{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "0270_prestamos.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNdbXS/1ar92ZwFeYoboFk0",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/financieras/pyCourse/blob/main/jupyter/calisto3/0270_prestamos.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Préstamos\n",
        "## Función Pago (pmt)\n",
        "* npf.pmt(rate, nper, pv, fv, when = ‘end’)\n",
        "* fv es optativo, por defecto es 0\n",
        "* when es optativo, por defecto es 'end', pagos a final de periodo, pospagable\n",
        "* Calcular el pago mensual periódico necesario para amortizar un préstamo de 350.000 €, a 20 años, al 6% TIN (Tipo de Interés Nominal)\n",
        "* con Excel: =PAGO(6%/12;20*12;-350000)"
      ],
      "metadata": {
        "id": "LD16NjKFRYL9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install numpy-financial   # instalamos con pip la librería financiera"
      ],
      "metadata": {
        "id": "mvvo9Yj4Jtcj",
        "outputId": "058ed5ef-2756-4733-c94b-8d4945433d20",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting numpy-financial\n",
            "  Downloading numpy_financial-1.0.0-py3-none-any.whl (14 kB)\n",
            "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.10/dist-packages (from numpy-financial) (1.23.5)\n",
            "Installing collected packages: numpy-financial\n",
            "Successfully installed numpy-financial-1.0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy_financial as npf\n",
        "\n",
        "pago = npf.pmt(0.06/12, 20 * 12, 350_000)\n",
        "\n",
        "# Here fv = 0 ; Also Default value of fv = 0\n",
        "print(f\"Pago mensual: {-pago:,.2f} €\" )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rnlAao1HP2IN",
        "outputId": "4eb4abb3-b0f9-4eda-9c98-f291acc4c173"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pago mensual: 2,507.51 €\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Cuadro de amortización de un préstamo\n",
        "Préstamo francés de 100.000 euros, a devolver en 10 años, al 5% anual."
      ],
      "metadata": {
        "id": "cyJ7l8jz0X3C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy_financial as npf\n",
        "import numpy as np\n",
        "import tabulate as tab\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# en primer lugar vamos a calcular la cuota constante\n",
        "\n",
        "C0 = 100000\n",
