Variaciones_en_la_distancia_entre_bobinas.ipynb

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      "name": "Variaciones en la distancia entre bobinas.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/fedemoss/HelmholtzCage/blob/main/Variaciones_en_la_distancia_entre_bobinas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DPpawPlxqZIe"
      },
      "source": [
        "**El objetivo de este código es estudiar como varía la homogeneidad para las bobinas cuadradas y circulares al modificar la distancia entre ellas**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s2kfFVOXyRuz"
      },
      "source": [
        "import math\n",
        "import numpy as np\n",
        "from scipy.integrate import quad\n",
        "from numpy import sqrt, sin, cos, pi\n",
        "from matplotlib import pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Sbrg-keyRu0"
      },
      "source": [
        "## Constantes:\n",
        "## Todo está en SI\n",
        "\n",
        "N = 40  ## número de vueltas\n",
        "I = 1   ## corriente\n",
        "u0 = 1.256637062* 10 ** (-6)  ## permeabilidad magnética del vacio\n",
        "C = u0 * N * I / (4 * np.pi)  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hPbRuHFtyRu0"
      },
      "source": [
        "**DEFINICIÓN CAMPO DE JAULA CIRCULAR (ES PARA UN PAR CON SEPARACIÓN D, HAY QUE INTEGRAR ESTAS EXPRESIONES)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g5mO4M7pyRu1"
      },
      "source": [
        "## Término que aparece en común para todos los campos\n",
        "\n",
        "def terminoDivisorup(u, z, x, y, r, d):\n",
        "    return ((z + d/2)**2 + (x - r * cos(u))**2 + (y - r * sin(u))**2)**(3/2)\n",
        "  \n",
        "def terminoDivisordown(u, z, x, y, r, d):\n",
        "    return ((z - d/2)**2 + (x - r * cos(u))**2 + (y - r * sin(u))**2)**(3/2) \n",
        "\n",
        "## Campo en la dirección axial para las dos bobinas en cartesianas (Bz)\n",
        "\n",
        "def dBzup(u, z, x, y, r, d):\n",
        "    return C * r * (r - x * cos(u) - y * sin(u)) / terminoDivisorup(u, z, x, y, r, d)\n",
        "\n",
        "def dBzdown(u, z, x, y, r, d):\n",
        "    return C * r * (r - x * cos(u) - y * sin(u)) / terminoDivisordown(u, z, x, y, r, d) \n",
        "\n",
        "## Componente x del campo (par de bobinas)\n",
        "\n",
        "def dBxup(u, z, x, y, r, d):\n",
        "    return C * r  * (z + d/2) * cos(u)/terminoDivisorup(u, z, x, y, r, d)\n",
        " \n",
        "def dBxdown(u, z, x, y, r, d):\n",
        "    return C * r (z - d/2) * cos(u)/terminoDivisordown(u, z, x, y, r, d)\n",
        "\n",
        "## Componente y del campo (par de bobinas)\n",
        "\n",
        "def dByup(u, z, x, y, r, d):\n",
        "    return C * r * (z + d/2) * sin(u)/terminoDivisorup(u, z, x, y, r, d)\n",
        " \n",
        "def dBydown(u, z, x, y, r, d):\n",
        "    return C * r * (z - d / 2) * sin(u)/terminoDivisordown(u, z, x, y, r, d)  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CEqK_zSLyRu1"
      },
      "source": [
        "**DEFINICIÓN DE CAMPO DE JAULA CUADRADA (PARA UN PAR CON SEPARACIÓN D)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lCcAklBUyRu1"
      },
      "source": [
        "## Campo en el eje axial en cartesianas (Bz)\n",
        "## La expresión es muy grande por lo tanto la separamos en varias partes\n",
        "## bobinas a d/2 y -d/2 en el eje z con respecto al centro\n",
        "\n",
        "def SBz1up(z, x, y, l, d):\n",
        "    factorcomun = (x + l / 2)/((x + l / 2) ** 2 + (z + d / 2) ** 2)\n",
        "    izquierda = (l / 2 - y) / np.sqrt((y - l /2) ** 2 + (x + l / 2) ** 2 + (z + d / 2) ** 2)\n",
        "    derecha = (l / 2 + y) / np.sqrt((y + l / 2) ** 2 + (x + l / 2) ** 2 + (z + d / 2) ** 2)\n",
        "    return C * factorcomun * (izquierda + derecha)\n",
        "\n",
        "def SBz2up(z, x, y, l, d):\n",
        "    factorcomun = (x - l / 2)/((x - l / 2) ** 2 + (z + d / 2) ** 2)\n",
        "    izquierda = (l / 2 - y) / np.sqrt((y - l /2) ** 2 + (x - l / 2) ** 2 + (z + d / 2) ** 2)\n",
        "    derecha = (l / 2 + y) / np.sqrt((y + l / 2) ** 2 + (x - l / 2) ** 2 + (z + d / 2) ** 2)\n",
        "    return C * factorcomun * (izquierda + derecha)\n",
        "\n",
        "def SBz3up(z, x, y, l, d):\n",
        "    factorcomun = (y + l / 2)/((y + l / 2) ** 2 + (z + d / 2) ** 2)\n",
        "    izquierda = (l / 2 - x) / np.sqrt((x - l /2) ** 2 + (y + l / 2) ** 2 + (z + d / 2) ** 2)     ## ojo acá\n",
        "    derecha = (l / 2 + x) / np.sqrt((y + l / 2) ** 2 + (x + l / 2) ** 2 + (z + d / 2) ** 2)\n",
