{ "cells": [ { "cell_type": "code", "execution_count": 524, "id": "f9716819", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Correr si esto no está corrido\n" ] } ], "source": [ "print(\"Correr si esto no está corrido\")" ] }, { "cell_type": "markdown", "id": "b56bc381", "metadata": {}, "source": [ "# Precódigo" ] }, { "cell_type": "markdown", "id": "25f06c5b", "metadata": {}, "source": [ "## pip install" ] }, { "cell_type": "code", "execution_count": 1, "id": "84dff70a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: matplotlib in c:\\users\\lord_fulgi\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (3.8.4)\n", "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\lord_fulgi\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (1.2.1)\n", "Requirement already satisfied: cycler>=0.10 in c:\\users\\lord_fulgi\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (0.12.1)\n", "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\lord_fulgi\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (4.51.0)\n", "Requirement already satisfied: kiwisolver>=1.3.1 in c:\\users\\lord_fulgi\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (1.4.5)\n", "Requirement already satisfied: numpy>=1.21 in c:\\users\\lord_fulgi\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (1.26.4)\n", "Requirement already satisfied: packaging>=20.0 in c:\\users\\lord_fulgi\\appdata\\roaming\\python\\python312\\site-packages (from matplotlib) (24.0)\n", "Requirement already satisfied: pillow>=8 in c:\\users\\lord_fulgi\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (10.3.0)\n", "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\lord_fulgi\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (3.1.2)\n", "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\lord_fulgi\\appdata\\roaming\\python\\python312\\site-packages (from matplotlib) (2.9.0.post0)\n", "Requirement already satisfied: six>=1.5 in c:\\users\\lord_fulgi\\appdata\\roaming\\python\\python312\\site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n", "Note: you may need to restart the kernel to use updated packages.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "[notice] A new release of pip is available: 24.0 -> 25.3\n", "[notice] To update, run: python.exe -m pip install --upgrade pip\n" ] } ], "source": [ "pip install matplotlib" ] }, { "cell_type": "code", "execution_count": 2, "id": "8f83987a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: sympy in c:\\users\\lord_fulgi\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (1.13.3)\n", "Requirement already satisfied: mpmath<1.4,>=1.1.0 in c:\\users\\lord_fulgi\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from sympy) (1.3.0)\n", "Note: you may need to restart the kernel to use updated packages.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "[notice] A new release of pip is available: 24.0 -> 25.3\n", "[notice] To update, run: python.exe -m pip install --upgrade pip\n" ] } ], "source": [ "pip install sympy" ] }, { "cell_type": "code", "execution_count": 3, "id": "608f15af", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Note: you may need to restart the kernel to use updated packages.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "ERROR: Could not find a version that satisfies the requirement math (from versions: none)\n", "ERROR: No matching distribution found for math\n", "\n", "[notice] A new release of pip is available: 24.0 -> 25.3\n", "[notice] To update, run: python.exe -m pip install --upgrade pip\n" ] } ], "source": [ "pip install math" ] }, { "cell_type": "code", "execution_count": 4, "id": "ae9bf6d6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: numpy in c:\\users\\lord_fulgi\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (1.26.4)\n", "Note: you may need to restart the kernel to use updated packages.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "[notice] A new release of pip is available: 24.0 -> 25.3\n", "[notice] To update, run: python.exe -m pip install --upgrade pip\n" ] } ], "source": [ "pip install numpy" ] }, { "cell_type": "code", "execution_count": 5, "id": "0b8f4b9b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: pandas in c:\\users\\lord_fulgi\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (2.2.3)\n", "Requirement already satisfied: numpy>=1.26.0 in c:\\users\\lord_fulgi\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from pandas) (1.26.4)\n", "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\lord_fulgi\\appdata\\roaming\\python\\python312\\site-packages (from pandas) (2.9.0.post0)\n", "Requirement already satisfied: pytz>=2020.1 in c:\\users\\lord_fulgi\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from pandas) (2024.2)\n", "Requirement already satisfied: tzdata>=2022.7 in c:\\users\\lord_fulgi\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from pandas) (2024.2)\n", "Requirement already satisfied: six>=1.5 in c:\\users\\lord_fulgi\\appdata\\roaming\\python\\python312\\site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n", "Note: you may need to restart the kernel to use updated packages.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "[notice] A new release of pip is available: 24.0 -> 25.3\n", "[notice] To update, run: python.exe -m pip install --upgrade pip\n" ] } ], "source": [ "pip install pandas" ] }, { "cell_type": "code", "execution_count": 6, "id": "dd417979", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Note: you may need to restart the kernel to use updated packages.Requirement already satisfied: scipy in c:\\users\\lord_fulgi\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (1.14.1)\n", "Requirement already satisfied: numpy<2.3,>=1.23.5 in c:\\users\\lord_fulgi\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from scipy) (1.26.4)\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "[notice] A new release of pip is available: 24.0 -> 25.3\n", "[notice] To update, run: python.exe -m pip install --upgrade pip\n" ] } ], "source": [ "pip install scipy" ] }, { "cell_type": "markdown", "id": "8728bfa8", "metadata": {}, "source": [ "## import" ] }, { "cell_type": "code", "execution_count": 7, "id": "bf108807", "metadata": {}, "outputs": [], "source": [ "from numpy import *\n", "import numpy as np\n", "from numpy.linalg import *\n", "import math\n", "import matplotlib.pyplot as plt\n", "\n", "%matplotlib inline\n", "\n", "from statistics import *\n", "\n", "from IPython.display import display, Latex\n", "from scipy import stats\n", "from scipy.stats import linregress\n", "import scipy.optimize as so\n", "from scipy.fft import fft\n", "from scipy.interpolate import interp1d\n", "\n", "import sympy as sp\n", "from sympy import *\n", "\n", "import pandas as pd\n", "\n", "\n", "pd.set_option('display.precision', 2) # Nos dará dos decimales en las tablas y arrays afectados por pandas\n" ] }, { "cell_type": "markdown", "id": "401702b3", "metadata": {}, "source": [ "## Funciones Técnicas I" ] }, { "cell_type": "markdown", "id": "283ab61d", "metadata": {}, "source": [ "Funciones usadas en Técnicas Experimentales 1. Repasar el archivo Tecnicas_1.ipynb para ver su uso o ejemplos.\n", "Tenemos:\n", "\n", " ·Comprobar si dos listas tienen mismo tamaño\n", " ·Parámetros para ajuste de recta sin término independiente\n", " ·Parámetros para ajuste de recta con término independiente\n", " ·Parámetros para ajuste de recta sin término independiente y su gráfica generada\n", " ·Parámetros para ajuste de recta con término independiente y su gráfica generada\n", " ·Media con exclusión\n", " ·Media cuadrada" ] }, { "cell_type": "code", "execution_count": 8, "id": "3e8bafd0", "metadata": {}, "outputs": [], "source": [ "# Chequea que dos listas tengan el mismo tamaño. Devuelve si son o no y cuantos valores tiene cada una\n", "def chequeo(x: list[float], y: list[float]) -> tuple[float]:\n", " list_size_bool = f\"Tamaño igual de listas: {len(x)==len(y)}\"\n", " x_info, y_info = f\"Tamaño x: {len(x)}\", f\"Tamaño y: {len(y)}\"\n", " return (list_size_bool, x_info, y_info)\n", "\n", "# Devuelve los parámetros de pendiente, desviación típica, incertidumbre de la pendiente y coeficiente de regresión para una recta sin término independiente (y = n*x) ajustada por mínimos cuadrados\n", "def min_cuadrados_sin_termino_indep(x: list[float], y: list[float]) -> tuple[float]:\n", " xy = [i*j for i,j in list(zip(x, y))]\n", " x2, y2 = [i*i for i in x], [j*j for j in y]\n", " sum_xy, sum_x2, sum_y2 = sum(xy), sum(x2), sum(y2)\n", " # Pendiente\n", " n = sum_xy / sum_x2\n", " # Desviacion Tipica\n", " num = len(x) - 1\n", " stdv = math.sqrt(sum([(j - n*i)**2 for i,j in zip(x, y)]) / num)\n", " # Incertidumbre de la pendiente (n)\n", " Sn = stdv / math.sqrt(sum_x2)\n", " # Coeficiente de Regresion\n", " r = sum_xy / math.sqrt(sum_x2 * sum_y2)\n", " return (n, stdv, Sn, r)\n", "\n", "# Devuelve la pendiente, término independiente, desviación típica, incertidumbre de la pendiente, incertidumbre del término independiente y coeficiente de regresión para una recta con término independiente (y = n*x + a) mediante el ajuste por mínimos cuadrados\n", "def min_cuadrados_con_termino_indep(x: list[float], y: list[float]) -> tuple [float]:\n", " xy = [i*j for i,j in list(zip(x,y))]\n", " x2, y2 = [i*i for i in x], [j*j for j in y]\n", " sum_x, sum_y, sum_xy, sum_x2, sum_y2 = sum(x), sum(y), sum(xy), sum(x2), sum(y2)\n", " # Pendiente y Término Independiente\n", " num = len(x)\n", " n = (num*sum_xy-sum_x*sum_y)/(num*sum_x2-sum_x**2)\n", " a =(sum_y*sum_x2-sum_x*sum_xy)/(num*sum_x2-sum_x**2)\n", " # Desviacion Tipica\n", " numero = len(x) -2\n", " stdv = math.sqrt(sum([(j - n*i)**2 for i,j in zip(x,y)]) / numero)\n", " # Incertidumbre de la pendiente (n)\n", " Sn = stdv * math.sqrt(num/(num*sum_x2-sum_x**2))\n", " # Incertidumbre del Término Independiente\n", " Sa = stdv * math.sqrt(sum_x2/(num*sum_x2-sum_x**2))\n", " # Coeficiente de Regresion\n", " r = (num*sum_xy-sum_x*sum_y)/math.sqrt((num*sum_x2-sum_x**2)*(num*sum_y2-sum_y**2))\n", " return (n, a, stdv, Sn, Sa, r)\n", "\n", "# Insertando dos listas (x,y) en la función, te devuelve los valores de min_cuadrados_sin_termino_indep() y te genera su gráfica\n", "def generar_grafica_sin_termino_indep(x: list[float], y: list[float], \n", " x_lbl: str = \"X\", y_lbl: str = \"Y\", m_lbl: str = \"m\", \n", " xerr: list[float] = None, yerr: list[float] = None,\n", " n: float = 0):\n", "\n", " # Regresión lineal mediante minimos cuadrados\n", " m, stdv, Sn, r = min_cuadrados_sin_termino_indep(x, y)\n", " \n", " # Recta de regresion (y = m*x + n) con n = 0\n", " recta_regresion = [m*i for i in x]\n", "\n", " # Grafica\n", " plt.clf()\n", " \n", " # plt.errorbar(x, y, xerr, yerr, fmt='x')\n", " plot, = plt.plot(x, recta_regresion, color='red', linestyle='-')\n", " points = plt.scatter(x, y)\n", " \n", " plt.xlabel(f\"{x_lbl}\")\n", " plt.ylabel(f\"{y_lbl}\")\n", " plt.legend([points, plot], [\"Valores Experimentales\", f\"{m_lbl} = {m:e}\"])\n", " plt.title(f\"{y_lbl} frente a {x_lbl}\")\n", " plt.grid()\n", "\n", " plt.show()\n", " print(f\"{m_lbl}:\\t{m:e}\")\n", " print(f\"S({m_lbl}): {Sn}\")\n", " print(f\"stdv:\\t{stdv}\")\n", " print(f\"r:\\t{r}\")\n", " return m, stdv, Sn, r\n", "\n", "# Insertando dos listas (x,y) en la función, te devuelve los valores de min_cuadrados_con_termino_indep() y te genera su gráfica\n", "def generar_grafica_con_termino_indep(x: list[float], y: list[float], \n", " x_lbl: str = \"X\", y_lbl: str = \"Y\", m_lbl: str = \"m\", \n", " xerr: list[float] = None, yerr: list[float] = None,\n", " n: float = 0):\n", " \n", " # Regresión lineal mediante minimos cuadrados\n", " m, n, stdv, Sn, Sa, r = min_cuadrados_con_termino_indep(x, y)\n", " \n", " # Recta de regresion (y = m*x + n)\n", " recta_regresion = [m*i + n for i in x]\n", "\n", " # Grafica\n", " plt.clf()\n", " \n", " # plt.errorbar(x, y, xerr, yerr, fmt='x')\n", " plot, = plt.plot(x, recta_regresion, color='red', linestyle='-')\n", " points = plt.scatter(x, y)\n", " \n", " plt.xlabel(f\"{x_lbl}\")\n", " plt.ylabel(f\"{y_lbl}\")\n", " plt.legend([points, plot], [\"Valores Experimentales\", f\"{m_lbl} = {m:e}\"])\n", " plt.grid()\n", "\n", " plt.show()\n", " print(f\"{m_lbl}:\\t{m:e}\") # Pendiente Recta\n", " print(f\"S({m_lbl}): {Sn}\") #Incertidumbre pendiente\n", " print(f\"a:\\t{n}\") # Termino Independiente\n", " print(f\"S(a):\\t{Sa}\") # Incertidumbre Termino Independiente\n", " print(f\"stdv:\\t{stdv}\") # Desviacion Tipica\n", " print(f\"r:\\t{r}\")\n", " return m, n, stdv, Sn, Sa, r\n", "\n", "# Esta funcion es el protocolo de la diapositiva 19 de {https://cv.usc.es/pluginfile.php/2390439/mod_resource/content/1/an%C3%A1lisis%20incertidumbres.pdf}\n", "# Se añade sb que es la incertidumbre de tipo b de los valores en x (que es constante)\n", "\n", "# Insertando la incertidumbre de tipo b (por ejemplo sensibilidad de la máquina) y una lista, te devuelve la media con la incertidumbre combinada, fi (fi = n - 1) y la nueva lista con los valores excluidos para k = 2\n", "def media_con_exclusion(sb, x: list[float]) -> tuple[float]:\n", " n = len(x)\n", " k = 2 # Usamos el valor k = 2 para calcular que valores se salen (utilizamos este k en Técnicas Experimentales I)\n", " # Calcular valor medio de la lista\n", " media_x = sum(x)/n\n", " # Calcular desviacion tipica\n", " dist_media_x = [(i-media_x)**2 for i in x]\n", " sa = math.sqrt(sum(dist_media_x)/(n-1))\n", " # Quitar valores discordantes\n", " for valor in x:\n", " if valor >= (media_x-k*sa) and valor <= (media_x+k*sa):\n", " pass\n", " else:\n", " x.remove(valor)\n", " # Calcular la nueva media\n", " media_x = sum(x)/n\n", " # Calculamos la nueva desviacion tipica de la media\n", " dist_media_x = [(i-media_x)**2 for i in x]\n", " sa = math.sqrt(sum(dist_media_x)/(n-1))\n", " sa_media = sa/math.sqrt(n)\n", " # Incertidumbre Combinada\n", " sc = math.sqrt(sa_media**2 + sb**2)\n", " # Resultado final\n", " phi = n - 1\n", " return (media_x, sc, phi, x)\n", "\n", "# Hace la media cuadrada entre dos valores (incertidumbres por ejemplo)\n", "def media_cuadrada(s1,s2):\n", " return math.sqrt(s1**2 + s2**2)" ] }, { "cell_type": "code", "execution_count": 9, "id": "be78a70d", "metadata": {}, "outputs": [], "source": [ "def media_ponderada(valores_lista, incertidumbres_lista):\n", " \"\"\"\n", " Calcula el valor medio ponderado y su incertidumbre.\n", " \n", " Parámetros:\n", " valores: lista o array de los valores medidos (x1, x2, ..., xn)\n", " incertidumbres: lista o array de las incertidumbres respectivas (u1, u2, ..., un)\n", " \n", " Retorna:\n", " (media_ponderada, incertidumbre_ponderada)\n", " \"\"\"\n", " # Convertir a arrays para evitar problemas con listas\n", " valores_lista = np.array(valores_lista)\n", " incertidumbres_lista = np.array(incertidumbres_lista)\n", " \n", " # Calcular la media ponderada\n", " media = np.sum(valores_lista / incertidumbres_lista**2) / np.sum(1 / incertidumbres_lista**2)\n", " \n", " # Calcular la incertidumbre ponderada\n", " incertidumbre = np.sqrt(1 / np.sum(1 / incertidumbres_lista**2))\n", " \n", " return media, incertidumbre" ] }, { "cell_type": "markdown", "id": "4590ffca", "metadata": {}, "source": [ "## Funciones Técnicas II" ] }, { "cell_type": "markdown", "id": "c0b5a1ac", "metadata": {}, "source": [ "### Ajuste Lineal por Mínimos Cuadrados" ] }, { "cell_type": "markdown", "id": "46b0e84e", "metadata": {}, "source": [ "La función de ajuste $y_i = a + b x_i$ pide tres listas de argumentos $\\{ x_i, y_i \\}$, y $\\sigma_i = \\sigma (y_i)$ donde $i=1,...,n$\n", "\n", "$$\\left( \\begin{array}{c} a \\\\ b \\end{array} \\right) = H^{-1} Z$$\n", "donde\n", "$$\n", "H = \\left( \\begin{array}{cc} \\sum_i \\frac{1}{\\sigma_i^{2}} & \\sum_i \\frac{x_i}{\\sigma_i^2} \\\\\n", "\\sum_i \\frac{x_i}{\\sigma_i^2} & \\sum_i \\frac{x^2_i}{\\sigma_i^2}\n", "\\end{array}\n", "\\right) ~~~; ~~~~ \n", "Z = \\frac{1}{\\Delta}\\left( \\begin{array}{c} \\sum_i\\frac{x_i^2}{\\sigma_i^2} \\\\ \n", " \\sum_i\\frac{1}{\\sigma_i^2} \\end{array}\n", "\\right)\n", "$$\n", "con $\\Delta= \\det H$.\n", "Además, las desviaciones estándar de $a$ y $b$ son \n", "$$\n", "\\sigma(a) =\\sqrt{ \\frac{1}{\\Delta} \\sum_i \\frac{x_i^2}{\\sigma_i^2}} ~~~; ~~~\n", "\\sigma(b) =\\sqrt{ \\frac{1}{\\Delta} \\sum_i \\frac{1}{\\sigma_i^2}}\\, .\n", "$$" ] }, { "cell_type": "markdown", "id": "aaadcc18", "metadata": {}, "source": [ "#### Funciones" ] }, { "cell_type": "code", "execution_count": 10, "id": "ed49b7dc", "metadata": {}, "outputs": [], "source": [ "def media(xlist):\n", " return sum(xlist)/len(xlist)\n", "\n", "def sigma(xlist):\n", " return np.sqrt(sum((xlist-media(xlist))**2)/(1.*len(xlist)*(len(xlist)-1.)))\n", "\n", "def sigmab(xlist):\n", " return np.sqrt(sum((xlist-media(xlist))**2)/(1.*len(xlist)*(len(xlist)-1)))\n", "\n", "def utotal(ua,ub):\n", " return np.sqrt(ua**2+ub**2)\n", "\n", "# Ajuste lineal y=a+bx\n", "def ajustelinealponderado(x,y,sig):\n", "\n", " H=np.array([[sum(1./sig**2),sum(x/sig**2)],[sum(x/sig**2),sum(x**2/sig**2)]])\n", " H_sympy = Matrix(H) #\n", " Delta=det(H_sympy) #_sympy\n", " Z=np.array([sum(y/sig**2),sum((x*y)/sig**2)])\n", " \n", " ([a,b])=np.matmul(inv(H),Z)\n", " \n", " siga= sqrt(sum(x**2/sig**2)/Delta)\n", " sigb= sqrt(sum(1./sig**2)/Delta)\n", " \n", " return a,b,siga,sigb\n" ] }, { "cell_type": "markdown", "id": "6bb7e1b6", "metadata": {}, "source": [ "#### Ejemplo" ] }, { "cell_type": "markdown", "id": "b9daed81", "metadata": {}, "source": [ "Los datos tomados, en forma matricial o en lista." ] }, { "cell_type": "code", "execution_count": 11, "id": "fc11fdc7", "metadata": {}, "outputs": [], "source": [ "x=np.array([ 25., 16., 11.1111, 8.16327, 6.25, 4.93827, 4., 2.77778, 1.77778, 1. ])\n", "y=np.array([ 901., 652., 443., 339., 283., 281., 240., 220., 180., 154. ])\n", "n=len(x)" ] }, { "cell_type": "markdown", "id": "8768f5dd", "metadata": {}, "source": [ "Muestra los datos frameados en una tabla de Panda" ] }, { "cell_type": "code", "execution_count": 12, "id": "47931e39", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " x y\n", "1 25.00 901.0\n", "2 16.00 652.0\n", "3 11.11 443.0\n", "4 8.16 339.0\n", "5 6.25 283.0\n", "6 4.94 281.0\n", "7 4.00 240.0\n", "8 2.78 220.0\n", "9 1.78 180.0\n", "10 1.00 154.0" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "datos = pd.DataFrame({'x': x, 'y': y},index=np.arange(n)+1)\n", "display(datos)" ] }, { "cell_type": "markdown", "id": "24d32df7", "metadata": {}, "source": [ "Muestra los datos frameados en una tabla de Latex" ] }, { "cell_type": "code", "execution_count": 13, "id": "4e807212", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "<>:3: SyntaxWarning: invalid escape sequence '\\h'\n", "<>:6: SyntaxWarning: invalid escape sequence '\\h'\n", "<>:3: SyntaxWarning: invalid escape sequence '\\h'\n", "<>:6: SyntaxWarning: invalid escape sequence '\\h'\n", "C:\\Users\\Lord_Fulgi\\AppData\\Local\\Temp\\ipykernel_64128\\1741227050.py:3: SyntaxWarning: invalid escape sequence '\\h'\n", " cadena = '\\\\begin{array}{|r|r|} \\hline x & y \\\\\\\\ \\hline'\n", "C:\\Users\\Lord_Fulgi\\AppData\\Local\\Temp\\ipykernel_64128\\1741227050.py:6: SyntaxWarning: invalid escape sequence '\\h'\n", " cadena += '\\hline \\end{array}'\n" ] }, { "data": { "text/latex": [ "\\begin{array}{|r|r|} \\hline x & y \\\\ \\hline25.00 & 901.00 \\\\16.00 & 652.00 \\\\11.11 & 443.00 \\\\8.16 & 339.00 \\\\6.25 & 283.00 \\\\4.94 & 281.00 \\\\4.00 & 240.00 \\\\2.78 & 220.00 \\\\1.78 & 180.00 \\\\1.00 & 154.00 \\\\\\hline \\end{array}" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Muestra los datos frameados en una tabla de Latex\n", "\n", "cadena = '\\\\begin{array}{|r|r|} \\hline x & y \\\\\\\\ \\hline'\n", "for i in range(n):\n", " cadena += '%3.2f & %3.2f \\\\\\\\' % (x[i],y[i])\n", "cadena += '\\hline \\end{array}'\n", "\n", "display(Latex(cadena))" ] }, { "cell_type": "markdown", "id": "c58e47fd", "metadata": {}, "source": [ "Calculemos la media $\\bar y = \\frac{1}{n}(\\sum_{i=1}^n y_i)$ y la desviación estándar de la media poblacional $u_A=\\sigma(\\bar y) = \\sqrt{\\frac{\\sum_{i=1}^n (y_i - \\bar y)^2}{n(n-1)}}$" ] }, { "cell_type": "code", "execution_count": 14, "id": "2f03dcfe", "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$ \\bar{y} =369 $" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ymed=media(y)\n", "display(Latex(r'$ \\bar{y} =%i $' % ymed))" ] }, { "cell_type": "code", "execution_count": 15, "id": "7125bb33", "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$\\sigma(\\bar y)=74 $" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sigbar=sigmab(y)\n", "display(Latex(r'$\\sigma(\\bar y)=%i $' % sigbar))" ] }, { "cell_type": "markdown", "id": "e2e6609e", "metadata": {}, "source": [ "En caso de ser conocida, la desviación estándar de cada dato es $\\sigma(y_i)$; $\\sigma(y_i) = \\sqrt{y_i}$" ] }, { "cell_type": "markdown", "id": "b115ed00", "metadata": {}, "source": [ "Tenemos la desviación para cada valor (que nos dará para luego la barra de error). Escogemos cuál de las dos es la que vamos a usar en el ajuste sigma = sigbar o sigma = sigi" ] }, { "cell_type": "code", "execution_count": 16, "id": "31bc8719", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([30.01666204, 25.53429067, 21.04756518, 18.41195264, 16.82260384,\n", " 16.76305461, 15.49193338, 14.83239697, 13.41640786, 12.40967365])" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sigi=np.sqrt(y)\n", "uy = sigi\n", "\n", "uy" ] }, { "cell_type": "markdown", "id": "f78f54f8", "metadata": {}, "source": [ "Aquí representamos en tabla Panda los valores con su respectiva desviación estándar de y." ] }, { "cell_type": "code", "execution_count": 17, "id": "97d58ae3", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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xysig
125.00901.030.02
216.00652.025.53
311.11443.021.05
48.16339.018.41
56.25283.016.82
64.94281.016.76
74.00240.015.49
82.78220.014.83
91.78180.013.42
101.00154.012.41
\n", "
" ], "text/plain": [ " x y sig\n", "1 25.00 901.0 30.02\n", "2 16.00 652.0 25.53\n", "3 11.11 443.0 21.05\n", "4 8.16 339.0 18.41\n", "5 6.25 283.0 16.82\n", "6 4.94 281.0 16.76\n", "7 4.00 240.0 15.49\n", "8 2.78 220.0 14.83\n", "9 1.78 180.0 13.42\n", "10 1.00 154.0 12.41" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "datossig = pd.DataFrame({'x': x,'y': y ,'sig': uy},index=np.arange(n)+1)\n", "display(datossig)" ] }, { "cell_type": "markdown", "id": "e2653630", "metadata": {}, "source": [ "Llamamos a la función de ajuste." ] }, { "cell_type": "code", "execution_count": 18, "id": "d0e59dd1", "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$a$=119.50 , $\\sigma_{a}$=7.57" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/latex": [ "$b$=30.70 , $\\sigma_{b}$=1.03" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "a,b,siga,sigb = ajustelinealponderado(x, y, uy)\n", "\n", "display(Latex(r'$a$=%3.2f , $\\sigma_{a}$=%3.2f' % (a,siga) ))\n", "display(Latex(r'$b$=%3.2f , $\\sigma_{b}$=%3.2f' % (b,sigb) ))" ] }, { "cell_type": "markdown", "id": "41e64daf", "metadata": {}, "source": [ "Graficamos los datos y el ajuste lineal (dos plots distintos en mismo frame)." ] }, { "cell_type": "code", "execution_count": 19, "id": "71957a91", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Y= a + b*x\n", "\n", "plt.figure(figsize=(5, 4)) # Medidas de la gráfica; por ejemplo: (7, 6)\n", "plt.plot(x,y, '.')\n", "plt.plot(x,Y)\n", "plt.grid()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "a2345e42", "metadata": {}, "source": [ "Podemos graficar lo mismo que antes pero con barras de error." ] }, { "cell_type": "code", "execution_count": 20, "id": "6ecccf21", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(5,4))\n", "plt.errorbar(x, y, fmt= '.', yerr=uy) # uy es la desviación estándar por valor de y\n", "plt.plot(x,Y)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "a2eedfa6", "metadata": {}, "source": [ "### Ajuste no Lineal" ] }, { "cell_type": "markdown", "id": "385e0f51", "metadata": {}, "source": [ "#### Ejemplo" ] }, { "cell_type": "markdown", "id": "4053a0be", "metadata": {}, "source": [ "Introducimos primero nuestros datos $\\{ x_i, y_i \\}$, $i=1,...,n$" ] }, { "cell_type": "code", "execution_count": 21, "id": "41c386c8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'nx1= 55'" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "'ny1= 55'" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x=array([5.5,6,6.5,7,7.5,8,8.5,9,9.5,10,10.5,11,11.5,12,12.5,13,13.5,14,14.5,15,15.5,\n", " 16.5,17,17.5,18,18.5,19,19.5,20,20.5,21,21.5,22,22.5,23,23.5,24,24.5,25,25.5,26,26.5,27,27.5,28,28.5,29,29.5,\n", " 30,30.5,31,31.5,32,32.5,33])-5.5 # Este -5.5 resta a cada valor de x -5.5\n", "nx=len(x)\n", "display('nx1=%3.2i'% nx) # Longitud lista x\n", "\n", "\n", "y=array([12.25,11,16.5,28,43.5,61.5,80.5,97.25,111,120.5,\n", " 124.75,123.75,118,108.25,96,82,68.5,56.5,47.25,41.5,\n", " 40,48.5,57,67.25,77.75,87.5,96,101.75,104.75,104.75,\n", " 102,96.75,89.75,82,74,67,61.25,57.75,56.25,57.25,60.5,65,70.5,76.75,82.5,87.25,90,93.25,93.75,92.5,89.75,86.25,81.75,77.25,77])\n", "ny=len(y)\n", "display('ny1=%3.2i'% ny) # Longitud lista y\n", "\n", "# El error estimado tipo B en y\n", "uy=array([0.5]*nx) # Lista con nx (número elementos de la lista x) veces el valor 0.5 (El error de \"la máquina\" con la que medimos por ejemplo)\n", "for i in range(0,20): # Cambia el error de los datos 1 al 20 (incluidos) a 1 en vez de 0.5 (como acababamos de hacer).\n", " uy[i]=1" ] }, { "cell_type": "markdown", "id": "011827c3", "metadata": {}, "source": [ "Podemos visualizar los datos en tabla Panda." ] }, { "cell_type": "code", "execution_count": 22, "id": "b5ff978f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " x y uy\n", "1 0.0 12.25 1.0\n", "2 0.5 11.00 1.0\n", "3 1.0 16.50 1.0\n", "4 1.5 28.00 1.0\n", "5 2.0 43.50 1.0\n", "6 2.5 61.50 1.0\n", "7 3.0 80.50 1.0\n", "8 3.5 97.25 1.0\n", "9 4.0 111.00 1.0\n", "10 4.5 120.50 1.0\n", "11 5.0 124.75 1.0\n", "12 5.5 123.75 1.0\n", "13 6.0 118.00 1.0\n", "14 6.5 108.25 1.0\n", "15 7.0 96.00 1.0\n", "16 7.5 82.00 1.0\n", "17 8.0 68.50 1.0\n", "18 8.5 56.50 1.0\n", "19 9.0 47.25 1.0\n", "20 9.5 41.50 1.0\n", "21 10.0 40.00 0.5\n", "22 11.0 48.50 0.5\n", "23 11.5 57.00 0.5\n", "24 12.0 67.25 0.5\n", "25 12.5 77.75 0.5\n", "26 13.0 87.50 0.5\n", "27 13.5 96.00 0.5\n", "28 14.0 101.75 0.5\n", "29 14.5 104.75 0.5\n", "30 15.0 104.75 0.5\n", "31 15.5 102.00 0.5\n", "32 16.0 96.75 0.5\n", "33 16.5 89.75 0.5\n", "34 17.0 82.00 0.5\n", "35 17.5 74.00 0.5\n", "36 18.0 67.00 0.5\n", "37 18.5 61.25 0.5\n", "38 19.0 57.75 0.5\n", "39 19.5 56.25 0.5\n", "40 20.0 57.25 0.5\n", "41 20.5 60.50 0.5\n", "42 21.0 65.00 0.5\n", "43 21.5 70.50 0.5\n", "44 22.0 76.75 0.5\n", "45 22.5 82.50 0.5\n", "46 23.0 87.25 0.5\n", "47 23.5 90.00 0.5\n", "48 24.0 93.25 0.5\n", "49 24.5 93.75 0.5\n", "50 25.0 92.50 0.5\n", "51 25.5 89.75 0.5\n", "52 26.0 86.25 0.5\n", "53 26.5 81.75 0.5\n", "54 27.0 77.25 0.5\n", "55 27.5 77.00 0.5" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "datos = pd.DataFrame({'x': x, 'y': y , 'uy' : uy },index=arange(nx)+1)\n", "display(datos)" ] }, { "cell_type": "markdown", "id": "3819d6cb", "metadata": {}, "source": [ "O visualizarlos en tabla tipo Latex." ] }, { "cell_type": "code", "execution_count": 23, "id": "ccd981a9", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "<>:1: SyntaxWarning: invalid escape sequence '\\h'\n", "<>:4: SyntaxWarning: invalid escape sequence '\\h'\n", "<>:1: SyntaxWarning: invalid escape sequence '\\h'\n", "<>:4: SyntaxWarning: invalid escape sequence '\\h'\n", "C:\\Users\\Lord_Fulgi\\AppData\\Local\\Temp\\ipykernel_64128\\3862766803.py:1: SyntaxWarning: invalid escape sequence '\\h'\n", " cadena = '\\\\begin{array}{|r|r|} \\hline x & y \\\\\\\\ \\hline'\n", "C:\\Users\\Lord_Fulgi\\AppData\\Local\\Temp\\ipykernel_64128\\3862766803.py:4: SyntaxWarning: invalid escape sequence '\\h'\n", " cadena += '\\hline \\end{array}'\n" ] }, { "data": { "text/latex": [ "\\begin{array}{|r|r|} \\hline x & y \\\\ \\hline0.00 & 12.25 \\\\0.50 & 11.00 \\\\1.00 & 16.50 \\\\1.50 & 28.00 \\\\2.00 & 43.50 \\\\2.50 & 61.50 \\\\3.00 & 80.50 \\\\3.50 & 97.25 \\\\4.00 & 111.00 \\\\4.50 & 120.50 \\\\5.00 & 124.75 \\\\5.50 & 123.75 \\\\6.00 & 118.00 \\\\6.50 & 108.25 \\\\7.00 & 96.00 \\\\7.50 & 82.00 \\\\8.00 & 68.50 \\\\8.50 & 56.50 \\\\9.00 & 47.25 \\\\9.50 & 41.50 \\\\10.00 & 40.00 \\\\11.00 & 48.50 \\\\11.50 & 57.00 \\\\12.00 & 67.25 \\\\12.50 & 77.75 \\\\13.00 & 87.50 \\\\13.50 & 96.00 \\\\14.00 & 101.75 \\\\14.50 & 104.75 \\\\15.00 & 104.75 \\\\15.50 & 102.00 \\\\16.00 & 96.75 \\\\16.50 & 89.75 \\\\17.00 & 82.00 \\\\17.50 & 74.00 \\\\18.00 & 67.00 \\\\18.50 & 61.25 \\\\19.00 & 57.75 \\\\19.50 & 56.25 \\\\20.00 & 57.25 \\\\20.50 & 60.50 \\\\21.00 & 65.00 \\\\21.50 & 70.50 \\\\22.00 & 76.75 \\\\22.50 & 82.50 \\\\23.00 & 87.25 \\\\23.50 & 90.00 \\\\24.00 & 93.25 \\\\24.50 & 93.75 \\\\25.00 & 92.50 \\\\25.50 & 89.75 \\\\26.00 & 86.25 \\\\26.50 & 81.75 \\\\27.00 & 77.25 \\\\27.50 & 77.00 \\\\\\hline \\end{array}" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cadena = '\\\\begin{array}{|r|r|} \\hline x & y \\\\\\\\ \\hline'\n", "for i in range(nx):\n", " cadena += '%3.2f & %3.2f \\\\\\\\' % (x[i],y[i])\n", "cadena += '\\hline \\end{array}'\n", "\n", "display(Latex(cadena))" ] }, { "cell_type": "markdown", "id": "11d5a706", "metadata": {}, "source": [ "Definimos la función a ajustar.\n", "\n", "Esta será una función genérica $f(t, p_1,...,p_n)$ donde $t$ es el agrumento y $p_1,...,p_n$ una serie de parámetros.\n", "\n", "por ejemplo, para una oscilación armónica amortiguada\n", "\n", "\n", "$ y(t, \\omega,\\gamma,A,\\phi,y_0) = y_0 + A e^{-\\gamma t} cos(\\omega t+\\phi)$\n", "\n", "Donde\n", " $\\omega$ es la frecuencia angular, $\\gamma$ es el coeficiente de amortiguamiento, $A$ la amplitud initial, $\\phi$ una fase inicial y $y_0$ la posición de equilibrio." ] }, { "cell_type": "code", "execution_count": 24, "id": "fb48addc", "metadata": {}, "outputs": [], "source": [ "# Definimos la función, el primer argumento es la variable independiente, y los que siguen son los parámetros.\n", "\n", "def fun(t,w,gamma,A,phi,y0):\n", " y = y0+ A*np.exp(-gamma*t)*np.cos(w*t+phi)\n", " return y" ] }, { "cell_type": "code", "execution_count": 25, "id": "2fc46fe6", "metadata": {}, "outputs": [], "source": [ "# Introducimos valores iniciales arbitrarios para los parámetros que hay que ajustar.\n", "# par = [w,gamma,A,phi,y0]\n", "\n", "par = [0.1 ,1. ,1. ,0., 0.]" ] }, { "cell_type": "markdown", "id": "2866d5e3", "metadata": {}, "source": [ "Realizamos el ajuste no lineal. Obtenemos de aquí los parametros [0] y las incertidumbres [1]. De esta última como solo queremos las varianzas (es una matriz de covarianza) tomamos la diagonal." ] }, { "cell_type": "code", "execution_count": 26, "id": "3fba7c5d", "metadata": {}, "outputs": [], "source": [ "# Usamos so.curve_fit\n", "''' scipy.optimize.curve_fit(fun,x,y,p0,sigma,absolute_sigma)\n", " fun: Función modelo que describimos; ha de tener la variable independiente como primer argumento, y los parámetros a ajustar a continuación.\n", " x: Array de datos de la variable independiente (por ejemplo, tiempo).\n", " y: Array de datos de la variable dependiente (por ejemplo, amplitud).\n", " p0: (Opcional) Array con valores iniciales para los parámetros que se van a ajustar. Si no se proporcionan, curve_fit intenta estimarlos automáticamente\n", " sigma: (Opcional) Array de desviaciones estándar de los datos de y. Si se proporciona, el curve_fit minimizará el error ponderado en lugar del error simple.\n", " absolute_sigma: (Opcional, por defecto False) Si True el argumento sigma se considera como errores absolutos. Si False, se considera que sigma son errores relativos y se ajusta el valor de covarianza.\n", "'''\n", "sol = so.curve_fit(fun,x,y,p0=(par),sigma=uy,absolute_sigma=True)\n", "\n", "# La salida tiene dos arrays: sol[0] con los parámetros \n", "w,gamma,A,phi,y0 = sol[0]\n", "\n", "# y sol[1] con las incertidumbres. Ésta es una matriz de covarianza. Sí solo queremos las varianzas tomamos la diagonal\n", "sw,sgamma,sA,sphi,sy0 = np.sqrt(np.diag(sol[1]))" ] }, { "cell_type": "markdown", "id": "ee2c3484", "metadata": {}, "source": [ "Los datos obtenidos de tal ajuste son:" ] }, { "cell_type": "code", "execution_count": 27, "id": "0d635eba", "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$\\omega$= -0.654 , $\\sigma(\\omega)$= 0.00057" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/latex": [ "$\\gamma$= 0.060 , $\\sigma(\\gamma)$= 0.00052" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/latex": [ "$A$= 67.303 , $\\sigma(A)$= 0.44740" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/latex": [ "$\\phi$= -2.821 , $\\sigma(\\phi)$= 0.00790" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/latex": [ "$y_0$= 77.515 , $\\sigma(y_0)$= 0.08198" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(Latex(r'$\\omega$= %6.3f , $\\sigma(\\omega)$= %6.5f'%(w,sw)))\n", "display(Latex(r'$\\gamma$= %6.3f , $\\sigma(\\gamma)$= %6.5f'%(gamma,sgamma)))\n", "display(Latex(r'$A$= %6.3f , $\\sigma(A)$= %6.5f'%(A,sA)))\n", "display(Latex(r'$\\phi$= %6.3f , $\\sigma(\\phi)$= %6.5f'%(phi,sphi)))\n", "display(Latex(r'$y_0$= %6.3f , $\\sigma(y_0)$= %6.5f'%(y0,sy0)))" ] }, { "cell_type": "markdown", "id": "7e11614d", "metadata": {}, "source": [ "Por tanto, con estos parámetros, completamos la función, que es:" ] }, { "cell_type": "code", "execution_count": 28, "id": "0a562de9", "metadata": {}, "outputs": [], "source": [ "# La función estimada\n", "yEst=fun(x,w,gamma,A,phi,y0) " ] }, { "cell_type": "markdown", "id": "2e4d9d76", "metadata": {}, "source": [ "Graficamos los datos y la curva obtenida (ambas sobre mismo plano)." ] }, { "cell_type": "code", "execution_count": 29, "id": "d023e0a3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, '$A$ frente a $t$')" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(5, 4))\n", "plt.plot(x,y, 'b.')