        "n = 10\n",
        "tasa = 0.05\n",
        "\n",
        "pago = npf.pmt(rate=tasa, nper=n, pv=-C0, fv=0, when='end')\n",
        "print(pago)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vq_37Go00KzZ",
        "outputId": "a6f109b6-982c-4ff1-e426-02052d6aec84"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "12950.457496545661\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Vamos a calcular el capital pendiente de cada periodo\n",
        "\n",
        "pendiente = npf.fv(rate=tasa, nper =range(n+1), pmt = pago, pv= -C0)\n",
        "print(pendiente)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "drPVFcwx1gfh",
        "outputId": "27f2d904-a922-410c-9306-f79d721b6ec0"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[100000.          92049.54250345  83701.56213208  74936.18274214\n",
            "  65732.5343827   56068.70360529  45921.68128901  35267.30785691\n",
            "  24080.21575321  12333.76904433     -0.        ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Calculamos la amortización de cada año\n",
        "\n",
        "amortz = npf.ppmt(rate=tasa, per=range(n+1), nper=n, pv=-C0, fv=0, when='end')\n",
        "print(amortz)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H9Xt-asI1hQB",
        "outputId": "a26d89ec-195c-47aa-fa3c-9278664ed8d3"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 7571.86428242  7950.45749655  8347.98037137  8765.37938994\n",
            "  9203.64835944  9663.83077741 10147.02231628 10654.3734321\n",
            " 11187.0921037  11746.44670888 12333.76904433]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Calculamos los intereses de cada periodo\n",
        "\n",
        "interes = npf.ipmt(rate=tasa, per=range(n+1), nper=n, pv=-C0, fv=0)\n",
        "print(interes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xdxxQESp1j_2",
        "outputId": "9c0e3f69-6350-433f-c8ac-05e52a0eaae2"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[5378.59321412 5000.         4602.47712517 4185.0781066  3746.80913711\n",
            " 3286.62671914 2803.43518026 2296.08406445 1763.36539285 1204.01078766\n",
            "  616.68845222]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Comprobamos\n",
        "\n",
        "comprob = np.array(amortz + interes)\n",
        "print(comprob)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZJCY9WWL1o2y",
