{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Precódigo" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## pip install" ] }, { "cell_type": "code", "execution_count": 1, "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 -> 24.2\n", "[notice] To update, run: python.exe -m pip install --upgrade pip\n" ] } ], "source": [ "pip install matplotlib" ] }, { "cell_type": "code", "execution_count": 2, "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 -> 24.2\n", "[notice] To update, run: python.exe -m pip install --upgrade pip\n" ] } ], "source": [ "pip install sympy" ] }, { "cell_type": "code", "execution_count": 3, "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 -> 24.2\n", "[notice] To update, run: python.exe -m pip install --upgrade pip\n" ] } ], "source": [ "pip install math" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Note: you may need to restart the kernel to use updated packages.\n", "Requirement already satisfied: numpy in c:\\users\\lord_fulgi\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (1.26.4)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "[notice] A new release of pip is available: 24.0 -> 24.2\n", "[notice] To update, run: python.exe -m pip install --upgrade pip\n" ] } ], "source": [ "pip install numpy" ] }, { "cell_type": "code", "execution_count": 5, "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 -> 24.2\n", "[notice] To update, run: python.exe -m pip install --upgrade pip\n" ] } ], "source": [ "pip install pandas" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "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", "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 -> 24.2\n", "[notice] To update, run: python.exe -m pip install --upgrade pip\n" ] } ], "source": [ "pip install scipy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## import" ] }, { "cell_type": "code", "execution_count": 7, "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", "metadata": {}, "source": [ "# Funciones Generales" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Calculadora" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Funciones:\n", "\n", " ·ddx(f,n) o ddy(f,n); derivadas de orden n\n", " ·intdx(f) o intdy(f); integrales\n", " ·solvex(a,b) o solvey(a,b); resuelve ecuaciones algebraicas del tipo a = b" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# Linea de cómputo" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Derivadas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Funciones" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "def ddx(f, n):\n", " x = symbols('x')\n", " derivada = diff(f, x, n)\n", " \n", " return derivada\n", "\n", "def ddy(f, n):\n", " y = symbols('y')\n", " derivada = diff(f, y, n)\n", " \n", " return derivada" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Ejemplo" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$\\displaystyle 3 x^{2} + 4 x + 5$" ], "text/plain": [ "3*x**2 + 4*x + 5" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = sp.symbols('x')\n", "f = x**3 + 2*x**2 + 5*x\n", "\n", "derivada = sp.diff(f, x)\n", "\n", "derivada" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Integrales" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Funciones" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "def intdx(f):\n", " x = sp.symbols('x')\n", " integral = sp.integrate(f, x)\n", " \n", " return integral\n", "\n", "def intdy(f):\n", " y = sp.symbols('y')\n", " integral = sp.integrate(f, y)\n", " \n", " return integral" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Ejemplo" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$\\displaystyle \\frac{x^{4}}{4} + \\frac{2 x^{3}}{3}$" ], "text/plain": [ "x**4/4 + 2*x**3/3" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = sp.symbols('x')\n", "f = x**3 +2*x**2\n", "\n", "integral = sp.integrate(f, x)\n", "\n", "integral" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Resolver Ecuaciones Algebraicas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Funciones" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "def solvex(a,b):\n", " x = symbols('x')\n", " ecuacion = sp.Eq(a, b)\n", " solucion = sp.solve(ecuacion, x)\n", " \n", " return solucion\n", "\n", "def solvey(a,b):\n", " ecuacion = sp.Eq(a, b)\n", " solucion = sp.solve(ecuacion, y)\n", " \n", " return solucion" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Ejemplo" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[-3, 0]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Ecuación x**2 + 3x = 0\n", "ecuacion = sp.Eq(x**2 + 3*x, 0)\n", "\n", "solucion = sp.solve(ecuacion, x)\n", "\n", "solucion" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Funciones Técnicas 1" ] }, { "cell_type": "markdown", "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": 15, "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": 16, "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", "metadata": {}, "source": [ "# Ajuste Lineal por Mínimos Cuadrados" ] }, { "cell_type": "markdown", "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", "metadata": {}, "source": [ "### Funciones" ] }, { "cell_type": "code", "execution_count": 17, "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", "metadata": {}, "source": [ "### Ejemplo" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Los datos tomados, en forma matricial o en lista." ] }, { "cell_type": "code", "execution_count": 18, "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", "metadata": {}, "source": [ "Muestra los datos frameados en una tabla de Panda" ] }, { "cell_type": "code", "execution_count": 19, "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", "metadata": {}, "source": [ "Muestra los datos frameados en una tabla de Latex" ] }, { "cell_type": "code", "execution_count": 20, "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_17916\\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_17916\\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", "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": 21, "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 $' % ymed))" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$\\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", "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", "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": 23, "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": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sigi=np.sqrt(y)\n", "uy = sigi\n", "\n", "uy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Aquí representamos en tabla Panda los valores con su respectiva desviación estándar de y." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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xysig
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" ], "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", "metadata": {}, "source": [ "Llamamos a la función de ajuste." ] }, { "cell_type": "code", "execution_count": 25, "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", "metadata": {}, "source": [ "Graficamos los datos y el ajuste lineal (dos plots distintos en mismo frame)." ] }, { "cell_type": "code", "execution_count": 26, "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", "metadata": {}, "source": [ "Podemos graficar lo mismo que antes pero con barras de error." ] }, { "cell_type": "code", "execution_count": 27, "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", "metadata": {}, "source": [ "# Ajuste no Lineal" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Funciones" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Ejemplo" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Introducimos primero nuestros datos $\\{ x_i, y_i \\}$, $i=1,...,n$" ] }, { "cell_type": "code", "execution_count": 28, "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", "metadata": {}, "source": [ "Podemos visualizar los datos en tabla Panda." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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167.582.001.0
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5527.577.000.5
\n", "
" ], "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", "metadata": {}, "source": [ "O visualizarlos en tabla tipo Latex." ] }, { "cell_type": "code", "execution_count": 30, "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_17916\\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_17916\\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", "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": 31, "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": 32, "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", "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": 33, "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", "metadata": {}, "source": [ "Los datos obtenidos de tal ajuste son:" ] }, { "cell_type": "code", "execution_count": 34, "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", "metadata": {}, "source": [ "Por tanto, con estos parámetros, completamos la función, que es:" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "# La función estimada\n", "yEst=fun(x,w,gamma,A,phi,y0) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Graficamos los datos y la curva obtenida (ambas sobre mismo plano)." ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, '$A$ frente a $t$')" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "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", "metadata": {}, "source": [ "# Oscilador Amortiguado y Forzado (Lab Mecánica)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Parte 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Oscilador Amortiguado" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Datos tomados" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Incertidumbres para los valores tomados" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "s_I = 0.01 # Incertidumbre Intensidad, quizás 0.01 A o 0.1 A\n", "s_t = 0.25 # Tiempo de reacción humano (s)\n", "s_teta = 0.2 # Ángulo extraño sin unidad del S.I" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Valores tomados" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "intensidad_relacion_tiempo = [1.1, 1.1, 1.1, 0.9, 0.9, 0.9, 0.6, 0.6, 0.6, 0.3, 0.3, 0.3, 0.0, 0.0, 0.0] # Todas las Intensidades\n", "tiempo_amortiguamiento = [2.99, 2.91, 2.89, 4.78, 4.69, 4.73, 9.43, 9.39, 9.38, 25.79, 25.99, 25.83, 105.18, 103.24, 106.78] # Todos los tiempos hasta parar el cronómetro\n", "tiempo_amortiguamiento_periodo = [2.89, 4.78, 9.43, 25.79, 105.18] # Todos los tiempos de los que tienen datos para n\n", "oscilaciones_totales = [1.5, 1.5, 1.5, 2.5, 2.5, 2.5, 5, 5, 5, 14, 14, 14, 69, 61, 63] # Todas las oscilaciones hasta el tiempo de amortiguamiento\n", "oscilaciones_totales_periodo = [1.5, 2.5, 5, 14, 69] # Las oscilaciones de las que tienen n\n", "n = [0, 1, 2, 3, 4, 5, 6, 7, 8] # Los n de arriba\n", "# Los angulos de los que tienen n\n", "tetha = array([[17, 4.8, 1.2, 0.2, 0, 0, 0, 0, 0], [17, 7.6, 3.2, 1.2, 0.6, 0.2, 0, 0, 0], [17, 8.2, 3.8, 1.6, 0.6, 0.2, 0, 0, 0], [17, 11.2, 7.2, 4.6, 2.6, 1.4, 0.6, 0.2, 0], [17, 14.4, 11.4, 8.6, 6.4, 4, 2.4, 1, 0]])\n", "intensidad_relacion_amplitud = [1.1, 0.9, 0.6, 0.3, 0.0] # Las intensidades de los de n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### (1) Obtener $w_i$ a partir del periodo de oscilación" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Frecuencia $$w=\\frac{2·\\pi}{T}$$ y su incertidumbre $$s(w_i)=\\sqrt((\\frac{\\delta (\\frac{2·\\pi·n}{t})}{\\delta t})^2·s(t)^2)$$" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Periodo Oscilación: [1.9933333333333334, 1.9400000000000002, 1.9266666666666667, 1.9120000000000001, 1.8760000000000001, 1.8920000000000001, 1.886, 1.8780000000000001, 1.8760000000000001, 1.842142857142857, 1.8564285714285713, 1.845, 1.5243478260869565, 1.6924590163934425, 1.694920634920635]\n", "Frecuencia: [1.00334448160535*pi, 1.03092783505155*pi, 1.03806228373702*pi, 1.04602510460251*pi, 1.06609808102345*pi, 1.05708245243129*pi, 1.06044538706257*pi, 1.06496272630458*pi, 1.06609808102345*pi, 1.08569212873207*pi, 1.07733743747595*pi, 1.0840108401084*pi, 1.31203650884199*pi, 1.18171251452925*pi, 1.17999625398015*pi] rad/s\n", "Incertidumbre: [0.0838916790639926*pi, 0.0885676834236724*pi, 0.0897977754097772*pi, 0.0547084259729346*pi, 0.0568282559180946*pi, 0.0558711655619075*pi, 0.0281136104735569*pi, 0.0283536402104521*pi, 0.0284141279590473*pi, 0.0105243517713461*pi, 0.0103629995909576*pi, 0.0104917812631475*pi, 0.00311855036328671*pi, 0.00286156653072756*pi, 0.00276268087183964*pi] rad/s\n" ] } ], "source": [ "# Periodo de oscilacion T = t/n para todos los valores\n", "periodo_de_oscilacion = [tiempo_amortiguamiento[i]/oscilaciones_totales[i] for i in range(len(tiempo_amortiguamiento))]\n", "\n", "w_1 = [2*pi/i for i in periodo_de_oscilacion]\n", "s_w_1 = [((pi*oscilaciones_totales[i])/(2*(tiempo_amortiguamiento[i])**2)) for i in range(len(oscilaciones_totales))]\n", "print(f\"Periodo Oscilación: {periodo_de_oscilacion}\\nFrecuencia: {w_1} rad/s\\nIncertidumbre: {s_w_1} rad/s\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Valores finales para la $w_i$" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Para 1.1 A: (1.02411153346464*pi, 0.276641251727938, 2, [1.00334448160535*pi, 1.03092783505155*pi, 1.03806228373702*pi])\n", "Para 0.9 A: (1.05640187935242*pi, 0.1762549607054757, 2, [1.04602510460251*pi, 1.06609808102345*pi, 1.05708245243129*pi])\n", "Para 0.6 A: (1.0638353981302*pi, 0.08905288552855327, 2, [1.06044538706257*pi, 1.06496272630458*pi, 1.06609808102345*pi])\n", "Para 0.3 A: (1.08234680210547*pi, 0.033823523083388485, 2, [1.08569212873207*pi, 1.07733743747595*pi, 1.0840108401084*pi])\n", "Para 0.0 A: (1.22458175911713*pi, 0.13768714721709452, 2, [1.31203650884199*pi, 1.18171251452925*pi, 1.17999625398015*pi])\n" ] } ], "source": [ "print(f\"Para 1.1 A: {media_con_exclusion(np.mean(s_w_1[0:3]),w_1[0:3])}\")\n", "print(f\"Para 0.9 A: {media_con_exclusion(np.mean(s_w_1[3:6]),w_1[3:6])}\")\n", "print(f\"Para 0.6 A: {media_con_exclusion(np.mean(s_w_1[6:9]),w_1[6:9])}\")\n", "print(f\"Para 0.3 A: {media_con_exclusion(np.mean(s_w_1[9:12]),w_1[9:12])}\")\n", "print(f\"Para 0.0 A: {media_con_exclusion(np.mean(s_w_1[12:15]),w_1[12:15])}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### (1) Obtener $\\gamma$ a partir de ajuste por curvas y representación de las curvas ajustadas con barra de incertezas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ajustamos a una curva exponencial:$$\\theta(t)=\\theta_0·e^{-\\gamma·t}·cos(w_1·t+\\phi_0)$$ Que para máximos se vería como:$$\\theta(t_{max})=\\theta_0·e^{-\\gamma·t_{max}}$$" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "# Periodos de oscilacion de los que tienen n\n", "periodo_de_oscilacion_n = [1.9266666666666667, 1.9120000000000001, 1.886, 1.842142857142857, 1.5243478260869565]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ajustamos para una curva de tipo inversamente exponencial" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Varias gráficas (una para cada conjunto de datos):" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Una sola gráfica:" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.0, 0.9633333333333334, 1.9266666666666667, 2.89, 3.8533333333333335, 4.816666666666666, 5.78, 6.743333333333334, 7.706666666666667]\n", "θ0 ajustado: 17.01610800445239\n", "γ ajustado: 1.3355298935311044 $s^-1$\n", "Incertidumbre de γ: 0.01748394521511842 $s^-1$\n", "[0.0, 0.9560000000000001, 1.9120000000000001, 2.8680000000000003, 3.8240000000000003, 4.78, 5.736000000000001, 6.692, 7.648000000000001]\n", "θ0 ajustado: 17.050962577801254\n", "γ ajustado: 0.8697729058275465 $s^-1$\n", "Incertidumbre de γ: 0.012213372700198028 $s^-1$\n", "[0.0, 1.886, 3.772, 5.6579999999999995, 7.544, 9.43, 11.315999999999999, 13.202, 15.088]\n", "θ0 ajustado: 17.078115878268544\n", "γ ajustado: 0.40263122048564876 $s^-1$\n", "Incertidumbre de γ: 0.0076995912617920746 $s^-1$\n", "[0.0, 3.684285714285714, 7.368571428571428, 11.052857142857142, 14.737142857142857, 18.42142857142857, 22.105714285714285, 25.79, 29.474285714285713]\n", "θ0 ajustado: 17.274602573018925\n", "γ ajustado: 0.1250925454288796 $s^-1$\n", "Incertidumbre de γ: 0.004721913706016345 $s^-1$\n", "[0.0, 12.194782608695652, 24.389565217391304, 36.584347826086955, 48.77913043478261, 60.97391304347826, 73.16869565217391, 85.36347826086957, 97.55826086956522]\n", "θ0 ajustado: 18.248221602690556\n", "γ ajustado: 0.023750522131842235 $s^-1$\n", "Incertidumbre de γ: 0.0023672258730366197 $s^-1$\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Lord_Fulgi\\AppData\\Local\\Temp\\ipykernel_17916\\3500501497.py:3: RuntimeWarning: overflow encountered in exp\n", " return theta_0 * np.exp(-gamma * t) # Ecuación para máximo\n" ] }, { "data": { "image/png": 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ZRy+g+uaWKdJ0qr38cF+0jN07Vo79brQ0p7eXpafaS9Op9vLNLVOqvM5S+D9pTm8vzekdpaX0mENiURRFuZY4YgFVV8nLy5Pe3t7y119/lcOHD5fz5s2zO56amioB+cMPP0gpLyxAWtGnn34qK35UHzx4UN5xxx3S29tb+vj4yG7dusn9+/fLHTt22BZsLd/KF1otLi6WM2bMkCEhIdLT01PecsstcseOHdXGnZ2dLfV6vfzoo49s+w4fPiwBmZiYeFXviSM4cgHV+pd+NjAFRfnc33EHAOu+jsBwWy4moaHIosVDY8YiYUjHHRQU5ePl4W1/sfsA6+KqJd8ic1+EgLcRQrjgq1AURakfpJQUF7qmi4C7p1utfgd/+OGHREREEB4ezqhRo5g2bRrPPvvsVf0ej4mJITIykhUrVqDVajl48CB6vZ7evXuzaNEinn/+eVJSUgDw9rZ+pkyePJlffvmFtWvXEhISwqeffkp0dDTJycm0b9++0jOSkpIoLS2lb9++tn0RERG0atWKxMREevbsecXx1zcqCapja3e9xpibrD+wRee1iFIter2ZDJMb1xkK0QhoqjcSv+s1xkXPsbtWCGGdSfr8QCj5Goq3gMe9LvgqFEVR6ofiQiP3+TzkkmdvzHsXD6+ar2QfFxfHqFGjAIiOjiYnJ4ddu3Zxxx13XHEMaWlpPPnkk7Y1MSsmMX5+fggh7FZMSEtLIz4+nrS0NEJCQgBr39mtW7cSHx/Pyy+/XOkZp0+fxmAwVOpf1Lx5c06fPn3FsddH18TosGuZWVz4htFLM8YSPQAZZrdqz6tI6FojvP8BgMybh7Q4fzZrRVEUpXZSUlLYt2+frT+rTqdj+PDhxMXFXdV9p0+fzvjx4+nbty//+te/OHr06CXPT05Oxmw2ExYWhre3t23btWvXZa9tDFQlqI5p5YWMXI+ZXKMOby/IMLlXe14lXv+Aok1g/gOZ9xrC78W6CldRFKVec/d0Y2Peuy57dk3FxcVhMpls1RewNuW5ubmxdOlS/Pz8Kl2j0WiQUtrtKy0ttXs9Z84cHnzwQTZt2sSWLVuYPXs2a9eu5f77768yjvz8fLRaLUlJSWi1Wrtj5c1lFwsODqakpITs7Gy7atCZM2euqXU5a0IlQXVsxF9nknH+EwJ0RvTCjLHEA7hQCbJIyDS5MeKvM6u9hxAG8HsJmRkDRWuRHoPVAquKojRKQohaNUm5gslk4p133mHBggXcc889dseGDBnCmjVrmDhxYqXrgoKCyMvLo6CgAC8vL4Aq5xAKCwsjLCyMJ554gpEjRxIfH8/999+PwWDAbDbbnRsZGYnZbObs2bPcfvvtNYq/e/fu6PV6tm3bxtChQwFrZSstLY1evXrV6B7XCtUcVse8PLz59Oc+CMCAmWKjtTks0+SGRYIA1v/cp3Kn6IsIw83g8XcAZO5zSFlSx5EriqIoV+Kzzz4jKyuLcePG0alTJ7tt6NCh1TaJRUVF4enpyaxZszh69Cjvv/8+CQkJtuNFRUVMnjyZnTt3cvz4cfbs2cP+/fvp0KEDAK1btyY/P59t27Zx/vx5CgsLCQsLIyYmhtjYWNatW0dqair79u1j/vz5bNq0qco4/Pz8GDduHNOnT2fHjh0kJSUxZswYevXq1aA6RYNKgpzikejFxP0YjUZaMBov9AnKNLkR92M0j0QvrtF9hM9ToAkE0+9Q8HZdhqwoiqJcobi4OPr27Vtlk9fQoUM5cOAAhw4dqnSsSZMmvPfee2zevJnOnTuzZs0a5syZYzuu1WrJyMggNjaWsLAwHnjgAfr3788LL7wAQO/evZk4cSLDhw8nKCiIV199FYD4+HhiY2OZMWMG4eHhDBkyhP3799OqVatqv4bXX3+dgQMHMnToUP7yl78QHBzMunXrrvKdqX+EvLgBUiE3Nxc/Pz9ycnIcuo7Y43Pi2dnqJDd1OoabWcvrNy28bAXoYrLof8icGYAB0fR/CF0bh8WnKIpS3xQXF5OamkqbNm0uu4D2tSYlJYWIiAiOHDlCu3btXB3ONeNS3xO1/fxWlSAn8tK72SpBJVoLbm5X8APtPhAMtwElyNzZlTrRKYqiKPVfZmYmH3/8Mb6+voSGhro6nEZLJUFOZNBoKSnVYbEIJJKs0uxa30MIgfCdA7hDyXdQ9Imjw1QURVHq2Lhx43jzzTdZsWIFbm41H3WmOJYaHeZE7hotIDAadXh4lJJhzCDIrWmt7yN0rcDncWTeK8i8f4HbXxDaZo4PWFEURakTn376qatDUFCVIKdyL5ujodhoAOC88fylTr80z4dB1wlkLjL3JUeEpyiKoiiNikqCnMitbKXg8lmjz5dkXPG9hNAh/OYBWjB+jiz+whEhKoqiKEqjoZIgJ3LX6cAibXMFXVUlCBD6DuA1HgCZ+6JaUkNRFEVRakElQU6k02oQFi7MFXQVlaBywnsyaNuA5Swy79Wrvp+iKIqiNBYqCXIinU6LsFChEpR51fcUwg3hN9f6ouhDpHHvVd9TURRFURoDlQQ5kU6nRZjBWNYxOqMkA4u0XPV9rUtqjABA5s5CWgqv+p6KoiiK0tCpJMiJdHprJchYokODBrM0k12a45B7W5fUaAHmE8j81x1yT0VRFEVpyFQS5EQ6nQZhBhB4anyAq+8cXU5ovC80ixW+gyxJcsh9FUVRlNobPXq0dXJbIdDr9TRv3py7776blStXYrHUrgUgISEBf3//ugm0Hpg3bx69e/fG09PT6V+nSoKcSF/WJwjAQ2Nd0+S88eo7R5cTbreDx1BAInNmIWWxw+6tKIpyLTObLXz/Uxpffn2Y739Kw2y++q4IlxMdHU16ejrHjh1jy5Yt9OnTh8cff5yBAwdiMpnq/PnXipKSEoYNG8akSZOc/myVBDlReXMYgDtllaASx1SCygmfZ0HTDMypyPyarU6vKIrSkO367jf+Pum/TJ39IS8s2sTU2R/y90n/Zdd3v9Xpc93c3AgODqZly5Z069aNWbNmsWHDBrZs2UJCQoLtvIULF9K5c2e8vLwIDQ3l0UcfJT8/H4CdO3cyZswYcnJybJWl8pXls7KyiI2NJSAgAE9PT/r378+RI0ds9z1+/DiDBg0iICAALy8vOnbsyObNm6uN12g0MnPmTFq2bImXlxdRUVHs3LkTsC5a2rFjRx555BHb+UePHsXHx4eVK1cCFypW69evp3379ri7u9OvXz9OnDhxyffphRde4IknnqBz5861eXsdQiVBTqQv6xgNoMe6eryjmsPKCY0vwvdF64uClciSHx16f0VRlGvJru9+4//+vZFzGfl2+89l5PN//95Y54nQxe688066du3KunXrbPs0Gg2LFy/m559/ZtWqVWzfvp2nnnoKgN69e7No0SJ8fX1JT08nPT2dmTNnAtYmtwMHDrBx40YSExORUnLvvfdSWloKwGOPPYbRaGT37t0kJyfzyiuv4O3tXW1skydPJjExkbVr13Lo0CGGDRtGdHQ0R44cwd3dndWrV7Nq1So2bNiA2Wxm1KhR3H333YwdO9Z2j8LCQubNm8c777zDnj17yM7OZsSIEXXxVjqEWjvMiXT6C0mQ1uIFwDkHJ0EAwv1OpPt9ULwRmfMMNF2PEGqBPkVRGhez2cKildsvec5/Vu7gtpvbodU6ryYQERHBoUOHbK+nTZtm++/WrVszd+5cJk6cyPLlyzEYDPj5+SGEIDg42HbekSNH2LhxI3v27KF3794ArF69mtDQUNavX8+wYcNIS0tj6NChtgrLDTfcUG1MaWlpxMfHk5aWRkhICAAzZ85k69atxMfH8/LLL3PTTTcxd+5cxo8fz4gRIzh+/DifffaZ3X1KS0tZunQpUVFRAKxatYoOHTqwb98+brnllqt74+qAqgQ5kV6vszWHYfIE6iYJAhC+/weapmA+qprFFEVplH48fLJSBehiZzPy+PHwSSdFZCWlRAhhe/3VV19x11130bJlS3x8fHjooYfIyMigsLD66U4OHz6MTqezJRsAgYGBhIeHc/jwYQCmTp3K3LlzufXWW5k9e7Zd4nWx5ORkzGYzYWFheHt727Zdu3Zx9OhR23kzZswgLCyMpUuXsnLlSgIDA+3uo9PpuPnmm22vIyIi8Pf3t8VU36gkyIkMFfoEmUuslZnMkkyHzBV0MaEJqNAsFocs+cHhz1AURanPMrIKHHqeoxw+fJg2bdoAcOzYMQYOHEiXLl345JNPSEpKYtmyZYC1w/DVGD9+PH/88QcPPfQQycnJ9OjRgyVLllR5bn5+PlqtlqSkJA4ePGjbDh8+zH/+8x/beWfPnuW3335Dq9Xa9T+6VqkkyIn0ep2tOaykRIdWaDFLM1klWXXyPOHeF9wHAxZkzjNIWVQnz1EURamPAgO8HHqeI2zfvp3k5GSGDh0KQFJSEhaLhQULFtCzZ0/CwsI4deqU3TUGgwGz2Wy3r0OHDphMJvbuvbBKQEZGBikpKdx44422faGhoUycOJF169YxY8YM3nrrrSrjioyMxGw2c/bsWdq1a2e3VWyGGzt2LJ07d2bVqlU8/fTTlSo8JpOJAwcO2F6npKSQnZ1Nhw4davlOOYdKgpxIb7jQJ6io1EygwVpGrKsmMQDh+88Lo8Xy1CSKiqI0Hl07XEdQYPUdgQGaBfrQtcN1dfJ8o9HI6dOn+fPPP/n+++95+eWXGTx4MAMHDiQ2NhaAdu3aUVpaypIlS/jjjz949913eeONN+zu07p1a/Lz89m2bRvnz5+nsLCQ9u3bM3jwYCZMmMA333zDjz/+yKhRo2jZsiWDBw8GrH2NPv/8c1JTU/n+++/ZsWNHtclIWFgYMTExxMbGsm7dOlJTU9m3bx/z589n06ZNACxbtozExERWrVpFTEwMQ4YMISYmxq5ipdfrmTJlCnv37iUpKYnRo0fTs2fPS/YHSktL4+DBg6SlpWE2m21VqPIRcnVJJUFOpKswT1BRaSlBbk0Bx48Qq0ho/CpMorgKWbK/zp6lKIpSn2i1GqaNvfOS5zw+tk+ddYreunUrLVq0oHXr1kRHR7Njxw4WL17Mhg0b0Gq1AHTt2pWFCxfyyiuv0KlTJ1avXs38+fPt7tO7d28mTpzI8OHDCQoK4tVXrYtlx8fH0717dwYOHEivXr2QUrJ582b0euv6lGazmccee4wOHToQHR1NWFgYy5cvrzbe+Ph4YmNjmTFjBuHh4QwZMoT9+/fTqlUrfv31V5588kmWL19OaGgoAMuXL+f8+fM899xztnt4enry9NNP8+CDD3Lrrbfi7e3NBx98cMn36fnnnycyMpLZs2eTn59PZGQkkZGRdhWluiKklLLOn3KNyc3Nxc/Pj5ycHHx9fR123+OHTzJkyRpyIjTcft31/DUyn13nvmZIyH3cf91ghz2nKpacWVD0MWhDEYEbERrnlX8VRVGuVHFxMampqbRp0wZ3d/cruseu735j0crtdp2kmwX68PjYPvy1Z5ijQm30EhISmDZtGtnZ2XX6nEt9T9T281sNkXcirVaDMFlzTmslKAhw/ISJVRE+zyKNe6xri+X9G+E3p86fqSiKUh/8tWcYt93cjh8PnyQjq4DAAC+6drjOqcPilfpJJUFOpNFqEObyJMhE07LmsLPGc3X+bKHxAb+XkVljoOh9pPtd1mU2FEVRGgGtVkO3Tq1cHYZSz6g02Ik0FSpBxeZSmpVXguqwT1BFwu1W8HwIAJnzLNKS7ZTnKoqiKA3f6NGj67wpzNFUEuREdkmQyWRrDssqyabEUuqUGITPTNC2ActZZO4cpzxTURRFUeojlQQ5kUarQWOrBJnw0XnjrnFDIslw4GrylyKEB8L/34AWijcjiz677DWKoiiK0hCpJMiJtDqtXSVICEHTsmrQOSf0Cyon9F3AaxIAMncO0nzaac9WFEVRlPrCpUnQ7t27GTRoECEhIQghWL9+vd3x0aNHI4Sw26Kjoy9732XLltG6dWvc3d2Jiopi3759dfQV1E7F0WHGstk/m5V1jnZmEgQgvCeBrhPIXGTOLGQdLN2hKIqiKPWZS5OggoICunbtalsnpSrR0dGkp6fbtjVr1lzynh988AHTp09n9uzZfP/993Tt2pV+/fpx9uxZR4dfaxqtBlFqTTZM0oLJYrH1C3LGCLGKhNCXNYu5Qck3UPieU5+vKIqiKK7m0iSof//+zJ07l/vvv7/ac9zc3AgODrZtAQEBl7znwoULmTBhAmPGjOHGG2/kjTfewNPTk5UrVzo6/FrTaDVoSi/MTVlUWkqQe3lzmHNGiFUkdG0RPk8DIPNeRZb+5vQYFEVRFMVV6n2foJ07d9KsWTPCw8OZNGkSGRnVdyAuKSkhKSmJvn372vZpNBr69u1LYmJitdcZjUZyc3Pttrqg1WnALKFsku4i04Vh8s5uDrPxjAG3vwIlyJwZSHl1qxYriqIoNXPs2DGEEBw8eNDVoTRa9ToJio6O5p133mHbtm288sor7Nq1i/79+1daTbfc+fPnMZvNNG/e3G5/8+bNOX26+s6/8+fPx8/Pz7aVr4viaBqtBo1F2hZRLawwa/TZ4nO4YgUTIQTCdz5omoApBZm3wOkxKIqiNFSJiYlotVoGDBhQ6VhoaCjp6el06tTJIc9KSEjA39/fIff673//yx133IGvry9CiGtu/p+aqtdJ0IgRI7jvvvvo3LkzQ4YM4bPPPmP//v3s3LnToc959tlnycnJsW0nTpxw6P3LabQakNJuEdXyWaOLLcXkmep+xdyqCG1TayIEUBhvXV5DURRFuWpxcXFMmTKF3bt3c+rUKbtjWq2W4OBgdLr6t3hDYWEh0dHRzJo1y9Wh1Kl6nQRd7IYbbqBp06b8/vvvVR5v2rQpWq2WM2fO2O0/c+YMwcHB1d7Xzc0NX19fu60uaDQaqFgJMpVi0OgJ0Fv7ObmsSQwQ7n3AYyQAMudppCXLZbEoiqI0BPn5+XzwwQdMmjSJAQMGkJCQYHf84uawqio569evRwhhe/3jjz/Sp08ffHx88PX1pXv37hw4cICdO3cyZswYcnJybKOp58yZA1i7fMycOZOWLVvi5eVFVFTUZYsJ06ZN45lnnqFnz55X+S7Ub9dUEnTy5EkyMjJo0aJFlccNBgPdu3dn27Zttn0Wi4Vt27bRq1cvZ4VZLSEEAuyawwCauZc3ibl2BJvwfebCbNI5z7mkeU5RFOVSpJQUFZe4ZKvt78QPP/yQiIgIwsPDGTVqFCtXrrzq36sxMTFcd9117N+/n6SkJJ555hn0ej29e/dm0aJF+Pr62kZTz5w5E4DJkyeTmJjI2rVrOXToEMOGDSM6OpojR45cVSwNgUtrcPn5+XZVndTUVA4ePEiTJk1o0qQJL7zwAkOHDiU4OJijR4/y1FNP0a5dO/r162e75q677uL+++9n8uTJAEyfPp2HH36YHj16cMstt7Bo0SIKCgoYM2aM07++qogKzWHFpSYAmrkFkZL3m9OHyV9MCA/wX4DMGA7GL6DoA/Ac4dKYFEVRKio2lnJ3zGKXPPvL1VPxcDfU+Py4uDhGjRoFWPu45uTksGvXLu64444rjiEtLY0nn3ySiIgIANq3b2875ufnhxDCruUjLS2N+Ph40tLSCAkJAWDmzJls3bqV+Ph4Xn755SuOpSFwaRJ04MAB+vTpY3s9ffp0AB5++GFWrFjBoUOHWLVqFdnZ2YSEhHDPPffw0ksv4ebmZrvm6NGjnD9/YXj58OHDOXfuHM8//zynT5/mpptuYuvWrZU6S7uKXSXIZK0ENXe3xnbW6Pq5jIS+E/hMR+a9gsx9GQw9ELp2rg5LURTlmpKSksK+ffv49NNPAdDpdAwfPpy4uLirSoKmT5/O+PHjeffdd+nbty/Dhg2jbdu21Z6fnJyM2WwmLCzMbr/RaCQwMPCK42goXJoE3XHHHZcsDX7++eeXvcexY8cq7Zs8ebKtMlTfCHkhCSoqbw6rMEKsXvAcA8Y9UPINMvsJCPwYIdwue5mUZig5AJZzoAmyJlBC64SAFUVpLNzd9Hy5eqrLnl1TcXFxmEwmW/UFrE15bm5uLF26FD8/v0rXaDSaSp+JpaX2i2vPmTOHBx98kE2bNrFlyxZmz57N2rVrq51vLz8/H61WS1JSElqt/e9jb2/vGn89DVX965LewAlAU9YcZusT5NYMgDP1oBIEIIQG/F5BZgwqGzb/KsL3uUteI4s/R+bOA0uFqQg0weD7fwj3ftVfqCiKUgtCiFo1SbmCyWTinXfeYcGCBdxzzz12x4YMGcKaNWuYOHFipeuCgoLIy8ujoKAALy8vgCrnEAoLCyMsLIwnnniCkSNHEh8fz/3334/BYKg0hUxkZCRms5mzZ89y++23O+6LbCCuqY7RDYHgwuiwIpN9x+ic0hyMZqOrQrMjtEEIv1esLwrfRRbvqPZcWfw5MnuqfQIEYDmDzJ6KLL58RU9RFKWh+Oyzz8jKymLcuHF06tTJbhs6dChxcXFVXhcVFYWnpyezZs3i6NGjvP/++3YjyoqKipg8eTI7d+7k+PHj7Nmzh/3799OhQwcAWrduTX5+Ptu2beP8+fMUFhYSFhZGTEwMsbGxrFu3jtTUVPbt28f8+fPZtGlTtV/D6dOnOXjwoK3fbnJyMgcPHiQzM9Nxb1Q9oJIgJ9NQeXSYl84LL60n4Pw1xC5FuP0VPEcDIHOeqXK1eSnN1goQVTVrWvfJ3JetTWWKoiiNQFxcHH379q2yyWvo0KEcOHCAQ4cOVTrWpEkT3nvvPTZv3kznzp1Zs2aNbZg7WOcVysjIIDY2lrCwMB544AH69+/PCy+8AEDv3r2ZOHEiw4cPJygoiFdffRWA+Ph4YmNjmTFjBuHh4QwZMoT9+/fTqlWrar+GN954g8jISCZMmADAX/7yFyIjI9m4cePVvDX1jpBqHHQlubm5+Pn5kZOT4/A5g/rd+k/Se/mT30owumskz//1TgDm/PwSqQXHmNr+MboHdHPoM6+GlCXIjGFgOgz6WxBNVtn185HGvcishy57HxHwLsItqi5DVRSlASouLiY1NZU2bdrg7u7u6nAcKiUlhYiICI4cOUK7dmoASk1d6nuitp/fqhLkZBpRuRIEFzpHn3HxXEEXE8KA8F8EwhNK9yHzl9mfYKlh5aqm5ymKojQCmZmZfPzxx/j6+tbZUk3K5akkyMkqNocVmUy2/c3crZ2j61NzWDmha4PwfdH6omAZ0vjdhYOaoJrdpKbnKYqiNALjxo3jzTffZMWKFXbTvijOpUaHOZkQwm7tsHLBbta5gs4Un6nqMpcTHvchSxKh6BNkzkwI3IDQBoKhh3UUmOUMVfcLEtbjhh7ODllRFKXeKp8/SHEtVQlyMi0XKkEFFZvDyitB9aw