eth_futures_yield.ipynb

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        "## Annualized basis yield for ETH futures ##\n",
        "\n",
        "Author: Alexis Plascencia  aplascenciac@gmail.com\n",
        "\n",
        "Downloads the ETH futures data using the Deribit API. \\\n",
        "Calculates the annualized yield for each contract. \\\n",
        "Generates plot of the annualized yield for the future contracts."
      ]
    },
    {
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      "source": [
        "import requests\n",
        "import pandas as pd\n",
        "import matplotlib.pylab as plt\n",
        "import matplotlib\n",
        "\n",
        "from datetime import datetime, timedelta"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "47KkJenSpeZe"
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      "source": [
        "def get_futures_data():\n",
        "  \"\"\"Connects to the Deribit API and gets the data on ETH Futures\n",
        "\n",
        "  Returns:\n",
        "      futures_data: Dictionary with the information on ETH Futures\n",
        "  \"\"\"\n",
        "\n",
        "  token = 'ETH'\n",
        "  r_summ = requests.get('https://test.deribit.com/api/v2/public/get_book_summary_by_currency?currency=' + token + '&kind=future')\n",
        "  summary = r_summ.json()\n",
        "  return summary\n",
        "\n",
        "futures_data = get_futures_data()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mLXM0mpcpeZe"
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      "source": [
        "def get_price_expiry(futures_data):\n",
        "  \"\"\"Creates two lists with the name and price of each contract\n",
        "\n",
        "  Arguments:\n",
        "      futures_data: Dictionary with the information of the ETH futures\n",
        "\n",
        "  Returns:\n",
        "      title: List with the names of the future contract\n",
        "      price: List with the price of each contract\n",
        "  \"\"\"\n",
        "\n",
        "  num_futures = len(futures_data['result'])\n",
        "  title = []\n",
        "  price = []\n",
        "  for i in range(num_futures):\n",
        "\n",
        "    title.append(futures_data['result'][i]['instrument_name'])\n",
        "\n",
        "    # The ETH/USD price is obtained from 'estimated_delivery_price' in the ETH Perpetual\n",
        "    # an alternative is to access 'get_index_price?index_name=eth_usd'\n",
        "    if futures_data['result'][i]['instrument_name'] == 'ETH-PERPETUAL':\n",
        "      price.append(futures_data['result'][i]['estimated_delivery_price'])\n",
        "    else:\n",
        "      price.append(futures_data['result'][i]['mid_price'])\n",
        "\n",
        "  return [title, price]\n",
        "\n",
        "\n",
        "[title, price] = get_price_expiry(futures_data)"
      ]
    },
    {
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        "def get_expiry_dates(title):\n",
        "  \"\"\"Obtain the expiry dates for each contract and saves it in 'datetime' format\n",
        "\n",
        "  Arguments:\n",
        "      title: List with the names of the future contract\n",
        "\n",
        "  Returns:\n",
        "      expiry_dates: List with the expiry date of each contract\n",
        "  \"\"\"\n",
        "  expiry_dates = []\n",
        "\n",
        "  for i in range(len(title)):\n",
        "    end = len(title[i])\n",
        "\n",
        "    # We obtain the expiry date\n",
        "    date_string = title[i][4:end]\n",
        "\n",
        "    if date_string == 'PERPETUAL':\n",
        "      expiry_dates.append(datetime.utcnow())\n",
        "    else:\n",
        "      # The Futures contract expire at 08:00 UTC\n",
        "      expiry_dates.append(datetime.strptime(date_string, \"%d%b%y\")+ timedelta(hours=8))\n",
        "\n",
        "  return expiry_dates\n",
        "\n",
        "expiry_dates = get_expiry_dates(title)"
      ]
    },
    {
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        "id": "S0omg-zLpeZf"
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      "source": [
        "# We construct the Dataframe with the contracts, price and expiry date\n",
        "df = pd.DataFrame({\"Name\": title})\n",
        "df[\"Price\"] = price\n",
        "df[\"Expiry Date\"] = expiry_dates\n",
        "df = df.sort_values(by=\"Expiry Date\")\n",
        "df = df.reset_index(drop=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "igWjw3HhpeZf",
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        "outputId": "22e154c7-7504-4bd1-875c-2e4bfe58bdb3"
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The current ETH price is:  3507.65\n"
          ]
        }
      ],
      "source": [
        "# Calculation of the anualized yield\n",
        "delta_days = []\n",
        "yield_annual = []\n",
        "eth_price = df['Price'][0]\n",
        "print('The current ETH price is: ', eth_price)\n",
        "\n",
        "for i in range(len(title)):\n",
        "    diff = df['Expiry Date'][i]-df['Expiry Date'][0]\n",
        "    delta = diff.days + diff.seconds/86400.\n",
        "    delta_days.append(delta)\n",
        "    yield_annual.append(100.*(df['Price'][i]-eth_price)/eth_price*(365./(delta+0.0001)))\n",
        "\n",
        "# Add new columns to dataframe\n",
        "df['Days to Expiry'] = delta_days\n",
        "df[\"Yield % (annual)\"] = yield_annual\n",
        "\n",
        "# Drop the Perpetual future\n",
        "df = df.drop(axis=0,index=0)"
      ]
    },
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        {
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            "text/plain": [
              "          Name    Price         Expiry Date  Days to Expiry  Yield % (annual)\n",
              "1   ETH-8MAR24  3520.88 2024-03-08 08:00:00        3.840301         35.847584\n",
              "2  ETH-15MAR24  3539.63 2024-03-15 08:00:00       10.840301         30.697975\n",
              "3  ETH-29MAR24  3564.00 2024-03-29 08:00:00       24.840301         23.605431\n",
              "4  ETH-26APR24  3621.63 2024-04-26 08:00:00       52.840301         22.446010\n",
              "5  ETH-28JUN24  3716.38 2024-06-28 08:00:00      115.840301         18.750007\n",
              "6  ETH-27SEP24  3805.75 2024-09-27 08:00:00      206.840301         14.996959\n",
              "7  ETH-27DEC24  3888.75 2024-12-27 08:00:00      297.840301         13.314717"
            ],
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              "      <th>1</th>\n",
              "      <td>ETH-8MAR24</td>\n",
              "      <td>3520.88</td>\n",
              "      <td>2024-03-08 08:00:00</td>\n",
              "      <td>3.840301</td>\n",
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              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"ETH-8MAR24\",\n          \"ETH-15MAR24\",\n          \"ETH-27SEP24\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 142.05874060033184,\n        \"min\": 3520.88,\n        \"max\": 3888.75,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          3520.88,\n          3539.63,\n          3805.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Expiry Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2024-03-08 08:00:00\",\n        \"max\": \"2024-12-27 08:00:00\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"2024-03-08 08:00:00\",\n          \"2024-03-15 08:00:00\",\n          \"2024-09-27 08:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Days to Expiry\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 112.43664882946307,\n        \"min\": 3.840300925925926,\n        \"max\": 297.84030092592593,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          3.840300925925926,\n          10.840300925925925,\n          206.84030092592593\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Yield % (annual)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.171612063114571,\n        \"min\": 13.31471740469988,\n        \"max\": 35.84758397584129,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          35.84758397584129,\n          30.697974932220372,\n          14.99695945905126\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 57
