22pZkfH\n",
              "69Nzl5sdacpQ5j1YvjVnpMzXluhrizeYHQeAH6ntaddzFcV69myxSjuaXO+PCQ7VJzOXaOecZVqd\n",
              "lMkgOwDwYIZh6OqUuVqbPEd7a0v1g2Mv6d3z9Xr4+F796vTr+stF6/WVBVcpIijE7KjAlGrp79b+\n",
              "2jLtqyvVgbpydQ71S5KuSMqkzGN65Kfl6h/f/D+91VSlzsF+fnoKwK06Bvv0fOUJPXu2WG82nXO9\n",
              "P9gSoC1p87VzzjJttuYyyA4AvIxhGMpPy9Uma7b2VJ3UfxS9rLKOZn3/2It69NRh/fXijfpi7hqu\n",
              "Q4bXsjnsOtZSo321pdpfV6YTbXXjHo8JDtW6lHnakjbfpITu4dbvyMrLy/XlL39Zra2tiomJ0a9+\n",
              "9SstXLhw3HMcDofuvfdevfDCC7LZbFq7dq1+9rOfKTg4WJL0/e9/X7/+9a8VHBys0NBQ/dd//ZdW\n",
              "r17tztgeIyNqpubGJOhMZ4sO1pfrutlLzI4EwMcM2IZVWFuiXWeLtK+2VEMXDLJbM2u2bpyTpx0Z\n",
              "i/hhIgD4AIth0ScyF2tb+kL977nj+o+iV1TV3aZ/eevPeuTkIf3tkk36i+xVCuaHtvACjX1d2l9b\n",
              "qn11ZTpUX66uoYFxjy+ZmaqNqdnaZM1RXkKaAi0BJiV1H7f+l3rHHXfo9ttvV0FBgf74xz+qoKBA\n",
              "b7311rjnPPbYYzp27JiOHTumoKAg3X777Xr44Yf1zW9+U8XFxfrpT3+qkydPKjIyUk8++aTuvPNO\n",
              "HTlyxJ2xPUq+NVdnOltUWFNCmQcwJRxOh15vPKdnzxZpd9W74/7Pb/6MWdo5J083zF6qFK4yAgCf\n",
              "FGCx6MY5ebpu9hL98cwx/Wdxoep6O/T/vfG/+tm7B/R3S/P16bnLfbL8wHsNO+w62lylfbWl2ldb\n",
              "qtPtjeMejw0J14bUedqUmq0NqdlKCIsyKen0cVuZb25u1tGjR/XSSy9Jkm666SbdeeedOnPmjObO\n",
              "net63vHjx7VlyxbXSvz27dt1//3365vf/KYMw9Dw8LB6e3sVGRmpjo4OWa3WD33NwcFBDQ4Ojnuf\n",
              "3W53w2c3fTan5eqRk4e0r65UDqeD86kALtmp8w3adbZI/1txXA19na73J4fHaOecZdqZlaf5cQyy\n",
              "AwB/EWQJ0F9kr9KNc/L0+7K39F/H96q2p0P3vPqMfnLigL6Rt0XXz17C958wTX1Ph/bVlWlfbakO\n",
              "N5xRzwU36RgytDTeqo3WbG1KzdGyeKsCLP7176rbynxNTY2Sk5MVGDjyEoZhKD09XdXV1ePK/IoV\n",
              "K/TII4/ozjvvVFhYmJ5++mlVVlZKkpYuXaqvf/3rmj17tuLi4hQSEqKDBw9+6Gt+97vf1T//8z+P\n",
              "e9+aNWum/pObRquTMhUVFKK2gV4Vt9ZqeUK62ZEAeJH6ng49W1GsXWeLxg2yiw4O1ScyF+vGOXm6\n",
              "gkF2AODXQgICVTD/St08b4V+U/KGfvLOAZ3ratWdB/6gHx3fp3uWX6Nt6QtlGIbZUeHjhuw2vdVU\n",
              "qb11ZdpfWzruexdJmhkaoQ2p2dqYmq0NqfM0MzTSpKSewfQDMQUFBaqqqtKGDRsUFhamLVu2uFbz\n",
              "z507p127dunMmTNKSUnRj3/8Y9188806fPjwRT/Wt771Ld19993j3vftb3/b7Z+DOwVZArQhNVvP\n",
              "V55QYU0JZR7Ax+oc7Nefq05o19kivdE4fpBdflqudmblabM1R6EMOgIAXCAsMFh3LFqvL+Rcof8+\n",
              "9ap+/u5BlXY06at7n9Timan65vKt2pSaTanHlKrtaXdtnT/ccFZ9tiHXYxbDUF58mjZZc7QxNVtL\n",
              "4lNZgLiA4XQ6ne74wM3NzZo7d67Onz+vwMBAOZ1OJScn6/Dhw+NW5t/vD3/4g37yk5/o0KFDevDB\n",
              "B1VWVqZf/OIXkuTabj84OOjalv9x7r77bj300ENT8jmZ5X/K39bXD/+PFsWl6IVP/a3ZcQB4oEG7\n",
              "TYU1JXq2okiFNSWuQXaSxg2yiw0JNzElAMCbdAz26ZcnD+vRk4fVO1qwViZm6JvLt2pt8hyT08Fb\n",
              "DdiGdaSpUvvqRgr8mc6WcY8nhEVqY2q2NqbmaH3KXM0IjTApqedz28p8YmKili9frieffFIFBQV6\n",
              "5plnZLVaP1DkBwYG1N/frxkzZqi1tVXf+9739J3vfEeSlJWVpccff1w9PT2KjIzU888/r+zs7AkX\n",
              "eV+xyZojQ4bePV+vxr4uzQqPNjsSAA/gcDr0ZlOldp0t0u7KE+q8YJBdTmySbpyTpxuylimVQXYA\n",
              "gEsQGxKuby7fqq8suEo/PXFQvzr9mo42V+nmF36ptclz9M28rVqZlGF2THiByq427R89+/5a41n1\n",
              "24ZdjwUYFq1ITNem1JHV94Uzk1l9nyC3brN/5JFHVFBQoAceeEDR0dF6/PHHJUm33Xabrr/+el1/\n",
              "/fXq7OzUxo0bZbFY5HA4dNddd+m6666TJO3cuVNvvfWWVq5cqZCQEEVEROh3v/udOyN7pPiwSC2N\n",
              "t6q4tUZ7a0v0+Wz/uJoPwMWdPt+oZyuK9FxFsep73xtkNys8Wjuz8rRzzjItiEs2MSEAwJfMDI3U\n",
              "t1ft0O0Lr9aP3tmn35Ye0asNZ/Vqw8+02Zqjb+Zt1eL4VLNjwoP024b1RmPFyPb5ujKd62od93hS\n",
              "eLTr2rh1yXO5AvcSuW2bvafwhW32kvTD4lf0H0Wv6Nr0BXos/0tmxwEwzep7OvTcuePadbZIJRdc\n",
              "xRIVFOIaZLdm1mx+kg0AcLvannY9fHyvni5/W3anQ5K0PWOhvpF3jXJncCuKP3I6nTrX1eqaPP96\n",
              "Y4UG7TbX44GGRSuTMrQpNUebrDmaP2MWsxemgOkD8DAx+dZc/UfRKzpUf0aDdptCAvjSAb6uc7Bf\n",
              "u6vedQ2yc2rkZ69BlgBttuboxjl5yrfmMsgOADCtrJEz9IO1N+mvFm/QD4sL9ezZYu2pOqkXqk7p\n",
              "U1lLdfeyLcqKiTc7Jtysb3hIrzdWaG9tqfbXlaqq+/y4x5PDY7TJmqNN1mxdnTxXUcGhJiX1XTRC\n",
              "L7FoZooSw6LU3N+tNxvPaX3qPLMjAXCDQbtNe2tL9OzZYhXWloz7qfYVSbO1c84yfSJzsWYwyA4A\n",
              "YLLZ0fH6r/U3668Xb9RDxa/oz5Un9FxFsf7v3Dv69Nzl+rulm5UWFWd2TEwRp9OpM50t2l9Xqn21\n",
              "ZXqz6dy471OCLAFanZQ5cvbdmq2c2CRW392MMu8lLIZFm6w5eqr8qAprSyjzgA9xOB060lSpXWeL\n",
              "9efKdz4wyG7nnDzdkLVU1sgZJqYEAODicmYk6ZFNX9CJ1jo9WPSyCmtL9FT5Ue06W6TPZ6/S3yzd\n",
              "zABnL9U7PKjD9WdGhtfVlaq2p2Pc49bIWNfW+auS5ygyKMScoH6KMu9F8q25I2W+pkT/fMV1ZscB\n",
              "cJlK2hv17NliPVdRrLreDtf7Z4VH64asZbpxzjLNn5HMT7UBAF5hcXyqfn1Ngd5urtIPjr2sww1n\n",
              "9OuSN/SH8qP6cu4a/dXijYoPizQ7Jj6C0+lUaUeT9tWWan9dmY40VWr4gutugy0BWjMrS5us2dqU\n",
              "mqM5MQl8n2IiyrwXWZ86T0GWAFV2t6mis0VZMQlmRwIwSfW9nfrfimI9W1GsU+cbXO+PCgrRjszF\n",
              "ujFrmdbMylKAhUF2AADvtCIxQ3/YdpteazirHxx7SW81V+kXJw/rydIjunXBWt2xaJ1iOS7mMbqH\n",
              "BnRodPV9f13puJtyJCkjKk4bU3O02ZqjK2dlKTzIv64J92SUeS8SGRSiK5Jm63DDGRXWllDmAS/R\n",
              "NTSg3ZUntOtskV6/yCC7naOD7MIYZAcA8CFXJc/Rrh1/qf11ZfrBsZf0TludfvTOPv265HXdvnCd\n",
              "bl2wlqFoJnA6nTrd3qC9tSPl/WhTlWyjtxJIUkhAoK6clTUyvC41R7OjZ7L67qEo814mPy1npMzX\n",
              "lOqrC9eZHQfARyisKdHTZ97WKzWn3zfILlM7s/L0idkMsgMA+DbDMLTJmqONqdl6sfqUfnDsJZV2\n",
              "NOnBopf12KlX9VeLN6hg/pUKC2S11506B/t1qL5c++rKtL+uTE19XeMenx0dr02j976vmZXFAoOX\n",
              "oMx7mXxrrv75yJ/1ZtM59QwPMmQC8FDPni3W3xz8g+v32bGJunFOnj41eymTfQEAfscwDG3LWKit\n",
              "6fP1f+dO6D+KXlZFV6v+7ege/eLkIf3Nkk36Qs4VXL88RRxOh062NWhf3cjZ97ebq2W/YPU9NCBI\n",
              "a5PnuH7Qkhk908S0uFT81+JlsmISlBk1U5XdbTpYV64dmYvMjgTgIp4qPypJ2pGxSHct3awFcQyy\n",
              "AwDAYlj0qayl+kTmIu06W6QfFheqpqdd//jm/+nn7x7UXUvz9dl5KxRkCTA7qtdpH+zTwbpy7ast\n",
              "1YH6MrX094x7fF5MojaODq5bnZSpUFbfvR5l3gvlp+XqsVOvqrC2hDIPeKD2gV693lghSfrWym2a\n",
              "HR1vciIAADxLoCVAn523UjdkLdNT5Uf1n8f3qr63U/e9tks/PbFfX1+2RTuzljEQ9iM4nA6901o3\n",
              "svpeW6ai1ho5nE7X4+GBwbr6gtV3dgb6Hsq8F8q3jpT5fbWlcjgdshj8JQd4kpdqTsvudGj+jFkU\n",
              "eQAAPkJwQKC+mLtGn567Qr8tfVM/emefqrrP6+8OPa2fvLNf38jboh2Zi/h+d9T5gd6RO99rS3Wg\n",
              "rlznB3vHPZ4Tm6SN1hxtTs3WqqRMBXNswafx1fVCV8yarfDAYDX3d+vdtnotibeaHQnABfZUvStJ\n",
              "7JwBAGCCwgKDdNvCq/UX2av0+OnX9bMTB1Te2ay/3P87LYhL1jfzrtGWtPl+d2TN7nCouLXWtfp+\n",
              "vLXWdSuONHLb1bqUudqYmqNNqdlKiYw1LyymHWXeC4UEBGpdyly9WH1KhbUllHnAg3QPDehgXbkk\n",
              "aXsGZR4AgMmICArRnUs26ku5a/TLk4f0y5OHdep8g/6fwt9oWXya7l2+VetS5vp0qW/p79aBujLt\n",
              "qy3TgfpydQz2jXt8QVyyNqXmaKM1WysTM5gv4Mco814qPy13pMzXlOrry7aYHQfAqL21pRpy2JUV\n",
              "Ha+c2CSz4wAA4JWig0P1jbxr9JX5V+ln7x7Uf596TcWtNfr8S4/piqTZunf5Vl0xa7bZMaeEzWFX\n",
              "UUuN9tWWal9dmU601Y17PDo4VOtT5mljarY2WnM0KzzapKTwNJR5L7XZmitJOt5aq9b+HsWHRZqc\n",
              "CIAk7R7dYr89Y5FPrxoAADAdZoRG6P9duV23LbhaPzmxX0+UvKE3m87ppj2PaENqtr6Zd42WJaSZ\n",
              "HXPSmvq6XGffD9WXq3NoYNzji2emauPove/LE9IUyOo7LoIy76VmhUdrUVyK3j1fr321pfrMvBVm\n",
              "RwL8Xr9tWHtrSyRxXh4AgKmUGB6lf77iOt2xcJ3+6519+kPZWzpQV6YDdWXamjZf9yzfqgVxyWbH\n",
              "/FDDDrvebq7Svtoy7asr1anzDeMejw0J14bR1fcNqdlKDI8yKSm8CWXei+Wn5erd8/UqrC2hzAMe\n",
              "4EBdmfptw0qNiNWSmalmxwEAwOekRMbqe1ft1NcWr9d/FhfqmbNFeqnmtF6qOa3rZy/R3cu2aG5s\n",
              "otkxJUn1vZ3aX1eqfbWlOlx/Rt3Dg67HDBlaEp+qTdaRwXXL4tO4hg+TRpn3YvlpuXr4+F4dqCvT\n",
              "sMPO8AvAZHtcW+wXssUeAAA3yoiaqR+u+6z+evFGPVT8iv507h396dw7er7yhG7MytPX8/KVETVz\n",
              "WjMN2W16q7lq5Ox7balKO5rGPR4XEqENqfO00ZqjjanzNDOUY7K4PJR5L7Ys3qqZoRFqG+jVW02V\n",
              "uip5jtmRAL81ZLfp5ZrTkphiDwDAdJkbm6ifbvy87lyyUQ8ee1kv1ZzWH88e03MVxfpc9ir97dLN\n",
              "SomIcdvr1/a0a//o1vnD9WfUaxtyPWbIUF5CmjZZs7UxNUdLZqay+o4pRZn3YhbDok2pOfrj2WMq\n",
              "rCmhzAMmeq2xQl1DA0oIi9TKxAyz4wAA4FcWxKXov7d8WUUtNXqw6GUdqCvTk6Vv6n/OvK1bcq7Q\n",
              "Xy/eOCXn0AftNh1pOje6+l6m8s7mcY/Hh0a6BtetT5mrGaERl/2awIehzHu5/LTckTJfW6Jvr/6E\n",
              "2XEAv7W7cmSL/bXpC/mpOwAAJslLSNNvt35Fbzae078fe0lvNp3TY6de1e/Kjuj/mX+VvrZo/aQL\n",
              "dlV3m2v1/dWGs+q3DbsesxiGViSkj559z9HCmcmyGHwfgOlBmfdy61PmKcCw6Exni6q626b9bBAA\n",
              "ye5w6MXqk5JGzssDAABzXTFrtv64/XYdqj+jfz/2kopba/TTEwf0m5I39NWFV+urC9cpOjj0on+2\n",
              "3zasNxorXFfHVXS1jns8KSxKG63Z2pSao6tT5io2JHw6PiXgAyjzXi4mJEyrkjL0RuM5FdaU6CsL\n",
              "1podCfA7bzVXqm2gVzHBoRx3AQDAQxiGofWp87QuZa4Ka0v078de0qnzDfphcaH++9Rr+triDfrK\n",
              "/KsUHhSsis7W0cnzZXq9sUID9vdW3wMMi1YmZrgmzy+IS2bQLTwCZd4H5FtzR8p8bSllHjDB2Bb7\n",
              "a9IWcKsEAAAexjAMbUmbr83WHO2ufFcPFr2sM50t+t7bL+jRk4cVERSiqu62cX9mVni0a+v81Slz\n",
              "P3QVHzATZd4H5Kfl6t+O7tEbjRXqGx5SeFCw2ZEAv+F0OrWnamSL/Y5MptgDAOCpLIZFn5y9RNsz\n",
              "FunZimL9sPgVVXWfV+tAj4IsAVo1tvpuzVFObBKr7/B4lHkfMC8mUWmRM1TT067DDWe0NX2B2ZEA\n",
              "v1HcWquGvk6FBwZrXco8s+MAAICPEWCx6NNzl+tTWUv1Ss1pBRgWXZU8R5FBIWZHAyaFUYs+wDAM\n",
              "bbbmSpIKa0pMTgP4lz1VI1vs8625CgsMMjkNAACYqCBLgLZnLNLW9AUUeXglyryPyE8bKfN7a0vl\n",
              "dDpNTgP4B6fT6TovzxR7AAAATCfKvI+4claWQgOC1NDXqdPtDWbHAfxCSXuTKrvbFBIQqM2jP1AD\n",
              "AAAApgNl3keEBQbp6pSRK7EKa0pNTgP4h91VJyRJ61PmsT0PAAAA04oy70Pyx87N13JuHpgOrin2\n",
              "GUyxBwAAwPSizPuQsSF4x1qq1T7Qa3IawLdVdLaqpL1RgYZFW9Lnmx0HAAAAfoYy70NSI2OVO2OW\n",
              "HE6n9tWVmR0H8GljU+yvSp6jGSHhJqcBAACAv6HM+xi22gPTY2yLPVPsAQAAYAbKvI8Zu6Juf22Z\n",
              "bA67yWkA31Tf06Hi1hoZMnRtOmUeAAAA048y72OWJ6QpJjhMnUP9OtZSY3YcwCftqR5ZlV+VlKHE\n",
              "8CiT0wAAAMAfUeZ9TKAlQBut2ZKkwhq22gPusLty5Lw8W+wBAABgFsq8D+LcPOA+Lf3dOtJUKUna\n",
              "zpV0AAAAMAll3gdtSs2WxTBU0t6oup4Os+MAPuXF6lNyyqklM1NljZxhdhwAAAD4Kcq8D5oRGqEV\n",
              "CemSpL2szgNTamyK/Y5MVuUBAABgHsq8jxqbas9We2DqdAz26dX6M5LYYg8AAABzUeZ91ObRc/OH\n",
              "68+q3zZschrAN7xSUyKb06Gc2CTNiUkwOw4AAAD8GGXeR82fMUvJ4TEasA/rtYazZscBfMKeqpEp\n",
              "9tuYYg8AAACTUeZ9lGEYrq32nJsHLl/v8KD215VJkj7BeXkAAACYjDLvw/KtOZJGzs07nU6T0wDe\n",
              "bV9dmQbtNmVExWn+jGSz4wAAAMDPUeZ92NrkuQoJCFRtT4fKOprNjgN4tT2VI1vst2cskmEYJqcB\n",
              "AACAv6PM+7DwoGBdOStLElPtgcsxYBvWKzWnJTHFHgAAAJ6BMu/j8ken2hfWUOaBS3W44Yx6bUNK\n",
              "Co9WXoLV7DgAAAAAZd7XjQ3BO9pcpY7BPpPTAN5pbIr99oyFshj8tQkAAADz8V2pj0uPitO8mETZ\n",
              "nQ4drCs3Ow7gdYYddr1YzRZ7AAAAeBbKvB/YPLo6z7l5YPLebDynjsE+xYVE6IqkTLPjAAAAAJIo\n",
              "835h7Iq6fbVlsjscJqcBvMvu0S32W9PnK9ASYHIaAAAAYARl3g+sSspUdHCozg/2qri11uw4gNdw\n",
              "OB16oeqkJGlH5mKT0wAAAADvocz7gSBLgNanzJMk7WWrPTBhbzdXq7m/W1FBIVqbPMfsOAAAAIAL\n",
              "Zd5PcEUdMHljU+y3pM1XSECgyWkAAACA91Dm/cQma44MGXr3fL0a+7rMjgN4PKfT6TovzxR7AAAA\n",
              "eBrKvJ+ID4vU0nirJLbaAxPxblu9ans6FBoQpE3WbLPjAAAAAONQ5v1IftrIVHu22gMfb2yL/SZr\n",
              "tsICg01OAwAAAIxHmfcjY+fmD9Wf0aDdZnIawLONbbHfkcEUewAAAHgeyrwfWTQzRYlhUeqzDenN\n",
              "xnNmxwE8VnlHs850tijIEqD8tFyz4wAAAAAfQJn3IxbDok3W0a32nJsHPtTuyhOSpHUpcxUdHGpy\n",
              "GgAAAOCDKPN+hivqgI+3p+qkJKbYAwAAwHNR5v3M+tR5CrIEqLK7TRWdLWbHATxOVXeb3j1fL4th\n",
              "aGv6fLPjAAAAABdFmfczkUEhWjNrtiS22gMX88LoqvyapNmaGRppchoAAADg4ijzfmhsq/0rbLUH\n",
              "PmB35cgUe7bYAwAAwJNR5v3Q5tEy/2bjOXUPDZicBvAcjX1derulWpK0LWOhyWkAAACAD0eZ90NZ\n",
              "MfGaHR0vm9Ohg/XlZscBPMbYFvvlCelKjogxOQ0AAADw4Sjzfip/9Iq6vZybB1z2VI1ssd/BFnsA\n",
              "AAB4OMq8n8pPG9lqv7e2VA6nw+Q0gPnOD/TqjcZzkqTtmWyxBwAAgGejzPupK5JmKyIwWC39PTrR\n",
              "Vm92HMB0L1Wfkt3p0MK4ZGVEzTQ7DgAAAPCRKPN+KjggUOtS5kmSCplqD2jP6Hl5ptgDAADAG1Dm\n",
              "/djYVnvum4e/6x4a0KHRYZA7MinzAAAA8HyUeT+2eXQI3vHWWrX0d5ucBjBPYW2Jhhx2zYlJ0LyY\n",
              "RLPjAAAAAB+LMu/HksKjtXhmqiRpX22pyWkA8+yufG+KvWEYJqcBAAAAPh5l3s+Nrc4XUubhp/pt\n",
              "Q9pXN/Lv//YMptgDAADAO1Dm/dzYufmDdWUadthNTgNMv/11Zeq3DcsaGevaqQIAAAB4Osq8n1sW\n",
              "b9XM0Ah1Dw/qSFOl2XGAaXfhFHu22AMAAMBbUOb9nMWwaFPqyFb7vVxRBz8zZLfplZrTkriSDgAA\n",
              "AN6FMg+uqIPferXhrLqGBpQYFqWVielmxwEAAAAmjDIPrU+ZpwDDojOdLarqbjM7DjBtxrbYX5u+\n",
              "QBaDvw4BAADgPfjuFYoJCdOqpAxJUiFb7eEn7A6HXqweKfM7MtliDwAAAO9CmYckKd86ttWeK+rg\n",
              "H440V6ptoFcxwWFaMyvL7DgAAADApFDmIem9c/NvNFaob3jI5DSA++2ufFfSyBb7IEuAyWkAAACA\n",
              "yaHMQ5I0LyZRaZEzNGi36XDDGbPjAG7lcDq0p2qkzG/PWGhyGgAAAGDyKPOQJBmG8d5Ue87Nw8cV\n",
              "t9aqsa9LEYHBWpcyz+w4AAAAwKRR5uEydm5+b22pnE6nyWkA99lTOTL4Lj8tV6GBQSanAQAAACaP\n",
              "Mg+XK2dlKSwwSA19nTrd3mB2HMAtnE6ndo9usd+RwRR7AAAAeCfKPFxCA4N0dfJcSdIrbLWHjzrd\n",
              "3qiq7jaFBARqkzXH7DgAAADAJaHMYxzXFXWUefiosVX5DSnzFBEUYnIaAAAA4NK4tcyXl5frqquu\n",
              "UnZ2tlatWqWTJ09+4DkOh0P33HOPFi1apNzcXN16660aGnrvarTq6mpdd911ysnJ0YIFC/SjH/3I\n",
              "nZH93ubRlcpjLTU6P9Brchpg6u0ZvZJuRyZb7AEAAOC93Frm77jjDt1+++0qKyvTfffdp4KCgg88\n",
              "57HHHtOxY8d07NgxnT59WhaLRQ8//LCkkbOtO3fu1Je+9CWVlpbq1KlT+uxnP+vOyH4vJTJW82fM\n",
              "klNO7asrMzsOMKUqOltU2tGkQMOiLWnzzY4DAAAAXDK3lfnm5mYdPXpUt9xyiyTppptuUk1Njc6c\n",
              "GX+H+fHjx7VlyxYFBwfLMAxt375dTzzxhCSpsLBQISEh+sxnPuN6flJSkrsiY9TYFXV72WoPH7O7\n",
              "amR30NrkOYoNCTc5DQAAAHDp3Fbma2pqlJycrMDAQEkj95inp6erurp63PNWrFihP/3pT+rq6tLw\n",
              "8LCefvppVVZWSpJOnTqlhIQEfe5zn1NeXp527typioqKD33NwcFBdXV1jXuz2+3u+hR91ti5+f11\n",
              "pbI5+N8PvmPP6Hn57UyxBwAAgJczfQBeQUGBtm3bpg0bNmjDhg3Kzs52/QDAZrNp7969+va3v62i\n",
              "oiJde+21H7nN/rvf/a5iYmLGvR05cmS6PhWfsTwhXbEh4eocGtDbzdUf/wcAL1DX06HjrbUyZOja\n",
              "jAVmxwEAAAAui9vKfFpamhoaGmSz2SSNnH+vrq5Wenr6uOcZhqH7779fRUVFeu2117RgwQItXLhQ\n",
              "kpSenq68vDzX77/4xS/q2LFjGh4evuhrfutb31JnZ+e4t9WrV7vrU/RZARaLNqZmS5IKa9lqD98w\n",
              "tiq/OilDCWFRJqcBAAAALo/bynxiYqKWL1+uJ598UpL0zDPPyGq1au7cueOeNzAwoPb2dklSa2ur\n",
              "vve97+nee++VJG3fvl21tbWqq6uTJO3evVvz589XUFDQRV8zJCRE0dHR494CAgLc9Sn6NK6og69h\n",
              "iz0AAAB8SaA7P/gjjzyigoICPfDAA4qOjtbjjz8uSbrtttt0/fXX6/rrr1dnZ6c2btwoi8Uih8Oh\n",
              "u+66S9ddd50kKSIiQj//+c/1iU98Qk6nUzExMfrDH/7gzsgYtdGaLYthqLSjSXU9HUqNjDU7EnDJ\n",
              "Wvq7daSpShJlHgAAAL7BrWU+JydHr7/++gfe/+ijj7p+nZSUpNOnT3/ox9i6dau2bt3qlnz4cDNC\n",
              "wrUiIV1vNVepsLZEX8pdY3Yk4JK9WHVKTjm1NN7KD6YAAADgE0wfgAfPNXZFHVvt4e3YYg8AAABf\n",
              "Q5nHh9o8em7+1Yaz6rddfOgg4Ok6Bvv0asNZSdKOjIUmpwEAAACmBmUeH2r+jFlKDo/RgH1Yr42W\n",
              "IcDbvFJzWjanQzmxScqKSTA7DgAAADAlKPP4UIZhuLba7+WKOnip3ZWjW+wz2WIPAAAA30GZx0fK\n",
              "t+ZIGrlv3ul0mpwGmJze4UEdqC+XJO3gvDwAAAB8CGUeH2lt8lyFBASqtqdDZR3NZscBJmVfbakG\n",
              "7TZlRM3U/BmzzI4DAAAATBnKPD5SeFCwrpyVJWlkdR7wJrtHp9jvyFgkwzBMTgMAAABMHco8Pla+\n",
              "lSvq4H0GbMOuf2e3ZzLFHgAAAL6FMo+PNTYE72hzlToG+0xOA0zMofpy9dqGlBweo2XxVrPjAAAA\n",
              "AFOKMo+PlR4Vp+zYRNmdDh2sKzc7DjAhe6pOSpK2ZSyUxeCvOgAAAPgWvsPFhGwe22rPuXl4gWGH\n",
              "XS9Wn5Ik7eBKOgAAAPggyjwm5L375ktldzhMTgN8tDcaK9Q51K+ZoRFanZhpdhwAAABgylHmMSEr\n",
              "EzMUHRyq9sE+FbfWmB0H+Ei7K0em2F+bvlABFv6aAwAAgO/hu1xMSJAlQBtSsiUx1R6eze5w6IXq\n",
              "kfPy2zOYYg8AAADfRJnHhOWn5Uji3Dw829st1Wrp71F0cKjWJs8xOw4AAADgFpR5TNgma44MGTp5\n",
              "vkENvZ1mxwEuak/VyBb7LWnzFRwQaHIaAAAAwD0o85iwmaGRWpYwcl/33tpSk9MAH+R0Ol1lni32\n",
              "AAAA8GWUeUxKvnVsqj1b7eF5TrTVqbanQ2GBQdqYmm12HAAAAMBtKPOYlLEyf6j+jAbtNpPTAOPt\n",
              "qRoZfLcpNUdhgcEmpwEAAADchzKPSVk0M0VJYVHqsw3pjcYKs+MALk6nU7tHt9jvyFxkchoAAADA\n",
              "vSjzmBTDMLTJOjrVnivq4EHKO5t1trNFwZYA1w4SAAAAwFdR5jFp+WkjRamwtlROp9PkNMCI3ZUj\n",
              "q/LrUuYpKjjU5DQAAACAe1HmMWnrUuYpyBKgqu42VXS1mh0HkCSm2AMAAMCvUOYxaZFBIVoza7Yk\n",
              "ttrDM1R1t+nk+QYFGBZtTV9gdhwAAADA7SjzuCRjZ5ILuaIOHmBP5cgU+zWzZisuNMLkNAAAAID7\n",
              "UeZxSTaPlvk3G8+pe2jA5DTwd64p9hlMsQcAAIB/oMzjkmTFxGt2dLxsTocO1pebHQd+rKG3U8da\n",
              "qiVJ13JeHgAAAH6CMo9Llj96Rd1ettrDRC9UjWyxX5mYoVnh0SanAQAAAKYHZR6XbOyKur21pXI4\n",
              "HSangb9iij0AAAD8EWUel+yKpNmKCAxWS3+PTrTVmx0Hfuj8QK/eaDonSdrOeXkAAAD4Eco8Lllw\n",
              "QKDWpcyTxBV1MMeL1afkcDq1KC5F6VFxZscBAAAApg1lHpdlSxpX1ME8bLEHAACAv6LM47KMXVF3\n",
              "vLVWLf3dJqeBP+kaGtCh+jOSpB2Zi01OAwAAAEwvyjwuS2J4lJbMTJUk7astNTkN/ElhTYmGHXbN\n",
              "jUnQvNhEs+MAAAAA04oyj8u2eXSr/Sucm8c02l11QpK0g8F3AAAA8EOUeVy2/NGt9gfryzVkt5mc\n",
              "Bv6g3zakfbVlkphiDwAAAP9EmcdlWxqfqvjQSPUMD+qtpkqz48AP7Kst04B9WGmRM7RoZorZcQAA\n",
              "AIBpR5nHZbMYFm2yZktiqj2mx4VT7A3DMDkNAAAAMP0o85gSY1vtCxmCBzcbtNv0Ss1pSdKODKbY\n",
              "AwAAwD9R5jEl1qdmK9Cw6Gxniyq72syOAx/2asNZdQ8PKiksSssT08yOAwAAAJiCMo8pER0cqlVJ\n",
              "mZLYag/3Gttivy1joSwGf4UBAADAP/GdMKbM2Fb7vVxRBzexOex6seqUJKbYAwAAwL9R5jFl8kfv\n",
              "m3+9sUK9w4Mmp4EvOtJUqfODvYoNCdcVs2abHQcAAAAwDWUeU2ZuTILSI+M05LDrcP0Zs+PAB+0e\n",
              "3WJ/bfp8BVkCTE4DAAAAmIcyjyljGIY2p+VIYqo9pp7D6dALVSclscUeAAAAoMxjSrnOzdeWyOl0\n",
              "mpwGvqSopVaNfV2KDArR1clzzY4DAAAAmIoyjyl15awshQUGqbGvS6fON5gdBz5kbIp9vjVXoYFB\n",
              "JqcBAAAAzEWZx5QKDQxyrZpyRR2mitPpdJX57ZlssQcAAAAo85hyY1vtC7miDlPkdHuDqrrPKyQg\n",
              "UJtSs82OAwAAAJiOMo8pt9k6MgTvWEuNzg/0mpwGvuDPlSOr8htTsxURFGJyGgAAAMB8lHlMuZTI\n",
              "WM2fMUtOObWvrszsOPABTLEHAAAAxqPMwy3y00an2rPVHpfpbGeLSjuaFGhYdE3afLPjAAAAAB6B\n",
              "Mg+3GDs3v7+uVDaH3eQ08GZjg+/WpsxVTEiYyWkAAAAAz0CZh1ssT0hXbEi4OocG9HZztdlx4MX2\n",
              "jG6x38EWewAAAMCFMg+3CLBYXFPHuaIOl6q2p13HW2tlyNC16QvMjgMAAAB4DMo83Gbs3DxX1OFS\n",
              "jQ2+W52UqfiwSJPTAAAAAJ6DMg+32ZCaLYthqLSjSbU97WbHgRfaPXpefkcmW+wBAACAC1Hm4TYz\n",
              "QsK1MjFDEqvzmLzmvm691VQlSdqevtDkNAAAAIBnoczDrTaPTrXn3Dwm68Xqk3LKqWXxaUqJjDU7\n",
              "DgAAAOBRKPNwq7Er6l5tOKt+25DJaeBNxqbYb89gVR4AAAB4P8o83Cp3RpJSImI0aLfptYYKs+PA\n",
              "S7QP9um1hrOSpO1cSQcAAAB8wKTLfHl5ubZv366UlBTFxcW53oCLMQzDtTrPVntM1CvVp2VzOpQ7\n",
              "Y5ayYuLNjgMAAAB4nEmX+a9+9asqKCjQjBkzdODAAX3605/WPffc445s8BEXXlHndDpNTgNv4Jpi\n",
              "z6o8AAAAcFGTLvNdXV26+eabZbFYtHjxYj3yyCN67rnn3BANvmJt8hyFBASqrrdDpR1NZseBh+sZ\n",
              "HtTB+nJJbLEHAAAAPsyky3xQUJAkKSoqSpWVlRocHFRra+uUB4PvCAsM1lWz5kjiijp8vH21pRq0\n",
              "2zQ7Ol65M5LMjgMAAAB4pEmX+fXr16utrU133nmnVqxYodmzZ+tTn/qUO7LBh4xttd/LuXl8jN2V\n",
              "I1vst2cslGEYJqcBAAAAPFPgZP/AD37wA0nS5z//ea1bt06dnZ1atIitsPho+dYc/YOko83V6hjs\n",
              "U2xIuNmR4IEGbMOuQYmclwcAAAA+3CVdTedwOFRfXy+n06no6GhVV1dPdS74mLSoOGXHJsrudOhA\n",
              "XbnZceChDtaXq882pOTwGC2Nt5odBwAAAPBYky7zv/rVrxQbG6vFixdrxYoVWrFihVauXOmObPAx\n",
              "m7miDh9jTxVb7AEAAICJmHSZ/853vqO33npLbW1tamlpUUtLi5qbm92RDT5m7Nz8vtpS2R0Ok9PA\n",
              "0ww77Hqp+rQkaUcmW+wBAACAjzLpMh8fH6+cnBx3ZIGPW5mYoejgULUP9qm4tcbsOPAwrzdUqHOo\n",
              "X/GhkVqVmGl2HAAAAMCjTbjMd3V1qaurSzfccIP+8z//U83Nza73dXV1uTMjfESQJUAbUrIlcUUd\n",
              "Pmj36Bb7a9MXKMBySeM8AAAAAL8x4Wn2sbGxMgxDTqdTknT33Xe7HjMMQ3a7ferTwefkp+Xo/yrf\n",
              "UWFtie5dca3ZceAh7A6HXqw+KUnazhZ7AAAA4GNNePnL4XDIbrfL4XB84I0ij4naZM2RIUMnzzeo\n",
              "obfT7DjwEEebq9TS36OY4FBdNSvL7DgAAACAx7ukvazV1dV68skn9dvf/lY1NZx9xsTNDI3UsoSR\n",
              "K8f21paanAaeYmyK/Za0+QoOmPCGIQAAAMBvTbrM/+53v1NeXp6eeeYZ/fGPf9Ty5cv1hz/8wR3Z\n",
              "4KPyR6+o28sVdZDkdDq1p2pki/2ODLbYAwAAABMx6SWwf/mXf9HRo0c1e/ZsSVJlZaW2bdumz33u\n",
              "c1MeDr4p35qrB4te1qH6Mxq02xTCSqxfe6etTnW9HQoPDNb61Gyz4wAAAABeYdIr8+Hh4a4iL0mZ\n",
              "mZkKDw+f0lDwbYtmpigpPFp9tiG90VhhdhyYbGyL/WZrjsICg0xOAwAAAHiHSZf5T3ziE7r//vtV\n",
              "W1urmpoa/cu//Iuuu+46rqjDhBmGoc3WHElcUefvnE6ndleOlPntbLEHAAAAJsxwjt01N0GWj7j/\n",
              "2ROvqLv77rv10EMPmR0D7/NC1UndtvcJZUTF6fBN35RhGGZHgglK25uU/9wPFWwJ0Duf/0dFBoWY\n",
              "HQkAAADwCpNemb/Y1XRcUYfJWpcyV8GWAFV1n9fZzhaz48Aku6tOSJLWp86jyAMAAACTcElX0wGX\n",
              "KyIoRGtG7xMvZKq93xqbYs8WewAAAGByJlzmZ8yYobi4ONfb2O/H/glMFufm/VtlV5tOnW9QgGHR\n",
              "1rT5ZscBAAAAvMqE7wQrLi52Ywz4o/y0XN1/5HkdaapU19CAooNDzY6EaTQ2xf7KWVmaERphchoA\n",
              "AADAu0y4zGdkZEiSmpubdf/996u4uFgDAwOux48dOzb16eDTZkfHKys6XhVdrTpYX65PZi42OxKm\n",
              "0e7RMr8jky32AAAAwGRN+sz8rbfeqoyMDLW2tuqf//mflZKSok984hPuyAY/kJ+WK0nay1Z7v1Lf\n",
              "26milhoZMnRt+gKz4wAAAABeZ9JlvqamRvfdd59CQkJ03XXXadeuXXrllVfckQ1+IN86WuZrS+Vw\n",
              "OkxOg+ny4ujgu5WJ6UoKjzY5DQAAAOB9Jl3mg4ODJUmhoaFqa2tTYGCgWltbpzwY/MPqpExFBoWo\n",
              "daBH77TWmR0H02Rsiz1T7AEAAIBLM+kyn52drba2Nt1yyy264oortHLlSq1YscId2eAHggMCtS5l\n",
              "riSuqPMXbQM9erPpnCRpe8ZCk9MAAAAA3mnCA/DGPPnkk5Kku+66SytXrlR7e7u2bds25cHgP/Kt\n",
              "udpTdVJ7a0v1jbxrzI4DN3ux+pQcTqcWz0xVWhTXWgIAAACXYtIr8xdau3atPvnJTyow8OI/Eygv\n",
              "L9dVV12l7OxsrVq1SidPnvzAcxwOh+655x4tWrRIubm5uvXWWzU0NPSB5xUUFMgwDHV0dFxOZHig\n",
              "zaPn5o+31qq5r9vkNHC3PZUjfw/sYIs9AAAAcMkuq8x/nDvuuEO33367ysrKdN9996mgoOADz3ns\n",
              "scd07NgxHTt2TKdPn5bFYtHDDz887jm7du1SUFCQO6PCRInhUVoyM1WStK+u1OQ0cKfOwX4dbjgj\n",
              "iS32AAAAwOVwW5lvbm7W0aNHdcstt0iSbrrpJtXU1OjMmTPjnnf8+HFt2bJFwcHBMgxD27dv1xNP\n",
              "POF6vKmpSQ888IAeeughd0WFB9g8ekVdIVfU+bTC2hINO+zKjk3U3NhEs+MAAAAAXsttZb6mpkbJ\n",
              "ycmuLfiGYSg9PV3V1dXjnrdixQr96U9/UldXl4aHh/X000+rsrLS9fhXv/pV/fu//7uioqI+9jUH\n",
              "BwfV1dU17s1ut0/p5wX3GLui7mB9uYbsNpPTwF12VzLFHgAAAJgKbt1mPxEFBQXatm2bNmzYoA0b\n",
              "Nig7O9v1A4BHH31U6enp2rx584Q+1ne/+13FxMSMezty5Ig742OKLI1PVXxopHqGB/VWU6XZceAG\n",
              "fcND2l9XJonz8gAAAMDlcluZT0tLU0NDg2y2kVVWp9Op6upqpaenj3ueYRi6//77VVRUpNdee00L\n",
              "FizQwoUjZ2n37dun//3f/1VmZqYyMzMlSUuWLFFRUdFFX/Nb3/qWOjs7x72tXr3aXZ8ippDFsGiT\n",
              "NVsSV9T5qn11pRqwDys9Mk4L4pLNjgMAAAB4NbeV+cTERC1fvtx1ld0zzzwjq9WquXPnjnvewMCA\n",
              "2tvbJUmtra363ve+p3vvvVeS9Nvf/lY1NTWqrKx0bb1/5513lJeXd9HXDAkJUXR09Li3gIAAN32G\n",
              "mGpjW+0LaxmC54v2VI1Msd+esVCGYZicBgAAAPBuk75nfjIeeeQRFRQU6IEHHlB0dLQef/xxSdJt\n",
              "t92m66+/Xtdff706Ozu1ceNGWSwWORwO3XXXXbruuuvcGQsean1qtgINi852tqiyq02Z0TPNjoQp\n",
              "Mmi3qbDmtCRpRyZb7AEAAIDL5dYyn5OTo9dff/0D73/00Uddv05KStLp06cn9PGcTueUZYPniQ4O\n",
              "1aqkTL3eWKHC2hLdumCt2ZEwRQ7Xn1H38KCSwqOVl5BmdhwAAADA65k+AA+40JbRK+r2ckWdT9lT\n",
              "NTLFflv6QlkM/toBAAAALhffVcOjjJ2bf72xQr3DgyanwVSwOex6sfqUJGlHxkKT0wAAAAC+gTIP\n",
              "jzInJkEZUXEacth1uP6M2XEwBd5sqlT7YJ9mhITrilmzzY4DAAAA+ATKPDyKYRjaPLo6/wpX1PmE\n",
              "3ZUjW+y3pi9QoIXbJQAAAICpQJmHx8kfOzdfW8rQQy/ncDr0QvXIlXQ7MphiDwAAAEwVyjw8zpqk\n",
              "2QoLDFJTX5dOnq83Ow4uQ1FLjZr6uhQVFKKrU+aaHQcAAADwGZR5eJzQwCCtSx4pfoVMtfdqu6tG\n",
              "VuXz0+YrJMCtN2ECAAAAfoUyD4+0eXSrfWFtqclJcKmcTqf2jJ6X384UewAAAGBKUebhkcaG4BW1\n",
              "1KhtoMfkNLgUp843qLrnvEIDgrQpNcfsOAAAAIBPoczDI6VExGhBXLKccmpfbZnZcXAJdleNrMpv\n",
              "TM1WeFCwyWkAAAAA30KZh8fKt45NtefcvDfaM1rmt2cyxR4AAACYapR5eKyxMn+grkw2h93kNJiM\n",
              "Mx3NKutoVpAlQFtGv44AAAAApg5lHh4rLyFNM0LC1Tk0oKPNVWbHwSTsGZ1ivzZ5jmJCwkxOAwAA\n",
              "APgeyjw8VoDFoo2p2ZKkwhqm2nuTsS32O9hiDwAAALgFZR4eLT+Nc/Pepqb7vN5pq5PFMHRt+gKz\n",
              "4wAAAAA+iTIPj7YhNVsWw1BpR5Nqe9rNjoMJeKF6ZIv9FUmzNTM00uQ0AAAAgG+izMOjzQgJ18rE\n",
              "DElSYQ2r895gd+XoFPuMhSYnAQAAAHwXZR4eb/PoNPRCttp7vKa+Lh1trpYkbcvgvDwAAADgLpR5\n",
              "eLyxK+pebTirftuQyWnwUV6sPiWnnMpLSFNKRIzZcQAAAACfRZmHx8udkaSUiBgN2m16raHC7Dj4\n",
              "CK4p9qzKAwAAAG5FmYfHMwzDtTrPVnvP1T7Q6/phy3bKPAAAAOBWlHl4hbEr6gprSuR0Ok1Og4t5\n",
              "uea07E6HFsQlKzN6ptlxAAAAAJ9GmYdXWJs8RyEBgarr7VBpR5PZcXARu6uYYg8AAABMF8o8vEJY\n",
              "YLDWJs+RxBV1nqhneFAH68olscUeAAAAmA6UeXiNsXPzezk373H21pRoyGFXVnS8cmKTzI4DAAAA\n",
              "+DzKPLzG2Ln5o83Vah/sMzkNLvTeFvtFMgzD5DQAAACA76PMw2tYI2coJzZJdqdDB+rKzI6DUf22\n",
              "Ye2tLZUk7chkiz0AAAAwHSjz8Cqbre9NtYdnOFhXpj7bkFIjYrVkZqrZcQAAAAC/QJmHV8lPy5Ek\n",
              "7a8rk93hMDkNJGlP1UlJI1Ps2WIPAAAATA/KPLzKysQMxQSHqn2wT0UtNWbH8XtDdpterjkliSn2\n",
              "AAAAwHSizMOrBFoCtCE1W5JUyFR7073eWKHOoQElhEVqZWKG2XEAAAAAv0GZh9dxnZunzJtud+XI\n",
              "FPtr0xcqwMJfJwAAAMB04btveJ1N1mwZMnTqfIPqezvNjuO37A6HXqwe2WK/gy32AAAAwLSizMPr\n",
              "zAyNVF5CmiRpL6vzpnmruVKtAz2KCQ7TlclZZscBAAAA/AplHl4p3zoy1X4vV9SZZmyK/db0+Qqy\n",
              "BJicBgAAAPAvlHl4pfy0kXPzhxrOaMA2bHIa/+N0OrWnauS8PFPsAQAAgOlHmYdXWhiXoqTwaPXb\n",
              "hvVG0zmz4/id4621qu/tVHhgsNalzDM7DgAAAOB3KPPwSoZhaPPoVvtCttpPu7Et9vnWXIUFBpmc\n",
              "BgAAAPA/lHl4rfzRK+r21pbI6XSanMZ/OJ1O7XZtsV9ochoAAADAP1Hm4bXWpcxVsCVAVd3ndbaz\n",
              "xew4fqO0o0nnuloVEhCozaOzCwAAAABML8o8vFZEUIjWzBq5Eq2QK+qmze7KkVX59SnzFBkUYnIa\n",