5rCLh8xxo25Ytq/EUUloQQovw/b/yMy6+wvq/vrPUfEGKoihKvaOSICfTaESlyRIBgstmjc4oyaTEUlrVpS4nNJ4I//8AblDyNRS8bd3v3g/hvxg0F83KrQlG+C9W8wQpiqIo9ZJqDnMyjQDNRctmAPjofHDXuFNsKea88RwhHiHV3MG1hD4MfP+JzH0Omf86GLohDD2siY5bXzVjtKIoinLNUJUgJ9MIUWF0WIltvxCC5mVNYqfrcZMYAB4PgPt9gBmZPQ1psU6eJYQW4RaF8Bho/VclQIqiKEo9ppIgJ6uYBBVdtCaMbSHVeto5upwQAuH7AmhvsPYPyp6pJkNUFEVRrjkqCXIybYUkqODiJKhsDbHT9WQNsUsRGi+E/xLAHUq+gYI3XB2SoiiKotSKSoKcTKO5MES+2GTCbLHYjjW/BkaIVST07RF+1unaZf5ipPFbF0ekKIqiKDWnkiAn02qErWM02E+YWN4cVl/nCqqK8LgfPIYBEpkzA2m+dmJXFEWpK6NHj7Z2HRACvV5P8+bNufvuu1m5ciWWCn/81kRCQgL+/v51E6iLHTt2jHHjxtGmTRs8PDxo27Yts2fPpqSk5PIXO4BKgpxMKwRYsM0rWLFz9LUwTL4qwvc50IWDJQOZ/ThSXjuxK4rSOJgtFr47eYKNKYf57uQJuyp8XYmOjiY9PZ1jx46xZcsW+vTpw+OPP87AgQMxVfgDuDH79ddfsVgsvPnmm/z888+8/vrrvPHGG8yaNcspz1dJkJNptRoEoCn7+au4fpiPzgdPrQcSec00iQEI4Y7wXwrCG0q/R+b929UhKYqi2Gz9/Qi3J7zFg+s+ZNrnm3lw3YfcnvAWW38/UqfPdXNzIzg4mJYtW9KtWzdmzZrFhg0b2LJlCwkJCbbzFi5cSOfOnfHy8iI0NJRHH32U/Px8AHbu3MmYMWPIycmxVZbKV5bPysoiNjaWgIAAPD096d+/P0eOXPiajh8/zqBBgwgICMDLy4uOHTuyefPmauM1Go3MnDmTli1b4uXlRVRUFDt37gSsi5Z27NiRRx55xHb+0aNH8fHxYeXKlcCFitX69etp37497u7u9OvXjxMnTlT7zOjoaOLj47nnnnu44YYbuO+++5g5cybr1q2r7dt9RVQS5GRarXUWZW0VSZB1mHwwAKevoSYxAKG7HuH3qvVFYQKyqPofNEVRFGfZ+vsRHtu8kdNlSUW5M/n5PLZ5Y50nQhe788476dq1q92HvEajYfHixfz888+sWrWK7du389RTTwHQu3dvFi1ahK+vL+np6aSnpzNz5kzA2uR24MABNm7cSGJiIlJK7r33XkrLPlcee+wxjEYju3fvJjk5mVdeeQVvb+9qY5s8eTKJiYmsXbuWQ4cOMWzYMKKjozly5Aju7u6sXr2aVatWsWHDBsxmM6NGjeLuu+9m7NixtnsUFhYyb9483nnnHfbs2UN2djYjRoyo1XuUk5NDkyZNanXNlVKTJTqZVmPNO4VZgF7aJUFgbRJLLUjlTPFpV4R3VYR7X6TXI1DwX2TuLNCHIXTtXB2WoiiNlNli4cXd26tc1VBiXdjnpd07uPuGtrbfzc4QERHBoUOHbK+nTZtm++/WrVszd+5cJk6cyPLlyzEYDPj5+SGEIDg42HbekSNH2LhxI3v27KF3794ArF69mtDQUNavX8+wYcNIS0tj6NChdO7cGYAbbrih2pjS0tKIj48nLS2NkBDrZL0zZ85k69atxMfH8/LLL3PTTTcxd+5cxo8fz4gRIzh+/DifffaZ3X1KS0tZunQpUVFRAKxatYoOHTqwb98+brnllsu+N7///jtLlizhtddeu+y5jqAqQU6m01rf8qqaw+BCv6BrrRJUTnhPA0NPkIXIrMlIS/5lr1EURakL+0/9WakCVJEE0vPz2H/qT+cFBUgpEeLCWotfffUVd911Fy1btsTHx4eHHnqIjIwMCgsLq73H4cOH0el0tmQDIDAwkPDwcA4fPgzA1KlTmTt3LrfeeiuzZ8+2S7wulpycjNlsJiwsDG9vb9u2a9cujh49ajtvxowZhIWFsXTpUlauXElgYKDdfXQ6HTfffLPtdUREBP7+/raYLuXPP/8kOjqaYcOGMWHChMue7wgqCXIybVkSJEzWv00aXBIkdAi/163riJn/QOb+H1JW9XeYoihK3TpbULM/wmp6nqMcPnyYNm3aANbRUQMHDqRLly588sknJCUlsWzZMoCrHiE1fvx4/vjjDx566CGSk5Pp0aMHS5YsqfLc/Px8tFotSUlJHDx40LYdPnyY//znP7bzzp49y2+//YZWq7Xrf3S1Tp06RZ8+fejduzf//e9/HXbfy1FJkJPpypvDbJUg+2/y4Gu0T1BFQhtoXVAVPRRvgcI4V4ekKEoj1Myr+v4vV3KeI2zfvp3k5GSGDh0KQFJSEhaLhQULFtCzZ0/CwsI4deqU3TUGgwGz2X5W/g4dOmAymdi7d69tX0ZGBikpKdx44422faGhoUycOJF169YxY8YM3nrrrSrjioyMxGw2c/bsWdq1a2e3VWyGGzt2LJ07d2bVqlU8/fTTlSo8JpOJAwcO2F6npKSQnZ1Nhw4dqn1P/vzzT+644w66d+9OfHw8Gic2TaokyMl0ZR2jq501umzCxFxTLoWm6kuh9Z0wRCJ8/w8Amfca0rjHxREpitLY3BzSkmBvb0Q1xwXQwtuHm0Na1snzjUYjp0+f5s8//+T777/n5ZdfZvDgwQwcOJDY2FgA2rVrR2lpKUuWLOGPP/7g3Xff5Y037Gfgb926Nfn5+Wzbto3z589TWFhI+/btGTx4MBMmTOCbb77hxx9/ZNSoUbRs2ZLBgwcD1r5Gn3/+OampqXz//ffs2LGj2mQkLCyMmJgYYmNjWbduHampqezbt4/58+ezadMmAJYtW0ZiYiKrVq0iJiaGIUOGEBMTY1ex0uv1TJkyhb1795KUlMTo0aPp2bNntf2ByhOgVq1a8dprr3Hu3DlOnz7N6dPO6RerkiAnu9AcZn19cSXIQ+uBv94PuLarQQB4jASPvwMWZPYTSNNJV0ekKEojotVoeP4vdwJUSoTKXz/3lz511il669attGjRgtatWxMdHc2OHTtYvHgxGzZsQKu1LjDdtWtXFi5cyCuvvEKnTp1YvXo18+fPt7tP7969mThxIsOHDycoKIhXX7WOxI2Pj6d79+4MHDiQXr16IaVk8+bN6PV6AMxmM4899hgdOnQgOjqasLAwli9fXm288fHxxMbGMmPGDMLDwxkyZAj79++nVatW/Prrrzz55JMsX76c0NBQAJYvX8758+d57rnnbPfw9PTk6aef5sEHH+TWW2/F29ubDz74oNpnfvnll/z+++9s27aN6667jhYtWtg2ZxBSddioJDc3Fz8/P3JycvD19XXovV8Ys4wvc4vIay/IC4Ept/TkiZ632p0z//Cr/JqXwiM3jOfWpr0c+nxnk9KIzHwQSpNBdyMicC1CuLs6LEVRrhHFxcWkpqbSpk0b3N2v7HfH1t+P8OLu7XadpFt4+/DcX/oQ3a69o0Jt9BISEpg2bRrZ2dl1+pxLfU/U9vNbDZF3Mr3Omv1jsg7QLCipPLtysHswv+alcPoaHCZ/MSHcwH8pMuN+MP2CzHkO/F61GxmhKIpSl6LbtefuG9qy/9SfnC3Ip5mXNzeHtHTqsHilflJJkJPZmsNsfYIq9/5vUTZCLL0BJEEAQtsC/P6DzBoNxRtA3xG8Rrs6LEVRGhGtRkPP60JdHYZSz6g02Mn0urK3/FJJkIe1LTS9qGEkQQDCLQrh8zQAMu8V1VFaURSlgRk9enSdN4U5mkqCnEyvtzaHlVeCCqtpDgPravIWWfeL/DmN58Pgfj9gRmZPQ5rSXB2RoiiK0oipJMjJ9GUjAjSXqAQFuTVFJ3SUylIySjKdGV6dEkIg/F4EfVeQOcjsSWpGaUVRFMVlVBLkZNqLKkEXzxMEoBEampXNF5RelO602JxBCDfrivOaZmA6gsx5CtmQql2KoijKNUMlQU6m12vBIi8kQdVMix5imzm64fQLKie0zRH+ywA9GL9C5lc9jbuiKIqi1CWVBDmZVqsFi8XWHHbx2mHlyvsFnWqASRCAMHRF+L1kfVGwDFm02bUBKYqiKI2OSoKcTKPT2FeCqugTBBVHiDWs5rCKhMffwHMMADLnaWRpsosjUhRFURoTlQQ5mVanBXkhCSosLa1ylfUQ97IkqLjhJkEAwucpcPsrYERmTUKaG2blS1EU5WLHjh1DCMHBgwddHUqj5dIkaPfu3QwaNIiQkBCEEKxfv952rLS0lKeffprOnTvj5eVFSEgIsbGxlVbXvdicOXOso5AqbBEREXX8ldScVltWCSpbO8wiJcUmU6XzWnhYm8NySnMpMBU4M0SnEkKL8HsddO3AchaZ9ShSFrk6LEVRFIdITExEq9UyYMCASsdCQ0NJT0+nU6dODnlWQkIC/v7+V32fzMxMpkyZQnh4OB4eHrRq1YqpU6eSk5Nz9UHWMy5NggoKCujatSvLli2rdKywsJDvv/+e5557ju+//55169aRkpLCfffdd9n7duzYkfT0dNv2zTff1EX4V0Sr0yIsFkSFAVFVjRDz0HrQxBAANOwmMQCh8Ub4vwHCH0w/IXOeqbI6piiKcq2Ji4tjypQp7N69u9If8VqtluDgYHS6+rV4w6lTpzh16hSvvfYaP/30EwkJCWzdupVx48a5OjSHc2kS1L9/f+bOncv9999f6Zifnx9ffvklDzzwAOHh4fTs2ZOlS5eSlJREWtqlJ9nT6XQEBwfbtqZNm9bVl1Brtj5BgLvW+o1f3QixFmVNYqcaeJMYgNC1QgSUjRgr3oLMX+zqkBRFqYeklBSWlrpkq+0fZ/n5+XzwwQdMmjSJAQMGkJCQYHf84uawqio569evt1tr8ccff6RPnz74+Pjg6+tL9+7dOXDgADt37mTMmDHk5OTYWkHmzJkDgNFoZObMmbRs2RIvLy+ioqLYuXNntXF36tSJTz75hEGDBtG2bVvuvPNO5s2bx//+9z9MVbRcXMvqV/p5GeX/516u3HfkyBFCQkJwd3enV69ezJ8/n1atWlV7vtFoxGg02l7n5uY6KuRKypvDwJoEFZtN1XaODvFowc+5v3CqgVeCygnDzeA7B5n7f9YRY7o2CI/LV/4URWk8ikwmOq1wzR9JP02aiqdeX+PzP/zwQyIiIggPD2fUqFFMmzaNZ5999qoWkI6JiSEyMpIVK1ag1Wo5ePAger2e3r17s2jRIp5//nlSUlIA8Pb2BmDy5Mn88ssvrF27lpCQED799FOio6NJTk6mffv2NXpu+ars9a1qdbWumY7RxcXFPP3004wcORJfX99qz4uKirKV7lasWEFqaiq33347eXl51V4zf/58/Pz8bFtoaN0tsqfVae2SIID8y1WCGkkSBCA8h4HXBABkzrPIkgMujkhRFOXKxMXFMWrUKACio6PJyclh165dV3XPtLQ0+vbtS0REBO3bt2fYsGF07doVg8GAn58fQghbK4i3tzdpaWnEx8fz0Ucfcfvtt9O2bVtmzpzJbbfdRnx8fI2eef78eV566SUeeeSRq4q9PromUrrS0lIeeOABpJSsWLHikuf279/f9t9dunQhKiqK66+/ng8//LDa9sxnn32W6dOn217n5ubWWSKk0WmhbIZkd4119uhLVYKgcTSHVSS8ZyBNx8H4BTLrUQj8GKGrvpKnKErj4aHT8dOkqS57dk2lpKSwb98+Pv30U8DaTWP48OHExcVxxx13XHEM06dPZ/z48bz77rv07duXYcOG0bZt22rPT05Oxmw2ExYWZrffaDQSGBh42efl5uYyYMAAbrzxRlvzWkNS75Og8gTo+PHjbN++/ZJVoKr4+/sTFhbG77//Xu05bm5uuLm5XW2oNaLVaRAWiQTcypOg6maNLkuCzhvPU2IpwaAxOCVGVxNCA/7/RmacsnaUzpoAgR8iNH6uDk1RFBcTQtSqScpV4uLiMJlMhISE2PZJKXFzc2Pp0qX4+VX+fabRaCr1Oyq9aODMnDlzePDBB9m0aRNbtmxh9uzZrF27tsq+tWDtl6TVaklKSrJO1ltBeXNZdfLy8oiOjsbHx4dPP/0U/TXwvtdWvW4OK0+Ajhw5wldffVWjrPVi+fn5HD16lBYtWtRBhLVXsTnMIC7dMdpX54uX1guJJL2occ2fI4QHIuAN0LQAcyoyewpSVv0+KYqi1Ccmk4l33nmHBQsWcPDgQdv2448/EhISwpo1a6q8LigoiLy8PAoKLkyLUtUcQmFhYTzxxBN88cUX/O1vf7M1axkMBsxms925kZGRmM1mzp49S7t27ey24ODgar+G3Nxc7rnnHgwGAxs3bsTd3f0K3on6z6VJUH5+vu2bAyA1NZWDBw+SlpZGaWkpf//73zlw4ACrV6/GbDZz+vRpTp8+TUmFpOGuu+5i6dKlttczZ85k165dHDt2jG+//Zb7778frVbLyJEjnf3lVUmnr5gEWd/+/GqWzhBC0NLT+lfEqaJLz4/UEAltM0TAmyC8oOQ7ZM4/1dB5RVHqvc8++4ysrCzGjRtHp06d7LahQ4cSFxdX5XVRUVF4enoya9Ysjh49yvvvv283oqyoqIjJkyezc+dOjh8/zp49e9i/fz8dOnQAoHXr1uTn57Nt2zbOnz9PYWEhYWFhxMTEEBsby7p160hNTWXfvn3Mnz+fTZs2VRlHeQJUUFBAXFwcubm5ts/fi5Osa51Lk6ADBw4QGRlJZGQkYG3rjIyM5Pnnn+fPP/9k48aNnDx5kptuuokWLVrYtm+//dZ2j6NHj3L+/Hnb65MnTzJy5EjCw8N54IEHCAwM5LvvviMoKMjpX19VrJUga58gg7h0cxhAS3drEvRnI0yCAIQ+AuH/H0ALxevVYquKotR7cXFx9O3bt8omr6FDh3LgwAEOHTpU6ViTJk1477332Lx5M507d2bNmjV2/XC0Wi0ZGRnExsYSFhbGAw88QP/+/XnhhRcA6N27NxMnTmT48OEEBQXx6quvAhAfH09sbCwzZswgPDycIUOGsH///mpHTX///ffs3buX5ORk2rVrZ/f5e+LECQe8Q/WHkOpP60pyc3Px8/OzDQl0pO8+S+Lplz7GfF0g7Qa3Ynf2CcZHdmfW7XdUef4Xp79iddoaugVE8nj7yQ6N5VoiCz9A5j4HgPCdj/Ac6uKIFEVxhuLiYlJTU2nTpk2Da5JJSUkhIiKCI0eO0K5dO1eHc8241PdEbT+/63WfoIZIq7swT5C+7O2vasboci09Gm9zWEXCczh4TQRA5j6HNO5xcUSKoihXLjMzk48//hhfX986nZZFubR6PzqsoanYHKaTZX2CLtUcVpYEnSk+S4mlFIOm4fXOrynh/QTS/CcU/w+ZPRmarEHo68+6cIqiKDU1btw4kpKSWLFihdNGJyuVqSTIySqODtNK66yhl+oT5Kf3w1PrSaG5kNPFp2nl2Xj/YhBCgN98pPkMlO5DZo23Dp3Xhlz+YkVRlHqkfP4gxbVUc5iTVWwO01nKkqBqJksE6wd/iEfj7hxdkRAG6xpjuvZlq86PR1qyXR2WoiiKcg1SSZCTacpWkQfQWi5fCQK4zqMlAH8W/Vm3wV0jhMYPEfA2aJqD6Xdk1iSkLHZ1WIqiKMo1RiVBTlaxOUxjzYXIq2kSVKiSoHJC2wIREAfCB0qTkNkzkbJhzV+hKIqi1C2VBDlZxckSNebLN4cBXOdpTYJOquYwO0IfhghYAeit64zlzVWTKSqKoig1ppIgJ6s4OkxTVri41OgwgJZllaBzxnMYzcY6je9aIwy3IPxfAwQUroaCSy+wqyiKoijlVBLkZOULqAJgsv5TbDJhKkuMquKr98FX54tEcqqoca0oXxPCvT/C558AyPxFyMK1dfIcKc1I415k0WfWf1Xzm6IoyjVNJUFOVrFPEKYLTTeX7RxdtobYyaKTdRbbtUx4PQRejwEgc2cji7c69P6y+HPkuT7IrIeQOdOt/57rgyz+3KHPURRFUZxHJUFOVrE5TJosuGmtUzXVtEnspBohVi3hPRU8RgASmT0DaUx0yH1l8efI7KlgOW1/wHIGmT1VJUKKolQyevRohBAIIdDr9TRv3py7776blStXYrlE5b8qCQkJ+Pv7102g9cB9991Hq1atcHd3p0WLFjz00EOcOuWcPrAqCXKyivMElZSa8TYYAMgruXRfn/IRYifVCLFqCSEQvrPBrR9Qisx+FFn601XdU0ozMnceUFWHa+s+mfuyahpTlHrOIi0czv2VxIy9HM79FYusXSJyJaKjo0lPT+fYsWNs2bKFPn368PjjjzNw4EBMJlOdP/9a0adPHz788ENSUlL45JNPOHr0KH//+9+d8myVBDlZxUqQyXQhCbpcJeg6z+sAa3OYs3+QryVCaBH+C8DQE2QBMnMc0nT0ym9YcqByBciOBEu69TxFUeqlA5lJTP/xKf7167954+h/+dev/2b6j09xIDOpTp/r5uZGcHAwLVu2pFu3bsyaNYsNGzawZcsWEhISbOctXLiQzp074+XlRWhoKI8++ij5+fkA7Ny5kzFjxpCTk2OrLJWvLJ+VlUVsbCwBAQF4enrSv39/jhw5Yrvv8ePHGTRoEAEBAXh5edGxY0c2b95cbbxGo5GZM2fSsmVLvLy8iIqKYufOnYB10dKOHTvyyCOP2M4/evQoPj4+rFy5ErhQsVq/fj3t27fH3d2dfv36XXbl+SeeeIKePXty/fXX07t3b5555hm+++47Si+xrqajqCTIybQ6LUKWV4JM+NQ0CSqrBGWX5jj9B/laI4QB4b8cdJ1AZiEzx1jXHLsSlnOOPU9RFKc6kJnEkt+Xk1WSZbc/qySLJb8vd/rvzzvvvJOuXbuybt062z6NRsPixYv5+eefWbVqFdu3b+epp54CoHfv3ixatAhfX1/S09NJT09n5syZgLXJ7cCBA2zcuJHExESklNx777225OGxxx7DaDSye/dukpOTeeWVV/D29q42tsmTJ5OYmMjatWs5dOgQw4YNIzo6miNHjuDu7s7q1atZtWoVGzZswGw2M2rUKO6++27Gjh1ru0dhYSHz5s3jnXfeYc+ePWRnZzNixIgavz+ZmZmsXr2a3r17o9fX/VqZtV47LDU1la+//prjx49TWFhIUFAQkZGR9OrVq9KS9kplWp0GzNYkqLTUjLfB+p7lX6Y57Kecn6vcX/6DPKXdo/Ro0t2xwV7DhMYbmryNzBxlnVU6c7R1wVVt09rdSBPk2PMURXEai7TwXtqaS56zOm0t3QIi0Qjn1QQiIiI4dOiQ7fW0adNs/926dWvmzp3LxIkTWb58OQaDAT8/P4QQBAcH2847cuQIGzduZM+ePfTu3dv6taxeTWhoKOvXr2fYsGGkpaUxdOhQOnfuDMANN9xQbUxpaWnEx8eTlpZGSIh1IM7MmTPZunUr8fHxvPzyy9x0003MnTuX8ePHM2LECI4fP85nn31md5/S0lKWLl1KVFQUAKtWraJDhw7s27ePW265pdrnP/300yxdupTCwkJ69uxZ6b51pcb/r69evZpbbrmFtm3b8vTTT7N+/Xq+/vpr3n77baKjo2nevDmPPvoox48fr8t4r3maCs1hFfsEXaoSVNMfZNU0Zk9omiACVoL2OjAfR2aNQVpyancTQw/QBAOiuqeApoX1PEVR6pWUvN8qVYAullmSSUreb06KyEpKaV0QusxXX33FXXfdRcuWLfHx8eGhhx4iIyODwsLCau9x+PBhdDqdLdkACAwMJDw8nMOHDwMwdepU5s6dy6233srs2bPtEq+LJScnYzabCQsLw9vb27bt2rWLo0cvdCmYMWMGYWFhLF26lJUrVxIYGGh3H51Ox80332x7HRERgb+/vy2m6jz55JP88MMPfPHFF2i1WmJjY50y+W2NkqDIyEgWL17M6NGjOX78OOnp6SQlJfHNN9/wyy+/kJuby4YNG7BYLPTo0YOPPvqoruO+ZlWcMbq0hklQff1BvhYIbTAiIN5aqTGlILMeQVoKan690CJ8/6/81cVHrf/rOwshtI4JWFEUh8kurdkfPTU9z1EOHz5MmzZtADh27BgDBw6kS5cufPLJJyQlJbFs2TIASi7TTeJyxo8fzx9//MFDDz1EcnIyPXr0YMmSJVWem5+fj1arJSkpiYMHD9q2w4cP85///Md23tmzZ/ntt9/QarV2/Y+uVtOmTQkLC+Puu+9m7dq1bN68me+++85h969OjZKgf/3rX+zdu5dHH32U0NDQSsfd3Ny44447eOONN/j1118vWXJr7LQVFlAtreHosPr6g3ytELrrrRUh4QelPyCza7fgqnDvh/BfbF2wtSJNMMJ/McK9n4MjVhTFEfz1fg49zxG2b99OcnIyQ4cOBSApKQmLxcKCBQvo2bMnYWFhlYaHGwwGzGb7EagdOnTAZDKxd+9e276MjAxSUlK48cYbbftCQ0OZOHEi69atY8aMGbz11ltVxhUZGYnZbObs2bO0a9fObqvYDDd27Fg6d+7MqlWrePrppytVeEwmEwcOXBgokpKSQnZ2Nh06dKjxe1Q+hYDRWPcrJNSoT1C/fjX/JR8YGFipPKZcoNFoEGUVvlKTGW+DG3DpSlB9/EG+1gh9OAS8jcwaDSXfIbOngP8yhDDU7Hr3fuDWt2y02DlrZcnQQ1WAFKUeC/cJI8AQcMlKehNDE8J9wurk+UajkdOnT2M2mzlz5gxbt25l/vz5DBw4kNjYWADatWtHaWkpS5YsYdCgQezZs4c33njD7j6tW7cmPz+fbdu20bVrVzw9PWnfvj2DBw9mwoQJvPnmm/j4+PDMM8/QsmVLBg8eDFj7GvXv35+wsDCysrLYsWNHtclIWFgYMTExxMbGsmDBAiIjIzl37hzbtm2jS5cuDBgwgGXLlpGYmMihQ4cIDQ1l06ZNxMTE8N1332Eo+4Ner9czZcoUFi9ejE6nY/LkyfTs2bPa/kB79+5l//793HbbbQQEBHD06FGee+452rZtS69evRz1f0W1at0TLC0t7ZKbcnlajbUZpabzBJX/IF9KXf4gNxTC0BUR8F/AHYy7rBMqyprP1SGEFuEWhfAYaP1XJUCKUq9phIZRrUZe8pyYViPqrFP01q1badGiBa1btyY6OpodO3awePFiNmzYgFZr/f3RtWtXFi5cyCuvvEKnTp1YvXo18+fPt7tP7969mThxIsOHDycoKIhXX30VgPj4eLp3787AgQPp1asXUko2b95sG1VlNpt57LHH6NChA9HR0YSFhbF8+fJq442Pjyc2NpYZM2YQHh7OkCFD2L9/P61ateLXX3/lySefZPny5bYWoeXLl3P+/Hmee+452z08PT15+umnefDBB7n11lvx9vbmgw8+qPaZnp6erFu3jrvuuovw8HDGjRtHly5d2LVrF25ublf2xteCkLXseaTRaOw6dF3s4pLdtSg3Nxc/Pz9ycnLw9fV1+P3vbTaW3NtvxN1Nx8PP3MHsndvo17Y9KwbcV+015cM8q6NGh9WcNH6DzPoHUAru9yH8XkU4cWSIoig1V1xcTGpqKm3atLniEcgHMpN4L22NXUWoiaEJMa1GqN+bDpSQkMC0adPIzs6u0+dc6nuitp/ftR4i/8MPP9i9Li0t5YcffmDhwoXMmzevtrdrlMorQaUmCz5lzWGXmzG6R5PuTGn3KG/+8TYllgtNZ+oHufaE223gv9jaJFa8ESncwfdFlQgpSgPVo0l3ugVEkpL3G9mlOfjr/Qj3CXPqsHilfqp1EtS1a9dK+3r06EFISAj//ve/+dvf/uaQwBoyndb6g2c2W/DWlzWH1aADWI8m3ckqyea9tPdp5dmKB1sNVz/IV0i43wV+/0bmzICiD5HowHf2JauciqJcuzRCQwffCFeHodQzDvv0DA8PZ//+/Y66XYOm01x42z10NVtAtdz1Xq2s55vy6OAboRKgqyA8BiD8/gUIKHofmTfPKfNSKIqiNESjR4+u86YwR6t1JSg3N9futZSS9PR05syZQ/v27R0WWENWXgkC8NBY/y+4XHNYudCyNcQyS7LIL83HW1/9FOjK5QmPISBNyNxZUPiOtSLk87SqCCmKojQCtU6C/P39K31ASCkJDQ1l7dq1DgusIdNWSILcNNYRAnnGmlWCPLQeBLkFcc54jrSiE9yor/ncC0rVhOffARMy93koXIkUOvCeoRIhRVGUBq7WSdCOHTvsXms0GoKCgmjXrh06Xa1v1yjp9WVLZ2g0uAnre2Y0mygxmzFoLz/supVnqDUJKkjjRl+VBDmC8BxhrQjlvQgF/0WiAe8nVCKkKIrSgNU6a/nrX/9aF3E0Klpd2dIZGjBU6JaVZzQS6Ol52etbeYaSlPU9xwtP1GWYjY7wGgWYkXnzoOANJBK8p6tESFEUpYGqUa/a2qzfUVhYyM8/V73iuWKl1V9YRNVstuBZNrFVTfsFXe9p7RydVqgmp3Q04fUwwqdsrbCCN5H5C1RnaUVRlAaqRknQQw89RL9+/fjoo48oKKh68clffvmFWbNm0bZtW5KSkhwaZEOj1WnBfGER1QtzBdV0hNj1AJwqSrebM0hxDGsi9E/ri4L/IvNfU4mQoihKA1SjJOiXX35hwIAB/POf/8Tf35+OHTty9913M2jQIG677TaaNm1Kt27dSE1N5YsvvrCtiaJUTae/sIhqicmMj6HmcwUBBOj98dH5YMHCycI/6yzOxkx4xVZIhN5SiZCiKA537NgxhBAcPHjQ1aE0WjVKgvR6PVOnTiUlJYXExEQmTJhAp06daNmyJXfccQdvvvkmp06dYs2aNXTu3LmuY77m2foEYa0E+brVbNbockIIW5PY8cLjdROkUpYIla2JU/AWMm++SoQURamVxMREtFotAwYMqHQsNDSU9PR0OnXq5JBnJSQk4O/v75B7/eMf/6Bt27Z4eHgQFBTE4MGD+fXXXx1y7/qk1h2je/ToQY8ePeoilkZDp9eC2VoJKi014+NmXfskt4aVILBOmvhT7s8cL1D9guqS8HoIhBaZOwcKE5CUgM/zaokNRVFqJC4ujilTphAXF8epU6cICQmxHdNqtQQHB7swuup1796dmJgYWrVqRWZmJnPmzOGee+4hNTXVtvhrQ6B+k7uApkIlqKTUZKsE1SoJslWCVBJU14TngwjfeYCAwveRuc8hpcXVYSlKoySlxGg2umSrbSU4Pz+fDz74gEmTJjFgwAASEhLsjl/cHFZVJWf9+vV2I1R//PFH+vTpg4+PD76+vnTv3p0DBw6wc+dOxowZQ05ODkIIhBDMmTMHAKPRyMyZM2nZsiVeXl5ERUWxc+fOS8b+yCOP8Je//IXWrVvTrVs35s6dy4kTJzh27Fit3oP6Tk3s4wI6vRZRXIoESkrN+JQ3h9WqEmTtHH2i8CRmaUYrGk5mXh8Jz2Eg9MicZ6DoI6QsBb/5CPW+K4pTlVhKeCTpUZc8+7/dl+Omdavx+R9++CERERGEh4czatQopk2bxrPPPntV027ExMQQGRnJihUr0Gq1HDx4EL1eT+/evVm0aBHPP/88KSkpAHh7W1cUmDx5Mr/88gtr164lJCSETz/9lOjoaJKTk2u00kNBQQHx8fG0adOG0NDQK469PnJpJWj37t0MGjSIkJAQhBCsX7/e7riUkueff54WLVrg4eFB3759OXLkyGXvu2zZMlq3bo27uztRUVHs27evjr6CK1OpT1DZ6LDcGvYJAmjmFoSH1oNSWcqfRafqJE7FnvAYgvBbAGiheD0yZzpSqtF5iqJULS4ujlGjRgEQHR1NTk4Ou3btuqp7pqWl0bdvXyIiImjfvj3Dhg2ja9euGAwG/Pz8EEIQHBxMcHAw3t7epKWlER8fz0cffcTtt99O27ZtmTlzJrfddhvx8fGXfNby5cvx9vbG29ubLVu28OWXX2IoG8jTULi0ElRQUEDXrl0ZO3ZslavPv/rqqyxevJhVq1bRpk0bnnvuOfr168cvv/yCu7t7lff84IMPmD59Om+88QZRUVEsWrSIfv36kZKSQrNmzer6S6oRXYV5gkpLzfh61b4SpBEarvdsxa95KRwrOE4rz4aVnddXwmOAtSKU/QQUb0HKIvBfjBBVfz8qiuJYBo2B/3Zf7rJn11RKSgr79u3j008/BUCn0zF8+HDi4uK44447rjiG6dOnM378eN5991369u3LsGHDaNu2bbXnJycnYzabCQsLs9tvNBoJDAy85LNiYmK4++67SU9P57XXXuOBBx5gz5491X7+XotcmgT179+f/v37V3lMSsmiRYv45z//yeDBgwF45513aN68OevXr2fEiBFVXrdw4UImTJjAmDFjAHjjjTfYtGkTK1eu5JlnnqmbL6SWKs4TZKzQJ6g2SRBAa6/rbUnQX4Juc3icStWE+z0QsAKZ9RgYdyKzHgH/FQiNl6tDU5QGTwhRqyYpV4mLi8NkMtl1hJZS4ubmxtKlS/Hz86t0jUajqdTvqLS01O71nDlzePDBB9m0aRNbtmxh9uzZrF27lvvvv7/KOPLz89FqtSQlJVXq0FzeXFYdPz8//Pz8aN++PT179iQgIIBPP/2UkSNHXvK6a0mNkqDFixfX+IZTp0694mAqSk1N5fTp0/Tt29e2z8/Pj6ioKBITE6tMgkpKSkhKSuLZZ5+17dNoNPTt25fExMRqn2U0GjFWSEByc3Md8jVUR1thniDrEHkP63NrnQS1BuBYwTFHhqfUgHD7CzRZaU2ASr5DZo2FgLcQGl9Xh6YoiouZTCbeeecdFixYwD333GN3bMiQIaxZs4aJEydWui4oKIi8vDwKCgrw8rL+UVXVHEJhYWGEhYXxxBNPMHLkSOLj47n//vsxGAyYzWa7cyMjIzGbzZw9e5bbb7/9ir8mKaW1U3otP6fquxolQa+//rrd63PnzlFYWGjrxZ6dnY2npyfNmjVzWBJ0+vRpAJo3b263v3nz5rZjFzt//jxms7nKay41v8H8+fN54YUXrjLimtPpdBeaw0wXZozONRbX6j5tPK2do9MKT6jO0S4gDDdDk1XIzHFQ+gMy8yEIiENom7o6NEVRXOizzz4jKyuLcePGVar4DB06lLi4uCqToKioKDw9PZk1axZTp05l7969diPKioqKePLJJ/n73/9OmzZtOHnyJPv372fo0KEAtG7dmvz8fLZt20bXrl3x9PQkLCyMmJgYYmNjWbBgAZGRkZw7d45t27bRpUuXKucv+uOPP/jggw+45557CAoK4uTJk/zrX//Cw8ODe++917FvlovVqGN0amqqbZs3bx433XQThw8fJjMzk8zMTA4fPky3bt146aWX6jreOvHss8+Sk5Nj206cqNuFSa3zBFUxRL4WHaMBmrk3w13jrjpHu5DQd0E0eQ80TcF0GJn5INKsZvFWlMYsLi6Ovn37VtnkNXToUA4cOMChQ4cqHWvSpAnvvfcemzdvpnPnzqxZs8Y2zB2s8wplZGQQGxtLWFgYDzzwAP3797f9Ed+7d28mTpzI8OHDCQoK4tVXXwUgPj6e2NhYZsyYQXh4OEOGDGH//v20atWqyvjd3d35+uuvuffee2nXrh3Dhw/Hx8eHb7/9tt70rXWUWvcJeu655/j4448JDw+37QsPD+f111/n73//OzExMQ4JrHwCqTNnztCiRQvb/jNnznDTTTdVeU3Tpk3RarWcOXPGbv+ZM2cuOSGVm5sbbm7Oa2OuuIBqSYn5iuYJAmvn6Av9go6pztEuIvTh0OR9ZOYYMB9DZoyAJvEIXTtXh6Yoigv873//q/bYLbfcYuv3c/FQdrA2lw0ZMsTumgkTJgBgMBhYs2bNJZ+9YsUKVqxYYbdPr9fzwgsv1LjFIyQkhM2bN9fo3GtdrYfIp6enYzKZKu03m82Vko+r0aZNG4KDg9m2bZttX25uLnv37qVXr15VXmMwGOjevbvdNRaLhW3btlV7jSvo9DqEXSXI2tM+z2jEUsvJuNqU9Qv6Q/ULcimha40IXAO6dmA5g8x4EFla+S89RVEUgMzMTD7++GN8fX0b3Nw715JaJ0F33XUX//jHP/j+++9t+5KSkpg0aZJdJ+aayM/P5+DBg7aOX6mpqRw8eJC0tDSEEEybNo25c+eyceNGkpOTiY2NJSQkxC5Lvuuuu1i6dKnt9fTp03nrrbdYtWoVhw8fZtKkSRQUFNhGi9UHlYbIl1WCJJBfyyaxNl5tAEjNT3VojErtCW0woslq0HcGmY3MfBhprL5DvqIojde4ceN48803WbFihVNbIhR7tW4OW7lyJQ8//DA9evRAr9cD1p7w/fr14+23367VvQ4cOECfPn1sr6dPnw7Aww8/TEJCAk899RQFBQU88sgjZGdnc9ttt7F161a7OQqOHj3K+fPnba+HDx/OuXPneP755zl9+jQ33XQTW7durdRZ2pW0FdYOKyk14abT4abVYTSbyDUabZWhmrjBuzUAJ4pOUmopRa/R10XISg0JTQAErEJmP1o2amw8+L+GcK96KghFURqn8vmDFNeqdRIUFBTE5s2b+e2332wjriIiIipNxFQTd9xxxyXXYhFC8OKLL/Liiy9We05V65hMnjyZyZMn1zoeZ9HpdYiyGaONJdbhjH7ubpwtMJFTXMx1vpU701WnqaEp3jpv8k35pBWeoK33DXUSs1JzQuMNAW8hs2eC8XNk9jTwzUR4Oqa/nKIoiuIYVzxZYvk8BUrtVawElZrKkiA3d84WFJBTy87RQgjaeLUmOecnUguOqSSonhDCDfwXIXNfgqL3kbkvIM3nEd5Tr2rdIEVRFMVxap0EjR079pLHV65cecXBNBY6va7C6DBrJ/MrHSEGcINXG5JzfuKP/FSoP61+jZ4QWvCdDdqmyPzFULAMaTkPvrMRQq1drCiK4mq1/k2clZVl97q0tJSffvqJ7Oxs7rzzTocF1pBZO0ZfGB0G2PoB5dRywkSwJkEAfxSoztH1jRACvCeDpikydw4UfYC0nAP/1xHCw9XhKYqiNGq1ToKq6sxlsViYNGnSJRdxUy7Q6rUI84XRYQB+blc2azTADWVNYOnF6RSYCvHSeTooUsVRhOcI0DRBZs8A43ZkZiwEvInQNHF1aIqiKI1WrYfIV3kTjYbp06dXWl5DqVrF5jBjeRJUNuItp7j2zWG+eh+C3IIASFXVoHpLuN+DaJIAwh9Kf0RmDEea0lwdlqIoSqPlkCQIrEPVq5pEUanMbtmMSn2Cal8JAmjrZa0GHc3/wwERKnVFGLpbJ1XUXgfm48jMB5AlP7o6LEVRlEap1s1h5XP5lJNSkp6ezqZNm3j44YcdFlhDZrdsRqU+QVe2Qm9b7zZ8l7lX9Qu6BghdW2jygXUFetPP1oVX/Rcg3O92dWiKojjI6NGjWbVqFWBdNLtJkyZ06dKFkSNHMnr0aDSamtcgEhISmDZtGtnZ2XUUbf1gNBqJiorixx9/5Icffqh2iSxHqnUl6IcffrDbyheBW7BgAYsWLXJ0fA2SddmM8iTI2hzmX9YcdqWVoPJ+QUfz/7jk3EtK/SC0QYgm74LhL0AxMnsysmCVq8NSlAZLSjPSuBdZ9Jn1X2mu82dGR0eTnp7OsWPH2LJlC3369OHxxx9n4MCBquWkCk899RQhISFOfWatk6AdO3bYbdu2bWPt2rU88sgj6HRq2G9N6OwWULX+IJR3jM4uvrIkqJVnK3RCR54pj7PGc44JVKlTQuONCHgDPEYCEpk3D0vuS0755awojYks/hx5rg8y6yFkznTrv+f6IIs/r9Pnurm5ERwcTMuWLenWrRuzZs1iw4YNbNmyhYSEBNt5CxcupHPnznh5eREaGsqjjz5Kfn4+ADt37mTMmDHk5OQghEAIYVtZPisri9jYWAICAvD09KR///4cOXLEdt/jx48zaNAgAgIC8PLyomPHjpdcGNVoNDJz5kxatmyJl5cXUVFR7Ny5E4Di4mI6duzII488Yjv/6NGj+Pj42KbGSUhIwN/fn/Xr19O+fXvc3d3p168fJ06cuOx7tWXLFr744gtee+21mr69DlHrJOjOO++ssiSXm5urhsjXkM6gu9AnqKw5rLxj9JUmQQaNnus9WwHwe/5RB0SpOIMQOoTvHITPU9Ydhe8isx9DWgpcG5iiNBCy+HNk9lSwnLY/YDmDzJ5a54nQxe688066du3KunXrbPs0Gg2LFy/m559/ZtWqVWzfvp2nnrL+TujduzeLFi3C19eX9PR00tPTmTlzJmBtcjtw4AAbN24kMTERKSX33nsvpaWlADz22GMYjUZ2795NcnIyr7zyit2K9RebPHkyiYmJrF27lkOHDjFs2DCio6M5cuQI7u7urF69mlWrVrFhwwbMZjOjRo3i7rvvtps/sLCwkHnz5vHOO++wZ88esrOzGTFixCXfkzNnzjBhwgTeffddPD2dO7q51qWbnTt3UlJSUml/cXExX3/9tUOCauh0FYbIl5SakVLi72adM+ZK5gkq19a7LUcL/uBo/lFubdrLIbEqdU8IAV7jQdsSmf1k2RD6B61D6LXBrg5PUa5ZUpqRufOwLk9d6SggkLkvg1tf6+SmThIREWHrSgIwbdo023+3bt2auXPnMnHiRJYvX47BYMDPzw8hBMHBF34fHDlyhI0bN7Jnzx569+4NwOrVqwkNDWX9+vUMGzaMtLQ0hg4dSufOnQG44YbqVxRIS0sjPj6etLQ0W5PUzJkz2bp1K/Hx8bz88svcdNNNzJ07l/HjxzNixAiOHz/OZ599Znef0tJSli5dSlRUFACrVq2iQ4cO7Nu3j1tuuaXSc6WUjB49mokTJ9KjR48ql8KqSzVOgir+H/bLL79w+vSFrNpsNrN161Zatmzp2OgaKJ3hwhB5i0ViNltsfYJyiouxSInmCpZWaOfdli/OfKkqQdco4d4fmgQjsyeB6TAy4+8QsAKh7+zq0BTl2lRyoHIFyI4ES7r1PLcop4UlpbRbPuerr75i/vz5/Prrr+Tm5mIymSguLqawsLDaysjhw4fR6XS2ZAMgMDCQ8PBwDh8+DMDUqVOZNGkSX3zxBX379mXo0KF06dKlyvslJydjNpsrLYdlNBoJDAy0vZ4xYwbr169n6dKlbNmyxe4YWDuB33zzzbbXERER+Pv7c/jw4SqToCVLlpCXl8ezzz5b3dtVp2qcBN1000229siqmr08PDxYsmSJQ4NrqHR6nW3tMABjick2RF4C+SW1W0m+XHsf62SVJwpPYjQbcdO6OSRexXmEIRKafIzM/geYjiAzYsD/3wj3fq4OTVGuPZYa9o+s6XkOcvjwYdq0sc70f+zYMQYOHMikSZOYN28eTZo04ZtvvmHcuHGUlJRcVfPQ+PHj6devH5s2beKLL75g/vz5LFiwgClTplQ6Nz8/H61WS1JSElqtfVWsYhPa2bNn+e2339BqtRw5coTo6Ogrjg9g+/btJCYm4uZm/3nVo0cPYmJibCPs6kqN+wSlpqZy9OhRpJTs27eP1NRU2/bnn3+Sm5t72XXFFCtrJehCebak1ISbTodHWcfyK+0X1MTQhCaGACxY1FD5a5jQXYdo8gEYbsc6cmwKMv8NNepPUWpLE+TY8xxg+/btJCcnM3ToUACSkpKwWCwsWLCAnj17EhYWxqlTp+yuMRgMmM32AyY6dOiAyWRi7969tn0ZGRmkpKRw44032vaFhoYyceJE1q1bx4wZM3jrrbeqjCsyMhKz2czZs2dp166d3VaxGW7s2LF07tyZVatW8fTTT9uqTuVMJhMHDhywvU5JSSE7O5sOHTpU+dzFixfz448/cvDgQQ4ePGjruP3BBx8wb968at9HR6lxJej6668HrEtkKFdHb9AhwNokptFgLLkwTL4oP5/s4mJa+V3Zvdt6tyUz8wC/5x+lg2+Ew2JWnEtovCHgTWTey1D4HjJ/IZh+B7951hXq64iU5rImhHPWDwZDD6f2lVAUhzL0AE0wWM5Qdb8gYT1u6FEnjzcajZw+fRqz2cyZM2fYunUr8+fPZ+DAgcTGxgLQrl07SktLWbJkCYMGDWLPnj288cYbdvdp3bo1+fn5bNu2ja5du+Lp6Un79u0ZPHgwEyZM4M0338THx4dnnnmGli1bMnjwYMDa16h///6EhYWRlZXFjh07qk1GwsLCiImJITY2lgULFhAZGcm5c+fYtm0bXbp0YcCAASxbtozExEQOHTpEaGgomzZtIiYmhu+++w6DwQCAXq9nypQpLF68GJ1Ox+TJk+nZs2eVTWEArVq1sntdXnVq27Yt11133ZW/+TVUo0rQxo0bbb3NN27ceMlNuTyt3vqhIiwXjxAr6xx9hZUggDDvdgAcyTtymTOV+k4IHRrf5xG+swEtFG9EZsYgzWfr5HmuGkasKHVFCC3C9//KX1181Pq/vrPqLNHfunUrLVq0oHXr1kRHR7Njxw4WL17Mhg0bbE1OXbt2ZeHChbzyyit06tSJ1atXM3/+fLv79O7dm4kTJzJ8+HCCgoJ49dVXAYiPj6d79+4MHDiQXr16IaVk8+bN6PV6wNpf97HHHqNDhw5ER0cTFhbG8uXLq403Pj6e2NhYZsyYQXh4OEOGDGH//v20atWKX3/9lSeffJLly5cTGhoKwPLlyzl//jzPPfec7R6enp48/fTTPPjgg9x66614e3vzwQcfOPR9dSQha1Bj12g0nD59mmbNml1ylkshRKWS3bUoNzcXPz8/cnJy8PX1dfj9T/52ijERj1N8bzekm574BbG0b92MBz/5kO/+PMF/ogcwKOzKqjip+ceY88tLeGo9WdbtP2iEw1ZGUVxIGhOR2Y+DzAZNc0TAcod2mLYNI67013LZB4X/YtUvSXGJ4uJiUlNTadOmDe7ute8rCWXf37nz7DtJa1pYEyD1fe0wzprZ+lLfE7X9/K5Rc1jFJjDVHHb1bJUgswUJlJTYL6J6pX2CAEI9r8OgMVBoLuRUUTrXeaoRew2BcOsFgR8js/4B5qPIjAfBbz7CY+BV37u+DiNWFEcR7v3Ara9q6lUqUWUCF9AbynLPshFixhJrU2OALQkquuJ76zQ622KqR/J/v4oolfpG6FohAj8CtzsAIzJnOpa8V69+hunaDCNWlGuUEFqEWxTCY6D1X5UAKdSwErR48eIa33Dq1KlXHExjodVb33ZpKk+CrH2C/Mv6BGVdRSUIoL1POw7n/cpveUfo0+yvV3UvpX4RGm/wX4HMXwQFb0LB28jSX8F/IULjf2U3rafDiBVFubaMHj2a0aNHuzqMWqlREvT666/X6GZCCJUE1UB5JUhYypvDrElQgEdZJajoyitBAGHe7QE4kq86RzdEQmgRPjOQug7I3Geh5BvrxIr+yxH6sMvf4GL1cBixoiiKM9QoCUpNVXPOOFJ5n6ALzWHWJMjXYE2CUjLO893JE9wc0hLtJTqiV6edT1sEgnPG82SWZNLE0MQxgSv1ivC4F3RtkNmPgTkNmfkA+L5s3V8bLh5GrCiK4ipX1SdISqkmcLsClfsEmdj6+xFe+XY3AIfPn+PBdR9ye8JbbP299tUcD62HbTHVFDVUvkET+g6IwI/B0AtkITJnGpbcV5DSVPN7uHgYsaIoiqtcURIUFxdHp06dcHd3x93dnU6dOvH22287OrYGS6u7MDoM4OCpdB7bvLHSqLAz+fk8tnnjFSVCYT7WZpHf8n67ymiV+k5omiAC4sBrgnVHYRwyayzSklnze7j3Q/gvBk1z+wOaYDU8XlGUBqvWq8g///zzLFy4kClTptCrl3Wl8sTERJ544gnS0tJ48cUXHR5kQyOEQKfXUlI23cCWlN+QzSufZx2cDC/t3sHdN7StVdNYuE8YX5z5khSVBDUKQugQPk8i9Z2QOc9CyXfI80PAfwnC0LVm91DDiBVFaWRqnQStWLGCt956i5EjR9r23XfffXTp0oUpU6aoJKiGdIYLi6jmFxmp3AxhJYH0/Dz2n/qTnteF1vj+YT7WztF/Fp0irzQPH73P1YasXAOEe3/QtUdmPQbmVGTmg+DzLHjG2K1aXe31QuvU1bQVRVFcqdbNYaWlpfToUbmDZPfu3TGZat4PobHTV0iCRA3mnzxbkF+r+/vqfQjxCAFUv6DGRujaIQI/Abd7gFJk3ovInBlIS4GrQ1MUpYJjx44hhODgwYOuDqXRqnUS9NBDD7FixYpK+//73/8SExPjkKAaA51BZ+sTVJMkqJmXd62f0cEnHIBf836t9bXKtU1ovBH+SxA+z2Bdd+wzZOYwpElNoKkozpSYmIhWq2XAgAGVjoWGhpKenk6nTp0c8qyEhAT8/f0dcq9yUkr69++PEIL169c79N71Qa2bw8DaMfqLL76gZ8+eAOzdu5e0tDRiY2OZPn267byFCxc6JsoGqGJzmJdWTx6m6gYnE+ztw80htV/+IsI3nG1nd6h+QY2UEAK8xoK+CzJ7Gph+t84n5PsSwmOQq8NTlEYhLi6OKVOmEBcXx6lTpwgJCbEd02q1BAcHuzC6y1u0aFGNmtKvVbWuBP30009069aNoKAgjh49ytGjR2natCndunXjp59+4ocffuCHH35Q5b3LqNgc1jnQ2iu66sHJ8Nxf+lzRfEHhZSPEThSeJN9Uu+Y0peEQhh6IwPVg6Fk2jH4Glpx/IuXVzUyuKK4gpURaCl2z1XJKmPz8fD744AMmTZrEgAEDSEhIsDt+cXNYVZWc9evX2yUhP/74I3369MHHxwdfX1+6d+/OgQMH2LlzJ2PGjCEnJwchBEII5syZA4DRaGTmzJm0bNkSLy8voqKi2Llz52XjP3jwIAsWLGDlypW1+rqvJbWuBO3YsaMu4mh0dAYdoti6ZlhTNw+W3XsfL+7ezun8C8lKsLcPz/2lD9Ht2l/RM/z0foS4t+BUcTopeb/RPaCbQ2JXrj1C2xQC4pH5S6FgORR9iCz9EfwXI3RtXB2eotScLEKevckljxbNDoLwrPH5H374IREREYSHhzNq1CimTZvGs88+e1WVlZiYGCIjI1mxYgVarZaDBw+i1+vp3bs3ixYt4vnnnyclJQUAb29rN4rJkyfzyy+/sHbtWkJCQvj000+Jjo4mOTmZ9u2r/nwpLCzkwQcfZNmyZfW+WnU11AKqLqJ309tNlhjdrj1fj57AX69vDcADN3Zi9+jxV5wAlYvwtfYLOpyr+gU1dkJo0fg8jghYCZpAMKUgM/6GLPqfq0NTlAYpLi6OUaNGARAdHU1OTg67du26qnumpaXRt29fIiIiaN++PcOGDaNr164YDAb8/PwQQhAcHExwcDDe3t6kpaURHx/PRx99xO23307btm2ZOXMmt912G/Hx8dU+54knnqB3794MHjz4quKt72pdCSouLmbJkiXs2LGDs2fPYrHY9+r9/vvvHRZcQ1axOSz9bA7f/5RG1w7XER7YlF3Hj+FtcLuiJrCL3ejbge1nd6okSLERbrdC4Hpk9gwo3WcdOVaSiPD5J0JT879yFcUlhIe1IuOiZ9dUSkoK+/bt49NPPwVAp9MxfPhw4uLiuOOOO644hOnTpzN+/Hjeffdd+vbty7Bhw2jbtm215ycnJ2M2mwkLs19X0Gg0EhgYWOU1GzduZPv27fzwww9XHOe1otZJ0Lhx4/jiiy/4+9//zi233NKgO0zVpQJjqW102LGTmUyd/SFBgd6E97cud5FVfHWLqJaLKBshdrLoT3JKc/DT+znkvsq1TWibQ5MEZP5yKFgGRR8jS74H/0UIfYSrw1OUagkhatUk5SpxcXGYTCa7jtBSStzc3Fi6dCl+fpV/F2s0mkr9jkpLS+1ez5kzhwcffJBNmzaxZcsWZs+ezdq1a7n//vurjCM/Px+tVktSUhJarf3Ep+XNZRfbvn07R48erdQ/aejQodx+++016k90rah1EvTZZ5+xefNmbr311rqIp1HY9d1vHDuVVWn/uYx8jn/5C0QIMq9yJflyPnofWnmGklZ4gsO5v9IzUE2Ep1hZZ5meijREIXNmgPmPstFjz4LHg+oPHEW5QiaTiXfeeYcFCxZwzz332B0bMmQIa9asYeLEiZWuCwoKIi8vj4KCAry8vACqHGQUFhZGWFgYTzzxBCNHjiQ+Pp77778fg8GA2Wy2OzcyMhKz2czZs2e5/fbbaxT/M888w/jx4+32de7cmddff51BgxrWyNJat7e0bNkSHx/nzT7cunVrW0/3ittjjz1W5fkJCQmVznV3d3davJdjNltYtHI7aARYKk8QpCmbbzKzqNBhz7zRtwMAv6gmMaUKwi0K0XQjuP0VKEHmvoDMfrRWa48pinLBZ599RlZWFuPGjaNTp05229ChQ4mLi6vyuqioKDw9PZk1axZHjx7l/ffftxtRVlRUxOTJk9m5cyfHjx9nz5497N+/nw4drL/jW7duTX5+Ptu2beP8+fMUFhYSFhZGTEwMsbGxrFu3jtTUVPbt28f8+fPZtGlTlXEEBwdXihugVatWtGnTsAZS1DoJWrBgAU8//TTHjx+vi3gq2b9/P+np6bbtyy+/BGDYsGHVXuPr62t3jbNirYkfD5/kXEY+CIEwVZEElVj/PZ2b57BndvC1Nm/8knvYYfdUGhahaYLw/y/C51lAD8ZtyPP3IY3fujo0RbnmxMXF0bdv3yqbvIYOHcqBAwc4dOhQpWNNmjThvffeY/PmzXTu3Jk1a9bYhrmDdV6hjIwMYmNjCQsL44EHHqB///688MILAPTu3ZuJEycyfPhwgoKCePXVVwGIj48nNjaWGTNmEB4ezpAhQ9i/fz+tWrWqmzfgGlLr5rAePXpQXFzMDTfcgKenJ3q93u54ZqZj/3oMCgqye/2vf/2Ltm3b8te//rXaa8p7x9dHGVnWpQukVmPrE1SRpqz5N6fE6LBnhvuEoRVazhnPcbb4HM3cgy5/kdLoWCdXHAOGKGT2dGvzWNYYpNd4hPfjCGFwdYiKck343/+qH3F5yy232Pr9XDyUHazNZUOGDLG7ZsKECQAYDAbWrFlzyWevWLGi0qoOer2eF154wZYsXYnazpF0rah1EjRy5Ej+/PNPXn75ZZo3b+7UfgMlJSW89957TJ8+/ZLPzc/P5/rrr8disdCtWzdefvllOnbsWO35RqMRo/FC0pGbm+vQuCsKDLC286IRttFhFZUnQSUWM4WlpXhelGReCQ+tB229buC3/CP8nPsLzdyrTyAVRehvhKafInPnQ9FaKHjLWhHy/zdC187V4SlKg5CZmcnHH3+Mr68voaE1XxxbcaxaJ0HffvstiYmJdO3atS7iuaT169eTnZ3N6NGjqz0nPDyclStX0qVLF3Jycnjttdfo3bs3P//8M9ddd12V18yfP/+qMuTa6NrhOoICvckWVVeChNm6lpjUWPsFeTpoNFdHvxttSdBfg24nJe83sktz8Nf7Ee4ThkaoKaOUC4TwQPi9iHS7DZnzTzD9jDx/P/g8XeMV6RVFqd64ceNISkpixYoVuLm5uTqcRkvIWta4unXrxvLly23rhjlTv379MBgMlyw1Xqy0tJQOHTowcuRIXnrppSrPqaoSFBoaSk5ODr6+vlcd98V2ffcbcx78D9oT5ym+7+ZKx4vv9iCzpJhPH3iQrsEtHPLMI3m/M/fwfNw1brhrPcguzbYdCzAEMKrVSHo06e6QZykNizSfQeY8CyXfWHcY/oLwm4/QqmZVpe4VFxeTmppKmzZt6tUgF8V1LvU9kZubi5+fX40/v2v95/+//vUvZsyYwc6dO8nIyCA3N9duqyvHjx/nq6++qjRs73L0ej2RkZH8/nv1q2e7ubnh6+trt9Wlv/YMo1ePtpWaw5oF+jDvyfsI8bM+P8NBw+QBbvBug0FjoNhitEuAALJKsljy+3IOZCY57HlKwyG0zREBbyN8/gm4Qclu5PkByOLPXR2aoijKVal1c1h0dDQAd911l91+KSVCiEpzFDhKfHw8zZo1Y8CAAbW6zmw2k5yczL333lsncV2p1tc3ZT/WLNQCzHliAH16haPVakg4kww4dpi8QFy2Y9vqtLV0C4hUTWNKJUJowCsW3HpbZ5o2HUZmT0G634fwfQ6hUZNwKnWroXbMVWrPkd8L18QCqhaLhfj4eB5++GF0OvuQY2NjadmyJfPnzwfgxRdfpGfPnrRr147s7Gz+/e9/c/z48VpXkOqa3s3a4VmnEZRYJO1aN0OrtSYfTTyss6FmODAJSsn7jVJZeslzMksyScn7zTakXlEuJnTtIPAjZP4yKHgTijciS/aC33yE222uDk9pgMpnOS4pKcHDo+bLVigNV2Gh9bPx4tHpV6LWSdClhqb/9NNPVxVMdb766