\n", "plt.plot(x,yEst,'b-')\n", "plt.plot(x,y0+0.*x,'b')\n", "plt.grid()\n", "\n", "# Nombre de los ejes\n", "plt.xlabel(r'$t$ (s)', fontsize = 12)\n", "plt.ylabel(r'$A$ (m)', fontsize = 12)\n", "\n", "# Título\n", "plt.title(r'$A$ frente a $t$', fontsize = 14)" ] }, { "cell_type": "markdown", "id": "fd84420f", "metadata": {}, "source": [ "# Líneas de Transmisión" ] }, { "cell_type": "markdown", "id": "f7df2fcf", "metadata": {}, "source": [ "## Teoría y procedimiento:" ] }, { "cell_type": "markdown", "id": "bb42a765", "metadata": {}, "source": [ "### Teoría" ] }, { "cell_type": "markdown", "id": "e5fc4537", "metadata": {}, "source": [ "$\\textbf{Teórico:}$\n", "\n", "En una línea de dos (o más) conductores, son posibles los modos normales TEM, donde la velocidad de propagación es $v_p=\\frac{1}{\\sqrt{\\pi·\\mu}}$\n", "\n", "Para modos que se propagan por la guía con dirección $\\pm \\hat{z}\\quad$; $Z=\\sqrt{\\frac{\\mu}{\\epsilon}}$ es la impedancia de propagación del medio que contiene la guía.\n", "\n", "Relación entre ondas:\n", "- $\\vec{H}=\\frac{1}{Z}·\\hat{z}\\times\\vec{E}$\n", "- $\\vec{E}=-Z· \\hat{z}\\times \\vec{H}$" ] }, { "cell_type": "markdown", "id": "d3561494", "metadata": {}, "source": [ "$\\textbf{Práctica de la línea de transmisión:}$\n", "\n", "La onda electromagnética viaja en el dieléctrico entre conductor central y exterior en campos confinados.\n", "\n", "Señal (suma de una componente incidente y otra reflejada):\n", "\n", "$V(z,t)=V^+·e^{i(w·t-\\beta·z)}+V^-·e^{i(w·t+\\beta·z)}$\n", "\n", "$I(z,t)=\\frac{1}{Z_0}·(V^+·e^{i(w·t-\\beta·z)}-V^-·e^{i(w·t+\\beta·z)})$\n", "\n", "Impedancia característica (de la línea):\n", "\n", "$Z_0=\\sqrt{\\frac{L_S}{C_P}}$\n", "; Donde $L_S$ es la inductancia por unidad de longitud y $C_P$ es la capacitancia por unidad de longitud.\n", "\n", "\n", "Primero se quita de las ecuaciones el régimen estacionario (igual en todo punto): quitamos $e^{i·w·t}$;\n", "\n", "Segundo, en z=0 hacemos cambio de medio a la impedancia externa:\n", "\n", "$Z_L=\\frac{V(0)}{I(0)}$\n", "\n", "Tercero, consideramos Coeficiente de Reflexión:\n", "\n", "$\\Gamma = \\frac{V^-}{V^+}$\n", "\n", "Cuarto, juntamos ambas ecuaciones y despejamos para $\\Gamma$:\n", "\n", "$\\Gamma = \\frac{Z_L-Z_0}{Z_L+Z_0}$\n", "\n", "Así:\n", "\n", "$\\Gamma = 0; \\quad Z_L=Z_0 \\quad$ Línea Adaptada\n", "\n", "$\\Gamma = 1; \\quad Z_L=\\infty \\quad$ Reflexión total (al aire)\n", "\n", "$\\Gamma = -1; \\quad Z_L=0 \\quad$ Reflexión total invertida: onda estacionaria (cortocircuito). V se refleja invertida, I se refleja sin invertir: $I_max$" ] }, { "cell_type": "markdown", "id": "24955333", "metadata": {}, "source": [ "$\\textbf{Aparte:}$\n", "\n", "Atenuación: $V(z)=V_0·e^{-\\alpha ·z} \\Rightarrow \\alpha=\\frac{1}{z}·ln(\\frac{V_0}{V(z)})$\n", "\n", "A partir de un circuito RL/RC con desfase $\\phi$:\n", "- $L=\\frac{R}{w}·tg(\\phi)$\n", "- $C=\\frac{cos(\\phi)}{w·R·[\\frac{V_{out}}{V_{in}}]}$" ] }, { "cell_type": "markdown", "id": "608d3a8f", "metadata": {}, "source": [ "### Procedimiento y Materiales" ] }, { "cell_type": "markdown", "id": "9c7e4aed", "metadata": {}, "source": [ "$\\textbf{Materiales:}$\n", "- Cable coaxial de longitud $l\\approx 100 \\text{ m}$ e impedancia característica $Z_0\\approx 50 \\space \\Omega$\n", "- Generador de funciones (impedancia de salida $50\\space\\Omega$)\n", "- Osciloscopio digital de dos canales\n", "- Módulo de cortocircuito\n", "- Resistencia variable\n", "- Adaptadores en T\n", "- Carga de $50\\space \\Omega$\n", "- Resistencia de $100\\space \\Omega$\n", "- Resistencia de $1\\space k\\Omega$" ] }, { "cell_type": "markdown", "id": "853bc294", "metadata": {}, "source": [ "$\\textbf{Procedimiento experimental:}$\n", "\n", "- 1:\n", "Conectamos dos adaptadores en T a las entradas del osciloscopio y extremos de la línea. Conectamos el generador al T del canal de entrada y la carga de $50\\space \\Omega$ en el de salida.\n", "\n", "- 2:\n", "($\\underline{\\text{Tiempo de propagación}}$) Introducimos una señal cuadrada de unos $100\\space kHz$. Medimos la señal con su retardo. Cambia mucho el retardo si aumentamos la frecuencia? Calculamos velocidad de propagación de la señal.\n", "\n", "- 3:\n", "($\\underline{\\text{Reflexión sin inversión}}$) Quitamos la terminación; ahora $Z_C\\rightarrow \\infty \\Rightarrow \\Gamma \\rightarrow 1$. Comentar cambios en la señal de entrada y salida; medir tiempos y tensión de los impulsos.\n", "\n", "- 4:\n", "($\\underline{\\text{Reflexión con inversión}}$) Conectamos cortocircuito en la salida (el canal de $V_2$ deja de interesar), ocurre $\\Gamma = -1$, en $z=l$ se refleja invertida la onda. Idealmente anularía la onda incidente, pero por el retardo no ocurrirá, ocurrirá un impulso de duración $2t_p$ (lo que tarda la onda en ir y volver), también la onda reflejada experimenta atenuación, por eso su tensión no es nula. Medir tiempos y tensión de los impulsos.\n", "\n", "- 5:\n", "Repetimos los puntos 2, 3 y 4 con señales triangulares y sinusoidales.\n", "\n", "- 6:\n", "($\\underline{\\text{Resonancias en la línea}}$) Introducimos señal sinusoidal con terminación en abierto. Variamos frecuencia desde $3 \\space MHz$ al mínimo posible de la escala (poco más de $400\\space kHz$) y vemos que $V_1$ varía presentando máximos y mínimos en función de la frecuencia. Explicar porque ocurre; anotar en qué frecuencias aparece y la amplitud; buscar patrón. Buscar relacionar esto con la velocidad y tiempo de retardo del apartado 2. Buscar que frecuencias se esperarían en ese tiempo.\n", "\n", "- 7:\n", "Repetir apartado 6 con cortocircuito y explicar si se relaciona.\n", "\n", "- 8:\n", "($\\underline{\\text{Medida de atenuación de línea}}$) Suponemos decaemiento del voltaje en caída exponencial con la distancia ($e^{-\\alpha z}$). Usamos las medidas de amplitud de los puntos 6 y 7 y hacemos una tabla de los coeficientes de atenuación ($\\alpha$) en las frecuencias medidas.\n", "\n", "- 9:\n", "Colocamos el adaptador de $50\\space \\Omega$ y exploramos la misma banda de frecuencias a ver si ocurre o no y porqué los máximos y mínimos de $V_1$.\n", "\n", "- 10:\n", "($\\underline{\\text{Impedancia característica de la línea}}$) Colocamos la resistencia variable al final del cable y la entrada directamente al generador, que pondremos en señal cuadrada en frecuencia del orden de $200\\space kHz$ con adaptador en T para ver la entrada en el osciloscopio. Con el destornillador vamos variando la resistencia hasta que la señal sea cuadrada sin escalones. Cuando lo tengamos quitamos la resistencia variable y medimos con el polímetro su valor, que será $Z_0$.\n", "\n", "- 11:\n", "($\\underline{\\text{Autoinducción de la línea}}$) Entre el generador y la entrada de la línea colocamos una resistencia de $100\\space \\Omega$ tal que medimos los voltajes antes y después de la resistencia; colocamos al final de la línea cortocircuito. Aplicamos señal sinusoidal de unos $30\\space kHz$. Medimos amplitudes pico a pico de las dos señales y el desfase entre ellos. Con esto, calculamos la autoinducción de la línea con las amplitudes y con el desfase.\n", "\n", "- 12:\n", "($\\underline{\\text{Capacidad de la línea}}$) Quitamos el cortocircuito y lo dejamos en abierto. Cambiamos la resistencia de $100 \\space \\Omega$ por la de $1\\space k\\Omega$. Repetimos la misma medida de antes. Con esto, calculamos la capacidad de la línea con las amplitudes y con el desfase.\n", "\n", "- 13:\n", "($\\underline{\\text{Desadaptación de entrada}}$) Con el montaje anterior le ponemos señal cuadrada y observamos que ocurre a diferentes frecuencias con la salida en a) circuito abierto, b) cortocircuito, c) adaptada." ] }, { "cell_type": "markdown", "id": "bd93ee5d", "metadata": {}, "source": [ "## Datos" ] }, { "cell_type": "markdown", "id": "2cc0219e", "metadata": {}, "source": [ "### Velocidad de propagación" ] }, { "cell_type": "markdown", "id": "14cd701f", "metadata": {}, "source": [ "- 2:\n", "\n", "Calculamos velocidad mediante $v=\\frac{l}{\\tau}\\space \\pm \\Delta v=\\frac{\\Delta \\tau ·l}{\\tau^2}$; donde $\\tau=540\\space \\text{ns}$ es el retardo y $\\Delta \\tau=2\\space\\text{ns}$ su incertidumbre. Este $\\tau$ es fácil medir pues es el espaciado temporal entre el canal 1 y canal 2 reflejado en FRR.\n", "\n", "Los resultados son:$$v=185185185\\pm 685871 \\text{ m/s}$$\n", "Que es eqivalente, y más fácil de apreciar, a $v=0.62c$ por lo que la longitud de onda sería $\\lambda=1852 \\pm 7 \\text{ m}$\n", "\n", "También probamos variando la frecuencia de la señal alejándonos considerablemente de los $100\\space kHz$ en los que tomamos la medida inicial. Así hemos observado que el retardo no variaba. Esto se explica al introducir la idea de que la línea no es dispersiva, es decir, no discrimina por frecuencias: $n=\\frac{c}{v}\\neq n(\\omega)$ lo que significa que la velocidad de la señal es independiente de la frecuencia." ] }, { "cell_type": "code", "execution_count": 265, "id": "c27a43a1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "v= 185185185.19 +/- 685871.06 m/s\n", "v= 0.62c\n", "\n", "λ= 1851.85 +/- 6.86 m\n" ] } ], "source": [ "l = 100\n", "t = 540e-9\n", "Dt = 2e-9\n", "tau=t\n", "\n", "# Calcular velocidad propagación\n", "v=l/t\n", "Dv=Dt*l/t**2\n", "c=299792458\n", "print(f\"v= {v:.2f} +/- {Dv:.2f} m/s\\nv= {(v/c):.2f}c\")\n", "\n", "# Calcular longitud de onda\n", "f=100e3\n", "Lambda=v/f\n", "Dlambda=Dv/f\n", "print(f\"\\nλ= {Lambda:.2f} +/- {Dlambda:.2f} m\")" ] }, { "cell_type": "markdown", "id": "aebe792e", "metadata": {}, "source": [ "### Reflexión sin inversión en señal cuadrada" ] }, { "cell_type": "markdown", "id": "3ce902a7", "metadata": {}, "source": [ "- 3:\n", "\n", "Introducimos señal cuadrada y dejamos el extremo final abierto tal que $$\\Gamma=\\frac{Z_L-Z_0}{Z_L+Z_0}\\space; \\text{con }Z_L\\rightarrow \\infty \\space \\Rightarrow \\Gamma=1\\space \\text{señal reflejada no invertida}$$\n", "Toda la señal se invierte, y, debido a la pérdida de energía por el camino (disipación de la línea por resistencia), la onda reflejada tendrá menor amplitud que la incidente. A esto le sumamos el retardo anteriormente observado adaptado a la distancia total recorrida, por tanto la suma de dos ondas cuadradas desfasadas de amplitudes diferentes.\n", "\n", "En el canal 1 pues, tendremos la medición de ambas ondas en forma de suma de la manera que se aprecia esquemáticamente en la imagen. Es evidente que, de esta suma, al sumarse las dos amplitudes, la amplitud de la señal de entrada será mayor que con la línea adaptada.\n", "\n", "Se ve que la anchura del primer escalón tiene de anchura el doble del retardo ya que ahí la onda reflejada no da aún peso al cambio de amplitud y queda sólo la de la onda incidente; es el doble ya que la onda tiene que ir hasta el extremo de la línea y volver, eso equivale al doble de la distancia respecto al retardo medido en el punto anterior, y por tanto, el doble del retardo medido (en el osciloscopio, un cuadrado equivale a $1.00\\space \\mu s$ y el ancho es de aproximadamente un cuadrado; lo que tiene sentido pues es más o menos el doble del retardo obtenido en el punto anterior ($2·\\tau \\approx 1\\space \\mu s$ para apreciar a ojo)).\n", "\n", "Todo esto son palabras que se pueden grabar más en el espacio de las ideas al visualizar el esquema gráfico a continuación.\n", "\n", "\n", "\n", "Al igual que mencionamos en el apartado de $\\textit{Velocidad de propagación}$, el medio es $\\textbf{no dispersivo}$ por lo que este retardo es constante para distintas frecuencias a las que realizemos la medición en este apartado y en los próximos. Es importante este punto para recalcar la capacidad de mandar información de frecuencias distintas a una misma velocidad, siendo un medio democrático que no prioriza frecuencias más altas o más bajas." ] }, { "cell_type": "code", "execution_count": 498, "id": "02598238", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ondas cadradas incidente e reflexada (con desfase e onda atenuada) e Suma de ambas ondas\n" ] }, { "data": { "image/png": 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", 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Parámetros\n", "z = np.linspace(0, 2, 1000)\n", "beta = 2 * np.pi # constante de propagación (1 λ = 1 unidad de z)\n", "A_i = 1 # amplitud incidente\n", "A_r = 0.8 # amplitud reflejada menor\n", "phi = 10000/np.pi\n", "\n", "# Ondas cuadradas aproximadas (por Fourier de coseno)\n", "def square_wave(z, beta, phi=0):\n", " # aproximación por los primeros términos de serie de Fourier\n", " wave = np.zeros_like(z)\n", " for n in range(1, 6000, 2): # solo términos impares\n", " wave += (1/n) * np.sin(n*(beta*z + phi))\n", " return (4/np.pi) * wave\n", "\n", "# Ondas\n", "Vi = A_i * square_wave(z, beta, phi=0) # incidente\n", "Vr = A_r * square_wave(z, -beta, phi=phi) # reflejada (viaja al revés, con desfase)\n", "Vtotal = Vi + Vr\n", "\n", "# Gráfico 1: incidente y reflejada\n", "plt.figure(figsize=(7,4))\n", "plt.plot(z, Vi, label='Incidente', color='blue')\n", "plt.plot(z, Vr, label='Reflexada', color='red')\n", "plt.title('Ondas cadradas incidente e reflexada')\n", "plt.xlabel('Tempo (s)')\n", "plt.ylabel('Amplitude')\n", "plt.grid(True)\n", "\n", "# Gráfico 2: suma total\n", "plt.figure(figsize=(7,4))\n", "plt.plot(z, Vtotal, color='black')\n", "plt.title('Suma de ambas ondas cadradas')\n", "plt.xlabel('Tempo (s)')\n", "plt.ylabel('Amplitude')\n", "plt.grid(True)\n", "\n", "print(\"Ondas cadradas incidente e reflexada (con desfase e onda atenuada) e Suma de ambas ondas\")\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "ff5151a6", "metadata": {}, "source": [ "### Reflexión con inversión en señal cuadrada" ] }, { "cell_type": "markdown", "id": "b52f261b", "metadata": {}, "source": [ "- 4:\n", "\n", "Conectamos ahora cortocircuito, por tanto $$\\Gamma=\\frac{Z_L-Z_0}{Z_L+Z_0}\\space; \\text{con }Z_L=0 \\space \\Rightarrow \\Gamma=-1\\space \\text{señal reflejada invertida}$$Se producirá una onda reflejada invertida, ligeramente atenuada debido a las pérdidas por el camino de la línea e, igual que antes, con retardo.\n", "\n", "La señal observada en el canal 1 es la siguiente:\n", "\n", "Se observa que hay zonas donde casi se anula del todo (se anularía del todo si la amplitud de la onda reflejada no fuera atenuada por la línea). Al igual que antes, los pico-escalones tienen un ancho de $2\\tau \\approx 1\\space \\mu s$. En esta fotografía del osciloscopio, al igual que antes, estamos midiendo el canal 1 para observar la suma de la onda emitida y reflectida, el canal 2 en esta foto se ha intentado poner en modo \"no molestar\" (bajando agresivamente la escala) para que se aprecie el canal 1 que es el que importa.\n", "\n", "La señal del osciloscopio es más fácil comprenderla con la suma esquemática de una onda emitida y otra reflejada invertida atenuada con desfase como a continuación." ] }, { "cell_type": "code", "execution_count": 497, "id": "77e4753a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ondas cadradas incidente e reflexada (con desfase e onda atenuada) e Suma de ambas ondas\n" ] }, { "data": { "image/png": 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zYLNnz64VV2ZmJkpLS43OONQVV32OHTuG//73v9i4cSPGjRtnWF7z6uG2bdsCQJ0fVdf8e19//TVcXFywe/duo9ugrF+/vta2crkcQ4YMwZAhQ7BkyRIsWLAA//d//4c9e/ZY5b5q+rO6x48fr5VwNcTUttbf+kelUlm0/vV9rNyxY0eUlpaa9bf0beHn52fRulqj3I4dO+LHH3/EI488UiuxrEmn0yEhIQGenp6YOnUqFixYgGeeeQZ/+9vfDOtYeozdunULmZmZmDNnDmbNmmVYLvSrHcSx0Hf0CKlm2rRpcHV1xUsvvVTrdgSFhYV4+eWX4ebmhmnTpgkue+jQoQCAjz/+2Gh5XVckKhSKWmdSli9fDq1Wa7SsZh2bNWuGTp06GX1MWZenn34af/75Z523JdH/Xf0Znur12L9/P7Kzs43Wj4+Ph0ajwcqVKw3LtFotli9f3mAdqqvrbzHG8NFHHxmtFxgYiNDQUGzcuNHoY6iMjAycPHmyVpkymcyozfLy8rBjxw6j9QoLC2vVR3+1Y2PtaK6YmBh4eHggNTUVd+/eNXqtoTNopra1n58fBg0ahH/961+4evVqrXLqutWLKdzd3et8szFy5EhkZ2dj9+7dtV4rKiqCRqOpt8zY2Fh4enpiwYIFRh9dNrWu1ih35MiR0Gq1mDdvXq3XNBqNUdssWbIE+/btw6effop58+ahX79+mDx5suH2RYDlx1hd5QF1zzFEOuiMHiHVdO7cGRs3bsSYMWPQs2fPWv8Z48aNG9i8eXODt1+oT2hoKEaPHo1PPvkEt2/fRr9+/ZCZmYlz587VWvfxxx/H559/Di8vL4SEhCA7Oxs//vgjWrRoYbReSEgIBg0ahLCwMPj4+OCPP/7Atm3bkJyc3GBdpk2bhm3btuHZZ5/Fiy++iLCwMBQWFmLnzp1YtWoVevfujccffxzbt2/HU089hWHDhiE3NxerVq1CSEgISktLDWU98cQTeOSRRzB9+nTk5eUhJCQE27dvF/R9oK5du6Jjx4548803cfnyZXh6euLrr7+u8/thqampGDZsGPr3748XX3wRhYWFWL58Obp3725Ur2HDhmHJkiWIi4vD888/j2vXrmHFihXo1KmT0cfzc+fOxd69ezFs2DC0bdsW165dwyeffILWrVvXeVbXEjw9PbF06VJMnDgRDz30EJ5//nk0b94cf/75J8rLy7Fx48Y6txPS1itWrED//v3Rs2dPJCUloUOHDigoKEB2djYuXbpkuJebEGFhYVi5ciXmz5+PTp06wc/PD4MHD8a0adOwc+dOPP7440hISEBYWBjKyspw7NgxbNu2DXl5eWjZsmW9bbFy5UqMHTsWDz74IEaNGgVfX19cvHgRP/zwAx555BH885//FFxXa5Q7cOBAvPTSS0hNTUVOTg5iYmKgUqlw9uxZfPXVV/joo4/wzDPP4NSpU5g5cyYSEhLwxBNPAKi6nU5oaCheeeUVfPnllwBg8THm6emJRx99FIsWLYJarUarVq2Qnp6O3Nxcwe1HHIg9LvUlROyOHj3KRo8ezQIDA5lKpWIBAQFs9OjR7NixY7XW1d9epebtOfS3bMjNzTUsu3PnDpsyZQpr0aIFc3d3Z0888QT73//+V+v2Krdu3WKJiYmsZcuWrFmzZiw2NpadPn261u085s+fz/r27cu8vb2Zq6sr69q1K3vvvfdYZWVlozHevHmTJScns1atWjEnJyfWunVrNn78eHbjxg3GWNWtOBYsWMDatm3LnJ2dWZ8+fdj3339f65YT+rLGjh3LPD09mZeXFxs7diw7cuRInbdXcXd3r7M+J0+eZFFRUaxZs2asZcuWLCkpif3555+1ymCMsa+//pp169aNOTs7s5CQELZ9+/Y667V27VrWuXNn5uzszLp27crWr19v6C+9zMxMNnz4cBYUFMScnJxYUFAQGz16dK1bhdSlvtur1LxNx549exgAtmfPHqPlO3fuZP369WOurq7M09OT9e3bl23evNmovcxta8YYO3/+PBs3bhwLCAhgKpWKtWrVij3++ONs27ZtZtU5Pz+fDRs2jHl4eDAARrdaKSkpYTNmzGCdOnViTk5OrGXLlqxfv35s8eLFJu2Pe/bsYbGxsczLy4u5uLiwjh07soSEBPbHH380uF199RdSrqm3V9H79NNPWVhYGHN1dWUeHh6sZ8+e7K233mJXrlxhGo2GPfTQQ6x169ZGt6ZhjLGPPvqIAWBbt25ljFlnjF26dIk99dRTzNvbm3l5ebFnn32WXblypdYcQ6RDxpiNvh1MCCGEEEJsir6jRwghhBDioCjRI4QQQghxUJToEUIIIYQ4KEr0CCGEEEIcFCV6hBBCCCEOihI9QgghhBAHRTdMtgCdTocrV67Aw8Oj3n8RRAghhBBiCYwxlJSUICgoqNb/Dq+JEj0LuHLlCoKDg+1dDUIIIYRIyP/+9z+0bt26wXUo0bMADw8PAFUN7unpafHy1Wo10tPTDf9uRyoobunELcWYAYpbSnFLMWZAmnHbIubi4mIEBwcb8o+GUKJnAfqPaz09Pa2W6Lm5ucHT01MyAwWguKUUtxRjBihuKcUtxZgBacZty5hN+boYXYxBCCGEEOKgKNEjhBBCCHFQlOgRQgghhDgoSvQIIYQQQhwUJXqEEEIIIQ6KEj1CCCGEEAdFiR4hhBBCiIPiKtHbu3cvnnjiCQQFBUEmk2HHjh2NbpOVlYUHH3wQzs7O6NSpEzZs2FBrnRUrVqBdu3ZwcXFBREQEDhw4YPnKE0IIIYTYGFeJXllZGXr37o0VK1aYtH5ubi6GDRuGxx57DDk5OZg6dSomTpyI3bt3G9bZunUrUlJSMHv2bBw+fBi9e/dGbGwsrl27Zq0wCCGEEEJsgqv/jDF06FAMHTrU5PVXrVqF9u3b48MPPwQAdOvWDb/++iuWLl2K2NhYAMCSJUuQlJSExMREwzY//PAD1q1bh+nTp1s+iCa6exe4cQMoKgIqKwG12vgnY1UPwPhnXcuq/2xMYzfftsbrGo0Mf/zhD8ZkEHpz8ZpxNfbcUtsAgFxe9VAoTPvp7g54eQEtWgAuLo3HRhwPY0BhYdW4vn27ajxrNFUPrdb4d0v8LXurPraV1Y5C1eeBmnOCqa9Za11Ty5HJqsaxm1vVo1mzqrFNiL1wlegJlZ2djaioKKNlsbGxmDp1KgCgsrIShw4dwowZMwyvy+VyREVFITs7u95yKyoqUFFRYXheXFwMoOrfnqjVagtGAEO5Bw/64513FDh+3OLFi5gSwMP2roTNyOUMPXoA779fdTS3xr4kVvpYpRQzAJSVqbFqVS+8+KIShYX2ro0tSWtsu7kxPP64DMOHKyW3j0txbNsiZiFlO3Sil5+fD39/f6Nl/v7+KC4uxp07d3Dr1i1otdo61zl9+nS95aampmLOnDm1lqenp8PNzc0yla+msNAZCxdGQ6Op+qRdodChWTM1lEodFAodlEpm+L36u8qqn8zoeX3LLMmUMwaMWe6PM2Z6LPrYjZdZbtuqs6cy6HQyMAbodDKjR13LKioUKC9XQaeT4ehRYNQoGVatUiIjI8O0ijkQqcX8/fftsWtXL8NzZ2cN3Nw0UKm0kMsZFIqqh/53udwyp+Pq2pdtraE5oPbZdFm9rzW0rbl/oynr6nQyqNVyVFQo7j2UKC+X4csvlaisfADu7tLax/WkNrYB68ZcXl5u8roOnehZy4wZM5CSkmJ4XlxcjODgYMTExMDT09Pify8rSwuNRgF3d4ZTpzTw9wdkMjk4+4qlYGq1GhkZGYiOjnb4f4bNmAaXLwMPPaTEzZvOOHu2Od58M9Th49aTUl9X9+GHVWP4739XY/58wNkZABT3Ho5LOv3NoFar8f77csybp8Bff3lLIGZj0unr+2wRs/6TRFM4dKIXEBCAgoICo2UFBQXw9PSEq6srFAoFFApFnesEBATUW66zszOcq2ZkIyqVyiqdqlBUvXts1QoIDpbGQKnOWu0qNu3bA23aADdvAjqddOKuTmoxnz9fdapo1CgZmjVz6Om4TlLob5UKiIkB5s0DCgrcJBFzXaQYtzVjFlKuQ58SioyMRGZmptGyjIwMREZGAgCcnJwQFhZmtI5Op0NmZqZhHTHQf2xgrY9aifhY8qNtIn4Khf0/SiXW06FD1c/r110hoa+qEZHgKtErLS1FTk4OcnJyAFTdPiUnJwcXL14EUPWR6rhx4wzrv/zyy/jrr7/w1ltv4fTp0/jkk0/w5Zdf4vXXXzesk5KSgtWrV2Pjxo04deoUJk+ejLKyMsNVuGJAiZ50UB9LixiugCXWFxAAKJUMOp0cNT5AIsTquPqs4I8//sBjjz1meK7/ntz48eOxYcMGXL161ZD0AUD79u3xww8/4PXXX8dHH32E1q1bY82aNYZbqwDAc889h+vXr2PWrFnIz89HaGgodu3aVesCDXuiRE96KAGQFhrbjk0uB5TKqlvk6HT2rg2RGq4SvUGDBoE1cASs679eDBo0CEeOHGmw3OTkZCQnJze1elZDiZ50UB9LCyX00kN9TmyNq49upYoSPemhg4G00Nh2fNTHxF4o0eMAJXrSQX0sLZTQSw/1ObE1SvQ4QImeFFFnSwmNbcdHfUzshRI9DlCiR4hjorM70kN9TmyNEj0OUKInHdTHhBBCLIkSPQ5Qoic99K5fGmhsSwf1MbEXSvQIERE6GBDi2OhNHLE1SvQ4QO/6pYcOBtJCY9vxUR8Te6FEjwP3Ez06+js6OhhICyX00kN9TmyNEj0O0Bk9KaLOlhIa246P+pjYCyV6HKBETzqoj6WFzu5ID/U5sTVK9DhAiZ700MFAWmhsOz7qY2IvlOhxgBI96aA+lhZK6KWH+pzYGiV6HKBET3roYCAtNLYdH/UxsRdK9DhAiZ50UB9LCyX00kN9TmyNEj0OUKInRdTZUkJj2/FRHxN7oUSPA5ToEeKY6OwOIcTaKNHjAB0MpIOSecKFPXuA7dvtXQsu0XxObI0SPY5QEiAddDCQBi7P1t+5AwweDDz9NHDzpr1rww2u+ri6CxeAt94Crl61d02ImZT2rgBpXPOzBzAURehxSwt8f2+20B8hTPkpZF1LbKv//cYNoKQEmDkTcHISHrgEcXswAIBPPwV++w3o3h3w8ADkckChqPpZ83e5/H6wMhlkGg0Cc3Igu3MHUCqNXjPpZ2OvmUpohi1k/TrWHapmKIMMHns0wHllo+ubXL41f9+79/6yu3eF1ZHw9yZu9mxg40bggw+AZcuqxqdKVfVToagaZ/oHYPxcJoNMq0VQTg5kZWVV2zWyfp3LrDkxWqFDZBoN/P74A+jQAejZ0+LlC0WJHgd6bZ2FNGQCJwE8Ye/amCEmBhgwwN614Ap3BwMAmDMHuHLFrE2VAPpatjZc2Kz/5TV71oLYArdv4i5fvv/71KmCN1cCeMhileGDEkAkAO2dO8DixfauDiV6PCgO6Iz/Hb8Nj2ZA124NnNkQehbEEj8beu2HH6p+r6gQFrCEcXswAIBbt6p+hoZWvZPV6e4/tNrazwFDRqvT6VBYWAgfH5/73ycx9SxyY+sIbVRrrl9j3QMHq5qid28GN9c6ymlKXaz5+08/CasXMeDuTVy3bsCPP1b9PmoUoNEYP2p+8lPjoWMMN2/cQAv92G5o/XrKMCznZILUMYbi27fh0aqVvasCgBI9LhxMWI4XflRiYJgOWVkcfa2yd2/g6FEOZzZilmbNqr6/9dlngj+u0KrV+C0tDfHx8ZCrVFaqoPhEezEUF8twcrMa3bpxFLeTE6BW09gWgJMcpTZ9H//f/wHz5wveXKtWY5/ExrZWrcbP92JW2LsyoIsxuMDlF7aB+xWmg4HJuOvj6rjdUe2H26FBY9ts3DYZjWtucZforVixAu3atYOLiwsiIiJw4MCBetcdNGgQZDJZrcewYcMM6yQkJNR6PS4uzhahmIzb4yd3FRYPxjhuO+p3wbhrMu4qbH/cNhm3mSnR4+qj261btyIlJQWrVq1CREQEli1bhtjYWJw5cwZ+fn611t++fTsqKysNz2/evInevXvj2WefNVovLi4O69evNzx3dna2XhBm4DbR06OJwmTc9jFA/WwG7puM+wBsj9sm43pykjauzugtWbIESUlJSExMREhICFatWgU3NzesW7euzvV9fHwQEBBgeGRkZMDNza1Woufs7Gy0XvPmzW0Rjsm4TfS4q7B4cHswAKjfzcBdk3FXYfvjtsm4nowIwNEZvcrKShw6dAgzZswwLJPL5YiKikJ2drZJZaxduxajRo2Cu7u70fKsrCz4+fmhefPmGDx4MObPn48WLVrUW05FRQUqql1JWlxcDABQq9VQq9VCwjKJRqMDoARjzCrlW4uSMcgAaNRqMDPqrY+Vp5ibTgH9+y/e4tb3t1qtrvqivgDS7GuAsaopWKPRCG0yu1Ki6r8xqysrBfc1IM3+1ve1Ws1XX8t1OigAaHU66KivTWKLmIWUzU2id+PGDWi1Wvj7+xst9/f3x+nTpxvd/sCBAzh+/DjWrl1rtDwuLg5/+9vf0L59e5w/fx7/+Mc/MHToUGRnZ0OhqPt6mdTUVMyZM6fW8vT0dLi5uQmIyjTHjrUGEIabN28iLc20pFYMBhYXwxvAwYMHcU1/Ow0zZGRkWKxOYnfzZj8AvgD4i3uoWg0nAHt/+QWleXlmlcFbzE2l1Q4DoMSvv/6Kc+fK7V0dkw3T6aAEsGfPHtyp42szppJSf6vVsQBckJ2djStXSuxdHZP1ystDewBnz53DmbQ0s8uRUl/rWTPm8nLT5wtuEr2mWrt2LXr27Im+fY1vyzpq1CjD7z179kSvXr3QsWNHZGVlYciQIXWWNWPGDKSkpBieFxcXIzg4GDExMfD09LR43a9d0wEAWrZsgfj4eIuXby3Ke8nwQ+HhYGZc4KJWq5GRkYHo6GioJHJZ/tKl999c8Ba3Ulk1nTz66KNV994SQIp9DQByeVV/DxjQH5078zMd698EPzZoENCuneDtpdjfTk5V/RsZGYnQUH76Wv6f/wAAOnfujI5mHH+k2Ne2iFn/SaIpuNnbWrZsCYVCgYKCAqPlBQUFCAgIaHDbsrIybNmyBXPnzm3073To0AEtW7bEuXPn6k30nJ2d67xgQ6VSWaVTFQrNvZ8yvgaKvOojSKX+X+aYyVrtKkb3mgyMybiNW+XkZHZ/8xqz+aq+/6RUKvmK+94XzlQqFY1tE8lkVX2tUPDZ1wqFAgrqa0GsGbOQcrm5GMPJyQlhYWHIzMw0LNPpdMjMzERkZGSD23711VeoqKjACy+80OjfuXTpEm7evInAwMAm19lSuL0YQ4++zCsN3O+otsd9k9HYNhn3fcxtAISbRA8AUlJSsHr1amzcuBGnTp3C5MmTUVZWhsTERADAuHHjjC7W0Fu7di1GjBhR6wKL0tJSTJs2Db///jvy8vKQmZmJ4cOHo1OnToiNjbVJTEJwN87opqqCcdfH1VE/SweNbbNx22RcT07Sxs1HtwDw3HPP4fr165g1axby8/MRGhqKXbt2GS7QuHjxIuRy49z1zJkz+PXXX5Genl6rPIVCgaNHj2Ljxo0oKipCUFAQYmJiMG/ePFHdS4/bN1TcVVg8uD0YANTvZuCuybirsP1x22RcT0YE4CzRA4Dk5GQkJyfX+VpWVlatZV26dAGrZ0d1dXXF7t27LVk9q+A20dOjicJk3PYxQP1sBu6bjPsAbI/bJuN6cpI2rj66lSqaGKSH2z4HqN/NwF2TcVdh++O2ybiejAhAiR4X6IyedHDbxwD1sxm4bzLuA7A9bpuM68lJ2ijR4wC3iR53FRYTjtuO+l0w7pqMuwrbH7dNxm1mSvQo0eMAt4meHk0UJuO2jwHqZzNw32TcB2B73DYZ15OTtFGixwHGOB1gNDGYjcuDAffvSOyHuyaj26sIxl0f69G45h4lehzgfpzRwcBk3PZxdQ4RhG3Q0CCiRzsp9yjR4wC3iR696zcbl03GZaWJWWhsC8bd/F0T9wFIFyV6HOFunHFXYWIR1O8m4/5NHHF8lMxzjxI9DnB7MNCjicJk9/uYw86mfpYe6nPBuG0ybg9AhBI9DnCb6HFXYWIR1O8mo7EtHdw2GbeZKdGjRI8D3B4M9GiiMBm3fQxQP0sR9blgdBcFYmuU6HGA20SPuwqLB9fHT+p3wbhrMu4qbH/cNhnXkxEBKNHjAreJnh5NFCbjto8BB9hRbY/7ocF9ALbHbZPRuOYWJXoc4Pb4SbdgMBuXTcZlpcWBxrbj466P9bg9ABE9SvQ4wO04467C9ucQTeYQQdgG5UnSw12fc1dhUhMlehzgfpxxH4A9cJgsUT+bjbvcmM7oCcZdH9fEfQDSRYkeB+iMnnQ4RJM5RBC2QVdgSg93uTF3FSY1UaLHAW4TPT2aKATjssm4rDRpEupzk3E7f+txH4B0UaLHEe7GGXcVtj+HaDKHCMK2uGsy7iosHtzlxtxVmNREiR4H6Iye9HDZZNzvqEQwLndU++B+WHAfgHRRoscBbo+f3FWYENupniNxN1ToYgzp4PYARPQo0eMA9+OMDgYm47aPAQfYUQmxPu6mQ+4qTGqiRI8D3B4/6V2/tFA/C0Jn9KSFuz6uifsApIsSPQ5wn+gRk90/fnLcdtTvhNSLu9yYuwqTmrhL9FasWIF27drBxcUFEREROHDgQL3rbtiwATKZzOjh4uJitA5jDLNmzUJgYCBcXV0RFRWFs2fPWjsMQbhN9PRoopAG6mezcTe26YyeYNz1cU3cByBdXCV6W7duRUpKCmbPno3Dhw+jd+/eiI2NxbVr1+rdxtPTE1evXjU8Lly4YPT6okWL8PHHH2PVqlXYv38/3N3dERsbi7t371o7HJNxm+hxV2H7c4gmc4ggrI/rHIn62Gzc9Tt3FSY1cZXoLVmyBElJSUhMTERISAhWrVoFNzc3rFu3rt5tZDIZAgICDA9/f3/Da4wxLFu2DO+88w6GDx+OXr164bPPPsOVK1ewY8cOG0RkGu7HGfcB2B41mbRwmzfRjiod3O6kRGnvCpiqsrIShw4dwowZMwzL5HI5oqKikJ2dXe92paWlaNu2LXQ6HR588EEsWLAA3bt3BwDk5uYiPz8fUVFRhvW9vLwQERGB7OxsjBo1qs4yKyoqUFFRYXheXFwMAFCr1VCr1U2Ksy5aLQOgAGM6qNVai5dvLQpUvZPQaDRgZrSLvi2t0aZixZi+1fiLW3Xvp1qjAQTWXYp9rdEA+larmjvsWh1BlDIZZDCvrwFp9nfVjCiDWq2BWs1PgqzQaiEHoNVqoaO+NoktYhZSNjeJ3o0bN6DVao3OyAGAv78/Tp8+Xec2Xbp0wbp169CrVy/cvn0bixcvRr9+/XDixAm0bt0a+fn5hjJqlql/rS6pqamYM2dOreXp6elwc3MTGlqjzp59AEA3XL58CWlpf1q8fGuJvH4dfgD+zMnBJW9vs8vJyMiwWJ3ErqCgL4BAAJzFzRiG3/v1x8xMVHp5mVUMVzE3kVYrA/AkAODnn7PQrBk/B8KYO3fgCuC3X37B7atXzS5HSv1dXj4EQDMcPHgQZWWF9q6OycKvXkUrACdOnkRuWprZ5Uipr/WsGXN5ebnJ63KT6JkjMjISkZGRhuf9+vVDt27d8K9//Qvz5s0zu9wZM2YgJSXF8Ly4uBjBwcGIiYmBp6dnk+pclz/+qHr3FxzcCvHxrSxevrUoVqwAAPTu3Ru94uMFb69Wq5GRkYHo6GioVKrGN3AA69crDL9zFbdOZ/g1KioK8PUVtLkU+7r6G/JBgwbBz4+fuJVubsDNm+j/yCNgYWGCt5dif7u7V43t8PCHMGiQopG1xUPx+ecAgO49eqAbzeMmsUXM+k8STcFNoteyZUsoFAoUFBQYLS8oKEBAQIBJZahUKvTp0wfnzp0DAMN2BQUFCAwMNCozNDS03nKcnZ3h7OxcZ/nW6FS5vOrjWoVCDpWKnwlC/50OpUIBNKFdrNWuYiS/961ZxmR8xV0t0VM5OZnd31zFbEG8xq1UKmlsm0gmq3rDrlQqoVJxc+g1zOMKhQIK6mtBrBmzkHK5uRjDyckJYWFhyMzMNCzT6XTIzMw0OmvXEK1Wi2PHjhmSuvbt2yMgIMCozOLiYuzfv9/kMm2B+6tu6Qvbjo/6WDC6YbI0cdtk3O2kRI+jtxVASkoKxo8fj/DwcPTt2xfLli1DWVkZEhMTAQDjxo1Dq1atkJqaCgCYO3cuHn74YXTq1AlFRUX44IMPcOHCBUycOBFA1RW5U6dOxfz589G5c2e0b98eM2fORFBQEEaMGGGvMGvhPtEjJnOI4yf1OyGOg+vJiACcJXrPPfccrl+/jlmzZiE/Px+hoaHYtWuX4WKKixcvQi6/f5Ly1q1bSEpKQn5+Ppo3b46wsDDs27cPISEhhnXeeustlJWVYdKkSSgqKkL//v2xa9euWjdWtiduEz09migcH/WxYHRGT1q46+OauA9AurhK9AAgOTkZycnJdb6WlZVl9Hzp0qVYunRpg+XJZDLMnTsXc+fOtVQVLY7bRI+7CtufQxw/qd8dH/Wx2bgb29xVmNTEzXf0pIzbRE+PJgrHx/XpKftwiCajsW0ybvtYj/sApIsSPQ5wO5fSxCDY/SajtiMiRmPbbNzN59yfaSCU6HGA+3HG3cxGBHOI01P2w22T0dg2Gbd9TLhHiR4HuE30HOILZ7bFXR/rUR8LxnWT0dg2G3dNxu0BiOhRoscBbscZdxUWD+4OBtVRvwtGTeb4uO1jricjAlCixwVuEz09mihMRn0sHVw3GZ3RMxu3Tcbt5EQo0eMAt4kedxUWD24PBgD1OyF14HZYcD0ZEYASPa7QROH4qI+lg+vrV+iMntm4bTLudlKiR4keB+iMnhRx3HbU746P+lgwbpuM2wMQ0aNEjyPcjjNu38ISk3F9eso+HKLJaGwLRk1GbI0SPQ5wOzFwe/SyH/pEjHCBdlTpoDN63KNEjwPcjzM6GDg+hzg9ZVvUZNJCfUzshRI9DnCbJ9G7fsG4bTLuKkyahNsd1f64azLuzzQQSvQ4wO04467CxCKo301CZ/Skhds+5i4zJTVRoscBbhM9PZooTEZ9TLhAZ/TMxm2TcTs5EUr0OMBtosddhYlFUL8LRk3m+LjtY24zU6JHiR4HuE309GiiMNn9EyWcdTZ9DikY18OCzuiZjdsmo3HNLUr0OMBtosddhQmxD+6GCncVtj9um4zbAxDRo0SPA9yPM27fwtqeQ/Qxt0HYlkMMC4cIwraoyYitUaLHEe6On9xVWDy4OxhwV2HSJPTRrWDcTofcn2kglOhxgPtxRgcDk3Hbx9U5RBDWRydBpYm76ZC7CpOaKNHjALeJHr3rlw7qY2mhsS2YTMZ5W3F3ACJ6lOhxgPtEj0gL9btJ6Iwe4QIl89yjRI8D3I8z7gOwHbq9CuECndEzG7dNRuOaW9wleitWrEC7du3g4uKCiIgIHDhwoN51V69ejQEDBqB58+Zo3rw5oqKiaq2fkJAAmUxm9IiLi7N2GILQGT1CHA/lxtLCbR9zewAielwlelu3bkVKSgpmz56Nw4cPo3fv3oiNjcW1a9fqXD8rKwujR4/Gnj17kJ2djeDgYMTExODy5ctG68XFxeHq1auGx+bNm20Rjsn0Z3e4HWfcvoW1PW5PlFDWIi3c7qj2R01GbI2rRG/JkiVISkpCYmIiQkJCsGrVKri5uWHdunV1rr9p0ya88sorCA0NRdeuXbFmzRrodDpkZmYarefs7IyAgADDo3nz5rYIx2TcvqHirsKE2A7XuTEleoJx18d63B6AiJ7S3hUwVWVlJQ4dOoQZM2YYlsnlckRFRSE7O9ukMsrLy6FWq+Hj42O0PCsrC35+fmjevDkGDx6M+fPno0WLFvWWU1FRgYqKCsPz4uJiAIBarYZarRYSlkm0WhkAORjTQq3WWbx8a1HodJAD0Go00JnRLvq2tEabihVjCujff3EVd2UlVPd+VWs0gE7YfirFvq4KVXXvdzXkHL3tVjIGGQCNRgNGY9skjFV1sEajgVrNT4KsYAxyUF8LYYuYhZTNTaJ348YNaLVa+Pv7Gy339/fH6dOnTSrj7bffRlBQEKKiogzL4uLi8Le//Q3t27fH+fPn8Y9//ANDhw5FdnY2FApFneWkpqZizpw5tZanp6fDzc1NQFSmuXQpFEBbnDt3DmlpZy1evrWEXb2K1gBOnjiBv9LSzC4nIyPDcpUSuUuX+gBoA4CvuJ1u38bQe7+npaXB3KyFp5ibqrjYCbjXaj/+mMHVCZOBJSXwBnDgwAFcb8LBTFr9/SiA5jhyJAdKZYG9q2OyftevwxdAzp9/4rKXl9nlSKmv9awZc3l5ucnrcpPoNdX777+PLVu2ICsrCy4uLoblo0aNMvzes2dP9OrVCx07dkRWVhaGDBlSZ1kzZsxASkqK4XlxcbHh+3+enp4Wr/vXX1cdATp37oT4+M4WL99aFFu2AABCQkLQNT5e8PZqtRoZGRmIjo6GSqVqfAMH8M03VW8uGJPxFff164Zf44cNE/wxjxT7ulqTITo6Gk5O/MStnD0bAND3oYfAYmIEby/F/p4/v+rNT2hoH8TH83P6VrFsGQAgNDQUvWkeN4ktYtZ/kmgKsxK9oqIibNu2DefPn8e0adPg4+ODw4cPw9/fH61atTKnyEa1bNkSCoUCBQXG74QKCgoQEBDQ4LaLFy/G+++/jx9//BG9evVqcN0OHTqgZcuWOHfuXL2JnrOzM5ydnWstV6lUVulUmazqYzClUgGVqu6zjKJ076yOQi6HogntYq12FaPqJ8K4ilt5fypRqVRmf5+Hq5ibqHqY3MV9b0dVKhTGgQjEXdxNIJdXzeMKhQIqFUfnWO6NZaVKRX0tkDVjFlKu4LcVR48exQMPPICFCxdi8eLFKCoqAgBs377d6Ptzlubk5ISwsDCjCyn0F1ZERkbWu92iRYswb9487Nq1C+Hh4Y3+nUuXLuHmzZsIDAy0SL0tiaePdgBwWGH7c4gmc4ggrI/rizGI2bi7foW7CpOaBCd6KSkpSEhIwNmzZ40+Ao2Pj8fevXstWrm6/vbq1auxceNGnDp1CpMnT0ZZWRkSExMBAOPGjTNKNhcuXIiZM2di3bp1aNeuHfLz85Gfn4/S0lIAQGlpKaZNm4bff/8deXl5yMzMxPDhw9GpUyfExsZaNRYhuL/oiSYKwbhrMu4qTJqErroVjNv5W4/7AKRL8PnjgwcP4l//+let5a1atUJ+fr5FKlWf5557DtevX8esWbOQn5+P0NBQ7Nq1y3CBxsWLFyGv9tnXypUrUVlZiWeeecaonNmzZ+Pdd9+FQqHA0aNHsXHjRhQVFSEoKAgxMTGYN29enR/N2gu3iR53FbY/ajLpoBxJmrjrd24PQERPcKLn7Oxc55cA//vf/8LX19cilWpIcnIykpOT63wtKyvL6HleXl6DZbm6umL37t0Wqpn1cDcx1MR9ALbHXZPRwUBa6IyeYDQ0iL0I/uj2ySefxNy5cw33cJHJZLh48SLefvttPP300xavIOH4GMpdhQmxnfvjmsNkiRI96eD2AET0BCd6H374IUpLS+Hn54c7d+5g4MCB6NSpEzw8PPDee+9Zo46Sx/04o4OBye73MWedzf1OSoht0HRIbE3wR7deXl7IyMjAr7/+iqNHj6K0tBQPPvig0U2IiWVxewyld/3SQX0sGLfjGqCxbQYu+xngfEclQBNumNy/f3/079/fknUh9eB2nHFXYfvj/vhJfU5Ig7gb29xVmNRkUqL38ccfm1zglClTzK4MqRu3iZ4eTRSOj/ud1PbuDwsOxwf370hsj/uhwX0A0mVSord06VKj59evX0d5eTm8vb0BVP2nDDc3N/j5+VGiZwXcHkO5q7D90fGTEMfG3djm9gBE9Ey6GCM3N9fweO+99xAaGopTp06hsLAQhYWFOHXqFB588EHMmzfP2vWVJO7HGXczGxGM+53UfrhsMnpHIhiX/UwcguCrbmfOnInly5ejS5cuhmVdunTB0qVL8c4771i0csQYdxMFdxW2P2oy6aAcSZq463d6E8c9wYne1atXodFoai3XarUoKCiwSKWIMe7HGXczGxGM+52UCEJn9ASjoUHsRXCiN2TIELz00ks4fPiwYdmhQ4cwefJkusWKlXB7DOWuwvZ3//jJWdvRAV8wbsc1QIleE3DXZFzvqAQwI9Fbt24dAgICEB4eDmdnZzg7O6Nv377w9/fHmjVrrFFHyeNuYqiJ+wCIyehgQEidaGgQexF8Hz1fX1+kpaXhv//9L06fPg0A6Nq1Kx544AGLV45U4fYNFb3rF4y7Ptbjdie1H7q9ijRx12Q0trln9g2TH3jgAUrubITbccZdhcWDu4MBIaRB3E6HNBlxT3Ci9+KLLzb4+rp168yuDKkbt4meHk0Ujo/7ndT2uG4yOqMnPVzuqAQwI9G7deuW0XO1Wo3jx4+jqKgIgwcPtljFyH3cHhC4q7D9UZMR4ti4y425PQARPcGJ3jfffFNrmU6nw+TJk9GxY0eLVIoYuz/OeJsh7uFuZiOC0cFAMK6bjM7oCcZlPxOHIPiq2zoLkcuRkpJS61+lEcvg9oDAXYXtj9vbqxBpoUTPbNw1GbcHIKJnkUQPAM6fP1/njZRJ03E/zrib2Yhg3O+ktsf1VbdEMBoaxF4Ef3SbkpJi9JwxhqtXr+KHH37A+PHjLVYxUht3EwW96xeM2ybjrsKkSbjdUe2PuyajN3HcE5zoHTlyxOi5XC6Hr68vPvzww0avyCVNw904467CpMmozwmpE7dDg7vMlNQkONHbs2ePNepBGsD9GyqaKEzGfR9zG4Dtcd1kdEbPbNw2GZc7KgHM+I7e4MGDUVRUVGt5cXEx3V7FSmhiIIQQvnE7HXL9joQAZiR6WVlZqKysrLX87t27+OWXXyxSKWKM+3HGbaZqe9xedcv9Tmp7XDcZndEzGzUZsTWTP7o9evSo4feTJ08iPz/f8Fyr1WLXrl1o1aqVZWtHAHA8MXB5BCOEEMvjdjrk+h0JAQSc0QsNDUWfPn0gk8kwePBghIaGGh5hYWGYP38+Zs2aZc26AgBWrFiBdu3awcXFBREREThw4ECD63/11Vfo2rUrXFxc0LNnT6SlpRm9zhjDrFmzEBgYCFdXV0RFReHs2bPWDEEw7scZt5mq7XHfx9wGYHtcDws6o2c2ajJiayYnerm5uTh//jwYYzhw4AByc3MNj8uXL6O4uNjqV91u3boVKSkpmD17Ng4fPozevXsjNjYW165dq3P9ffv2YfTo0ZgwYQKOHDmCESNGYMSIETh+/LhhnUWLFuHjjz/GqlWrsH//fri7uyM2NhZ37961aixCcHsM5a7C4kEHAyJqlOgJxu10yO0BiOiZnOi1bdsW7dq1g06nQ3h4ONq2bWt4BAYGQqFQWLOeAIAlS5YgKSkJiYmJCAkJwapVq+Dm5oZ169bVuf5HH32EuLg4TJs2Dd26dcO8efPw4IMP4p///CeAqrN5y5YtwzvvvIPhw4ejV69e+Oyzz3DlyhXs2LHD6vGYivtxRgcDx8f9Tmp73P9rQ0JInY4eBfbtC0S1c0p2ZdJ39Hbu3ImhQ4dCpVJh586dDa775JNPWqRiNVVWVuLQoUOYMWOGYZlcLkdUVBSys7Pr3CY7O7vWDZ5jY2MNSVxubi7y8/MRFRVleN3LywsRERHIzs7GqFGj6iy3oqICFRUVhufFxcUAALVaDbVabVZ8DdFqq/JxnU4LtZqfg4KcMSgAaDUa6MxoF31bWqNNxYoxOYCqN01cxV1ZCRUAJpNBQ31tkqpQVfd+5ytuBarOEmjUajDqb5PodFXzuEbD1zyu1OkgA6DRaqmvTbRxI7B8eV+o1Wr06GGduIW0p0mJ3ogRI5Cfnw8/Pz+MGDGi3vVkMhm0Wq3Jf1yIGzduQKvVwt/f32i5v78/Tp8+Xec2+fn5da6vv5BE/7OhdeqSmpqKOXPm1Fqenp4ONze3xoMRqFmzHujWzRsXL55BWtp1i5dvLT0vXEAHAOfOncPpGt+NFCIjI8NylRK53NweADoC4CvuZpcuYQiqJp//UF+b5MoVdwBRkMn4i7vfjRvwBZCTk4PLXl5ml8Nb3E1RWBgJwA/Hjx9HWtole1fHZIOKi+EF4MCBA7jehGRNSn194UJ3AJ1w4UIe0tJOWuVvlJeXm7yuSYmeTqer83epmjFjhtGZwuLiYgQHByMmJgaenp4W/3vR0WpkZGQgOjoaKpXK4uVbi/zewO7UsSM6xMcL3l6t5jPuptizp+pdP2MyvuI+dQoAoHJyQjz1tUmqX/PFW9yK5csBAKG9e6M39bdJli+vGtvdu/dAfHwvO9fGdMqZMwEAfSMiwKp9+mUqKfa1Pqdt374d4uPbWeVv6D9JNIXg/4xhLy1btoRCoUBBQYHR8oKCAgQEBNS5TUBAQIPr638WFBQgMDDQaJ3Q0NB66+Ls7AxnZ+day1UqlVV3ZGuXb3H3vrepUCigaEK9uYu7CeTVvjXLVdz36ikDmlRnrmJuImW12Ze7uO99F1OpVBr63hzcxd0ECoXu3k8FVCpuDr0GSpWK+tpECoX23k85VCrrXL8gpC1N2ts+/vhjkwucMmWKyesK4eTkhLCwMGRmZho+PtbpdMjMzERycnKd20RGRiIzMxNTp041LMvIyEBkZCQAoH379ggICEBmZqYhsSsuLsb+/fsxefJkq8QhSXQxhsm4vZiRLsaQFm53VPujJnN8YutjkxK9