              "AAAAwD9R5uHVODc//cam2O9gij0AAABgGso8vNrYFXVHmirVNTRgchrfd66rVafbGxVoWLQlfb7Z\n",
              "cQAAAAC/RZmHV5sdHa+s6HjZnA4drC83O47PG5tif1XyHM0ICTc5DQAAAOC/KPPwemOr83vZau92\n",
              "Y+flmWIPAAAAmIsyD6+3xXVFXakcTofJaXxXfU+HiltrZMjQtemUeQAAAMBMlHl4vVVJmYoMClHr\n",
              "QI/eaa0zO47PeqF6ZIv9qqQMJYZHmZwGAAAA8G+UeXi94IBArU+ZJ4kr6txpN1PsAQAAAI9BmYdP\n",
              "GDs3zxV17tHa36MjTZWSpG2clwcAAABMR5mHT9iUOnLf/DttdWrq6zI5je95sfqUHE6nlsxMlTVy\n",
              "htlxAAAAAL9HmYdPSAyP0tJ4qyRpX22pyWl8z56xLfaZbLEHAAAAPAFlHj5js3VkdZ5z81Orc7Bf\n",
              "rzaclSRt57w8AAAA4BEo8/AZ+aNX1B2qP6Mhu83kNL7jldoSDTvsyolN0pyYBLPjAAAAABBlHj5k\n",
              "SXyqEsIi1TM86BrWhsu3u/KEJAbfAQAAAJ6EMg+fYTEsrkF4bLWfGn3DQ9pfVyZJ+gTn5QEAAACP\n",
              "QZmHT9nMFXVTam9dqQbtNmVExWn+jGSz4wAAAAAYRZmHT1mfMk+BhkUVXa0619VqdhyvNzbFfnvG\n",
              "IhmGYXIaAAAAAGMo8/Ap0cGhWp2UKYnV+cs1aLe5/jdkij0AAADgWSjz8Dn5o1vt93Lf/GU5XH9G\n",
              "PcODmhUerbwEq9lxAAAAAFyAMg+fM3ZF3RuNFeodHjQ5jffaPbrFflvGQlkM/qoAAAAAPAnfocPn\n",
              "zIlJUEZUnIYcdh2qP2N2HK9kc9j1UvUpSdIOttgDAAAAHocyD59jGIY2j67Oc0XdpXmz8ZzaB/sU\n",
              "FxLhmkEAAAAAwHNQ5uGTLjw373Q6TU7jfca22G9Nn69AS4DJaQAAAAC8H2UePmlN0myFBQapqa9L\n",
              "J8/Xmx3HqzicDr1QdVKStCNzsclpAAAAAFwMZR4+KTQwSOuS50riirrJOtZco6b+bkUFhWht8hyz\n",
              "4wAAAAC4CMo8fNbmtLFz81xRNxl7RrfYb0mbr5CAQJPTAAAAALgYyjx81tgQvKKWGrUN9Jicxjs4\n",
              "nU7XefntTLEHAAAAPBZlHj4rJSJGC+KS5ZRT+2rLzI7jFU6er1dNT7tCA4K0yZptdhwAAAAAH4Iy\n",
              "D5+2xTo21Z5z8xOxu3JkVX6TNVthgcEmpwEAAADwYSjz8GljV9QdqCuTzWE3OY3n2zM2xT6DKfYA\n",
              "AACAJ6PMw6cti09TXEiEOocGdLS5yuw4Hq28o1nlnc0KsgS4fggCAAAAwDNR5uHTAiwWbRw9+/0K\n",
              "V9R9pLEp9utS5io6ONTkNAAAAAA+CmUePi+fc/MTMrbFnin2AAAAgOejzMPnbUidpwDDorKOZtV0\n",
              "nzc7jkeq7j6vE211shiGrk1fYHYcAAAAAB+DMg+fFxsSrpWJ6ZKkwtpSk9N4phdGV+XXJM1WXGiE\n",
              "yWkAAAAAfBzKPPzC5tGt9oWcm7+o3aPn5dliDwAAAHgHyjz8wth09tcaz6rfNmRyGs/S1NflmvS/\n",
              "LWOhyWkAAAAATARlHn4hJzZJqRGxGrTb9GrDWbPjeJSxLfbLE9KVHBFjchoAAAAAE0GZh18wDMO1\n",
              "Os9W+/HGptjvYIs9AAAA4DUo8/AbY1fUFdaWyOl0mpzGM7QP9Or1xgpJ0vZMttgDAAAA3oIyD79x\n",
              "VXKWQgICVd/bqZL2JrPjeISXak7L7nRoYVyyMqJmmh0HAAAAwARR5uE3wgKDtTZ5jqSR1XlIe5hi\n",
              "DwAAAHglyjz8ythW+72UeXUPDehgXbkkaUcmZR4AAADwJpR5+JWxIXhHm6vUPthnchpz7a0t1ZDD\n",
              "rjkxCZoXk2h2HAAAAACTQJmHX7FGzlBObJIcTqcO1JWZHcdUu0e32O/IWCTDMExOAwAAAGAyKPPw\n",
              "O5utXFHXbxt2HTXYnsEUewAAAMDbUObhd/LTciRJ++vKZHc4TE5jjgN1Zeq3DcsaGavFM1PNjgMA\n",
              "AABgkijz8DsrEzMUExyq9sE+FbXUmB3HFBdOsWeLPQAAAOB9KPPwO4GWAG1IzZbkn1fUDdlternm\n",
              "tCSupAMAAAC8FWUefsl1bt4Py/xrjRXqGhpQYliUViammx0HAAAAwCVwa5kvLy/XVVddpezsbK1a\n",
              "tUonT578wHMcDofuueceLVq0SLm5ubr11ls1NDQkSTpx4oTWr1+v3NxcLVq0SF/5ylfU39/vzsjw\n",
              "E5utOTJk6NT5BtX3dpodZ1rtrhzZYn9t+gJZDH6eBwAAAHgjt34nf8cdd+j2229XWVmZ7rvvPhUU\n",
              "FHzgOY899piOHTumY8eO6fTp07JYLHr44YclSaGhofrxj3+skpISHT9+XL29vfr+97/vzsjwE3Gh\n",
              "EVqekCZJrqnu/sDucOjF6pEfqu3IZIs9AAAA4K3cVuabm5t19OhR3XLLLZKkm266STU1NTpz5sy4\n",
              "5x0/flxbtmxRcHCwDMPQ9u3b9cQTT0iS5s2bpyVLlkiSAgICtGrVKlVWVn7oaw4ODqqrq2vcm91u\n",
              "d88nCK+Xnzay1X6vH11Rd6S5Um0DvYoJDtOaWVlmxwEAAABwidxW5mtqapScnKzAwEBJkmEYSk9P\n",
              "V3V19bjnrVixQn/605/U1dWl4eFhPf300xct7L29vXr00Uf1qU996kNf87vf/a5iYmLGvR05cmRK\n",
              "Py/4jvzRc/OHGs5owDZscprpseeCLfZBlgCT0wAAAAC4VKYfmC0oKNC2bdu0YcMGbdiwQdnZ2a4f\n",
              "AIwZGhrSzTffrK1bt2rnzp0f+rG+9a1vqbOzc9zb6tWr3f0pwEstiEvWrPBo9duG9Xpjhdlx3M7p\n",
              "dGpP1cgW++0ZC01OAwAAAOByuK3Mp6WlqaGhQTabTdJIkaiurlZ6+vjp2YZh6P7771dRUZFee+01\n",
              "LViwQAsXvlc0hoeHdfPNNys5Odl1lv7DhISEKDo6etxbQACrj7g4wzD8aqp9cWutGvo6FREYrHUp\n",
              "88yOAwAAAOAyuK3MJyYmavny5XryySclSc8884ysVqvmzp077nkDAwNqb2+XJLW2tup73/ue7r33\n",
              "XkmSzWbT5z73OcXFxekXv/iFDMNwV1z4qXxrjiRpb02pnE6nyWnca0/VyBb7/LRchQYGmZwGAAAA\n",
              "wOUI/PinXLpHHnlEBQUFeuCBBxQdHa3HH39cknTbbbfp+uuv1/XXX6/Ozk5t3LhRFotFDodDd911\n",
              "l6677jpJ0lNPPaVdu3ZpyZIlysvLkyStXbtWP/nJT9wZG37k6pS5CrYEqLrnvM50tmhebKLZkdzC\n",
              "6XS6rqTbkcEUewAAAMDbubXM5+Tk6PXXX//A+x999FHXr5OSknT69OmL/vkvfOEL+sIXvuC2fEBE\n",
              "UIiunJWlA/XlKqwp8dkyX9LepMruNoUEBGrT6G4EAAAAAN7L9AF4gNk2p/n+ufndVSckSRtTsxUR\n",
              "FGJyGgAAAACXizIPvzd2Rd1bTZXqGhowOY17MMUeAAAA8C2Uefi9zOiZmhOTIJvToYN1ZWbHmXIV\n",
              "na0qaW9UoGHRlrT5ZscBAAAAMAUo84Dem2rvi1vtx6bYr02eo9iQcJPTAAAAAJgKlHlA722131db\n",
              "JofTYXKaqbV7tMxvZ4o9AAAA4DMo84CkVUmZigwKUetAj4631pkdZ8rU9XToeGutDBm6NmOB2XEA\n",
              "AAAATBHKPCApOCBQ61PmSfKtrfYvVI8MvludlKGEsCiT0wAAAACYKpR5YFT+6BV1e2t8p8zvrmSL\n",
              "PQAAAOCLKPPAqE2pI0Pw3mmrU1Nfl8lpLl9Lf7eONFVKoswDAAAAvoYyD4xKDI/S0nirJGlfbanJ\n",
              "aS7fi9Wn5JRTS+OtSo2MNTsOAAAAgClEmQcusNmHrqjbUzVyXp5VeQAAAMD3UOaBC4xdUXeo/oyG\n",
              "7DaT01y6jsE+vVp/RpK0I2OhyWkAAAAATDXKPHCBJfGpSgiLVM/woOu8uTd6paZENqdDObFJyopJ\n",
              "MDsOAAAAgClGmQcuYDEsrkF43rzVfk/VyBT7HZlssQcAAAB8EWUeeJ+xK+oKvfSKut7hQe2vK5PE\n",
              "eXkAAADAV1HmgfdZnzJPQZYAVXS16lxXq9lxJm1fbakG7TZlRM3U/BmzzI4DAAAAwA0o88D7RAWH\n",
              "anVSpiTvXJ0fm2K/I2ORDMMwOQ0AAAAAd6DMAxcxNtXe287ND9iG9UrNaUnS9kym2AMAAAC+ijIP\n",
              "XMTYufk3Gs+pZ3jQ5DQTd7jhjHptQ0oOj9GyeKvZcQAAAAC4CWUeuIis6HhlRM3UsMOuQ/XlZseZ\n",
              "sLEp9tsyFspi8J83AAAA4Kv4bh+4CMMwlG8dvaLOS87NDzvserF6ZIs9V9IBAAAAvo0yD3yIsa32\n",
              "+2pL5XQ6TU7z8d5sPKeOwT7NDI3Q6sRMs+MAAAAAcCPKPPAh1szKUnhgsJr6u/VuW73ZcT7W7tEt\n",
              "9temL1SAhf+0AQAAAF/Gd/zAhwgJCNS6lLmSPH+qvcPp0AujV9Jtz2CKPQAAAODrKPPAR9jsJVfU\n",
              "vd1creb+bkUHh2pt8hyz4wAAAABwM8o88BE2jw7BK26pVdtAj8lpPtzYFPstafMVHBBochoAAAAA\n",
              "7kaZBz5CckSMFsYlyymn9tWWmh3nopxOp+u8/I4MptgDAAAA/oAyD3yM/LGt9jWeWebfbatXbU+H\n",
              "wgKDtCF1ntlxAAAAAEwDyjzwMcauqDtQX6Zhh93kNB80tiq/2ZqrsMBgk9MAAAAAmA6UeeBjLItP\n",
              "U1xIhLqGBnS0ucrsOB8wdl6eKfYAAACA/6DMAx8jwGLRRmu2JKmwxrOm2pd3NOtMZ4uCLQGu4wAA\n",
              "AAAAfB9lHpiAsaK818OuqNtdeUKStC5lnqKCQ01OAwAAAGC6UOaBCdiQOk8BhkVlHc2q6T5vdhyX\n",
              "PVUnJbHFHgAAAPA3lHlgAmJDwrUyMV2SVOghV9RVdbfp3fP1CjAs2pq+wOw4AAAAAKYRZR6YoM2u\n",
              "K+o8Y6v9C6Or8mtmzVZcaITJaQAAAABMJ8o8MEFjV9S91nhW/bYhk9NIuytHptjvyFhkchIAAAAA\n",
              "040yD0xQTmySrJGxGrTb9GrDWVOzNPZ16e2WaknStZyXBwAAAPwOZR6YIMMwXFPtzd5qP7bFfmVi\n",
              "hmaFR5uaBQAAAMD0o8wDk+A6N19bIqfTaVqOPVUjW+yZYg8AAAD4J8o8MAlXJc9RaECQ6ns7VdLe\n",
              "ZEqG8wO9eqPxnCRpO+flAQAAAL9EmQcmISwwSGuT50iSCmtPm5LhpepTsjsdWhSXovSoOFMyAAAA\n",
              "ADAXZR6YpLGp9madm98zel5+Ryar8gAAAIC/oswDkzQ2BO/tlmq1D/RO62t3Dw3oUH25JLbYAwAA\n",
              "AP6MMg9MUmpkrHJik+RwOrW/rnxaX7uwtkRDDrvmxiRoXmzitL42AAAAAM9BmQcugWurfe30brXf\n",
              "XTkyxX4Hq/IAAACAX6PMA5dgbKv9/roy2R2OaXnNftuQ9tWVSmKLPQAAAODvKPPAJViRmK6Y4DB1\n",
              "DPbpWEv1tLzm/roy9duGlRY5Q4tmpkzLawIAAADwTJR54BIEWgK0MTVb0vRttR+bYr89Y6EMw5iW\n",
              "1wQAAADgmSjzwCXaPI1X1A3ZbXqlZuRe+x0Zi93+egAAAAA8G2UeuESbUrNlyNDp9kbV93S49bVe\n",
              "bTirrqEBJYVFaXlimltfCwAAAIDno8wDlyguNELLE0aK9d7aUre+1u6qkSn22zIWymLwny0AAADg\n",
              "72gFwGWYjivq7A6HXqw6JYkp9gAAAABGUOaByzB2Rd3hhjMasA275TWONFfq/GCvYkPCtWbWbLe8\n",
              "BgAAAADvQpkHLsOCuGTNCo9Wv21YrzdWuOU1dleObLG/Nn2+Ai0BbnkNAAAAAN6FMg9cBsMwtNnq\n",
              "vq32DqdDe0bPyzPFHgAAAMAYyjxwmfKtOZKkvTWlcjqdU/qxi1tr1djXpcigEK1NnjOlHxsAAACA\n",
              "96LMA5fp6pS5CrYEqLrnvM50tkzpx95TeVLSyNn80MCgKf3YAAAAALwXZR64TBFBIbpyVpYkqbBm\n",
              "6rbaO51O15V02zOZYg8AAADgPZR5YApsdsMVdafbG1XV3aaQgEBtSs2eso8LAAAAwPtR5oEpMHZF\n",
              "3VtNleoaGpiSjzm2Kr8xNVsRQSFT8jEBAAAA+AbKPDAFMqNnam5MgmxOhw7WlU3Jx9wzeiXd9gy2\n",
              "2AMAAAAYjzIPTJH8KbyirqKzRaUdTQo0LLombf5lfzwAAAAAvoUyD0yRsXPze2tL5XA6Lutj7a4a\n",
              "mWK/NmWuYkLCLjsbAAAAAN9CmQemyOqkTEUFhahtoFfFrbWX9bH2jJ6X38EWewAAAAAXQZkHpkiQ\n",
              "JUDrR6fOX84VdXU9HTreWiuLYeja9AVTFQ8AAACAD6HMA1Mo35ojaWSr/aUaW5VfnZSp+LDIKckF\n",
              "AAAAwLdQ5oEptGm0zJ9oq1NTX9clfYyxMs8UewAAAAAfhjIPTKGEsCgtjbdKurTV+Zb+bh1pqpIk\n",
              "bU9fOKXZAAAAAPgOyjwwxVxX1F3CufkXq07JKaeWxacpJTJ2ipMBAAAA8BWUeWCK5Y9eUXeovlyD\n",
              "dtuk/ux7W+xZlQcAAADw4SjzwBRbPDNFCWGR6rUN6UjTuQn/uY7BPr3acFYS5+UBAAAAfDTKPDDF\n",
              "LIZFm0cH4U1mq/0rNadlczqUO2OWsmLi3RUPAAAAgA+gzANusHns3PwkhuDtrhzZYr+DVXkAAAAA\n",
              "H4MyD7jB+pR5CrIE6FxXqyo6Wz/2+b3DgzpQXy6JLfYAAAAAPh5lHnCDqOBQrU7KlCTtrf34rfZ7\n",
              "a0s1aLdpdnS8cmckuTkdAAAAAG9HmQfcxHVF3QTK/IVT7A3DcGsuAAAAAN6PMg+4ydgVdW80nlPP\n",
              "8OCHPm/ANuwalMd5eQAAAAATQZkH3CQrOl4ZUTM17LDr0Oh5+Is5VF+uXtuQUiJitDTeOo0JAQAA\n",
              "AHgryjzgJoZhKH8CV9TtqTopaWTwHVvsAQAAAEwEZR5wo7Gt9vtqS+V0Oj/w+LDDrherT0kaOS8P\n",
              "AAAAABNBmQfcaM2sLIUHBqupv1vvttV/4PE3GivUOdSv+NBIrUrMnP6AAAAAALwSZR5wo5CAQK1L\n",
              "mSvp4lPtd1eOTLG/Nn2BAiz85wgAAABgYmgPgJuNbbV/f5m3Oxx6oXr0vHwmU+wBAAAATBxlHnCz\n",
              "zaP3zRe31KptoMf1/rdbqtXS36OY4FBdNSvLrHgAAAAAvBBlHnCzWeHRWhSXIqec2ldb6nr/nqqR\n",
              "LfZb0uYrOCDQrHgAAAAAvBBlHpgGY1vtXxm9os7pdLrK/I4MttgDAAAAmBzKPDANxrbaH6gr07DD\n",
              "rhNtdart6VB4YLDWp2abnA4AAACAt2FvLzANlsVbFRcSofODvXqrqVKH6s9IkjZbcxQWGGRyOgAA\n",
              "AADehpV5YBoEWCzaZB1ZgS+sLdXu0S3229liDwAAAOASUOaBaZI/utX+qfKjOtvZomBLgOssPQAA\n",
              "AABMhlvLfHl5ua666iplZ2dr1apVOnny5Aee43A4dM8992jRokXKzc3VrbfeqqGhIdfjzz//vHJz\n",
              "czVv3jzdeOON6urqcmdkwG02pGYrwLCoY7BPkrQ+dZ4ig0JMTgUAAADAG7m1zN9xxx26/fbbVVZW\n",
              "pvvuu08FBQUfeM5jjz2mY8eO6dixYzp9+rQsFosefvhhSVJPT49uvfVWPffccyovL1dKSoq+853v\n",
              "uDMy4DYxIWFamZjh+j1T7AEAAABcKreV+ebmZh09elS33HKLJOmmm25STU2Nzpw5M+55x48f15Yt\n",
              "WxQcHCzDMLR9+3Y98cQTkqQ9e/YoLy9PubkjW5H/6q/+Sr///e8/9DUHBwfV1dU17s1ut7vpMwQm\n",
              "b2xbfYBh0TVp801OAwAAAMBbua3M19TUKDk5WYGBIwPzDcNQenq6qqurxz1vxYoV+tOf/qSuri4N\n",
              "Dw/r6aefVmVlpSSpurpaGRnvrWRmZmaqoaFBNpvtoq/53e9+VzExMePejhw54p5PELgEn5q9VDNC\n",
              "wvXpuXmaERphdhwAAAAAXsr0AXgFBQXatm2bNmzYoA0bNig7O9v1A4DJ+ta3vqXOzs5xb6tXr57i\n",
              "xMClS42M1YnP/6P+4+rPmB0FAAAAgBdzW5lPS0sbt4rudDpVXV2t9PT0cc8zDEP333+/ioqK9Npr\n",
              "r2nBggVauHChJCk9PV1VVVWu51ZWVo5b7X+/kJAQRUdHj3sLCAhw02cIAAAAAIA53FbmExMTtXz5\n",
              "cj355JOSpGeeeUZWq1Vz584d97yBgQG1t7dLklpbW/W9731P9957ryRp27ZtOnbsmEpKSiRJP/3p\n",
              "T/W5z33OXZEBAAAAAPAKl7affYIeeeQRFRQU6IEHHlB0dLQef/xxSdJtt92m66+/Xtdff706Ozu1\n",
              "ceNGWSwWORwO3XXXXbruuuskSVFRUXr00Ud1ww03yGazadGiRfr1r3/tzsgAAAAAAHg8w+l0Os0O\n",
              "4U533323HnroIbNjAAAAAAAwZUwfgAcAAAAAACaHMg8AAAAAgJehzAMAAAAA4GUo8wAAAAAAeBnK\n",
              "PAAAAAAAXoYyDwAAAACAl6HMAwAAAADgZSjzAAAAAAB4Gco8AAAAAABehjIPAAAAAICXocwDAAAA\n",
              "AOBlKPMAAAAAAHgZyjwAAAAAAF6GMg8AAAAAgJehzAMAAAAA4GUo8wAAAAAAeBnD6XQ6zQ7hTjfe\n",
              "eKMyMzPNjvGx7Ha7jhw5otWrVysgIMDsOHAjvtb+g6+1f+Dr7D/4WvsPvtb+g6+1//C2r3VGRobu\n",
              "uuuuj3yOz5d5b9HV1aWYmBh1dnYqOjra7DhwI77W/oOvtX/g6+w/+Fr7D77W/oOvtf/wxa812+wB\n",
              "AAAAAPAylHkAAAAAALwMZR4AAAAAAC9DmfcQISEh+qd/+ieFhISYHQVuxtfaf/C19g98nf0HX2v/\n",
              "wdfaf/C19h+++LVmAB4AAAAAAF6GlXkAAAAAALwMZR4AAAAAAC9DmQcAAAAAwMtQ5j1AeXm5rrrq\n",
              "KmVnZ2vVqlU6efKk2ZHgBn/7t3+rzMxMGYah4uJis+PAjQYGBnTDDTcoOztbS5cu1TXXXKMzZ86Y\n",
              "HQtusnXrVi1ZskTLli3TunXrVFRUZHYkuNHjjz8uwzD03HPPmR0FbpKZmamcnBwtW7ZMy5Yt01NP\n",
              "PWV2JLjJ4OCg7rzzTs2bN0+LFy/WLbfcYnYkuEFbW5vrv+dly5YpOztbgYGBOn/+vNnRLlug2QEg\n",
              "3XHHHbr99ttVUFCgP/7xjyooKNBbb71ldixMsU9/+tO69957dfXVV5sdBdPg9ttv1/bt22UYhn78\n",
              "4x/rtttu0/79+82OBTd4+umnFRsbK0l69tlnVVBQoOPHj5sbCm5RWVmpX/7yl1qzZo3ZUeBmTz31\n",
              "lJYtW2Z2DLjZ3//938swDJWVlckwDDU2NpodCW4wc+bMcQtpDz74oA4cOKC4uDjzQk0RVuZN1tzc\n",
              "rKNHj7p+EnjTTTeppqaGVTwftH79elmtVrNjYBqEhoZqx44dMgxDkrRmzRpVVlaaGwpuM1bkJamz\n",
              "s9P1dYdvcTgcuu222/SjH/3Ip641AvxVb2+vHnvsMf3bv/2b6+/tWbNmmZwK0+Gxxx7TrbfeanaM\n",
              "KUGZN1lNTY2Sk5MVGDiyScIwDKWnp6u6utrkZACmysMPP6xPfepTZseAG33pS19SWlqavv3tb+uJ\n",
              "J54wOw7c4KGHHtLatWu1YsUKs6NgGnzpS1/S4sWLdeutt6qlpcXsOHCDs2fPKi4uTg888IBWrlyp\n",
              "devWqbCw0OxYcLPXXntN7e3t+uQnP2l2lClBmQcAN3rggQd05swZffe73zU7CtzoN7/5jWpqavSv\n",
              "//qvuu+++8yOgyn27rvv6plnntE//MM/mB0F0+DgwYN65513dOzYMcXHx+vLX/6y2ZHgBjabTVVV\n",
              "VVqwYIGOHj2q//qv/9LNN9+spqYms6PBjR577DF96Utfci2kejvf+Cy8WFpamhoaGmSz2RQYGCin\n",
              "06nq6mqlp6ebHQ3AZXrwwQe1a9cuvfLKKwoPDzc7DqbBl7/8Zf3lX/6l2traNHPmTLPjYIocOnRI\n",
              "lZWVmjdvniSpsbFRt99+uxoaGvS1r33N5HSYamPfgwUFBenv/u7vlJ2dbXIiuEN6erosFou+8IUv\n",
              "SJLy8vI0e/ZsnThxQklJSSangzv09PTo6aef9qnZZKzMmywxMVHLly/Xk08+KUl65plnZLVaNXfu\n",
              "XJOTAbgcDz30kH7/+9/r5ZdfHnemGr6lo6ND9fX1rt8/99xzmjlzpk8M1cF7vva1r6mhoUGVlZWq\n",
              "rKzUmjVr9Itf/IIi74N6e3vV0dHh+v3vf/975eXlmRcIbhMfH6/8/Hy9+OKLkqRz587p3Llzmj9/\n",
              "vsnJ4C5PPfWUli5dqtzcXLOjTBlW5j3AI488ooKCAj3wwAOKjo7W448/bnYkuMEdd9yhP//5z2ps\n",
              "bNS1116rqKgoBh36qNraWn3jG99QVlaWNm3aJEkKCQnRm2++aXIyTLXOzk595jOfUX9/vywWixIS\n",
              "EvT8888zBA/wUk1NTbrppptkt9vldDqVlZWl3/zmN2bHgpv8/Oc/16233qr77rtPFotFjzzyiFJT\n",
              "U82OBTd57LHH9NWvftXsGFPKcDqdTrNDAAAAAACAiWObPQAAAAAAXoYyDwAAAACAl6HMAwAAAADg\n",
              "ZSjzAAD4uWXLlqm7u3vSf+7Tn/60fvWrX019IAAA8LGYZg8AgJ8rLi42OwIAAJgkVuYBAPBzhmG4\n",
              "7tbOzMzUP/7jP+rKK6/U7Nmz9a//+q+u55WUlOiqq67SwoULdcMNN6irq8v1WHd3t7761a9q9erV\n",
              "WrJkiW6//XYNDQ2ptLRUVqtVFRUVkqQHH3xQ27Ztk8PhmNbPEQAAX0OZBwAA43R0dOj111/XW2+9\n",
              "pR/84Aeqq6uTJH3xi1/UrbfeqpMnT+o73/mODhw44Poz3/jGN7Ru3TodOXJEx48fl8Ph0MMPP6yc\n",
              "nBz94Ac/0Gc/+1nt379fP/nJT/TEE0/IYuFbEAAALgfb7AEAwDif//znJUnx8fHKysrSuXPnFBUV\n",
              "peLiYhUUFEiSFi9erKuvvtr1Z5577jm9/vrreuihhyRJ/f39CggIkCT9xV/8hfbt26drr71WhYWF\n",
              "SkhImN5PCAAAH0SZBwAA44SGhrp+HRAQIJvNdtHnGYbh+rXT6dQzzzyj7OzsDzzPZrPp3XffVVxc\n",
              "nGuVHwAAXB72uAEAgI8VHR2tvLw8/eY3v5EknTx5UocPH3Y9fsMNN+j73/++q/i3t7frzJkzkqS/\n",
              "//u/V05Ojg4dOqR77rnH9X4AAHDpKPMAAGBCfvOb3+gXv/iFFi1apH/4h3/Q+vXrXY/98Ic/VFhY\n",
              "mJYtW6YlS5YoPz9flZWVev755/XCCy/oJz/5iebOnauHHnpIn/3sZzUwMGDiZwIAgPcznE6n0+wQ\n",
              "AAAAAABg4liZBwAAAADAy1DmAQAAAADwMpR5AAAAAAC8DGUeAAAAAAAvQ5kHAAAAAMDLUOYBAAAA\n",
              "APAylHkAAAAAALwMZR4AAAAAAC9DmQcAAAAAwMtQ5gEAAAAA8DL/P8BTs6dF3qC3AAAAAElFTkSu\n",
              "QmCC\n",
              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-f620d9ad-7972-42ab-8166-3319277e8bd9\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-f620d9ad-7972-42ab-8166-3319277e8bd9\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "import seaborn as sns\n",
              "def _plot_series(series, series_name, series_index=0):\n",
              "  palette = list(sns.palettes.mpl_palette('Dark2'))\n",
              "  xs = series['index']\n",
              "  ys = series['process fidelity']\n",
              "  \n",
              "  plt.plot(xs, ys, label=series_name, color=palette[series_index % len(palette)])\n",
              "\n",
              "fig, ax = plt.subplots(figsize=(10, 5.2), layout='constrained')\n",
              "df_sorted = _df_6.sort_values('index', ascending=True)\n",
              "_plot_series(df_sorted, '')\n",
              "sns.despine(fig=fig, ax=ax)\n",
              "plt.xlabel('index')\n",
              "_ = plt.ylabel('process fidelity')"
            ],
            "text/html": [
              "      <div class=\"colab-quickchart-chart-with-code\" id=\"chart-f9aaa0f1-f86e-44b4-9715-1ecc5880670e\">\n",
              "        <img style=\"width: 180px;\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA/MAAAITCAYAAABLz0yVAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90\n",
              "bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9h\n",
              "AAAPYQGoP6dpAABsu0lEQVR4nO3deXzdZZ3+/+tzTva9zdYkJ0vTNkn3pFAolK4phVZZCo6Oo2J/\n",
              "gqAzfHXcYPx+xxmdUcCRQVFEEBgYRAWUgogtW7qzldKmdEvSJM2+p82+npzz+yPJoaEFkjYnn7O8\n",
              "no9HH7Q5J8kVAzXXue/7fRtOp9MpAAAAAADgNSxmBwAAAAAAABNDmQcAAAAAwMtQ5gEAAAAA8DKU\n",
              "eQAAAAAAvAxlHgAAAAAAL0OZBwAAAADAy1DmAQAAAADwMpR5AAAAAAC8jM+X+fvvv9/sCAAAAAAA\n",
              "TCqfL/OVlZVmRwAAAAAAYFL5fJkHAAAAAMDXUOYBAAAAAPAylHkAAAAAALwMZR4AAAAAAC9DmQcA\n",
              "AAAAwMtQ5gEAAAAA8DKUeQAAAAAAvAxlHgAAAAAAL0OZBwAAAADAy1DmAQAAAADwMpR5AAAAAAC8\n",
              "DGUeAAAAAAAvQ5kHAAAAAMDLUOYBAAAAAPAylHkAAAAAALwMZR4AAAAAAC9DmQcAAAAAwMtQ5gEA\n",
              "AAAA8DKUeQAAAAAAvEyA2QEAAAA8Qa99QI09nWru7VRTb6eazvx9b6eae7vUPdiv6OBQTQsOU0xw\n",
              "2Mg/QzUtOPyM33/wWERgsAzDMPtLAwD4IMo8AADwWUMOh071d6u5t/OMot6lpp4ONfd2nVHUO9U1\n",
              "2D/pnz/AsCjmHCX/7BcDzvxzmEIDAnkRAADwsSjzAADA6/QMDqix94xCfmY5Hyntzb2daunr1pDT\n",
              "Me6PG2INVGJYpOJDI5UQOvrPCCWERSkhNFJhgUFq7+/V6f4etfX3jPyzV6f7u0f++cHb+4fssjsd\n",
              "aunrUktf14S+vmBrgGKCQkdeCBh5ASAkTDFBI/88xwsA04LDFGzlRzsA8Bf8jQ8AADzCkMOh1r7u\n",
              "D21t/2BF/czfd9sHxv1xDRmKDQlXQtgZ5Tw0SvGhEUoIjVRC2AfFfTK3xffaB8eU+9HfD/+5V23n\n",
              "eAHgdF+P7E6H+ofsauztVGNv54Q+Z2hA4IR2AEwLDlN0cKgCLdZJ+ZoBAFOHMg8AANyqe7D/I86g\n",
              "d6qpp0tNIyvsLX1dcjid4/64oQGBSgiNcq2cu8p56Acr6wlhkYoNCVeACWU1NCBQoQHRSg6PHvf7\n",
              "OJ1OddsHhst9X8+HdgCcuRNg7J/bBnrkcDrVax9Ur71ddd3tE8oaGRh81gsA5zoe8MFzQhUVFCqr\n",
              "hVnKAGAWyjwAAJiwIcfw9vGPO4Pe1DP8+54JrqLHhYZ/UMZHi7lr9Xx4VT0hLFLhAUE+d67cMAxF\n",
              "BAYrIjBYtohp434/h9OhzoH+MTsAPvEFgP5utQ/0SZI6B/vVOdiv6q7T488qQ1FBIWcfAQgK/dCR\n",
              "gLGDAiMZCggAk4IyDwAAXLoG+z9iBX3sn1v7uie0ih4WEORaKR9b1EfPo0coPtS8VXRvZzEsig4O\n",
              "VXRwqDIUO+73G3I41D7w4aL/0S8AjM4G6Brsl1NOtQ/0qn2gVxWdreP+nFbDco4V/4/aAfDB7xkK\n",
              "CABjUeYBAPBxdseQWvu6XYX8w9etNfV0qKl3eJV9IqvoFsNQXEiE4keK+OjguPjQSCV+aEU9PDDY\n",
              "jV8hzpfVYtH0kHBNDwmf0PsNDNldLwKc7vu4FwDGvr1vaFBDzuHZCK193RP6nEEW6/iOAHxol0BI\n",
              "QOCEPg8AeAvKPAAAXmj0bPVwER8u5Y0jW92bRwanja6ot/Z1y6nxr6KHBwSdXc7PUdRjQ8I5M+2n\n",
              "gqwBrn8fJqLXPjiOHQCjv3pdxwYGHUMacAyd91DAsUcAws86CvDhXQIxwWEMBQTg8SjzAAB4ELtj\n",
              "SC193Wru+eiJ7qNv77UPjvvjjq6if3AG/YyJ7iPXro0OkGMVHe4yOhQwaYJDAXvsA65t/uPZATD6\n",
              "YsCQ0+EaCljfM7GhgHOiE/R/Fq/RdTMX86IVAI9EmQcAwM2cTufwWfTRct5z7vPozb1dE15FjwgM\n",
              "Pvu6tbCzJ7pPD2YVHd7JMAyFBwYrPDBYKREx434/h9OhrsEB1zn/s48EnHtnQMdAn5xy6kR7k76x\n",
              "+xk98P4OfSfvSm1Iny+LwX9DADwHZR4AgAtgdwyp6HSDGns61djbMVLUP7hubXRVvW9oYqvo8SER\n",
              "rjPnZ25tjw874zx6aKTCAoPc+NUB3stiWBQVFKKooBClT+A0wJDDodP9PXr6xLv6zeFdKmlr0m07\n",
              "fq8F05P1vSXrtdaWzSA+AB7BcDonMIrWC33729/WfffdZ3YMAIAPcTqdKmyp0Zayg/rryffV0tc1\n",
              "rveLCAw+a8Xctap+xkR3VtEBz9De36tHju3VI0f2qHtkOOSS+DTdsWS9rkiebXI6AP6OMg8AwDid\n",
              "7GjR82WFer68UCc7Wlxvjw4KUVpk7Jhz5wlhUWf8PlLxIayiA97qVF+3fnN4tx4//qZrl81lMzJ1\n",
              "x5L1WpqYYW44AH6LMg8AwMdo7evSi+Xva0v5QR1srna9PcQaqKvS5+mGzDytTJnD5GvADzT2dOiB\n",
              "93fq98XvaMAxJElanZKl7y1Zr8VxNpPTAfA3lHkAAD6kZ3BAr1Qd0/PlB7Wr9oSGnA5Jw2fZVyTP\n",
              "0abMXF2dPl8RTH0H/FJtV5vuP7Rdz57YL/vI3w9Xpc3Td/PWa+70GSanA+AvKPMAAGh4kN3e+jJt\n",
              "KTuolyuPqmfkfKwkLY6zaVNmrq6duVgJYRO7VxuA76roaNXPC1/XlrJCOeWUIUPXzlykb+et06zo\n",
              "eLPjAfBxlHkP4XQ61drXrbjQCLOjAIDfcDqdOtRSo+fLC/XiyUNq7v1gkF165HRdn5mrG2bl8UM5\n",
              "gI9V0tao+w6+rpcqDksa3sXzd7OX6J8X5ys1crrJ6QD4Kq6m8wAn2pp002uPy+5waN9n/4XrTgDA\n",
              "zSo6WvV8+UE9X1ao8jMG2U0LDtO1Mxfrhlm5WhKfxt/HAMYlKyZRD635go621uneg6/pterjeubE\n",
              "e9pSVqjPZy3V/1m0Rknh0WbHBOBjKPMewBYxTc29XeobGtTx0w2aNz3J7EgA4HNa+7r015OHtaXs\n",
              "oA40V7neHmIN1FVp87RpVq5WpWQxyA7AeZsfm6zH131ZB5qrdO+B17S77oSeLHpbz5zYr5tylumf\n",
              "Fq5mFyaASUOZ9wChAYFanjRLBTVF2l5TRJkHgEnSax8ZZFdWqF21Ja5BVRbD0BVJs3XDrFxdnb6A\n",
              "QXYAJtWS+DT94aqb9VZDue498KreaazQI0f36qnid3TzvOW6bcFKTQsOMzsmAC9HmfcQ61JzVFBT\n",
              "pILqIt2+aI3ZcQDAa9kdQ3rjjEF23WcMslsUm6JNs4YH2SWGRZmYEoA/uGxGpv684Tbtrjuh/zrw\n",
              "qg611OiB93fqf4+/pVsXrNAt865QZFCI2TEBeCnKvIdYa8uRJL3XXKXTfd2aFhJuciIA8B5Op1Pv\n",
              "t9bq+bKD+suHBtmlRkzTDbPytCkzV7NjEkxMCcAfGYahVSlZWpk8R69VH9fPDryq46cb9N8HX9dj\n",
              "x97UPy5cpc05lyksMMjsqAC8DGXeQ6RExChn2gwVnW7QjtoS3TArz+xIAODxKjtb9UJZobaUF6qs\n",
              "vdn19mnBYbpm5iLdkJmnixIYZAfAfIZhaH3aPK1LzdHfKo7o3oOvqay9WXft36ZHju7R7YvW6AtZ\n",
              "lygkINDsqAC8BGXeg+TbclR0ukEFNUWUeQD4CKf6uvXXk+/r+fJC7W+qdL092BowPMguc3iQXZCV\n",
              "/4sD4HkshkXXzFykDenz9UJ5oe47WKCqrlP693f+qocO79Y3c9fqc3MuZhgngE/ETzoeJD81R78+\n",
              "vFM7a0pkdwwpgL/EAUDS8CC716qOa0v5weG/I88YZLc8aZZuyMzT1enzOXsKwGsEWKz6zOyLdF1m\n",
              "rp45sV/3F25XfU+7/uXN5/Wbw7v0rdx8bcrMk9ViMTsqAA9FmfcgS+JTFR0UqvaBXh1ortYliRlm\n",
              "RwIA0ww5HHqjvkzPlx/U1oojYwbZLYxN0abMXF2buVgzGGQHwIsFWqz6Yval+sysJfp9yT498P4O\n",
              "VXae0j/v+ZMeeH+nvpN3pT6VsUAWg1IPYCzKvAcJsFi12palv5QfUkF1EWUegN9xOp060lqnLeUH\n",
              "9WL5ITX2droeS42Ypk2Zudo0K09zGGQHwMeEBATq5nnL9fk5S/VE0Vt68PAulbY36+s7/6C502bo\n",
              "e0vW68rUucwAAeBCmfcw+bac4TJfU6TvX3y12XEAYEpUdZ7SC+WF2lJ2UKVnDLKLCQ7TNRkLdcOs\n",
              "PF2ckM4PsQB8XlhgkP5x4Sp9KftSPXpsr357ZI+On27QVwqe1OI4m+5Ysl4rk+fw9yEAyrynWZOS\n",
              "JYthqOh0g2q72pQSEWN2JABwi9N93Xqp4rC2lB3Uux8aZHdl6lzdMCtPqxlkB8BPRQaF6Fu567R5\n",
              "7uV6+MhuPXbsDR1qqdEXXv0fXZqYoe8tWa9lMzLNjgnARPyE5GGmhYRrSXya9jdVantNkb6Us8zs\n",
              "SAAwaXrtg3q9+rieLzuoHbUlGnQMSZIMDQ+y2zQrVxvSFyiKQXYAIGn4qs1/uehq3TxvuR48vEtP\n",
              "Fr2tdxor9Jltv9XK5Dn67pIrtSQ+zeyYAExAmfdA+bYc7W+qVAFlHoAPGHI49FZDubaUHdTWyiPq\n",
              "Gux3PTZ/epJumJWna2cuVlJ4tIkpAcCzxYdG6t8v+bRunb9Cv3p/h/5QvE+7605od90JXZk6V9/N\n",
              "u1LzY5PNjglgClHmPVB+arZ+euAV7a0rU699UKEBgWZHAoAJcTqdOnqqTs+XFeqFk4fU2NPheswW\n",
              "EaPrM3O1KTNP2dMSTUwJAN4nKTxad112vb62YKV+UVigP5cd0GvVx/Va9XF9OmOhvpN3JUNCAT9B\n",
              "mfdAc6clKSksWvU97XqroVxrbdlmRwKAcanuPKUXyg/p+fKDKmlrcr09OihU18xcpE2ZuVqamM4V\n",
              "SwBwgdIip+u+FX+nf1q0WvcVvq4Xy9/XSxWHtbXyiDZl5upbueuUERVrdkwAbkSZ90CGYWitLVu/\n",
              "L9mnguoiyjwAj3a6v0d/O3lYW8oPal9jhevtwdYArUudqxsyc7Xalq1gBtkBwKSbFR2vX6/6vG5f\n",
              "uEb3HnxVr1Qd03NlB/VC+SF9bs7F+ubitQxUBnwUP1l5qPzUHP2+ZJ+21xTJ6byW60cAeJRe+6AK\n",
              "qo/r+fJCba8pHjPI7vKkTG3KzNXGjIUMsgOAKTJ3+gw9ln+TDrXU6GcHXtXO2hL9oWSf/lz6nr6Q\n",
              "faluX7RaiWFRZscEMIko8x7qiqTZCrYGqLrrtE60NykrhnOlAMw15HDo7YZybSk/qK0VR9R5xiC7\n",
              "edOTdENmnq7NXKxkBtkBgGkWx9n01PqvaF9jhf7rwCt6u+GkHj/+pv5Y8q42z71M/7hwlaaHhJsd\n",
              "E8AkoMx7qLDAIC2bkaldtSUqqC6izAMwhdPp1LFT9dpSXqgXygvHDLJLCR8ZZDcrVznTZpiYEgDw\n",
              "YZckZuhPV9+qN+rL9F8HXtWB5io9dGS3flf0tm6Zf4Vunb9C0cGhZscE3G7I4VBhS7W21xRrZlSc\n",
              "PjN7idmRJg1l3oPl27KHy3xNkb6+cJXZcQD4kZqu03qhvFDPlxWquK3R9fbooBB9OmORNs3K1SWJ\n",
              "GQyyAwAPZhiGrkiereVJs7S9plg/O/Cqjpyq0/2HtuuJ42/pawtW6ivzLld4YLDZUYFJ1dzbqZ01\n",
              "JdpRW6xdtSfUPtArSbo0MYMyj6mRn5qjf3vnr3q3sVLt/b28egrArdr6e/RSxWE9X1aodxpPut4e\n",
              "ZLFqXepcbZqVq7W2HAbZAYCXMQxD+ak5WmPL0rbKo/rvg6+ppK1JPz3wih49tlf/tHC1vpSzjOuQ\n",
              "4bXsjiEdaK7Wjppi7awt0eHW2jGPRweFaEXyHK1LnWtSQvfgJzIPlh4Zq9nR8Sptb9buuhO6ZuYi\n",
              "syMB8DF99kEV1BRpS9lB7agp1sAZg+yWzZipG2blaWP6Al5MBAAfYDEs+lTGQl2dNl9/OXlI/33w\n",
              "dVV2tuo/3v2bHj66R99YtEafz1qqIF60hRdo6OnQzppi7agt0Z66E+oY6Bvz+KLYFK1OydIaW7by\n",
              "4lMVYLGalNR9+C/Vw+XbclTa3qyC6iLKPIBJ4XA69FbDST1fdlBbK4+M+T+/udNmaNOsPF0/c7GS\n",
              "ucoIAHyS1WLRDbPydM3MRfpz6QH9orBAtd1t+n9v/0W/ObJL/7w4X5+ZvcQnyw+816BjSPubKrWj\n",
              "plg7aop1/HTDmMdjgsO0KmWO1qRkaVVKluJDI01KOnUo8x5ubWqOHj66Rztqi+VwOjifCuC8HTtV\n",
              "ry1lB/WX8kOq72l3vT0pLFqbZuVqU2ae5k5nkB0A+ItAi1Wfz1qqG2bl6Y8l7+qXh7arpqtN333j\n",