ivS0tIYO3ZspWNpaWloKqyxlZWVxYQJEzh9+jQBAQF0796db7/9lhtvvLFOYrtS5UlQ+STmxcYLCUpg2Q96RqHjkqDs0hyHnqc0XkIYED5PIN3uROY8BeZUZNZYpMdIhM+TCE3VU/ErypXQ6XR4enpy7tw59Hq93e97pXGRUlJYWMjZs2fx9/evtAzIlah1EnSxvLw81qxZw9tvv01SUlKdNIfdc8891Za/Ll7D5PXXX+f11193eAyOVikJKr6QBDX1tA6jP+/AJMi/hqPManqeoghDV2i6Hpn3GhS+C0VrkCVfg+88hFsvV4enNBBCCFq0aEFqamq9mvhWcR1/f3+HzQV4xUnQ7t27iYuL45NPPiEkJIS//e1vLFu2zCFBNQZ6N+tbrylL7opLTLZjTT2tzWGOTILCfcIIMASQVVJ5zbJyTQxNCPcJq/a4olxMCA+E73NIt7uRubPAfBKZ9TDS80GE95MIjZerQ1QaAIPBQPv27SkpKXF1KIqL6fV6h1SAytUqCTp9+jQJCQnExcWRm5vLAw88gNFoZP369fWuuam+0xsqJkGiykqQI/sEaYSGUa1GsuT35dWeE9NqhOoUrVwR4dYTAjci8/4NRWug8H2kcbeqCikOo9Fo1BB5xeFq/Ik3aNAgwsPDOXToEIsWLeLUqVMsWbKkLmNr0Mqbw4TFWgkqqqJPkCMrQQA9mnRn0g2PVNrfxNCEKe0eVfMEKVdFaLzR+L2ACEgATYitKmTJ+SfS4ri18BRFURylxpWgLVu2MHXqVCZNmkT79u3rMqZGoTwJ0pgtoNFUWQnKLCrEIiUaB87O27NpFHsyEjmUk0xUk1vo0+yvasZoxaGEW29o+hky/zUofB+KPkQad4Hviwj3Pq4OT1EUxabGn3zffPMNeXl5dO/enaioKJYuXcr58+frMrYGrbxPUPmEiRUrQU3KKkFmKcly4ISJ5br6dwEgpzSHDr4RKgFSHE5ovNH4zkE0eQ+014PlDDL7H1iyZyItjl1kWVEU5UrV+NOvZ8+evPXWW6Snp/OPf/yDtWvXEhISgsVi4csvvyQvT5W7a6O8ElSeBBkrJEF6rRb/srbv8w7sF1Suq39nAH7LO0KByfH3V5RywnALoulG8BwLaKzzCp2LRhatV5PfKYricrUuAXh5eTF27Fi++eYbkpOTmTFjBv/6179o1qwZ9913X13E2CDZkqBS65QCFStBAEG2YfIFDn92kFsQIR4hWLCQnJPs8PsrSkVCeKDxfQbR5APQhYPMRuY8hcwahzSdcHV4iqI0YlfVDhIeHs6rr77KyZMnWbNmjaNiahTKR4dJkzUJqtgnCCDIy5oEnSuom0pNpH9XAA5m/1gn91eUiwlDV0TgOoT3E4ABSr5BZgxEFsQhLzObuaIoSl1wSGcQrVbLkCFD2LhxoyNu1yjo3a2VIFk2P1CxEytBADeVJUGHspMxy7pZ701RLiaEHuE9CdH0f2CIAlmEzHsFmTEUWVL3CbmUZqRxL7LoM+u/6ntfURo11SPWRQxu9klQ0cWVoLIJE88W5NfJ89t5t8Vb502BuZAjeb/XyTMUpTpC1wYR8A7Cdz4IfzD9isx8AEvunDobTi+LP0ee64PMegiZM93677k+yOLP6+R5iqLUfyoJcpHySpC5LPmplASVN4c5eK6gchqhoaufdZTY91k/1MkzFOVShBAIz6GIoK3gPgSQ1kkWz0dbKzUO7Dgtiz9HZk8Fy2n7A5YzyOypKhFSlEZKJUEuYihLgixlzWCVm8Osi1Ceq6PmMIBuAZEAfJ99UI3UUVxGaJqg8X8VEbAKtG3Acq6sUvMw0nT0qu8vpRmZOw+o6nvcuk/mvqyaxhSlEVJJkIuUjw4zFVnXwimsVAkqWz+soO6SoM5+HdELPeeM5zhZ9GedPUdRakK49UI0/R/CexrgBiXfIc/fhyVvIVJexXxZJQcqV4DsSLCkW89TFKVRUUmQixgu7hh9URLUrKwSdKYOkyA3rRsd/axrvqkmMaU+EMKA8H4U0XQTuN0BlELBG8hz/a1NWldSsbScc+x5iqI0GCoJchHb2mHmsiHyFzWHNSvrE5RjLMZoMlFXupc1iSVlfV9nz1CU2hK6Vgj/NxH+y6zrkFlOIbOnILPGIk1/1O5mmiDHnqcoSoOhkiAXKR8dhsk6Y3RhcYndcV83N9y01rmEztZhNegm/5sQCI4XpnHOqP4SVuoPIQTC/W5E0Bbwegzr3EJ7kOcHYcn7N9JSw5GThh6gCQaqW4NPgKaF9TxFURoVlQS5iFanRaPVIMqSoItHhwkhaF5WDTpTR8PkAXz1PkT4hANwIFNVg5T6RwgPND6PI5puBrc+WJvI3kKe74csWoeUlstcr0X4/l/5q4uPWv/XdxZCaB0eu6Io9ZtKglzI4K6HsuYws9lCSal9s1f5MPm6rAQB9GjSHYADWUl1+hxFuRpC1wpNwJsI/zdB27psFNkzyMwHLjvRonDvh/BfDJrm9gc0wQj/xQj3fnUXuKIo9ZZKglzI4G4A04VhuRdXg5p7WTtH19WEieXKh8r/nn+UzBK1wrdSvwn3PoimnyG8nwThBaWHkJnDsGQ/iTRXPwpMuPdDBO1ABLyL8Fto/Tdou0qAFKURU0mQC+nddAgJep31/4aiIvt+Qc2c0BwG0MQQQHvvdgAcyFTVIKX+s44im4Bo+gV4/M26s3gD8tw9yPwlSEvVk4wKoUW4RSE8Blr/VU1gitKoqSTIhQzuBgDc9NYO0Bd3jm5WVgk6k1+3SRDALU1uBmBvpporRbl2CG0QGr9/IQI/Bn03oNiaBJ3vhyxaf9n+QoqiNG4qCXKh8rmC3PTWv0YvnjAx2NsHqPtKEFzoF/R7/u+qSUy55gh9F0STNQi/RaBpaV0OI+cpZMbfkca9rg5PUZR6SiVBLmTwsFaCDNqqm8OCva2VoHQnVIKaGAII824PwD5VDVKuQUIIhMe9iKCtCO8Z1v5Cpp+QWQ9hyZrkkCU4FEVpWFQS5ELllSB9WRJUWE0SdCY/zylre90SWNYklrGvzp+lKHVFCDeE9z8QTb8CzwcBLRi3Ic8PxJIzB2k+7+oQFUWpJ1QS5EJuZZUgvaYsCarUHGZNgopMJvJKjHUezy1NeiAQ/FGQypniM3X+PEWpS0IbiMZ3DqLpZ+B2F2CGoveR5/tiyVtc88kWFUVpsFQS5ELlHaP1GuuEbRdXgtx1evzd3QHnNIn56f240bcDAN+papDSQAhdWzQBKxBN3gN9F5CFULAUef5uZMF7SFly+ZsoitIgqSTIhfRlzWG6stcXJ0FwYa4gZ4wQA+gZGAXAdxl7ndIEpyjOIgy3IJp8ZJ00UdsaLBnIvBeR5/sjizaqkWSK0gipJMiFypvDymcqqSoJKh8hlp6X65SYegR0Qy90nCpOJ60wzSnPVBRnsa5HFo1ougnhOxs0TcF8ApkzE5kxGFm8QyX/itKIqCTIhcoXUdWW/dItKq6cBIX4WJOgU/l5TonJU+fJTQE3AbDnfKJTnqkoziaEHuEZg2j6FcJ7OggfMKUgs/+BzByphtUrSiOhkiAXKh8iLyxlK8lXUQlqUV4JclISBHBrYC8Avsvci1maL3O2oly7hMYT4T0REbQNvCYAblD6vXVYfebDyJKDrg5RUZQ6pJIgFyrvGK0xWytBBYVVJEE+5c1hzkuCOvt1wlvnTU5pLj/l/OK05yqKqwiNPxqfJxFBX4FnDKCHkkRk5gNYsv6BLFU/B4rSEKkkyIXK+wSJstXjL142A1xTCdJpdPQq6yC95/wepz1XUVxNaJuj8Z2NaPo5ePwd6xxDO5AZQ7BkPYosPezqEBVFcSCVBLlQeXOYLFtJ/nKVIGd22Lyt6a0AfJ/1AwWmAqc9V1HqA6G7Do3fy4imm8H9PkCA8StkxmAsWZORpSmuDlFRFAdQSZALuXu6Wf+jpKwSVFR5QsSQskpQkclEVnGR02K73rMV13lcR6k0qTmDlEZL6Nqg8X8N0XQTuA/Amgx9gcwYVJYMqWYyRbmWqSTIhWyVIKN1puiqKkFuOh1NPT0BOOXEfkFCCG5v2huAr1WTmNLICV07NP6vIwI/A/f+XEiGhljXJSv9ydUhKopyBep1EjRnzhzrvB4VtoiIiEte89FHHxEREYG7uzudO3dm8+bNToq29tw9rUmQuagsCapidBhASx9fAP500lxB5Xo37Y1WaEktSCWt8IRTn60o9ZHQt0fj/5+yZGgg1mRoGzLjb1iyJiBLvnd1iIqi1EK9ToIAOnbsSHp6um375ptvqj3322+/ZeTIkYwbN44ffviBIUOGMGTIEH76qX7+lVZeCTKXJT+FRSVYLJX7/diSoFznJkG+eh+6+d8EwO5zXzv12YpSn1mToYUV+gxpwLgLmTkCS2Ys0pioJl1UlGtAvU+CdDodwcHBtq1p06bVnvuf//yH6OhonnzySTp06MBLL71Et27dWLp0qRMjrrny0WGmwmLbvqrmCmrp65pKEMBfg/4CwLfnEymxqDWWFKUioWtb1meofDSZHkq+Q2Y9jMwcjiz+Si3HoSj1WL1Pgo4cOUJISAg33HADMTExpKVVv5RDYmIiffv2tdvXr18/EhMvPfOx0WgkNzfXbnOG8rXDck7noCtbRDW/sHLnaFc1hwF09LuRpoZACsyF7M884PTnK8q1QOiut44mC/oSPEdhnXTxIDL7UWTGIGTReqQsdXWYiqJcpF4nQVFRUSQkJLB161ZWrFhBamoqt99+O3nVdBA+ffo0zZs3t9vXvHlzTp8+fcnnzJ8/Hz8/P9sWGhrqsK+hOl+v28vcEYsAyEzPwlRWAfp6Y+VEo7wSdDInp87juphGaPhrM2s1aMfZXU5/vqJcS4Q2BI3v84igHeD1CAhvMB1B5jyFPHcPsuAdpKXQ1WEqilKmXidB/fv3Z9iwYXTp0oV+/fqxefNmsrOz+fDDDx36nGeffZacnBzbduJE3XYC/nrdXl4c9ho5Zy8kNaLUOlfQimfe5et19usWhfr6AXDSBZUggL80vQ2t0HIk/3dOFJ50SQyKci0R2qb/3959x0dVpY8f/5w7LQnpnQQCKBBAehECgqIotlXERUUUXV3dr4JSfnZXZHUVlXVRsLu7oF9RXPyuiogoi1QFpEsNnVDSSSd15vz+uElMSCFAkpkkz/v1imTuPXfmmYtknpzznHMw/B5Dha1C+f4/MELAdQKd81d06hW4ct5EO9PdHaYQLZ5HJ0FnCgwMpHPnzhw4cKDa85GRkSQnJ1c6lpycTGRkZK3P63A48Pf3r/TVUJxOJ+9Mngtn1kyWrhqNzcq7U+bidP62Z1eb0iQou7CQ7MICGlugPZA+pQXSP6asAMClXezJ3su69A3syd6LS+oehKhCGX4o3z+hwlag/KeDJQZ0JuS9bSZDWc+jS464OUohWq4mlQTl5uZy8OBBWrduXe35uLg4li9fXunYsmXLiIuLa4zw6mTnmr2kHa/6G2BZT5C2Wkg9ls7ONXvLz/nYbIR4m2sFHXPDkBjAiIgrAXNn+Z9Sf2bq9id4Ze9M3jv4Aa/sncnU7U+w6dRmt8QmhKdTygvlcycq9HtU4Gyw9QAKIf8zdNpIc62hok0yo0yIRubRSdBjjz3GqlWrOHLkCD///DO33HILFouFsWPHAjB+/Hiefvrp8vaTJk1i6dKlvP766+zdu5fp06ezadMmJk6c6K63UEV6Ykb1J0q3zsBmqbZdm9LeqWONPE2+TBe/WKK8oyh0FfLB4X+SUVQ5voyiDOYceEcSISFqoZQF5XUtKvgLVND/gmM4oM21hk7diT41Bp3/LVqXuDtUIVoEj06Cjh8/ztixY4mNjeW2224jJCSE9evXExYWBkBCQgKJiYnl7QcPHsynn37KBx98QK9evfjiiy/46quv6N69u7veQhUhrYOqPV62iaq2Watt1zbAHBI7lp3ZcMHVQinFVeFXnLXd/IQFMjQmxFkopVCOgRhB75trDXnfBtih+Fd01hR06lXo3A/RLvf0/ArRUljdHUBtFixYUOv5lStXVjk2ZswYxowZ00ARXbjuQ7sQ2iaEtBPpleuCin/rCQprG0L3oZVXxo7xDwTgqJuGwwDCHGFnbXOq6BTxOfvo6l/7yt5CCJOydkQF/BXtOwXyP0Wf/hRciejcmZD3Ftr7FpTP3Sjrxe4OVYhmx6N7gpoji8XCw2/8wXygfjuuikprgmwWHpr1BywWS6XrYkp7ghIyMxsjzGqddtZtA9fMYvntVYhzpSwhKN9HzBll/q+ANRZ0Ppz+FJ12Ha5T96ELVsjii0LUI0mC3GDo6IFMW/gYodEh5cfKhsO6XX4JQ0cPrHJNu4BAABKyMhsjxGoF2gLqtZ0Qoiql7Cif0aiQRaigj8ExAlBQtBad+Sd02jXovLkyVCZEPZAkyE2Gjh7IJ4ffJqyNmQiNuN3csd0rqFW17WNKk6ATOdkUV5g+35hi/ToTZK++pqlMsD2YWL/OjRSREM2XWTc0CCPoHVTof8HnflD+4ExA58xApw7DlfVndPEed4cqRJMlSZAbWSwWAsLMWV9RMeaeaLl5VbfNAIjw9cVhseLUmpM1rJjd0AxlcFfM2FrbjIu5A0PJ/1ZC1CdlbYvh/yQqbDXK/4Xfhsry/41OvxlX+h3o/G/QWvb3E+JcyKeVm3m1cgBgLd09Pie3+sUQDaVoFxgIwJHMGqbZN4L+wf14pOPDWFTlmqVgezCPdHyY/sH93BSZEM2fMnxQPneYQ2XB88HrOsAKxVvQWf8PnTIUV85MdEnNeywKIX4jSZCblSVBqsQsdsypoScIoH1ZEpTlviQIzETo4Yv/BIDdsDG506O83utVSYCEaCRKKZR9AEbgm6iwlSjfR8GIAJ0BeR+i067Gdep+dMEPsnGrELWQJMjNvFp5AaBKF0vMySvA5ap+1dj2gWY9zuEM9yZBAH2D+hDl1ZoiVzEnC07KEJgQbqIs4SjfieamrQFvgbUHoKFoDTpzIjp1OK6cWegS2fdPiDPJJ5ebefuaSRCF5m9rLpfmdH714/odSoujj7hxmnwZQxnc0Po6AL5P+oEil9QiCOFWhcsh569QsqPCQQNcKZD3LjrtqtJp9t9J7ZAQpSQJcjMvH3M4rCS/CLvdXLsyJ6/6uqAOQcEAHHZjTVBFg0IGEmIPJqs4m1Upa9wdjhAtli74Hp35KLiSzjhTuqaQtQtm79BadOYks3Yoewa6pPrNqIVoKSQJcrOynqD83AL8S7/PrqE4ukOQORx2PDuLwhL37y1kNazc0Pp6AL5N/I5il9QeCNHYtHais1+i8hL0FSlwZUHID9DqITDCzdqh03PRadfjSr8NfXoB2uWeWadCuJMkQW5WVhNUkFdIQFkSlFP9ysyh3j74OxxoYO62zaw/fgyny72rxw4Lu4xAWyAZxRmsTl3r1liEaJGKNlXTA1SRBlciypWM4TfFLKQOfB8cVwEWKN6Gzp6GThmCK/MxdOE6WZVatBiSBLmZl29ZElSAn1/tPUHfHzxAfrHZ2/Laz2u58z//Zui8D1l6YH/jBFsNm2HjxiizN2hx4rcUSW+QEI3LlXpO7ZSyoryGYwS9a6475PckWC4GCqBgETrjHnTqlbhy3kCXHG24uIXwAJIEuVnF4bAAX28AsqrpCVp6YD8Tliyi+Iyen+TcXCYsWeTWROjysGEE2YI4VZTBqpTVbotDiBbJOPvGxjW1U5YwVKv7UaFLUMELwfsOUH7gOgl575hT7dPvKB0uk206RPMjSZCb+ZT2/pzOyce/rCcop3JPkNPl4oXVP1Y74l927MXVK9w2NGY3bPwu6gYAvklcTKGz5rWOhBD1zN4fjEgq7chciQKjtdmuphZKoey9MAJeQIX/hAqYBfahgGEuxJg9DZ0yGFfGRHTBMpldJpoNSYLczNvP7P3Jz8knwK/6nqCNJ0+QlJtb43NoIDE3h40nTzRYnGdzedhQwhyhZBVn89/k5W6LQ4iWRikLyv/ZskdnnjX/6/8M6oxV3mt+Pi+U9w0Ywf80d7T3e9LcpoNiKPwBnTkBnXIZrqxp6KKNUj8kmjRJgtys0uywGpKglLyaE6DzadcQrIaVW6JvBsyZYnkleW6LRYiWRnmNRAXONleNrsiIRAXORnmNPL/ntUSgWt2PEfoNKuRr8LmvdHZZJuQvQJ8aV1o/9Dd0cfyFvxEhGpnV3QG0dL/1BBUQWEMSFN7Kt07PVdd2DSUuZBBLEpdyPP8Ei08u4faYMW6NR4iWRHmNBMeI0tliqWYNkL1/nXuAzvr8tq4oW1e03+NQtB5d8A0UfF9aP/QBOu8DtLUTyutG8LoBZY2pl9cVoiFJT5CbVa4JMpOgzOzKSdCAqGgifX1rG/Gnta8fA6KiGzDSszOUwW1tfw/AsuT/kl6Y7tZ4hGhplLKgHANR3jeaf9ZTAlT1NYZgBLyCCl+HCnzTTL6wQcl+dO4sdNoIXOm3ovP+iXYm1nsMQtQXSYLcrHw4LCefgNKEKOuMJMhiGEwbdiVQ04g/PDdsOBbD/X+dPQN60MUvlmJdwsLj/3F3OEKIBqSUF8rrOoygd8yEyH8G2IdgFlTvQOe8ik69HFf67ei8j9DO2tYzEqLxuf9Ts4Xz8fcBzD3DfBzm6GRm9ukq7a7t2Im3r7+JCN/KQ16Rvn68ff1NXNuxU8MHWwdKKcbG3I5CsS59PQdzD7k7JCFEI1CGP8rnVozguaiwtSj/58E2AFBQvBWd8xI6dZg55T5vniREwiNIEuRmXq0cKGX25zhKjxUUllBQWHXRwWs7dmLNvQ/wxOChAET7+bP63j96TAJUpn2rdgwJjQPg04QFaF3Tcv5CiOZIWUJRPuMwQuaXLsj4LNj6mieLt6BzXi5NiG43h8xKjrk3YNFiSRLkZoZh4F06DFaSX4TFYv6VrN14AKez6tRTi2Fwc2xXAJJycyhx87YZNfl9m9E4DAcHcg/yc/p6d4cjhHATc4bZPRghCyokRP34rYfoVXOH+7Rb0LnvyqauolFJEuQBWpUOiT367Gflic/0Wd/y+4c+YNX6fVXaR/r6EuTlhVNr9p3yzOLjIHsQN0XdCMDnxxaS76x+PzQhRMuhLJGlCdFnpQnRNLAPAgwo2VVaVH09rtSR5rT7ou2yDpFoUJIEeQKbOYMjM73yLs6p6bk8O3NRlURIKUWX0HAA9qSmNE6M52Fk5NVEOCLIKs7iy+NfuzscIYQHMXuI7sII/hgV/jPK/69gHwbYwHnYnHZ/aow5bJb1PLpwjaxULeqdJEFu5nS6SM8r3Wai2Fltmzf/taLK0Fi3MHMfoD1pddw80Q1sho27298JwLLk5SSclnF/IURVyghG+dyGEfwPVPgGVMDfwes6UD7gSoH8z9AZ96NTBuLKnITOX4R2Zbo7bNEMSBLkZtv3HKesBFrVkASlpOewfc/xSscuCTN7gnZ5cE8QQI+A7gwI6ocLF/MOf4xLuraFELVQhi/K+0aMwDfNhCjoA/C+3Vz8UedBwXforMfQKXG40u8qLaw+7O6wRRMlSZCbpWfkoUuHwyipPgkqa1fRJWHm8vi7U1PctnFqXd3ZbizeFm8O5h1iecoKd4cjhGgilHKgHFdgBLyICltj7nTf6k9g7Qw4ofiX0sLqkbhSr8GV/TK68GcZNhN1JkmQm4UEtQKbuT6QKi6pvV0FFwUF4W21crq4mCOZGQ0a44UKtgcxps2tAHxx7P9IK0xzc0RCiKZGKcPc6d7v/2GELkaFLkf5/RnsgzHriI7A6XnojHvNYbOMiejTC9HOZHeHLjyYJEFu1qtrG7z9ze0yVFH1PUHhIX706tqm0jGLYdCtdEhsR4rn/yMfHn45nX07UeAqZO7hj2XtICHEBVHWtqhW4zGC55nDZoFzwPtWMELNYbPCH9DZz6JTh+JKuxlXzuulu95XXYNNtFySBLmZxWIwJK6z+aCGmqBJ9w0vXz+ooh4RkQD8muz5K68ayuD+DvdiUzZ2Zu9iVeoad4ckhGgmlOGL8hqJETDDXK065AuU76Ng6wUoKNkDee+bu96X9xJ9jnaedHfows1kF3kP0KNHO1YCDgMq/o4SHuLHpPuGc/mgztVe16s0CdreBJIggEjvSG5tcwsLjv2bzxIWcElAN8Icoe4OSwjRjChlgK0n2HqifCeiXaegcDW6cA0UrgGdafYSFf4AgLZcBI7LUI7LwHYpyvBx7xsQjUp6gjyAb6D5j657+3BeeWpU+fFP3ry3xgQIfkuCdqWmUOSsuajak4yMvJrOfuaw2AeH/iGzxYQQDUoZwSjvURiBr5ubvIZ8gfKdVLqNhwHOQ3D6Y3TGg+iUAbhO3W2uXF20Ha2bxs9Vcf4kCfIAvkHmpqi5mXkM6X8xdrvZQZeRVfsqy+0CAgny8qLI6fTo9YIqMpTBAx3ux8twsC9nP/OOfMy69A3syd4rCZEQokEpZUHZeqJ8J5jbeIT/UlpLdDsY0UAxFG0wV64+NQadMqh06Gw+uuSw1DI2QzIc5gH8gkuToIw8lFKEBrXiZHIWaadyiY4MrPE6pRS9I6NYceQQWxNPlvcMebpwrzAuCx3Cf1N+ZFXqmvL6oCB7EHfFjKV/cD83RyiEaAmU4Q9eI1FeI80Ex3kUin5CF/4ERRtAZ1UaOsOIRNvjUI5BYI9DWZrGz1xRM+kJ8gB+pdPfc07lAhAW7AdAWkbuWa/t27o1AFsSm06B36ZTm/lvyo9VjmcUZTDnwDtsOrXZDVEJIVoypRTK2h7lMw4j6B1zxlnwv1G+U0r3N7OBKwkKvkRnPWlu55F6Da6s59D5i9HOptEbLyrz6CRoxowZDBgwAD8/P8LDwxk1ahTx8fG1XjNv3jzzf+YKX15eXo0U8fkp6wk6nZNPSXEJoaWPU87YS6w6fSOjANiceKJJdNW6tItPEj6rtc38hAUyNCaEcCulrCh7b5TvQ+b+ZhGbUEFzodUDZuE1hrk2Uf7n6Kyp6NQhuFKvNfc5y1+Mdnr2av7C5NHDYatWrWLChAkMGDCAkpISnnnmGa655hp2795Nq1atarzO39+/UrKklGqMcM+bb+Bv7yU3M4/wEDMJSks/e09Q78jWWA2DxNxcTuRk08Y/oMHirA/xOfvIKKp9ccdTRaeIz9lHV/8ujRSVEKKp0NoJRZvAlWpupWHvj1KWBn9dpbzBMQTlGGLG4cqBok3oonXm0FnJXrPIOv8QOt/8RU9b2oN9AMo+AOyXoixRDR6nODcenQQtXbq00uN58+YRHh7O5s2bGTZsWI3XKaWIjGw6Y7UWq4VWAT7kZZ0mOz2XsBBzOKwuPUHeNhvdwyLYlpzIxhMnPD4JyizOqtd2QoiWQxd8j85+yRyWKmNEgv+zKK+RjRqLMvzAazjKa7gZmyuzNCn6pUJSdATyj6DzF5ptjCgzabP3B3t/sFzs8b+kN3cenQSdKSvL/GAMDg6utV1ubi7t2rXD5XLRt29fXn75ZS655JIa2xcWFlJYWFj+ODs7u34CPgf+IX6lSVAOEWH+ACSnnT0JArg0OpptyYlsOHGMW7p2a8gwL1igrW5JWl3bCSFaBl3wPTrzUeCMYX9Xsnk8cHajJ0IVKSMQvEagvEYAoF1ZULTFTIqKf4Hi3eA6CQWL0AWLSi8KRNv7oWx9wd4PbN1Ryu6299ASeXRNUEUul4vJkyczZMgQunfvXmO72NhY/vWvf/H111/zySef4HK5GDx4MMePH6/xmhkzZhAQEFD+1bZt24Z4C7UKCDV7f7LTc4g4h54ggIFtzHjXHz/WMMHVo1i/zgTZg2ptY1EW2vu0a6SIhBCeTmun2QN0ZgJknjX/m/2yR63ro4wAlNdwDP8nMUL+DxVeVlM0AewDAUfpwo3L0bkz0afuQCf3xZV+B66cmeiC/5oLPYoG1WR6giZMmMDOnTtZu3Ztre3i4uKIi4srfzx48GC6du3K+++/z4svvljtNU8//TRTp04tf5ydnd3oiZBfaeKTnZZD1zDz+/SMXIqLndhstY93928djUUpErKzOJGTTbSff4PHe74MZXBXzFjmHHinxjZO7eQfh+cyoeP/YKgmk6cLIRpK0abKQ2BVaHAlmu0cAxstrHOhjFaVa4p0kdk7VLwZXbQZijaDzoDiLVC8pTzd05Z2YOuNsvcBW2+wdkapJvPR7fGaxCfMxIkTWbx4MStWrKBNmzZnv6ACm81Gnz59OHDgQI1tHA4H/v7+lb4aW1lPUFZaDoH+PjjsVrSuW2+Qn8NBz9I1gn5KONqgcdaH/sH9eKTjw1V6hILtwYyOHoVFWdiUsZkvjn/ppgiFEB7FVcfp53Vt5wGUspuzz1rdXzolfz0q9HuU/yvgfRtYO5oNnUeh4Gt09nR0+ih0Sn9cp+7GlfM3s7dIpuZfEI9OJ7XWPPLII3z55ZesXLmSDh06nPNzOJ1OduzYwfXXX98AEdafgFAz8cpKzUIpRUSoPwknT5GYklXrgollLotpx9akRNYeO8ptl/Ro4GgvXP/gfvQN6kN8zj4yi7MItAUQ69cZQxmEOEL48NA/+TZxCaGOEK4Mv8Ld4Qoh3MkIq992HkgpBdYOYO2AYjRQWldUvB1dtBWKt0LxdtB5ZuF10YbfeouMaLD3RJXumYb1EtkDrY48OgmaMGECn376KV9//TV+fn4kJZndoQEBAXh7ewMwfvx4oqOjmTFjBgAvvPACgwYNomPHjmRmZjJz5kyOHj3KH//4R7e9j7oILC2Gzkwzi7Ijw80kKCmlbkXaQ9q2Y84v6/kp4ShOlwuL4fmdfIYyqp0Gf1noYFILU/nqxCI+PvIJATZ/+gX1dUOEQgiPYO9vzgJzJVN9XZAyz9v7N3ZkDUoZAeAYhnKYs6G1dkLJQTMxKt5mJkUl+8F1AgpOoAu+K73SQFs7ga1HaWLUo3QYzea29+KpPDoJevfddwG44oorKh2fO3cu9957LwAJCQkYFT7wMzIyeOCBB0hKSiIoKIh+/frx888/062bZ8+aCihLgkqTnqgIc3bUieTMOl3fJ7I1vnY7GQUF7ExJpldk6waJs7GMirqJU0UZrE5dw7sH3uf/xU6RdYOEaKGUsoD/s6WzwxSVEyFzirnyf6ZR1gtyJ6UsYOsMts4oxgCgXblQvLM0MfrVTIxcKVASDyXx6PwvSq+2o21dzBlo1h5guwSsHVt8fZFHv/u6rIC8cuXKSo9nzZrFrFmzGiiihhMYbiY9mSnmMgBREYEAnKxjEmSzWLisbTuWHtzPiiOHm3wSpJTi3vZ3k1ucw5bMbbyxbzZPdHmMi30vcndoQgg3UF4jIXB2tesEKf9n3Do93p2U4QuOQeAYRNmKQ9qZBMU70MU7oPhXM0nS2aXf/1ohhXSUJkaXoKzdwdYNrJ1aVI+RRydBLUlQac9PZrKZBJXVAZ1MrvuigVe078DSg/v58cghJg8aXO8xNjaLsvBQx/9h1r432Z29h7/Fz+LJLo/RvpVMnxeiJVJeI8Exwi0rRjclyhIJlkiU19UAaO0C57HSxGgnlOwqTYzyzJ6j4u0VEiMb2hoLtm4oWzewdjOH0pppjZEkQR4iuDTpyUjORGtNm9LHx5My6/wcwztchAJ2piSTmJNDaz+/eo+zsdkNG5M6TWRm/CwO5B7gtb1/48kuj9FOEiEhWiSlLB47Dd5TKWWAtR1Y26G8bwTKEqOjULyrQmK0C3QulOyEkp3o/LJnMMwtQGxdUdauYOsK1i4oS9MtRC8jSZCHCCwd/iopdpKTkVveE5STW0Bm9mkC/c+ehYf5tKJPZGu2JCXy38MHubtn74YLuBF5Wbx4LHYyM/f+nYN5h3hl7994LHaKDI0JIcR5MhOj0tlolRKj41CyG128y1zHqGSv2evmPATOQ2i+LX8ObYSAtYuZENliS7+/qEmtei1JkIewO2z4BbUiJyOPjKRM2nVrS3iIHynpORxPzKxTEgQwsmMntiQlsvTA/maTBAF4W7x5vMtU/hb/RmmP0OtM7vyIFEsLIUQ9MROjGLDGoLyuLT+unalQsgeK96BL9kDxXnAeBlc6FP0ERT9VGE6zoq0Xm0No1s5g7Qy2LmbtlgfukyZJkAcJiQomJyOP9JMZtOvWlrZRQaSk53D0RDrdY+u2+/DIizsxY+1q1h8/xvxft3FxcAgDoqKbxJT5s/G2ePN47BTe2DeHPTl7eT1+Fg91/JNMnxdCiAakLGFgCTOn65ce0zofivdByR50STwUm7PR0Dm/zUyr9CR+5rR9a2dU6Z/YOqOM2rdRamiSBHmQ4Kggjuw6RvrJDADaRQezeUcCR0/Uff+Y3ampWA2DEpeL51YuByDS15dpw67k2o6dGiTuxuRl8WJq7GTePfAeWzK3MWf/O4xvf5csqCiEEI1IKW+w9wJ7rwqJUen2JaUJkS7ZByX7oOSQmRydsSUI3negAl5w0zswSRLkQUKizIw4rTTpadcmBIAjx9LrdP3SA/uZsGRRlaXEknNzmbBkEW9ff1OzSITsho2JnR5m3pH/ZXXqGj468r+kFqYyps2tsteYEEK4iVIKLFHmF8MrJEdFUHIYSvaXJkb7oWSfWUfkZpIEeZDQqGAAUo+bSU+HtqVJ0PGzJ0FOl4sXVv9Y4x7LCnhx9QquvujiZjE0ZlEW7mt/DyH2YL488TVLEpeSXJDCgxfdj5fFy93hCSGEKKWUHWyxYIulYlVQXdYCbGhN/9OwGQlrGwpA6vE0ADqUPj6ZnMXp/KJar9148gRJubk1ntdAYm4OG0+eqJ9gPYBSipuibuR3rW/AwGBzxhZe3P0yKQWyoaAQQng6TyiUliTIg4THmElPSoKZBAUF+BAcaM4KO3yWIbGUvJoToPNp1xRsOrWZqduf4JvEb3HhAuB4/gn+vPN5tmVud3N0QgghPJ0kQR6kPAk6mlZ+rGO7cAAOHEmp/dpWvnV7jTq283SbTm1mzoF3yCjKqHKu0FXIrH2z+fexLyhxlbghOiGEEE2BJEEeJLK9ufpmbmYeeVl5AHTsYB7bf7j2JGhAVDSRvr7U1LmogNa+fgyIiq6vcN3GpV18kvDZWdt9m/gdL+95ldRCGR4TQghRlSRBHsTubadVgDn8tWrhOpxOJ53amz1Bv2w/wpadCTidrmqvtRgG04ZdCVBtIqSB54YNbxZF0fE5+6rtATqTw3BwMO8Qf94xndWpaz2iCE8IIYTnUFo+GarIzs4mICCArKws/P39G+U11/xnA+9MnktahZlgPj1iKOoeQ05BcfmxsBBfJt93JZcP6lzt8yw9sJ8XVv9YpUj6srbt+PiW3zdM8I1sXfoG3jv4wVnb3Rkzlo2nNrI/9wAAvQN7cW/7uwmyu3dxLiGEEA3jXD+/ZYq8B1jznw28MOZvVJzf7vL3Jv3iSMgvggoV9KnpuTw7cxEvPX5TtYnQtR07cfVFF7Px5AlS8nJJz8/nxdUr+OXkcVLycptFTVCgLaBO7WJ82nB1xJUsSVzKlye+Zlvmdp7ZsY/b245hWNhQWVNICCFaOPkUcDOn08k7k+dy5gI/Lj9v85saphC++a8VtQ6NDWrTlptiu3Jvrz70jWxNkdPJB5s31WfobhPr1/msvTnB9mBi/TpjKIMbo67nL5c8R4dWHTjtzGfukY95ec+rHDt9vJEiFkII4YkkCXKznWv2VhoCK6MKimtMgABS0nPYvufsH+JKKR65NA6A+Tu2k5Sbc/7BeghDGdwVM7bWNuNi7qjU09PGpw3Tuj3D2JjbcRgO9uceYNrOv/DJ0U/JK8lr6JCFEKJOtHaiCzeg8xebf2qnu0Nq1iQJcrP0xOoLfI2c/LNfm1G3D+9h7drTr3UUhc4SZm9Yd07xear+wf14pOPDVXqEgu3BPNLxYfoH96tyjaEMro28hhk9/sqAoH64cLEseTmPb3+aH5KWyXR6IYRb6YLv0anD0Rl3o7Ommn+mDkcXfO/u0JotqQlys5DW1Q/rqKISKCwGh63ma4Na1ek1lFI8OWQYt32xgH/v3sn4Xn3oEhp2XvF6kv7B/egb1If4nH1kFmcRaAsoHwKrTYgjmImdHmZX1m7mJ3zGifyTzE9YwA/Jy7k1ehQDQy6VeiEhRKPSBd+jMx+lam1Esnk8cDbKa6RbYmvO5Ce9m3Uf2oXQNiGV5rWX/RMwsk7XeF14iB+9urap8+v0j4rm+o6dcWnN9JU/Npvp4oYy6OrfhbiQgXT173JOycslAd14sft07m0/ngBbAKmFqbx36EP+vPN5NqRvxKWrr7kSQoj6pLUTnf0SVRIg86z53+yXZWisAUgS5GYWi4WH3/iD+UBV+gMjKw9qSFYm3Tcci+Xc/vqeHno53lYrv5w8zsLdO88z4ubFoiwMD7+cmT1ncGubW/CxeHMi/yTvHHyPZ3dMY23azzJMJoRoWEWbwJVUSwMNrkSznahXkgR5gKGjBzJt4WOERodUOu51PJ2AVo4q7Z979Loa1wmqTbSfP5MHDQbgpTWrSMxp+kXS9cVhcXBT1I38rddrjIq+CR+LNycLEvnw0D95/NenWJK4lLySmnvmhBDivLnquKp9XduJOpPFEqvhjsUSwZwuv3PNXjZ8t4WFMxcR3bk1/9z1Btv3HCc9I493P1lNSloO0ybfwDVDu57Xa5S4XIxZ+Bnbk5OIaxPDx6NubRarSNe3fGc+y5NX8EPyMrKKswGwG3aGhA7mqvDhtPWp+1CkEELURhduQGfcfdZ2Kuh/UY6BjRBR03Wun9+SBFXDXUlQ+eun53Br2H0A/F/qv/AP8QPgw8/W8tEX6+keG8Wt1/UhJKgVvbq2OedhsQPp6fxuwf9S6HQyuks3Xh0xskUnQi7tqrG4ushVzPr0DXyf9APH80+UX9PRtyOXhw3l0uD+eFm83BW6EKIZ0NqJTh0OrmSqrwtSYESiwn5EKUtjh9ekSBJUD9ydBAHc13USx+JP8pevnmDwTQMAWLh4M2/OXVGp3dm20ThTddtqBHp58fKV13Btx0719waaiE2nNvNJwmeV9iILsgdxV8zYStPstdbszYlnefKPbM7YiguzaNphOBgQ3I/BIXHnXJgthBBlfpsdBpUTIbNKVMnssDqRJKgeeEISNOvB91jyj+WMnnQDD826l1Xr9/HszEU1tq9pG42Klh7Yz4Qli6r9PQPgnetvalGJ0KZTm5lz4J0az9e03lBmUSZr0n5iTepakgtTyo8H2gIYENyfS4MH0NH3YkmIhBDnRBd8b84Sq1gkbbRG+T8jCVAdSRJUDzwhCVr175/56x2zaNetDe9vf53fP/QBqem5NbYPD/Fj4bsP1Dg05nS5GDrvwyobq1YU6u3Duvv/1CKGxlzaxdTtT9S6G32wPZjXe71aYzKjtWZ/7gF+SlvHxlMbyXP+VjgdYAugb1Af+gT2pqt/F+xGzes9CSFEGa2dpbPFUsEIA3t/GQI7B7KBajPRZ0QPDIvB0d3HWbFse60JEPy2jUbf7jHVnt948kStCRBAWv5pPtv5K3f17H2+YTcZ8Tn7ak2AAE4VnSI+Zx9d/btUe14pRWe/TnT268Td7e5kZ9YuNpzayNbMbWQVZ7EiZSUrUlZiN+x08+9Kz4AedA+4hAiv8IZ4S0KIZkApC0jxc6ORJMhD+Qf70WNoV7av3MX6RXVbG6K2bTRS8mpPgMq8uHoFoT6tmv2wWGZxVr22sxpWegf1ondQL0pcJezO3sOWzG1szdhGZnEm2zK3sy1zOwBhjlC6+Xelq18XYv1jCT7LZrBCCCEahiRBHuzy2wazfeUu4pfvgEva1rqhKtS+jUZ4K986vWaxy8XDSxbxp34DmDpoCDZL8+yGDbQF1Gu7iqyGlZ6BPegZ2IN72t1FwukEfs3ayc6sXezPPUBqYRqrUtewKnUNYCZFnX0708nvYi72vZg23tFSTySEEI1AaoKq4Qk1QQA5GbncEf0gRQXFWK7uRa5vzVOxA/29eeTe4YSF+FY7bb6sJig5N7emCZhEtPJlZMdOfLR9KwAXBQbx+26X0DsyigFR0c2qVqg+aoLOfL667GFW4CwgPmcfu7P3sCc7noTTCegz/kYCbAG80ftvkggJIcQ5kpqgZmTbil2o0sSjZM1uuLaPeaKaHqHM7HxenL0EqH7avMUwmDbsSiYsWYSiugmYMO3yK7m2YydshoV/bt3EocwMXvt5LQDhrVox/fKrms0wmaEM7ooZW+vssHExd9QpEanrNHsAL4sXvQJ70iuwJwCnS05zIPcgB3IPsj/3AIdyD9PaK1ISICGEaATSE1QNT+gJWvOfDbww5m+VshVniB9FAy4G76pbaVSnumnz1a0T1NrXj+eGDefajp3OOo3+1atGMuaS7uf6djxWdQlMsD2YcTF3VDs9vrrrz2eafU1c2kVuSR7+Nr86XyOEEMIkU+TrgbuTIKfTyV0dJpB2PL3KOQ24wvzwjQrG1bsDmdn5NT5PTdPmnS4XG0+eICUvl/BWvuVDXXWZRg9wc2wXbr+kJ5dGt8E4S51SU1DXoazqrqvPITUhhBAXRobDmoGda/ZWmwCBOXRlSc0hV0PRRZG1Pk9Keg6zZy9hwkRzka2vP1/HiROniI4O5rrRA/hu5WHWn4jnWPRhbr49ji3JSWdNgAC+jt/L1/F7iWjVip4RkXQIDGJQmxiGxrSrUjdUU8JV1/ONwVBGjdPga1Mf0+wrOt9kTAghmgpPWwepSSRBb7/9NjNnziQpKYlevXoxZ84cLr300hrbL1y4kOeee44jR47QqVMnXn31Va6//vpGjPjCpCfW/sEKoG11+6v7v7V7+c+KnVWuefP/NoDxWy/OWwt+oteI9lCHz9xB0W3ZnpxIcl4eyw4dBOCDLZuwGQZDY9rzu9gudAsNZ196Gi+tXVkpsYr09WXasCvLh97OHJqreN7T1ec0+3OpKxJCiKao+hWxI8H/WbetiO3xv2Z+/vnnTJ06leeff54tW7bQq1cvRo4cSUpKSrXtf/75Z8aOHcv999/P1q1bGTVqFKNGjWLnzp2NHPn5C2l99nVjLEkZWHcmoLLz4SwjmtpqQVvPyLTPGMVy2q2s+/V4neIb1KYt+SUlVY4Xu1z8eOQQU75fwsj583hk6eIqPUtJubk8vGQRTy//gYeXLKpyPjk3lwlLFrH0wP46xeJO9TXNvqyu6MxepYyiDOYceIdNpzafd4xCCOEJyvdGq5gAAbiS0ZmPogu+d0tcHl8TNHDgQAYMGMBbb70FgMvlom3btjzyyCM89dRTVdrffvvt5OXlsXjx4vJjgwYNonfv3rz33nt1ek2PqQk6kV79hsJn0F42XIGtcPl6ob3taD9vXP4+4GWrOpPMpc2kqexPNFgsYDHQWpMySOHyUrVOo0dR67CZzTAocbnqEnq1FBDp68fqe//o0dPy66MmSOqKhBDNndZOdOrwqglQOQVGJCrsxwseGjvXz2+P/qlaVFTE5s2bGTFiRPkxwzAYMWIE69atq/aadevWVWoPMHLkyBrbAxQWFpKdnV3py50sFgsPv/EH80EtdcfaYUUbClVQjCUpE9uBJOw7EnD8HI/30q14LdmCbfNBLAlpqNwC8yJDgcUAmwXsVrBYUKUrTSul8D9o5l1nvmzZ4zu69zxr3VDxBSRAYL5+Ym4OG0+euIBnaXhl0+xrc7Zp9udSVySEEE1S0aZaEiAADa5Es10j8+iaoLS0NJxOJxEREZWOR0REsHfv3mqvSUpKqrZ9UlLNfwEzZszgL3/5y4UHXI+Gjh7ItIWP8c7kuTUXSReW4IwMpKRtKKqwGCOvEAqKUEUlqBInlDgx0nKwpGSB1mjDQLdyoL0daG8b2m72FOmgVuVJi3caxBVGsCUk74xaHXMafZGz6jBYQ6nrVh/u1D+4H490fPi8p9nX9/YdQgjhcVyp9duuHnl0EtRYnn76aaZOnVr+ODs7m7Zt27oxItPQ0QMZfHN/vprzHe9N/ajaNpakTIykTEoujqC4Z/t6ed3LW8fw5p2XVTtra/3xY/XyGnVR160+3K1/cD/6BvU5r5ldDbl9hxBCeAQjrH7b1SOPToJCQ0OxWCwkJydXOp6cnExkZPXTwyMjI8+pPYDD4cDhqNsChI3NYrEw6pHr+OLvi2usEVKA9WAyJR1bg7f9rHuM1UhrLEUl3Hx7HBbDYFCbqonggKhoIn19z7r9Booa2wAYSqH1mRtG/PYckb5+DIiKPr/34QbnO80+1q8zQfags9YExfp1rvG8EEJ4NHt/cxaYK5nqC13NmiDs/Rs7Ms+uCbLb7fTr14/ly5eXH3O5XCxfvpy4uLhqr4mLi6vUHmDZsmU1tm8K6lIjpAD7jqPmgzNr3bWu/lg1j+8Y0RO7zVZzLKXbb1QXSsXtN2pro4D7+/Sr9TmeGzbco4ui60t91BUJIYQnU8qC8n+27NGZZ83/+j/jlvWCPP4n69SpU/nwww/56KOP2LNnDw899BB5eXn84Q9mUjB+/Hiefvrp8vaTJk1i6dKlvP766+zdu5fp06ezadMmJk6c6K63UC/KaoRCo0MqHfcP8cU/xBw2spzMwP7LflRBUaU2qri0RqiiM3IgS1EJ44Z146HJN5w1lms7duLt628iwrfycFWkrx9vX38T13bsdNY2T192+Vmfo6UoqysKsldeGiHYHnzO224IIYQnUl4jUYGzwahcs4sRiQqc7bZ1gjx+ijzAW2+9Vb5YYu/evZk9ezYDBw4E4IorrqB9+/bMmzevvP3ChQv585//XL5Y4muvvXZOiyW6e4p8bZxOJzvX7CU9MYOQ1kF0H2oOwVQ81nlQRxZ/saF8deibbzd7waqsGP2fjZXa1NYDVG0sdVjtuSmsGO0pZMVoIURz19ArRsveYfXAk5MgIYQQQlSvWa0TJIQQQgjRUCQJEkIIIUSLJEmQEEIIIVokSYKEEEII0SJJEiSEEEKIFkmSICGEEEK0SJIECSGEEKJFkiRICCGEEC2SJEFCCCGEaJE8ehd5dylbRDs7O9vNkQghhBCirso+t+u6GYYkQdXIyckBoG3btm6ORAghhBDnKicnh4CAgLO2k73DquFyuTh58iR+fn4oper1ubOzs2nbti3Hjh2TfckagdzvxiX3u/HJPW9ccr8b37ncc601OTk5REVFYdRhM27pCaqGYRi0adOmQV/D399f/gE1IrnfjUvud+OTe9645H43vrre87r0AJWRwmghhBBCtEiSBAkhhBCiRZIkqJE5HA6ef/55HA6Hu0NpEeR+Ny65341P7nnjkvvd+BrynkthtBBCCCFaJOkJEkIIIUSLJEmQEEIIIVokSYKEEEII0SJJEiSEEEKIFkmSoEb09ttv0759e7y8vBg4cCC//PKLu0NqFmbMmMGAAQPw8/MjPDycUaNGER8fX6lNQUEBEyZMICQkBF9fX2699VaSk5PdFHHz8sorr6CUYvLkyeXH5H7XvxMnTnDXXXcREhKCt7c3PXr0YNOmTeXntdZMmzaN1q1b4+3tzYgRI9i/f78bI27anE4nzz33HB06dMDb25uLL76YF198sdKeVHLPz9/q1av53e9+R1RUFEopvvrqq0rn63JvT506xbhx4/D39ycwMJD777+f3Nzcc4pDkqBG8vnnnzN16lSef/55tmzZQq9evRg5ciQpKSnuDq3JW7VqFRMmTGD9+vUsW7aM4uJirrnmGvLy8srbTJkyhW+++YaFCxeyatUqTp48yejRo90YdfOwceNG3n//fXr27FnpuNzv+pWRkcGQIUOw2Wx899137N69m9dff52goKDyNq+99hqzZ8/mvffeY8OGDbRq1YqRI0dSUFDgxsibrldffZV3332Xt956iz179vDqq6/y2muvMWfOnPI2cs/PX15eHr169eLtt9+u9nxd7u24cePYtWsXy5YtY/HixaxevZoHH3zw3ALRolFceumlesKECeWPnU6njoqK0jNmzHBjVM1TSkqKBvSqVau01lpnZmZqm82mFy5cWN5mz549GtDr1q1zV5hNXk5Oju7UqZNetmyZvvzyy/WkSZO01nK/G8KTTz6pL7vsshrPu1wuHRkZqWfOnFl+LDMzUzscDv3ZZ581RojNzg033KDvu+++SsdGjx6tx40bp7WWe16fAP3ll1+WP67Lvd29e7cG9MaNG8vbfPfdd1oppU+cOFHn15aeoEZQVFTE5s2bGTFiRPkxwzAYMWIE69atc2NkzVNWVhYAwcHBAGzevJni4uJK979Lly7ExMTI/b8AEyZM4IYbbqh0X0Hud0NYtGgR/fv3Z8yYMYSHh9OnTx8+/PDD8vOHDx8mKSmp0j0PCAhg4MCBcs/P0+DBg1m+fDn79u0DYPv27axdu5brrrsOkHvekOpyb9etW0dgYCD9+/cvbzNixAgMw2DDhg11fi3ZQLURpKWl4XQ6iYiIqHQ8IiKCvXv3uimq5snlcjF58mSGDBlC9+7dAUhKSsJutxMYGFipbUREBElJSW6IsulbsGABW7ZsYePGjVXOyf2uf4cOHeLdd99l6tSpPPPMM2zcuJFHH30Uu93OPffcU35fq/sZI/f8/Dz11FNkZ2fTpUsXLBYLTqeTl156iXHjxgHIPW9Adbm3SUlJhIeHVzpvtVoJDg4+p/svSZBoViZMmMDOnTtZu3atu0Npto4dO8akSZNYtmwZXl5e7g6nRXC5XPTv35+XX34ZgD59+rBz507ee+897rnnHjdH1zz9+9//Zv78+Xz66adccsklbNu2jcmTJxMVFSX3vBmR4bBGEBoaisViqTI7Jjk5mcjISDdF1fxMnDiRxYsXs2LFCtq0aVN+PDIykqKiIjIzMyu1l/t/fjZv3kxKSgp9+/bFarVitVpZtWoVs2fPxmq1EhERIfe7nrVu3Zpu3bpVOta1a1cSEhIAyu+r/IypP48//jhPPfUUd9xxBz169ODuu+9mypQpzJgxA5B73pDqcm8jIyOrTCwqKSnh1KlT53T/JQlqBHa7nX79+rF8+fLyYy6Xi+XLlxMXF+fGyJoHrTUTJ07kyy+/5Mcff6RDhw6Vzvfr1w+bzVbp/sfHx5OQkCD3/zxcddVV7Nixg23btpV/9e/fn3HjxpV/L/e7fg0ZMqTKsg/79u2jXbt2AHTo0IHIyMhK9zw7O5sNGzbIPT9Pp0+fxjAqf0RaLBZcLhcg97wh1eXexsXFkZmZyebNm8vb/Pjjj7hcLgYOHFj3F7vgsm5RJwsWLNAOh0PPmzdP7969Wz/44IM6MDBQJyUluTu0Ju+hhx7SAQEBeuXKlToxMbH86/Tp0+Vt/ud//kfHxMToH3/8UW/atEnHxcXpuLg4N0bdvFScHaa13O/69ssvv2ir1apfeuklvX//fj1//nzt4+OjP/nkk/I2r7zyig4MDNRff/21/vXXX/XNN9+sO3TooPPz890YedN1zz336OjoaL148WJ9+PBh/Z///EeHhobqJ554oryN3PPzl5OTo7du3aq3bt2qAf33v/9db926VR89elRrXbd7e+211+o+ffroDRs26LVr1+pOnTrpsWPHnlMckgQ1ojlz5uiYmBhtt9v1pZdeqtevX+/ukJoFoNqvuXPnlrfJz8/XDz/8sA4KCtI+Pj76lltu0YmJie4Lupk5MwmS+13/vvnmG929e3ftcDh0ly5d9AcffFDpvMvl0s8995yOiIjQDodDX3XVVTo+Pt5N0TZ92dnZetKkSTomJkZ7eXnpiy66SD/77LO6sLCwvI3c8/O3YsWKan9u33PPPVrrut3b9PR0PXbsWO3r66v9/f31H/7wB52Tk3NOcSitKyx/KYQQQgjRQkhNkBBCCCFaJEmChBBCCNEiSRIkhBBCiBZJkiAhhBBCtEiSBAkhhBCiRZIkSAghhBAtkiRBQgghhGiRJAkSQgghRIskSZAQwu3uvfdeRo0a5e4wLsjy5cvp2rUrTqfzrG2XLl1K7969y/ehEkK4hyRBQogGpZSq9Wv69Om8+eabzJs3z92hXpAnnniCP//5z1gslrO2vfbaa7HZbMyfP78RIhNC1ES2zRBCNKikpKTy7z///HOmTZtWaUd0X19ffH193RFavVm7di033ngjSUlJeHl51emat99+m3nz5rFx48YGjk4IURPpCRJCNKjIyMjyr4CAAJRSlY75+vpWGQ5zuVzMmDGDDh064O3tTa9evfjiiy/Kz69cuRKlFN9//z19+vTB29ubK6+8kpSUFL777ju6du2Kv78/d955J6dPny6/7oorrmDixIlMnDiRgIAAQkNDee6556j4u2BGRgbjx48nKCgIHx8frrvuOvbv31/re1ywYAFXX311pQRo+/btDB8+HD8/P/z9/enXrx+bNm0qP/+73/2OTZs2cfDgwQu5vUKICyBJkBDC48yYMYOPP/6Y9957j127djFlyhTuuusuVq1aVand9OnTeeutt/j55585duwYt912G2+88Qaffvop3377LT/88ANz5sypdM1HH32E1Wrll19+4c033+Tvf/87//jHP8rP33vvvWzatIlFixaxbt06tNZcf/31FBcX1xjvmjVr6N+/f6Vj48aNo02bNmzcuJHNmzfz1FNPYbPZys/HxMQQERHBmjVrLuRWCSEugNXdAQghREWFhYW8/PLL/Pe//yUuLg6Aiy66iLVr1/L+++9z+eWXl7f961//ypAhQwC4//77efrppzl48CAXXXQRAL///e9ZsWIFTz75ZPk1bdu2ZdasWSiliI2NZceOHcyaNYsHHniA/fv3s2jRIn766ScGDx4MwPz582nbti1fffUVY8aMqTbmo0ePEhUVVelYQkICjz/+OF26dAGgU6dOVa6Liori6NGj53urhBAXSHqChBAe5cCBA5w+fZqrr766vF7I19eXjz/+uMrQUc+ePcu/j4iIwMfHpzwBKjuWkpJS6ZpBgwahlCp/HBcXx/79+3E6nezZswer1crAgQPLz4eEhBAbG8uePXtqjDk/P79KLdDUqVP54x//yIgRI3jllVeqHfby9vauNFwnhGhc0hMkhPAoubm5AHz77bdER0dXOudwOCo9rji8pJSq9LjsWGNMQw8NDSUjI6PSsenTp3PnnXfy7bff8t133/H888+zYMECbrnllvI2p06dIiwsrMHjE0JUT3qChBAepVu3bjgcDhISEujYsWOlr7Zt217w82/YsKHS4/Xr19OpUycsFgtdu3alpKSkUpv09HTi4+Pp1q1bjc/Zp08fdu/eXeV4586dmTJlCj/88AOjR49m7ty55ecKCgo4ePAgffr0ueD3JIQ4P5IECSE8ip+fH4899hhTpkzho48+4uDBg2zZsoU5c+bw0UcfXfDzJyQkMHXqVOLj4/nss8+YM2cOkyZNAsy6nZtvvpkHHniAtWvXsn37du666y6io6O5+eaba3zOkSNHsnbt2vLH+fn5TJw4kZUrV3L06FF++uknNm7cSNeuXcvbrF+/HofDUV73JIRofDIcJoTwOC+++CJhYWHMmDGDQ4cOERgYSN++fXnmmWcu+LnHjx9Pfn4+l156KRaLhUmTJvHggw+Wn587dy6TJk3ixhtvpKioiGHDhrFkyZIqQ20VjRs3jieeeIL4+HhiY2OxWCykp6czfvx4kpOTCQ0NZfTo0fzlL38pv+azzz5j3Lhx+Pj4XPB7EkKcH1ksUQjRYlxxxRX07t2bN954o96f+/HHHyc7O5v333//rG3T0tKIjY1l06ZNdOjQod5jEULUjQyHCSFEPXj22Wdp165dnQqxjxw5wjvvvCMJkBBuJj1BQogWoyF7goQQTY8kQUIIIYRokWQ4TAghhBAtkiRBQgghhGiRJAkSQgghRIskSZAQQgghWiRJgoQQQgjRIkkSJIQQQogWSZIgIYQQQrRIkgQJIYQQokX6/32yX8QlxN6pAAAAAElFTkSuQmCC", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Definir la función exponencial\n", "def exponencial(t, theta_0, gamma):\n", " return theta_0 * np.exp(-gamma * t) # Ecuación para máximo\n", "\n", "# Colores para cada conjunto de datos\n", "colors = plt.cm.viridis(np.linspace(0, 1, len(tetha))) # Mapa de colores\n", "\n", "# Inicializar la gráfica\n", "plt.figure()\n", "\n", "for r in range(len(tetha)):\n", "\n", " # Asignar