pUuXmlSYTCazWqIHACkpKRg/fjzCw8PRt29fLFu2DGVlZUhMTAQAjBs3Dq1atUJqaioA4O9//zsGDhyIDz/8EMOGDcOWLVvwxx9/4NNPPzXUd+rUqZg/fz46d+6M9u3bY+bMmQgKCmrwu4jERHTQJ4QQABxPh/QmTjCxNZlJiV5ubq6162GS5557DtevX8esWbOQn5+P0NBQ7Nq1y3AxxcWLFyGv9tlXv3798MUXX+Cdd97BP/7xD3Tu3Bk7duxAjx49DOu89dZbKCsrw6RJk1BUVIT+/ftj165dcHFxsXl8Dktsb29EjNsTJWKb2ThgfHsVztqN2x3V/qjJHJ/YpsMmfVGA3YtGZsNokpOT6/2oNisrq9ayZ599Fs8++2y95clkMsydOxdz5861VBWJnlj2cmJ9dPSSFkr0BON2OhRb1sIBsTWZyTdMrm7t2rXo0aMHXFxc4OLigh49emDNmjWWrhtxFHQwMJlYJgazcR+A7YjtYEAIsQyxjW3BZ/RmzZqFJUuW4LXXXjNc1JCdnY3XX38dFy9epDNj5D561y8dYpvZiHXR2BaM26FBY1swsTWZ4ERv5cqVWL16NUaPHm1Y9uSTT6JXr1547bXXKNEj94llL+cQY9R2jk5sBwNiG9zlxrSjCia2JhP80a1arUZ4eHit5WFhYdBoNBapFHEw3M1s9iOWiUEwsc1sxLrojB4h9RLbdCg40Rs7dixWrlxZa/mnn36KMWPGWKRSxEGIZS8nRITu50iULEkBt9Oh2LIWDoitycy66nbt2rVIT0/Hww8/DADYv38/Ll68iHHjxhn9a7AlS5ZYppaEb/Su32TcnigR28xGrIvbHdX+qMkcn9imQ8GJ3vHjx/Hggw8CAM6fPw+g6t+TtWzZEsePHzesZ8tbrhCRon2AkHqJ7WBgFspaHJ9D7Ki2pf+OtViaTHCit2fPHmvUgzgyOhiYTCwTg2B0MJAW6mez0XRIbM2s++gRYhL6eEc6KNETjOthQWNbMG6HBo1twcTWZILP6N29exfLly/Hnj17cO3aNeh0OqPXDx8+bLHKEc6JZS/nyP3jJ7WdVNAwkRbKjR0f94nehAkTkJ6ejmeeeQZ9+/al7+KRxtHM5vjENrMR66IzeoJxOzRobAsmtiYTnOh9//33SEtLwyOPPGKN+hBHIpa9nCN0/JQOur2KNHE3tsWWtXBAbE0m+Dt6rVq1goeHhzXqQhwVdzMbEUxsMxuxLnpHIhgNDekQ23QoONH78MMP8fbbb+PChQvWqA9xJGLZyzlCTSYdYjsYENvgLjemHVUwsTWZ4I9uw8PDcffuXXTo0AFubm5QqVRGrxcWFlqscsRBcDezEcHENrMR66IzeoLR0JAOsU2HghO90aNH4/Lly1iwYAH8/f3pYgxSP9o3zMbtVbfU5yYT28HALJToOT6H2FFtS2xNJjjR27dvH7Kzs9G7d29r1Ic4IjoYmEwsE4Ng1MfSwu2Oan/cvokjJhNboif4O3pdu3bFnTt3rFEX4mjo4x3pENvMxgGuhwWNbcG4HRo0tgUT27AQnOi9//77eOONN5CVlYWbN2+iuLjY6EGIAU0MgtHxkxDHxu3YpvncZGLLjQV/dBsXFwcAGDJkiNFyxhhkMhm0Wq1lakYcB7czGzGZ2GY2DnA9LOgdiWDcDg3qY7OJpc8FJ3p79uyp97Vjx441qTLEwYhlL+cINZn0yGR0IJUS7vImehMnmNiaTHCiN3DgQKPnJSUl2Lx5M9asWYNDhw4hOTnZYpUjDoK7mY0IJraZjQNcDws6oycYDQ3pENt0KPg7enp79+7F+PHjERgYiMWLF2Pw4MH4/fffLVk3wjux7OUcuX/8pLaTCq6HCSV6gnHXZGLLWjggtiYTdEYvPz8fGzZswNq1a1FcXIyRI0eioqICO3bsQEhIiLXqSHjH3cxGBBPbzEasi/pZMGoy6RDbdGjyGb0nnngCXbp0wdGjR7Fs2TJcuXIFy5cvt2bdjBQWFmLMmDHw9PSEt7c3JkyYgNLS0gbXf+2119ClSxe4urqiTZs2mDJlCm7fvm20nkwmq/XYsmWLtcORBrHs5Rzh9hMxsc1sHOCuj+viEEHYFndNRmNbMLE1mcln9P7zn/9gypQpmDx5Mjp37mzNOtVpzJgxuHr1KjIyMqBWq5GYmIhJkybhiy++qHP9K1eu4MqVK1i8eDFCQkJw4cIFvPzyy7hy5Qq2bdtmtO769esNVxMDgLe3tzVDkR7uZjZCSIPEcgTjCDWZdHCb6P36669Yu3YtwsLC0K1bN4wdOxajRo2yZt0MTp06hV27duHgwYMIDw8HACxfvhzx8fFYvHgxgoKCam3To0cPfP3114bnHTt2xHvvvYcXXngBGo0GSuX90L29vREQEGD9QKSG29NT9iOWiUEwsc1sHLjfZByODxrbZuOuyWhsCya2JjM50Xv44Yfx8MMPY9myZdi6dSvWrVuHlJQU6HQ6ZGRkIDg4GB4eHlapZHZ2Nry9vQ1JHgBERUVBLpdj//79eOqpp0wq5/bt2/D09DRK8gDg1VdfxcSJE9GhQwe8/PLLSExMbPB/+FZUVKCiosLwXH+jaLVaDbVaLSQ0k+jLtEbZ1iTX6aAAoNXpoDOj7rzG3RRarRyAAgBfccs0GigBMAAa6muTaDQy6Kdg3uJW6HSQA9BqtTS2TSYDIIdWq4VarbN3ZUymZAwyAGqtFqC+NolOV9XXOp31+lpIewq+vYq7uztefPFFvPjiizhz5gzWrl2L999/H9OnT0d0dDR27twptMhG5efnw8/Pz2iZUqmEj48P8vPzTSrjxo0bmDdvHiZNmmS0fO7cuRg8eDDc3NyQnp6OV155BaWlpZgyZUq9ZaWmpmLOnDm1lqenp8PNzc2k+pgjIyPDamVbQ5ezZ9EVwMULF3A0Lc3scniLuynOnu0CoCsY4ytun5MnMQBAWXk5MqmvTXL2rDeAgZDJ+Is7LD8frQGcPHECf1F/myQ/PxxAK5w+fRppabn2ro7JYu7ehSuA3379FbevXjW7HCn1dUHBQwCCcOrUKaSl5Vnlb5SXl5u8ruBEr7ouXbpg0aJFSE1NxXfffYd169YJ2n769OlYuHBhg+ucOnWqKVUEUHXGbdiwYQgJCcG7775r9NrMmTMNv/fp0wdlZWX44IMPGkz0ZsyYgZSUFKPyg4ODERMTA09PzybXtya1Wo2MjAxER0dDpVJZvHxrkR8+DABo06YNWsfHC96e17ib4vDh+9dH8RS3zMsLAODu5oZ46muT/PHH/U8NeItbsWkTACAkJARdqb9N8vnnVf3dpUtXxMd3s3NtTKd0dgYAPNK/P9Cnj+DtpdjXa9dW9XVISDfEx1vnjiRC/uVskxI9PYVCgREjRmDEiBGCtnvjjTeQkJDQ4DodOnRAQEAArl27ZrRco9GgsLCw0e/WlZSUIC4uDh4eHvjmm28a3dEiIiIwb948VFRUwPneDl6Ts7Nzna+pVCqr7sjWLt/iFFUfQSpkMiiaUG/u4m6Ce00GxmR8xX2v4jK5vEl15irmJtL3NcBh3PqxLZfT2DaRXF71EZ5CoYBKpWhkbfFRqVQA9bVJZLKqvlYqrdfXQtrSIomeuXx9feHr69voepGRkSgqKsKhQ4cQFhYGAPjpp5+g0+kQERFR73bFxcWIjY2Fs7Mzdu7cCRcXl0b/Vk5ODpo3b15vkkcEEMs3UTnCfZNxHwARhLsrC+yPuyYT25UFHBDbhVZ2TfRM1a1bN8TFxSEpKQmrVq2CWq1GcnIyRo0aZbji9vLlyxgyZAg+++wz9O3bF8XFxYiJiUF5eTn+/e9/o7i42HCq09fXFwqFAt999x0KCgrw8MMPw8XFBRkZGViwYAHefPNNe4breLib2Yhg1MeCcd1kdNAXjJpMOsSWG3OR6AHApk2bkJycjCFDhkAul+Ppp5/Gxx9/bHhdrVbjzJkzhi8oHj58GPv37wcAdOrUyais3NxctGvXDiqVCitWrMDrr78Oxhg6deqEJUuWICkpyXaBOTK6BYNg3DaZ2GY2jojlXb9ZuNtR7Y+7JqOxLZjYmoybRM/Hx6femyMDQLt27cCqjaBBgwYZPa9LXFyc0Y2SiYWJZS8nRIS4O+BXR2NbMO6bjPsAbEdsiZ7J/wKNELNxfUSzLbFMDIKJbWbjCJdNxu2pZ/vjrsm4q7B4iGVsU6JHrEcsezkhhNgZt9MhvYkTTGxNRokesT56R2iy+ydKRDJDmEpsMxsHuB4WdEbPbNRkjk9s0yElesR6xLKXE0KInXE7HYota+GA2JJ5SvSI9Yltrxcxbk+U0MFAMLHda0sQbndU+6Mmc3xi62NK9Ij10EFfeqjPpUVsRzQR43Zo0Js4wcTWZJToEeujg4Hjoz4WTGwHA0G4rDRpEupzk4ltbFOiR6yHPt4RTCwTg2Bim9mIbdDYFoy7JuOuwuIhlumQEj1iPWLZyznE3VW3RDCuj580tgXjvsm4D8B2xPa+lxI9Yn1cH9FsSywTg2Bim9k4wmWT0dl6s3HXZNxV2P7ENh1SokesRyx7OSEiRMdPaeF2OhRb1sIBsTUZJXrE+uiIZjJuT5SIbWYj1sXtjmp/1GSOT2zTISV6xHrEspcT26E+N5lDHPAdIgjb4HZoiC1r4YDYmowSPWJ9dDAwmVgmBsGoj83G9Q2TiWA0VBwfJXpEOsSylxPrE9vMRmyDshaTcTs0aGwLJrYmo0SPWB8dDEx2/6tPIpkhiNWI7WAgCJeVFgdup0Pqc5OJrY8p0SPWQ1/Ylg6usxZiNhrbJuN2aFAfCya26ZASPWI9YtnLOUK5sXRw3cc0ts3Gbb9Tn5uMEj0iPdzObMRkYpvZiHXROxLBuB0a1MeCiW06pESPWI9Y9nJiO9TnJrt/MKADKRExsWUtHBFLk1GiR6yP3hGaTCwTg2DUx9JCZ/TMRk3m+MSWG1OiR6xHLHs5h7g9GFCfm4zbPq7OIYKwDW6HhtiyFg6Ircko0SPWRwcDk4llYhCM+thsXPY5l5UWB26HCvW5ySjRI9JBH++Yjbv76IltZuOAQwwLhwjCNrgdGtTHgoltOuQm0SssLMSYMWPg6ekJb29vTJgwAaWlpQ1uM2jQIMhkMqPHyy+/bLTOxYsXMWzYMLi5ucHPzw/Tpk2DRqOxZijSIZa9nCPUZIQLtKOajdu8ifrcZGJL9JT2roCpxowZg6tXryIjIwNqtRqJiYmYNGkSvvjiiwa3S0pKwty5cw3P3dzcDL9rtVoMGzYMAQEB2LdvH65evYpx48ZBpVJhwYIFVotFcrid2YjJxDazccAhhoVDBGEb3A4N6mPBxDYdcpHonTp1Crt27cLBgwcRHh4OAFi+fDni4+OxePFiBAUF1butm5sbAgIC6nwtPT0dJ0+exI8//gh/f3+EhoZi3rx5ePvtt/Huu+/CycnJKvFIhlj2co5Qk0kPl7dXoR3VbNzlTWLLWjggtj7mItHLzs6Gt7e3IckDgKioKMjlcuzfvx9PPfVUvdtu2rQJ//73vxEQEIAnnngCM2fONJzVy87ORs+ePeHv729YPzY2FpMnT8aJEyfQp0+fOsusqKhARUWF4XlxcTEAQK1WQ61WNynWuujLtEbZ1iTXaqEAoNNqoTWj7rzG3RRarRyAAgBfccvUaigB6ADqaxNpNDLop2De4pYzBgUArUYDHfW3SaoO/nLodFqo1Tp7V8dkSgAy3Osr6muT6HRVc7hWq4FabZ2sT0h7cpHo5efnw8/Pz2iZUqmEj48P8vPz693u+eefR9u2bREUFISjR4/i7bffxpkzZ7B9+3ZDudWTPACG5w2Vm5qaijlz5tRanp6ebvTRsKVlZGRYrWxraH/yJHoBuHr1Kv5ISzO7HN7ibopTpzoA6AnG+Io74NAhRAAoKirCL9TXJsnJ8QXQDzIZf3H3unAB7QGcPXcOZ6i/TXLpUm8A7XD27Hmkpf3X3tUx2eP33rDvycrCHV9fs8uRUl8XFw8E4I2cnCNQKK5Z5W+Ul5ebvK5dE73p06dj4cKFDa5z6tQps8ufNGmS4feePXsiMDAQQ4YMwfnz59GxY0ezy50xYwZSUlIMz4uLixEcHIyYmBh4enqaXW591Go1MjIyEB0dDZVKZfHyrUV+4QIAIDAgAPHx8YK35zXupvjrr/vXR/EUt+zeu0vv5s2pr03k5HT/ozDe4pbv3g0A6NyxIzpSf5vku++qfnbq1BHx8Z3sWxkB5PKqOemxxx4D2rQRvL0U+3rWrKozeg8+2Afx8Qqr/A39J4mmsGui98YbbyAhIaHBdTp06ICAgABcu2acFWs0GhQWFtb7/bu6REREAADOnTuHjh07IiAgAAcOHDBap6CgAAAaLNfZ2RnOzs61lqtUKqvuyNYu3+KUVbuXXC6HvAn15i7uJlDcmxMYk/EVN/W1YIpq8z93cd87+Cvkciiov00il2sBADKZAiqVdQ7+VnHvC2cqJyeA+tpE99pMpYRKZZ00S0hb2jXR8/X1ha8Jp4IjIyNRVFSEQ4cOISwsDADw008/QafTGZI3U+Tk5AAAAgMDDeW+9957uHbtmuGj4YyMDHh6eiIkJERgNKReYvtmqohx+31n6mNp4XZHtR/um4z7AGxPLE3GxX30unXrhri4OCQlJeHAgQP47bffkJycjFGjRhmuuL18+TK6du1qOEN3/vx5zJs3D4cOHUJeXh527tyJcePG4dFHH0WvXr0AADExMQgJCcHYsWPx559/Yvfu3XjnnXfw6quv1nnGjghEN0w2G3dNRlfmCcZdH9fFIYIgDaI+Fkxs0yEXiR5QdfVs165dMWTIEMTHx6N///749NNPDa+r1WqcOXPG8AVFJycn/Pjjj4iJiUHXrl3xxhtv4Omnn8Z3+i9KAFAoFPj++++hUCgQGRmJF154AePGjTO67x5pArHs5RyhJpMeur2KtHCbN1Gfm0xsiR4XV90CgI+PT4M3R27Xrh1YtREUHByMn3/+udFy27Zti7QmXDVGTMDtzEZMJraZjQMOMSwcIgjb4HZoUB8LJrbpkJszeoRDYtnLOcJ9k3EfgO1x2WRcVlocuMubxJa1cEBsTUaJHrE+7mY2+2NMJDOEqaiPBeO6yej7t4KJ5aBPrI8SPSIdYtnLOcJ9k3EfABGEEj3BuGsysWUtHBBbH1OiR6xPbHs9sTzqY8G4bjI66AvGfZNxH4DtiC03pkSPWI9Y9nKOcPuJmNhmNo5w3WTc7aj2x12TcVdh+xPbdEiJHrE+migIcSxiOYJxhPsm4z4A26FEj0gHt6en7EcsE4NgYpvZOHC/yTgeHzS2BeOuybirsP2JbTqkRI9Yj1j2cmI71OfSQP0sGPdNxn0AtieWJqNEj1gfvSMUjG6v4vjE9q7fLNTvglGTSYdYxjYlesR6xLKXc4T7JuM+AGIS6mdpqJ6VUp+bTGxv4ijRI9ZHb2EF467JuKuw/XHdZPT9W8HEctAn1keJHpEOsezlHOG+ybgPgAhCiZ5gXDUZndEzCyV6RHq4mtmIWaiPBeO6ycRyBOMI903GfQC2Q4kekQ76eEcwsUwMgoltZuMI3V5FWrhqMq4qKx5iazZK9Ij10EHfbGKbKIjlcd3HNLYF477JuA/AdsT2vpcSPWJ9XB/RbEssE4NgYpvZOMJ1k9HYFoyrJuOqsuIhtumQEj1iPWLZyznE3X309KjPTcb1MZT6WTDum4z7AGyHEj0iPVwf0WxLLBODYNTH0kT9LhhXTcZVZcVDbP/ekBI9Yj3cZi3EbNTn0kAXWgnG5dCg26uYhc7oEemhg4HJxDIxCEZ9LJjY3vULwu2Oan/cDhXqc5OJrY8p0SPWQxOD9FCfS4vYjmgixuXQoP5tErH0OSV6xPposhCMuybjrsL2x3WTieUIRmyH+txk9NEtkQ76Ho9gYpkYBBPbzMYRrpuMxrZgXDUZV5UVD7FNh5ToEesRy17OIbq9iuPj+hhK/SwY903GfQC2Q4memQoLCzFmzBh4enrC29sbEyZMQGlpab3r5+XlQSaT1fn46quvDOvV9fqWLVtsEZJ0cH1Esy2xTAyCUR9LE/W7Y6P+NYvYmk1p7wqYasyYMbh69SoyMjKgVquRmJiISZMm4Ysvvqhz/eDgYFy9etVo2aeffooPPvgAQ4cONVq+fv16xMXFGZ57e3tbvP6SxG3WQsxGfW4ysR0MBKF+lga6vYpZxHZGj4tE79SpU9i1axcOHjyI8PBwAMDy5csRHx+PxYsXIygoqNY2CoUCAQEBRsu++eYbjBw5Es2aNTNa7u3tXWtdYkFcH9FsSywTg2DUx2bj8vYqetTvglGTOT5K9MyQnZ0Nb29vQ5IHAFFRUZDL5di/fz+eeuqpRss4dOgQcnJysGLFilqvvfrqq5g4cSI6dOiAl19+GYmJiZA10EMVFRWoqKgwPC8uLgYAqNVqqNVqIaGZRF+mNcq2JplWCyUAHWPQmlF3XuNuCo1GBkAJxviKm/paOLW6qq+rfucrbrlOBwUArVYLHfW3SaoO/gpotTqo1Tp7V8c0lZVQ3ftVrdEA1NcmYaxqXGu1GnOazCRC2pOLRC8/Px9+fn5Gy5RKJXx8fJCfn29SGWvXrkW3bt3Qr18/o+Vz587F4MGD4ebmhvT0dLzyyisoLS3FlClT6i0rNTUVc+bMqbU8PT0dbm5uJtXHHBkZGVYr2xpa5eQgHMDNGzewLy3N7HJ4i7spjh0LBvAgAL7ibvPnn+gD4Nq1a9hPfW2SI0eCADwEmYy/uLucP4+uAC5euICj1N8mycsLAdAZeXkXkJZ2wt7VMYni7l08fu/33enp0Do7m12WlPq6snIoACdkZ+/DpUv1X0vQFOXl5Sava9dEb/r06Vi4cGGD65w6darJf+fOnTv44osvMHPmzFqvVV/Wp08flJWV4YMPPmgw0ZsxYwZSUlIMz4uLixEcHIyYmBh4eno2ub41qdVqZGRkIDo6GiqVqvENREJ272KZFi1aID4+XvD2vMbdFDdvVp1JZkzGVdyye2+4/Pz9qa9NVFZ2/1MD3uKWHzwIAGjTpg1aU3+bZO/eqp9t27ZFfHxb+1bGVNUueIyNjQXMOJEhxb5WKqtSq0ce6YcePayTZuk/STSpPlapgYneeOMNJCQkNLhOhw4dEBAQgGvXrhkt12g0KCwsNOm7ddu2bUN5eTnGjRvX6LoRERGYN28eKioq4FzPuxdnZ+c6X1OpVFbdka1dvsXd29nlAORNqDd3cTdB9TC5iluhAADI5XLqaxMpq82+3MV9r/IKuRwK6m+TyOXaez/lUKkUdq6Niar1jcrJyXiCElyUdPqa3fuSnkqltFrMQsq1a6Ln6+sLX1/fRteLjIxEUVERDh06hLCwMADATz/9BJ1Oh4iIiEa3X7t2LZ588kmT/lZOTg6aN29eb5JHBKAbJkuH2L59TGyDxrbJuBwa1L9NIpY+5+I7et26dUNcXBySkpKwatUqqNVqJCcnY9SoUYYrbi9fvowhQ4bgs88+Q9++fQ3bnjt3Dnv37kVaHd8j+e6771BQUICHH34YLi4uyMjIwIIFC/Dmm2/aLDaHJpa9nCPc58bU5ya7nxtz2NnUz2ajse34xPa+l4tEDwA2bdqE5ORkDBkyBHK5HE8//TQ+/vhjw+tqtRpnzpyp9QXFdevWoXXr1oiJialVpkqlwooVK/D666+DMYZOnTphyZIlSEpKsno8kpKbCxw8CFRUAFotoNPV/qnT3R8d937K1GoEHDoEWWVl1UdFNV436WfNZeYQOlqbsH7b34GRANreug3Zl+7Gn+9Zqj51bd/YOo1t8+23wupEHMPRo8D+/YD+ikyN5v5YrusBAIxVje0//oBMra762L++9evY1ui5JdiorLATOoyDHA+d0gEbTfxfBdbICmWy+2NX/3t9j5ISy/99CaBEz0w+Pj713hwZANq1a2f4XLy6BQsWYMGCBXVuExcXZ3SjZGJh+r384kWg2llWUykBNP7BvGMZcO+BiwBesG9dzHLxor1rwA1uz+wA98f2vn3Aww8L3lyKY/u5ew9k3HvwRixZCwco0SPSUf07kcHBgItL1bt3ubzqof9doTB+FwkAMhl0jKGoqAjezZtDLpcbvwut76cprwkh9GjcxPXzC4CTJ4FmzSoRHq6CvGadm1qfurZvbB1TtjlwoOpsjhkJvdSJ5WAgyCOPAM7OgKsr4OVV9SV9larqDLR+rNb3AKADUHT7tvHYNuUB1H5uTRb8G2fOMPyVK0PbNgwhISacMbeGhs6Q1vfIzwc6d67qb2ISsb2Jo0SPWM+AAUBaGtCrF9CqleDNtWo1fklLQ3x8fJOu5ORJ5ibghReAXh2u4490b37i1mqBn34CHnrI3jXhhtgOBoIMGVL1sZ5SaVaSIsWxvX66FgsXKvDacC0+/piTq26JWeiMHpEOuRyo8X+FScPEMjEIplAA0dH2rgWxJYkkaJbGdYJPTCK2RM/Eb4QSQmyJDgaOj/pYWsRy0CfWR4keIaReYpkYiO1Qn0sLJfiOjxI9QgghdMCXGLEc9In1UaJHCKmXWCYGYjtc3jCZmI0SfMdHiR4hpFF0MHB81MfSIpaDPrE+SvQIIfUSy8RACLEOSvClQyzzOSV6hIgQYyKZIQghFiGWgz6xPjqjRwipl1gmBmJ9dGZHmqjfHZ/Y+pgSPUIIsSNK7qWB+lk66IweIYQQ0b3rJ4RYhv6rN5ToEUJqEcvEQGyHbq8iLZTgS4dY5nNK9AgRIToYOD7qY2kRy0GfWFf1cS2WPqdEjxAREcvEQGyH+lxaKMF3bJToEUJMQrdXcXx0wJcWsRz0iXVRokcIaZBYJgZCiHVQgu/YKNEjhBACgA74UiOWgz6xLkr0CCENEsvEQGyHrrqVFkrwHRsleoQQk9DBwPFRH0uLWA76xHbE0ueU6BEiImKZGIjtUJ9LC11o5djE+AaOEj1CRIgOBoQ4FkropYE+uiWENMjbu+pnSYmTXetBrE+M7/yJ9VG/OzZK9JrgvffeQ79+/eDm5gZv/dGwEYwxzJo1C4GBgXB1dUVUVBTOnj1rtE5hYSHGjBkDT09PeHt7Y8KECSgtLbVCBIQ0rn37qp/XrrlCp7NvXQghliOWgz6xLkr0mqCyshLPPvssJk+ebPI2ixYtwscff4xVq1Zh//79cHd3R2xsLO7evWtYZ8yYMThx4gQyMjLw/fffY+/evZg0aZI1QiCkUa1bA0olg0ajwJUr9q4NsaZLl6p+urho7FsRYlOVlfauAbEmMSZ6SntXwFRz5swBAGzYsMGk9RljWLZsGd555x0MHz4cAPDZZ5/B398fO3bswKhRo3Dq1Cns2rULBw8eRHh4OABg+fLliI+Px+LFixEUFGSVWAipj1IJtG0LnD8P/O1vSgwcCLRoAbi6AiqV8UOpBOTV3qpVn1RM+V1MNBoZjhwJQmmpDApF1TL9hFn9Z13LmvqaLbfX6YBbt4CCAuDLL6uW9elzHYB/g+1D+NemTdVO8NVXMiiVgJ8f4OUFuLlVjWWFoupn9d/1Y6Gmuj7+tcRHwqbOD0Lmkaqx3QolJVVxV69nfb839JrYftfpgPJyoLS06nHyZNVypVIHlQqiwE2iJ1Rubi7y8/MRFRVlWObl5YWIiAhkZ2dj1KhRyM7Ohre3tyHJA4CoqCjI5XLs378fTz31VJ1lV1RUoKKiwvC8uLgYAKBWq6FWqy0ei75Ma5QtZlKNOzmZ4fXXnZCTI0NOjr1rYytKAA/ZuxI216GDDg8/fAVqdRd7V8WmpDi2R4xQo3PnEpw92xwmnq9wEEoA4Y2u5Wieeuos5PJgWGsXFzJ2HDbRy8/PBwD4+xu/U/b39ze8lp+fDz8/P6PXlUolfHx8DOvUJTU11XCGsbr09HS4ubk1ter1ysjIsFrZYia1uNu3Bz76yAPHjvmisNAFJSUqqNUKaLUyaDRyo596+qt0G3qnb80reS1109+aZwr0z6uXX3OZ8RnL2svqX7fxsqsvt1TZzZpVwsurEn5+5QgPz4erq1Zy+7ie1OJesECOQ4f8cOGCJ8rKVCgrU6GyUgGdTgatVgadTmb0u1YrE+1Z+MYwVv+Zv7rGrJD16i/XUus0Xr/qY9zZWQsXFy1cXDTw8qpA166F6Nr1FjIyTtf9ByygvLzc5HXtmuhNnz4dCxcubHCdU6dOoWvXrjaqkWlmzJiBlJQUw/Pi4mIEBwcjJiYGnp6eFv97arUaGRkZiI6Ohkos54JtQOpxT5z4sGTilm5fh0g0bun1tz7mmTN7SiZmQNp9bc2Y9Z8kmsKuid4bb7yBhISEBtfp0KGDWWUHBAQAAAoKChAYGGhYXlBQgNDQUMM6165dM9pOo9GgsLDQsH1dnJ2d4ezsXGu5SqWy6o5s7fLFiuKWDinGDFDcUiLFmAFpxm3NmIWUa9dEz9fXF76+vlYpu3379ggICEBmZqYhsSsuLsb+/fsNV+5GRkaiqKgIhw4dQlhYGADgp59+gk6nQ0REhFXqRQghhBBiK9zcXuXixYvIycnBxYsXodVqkZOTg5ycHKN73nXt2hXffPMNAEAmk2Hq1KmYP38+du7ciWPHjmHcuHEICgrCiBEjAADdunVDXFwckpKScODAAfz2229ITk7GqFGj6IpbQgghhHCPm4sxZs2ahY0bNxqe9+nTBwCwZ88eDBo0CABw5swZ3L5927DOW2+9hbKyMkyaNAlFRUXo378/du3aBRcXF8M6mzZtQnJyMoYMGQK5XI6nn34aH3/8sW2CIoQQQgixIm4SvQ0bNjR6Dz3Gal4pJ8PcuXMxd+7cerfx8fHBF198YYkqEkIIIYSICjcf3RJCCCGEEGEo0SOEEEIIcVCU6BFCCCGEOChK9AghhBBCHBQleoQQQgghDooSPUIIIYQQB8XN7VXETH9bFyH/e04ItVqN8vJyFBcXS+pfyFDc0olbijEDFLeU4pZizIA047ZFzPp8o+Zt5epCiZ4FlJSUAACCg4PtXBNCCCGESEVJSQm8vLwaXEfGTEkHSYN0Oh2uXLkCDw8PyGQyi5dfXFyM4OBg/O9//4Onp6fFyxcrils6cUsxZoDillLcUowZkGbctoiZMYaSkhIEBQVBLm/4W3h0Rs8C5HI5WrdubfW/4+npKZmBUh3FLR1SjBmguKVEijED0ozb2jE3diZPjy7GIIQQQghxUJToEUIIIYQ4KEr0OODs7IzZs2fD2dnZ3lWxKYpbOnFLMWaA4pZS3FKMGZBm3GKLmS7GIIQQQghxUHRGjxBCCCHEQVGiRwghhBDioCjRI4QQQghxUJToEUIIIYQ4KEr07GTFihVo164dXFxcEBERgQMHDjS4/ldffYWuXbvCxcUFPXv2RFpamtHrjDHMmjULgYGBcHV1RVRUFM6ePWvNEMwiJO7Vq1djwIABaN68OZo3b46oqKha6yckJEAmkxk94uLirB2GIEJi3rBhQ614XFxcjNZxxL4eNGhQrbhlMhmGDRtmWEfsfb1371488cQTCAoKgkwmw44dOxrdJisrCw8++CCcnZ3RqVMnbNiwodY6QucKWxMa9/bt2xEdHQ1fX194enoiMjISu3fvNlrn3XffrdXXXbt2tWIUwgiNOSsrq879Oz8/32g9R+vrusasTCZD9+7dDeuIva9TU1Px0EMPwcPDA35+fhgxYgTOnDnT6HZiOmZTomcHW7duRUpKCmbPno3Dhw+jd+/eiI2NxbVr1+pcf9++fRg9ejQmTJiAI0eOYMSIERgxYgSOHz9uWGfRokX4+OOPsWrVKuzfvx/u7u6IjY3F3bt3bRVWo4TGnZWVhdGjR2PPnj3Izs5GcHAwYmJicPnyZaP14uLicPXqVcNj8+bNtgjHJEJjBqrupl49ngsXLhi97oh9vX37dqOYjx8/DoVCgWeffdZoPTH3dVlZGXr37o0VK1aYtH5ubi6GDRuGxx57DDk5OZg6dSomTpxolPSYs//YmtC49+7di+joaKSlpeHQoUN47LHH8MQTT+DIkSNG63Xv3t2or3/99VdrVN8sQmPWO3PmjFFMfn5+htccsa8/+ugjo3j/97//wcfHp9a4FnNf//zzz3j11Vfx+++/IyMjA2q1GjExMSgrK6t3G9Edsxmxub59+7JXX33V8Fyr1bKgoCCWmppa5/ojR45kw4YNM1oWERHBXnrpJcYYYzqdjgUEBLAPPvjA8HpRURFzdnZmmzdvtkIE5hEad00ajYZ5eHiwjRs3GpaNHz+eDR8+3NJVtRihMa9fv555eXnVW55U+nrp0qXMw8ODlZaWGpaJva+rA8C++eabBtd56623WPfu3Y2WPffccyw2NtbwvKntaGumxF2XkJAQNmfOHMPz2bNns969e1uuYlZkSsx79uxhANitW7fqXUcKff3NN98wmUzG8vLyDMt46mvGGLt27RoDwH7++ed61xHbMZvO6NlYZWUlDh06hKioKMMyuVyOqKgoZGdn17lNdna20foAEBsba1g/NzcX+fn5Rut4eXkhIiKi3jJtzZy4ayovL4darYaPj4/R8qysLPj5+aFLly6YPHkybt68adG6m8vcmEtLS9G2bVsEBwdj+PDhOHHihOE1qfT12rVrMWrUKLi7uxstF2tfm6OxcW2JduSBTqdDSUlJrXF99uxZBAUFoUOHDhgzZgwuXrxopxpaTmhoKAIDAxEdHY3ffvvNsFwqfb127VpERUWhbdu2Rst56uvbt28DQK39tTqxHbMp0bOxGzduQKvVwt/f32i5v79/re9r6OXn5ze4vv6nkDJtzZy4a3r77bcRFBRkNDji4uLw2WefITMzEwsXLsTPP/+MoUOHQqvVWrT+5jAn5i5dumDdunX49ttv8e9//xs6nQ79+vXDpUuXAEijrw8cOIDjx49j4sSJRsvF3NfmqG9cFxcX486dOxYZMzxYvHgxSktLMXLkSMOyiIgIbNiwAbt27cLKlSuRm5uLAQMGoKSkxI41NV9gYCBWrVqFr7/+Gl9//TWCg4MxaNAgHD58GIBl5kexu3LlCv7zn//UGtc89bVOp8PUqVPxyCOPoEePHvWuJ7ZjttLiJRJiBe+//z62bNmCrKwso4sTRo0aZfi9Z8+e6NWrFzp27IisrCwMGTLEHlVtksjISERGRhqe9+vXD926dcO//vUvzJs3z441s521a9eiZ8+e6Nu3r9FyR+trAnzxxReYM2cOvv32W6Pvqw0dOtTwe69evRAREYG2bdviyy+/xIQJE+xR1Sbp0qULunTpYnjer18/nD9/HkuXLsXnn39ux5rZzsaNG+Ht7Y0RI0YYLeepr1999VUcP35cVN8hNAWd0bOxli1bQqFQoKCgwGh5QUEBAgIC6twmICCgwfX1P4WUaWvmxK23ePFivP/++0hPT0evXr0aXLdDhw5o2bIlzp071+Q6N1VTYtZTqVTo06ePIR5H7+uysjJs2bLFpAleTH1tjvrGtaenJ1xdXS2y/4jZli1bMHHiRHz55Ze1PuaqydvbGw888AC3fV2Xvn37GuJx9L5mjGHdunUYO3YsnJycGlxXrH2dnJyM77//Hnv27EHr1q0bXFdsx2xK9GzMyckJYWFhyMzMNCzT6XTIzMw0OpNTXWRkpNH6AJCRkWFYv3379ggICDBap7i4GPv376+3TFszJ26g6sqkefPmYdeuXQgPD2/071y6dAk3b95EYGCgRerdFObGXJ1Wq8WxY8cM8ThyXwNVtySoqKjACy+80OjfEVNfm6OxcW2J/UesNm/ejMTERGzevNnoFjr1KS0txfnz57nt67rk5OQY4nHkvgaqrlw9d+6cSW/gxNbXjDEkJyfjm2++wU8//YT27ds3uo3ojtkWv7yDNGrLli3M2dmZbdiwgZ08eZJNmjSJeXt7s/z8fMYYY2PHjmXTp083rP/bb78xpVLJFi9ezE6dOsVmz57NVCoVO3bsmGGd999/n3l7e7Nvv/2WHT16lA0fPpy1b9+e3blzx+bx1Udo3O+//z5zcnJi27ZtY1evXjU8SkpKGGOMlZSUsDfffJNlZ2ez3Nxc9uOPP7IHH3yQde7cmd29e9cuMdYkNOY5c+aw3bt3s/Pnz7NDhw6xUaNGMRcXF3bixAnDOo7Y13r9+/dnzz33XK3lPPR1SUkJO3LkCDty5AgDwJYsWcKOHDnCLly4wBhjbPr06Wzs2LGG9f/66y/m5ubGpk2bxk6dOsVWrFjBFAoF27Vrl2GdxtpRDITGvWnTJqZUKtmKFSuMxnVRUZFhnTfeeINlZWWx3Nxc9ttvv7GoqCjWsmVLdu3aNZvHVxehMS9dupTt2LGDnT17lh07doz9/e9/Z3K5nP3444+GdRyxr/VeeOEFFhERUWeZYu/ryZMnMy8vL5aVlWW0v5aXlxvWEfsxmxI9O1m+fDlr06YNc3JyYn379mW///674bWBAwey8ePHG63/5ZdfsgceeIA5OTmx7t27sx9++MHodZ1Ox2bOnMn8/f2Zs7MzGzJkCDtz5owtQhFESNxt27ZlAGo9Zs+ezRhjrLy8nMXExDBfX1+mUqlY27ZtWVJSkqgmRsaExTx16lTDuv7+/iw+Pp4dPnzYqDxH7GvGGDt9+jQDwNLT02uVxUNf62+hUfOhj3P8+PFs4MCBtbYJDQ1lTk5OrEOHDmz9+vW1ym2oHcVAaNwDBw5scH3Gqm4zExgYyJycnFirVq3Yc889x86dO2fbwBogNOaFCxeyjh07MhcXF+bj48MGDRrEfvrpp1rlOlpfM1Z12xBXV1f26aef1lmm2Pu6rngBGI1VsR+zZfcCIYQQQgghDoa+o0cIIYQQ4qAo0SOEEEIIcVCU6BFCCCGEOChK9AghhBBCHBQleoQQQgghDooSPUIIIYQQB0WJHiGEEEKIg6JEjxBCCCHEQVGiRwghDuDMmTMICAhASUlJo+uePHkSrVu3RllZmQ1qRgixJ0r0CCGSJJPJGny8++679q6iIDNmzMBrr70GDw+PRtcNCQnBww8/jCVLltigZoQQe6J/gUYIkaT8/HzD71u3bsWsWbNw5swZw7JmzZqhWbNm9qiaYBcvXkSnTp2Qm5uLVq1ambTNDz/8gKSkJFy8eBFKpdLKNSSE2Aud0SOESFJAQIDh4eXlBZlMZrRsy5Yt6NatG1xcXNC1a1d88sknhm3z8vIgk8nw5ZdfYsCAAXB1dcVDDz2E//73vzh48CDCw8PRrFkzDB06FNevXzdsl5CQgBEjRmDOnDnw9fWFp6cnXn75ZVRWVhrWqaiowJQpU+Dn5wcXFxf0798fBw8ebDCWL7/8Er179zZK8i5cuIAnnngCzZs3h7u7O7p37460tDTD69HR0SgsLMTPP/9sieYkhIgUvY0jhJAaNm3ahFmzZuGf//wn+vTpgyNHjiApKQnu7u4YP368Yb3Zs2dj2bJlaNOmDV588UU8//zz8PDwwEcffQQ3NzeMHDkSs2bNwsqVKw3bZGZmwsXFBVlZWcjLy0NiYiJatGiB9957DwDw1ltv4euvv8bGjRvRtm1bLFq0CLGxsTh37hx8fHzqrO8vv/yC8PBwo2WvvvoqKisrsXfvXri7u+PkyZNGZyidnJwQGhqKX375BUOGDLFk8xFCxIQRQojErV+/nnl5eRmed+zYkX3xxRdG68ybN49FRkYyxhjLzc1lANiaNWsMr2/evJkBYJmZmYZlqamprEuXLobn48ePZz4+PqysrMywbOXKlaxZs2ZMq9Wy0tJSplKp2KZNmwyvV1ZWsqCgILZo0aJ669+7d282d+5co2U9e/Zk7777boNxP/XUUywhIaHBdQghfKMzeoQQUk1ZWRnOnz+PCRMmICkpybBco9HAy8vLaN1evXoZfvf39wcA9OzZ02jZtWvXjLbp3bs33NzcDM8jIyNRWlqK//3vf7h9+zbUajUeeeQRw+sqlQp9+/bFqVOn6q3znTt34OLiYrRsypQpmDx5MtLT0xEVFYWnn37aqL4A4OrqivLy8nrLJYTwj76jRwgh1ZSWlgIAVq9ejZycHMPj+PHj+P33343WValUht9lMlmdy3Q6ndXr3LJlS9y6dcto2cSJE/HXX39h7NixOHbsGMLDw7F8+XKjdQoLC+Hr62v1+hFC7IcSPUIIqcbf3x9BQUH466+/0KlTJ6NH+/btm1z+n3/+iTt37hie//7772jWrBmCg4PRsWNHODk54bfffjO8rlarcfDgQYSEhNRbZp8+fXDy5Mlay4ODg/Hyyy9j+/bteOONN7B69Wqj148fP44+ffo0OSZCiHjRR7eEEFLDnDlzMGXKFHh5eSEuLg4VFRX4448/cOvWLaSkpDSp7MrKSkyYMAHvvPMO8vLyMHv2bCQnJ0Mul8Pd3R2TJ0/GtGnT4OPjgzZt2mDRokUoLy/HhAkT6i0zNjYWEydOhFarhUKhAABMnToVQ4cOxQMPPIBbt25hz5496Natm2GbvLw8XL58GVFRUU2KhxAibpToEUJIDRMnToSbmxs++OADTJs2De7u7ujZsyemTp3a5LKHDBmCzp0749FHH0VFRQVGjx5tdHPm999/HzqdDmPHjkVJSQnCw8Oxe/duNG/evN4yhw4dCqVSiR9//BGxsbEAAK1Wi1dffRWXLl2Cp6cn4uLisHTpUsM2mzdvRkxMDNq2bdvkmAgh4kU3TCaEEBtJSEhAUVERduzYYfGyV6xYgZ07d2L37t2NrltZWYnOnTvjiy++MLrwgxDieOiMHiGEOICXXnoJRUVFKCkpafTfoF28eBH/+Mc/KMkjRALojB4hhNiINc/oEUJIXSjRI4QQQghxUHR7FUIIIYQQB0WJHiGEEEKIg6JEjxBCCCHEQVGiRwghhBDioCjRI4QQQghxUJToEUIIIYQ4KEr0CCGEEEIcFCV6hBBCCCEO6v8BaNMwMHA8I5MAAAAASUVORK5CYII=", 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Parámetros\n", "z = np.linspace(0, 2, 1000)\n", "beta = 2 * np.pi # constante de propagación (1 λ = 1 unidad de z)\n", "A_i = 1 # amplitud incidente\n", "A_r = 0.8 # amplitud reflejada menor\n", "phi = 1230/np.pi\n", "\n", "# Ondas cuadradas aproximadas (por Fourier de coseno)\n", "def square_wave(z, beta, phi=0):\n", " # aproximación por los primeros términos de serie de Fourier\n", " wave = np.zeros_like(z)\n", " for n in range(1, 6000, 2): # solo términos impares\n", " wave += (1/n) * np.sin(n*(beta*z + phi))\n", " return (4/np.pi) * wave\n", "\n", "# Ondas\n", "Vi = A_i * square_wave(z, beta, phi=0) # incidente\n", "Vr = A_r * square_wave(-z, -beta, phi=phi) # reflejada (viaja al revés, con desfase)\n", "Vtotal = Vi + Vr\n", "\n", "# Gráfico 1: incidente y reflejada\n", "plt.figure(figsize=(7,4))\n", "plt.plot(z, Vi, label='Incidente', color='blue')\n", "plt.plot(z, Vr, label='Reflejada', color='red')\n", "plt.title('Ondas cadradas incidente e reflexada')\n", "plt.xlabel('Tempo (s)')\n", "plt.ylabel('Amplitude')\n", "#plt.legend()\n", "plt.grid(True)\n", "\n", "# Gráfico 2: suma total\n", "plt.figure(figsize=(7,4))\n", "plt.plot(z, Vtotal, color='black')\n", "plt.title('Suma de ambas ondas cadradas')\n", "plt.xlabel('Tempo (s)')\n", "plt.ylabel('Amplitude')\n", "plt.grid(True)\n", "\n", "print(\"Ondas cadradas incidente e reflexada (con desfase e onda atenuada) e Suma de ambas ondas\")\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "925ed914", "metadata": {}, "source": [ "### Reflexión sin inversión en señal triangular" ] }, { "cell_type": "markdown", "id": "c0c3a967", "metadata": {}, "source": [ "- 5:\n", "\n", "Este caso y los siguientes ya son repetir lo hecho con la señal cuadrada (primer caso onda reflejada sin inversión por terminal abierto y segundo caso onda reflejada invertida por terminal en cortocircuito) pero con señal triangular y, más adelante, sinusoidal.\n", "\n", "Ahora nos compete el caso de la señal triangular con el terminal de la línea en abierto. Se forma en el canal 1 la suma de ambas ondas como de costumbre, lo único estéticamente extraño a observar es la punta amorfa como si de una esquina abollada de una mesa se tratase. Efectivamente, esto se explica por la suma de ambas ondas al no ser los dos picos máximos (de la onda incidente y reflectida) iguales en amplitud y desplazado uno respecto al otro por el retardo.\n", "\n", "Dicha figura es sencilla de apreciar al ver el esquema siguiente:" ] }, { "cell_type": "code", "execution_count": 496, "id": "374bcea9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ondas triangulares incidente e reflexada (con desfase e onda atenuada) e Suma de ambas ondas\n" ] }, { "data": { "image/png": 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", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Parámetros\n", "z = np.linspace(0, 2, 1000)\n", "beta = 2 * np.pi # constante de propagación (1 λ = 1 unidad de z)\n", "A_i = 1 # amplitud incidente\n", "A_r = 0.8 # amplitud reflejada menor\n", "phi = 12 / np.pi\n", "\n", "# Onda triangular aproximada por serie de Fourier\n", "def triangular_wave(z, beta, phi=0):\n", " wave = np.zeros_like(z)\n", " for n in range(1, 100, 2): # solo términos impares\n", " wave += ((-1)**((n-1)//2) / n**2) * np.sin(n * (beta*z + phi))\n", " return (8/np.pi**2) * wave\n", "\n", "# Ondas\n", "Vi = A_i * triangular_wave(z, beta, phi=0) # incidente\n", "Vr = A_r * triangular_wave(z, -beta, phi=phi) # reflejada (viaja al revés, con desfase)\n", "Vtotal = Vi + Vr\n", "\n", "# Gráfico 1: incidente y reflejada\n", "plt.figure(figsize=(7,4))\n", "plt.plot(z, Vi, label='Incidente', color='blue')\n", "plt.plot(z, Vr, label='Reflejada', color='red')\n", "plt.title('Ondas triangulares incidente e reflexada')\n", "plt.xlabel('Tempo (s)')\n", "plt.ylabel('Amplitude')\n", "plt.grid(True)\n", "\n", "# Gráfico 2: suma total\n", "plt.figure(figsize=(7,4))\n", "plt.plot(z, Vtotal, color='black')\n", "plt.title('Suma de ambas ondas triangulares')\n", "plt.xlabel('Tempo (s)')\n", "plt.ylabel('Amplitude')\n", "plt.grid(True)\n", "\n", "print(\"Ondas triangulares incidente e reflexada (con desfase e onda atenuada) e Suma de ambas ondas\")\n", "\n", "plt.show()\n" ] }, { "cell_type": "markdown", "id": "58cebc16", "metadata": {}, "source": [ "### Reflexión con inversión en señal triangular" ] }, { "cell_type": "markdown", "id": "38dcfd53", "metadata": {}, "source": [ "- 5:\n", "\n", "Seguimos con señal triangular pero con cortocircuito al final de la línea para forzar que vuelva una onda invertida a juntarse con la emitida en la región de la entrada de la señal.\n", "\n", "La forma vista en el osciloscopio podría definirse como un sombrero o más como una serpiente que se comió un elefante.\n", "\n", "Tal extraña figura es fácil de componer y comprender con estas dos señales sumadas como se ve en la siguiente imagen:" ] }, { "cell_type": "code", "execution_count": 495, "id": "d174bd20", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ondas triangulares incidente e reflexada (con desfase e onda atenuada) e Suma de ambas ondas\n" ] }, { "data": { "image/png": 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x+Pn1efLkwZYtW1J9Pi2nKilJV6612W5qtjZu3Bjjxo1L9fkSJUok+/+pU6dUk5SbN2+ievXqyZ5X93dEXSIiIlC3bl3Y2tpi+vTpcHV1hZWVFa5du4bx48dnqk2GYcEcPYbR07JlS6xZswZnz55FrVq1Ujx/5swZPH36FJ6enhq3XbhwYSiVSjx69CjZTPn+/fspzt25cyd69eqFhQsXqh6LiYlBREREinNz5cqFPn36oE+fPvj69Svq1KmDqVOnpuvopbd9pwk7d+5E/fr1ERwcnOzxiIgIODg4aNxe4cKFAdAqTv369VWPJyQk4OnTp8lWKvnVwJ/fE35VMD127doFKysrHDlyJJnEyLp169Syk8+aTkxMRKNGjTI8P1u2bOjUqRM6deqEuLg4/Pbbb5g5cyYmTpyoEwkVV1dXHDlyBJ8+fdJoVa9w4cKphgH8/BnlV6Xz5MmjVv/VIa3PJC9jZG5unqlrubq64tixY6hZs6baExix2nV1dcXXr1/V6uebN28wbNgwNGnSBBYWFipHmP8OAer/jqh730+dOoWPHz9i9+7dqFOnjurxJ0+eqNtFhoHDYvQYRs/YsWNhbW0NT09PfPz4Mdlznz59wqBBg2BjY4OxY8dq3HazZs0AAMuWLUv2+JIlS1Kcq1AoUqxyLV++PMVq1c82Zs+eHcWKFUu2FZkafFZeao6jJqRm544dO9KNmUqPKlWqIHfu3FizZg0SEhJUj2/ZsiXFNh/vbCSNW0pMTExzK/Vnu2UyWbL38+nTp9izZ49adioUCrRv3x67du3CrVu3Ujz//v171fHP98jCwgKlS5cGx3GIj49X63qa0r59e3Acl+qKYXqrp82bN8eFCxdw6dIl1WPv379PsWrl4eEBW1tbzJo1K9U+JO2/uvAZoD9/JvPkyYN69eohICAAb9680fhaHTt2RGJiIvz8/FI8l5CQkOnvgC7a7dixI0JDQ3HkyJEUz0VERCT7TgwYMABKpRLBwcEIDAyEmZkZ+vXrl+z+qvs7ou5951dmk7YZFxeHVatWadxXhmHCVvQYRk/x4sWxYcMGdOvWDWXLlk1RGePDhw/4888/U8TZqUOFChXQpUsXrFq1CpGRkahRowaOHz+eqo5cy5YtsWnTJtjZ2aF06dIIDQ3FsWPHUsS9lS5dGvXq1UPlypWRK1cuXLlyBTt37oSXl1eGtigUCsydOxeRkZGwtLREgwYN0ow3SouWLVti+vTp6NOnD2rUqIGbN29iy5YtmRaTtrCwwNSpUzFs2DA0aNAAHTt2xNOnT7F+/Xq4uromW/X55ZdfUK1aNUycOFG1crVt27Zkg2FatGjRAosWLULTpk3RtWtXvHv3DitXrkSxYsUy3LrnmTNnDk6ePAl3d3cMGDAApUuXxqdPn3Dt2jUcO3YMnz59AgA0adIETk5OqFmzJvLmzYu7d+9ixYoVaNGiRYaJP5mlfv366NGjB5YtW4YHDx6gadOmUCqVOHPmDOrXr5/m52PcuHHYtGkTmjZtihEjRqhkNgoXLpzsfbG1tYW/vz969OiBSpUqoXPnznB0dMTz589x8OBB1KxZEytWrNDIZmtra5QuXRrbt29HiRIlkCtXLpQpUwZlypTBypUrUatWLZQtWxYDBgyAi4sL3r59i9DQULx8+TKFbmNS6tatC09PT8yePRthYWFo0qQJzM3N8eDBA+zYsQNLly7F77//rpGtump37Nix2LdvH1q2bInevXujcuXK+PbtG27evImdO3fi6dOncHBwwLp163Dw4EGsX79eFTu4fPlydO/eHf7+/hgyZAgA9X9H1L3vNWrUgL29PXr16oXhw4dDJpNh06ZNmQq9YBgo+k/0ZTDE4caNG1yXLl24fPnycebm5pyTkxPXpUsXlQxIUnh5lffv3yd7PDX5j+/fv3PDhw/ncufOzWXLlo1r1aoV9+LFixSyEp8/f+b69OnDOTg4cNmzZ+c8PDy4e/fupZBjmDFjBle1alUuZ86cnLW1Nefm5sbNnDmTi4uLy7CPa9as4VxcXDiFQpFMjqRw4cJpSrakJq8yevRoLl++fJy1tTVXs2ZNLjQ0lKtbt24yKRRe8mTHjh3J2ktNQoTjOG7ZsmVc4cKFOUtLS65q1arcuXPnuMqVK3NNmzZNdt6jR4+4Ro0acZaWllzevHm5SZMmcSEhIWrJqwQHB3PFixfnLC0tOTc3N27dunWqe5kUANzQoUNTfT/evn3LDR06lHN2dlZ9Tho2bMgFBgaqzgkICODq1KnD5c6dm7O0tORcXV25sWPHcpGRkam2md57k5ZMR2p2JyQkcPPnz+fc3Nw4CwsLztHRkWvWrBl39epV1Tk/30+Oo89+3bp1OSsrK65AgQKcn58fFxwcnKaUjYeHB2dnZ8dZWVlxrq6uXO/evbkrV65kyubz589zlStX5iwsLFJ8Jx49esT17NmTc3Jy4szNzbkCBQpwLVu25Hbu3JnWW5iMwMBArnLlypy1tTWXI0cOrmzZsty4ceO4169fp/u6tOzXpF115VU4jqRrJk6cyBUrVoyzsLDgHBwcuBo1anALFizg4uLiuBcvXnB2dnZcq1atUrTXrl07Llu2bNzjx485jlP/d4Tj1L/v586d46pVq8ZZW1tz+fPn58aNG8cdOXIkTbkmhrSQcRxz2xkMhv5RKpVwdHTEb7/9hjVr1ohtDoPBYBglLEaPwWDonJiYmBRbQRs3bsSnT5+Mrtg9g8FgGBJsRY/BYOicU6dOYdSoUejQoQNy586Na9euITg4GKVKlcLVq1dZYXgGg8HQESwZg8Fg6JwiRYrA2dkZy5YtUyVZ9OzZE3PmzGFOHoPBYOgQtqLHYDAYDAaDYaSwGD0Gg8FgMBgMI4U5egwGg8FgMBhGCovR0wJKpRKvX79Gjhw5tFaGisFgMBgMBiM1OI7Dly9fkD9//hQ1m3+GOXpa4PXr13B2dhbbDAaDwWAwGCbEixcvVJVU0oI5elqAL3n04sUL2Nraar39+Ph4HD16VFWOx1Rg/TadfptinwHWb1Pqtyn2GTDNfuujz1FRUXB2dlar5CJz9LQAv11ra2urM0fPxsYGtra2JvNFAVi/TanfpthngPXblPptin0GTLPf+uyzOuFiLBmDwWAwGAwGw0hhjh6DwWAwGAyGkcIcPQaDwWAwGAwjhTl6DAaDwWAwGEYKc/QYDAaDwWAwjBTm6DEYDAaDwWAYKZJy9E6fPo1WrVohf/78kMlk2LNnT4avOXXqFCpVqgRLS0sUK1YM69evT3HOypUrUaRIEVhZWcHd3R2XLl3SvvEMBoPBYDAYekZSjt63b99Qvnx5rFy5Uq3znzx5ghYtWqB+/foICwvDyJEj0b9/fxw5ckR1zvbt2+Ht7Q1fX19cu3YN5cuXh4eHB969e6erbjAYDAaDwWDoBUkJJjdr1gzNmjVT+/zVq1ejaNGiWLhwIQCgVKlSOHv2LBYvXgwPDw8AwKJFizBgwAD06dNH9ZqDBw9i7dq1mDBhgvY7wWAwGAwGg6EnJOXoaUpoaCgaNWqU7DEPDw+MHDkSABAXF4erV69i4sSJquflcjkaNWqE0NDQNNuNjY1FbGys6v9RUVEASA07Pj5eiz2Aqt2k/5oK9+8n4M2bbCbXb1O836bYZ8A0+x0TA9y4kYD4eJlJ9dsU7zVgmv3WR581aduoHb3w8HDkzZs32WN58+ZFVFQUvn//js+fPyMxMTHVc+7du5dmu7Nnz8a0adNSPH706FHY2Nhox/hUCAkJ0Vnbhsbly3kxe3ZVyOX18fnzeZQu/Ulsk/SOKd1vHlPsM2Ba/V66tCJOniwEN7eakMtDoFCIbZF+MaV7nRRT7Lcu+xwdHa32uUbt6OmKiRMnwtvbW/V/vrhwkyZNdFbrNiQkBI0bNzaJWoEcB0ybZgalUgalEjh0qAbGjFGKbZbeMLX7DZhmnwHT6/fTp8C//9Kwc+9ebkRENEWPHpIKFc80pnaveUyx3/roM7+TqA5G7eg5OTnh7du3yR57+/YtbG1tYW1tDYVCAYVCkeo5Tk5OabZraWkJS0vLFI+bm5vr9IOs6/YNhQsXgOvXhf+fPavArVsKVKwonk1iYCr3Oymm2GfAdPq9ahWgTDJnCwoyR9++puHo8ZjKvf4ZU+y3LvusSbtG/Q2rXr06jh8/nuyxkJAQVK9eHQBgYWGBypUrJztHqVTi+PHjqnMY+mfZMvq3Rw8latd+CQBYulREgxgMRpaJiACCgug4KCgBCoUSoaFy3LghqlkMhtEjKUfv69evCAsLQ1hYGACSTwkLC8Pz588B0JZqz549VecPGjQIjx8/xrhx43Dv3j2sWrUKf/31F0aNGqU6x9vbG2vWrMGGDRtw9+5dDB48GN++fVNl4TL0y+vXwI4ddOzllYhWrR4BAP78EwgPF9EwBoORJQIDga9fgTJlgB49OLi7vwEArF4tsmEMhpEjKUfvypUrqFixIir+2MPz9vZGxYoV4ePjAwB48+aNyukDgKJFi+LgwYMICQlB+fLlsXDhQgQFBamkVQCgU6dOWLBgAXx8fFChQgWEhYXh8OHDKRI0GPrB3x9ISABq1QIqVgRKlIiAu7sScXFsQGAwpEpcnLBS7+0NyGSAh8dTAMDGjbTax2AwdIOkYvTq1asHjuPSfD61qhf16tXD9aQBX6ng5eUFLy+vrJrHyCLfvwvO3IgRwuPDhilx8aIc/v7AxIlAKuGRDAbDgNm+HXj1CsiXD+jalR4rV+4DfvmFw+3bMgQGAuPGiWsjg2GsSGpFj2HcbNkCfPgAFC4MtG0rPN6uHYcCBYB374Bt20Qzj8FgZAKOAxYsoOPhw4WJmkwGjByZCIBW+0xIZo3B0CvM0WMYBBwHLFlCx8OGAWZJ1prNzQF+wXXpUjqXwWBIgxMngBs3gGzZAE/P5M917swhb15a7fvrL3HsYzCMHeboMQyCY8eA27eB7NmB/v1TPj9gAGBtTbIrZ87o3z4Gg5E5Fi+mf/v0Aeztkz9nackmcQyGrmGOHsMg4Ffz+vQB7OxSPp87N9CjBx0zqRUGQxr89x9w8CBt0w4fnvo5np7k8F2+TBqaDAZDuzBHjyE69+8Dhw6lPxgAwnN79pDCPoPBMGz4TNuWLYHixVM/x9ER6NaNjvkJH4PB0B7M0WOIDj8YtGoFFCuW9nm//AI0bkzK+itW6Mc2BoOROT5/Btato+ORI9M/l8+y37ULePFCp2YxGCYHc/QYovLpE8Cr4mQ0GADCgBAUROKrDAbDMAkKAqKjgbJlgfr10z+3XDk6JzERWLlSP/YxGKYCc/QYosIPBuXKAfXqZXx+s2a0BRQZCWzYoHPzGAxGJkhIAJYvp+ORIyksIyP4iV5gIPDtm64sYzBMD+boMUQjPl4YDEaNUm8wkMuFWL2lS5MXSGcwGIbB33/TFqyjoyCQnBEtWgCurrTlu3Gjbu1jMEwJ5ugxRGP3buDlSyBPHqBzZ/Vf17s3ZeY+eAAcPqwz8xgMRibhkyoGDwasrNR7jUIhTOKWLWOTOAZDWzBHjyEamRkMANLa69cveRsMBsMwuHQJOH+ehM4HD9bstX36ALa2wL17wJEjurGPwTA1mKPHEIULF+jPwkLzwQAgkVW5HAgJIaFlBoNhGPCTry5dACcnzV6bI4cgmM4mcQyGdmCOHkMU+B/xrl2BvHk1f33RokI9XF6ehcFgiMvLl8COHXSsThZ9agwbRpO4o0fZJI7B0AbM0WPonRcvgJ076TizgwEgSK1s3Ah8/JhlsxgMRhZZtYoybuvWBSpWzFwbRYoA7drRMauCw2BkHeboMfTOypWkl1W/PlC+fObbqV2bBpOYGGDNGu3Zx2AwNCc6GggIoOOsTOCSvn7TJuDDh6y1xWCYOszRY+iVb99IJwvI+mAgkwmreitWkFwLg8EQh02bSADdxYWq3GSFmjWBypVpEsc7jwwGI3MwR4+hVzZuJJ0sV1fSzcoqnTuTPMurVyTXwmAw9I9SKcTdDh9OUilZQSYjbU2AdgDi4rLWHoNhyjBHj6E3lEoh5kYbgwEAWFoKWbssS4/BEIeQEJJEyZGDJFK0QYcOQL58wJs3wF9/aadNBsMUYY4eQ28cOQLcv086WdoaDABg0CCSablwAbh4UXvtMhgM9eAnWf360fdbG1hYkIwSACxeDHCcdtplMEwN5ugx9MbixfRv//4089cWTk5CZQ2Wpcdg6Je7d6lCjUxG0ijaZOBAElO/dg04d067bTMYpgJz9Bh64fZt2t6Ry7U/GABCUsaOHRSvx2DohehoKgMRGyu2JaLBT67atqVEDG3i4AD06EHH/ESRwdALz58D//uf2FZoBeboMfQCPxi0a0c6WdqmUiWgTh3S8Fq5UvvtMxip0qULpYg2aGCSad8fP1KCFZD1LPq04Nvdswd48kQ312AwknH2LFCiBFChAhAUJLY1WYY5egyd8+EDSS8AuhsMkrYdEEALLQyGTjl+HNi3j47PnzdJHZA1a4Dv30nPsnZt3VyjdGmgSRNK5lq+XDfXYDBUcBwwerSwSj9mDOkGSRjm6DF0TkAA6WFVrkyLH7qidWsqjfbpk+BYMhg6QakExo6lYz59fNYs8npMhPh40q8EaJIlk+nuWrzUSlAQEBWlu+swGNi7F7h0CbC2pgDwyEhg9myxrcoSknP0Vq5ciSJFisDKygru7u64dOlSmufWq1cPMpksxV+LJAJuvXv3TvF806ZN9dEVkyAuTthKHTVKt4OBQkGyLQBlASqVursWw8TZuhW4fp2yip49AwoXJh0Qf3+xLdMbO3dSPKyTE9Cpk26v1aQJ4OYGfPkCrF+v22sxTJiEBGDSJDr29gbWrqXj5cupdqdEkZSjt337dnh7e8PX1xfXrl1D+fLl4eHhgXfv3qV6/u7du/HmzRvV361bt6BQKNChQ4dk5zVt2jTZeX/++ac+umMS/PUXjX/58pEulq7p25fG3nv3KPmDwdA6MTHA5Ml0PGECUKAAMGUK/X/2bFoBMHI4TkiOGDKE9Cx1iVwuhGYsXUolFBkMrRMURGnkuXPTlm3TplS4OTYWmDpVbOsyjaQcvUWLFmHAgAHo06cPSpcujdWrV8PGxgZrea/7J3LlygUnJyfVX0hICGxsbFI4epaWlsnOs7e310d3jJ6kg8HQoaSLpWtsbUnLC2ACygwdsWwZZeQVKCB4H7160ZLThw+S3+ZRhwsXgMuXycHz9NTPNXv0AOztgcePgQMH9HNNhgkRFQX4+NCxry+QMydtQc2ZQ4+tXw/cuSOWdVnCTGwD1CUuLg5Xr17FxIkTVY/J5XI0atQIoaGharURHByMzp07I1u2bMkeP3XqFPLkyQN7e3s0aNAAM2bMQO7cudNsJzY2FrFJ5BSifgSNxMfHI14HmXd8m7poW5ecOyfDtWtmsLLi0LdvgsZJiZnt96BBwNKlZjh8WIYbN+JRqpRm1xUbqd7vrCCZPn/8CLNZsyADkDBtGjhzc1W2rWz2bJi1awduyRIk9O9P27kZIJl+/8TChQoAcnTpooS9faJevtvm5kD//nLMn6/AokVKNG8urWU9qd7rrCKVfstnzoTi/XtwxYsjoV8/IYu+cmUo2rSBfO9eKCdOROLOnRm2pY8+a9K2jOOkoTf++vVrFChQAOfPn0f16tVVj48bNw7//vsvLmZQEuHSpUtwd3fHxYsXUbVqVdXj27Ztg42NDYoWLYpHjx5h0qRJyJ49O0JDQ6FIo0bX1KlTMW3atBSPb926FTY2NpnsofExd+6vCA3Nj8aNn2LoUP3qEc2eXRUXL+aDh8cTDB58Q6/XZhgvZYKC4HrgACKLFMGphQuT1/HjONTw8YHjzZt4UbcurvEZBEbGu3fWGDSoMZRKGZYsOYEiRb7o7dofPljB07MxEhPlWLToFFxcjH+bnKF7rN+/R8OhQ6GIi8PFiRMR7u6e7PnsL16gwYgRkCmVOD1nDj67uYlkqUB0dDS6du2KyMhI2GZQjsZkHD1PT0+Ehobixo30B/3Hjx/D1dUVx44dQ8OGDVM9J7UVPWdnZ3z48CHDNzwzxMfHIyQkBI0bN4a5ubnW29cFT54ApUqZQamU4fr1ePzyi+ZtZKXfZ87I0LChGaytOTx+nIB0FmgNDine76wiiT4/egSzcuUgi49HwsGD4Bo3TnnOtWswr1YNnEyGhAsXSHckHSTR75+YMEGORYsUaNBAicOHM7eqlpV+9+ihwPbtcnTvrsTatdJZ1ZPivdYGUui3ondvyLduhbJOHSSGhKSaNajw9IR83Tooa9ZE4okT6WYW6qPPUVFRcHBwUMvRk8zWrYODAxQKBd6+fZvs8bdv38LJySnd13779g3btm3D9OnTM7yOi4sLHBwc8PDhwzQdPUtLS1imEn1sbm6u0w+yrtvXJgEBlPXapAlQoULWbM5Mv+vXpzH2+nUZ1q0zR5Idf8kgpfutLQy6zz4+tJ3TpAnMmjdP/Rx3d6BrV8i2boX5H38AR4+q1bRB9zsJX78KiYijRslhbp61MO/M9Hv0aGD7duCvv+SYP1+ODH7+DQ6p3GttY7D9vnqVsugByBcuhDytYHI/P2DbNsjPnYP80CEqBZMBuuyzJu1KJhnDwsIClStXxvHjx1WPKZVKHD9+PNkKX2rs2LEDsbGx6N69e4bXefnyJT5+/Ih8+fJl2WZT5csXQUxclwLJ6SGTCddescIkixYwtMnFi1RfTyYD5s9P/9wZMyjzKCREbUdPKmzYAEREAMWKAWn5urrm11+BGjVIusmE1GwYuoDjBD3Mbt2AKlXSPrdAAZJcAYBx4yQ1qEjG0QMAb29vrFmzBhs2bMDdu3cxePBgfPv2DX369AEA9OzZM1myBk9wcDDatm2bIsHi69evGDt2LC5cuICnT5/i+PHjaNOmDYoVKwYPDw+99MkYWbeOEphKlgTEfBs7dSKNr9evSfOLwcgUHEdSCwBl15Yrl/75RYtSmjlAA4KRCDoqlUIpwxEjSPJELPhJnL8/qd0wGJni8GHg5EmamM2cmfH548cDefIADx4AgYG6t09LSMrR69SpExYsWAAfHx9UqFABYWFhOHz4MPLmzQsAeP78Od68eZPsNffv38fZs2fRj9fcSIJCocCNGzfQunVrlChRAv369UPlypVx5syZVLdmGRmTmEjqEwD9GIs5GFhaksYXQDIv0ohGZRgce/dS7UsrK9q+UYfJkwE7OyqKvnmzbu3TE//8Q+ObnR3Qu7e4trRrBxQqBLx/r9p1YzA0IzGRJmIAKe2rkSWPHDkEPb2pUyWjmSkpRw8AvLy88OzZM8TGxuLixYtwT5Idc+rUKaz/STa9ZMmS4DgOjVMJnLa2tsaRI0fw7t07xMXF4enTpwgMDFQ5jgzNOXgQePSI9K569BDbGtL4srQkzS81VXgYDIH4eJrFA1TapWBB9V6XO7egsP/HH0ZRGo3XpRwwAMieXVRTYGYGDBtGx0uWsEkcIxNs3AjcukWDFf9dVYf+/QXNTF5jz8CRnKPHMGx4geSBA4Gf5ApFIU8egA/N5G1jMNRmzRrgv/8ABwfB4VOXYcMAZ2cqncQvc0uUW7eAY8dohd7LS2xriH796Dfm5k3afWMw1CY6WqhmM3kyOXvqYm4OzJtHx0uWkHi6gcMcPYbWCAsDTp0iaTE+RMkQGDGC/t29m8qSMhhqERUlbNP4+tKepSZYW1NiBgDMmkUrABKFX8377Tf1drj0gb29sIXMJnEMjVi6lAo1Fy6cucGqZUsqjRYTI1TTMGCYo8fQGnygdocOtJBhKJQtCzRqRMHkK1aIbQ1DMsybR0FgxYtnvs5X9+5A+fLkNPJOn8R4/14IMzQ0DWh+EnfgAMUPMhgZ8v69UKZw5kyKvdWUpNn3GzdSLK4Bwxw9hlYIDxeCosWSVEkP3qY1a0gLjMFIl1evgEWL6HjOHNquyQxyuTAgrFpFAawSIyCAarr/+iuQgZKV3ilenBZXAMnvjjP0xYwZpAFWsSLQpUvm2/n1V6BzZwoQ5ZM6DBTm6DG0wurVpGtVrRppxhoazZrRoBAZSVpgDEa6+PhQAkWNGpTimRUaNyadofh4igeSEHFxwMqVdDxyZLrFAESDn8StW0cafwxGmjx8SBMugCZgWZWFmDmTJoFHjxq0ZiZz9BhZJiZGEC41tK0dHrlc2OZZutRopM0YuuDmTfIaABoMtOHdzJ1L7WzfDly6lPX29MT27bRanz8/8PvvYluTOg0aUHjGt2+CUDuDkSqTJwMJCUDTpkAala80wsVFEpqZzNFjZJlt24B37ygu77ffxLYmbXr1AnLmpFieQ4fEtoZhsIwbR9sx7dvTip42KF8e6NmTjseOlYQeCMcJSQ5eXqQpa4gkrYKzfDmN4wxGCi5dAv76iz4wc+dqr90//hA0M7ds0V67WoQ5eows8fNgYGbA1ZOzZycNMEDIImQwknHsGKnlm5kJAdvaws+PAr9Pnwb279du2zrg7Fng+nUyeeBAsa1Jn65dAUdHUrr4+2+xrWEYHElLnfXsmXF1G01Iqpk5ebJBlmphjh4jS5w6Bdy4AdjYCE6UIePlRfIvx4/TDh2DoUKpFAaDwYMpqFObODsLS0/jxxv80hM/GerZk8YyQ8bKim4ZwCZxjFTYv58mWJpUt9EEA9fMZI4eI0vwP6q9e2umOSkWhQoJ28tsQGAkY/NmEoO0tdWdNtaECeQ13bsHBAfr5hpa4MkTYM8eOuZjWw2dwYMpLv78eUmFQTJ0TUKCIHY+cqRutL9+1sz8+FH718gCzNFjZJqHD4UdqOHDxbVFE/iEkS1bKLaQwcD37xRrAwATJ1IlDF1gZyc4kb6+Bqv1s3w5LXB6eAClS4ttjXo4OQlqGWwSx1Cxdi1NrHLnpomWrujWjWJxIyMhN7DSaMzRY2SaZcso9KFFC6BkSbGtUZ9q1YCqVUkbLCBAbGsYBsGyZbTt4uys+yWsQYMAV1fg7VvIea0+AyIqSsheNURNzPTg7d2xA3j5UlRTGIbA16/CxMrHR/PqNpqgUKg0M+WrVsHm7VvdXUtDmKMnFQwsSy8igiZKgPQGg6RZeqtWkcPHMGE+fKDtFoC2X6ytdXs9CwtVood88WJYfvqk2+tpyPr1pCfr5gY0aSK2NZpRsSJVpkpIEOTSGCbMwoXA27c0sRo0SPfXa9wYaNIEsvh4lOLLyRgAzNEzdJRKyDZuRP0RI6h0i4EQHEy6VWXKaEeOSN/8/jtQoABphG3fLrY1DFHx86NlrPLlaftFH/z+O+DuDtm3b3Dbtk0/11SDxEShlOGIEVnXkxUDfhIXEEC16xkmSni4UJVm9mz96QPNmwdOJkPBM2cgu3JFP9fMAAl+jU0PxYoVsH3+HHJtyz1kkoQEiuEBDFctPyPMzSkDF6B4HgNbMGXoi5+V8hUK/Vw3Sa3MwseOAXfv6ue6GXDgAPD4MSVW8bJ/UqNVK9Kx/fQJ2LRJbGsYojF1Kq1GVK2qX7Xv8uXBde8OAJDPnKm/66YDc/QMHbkciT8CO+WrV9PAJDJ79wLPnlG8eteuYluTeQYMoF2669eBM2fEtoYhChMn0szFw4O2XfRJ7dpQtmoFmVIJBa/DJTJ8EoOnJ0kmSRGFQkgOW7LEYIsVMHTJ3btCoOmCBXpfjUicOhUPW7dGooGUamGOngTgGjTA20qVIEtIEIQZRYQXSB40SPfhTLokd25h1YJl6ZkgoaHAzp00CMybJ4oJiTNnQimXQ37wIPDvv6LYwBMWRrqYCoVQ1Umq9OkD5MhByZYhIWJbw9A7EydSHEKbNkDt2vq/vrMzbvftazAClMzRkwh3evYEJ5NROtnFi6LZcfkycO4cbX0OGSKaGVqDT7Dcs4e2rBgmQlKl/N69tauUrwlubnjGZzyMHSvq8hM/2enQAShYUDQztIKtLdC/Px3zE1OGiXDmDG07KRSAgcmciAVz9CRCVJEi4PjlpzFjRAsq4wO1O3cG8uUTxQStUqoU1bfmOCHukGEC7NlDMxZra90o5WvA/c6dwWXLRrOoHTtEsSE8HPjzTzrmdSalzrBhlExy5Ahw547Y1jD0AsfR+AiQp+/mJq49BgJz9CREoq8vlXA5exbYt0/v13/9WshQlYpavjrwWXrBwZR8yTBy4uMFpXxvb0q/FpHYnDmhHD2a/jNxoih6P6tXA3FxQPXqFLtuDBQtSjt3gEFWpWLogh07qCxKtmyUjMEAwBw9aVGwoDDdFqFW5sqVdMnatYHKlfV6aZ3SpAmt7H35ImgDMoyYwEDgwQPA0REYN05sawAAylGjqLTDkyeAv79erx0TI1xSapqYGcH3Z+NGg6tKxdA2cXE0UQIoDMLJSVx7DAjm6EmN8eMp3fX+fb3WyoyOFqpIGMvWDk9SAeVlyyiGl2GkREUB06bRsa8vBXMZAtmyAdOn07GfHymS64k//6RSgM7OQh1oY6F2baBSJapwFxgotjUMnbJ6NQVaOzkB/Ao5AwBz9KTHz7Uyv3zRy2W3bKEZcZEiQOvWermkXuneHciVixZU+Pq9DCNk7lwSHi9RAhg4UGxrktOnDy0tf/qkqpyhazhOSMIYNgwwM9PLZfVG0kncihW0a88wQiIihInStGlA9uyimmNoMEdPinh6qmplYuFCnV8u6WAwfLj+NGX1iY0Nva0Ak1oxWl6+BPjasnPmUOq4IWFmJsi8LF0KPH+u80ueOgXcuEGffz5L1djo1IkWeV6/JjUdhhEydy6tRJQqBfTtK7Y1Bgdz9KRIklqZWLAAePNGp5cLCaGstRw5gH79dHopURk6lMbaf/8lEWWGkeHjQwFpNWsCbduKbU3qtGhBxVpjY4E//tD55fhJTe/eVA3DGLGwEHQBFy9mVXCMjhcvhA/y3LnGtyytBSTn6K1cuRJFihSBlZUV3N3dcenSpTTPXb9+PWQyWbI/KyurZOdwHAcfHx/ky5cP1tbWaNSoER48eKDrbmSdH7Uy8e2bEHOkI/jvUN++hhPSpAsKFAA6dqRjtqpnZNy4AaxfT8ciKOWrTZLSaNi8WaczjocPhTAFvpKEseLpCVhakoJNaKjY1jC0ypQpNIGrUwdo2VJsawwSSTl627dvh7e3N3x9fXHt2jWUL18eHh4eePfuXZqvsbW1xZs3b1R/z549S/b8vHnzsGzZMqxevRoXL15EtmzZ4OHhgZiYGF13J2skHRCCgnRWK/PuXeCff+hyw4bp5BIGBS8b8+efpC3GMBLGjaOlnA4dgGrVxLYmfX79lYQqOU6QgdEBy5bRJZo3B0qW1NllDAJHR4rDBdgkzqj43/8opRqg8dBQJ3AiI6k1zkWLFmHAgAHo06cPAGD16tU4ePAg1q5diwkTJqT6GplMBqc00qw5jsOSJUvwxx9/oM0PwaWNGzcib9682LNnDzp37pzq62JjYxGbROsq6of4Wnx8POJ1EO3Lt5mi7WrVoGjVCvL9+6EcNw6Ju3dr/dpLlsgBKNCqlRKFCiXqNZg5zX7rkIoVgerVFQgNlWPFikT4+uq/UoEY/RYbXfZZduwYzI4cAWdujoTp0w0qIj/Nfk+bBrNduyALCUHCwYPg+OoZWiIiAli71gyADMOGJSA+Xr/7mWJ8xocMAYKDzbFrF4eHDxNQuLDeLg3ANL/XgG77rRg3DnKOg7JDByRWrGgw32193GtN2pZxnDQiFuLi4mBjY4OdO3eibZL4ml69eiEiIgJ79+5N8Zr169ejf//+KFCgAJRKJSpVqoRZs2bhl19+AQA8fvwYrq6uuH79OipUqKB6Xd26dVGhQgUs5ctA/MTUqVMxLZXt0q1bt8JGz5XAs798ifrDh0OuVOLszJn4+KNv2uDLF3P069cEcXFmmDHjLMqUMQ0hqvPn82HevKqws4vFmjVHYWHBqqJLlsRE1Bs9GnZPn+JRy5a4JaGMg1/WrkWxffsQWaQITi1cqNUsqL17XbFuXRkUKhSFpUtPmsxCiK9vdfzvf3nQtu0D9O7NymVIGcewMNSYOhVKMzMcX74c0cZQqkkDoqOj0bVrV0RGRsI2g5gqyazoffjwAYmJicibN2+yx/PmzYt79+6l+pqSJUti7dq1KFeuHCIjI7FgwQLUqFEDt2/fRsGCBRH+Y28utTbD09m3mzhxIry9vVX/j4qKgrOzM5o0aZLhG54Z4uPjERISgsaNG8M8lUxB7sYNIDAQNfbsQeKYMVpbvp43T464OAXKl+cwdqy73geDjPqtK5o0AbZt4/D8uSUiIpqhd2/9r3aI0W8x0VWfZRs3wuzpU3C2tigUGIhCDg5aa1sbpNvvatXAnT4Nu6dP0eLzZ6EEYhZJSABGjKCf/kmTbNCiRXOttKsJ4n3GZWjbFjh5shjWri2iVxUOU/xeAzrqt1IJsx8yY9zgwahnYFmC+rjXURqUcZKMo5cZqlevjurVq6v+X6NGDZQqVQoBAQHwy0J9S0tLS1haWqZ43NzcXKdf4DTbnzYN2LIF8suXId+zR8goyALx8YJavre3DBYW4v0w6fp9TXk9ikccOxZYvtwM/fuLE/qh734bAlrt8/fvqjJIskmTYG7AM/5U+503LzBpEjBuHMx8fYGuXak2bxbZtw949ox013v2NBNVZUbfn/FWrUhC8b//ZNi61VyVjatPTPF7DWi535s2UYKVnR0Uvr5QGOj7qct7rUm7kknGcHBwgEKhwNu3b5M9/vbt2zRj8H7G3NwcFStWxMOHDwFA9bqstGkQODmRVwJQCZi4uCw3uWsX8OoVjTWdOmW5OcnRvz8VK7h5EzhxQmxrGJli6VLSznN2lm5a6bBhQKFC9GXUUhYB38ygQVrxGyWFXC4kXC1dCihZVIb0+P4dmDyZjidOBHLnFtceCSAZR8/CwgKVK1fG8ePHVY8plUocP3482apdeiQmJuLmzZvI92NmX7RoUTg5OSVrMyoqChcvXlS7TYNh9Ghy+B4/plIwWYQfDIYMIVkCUyNnTipUALAsPUny/r2gNTlzpnQ9Gisrsh+g/rx/n6XmrlwBzp6lVevBg7VgnwTp2ZO+3w8eAIcOiW0NQ2OWLyftPClP4PSMZBw9APD29saaNWuwYcMG3L17F4MHD8a3b99UWbg9e/bERL6oMYDp06fj6NGjePz4Ma5du4bu3bvj2bNn6P8jIFsmk2HkyJGYMWMG9u3bh5s3b6Jnz57Inz9/soQPSZA9u6CnN306EBmZ6aZCQ4GLF8nBGzRIS/ZJEP435MABGhQYEsLPj+raVqwIdOsmtjVZo2tX6seXL9SvLMDnl3XqBOTPrwXbJEj27MCAAXTMJnES4+NHYNYsOp4xQ7oTOD0jKUevU6dOWLBgAXx8fFChQgWEhYXh8OHDqmSK58+f402SKhGfP3/GgAEDUKpUKTRv3hxRUVE4f/48SpcurTpn3LhxGDZsGAYOHIhff/0VX79+xeHDh1MIK0uCvn0BNzf6MsyZk+lm+B+/bt2APHm0Y5oUKV5c0N9ctkxcWxga8OCBEGA6fz7t10kZuVzQzPT3J6XjTPD6NbBtGx3z9V9NFS8vSmI+fpxCvRgSYcYMWsQoX176Ezg9IrlfQC8vLzx79gyxsbG4ePEi3N3dVc+dOnUK63n1ewCLFy9WnRseHo6DBw+iYsWKydqTyWSYPn06wsPDERMTg2PHjqFEiRL66o52MTOjEjAAeWsvXmjcxPPnFJ8HCLEspgw/IK5bR9pjDAkwcSKlljZtCjRsKLY12qFhQ6BZM+rXpEmZamLVKnp57dpA5cpatk9iFCoEtG9Px2moaDEMjcePgZUr6XjePOMsuq4jJOfoMTKgVSsqBRMTQ7U9NWTFCiAxEWjQAChXTgf2SYwGDYCyZanSXFCQ2NYwMuT8eZqpyOU0GBgTc+dS+veOHcCFCxq99Pt3IXTX1FfzePj3YcsWIJ3iSgxDYfJkkoNo0oT+GGrDHD1jI2lptA0bNNqX+PoVWLOGjkeN0oFtEkQmEwaE5ctpRYRhoHAcMGYMHffuTR66MVG2LPULoCx7DbTut2yhiI4iRYAfRYBMnmrVgKpVgdhYICBAbGsY6XL5MsUdyGTCrhVDbZijZ4xUrUpaehxHNT7VZONG2p4sVozqXzKIrl2pVubz58CePWJbw0iTv/+mTCJra0pIMkamT6f+nT0LpFINKDU4Toi7HTaM7XjxyGTChHblSnL4GAYIxwnyYT16AEmqWDHUgzl6xsqsWaShcOQIEBKS4elKpTAYjBgh/fh1bWJlJUhRLF4sri2MNIiPB/h616NHAwUKiGuPrihYUPBOxo9Xq7bnsWPA7duUbWpgBQREp317+qi8fQts3y62NYxUOXgQ+PdfkoGYMUNsayQJG86NFVdXEsEDaFUvA2XQf/6hZEU7O2F3iCEweDD5zefPA5cuiW0NIwUBAfQBzpNHo1VsSTJuHJW1+O8/IDg4w9P5CVzfvvT9ZgiYm1MGLkDvkzQqv5sQCQnC93nkSNLOY2gMc/SMmT/+AGxtgbAwCtJJB34w6N8feq3/KBWcnIAuXeiYZekZGJGRgobk1KlAjhyimqNz7OwAX1869vUlfb00uH+fRIFlMtq2ZaRk4EDaDb9+HTh9WmxrGMlYtw64e5eqX/Ar9gyNYY6eMePgQFITAGUsxcSketqtW7S9I5ezwSA9+KSMv/6iilQMA2HuXODDB6BkSZqpmAIDB1Iw7bt3wIIFaZ7GT0pataLTGSnJlQvo1YuOmYCyAfHtm6AcMWUKlTNhZArm6Bk7I0ZQXM+LF5Q2mgr8j1v79kDhwvozTWpUrAjUrUu7CbycE0NkXrwQAifnzKG9OFPAwkIo8bZgAZBEKJ7n0ydKvAdYFn1G8FVw9u4luTaGAbBwIRAeDri4mG69Pi3BHD1jx9paCGCdOZM0FpLw/j2weTMdM32tjOHfo4AAIDpaVFMYAM30Y2KAWrVMTzekfXvSCImOFrZykxAURE+VL08TFEbalCpF+tocl+Z8mKFP3r4VdDBnzaKJDSPTMEfPFOjendSPIyOFAuk/CAggWYFffwWqVxfJPgnRqhVQtCitlvAOMkMk/vc/0gQCaFVLJhPXHn2TVDMzOBi4c0f1VHy84LCMHGl6b01m4Fc9g4OpTDJDRKZNo63bX38lqTBGlmCOnimgUAgDwooVwJMnAMjB47cg2WCgHgqFsM3DsvREZtw4ugEdOwJJSiGaFLVqAW3bUlZ9kmD13buBly8pCblzZ/HMkxKNG9PK3pcvwNq1Yltjwty7BwQG0vH8+Wxg0gLM0TMVmjShX7L4eFWtzL/+ohCI/PmBDh1Etk9C9O1LiZ137wJHj4ptjYly9Cj9mZsLsWqmypw5NAPZv5/0xiDE3Q4ZQjqQjIxJWgVn2TIqBckQgYkT6c1v1YrFHGgJ5uiZEvPm0a/Ztm3gLl1WxbB7eZlODLs2sLUVhGdZlp4IJCYKSvlDh1KwtilTsiRl4QLA2LG4cF6JCxcorGnQIHFNkxrdu1MW7pMnwL59Yltjgpw9S+WH5HJW6kyLMEfPlKhQgUrIAIj0HIvr1zlYWQljBEN9hg0jn/nwYVrZY+iRzZuphrOdHWlFMigZI3t24PJlXBy7AwCV7subV2S7JIaNjeAcs0mcnklaq7p/f9pHZ2gF5uiZGn5+gKUlcob9ixY4iJ49SYuSoRkuLhQaBTABZb3y/bvg3E2axD68PHnzqioItDo/ERaIZVn0mWTIEMDMjMSTr10T2xoTYtcu4OJFIFs2QQCdoRWYo2dqFCqEiF4jAABzMR4jhiaIbJB04QfSjRtTqNYwdMXSpZRl4OzM1L1/xtsbUdmc4IInWFTMH+XLi22QNClQQEj0ZJM4PREXJyQTjRlDpYgYWoM5eibIPMVEfEQu/II7KH1pvdjmSJbatUlE+ft3YM0asa0xAT58EBIvZs4kjUiGim/IBh9uOgBgwFs/ICJCXIMkDC+18uefqWpRM7RNQADw6BGtTI8eLbY1Rgdz9EyMqChgxeac8MMUesDHh/SKGBqTNEtvxQpKaGbokBkz6ANcvjzQrZvY1hgcmzYBK6L74KF5KVh8+UTZuIxMUaUKULMmfaf9/cW2xsiJjASm0wTFJGpViwBz9EyMdetIJ+pkycHgihal6eqiRWKbJVk6daJdhlevgJ07xbbGiHn0CFi1io7nz6esPIYKpZK2GRNhhju9f1QUWLIEeP5cVLukDD+J8/dPs0w4QxuYYq1qPcN+LU2IxETShwKAIaMsIeO3webNo5IzDI2xtKTgbYBKrjIBZR0xaRItr3h4kB4kIxlHjpDOrK0tUH9BC9Ifi42lEnGMTNG2LdX+/vAB2LJFbGuMlJcvhVrVc+dSFgxD6zBHz4Q4cIAKdtvb/1BZ6dCBSsx8/cqynLKApyc5fJcvAxcuiG2NEXLxIql7y2RMWysNeCmQ/v2BHLZJSqNt2gSEhYlllqQxMxPyfVgVHB3h4yPUqm7dWmxrjBbm6JkQ/GDg6Ul6UZDLhQEhMBC4f18s0yRNnjxCyBg/OWVoCY4TxJF79gRLJU3J7dtUJEQuJ/FzADSB69yZ3r8fsisMzenXj9Q+bt0CTpwQ2xoj4+ZNYP16OjbFWtV6hDl6JkJYGHDqFM1Shw5N8kTdulRqJjGRSs8wMgUfz7NrF/DsmaimGBf79wNnzlAdLz8/sa0xSPhwjLZtgaJFkzwxcyaVvAkJYbX6MknOnECfPnTMBJS1DF+rukMH061VrSeYo2ci8D9SHToABQv+9OScObQc8PffVIKGoTFlywING1JQ/MqVYltjJCQkAOPH0/HIkaSdx0jGhw+k4wgIkiAqXFyEWd24cax4ayYZPpwWmw4cAP77T2xrjIRjx6iskLk5MGuW2NYYPZJz9FauXIkiRYrAysoK7u7uuHTpUprnrlmzBrVr14a9vT3s7e3RqFGjFOf37t0bMpks2V/Tpk113Q29Eh5OelAAUlfLL11aKN46diwLRskk/Hu7Zg2FPTKySHAwZRjkzi2IqTKSERhIIU6VK5McSAr++INKxf3vf1Q6jqExxYsDLVvSMb96ysgCSqUQTjB4MFCsmLj2mACScvS2b98Ob29v+Pr64tq1ayhfvjw8PDzw7t27VM8/deoUunTpgpMnTyI0NBTOzs5o0qQJXr16ley8pk2b4s2bN6q/P3mvyEjw9yfh8erVgapV0zhp2jQK3LtwAdi9W6/2GQvNm9OgEBEBbNggtjUS5+tXqt8KUMC2nZ249hggcXHC6vHIkWmEOOXOTRnLADl937/ryzyjgp/ErVsHfP4sqinSZ+tW4Pp1ShFntar1gqQcvUWLFmHAgAHo06cPSpcujdWrV8PGxgZr165N9fwtW7ZgyJAhqFChAtzc3BAUFASlUonjx48nO8/S0hJOTk6qP3t7e310Ry/ExAiCnym2dpKSL59QUHrCBBpFGBohlwMjqLocli6liSsjkyxcSJI/rq5ClXlGMnbuBF6/Jh1HvmRXqgwbRtveL1+yml6ZpH59oFw5IDoaCAoS2xoJExMDTJ5MxxMmAI6O4tpjIkhGtCYuLg5Xr17FxCQJA3K5HI0aNUJoaKhabURHRyM+Ph65cuVK9vipU6eQJ08e2Nvbo0GDBpgxYwZyp1MsPTY2FrGxsar/R0VFAQDi4+MRr4PyCHybmWl782YZ3r83g7Mzh5YtE9Kv3jBiBMxWr4bs4UMk+vtDyQvEiURW+i0WXbsCkyeb4cEDGfbvT0Dz5ppvg0ux31klWZ/Dw2E2fz5kABL8/MDJZEZbdiSz95rjgEWLFADkGDQoETKZMu23yMwMsmnTYNa3L7jZs5HQqxfg4JA1w7OIFD/jw4bJMGCAGZYv5+DllaCx5JsU+6wNkvZbvnw5FM+fgytYEAlDh7LvtRauoQ4yjpNGQNbr169RoEABnD9/HtWrV1c9Pm7cOPz777+4ePFihm0MGTIER44cwe3bt2FlZQUA2LZtG2xsbFC0aFE8evQIkyZNQvbs2REaGgqFQpFqO1OnTsW0VHTntm7dChsbm0z2UPtwHDBqVD08fWqHXr1uo127hxm+psjhwyi/ejVibW1xbPVqJBhQf6TC+vWlsWdPcZQr9x7Tp58X2xzJUc7fH0WPHMHn4sVxet48JruQCnfv5sLEibVhYZGINWuOws4ugxV4pRL1vL1h9/QpHrVsiVusAoHGxMXJMWBAE0RGWmLs2MuoWfO12CZJCvMvX9Bo0CBYfPuGa8OG4UXDhmKbJGmio6PRtWtXREZGwtbWNt1zTcbRmzNnDubNm4dTp06hXLlyaZ73+PFjuLq64tixY2iYxgcxtRU9Z2dnfPjwIcM3PDPEx8cjJCQEjRs3hrm5udqvO3lSBg8PM2TLxuHx4wSotSMdHw+zihUh++8/JI4fD6WIkhaZ7bfYPH8OlCxphsREGa5ejUfZspq9Xqr9zgp8n5s4O8OqalXIEhORcOIEuFq1xDZNp2T2XnfurMDu3XL07avE6tXqZdPKjh2DWfPm4MzNkXDjBm2Li4RUP+PTpskxc6YC1asr8e+/mmUxS7XPWYXvd/Pjx2G+fDm4MmWQcPkykMZCijGgj3sdFRUFBwcHtRy9TG3dRkREYOfOnXj06BHGjh2LXLly4dq1a8ibNy8KFCiQKaMzwsHBAQqFAm9/KtX19u1bODk5pfvaBQsWYM6cOTh27Fi6Th4AuLi4wMHBAQ8fPkzT0bO0tISlpWWKx83NzXX6Bda0/RUr6N/evWXIk0fN15mbU/WBdu2gWLoUCi+vVPRY9Iuu31dt4+oK/PYbsGMHsGqVeaZjeqTWb21g6esLWWIi0KYNzOrXF9scvaHJvX72DNizh45HjZLD3FzNUOtmzYAmTSA7ehTmU6cC27ZlylZtIrXPuJcXacyHhspx/bo87eS2dJBan7WBzdu3MAsIAADIFiyA+Y8dNWNHl/dak3Y1Tsa4ceMGSpQogblz52LBggWIiIgAAOzevTtZ/Jy2sbCwQOXKlZMlUvCJFUlX+H5m3rx58PPzw+HDh1GlSpUMr/Py5Ut8/PgR+fLl04rdYvHgAek+AaQDpRFt2lBJmpgYIfORoRF8lt7mzcD796KaIhly374N+YEDNNOfM0dscwyW5csp0adRI6BMGQ1fzG+Fb98OpCNNxUgdJyegSxc6ZgLK6lNq82bI4uLoQ9ukidjmmBwaO3re3t7o3bs3Hjx4oIpzA4DmzZvj9OnTWjUutWuvWbMGGzZswN27dzF48GB8+/YNfX5Il/fs2TOZszl37lxMmTIFa9euRZEiRRAeHo7w8HB8/SFy9vXrV4wdOxYXLlzA06dPcfz4cbRp0wbFihWDh4eHTvuia5Yvpxi9Fi2AEiU0fLEsSa3MdeuoVA1DI6pXpypUsbHA6tViWyMBOA6/8OWQ+vcH3NxENcdQ+fJFyPpMN4s+LcqXp1JyANPMzCR8Zv2OHZTIzEgf2dWrKHjmDCVVsZhbUdDY0bt8+TI8PT1TPF6gQAGEh4drxai06NSpExYsWAAfHx9UqFABYWFhOHz4MPLmzQsAeP78Od68eaM639/fH3Fxcfj999+RL18+1d+CBQsAAAqFAjdu3EDr1q1RokQJ9OvXD5UrV8aZM2dS3ZqVChERAK84k6nBAACqVQN+/50GAr46AUNtZDLhvV+1ihw+RtrIdu6E/YMH4LJlA6ZOFdscg2XDBiAykiZvmdZ19/MDLC2B06eFZX+G2lSsSJUjExLou81IB46D/IfYOde1K715DL2jsaNnaWmpkhNJyn///QdHPWjieHl54dmzZ4iNjcXFixfhnqRG3qlTp7CeXxUA8PTpU3Acl+Jv6o+BxNraGkeOHMG7d+8QFxeHp0+fIjAwUOU4SpXgYODbN9rWadAgCw3Nnk3Fcf/5B/hJe5CRMb//DuTPT5VJ/vpLbGsMmLg4KKZMAQAovb1pf4yRAqVSkMEbMYJ0GzOFs7MQWzB+PHksDI3gJ3EBAaStx0iDQ4cg//dfJJqbI5FN4ERD45+K1q1bY/r06SoNF5lMhufPn2P8+PFo37691g1kaEZCAm3bAumo5atLsWJUogagbR6mAKwR5uYUvA0AixezXbI0Wb0assePEWNvD2Wml6CNn4MHgYcPgZw5hd3XTDNhAlXNuHuXwjMYGtGyJZUS/vQJ2LRJbGsMlIQEVamzxy1aAIULi2yQ6aKxo7dw4UJ8/foVefLkwffv31G3bl0UK1YMOXLkwMyZM3VhI0MD9uyhrDwHBxLvzTJTplCpmuvXhYK5DLUZOBCwtqa378wZsa0xQCIigOnTAQD3OncGsmcX1x4Dhg/+HzhQC29Tzpz03QaoxBwrzqwRCoWQ5LZkCZsDp8qGDcCdO+By5cJ/v/8utjUmjcaOnp2dHUJCQrB//34sW7YMXl5eOHToEP79919ky5ZNFzYyNIAfDAYPJgcjyzg6CgXlJ0+mTFyG2uTOLay+sCy9VJg7F/j4EVzJknjeqJHY1hgsN24AJ06QgzF0qJYaHTyYlqXCw4FFi7TUqOnQty/Nge/dA44eFdsaA+PbN9VEQjlxIhLYBE5UMl3rtlatWhgyZAjGjRuHRuwH2iC4fBk4d462DPkdV60wYgRQoAAtFfLifAy14Wf+e/YAjx+Laoph8eKFyvtNnDULnBELqGYVPjavfXugUCEtNWphAcyaRcfz5lFtYYba5MgB9OtHx2wS9xOLFwNv3gBFi0LJalWLjlqCycuWLVO7weEai7YxtAU/GHTuDGhVBtDGhjL1+vYFZs6kf3+qF8xIm9KlAQ8P4MgRip9cvFhsiwyEKVNohbhOHXAtW1LSDyMF794BW7bQMZ9DoTU6dgQWLqRZ4rRpLI1UQ4YNo9/dI0eAO3fou27yvHtHK/UATSQkrGBhLKjl6C3+aWR6//49oqOjkTNnTgBUKcPGxgZ58uRhjp5IvHpFGqiADgYDgPYfFy8mTb1Zs4AfEjUM9Rg1igaD4GAaT3VQKU9a/O9/wMaNdDx/PtPWSofVq0mex92d9Bm1Cq+ZWa8eEBhIy89Mw1BtihYlffm//waWLWOamQDoB+7rV6BKFZpIJGpWKo6hfdTaun3y5Inqb+bMmahQoQLu3r2LT58+4dOnT7h79y4qVaoEPxHropo6q1ZRklOdOkClSjq4gEJB2zsALUs9eaKDixgvTZoApUqR4C1LcgTJenAcDQSZqSNlIsTGCotsOpnAASQK16oVDcg6rG5krPCJ4hs3Ah8/imuL6Ny/T5ozAE0gMq0BxNAmGt+FKVOmYPny5ShZsqTqsZIlS2Lx4sX4448/tGocQz2io4Xvls4GA4D2Hxs2BOLiAHavNUImExT1ly0z8UluSAgtb5qbCzFijFTZvp1C5woUoPg8nTFnDg3Ke/YAZ8/q8ELGR61aNLn+/p0WRU2aSZPox61lS1olZhgEGjt6b968QUIqApuJiYl4y4J5RWHLFppJFi0KtG6twwslLY22dStw9aoOL2Z89OhBoY2PHwP794ttjUgolaTJCABDhgCuruLaY8BwnBDk7+VFfrHOKF1ayCxgpdE0QiYTJtgrVgA/JGZNj3PngN27acLAalUbFBo7eg0bNoSnpyeuXbumeuzq1asYPHgwy74VgaSDwfDhtMOqUypWBLp3p2M2IGiEjQ3AVw802Sy9LVsoPs/OTtBxY6TK6dOkv2htTdp5OmfaNPqQXrhAAzZDbTp1ooIur19TDVyTg+OECVzfvsAvv4hrDyMZGjt6a9euhZOTE6pUqQJLS0tYWlqiatWqyJs3L4L4atsMvRESQtleOXLQ90svzJhBmVQnT7JMSQ0ZMoSqyv37Lw3iJsX376TFCFAsWO7c4tpj4PCTgV699JTkni8fMGYMHU+YQCEaDLWwsBD0DU2yCs7ffwOhoTRRmDZNbGsYP6Gxo+fo6IhDhw7h3r172LFjB3bs2IG7d+/i0KFDyJMnjy5sZKQDPxjw4p16oXBhQRxu3DgTDzjTjIIFgQ4d6JiXwzEZli8n7TxnZ+Hzw0iVx4+BvXvpWK9v1ZgxQJ48VGvN5APONMPTk+a/V66Qz2MyxMcLovqjR1OBb4ZBkemUmBIlSqB169Zo3bo1SpQooU2bGGpy9y4tqMlkpOekVyZOBOztgdu3gfXr9XxxacPH8/z5JxUlMAk+fhQSL2bM0FLZFuNl+XJaFWralLK19UaOHABffH7aNCAqSo8XlzaOjkJUi0mFZgQGAg8e0ASB375lGBRq6eglpW8G+4Nr167NtDEMzeB1rNu0ESGm3d6eMm9Hj6ZamZ07A6wEnlpUrQrUqAGcPw/4+5vITseMGUBkJFC+PNCtm9jWGDRRUaS3CAjSHXqlf3/yVP77jySVZswQwQhpMnIk3btdu6iQUOHCYlukY6KihB+wqVNposAwODRe0fv8+XOyv3fv3uHEiRPYvXs3IiIidGAiIzU+faKa0YCOJVXSY+hQoEgRikA2qSls1uHvmb+/CZQPfvwYWLmSjufN00PGkLRZu5b0FkuVAho3FsEAc3OhssGiRaTGzlCLMmWARo0oudwkqkXOmwe8fw+UKEETBIZBorGj9/fffyf7O3DgAB4/foxOnTqhWrVqurCRkQpr1lBse4UKJJIsCpaWwnbc3LlU+oahFu3aUc3S9+9pC9eomTyZ4niaNKE/RpokJgor9SNHilgwpE0boGZN+pHx8RHJCGnCT+LWrKECEUbLq1c0EQBITkWn+j+MrKAV2Wq5XA5vb+8UpdIYuiE+nmJ4AJEHA4B0BSpXpiWI6dNFNERamJkJcZVGnaV3+TKwbRt9SPlVIkaa7N9PRWdy5RLivUQhqWbm+vVU+pChFs2a0QJXZKSRhy/7+tJEoGZNoG1bsa1hpIPW6pM8evQoVSFlhvbZtYsmU3nzUmicqMjlwoAQEEBxPQy16NePwhpv3iSlGqMjqbZWjx60/MxIF36uPGgQKVWISvXqVI5DqRSyKhkZIpcLVXCWLqW3z+i4dUuo5chqVRs8GidjeHt7J/s/x3F48+YNDh48iF69emnNMEba8OFwQ4bQ7qno1K8PtGgBHDxIJXB27hTbIklgbw/07k3ha0uWAA0aiG2Rljl4kAQDLS1ZQL8aXLtGIslmZvTdNghmzyadl0OHgBMnjPBDqht69qSIhYcP6a1r2VJsi7TM+PHkwbZvTxMChkGj8Yre9evXk/3duHEDALBw4UIsYQH5OufCBeDiRRo7Bw0S25ok8LUyd+2idFKGWvAaaQcOkEKB0ZCQQBqLAMUXODuLao4U4HUVO3ak2rYGQfHiwg/N2LFGujylfbJnBwYMoGOjGxZPnCDv1cyMJgIMg0fjFb2TRrnHJB34rZ1u3Ui2yGAoUwbo04e0BcaOpcLobDk/Q0qUoNn+gQMUhM/HXkqedetI6DF3brbtpwbh4UJSjmhZ9GkxZQql+F+7RvGWXbuKbZEk8PKiXIXjx4EbN/Ssh6grktaqHjSIJgIMg0fjFb0GDRqkKqMSFRWFBmxZX6c8f04LZoAQA2JQTJ9OQrjnzwN79ohtjWTgB/Z16wCjUCj69k3I1JwyBciZU1RzpMDq1XLEx5O+4q+/im3NT+TJQ1t1AIVmxMaKa49EKFSIdjYBI6qCs20bOfw5crBsbAmhsaN36tQpxKVSAzEmJgZnzpzRilGM1PH3lyMxkcJkypUT25pUyJ+fBJQBGhji48W1RyI0aACULUv+ES+UK2kWLqQlKhcXYPBgsa0xeOLi5AgMpJ9iUQSS1WHUKPp+P3smaCIyMoSfxG3ZYgTqU7Gx5OgDtErv6CiuPQy1UdvRu3Hjhioe786dO6r/37hxA9evX0dwcDAKGExgifERE6NAcDDdLoPb2knK2LH0A/DgAQlJMTJEJhPu6bJlFN4mWd6+JRFVgDQWLSzEtUcCnD5dEB8+yFCokAGrVNjYAH5+dDxjBvD5s7j2SITq1QF3d/KReGdesqxYQY5+/vwGPggxfkbtT16FChVQsWJFyGQyNGjQABUqVFD9Va5cGTNmzICPHpZyV65ciSJFisDKygru7u64dOlSuufv2LEDbm5usLKyQtmyZXHo0KFkz3McBx8fH+TLlw/W1tZo1KgRHhhgVPyJE86IiJChWDFKcDVYbG2FWplTp5K+HiNDunYFHBxoe37vXgnHNk6dSkuTv/5KWQWMdOE4YP9+FwCkq2imcdS0HunVC/jlF3LyeKF0RobwPlFAgBzx8RJ19j59EjLn/fwMQPuHoQlqf+qePHmCR48egeM4XLp0CU+ePFH9vXr1ClFRURnWwc0q27dvh7e3N3x9fXHt2jWUL18eHh4eeJfGmvj58+fRpUsX9OvXD9evX0fbtm3Rtm1b3Lp1S3XOvHnzsGzZMqxevRoXL15EtmzZ4OHhgRgDqkulVAIHDlAx2xEjKLnVoBkwgIJ0378XNPYY6WJlJexyLl9u6Dc4De7dE1ZxmbaWWpw8KcOzZ3bIlo0z/ApSCoWwWrtsGfD0qajmSIX27SmL+u1bGc6eleiu1+zZFEBcpgw5/AxpwUmIqlWrckOHDlX9PzExkcufPz83e/bsVM/v2LEj16JFi2SPubu7c56enhzHcZxSqeScnJy4+fPnq56PiIjgLC0tuT///FNtuyIjIzkAXGRkpCbdUZu9e+M5gOPs7JTcly86uYT22bWL4wCOs7HhuFevMtVEXFwct2fPHi4uLk7Lxhkmr19znLk5vW3z55+SXr/btiXjW7XS+KWmdq95mjdP5ACOGzIkQWxT1EOp5Lj69ek+d++e6WZM7X7Pnk1vmYvLZy42VmJ9fvKE4ywsqAOHDmn8clO71xzHcQ8exHEDB4Zxnz/rrs+a+B1qbRTs27cPzZo1g7m5Ofbt25fuua1bt9aC+5mSuLg4XL16FRMnTlQ9JpfL0ahRI4SGhqb6mtDQ0BQCzx4eHtjzIyP0yZMnCA8PR6NGjVTP29nZwd3dHaGhoeicRtmJ2NhYxCbJPIuKigIAxMfHI14HCQhLltAKT69eCbC0lEiOQ8uWUFSvDnloKJRTpiBx9WqNm+DfS128p4aIgwPQqZMCmzfLsX+/C4YMkU6/ZWfPwmzPHnByORJmzND4Q2pq9xqgMNZDh6g+qKdnHOLjDXnfNgmzZ8O8WjVg82bEDxsGVKyocROmdr/79AGmTzfD48c5cfJkjKR0pxWTJkEeFwdl/fpIbNiQfbfVYNkyIDCwPN68ScCuXbrptybvp1q/LG3btkV4eDjy5MmDtulEC8tkMiQmJqp9cU348OEDEhMTkTdv3mSP582bF/fu3Uv1NeHh4ameHx4ernqefyytc1Jj9uzZmDZtWorHjx49Chstxy4olUCePCVhb18EZcqcxqFD37Xavi6xb9MGdUJDIVu/HmcqVMCXQoUy1U5ISIiWLTNcKla0w+bN9XDuXAFs2xaC3LkNJ4QgTTgOtcePRy4Azxo1wv+ePKGCrZnAlO51YGBZAC6oUiUcT55czOxbJgqV6tSB8+nTiOjfH+enT8/0Nr0p3e+6dcvh8OGi8PX9jJiY9GPLDQW7R49Q74fA4+kWLRD5zz+ZbstU7vX372YIDm4CAChf/jIOHdJNunV0dLTa56rl6CmTqKErmTI6Jk6cmGylMCoqCs7OzmjSpAlsbW21fj0Pj3gcPnwUTZs2grm5udbb1xnNm0N54QLke/ag3uHDSNRQWy8+Ph4hISFo3LixtPqdRf7+OxFnzypw714DzJwptjUZI9u1C2b//QcuWzYUCApCAScnjdswtXsdEQF060Y/v61bP5Jev0uVAle2LBxv3kQLMzNwHh4avdzU7jcAFCqUgMOHgUuXnODm1hwuLmJblAEcB0XTpgAAZZcuqMmX8dEQU7vXK1fKER2tQIECXzB+fHlYWuqmz/xOojpIZK8AcHBwgEKhwNu3b5M9/vbtWzilMbA4OTmlez7/79u3b5EvX75k51RIpwC7paUlLFMpMmtubq6zD7JCwem0fZ0xdy5w4ADkhw5BfvYs1cXVEEn2OwsMH56As2eB4GAzTJ0qM+wEt7g44I8/AACy0aNhnsVSZ6ZyrzdsoOTkMmU4lC37QXr9LlFCVfrBbNIkoFkzStbQEMn1OwuUKQNUqvQW167lhb+/ueGXRvvnH+DkScDCAvJZsyDP4n0yhXutVJIKDQC0bPkYlpalddZnTdpVy9FbtmyZ2g0Oz6TXnxEWFhaoXLkyjh8/rto+ViqVOH78OLy8vFJ9TfXq1XH8+HGMTKL5ExISguo/ijAXLVoUTk5OOH78uMqxi4qKwsWLFzGYCb1qhxIlAE9PElkdN44K9Rp82rC4tGrFIW/eb3j7Nhs2bwYGDhTbonQICAAePQLy5hVKIzHSJSFBKHU3fHiidJOTJ08G1q4Fbt4ENm0CevcW2yKDp1WrR7h2LS/WrqVCQjrYANIOiYlCrephw4AiRUQ1RyocOEA/h/b2HOrXfwGgtNgmAVDT0VvMF1jNAJlMpjNHDwC8vb3Rq1cvVKlSBVWrVsWSJUvw7ds39OnTBwDQs2dPFChQALN/FFoeMWIE6tati4ULF6JFixbYtm0brly5gsDAQJW9I0eOxIwZM1C8eHEULVoUU6ZMQf78+dONRWRoiI8PsHEjcOUKsH070KWL2BYZNAoF0KLFY6xdWxZLlpBajUE6A5GRNFoBwLRpVMmdkSF//016iY6OQOfOHE6cENuiTJIrFzl7Y8fSqm7HjkxfLQMqVHgPNzcO9+7JsHatAesOb9gA3LoF2NvTPWaoBb9K26+fElZWuslXyAxqLa0k1cxL7+/x48c6NbZTp05YsGABfHx8UKFCBYSFheHw4cOqZIrnz5/jzZs3qvNr1KiBrVu3IjAwEOXLl8fOnTuxZ88elClTRnXOuHHjMGzYMAwcOBC//vorvn79isOHD8PKykqnfTEpWK1MjWnU6Dly5OBw9y5gsDHMc+cCHz4AJUsC/fqJbY1k4AeDwYNJP1HSeHlRUddXr4yooKvukMloFRegzEwd5S5mjehoqlENkJNnby+uPRLhf/+jnW6FAhg82LByGbK0h8ZxHDiO05YtauHl5YVnz54hNjYWFy9ehLu7u+q5U6dOYf369cnO79ChA+7fv4/Y2FjcunULzZs3T/a8TCbD9OnTER4ejpiYGBw7dgwlSpTQR1dMC75W5tOnwKpVYltj8NjYJKBPH/qxUHNBXb+8fCkYNneugZd0MBwuXQLOnwfMzY2kDLCVFVQZQ7Nnk0g6I126deOQOzclpmegViYOS5YAr18DhQsDQ4eKbY1k4Cdwv/8OZDFUWetkytELDg5GmTJlYGVlBSsrK5QpUwZBQUHato1hTNjYCNt8fn6sVqYaDBmihEwGHD4M3L0rtjU/4eMDxMQAtWsDOtLONEb4Ra8uXYBMJCcbJl27kpbely9CmSxGmlhbU9gyAMNLyHj3Dpgzh45nzTKCJWf98PYtsHUrHY8aJa4tqaGxo+fj44MRI0agVatW2LFjB3bs2IFWrVph1KhReql1y5AwvXsLtTJ/xFEy0sbFBWjTho41yIfSPTduAPzKOSt1pjavXgF//UXHBhublRnkcqHU4apVwMOH4tojAYYMoUXw06eBa9fEtiYJfn7ksFeqBKRRMICRktWrSYCgWjUgySajwaCxo+fv7481a9Zg9uzZaN26NVq3bo3Zs2cjMDAQq9iWHCM9fq6V+eyZuPZIAN4h2LCB6oobBOPHAxwHdOhgmL9qBsrKlZRxW7dupopJGDYNGwJNm1IHJ00S2xqDp0ABoFMnOjaYVb0HD8hjAchxZ+oIahEbK0QjGeoETuM7GR8fjypVqqR4vHLlykhISNCKUQwjplkz0tKLjRUCfhlpUqcOOQXfvwM/ksXF5dgx2ks2N2ershoQHU1KNIDhDgZZZu5cWt3dsYNklBjpwn8Otm0DkuQQisfEieSoN28OSdVoE5k//6Qdb2dnoH17sa1JHY0dvR49esDf3z/F44GBgejWrZtWjGIYMTKZsM2zeTNw/bq49hg4MpkwIKxYIXKdY6VS0NYaPBhwdRXRGGmxeTOtyLq4AK1aiW2NjihXDujVi47HjqVVX0aaVKkC1KxJ3+lUhlT9EhoK7NpFq3hz54psjHTgOGFF1svLcHPSspSM0b9/f/Tv3x9ly5bFmjVrIJfL4e3trfpjMFKlcmUK4OY4NiCoQadOpEf86hX9FovG1q3kmNvastVYDUg6GAwfnqkCEtLBz48C+M+cAfbvF9sag4efxPn7U26TKPC/wwDFUSeRH2Okz7//kqyKjQ3Qv7/Y1qSNxo7erVu3UKlSJTg6OuLRo0d49OgRHBwcUKlSJdy6dQvXr1/H9evXERYWpgNzGUbDzJmAhQVw/Dhw9KjY1hg0lpYUvA2QookofnFMjCCcOnEi4OAgghHS5OhRyprOkQP4oe1uvBQsKKQdjh9PW4GMNGnbllRMPnwAtmwRyYg9e4Bz5ygdmFdGYKgFP4Hr1Yv0ww0VjRcaT548qQs7GKZGkSJUWmfhQtoObNTIyJc6ssagQaR2cOkScOEC8KOKn/5YvpzKORQsCIwYoeeLSxtBLd+AS15pk/HjKaD03j0gOFjQEmGkwMyMfgbHjKHPSd++ek5ij48HJkygY29vyhJhqMXDh4IOoqH/JLK0GoZ4TJoE5MxJch2bNoltjUGTJw/Ah8DqPUvv40dBFNfPj2b+DLW4e5dyV2QyGtBNAjs70lkEAF9f4OtXce0xcPr1A7Jlo4pjei+HFxQE/Pcf1ePj428ZarF8Oe2uNG9OxYEMGY0dvZiYGMyfPx/NmzdHlSpVUKlSpWR/DIba8LUyAaqV+f27uPYYOHw8z65dtLimN2bNorq25coBPXro8cLShxdIbtuWEjFMhkGDKFnn7VtatWekSc6ctJIH6LkKzpcvwNSpdOzrayLLzdohMhJYu5aOpZBFr7Gj169fP8ybNw+FCxdGy5Yt0aZNm2R/DIZGeHlRkAqrlZkhZcuSXFliImXg6oUnT4SLzZvHttc14ONHYONGOpbCYKBVLCwE+Z3584HwcHHtMXCGDaNV34MHaYFNL8yfT7ogxYsDAwfq6aLGwdq1tFBdujRFHRk6GsfoHThwAIcOHULNmjV1YQ/D1OBrZXbvTgNDv360jcBIlZEjKX9lzRraHcueXccXnDyZJN8bNQKaNNHxxYyLNWtokbpiRaoUZ3L8/jtQtSoFlk6dKojxMlJQvDjQsiUlKi9bpoeJ3OvXwkrr7Nmki8lQi8REoVLRyJHSKAyk8YpegQIFkCNHDl3YwjBVunSh0TAqitXKzIDmzYFixYCICGG1SGdcuUJqoDIZreZJ4RfNQIiPFwZrqQwGWiepZmZQkAEWbDYs+FXfdev0UArc15dUvKtXB377TccXMy727gWePgVy56b1CSmgsaO3cOFCjB8/Hs9Y+SqGtmC1MtVGLhcyvJYuJQ1jnZBUW6t7dyOs2aVbdu6kaAQnJ6HUlUlSpw7QujUtg0ycKLY1Bk39+hQGGx1NfrHOuH1bCDBjtao1hk+GGzRIOnlpGjt6VapUQUxMDFxcXJAjRw7kypUr2R+DkSkaNqTyaKxWZob07k2Jjf/9B/zzj44ucugQcOoUifj5+enoIsYJxwlB9UOG0Fto0sydS7Gde/eSkDIjVZJWwVm+XIcShBMm0AyxXTsqzcFQm6tX6SNsZiZom0oBjWP0unTpglevXmHWrFnImzcvZGw2wNAWc+eSFgVfK5NlcadK9uzAgAHAggU0u2zRQssXSEgQpBZGjKBkGYbaXLgAXL5MDh6TkAPg5kZlAwICSDDuwgWxLTJYunQhP+zFC+Dvv4EOHbR8gVOngAMHyPGeM0fLjRs/fL5gp05A/vzi2qIJGjt658+fR2hoKMqXL68LeximTNmytFy1bh1tGx47JrZFBouXF7BoEb1Ft25puWrR+vXAnTskf8O22zSGX83r3p30DxmgZIzNmykxY+dO0pthpMDKispIT5tGnyOtOnpKpRCO4ekJlCihxcaNnzdvgG3b6Jgv/iIVNN66dXNzw3emd8bQFdOnU+DDmTOQHTggtjUGS+HCQgy1VgWUv30TxG7/+INEvhhq8+yZUI/Y0NXy9YqTE63mATR5iIsT1x4DZtAgUqcJDaWNDa3x11+UYJU9u/AdZ6jNqlWUZFWrFpVrlxIaO3pz5szB6NGjcerUKXz8+BFRUVHJ/hiMLJGkVqZi0iTIEhNFNshw4WeVmzcD799rqdFFi2jqWrSotIJQDISVK2nhpGFDWqBmJGHMGCBvXuDRI8jXrBHbGoPFyYm2cAEtSovGxgqr8+PH031gqM3374I6kBQ1MTV29Jo2bYrQ0FA0bNgQefLkgb29Pezt7ZEzZ07Y29vrwkaGqTFuHODgANn9+yjEtm/TpHp14Ndf6Tc8IEALDb59SzIqAFXDMPksAs34+pW08wBpDgY6J3t22pMEIJ8xA2bfvolskOHCf3527ABevtRCg6tWkSZIvnzS23c0ALZuBT58oJ0UKdaF0NjRO3nyJE6ePIkTJ04k+zt58iSWssoGDG1gZ0c6TwDc/vyT1cpMg6RZeitXamE3bPp0eq9//RXo2DGr5pkcGzaQvmHx4qR3yEiFfv2AkiUh+/gRxXfvFtsag6VCBaBePcqLWrkyi419/ixkzk+fToV1GWqTNIt++HDKuJUaGjt6devWTfZXqVIl3L9/H2PHjsUIFpTC0BYDB4IrVgxWERGQL1oktjUGS4cOlP0VHg5s356Fhu7fF5YF580jwT6G2iiVwjbbiBHs7UsTMzPKrgfgun+/lparjBN+EhcQQNp6mWb2bHL2fvmFkt0YGnH8OEkPZs9O8xQpkumfo9OnT6NXr17Ily8fFixYgAYNGuACS5tnaAsLCyT+mIXKFy+muDFGCszNKQMXoFknx2WyoYkTSdS2ZUtaSmBoxD//AA8e0GJ0r15iW2PgtG4NZa1aUMTFQfFjK5eRkpYtARcX8tE2bcpkI8+eCfW65s6V5nKUyPDJbn360Pdbimjk6IWHh2POnDkoXrw4OnToAFtbW8TGxmLPnj2YM2cOfv31V13ZyTBBuN9+w6eSJSH79o3kGRipMnAgyTJcvw6cPZuJBs6dI9EuuVy12sLQDH5rZ8AAPdQfljoyGZQ/NNxkGzcCN26IbJBholAImdtLlmSyCs6UKRTEW68eiyfIBP/9Bxw8SGEyw4eLbU3mUdvRa9WqFUqWLIkbN25gyZIleP36NZYvX65L25Lx6dMndOvWDba2tsiZMyf69euHr+nEbn369AnDhg1DyZIlYW1tjUKFCmH48OGIjIxMdp5MJkvxt40Xy2GIi0yG2/zySHAwq5WZBrlzAz170rHGUitJS5316weULq1N00yCmzdpe0ehAIYNE9saacBVrYpXNWpAxnGUBcpIlT59AFtb4N494OhRDV98/Tql5AOs1Fkm4cMxWrWiGuNSRW1H759//kG/fv0wbdo0tGjRAgqFQpd2paBbt264ffs2QkJCcODAAZw+fRoDBw5M8/zXr1/j9evXWLBgAW7duoX169fj8OHD6JfKJvu6devw5s0b1V9bJuZpMHwqXRpKvlbmhAlim2Ow8DP/PXuAJ080eOHu3STYZWOjyohkaAY/GPz2G1CokLi2SIm7PXqAMzOjajgsuz5VcuQQ4sI0msTxEziOI62WKlV0YZ5R8/kzaccD0s+iV9vRO3v2LL58+YLKlSvD3d0dK1aswIcPH3Rpm4q7d+/i8OHDCAoKgru7O2rVqoXly5dj27ZteP36daqvKVOmDHbt2oVWrVrB1dUVDRo0wMyZM7F//34k/FREMGfOnHByclL9WVlZ6aNbDDVJnDmTlkv27QNOnxbbHIOkdGnAw4O2d9ReaI+PF5znMWNIeoGhEe/fC4smUh8M9M23fPmg5GvEjRuXyb1J42fYMIqqOHKECtaoxdGjtMxsYQHMnKlT+4yVoCBKgilXTvphy2pHZlarVg3VqlXDkiVLsH37dqxduxbe3t5QKpUICQmBs7MzcuTIoRMjQ0NDkTNnTlRJMitp1KgR5HI5Ll68iHbt2qnVTmRkJGxtbWH2U0Dq0KFD0b9/f7i4uGDQoEHo06dPujV8Y2NjERsbq/o/LxQdHx+P+Ph4TbqmFnybumjbkFH128UF8v79oQgIgHLMGCSePWvU2xCZvd9eXjIcOWKGoCAOkycnwNY2/fPlq1ZB8fAhuDx5kDBiBDl+IiHVz/jKlXLExipQpYoSVaokavwWSrXfWYXvb+zYsbDetAmy69eRsGkTuK5dRbZMd2T2XhcsCLRurcCePXIsXpyIVasycIgTE2E2ZgxkABKHDIGyYEH23daQhARg+XIzADIMG5aAhATNstz00WdN2pZxXKbz9HD//n0EBwdj06ZNiIiIQOPGjbFv377MNpcms2bNwoYNG3D//v1kj+fJkwfTpk3D4MGDM2zjw4cPqFy5Mrp3746ZSWY4fn5+aNCgAWxsbHD06FH4+vpi3rx5GJ5O5OXUqVMxLZVtrq1bt8LGxkaDnjHUxTIiAo0GDYJZTAwujxmD17VqiW2SwaFUAsOHN8DLlznQr99NtGr1OM1zzaKj0WjQIFhGReF/np542qyZHi01DuLj5Rg4sDE+f7aCt/cV1KnzSmyTJEnxnTtRevNmRDs64vjKlVBaWIhtksFx+3YuTJ5cGxYWiQgKOgJb27QHeefjx1Fp+XLEZcuGY6tXI15HCzDGzNmz+bFgwa+ws4vBmjUhsLAwvNXm6OhodO3aVbWAlR5ZcvR4EhMTsX//fqxdu1YjR2/ChAmYm0GW3927d7F79+4sOXpRUVFo3LgxcuXKhX379sHc3DzNc318fLBu3Tq8ePEizXNSW9FzdnbGhw8fMnzDM0N8fDxCQkLQuHHjdG03Nn7ut3zmTCimTQPn4oKEGzdoW8IIycr9XrNGjqFDFXBx4XD7dgLSCqWV+/hAMWcOuOLFkRAWRjotIiLFz/jmzTL07WuG/Pk5PHiQkKm3UIr91gbJ+h0fD7NffoHs1SskzpkDpbe32ObphKzca44DqlUzw/XrMvj5JWL8+DQcj+/fYVa6NL2Xs2dDOXq0FizPGlL8jNepo8CFC3L88UcifHw0d/L00eeoqCg4ODio5ehpRVRHoVCgbdu2GicxjB49Gr0zEHB0cXGBk5MT3r17l+zxhIQEfPr0CU5OTum+/suXL2jatCly5MiBv//+O8M33d3dHX5+foiNjYVlGiWgLC0tU33O3Nxcpx9kXbdvqKj6PXYsEBAA2ePHMA8KMvqq8Zm53717A3/8ATx+LMORI+apl+t59UqVQSCbNw/mBrQKLZXPOMcJsZBeXjLY2GTNZqn0W9uYm5vT58/PD+jbF4o5c6AYMADIlUts03RGZu+1tzfQowfg76/A+PGK1CcWCxbQ97tQIShGjoTCgD5TUvmMX7wIXLhA6whDhypgbp75xFNd9lmTdkXVb3d0dISbm1u6fxYWFqhevToiIiJw9epV1WtPnDgBpVIJd3f3NNuPiopCkyZNYGFhgX379qmVZBEWFgZ7e/s0nTyGiGTLRiV8ABoYIiJENccQsbEB+Pj2NLP0fHyoSnfNmtIs3GgAnDlD6hXW1qRjyMgiPXsCZcvSd3rWLLGtMUg6dgScnIDXr6kGbgo+fAB+6BNi5kwS12RoDJ9F36ULvd/GgCQK9ZQqVQpNmzbFgAEDcOnSJZw7dw5eXl7o3Lkz8ufPDwB49eoV3NzccOnSJQCCk/ft2zcEBwcjKioK4eHhCA8PR2JiIgBg//79CAoKwq1bt/Dw4UP4+/tj1qxZGMbEsAyXPn0oxfTjR+FHjZGMoUNJAP/UKSAs7Kcnb94UNAOYtlam4Z3onj1Jx5CRRRQKKr0H0FKpRhpBpgGtMNFxqlVw/PyAqCgqlGvESS265OVLwYk2pix6STh6ALBlyxa4ubmhYcOGaN68OWrVqoXAwEDV8/Hx8bh//z6ifxQFvHbtGi5evIibN2+iWLFiyJcvn+qPj78zNzfHypUrUb16dVSoUAEBAQFYtGgRfH19RekjQw2S1MrEkiXA8+eimmOIFCxINXCBVFb1JkygrI3ffweqV9e3aUbB48ekVwhIWy3f4PDwABo2BOLiKP6AkQJPT8DSErhyheQvVTx8CKxaRccLFrBiy5lk5UrKuK1Xj/xlY0Eyn4ZcuXJh69at+PLlCyIjI7F27VpkT1JrqEiRIuA4DvV+CN7Uq1cPHMel+lekSBEAQNOmTXH9+nV8+fIFX79+RVhYGDw9PSFnXxLDpkULoG5dKu3j4yO2NQYJPxv9808gPPzHgydOAIcOkbPMtscyzYoVtJri4cEKiWgVmUxY1du6FUgSqsMgHB0pTg8Qyu4BACZNIg+laVNylhkaEx0NBATQsTGt5gEScvQYDBUyGW07AsDGjcD//ieuPQZI1aq0YBcXB6xeDVrF40udDRoEFC8uqn1SJSqKhFQB4xsMDIJKlYDu3emYr+zASAafg7Z7N/DsGShzYMcO+l1ktaozzaZNVA3DxQVo2VJsa7QLc/QY0uTXX4HOnWkgGDdObGsMklGj6N9Vq4C4jduAa9eophJbBc0069YBX74ApUrRih5DB8yYQQFpJ09SeTRGMsqUARo1ornbiuVJalX37k1lHBgao1QKYS7DhyNNWSqpwhw9hnSZOZP0344ezUTFb+OnXTvA2RmIfB+L2DGT6MEJE2j/h6ExiYnAsmV0PGIEy2PRGYULC8GP48bRG89IBj+Je+W/Dzh7ljJseUUChsYcPQrcuwfY2gJ9+4ptjfZhjh5Duri4AF5edMwGhBSYmVGdTC+sQI6Pz8Dlz8/2G7PA/v2UiJErlxAnxdARkyYB9vbArVvAhg1iW2NwNG0KlC4eD5/o8fTAqFGUhcXIFPxqXr9+tOlhbDBHjyFtJk8G7OwoTm/LFrGtMTgGtP+EPzADAHCvqx8J7TEyBT8YeHqyt1Hn2NvTdxsApkyhSHmGCrkcWFU5GG64j09yByjHjhfbJMly5w5w5Ai9p8aqrMYcPYa0yZ2bZv8ASTJ8/y6uPQZGTv/ZsEcEbqIMJt7rJbY5kuX6deDff2mVdMgQsa0xEby8gCJFSCE4TfVvE+XLF9Q+TjJgvkofHDpnJ7JB0oUXSG7TBihaVFxbdAVz9BjSZ/hwoFAh4MULIYiKATx9qno/xmEe9h1U4MEDcU2SKryf0aED2yHTG5aWFIcLkDj6T2UwTZoFCyB//w4fchZDADyTS60w1ObjRxJuAIw7qoU5egzpY2VFmXoA6cN9+CCuPYbCH3+QvkqDBlA0b5qsPitDfcLDSY8QMO7BwCDp3JkkV758ocoPDODNGxJFBsDNmg2lwgInTgA3bohslwQJDARiYugjVru22NboDuboMYyDbt1IyjwqSnD6TJlr14SYxXnzMMqbUkTXrmUlgjXF3x+Ijwdq1CB9QoYekcsFzczVq8GWpAH4+lLMYrVqcBzUHu3b08P8FiRDPeLjSfwcoAmcMWfRM0ePYRwkHRBWrQIePRLXHjHhkmhrdesGVK6MBg1If+vbNyA4WFzzpERMDDl6AFvNE40GDYDmzanyAx+Pa6rcuSN8gX/UqualVrZsYbvbmrBjB4V/OjkBnTqJbY1uYY4ew3ho1IhUbOPjhYw9U+TwYSp3ZmGhWt2UyQRHZflyGjMZGbN1K/D+PYWAtmsntjUmzNy5NJnbuZMqQZgqfK3qtm2BWrUAANWqAe7uVBFy9WpxzZMKHCeUkBs6lH4qjRnm6DGMi3nzyKvZvh24dElsa/RPYqJQKWTYMMpa/EHXroCDA5VN2rtXHPOkBMcJSRjDhlHGLUMkypShyg8AMGaMaZZG+/dfEnNUKCg5JQn8JG7VKnL4GOkTGgpcuUL5Pp6eYluje5ijxzAuypUDev2QETHFWpkbN5LIbM6cKba5rK2BwYPpmGXpZczJk8DNm0C2bCSkyhCZ6dPpQ3zunOnNVJRKcnABYOBAoGTJZE+3b0/Z4G/f0hyXkT7871/37qZRKIg5egzjY/p0ysQ9fRo4cEBsa/RHdDRl2gL0b65cKU4ZPJiqxp07B1y+rGf7JAY/GPTuTfq9DJEpUADw9qbj8eMpRMNU2LGDlqCyZ6dkjJ8wNxeKBC1ebHrzW0149gzYvZuOTSXuljl6DOPD2Vn4Bo8bZzoBaUuWUHRx4cIUeJIK+fKRYgXAsvTS48EDYY4wYoS4tjCSMG4cxR/895/pZBXFxgITJ9LxuHFA3rypnjZgAC14hoXRHJeROitW0AJpo0YUEWAKMEePYZxMmEBVM+7dI00RY+f9eyFuZ+ZMWtFMA94H3r4dePVK96ZJEV53u2VLoHhxcW1hJMHWVljR8vUlfT1jx98fePKEZmn8imYq5MolRK2wQiKp8/UrsGYNHZvKah7AHD2GsWJnB/j40LGvL33DjRk/Pxr0KlUCunRJ99RKlYA6dWihc9UqPdknISIigHXr6NiUBgPJMHAgUKwYaYn8EA42WiIiBKHo6dMpYDQd+NXnvXuBx491a5oUWb8eiIwESpQAmjUT2xr9wRw9hvEyaBDg6kqlDRYuFNsa3fHggSD2Nn8+yVBkAO/ABASwevE/ExREeoNly5KEG8PAsLAAZs+m4wULqFKEsTJ7NvDpE1C6tJB1nA5ubuTAcByrBvkzSqUQrjJihFo/k0aDCXWVYXIkHRDmzyeHzxiZNImW55o1U9szad2aCnh//CgU0GDQ28iXiTN2tXxJ0749CchFRwNTp4ptjW54/lzwTObOVVvfh5/ErV1LhYIYxMGDwMOHJEjQs6fY1ugX5ugxjJvff6e6Vd++AdOmiW2N9rlwgURkZTIaDNREoQCGD6fjJUtYlh7Pnj00vjo4kO4gw0CRyYRKOEFBVDHC2PjjD0rEqFcPaNFC7Zc1bkwLgF++mEZ4srrwcYsDBlDysinBHD2GcSOTCXE8a9ZQcoaxkLTUWe/etNeoAX37Ajly0BgZEqJ986QIL6kyeHC6+SwMQ6BWLaoQoVRS8pUxcf06sHkzHf8odaYuSavgLFtGGuqmzo0bVCxIoRBkaEwJ5ugxjJ/atYE2begXz5gGhL17gbNnSVNh+nSNX25rS84ewLL0ACqkcv48aZINGSK2NQy1mDOHRu/9+6lyhLEwfjxN5Lp0AapU0fjl3buT6MCTJ8C+fTqwT2Lwv2/t21M5Q1ODOXoM04AfEPbuNQ6Rqfh4GgwAYNQoksXPBMOG0QrAP/8Y12JnZuDDobp0oULnDAlQsiRl4QLGUwnnyBFaYjc3J6mkTGBtLZT2MvVJ3Lt3QhyyqWbRM0ePYRq4uQH9+9OxMQwIwcEkGuvgINS2zQSurrTYCZi2gPKrV8Bff9GxqQ4GksXXl2RHLl+mChJSJmmtai8vypjKJEOHUv7G6dPAtWtask+CrF4NxMUB7u5A9epiWyMOknH0Pn36hG7dusHW1hY5c+ZEv3798DUDbbR69epBJpMl+xs0aFCyc54/f44WLVrAxsYGefLkwdixY5FgKpUUTI2pU2lAuHSJEhikypcvgmisjw9pBmYB3rHZsIGUHEyRlSsp47ZuXaBiRbGtYWhE3ryCczRxIiUwSJVNmyigLGdOoZxhJsmfH+jUiY5NdVUvNlbQCjXlCZxkHL1u3brh9u3bCAkJwYEDB3D69GkM5Jfs02HAgAF48+aN6m/evHmq5xITE9GiRQvExcXh/Pnz2LBhA9avXw8fXmiXYVw4OQnJCxMn0jRPiixYQPsRxYoJ+zNZoE4doEIF4Pt3QTXelIiOJj1BwLQHA0nj7U3f78ePaQlHinz/DkyZQseTJqVaq1pT+M/ztm3GLTeYFtu2AW/fUpnk9u3FtkY8JOHo3b17F4cPH0ZQUBDc3d1Rq1YtLF++HNu2bcPr16/Tfa2NjQ2cnJxUf7a2tqrnjh49ijt37mDz5s2oUKECmjVrBj8/P6xcuRJxUnUCGOkzejStADx6JM0B4c0bIYt49mzSCswiSbP0li83rVrxACU3fvoEuLgArVqJbQ0jU2TPLsgn+flRRQmpsXQp8PIlZQsMG6aVJqtUAWrWpO80r6luKnCcsJLp5UUhj6aKegqMIhMaGoqcOXOiSpLso0aNGkEul+PixYto165dmq/dsmULNm/eDCcnJ7Rq1QpTpkyBjY2Nqt2yZcsib5Ii0R4eHhg8eDBu376Nimns4cTGxiI2yfZA1A9Vyvj4eMTrYJTk29RF24aMTvptaQm5jw8UQ4eCmz4dCV27ZnnrU9uk12/FlCmQR0dD6e6OxNatteaVtW8PjB9vhlevZNi+PQGdOuk3hlGszzjHAYsXmwGQYejQRCiVSiiV+rs++25rsd89esBs8WLI7t1D4qxZUGYykUFXpNvnDx9gNns2ZAASpk0Dp1Bo7bs9bJgM586Zwd+fw5gxCbC21kqzaiPWZ/z0aRnCwsxgbc2hT58EvU5g9dFnTdqWhKMXHh6OPHnyJHvMzMwMuXLlQng61Q66du2KwoULI3/+/Lhx4wbGjx+P+/fvY/fu3ap2kzp5AFT/T6/d2bNnY1oq4rtHjx5VOZG6IMRExc603W+ZkxPqFyyIHC9f4smgQbjbo4dW29cWP/c7x4sXqP+jCOu5Nm3w6Z9/tHq9Bg1K4M8/S8HPLwo5cpzRatvqou/P+PXrjrh3rwasreORL99RHDokTnwu+25rh7y//YZqs2YBS5fiRIkSiHF01Gr72iC1PpcJCoJrVBQiihbFv3Z2wKFDWrueubkMjo6N8P69DSZNuoXGjZ9rrW1N0PdnfNasqgDyoW7dp7hw4YZer82jyz5Ha1C7UlRHb8KECZibgZr/3bt3M91+0hi+smXLIl++fGjYsCEePXoEV1fXTLc7ceJEeHt7q/4fFRUFZ2dnNGnSJNnWsLaIj49HSEgIGjduDHMTWn/WZb9lANC+PYofPIii8+dnWp5EF6TVb0W7dpAplVC2bo1qY8Zo/bpVqgC7dnH4779cyJ27Bdzd9beqJ9Zn3N9fAQAYMECO339vorfr8rDvtpb73awZlGfOQHHmDBqfPo3E4GDttZ1F0uzzo0cwO3IEAJB91So0b9hQ69d++FCO8eOBU6cqYNGiMnot7SfGZ/zRI+DyZXJv5s0rCDc3/f6+66PPURrUtxPV0Rs9ejR6Z1Co2cXFBU5OTnj37l2yxxMSEvDp0yc4aSB45e7uDgB4+PAhXF1d4eTkhEuXLiU75+3btwCQbruWlpawtLRM8bi5ublOP8i6bt9Q0Um/27UDateG7MwZmE+fDvxYKTMkkvX733+pWKNCAfm8eZDr4HNQoADQrRu9FcuXm6FWLa1fIkP0+Rm/e5cky2QyYMQIBczNFXq5bmqw77YWWbAAcHeHfPNmyEePBsqX1277WSRFn319aZu2aVOYNW2qk2t6elLo4u3bMpw5Yw4d+JIZos/P+OrVFJbRrBlQtqx43ytd9lmTdkVNxnB0dISbm1u6fxYWFqhevToiIiJw9epV1WtPnDgBpVKpct7UISwsDACQL18+AED16tVx8+bNZE5kSEgIbG1tUbp0ae10kmGYJC2NtmEDSRoYKkolwK/gDRxIIrE6gk/K2LWLar4aM7xuYJs2lIjBMBKqViVdEY4TRMUNlYsXScBRw1rVmmJnB/TpQ8d8mT9jJTKSZEYBlkXPI4ms21KlSqFp06YYMGAALl26hHPnzsHLywudO3dG/vz5AQCvXr2Cm5ubaoXu0aNH8PPzw9WrV/H06VPs27cPPXv2RJ06dVCuXDkAQJMmTVC6dGn06NED//vf/3DkyBH88ccfGDp0aKordgwjo2pVoGNHwx8QduwArlyhzEJeP09HlCsHNGhAuq0rV+r0UqLy8SOwcSMdjxolri0MHTBzJqVZ8lUmDBGOE/T/evWiL58O4avgHDxIWuvGytq1wNevQKlSQOPGYltjGEjC0QMoe9bNzQ0NGzZE8+bNUatWLQQGBqqej4+Px/3791UBihYWFjh27BiaNGkCNzc3jB49Gu3bt8f+/ftVr1EoFDhw4AAUCgWqV6+O7t27o2fPnpieibqhDIkyaxYNCIcPA8eOiW1NSmJjSfMPoEHhp+QhXcDPggMDgW/fdH45UVizhmTLKlakUsgMI8PVVShYPG4c9JpKrS4HDlDZCisr2lfVMcWLAy1b0vGyZTq/nCgkJgp9GzkSeo1FNGQkkXULALly5cLWrVvTfL5IkSLgkpS1cnZ2xr9qFLkuXLgwDmkxw4khMVxdgcGD6ddh7Fjg6lVAbkDzH39/qkzu5ESisHqgRQvSYn74kHa1+fHSWIiPB1asoGM2GBgxf/xBAadhYVTs1JCy6xMStFKrWlNGjQL276e3xc8PsLfXy2X1xt69wNOnQO7chnW7xcaARjQGQySmTAFsbWlASGcyoXciIoSZ/vTpVL5ND8jlwIgRdLx0qWEuhmSFnTuptq2Tk1AiimGEODhQhQkAmDwZiIkR156krF1L2UC5c+s1bKRePdohjo4GgoL0dlm9wQske3pC73qBhgxz9BgMBwdhe9SABgT5vHlUsqF0aSGSWk/07k0B3P/9R7vaxgIJJNPxkCEAC8U1coYPp9WyFy+o7Ish8PWrVmtVa8LPVXCMqaz71avAmTOAmZnx7UJkFeboMRgALWEVLEippgYwIFi/fw85b8fcufTrpUeyZwf696djYyqIHhoKXL5MDp4WygQzDB1ra2DGDDqeOZOycERGvmQJEB5Oqd6DBun9+l26AHnykO/79996v7zO4H+nOnUiqSiGAHP0GAzA4AYEt61bIYuNBerWpaA5EfDyom3ckBDg1i1RTNA6/GDQrRsNdgwToHt32q+MjKTvtohYRkRAvnAh/UdLtao1xcqKwpIB45Faef0a2L6djpmkSkqYo8dg8CQdEGbNEs+OsDA4nzpFx/Pni5YtUKQI8NtvdMxrzkmZZ89IHxBgg4FJoVAA8+bR8YoVlNwkEiW3bYPs2zeSdurQQTQ7Bg0iHzM0lKT8pI6/PyVZ1axJFX4YyWGOHoPBYyADgmLSJMg4DsqOHYFffxXFBh7eIdq0CXj/XlRTssyKFZRY0rAhULas2NYw9IqHB4mqxcdTHK4Y3L+PwkeP0rGIEziAEpG6dKFjqU/ivn+nShgAm8ClBXP0GIyk8ANCXJw4A8LRo5AfOwalmRkSDUDPsUYNmiHHxgIBAWJbk3m+fiXtPIANBibL3LnkXP35JwmQ6xnF5MmQK5VQtmgB1Kmj9+v/DP892LEDePlSVFOyxJYtwIcPQOHCQNu2YltjmDBHj8H4GbEGhMRElVL+4+bNDaIuV9IsvZUryf+VIhs20I588eJA8+ZiW8MQhYoVKTwDIM3MJLqrOufMGcj37YNSLkeimGEhSahQgeRWEhKkWwWH44S422HD9J6zJhmYo8dg/IxYA8LmzcD//gfOzg7//f67fq6pBh06APnzU6LgX3+JbY3mKJXC9tSIEYalh83QM35+lHJ96hSgL6F8jqPfEQDPGjem2lwGAj+JCwggbT2pcfw4cPs2SYz26ye2NYYL+8ljMFJD3wPC9++k5A9AOX484m1tdX9NNbGwAIYOpePFi/W7EKINDh0CHjwgubJevcS2hiEqhQsLauDjxulHSG7nTuDiRXDZsuF+5866v54GtGxJxYE+f6Y4XKnBZw337QvkzCmqKQYNc/QYjNTQ94CwbBkFyhQqBKWXl26vlQkGDiRZhmvXgLNnxbZGM/itnQEDSB+QYeJMnAjkygXcuUN7+rokLk4lxq709kasgdUcUyhIUxqg74mUquDcv0+TOJmMtm0ZacMcPQYjLfQ1IHz4IMi5zJhBHpWB4eAg1I6UkoDyzZu0vaNQsMGA8YOcOVWr55gyBfj2TXfXWr0aePQIcHKCctQo3V0nC/TpQxUg790D+KRgKbBsGf3bsiXF3jLShjl6DEZa6GtAmDEDiIqi6Ohu3XRzDS3Ax/Ps2SOqFJlG8E7pb78BhQqJagrDkBgyBChaFHjzRneqwRERVKMaoH8NdDk5Rw4hvk0qk7jPn4H16+mYZdFnDHP0GIz00PWA8OgRsGoVHc+fb9CZAqVLA02a0PbOihViW5Mx796R9ALABgPGT1haCqvoc+fSh0XbzJlDFXZKldJ7rWpNGTaMfnqOHKENDENnzRpKHilXDqhfX2xrDB/DHVUYDENA1wPCpEkk4urhATRqpN22dQDvMAUFAV++iGpKhgQEkP7fr78C1auLbQ3D4OjYkUQiv34VVt60xfPnwvLYvHkGr/tRtKigQWfoAsrx8UI58pEjRdWdlgzM0WMwMkJXA8LFi6RXIpMJFTkMHA8PoGRJ2mlet05sa9ImNlZYKB01ig0GjFSQy4XvXUAA8N9/2mt7yhT6EIpYq1pT+Encxo2il/pOl7//prw1R0ehugcjfZijx2BkhFxO26oABVffv5/1NjlOJY6MXr1oD0ICyOXCgLBsGWk8GyLbt5PuX/78gAFJEjIMjfr1yRFLSFBlx2aZsDBBq2TBAsnMMmrVAipXBmJigMBAsa1JGz6CZvBgg8xbM0iYo8dgqEO9epTelZionQFh/37g9Gn6pTKAUmea0KMHYG9P4YUHD4ptTUqSquV7eQHm5qKawzB05s6lGczu3cD581lvb9w4+hB26UI7ARIhaRWcFSsMswrOhQv0Z2FBjh5DPZijx2CoCz8g/P131sTkEhKA8ePpeORIwNlZK+bpi2zZSFcP0F3CYlY4cwa4fh2wthbsZDDS5JdfSHEXyHolnCNHgJAQ8kRmztSOfXqkY0fAyQl4/Zp0ng0NPn6wSxeyk6EezNFjMNSldGlBhyArA0JwMIlW5c4NTJigPfv0yNChpE136hTtVBkS/Gpejx70FjMYGTJtGs0Mzp8n/aDMkKRWNby8KMNBYhhyFZyXL4EdO+iYZdFrBnP0GAxNmDaNlrQuXMjclPfrV8DXl459fKgulwRxdqYauIBhZek9fiyM03xhEwYjQ/LnB0aPpuPx4ym1U1M2bwZu3CD9zcmTtWqePvH0pIiSK1eA0FCxrRFYsYJ86Xr1SHKUoT7M0WMwNCFfPmDMGDqeOFHzQJYFC4C3b6nA5KBB2rdPj/Cz6q1bKfHBEFi+nFYhPDxoAZbBUJuxYymV88EDEmrThCS1qjFpElXUkSiOjkD37nRsKKEZ374JCSJsNU9zmKPHYGjKmDEUIPLoEeDvr/7r3rwhRw8AZs+mfRIJ4+4OVKtGvu7q1WJbQ5IvwcF0zAYDhsbY2gJTp9Lx1KmaCUUuXaqqVW0Mtfb41fDdu4Fnz8S1BaAk5s+fARcXyoljaAZz9BgMTcmenbZwAcqYjYhQ73XTptHU1N3daDQ/eIfK359kGcRk3Toam93cqIIHg6ExAwZQ4dT37wVJpYz48IEmbgAlYBiB5keZMkDjxoZRBUepFMJDhg+n2GCGZkjG0fv06RO6desGW1tb5MyZE/369cPXr1/TPP/p06eQyWSp/u3gIzqBVJ/ftm2bPrrEkDJ9+1Jpo0+fhB/59Lh7l8pJADSASERbKyPat6d4vXfvADG/NomJQpHzESMMupIcw5AxN6fSZQCwcCGln2YEX6u6YkWga1fd2qdH+EncmjUUWiwWR45Q7pqtrZAczdAMyfwcduvWDbdv30ZISAgOHDiA06dPY2A62gnOzs548+ZNsr9p06Yhe/bsaNasWbJz161bl+y8tnwtGAYjLczMBFX9pUup5FF6TJxI3kibNkDt2rq3T0+YmVGCISBult6BA5SIYW9P2bYMRqZp1w6oUYOKqfKJU2khoVrVmtK0KVCiBBAZCaxfL54dfBZ9v35Ajhzi2SFlJPGpvHv3Lg4fPoygoCC4u7ujVq1aWL58ObZt24bXacy4FAoFnJyckv39/fff6NixI7Jnz57s3Jw5cyY7z8oIlt4ZeqBFC0oBi40VArFT48wZYO9e2nPgVwuMiP79ARsbSjg8dUocG/jBYOBASopmMDKNTCZs265dC9y+nfa5fK3qpk2Bhg31Y5+ekMuFWL2lS2kLVd/cvg0cPUq2GEHoo2gYdqXlH4SGhiJnzpyokkRlvFGjRpDL5bh48SLatWuXYRtXr15FWFgYVq5cmeK5oUOHon///nBxccGgQYPQp08fyNLZWouNjUVsbKzq/1FRUQCA+Ph4xGcmLT8D+DZ10bYhI4V+y2bPhln16uA2b0aClxdt3ySF46AYMwZyAIn9+kHp6pqhdIMU+p2UHDmAHj3kCAhQYNEiJWrV0rwuWlb6HBYGnDplDoWCw8CBCZlSxhALqd1rbWHw/f71VyjatoV8zx4ox41DYiraerLLl2H211/gZDIkzJhhdN9rgHaiJ082w8OHMuzbl4AWLTRfss9KvxcvVgCQo1UrJQoWTJTMd1sf91qTtiXh6IWHhyNPnjzJHjMzM0OuXLkQrqauQ3BwMEqVKoUaNWoke3z69Olo0KABbGxscPToUQwZMgRfv37F8OHD02xr9uzZmMYH4yfh6NGjsLGxUcuezBASEqKztg0ZQ+93pTp14Hz6NCL698f56dOTxd/lP3sWv166hAQrKxyrXh2xhw6p3a6h9zsp5cplB9AQBw/KEBz8L/Ll+5apdjLT52XLKgIohBo1XuHmzau4eTNTlxYVKd1rbWLI/c7WuDEa7NsH+aFDOD93Lj6WLSs8yXGoOXkyHAC8qF8f11++pKxbNTDkPqdG/fql8fffxeHj8xkyWeZLxGna76goC2zaRFlVVauew6FDnzJ9bbHQ5b2Ojo5W+1wZx4mnfT1hwgTMnTs33XPu3r2L3bt3Y8OGDbj/UzH5PHnyYNq0aRicQdG779+/I1++fJgyZQpG86KYaeDj44N169bhxYsXaZ6T2oqes7MzPnz4AFtb23Tbzwzx8fEICQlB48aNYW5ChTsl0++nT2FWpgxkcXFI2LsXHB8DGhMDs7JlIXv2DIm+vlCqKaIqmX7/RJs2CvzzjxxDhyZi8WLN9nky22eSJDRDXJwMZ88moGpVA5LyVwOp3uusIpV+y4cPh2L1aigrV0biuXOqGDzZvn0w+/13cFZWSLhzByhYMMO2pNLnn3nxAihRwgyJiTJcuRKPcuU0e31m+z1njhw+PgpUrMjhwoUESeWv6eNeR0VFwcHBAZGRkRn6HaKu6I0ePRq9e/dO9xwXFxc4OTnh3bt3yR5PSEjAp0+f4KRGwbudO3ciOjoaPXv2zPBcd3d3+Pn5ITY2FpaWlqmeY2lpmepz5ubmOv0C67p9Q8Xg+128OOX9L1gAs0mTKHZPoaDshGfPgAIFoBg3DgoN+2Dw/f6JUaOAf/4B1q9XYMYMBXLm1LwNTfscFEQ6ftWqATVrSmKDIlWkdq+1hcH3e+pUYPNmyK9ehfzvv4HOnalW9Y+YXNmoUTDXsNSZwff5J1xcSA1q+3Zg1SpzlValpmjS76TanKNGyWBhIZ33Kym6vNeatCtqMoajoyPc3NzS/bOwsED16tURERGBq1evql574sQJKJVKuLu7Z3id4OBgtG7dGo6OjhmeGxYWBnt7+zSdPAYjVSZNopTP27eBP/8kuQV+tXrmTMpWMHIaNaL68N++UQy7romJEfSqmUAyQyfkzUsl0QD6jsfGCrWqHRyE54wc/vu1ZQtJKemaHTtI2cbJCejUSffXM3YkkXVbqlQpNG3aFAMGDMClS5dw7tw5eHl5oXPnzsifPz8A4NWrV3Bzc8OlS5eSvfbhw4c4ffo0+vfvn6Ld/fv3IygoCLdu3cLDhw/h7++PWbNmYRhL72Foir29UNB86lRg0SLS2CtZUqgnZOTIZMKAsGwZLXzokm3baNApWBD47TfdXothwowaRaUPnzwhbT2+esaUKZKtVa0p1aqRzntsrO6r4HCckEU/dKjkCwgZBJJw9ABgy5YtcHNzQ8OGDdG8eXPUqlULgXzxO9Ce+P3791MEKK5duxYFCxZEk1Sk8s3NzbFy5UpUr14dFSpUQEBAABYtWgTfjLSTGIzUGDaMCkU+eiRUzpg61aSk3Lt1A3Lnph3rvXt1d52kg4GXF+ncMhg6IVs2qoADAJMnU2HnwoUlX6taU/hJ3KpV5PDpivPngStXAEtLwNNTd9cxJSTj6OXKlQtbt27Fly9fEBkZibVr1ybTwytSpAg4jkO9evWSvW7WrFl4/vw55KkIWTZt2hTXr1/Hly9f8PXrV4SFhcHT0zPVcxmMDMmWjbZ3eCpWBDp2FM8eEbC2FsY/3hHTBf/+C/zvf3S9AQN0dx0GAwDQuzfFJfD4+prcUlP79rR6/vYtxevpCv53o3t3mjczsg7zaBgMbTJ8OMXmde0K7NplVEr56jJkCK2wnT1LM3NdwA8GvXoBuXLp5hoMhgozM2DfPpq4zZlDjp+JYW6u+yo4T58Cu3fTMYu71R6mNwoxGLpELqdYvS1bAA2z8YyF/PmFAGpdrOo9ekRjLiAo9zMYOsfFhZayxo83mlrVmjJgAOWVhYUBp09rv/0VK6gCR6NGQJky2m/fVGGOHoPB0Dr8bHz7dvXqwmvC8uW0mtCsGeDmpt22GQxG2uTKRavogPYncV++kFwSwCZw2oY5egwGQ+tUrgzUqkWZt3zNd20QFSVIt7CtHQZD//BFo/bupdV1bbFhAxAZCZQoATRvrr12GczRYzAYOoJ3xFavBr5/106bwcE08y9VCmjcWDttMhgM9XFzo9V0jqPVdW2gVAJLl9LxiBEmGdqsU9jbyWAwdELbtkCRIsDHj8DmzVlvLzGR9PkAciJNNEyKwRAdfhK3di2tsmeVgweBhw+BnDkBNQpYMTSEOXoMBkMnKBQkLQhQPE9Ws/T27aOsvFy5TEaDmsEwSBo3BkqXptV1bVTB4eP9BgwAkqimMbQEc/QYDIbO6NePfrjv3AGOHctaW4sX07+DBplERTkG4//t3XlUk1f6B/BvWAPIIiNrRYr7MoC2FIrVag9BcKxHejwqjjroALYWaqmOjp3TAdHpcamntlqrHbVipwplxq3TWhVRtMPgUoUpqENdULQWmVaRAMp6f3/kl1cjayAJIfl+zskhuXney324JO/DS977Gq0nr4LT2Nj5vr7/Hjh2TPWHoXr5FtItFnpEpDfOzsC8ear7XTlL79w54NtvVcuZvf66ToZGRF0we7bqKjglJY+WO+oM9fvC1KlAv346GRo9gYUeEenVwoWqIwAHD6quBd8Z6g9qT58OPPWU7sZGRJ2ji6vglJerlhwFeBa9PrHQIyK9GjgQmDxZdV99MoU2fvoJyMhQ3X/rLd2Ni4i65vXXVUfZT54Ezp/XfvstW4C6OiA4GHj+ed2Pj1RY6BGR3qn/Wt+5E7h7V7ttP/4YqK8HXngBCArS+dCIqJO6chWc2tpHa2y++SbPotcnFnpEpHfjxwMBAUBNDbB1a8e3e/BA9Vc/wH/tEBkj9esyI0N19L2jMjKAO3dUH8WYNk0vQ6P/x0KPiPROJnv0b9ePPlIdoeuIXbuAn38GfH1V6/IRkXEJClJdBae+Hti8uWPbCPHoCGBiImBtrbfhEVjoEZGBREcD7u7ArVvA3r3txz++M3jjDdVngYjI+KiP6m3e3LGr4Jw8CRQUqE7omD9fnyMjgIUeERmIXA4sWKC6rz6Lti3Z2cCFC4CDg2o9PiIyTlOmqI66//wzsHt3+/HqNTFjYlQLoJN+sdAjIoN57TXVv2ny8oAzZ9qOVe8M5s1TXRqJiIyTlZVqGSWg/avgXL36aN099TakXyz0iMhgPD2BmTNV99s6qldcrFp3TybjzoCoJ1BfBaeoSHU0vjUbNqgKwchIYNgww43PnLHQIyKDevNN1dfMTODHH1uOUa+39/LLwKBBhhkXEXVeR66Cc//+o2vjck1Mw2GhR0QG9cwzwIsvAg0Nj9bRety9e0Bamuo+l1Qh6jnUV8H5+mvghx+aP799O1BVpTqSFx5u+PGZKxZ6RGRw6qN6n3zS/Cy9bdtU6+0FBAAvvWT4sRFR57R1FZyGhkcf11i0iAskGxILPSIyuClTgKefBn75Bfj880ftDQ3Axo2q+0lJ3BkQ9TTqo/A7dqiOzqvt3StDaSng5gbMnt0tQzNbLPSIyOAsLR+dZLFuHdDYqLr/5Zcy3Lyp2hmoT9ogop7j8avgbNumalOtiakqNxISVEstkeGw0COibhEXB/Turfosz4EDqkN3mzer3pLmz+fOgKgnevwqOBs3qo7SFxf3xnffWUAuB15/vXvHZ456TKH37rvvYvTo0bC3t4dLBxfVEkIgOTkZXl5esLOzg0KhwOXLlzVi7t69i1mzZsHJyQkuLi6IjY1FVVWVHjIgosc5Oj5aQHnrVgvcuOGIEycsYGkJvPpq946NiDpPfRWcmzdVf8QdOfI0ANVReje37h2bOeoxhV5dXR2mTZuGBeo9QwesXbsWGzZswJYtW3D69Gk4ODggIiICDx8+lGJmzZqFCxcuICsrC1999RVOnjyJ+bwmC5FBxMWpvmZnW2DnzhEAVNe09fHpvjERUdfI5UB8vOr+++9bIDfXG8CjNjKsHlPopaam4q233oK/v3+H4oUQ+OCDD/DOO+9gypQpCAgIwGeffYbbt29j//79AIBLly7h0KFD2LZtG0JCQjBmzBhs3LgRGRkZuH37th6zISIA8PMDwsJU98+f9wCgusg5EfVs8fGqf+OePWuB2lorDBsm8Pzz3T0q82SylwkvKSlBWVkZFAqF1Obs7IyQkBDk5eUhOjoaeXl5cHFxQVBQkBSjUChgYWGB06dP45VXXmmx79raWtTW1kqPKysrAQD19fWor6/XeS7qPvXRtzFj3uaR91tvyZCdrXorevbZRowe3QQzSd3s5lrNHPM2t5y9vYGXX7bEP/+pOp60cGEdGhp6zLGlLjHEXGvTt8kWemVlZQAADw8PjXYPDw/pubKyMri7u2s8b2VlBVdXVymmJatWrUJqamqz9iNHjsDe3r6rQ29VVlaW3vo2Zszb9CUk9ENhYR/MnPlffPNNTXcPx+DMaa4fZ455m1PO06bZQKn8Nfr1q4Sn5xUcPNjdIzIsfc51TU3H3ye7tdBbtmwZ1qxZ02bMpUuXMHToUAONqGPefvttLFq0SHpcWVkJHx8fTJgwAU5OTjr/fvX19cjKykJ4eDisra113r+xYt7mk3d4uPnlDJjnXAPmmbc55gwAU6fWIyvrvFnlbYi5Vv8nsSO6tdBbvHgx5s6d22ZM//79O9W3p6cnAODOnTvw8vKS2u/cuYORI0dKMeXl5RrbNTQ04O7du9L2LbG1tYWtrW2zdmtra73+Iuu7f2PFvM2HOeYMMG9zYo45A+aZtz5z1qbfbi303Nzc4Kanc639/Pzg6emJ7OxsqbCrrKzE6dOnpTN3Q0NDUVFRgXPnzuHZZ58FABw7dgxNTU0ICQnRy7iIiIiIDKXHfDKytLQUBQUFKC0tRWNjIwoKClBQUKCx5t3QoUOxb98+AIBMJkNSUhL+8pe/4Msvv0RhYSF+97vfwdvbG1FRUQCAYcOGITIyEvHx8Thz5gxyc3ORmJiI6OhoeHt7d0eaRERERDrTY07GSE5Oxs6dO6XHo0aNAgAcP34c48ePBwAUFxfj/v37UszSpUtRXV2N+fPno6KiAmPGjMGhQ4cgf2zJ/V27diExMRFhYWGwsLDA1KlTseHJqzETERER9UA9ptBLS0tDWlpamzFCCI3HMpkMK1aswIoVK1rdxtXVFbt379bFEImIiIiMSo/51y0RERERaYeFHhEREZGJYqFHREREZKJY6BERERGZKBZ6RERERCaKhR4RERGRieoxy6sYM/WyLtpce04b9fX1qKmpQWVlpVldQoZ5m0/e5pgzwLzNKW9zzBkwz7wNkbO63nhyWbmWsNDTAaVSCQDw8fHp5pEQERGRuVAqlXB2dm4zRiY6Ug5Sm5qamnD79m04OjpCJpPpvP/Kykr4+Pjg5s2bcHJy0nn/xop5m0/e5pgzwLzNKW9zzBkwz7wNkbMQAkqlEt7e3rCwaPtTeDyipwMWFhbo27ev3r+Pk5OT2bxQHse8zYc55gwwb3NijjkD5pm3vnNu70ieGk/GICIiIjJRLPSIiIiITBQLvR7A1tYWKSkpsLW17e6hGBTzNp+8zTFngHmbU97mmDNgnnkbW848GYOIiIjIRPGIHhEREZGJYqFHREREZKJY6BERERGZKBZ6RERERCaKhV432bRpE55++mnI5XKEhITgzJkzbcb//e9/x9ChQyGXy+Hv74+DBw9qPC+EQHJyMry8vGBnZweFQoHLly/rM4VO0SbvrVu3YuzYsejduzd69+4NhULRLH7u3LmQyWQat8jISH2noRVtck5LS2uWj1wu14gxxbkeP358s7xlMhkmTZokxRj7XJ88eRKTJ0+Gt7c3ZDIZ9u/f3+42OTk5eOaZZ2Bra4uBAwciLS2tWYy27xWGpm3ee/fuRXh4ONzc3ODk5ITQ0FAcPnxYI2b58uXN5nro0KF6zEI72uack5PT4u93WVmZRpypzXVLr1mZTIYRI0ZIMcY+16tWrcJzzz0HR0dHuLu7IyoqCsXFxe1uZ0z7bBZ63eCLL77AokWLkJKSgvPnzyMwMBAREREoLy9vMf7f//43Zs6cidjYWOTn5yMqKgpRUVEoKiqSYtauXYsNGzZgy5YtOH36NBwcHBAREYGHDx8aKq12aZt3Tk4OZs6ciePHjyMvLw8+Pj6YMGECfvzxR424yMhI/PTTT9ItPT3dEOl0iLY5A6rV1B/P58aNGxrPm+Jc7927VyPnoqIiWFpaYtq0aRpxxjzX1dXVCAwMxKZNmzoUX1JSgkmTJuGll15CQUEBkpKSEBcXp1H0dOb3x9C0zfvkyZMIDw/HwYMHce7cObz00kuYPHky8vPzNeJGjBihMdf/+te/9DH8TtE2Z7Xi4mKNnNzd3aXnTHGuP/zwQ418b968CVdX12ava2Oe6xMnTiAhIQGnTp1CVlYW6uvrMWHCBFRXV7e6jdHtswUZXHBwsEhISJAeNzY2Cm9vb7Fq1aoW46dPny4mTZqk0RYSEiJeffVVIYQQTU1NwtPTU7z33nvS8xUVFcLW1lakp6frIYPO0TbvJzU0NAhHR0exc+dOqS0mJkZMmTJF10PVGW1z3rFjh3B2dm61P3OZ6/Xr1wtHR0dRVVUltRn7XD8OgNi3b1+bMUuXLhUjRozQaJsxY4aIiIiQHnf152hoHcm7JcOHDxepqanS45SUFBEYGKi7gelRR3I+fvy4ACDu3bvXaow5zPW+ffuETCYT169fl9p60lwLIUR5ebkAIE6cONFqjLHts3lEz8Dq6upw7tw5KBQKqc3CwgIKhQJ5eXktbpOXl6cRDwARERFSfElJCcrKyjRinJ2dERIS0mqfhtaZvJ9UU1OD+vp6uLq6arTn5OTA3d0dQ4YMwYIFC/DLL7/odOyd1dmcq6qq4OvrCx8fH0yZMgUXLlyQnjOXud6+fTuio6Ph4OCg0W6sc90Z7b2udfFz7AmampqgVCqbva4vX74Mb29v9O/fH7NmzUJpaWk3jVB3Ro4cCS8vL4SHhyM3N1dqN5e53r59OxQKBXx9fTXae9Jc379/HwCa/b4+ztj22Sz0DOznn39GY2MjPDw8NNo9PDyafV5DraysrM149Vdt+jS0zuT9pD/+8Y/w9vbWeHFERkbis88+Q3Z2NtasWYMTJ05g4sSJaGxs1On4O6MzOQ8ZMgSffvopDhw4gM8//xxNTU0YPXo0bt26BcA85vrMmTMoKipCXFycRrsxz3VntPa6rqysxIMHD3TymukJ1q1bh6qqKkyfPl1qCwkJQVpaGg4dOoTNmzejpKQEY8eOhVKp7MaRdp6Xlxe2bNmCPXv2YM+ePfDx8cH48eNx/vx5ALp5fzR2t2/fxjfffNPsdd2T5rqpqQlJSUl44YUX8Otf/7rVOGPbZ1vpvEciPVi9ejUyMjKQk5OjcXJCdHS0dN/f3x8BAQEYMGAAcnJyEBYW1h1D7ZLQ0FCEhoZKj0ePHo1hw4bhk08+wcqVK7txZIazfft2+Pv7Izg4WKPd1OaagN27dyM1NRUHDhzQ+LzaxIkTpfsBAQEICQmBr68vMjMzERsb2x1D7ZIhQ4ZgyJAh0uPRo0fj6tWrWL9+Pf72t79148gMZ+fOnXBxcUFUVJRGe0+a64SEBBQVFRnVZwg7gkf0DKxPnz6wtLTEnTt3NNrv3LkDT0/PFrfx9PRsM179VZs+Da0zeautW7cOq1evxpEjRxAQENBmbP/+/dGnTx9cuXKly2Puqq7krGZtbY1Ro0ZJ+Zj6XFdXVyMjI6NDb/DGNNed0drr2snJCXZ2djr5/TFmGRkZiIuLQ2ZmZrN/cz3JxcUFgwcP7rFz3ZLg4GApH1OfayEEPv30U8yZMwc2NjZtxhrrXCcmJuKrr77C8ePH0bdv3zZjjW2fzULPwGxsbPDss88iOztbamtqakJ2drbGkZzHhYaGasQDQFZWlhTv5+cHT09PjZjKykqcPn261T4NrTN5A6ozk1auXIlDhw4hKCio3e9z69Yt/PLLL/Dy8tLJuLuiszk/rrGxEYWFhVI+pjzXgGpJgtraWsyePbvd72NMc90Z7b2udfH7Y6zS09Mxb948pKenayyh05qqqipcvXq1x851SwoKCqR8THmuAdWZq1euXOnQH3DGNtdCCCQmJmLfvn04duwY/Pz82t3G6PbZOj+9g9qVkZEhbG1tRVpamrh48aKYP3++cHFxEWVlZUIIIebMmSOWLVsmxefm5gorKyuxbt06cenSJZGSkiKsra1FYWGhFLN69Wrh4uIiDhw4IL7//nsxZcoU4efnJx48eGDw/Fqjbd6rV68WNjY24h//+If46aefpJtSqRRCCKFUKsUf/vAHkZeXJ0pKSsTRo0fFM888IwYNGiQePnzYLTk+SducU1NTxeHDh8XVq1fFuXPnRHR0tJDL5eLChQtSjCnOtdqYMWPEjBkzmrX3hLlWKpUiPz9f5OfnCwDi/fffF/n5+eLGjRtCCCGWLVsm5syZI8Vfu3ZN2NvbiyVLlohLly6JTZs2CUtLS3Ho0CEppr2fozHQNu9du3YJKysrsWnTJo3XdUVFhRSzePFikZOTI0pKSkRubq5QKBSiT58+ory83OD5tUTbnNevXy/2798vLl++LAoLC8Wbb74pLCwsxNGjR6UYU5xrtdmzZ4uQkJAW+zT2uV6wYIFwdnYWOTk5Gr+vNTU1Uoyx77NZ6HWTjRs3in79+gkbGxsRHBwsTp06JT03btw4ERMToxGfmZkpBg8eLGxsbMSIESPE119/rfF8U1OT+POf/yw8PDyEra2tCAsLE8XFxYZIRSva5O3r6ysANLulpKQIIYSoqakREyZMEG5ubsLa2lr4+vqK+Ph4o3pjFEK7nJOSkqRYDw8P8Zvf/EacP39eoz9TnGshhPjvf/8rAIgjR44066snzLV6CY0nb+o8Y2JixLhx45ptM3LkSGFjYyP69+8vduzY0azftn6OxkDbvMeNG9dmvBCqZWa8vLyEjY2NeOqpp8SMGTPElStXDJtYG7TNec2aNWLAgAFCLpcLV1dXMX78eHHs2LFm/ZraXAuhWjbEzs5O/PWvf22xT2Of65byBaDxWjX2fbbs/xMhIiIiIhPDz+gRERERmSgWekREREQmioUeERERkYlioUdERERkoljoEREREZkoFnpEREREJoqFHhEREZGJYqFHREREZKJY6BERmYDi4mJ4enpCqVS2G3vx4kX07dsX1dXVBhgZEXUnFnpEZJZkMlmbt+XLl3f3ELXy9ttv44033oCjo2O7scOHD8fzzz+P999/3wAjI6LuxEugEZFZKisrk+5/8cUXSE5ORnFxsdTWq1cv9OrVqzuGprXS0lIMHDgQJSUleOqppzq0zddff434+HiUlpbCyspKzyMkou7CI3pEZJY8PT2lm7OzM2QymUZbRkYGhg0bBrlcjqFDh+Ljjz+Wtr1+/TpkMhkyMzMxduxY2NnZ4bnnnsMPP/yAs2fPIigoCL169cLEiRPxv//9T9pu7ty5iIqKQmpqKtzc3ODk5ITXXnsNdXV1UkxtbS0WLlwId3d3yOVyjBkzBmfPnm0zl8zMTAQGBmoUeTdu3MDkyZPRu3dvODg4YMSIETh48KD0fHh4OO7evYsTJ07o4sdJREaKf8YRET1h165dSE5OxkcffYRRo0YhPz8f8fHxcHBwQExMjBSXkpKCDz74AP369cPvf/97/Pa3v4WjoyM+/PBD2NvbY/r06UhOTsbmzZulbbKzsyGXy5GTk4Pr169j3rx5+NWvfoV3330XALB06VLs2bMHO3fuhK+vL9auXYuIiAhcuXIFrq6uLY7322+/RVBQkEZbQkIC6urqcPLkSTg4OODixYsaRyhtbGwwcuRIfPvttwgLC9Plj4+IjIkgIjJzO3bsEM7OztLjAQMGiN27d2vErFy5UoSGhgohhCgpKREAxLZt26Tn09PTBQCRnZ0tta1atUoMGTJEehwTEyNcXV1FdXW11LZ582bRq1cv0djYKKqqqoS1tbXYtWuX9HxdXZ3w9vYWa9eubXX8gYGBYsWKFRpt/v7+Yvny5W3m/corr4i5c+e2GUNEPRuP6BERPaa6uhpXr15FbGws4uPjpfaGhgY4OztrxAYEBEj3PTw8AAD+/v4abeXl5RrbBAYGwt7eXnocGhqKqqoq3Lx5E/fv30d9fT1eeOEF6Xlra2sEBwfj0qVLrY75wYMHkMvlGm0LFy7EggULcOTIESgUCkydOlVjvABgZ2eHmpqaVvslop6Pn9EjInpMVVUVAGDr1q0oKCiQbkVFRTh16pRGrLW1tXRfJpO12NbU1KT3Mffp0wf37t3TaIuLi8O1a9cwZ84cFBYWIigoCBs3btSIuXv3Ltzc3PQ+PiLqPiz0iIge4+HhAW9vb1y7dg0DBw7UuPn5+XW5///85z948OCB9PjUqVPo1asXfHx8MGDAANjY2CA3N1d6vr6+HmfPnsXw4cNb7XPUqFG4ePFis3YfHx+89tpr2Lt3LxYvXoytW7dqPF9UVIRRo0Z1OSciMl781y0R0RNSU1OxcOFCODs7IzIyErW1tfjuu+9w7949LFq0qEt919XVITY2Fu+88w6uX7+OlJQUJCYmwsLCAg4ODliwYAGWLFkCV1dX9OvXD2vXrkVNTQ1iY2Nb7TMiIgJxcXFobGyEpaUlACApKQkTJ07E4MGDce/ePRw/fhzDhg2Ttrl+/Tp+/PFHKBSKLuVDRMaNhR4R0RPi4uJgb2+P9957D0uWLIGDgwP8/f2RlJTU5b7DwsIwaNAgvPjii6itrcXMmTM1FmdevXo1mpqaMGfOHCiVSgQFBeHw4cPo3bt3q31OnDgRVlZWOHr0KCIiIgAAjY2NSEhIwK1bt+Dk5ITIyEisX79e2iY9PR0TJkyAr69vl3MiIuPFBZOJiAxk7ty5qKiowP79+3Xe96ZNm/Dll1/i8OHD7cbW1dVh0KBB2L17t8aJH0RkenhEj4jIBLz66quoqKiAUqls9zJopaWl+NOf/sQij8gM8IgeEZGB6POIHhFRS1joEREREZkoLq9CREREZKJY6BERERGZKBZ6RERERCaKhR4RERGRiWKhR0RERGSiWOgRERERmSgWekREREQmioUeERERkYn6P80b/IC1B6XmAAAAAElFTkSuQmCC", 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Parámetros\n", "z = np.linspace(0, 2, 1000)\n", "beta = 2 * np.pi # constante de propagación (1 λ = 1 unidad de z)\n", "A_i = 1 # amplitud incidente\n", "A_r = 0.8 # amplitud reflejada menor\n", "phi = 1230 / np.pi\n", "\n", "# Onda triangular aproximada por serie de Fourier\n", "def triangular_wave(z, beta, phi=0):\n", " wave = np.zeros_like(z)\n", " for n in range(1, 100, 2): # solo términos impares\n", " wave += ((-1)**((n-1)//2) / n**2) * np.sin(n * (beta*z + phi))\n", " return (8/np.pi**2) * wave\n", "\n", "# Ondas\n", "Vi = A_i * triangular_wave(z, beta, phi=0) # incidente\n", "Vr = A_r * triangular_wave(-z, -beta, phi=phi) # reflejada (viaja al revés, con desfase)\n", "Vtotal = Vi + Vr\n", "\n", "# Gráfico 1: incidente y reflejada\n", "plt.figure(figsize=(7,4))\n", "plt.plot(z, Vi, label='Incidente', color='blue')\n", "plt.plot(z, Vr, label='Reflejada', color='red')\n", "plt.title('Ondas triangulares incidente e reflexada')\n", "plt.xlabel('Tempo (s)')\n", "plt.ylabel('Amplitude')\n", "plt.grid(True)\n", "\n", "# Gráfico 2: suma total\n", "plt.figure(figsize=(7,4))\n", "plt.plot(z, Vtotal, color='black')\n", "plt.title('Suma de ambas ondas triangulares')\n", "plt.xlabel('Tempo (s)')\n", "plt.ylabel('Amplitude')\n", "plt.grid(True)\n", "\n", "print(\"Ondas triangulares incidente e reflexada (con desfase e onda atenuada) e Suma de ambas ondas\")\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "0eff9518", "metadata": {}, "source": [ "### Teoría de circuitos y su validez" ] }, { "cell_type": "markdown", "id": "a7744324", "metadata": {}, "source": [ "Antes de pasar al siguiente punto, es importante hablar brevemente sobre la teoría de circuitos para calibrar nuestra intuición en el caso de la señal sinusoidal.\n", "\n", "Se dice que la teoría de circuitos es válida cuando $\\lambda >> l$ al considerarse la longitud de onda tan grande respecto al circuito a evaluar que (en régimen de posición $k·z=\\frac{2\\pi}{\\lambda}·z$) el término espacial sea común (por ser altamente similar de forma aproximada) y por tanto lo podamos considerar constante a todo punto en las ecuaciones.\n", "\n", "Ahora bien, en estos casos prácticos, la validez de la teoría de circuitos se reduce a que el desfase $\\phi$ sea pequeño y por tanto se pueda caracterizar la naturaleza de la línea como una Capacitancia o Inductancia característica.\n", "\n", "Vemos que el desfase se ve como $\\phi=\\omega·\\tau \\Rightarrow \\phi\\propto f·\\tau$, por tanto a una frecuencia baja se consideraría válida la teoría de circuitos (tiene sentido porque para frecuencia pequeña, su longitud de onda es grande) y para una frecuencia alta no y habría que considerar los términos de posición; el desfase ya no puede anularse en este caso.\n", "\n", "Con esto tenido en cuenta, podemos explicar porque en los siguientes apartados, ocurren máximos y mínimos de amplitud de onda de nuestra señal suma (suma de onda sinusoidal incidente con una reflejada con desfase y menor amplitud), ya que con la frecuencia varía el desfase. Como punto aparte, la ya mencionada barrera de cuando podemos considerar válida o no la teoría de circuitos." ] }, { "cell_type": "markdown", "id": "b8a48ff3", "metadata": {}, "source": [ "### Reflexión sin inversión en señal sinusoidal" ] }, { "cell_type": "markdown", "id": "75ffbe3f", "metadata": {}, "source": [ "- 5:\n", "\n", "Lo mismo que antes y que todo el rato pero ahora con una señal sinusoidal, este primer caso es sin inversión, así que, para nuestra sorpresa, para que esto suceda dejaremos el términal final de la línea de transmisión abierto para que así $Z_L \\rightarrow \\infty \\Rightarrow \\Gamma =1$\n", "\n", "Este caso, con señal sinusoidal es especial ya que según el desfase, la onda final puede intentar anularse o potenciarse (no podrá anularse del todo debido a que en amplitud $V_{in}\\neq V_r$). Y ocurre que el desfase es proporcional al producto del retardo con la frecuencia (mencionado anteriormente), por lo que es lógico que ambas ondas tengan formas de juntarse distintas según la frecuencia, a veces teniendo una relación más destructiva o constructiva.\n", "\n", "Podemos observar un gráfico como ejemplo de las dos ondas, con un desfase no muy grande y su onda resultado potenciada (no máximo pero bastante potenciada):" ] }, { "cell_type": "code", "execution_count": 501, "id": "ce82e024", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ondas triangulares incidente e reflexada (con desfase e onda atenuada) e Suma de ambas ondas\n" ] }, { "data": { "image/png": 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4NGnSBD/++GOeClt+j9OGIkWKZCtrdtY3uVyOli1bomXLlli4cCFmzZqF77//HqdOnYKvry9KlSqFGzduQKVSZXoZ89NIpUqVylYGFxcX2NjYZDs9/el3fujQISQnJ+PgwYOZLJbaTs/Z2dmhe/fu6N69O1JSUtC5c2fMnDkTEydONEgKFU9PTxw/fhxv377VyapXqlQprb4P3urs6uqqtjYVlJyUfz5NkVKpzNe5PD09ceLECTRq1Cjb4IL8Yoh2PT09ER8fr1U/IyMj8c0338Df3x+WlpZqRTjj713b54S21/306dN48+YN9u3bhyZNmqi3P378WNsuMiQGm7plmB1jx46FjY0Nhg4dijdv3mT639u3b/H111/D1tYWY8eO1bntVq1aAQCWLFmSafvixYuz7KtQKLJYApcuXZrFIvapjPb29ihXrpzaCpAT+T1OWzw9PfHu3btM1tHIyEjs378/035v377NcixfPoqXpXXr1oiKisLOnTvV+6SlpWHp0qWwt7dH06ZNs5VBoVAgICAABw4cwLNnz9Tb79y5g+PHj2fZF8hslXz37h02bNiQZ18//S4tLS1RuXJlcByH1NTUPI/PD126dAHHcdlaDHOzILdu3Rp//PEHLl++rN726tWrLFargIAAODg4YNasWdn24dWrVzrLzEeAfqqEuLq6olmzZli1ahUiIyN1Ple3bt2Qnp6OGTNmZPlfWlpatgMObTBEu926dUN4eHiW3x9A30taWpr67yFDhkClUmHdunVYvXo1LCwsMGjQoEzXV9vnhLbXPbv7ICUlBcuXL9e5rwxpwCx6DLOjfPny2LRpE3r37o1q1aplqYzx+vVrbN++PYufnTbUrFkTPXv2xPLly/Hu3Ts0bNgQYWFhePjwYZZ927Zti82bN6Nw4cKoXLkywsPDceLEiSzpSypXroxmzZqhTp06cHJywpUrV7Bnzx6MHDkyV1nye5y29OjRA+PHj0enTp0watQodWqOChUqZHISnz59Os6ePYs2bdqgVKlSiImJwfLly1GyZEm1RfWrr77CqlWrMGDAAFy9ehWlS5fGnj17cOHCBSxevDjX4Jlp06YhODgYjRs3xvDhw9UKYpUqVTIpobzVpF27dhg6dCji4+OxZs0auLq6Zqt8ZMTf3x/u7u5o1KgR3NzccOfOHfz6669o06ZNnoE9+aV58+bo27cvlixZggcPHiAwMBAqlQrnzp1D8+bNc7yO48aNw+bNmxEYGIjRo0er02zwVlMeBwcHrFixAn379kXt2rXRo0cPuLi44NmzZzhy5AgaNWqEX3/9VSeZbWxsULlyZezcuRMVKlSAk5MTqlatiqpVq2LZsmXw8fFBtWrVMGTIEJQtWxbR0dEIDw/Hv//+i7/++ivHdps2bYqhQ4di9uzZiIiIgL+/P5RKJR48eIDdu3fjl19+QdeuXXWS1VDtjh07FgcPHkTbtm0xYMAA1KlTBwkJCbh58yb27NmDJ0+ewNnZGRs2bMCRI0ewceNGte/g0qVL0adPH6xYsQLDhw8HoP1zQtvr3rBhQxQpUgT9+/fHqFGjIJPJsHnz5ny5nzAkgjDBvgyG8Ny4cYPr2bMnV6xYMU6pVHLu7u5cz549uZs3b2bZl08Z8mmKkA0bNmRJXZCUlMSNGjWKK1q0KGdnZ8e1a9eOe/78eZa0E//99x83cOBAztnZmbO3t+cCAgK4u3fvZknX8NNPP3H16tXjHB0dORsbG87Ly4ubOXMml5KSkmv/tDkup/QqI0aMyNLep3JxHMeFhIRwVatW5SwtLbmKFStyW7ZsydJmWFgY16FDB6548eKcpaUlV7x4ca5nz55Z0k9ER0ervw9LS0uuWrVqmdKSZJTv0/QdZ86c4erUqcNZWlpyZcuW5VauXJlt3w4ePMhVr16ds7a25kqXLs3NnTuXW79+fZZr+Gl6lVWrVnFNmjThihYtyllZWXGenp7c2LFjuXfv3mWRLyM5pVfJLk1HdvKmpaVx8+bN47y8vDhLS0vOxcWFa9WqFXf16lX1Ptldlxs3bnBNmzblrK2tuRIlSnAzZszg1q1bl6WfHMdxp06d4gICArjChQtz1tbWnKenJzdgwADuypUr+ZL54sWL6mvx6bV69OgR169fP87d3Z1TKpVciRIluLZt23J79uzJ6SvMxOrVq7k6depwNjY2XKFChbhq1apx48aN416+fJnrcTnJr0u72qZX4ThKXTNx4kSuXLlynKWlJefs7Mw1bNiQmz9/PpeSksI9f/6cK1y4MNeuXbss7XXq1Imzs7Pj/vnnH47jtH9OcJz21/3ChQtc/fr1ORsbG6548eLcuHHjuOPHj3MAuFOnTuX6XTKkh4zjmBrPYDAYDAaDYYowHz0Gg8FgMBgME4UpegwGg8FgMBgmClP0GAwGg8FgMEwUpugxGAwGg8FgmChM0WMwGAwGg8EwUZiix2AwGAwGg2GisITJekClUuHly5coVKhQjiWAGAwGg8FgMPQBx3F4//49ihcvnqWG86cwRU8PvHz5Eh4eHkKLwWAwGAwGw4x4/vy5urJKTjBFTw/wJZCeP38OBwcHvbefmpqKkJAQdXkec4H123z6bY59Bli/zanf5thnwDz7bYw+x8XFwcPDQ6sSjEzR0wP8dK2Dg4PBFD1bW1s4ODiYzY0CsH6bU7/Nsc8A67c59dsc+wyYZ7+N2Wdt3MVYMAaDwWAwGAyGicIUPQaDwWAwGAwThSl6DAaDwWAwGCYKU/QYDAaDwWAwTBSm6DEYDAaDwWCYKEzRYzAYDAaDwTBRmKLHYDAYDAaDYaJIStE7e/Ys2rVrh+LFi0Mmk+HAgQN5HnP69GnUrl0bVlZWKFeuHDZu3Jhln2XLlqF06dKwtraGt7c3Ll++rH/hGQwGg8FgMIyMpBS9hIQE1KhRA8uWLdNq/8ePH6NNmzZo3rw5IiIiMGbMGAwePBjHjx9X77Nz504EBQVh6tSpuHbtGmrUqIGAgADExMQYqhsMBoPBYDAYRkFSlTFatWqFVq1aab3/ypUrUaZMGSxYsAAAUKlSJZw/fx6LFi1CQEAAAGDhwoUYMmQIBg4cqD7myJEjWL9+PSZMmKD/TjDy5N074O5dIDpahitX3ODuDlSuDGhR6YXBYIgUjgOePgUePQLevZPh1i1nlCsHVKoE5FGTncFgFABJKXq6Eh4eDl9f30zbAgICMGbMGABASkoKrl69iokTJ6r/L5fL4evri/Dw8BzbTU5ORnJysvrvuLg4AFT2JDU1VY89gLrdjEtTJDoaWL9ejgMH5Lh+nS/pYgGgPn76CVAoONSuzaFbNw59+qhQtKiQ0hoWc7jen2KOfQZMv98cB1y4IMPGjXIcPSrD69cZ7+1GmDoVcHTk4O9P97W/P2eySp+pX+ucMMd+G6PPurRt0opeVFQU3NzcMm1zc3NDXFwckpKS8N9//yE9PT3bfe7evZtju7Nnz8a0adOybA8JCYGtra1+hM+G0NBQg7UtFAkJFti1qyKOHCmDtDSFeruTUxKKFCFl+s0ba8TGWuPPP2X480/ghx84tGv3CJ07P4SNTZpQohscU7zeeWGOfQZMs9/37xfB2rVVcf++k3qbhYUK7u4JsLFJw4cPCkRH2yI21gK7dsmwa5ccpUq9Q9++d1C3brSAkhsWU7zW2mCO/TZknxMTE7Xe16QVPUMxceJEBAUFqf+Oi4uDh4cH/P394eDgoPfzpaamIjQ0FH5+fiZVFPrQIRnGjFHg1Ssa5Xt7qzBokAqtWnFwc7NAaiqn7ndUVCqOHpVjzRo5btywwO7dFXHpUgWsXJkOX19O4J7oF1O93rlhjn0GTLPfiYnA//4nx9q1NHCztubQqxeHHj1UaNiQg6WltbrfzZr54eZNYOdOGTZtkuPp08L46af66NxZheXL0+HklMfJJIQpXmttMMd+G6PP/EyiNpi0oufu7o7o6Mwjw+joaDg4OMDGxgYKhQIKhSLbfdzd3XNs18rKClZWVlm2K5VKg/6QDd2+sUhNBSZMABYupL+9vIBFi4DAQDmyiw9SKpUoW1aJkSOBESOA338HgoKAx49laNPGAjNnUnsyWZZDJY2pXG9dMMc+A6bT7wcPgC5dgJs36e/+/YE5c2Rwd5chu3vb1lYJHx8L+PgA06YBs2fTs2DfPjmuX5dj716gVi3j9sHQmMq11hVz7Lch+6xLuybqDUE0aNAAYWFhmbaFhoaiQYMGAABLS0vUqVMn0z4qlQphYWHqfRj6JSkJ6NxZo+QFBQF//QUEBmp3vEwGdOxIL5IhQ8gHaNIkoHdvUiAZDIYwhIcD9erRvenmBpw8CWzcCOQyZs6EkxMwbx5w+TJQtizw+DHQsCFw5IhBxWYwTB5JKXrx8fGIiIhAREQEAEqfEhERgWfPngGgKdV+/fqp9//666/xzz//YNy4cbh79y6WL1+OXbt24dtvv1XvExQUhDVr1mDTpk24c+cOhg0bhoSEBHUULkN/xMUBAQHA4cOAtTWwbx+wYAFgaal7W3Z2wOrVwMqVgFIJbN8OdO8OpKToX24Gg5E7J08Cfn5AbCxQvz5w7RrQvHn+2qpdG7hyBWjVCvjwAejUCdi7V6/iMhhmhaQUvStXrqBWrVqo9dGWHxQUhFq1amHKlCkAgMjISLXSBwBlypTBkSNHEBoaiho1amDBggVYu3atOrUKAHTv3h3z58/HlClTULNmTURERCA4ODhLgAajYCQn0wP73DmgcGEgNJT+LihDh9JUrpUVsH8/0K0bkGa68RkMhugIDwfatAESEkjZO3ECKF68YG0WKUL3dY8eZKnv1g04dEg/8jIY5oakfPSaNWsGjsvZ8T67qhfNmjXD9evXc2135MiRGDlyZEHFY+RAejrQpw+N+u3t6UVQt67+2m/Vil4C7dvTy2H0aODXX03PZ4/BEBt37wJt25LlrVUrGmxl476cL5RKYMsWsvj/9htZ7E+dAry99dM+g2EuSMqix5AmU6YAe/bQA/vAAf0qeTx+fvRSkMmA5cvJoZvBYBiON29IuXv7lnzzdu/Wn5LHo1AAa9eSD29SEimVT57o9xwMhqnDFD2GQdm/H5g1i9bXrwdatjTcubp0AebPp/WxY4EzZwx3LgbDnFGpyEr/5AkFThw+TH6zhkCpJCWydm3g9Wvgiy/IFYTBYGgHU/QYBuPBA0qvAADffkuRsYbm22/pnCoV+fdERRn+nAyGuTFjBhAcDNjYUFCVi4thz2dvT+dxcqJAjY/FjRgMhhYwRY9hENLSgH79gPfvgaZNgZ9/Ns55ZTJg2TKgShVS8vr2JaWPwWDohwsXgOnTaX3lSqBGDeOct1QpYOtWusdXriTFj8Fg5A1T9BgG4eefgT/+oAjbzZsBCyOG/djZ0VSPjQ0FfqxcabxzMximTHw8DeBUKlpmyGZlFAIDgfHjaX3oUCAmxrjnZzCkCFP0GHrn+nVg6lRa//VXwMPD+DJUqgTMnUvrY8cC//xjfBkYDFPjf/+je8nDA1iyRBgZfvwRqF6d/PWGDqWk6QwGI2eYosfQK2lpwMCBtOzSxTh+eTkxYgRNGycmAoMHsxcCg1EQzp8HVq2i9Y0byVovBFZWlG5FqaQofpZMmcHIHaboMfTKsmVU0szJCVixQthcdnI5Rfra2FD+rR07hJOFwZAyqanAsGG0Pngw0KKFsPLUqAFMnEjrY8aQLzCDwcgepugx9EZkJOXMA6g4uaEj8bShbFng++9p/bvvqAwbg8HQjSVLgFu3gKJFgTlzhJaGmDCB7u8XLzTBIQwGIytM0WPojbFjSZH6/HNg0CChpdHwv/8B5cqRIjptmtDSMBjS4t9/NT63P/9Myp4YsLEBli6l9cWLSRFlMBhZYYoeQy+cP69JfbB8OWW0FwtWVpoXwi+/AH//Law8DIaU+P57qmPbsCEwYIDQ0mSmdWuqmZ2WRjk0GQxGVpiixygwHAeMG0frgwcbpsRZQQkMBDp0oLq7vG8Pg8HInYgISo8E0CBJLsI3xsKFVF7xxAkgJERoaRgM8SHC25YhNX7/HQgPB2xtxT01OmcOvagOHqSkrwwGI3fGj6eBXI8e4hzAAUDp0hRhD9CAkyVIZzAywxQ9RoFIS9NYyL79FihWTFh5csPLS+M7yL/AGAxG9oSGkoVMqQRmzhRamtz5/ntK9/LXX+RCwmAwNDBFj1EgNm4E7t4lB+2xY4WWJm+mTiUn7gsXqBA7g8HICsdRVCsADB9O0a1ipmhRjbyTJwPJycLKw2CICaboMfJNSoomrQE/ohY7JUoAo0fT+vffs2keBiM7Dh0Crl0D7O1JcZICo0cDxYsDz54BGzYILQ2DIR6YosfIN5s2Ac+f03Qtn0xVCowbBzg4ADdvkr8eg8HQwHEaX9uRIwFnZ2Hl0RYbG41Vb/ZsGogyGAym6DHySWoqPUwBUpysrYWVRxeKFKEXGADMmMF89RiMjBw5QtY8OztKMi4lBg8G3N3Jqvfbb0JLw2CIA6boMfLF1q3A48eAqyvw1VdCS6M7335LL7Jr14Bjx4SWhsEQBxmtecOHS8eax2Njo0n1NGsWDUgZDHOHKXqmCseRJnbmDA3Rg4MpJE0PNcDS0+khClDVCVvbAjdpdJydNdPN06czqx5DQiQkkN/BsWP0CQujez09vcBNHz8OXLlCCtP//qcHWQVg6FAagD5+zCJwGQwAsBBaAIYeiYsDDhwA9uwhBS87pU4mo4rg/v6U5r5SJZ1Ps28f8OAB4OQkLd+8T/nuO+DXX4FLl4BTp4Qv1M5g5Mj9+zQXGRxMZujsRia2tvQjbteOEt85OOh8mvnzackrS1LE1paU1HHjgHnzgP796bHHYJgrzKJnCrx6RSGkn31GT7VDh0jJs7QEKlSgTKc1agAuLvSCiIigopWVKwNNm5JSqAMLF9Jy5EiKypMq7u7Al1/S+qJFwsrCYGTLxYtAQABQsSIls7t6le5hJye6p2vXpgSRlpZAYiLlDBo6FChZkvwTYmK0PlVEBBkHFQpgzBiD9cgofPUVUKgQlTsMDhZaGgZDWJiiJ2VUKmDVKlLmZs0C3r0DypenZHFXrwLv3wP37gF//klP8ZgY4OVLYPt2qgemUABnzwLNmgFt21IIbR5cvAj88QfVjx0+3OA9NDijR9No//Bh+qoYDFHw4gXQvTvQqBFlLZbJ6B7dtInu4Tdv6J6+ehW4c4eUvOvXSRn08qJ7f/Fieh4sXEiZzfNgwQJafvEFUKqUQXtncAoXBoYMoXW+XwyGucIUPany8iXQvDnw9ddAbCxQsyawfz9lL/7xRxrpW1pmPa5YMZrWOXAAePqU5l4tLMiPr2pVyoCcC7w1r08fwM1Nv10SggoVaKYLoPcigyE4u3cD1aoBu3ZRzb7Bg4FHj8hS369f9uVnFAp6BkyaBNy+Tb57deqQZf+778hy/+RJjqf8919gxw5al1qkbU6MGkVfS1gY6cQMhrkiOUVv2bJlKF26NKytreHt7Y3Lly/nuG+zZs0gk8myfNq0aaPeZ8CAAVn+HxgYaIyu5J+wMHqonz1Lc6dLlpAHdceOulUdL1ECWL6cHLvr16eXwsCBpDxmk4Tqn39IlwRoVshUCAqi5aZNwOvXwsrCMGPS0sjE3K0b8N9/pKhdvw6sWQOUKaN9O3I5EBgIXL5Mxzo4kCm+Ro0cQ8yXLKHTN20q3pq2ulKqFNC1K63zA1QGwxyRlKK3c+dOBAUFYerUqbh27Rpq1KiBgIAAxOTgh7Jv3z5ERkaqP7du3YJCocAXX3yRab/AwMBM+23fvt0Y3ckfmzfTQ/zVK1L2rl0DvvmGhq75xcsLOH8e+OknmiJatQrw9SVLYQYWL6bZ4sBAoEqVgnRCXDRpQgbQpCTqOoNhdN6/J9PykiX096RJQHg4UL16/tvkrYEREUDDhjSQa9sWWLEi025xcZrfvalY83j4/mzfTrPhDIY5IilFb+HChRgyZAgGDhyIypUrY+XKlbC1tcX69euz3d/JyQnu7u7qT2hoKGxtbbMoelZWVpn2K1KkiDG6ozPyRYto6iYtDejZk14E5cvrp3GFggI6jhwhB5dz52hq+NUrAKTz8V+zqb0MZDKNVe/XX1mdTIZxsYiPh6J1a4oasLGhqPmZMwGlUj8nKFOGwsoHDqSR2vDhlCn8I7/9RspexYpAhskOk+Dzz4HGjemRuWyZ0NIwGMIgmfQqKSkpuHr1KiZOnKjeJpfL4evri/DwcK3aWLduHXr06AE7O7tM20+fPg1XV1cUKVIELVq0wE8//YSiRYvm2E5ycjKSM2gDcR/TmKSmpiLVABk6U1NTUfbgQSg+alrp334L1ezZNGLX9/l8fYETJ2DRpg1kERHgmjZFWlgY1m93RUKCAlWqcGjSJM0oiUj579IQ3+mndOoElChhgRcvZNi5Mw09ewqXWM+Y/RYL5thnAEiNiUHDH3+E/OFDcE5OSD98GFzduvq/r2UyYOVKyD08oJg+HZgyBelpaUj/fjKWLbMAIMOwYelIT1fpIx1fnhjzeo8cKcO5cxZYt47D99+nZeu6bAzM9jduhv1OTU2FIinJoH3WpW0Zx0kjVezLly9RokQJXLx4EQ0aNFBvHzduHM6cOYNLly7levzly5fh7e2NS5cuoV69eurtO3bsgK2tLcqUKYNHjx5h0qRJsLe3R3h4OBQ5TIf++OOPmManj8/Atm3bYGuA7MGljx5FjdWrAQB3u3fHvZ499X6OT7F78QKNpkyBzZs3+K9cOTRIPIN7L4vjq6/+QuvWTwx+fiHYubMCtm+vhMqVX2PWrAtCi8MwceQpKWgwbRqcb99GsoMDLk6bhjhdfPHySbl9+1DlY32wkNbfIODoElhbp2HduuOws8s7OldqpKfLMGSIH96+tcF3311B48ZsDpdhWJxv3EDdefNwNSgIr2rVMsg5EhMT0atXL7x79w4OeeTMNBtFb+jQoQgPD8eNGzdy3e+ff/6Bp6cnTpw4gZYtW2a7T3YWPQ8PD7x+/TrPL1xnkpOhqFcP8jt3kPrdd5RGxVjZP+/cgUXLlpC9fo0TaImutkfx6JksP3lY80VqaipCQ0Ph5+cHpb6msXLh5UvA09MC6ekyXLuWiqpVDX7KbDF2v8WA2fVZpYKid2/I9+5Fqq0t0k6ehEXt2kY7vXzOHCimTIEKMnTFHhQd3AHLl6uMdn5jX+8ZM+SYMUMBHx8VTp40gskyG8zuN/4Rs+v333/DomlTyN69Q1qPHuAMVHQ5Li4Ozs7OWil6kpm6dXZ2hkKhQHR0dKbt0dHRcHd3z/XYhIQE7NixA9OnT8/zPGXLloWzszMePnyYo6JnZWUFKyurLNuVSqX+f8hKJVJDQ/H3Dz+g4qxZUBpz3qF6dSA4GEn1m8E3LQz7y3yHokWXGu/8HzHI95oNpUpResF9+4B165T49VeDnzJXjNVvMWE2ff7f/4C9e8Eplbg8cSLq1a5t3H5PnoyER5Gw27QCW9Ebz31OQqlskPdxesZY13voUBojnz8vx717csEGcYAZ/cY/wSz6HR1N2S/evcObSpXgsHq1wfqsS7uSCcawtLREnTp1EBYWpt6mUqkQFhaWycKXHbt370ZycjL69OmT53n+/fdfvHnzBsWyy1UlFK6ueNShgyB1fCKL10Fv1RYAQPPbv1K6BhOGL+n2229AfLywsjBMlO3b1Vl809etw+tq1Ywvg0yGxWWW4BDawgYfUGF8JyAy0vhyGIkSJWgQBwArVworC8NESUujJOdPnoArVw6XJ04ErK2FlgqAhBQ9AAgKCsKaNWuwadMm3LlzB8OGDUNCQgIGDhwIAOjXr1+mYA2edevWoWPHjlkCLOLj4zF27Fj88ccfePLkCcLCwtChQweUK1cOAQEBRumT2Fm3Dtiv6oDVHh+j9EaMoJxcJkqLFkC5cpTtQsxZdhgS5fZtSnkCAJMmgevRQxAx0tKAlWst0AM78F/JqmSJ6NlTqwoaUoWv5MMGcQyDMHkylRMtVAhp+/cjxVg+TlogKUWve/fumD9/PqZMmYKaNWsiIiICwcHBcPtYouHZs2eI/GRUeu/ePZw/fx6DBg3K0p5CocCNGzfQvn17VKhQAYMGDUKdOnVw7ty5bKdmzY30dOBjDAhsfvqeaiOlpgK9emXJsWcqyOWULxqgdGPS8GBlSIL374HOnalcma8voIUriaE4doyqYdg628H2yB5KvH7mDPDDD4LJZGhatKBKOO/fA1u3Ci0Nw6Q4eBCYO5fW162jXEUiQlKKHgCMHDkST58+RXJyMi5dugRvb2/1/06fPo2Nn5TwqlixIjiOg5+fX5a2bGxscPz4ccTExCAlJQVPnjzB6tWr1YqjuRMSQuVvnZyAL7rJgLVrKSfX06ekDZmoFjRgANXyvX6dSokyGHph9Gjg/n3AwwPYtq1gSc4LCJ8Ts18/wKp6RXo5AcCcOXTjmyAyGfnqAZr+MxgF5sULoH9/Wh89mgwiIkNyih7DeGzYQMvevT+6Gjg4aF5QO3dSzTATpGhRMrwAmu+AwSgQv/9OPyaZjMxJLi6CiRITAxw+TOsfvV6o7Bo/t/nll1SCzQTp25dKe1++TLPoDEaB4Dhg0CCa4apbF/j5Z6Elyham6DGy5e1bejcBGV4GANXE5aecRo0ik58Jwvd52zbgwwdhZWFInJgYYMgQWh87lko1CMiWLeSK9/nnyBx9Om8eVdp58YLubRPExYUqzQFsEMfQA6tWAcePkyVk82YIlo07D5iix8iW7duBlBSqg54l3+P48VQ78/17mgsxwSncFi1ohi02FjhwQGhpGJLm66+plGD16oL65QF0q/IKzpdffvJPW1uKVJDLSRvct8/o8hkDvt+//ab/AiQMM+LRI0qTBACzZ1PNeJHCFD1GtvAvg0zWPB6Fgvz1LC3Jq9sEPZsVCo3bBRv5M/LNgQPA/v00X7h5Mzl/CsjVq8CtW2SAyDbgt359GsgBFGFvgkFXgYGAuzvp3keOCC0NQ5JwHOXiSkgAmjUTvQWcKXqMLNy8SS8EpZL887KlUiVgyhRaHz2apqdMjAEDaBkaarIz1AxD8v49MHIkrY8bRxY9geGDEDp3Bhwdc9hp6lQKT42KAr7/3liiGQ0LCwpCAdggjpFPduygF4OVFeWWlYtblRK3dAxB4B9+bdsCzs657DhuHM3tvn0LTJhgFNmMiacn0KQJDd4MVMWGYcpMnkz+bmXL0rrAJCVpckNma6nnsbLSZBVesYIiF0wMvv9HjpA+y2BoTWws8O23tD55MiVeFTlM0WNkIjWV3HOAPF4GAJn8Vqyg9Q0bgD/+MKhsQsB/Bxs3mqQrIsNQXL8OdQ29FSsAGxth5QHNIsfGAp99Rj6oudK8OYWochz54ZpYImUvL6BBA8oVyj/vGAytmDSJEox7eVFwlQRgih4jE0eOkO+KmxvQqpUWBzRooJnjHDmSnpwmRNeulEv24UMgPFxoaRiSgOOAMWMAlYoc4fz9hZYIAA1WALpdtZppmj8fKFIEiIjQ5NkzIXgfXKboMbTmr78yW7slUliBKXqMTGzeTEs+35RWzJlDOfauXjW5F4K9PdCpE62bYMwJwxDs2wecPUtWPJHk1YqKAk6coPW+fbU8yNUVmDaN1n/4AXj3ziCyCcUXX9CkxF9/sZx6DC3gOCAoiJbdulEQhkRgih5DTWysJgpN65cBQOY/Pm3E5MlAXJy+RRMUPiBl1y6WjoGRB8nJmumcsWMpR48I2LWLDIze3jq6FH39NZVzevWKUkiYEE5OmlkLNohj5Mnhw8DJk2TF48udSQSm6DHU7N9P76nKlYFq1XQ8ePhwitR79YqmfEyIli3JuPH6NQVaMRg58ssvwOPHQPHiFKwkEnhFplcvHQ9UKjX386JF1DcTgh/EbdtGijCDkS0pKZqced9+C5QuLag4usIUPYaabdto2asXVWrSCaUSmDWL1hcsACIj9SqbkFhYAN270zob+TNyJCYG+OknWp89G7CzE1aejzx8SIGzcrnmd6wTbdrQaCclxeSi69u1AwoVovLdFy8KLQ1DtKxcSXWqXV2BiROFlkZnmKLHAEB62cmTtN6zZz4b6dyZEq4mJmp8e0wEfuR/4AAQHy+oKAyxMns25c6rUwfo00doadTwAzhfX/Ky0BmZDFi4kJa7dplUuhUbG01dazaIY2RLXJzGNWnGDPJHlxhM0WMA0Pjw1K9Pab/yhUxG9TIBqpxx967e5BOaevUor15iInDwoNDSMETHv/9qUg3Nni2aBKocp1H0ckx+rg3Vq2uyDP/wQ4HlEhMZfXBTUoSVhSFCFi8G3ryhdCpZ6gZKA3E8jRiCk3HatkD4+ADt21OalUmTCiyXWJDJNN8NG/kzsjBjBjm4Nm1KpjORcO0acO8elTzr2LGAjU2dSn4MISEUVWwitGhBJdHevgWCg4WWhiEq3r4lVySAZqm0TkUhLpiix8jkw9Otmx4anD2bNKP9+ykHl4nAK3rHj1PMCYMBgG4gPq3QzJn5cHA1HPwArn17Pcw4lSkDDB5M65Mnm0wGcYVCU/eXDeIYmZg3j6Zua9SgpKoShSl6DHVZpHz78HxK5coar2/et8EE8PICatcmY+WuXUJLwxANP/5IP4pWrYBGjYSWRk16uubeLrClnuf77ym9xLlzmsR8JgA/fXvwIPPBZXwkOhpYsoTWZ8wQjTtGfpCu5Ay9kNGHR28vA4D8eEzQqpfRn4fBwN9/a24gPuJWJJw7R0FWjo5AYKCeGi1ZklIpASZl1atTh3yTP3zQ5BJlmDlz5pBTdr16VPhdwjBFz8y5dYtiJqysNBUg9IKJWvV46z3/EmWYOXPmkLLTqROZe0XE7t207NRJz5WaJkyg1DGXL5Mfgwkgk1GlDEDzvTHMmOhoTamzn34SlTtGfmCKnpnDP9QCAw0QNW6CVr3PPqPqAhxHla4YZsw//2ised9/L6wsn5CeDuzdS+u8AqM3XF2pYgZAPokmAu+ffPQokJAgrCwMgVm8mMy79euLKrgqvzBFz8zZs4eWBvEzzWjVmzHDACcQBjbyZwCgOrbp6UBAAM39iYgLF8go4ehIuY71TlAQYGkJnD9vMhG4tWrR9G1SEpu+NWtiY4Fly2h90iTJW/MApuiZNbdvA3fu0PO6XTsDnWTyZFru3095HkwAXik+e5aKxTPMkJcvgQ0baF2EaYT4QUjHjnR/653ixTU5xfiKOBKHTd8yAJCS9/49ULUqVYUxAZiiZ8bw1jx/f6BwYQOdpEoV0iI5TpOPSOKUKkX+uRxH+ivDDFmwgLLr+vgATZoILU0mVCoDTttmZNw4yk1y/Dhw5YoBT2Q8+O/ryBE2fWuWJCbStC1Apc4kHGmbEdPoBSNf8KNWg74MAE1x902bTCaCgY38zZjXrzWO2iLzzQOoZmtkJA3eDOpeVKaMJlTfRKx6tWuz6VuzZu1aur/LltVTUllxIDlFb9myZShdujSsra3h7e2Ny7nUXdy4cSNkMlmmj7W1daZ9OI7DlClTUKxYMdjY2MDX1xcPHjwwdDcE584dmrpVKg04bcvj4wM0bEgWED4vkcThp2/PnKFa9gwzYskSGvnXrk3+eSKDH3x06GCgaduMTJigCbgyAdcMNn1rxqSkaEp4jh8v2SoY2SEpRW/nzp0ICgrC1KlTce3aNdSoUQMBAQGIyeVN6+DggMjISPXn6dOnmf7/888/Y8mSJVi5ciUuXboEOzs7BAQE4MOHD4bujqDwUzu+vkCRIkY4IW/VW7GCMo1LnNKlgc8/p2kyFn1rRiQmAsuX0zqv5IgIlUrjkmFwSz1AAVf8SJGf8pI4bPrWTNm+nWpWFysG9O8vtDR6RVKK3sKFCzFkyBAMHDgQlStXxsqVK2Fra4v169fneIxMJoO7u7v645ah9APHcVi8eDEmT56MDh06oHr16vjtt9/w8uVLHDhwwAg9Eg6jTdvytGsHVKoEvHsHrFplpJMaFt6qx0b+ZsTmzVTgvEwZoHNnoaXJQng4xYk4OAB+fkY6aVAQLTdupGkviZNx+vboUaGlYRgFjgMWLqT10aP1nHhSeCRjm0xJScHVq1cxceJE9Ta5XA5fX1+Eh4fneFx8fDxKlSoFlUqF2rVrY9asWahSpQoA4PHjx4iKioJvBkeWwoULw9vbG+Hh4ejBF0D8hOTkZCQnJ6v/jvtooUpNTUVqamqB+pkdfJv6avv+feDGDSUsLDi0bp0GA4icLbKgIFgMGQJu0SKkDRuW582k737rm44dgfHjlTh9msOLF2lwddVPu2LvtyGQRJ9VKlgsXAgZgPSRI6FSqciEVgD03e9du+QAFGjbVgW5PN0493aDBrCoVQuy69eRvmwZVFpEIYv9enfuLMf8+Qrs2KFCx47pemlT7H02FFLot+zUKVjcuAHO1hZpAweioDeOMfqsS9uSUfRev36N9PT0TBY5AHBzc8Pdu3ezPaZixYpYv349qlevjnfv3mH+/Plo2LAhbt++jZIlSyLqY26M7NqMyiVvxuzZszFt2rQs20NCQmBra6tr17QmNDRUL+3s3l0eQGVUqxaDP/74Qy9taoPc0RG+Tk6wiYzEjcmT8W/z5lodp69+GwJPz6Z49MgRM2feRkDA07wP0AEx99tQiLnPrleuoMH9+0i1tUVI8eJI06O5Rx/9VqmArVv9AdigdOk/cfSo8XL/lGjWDHWvX0fq4sUIrVIFKqVSq+PEer3d3QsDaIYjR1TYvz8YVlYFU+gzItY+Gxox99v7p5/gDuBxs2a4mYvhSFcM2efExESt95WMopcfGjRogAYNGqj/btiwISpVqoRVq1ZhRgES+E6cOBFB/HQFyKLn4eEBf39/OOi9vARp7qGhofDz84NSywdobvz4I132oUOLonXr1gVuTxfkd+4AU6ag1rlzqP7zz7n6OOm734bg1i05Jk8G/vmnOlq3rqKXNqXQb30jhT4rPgYSyYcOhX+XLnppU5/9/vNPGd68sUChQhwmTKiNT+LODIufH7hdu2D94gVaxcaCy8PHSezXm+OAJUs4PHtmAQuLVmjduuA1fcXeZ0Mh+n7fuwfllSvgZDJ4zJsHj/LlC9ykMfocp4Ovu2QUPWdnZygUCkRHR2faHh0dDXd3d63aUCqVqFWrFh4+fAgA6uOio6NRrFixTG3WrFkzx3asrKxglc20o1KpNOgPWR/tP35M1cjkcqBLFwsY/b4bNgyYORPya9cgv3KFonHzwNDfa0Ho2pVyQp88KUdSklyvZeTE3G9DIdo+//UXcPIkoFBAMXo0FHqWUR/9PnyYlq1by1CokJG/Q6USGDUKGD8eFr/8AgwapFWgimivN8g1Y8kS4PBhC726Y4q5z4ZEtP3+GFwla9sWysqV9dq0IfusS7uSCcawtLREnTp1EBYWpt6mUqkQFhaWyWqXG+np6bh586ZaqStTpgzc3d0ztRkXF4dLly5p3abUOHiQlo0bA87OAgjg7Az07k3rv/wigAD6pWJFwMuLXDqOHRNaGobBWLSIll27UsZsEcLHj3XsKJAAX30F2NkBt24BIp6m0xb+ezx4EEhLE1QUhqF4+5byuwKaoCITRDKKHgAEBQVhzZo12LRpE+7cuYNhw4YhISEBAwcOBAD069cvU7DG9OnTERISgn/++QfXrl1Dnz598PTpUwwePBgAReSOGTMGP/30Ew4ePIibN2+iX79+KF68ODoK9rQ0LIK/DAAa+QOU4+XffwUURD/w36WJB2qbL9HRwLZttC7Sl8G9e5QbU6kEWrUSSAhHR7LkAcDSpQIJoT8aNwacnCjI+sIFoaVhGITVqyllUs2aQNOmQktjMCSl6HXv3h3z58/HlClTULNmTURERCA4OFgdTPHs2TNEZqi88N9//2HIkCGoVKkSWrdujbi4OFy8eBGVM5hnx40bh2+++QZfffUVPv/8c8THxyM4ODhLYmVT4M0bTf3xDh0EFKRGDbqp0tM1OckkDK/oHTkCZAjGZpgKa9aQybZ+fap9J0J+/52WLVoYsJyhNowYQcsjR8hPRMJYWGhSBLJShyZIWhrVtQWAb78VXU5MfSIpRQ8ARo4ciadPnyI5ORmXLl2Ct7e3+n+nT5/Gxo0b1X8vWrRIvW9UVBSOHDmCWrVqZWpPJpNh+vTpiIqKwocPH3DixAlUqFDBWN0xKkeOUGRe9eqUBkxQRo+m5erVlLBKwnz+OeXYfP8eOH1aaGkYeiUtTZP3kVdiRIgoLPUAUKECJfDjOE2ZOAnTqRMtDxygLjFMiMOHaUbJ2Rno3l1oaQyK5BQ9Rv4RzcsAANq3J1+nN28002ISRS7XWEjZ9K2JkfFlwGfIFhmRkQCfJal9e2FlAaBRiNetAyReYcjPD7CxAZ4+pSA2hgnBzyYNHmxyCZI/hSl6ZkJSEnD8OK0LOm3Lo1AAI0fS+rJlkh8u88rz778XOIcuQ0zwUzuDB8O4+Uq059Ahun3q1QOKFxdaGgBt2wKffUaDuJ07hZamQNjaAoGBtM4GcSbE/fsUMCSTAUOHCi2NwWGKnplw4gT5nHp4AJ/MXgvHwIE0krp+HfjzT6GlKRDNm1PZqchIyXeFwXPvHt04In8Z8P55orDUAzSI+/prWucVZQnDf6/MT8+E4N0K2rShwuUmDlP0zISM07ai8TktWhTo1o3WJe7PY2kJ8Lmn2cjfRFixgpZt24r2ZfD+PemigIgUPYAsoJaWNOqR+MinbVvSXW/eBB49EloaRoFJTAQ2bKD1YcOElcVIMEXPDEhPp+kdQCTTthnhR/47dgD//SesLAWEpVkxIRISAD6wa/hwQUXJjeBgICWFYiC8vISWJgMuLppBnMQj652cNJk32L1tAuzYAcTGUkRiQIDQ0hgFpuiZAeHhwKtXlOaqSROhpfmEBg2AatXIiXDzZqGlKRCtWlEes7t36cOQMNu3A+/eAZ6egL+/0NLkiCgt9Ty8grxjB/nrSRg++pZN30ocjtO4E3z9NZlqzQCm6JkB/MugTRsYv+RZXshkGqveqlWSDspwcABatqR13m+KIUEyvgyGDaOwahGSkkIpkwARWuoByjtYqxZF3vJTZRKFj2YODwdevxZWFkYB+PNP4No18g3/8kuhpTEa4nyCMfQGx4ksrUp29OlDpZP+/hs4f15oaQoEc9w2Aa5coVwa1tYUMCRSzpwho6ObG5Ahnah4kMk0PlBr10p6EPfZZ5R/VKVipQ4lDe93262bQDVAhYEpeibO33+TA7GlpYjdERwcgF69aF3iQRn8yP/SJaqcxZAga9bQsmtXctASKbzVuF07Ec9A9ehBg7h79yQ/iOOrZBw+LKwcjHzy7p0m3Q8/i2QmMEXPxDl4kJYtWwKFCgkrS67w6Sv27CGHQolSrBhQty6tHz0qrCyMfBAfT/55AEWOihSO0ygcopy25SlUiJQ9gKx6EoZX9PgAGIbE2L6dfMErVybfcDOCKXomDv8yEEXG/NyoU4dqiaWkSN6fp21bWrKRvwTZtYuUvfLlRRi5pOH2barWYG1N9W1FDa8w795N0Y4S5fPPAVdXIC5O8sZJ84QfaAweLMLIJcPCFD0T5vVrch4GKBBD9PBWvXXrJO3Pwyt6ISFAcrKwsjB0RCIvA34Q0bIlVW8QNd7eQNWqZE2RcLlDuVzzHOXTVTEkQkQEcPUqRSP27Su0NEaHKXomTHAw6UvVq1NFDNHTrRv589y/D1y8KLQ0+aZWLZrCjY8Hzp4VWhqG1ty+TSMjCwugXz+hpckVPtpWEgM4mUxj1VuzxiQGcXzZOYZEWLeOlp06mVUQBg9T9EwYftTPP5xET6FCmiSr/I0pQdjIX6Lwv7l27QB3d2FlyYU3bzTjIEkoegBZUaysyLJy7ZrQ0uQbPz8KbHv0iOJLGBIgKQnYsoXWRex3a0iYomeipKaSRQ+QkKIHAIMG0XLXLqrvJFEy+umxkb8ESE4GfvuN1kX+MggOpjQf1atT2g9J4OQEdO5M6xIOyihUCGjWjNbZIE4i7NtHvqGlSmkSnZoZTNEzUS5epGhyZ2egXj2hpdGBhg2pnlNCAmR79ggtTb7x9SUDxuPHwJ07QkvDyJMDB8hUVrKkiPMQEZKz1PMMGULLrVupxJxEYWlWJAY/sBg0SLTJzw2NefbaDOAfQq1aiTjHVnbIZOqM5XK+1qgEsbPTREOyF4IE4F8GX34p6htGspZ6gArGenoC799Dtnev0NLkG/57v3ABePtWWFkYefDgAXD6NCl4AwYILY1gMEXPRJHsqB8gR3iFAvLwcNj/+6/Q0uQblmZFIjx+DJw4QYMMEVfCAMhSHxsrQUs9QC/bj9Picgn74JYuTUHE6ekapZshUtavp2VgoEQiEg0DU/RMkEePgLt3yTAh4nrsOVOsGNC6NQDgsxMnBBYm//CO8mzkL3L4vI1+fvQWFzF8tK3kLPU8/furB3F2L14ILU2+yRh9yxApaWkAPyskcr9bQ8MUPROEfxk0bgw4OgoqSv75GJThcfo0zVdJkFKlgGrVyHGejfxFikoFbNpE6yK35gESt9QDNIj76AP52alTAguTfzJWyZDo48n0CQ4GoqIoy7Vkbxj9wBQ9E0TyLwMAaN0anKsrrGNjIZNwFXE2fStyzpwBnj0DChcWeS0xstTfuUNp/iRpqefp3x8A4HHqFM1/ShBvb5o+j40liz1DhPADuN69KVGyGcMUPRPj/Xt6dwESyrGVHUolVH36AJB2UAav6B07RjMJDJHBvwy6dQNsbISVJQ9MwlIPAO3bg3N0hM2bN5CdPi20NPlCoVB7l7BBnBh5+1ZT6P3jwMKcYYqeiXHiBJWL9fQEKlYUWpqCofoYJSU7dgyIjhZWmHzi7Q0ULUojfwkX+zBN4uMBPoWPBCLyTMJSDwDW1lB9TIwu37xZYGHyD0uzImJ27qQXYY0a9DFzmKJnYmR8GYi4VKd2eHnhbYUKkKWnA9u3Cy1NvmAjfxGzbx/lcytfHmjQQGhpcuX9e8oSAZiAogeA+1hiTrZ/PxAXJ7A0+cPPj+7ve/eAf/4RWhpGJvjk58yaB0CCit6yZctQunRpWFtbw9vbG5cvX85x3zVr1qBx48YoUqQIihQpAl9f3yz7DxgwADKZLNMnMDDQ0N0wCCoVcPQorUt62jYDz/k09PyNK0FYhJ5I4adt+/UT/ajoxAly+i9XjvKJSx3u88/xvkQJyJKSNFZViVG4MNCoEa1L2I3Y9Lh3D/jjD9LCe/USWhpRIClFb+fOnQgKCsLUqVNx7do11KhRAwEBAYiJicl2/9OnT6Nnz544deoUwsPD4eHhAX9/f7z4JKw/MDAQkZGR6s92iVqPrl2jICN7e6BJE6Gl0Q8vfHzAKZXA9evAzZtCi5Mv/P3pmXP3LqVsY4iAZ88APuqzb19hZdECk5m25ZHJ8JzPKM4r3BKEt9YzRU9E8EaBwEDAzU1YWUSCpBS9hQsXYsiQIRg4cCAqV66MlStXwtbWFuv5pIifsHXrVgwfPhw1a9aEl5cX1q5dC5VKhbCwsEz7WVlZwd3dXf0pUqSIMbqjd/iXgb8/ld8yBVIdHMDxT1OJ+vM4OlJlN4C9EETD5s1UhLh5c8qDI2JUKk0ghskoegCeN20KTiYDzp6V7Nwn/2g6eRL48EFYWRigm4V/T7BpWzUWQgugLSkpKbh69SomTpyo3iaXy+Hr64vw8HCt2khMTERqaiqcnJwybT99+jRcXV1RpEgRtGjRAj/99BOKFi2aYzvJyclITk5W/x330cckNTUVqQZIqsS3mVfbhw8rAMgRGJiG1FRO73IYG3W/e/aE1e+/g9u6FWnTp0syU6y/vxznzilw5IgKQ4bknlJC2+ttShi1zxwHi40bIQOQ1rs3OAG/Z236ff06EB2thL09h/r100wib1tqaio+ODtD1aIFFGFhSN+wAaopU4QWS2cqVgRKlLDAixcyhIWlwd8/5+euOd7XgHH7LTt5EhbPn4NzdERaYKBgSQ6N0Wdd2paMovf69Wukp6fD7RNTrJubG+7evatVG+PHj0fx4sXh6+ur3hYYGIjOnTujTJkyePToESZNmoRWrVohPDwcihwUitmzZ2PatGlZtoeEhMDW1laHXulGaGhojv+LjbXE1autAAAWFidw9GhyjvtKjeMKBQILFYLly5f4c+5cvKpZU2iRdMbOzgFAc4SFqXDgQDAsLVV5HpPb9TZVjNHnInfvosnDh0iztkawnR3SecdWAcmt37t3VwBQCVWqROHEiZx9kqXI9erVUTcsDB9Wr8aJ2rUlWXS+cuUaePGiNFaufIq0tFt57m+O9zVgnH7XXrwYHgCe1K+PGydPGvx8eWHIPicmJmq9r2QUvYIyZ84c7NixA6dPn4a1tbV6e48ePdTr1apVQ/Xq1eHp6YnTp0+jZcuW2bY1ceJEBAUFqf+Oi4tT+/85ODjoXfbU1FSEhobCz88PyhwSP27ZQs7kNWpw6NMne7mlBt9v39atoejTB1ixAvXv30f6pElCi6YzHAfMm8chMtIC9vat4Oub+8g/r+ttahizz/KPPg7yrl0R0KWLQc+VF9r0e948GnD26+eK1vxcocTh+1150iRwa9fCLiYGbRwcwEnQuTglRYbQUODu3bJo3fqzHPczx/saMGK/37+HxcfgC4/vv0dJb2/DnSsPjNHnOB2i1fOl6MXGxmLPnj149OgRxo4dCycnJ1y7dg1ubm4oUaJEfprME2dnZygUCkR/kk8tOjoa7u7uuR47f/58zJkzBydOnED16tVz3bds2bJwdnbGw4cPc1T0rKysYJWNE5xSqTToDzm39vmSsK1by0zuIaJUKqHo3x9YsQLyAwcg//ABKFRIaLF0plUrqrEdGmqBVq3y3t/QvycxYvA+JyUBu3cDAOQDB0Iuku83p37HxlIAIQC0aaOAUik9t4XcUBYuDFn37sDatbDYsgXI4ZkrZgICqPDCw4cyPHmiRPnyue9vjvc1YIR+HzwIJCYCFSrAolEjUUTSG7LPurSrs538xo0bqFChAubOnYv58+cjNjYWALBv375M/nP6xtLSEnXq1MkUSMEHVjTIJQfWzz//jBkzZiA4OBh169bN8zz//vsv3rx5g2LFiulFbmOQng4cP07rEs0Mkzf16lFeicREyn8mQXjljgVkCMjBg8C7d8BnnwF86h4Rc+IE3d+VKok+ZiT/8E7ze/bQ/S0xHByoWgnA7m1B4aNtJZAuydjorOgFBQVhwIABePDgQaYp0NatW+Ps2bN6FS67c69ZswabNm3CnTt3MGzYMCQkJGDgx2Lk/fr1y6Rszp07Fz/88APWr1+P0qVLIyoqClFRUYiPjwcAxMfHY+zYsfjjjz/w5MkThIWFoUOHDihXrhwCPhbelgJXrwJv3tADR+R5X/OPTEY3MCDZnHq+vizNiuBkzJ0nAX8wXnEw2QEcQMnoSpemSiV82SqJwQZxAvP0KaVLkskkkS7J2Oj8pPvzzz8xdOjQLNtLlCiBqKgovQiVE927d8f8+fMxZcoU1KxZExEREQgODlYHaDx79gyRkZHq/VesWIGUlBR07doVxYoVU3/mz58PAFAoFLhx4wbat2+PChUqYNCgQahTpw7OnTuX7dSsWOEfLn5+Jl67+WPtW5w6RXnQJAZLsyIw0dEa0zc/aBAxHAcEB9O6NlP9kkUmo8LzALB1q7Cy5BPedfLUKUkaJaUPn1KleXOy1jMyobOPnpWVVbZOgPfv34eLi4tehMqNkSNHYuTIkdn+7/QnBbKfPHmSa1s2NjY4zj/4JQz/MjDpUT9Ac1fNmlEtqK1bAQO6ChiKVq2Ac+dI0Rs+XGhpzIydOynPVr16yNORSgTcvAm8fAnY2mqmBk2W3r2BmTPpYfbqFWCEd4k+qVSJ9Itnz+jxZCIxM9KA44AtW2hdAgM4IdDZote+fXtMnz5dncNFJpPh2bNnGD9+PLoIHMFmjrx5A1y6ROsmr+gBmhuZT3grMViCVQHhrUW89Ujk8AO45s2BDF4ypkmlSkCdOkBaGrBrl9DS6IxMprm3RZCtx7y4fp3KnllbA506CS2NKNFZ0VuwYAHi4+Ph6uqKpKQkNG3aFOXKlUOhQoUwc+ZMQ8jIyIXQUNJ3qlYFSpYUWhoj0KULYGMD3LlDzokSo3p1oHhxmt45d05oacyIhw+By5fJSbJ7d6Gl0Qqz8M/LCO+aIfHp22PHJDkGlS7bttGyfXtyVGdkQWdFr3DhwggNDcWhQ4ewZMkSjBw5EkePHsWZM2dgZ2dnCBkZucC/DEzahycjDg5Ax460LsGSaDKZ5sXN/PSMCP8y8PWVRP3L9++B8+dp3Wzu7R49KEAmPBx49EhoaXSmRQvA0pKqud2/L7Q0ZkJ6OsDXpv+YQ4+RlXyHnfn4+GD48OEYN25cpkoTDOOhUplBWpXs4Ef+O3bQVI/E4F/cbIrHSHCc5KZtw8Lop12uHODpKbQ0RsLdnRRxQJJWPTs7oGlTWmeDOCNx9iw5shYpYkYjIt3RKhhjyZIlWjc4atSofAvD0I2ICAoktLMDfHyElsaI+PkBzs5ATAw5u/n7Cy2RTvBpVu7dozQrZcoILZGJc/UqmVhsbDTWYJFjFtG22dGnDxASQoreDz9ILh9aq1bkTnP0KDBmjNDSmAH8gKBrVzKnMrJFK0Vv0aJFmf5+9eoVEhMT4ejoCIAqZdja2sLV1ZUpekaEfxm0bGlmv3GlEujWDVi+nG50iSl6fJoVFn1rJPiXQfv2kqiokjGtillZ6gFSxG1sSDG/cgX4/HOhJdKJ1q2BoCDgzBlKC2hvL7REJsyHD5RkG5CMpV4otJq6ffz4sfozc+ZM1KxZE3fu3MHbt2/x9u1b3LlzB7Vr18aMGTMMLS8jA2bnn5cR/sbet0+SiasyOm4zDEh6Ok3xA5J5Gdy9S/lfrawkUbxDvxQqpLG68ikzJESFCkDZskBKCuXUYxiQY8eoyk3JkmaQf6hg6Oyj98MPP2Dp0qWoWLGielvFihWxaNEiTJ48Wa/CMXImNpZ8lgEzHPUDVAKEz6b/sUi9lOCVc5ZmxcCcPAlERQFOTlSUVALw1rymTSmHntkhYR9cmYz54BoN3lLfs6ckqtwIic7fTmRkJNKyufnS09MRHR2tF6EYecPXwPTyIn3H7JDJNFFWEnTczphmxcCVA80b/rfRrZtk/BvMLq3Kp/j5UcLkmBh60EmMjOXQWJoVA/HunWaALxFLvZDorOi1bNkSQ4cOxbVr19Tbrl69imHDhrHoWyNitj48GeFv8GPHgLdvhZVFR1iaFSOQlERT+4BkXgYJCeTfBZipSwZAPrh8rkMJTt82b07T7k+f0jQ8wwDs2wckJwOVK9OomZErOit669evh7u7O+rWrQsrKytYWVmhXr16cHNzw9q1aw0hI+MTzKYGZl5UrgzUqAGkpmqcciUEK4RuYA4dooR0pUppigyLnNOnyb+rVCkgg3eM+cFP3+7fT+4ZEsLWVpNmhX9OM/RMxnRJEovMFgKdFT0XFxccPXoUd+/exe7du7F7927cuXMHR48ehaurqyFkZHzCrVvAixcUnNakidDSCIyEi6F/mmaFoWf4JMm9eknGhyfjAM6s31/16lESwcRE4PffhZZGZ9ggzoBERpLvLUD+eYw8yffTr0KFCmjfvj3at2+PChUq6FMmRh7wDw+zqIGZFz170hvx7Fng+XOhpdEJR0egUSNaZy8EPfP2rcYbXiLTtgDzz1Mjk2mumwSnb3lF78wZmo5n6JEdO2haq2FDloRUS7TKo5eRL7/8Mtf/r1+/Pt/CMLSD+edloGRJMmueOUOlcMaNE1oinQgMJB01OJjl09Mre/bQlH716kCVKkJLoxUPH1LlL6WSymmZPb17A9OmUQLl6GhJlK7jqVCBguSePKE0K23bCi2RCZHRUs/QCp0tev/991+mT0xMDE6ePIl9+/YhNjbWACIyMmKWNTDzQsLTtxnTrCQnCyuLSSGxkmeAxprn4yOJvM6Gp3x5wNubaj3u3Cm0NDqRMc0Ks9brET6RtkJBkfQMrdDZord///4s21QqFYYNGwZPsynKKBwnT5Kholw5+jBA5W9GjABu3CAHxqpVhZZIa2rUoBKfUVFUKYMFruuBZ8/ITCqTScqHhwVYZUPv3sClS6S4S6zqUqtWwIoVLM2KXuEHcP7+lIKHoRV68VCWy+UICgrKUiqNoX+YD082FCmiKTXBm/UlQsY0KyxCT09s307LJk0ADw9hZdGSDx80lRTYvZ2Bbt3IenP5MvDggdDS6ESLFpS68fFjyYkuTjhO83yXkKVeDOgtFO3Ro0fZJlJm6A+WViUX+Bt/2zaa6pEQbIpHz0hw2vbcORmSkoASJSRlkDY8bm4aM7fEBnF2dpqsCOze1gN//kmOrLa2QIcOQksjKXSeug0KCsr0N8dxiIyMxJEjR9C/f3+9CcbIilnXwMyLtm3JsenpU6oNx4ezSgA/P8r+8fffNOtYrJjQEkmYmzfpY2lJU/oSISSEcqkEBpp5WpXs6N0bOH6cFPgpUyT1BQUGUnGPY8dYsFWB4RX9Dh0Ae3thZZEYOlv0rl+/nulz48YNAMCCBQuwePFifcvHyEBICF0us62BmRs2NkDnzrQusaCMIkWA+vVpnU3fFhD+2rduTV+sRAgOpnubTdtmQ8eOdH8/eECO+BKCt9afPk0pARn5JC2N0qoAkrLUiwWdLXqneEcShtE5flwz6mdkQ+/ewKZNwK5dwC+/UJ4KidCqFXDxIo38Bw4UWhqJolJJ0ocnOtoG9+7JoFCwYJxsKVSIrDg7dtD1/fxzoSXSmkqVgM8+4+ODpGOJFB2nTlGKnaJFKRCDoRM6W/RatGiRbRqVuLg4tGDJnwzGhw8K9YOC+eflQIsW5NPz5g3l3pIQvPIeFkYlsBj54MIFSppdqBDQpo3Q0mjN9euUH65BA0qizcgGXnHfsQNITxdWFh3ImGaFH6gz8gFvqe/WTVIDeLGgs6J3+vRppGTzJvrw4QPOnTunF6EYWbl1yxkpKTJWAzM3FAqgRw9al9j0be3agKsr5UkMD2cvhHzBX/MuXWiqTyJcu0alI9kALhcCAsiaExWlKX8lETSKnjTK8ImOpCRg3z5aZ0mS84XWv7wbN26o/fH+/vtv9d83btzA9evXsW7dOpQoUcJggpo7GV8GEvJFNj78yP/33yVVDF0up3cZAAQHswusMykpwO7dtC6haduUFODGDcoHxlwyckGp1CTIldggrkULEv/hQxkiI+2EFkd6HD5MI+BSpajsGUNntFb0atasiVq1akEmk6FFixaoWbOm+lOnTh389NNPmDJliiFlBQAsW7YMpUuXhrW1Nby9vXH58uVc99+9eze8vLxgbW2NatWq4Shf//IjHMdhypQpKFasGGxsbODr64sHIkx6xCt67GWQB3XrSrYYOn9t+aAbhg4EB1N922LFqAi0RLh4UYYPHyzg5sahZk2hpRE5vDVn3z6y8kiEQoWo2gkAXL3qKqwwUoRX7Hv2pBExQ2e0/tYeP36MR48egeM4XL58GY8fP1Z/Xrx4gbi4uDzr4BaUnTt3IigoCFOnTsW1a9dQo0YNBAQEICYmJtv9L168iJ49e2LQoEG4fv06OnbsiI4dO+LWrVvqfX7++WcsWbIEK1euxKVLl2BnZ4eAgAB8+PDBoH3RhYcPgagoeyiVHKuBmRcZi6FLbOTv70/i37wpw5s31kKLIy34IIzu3WkKXyLw1ls/P469w/KiYUOy6rx/Dxw6JLQ0OsFP316/zhQ9nfjvP4A3zkjIUi82tH60lCpVCqVLl4ZKpULdunVRqlQp9adYsWJQGOHhunDhQgwZMgQDBw5E5cqVsXLlStja2mL9+vXZ7v/LL78gMDAQY8eORaVKlTBjxgzUrl0bv/76KwCy5i1evBiTJ09Ghw4dUL16dfz22294+fIlDhw4YPD+aAvv29GoEcdqYGoDP/IPCQFevRJWFh1wdgbq1aN13oLL0IL374GDB2ldYi8D3nobECCtJN+CIJdr7m2JDeJ4Re/mTWeIyIYgfvbsoZqf1atLKpP47dvA6dMlkYMNyuholV7l4MGDaNWqFZRKJQ7yD9QcaN++vV4E+5SUlBRcvXoVEydOVG+Ty+Xw9fVFeHh4tseEh4dnSfAcEBCgVuIeP36MqKgo+GbIaVC4cGF4e3sjPDwcPXjH/k9ITk5GcoYK9HFxcQCA1NRUpKam5qt/ucHn2PL1TUNqqvn4b/Hfpc7faZkyUNSpA/nVq0jfvh2qYcMMIJ1h8POT49IlBa5fdzXIb0ms5PtaA5Dt3QuLpCRw5cohrXp1ejFIgH//BW7dUkIu59C0aQpSU82nIGq+r3e3blDOng3u2DGkRUcDTk4GkE7/VKgAlCihwIsXFjh58oO6YqM5UJB7W7FlC+QA0rt3h0oi9zUAbNkCLF5cB5GRadi82TBy6/J9aqXodezYEVFRUXB1dUXHjh1z3E8mkyHdQKHvr1+/Rnp6Otzc3DJtd3Nzw927d7M9JioqKtv9o6Ki1P/nt+W0T3bMnj0b06ZNy7I9JCQEtgbIZGxrWxnu7sVgb38ZR4++13v7Yic0NFTnY8rWqIFqV6/i3fLlOFeqlAGkMgwODkUANEFEhCuCg49BoTCflz+Qv2tdf+lSuAG4V7cu7kmo1lRo6GcAaqF8+f9w7Zp5ZizIz/VuVro0Cj95gtvTpuEpH8EkASpXroEXL0pj7doXAG7lub+poeu1tn71Cv4fM3mEuboi6RP/ejGzd28zAIVRvPhfOHr0X4OcI1GHDNxaKXqqDLVDVRKrI2oIJk6cmMlSGBcXBw8PD/j7+8PBwUHv5/PzS0VoaCh8ff1gaWk+OYRSU6nffn5+UOqaO6lWLXAbN8Lp3j209vICypY1jJB6JiAAmDuXw5s3ShQq5I9mzaTjb1YQ8n2tY2Jg8ddfAADPyZPhWaGCgSTUP5s20bWtVSsmf79xCVOQe1t+5w4wcSKq37yJKr/8YiAJ9U9iogqhocC9e2XQuvVnQotjNPJ7reULFkDGcVD5+KC5hMqrvnwJPHmihEzGYfToSihevLpBzsPPJGqDzpUxhMLZ2RkKhQLR0dGZtkdHR8Pd3T3bY9zd3XPdn19GR0ejWIYCo9HR0aiZSwiclZUVrKyssmxXKpUGfVhbWhq2fbGSr+/1s88or8GJE1Du2QN8/71hhNMzSiXg66vCzp0yhIUp4ednHooej87X+sABSqBbty6UVaoYTC59k5pKybEBoHbtaCiVnuze1pY+fYBJkyA/fx7yyEi61yWAv38qFAoVHjyQ4/lzuVTGnnpD52u9cycAQN6nD+QSujf44mHlysWieHF7g93XurSrlaK3ZMkSrRscNWqU1vvqgqWlJerUqYOwsDD19LFKpUJYWBhGjhyZ7TENGjRAWFgYxowZo94WGhqKBg0aAADKlCkDd3d3hIWFqRW7uLg4XLp0CcMk5NfFyIHevami+NatwKRJkklAGBCgws6dchw/LsecOUJLI3IkWPIMAC5dAuLigKJFOXh6xgotjrQoWZIKfp8+DWzfDowfL7REWuHgAHh5vcXt284IDgaGDxdaIhHz999ARARgYQF07Sq0NDrB1yuvVSsGgL2gsvBopegtWrRIq8ZkMpnBFD0ACAoKQv/+/VG3bl3Uq1cPixcvRkJCAgZ+LA7ar18/lChRArNnzwYAjB49Gk2bNsWCBQvQpk0b7NixA1euXMHq1avV8o4ZMwY//fQTypcvjzJlyuCHH35A8eLFc/VFZEiETp2Ar78G7twB/voLUklU5udHfnkRETJERQE5GKwZjx9TgWCZjNKqSAjeldDXl5NSNhjx0Ls3KXpbt0pG0QOAOnWicfu2M44dY4pervBR1a1aUUUUiZCWpqm+Wbt2NABxmG21UvQeP35saDm0onv37nj16hWmTJmCqKgo1KxZE8HBwepgimfPnkGeIRlVw4YNsW3bNkyePBmTJk1C+fLlceDAAVTNEKY9btw4JCQk4KuvvkJsbCx8fHwQHBwMa2uWx0zyFC4MtGtHIfpbt0pG0XNzAzw9Y/HokSOOHwck5J5iXLZvp2WLFpQoWULwo36WViWfdOkCjBgB3LxJn2rVhJZIK2rVisFvv1XByZPAhw8Ae81kA8dJ1lL/55+U+q9IEQ7ly8cKLY6aAqXo5DgOHGfcqMCRI0fi6dOnSE5OxqVLl+Dt7a3+3+nTp7Fx48ZM+3/xxRe4d+8ekpOTcevWLbT+JK5dJpNh+vTpiIqKwocPH3DixAlUkJBDNyMP+AfF9u2AhAKJaDSosfwwPoHjNKN+idW/jIoCrl2jdd56y9CRIkWgzlEioZx6pUvHoXhxDomJACsNnwPh4cCTJ4C9PQ3UJQQ/gGvZkhNVxoR8KXrr1q1D1apVYW1tDWtra1StWhVr167Vt2wMRsFp1QpwdARevADOnhVaGq2pXZsybYaEUKwB4xNu3iQ/Hisrsu5IiOPHaVmnDllvGfmEH8Rt2yaZQZxMBvj7kwLAKwWMT+AV906dAAOkKzMkYrXU66zoTZkyBaNHj0a7du2we/du7N69G+3atcO3335rlFq3DIZOWFlpnHklNPKvUOE/ODpy+O8/II9yzuYJfy3btKEpegnBvwxY3eoC0rYtRTg8fw6cPy+0NFrDKwHMWp8NqanArl20LrFp21evaOoWEJ+lXmdFb8WKFVizZg1mz56N9u3bo3379pg9ezZWr16N5cuXG0JGBqNg8A+MPXuADBVNxIxCwaFlS3pYsBfCJ6hUGv88iU3bpqdrnLX5sliMfGJtrbHmSmgQR9N6FCP29KnQ0oiM0FDg9WvA1RVo2VJoaXQiNJQ8SqpXB4oXF1qazOis6KWmpqJu3bpZttepUwdpaWl6EYrB0CtNmgAlSgCxsZLSmgIDaeTPpng+4cIFsuI4OJBFT0L8+Sfw9i15E2RwL2bkF34Qt3s3kJIirCxa4ugIfMzwJaXHkXHgFfbu3Sm1ioTgn9NiHMDprOj17dsXK1asyLJ99erV6C0xUyvDTJDLgZ49aZ2P5pIAvC/PlSs0LcD4CH8Nu3SRXNgi/2L385Pce0ycNGtGEdf//ScprYlXBiQksuFJSKAE6IDkpm1VKo3vrRhdMgoUjDF48GAMHjwY1apVw5o1ayCXyxEUFKT+MBiigZ/iO3SIMtVKgGLFgBo1aDqAn+4ze1JSND48Epu2BcQ96pckCoUkB3H89Q8Lk4wh0vD8/juQmAh4egL16gktjU5cvw7ExFCgcMOGQkuTFZ0VvVu3bqF27dpwcXHBo0eP8OjRIzg7O6N27dq4desWrl+/juvXryMiIsIA4jIY+aRmTaBSJUpetW+f0NJoDRv5f0JICM19urkBzZsLLY1OZHTWDggQVhaTglf4Dx6UzCCuZk1KhJ6QIKk4EsOSMV2SRKoY8WjSqgCWlsLKkh06Tx6c4gu5MRhSQiaj6YDJk2nkP2CA0BJpRatWwJw5NC2gUtEstFnDW2169IDUSkrwzto1aojPWVvS1K4NVKwI3LsH7N8viQzjMhkp+5s20SCuRQuhJRKYV680c5/MUq93zP21wTAn+CmesDDKWisBGjSgmIPXr4GrV4WWRmDi42l6B5CcDw+gscqK0YdH0vCDOEBS0bfMWp+B3bspJL12bcDLS2hpdCI2lnI8A+K11Ous6H348AHz5s1D69atUbduXdSuXTvTh8EQLWXLkuakUgE7dggtjVYolYCvL62b/QuB9+EpVw7IJvJfzGR01hbrqF/S8FYgCQ3i/PzIQn/7NgWRmzW8gi7BAdyJE6SjenkBpUsLLU326KzoDRo0CD///DNKlSqFtm3bokOHDpk+DIao4V8IEnLc5i1AZq/o8ddMgj48167R7FShQuJ01pY8np5A/fqSGsQ5OZHIgJmnUHr8GLh4ke7pHj2ElkZnpJAAXWcfvcOHD+Po0aNo1KiRIeRhMAxLt27AmDHkFf/gAVC+vNAS5QlvAbp8GXjzBihaVFh5BEHiPjy8ku7rS1ZahgHo3Rv44w+yDo0ZI7Q0WtGqFek4x44BQ4YILY1A8MnPmzeXnPMqx4nfPw/Ih0WvRIkSKFSokCFkYTAMj6sr4O9P6xKx6pUsCVStSsaK0FChpRGIPXtofqROHXK8lxhSeBlInm7dKEDnyhXg/n2hpdEK3gp04oSZplnhOElP296+TWXUbWwoL79Y0VnRW7BgAcaPH4+nrHYLQ6rwFqGtW+lBIwH4F4LZTvFkTL0gMd6+JUMTIO7pHcmTcRAnkaCM2rVJ7PfvybJndty4Afz9N9Uk58vZSQjeUt+smbhzt+us6NWtWxcfPnxA2bJlUahQITg5OWX6MBiip2NHwNaWpm6vXBFaGq3gLUHBwWTZMyuePKGyZzIZlUaSGCdO0DWrUgXw8BBaGhMnow+uBAZxcrkmUtMsfXB5hbxNG6BwYWFlyQdS8M8D8uGj17NnT7x48QKzZs2Cm5sbZBJzimYwYG8PdOhAviHbtgGffy60RHni4wPY2QHR0UBEBFkCzAb+ZdC8OdUslhgsrYoR4QdxDx+SH64EKiy0agVs3kxKw9y5QktjRFQqjX+eBKdt4+OBc+doXez3ts6K3sWLFxEeHo4aNWoYQh4Gwzj06kUPmR07gPnzRZ9819KSsq4fPEgvBLNR9DiO3oIA0LevsLLkA6k4a5sMGQdxW7dKQtHz9ydj9Y0b5O8lwbFM/jh3Dvj3X7LktW4ttDQ6c+oUkJpKWbvEHtOn89Stl5cXkpKSDCELg2E8AgIofDUqCjh5UmhptMIsE6xeuUIVD2xsJOnD89df9BOzsyOrLMMI8NahHTuAtDRhZdGCokU1+qhZ+eDylvouXcTt4JYDGS31Yp/Y1FnRmzNnDr777jucPn0ab968QVxcXKYPgyEJlErgiy9oXSLRt/z0QHg4ZWM3C377jZYdO1ISOonBvwxatCB/c4YR8PcHnJ2pynxYmNDSaIXZDeI+fKBqGIAkp205TlouGToreoGBgQgPD0fLli3h6uqKIkWKoEiRInB0dESRIkUMISODYRj4B8zevYAErNSlS1P29fR0cvA3eVJTNclvJThtC7BpW0FQKinVCiCZ6Fv+9xEaSj97k+fwYRqtenhQyKrEePCAYsQsLcl1WOzo7KN36tSpHP938+bNAgnDYBiVhg2BUqWAp0+BI0eArl2FlihPWrUC7t6l0aQExC0YwcFU5NfNjepFSYx37yhYGJDGqN+k6N0bWL4c2L+fyubZ2gotUa7UrUtGyNevKRVP48ZCS2RgeEt9nz4UeiwxeGuejw+5hYodnb/hpk2bZvrUrl0b9+7dw9ixYzF69GhDyMhgGAa5HOjZk9a3bBFWFi3JmE9PAtkjCgYfhNGzJ2Ch85hUcMLCyPpasSJQpozQ0pgZDRrQlx4fTxFMIses0qzExGg6KVFL/dGjtJRKDEm+VemzZ8+if//+KFasGObPn48WLVrgDz4rKIMhFfr0oeXRozScFjlNmlBcwsuXgEkb0GNjNS/ofv0EFSW/SMmHx+SQyTInRpcAZlPTmg+S+fxzoFIloaXRmfh44PRpWjdJRS8qKgpz5sxB+fLl8cUXX8DBwQHJyck4cOAA5syZg88lkI+MwchElSpUVis1VZPTScRYW2t8Qkz6hbBnD5CcTNenZk2hpdEZllZFBPCKHu8CIHICAkg/jYgAIiOFlsaA8NO2Eh3AnTxJ5erKlCGfaSmgtaLXrl07VKxYETdu3MDixYvx8uVLLF261JCyZeLt27fo3bs3HBwc4OjoiEGDBiE+Pj7X/b/55htUrFgRNjY2+OyzzzBq1Ci8e/cu034ymSzLZwfvAM4wD/r3p+WmTcLKoSUZq2SYLBlz54k9d0E23L5NKcKsrcVdA9OkqVyZBglpaTRwEDkuLuSrB5jwvX37NnD1Krli9OghtDT54sgRWrZuLZ1Hk9aK3rFjxzBo0CBMmzYNbdq0gcLICWZ79+6N27dvIzQ0FIcPH8bZs2fx1Vdf5bj/y5cv8fLlS8yfPx+3bt3Cxo0bERwcjEGDBmXZd8OGDYiMjFR/OnbsaMCeMERHz54UqXf1Kj2IRA6v6J0/D5hkRqMnT4CzZ+kpKsHUC4DmZdC8OU21MwSC//1IZPrW5Adx/ACuTRuKPpEYHKfxz2vTRlhZdEFrRe/8+fN4//496tSpA29vb/z66694bSRz+J07dxAcHIy1a9fC29sbPj4+WLp0KXbs2IGXL19me0zVqlWxd+9etGvXDp6enmjRogVmzpyJQ4cOIe2TJJqOjo5wd3dXf6wlmLyRUQCcnTV3rQSsep6eQLlyZKiQSJow3chY8qxkSWFlySe8ote2rbBymD09e9KA4fx5iq4XObyiFxIiiVzPupGergl6k+i07c2bZKm3sZFWVhitQ9nq16+P+vXrY/Hixdi5cyfWr1+PoKAgqFQqhIaGwsPDA4UMlNA0PDwcjo6OqMvbtQH4+vpCLpfj0qVL6NSpk1btvHv3Dg4ODrD4JIJvxIgRGDx4MMqWLYuvv/4aAwcOzLWGb3JyMpKTk9V/84miU1NTkWqAJEh8m4ZoW8wYs9+y3r1hceAAuC1bkDZtmqBRntr0OyBAjocPFTh6NB1t26qMJZrBUPc5JQUWmzZBBiCtVy9wEvzN//cfcPGiBQAZ/P1Tc82Lxu5tA/fb1RWKpk0hP30a6b/9BtWECYY9Xy5o0+eaNQEnJwu8fSvDhQtpaNhQ+qH1fH/TT5yA8sULcEWKIM3fX5IJAw8dkgNQoHlzFSws0nPsgjF+37q0rfPbzM7ODl9++SW+/PJL3Lt3D+vWrcOcOXMwYcIE+Pn54aABQtmjoqLg6uqaaZuFhQWcnJwQFRWlVRuvX7/GjBkzskz3Tp8+HS1atICtrS1CQkIwfPhwxMfHY9SoUTm2NXv2bEybNi3L9pCQENgaMF9TaGiowdoWM8botwxAQKFCsIqMxJU5cxAjgmKyufXbyckVQAMcOJCMNm1CJeMrkhd/Ll+Opg8eIM3SEsft7JDGz5NIiHPnSiA9vS4++ywOt2+f0sobgN3bhuOzatVQ6/RpJK1cibBq1QR3rMqrz1Wq1MG5cyWxbNkjxMbeNZJUhufVggXwAPDE2xs3JDoVsXWrD4CiKFnyJo4efZLn/ob8fScmJmq9r4zjCp6NKz09HYcOHcL69et1UvQmTJiAuXPn5rrPnTt3sG/fPmzatAn37t3L9D9XV1dMmzYNw4YNy7WNuLg4+Pn5wcnJCQcPHoRSqcxx3ylTpmDDhg14/vx5jvtkZ9Hz8PDA69ev4eDgkKss+SE1NRWhoaHw8/PLVXZTw9j9ln/7LRTLlkHVrRvSBcyrp02/ExMBNzcLJCfLEBGRisqVjSyknuH73Do4GMqVK6Hq3h3pvD+PxBgwQIFt2+T47rt0zJ6du7WV3dtG6Pf797Dw8IAsMRFpZ86Aa9DAsOfLAW37vHmzDIMGWaBWLQ6XLkl//jY1NRUnDx5Em0GD6BqcOwfO21tosXTmv/+AYsUsoFLJ8OBBKkqVynlfY/y+4+Li4OzsrJ6pzA29zE8pFAp07NhR5yCG7777DgMGDMh1n7Jly8Ld3R0xMTGZtqelpeHt27dwd3fP9fj3798jMDAQhQoVwv79+/P80r29vTFjxgwkJyfDKofilFZWVtn+T6lUGvShZej2xYrR+j1gALBsGeQHD0KemAgULmz4c+ZCbv0uXJh8RI4fB06cUKJGDePKZgjkqamw2LWL1gcMgFyCv/X0dLomANC+vQJKpXZBa+zeNiBOTlTXetMmWGzeLHgYdF595t2Fr1+X4e1bJdzcjCSYASn+xx+QJSYC5cvDolEjwa2q+eHkSUClooxP5cpp95s15O9bl3YFrT3i4uICLy+vXD+WlpZo0KABYmNjcfXqVfWxJ0+ehEqlgncuI4O4uDj4+/vD0tISBw8e1CrIIiIiAkWKFMlRyWOYMHXqUEqGDx+AjwqHmDG1BKvuly9D9vYtBWBIsOQZAFy6BLx5Azg6UoU9hkgYOJCWO3cCCQnCypIHbm70KAI0gwap48GXTu3XT5JKHiC9ahgZkUSRuUqVKiEwMBBDhgzB5cuXceHCBYwcORI9evRA8eLFAQAvXryAl5cXLl++DECj5CUkJGDdunWIi4tDVFQUoqKikJ6eDgA4dOgQ1q5di1u3buHhw4dYsWIFZs2ahW+++UawvjIERCaTVE49PkLv3DnK1i51PuP9dvr1A4ycvklf8NG2gYGSrNpmujRuTBlu378H9u0TWpo8MalB3PPncObL+PCViCRGerrmWkgprQqPJBQ9ANi6dSu8vLzQsmVLtG7dGj4+Pli9erX6/6mpqbh3757aQfHatWu4dOkSbt68iXLlyqFYsWLqD+9/p1QqsWzZMjRo0AA1a9bEqlWrsHDhQkydOlWQPjJEAF9k+8IF4OFDoaXJlQoV6N2VkgLwA2bJ8uIFXCMiaD0Pdw4xwyt6UnwZmDRyueZ3tXGjkJJoRcY0Kx/tEpJFvnkzZBwHVZMmQOnSQouTL65coeIqhQtL01IvGUXPyckJ27Ztw/v37/Hu3TusX78e9vb26v+XLl0aHMeh2cfkNs2aNQPHcdl+Sn/8sQUGBuL69et4//494uPjERERgaFDh0Iul8zXwtA3xYtrpg35Uj0iRSbTjPwlGJyaCfmWLZCpVFD5+ADlywstTr54/hz466/M14UhIvr3p4tz8iQl5RYx3t40/f/2LfBxkkqaqFSQf3yOqvjZEgnCD+D8/Sm3vtRgGg2D8Sn8A+m338j7VsTwlqMjRyhruyThOMg/TpVL+WXAK9sNGkgy6b/pU6oU0KIFrYvcNcPCgpQKQOJVMs6cgeyff5BqYwOuSxehpck3UvbPA5iix2BkpWNHwMGBMumLfE60RQvK0s5bkyTJxYuQPXyINGtrSb8M2LStBMg4fSvyQZxJ+OmtXw8AeNG4MWDAHLOGJDKSqmMCmil1qcEUPQbjU2xsgF69aH3dOmFlyQMbG81M86FDwsqSbzZsAAC8bNgQyOCOISU+fNCUo2OKnojp3JkGcXw9ZRHDK3pXrgCvXgkrS76IjQX27AEAPPX1FVaWAsBbVOvWhWRT3TBFj8HIjsGDabl3L+XLEDHt2tFSkopeQgKlvADwrGVLgYXJP6dPUxLrkiWB6tWFloaRI7a2QPfutP5xgCFWihWjkmgcJ9Hp2+3bgQ8fwFWujFiJ+t0CpmGpZ4oeg5EdtWvTUzYlBdi6VWhpcoV/AP35J00zSIo9e4D4eHDlyuGNhMt7HD5My9atJZsmzHzgc+rt2UPpVkQMf2/zvy9J8XHaVjVwoGRvipQUinwGpOufBzBFj8HIHplMY9Vbs0bUkQ7FigGff07r/OhTMny0qqj69pXsy4DjNN9727bCysLQgvr1gYoVyQT70ZosVtq3p+WxY6R0SIYbN2jOWamEineDkSBnztBYwN2dpm6lClP0GIyc6NULsLYGbt0ic5mIkeT07cOH9CSVyaCSaCJVALhzh1y+rKw0QZ0MESOTAV9+Setr1worSx7UrUtKxvv3dKtIho/WPLRvD7i4CCtLATh4kJbt2lEqRqkiYdEZDANTpAjQtSuti/yFwCt6oaFAUpKwsmjNmjW0DAwEPDyElaUA8Na85s0BOzthZWFoyYABlBDt0iVRh6vL5RorMa90iJ7kZGDzZlofNEhYWQoAx2m+c96yKlWYosdg5AY/fbt9u6jrjNWoQYEASUmUD1b0pKRonOGHDhVWlgLCW1Gl7Kxtdri6UholAMhQYUmM8ErGwYOi9iDRcPAgZXouUUKTDFCC3LgBPHtGmQ0kHCcGgCl6DEbuNGlClRri44Fdu4SWJkdkMs3IXxLTtwcOUM6I4sUlrSG9fk3V8gCNVZUhEfgBxpYtFP0tUlq2JGXj2TNSPkQPP/sxYIBka1YDGmuenx99/1KGKXoMRm5kDMqQyPTt4cMSGPnzVpRBg6gMgEQ5fJjy7tasSYUXGBKieXPA0xOIixN1UIatrSZXpuinbx89ojDVjH6QEsVUpm0BpugxGHnTrx8pI+HhwO3bQkuTIy1a0EvhxQvg+nWhpcmFhw8pu7BMJmkfHkDzMujQQVg5GPlALgeGDKF1CU3fihr+ewwIAMqWFVaWAvDyJQUNZ5wpkTJM0WMw8sLdXWMuE7FVz9paIlUyMgZhSNgMlpQEHD9O60zRkygDB0oiKKNtW1I6rlwhJUSUJCdrom2//lpYWQoIn7fQ21u61TAywhQ9BkMbvvqKlhs3Uv4tkSL6NCsmFIQRFkY/BQ8PmrplSBCJBGW4uZHSAYg4efLeveS0WrKkpP1uAdOatgWYosdgaIe/P01FxMZSBK5I4Z+vV6+KdOT/++8UhFGsmORfBr//Tsv27SWb65kBaAZxIg/KEP307cqVtBwyRNJ+twkJwIkTtM4UPQbDnJDLgWHDaH3ZMtFGO7i7A/Xq0booR/6rVtFS4kEYKpXGasqmbSVOixaSCMrglY4TJ0Soj96+DZw7R1G2Eve7DQ2lWeiyZQEJV2XMBFP0GAxtGTiQyh9cvw5cviy0NDnCvxB4i5NouHdPE4TBRzJLlEuXgOhowMEBaNpUaGkYBSJjUAY/EBEhlSuT8pGcTMqIqOC/t/btKX+ehMk4bWsqlnqm6DEY2lK0KNCjB60vWyasLLnQqRMtT5wgI4VoWL6clm3bSjoIA9Ao0a1bA5aWwsrC0AN8pYzLl0Vb7lAmE+n0bUIC8NtvtC7xIIz0dM1MiCnlxWSKHoOhCyNG0HLnTnI8FiGVKgEVKlDcw9GjQkvzkffvNUEYI0cKK4se4BU9Nm1rIri5Ad270/rSpcLKkgu8onf4MCklomDnTuDdOzI3+voKLU2BuHSJXIgLFwYaNxZaGv3BFD0GQxc+/5wqjaekaFIJiAyZTGPV279fWFnUbN5Myl7FipJ/Gdy/D9y9SwagVq2EloahN775hpY7dwIxMcLKkgM+PoCjIykjf/whtDQgX+Vff6X1oUNpGlzC8M/L1q3p/jYVpH1VGAwhGD6clitXimhYnZnOnWl59Cjw4YOwsmR6GYwYIfmXAW/Na9aMRv4ME6FePcphkpIi2lQrSiUpIQBVERSc8+fJZ9nGRvJBGBwH7NtH6126CCuLvpH2E5fBEILu3YEiRYDHj4HgYKGlyZa6dcknOj6e4h8E5eRJ4M4dwN4e6N9fYGEKDpu2NWF4q96KFUBqqrCy5AA/iNu7VwTB/7/8Qss+fciHWcLcuAH88w8lng8MFFoa/cIUPQZDV2xtKQIXAJYsEVaWHJDLNXlg+VGqYPDWvP79KUxVwsTEABcv0ropOWszPvLFF+Sv9/KlCG6c7AkMJAPa48cCF/N4+lQz1zlqlICC6Af+cgcGAnZ2wsqib5iix2Dkh5EjSZsKCQFu3RJammzh/fQOHgTS0gQS4ulTTYggH8giYQ4cICtK7drAZ58JLQ1D71haaiJHRRqUYWensTgJqosuW0YJJVu0AKpWFVAQ/cB/l7zF1JSQjKL39u1b9O7dGw4ODnB0dMSgQYMQHx+f6zHNmjWDTCbL9Pn6k/DvZ8+eoU2bNrC1tYWrqyvGjh2LNMHeigzJUKaMRpNavFhQUXKiSRPAyYmCgy9cEEgI/mXg60vhwBJn715adu0qrBwMAzJ0KCXzvnABuHZNaGmyJeP0rSAkJGhqVo8eLZAQ+uP+fRqvW1hQ9idTQzKKXu/evXH79m2Ehobi8OHDOHv2LL7iS9fkwpAhQxAZGan+/Pzzz+r/paeno02bNkhJScHFixexadMmbNy4EVOmTDFkVximQlAQLbdsEWWUnlKpmV4UZOQfF6dJpGoCL4O3b8ndEDA9Z21GBooVA7p1o3WRWvXatqX7+++/KQLc6GzZQuUgy5aVfClDQDMD3bw5uV+bGpJQ9O7cuYPg4GCsXbsW3t7e8PHxwdKlS7Fjxw68zKOgp62tLdzd3dUfhww+QiEhIfj777+xZcsW1KxZE61atcKMGTOwbNkypKSkGLpbDKnToAFF6iUnk/O2COGNjvyUo1FZu5aUvUqVNKGCEoafAq9WjfIUMkwY3uds2zYgMlJYWbLB0RFo2ZLWjZ5CieM0vsnffENlzySOKU/bAoAkik2Gh4fD0dERdevWVW/z9fWFXC7HpUuX0Il/m2XD1q1bsWXLFri7u6Ndu3b44YcfYGtrq263WrVqcHNzU+8fEBCAYcOG4fbt26hVq1a2bSYnJyM5OVn9d9zH8gOpqalINUCkFt+mIdoWM1Lot2zUKFj06QNu2TKkBQVRyFYB0We/mzcHbG0t8OyZDJcvp6J27QI3qR2pqbD45RfIAKSNHg0uPT3XVDRSuNa7dysAyNGpUzpSU1V6aVMK/TYEou937dpQNGwI+cWLSF+0CKqZMwvcpL773KGDDMHBFtizR4X//c94aZ5kwcGw+PtvcPb2SOvTJ8/oZLFf6+fPgcuXlZDJOLRunaaXYGtj9FmXtiWh6EVFRcHV1TXTNgsLCzg5OSEqKirH43r16oVSpUqhePHiuHHjBsaPH4979+5h30f1PSoqKpOSB0D9d27tzp49G9OmTcuyPSQkRK1EGoJQ0RU4NA5i7rfMxga+zs6wffUKtyZNwjM9JgPWV79r1Pgc4eHFsWDBP+jd2zjzPCXOnkXdZ8/woXBhhDo5QaVliQ6xXuvERAuEhJAHvLPzGRw9+l6v7Yu134ZGzP12b9YM3hcvQrVsGUJq10aajY1e2tVXn21sLCGXB+LaNTk2bjwBV9ckvbSbF42+/x7OAB61aIHbOjj/ivVaHz5cBkB1eHm9xfXr53H9uv7aNmSfExMTtd5XUEVvwoQJmDt3bq773LlzJ9/tZ/Thq1atGooVK4aWLVvi0aNH8PT0zHe7EydORBDvnwWy6Hl4eMDf3z/T1LC+SE1NRWhoKPz8/KA0pXTdeSCVfsvv3wcmTkTNkydRdcGCAlfC1ne/Y2NlCA8Hbt6sgNatyxa4vTzhOFh8HAgpv/0WgXyel1wQ+7Xevl2GtDQFKlbkMHRoY70VOxd7vw2FJPodGAhu714o791D4PPnUI0ZU6DmDNHn9es5nD0rQ1xcSwwYoB8rc27ILl+Gxe3b4JRKlFq8GKVKlszzGLFf60WLaOp54EBHtNaTi4kx+hynQyFzQRW97777DgMGDMh1n7Jly8Ld3R0xnzi7p6Wl4e3bt3B3d9f6fN7e3gCAhw8fwtPTE+7u7rh8+XKmfaKjowEg13atrKxgZWWVZbtSqTToD9nQ7YsV0ff766+BmTMh+/tvKENC9Ba2pa9+d+hAjtt378pw/74SVaroQbjcOHVKnS1fMXIkFDr0QazXmq9C0LWrDJaW+pdPrP02NKLv99ixwODBUCxZAsXo0ZR+pYDos89dugBnzwIHDijw3XdG8JVbsAAAIOvdG8oyZXQ6VIzX+tUr4Nw5Wu/aVQGlUr/foSH7rEu7ggZjuLi4wMvLK9ePpaUlGjRogNjYWFy9elV97MmTJ6FSqdTKmzZEREQAAIoVKwYAaNCgAW7evJlJiQwNDYWDgwMqV66sn04yTB9HR01ZtJkzRZCuPjOOjkBAAK3v2mWEE86fT8uBAyWfLR+gTBJ8ARSWVsXM6NMHcHcH/v0X2LFDaGmywLunX7gA5OJtpB/u3dOMeMaONfDJjMOBA5T9qVYtyphlqkgi6rZSpUoIDAzEkCFDcPnyZVy4cAEjR45Ejx49ULx4cQDAixcv4OXlpbbQPXr0CDNmzMDVq1fx5MkTHDx4EP369UOTJk1QvXp1AIC/vz8qV66Mvn374q+//sLx48cxefJkjBgxIluLHYORI99+C1hZUaXx06eFliYLfLaInTsNrIdGRFCBXbmcvhMT4NgxICmJMknUqCG0NAyjYmWlSQ00b57oBnEeHhT4n7FOq8GYP59O1K4dYCKGkJ07ack/H00VSSh6AEXPenl5oWXLlmjdujV8fHywOkPh6dTUVNy7d0/toGhpaYkTJ07A398fXl5e+O6779ClSxccOnRIfYxCocDhw4ehUCjQoEED9OnTB/369cP06dON3j+GxHF31xT1njVLWFmyoUMHemfduwfcvGnAE/HRid27A+XKGfBExmPPHlp27Vpg90uGFPn6a6rTfOsWDWJERsZBnMGIjAR++43Wx4834ImMR3Q0eZkApq/oSSLqFgCcnJywbdu2HP9funRpcBlGWx4eHjhz5kye7ZYqVQpHRXjzMiTIuHHA6tXAiRPA5cs01BYJDg5Aq1Y0VbFzJ/DRqK1f/v5bk6p/0iQDnMD4JCUBR47QOkuSbKY4OgLDhpFFb/p0ygkpIo2/Wzfgf/8jX7N//wW0iI/QnYULgZQUoFEj+pgAe/fStG3dumStN2UkY9FjMERPqVJA7960LkKrXvfutNy1y0AzULNmUcOdO5tE7UuADDjx8VTX9vPPhZaGIRjffQfY2NAA7vhxoaXJhIcH4ONDt97u3QY4QUwMsHw5rU+caIATCANvAeWfi6YMU/QYDH0yYQKN9n//naZ6RETbtvSuevgQes0VBQB48ADYvp3Wv/9ez40LB9+lHj1EZcRhGBs3N7LqAcC0aaLz1evRg5YGiReZNw9ITKSRjglUuAGAly810bamPm0LMEWPwdAvXl6aOb5skmoLib29piyl3v155syheZA2bWC88huGJS4OOHyY1nv2FFYWhggYO5Yq3/zxB7lniIiuXSn+6fJl4J9/9NhwTAywbBmt//ijyYx29uwhXb1BA7LWmzpM0WMw9M3UqfRA3LPHAKazgmGQ6dsHD4BNm2h98mQ9NSo8v/9OZYwrVmTRtgxQwNXQobQuMquemxuVOwT0nELp55/JUbVePXLyNRHMadoWYIoeg6F/qlbVmIB++EFYWT6hdWvAzg548gT48089NTplCtWxbdMGqF9fT40KDz9t27OnyRgyGAVl3DgKX79wARBZSS+9T99GR2t880zImvf8OXDxInXHXPJiMkWPwTAEP/4IKBQUshkeLrQ0amxtKQ0WoKfp27/+0rxZ9FD4XSy8fq15j7NpW4aa4sUp3QpAgQkqw5cd05bOnQELC7olC1A5VMPs2WTN8/YGAgP10KA44C2ePj5AiRLCymIsmKLHYBiC8uUBvryfyIITMk7fFvg9xfetRw+Tmt/cswdISyN3wwoVhJaGISq+/x4oVAi4ds1IpWa0w8lJUwGnwIO4f/7RWPNmzDAZax6gsdSby7QtwBQ9BsNwTJlCtTFPnRKV83ZgIFC4MOXcOnu2AA1duEAWS4WC8ouZEBmjbRmMTLi4aEqATZ5M+eVEAq+87NhRQBfC778HUlMBf3/Az08vsomBO3eAq1fpkWUO0bY8TNFjMAzFZ59ppnm++4782ESAtTXwxRe0vnlzPhvhOEolAwBffkkWTBPh3381qRfMadTP0IFvv6UIiEePgDVrhJZGTcYKOB9Lu+vOlSukKcpkwNy5+hRPcLZsoWVgIOnr5gJT9BgMQzJlCmXWv3ED2LBBaGnU9O1Lyz17yA1HZ/bsAc6fp8R8U6boVTah4SOSfXzMI/UCIx/Y21N0PUDW7PfvhZXnIw4OGh/cfA3iOE5jrezTB6hZU1+iCY5KpVH0+OefucAUPQbDkBQtqlGEJk8WzQvBx4cKecTFAQcP6nhwUhLVXAKo7qVBai4JB1/SkwVhMHJl8GCq5xwTI6pApH79aLl1K/mZ6sTRo8Dp0+RyMmOGvkUTlPPngWfPSBlu315oaYwLU/QYDEMzYgRNbUZHUySbCJDLacAO5GPkv2ABPTE9PDSjfxMhIoKiFi0tmX8eIw+USqoBC9Dy/n1h5fkIPy0ZEwOEhOhwYHIyTUkDwKhRNBI0IfjnXNeuNBFhTjBFj8EwNJaWwPz5tL5wIfD4sbDyfISfvggOppeCVrx4oVFW586lfC0mBJ/3uX17imJkMHKlbVtKJJyaCoweLYokykol0KsXrfO/Z61YtIiSn7u5iS7/Z0H58EFTB9jcpm0BpugxGMahXTugZUsaNY8aJYoXQsWKVL4yPV2HJKvjx1Pdy0aNTM7klZpK010A0L+/sLIwJIJMBvzyCw3mgoOBQ4eElgiAZvr299+B2FgtDnj+XDNVO28ezW+aEIcOAe/e0SREkyZCS2N8mKLHYBgDmQz49Vcabh8+DOzfL7READSjW62mb0NDSROSyYDFi00qtxYAHDsGvHoFuLpq8pExGHlSvjwQFETrY8bkM7pJv9SqRQV6kpO1TPX33Xc0gPPx0fh0mBB8EEbv3uS2Ym6YYZcZDIHw8tKkJPnmG4qEEJgePSib/pUreWTTT0zUpIr55hugbl2jyGdM+GmuPn1IH2cwtOb776nMwuPHVAdXYGQyjVWPDy7KkUOHaF5TLgeWLjW5Adzr1xRjApjntC3AFD0Gw7hMmkSRei9fiqJihouLplb5xo257DhtGmXL9/AAfvrJGKIZlTdvNLNubNqWoTP29sCyZbQ+fz6NnASGt15duAA8fJjDTrGxmXN9mlA6FZ4tWyj6uE4doHJloaURBqboMRjGxNoaWLGC1n/9lapmCMyXX9Jy0ybyU8vC1asUaQvQy6xQIaPJZiy2b6e+16wJVK8utDQMSdKhA2XYTk+nm0rgihnFi2uKWuRo1Rs7lgad5cuLwhKpbzgOWLuW1gcNElYWIWGKHoNhbHx9ga++ovUBAwSfwm3ThgLtoqOpolkmEhPJNJCeTjWD+GysJgY/bcuseYwCsXQp4OwM3Lwpitx6/O9506ZsCvOEhmq0oLVrTTLnyKVLwO3b1DVzzovJFD0GQwgWLADKlqV8dKNHCyqKUkn6JqB57qsZN47qKRUvrilybmL89RfNtFlYaNJSMBj5wsWFlD2AXBzOnxdUnE6dgCJF6DGTKadeTIzGiW/ECJMNRV23jpZffEEFiswVpugxGEJgb0/DbJmMnOO0Co0zHPy0xrFjVOsVAHkw835HGzdSlQ8TZNUqWnbsSBG3DEaB6NGDvP5VKho5/PefYKJYW2useqtXf9yoUtHILioKqFKF0qmYIO/fk0sGQEVMzBmm6DEYQuHjo4nCHTQIuHtXMFHKlweaNqV3wMaNAJ4+1Yz4R4/WOPuYGAkJmtQLQ4cKKwvDhFi2jIKunj8nLUPAvJlDhtDy0CFyx8Mvv9CIztqaEmia4JQtQGPnhASgQgV61JozTNFjMIRk+nSgWTMgPp5q8yQkCCYKP+rdsiYJXOfOFIpapw4wZ45gMhmaHTto5O/pCbRoIbQ0DJOhUCEyJ1lYAPv2CWo1q1yZFJ30dCDsh9PkjgFQlZ6qVQWTy9Dw07aDBplcxhidYYoegyEkFhb0QihWjLyGv/ySzGoC0KULUNiBw/hnwyG7do2cyvfto5G/icJP2371lXkmUmUYkLp1KbE4AEyYABmfzE0AvvoKKIN/0GZjV8o10quXJq2KCXLzJhAeTo9XfmLCnJHMo+3t27fo3bs3HBwc4OjoiEGDBiE+Pj7H/Z88eQKZTJbtZzdf9A7I9v87tK4HxWDoAXd3mmdQKoFduyAXKL+ejQ3wW5W5GIiNSIeczF2ffSaILMbg+nXgzz8zB6MwGHpl+HDyCeA4KPr2RaFnzwQRo2vAexyWd4CT6g3ela9LUVcmbObiXYs7dqTHq7kjGUWvd+/euH37NkJDQ3H48GGcPXsWX/EpKrLBw8MDkZGRmT7Tpk2Dvb09WvEZYj+yYcOGTPt17NjRwL1hMD7Bx0c916BYsABlsuQ5MQKbNqF9+EQAQJBsEZ54tjS+DEaEt+Z17syCMBgGQiYDliwBmjSB7P17NJg2japnGJOkJNh0b4/KqluIhDu+LXPAZP3yAMoBzZd0HDlSUFFEgyQUvTt37iA4OBhr166Ft7c3fHx8sHTpUuzYsQMvX77M9hiFQgF3d/dMn/3796Nbt26wt7fPtK+jo2Om/axNeKqKIWL69gVmzQIAVFu7FjKtCtDqiT171E56O0uNxRJulDqvsykSF0dlewEWhMEwMJaWwL594CpVgs2bN7AIDARevDDOuVNTKYnz6dNItyuEtjiMTSdKGF3XNCYbN1L6z6pVTTZrjM5YCC2ANoSHh8PR0RF1M9TX9PX1hVwux6VLl9CpU6c827h69SoiIiKwjLfpZmDEiBEYPHgwypYti6+//hoDBw6ELBezdnJyMpKTk9V/x31MeJuamorUbEsLFAy+TUO0LWbMst/ffQc8fQrlqlVQDB6MNJUKnIGz+Mp27YKif3/I0tOh6tMHyg4/AV8Aa9dy+P77NKMM/o19rdeskSM+XgEvLw6NGqVlXxHECJjlbxxm2G8HB6QdOoR0Hx/YP34MrkULpB07RiUFDUVSEhQ9e0J+9Cg4a2twv++H05xaUJ0Ali5Nx9y5xvEFNua1VqmAZcssAMgwbFg60tKE8Xc2Rp91aVsSil5UVBRcP5lbsbCwgJOTE6KiorRqY926dahUqRIaNmyYafv06dPRokUL2NraIiQkBMOHD0d8fDxGjRqVY1uzZ8/GtGzKxYSEhMDW1lYrefJDaGiowdoWM2bX74AAVH/2DGWOHYPFkCH4+8wZPOjSRf8+NRyHskeOoOr69ZCpVHjWvDmud+oE4BhcXPzw6pUtvv/+Fnx9jedXZIxrnZ4OzJvnC8AOzZv/hWPHnhr8nHlhdr/xj5hbv22mT4fPpEmwvX8fafXqIXzqVLw3gB+sMj4e9WbPhvPt20i3tMTlsWMREx+P+vUv48SJ+li1SoV69Y7DxubTchmGwxjX+vp1Fzx82BC2tqlwcjqOo0eN17/sMGSfExMTtd5XUEVvwoQJmDt3bq773Llzp8DnSUpKwrZt2/DDDz9k+V/GbbVq1UJCQgLmzZuXq6I3ceJEBAUFqf+Oi4uDh4cH/P394eDgUGB5PyU1NRWhoaHw8/ODUqnUe/tixaz7LZOhZLlyUC5dispbtsBLJkP6r7/qz7cmORny//0Pio+lMNIHD0axX39FsY+hp/fuyTFpEnDuXE0sWFDV4H7bxrzWv/8uQ3S0BZycOMyeXQW2tlUMer7cMOvfuJn1m++z7Px5cB07wubuXTSfPBnpa9aA02JWSmtu3oRFt26QPXoEzsEB3IEDqPsxkVxgILBjB4eHD5V4/boVhg41vMXLmNd69WoFAGDwYDm6dAkw6Llywxh9jtOhdKagit53332HAXmEu5UtWxbu7u6IiYnJtD0tLQ1v376FuxYhNXv27EFiYiL6aRFn7e3tjRkzZiA5ORlWVlbZ7mNlZZXt/5RKpUF/yIZuX6yYZb9lMiqTVrEiMGoU5Js3Q37lCjmW1apVsLbv3KH0ChERdJ65c6H43/+gyKDNffUVpfj76y8ZLl9WGi3hqDGu9a+/0nLoUBkKFxbH78osf+Mwz35blC0L2YULQMeOkJ07B4vu3YFRo8g/184u/w2rVMCKFZQnLzERKF0asgMHYFGjRqbdvvmGcqAvW6bAiBEKowXfGvpaP3xIeaABYORIBZRKhcHOpS2G7LMu7QoajOHi4gIvL69cP5aWlmjQoAFiY2Nx9epV9bEnT56ESqWCt7d3nudZt24d2rdvDxcXlzz3jYiIQJEiRXJU8hgMozJiBHD8OOUIuHMHqFcPGDMGePtW97bi4oDx44GaNUnJc3YGDh4Exo7NMi1ctCjQpw+tm1KFpOvXgTNnKL/W8OFCS8MwW5ycgLAw4H//o7+XLKFyZPv356+Kxp9/UuL1kSNJyfP1pQLOnyh5AKUSKlSICvGY0sz5ggX01bVpQ5V+GBokEXVbqVIlBAYGYsiQIbh8+TIuXLiAkSNHokePHihevDgA4MWLF/Dy8sLly5czHfvw4UOcPXsWg7Mpdnfo0CGsXbsWt27dwsOHD7FixQrMmjUL33zzjVH6xWBoha8vZQDt3JmSnf7yC1C2LBAUBNy/n/fxjx9TqbUyZYCffwZSUoDWrYEbN4C2bXM87H//I/3v4EHg77/12B8B+eUXWn7xBVCypLCyMMwcpZJGUUePAqVKUdnBzp1pIPbbbzQwy420NODIEaB9exoAnjtHFsGlS2lwmENtagcHYOBAWl+4UL9dEoqYmI+lG0HjVkZmJBGMAQBbt27FyJEj0bJlS8jlcnTp0gVLlixR/z81NRX37t3L4qC4fv16lCxZEv7+/lnaVCqVWLZsGb799ltwHIdy5cph4cKFGMIXB2QwxIKzM7B3Lw3Bg4KAW7eARYvoU6EC0Lw5LfmgpVevSAk8ezazllahAg1927TJM7ijYkWgQwfgwAFg/nxg/XrDdc8Y/PsvsG0brY8eLawsDIaaVq2oKs6sWWTZu3ED6N8fsLKi/CB16tAgrVAhstZFRgJXrwLnzwOvX1MbcjmlZ5o2jZTGPBgzhpIKHz8OXLsG1K5t2C4aml9/BT58IH2XpVTJimQUPScnJ2zjn9LZULp0aXDZmLxnzZqFWR9zk31KYGAgAgMD9SYjg2Fw/PyAv/4CgoNp5B4aSgpdbpY9uZwUwW++IQueQnvflfHjSdHbsoV89qRsBVuwgNKKNW0KaOHxwWAYDzs7YOZMSq+0fDndcPfu0f2d2/yqszP5WAwdCnh5aX26MmWAnj3pNLNnAxmKRUmOhARNJYxsvFAYkJCix2AwPiKX09Rr69bAu3fAiRM0LH/wAPjvP3rSOTiQo0rNmqQcOjnl61T169MI+exZKts5f75ee2I0Xr3SVMKYNElYWRiMHHFyAiZPBr7/nqz2Fy/SvR0VBbx/T1H3bm5ApUpUTaduXZoCzgcTJpCit3cv+evpoCeKivXryWXZ0xPQZ/CyKcEUPQZDyhQuDHTpQh8DMW4cKXqrVtH7p0gRg53KYPzyC5CURO9FPz+hpWEw8kAmA6pVo4+BqFKFXDN+/x2YOxfYsMFgpzIYHz6Q7AD5FOswWWFWSCIYg8FgCEfr1lROKD6erHpS4907TUqVSZPY1A6DwTORSltjyxbgmfHyouuNdeuomlzJkpoAE0ZWmKLHYDByRSYDpkyh9UWLgDdvhJVHV5YsIWWvUiWyYDAYDMLbG2jRggJ486hdIDqSk8m/ECCFlWVEyxmm6DEYjDzp0gWoXp3chKTkp/fmjUbeKVPIvZHBYGjgi0OtWUOZmKQCb80rUQIYNEhoacQNe+wxGIw8kcsp6hYgC9knhWpEy88/UzqyGjWAbt2ElobBEB/NmpHfamoqMHWq0NJoR1ISZaMByB2DWfNyhyl6DAZDK9q3p2CGxERgzhyhpcmbly9JKQUocwWz5jEY2cMrTVu2ULCv2FmyhKx5Hh7MmqcN7NHHYDC0QiYDZsyg9WXLgH/+EVaevPjpJ4rKa9iQAkoYDEb21K1L7hkcR9ldxMyrVxrFdOZMZs3TBqboMRgMrQkIoGmelBRKuyJWbt0CVq+m9VmzWKQtg5EXM2aQ1fv336ketFiZMYPcMWrVAnr3FloaacAUPQaDoTUyGdXHlMsp0erZs0JLlBWOoxJP6emUQLVpU6ElYjDET6VKVGADAEaNokhcsfHgAbBiBa3Pn8/cMbSFfU0MBkMnqlYF+HLQ334LqFTCyvMpBw4AYWE0pbNggdDSMBjSYcYMSoh+44bGIi4WOI5qVKelkStGixZCSyQdmKLHYDB0Zvp0qrJ27Zq4XggfPgBBQbT+v/9RTU8Gg6EdRYuSbytAvnqvXwsrT0b27gWOHQMsLWlWgaE9TNFjMBg64+qqCcwYN44i4MTAjBnAkyeUW4vP+s9gMLTnq68oZ+Z//4nHDzcujqx5ANXorVhRWHmkBlP0GAxGvhgxgjLrv39P6xwnrDwREZrs/kuWAHZ2gorDYEgSCwuKqpfJqP5tcLDQElFS55cvgXLl2AAuPzBFj8Fg5AuFgrLpW1hQpN7evcLJkpoKfPklBWB07Qp07iycLAyG1PHxoYAMgPxx370TTpYzZ4ClS2l9+XLA2lo4WaQKU/QYDEa+qVaNplIA4OuvhZvCnT4duH6dHMl//VUYGRgMU2LmTMDTE/j3X+C774SR4d07oF8/mi348ktK7cTQHaboMRiMAjF5MuW0evOGHsrp6cY9/6lT9FICKPWCm5txz89gmCJ2djR1K5NRXdldu4x7fo4DRo4Enj0DypYFFi827vlNCaboMRiMAmFlBWzfDtjaAidPArNnG+/cMTFAnz70Uhg0COje3XjnZjBMncaNNRb7wYOB+/eNd+7Vq6kkm1wO/PYbUKiQ8c5tajBFj8FgFJiKFcmBGwCmTAEOHjT8OZOTyRfv5UvAywv45RfDn5PBMDemTweaNKGgq65daWlo/vgD+OYbWp81C2jUyPDnNGWYosdgMPTCgAHA8OFkXevdG7h503Dn4jhg2DDgwgWgcGFg/34WZctgGAILC2DHDnKJuHkT6NaNgp8MxePHNIBLTaX6u2JJ8SJlmKLHYDD0xuLFQPPmQHw81cV9+FD/5+A4YNIk8h+Sy8l3yMtL/+dhMBhEsWLA4cPknhEcTIMsQ1TEiYkB/P2ByEgK9OJ9BBkFgyl6DAZDbyiVwJ49VCYtMhJo2RJ4+lS/55gxA5gzh9aXLaMXA4PBMCx165IvLh+c8fXX+lX2oqPpXn74EChdmhRK5penH5iix2Aw9IqTE3DiBFChAkXMNWxIyYwLSno6MGYMMHUq/b1wIb1sGAyGcWjfHti4kSzpa9aQu0ZycsHbffqUcvf99RdNER8/DhQvXvB2GQRT9BgMht5xcwPCwoAqVShYonFjYN++/Lf333/kt8MHXMybB3z7rX5kZTAY2tOvH7B5Myl7mzeTq0ZkZP7bCw0FPv9cY8k7f54GiQz9IRlFb+bMmWjYsCFsbW3h6Oio1TEcx2HKlCkoVqwYbGxs4OvriwcPHmTa5+3bt+jduzccHBzg6OiIQYMGIT4+3gA9YDDMi5Il6aHN++x16QIMHEhKmy4cO0ZTwQcPUiqXnTuB//3PMDIzGIy86dULOHoUcHQEwsOpNu7mzbqVQUxIAMaPJ1/eV6+AGjXoeVGunMHENlsko+ilpKTgiy++wLBhw7Q+5ueff8aSJUuwcuVKXLp0CXZ2dggICMCHDx/U+/Tu3Ru3b99GaGgoDh8+jLNnz+Krr74yRBcYDLPD0ZF8bcaPJ9+e/7d370FRXXccwL/LG3lK5alI8IVKEUkIBKNRh1VAx0omU8U2RFLAxIFaRqM1nRRE0xGNE2MsNa01YlqF0PrKNBYfKGoovhASCIT6ANFEJFGRBQwge/rHljteebk8lmX3+5nZkT33dw/nu4fLHi+7dzMyNFfbT0sDfvih6/2EAM6cAcLDgXnzNGcFJ0wAzp7VvOuPiAZXWBhw4YJmkffDD5ozfS+9pDneu7toukql+SxqHx9g82bNsb5smWbBOHKk7sZvTMwGewBPKzU1FQCQkZHxVPVCCHzwwQd45513sHDhQgDAJ598AldXVxw6dAhRUVEoLy9HTk4OLl68iMDAQADA9u3bMW/ePGzZsgUefJEAUZ9ZWGgWdvPna15TV1am+WDylBRg5kzNNbJGjVKgrGwkKitNUF6ueY3f9eua/c3MNNfUevddzbv+iEg/jB8PXLoEbNmiud7eF18AERGad+mGhwNTpwLDhytQWOiJL780waVLmj/Vtp9reeYZzcsxfvazwUxh+IbMQk9blZWVqKmpgVKplNocHBwQHByMgoICREVFoaCgAI6OjtIiDwCUSiVMTExw/vx5vPzyy5323dzcjObHXoFaX18PAGhtbUXrAFxgqL3PgehbnzG3YeV+4QWgsBDYu1eB9HRTFBUpcPy45he/5ldRoKzexkYgKkrgrbfaMHasps3AHhKDneueGGNuQ8781ltAVBSwbZsJ9uwxwe3bCuze3b7VDMCzsvrx4wVWrFDjtdfUsLbmcd2X7/E0DHahV1NTAwBwfeKDL11dXaVtNTU1cHFxkW03MzODk5OTVNOZjRs3SmcYH3fs2DEMG8BTDsc1z4hGh7kNy4gRmrN5N2/aorjYBTdu2OP7762hVitgY9MKN7dGTJ58F35+d2Ft/QgVFUBFxWCPemAZ6lz3xBhzG3Lm2bOB6dMV+OorZ3zzjRNu3bJDQ4M5TE0FHBya4e39AFOmfA9v73ooFJrPqTZkAznXTU1NT107qAu9tWvXYtOmTd3WlJeXY6KeXQ317bffxsqVK6X79fX18PT0xNy5c2Fvb9/v36+1tRXHjx/HnDlzYG5u3u/96yvmNp7c8szOAJ4Z7CHphDHONWCcuY0p8/9fLQXgydxuAHwGbVy6oou5bv9L4tMY1IXeqlWrEBMT023NmDFjetW3m5sbAODOnTtwd3eX2u/cuYOpU6dKNbW1tbL9Hj16hHv37kn7d8bS0hKWlpYd2s3NzQf0AB7o/vUVcxsPY8wMMLcxMcbMgHHmHsjM2vQ7qAs9Z2dnODs7D0jf3t7ecHNzQ25urrSwq6+vx/nz56V37oaEhKCurg6FhYV47rnnAAAnT56EWq1GcHDwgIyLiIiISFeGzOVVqqurUVxcjOrqarS1taG4uBjFxcWya95NnDgRBw8eBAAoFAokJSXh3XffxWeffYaSkhK89tpr8PDwQGRkJABg0qRJCA8PR3x8PC5cuID8/HwkJiYiKiqK77glIiKiIW/IvBkjOTkZe/bske4HBAQAAE6dOoVZs2YBACoqKvDgwQOpZs2aNWhsbMSyZctQV1eH6dOnIycnB1ZWVlLN3r17kZiYiNDQUJiYmOCVV17Bhx9+qJtQRERERANoyCz0MjIyeryGnnjistwKhQLr16/H+vXru9zHyckJ+/bt648hEhEREemVIfOnWyIiIiLSDhd6RERERAaKCz0iIiIiA8WFHhEREZGB4kKPiIiIyEBxoUdERERkoIbM5VX0WftlXbT57DlttLa2oqmpCfX19Ub1ETLMbTy5jTEzwNzGlNsYMwPGmVsXmdvXG09eVq4zXOj1A5VKBQDw9PQc5JEQERGRsVCpVHBwcOi2RiGeZjlI3VKr1fjuu+9gZ2cHhULR7/3X19fD09MTN2/ehL29fb/3r6+Y23hyG2NmgLmNKbcxZgaMM7cuMgshoFKp4OHhAROT7l+FxzN6/cDExASjRo0a8O9jb29vNAfK45jbeBhjZoC5jYkxZgaMM/dAZ+7pTF47vhmDiIiIyEBxoUdERERkoLjQGwIsLS2RkpICS0vLwR6KTjG38eQ2xswAcxtTbmPMDBhnbn3LzDdjEBERERkontEjIiIiMlBc6BEREREZKC70iIiIiAwUF3pEREREBooLvUGSnp6OZ555BlZWVggODsaFCxe6rf/HP/6BiRMnwsrKCn5+fjhy5IhsuxACycnJcHd3h7W1NZRKJa5cuTKQEXpFm9w7d+7EjBkzMHz4cAwfPhxKpbJDfUxMDBQKhewWHh4+0DG0ok3mjIyMDnmsrKxkNYY417NmzeqQW6FQYP78+VKNvs/1mTNnsGDBAnh4eEChUODQoUM97pOXl4dnn30WlpaWGDduHDIyMjrUaPu7Qte0zX3gwAHMmTMHzs7OsLe3R0hICI4ePSqrWbduXYe5njhx4gCm0I62mfPy8jr9+a6pqZHVGdpcd3bMKhQK+Pr6SjX6PtcbN27E888/Dzs7O7i4uCAyMhIVFRU97qdPz9lc6A2CTz/9FCtXrkRKSgouX74Mf39/hIWFoba2ttP6//znP1iyZAliY2NRVFSEyMhIREZGorS0VKrZvHkzPvzwQ3z00Uc4f/48bGxsEBYWhh9//FFXsXqkbe68vDwsWbIEp06dQkFBATw9PTF37lx8++23srrw8HDcvn1bumVmZuoizlPRNjOguZr643lu3Lgh226Ic33gwAFZ5tLSUpiamuLnP/+5rE6f57qxsRH+/v5IT09/qvrKykrMnz8fs2fPRnFxMZKSkhAXFydb9PTm50fXtM195swZzJkzB0eOHEFhYSFmz56NBQsWoKioSFbn6+srm+svvvhiIIbfK9pmbldRUSHL5OLiIm0zxLnetm2bLO/Nmzfh5OTU4bjW57k+ffo0EhIScO7cORw/fhytra2YO3cuGhsbu9xH756zBelcUFCQSEhIkO63tbUJDw8PsXHjxk7rFy1aJObPny9rCw4OFm+88YYQQgi1Wi3c3NzEe++9J22vq6sTlpaWIjMzcwAS9I62uZ/06NEjYWdnJ/bs2SO1LV26VCxcuLC/h9pvtM28e/du4eDg0GV/xjLXW7duFXZ2dqKhoUFq0/e5fhwAcfDgwW5r1qxZI3x9fWVtixcvFmFhYdL9vj6OuvY0uTszefJkkZqaKt1PSUkR/v7+/TewAfQ0mU+dOiUAiPv373dZYwxzffDgQaFQKERVVZXUNpTmWgghamtrBQBx+vTpLmv07TmbZ/R0rKWlBYWFhVAqlVKbiYkJlEolCgoKOt2noKBAVg8AYWFhUn1lZSVqampkNQ4ODggODu6yT13rTe4nNTU1obW1FU5OTrL2vLw8uLi4wMfHB8uXL8fdu3f7dey91dvMDQ0N8PLygqenJxYuXIivv/5a2mYsc71r1y5ERUXBxsZG1q6vc90bPR3X/fE4DgVqtRoqlarDcX3lyhV4eHhgzJgx+OUvf4nq6upBGmH/mTp1Ktzd3TFnzhzk5+dL7cYy17t27YJSqYSXl5esfSjN9YMHDwCgw8/r4/TtOZsLPR374Ycf0NbWBldXV1m7q6trh9drtKupqem2vv1fbfrUtd7kftJvf/tbeHh4yA6O8PBwfPLJJ8jNzcWmTZtw+vRpREREoK2trV/H3xu9yezj44OPP/4Yhw8fxt///neo1WpMmzYNt27dAmAcc33hwgWUlpYiLi5O1q7Pc90bXR3X9fX1ePjwYb8cM0PBli1b0NDQgEWLFkltwcHByMjIQE5ODnbs2IHKykrMmDEDKpVqEEfae+7u7vjoo4+wf/9+7N+/H56enpg1axYuX74MoH9+P+q77777Dv/+9787HNdDaa7VajWSkpLw4osv4qc//WmXdfr2nG3W7z0SDYC0tDRkZWUhLy9P9uaEqKgo6Ws/Pz9MmTIFY8eORV5eHkJDQwdjqH0SEhKCkJAQ6f60adMwadIk/PnPf8aGDRsGcWS6s2vXLvj5+SEoKEjWbmhzTcC+ffuQmpqKw4cPy16vFhERIX09ZcoUBAcHw8vLC9nZ2YiNjR2MofaJj48PfHx8pPvTpk3DtWvXsHXrVvztb38bxJHpzp49e+Do6IjIyEhZ+1Ca64SEBJSWlurVawifBs/o6diIESNgamqKO3fuyNrv3LkDNze3Tvdxc3Prtr79X2361LXe5G63ZcsWpKWl4dixY5gyZUq3tWPGjMGIESNw9erVPo+5r/qSuZ25uTkCAgKkPIY+142NjcjKynqqX/D6NNe90dVxbW9vD2tr6375+dFnWVlZiIuLQ3Z2doc/cz3J0dEREyZMGLJz3ZmgoCApj6HPtRACH3/8MaKjo2FhYdFtrb7OdWJiIv71r3/h1KlTGDVqVLe1+vaczYWejllYWOC5555Dbm6u1KZWq5Gbmys7k/O4kJAQWT0AHD9+XKr39vaGm5ubrKa+vh7nz5/vsk9d601uQPPOpA0bNiAnJweBgYE9fp9bt27h7t27cHd375dx90VvMz+ura0NJSUlUh5DnmtAc0mC5uZmvPrqqz1+H32a697o6bjuj58ffZWZmYnXX38dmZmZskvodKWhoQHXrl0bsnPdmeLiYimPIc81oHnn6tWrV5/qP3D6NtdCCCQmJuLgwYM4efIkvL29e9xH756z+/3tHdSjrKwsYWlpKTIyMkRZWZlYtmyZcHR0FDU1NUIIIaKjo8XatWul+vz8fGFmZia2bNkiysvLRUpKijA3NxclJSVSTVpamnB0dBSHDx8WX331lVi4cKHw9vYWDx8+1Hm+rmibOy0tTVhYWIh//vOf4vbt29JNpVIJIYRQqVTirbfeEgUFBaKyslKcOHFCPPvss2L8+PHixx9/HJSMT9I2c2pqqjh69Ki4du2aKCwsFFFRUcLKykp8/fXXUo0hznW76dOni8WLF3doHwpzrVKpRFFRkSgqKhIAxPvvvy+KiorEjRs3hBBCrF27VkRHR0v1169fF8OGDROrV68W5eXlIj09XZiamoqcnByppqfHUR9om3vv3r3CzMxMpKeny47ruro6qWbVqlUiLy9PVFZWivz8fKFUKsWIESNEbW2tzvN1RtvMW7duFYcOHRJXrlwRJSUl4je/+Y0wMTERJ06ckGoMca7bvfrqqyI4OLjTPvV9rpcvXy4cHBxEXl6e7Oe1qalJqtH352wu9AbJ9u3bxejRo4WFhYUICgoS586dk7bNnDlTLF26VFafnZ0tJkyYICwsLISvr6/4/PPPZdvVarX4/e9/L1xdXYWlpaUIDQ0VFRUVuoiiFW1ye3l5CQAdbikpKUIIIZqamsTcuXOFs7OzMDc3F15eXiI+Pl6vfjEKoV3mpKQkqdbV1VXMmzdPXL58WdafIc61EEJ88803AoA4duxYh76Gwly3X0LjyVt7zqVLl4qZM2d22Gfq1KnCwsJCjBkzRuzevbtDv909jvpA29wzZ87stl4IzWVm3N3dhYWFhRg5cqRYvHixuHr1qm6DdUPbzJs2bRJjx44VVlZWwsnJScyaNUucPHmyQ7+GNtdCaC4bYm1tLf7yl7902qe+z3VneQHIjlV9f85W/D8IERERERkYvkaPiIiIyEBxoUdERERkoLjQIyIiIjJQXOgRERERGSgu9IiIiIgMFBd6RERERAaKCz0iIiIiA8WFHhEREZGB4kKPiMgAVFRUwM3NDSqVqsfasrIyjBo1Co2NjToYGRENJi70iMgoKRSKbm/r1q0b7CFq5e2338avf/1r2NnZ9Vg7efJkvPDCC3j//fd1MDIiGkz8CDQiMko1NTXS159++imSk5NRUVEhtdna2sLW1nYwhqa16upqjBs3DpWVlRg5cuRT7fP5558jPj4e1dXVMDMzG+AREtFg4Rk9IjJKbm5u0s3BwQEKhULWlpWVhUmTJsHKygoTJ07En/70J2nfqqoqKBQKZGdnY8aMGbC2tsbzzz+P//73v7h48SICAwNha2uLiIgIfP/999J+MTExiIyMRGpqKpydnWFvb48333wTLS0tUk1zczNWrFgBFxcXWFlZYfr06bh48WK3WbKzs+Hv7y9b5N24cQMLFizA8OHDYWNjA19fXxw5ckTaPmfOHNy7dw+nT5/uj4eTiPQU/xtHRPSEvXv3Ijk5GX/84x8REBCAoqIixMfHw8bGBkuXLpXqUlJS8MEHH2D06NH41a9+hV/84hews7PDtm3bMGzYMCxatAjJycnYsWOHtE9ubi6srKyQl5eHqqoqvP766/jJT36CP/zhDwCANWvWYP/+/dizZw+8vLywefNmhIWF4erVq3Bycup0vGfPnkVgYKCsLSEhAS0tLThz5gxsbGxQVlYmO0NpYWGBqVOn4uzZswgNDe3Ph4+I9IkgIjJyu3fvFg4ODtL9sWPHin379slqNmzYIEJCQoQQQlRWVgoA4q9//au0PTMzUwAQubm5UtvGjRuFj4+PdH/p0qXCyclJNDY2Sm07duwQtra2oq2tTTQ0NAhzc3Oxd+9eaXtLS4vw8PAQmzdv7nL8/v7+Yv369bI2Pz8/sW7dum5zv/zyyyImJqbbGiIa2nhGj4joMY2Njbh27RpiY2MRHx8vtT969AgODg6y2ilTpkhfu7q6AgD8/PxkbbW1tbJ9/P39MWzYMOl+SEgIGhoacPPmTTx48ACtra148cUXpe3m5uYICgpCeXl5l2N++PAhrKysZG0rVqzA8uXLcezYMSiVSrzyyiuy8QKAtbU1mpqauuyXiIY+vkaPiOgxDQ0NAICdO3eiuLhYupWWluLcuXOyWnNzc+lrhULRaZtarR7wMY8YMQL379+XtcXFxeH69euIjo5GSUkJAgMDsX37dlnNvXv34OzsPODjI6LBw4UeEdFjXF1d4eHhgevXr2PcuHGym7e3d5/7//LLL/Hw4UPp/rlz52BrawtPT0+MHTsWFhYWyM/Pl7a3trbi4sWLmDx5cpd9BgQEoKysrEO7p6cn3nzzTRw4cACrVq3Czp07ZdtLS0sREBDQ50xEpL/4p1sioiekpqZixYoVcHBwQHh4OJqbm3Hp0iXcv38fK1eu7FPfLS0tiI2NxTvvvIOqqiqkpKQgMTERJiYmsLGxwfLly7F69Wo4OTlh9OjR2Lx5M5qamhAbG9tln2FhYYiLi0NbWxtMTU0BAElJSYiIiMCECRNw//59nDp1CpMmTZL2qaqqwrfffgulUtmnPESk37jQIyJ6QlxcHIYNG4b33nsPq1evho2NDfz8/JCUlNTnvkNDQzF+/Hi89NJLaG5uxpIlS2QXZ05LS4NarUZ0dDRUKhUCAwNx9OhRDB8+vMs+IyIiYGZmhhMnTiAsLAwA0NbWhoSEBNy6dQv29vYIDw/H1q1bpX0yMzMxd+5ceHl59TkTEekvXjCZiEhHYmJiUFdXh0OHDvV73+np6fjss89w9OjRHmtbWlowfvx47Nu3T/bGDyIyPDyjR0RkAN544w3U1dVBpVL1+DFo1dXV+N3vfsdFHpER4Bk9IiIdGcgzekREneFCj4iIiMhA8fIqRERERAaKCz0iIiIiA8WFHhEREZGB4kKPiIiIyEBxoUdERERkoLjQIyIiIjJQXOgRERERGSgu9IiIiIgM1P8AGwOenuCSY7YAAAAASUVORK5CYII=", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Parámetros\n", "z = np.linspace(0, 2, 1000)\n", "beta = 2 * np.pi # constante de propagación (1 λ = 1 unidad de z)\n", "A_i = 1 # amplitud incidente\n", "A_r = 0.8 # amplitud reflejada menor\n", "phi = 1230 / np.pi\n", "\n", "# Onda triangular aproximada por serie de Fourier\n", "def triangular_wave(z, beta, phi=0):\n", " wave = np.zeros_like(z)\n", " for n in range(1, 100, 2): # solo términos impares\n", " wave += ((-1)**((n-1)//2) / n**2) * np.sin(n * (beta*z + phi))\n", " return (8/np.pi**2) * wave\n", "\n", "\n", "Vi = A_i * np.sin(beta * z)\n", "Vr = A_r * np.sin(-beta * z + phi)\n", "Vtotal = Vi + Vr\n", "\"\"\"\n", "# Ondas\n", "Vi = A_i * triangular_wave(z, beta, phi=0) # incidente\n", "Vr = A_r * triangular_wave(-z, -beta, phi=phi) # reflejada (viaja al revés, con desfase)\n", "Vtotal = Vi + Vr\n", "\"\"\"\n", "\n", "# Gráfico 1: incidente y reflejada\n", "plt.figure(figsize=(7,4))\n", "plt.plot(z, Vi, label='Incidente', color='blue')\n", "plt.plot(z, Vr, label='Reflejada', color='red')\n", "plt.title('Ondas sinusoidais incidente e reflexada')\n", "plt.xlabel('Tempo (s)')\n", "plt.ylabel('Amplitude')\n", "plt.grid(True)\n", "\n", "# Gráfico 2: suma total\n", "plt.figure(figsize=(7,4))\n", "plt.plot(z, Vtotal, color='black')\n", "plt.title('Suma de ambas ondas sinusoidais')\n", "plt.xlabel('Tempo (s)')\n", "plt.ylabel('Amplitude')\n", "plt.grid(True)\n", "\n", "print(\"Ondas triangulares incidente e reflexada (con desfase e onda atenuada) e Suma de ambas ondas\")\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "323dde0a", "metadata": {}, "source": [ "Eso es lo que observaremos, con terminal de línea abierto, las frecuencias en las que anotamos un máximo y un mínimo y su amplitud, osea, los puntos de resonancia de la línea de transmisión.\n", "\n", "A continuación, la tabla en los rangos que se nos pidió buscar:" ] }, { "cell_type": "code", "execution_count": 271, "id": "9ce3cecc", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Tipo de ondaFrecuencia (kHz)Amplitud (V)
0Máximo9479.20
1Mínimo13981.92
2Máximo18578.56
3Mínimo23672.48
4Máximo28298.24
5Mínimo33152.64
6Máximo38587.84
7Mínimo42643.04
8Máximo47857.60
\n", "
" ], "text/plain": [ " Tipo de onda Frecuencia (kHz) Amplitud (V)\n", "0 Máximo 947 9.20\n", "1 Mínimo 1398 1.92\n", "2 Máximo 1857 8.56\n", "3 Mínimo 2367 2.48\n", "4 Máximo 2829 8.24\n", "5 Mínimo 3315 2.64\n", "6 Máximo 3858 7.84\n", "7 Mínimo 4264 3.04\n", "8 Máximo 4785 7.60" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\\begin{table}[t]\n",
       "    \\centering\n",
       "    \\begin{tabular}{| c | c | c |}\n",
       "        \\hline\n",
       "        Tipo de onda & Frecuencia (kHz) & Amplitud (V) \\\\ \\hline\n",
       "        Máximo & 947 & 9.2 \\\\ \n",
       "        Mínimo & 1398 & 1.92 \\\\ \n",
       "        Máximo & 1857 & 8.56 \\\\ \n",
       "        Mínimo & 2367 & 2.48 \\\\ \n",
       "        Máximo & 2829 & 8.24 \\\\ \n",
       "        Mínimo & 3315 & 2.64 \\\\ \n",
       "        Máximo & 3858 & 7.84 \\\\ \n",
       "        Mínimo & 4264 & 3.04 \\\\ \n",
       "        Máximo & 4785 & 7.6 \\\\ \n",
       "        \\hline\n",
       "    \\end{tabular}\n",
       "    \\caption{Circuito abierto}\n",
       "    \\label{tab:resonancia_circuito_abierto}\n",
       "\\end{table}
" ], "text/plain": [ "" ] }, "execution_count": 271, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "from IPython.display import HTML\n", "\n", "# --- Tus datos ---\n", "data = [\n", " [\"Máximo\", 947, 9.2],\n", " [\"Mínimo\", 1398, 1.92],\n", " [\"Máximo\", 1857, 8.56],\n", " [\"Mínimo\", 2367, 2.48],\n", " [\"Máximo\", 2829, 8.24],\n", " [\"Mínimo\", 3315, 2.64],\n", " [\"Máximo\", 3858, 7.84],\n", " [\"Mínimo\", 4264, 3.04],\n", " [\"Máximo\", 4785, 7.6],\n", "]\n", "data_abierto = data\n", "\n", "columnas = [\"Tipo de onda\", \"Frecuencia (kHz)\", \"Amplitud (V)\"]\n", "\n", "df = pd.DataFrame(data, columns=columnas)\n", "\n", "# --- 1: Mostrar tabla bonita en Jupyter ---\n", "display(df)\n", "\n", "\n", "# --- 2: Generar código LaTeX ---\n", "def df_to_latex_hline(df, caption=\"\", label=\"\"):\n", " latex = \"\\\\begin{table}[t]\\n\"\n", " latex += \" \\\\centering\\n\"\n", " latex += f\" \\\\begin{{tabular}}{{| {' | '.join(['c']*len(df.columns))} |}}\\n\"\n", " latex += \" \\\\hline\\n\"\n", " latex += \" \" + \" & \".join(df.columns) + \" \\\\\\\\ \\\\hline\\n\"\n", " for _, row in df.iterrows():\n", " latex += \" \" + \" & \".join(row.astype(str)) + \" \\\\\\\\ \\n\"\n", " latex += \" \\\\hline\\n\"\n", " latex += \" \\\\end{tabular}\\n\"\n", " latex += f\" \\\\caption{{{caption}}}\\n\"\n", " latex += f\" \\\\label{{{label}}}\\n\"\n", " latex += \"\\\\end{table}\"\n", " return latex\n", "\n", "codigo = df_to_latex_hline(df, \"Circuito abierto\", \"tab:resonancia_circuito_abierto\")\n", "\n", "\n", "# --- 3: Mostrar el código LaTeX formateado en Jupyter ---\n", "HTML(f\"
{codigo}
\")" ] }, { "cell_type": "markdown", "id": "9cbd1190", "metadata": {}, "source": [ "### Reflexión con inversión en señal sinusoidal" ] }, { "cell_type": "markdown", "id": "eb3849bd", "metadata": {}, "source": [ "- 5:\n", "\n", "Exactamente lo mismo que sucede con el caso sin inversión de la señal sinusoidal ocurre para el que sí muestra inversión, en este el desfase se acerca a los 180º ocasionando una destrucción completa de no ser por que la amplitud de ida no es igual al de la de venida, por lo que tendremos mínimos de la onda suma de poco voltaje\n", "\n", "Representamos gráficamente el caso de las ondas (préstese atención a la escala):" ] }, { "cell_type": "code", "execution_count": 510, "id": "65764ffa", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ondas triangulares incidente e reflexada (con desfase e onda atenuada) e Suma de ambas ondas\n" ] }, { "data": { "image/png": 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", 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Parámetros\n", "z = np.linspace(0, 2, 1000)\n", "beta = 2 * np.pi # constante de propagación (1 λ = 1 unidad de z)\n", "A_i = 1 # amplitud incidente\n", "A_r = 0.8 # amplitud reflejada menor\n", "phi = 2*np.pi\n", "\n", "# Onda triangular aproximada por serie de Fourier\n", "def triangular_wave(z, beta, phi=0):\n", " wave = np.zeros_like(z)\n", " for n in range(1, 100, 2): # solo términos impares\n", " wave += ((-1)**((n-1)//2) / n**2) * np.sin(n * (beta*z + phi))\n", " return (8/np.pi**2) * wave\n", "\n", "\n", "Vi = A_i * np.sin(beta * z)\n", "Vr = A_r * np.sin(-beta * z + phi)\n", "Vtotal = Vi + Vr\n", "\"\"\"\n", "# Ondas\n", "Vi = A_i * triangular_wave(z, beta, phi=0) # incidente\n", "Vr = A_r * triangular_wave(-z, -beta, phi=phi) # reflejada (viaja al revés, con desfase)\n", "Vtotal = Vi + Vr\n", "\"\"\"\n", "\n", "# Gráfico 1: incidente y reflejada\n", "plt.figure(figsize=(7,4))\n", "plt.plot(z, Vi, label='Incidente', color='blue')\n", "plt.plot(z, Vr, label='Reflejada', color='red')\n", "plt.title('Ondas sinusoidais incidente e reflexada')\n", "plt.xlabel('Tempo (s)')\n", "plt.ylabel('Amplitude')\n", "plt.grid(True)\n", "\n", "# Gráfico 2: suma total\n", "plt.figure(figsize=(7,4))\n", "plt.plot(z, Vtotal, color='black')\n", "plt.title('Suma de ambas ondas sinusoidais')\n", "plt.xlabel('Tempo (s)')\n", "plt.ylabel('Amplitude')\n", "plt.grid(True)\n", "\n", "print(\"Ondas triangulares incidente e reflexada (con desfase e onda atenuada) e Suma de ambas ondas\")\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "6ec35f4c", "metadata": {}, "source": [ "Ahora vamos recorriendo frecuencias y buscando máximos y mínimos con la amplitud tal que en el caso anterior pero ahora con la línea en cortocircuito.\n", "\n", "Tabla con valores obtenidos:" ] }, { "cell_type": "code", "execution_count": 511, "id": "cd140319", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Tipo de ondaFrecuencia (kHz)Amplitud (V)
0Máximo4379.68
1Mínimo9291.60
2Máximo14068.88
3Mínimo18752.24
4Máximo23328.40
5Mínimo28392.64
6Máximo33508.08
7Mínimo38192.96
8Máximo43217.76
9Mínimo48623.20
\n", "
" ], "text/plain": [ " Tipo de onda Frecuencia (kHz) Amplitud (V)\n", "0 Máximo 437 9.68\n", "1 Mínimo 929 1.60\n", "2 Máximo 1406 8.88\n", "3 Mínimo 1875 2.24\n", "4 Máximo 2332 8.40\n", "5 Mínimo 2839 2.64\n", "6 Máximo 3350 8.08\n", "7 Mínimo 3819 2.96\n", "8 Máximo 4321 7.76\n", "9 Mínimo 4862 3.20" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\\begin{table}[t]\n",
       "    \\centering\n",
       "    \\begin{tabular}{| c | c | c |}\n",
       "        \\hline\n",
       "        Tipo de onda & Frecuencia (kHz) & Amplitud (V) \\\\ \\hline\n",
       "        Máximo & 437 & 9.68 \\\\ \n",
       "        Mínimo & 929 & 1.6 \\\\ \n",
       "        Máximo & 1406 & 8.88 \\\\ \n",
       "        Mínimo & 1875 & 2.24 \\\\ \n",
       "        Máximo & 2332 & 8.4 \\\\ \n",
       "        Mínimo & 2839 & 2.64 \\\\ \n",
       "        Máximo & 3350 & 8.08 \\\\ \n",
       "        Mínimo & 3819 & 2.96 \\\\ \n",
       "        Máximo & 4321 & 7.76 \\\\ \n",
       "        Mínimo & 4862 & 3.2 \\\\ \n",
       "        \\hline\n",
       "    \\end{tabular}\n",
       "    \\caption{Cortocircuito}\n",
       "    \\label{tab:resonancia_cortocircuito}\n",
       "\\end{table}
" ], "text/plain": [ "" ] }, "execution_count": 511, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "from IPython.display import HTML\n", "\n", "# --- Tus datos ---\n", "data = [\n", " [\"Máximo\", 437, 9.68],\n", " [\"Mínimo\", 929, 1.60],\n", " [\"Máximo\", 1406, 8.88],\n", " [\"Mínimo\", 1875, 2.24],\n", " [\"Máximo\", 2332, 8.40],\n", " [\"Mínimo\", 2839, 2.64],\n", " [\"Máximo\", 3350, 8.08],\n", " [\"Mínimo\", 3819, 2.96],\n", " [\"Máximo\", 4321, 7.76],\n", " [\"Mínimo\", 4862, 3.20]\n", "]\n", "data_abierto = data\n", "\n", "columnas = [\"Tipo de onda\", \"Frecuencia (kHz)\", \"Amplitud (V)\"]\n", "\n", "df = pd.DataFrame(data, columns=columnas)\n", "\n", "# --- 1: Mostrar tabla bonita en Jupyter ---\n", "display(df)\n", "\n", "\n", "# --- 2: Generar código LaTeX ---\n", "def df_to_latex_hline(df, caption=\"\", label=\"\"):\n", " latex = \"\\\\begin{table}[t]\\n\"\n", " latex += \" \\\\centering\\n\"\n", " latex += f\" \\\\begin{{tabular}}{{| {' | '.join(['c']*len(df.columns))} |}}\\n\"\n", " latex += \" \\\\hline\\n\"\n", " latex += \" \" + \" & \".join(df.columns) + \" \\\\\\\\ \\\\hline\\n\"\n", " for _, row in df.iterrows():\n", " latex += \" \" + \" & \".join(row.astype(str)) + \" \\\\\\\\ \\n\"\n", " latex += \" \\\\hline\\n\"\n", " latex += \" \\\\end{tabular}\\n\"\n", " latex += f\" \\\\caption{{{caption}}}\\n\"\n", " latex += f\" \\\\label{{{label}}}\\n\"\n", " latex += \"\\\\end{table}\"\n", " return latex\n", "\n", "codigo = df_to_latex_hline(df, \"Cortocircuito\", \"tab:resonancia_cortocircuito\")\n", "\n", "\n", "# --- 3: Mostrar el código LaTeX formateado en Jupyter ---\n", "HTML(f\"
{codigo}
\")" ] }, { "cell_type": "markdown", "id": "c96354f6", "metadata": {}, "source": [ "### Resonancias en la línea" ] }, { "cell_type": "markdown", "id": "53427aef", "metadata": {}, "source": [ "- 6:\n", "\n", "Como estamos a estudiar en el rango de frecuencias aproximado de $0\\space-\\space 5\\space\\text{MHz}$ los máximos y mínimos de amplitud en función de la frecuencia en los casos de la señal sinusoidal con terminal abierto y con cortocircuito, desarrollamos este nuevo apartado para buscar el patrón, relacionarlo con la velocidad y tiempo de retardo y buscar que frecuencias se esperarían.\n", "\n", "Usaremos los datos experimentales de las tablas {TABLA refleion sin inversion} y {TABLA refleion con inversion} de los apartados anteriores. Para ello primero buscaremos la función con la que ajustar los datos experimentales (lo representaremos para ver a ojo artístico que tal quedan los puntos tal que bolas de adorno a árbol de navidad) y su relación con la velocidad el retardo, luego podremos predecir máximos y mínimos." ] }, { "cell_type": "markdown", "id": "bcf65595", "metadata": {}, "source": [ "El proceso matemático teórico es el siguiente:\n", "\n", "Tenemos la onda incidente y reflejada:\n", "\n", "$V_+=V_{0+}·cos(w·t-k·x)$\n", "\n", "y\n", "\n", "$V_-=V_{0-}·cos(w·t-k·x+\\Delta\\phi)$\n", "\n", "donde\n", "\n", "$\\Delta\\phi=\\vec{k}·\\vec{z_+}+\\vec{k}·\\vec{z_-}=2·l·k=4·\\pi·f·\\tau$\n", "\n", "Para considerar los máximos y mínimos tomamos $t=0$ y medimos en el principio de la línea, por tanto $z=0$, así:\n", "\n", "$V_+=V_{0+}$ y $V_-=V_{0-}·cos(\\Delta\\phi)$\n", "\n", "$\\Rightarrow V=V_++V_-=V_{0+}+V_{0-}·cos(\\Delta\\phi)$\n", "\n", "Estudiamos ahora para los dos casos, en abierto y en cortocircuito.\n", "\n", "- Circuito abierto:\n", "\n", "$\\Gamma =1 \\Rightarrow V_{0+}=V_{0-}\\space \\text{Despreciamos atenuación ya que para saber donde hay máximos y mínimos no afecta, tan solo afecta a la amplitud}\\space \\rightarrow V=V_{0+}·(1+cos(\\Delta\\phi))$\n", "\n", "Derivamos V con respecto de f y para buscar máximos hay que maximizar la función coseno, para el caso de mínimos habría que minimizar la funcion coseno:\n", "\n", "$\\Delta\\phi_n=2·\\pi·n\\rightarrow f_{max}=\\frac{n}{2·\\tau}$\n", "\n", "$\\Delta\\phi_n=(2n+1)·\\pi\\rightarrow f_{min}=\\frac{2n+1}{4·\\tau}$\n", "\n", "con $n=1,2,3,...$\n", "\n", "-Cortocircuito:\n", "\n", "$\\Gamma =1 \\Rightarrow V_{0+}=-V_{0-}\\space \\rightarrow V=V_{0+}·(1-cos(\\Delta\\phi))$\n", "\n", "Derivamos V con respecto de f y para buscar máximos hay que minimizar la función coseno y para buscar mínimos hay que maximizar la función coseno, lo opuesto a antes:\n", "\n", "$\\Delta\\phi_n=(2n+1)·\\pi\\rightarrow f_{max}=\\frac{2n+1}{4·\\tau}$\n", "\n", "$\\Delta\\phi_n=2·\\pi·n\\rightarrow f_{min}=\\frac{n}{2·\\tau}$\n", "\n", "con $n=1,2,3,...$\n", "\n", "Tras esto vamos a comparar los valores teóricos siguiendo estas ecuaciones con nuestro $\\tau$ y comparándolas con las obtenidas." ] }, { "cell_type": "code", "execution_count": 512, "id": "81428e59", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Lord_Fulgi\\AppData\\Local\\Temp\\ipykernel_64128\\517523746.py:10: RuntimeWarning: divide by zero encountered in divide\n", " f_teo_kHz = n_max / (2 * tau * 1000) # convertir a kHz\n" ] }, { "ename": "TypeError", "evalue": "unsupported format string passed to numpy.ndarray.__format__", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "Cell \u001b[1;32mIn[512], line 11\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tipo \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMáximo\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m 10\u001b[0m f_teo_kHz \u001b[38;5;241m=\u001b[39m n_max \u001b[38;5;241m/\u001b[39m (\u001b[38;5;241m2\u001b[39m \u001b[38;5;241m*\u001b[39m tau \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m1000\u001b[39m) \u001b[38;5;66;03m# convertir a kHz\u001b[39;00m\n\u001b[1;32m---> 11\u001b[0m latex_rows\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtipo\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m & \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mf_exp\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m & \u001b[39m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mf_teo_kHz\u001b[49m\u001b[38;5;132;43;01m:\u001b[39;49;00m\u001b[38;5;124;43m.0f\u001b[39;49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 12\u001b[0m n_max \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m 13\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", "\u001b[1;31mTypeError\u001b[0m: unsupported format string passed to numpy.ndarray.__format__" ] } ], "source": [ "# Caso terminal abierto\n", "tau = t\n", "\n", "n_max = 1\n", "n_min = 1\n", "latex_rows = []\n", "\n", "for tipo, f_exp, A in data_abierto:\n", " if tipo == \"Máximo\":\n", " f_teo_kHz = n_max / (2 * tau * 1000) # convertir a kHz\n", " latex_rows.append(f\"{tipo} & {f_exp} & {f_teo_kHz:.0f} \\\\\\\\\")\n", " n_max += 1\n", " else:\n", " f_teo_kHz = (2*n_min + 1) / (4 * tau * 1000) # convertir a kHz\n", " latex_rows.append(f\"{tipo} & {f_exp} & {f_teo_kHz:.0f} \\\\\\\\\")\n", " n_min += 1\n", "\n", "latex_table = \"\\\\begin{tabular}{ccc}\\n\\\\hline\\n\"\n", "latex_table += \"Tipo & $f_{\\\\frecuencia_{exp}}$ (kHz) & $f_{\\\\frecuencia_{teo}}$ (kHz) \\\\\\\\\\n\\\\hline\\n\"\n", "latex_table += \"\\n\".join(latex_rows)\n", "latex_table += \"\\n\\\\hline\\n\\\\end{tabular}\"\n", "\n", "print(latex_table)" ] }, { "cell_type": "code", "execution_count": null, "id": "4ca69625", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Lord_Fulgi\\AppData\\Local\\Temp\\ipykernel_64128\\3997538524.py:10: RuntimeWarning: divide by zero encountered in divide\n", " f_teo_kHz = (2*n_min + 1) / (4 * tau * 1000)\n" ] }, { "ename": "TypeError", "evalue": "unsupported format string passed to numpy.ndarray.__format__", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "Cell \u001b[1;32mIn[509], line 11\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tipo \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMáximo\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m 10\u001b[0m f_teo_kHz \u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m2\u001b[39m\u001b[38;5;241m*\u001b[39mn_min \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m) \u001b[38;5;241m/\u001b[39m (\u001b[38;5;241m4\u001b[39m \u001b[38;5;241m*\u001b[39m tau \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m1000\u001b[39m)\n\u001b[1;32m---> 11\u001b[0m latex_rows\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtipo\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m & \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mf_exp\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m & \u001b[39m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mf_teo_kHz\u001b[49m\u001b[38;5;132;43;01m:\u001b[39;49;00m\u001b[38;5;124;43m.0f\u001b[39;49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 12\u001b[0m n_max \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m 13\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", "\u001b[1;31mTypeError\u001b[0m: unsupported format string passed to numpy.ndarray.__format__" ] } ], "source": [ "# Caso cortocircuito\n", "tau = t\n", "\n", "n_max = 0\n", "n_min = 0\n", "latex_rows = []\n", "\n", "for tipo, f_exp, A in data_corto:\n", " if tipo == \"Máximo\":\n", " f_teo_kHz = (2*n_min + 1) / (4 * tau * 1000)\n", " latex_rows.append(f\"{tipo} & {f_exp} & {f_teo_kHz:.0f} \\\\\\\\\")\n", " n_max += 1\n", " else:\n", " f_teo_kHz = n_max / (2 * tau * 1000)\n", " latex_rows.append(f\"{tipo} & {f_exp} & {f_teo_kHz:.0f} \\\\\\\\\")\n", " n_min += 1\n", "\n", "latex_table = \"\\\\begin{tabular}{ccc}\\n\\\\hline\\n\"\n", "latex_table += \"Tipo & $f_{\\\\frecuencia_{exp}}$ (kHz) & $f_{\\\\frecuencia_{teo}}$ (kHz) \\\\\\\\\\n\\\\hline\\n\"\n", "latex_table += \"\\n\".join(latex_rows)\n", "latex_table += \"\\n\\\\hline\\n\\\\end{tabular}\"\n", "\n", "print(latex_table)" ] }, { "cell_type": "markdown", "id": "dbae0d37", "metadata": {}, "source": [ "Vemos que en general, sobre todo en el caso del terminal abierto, los datos son muy similares; en el caso del cortocircuito difieren un poco más pero habría que tener en cuenta que el valor del retardo tiene una incertidumbre que da un margen de error." ] }, { "cell_type": "markdown", "id": "51c085e2", "metadata": {}, "source": [ "Para graficar las variaciones de amplitud por frecuencia con sus máximos y mínimos, procederemos.\n", "\n", "Esto visto antes de maximizar la función coseno para obtener las frecuencias máximas y viceversa se va a apreciar gráficamente al ver que las ondas están desfasadas de sí $\\pi/2$. Lo que haremos será representar gráficamente los puntos experimentales con un ajuste de la función, para el caso de cortocircuito y de línea libre.\n", "\n", "El ajuste será siguiendo la función $V=V_0(1+e^{-\\alpha·2l·f}·cos(4\\pi·\\tau·f))$ que, para mayor exactitud usaremos para el ajuste: $V=A·e^{-\\alpha·2l·f}·cos(4\\pi·\\tau·f)+B$ de aquí nada es de sorprender salvo el término $e^{-\\alpha·2l·f}$, este es la disipación de la línea, que es un decaemiento exponencial (unidades [1/m], por lo que se multiplica por la ida y vuelta de la línea ($2·l$)) que a la vez se multiplica por la frecuencia, debido a la resistencia AC y el efecto pelicular (y porque el ajuste sin esto daba muy raruno).\n", "\n", "Se relaciona la ecuación con la velocidad de propagación por el retardo, ya que $v=\\frac{l}{\\tau}$.