              "Of368C59J2+drp25iJ8/YZq6rjbtqC3Rjppi7a0vVdcZN+kYMrQ4zqbVtiytSclWbpxNVot//btK\n",
              "mfdwlyRmKDIwWK193SpsqdGS+DSzIwHwInVdbXq+vFBbyg6OGWQXFRSiT2Us1A2z8nQpg+wAwK8F\n",
              "WwO0ee5l+tyci/Rk0dv69fu7dLKjRbfvelq/OrRD311ypa5Omy/DMMyOCh83MGTXu40V2l5bop01\n",
              "xWN+dpGk2JBwrUrJ0uqULK1KmaPYkAiTknoGyryHC7RYtSolSy9VHFZBdRFlHsAnau/v1d8qD2tL\n",
              "2UG93TB2kF1+ao42ZeZprS1bIQw6AgCcITQgSLctWKkvZF+q/zn2hh46slvFbY366vantDA2Rd9b\n",
              "sl5rUrIo9ZhUNV2nXVvn99aXqcc+4HrMYhjKi0vVGlu2VqdkaVFcCgsQZ6DMe4F8W46rzH9vyXqz\n",
              "4wDwQP1DdhVUF+n58oMqqC5yDbKTNGaQXUxwmIkpAQDeICIwWN9YvFY35SzTI0f36tGje3W4tVY3\n",
              "vfa4Lk5I1/eWrNfypFlmx4SX6rMPal9jhXbUDhf40vbmMY/Hh0ZodUqWVqdka2XybE0LCTcpqedz\n",
              "a5k/ceKEvvzlL6ulpUXR0dF64oknNH/+/DHPcTgcuuOOO/Tyyy/Lbrdr+fLl+s1vfqOgoCBJ0k9/\n",
              "+lP97//+r4KCghQSEqJf/vKXuuSSS9wZ2+OssWXLkKEjp+rU0NOhGWFRZkcC4AEcTofeaazQlrKD\n",
              "2lpxWO1nDLLLjknUDbPydH1mrlIYZAcAOA8xwWH63pL1+sq8y/Xg4d164vib2t9Uqc+9/IiWJ83S\n",
              "9/LW6+LEdLNjwgtUdLRq58jZ9zcbytRrH3Q9ZjUsuighTWtShlff58cmsfo+Tm4t87fddptuvfVW\n",
              "bd68WX/+85+1efNmvfvuu2Oe89hjj+nAgQM6cOCAAgMDdeutt+r+++/X9773PRUWFurBBx/U0aNH\n",
              "FRERoaeeekq333679u3b587YHicuNEKL42wqbKnW9poi/UOWf72YAWCs46ca9Hz5Qb1QXqi67g8G\n",
              "2c0Ii9KmzDxtmpWredOTTEwIAPAlsSER+sHSjbp1/hX61fs79PvifXqjvkxv1P9Ga23Z+l7eei2M\n",
              "SzE7JjxIr31QbzeUD2+fry3RyY6WMY8nhkW5ro1bkTSbK3DPk9vKfFNTk/bv369XX31VknTjjTfq\n",
              "9ttvV2lpqWbPnu163qFDh7Ru3TrXSvyGDRv0wx/+UN/73vdkGIYGBwfV3d2tiIgItbW1yWazuSuy\n",
              "R8tPzVZhS7UKqinzgD+q62rTCycPaUvZQRWdcRVLZGCwa5DdshkzeSUbAOA2iWFR+vGy6/S1BSt1\n",
              "/6HtevbEe9peU6ztNcXakD5f38m7UjnTuBXFHzmdTp3saHFNnn+roVz9Q3bX4wGGRRcnpmtNSrbW\n",
              "2LI1d9oMZi9MAreV+erqaiUlJSkgYPhTGIahtLQ0VVVVjSnzF110kR5++GHdfvvtCg0N1bPPPquK\n",
              "igpJ0uLFi/Wtb31LM2fO1PTp0xUcHKzdu3d/5Ofs7+9Xf3//mLcNDQ19xLO9S74tR/998HXtqStV\n",
              "/5BdwVbGHQC+rr2/V1srj7gG2TnllDQ8GHOtLVs3zMpTvi2HQXYAgClli5imny2/Uf+4cJV+Xlig\n",
              "58sKta3yqF6uPKbrMhfr27nrlBkdZ3ZMuFnP4IDeaijX9ppi7awtVmXnqTGPJ4VFa40tW2tsWboi\n",
              "abYig0JMSuq7TG+EmzdvVmVlpVatWqXQ0FCtW7fOtZp/8uRJbdmyRaWlpUpOTtYDDzygz33uc9q7\n",
              "d+85P9bdd9+tH/3oR2PetmzZMrd/DVNhQWyyEkIj1dTbqXcaTmplyhyzIwFwg/4hu7bXFOn5skIV\n",
              "1BSNeVX70sSZ2jQrV5/KWKhpDLIDAJhsZlScfrnyc/qnhat1X+Hr+lvFYb1QXqi/nnxfn5m9RP+8\n",
              "eK1SI6ebHROTxOl0qrS9WTtri7WjpkTvNJ4c83NKoMWqSxIzhs++27KUHZPI6rubGU6n0+mOD9zU\n",
              "1KTZs2fr1KlTCggIkNPpVFJSkvbu3TtmZf7Dnn76af3617/Wnj17dO+996qkpES//e1vJcm13b6/\n",
              "v9+1Lf9M51qZ/8EPfqD7779/cr84k3xn75/1zIn9unnecv3o0mvMjgNgkjicDu1rrNCWskL9reL9\n",
              "swbZbZqVp+szF8sWMc3ElAAAfLzDLbW69+BrKqgpkjRc7v4ha6n+z+K1DHD2Ut2D/dpbVzo8vK62\n",
              "WDVdbWMet0XEuLbOX540SxGBweYE9VNuW5lPSEjQkiVL9NRTT2nz5s167rnnZLPZziryfX196u3t\n",
              "1bRp09TS0qJ77rlH//mf/ylJyszM1OOPP66uri5FRETopZdeUlZW1jmLvCQFBwcrOHjsv0BWq9U9\n",
              "X6AJ8m05eubEfhVUF1HmAR9QdLpBz5cV6oXyQtV2t7nePiMsStdn5uqGWbmaOy2JV7UBAF5hYVyK\n",
              "/vfKzXqvqVI/O/Ca9taX6n+L3tbTJ/bryznL9I8LVysuNMLsmPgYTqdTxW2N2lFTrJ21JdrXWKHB\n",
              "M667DbJYtWxGptbYsrQmJVuzouP5OcVEbt1m//DDD2vz5s266667FBUVpccff1ySdMstt+jaa6/V\n",
              "tddeq/b2dq1evVoWi0UOh0Pf/OY3dc01w0V106ZNevfdd3XxxRcrODhY4eHh+sMf/uDOyB5tZcoc\n",
              "BVqsquhsVXl7szKj482OBGCC6rrb9ZfyQj1fXqhjp+pdb48MDNbGjIW6ITNXy2ZkymphkB0AwDtd\n",
              "lJCup6++RW/Wl+lnB17Vu02V+u3RvXqqeJ9unrdcty1YoRiOi3mMzoE+7RlZfd9ZWzzmphxJSo+c\n",
              "rtUp2Vpry9ZlMzIVFnjuhVVMPbdts/cU3/72t3XfffeZHWPS/P3Lj2pvfan+/ZJP6avzV5gdB8A4\n",
              "dAz0aWvFYW0pO6i3zjHIbtPIILtQBtkBAHyM0+nUztoS/ezAq3q/tVaSFBUUolvnr9DN85YzFM0E\n",
              "TqdTx0/Xa3vNcHnf31gpu9PhejzYGqDLZmQOD69LydbMqFhW3z2U6QPwMDH5qdnaW1+qgupiyjzg\n",
              "4Qqqi/Rs6Xt6vfr4hwbZZWhTZp4+NZNBdgAA32YYhtbYsrU6JUuvVB3Tzw68quK2Rt178DU9duwN\n",
              "/ePCVdo89zKFBrDa607t/b3aU3dCO2pLtLO2RI09HWMenxkVpzUj974vm5HJAoOXoMx7mXxbjn60\n",
              "7296p/Gkugb7GTIBeKjnywr1f3Y/7fpzVkyCbpiVp+tmLmayLwDA7xiGoavT52t92lz99eRh/ffB\n",
              "11Te0aKf7N+m3x7do/+zaI2+kH0p1y9PEofToaOt9dpRO3z2/b2mKg2dsfoeYg3U8qRZrhdaMqJi\n",
              "TUyL88V/LV4mMzpeGZGxquhs1e7aE9qYscDsSADO4ZkT+yVJG9MX6JuL12redAbZAQBgMSy6LnOx\n",
              "PpWxQFvKDurnhQWq7jqtf3vnr3royG59c3G+PjvnIgVafGeI9VQ53d+j3bUntKOmWLvqStTc2zXm\n",
              "8TnRCVo9MrjuksQMhbD67vUo814oPzVHjx17QwU1RZR5wAOd7uvWWw3lkqTvX3y1ZkbFmZwIAADP\n",
              "EmCx6rNzLtb1mbl65sR+/eLQdtV1t+vON7fowcM79a3cddqUmctA2I/hcDr0fkvt8Op7TYkOtlTL\n",
              "ccY4tLCAIF1xxuo7OwN9D2XeC+Xbhsv8jppiOZwOWQz+kgM8yavVxzXkdGjutBkUeQAAPkaQNUBf\n",
              "ylmmz8y+SL8vfke/en+HKjtP6Z/3PKtfv79T38lbp40ZC/h5d8Spvu7hO99rirWr9oRO9XePeTw7\n",
              "JlGrbdlam5KlpYkZCuLYgk/ju+uFLp0xU2EBQWrq7dSR1jotirOZHQnAGbZVHpEkds4AADBOoQGB\n",
              "umX+Ffp81lI9fvwt/ebwLp1ob9LXdv5B86Yn6Xt5V2pd6ly/O7I25HCosKXGtfp+qKXGdSuOJEUE\n",
              "BmtF8mytTsnWmpQsJUfEmBcWU44y74WCrQFakTxbr1QdU0FNEWUe8CCdA33aXXtCkrQhnTIPAMBE\n",
              "hAcG6/ZFq3VTzjI9cnSPHjm6V8dO1ev/K3hSuXGpumPJeq1Inu3Tpb65t1O7aku0o6ZEu+pOqK2/\n",
              "Z8zj86YnaU1KtlbbsnRxQjrzBfwYZd5L5afmDJf56mJ9K3ed2XEAjNheU6wBx5Ayo+KUHZNodhwA\n",
              "ALxSVFCIvpN3pb4y93L95shu/c+xN1XYUq1/ePUxXZo4U3csWa9LZ8w0O+aksDuGdLC5WjtqirWj\n",
              "tkSHW2vHPB4VFKKVyXO0OiVLq23ZmhEWZVJSeBrKvJdaa8uRJB1qqVFLb5fiQiNMTgRAkraObLHf\n",
              "kL7Ap1cNAACYCtNCwvV/L96gW+ZdoV8f3qnfFb2tdxpP6sZtD2tVSpa+l3elcuNTzY45YY09Ha6z\n",
              "73vqTqh9oG/M4wtjU7R65N73JfGpCmD1HedAmfdSM8KitGB6so6cqtOOmmL93ZyLzI4E+L1e+6C2\n",
              "1xRJ4rw8AACTKSEsUj+69BrdNn+Ffvn+Dj1d8q521ZZoV22J1qfO1XeXrNe86Ulmx/xIg44hvddU\n",
              "qR01JdpRW6xjp+rHPB4THKZVI6vvq1KylBAWaVJSeBPKvBfLT83RkVN1KqgposwDHmBXbYl67YNK\n",
              "CY/RotgUs+MAAOBzkiNidM/lm/T1hSv1i8ICPVd2UK9WH9er1cd17cxF+nbuOs2OSTA7piSprrtd\n",
              "O2uLtaOmWHvrStU52O96zJChRXEpWmMbHlyXG5fKNXyYMMq8F8tPzdH9h7ZrV22JBh1DDL8ATLbN\n",
              "tcV+PlvsAQBwo/TIWP18xWf1TwtX677C1/Xiyff14sn39VLFYd2Qmadv5eUrPTJ2SjMNDNn1blPl\n",
              "8Nn3mmIVtzWOeXx6cLhWpczRalu2VqfMUWwIx2RxYSjzXiw3zqbYkHC19nXr3cYKXZ40y+xIgN8a\n",
              "GLLrterjkphiDwDAVJkdk6AHV/+Dbl+0WvceeE2vVh/Xn8sO6IXyQv191lJ9Y/FaJYdHu+3z13Sd\n",
              "1s6RrfN760rVbR9wPWbIUF58qtbYsrQ6JVuLYlNYfcekosx7MYth0ZqUbP257IAKqoso84CJ3mwo\n",
              "V8dAn+JDI3RxQrrZcQAA8Cvzpifrf9Z9WQebq3Xvwde0q7ZETxW/oz+VvqcvZl+qf1q4elLOofcP\n",
              "2bWv8eTI6nuJTrQ3jXk8LiTCNbhuZfJsTQsJv+DPCXwUyryXy0/NGS7zNUX6wSWfMjsO4Le2Vgxv\n",
              "sb8qbT6vugMAYJK8+FT9fv1X9E7DSf3XgVf1TuNJPXbsDf2hZJ/+v7mX6+sLVk64YFd2trpW39+o\n",
              "L1OvfdD1mMUwdFF82sjZ92zNj02SxeDnAEwNyryXW5k8R1bDotL2ZlV2tk752SAA0pDDoVeqjkoa\n",
              "Pi8PAADMdemMmfrzhlu1p65U/3XgVRW2VOvBw7v0ZNHb+ur8K/TV+SsUFRRyzvfttQ/q7YZy19Vx\n",
              "5R0tYx5PDI3UaluW1qRk64rk2YoJDpuKLwk4C2Xey0UHh2ppYrrebjipguoifWXecrMjAX7n3aYK\n",
              "tfZ1KzoohOMuAAB4CMMwtDJljlYkz1ZBTZH+68CrOnaqXj8vLND/HHtTX1+4Sl+Ze7nCAoNU3t4y\n",
              "Mnm+RG81lKtv6IPVd6th0cUJ6a7J8/OmJzHoFh6BMu8D8m05w2W+ppgyD5hgdIv9lanzuFUCAAAP\n",
              "YxiG1qXO1VpbtrZWHNG9B19TaXuz7nnvZT16dK/CA4NV2dk65n1mhEW5ts5fkTz7I1fxATNR5n1A\n",
              "fmqOfrJ/m95uKFfP4IDCAoPMjgT4DafTqW2Vw1vsN2YwxR4AAE9lMSz69MxF2pC+QM+XF+rnha+r\n",
              "svOUWvq6FGixauno6rstW9kxiay+w+NR5n3AnOgEpUZMU3XXae2tL9X6tHlmRwL8RmFLjep72hUW\n",
              "EKQVyXPMjgMAAD6B1WLRZ2Yv0XWZi/V69XFZDYsuT5qliMBgs6MBE8KoRR9gGIbW2nIkSQXVRSan\n",
              "AfzLtsrhLfb5thyFBgSanAYAAIxXoMWqDekLtD5tHkUeXoky7yPyU4fL/PaaYjmdTpPTAP7B6XS6\n",
              "zsszxR4AAABTiTLvIy6bkakQa6Dqe9p1/HS92XEAv1B0ulEVna0KtgZo7cgLagAAAMBUoMz7iNCA\n",
              "QF2RPHwlVkF1sclpAP+wtfKwJGll8hy25wEAAGBKUeZ9SP7oufkazs0DU8E1xT6dKfYAAACYWpR5\n",
              "HzI6BO9Ac5VO93WbnAbwbeXtLSo63aAAw6J1aXPNjgMAAAA/Q5n3ISkRMcqZNkMOp1M7akvMjgP4\n",
              "tNEp9pcnzdK04DCT0wAAAMDfUOZ9DFvtgakxusWeKfYAAAAwA2Xex4xeUbezpkR2x5DJaQDfVNfV\n",
              "psKWahkydFUaZR4AAABTjzLvY5bEpyo6KFTtA7060FxtdhzAJ22rGl6VX5qYroSwSJPTAAAAwB9R\n",
              "5n1MgMWq1bYsSVJBNVvtAXfYWjF8Xp4t9gAAADALZd4HcW4ecJ/m3k7ta6yQJG3gSjoAAACYhDLv\n",
              "g9akZMliGCo63aDarjaz4wA+5ZWqY3LKqUWxKbJFTDM7DgAAAPwUZd4HTQsJ10XxaZKk7azOA5Nq\n",
              "dIr9xgxW5QEAAGAeyryPGp1qz1Z7YPK09ffojbpSSWyxBwAAgLko8z5q7ci5+b11Zeq1D5qcBvAN\n",
              "r1cXye50KDsmUbOi482OAwAAAD9GmfdRc6fNUFJYtPqGBvVmfZnZcQCfsK1yeIr91UyxBwAAgMko\n",
              "8z7KMAzXVnvOzQMXrnuwXztrSyRJn+K8PAAAAExGmfdh+bZsScPn5p1Op8lpAO+2o7ZE/UN2pUdO\n",
              "19xpSWbHAQAAgJ+jzPuw5UmzFWwNUE1Xm0ramsyOA3i1bRXDW+w3pC+QYRgmpwEAAIC/o8z7sLDA\n",
              "IF02I1MSU+2BC9FnH9Tr1cclMcUeAAAAnoEy7+PyR6baF1RT5oHztbe+VN32ASWGRSkv3mZ2HAAA\n",
              "AIAy7+tGh+Dtb6pUW3+PyWkA7zQ6xX5D+nxZDP7aBAAAgPn4qdTHpUVO15zoBA05Hdpde8LsOIDX\n",
              "GXQM6ZUqttgDAADAs1Dm/cDakdV5zs0DE/dOw0m19fdoenC4Lk3MMDsOAAAAIIky7xdGr6jbUVOi\n",
              "IYfD5DSAd9k6ssV+fdpcBVisJqcBAAAAhlHm/cDSxAxFBYXoVH+3CltqzI4DeA2H06GXK49KkjZm\n",
              "LDQ5DQAAAPAByrwfCLRYtTJ5jiRpO1vtgXF7r6lKTb2digwM1vKkWWbHAQAAAFwo836CK+qAiRud\n",
              "Yr8uda6CrQEmpwEAAAA+QJn3E2ts2TJk6MipOjX0dJgdB/B4TqfTdV6eKfYAAADwNJR5PxEXGqHF\n",
              "cTZJbLUHxuNIa51qutoUYg3UGluW2XEAAACAMSjzfiQ/dXiqPVvtgU82usV+jS1LoQFBJqcBAAAA\n",
              "xqLM+5HRc/N76krVP2Q3OQ3g2Ua32G9MZ4o9AAAAPA9l3o8siE1WQmikeuwDeqfhpNlxAI91oq1J\n",
              "pe3NCrRYlZ+aY3YcAAAA4CyUeT9iMSxaYxvZas+5eeAjba04LElakTxbUUEhJqcBAAAAzkaZ9zNc\n",
              "UQd8sm2VRyUxxR4AAACeizLvZ1amzFGgxaqKzlaVtzebHQfwOJWdrTpyqk4Ww9D6tLlmxwEAAADO\n",
              "iTLvZyICg7VsxkxJbLUHzuXlkVX5ZYkzFRsSYXIaAAAA4Nwo835odKv962y1B86ytWJ4ij1b7AEA\n",
              "AODJKPN+aO1ImX+n4aQ6B/pMTgN4joaeDr3XXCVJujp9vslpAAAAgI9GmfdDmdFxmhkVJ7vTod11\n",
              "J8yOA3iM0S32S+LTlBQebXIaAAAA4KNR5v1U/sgVdds5Nw+4bKsc3mK/kS32AAAA8HCUeT+Vnzq8\n",
              "1X57TbEcTofJaQDznerr1tsNJyVJGzLYYg8AAADPRpn3U5cmzlR4QJCae7t0uLXO7DiA6V6tOqYh\n",
              "p0PzpycpPTLW7DgAAADAx6LM+6kga4BWJM+RJBUw1R7QtpHz8kyxBwAAgDegzPux0a323DcPf9c5\n",
              "0Kc9I8MgN2ZQ5gEAAOD5KPN+bO3IELxDLTVq7u00OQ1gnoKaIg04hjQrOl5zohPMjgMAAAB8Isq8\n",
              "H0sMi9LC2BRJ0o6aYpPTAObZWvHBFHvDMExOAwAAAHwyyryfG12dL6DMw0/12ge0o3b43/8N6Uyx\n",
              "BwAAgHegzPu50XPzu2tLNOgYMjkNMPV21pao1z4oW0SMa6cKAAAA4Oko834uN86m2JBwdQ72a19j\n",
              "hdlxgCl35hR7ttgDAADAW1Dm/ZzFsGhNyvBW++1cUQc/MzBk1+vVxyVxJR0AAAC8C2UeXFEHv/VG\n",
              "fZk6BvqUEBqpixPSzI4DAAAAjBtlHlqZPEdWw6LS9mZVdraaHQeYMqNb7K9KmyeLwV+HAAAA8B78\n",
              "9ApFB4dqaWK6JKmArfbwE0MOh16pGi7zGzPYYg8AAADvQpmHJCnfNrrVnivq4B/2NVWota9b0UGh\n",
              "WjYj0+w4AAAAwIRQ5iHpg3PzbzeUq2dwwOQ0gPttrTgiaXiLfaDFanIaAAAAYGIo85AkzYlOUGrE\n",
              "NPUP2bW3vtTsOIBbOZwObascLvMb0uebnAYAAACYOMo8JEmGYXww1Z5z8/BxhS01aujpUHhAkFYk\n",
              "zzE7DgAAADBhlHm4jJ6b315TLKfTaXIawH22VQwPvstPzVFIQKDJaQAAAICJo8zD5bIZmQoNCFR9\n",
              "T7uOn643Ow7gFk6nU1tHtthvTGeKPQAAALwTZR4uIQGBuiJptiTpdbbaw0cdP92gys5WBVsDtMaW\n",
              "bXYcAAAA4LxQ5jGG64o6yjx81Oiq/KrkOQoPDDY5DQAAAHB+KPMYY+3ISuWB5mqd6us2OQ0w+baN\n",
              "XEm3MYMt9gAAAPBelHmMkRwRo7nTZsgpp3bUlpgdB5hU5e3NKm5rVIBh0brUuWbHAQAAAM4bZR5n\n",
              "Gb2ibjtb7eFjtlYOT7FfnjRLMcFhJqcBAAAAzh9lHmcZPTe/s7ZYdseQyWmAybNt5Lz8BqbYAwAA\n",
              "wMtR5nGWJfFpigkOU/tAn95rqjI7DjAparvadKilRoYMXZU+z+w4AAAAwAWhzOMsVotFq1OyJEkF\n",
              "NWy1h28YXZW/JDFd8aGRJqcBAAAALgxlHufEFXXwNWyxBwAAgC+hzOOcVtuyZDEMFbc1qrarzew4\n",
              "wAVp7u3UvsZKSZR5AAAA+AbKPM5pWnCYLopPk8RWe3i/VyqPySmnFsfZlBIRY3YcAAAA4IJR5vGR\n",
              "Rq+oY6s9vB1b7AEAAOBrKPP4SGtHzs2/UV+mXvugyWmA89PW36M36sskSRvT55ucBgAAAJgclHl8\n",
              "pLnTZigpLFp9Q4N6c6QMAd7m9erjsjsdyo5JVGZ0vNlxAAAAgElBmcdHMgzDtdV+O+fm4aW2Voxs\n",
              "sc9giz0AAAB8B2UeHyvfli1peAie0+k0OQ0wMd2D/dpVd0KStJHz8gAAAPAhbi3zJ06c0OWXX66s\n",
              "rCwtXbpUR48ePes5DodD3/3ud7VgwQLl5OTo5ptv1sDAgOvxqqoqXXPNNcrOzta8efP0q1/9yp2R\n",
              "8SHLk2Yr2Bqgmq42lbQ1mR0HmJAdNcXqH7IrPTJWc6fNMDsOAAAAMGncWuZvu+023XrrrSopKdGd\n",
              "d96pzZs3n/Wcxx57TAcOHNCBAwd0/PhxWSwW3X///ZIkp9OpTZs26aabblJxcbGOHTumz372s+6M\n",
              "jA8JCwzSZTMyJXFFHbzP1pEp9hvTF8gwDJPTAAAAAJPHbWW+qalJ+/fv1xe/+EVJ0o033qjq6mqV\n",
              "lpaOed6hQ4e0bt06BQUFyTAMbdiwQb/73e8kSQUFBQoODtbf/d3fuZ6fmJj4kZ+zv79fHR0dY34N\n",
              "DQ254avzL/k2rqiD9+mzD7r+nd2QwRR7AAAA+Ba3lfnq6molJSUpICBA0vAwtbS0NFVVVY153kUX\n",
              "XaQXX3xRHR0dGhwc1LPPPquKigpJ0rFjxxQfH6+///u/V15enjZt2qTy8vKP/Jx33323oqOjx/za\n",
              "t2+fu75EvzE6BG9/U6Xa+ntMTgOMz566E+q2DygpLFq5cTaz4wAAAACTyvQBeJs3b9bVV1+tVatW\n",
              "adWqVcrKynK9AGC327V9+3b94Ac/0MGDB3XVVVd97Db773//+2pvbx/z65JLLpmqL8VnpUVOV1ZM\n",
              "goacDu2uPWF2HGBctlUOz+i4On2+LIbpf9UBAAAAk8ptP+Gmpqaqvr5edrtd0vD596qqKqWlpY15\n",
              "nmEY+uEPf6iDBw/qzTff1Lx58zR//vCW2LS0NOXl5bn+/KUvfUkHDhzQ4ODgOT9ncHCwoqKixvyy\n",
              "Wq3u+hL9ytrRrfacm4cXGHQM6ZWqY5KkjVxJBwAAAB/ktjKfkJCgJUuW6KmnnpIkPffcc7LZbJo9\n",
              "e/aY5/X19en06dOSpJaWFt1zzz264447JEkbNmxQTU2NamtrJUlbt27V3LlzFRgY6K7Y+Agf3Ddf\n",
              "rCGHw+Q0wMd7u6Fc7QO9ig0J1yUJGWbHAQAAACZdgDs/+MMPP6zNmzfrrrvuUlRUlB5//HFJ0i23\n",
              "3KJrr71W1157rdrb27V69WpZLBY5HA5985vf1DXXXCNJCg8P10MPPaRPfepTcjqdio6O1tNPP+3O\n",
              "yPgIFyekKyooRKf7e1TYUq2LEtLNjgR8pK0Vw1Psr0qbL6uFLfYAAADwPW4t89nZ2XrrrbfOevuj\n",
              "jz7q+n1iYqKOHz/+kR9j/fr1Wr9+vVvyYfwCLVatSs7SXyveV0F1EWUeHmvI4dDLVcPn5TekM8Ue\n",
              "AAAAvoklK4xbfmq2JM7Nw7O911yl5t4uRQWFaHnSLLPjAAAAAG5Bmce4rbFly5Cho6fqVd/dbnYc\n",
              "4Jy2VQ5vsV+XOldBVrduPgIAAABMQ5nHuMWGRCg3fvi+7u01xSanAc7mdDpdZZ4t9gAAAPBllHlM\n",
              "SL5tdKo9W+3heQ631qqmq02hAYFanZJldhwAAADAbSjzmJDRMr+nrlT9Q3aT0wBjbascHny3JiVb\n",
              "oQFBJqcBAAAA3IcyjwlZEJusxNBI9dgH9HZDudlxABen06mtI1vsN2YsMDkNAAAA4F6UeUyIYRha\n",
              "YxuZal/NVnt4jhPtTSprb1aQxeraQQIAAAD4Kso8Jiw/dbgoFdQUy+l0mpwGGLa1YnhVfkXyHEUG\n",
              "hZicBgAAAHAvyjwmbEXyHAVarKrsbFV5R4vZcQBJYoo9AAAA/AplHhMWERisZTNmSmKrPTxDZWer\n",
              "jp6ql9WwaH3aPLPjAAAAAG5Hmcd5GT2TXMAVdfAA2yqGp9gvmzFT00PCTU4DAAAAuB9lHudl7UiZ\n",
              "f6fhpDoH+kxOA3/nmmKfzhR7AAAA+AfKPM5LZnScZkbFye50aHfdCbPjwI/Vd7frQHOVJOkqzssD\n",
              "AADAT1Dmcd7yR66o285We5jo5crhLfYXJ6RrRliUyWkAAACAqUGZx3kbvaJue02xHE6HyWngr5hi\n",
              "DwAAAH9Emcd5uzRxpsIDgtTc26XDrXVmx4EfOtXXrbcbT0qSNnBeHgAAAH6EMo/zFmQN0IrkOZK4\n",
              "og7meKXqmBxOpxZMT1Za5HSz4wAAAABThjKPC7IulSvqYB622AMAAMBfTajMOxyci8ZYo1fUHWqp\n",
              "UXNvp8lp4E86Bvq0p65UkrQxY6HJaQAAAICpNaEyn5GRobvuukvNzc3uygMvkxAWqUWxKZKkHTXF\n",
              "JqeBPymoLtKgY0izo+M1JybB7DgAAADAlJpQmX/ttdfU1NSkBQsW6Etf+pLeeecdd+WCF1k7stX+\n",
              "dc7NYwptrTwsSdrI4DsAAAD4oQmV+ezsbP3iF7/QyZMntWLFCn32s5/V0qVL9Yc//EFOp9NdGeHh\n",
              "8ke22u+uO6GBIbvJaeAPeu0D2lFTIokp9gAAAPBPEx6A53Q69eqrr+qZZ55RRESEPv/5z+uPf/yj\n",
              "brjhBnfkgxdYHJeiuJAIdQ32693GCrPjwA/sqClR39CgUiOmaUFsstlxAAAAgCk3oTJ/9913KzMz\n",
              "U48++qjuvPNOHT16VN/+9rf117/+VUeOHHFXRng4i2HRGluWJKbaY2qcOcXeMAyT0wAAAABTb0Jl\n",
              "vq6uTq+88opeeuklrV+/fsxjTz/99KQGg3cZ3WpfwBA8uFn/kF2vVx+XJG1MZ4o9AAAA/NOEynxi\n",
              "YqKysrLGvO3HP/6xJOmiiy6avFTwOitTshRgWFTW3qyKjlaz48CHvVFfps7BfiWGRmpJQqrZcQAA\n",
              "AABTTKjMb9myZVxvg/+JCgrR0sQMSWy1h3uNbrG/On2+LMaEx34AAAAAPiFgPE965ZVX9PLLL6u2\n",
              "tlbf/va3XW9vb293WzB4n3xbjt5qKNf26iLdPG+52XHgg+yOIb1SeUwSU+wBAADg38a1rBUSEqKY\n",
              "mBhZLBZFR0e7fi1YsICVebjkj9w3/1ZDuboH+01OA1+0r7FCp/q7FRMcpktnzDQ7DgAAAGCaca3M\n",
              "r1q1SqtWrdL111+vxYsXuzsTvNTs6HilRUxXVdcp7a0r1VXp882OBB+zdWSL/VVpcxVosZqcBgAA\n",
              "ADDPuMr8H//4R33+85/Xrl27tGvXrrMe/8Y3vjHpweB9DMPQ2tRsPXH8LRXUFFPmMakcToderjwq\n",
              "iS32AAAAwLjKfFHR8ECzgwcPnvUYdzzjTPm2HD1x/C1trymS0+nk3w9MmoPNNWro6VBEYLCuSJpt\n",
              "dhwAAADAVOMq8z/60Y8kSY8//rhbw8D7XTYjU6EBgWro6dCxU/WaH5tsdiT4iNEp9vm2HIUEBJqc\n",
              "BgAAADDXuMr8iy+++LGPX3vttZMSBt4vJCBQVyTN1mvVx1VQU0SZx6RwOp2uMr8hgy32AAAAwLjK\n",
              "/M9//vOPfMwwDMo8xsi35QyX+eoifWPxWrPjwAccP12vys5TCrYGaE1KltlxAAAAANONq8zv2LHD\n",
              "3TngQ9basiVJB5qrdaqvW9NDwk1OBG/3t4rhVfnVKVkKDww2OQ0AAABgvnHdMz/Kbrfrv//7v/WP\n",
              "//iPkqSysjJt377dLcHgvZIjYjR32gw55dSO2hKz48AHMMUeAAAAGGtCZf72229XUVGRa6U+NjZW\n",
              "d9xxh1uCwbvlp+ZIkrZXF5mcBN6urL1ZxW2NCjAsujJ1rtlxAAAAAI8woTL/9ttv65FHHlFISIgk\n",
              "KSYmRoODg24JBu+Wbxsu8ztri2V3DJmcBt5sdPDd8uTZig4ONTkNAAAA4BkmVOZHS/yooaEhORyO\n",
              "SQ0E37AkPk0xwWFqH+jTe01VZseBF9s2ssV+I1vsAQAAAJcJlflFixbpqaeeksPhUGlpqb72ta9p\n",
              "9erVbooGb2a1WFxTxwtq2GqP81PTdVqHWmpkyNBVafPMjgMAAAB4jAmV+fvuu0979uxRQ0ODli9f\n",
              "LovFonvuucdd2eDlRs/NF3BuHudpdPDdJYkZiguNMDkNAAAA4DnGdTXdqIiICD388MN6+OGH3ZUH\n",
              "PmRVSpYshqHitkbVdJ2WLWKa2ZHgZbaOnJffmMEWewAAAOBM4yrzTz755Mc+ftNNN01KGPiWacFh\n",
              "ujghXfsaK1RQXaQvz73M7EjwIk09nXq3sVKStCFtvslpAAAAAM8yrjL/17/+VZLU0dGhXbt26Yor\n",
              "rpBhGNq7d69WrVpFmcdHWmvLGS7zNZR5TMwrVUfllFO5calKjogxOw4AAADgUcZ1Zv5Pf/qT/vSn\n",
              "PyksLEz79+/X66+/rtdee0379+9XeHi4uzPCi41eUfdGfZl67QMmp4E3GZ1ivyGdVXkAAADgwyY0\n",
              "AK+0tFQLFnxwdnX+/Pk6ceLEpIeC78iZlqjk8Gj1D9n1Zn252XHgJU739+jN+jJJ0gaupAMAAADO\n",
              "MqEyHxUVpSeeeEJOp1NOp1NPPPGEIiKYMI2PZhiGa3WeK+owXq9XHZfd6VDOtBnKjI4zOw4AAADg\n",
              "cSZU5v/nf/5HDz74oIKDgxUSEqKHHnpIjz/+uLuywUeceUWd0+k0OQ28gWuKPavyAAAAwDlN6Gq6\n",
              "7Oxs7du3T52dnZKkyMhIt4SCb1meNEvB1gDVdrepuK1ROdNmmB0JHqxrsF+764aP77DFHgAAADi3\n",
              "cZX5EydOaM6cOXr//ffP+fiiRYsmNRR8S2hAkC6fMUs7aotVUF1EmcfH2lFTrP4hu2ZGxSlnWqLZ\n",
              "cQAAAACPNK4y/61vfUsvvfSSrrvuurMeMwxD5eUMNsPHy0/N0Y7aYm2vKdI/LVptdhx4sK0Vw1vs\n",
              "N6TPl2EYJqcBAAAAPNO4yvw3vvENSVJhYaGio6PdGgi+Kd+WrX+VtL+pSm39PYoJDjM7EjxQn33Q\n",
              "NSiR8/IAAADARxvXALzvf//7kqQ1a9a4NQx8V2rkdGXFJGjI6dCuWq4zxLntrjuhHvuAksKitTjO\n",
              "ZnYcAAAAwGONa2V+cHBQP/3pT9XU1KRf/vKXZz0+unIPfJy1thyVtDWpoKZI12UuNjsOPNC2SrbY\n",
              "AwAAAOMxrjL/yCOP6H//93/V29urgwcPjnmMH7gxXvmpOXroyG7tqCnWkMMhq2VCNyPCxw06hvRq\n",
              "1XFJ0sYMttgDAAAAH2dcZf7SSy/VpZdeqvT0dN15553uzgQfdXFCuqKCQnS6v0eFLdW6KCHd7Ejw\n",
              "IG/Vl6t9oFdxIRFampBhdhwAAADAo01oaZQijwsRaLFqVXKWJKmgusjkNPA0W0e22F+VNo9dGwAA\n",
              "AMAn4CdmTKn81GxJck0sByRpyOHQK1VHJUkb2GIPAAAAfCLKPKbUGlu2DBk6eqpe9d3tZseBh9jf\n",
              "VKnm3i5FB4Xo8hmZZscBAAAAPN55l3mn06nOzs7JzAI/EBsSodz44SvHttcUm5wGnmJ0iv261LkK\n",
              "so5rlAcAAADg1yZU5m+++Wa1tbVpYGBAubm5SkxM1IMPPuiubPBR+bYcSdJ2ttpDwy8Mbqsc3mK/\n",
              "MZ0t9gAAAMB4TKjMv/fee4qJidHLL7+svLw8NTQ06KGHHnJXNvio0TK/p65U/UN2k9PAbO+31qq2\n",
              "u01hAUFamZJldhwAAADAK0yozDudTknSnj179OlPf1pRUVGyWq1uCQbftSA2WYlhUeqxD+jthnKz\n",
              "48Bko1vs19qyFRoQaHIaAAAAwDtMqMzPmDFDX//61/WnP/1J69at0+DgoIaGhtyVDT7KMAyttY1M\n",
              "teeKOr/mdDq1tWK4zG9giz0AAAAwbhMq87///e+VnZ2tp59+WjExMaqtrdW3v/1td2WDDxvdal9Q\n",
              "U+Ta8QH/U9LWpPKOFgVZrMpPzTE7DgAAAOA1JjQ2OioqSt/85jdlGIbKy8t17NgxfelLX3JXNviw\n",
              "FcmzFWSxqrLzlMramzU7JsHsSDDB1srDkqSVKXMUERhschoAAADAe0xoZX758uXq6upSa2urVqxY\n",
              "obvvvlu33367u7LBh4UHBmvZyH3iBUy191ujU+zZYg8AAABMzITKvN1uV2RkpP72t7/py1/+st54\n",
              "4w3t2bPHXdng4zg3798qOlp17FS9rIZF61Pnmh0HAAAA8CoTKvMDAwOSpJ07d2rt2rWSpICACe3U\n",
              "B1xGz0jva6xQx0CfyWkw1Uan2F82I1PTQsJNTgMAAAB4lwmV+TVr1mjevHl64403tGrVKp0+fZoy\n",
              "j/M2MypOmVFxsjsd2l13wuw4mGJbR8r8xgy22AMAAAATNaEy/6tf/Up/+MMf9O677yowMFBDQ0N6\n",
              "5JFH3JUNfmB0dX47W+39Sl13uw42V8uQoavS5pkdBwAAAPA6EyrzhmGorKxMDzzwgCSpv7+flXlc\n",
              "kNEr6rbXFMvhdJicBlPllZHBdxcnpCkxLMrkNAAAAID3mVCZ/7d/+zc9+uijeuKJJ4bf2WLRbbfd\n",
              "5o5c8BOXJGYoIjBYLX1der+l1uw4mCKjW+yZYg8AAACcnwmV+b/85S966aWXFB4+PKwqKSlJXV1d\n",
              "bgkG/xBkDdCK5NmSuKLOX7T2demdxpOSpA3p801OAwAAAHinCZX50NBQWa3WMW9zOp2TGgj+58yt\n",
              "9vB9r1Qdk8Pp1MLYFKVGTjc7DgAAAOCVJlTm09PTtWfPHhmGocHBQf3oRz9Sbm6um6LBX6wdKfOH\n",
              "WmrU1NNpchq427aK4fPyG9liDwAAAJy3CZX5X/7yl/rJT36iw4cPKzw8XG+++abuu+8+d2WDn0gI\n",
              "i9Si2BRJ0o5aVud9WXt/r/bWl0piiz0AAABwISY0ij4xMVEvv/yyenp65HQ6XWfngQu1NjVH77fW\n",
              "qqC6SJ+bc7HZceAmBTVFGnQMKSsmQbNjEsyOAwAAAHitCa3M//a3v9WpU6cUFham8PBwtba2cs88\n",
              "JsXoufnddSc0MGQ3OQ3cZWsFU+wBAACAyTChMv/ggw9q+vQPBlbFxsbqwQcfnPRQ8D+L41IUFxKh\n",
              "rsF+vdtYYXYcuEHP4IB21pZI4rw8AAAAcKEmVObPNbl+aGho0sLAf1kMi9bYsiRxRZ2v2lFbrL6h\n",
              "QaVFTNe86UlmxwEAAAC82oTKfFJSkp599lnXn5955hklJfFDOSbH6Fb7Aq6o80nbKoen2G9Iny/D\n",
              "MExOAwAAAHi3CQ3A+8UvfqHrrrtOd9xxhyQpLCxMf/nLX9wSDP5nZUqWAgyLytqbVdHRqoyoWLMj\n",
              "YZL0D9lVUH1ckrQxgy32AAAAwIWaUJnPycnRsWPHVFw8vHKanZ0tq9XqlmDwP1FBIVqamKG3GspV\n",
              "UFOkm+ctNzsSJsneulJ1DvYrMSxKefGpZscBAAAAvN6EttlL0nvvvacXXnhBL7zwgg4ePOiOTPBj\n",
              "61KHt9pvr+bcvC/ZVjk8xf7qtPmyGBP+awcAAADAh0z4arrPfOYzampqUnNzs2688UY9+uij7soG\n",
              "PzR6bv6thnJ1D/abnAaTwe4Y0itVxyRJG9Pnm5wGAAAA8A0T2mb/wAMP6L333lN8fLwk6f/+3/+r\n",
              "/Px83XLLLW4JB/8zKzpe6ZHTVdl5SnvrSnUV5c/rvdNYodP9PZoWHKZLZ8w0Ow4AAADgEya833W0\n",
              "yH/498BkMAxDa0dW51/nijqfsLVieIv9+rR5CrAwYwMAAACYDBMq83PmzNH/+3//T1VVVaqqqtIP\n",
              "fvADzZkzx13Z4KfyR8/N1xTL6XSanAYXwuF06OWq4SvpNqYzxR4AAACYLBMq8w899JDKysq0ZMkS\n",
              "XXTRRSotLdVvfvMbd2WDn1qWOFOhAYFq7OnQ0VN1ZsfBBTjYXK3Gng5FBgbriuTZZscBAAAAfMa4\n",
              "z8wPDQ3p3nvv1dNPP+3OPIBCAgK1Imm2Xq0+roLqIi2ITTE7Es7T1srhVfn81LkKtk5oRAcAAACA\n",
              "jzHulXmr1aodO3a4MwvgsnZkq31BTbHJSXC+nE6nto2cl9/AIEMAAABgUk1om/3GjRv1k5/8RHV1\n",
              "dero6HD9Aibb6BC8g83Vau3rMjkNzsexU/Wq6jqlEGug1qRkmx0HAAAA8CkTKvP/8R//oR/84Aey\n",
              "2WyKiYlRTEyMpk2b5q5s8GPJ4dGaNz1JTjm1o6bE7Dg4D1srh1flV6dkKSwwyOQ0AAAAgG+ZUJl3\n",
              "OBxn/RoaGnJXNvi5fNvoVHuuqPNG20bK/IYMptgDAAAAk23C98xXVVXpqaee0u9//3tVV1d/7HNP\n",
              "nDihyy+/XFlZWVq6dKmOHj161nMcDoe++93vasGCBcrJydHNN9+sgYGBs563efNmGYahtra2iUaG\n",
              "lxot87tqS2R38KKRNylta1JJW5MCLVatG/k+AgAAAJg8Eyrzf/jDH5SXl6fnnntOf/7zn7VkyZKP\n",
              "nW5/22236dZbb1VJSYnuvPNObd68+aznPPbYYzpw4IAOHDig48ePy2Kx6P777x/znC1btigwMHAi\n",
              "UeED8uJTNS04TO0DfdrfVGl2HEzAtpEp9suTZik6ONTkNAAAAIDvmfCZ+f379+v555/X888/r3ff\n",
              "fVc//OEPz/ncpqYm7d+/X1/84hclSTfeeKOqq6tVWlo65nmHDh3SunXrFBQUJMMwtGHDBv3ud79z\n",
              "Pd7Y2Ki77rpL99133yfm6+/vHzOYr6Ojg2MAXsxqsWh1SpYkqaCaqfbeZHSL/Ua22AMAAABuMaEy\n",
              "HxYWppkzZ7r+nJGRobCwsHM+t7q6WklJSQoIGL5b2jAMpaWlqaqqaszzLrroIr344ovq6OjQ4OCg\n",
              "nn32WVVUVLge/+pXv6r/+q//UmRk5Cfmu/vuuxUdHT3m1759+ybyJcLD5Kdybt7bVHee0vuttbIY\n",
              "hq5Km2d2HAAAAMAnTajMf+pTn9IPf/hD1dTUqLq6Wv/xH/+ha6655oKuqNu8ebOuvvpqrVq1SqtW\n",
              "rVJWVpbrBYBHH31UaWlpWrt27bg+1ve//321t7eP+XXJJZecVy54hlUpWbIYhorbGlXTddrsOBiH\n",
              "l6uGt9hfmjhTsSERJqcBAAAAfFPARJ78k5/8RNLwdvsz/ed//qcMwxizpT01NVX19fWy2+0KCAiQ\n",
              "0+lUVVWV0tLSxryvYRj64Q9/6Nqu//TTT2v+/PmSpB07dmj37t166aWXXM9ftGiR/vKXvygvL++s\n",
              "fMHBwQoODh7zNqvVOpEvER5mWnCYLk5I177GChVUF+nLcy8zOxI+wdaKkSn26fNNTgIAAAD4rgu+\n",
              "mu6jrqhLSEjQkiVL9NRTT0mSnnvuOdlsNs2ePXvM8/r6+nT69PCKa0tLi+655x7dcccdkuSamF9R\n",
              "UeHaev/++++fs8jDd60dmYZewFZ7j9fY06H9TcNHaa5O57w8AAAA4C4TvppuIh5++GE9/PDDysrK\n",
              "0j333KPHH39cknTLLbfoxRdflCS1t7fr8ssv1/z587VixQp97Wtf0zXXXOPOWPAyo1fUvVFfpl77\n",
              "2dcWwnO8UnVMTjmVF5+q5PBos+MAAAAAPmtC2+wnKjs7W2+99dZZb3/00Uddv09MTNTx48fH9fGc\n",
              "TuekZYP3yJmWqOTwaNV1t+vN+nLXUDx4HtcUe1blAQAAALdy68o8MBkMw3CtzrPV3nOd7uvWm/Xl\n",
              "kqQNlHkAAADArSjz8Aqjq/EF1UXs0PBQr1Uf15DToXnTk5QRFWt2HAAAAMCnUebhFZYnzVKwNUC1\n",
              "3W0qbms0Ow7OYWslU+wBAACAqUKZh1cIDQjS8qRZkoZX5+FZugb7tbv2hCS22AMAAABTgTIPrzF6\n",
              "bn475+Y9zvbqIg04hpQZFafsmESz4wAAAAA+jzIPrzF6bn5/U5VO9/eYnAZn+mCL/QIZhmFyGgAA\n",