        "    return C * factorcomun * (izquierda + derecha)\n",
        "\n",
        "def SBz4up(z, x, y, l, d):\n",
        "    factorcomun = (y - l / 2)/((y - l / 2) ** 2 + (z + d / 2) ** 2)\n",
        "    izquierda = (l / 2 - x) / np.sqrt((x - l /2) ** 2 + (y - l / 2) ** 2 + (z + d / 2) ** 2)\n",
        "    derecha = (l / 2 + x) / np.sqrt((y - l / 2) ** 2 + (x + l / 2) ** 2 + (z + d / 2) ** 2)\n",
        "    return C * factorcomun * (izquierda + derecha)\n",
        "\n",
        "## Este es la función que importa\n",
        "\n",
        "def SBzup(z, x, y, l, d):\n",
        "    return SBz1up(z, x, y, l, d) - SBz2up(z, x, y, l, d) + SBz3up(z, x, y, l, d) - SBz4up(z, x, y, l, d)\n",
        "\n",
        "## Campo en el eje axial para la otra bobina\n",
        "\n",
        "def SBz1down(z, x, y, l, d):\n",
        "    factorcomun = (x + l / 2)/((x + l / 2) ** 2 + (z - d / 2) ** 2)\n",
        "    izquierda = (l / 2 - y) / np.sqrt((y - l /2) ** 2 + (x + l / 2) ** 2 + (z - d / 2) ** 2)\n",
        "    derecha = (l / 2 + y) / np.sqrt((y + l / 2) ** 2 + (x + l / 2) ** 2 + (z - d / 2) ** 2)\n",
        "    return C * factorcomun * (izquierda + derecha)\n",
        "\n",
        "def SBz2down(z, x, y, l, d):\n",
        "    factorcomun = (x - l / 2)/((x - l / 2) ** 2 + (z - d / 2) ** 2)\n",
        "    izquierda = (l / 2 - y) / np.sqrt((y - l /2) ** 2 + (x - l / 2) ** 2 + (z - d / 2) ** 2)\n",
        "    derecha = (l / 2 + y) / np.sqrt((y + l / 2) ** 2 + (x - l / 2) ** 2 + (z - d / 2) ** 2)\n",
        "    return C * factorcomun * (izquierda + derecha)\n",
        "\n",
        "def SBz3down(z, x, y, l, d):\n",
        "    factorcomun = (y + l / 2)/((y + l / 2) ** 2 + (z - d / 2) ** 2)\n",
        "    izquierda = (l / 2 - x) / np.sqrt((x - l /2) ** 2 + (y + l / 2) ** 2 + (z - d / 2) ** 2)\n",
        "    derecha = (l / 2 + x) / np.sqrt((y + l / 2) ** 2 + (x + l / 2) ** 2 + (z - d / 2) ** 2)\n",
        "    return C * factorcomun * (izquierda + derecha)\n",
        "\n",
        "def SBz4down(z, x, y, l, d):\n",
        "    factorcomun = (y - l / 2)/((y - l / 2) ** 2 + (z - d / 2) ** 2)\n",
        "    izquierda = (l / 2 - x) / np.sqrt((x - l /2) ** 2 + (y - l / 2) ** 2 + (z - d / 2) ** 2)\n",
        "    derecha = (l / 2 + x) / np.sqrt((y - l / 2) ** 2 + (x + l / 2) ** 2 + (z - d / 2) ** 2)\n",
        "    return C * factorcomun * (izquierda + derecha)\n",
        "\n",
        "## Este es la otra función que importa\n",
        "\n",
        "def SBzdown(z, x, y, l, d):\n",
        "    return SBz1down(z, x, y, l, d) - SBz2down(z, x, y, l, d) + SBz3down(z, x, y, l, d) - SBz4down(z, x, y, l, d)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hZiTw4layRu1"
      },
      "source": [
        "## Campo en la componente x\n",
        "\n",
        "def SBx1up(z, x, y, l, d):\n",
        "    factorcomun = (z + d/2)/((x - l / 2) ** 2 + (z + d / 2) ** 2)\n",
        "    izquierda = (l / 2 - y) / np.sqrt((y - l /2) ** 2 + (x - l / 2) ** 2 + (z + d / 2) ** 2)\n",
        "    derecha = (l / 2 + y) / np.sqrt((y + l / 2) ** 2 + (x - l / 2) ** 2 + (z + d / 2) ** 2)\n",
        "    return C * factorcomun * (izquierda + derecha)\n",
        "\n",
        "def SBx2up(z, x, y, l, d):\n",
        "    factorcomun = (z + d/2)/((x + l / 2) ** 2 + (z + d / 2) ** 2)\n",
        "    izquierda = (l / 2 - y) / np.sqrt((y - l /2) ** 2 + (x + l / 2) ** 2 + (z + d / 2) ** 2)\n",
        "    derecha = (l / 2 + y) / np.sqrt((y + l / 2) ** 2 + (x + l / 2) ** 2 + (z + d / 2) ** 2)\n",
        "    return C * factorcomun * (izquierda + derecha)\n",
        "\n",
        "def SBxup(z, x, y, l, d):\n",
        "    return SBx1up(z, x, y, l, d) - SBx2up(z, x, y, l, d)\n",
        "\n",
        "def SBx1down(z, x, y, l, d):\n",
        "    factorcomun = (z - d/2)/((x - l / 2) ** 2 + (z - d / 2) ** 2)\n",
        "    izquierda = (l / 2 - y) / np.sqrt((y - l /2) ** 2 + (x - l / 2) ** 2 + (z - d / 2) ** 2)\n",
        "    derecha = (l / 2 + y) / np.sqrt((y + l / 2) ** 2 + (x - l / 2) ** 2 + (z - d / 2) ** 2)\n",
        "    return C * factorcomun * (izquierda + derecha)\n",
        "\n",
        "def SBx2down(z, x, y, l, d):\n",
        "    factorcomun = (z - d/2)/((x + l / 2) ** 2 + (z - d / 2) ** 2)\n",
        "    izquierda = (l / 2 - y) / np.sqrt((y - l /2) ** 2 + (x + l / 2) ** 2 + (z - d / 2) ** 2)\n",
        "    derecha = (l / 2 + y) / np.sqrt((y + l / 2) ** 2 + (x + l / 2) ** 2 + (z - d / 2) ** 2)\n",
        "    return C * factorcomun * (izquierda + derecha)\n",
        "\n",
        "def SBxdown(z, x, y, l, d):\n",