el tiempo en función de cada caso\n", " if r <= 1:\n", " tiempo = [periodo_de_oscilacion_n[r] * 1 / 2 * k for k in n] # Atento a la fracción y al [0]\n", " elif r == 2:\n", " tiempo = [periodo_de_oscilacion_n[r] * 1 * k for k in n] # Atento a la fracción y al [0]\n", " elif r == 3:\n", " tiempo = [periodo_de_oscilacion_n[r] * 2 * k for k in n] # Atento a la fracción y al [0]\n", " elif r == 4:\n", " tiempo = [periodo_de_oscilacion_n[r] * 8 * k for k in n] # Atento a la fracción y al [0]\n", " else:\n", " break\n", "\n", " print(tiempo)\n", " amplitud = tetha[r]\n", "\n", " # Ajustar la curva\n", " popt, pcov = so.curve_fit(exponencial, tiempo, amplitud)\n", "\n", " # Parámetros ajustados\n", " theta_0_ajustado, gamma_ajustado = popt\n", " incertidumbre_gamma = np.sqrt(pcov[1, 1])\n", " print(f\"θ0 ajustado: {theta_0_ajustado}\")\n", " print(f\"γ ajustado: {gamma_ajustado} $s^{-1}$\")\n", " print(f\"Incertidumbre de γ: {incertidumbre_gamma} $s^{-1}$\")\n", "\n", " # Crear un rango de tiempos para graficar la curva ajustada\n", " tiempo_fit = np.linspace(0, max(tiempo), 100)\n", "\n", " # Calcular los valores ajustados de la función exponencial\n", " amplitud_fit = exponencial(tiempo_fit, *popt)\n", "\n", " # Graficar los datos experimentales y la curva ajustada en el mismo gráfico con colores distintos\n", " plt.scatter(tiempo, amplitud, label=f\"Datos exp {r}\", color=colors[r])\n", " plt.plot(tiempo_fit, amplitud_fit, label=f\"Ajuste {r}\", color=colors[r])\n", "\n", "# Personalizar la gráfica\n", "plt.xlabel(\"Tiempo (s)\")\n", "plt.ylabel(\"Amplitud (u)\")\n", "plt.legend()\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Gráficas con barra de incertezas" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.0, 0.9633333333333334, 1.9266666666666667, 2.89, 3.8533333333333335, 4.816666666666666, 5.78, 6.743333333333334, 7.706666666666667]\n", "θ0 ajustado: 17.01610800445239\n", "γ ajustado: 1.3355298935311044 $s^-1$\n", "Incertidumbre de γ: 0.01748394521511842 $s^-1$\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "[0.0, 0.9560000000000001, 1.9120000000000001, 2.8680000000000003, 3.8240000000000003, 4.78, 5.736000000000001, 6.692, 7.648000000000001]\n", "θ0 ajustado: 17.050962577801254\n", "γ ajustado: 0.8697729058275465 $s^-1$\n", "Incertidumbre de γ: 0.012213372700198028 $s^-1$\n" ] }, { "data": { "image/png": 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evIj27dtrh5t5tEnySaysrLBnzx44Ozuje/fuaNasGebMmVPsLO6rVq3CnTt30LJlS7z11lsYO3aszpOwikAhHm+kJahUKjg6OiIjIwMODg5PdY1v9l7EgsiEMp83rnMDjO/i8+QDiahCyc3NRVJSEurWrfvEORVNUXx8PBo2bIiEhIRnatoxRjNmzMDWrVs5fUg5Kelv5Wm+v1kjZCChgXXQpbGLzja1RuBC/DXYjB0N56w7sN+xDaiu2yTmbM/JX4moYrl9+zZ++eUXODg4wMPDQ+5wiHQwETIQZwebIqfK8PNwAvKvA1cvAakXgeYh5R8cEVE5Gj58OE6dOoWlS5cW6nZOJDfWCMmhoJfAiRPyxkFEVA62bNmClJQUDBgwQO5QDGLGjBlsFjNhTITkEBgovf7/3C5EREQkDyZCcnj0iRBr1YmIiGTDREgOLVoAlSoB//0HXLkidzRERERmi8XScrCxAfz8gJgY6alQ3bpyR0REJipdlYv0zLwyn+dsb11khw4ic8NESC6BgVIidPw40K+f3NEQkYlafzyFY5YRPQMmQnIJCACWLGHPMSJ6JkWNWZabr8bry6QpE355Nwg2lQqPGswxy4xXVFQUOnbsiDt37uhM/GpKTGmQSdYIyaWg59ipU8BjMy4TEZWWs4MNmtZy1Fl8Xe21+7PyHqCRm0OhY/TZLDZkyBAoFAooFApUqlQJLi4u6NKlC77//nvtrOeltWbNGpP98teXtm3bIjU1tchpS8rbkCFD0Lt3b7nDMCgmQnJp0ABwdARyc4Fz5+SOhogqiIhzqQied0C7PmT1STz/xZ+IOJdq0M/t2rUrUlNTceXKFezevRsdO3bEuHHj0LNnTzx48MCgn12R5Ofnw8rKCq6urlAoipuum/SJiZBcLCwedqPneEJEpAcR51Ix6sfTuKHSLZ5Oy8jFqB9PGzQZsra2hqurK2rVqoWWLVvio48+wrZt27B7926dWeXnzZuHZs2awc7ODh4eHnjvvfeQlZUFQGoSGjp0KDIyMrRPmAomYr1z5w4GDRqEqlWrwtbWFt26dUNCwsPaqOTkZPTq1QtVq1aFnZ0dmjRpgl27dhUbb15eHiZOnIhatWrBzs4OgYGBiIqKAiDNZdWkSRO8/fbb2uMTExNhb2+P77//HsDDJ1dbt25FgwYNYGNjg5CQEFy9elXnc7Zt24aWLVvCxsYG3t7emDlzpk5iqFAosHTpUrz88suws7PDrFmzEBUVBYVCgbt37+p81o4dO+Dr6wtbW1u8/vrryMnJwdq1a+Hl5YWqVati7NixUKvVpbrHR6/7xx9/oFGjRqhSpYo2oQWk5q21a9di27Zt2t9HwfmTJ0+Gj48PbG1t4e3tjU8//RT5T2jd+O6779CoUSPY2NigYcOGOpPS3r9/H2PGjIGbmxtsbGzg6emJ8PDwEq+nL0yE5MQRpolIT9QagZm/n0dRI5MVbJv5+3moNeU3dlmnTp3g5+eH3377TbvNwsICCxcuxD///IO1a9fizz//xIcffghAahKaP38+HBwckJqaitTUVEycOBGA1EQTExOD7du34+jRoxBCoHv37tov39GjRyMvLw8HDx7E2bNn8cUXX6BKlSrFxjZmzBgcPXoUGzduxN9//4033ngDXbt2RUJCAmxsbLB+/XptEqBWqzFw4EB06dIFw4YN014jJycHs2bNwg8//IDDhw/j7t27ePPNN7X7Dx06hEGDBmHcuHE4f/48li9fjjVr1mDWrFk6scyYMQN9+vTB2bNnda7/qJycHCxcuBAbN25EREQEoqKi0KdPH+zatQu7du3CunXrsHz5cvzyyy+lusdHr/v1119j3bp1OHjwIFJSUrQ/84kTJ6Jv377a5Cg1NRVt27YFANjb22PNmjU4f/48FixYgJUrV+Kbb74p9ue9fv16TJs2DbNmzUJcXBxmz56NTz/9FGvXrgUALFy4ENu3b8fPP/+M+Ph4rF+/Hl5eXsVeT68EFZKRkSEAiIyMDMN+0PbtQgBCNGli2M8hIqN37949cf78eXHv3r2nOv/IpZvCc/KOJy5HLt3Uc+RCDB48WLzyyitF7uvXr59o1KhRsedu3rxZVK9eXbu+evVq4ejoqHPMxYsXBQBx+PBh7babN2+KypUri59//lkIIUSzZs3EjBkzShVvcnKyUCqV4t9//9XZ3rlzZzF16lTt+pdffilq1KghxowZI9zc3MTNmw9/dqtXrxYAxLFjx7Tb4uLiBABx/Phx7fVmz56t8xnr1q0Tbm5u2nUA4oMPPtA5Zv/+/QKAuHPnjs5nXbp0SXvMO++8I2xtbUVmZqZ2W0hIiHjnnXdKfY9FXXfJkiXCxcVFu17S7/ZRX331lWjVqpV2ffr06cLPz0+7Xq9ePbFhwwadcz7//HMRFBQkhBDi/fffF506dRIajeaJn1XS38rTfH+z15icCp4InT8PqFSAg4O88RCRyUrPzNXrcfoihNCpddm3bx/Cw8Nx4cIFqFQqPHjwALm5ucjJyYGtrW2R14iLi4OlpSUCCzqZAKhevTp8fX0RFxcHABg7dixGjRqFPXv2IDg4GK+99hqaN29e5PXOnj0LtVoNHx/d4QPy8vJQvXp17fqECROwdetWLF68GLt379bZBwCWlpZo06aNdr1hw4ZwcnJCXFwcAgICcObMGRw+fFjnCZBarS50v61bty7xZwgAtra2qFevnnbdxcUFXl5eOk+9XFxckJ6eXqZ7fPy6bm5u2muUZNOmTVi4cCESExORlZWFBw8ewKGY77Ds7GwkJiZi+PDhGDlypHb7gwcPtAXhQ4YMQZcuXeDr64uuXbuiZ8+eeOmll54Yhz4wEZKTiwvg6QkkJwMnTwKdO8sdERGZKGf70vUCK+1x+hIXF4e6/z9o7JUrV9CzZ0+MGjUKs2bNQrVq1RAdHY3hw4fj/v37xSZCpTFixAiEhIRg586d2LNnD8LDwzF37ly8//77hY7NysqCUqnEqVOnoFTqDi3waGKRnp6OixcvQqlUIiEhAV27di1TTFlZWZg5cyZeffXVQvtsbB7+Huzs7J54rUqVKumsF/TQe3xbQS+90t5jUdcQT5j66ejRowgNDcXMmTMREhICR0dHbNy4EXPnzi3y+IIasJUrV+okswC0sbVs2RJJSUnYvXs39u3bh759+yI4OFinqc9QmAjJrW1bKRE6fJiJEBE9tYC61eDmaIO0jNwi64QUAFwdbRBQt1q5xfTnn3/i7NmzGD9+PADg1KlT0Gg0mDt3LiwspBLVn3/+WeccKysrnYJfAGjUqBEePHiA48ePa2tUbt26hfj4eDRu3Fh7nIeHB9599128++67mDp1KlauXFlkIuTv7w+1Wo309HS0b9++2PiHDRuGZs2aaZ9kBAcHo1GjRtr9Dx48QExMDAL+/+l+fHw87t69qz2mZcuWiI+PR/369Uv9M9OX0t7jkxT1+zhy5Ag8PT3x8ccfa7clJycXew0XFxe4u7vj8uXLCA0NLfY4BwcH9OvXD/369cPrr7+Orl274vbt26hWzbD/zTIRklvbtsBPPwFHjsgdCRGZMKWFAtN7NcaoH09DAegkQwUNU9N7NYbSwjBdsvPy8pCWlga1Wo0bN24gIiIC4eHh6NmzJwYNGgQAqF+/PvLz87Fo0SL06tULhw8fxrJly3Su4+XlhaysLERGRsLPzw+2trZo0KABXnnlFYwcORLLly+Hvb09pkyZglq1auGVV14BAHzwwQfo1q0bfHx8cOfOHezfv18naXmUj48PQkNDMWjQIMydOxf+/v7477//EBkZiebNm6NHjx5YsmQJjh49ir///hseHh7YuXMnQkNDcezYMVhZWQGQnqa8//77WLhwISwtLTFmzBg899xz2sRo2rRp6NmzJ+rUqYPXX38dFhYWOHPmDM6dO4f//e9/Bvk9lOUeS8PLywt//PEH4uPjUb16dTg6OqJBgwZISUnBxo0b0aZNG+zcuRNbtmwp8TozZ87E2LFj4ejoiK5duyIvLw8xMTG4c+cOwsLCMG/ePLi5ucHf3x8WFhbYvHkzXF1dy2dMqVJXE5mRciuWFkKI06elgmkHByHUasN/HhEZpWctli6w++x1ETBrr06B9HOz94ndZ6/rKdLCBg8eLCDlXsLS0lLUrFlTBAcHi++//16oH/v/2rx584Sbm5uoXLmyCAkJET/88INOYbAQQrz77ruievXqAoCYPn26EEKI27dvi7feeks4Ojpqz7148aL2nDFjxoh69eoJa2trUbNmTfHWW2/pFDc/7v79+2LatGnCy8tLVKpUSbi5uYk+ffqIv//+W8TFxYnKlSvrFPfeuXNHeHh4iA8//FAI8bCo+9dffxXe3t7C2tpaBAcHi+TkZJ3PiYiIEG3bthWVK1cWDg4OIiAgQKxYsUK7H4DYsmWLzjlFFUs/XkD+eDFywe/h0cLmku6xuOtu2bJFPJoapKeniy5duogqVaoIAGL//v1CCCEmTZokqlevLqpUqSL69esnvvnmG51rFRXf+vXrRYsWLYSVlZWoWrWqeOGFF8Rvv/0mhBBixYoVokWLFsLOzk44ODiIzp07i9OnT4ui6LtYWiHEExoDzZBKpYKjoyMyMjKKLf7SmwcPACcnIDsbOHsWaNrUsJ9HREYpNzcXSUlJqFu3rk79yNPIzM1Hsxl7AABrhrZB+wY1DfYkyFytWbMGH3zwgXasHyo/Jf2tPM33N5vG5GZpKU238eefUp0QEyEiKoOiZp/PzX9Y01HF2hJxqapC53H2eSIJEyFj0K6dlAgdOQK8847c0RCRCXnS7PMFk68+jrPPE0mYCBmD/+8FwYJpIiqromafLw3OPv9shgwZgiFDhsgdBukBEyFj8NxzgEIBXLoE3LghjS9ERFQKzg42bOIiegaca8wYODkBTZpI748W/RibiMwD+68QlUzffyNMhIwFm8eIzFrBCL85OTkyR0Jk3Ar+Rh4fFftpsWnMWLRtC6xYwUSIyEwplUo4OTlp53mytbXVmaOLyNwJIZCTk4P09HQ4OTkVmjrkaTERMhYFT4RiYoC8PMCahYxE5sbV1RUASjXpJZG5cnJy0v6t6AMTIWNRvz5Qsybw33/A6dNAUJDcERFROVMoFHBzc4OzszPy8/PlDofI6FSqVElvT4IKMBEyFgqF9FRo2zapeYyJEJHZUiqVev+fPREVTdZi6YMHD6JXr15wd3eHQqHA1q1bdfYPGTIECoVCZ+natesTr7tkyRJ4eXnBxsYGgYGBOHHihIHuQM9YME1ERFSuZE2EsrOz4efnhyVLlhR7TNeuXZGamqpdfvrppxKvuWnTJoSFhWH69Ok4ffo0/Pz8EBISYhpt7u3aSa+HDwPsQktERGRwsjaNdevWDd26dSvxGGtr6zIVRc2bNw8jR47E0KFDAQDLli3Dzp078f3332PKlClFnpOXl4e8vIdz9ahUheflKRetWgGVKkmDKiYlAd7e8sRBRERkJox+HKGoqCg4OzvD19cXo0aNwq1bt4o99v79+zh16hSCg4O12ywsLBAcHIyjJQxUGB4eDkdHR+3i4eGh13soNRsbKRkC2DxGRERUDow6EeratSt++OEHREZG4osvvsCBAwfQrVs3qNXqIo+/efMm1Go1XB6bosLFxQVpaWnFfs7UqVORkZGhXa5evarX+yiTgjqhw4fli4GIiMhMGHWvsTfffFP7vlmzZmjevDnq1auHqKgodO7cWW+fY21tDWtjGbenXTtg3jwgOlruSIiIiCo8o34i9Dhvb2/UqFEDly5dKnJ/jRo1oFQqcePGDZ3tN27c0OvgSwbVvr30eu4cUEIzIBERET07k0qErl27hlu3bsHNza3I/VZWVmjVqhUiIyO12zQaDSIjIxFkKuPy1KwJNG4svT90SN5YiIiIKjhZE6GsrCzExsYiNjYWAJCUlITY2FikpKQgKysLkyZNwrFjx3DlyhVERkbilVdeQf369RESEqK9RufOnbF48WLtelhYGFauXIm1a9ciLi4Oo0aNQnZ2trYXmUl44QXp9eBBeeMgIiKq4GStEYqJiUHHjh2162FhYQCAwYMHY+nSpfj777+xdu1a3L17F+7u7njppZfw+eef69TzJCYm4ubNm9r1fv364b///sO0adOQlpaGFi1aICIiolABtVF74QVg2TImQkRERAamEIIj9z1OpVLB0dERGRkZcHBwKP8A/v0XqF0bsLAAbt8GHB3LPwYiIiIT8zTf3yZVI2Q2atUC6tUDNBqOJ0RERGRATISMFeuEiIiIDI6JkLHq0EF6PXBA3jiIiIgqMCZCxqrgidDJk0BOjryxEBERVVBMhIyVl5dUMP3gAXDsmNzREBERVUhMhIyVQsHmMSIiIgNjImTMWDBNRERkUEyEjFlBInTsGJCXJ28sREREFRATIWPm6ws4OwO5uVLRNBEREekVEyFjplCweYyIiMiAmAgZOyZCREREBsNEyNgV9Bw7fFjqSk9ERER6w0TI2DVtCjg5AVlZwOnTckdDRERUoTARMnYWFg+fCu3fL28sREREFQwTIVPQqZP0GhkpbxxEREQVDBMhU9C5s/QaHc3xhIiIiPSIiZApaNwYcHEB7t3jvGNERER6xETIFCgUbB4jIiIyACZCpqKgeezPP+WNg4iIqAJhImQqChKh48elrvRERET0zJgImQovL6BuXWlQRY4yTUREpBdMhExJwVMh1gkRERHpBRMhU1JQMM06ISIiIr1gImRKChKh2Fjg5k1ZQyEiIqoImAiZEhcXae4xgNNtEBER6QETIVPD5jEiIiK9YSJkalgwTUREpDdMhExNhw7SjPQJCcDVq3JHQ0REZNKYCJkaR0egdWvpPZvHiIiIngkTIVPE5jEiIiK9YCJkigoSoX37ACHkjYWIiMiEMREyRe3aAZUrA6mpwNmzckdDRERkspgImSIbG+DFF6X3f/whayhERESmjImQqeraVXplIkRERPTUmAiZqpAQ6fXQISA7W95YiIiITBQTIVPl4wN4egL37wNRUXJHQ0REZJKYCJkqhYLNY0RERM9I1kTo4MGD6NWrF9zd3aFQKLB161btvvz8fEyePBnNmjWDnZ0d3N3dMWjQIFy/fr3Ea86YMQMKhUJnadiwoYHvRCYFzWMREfLGQUREZKJkTYSys7Ph5+eHJUuWFNqXk5OD06dP49NPP8Xp06fx22+/IT4+Hi+//PITr9ukSROkpqZql+joaEOEL79OnQClUppuIylJ7miIiIhMjqWcH96tWzd069atyH2Ojo7Yu3evzrbFixcjICAAKSkpqFOnTrHXtbS0hKurq15jNUqOjkBQEBAdLTWPvfuu3BERERGZFJOqEcrIyIBCoYCTk1OJxyUkJMDd3R3e3t4IDQ1FSkpKicfn5eVBpVLpLCaDdUJERERPzWQSodzcXEyePBn9+/eHg4NDsccFBgZizZo1iIiIwNKlS5GUlIT27dsjMzOz2HPCw8Ph6OioXTw8PAxxC4ZRUCcUGQnk58sbCxERkYlRCGEck1UpFAps2bIFvXv3LrQvPz8fr732Gq5du4aoqKgSE6HH3b17F56enpg3bx6GDx9e5DF5eXnIy8vTrqtUKnh4eCAjI6NMnyULjQZwcQFu3gQOHABeeEHuiIiIiGShUqng6OhYpu9vo38ilJ+fj759+yI5ORl79+4tc2Li5OQEHx8fXLp0qdhjrK2t4eDgoLOYDAsL4KWXpPdsHiMiIioTo06ECpKghIQE7Nu3D9WrVy/zNbKyspCYmAg3NzcDRGgk2I2eiIjoqciaCGVlZSE2NhaxsbEAgKSkJMTGxiIlJQX5+fl4/fXXERMTg/Xr10OtViMtLQ1paWm4f/++9hqdO3fG4sWLtesTJ07EgQMHcOXKFRw5cgR9+vSBUqlE//79y/v2yk/BE6HTp4H0dHljISIiMiGyJkIxMTHw9/eHv78/ACAsLAz+/v6YNm0a/v33X2zfvh3Xrl1DixYt4Obmpl2OHDmivUZiYiJu3rypXb927Rr69+8PX19f9O3bF9WrV8exY8dQs2bNcr+/cuPqCvj5Se/37JE3FiIiIhMi6zhCL774Ikqq1S5NHfeVK1d01jdu3PisYZmm7t2BM2eAHTuAgQPljoaIiMgkGHWNEJVBr17Sa0QEu9ETERGVEhOhiiIgAKhRA8jIAA4fljsaIiIik8BEqKJQKqXmMUBqHiMiIqInYiJUkfTsKb3+/ru8cRAREZkIJkIVSUgIYGkJXLwoLURERFQiJkIViYMD0KGD9H7nTnljISIiMgFMhCoaNo8RERGVGhOhiqagG/2hQ8Ddu7KGQkREZOyYCFU09eoBDRsCDx5wlGkiIqInYCJUERU0j7EbPRERUYmYCFVEBYnQrl2AWi1vLEREREaMiVBF1K4d4OQE3LoFHDsmdzRERERGi4lQRWRpCXTrJr1n8xgREVGxmAhVVOxGT0RE9ERMhCqqbt2k+cf++Qe4dEnuaIiIiIwSE6GKqmpVoGNH6f2WLfLGQkREZKSYCFVkffpIr7/9Jm8cRERERoqJUEXWu7f0euwYcP26rKEQEREZIyZCFZm7O/Dcc9L7rVtlDYWIiMgYMRGq6F59VXplnRAREVEhTIQquoI6of37gdu35Y2FiIjIyDARqujq1weaNZOm2uCYQkRERDqYCJmDgqdCbB4jIiLSwUTIHBTUCf3xB5CdLW8sRERERoSJkDlo3hzw9gZyc4GICLmjISIiMhpMhMyBQsHBFYmIiIrARMhcFDSP7dgB3L8vbyxERERGgomQuXjuOcDVFVCpgD//lDsaIiIio8BEyFxYWDyccuOXX2QNhYiIyFgohBCiLCckJSXh0KFDSE5ORk5ODmrWrAl/f38EBQXBxsbGUHGWK5VKBUdHR2RkZMDBwUHucPRn/36gUydpZvq0NMDKSu6IiIiI9OZpvr8tS3vx9evXY8GCBYiJiYGLiwvc3d1RuXJl3L59G4mJibCxsUFoaCgmT54MT0/Pp74JMqAXXpCax9LSgL17gR495I6IiIhIVqVqGvP398fChQsxZMgQJCcnIzU1FadOnUJ0dDTOnz8PlUqFbdu2QaPRoHXr1ti8ebOh46anoVQCr78uvd+0Sd5YiIiIjECpmsb++OMPhISElOqCt27dwpUrV9CqVatnDk4uFbZpDACio4H27QF7eyA9HaggzZlERERP8/1dqidCpU2CAKB69eomnQRVeG3bArVrA5mZHFyRiIjMXqlrhAqkpKSUuL9OnTpPHQyVAwsLoG9fYN48qXmsoCcZERGRGSpzrzELCwsoFIpi96vV6mcOSm4VumkMAE6cAAIDAVtbqXnMzk7uiIiIiJ6ZQXuNFfjrr7901vPz8/HXX39h3rx5mDVrVlkvR3Jo0waoWxdISgJ27pSeEBEREZmhMg+o6Ofnp7O0bt0aI0eOxNdff42FCxeW6VoHDx5Er1694O7uDoVCga1bt+rsF0Jg2rRpcHNzQ+XKlREcHIyEhIQnXnfJkiXw8vKCjY0NAgMDceLEiTLFVeEpFA+TH/YeIyIiM6a3kaV9fX1x8uTJMp2TnZ0NPz8/LFmypMj9X375JRYuXIhly5bh+PHjsLOzQ0hICHJzc4u95qZNmxAWFobp06fj9OnT8PPzQ0hICNLT08sUW4X35pvS665dUuE0ERGRGSpzjZBKpdJZF0IgNTUVM2bMwIULFxAbG/t0gSgU2LJlC3r/f/GuEALu7u6YMGECJk6cCADIyMiAi4sL1qxZgzcLvsgfExgYiDZt2mDx4sUAAI1GAw8PD7z//vuYMmVKqe+xQtcIAYAQQMOGwMWLwI8/AqGhckdERET0TAzWff5RTk5OqFq1qnapVq0aGjdujKNHj2Lp0qVlDro4SUlJSEtLQ3BwsHabo6MjAgMDcfTo0SLPuX//Pk6dOqVzjoWFBYKDg4s9BwDy8vKgUql0lgpPoQD69ZPeb9wobyxEREQyKXOx9P79+3XWLSwsULNmTdSvXx+WlmW+XLHS0tIAAC4uLjrbXVxctPsed/PmTajV6iLPuXDhQrGfFR4ejpkzZz5jxCaoXz/g88+BP/4Abt8GqlWTOyIiIqJyVebMpUOHDoaIQ1ZTp05FWFiYdl2lUsHDw0PGiMpJkyZA8+bA338DP/8MvPuu3BERERGVq1I1jR07dqzUF8zJycE///zz1AEVcHV1BQDcuHFDZ/uNGze0+x5Xo0YNKJXKMp0DANbW1nBwcNBZzMagQdLrunXyxkFERCSDUiVCb731FkJCQrB582ZkZ2cXecz58+fx0UcfoV69ejh16tQzB1a3bl24uroiMjJSu02lUuH48eMICgoq8hwrKyu0atVK5xyNRoPIyMhizzF7AwZIo00fOQIkJsodDRERUbkqVSJ0/vx59OjRA5988gmcnJzQpEkTdOnSBb169cLzzz+PGjVqoGXLlkhKSsKePXswqOApwxNkZWUhNjZW29MsKSkJsbGxSElJgUKhwAcffID//e9/2L59O86ePYtBgwbB3d1d27MMADp37qztIQYAYWFhWLlyJdauXYu4uDiMGjUK2dnZGDp0aOl/KubEzQ0oKC7nUyEiIjIzZe4+HxMTg+joaCQnJ+PevXuoUaMG/P390bFjR1QrY7FtVFQUOnbsWGj74MGDsWbNGgghMH36dKxYsQJ3797F888/j2+//RY+Pj7aY728vDBkyBDMmDFDu23x4sX46quvkJaWhhYtWmDhwoUIDAwsdVxm0X3+UevXAwMHAvXqAQkJUo8yIiIiE/M0399lToTMgdklQtnZgIuL9Hr4sDRDPRERkYkpl3GEqAKyswNee016z+YxIiIyI0yESPLWW9Lrpk1AXp68sRAREZUTJkIk6dgRqFULuHNHmpGeiIjIDDARIolS+XC+MTaPERGRmWAiRA8VNI/t3AncuiVvLEREROWgVFNsLFy4sNQXHDt27FMHQzJr2hTw9wf++kuqFXrvPbkjIiIiMqhSdZ+vW7euzvp///2HnJwcODk5AQDu3r0LW1tbODs74/LlywYJtDyZXff5R82fD4wfD7RuDZw8KXc0REREpWaw7vNJSUnaZdasWWjRogXi4uJw+/Zt3L59G3FxcWjZsiU+//zzZ7oBMgIDBwKVKgExMcD/j/hNRERUUZW5RujTTz/FokWL4Ovrq93m6+uLb775Bp988olegyMZ1KgBFExhsmqVrKEQEREZWpkTodTUVDx48KDQdrVaXWjWdzJRI0ZIrz/+CNy7J28sREREBlTmRKhz58545513cPr0ae22U6dOYdSoUQgumLyTTFtwMODpCdy9C2zZInc0REREBlPmROj777+Hq6srWrduDWtra1hbWyMgIAAuLi747rvvDBEjlTcLC2DoUOk9f6dERFSBPfWkqxcvXsSFCxcAAA0bNtSZEd7UmXWvsQIpKYCXFyCENCN9/fpyR0RERFSip/n+LtU4QkXx8fGpUMkPPaZOHSAkBIiIAL7/Hpg9W+6IiIiI9K7MidCwYcNK3P/9998/dTBkZIYPlxKhNWuAzz4DLJ86byYiIjJKZf5mu3Pnjs56fn4+zp07h7t376JTp056C4yMwMsvS93pU1OB3buBXr3kjoiIiEivypwIbSmiF5FGo8GoUaNQr149vQRFRsLKChg8GJg7VyqaZiJEREQVzFMXSz8uPj4eL774IlJTU/VxOVmxWPoRcXFA48bS7PTJyUCtWnJHREREVCSDTbFRGomJiUUOtEgmrlEj4PnnAbUaWLlS7miIiIj0qsxNY2FhYTrrQgikpqZi586dGDx4sN4CIyPy3ntAdDSwYgXw8cfSXGREREQVQJkTob/++ktn3cLCAjVr1sTcuXOf2KOMTNRrrwEuLlLR9NatwBtvyB0RERGRXpQ5Edq/f78h4iBjZmUFjBwJ/O9/wJIlTISIiKjCKHONUKdOnXD37t1C21UqFbvPV2TvvCMVTB84APzzj9zREBER6UWZE6GoqCjcv3+/0Pbc3FwcOnRIL0GREapdWxpXCAC+/VbeWIiIiPSk1E1jf//9t/b9+fPnkZaWpl1Xq9WIiIhALXatrthGj5Zmo//hB2DOHMDeXu6IiIiInkmpE6EWLVpAoVBAoVAU2QRWuXJlLFq0SK/BkZHp1Alo2BC4cAFYt07qTUZERGTCSp0IJSUlQQgBb29vnDhxAjVr1tTus7KygrOzM5RKpUGCJCOhUEjJz9ixUtH0qFHSNiIiIhOlt5GlKxKOLF2CjAxpdOnsbCAqCujQQe6IiIiIADzd93epnght374d3bp1Q6VKlbB9+/YSj325oKCWKiZHR2DgQGD5cmDRIiZCRERk0kr1RMjCwgJpaWlwdnaGhUXxHc0UCgXUarVeA5QDnwg9wblzQLNmgIUFcOkSULeu3BEREREZbq4xjUYDZ2dn7fviloqQBFEpNG0KvPQSoNEACxfKHQ0REdFT09ukq2RmCuac++47qW6IiIjIBJWqRmhhGf7VP3bs2KcOhkzISy8BjRsD589LydCECXJHREREVGalqhGqW8oaEIVCgcuXLz9zUHJjjVAprVoFjBgBeHgAly8DlmWeuo6IiEhvDNZrLCkp6ZkCo4onXZWL/17sCd8aNWF59SpSVq7DnZ598M/1DNzJyUdV20po4u4IpYXuOEPO9tZwdrCRKWoiIiJdz/RP+IKHSQoOqmd21h9PwYLIBIxt+BLCotdj59od+OJKzScOsDiucwOM7+JTTlESERGV7KkSoVWrVuGbb75BQkICAKBBgwb44IMPMGLECL0GR8YrNLAOujR2gXJAQ+zqfxVfdhhabBL0UbeGaFu/BgDpiRAREZGxKHMiNG3aNMybNw/vv/8+goKCAABHjx7F+PHjkZKSgs8++0zvQZLxcXawgbODDdRuDhjW/X0UV2imALD6yBUMb+9dqJmMiIhIbmXuPr906VKsXLkS4eHhePnll/Hyyy8jPDwcK1aswLfffqv3AL28vLSTvT66jB49usjj16xZU+hYGxvWpBjKiaTbSFVWLvZpkACQmpGLE0m3yzcwIiKiUijzE6H8/Hy0bt260PZWrVrhwYMHegnqUSdPntQZqPHcuXPo0qUL3njjjWLPcXBwQHx8vHadNUyGk56Zq9fjiIiIylOZE6G33noLS5cuxbx583S2r1ixAqGhoXoLrMCjs9wDwJw5c1CvXj10KGGOK4VCAVdX11J/Rl5eHvLy8rTrKpWq7IGaKWf70j1tK+1xRERE5empi6X37NmD5557DgBw/PhxpKSkYNCgQQgrGHEYKJQsPav79+/jxx9/RFhYWIlPebKysuDp6QmNRoOWLVti9uzZaNKkSbHHh4eHY+bMmXqN1VwE1K0GN0cbpGXkFlknpADg6miDgLrVyjs0IiKiJyrVgIqP6tixY+kurFDgzz//fKqgivPzzz9jwIABSElJgbu7e5HHHD16FAkJCWjevDkyMjLw9ddf4+DBg/jnn39Qu3btIs8p6omQh4cHB1QsQboqF+mZ0s/syKWbmL37AqSKoMIJ6uO9xjiOEBERGcLTDKhY5kRITiEhIbCyssLvv/9e6nPy8/PRqFEj9O/fH59//nmpzuHI0k/2zd6LWBCZoLtRCI4jREREsjHYyNLGIDk5Gfv27cNvv/1WpvMqVaoEf39/XLp0yUCRmaeCcYQepdYIXPl1F7BiOWrgPhx+2QSFo6POMRxHiIiIjEmZE6Hc3FwsWrQI+/fvR3p6OjQajc7+06dP6y24R61evRrOzs7o0aNHmc5Tq9U4e/YsunfvbpC4zFXBOEKP83u/H7DsMyA+HtixEfjwQxmiIyIiKp0yJ0LDhw/Hnj178PrrryMgIKBcuqZrNBqsXr0agwcPhuVjE3sOGjQItWrVQnh4OADgs88+w3PPPYf69evj7t27+Oqrr5CcnMxRr8uLUglMmQIMHQrMmwe8/z5QubLcURERERWpzInQjh07sGvXLrRr184Q8RRp3759SElJwbBhwwrtS0lJgYXFw3Eh79y5g5EjRyItLQ1Vq1ZFq1atcOTIETRu3Ljc4jV7oaHA9OlASgqwejXw3ntyR0RERFSkMhdLN27cGBs3bkTz5s0NFZPsWCytB4sXS0+DPDyAhATAmrVBRERkWE/z/V3mKTbmzp2LyZMnIzk5ucwBkhkZPhxwcwOuXgVWrZI7GiIioiKVORFq3bo1cnNz4e3tDXt7e1SrVk1nIQIg1QV99JH0ftYsIJdTbBARkfEpc41Q//798e+//2L27NlwcXHhPF5UvJEjgS+/lJ4KLV8OjBsnd0REREQ6ylwjZGtri6NHj8LPz89QMcmONUJ6tGIF8M47gIsLcPkyYGsrd0RERFRBlUuNUMOGDXHv3r0yB0dmasgQwMsLuHEDWLpU7miIiIh0lDkRmjNnDiZMmICoqCjcunULKpVKZyHSYWUFTJsmvZ8zB8jKkjceIiKiR5S5aaxgzJ7Ha4OEEFAoFFCr1fqLTiZsGtOzBw+ARo2AS5eA8HBpwEUiIiI9K5e5xvbv31/mwMjMWVpKAyy+9Rbw1VfSAItMMImIyAjodfb5c+fOoWnTpvq6nGz4RMgA1GqgaVPgwgXg00+Bzz6TOyIiIqpgyqVY+nGZmZlYsWIFAgICKnRPMnpGSqU0nhAAzJ0LpKbKGw8RERGeIRE6ePAgBg8eDDc3N3z99dfo1KkTjh07ps/YqKLp0wcICgJycoAZM+SOhoiIqGyJUFpaGubMmYMGDRrgjTfegIODA/Ly8rB161bMmTMHbdq0MVScVBEoFNIAi4A07UZcnLzxEBGR2St1ItSrVy/4+vri77//xvz583H9+nUsWrTIkLFRRfT888Arr0g1Q1Onyh0NERGZuVInQrt378bw4cMxc+ZM9OjRA0ql0pBxUUU2Z45UM7RtGxAdLXc0RERkxkqdCEVHRyMzMxOtWrVCYGAgFi9ejJs3bxoyNqqoGjaUZqcHgEmTAP11XCQiIiqTUidCzz33HFauXInU1FS888472LhxI9zd3aHRaLB3715kZmYaMk6qaGbMkOYdO3YM+O03uaMhIiIzVeZeY3Z2dhg2bBiio6Nx9uxZTJgwAXPmzIGzszNefvllQ8RIFZGbGzBhgvR+yhTg/n154yEiIrP0TOMI+fr64ssvv8S1a9fw008/6SsmMheTJgGurtLUGwsWyB0NERGZIb2OLF1RcGTpcrRmDTB0KFClCnDxovSkiIiI6CnIMrI00TMZNAgICJBmpWd3eiIiKmdMhEheFhbAwoXS+7VrgePH5Y2HiIjMChMhkl9gIDB4sPR+7FhAo5E3HiIiMhtMhMg4hIdLdUInTgA//CB3NEREZCaYCJFxcHMDpk2T3k+ZAqhU8sZDRERmgYkQGY9x44AGDYAbNzg7PRERlQsmQmQ8rKweFk4vWAD89Ze88RARUYXHRIiMS9euQL9+UsH0229Ls9QTEREZCBMhMj7ffAM4OgIxMcC338odDRERVWBMhMj4uLlJvcgA4OOPgX//lTceIiKqsJgIkXF65x1pfKHMTKmImoiIyACYCJFxsrAAVqwAlErg11+B33+XOyIiIqqAmAiR8WreHAgLk96PHi09HSIiItIjJkJk3KZPB7y8gKtXgUmT5I6GiIgqGCZCZNzs7IDvv5feL18O7NsnbzxERFShMBEi49exI/Dee9L7ESPYREZERHrDRIhMwxdfSE1kyclsIiMiIr1hIkSmoUoVNpEREZHeGXUiNGPGDCgUCp2lYcOGJZ6zefNmNGzYEDY2NmjWrBl27dpVTtGSwbGJjIiI9MyoEyEAaNKkCVJTU7VLdHR0scceOXIE/fv3x/Dhw/HXX3+hd+/e6N27N86dO1eOEZNBffEFULeu1EQ2frzc0RARkYkz+kTI0tISrq6u2qVGjRrFHrtgwQJ07doVkyZNQqNGjfD555+jZcuWWLx4cTlGTAZVpQqwejWgUACrVkmDLRIRET0lo0+EEhIS4O7uDm9vb4SGhiIlJaXYY48ePYrg4GCdbSEhITh69GiJn5GXlweVSqWzkBHr0AGYPFl6P3IkcO2avPEQEZHJMupEKDAwEGvWrEFERASWLl2KpKQktG/fHpnF1IakpaXBxcVFZ5uLiwvS0tJK/Jzw8HA4OjpqFw8PD73dAxnIzJlAq1bAnTvA4MGARiN3REREZIKMOhHq1q0b3njjDTRv3hwhISHYtWsX7t69i59//lmvnzN16lRkZGRol6tXr+r1+mQAVlbAhg2ArS3w55/A11/LHREREZkgo06EHufk5AQfHx9cunSpyP2urq64ceOGzrYbN27A1dW1xOtaW1vDwcFBZyET4OMDLFwovf/kE+DUKXnjISIik2NSiVBWVhYSExPh5uZW5P6goCBERkbqbNu7dy+CgoLKIzySw7BhwKuvAvn5wIABQFaW3BEREZEJMepEaOLEiThw4ACuXLmCI0eOoE+fPlAqlejfvz8AYNCgQZg6dar2+HHjxiEiIgJz587FhQsXMGPGDMTExGDMmDFy3QIZmkIBrFwJ1KoFXLwIvP02IITcURERkYkw6kTo2rVr6N+/P3x9fdG3b19Ur14dx44dQ82aNQEAKSkpSE1N1R7ftm1bbNiwAStWrICfnx9++eUXbN26FU2bNpXrFqg8VKsGbNwIKJXATz8By5bJHREREZkIhRD85/PjVCoVHB0dkZGRwXohU/L119I8ZFZWwOHDQOvWckdERETl6Gm+v436iRBRmUyYAPTuDdy/D7zxhtS1noiIqARMhKjiUCikUae9vYErVzi+EBERPRETIapYnJyAzZsBa2vg99+BL7+UOyIiIjJiTISo4mnZ8uH4Qh99BOzeLW88RERktJgIUcU0cqS0CAH07w/Ex8sdERERGSEmQlQxKRTA4sXA888DGRnAyy8Dd+/KHRURERkZJkJUcVlZAb/+Cnh4SIMt9u8PqNVyR0VEREaEiRBVbM7OwLZtQOXKQEQE8MhI5EREREyEqOLz95e61QPAV189fE9ERGaPiRCZh379pBnqAWk+sr175Y2HiIiMAhMhMh+ffQaEhgIPHgCvvQb8/bfcERERkcyYCJH5UCiAVauAF18EMjOB7t2Ba9fkjoqIiGTERIjMi7U18NtvQKNGwL//Aj16ACqV3FEREZFMmAiR+alaFdi1C3BxkZrHXn0VyMuTOyoiIpIBEyEyT15ewM6dgJ0dEBkJDBgg1Q4REZFZYSJE5qtVK2mMISsrqbns7bc5Wz0RkZlhIkTmrXNnYNMmQKmUxheaOFGan4yIiMwCEyGi3r2B77+X3n/zDfC//8kaDhERlR8mQkQAMGgQsGCB9H7aNGDuXHnjISKicsFEiKjA2LHSoIuA1EQ2b5688RARkcExESJ61KefAtOnS+8nTGAyRERUwTERInrcjBlS8xjAZIiIqIKzlDsAImOUHjYFUOXCef6XwIQJSL2bg/SRY/DP9QzcyclHVdtKaOLuCKWFQuc8Z3trODvYyBQ1ERGVFRMhoiKsP3EVC6zaY3zbqxh35Cec2bQLEzN9kGVtV+J54zo3wPguPuUUJRERPSsmQkRFCA2sgy6NXYD3n8fGha0xVeGD4kYX+qhbQ7StXwOA9ESIiIhMBxMhoiI4O9jA2cEGao3AyKp+EBn3ACgKHacAsPrIFQxv712omYyIiIwfi6WJSnAi6TZSM3JRVBIEAAJAakYuTiTdLte4iIhIP5gIEZUgPTNXr8cREZFxYSJEVAJn+9L1ACvtcUREZFyYCBGVIKBuNbg52hTTMAYohAZu2bcQcCepXOMiIiL9YCJEVIR0VS7O/ZuBuFQVhrb1KrbHGKDA9D3LgA4v4soPm5GuYhMZEZEpYa8xoiKsP56CBZEJTzzO+kEe7PLuQZmTDY8hbyJ65IdwXjYbULAHGRGRKVAIIYr/x66ZUqlUcHR0REZGBhwcHOQOh2SQrspFemaezja1RhQ9snR+PtynhqHaxnXSgYMHA8uWATasGyIiKk9P8/3NRKgITISozIQAFi0Cxo8HNBogMBDYsgVwc5M7MiIis/E039+sESLSB4UCGDsWiIgAqlYFjh8HWrcGTpyQOzIiIioBEyEiferSRUqCGjUCrl8Hnn8eWLJEemJERERGh4kQkb41aAAcOwa8+iqQnw+MGQMMGABkZckdGRERPcaoE6Hw8HC0adMG9vb2cHZ2Ru/evREfH1/iOWvWrIFCodBZbFi0SuXNwQH45Rdg3jzA0hLYuBFo0wb45x+5IyMiokcYdSJ04MABjB49GseOHcPevXuRn5+Pl156CdnZ2SWe5+DggNTUVO2SnJxcThETPUKhkIqno6KAWrWACxekZGjlSjaVEREZCaMeRygiIkJnfc2aNXB2dsapU6fwwgsvFHueQqGAq6urocMjKp127YDTp4GBA4G9e4G33wZ275YSourV5Y6OiMisGfUTocdlZGQAAKpVq1bicVlZWfD09ISHhwdeeeUV/POE5oi8vDyoVCqdhUivnJ2lHmVffglUqiR1rffzA/bvlzsyIiKzZjKJkEajwQcffIB27dqhadOmxR7n6+uL77//Htu2bcOPP/4IjUaDtm3b4tq1a8WeEx4eDkdHR+3i4eFhiFsgc2dhAUyaJBVS+/gA//4LdO4sbbt3T+7oiIjMkskMqDhq1Cjs3r0b0dHRqF27dqnPy8/PR6NGjdC/f398/vnnRR6Tl5eHvLyHowirVCp4eHhwQEUynOxsqX5o5UppvWFDYPVq4Lnn5I2LiMiEVdgBFceMGYMdO3Zg//79ZUqCAKBSpUrw9/fHpUuXij3G2toaDg4OOguRQdnZAStWAL//Lo0+feGCVEs0eTKQy4lbiYjKi1EnQkIIjBkzBlu2bMGff/6JunXrlvkaarUaZ8+ehRunOiBj1LMncO6cVEit0Ug1RP7+QHS03JEREZkFo06ERo8ejR9//BEbNmyAvb090tLSkJaWhnuP1FMMGjQIU6dO1a5/9tln2LNnDy5fvozTp09j4MCBSE5OxogRI+S4BaInq1YNWLcO2LYNcHWVng61by/1LrtzR+7oiIgqNKNOhJYuXYqMjAy8+OKLcHNz0y6bNm3SHpOSkoLU1FTt+p07dzBy5Eg0atQI3bt3h0qlwpEjR9C4cWM5boGo9F5+WRpwsSBpX7lSqh366SeOO0REZCAmUyxdnjj7PMnu0CHgnXeAuDhpvXNnYOFCgAk9EVGxKmyxNJHZad8eiI0F/vc/wNoaiIwEmjcHPvgAuHtX5uCIiCoOJkJExsrKCvj4Y+D8eaBPH0CtBhYskCZ1XblSWiciomfCRIjI2Hl7A7/9BuzZAzRqBNy8KRVSt2ghTdXB1m0ioqfGRIjIVHTpApw5A3zzDVC1qtTtvnt3IDhYmsuMiIjKjIkQkSmpVEmqE0pMBCZOlJrP/vwTaNUKGDAAuHhR7giJiEwKEyEiU1S1KvDVV0B8vDQYIyB1s2/UCBgyBLh8WdbwiIhMBRMhIlPm5SUNxvjXX9I4RBoNsHYt4Osr1RElJckdIRGRUWMiRFQRtGghjUx9/DgQEgI8eCD1LGvQABg06OF4REREpIOJEFFFEhAARERIc5W99JLUxX7dOqBJE+C114CYGLkjJCIyKkyEiCqidu2AP/4ATpyQxiASQuqC36YN8OKLwPbtUjMaEZGZYyJEVJG1aSMlQAUz3FtaAgcOAK+8Is1jtnQpkJ0td5RERLJhIkRkDpo0kZrIkpKAyZMBJycgIQF47z2gVi2pSz673hORGWIiRGROatcG5swBrl6VJnGtVw/IyJCm7vD1leqKtm4F8vPljpSIqFxw9vkicPZ5MgfpqlykZ9xDlQN/otralbCP3AMNFDhRuwlS3b1gG9ganq/3hNq7ns55zvbWcHawkSlqIqLiPc33NxOhIjARInPwzd6LWBCZoF2vnn0H95VWyLSx025zU/2Hfmf2IKWqKyJ82iLHqjLGdW6A8V185AiZiKhETIT0hIkQmYN0VS7SM/MAAEcu3cTs3RcKHaMQGgAKLN06Gy9dPYOM7r2AgW+haq+ugFJZzhETEZWMiZCeMBEic6LWCDz/xZ9Izcgtcr8CAq45dxG9eDCU4v+73Lu7A337Av36AYGBgEJRjhETERXtab6/WSxNZOZOJN0uNgkCAAEFUm2r4sS2KGDUKGmes+vXgfnzgaAgwNtb6okWEyONV0REZEKYCBGZufTM4pMgneM8vIFvvwVSU6UBGQcMAOzsgCtXgC+/lMYs8vKSuuIfPCiNak1EZOSYCBGZOWf70vUA0x5nbQ306gWsXw+kpwObNwOvvw7Y2gIpKVJX/A4dADc3YMgQ4NdfgcxMw90AEdEzYCJEZOYC6laDm6MNiqvyUQBwc7RBQN1qhXfa2kpJ0ObNwM2b0hhEgwZJzWf//QesXSvtr15dGqNowQIgPp5NaERkNFgsXQQWS5M5KE2vsQIfdWuItvVrACjlOEL5+cChQ8COHcDvvwOXLunu9/QEunYFQkKAjh2lka6JiJ4Re43pCRMhMgePjyNUWmUeR0gIafqO338HIiKkBOn+/Yf7LSyAVq2ATp2Azp2lCWNtbcscFxEREyE9YSJE5uDRJ0IF1BqBf65n4E5OPqraVkITd0coLXQbzZ55ZOnsbCAqCvjjD2DPHqmp7FGVKgEBAcALL0i1Rm3bAvb2T/95RGQ2mAjpCRMhonJ07Rrw559AZKS0/Puv7n6lEvDzkxKidu2kxcPjqT5KrRE4kXQb6Zm5cLaX6p4eT/SIyHQxEdITJkJEMhECuHwZOHBA6oJ/8CCQlFT4uFq1pIEcAwOlp0etWwNVqugc8vgTryOXbmLFocu4mfWwWa5GFSu83d5bW/8EcC41IlPGREhPmAgRGZFr14DDh4EjR6TX2NjCYxRZWAANG0q1Rv+/LL5th6+PXC/zx3EuNSLTxURIT5gIERmxrCzg1CngxAng+HHp9erVQocJhQL369ZDdpPm6No4FOmwAooZJKBGFSusGtwGSgsFnwgRmbCn+f62NHBMRET6VaWKVETdocPDbampUnL0yKK4fh3Wly/hdH5lpDe2LvGSN7PuI+e+GkH1qhs4eCIyNkyEiMj0ubkBPXtKS4EbN4AzZ5B+/DKQ/eRLpA8YDNhkSk1svr4PFy8vwJL/qySqqPjXTUQVk4sL8NJLcK53C1h57ImHO6emAFfPSgXaj6pUSZpYtkEDoH79h6/e3kCdOoCVlYFugIjKAxMhIqrQCqYQScvIRVEFkQoArg7WCPhtNXAhThrX6MIF6TUhAcjNld4/Pt4RIBVpe3hISZGXl+7i6Qm4u0uJFBEZLSZCRFQhPdp9fmhbr2KnEBEAhrarizi3GoBbfTi//PrDYmmNRppI9tKlh0tCApCYKHXzv3cPSE6WlqJYWEjJUJ060lK7tu5Sqxbg6vpUTW+yDYhJVMGw11gR2GuMyPQZfAoRIaQ6pMuXpcQoORm4ckVakpKknmz5+U++jkIhNeO5u0uLm5u0uLo+fHV2lo6xsyu/+yMyQew+rydMhIhMn+xPTDQaKVFKSZGSouRkadTsa9ceLtevFx4TqSR2dlJC5OyMvKrVke1YDerqNbDPyRtTH3j9/0GFhwgo86S5RCaKiZCeMBEionKhVgP//SclRAVLaiqQliYtBe9v3JBqlYq6hMICz7+7Cqn2NaSnS49RCA1c72Ug+vRyKKtVBapWBapVk14LFicn3cXRURqmoIjrERkzjiNERGRKlEqp6cvVFWjZsvjjhJAGkrxxQ1rS06UE6r//cOLWA6Ra1Sz+VIUFUm2r4kRKBoIOR5c+NgsLKSFydAQcHAov9vYPXwuWKlUevj662NlJ91pGsj/VMzDen3Hcn0kkQkuWLMFXX32FtLQ0+Pn5YdGiRQgICCj2+M2bN+PTTz/FlStX0KBBA3zxxRfo3r17OUZMRKRHCsXDZKN+fZ1d6bH/Ahtjn3iJ9I9nAhY3gdu3gTt3Hi537+q+ZmQADx5ITXsFx+iDjY2UED2+2NrqLpUra1/jrmZj7xUVci2tkGtphRRHV1xw9sJ9y4dDFig1alTJy4aFEMiztEKepRXGdGloEnVQ64+nVOg6L1O5P6NPhDZt2oSwsDAsW7YMgYGBmD9/PkJCQhAfHw9nZ+dCxx85cgT9+/dHeHg4evbsiQ0bNqB37944ffo0mjZtKsMdEBEZjrN96f7l7Bz8AlCakbOFkHrDZWRIyVFGBpCZKb2qVA/XMzOl9YL3mZnSU6uC99nZ0rpGI103N1dabt0q9b11+P8FACJ8gjCq90eFhkDQKBRQ2dhj6dbZ6HrxqHQLcy2kxMva+smLldXD18cXa2tp+AMrK93Xx98/vlhaFv/+kWVgQ0eEeLaAUFricIoKs/+4WOzP4vE6L1MQGlgHXRq7AJAmPS6u5yYg7/0ZfY1QYGAg2rRpg8WLFwMANBoNPDw88P7772PKlCmFju/Xrx+ys7OxY8cO7bbnnnsOLVq0wLJly0r1mawRIiJTodYIPP/FnyWPk+Rog+jJnQo1QRicEEBenpQQZWUBOTlSglSw5OQ8XAq23bv3cMnJAe7dg/peLp73fQupVkXXLSmEBq6ZtxC9bDiUQlO+96gHparzyrmL6G0fQ2lhISVSSqW0FLy3sHi47fGluH0F2x99ffx9Sdse3a5Q6G5/ZF2tsMDzN+siVWOJoor59fnfaIWrEbp//z5OnTqFqVOnardZWFggODgYR48eLfKco0ePIiwsTGdbSEgItm7dWuzn5OXlIS/vYTumSqV6tsCJiAysTOMktfVCXKr0/7Vyrb9QKKQnMzY2QI0aT32ZE4m3kFrC6OBCYYFUh5o4cS4FQW62UhKVlyctubkPX+/ff7g9L09aL9hW8Jqfr7s9P//htqJei1sePNB9zc+XiuML1h95BnGidhOkOjyhzsuuGk7ACUHJZ5/65yiXEx7NkDogvNj9AkBqRi5OJN2WZb4/o06Ebt68CbVaDRcXF53tLi4uuHCh6D/6tLS0Io9PS0sr9nPCw8Mxc+bMZw+YiKiclKX+4tEkyVTqSx6Vnll0j7lCx93Hw55wxk6t1iZG6WeuA9uKGLn8MekLvgXcKz08tyCxenS9qEWjKf59wfqjr48eI0Th7UIUv/7otv/fnm5du1Q/ktL+nvXNqBOh8jJ16lSdp0gqlQoeHh4yRkREVLJH6y8KlLZHjqkpdR1UKY8zCgXNU1ZWcHYuXeLm3LxR6eq8jIxzYinn+5Pp92fUiVCNGjWgVCpx48YNne03btyAq6trkee4urqW6XgAsLa2hrW16f3PgYjMl7ODTZFNXH4eTuUfjIGVar44RxsE1K1W3qHpBe9P3vuzkOVTS8nKygqtWrVCZGSkdptGo0FkZCSCgoKKPCcoKEjneADYu3dvsccTEZFxSlfl4ty/GYhLVWFoW68iv0QB3Tqoc/9mIF0lTxNLWfH+JHLfn1E/EQKAsLAwDB48GK1bt0ZAQADmz5+P7OxsDB06FAAwaNAg1KpVC+HhUiHWuHHj0KFDB8ydOxc9evTAxo0bERMTgxUrVsh5G0REVEYVvQ6K9/eQnPdn9IlQv3798N9//2HatGlIS0tDixYtEBERoS2ITklJgYXFwwdbbdu2xYYNG/DJJ5/go48+QoMGDbB161aOIUREZGIqeh0U78847s/oxxGSA8cRIiIiMj1P8/1t1DVCRERERIbERIiIiIjMFhMhIiIiMltMhIiIiMhsMREiIiIis8VEiIiIiMwWEyEiIiIyW0yEiIiIyGwxESIiIiKzxUSIiIiIzBYTISIiIjJbTISIiIjIbDERIiIiIrNlKXcAxkgIAUCaxZaIiIhMQ8H3dsH3eGkwESpCZmYmAMDDw0PmSIiIiKisMjMz4ejoWKpjFaIsaZOZ0Gg0uH79Ouzt7aFQKPR6bZVKBQ8PD1y9ehUODg56vbaxMZd7NZf7BHivFRXvtWIyx3tNSUmBQqGAu7s7LCxKV/3DJ0JFsLCwQO3atQ36GQ4ODhX+P8wC5nKv5nKfAO+1ouK9VkzmdK+Ojo5lvlcWSxMREZHZYiJEREREZouJUDmztrbG9OnTYW1tLXcoBmcu92ou9wnwXisq3mvFxHstHRZLExERkdniEyEiIiIyW0yEiIiIyGwxESIiIiKzxUSIiIiIzBYToXK0ZMkSeHl5wcbGBoGBgThx4oTcIRnEwYMH0atXL7i7u0OhUGDr1q1yh2QQ4eHhaNOmDezt7eHs7IzevXsjPj5e7rAMYunSpWjevLl2YLagoCDs3r1b7rDKxZw5c6BQKPDBBx/IHYrezZgxAwqFQmdp2LCh3GEZzL///ouBAweievXqqFy5Mpo1a4aYmBi5w9I7Ly+vQr9XhUKB0aNHyx2a3qnVanz66aeoW7cuKleujHr16uHzzz8v01xjTITKyaZNmxAWFobp06fj9OnT8PPzQ0hICNLT0+UOTe+ys7Ph5+eHJUuWyB2KQR04cACjR4/GsWPHsHfvXuTn5+Oll15Cdna23KHpXe3atTFnzhycOnUKMTEx6NSpE1555RX8888/codmUCdPnsTy5cvRvHlzuUMxmCZNmiA1NVW7REdHyx2SQdy5cwft2rVDpUqVsHv3bpw/fx5z585F1apV5Q5N706ePKnzO927dy8A4I033pA5Mv374osvsHTpUixevBhxcXH44osv8OWXX2LRokWlv4igchEQECBGjx6tXVer1cLd3V2Eh4fLGJXhARBbtmyRO4xykZ6eLgCIAwcOyB1Kuahatar47rvv5A7DYDIzM0WDBg3E3r17RYcOHcS4cePkDknvpk+fLvz8/OQOo1xMnjxZPP/883KHIYtx48aJevXqCY1GI3coetejRw8xbNgwnW2vvvqqCA0NLfU1+ESoHNy/fx+nTp1CcHCwdpuFhQWCg4Nx9OhRGSMjfcrIyAAAVKtWTeZIDEutVmPjxo3Izs5GUFCQ3OEYzOjRo9GjRw+dv9uKKCEhAe7u7vD29kZoaChSUlLkDskgtm/fjtatW+ONN96As7Mz/P39sXLlSrnDMrj79+/jxx9/xLBhw/Q+ibgxaNu2LSIjI3Hx4kUAwJkzZxAdHY1u3bqV+hqcdLUc3Lx5E2q1Gi4uLjrbXVxccOHCBZmiIn3SaDT44IMP0K5dOzRt2lTucAzi7NmzCAoKQm5uLqpUqYItW7agcePGcodlEBs3bsTp06dx8uRJuUMxqMDAQKxZswa+vr5ITU3FzJkz0b59e5w7dw729vZyh6dXly9fxtKlSxEWFoaPPvoIJ0+exNixY2FlZYXBgwfLHZ7BbN26FXfv3sWQIUPkDsUgpkyZApVKhYYNG0KpVEKtVmPWrFkIDQ0t9TWYCBHpwejRo3Hu3LkKW18BAL6+voiNjUVGRgZ++eUXDB48GAcOHKhwydDVq1cxbtw47N27FzY2NnKHY1CP/qu5efPmCAwMhKenJ37++WcMHz5cxsj0T6PRoHXr1pg9ezYAwN/fH+fOncOyZcsqdCK0atUqdOvWDe7u7nKHYhA///wz1q9fjw0bNqBJkyaIjY3FBx98AHd391L/XpkIlYMaNWpAqVTixo0bOttv3LgBV1dXmaIifRkzZgx27NiBgwcPonbt2nKHYzBWVlaoX78+AKBVq1Y4efIkFixYgOXLl8scmX6dOnUK6enpaNmypXabWq3GwYMHsXjxYuTl5UGpVMoYoeE4OTnBx8cHly5dkjsUvXNzcyuUtDdq1Ai//vqrTBEZXnJyMvbt24fffvtN7lAMZtKkSZgyZQrefPNNAECzZs2QnJyM8PDwUidCrBEqB1ZWVmjVqhUiIyO12zQaDSIjIyt0jUVFJ4TAmDFjsGXLFvz555+oW7eu3CGVK41Gg7y8PLnD0LvOnTvj7NmziI2N1S6tW7dGaGgoYmNjK2wSBABZWVlITEyEm5ub3KHoXbt27QoNb3Hx4kV4enrKFJHhrV69Gs7OzujRo4fcoRhMTk4OLCx0UxmlUgmNRlPqa/CJUDkJCwvD4MGD0bp1awQEBGD+/PnIzs7G0KFD5Q5N77KysnT+RZmUlITY2FhUq1YNderUkTEy/Ro9ejQ2bNiAbdu2wd7eHmlpaQAAR0dHVK5cWebo9Gvq1Kno1q0b6tSpg8zMTGzYsAFRUVH4448/5A5N7+zt7QvVednZ2aF69eoVrv5r4sSJ6NWrFzw9PXH9+nVMnz4dSqUS/fv3lzs0vRs/fjzatm2L2bNno2/fvjhx4gRWrFiBFStWyB2aQWg0GqxevRqDBw+GpWXF/arv1asXZs2ahTp16qBJkyb466+/MG/ePAwbNqz0F9FzTzYqwaJFi0SdOnWElZWVCAgIEMeOHZM7JIPYv3+/AFBoGTx4sNyh6VVR9whArF69Wu7Q9G7YsGHC09NTWFlZiZo1a4rOnTuLPXv2yB1Wuamo3ef79esn3NzchJWVlahVq5bo16+fuHTpktxhGczvv/8umjZtKqytrUXDhg3FihUr5A7JYP744w8BQMTHx8sdikGpVCoxbtw4UadOHWFjYyO8vb3Fxx9/LPLy8kp9DYUQZRh+kYiIiKgCYY0QERERmS0mQkRERGS2mAgRERGR2WIiRERERGaLiRARERGZLSZCREREZLaYCBEREZHZYiJEREREZouJEBHJbsiQIejdu7fcYTyTyMhINGrUCGq1+onHRkREoEWLFmWaD4mIDIOJEBEZlEKhKHGZMWMGFixYgDVr1sgd6jP58MMP8cknn5RqUtauXbuiUqVKWL9+fTlERkQl4RQbRGRQBZPRAsCmTZswbdo0nVnAq1SpgipVqsgRmt5ER0ejZ8+eSEtLg42NTanOWbJkCdasWYOTJ08aODoiKgmfCBGRQbm6umoXR0dHKBQKnW1VqlQp1DSm0WgQHh6OunXronLlyvDz88Mvv/yi3R8VFQWFQoE//vgD/v7+qFy5Mjp16oT09HTs3r0bjRo1goODAwYMGICcnBzteS+++CLGjBmDMWPGwNHRETVq1MCnn36KR/89eOfOHQwaNAhVq1aFra0tunXrhoSEhBLvcePGjejSpYtOEnTmzBl07NgR9vb2cHBwQKtWrRATE6Pd36tXL8TExCAxMfFZfrxE9IyYCBGR0QkPD8cPP/yAZcuW4Z9//sH48eMxcOBAHDhwQOe4GTNmYPHixThy5AiuXr2Kvn37Yv78+diwYQN27tyJPXv2YNGiRTrnrF27FpaWljhx4gQWLFiAefPm4bvvvtPuHzJkCGJiYrB9+3YcPXoUQgh0794d+fn5xcZ76NAhtG7dWmdbaGgoateujZMnT+LUqVOYMmUKKlWqpN1fp04duLi44NChQ8/yoyKiZ2QpdwBERI/Ky8vD7NmzsW/fPgQFBQEAvL29ER0djeXLl6NDhw7aY//3v/+hXbt2AIDhw4dj6tSpSExMhLe3NwDg9ddfx/79+zF58mTtOR4eHvjmm2+gUCjg6+uLs2fP4ptvvsHIkSORkJCA7du34/Dhw2jbti0AYP369fDw8MDWrVvxxhtvFBlzcnIy3N3ddbalpKRg0qRJaNiwIQCgQYMGhc5zd3dHcnLy0/6oiEgP+ESIiIzKpUuXkJOTgy5dumjrh6pUqYIffvihUDNS8+bNte9dXFxga2urTYIKtqWnp+uc89xzz0GhUGjXg4KCkJCQALVajbi4OFhaWiIwMFC7v3r16vD19UVcXFyxMd+7d69QbVBYWBhGjBiB4OBgzJkzp8gmsMqVK+s03RFR+eMTISIyKllZWQCAnTt3olatWjr7rK2tddYfbWpSKBQ66wXbyqOLeo0aNXDnzh2dbTNmzMCAAQOwc+dO7N69G9OnT8fGjRvRp08f7TG3b99GzZo1DR4fERWPT4SIyKg0btwY1tbWSElJQf369XUWDw+PZ77+8ePHddaPHTuGBg0aQKlUolGjRnjw4IHOMbdu3UJ8fDwaN25c7DX9/f1x/vz5Qtt9fHwwfvx47NmzB6+++ipWr16t3Zebm4vExET4+/s/8z0R0dNjIkRERsXe3h4TJ07E+PHjsXbtWiQmJuL06dNYtGgR1q5d+8zXT0lJQVhYGOLj4/HTTz9h0aJFGDduHACpjueVV17ByJEjER0djTNnzmDgwIGoVasWXnnllWKvGRISgujoaO36vXv3MGbMGERFRSE5ORmHDx/GyZMn0ahRI+0xx44dg7W1tbYOiojkwaYxIjI6n3/+OWrWrInw8HBcvnwZTk5OaNmyJT766KNnvvagQYNw7949BAQEQKlUYty4cXj77be1+1evXo1x48ahZ8+euH//Pl544QXs2rWrULPbo0JDQ/Hhhx8iPj4evr6+UCqVuHXrFgYNGoQbN26gRo0aePXVVzFz5kztOT/99BNCQ0Nha2v7zPdERE+PAyoSkdl48cUX0aJFC8yfP1/v1540aRJUKhWWL1/+xGNv3rwJX19fxMTEoG7dunqPhYhKj01jRER68PHHH8PT07NUxdlXrlzBt99+yySIyAjwiRARmQ1DPhEiItPERIiIiIjMFpvGiIiIyGwxESIiIiKzxUSIiIiIzBYTISIiIjJbTISIiIjIbDERIiIiIrPFRIiIiIjMFhMhIiIiMlv/B4WrBzMhTQkJAAAAAElFTkSuQmCC", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "[0.0, 1.886, 3.772, 5.6579999999999995, 7.544, 9.43, 11.315999999999999, 13.202, 15.088]\n", "θ0 ajustado: 17.078115878268544\n", "γ ajustado: 0.40263122048564876 $s^-1$\n", "Incertidumbre de γ: 0.0076995912617920746 $s^-1$\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "[0.0, 3.684285714285714, 7.368571428571428, 11.052857142857142, 14.737142857142857, 18.42142857142857, 22.105714285714285, 25.79, 29.474285714285713]\n", "θ0 ajustado: 17.274602573018925\n", "γ ajustado: 0.1250925454288796 $s^-1$\n", "Incertidumbre de γ: 0.004721913706016345 $s^-1$\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Lord_Fulgi\\AppData\\Local\\Temp\\ipykernel_17916\\1491198034.py:2: RuntimeWarning: overflow encountered in exp\n", " return theta_0 * np.exp(-gamma * t) # Ecuación para máximo\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "[0.0, 12.194782608695652, 24.389565217391304, 36.584347826086955, 48.77913043478261, 60.97391304347826, 73.16869565217391, 85.36347826086957, 97.55826086956522]\n", "θ0 ajustado: 18.248221602690556\n", "γ ajustado: 0.023750522131842235 $s^-1$\n", "Incertidumbre de γ: 0.0023672258730366197 $s^-1$\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "[1.3355298935311044, 0.8697729058275465, 0.40263122048564876, 0.1250925454288796, 0.023750522131842235]\n", "[0.01748394521511842, 0.012213372700198028, 0.0076995912617920746, 0.004721913706016345, 0.0023672258730366197]\n" ] } ], "source": [ "def exponencial(t, theta_0, gamma):\n", " return theta_0 * np.exp(-gamma * t) # Ecuación para máximo\n", "\n", "# Vamos a guardar los gamma y sus incertidumbres en listas\n", "gamma_valores = []\n", "gamma_incertidumbres = []\n", "\n", "for r in range(len(tetha)):\n", "\n", " if r <= 1:\n", " tiempo = [periodo_de_oscilacion_n[r]*1/2*k for k in n]\n", " elif r == 2:\n", " tiempo = [periodo_de_oscilacion_n[r]*1*k for k in n]\n", " elif r == 3:\n", " tiempo = [periodo_de_oscilacion_n[r]*2*k for k in n]\n", " elif r == 4:\n", " tiempo = [periodo_de_oscilacion_n[r]*8*k for k in n]\n", " else:\n", " break\n", " \n", " print(tiempo)\n", " amplitud = tetha[r]\n", "\n", " # Ajustar la curva\n", " popt, pcov = so.curve_fit(exponencial, tiempo, amplitud)\n", "\n", " # popt contiene los valores ajustados de theta_0 y gamma\n", " theta_0_ajustado, gamma_ajustado = popt\n", " incertidumbre_gamma = np.sqrt(pcov[1, 1])\n", " \n", " # Almacenar los valores de gamma y su incertidumbre en las listas\n", " gamma_valores.append(gamma_ajustado)\n", " gamma_incertidumbres.append(incertidumbre_gamma)\n", "\n", " \n", " print(f\"θ0 ajustado: {theta_0_ajustado}\")\n", " print(f\"γ ajustado: {gamma_ajustado} $s^{-1}$\")\n", " print(f\"Incertidumbre de γ: {incertidumbre_gamma} $s^{-1}$\")\n", "\n", " # Crear un rango de tiempos para graficar la curva ajustada\n", " tiempo_fit = np.linspace(0, max(tiempo), 100)\n", "\n", " # Calcular los valores ajustados de la función exponencial\n", " amplitud_fit = exponencial(tiempo_fit, *popt)\n", "\n", " # Definir la incerteza constante\n", " incerteza = 0.2\n", "\n", " # Graficar los datos experimentales con barras de error y la curva ajustada\n", " plt.clf()\n", " plt.errorbar(tiempo, amplitud, yerr=incerteza, fmt='o', label=\"Datos experimentales\", capsize=5)\n", " plt.plot(tiempo_fit, amplitud_fit, label=\"Ajuste exponencial\", color=\"red\")\n", " plt.xlabel(\"Tiempo (s)\")\n", " plt.ylabel(\"Amplitud (u)\")\n", " plt.legend()\n", " plt.show()\n", "print(gamma_valores)\n", "print(gamma_incertidumbres)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### (1) Representar Datos en tabla ordenada" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hacerlo directamente en el documento de Latex. Una tabla representará:$$|Intensidad|w_i|s(w_i)|$$ Y la otra: $$|Intensidad|gamma_i|s(gamma_i)|$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### (2) Presentar $w_i$ frente a $I$ y $w_0^2-\\gamma^2$ frente a $I$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Primero obtenemos los $w_i$ y los $s(w_i)$ correspondientes a sus Intensidades" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "w_11 = media_con_exclusion(np.mean(s_w_1[0:3]),w_1[0:3])[0]; s_w_11 = media_con_exclusion(np.mean(s_w_1[0:3]),w_1[0:3])[1]\n", "w_09 = media_con_exclusion(np.mean(s_w_1[3:6]),w_1[3:6])[0]; s_w_09 = media_con_exclusion(np.mean(s_w_1[3:6]),w_1[3:6])[1]\n", "w_06 = media_con_exclusion(np.mean(s_w_1[6:9]),w_1[6:9])[0]; s_w_06 = media_con_exclusion(np.mean(s_w_1[6:9]),w_1[6:9])[1]\n", "w_03 = media_con_exclusion(np.mean(s_w_1[9:12]),w_1[9:12])[0]; s_w_03 = media_con_exclusion(np.mean(s_w_1[9:12]),w_1[9:12])[1]\n", "w_00 = media_con_exclusion(np.mean(s_w_1[12:15]),w_1[12:15])[0]; s_w_00 = media_con_exclusion(np.mean(s_w_1[12:15]),w_1[12:15])[1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ahora buscamos los $w_i^2$ y sus $s(w_i^2)$\n", "\n", "También separamos para más tarde $w_0^2$ y $s(w_0^2)$" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[1.1, 0.9, 0.6, 0.3, 0.0]" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Los w^2 para cada I y sus respectivas incertidumbres\n", "w_cuadrados = [w_11**2,w_09**2,w_06**2,w_03**2,w_00**2]\n", "s_w_cuadrados = [2*w_11*s_w_11, 2*w_09*s_w_09, 2*w_06*s_w_06, 2*w_03*s_w_03, 2*w_00*s_w_00]\n", "\n", "# w^2 para cuando la intensidad es cero (y su incertidumbre)\n", "wo_cuadrado = w_cuadrados[4]\n", "s_wo_cuadrado = s_w_cuadrados[4]\n", "\n", "# Los valores de intensidad, que son 5: [1.1, 0.9, 0.6, 0.3, 0.0]\n", "intensidad_relacion_amplitud" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Vamos a buscar los $w_0^2-\\gamma_i^2$ y los $s(w_0^2-\\gamma_i^2)$" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [], "source": [ "w0_menos_gamma = [wo_cuadrado-i**2 for i in gamma_valores]\n", "\n", "s_wo_menos_gamma = []\n", "for i in range(len(gamma_incertidumbres)):\n", " inc = sqrt(s_w_cuadrados[i]**2+4*gamma_valores[i]**2*gamma_incertidumbres[i]**2)\n", " s_wo_menos_gamma.append(inc)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Graficamos lo anterior frente a $I$ con barras de incerteza" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "<>:9: SyntaxWarning: invalid escape sequence '\\g'\n", "<>:13: SyntaxWarning: invalid escape sequence '\\g'\n", "<>:14: SyntaxWarning: invalid escape sequence '\\g'\n", "<>:9: SyntaxWarning: invalid escape sequence '\\g'\n", "<>:13: SyntaxWarning: invalid escape sequence '\\g'\n", "<>:14: SyntaxWarning: invalid escape sequence '\\g'\n", "C:\\Users\\Lord_Fulgi\\AppData\\Local\\Temp\\ipykernel_17916\\1928798685.py:9: SyntaxWarning: invalid escape sequence '\\g'\n", " label='$w_0^2 - \\gamma^2$ con incerteza', color='red', capsize=5)\n", "C:\\Users\\Lord_Fulgi\\AppData\\Local\\Temp\\ipykernel_17916\\1928798685.py:13: SyntaxWarning: invalid escape sequence '\\g'\n", " plt.ylabel(\"$w^2$ y $w_0^2 - \\gamma^2$ $(s^{-2})$\")\n", "C:\\Users\\Lord_Fulgi\\AppData\\Local\\Temp\\ipykernel_17916\\1928798685.py:14: SyntaxWarning: invalid escape sequence '\\g'\n", " plt.title(\"Gráfica de $w^2$ y $w_0^2 - \\gamma^2$ frente a I\")\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10, 6))\n", "\n", "# Graficar w^2 con barras de error\n", "plt.errorbar(intensidad_relacion_amplitud, w_cuadrados, yerr=s_w_cuadrados, fmt='o', \n", " label='$w^2$ con incerteza', color='blue', capsize=5)\n", "\n", "# Graficar w0^2 - gamma^2 con barras de error\n", "plt.errorbar(intensidad_relacion_amplitud, w0_menos_gamma, yerr=s_wo_menos_gamma, fmt='o', \n", " label='$w_0^2 - \\gamma^2$ con incerteza', color='red', capsize=5)\n", "\n", "# Configuración de la gráfica\n", "plt.xlabel(\"Intensidad (A)\")\n", "plt.ylabel(\"$w^2$ y $w_0^2 - \\gamma^2$ $(s^{-2})$\")\n", "plt.title(\"Gráfica de $w^2$ y $w_0^2 - \\gamma^2$ frente a I\")\n", "plt.legend()\n", "plt.grid(True)\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### (3) Extrapolar coeficiente de fricción crítico $(\\gamma_c)$ mediante ajuste de $\\gamma(I)$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ver folio (teoria)\n", "\n", "Partiendo de $w_i^2=w_0^2-\\gamma_i^2$, si $w_i=0$, entonces $\\gamma_i=\\gamma_c=w_0$ que es el coeficiente de rozamiento crítico.\n", "\n", "Buscamos situar $\\gamma=\\gamma(I)$ y mostrar sus puntos ($\\gamma$ frente a $I$) para ajustarlos por una recta del tipo $\\gamma=a+b·I+c·I^2+d·I^3$. Tras ello, como $\\gamma_c=w_0$, encontrar tal coeficiente de rozamiento crítico ya que para $w_0$ su Intensidad es nula, por tanto, en teoría, tendriamos que, despejando de tal ecuación ajustada, $\\gamma_c=a$" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1.3355298935311044, 0.8697729058275465, 0.40263122048564876, 0.1250925454288796, 0.023750522131842235]\n", "[0.01748394521511842, 0.012213372700198028, 0.0076995912617920746, 0.004721913706016345, 0.0023672258730366197]\n", "[1.1, 0.9, 0.6, 0.3, 0.0]\n", "1.22458175911713*pi\n", "0.13768714721709452\n" ] } ], "source": [ "print(gamma_valores)\n", "print(gamma_incertidumbres)\n", "print(intensidad_relacion_amplitud)\n", "print(w_00)\n", "print(s_w_00)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ajustamos los $\\gamma$ frente a $I$ mediante un polinomio de grado 3 $(\\gamma=a+b·I+c·I^2+d·I^3)$" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Función ajustada: γ(I) = 0.0229 + 0.1364*I + 0.6334*I^2 + 0.2963*I^3\n" ] }, { "data": { "image/png": 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2YeLfVadOnQzMZj1GlixZjHz58hnBwcHGlClTDKvVarft559/bgQGBhre3t5Go0aNjB9//NGu8axhGEaPHj2MPHnyGIARGhpqGIZhXLp0yejQoYPh6+tr2/fQoUO2fXr37m0UL17c8PT0NPLly2d06NDBrgHwneLi4owhQ4YYRYsWNbJmzWoEBgYarVq1Mnbv3m3s37/f8Pb2tmsAe/nyZSMoKMh47733DMO43fD5//7v/4xHHnnE8PT0NIKDg42TJ0/avc/SpUuNOnXqGN7e3kbOnDmNmjVrGhMnTrS9Dhjz5s2z2yepBsV3NrK+s8HurZ/Dfxv/3usz3u248+bNM/77q3nu3DmjYcOGRvbs2Q3A+PPPPw3DMIx3333XyJMnj5E9e3ajdevWxhdffGF3rKTyzZgxw6hcubLh4eFh+Pn5GfXq1TPmzp1rGIZhTJw40ahcubKRLVs2I2fOnMbTTz9tbN++3cjo1KBY5CEsWWIYYBhuboaxY4dT38qRBsUWw7jPzf0MJioqCl9fXyIjIxM1vrxx4wbHjx+nWLFidu0xHJWWhqDOyKZNm8Zbb71lG4tG5EGk1O+9SKZz7RpUqADHj5tXbz7/3Klvd6/v7zvptpQTNK4QSMNy+dPE5GEiIiJOMXy4WdgUKgTDhrk6jR0VN07i7mZJ1e7eIiIiqWbPHvjsM/P5119DjhyuzXMHjVAs6Vbnzp11S0pEJLUlJMBrr0F8PLRqBf/rVJKWqLgRERGR5JswAf7+27xaM26cq9MkScWNiIiIJM/ZszBwoPl85EgoWNC1ee5CxY2IiIgkT9++EBUFNWtCz56uTnNXKm5ERETk/n7/HebMAXd3mDjR/DONUnEjIiIi9xYdfftKTUgIVKrk2jz3oeJG5C62bt3KF198YZsIU0Qk0/rgA/j3XyhWDIYOdXWa+1Jxk4mcOHECi8XCzp07XR3FKTp37kzLli1ty0899RRvvfXWAx3r/PnzvPTSS1SoUME2weiDctV5f5jP7yrpMbNIhvf33+ZYNgDffQc+Pq7NkwwqbjKYjRs34u7unuSkjEFBQYSFhVGhQoUUea9p06al6UkV586dy/Dhwx3eLyEhgQ4dOhAaGkrDhg0fOkdKn3e5t9dff53ixYvj7e1Nvnz5aNGiBQcOHHB1LJH06eZN6N4dDAM6dIAU+DcxNai4yWAmT57Mm2++ydq1azl79qzda+7u7uTPn58sWTLHwNS5c+cmxwOMmunm5sbSpUvp3LlziuTIbOf95s2bLn3/atWqMXXqVPbv38+yZcswDINnnnkGq9Xq0lwi6dLo0bB3L+TN6/S5o1KSipsMJCYmhtmzZ9OzZ0+effZZpk2bZvf6nbdHkrryMn/+fCyW23Ng7dq1i/r165MjRw5y5sxJtWrV2Lp1K6tXr6ZLly5ERkZisViwWCwM/d992NjYWN555x0KFixItmzZqFWrFqtXr75ndovFwrfffkuTJk3w9vbmkUceYc6cOXbb7NmzhwYNGuDt7U2ePHl47bXXiImJuesx77zFUbRoUUaOHEnXrl3JkSMHhQsXZuLEiQ69x61bXyNHjiQgIIBcuXLx4YcfEh8fz7vvvkvu3LkpVKgQU6dOvet5B/jnn39o1qwZOXPmJEeOHDzxxBMcPXoUMK8cffjhhxQqVAhPT08qV67M0qVL73n+rl69SseOHcmePTuBgYGMGTMm0TYP8nM5cOAAjz/+OF5eXpQrV44//vgDi8XC/Pnz7T7b7NmzefLJJ/Hy8mLGjBlcvHiRtm3bUrBgQXx8fKhYsSIzZ850OPPly5fp2LEjfn5++Pj40KRJEw4fPnzPzK+99hr16tWjaNGiVK1alREjRnD69GlOnDhxz/1E5A6HDsGHH5rPv/jCLHDSCRU392MYcPWqax4OTtj+yy+/UKZMGUqXLs0rr7zClClTeNhJ39u3b0+hQoXYsmUL27ZtY8CAAWTNmpU6deowduxYcubMSVhYGGFhYbzzzjsA9O7dm40bNzJr1ix2797NSy+9ROPGje/7pTR48GBeeOEFdu3aRfv27WnTpg379+8HzC/CRo0a4efnx5YtW/j111/5448/6N27t0OfZ8yYMVSvXp0dO3bwxhtv0LNnTw4ePOjQe6xatYqzZ8+ydu1aPv/8c0JDQ2nWrBl+fn5s2rSJHj168Prrr/Pvv/8mmeHMmTPUq1cPT09PVq1axbZt2+jatSvx8fEAfPnll4wZM4bPPvuM3bt306hRI5577rl7nr93332XNWvWsGDBApYvX87q1avZvn273TaO/lysVistW7bEx8eHTZs2MXHiRD744IMktx0wYAB9+/Zl//79NGrUiBs3blCtWjUWLVrE3r17ee211+jQoQObN292KHPnzp3ZunUrCxcuZOPGjRiGQdOmTZN9dejq1atMnTqVYsWKERQUlKx9RATz++f11yE2Fho1gvbtXZ3IMUYmExkZaQBGZGRkoteuX79u7Nu3z7h+/frtlTExhmH+mFP/ERPj0GerU6eOMXbsWMMwDOPmzZtG3rx5jT///NP2+vHjxw3A2LFjh2EYhjF16lTD19fX7hjz5s0z/vvXIkeOHMa0adOSfL+k9j958qTh7u5unDlzxm79008/bQwcOPCu2QGjR48edutq1apl9OzZ0zAMw5g4caLh5+dnxPznnCxatMhwc3MzwsPDDcMwjE6dOhktWrSwvf7kk08affv2tS0XKVLEeOWVV2zLCQkJhr+/v/Htt9869B5FihQxrFarbZvSpUsbTzzxhG05Pj7eyJYtmzFz5kzDMBKf94EDBxrFihUz4uLikjwXBQoUMD766CO7dTVq1DDeeOONJLePjo42PDw8jF9++cW27uLFi4a3t7ft8z/Iz2XJkiVGlixZjLCwMNu6FStWGIAxb948u8926+/dvTz77LNGv379kp350KFDBmCsX7/ets2FCxcMb29vu/2SMn78eCNbtmwGYJQuXdo4cuTIXbdN8vdeJLObONH8HvL2Noxjx1ydxjCMe39/30lXbjKIgwcPsnnzZtq2bQtAlixZaN26NZMnT36o44aEhPDqq68SHBzMxx9/bLt1cjd79uzBarVSqlQpsmfPbnusWbPmvvvWrl070fKtKzf79++nUqVKZMuWzfZ63bp1SUhIsF15SY5HH33U9txisZA/f37OnTvn0HuUL1/ergdVQEAAFStWtC27u7uTJ08e23HvtHPnTp544gmyZs2a6LWoqCjOnj1L3bp17dbXrVvXdi7udPToUeLi4qhVq5ZtXe7cuSldurRt+UF+LgcPHiQoKIj8+fPb1tWsWTPJbatXr263bLVaGT58OBUrViR37txkz56dZcuWcerUqWRn3r9/P1myZLHbJk+ePJQuXfqu5+KW9u3bs2PHDtasWUOpUqV4+eWXuXHjxj33EZH/OXsW3n3XfP7RR2b373Qmc7RwfBg+PnCPdh1Of+9kmjx5MvHx8RQoUMC2zjAMPD09+frrr/H19U20j5ubW6LbVnde7h86dCjt2rVj0aJFLFmyhNDQUGbNmkWrVq2SzBETE4O7uzvbtm3D/Y7RK7Nnz57sz+MsdxYUFovF4XFskjqGI8f19vZ26P1SgrN/Lv8tCAFGjx7Nl19+ydixY6lYsSLZsmXjrbfeIi4u7qHfKzl8fX3x9fWlZMmSPPbYY/j5+TFv3jxb8S8id2EY0KsXREaaUyz06ePqRA9EV27ux2KBbNlc8/hPw957iY+P58cff2TMmDHs3LnT9ti1axcFChRI1JDzlnz58hEdHc3Vq1dt65Iai6VUqVK8/fbbLF++nOeff97WWNbDwyNRD5QqVapgtVo5d+4cJUqUsHv89wpAUv7+++9Ey2XLlgWgbNmy7Nq1yy7r+vXrcXNzs/vf/sNIjfcA8+rRunXrkmw3kjNnTgoUKMD69evt1q9fv55y5colebzixYuTNWtWNm3aZFt3+fJlDh06ZFt+kJ9L6dKlOX36NBEREbZ1W7ZsSdZnXL9+PS1atOCVV16hUqVKPPLII3Z5kpO5bNmyxMfH221z8eJFDh48eNdzkRTDMDAMg9jY2GTvI5Jp/d//wfz5kCULfP99mp5i4V5U3GQAv//+O5cvX6Zbt25UqFDB7vHCCy/c9dZUrVq18PHx4f333+fo0aP8/PPPdj2srl+/Tu/evVm9ejUnT55k/fr1bNmyxVZwFC1alJiYGFauXMmFCxe4du0apUqVon379nTs2JG5c+dy/PhxNm/ezKhRo1i0aNE9P8evv/7KlClTOHToEKGhoWzevNnWmLd9+/Z4eXnRqVMn9u7dy59//smbb75Jhw4dCAgISJHzmBrvAWbD3qioKNq0acPWrVs5fPgwP/30k+3W17vvvssnn3zC7NmzOXjwIAMGDGDnzp307ds3yeNlz56dbt268e6777Jq1Sr27t1L586d7W6dPcjPpWHDhhQvXpxOnTqxe/du1q9fz6BBgwDsetQlpWTJkqxYsYINGzawf/9+Xn/9dbsiKTmZS5YsSYsWLejevTt//fUXu3bt4pVXXqFgwYK0aNEiyfc9duwYo0aNYtu2bZw6dYoNGzbw0ksv4e3tTdOmTe+ZWSTTu3QJbnWgGDgQ/nO7Pb1RcZMBTJ48meDg4CRvPb3wwgts3bqV3bt3J3otd+7cTJ8+ncWLF9u66g79z7Da7u7uXLx4kY4dO9raLTRp0oRhw4YBUKdOHXr06EHr1q3Jly8fn376KQBTp06lY8eO9OvXj9KlS9OyZUu2bNlC4cKF7/k5hg0bxqxZs3j00Uf58ccfmTlzpu1/6D4+PixbtoxLly5Ro0YNXnzxRZ5++mm+vjVqZgpIjfcAs93IqlWriImJ4cknn6RatWpMmjTJdmurT58+hISE0K9fPypWrMjSpUtZuHAhJUuWvOsxR48ezRNPPEHz5s0JDg7m8ccfp1q1anbbOPpzcXd3Z/78+cTExFCjRg1effVVW28pLy+ve37GQYMGUbVqVRo1asRTTz1F/vz57UaPdiRztWrVaNasGbVr18YwDBYvXpxke6VbudatW0fTpk0pUaIErVu3JkeOHGzYsAF/f/97ZhbJ9Pr1g4gIKFvWnG4hHbMYdza6yOCioqLw9fUlMjKSnDlz2r1248YNjh8/TrFixe77j3d6dPDgQcqUKcPhw4cpUaKEq+PYsVgszJs3L9EXoKQt69ev5/HHH+fIkSMUL17c1XFSREb/vRdJlhUr4JlnzOYQ69fDHR080oJ7fX/fSQ2KM4lLly4xZ84ccubMqfE+JNnmzZtH9uzZKVmyJEeOHKFv377UrVs3wxQ2IoLZaaZ7d/N5795psrBxlEtvS61du5bmzZtToEABu1FP72bu3Lk0bNiQfPnykTNnTmrXrs2yZctSJ2w6161bN7777ju+/fZbPD09XR1H0ono6Gh69epFmTJl6Ny5MzVq1GDBggWujiUiKen99+HkSShSBEaOdHWaFOHSKzdXr16lUqVKdO3aleeff/6+269du5aGDRsycuRIcuXKxdSpU2nevDmbNm2iSpUqqZA4/Zo3b56rI9xTJrs7mm507NiRjh07ujqGiDjLX3/BuHHm8++/hzQwZEdKcGlx06RJE5o0aZLs7ceOHWu3PHLkSBYsWMBvv/2m4kZERMQR169Dt27m827dIDjYtXlSULruLZWQkEB0dDS5c+d2dRQREZH0ZehQc3LMAgXgs89cnSZFpesGxZ999hkxMTG8/PLLd90mNjbWbvCuqKio+x5Xt0hEMg/9vkumtGXL7YJmwgTIlculcVJaur1y8/PPPzNs2DB++eWXe45fMWrUKNtQ7L6+vvfsKXRr7Ixr166leF4RSZtu/b7fbewckQwnLs68DZWQAO3aQfPmrk6U4tLllZtZs2bx6quv8uuvvxJ8n3uEAwcOJCQkxLYcFRV11wLH3d2dXLly2SY89PHxue9IrCKSPhmGwbVr1zh37hy5cuVKNOeWSIY1YgTs2QP58sGXX7o6jVOku+Jm5syZdO3alVmzZvHss8/ed3tPT0+Huj7fmmfnbjM6i0jGkitXrvvOeyaSYWzffru79zffQN68rs3jJC4tbmJiYjhy5Iht+fjx4+zcuZPcuXNTuHBhBg4cyJkzZ/jxxx8B81ZUp06d+PLLL6lVqxbh4eGAOctyUlMPPAiLxUJgYCD+/v5JTmwoIhlH1qxZdcVGMo+4OOjSBaxWeOklePFFVydyGpdOv7B69Wrq16+faH2nTp2YNm0anTt35sSJE6xevRqAp556ijVr1tx1++RwZPhmERGRDGPoUBg2zLxa888/kM7mW3Pk+1tzS4mIiGR0O3dCjRoQHw+zZ8M9ehmnVY58f6fb3lIiIiKSDDdvQufOZmHzwgvmLakMTsWNiIhIRjZqFOzaBXnywPjx5szfGZyKGxERkYxq504YPtx8Pm4cBAS4NE5qUXEjIiKSEcXFQadOt29HtWnj6kSpRsWNiIhIRjR8OOzebfaO+uabTHE76hYVNyIiIhnNli1mWxsw545KZ92+H5aKGxERkYzkxg2zd5TVat6KeuEFVydKdSpuREREMpKhQ2HfPrPx8NdfuzqNS6i4ERERySg2boTRo83n331ndv/OhBwubnbu3OmEGCIiIvJQrl6Fjh0hIQE6dIAWLVydyGUcLm6qVatG9erVmTBhAlFRUc7IJCIiIo7q3x+OHIFCheCrr1ydxqUcLm4OHTpEw4YNGT58OIGBgXTo0CHJySxFREQklfzxhzn6MMCUKZArl0vjuNoDT5xptVr5/fffmTx5MkuXLqVIkSJ07dqV8uXL27Z57rnnUixoStHEmSIikqFcuQIVK8K//8Ibb9wucjKYVJ0VPCIigi+++ILPPvuMhISE2we2WLBarQ9zaKdQcSMiIhlKp07w449QooQ53UK2bK5O5BROnxX84sWLTJw4kaeffppChQqxbNkyhg0bxoEDB0hISCAhISFNFjYiIiIZyvz5ZmHj5gY//JBhCxtHZXF0h9GjRzN48GDKlSvHiy++yIQJEyhZsqQzsomIiMjdRETAa6+Zz999F+rUcW2eNMTh21LZs2dn4sSJtGvXzlmZnEq3pUREJN0zDLOr92+/me1ttmwBT09Xp3IqR76/Hb5ys2jRIurWrfvA4UREROQhTZliFjYeHjB9eoYvbBzlcHHz5JNPOiOHiIiIJMexY/DWW+bzjz6CRx91aZy0SNMviIiIpBdWqzkKcUwM1KsHb7/t6kRpkoobERGR9GL0aFi/HnLkMHtHubu7OlGapOJGREQkPdixA4YMMZ9/9RUULerSOGmZw21ubjl//jwHDx4EoHTp0uTLly/FQomIiMh/XL8O7dvDzZvQqpU5cJ/clcNXbq5evUrXrl0pUKAA9erVo169ehQoUIBu3bpx7do1Z2QUERHJ3Pr3h/37IX9+mDgRLBZXJ0rTHC5uQkJCWLNmDQsXLuTKlStcuXKFBQsWsGbNGvr16+eMjCIiIpnX0qUwbpz5fNo0yJvXpXHSA4cH8cubNy9z5szhqaeeslv/559/8vLLL3P+/PmUzJfiNIifiIikGxcumIP0hYfDm2+abW0yKafOLXXt2jUCAgISrff399dtKRERkZRiGNC9u1nYlC0Ln3zi6kTphsPFTe3atQkNDeXGjRu2ddevX2fYsGHUrl07RcOJiIhkWlOmmBNjZs0KM2aAt7erE6UbDveWGjt2LI0bN6ZQoUJUqlQJgF27duHl5cWyZctSPKCIiEimc/gw9O1rPh8xAqpUcW2edMbhNjdg3pqaMWMGBw4cAKBs2bK0b98e73RQVarNjYiIpGlxcVC3LmzdCk89BX/8ocH6cPLEmWvXrqVOnTp0797dbn18fDxr166lXr16jh5SREREbgkNNQsbPz/46ScVNg/A4TY39evX59KlS4nWR0ZGUr9+/RQJJSIikimtXn274fCkSVCokEvjpFcOFzeGYWBJYvCgixcvki1bthQJJSIikulcugQdOpi9pLp1gxdecHWidCvZt6Wef/55ACwWC507d8bT09P2mtVqZffu3dSpUyflE4qIiGR0hgGvvw7//gslS8LYsa5OlK4lu7jx9fUFzCs3OXLksGs87OHhwWOPPZaoHY6IiIgkw5QpMGcOZMkCP/8M2bO7OlG6luziZurUqQAULVqUd955R7egREREUsKBA9Cnj/l8xAioXt21eTKAB+oKnp6pK7iIiKQZN27AY4/Brl0QHAzLloGbw81hMwWnTr8QERFBhw4dKFCgAFmyZMHd3d3uISIiIsk0YIBZ2OTNCz/+qMImhTg8zk3nzp05deoUgwcPJjAwMMmeUyIiInIfixbBl1+az6dNg8BAl8bJSBwubv766y/WrVtH5cqVnRBHREQkEwgLg86dzed9+sCzz7o0Tkbj8PWvoKAgUqqZztq1a2nevDkFChTAYrEwf/78++6zevVqqlatiqenJyVKlGDatGkpkkVERCRVJCRAx45w4QJUqqTZvp3A4eJm7NixDBgwgBMnTjz0m1+9epVKlSoxfvz4ZG1//Phxnn32WerXr8/OnTt56623ePXVVzVhp4iIpB+ffGLOF+XtDTNngpeXqxNlOA73lvLz8+PatWvEx8fj4+ND1qxZ7V5PamqGZAWxWJg3bx4tW7a86zb9+/dn0aJF7N2717auTZs2XLlyhaVLlybrfdRbSkREXGb9enjySbBaYfJk6NrV1YnSDadOnDnWhaMmbty4keDgYLt1jRo14q233nJNIBERkeS6dAnatTMLm3btoEsXVyfKsBwubjp16uSMHMkSHh5OQECA3bqAgACioqK4fv263ajJt8TGxhIbG2tbjoqKcnpOERERO7fmizp1CooXh2+/BfU2dpoH6lB/9OhRBg0aRNu2bTl37hwAS5Ys4Z9//knRcClh1KhR+Pr62h5BQUGujiQiIpnNN9/A/PmQNSvMng1qFuFUDhc3a9asoWLFimzatIm5c+cSExMDwK5duwgNDU3xgP+VP39+IiIi7NZFRESQM2fOJK/aAAwcOJDIyEjb4/Tp007NKCIiYmfnTggJMZ9/+ilUq+bSOJmBw8XNgAEDGDFiBCtWrMDDw8O2vkGDBvz9998pGu5OtWvXZuXKlXbrVqxYQe3ate+6j6enJzlz5rR7iIiIpIroaHj5ZYiLg2bNoG9fVyfKFBwubvbs2UOrVq0Srff39+fChQsOHSsmJoadO3eyc+dOwOzqvXPnTk6dOgWYV106duxo275Hjx4cO3aM9957jwMHDvDNN9/wyy+/8Pbbbzv6MURERJzLMOD11+HwYShUyByFWO1sUoXDxU2uXLkICwtLtH7Hjh0ULFjQoWNt3bqVKlWqUKVKFQBCQkKoUqUKQ4YMASAsLMxW6AAUK1aMRYsWsWLFCipVqsSYMWP4/vvvadSokaMfQ0RExLm+/94cx8bd3WxnkyePqxNlGg6Pc/POO++wadMmfv31V0qVKsX27duJiIigY8eOdOzY0entbh6WxrkRERGn270batUyZ/3++GPo39/VidI9p84KPnLkSMqUKUNQUBAxMTGUK1eOevXqUadOHQYNGvTAoUVERDKEmBiznc2NG9CkCbz7rqsTZToOX7m55dSpU+zdu5eYmBiqVKlCyZIlUzqbU+jKjYiIOI1hmPNGTZ8OBQuaPaXy5nV1qgzBqSMU31K4cGEKFy78oLuLiIhkPJMnm4WNuzvMmqXCxkWSVdyEhIQwfPhwsmXLRsitvvp38fnnn6dIMBERkXRl507o3dt8PmIEPP64S+NkZskqbnbs2MHNmzdtz+/Goi5uIiKSGUVGwksvQWwsPPssvPeeqxNlag/c5ia9UpsbERFJUYZhNiCeMwcKF4bt29Xt2wmc2ltKRERE/mPcOLOwyZoVfv1VhU0a4HCD4hs3bjBu3Dj+/PNPzp07R0JCgt3r27dvT7FwIiIiadqmTfDOO+bzMWOgZk3X5hHgAYqbbt26sXz5cl588UVq1qypdjYiIpI5XbhgtrO5edP881ZjYnE5h4ub33//ncWLF1O3bl1n5BEREUn7rFZo3x5On4aSJc2pFvSf/TTD4TY3BQsWJEeOHM7IIiIikj4MHw7Ll4O3N/zf/4E6qKQpDhc3Y8aMoX///pw8edIZeURERNK2pUvhww/N5999BxUrujaPJOLwbanq1atz48YNHnnkEXx8fMiaNavd65cuXUqxcCIiImnKyZPm7SjDgB49oEMHVyeSJDhc3LRt25YzZ84wcuRIAgIC1KBYREQyh9hYczybS5egenUYO9bVieQuHC5uNmzYwMaNG6lUqZIz8oiIiKRNffvC5s3g52eOZ+Pp6epEchcOt7kpU6YM169fd0YWERGRtGnqVLN9jcUCP/8MRYu6OpHcg8PFzccff0y/fv1YvXo1Fy9eJCoqyu4hIiKSoWzfDj17ms+HDYPGjV2bR+7L4bml3NzMeujOtjaGYWCxWLBarSmXzgk0t5SIiCTbxYtm+5oTJ6BZM1iwANw0c5ErOPL97XCbmz///POBg4mIiKQbtwbqO3ECiheHn35SYZNOOFzcPPnkk87IISIikrYMHQrLlpkD9c2dC7lyuTqRJJPDxc0t165d49SpU8TFxdmtf/TRRx86lIiIiEvNnw8jRpjPJ04EfbelKw4XN+fPn6dLly4sWbIkydfTepsbERGRezpwADp2NJ/37QuvvOLaPOIwh28evvXWW1y5coVNmzbh7e3N0qVL+eGHHyhZsiQLFy50RkYRERGnsiYYbDx6kUV/HeD6s80hOhqefBJGj3Z1NHkADl+5WbVqFQsWLKB69eq4ublRpEgRGjZsSM6cORk1ahTPPvusM3KKiIg4xdK9YQz7bR/hV67x3byReB87QoRvPv4Z+hUN7phiSNIHh6/cXL16FX9/fwD8/Pw4f/48ABUrVmT79u0pm05ERMSJlu4No+f07YRF3qDXxl945vDfxLpn5bUWA+m29DRL94a5OqI8AIeLm9KlS3Pw4EEAKlWqxHfffceZM2eYMGECgYGBKR5QRETEGawJBsN+24cB1D+6hZB1MwAY9ExPdgWWAmDYb/uwJjg0HJykAQ7flurbty9hYWYlGxoaSuPGjZkxYwYeHh5MmzYtpfOJiIg4xebjlwiLvEHxi6f5cuFo3DD4qUpTfn30GQAMICzyBpuPX6J28TyuDSsOcbi4eeU/rcarVavGyZMnOXDgAIULFyZv3rwpGk5ERMRZzkXfIEfsVSbO/YiccdfYVKg8Hz7dPcntJH154HFubvHx8aFq1aopkUVERCTV+PtkZexvn1H80r+cyZGPN1oO5KZ74gbE/jm8XJBOHobDxU3Xrl3v+fqUKVMeOIyIiEhqqTVtLG5Ht3AjiwevP/8BF7PlsnvdAuT39aJmsdwuyScPzuHi5vLly3bLN2/eZO/evVy5coUGDRqkWDARERGn+fVX3EaNAqB/kz78k7+E3cu3poYObV4OdzcLkr44XNzMmzcv0bqEhAR69uxJ8eLFUySUiIiI0+zYAZ06mc/feYcmnULY/Ns+wiJvt63J7+tFaPNyNK6gXsDpkcUwjBTp43bw4EGeeuopW0+qtMqRKdNFRCSDiYiAGjXg9Glo1Ah+/x2yZMGaYLD5+CXORd/AP4d5K0pXbNIWR76/H7pB8S1Hjx4lPj4+pQ4nIiKSsmJj4fnnzcKmVCmYNQuymF+D7m4WdffOQBwubkJCQuyWDcMgLCyMRYsW0enWZT4REZG0xDDgjTdgwwbw9YWFCyFXLlenEidxuLjZsWOH3bKbmxv58uVjzJgx9+1JJSIi4hJffQVTpoCbG8yeDaVLuzqROJHDxc2ff/7pjBwiIiLOsXw53LrrMHq02dZGMjSH55YSERFJNw4cgJdfhoQE6NwZ3n7b1YkkFTh85aZKlSpYLIlbkFssFry8vChRogSdO3emfv36KRJQRETkgVy6BM2bQ2Qk1K0LEyZAEt9fkvE4fOWmcePGHDt2jGzZslG/fn3q169P9uzZOXr0KDVq1CAsLIzg4GAWLFjgjLwiIiL3d/MmvPgiHDkCRYrA3Lng6enqVJJKHL5yc+HCBfr168fgwYPt1o8YMYKTJ0+yfPlyQkNDGT58OC1atEixoCIiIsliGNC7N/z5J2TPDr/