              "AMD3UebhNWwR05Qdk6ghp0O7akvMjoMRvfZBba8pliRtzGCLPQAAADAVKPPwKmttH0y1h2fYXVui\n",
              "HvuAUsJjtCg2xew4AAAAgF+gzMOr5KdmS5J21pZoyOEwOQ0kaVvlUUnDU+zZYg8AAABMDco8vMrF\n",
              "CemKDgrR6f4eHWyuNjuO3xsYsuu16mOSmGIPAAAATCXKPLxKgMWqVSlZkqQCptqb7q2GcrUP9Ck+\n",
              "NEIXJ6SbHQcAAADwG5R5eB3XuXnKvOm2VgxPsb8qbb6sFv46AQAAAKYKP33D66yxZcmQoWOn6lXX\n",
              "3W52HL815HDolarhLfYb2WIPAAAATCnKPLxObEiE8uJTJUnbWZ03zbtNFWrp61J0UKguS8o0Ow4A\n",
              "AADgVyjz8Er5tuGp9tu5os40o1Ps16fNVaDFanIaAAAAwL9Q5uGV8lOHz83vqS9Vn33Q5DT+x+l0\n",
              "alvl8Hl5ptgDAAAAU48yD680f3qyEsOi1Gsf1NuNJ82O43cOtdSorrtdYQFBWpE8x+w4AAAAgN+h\n",
              "zMMrGYahtSNb7QvYaj/lRrfY59tyFBoQaHIaAAAAwP9Q5uG18keuqNteUySn02lyGv/hdDq11bXF\n",
              "fr7JaQAAAAD/RJmH11qRPFtBFqsqO0+prL3Z7Dh+o7itUSc7WhRsDdDakdkFAAAAAKYWZR5eKzww\n",
              "WMtmDF+JVsAVdVNma8XwqvzK5DmKCAw2OQ0AAADgnyjz8Gqcm596o1PsNzLFHgAAADANZR5ebfSK\n",
              "un2NFeoY6DM5je872dGi46cbFGBYtC5trtlxAAAAAL9FmYdXmxkVp8yoONmdDu2uO2F2HJ83OsX+\n",
              "8qRZmhYcZnIaAAAAwH9R5uH1Rlfnt7PV3u1Gz8szxR4AAAAwF2UeXm+d64q6YjmcDpPT+K66rjYV\n",
              "tlTLkKGr0ijzAAAAgJko8/B6SxMzFBEYrJa+Lr3fUmt2HJ/1ctXwFvulielKCIs0OQ0AAADg3yjz\n",
              "8HpB1gCtTJ4jiSvq3GkrU+wBAAAAj0GZh08YPTfPFXXu0dLbpX2NFZKkqzkvDwAAAJiOMg+fsCZl\n",
              "+L7591tr1djTYXIa3/NK1TE5nE4tik2RLWKa2XEAAAAAv0eZh09ICIvU4jibJGlHTbHJaXzPttEt\n",
              "9hlssQcAAAA8AWUePmOtbXh1nnPzk6u9v1dv1JdJkjZwXh4AAADwCJR5+Iz8kSvq9tSVamDIbnIa\n",
              "3/F6TZEGHUPKjknUrOh4s+MAAAAAEGUePmRRXIriQyPUNdjvGtaGC7e14rAkBt8BAAAAnoQyD59h\n",
              "MSyuQXhstZ8cPYMD2llbIkn6FOflAQAAAI9BmYdPWcsVdZNqe22x+ofsSo+crrnTksyOAwAAAGAE\n",
              "ZR4+ZWXyHAUYFpV3tOhkR4vZcbze6BT7DekLZBiGyWkAAAAAjKLMw6dEBYXoksQMSazOX6j+Ibvr\n",
              "f0Om2AMAAACehTIPn5M/stV+O/fNX5C9daXqGuzXjLAo5cXbzI4DAAAA4AyUefic0Svq3m4oV/dg\n",
              "v8lpvNfWkS32V6fPl8XgrwoAAADAk/ATOnzOrOh4pUdO14BjSHvqSs2O45XsjiG9WnVMkrSRLfYA\n",
              "AACAx6HMw+cYhqG1I6vzXFF3ft5pOKnT/T2aHhzumkEAAAAAwHNQ5uGTzjw373Q6TU7jfUa32K9P\n",
              "m6sAi9XkNAAAAAA+jDIPn7QscaZCAwLV2NOho6fqzI7jVRxOh16uPCpJ2pix0OQ0AAAAAM6FMg+f\n",
              "FBIQqBVJsyVxRd1EHWiqVmNvpyIDg7U8aZbZcQAAAACcA2UePmtt6ui5ea6om4htI1vs16XOVbA1\n",
              "wOQ0AAAAAM6FMg+fNToE72BztVr7ukxO4x2cTqfrvPwGptgDAAAAHosyD5+VHB6tedOT5JRTO2pK\n",
              "zI7jFY6eqlN112mFWAO1xpZldhwAAAAAH4EyD5+2zjY61Z5z8+OxtWJ4VX6NLUuhAUEmpwEAAADw\n",
              "USjz8GmjV9Ttqi2R3TFkchrPt210in06U+wBAAAAT0aZh0/LjUvV9OBwtQ/0aX9TpdlxPNqJtiad\n",
              "aG9SoMXqehEEAAAAgGeizMOnWS0WrR45+/06V9R9rNEp9iuSZysqKMTkNAAAAAA+DmUePi+fc/Pj\n",
              "MrrFnin2AAAAgOejzMPnrUqZI6thUUlbk6o7T5kdxyNVdZ7S4dZaWQxDV6XNMzsOAAAAgE9AmYfP\n",
              "iwkO08UJaZKkgppik9N4ppdHVuWXJc7U9JBwk9MAAAAA+CSUefiFtSNb7Qs4N39OW0fOy7PFHgAA\n",
              "APAOlHn4hdHp7G82lKnXPmByGs/S2NPhmvR/dfp8k9MAAAAAGA/KPPxCdkyiUsJj1D9k1xv1ZWbH\n",
              "8SijW+yXxKcpKTza5DQAAAAAxoMyD79gGIZrdZ6t9mONTrHfyBZ7AAAAwGtQ5uE3Rq+oK6gpktPp\n",
              "NDmNZzjd1623GsolSRsy2GIPAAAAeAvKPPzG5UmZCrYGqK67XUWnG82O4xFerT6uIadD86cnKT0y\n",
              "1uw4AAAAAMaJMg+/ERoQpOVJsyQNr85D2sYUewAAAMArUebhV0a32m+nzKtzoE+7a09IkjZmUOYB\n",
              "AAAAb0KZh18ZHYK3v6lSp/t7TE5jru01xRpwDGlWdLzmRCeYHQcAAADABFDm4VdsEdOUHZMoh9Op\n",
              "XbUlZscx1daRLfYb0xfIMAyT0wAAAACYCMo8/M5aG1fU9doHXUcNNqQzxR4AAADwNpR5+J381GxJ\n",
              "0s7aEg05HCanMceu2hL12gdli4jRwtgUs+MAAAAAmCDKPPzOxQnpig4K0en+Hh1srjY7jinOnGLP\n",
              "FnsAAADA+1Dm4XcCLFatSsmS5J9X1A0M2fVa9XFJXEkHAAAAeCvKPPyS69y8H5b5NxvK1THQp4TQ\n",
              "SF2ckGZ2HAAAAADngTIPv7TWli1Dho6dqlddd7vZcabU1orhLfZXpc2TxeCvAAAAAMAb8ZM8/NL0\n",
              "kHAtiU+VJNdUd38w5HDolaqjkqSNGWyxBwAAALwVZR5+Kz91eKv9dj+6om5fU4Va+7oVHRSqZTMy\n",
              "zY4DAAAA4DxR5uG38kfOze+pL1WffdDkNFNj2xlb7AMtVpPTAAAAADhflHn4rXnTkzQjLEq99kG9\n",
              "1VBudhy3czqd2lY5vMV+Q/p8k9MAAAAAuBCUefgtwzD8aqp9YUuN6nvaFR4QpBXJc8yOAwAAAOAC\n",
              "UObh1/Jt2ZKk7dXFcjqdJqdxr22Vw1vs81NzFBIQaHIaAAAAABeCMg+/dkXybAVZrKrqOqXS9maz\n",
              "47iN0+l0XUm3MZ0p9gAAAIC3o8zDr4UHBuuykanuBT481b7odKMqOlsVbA3QmpHdCAAAAAC8F2Ue\n",
              "fm9tqu+fm99aeViStDolS+GBwSanAQAAAHChKPPwe6NX1L3bWKGOgT6T07gHU+wBAAAA30KZh9/L\n",
              "iIrVrOh42Z0O7a4tMTvOpCtvb1HR6QYFGBatS51rdhwAAAAAk4AyD+iDqfa+uNV+dIr98qRZigkO\n",
              "MzkNAAAAgMlAmQf0wVb7HTUlcjgdJqeZXFtHyvwGptgDAAAAPoMyD0hampihiMBgtfR16VBLrdlx\n",
              "Jk1tV5sOtdTIkKGr0ueZHQcAAADAJKHMA5KCrAFamTxHkm9ttX+5anjw3SWJ6YoPjTQ5DQAAAIDJ\n",
              "QpkHRuSPXFG33Yfum99awRZ7AAAAwBdR5oERa1KGh+C931qrxp4Ok9NcuObeTu1rrJBEmQcAAAB8\n",
              "DWUeGJEQFqnFcTZJ0o6aYpPTXLhXqo7JKacWx9mUEhFjdhwAAAAAk4gyD5xhrQ9dUbetcvi8PKvy\n",
              "AAAAgO+hzANnGL2ibk9dqQaG7CanOX9t/T16o65UkrQxfb7JaQAAAABMNso8cIZFcSmKD41Q12C/\n",
              "67y5N3q9ukh2p0PZMYnKjI43Ow4AAACASUaZB85gMSyuQXjevNV+W+XwFPuNGWyxBwAAAHwRZR74\n",
              "kNEr6gq89Iq67sF+7awtkcR5eQAAAMBXUeaBD1mZPEeBFqvKO1p0sqPF7DgTtqOmWP1DdqVHxmru\n",
              "tBlmxwEAAADgBpR54EMig0J0SWKGJO9cnR+dYr8xfYEMwzA5DQAAAAB3cGuZP3HihC6//HJlZWVp\n",
              "6dKlOnr06FnPcTgc+u53v6sFCxYoJydHN998swYGBiRJhw8f1sqVK5WTk6MFCxboK1/5inp7e90Z\n",
              "GZD0wVR7bzs332cf1OvVxyVJGzKYYg8AAAD4KreW+dtuu0233nqrSkpKdOedd2rz5s1nPeexxx7T\n",
              "gQMHdODAAR0/flwWi0X333+/JCkkJEQPPPCAioqKdOjQIXV3d+unP/2pOyMDkj44N/92w0l1Dfab\n",
              "nGb89taXqts+oKSwaOXG2cyOAwAAAMBN3Fbmm5qatH//fn3xi1+UJN14442qrq5WaWnpmOcdOnRI\n",
              "69atU1BQkAzD0IYNG/S73/1OkjRnzhwtWrRIkmS1WrV06VJVVFS4KzLgkhkVp/TIWA06hrSn7oTZ\n",
              "ccZtdIr91enzZTE4RQMAAAD4Krf9tF9dXa2kpCQFBARIkgzDUFpamqqqqsY876KLLtKLL76ojo4O\n",
              "DQ4O6tlnnz1nYe/u7tajjz6q66677iM/Z39/vzo6Osb8GhoamtSvC/7BMAzl20auqPOSc/ODjiG9\n",
              "UjW8xZ4r6QAAAADfZvrS3ebNm3X11Vdr1apVWrVqlbKyslwvAIwaGBjQ5z73Oa1fv16bNm36yI91\n",
              "9913Kzo6esyvffv2uftLgI8a3Wq/o6ZYTqfT5DSf7J2Gk2rr71FsSLguScgwOw4AAAAAN3JbmU9N\n",
              "TVV9fb3sdrskyel0qqqqSmlpaWOeZxiGfvjDH+rgwYN68803NW/ePM2f/8HgrsHBQX3uc59TUlKS\n",
              "6yz9R/n+97+v9vb2Mb8uueSSyf/i4BeWzchUWECQGns7daS1zuw4n2jryBb7q9Lmy2ox/XU6AAAA\n",
              "AG7ktp/4ExIStGTJEj311FOSpOeee042m02zZ88e87y+vj6dPn1aktTS0qJ77rlHd9xxhyTJbrfr\n",
              "7//+7zV9+nT99re//cRrtoKDgxUVFTXml9VqdcNXB38QbA3QiuThf189faq9w+nQyyNX0m1IZ4o9\n",
              "AAAA4Ovcunz38MMP6+GHH1ZWVpbuuecePf7445KkW265RS+++KIkqb29XZdffrnmz5+vFStW6Gtf\n",
              "+5quueYaSdIzzzyjLVu2aP/+/crLy1Nubq7+6Z/+yZ2RgTHWeskVde81Vampt1NRQSFanjTL7DgA\n",
              "AAAA3Czgk59y/rKzs/XWW2+d9fZHH33U9fvExEQdP378nO//hS98QV/4whfclg/4JGtHhuAVNteo\n",
              "ta9LsSERJic6t9Ep9utS5yrI6tb/rAEAAAB4AA7WAh8jKTxa86cnySmndtQUmx3nnJxOp+u8/MZ0\n",
              "ptgDAAAA/oAyD3yC/NGt9tWeWeaPtNappqtNoQGBWpUyx+w4AAAAAKYAZR74BKNX1O2qK9GgY8jk\n",
              "NGcbXZVfa8tRaECQyWkAAAAATAXKPPAJcuNSNT04XB0DfdrfVGl2nLOMnpdnij0AAADgPyjzwCew\n",
              "WixabcuSJBVUe9ZU+xNtTSptb1aQxeo6DgAAAADA91HmgXEYLcrbPeyKuq0VhyVJK5LnKDIoxOQ0\n",
              "AAAAAKYKZR4Yh1Upc2Q1LCppa1J15ymz47hsqzwqiS32AAAAgL+hzAPjEBMcposT0iRJBR5yRV1l\n",
              "Z6uOnKqT1bBofdo8s+MAAAAAmEKUeWCc1rquqPOMrfYvj6zKL5sxU9NDwk1OAwAAAGAqUeaBcRq9\n",
              "ou7NhjL12gdMTiNtrRieYr8xfYHJSQAAAABMNco8ME7ZMYmyRcSof8iuN+rLTM3S0NOh95qrJElX\n",
              "cV4eAAAA8DuUeWCcDMNwTbU3e6v96Bb7ixPSNSMsytQsAAAAAKYeZR6YANe5+ZoiOZ1O03Jsqxze\n",
              "Ys8UewAAAMA/UeaBCbg8aZZCrIGq625X0elGUzKc6uvW2w0nJUkbOC8PAAAA+CXKPDABoQGBWp40\n",
              "S5JUUHPclAyvVh3TkNOhBdOTlRY53ZQMAAAAAMxFmQcmaHSqvVnn5reNnJffmMGqPAAAAOCvKPPA\n",
              "BI0OwXuvuUqn+7qn9HN3DvRpT90JSWyxBwAAAPwZZR6YoJSIGGXHJMrhdGpn7Ykp/dwFNUUacAxp\n",
              "dnS85sQkTOnnBgAAAOA5KPPAeXBtta+Z2q32WyuGp9hvZFUeAAAA8GuUeeA8jG6131lboiGHY0o+\n",
              "Z699QDtqiyWxxR4AAADwd5R54DxclJCm6KBQtfX36EBz1ZR8zp21Jeq1Dyo1YpoWxCZPyecEAAAA\n",
              "4Jko88B5CLBYtTolS9LUbbUfnWK/IX2+DMOYks8JAAAAwDNR5oHztHYKr6gbGLLr9erhe+03pi90\n",
              "++cDAAAA4Nko88B5WpOSJUOGjp9uUF1Xm1s/1xv1ZeoY6FNiaKSWJKS69XMBAAAA8HyUeeA8TQ8J\n",
              "15L44WK9vabYrZ9ra+XwFPur0+fLYvCfLQAAAODvaAXABZiKK+qGHA69UnlMElPsAQAAAAyjzAMX\n",
              "YPSKur31peqzD7rlc+xrqtCp/m7FBIdp2YyZbvkcAAAAALwLZR64APOmJ2lGWJR67YN6q6HcLZ9j\n",
              "a8XwFvur0uYqwGJ1y+cAAAAA4F0o88AFMAxDa23u22rvcDq0beS8PFPsAQAAAIyizAMXKN+WLUna\n",
              "Xl0sp9M5qR+7sKVGDT0diggM1vKkWZP6sQEAAAB4L8o8cIGuSJ6tIItVVV2nVNrePKkfe1vFUUnD\n",
              "Z/NDAgIn9WMDAAAA8F6UeeAChQcG67IZmZKkgurJ22rvdDpdV9JtyGCKPQAAAIAPUOaBSbDWDVfU\n",
              "HT/doMrOVgVbA7QmJWvSPi4AAAAA70eZBybB6BV17zZWqGOgb1I+5uiq/OqULIUHBk/KxwQAAADg\n",
              "GyjzwCTIiIrV7Oh42Z0O7a4tmZSPuW3kSroN6WyxBwAAADAWZR6YJPmTeEVdeXuzitsaFWBYdGXq\n",
              "3Av+eAAAAAB8C2UemCSj5+a31xTL4XRc0MfaWjk8xX558mxFB4decDYAAAAAvoUyD0ySSxIzFBkY\n",
              "rNa+bhW21FzQx9o2cl5+I1vsAQAAAJwDZR6YJIEWq1aOTJ2/kCvqarvadKilRhbD0FVp8yYrHgAA\n",
              "AAAfQpkHJlG+LVvS8Fb78zW6Kn9JYobiQiMmJRcAAAAA30KZBybRmpEyf7i1Vo09Hef1MUbLPFPs\n",
              "AQAAAHwUyjwwieJDI7U4zibp/Fbnm3s7ta+xUpK0IW3+pGYDAAAA4Dso88Akc11Rdx7n5l+pPCan\n",
              "nMqNS1VyRMwkJwMAAADgKyjzwCTLH7mibk/dCfUP2Sf0vh9ssWdVHgAAAMBHo8wDk2xhbLLiQyPU\n",
              "bR/QvsaT436/tv4evVFfJonz8gAAAAA+HmUemGQWw6K1I4PwJrLV/vXq47I7HcqZNkOZ0XHuigcA\n",
              "AADAB1DmATdYO3pufgJD8LZWDG+x38iqPAAAAIBPQJkH3GBl8hwFWqw62dGi8vaWT3x+92C/dtWd\n",
              "kMQWewAAAACfjDIPuEFkUIguScyQJG2v+eSt9ttritU/ZNfMqDjlTEt0czoAAAAA3o4yD7iJ64q6\n",
              "cZT5M6fYG4bh1lwAAAAAvB9lHnCT0Svq3m44qa7B/o98Xp990DUoj/PyAAAAAMaDMg+4SWZUnNIj\n",
              "YzXoGNKekfPw57Kn7oS67QNKDo/W4jjbFCYEAAAA4K0o84CbGIah/HFcUbet8qik4cF3bLEHAAAA\n",
              "MB6UecCNRrfa76gpltPpPOvxQceQXqk6Jmn4vDwAAAAAjAdlHnCjZTMyFRYQpMbeTh1prTvr8bcb\n",
              "ytU+0Ku4kAgtTciY+oAAAAAAvBJlHnCjYGuAViTPlnTuqfZbK4an2F+VNk9WC/85AgAAABgf2gPg\n",
              "ZqNb7T9c5occDr1cNXJePoMp9gAAAADGjzIPuNnakfvmC5tr1NrX5Xr7e81Vau7tUnRQiC6fkWlW\n",
              "PAAAAABeiDIPuNmMsCgtmJ4sp5zaUVPsevu2yuEt9utS5yrIGmBWPAAAAABeiDIPTIHRrfavj1xR\n",
              "53Q6XWV+Yzpb7AEAAABMDGUemAKjW+131ZZo0DGkw621qulqU1hAkFamZJmcDgAAAIC3YW8vMAVy\n",
              "42yaHhyuU/3derexQnvqSiVJa23ZCg0INDkdAAAAAG/DyjwwBawWi9bYhlfgC2qKtXVki/0GttgD\n",
              "AAAAOA+UeWCK5I9stX/mxH6VtTcryGJ1naUHAAAAgImgzANTZFVKlqyGRW39PZKklSlzFBEYbHIq\n",
              "AAAAAN6IMg9MkejgUF2ckO76M1PsAQAAAJwvyjwwhUa31VsNi65MnWtyGgAAAADeijIPTKHrZi7W\n",
              "tOAwfWZ2nqaFhJsdBwAAAICX4mo6YAqlRMTo8D/8m9kxAAAAAHg5VuYBAAAAAPAylHkAAAAAALwM\n",
              "ZR4AAAAAAC9DmQcAAAAAwMtQ5gEAAAAA8DKUeQAAAAAAvAxlHgAAAAAAL0OZBwAAAADAy1DmAQAA\n",
              "AADwMpR5AAAAAAC8DGUeAAAAAAAvQ5kHAAAAAMDLUOYBAAAAAPAylHkAAAAAALwMZR4AAAAAAC9D\n",
              "mQcAAAAAwMtQ5gEAAAAA8DKUeQAAAAAAvAxlHgAAAAAAL0OZBwAAAADAyxhOp9Npdgh3uuGGG5SR\n",
              "kWF2jE80NDSkffv26ZJLLpHVajU7DtyI77X/4HvtH/g++w++1/6D77X/4HvtP7zte52enq5vfvOb\n",
              "H/scny/z3qKjo0PR0dFqb29XVFSU2XHgRnyv/Qffa//A99l/8L32H3yv/Qffa//hi99rttkDAAAA\n",
              "AOBlKPMAAAAAAHgZyjwAAAAAAF6GMu8hgoOD9e///u8KDg42OwrcjO+1/+B77R/4PvsPvtf+g++1\n",
              "/+B77T988XvNADwAAAAAALwMK/MAAAAAAHgZyjwAAAAAAF6GMg8AAAAAgJehzHuAEydO6PLLL1dW\n",
              "VpaWLl2qo0ePmh0JbvCNb3xDGRkZMgxDhYWFZseBG/X19en6669XVlaWFi9erCuvvFKlpaVmx4Kb\n",
              "rF+/XosWLVJubq5WrFihgwcPmh0JbvT444/LMAy98MILZkeBm2RkZCg7O1u5ubnKzc3VM888Y3Yk\n",
              "uEl/f79uv/12zZkzRwsXLtQXv/hFsyPBDVpbW13/Pefm5iorK0sBAQE6deqU2dEuWIDZASDddttt\n",
              "uvXWW7V582b9+c9/1ubNm/Xuu++aHQuT7DOf+YzuuOMOXXHFFWZHwRS49dZbtWHDBhmGoQceeEC3\n",
              "3HKLdu7caXYsuMGzzz6rmJgYSdLzzz+vzZs369ChQ+aGgltUVFTokUce0bJly8yOAjd75plnlJub\n",
              "a3YMuNm//Mu/yDAMlZSUyDAMNTQ0mB0JbhAbGztmIe3ee+/Vrl27NH36dPNCTRJW5k3W1NSk/fv3\n",
              "u14JvPHGG1VdXc0qng9auXKlbDab2TEwBUJCQrRx40YZhiFJWrZsmSoqKswNBbcZLfKS1N7e7vq+\n",
              "w7c4HA7dcsst+tWvfuVT1xoB/qq7u1uPPfaYfvKTn7j+3p4xY4bJqTAVHnvsMd18881mx5gUlHmT\n",
              "VVdXKykpSQEBw5skDMNQWlqaqqqqTE4GYLLcf//9uu6668yOATe66aablJqaqh/84Af63e9+Z3Yc\n",
              "uMF9992n5cuX66KLLjI7CqbATTfdpIULF+rmm29Wc3Oz2XHgBmVlZZo+fbruuusuXXzxxVqxYoUK\n",
              "CgrMjgU3e/PNN3X69Gl9+tOfNjvKpKDMA4Ab3XXXXSotLdXdd99tdhS40ZNPPqnq6mr9+Mc/1p13\n",
              "3ml2HEyyI0eO6LnnntO//uu/mh0FU2D37t16//33deDAAcXFxenLX/6y2ZHgBna7XZWVlZo3b572\n",
              "79+vX/7yl/rc5z6nxsZGs6PBjR577DHddNNNroVUb+cbX4UXS01NVX19vex2uwICAuR0OlVVVaW0\n",
              "tDSzowG4QPfee6+2bNmi119/XWFhYWbHwRT48pe/rK997WtqbW1VbGys2XEwSfbs2aOKigrNmTNH\n",
              "ktTQ0KBbb71V9fX1+vrXv25yOky20Z/BAgMD9c///M/KysoyORHcIS0tTRaLRV/4whckSXl5eZo5\n",
              "c6YOHz6sxMREk9PBHbq6uvTss8/61GwyVuZNlpCQoCVLluipp56SJD333HOy2WyaPXu2yckAXIj7\n",
              "7rtPf/zjH/Xaa6+NOVMN39LW1qa6ujrXn1944QXFxsb6xFAdfODrX/+66uvrVVFRoYqKCi1btky/\n",
              "/e1vKfI+qLu7W21tba4///GPf1ReXp55geA2cXFxys/P1yuvvCJJOnnypE6ePKm5c+eanAzu8swz\n",
              "z2jx4sXKyckxO8qkYWXeAzz88MPavHmz7rrrLkVFRenxxx83OxLc4LbbbtPf/vY3NTQ06KqrrlJk\n",
              "ZCSDDn1UTU2NvvOd7ygzM1Nr1qyRJAUHB+udd94xORkmW3t7u/7u7/5Ovb29slgsio+P10svvcQQ\n",
              "PMBLNTY26sYbb9TQ0JCcTqcyMzP15JNPmh0LbvLQQw/p5ptv1p133imLxaKHH35YKSkpZseCmzz2\n",
              "2GP66le/anaMSWU4nU6n2SEAAAAAAMD4sc0eAAAAAAAvQ5kHAAAAAMDLUOYBAAAAAPAylHkAAPxc\n",
              "bm6uOjs7J/x+n/nMZ/TEE09MfiAAAPCJmGYPAICfKywsNDsCAACYIFbmAQDwc4ZhuO7WzsjI0L/9\n",
              "27/psssu08yZM/XjH//Y9byioiJdfvnlmj9/vq6//np1dHS4Huvs7NRXv/pVXXLJJVq0aJFuvfVW\n",
              "DQwMqLi4WDabTeXl5ZKke++9V1dffbUcDseUfo0AAPgayjwAABijra1Nb731lt5991397Gc/U21t\n",
              "rSTpS1/6km6++WYdPXpU//mf/6ldu3a53uc73/mOVqxYoX379unQoUNyOBy6//77lZ2drZ/97Gf6\n",
              "7Gc/q507d+rXv/61fve738li4UcQAAAuBNvsAQDAGP/wD/8gSYqLi1NmZqZOnjypyMhIFRYWavPm\n",
              "zZKkhQsX6oorrnC9zwsvvKC33npL9913nySpt7dXVqtVkvT5z39eO3bs0FVXXaWCggLFx8dP7RcE\n",
              "AIAPoswDAIAxQkJCXL+3Wq2y2+3nfJ5hGK7fO51OPffcc8rKyjrreXa7XUeOHNH06dNdq/wAAODC\n",
              "sMcNAAB8oqioKOXl5enJJ5+UJB09elR79+51PX799dfrpz/9qav4nz59WqWlpZKkf/mXf1F2drb2\n",
              "7Nmj7373u663AwCA80eZBwAA4/Lkk0/qt7/9rRYsWKB//dd/1cqVK12P/fznP1doaKhyc3O1aNEi\n",
              "5efnq6KiQi+99JJefvll/frXv9bs2bN133336bOf/az6+vpM/EoAAPB+htPpdJodAgAAAAAAjB8r\n",
              "8wAAAAAAeBnKPAAAAAAAXoYyDwAAAACAl6HMAwAAAADgZSjzAAAAAAB4Gco8AAAAAABehjIPAAAA\n",
              "AICXocwDAAAAAOBlKPMAAAAAAHgZyjwAAAAAAF7m/we+HVo8oqgKDAAAAABJRU5ErkJggg==\n",
              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-f9aaa0f1-f86e-44b4-9715-1ecc5880670e\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-f9aaa0f1-f86e-44b4-9715-1ecc5880670e\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "import seaborn as sns\n",
              "def _plot_series(series, series_name, series_index=0):\n",
              "  palette = list(sns.palettes.mpl_palette('Dark2'))\n",
              "  counted = (series['index']\n",
              "                .value_counts()\n",
              "              .reset_index(name='counts')\n",
              "              .rename({'index': 'index'}, axis=1)\n",
              "              .sort_values('index', ascending=True))\n",
              "  xs = counted['index']\n",
              "  ys = counted['counts']\n",
              "  plt.plot(xs, ys, label=series_name, color=palette[series_index % len(palette)])\n",
              "\n",
              "fig, ax = plt.subplots(figsize=(10, 5.2), layout='constrained')\n",
              "df_sorted = _df_7.sort_values('index', ascending=True)\n",
              "_plot_series(df_sorted, '')\n",
              "sns.despine(fig=fig, ax=ax)\n",
              "plt.xlabel('index')\n",
              "_ = plt.ylabel('count()')"
            ],
            "text/html": [
              "      <div class=\"colab-quickchart-chart-with-code\" id=\"chart-950f3c88-2845-4461-8414-71851e7039d9\">\n",
              "        <img style=\"width: 180px;\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA/MAAAITCAYAAABLz0yVAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90\n",
              "bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9h\n",
              "AAAPYQGoP6dpAAAjjklEQVR4nO3df5BV9X3/8dcFdP1R+aEg2K4rIqKi6OIPxt8kNS2YKlExxibG\n",
              "bMsvM3G0EosylVbzA5LGYohJB8wwtujU6IBxGNNppzGNRbFFi5gENYphXVDBH1VQCwjs/f6R6fa7\n",
              "FZRN9nr5wOMxc2a453z27vvOGR2enLNnK9VqtRoAAACgGD3qPQAAAADQNWIeAAAACiPmAQAAoDBi\n",
              "HgAAAAoj5gEAAKAwYh4AAAAKI+YBAACgMGIeAAAACrPHx/ycOXPqPQIAAAB0qz0+5l988cV6jwAA\n",
              "AADdao+PeQAAANjTiHkAAAAojJgHAACAwoh5AAAAKIyYBwAAgMKIeQAAACiMmAcAAIDCiHkAAAAo\n",
              "jJgHAACAwoh5AAAAKIyYBwAAgMKIeQAAACiMmAcAAIDCiHkAAAAojJgHAACAwoh5AAAAKIyYBwAA\n",
              "gMKIeQAAACiMmAcAAIDCiHkAAAAojJgHAACAwoh5AAAAKIyYBwAAgMKIeQAAACiMmAcAAIDCiHkA\n",
              "AAAojJgHAACAwoh5AAAAKIyYBwAAgMKIeQAAACiMmAcAAIDCiHkAAAAojJgHAACAwoh5AAAAKIyY\n",
              "BwAAgMKIeQAAACiMmAcAAIDCiHkAAAAojJgHAACAwoh5AAAAKIyYBwAAgMKIeQAAACiMmAcAAIDC\n",
              "iHkAAAAojJgHAACAwoh5AAAAKIyYBwAAgMKIeQAAACiMmAcAAIDCiHkAAAAojJgHAACAwoh5AAAA\n",
              "KIyYBwAAgMKIeQAAACiMmAcAAIDCiHkAAAAojJgHAACAwoh5AAAAKIyYBwAAgMKIeQAAACiMmAcA\n",
              "AIDC1DTmr7nmmgwePDiVSiUrVqzY6br58+fn6KOPzlFHHZVJkyZl69atnY5Xq9X8/u//fvr27VvL\n",
              "cQEAAKAINY35Sy+9NI888kiOOOKIna5ZvXp1ZsyYkSVLlmTVqlVZv3597rjjjk5rbrvtthx11FG1\n",
              "HBUAAACKUdOYP/fcc9PY2PiBaxYuXJhx48Zl0KBBqVQqueqqq3LPPfd0HF+5cmUeeOCB3HjjjR/6\n",
              "/bZs2ZKNGzd22rZv3/5bfw4AAADYndT9Z+bb2to6XbkfPHhw2trakiRbt27NpEmTMm/evPTs2fND\n",
              "32vWrFnp06dPp23ZsmU1mx0AAADqoe4x/0FuueWWXHLJJTnuuON2af306dOzYcOGTtuoUaNqPCUA\n",
              "AAB8tHrVe4Cmpqa88MILHa9bW1vT1NSUJHn44YfT1taW7373u9m2bVs2btyYwYMH5/HHH8+AAQPe\n",
              "914NDQ1paGjotG9XrugDAABASep+ZX78+PFZvHhx1q1bl2q1mrlz5+byyy9PkixZsiQvvvhiWltb\n",
              "88gjj6R3795pbW3dYcgDAADA3qKmMT9lypQ0NjZm7dq1GTNmTIYOHZokmThxYhYvXpwkGTJkSG65\n",
              "5ZacddZZGTp0aAYMGJApU6bUciwAAAAoWqVarVbrPUQtTZ06NbNnz673GAAAANBt6n6bPQAAANA1\n",
              "Yh4AAAAKI+YBAACgMGIeAAAACiPmAQAAoDBiHgAAAAoj5gEAAKAwYh4AAAAKI+YBAACgMGIeAAAA\n",
              "CiPmAQAAoDBiHgAAAAoj5gEAAKAwYh4AAAAKI+YBAACgMGIeAAAACiPmAQAAoDBiHgAAAAoj5gEA\n",
              "AKAwYh4AAAAKI+YBAACgMGIeAAAACiPmAQAAoDBiHgAAAAoj5gEAAKAwYh4AAAAKI+YBAACgMGIe\n",
              "AAAACiPmAQAAoDBiHgAAAAoj5gEAAKAwYh4AAAAKI+YBAACgMGIeAAAACiPmAQAAoDBiHgAAAAoj\n",
              "5gEAAKAwYh4AAAAKI+YBAACgMGIeAAAACiPmAQAAoDBiHgAAAAoj5gEAAKAwYh4AAAAKI+YBAACg\n",
              "MGIeAAAACiPmAQAAoDBiHgAAAAoj5gEAAKAwYh4AAAAKI+YBAACgMGIeAAAACiPmAQAAoDBiHgAA\n",
              "AAoj5gEAAKAwYh4AAAAKI+YBAACgMGIeAAAACiPmAQAAoDBiHgAAAAoj5gEAAKAwYh4AAAAKI+YB\n",
              "AACgMGIeAAAACiPmAQAAoDBiHgAAAAoj5gEAAKAwYh4AAAAKI+YBAACgMGIeAAAACiPmAQAAoDBi\n",
              "HgAAAAoj5gEAAKAwYh4AAAAKI+YBAACgMGIeAAAACiPmAQAAoDBiHgAAAApT05i/5pprMnjw4FQq\n",
              "laxYsWKn6+bPn5+jjz46Rx11VCZNmpStW7cmSX7yk59k1KhRGT58eI4//vhMmzYt7e3ttRwZAAAA\n",
              "dns1jflLL700jzzySI444oidrlm9enVmzJiRJUuWZNWqVVm/fn3uuOOOJEm/fv3ygx/8IE8//XT+\n",
              "8z//M0uXLs2CBQtqOTIAAADs9moa8+eee24aGxs/cM3ChQszbty4DBo0KJVKJVdddVXuueeeJMnI\n",
              "kSMzZMiQJMl+++2X5ubmtLa27vS9tmzZko0bN3batm/f3m2fBwAAAHYHdf+Z+ba2tk5X7gcPHpy2\n",
              "trb3rVu3bl0WLlyYCy64YKfvNWvWrPTp06fTtmzZsprMDQAAAPVS95jfFRs3bsyFF16YadOm5dRT\n",
              "T93puunTp2fDhg2dtlGjRn2EkwIAAEDt9ar3AE1NTXnhhRc6Xre2tqapqanj9dtvv52xY8fmU5/6\n",
              "VKZOnfqB79XQ0JCGhoZO+3r27Nm9AwMAAECd1f3K/Pjx47N48eKsW7cu1Wo1c+fOzeWXX54keeed\n",
              "dzJ27NiMHTs2N910U50nBQAAgN1DTWN+ypQpaWxszNq1azNmzJgMHTo0STJx4sQsXrw4STJkyJDc\n",
              "csstOeusszJ06NAMGDAgU6ZMSZLMmTMny5Yty/3335/m5uY0Nzfn61//ei1HBgAAgN1epVqtVus9\n",
              "RC1NnTo1s2fPrvcYAAAA0G3qfps9AAAA0DViHgAAAAoj5gEAAKAwYh4AAAAKI+YBAACgMGIeAAAA\n",
              "CiPmAQAAoDBiHgAAAAoj5gEAAKAwYh4AAAAKI+YBAACgMGIeAAAACiPmAQAAoDBiHgAAAAoj5gEA\n",
              "AKAwYh4AAAAKI+YBAACgMGIeAAAACiPmAQAAoDBiHgAAAAoj5gEAAKAwYh4AAAAKI+YBAACgMGIe\n",
              "AAAACiPmAQAAoDBiHgAAAAoj5gEAAKAwYh4AAAAKI+YBAACgMGIeAAAACiPmAQAAoDBiHgAAAAoj\n",
              "5gEAAKAwYh4AAAAKI+YBAACgMGIeAAAACiPmAQAAoDBiHgAAAAoj5gEAAKAwYh4AAAAKI+YBAACg\n",
              "MGIeAAAACiPmAQAAoDBiHgAAAAoj5gEAAKAwYh4AAAAKI+YBAACgMGIeAAAACiPmAQAAoDBiHgAA\n",
              "AAoj5gEAAKAwYh4AAAAKI+YBAACgMGIeAAAACiPmAQAAoDBiHgAAAAoj5gEAAKAwYh4AAAAKI+YB\n",
              "AACgMGIeAAAACiPmAQAAoDBiHgAAAAoj5gEAAKAwYh4AAAAK0+s3+aKnn346a9euzf77758RI0ak\n",
              "b9++3TwWAAAAsDO7HPNvv/12br311nz/+9/PAQcckIEDB2bz5s154YUXMmrUqNxwww0577zzajkr\n",
              "AAAAkC7E/Mc//vFceeWVefLJJzNw4MCO/e3t7VmyZEnmzp2bVatWZcqUKTUZFAAAAPi1XY75Rx99\n",
              "NA0NDe/b36NHj4wePTqjR4/Oli1bunU4AAAA4P12+QF4Owr532QNAAAA8NvZ5Zi/4IILsnTp0h0e\n",
              "e+utt/I3f/M3mTdvXrcNBgAAAOzYLt9mP3PmzEyfPj1PP/10TjvttI4H4D377LN56aWX8qUvfSl/\n",
              "+qd/WstZAQAAgHQh5k888cT86Ec/ypo1a/Lwww9n7dq1OeCAA/K5z30uZ599dnr1+o1+yx0AAADQ\n",
              "RV0u8DfffDNXXHFFp30/+9nPcuKJJ3bbUAAAAMDO7fLPzP+PlpaWXdoHAAAA1MYuX5l/9dVXs27d\n",
              "umzatCk///nPU61WkyQbNmzIu+++W7MBAQAAgM52OebvueeefPvb387LL7+ccePGdezv06dPpk2b\n",
              "VpPhAAAAgPfb5Zi/9tprc+211+arX/1qZsyYUcuZAAAAgA/Q5QfgzZgxI+3t7Vm3bl22bdvWsb+p\n",
              "qalbBwMAAAB2rMsPwPv7v//79O3bNyNGjMgpp5ySU045JaeeeuoO115zzTUZPHhwKpVKVqxYsdP3\n",
              "nD9/fo4++ugcddRRmTRpUrZu3bpLxwAAAGBv1OWY/8pXvpLHH388b7zxRl577bW89tprefXVV3e4\n",
              "9tJLL80jjzySI444Yqfvt3r16syYMSNLlizJqlWrsn79+txxxx0fegwAAAD2Vl2+zb5///455phj\n",
              "dmntueee+6FrFi5cmHHjxmXQoEFJkquuuiozZ87Ml770pQ88tiepVqvZtM0dBwAAALW0f699UqlU\n",
              "6j1Gt+hyzF900UX59re/nc9+9rPZb7/9Ovb37t37Nxqgra2t05X7wYMHp62t7UOP7ciWLVuyZcuW\n",
              "Tvu2b9/+G831Udq0bWuG3f2X9R4DAABgj/bcFV/JAfvsW+8xukWXb7P/i7/4i0ydOjWDBg1Kv379\n",
              "0rdv3/Tr168Ws3XZrFmz0qdPn07bsmXL6j0WAAAAdKsuX5lvb2/v1gGamprywgsvdLxubW3teDL+\n",
              "Bx3bkenTp2fq1Kmd9pXwa/T277VPnrviK/UeAwAAYI+2f6996j1Ct+lyzHe38ePH5+yzz87NN9+c\n",
              "gQMHZu7cubn88ss/9NiONDQ0pKGhodO+nj171nT+7lCpVPaYWz0AAACovS7fZt+jR4/07NnzfduO\n",
              "TJkyJY2NjVm7dm3GjBmToUOHJkkmTpyYxYsXJ0mGDBmSW265JWeddVaGDh2aAQMGZMqUKR96DAAA\n",
              "APZWlWq1Wu3KF7z77rsdf960aVMWLFiQ7du358///M+7fbjuMHXq1MyePbveYwAAAEC36fKV+QMP\n",
              "PLBj69+/f6ZOnZqFCxfWYjYAAABgB7oc8//Xs88+m9dff707ZgEAAAB2QZcfgNevX79UKpUkybZt\n",
              "25Ikt99+e/dOBQAAAOxUl2N+xYoV//vFvXpl0KBBRTwxHgAAAPYUXY75I444Iv/93//dEfX9+vXL\n",
              "AQcc0N1zAQAAADvR5ZhfunRpxo8fn0GDBiVJ1q9fn0WLFuWMM87o9uEAAACA9+tyzP/P0+vPOuus\n",
              "JL+O++uuuy7//u//3u3DAQAAAO/X5afZb9q0qSPkk+TMM8/M5s2bu3UoAAAAYOe6HPO/8zu/kx//\n",
              "+Mcdrx966KEceOCB3ToUAAAAsHNdvs3+O9/5Ti655JKOJ9i3t7fn/vvv7/bBAAAAgB3rcsy//PLL\n",
              "eeKJJ7J+/fokycCBA7Ns2bJuHwwAAADYsS7fZj9jxowMGDAgJ5xwQk444YT0798/M2bMqMVsAAAA\n",
              "wA50Oeb/r0qlku3bt3fHLAAAAMAu6HLMH3TQQVm6dGnH60cffTQHHXRQtw4FAAAA7FyXf2b+r//6\n",
              "r3PxxRfn2GOPTZI8//zz+eEPf9jtgwEAAAA71uWYP+OMM/LMM8/kscceS/Lr3zPft2/f7p4LAAAA\n",
              "2Ikux3yS9OvXL5/85Ce7exYAAABgF/zWD8ADAAAAPlpiHgAAAAoj5gEAAKAwYh4AAAAKI+YBAACg\n",
              "MGIeAAAACiPmAQAAoDBiHgAAAAoj5gEAAKAwYh4AAAAKI+YBAACgMGIeAAAACiPmAQAAoDBiHgAA\n",
              "AAoj5gEAAKAwYh4AAAAKI+YBAACgMGIeAAAACiPmAQAAoDBiHgAAAAoj5gEAAKAwYh4AAAAKI+YB\n",
              "AACgMGIeAAAACiPmAQAAoDBiHgAAAAoj5gEAAKAwYh4AAAAKI+YBAACgMGIeAAAACiPmAQAAoDBi\n",
              "HgAAAAoj5gEAAKAwYh4AAAAKI+YBAACgMGIeAAAACiPmAQAAoDBiHgAAAAoj5gEAAKAwYh4AAAAK\n",
              "I+YBAACgMGIeAAAACiPmAQAAoDBiHgAAAAoj5gEAAKAwYh4AAAAKI+YBAACgMGIeAAAACiPmAQAA\n",
              "oDBiHgAAAAoj5gEAAKAwYh4AAAAKI+YBAACgMGIeAAAACiPmAQAAoDBiHgAAAAoj5gEAAKAwYh4A\n",
              "AAAKI+YBAACgMGIeAAAACiPmAQAAoDBiHgAAAApT05h//vnnc+aZZ2bYsGE57bTTsnLlyvetaW9v\n",
              "z/XXX58TTjghxx57bCZMmJD33nuv4/g3v/nNDB8+PM3NzTn99NOzbNmyWo4MAAAAu72axvyUKVMy\n",
              "efLkPPfcc7nhhhvS0tLyvjXz58/P8uXLs3z58jzzzDPp0aNH5syZkyRZsWJF/vZv/zbLli3LihUr\n",