        "outputId": "a25097d4-82c5-4fe7-e709-9b6c97c906be"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[12950.45749655 12950.45749655 12950.45749655 12950.45749655\n",
            " 12950.45749655 12950.45749655 12950.45749655 12950.45749655\n",
            " 12950.45749655 12950.45749655 12950.45749655]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Hacemos un cuadro de amortización para ver los datos con la librería tabulate\n",
        "datos =[]\n",
        "saldo = C0\n",
        "saldo2 = 0\n",
        "linea1 = [0,0,0,0,C0,0]\n",
        "datos.append(linea1)\n",
        "\n",
        "Anualidad = npf.pmt(rate=tasa, nper=n, pv=-C0, fv=0, when='end')\n",
        "\n",
        "for i in range(1, n+1):\n",
        "    pago_capital = npf.ppmt(rate=tasa, per=i, nper=n, pv=-C0, fv=0, when='end')\n",
        "    pago_int = Anualidad - pago_capital\n",
        "    saldo -= pago_capital\n",
        "    saldo2 += pago_capital\n",
        "\n",
        "    linea = [i, format(Anualidad, '.0f'),  format(pago_int, '.0f'), format(pago_capital, '.0f'), format(saldo, '.0f'), format(saldo2, '.0f')]\n",
        "\n",
        "    datos.append(linea)\n",
        "\n",
        "print(tab.tabulate(datos, headers= ['Periodo', 'Anualidad', 'Intereses', 'Amortización','Capital Vivo','Capital Amortizado'], tablefmt = 'psql'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_QvkiPUm1scP",
        "outputId": "2d5ee5bb-1fe8-4c97-8223-7fd3b445a27d"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-----------+-------------+-------------+----------------+----------------+----------------------+\n",
            "|   Periodo |   Anualidad |   Intereses |   Amortización |   Capital Vivo |   Capital Amortizado |\n",
            "|-----------+-------------+-------------+----------------+----------------+----------------------|\n",
            "|         0 |           0 |           0 |              0 |         100000 |                    0 |\n",
            "|         1 |       12950 |        5000 |           7950 |          92050 |                 7950 |\n",
            "|         2 |       12950 |        4602 |           8348 |          83702 |                16298 |\n",
            "|         3 |       12950 |        4185 |           8765 |          74936 |                25064 |\n",
            "|         4 |       12950 |        3747 |           9204 |          65733 |                34267 |\n",