        "    return SBx1down(z, x, y, l, d) - SBx2down(z, x, y, l, d)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LXdkNVzLyRu2"
      },
      "source": [
        "## Campo en la componente y\n",
        "\n",
        "def SBy1up(z, x, y, l, d):\n",
        "    factorcomun = (z + d/2)/((y - l / 2) ** 2 + (z + d / 2) ** 2)\n",
        "    izquierda = (l / 2 - x) / np.sqrt((y - l /2) ** 2 + (x - l / 2) ** 2 + (z + d / 2) ** 2)\n",
        "    derecha = (l / 2 + x) / np.sqrt((y - l / 2) ** 2 + (x + l / 2) ** 2 + (z + d / 2) ** 2)\n",
        "    return C * factorcomun * (izquierda + derecha)\n",
        "\n",
        "def SBy2up(z, x, y, l, d):\n",
        "    factorcomun = (z + d/2)/((y + l / 2) ** 2 + (z + d / 2) ** 2)\n",
        "    izquierda = (l / 2 - x) / np.sqrt((y + l /2) ** 2 + (x - l / 2) ** 2 + (z + d / 2) ** 2)\n",
        "    derecha = (l / 2 + x) / np.sqrt((y + l / 2) ** 2 + (x + l / 2) ** 2 + (z + d / 2) ** 2)\n",
        "    return C * factorcomun * (izquierda + derecha)\n",
        "\n",
        "def SByup(z, x, y, l, d):\n",
        "    return SBy1up(z, x, y, l, d) - SBy2up(z, x, y, l, d)\n",
        "\n",
        "def SBy1down(z, x, y, l, d):\n",
        "    factorcomun = (z - d/2)/((y - l / 2) ** 2 + (z - d / 2) ** 2)\n",
        "    izquierda = (l / 2 - x) / np.sqrt((y - l /2) ** 2 + (x - l / 2) ** 2 + (z - d / 2) ** 2)\n",
        "    derecha = (l / 2 + x) / np.sqrt((y - l / 2) ** 2 + (x + l / 2) ** 2 + (z - d / 2) ** 2)\n",
        "    return C * factorcomun * (izquierda + derecha)\n",
        "\n",
        "def SBy2down(z, x, y, l, d):\n",
        "    factorcomun = (z - d/2)/((y + l / 2) ** 2 + (z - d / 2) ** 2)\n",
        "    izquierda = (l / 2 - x) / np.sqrt((y + l /2) ** 2 + (x - l / 2) ** 2 + (z - d / 2) ** 2)\n",
        "    derecha = (l / 2 + x) / np.sqrt((y + l / 2) ** 2 + (x + l / 2) ** 2 + (z - d / 2) ** 2)\n",
        "    return C * factorcomun * (izquierda + derecha)\n",
        "\n",
        "def SBydown(z, x, y, l, d):\n",
        "    return SBy1down(z, x, y, l, d) - SBy2down(z, x, y, l, d)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u-tgXS5eLpj-"
      },
      "source": [
        "**ESTUDIO DE ERRORES PRODUCIDOS POR LA VARIACIÓN DE LA DISTANCIA ENTRE BOBINAS (Bobina cuadrada)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cZwo4n7rhVvW"
      },
      "source": [
        "# la distancia ideal está dada por d = 0.5445 * l\n",
        "l = 1   # longitud de la bobina\n",
        "d = np.linspace(0.5445 * l - 0.1, 0.5445 * l + 0.1, 51)  # intervalo de variación de d que contiene a la distancia ideal\n",
        "dimensiony = 1  # tamaño del cubesat (luego se divide para obtenerlo en cm)\n",
        "dimensionz = 1  # tamaño del cubesat\n",
        "z = np.linspace(0, dimensionz / 20, 200) # vamos a ver puntos desde el centro, hasta el borde del cubesat\n",
        "y = np.linspace(0, dimensiony / 20, 200)\n",
        "Bzsquarez = np.zeros(len(z))  # array para el campo en z, variando en z (desde 0 a 5cm) x = 0, y = 0\n",
        "Bzsquarey = np.zeros(len(y))  # \"\" \"\" variando y desde 0 a 5 cm (z = 0, x = 0)\n",
        "HomogeneidadSz = np.zeros(len(d)) # array donde se guardan los valores de homogeneidad para la variación en z\n",
        "HomogeneidadSy = np.zeros(len(d)) # \"\"  \"\" para la variación en y\n",
        "\n",
        "## En cada iteración calculamos el campo (variando en y y en z), luego calculamos la homogeneidad\n",
        "## y guardamos el máximo valor de la homogeneidad para una dada distancia, \n",
        "## así vamos completando el array de homogeneidad\n",
        "\n",
        "for h in range(len(d)):\n",
        "    for i in range(len(z)):\n",
        "        Bzsquarez[i] = SBzdown(z[i], 0, 0, l, d[h]) + SBzup(z[i], 0, 0, l, d[h]) \n",
        "    \n",
        "    Bz0 = SBzdown(0, 0, 0, l, d[h]) + SBzup(0, 0, 0, l, d[h])\n",
        "    HomogeneidadSz[h] = max(abs(Bzsquarez - Bz0) / abs(Bz0)) * 10 ** 6  ## lo queremos en micro tesla\n",
        "\n",
        "for h in range(len(d)):\n",
        "    for i in range(len(y)):\n",
        "        Bzsquarey[i] = SBzdown(0, 0, y[i], l, d[h]) + SBzup(0, 0, y[i], l, d[h])\n",
        "    Bz0 = SBzdown(0, 0, 0, l, d[h]) + SBzup(0, 0, 0, l, d[h])\n",
        "    HomogeneidadSy[h] = max(abs(Bzsquarey - Bz0) / abs(Bz0)) * 10 ** 6\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2HYx-O_RhWW7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "outputId": "477edd61-8026-4529-8f19-ba34e9c06de3"
      },
      "source": [
        "## gráfico de homogeneidad versus distancia de separación para bobinas cuadradas\n",
        "\n",
        "plt.plot(d, HomogeneidadSz)\n",
        "plt.plot(d, HomogeneidadSy, \"red\")\n",
        "plt.xlabel(\"Distancia de separación [m]\")\n",
        "plt.ylabel (\"Homogeneidad [ppm]\")\n",
        "plt.legend(['Variación en z', 'Variaciones en y'])\n",
        "plt.title('Variaciones con respecto a la distancia ideal para un par de bobinas cuadradas')\n",
        "plt.grid()\n",
        "plt.ylim(0, 500)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 500.0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dkI40J88nL02"
      },
      "source": [
        "# la distancia ideal está dada por d = r\n",
        "r = 0.5\n",
        "dcircle = np.linspace(r - 0.1, r + 0.1, 51)  # intervalo de variación de d con respecto a la distancia ideal\n",
        "dimensiony = 1  # tamaño del cubesat (luego se divide para obtenerlo en cm)\n",
        "dimensionz = 1  # tamaño del cubesat\n",
        "z = np.linspace(0, dimensionz / 20, 200) ## vamos a ver puntos desde el centro, hasta el borde del cubesat\n",
        "y = np.linspace(0, dimensiony / 20, 200)\n",
        "Bzcirclez = np.zeros(len(z))  # array para el campo en z, variando en z\n",
        "Bzcircley = np.zeros(len(y))  # \"\" \"\" variando y\n",
        "HomogeneidadCz = np.zeros(len(dcircle)) # array donde se guardan los valores de homogeneidad para la variación en z\n",
        "HomogeneidadCy = np.zeros(len(dcircle)) # \"\"  \"\" para la variación en y\n",
        "\n",
        "## lo mismo que antes, pero integrando numéricamente\n",
        "\n",
        "for h in range(len(dcircle)):\n",
        "  for i in range(len(z)):\n",
        "    ## integral numérica en cuadraturas\n",
        "    Lz = quad(dBzup, 0, 2*np.pi, args=(z[i], 0, 0, r, dcircle[h])) + quad(dBzdown, 0, 2*np.pi, args=(z[i],0 ,0 ,r, dcircle[h]))   \n",
        "    Bzcirclez[i] = (Lz[0] + Lz[2]) * 10 **6 \n",
        "  Bz0 = Bzcirclez[0] # Campo en el centro\n",
        "  # Cálculo de la homogeneidad\n",
        "  HomogeneidadCz[h] = max(abs(Bzcirclez - Bz0) / abs(Bz0)) * 10 ** 6 \n",
        "\n",
        "for h in range(len(dcircle)):\n",
        "  for i in range(len(y)):\n",
        "    ## integral numérica en cuadraturas\n",
        "    Ly = quad(dBzup, 0, 2*np.pi, args=(0, y[i], 0, r, dcircle[h])) + quad(dBzdown, 0, 2*np.pi, args=(0, y[i], 0, r, dcircle[h]))   \n",
        "    Bzcircley[i] = (Ly[0] + Ly[2]) * 10 **6 \n",
        "    \n",
        "  Bz0 = Bzcircley[0]\n",
        "  HomogeneidadCy[h] = max(abs(Bzcircley - Bz0) / abs(Bz0)) * 10 ** 6  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "amYDDQlQpcgE",
        "outputId": "639b3e95-dc87-4f53-93a8-eaaadaa18d29"
      },
      "source": [
        "## Gráfico de homogeneidad para variaciones en la distancia de separación para bobinas circulares\n",
        "## Se ve que la distancia ideal depende del punto de observación ya que solo vale en el centro (aunque el corrimiento es despreciable)\n",
        "plt.plot(dcircle, HomogeneidadCz)\n",
        "plt.plot(dcircle, HomogeneidadCy, \"red\")\n",
        "plt.xlabel(\"Distancia [m]\")\n",
        "plt.ylabel (\"Homogeneidad [ppm]\")\n",
        "plt.legend(['Variación en z', 'Variaciones en y'])\n",
        "plt.title('Variaciones con respecto a la distancia ideal para un par de bobinas circulares')\n",
        "plt.grid()\n",
        "#plt.ylim(0, 500)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "tHIr4sphfDzq",
        "outputId": "846a7d63-a435-44db-c759-41d2d4447f7e"
      },
      "source": [
        "## Gráfico comparativo de homogeneidad en función de la distancia para la bobina circular y la cuadrada\n",
        "\n",
        "plt.plot(dcircle, HomogeneidadCz)\n",
        "plt.plot(d, HomogeneidadSz, \"red\")\n",
        "plt.xlabel(\"Distancia [m]\")\n",
        "plt.ylabel (\"Homogeneidad [ppm]\")\n",
        "plt.legend(['Bobina circular', 'Bobina cuadrada'])\n",
        "plt.title('Variaciones con respecto a la distancia ideal')\n",
        "plt.ylim(0, 1000)\n",
        "plt.grid()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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cBw5Abe2AL62tDzAyI40JY7JjIJiPqKqCWbMgPz/RkhgRMMVhJCVlRbm8t7+N1vauRIvinSE4yGvrmykryvVHRuBYoeooDjNTJT2mOIykpNR1kL+7x0ejjmOOcRYeGoTiqKsPWHLDzZuhsdEUhw8wxWEkJcF8Tb4yV40YAbNnD1hxHOzsYsueFouoMse4bzDFYSQlwRHHJj86yF9/3TG7eOTdxha6FZvDUVUFWVmO8jWSGlMcRlKSNzKD4vyR/hpxgKM49uyBLVs8X9KTo8oUh5PzKzPFU674AFMcRtJSVujTkFyAFSs8XxKcw1Geyqaqzk7nMzMzlS8wxWEkLWVFPkuvDnDccc5kwJUrPV9SWx/giFFZ5I7MiKFgSc66dU6uL1McvsAUh5G0lBXn0hBop6mtI9GieCc724muGoCDvLa+mWnjUni0AeYY9xmmOIykpWf9cb+NOoIOcg+oKnW7LRSXqioYOxamTUu0JIYHTHEYSYsvkx2Cozh27oQdO/otWh84yIGDnbZcbHDin6TwBEgfYYrDSFqmFOQgAnV+jKwCT6OO2t2W3JDmZsfHYWYq32CKw0hasjLTmTgm23+RVXPmOP+cvSgOC8W1pWJ9iCkOI6nxZWRVfj4cdZSnkNy6+mayM9M5YlRWHARLUmypWN9hisNIaoJZcnUAM7GTAo8OcmfVv1zSUjm5YVUVTJkCJSWJlsTwiCkOI6kpK8rlwMFOGpvbEy3KwKishK1bob4+YrFaS25oGXF9iCkOI6nxdWQVRJwI2NbRxfZ9ramtOOrrYdMmUxw+wxSHkdSU+TFLLjg5lyCiuWpTQzOqKZ5qpLraeTXF4StMcRhJzcQx2WSmi/+y5I4ZA+XlERWHRVThmKnS0hzTnuEbTHEYSU1GehpTCnL8N+KAfh3kdfXNiBwaVaUkVVVOipa8FFaePsQUh5H0+DIkFxzFUVsL+/aFPV1bH2DC6GyyR6THWbAkQdUxVZmZyneY4jCSnrIiJ716d7cPQ3IBVq0Ke7q2PpDaM8Y3b4aGBlMcPsQUh5H0lBXlcbCzmx1NbYkWZWBESD2iqtTVN6f2crGWEde3JERxiMjXRGSdiKwVkYdEJEtEykTkNRHZKCKPiMgIt+xI9/1G93xpImQ2EodvI6uKi2Hy5LAzyHc2tdHS3pXay8VWVcHIkbZUrA+Ju+IQkYnAV4H5qjobSAc+A/wA+JGqTgf2Al9wL/kCsNc9/iO3nJFCHJrLEUiwJIOgDwd5T3LDVB9xVFTYUrE+JFGmqgwgW0QygBxgB3Aq8Kh7/n7gPHf/XPc97vkPiVju5VSiZNRIsjPT2dTQkmhRBk5FBbz9NgQOV3p1rhKcnqojjs5OR6GamcqXxH2tSlXdLiJ3AluAVuD/gBXAPlXtdIttAya6+xOBre61nSKyHygEGkLrFZErgSsBSkpKWL58+aBlDAQCQ7reryRzv4uzlJp3trB8+e6o1x3LfhdmZnKsKq/fey9Nxx7bc/yF9QfJSod1K15hfYL+ByXyfufW1rKgpYX1ubnsToAMyfysx5Ko9VtV47oBY4HngWIgE/grcBGwMaTMZGCtu78WmBRyrhYoitRGZWWlDoVly5YN6Xq/ksz9/vIfV+gH7ng+JnXHtN/bt6uC6k9/etjhz939qn7sZ/+MXbseSOj9/u1vnc/lnXcS0nwyP+uxpL9+AzXq4Xc8EaaqDwObVLVeVTuApcCJwBjXdAUwCdju7m/HUSS450cDjfEV2Ug0ZUW5bN3bSkdXd6JFGRjjxztZX3v5OerqA+YYHzMGpk9PtCTGIEiE4tgCLBaRHNdX8SFgPbAM+KRb5hLgMXf/cfc97vnnXc1opBBlRbl0dStb9/jMzyHyPgd5S3sn7+1vM8f4ggW2VKxPibviUNXXcJzcrwNrXBnuAq4Dvi4iG3F8GPe4l9wDFLrHvw5cH2+ZjcRTVuzTLLng5GFatw7anHkowaVwUzZHVWsrrFljCzf5mLg7xwFU9Wbg5l6H64D3hVioahvwqXjIZSQvZYU+VhwVFc7SqKtXw8KFPckNU9ZUZUvF+h6bOW74grG5IxiTk+lfxQE95qq6+mbSBKYW5iRQqARiqdR9jykOwzf4NtnhlClQUNCjOGrrA0wam0NWZoomN6yqgokTncABw5eY4jB8Q1mhTxVHLwe55aiypWL9jikOwzeUFeWyY38bre1diRZl4FRWwpo1dLcdpK4hhUNx9+yBjRvNMe5zTHEYviEYWbXZb6sBgjPiaG+noep12jq6UzeiqqbGebURh68xxWH4hlK/R1YB+/75GpDC64wHHePz5ydWDmNI9BmOKyIVHq7vUNU1UZTHMPrkUJZcHyqOadNg9Gi6a2pgxpGpqziqqmDGDBg9OtGSGEMg0jyOF4BqINLUzjKgNJoCGUZf5I7MoGTUSH8qDhGYN4/c9WvIn/N5ivNGJlqi+KPqKI7TTku0JMYQiaQ4qlX11EgXi8jzUZbHMCLi25BcgIoKSv71c6YXZJGSKwNs3w47d5pjfBjQp4+jP6XhtYxhRJOyolzq6n24oBNAZSUjOto5oT36qeF9gS0VO2zwlHJERI7DMUn1lFfVpTGSyTD6pKwol70tHexraWdMzohEizMgWmYfRw4wr2FTokVJDNXVzmp/c+YkWhJjiPSrOETkd8BxwDogmNNacdKhG0ZcKStywlg3NTQzb4q/FEfdmAmUZWYxfds7iRYlMVRVwXHHQVZWoiUxhoiXEcdiVT0m5pIYhgeCkVWbG5uZN2VsgqUZGLV7WmkdV87sDesSLUr86e525nB87nOJlsSIAl7mcbwiIqY4jKRgSkEOaQKb6v3nIK+tb2btEdPIWrfGyQ6bSrzzDjQ1mWN8mOBFcfweR3m8LSKrRWSNiKyOtWCGEY4RGWlMLsihzoeRVbX1AXZMOwZpboYNGxItTnwxx/iwwoup6h7g8ziLLvls3U5jOOLXkNy6+mYKZs+BPwErVsDMmYkWKX5UV0Nubmr1eRjjZcRRr6qPq+omVX03uMVcMsPog1I3S66fVhDu7lY2NQTIOm6W4xzutQb5sKe62kn0mJ6iqeSHGV5GHCtF5EHgCeBg8KCF4xqJorw4l5b2LnYfOEjJKH9E6Ly3v5W2jm7Kx49xwlFTSXG0tzur/n31q4mWxIgSXhRHNo7COD3kmIXjGgkjGFlVV9/sG8VR6zrzy4tynYSHDzzgRBqlpUCe0TVrHOVhjvFhQ7+KQ1Uvi4cghuGV0JDc46cVJlgabwRnu08bl+eYbH71K6irg+nTEyxZHAhmxDXFMWzo9++OiJSLyBMiUi8iu0XkMREpi4dwhhGOCaOzGZGR5isHeW19gFFZGRTmjji0BvmKFYkVKl5UV0NhIZSWJloSI0p4GSc/iBMHMh6YAPwv8HAshTKMSKSlCWWFudT5aC5HXX0z08blOckNZ81yUm+kip8juFRsKiZ2HKZ4URw5qvoHVe10tz8C/jAsG8MWJyTXP8kOa+sDlLvpUhgxAo49NjUUR3MzrF9vZqphhhfF8bSIXC8ipSIyVUS+BTwlIgUiUhBrAQ0jHGXFuWzZ00JnV/JPLQoc7GRX00GmjQtZvKmy0jFV+SikeFC8/roTBGCKY1jhJarq0+7rv/c6/hmc6KryqEpkGB4oK8ylo0vZvq+VqYXJvZpe0DHeM+IAx89x993w7rvD2/YfnDFuimNY4SWqyhzhRtJR5i69WtfQ7APF4fhipoeOOEId5MNZcVRXw5QpUFKSaEmMKOIlqipLRL4uIktF5M8icq2ImI/DSCg964/7wEFeWx8gPU2YUhCiOI47zplFPdz9HNXVNtoYhnhNcjgL+Bnwc3f/D7EUyjD6ozB3BPlZGWxuTH7FUVffzJSCHEZkhHzdsrKc6KrhrDgaG525KqY4hh1efByze63HsUxE1sdKIMPwgohQ7pNkh05EVRhzWmUlPPmk4yAfjqGqNvFv2OJlxPG6iCwOvhGRRUBN7EQyDG84648nt+Jwkhs2U14cRnFUVEB9PWzbFn/B4kF1taMQKysTLYkRZbwojkrgZRHZLCKbgVeABbYuh5Foyory3OSBybso0vZ9rRzs7Ka8OO/9J4M/qMPVXFVdDTNmwOjRiZbEiDJeTFVnxFwKwxgEpUU5qMK7jS3MOCI/0eKEJbjg1LRwimPOHCfJ4YoVcO65cZYsxqg6obinn95/WcN3eAnHtbU3jKQkOC9iU0MgaRVH7W53Dkc4U1VODhx99PAccWzbBrt2mX9jmNKnqUpE+n2avZQxjFhRWpQDkNTLyNY1hCQ3DEdwBvlwI+gYt6VihyWRfBxHu2uM97WtAYoG06iIjBGRR0XkLRF5U0SOd1OYPCsiG9zXsW5ZEZGfishGt92KwbRpDD/yszIpzh/J5mRWHPXNlBe7yQ3DUVEBO3fCjh3xFSzWVFdDRoZjjjOGHZFMVV4WBx6sV/InwN9V9ZMiMgLIAW4E/qGqt4vI9cD1wHXAmcCR7rYI+JX7ahhJv/54XX0zJ06P8P8q6CBfsQLOOSc+QsWD6mpnkmOWzRUejvQ54ghdXzzCNuA4QhEZDZwC3OO2066q+4BzgfvdYvcD57n75wK/V4dXgTEiMn6g7RrDk2SeyxE42MnOprbw/o0gc+c6IavDyc/R3W0zxoc5XqKqok0ZUA/cKyJzgBXANUCJqgbH6zuBYHKbicDWkOu3uccOG9uLyJXAlQAlJSUsX7580AIGAoEhXe9X/Njv7qZ2GgId/O3ZZeRmDm4SXaz6vXm/MyBv3bWZ5cv7/o+1YPJkWp95hrWnnBJ1GSIRq35nb9nCoqYm3ho1ip1J+jz58VmPBlHrt6rGdQPmA53AIvf9T4BbgX29yu11X58ETgo5/g9gfqQ2KisrdSgsW7ZsSNf7FT/2++9rd+jU657UVVv2DrqOWPX7ryu36dTrntS3dzZFLvjZz6pOmhQTGSIRs/v9hz+ogurq1bGpPwr48VmPBv31G6hRD7/jXiYARpttwDZVfc19/yhQAewKmqDc193u+e3A5JDrJ7nHDKMnlUcymqtqdwdIE5hamBO5YGWlE766e3fkcn6huvpQqLExLIkUjntARJr62gbboKruBLaKyAz30IeA9cDjwCXusUuAx9z9x4GL3eiqxcB+PWTSMlKcKYU5iCRnSG5tQzOTC3IYmZEeuWAwxfpw8XNUVzt9ykiEJdyIB33eWVXNBxCRW3H8CX8ABPgczvrjQ+Fq4AE3oqoOuAxHif1JRL4AvMuhBaSeAs4CNgItblnDAGBkRjqTxmYnZUhuXX1z+OSGvZk3z3ldsQLO8Hmiho4OWLkSvvSlREtixBAvfwk+pqqhwdi/EpE3gP8cbKOqugrH19GbD4Upq8BVg23LGP6UFeVRl2TrjzvJDQOcMK2w/8KjR8P06cNjIuC6ddDWZhFVwxwvPo5mEfmciKSLSJqIfA5Ivr93RspSXpTL5oaWYPBEUuAkX+yOHIobSmXl8DBVWSr1lMCL4vgsjtlol7t9yj1mGElBWVEugYOd1AcOJlqUHoLp3sMmNwxHZaWz/nhjYwyligPV1TB2LEyblmhJjBjSr+JQ1c2qeq6qFqlqsaqep6qb4yCbYXgiGZeRrauPkNwwHMPFQV5dDfPnD8+FqYwevK45fpWI/FJEfhfc4iGcYXihLAlDcusamskfmUFx3khvFwQVh5/9HK2tsGaNmalSAC+mqj8ARwAfAV7AmUdxIJZCGcZAmDAmmxEZaUmlOGrrA5QX5/ad3LA3Y8dCWZm/RxyrVkFXlymOFMCL4piuqt8GmlX1fuBsLMmgkUSkpwmlhTlJNZejrr7Zu38jiN9TrJtjPGXwojg63Nd9IjIbGA2Mi51IhjFwkilLbkt7Jzv295PcMBwVFVBXB3v3xkawWFNdDePHw8SJiZbEiDFeFMdd7toY38aZxb0euCOmUhnGACkryuPdxma6uhMfkhuMqAq7zngkginWV66MskRxwjLipgxeoqp+q6p7VfUFVS1X1XGq+ut4CGcYXikvyqWjS9m+tzXRolA70IiqIH52kO/fD2+/bYojRehz5riIfD3Shar6w+iLYxiDo8z9ka5rCDClv6SCMaauvhkRKC0coOIoKoIpU/zpIFQpRd4AACAASURBVA8qO1McKUGkEUe+u80HvoSzBsZE4Is42WwNI2lIppDcuoZmJo3NJiuzn+SG4fCrgzzoGJ8fLpOQMdyIlOTwFgAReRGoUNUD7vvvAH+Li3SG4ZHC3BHkZ2UkheKo3R2gvGiA/o0glZXwl784pp/Ro6MrWCyprobycij0kJvL8D1enOMlQHvI+3YOrc5nGEmBiCTFMrJOcsPmgfs3ggT9HH5zkJtjPKXwojh+D1SJyHfc0cZrwH2xFMowBkNZUW5PRFOi2NnURmtH18DncAQJRlb5yc+xezds2WKKI4XwElX1PZw1MPa622WqelusBTOMgVJWlOdmpe1KmAyHQnEHOeIYNw4mTfKXn8Mm/qUckaKqRqlqk4gUAJvdLXiuQFX3xF48w/BOWXEuqvBuYwszjshPiAzBUNxBjzjAfw7y6mpISztkZjOGPZFGHA+6ryuAmjCvhpFUHFp/PHGLOtXVB8gdkc64fI/JDcNRUQHvvAMHfJISrrraWV88bwjK0vAVkaKqznFfy+InjmEMntKi4FyOxPk56hqamTYuz3tyw3BUVoKqkzTw5JOjJ1wsUHUUx9lnJ1oSI454SasuInKRiHzbfT9FRBbGXjTDGBh5IzMYlz8yoetyeF5nPBJBB7kfzFVbtkB9vfk3UgwvUVW/BI7n0Kp/B4BfxEwiwxgCiUx22NrexfZ9rQPPUdWbI46ACRP8oTjMMZ6SeFEci1T1KqANQFX3AiNiKpVhDJLy4sQpjrqGQeaoCkdFhT9CcqurITMTjjsu0ZIYccRTWnURSQcUQESKge6YSmUYg6SsKJfG5nb2t3T0XzjK9ITiDnbWeCiVlfDWW9Cc+JnwEamuhjlzYOQQggEM3+FFcfwU+AswTkS+B7wEfD+mUhnGIClzf7Q3Ncb/BzeoOMqG6uMAR3F0dzsO8mSlu9sxp5mZKuXwMgHwAeBbwG3ADuA8Vf3fWAtmGIOhLIEhuXUNASaOySZ7xCCSG/bGDw7yDRugqckURwrSZzhuLzYATcHyIjJFVbfETCrDGCRTCnJIExISWVVXP4QcVb0ZPx5KSpLbz2EZcVOWfhWHiFwN3AzsAroAwfF3mDfMSDpGZKQxuSD+64+rKnX1AT5ZOSk6FYok/wzy6mrIyXEm/xkphZcRxzXADFVtjLUwhhENEhGSu/vAQZrbu4YeihtKZSX8/e/Q0uL8QCcbNTVO9FeGV8OFMVzw4hzfCuyPtSCGES2CikM1fuuPD3q52EgEHeSrV0evzmjR2emkfjczVUri5a9CHbBcRP4GHAwetKVjjWSlvCiXlvYudh84SMmorLi0eSgrbhRHHKFrkC9eHL16o8G6ddDaao7xFMWL4tjibiOwiX+GDwiG5NbVN8dVcWRlpjE+mu1NmgTFxcnp57AZ4ylNv4ojZAnZHFVtib1IhjE0yooPrT9+/LT4LGVa1xCgrCiPtLQhJDfsTTI7yGtqnKVtp09PtCRGAvCS5PB4EVkPvOW+nyMiv4y5ZIYxSMaPymJkRlpc53LU1TczLZr+jSCVlY5ZqK0t+nUPhepqx78xlCzAhm/x4hz/MfARoBFAVd8ATomlUIYxFNLSJK6RVQc7u9i2tyW6/o0gFRXQ1ZVcDvK2NkceM1OlLF4UB6q6tdehxK3NaRgeiOf64+82ttCtxG7EAcllrlq92omqMsWRsngKxxWREwAVkUwR+QbwZozlMowhUVaUy5Y9LXR2xT4fZ+1uNxQ3GskNezNlChQWJpfiMMd4yuNFcXwRuAqYCGwH5rrvh4SIpIvIShF50n1fJiKvichGEXlEREa4x0e67ze650uH2rYx/CkryqWzW9m2tzXmbQVnqZfFYsSRjA7y6moYN86J+jJSEi9JDhtU9XOqWqKq41T1oijNIr+Gw0cuPwB+pKrTgb3AF9zjXwD2usd/5JYzjIgE/Q3x8HPU1gcoGTWSvJExmkFdWQlr1yaPg7y62hltmGM8ZfESVfXTMNutInLuYBsVkUnA2cBv3fcCnAo86ha5HzjP3T/XfY97/kMypAWdjVQguHxrcEZ3LHGWi42BmSpIZaXjU1izJnZteCUQgDffNDNViuPlL1IWMBMIplL/BLAJmCMiH1TVawfR7o9xUrXnu+8LgX2q2um+34ZjGsN93Qqgqp0ist8t3xBaoYhcCVwJUFJSwvLlywchlkMgEBjS9X5luPU7NxP+tXoD07siJ3IeSr9VlXd2tLBofEbMPrusjg4WA+889BDvRXFhp8H0e/QbbzBPldUjRrDHx8/KcHvWvRK1fqtqxA14FUgPeZ8BvAKkA+v7uz5MfecAv3T3lwBPAkXAxpAyk4G17v5aYFLIuVqgKFIblZWVOhSWLVs2pOv9ynDr93m/eEkvvOuVfssNpd/1B9p06nVP6m//WTfoOvqlu1u1oED18sujWu2g+n3nnaqgumtXVGWJN8PtWfdKf/0GatTD77gX5/hYIHQcngsUqGoXIbmrBsCJwMdEZDPwMI6J6ifAGBEJjoAm4TjicV8nA7jnR+POKTGMSMRjLsehHFUxcIwHSSYHeXW1E+k1blyiJTESiBfFcQewSkTuFZH7gJXAf4tILvDcQBtU1RtUdZKqlgKfAZ5X1c8By4BPusUuAR5z9x933+Oef97VjIYRkfKiXHbsb6OlvbP/woOkzvWhTIuljwMOOcgPDua/WhSpqTH/huEpquoe4ATgrzhrj5+kqr9V1WZV/WYUZbkO+LqIbMTxYdzjHr8HKHSPfx24PoptGsOYYLLDzQ2xS7FW19DMiIw0Jo7NjlkbgKM4OjoS6yDfswdqay2VuuF56dgFwMnufjfwXjQaV9XlwHJ3vw5YGKZMG/CpaLRnpBZB81FdQ4BjJoyKSRt19QFKC3NIj2Zyw3CEziBP1A93TY3zaiOOlMdLOO7tOHMu1rvbV0Xk+7EWzDCGSmmhmyU3hqlHYh6KG6S0FMaOTayfI6g4gkrMSFm8jDjOAuaqajeAiNyP4+e4MZaCGcZQyR6RzoTRWTFzkHd0dbNlTwtnHntETOo/DBEn4WEiFUd1NRx5JIwZkzgZjKTAU5JDIPRJGR0LQQwjFpQV5/akBIk2W/a00Nmt8RlxgPNPf82axDnIzTFuuHhRHLcBK0XkPne0sQL4XmzFMozo4GTJDcRk/fG4hOKGEnSQr10bn/ZC2bkTtm0zx7gBeIuqeghYDCwF/gwcr6qPxFoww4gGZUV5NLV1srelI+p1B9OZxGQdjnAkMsW6OcaNELyaqord1wzgBBH5eIzkMYyo0hNZFYOcVXX1AYryRjA6OzPqdYelvNzxLyRKcaSlwbx58W/bSDr6dY6LyO+A44B1OKG4AIozAjGMpCaY7LCuoZn5pQVRrTtuEVVBEukgr66GY46B3DiZ5YykxktU1WJVPSbmkhhGDJg4JpvMdIlJZFVdQzOnH1MS9XojUlkJP/kJtLfDiBHxaVPVGXGcdVZ82jOSHi+mqldExBSH4Usy0tOYUpAT9bkc+1ra2dPcHj/HeJDKSkdpxNNBvnUr7N5t/g2jBy8jjt/jKI+dOEkNBVBVPS6mkhlGlCgryov6iCMY4htXUxUc7iCvqIhPm0HHuEVUGS5eRhz3AJ8HzgA+ipMW/aOxFMowokl5cS6bGpvp7o5eSG4wFDcmy8VGYto0GD06vn6O6mrIyIDj7L+i4eBlxFGvqo/HXBLDiBFlRbm0d3azfV8rkwtyolJnXX2AjDRhSpTq80wiHOQ1NY7SyMqKX5tGUuNlxLFSRB4UkQtF5OPBLeaSGUaUCEZWRdNcVVffzJSCHDLTvUa0R5HKSli92vF1xJqgY9zMVEYIXp76bBzfxuk4JqqgucowfEHQnBRVxdEQiL9jPEjQQb5uXezbqq2FffvMMW4cRr+mKlW9LB6CGEasKM4bSd7IjKgpjq5uZXNjC0tmJGgVvFAHeawn5FVXO6824jBC8JJWfZKI/EVEdrvbn0VkUjyEM4xoICJOzqooKY739rXS3tndYwKLO9OmwahR8fFz1NQ4vo1Zs2LfluEbvJiq7sVZvnWCuz3hHjMM3+CsPx6dtCNxz1HVm7S0+DnIa2pg7lzIjFNaFcMXeFEcxap6r6p2utt9HMpdZRi+oKwol217W2nr6BpyXXHPihuO+fNj7yDv6nKUk/k3jF54URyNInKRiKS720VAY6wFM4xoUl6ci6qzhsZQqWsIkJ+VQWFunFJ+hKOy0lmXI5YO8rffhuZm828Y78OL4vg34NPATmAH8EnAHOaGrwjO8K6LQuqRuvpmyovzEInxOuORiEeK9aBj3EYcRi+8rMfxrqp+TFWLVXWcqp6nqlviIZxhRIvSImeiXjQiq+rqm5mWKMd4kHg4yGtqIC8Pjjoqdm0YvqTPcFwR+RlO+vSwqOpXYyKRYcSA/KxMivNHDtlB3nywk51NbYn1b0B8HOTV1U4b6emxa8PwJZFGHDU4y8SuAD4Wsh/cDMNXOMvIDm3EERyxJCyiKpRYOsg7OmDVKjNTGWHpc8ShqvcH90Xk2tD3huFHyotyeXb9riHV0ZMVN9EjDjjcQR7tiYBr1zp1m2PcCIPXRDvRSytqGAmirCiXxuZ29g9h/fG6+gAiUFqYJIoDYmOuslTqRgQSkKHNMBJD0LxUNwQ/R119MxPHZJOVmQR2/1g6yGtqnPXNp02Lft2G74nkHD/AoZFGjog0BU/hLOQ0KtbCGUY0KQvJkjtvythB1VHXEOipJ+HE0kEezIibyJBjI2npc8ShqvmqOsrdMkL2801pGH5kSkEO6WmDX39cVdlU38y0ZHCMB4mFg7ytDdasMTOV0SdmqjJShhEZaUwemz3oyKrdBw7S3N6VHI7xILGYQb5mjRNVZRFVRh+Y4jBSiqFkye1JbhjvdcYjEQsHuaVSN/rBFIeRUpQX57G5YXDrjydFcsPexMJBXlMDxcUweXL06jSGFaY4jJSirCiX1o4udja1DfjauvpmsjPTOWJUEq29HQsHuTnGjX4wxWGkFENZfzwYUZWWlmQ/qNF0kLe0OP4SM1MZETDFYaQUh+ZyDEJx1Dcnl5kqSDQd5KtWQXe3KQ4jInFXHCIyWUSWich6EVknIte4xwtE5FkR2eC+jnWPi4j8VEQ2ishqEamIt8zG8KFk1EiyM9Opqx/YJMCDnV1s29uSuOViIxFNB7nNGDc8kIgRRyfwH6p6DLAYuEpEjgGuB/6hqkcC/3DfA5wJHOluVwK/ir/IxnAhuP74QE1VWxpb6NYkSW7Ym2g6yKurYcIEZzOMPoi74lDVHar6urt/AHgTmAicCwQTKd4PnOfunwv8Xh1eBcaIyPg4i20MI8qLB644apMxoipINB3kQce4YUSgz5Qj8UBESoF5wGtAiarucE/tBErc/YnA1pDLtrnHdoQcQ0SuxBmRUFJSwvLlywctVyAQGNL1fiVV+p3W3M6Wxg6efX4ZmWniqd/P1TmO5+1vrWTPxiRzjgPlJSVMWrqUfz77LJqZ6ema3v1Ob27mpLffZvPxx/PuMH8OUuVZ703U+q2qCdmAPJx1PT7uvt/X6/xe9/VJ4KSQ4/8A5kequ7KyUofCsmXLhnS9X0mVfi99fatOve5J3bCrSVW99fs//rRKF/zXszGWbAg89JAqqL7+uudL3tfv5cudOp56KrqyJSGp8qz3pr9+AzXq4fc7IVFVIpIJ/Bl4QFWXuod3BU1Q7utu9/h2IHQm0iT3mGEMisGsP15XH0hOM1WQaDjIg47xYF2G0QeJiKoS4B7gTVX9Ycipx4FL3P1LgMdCjl/sRlctBvbrIZOWYQyYUjcyymtIrqqyYXeAI8flx1KsoTFtGowePXTFMWUKjBsXPbmMYUkifBwnAp8H1ojIKvfYjcDtwJ9E5AvAu8Cn3XNPAWcBG4EW4LL4imsMN0ZnZ1KUN4JNHkccuw8c5EBbJ0eWJGFEVZCggzw4ahgM1dXmGDc8EXfFoaov4azpEY4PhSmvwFUxFcpIOcqL8jxHVm3Y5cz5mD4uiRUHOD/6P/mJM4N8xIiBXbt3L9TWwuWXx0Y2Y1iR0KiqeNLR0cG2bdtoa+s/R9Ho0aN588034yBVcuHnfmdlZTFp0iQyPUYUlRXl8o+3vK0/vmH3AYDkNlWB45tob3fWC68Y4DzZoInLRhyGB1JGcWzbto38/HxKS0uRfpK3HThwgPz8JP+RiAF+7beq0tjYyLZt2ygrK/N0TVlxLg017exv7X/98Xd2BRib45i3kppQB/lAFYc5xo0BkDK5qtra2igsLOxXaRj+Q0QoLCz0NJoMEkwdstmDuWrj7gMcOS4/+Z+doTjIa2qc68cObkldI7VIGcUBJP8X3xg0A723wdDauobIOatUlXd2BZiezI7xICLOiGEwDnKbMW4MgJRSHIYRZHJBDmlCv5FVDQHHnHVksjvGgwRTrB886P2a+np4911THIZnTHHEkfT0dObOncucOXOoqKjg5Zdfjlh++fLlnHPOOWHPnXXWWezbty8WYka1/kh9SCQjM9KZXJDT71wO3zjGg1RWOuuFr13r/RrLiGsMkJRxjicD2dnZrFrlTF155plnuOGGG3jhhRcGVddTTz0VTdE81d+TbiAtdv83Ojs7yciIz2NZVpTrzB6PkAg2GIp7lB9MVXDox3/FCu+O7qoqx8w1UIe6kbKkpOK45Yl1rH+vqc/zXV1dpKenD6jOYyaM4uaPzvJcvqmpibGuI1JV+da3vsXTTz+NiHDTTTdxwQUX9JQ7++yz2bhxIx/84Af55S9/SVpaGqWlpdTU1BAIBDjzzDM56aSTePnll5k4cSKPPfYY2dnZ3H333dx11120t7czffp0/vCHP5CTk3OYHIFAgKuvvpqamhpUlVtuuYVPfOITh9X/kY98hEWLFrFixQqeeuopHn74Yf74xz+SlpbGmWeeye23386SJUu48847mT9/Pg0NDcyfP5/Nmzcf1lZVVRXXXHMNbW1tZGdnc++99zJjxgzuu+8+li5dSiAQoKura9DKdKCUFeXyWt0eVEf2WWbD7gOMysqgOL/vMklFWZnj4B6Ig/y112DWLCc1u2F4ICUVR6JobW1l7ty5tLW1sWPHDp5//nkAli5dyqpVq3jjjTdoaGhgwYIFnHLKKYDzY7t+/XqmTp3KGWecwdKlS/nkJz95WL0bNmzgoYce4u677+bTn/40f/7zn7nooov4+Mc/zhVXXAHATTfdxD333MPVV1992LW33noro0ePZs2aNRw4cIDOzs73yb1hwwbuv/9+Fi9ezNNPP81jjz3Ga6+9Rk5ODnv27PHc/5kzZ/LPf/6TjIwMnnvuOW688Ub+/Oc/A/D666+zevVqCgoKvH+gQ6S8OI/Wji72HdQ+y2zYFeDIEh9EVAUZqINc1VEc558fW7mMYUVKKo7+Rgaxms8Qaqp65ZVXuPjii1m7di0vvfQSF154Ienp6ZSUlPCBD3yA6upqRo0axcKFCykvLwfgwgsv5KWXXnqf4igrK2Pu3LkAVFZW9vzTX7t2LTfddBP79u3rGTn05rnnnuPhhx/ueT82TDjm1KlTWbx4cU/5yy67rGfkMpAf+v3793PJJZewYcMGRISOjkNzKE477bS4Kg04FJK7o7lvxbFxd4DTjinp83xSUlkJP/yh4yAf2c9IaeNG2LMHFi2Kj2zGsMCc4wni+OOPp6Ghgfr6+ojlev/TDffPd2TIj0N6enrPqOHSSy/l5z//OWvWrOHmm28e0DyHUHJz+88Km5GRQXd3N0Cf7Xz729/mgx/8IGvXruWJJ544rJyXNqJNmas4djZ3hz3fGDhIY3N78qca6U3QQb5mTf9lX3vNeTXFYQwAUxwJ4q233qKrq4vCwkJOPvlkHnnkEbq6uqivr+fFF19k4cKFgGOq2rRpE93d3TzyyCOcdNJJnts4cOAA48ePp6OjgwceeCBsmdNOO41f/OIXPe/37t0bsc7TTjuNe++9l5aWFoAeU1VpaSkrXLv6o48+Gvba/fv3M3HiRADuu+8+z/2IFUeMyiI7M51dfSiOjbuDjnGfRFQFCXWQ98drr0FuruPjMAyPmOKII0Efx9y5c7ngggu4//77SU9P5/zzz+e4445jzpw5nHrqqdxxxx0cccQRACxYsICvfOUrHH300ZSVlXH+AGzRt956K4sWLeLEE09k5syZYcvcdNNN7N27l9mzZ3PCCSewbNmyiHWeccYZfOxjH2P+/PnMnTuXO++8E4BvfOMb/OpXv2LevHk0NDSEvfZb3/oWN9xwA/PmzQvrS4k3aWlCaVFun6aqd1zFkdRZccNRWurdQf7qq7BgAQwwGMRIcbys9uS3LdwKgOvXr4+48lUoTU1NnssOJ/ze74Hc4yA3LF2tR934pDYf7Hjfuf/86xqd9Z9/1+7u7miIF19OO0113ryIRV545hnVzEzV666Lk1DJg60AGB6SeQVAw0gWzps7kYNd8My6ne87t2F3gOnj8vwTURVKZaUzCTCCXytvwwbHF2L+DWOAmOIwUpr5U8dSlC0sff39qxE7q/75zEwVZP78fh3ko4Ip9E1xGAPEFIeR0qSlCcdPyOBfGxvY1XTo3/m+lnbqDxz0n38jiIc1yEetXw+TJ8OECFPnDSMMpjiMlOfECRl0Kzy26tCoY0OPY9xnEVVBpk6FgoKIiiP/zTdttGEMClMcRspzRG4acyaPOcxcFcxR5VtTlYhjruprBvnu3WTv3GmKwxgUpjgMA/j4vIm8tfMAb+5wcpht2H2AnBHpTBidnWDJhkAkB3lw4p+bEcAwBoIpjjjip7TqQ6W0tLTP+RxeiHc69o/OmUBGmvCXlc6oY6MbUZWW5sOIqiAnnACdnRBuQuarr6JpaZYR1xgUpjjiSDBX1RtvvMFtt93GDTfcMOi6nnrqKcaMGRNF6WKPqvakJUk2CnJHsGRGMY+t2k5Xt7JhV8B/qUZ6c9ZZzqjj+uuhude6I6+9RmDaNOiVLdkwvJCaiuPaa2HJkj637LPOing+7HbttQMSoXda9W9+85vMnj2bY489lkceeeSwcmeffTYzZszgi1/8Ys8Pb/Af/ebNmzn66KO54oormDVrFqeffjqtra0A3H333SxYsIA5c+bwiU98oidNSCiBQIDLLruMY489luOPP74nW21e3qEfzUcffZRLL70UgCeeeIJFixYxb948PvzhD7Nr1y4AGhsbOf3005k1axaXX345zlwi2Lx5MzNmzODiiy9m9uzZbN26lS996UvMnz+fWbNmcfPNN/e08/e//52ZM2dSUVHB0qVLe45XVVVx/PHHM2/ePE444QTefvvtAX3WXjl/3iR2NR3kmXU72dnU5r9UI71JS4Mf/xi2b4f//u9Dx7u7obqapqOPTpxshq9JTcWRIIIpR2bOnMnll1/Ot7/9beDwtOrPPfcc3/zmN9mxYwfg/Gj+7Gc/Y/369dTW1h72gxpkw4YNXHXVVaxbt44xY8b0/Ph//OMfp7q6mjfeeIOjjz6ae+65533XhqZVf+WVVzj11FMj9uGkk07i1VdfZeXKlXzmM5/hjjvuAOCWW27hpJNOYt26dZx//vls2bLlMPm+/OUvs27dOqZOncr3vvc9ampqWL16NS+88AKrV6+mra2NK664gieeeIIVK1awc+ehCXnBdOwrV67ku9/9LjfeeOMAP3lvfOjoceRnZfA//+coJt86xkM56SS44AK44w7YutU59tZb0NRkisMYNCmZVp0f/zji6VZLq94n27Zt44ILLmDHjh20t7dTVlYGwIsvvtij1M4+++zD6glNyw7wpz/9ibvuuovOzk527NjB+vXr6e7upqysjCOPPBKAiy66iLvuuguInI49mmRlpnP2seN5uNr5gfXNcrH98YMfwGOPOSarBx5w8lMBB445JsGCGX7FRhwJItnTqoe2E3rd1VdfzVe+8hXWrFnDb37zG091hqZM37RpE3feeSf/+Mc/WL16NWeffXa/dURKxx5tzp/nZO/Nykxj4lgfR1SFMnUqfOMb8OCD8MorTkTV6NG0TJqUaMkMn2KKI0Eke1r1kpIS3nzzTbq7u/nLX/7Scz40Nfr999/fc/yUU07hwQcfBODpp5/uMz17U1MTubm5jB49ml27dvH0008Djjlq8+bN1NbWAvDQQw+FbTPW6dgXlBYwcUw208flke7niKreXHcdjB8P11zjjDgWLnR8IIYxCOzJiSN+Sqt+++23c84553DCCScwfvz4nvLf+c53+NSnPkVlZSVFRUU9x2+++WZefPFFZs2axdKlS5kyZUrY9ubMmcO8efOYOXMmn/3sZznxxBMByMrK4q677uLss8+moqKCcePG9VwTz3TsaWnCry6q4PvnHxvTduJOXh7cfjtUV8Pq1TZ/wxgSEox+GU7Mnz9fa3rNmH3zzTc52qMzMFZLxyY7fu/3QO5xKMuXL2fJkiXRFyjZ6O52FEZ1NTz5JMtzc1Oj32FImXvei/76LSIrVHV+f/XYiMMwUoW0NPj1r+HMM+GUUxItjeFjUjOqyjBSlYoKeOqpREth+JyUGnEMR7Oc4WD31jDiR8oojqysLBobG+0HZhiiqjQ2NpKVlZVoUQwjJUgZU9WkSZPYtm1bv/MmwJm3kIo/Qn7ud1ZWFpNsXoJhxIWUURyZmZk9s5z7Y/ny5cybNy/GEiUfqdpvwzAGhm9MVSJyhoi8LSIbReT6RMtjGIaRqvhCcYhIOvAL4EzgGOBCEbFEO4ZhGAnAF4oDWAhsVNU6VW0HHgbOTbBMhmEYKYlffBwTga0h77cBhy2WLCJXAle6bwMiMpRFG4qAwS9f51+s36lFqvYbUrfv/fV7qpdK/KI4+kVV7wLuikZdIlLjZdr9cMP6nVqkar8hdfserX77xVS1HZgc8n6Se8wwDMOIM35RHNXAkSJSJiIjgM8AjydYJsMwjJTEF6YqVe0Uka8AzwDpwO9UMZtdJgAABlZJREFUdV0Mm4yKycuHWL9Ti1TtN6Ru36NjzrcUHIZhGMZA8IupyjAMw0gSTHEYhmEYAyKlFIfXtCUi8gkRURGZH3LsBve6t0XkI/GRODoMtt8iUioirSKyyt1+HT+po0N/fReRS0WkPqSPl4ecu0RENrjbJfGVfGgMsd9dIcd9FYTi5VkXkU+LyHoRWSciD4YcH7b32y3TV78Hfr9VNSU2HKd6LVAOjADeAI4JUy4feBF4FZjvHjvGLT8SKHPrSU90n+LQ71JgbaL7EMu+A5cCPw9zbQFQ576OdffHJrpPse63ey6Q6D7EsN9HAiuD9xIYlyL3O2y/B3u/U2nE4TVtya3AD4C2kGPnAg+r6kFV3QRsdOvzA0Ppt98ZSqqajwDPquoeVd0LPAucESM5o02qpujx0u8rgF+49xRV3e0eH+73u69+D4pUUhzh0pZMDC0gIhXAZFX920CvTWKG0m+AMhFZKSIviMjJMZQzFni9b58QkdUi8qiIBCeaDut77hKu3wBZIlIjIq+KyHkxlTS6eOn3UcBRIvIvt39nDODaZGUo/YZB3G9fzOOIByKSBvwQZwifMvTT7x3AFFVtFJFK4K8iMktVm+IpY4x5AnhIVQ+KyL8D9wOnJlimeBCp31NVdbuIlAPPi8gaVa1NmKTRJQPHbLMEJwPFiyJybEIlig9h+62q+xjE/U6lEUd/aUvygdnAchHZDCwGHncdxX5OeTLofrumuUYAVV2BY0c9Ki5SR4d+75uqNqrqQfftb4FKr9cmMUPpN6q63X2tA5YDflndy8s92wY8rqodrtn5HZwf1GF9v+m734O734l27MTRgZSB4/Aq45ADaVaE8ss55CSexeHO8Tr84xwfSr+Lg/3EcbxtBwoS3ado9h0YH7J/PvCqu18AbMJxlI51933R9yH2eyww0t0vAjYQJpgiGTeP/T4DuD+kf1uBwhS43331e1D3O2VMVdpH2hIR+S5Qo6p9hqG55f4ErAc6gatUtSsugg+RofQbOAX4roh0AN3AF1V1T+yljg4e+/5VEfkYzn3dg2uyU9U9InIrTp40gO/6pe9D6TdwNPAbEenGsUjcrqrr496JQeCx388Ap4vIeqAL+Ka6o+phfr/D9ltETmAQ99tSjhiGYRgDIpV8HIZhGEYUMMVhGIZhDAhTHIZhGMaAMMVhGIZhDAhTHIZhGMaAMMVhGIZhDAhTHEZKEZJCep2IvCEi/+GmXUFE5ovITyNcWyoin42BTBHbDVN+iYjsF5GnBtjO10Rki4j8fOBSGsYhUmYCoGG4tKrqXAARGQc8CIwCblbVGqAmwrWlwGfda6KGh3bD8U9VPWeA7fxIRPYC8/stbBgRsBGHkbKok1r6SuAr4rBERJ4EEJEPhCxus1JE8oHbgZPdY19zRyD/FJHX3e0E99olIrLczTr7log8ICLinlsgIi+7o50qEcnv1e5CEXnFbfNlEZnRXz/c618QkcdEpE5EbheRz7n1rxGRabH6DI3UxEYcRkqjqnUikg6M63XqGzipZf4lInk465RcD3wj+E9fRHKA01S1TUSOBB7i0L/5eTg5zt4D/gWcKCJVwCPABapaLSKjgNZe7b4FnOymkfgw8H3gEx66MgcnXcgenLxFv1XVhSJyDXA1cK3Xz8Qw+sMUh2GE51/AD0XkAWCpqm5zBw2hZAI/F5G5OPl/QjMHV6nqNgARWYVj5toP7FDVagB109P3qnc0cL+riNRtwwvVqrrDra8W+D/3+Brggx7rMAxPmKnKSGncNQi6gMNWRFPV24HLgWzgXyIyM8zlXwN24fzbn4+TmTTIwZD9Lrz/SbsVWKaqs4GPAlkerwttrzvkffcA2jYMT5jiMFIWESkGfo2z9rb2OjdNVdeo6g9wMqbOBA7grF8SZDTOCKIb+DxOZtJIvA2MF5EFbhv5ItL7R300h9ZSuHTgvTKM2GOKw0g1soPhuMBzOCadW8KUu1ZE1orIaqADeBpYDXS5ju2vAb8ELhGRN3AUS3OkhtVZD/oC4GfuNc/y/hHFHcBtIrISGykYSYqlVTcMnyEiSwhx0g/w2ktxFur6SrTlMlIHG3EYhv9oB2YPZgIgcAMwnNaMNxKAjTgMwzCMAWEjDsMwDGNAmOIwDMMwBoQpDsMwDGNAmOIwDMMwBsT/B0msv/9HYmb5AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}