              "cvXVV+fqq6+u5cgAAACw26tZzL/66qt54okncsUVVyRJxo8fnzVr1mTVqlWd1j311FP5xCc+kX33\n",
              "3TeVSiXnn39+7rrrriRJpVLJ1q1b8+677yZJ3nrrrTQ2Nu70e27ZsiUbN27stG3fvr1GnxAAAADq\n",
              "o2Yxv2bNmhx22GHp1atXkl+HeVNTU9ra2jqtO+WUU7J48eJs3LgxW7duzX333ZfW1tYkyUknnZTr\n",
              "rrsuRx55ZBobG3Pbbbfl9ttv3+n3nDVrVvr06dNpc1s+AAAAe5q6PwCvpaUlY8eOzejRozN69OgM\n",
              "Gzas4x8AVq9enfvvvz+rVq3K2rVrc9111+Uzn/nMTt9r+vTp2bBhQ6dt1KhRH9VHAQAAgI9EzWL+\n",
              "8MMPzyuvvJJt27YlSarVatra2tLU1NRpXaVSyc0335wnn3wyS5cuzfDhw3P88ccnSRYtWpQRI0bk\n",
              "d3/3d5Mkf/Inf5JHH3200wPy/n8NDQ3p3bt3p61nz561+ogAAABQFzWL+UMPPTQnn3xy7r777iS/\n",
              "DvPGxsYMHTq007rNmzfnzTffTJK8/vrr+cY3vpFp06YlSYYMGZJHH30077zzTpLkwQcfzLBhw7Lv\n",
              "vvvWamwAAADY7fWq5ZvPmzcvLS0tmTlzZnr37p0777wzSTJx4sSMGzcu48aNy4YNG/Kxj30sPXr0\n",
              "SHt7e6699tpceOGFSZKLL744jz/+eE499dQ0NDTkwAMPzD/8wz/UcmQAAADY7VWq1Wq13kPU0tSp\n",
              "UzN79ux6jwEAAADdpu4PwAMAAAC6RswDAABAYcQ8AAAAFEbMAwAAQGHEPAAAABRGzAMAAEBhxDwA\n",
              "AAAURswDAABAYcQ8AAAAFEbMAwAAQGHEPAAAABRGzAMAAEBhxDwAAAAURswDAABAYcQ8AAAAFEbM\n",
              "AwAAQGHEPAAAABRGzAMAAEBhxDwAAAAURswDAABAYcQ8AAAAFEbMAwAAQGHEPAAAABRGzAMAAEBh\n",
              "xDwAAAAURswDAABAYcQ8AAAAFEbMAwAAQGHEPAAAABRGzAMAAEBhxDwAAAAURswDAABAYcQ8AAAA\n",
              "FEbMAwAAQGHEPAAAABRGzAMAAEBhxDwAAAAURswDAABAYcQ8AAAAFEbMAwAAQGHEPAAAABRGzAMA\n",
              "AEBhxDwAAAAURswDAABAYcQ8AAAAFEbMAwAAQGHEPAAAABRGzAMAAEBhxDwAAAAURswDAABAYcQ8\n",
              "AAAAFEbMAwAAQGHEPAAAABRGzAMAAEBhxDwAAAAURswDAABAYcQ8AAAAFEbMAwAAQGHEPAAAABRG\n",
              "zAMAAEBhxDwAAAAURswDAABAYcQ8AAAAFEbMAwAAQGHEPAAAABRGzAMAAEBhxDwAAAAURswDAABA\n",
              "YcQ8AAAAFEbMAwAAQGHEPAAAABRGzAMAAEBhxDwAAAAURswDAABAYcQ8AAAAFEbMAwAAQGHEPAAA\n",
              "ABRGzAMAAEBhahrzzz//fM4888wMGzYsp512WlauXPm+Ne3t7bn++utzwgkn5Nhjj82ECRPy3nvv\n",
              "dRxva2vLhRdemGOOOSbDhw/P7bffXsuRAQAAYLdX05ifMmVKJk+enOeeey433HBDWlpa3rdm/vz5\n",
              "Wb58eZYvX55nnnkmPXr0yJw5c5Ik1Wo1F198ca688sr88pe/zNNPP53LLrusliMDAADAbq9mMf/q\n",
              "q6/miSeeyBVXXJEkGT9+fNasWZNVq1Z1WvfUU0/lE5/4RPbdd99UKpWcf/75ueuuu5IkDz30UBoa\n",
              "GvLpT3+6Y/3AgQN3+j23bNmSjRs3dtq2b99eg08HAAAA9VOzmF+zZk0OO+yw9OrVK0lSqVTS1NSU\n",
              "tra2TutOOeWULF68OBs3bszWrVtz3333pbW1NUny9NNPZ8CAAbn88sszcuTIXHzxxfnVr3610+85\n",
              "a9as9OnTp9O2bNmyWn1EAAAAqIu6PwCvpaUlY8eOzejRozN69OgMGzas4x8Atm3blp/85CeZMWNG\n",
              "nnzyyYwZM+YDb7OfPn16NmzY0GkbNWrUR/VRAAAA4CNRs5g//PDD88orr2Tbtm1Jfv3z721tbWlq\n",
              "auq0rlKp5Oabb86TTz6ZpUuXZvjw4Tn++OOTJE1NTRk5cmTH689//vNZvnx5tm7dusPv2dDQkN69\n",
              "e3faevbsWauPCAAAAHVRs5g/9NBDc/LJJ+fuu+9OkixatCiNjY0ZOnRop3WbN2/Om2++mSR5/fXX\n",
              "841vfCPTpk1Lkpx//vlZu3ZtXnrppSTJP/7jP+a4447LPvvsU6uxAQAAYLfXq5ZvPm/evLS0tGTm\n",
              "zJnp3bt37rzzziTJxIkTM27cuIwbNy4bNmzIxz72sfTo0SPt7e259tprc+GFFyZJDjzwwMydOzd/\n",
              "9Ed/lGq1mj59+uQHP/hBLUcGAACA3V6lWq1W6z1ELU2dOjWzZ8+u9xgAAADQber+ADwAAACga8Q8\n",
              "AAAAFEbMAwAAQGHEPAAAABRGzAMAAEBhxDwAAAAURswDAABAYcQ8AAAAFEbMAwAAQGHEPAAAABRG\n",
              "zAMAAEBhxDwAAAAURswDAABAYcQ8AAAAFEbMAwAAQGHEPAAAABRGzAMAAEBhxDwAAAAURswDAABA\n",
              "YcQ8AAAAFEbMAwAAQGHEPAAAABRGzAMAAEBhxDwAAAAURswDAABAYcQ8AAAAFEbMAwAAQGHEPAAA\n",
              "ABRGzAMAAEBhxDwAAAAURswDAABAYcQ8AAAAFEbMAwAAQGHEPAAAABRGzAMAAEBhxDwAAAAURswD\n",
              "AABAYcQ8AAAAFEbMAwAAQGHEPAAAABRGzAMAAEBhxDwAAAAURswDAABAYcQ8AAAAFEbMAwAAQGHE\n",
              "PAAAABRGzAMAAEBhxDwAAAAURswDAABAYcQ8AAAAFEbMAwAAQGHEPAAAABRGzAMAAEBhxDwAAAAU\n",
              "RswDAABAYcQ8AAAAFEbMAwAAQGHEPAAAABRGzAMAAEBhxDwAAAAUplKtVqv1HqKWLrnkkgwePLje\n",
              "Y3yo7du3Z9myZRk1alR69uxZ73GoIed67+Fc7x2c572Hc733cK73Hs713qO0c33EEUfk2muv/cA1\n",
              "e3zMl2Ljxo3p06dPNmzYkN69e9d7HGrIud57ONd7B+d57+Fc7z2c672Hc7332BPPtdvsAQAAoDBi\n",
              "HgAAAAoj5gEAAKAwYn430dDQkL/6q79KQ0NDvUehxpzrvYdzvXdwnvcezvXew7neezjXe4898Vx7\n",
              "AB4AAAAUxpV5AAAAKIyYBwAAgMKIeQAAACiMmN8NPP/88znzzDMzbNiwnHbaaVm5cmW9R6IGrrnm\n",
              "mgwePDiVSiUrVqyo9zjU0ObNm3PRRRdl2LBhOemkk/IHf/AHWbVqVb3Hokb+8A//MCeeeGKam5tz\n",
              "zjnn5Mknn6z3SNTQnXfemUqlkgceeKDeo1AjgwcPzjHHHJPm5uY0Nzfn3nvvrfdI1MiWLVty9dVX\n",
              "5+ijj86IESNyxRVX1HskauCNN97o+O+5ubk5w4YNS69evfJf//Vf9R7tt9ar3gOQTJkyJZMnT05L\n",
              "S0sWLlyYlpaWPP744/Uei2526aWXZtq0aTn77LPrPQofgcmTJ+f8889PpVLJd7/73UycODE//elP\n",
              "6z0WNXDfffelb9++SZIf/vCHaWlpyVNPPVXfoaiJ1tbWfP/738/pp59e71GosXvvvTfNzc31HoMa\n",
              "u/HGG1OpVPLcc8+lUqlk3bp19R6JGjjkkEM6XUi79dZb8/DDD+fggw+u31DdxJX5Onv11VfzxBNP\n",
              "dPxL4Pjx47NmzRpX8fZA5557bhobG+s9Bh+B/fbbL5/85CdTqVSSJKeffnpaW1vrOxQ18z8hnyQb\n",
              "NmzoOO/sWdrb2zNx4sTcfvvte9SvNYK91bvvvpv58+fn61//esf/twcNGlTnqfgozJ8/PxMmTKj3\n",
              "GN1CzNfZmjVrcthhh6VXr1/fJFGpVNLU1JS2trY6TwZ0lzlz5uRTn/pUvceghq688socfvjhmTFj\n",
              "Ru666656j0MNzJ49O2eddVZOOeWUeo/CR+DKK6/MiBEjMmHChLz22mv1HocaeOGFF3LwwQdn5syZ\n",
              "OfXUU3POOefkoYceqvdY1NjSpUvz5ptv5oILLqj3KN1CzAPU0MyZM7Nq1arMmjWr3qNQQwsWLMia\n",
              "NWvyta99LTfccEO9x6Gb/eIXv8iiRYty00031XsUPgL/9m//lp/97GdZvnx5+vfvny984Qv1Hoka\n",
              "2LZtW1588cUMHz48TzzxRL7zne/kM5/5TNavX1/v0aih+fPn58orr+y4kFq6PeNTFOzwww/PK6+8\n",
              "km3btqVXr16pVqtpa2tLU1NTvUcDfku33npr7r///vz4xz/OAQccUO9x+Ah84QtfyFVXXZU33ngj\n",
              "hxxySL3HoZssWbIkra2tOfroo5Mk69aty+TJk/PKK6/ki1/8Yp2no7v9z9/B9tlnn/zZn/1Zhg0b\n",
              "VueJqIWmpqb06NEjn/vc55IkI0eOzJFHHpmf//znGThwYJ2noxbeeeed3HfffXvUs8lcma+zQw89\n",
              "NCeffHLuvvvuJMmiRYvS2NiYoUOH1nky4Lcxe/bs3HPPPfmXf/mXTj9TzZ7lrbfeyssvv9zx+oEH\n",
              "HsghhxyyRzxUh//1xS9+Ma+88kpaW1vT2tqa008/PXfccYeQ3wO9++67eeuttzpe33PPPRk5cmT9\n",
              "BqJm+vfvn/POOy///M//nCRZvXp1Vq9eneOOO67Ok1Er9957b0466aQce+yx9R6l27gyvxuYN29e\n",
              "WlpaMnPmzPTu3Tt33nlnvUeiBqZMmZIf/ehHWbduXcaMGZODDjrIgw73UGvXrs2Xv/zlDBkyJB//\n",
              "+MeTJA0NDfmP//iPOk9Gd9uwYUM+/elPZ9OmTenRo0cGDBiQBx980EPwoFDr16/P+PHjs3379lSr\n",
              "1QwZMiQLFiyo91jUyNy5czNhwoTccMMN6dGjR+bNm5ff+73fq/dY1Mj8+fMzadKkeo/RrSrVarVa\n",
              "7yEAAACAXec2ewAAACiMmAcAAIDCiHkAAAAojJgHgL1cc3Nz3n777S5/3aWXXpq/+7u/6/6BAIAP\n",
              "5Wn2ALCXW7FiRb1HAAC6yJV5ANjLVSqVjt+tPXjw4PzlX/5lzjjjjBx55JH52te+1rHu2WefzZln\n",
              "npnjjz8+F110UTZu3Nhx7O23386kSZMyatSonHjiiZk8eXLee++9/PKXv0xjY2N+9atfJUluvfXW\n",
              "jB07Nu3t7R/pZwSAPY2YBwA6eeutt/LYY4/l8ccfz7e+9a289NJLSZLPf/7zmTBhQlauXJmvfvWr\n",
              "efjhhzu+5stf/nLOOeecLFu2LE899VTa29szZ86cHHPMMfnWt76Vyy67LD/96U/zve99L3fddVd6\n",
              "9PBXEAD4bbjNHgDo5LOf/WySpH///hkyZEhWr16dgw46KCtWrEhLS0uSZMSIETn77LM7vuaBBx7I\n",
              "Y489ltmzZydJNm3alJ49eyZJ/viP/zj/+q//mjFjxuShhx7KgAEDPtoPBAB7IDEPAHSy3377dfy5\n",
              "Z8+e2bZt2w7XVSqVjj9Xq9UsWrQow4YNe9+6bdu25Re/+EUOPvjgjqv8AMBvxz1uAMCH6t27d0aO\n",
              "HJkFCxYkSVauXJlHHnmk4/hFF12Ub37zmx3h/+abb2bVqlVJkhtvvDHHHHNMlixZkuuvv75jPwDw\n",
              "mxPzAMAuWbBgQe64446ccMIJuemmm3Luued2HLvtttuy//77p7m5OSeeeGLOO++8tLa25sEHH8w/\n",
              "/dM/5Xvf+16GDh2a2bNn57LLLsvmzZvr+EkAoHyVarVarfcQAAAAwK5zZR4AAAAKI+YBAACgMGIe\n",
              "AAAACiPmAQAAoDBiHgAAAAoj5gEAAKAwYh4AAAAKI+YBAACgMGIeAAAACiPmAQAAoDD/DwG6k+jH\n",
              "Pqn4AAAAAElFTkSuQmCC\n",
              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-950f3c88-2845-4461-8414-71851e7039d9\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-950f3c88-2845-4461-8414-71851e7039d9\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x7c36ddf65000>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">Values</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "_df_8['index'].plot(kind='line', figsize=(8, 4), title='index')\n",
              "plt.gca().spines[['top', 'right']].set_visible(False)"
            ],
            "text/html": [
              "      <div class=\"colab-quickchart-chart-with-code\" id=\"chart-3a7a0ac5-d011-4bcb-a508-bc802cfd1fb5\">\n",
              "        <img style=\"width: 180px;\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAApAAAAFuCAYAAAA2xSikAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90\n",
              "bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9h\n",
              "AAAPYQGoP6dpAAA2LElEQVR4nO3deXSUhaH+8ScLTCAQQsJOyDoTFWUXDIR9qK1Wj1631qWAgiiK\n",
              "gVPPrfWccq7c3iu9vV5PAygiUuteUKq3bj/bhIRdBAXFlZnsIQkhgYQsZJLMvL8/JgRypZZJMpkl\n",
              "3885ntO8mTDPvDbh6zuZJMQwDEMAAADAJQr19QAAAAAEFgISAAAAHiEgAQAA4BECEgAAAB4hIAEA\n",
              "AOARAhIAAAAeISABAADgEQISAAAAHiEgAQAA4BECEkCvdd111+m3v/1tpz9+5syZeuKJJ7pvEAAE\n",
              "iHBfDwAAX/nwww99PQEAAhJXIAEAAOARAhJArzV37lz95je/kSSFhIRo/fr1Sk9P14ABAzRu3Djt\n",
              "2bOn/batra361a9+pREjRmjo0KF6/PHHv/fnHT9+XHfddZdGjx6tYcOG6c4779TJkyclSbm5uYqM\n",
              "jNTBgwclSYZh6Kc//aluvPFGGYbRA48WALoPAQkAbV544QW99NJLqqmpkdVq1d13393+vt///vfa\n",
              "tm2bduzYodLSUoWHh+vAgQPt73c4HLJarRo1apSOHTum/Px8hYeH66677pLkjtUnnnhCt912m6qr\n",
              "q/Xb3/5W3377rV555RWFhIT0+GMFgK4gIAGgzaOPPiqz2azw8HDdf//9Ki4u1okTJyRJL774oh59\n",
              "9FGNHTtWJpNJTzzxhAYPHtz+se+//77q6ur03//934qMjNSAAQP0u9/9TllZWSotLZUk/eu//qum\n",
              "Tp0qq9Wq3//+99q+fbuio6N98VABoEt4EQ0AtBk1alT7/46MjJQk1dXVafjw4SotLVVSUlL7+8PC\n",
              "whQfH9/+ts1m04kTJzpEpSSZTCYVFxcrLi5OkvTLX/5S6enpuvPOOzVx4kQvPhoA8B4CEgAuQVxc\n",
              "nAoLC9vfdjqdKikpaX97xIgRSkhIUF5e3j/8M2pra7Vo0SItWbJE27Zt07vvvqsbb7zRm7MBwCt4\n",
              "ChsALsGiRYv0P//zP/r222/lcDj07//+7zp16lT7+2+55Ra1tLRo9erVqq2tlSRVVlZq69atktwv\n",
              "mlm0aJEuu+wybd68WZs2bdLChQuVn5/vk8cDAF1BQALAJXjsscd0yy23aM6cOYqLi1Nzc7Ouueaa\n",
              "9vcPHDhQ+/fvV3FxscaNG6eoqCjNmDFDu3btkiT913/9l7744ov2F83ceeeduueee3Trrbfq7Nmz\n",
              "vnpYANApIQY/PwIAAAAe4AokAAAAPEJAAgAAwCMEJAAAADxCQAIAAMAjBCQAAAA8QkACAADAIwQk\n",
              "AAAAPNKjAZmZmdmTdwcAAAAv6NGALCoq6sm7AwAAgBfwFDYAAAA8QkACAADAIwQkAAAAPEJAAgAA\n",
              "wCMEJAAAADxCQAIAAMAjBCQAAAA8QkACAADAI+Ge3Li6ulpWq7X97cbGRuXn56uyslIxMTHdPg4A\n",
              "AAD+x6OAjI2N1ZEjR9rffuqpp7Rz507iEQAAoBfp0lPYW7Zs0ZIlS7prCwAAAC5gGIZanS5fz/ge\n",
              "j65AXmjfvn06ffq0brjhhou+3+FwyOFwdDjmdDo7e3cAAAC9hmEY+vvXJ7Ruh003TxytpbOSfT2p\n",
              "g04H5JYtW7Rw4UKFh1/8j1i7dq3WrFnT4VhaWlpn7w4AACDouVyG/vb1Ca3Ltunr8jOSpPqmVt2X\n",
              "nqTQ0BAfrzsvxDAMw9MPqq+v18iRI3Xw4EFdfvnlF73Nxa5Arl69WpmZmZ1bCgAAEKTc4VihP2TZ\n",
              "9G1FnSQpsm+YFqcnasnMZMVE9vXxwo46dQVy69atmjBhwj+MR0kymUwymUwdjoWFhXXm7gAAAIKS\n",
              "y2Xo/31VoXXZ58NxgClci2ckasnMJA32s3A8p1MBuWXLFt1///3dvQUAAKBXcLkMffBludZn2/Xd\n",
              "CXc4DjSF6970RN03M0nR/f0zHM/pVEDu27evu3cAAAAEPafL0AdHy7Uu2yZbZb0kaWBEuO5LT9J9\n",
              "6Uka1L+Pjxdemk6/iAYAAACXxuky9N4XZVq/wy57WzhGRYTrvplJujc9SYP6BUY4nkNAAgAAeInT\n",
              "Zejdz8u0fodNeScbJLnDcemsZC1OT1RURGCF4zkEJAAAQDdrdbr07hdlWp9tV36VOxwH9eujpTOT\n",
              "tCiAw/EcAhIAAKCbtDpd+t8jZdqQY1dBWzhG9++j+2cla+H0BA0M8HA8h4AEAADoolanS28fPq5n\n",
              "cuwqrG6UJA3u30f3z07WwumJGmAKruQKrkcDAADQg1rawnHDDruKT7nDMSayb/sVx8ggC8dzgvNR\n",
              "AQAAeFGL06W/fFaqDTl2lZw6K0mKjeyrZbOTdU9a8IbjOcH96AAAALpRc6tL2z8r1TM5dpWedofj\n",
              "kAF99cDsFN2dFq/+fXtHWvWORwkAANAFza0uvfWpOxyP15wLR5MenJOsu69JUL++vevXNROQAAAA\n",
              "/4Cj1ak3D5VqY25eezgOHWjSg3NSdNe0+F4XjucQkAAAAP+Ho9WpbQdL9GxunsprmyRJwwaatHxu\n",
              "iu6cFq+IPr0zHM8hIAEAANo0tTi17VCJns3JU8UZdzgOjzJp+ZwU/ZxwbEdAAgCAXq+pxak/f1Ks\n",
              "jTvzdOKMQ5I0IipCD81L0R1XjyEc/w8CEgAA9FpNLU69fqBYz+3MU2WdOxxHDorQQ/PMuuPqOJnC\n",
              "CceLISABAECv09Ti1Gtt4XiyLRxHtYXj7YTjP0VAAgCAXuNss1OvHSjSczvzVVXvDsfR0f308Dyz\n",
              "bpsSp77hoT5eGBgISAAAEPQam1v16sdFen5XvqrqmyVJcYP7acU8s26ZTDh6ioAEAABBq8HRqlc+\n",
              "LtLmXfmqbnCH45iY8+HYJ4xw7AwCEgAABJ0GR6te3l+kzbvzdaotHBNi++vheWb9y6TRhGMXEZAA\n",
              "ACBo1Dta9dK+Qr2wO1+nG1skucPxkfkW3TxxlMIJx25BQAIAgIBX19TiDsc9BappC8ekIZFaMc+s\n",
              "mwjHbkdAAgCAgHWmqUUv7XWHY+1ZdzgmD4nUI1azbhxPOHoLAQkAAALOmaYWvbinUFv25OtMU6sk\n",
              "KXlopDLmW3TjhFEKCw3x8cLgRkACAICAUXu2RX/cU6A/7i1QXVs4mocN0CPzzbphPOHYUwhIAADg\n",
              "92obW7Rlb4FevCAcLcMGKMNq0fXjRhKOPYyABAAAfqumsVlb9hToT3sLVedwh2Pq8LZwvGqkQglH\n",
              "nyAgAQCA3znd0BaO+wpV3xaOl48YqAyrRT+5cgTh6GMEJAAA8BunGpr1wu58vbSvUA3NTknucFy1\n",
              "wKJrxxKO/oKABAAAPldd79Dm3QV6eX+hGtvCcezIKGVYLbp27HDC0c8QkAAAwGeq6h3avCtfr3xc\n",
              "1B6OV46K0kqrRT8aO1whIYSjPyIgAQBAj6uqd+j5Xfl6ZX+Rzra4w/Gq0VFaZU2V9YphhKOf8zgg\n",
              "HQ6HHn30UX300UeKiIjQhAkT9Oqrr3pjGwAACDKVdU16fme+Xj1QpKYWlyRpfNwgrbRaNP9ywjFQ\n",
              "eByQv/71rxUSEqJjx44pJCREFRUV3tgFAACCSOWZJj23M1+vHSiSo9UdjhPGRGuV1aK5lw0lHAOM\n",
              "RwHZ0NCgLVu2qLS0tP1f9IgRI7wyDAAABL7KM03auDNPrx8obg/HiWOitWqBRXNSCcdA5VFA5uXl\n",
              "KSYmRk8++aSysrLUr18/PfHEE7Jard+7rcPhkMPh6HDM6XR2bS0AAAgIFbVNem5nnl7/pFjNbeE4\n",
              "OT5aKxekarZlCOEY4DwKyNbWVhUVFWns2LH63e9+p8OHD+tHP/qRvvrqKw0fPrzDbdeuXas1a9Z0\n",
              "OJaWltb1xQAAwG+V157Vxtw8/flgSXs4Xp0wWCsXWDTTTDgGixDDMIxLvXFVVZWGDx+u5uZmhYWF\n",
              "SZKmTp2qtWvXasGCBR1ue7ErkKtXr1ZmZmY3zAYAAP6krMYdjlsPlqjZ6Q7HqYmDtdKaqnRzLOEY\n",
              "ZDy6AjlkyBBZrVZ99NFHuv7661VQUKCCggJdccUV37utyWSSyWTqcOxcdAIAgOBwvOasns2x681D\n",
              "pe3hOC0pRqusFk1PIRyDlcevwn7uuee0ZMkSPfbYYwoNDdWmTZs0evRob2wDAAB+qvR0o57NzdOb\n",
              "h0rU4nQ/mXlNUoxWLUjV9JRYH6+Dt3kckMnJycrJyfHGFgAA4OdKTjXq2Vy73vq0tD0cpyfHauUC\n",
              "i9KSCcfegt9EAwAA/qni6kY9k2PX9s9K1epyh2O6OVYrramalhTj43XoaQQkAAD4h4qrG7Uhx6bt\n",
              "nx2Xsy0cZ5qHaOUCi6YmEo69FQEJAAC+p7CqQRty7Hr78PlwnGUZolULLJqSQDj2dgQkAABoV1DV\n",
              "oPU7bPrfI2Xt4TgndagyrBZNSRjs43XwFwQkAABQ/sl6bdhh1ztHjqutGzX3sqFaabVoUjzhiI4I\n",
              "SAAAejF7Zb027LDpr5+XtYfj/MuHKcNq0cQx0T7dBv9FQAIA0AvZK+u0Ltuud78o07nfSbfgCnc4\n",
              "jo+L9uk2+D8CEgCAXsR2ok7rdtj1XodwHK6VVovGxQ3y7TgEDAISAIBe4LuKOq3bYdMHR8vbw/Ha\n",
              "scOVYbXoqtGEIzxDQAIAEMS+rTijddk2fXC0ov3YT64coUesZl05inBE5xCQAAAEoW/K3eH44Zfn\n",
              "w/G6q0Yow2rRFSOjfLgMwYCABAAgiHxVVqt12TZ99NUJSVJIiHT9VSP1iNWsy0cQjugeBCQAAEHg\n",
              "y+O1ysy26e9fnw/Hn44bqQyrRanDB/p4HYINAQkAQAA7WuoOx6xvzofjDeNHKWO+WRbCEV5CQAIA\n",
              "EIC+KK1RZpZN2d9WSpJCQ6QbJ4zSI/PNMg8jHOFdBCQAAAHkSEmNMrOOKee7k5Lc4XjTxNFaMd+s\n",
              "lKEDfLwOvQUBCQBAADhcfFqZ2TblXhCON7eFYzLhiB5GQAIA4Mc+LXKH465j7nAMCw1pD8ekIZE+\n",
              "XofeioAEAMAPfVp0Sn/Ismm3rUqSOxxvmTRaD88zK5FwhI8RkAAA+JGDhaeUmWXTHrs7HMNDQ3TL\n",
              "ZHc4JsQSjvAPBCQAAH7gQH61MrNt2pdXLckdjrdNidPD88waE9Pfx+uAjghIAAB86OP8amVm2bQ/\n",
              "/3w43n71GD00N4VwhN8iIAEA6GGGYWh/WzgeKDglSeoTdj4c4wYTjvBvBCQAAD3EMAztz6vWH7Js\n",
              "+qTQHY59w0J1x9Q4LZ9r1ujofj5eCFwaAhIAAC8zDEN77dXKzD6mg4WnJbnD8WdTx2j53BSNIhwR\n",
              "YAhIAAC8xDAM7bZVKTPbpk+L2sIxPFR3Th2jB+emaOQgwhGBiYAEAKCbGYahXbYq/SHrmA4X10hy\n",
              "h+Nd0+K1fG6KhkdF+HYg0EUEJAAA3cQwDOUeO6nMLJuOlNRIkkzhobrrmng9OIdwRPAgIAEA6CLD\n",
              "MJTzXaUys+36vC0cI/qE6u5rEvTA7GQNIxwRZAhIAAA6yTAM7fi2UpnZNn1RWivJHY6/SEvQstkp\n",
              "GjrQ5OOFgHcQkAAAeMgwDGV9U6l12TYdPe4Ox359wvSL6Qm6f1Yy4Yig53FAJiYmymQyqV8/9yvH\n",
              "Hn/8cf3sZz/r9mEAAPgbwzD0t69PaF22TV+VnZEk9e97PhyHDCAc0Tt06grk1q1bNXHixG6eAgCA\n",
              "f3K53OGYmW3TN+XucIzsG6aFMxK1dGaSYglH9DI8hQ0AwD/gchn66KsKZWbb9G1FnSR3OC6akail\n",
              "s5IVE9nXxwsB3+hUQC5cuFCGYWjatGn63e9+p6FDh37vNg6HQw6Ho8Mxp9PZuZUAAPQgl8vQh19W\n",
              "aP2O8+E4wBSuxTMStWRmkgYTjujlQgzDMDz5gOLiYsXHx6ulpUW/+c1vdPToUX3wwQffu90TTzyh\n",
              "NWvWdDiWlpam/fv3d20xAABe4nIZ+uDLcq3LtunYiXpJ0kBTuO5NT9R9M5MU3Z9wBKROBOSFysvL\n",
              "lZqaqrq6uu+972JXIFevXq3MzMzO3h0AAF7hdBl6/2i51mfbZKtsC8eIcN2bnqQl6Uka1L+PjxcC\n",
              "/sWjp7AbGhrU0tKi6OhoSdIbb7yhSZMmXfS2JpNJJlPHbyoOCwvr3EoAALzA6TL03hdlWr/DLvsF\n",
              "4bhkZpLuTU/SoH6EI3AxHgXkiRMndOutt8rpdMowDCUnJ+vll1/21jYAALzC6TL07udlWrfDpvyT\n",
              "DZKkqIhwLZmZrMXpiYQj8E94FJDJyck6fPiwt7YAAOBVrU6X/vp5mTbssCu/yh2Og/r10dKZSVqU\n",
              "nqioCMIRuBT8GB8AQNBrdbr0zpEyPZNjV0FbOEb376P7ZyVr4fQEDSQcAY8QkACAoNXqdOntw8e1\n",
              "IceuoupGSdLg/n20dFayFs1I1AATfw0CncFnDgAg6LQ4XXr7M3c4Fp9yh2NMZN/2K46RhCPQJXwG\n",
              "AQCCRovTpe2fluqZXLtKTp2VJMVG9tWy2cm6J41wBLoLn0kAgIDX3OrS9s9KtWGHXcdr3OE4ZMD5\n",
              "cOzfl7/ugO7EZxQAIGA1t7r05qclejYn74JwNOnBOcm6+5oE9evLzx8GvIGABAAEHEerU9sOlWpj\n",
              "jl1ltU2SpKEDTXpwTorumhZPOAJeRkACAAKGo9WpbQdL9GxunsrbwnHYuXC8Jl4RfQhHoCcQkAAA\n",
              "v9fU4tTWgyXamJunijPucBweZdLyOSn6+TTCEehpBCQAwG81tTj1xifFem5nnk6ccUiSRkRF6KF5\n",
              "Kbrj6jGEI+AjBCQAwO80tTj1+gF3OFbWucNx5KAIPTQ3RXdMHSNTOOEI+BIBCQDwG2ebnXrtQJE2\n",
              "7crXybZwHDUoQg/NM+v2q+MIR8BPEJAAAJ9rbG7Vax8Xa9OufFXVu8NxdHQ/PTzPrNumxKlveKiP\n",
              "FwK4EAEJAPCZxuZWvfpxkZ7fla+q+mZJUtxgdzjeOplwBPwVAQkA6HENjla98nGRNu/KV3WDOxzH\n",
              "xPTTinlm3TI5Tn3CCEfAnxGQAIAeU+9o1cv7C/XC7gKdagvH+Jj+WjHfrH+ZNJpwBAIEAQkA8Lp6\n",
              "R6te2leoF3bn63RjiyQpIba/Vswz62bCEQg4BCQAwGvqmlrc4binQDVt4Zg0JFIr5pl108RRCicc\n",
              "gYBEQAIAut2Zpha9tNcdjrVn3eGYPCRSj1jNunE84QgEOgISANBtas+26E97C7VlT77ONLVKkpKH\n",
              "RipjvkU3ThilsNAQHy8E0B0ISABAl9WebdEf9xToj3sLVNcWjuZhA/TIfLNuGE84AsGGgAQAdFpt\n",
              "Y4u27C3QixeEo2XYAGVYLbp+3EjCEQhSBCQAwGM1jc3asqdAf9pbqDqHOxxTh7eF41UjFUo4AkGN\n",
              "gAQAXLLTDc16YU++XtpXpPq2cLx8xEBlWC36yZUjCEeglyAgAQD/1KmGZr2wO18v7StUQ7NTkjsc\n",
              "Vy2w6NqxhCPQ2xCQAIB/qLreoc27C/Ty/kI1toXj2JFRyrBadO3Y4YQj0EsRkACA76mqd2jzrny9\n",
              "8nFRezheOSpKK60W/WjscIWEEI5Ab0ZAAgDaVdU79PyufL2yv0hnW9zheNXoKK2ypsp6xTDCEYAk\n",
              "AhIAIKmyrknP78zXqweK1NTikiSNjxuklVaL5l9OOALoiIAEgF6s8kyTntuZr9cOFMnR6g7HCWOi\n",
              "tcpq0dzLhhKOAC6KgASAXqjyTJM27szT6weK28Nx4phorVpg0ZxUwhHAD+t0QL744ou677779Pbb\n",
              "b+vmm2/uxkkAAG+pqG3Sczvz9PonxWpuC8fJ8dFauSBVsy1DCEcAl6RTAVlYWKjNmzcrLS2tu/cA\n",
              "ALygvPasNubm6c8HS9rD8eqEwVq5wKKZZsIRgGc8DkiXy6WlS5dq/fr1evTRR72xCQDQTcpq3OG4\n",
              "9WCJmp3ucJyaOFirFqRqRkos4QigUzwOyKefflrp6emaMmXKD97O4XDI4XB0OOZ0Oj29OwBAJxyv\n",
              "Oatnc+x681BpezhOS4rRKqtF0wlHAF3kUUB++eWX2r59u3bt2vVPb7t27VqtWbOmwzGe8gYA7yo9\n",
              "3ahnc/P05qEStTgNSdI1STFatSBV01NifbwOQLDwKCB3796twsJCWSwWSVJFRYWWLVum8vJyLV++\n",
              "vMNtH3/8cf3yl7/scGz16tVdnAsAuJiSU416Nteutz4tbQ/H6cmxWrnAorRkwhFA9/IoIJcvX94h\n",
              "FOfOnatVq1Zd9FXYJpNJJpOpw7GwsLDOrQQAXFRxdaOeybFr+2elanW5wzHdHKuV1lRNS4rx8ToA\n",
              "wYqfAwkAAai4ulEbcmza/tlxOdvCcaZ5iFYusGhqIuEIwLu6FJC5ubndNAMAcCkKqxq0Iceutw+f\n",
              "D8dZliFatcCiKQmEI4CewRVIAAgABVUNWr/Dpv89UtYejnNShyrDatGUhME+XgegtyEgAcCP5Z+s\n",
              "14Yddr1z5LjaulFzLxuqlVaLJsUTjgB8g4AEAD9kr6zXhh02/fXzsvZwnH/5MGVYLZo4Jtqn2wCA\n",
              "gAQAP2KvrNO6bLve/aJMRls4LrjCHY7j46J9ug0AziEgAcAP2E7Uad0Ou97rEI7DtdJq0bi4Qb4d\n",
              "BwD/BwEJAD70XUWd1u2w6YOj5e3heO3Y4cqwWnTVaMIRgH8iIAHAB76tOKN12TZ9cLSi/dhPrhyh\n",
              "R6xmXTmKcATg3whIAOhB35S7w/HDL8+H43VXjVCG1aIrRkb5cBkAXDoCEgB6wFdltVqXbdNHX52Q\n",
              "JIWESNdfNVKPWM26fAThCCCwEJAA4EVfHq9VZrZNf//6fDj+dNxIZVgtSh0+0MfrAKBzCEgA8IKj\n",
              "pe5wzPrmfDjeMH6UMuabZSEcAQQ4AhIAutEXpTXKzLIp+9tKSVJoiHTjhFF6ZL5Z5mGEI4DgQEAC\n",
              "QDc4UlKjzKxjyvnupCR3ON40cbRWzDcrZegAH68DgO5FQAJAFxwuPq3MbJtyLwjHm9vCMZlwBBCk\n",
              "CEgA6IRPi9zhuOuYOxzDQkPawzFpSKSP1wGAdxGQAOCBQ4WnlJlt025blSR3ON4yabQenmdWIuEI\n",
              "oJcgIAHgEhwsPKXMLJv22N3hGB4aolsmu8MxIZZwBNC7EJAA8AMO5FcrM9umfXnVktzheNuUOD08\n",
              "z6wxMf19vA4AfIOABICL+Di/WplZNu3PPx+Ot189Rg/NTSEcAfR6BCQAtDEMQ/vbwvFAwSlJUp+w\n",
              "8+EYN5hwBACJgAQAdzjmVesPWTZ9UugOx75hobpjapyWzzVrdHQ/Hy8EAP9CQALotQzD0F57tTKz\n",
              "j+lg4WlJ7nD82dQxWj43RaMIRwC4KAISQK9jGIZ226qUmW3Tp0Vt4RgeqjunjtGDc1M0chDhCAA/\n",
              "hIAE0GsYhqFdtir9IeuYDhfXSHKH413T4rV8boqGR0X4diAABAgCEkDQMwxDucdOKjPLpiMlNZIk\n",
              "U3io7romXg/OIRwBwFMEJICgZRiGcr6rVGa2XZ+3hWNEn1DdfU2CHpidrGGEIwB0CgEJIOgYhqEd\n",
              "31YqM9umL0prJbnD8RdpCVo2O0VDB5p8vBAAAhsBCSBoGIahrG8qtS7bpqPH3eHYr0+YfjE9QffP\n",
              "SiYcAaCbEJAAAp5hGPrb1ye0Ltumr8rOSJL69z0fjkMGEI4A0J0ISAABy+Vyh2Nmtk3flLvDMbJv\n",
              "mBbOSNT9s5IVE9nXxwsBIDgRkAACjstl6KOvKpSZbdO3FXWS3OG4aEailhKOAOB1Hgfktddeq4qK\n",
              "CoWGhmrgwIFat26dJk2a5I1tANCBy2Xowy8rtH7H+XAcYArX4hmJWjIzSYMJRwDoER4H5LZt2xQd\n",
              "HS1Jevvtt7V48WJ9/vnn3b0LANq5XIY++LJc67JtOnaiXpI00BSue9MTdd/MJEX3JxwBoCd5HJDn\n",
              "4lGSamtrFRIS0p17AKCd02Xo/aPlWp9tk62yLRwjwnVvepKWpCdpUP8+Pl4IAL1Tp74HcuHChcrJ\n",
              "yZEkffDBBxe9jcPhkMPh6HDM6XR25u4A9DJOl6H3vijT+h122S8IxyUzk3RvepIG9SMcAcCXQgzD\n",
              "MDr7wS+99JK2bt160Yh84okntGbNmg7H0tLStH///s7eHYAg53QZevfzMq3bYVP+yQZJUlREuJbM\n",
              "TNbi9ETCEQD8RJcCUpL69eun0tJSxcbGdjh+sSuQq1evVmZmZlfuDkAQanW69NfPy7Rhh135Ve5w\n",
              "HNSvj5bOTNKi9ERFRRCOAOBPPHoKu6amRo2NjRo1apQk6Z133lFsbKxiYmK+d1uTySSTqeMP7w0L\n",
              "C+vCVADBptXp0jtHyvRMjl0FbeEY3b+P7p+VrIXTEzSQcAQAv+RRQNbW1ur222/X2bNnFRoaqqFD\n",
              "h+q9997jhTQAPNLqdOntw8e1IceuoupGSdLg/n20dFayFs1I1AATP6IWAPyZR1+lExIS9Mknn3hr\n",
              "C4Ag1+J06e3P3OFYfModjjGRfduvOEYSjgAQEPhqDcDrWpwubf+0VM/k2lVy6qwkKTayr5bNTtY9\n",
              "aYQjAAQavmoD8JrmVpe2f1aqDTvsOl7jDschA86HY/++fAkCgEDEV28A3a651aU3Py3Rszl5F4Sj\n",
              "SQ/OSdbd1ySoX19eUAcAgYyABNBtHK1ObTtUqo05dpXVNkmShg406cE5KbprWjzhCABBgoAE0GWO\n",
              "Vqe2HSzRs7l5Km8Lx2HnwvGaeEX0IRwBIJgQkAA6ranFqa0HS7QxN08VZ9zhODzKpOVzUvTzaYQj\n",
              "AAQrAhKAx5panHrjk2I9tzNPJ864f+PUiKgIPTQvRXdcPYZwBIAgR0ACuGRNLU69fsAdjpV17nAc\n",
              "OShCD81N0R1Tx8gUTjgCQG9AQAL4p842O/XagSJt2pWvk23hOGpQhB6aZ9btV8cRjgDQyxCQAP6h\n",
              "xuZWvfZxsTbtyldVvTscR0f308PzzLptSpz6hof6eCEAwBcISADf09jcqlc/LtLzu/JVVd8sSYob\n",
              "7A7HWycTjgDQ2xGQANo1OFr1ysdF2rwrX9UN7nAcE9NPK+aZdcvkOPUJIxwBAAQkAEn1jla9vL9Q\n",
              "L+wu0Km2cIyP6a8V8836l0mjCUcAQAcEJNCL1Tta9dK+Qr2wO1+nG1skSQmx/bVinlk3E44AgH+A\n",
              "gAR6obqmFnc47ilQTVs4Jg2J1Ip5Zt00cZTCCUcAwA8gIIFe5ExTi/60t1Bb9hSo9qw7HJOHROoR\n",
              "q1k3jiccAQCXhoAEeoHas+fCMV9nmlolSclDI5Ux36IbJ4xSWGiIjxcCAAIJAQkEsdqzLfrjngL9\n",
              "cW+B6trC0TxsgB6Zb9YN4wlHAEDnEJBAEKptbNGWvQV68YJwtAwboAyrRdePG0k4AgC6hIAEgkhN\n",
              "Y7O27CnQn/YWqs7hDsfU4W3heNVIhRKOAIBuQEACQeB0Q7Ne2JOvl/YVqb4tHC8fMVAZVot+cuUI\n",
              "whEA0K0ISCCAnWpo1gu78/XSvkI1NDslucNx1QKLrh1LOAIAvIOABAJQdb1Dm3cX6OX9hWpsC8ex\n",
              "I6OUYbXo2rHDCUcAgFcRkEAAqap3aPOufL3ycVF7OF45KkorrRb9aOxwhYQQjgAA7yMggQBQVe/Q\n",
              "87vy9cr+Ip1tcYfjVaOjtMqaKusVwwhHAECPIiABP1ZZ16Tnd+br1QNFampxSZLGxw3SSqtF8y8n\n",
              "HAEAvkFAAn6o8kyTntuZr9cOFMnR6g7HCWOitcpq0dzLhhKOAACfIiABP1J5pkkbd+bp9QPF7eE4\n",
              "cUy0Vi2waE4q4QgA8A8EJOAHKmqb9NzOPL3+SbGa28Jxcny0Vi5I1WzLEMIRAOBXCEjAh8prz2pj\n",
              "bp7+fLCkPRyvThislQssmmkmHAEA/omABHygrMYdjlsPlqjZ6Q7HqYmDtWpBqmakxBKOAAC/5lFA\n",
              "NjU16ec//7m+/vpr9evXT8OGDdPGjRtlNpu9tQ8IKsdrzurZHLvePFTaHo7TkmK0ymrRdMIRABAg\n",
              "PL4CuWzZMl133XUKCQnRhg0btHTpUuXm5nphGhA8Sk836tncPL15qEQtTkOSdE1SjFYtSNX0lFgf\n",
              "rwMAwDMeBWRERISuv/769rfT0tL01FNPdfsoIFiUnGrUs7l2vfVpaXs4Tk+O1coFFqUlE44AgMDU\n",
              "pe+BzMzM1E033XTR9zkcDjkcjg7HnE5nV+4OCBjF1Y16Jseu7Z+VqtXlDsd0c6xWWlM1LSnGx+sA\n",
              "AOiaTgfkk08+Kbvdruzs7Iu+f+3atVqzZk2HY2lpaZ29OyAgFFc3akOOTds/Oy5nWzjONA/RygUW\n",
              "TU0kHAEAwSHEMAzD0w966qmn9Oc//1lZWVmKjo6+6G0udgVy9erVyszM7NRQwJ8VVjVoQ45dbx8+\n",
              "H46zLEO0aoFFUxIIRwBAcPH4CuTTTz+tN9544wfjUZJMJpNMJlOHY2FhYR4PBPxZQVWD1u+w6X+P\n",
              "lLWH45zUocqwWjQlYbCP1wEA4B0eBWRpaakeffRRJScna968eZLcoXjgwAGvjAP8Vf7Jem3YYdc7\n",
              "R46rrRs197KhWmm1aFI84QgACG4eBWRcXJw68Yw3EDTslfXasMOmv35e1h6O8y8fpgyrRRPHRPt0\n",
              "GwAAPYXfRANcAntlndZl2/XuF2U6999QC65wh+P4uGifbgMAoKcRkMAPsJ2o07oddr3XIRyHa6XV\n",
              "onFxg3w7DgAAHyEggYv4rqJO63bY9MHR8vZwvHbscGVYLbpqNOEIAOjdCEjgAt9WnNG6bJs+OFrR\n",