        }
      ],
      "source": [
        "# We show the dataframe that contais the results:\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k-bmuQT2peZg"
      },
      "source": [
        "**Plot for the annual yield (%) for each of the ETH futures contract:**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "ZBtU3TTbpeZh",
        "outputId": "fcfb1e25-dabc-4114-8ed7-eecea929637e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1100x600 with 1 Axes>"
            ],
            "image/png": 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T1D/CpJtqmeRmMhXr7wfXVRLnF3AlzjGUNM4xlCTOL5Q0zjGUtLJwjg0dOrTA67S0NFmtVjVr1ky7du0q1DFdcqKjefPmkqSoqCi1a9dOLVu21MKFC/9wYsPNzU133nmnJkyYoKysLPn4+Fz1uBaLpdB/UEZZsmSJ+vTpU+zHvVXSrvgUjf9ipw6evaB8u/TlIbtOWCtp6l0tVKeKb7H/nnA9JXV+AZdwjqGkcY6hJHF+oaRxjqGklYVzLCkpqcDrS4MKReGS9+i4UvPmzeXh4aGDBw8aHaXcaV4rWN891lmjO9+gS0Mcm48mqc8ba/TppmNywWEfAAAAAAD+lMsXHZs2bVJeXp4iIiL+cLvNZtP8+fPVpEmTP53mwB/z9jDrn7dZ9PmDHVSrkuPPLzPXqv9buEcjZ/2i06nZBicEAAAAAODaudSlKwMHDlTbtm3VvHlz+fj4aOfOnXr11VfVvHlz3XHHHTp27JhGjBihe+65R/Xr11dycrLeffddbdmyRV9++aXR8cu09hFVtGRcV730/T59tvm4JGl13Dn1fn2NJvdvon4tasrEvTsAAAAAAC7OpYqOG2+8UfPmzdPLL78sm82m8PBwPfjgg3r66afl6empgIAABQUF6cUXX9TZs2fl6emptm3b6ocfflDv3r2Njl/m+Xu5678Dm+kWS3U9++UunU3PUWpWnp74fIeW7j2tF+9opsp+nkbHBAAAAADgqlyq6Hjuuef03HPPXXV75cqV9fXXX5diooqpR6NqWvZkVz3/9V59s/OkJGnx7tPafCRZLw9spl6W6gYnBAAAAADgj7n8PTpgjGBfT715Tyu9PbSVgn09JEnnL+Ro9P+26Jn5O5WenWdwQgAAAAAAfo+iA3/qtuY1tWxcV/VsVM25Nn9rvPq8vlY/HzpvYDIAAAAAAH6PogN/qVqgtz4c0Vav3Nlc/l6Oq50SUrI09INNmvTNXmXlWg1OCAAAAACAA0UHronJZNLgdrX1wxNd1CGisnN99s9Hdeuba7X9eLKB6QAAAAAAcKDowHWpXdlXc0d30PO3WeTl7jh9Dp/P0J3v/qypS39Vbr7N4IQAAAAAgIqMogPXzc3NpAc636DvH++iFrWCJEk2u/T2yoO6I2a99p9OMzghAAAAAKCiouhAodWv5q8vH+6op26OlLubSZIUeypNt7+1Tu+uOiSrzW5wQgAAAABARUPRgSJxN7vpsZsaaFF0JzWsHiBJyrPaNWXJfg1+b4OOns8wOCEAAAAAoCKh6ECxaBoWpG8e66Qx3SJkcgx3aOuxZPV9Y63mbDgqu53pDgAAAABAyaPoQLHxcjfr730ba/6YKNWt4itJysqzauLXe3XfR5t1MiXL4IQAAAAAgPKOogPFrm14ZS1+vIuGd6jjXFt74Lx6v75GX22LZ7oDAAAAAFBiKDpQIvy83PXiHc30vwduVI1Ab0lSena+xn+xU2M/2arzF3IMTggAAAAAKI8oOlCiukZW1dJxXTWgVZhzbeneM+r92hot2XPawGQAAAAAgPKIogMlLsjXQ68NaakZw1ursp+nJCkxI1djP9mq8fN2KDUrz+CEAAAAAIDyokIVHXFxcbJYLIqJiTE6SoXUp2molo7rqpst1Z1rX21PUJ/X12jtgXMGJgMAAAAAGCkmJkYWi0VxcXFFPlaFKjoiIyMVGxur6Ohoo6NUWFUDvPT+vW007a4WCvBylySdSs3WvR9u1sRFe5SZm29wQgAAAABAaYuOjlZsbKwiIyOLfKwKVXTANZhMJt3ZppaWPNlVnepXca7P2XhMf3tjrbYeSzIwHQAAAACgLKPogGHCgn0054H2mty/ibw9HKfi0cRM3TVjg17+Yb9y8q0GJwQAAAAAlDUUHTCUm5tJ90WFa/HjXdSqTrAkyWaXZqw+pP5vr9fek6nGBgQAAAAAlCkUHXAJEVX9tWBsR03o01AeZpMkaf/pdN0Rs15vrzigfKvN4IQAAAAAgLKAogMuw+xm0iPd6+ubRzurUY0ASVKe1a6py+I0aMYGHTp3weCEAAAAAABXR9EBl9M4NFDfPNpZ0T3qyc0x3KEdJ1J065trNWv9EdlsdmMDAgAAAABcFkUHXJKnu5ue6d1ICx7uqBtC/CRJ2Xk2/fvbWA2buUnxyZkGJwQAAAAAuCKKDri01nUqafHjXTSyY7hzbcPhRPV5fa2+2HJCdjvTHQAAAACAyyg64PJ8PM2a1K+JPh3dXjWDvCVJF3LyNWHBLj34vy06m55tcEIAAAAAgKug6ECZ0al+iJY82VWD2tRyrv2076x6v7ZGi3efMjAZAAAAAMBVUHSgTAn09tDUu1rog/vaKsTfU5KUnJmnRz7dpic+366UzFyDEwIAAAAAjETRgTLpZkt1LR3XVX2b1nCufb3jpHq/vkarfj1rYDIAAAAAgJEoOlBmVfH30jvDWuv1IS0V6O0uSTqTlqORs37RPxbuVkZOvsEJAQAAAACljaIDZZrJZNIdrcK07Mlu6hpZ1bk+d9Nx9XljjTYfSTIwHQAAAACgtFF0oFyoEeStj+9vpxfvaCpfT7Mk6URSloa8v0EvfR+r7DyrwQkBAAAAAKWBogPlhslk0vAOdfXDE13Utm4lSZLdLn2w9ohuf2uddsenGpwQAAAAAFDSKDpQ7tSt4qd5Y6L0976N5Gl2nOIHzl7QgHfW6/Wf4pRntRmcEAAAAABQUig6UC6Z3Uwa062evn2ss5rUDJQk5dvsev2nA7rz3Z914Ey6wQkBAAAAACWhQhUdcXFxslgsiomJMToKSknDGgFa+EgnPd6zvsxuJknSrvhU3frWOs1ce1g2m93ghAAAAACAmJgYWSwWxcXFFflYFaroiIyMVGxsrKKjo42OglLk6e6m8bc01FcPd1S9qn6SpNx8m178fp/u/mCjTiRlGpwQAAAAACq26OhoxcbGKjIyssjHqlBFByq2FrWD9f3jXTSq8w0yOYY7tPlIkvq8vkafbT4uu53pDgAAAAAo6yg6UKF4e5g18TaL5o7uoFqVfCRJGblW/f2r3bp/9i86k5ZtcEIAAAAAQFFQdKBCiqpXRUvGddXd7Wo711b9ek63vLZG3+w8aWAyAAAAAEBRUHSgwvL3ctfLdzbXRyPbqmqAlyQpNStPj3+2XdFztyk5I9fghAAAAACA60XRgQqvZ6PqWjauq25rHupc+37XKd3y+hqt2H/GwGQAAAAAgOtF0QFIquTnqbeHttZb97RSsK+HJOlceo4emL1FExbsVHp2nsEJAQAAAADXgqIDuMLtLWpq2biu6tGwqnPtiy3x6vP6Wm04lGhgMgAAAADAtaDoAH6jWqC3PhrZTi8PbCY/T7MkKSElS/d8sFH//navsvOsBicEAAAAAFwNRQfwB0wmk+6+sY6WjOuq9jdUdq7PWn9Uf3tzrXacSDEuHAAAAADgqig6gD9Ru7KvPnuwg/55a2N5ujv+czl8LkN3vvuzpi37Vbn5NoMTAgAAAACuRNEB/AU3N5NGd4nQ4sc7q3mtIEmS1WbXWysOasA76/Xr6XSDEwIAAAAALqHoAK5R/WoB+vLhjhp/c6Tc3UySpL0n03T7W+s0Y/UhWW12gxMCAAAAACg6gOvgYXbT4zc10KLoTmpQzV+SlGu16eUf9mvIext09HyGwQkBAAAAoGKj6AAKoWlYkL59rLPGdI2QyTHcoS3HktX3jbWas/GY7HamOwAAAADACBQdQCF5e5j197811ryHolSnsq8kKSvPqomL9ui+jzbrVGqWwQkBAAAAoOKh6ACK6MYbKuuHJ7poWPs6zrW1B87rltfWaOH2eKY7AAAAAKAUUXQAxcDPy10vDWim2fe3U/VAL0lSena+npy3Uw9/sk2JF3IMTggAAAAAFQNFB1CMujespmXjuumOljWda0v2nlbv19do2d7TBiYDAAAAgIqhQhUdcXFxslgsiomJMToKyrEgXw+9fncrvTOstSr5ekiSzl/I1UNztuqpL3YqLTvP4IQAAAAA4FpiYmJksVgUFxdX5GNVqKIjMjJSsbGxio6ONjoKKoC/NQvVsie7qVfj6s61L7fFq89ra7T+4HkDkwEAAACAa4mOjlZsbKwiIyOLfKwKVXQApa1qgJc+uK+NXh3UXAFe7pKkk6nZGjZzk/719R5l5VoNTggAAAAA5QtFB1DCTCaT7mpbW0ue7KqO9ao41z/ecEx/e3Otth5LNjAdAAAAAJQvFB1AKQkL9tEno9pr0u0WeXs4/tM7cj5Dd834WVOW7FdOPtMdAAAAAFBUFB1AKXJzM2lkpxu0+PEualk7WJJks0vvrjqk/m+vV+zJNGMDAgAAAEAZR9EBGCCiqr8WjI3SM70bysNskiTtP52u/jHrFLPyoPKtNoMTAgAAAEDZRNEBGMTd7KboHvX1dXRnNaoRIEnKs9r16tJfddd7G3T43AWDEwIAAABA2UPRARjMUjNQXz/aSQ93ryc3x3CHth9P0d/eXKvZ64/IZrMbGxAAAAAAyhCKDsAFeLmb9WyfRpo/NkrhVXwlSdl5Nk36NlbDP9ykhJQsgxMCAAAAQNlA0QG4kDZ1K2vxE110X1Rd59rPhxLV57U1mr/lhOx2pjsAAAAA4M9QdAAuxtfTXZP7N9Uno9orNMhbkpSek69nFuzSg//bqnPpOQYnBAAAAADXRdEBuKjODUK0ZFxX3dm6lnPtp31n1Pv1Nfph9ykDkwEAAACA66LoAFxYkI+Hpg1uoffubaMqfp6SpKSMXD386TaN+3y7UjPzDE4IAAAAAK6FogMoA3o3qaGlT3ZV7ybVnWuLdpzULa+v1uq4cwYmAwAAAADXQtEBlBEh/l6aMbyNXhvSQgHe7pKkM2k5GvHRZv3fwt3KyMk3OCEAAAAAGI+iAyhDTCaTBrSqpWVPdlWXBiHO9U83HVffN9bql6NJBqYDAAAAAONRdABlUGiQj/73wI164Y6m8vEwS5KOJ2Vq8Hsb9N/F+5SdZzU4IQAAAAAYg6IDKKNMJpPu7VBXPzzRRW3qVpIk2e3Se2sOq9/b67QnIdXghAAAAABQ+ig6gDIuPMRPX4yJ0nN9G8nT7PhPOu7MBd0Rs15vLj+gfKvN4IQAAAAAUHoqVNERFxcni8WimJgYo6MAxcrsZtLYbvX0zWOdZAkNlCTl2+ya/mOc7nz3Zx08m25wQgAAAAC4upiYGFksFsXFxRX5WBWq6IiMjFRsbKyio6ONjgKUiEY1ArUoupMe61lfZjeTJGlnfKpufXOdZq49LJvNbnBCAAAAAPi96OhoxcbGKjIyssjHqlBFB1AReLq76albGurLhzsqoqqfJCkn36YXv9+nez7YqBNJmbJapVWrpOxsx2cr9y4FAAAAUE5QdADlVMvawfr+sS66v1O4c23TkST1mrpGN9x0XD162JWSIvXoIYWHS199ZVRSAAAAACg+FB1AOebjada/bm+iuQ+2V1iwjyQpx2aVW4fdqnrnFqXnOy5lSUiQBg2i7AAAAABQ9lF0ABVAx3oh+v6xLrIdrOVc861/Vu8ctsk7/JzsF2/dMW4cl7EAAAAAKNsoOoAKYscvHjrxZQudXdBW1gtekqQsm1Ttzi3yiTwlu106cUJau9bgoAAAAABQBBQdQAVx6pTjc9ah6jr5YVdlHqgmSTK521S1/zb5NY0vsB8AAAAAlEUUHUAFERp6+de2bE+dW9hGLQIdj6A1uUkht+5UQOujBfYDAAAAgLKGogOoILp0kWrVkkymiwt2N/UPNSltS7hzn8o379Wu/AOyX7ppBwAAAACUMRQdQAVhNktvvOH49aWyw81kUvJyi1J/ru/cb/pPcXr5h/2UHQAAAADKJIoOoAIZOFBasEAKC7ty1aSAow11e+1GzpX31hzW/y3aI6uNsgMAAABA2ULRAVQwAwdKR49KK1dKwcGOz0eOSG9F19N/BjRzTnvM3XRcT87boTyrzci4AAAAAHBdKDqACshslrp3l7y9HZ/NZsf60PZ19PqQlnJ3c7Qd3+w8qbFztio7z2pYVgAAAAC4HhQdAAro3zJM793bRp7ujm8Py/ef1f2zftGFnHyDkwEAAADAX3OpomPx4sXq1q2bqlatKi8vL0VERGj8+PFKTU0tsN+3336rFi1ayNvbW5GRkZo1a5ZBiYHy6abG1TX7/nby83SMemw4nKhhMzcpJTPX4GQAAAAA8OdcquhISkpS+/btNWPGDC1dulTjx4/X//73P911113OfdatW6cBAwYoKipKP/zwg4YMGaJRo0ZpwYIFBiYHyp+O9UL06YMdFOTjIUnaeSJFQ97bqLNp2QYnAwAAAICrczc6wJWGDx9e4HX37t3l5eWlhx56SCdPnlTNmjX1wgsvOMsQSerRo4cOHTqk559/XoMGDTIiNlButawdrC/GRGn4h5t0Lj1Hv55J113vbdAno9qrdmVfo+MBAAAAwO+41ETHH6lSpYokKTc3Vzk5OVq5cmWBCQ9Juvvuu7Vv3z4dPXrUgIRA+dawRoDmj4lSWLCPJOlYYqYGv7dBB89eMDgZAAAAAPyeSxYdVqtV2dnZ2rZtmyZPnqx+/fopPDxchw4dUl5enho1alRg/8aNG0uS9u/fb0RcoNwLD/HTgoejVK+qnyTpVGq2hry3QXsSUv/inQAAAABQulyy6Khbt658fHzUpk0bhYaGau7cuZKk5ORkSVJwcHCB/StVqiTJcY+PP7Nv3z6FhoY6PwBcu9AgH30xJkpNagZKkhIzcnXPBxu15eif/3cHAAAAAFdz5c/ooaGh2rdvX5GP6VL36Lhk8eLFysjI0N69e/Xiiy/q9ttv148//ljk49rtduXk5DhfL1mypMjHLGk5OTllIifKpsKcX6NusCvmgnQoTUrPztfQ9zdobFM3WSqbSiglyjK+h6GkcY6hJHF+oaRxjqGklYVz7Mqf0SXHz+1F5ZJFR/PmzSVJUVFRateunVq2bKmFCxfKYrFI0u8eN3tp0qNy5cp/elyLxaJdu3aVQOKSs2TJEvXp08foGCinCnt+9emdrzFztmrtgfPKtUkz9kpv3tNCfZoyKYWC+B6GksY5hpLE+YWSxjmGklYWzrHfXpnRvHlz7d69u0jHdMlLV67UvHlzeXh46ODBg6pXr548PDx+dy+OS69/e+8OACXD19NdM0e0Ve8m1SVJuVabHvl0mxZsjTc4GQAAAICKzuWLjk2bNikvL08RERHy8vJSjx49tGDBggL7zJs3T40bN1Z4eLgxIYEKyMvdrJihrTWwdZgkyWaXnp6/Ux//fNTYYAAAAAAqNJe6dGXgwIFq27atmjdvLh8fH+3cuVOvvvqqmjdvrjvuuEOSNHHiRHXv3l2PPPKIBg8erJUrV2ru3LmaN2+eseGBCsjd7Kapg1oowMtdH284Jkn61zd7lZ6dp+ge9WUycd8OAAAAAKXLpYqOG2+8UfPmzdPLL78sm82m8PBwPfjgg3r66afl6ekpSercubO++uor/fOf/9SHH36oOnXqaObMmbrrrrsMTg9UTG5uJk3q10QB3h56e+VBSdLUZXFKz87Xc30bUXYAAAAAKFUuVXQ899xzeu655/5yv379+qlfv36lkAjAtTCZTHq6d0MFeLvrvz847pnz3prDSs/J1wv9m8rsRtkBAAAAoHS4/D06AJQdY7rV00sDmurSEMfcTcf15LwdyrPajA0GAAAAoMKg6ABQrIa1r6vXh7R0TnF8s/Okxs7Zquw8q8HJAAAAAFQEFB0Ail3/lmF6b3gbebo7vsUs339W98/6RRdy8g1OBgAAAKC8o+gAUCJ6Wapr9v3t5OdpliRtOJyoYTM3KSUz1+BkAAAAAMozig4AJaZjvRB9Mrq9gnw8JEk7T6RoyHsbdTYt2+BkAAAAAMorig4AJapVnUqaN6aDQvy9JEm/nknXXe9t0ImkTIOTAQAAACiPKDoAlLhGNQK1YGyUwoJ9JEnHEjM1+L0NOnj2gsHJAAAAAJQ3FB0ASkV4iJ/mj41SRFU/SdKp1GwNeW+D9iSkGpwMAAAAQHlC0QGg1NQM9tEXY6JkCQ2UJCVm5OqeDzZqy9Ekg5MBAAAAKC8oOgCUqhB/L332UAe1qVtJkpSena97P9ysNXHnDE4GAAAAoDyg6ABQ6oJ8PDRn1I3q0iBEkpSVZ9Xoj7doyZ5TBicDAAAAUNZRdAAwhK+nu2aOaKveTapLknKtNj3y6TYt2BpvcDIAAAAAZRlFBwDDeLmbFTO0tQa2DpMk2ezS0/N36uOfjxobDAAAAECZVaGKjri4OFksFsXExBgdBcBF7mY3TR3UQiOi6jrX/vXNXr294oDsdruByQAAAACUlpiYGFksFsXFxRX5WBWq6IiMjFRsbKyio6ONjgLgCm5uJk3q10SP9qjvXJu6LE4v/7CfsgMAAACoAKKjoxUbG6vIyMgiH6tCFR0AXJfJZNLTvRvq730bOdfeW3NY/7doj6w2yg4AAAAA14aiA4BLGdOtnl4a0FQmk+P13E3HNf6LHcqz2owNBgAAAKBMoOgA4HKGta+r14e0lNnN0XZ8veOkHv5kq7LzrAYnAwAAAODqKDoAuKT+LcP03vA28nR3fJv6ad9Z3T/rF13IyTc4GQAAAABXRtEBwGX1slTX7Pvbyc/TLEnacDhRw2duUkpmrsHJAAAAALgqig4ALq1jvRB9Mrq9gnw8JEk7TqTo7vc36mx6tsHJAAAAALgiig4ALq9VnUqaN6aDQvy9JEn7T6dr8IwNik/ONDgZAAAAAFdD0QGgTGhUI1ALxkYpLNhHknQ0MVN3zdigQ+cuGJwMAAAAgCuh6ABQZoSH+Gn+2ChFVPWTJJ1KzdbgGRu092SqwckAAAAAuAqKDgBlSs1gH30xJkqW0EBJUmJGru5+f6O2HksyOBkAAAAAV0DRAaDMCfH30mcPdVCbupUkSenZ+Ro+c7PWHjhncDIAAAAARqPoAFAmBfl4aM6oG9WlQYgkKSvPqlGzt2jJntMGJwMAAABgJIoOAGWWr6e7Zo5oq95NqkuScq02Rc/dpi+3xhucDAAAAIBRKDoAlGle7mbFDG2tga3CJElWm11Pzd+p/204amwwAAAAAIag6ABQ5rmb3TT1rha6L6quc+35r/cqZuVB2e12A5MBAAAAKG0UHQDKBTc3k/7dr4mie9Rzrr269Fe9vGQ/ZQcAAABQgbhf7xvOnj2rRYsWadWqVdq7d6/Onj0rk8mkqlWrqmnTpurevbv69++vatWqlUReALgqk8mkZ3o3UoC3h17+Yb8k6b3Vh5Wena8X+jeV2c1kcEIAAAAAJe2aJzp27dqlIUOGqE6dOho7dqwWLlyozMxM1a1bV7Vr11ZmZqa+/PJLjRkzRnXq1NHdd9+t3bt3l2R2APhDY7vV04t3NJXpYq8xd9Nxjf9ih/KsNmODAQAAAChx11R0PPDAA2rdurW2bt2q5557Ths2bFBaWpoOHDigjRs3atOmTTpw4IDS0tK0YcMGTZgwQb/88otat26t0aNHl/TXcM3i4uJksVgUExNjdBQAJWx4h7p6fUhL5xTH1ztO6uFPtio7z2pwMgAAAAC/FRMTI4vFori4uCIf65qKjp07d2rhwoU6ePCgJk2apPbt28vDw+N3+3l6eqp9+/aaPHmyDh06pAULFmjbtm1FDllcIiMjFRsbq+joaKOjACgF/VuG6b3hbeTp7vhW99O+s7p/1i+6kJNvcDIAAAAAV4qOjlZsbKwiIyOLfKxrKjq2bt2q22+//boP3r9/f5cqOgBUPL0s1TV7ZDv5epolSRsOJ2r4zE1Kycw1OBkAAACAksBTVwCUex3rh+jT0e0V5OOYRNtxIkV3v79RZ9OzDU4GAAAAoLgVS9GRkZGhefPmaerUqZo7d65SU1OL47AAUGxa1amkzx/qoBB/L0nS/tPpGjxjg+KTMw1OBgAAAKA4Fbno2LNnj+rVq6cRI0Zo+vTpGjFihOrXr69ffvmlOPIBQLFpHBqo+WOjFBbsI0k6mpipu2Zs0KFzFwxOBgAAAKC4FLnoeOKJJ3T77bcrOTlZJ0+eVHx8vCIjI/XYY48VRz4AKFY3hPhp/tgoRYT4SZJOpWZr8IwN2nuSSTQAAACgPLjmomPq1Kmy2Wy/W9+3b5+io6Pl4+P4F9Lq1avr3nvvVWxsbPGlBIBiVDPYR1+MjZIlNFCSlJiRq7vf36itx5IMTgYAAACgqK656HjzzTd14403aseOHQXWmzRponfeeUc5OTmSpHPnzmnOnDmyWCzFGhQAilOIv5c+e6iD2tStJElKz87X8JmbtfbAOYOTAQAAACiKay469u7dq/bt26tDhw569tlnlZ3teFrB9OnT9c033yg4OFhhYWEKCwvT/v379dZbb5VYaAAoDkE+Hpoz6kZ1aRAiScrKs2rU7C1asue0wckAAAAAFNY1Fx0BAQGKiYnRihUr9P3336tp06Zavny5mjVrpoMHD+rDDz/UuHHjNGvWLB06dEjt2rUrydwAUCx8Pd01c0Rb9W5SXZKUa7Upeu42fbk13uBkAAAAAArD/Xrf0LFjR23fvl3//e9/ddttt2nIkCF67bXXNHTo0JLIBwAlzsvdrJihrTVhwS59tT1BVptdT83fqYzcfN0XFW50PAAAAADXoVBPXfHw8NDzzz+v7du36/Dhw2rUqJHmzp1b3NkAoNS4m9009a4Wui+qrnPt+a/3KmblQdntdgOTAQAAALge11V0ZGRkaPny5frmm2+UkJCgRo0aac2aNZo8ebKio6PVt29fHT9+vKSyAkCJcnMz6d/9mii6Rz3n2qtLf9XLS/ZTdgAAAABlxDUXHTt27FDDhg11880364477lD9+vX1wQcfSJLGjBmj2NhY+fr6qkmTJnrttdf4oQBAmWQymfRM70Z6rm8j59p7qw/r/xbtkdXG9zUAAADA1V1z0fH444+rZs2aOnz4sJKTkzV69Gg98cQTSklJkSSFhobqyy+/1Jw5czR9+nS1b9++pDIDQIkb262eXryjqUwmx+u5m45r/Bc7lGe1GRsMAAAAwJ+65qJj+/btGj16tMLDwxUUFKRnnnlG2dnZiouLK7DfHXfcob179/LUFQBl3vAOdfX6kJYyuznajq93nNTDn2xVdp7V4GQAAAAAruaai45atWpp9erVztdr1qyRyWRSWFjY7/YNDAxUTExM8SQEAAP1bxmm94a3kae749vlT/vO6v5Zv+hCTr7ByQAAAAD8kWsuOiZOnKjPP/9c9evXV9u2bTVixAiNGDHiD4sOAChPelmqa/bIdvL1NEuSNhxO1PCZm5SSmWtwMgAAAAC/dc1Fx9ChQ7VhwwYNHjxYXbp00WeffaaPPvqoJLMBgMvoWD9En45uryAfD0nSjhMpuvv9jTqbnm1wMgAAAABXuq7Hy9544436z3/+o9dee02DBw8uqUwlJi4uThaLhctqABRKqzqV9PlDHRTi7yVJ2n86XYNnbFB8cqbByQAAAICyLSYmRhaL5Xf3AS2M6yo6yrrIyEjFxsYqOjra6CgAyqjGoYGaPzZKYcE+kqSjiZkaPGODDp27YHAyAAAAoOyKjo5WbGysIiMji3ysayo67r33Xh0+fPi6D37gwAENHz78ut8HAK7shhA/zR8bpYgQP0nSydRsDZ6xQXtPphqcDAAAAMA1FR0HDx5U48aNNWjQIH3zzTfKysq66r4XLlzQggUL1L9/fzVp0kRHjhwptrAA4CpqBvvoi7FRsoQGSpISM3J19/sbtfVYksHJAAAAgIrN/Vp22rBhg+bOnasXXnhBd9xxh9zd3dWkSRPVq1dPlStXlt1uV1JSkg4cOKB9+/bJarXKYrFozpw5GjJkSEl/DQBgiBB/L332UAc9MPsXbT2WrPTsfA2fuVkf3NdWnRuEGB0PAAAAqJCuqeiQHE9dGTp0qJYvX6758+drzZo1WrRokWw2myTJzc1NjRs31tixY3XXXXepa9euJRYaAFxFkI+H5oy6UWPmbNXaA+eVlWfVA7N/0VtDW6l3kxpGxwMAAAAqnGsuOi656aabdNNNN0mSbDabEhMTZTKZVKVKFZlMpmIPCACuztfTXTNHtNXjn23X0r1nlGu16ZFPt+nVQc01sHUto+MBAAAAFUqRnrri5uamqlWrKiQkhJIDQIXm5W5WzNDWGtgqTJJktdk1/oudmrPhqLHBAAAAgAqmQj1eFgBKkrvZTVPvaqF7O9R1rk38eq9iVh40MBUAAABQsVB0AEAxcnMzaXL/Jnqkez3n2qtLf9XLP+yX3W43MBkAAABQMVB0AEAxM5lMmtCnkZ7t08i5NmP1If1z0R7ZbJQdAAAAQEmi6ACAEvJw93p64Y6munQLo083HdeTX+xQntVmbDAAAACgHKPoAIASdG+HunptcEuZ3Rxtx9c7TurhT7YpO89qcDIAAACgfKLoAIASdkerMM0Y3kae7o5vuT/tO6MHZv+ijJx8g5MBAAAA5Q9FBwCUgpst1TVrZDv5epolST8fStSwmZuUkplrcDIAAACgfLmmosPNzU1ms/m6Ptzd3Us6OwCUKZ3qh+iT0e0V6O34/rjjRIrufn+jzqZnG5wMAAAAKD+uqY247777ZLp0N72Ltm7dqj179qhhw4Zq3LixJCk2NlZxcXFq2rSp2rRpU/xpAaCMa12nkuaNidK9H27W+Qs52n86XYNnbNAno9urViVfo+MBAAAAZd41FR2zZ88u8PrHH3/UggULtGjRIvXr16/AtkWLFunee+/V9OnTiy0kAJQnjUMDNX9slIbP3KSElCwdTczU4BkbNGd0e9Wr6m90PAAAAKBMK9Q9OiZOnKgxY8b8ruSQpDvuuEMPPfSQ/vnPfxY5HACUVzeE+OmLsVGKCPGTJJ1MzdbgGRu092SqwckAAACAsq1QRceuXbtUr169q26vX7++du/eXehQAFARhAX7aN6YKDUODZQkJWbk6u73N2rrsSSDkwEAAABlV6GKjkqVKmnZsmVX3b5kyRIFBQUVOhQAVBRVA7z0+YMd1LpOsCQpPTtfw2du1roD540NBgAAAJRRhSo6hg4dqq+//lqjRo3Svn37ZLVaZbVatW/fPj3wwAP67rvvNGzYsOLOWmRxcXGyWCyKiYkxOgoAOAX5emjOqPbqXD9EkpSVZ9UDs3/R0r2nDU4GAAAAlI6YmBhZLBbFxcUV+ViFKjpefPFF9e/fX7NmzVLTpk3l7e0tb29vNW3aVLNnz9Ztt92mF198scjhiltkZKRiY2MVHR1tdBQAKMDPy10zR7TVLZbqkqRcq02PfLpNX22LNzgZAAAAUPKio6MVGxuryMjIIh/rmp668lteXl5auHChli1bpq+//lqHDx+WJEVERKh///665ZZbihwMACoabw+z3hnWWhMW7NJX2xNktdk1/oudysjJ171R4UbHAwAAAMqEQhUdl9xyyy2UGgBQjNzNbpp6Vwv5eblrzsZjkqSJX+9VWna+onvUNzgdAAAA4PoKdekKAKDkuLmZNLl/Ez3S/fLTrV5d+qte/mG/7Ha7gckAAAAA13dNEx2TJ0++7gObTCZNnDjxut8HAHB8D53Qp5ECvD00Zcl+SdKM1YeUnp2nF/o3lZubyeCEAAAAgGu6pqJj0qRJ131gig4AKLqHu9eTv7e7nv96j+x26dNNx3UhJ19T72ohDzNDeQAAAMBvXVPRceTIkZLOAQC4ins71FWAl7uemr9TVptdX+84qYwcq94e2kreHmaj4wEAAAAu5ZqKjrp165Z0DgDAn7ijVZj8vNwVPXebcvNt+mnfGT0w+xd9cF9b+XkV6b7SAAAAQLlS5LnnnJwcJSQkKDc3tzjyAACu4mZLdc0a2U6+no4pjp8PJWrYzE1KyeT7LwAAAHBJoYuObdu2qWfPngoICFCdOnW0bt06SdLZs2d100036aeffiq2kAAAh071Q/TJ6PYK9HZMcew4kaK739+os+nZBicDAAAAXEOhio4dO3aoS5cuOnTokO67774C26pVq6asrCx9/PHHxRIQAFBQ6zqVNG9MlEL8vSRJ+0+na/CMDYpPzjQ4GQAAAGC8QhUdzz//vGrWrKm9e/fq5Zdflt1uL7D9pptu0ubNm4slIADg9xqHBmr+2CiFBftIko4mZmrwjA06dO6CwckAAAAAYxWq6Fi7dq0efPBB+fv7y2Qy/W57nTp1dPLkySKHAwBc3Q0hfvpibJQiQvwkSSdTszV4xgbtPZlqcDIAAADAOIUqOrKzsxUUFHTV7WlpaYUKM3/+fPXv31+1atWSn5+fWrZsqY8++qjAxEj37t1lMpl+97F///5C/Z4AUJaFBfto3pgoNQ4NlCQlZuTq7vc3auuxJIOTAQAAAMYoVNFRr149bd269arbV6xYIYvFct3HnT59unx9fTVt2jR9++236tu3rx588EFNnjy5wH6dOnXShg0bCnyEh4df9+8HAOVB1QAvff5gB7WuEyxJSs/O1/CZm7XuwHljgwEAAAAGcC/Mm4YOHaoXXnhBgwcPVqtWrSTJeQnLtGnTtGTJEr3xxhvXfdxvv/1WISEhztc9e/ZUYmKipk+frokTJ8rNzdHLBAcHq0OHDoWJDgDlUpCvh+aMaq8xc7Zq3cHzysqz6oHZv+itoa3Uu0kNo+MBAAAApaZQEx1PP/20OnTooN69e6tr164ymUx68sknFRYWpgkTJujmm2/WI488ct3HvbLkuKRVq1ZKS0tTRkZGYaICQIXh5+WumSPa6hZLdUlSrtWmRz7dpq+2xRucDAAAACg9hSo6PD099eOPP2rq1Kny8fGRt7e34uLiFBISoldeeUXfffedc/qiqNatW6ewsDAFBAQ411avXi0/Pz95e3urW7duWrNmTbH8XgBQ1nl7mPXOsNYa2CpMkmS12TX+i52as+GoscEAAACAUlKoS1ckyd3dXU8++aSefPLJ4sxTwLp16/T5559r2rRpzrVu3brpvvvuU4MGDXTy5ElNnTpVvXr10urVqxUVFfWnx9u3b59CQ0Odr0+dOlVi2QHAKO5mN029q4X8vNw1Z+MxSdLEr/cqPSdfj3Svb3A6AAAA4LIrf0aXpPPni36fOZP9ykeauJD4+Hi1b99ejRs31rJly646IZKRkaEmTZrIYrFo8eLFf7hP8+bNtXv3bpnNZgUGBjrX586dWyLZi1NOTo68vLyMjoFyivOrfLPb7fr6iF1Ljl/+Nt+7tkl3RJj+8NHgJYFzDCWNcwwlifMLJY1zDCWtLJxjQ4cOLfA6LS1NVqtVzZo1065duwp1zGua6Lh0aUjXrl0LvP4rl/a/XikpKerbt6+qVKmiL7/88k8vg/Hz89Ott96qBQsW/OVxLRZLof+gjLJkyRL16dPH6Bgopzi/yr++kt5ZdVCvLPlVkrT0hF1Vw2prcr+mcnMr+bKDcwwljXMMJYnzCyWNcwwlrSycY0lJSQVeXxpUKIprKjq6d+8uk8mkrKwseXp6Ol9fjd1ul8lkktVqve5AWVlZuu2225SamqoNGzYoKCjouo8BALjske71FeDtoee/3iO7Xfpk43FdyM7Xq3e1kIe5eO6nBAAAALiKayo6PvroI5lMJnl4eEiSZs2aVSJh8vPzNXjwYO3bt09r165VWFjYX74nIyND3333ndq1a1cimQCgPLi3Q135e5n19PxdstrsWrTjpDJyrXrrnlby9jAbHQ8AAAAoNtdUdIwcOVI7duxwTnGMGDGiRMI88sgj+u677zRt2jSlpaVp48aNzm2tWrXS5s2b9eqrr2rAgAEKDw/XyZMnNW3aNJ0+fVrz588vkUwAUF4MaFVLfp7uenTuduVabfox9oxGffyL3r+3rfy8Cn1vagAAAMClXPPfbDt06KCJEyfq73//e7E9Ova3li1bJkl66qmnfrftyJEjCg0NVW5urv7xj38oMTFRfn5+6tixo2bMmKEbb7yxRDIBQHlyS5MamnV/Oz34vy3KzLVq/cFEDf9wk2aPvFFBvh5GxwMAAACK7Jobi27dumnixInq2LGj4uLiSiTM0aNHZbfb//AjPDxc9evX15IlS3Tq1Cnl5uYqOTlZ33//PSUHAFyHTvVDNGdUewV6O7ru7cdTNOT9DTqXnmNwMgAAAKDorrnoWLp0qd59913t27dPrVq10uuvv16CsQAAJalN3Ur6/KEohfh7SpL2n07X4Pc2KCEly+BkAAAAQNFc1zUoY8aM0a5du9ShQweNHz9ePXr00LFjx0oqGwCgBFlqBuqLMVGqGeQtSTpyPkN3vfuzDp+7YHAyAAAAoPCu+2YbdevW1fLly/XWW29py5YtslgsioiI+N1HvXr1SiIvAKAYRVT11/yHO+qGED9J0snUbA1+b4NiT6YZnAwAAAAonELfVTQvL082m+2q99Sw2WzFmRMAUELCgn30xZgoNaoRIEk6fyFXd7+/QVuPJRucDAAAALh+1110HDt2TD179tRTTz2lNm3aaO/evTpy5MgffgAAyoaqAV6a91CUWtUJliSlZefr3g83ad2B88YGAwAAAK7TdRUd77//vpo3b64NGzbolVde0erVq3XDDTeUVDYAQCkK8vXQJ6Paq1P9KpKkzFyrHpj9i5btPW1wMgAAAODaXXPR0bdvXz388MOKjIzUtm3b9NRTT8lkMpVkNgBAKfPzcteHI9rpZkt1SVKu1aaHP92mhdvjDU4GAAAAXJtrLjpWrFihSZMmaePGjWrcuHFJZgIAGMjbw6x3hrXWgFZhkiSrza4n5+3UnA1HjQ0GAAAAXAP3a91x48aNatWqVUlmAQC4CA+zm6bd1UJ+XmZ9svG4JGni13uVnpOvR7rXNzgdAAAAcHXXPNFByQEAFYubm0kv9G+qh7tfflz4K0t+1cs/7JfdbjcwGQAAAHB1hX68LACg/DOZTHq2TyNN6NPQuTZj9SFN/HqPbDbKDgAAALgeig4AwF96pHt9vXBHU126B/UnG49r/Bc7lGe1GRsMAAAA+A2KDgDANbm3Q11NH9xCZjdH27Fox0k98uk2ZedZDU4GAAAAXFahio64uDhZLBbFxMQYHQUAyqQBrWrp3WGt5Wl2/N/Hj7FnNOrjX5SRk29wMgAAAJRlMTExslgsiouLK/KxKlTRERkZqdjYWEVHRxsdBQDKrFua1NCs+9vJ19MsSVp/MFHDP9yk1Mw8g5MBAACgrIqOjlZsbKwiIyOLfKwKVXQAAIpHp/ohmjOqvQK9HU8p3348RUPe36Bz6TkGJwMAAEBFd01Fh5ubm8xm83V9uLu7l3R2AICB2tStpM8filKIv6ckaf/pdA1+b4MSUrJktUqrVknZ2Y7PVm7jAQAAgFJyTW3EfffdJ9OlW+0DAHCRpWagvhgTpeEzN+lkaraOnM/QrdN/VuKC9orf569Zs6QBA6RataQ33pAGDjQ6MQAAAMq7ayo6Zs+eXcIxAABlVURVf81/uKOGz9ykI+czlJKbLd20QR7n2zv3SUiQBg2SFiyg7AAAAEDJ4h4dAIAiCwv20ecPRsmeHCBJMvvlqvrQDTqWaZdkl93u2G/cOC5jAQAAQMmi6AAAFIt9270U/3GUchKCJUlm73zNOm5T7XHLVOPedar8tx1KrXVA0+ef0q+n05WdR+MBAACA4lfoO4auX79e//3vf7Vp0yYlJyfLfumf6y4ymUzKz88vckAAQNlw6pRky/HQmXntVXXgFvmEJ0qS3Lzy5VUzVV41UyVJMTscHyaTVKuSj+pV9VdEiL/qVfNzfK7qp6oBXtwbCgAAAIVSqKJjzZo16tWrl4KCgtS+fXstXrxYPXv21IULF7R582Y1a9ZMrVu3Lu6sAAAXFhrq+GzPc9fZBe0U2O6I2v3tV+1P8JE5KEu/7S3sdulEUpZOJGVp1a/nCmwL8HJXRFU/RwlS1U8RVf1Vr6q/6lbxlbeHuZS+IgAAAJRFhSo6XnrpJYWGhmrLli0ymUyqVq2a/vGPf6hnz55atmyZBg0apHfeeae4swIAXFiXLo6nqyQkSHarWWkb62v4mIO6//meMrlb5VE5Q9XrZ+ipf1/QkfMXdPh8hg6dvaCM3N9fwpKek6+d8anaGZ9aYN1kkmpX8nWUH1dOgVTzU1V/pkAAAABQyKJj8+bNGj9+vKpWraqkpCRJks1mkyTdcsstuvfeezVx4kStWLGi+JICAFya2ex4hOygQY5CosAVjVaz8s4F6vV3AzWw1+Vlu92us+k5OnTugg6dy9DhKz4npGTpN1dFym6Xjidl6nhS5l9OgTg+MwUCAABQ0RSq6MjJyVFYWJgkycvLS5KUnp7u3N6yZUt98sknxRAPAFCWDBzoeITsE09I8fGX12vVkl5//fePljWZTKoe6K3qgd7qWC+kwLbsPKuOnM/QYWcBUrgpEDeTVOviFIjzUhimQAAAAMqtQhUdoaGhir/4N1g/Pz8FBwdrz549GjBggCQpPj5e7u6Fvs8pAKAMGzhQ6t9fWrtWSkmRVq50XNZivs6hCm8PsxqHBqpxaGCB9eudArExBQIAAFChFKqNaNeundavX+98fcstt+i1115T3bp1ZbPZ9Pbbb6t9+/bFFhIAULaYzVL37tKSJY7PxYkpEAAAAPyZQhUdo0aN0uzZs5WVlSUfHx/95z//0dq1azVy5EhJUo0aNfTKK68UZ04AAP7SX06BnL2gQ+eLYQqkmr/qhfgxBQIAAOCCClV03Hzzzbr55pudryMiIhQXF6fly5fLbDarc+fOCgoKKraQAAAURYEpkPpXnwI5dO6CDl/LFMiJFO08kVJg/Y+mQC59ZgoEAACg9BTbjTT8/PzUr1+/4jocAAClorSnQOpV81dEiB9TIAAAACWEO4YCAPAHmAIBAAAomwpVdERERPzlPiaTSYcOHSrM4QEAcGmlMgXi7a6IqkyBAAAAXK9CFR116tT53b8y5efn68iRIzp58qTq16+vsLCwYglYnOLi4mSxWBQdHa3o6Gij4wAAypnrnQK5VIL84RRI9p9PgdSr6ig+mAIBAADlQUxMjGJiYnT48OEiH6tQRceqVauuuu2zzz7TU089pRkzZhQ2U4mJjIzUrl27jI4BAKiASmIKZOU1TIHUq+aYAvFyZwoEAAC4rksDCc2bN9fu3buLdKxiv0fHPffco7Vr1+qpp57Sd999V9yHBwCgXDFiCuTSBAhTIAAAoDwqkZuRtmzZUp988klJHBoAgAqDKRAAAIDrVyJFx44dO+Tm5lYShwYAoMK7likQxwRIRpGmQGpX9nXeBJUpEAAAUFYUquhYs2bNH64nJSXpp59+0gcffKCBAwcWKRgAALh+1zoFcuis45G4fzYFciwxU8cS/2QK5NKNUJkCAQAALqRQRUf37t3/8F9y7Bf/ltSrVy+99dZbRUsGAACKjZFTIPWq+ivE35MpEAAAUCoKVXTMmjXrd2smk0mVK1dWZGSkIiMjixwMAACUDiOmQC7dGLWwUyBWq7R2rZSdLa1aJXXpIpkZJgEAACpk0TFixIjizgEAAFxMaU+BOCZA/noK5KuvpCeekOLjpVmzpAEDpFq1pDfekLhyFgAAlMjNSAEAQPn2Z1MgZ9JyHMXHFVMgh85e0MnU658CufLyl3pV/XR4h78evd9X9vyC4xsJCdKgQdKCBZQdAABUdIUuOjIyMjR37lwdOHBAiYmJzvtzXGIymfThhx8WOSAAACg7TCaTagR5q0ZQ8UyB7DiRoh2/mQKp/aSUn+qrvER/bUm2SWar7FazTCZp3Dipf38uYwEAoCIrVNGxefNm3XbbbTp//vxV96HoAAAAVyquKRCTm+RRKVMelTL13RkpbMwqpW2M0IVddXTihFlr10rdu5fe1wUAAFxLoYqO8ePHKzc3V1988YV69uypypUrF3cuAABQQVzrFMiiFRlatPyCPCpnyKPyBbl5OaZA3AOyVfnmWAVGHVLa5ggdi68jrs4FAKDiKtTfArZu3ap//OMfGjRoUHHnAQAAcLpyCsQ/SfrwyUtb7PKskaru0ev16wXHirt/jir33KepcYeUuSpC90bVlb8XhQcAABWNW2HeFBgYqCpVqhR3FgAAgKvq0sXxdBXHg1hMyj0drHtqmXXyoy7K2F/DeYlLem6upizZr85TVujtFQeUlp1nZGwAAFDKClV0DBw4UEuXLi3uLAAAAFdlNjseIStdKjsc8s4FKvGbNjr9UVe1qlzTuS0lM09Tl8Wp88sr9NqPcUrNpPAAAKAiKFTRMWXKFJ09e1aPPfaYDh069LsnrgAAAJSEgQMdj5ANCyu4XquW9Nl7AVo4oZV+Gt9NA1uHyezmaDzSsvP1xvID6jRlhV5dul9JGbkGJAcAAKWlUEVHcHCwNm/erHfeeUeRkZFyd3eX2Wwu8OHuzjWxAACg+A0cKB09Kq1cKQUHOz4fOeJYl6R6Vf01fXBLLR/fTYPb1pL7xcLjQk6+YlYeUucpK/Tfxft0Lj3HsK8BAACUnEK1Effdd59MV86MAgAAlCKz2fEI2SVLrv4o2fAQP70yqIUe69lA764+pPlbTijPaldmrlXvrTmsjzcc1dAb62pMtwhVD/QuzfgAAKAEFaromD17djHHAAAAKBm1K/vqPwOa6dEe9fXe6kP67JcTys23KTvPpo/WH9Enm47p7na1NbZbPdUM9jE6LgAAKKJCXboCAABQ1tQM9tG/+zfVugk9NKrzDfL2cPw1KDffpv9tOKZur67U37/arRNJmQYnBQAARVGkG2lkZmbq6NGjSkxM/MMbknbt2rUohy92cXFxslgsio6OVnR0tNFxAACAAaoFemvibRY93L2ePlh7WHM2HFNmrlV5Vrs+23xc87ec0IBWYYruUV/hIX5GxwUAoEKIiYlRTEyMDh8+XORjFaroyMzM1Pjx4zVr1izl5+f/brvdbpfJZJLVai1ywOIUGRmpXbt2GR0DAAC4gBB/L/29b2ON6VpPH607otk/H9WFnHzl2+yavzVeX26LV/+WjsKjfjV/o+MCAFCuXRpIaN68uXbv3l2kYxWq6HjiiSf04Ycf6m9/+5t69uypKlWqFCkEAACAUSr7eerp3g31YJcIzfr5iD5ad0Rp2fmy2aWF2xO0aEeCbmteU4/1rK/I6gFGxwUAAH+hUEXHwoULdc899+jTTz8t7jwAAACGCPL10LhekXqg8w2as+GYPlh7WCmZebLbpW93ntS3O0+qb9MaerRnfTWpGWR0XAAAcBWFuhlpdna2ul/tWW4AAABlWKC3h6J71Ne6Z3vqub6NVMXP07nthz2ndeub6zT64y3aFZ9iXEgAAHBVhSo62rZtqwMHDhR3FgAAAJfh7+Wusd3qae2zPfTPWxuraoCXc9tP+86o39vrNXLWZm09lmxgSgAA8FuFKjpefvllzZo1S1u2bCnuPAAAAC7F19Ndo7tEaO2EHvp3vyaqEejt3Lbq13O6892fNXzmJm0+kmRgSgAAcEmh7tHx/vvvq1atWurQoYOioqIUEREhs9lcYB+TyaQPP/ywWEICAAAYzdvDrBEdw3X3jbW1YGu83ll5SAkpWZKkdQfPa93B8+oQUVmP92ygqHpVZDKZDE4MAEDFVKiiY/bs2c5fr1+/XuvXr//dPhQdAACgPPJyN2tY+7oa3La2Fm5L0NsrD+p4UqYkaePhJG08vElt61bSYzc1UNcGIRQeAACUskJdumKz2f7yw2q1FndWAAAAl+FhdtPgdrW14qlumj64hSJC/JzbthxL1oiPNuuOd37W8n1nZLfbDUwKAEDFUqii41rk5OSU1KEBAABchrvZTQNb19KP47vpzXtaqUE1f+e2nSdSNOrjLbrtrXVasue0bDYKDwAASlqxFx1bt27VI488opo1axb3oQEAAFyW2c2kfi1qaum4rnpnWGs1qhHg3Lb3ZJrGfrJVf3tzrb7bdVJWCg8AAEpMoe7R8VtJSUn65JNP9NFHH2n37t2y2+2KjIwsjkMDAACUKW5uJv2tWaj6NKmhn/ad0ZsrDmhPQpokaf/pdD06d7vqVzugx3rW123Na8rsxj08AAAoTkWa6Fi6dKmGDBmisLAwPfnkk8rJydG//vUv7d69W/v37y+ujAAAAGWOm5tJtzSpoW8f7axZI9upZe1g57aDZy/oic93qNf01VqwNV75VptxQQEAKGeue6Lj6NGj+uijj/Txxx8rPj5eISEhGjRokObOnauXXnpJAwcOLImcAAAAZZLJZFKPRtXUvWFVrTt4Xm8uP6BfjiZLko6cz9DT83fqzeUH9Ej3ehrYupY83UvsFmoAAFQI1/z/pJ9++qluuukm1a9fX1OmTFHbtm21cOFCJSQkaNKkSdxNHAAA4E+YTCZ1aVBVX4yJ0mcPdlBURBXntuNJmXruq93qMXWV5mw8ppx8nl4HAEBhXfNEx7333quIiAi9/vrruueee1SlSpW/fhMAAAAKMJlMiqpXRVH1quiXo0l6c/kBrT1wXpKUkJKliYv2KGbFQY3pFqF7bqwjbw+zwYkBAChbrnmiw8vLS0ePHtXXX3+tJUuWKCsrqyRzAQAAlHvtwitrzqj2WvhIR/VsVM25fjotW//+Nladp6zUB2sOKzM338CUAACULddcdJw6dUqvv/66EhMTde+996pGjRoaNWqU1qxZw2UrAAAARdCqTiV9NLKdvn20s26xVHeun7+Qo5cW71PnKSv17qpDupBD4QEAwF+55qIjODhYjz76qLZt26YtW7Zo+PDhWrhwoXr06KHOnTvLZDIpNTW1JLMCAACUa81qBen9+9rqhye66NZmoTJdfPJsUkaupizZr85TVuit5QeUlp1nbFAAAFxYoW7r3bp1a8XExOjUqVOaM2eOmjRpIkkaPXq0WrZsqRdffFF79+4t1qDFIS4uThaLRTExMUZHAQAAuKrGoYGKGdZaS8d1Vb8WNeV2sfBIyczTtB/j1OnlFZr+Y5xSMnONDQoAQDGJiYmRxWJRXFxckY9VpOeXeXl5aejQoVq+fLkOHTqk//u//1NycrKef/55tWjRosjhiltkZKRiY2MVHR1tdBQAAIC/FFk9QG/e00o/ju+mga3DZL7YeKRn5+vN5QfUecpKvbJkv5IyKDwAAGVbdHS0YmNjFRkZWeRjFduD2sPDwzV58mQdPXpUixcv1sCBA4vr0AAAABVavar+mj64pVY81U1D2taW+8XC40JOvt5ZdUidXl6h/yzep3PpOQYnBQDAeMVWdFxiMpnUp08fffHFF8V9aAAAgAqtbhU/TRnUXCuf7q5h7evIw+woPLLyrHp/zWF1nrJC//52r86kZRucFAAA4xR70QEAAICSVbuyr14a0ExrJvTQyI7h8nR3/JUuJ9+mWeuPqssrKzVx0R4lpGQZnBQAgNJH0QEAAFBGhQb5aFK/Jlo3oYdGd75B3h6Ov9rl5ts0Z+MxdX91pf7+1W6dSMo0OCkAAKWHogMAAKCMqxborX/eZtG6Z3tqTLcI+XqaJUl5Vrs+23xc3aeu0jPzd+ro+QyDkwIAUPIoOgAAAMqJEH8v/b1vY617tqce7VFfAV7ukiSrza75W+PVc9oqPTlvhw6evWBwUgAASg5FBwAAQDlT2c9TT/duqHXP9tS4Xg0U6O0oPGx2aeH2BN382mo9Onebfj2dbnBSAACKH0UHAABAORXk66FxvSK17rmeeqZ3QwX7ekiS7Hbpu12n1Pv1NRo7Z6v2nkw1OCkAAMWHogMAAKCcC/T2UHSP+lr/bE/9vW8jVfHzdG5bsve0bn1znUZ//It2nkgxLiQAAMWEogMAAKCC8PNy15hu9bTu2Z76562NVTXAy7ntp31n1T9mvUZ8tFlbjyUbmBIAgKKh6AAAAKhgfDzNGt0lQmsn9NC/+zVRaJC3c9vquHO6892fNWzmRm06nGhgSgAACsfd6AAAAAAwhreHWSM6huvuG2trwdZ4vbPykBJSsiRJ6w8mav3BRLW/obIev6mBOtarIpPJZHBiAAD+mktNdMyfP1/9+/dXrVq15Ofnp5YtW+qjjz6S3W4vsN+HH36oyMhIeXt7q0WLFvruu+8MSgwAAFD2ebmbNax9Xa16prteubO56lbxdW7bdCRJw2Zu0qAZG7Tq17O/+3sZAACuxqWKjunTp8vX11fTpk3Tt99+q759++rBBx/U5MmTnft8/vnnevDBBzVkyBD98MMPioqK0oABA7Rx40YDkwMAAJR9HmY3DW5XW8vHd9P0wS0UUdXPuW3rsWSNnPWL7ohZr+X7zlB4AABclktduvLtt98qJCTE+bpnz55KTEzU9OnTNXHiRLm5uelf//qX7r77br3wwguSpB49emjXrl2aPHmyFi9ebFR0AACAcsPd7KaBrWupf8swfb/7lN5afkAHzl6QJO2MT9Woj7eoSc1APdazgW6xVJebG5e0AABch0tNdFxZclzSqlUrpaWlKSMjQ4cPH1ZcXJwGDx5cYJ+7775by5cvV05OTmlFBQAAKPfMbib1a1FTS8d11TvDWqtRjQDntr0n0zT2k63q+8ZafbfrpKw2JjwAAK7BpYqOP7Ju3TqFhYUpICBA+/fvlyQ1atSowD6NGzdWbm6ujhw5YkREAACAcs3NzaS/NQvV4se76P1726hZWJBz269n0vXo3O265bXVWrQ9QflWm4FJAQBwsUtXfmvdunX6/PPPNW3aNElScrLjme7BwcEF9qtUqZIkKSkp6U+Pt2/fPoWGhjpfnzp1qhjTAgAAlG9ubibd0qSGbrZU16pfz+mN5Qe040SKJOnQuQyNm7dDbyw/oEe619MdrcLkYXb5f1MDABjsyp/RJen8+fNFPqbJ7qJ3koqPj1f79u3VuHFjLVu2TG5ubvr00081fPhwnTp1SjVq1HDuu2XLFrVr107r169Xx44df3es5s2ba/fu3TKbzQoMDHSuz507t1S+lqLIycmRl5eX0TFQTnF+oaRxjqGkcY4Zy263a1+ytPiYTQdTC24L8ZZ61zEpqoZJ7mX0Hh6cXyhpnGMoaWXhHBs6dGiB12lpabJarWrWrJl27dpVqGO65ERHSkqK+vbtqypVqujLL7+Um5vjXwMuTW6kpqYWKDouTXpUrlz5T49rsVgK/QdllCVLlqhPnz5Gx0A5xfmFksY5hpLGOWa8vpKetNu18XCS3lx+QBsOJ0qSzmdLn8bZtfKMlx7uXk93ta0tbw+zsWGvE+cXShrnGEpaWTjHfntlxqVBhaJwuXnCrKws3XbbbUpNTdUPP/ygoKDL14BeujfHpXt1XLJ//355enoqIiKiVLMCAABAMplMiqpXRZ891EHzx0apS4PLN5g/mZqtiV/vVbdXV+qjdUeUnWc1MCkAoCJwqaIjPz9fgwcP1r59+7RkyRKFhYUV2B4REaHIyEjNnz+/wPq8efN00003ydPTszTjAgAA4DfahVfWnFHttfCRjurZqJpz/UxajiZ/F6vOU1bqgzWHlZmbb2BKAEB55lKXrjzyyCP67rvvNG3aNKWlpWnjxo3Oba1atZKXl5cmTZqkYcOGqV69eurRo4fmzZunTZs2ac2aNQYmBwAAwJVa1amkj0a20+74VL214oCWxZ6RJJ2/kKOXFu/Tu6sPaXSXG3RfVLj8vVzqr6QAgDLOpf5fZdmyZZKkp5566nfbjhw5ovDwcN1zzz3KzMzUyy+/rJdfflkNGzbUwoULFRUVVdpxAQAA8Bea1QrS+/e11b5TaXp7xUEt3nNKdruUlJGrV5b8qvdWH9aozjdoRMdwBfl4GB0XAFAOuFTRcfTo0Wvab9SoURo1alTJhgEAAECxaRwaqJhhrXXgTLreXnlQ3+48KZtdSs3K0/Qf4/TB2sO6v2O4Huh8g4J9uRwZAFB4LnWPDgAAAJRvDaoH6I27W+nH8d00sHWYzBcfPZuena83VxxUp5dXaMqS/Uq8kGNwUgBAWUXRAQAAgFJXr6q/pg9uqRVPddOQtrXlfrHwyMi16t1Vh9R5ykq99H2szqZnG5wUAFDWUHQAAADAMHWr+GnKoOZa9Ux3DWtfR55mx19Ps/Ks+mDtEXWZslL//navTqdSeAAArg1FBwAAAAxXq5KvXhrQTKsndNfIjuHycnf8NTUn36ZZ64+q6ysrNXHRHiWkZBmcFADg6ig6AAAA4DJCg3w0qV8TrZ3QQ6M73yBvD8dfV3OtNs3ZeEzdX12pv3+1SyeSMg1OCgBwVRQdAAAAcDnVAr31z9ssWvdsT43tVk++nmZJUp7Vrs82n1D3qav09PydOnI+w+CkAABXQ9EBAAAAlxXi76Xn+jbS+md76rGe9RXg5S5JstrsWrA1XjdNW6Vxn2/XwbPpBicFALgKig4AAAC4vEp+nnrqloZa92xPjevVQIHejsLDZpcW7Tipm19bo+i527T/dJrBSQEARqPoAAAAQJkR5Ouhcb0itf65nnqmd0NV8vWQJNnt0ve7TqnP62s1Zs4W7UlINTgpAMAoFB0AAAAocwK8PRTdo77WPdtTf+/bSCH+ns5tS/ee0W1vrdPoj3/RzhMpxoUEABiCogMAAABllp+Xu8Z0q6e1E3pq4m0WVQvwcm77ad9Z9Y9ZrxEfbdbWY0kGpgQAlCaKDgAAAJR5Pp5mjep8g9ZM6KHJ/ZsoNMjbuW113Dnd+e4GDZu5URsPJxqYEgBQGtyNDgAAAAAUF28Ps+6LCteQdrX15dYExaw8qISULEnS+oOJWn8wUTfeUFlP3NRAHetVkclkMjgxAKC4MdEBAACAcsfL3ayh7eto1TPd9cqg5qpbxde5bfORJA2buUl3vvuzVv16Vna73cCkAIDiVqGKjri4OFksFsXExBgdBQAAAKXAw+ymwW1ra/n4bnptSAtFVPVzbtt2PEUjZ/2i/jHr9VPsGWfhYbVKq1ZJ2dmOz1arMdkBoCKJiYmRxWJRXFxckY9VoYqOyMhIxcbGKjo62ugoAAAAKEXuZjcNaFVLPz7ZTW/e00oNqvk7t+2KT9Xo/23RrW+u06SZpxQeblePHlJKitSjhxQeLn31lWHRAaBCiI6OVmxsrCIjI4t8rApVdAAAAKBiM7uZ1K9FTS0d11XvDmutxqGBzm2xp9I0++A2WW9ZK99GJ2W7OOGRkCANGkTZAQBlBUUHAAAAKhw3N5P6NgvV4sc764P72qppzSDnNs+q6araf7veOWKTnyVBdjkKj3HjuIwFAMoCig4AAABUWCaTSTdbquupZp10Zn475SQEO7edz5VCbt+hmqNWy7dxvE7E27R2rXFZAQDXhsfLAgAAoMI7fdqk7MPVdPpwVXnXTVRQpzh5106WJHlUyVDI7TsV1PGgvt9bX5271JS7mX8vBABXxXdoAAAAVHihoZd+ZVL2sRCdmRulEbXdlH28snMfjyoZmn9ip25+bY2+3BqvfKvNkKwAgD9H0QEAAIAKr0sXqVYtyWS6tGLSDX4mnfksSqfndihQeBw5n6Gn5u9Ur+mrtYDCAwBcDkUHAAAAKjyzWXrjDcevL5cdDrnxVXT28yg90qiDOkRcLjyOJmbq6fk7ddP01Zq/5QSFBwC4CIoOAAAAQNLAgdKCBVJYWMH1WrUc6xNGVtHnD0Xp84c6KCqiinP7scRMPbNgl26avlpfbDmhPAoPADAURQcAAABw0cCB0tGj0sqVUnCw4/ORI471SzpEVNFnD3XQvIc6qGO9goXHhAW7dNM0Cg8AMBJFBwAAAHAFs1nq3l3y9nZ8Npv/eL/2EVU098EO+mJMlDrVv1x4HE9yFB49p63SF79QeABAaaPoAAAAAIrgxhsq69PRHTR/bJQ61w9xrp9IytKELx2Fx7xfjlN4AEApoegAAAAAikG78Mr6ZHR7LRgbpS4NChYez365Wz2mrtLnm48rN5/CAwBKEkUHAAAAUIzahlfWnFHt9eXDBQuP+OQsPfeVo/D4jMIDAEoMRQcAAABQAtrUvVR4dFTXyKrO9YSULP39YuExdxOFBwAUN4oOAAAAoAS1qVtJ/3vgRn31SEd1+03h8Y+FjsLj003HKDwAoJhQdAAAAACloHWdSvr4gRu18JGO6t6wYOHxfwv3qPurK/XJxmPKybcamBIAyj6KDgAAAKAUtapTSbPvv1GLojupxxWFx8nUbP1z0R51f3WV5lB4AEChUXQAAAAABmhZO1izLhYePRtVc66fSs3WxEuFx4ajFB4AcJ0qVNERFxcni8WimJgYo6MAAAAAkhyFx0cj2+nr6E666beFx9d71e2VVfrfhqPKzqPwAFB+xcTEyGKxKC4ursjHqlBFR2RkpGJjYxUdHW10FAAAAKCAFrWD9eHIdvrm0U7q1fhy4XE6LVvPf71X3V5dqY9/pvAAUD5FR0crNjZWkZGRRT5WhSo6AAAAAFfXvFawZo5op28f7axejas718+k5ehf3zgKj9nrj1B4AMBVUHQAAAAALqhZrSDNHNFW3z3WWTdbChYek76NVddXVmoWhQcA/A5FBwAAAODCmoYF6YP7HIXHLVcUHmfTc/Tvb2PV5ZWV+mgdhQcAXELRAQAAAJQBTcOC9P59bfX9453Vu8nlwuNceo4mf+coPD6k8AAAig4AAACgLGlSM0jv3dtWix/voj5NajjXz6Xn6IXvYtV5ykrNXHtYWbkUHgAqJooOAAAAoAyy1AzUjHvb6Icnuqhv08uFx/kLOXrx+33q8gqFB4CKiaIDAAAAKMMahwbq3eGOwuNvzf6o8FihD9YcVmZuvoEpAaD0UHQAAAAA5UDj0EC9M6yNlozrolubhTrXz1/I1UuL96nLlJV6f80hCg8A5R5FBwAAAFCONKoRqJhhrbV0XFfd2jxUJpNjPTEjV/9ZvF9dpqzUe6spPACUXxQdAAAAQDnUsEaAYoY6Co/bflN4/PeH/eo8ZaVmrD6kjBwKDwDlC0UHAAAAUI5FVg/Q20Nba9m4rrq9RU1n4ZGUkauXf9ivLq+s1LurKDwAlB8UHQAAAEAF0KB6gN66p5WWjeuqfr8pPKYs2a/OU1bonVUHdYHCA0AZR9EBAAAAVCANqgfozXta6ccnu6p/y8uFR3Jmnl5Z8qs6T1mhmJUUHgDKLooOAAAAoAKqXy1Ab9zdSj8+2U13tKwpt4uFR0pmnl5dernwSM/OMzYoAFwnig4AAACgAqtfzV+v391KP47vpgGtwn5XeHR5ZaXeXnGAwgNAmUHRAQAAAED1qvrrtSEt9dP4bhr4m8Jj6rI4dZ6yUm8tp/AA4PooOgAAAAA4RVT11/RLhUfry4VHalaepv3oKDzeXH5AaRQeAFwURQcAAACA34mo6q/pg1tq+VPddWfrWjJfbDxSs/I0/cc4dX55hd74icIDgOupUEVHXFycLBaLYmJijI4CAAAAlAk3hPhp2uAWWj6+mwa1uVx4pGXn67WfHIXH6z/FKTWLwgNA4cXExMhisSguLq7Ix6pQRUdkZKRiY2MVHR1tdBQAAACgTAkP8dPUu1poxVPddNdvCo/XfzqgzlNW6LUfKTwAFE50dLRiY2MVGRlZ5GNVqKIDAAAAQNHUreKnVy8WHoPbXi480rPz9cZyR+Ex/cc4pWZSeAAwBkUHAAAAgOtWt4qfXhnUQiuf6q4hbWvL/YrC481LhceyXyk8AJQ6ig4AAAAAhVaniq+mDGqulU93193trig8cvL15oqD6jxlhaYt+1UpmbkGJwVQUVB0AAAAACiy2pV99fKdjsLjnhsLFh5vrTiozlNWaupSCg8AJY+iAwAAAECxqV3ZV/8deKnwqOMsPC7k5OvtlY7C49Wl+5WcQeEBoGRQdAAAAAAodo7Co5lWPdNdQ9vXkYf5cuERs/KQOk9ZoVeW7FcShQeAYkbRAQAAAKDE1Krkq/8MaKZVz/TQsCsKj4xcq95ZdUhdpqzQFAoPAMWIogMAAABAiQsL9tFLFwuP4R0KFh7vrnJMeLz8w34lXsgxOCmAso6iAwAAAECpCQv20Yt3NNPqZ3ro3g515Wl2/EiSmWvVjNWH1OWVlfrvD/soPAAUGkUHAAAAgFJXM9hHL9zRVKsndNd9UQULj/dWH1bnKSv138X7dJ7CA8B1ougAAAAAYJjQIB9N7u8oPEZcUXhk5Vn13prD6jJlpf5D4QHgOlB0AAAAADBcaJCP/n1l4eF+ufB4/2Lh8dL3sTqXTuEB4M9RdAAAAABwGZcKjzXP9NDIjuEFCo8P1h5Rl1dW6MXvYnU2PdvgpABcFUUHAAAAAJdTI8hbk/o10doJBQuP7DybZq47oq6vrNQLFB4A/gBFBwAAAACXVT3wcuFxf6dweV1ReHy47oi6TFmpyd/G6mwahQcAB4oOAAAAAC6veqC3/nW7o/B4oNMNzsIjJ9+mj9YfUZdXVurf3+6l8ABA0QEAAACg7KgW6K3nb7do7bM9NKrzDfL2uFx4zFp/VF1eWalJ3+zVGQoPoMKi6AAAAABQ5lQL8NbE2yxaM6GHRv+m8Jj98+XC43QqhQdQ0VSooiMuLk4Wi0UxMTFGRwEAAABQDKoFeOuft1m0dkJPPdjlcuGRe7Hw6PrqSv3r6z0UHoCLi4mJkcViUVxcXJGPVaGKjsjISMXGxio6OtroKAAAAACKUdUAL/3frY7C46GuEfLxMEtyFB4fbzimrq9QeACuLDo6WrGxsYqMjCzysSpU0QEAAACgfKsa4KV//K2x1j7bQ2OuLDyslwuP57/eo1OpWQYnBVBSKDoAAAAAlDsh/l76+98aa92zPTSmW4R8PS8XHv/bcEzdXlmliYv26GQKhQdQ3lB0AAAAACi3qvh76e99G2vthB4a261egcJjzsZj6v7qKv1z0W4KD6AcoegAAAAAUO5V8ffSc30bad2zPfVw93ryu6Lw+GTjcXV7daX+b+FuJVB4AGUeRQcAAACACqOyn6ee7dNIa5/tqUeuKDzyrHZ9uum4ur+6Uv9YuFvxyZkGJwVQWBQdAAAAACqcyn6emtDHMeER3aNg4TF303H1mLpKf/+KwgMoiyg6AAAAAFRYlfw89UxvR+HxaI/68vdyl+QoPD7bfFzdX12lv3+1SyeSKDyAsoKiAwAAAECFV8nPU0/3bqh1z/bQYz0vFx75Nrs+23xCPaau0nNfUngAZQFFBwAAAABcFOzrqaducRQej/esr4ArCo/Pf3EUHs8u2KXjiRQegKui6AAAAACA3wj29dT4Wxpq3bM99fhNDQoUHvO2nFCPaas0YcFOCg/ABVF0AAAAAMBVBPl6aPzNkVr3bE89cVMDBXg7Cg+rza4vtsSrx7RVemb+Th1LzDA4KYBLKDoAAAAA4C8E+XroyYuFx7heBQuP+Vvj1XPaaj09f6eOnqfwAIxG0QEAAAAA1yjIx0PjejkKjyd7RSrwisJjwdZ43TR9tZ76gsIDMJJLFR0HDx7U2LFj1bJlS7m7u6tp06a/26d79+4ymUy/+9i/f78BiQEAAABUREE+HnqiVwOte66nxt9csPD4clu8ek5bpfFf7NARCg+g1LkbHeBKe/fu1ffff6/27dvLZrPJZrP94X6dOnXS1KlTC6yFh4eXQkIAAAAAuCzQ20OP39RAIzuF6+P1RzVz3RGlZuXJZpe+2pagRdsTdEfLMD3as74iqvpLkqxWae1aKTtbWrVK6tJFMpuN/TqA8sSlio7bb79d/fv3lySNHDlSW7Zs+cP9goOD1aFDh9KMBgAAAABXFejtoccuFR4/OwqPlMyLhcf2BC3akaD+LcPUML++XnrOX/Hx0qxZ0oABUq1a0htvSAMHGv1VAOWDS1264ubmUnEAAAAA4LoEeHvo0Z4NtHZCDz3Tu6GCfT0kSTa7tHB7gv67c7WyWm6Xe+ULzvckJEiDBklffWVUaqB8KZPNwurVq+Xn5ydvb29169ZNa9asMToSAAAAADgFeHsoukd9rXu2p57p3VCVLhYeJjfJv8lJ1Ry9Wp/HWxXQ5og8qqdIbjaNG+e4rAVA0bjUpSvXolu3brrvvvvUoEEDnTx5UlOnTlWvXr20evVqRUVF/el79+3bp9DQUOfrU6dOlXRcAAAAABWYv5e7onvUV4Q1XHf/86gCbzwss2+eTCZp/wWpcq9YSZItz025p4P12Mxg9e9cSa3rVFLVAC+D0wMl78qf0SXp/PnzRT6myW6324t8lBJw6R4de/bs+dP9MjIy1KRJE1ksFi1evPgP92nevLl2794ts9mswMBA5/rcuXOLNXNJyMnJkZcX3+BQMji/UNI4x1DSOMdQkji/UJyys6WUFCnHZtcvyXb9nGRX5l9Mb4R4SxGBJkUEOT6H+UlmN1Op5EX5UBa+jw0dOrTA67S0NFmtVjVr1ky7du0q1DHL3ETHb/n5+enWW2/VggUL/nJfi8VS6D8ooyxZskR9+vQxOgbKKc4vlDTOMZQ0zjGUJM4vFKdVqxw3HnUy2fTSO0v13/cbyyssWV5hyfIIzirwnvPZ0vlsuzaflSS7fDzMal4rSK3rVlKbOpXUqk6wqvi79g+xMFZZ+D6WlJRU4PWlQYWiKPNFBwAAAAC4ui5dHE9XSUiQ7HZJdjfV9DbpwvZwXdgeLpNJqlU/R+99mawd8cnafixFO+NTlJNvcx4jK8+qTUeStOnI5R8Mw6v4qnWdSmpVt5Ja1wlWw+oBcjeXyVsxAsWmzBcdGRkZ+u6779SuXTujowAAAADAHzKbHY+QHTRIMpkulh0XmS5ejfL6y17q26yG+jarIUnKzbdp36k0bTuerG3HU7TtWLISUgpOfRxNzNTRxEx9tT1BkuTnaVaL2sFqXaeSWtcNVqvalVTJz7NUvkbAVbhU0ZGZmem8z8axY8eUlpbmvCSlW7du2r9/v1599VUNGDBA4eHhOnnypKZNm6bTp09r/vz5RkYHAAAAgD81cKC0YIH0xBNSfPzl9Vq1pNdfd2y/kqe7m1rUDlaL2sG6v5Nj7WxatrP42HosWbsTUpV7xdRHRq5VPx9K1M+HEp1rESF+alWnktrUdZQfDaoFcK8PlGsuVXScPXtWd911V4G1S69XrlypWrVqKTc3V//4xz+UmJgoPz8/dezYUTNmzNCNN95oRGQAAAAAuGYDB0r9+0tr1zpuTrpypeOyFrP52t5fLdBbfZqGqk9Tx5MqcvNt2nsy1THxcTxZ248l62RqdoH3HD6focPnM/TlNke74u/lrpa1g9W6TrDjkpfalRR08fG3QHngUkVHeHi4/uohMEuWLCmlNAAAAABQ/MxmqXt3ackSx+ei8HR3U6s6ldSqTiWN0g2SpFOpWdp2LOXi5Eey9iakKdd6eerjQk6+1h08r3UHLz/Gs341f7Wuc+mSl0qqX9Vfbkx9oIxyqaIDAAAAAFA0oUE+urW5j25t7pj6yM6zau/JNG07luwsP86k5RR4z8GzF3Tw7AV9scUx9RHg7Zj6aFO3klrXqaSWdYIV6M3UB8oGig4AAAAAKMe8PcxqU9dxjw5JstvtOpmafUXxkaK9CanKt12erk/PztfaA+e19oBj6sNkkhpU83dMfFy80WlECFMfcE0UHQAAAABQgZhMJoUF+ygs2Ee3t6gpyTH1sTshtUD5cS798tSH3S7FnbmguDMX9PkvJyRJQT4eanXpcpc6ldSidpACmPqAC6DoAAAAAIAKztvDrHbhldUuvLIkx9RHfHKWo/Q45ig+Yk+lyXrF1EdqVp5W/XpOq349J8kx9dGweoBaX7zcpXWdYN0Q4ieTiakPlC6KDgAAAABAASaTSbUr+6p2ZV/1bxkmScrKtWpXfIrzCS/bjiUrMSPX+R67Xdp/Ol37T6dr7qbjkqRKvh5qdbH0cEx9BMvPix9DUbI4wwAAAAAAf8nH06z2EVXUPqKKJMfUx/GkzIulh6P82H86vcDUR3JmnlbsP6sV+89KktxMUqMagWpd9/IlL3Wr+DL1gWJF0QEAAAAAuG4mk0l1q/ipbhU/DWhVS5KUkZOvXfGpV1zykqzkzDzne2x2KfZUmmJPpemTjY6pjyp+no6pj4vlR/NaQfL15EdVFB5nDwAAAACgWPh5uSuqXhVF1bs89XE0MdNZemw9lqy4M+m6YuhDiRm5+mnfGf2074wkyexmUuPQgMtPeKlTSbUr+zD1gWtG0QEAAAAAKBEmk0k3hPjphhA/3dnGMfVxISdfO0+kFHjCS2rW5akPq82uPQlp2pOQpv9tOCZJCvH3ctzn4+KNTpvXCpK3h9mQrwmuj6IDAAAAAFBq/L3c1al+iDrVD5Ek2Wx2HT6foW3Hk7X94v0+4s6my37F1Mf5CzlaFntGy2IdUx/ubiY1qRl48ZIXx81Ow4KZ+oADRQcAAAAAwDBubibVr+av+tX8NbhtbUlSWnbexamPFG29WICkZ+c735Nvs2tnfKp2xqdq9s9HJUnVArwcl7pcvNdH0zCmPioqig4AAAAAgEsJ9PZQlwZV1aVBVUmOqY9D5y4UeMLLgbMXCrznbHqOluw9rSV7T0uSPMwmNakZVKD8qBnsU+pfC0ofRQcAAAAAwKW5uZnUoHqAGlQP0JB2dSRJqZl52n7CcY+P7ceTteN4itJzLk995Fnt2nEiRTtOpOij9Y61GoHealO3klpdvN9Hk5qB8nJn6qO8qVBFR1xcnCwWi6KjoxUdHW10HAAAAABAIQX5eqh7w2rq3rCaJMdNTA+eveB8usu248k6fC6jwHtOp2Xr+92n9P3uU5IkT3c3Na0ZeHHqo5La1K2k6oHepf61QIqJiVFMTIwOHz5c5GNVqKIjMjJSu3btMjoGAAAAAKCYmd1MalgjQA1rBOieGx1THymZudp+POXi010cUx8ZuVbne3Lzbdp2PEXbjqdI645IksKCfRwTHxfLD0tooDzd3Yz4kiqUSwMJzZs31+7du4t0rApVdAAAAAAAKo5gX0/1aFRNPRpdnvr49XS6s/jYfjxFR84XnPpISMlSQkqWvtvlmPrwcndT81qOe320uni/j2oBTH24MooOAAAAAECFYHYzyVIzUJaagRreoa4kKfFCToGpj50nUpWVd3nqIyffpl+OJuuXo8nOtVqVfBwTH3WC1aZuZTUKDZCHmakPV0HRAQAAAACosKr4e6mXpbp6WapLkvKtNu0/na7tznt9pOh4UmaB98QnZyk+OUvf7DwpSfL2cFPzWsHO8qN13UoK8fcq9a8FDhQdAAAAAABc5G52U9OwIDUNC9K9UeGSpHPpOdp+PPni/TyStSs+Rdl5Nud7svNs2nwkSZuPJDnX6lbxdRYfrepUUqMaAXJn6qNUUHQAAAAAAPAnqgZ46ZYmNXRLkxqSpDyrTftOpWnbscvlR3xyVoH3HEvM1LHETC3cniBJ8vU0O+/14XjEbSVV9vMs9a+lIqDoAAAAAADgOniYHZeqNK8VrJGdHGtn07Kdpce2Y8nalZCq3PzLUx+ZuVZtPJykjYcvT33cEOJ3+QkvdSqpYY0Amd1Mpf3llDsUHQAAAAAAFFG1QG/1aVpDfZo6pj5y822KdU59OMqPk6nZBd5z5HyGjpzP0FfbHFMffp5mtbyi+GhVJ1jBvkx9XC+KDgAAAAAAipmnu5ta1g5Wy9rBekA3SJJOp2Y7S49tx5O1JyFNudbLUx8ZuVatP5io9QcTnWsRVf2cl7u0rlNJDar5y+0vpj6sVmntWik7W1q1SurSRTKbS+TLdEkUHQAAAAAAlIIaQd76W7NQ/a1ZqCQpJ9+qvSevnPpI0em0glMfh89l6PC5DC3YGi9JCvByV8uLNzi9dKPTIB8P5/5ffSU98YQUHy/NmiUNGCDVqiW98YY0cGDpfa1GougAAAAAAMAAXu5m52Uql5xMyXKWHluPJyv2ZKryrHbn9vScfK09cF5rD5x3rjWo5q/WdSrJnhis1/9ZSXnn/SVdnvpISJAGDZIWLKgYZQdFBwAAAAAALqJmsI9qBvvotuY1JUnZeVbtSUh1lh/bjifrbHpOgfccOHtBB85ekHRCNUdJ1mx35Z6spFXnbTIHZMma7iOTSRo3Turfv/xfxkLRAQAAAACAi/L2MKtteGW1Da8sSbLb7UpIydLWY8nafvEpL7En05Rvuzz1YfbOl0/EOa06L7l55cuaLtnt0on/b+/O46Kq/v+Bv4ZFlgFUFnFB2RTN/Jhm4orgAi5o7iiYYpaGaYLhWrK4Zy5YmuYWlLGYitbPNSlQ3DFbUFQwBbWkREUEUbbz+4Pv3A/jzLC4fGTG1/PxmEfNuWfOPffy9t47Z85yvXzuDg+PF3Qw/yNs6CAiIiIiIiLSEjKZDHb1TWFX3xSD2zUBABQWlSLi63tYtvkujJqUv/TlRTDSw/8NY/mvmzdfRK3/t9jQQURERERERKTFTOroo2sLS+Sdtvy/FAGDuoWYs/gwFkF5hZZGjf739ftf03vRFSAiIiIiIiKip+PmVr66ikwGADKU3DOFs/y/jRwyGdC0aXk+XceGDiIiIiIiIiItp69fvoQsoGjs+C/F+9WrdX8iUoANHUREREREREQ6Ydiw8iVkmzRRTreze3mWlgU4RwcRERERERGRzhg2rHwJ2eRkIDcXSEwsH67yMvTkUHipenSkp6ejdevW+OKLL150VYiIiIiIiIieC3398iVkjY3L/6sNjRxffPEFWrdujfT09Kcu66Vq6HBxcUFaWhqmTJnyoqtCRERERERERP9nypQpSEtLg4uLy1OX9VI1dBARERERERGRbmNDBxERERERERHpDDZ0EBEREREREZHOYEMHEREREREREekMNnQQERERERERkc5gQwcRERERERER6Qw2dBARERERERGRzmBDBxERERERERHpDDZ0EBEREREREZHOYEMHEREREREREekMgxddAdKsUaNGePToEe7cufOiq0I6iPFFzxtjjJ43xhg9T4wvet4YY/S8vcwxxh4dRERERERERKQz2NBBRERERERERDqDDR1EREREREREpDNeijk6srKyAAAZGRlo27btC65N9eXk5EAIoVV1Ju3B+KLnjTFGzxtjjJ4nxhc9b4wxet60NcYyMjIA/Pd7/JOQCSHEs6pQbWViYoKHDx++6GoQERERERERUTUYGxujsLDwiT77UvTosLS0xJ07d1CnTh3Y29u/6OoQERERERERkRpZWVkoKiqCpaXlE5fxUvToICIiIiIiIqKXAycjJSIiIiIiIiKdofMNHeHh4ZDJZGpfn3zyicZtFV+KcszMzDTuQ9O2x/3www/o1KkTzM3N0ahRI/j4+ODKlStKeRwcHCCTyTBnzhyVz2dkZEj1SkpKUtl+69YtGBoawtzcXO14pqioKKVjq1u3Ljp06ICtW7cq5SstLcWnn36KHj16wNraGpaWlujZsyeSk5MrPb6goCDIZDJMnTq1GmdDu9SmWLp8+TICAgLQrl07GBgYoE2bNmrzeXh4qK3HxYsXpTxJSUlq0xU+/vhjyGQyODg4qN1HREQEZDIZ3nnnnSrroKenBzs7OwwbNgxpaWlK+S5duoSpU6eidevWMDU1haOjIyZPnoycnByN5yE/Px92dnaQyWQ4c+aMxnzaojbF2Pbt2zF48GDY2dlBLpejXbt2+Oqrr/B4J8CioiLMnj0bjRs3homJCVxdXfHTTz8p5cnMzJTqd+DAAZV9bdq0San+j9u1axdkMhl69+6tdvv48eOVzoGtrS28vLxw4sQJpXw3b97ErFmz0K5dO5ibm8POzg5+fn6VTnRVVlaGDh06QCaTYceOHRrzaRttjDUAyM3NxbRp09C4cWMYGxvD2dkZK1euVFtuTa9NzZo1U4mHx8+TlZUVunfvjn379imVlZeXh/DwcLi6uqJevXqwtbXFoEGDkJqaWumxDxkyBDKZDCtWrKjqNGklbYyzBw8eYO7cuXBycoKpqSlcXFywZMkSlJSUSHkU982K9x0HBweNzz7t2rXD+PHjVc5Ljx49VPIGBQUp3W95b9RM2+Kr4r3w8ZexsbFSeSdPnkT//v3RsGFDmJiYwMHBASNGjMCpU6eqffwKiu8VMpkMBgYGcHJyUomhDRs2wMvLCw0bNoSFhQU6d+6M77//vtJjXr16NWQyGQYOHFjl+dE2uhpbFZ/5ZTIZ5HI5nJycMHr0aBw6dKjG56Gi/Px8zJ8/H23atIGpqSnkcjlcXV2xatUqaa7MlJQUTJgwAc2bN4epqSlatGiBuXPnoqCgQOPx37hxA2ZmZpDJZJVe99R5KeboMDExwc8//6yS3qhRI3h4eEjv9+7di0WLFuHAgQOoW7fuM69HUlIShg4dinHjxmHx4sW4ffs2QkND4eXlhdTUVJiYmEh5zczMsG3bNpUgio2NhZmZGfLz89XuY9u2bSgpKUF+fj5++OEHjBo1Sm0+xTHm5OTg888/x7hx42BoaIjRo0cDAAoLC7F06VKMHz8es2fPhr6+PjZu3IiePXvixx9/RK9evVTKTE1NxVdffQULC4snPUW1Xm2JpfPnz2Pv3r3o1KkTysrKUFZWpjFvt27dVB6i1TVamJmZIS4uDuHh4UrpcXFxlV6Io6OjAQDx8fFYt24djIyMNNahtLQUaWlpmDdvHvr06YPz58+jfv36AIBDhw4hOTkZ7733Hl577TVkZWUhNDQUSUlJ+O2339SWu3DhQqWHT11QW2Js1apVcHBwwMqVK2FjY4NDhw5h4sSJuH79OsLCwqR8QUFB+Oabb7B48WK0bNkSkZGRGDBgAE6cOIHXX39dqUxFjPXr108pvarrmiLGkpKS8Pfff6Nx48YqeZycnBAdHQ0hBK5cuYKwsDD06dMHqampcHJyAgD88ssviI+Px4QJE9C5c2fk5ORg4cKFcHV1xblz52BjY6NS7oYNG/DXX3/V7ORpCW2LtYKCAnh4eMDAwAARERGwtbVFeno68vLy1JZb02tTamoq5s2bh9OnT+OPP/6AqakpAOXz9Pfff2PJkiUYNGgQkpOT0bVrVwDAtWvXsGHDBrzzzjtYtGgRHj58iBUrVqBz5844c+YMXnnlFZV979+/HydPnny6k6cFtC3Opk6dip07d2LJkiVo3bo1Tpw4gdDQUBQUFGDx4sXPtE7JyclISkpSOg+P472xctoUX40aNVJpgBdCoF+/fkrP1ceOHYOHhwf69euHL7/8EhYWFsjIyMDu3btx+vRpdOrUScqr6fibNWum9H7EiBEIDg5GcXExTp48ifDwcKSmpuLIkSPQ09PD4sWL0bdvX0yePBlmZmbYvn07hgwZgqioKPj7+6uUn52djfnz56NBgwZPdd5qM12MLYXIyEi0atUKDx8+xJUrVxAbGwsvLy+8//77+OKLL5TyVifGcnJy0LNnT1y/fh1BQUHo3r07AODEiRP45JNPoK+vj8DAQGzbtg0ZGRmYNWsWXFxccP78eYSGhuLUqVNq9wEAwcHBMDMzq7QxRCOh48LCwoRcLq9W3sjISAFA3Lp1q0blVHcf7733nnB0dBRlZWVS2s8//ywAiCNHjkhp9vb2YtSoUcLAwEAcP35cqYxWrVqJt956SwAQiYmJKvvo0qWLeOWVV0STJk3EoEGDqnWM+fn5on79+sLLy0tKKykpEXfu3FH6bElJiWjVqpUYOHCg2uPr0aOHCA0NFfb29mLKlCmVnwwtVJtiqbS0VPp/f39/8eqrr6rN5+7uLry9vSstKzExUQAQY8aMES