            "|         5 |       12950 |        3287 |           9664 |          56069 |                43931 |\n",
            "|         6 |       12950 |        2803 |          10147 |          45922 |                54078 |\n",
            "|         7 |       12950 |        2296 |          10654 |          35267 |                64733 |\n",
            "|         8 |       12950 |        1763 |          11187 |          24080 |                75920 |\n",
            "|         9 |       12950 |        1204 |          11746 |          12334 |                87666 |\n",
            "|        10 |       12950 |         617 |          12334 |              0 |               100000 |\n",
            "+-----------+-------------+-------------+----------------+----------------+----------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Comprobemos como evoluciona nuestro prestamo en la siguiente gráfica\n",
        "\n",
        "plt.figure(figsize = (12, 8))\n",
        "plt.bar(range(0, n + 1), pendiente)\n",
        "plt.title(\"Capital Pendiente\", fontsize = 15)\n",
        "plt.xlabel(\"Periodos(años)\", fontsize = 12)\n",
        "plt.ylabel(\"Balance del préstamo (euros)\", fontsize = 12)\n",
        "plt.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 741
        },
        "id": "88V5kf4b1vMJ",
        "outputId": "01e11132-6495-44a4-939e-10467545d964"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (12, 8))\n",
        "plt.bar(range(0, n + 1), amortz, label = 'amortizacion')\n",
        "plt.bar(range(0, n + 1), interes, bottom= amortz, label='intereses')\n",
        "plt.legend(fontsize = 20)\n",
        "plt.title(\"Pagos del Préstamo\", fontsize = 15)\n",
        "plt.xlabel(\"Periodos en años\", fontsize = 12)\n",
        "plt.ylabel(\"Pagos en euros\", fontsize = 12)\n",
        "plt.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 743
        },
        "id": "6PEh-BR41xoq",
        "outputId": "36028a6d-b94e-4898-88f7-aec822804ae0"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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2Ozncua5JkyaaNWuWzp49qxYtWqhatWoKDQ1VWFiYHn74YRUtWlR9+/a1hBsXL15Unz59VKxYMTVv3lzh4eHq0qWLypQpoy1btqhu3bpWb1To1KmTTCaTXnnlFXXp0kXPPPOMnnnmGf3zzz82n0v2dqfnGQDg/kAYAADAHXr99dc1d+5cVa9eXZs2bdKvv/6qGjVqWC4GM1O+fHlt3bpVTzzxhC5evKhly5YpISHBMjO9pAyvAXR2dtby5cs1bdo0PfDAA/rll1+0atUqVahQQbNmzdI333xjNVFcx44dNXLkSD300EPavXu3vv76a+3bt09NmjTRt99+q2nTpt32Pr7//vv65JNPVKlSJa1du1Zr165