              "fuwnV47QI1azrhxFOAIAIBGQgCTpm3J3OH745flwvO6qEcqwWnTFyCgfLgMAwP8QkOjVviqr1bps\n",
              "mz766oQkKSREuv6qkXrEatblIwhHAAAuhoBEr/Tl8VplZtv096/Ph+NPx41UhtWi1OEDfbwOAAD/\n",
              "RkCiVzla6g7HrG/Oh+MN40cpY75ZFsIRAIBLQkCiV/iitEaZWTZlf1spSQoNkW6cMEqPzDfLPIxw\n",
              "BADAEwQkgtqRkhplZh1TzncnJbnD8aaJo7VivlkpQwf4eB0AAIGJgERQOlx8WpnZNuVeEI43t4Vj\n",
              "MuEIAECXEJAIKp8WucNx1zF3OIaFhrSHY9KQSB+vAwAgOBCQCAqHCk8pM9um3bYqSe5wvGXSaD08\n",
              "z6xEwhEAgG5FQCKgHSw8pcwsm/bY3eEYHhqiWya7wzEhlnAEAMAbCEgEpAP51crMtmlfXrUkdzje\n",
              "NiVOD88za0xMfx+vAwAguBGQCCj786qVmX1MH+efkuQOx9uvHqOH5qYQjgAA9BACEn7PMAztz69W\n",
              "ZpZNBwrc4dgn7Hw4xg0mHAEA6EkEJPyWYRjan1etP2TZ9EmhOxz7hoXqjqlxWj7XrNHR/Xy8EACA\n",
              "3omAhN8xDEN77e6nqg8WnpbkDsefTR2j5XNTNIpwBADApwhI+A3DMLTbVqXMbJs+LWoLx/BQ3Tl1\n",
              "jB6cm6KRgwhHAAD8AQEJnzMMQ7tsVfpD1jEdLq6R5A7Hu6bFa/ncFA2PivDtQAAA0AEBCZ8xDEO5\n",
              "x04qM8umIyU1kiRTeKjuuiZeD84hHAEA8FcEJHqcYRjK+a5Smdl2fd4WjhF9QnX3NQl6YHayhhGO\n",
              "AAD4NQISPcYwDO34tlKZ2TZ9UVoryR2Ov0hL0LLZKRo60OTjhQAA4FIQkPA6wzCU9U2l1mXbdPS4\n",
              "Oxz79QnTL6Yn6P5ZyYQjAAABhoCE1xiGob99fULrsm36quyMJKl/3/PhOGQA4QgAQCAiINHtXC53\n",
              "OGZm2/RNuTscI/uGaeGMRN0/K1kxkX19vBAAAHQFAYlu43IZ+uirCmVm2/RtRZ0kdzgumpGopYQj\n",
              "AABBg4BEl7lchj78skLrd5wPxwGmcC2ekaglM5M0mHAEACCoEJDoNJfL0Adflmtdtk3HTtRLkgaa\n",
              "wnVveqLum5mk6P6EIwAAwYiAhMecLkPvHy3X+mybbJVt4RgRrnvTk7QkPUmD+vfx8UIAAOBNHgVk\n",
              "RkaG/vrXv6qoqEiHDx/WxIkTvTQL/sjpMvTeF2Vav8Mu+wXhuGRmku5NT9KgfoQjAAC9gUcBedtt\n",
              "t+lXv/qVZs6c6a098ENOl6F3Py/Tuh025Z9skCRFRYRrycxkLU5PJBwBAOhlPArI2bNne2sH/FCr\n",
              "06W/fl6mDTvsyq9yh+Ogfn20dGaSFqUnKiqCcAQAoDfy2vdAOhwOORyODsecTqe37g7dqNXp0jtH\n",
              "yvRMjl0FbeEY3b+P7p+VrIXTEzSQcAQAoFfzWkCuXbtWa9as6XAsLS3NW3eHbtDqdOntw8e1Iceu\n",
              "oupGSdLg/n20dFayFs1I1AATr7kCAABeDMjHH39cv/zlLzscW716tbfuDl3Q4nTp7c/c4Vh8yh2O\n",
              "MZF9df+sZP1iegLhCAAAOvBaGZhMJplMHX/XcVhYmLfuDp3Q4nRp+6eleibXrpJTZyVJsZF9tWx2\n",
              "su5JS1Ak4QgAAC7Co0J44IEH9P7776uiokI//vGPNXDgQNntdm9tg5c0t7q0/bNSbdhh1/EadzgO\n",
              "GXA+HPv3JRwBAMA/5lEpbNq0yVs70AOaW11689MSPZuTd0E4mvTgnGTdfU2C+vXlCjEAAPjnuNTU\n",
              "Czhandp2qFQbc+wqq22SJA0daNKDc1J017R4whEAAHiEgAxijlanth0s0bO5eSpvC8dh58LxmnhF\n",
              "9CEcAQCA5wjIINTU4tTWgyXamJunijPucBweZdLyOSn6+TTCEQAAdA0BGUSaWpz68yfF2rgzTyfO\n",
              "uH+I+4ioCD00L0V3XD2GcAQAAN2CgAwCTS1OvX6gWM/tzFNlnTscRw6K0ENzU3TH1DEyhROOAACg\n",
              "+xCQAexss1OvHSjSpl35OtkWjqMGReiheWbdfnUc4QgAALyCgAxA58LxuZ35qqp3h+Po6H56eJ5Z\n",
              "t02JU9/wUB8vBAAAwYyADCCNza169eMiPb8rX1X1zZKkuMHucLx1MuEIAAB6BgEZABocrXrl4yJt\n",
              "3pWv6gZ3OI6J6acV88y6ZXKc+oQRjgAAoOcQkH6swdGql/cXafPufJ1qC8f4mP5aMd+sf5k0mnAE\n",
              "AAA+QUD6oXpHq17aV6gXdufrdGOLJCkhtr9WzDPrZsIRAAD4GAHpR+qaWtzhuKdANW3hmDQkUivm\n",
              "mXXTxFEKJxwBAIAfICD9wJmmFr201x2OtWfd4Zg8JFKPWM26cTzhCAAA/AsB6UO1Z1v0p72F2rIn\n",
              "X2eaWiVJyUMjlTHfohsnjFJYaIiPFwIAAHwfAekDtWdb9Mc9Bfrj3gLVtYWjedgAPTLfrBvGE44A\n",
              "AMC/EZA9qLaxRVv2FujFC8LRMmyAMqwWXT9uJOEIAAACAgHZA2oam7VlT4H+tLdQdQ53OKYObwvH\n",
              "q0YqlHAEAAABhID0otMNzXphT75e2lek+rZwvHzEQGVYLfrJlSMIRwAAEJAISC841dCsF3bn66V9\n",
              "hWpodkpyh+OqBRZdO5ZwBAAAgY2A7EbV9Q5t3l2gl/cXqrEtHMeOjFKG1aJrxw4nHAEAQFAgILtB\n",
              "db1Dz+/O1yv7i9rD8cpRUVpptehHY4crJIRwBAAAwYOA7IKqeoee3+UOx7Mt7nC8anSUVllTZb1i\n",
              "GOEIAACCEgHZCZV1TXp+Z75ePVCkphaXJGl83CCttFo0/3LCEQAABDcC0gOVdU3atDNfr10QjhPG\n",
              "RGuV1aK5lw0lHAEAQK9AQF6CyjNN2rgzT68fKJaj1R2OE8dEa9UCi+akEo4AAKB3ISB/QEVtk57b\n",
              "mafXPylWc1s4To6P1soFqZptGUI4AgCAXomAvIjy2rN6LjdPbxwsaQ/HKQmDtWqBRTPNhCMAAOjd\n",
              "CMgLlNWc1cbcPG09WKJmpzscpyYO1kprqtLNsYQjAACACEhJ0vGas9qYa9e2g6Xt4TgtKUarrBZN\n",
              "TyEcAQAALtSrA7L0dKOezc3Tm4dK1OI0JEnXJMVo1YJUTU+J9fE6AAAA/9QrA7LkVKOezbXrrU9L\n",
              "28NxenKsVi6wKC2ZcAQAAPghHgekzWbTokWLVFVVpUGDBulPf/qTrrzySm9s63Ylpxr1TI47HFtd\n",
              "7nBMN8dqpTVV05JifLwOAAAgMHgckA888ICWLVumxYsX66233tLixYt18OBBb2zrNsXVjdqQY9Nf\n",
              "PjveHo4zzUO0coFFUxMJRwAAAE94FJCVlZU6dOiQ/va3v0mSbr31Vq1YsUJ2u11ms9krA7uisKpB\n",
              "G3LsevvwcTnbwnGWZYhWLbBoSgLhCAAA0BkeBWRJSYlGjhyp8HD3h4WEhCg+Pl7FxcXfC0iHwyGH\n",
              "w9HhmNPp7OLcS1N5pkn/9f++0ztHzofjnNShyrBaNCVhcI9sAAAACFZeexHN2rVrtWbNmg7H0tLS\n",
              "vHV3HYSHherDL8vldBmae9lQrbRaNCmecAQAAOgOIYZhGJd648rKSpnNZp06dUrh4eEyDEMjR47U\n",
              "nj17LukK5OrVq5WZmdk9y/+Jv3xWquShAzRxTHSP3B8AAEBvEerJjYcNG6bJkyfr1VdflSRt375d\n",
              "cXFxF/3+R5PJpKioqA7/hIWFdc/qS3DL5DjiEQAAwAs8fgp706ZNWrx4sZ588klFRUXpxRdf9MYu\n",
              "AAAA+CmPA/Kyyy7T/v37vbEFAAAAAcCjp7ABAAAAAhIAAAAeISABAADgEQISAAAAHiEgAQAA4BEC\n",
              "EgAAAB4hIAEAAOARAhIAAAAeISABAADgkRDDMIyeurNbbrlFiYmJPXJfTqdTn3zyiaZNm9ajv4M7\n",
              "mHAOu4bz13Wcw67h/HUd57BrOH9d54tzmJCQoJUrV/7gbXo0IHvSmTNnNGjQINXW1ioqKsrXcwIS\n",
              "57BrOH9dxznsGs5f13EOu4bz13X+eg55ChsAAAAeISABAADgEQISAAAAHgnagDSZTPq3f/s3mUwm\n",
              "X08JWJzDruH8dR3nsGs4f13HOewazl/X+es5DNoX0QAAAMA7gvYKJAAAALyDgAQAAIBHCEgAAAB4\n",
              "JCgD0mazacaMGUpNTdXUqVP11Vdf+XpSQMnIyFBiYqJCQkJ05MgRX88JOE1NTbr55puVmpqqCRMm\n",
              "6Ec/+pHsdruvZwWca6+9VuPHj9fEiRM1a9YsHT582NeTAtKLL76okJAQvfPOO76eEnASExN12WWX\n",
              "aeLEiZo4caK2bt3q60kBxeFwaMWKFbJYLBo3bpzuueceX08KKNXV1e3/35s4caJSU1MVHh6uU6dO\n",
              "+XqaJCnc1wO84YEHHtCyZcu0ePFivfXWW1q8eLEOHjzo61kB47bbbtOvfvUrzZw509dTAtayZct0\n",
              "3XXXKSQkRBs2bNDSpUuVm5vr61kBZdu2bYqOjpYkvf3221q8eLE+//xz344KMIWFhdq8ebPS0tJ8\n",
              "PSVgbd26VRMnTvT1jID061//WiEhITp27JhCQkJUUVHh60kBJTY2tsNFnKeeeko7d+5UTEyM70Zd\n",
              "IOiuQFZWVurQoUPt/6Vz6623qqSkhCtAHpg9e7bi4uJ8PSNgRURE6Prrr1dISIgkKS0tTYWFhb4d\n",
              "FYDOxaMk1dbWtp9PXBqXy6WlS5dq/fr1fvfjPxD8GhoatGXLFv3nf/5n++fuiBEjfLwqsG3ZskVL\n",
              "lizx9Yx2QReQJSUlGjlypMLD3RdXQ0JCFB8fr+LiYh8vQ2+VmZmpm266ydczAtLChQs1ZswYrV69\n",
              "Wq+88oqv5wSUp59+Wunp6ZoyZYqvpwS0hQsXaty4cVqyZIlOnjzp6zkBIy8vTzExMXryySd19dVX\n",
              "a9asWcrOzvb1rIC1b98+nT59WjfccIOvp7QLuoAE/MmTTz4pu92utWvX+npKQHr55ZdVUlKi//iP\n",
              "/9Bjjz3m6zkB48svv9T27dv1m9/8xtdTAtquXbv0xRdf6LPPPtOQIUO0aNEiX08KGK2trSoqKtLY\n",
              "sWN16NAhrVu3Tj/72c904sQJX08LSFu2bNHChQvbL475A/9Z0k3GjBmj8vJytba2Kjw8XIZhqLi4\n",
              "WPHx8b6ehl7mqaee0l/+8hdlZWWpf//+vp4T0BYtWqQHH3xQ1dXVio2N9fUcv7d7924VFhbKYrFI\n",
              "kioqKrRs2TKVl5dr+fLlPl4XOM79vdGnTx+tWrVKqampPl4UOOLj4xUaGqq7775bkjRp0iQlJSXp\n",
              "6NGjGj58uI/XBZb6+npt27bN717LEXRXIIcNG6bJkyfr1VdflSRt375dcXFxMpvNPl6G3uTpp5/W\n",
              "G2+8ob///e8dvpcPl6ampkZlZWXtb7/zzjuKjY31m28e93fLly9XeXm5CgsLVVhYqLS0ND3//PPE\n",
              "owcaGhpUU1PT/vYbb7yhSZMm+W5QgBkyZIisVqs++ugjSVJBQYEKCgp0xRVX+HhZ4Nm6dasmTJig\n",
              "yy+/3NdTOgi6K5CStGnTJi1evFhPPvmkoqKi9OKLL/p6UkB54IEH9P7776uiokI//vGPNXDgQF6E\n",
              "5IHS0lI9+uijSk5O1rx58yS5f5fpgQMHfLwscNTW1ur222/X2bNnFRoaqqFDh+q9997jhTToMSdO\n",
              "nNCtt94qp9MpwzCUnJysl19+2dezAspzzz2nJUuW6LHHHlNoaKg2bdqk0aNH+3pWwNmyZYvuv/9+\n",
              "X8/4Hn4XNgAAADwSdE9hAwAAwLsISAAAAHiEgAQAAIBHCEgAAAB4hIAEAACARwhIAAAAeISABAAA\n",
              "gEcISAAAAHiEgAQAAIBHCEgAAAB4hIAEAACAR/4/WBsAQQVTOMoAAAAASUVORK5CYII=\n",
              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-3a7a0ac5-d011-4bcb-a508-bc802cfd1fb5\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-3a7a0ac5-d011-4bcb-a508-bc802cfd1fb5\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "_df_9['alpha'].plot(kind='line', figsize=(8, 4), title='alpha')\n",
              "plt.gca().spines[['top', 'right']].set_visible(False)"
            ],
            "text/html": [
              "      <div class=\"colab-quickchart-chart-with-code\" id=\"chart-98e9f10f-160a-49ae-88ca-0e8451764606\">\n",
              "        <img style=\"width: 180px;\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqIAAAFuCAYAAABaw3qhAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90\n",
              "bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9h\n",
              "AAAPYQGoP6dpAABBtElEQVR4nO3deXjU9bn//9dMdiALITtJ2JIQIEAAQWSRJYgEBWs5Vr9Hq6lQ\n",
              "sK3Wnh6r9Vyn1/F824o9X3/2YG0rVkoVWqunuHAUXABZFCvQQNgCSYCQsGUBkpA9k5nfHzMTEgHJ\n",
              "kOUzy/NxXbmEySeZe0Y0Lz73+/2+TTabzSYAAACgl5mNLgAAAAC+iSAKAAAAQxBEAQAAYAiCKAAA\n",
              "AAxBEAUAAIAhCKIAAAAwBEEUAAAAhiCIAgAAwBAEUQAAABiCIAoAPeiZZ57RtGnTOn391q1bZTKZ\n",
              "ZLFYerAqAHAPBFEAAAAYgiAKAAAAQxBEAaCLfvvb3yojI0NhYWGKi4vTt7/9bVVWVl712pkzZ+rR\n",
              "Rx/VN7/5TYWGhiolJUWvv/76Fde9++67SktLU2hoqG677TadPn36hp4PANwZQRQAuiguLk5vv/22\n",
              "qqqq9OWXX6qgoECPPfbYNa9ftWqVvvOd7+jixYt68cUXtWTJEn3++ecdrnnnnXe0e/dunTp1SvX1\n",
              "9fq3f/u3G34+AHBXBFEA6KJFixYpLS1NZrNZgwYN0k9/+lN9/PHH17x+/vz5WrBggfz9/TV//nzd\n",
              "fffd+uMf/9jhmuXLlys8PFzh4eH653/+Z+3ateuGnw8A3JW/0QUAgKd7++239fzzz6uoqEiNjY2y\n",
              "Wq2qq6tTa2vrVa8fMmTIFb/Pzc3t8FhCQkLbr/v27atLly516vn8/Py68ZUBQM/ijigAdMGpU6d0\n",
              "zz336LHHHlNJSYlqamq0Zs0aSZLNZrvq1xQXF1/x+8TExB57PgBwVwRRAOiC2tpaWa1WRUVFKTg4\n",
              "WIWFhVq+fPnXfs2GDRv0wQcfqLW1VR9++KHeeecdfec73+mx5wMAd0VrHgC6ID09XcuXL9eDDz6o\n",
              "2tpajR49Wg888IB27959za95+OGHtWrVKt13332KiYnRyy+/rOnTp/fY8wGAuzLZ6OUAQK+ZOXOm\n",
              "pk2bpl/84hdGlwIAhqM1DwAAAEMQRAEAAGAIWvMAAAAwBHdEAQAAYAiCKAAAAAxBEAUAAIAhCKIA\n",
              "AAAwhEcG0RUrVhhdAgAAALrII4PoyZMnjS4BAAAAXeSRQRQAAACejyAKAAAAQxBEAQAAYAiCKAAA\n",
              "AAxBEAUAAIAhCKIAAAAwBEEUAAAAhiCIAgAAwBAEUQAAABiCIAoAAABDEEQBAABgCH+jCwAAGMtq\n",
              "telCfbMqLjWpsrZJFZfafdTaHzObTIroE6iIkABF9AlQeEiAIvoEqn8f5+8D2x4P8OMeB4DOIYgC\n",
              "gBey2WyqabR0DJSOfzofc4bO83XNarXauu25+wX5O4JqgPr3CVR4n4C2ABsR0v739vDqDLBB/n7d\n",
              "VgMAz0AQBQAPUt9sUeWlZlXUNrYLmc1XDZzNFmunv6/JJEX2CVR0aJCiQ4MU1c/+z+h+QYoKDZTV\n",
              "KlU1tKi6vllVDS26WN+iqvpmVTe0qMrx65pGiySptsmi2iaLTlc1uPTa+gT6KSIkQOHt7rzaP9rf\n",
              "ib0cXiMcvw4OIMACnoogCgAGa7ZYdb7uKy1x5x3Lr7TK65pbXfreocH+bYGyQ8AMDerweGTfwC63\n",
              "1FutNtU0tDiCarOq61tU1dDsCKotjtB6Ocg6Q211Q4tsNqm+uVX1za06U93o0vMG+ZvVv09guyUD\n",
              "l0NquOPX/dv92hlkQwL8ZDKZuvSaAXQNQRQAekCr1aYLdc0d11zWNl25DrO2SVX1LS597+AAc4cQ\n",
              "+dU7mO0f6827hX5mk/r3DVT/voEaor6d/jqr1aZLjZbLodUZWB0BtqrBGWovB1nnXVirTWqyWHWu\n",
              "plHnalwLsIF+5rZlAlcsIegTeEWodT7eN5AAC3QXgigAdJLNZlNNg8XRFm++4m5lW8CsbdL52ia5\n",
              "suzS32zqeLey7Q5moKJDgzvcxfS2IGQ2mxTuuGM5aEDnv85qtam22WIPqe3vvja0qKrucmCt7hBw\n",
              "7QHWYrWpudXa9u/OFf5mU4cNW/blBPYw27aEoP3ygpBARYUGqk8gP3KBr+K/CgA+r77ZcmWYbL+x\n",
              "p7bZvu7yUpOaW11bdzmgb+A171a2D53hIQEym70nXPYGs9mksOAAhQUHKCmy819ns9lU19zadte1\n",
              "bZ1rw+XfX3QE2a8uL2hutcpitamytlmVtc2S6jr1nH5mk76ROVA/zErRoAGdv1sMeDuCKACvdrGu\n",
              "WbklF1V+6cpd486NPa6uuwxzrLu82nrLKMc/YxzrLv05ysjtmEwm9QvyV78gfyX27/zX2Ww2NbZY\n",
              "VdXQrIt1X10y0O73XwmvVQ3Namyxal3uKb2777S+OW6gHpudquQBfXruRQIegiAKwOvUNVm0Kb9M\n",
              "6/ed0baCClk60SMPDjArxtECt7fDgxTdL7jdHcxAQ9Zdwn2YTCaFBPopJDBE8eEhLn3tvtIq/fqT\n",
              "Am0rqND//OOU3tl7WovGJ+rR2SlKiiSQwncRRAF4hWaLVdsKKrQ+74w2HS5TQ8vlu5wpMf00KLLP\n",
              "Ve9iOn/vbesu4V4ykyL02sOT9I+TF/Xfmwq0o7BSb+4p1brcU7rnpkT9YFaKEvsTSOF7CKIAPFar\n",
              "1aYvT5zX+n1ntPHgOVU3XN59PnhAHy0cm6CFmQlKiQk1sErgsgmD+mvN4pv1j5MX9OtPCvVZUaXe\n",
              "2FWqv/3jlO65KUk/mJWigRGu3W0FPBlB9Gu0tFq1u/iCWq02TU+NNrocALKv09t/qlrr887of/PO\n",
              "qLzdjueY0CAtGJughWMTNCYxnDuccFsTBkVq7ZKbtbv4gn79SYF2Hjuvv3xZov/ZU6r7Jibr+7OG\n",
              "udz+BzwRQfRrvJN7Wk+u26+xieEEUcBgReWXtH7fGa3PO6Pi8/Vtj4cF+2v+6HgtzEzQzUMGyI+d\n",
              "5/AgEwdH6i/fnawvj5/XrzcV6O/HL2jN30/qzd2l+j+TkvT9WSmKDQs2ukygxxBEv8bM4fbwmXeq\n",
              "WuU1jYrhfwZArzpd1aD/zTuj9fvO6PDZmrbHgwPMum1knBaOTdCtaVHMKIfHu3noAP116S3aeaxS\n",
              "//1JoXYVX9BrX5zUG7tL9c+TkvX9mcP4GQSvRBD9GjFhwRqbGK68U9X69Gi57p2YbHRJgNc7X9uk\n",
              "DQfPaf2+09pdfLHtcX+zSbemReuuzATNGRGrvkH87wveZ8qwKN0ydIB2HjuvX39SoD0nL+pPO4v1\n",
              "xq4S3X/zID0yc6hiQgmk8B78n/w6skbEKu9UtTblE0SBnlLbZNHHh85pfd4Z7SisVKvjuCWTSZo0\n",
              "OFJ3ZQ5Udkac+vcNNLhSoOeZTCZNTYnSlGED9FlRpX79SYFyS6r0x89P6C+7TuqBmwdp2Yxhig4N\n",
              "MrpUoMsIotcxOz1GL3xSoM8KK9XY0sr5gUA3abK0auvRCq3fd0ab8svUZLk8sWj0wHAtHJugO8fG\n",
              "s2EDPstkMml6arSmpURpe6E9kO4rrdKrn53Qn78s0bdvGaRltw7VgH4EUnguguh1jEoIU3x4sM5W\n",
              "N+qLY+c1Kz3G6JIAj9VqtemLY+f13r7T+vDQOV1qtLR9bmhUXy3MtO94Hxrdz8AqAfdiMpk0Iy1a\n",
              "t6ZGaWtBhf77kwLlnarWK9uPa80XJ/XglEFaduswRdIxgAciiF6HyWTS7PQY/fnLEm3KLyOIAi6y\n",
              "2WzaW1ql9fvO6P39Z1VZe/m4pbiwYC0YG6+7MgdqVEIYxy0BX8NkMmnW8BjNTIvWp0fL9etPCnXg\n",
              "dLVWbrMH0oemDNbS6UNZwgKPQhDthDkjYvXnL0u05Ui5bDYbPyyBTigou6T39p3W/+adVcmFy8ct\n",
              "RfQJsB+3NDZBkwZHysxxS4BL7DdIYjVreIw255fr15sKdOhMjX6/9Zhe31ms70wdoiXThyiiD4EU\n",
              "7o8g2gm3DBug4ACzzlY36vDZGo1KCDe6JMAtlV6obzto/si5S22P9wn009yRsVqYmaBpKdEK9Dcb\n",
              "WCXgHUwmk+aMjFXWiBh9crhM/72pUIfP1uilT4v02s5ifWfqYC2eNlThfQKMLhW4JoJoJwQH+Gla\n",
              "SrQ25Zdpc345QRRop+JSkzYcOKv39p1WbklV2+MBfibNSIvRXZkJyhoRoz6B/O8G6Akmk0lzR8Vp\n",
              "zohYfXy4TP+9qUBHzl3Si1uKtHpnsR6eOkQPTxui8BACKdwPPxk6ac6IGEcQLdMPs1KNLgcwVE1j\n",
              "iz46aD9u6fOiSjlOW5LJJN0ydIAWjk1QdkY8d2KAXmQ2mzQvI05zR8bqw0PntGJToY6WXdKKzYVa\n",
              "/fkJLZ42VN+ZNlhhwfx3CfdBEO2k2Y5NSnmnqlV+qZEDheFzGlta9emRcr2374y2HC1Xc7vjlsYm\n",
              "hmth5kDdOSaecYSAwcxmk+aPjte8UXHacPCsVmwqVGF5rX69qUB//PyElkwbopypgxVKIPUpNptN\n",
              "Npvcbl2+y0G0sLBQDz30kCorKxUeHq4//elPGjVqVIdrrFarnnzySX344YeyWCyaOnWqfv/73ysw\n",
              "0L5w+le/+pVee+01BQYGKjg4WC+++KImTZrUPa+oh8SEBWtMYrj2n6rWp0c43B6+wdJq1efHzmv9\n",
              "vjP66NA51TZdPm4pJaaf7hqboAVjEzQ4qq+BVQK4GrPZpDvH2LsTHxw4qxWbCnSsok7/3ycFWvX5\n",
              "CX13+lA9NGWw+jGlzGtV1Tfrs6JKbTtaoR2Flfrl3RnKGhFrdFkduPynb9myZVq6dKlycnL0t7/9\n",
              "TTk5Odq9e3eHa1atWqXc3Fzl5uYqICBAS5cu1YoVK/STn/xE+/bt0+9+9zsdOnRI/fr109q1a/Xo\n",
              "o49q165d3faiekpWeqz2M2UJXs5qtSm35KLW553RB/vP6nxdc9vnBkaE6M6x8bpr7ECNiA/lBAnA\n",
              "A/iZTVo4NkF3jI7X+/vPaMXmQh2vqNP/++ioXt1xXEtvHaYHbxnE2FwvYGm1Ku9UlbYVVGp7QYX2\n",
              "n6pqWzolSTsKKz07iJaXl2vPnj36+OOPJUmLFi3So48+qqKiIqWkpLRdl5eXpzlz5rTdAc3OztYz\n",
              "zzyjn/zkJzKZTGppaVFdXZ369eunqqoqJSYmduNL6jlZI2L0601MWYL3sdlsOnLukt7bZ9/xfrqq\n",
              "oe1zkX0DdcfoeC3MTNCE5P5u19YB0Dl+ZpPuyhyoO8ckaH3eab24uUgnKuv0qw+P6A87jmvZrUP1\n",
              "7VsGsbHQw5yuatD2ggptL6jQ50WVqmk3KESS0mL76dbUaN2aFq1JQyINqvLaXPrTVlpaqvj4ePn7\n",
              "27/MZDIpOTlZJSUlHYLohAkTtHLlSj366KMKCQnRW2+9peLiYknS2LFj9S//8i8aMmSIIiMjFRQU\n",
              "pO3bt1/zOZuamtTU1NThsdbWVlfK7jYdpiwdP69ZwzncHp6t5Hy91ued1nv7zqiwvLbt8b6Bfro9\n",
              "I04LxyZoakqUAvw4bgnwFn5mk+4el6gFYxL03r4zenFLoU6er9fyjc5AOkwPTB6kkEButrijhuZW\n",
              "/f3E+bbweayirsPnw0MCNC0lSremRenWtGi3H5PcI3/tycnJ0cmTJzVjxgyFhIRozpw5bXdRT5w4\n",
              "obfffltFRUVKSEjQSy+9pHvvvVefffbZVb/X8uXL9Z//+Z8dHps8eXJPlH1d7acsbc4vI4jCI5XX\n",
              "NOr9/We1Pu+M9pVWtT0e6GfWrPRo3ZU5ULPTY7jjD3g5fz+zFk1I1F2ZCXpn72n9ZkuRSi7U65cb\n",
              "8rVy+3E9MmOoHpg8iP8XGMxms6mgrNYePAsr9OWJCx02i5pNUmZShG5Ns9/1HJsYIT8P6lyZbDab\n",
              "7fqX2ZWXlyslJUUXLlyQv7+/bDab4uPj9dlnn3W4I/pVf/3rX/Xb3/5WO3bs0PPPP6+CggK98sor\n",
              "ktTWom9qampr5bd3tTuiP/vZz7RixYrOlt2tthwp08N/2qOE8GB9/tPZrJGDR6iub9GHh+zh84tj\n",
              "59vWDJlN0tSUKC0Ym6DbR8VxziDgw1parXon97Re3FKoUxfty3OiQ4P0vRnD9M83JxNIe9HFOvsm\n",
              "o+0F9k1G52oaO3w+ITy4LXhOHRbl0UfluXRHNCYmRuPHj9fatWuVk5OjdevWKTEx8YoQ2tjYqIaG\n",
              "BvXv31+VlZV67rnn9POf/1ySNHToUK1evVq1tbXq16+f3n//faWlpV01hEpSUFCQgoKCOjzm52fc\n",
              "fwxThkUpOMCsM0xZgptraG7V5iNlem/fGW07WqHm1st/gx6XHKG7xiZo/ph4jiIDIEkK8DPrWxOT\n",
              "dPf4gVr3j1P6zZYina5q0P99/7Be3nZM3585TPdNIpD2BEurVftKq7S9oELbCiu1/1SV2t8mDPI3\n",
              "a/LQAbo1LVoz0qI0LLqf19wIc7k1v3LlSuXk5OjZZ59VWFiYVq9eLUlasmSJFi5cqIULF6q6uloz\n",
              "Z86U2WyW1WrV448/rgULFkiS7r77bu3evVs33XSTgoKC1LdvX/3lL3/p3lfVg9pPWdrClCW4mZZW\n",
              "qz4rrNT6vDP6+NA51TVfXk89PDZUCzMTtGBMgpIH9DGwSgDuLMDPrPsmJeub4xP1P/8o1W+3FOlM\n",
              "daOe+d/DennbcX1/1jDdOzFJQf4E0q5wbjLadrRCnx+r1KXrbDLy1r8AuNSadxc//vGP9cILLxj2\n",
              "/H/dVaKfvn1AY5Mi9N4PphpWByDZj1vaXXxB6/POaMOBs7pY39L2ucT+IVo4NkELMxOUHhdmYJUA\n",
              "PFWTpVVv7Tml331apLPV9hZxfHiwfjArRd+6KUmB/mxm7IxObTJKjdKM1GhNT4ty+01G3YUzGm5A\n",
              "25Sl0iqmLMEQNptNh87UaH2e/bgl5w8HSYrqF6g7x9gPmh+fHOE17RsAxgjy99O3Jw/St25K1Ju7\n",
              "S/VbRyD993cP6vdbj+kHs1L0TxMSCaRfYbPZdLTskiN4VmpX8ZWbjMYl93fc9YzSGA/bZNRdCKI3\n",
              "gClLMMqJyjqt33dG7+Wd1vF2f5sODfLX7RlxuiszQbcMHSB/jlsC0M2C/P304C2D9a2bkvTXXSX6\n",
              "3dZjOl3VoH9754B++2mRHpudokUTEn36uLeLdc3a0bbJqEJlNR03W3vTJqPuQhC9Qc4pS5uZsoQe\n",
              "Vl7TqPV5Z7Q+74z2n6puezzQ36w5I2K0cGyCZg7nuCUAvSM4wE85U4fovknJ+suXJfr9Nnsg/enb\n",
              "B/TbrUV6bFaq7h4/0CcC6fU2GQUHmHXzEO/cZNRdCKI3yDllaQdTltCDTl2s152/+UxVjnWffmaT\n",
              "pqZE6a6xCZo7KlahwfxtGoAxggP89PC0Ifo/k5L15y9P6uVtx1R6oUFPrtuvlxx3SO8eN9DrOjSn\n",
              "LtZru2OE5tU2GQ2PDW07TH7iYO/dZNRdCKI3aFRCmOLCgnWuhilL6Dnv7TujqvoWJUWGaOn0ocoe\n",
              "Ha+ofkHX/0IA6CUhgX5aMn2o7r95kNb+3R5ISy7U6yd/2+9o2afqrswEjw2kDc2t+vvx89rmOFD+\n",
              "+FU2GU1PtQfPW1OjFRfOvhFXEERvkMlk0uwRMfoLU5bQgz7Yf1aS9OisFJaAAHBrIYF++u6tQ3X/\n",
              "5GSt+eKkVm4/ruLz9frX/8mzB9KsFC0cO9DtN+S032S0raBCu09c7HAOM5uMuhdBtAvmOILolvxy\n",
              "2e6yse4D3aq4sk6Hz9bIz2zSbSPjjC4HADqlT6C/ls2wz6t/7YtivbL9uI5X1ulf3szTb7YU6fGs\n",
              "VN05JsGtwtv1NhkNjAixt9tTozUlJYopdN2IINoF7acs5Z+9pJEJnNOI7rPx4DlJ0i1DByiy79Un\n",
              "jwGAu+ob5K/vz0zRg7cM1ms7HYG0ok6P/3WffrOlSD/MStWdo+NlNiCQWlqt2uvYZLS9oEL7T1df\n",
              "sclo8tABbQfKD4vuy82mHkIQ7QL7lKUobcov1+b8MoIoutWGA/a2/PzR8QZXAgA3rl+Qv34wK0UP\n",
              "3jJIf/q8WH/YcVxF5bX64Rt79dKWQj2elabsjLgeD6SlF+q1vdAePHcWndelJjYZuQOCaBdljYjV\n",
              "pvxybTpSrseyUo0uB16i9EK9DpyultkkzR0Va3Q5ANBlocEBeiwrVQ9NHazVnxXr1c+Oq6CsVj/4\n",
              "S67S40L1eFaqbh/VfYG0vtmiL49fuOYmo4g+AZqWwiYjoxFEuyiLKUvoAc67oTcPGcAueQBeJSw4\n",
              "QI/PSVXO1MH642cn9MfPTujIuUv63p/tgfRHc9J0+6hYl1vhNptNR845JhkVXrnJyM9s0rikiLYD\n",
              "5UcPDHerdaq+iiDaRe2nLG09UqFvTUwyuiR4gQ2O9aHzx9CWB+CdwkMC9C+3penhqUO06rPj+uPn\n",
              "xTpy7pIeWfsPjYwP04/mpOq2kV8fSC/UNWtHoX2E5o7CCpVfuvomoxlp0bplGJuM3BFBtBs4pyxt\n",
              "yi8jiKLLTl2sV15plUwm6Xba8gC8XHifAP147nA9PG2I/rDjuP70ebEOn63R0jX/UMbAMP0oK01Z\n",
              "I2JkMpnU0n6SUUGFDrDJyOMRRLsBU5bQnT503A2dODiSpR4AfEZEn0D95PZ0LZ42VH/YcVyv7SzW\n",
              "wdM1WvL6Ho1JDFd8ePBVNxmlx4W2rfO8aXB/fgZ7GIJoN2DKErqTc33oHeyWB+CDIvsG6ql56Voy\n",
              "bYhe2XFcr+88qf2nqrX/VLWky5uMZjjWesaG8Rd2T0YQ7QbtpyxtyS8niOKGna1uUG5JlSRpXgaH\n",
              "2APwXQP6Benp7BH67vShenN3qaxWm6azycjreObgVzc0Z4Q9fG7OL5Ot/YIVwAUbD9jb8jcN6s/f\n",
              "8gFAUlS/IP1gVooey0pVZhLjNL0NQbSbfHXKEnAjNh7kEHsAgO8giHYT55QlyX5XFHBVWU2j9py8\n",
              "KIm2PADANxBEu1HWCPtRO5uPlBtcCTzRhwfPyWaTxiVHKCEixOhyAADocQTRbjTbOWXpVJUqvnKo\n",
              "LnA97JYHAPgagmg3ig0L1uiB4bLZpE+5KwoXVFxq0q7iC5JoywMAfAdBtJtlOXbPb2KdKFzw4SF7\n",
              "W35sYrgS+/cxuhwAAHoFQbSbzXGsE3VOWQI6Y+MBdssDAHwPQbSbjUoIU2xYkBpaWvX34+eNLgce\n",
              "4HxtU9ufFYIoAMCXEES7mclk0ux0x+75fNaJ4vo+OlQmq00aPTBcSZG05QEAvoMg2gOYsgRXOA+x\n",
              "zx7NJiUAgG8hiPaAqSlMWULnXKhr1s5jjrZ8Bm15AIBvIYj2gPZTlrYcYfc8ru2Tw+fUarVpZHyY\n",
              "Bkf1NbocAAB6FUG0hzinLG1inSi+xoYD5yRJ82nLAwB8EEG0hzBlCddTVd+sz4sqJUnZ7JYHAPgg\n",
              "gmgPYcoSrueTw2WyWG1KjwvVsOh+RpcDAECvI4j2IOeUpc2sE8VVbDxob8tns0kJAOCjCKI9iClL\n",
              "uJbqhhbtKKyQxPpQAIDvIoj2IOeUpfpmpiyho835ZWpptSk1pp9SY0ONLgcAAEMQRHsQU5ZwLc7d\n",
              "8mxSAgD4MoJoD3NOWdpypJwpS5AkXWps0Xba8gAAEER7mnPK0umqBh05x5Ql2P9S0myxamh0Xw2n\n",
              "LQ8A8GEE0R4WHOCnqcPsU5Y257N7HtKGA/bZ8vMz4mUymQyuBgAA4xBEewFTluBU12TR1qP2tnw2\n",
              "bXkAgI8jiPYC53miTFnCliPlarJYNXhAH42MDzO6HAAADEUQ7QUdpiwd5a6oL9t40N6Wzx5NWx4A\n",
              "AIJoL2mbssQ6UZ9V32zRFse41/lMUwIAgCDaW7LSmbLk67YerVBji1VJkSHKGEhbHgAAgmgvyRjI\n",
              "lCVfx255AAA6Ioj2kvZTlpztWfiOxpbWy215pikBACCJINqr5rStE2XKkq/ZerRC9c2tGhgRojGJ\n",
              "4UaXAwCAWyCI9qIpw6IU5M+UJV/kbMtnZ8TRlgcAwIEg2otCAv00LYUpS76msaW17d/3/DG05QEA\n",
              "cCKI9jLnlKXNrBP1GTsKK1XX3Kr48GBlJkYYXQ4AAG6DINrLZqfb14nuK61SZS1TlnyBsy0/LyNO\n",
              "ZjNteQAAnAiivSwuPFgZA8Nks7F73hc0WVq16bC9LX8Hu+UBAOiAIGoA5+H2rBP1fp8XVepSk0Ux\n",
              "oUEan9zf6HIAAHArBFEDzBlxecpSk4UpS97sg/3nJNl3y9OWBwCgI5eDaGFhoaZMmaK0tDRNnDhR\n",
              "hw4duuIaq9WqJ554QhkZGUpPT9fixYvV3Nzc9vmSkhItWLBAw4cP18iRI/Wb3/yma6/Cw3ScsnTB\n",
              "6HLQQ5otVn1y2B5EOcQeAIAruRxEly1bpqVLl6qgoEBPPfWUcnJyrrhm1apVys3NVW5urvLz82U2\n",
              "m7VixQpJks1m0913360HH3xQR48e1eHDh/Wtb32ryy/Ek9inLDkPt6c97612HqtUTaNFUf2CdNPg\n",
              "SKPLAQDA7bgURMvLy7Vnzx498MADkqRFixaptLRURUVFHa7Ly8vTnDlzFBgYKJPJpOzsbK1Zs0aS\n",
              "tHnzZgUFBemee+5puz42Nrarr8PjXF4nypQlb3V5t3ys/GjLAwBwBZeCaGlpqeLj4+Xv7y/Jfmcv\n",
              "OTlZJSUlHa6bMGGC1q9fr5qaGrW0tOitt95ScXGxJOnw4cOKjo7Wfffdp3Hjxunuu+/W8ePHr/mc\n",
              "TU1Nqqmp6fDR2ur56yqnplyesnS0jClL3qal1aqPHbvlacsDAHB1PbJZKScnR/PmzdOMGTM0Y8YM\n",
              "paWltYVXi8WiLVu26Gc/+5n27t2r22+//Wtb88uXL1d4eHiHj127dvVE2b2q45QljnHyNn8/fl5V\n",
              "9S0a0DdQk2jLAwBwVS4F0aSkJJ09e1YWi0WSfb1nSUmJkpOTO1xnMpn0zDPPaO/evdq5c6dGjhyp\n",
              "UaNGSZKSk5M1bty4tt9/+9vfVm5urlpaWq76nE8//bSqq6s7fEyaNMnlF+qOnFOWNrFO1Os42/Jz\n",
              "R8XJ34/DKQAAuBqXfkLGxMRo/PjxWrt2rSRp3bp1SkxMVEpKSofrGhsbdfHiRUlSZWWlnnvuOT35\n",
              "5JOSpOzsbJ06dUqnT5+WJG3YsEEjRoxQQEDAVZ8zKChIYWFhHT78/Pxce5VuiilL3snSatVHhzjE\n",
              "HgCA6/F39QtWrlypnJwcPfvsswoLC9Pq1aslSUuWLNHChQu1cOFCVVdXa+bMmTKbzbJarXr88ce1\n",
              "YMECSVLfvn318ssv64477pDNZlN4eLj++te/du+r8hDOKUsHT9doy5FyfeumJKNLQjfYdeKCLtQ1\n",
              "q3+fAN08lLY8AADX4nIQHT58uL744osrHn/11Vfbfh0bG6v8/Pxrfo+5c+dq7ty5rj61V8pKj7UH\n",
              "0XyCqLf4wNmWHxmnANryAABcEz8lDXZ5ylIFU5a8QKvVpo8OOQ6xH0NbHgCAr0MQNdiohDDFhAap\n",
              "jilLXmF38QVV1jYrPCRAU4YNMLocAADcGkHUYGazSVkjmLLkLdp2y4+MpS0PAMB18JPSDTBlyTtY\n",
              "rTZtPMhseQAAOosg6gaYsuQd9py8qIpLTQoN9tdUx7ACAABwbQRRNxAS6NcWXJiy5LmcbfnbRsYq\n",
              "0J//tAAAuB5+WroJ5zpRpix5JqvVpg+dbfkM2vIAAHQGQdRNONeJMmXJM+0tvahzNY3qF+Svaam0\n",
              "5QEA6AyCqJtwTlmy2aRPj9Ce9zQbDtjvhs4ZEaPgAO8YQQsAQE8jiLqR2e12z8Nz2Gw2bXSsD81m\n",
              "tzwAAJ1GEHUjcxzrRJmy5Fn2lVbpTHWj+gb6aUZatNHlAADgMQiibiQjIbxtytKXTFnyGM6zQ2eP\n",
              "iKUtDwCACwiiboQpS57HZrO1Hds0PyPO4GoAAPAsBFE349w9v4kpSx7hwOlqnbrYoJAAP80cHmN0\n",
              "OQAAeBSCqJthypJnce6Wn50eo5BA2vIAALiCIOpmmLLkOWw2mzYedO6Wpy0PAICrCKJuiHWinuHQ\n",
              "mRqdPF+vIH+zZtGWBwDAZQRRN+RcJ7qXKUtuzXk3dNbwGPUN8je4GgAAPA9B1A3FhQdrVAJTltyZ\n",
              "fbe8fX0obXkAAG4MQdRNZY1gypI7O3Lukk5U1inQ39z27woAALiGIOqmmLLk3pwjPWekRasfbXkA\n",
              "AG4IQdRNMWXJfdlsNn3gPMSetjwAADeMIOqmzGaTZqeze94dFZbX6lhFnQL9aMsDANAVBFE35gw5\n",
              "TFlyL86RntNToxQWHGBwNQAAeC6CqBub1m7KUkFZrdHlwMEZRLNHxxtcCQAAno0g6sbaT1naRHve\n",
              "LRSVX1JBWa0C/Ey6jbY8AABdQhB1c6wTdS8bHWeHTk2JUngf2vIAAHQFQdTNOcd9MmXJPbTtls+g\n",
              "LQ8AQFcRRN1cfHhI25SlrUcrjC7Hpx2vqNWRc5fkbzZp7ija8gAAdBVB1ANcnrJEe95IGw/a2/K3\n",
              "DBugiD6BBlcDAIDnI4h6AOeUpe0FTFky0oa2Q+xpywMA0B0Ioh4gIyFc0UxZMtTJ83U6dKZGfmaT\n",
              "bh/FNCUAALoDQdQDmM0mZTl2z285Um5wNb7J2ZafPDRSkX1pywMA0B0Ioh7i8pSlMqYsGaDtEHt2\n",
              "ywMA0G0Ioh7COWXp1EWmLPW20gv12n+qWmaTaMsDANCNCKIeIiTQT1OGDZDElKXe9qGjLT9pSKSi\n",
              "Q4MMrgYAAO9BEPUgHONkjA/YLQ8AQI8giHqQ9lOWzjNlqVecrmrQvtIqmUzSPNryAAB0K4KoB2k/\n",