4uLkrbTp48KfT19YWvr6+wt7dX+eylS5cEANGnTx8BQOzcubNadYiOjhYARExMjJSWk5Oj9G9DCCGOHTsmAIgdO3aolHvhwgUhl8vFl19+KQCIlJSUSo9TG9SmGFNX7sSJE4WFhYUUfzdu3BD6+vri888/l/KUlZWJ//znP+LNN9+U0q5evSrFmIWFhSgsLJS2/f3330JPT0+6rj3u3r17wtjYWIqxlStXquRR929AETtLliyR0u7evSuKi4uV8l2/fl3IZDKxYsUKtefA0tJSfPXVVwKA2L59u0oebaVtsSaEEPPmzRNOTk4iPz+/yjKf9Nr0zTffKP2t1R3DjRs3hEwmE5MmTZLS8vPzRUFBgVK++/fvC0tLSzF16lSVfT98+FA0b95ciq3ly5dXeUzaSNvirLS0VJiamoqwsDClfOPGjRNOTk7Se8V9s+J9p7Jnn9dee034+/ur1NnV1VX06tVLKW9gYKDS/Zb3Rs20Lb7UUcTSd999J6WNGTNGNG/eXJSUlKjkr1hWdeumLjYXLFigFB/q6u/p6SnatGmjtsyxY8eKcePGVes5Uxvpamypu3YpzJ07VwAQ3377bY3rOHLkSGFqaipSU1NVtt2+fVscO3ZMCCHEv//+q7Jd8Z3gzJkzKtt++uknYWlpKVasWKHxHFdG54eu1CbFxcUwNzdX6p6taPkTj3WdtLa2Rp8+fRAbGyul/frrr0hPT5d6XTzu6tWrOHHiBMaMGYPRo0fjwIEDuHPnTpX1ksvlaNGiBa5duyal6evrS7+0V0xr27Yt/v77b5UyoqOjcfXqVcyePbvK/dHT09N79v90fXx8cPnyZZw9e1ZKi4mJQe/evTW22MfExEAmk2Hjxo2wtbWVfkGtSvv27QFAKeasrKxUhi4o8qmLuQ8++AABAQFo2bJltfZJNWNtba2S1r59e+Tl5Umt6n/88QdKS0vh5eUl5ZHJZPDy8sLBgwdRVFSk9Pn+/ftDJpMpdfuPi4tD8+bN0aFDB7X1iI+Px8OHDxEeHo4OHTo8VYzVq1cPBgbKHRnt7OxgY2OjNsbmzp2Lnj17omfPntXaJz2Z6sQaAGzevBkTJkyAXC6vsswnvTa98cYbAMrvp5o0adIENjY2SrEll8ulHiAKZmZmaN68udrYWrFiBerXr680nIGer+rEmRACJSUlKr/K1q1bV+1QqqcVEhKCn3/+GcePH9eYh/dG7VDd69jjYmJiYGFhgUGDBklpd+/eRYMGDaCvr6+S/1k9/z1+rdNUf3UxdvToUezevVul1zk9H88ytiqzYMECNGrUSKVHR1WysrKwY8cOBAQEqB1Kb2lpKfV+VNdzVtP1rLi4GFOnTsX8+fNhZWVVozopvDQNHSUlJSqvZ1VOZcMGKho/fjzS0tKwbt063Lt3D1euXMFHH32E9u3bo1u3bir5fX19sX37dqn82NhYuLm5oUmTJmrLj4mJAQD4+fnBz88PxcXF2L59e5X1Kisrw40bN+Do6FhpvpKSEpw8eVKlC+79+/cxc+ZMLF++XOVBTxfVhliqicOHD0Mul8PY2Bju7u44cuSI2nyNGzeGu7u71LhWVlaG7777Dr6+vhrLjomJgZubGxwdHeHj44O9e/fi3r17VdZJMQa+qpg7evQoAKjE3I4dO5CamorQ0NAq96WNamuMHT16FE2aNIG5uTkASGMuH+86bWRkhEePHql8WTQyMsKwYcOUGnBjY2MrjbHo6Gg4ODiga9eu8PPzw9mzZ3Hp0qUq61rdGEtPT8e///6rEmOnT59GTEyMzs6doKAtsZaZmYns7GxYW1vjzTffhJGRESwtLTFx4kS1Q56e9NqkiFl1w6MU8vPzcefOnSpjKzc3F+fOnVOJrWvXrmHp0qX4/PPPNc5Lo2u0Jc709fUxfvx4rF27FikpKcjPz0dCQgK2bt36XOYeGzhwINq3b4/58+fXuN7Ay3dv1ERb4utxxcXF2LlzJ4YOHao0j0KHDh1w/PhxhISEqJ07rTr1rkp1rnVHjx5VibHS0lJMnToVH3/8MRo1alTlfrSdrsVWZQwMDNCrVy+cOXMGxcXFVdZfITk5WRom86THAgCtWrVSSv/ss8+gr6+PyZMnP1G5wEvS0FFQUABDQ0OVl+LEPm05CxcurNbn3dzcsGvXLsyZMwf16tWDs7Mz/vnnH+zfv19tq+2QIUOQm5uLxMRECCEQFxcHPz8/jeXHxsaic+fOcHR0xOuvv45WrVpp/BWrtLQUJSUlyM7ORnBwMG7fvo25c+dWWv9PP/0Uf/31F6ZPn66UHh4ejubNm2ucD0SX1JZYqi53d3d89tlnOHDgAL7++ms8ePAAffr0URnHp+Dr64tt27ZBCIHExETk5uZi2LBhavOmpKQgIyNDikk/Pz88evQIO3fuVMmr+JWsqKgIv/76K2bPno327dtj8ODBGuv+8OFDzJgxA+3bt1eahPLBgwf48MMPsWTJEp2cD6a2xtjRo0cRFxeHGTNmSGktWrQAUN4oUJFi3gF1Pcp8fX2xd+9e5Ofn488//0RKSorG61p2djYSExMxevRoyGQyjB49Gnp6ehqvayUlJSguLkZ6ejref/992Nvb4+2339Z4TEIIaWLLio0tZWVlmDJlCoKDgzVOwqsLtCnWsrOzAQAzZsxA/fr1sW/fPixZsgTbt2/HxIkTlT7/pNemX375BTNnzkS9evXQp08fpXyKB7tr167h3Xffhbm5OQIDAys9jlmzZkEmkyEgIEApffr06Rg2bBg6d+5c/ZOixbQpzgBg3bp16NWrF1xdXWFubg5PT09MnjwZH3744RPtpyrz5s3Djz/+qHId1eRlvTdqom3xVdH+/ftx584dlXvgzJkz4enpiUWLFuGVV16BlZUVxowZo3ZBgOoev+JaV1hYiKSkJCxevBhOTk4qc2kpxMTE4Pjx42r/fRQUFKh8F9BFuhhbVWnatCmKi4uVnt+qOg+KecwenxemOnJychAeHo7BgwdLz5RAee+OBQsWSI0dT+qlmYxU3a/Yj7ccPWk5GzdulHpTAOWNCBW7OOrr60Mmk+H48eMYO3YsJk6ciIEDB+L27dtYuHAhvL29kZycrDQZKQBYWFjA29sbsbGxMDIyQnZ2NkaMGKHUXVbh999/x/nz5/H5559Lab6+vggPD8e1a9dUgq9hw4ZK79evXw83NzeNx37o0CGEhYUhNDRUqYv5+fPn8cUXX7wUE6oBtSeWquvxX4kGDhyIV199FQsXLlRZNQAAhg8fjilTpuDYsWOIjY3FgAEDND4wxcTEwNDQECNHjgQAdO7cWZoQcsKECUp59+3bB0NDQ+l9gwYNkJKSonYSNYWAgABcvXoVx48fVzrmRYsWwdbWttIvsNqsNsbYjRs3MGrUKPTs2RPTpk2T0tu0aQM3NzfMnj0bTZs2hYuLCyIjI3H48GEAUBurvXr1grm5OXbv3o3MzEy8/vrrcHFxURuP27ZtQ2lpqXSjVvQ6iomJwYIFC5Tynj9/XinGTE1NkZycrLabpEJ4eDh++uknHDhwQGk4xObNm5Gdna125Stdok2xpvjly8XFBV9//TUAoHfv3jAwMMDEiROlh3bg6a5NLi4uiI+Ph62trZSmeMirWO/vv/++0qEBkZGR2LRpE6KiomBnZyel//jjj/jxxx+r1StJV2hTnAHAnDlzsHfvXmzevBktWrTAyZMnMX/+fNSvXx8zZ86sUZ2rY+jQoWjTpg0WLFiAPXv2VJn/Zb03aqJt8VVRdHQ0bG1tVVYUMzc3lxq/9u7di6NHj2L79u2IjY3Fxo0b8e6771ZZ78ePf926dVi3bp30vmPHjti4caPKdw+gfFhqQEAA3n77bQwZMkRK//fffxEaGopvvvkGderU0XhcukIXY6sqiv1X3G91z0NNeygWFxdL0zGsX79eaduMGTPg6empdiLVmngpGjr09PSksWjPo5zHb0y9e/eWHvQBIDExER4eHpg2bRp69eqltBRe586d0axZM2zduhWTJk1SKdvX11f6tapv376wtLRU29ARHR0NPT099O3bF7m5uQCAAQMGICwsDLGxsSpzZyQkJMDCwgLXr19HaGgoAgMD0bVrV7Rt21al7LNnz2L48OHw8/NT6Q4ZHByMkSNHwsHBQdpvWVkZioqKkJubCwsLi+cyn8SLUlti6UnJ5XJ4e3trXCbT0tISffv2RVRUFHbu3InNmzerzVdWVoa4uDh4eHhAT09P+tsPHjwYn332mcrKGN27d0dERASKi4tx7NgxfPzxx/D19UVycrLa+Jg3bx6io6OxZ88epfF+WVlZWLlyJXbt2iV1Q1d0W8/Pz0d+fn61l3qurWpbjOXm5qJ///6wsrLCzp07Vf5eX3/9NXx8fKTxl/b29ggNDUVYWJjabq36+vrw8fFBbGwsMjMzVb54VhQdHY2WLVuiadOmUoy9+eabmD59Ok6dOqU087yzszPi4uJQWlqK33//HbNmzYKPj4/S6hkVbdq0CQsWLMCWLVuUHgTy8/Px0UcfYfHixSgqKkJRUZG0qseDBw+Ql5enM7+WalOsKeaMeny+FMXf7vz583Bycnria5O+vj6aNGmidj4ixUNeWVkZMjIyMGfOHIwbNw7nzp1TG+P79+/HpEmTEBISorJawbRp0zBt2jSYmppKdQPKf6XPzc1FvXr1VMrTdtoUZ+fOncOKFSvwww8/SOPae/TogeLiYoSEhCAgIEBjV3EDAwOUlpaq3VZaWqrUWFaRTCaT7okV58hS52W+N2qiTfFVUX5+Pv7f//t/mDhxosZfrF1dXeHq6gqgfKiJu7s7Zs+erdTQUd3j9/HxwcyZM2FoaIimTZvC0tJSbb6srCz0798frq6u2LBhg9K20NBQtG3bFm5ubtL1S9HbLTc3F2ZmZirzYGkzXY4tTW7cuIE6deooxUdV50ExpcK1a9fg4uJSrf0IITBhwgScPn0aycnJSvfSEydOYMeOHTh16pQUZw8ePABQvpS7qalptadK0J1orEU2bNiA+/fvS+8Vv/qkpaWpdNW3s7ODtbU1/vzzT7VleXt7o6SkBJGRkdi6davaPIphLWVlZWp/YYqOjlZp6HjttddgbW2Njh07omPHjmjVqhVmz56N/fv3K+W7fPky+vfvj65du6r90nvx4kUcPHgQ3377rVL6pk2bsGnTJly4cKHGLZ/0X5pi6Xny9fXF2LFjYWZmBm9vb7V5fv75Z2RnZyM7O1tl0lqgfILJit1869atK10ku3TpAj09PQQHB2P79u0qQ57WrFmDJUuWICoqCn379lXadvXqVRQVFamtV8+ePdGpU6eXpnfRs1JZjBUWFmLgwIG4d+8eTpw4oXbZNEdHR6SkpCAzMxMPHjxAy5YtsWrVKjRq1Aj29vZq9+nr6yv1INM05O3y5ctISUkBALUxFh0drdTQYWxsLMVYp06dYG1tjeHDh2PNmjUq179du3Zh8uTJWLBggUpDS05ODm7fvo2AgACVIQf+/v6wtbWVhlFQzTxNrDk7O1faA0wxX8zTXJs0qfiQ5+rqipYtW6JTp05YsGCByq9QJ0+exIgRI+Dv76/S6wgALl26hCVLlmDJkiVK6SEhIQgJCUFhYWG1x1OTek8TZ2lpaQCAdu3aKaW3b98ejx49wo0bN9QuFQyUT7Kn6dpw8+bNSpfh9PHxQXh4OBYuXKjxusl7Y+3wtPdMhV27dqGwsLDaQwscHR0xcuRIrFq1Cv/8849Sr7PqsLGxqfJal5OTg759+6JBgwaIj49XaZy7ePEijhw5ovbaWr9+fezfv/+J52mgFxdbCiUlJfj555/RsWPHGjVY9ejRAzKZDAcPHlQZ9qnJjBkz8N1332Hfvn147bXXlLZdunQJxcXFaodVOTs7Y9SoUYiLi6vWftjQ8Rxo+jJqb2+v0lqflZWFnJwcjePAjY2N8dFHH+H06dMa5zNITk7G9evXER4eDnd3d6VtBw4cwLJly5Camor//Oc/aj/ftGlTTJ8+HYsXL8avv/4qzX578+ZNeHl5oVmzZtixY4faXyPi4uKkB0yF0aNHo0uXLggMDHyi8Vr0X8+6YaOgoAB79uxBx44dNeYZPHgwBg8ejE6dOml84I6JiYFcLsf333+v0locFBSE6OjoSsczf/DBB1i7di2WLl2q9EU3NjYWgYGBWLp0KcaNG6fyuXbt2iExMVEp7bfffsP06dPx5ZdfVnpcpJ6mGCspKYGPjw8uXLiA5ORkjZMgKyiuYYWFhdiyZYvSL06P69KlC/z8/NCgQQOlbv0VKVbNiI+PV/mV+5NPPsG2bdukX+LVGTZsGLp164aIiAgEBgZKsZyUlCT1lAsJCVH5XMOGDVViLDs7WxoK6OnpqfG4qHJPE2t16tSBl5cXfvrpJ6X0Q4cOAYD0QPS016bqeOONN+Dr64vIyEiEhYVJQ0HT0tLg7e2NXr164csvv1T72cdjCyj/IhoQEIBRo0a9FN3Bn7eniTNFI8PZs2fRtGlTKf2XX36BTCbT2AgBlM+JtWHDBty7d0/pS0hycjJu376NHj16aPysnp4ePv74Y/j7+6vttcl7Y+3xrO6ZMTExcHZ2VmqwV9DUkJGeng4jI6Pn0vMrPz8f/fv3R1FRERITE9X2XFy9erVSTzSg/LpqYmKCpUuXqu0VTtX3v4ityoSGhuLmzZtYtWpVjT7XrFkzjBgxAuvXr8fbb7+N1q1bK23Pzc3FhQsX0KVLFwDlz3ARERGIjo5WO7SmX79+KtczxffZ3bt3K83lUZWXoqGjrKxMbUt2gwYNpDG9/wsBAQEICgpCYGAgBg0ahNu3b2PRokVo0KABfHx8NH6uqnHi0dHRkMvlCA4OVuma2KZNG6xatQoxMTFYunSpxjI+/PBDrFmzBsuWLUNcXBwKCwvRv39/5OTk4LPPPsO5c+ekvEZGRlJjiLrJ1IyNjdGkSZOnGmJRW9WWWHrw4IE0p0FWVhby8vKk4Sju7u6wsbFBcnIyli9fjqFDh8LBwQF///03Vq5ciezs7EpX45HL5YiPj9e4/eHDh4iPj8fw4cPVXqAmTJiAwMBAXLp0SeNF29DQEB999BEmTpyIAwcOoF+/fjh8+DD8/f3Rq1cvuLu7K51nOzs72NnZoV69ehrjqkOHDhon1dImtSXG3n//fezZswcrV65EXl6eUp3at28v/bq+du1a1K1bF02bNkVmZiZWrVoFY2PjSpealslkGnuoKShWzag4PlghLy8PgwcPRkJCgsovmxUpGiaioqIQEBCACxcuYMiQIWjRogXGjh2rdEw2NjZwdnaGsbGxSoxlZmYCAF599VVpiI4u0LZYCwsLQ9euXTFmzBj4+/sjIyMDc+fOxZgxY+Ds7PxMrk3VFRISgri4OKxevRqffPIJ/v33X/Tt2xcmJiaYPn06zpw5I+W1sLCQHvw0Xb+cnZ118p4JaFecvfHGG3jjjTfw3nvv4Z9//kHz5s1x6tQpLF26FBMmTFDpLl1xTHpgYCC2bNkCNzc3zJo1C40bN8a5c+cwf/58uLm5VdlI6ufnh/nz5yMxMVGpQYX3xsppU3wp3Lp1CwkJCRqf7ydOnIiSkhIMHz4cLVq0kJ7x9uzZg6CgIKWyntXxDxs2DL/99hu++uorZGVlSSuXAf991n+8pxNQvmy7mZmZTl6/dDG2FM6dO4eSkhI8evQIV65cQUxMDBISEvDBBx9I82YoVOc8rFu3Dh4eHujWrRumT58urSZ66tQprFmzBnPmzEGXLl0QExODuXPn4q233oKjo6NSuc7OzrCxsUHDhg1V5pJUPId169ZN7XK7GgkdFxYWJgCofb3zzjtKeSMjIwUAcevWLbXlyOVyjfvQtK2isrIysX79etG2bVshl8tFw4YNxdChQ8WFCxeU8tnb24spU6ZoLOfXX38VAERiYqIoKioSlpaW4q233tKYf8iQIaJZs2airKys0mP8+OOPhb6+vrh8+bK4evWqxvNmb29f6XFWVX9tVZtiqbK/T2JiohBCiIyMDNG3b1/RsGFDYWhoKOrVqycGDBggTp06pVRWYmKiACBSUlI07i8wMFD6