V69attXnzZhUuXDjLffzjjz/0zjvvqFSpUtqyZYu+++47HTp0SLVq1dKsWbP05JNP3nYNd8tzzz2nHTt2qFevXrp8+bJWrFihX375RVeuXFF4eLhWrFghb29vSdKDDz6oDz74QEFBQTpx4oS+++47bdy4UQEBAZoyZYrWrVsnNzc3y9h16tTR/PnzVblyZa1atUqfffaZPvvsM8sbAmw5l+ztTs8zAMD9wWS+G9MdAwCALL333nsaMWKE3nvvPb322muOLgcAABgAYQAAAHdBYmKijh49qsqVK1st/+2339S5c2ddv35dR44cyfJ97wAAAPbEqwUBALgL4uLiVKVKFVWoUEHly5eXu7u7Dh8+bJmgb9KkSQQBAADgruHOAAAA7oLr169r1KhRioyM1MmTJ5WQkCAfHx/Vq1dPL7zwgtq1a+foEgEAgIEQBgAAAAAAYDBMDQsAAAAAgMEQBgAAAAAAYDCEAQAAAAAAGAxvE8glaWlpOnPmjAoWLCiTyeTocgAAAAAA9zmz2azLly/L399fTk7Zf/dPGJBLzpw5o9KlSzu6DAAAAACAwZw8eVKlSpXKtg9hQC4pWLCgpJt/BC8vLwdXAwAAAAC43yUkJKh06dKW69HsEAbkkvRHA7y8vAgDAAAAAAB3ze08qs4EggAAAAAAGAxhAAAAAAAABkMYAAAAAACAwRAGAAAAAABgMIQBAAAAAAAYDGEAAAAAAAAGQxgAAAAAAIDBEAYAAAAAAGAwhAEAAAAAABgMYQAAAAAAAAbj4ugC8N/S0tKUkpKitLQ0R5cC4B7j5OSkfPnyyWQyOboUAAAA5CGEAXlYfHy8EhISdO3aNYIAADbLly+fChYsqKJFi8rZ2dnR5QAAACAPIAzIg8xms2JjY3Xp0iV5enqqaNGicnd3l5OTE9/uAbhtZrNZqampunLliuLi4nT9+nWVLl2aQAAAAACEAXnRpUuXdOnSJfn5+alQoUKOLgfAPa5AgQLy9vbWiRMndOHCBfn6+jq6JAAAADgYEwjmMWazWXFxcSpYsCBBAAC78fDwkJeXly5fviyz2ezocgAAAOBghAF5TEpKipKSkuTt7e3oUgDcZwoWLKjk5GQlJyc7uhQAAAA4GGFAHpOamipJcnHhCQ4A9pU+VwATkgIAAIAwII9iokAA9sa/KwAAAEhHGAAAAAAAgMEQBgAAAAAAYDCEAQAAAAAAGAxhAAAAAAAABkMYAMBuTCaTTCaTRo8e7ehS7Grt2rWWfVu7dq2jywEAAAByjDAAAAAAAACD4WX297iyw390dAm56th7HRxdguGVLVtWx48fV69evRQREeHocgAAAADYAWEAALsxm82OLiFXBAUF3bf7BgAAAGMiDEDeduaP3B3fv1bO1s/t+vKC1Bs3//faxf/tL8fNdjk5djk9bilmKe68NLObdOVkzsa620bH53B9b/vUca/huNmG42a7nBw7jlsO1jfoseO42Y7/Vm2T03Muj2HOAAAAAAAADIYwAJC0d+9evfPOOwoJCVGpUqXk5uamAgUKqHz58urVq5e2bNmS5bqjP5gtU8naMpWsLUlKuHxFoz+YrWqtHleB8o1VvHortX/qBf2+fbfVeucuXNTICbNUpcVjyl+ukYpUaaHOfYboj71//We9aWlpmv/tj2r/1Avyq9larmXrq1i1lmrxWH99GLFEN24k33a98QmX9faUT1SrzRPyqdRMppK1FbF4uYIe6ydTydo6fuqsJGnu1z9Y1kufWT8oKMhq7KzeJnDrbPwmk+l/42Tx03vwWxnq3rLzT42cMEtBj/Wz7LNXhaaqHBSq54e/q/2Hjv7ncUv30+qNevKFN/RAw07KX66R3B94WIEPd1Rov1cUsXi5rl2/bl3/7zssta39fUeW4165ek3vzfxCDTv1UuEqQXILbKBSddrqsX6vakXk+mxrCgoKsjqmp0+f1tChQ1WuXDl5eHioSJEiCukxQD+v2XTb+wkAAABkhccEYHhr165VixYtMiy/ceOGjhw5oiNHjmjevHkaPny4xo8fn+1YJ0/HKDjseR06etyy7Oq16/p5zSatWrdFX816V906tdaf+w+p/VMv6nTMOUu/a9cTtXzVOv2ybrN+/nKGWjSul+k2Ll6K1yN9hmjT9iir5Rcuxmnt5h1au3mHZkYs1s/zZyiglH+29R4+ekJtegzQsZNnsu3naBGLl6vP0NEZlicnp+jA4WgdOBytTxYu1fSxr2pA78ezHOefi3Hq/vxwrd64LUPbsZNndOzkGX330xpJUu/uj9xRjX/s/Usde72kMzHnrZafjjmnb39arW9/Wq2u7Vtqwbc/yt3dPduxNm3apC5duujChQuWZYmJiVq1botWrdui998crFee63lH9QEAAAC3IgyA4aWkpCh//vzq0KGDWrZsqYoVK8rLy0vnzp3Tvn37NH36dB0/flzvvfeeHnroIfXp0yfLsbo9O0ynzsZqxKA+atuikTw9PLRx2x9664OPlXD5ivq+MlZ1a1RWx16DdT0xSeNeG6jmDeson4uLVq79XeOmf6akpBvqPeQtHd74vVxd81mNn5qaqo69XtLmnX9Kkpo3rKNBvbsrsIy/zsSc1+eLl2vZyt904HC0WnV/TlGrFqlAfs8s632s/6s6HXNOLzwdpkdaN1chn4I6HH1SASX99MXk0bp67bpCwgfqTMx5dQ4J0jvDBtxcsXglSVL+/Plv6xjXq1dPe/bs+d+Ccwcy9JnyyQJ9vuh7SVJAKT+rtpTUVBXy8VLnNs3V7OHaKh9YRvk9PXQm5rx27flL0z//ShcuxmnQyAmqWK6sWjapn2H8a9evq8Xj/bXnwBFJUp3qldQ/vKuqVignN7d8OnkmVuu37NLiH1bd1j7d6vTZc2rV/TldikuQyWRS78c7KaxziIoU8tb+Q0f1wcfztXv/IX330xr17t1bixYtynKss2fPqkuXLnJyctJ7772nJk2ayNXVVRs3btTYMW8pLv6yRoyfqXYtGqtKhQfvuFYAAABAIgwAVLNmTZ06dUo+Pj4Z2kJCQjRo0CB17NhRkZGRGjNmjHr27ClnZ+dMx4raf0jrvvlEDWpXsyyrW6OyygeWUcdeL+nylatq0LGnzGaztv04Tw+WLW3pV79WVRUt5KOBb7ynE6dj9OPqDXq0XUur8Wd/+Y0lCOj5WEdFTB0jk8kkSapTXerUprneeG+m3p3xuf4+dkpvT/1EE954Kct933vwb/08f4baNG9oWVanemWrPvlcbv4z4eNVUFUrlru50L9qlmNmJn/+/Kpa9ZZ1zlg/xvDDqnWKWPKDJCmoYV2NfOkZq/Z2LRqrx6Nt5enhYbW8VtWK6hDcVC/2DVOzrs/ozwOH9dYHszMNA0ZO+NASBAzs/bhmvPOa5dhJN/e7S9sWeu/1F3UpPuGO9m/wW5N0Ke7mOp+8/6b6PtHFatzHO7VRuycH6bffd2jx4sXq1auX2rVrl+lYhw4dUkBAgDZt2qSSJUtalterV0/1Ar3VrOszSklJ0ZwF32na2FfvqE4AAAAgHXMGwPCKFi2aaRCQztXVVe+//74k6fjx44qKisqy7+BnelgFAek6BDdVQKkSkqTz/1zS268OsAoC0vXp/ojc3d0kSRu2Zpw5flbEEklSsSKFNHOc9cVsujGvPKeK5cpKkj5ZuFRJSTeyrLf3452sggBH2PvXEYW/MFJpaWl6IKCUvpkzUfnyWd8RUbJE8QxBwK28vQpq7KvPS5I2bovSPxfjrNrj4i/r4/nfSrp5R8C0sa9meuwkydU1n3yLFbnt+s/EnNfSlb9Jktq2aGQVBKRzc3PV55NHy+X/g5WZM2dmO+aMGTOsgoB0TerXUoNaN0OVzM4PAAAA4HYRBgD/kpSUpBMnTmj//v3au3ev9u7da/WO+d27d2e5btgjIVm2Va9UXtLNSfa6P9Im0z4eHu4qH3gzJDh64rRV25mY8zpwOFqS9Hin1ipYIPNb9F1cXNTn/593vxSXoF17Mt6Sny780cy/nb5bzv9zSZ16D9blK1dVsEB+Lf9iiooU9vnP9a5eu65jJ89o38G/tfevI9r71xHLHQyStHv/Iav+azZt07XriZKkF59+Iss7O2yxdvMOpaamSpL6hnXJsl/Z0v5q3azBzXXWrrWs828+Pj7q0KFDluPUqX7zEY2jJ07ZWDEAAADAYwKAJOnq1auaPn26Fi1apH379mV5oSbJalK3f3vowTJZtvl4FZQkFS3so0I+Xv/Z7/KVq1bL9x48Yvk9/dvhrDSo9b+7E/Ye/FsN69bItF/1yuWzHSc33biRrNB+r+jYyTNycnLSV7PezfYZ+AsXL2nyx/P17U9rdDj6hFVAk7FvnNXnP/YetPzetEGtHNd+q71/3cnfpap+XrNJ165d09GjR1W+fMbjX758eTk5ZZ3TFva5+W7fy1eu2VgxAAAAQBgA6NixY2rZsqWio6Nvq//1f7127lbZ3cqefoHn6ZH9TPLp/VLT0qyWX4z733PsxYsWznYMv1tuc791vX8r5J11KJHbnh/xruVW9/def0Edgptm2Xfnn/sV0mOQ/rkUd1tjX09Msvp8azhQonjRO641Ozb/XS5ezLSPp2fWEz5KkpPTzccb0v51fgAAAAB3gjAAhvfUU08pOjpaJpNJffr0UVhYmCpVqqRixYrJ1dVVJpNJaWlpllvLs/tG+m7J6nn3O2XP2+XvxOSP51veHNCrWye9+nyvLPveuJGsx58brn8uxSlfPhe90CdMnUOa66EHAlTI20tubq6SpKPHT+nBRjcfj3DU38hefxcAAAAgtxEGwND++usvbdy4UZL0+uuv65133sm0X1bf4t5NhW95tCD2/D/Z9o25pb1wNo8kOMLPP/+sV9+ZKklqWKe6Pp7wRrb912zapqPHbz4f/+G7I/RMj0cz7ZfdHRBFb5mH4Oy5Cwosk3FyPlv9++9SuqRfln2t/i6Fs7+LAAAAAMhNTCAIQ9u3b5/l9+7du2fZb8eOHXejnGxVrVDO8vvWP/Zm23db1P/aq+bwXfT2/LZ7//79CgsLU1pamsqU9NPSzz6wfLOflX0Hj1p+z2riRUnasXt/lm21q1W0/L5+y647qPi/WV63qNv4u/xx83zz9PTUAw88YNc6AAAAgDtBGABDS0lJsfx+9erVLPvNnj37bpSTLX+/YqpUPlCStOSHSF25mvkEcqmpqYpY8oMkqZCPl2pXq5Sj7br//8V60o2sX1F4O/755x898sgjSkhIUH5PD33/+ZTbeoVfyi2TOV69lvl8DWlpafpk4XdZjtGiUT3l97w5n8OMLxZlO0HknQpqWNfyuMXni7/Pst+J02cVuWHLzXWCghz2iAYAAAAgEQbA4G6dzT0iIiLTPh999JG+/z7ri7y7aWDvxyXdfCXfi29OzLTPmMlztP/QzW/T+/V49D+/ef8v6RPu/X3c9lfZJScn67HHHtPff/8tk8mkedPGqmbVCre1bvqrFiVZQo5/GzF+hnbt+SvLMXy8C+rZJ0MlSTv/PKDBb03Kcl6B5ORknbtw+4+F+PsV06NtW0iSfl6zSXMzqfHGjWQ9PXSMkpNvhk+DBg267fEBAACA3MCcATC0WrVqqWrVqtq7d68+/vhjXbp0SU899ZRKlCihU6dOaf78+frmm2/UuHFjbdq0ydHl6rmnHtOC737W5p1/6ovFy3X81FkN6PW4Asv462zsBX2++Ht999MaSdKDZUvpzcH9crzNRnVr6Lffd2h71D69N/MLtWvRWPmv3Xz9oYeHh0qW/O/n799++22tXbtWkvTkk0/qoQcCrF7J92+FvL1UskRxSVJIUCMVL1pY5y5c1MiJH+rYyTN6tF0LFS3soyPRJ/XJwqVavXGbGterqU3bo7KuYdjzitywRXsOHNHMLxZr884/9eyToapWsZxcXfPp1Nlz2rB1l75a9oveGTZAvbs/ctvHaMrol7V60zZdikvQ0y+P0cbtUereqY0K+RTUX0eOadLsLxW17+brDR9//HG1a9futscGAAAAcgNhAAzNZDLpyy+/VMuWLXXp0iUtWbJES5YssepTrVo1ff311/L393dQlf/j7OysFXOn6ZE+Q7Rpe5TWbNquNZu2Z+hXqXygfp4/QwXyZ/+autvxfM9u+mjeN7oYF68R42doxPgZlrbmzZtbLvKzc+LECcvvX375pb788sts+/fq1kkRU8dIkvJ7emjetLHq0vdlJSYm6eP53+rj+d9a9Q9qWFczx72mqi27ZTmmp4eH1iz5WKH9XtX6Lbu0888D6j8s8wkj71Qpf1+tXjxbHXu9pDMx5/XpwqX6dOHSDP26tm+puXPn2mWbAAAAQE7wmAAMr2bNmoqKitJzzz2ngIAA5cuXT4ULF1b9+vU1adIkbdu2TSVKlHB0mRaFC3lr/Xefat60sWrbopF8ixVRvnwuKlLIx3JRHLVqkQJK2Se8KFmiuLb9OE99n+iicmVLy93dzS7j3omQoEba8dN8Pdm1vfz9iilfPhcVK1JIzRvW0ZyJI7V6yWzLnADZKVq4kNZ9+6m++3SSHusQrFIlfOXm5ip3dzc9EFBK3Tq21oKZ4/REl7Z3XGOtqhV1cP1SjR/xghrUqiof74Jydc0nf79i6tq+pZZ/MUXffjJJ7u7uthwCAAAAwK5M5rzw0vT7UEJCgry9vRUfHy8vr9t/tVtiYqKio6MVGBh49y4azvxxd7aTF/nXytn6Rj12HDfb5eTY5fC4JaaYFX36vAI3vSz3KydzNNZdNzo+h+t726eOew3HzTYcN9vl5Nhx3HKwvkGPHcfNdvy3apucnnN3wZ1ch3JnAAAAAAAABkMYAAAAAACAwRAGAAAAAABgMIQBAAAAAAAYDGEAAAAAAAAGQxgAAAAAAIDBEAYAAAAAAGAwhAEAAAAAABgMYQAAAAAAAAbj4ugCAAAAAAD3hrKJCx1dgsMcc3QBdsadAQAAAAAAGAxhAAAAAAAABkMYAAAAAACAwRAGAAAAAABgMIQBAAAAAAAYDGEAAAAAAAAGQxgAAAAAAIDBEAYAAAAAAGAwhAEAAAAAABgMYQAMLyIiQiaTSSaTSceOHXN0OQAAAACQ6/JUGLB+/Xp16tRJ/v7+MplMWrZsmaUtOTlZr732mqpVq6b8+fPL399fPXv21JkzZ6zGuHjxosLDw+Xl5SUfHx/17dtXV65cserz559/qmnTpnJ3d1fp0qU1ceLEDLV8/fXXqlixotzd3VWtWjX99NNPubLPAAAAAADcbS6OLuBWV69eVY0aNfT000+ra9euVm3Xrl3Trl279Oabb6pGjRq6dOmSXnrpJT3yyCPasWOHpV94eLjOnj2ryMhIJScnq0+fPurfv78WLlwoSUpISFCbNm0UHBys2bNna8+ePXr66afl4+Oj/v37S5J+//13PfHEExo/frw6duyohQsXqkuXLtq1a5eqVq169w7I7Rjt7egKclf/tY6uwC5MJWtLkt4a2l+jX37OwdUAAAAAMLo8FQa0a9dO7dq1y7TN29tbkZGRVstmzpyp+vXr68SJEypTpowOHDiglStXavv27apbt64kacaMGWrfvr0mTZokf39/LViwQDdu3NDnn38uV1dXValSRVFRUZo8ebIlDJg2bZratm2rV199VZL09ttvKzIyUjNnztTs2bNz8QjAEXr37q3evXs7ugwAAAAAuGvy1GMCdyo+Pl4mk0k+Pj6SpM2bN8vHx8cSBEhScHCwnJyctHXrVkufZs2aydXV1dInJCREBw8e1KVLlyx9goODrbYVEhKizZs3Z1lLUlKSEhISrH4AAAAAAMiL7tkwIDExUa+99pqeeOIJeXl5SZJiYmJUvHhxq34uLi4qXLiwYmJiLH18fX2t+qR//q8+6e2ZGT9+vLy9vS0/pUuXztkOAgAAAACQS+7JMCA5OVmPP/64zGazPvroI0eXI0kaMWKE4uPjLT8nT550dEm4Tdm9TSAoKEgmk0lBQUGSpNOnT2vo0KEqV66cPDw8VKRKC4X0GKCf12zKdOyyDTpY5guQpDGT58hUsrbVT+/Bb2W67pHoExry1iRVa/W4vCs2k8eDDfVAw07qPfgt7di9P8v9Wfv7DsvYa3/fobS0NH2+aJlaPNZfvjWC5VSqTqbb3LXngJ57bZwqNH1UBco3Vv5yjVSh6aN6fvi7OvT38WyPYVxcnMaNG6eGDRuqUKFCypcvn4oVK6bKlSvr0Ucf1UcffaTY2Ngs109MTNLMLxap1ePPyq9ma7mWra/i1VspuPtz+uyrZUpJScl2+2s2btMTA0Yo8OGO8niwoTwfbKSA+u31cMeeemXsFK3ZuC3b9QEAAACjyVNzBtyO9CDg+PHjWrNmjeWuAEny8/PTuXPnrPqnpKTo4sWL8vPzs/T590VJ+uf/6pPenhk3Nze5ubnZvmPI8zZt2qQuXbrowoULlmWJiYlatW6LVq3bovffHKxXnutpl21Nmj1Pr783U8nJ1hfB0SdOK/rEac37ZoVGvvSMxr76fLbjJCYlKaTHQP26YWuWfdLS0vTK2Cma+ulCmc1mq7ZDR4/r0NHj+vSrZZo17jX1fzI0w/oHDhxQcHBwhjd7XLhwQRcuXNCBAwe0bNkypaamatCgQRnW373vkDo/PUTHT521Wn7+n0tavXGbVm/cpo/nf6sfIqbKt1iRDOsPeWuSpn66MMPyE6djdOJ0jLb+sVcRS37Qhb1rsjwGAAAAgNHcU2FAehBw+PBh/fbbbypSxPrCoGHDhoqLi9POnTtVp04dSdKaNWuUlpamBg0aWPq88cYbSk5OVr58+SRJkZGRqlChggoVKmTps3r1ag0ePNgydmRkpBo2bHgX9hJ50dmzZ9WlSxc5OTnpvffeU5MmTeTq6qqNPy7S2KmfKC7+skaMn6l2LRqrSoUHLeutWvihbiQnq1qrxyVJz/fspgG9ulmNXcjby+rz+x/N1bB3pkmSqlcqr+d7dlP5wNLy8S6og38f18wvFmvzzj/19tRPVLSwj17s+0SWdb82brr+PHBYj7Rprt6Pd1JAqRKKPX9RCZf/97rNF0ZO0Idzv5YkNXu4tno/3kkPlCklTw937d5/SFM/Xah9B//Ws6+Nk1/xonqkTXOrbTz11FM6c+aM8uXLp379+qldu3by8/NTWlqaTp06pS1btmjp0qWZ1nck+oSaP/aM4hOuyKtgAQ3s1U31a1VVaX9f/XMpXstXrdPH87/T9qh96txniDYs/czy360krYhcbwkC0o9VpfKB8i5YQHEJl7Xv0N/6dcM2bYvam+UxAgAAAIwoT4UBV65c0ZEjRyyfo6OjFRUVpcKFC6tEiRJ67LHHtGvXLq1YsUKpqamWZ/gLFy4sV1dXVapUSW3btlW/fv00e/ZsJScna9CgQQoLC5O/v78kqUePHhozZoz69u2r1157TXv37tW0adM0ZcoUy3ZfeuklNW/eXB988IE6dOigRYsWaceOHZozZ87dPSDIMw4dOqSAgABt2rRJJUuWtCyvV9JF9WpWUbOuzyglJUVzFnynaWNftbQ/9GCA1TjFixZS1YrlstzO/kNH9caEWZJuvobwraHPymQyWdrrVK+ssM4h6vXSKM3/7ie9MWGWngrtoEI+XpmO9+eBwxr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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}