              "ZelTpiz1CmdbfuKgSMWEBRtcDQAA3oUg6mGcxzjRnu8dbbvlR3M3FACA7kYQ9TDOdaJMWep556ob\n",
              "9Y+TFyVxbBMAAD2BIOphRg+8PGVp1wmmLPWkjQftd0MnDOqvuHDa8gAAdDeCqIdpP2Vpcz5TlnrS\n",
              "xgP29aHZGbTlAQDoCQRRDzTbEUSZstRzymsatfuk/Y5zNsc2AQDQIwiiHmhaapQCmbLUoz48dE42\n",
              "m5SZFKGBESFGlwMAgFciiHqgPoH+muqYsrT5CLvne4Jzt/wd3A0FAKDHEEQ91OUpS6wT7W4Vl5ra\n",
              "NoLNY30oAAA9hiDqoZxTlnJLLjJlqZt9dOicrDZpTGK4kiL7GF0OAABeiyDqoeLDQzQynilLPcF5\n",
              "bBOz5QEA6FkEUQ82x3FXdAvrRLvN+dom/f24Y7c8bXkAAHoUQdSDXZ6yVKlmi9XgarzDx4fL1Gq1\n",
              "aVRCmAYN6Gt0OQAAeDWCqAdzTlmqbbLoyxPnjS7HKzh3y9OWBwCg5xFEPZjZbNLs4UxZ6i4X65q1\n",
              "85g90NOWBwCg5xFEPZxz9zxTlrruE0dbPj0uVEOj+xldDgAAXo8g6uHaT1kqLGfKUldsOMgh9gAA\n",
              "9CaXg2hhYaGmTJmitLQ0TZw4UYcOHbriGqvVqieeeEIZGRlKT0/X4sWL1dzcfMV1OTk5MplMqqqq\n",
              "uqHi0XHK0qZ8ds/fqOr6Fn1eVCmJ2fIAAPQWl4PosmXLtHTpUhUUFOipp55STk7OFdesWrVKubm5\n",
              "ys3NVX5+vsxms1asWNHhmrffflsBAQE3XDgum82UpS77JL9MLa02pcX2U0oMbXkAAHqDS0G0vLxc\n",
              "e/bs0QMPPCBJWrRokUpLS1VUVNThury8PM2ZM0eBgYEymUzKzs7WmjVr2j5fVlamZ599Vi+88EI3\n",
              "vARkpTNlqas2slseAIBe51IQLS0tVXx8vPz9/SVJJpNJycnJKikp6XDdhAkTtH79etXU1KilpUVv\n",
              "vfWWiouL2z7/3e9+V//1X/+l0NDQ6z5nU1OTampqOny0tra6UrbXS4i4PGVpK1OWXFbT2KIdhfa2\n",
              "PEEUAIDe0yOblXJycjRv3jzNmDFDM2bMUFpaWlt4ffXVV5WcnKzZs2d36nstX75c4eHhHT527drV\n",
              "E2V7NOeUpc1MWXLZ5vwyNbdaNSy6r1JpywMA0GtcCqJJSUk6e/asLBaLJMlms6mkpETJyckdrjOZ\n",
              "THrmmWe0d+9e7dy5UyNHjtSoUaMkSZ9++qnee+89DR48WIMHD5YkjRkzRnv37r3qcz799NOqrq7u\n",
              "8DFp0iRXX6fXm82UpRu24cA5Sfbd8iaTyeBqAADwHf6uXBwTE6Px48dr7dq1ysnJ0bp165SYmKiU\n",
              "lJQO1zU2NqqhoUH9+/dXZWWlnnvuOf385z+XJP35z3/ucK3JZNL+/fsVERFx1ecMCgpSUFBQh8f8\n",
              "/PxcKdsnjHFMWaq41KQvT5zX9NRoo0vyCJcaW7StwL6cgd3yAAD0Lpdb8ytXrtTKlSuVlpam5557\n",
              "TqtXr5YkLVmyROvXr5ckVVdXa8qUKRo1apSmT5+uRx55RAsWLOjeytEBU5ZuzJYj5Wq2WDUkqq/S\n",
              "466/ZhkAAHQfl+6IStLw4cP1xRdfXPH4q6++2vbr2NhY5efnd+r7MQ2o+2SNiNGbe0q1+UiZ/mPB\n",
              "SNrMnbDR0ZafPzqO9wsAgF7GZCUv4pyyVHqBKUudUddk0adH7XePszNoywMA0NsIol6kT6C/pjBl\n",
              "qdM+PVquJotVgwb00aiEMKPLAQDA5xBEvUyWY/f8FtaJXpezLZ+dwW55AACMQBD1Mu2nLF2oaza4\n",
              "GvfV0NyqLUfsYX3+6DiDqwEAwDcRRL2Mc8qS1SZ9eoS7otey9Wi5Glpaldg/RKMHhhtdDgAAPokg\n",
              "6oWymLJ0XRsOOnfL05YHAMAoBFEvlMWUpa/V2NKqzY7NXNkZtOUBADAKQdQLOacs1TZZtOvEBaPL\n",
              "cTvbCipU39yqhPBgZSZFGF0OAAA+iyDqhdpPWeIYpyttPHBWkn2kJ215AACMQxD1UrPbrRNletVl\n",
              "jS2t2pTPbnkAANwBQdRLTWfK0lV9Vlip2iaL4sKCNS6pv9HlAADg0wiiXqr9lKXNHG7fZsNBe1t+\n",
              "XkaczGba8gAAGIkg6sWcu+c3s05UktRkadUnh+3vxfzRzJYHAMBoBFEvNpspSx3sLDqvS40WxYQG\n",
              "6aZBtOUBADAaQdSLDYwI0QimLLX54ABteQAA3AlB1MvNceye3+LjQbTZYtXHh+zTlLIzaMsDAOAO\n",
              "CKJezrlOdFtBhU9PWfri+HnVNFoU1S9Qk4ZEGl0OAAAQQdTrjRkYrqh+TFnasN/elr99VJz8aMsD\n",
              "AOAWCKJezmw2aXZ6tCTfnbLU0mrVR4ftbXl2ywMA4D4Ioj6g7RgnH52y9OXxC6qqb1Fk30DdTFse\n",
              "AAC3QRD1Ae2nLBX54JQl527520fFyt+PP/IAALgLfir7gPZTljb52JQlS+vl3fK05QEAcC8EUR+R\n",
              "5Tjc3temLO0qvqDzdc2K6BOgyUMHGF0OAABohyDqI2Y71on62pSlDY62/NyRsQqgLQ8AgFvhJ7OP\n",
              "aD9laetR32jPt1pt+vAgs+UBAHBXBFEf4pyytNlH1onuKb6gytomhQX7a8qwKKPLAQAAX0EQ9SGz\n",
              "HetEfWXKkrMtf9vIOAX680cdAAB3w09nHzI2McJnpixZrTZtPGjfLX/HmDiDqwEAAFdDEPUh7acs\n",
              "bT7i3bvnc0suqvxSk0KD/DU1hbY8AADuiCDqY9qmLOWXe/WUJech9nNGxirI38/gagAAwNUQRH3M\n",
              "tJQoBfqZVXKh3munLFmtNn14kEPsAQBwdwRRH9M3yF+3ePmUpX2nqnS2ulF9A/00PZW2PAAA7oog\n",
              "6oOcxzht8dJ1ohv229vyWSNiFRxAWx4AAHdFEPVBzilL/zh5URe9bMqSzXZ5tzxteQAA3BtB1Ae1\n",
              "n7L0qZdNWco7Va3TVQ3qE+inmcOjjS4HAAB8DYKoj8pK984pSxsdu+VnpcfQlgcAwM0RRH1UlmOd\n",
              "6HYvmrJks9m04aA9iN5BWx4AALdHEPVRzilLl5os2l3sHVOWDp6uUemFBgUHmGnLAwDgAQiiPqr9\n",
              "lKVN+d6xe955N3TW8Bj1CfQ3uBoAAHA9BFEfNjvde6Ys2Wy2tvWh7JYHAMAzEER92PTUy1OWjlV4\n",
              "9pSlw2drVHy+XkH+Zs1ybMQCAADujSDqw7xpytLGA/azQ2ekRatfEG15AAA8AUHUxzmnLG324HWi\n",
              "NptNGxxt+TvG0JYHAMBTEER9nLON7clTlo6WXdLxyjoF+ps1m7Y8AAAegyDq4xL791F6XKisNmlr\n",
              "gWe25zc42vK3pkYrNDjA4GoAAEBnEUShOY7Z8566TvTybvk4gysBAACuIIji8pSlo543Zamw7JIK\n",
              "y2sV4GdSliNQAwAAz0AQhWPKUqBHTllytuWnp0YrPIS2PAAAnoQgCpnNJs0abr8r6mlTljY6pill\n",
              "Z9CWBwDA0xBEIUltbW1PmrJ0rKJWR85dkr/ZpNtG0pYHAMDTEEQhyTOnLDk3KU1NiVJEn0CDqwEA\n",
              "AK4iiEKSZ05Zcq4PZbc8AACeiSCKNlkeNGWpuLJOh8/WyM9s0m0jCaIAAHgigijazPagKUsbHJuU\n",
              "pgwboMi+tOUBAPBELgfRwsJCTZkyRWlpaZo4caIOHTp0xTVWq1VPPPGEMjIylJ6ersWLF6u52R5s\n",
              "Dhw4oFtvvVXp6enKyMjQww8/rIaGhq6/EnSZJ01Zcs6Wz85gtjwAAJ7K5SC6bNkyLV26VAUFBXrq\n",
              "qaeUk5NzxTWrVq1Sbm6ucnNzlZ+fL7PZrBUrVkiSgoOD9dJLL+nIkSPKy8tTXV2dfvWrX3X5haB7\n",
              "eMKUpZLz9Tp4ukZmkzR3FLvlAQDwVC4F0fLycu3Zs0cPPPCAJGnRokUqLS1VUVFRh+vy8vI0Z84c\n",
              "BQYGymQyKTs7W2vWrJEkpaamasyYMZIkPz8/TZw4UcXFxd3wUtAdZnvAlCXn2aGThw5QVL8gg6sB\n",
              "AAA3yqUgWlpaqvj4ePn7+0uSTCaTkpOTVVJS0uG6CRMmaP369aqpqVFLS4veeuutq4bNuro6vfrq\n",
              "q7rrrruu+ZxNTU2qqanp8NHa2upK2XBBZrspS3vcdMpSW1t+NG15AAA8WY9sVsrJydG8efM0Y8YM\n",
              "zZgxQ2lpaW3h1am5uVn33nuv5s6dq7vvvvua32v58uUKDw/v8LFr166eKBv66pQl92vPn7pYr7xT\n",
              "1TKZpNtpywMA4NFcCqJJSUk6e/asLBaLJMlms6mkpETJyckdrjOZTHrmmWe0d+9e7dy5UyNHjtSo\n",
              "UaPaPt/S0qJ7771X8fHxbWtHr+Xpp59WdXV1h49Jkya5UjZc1DZl6UiZ201Z+vCg/ezQSYMjFRMa\n",
              "bHA1AACgK1wKojExMRo/frzWrl0rSVq3bp0SExOVkpLS4brGxkZdvHhRklRZWannnntOTz75pCTJ\n",
              "YrHovvvuU2RkpF555RWZTKavfc6goCCFhYV1+PDz83OlbLjIOWXp5Hn3m7L0gaMtP5+2PAAAHs/l\n",
              "1vzKlSu1cuVKpaWl6bnnntPq1aslSUuWLNH69eslSdXV1ZoyZYpGjRql6dOn65FHHtGCBQskSW++\n",
              "+abefvtt7dmzR+PGjVNmZqZ+8IMfdONLQlf1DfLXZMeUpc1u1J4/U9WgvSVVMpmkeRkcYg8AgKfz\n",
              "v/4lHQ0fPlxffPHFFY+/+uqrbb+OjY1Vfn7+Vb/+/vvv1/333+/q06KXzRkRo+0FFdqcX65lM4YZ\n",
              "XY6ky235mwb1V2wYbXkAADwdk5VwVc4pS3tOXnCbKUscYg8AgHchiOKq3G3K0rnqRu05aV93nD2a\n",
              "tjwAAN6AIIpryhrhPsc4fXTI3pYfnxyh+PAQg6sBAADdgSCKa3Ie47T9aIVaWo2dssRueQAAvA9B\n",
              "FNfUfsrS7hPGTVkqv9So3Y4pT0xTAgDAexBEcU3uMmXpo0NlstmksUkRGhhBWx4AAG9BEMXXcq4T\n",
              "NXLK0ob9jrY8Z4cCAOBVCKL4WtNTo9tNWarr9eevrG3SlyfOS2J9KAAA3oYgiq/VccpSWa8//0eH\n",
              "zslqk0YPDFdSZJ9ef34AANBzCKK4rjnO9rwB60Q3HrAf28TZoQAAeB+CKK7LqClLF+qa9cVxR1ue\n",
              "aUoAAHgdgiiuq/2UpW0FFb32vB8fOqdWq00j48M0OKpvrz0vAADoHQRRdMrlKUu9t050w0F7W34+\n",
              "bXkAALwSQRSd4pyytK2gd6YsVdU3a2dRpSR2ywMA4K0IouiUsYkRGtA3UJcae2fK0seHy2Sx2pQe\n",
              "F6qh0f16/PkAAEDvI4iiU/zMJs1Kdx5u3/O75zc6Zstns0kJAACvRRBFp10+xqlnpyxVN7ToM0db\n",
              "/o4xrA8FAMBbEUTRac4pS8U9PGVp0+EytbTalBrTTykxoT32PAAAwFgEUXRab01Z2njQ0ZZnkxIA\n",
              "AF6NIAqXZPXwOtFLjS3aXuBoyxNEAQDwagRRuMR5nug/Tl5UVX33T1nanF+u5larhkb3VVosu+UB\n",
              "APBmBFG4xDllqdVq09aj3T9laYNjt/wdo+NlMpm6/fsDAAD3QRCFy3pqylJtk0VbHSNEObYJAADv\n",
              "RxCFy2an98yUpS1HytVssWrwgD4aEc9ueQAAvB1BFC7LTGo3Zam4+6YsOQ+xn09bHgAAn0AQhcs6\n",
              "TFnK757d8/XNFn161P69mC0PAIBvIIjihnT3lKVPj1SoscWqpMgQjUoI6/L3AwAA7o8gihsyrZun\n",
              "LG04SFseAABfQxDFDekX5K+bh0ZKkrYc6dru+YbmVm1xtPjns1seAACfQRDFDZszwr57flMX14lu\n",
              "KyhXQ0urBkaEaExieHeUBgAAPABBFDdsdnr3TFnacOCcJGn+6Dja8gAA+BCCKG5YUmQfDY/t2pSl\n",
              "xpZWbXYcjJ/NbnkAAHwKQRRd4pyytPnIjbXntxdUqK65VfHhwcpMjOjGygAAgLsjiKJLshzrRLce\n",
              "Lb+hKUsbD9rb8tkZ8TKbacsDAOBLCKLokq5MWWqytGrTYXtbfv7ouJ4oDwAAuDGCKLqkK1OWPius\n",
              "1KUmi2LDgjQ+uX9PlAcAANwYQRRdluUIoltcXCfq3C1PWx4AAN9EEEWXTU+zT1k6UVmnYxW1nfqa\n",
              "ZotVnxx2BlHa8gAA+CKCKLqs/ZQl51FM1/P5sUrVNFoU1S9INw2O7MnyAACAmyKIolu4OmVp4wH7\n",
              "bPnsjDj50ZYHAMAnEUTRLVyZstTSatXHh52H2NOWBwDAVxFE0S3aT1naVvD1U5a+OHZeVfUtGtA3\n",
              "UJNoywMA4LMIoug2zilL12vPbzxob8vfnhEnfz/+CAIA4KtIAeg2nZmyZGm16qNDjkPsM5gtDwCA\n",
              "LyOIottkJkUo8jpTlr48cUEX6prVv0+AJg+lLQ8AgC8jiKLb+JlNmjXccbj9NdrzGxy75W8fRVse\n",
              "AABfRxJAt5rjWCe6+SpTllqtNn10yHGI/Wja8gAA+DqCKLrV9LRoBfiZrjpladeJC6qsbVZ4SICm\n",
              "DBtgUIUAAMBdEETRrfoF+WvyUHvI/OqUJWdbfu7IWAXQlgcAwOeRBtDtshyH229ut0601WrTh462\n",
              "/Hza8gAAQARR9ADnMU57Tl5UdX2LJPvEpYpLTQoN9tfUlCgjywMAAG6CIIpu137K0tYC+11RZ1v+\n",
              "tpGxCvTnjx0AACCIooe0n7JktdrapilxiD0AAHAiiKJHOIPotqPl2lV8QWU1TeoX5K/pabTlAQCA\n",
              "nctBtLCwUFOmTFFaWpomTpyoQ4cOXXGN1WrVE088oYyMDKWnp2vx4sVqbm5u+/z777+v9PR0paam\n",
              "6pvf/KZqamq69irgdjKT+iuyb6BqGi365Qf5kuxnjAb5+xlcGQAAcBcuB9Fly5Zp6dKlKigo0FNP\n",
              "PaWcnJwrrlm1apVyc3OVm5ur/Px8mc1mrVixQpJUW1urxYsX691331VhYaESEhL085//vMsvBO6l\n",
              "/ZSlA6erJXGIPQAA6MilIFpeXq49e/bogQcekCQtWrRIpaWlKioq6nBdXl6e5syZo8DAQJlMJmVn\n",
              "Z2vNmjWSpI0bN2rcuHFKT0+XJH3/+9/XG2+80R2vBW7GOWVJkvoG+mlGWrSB1QAAAHfjUhAtLS1V\n",
              "fHy8/P39JUkmk0nJyckqKSnpcN2ECRO0fv161dTUqKWlRW+99ZaKi4slSSUlJRo0aFDbtYMHD9bZ\n",
              "s2dlsViu+pxNTU2qqanp8NHa2upK2TDItNQoBfiZJEmzR8QqOIC2PAAAuKxHNivl5ORo3rx5mjFj\n",
              "hmbMmKG0tLS28Oqq5cuXKzw8vMPHrl27urli9ITQ4ABlpdvPFF00fqDB1QAAAHfjUhBNSkrqcPfS\n",
              "ZrOppKREycnJHa4zmUx65plntHfvXu3cuVMjR47UqFGjJEnJyck6efJk27XFxcUd7rJ+1dNPP63q\n",
              "6uoOH5MmTXLpRcI4/++eMXr/sWmaOTzm+hcDAACf4lIQjYmJ0fjx47V27VpJ0rp165SYmKiUlJQO\n",
              "1zU2NurixYuSpMrKSj333HN68sknJUnz5s1Tbm6ujhw5Ikn63e9+p/vuu++azxkUFKSwsLAOH35+\n",
              "tHg9RWhwgDIGhhtdBgAAcEMu98tXrlypnJwcPfvsswoLC9Pq1aslSUuWLNHChQu1cOFCVVdXa+bM\n",
              "mTKbzbJarXr88ce1YMECSVJoaKheffVVfeMb35DFYlFGRoZee+217n1VAAAAcHsmm81mM7oIV/34\n",
              "xz/WCy+8YHQZAAAA6AImKwEAAMAQBFEAAAAYgiAKAAAAQxBEAQAAYAiCKAAAAAxBEAUAAIAhCKIA\n",
              "AAAwBEEUAAAAhiCIAgAAwBAeOVnpm9/8pgYPHtwrz9Xa2qpdu3Zp0qRJzLi/Abx/Xcd72DW8f13H\n",
              "e9g1vH9dx3vYNUa9f4MGDdLjjz/+tdd4ZBDtTTU1NQoPD1d1dbXCwsKMLsfj8P51He9h1/D+dR3v\n",
              "Ydfw/nUd72HXuPP7R2seAAAAhiCIAgAAwBAEUQAAABiCIHodQUFB+o//+A8FBQUZXYpH4v3rOt7D\n",
              "ruH96zrew67h/es63sOucef3j81KAAAAMAR3RAEAAGAIgigAAAAMQRAFAACAIQiiX6OwsFBTpkxR\n",
              "WlqaJk6cqEOHDhldkkf54Q9/qMGDB8tkMmnfvn1Gl+NxGhsb9Y1vfENpaWkaO3asbrvtNhUVFRld\n",
              "lseZO3euxowZo8zMTE2fPl179+41uiSPtHr1aplMJr377rtGl+JxBg8erOHDhyszM1OZmZl68803\n",
              "jS7JozQ1NenRRx9VamqqRo8erQceeMDokjzK+fPn2/7sZWZmKi0tTf7+/rpw4YLRpUmS/I0uwJ0t\n",
              "W7ZMS5cuVU5Ojv72t78pJydHu3fvNrosj/FP//RPevLJJzVt2jSjS/FYS5cuVXZ2tkwmk1566SUt\n",
              "WbJEW7duNbosj/LWW28pIiJCkvTOO+8oJydHeXl5xhblYYqLi/WHP/xBkydPNroUj/Xmm28qMzPT\n",
              "6DI80k9/+lOZTCYVFBTIZDLp3LlzRpfkUQYMGNDhZtDzzz+vbdu2KTIy0rii2uGO6DWUl5drz549\n",
              "bX/zWrRokUpLS7kj5YJbb71ViYmJRpfhsYKDgzV//nyZTCZJ0uTJk1VcXGxsUR7IGUIlqbq6uu39\n",
              "ROdYrVYtWbJEv/nNb9zy6Bd4t7q6Oq1atUq//OUv2/7bjYuLM7gqz7Zq1SotXrzY6DLaEESvobS0\n",
              "VPHx8fL3t980NplMSk5OVklJicGVwVetWLFCd911l9FleKQHH3xQSUlJ+tnPfqY1a9YYXY5HeeGF\n",
              "FzR16lRNmDDB6FI82oMPPqjRo0dr8eLFqqioMLocj3Hs2DFFRkbq2Wef1U033aTp06dr8+bNRpfl\n",
              "sXbu3KmLFy/qzjvvNLqUNgRRwAM8++yzKioq0vLly40uxSO9/vrrKi0t1S9+8Qs99dRTRpfjMQ4e\n",
              "PKh169bp3//9340uxaNt375d+/fvV25urqKiovTQQw8ZXZLHsFgsOnnypEaOHKk9e/boxRdf1L33\n",
              "3quysjKjS/NIq1at0oMPPth2k80duE8lbiYpKUlnz56VxWKRv7+/bDabSkpKlJycbHRp8DHPP/+8\n",
              "3n77bW3atEl9+vQxuhyP9tBDD+mRRx7R+fPnNWDAAKPLcXs7duxQcXGxUlNTJUnnzp3T0qVLdfbs\n",
              "WX3ve98zuDrP4fy5ERAQoB/96EdKS0szuCLPkZycLLPZrPvvv1+SNG7cOA0ZMkQHDhxQbGyswdV5\n",
              "ltraWr311ltut9eFO6LXEBMTo/Hjx2vt2rWSpHXr1ikxMVEpKSkGVwZf8sILL+iNN97QJ5980mGt\n",
              "IzqnqqpKZ86cafv9u+++qwEDBrjNIn13973vfU9nz55VcXGxiouLNXnyZL3yyiuEUBfU1dWpqqqq\n",
              "7fdvvPGGxo0bZ1xBHiYqKkpZWVn66KOPJEknTpzQiRMnNGLECIMr8zxvvvmmxo4dq/T0dKNL6YA7\n",
              "ol9j5cqVysnJ0bPPPquwsDCtXr3a6JI8yrJly/TBBx/o3Llzuv322xUaGspmLxecOnVK//qv/6qh\n",
              "Q4dq1qxZkuzzgr/88kuDK/Mc1dXVuueee9TQ0CCz2azo6Gi9//77bFhCrykrK9OiRYvU2toqm82m\n",
              "oUOH6vXXXze6LI/y8ssva/HixXrqqadkNpu1cuVKDRw40OiyPM6qVav03e9+1+gyrsCseQAAABiC\n",
              "1jwAAAAMQRAFAACAIQiiAAAAMARBFAAAAIYgiAIAAMAQBFEAAAAYgiAKAAAAQxBEAQAAYAiCKAAA\n",
              "AAxBEAUAAIAhCKIAAAAwxP8PsaCufZkAOKoAAAAASUVORK5CYII=\n",
              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-98e9f10f-160a-49ae-88ca-0e8451764606\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-98e9f10f-160a-49ae-88ca-0e8451764606\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "_df_10['process fidelity'].plot(kind='line', figsize=(8, 4), title='process fidelity')\n",
              "plt.gca().spines[['top', 'right']].set_visible(False)"
            ],
            "text/html": [
              "      <div class=\"colab-quickchart-chart-with-code\" id=\"chart-47d9c5e1-6ec9-4f63-a6b6-970db6f50302\">\n",
              "        <img style=\"width: 180px;\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqIAAAFuCAYAAABaw3qhAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90\n",
              "bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9h\n",
              "AAAPYQGoP6dpAABEyklEQVR4nO3deXjUZZrv/09V9pCN7AkhbCEECBBAUNkhuIACTdu29uhoWmlc\n",
              "u53pURxnTp/j6dODOtM/e9BeRKVpl3Zr0ZZRwGYHBVkaCDtJgJCwZSEbCdlq+f1RqSIREEKWby3v\n",
              "13XlklRVUneVQD587+d5bpPdbrcLAAAA6GZmowsAAACAbyKIAgAAwBAEUQAAABiCIAoAAABDEEQB\n",
              "AABgCIIoAAAADEEQBQAAgCEIogAAADAEQRQAAACGIIgCgBsrLCzUxIkTFRERoXHjxqmoqEhhYWE6\n",
              "duzYFb/m/vvvV05OzjU/R05Oju6//37X50OHDtVbb73VkbIB4Jr4G10AAODKXnjhBfXs2VNVVVUy\n",
              "mx3XDmpra7v0OQ8cOOD6dWFhofr166f8/HylpaV16fMC8D1cEQXgU6xWq2w2m9FlXLOjR49q+PDh\n",
              "rhAKAN6Ev9kAeLQpU6boySef1Pe//32Fh4crLS1Nb7/9tuv+DRs2yGQy6YMPPlB6erpCQ0NVWlqq\n",
              "U6dO6Yc//KESEhKUkJCge+65R6dPn3Z9ncVi0csvv6whQ4YoPDxcKSkpeumll1z3b9u2TVOmTFFM\n",
              "TIz69OmjX/ziF7JYLJKkpqYmPf7440pMTFR4eLj69u2rV199VZJUVVWle++9V7GxsYqIiFB6ero+\n",
              "/vjjy762AQMGaP369frP//xPhYWFaeHChSosLJTJZFJBQYHrcf/1X/+l1NRURUVFad68eWpqamrz\n",
              "faqqqvTYY4+pT58+iomJ0cyZM7+ztd+3b1+9+eabkhxtekkaMWKEwsLC9Oijj2rJkiUaMGCA7Ha7\n",
              "62saGxsVGxurv/71r9/5/wsAWiOIAvB4S5Ys0Y9//GNVVlbqlVde0bx58/T111+3ecyHH36orVu3\n",
              "qqamRnFxcbrzzjvl5+envLw8HTlyRHa7XbNnz5bVapUkPf/88/r973+vP/3pT6qurtbevXs1adIk\n",
              "SdKRI0eUnZ2tRx99VCUlJdq0aZOWL1/uCqpvvfWWtm7dqv379+v8+fP65ptvNH78eEmO0Hj+/Hkd\n",
              "P35c1dXVWr16tYYMGXLZ13X06FFNnDhRCxYsUG1trf7t3/7tkse89957WrhwoT744AOVlZVp7Nix\n",
              "+vTTT1332+12zZ07VzU1Ndq9e7dOnz6tYcOG6c4771Rzc/NV31tnmz43N1e1tbV67bXX9KMf/Ujn\n",
              "zp3TmjVrXI/7+OOPFRwcrFmzZl31ewKAE0EUgMebOXOmZs2aJX9/f82cOVNz587VH//4xzaPefHF\n",
              "FxUTE6OgoCBt375dubm5+v3vf6/IyEhFRUXpD3/4g3bt2qUdO3bIbrdr0aJFevHFFzV27FiZzWZF\n",
              "R0fr5ptvliT97ne/06xZs3TvvffK399fffr00YIFC7R06VJJUmBgoGpra3Xw4EE1NzcrMTFRo0aN\n",
              "ct137tw5HT58WHa7XX369LliEL0WS5cu1UMPPaRx48YpICBA8+fP1/Dhw1337969W19//bUWL16s\n",
              "6OhoBQUFaeHChTp+/Li2bdt2Xc8ZGhqqBx98UK+//rrrtsWLF2vevHny8/O77tcCwPewWQmAx+vX\n",
              "r98ln+/ateuKjykuLlZ0dLR69uzpui0mJkY9e/ZUUVGRBgwYoNraWg0aNOiyz5efn6/169crKirK\n",
              "dZvNZnOtPb3//vtVVlamZ555RocPH9a4ceP0q1/9SqNHj9Yzzzwjq9WqefPmqaioSNOnT9cLL7xw\n",
              "3RuBTp48qTlz5lzxtebn58tisSglJeWSry0uLr6u55Skxx57TCNGjFBJSYkqKiq0ZcsWvffee9f9\n",
              "/QD4Jq6IAvB4hYWFl3z+7eDVerNP7969VVlZqcrKStdtFRUVqqysVGpqqmJjYxUWFqa8vLzLPl9i\n",
              "YqL+4R/+QVVVVa6Pmpoa1252Pz8/Pf3009q2bZtOnTqlwYMHu8JiaGiofvnLXyo3N1dHjx6Vv7+/\n",
              "Hnzwwet+7SkpKZd9/a1rDQwMVFlZWZt66+vr9aMf/eiq3/9Km6QyMjI0fvx4LV26VIsXL9Ydd9xx\n",
              "2bALAN+FIArA461YsUJffPGFrFarVq1apU8//VQ//vGPr/j4sWPHKjMzU08++aRqampUXV2tJ554\n",
              "QllZWRozZoxMJpN++tOf6rnnntPOnTtlt9tVUVGhrVu3SpIef/xxffzxx/rLX/6ipqYmWa1WFRQU\n",
              "aNWqVZKkdevWaefOnWpqalJwcLDCwsJcLevly5frwIEDslgsCg0NVUhIiPz9r7859eCDD+qPf/yj\n",
              "vvnmG1ksFr355pvKzc113T9hwgRlZmbqscceU2lpqSSpsrJSy5Yt04ULF676/ePi4mQ2m3XkyJFL\n",
              "7nv88cf1+uuv6+2339Yjjzxy3a8BgO8iiALweA899JCWLFmiqKgoPfHEE3rttdc0ceLEKz7ez89P\n",
              "n3/+uRobG5WWlqaBAwfKYrFo+fLlrsD4y1/+UvPmzdN9992n8PBwDR8+XJs3b5YkjRkzRqtXr9Yb\n",
              "b7yhXr16KSYmRj/4wQ904sQJSVJpaalycnIUHR2tuLg4bdy40bUz/vjx4/re976nqKgo9erVSyUl\n",
              "JVqyZMl1v/b77rtPCxYs0N13363Y2Fh98803mjt3bpvXunr1aoWGhurGG29UeHi4RowYoU8//VQm\n",
              "k+mq3z8kJEQLFy7UvHnzFBUVpccff9x13/e+9z01NDQoIiJCt99++3W/BgC+y2Rvff4GAHiYKVOm\n",
              "aMKECfrVr35ldCk+6cYbb9Ts2bP17//+70aXAsADsVkJAHBdVqxYof379+uLL74wuhQAHoogCgBo\n",
              "t969e6u+vl6vvfaaYmNjjS4HgIeiNQ8AAABDsFkJAAAAhiCIAgAAwBAEUQAAABiCIAoAAABDeGQQ\n",
              "XbRokdElAAAAoIM8Mog6p5cAAADAc3lkEAUAAIDnI4gCAADAEARRAAAAGIIgCgAAAEMQRAEAAGAI\n",
              "gigAAAAMQRAFAACAIQiiAAAAMARBFAAAAIYgiAIAAMAQBFEAAAAYwt/oAgAAxrLZ7Kq40KSy840q\n",
              "r21U2flWH7WO28wmk6JCAxUVEqCo0ABFhgQoKjRQPUOdnwe6bg/w4xoHgGtDEAUAL2S321XTYGkb\n",
              "KFv+67zNGTrP1TXJarN32nOHBfm3BNUA9QwNVGRogCvARoW0/twRXp0BNsjfr9NqAOAZCKIA4EEu\n",
              "NFlUfr5JZbUNrUJm02UDZ5PFds3f12SSokMDFRcepLjwIMWGOf4bFxak2PBA2WxSVX2zqi80qaq+\n",
              "WZUXmlV1oUnV9c2qavl1TYNFklTbaFFto0Wnqurb9dpCA/0UFRKgyFZXXh0fra/EXgyvUS2/Dg4g\n",
              "wAKeiiAKAAZrsth0ru5bLXHnFctvtcrrmqzt+t7hwf6uQNkmYIYHtbk9ukdgh1vqVptdNfXNLUG1\n",
              "SdUXmlVV39QSVJtbQuvFIOsMtdX1zbLbpQtNVl1osup0dUO7njfI36yeoYGtlgxcDKmRLb/u2erX\n",
              "ziAbEuAnk8nUodcMoGMIogDQBaw2uyrqmtquuaxtvHQdZm2jqi40t+t7BweY24TIb1/BbH1bd14t\n",
              "9DOb1LNHoHr2CFQ/9bjmr7PZ7DrfYLkYWp2BtSXAVtU7Q+3FIOu8CmuzS40Wm87WNOhsTfsCbKCf\n",
              "2bVM4JIlBKGBl4Ra5+09AgmwQGchiALANbLb7aqpt7S0xZsuuVrpCpi1jTpX26j2LLv0N5vaXq10\n",
              "XcEMVFx4cJurmN4WhMxmkyJbrlj2ibn2r7PZ7KptsjhCauurr/XNqqq7GFir2wRcR4C12Oxqstpc\n",
              "/+/aw99sarNhy7GcwBFmXUsIWi8vCAlUbHigQgP5kQt8G38qAPi8C02WS8Nk6409tU2OdZfnG9Vk\n",
              "bd+6y5gegVe8Wtk6dEaGBMhs9p5w2R3MZpMiggMUERyg3tHX/nV2u111TVbXVVfXOtf6i59XtgTZ\n",
              "by8vaLLaZLHZVV7bpPLaJkl11/ScfmaTvpfVSz/LTlOfmGu/Wgx4O4IoAK9WWdekXUWVKj1/6a5x\n",
              "58ae9q67jGhZd3m59ZaxLf+Nb1l36c9RRm7HZDIpLMhfYUH+Sul57V9nt9vV0GxTVX2TKuu+vWSg\n",
              "1effCq9V9U1qaLZp2a6T+uueU/r+yF766bSBSo0J7boXCXgIgigAr1PXaNGaQyVavue0NuaVyXIN\n",
              "PfLgALPiW1rgjnZ4kOLCgltdwQw0ZN0l3IfJZFJIoJ9CAkOUFBnSrq/dU1yl36zO08a8Mv3l7yf1\n",
              "6e5TumtUip6clqbe0QRS+C6CKACv0GSxaWNemZbnntaagyWqb754lTMtPkx9okMvexXT+bm3rbuE\n",
              "e8nqHaW3Hhqrv5+o1H+vydPm/HJ9uLNYy3ad1N03pOiJqWlK6Ukghe8hiALwWFabXduOn9PyPae1\n",
              "cv9ZVddf3H3eNyZUs0cka3ZWstLiww2sErhodJ+eeufhG/X3ExX6zep8fVVQrve3F+vjv5/U3Tf0\n",
              "1hNT09Qrqn1XWwFPRhD9Ds1Wm3YUVshqs2viwDijywEgxzq9vSertTz3tP4n97RKW+14jg8P0qwR\n",
              "yZo9IlnDUyK5wgm3NbpPtN6dd6N2FFboN6vztOXoOb23rUh/2Vmse8ek6vGpA9rd/gc8EUH0O3y6\n",
              "65QWLNurESmRBFHAYAWl57V8z2ktzz2twnMXXLdHBPtr5rAkzc5K1o39YuTHznN4kDF9o/XeT27S\n",
              "tmPn9Js1efrmWIXe+eaEPtxRrB+N7a3Hp6YpISLY6DKBLkMQ/Q5TBjnCZ+7JapXWNCievwyAbnWq\n",
              "ql7/k3tay/ec1sEzNa7bgwPMumVIomaPSNak9FhmlMPj3dg/Rh/Mv1lbjpbrv1fna3thhd7aekLv\n",
              "7yjWP4xN1eNTBvAzCF6JIPod4iOCNSIlUrknq7X+SKnuGZNqdEmA1ztX26gV+89q+Z5T2lFY6brd\n",
              "32zSpPQ4zclK1vTBCeoRxF9f8D7jBsTq5v4x2nL0nH6zOk87T1TqT1sK9f72It13Yx89OqW/4sMJ\n",
              "pPAe/E1+FdmDE5R7slprDhFEga5S22jR3w6c1fLc09qcXy5ry3FLJpM0tm+05mT10ozMRPXsEWhw\n",
              "pUDXM5lMGp8Wq3EDYvRVQbl+szpPu4qq9Mevj+u97Sd0/4199MjkAYoLDzK6VKDDCKJXMS0jXi+v\n",
              "ztNX+eVqaLZyfiDQSRotVm04Uqble05rzaESNVouTiwa1itSs0ck684RSWzYgM8ymUyaODBOE9Ji\n",
              "tSnfEUj3FFfpza+O68/bivSPN/fRI5P6KyaMQArPRRC9iqHJEUqKDNaZ6gZtPXpOUzPijS4J8FhW\n",
              "m11bj57TZ3tOadWBszrfYHHd1z+2h2ZnOXa8948LM7BKwL2YTCZNTo/TpIGx2pBXpv9enafck9V6\n",
              "fdMxvbP1hB4Y10ePTBqgaDoG8EAE0aswmUyalhGvP28r0ppDJQRRoJ3sdrt2F1dp+Z7T+nzvGZXX\n",
              "XjxuKTEiWLNGJGlOVi8NTY7guCXgO5hMJk0dFK8p6XFaf6RUv1mdr32nqrV4oyOQPjiur+ZP7M8S\n",
              "FngUgug1mD44QX/eVqR1h0tlt9v5YQlcg7yS8/pszyn9T+4ZFVVcPG4pKjTAcdzSiGSN7RstM8ct\n",
              "Ae3iuECSoKmD4rX2UKl+syZPB07X6A8bjurtLYX68fh+mjexn6JCCaRwfwTRa3DzgBgFB5h1prpB\n",
              "B8/UaGhypNElAW6puOKC66D5w2fPu24PDfTTrUMSNDsrWRPS4hTobzawSsA7mEwmTR+SoOzB8Vp9\n",
              "sET/vSZfB8/U6LfrC/TWlkL9eHxfPTyhvyJDA4wuFbgigug1CA7w04S0OK05VKK1h0oJokArZecb\n",
              "tWLfGX2255R2FVW5bg/wM2lyerzmZCUre3C8QgP56wboCiaTSbcOTdT0wQn628ES/feaPB0+e16v\n",
              "rCvQ0i2Femh8Pz00oZ8iQwikcD/8ZLhG0wfHtwTREv0se6DR5QCGqmlo1pf7HcctfV1QrpbTlmQy\n",
              "STf3j9HsEcmakZnElRigG5nNJt2emahbhyRo1YGzWrQmX0dKzmvR2nwt/fq4Hp7QXz+e0FcRwfy5\n",
              "hPsgiF6jaS2blHJPVqv0fAMHCsPnNDRbtf5wqT7bc1rrjpSqqdVxSyNSIjU7q5fuHJ7EOELAYGaz\n",
              "STOHJen2oYlasf+MFq3JV35prX6zJk9//Pq45k3op5zxfRVOIPUpdrtddrvcbl0+QfQaxUcEa3hK\n",
              "pPaerNb6wxxuD99gsdr09dFzWr7ntL48cFa1jRePW0qLD9OcEcmaNSJZfWN7GFglgMsxm026c7ij\n",
              "O/HFvjNatCZPR8vq9P+tztOSr4/rJxP768FxfRXGlDKvVXWhSV8VlGvjkTJtzi/Xf8zNVPbgBKPL\n",
              "aoPffe2QnZGgvUxZgpez2ezaVVSp5bmn9cXeMzpX1+S6r1dUiO4ckaQ5I3ppcFI4J0gAHsDPbNLs\n",
              "Ecm6Y1iSPt97WovW5utYWZ3+68sjenPzMc2fNEAP3NyHsblewGK1KfdklTbmlWtTXpn2nqxyLZ2S\n",
              "pM355QRRT5Y9OF6/WcOUJXgfu92uw2fP67M9jh3vp6rqXfdF9wjUHcOSNDsrWaNTe7pdWwfAtfEz\n",
              "mzQnq5fuHJ6s5bmn9MraAh0vr9NLqw7rjc3H9Mik/vrHm/uwsdDDnKqq16a8Mm3KK9PXBeWqaTUo\n",
              "RJLSE8I0aWCcJqXHaWy/aIOqvDJ+t7VDmylLx85p6iAOt4dnKzp3QctzT+mzPaeVX1rrur1HoJ9u\n",
              "y0zU7BHJGp8WqwA/jlsCvIWf2aS5I1M0a3iyPttzWq+sy9eJcxf0wkpnIB2g+2/qo5BALra4o/om\n",
              "q745fs4VPo+W1bW5PzIkQBPSYjUpPVaT0uPcfkxyu4Nofn6+HnzwQZWXlysyMlJ/+tOfNHTo0DaP\n",
              "sdlsWrBggVatWiWLxaLx48frD3/4gwIDHYfrvvTSS3rrrbcUGBio4OBgvfLKKxo7dmznvKIu1HrK\n",
              "0tpDJQRReKTSmgZ9vveMluee1p7iKtftgX5mTc2I05ysXpqWEc8Vf8DL+fuZddfoFM3JStanu0/p\n",
              "1XUFKqq4oP9YcUiLNx3To5P76/6b+vB3gcHsdrvySmodwTO/TNuOV7TZLGo2SVm9ozQp3XHVc0RK\n",
              "lPw8qHNlstvt9qs/7KJp06bpgQceUE5Ojj7++GO99NJL2rFjR5vHvPHGG3r//fe1atUqBQQEaP78\n",
              "+UpPT9czzzyjPXv2aM6cOTpw4IDCwsL07rvv6pVXXtH27duvuYaf//znevnll9tTdqdZd7hED/1p\n",
              "p5Ijg/X1v05jjRw8QvWFZq064AifW4+ec60ZMpuk8WmxmjUiWbcNTeScQcCHNVtt+nTXKb2yLl8n\n",
              "Kx3Lc+LCg/TY5AH6hxtTCaTdqLLOscloU55jk9HZmoY29ydHBruC5/gBsR59VF67roiWlpZq586d\n",
              "+tvf/iZJuuuuu/Tkk0+qoKBAaWlprsfl5uZq+vTpriugM2bM0PPPP69nnnlGJpNJzc3NqqurU1hY\n",
              "mKqqqpSSktKJL6lrjRsQq+AAs04zZQlurr7JqrWHS/TZntPaeKRMTdaL/4IemRqlOSOSNXN4EkeR\n",
              "AZAkBfiZ9cMxvTV3VC8t+/tJvbquQKeq6vXLzw/qtY1H9fiUAbp3LIG0K1isNu0prtKmvDJtzC/X\n",
              "3pNVan2ZMMjfrJv6x2hSepwmp8dqQFyY11wIa1cQLS4uVlJSkvz9HV9mMpmUmpqqoqKiNkF09OjR\n",
              "Wrx4sZ588kmFhIToo48+UmFhoSRpxIgR+ud//mf169dP0dHRCgoK0qZNm674nI2NjWpsbGxzm9Vq\n",
              "bU/Znar1lKV1TFmCm2m22vRVfrmW557W3w6cVV3TxT8rgxLCNTsrWbOGJys1JtTAKgG4swA/s+4d\n",
              "m6rvj0rRX/5erN+tK9Dp6gY9/z8H9drGY3p86gDdM6a3gvwJpB3h3GS08UiZvj5arvNX2WTkrf8A\n",
              "6JLNSjk5OTpx4oQmT56skJAQTZ8+3XUV9fjx4/rkk09UUFCg5ORk/fa3v9U999yjr7766rLf64UX\n",
              "XtD//b//t81tN910U1eUfc2cU5bWHC7VT5myBIPZbHbtKKzQ8tzTWrHvjCovNLvuS+kZotkjkjU7\n",
              "K1kZiREGVgnA0wT6m3XfjX30g9Ep+mjnSf1+fYHOVDfof392QH/YcFRPTE3TD2/orUB/NjNei2va\n",
              "ZDQwVpMHxmlieqzbbzLqLO1aI1paWqq0tDRVVFTI399fdrtdSUlJ+uqrr9pcEf22Dz74QL/73e+0\n",
              "efNm/frXv1ZeXp5ef/11SXK16BsbG12t/NYud0X0F7/4hRYtWnStZXe60poGjV24VpK0/d+zaW2i\n",
              "29ntdh04XaPluY7jls5UX1w/FBsWqDuHOw6aH5Ua5TXtGwDGarRY9eGOYv1ufYFKahw/l3tFheiJ\n",
              "qWn6wegUAum32O12HSk53xI8y7W98NJNRiNTe7Zc9YzVcA/bZNRZ2nVFND4+XqNGjdK7776rnJwc\n",
              "LVu2TCkpKZeE0IaGBtXX16tnz54qLy/Xiy++qP/3//6fJKl///5aunSpamtrFRYWps8//1zp6emX\n",
              "DaGSFBQUpKCgoDa3+fkZe3maKUswyvHyOi3fc1qf5Z7SsVb/mg4P8tdtmYmak5Wsm/vHyJ/jlgB0\n",
              "siB/Pz1wc1/98Ibe+mB7kX6/4ahOVdXr3z7dp9+tL9BPp6XprtEpPn3cW2Vdkza7NhmVuQK7kzdt\n",