u+/YsUMAEAkJCWrz3rp1SxgaGoqQkBAhhBDu7u7C29tbJV9RUZFwdHQU7u7u0rFrOqawsDCNdatO/bVFbYoxe3t7jXW5evWqlG/FihXCyclJ1KlTRzRq1EhMmTJF3LlzR6ksRbxu375d4/4iIiKE4pZ05swZAUBs3rxZbd6ioiJhY2Mjxo4dK4QQwt/fX7z66qtq83bv3l04OzuLkpIS6Zype/n7+2usW3Xqr220MdaEECIhIUG88cYbwsjISDRs2FAEBweLhw8fCiGe3bWpuscwZswYYWFhIXJzc6XrkLqX4hqnCQCxfPnySvNoK22Ms5s3b4p3331X2NvbCxMTE+Hi4iLCwsLEgwcPpDz79u0TAERqaqrSPtLT08XIkSOFlZWVMDAwEM2aNRNBQUEiLy+vWnXevHmzynMW742aaWN8CSHE2rVrBQCRlpamtqwDBw4IPz8/4eTkJExMTISVlZVwdXUVW7ZsESUlJTU+/uo8l2sqR3Ff1qQ611FtpKux9fi9ysTERDg4OIhRo0aJQ4cOPdV5yMvLE+Hh4aJ169bC2NhYmJqaio4dO4qIiAhRWFgohCh/XtNUXmRkpMZzUNk5roxMiArTuxIRERERkUaff/45goKCcPfu3UrHyhMR0YvzUgxdISIiIiJ6GllZWTh16hSWL18OT09PNnIQEdViurPuJxERERHRcxIREYEpU6agS5cuKqvNERFR7cKhK0RERERERESkM9ijg4iIiIiIiIh0Bhs6iIiIiIiIiEhnsKGDiIiIiIiIiHQGGzqIiIiIiIiISGewoYOIiIiIiIiIdAYbOoiIiIiIiIhIZ7Chg4iISAskJSVBJpNpfJ08ebLGZUZFRWH16tXPvrLPyYMHD7B69Wq4ubnB0tIShoaGsLW1xYABAxAVFYWSkpLnXoekpCSEh4cjNzf3ue+rOsLDw7F79+4XXQ0iIqJaRSaEEC+6EkRERFS5pKQk9OzZE76+vhgwYIDK9n79+sHa2rpGZXp4eCAzMxOZmZnPqJbPz+XLl+Ht7Y309HT06dMHXl5esLa2xr///ouEhAQkJCRg5syZ+PTTT59rPcLDwzF//nxcvXoVDg4Oz3Vf1SGTyeDv74+oqKgXXRUiIqJaw+BFV4CIiIiq7/XXX8dbb731oqtRpdLSUjx69AimpqZPXVZhYSEGDhyIK1euYOfOnRg2bJjS9tmzZyMlJQUpKSlPva9nrbi4GKWlpTA2Nn7RVSEiInppcOgKERGRjlAMb1H36/748eMhk8mk9w4ODjh8+DCysrKUhsAkJSVJ2z08PKq1j6ioKMhkMiQkJGDhwoVwdnaGsbExvvvuOwCAEALr169Hhw4dYGpqCjMzM/Ts2ROJiYnVOq7Nmzfj0qVLCA4OVmnkUOjYsSPef/99pbTdu3ejW7dukMvlMDMzQ7du3fD999+rfFZxrBcvXoS3tzfMzc1Rt25djBgxAtnZ2UrncP78+QAAR0dH6ZyFh4cDKO/tIZPJcP78eXz44Yews7ODsbGxNKxo27ZtePPNN9GsWTMYGRnB2toaQ4YMwR9//KH2mH799VeMHDkStra2MDIyQtOmTeHr64s///wTmZmZ0t/z66+/VvobEhERvezYo4OIiEiLPHjwADk5OUppRkZGMDc3r1E5q1evxty5c5GTk4OIiAgp/ZVXXnnius2YMQPFxcWYOHEiLCws0LJlSwDA2LFjERsbixEjRuDtt9/Go0ePEB0dDU9PT8THx+PNN9+stNwdO3YAACZNmlTtuqxbtw5TpkxBq1atEBoaCqC8QWbIkCHYsGGDSll//fUXPDw8MHToUCxfvhy///47NmzYgLy8PPz4448AgPfeew95eXnYtWsXIiIipKFCbdu2VSprzJgxMDExQXBwMGQyGRo1agQAWLt2LaysrDBp0iQ0bNgQf/75JzZu3Ihu3brh7NmzaNGihVTGnj17MHz4cMjlcrz77rto3rw5srOzcfDgQZw7dw59+vTB1q1bMXbsWLi5udXo3BAREek8QURERLVeYmKiAKD2NWrUKKU8kZGRKp/39/cXj9/23d3dhb29vdr92dvbC3d3d431qLiPyMhIAUC4uLiIgoICpfzx8fECgNiwYYNSenFxsejQoYNwcHAQZWVllR67paWlsLCwqDRPRXfu3BFyuVw4OzuLe/fuSen37t0TTk5OwszMTNy9e1fpWAGIbdu2KZXz/vvvCwDi4sWLUlpYWJgAIK5evaqyX8U2d3d3UVxcrLI9Pz9fJS0tLU3UqVNHTJ48WUorKCgQ1tbWwsbGRty4cUPlM6WlpdL/AxD+/v5qzwMREdHLikNXiIiItMikSZNw6NAhpde8efNedLUAAJMnT1aZk+Pbb7+Fubk5hgwZgpycHOmVm5uLQYMGITMzExkZGZWWm5eXV6MeK4cOHUJBQQGmTZsGCwsLKd3CwgLTpk1Dfn4+EhISlD7TuHFj+Pj4KKX16tULAKqs3+OCgoJgYKDaaVYulwMoH8qTl5eHnJwc2NjYoGXLljh16pSU7+DBg8jJyUFwcDCaNGmiUo6eHh/fiIiIKsOhK0RERFqkRYsW6NOnz4uuhlouLi4qaRcuXMD9+/dha2ur8XP//POP2s8qWFhY4P79+9Wux9WrVwEAr776qso2RdqVK1eU0p2cnFTyWllZAQBu375d7X0D6s8DUD7nRkhICJKSklBQUKC0zdHRUfp/RcNK+/bta7RfIiIiKseGDiIiIh1R2USUJSUlz6SsyspRt8KKEAI2NjaIiYnR+Lk2bdpUWpc2bdrgyJEjuHLlitoGiWdBX19f4zYhRI3KUncerl27hh49esDCwgIhISFo2bIl5HI5ZDIZgoKCkJ+fX+M6ExERkXps6CAiItIRlpaWAIA7d+6obHu8BwNQecOIpaVltcupTIsWLZCeno7OnTvDzMysRp9VGD58OI4cOYLNmzdjyZIlVeZXNIacP38evXv3VtqWlpamlKemnnRVk127diE/Px8//PADevbsqbTt9u3bMDIykt4reoT89ttv8PLyeqL9ERERvcw4yJOIiEhHODo6wsDAQGX+iePHj0tLnFZkZmaGu3fvqu2x4OLigosXL+Kvv/6S0h49eoQvvviiRnUaN24cysrKMHfuXLXb//nnnyrLePfdd9GyZUusWLFC7fKwAPDLL79g3bp1AABPT0/I5XKsWbNGacjL/fv3sWbNGpiZmcHT07NGx6GgaKxR1whUGUWPkcfP9aZNm5SWsAUALy8vWFtbY+XKlbh586ZKWRXLMDMzq3FdiIiIdB17dBAREekIMzMzjB8/Hps3b4avry88PDyQkZGByMhItG3bFr///rtS/s6dO2PPnj2YOnUqunbtCn19ffTq1QsNGjTA1KlTERcXhz59+iAgIABFRUXYunWr2mEZlVEsKbt27VqcPXsWAwcOhLW1NW7cuIETJ07g8uXLVfYSMTU1xZ49e+Dt7Y0hQ4bAy8sLnp6esLKywq1bt5CYmIiDBw9i1qxZAIB69erh008/xZQpU9CpUyeMHz8eQPnyspcvX8aGDRtQt27dGh1HxXMGALNnz8aYMWNgbGyMNm3aVDn8pn///jA1NcXYsWMxdepU1K9fH8eOHcO+ffvg7OysNCTI1NQUW7ZswYgRI9CmTRtpedlbt27h4MGD+PDDDzF48GCpPgkJCVi2bBmaNWsGmUyG0aNHP9GxERER6YwXueQLERERVY9iWdfly5dXmu/+/fvinXfeEZaWlsLExER0795dHDt2TO3ysgUFBWLChAmiQYMGQk9PTwAQiYmJ0vaoqCjh4uIiDA0NhYODg1i2bJn46aefNC4vW/Gzj/vmm29E9+7dhbm5uTAyMhL29vZi6NChIi4urtrnoKCgQKxatUp069ZN1KtXTxgYGIgGDRqIAQMGiG+++UaUlJQo5Y+PjxddunQRpqamwtTUVHTp0kXs2rVLpdyaLKUrhBDLli0Tjo6OwsDAQAAQYWFhQojKl54VQojDhw+Lbt26CTMzM1G3bl0xYMAAkZqaqnGZ31OnTonBgwcLKysrUadOHdG0aVPh5+cn/vzzTylPenq68PT0FObm5tJyw0RERC87mRA1nGGLiIiIiIiIiKiW4hwdRERERERERKQz2NBBRERERERERDqDDR1EREREREREpDPY0EFEREREREREOoMNHURERERERESkM9jQQUREREREREQ6gw0dRERERERERKQz2NBBRERERERERDqDDR1EREREREREpDPY0EFEREREREREOoMNHURERERERESkM9jQQUREREREREQ6gw0dRERERERERKQz/j9AsORwo938VAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "def make_yield_plot():\n",
        "  matplotlib.rcParams['axes.linewidth'] = 1.9\n",
        "  fig = plt.figure(figsize=(11,6))\n",
        "  ax = fig.gca()\n",
        "  ax.yaxis.set_minor_locator(plt.MultipleLocator(1))\n",
        "  ax.tick_params(axis='both', which='both', direction='in', labelsize=11)\n",
        "  ax.set_xlabel('Future Contract', fontsize=13)\n",
        "  ax.set_ylabel('Annual Yield (%)', fontsize=13)\n",
        "\n",
        "  plt.plot(df[\"Name\"], df[\"Yield % (annual)\"],color='tab:blue', linewidth=2)\n",
        "  plt.scatter(df[\"Name\"], df[\"Yield % (annual)\"], color='blue')\n",
        "  ax.set_title('ETH Futures', fontsize=15, color='black')\n",
        "  ax.grid(linewidth=0.65)\n",
        "\n",
        "  ax.xaxis.set_tick_params(width=1.3)\n",
        "  ax.yaxis.set_tick_params(width=1.3)\n",
        "\n",
        "  plt.tight_layout()\n",
        "  ax.xaxis.set_ticks_position('both')\n",
        "  ax.yaxis.set_ticks_position('both')\n",
        "  plt.show()\n",
        "  fig.savefig('yield.png')\n",
        "  fig.savefig('yield.pdf', format='pdf')\n",
        "\n",
        "make_yield_plot()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dhoC-p3kpeZh"
      },
      "source": [
        "**The code below can generate other plots.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lajC3USKpeZh"
      },
      "outputs": [],
      "source": [
        "def make_yield_days_plot():\n",
        "  fig = plt.figure(figsize=(11,6))\n",
        "  ax = fig.gca()\n",
        "  ax.tick_params(axis='both', which='major', direction='in', labelsize=13)\n",
        "  #ax.tick_params(axis='both', which='both', direction='in')\n",
        "  ax.set_xlabel('Days to Expiry (Future Contract)', fontsize=14)\n",
        "  ax.set_ylabel('Annual Yield (%)', fontsize=14)\n",
        "\n",
        "  plt.plot(df[\"Days to Expiry\"], df[\"Yield % (annual)\"],color='tab:blue', linewidth=2)\n",
        "  plt.scatter(df[\"Days to Expiry\"], df[\"Yield % (annual)\"], color='blue')\n",
        "  ax.set_title('ETH Futures', fontsize=15, color='black')\n",
        "  ax.grid(linewidth=0.65)\n",
        "\n",
        "  plt.tight_layout()\n",
        "  ax.xaxis.set_ticks_position('both');\n",
        "  ax.yaxis.set_ticks_position('both')\n",
        "  plt.show()\n",
        "  fig.savefig('days.png')\n",
        "  #fig.savefig('days.pdf', format='pdf')\n",
        "\n",
        "#make_yield_days_plot()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MKEW5DQXpeZi"
      },
      "outputs": [],
      "source": [
        "def make_price_plot():\n",
        "  fig = plt.figure(figsize=(10,5))\n",
        "  ax = fig.gca()\n",
        "  ax.tick_params(axis='both', which='major', direction='in', labelsize=9)\n",
        "  #ax.tick_params(axis='both', which='both', direction='in')\n",
        "  ax.set_xlabel(r'Future Contract', fontsize=13)\n",
        "  ax.set_ylabel(r'USD', fontsize=15)\n",
        "\n",
        "  plt.plot(df[\"Name\"],df[\"Price\"],color='red', linewidth=2)\n",
        "  ax.set_title('ETH Futures Price', fontsize=15, color='black')\n",
        "  ax.grid(linewidth=0.5)\n",
        "\n",
        "  plt.tight_layout()\n",
        "  ax.xaxis.set_ticks_position('both');\n",
        "  ax.yaxis.set_ticks_position('both')\n",
        "  plt.show()\n",
        "  fig.savefig('price.png')\n",
        "  #fig.savefig('price.pdf', format='pdf')\n",
        "\n",
        "#make_price_plot()"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tRs_Vds8_Ew2"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.10.12"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}