\n", "\n", "Con esto dado, si graficamos tenemos:\n", "\n", "Donde se aprecia que a la frecuencia en la que un caso es máximo, en el otro es mínimo como predeciamos." ] }, { "cell_type": "code", "execution_count": 513, "id": "81c32516", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tau fit0 (s) = 5.177594655880982e-07\n", "tau fit1 (s) = 4.7616273917575455e-07\n", "Resultados del ajuste:\n", "A0 = 4.5805 ± 52\n", "A1 = 5.0292 ± 52\n", "alpha0 = 6.87e-10 1/m ± 1.4e-07\n", "alpha1 = 8.8076e-10 1/m ± 1.4e-07\n", "B0 (offset) = 5.3986 ± 0.94\n", "B1 (offset) = 5.3941 ± 0.94\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from scipy.optimize import curve_fit\n", "\n", "# -------------------------\n", "# Tus datos (frecuencias en kHz y amplitudes)\n", "# -------------------------\n", "data_abierto = [ \n", " [\"Máximo\", 947, 9.2],\n", " [\"Mínimo\", 1398, 1.92],\n", " [\"Máximo\", 1857, 8.56],\n", " [\"Mínimo\", 2367, 2.48],\n", " [\"Máximo\", 2829, 8.24],\n", " [\"Mínimo\", 3315, 2.64],\n", " [\"Máximo\", 3858, 7.84],\n", " [\"Mínimo\", 4264, 3.04],\n", " [\"Máximo\", 4785, 7.6],\n", "]\n", "\n", "data_corto = [\n", " [\"Máximo\", 437, 9.68],\n", " [\"Mínimo\", 929, 1.60],\n", " [\"Máximo\", 1406, 8.88],\n", " [\"Mínimo\", 1875, 2.24],\n", " [\"Máximo\", 2332, 8.40],\n", " [\"Mínimo\", 2839, 2.64],\n", " [\"Máximo\", 3350, 8.08],\n", " [\"Mínimo\", 3819, 2.96],\n", " [\"Máximo\", 4321, 7.76],\n", " [\"Mínimo\", 4862, 3.20]\n", "]\n", "\n", "freqs_khz_abierto = np.array([d[1] for d in data_abierto], dtype=float)\n", "amps_abierto = np.array([d[2] for d in data_abierto], dtype=float)\n", "\n", "freqs_khz_corto = np.array([d[1] for d in data_corto], dtype=float)\n", "amps_corto = np.array([d[2] for d in data_corto], dtype=float)\n", "\n", "# pasar a Hz\n", "freqs_abierto = freqs_khz_abierto * 1e3\n", "freqs_corto = freqs_khz_corto * 1e3\n", "\n", "# -------------------------\n", "# Parámetros físicos que ya conoces: pon aquí tus valores reales\n", "# -------------------------\n", "l = 100 # longitud en metros (ejemplo)\n", "\n", "# -------------------------\n", "# Modelo: V(f) = B + A * exp(-2*alpha*l) * cos(4*pi*tau*f)\n", "A0 = 3.5 # amplitud aproximada\n", "alpha0 = 1e-9\n", "B0 = 5\n", "#phi0 = -0.6\n", "tau0 = 520e-9\n", "\n", "A1 = 3\n", "alpha1 = 6e-9\n", "B1 = 5.5\n", "#phi1 = -0.6\n", "tau1 = 530e-9\n", "\n", "# -------------------------\n", "def modelo_tau_libre_0(f, A, alpha, B, tau_fit):\n", " env = A * np.exp(-2.0 * alpha * l * f)\n", " return B + env * np.cos(4.0 * np.pi * tau_fit * f)\n", "\n", "def modelo_tau_libre_1(f, A, alpha, B, tau_fit):\n", " env = A * np.exp(-2.0 * alpha * l * f)\n", " return B + env * np.sin(4.0 * np.pi * tau_fit * f)\n", "\n", "p0 = [A0, alpha0, B0, tau0]\n", "p1 = [A1, alpha1, B1, tau1]\n", "\n", "bounds_lower = [0*np.abs(A0), 0.0, np.min(amps)-5, 0.0]\n", "bounds_upper = [10*np.abs(A0), 1.0, np.max(amps)+5, 5e-6]\n", "\n", "popt_0, pcov_0 = curve_fit(modelo_tau_libre_0, freqs_abierto, amps_abierto, p0=p0, bounds=(bounds_lower,bounds_upper), maxfev=200000)\n", "popt_1, pcov_1 = curve_fit(modelo_tau_libre_1, freqs_corto, amps_corto, p0=p1, bounds=(bounds_lower,bounds_upper), maxfev=200000)\n", "A_fit_0, alpha_fit_0, B_fit_0, tau_fit_0 = popt_0\n", "A_fit_1, alpha_fit_1, B_fit_1, tau_fit_1 = popt_1\n", "print(\"tau fit0 (s) =\", tau_fit_0)\n", "print(\"tau fit1 (s) =\", tau_fit_1)\n", "\n", "\n", "\n", "print(\"Resultados del ajuste:\")\n", "print(f\"A0 = {A_fit_0:.5g} ± {perr[0]:.2g}\")\n", "print(f\"A1 = {A_fit_1:.5g} ± {perr[0]:.2g}\")\n", "print(f\"alpha0 = {alpha_fit_0:.5g} 1/m ± {perr[1]:.2g}\")\n", "print(f\"alpha1 = {alpha_fit_1:.5g} 1/m ± {perr[1]:.2g}\")\n", "print(f\"B0 (offset) = {B_fit_0:.5g} ± {perr[2]:.2g}\")\n", "print(f\"B1 (offset) = {B_fit_1:.5g} ± {perr[2]:.2g}\")\n", "\n", "# -------------------------\n", "# Gráfica comparativa\n", "# -------------------------\n", "f_plot_0 = np.linspace(freqs_abierto.min(), freqs_abierto.max(), 3000)\n", "f_plot_1 = np.linspace(freqs_corto.min(), freqs_corto.max(), 3000)\n", "V_fit_0 = modelo_tau_libre_0(f_plot_0, *popt_0)\n", "V_fit_1 = modelo_tau_libre_1(f_plot_1, *popt_1)\n", "\"\"\"\n", "plt.figure(figsize=(9,5))\n", "plt.scatter(freqs/1e3, amps, label='Datos medidos', zorder=3)\n", "plt.plot(f_plot/1e3, V_fit, label='Ajuste: B + A e^{-2αl} cos(4π τ f)', linewidth=2)\n", "plt.xlabel('Frecuencia (kHz)')\n", "plt.ylabel('Tensión (u.a.)')\n", "plt.title('Ajuste del modelo pedido')\n", "plt.grid(True)\n", "plt.legend()\n", "plt.show()\n", "\"\"\"\n", "plt.figure(figsize=(10,6))\n", "\n", "# Puntos experimentales\n", "plt.scatter(freqs_abierto, amps_abierto, color='blue', label='Datos aberto')\n", "plt.scatter(freqs_corto, amps_corto, color='red', label='Datos cortocircuito')\n", "\n", "# Ajustes\n", "plt.plot(f_plot_0, V_fit_0, color='blue', linestyle='--', label='Axuste aberto')\n", "plt.plot(f_plot_1, V_fit_1, color='red', linestyle='--', label='Axuste cortocircuito')\n", "\n", "# Estética\n", "plt.xlabel(\"Frecuencia (MHz)\")\n", "plt.ylabel(\"Amplitude (V)\")\n", "plt.title(\"Resonancia na liña (amplitude vs frecuencia)\")\n", "plt.legend()\n", "plt.grid(True)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "5a1b933a", "metadata": {}, "source": [ "### Atenuación de línea" ] }, { "cell_type": "markdown", "id": "9cc3b792", "metadata": {}, "source": [ "La atenuación de la amplitud con el voltaje la suponemos como caida exponencial con la distancia del modo $V(z)=V_0·e^{-\\alpha z}$. Es evidente que en nuestro caso, al recorrer toda la línea de ida y vuelta, $z=2l=200\\space \\text{m}$ para el caso $V(z=2l)$.\n", "\n", "Desde ahí deducimos: $$V_{max}=V_0+V(z=2l);\\quad V_{min}=V_0-V(z=2l)$$Que operando tenemos:\n", "$$V_0=\\frac{V_{max}+V_{min}}{2};\\quad V(z=2l)=\\frac{V_{max}-V_{min}}{2}$$\n", "Por lo que, en nuestro caso, sacamos la constante alpha de $V(z=2l)=V_0·e^{-\\alpha 2l}$ así obteniendo:\n", "$$\\alpha=\\frac{1}{2l}·ln(\\frac{V_0}{V(z=2l)})$$\n", "Lo que haremos será usar los datos de amplitud máxima y mínima del apartado anterior, que por cada máximo hay un mínimo que ambos se miden en una misma frecuencia (teórica, de forma práctica son frecuencias muy similares que supondremos idénticas). Por ello haremos una tabla donde con estos datos obtenemos $V_0$, $V(z=200\\text{m})$ y $\\alpha$ a cada caso y después lo representaremos gráficamente $\\alpha$ frente a $f$ para hacer una regresión lineal y obtener la constante en la forma $\\alpha=a+b·f$ así como el $r$ de la regresión." ] }, { "cell_type": "code", "execution_count": null, "id": "f99f08c7", "metadata": {}, "outputs": [], "source": [ "data_abierto = [ \n", " [\"Máximo\", 947, 9.2],\n", " [\"Mínimo\", 1398, 1.92],\n", " [\"Máximo\", 1857, 8.56],\n", " [\"Mínimo\", 2367, 2.48],\n", " [\"Máximo\", 2829, 8.24],\n", " [\"Mínimo\", 3315, 2.64],\n", " [\"Máximo\", 3858, 7.84],\n", " [\"Mínimo\", 4264, 3.04],\n", " [\"Máximo\", 4785, 7.6],\n", "]\n", "\n", "data_corto = [\n", " [\"Máximo\", 437, 9.68],\n", " [\"Mínimo\", 929, 1.60],\n", " [\"Máximo\", 1406, 8.88],\n", " [\"Mínimo\", 1875, 2.24],\n", " [\"Máximo\", 2332, 8.40],\n", " [\"Mínimo\", 2839, 2.64],\n", " [\"Máximo\", 3350, 8.08],\n", " [\"Mínimo\", 3819, 2.96],\n", " [\"Máximo\", 4321, 7.76],\n", " [\"Mínimo\", 4862, 3.20]\n", "]\n", "\n", "# [frecuencia (kHz), Vmax (V), Vmin (V)]\n", "data_atenuacion = [\n", " [947, 9.20, 1.60],\n", " [1398, 8.88, 1.92],\n", " [1857, 8.56, 2.24],\n", " [2367, 8.40, 2.48],\n", " [2829, 8.24, 2.64],\n", " [3315, 8.08, 2.64],\n", " [3858, 7.84, 2.96],\n", " [4264, 7.76, 3.04],\n", " [4785, 7.60, 3.20]\n", "]" ] }, { "cell_type": "code", "execution_count": null, "id": "9a86e384", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ecuación de la recta: alpha(f) = 6.810628e-07 * f + 1.282215e-03\n", "Coeficiente de correlación r = 0.991349\n" ] }, { "data": { "text/plain": [ "( f_kHz Vmax Vmin V0 Vz200 alpha\n", " 0 947 9.20 1.60 5.40 3.80 1.76e-03\n", " 1 1398 8.88 1.92 5.40 3.48 2.20e-03\n", " 2 1857 8.56 2.24 5.40 3.16 2.68e-03\n", " 3 2367 8.40 2.48 5.44 2.96 3.04e-03\n", " 4 2829 8.24 2.64 5.44 2.80 3.32e-03\n", " 5 3315 8.08 2.64 5.36 2.72 3.39e-03\n", " 6 3858 7.84 2.96 5.40 2.44 3.97e-03\n", " 7 4264 7.76 3.04 5.40 2.36 4.14e-03\n", " 8 4785 7.60 3.20 5.40 2.20 4.49e-03,\n", " (6.810627998920114e-07, 0.0012822150327464676))" ] }, "execution_count": 471, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "df = pd.DataFrame(data_atenuacion, columns=[\"f_kHz\",\"Vmax\",\"Vmin\"])\n", "df[\"V0\"] = (df[\"Vmax\"]+df[\"Vmin\"])/2\n", "df[\"Vz200\"] = (df[\"Vmax\"]-df[\"Vmin\"])/2\n", "df[\"alpha\"] = (1/200)*np.log(df[\"V0\"]/df[\"Vz200\"])\n", "\n", "# regression alpha vs f\n", "f = df[\"f_kHz\"].values\n", "alpha = df[\"alpha\"].values\n", "coeffs = np.polyfit(f, alpha, 1)\n", "a, b = coeffs\n", "\n", "r_matrix = np.corrcoef(f, alpha)\n", "r = r_matrix[0, 1]\n", "\n", "# print equation and r\n", "print(f\"Ecuación de la recta: alpha(f) = {a:.6e} * f + {b:.6e}\")\n", "print(f\"Coeficiente de correlación r = {r:.6f}\")\n", "\n", "# plot\n", "plt.figure()\n", "plt.scatter(f, alpha)\n", "plt.plot(f, a*f+b)\n", "plt.xlabel(\"f (kHz)\")\n", "plt.ylabel(\"alpha (1/m)\")\n", "plt.title(\"Regresión lineal de α(f)\")\n", "plt.tight_layout()\n", "\n", "df, (a,b)\n" ] }, { "cell_type": "code", "execution_count": null, "id": "1006eb0f", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "<>:1: SyntaxWarning: invalid escape sequence '\\c'\n", "<>:1: SyntaxWarning: invalid escape sequence '\\c'\n", "C:\\Users\\Lord_Fulgi\\AppData\\Local\\Temp\\ipykernel_64128\\4243951484.py:1: SyntaxWarning: invalid escape sequence '\\c'\n", " \"\"\"\n" ] }, { "data": { "text/plain": [ "'\\n\\x08egin{table}[h!]\\n\\\\centering\\n\\x08egin{tabular}{c|c|c|c|c|c}\\n\\\\hline\\n$f$ (kHz) & $V_{\\\\max}$ (V) & $V_{\\\\min}$ (V) & $V_0$ (V) & $V(200\\text{m})$ (V) & $\\x07lpha$ (m$^{-1}$) \\\\\\n\\\\hline\\n947 & 9.20 & 1.60 & 5.40 & 3.80 & 0.00176 \\\\\\n1398 & 8.88 & 1.92 & 5.40 & 3.48 & 0.00220 \\\\\\n1857 & 8.56 & 2.24 & 5.40 & 3.16 & 0.00268 \\\\\\n2367 & 8.40 & 2.48 & 5.44 & 2.96 & 0.00304 \\\\\\n2829 & 8.24 & 2.64 & 5.44 & 2.80 & 0.00332 \\\\\\n3315 & 8.08 & 2.64 & 5.36 & 2.72 & 0.00339 \\\\\\n3858 & 7.84 & 2.96 & 5.40 & 2.44 & 0.00397 \\\\\\n4264 & 7.76 & 3.04 & 5.40 & 2.36 & 0.00414 \\\\\\n4785 & 7.60 & 3.20 & 5.40 & 2.20 & 0.00449 \\\\\\n\\\\hline\\n\\\\end{tabular}\\n\\\\end{table}\\n'" ] }, "execution_count": 469, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"\"\"\n", "\\begin{table}[h!]\n", "\\centering\n", "\\begin{tabular}{c|c|c|c|c|c}\n", "\\hline\n", "$f$ (kHz) & $V_{\\max}$ (V) & $V_{\\min}$ (V) & $V_0$ (V) & $V(200\\text{m})$ (V) & $\\alpha$ (m$^{-1}$) \\\\\n", "\\hline\n", "947 & 9.20 & 1.60 & 5.40 & 3.80 & 0.00176 \\\\\n", "1398 & 8.88 & 1.92 & 5.40 & 3.48 & 0.00220 \\\\\n", "1857 & 8.56 & 2.24 & 5.40 & 3.16 & 0.00268 \\\\\n", "2367 & 8.40 & 2.48 & 5.44 & 2.96 & 0.00304 \\\\\n", "2829 & 8.24 & 2.64 & 5.44 & 2.80 & 0.00332 \\\\\n", "3315 & 8.08 & 2.64 & 5.36 & 2.72 & 0.00339 \\\\\n", "3858 & 7.84 & 2.96 & 5.40 & 2.44 & 0.00397 \\\\\n", "4264 & 7.76 & 3.04 & 5.40 & 2.36 & 0.00414 \\\\\n", "4785 & 7.60 & 3.20 & 5.40 & 2.20 & 0.00449 \\\\\n", "\\hline\n", "\\end{tabular}\n", "\\end{table}\n", "\"\"\"" ] }, { "cell_type": "markdown", "id": "d7cb2db3", "metadata": {}, "source": [ "Obtenemos con un coeficiente de regresión de $r=0.99135$, que es aceptablemente bueno, la ecuación de la constante:$$\\alpha = 1.282215·10^{-3} + 6.810628·10^{-7} · f$$" ] }, { "cell_type": "markdown", "id": "3c6b7412", "metadata": {}, "source": [ "### Línea adaptada con señal sinusoidal" ] }, { "cell_type": "markdown", "id": "ab6f758d", "metadata": {}, "source": [ "Al poner al final de la línea el adaptador de $50\\space\\Omega$, lo que hacemos es adaptar la línea pues la resistencia de esa línea es la misma que la impedancia característica de la línea, por lo que $\\Gamma=0$ así que no habrá máximos ni mínimos porque no hay onda estacionaria, toda la onda es en una dirección.\n", "\n", "En este caso, variando la frecuencia sería imposible encontrar máximos o mínimos." ] }, { "cell_type": "markdown", "id": "0aaf1188", "metadata": {}, "source": [ "### Impedancia característica de la línea" ] }, { "cell_type": "markdown", "id": "448f11f2", "metadata": {}, "source": [ "Buscamos hallar $Z_0$, la impedancia característica de la línea de transmisión y compararlo con el \"valor de fábrica\" que se nos dice que es $50\\space\\Omega$.\n", "\n", "Lo que hacemos es colocar al final de la línea un potenciómetro para ir variando manualmente la impedancia, además introducimos señal cuadrada, así, cuando el potenciómetro equilibre su impedancia con la del cable, no habrá onda reflejada, es decir, deberíamos ver en el osciloscopio una señal cuadrada sin interferencias (o con poca interferencia, es decir, ver un cuadrado no una forma extraña de dragón). Una vez encontrado este punto, medimos con el polímetro la resistencia del polímetro.\n", "\n", "Hecho esto, obtuvimos que el cable de la línea tenía una impedancia característica de $52\\space\\Omega$, muy similar a los $50\\space\\Omega$ esperados." ] }, { "cell_type": "markdown", "id": "4889233f", "metadata": {}, "source": [ "### Autoinducción de la línea" ] }, { "cell_type": "markdown", "id": "11d28c38", "metadata": {}, "source": [ "Colocamos el generador en señal sinusoidal a 30 kHz y la línea en cortocircuito. Antes de la línea colocamos una resistencia de $100\\space\\Omega$ para medir el voltaje antes y después de la resistencia. Al medir $V_1$ y $V_2$ y el desfase entre ellos, podemos calcular la autoinducción de la línea.\n", "\n", "Hemos medido$$V_1=6.88\\space V;\\quad V_2=536\\space mV;\\quad\\phi=313.7º$$\n", "\n", "Para poder hallarlo, partimos de un circuito con tres elementos en serie, la resistencia de $100\\space\\Omega$, la resistencia de línea y la inductancia:\n", "$$V_1=I·(R+R_L+iwL);\\quad V_2=I·(R_L+iwL)$$\n", "Aplicando la ley de nodos:\n", "$$\\frac{V_1-V_2}{R}=\\frac{V_2-0}{R_L+iwL}\\Rightarrow\\frac{V_1}{V_2}=1+\\frac{R}{R_L+iwL}\\approx\\frac{R}{R_L+iwL}$$\n", "Que aproximamos ya que $R>>R_L$ y $R>>wL$. También tenemos el valor $R_L=5\\space\\Omega$.\n", "\n", "Ahora hay dos maneras de encontrar la Inductancia, partiendo de módulos o de desfase.\n", "\n", "Para módulos:$$\\left|\\frac{V_1}{V_2}\\right|^2=\\frac{R^2}{R_L^2+(wL)^2}\\Rightarrow L=\\frac{1}{w}·\\sqrt{\\frac{R^2}{\\left|\\frac{V_1}{V_2}\\right|^2}-R_L^2}$$\n", "Y con el desfase:$$tg(\\phi)=\\frac{-wL}{R_L}\\Rightarrow L=-\\frac{R_L·tg(\\phi)}{w}$$\n", "De resultados obtenidos tendríamos:$$L_{módulo}=3.17·10^{-5}\\space H;\\quad L_{desfase}=2.78·10^{-5}\\space H$$" ] }, { "cell_type": "code", "execution_count": null, "id": "d8d62773", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "L_mod = 0.0000316958489931722 H\n", "L_teo = 0.0000277576890846979 H\n" ] } ], "source": [ "freq = 30e3\n", "V1 = 6.88\n", "V2 = 536e-3\n", "phi = 313.7\n", "\n", "L_mod = (1/(2*pi*freq))*sqrt(((100**2)/((V1/V2))**2)-5**2)\n", "L_teo = -(5/(2*pi*freq))*tan(radians(phi))\n", "\n", "print(f\"L_mod = {L_mod} H\")\n", "print(f\"L_teo = {L_teo} H\")" ] }, { "cell_type": "markdown", "id": "6be59c04", "metadata": {}, "source": [ "### Capacidad de la línea" ] }, { "cell_type": "markdown", "id": "a7fbb86d", "metadata": {}, "source": [ "Con el mismo tipo de señal y misma frecuencia, quitamos el cortocircuito y cambiamos la resistencia de $100\\space\\Omega$ por una de $1\\space k\\Omega$.\n", "Los datos obtenidos ahora son$$V_1=9.84\\space V;\\quad V_2=4.56\\space V;\\quad\\phi=61.29º$$\n", "\n", "Volvemos a aplicar teóricamente nodos:$$\\frac{V_1-V_2}{R}=\\frac{V_2-0}{(1/iwC)}\\Rightarrow \\frac{V_1}{V_2}=1+iwRC$$\n", "Al igual que antes, buscamos $C$ mediante módulos y desfase. Mediante módulo:$$\\left|\\frac{V_1}{V_2}\\right|^2=1+(wRC)^2\\Rightarrow C=\\frac{1}{wR}\\sqrt{\\left|\\frac{V_1}{V_2}\\right|^2-1}$$\n", "Por desfase:$$tg(\\phi)=wRC\\Rightarrow C=\\frac{tg(\\phi)}{wR}$$\n", "Los resultados obtenidos entonces son:$$C_{módulo}= 10.1\\space nF;\\quad C_{desfase}=9.7\\space nF$$" ] }, { "cell_type": "code", "execution_count": null, "id": "9371621b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "C_mod = 1.01445372591272E-8 F\n", "C_teo = 9.68606794593012E-9 F\n" ] } ], "source": [ "freq = 30e3\n", "V1 = 9.84\n", "V2 = 4.56\n", "phi = 61.29\n", "\n", "C_mod = (1/(2*pi*freq*1000))*sqrt((V1/V2)**2 -1)\n", "C_teo = tan(radians(phi))/(2*pi*freq*1000)\n", "\n", "print(f\"C_mod = {C_mod} F\")\n", "print(f\"C_teo = {C_teo} F\")" ] }, { "cell_type": "markdown", "id": "24b447ec", "metadata": {}, "source": [ "### Desadaptación de entrada" ] }, { "cell_type": "markdown", "id": "6b10646d", "metadata": {}, "source": [ "Mantenemos el montaje del apartado anterior y cambiamos el tipo de señal a cuadrada. Estudiaremos el comportamiento de la señal en el circuito variando la frecuencia para los tres casos que conocemos: terminal abierto, en cortocircuito y adaptada.\n", "\n", "Por la composición del circuito, es importante calcular el coeficiente de reflexión en la resistencia, $\\Gamma=\\frac{R-Z_0}{R+Z_0}=\\frac{1000-50}{1000+50} \\approx 0.9$ que se traduce con que el 90% de la señal de entrada se refleja y un 10% cruza la resistencia. Ahora, ese 10% llegará al final de la línea y dependiendo del caso o se reflejará del todo sin invertir (terminal abierto), se reflejará del todo invertida (cortocircuito) o no reflejará nada (terminal adaptado). A su vez, esta parte reflejada volverá \"al volver\" a encontrarse la resistencia, una parte pasará y llegará al comienzo pero otra gran parte volverá a reflejarse y volver al terminal y volver a poder pasar o poder reflejarse y así las veces que haga falta. A cada distancia extra que recorre una señal, gana retardo, aparte puede venir invertida o no y también al recorrer más distancia, su amplitud inicial se irá disipando más y más.\n", "\n", "En el caso del circuito abierto, $\\Gamma =1$, la señal se refleja del todo, lo que tendríamos aquí sería una suma de señales cuadradas con retardo y menor amplitud que si sumasemos nos daría una forma de nacho escalonado. Lo observado en el osciloscopio es:\n", "{meter foto y diagrama del fernandesito}\n", "\n", "Para cortocircuito en terminal, $\\Gamma =-1$, la señal se va invirtiendo a medida que \"rebota\" dentro del circuito, por lo que produce unos máximos y mínimos retardados que se van disipando ya que esta señal se va potenciando mucho o anulando mucho a medida que se disipa y pierde amplitud; se podría describir casi como un paquete de ondas. Se aprecia en el osciloscopio.\n", "{meter foto}\n", "\n", "Con el extremo adaptado, $\\Gamma =0$, no hay onda reflejada, por lo que es muy similar a la onda emitida, esto es salvo por la desadaptación de la resistencia, que hace que una parte sí se refleje, por eso se ve un pequeño \"pico\" al comienzo, apenas hay deformación.\n", "{meter foto de fernandesito}\n" ] }, { "cell_type": "markdown", "id": "a8697a77", "metadata": {}, "source": [ "Vemos que afecta considerablemente la respuesta según el terminal que situemos al final de la línea; ahora bien, la frecuencia no afecta al retardo (como vimos antes) por lo que la figura se mantiene idéntica $\\textbf{salvo}$ por la pérdida de amplitud que depende de la frecuencia. $\\alpha=\\alpha(f)$, es decir, solo el tamaño o la agresividad del cambio de tamaño en el eje de las amplitudes es lo que puede variar con la frecuencia (bueno, aparte de la evidente frecuencia de la señal, pero con esto queríamos recalcar que el retardo se mantiene constante, sus escalones no varían).\n", "\n", "Es más fácil apreciarlo con imágenes:\n", "Puede parecer que sí cambia considerablemente la forma, pero es lo dicho, cambia el marco y el pincel, la pintura es la misma vista \"desde otro ángulo\". En la segunda imagen ya no se ve tan similar a su forma original, en parte tenemos que considerar que a estas tan altas frecuencias, en nuestro circuito, la teoría de circuitos ya no es válida, se llena de ruido la señal y esta reflexión inicial se potencia y distorsiona.\n", "\n", "Este último apartado puede tener finalidad práctica, pues midiendo el retardo (con los \"escalones\") podríamos averiguar en que posición del cable ocurre la desadaptación." ] }, { "cell_type": "markdown", "id": "f96b81bb", "metadata": {}, "source": [ "# Relación Carga-Masa Electrón" ] }, { "cell_type": "markdown", "id": "4b615821", "metadata": {}, "source": [ "## Teoría e Procedemento" ] }, { "cell_type": "markdown", "id": "697ac415", "metadata": {}, "source": [ "### Introdución" ] }, { "cell_type": "markdown", "id": "92d477b5", "metadata": {}, "source": [ "Este experimento é unha adaptación do feito por Joseph John Thomson en 1897. Neste, J. J. Thomson descubriu o electrón no laboratorio Cavendish da Universidade de Cambridge.\n", "\n", "O que buscamos é achar a relación carga-masa do electrón. Para elo primero deberemos coñecer o procedemento e á máquina que nos vai permitir encontrar este valor.\n", "\n", "A idea é sinxela en temas de electrodinámica, pois consiste en xerar unha nube de electróns, acelerala con un potencial eléctrico e curvar este feixe de electróns con un campo magnético uniforme. A partir do radio de electróns deflectado e sabendo o potencial cos que aceleramos os electróns así como a intensidade do campo magnético, acharemos a relación $\\frac{e}{m}$, onde $e$ é a carga do electrón e $m$ a súa masa, veremos que pode darse como $\\frac{e}{m}=\\frac{2 V}{B^2 r^2}$.\n", "\n", "Esta idea a afondaremos en breves para achar a relación matemática, mais primeiro coñeceremos a máquina.\n", "\n", "A máquina PASCO Modelo SE-9638 consiste nunha esfera de vacío parcial (presión interna moi baixa, preto do vacío) chea de Helio, que contén un canón de electróns e unha \"escaleira\" para medir o radio do feixe.\n", "\n", "O canón de electróns funciona cun quentador que quenta un cátodo e así emite electróns, despois, cun potencial aplicado, aceléranse a gusto xa que un cátodo e un ánodo concentran o raio. Este fenómeno ocorre polo efecto termoiónico, que é que o quentar dá enerxía térmica (cinética) ós electróns libres do material do cátodo para superar a barreira de potencial da superficie e \"evaporarse\" do metal. Créase unha nube de electróns libres arredor do cátodo que logo, con un potencial, poderan dirixirse e acelerarse. Se estas dúas cousas as facemos de forma continua, creamos un feixe de electróns.\n", "\n", "O feixe existente de por sí no baleiro non o poderíamos ver, por iso é que a esfera ten un pouco de helio, para que o chorro de electróns deixe unha marca ó seu paso ao chocar cos átomos de Helio, que se excitan e logo liberan luz visible para volver ao seu estado inicial.\n", "\n", "Agora, o campo magnético será producido por un par de bobinas de Helmholtz que se colocan como pan nun sandwich no que o interior é a esfera co feixe de electróns." ] }, { "cell_type": "markdown", "id": "b6647738", "metadata": {}, "source": [ "### Materiais" ] }, { "cell_type": "markdown", "id": "18b24f70", "metadata": {}, "source": [ "Usaremos:\n", "- Aparato PASCO Modelo SE-9638\n", "- Un par de bobinas de Helmholtz de 130 voltas ($N$), radio ($r$) e distancia entre elas ($a$): $r=a=0.15\\text{ m}$\n", "- Un par de polímetros (un para medir Intensidade e outo Voltaxe)" ] }, { "cell_type": "markdown", "id": "2e7cab45", "metadata": {}, "source": [ "### Teoría" ] }, { "cell_type": "markdown", "id": "fb3e5169", "metadata": {}, "source": [ "Empezamos partindo da ecuación da forza de Lorentz $\\vec{F}=q·(\\vec{E}+\\vec{v}\\times\\vec{B})$ para un campo eléctrico nulo $\\vec{E}=0$, que de forma escalar obteríamos para o noso caso:\n", "$$F_m=evB$$\n", "Que traduciráse nunha forza centrípeta que levará ao noso feixe a ter carácter circuferencial con radio $r$:\n", "$$F_c=\\frac{mv^2}{r}$$\n", "Ao igualar ambas ecuacións e despexando para a relación carga-masa chegamos a:\n", "$$\\frac{e}{m}=\\frac{v}{Br}$$\n", "Teremos que atopar as formas de representar $v$ e $B$ en base a outras variables que sí coñezamos. Para sacar a velocidade partimos da enerxía potencia $eV$ e ad enerxía cinética $\\frac{mv^2}{2}$, combinadas e despexada a velocidade obtemos:\n", "$$v=\\sqrt{\\frac{2eV}{m}}$$\n", "E para o campo magnético producido cerca do eixe dun par de bobinas de Helmholtz de $N$ voltas e separadas entre sí unha distancia $a$:\n", "$$B=\\frac{N \\mu _0}{a\\left(\\frac{5}{4}\\right)^{\\frac{3}{2}}}I$$\n", "Combinando todo e despexando para a relación que nos interesa chegaríamos a ecuación coa que atoparemos no proceso experimental a relación carga/masa:\n", "\\[\n", "\\fbox{$\\frac{e}{m}=2V\\left(\\frac{5}{4}\\right)^3\\left(\\frac{a}{N\\mu _0 r I}\\right)^2$}\n", "\\]" ] }, { "cell_type": "markdown", "id": "067fd472", "metadata": {}, "source": [ "### Extra" ] }, { "cell_type": "markdown", "id": "7afedf9f", "metadata": {}, "source": [ "Como mención curiosa extra podemos falar sobre os raios catódicos e os televisores antiguos xa que é o mesmo principio ca no experimento.\n", "\n", "Os raios catódicos son feixes de electróns emitidos desnde o cátodo e acelerados cun potencial nun tubo ao baleiro. Os televisores antiguos tiñan un tubo de raios catódicos no interior, estes raios desvíanse por campos eléctricos e magnéticos ata impactar cada punto cunha superficie fluorescente (que se excita e produce luz visible que nós observamos). O feixe de electróns varre a pantalla varias veces por segundo (50-60 Hz). O brillo observado en cada punto ven dado pola intensidade do feixe nese punto.\n", "\n", "O caso do osciloscopio non eléctrico é igual solo que o feixe desvíase segundo o sinal medido.\n", "\n", "O cambio de televisor en branco e negro (o explicado anteriormente) e o televisor a cor é basicamente que o que mostra sinal en branco e negro funciona cun só feixe de electróns e a pantalla é dun só tipo de fósforo que emite luz branca. No televisor a cor, cambia en que posée 3 feixes de electróns distintos e 3 tipos de fósforo (RGB (Vermello, Verde e Azul)) que son apuntados cunha \"máscara de sombras\" (lámina metálica fina furada con moitos buratos para dirixir os raios aos puntos de cor específicos).\n", "\n", "Se situasemos un imán na pantalla, xeraríase un cambo $\\vec{B}$ que ocasionaría un desvío dos electróns e, polo tanto, a imaxe deformaríase. Nun televisor en branco e negro non pasaría nada salvo a distorsión da imaxe, mais no televisor a cor non só habería una deformación da imaxe, tamén pódese dañar permanentemente por magnetizar a máscara metálica e impedir que se dirixan apropiadamente os feixes ós buratos de fósforo de cor.\n", "\n", "Riscos para a saúde non ten moitos, o que si é perigoso e os elementos a alta tensión no interior do televisor ou a carga estática que pode formarse na pantalla. Aparte, estes televisores, no proceso de xerar electróns, xeran tamén baixas doses de radiación X, pero non é moito aunque si existente." ] }, { "cell_type": "markdown", "id": "65733396", "metadata": {}, "source": [ "## Datos" ] }, { "cell_type": "markdown", "id": "d709dd19", "metadata": {}, "source": [ "Primeiro deberemos mencionar e acordar as incertezas, que serán no caso da Voltaxe e Intensidade (debido ao polímetro): $s(V)= 1\\text{ V}$ e $s(I)=0.01\\text{ A}$, mais no caso do radio, ao ser unha escaleira cos escalóns bastante finos, ter 0.5 cm de incerteza é pasarse moito de incerteza, xa que cando o feixe toca o escalón formase un claro semicírculo en vez dunha circunferencia completa ao estar no escalón, a incerteza debería ser a asociada ó grosor do escalón (que non houbo maneira de medila), así que, por decisión propia e aproximada tomaremos $s(r)=0.1 \\text{ cm}$.\n", "\n", "Os datos medidos variando Voltaxe, Intensidade do campo magnético e radios foron:\n", "\n", "\\begin{table}[H]\n", "\\centering\n", "\\begin{tabular}{c c c}\n", "\\hline\n", "$\\mathbf{V\\,(V)}$ & $\\mathbf{I\\,(A)}$ & $\\mathbf{r\\,(cm)}$ \\\\\n", "\\hline\n", "176 & 3.08 & 2 \\\\\n", "176 & 2.04 & 3 \\\\\n", "176 & 1.48 & 4 \\\\\n", "176 & 1.18 & 5 \\\\\n", "200 & 1.27 & 5 \\\\\n", "200 & 1.59 & 4 \\\\\n", "200 & 2.14 & 3 \\\\\n", "200 & 3.28 & 2 \\\\\n", "215 & 3.40 & 2 \\\\\n", "215 & 2.24 & 3 \\\\\n", "215 & 1.65 & 4 \\\\\n", "215 & 1.32 & 5 \\\\\n", "\\hline\n", "\\end{tabular}\n", "\\end{table}" ] }, { "cell_type": "code", "execution_count": 514, "id": "46afc119", "metadata": {}, "outputs": [], "source": [ "# Usaremos a ecuación $\\frac{e}{m}=2V\\left(\\frac{5}{4}\\right)^3\\left(\\frac{a}{N\\mu _0 r I}\\right)^2$\n", "# V(V), I(A), r(cm)\n", "\n", "sV = 1\n", "sI = 0.01\n", "sr = 0.1\n", "\n", "datos = [\n", " [176, 3.08, 2],\n", " [176, 2.04, 3],\n", " [176, 1.48, 4],\n", " [176, 1.18, 5],\n", " [200, 1.27, 5],\n", " [200, 1.59, 4],\n", " [200, 2.14, 3],\n", " [200, 3.28, 2],\n", " [215, 3.40, 2],\n", " [215, 2.24, 3],\n", " [215, 1.65, 4],\n", " [215, 1.32, 5],\n", "]" ] }, { "cell_type": "code", "execution_count": 523, "id": "6de0aa32", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fórmula usada (símbolo):\n", " e/m = K * V / (r^2 I^2),\n", " con K = 2*(5/4)^3 * (a/(N μ0))^2\n", "\n", "Fórmula de propagación usada (símbolo):\n", " s_{e/m} = (e/m) * sqrt( (sV/V)^2 + (2 s_r / r)^2 + (2 sI / I)^2 )\n", "\n", "Tabla de resultados (valores y sus incertidumbres):\n", "\n", " V (V) I (A) r (cm) r (m) e/m (C/kg) s(e/m) (C/kg) rel_unc (%)\n", "0 1.7600e+02 3.0800e+00 2.0000e+00 2.0000e-02 1.5275e+11 1.5332e+10 1.0037e+01\n", "1 1.7600e+02 2.0400e+00 3.0000e+00 3.0000e-02 1.5476e+11 1.0465e+10 6.7623e+00\n", "2 1.7600e+02 1.4800e+00 4.0000e+00 4.0000e-02 1.6539e+11 8.6175e+09 5.2105e+00\n", "3 1.7600e+02 1.1800e+00 5.0000e+00 5.0000e-02 1.6651e+11 7.2953e+09 4.3813e+00\n", "4 2.0000e+02 1.2700e+00 5.0000e+00 5.0000e-02 1.6335e+11 7.0695e+09 4.3278e+00\n", "5 2.0000e+02 1.5900e+00 4.0000e+00 4.0000e-02 1.6284e+11 8.4349e+09 5.1800e+00\n", "6 2.0000e+02 2.1400e+00 3.0000e+00 3.0000e-02 1.5981e+11 1.0788e+10 6.7504e+00\n", "7 2.0000e+02 3.2800e+00 2.0000e+00 2.0000e-02 1.5306e+11 1.5353e+10 1.0031e+01\n", "8 2.1500e+02 3.4000e+00 2.0000e+00 2.0000e-02 1.5313e+11 1.5356e+10 1.0028e+01\n", "9 2.1500e+02 2.2400e+00 3.0000e+00 3.0000e-02 1.5680e+11 1.0572e+10 6.7423e+00\n", "10 2.1500e+02 1.6500e+00 4.0000e+00 4.0000e-02 1.6255e+11 8.3970e+09 5.1658e+00\n", "11 2.1500e+02 1.3200e+00 5.0000e+00 5.0000e-02 1.6255e+11 6.9938e+09 4.3026e+00\n", "\n", "Estadísticos finales:\n", " - K = 3.293337e+06\n", " - Media ponderada e/m = 1.615851e+11 ± 2.67e+09 C/kg\n", " - Chi2 = 2.37, dof = 11, Chi2_red = 0.22\n", " - Media simple = 1.594571e+11 ± 1.47e+09 (s.e.m.)\n", " - Desviación estándar muestral = 5.098157e+09\n" ] } ], "source": [ "# Cálculo de e/m para cada medida con propagación de errores (salida visible)\n", "import numpy as np\n", "import pandas as pd\n", "from math import pi, sqrt\n", "\n", "# Datos y constantes pedidos\n", "datos = np.array([\n", " [176, 3.08, 2],\n", " [176, 2.04, 3],\n", " [176, 1.48, 4],\n", " [176, 1.18, 5],\n", " [200, 1.27, 5],\n", " [200, 1.59, 4],\n", " [200, 2.14, 3],\n", " [200, 3.28, 2],\n", " [215, 3.40, 2],\n", " [215, 2.24, 3],\n", " [215, 1.65, 4],\n", " [215, 1.32, 5],\n", "])\n", "\n", "sV = 1.0 # V\n", "sI = 0.01 # A\n", "sr_cm = 0.1 # cm\n", "sr = sr_cm * 1e-2 # m\n", "\n", "N = 130\n", "a = 0.15 # m\n", "mu0 = 4*pi*1e-7\n", "\n", "V = datos[:,0].astype(float)\n", "I = datos[:,1].astype(float)\n", "r_cm = datos[:,2].astype(float)\n", "r = r_cm * 1e-2 # m\n", "\n", "# Constante K tal que e/m = K * V / (r^2 I^2)\n", "K = 2 * (5/4)**3 * (a / (N * mu0))**2\n", "\n", "# Cálculo punto a punto\n", "e_over_m = K * V / (r**2 * I**2)\n", "\n", "# Incertidumbre por propagación (usando la fórmula deducida):\n", "# s_f = f * sqrt( (sV/V)^2 + (2 s_r / r)^2 + (2 sI / I)^2 )\n", "rel_unc = np.sqrt( (sV / V)**2 + (2*sr / r)**2 + (2*sI / I)**2 )\n", "s_e_over_m = e_over_m * rel_unc\n", "\n", "# Tabla con resultados\n", "df = pd.DataFrame({\n", " \"V (V)\": V,\n", " \"I (A)\": I,\n", " \"r (cm)\": r_cm,\n", " \"r (m)\": r,\n", " \"e/m (C/kg)\": e_over_m,\n", " \"s(e/m) (C/kg)\": s_e_over_m,\n", " \"rel_unc (%)\": rel_unc * 100\n", "})\n", "\n", "# Estadísticas: media ponderada usando las incertidumbres individuales\n", "weights = 1 / (s_e_over_m**2)\n", "e_weighted_mean = np.sum(e_over_m * weights) / np.sum(weights)\n", "e_weighted_std = sqrt(1 / np.sum(weights)) # incertidumbre de la media ponderada\n", "\n", "# Chi2 respecto media ponderada\n", "chi2 = np.sum(((e_over_m - e_weighted_mean)**2) * weights)\n", "dof = len(e_over_m) - 1\n", "chi2_red = chi2 / dof\n", "\n", "# Media simple y desviación estándar de la media simple\n", "e_mean_simple = np.mean(e_over_m)\n", "e_std_sample = np.std(e_over_m, ddof=1)\n", "e_std_mean = e_std_sample / np.sqrt(len(e_over_m))\n", "\n", "# Mostrar resultados\n", "print(\"Fórmula usada (símbolo):\\n e/m = K * V / (r^2 I^2),\\n con K = 2*(5/4)^3 * (a/(N μ0))^2\\n\")\n", "print(\"Fórmula de propagación usada (símbolo):\\n s_{e/m} = (e/m) * sqrt( (sV/V)^2 + (2 s_r / r)^2 + (2 sI / I)^2 )\\n\")\n", "\n", "print(\"Tabla de resultados (valores y sus incertidumbres):\\n\")\n", "pd.set_option('display.float_format', '{:0.4e}'.format)\n", "display_df = df.copy()\n", "display_df[\"e/m (C/kg)\"] = display_df[\"e/m (C/kg)\"]\n", "display_df[\"s(e/m) (C/kg)\"] = display_df[\"s(e/m) (C/kg)\"]\n", "display_df[\"rel_unc (%)\"] = display_df[\"rel_unc (%)\"]\n", "print(display_df.to_string(index=True))\n", "\n", "print(\"\\nEstadísticos finales:\")\n", "print(f\" - K = {K:.6e}\")\n", "print(f\" - Media ponderada e/m = {e_weighted_mean:.6e} ± {e_weighted_std:.2e} C/kg\")\n", "print(f\" - Chi2 = {chi2:.2f}, dof = {dof}, Chi2_red = {chi2_red:.2f}\")\n", "print(f\" - Media simple = {e_mean_simple:.6e} ± {e_std_mean:.2e} (s.e.m.)\")\n", "print(f\" - Desviación estándar muestral = {e_std_sample:.6e}\")" ] }, { "cell_type": "markdown", "id": "0e7fa6d8", "metadata": {}, "source": [ "Como buscamos achar a ecuación\n", "$$f=\\frac{e}{m}$$\n", "A incertidumbre, por propagación de erros será:\n", "$$s_f^2=\\sqrt{\\left({\\frac{s_V}{V}}\\right)^2+\\left({2\\frac{s_r}{r}}\\right)^2+\\left({2\\frac{s_I}{I}}\\right)^2}$$\n", "Facendo isto para cada set de datos e unha media ponderada destes, acharíamos o valor final buscado:\n", "\\[\n", "\\fbox{$\\frac{e}{m}=(1.616\\pm0.027)·10^{11}\\text{ C/Kg}$}\n", "\\]\n", "Que podemos comparar con valor real:\n", "$$\\frac{e}{m}=1.759·10^{11}\\text{ C/Kg}$$\n", "Son distintos e o real non entra no marxe de erro do calculado experimentalmente. Aínda así son considerablemente similares." ] } ], "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": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 5 }