              "Muos7W7NL168WDk5OVq4cKEiIiK0dOlSSdK8efM0e/ZszZ49W9XV1ZoyZYrMZrNsNpueeuopzZo1\n",
              "S5I0d+5c7dixQzfccIOCgoLUo0cPvffee537qrqBc8rSWqYsoYuV1jRoee5pLc89rb0nq123B/qb\n",
              "NX1wvGaPSNaUQRy3BKB7BAf4KWd8P907NlXvbSvSHzY6Aum/frJPv9tQoJ9OHai5o3r5RCC92iaj\n",
              "4ACzbuznnZuMOku7j29yB0Ye3+S0/1S17nz1K4UE+Gn3/76FEIAucbLygu589StVtaz79DObND4t\n",
              "VnNGJOvWoQkKD+Zf0wCMVd9k1Z+3ndBrG4+qvNYxEjg1OlQ/nZamuSN7eV2H5mTlBW1qGaF5uU1G\n",
              "gxLCXYfJj+nrvZuMOguTla7T0OQIJUYE62wNU5bQdT7bc1pVF5rVOzpE8yf214xhSYoNC7r6FwJA\n",
              "NwkJ9NO8if1134199O43jkBaVHFBz3y8t6VlP1BzspI9NpDWN1n1zbFz2thyoPyxy2wymjjQETwn\n",
              "DYxTYiT7RtqDIHqdTCaTpg2O13tMWUIX+mLvGUnSk1PTWAICwK2FBPrpJ5P6676bUvXO1hNavOmY\n",
              "Cs9d0L/8JdcRSLPTNHtEL7ffkNN6k9HGvDLtOF7Z5hxmNhl1LoJoB0xvCaLrDpXKPsfOug90qsLy\n",
              "Oh08UyM/s0m3DEk0uhwAuCahgf56ZLJjXv1bWwv1+qZjOlZep3/+MFevrivQU9kDdefwZLcKb1fb\n",
              "ZNQrKsTRbh8Yp3FpsUyh60QE0Q5oPWXp0JnzGpLMOY3oPCv3n5Uk3dw/RtE9Ln+qBAC4qx5B/np8\n",
              "SpoeuLmv3trSEkjL6vTUB3v06roC/Sx7oO4cliSzAYHUYrVpd8smo015Zdp7qvqSTUY39Y9xHSg/\n",
              "IK4HF5u6CEG0AxxTlmK15lCp1h4qIYiiU63Y52jLzxyWZHAlAHD9woL89cTUND1wcx/96etCvbH5\n",
              "mApKa/Wz93frt+vy9VR2umZkJnZ5IC2uuKBN+Y7guaXgnM43ssnIHRBEOyh7cILWHCplyhI6VXHF\n",
              "Be07VS2zSbp1aILR5QBAh4UHB+in2QP14Pi+WvpVod786pjySmr1xHu7lJEYrqeyB+q2oZ0XSC80\n",
              "WbTtWMUVNxlFhQZoQhqbjIxGEO2g7AzHJqXc4iqVnm9gyhI6hfNq6I39YtglD8CrRAQH6KnpA5Uz\n",
              "vq/++NVx/fGr4zp89rwe+7MjkP7T9HTdNjSh3a1wu92uw2dbJhnlX7rJyM9s0sjeUa4D5Yf1inSr\n",
              "daq+iiDaQa2nLG04XKYfjultdEnwAita1ofOHE5bHoB3igwJ0D/fkq6HxvfTkq+O6Y9fF+rw2fN6\n",
              "9N2/a0hShP5p+kDdMuS7A2lFXZM25ztGaG7OL1Pp+ctvMpqcHqebB7DJyB0RRDuBc8rSmkMlBFF0\n",
              "2MnKC8otrpLJJN1GWx6Al4sMDdDPbx2khyb00xubj+lPXxfq4JkazX/n78rsFaF/yk5X9uB4mUwm\n",
              "NbeeZJRXpn1sMvJ4BNFOkD04Xr9Zk6fN+eVqaLaywBkdsqrlauiYvtEs9QDgM6JCA/XMbRl6eEJ/\n",
              "vbH5mN7aUqj9p2o07+2dGp4SqaTI4MtuMspIDHet87yhb09+BnsYgmgnYMoSOpNzfegd7JYH4IOi\n",
              "ewTq2dszNG9CP72++Zje3nJCe09Wa+/JakkXNxlNblnrmRDBP9g9GUG0E7SesrTuUClBFNftTHW9\n",
              "dhVVSZJuz+QQewC+KyYsSM/NGKyfTOyvD3cUy2azayKbjLyOZw5+dUPTBzvC59pDJbK3XrACtMPK\n",
              "fY62/A19evKvfACQFBsWpCempumn2QOV1Ztxmt6GINpJvj1lCbgeK/dziD0AwHcQRDuJc8qS5Lgq\n",
              "CrRXSU2Ddp6olERbHgDgGwiinSh7sOOonbWHSw2uBJ5o1f6zstulkalRSo4KMbocAAC6HEG0E01z\n",
              "Tlk6WaWybx2qC1wNu+UBAL6GINqJEiKCNaxXpOx2aT1XRdEOZecbtb2wQhJteQCA7yCIdrLslt3z\n",
              "a1gninZYdcDRlh+REqmUnqFGlwMAQLcgiHay6S3rRJ1TloBrsXIfu+UBAL6HINrJhiZHKCEiSPXN\n",
              "Vn1z7JzR5cADnKttdP1eIYgCAHwJQbSTmUwmTcto2T1/iHWiuLovD5TIZpeG9YpU72ja8gAA30EQ\n",
              "7QJMWUJ7OA+xnzGMTUoAAN9CEO0C49OYsoRrU1HXpC1HW9rymbTlAQC+hSDaBVpPWVp3mN3zuLLV\n",
              "B8/KarNrSFKE+sb2MLocAAC6FUG0izinLK1hnSi+w4p9ZyVJM2nLAwB8EEG0izBlCVdTdaFJXxeU\n",
              "S5JmsFseAOCDCKJdhClLuJrVB0tksdmVkRiuAXFhRpcDAEC3I4h2IeeUpbWsE8VlrNzvaMvPYJMS\n",
              "AMBHEUS7EFOWcCXV9c3anF8mifWhAADfRRDtQs4pSxeamLKEttYeKlGz1a6B8WEamBBudDkAABiC\n",
              "INqFmLKEK3HulmeTEgDAlxFEu5hzytK6w6VMWYIk6XxDszbRlgcAgCDa1ZxTlk5V1evwWaYswfGP\n",
              "kiaLTf3jemgQbXkAgA8jiHax4AA/jR/gmLK09hC75yGt2OeYLT8zM0kmk8ngagAAMA5BtBswZQlO\n",
              "dY0WbTjiaMvPoC0PAPBxBNFu4DxPlClLWHe4VI0Wm/rGhGpIUoTR5QAAYCiCaDdoM2XpCFdFfdnK\n",
              "/Y62/IxhtOUBACCIdhPXlCXWifqsC00WrWsZ9zqTaUoAABBEu0t2BlOWfN2GI2VqaLapd3SIMnvR\n",
              "lgcAgCDaTTJ7MWXJ17FbHgCAtgii3aT1lCVnexa+o6HZerEtzzQlAAAkEUS71XTXOlGmLPmaDUfK\n",
              "dKHJql5RIRqeEml0OQAAuAWCaDcaNyBWQf5MWfJFzrb8jMxE2vIAALQgiHajkEA/TUhjypKvaWi2\n",
              "uv5/zxxOWx4AACeCaDdzTllayzpRn7E5v1x1TVYlRQYrKyXK6HIAAHAbBNFuNi3DsU50T3GVymuZ\n",
              "suQLnG352zMTZTbTlgcAwIkg2s0SI4OV2StCdju7531Bo8WqNQcdbfk72C0PAEAbBFEDOA+3Z52o\n",
              "9/u6oFznGy2KDw/SqNSeRpcDAIBbIYgaYPrgi1OWGi1MWfJmX+w9K8mxW562PAAAbRFEDdB2ylKF\n",
              "0eWgizRZbFp90BFEOcQeAIBLEUQN4Jiy5Dzcnva8t9pytFw1DRbFhgXphr7RRpcDAIDbIYga5OI6\n",
              "UaYseauLu+UT5EdbHgCASxBEDTI+7eKUpSMlTFnyNs1Wm/7WsluetjwAAJdHEDVI2ylLHOPkbb45\n",
              "dk5VF5oV0yNQY2nLAwBwWQRRAzmnLK1hnajXcbblbx2aKH8//pgBAHA5/IQ0EFOWvJPFatOXBzjE\n",
              "HgCAqyGIGogpS95p+/EKVdQ1qWdogG7sT1seAIArIYgazLl7fh3rRL3GF862/JBEBdCWBwDgitr9\n",
              "UzI/P1/jxo1Tenq6xowZowMHDlzyGJvNpqefflqZmZnKyMjQww8/rKamJtf9RUVFmjVrlgYNGqQh\n",
              "Q4bo1Vdf7dir8GAXpyyVMWXJC1htdn15oOUQ++G05QEA+C7tDqKPPPKI5s+fr7y8PD377LPKycm5\n",
              "5DFLlizRrl27tGvXLh06dEhms1mLFi2SJNntds2dO1cPPPCAjhw5ooMHD+qHP/xhh1+IpxqaHKH4\n",
              "8CDVMWXJK+worFB5bZMiQwI0bkCM0eUAAODW2hVES0tLtXPnTt1///2SpLvuukvFxcUqKCho87jc\n",
              "3FxNnz5dgYGBMplMmjFjht555x1J0tq1axUUFKS7777b9fiEhISOvg6PZTablD2YKUvewrVbfkgC\n",
              "bXkAAK6iXT8pi4uLlZSUJH9/f0mOUZWpqakqKipq87jRo0dr+fLlqqmpUXNzsz766CMVFhZKkg4e\n",
              "PKi4uDjde++9GjlypObOnatjx45d8TkbGxtVU1PT5sNq9a4WNlOWvIPNZtfK/cyWBwDgWnXJJZuc\n",
              "nBzdfvvtmjx5siZPnqz09HRXeLVYLFq3bp1+8YtfaPfu3brtttu+szX/wgsvKDIyss3H9u3bu6Js\n",
              "wzBlyTvsPFGpsvONCg/21/iWYQUAAODK2hVEe/furTNnzshisUhyrPcsKipSampqm8eZTCY9//zz\n",
              "2r17t7Zs2aIhQ4Zo6NChkqTU1FSNHDnS9fk//uM/ateuXWpubr7scz733HOqrq5u8zF27Nh2v1B3\n",
              "FhLo5wouTFnyXM62/C1DEhToT1seAICraddPy/j4eI0aNUrvvvuuJGnZsmVKSUlRWlpam8c1NDSo\n",
              "srJSklReXq4XX3xRCxYskCTNmDFDJ0+e1KlTpyRJK1as0ODBgxUQEHDZ5wwKClJERESbDz8/v/a9\n",
              "Sg/gXCfKlCXPZLPZtcrZls+kLQ8AwLXwb+8XLF68WDk5OVq4cKEiIiK0dOlSSdK8efM0e/ZszZ49\n",
              "W9XV1ZoyZYrMZrNsNpueeuopzZo1S5LUo0cPvfbaa7rjjjtkt9sVGRmpDz74oHNflQfKzkjQv2u/\n",
              "a8pSbFiQ0SWhHXYXV+psTYPCgvw1YSBteQAArkW7g+igQYO0devWS25/8803Xb9OSEjQoUOHrvg9\n",
              "br31Vt16663tfWqv5pyytP9UjdYfLtXdN/Q2uiS0w4p9jquh0wfHKzjA+67YAwDQFVjI5kamtdo9\n",
              "D89ht9u1smV96Ax2ywMAcM0Iom5kess6UaYseZY9xVU6Xd2gHoF+mpweZ3Q5AAB4DIKoG8lMjnRN\n",
              "WdrGlCWP4Tw7dNrgBNryAAC0A0HUjTBlyfPY7XbXsU0zMxMNrgYAAM9CEHUzzilLa5iy5BH2narW\n",
              "ycp6hQT4acqgeKPLAQDAoxBE3QxTljyLc7f8tIx4hQTSlgcAoD0Iom6GKUuew263a+V+52552vIA\n",
              "ALQXQdQNsU7UMxw4XaMT5y4oyN+sqbTlAQBoN4KoG3KuE93dMmUJ7sl5NXTqoHj1CGr3bAgAAHwe\n",
              "QdQNJUYGa2hyhOx2af1h2vPuyLFb3rE+lLY8AADXhyDqprIHM2XJnR0+e17Hy+sU6G92/b8CAADt\n",
              "QxB1U0xZcm/OkZ6T0+MURlseAIDrQhB1U0xZcl92u11fOA+xpy0PAMB1I4i6KbPZpGkZ7J53R/ml\n",
              "tTpaVqdAP9ryAAB0BEHUjTlDDlOW3ItzpOfEgbGKCA4wuBoAADwXQdSNTWg1ZSmvpNboctDCGURn\n",
              "DEsyuBIAADwbQdSNtZ6ytIb2vFsoKD2vvJJaBfiZdAtteQAAOoQg6uZYJ+peVracHTo+LVaRobTl\n",
              "AQDoCIKom3OO+2TKkntw7ZbPpC0PAEBHEUTdXFJkiGvK0oYjZUaX49OOldXq8Nnz8jebdOtQ2vIA\n",
              "AHQUQdQDXJyyRHveSCv3O9ryNw+IUVRooMHVAADg+QiiHsA5ZWlTHlOWjLTCdYg9bXkAADoDQdQD\n",
              "ZCZHKo4pS4Y6ca5OB07XyM9s0m1DmaYEAEBnIIh6ALPZpOyW3fPrDpcaXI1vcrblb+ofregetOUB\n",
              "AOgMBFEPcXHKUglTlgzgOsSe3fIAAHQagqiHcE5ZOlnJlKXuVlxxQXtPVstsEm15AAA6EUHUQ4QE\n",
              "+mncgBhJTFnqbqta2vJj+0UrLjzI4GoAAPAeBFEPwjFOxviC3fIAAHQJgqgHaT1l6RxTlrrFqap6\n",
              "7Smukskk3U5bHgCATkUQ9SCtpyytZ8pSt3C25cf0iVZ8RLDB1QAA4F0Ioh7GeYwT7fnu4dotP4yr\n",
              "oQAAdDaCqIdxrhNlylLXO1vdoL+fqJTEsU0AAHQFgqiHGdbr4pSl7ceZstSVVu53XA0d3aenEiNp\n",
              "ywMA0NkIoh6m9ZSltYeYstSVVu5zrA+dkUlbHgCArkAQ9UDTWoIoU5a6TmlNg3accFxxnsGxTQAA\n",
              "dAmCqAeaMDBWgUxZ6lKrDpyV3S5l9Y5Sr6gQo8sBAMArEUQ9UGigv8a3TFlae5jd813BuVv+Dq6G\n",
              "AgDQZQiiHurilCXWiXa2svONro1gt7M+FACALkMQ9VDOKUu7iiqZstTJvjxwVja7NDwlUr2jQ40u\n",
              "BwAAr0UQ9VBJkSEaksSUpa7gPLaJ2fIAAHQtgqgHm95yVXQd60Q7zbnaRn1zrGW3PG15AAC6FEHU\n",
              "g12cslSuJovN4Gq8w98Olshqs2tocoT6xPQwuhwAALwaQdSDOacs1TZatO34OaPL8QrO3fK05QEA\n",
              "6HoEUQ9mNps0bRBTljpLZV2Tthx1BHra8gAAdD2CqIdz7p5nylLHrW5py2ckhqt/XJjR5QAA4PUI\n",
              "oh6u9ZSl/FKmLHXEiv0cYg8AQHciiHq41lOW1hxi9/z1qr7QrK8LyiUxWx4AgO5CEPUC05iy1GGr\n",
              "D5Wo2WpXekKY0uJpywMA0B0Iol4gO4MpSx21kt3yAAB0O4KoF0iOujhlaQNTltqtpqFZm/MdbXmC\n",
              "KAAA3Ycg6iWcU5bWMmWp3dYeKlGT1aYBcT00kLY8AADdhiDqJaYxZem6rdh3VpJjt7zJZDK4GgAA\n",
              "fAdB1EsMZ8rSdTnf0KyNeY7lDOyWBwCgexFEvQRTlq7PusOlarLY1C+2hzISw40uBwAAn0IQ9SLZ\n",
              "rdaJMmXp2qxsacvPHJZIWx4AgG5GEPUizilLxRVMWboWdY0WrT/iuHo8I5O2PAAA3Y0g6kVCA/01\n",
              "jilL12z9kVI1WmzqExOqockRRpcDAIDPIYh6meyW3fPrWCd6Vc62/IxMdssDAGCEdgfR/Px8jRs3\n",
              "Tunp6RozZowOHDhwyWNsNpuefvppZWZmKiMjQw8//LCampoueVxOTo5MJpOqqqquq3hcqvWUpYq6\n",
              "S99zONQ3WbXusCOszxyWaHA1AAD4pnYH0UceeUTz589XXl6enn32WeXk5FzymCVLlmjXrl3atWuX\n",
              "Dh06JLPZrEWLFrV5zCeffKKAgIDrLhyX55yyZLNL6w9zVfRKNhwpVX2zVSk9QzSsV6TR5QAA4JPa\n",
              "FURLS0u1c+dO3X///ZKku+66S8XFxSooKGjzuNzcXE2fPl2BgYEymUyaMWOG3nnnHdf9JSUlWrhw\n",
              "oV5++eVOeAn4tmymLF3Viv3O3fK05QEAMEq7gmhxcbGSkpLk7+8vSTKZTEpNTVVRUVGbx40ePVrL\n",
              "ly9XTU2Nmpub9dFHH6mwsNB1/09+8hP953/+p8LDr35uY2Njo2pqatp8WK3W9pTtc7KZsvSdGpqt\n",
              "WtuymWtGJm15AACM0iWblXJycnT77bdr8uTJmjx5stLT013h9c0331RqaqqmTZt2Td/rhRdeUGRk\n",
              "ZJuP7du3d0XZXqP1lKXtxyuMLsftbMwr04Umq5Ijg5XVO8rocgAA8FntCqK9e/fWmTNnZLFYJEl2\n",
              "u11FRUVKTU1t8ziTyaTnn39eu3fv1pYtWzRkyBANHTpUkrR+/Xp99tln6tu3r/r27StJGj58uHbv\n",
              "3n3Z53zuuedUXV3d5mPs2LHtfZ0+pfWUJY5xutTKfWckOUZ60pYHAMA47Qqi8fHxGjVqlN59911J\n",
              "0rJly5SSkqK0tLQ2j2toaFBlZaUkqby8XC+++KIWLFggSfrzn/+s4uJiFRYWutr1e/fu1ciRIy/7\n",
              "nEFBQYqIiGjz4efn164X6YumMWXpshqarVpziN3yAAC4A//2fsHixYuVk5OjhQsXKiIiQkuXLpUk\n",
              "zZs3T7Nnz9bs2bNVXV2tKVOmyGw2y2az6amnntKsWbM6vXhc2cRvTVlKT2COuiR9lV+u2kaLEiOC\n",
              "NbJ3T6PLAQDAp7U7iA4aNEhbt2695PY333zT9euEhAQdOnTomr4fV+u6hnPK0oYjZVp7qJQg2mLF\n",
              "fkdb/vbMRJnNtOUBADASk5W8mHP3/FrWiUqSGi1WrT7oeC9mDmO2PAAARiOIerFpTFlqY0vBOZ1v\n",
              "sCg+PEg39KEtDwCA0QiiXqxXVIgGM2XJ5Yt9tOUBAHAnBFEvN71l9/w6Hw+iTRab/nbAMU1pRiZt\n",
              "eQAA3AFB1Ms514luzCvz6SlLW4+dU02DRbFhgRrbL9rocgAAgAiiXm94r0jFhjFlacVeR1v+tqGJ\n",
              "8qMtDwCAWyCIejmz2aRpGXGSfHfKUrPVpi8POtry7JYHAMB9EER9gOsYJx+dsrTtWIWqLjQrukeg\n",
              "bqQtDwCA2yCI+oDWU5YKSmuNLqfbOXfL3zY0Qf5+/JYHAMBd8FPZBzinLElyzVn3FRbrxd3ytOUB\n",
              "AHAvBFEfkd1yuL2vTVnaXlihc3VNigoN0E39Y4wuBwAAtEIQ9RHTWtaJ+tqUpRUtbflbhyQogLY8\n",
              "AABuhZ/MPqL1lKUNR3yjPW+12bVqP7PlAQBwVwRRH+KcsrTWR9aJ7iysUHltoyKC/TVuQKzR5QAA\n",
              "gG8hiPqQaS3rRH1lypKzLX/LkEQF+vNbHQAAd8NPZx8yIiXKZ6Ys2Wx2rdzv2C1/x/BEg6sBAACX\n",
              "QxD1Ia2nLK097N2753cVVar0fKPCg/w1Po22PAAA7ogg6mNcU5YOlXr1lCXnIfbThyQoyN/P4GoA\n",
              "AMDlEER9zIS0WAX6mVVUccFrpyzZbHat2s8h9gAAuDuCqI/pEeSvm718ytKek1U6U92gHoF+mjiQ\n",
              "tjwAAO6KIOqDnMc4rfPSdaIr9jra8tmDExQcQFseAAB3RRD1Qc4pS38/UalKL5uyZLdf3C1PWx4A\n",
              "APdGEPVBracsrfeyKUu5J6t1qqpeoYF+mjIozuhyAADAdyCI+qjsDO+csrSyZbf81Ix42vIAALg5\n",
              "gqiPym5ZJ7rJi6Ys2e12rdjvCKJ30JYHAMDtEUR9lHPK0vlGi3YUeseUpf2nalRcUa/gADNteQAA\n",
              "PABB1Ee1nrK05pB37J53Xg2dOiheoYH+BlcDAACuhiDqw6ZleM+UJbvd7lofym55AAA8A0HUh00c\n",
              "eHHK0tEyz56ydPBMjQrPXVCQv1lTWzZiAQAA90YQ9WHeNGVp5T7H2aGT0+MUFkRbHgAAT0AQ9XHO\n",
              "KUtrPXidqN1u14qWtvwdw2nLAwDgKQiiPs7ZxvbkKUtHSs7rWHmdAv3NmkZbHgAAj0EQ9XEpPUOV\n",
              "kRgum13akOeZ7fkVLW35SQPjFB4cYHA1AADgWhFEoekts+c9dZ3oxd3yiQZXAgAA2oMgiotTlo54\n",
              "3pSl/JLzyi+tVYCfSdktgRoAAHgGgihapiwFeuSUJWdbfuLAOEWG0JYHAMCTEEQhs9mkqYMcV0U9\n",
              "bcrSypZpSjMyacsDAOBpCKKQJFdb25OmLB0tq9Xhs+flbzbpliG05QEA8DQEUUjyzClLzk1K49Ni\n",
              "FRUaaHA1AACgvQiikOSZU5ac60PZLQ8AgGciiMIl24OmLBWW1+ngmRr5mU26ZQhBFAAAT0QQhcs0\n",
              "D5qytKJlk9K4ATGK7kFbHgAAT0QQhYsnTVlyzpafkclseQAAPBVBFG14wpSlonMXtP9Ujcwm6dah\n",
              "7JYHAMBTEUTRxjQPmLLkPDv0pv4xig0LMrgaAABwvQiiaCOr1ZSlnW46ZcnVlh9GWx4AAE9GEEUb\n",
              "bacsuV97/mTlBeWerJbJJN1GWx4AAI9GEMUlXFOWDpe43ZSlVfsdZ4eO7Rut+PBgg6sBAAAdQRDF\n",
              "JZxTlk6cc78pS1+0tOVn0pYHAMDjEURxiR5B/rqpZcrSWjdqz5+uqtfuoiqZTNLtmRxiDwCApyOI\n",
              "4rKmu6YsuU8Qdbblb+jTUwkRtOUBAPB0BFFclnPK0s4TFW4zZYlD7AEA8C4EUVyWu01ZOlvdoJ0n\n",
              "KiVJM4bRlgcAwBsQRHFF2YPd5xinLw842vKjUqOUFBlicDUAAKAzEERxRc5jnDYdKVOz1dgpS+yW\n",
              "BwDA+xBEcUWtpyztOG7clKXS8w3a0TLliWlKAAB4D4Iorshdpix9eaBEdrs0oneUekXRlgcAwFsQ\n",
              "RPGdnOtEjZyytGJvS1ues0MBAPAqBFF8p4kD41pNWarr9ucvr23UtuPnJLE+FAAAb9PuIJqfn69x\n",
              "48YpPT1dY8aM0YEDBy55jM1m09NPP63MzExlZGTo4YcfVlOT4yzKffv2adKkScrIyFBmZqYeeugh\n",
              "1dfXd/yVoEu0nbJU0u3P/+WBs7LZpWG9ItU7OrTbnx8AAHSddgfRRx55RPPnz1deXp6effZZ5eTk\n",
              "XPKYJUuWaNeuXdq1a5cOHToks9msRYsWSZKCg4P129/+VocPH1Zubq7q6ur00ksvdfiFoOsYOWVp\n",
              "5T7HsU2cHQoAgPdpVxAtLS3Vzp07df/990uS7rrrLhUXF6ugoKDN43JzczV9+nQFBgbKZDJpxowZ\n",
              "eueddyRJAwcO1PDhwyVJfn5+GjNmjAoLCzvhpaCrGDVlqaKuSVuPtbTlmaYEAIDXaVcQLS4uVlJS\n",
              "kvz9/SVJJpNJqampKioqavO40aNHa/ny5aqpqVFzc7M++uijy4bNuro6vfnmm5ozZ84Vn7OxsVE1\n",
              "NTVtPqxWa3vKRge1nrK0Ma+s2573bwfOymqza0hShPrG9ui25wUAAN2jSzYr5eTk6Pbbb9fkyZM1\n",
              "efJkpaenu8KrU1NTk+655x7deuutmjt37hW/1wsvvKDIyMg2H9u3b++KsvEdLk5Z6r51oiv2O9ry\n",
              "M2nLAwDgldoVRHv37q0zZ87IYrFIkux2u4qKipSamtrmcSaTSc8//7x2796tLVu2aMiQIRo6dKjr\n",
              "/ubmZt1zzz1KSkpyrR29kueee07V1dVtPsaOHduestEJnFOWNuZ1z5SlqgtN2lJQLond8gAAeKt2\n",
              "BdH4+HiNGjVK7777riRp2bJlSklJUVpaWpvHNTQ0qLKyUpJUXl6uF198UQsWLJAkWSwW3XvvvYqO\n",
              "jtbrr78uk8n0nc8ZFBSkiIiINh9+fn7tKRudYERKlGJ6BOp8Q/dMWfrbwRJZbHZlJIarf1xYlz8f\n",
              "AADofu1uzS9evFiLFy9Wenq6XnzxRS1dulSSNG/ePC1fvlySVF1drXHjxmno0KGaOHGiHn30Uc2a\n",
              "NUuS9OGHH+qTTz7Rzp07NXLkSGVlZemJJ57oxJeEruBnNmlqhvNw+67fPb+yZbb8DDYpAQDgtUx2\n",
              "o8bldMDPf/5zvfzyy0aX4XNW7T+jR9/dpb4xoVr/9JSrXs2+XtX1zbrhV6vVbLVrzc8nKS0+vEue\n",
              "BwAAGIvJSrhmzilLhV08ZWnNwRI1W+0aGB9GCAUAwIsRRHHNumvK0sr9LW15NikBAODVCKJol+wu\n",
              "Xid6vqFZm/Icu+XvIIgCAODVCKJoF+d5on8/UamqC50/ZWntoVI1WW3qH9dD6QnslgcAwJsRRNEu\n",
              "zilLVptdG450/pSlFS275e8YltRlm6EAAIB7IIii3bpqylJto0UbWkaIcmwTAADejyCKdpuW0TVT\n",
              "ltYdLlWTxaa+MaEanMRueQAAvB1BFO2W1bvVlKXCzpuy5DzEfiZteQAAfAJBFO3WZsrSoc7ZPX+h\n",
              "yaL1Rxzfi9nyAAD4BoIorsv0wc4gWqLOGM61/nCZGppt6h0doqHJER3+fgAAwP0RRHFdJnTylKUV\n",
              "+2nLAwDgawiiuC5hQf66sX+0JGnd4Y7tnq9vsmpdS4t/JrvlAQDwGQRRXLfpgx2759d0cJ3oxrxS\n",
              "1Tdb1SsqRMNTIjujNAAA4AEIorhu0zI6Z8rSin1nJUkzhyXSlgcAwIcQRHHdekeHalBCx6YsNTRb\n",
              "tbblYPwZ7JYHAMCnEETRIc4pS2sPX197flNemeqarEqKDFZWSlQnVgYAANwdQRQdkt2yTnTDkdLr\n",
              "mrK0cr+jLT8jM0lmM215AAB8CUEUHdKRKUuNFqvWHHS05WcOS+yK8gAAgBsjiKJDOjJl6av8cp1v\n",
              "tCghIkijUnt2RXkAAMCNEUTRYdktQXRdO9eJOnfL05YHAMA3EUTRYRPTHVOWjpfX6WhZ7TV9TZPF\n",
              "ptUHnUGUtjwAAL6IIIoOaz1lyXkU09V8fbRcNQ0WxYYF6Ya+0V1ZHgAAcFMEUXSK9k5ZWrnPMVt+\n",
              "Rmai/GjLAwDgkwii6BTtmbLUbLXpbwedh9jTlgcAwFcRRNEpWk9Z2pj33VOWth49p6oLzYrpEaix\n",
              "tOUBAPBZBFF0GueUpau151fud7Tlb8tMlL8fvwUBAPBVpAB0mmuZsmSx2vTlgZZD7DOZLQ8AgC8j\n",
              "iKLTZPWOUvRVpixtO16hirom9QwN0E39acsDAODLCKLoNH5mk6YOajnc/grt+RUtu+VvG0pbHgAA\n",
              "X0cSQKea3rJOdO1lpixZbXZ9eaDlEPthtOUBAPB1BFF0qonpcQrwM112ytL24xUqr21SZEiAxg2I\n",
              "MahCAADgLgii6FRhQf66qb8jZH57ypKzLX/rkAQF0JYHAMDnkQbQ6bJbDrdf22qdqNVm16qWtvxM\n",
              "2vIAAEAEUXQB5zFOO09UqvpCsyTHxKWy840KD/bX+LRYI8sDAABugiCKTtd6ytKGPMdVUWdb/pYh\n",
              "CQr057cdAAAgiKKLtJ6yZLPZXdOUOMQeAAA4EUTRJZxBdOORUm0vrFBJTaPCgvw1MZ22PAAAcCCI\n",
              "oktk9e6p6B6Bqmmw6D++OCTJccZokL+fwZUBAAB3QRBFl2g9ZWnfqWpJHGIPAADaIoiiyzinLElS\n",
              "j0A/TU6PM7AaAADgbgii6DITBsYqwM8kSZo2OEHBAbTlAQDARQRRdJnw4ABlZzjOFL1rVC+DqwEA\n",
              "AO7G3+gC4N3+6+7henJamjJ7RRpdCgAAcDNcEUWXCg8OIIQCAIDLIogCAADAEARRAAAAGIIgCgAA\n",
              "AEMQRAEAAGAIgigAAAAMQRAFAACAIQiiAAAAMARBFAAAAIYgiAIAAMAQBFEAAAAYgiAKAAAAQ5js\n",
              "drvd6CLa6/vf/7769u3bLc9ltVq1fft2jR07Vn5+ft3ynN6E96/jeA87hvev43gPO4b3r+N4DzvG\n",
              "qPevT58+euqpp77zMR4ZRLtTTU2NIiMjVV1drYiICKPL8Ti8fx3He9gxvH8dx3vYMbx/Hcd72DHu\n",
              "/P7RmgcAAIAhCKIAAAAwBEEUAAAAhiCIXkVQUJD+z//5PwoKCjK6FI/E+9dxvIcdw/vXcbyHHcP7\n",
              "13G8hx3jzu8fm5UAAABgCK6IAgAAwBAEUQAAABiCIAoAAABDEES/Q35+vsaNG6f09HSNGTNGBw4c\n",
              "MLokj/Kzn/1Mffv2lclk0p49e4wux+M0NDToe9/7ntLT0zVixAjdcsstKigoMLosj3Prrbdq+PDh\n",
              "ysrK0sSJE7V7926jS/JIS5culclk0l//+lejS/E4ffv21aBBg5SVlaWsrCx9+OGHRpfkURobG/Xk\n",
              "k09q4MCBGjZsmO6//36jS/Io586dc/3ey8rKUnp6uvz9/VVRUWF0aZIkf6MLcGePPPKI5s+fr5yc\n",
              "HH388cfKycnRjh07jC7LY/zgBz/QggULNGHCBKNL8Vjz58/XjBkzZDKZ9Nvf/lbz5s3Thg0bjC7L\n",
              "o3z00UeKioqSJH366afKyclRbm6usUV5mMLCQr3xxhu66aabjC7FY3344YfKysoyugyP9K//+q8y\n",
              "mUzKy8uTyWTS2bNnjS7Jo8TExLS5GPTrX/9aGzduVHR0tHFFtcIV0SsoLS3Vzp07Xf/yuuuuu1Rc\n",
              "XMwVqXaYNGmSUlJSjC7DYwUHB2vmzJkymUySpJtuukmFhYXGFuWBnCFUkqqrq13vJ66NzWbTvHnz\n",
              "9Oqrr7rl0S/wbnV1dVqyZIn+4z/+w/VnNzEx0eCqPNuSJUv08MMPG12GC0H0CoqLi5WUlCR/f8dF\n",
              "Y5PJpNTUVBUVFRlcGXzVokWLNGfOHKPL8EgPPPCAevfurV/84hd65513jC7Ho7z88ssaP368Ro8e\n",
              "bXQpHu2BBx7QsGHD9PDDD6usrMzocjzG0aNHFR0drYULF+qGG27QxIkTtXbtWqPL8lhbtmxRZWWl\n",
              "7rzzTqNLcSGIAh5g4cKFKigo0AsvvGB0KR7p7bffVnFxsX71q1/p2WefNbocj7F//34tW7ZM/+t/\n",
              "/S+jS/FomzZt0t69e7Vr1y7FxsbqwQcfNLokj2GxWHTixAkNGTJEO3fu1CuvvKJ77rlHJSUlRpfm\n",
              "kZYsWaIHHnjAdZHNHbhPJW6md+/eOnPmjCwWi/z9/WW321VUVKTU1FSjS4OP+fWvf61PPvlEa9as\n",
              "UWhoqNHleLQHH3xQjz76qM6dO6eYmBijy3F7mzdvVmFhoQYOHChJOnv2rObPn68zZ87oscceM7g6\n",
              "z+H8uREQEKB/+qd/Unp6usEVeY7U1FSZzWbdd999kqSRI0eqX79+2rdvnxISEgyuzrPU1tbqo48+\n",
              "cru9LlwRvYL4+HiNGjVK7777riRp2bJlSklJUVpamsGVwZe8/PLLev/997V69eo2ax1xbaqqqnT6\n",
              "9GnX53/9618VExPjNov03d1jjz2mM2fOqLCwUIWFhbrpppv0+uuvE0Lboa6uTlVVVa7P33//fY0c\n",
              "OdK4gjxMbGyssrOz9eWXX0qSjh8/ruPHj2vw4MEGV+Z5PvzwQ40YMUIZGRlGl9IGV0S/w+LFi5WT\n",
              "k6OFCxcqIiJCS5cuNbokj/LII4/oiy++0NmzZ3XbbbcpPDyczV7tcPLkSf3Lv/yL+vfvr6lTp0py\n",
              "zAvetm2bwZV5jurqat19992qr6+X2WxWXFycPv/8czYsoduUlJTorrvuktVqld1uV//+/fX2228b\n",
              "XZZHee211/Twww/r2Wefldls1uLFi9WrVy+jy/I4S5Ys0U9+8hOjy7gEs+YBAABgCFrzAAAAMARB\n",
              "FAAAAIYgiAIAAMAQBFEAAAAYgiAKAAAAQxBEAQAAYAiCKAAAAAxBEAUAAIAhCKIAAAAwBEEUAAAA\n",
              "hiCIAgAAwBD/P4lKjg9I3XZBAAAAAElFTkSuQmCC\n",
              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-47d9c5e1-6ec9-4f63-a6b6-970db6f50302\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-47d9c5e1-6ec9-4f63-a6b6-970db6f50302\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "qubit_pairs = [item for pair in zip(LAYER_1_ANSWER, LAYER_2_ANSWER) for item in pair]\n",
        "pfs = [cal_process_fid(alpha) for alpha in alphas]\n",
        "df = pd.DataFrame(zip(qubit_pairs, alphas, pfs), columns=['qubit pair', 'alpha', 'process fidelity'])\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O-opBORKKkk1"
      },
      "source": [
        "We now proceed to use the 2-qubit process fidelity `pfs` calculated above to compute the Layer Fidelity. For each value of the chain size, we take the largest Layer Fidelity from all the subchains measured. At the two ends of the chain, the 1-qubit process fidelity is approximated by the square root of the 2-qubit process fidelity. The data analysis code is adapted from the notebook [here](https://github.com/qiskit-community/qiskit-device-benchmarking/blob/main/notebooks/layer_fidelity.ipynb).  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PDrqdDmeKkk1"
      },
      "outputs": [],
      "source": [
        "# Compute layer fidelity\n",
        "chain_lens = np.arange(2, len(pfs), 1)\n",
        "chain_fids = []\n",
        "for length in chain_lens:\n",
        "     w = length + 1  # window size\n",
        "     fid_w = max(\n",
        "          np.sqrt(pfs[s]) * np.prod(pfs[s + 1 : s + w - 1]) * np.sqrt(pfs[s + w - 1]) for s in range(len(pfs) - w + 1))\n",
        "     chain_fids.append(fid_w)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zokpOOhtKkk2"
      },
      "source": [
        "Now we can present the Layer Fidelity as a function of our chain length. This should decrease for larger chains."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "ozz-1_f0Kkk2",
        "outputId": "64bf5b1c-315c-4581-aea6-79eb0e35dc6e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Plot LF by chain length\n",
        "plt.title(f\"Backend: {BACKEND.name}\")\n",
        "plt.plot(\n",
        "    chain_lens,\n",
        "    chain_fids,\n",
        "    marker=\"o\",\n",
        "    linestyle=\"-\",\n",
        ")\n",
        "plt.xlim(0, chain_lens[-1] * 1.05)\n",
        "plt.ylim(0.95 * min(chain_fids), 1)\n",
        "plt.ylabel(\"Layer Fidelity\")\n",
        "plt.xlabel(\"Chain Length\")\n",
        "plt.grid()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ya-zFtb1Kkk2"
      },
      "source": [
        "Then, at long last, we can calculate the EPLG as a function of chain length. EPLG is defined as\n",
        "$$\n",
        "EPLG = 1 − LF^{1/n_{2q}}\n",
        "$$\n",
        "where $n_{2q}$ is the number of two-qubit gates in all the layers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "SamOpSZkKkk2",
        "outputId": "07c83f94-4321-467d-dfac-c5f89e23bf63"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Plot EPLG by chain length\n",
        "num_2q_gates = [length - 1 for length in chain_lens]\n",
        "chain_eplgs = [\n",
        "    1 - (fid ** (1 / num_2q)) for num_2q, fid in zip(num_2q_gates, chain_fids)\n",
        "]\n",
        "plt.title(f\"Backend: {BACKEND.name}\")\n",
        "plt.plot(\n",
        "    chain_lens,\n",
        "    chain_eplgs,\n",
        "    marker=\"o\",\n",
        "    linestyle=\"-\",\n",
        ")\n",
        "plt.xlim(0, chain_lens[-1] * 1.05)\n",
        "plt.ylabel(\"Error per Layered Gates\")\n",
        "plt.xlabel(\"Chain Length\")\n",
        "plt.grid()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fk2EjVD8Kkk2"
      },
      "source": [
        "This analysis is very useful in diagnosing which qubits and devices to use for utility-scale workloads."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vf6SD1wlKkk2",
        "outputId": "ba2c1aa5-2db2-4ef0-f478-a54b1f738322"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><p>&copy; Copyright IBM 2017, 2024.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import datetime\n",
        "from IPython.display import HTML, display\n",
        "\n",
        "\n",
        "def qiskit_copyright(line=\"\", cell=None):\n",
        "    \"\"\"IBM copyright\"\"\"\n",
        "    now = datetime.datetime.now()\n",
        "\n",
        "    html = \"<div style='width: 100%; background-color:#d5d9e0;\"\n",
        "    html += \"padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'>\"\n",
        "    html += \"<p>&copy; Copyright IBM 2017, %s.</p>\" % now.year\n",
        "    html += \"<p>This code is licensed under the Apache License, Version 2.0. You may<br>\"\n",
        "    html += \"obtain a copy of this license in the LICENSE.txt file in the root directory<br> \"\n",
        "    html += \"of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.\"\n",
        "\n",
        "    html += \"<p>Any modifications or derivative works of this code must retain this<br>\"\n",
        "    html += \"copyright notice, and modified files need to carry a notice indicating<br>\"\n",
        "    html += \"that they have been altered from the originals.</p>\"\n",
        "    html += \"</div>\"\n",
        "    return display(HTML(html))\n",
        "\n",
        "\n",
        "qiskit_copyright()"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "runtime-latest",
      "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": "3.11.6"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}