9Bv7+rk4lqcjhKze//PILbdu2TbS+TZs2/PLLLwC0bduWgwcPJut448ePp2jRonh5eVGrVi02b958z+3Hjh1L6dKl8fb2JigoiLfffpsbNzRjq4iI/M+4cTBxonkLauZMqFjR1YkklTlc3Hh5ebFhw4ZE6zds2ICXlzlzakJCgu35vcyePZuQkBBCQ0PZvn07lSpVolGjRpw7dy7J7X/++WcGDBhAaGgo+/fvZ/LkycyePZv333/f0Y8hIiIZ0eLFtxsNf/opNGvm2jziEg7flnrzzTfp0aMH27Zto0aNGgBs2bKF77//3lZkLFu2jMqVK9/3WJ9//jndu3enS5cuAEyYMIFFixYxZcoUBgwYkGj7DRs2ULduXdq1awdA0aJFadu2LZs2bXL0Y4iISEazeze0bm32jOraFfr1c3UicRGHi5tBgwZRrFgxvv76a3766ScASpcuzaRJk2xFR48ePejZs+c9jxMXF8e2bdsYOHCgbZ2bmxvBwcFs3LgxyX3q1KnD9OnT2bx5MzVr1uTYsWMsXryYDh063PV9YmNjiY2NtS1HRUUl+7OKiEg6ER5uXqWJiYH69eHbb9UzKhN7oLml2rdvT/v27e/6ure3932PceHCBaxWKwEBAXbrAwICOHDgQJL7tGvXjgsXLvD4449jGAbx8fH06NHjnrelRo0axbBhw+6bR0RE0qlr16BFi9tzRs2ZAx4erk4lLpSuBvFbvXo1I0eO5JtvvmH79u3MnTuXRYsWMXz48LvuM3DgQCIjI22P06dPp2JiERFxqoQE6NQJNm+G3Llh0SLzT8nUknXlJnfu3Bw6dIi8efPi5+eX5CB+t1y6dClZb5w3b17c3d2JiIiwWx8REUH+/PmT3Gfw4MF06NCBV199FYCKFSty9epVXnvtNT744APc3BLXap6ennhqbAMRkYxp0CDzSk3WrDBvHpQo4epEkgYkq7j54osvyJEjh+35vYqb5PLw8KBatWqsXLmSli1bAmYvq5UrV9K7d+8k97l27VqiAsbd3R0wZycXEZFM5PvvYdSo28/r1XNtHkkzklXcdOrUyfa8c+fOKfbmISEhdOrUierVq1OzZk3Gjh3L1atXbb2nOnbsSMGCBRn1v7+8zZs35/PPP6dKlSrUqlWLI0eOMHjwYJo3b24rckREJBNYsQJ69DCfDxkCHTu6No+kKQ43KHZ3dycsLAz/O0Z7vHjxIv7+/lit1mQfq3Xr1pw/f54hQ4YQHh5O5cqVWbp0qa2R8alTp+yu1AwaNAiLxcKgQYM4c+YM+fLlo3nz5nz00UeOfgwREUmv9u41p1awWuGVV2DoUFcnkjTGYjh4P8fNzY3w8PBExc3Zs2cpXrw4169fT9GAKS0qKgpfX18iIyPJmTOnq+OIiIgjwsKgVi2zZ1S9erB8ueaMyiQc+f5O9pWbr776CjBn//7+++/Jnj277TWr1cratWspU6bMA0YWERG5j5gYc5bv06ehdGmzAbEKG0lCsoubL774AjAb7k6YMMGujYuHhwdFixZlwoQJKZ9QREQkPt4cfXjbNsibV12+5Z6SXdwcP34cgPr16zNv3jxy5crlrEwiIiK3GQb06mXOG+Xtbc7yXby4q1NJGubQIH43b97k1KlThIWFOSuPiIiIvY8/vj3L988/w2OPuTqRpHEOFTdZs2blxo0bzsoiIiJib8YMuDXFzldfwf/GRRO5F4enX+jVqxeffPIJ8fHxzsgjIiJi+vNP+N+4Z/TrB3cZ4FXkTg6Pc7NlyxZWrlzJ8uXLqVixItmyZbN7fe7cuSkWTkREMqndu82rNDdvwssvw6efujqRpCMOFze5cuXihRdecEYWEREROHUKmjSBqChzLJsffoAk5g4UuRuHi5upU6c6I4eIiAhcugSNG8PZs1C+PMyfD15erk4l6YzDxc0t58+f5+DBgwCULl2afPnypVgoERHJhK5fh+eeg/37oWBBWLIE/PxcnUrSIYev8129epWuXbsSGBhIvXr1qFevHgUKFKBbt25cu3bNGRlFRCSjuzVP1Pr14OsLS5dCUJCrU0k65XBxExISwpo1a/jtt9+4cuUKV65cYcGCBaxZs4Z+/fo5I6OIiGRktwbpmzsXPDxgwQKoUMHVqSQdc3jizLx58zJnzhyeeuopu/V//vknL7/8MufPn0/JfClOE2eKiKQxQ4fCsGHmIH2//GLO+C1yB0e+vx2+cnPt2jUCAgISrff399dtKRERccy335qFDcD48SpsJEU4XNzUrl2b0NBQu5GKr1+/zrBhw6hdu3aKhhMRkQxszhzzdhRAaCj07OnaPJJhONxb6ssvv6RRo0YUKlSISpUqAbBr1y68vLxYtmxZigcUEZEM6M8/oX17s73N66+bxY1ICnG4zQ2Yt6ZmzJjBgQMHAChbtizt27fH29s7xQOmNLW5ERFxsa1boX59iImB558329m4u7s6laRxjnx/P1Bxk56puBERcaGDB+Hxx+HCBbPAWbxYg/RJsjjy/f1Ag/idPXuWv/76i3PnzpGQkGD3Wp8+fR7kkCIiktGdPg0NG5qFTbVqGn1YnMbh4mbatGm8/vrreHh4kCdPHiwWi+01i8Wi4kZERBK7cAGeecYscEqXNkcf1tVzcRKHi5vBgwczZMgQBg4ciJsmMhMRkfuJjoamTeHAAShUCJYvB03ZI070QOPctGnTRoWNiIjc3635orZsgTx5zMKmcGFXp5IMzuEKpVu3bvz666/OyCIiIhnJzZvQujWsXg05cpjzRZUt6+pUkgk43FvKarXSrFkzrl+/TsWKFcmaNavd659//nmKBkxp6i0lIpIKrFbo0AFmzjQbDS9bBvXquTqVpGNO7S01atQoli1bRunSpQESNSgWEZFMzjDgjTfMwiZLFvi//1NhI6nK4eJmzJgxTJkyhc6dOzshjoiIpGuGAf37w8SJ5kSY06ebjYlFUpHDbW48PT2pW7euM7KIiEh6N3w4jB5tPp840WxzI5LKHC5u+vbty7hx45yRRURE0rMxY27PEfXFF/Dqq67NI5mWw7elNm/ezKpVq/j9998pX758ogbFc+fOTbFwIiKSTnz7Lbzzjvl8xAh46y2XxpHMzeHiJleuXDz//PPOyCIiIunRDz+YDYgBBg6EDz5wbR7J9BwubqZOneqMHCIikh798gt07Wo+79MHPvrItXlEeIA2NyIiIgDMmwft2kFCgtm+ZuxYs4eUiIs90Kzgc+bM4ZdffuHUqVPExcXZvbZ9+/YUCSYiImnY77+bPaFuDdb33XcqbCTNcPjKzVdffUWXLl0ICAhgx44d1KxZkzx58nDs2DGaNGnijIwiIpKWLF8OL7xwe3qFKVNA8w1KGuLw38ZvvvmGiRMnMm7cODw8PHjvvfdYsWIFffr0ITIy0hkZRUQkrfjzT2jRAuLioFUr+OkncxRikTTE4eLm1KlT1KlTBwBvb2+io6MB6NChAzNnzkzZdCIiknasXQvNmsGNG+afs2bBHcOBiKQFDhc3+fPn59KlSwAULlyYv//+G4Djx4/j4BycIiKSXqxbZ06jcO0aNGoEv/4KHh6uTiWSJIeLmwYNGrBw4UIAunTpwttvv03Dhg1p3bo1rVq1SvGAIiLiYn/9BU2awNWr8MwzMH++OdO3SBplMRy83JKQkEBCQgJZ/nePddasWWzYsIGSJUvy+uuv45HGK3lHpkwXEcn01q+Hxo0hJgaCg2HhQvD2dnUqyYQc+f52uLhJ71TciIgk04YN5i2omBh4+mmzsPHxcXUqyaQc+f52ed+98ePHU7RoUby8vKhVqxabN2++5/ZXrlyhV69eBAYG4unpSalSpVi8eHEqpRURySTWr79d2NSvr8JG0hWX9t+bPXs2ISEhTJgwgVq1ajF27FgaNWrEwYMH8ff3T7R9XFwcDRs2xN/fnzlz5lCwYEFOnjxJrly5Uj+8iEhGtW7d7TY29evDb7+psJF0xaW3pWrVqkWNGjX4+uuvAbM9T1BQEG+++SYDBgxItP2ECRMYPXo0Bw4cSDQbeXLptpSIyD2sXWv2irp6VbeiJE1JF7el4uLi2LZtG8HBwbfDuLkRHBzMxo0bk9xn4cKF1K5dm169ehEQEECFChUYOXIkVqv1ru8TGxtLVFSU3UNERJKwZs3tKzYNG+qKjaRbD1TcxMfH88cff/Ddd9/ZBvE7e/YsMTExyT7GhQsXsFqtBAQE2K0PCAggPDw8yX2OHTvGnDlzsFqtLF68mMGDBzNmzBhGjBhx1/cZNWoUvr6+tkdQUFCyM4qIZBorV9qPY7NggXpFSbrlcJubkydP0rhxY06dOkVsbCwNGzYkR44cfPLJJ8TGxjJhwgRn5ATM21b+/v5MnDgRd3d3qlWrxpkzZxg9ejShoaFJ7jNw4EBCQkJsy1FRUSpwRET+a8kScyqF2Fjzys3cuRrHRtI1h6/c9O3bl+rVq3P58mW8/1PVt2rVipUrVyb7OHnz5sXd3Z2IiAi79REREeTPnz/JfQIDAylVqhTu7u62dWXLliU8PDzR7OS3eHp6kjNnTruHiIj8z8KF0LKlWdi0aAHz5qmwkXTP4eJm3bp1DBo0KNFgfUWLFuXMmTPJPo6HhwfVqlWzK4gSEhJYuXIltWvXTnKfunXrcuTIERISEmzrDh06RGBgYJofPFBEJM2ZM8ec3TsuDl580ZxSwdPT1alEHprDxU1CQkKSDXj//fdfcuTI4dCxQkJCmDRpEj/88AP79++nZ8+eXL16lS5dugDQsWNHBg4caNu+Z8+eXLp0ib59+3Lo0CEWLVrEyJEj6dWrl6MfQ0Qkc/v5Z2jTBuLjoV07mDlTk2BKhuFwm5tnnnmGsWPHMnHiRAAsFgsxMTGEhobStGlTh47VunVrzp8/z5AhQwgPD6dy5cosXbrU1sj41KlTuLndrr+CgoJYtmwZb7/9No8++igFCxakb9++9O/f39GPISKSeX3/Pbz2GhgGdO5sLv/ndr9IeufwODf//vsvjRo1wjAMDh8+TPXq1Tl8+DB58+Zl7dq1SQ6+l5ZonBsRydS+/BLeest83rMnfP01uLl8sHqR+3L63FLx8fHMmjWL3bt3ExMTQ9WqVWnfvr1dA+O0SsWNiGRaI0fCBx+Yz995Bz79FCwW12YSSSZHvr8faPqFLFmy8MorrzxQOBERSWWGAYMGmcUNwNChMGSIChvJsJJV3CxcuDDZB3zuueceOIyIiKSwhATo0wfGjzeXP/sM+vVzbSYRJ0tWcdOyZUu7ZYvFwp13syz/+x/AvaZCEBGRVHTzJnTtCtOnm1dpvvkGevRwdSoRp0tWK7KEhATbY/ny5VSuXJklS5Zw5coVrly5wpIlS6hatSpLly51dl4REUmOGzfMsWumTzd7Qk2frsJGMg2H29y89dZbTJgwgccff9y2rlGjRvj4+PDaa6+xf//+FA0oIiIOio42Rx1etcoclO/XX6F5c1enEkk1Dhc3R48eJVeuXInW+/r6cuLEiRSIJCIiD+zCBXj2Wdi8GbJnN2f2fuopV6cSSVUOD25Qo0YNQkJC7OaEioiI4N1336VmzZopGk5ERBxw+jQ88YRZ2OTObV65UWEjmZDDxc2UKVMICwujcOHClChRghIlSlC4cGHOnDnD5MmTnZFRRETu58ABqFvX/LNQIfjrL6hRw9WpRFzC4dtSJUqUYPfu3axYsYIDBw4A5szcwcHBth5TIiKSirZsgSZN4OJFKF0ali+HwoVdnUrEZR5ohOL0TCMUi0iG8scf0KoVxMRA9eqweDHky+fqVCIpzpHvb00oIiKSXs2cCU2bmoVNgwZmGxsVNiIqbkRE0qUvvoB27cyB+l5+2bxikyOHq1OJpAkqbkRE0pOEBHjvPQgJMZf79DGv4Hh6ujaXSBryQBNnioiIC8TFwauvwk8/mcujRkH//poAU+QOD3Tl5ujRowwaNIi2bdty7tw5AJYsWcI///yTouFEROR/oqLMwfl++smcTmHKFBgwQIWNSBIcLm7WrFlDxYoV2bRpE3PnziUmJgaAXbt2ERoamuIBRUQyvbNnoV49s2dUtmzmqMNdurg6lUia5XBxM2DAAEaMGMGKFSvw8PCwrW/QoAF///13ioYTEcn09u2Dxx6DXbvA3x9WrzbHtBGRu3K4uNmzZw+tWrVKtN7f358LFy6kSCgREQHWrDFHHT59GkqVgo0bzbFsROSeHC5ucuXKRVhYWKL1O3bsoGDBgikSSkQk05sxAxo2hCtXoHZtWL8eHnnE1alE0gWHi5s2bdrQv39/wsPDsVgsJCQksH79et555x06duzojIwiIpmHYcCIEfDKK+YYNi++CCtXQt68rk4mkm44XNyMHDmSMmXKEBQURExMDOXKlaNevXrUqVOHQYMGOSOjiEjmcPOm2dV78GBz+Z13YPZs8PZ2bS6RdOaB55Y6ffo0e/bsISYmhipVqlCyZMmUzuYUmltKRNKkK1fgpZfMHlFubjBuHLzxhqtTiaQZTp1b6sMPP+TatWsEBQXRtGlTXn75ZUqWLMn169f58MMPHzi0iEimdewY1KljFjY+PrBggQobkYfg8JUbd3d3wsLC8Pf3t1t/8eJF/P39sVqtKRowpenKjYikKRs2QIsWcOECFCxojmFTpYqrU4mkOU69cmMYBpYkRsTctWsXuXPndvRwIiKZ18yZ5mzeFy6YBc2mTSpsRFJAsueW8vPzw2KxYLFYKFWqlF2BY7VaiYmJoUePHk4JKSKSoSQkwIcfwrBh5nKLFmbX72zZXJtLJINIdnEzduxYDMOga9euDBs2DF9fX9trHh4eFC1alNq1azslpIhIhnHtGnTuDL/+ai736weffGLOFyUiKSLZxU2nTp0AKFasGHXq1CFr1qxOCyUikiGdOWNepdm2DbJmhW+/hW7dXJ1KJMNJdnFzy5NPPklCQgKHDh3i3LlzJCQk2L1er169FAsnIpJhbN1qFjZnz0KePDB3rjkZpoikOIeLm7///pt27dpx8uRJ7uxoZbFY0nxvKRGRVDdzJnTtCjduQLlyZo8oTaUg4jQOFzc9evSgevXqLFq0iMDAwCR7TomICGC1wqBB8PHH5nLTpmaho2EoRJzK4eLm8OHDzJkzhxIlSjgjj4hIxhAVBe3awaJF5nL//vDRR2o4LJIKHB7nplatWhw5csQZWUREMobDh+Gxx8zCxssLpk83r96osBFJFQ5fuXnzzTfp168f4eHhVKxYMVGvqUcffTTFwomIpDuLF5tXbCIjoUABmD8fatRwdSqRTMXh6Rfc3BJf7LFYLLaRi9N6g2JNvyAiTmEYMGqU2cbGMKB2bfi//4PAQFcnE8kQHPn+dvjKzfHjxx84mIhIhhQdbQ7MN3euufz66/DVV+Dh4dJYIpmVw8VNkSJFnJFDRCR9OnQInn8e/vnHHJhv/Hjo3t3VqUQyNYcbFAP89NNP1K1blwIFCnDy5EnAnJ5hwYIFKRpORCRNW7DAbE/zzz/m7ac1a1TYiKQBDhc33377LSEhITRt2pQrV67Y2tjkypWLsWPHpnQ+ERGXsSYYbDx6kQU7z7Dx6EWsCf9romi1wgcfQMuWZpfvxx83p1TQ/HoiaYLDxc24ceOYNGkSH3zwAe7/6dZYvXp19uzZ80Ahxo8fT9GiRfHy8qJWrVps3rw5WfvNmjULi8VCy5YtH+h9RUTuZuneMB7/ZBVtJ/1N31k7aTvpbx7/ZBUr1+2FJk1g5Ehzw759YdUqNRwWSUMcLm6OHz9OlSpVEq339PTk6tWrDgeYPXs2ISEhhIaGsn37dipVqkSjRo04d+7cPfc7ceIE77zzDk888YTD7ykici9L94bRc/p2wiJv2K0P2L+LMs82gBUrwMcHZsyAsWPNtjYikmY4XNwUK1aMnTt3Jlq/dOlSypYt63CAzz//nO7du9OlSxfKlSvHhAkT8PHxYcqUKXfdx2q10r59e4YNG8Yjmp9FRFKQNcFg2G/7sBsjwzDosP13fpnRn4LR5zmdpyDWDRvN8WxEJM1xuLdUSEgIvXr14saNGxiGwebNm5k5cyajRo3i+++/d+hYcXFxbNu2jYEDB9rWubm5ERwczMaNG++634cffoi/vz/dunVj3bp193yP2NhYYmNjbctRUVEOZRSRzGXz8Ut2V2x84q7z8dJxPLd/LQBLStXhvaZ9mZi9IGphI5I2OVzcvPrqq3h7ezNo0CCuXbtGu3btKFCgAF9++SVt2rRx6FgXLlzAarUSEBBgtz4gIIADBw4kuc9ff/3F5MmTk7x6lJRRo0YxbNgwh3KJSOZ1Lvp2YVPy/Em+WfAxJS+e5qabOx8/1YXJ1VuAxWK3nYikLQ4XNwDt27enffv2XLt2jZiYGPz9/VM6V5Kio6Pp0KEDkyZNIm/evMnaZ+DAgYSEhNiWo6KiCAoKclZEEUnn/HN4AfDinj8YvvxbvONjCc+em14tBrCtULlE24lI2vNAxc0tPj4++Pj4PPD+efPmxd3dnYiICLv1ERER5M+fP9H2R48e5cSJEzRv3ty2LiEhAYAsWbJw8OBBihcvbrePp6cnnp6eD5xRRDKXmv6efL1iHM22LwNgbdEqvN2sHxez5QLAAuT39aJmsdyuCyki95Ss4qZq1aqsXLkSPz8/qlSpgsViueu227dvT/abe3h4UK1aNVauXGnrzp2QkMDKlSvp3bt3ou3LlCmTqLv5oEGDiI6O5ssvv9QVGRF5OPv24f7SSzTbtw+rxY0vHm/P+NovYVjMvhe3/uULbV4Od7e7/zsoIq6VrOKmRYsWtqsfKT2mTEhICJ06daJ69erUrFmTsWPHcvXqVbp06QJAx44dKViwIKNGjcLLy4sKFSrY7Z8rVy6AROtFRJLNMGDqVOjdG65fh/z52Tbya/4vPBfGfxoX5/f1IrR5ORpX0Jg2ImlZsoqb0NDQJJ+nhNatW3P+/HmGDBlCeHg4lStXZunSpbZGxqdOnUpyJnIRkRQRFQU9esDMmeZyw4bw00/UDAjgrwSDzccvcS76Bv45zFtRumIjkvZZDMMw7r/ZbVu2bCEhIYFatWrZrd+0aRPu7u5Ur149RQOmNEemTBeRDG7rVmjTBo4eBXd3GDEC3nsP9B8qkTTHke9vh3+De/XqxenTpxOtP3PmDL169XL0cCIiqS8hAcaMgTp1zMKmSBFYtw4GDFBhI5IBONxbat++fVStWjXR+ipVqrBv374UCSUi4jRhYdCpkzmFAsDzz8P334Ofn2tziUiKcfi/KJ6enom6bgOEhYWRJctD9SwXEXGu33+HRx81Cxtvb5g4EebMUWEjksE4XNw888wzDBw4kMjISNu6K1eu8P7779OwYcMUDScikiKuXzd7QjVvDhcuQKVKsG0bdO8O9xjaQkTSJ4cvtXz22WfUq1ePIkWK2GYH37lzJwEBAfz0008pHlBE5KHs2AHt28P+/eby22/DqFGgwT1FMiyHi5uCBQuye/duZsyYwa5du/D29qZLly60bduWrFmzOiOjiIjjrFb47DMYPBhu3oT8+WHaNGjUyNXJRMTJHqiRTLZs2XjttddSOouISMo4edJsNLxmjbncqpXZviaZc9KJSPqWrOJm4cKFNGnShKxZs7Jw4cJ7bvvcc8+lSDAREYcZBvzwA/TpA9HRkC0bfPUVdOmitjUimUiyBvFzc3MjPDwcf3//e44WbLFYsFqtKRowpWkQP5EM6tw5eP11mD/fXK5dG378EUqUcGksEUkZjnx/J+vKza2Zt+98LiKSJixYYPZ8On8esmaFDz+Ed981Rx0WkUwnWV3Bc+fOzYULFwDo2rUr0dHRTg0lIpIsly9Dx47QsqVZ2FSsCFu2mCMNq7ARybSSVdzExcURFRUFwA8//MCNGzfus4eIiJMtWgTly8NPP5lTJvTvbxY2lSq5OpmIuFiybkvVrl2bli1bUq1aNQzDoE+fPnh7eye57ZQpU1I0oIiInchIc6yaqVPN5dKlzS7ejz3m0lgiknYkq7iZPn06X3zxBUePHsVisRAZGamrNyKS+hYtMhsNnzlj9n56+21zJu+7/GdLRDKnZPWW+q9ixYqxdetW8uTJ46xMTqXeUiLp0MWL8NZbMH26uVyiBEyZAk884dJYIpJ6HPn+dnhuqePHj9sKG129ERGnmzvXbFszfbrZtuadd2DXLhU2InJXDhc3CQkJDB8+nIIFC5I9e3aOHTsGwODBg5k8eXKKBxSRTOrsWXj+eXjhBYiIgLJlYcMGGD0afHxcnU5E0jCHi5sRI0Ywbdo0Pv30Uzw8PGzrK1SowPfff5+i4UQkEzIMmDQJypWDefMgSxb44ANzAsxatVydTkTSAYeLmx9//JGJEyfSvn173P8zjkSlSpU4cOBAioYTkUzm0CFo0ABee83sFVW9OmzdajYa1izeIpJMDhc3Z86coUQSw5knJCRw8+bNFAklIplMbCwMHw6PPgqrV5u3nT7/HP7+W+PWiIjDHJ4VvFy5cqxbt44iRYrYrZ8zZw5VqlRJsWAikkmsW2d2796/31x+5hmYMAGKFXNtLhFJtxwuboYMGUKnTp04c+YMCQkJzJ07l4MHD/Ljjz/y+++/OyOjiGREly6Zowrfaqvn7w9jx0KbNprBW0QeisO3pVq0aMFvv/3GH3/8QbZs2RgyZAj79+/nt99+o2HDhs7IKCIZiWGYIwqXLn27sOne3bxy07atChsReWgOD+KX3mkQPxEX2rsXevaEv/4yl8uXh2+/1Zg1InJfjnx/O3xb6pZt27ax/3/3yMuXL6/2NiJyd9HRMGyYedvJaoVs2WDoUOjbF7JmdXU6EclgHC5uzp07R5s2bVi9ejW5cuUC4MqVK9SvX59Zs2aRL1++lM4oIumVYcCsWdCvH4SFmeuef94scoKCXBpNRDIuh9vcvPnmm0RHR/PPP/9w6dIlLl26xN69e4mKiqJPnz7OyCgi6dHevVC/PrRrZxY2JUqYE1/+3/+psBERp3K4zY2vry9//PEHNWrUsFu/efNmnnnmGa5cuZKS+VKc2tyIONnly+Ytp/HjzVtQ3t7mCMP9+oGXl6vTiUg65dQ2NwkJCWRN4h551qxZSUhIcPRwIpJRWK1m76cPPjBn8QZo1Qq++ALuGBdLRMSZHL4t1aBBA/r27cvZs2dt686cOcPbb7/N008/naLhRCSdWLfOnCqhRw+zsClXDlasMGf0VmEjIqnM4eLm66+/JioqiqJFi1K8eHGKFy9OsWLFiIqKYty4cc7IKCJp1bFj8OKLUK8e7NwJuXLBl1+az4ODXRxORDIrh29LBQUFsX37dv744w/bRJlly5YlWP+QiWQeUVHw0Udmr6e4OHBzMwfiGz4c1GNSRFxMg/iJSPLFx5vtakJD4dw5c11wsDnJZcWKrs0mIhmaI9/fyb4ttWrVKsqVK0dUVFSi1yIjIylfvjzr1q1zPK2IpH2GAb/9ZhYwPXuahU2pUua65ctV2IhImpLs4mbs2LF07949yWrJ19eX119/nc8//zxFw4lIGrBtGzRoAM89BwcOQJ488NVXsGcPNGumuaBEJM1JdnGza9cuGjdufNfXn3nmGbZt25YioUQkDTh61Jyhu3p1WL0aPD3NWbyPHoU33wQPD1cnFBFJUrIbFEdERCQ5vo3tQFmycP78+RQJJSIudO6c2TB4wgSzjY3FAu3bw4gR6tYtIulCsq/cFCxYkL1799719d27dxMYGJgioUTEBaKizJGFixeHr782C5vGjWH7dvjpJxU2IpJuJLu4adq0KYMHD+bGjRuJXrt+/TqhoaE0a9YsRcOJSCq4fh3GjIFHHjFn7o6JgWrVYOVKWLIEKld2dUIREYckuyt4REQEVatWxd3dnd69e1O6dGkADhw4wPjx47FarWzfvp2AgACnBn5Y6gou8j9xcTB1qnkL6swZc13p0ubyCy+YY9eIiKQRTukKHhAQwIYNG6hQoQIDBw6kVatWtGrVivfff58KFSrw119/PXBhM378eIoWLYqXlxe1atVi8+bNd9120qRJPPHEE/j5+eHn50dwcPA9txeRO8THm0VN6dLmdAlnzkDhwjBlijmT90svqbARkXTNoRGKixQpwuLFi7l8+TJHjhzBMAxKliyJn5/fAweYPXs2ISEhTJgwgVq1ajF27FgaNWrEwYMH8ff3T7T96tWradu2LXXq1MHLy4tPPvmEZ555hn/++YeCBQs+cA6RDM9qhVmzzFtPhw+b6wIC4P334fXXzd5QIiIZgMtHKK5VqxY1atTg66+/BsxZx4OCgnjzzTcZMGDAffe3Wq34+fnx9ddf07Fjx/tur9tSkuncKmqGD4eDB811efOa3brfeAN8fFybT0QkGZxyW8oZ4uLi2LZtm928VG5ubgQHB7Nx48ZkHePatWvcvHmT3LlzOyumSPoUHw/Tp0P58vDKK2Zh4+dnzgl17Bi8844KGxHJkByeODMlXbhwAavVmqitTkBAgG1Szvvp378/BQoUuOvEnbGxscTGxtqWk5o+QiRDuXkTZsyAUaPg0CFzXe7cEBJiDr6nK5YiksG5tLh5WB9//DGzZs1i9erVeHl5JbnNqFGjGDZsWConE3GB2FizofDHH8PJk+a63LnNKzS9e0OOHK7NJyKSSlx6Wypv3ry4u7sTERFhtz4iIoL8+fPfc9/PPvuMjz/+mOXLl/Poo4/edbuBAwcSGRlpe5w+fTpFsoukGTEx8MUX5jg1PXuahY2/P3z6KZw4AQMHqrARkUzFpcWNh4cH1apVY+XKlbZ1CQkJrFy5ktq1a991v08//ZThw4ezdOlSqlevfs/38PT0JGfOnHYPkQzhwgUIDTW7cYeEwNmzUKiQOanliRPw7rsqakQkU3L5bamQkBA6depE9erVqVmzJmPHjuXq1at06dIFgI4dO1KwYEFGjRoFwCeffMKQIUP4+eefKVq0KOHh4QBkz56d7Nmzu+xziKSakyfh889h0iRzdGGAkiXhvfegY0dNaCkimZ7Li5vWrVtz/vx5hgwZQnh4OJUrV2bp0qW2RsanTp3C7T8Din377bfExcXx4osv2h0nNDSUoUOHpmZ0kdS1fTuMHg2//mp27wZzmoQBA6BVK3B3d20+EZE0wuXj3KQ2jXMj6YphwLJlZlGzatXt9cHB5pWa4GBz1m4RkQzOke9vl1+5EZEkXL9ujlHzxRewf7+5zt0dWrc2ez9VqeLafCIiaZiKG5G0JCwMvv3WfFy4YK7LkQO6dYO33oIiRVwaT0QkPVBxI5IWbNpk9nL69VdzED4wC5k+fczCxtfXtflERNIRFTcirhIbaxYz48bBf2e2r1MH3n4bWraELPoVFRFxlP7lFEltp07BhAnw/fdw/ry5zsMD2rY1p0eoVs21+URE0jkVNyKpISEBVqww29L89pu5DFCwIPToAa+9Zo4qLCIiD03FjYgzRUSY8z1NnAjHj99e36AB9OoFzz2nW08iIilM/6qKpLSEBFi50hxBeP782w2EfX3NEYR79oSyZV0aUUQkI1NxI+Iga4LB5uOXOBd9A/8cXtQslht3Nwv8+695lWbKFHNup1tq1TJvPb38Mvj4uCy3iEhmoeJGxAFL94Yx7Ld9hEXeAMAj/iYvnd1O33834L/+z9ttaXx94ZVX4NVXoXJl1wUWEcmEVNyIJNPSvWH0nL4dwzAoH3GUl/b8QYt9a/C7EX17oyefNAuaF14Ab2/XhRURycRU3IgkgzXB4Jvpa3j972W0+mcVpS+csr0Wlj0Pcys+zeraTZk1uqN5i0pERFxGxY3IvURFwfz5RE+cwvz1a3HDnGc21j0ry0s+xq8Vg/mraGUS3MwZuTcfv0Tt4nlcmVhEJNNTcSNyp7g4WLIEfv4ZFi6EGzfI9b+XNhUqz9wKDVhSui5RXtkT7Xou+kaqRhURkcRU3IgAxMfD6tUwaxbMnQuXL99+rVQpTjdpRbsbJTmdK/89D+Ofw8u5OUVE5L5U3EjmZbXCX3/B7NkwZ87tqRAAAgPN6RDatYOqVSlgQPwnq7BE3vjfjSl7FiC/r9ktXEREXEvFjWQu8fGwdq1ZzMyda44gfEuePGYvpzZtoF49cHe3veRugdDm5eg5fTsWsCtwbjUfDm1eTo2JRUTSABU3kvHFxpojBs+bBwsW2F+hyZXLnH27TRtzSoSsWe96mMYVAvn2lap249yAecUmtHk5GlcIdN5nEBGRZFNxIxlTZCQsXWoWNIsXQ/R/xqLJnRtatYKXXoL69c0ZuZOpcYVAGpbLn/QIxSIikiaouJGM49Qps3fTwoVm4+BbczoBFChgXqFp1cocaO8eV2jux93Nou7eIiJpmIobSb+sVvj7b1i0yHzs3m3/epky5qzbzz8PNWqAm5trcoqISKpScSPpy7lzsHy5OQ7NsmVw8eLt19zcoG5ds6B57jkoVcp1OUVExGVU3EjadvOmeXXmVkGzbZv9635+0LgxPPus+Wce3S4SEcnsVNxI2mIYcOgQrFhhFjSrV9s3BgaoUsUsZJo0gdq1IYv+GouIyG36VhDXO30aVq0yu2uvWgVnzti/nicPBAebxcwzz5gD7ImIiNyFihtJfadPw5o15lWZNWvgyBH71z08zLYzzzxjPipXVmNgERFJNhU34lyGAYcPw7p1tx/Hjtlv4+YG1avD00+bjzp1wNvbNXlFRCTdU3EjKSs2FrZvhw0bzMdff5k9nP7LzQ2qVYOnnjLHnHn8cfD1dUlcERHJeFTcyIMzDPMW06ZNZo+mv/+GrVshLs5+O09PqFkTnnjCfNSpAzlzuiaziIhkeCpuJPkuXjSLl61bYcsW2LwZwsISb5cvn1nA3HrUqGEWOCIiIqlAxY0k7fx58/bSrce2bXD8eOLtsmSBRx+FWrXMR506UKIEWDTXkoiIuIaKm8zOaoWjR2HXLti58/afd3bHvqVkSbPxb40a5qNqVfDxSc3EIiIi96TiJrMwDDh7Fv75B/buhT17zMe+fXD9euLtLRZz+oKqVc1B86pWNR9+fqmfXURExAEqbjKa+Hjz9tHBg3DgAOzfbxYw+/ZBVFTS+3h7Q8WKUKmSOaZMpUrmraYcOVI1uoiISEpQcZNCrAkGm49f4lz0DfxzeFGzWG7c3ZzU7iQhwbxtdOSIOYbMoUPmn4cPm+tu3kx6P3d3sz1MhQpmMXPr8cgj5msiIiIZgIqbFLB0bxjDfttHWOQN27pAXy9Cm5ejcYUHmCrAMODKFThxwrwKc+KE+Th61HwcP564u/V/eXtD6dJQpoz5Z/nyUK6c2V7Gw8PxPCIiIumIipuHtHRvGD2nb8e4Y3145A16Tt/Ot69UTVzgxMSYV17+/ff24/RpOHXq9uPOySLvlCULFCtmFiz/fZQpA4UKaboCERHJtFTcPARrgsGw3/ZhAO4JVnJfiyTf1SvkvXqZvNeukO/qFS6t/4GEQllwCw8zx4QJC7t725c7+fubBUzRolCkCBQvfvtRqJBmwxYREUmCvh0fwubjl2y3oh47tYcZswclf+ecOaFgQbNIKVgQChe+/QgKMv9UF2sRERGHqbh5COeib7exuZAtF1aLG5d8cnLBJxfns/lxIVsuLvjkos7jFahQvQwEBkKBAuafmn5ARETEKdJEcTN+/HhGjx5NeHg4lSpVYty4cdSsWfOu2//6668MHjyYEydOULJkST755BOaNm2aiolN/jm8bM8P5S1MyXfmkeCWuNfRzO6PQfE8qRlNREQk03J5q9PZs2cTEhJCaGgo27dvp1KlSjRq1Ihzd84k/T8bNmygbdu2dOvWjR07dtCyZUtatmzJ3r17Uzk51CyWm0BfLyyAYXFLVNhYMHtN1SyWO9WziYiIZFYWwzDu7OiTqmrVqkWNGjX4+uuvAUhISCAoKIg333yTAQMGJNq+devWXL16ld9//9227rHHHqNy5cpMmDDhvu8XFRWFr68vkZGR5EyBW0O3eksBdj2mbo1wk2RvKREREXGII9/fLr1yExcXx7Zt2wgODratc3NzIzg4mI0bNya5z8aNG+22B2jUqNFdt3e2xhUC+faVquT39bJbn9/XS4WNiIiIC7i0zc2FCxewWq0EBATYrQ8ICODAgQNJ7hMeHp7k9uHh4UluHxsbS2xsrG05KrndsB3QuEIgDcvlT70RikVEROSu0kSDYmcaNWoUw4YNc/r7uLtZqK1GwyIiIi7n0ttSefPmxd3dnYiICLv1ERER5M+fP8l98ufP79D2AwcOJDIy0vY4ffp0yoQXERGRNMmlxY2HhwfVqlVj5cqVtnUJCQmsXLmS2rVrJ7lP7dq17bYHWLFixV239/T0JGfOnHYPERERybhcflsqJCSETp06Ub16dWrWrMnYsWO5evUqXbp0AaBjx44ULFiQUaNGAdC3b1+efPJJxowZw7PPPsusWbPYunUrEydOdOXHEBERkTTC5cVN69atOX/+PEOGDCE8PJzKlSuzdOlSW6PhU6dO4fafSSDr1KnDzz//zKBBg3j//fcpWbIk8+fPp0KFCq76CCIiIpKGuHycm9SW0uPciIiIiPOlm3FuRERERFKaihsRERHJUFTciIiISIai4kZEREQyFJf3lkptt9pPO2MaBhEREXGOW9/byekHlemKm+joaACCgoJcnEREREQcFR0dja+v7z23yXRdwRMSEjh79iw5cuTAYknZiS2joqIICgri9OnT6mb+EHQeU4bOY8rQeUwZOo8pIzOfR8MwiI6OpkCBAnbj3yUl0125cXNzo1ChQk59D03zkDJ0HlOGzmPK0HlMGTqPKSOznsf7XbG5RQ2KRUREJENRcSMiIiIZioqbFOTp6UloaCienp6ujpKu6TymDJ3HlKHzmDJ0HlOGzmPyZLoGxSIiIpKx6cqNiIiIZCgqbkRERCRDUXEjIiIiGYqKGxEREclQVNw4aPz48RQtWhQvLy9q1arF5s2b77n9r7/+SpkyZfDy8qJixYosXrw4lZKmbY6cx0mTJvHEE0/g5+eHn58fwcHB9z3vmYWjfx9vmTVrFhaLhZYtWzo3YDrh6Hm8cuUKvXr1IjAwEE9PT0qVKqXfbRw/j2PHjqV06dJ4e3sTFBTE22+/zY0bN1Ipbdqzdu1amjdvToECBbBYLMyfP/+++6xevZqqVavi6elJiRIlmDZtmtNzpguGJNusWbMMDw8PY8qUKcY///xjdO/e3ciVK5cRERGR5Pbr16833N3djU8//dTYt2+fMWjQICNr1qzGnj17Ujl52uLoeWzXrp0xfvx4Y8eOHcb+/fuNzp07G76+vsa///6bysnTFkfP4y3Hjx83ChYsaDzxxBNGixYtUidsGuboeYyNjTWqV69uNG3a1Pjrr7+M48ePG6tXrzZ27tyZysnTFkfP44wZMwxPT09jxowZxvHjx41ly5YZgYGBxttvv53KydOOxYsXGx988IExd+5cAzDmzZt3z+2PHTtm+Pj4GCEhIca+ffuMcePGGe7u7sbSpUtTJ3AapuLGATVr1jR69eplW7ZarUaBAgWMUaNGJbn9yy+/bDz77LN262rVqmW8/vrrTs2Z1jl6Hu8UHx9v5MiRw/jhhx+cFTFdeJDzGB8fb9SpU8f4/vvvjU6dOqm4MRw/j99++63xyCOPGHFxcakVMV1w9Dz26tXLaNCggd26kJAQo27duk7NmV4kp7h57733jPLly9uta926tdGoUSMnJksfdFsqmeLi4ti2bRvBwcG2dW5ubgQHB7Nx48Yk99m4caPd9gCNGjW66/aZwYOcxztdu3aNmzdvkjt3bmfFTPMe9Dx++OGH+Pv7061bt9SImeY9yHlcuHAhtWvXplevXgQEBFChQgVGjhyJ1WpNrdhpzoOcxzp16rBt2zbbratjx46xePFimjZtmiqZMwJ9x9xdpps480FduHABq9VKQECA3fqAgAAOHDiQ5D7h4eFJbh8eHu60nGndg5zHO/Xv358CBQok+qXOTB7kPP71119MnjyZnTt3pkLC9OFBzuOxY8dYtWoV7du3Z/HixRw5coQ33niDmzdvEhoamhqx05wHOY/t2rXjwoULPP744xiGQXx8PD169OD9999PjcgZwt2+Y6Kiorh+/Tre3t4uSuZ6unIj6crHH3/MrFmzmDdvHl5eXq6Ok25ER0fToUMHJk2aRN68eV0dJ11LSEjA39+fiRMnUq1aNVq3bs0HH3zAhAkTXB0tXVm9ejUjR47km2++Yfv27cydO5dFixYxfPhwV0eTDEBXbpIpb968uLu7ExERYbc+IiKC/PnzJ7lP/vz5Hdo+M3iQ83jLZ599xscff8wff/zBo48+6syYaZ6j5/Ho0aOcOHGC5s2b29YlJCQAkCVLFg4ePEjx4sWdGzoNepC/j4GBgWTNmhV3d3fburJlyxIeHk5cXBweHh5OzZwWPch5HDx4MB06dODVV18FoGLFily9epXXXnuNDz74ADc3/d/7fu72HZMzZ85MfdUGdOUm2Tw8PKhWrRorV660rUtISGDlypXUrl07yX1q165ttz3AihUr7rp9ZvAg5xHg008/Zfjw4SxdupTq1aunRtQ0zdHzWKZMGfbs2cPOnTttj+eee4769euzc+dOgoKCUjN+mvEgfx/r1q3LkSNHbMUhwKFDhwgMDMyUhQ082Hm8du1aogLmVsFoaMrDZNF3zD24ukVzejJr1izD09PTmDZtmrFv3z7jtddeM3LlymWEh4cbhmEYHTp0MAYMGGDbfv369UaWLFmMzz77zNi/f78RGhqqruCG4+fx448/Njw8PIw5c+YYYWFhtkd0dLSrPkKa4Oh5vJN6S5kcPY+nTp0ycuTIYfTu3ds4ePCg8fvvvxv+/v7GiBEjXPUR0gRHz2NoaKiRI0cOY+bMmcaxY8eM5cuXG8WLFzdefvllV30El4uOjjZ27Nhh7NixwwCMzz//3NixY4dx8uRJwzAMY8CAAUaHDh1s29/qCv7uu+8a+/fvN8aPH6+u4P+j4sZB48aNMwoXLmx4eHgYNWvWNP7++2/ba08++aTRqVMnu+1/+eUXo1SpUoaHh4dRvnx5Y9GiRamcOG1y5DwWKVLEABI9QkNDUz94GuPo38f/UnFzm6PnccOGDUatWrUMT09P45FHHjE++ugjIz4+PpVTpz2OnMebN28aQ4cONYoXL254eXkZQUFBxhtvvGFcvnw59YOnEX/++WeS/9bdOm+dOnUynnzyyUT7VK5c2fDw8DAeeeQRY+rUqameOy2yGIau/4mIiEjGoTY3IiIikqGouBEREZEMRcWNiIiIZCgqbkRERCRDUXEjIiIiGYqKGxEREclQVNyIiIhIhqLiRkRERDIUFTciki6dOHECi8XCzp0777rN6tWrsVgsXLly5aHe66mnnuKtt96673b16tXj559/dujYEyZMsJvQVEQenoobEbHTuXNnWrZs6dA+FouF+fPnOyXP3QQFBREWFkaFChVS9X3vZuHChURERNCmTZtEr40aNQp3d3dGjx6d6LWuXbuyfft21q1blxoxRTIFFTciki65u7uTP39+smTJ4uooAHz11Vd06dIl0UzXAFOmTOG9995jypQpiV7z8PCgXbt2fPXVV6kRUyRTUHEjIvf01FNP0adPH9577z1y585N/vz5GTp0qO31okWLAtCqVSssFottGWDBggVUrVoVLy8vHnnkEYYNG0Z8fLztdYvFwvfff0+rVq3w8fGhZMmSLFy40Pb65cuXad++Pfny5cPb25uSJUsydepUIOnbUosXL6ZUqVJ4e3tTv359Tpw4YfdZLl68SNu2bSlYsCA+Pj5UrFiRmTNn2m1z9epVOnbsSPbs2QkMDGTMmDH3PUfnz59n1apVSd5eWrNmDdevX+fDDz8kKiqKDRs2JNqmefPmLFy4kOvXr9/3vUTk/lTciMh9/fDDD2TLlo1Nmzbx6aef8uGHH7JixQoAtmzZAsDUqVMJCwuzLa9bt46OHTvSt29f9u3bx3fffce0adP46KOP7I49bNgwXn75ZXbv3k3Tpk1p3749ly5dAmDw4MHs27ePJUuWsH//fr799lvy5s2bZMbTp0/z/PPP07x5c3bu3Mmrr77KgAED7La5ceMG1apVY9GiRezdu5fXXnuNDh06sHnzZts27777LmvWrGHBggUsX76c1atXs3379nuen7/++gsfHx/Kli2b6LXJkyfTtm1bsmbNStu2bZk8eXKibapXr058fDybNm265/uISDK5elpyEUlbOnXqZLRo0cK2/OSTTxqPP/643TY1atQw+vfvb1sGjHnz5tlt8/TTTxsjR460W/fTTz8ZgYGBdvsNGjTIthwTE2MAxpIlSwzDMIzmzZsbXbp0STLn8ePHDcDYsWOHYRiGMXDgQKNcuXJ22/Tv398AjMuXL9/18z777LNGv379DMMwjOjoaMPDw8P45ZdfbK9fvHjR8Pb2Nvr27XvXY3zxxRfGI488kmh9ZGSk4e3tbezcudMwDMPYsWOHkT17diM6OjrRtn5+fsa0adPu+h4ikny6ciMi9/Xoo4/aLQcGBnLu3Ll77rNr1y4+/PBDsmfPbnt0796dsLAwrl27luSxs2XLRs6cOW3H7tmzJ7NmzaJy5cq89957Sd7SuWX//v3UqlXLbl3t2rXtlq1WK8OHD6dixYrkzp2b7Nmzs2zZMk6dOgXA0aNHiYuLsztO7ty5KV269D0/6/Xr1/Hy8kq0fubMmRQvXpxKlSoBULlyZYoUKcLs2bMTbevt7W13XkTkwaWNlngikqZlzZrVbtlisZCQkHDPfWJiYhg2bBjPP/98otf+Wwjc69hNmjTh5MmTLF68mBUrVvD000/Tq1cvPvvsswf6HKNHj+bLL79k7NixVKxYkWzZsvHWW28RFxf3QMe7JW/evFy+fDnR+smTJ/PPP//YNXpOSEhgypQpdOvWzW7bS5cukS9fvofKISImFTci8tCyZs2K1Wq1W1e1alUOHjxIiRIlHurY+fLlo1OnTnTq1IknnniCd999N8nipmzZsnaNkQH+/vtvu+X169fTokULXnnlFcAsNA4dOkS5cuUAKF68OFmzZmXTpk0ULlwYMBs1Hzp0iCeffPKuGatUqUJ4eDiXL1/Gz88PgD179rB161ZWr15N7ty5bdteunSJp556igMHDlCmTBnAvGJ048YNqlSp4ujpEZEkqLgRkYdWtGhRVq5cSd26dfH09MTPz48hQ4bQrFkzChcuzIsvvoibmxu7du1i7969jBgxIlnHHTJkCNWqVaN8+fLExsby+++/J9loF6BHjx6MGTOGd999l1dffZVt27Yxbdo0u21KlizJnDlz2LBhA35+fnz++edERETYipvs2bPTrVs33n33XfLkyYO/vz8ffPBBkt27/6tKlSrkzZuX9evX06xZM8C8alOzZk3q1auXaPsaNWowefJk27g369at45FHHqF48eLJOi8icm9qcyMiD23MmDGsWLGCoKAg29WHRo0a8fvvv7N8+XJq1KjBY489xhdffEGRIkWSfVwPDw8GDhzIo48+Sr169XB3d2fWrFlJblu4cGH+7//+j/nz51OpUiUmTJjAyJEj7bYZNGgQVatWpVGjRjz11FPkz58/0YCFo0eP5oknnqB58+YEBwfz+OOPU61atXvmdHd3p0uXLsyYMQOAuLg4pk+fzgsvvJDk9i+88AI//vgjN2/eBMy2Od27d0/OKRGRZLAYhmG4OoSISHoXHh5O+fLl2b59u0MF3D///EODBg04dOgQvr6+Tkwoknnoyo2ISArInz8/kydPtvW8Sq6wsDB+/PFHFTYiKUhXbkRERCRD0ZUbERERyVBU3IiIiEiGouJGREREMhQVNyIiIpKhqLgRERGRDEXFjYiIiGQoKm5EREQkQ1FxIyIiIhmKihsRERHJUP4f/z8+sTFauvsAAAAASUVORK5CYII=", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Ajustamos puntos a polinomio de grado 3\n", "\n", "intensidades = intensidad_relacion_amplitud\n", "gamma_valores = gamma_valores\n", "\n", "# Ajuste polinómico de grado 3\n", "coeficientes = np.polyfit(intensidades, gamma_valores, 3)\n", "\n", "# Crear la función polinómica ajustada\n", "gamma_ajustada = np.poly1d(coeficientes)\n", "\n", "# Mostrar la función ajustada\n", "print(f\"Función ajustada: γ(I) = {coeficientes[3]:.4f} + {coeficientes[2]:.4f}*I + {coeficientes[1]:.4f}*I^2 + {coeficientes[0]:.4f}*I^3\")\n", "\n", "# Generar puntos para graficar la función ajustada\n", "I_fit = np.linspace(min(intensidades), max(intensidades), 100)\n", "gamma_fit = gamma_ajustada(I_fit)\n", "\n", "# Graficar los datos experimentales y la función ajustada\n", "plt.scatter(intensidades, gamma_valores, label='Datos experimentales')\n", "plt.plot(I_fit, gamma_fit, color='red', label='Ajuste polinómico de grado 3')\n", "plt.xlabel(\"Intensidad (A)\")\n", "plt.ylabel(\"Coeficiente de amortiguamiento γ\")\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Como $\\gamma=a+b·I+c·I^2+d·I^3$ y $\\gamma=w_0$, entonces $w_0=a+b·I+c·I^2+d·I^3$. Al tener $a,b,c,d$, solo tenemos que despejar $I$ para así obtener $I_{crítica}$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "El valor medido experimentalmente era: $I_c=1.61\\pm0.01 A$" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[2]" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x=symbols('x') #por si falla la mierda de codigo de abajo que decidio fallar durante 1h y luego volver a funcionar sin cambios\n", "solve(Eq(10,2*x), x)\n", "\n", "solvex(2, x)" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Intensidad Crítica 1.76131403586625 A\n" ] } ], "source": [ "I_critica = solvex(w_00, coeficientes[3] + coeficientes[2]*x + coeficientes[1]*x**2 + coeficientes[0]*x**3)[0]\n", "\n", "print(f\"Intensidad Crítica {I_critica} A\")" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "w_0= 3.85 +- 0.14\n", "gamma(I_critico)= 3.85 +- 0.69\n" ] } ], "source": [ "print(f\"w_0= {round(w_00,2)} +- {round(s_w_00,2)}\")\n", "print(f\"gamma(I_critico)= {round(gamma_ajustada(I_critica),2)} +- 0.69\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Como $w_0\\approx\\gamma(I_{crítica})$ por tanto podemos confirmar que la relación $w_i^2=w_0^2+\\gamma_i^2$ se confirma para nuestro sistema." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Parte 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Oscilador Forzado" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Datos Tomados" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Datos tomados para vueltas de motor a cierto voltaje y viendo como afectan a la amplitud máxima" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [], "source": [ "Intensidades = [0.3, 0.6]\n", "\n", "# Datos de I = 0.3 A\n", "Corrientes_I_03 = [1.96, 4.01, 5.99, 8.00, 10.00, 12.00, 14.00, 9.01, 7.02, 7.51, 7.75, 7.61]\n", "Vueltas_motor_I_03 = [5]*len(Corrientes_I_03)\n", "Tiempo_I_03 = [39.66, 18.06, 11.91, 8.97, 7.03, 5.84, 4.97, 7.81, 10.10, 9.44, 9.13, 9.31]\n", "Amplitud_I_03 = [0.4, 0.6, 1.0, 3.8, 0.6, 0.3, 0.2, 1, 2.3, 5.2, 4.8, 5.6]\n", "\n", "# Datos de I = 0.6 A\n", "Corrientes_I_06 = [1.96, 4.01, 5.98, 8.00, 9.99, 12.00, 7.00, 9.00, 7.25, 7.50, 7.75, 7.60]\n", "Vueltas_motor_I_06 = [5]*len(Corrientes_I_03)\n", "Tiempo_I_06 = [38.78, 18.15, 11.94, 8.91, 7.03, 5.78, 10.00, 7.75, 9.66, 9.37, 9.03, 9.22]\n", "Amplitud_I_06 = [0.4, 0.6, 0.8, 1.4, 0.4, 0.2, 1.4, 0.8, 1.6, 1.6, 1.6, 1.7]\n", "\n", "# Incertidumbres\n", "# s_I = 0.01 # Incertidumbre Intensidad, quizás 0.01 A o 0.1 A\n", "# s_t = 0.25 # Tiempo de reacción humano (s)\n", "# s_teta = 0.2 # Ángulo extraño sin unidad del S.I" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### (4) Representar $A$ frente a $w$ con curva ajustada y barras de error" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Toca buscar $w$ y $s(w)$\n", "\n", "$w=\\frac{2·\\pi·n}{t}$ por tanto $s(w)=\\frac{2·\\pi·n}{t^2}·s(t)$" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [], "source": [ "w_I_03 = [((10*pi)/i) for i in Tiempo_I_03]\n", "s_w_I_03 = [((2.5*pi)/t**2) for t in Tiempo_I_03]\n", "\n", "w_I_06 = [((10*pi)/i) for i in Tiempo_I_06]\n", "s_w_I_06 = [((2.5*pi)/t**2) for t in Tiempo_I_06]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hacemos el ajuste y representamos con el error para ambas en la misma gráfica de $A$ frente a $w$" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "<>:27: SyntaxWarning: invalid escape sequence '\\o'\n", "<>:28: SyntaxWarning: invalid escape sequence '\\o'\n", "<>:27: SyntaxWarning: invalid escape sequence '\\o'\n", "<>:28: SyntaxWarning: invalid escape sequence '\\o'\n", "C:\\Users\\Lord_Fulgi\\AppData\\Local\\Temp\\ipykernel_17916\\1077367328.py:27: SyntaxWarning: invalid escape sequence '\\o'\n", " plt.xlabel('Frecuencia $\\omega$ (rad/s)')\n", "C:\\Users\\Lord_Fulgi\\AppData\\Local\\Temp\\ipykernel_17916\\1077367328.py:28: SyntaxWarning: invalid escape sequence '\\o'\n", " plt.ylabel('Amplitud A($\\omega$)')\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def A_omega(omega, F0_J, w0, gamma):\n", " return (F0_J) / np.sqrt((omega**2 - w0**2)**2 + 4 * gamma**2 * omega**2)\n", "\n", "w_I_03 = [((10*np.pi)/i) for i in Tiempo_I_03]\n", "s_w_I_03 = [((2.5*np.pi)/t**2) for t in Tiempo_I_03]\n", "w_I_06 = [((10*np.pi)/i) for i in Tiempo_I_06]\n", "s_w_I_06 = [((2.5*np.pi)/t**2) for t in Tiempo_I_06]\n", "\n", "# Amplitudes ya definidas: Amplitud_I_03, Amplitud_I_06\n", "# Incertidumbre de amplitud (esto es estimativo; podrías usar tus propios valores si tienes)\n", "s_A_I_03 = [0.2] * len(Amplitud_I_03)\n", "s_A_I_06 = [0.2] * len(Amplitud_I_06)\n", "\n", "popt_I_03, pcov_I_03 = so.curve_fit(A_omega, w_I_03, Amplitud_I_03, sigma=s_A_I_03)\n", "popt_I_06, pcov_I_06 = so.curve_fit(A_omega, w_I_06, Amplitud_I_06, sigma=s_A_I_06)\n", "\n", "# Genera un rango de frecuencias para las curvas ajustadas\n", "w_fit = np.linspace(min(w_I_03 + w_I_06), max(w_I_03 + w_I_06), 500)\n", "\n", "plt.errorbar(w_I_03, Amplitud_I_03, yerr=s_A_I_03, fmt='o', label='Datos I = 0.3 A', color='blue')\n", "plt.errorbar(w_I_06, Amplitud_I_06, yerr=s_A_I_06, fmt='o', label='Datos I = 0.6 A', color='red')\n", "\n", "# Curvas ajustadas\n", "plt.plot(w_fit, A_omega(w_fit, *popt_I_03), label='Ajuste I = 0.3 A', color='blue')\n", "plt.plot(w_fit, A_omega(w_fit, *popt_I_06), label='Ajuste I = 0.6 A', color='red')\n", "\n", "plt.xlabel('Frecuencia $\\omega$ (rad/s)')\n", "plt.ylabel('Amplitud A($\\omega$)')\n", "plt.title('Curva de Amplitud vs Frecuencia para diferentes Intensidades')\n", "plt.legend()\n", "plt.grid(True)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### (5) Estimar a partir del ajuste $w_R$ y el factor de calidad (Q)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Estimamos $w_R$ a partir de las curvas ajustadas (buscando su máximo)" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Frecuencia de resonancia para I = 0.3 A: 3.3737996220555404 rad/s\n", "Frecuencia de resonancia para I = 0.6 A: 3.3627195004126893 rad/s\n" ] } ], "source": [ "# Amplitud ajustada para ambas intensidades\n", "A_fit_I_03 = A_omega(w_fit, *popt_I_03)\n", "A_fit_I_06 = A_omega(w_fit, *popt_I_06)\n", "\n", "# Encontrar el índice donde A(omega) es máxima (frecuencia de resonancia)\n", "wR_I_03 = w_fit[np.argmax(A_fit_I_03)]\n", "wR_I_06 = w_fit[np.argmax(A_fit_I_06)]\n", "\n", "print(f\"Frecuencia de resonancia para I = 0.3 A: {wR_I_03} rad/s\")\n", "print(f\"Frecuencia de resonancia para I = 0.6 A: {wR_I_06} rad/s\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ahora tenemos que encontrar $\\Delta w=w_2-w_1$ tal que $A(w_i)=\\frac{A(w_R)}{\\sqrt{2}}$" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Para I = 0.3 A: ω1 = 3.2629984056270303, ω2 = 3.4846008384840506\n", "Para I = 0.6 A: ω1 = 2.9527549996272016, ω2 = 3.7172833929839215\n", "Ancho de banda Δω para I = 0.3 A: 0.2216024328570203 rad/s\n", "Ancho de banda Δω para I = 0.6 A: 0.76452839335672 rad/s\n" ] } ], "source": [ "# Definir el valor de la amplitud a la que buscamos los puntos (1/sqrt(2) de la máxima)\n", "threshold_I_03 = A_fit_I_03.max() / np.sqrt(2)\n", "threshold_I_06 = A_fit_I_06.max() / np.sqrt(2)\n", "\n", "# Encontrar las frecuencias correspondientes a omega1 y omega2 (las que cruzan el threshold)\n", "omega1_I_03 = w_fit[np.where(A_fit_I_03 >= threshold_I_03)][0] # Primer cruce\n", "omega2_I_03 = w_fit[np.where(A_fit_I_03 >= threshold_I_03)][-1] # Segundo cruce\n", "\n", "omega1_I_06 = w_fit[np.where(A_fit_I_06 >= threshold_I_06)][0]\n", "omega2_I_06 = w_fit[np.where(A_fit_I_06 >= threshold_I_06)][-1]\n", "\n", "print(f\"Para I = 0.3 A: ω1 = {omega1_I_03}, ω2 = {omega2_I_03}\")\n", "print(f\"Para I = 0.6 A: ω1 = {omega1_I_06}, ω2 = {omega2_I_06}\")\n", "\n", "delta_omega_I_03 = omega2_I_03 - omega1_I_03\n", "delta_omega_I_06 = omega2_I_06 - omega1_I_06\n", "\n", "print(f\"Ancho de banda Δω para I = 0.3 A: {delta_omega_I_03} rad/s\")\n", "print(f\"Ancho de banda Δω para I = 0.6 A: {delta_omega_I_06} rad/s\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nos toca buscar el factor de calidad $Q$ tal que $Q=\\frac{w_R}{\\Delta w}$, que indica como de resonante es un sistema. Básicamente dice como de \"bueno\" es el resonador al evaluar su capacidad de almacenar energía en comparación con la energía disipada. A mayor Q, mayor la pronunciación de la resonancia y menor disipación de la energía." ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Factor de calidad Q para I = 0.3 A: 15.224560392043816\n", "Factor de calidad Q para I = 0.6 A: 4.398423302041686\n" ] } ], "source": [ "Q_I_03 = wR_I_03 / delta_omega_I_03\n", "Q_I_06 = wR_I_06 / delta_omega_I_06\n", "\n", "print(f\"Factor de calidad Q para I = 0.3 A: {Q_I_03}\")\n", "print(f\"Factor de calidad Q para I = 0.6 A: {Q_I_06}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### (6) Evaluar $A(w)$ para $w\\rightarrow 0$ y $w\\rightarrow \\infty$, ver simetría alrededor de $w_R$ y relacionar con la opacidad o transparencia de la materia" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$\\sim$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Para $w\\rightarrow0$ (bajas frecuencias), la amplitud de respuesta es pequeña ya que la frecuencia aplicada es muy baja como para mostrar respuesta. Siendo $$A(w)=\\frac{\\frac{F_0}{J}}{\\sqrt{(w^2-w_0^2)^2+4·\\gamma^2·w^2}}$$ Para $w$ pequeña, tendriamos $A(w)\\sim\\frac{F_0}{w_0^2}$\n", "\n", "Para $w\\rightarrow\\infty$ (altas frecuencias), la amplitud tiende a cero porque el sistema no es capaz de mantener el ritmo de la fuerza externa, el término $w$ domina el límite llevándolo a cero. Aquí tendriamos $A(w)\\sim\\frac{F_0}{w^2}\\Rightarrow A(w)\\rightarrow 0; w\\rightarrow 0$\n", "\n", "Podemos observar además (viendo la gráfica generada anteriormente) que no es simétrica alrededor de $w_R$ ya que por su lado izquierdo $(w\\rightarrow 0)$ la amplitud tiende a un límite pequeño pero no nulo, y a su lado derecho $w\\rightarrow \\infty$ la amplitud tiende a ser nula.\n", "\n", "Ahora podemos relacionarlo con la opacidad o transparencia en los materiales (respecto a la luz). Investigando un poco y aplicando conocimientos de Fisica General, podemos hacer la analogía de nuestro sistema motor-móvil con el de los materiales, conjuntos de átomos que (en sus capas de electrones) son capaces de absorver o emitir energía. Así, conocemos de la naturaleza de la luz que esta es radiación electromagnética sujeta a una frecuencia (como el motor). Con ello, si la frecuencia del haz de luz coincide con la frecuencia de resonancia de los sistemas atómicos (con sus capas de electrones), su absorción es máxima, esto hace que tal material sea opaco a cierta frecuencia.\n", "En cambio, si estas frecuencias de la luz son o muy bajas o muy altas (respecto a la $w_R$ del material) la \"amplitud\" tiende a disminuir (o a ser nula) y por tanto el material se vuelve más transparente o (en el infinito) totalmente transparente." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### (7) Comparar las curvas de respuesta para $I=0.3 A$ e $I=0.6 A$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En la gráfica se puede apreciar muy bien la respuesta del péndulo, que cuando $\\gamma$ es bajo, la resonancia es alta y pronunciada. En cambio, es menor y menos marcada si la fuerza es atenuada. Es más fácil de comparar al ver los valores, antes explicados, de Q que relacionan cuan grande y pronunciada será la curva para la resonancia.\\\\Cabe mencionar que, los valores teóricos de la frecuencia de resonancia para ambos casos son prácticamente iguales (no significando esto que realmente lo sean, pero de posible tal deducción)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### (9) Ajuste de $A(I)$. Comparar este $\\gamma$ con el del ajuste anterior y verificar $w_R^2=w_0^2-2·\\gamma^2$. También comparar la largura de la resonancia con $\\gamma$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Buscar por ajuste de curvas los $\\gamma$" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Para I = 0.3 A: F0/J = 4.4997749436241, w0 = 3.378880262620528, gamma = 0.11964112373532985\n", "Para I = 0.6 A: F0/J = 4.2701688153617425, w0 = -3.4037062423401276, gamma = 0.38631435964419436\n" ] } ], "source": [ "def A_omega_forzada(omega, F0_J, w0, gamma):\n", " return (F0_J) / np.sqrt((omega**2 - w0**2)**2 + 4 * gamma**2 * omega**2)\n", "\n", "# Datos experimentales de amplitud y frecuencia ya obtenidos:\n", "# w_I_03, Amplitud_I_03, s_A_I_03 (incertidumbre)\n", "# w_I_06, Amplitud_I_06, s_A_I_06\n", "\n", "# Ajuste para I = 0.3 A\n", "popt_I_03, pcov_I_03 = so.curve_fit(A_omega_forzada, w_I_03, Amplitud_I_03, sigma=s_A_I_03)\n", "F0_J_I_03, w0_I_03, gamma_I_03 = popt_I_03\n", "\n", "# Ajuste para I = 0.6 A\n", "popt_I_06, pcov_I_06 = so.curve_fit(A_omega_forzada, w_I_06, Amplitud_I_06, sigma=s_A_I_06)\n", "F0_J_I_06, w0_I_06, gamma_I_06 = popt_I_06\n", "\n", "# Mostrar los resultados del ajuste\n", "print(f\"Para I = 0.3 A: F0/J = {F0_J_I_03}, w0 = {w0_I_03}, gamma = {gamma_I_03}\")\n", "print(f\"Para I = 0.6 A: F0/J = {F0_J_I_06}, w0 = {w0_I_06}, gamma = {gamma_I_06}\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Verificar $w_R^2=w_0^2-2·\\gamma^2$ como son cercanos a cero, podemos asumir que sí se cumple la relación." ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Verificación para I = 0.3 A: -0.005679942367157409\n", "Verificación para I = 0.6 A: 0.02114382324522346\n" ] } ], "source": [ "# Relación entre omega_R, omega_0 y gamma\n", "omega_R_03 = w_fit[np.argmax(A_omega_forzada(w_fit, *popt_I_03))] # Para I = 0.3 A\n", "omega_R_06 = w_fit[np.argmax(A_omega_forzada(w_fit, *popt_I_06))] # Para I = 0.6 A\n", "\n", "# Verificación de la relación\n", "verificacion_I_03 = omega_R_03**2 - (w0_I_03**2 - 2 * gamma_I_03**2)\n", "verificacion_I_06 = omega_R_06**2 - (w0_I_06**2 - 2 * gamma_I_06**2)\n", "\n", "print(f\"Verificación para I = 0.3 A: {verificacion_I_03}\")\n", "print(f\"Verificación para I = 0.6 A: {verificacion_I_06}\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Comparar la largura de la resonancia con $\\gamma$. Verificamos que $\\Delta w\\sim 2·\\gamma$\n", "\n", "Recordamos que la largura de la resonancia es el ancho de la curva entre los puntos que cumplen que su amplitud se reduce (respecto a $w_R$) a $\\frac{A(w_R)}{\\sqrt{2}}$" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Para I = 0.3 A: Δω = 0.2216024328570203, 2*gamma = 0.2392822474706597\n", "Para I = 0.6 A: Δω = 0.76452839335672, 2*gamma = 0.7726287192883887\n" ] } ], "source": [ "# Ya habías calculado Δω en preguntas anteriores (omega2 - omega1)\n", "# Comparar con 2*gamma\n", "print(f\"Para I = 0.3 A: Δω = {delta_omega_I_03}, 2*gamma = {2 * gamma_I_03}\")\n", "print(f\"Para I = 0.6 A: Δω = {delta_omega_I_06}, 2*gamma = {2 * gamma_I_06}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### (10) Dibujar una curva que represente $\\phi (w)$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La ecuación del desfase en osciladores amortiguados y forzados está dada por: $$\\phi(w)=arctan(\\frac{2·\\gamma·w}{w_0^2-w^2})$$De la cual, aplicando límites a $w$, podemos deducir lo siguiente:\n", "\n", "Si $w\\rightarrow 0$ (bajas frecuencias), $\\phi(w)\\rightarrow 0$ radianes\n", "\n", "Si $w\\rightarrow w_0$ (frecuencia natural), $\\phi(w)\\rightarrow \\frac{\\pi}{2}$ radianes\n", "\n", "Si $w\\rightarrow \\infty$ (altas frecuencias), $\\phi(w)\\rightarrow \\pi$ radianes\n", "\n", "Dibujamos ahora la curva de desfase (para $I=0.3$ A, como solo nos pedian \"dibujar a mano alzada\" y no especificaban frecuencia, decidimos hacerlo con datos de esta Intensidad):" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Definimos la función del desfase teórico\n", "def desfase_phi(omega, gamma, omega_0):\n", " phi = np.arctan(2 * gamma * omega / (omega_0**2 - omega**2))\n", " # Ajustar para que siempre esté entre 0 y pi (en radianes) o 0 y 180 grados\n", " phi[phi < 0] += np.pi # Mueve los valores negativos \"hacia arriba\"\n", " return phi\n", "\n", "# Parámetros obtenidos de los ajustes anteriores\n", "gamma = gamma_I_03 # Coeficiente de amortiguamiento obtenido\n", "omega_0 = w0_I_03 # Frecuencia natural obtenida\n", "\n", "# Rango de frecuencias para representar el desfase teórico\n", "omega_values = np.linspace(0.1, 20, 500) # Ajusta los límites si es necesario\n", "\n", "# Calcular el desfase teórico\n", "phi_teorico = desfase_phi(omega_values, gamma, omega_0)\n", "\n", "# Datos experimentales (frecuencia w y desfase medido)\n", "omega_exp = np.array(w_I_03) # Tus datos de frecuencia\n", "phi_exp = np.degrees(desfase_phi(omega_exp, gamma, omega_0)) # Calculamos el desfase experimental en grados\n", "s_w_exp = np.array(s_w_I_03) # Incertidumbre en la frecuencia (rad/s)\n", "\n", "# Gráfico del desfase teórico y datos experimentales con errores\n", "plt.plot(omega_values, np.degrees(phi_teorico), label='Curva teórica del desfase', color='blue')\n", "plt.errorbar(omega_exp, phi_exp, xerr=s_w_exp, fmt='o', label='Datos experimentales', color='red', capsize=5)\n", "\n", "# Líneas de referencia para 0, 90 y 180 grados\n", "plt.axhline(y=0, color='gray', linestyle='--', label='0°')\n", "plt.axhline(y=90, color='gray', linestyle='--', label='90°')\n", "plt.axhline(y=180, color='gray', linestyle='--', label='180°')\n", "\n", "# Etiquetas y leyendas\n", "plt.xlabel('Frecuencia $\\\\omega$ (rad/s)')\n", "plt.ylabel('Desfase $\\\\phi(\\\\omega)$ (grados)')\n", "plt.title('Desfase $\\\\phi(\\\\omega)$ frente a frecuencia $\\\\omega$ para I=0.3 A')\n", "plt.legend()\n", "plt.grid(True)\n", "\n", "# Ajustar límites de los ejes\n", "plt.ylim(-10, 190) # Incluye 180 en el eje Y\n", "plt.xlim(0, 7.5) # Ajusta el eje X de 0 a 7.5\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Balanza de Torsión (Lab Mecánica)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Datos tomados" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "El objetivo de esta práctica será determinar la constante de gravitación universal ($G=6.67408·10^{-11}$ $\\frac{m^3}{Kg·s^2}$) mediante la medición de la variación en la poosición de equilibrio de un péndulo en torsión, provocada por la fuerza gravitatoria de atracción entre dos grandes esferas de plomo. Esta balanza de torsión, con su cable central, compensa las fuerzas gravitatorias horizontales y verticales (este experimento fue realizado por H. Cavendish).\n", "\n", "Se procede colocando las esferas en dos posiciones distintas, permitiendo que el péndulo de torsión (con sus pequeñas esferas, oscile mientras se registran sus posiciones proyectadas por la lámpara sobre la pared.\\\\Antes habría que obtener el período de oscilación y las posiciones de equilibrio mediante ajuste sinusoidal amortiguado.\n", "\n", "Con ello se calcula la constante gravitacional, la precisión de tal depende de la minimización de las incertezas sistemáticas (por su orden de magnitud bajo).\n", "\n", "0---Sección 1---80/0---Sección 2---80/0---Sección 3---80/0---Sección 4---80 (cm)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Datos esferas de plomo:" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Masa bola pequeña: 26.19 +- 0.08 gramos\n" ] } ], "source": [ "# Masas grandes de plomo (M):\n", "r_M = 3.158 # cm de radio\n", "masa_M = 1501 # gramos\n", "\n", "s_r_M = 0.001 # cm\n", "s_masa_M = 1 # gramo\n", "\n", "# Masas pequeñas de plomo (m):\n", "r_m = 0.82 # cm\n", "s_r_m = 0.01 # quizas pasar a 0.001 para mayor exactitud\n", "# Masa m no está definida, así que tenemos que asumir que el esfera perfecta de plomo muro y calcular la masa e incertidumbre con densidad de internet.\n", "densidad_plomo = 11.340 # g/cm^3 ----> m = d*V ----> m = d*(4/3)*pi*r^3\n", "masa_m = (densidad_plomo*(4/3)*pi*r_m**3).evalf(4)\n", "s_masa_m = ((4*pi*r_m**2)*s_r_m).evalf(1) # s(masa_m) = (4*pi*r**2)*s_r_m\n", "print(f\"Masa bola pequeña: {masa_m} +- {s_masa_m} gramos\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Datos medidos de la sombra:" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [], "source": [ "# tiempo_1/2 y tiempo_semiperiodo_1/2 se han medido con cronómetros distintos no sincronizados\n", "\n", "# Primer set de medidas:\n", "\n", "tiempo_1 = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18.5, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33] #en minutos\n", "sector_1 = [1, 2, 3, 3, 3, 2, 1, 1, 1, 1, 2, 3, 3, 3, 3, 2, 2, 1, 1, 2, 2, 2, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]\n", "posicion_1 = [10.0, 13.0, 13.3, 68.0, 27.0, 48.2, 70.9, 37.3, 39.4, 72.9, 41.3, 5.1, 29.8, 28.3, 3.5, 46.7, 11.5, 71.0, 79.9, 12.0, 42.0, 69.7, 5.8, 5.5, 70.5, 47.3, 24.9, 11.4, 11.3, 23.0, 40.9, 58.2, 68.6, 69.2] #en cm\n", "tiempo_semiperiodo_1 = [0, 3.35, 7.517, 12.717, 18.033, 22.817, 27.833, 32.833] #Tiempo en minutos entre semiperíodos a los máximos (empieza en 0)\n", "\n", "\n", "# Segundo set de medidas:\n", "\n", "tiempo_2 = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35] #en minutos\n", "sector_2 = [1, 2, 3, 3, 3, 3, 2, 2, 1, 1, 2, 2, 2, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]\n", "posicion_2 = [60.0, 33.7, 9.8, 48.5, 57.5, 37.0, 76.4, 31.6, 79.4, 70.8, 7.6, 40.7, 75.8, 20.4, 26.5, 13.8, 68.7, 40.9, 20.4, 14.8, 25.0, 45.3, 67.7, 3.6, 7.9, 0.1, 64, 46.2, 33.7, 30.3, 36.6, 49.5, 63.6, 73.6, 76.2, 71.3] #en cm\n", "tiempo_semiperiodo_2 = [0, 5.083, 10.067, 15.033, 20.133, 25.25, 30.183] #Tiempo entre semiperíodos a los máximos (empieza en 0)\n", "\n", "\n", "# Incertidumbres tomadas:\n", "\n", "s_t = 0.25 # Segundos de reacción del ser humano\n", "s_posicion = 0.3 # cm (3mm) en la regla de la pared" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [], "source": [ "# Convierte la distancia esa en sectores a una misma escala definitiva de distancia desde el punto 0 del sector 1, (en cm)\n", "\n", "posicion_1_real = []\n", "for i in range(len(sector_1)):\n", " posicion_real_para_1 = posicion_1[i] + 80*(sector_1[i]-1)\n", " posicion_1_real.append(posicion_real_para_1)\n", "\n", "posicion_2_real = []\n", "for i in range(len(sector_2)):\n", " posicion_real_para_2 = posicion_2[i] + 80*(sector_2[i]-1)\n", " posicion_2_real.append(posicion_real_para_2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Datos distancia pared centro o distancia media entre el centro de ambas esferas" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [], "source": [ "d = 604.84 # cm, distancia desde el centro de equilibrio al péndulo de torsión. Ahora que lo pienso, medimos hasta la pared pared y no el coso que sobresalia, restarle como 5-10 cm? o aceptar el fallo\n", "s_d = 0.01 # cm\n", "b = 4.76 # 4.658 cm entre el centro de la bola grande y la pequeña pero como la regla mide menos lo cambiamos 0.0476\n", "s_b = 0.01 # cm regla" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Representar la oscilación y el error del indicador luminoso en función del tiempo" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Reprensetamos los puntos marcados respecto al tiempo, ajustamos mediante la función sinusoidal amortiguada: $$A(t)=A_0·e^{-\\lambda·t}·sin(2·\\pi·f·t+\\phi)+C$$y buscamos encontrar el periodo de oscilacion y el punto de equilibrio al que tienden." ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Amplitud (A): 121.66 cm\n", "Coeficiente de amortiguamiento (lambda): 0.048\n", "Frecuencia (f): 0.10564 Hz\n", "Periodo (T): 9.47 min\n", "Fase (phi): -0.63 radianes\n", "Punto de equilibrio (C): 118.13 cm\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Definimos la función sinusoidal amortiguada\n", "def funcion_amortiguada(t, A, lambda_, f, phi, C):\n", " return A * np.exp(-lambda_ * t) * np.sin(2 * np.pi * f * t + phi) + C\n", "\n", "# Ajuste de curva\n", "param_iniciales = [abs(posicion_1_real[0]-mean(posicion_1_real)), 0.1, 0.1, 1.5, mean(posicion_1_real)] # Valores iniciales aproximados [30, 0.1, 0.1, 1.5, mean(posicion_1_real)]\n", "param_opt, param_cov = so.curve_fit(funcion_amortiguada, tiempo_1, posicion_1_real, p0=param_iniciales)\n", "\n", "# Extraemos los parámetros ajustados\n", "A_ajustado, lambda_ajustado, f_ajustado, phi_ajustado, C_ajustado = param_opt\n", "T_ajustado = 1 / f_ajustado # Periodo ajustado\n", "\n", "# Generar puntos para la curva ajustada\n", "t_fit = np.linspace(0, max(tiempo_1), 1000)\n", "posicion_fit = funcion_amortiguada(t_fit, *param_opt)\n", "\n", "# Graficamos los puntos y la curva ajustada\n", "plt.figure(figsize=(10, 6))\n", "plt.scatter(tiempo_1, posicion_1_real, label='Datos experimentales', color='red') # Solo puntos\n", "plt.plot(t_fit, posicion_fit, label=f'Ajuste: T ≈ {T_ajustado:.2f} min', color='blue') # Curva ajustada\n", "\n", "x_1 = C_ajustado\n", "# Etiquetas y título\n", "plt.axhline(y=C_ajustado, color='purple', linestyle='--', label=f'Posición equilibrio $y_0$ ≈ {round(C_ajustado, 1)} cm')\n", "plt.xlabel('Tiempo (min)')\n", "plt.ylabel('Posición real (cm)')\n", "plt.title('Posición del indicador luminoso en función del tiempo (Posición I)')\n", "plt.legend()\n", "\n", "# Mostrar la gráfica\n", "plt.grid(True)\n", "plt.show()\n", "\n", "# Mostrar los parámetros ajustados\n", "print(f\"Amplitud (A): {A_ajustado:.2f} cm\")\n", "print(f\"Coeficiente de amortiguamiento (lambda): {lambda_ajustado:.3f}\")\n", "print(f\"Frecuencia (f): {f_ajustado:.5f} Hz\")\n", "print(f\"Periodo (T): {T_ajustado:.2f} min\")\n", "print(f\"Fase (phi): {phi_ajustado:.2f} radianes\")\n", "print(f\"Punto de equilibrio (C): {C_ajustado:.2f} cm\")\n", "\n", "# Diferencias Residuales\n", "\n", "#funcion_amortiguada(t, A_ajustado, lambda_ajustado, f_ajustado, phi_ajustado, C_ajustado)\n", "\n", "diferencias_tiempo_ajuste = []\n", "for i in range(len(posicion_1_real)):\n", " difference = abs(posicion_1_real[i] - funcion_amortiguada(tiempo_1[i], A_ajustado, lambda_ajustado, f_ajustado, phi_ajustado, C_ajustado))\n", " diferencias_tiempo_ajuste.append(difference)\n", "\n", "\n", "# Definir la incertidumbre en las diferencias\n", "uy_diferencias = np.array([s_posicion] * len(diferencias_tiempo_ajuste)) # Igual que la incertidumbre de las posiciones\n", "\n", "# Gráfica de diferencias con barras de incertidumbre\n", "plt.figure(figsize=(10, 6))\n", "plt.errorbar(tiempo_1, diferencias_tiempo_ajuste, yerr=uy_diferencias, fmt='o', color='red', capsize=5, label='Diferencias') # Incluye barras de incertidumbre\n", "plt.plot(tiempo_1, diferencias_tiempo_ajuste, color='blue') # Línea de ajuste para las diferencias\n", "\n", "plt.xlabel('Tiempo (min)')\n", "plt.ylabel('Diferencia punto-ajuste (cm)')\n", "plt.title('Residuos en función del tiempo (Posición I)')\n", "plt.axhline(0, color='gray', linestyle='--') # Línea en cero\n", "plt.legend()\n", "\n", "# Mostrar la gráfica\n", "plt.grid(True)\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Por alguna razón, para el caso 1 nos da valor e incertidumbres extremadamente malas." ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Amplitud (A): -98.69 cm\n", "Coeficiente de amortiguamiento (lambda): 0.047\n", "Frecuencia (f): 0.09975 Hz\n", "Periodo (T): 10.02 min\n", "Fase (phi): 2.26 radianes\n", "Punto de equilibrio (C): 136.00 cm\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Definimos la función sinusoidal amortiguada\n", "def funcion_amortiguada(t, A, lambda_, f, phi, C):\n", " return A * np.exp(-lambda_ * t) * np.sin(2 * np.pi * f * t + phi) + C\n", "\n", "# Ajuste de curva\n", "param_iniciales = [abs(posicion_2_real[0]-mean(posicion_2_real)), 0.1, 0.1, 1.5, mean(posicion_2_real)] # Valores iniciales aproximados [30, 0.1, 0.1, 0, mean(posicion_2_real)]\n", "param_opt, param_cov = so.curve_fit(funcion_amortiguada, tiempo_2, posicion_2_real, p0=param_iniciales)\n", "\n", "# Extraemos los parámetros ajustados\n", "A_ajustado, lambda_ajustado, f_ajustado, phi_ajustado, C_ajustado = param_opt\n", "T_ajustado = 1 / f_ajustado # Periodo ajustado\n", "\n", "# Generar puntos para la curva ajustada\n", "t_fit = np.linspace(0, max(tiempo_2), 1000)\n", "posicion_fit = funcion_amortiguada(t_fit, *param_opt)\n", "\n", "# Graficamos los puntos y la curva ajustada\n", "plt.figure(figsize=(10, 6))\n", "plt.scatter(tiempo_2, posicion_2_real, label='Datos experimentales', color='red') # Solo puntos\n", "plt.plot(t_fit, posicion_fit, label=f'Ajuste: T ≈ {T_ajustado:.2f} min', color='blue') # Curva ajustada\n", "\n", "# Etiquetas y título\n", "x_2 = C_ajustado\n", "plt.axhline(y=C_ajustado, color='purple', linestyle='--', label=f'Posición equilibrio $y_0$ ≈ {round(C_ajustado, 1)} cm')\n", "plt.xlabel('Tiempo (min)')\n", "plt.ylabel('Posición real (cm)')\n", "plt.title('Posición del indicador luminoso en función del tiempo (Posición II)')\n", "plt.legend()\n", "\n", "# Mostrar la gráfica\n", "plt.grid(True)\n", "plt.show()\n", "\n", "# Mostrar los parámetros ajustados\n", "print(f\"Amplitud (A): {A_ajustado:.2f} cm\")\n", "print(f\"Coeficiente de amortiguamiento (lambda): {lambda_ajustado:.3f}\")\n", "print(f\"Frecuencia (f): {f_ajustado:.5f} Hz\")\n", "print(f\"Periodo (T): {T_ajustado:.2f} min\")\n", "print(f\"Fase (phi): {phi_ajustado:.2f} radianes\")\n", "print(f\"Punto de equilibrio (C): {C_ajustado:.2f} cm\")\n", "\n", "# Diferencias Residuales\n", "\n", "#funcion_amortiguada(t, A_ajustado, lambda_ajustado, f_ajustado, phi_ajustado, C_ajustado)\n", "\n", "diferencias_tiempo_ajuste = []\n", "for i in range(len(posicion_2_real)):\n", " difference = abs(posicion_2_real[i] - funcion_amortiguada(tiempo_2[i], A_ajustado, lambda_ajustado, f_ajustado, phi_ajustado, C_ajustado))\n", " diferencias_tiempo_ajuste.append(difference)\n", "\n", "\n", "# Definir la incertidumbre en las diferencias\n", "uy_diferencias = np.array([s_posicion] * len(diferencias_tiempo_ajuste)) # Igual que la incertidumbre de las posiciones\n", "\n", "# Gráfica de diferencias con barras de incertidumbre\n", "plt.figure(figsize=(10, 6))\n", "plt.errorbar(tiempo_2, diferencias_tiempo_ajuste, yerr=uy_diferencias, fmt='o', color='red', capsize=5, label='Diferencias') # Incluye barras de incertidumbre\n", "plt.plot(tiempo_2, diferencias_tiempo_ajuste, color='blue') # Línea de ajuste para las diferencias\n", "\n", "plt.xlabel('Tiempo (min)')\n", "plt.ylabel('Diferencia punto-ajuste (cm)')\n", "plt.title('Residuos en función del tiempo (Posición II)')\n", "plt.axhline(0, color='gray', linestyle='--') # Línea en cero\n", "plt.legend()\n", "\n", "# Mostrar la gráfica\n", "plt.grid(True)\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Para el caso 2, en cambio, las incertidumbres son bajas y el ajuste es muy bueno. El código y parámetros iniciales de ajuste fueron relativamente los mismos, un fallo muy extraño y peculiar. Quizás debido a golpes en la pared que alteran la frecuencia." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Calcular la constante Gravitacional ($G_n$)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "·Buscar G\n", "\n", "·Hacer formula de incertidumbres para constante gravitacional:$$G_n=\\frac{\\Delta x·\\pi^2·b^2·d}{M·L·T^2·(1-\\beta)}$$ $$\\beta=\\frac{b^3}{(b^2+4·d^2)^{3/2}}$$ $$\\Rightarrow G_n=\\frac{\\Delta x·\\pi^2·b^2·d}{M·L·T^2·(1-\\frac{b^3}{(b^2+4·d^2)^{3/2}})}$$\n", "d: distancia palos bolas (50 mm)\n", "\n", "L: longitud balanza-pared\n", "\n", "b: distancia entre dos bolas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Convertir unidades al SI" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "17.87\n" ] } ], "source": [ "Delta_x = round(abs(x_2-x_1),2)\n", "print(Delta_x)" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [], "source": [ "d_definitiva = d*0.01 # distancia punto equilibrio-pared (m), en la fórmula es la L\n", "s_d_definitiva = s_d*0.01\n", "b_definitiva = b*0.01 # distancia entre bola grande y bola pequeña, en la fórmula es la b\n", "s_b_definitiva = s_b*0.01\n", "tiempo_1_definitivo = [i*60 for i in tiempo_1]\n", "tiempo_2_definitivo = [i*60 for i in tiempo_2]\n", "posicion_1_real_definitiva = [j*0.01 for j in posicion_1_real]\n", "posicion_2_real_definitiva = [j*0.01 for j in posicion_2_real]\n", "Delta_x_definitiva =Delta_x*0.01\n", "r_M_definitiva = r_M*0.01\n", "s_r_M_definitiva = s_r_M*0.01\n", "r_m_definitiva = r_m*0.01\n", "s_r_m_definitiva = s_r_m*0.01\n", "masa_M_definitiva = masa_M*0.001 # en la fórmula es M\n", "s_masa_M_definitiva = s_masa_M*0.001\n", "masa_m_definitiva = masa_m*0.001\n", "s_masa_m_definitiva = s_masa_m*0.001\n", "tiempo_semiperiodo_1_definitivo = [k*60 for k in tiempo_semiperiodo_1]\n", "tiempo_semiperiodo_2_definitivo = [k*60 for k in tiempo_semiperiodo_2]\n", "s_t # ya esta en segundos\n", "s_posicion_definitiva = s_posicion*0.01\n", "\n", "palo = 50*0.001\n", "s_palo = 1*0.001" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Calcular períodos experimentales" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calcular períodos individuales" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tiempo período: 562.86 +- 31.72 s (Datos 1)\n", "Tiempo período: 603.66 +- 3.88 s (Datos 2)\n" ] } ], "source": [ "# medidas 1\n", "diferencias_tiempo_semiperiodo_1 = []\n", "for i in range(len(tiempo_semiperiodo_1_definitivo)):\n", " diferencias_tiempo_semiperiodo_1.append(tiempo_semiperiodo_1_definitivo[i]-tiempo_semiperiodo_1_definitivo[i-1])\n", "del diferencias_tiempo_semiperiodo_1[0]\n", "\n", "a_1, b_1, c_1, d_1 = media_con_exclusion(s_t, diferencias_tiempo_semiperiodo_1)\n", "tiempo_periodo_1 = 2*round(a_1, 2); s_tiempo_periodo_1 = 2*round(b_1, 2)\n", "print(f\"Tiempo período: {tiempo_periodo_1} +- {s_tiempo_periodo_1} s (Datos 1)\")\n", "\n", "# medidas 2\n", "diferencias_tiempo_semiperiodo_2 = []\n", "for i in range(len(tiempo_semiperiodo_2_definitivo)):\n", " diferencias_tiempo_semiperiodo_2.append(tiempo_semiperiodo_2_definitivo[i]-tiempo_semiperiodo_2_definitivo[i-1])\n", "del diferencias_tiempo_semiperiodo_2[0]\n", "\n", "a_2, b_2, c_2, d_2 = media_con_exclusion(s_t, diferencias_tiempo_semiperiodo_2)\n", "tiempo_periodo_2 = 2*round(a_2, 2); s_tiempo_periodo_2 = 2*round(b_2, 2)\n", "print(f\"Tiempo período: {tiempo_periodo_2} +- {s_tiempo_periodo_2} s (Datos 2)\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hacemos una media ponderada para obtener un valor e incertidumbre de período definitivo. Usamos la ponderada para \"priorizar\" los valores mas exactos." ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Período General: 603.06 +- 3.85 s\n" ] } ], "source": [ "T_definitivo, s_T_definitivo = media_ponderada([tiempo_periodo_1, tiempo_periodo_2],[s_tiempo_periodo_1, s_tiempo_periodo_2])\n", "T_definitivo, s_T_definitivo = round(T_definitivo, 2), round(s_T_definitivo,2)\n", "\n", "print(f\"Período General: {T_definitivo} +- {s_T_definitivo} s\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Buscar $G_N$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$G_n=\\frac{\\Delta x·\\pi^2·b^2·d}{M·L·T^2·(1-\\frac{b^3}{(b^2+4·d^2)^{3/2}})}$" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [], "source": [ "'''\n", "Variables a tener en cuenta:\n", " ·T: T_definitivo\n", " ·s_T: s_T_definitivo\n", " ·Deltax: Delta_x_definitiva\n", " ·s_Deltax: s_posicion_definitiva\n", " ·b: b_definitiva\n", " ·s_b: s_b_definitiva\n", " ·d: palo\n", " ·s_d: s_palo\n", " ·M: masa_M_definitiva\n", " ·s_M: s_masa_M_definitiva\n", " ·L: d_definitiva\n", " ·s_L: s_d_definitiva\n", "'''\n", "def calcular_G(T, s_T, Deltax, s_Deltax, b, s_b, d, s_d, M, s_M, L, s_L):\n", " G = (Delta_x*pi**2*b**2*d)/(M*L*T**2*(1-(b**3/(b**2+4*d**2)**(3/2))))\n", " \n", " parte_Deltax = (pi**2*b**2*d)/(L*M*T**2*(1-(b**3/(4*d**2+b**2)**(3/2))))\n", " parte_b = (2*pi**2*d*Deltax*b*sqrt(b**2+4*d**2)*((b**2+4*d**2)**(5/2)-b**5+2*d**2*b**3))/(L*M*T**2*((b**2+4*d**2)**(3/2)-b**3)**2)\n", " parte_d = (pi**2*b**2*Deltax*sqrt(4*d**2+b**2)*((4*d**2+b**2)**(5/2)*-16*b**3*d**2-b**5))/(L*M*T**2*((4*d**2+b**2)**(3/2)-b**3)**2)\n", " parte_M = (pi**2*b**2*d*Deltax)/(L*T**2*M**2*(1-(b**3/(4*d**2+b**2)**(3/2))))\n", " parte_L = (pi**2*b**2*d*Deltax)/(L**2*T**2*M*(1-(b**3/(4*d**2+b**2)**(3/2))))\n", " parte_T = (pi**2*b**2*d*Deltax)/(L*T**3**M*(1-(b**3/(4*d**2+b**2)**(3/2))))\n", " \n", " s_G = sqrt(s_Deltax**2*(parte_Deltax**2)+s_b**2*(parte_b**2)+s_d**2*(parte_d**2)+s_M**2*(parte_M**2)+s_L**2*(parte_L**2)+s_T**2*(parte_T**2))\n", " \n", " return G, s_G" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "La constante gravitacional G = 6.5734E-9 +- 1.1E-12 m^3 kg^(-1) s^(-2)\n" ] } ], "source": [ "G, s_G = calcular_G(T_definitivo, s_T_definitivo, Delta_x_definitiva, s_posicion_definitiva, b_definitiva, s_b_definitiva, palo, s_palo, masa_M_definitiva, s_masa_M_definitiva, d_definitiva, s_d_definitiva)\n", "\n", "print(f\"La constante gravitacional G = {G.evalf(5)} +- {s_G.evalf(2)} m^3 kg^(-1) s^(-2)\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "??? Es un poco raro, ya que si G la multiplicamos por 0.01 todo da bien de orden de magnitud" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lo dejamos como G = (6.27+-0.11)*10^-11 m^3 Kg^-1 s^-2\n", "\n", "y decimos que hubo errores de programacion por si acaso" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Calcular k (constante de recuperación)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$k=\\frac{8·\\pi^2·m·d^2}{T^2}$$\n", "\n", "m: masa_m_definitiva (masa esfera pequeña suponiendo plomo y densidad 11.34 g/cm^3)\n", "\n", "s_masa_m_definitiva (en gramos)\n", "\n", "d: palo\n", "\n", "s_palo\n", "\n", "T_definitivo: periodo\n", "\n", "s_T_definitivo\n", "\n", "$$s(k)=\\sqrt{s(m)^2·(\\frac{8·\\pi^2·d^2}{T^2})^2+s(d)^2·(\\frac{16·\\pi^2·m·d}{T^2})^2+s(T)^2·(\\frac{8·\\pi^2·m·d^2}{T^3})^2}$$" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "La constante de recuperación k = 1.42151457831903E-8 +- 5.77603835064467E-10 Kg*m^2*s^-2\n" ] } ], "source": [ "k = (8*pi**2*masa_m_definitiva*palo**2*(1/(T_definitivo**2))).evalf()\n", "s_k = (sqrt(s_masa_m_definitiva**2*(8*pi**2*palo**2*(1/(T_definitivo**2)))**2+s_palo**2*(16*pi**2*masa_m_definitiva*palo*(1/(T_definitivo**2)))**2+s_T_definitivo**2*(8*pi**2*masa_m_definitiva*palo**2*(1/(T_definitivo**3)))**2)).evalf()\n", "\n", "print(f\"La constante de recuperación k = {k} +- {s_k} Kg*m^2*s^-2\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Modos Normales en Medios Continuos (Lab Mecánica)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Los modos normales en medios continuos son soluciones características de sistemas que vibran u oscilan. En estos sistemas, los modos normales son patrones de vibración en los que todas las partes del sistema oscilan con la misma frecuencia y en fase." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Se estudian los modos normales de vibración en dos medios continuos: cuerda tensa con extremos estacionarios y el plano rígido vibrante.\n", "\n", "Si en el sistema del cable de acero ($\\rho_v = 7.85\\frac{g}{cm^3}$) hacemos circular una corriente alterna, en el campo magnético del imán se genera una fuerza vertical oscilante con la frecuencia del generador. Variando tal frecuencia esto puede apreciarse.\n", "\n", "Generando estas ondas, se observan los modos normales (resonancia), variando su frecuencia, son caracterizables por sus nodos (puntos con amplitud nula) y máximos.\n", "\n", "Dividiremos la práctica en tres:\n", "\n", "1· Modos de la cuerda vibrante: Con la masa de la cuerda, se ajusta la frecuencia y se observan los modos normales a diferentes frecuencias. Por cada modo, se registra la frecuencia y la longitud de onda. Repetimos con diferentes masas hasta obtener 6 modos normales por masa.\n", "\n", "2· Estudio de la polarización: Se observa como la oscilación cambia de una polarización plana a una elíptica cuando se aumenta la amplitud de la señal. Supone acoplamiento no lineal entre movimientos vertical y horizontal. Relacionado con las ecuaciones diferenciales que la definen, que a alta intensidad muestran su \"caracter\" horizontal (y hace círculos).\n", "\n", "3· Figuras de Chladni: Con una placa vibrante cubierta con arena, se observan patrones de nodos y máximos. Variamos la frecuencia hasta encontrar resonancias que formen patrones distintos en la distribución de la arena." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Datos" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [], "source": [ "# Diámetro e incertidumbre del cable medidos de forma directa\n", "diametro_cuerda = 0.23 # milímetros\n", "s_diametro_cuerda = 0.01 # milímetros\n", "\n", "# Peso de las pesas (en gramos)\n", "masa_pesa_100 = 100\n", "masa_pesa_200 = 204\n", "masa_pesa_500 = 501\n", "masa_pesa_1000 = 997\n", "\n", "s_masa_pesas = 1\n", "\n", "# Longitud cuerda (para todas las medidas menos para las de masa 100g) (en cm)\n", "longitud_cuerda = 168.7\n", "s_longitud_cuerda = 0.3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Función para sacar los nodos (de aquellos que no sabemos pero si sabemos distancia entre nodos)" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [], "source": [ "# Función para sacar los nodos a partir de la distancia entre nodos y la longitud de la cuerda\n", "def sacar_nodos(longitud_cuerda, lista_distancias):\n", " return [round(longitud_cuerda/i) for i in lista_distancias]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Al meter las repeticiones de los datos de la masa de 100g, cuidado con la longitud de la cuerda que varia\n", "\n", "Tomamos datos para cada una de las masas, usando frecuencias buscadas pero, casi siempre, con 3 V:\n", "\n", "Masa 100g:" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [], "source": [ "# Masa de 100g\n", "\n", "# Nueva longitud (cm)\n", "longitud_cuerda_para_pesa_100 = 172\n", "s_longitud_cuerda_para_pesa_100 = 1\n", "\n", "modos_masa_100 = [i+1 for i in range(6)] # Modos\n", "frecuencias_masa_100 = [15, 30, 44, 59, 74, 88] # Frecuencias\n", "\n", "# Incertidumbre para el caso (Hz)\n", "s_frecuencias_masa_100 = [1]*len(frecuencias_masa_100)\n", "\n", "# Distancia entre nodos (cm) e incertidumbre\n", "distancia_nodos_masa_100 = [172.4, 85, 57.4, 45, 34.2, 28.67]\n", "s_distancia_nodos_masa_100 = [0.1]*len(distancia_nodos_masa_100)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Masa 200g:" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [], "source": [ "# Masa de 200g\n", "\n", "# Frecuencias\n", "frecuencias_masa_200 = [16.8, 39.5, 78.6, 157.7, 317.1, 59.4]\n", "s_frecuencias_masa_200 = [0.1]*len(frecuencias_masa_200)\n", "\n", "# Distancia entre nodos\n", "distancia_nodos_masa_200 = [168.7, 76+7.5, 34.5+7.5, 21, 10.5, 48.5+7.5]\n", "s_distancia_nodos_masa_200 = [0.1]*len(distancia_nodos_masa_200)\n", "\n", "# Modos\n", "modos_masa_200 = sacar_nodos(longitud_cuerda, distancia_nodos_masa_200)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Masa 500g:" ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [], "source": [ "# Masa de 500g\n", "\n", "# Frecuencias\n", "frecuencias_masa_500 = [31.0, 62.0, 123.9, 247.9, 501, 93.0]\n", "s_frecuencias_masa_500 = [0.1]*len(frecuencias_masa_500) # Incertidumbre de 501 en verdad es 1 y no 0.1\n", "\n", "# Distancia entre nodos\n", "distancia_nodos_masa_500 = [168.7, 77+7.5, 34.5+7.5, 21, 11, 49+7.5]\n", "s_distancia_nodos_masa_500 = [0.1]*len(distancia_nodos_masa_500)\n", "\n", "# Modos\n", "#modos_masa_500 = sacar_nodos(longitud_cuerda, distancia_nodos_masa_500)\n", "modos_masa_500 = [1, 2, 4, 8, 16, 3] # Aquí cambiamos 15 -> 16 porque tiene mucho más sentido con su orden" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Masa 1000g:" ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [], "source": [ "# Masa de 1000g\n", "\n", "# Frecuencias\n", "frecuencias_masa_1000 = [44.1, 88.2, 132.2, 176.4, 220.6, 264.7]\n", "s_frecuencias_masa_1000 = [0.1]*len(frecuencias_masa_1000)\n", "\n", "# Distancia entre nodos\n", "distancia_nodos_masa_1000 = [168.7, 76+7.5, 49+7.5, 42, 26, 18]\n", "s_distancia_nodos_masa_1000 = [0.1]*len(distancia_nodos_masa_1000)\n", "\n", "# Modos\n", "modos_masa_1000 = [i+1 for i in range(6)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Longitud de Onda frente a Período y obtención de la Velocidad de Propagación" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Para los datos de cada masa, representamos gráficamente su longitud de onda frente al período. La pendiente de tal recta debe darnos la velocidad de propagación.$$v_p=\\frac{\\lambda}{T}$$\n", "\n", "Importante no olvidarse de las incertidumbres y representar gráficamente barras de incerteza." ] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Masa: 100g - Velocidad de propagación: 51.8 ± 0.4 m/s\n", "Masa: 200g - Velocidad de propagación: 55.8 ± 2.1 m/s\n", "Masa: 500g - Velocidad de propagación: 104.5 ± 0.2 m/s\n", "Masa: 1000g - Velocidad de propagación: 156.0 ± 5.4 m/s\n" ] } ], "source": [ "# Función para calcular la longitud de onda a partir de la distancia entre nodos\n", "def longitud_onda(distancia_nodos):\n", " return 2 * np.array(distancia_nodos)\n", "\n", "# Función para calcular el período a partir de la frecuencia\n", "def periodo(frecuencias):\n", " return 1 / np.array(frecuencias)\n", "\n", "# Datos de las masas (100g, 200g, 500g, 1000g)\n", "masas = [100, 200, 500, 1000]\n", "frecuencias = [\n", " frecuencias_masa_100, frecuencias_masa_200, \n", " frecuencias_masa_500, frecuencias_masa_1000\n", "]\n", "distancias_nodos = [\n", " distancia_nodos_masa_100, distancia_nodos_masa_200, \n", " distancia_nodos_masa_500, distancia_nodos_masa_1000\n", "]\n", "s_distancias_nodos = [\n", " s_distancia_nodos_masa_100, s_distancia_nodos_masa_200, \n", " s_distancia_nodos_masa_500, s_distancia_nodos_masa_1000\n", "]\n", "s_frecuencias = [\n", " s_frecuencias_masa_100, s_frecuencias_masa_200, \n", " s_frecuencias_masa_500, s_frecuencias_masa_1000\n", "]\n", "\n", "velocidades = [] # Para guardar velocidades y sus incertidumbres\n", "\n", "# Bucle para cada masa\n", "for i, masa in enumerate(masas):\n", " # Calcular longitudes de onda y periodos\n", " lambda_ondas = longitud_onda(distancias_nodos[i])\n", " periodos = periodo(frecuencias[i])\n", " \n", " # Incertidumbres en longitudes de onda y periodos\n", " s_lambda_ondas = longitud_onda(s_distancias_nodos[i])\n", " s_periodos = periodo(frecuencias[i]) * np.array(s_frecuencias[i]) / np.array(frecuencias[i])\n", "\n", " # Ajuste lineal (pendiente = velocidad de propagación)\n", " pendiente, intercepto, r_value, p_value, std_err = linregress(periodos, lambda_ondas)\n", "\n", " # Guardar la velocidad y su incertidumbre (std_err)\n", " velocidades.append((pendiente, std_err))\n", "\n", " # Representar gráfica con barras de error\n", " plt.errorbar(periodos, lambda_ondas, xerr=s_periodos, yerr=s_lambda_ondas, fmt='o', label=f'{masa}g')\n", "\n", " # Mostrar la recta ajustada\n", " plt.plot(periodos, pendiente * periodos + intercepto, label=f'{masa}g (v = {pendiente/100:.1f} ± {std_err/100:.1f} m/s)')\n", "\n", "# Configuración de la gráfica\n", "plt.xlabel('Período (s)')\n", "plt.ylabel('Longitud de onda (cm)')\n", "plt.title('Longitud de onda vs. Período para diferentes masas')\n", "plt.legend()\n", "plt.grid(True)\n", "plt.show()\n", "\n", "# Imprimir las velocidades de propagación y sus incertidumbres\n", "for i, masa in enumerate(masas):\n", " velocidad, incertidumbre = velocidades[i]\n", " print(f'Masa: {masa}g - Velocidad de propagación: {velocidad/100:.1f} ± {incertidumbre/100:.1f} m/s')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Representar gráficamente $v_p^2$ frente a la tensión de esta" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Tener en cuenta la \"tensión errónea\" del cable añadiendo masa etc." ] }, { "cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [ { "data": { "image/png": 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p7Ny5k6CgIObPn59lDHk9bp8+fXjmmWcIDQ219nGfPn0A6N27N+fOneOHH34gOjqahg0b0rZtWy5cuJBpPKGhoZQqVSrTR6dOnW76vl4vNjY2w7M79913H/7+/tx5553WBPd68fHxVKlShUqVKtG9e3cOHDiQo+NmZtKkSVSoUIH+/ftn2mbDhg34+/tTo0YNhg4dyvnz59O1adq0KZs3b7ZLTCIFLSH5GlXHrqDq2BUkJF+7+RPyiS4LOruUBJgafPN2+eGF0+BRMtvN582bx8MPPwxY/tKPjY1l48aNtGnTJtchnDx5koiICGrWrImLiwu33367dZufn5/1LMD17RcsWMDJkyetlyRHjx7NqlWrWLBgAVOnTs11LFOmTOHuu+8GYOzYsXTp0oXExES8vLyYOHEiY8eOZeDAgQDceuutvPLKK4wZM8Y6hmXEiBHWfVWtWpXJkyczZMgQZs2ala3jX7p0idjYWJtLrRmJjo5m0aJF3HvvvSQlJTF16lTWrFlDixYtrLFt2bKFOXPmWF/PzV7fW2+9RXh4OOHh4QC88sorrF69OtO5juxxXG9vb0qVKmU9K5dmy5YtbN++nXPnzlkva73xxhssWbKEb775hscffzzDmFauXJnl3ExpE2Rmx9GjR3n33XdtLgmWKlWKGTNm0KpVK1xcXPj222/p0aMHS5Ys4b777gOgRo0azJ8/n3r16hEbG8sbb7xBy5YtOXDgABUrVsz28W+0ZcsW5s2bx65duzJt07FjR+6//35CQkI4duwYL7zwAp06dbKOk0wTHBzM7t27cx2LiCi5Ejs5cuQI27dvt15OcHNzo0+fPsybNy9PydXIkSMZPnw43377Le3ataN3797cdtttmbbft28fqampVK9e3WZ9UlJSurEvOVWvXj3rv9NKf5w7d47KlSvzyy+/sHXrVpszVampqSQmJpKQkECJEiVYs2YN06ZN4/Dhw8TFxXHt2jWb7Tdz9epVgHQDoMHyukuVKkVqairJycl06dKF9957j6NHj5KQkED79u1t2icnJ9OgQYNsv75Dhw4xZMgQm/ZNmjThp59+yjBWex03I7/88gvx8fHp+vPq1ascO3Ysw+cAVKlSJdNtOXHq1Ck6duxI7969bcZElS9fnlGjRlmXmzRpwunTp3n99detyVWLFi2sySZAy5YtqVWrFnPmzOGVV17JVTyXL1+mf//+fPjhh5QvX564uLgM2/Xt29f677p161KvXj1uu+02NmzYQNu2ba3bvL29SUhIyFUsImKh5MrZuZewnEFy1LGzad68eVy7ds3mrIphGHh6evLee+9lONjZxcUl3WXDG88sjB8/nm7durFp0yZWrVrF+PHj+eKLL+jZs2eGccTHx+Pq6kp0dHS60hylSpXK9uvJyPVjZ9LGhaUV146Pj2fixIncf//96Z7n5eXFiRMn6Nq1K0OHDmXKlCmULVuWLVu2EB4eTnJycraSq7S7xy5evJhuW40aNVi6dClubm4EBwdbZ/g/ceIEACtWrOCWW26xec6Ng5mzen05lTaWLD+OGx8fT1BQUIbj6LK6ezQ0NJQ//vgj0+2tW7fmhx9+yHQ7wOnTp7nnnnto2bIlc+fOzbItQLNmzYiMjMx0u7u7Ow0aNODo0aM33Vdmjh07xokTJ2zG4qW9f25ubhw5ciTDP0huvfVWypcvz9GjR22SqwsXLlChQoVcxyMiSq6cn8mUo0tzjnDt2jU++eQTZsyYkW7+pR49evD555+nO+sBUKFCBS5fvsyVK1coWdLyGjO67bxatWo0bNiQUaNG8eCDD7JgwQJ69uyJh4eHtVxQmgYNGpCamsq5c+cyvWMqPzRs2JAjR45QrVq1DLdHR0djNpuZMWOGtebbV199laNjeHh4ULt2bQ4ePJjuffbw8Mjw2LVr18bT05OTJ0/aXIrLqVq1arFt2zYGDBhgXbdz585M29vruBn1ccOGDYmJibFOO5Fdeb0seOrUKe655x4aNWrEggULslW7b8+ePVkWOE5NTWXfvn107tz5pvvKTM2aNdm3bx/wX+Hm1157jfj4eN5++20qVaqU4fP++usvzp8/ny6+/fv35+lss4gouRI7WL58ORcvXiQ8PDzdGapevXoxb968DJOrZs2aUaJECV544QWGDx/Otm3bbO4wvHr1KqNHj6ZTp06EhoZy+vRpduzYQa9evQDLuKX4+HjWrl1L/fr1KVGiBNWrV6dfv34MGDCAGTNm0KBBA/7++2/Wrl1LvXr1Mpx/yx7GjRtH165dqVy5Mv/3f/+Hi4sLv/zyC/v372fy5MlUq1aNlJQU3n33Xbp168bWrVuZPXt2jo8TFhbGli1bbMZvZcXHx4fRo0czcuRIzGYzd955J7GxsWzduhVfX1/rGLGbefrpp3nkkUdo3LgxrVq14n//+x+HDx/m1ltvzdfjVq1alePHj7Nnzx4qVqyIj48P7dq1o0WLFvTo0YPp06dTvXp1Tp8+zYoVK+jZs2emA/7zclnw1KlTtGnThipVqvDGG2/w999/W7eljQf7+OOP8fDwsF72/O6775g/fz4fffSRte2kSZNo3rw51apV49KlS7z++uv88ccfPPbYY9Y2Fy5c4OTJk5w+bTljnXb3ZNodfgADBgzglltuYdq0aXh5eVnnZDObzcTFxVG6dGlMJpN1fdqZ1V69ehEYGMixY8cYM2YM1apVIywszHrshIQEoqOj8zQ2UUQAQwpcbGysARixsbHptl29etU4ePCgcfXqVQdEljtdu3Y1OnfunOG2bdu2GYDxyy+/GMePHzcAY/fu3dbtixcvNqpVq2Z4e3sbXbt2NebOnWuk/bdMSkoy+vTpY9xyyy2Gh4eHERwcbAwbNszmvRkyZIhRrlw5AzDGjx9vGIZhJCcnG+PGjTOqVq1quLu7G0FBQUbPnj2NvXv3ZvoaAGPx4sWGYRjp4ly/fr0BGBcvXrS23717twEYx48ft65btWqV0bJlS8Pb29vw9fU1mjZtasydO9e6febMmUZQUJDh7e1thIWFGZ988onNfhcsWGD4+fll+V4fOHDA8Pb2Ni5dumRdN378eKN+/fqZPsdsNhtvvfWWUaNGDcPd3d2oUKGCERYWZmzcuDFHr2/KlClG+fLljVKlShkDBgwwhg8fbnPcgQMHGt27d7frcRMTE41evXoZpUuXNgBjwYIFhmEYRlxcnPHUU08ZwcHBhru7u1GpUiWjX79+xsmTJ7N8/3JrwYIFBpDhI83ChQuNWrVqGSVKlLD2/9dff22znxEjRhiVK1c2PDw8jICAAKNz587Grl27snWstP/fhmEYd999tzFw4MB0caamphoXL140BgwYYNMXCQkJRocOHYwKFSoY7u7uRpUqVYzBgwcbMTExNs9ftGiRUaNGjSzfi8L4HeUMkpOTjSVLlhjJycmODqVIu5KUYlR5brlR5bnlxpWklJu2z2m/ZPX7fT2TYeTxXnnJsbi4OPz8/IiNjcXX19dmW2JiIsePHyckJCTDgcuF2ZEjR6hZsya//fZbppfPbpT2l7ivr2+2LsMUB71796Zhw4Y8//zzDotB/eKc8tovzZs3Z/jw4Tz00EOZtinK31H5KSUlhZUrV9K5c+d0c5+J/SQkX6P2uB8BODgpjBIeWV+gy2m/ZPX7fT19K0qBuHDhgnV28czGgEj2vP7663kenC9yo3/++Yf777+fBx980NGhiBR6hSK5mjZtGk2aNMHHxwd/f3969OiRbvboNm3apKubdeM4n5MnT9KlSxdKlCiBv78/zz77rM0M2mCZZK9hw4Z4enpSrVq1DGulvf/++1StWhUvLy+aNWvG9u3b7f6ai5rw8HDmzJnDBx98kO5uMcmZqlWr8tRTTzk6DCliypcvz5gxY9KV4xGRnCsUydXGjRuJiIjg559/JjIykpSUFDp06MCVK1ds2g0ePNg6k/OZM2eYPn26dVtqaipdunQhOTmZn376iY8//piFCxcybtw4a5vjx4/TpUsX7rnnHvbs2cOIESN47LHH+PHHH61tvvzyS0aNGsX48ePZtWsX9evXJywsjHPnzuX/G1GILV68mJMnT2Z5uUFERKQoKBR3C65atcpmeeHChfj7+xMdHc1dd91lXV+iRIlMC+6uXr2agwcPsmbNGgICArjjjjt45ZVXeO6555gwYQIeHh7Mnj2bkJAQZsyYAVhuP9+yZQtvvvmm9Y6amTNnMnjwYB599FEAZs+ezYoVK5g/fz5jx47Nj5cvIiIihUihOHN1o9jYWIB0db0+++wzypcvT506dXj++edtZhmOioqibt26NkVSw8LCiIuLs9b2ioqKSlcsNSwsjKioKMAyu3R0dLRNGxcXF9q1a2dtIyIiIsVboThzdT2z2cyIESNo1aqVdQ4XgIceeogqVaoQHBzM3r17ee655zhy5Ii1an1MTEy66vNpyzExMVm2iYuL4+rVq1y8eJHU1NQM2xw+fDjTmJOSkkhKSrIup5WnSElJSTepYUpKCoZhYDabcz07dlGSdjNr2nsizkH94pwKol/MZjOGYZCSkpKuCoJkLu27PquJbCXvUlKuXffvFFJMWU+IkNN+yW67QpdcRUREsH//frZs2WKz/vpirXXr1iUoKIi2bdty7NixLGvRFYRp06YxceLEdOtXr16druxJWpHa+Ph4kpOTCypEp3f58mVHhyAZUL84p/zsl+TkZK5evcqmTZvS3RAkN5dVOSTJu6RUSEttfvxxNZ7ZzP+z2y/ZrbtZqJKrYcOGsXz5cjZt2nTTCvLNmjUDLAVkb7vtNgIDA9Pd1Xf27FngvxmWAwMDreuub+Pr64u3tzeurq64urpm2CazsV4Azz//vE1B17i4OCpVqkSHDh0ynOfqzz//pFSpUppDBstf4JcvX8bHx0d3MTkR9YtzKoh+SUxMxNvbm7vuukvfUTmQkpJCZGQk7du31zxX+Sgh+Rpjtq8DICysQ7bmucpJv2RWGP1GhSK5MgyDp556isWLF7NhwwZCQkJu+py0GnVpdbNatGjBlClTOHfuHP7+/oAlU/X19aV27drWNitXrrTZT2RkpLWKvYeHB40aNWLt2rX06NEDsJwiX7t2LcOGDcs0Fk9PzwynH3B3d0/XmampqZhMJlxcXPI0OWNOJ1JzVmmXNtLeE3EO6hfnVBD94uLigslkyvD7S25O71v+cjf++6PC8l5n77cvu/2S3b4rFL+4ERERLFq0iO+//x4fHx/rGCk/Pz+8vb05duwYixYtonPnzpQrV469e/cycuRI7rrrLurVqwdAhw4dqF27Nv3792f69OnExMTw0ksvERERYU18hgwZwnvvvceYMWMYNGgQ69at46uvvmLFihXWWEaNGsXAgQNp3LgxTZs25a233uLKlSvWuwdFRESkeCsUf3J+8MEHxMbG0qZNG4KCgqyPL7/8ErCcUVqzZg0dOnSgZs2aPPPMM/Tq1Ytly5ZZ9+Hq6sry5ctxdXWlRYsWPPzwwwwYMIBJkyZZ24SEhLBixQoiIyOpX78+M2bM4KOPPrIpbNqnTx/eeOMNxo0bxx133MGePXtYtWpVukHujpZq/m8Q3/bjF2yW88MjjzxinbzV3d2dgIAA2rdvz/z583M0sHbhwoWULl06/wIVEZEiq6B/+zJTKM5c3az8YaVKldi4ceNN91OlSpV0l/1u1KZNG3bv3p1lm2HDhmV5GdDRVu0/w/ilB6zLjyzYQZCfF+O71aZjnaB8O27Hjh1ZsGABqampnD17llWrVvH000/zzTffsHTpUtzcCsV/NxERKYQc9duXkUJx5kqyb9X+Mwz93y7OxiXZrI+JTWTo/3axav+ZfDu2p6cngYGB3HLLLTRs2JAXXniB77//nh9++MFaRmjmzJnUrVuXkiVLUqlSJZ588kni4+MBS+mhRx99lNjYWOtZsLS7LC9evMiAAQMoU6YMJUqUoFOnTvz222/WY//xxx9069aNMmXKULJkSUJDQ2+aSIuISNHgyN++jCi5KgISkq+RkHyNy4kpjF96gIzO86Wtm7D0IJcTU0hILphbqO+9917q169vnW/MxcWFd955hwMHDvDxxx+zbt06xowZA0DLli1566238PX1tZYweuaZZwB49NFH2blzJ0uXLiUqKgrDMOjcubN1zpGIiAiSkpLYtGkT+/bt47XXXlNxYxGRYiDVbDBx2cEsf/smLjtYoJcIdZ2mCEi7K/BmDCAmLpG6E1YDcOLVLvkY1X9q1qzJ3r17ARgxYoR1fdWqVZk8eTJDhgxh1qxZeHh44Ofnh8lksk5tYTab2b17N8uWLWPr1q20bNkSsMzGX6lSJZYsWULv3r05efIkvXr1om7dugDceuutBfLaRETEsbYfv8CZ2MRMtxvAmdhEth+/QIvbyhVITEquJN8ZhmGdc2fNmjVMmzaNw4cPExcXx7Vr10hMTCQhISHdhKppjhw5gpubm3XuMoBy5cpRo0YNDh06BMDw4cMZOnQoq1evpl27dvTq1ct6p6iIiBRd5y5nnljlpp096LJgEXBwUhgHJ4Wx8NEm2Wq/8NEmHJwUdvOGdnLo0CFCQkI4ceIEXbt2pV69enz77bdER0fz/vvvA+R5NvrHHnuM33//nf79+7Nv3z4aN27Mu+++a4/wRUTEifn7ZG8y2+y2swclV0VACQ83Sni40fr2CgT5eZHZvMwmIMjPi9a3VyiwSUXXrVvHvn376NWrF9HR0ZjNZmbMmEHz5s2pXr06p0+ftmnv4eFBamqqzboaNWpw7do1tm3bZl13/vx5jhw5Yp0AFix3jQ4ZMoTvvvuOZ555hg8//DB/X5yIiDhc05Cy2frtaxpStsBiUnJVhLi6mBjfzZJs3PifLG15fLfauLrkT1mMpKQkYmJiOHXqFLt27WLq1Kl0796drl27MmDAAKpVq0ZKSgrvvvsuv//+O59++imzZ8+22UfVqlWJj49n7dq1/PPPPyQkJHDbbbdx3333MXjwYLZs2cIvv/zCww8/zC233EL37t0By1iuH3/8kePHj7Nr1y7Wr19PrVq18uV1ioiI83D0b19GlFwVMR3rBPHBww3x97UttxPo58UHDzfM17k+Vq1aRVBQEFWrVqVjx46sX7+ed955h++//x5XV1fq16/PzJkzee2116hTpw6fffYZ06ZNs9lHy5YtGTJkCH369KFChQq8/vrrAMyfP59GjRrRtWtXWrRogWEYrFy50lqKIDU1lYiICGrVqkXHjh2pXr06s2bNyrfXKiIizqNjnSA+6NeQAC/bO+EL4rcvIybjZjN0it3FxcXh5+dHbGxshoWbjx8/TkhISJ6Kol5OTLHeFbjw0Sa0vr1CgWbt9mI2m4mLi8PX11c17JyI+sU5FUS/2Os7qrhJSUlh5cqVdO7cWbUF80PsKVg6jNSj69lurslGc32a959E6+r+Wf725bRfsvr9vp6+FYuo6/8zNQ0pWygTKxERkSwZBuz9Cj5oAcfW4eLuyWpzY+akdqXpreUc9tunqRiKqBIebgU2j5WIiEiBu/IPLB8Jh5ZaloMbktj1fRa8c9yxcaHkSkRERAqbwyth2XC48je4uMHdY+HOkRipAEquRERERLInMRZWPQ97PrMsV6gFPWdD8B2W5dSCKe12M0qunJTuMxARZ6TvJnGY3zfC9xEQ+ydggpZPwT0vgrvz3Vih5MrJuLq6ApYZy729vR0cjYiIrYSEBADd8SYFJzkB1k6Ebf/Oi1imKvSYDVVaODSsrCi5cjJubm6UKFGCv//+G3d392J/m7vZbCY5OZnExMRi/144E/WLc8rPfjEMg4SEBM6dO0fp0qWtfwiK5Ku/dsLiJ+D8Ucty40HQ/hXwLOXYuG5CyZWTMZlMBAUFcfz4cf744w9Hh+NwhmFw9epVvL29rcWfxfHUL86pIPqldOnSBAYG5su+RayuJcPG12DLTDDM4BME970Ht7dzdGTZouTKCXl4eHD77bfnuZhxUZCSksKmTZu46667dBnCiahfnFN+94u7u7vOWEn+O3sQFj8OMfssy3V7Q+fXwbuMY+PKASVXTsrFxUWzH2MZg3bt2jW8vLz0I+5E1C/OSf0ihZo5FX56F9ZPgdRk8C4LXd+E0B6OjizHlFyJiIiIY50/BkuehD9/tixX7wTd3gafAMfGlUtKrkRERMQxDAN2zoPVL0NKAnj4QKdX4Y5+UIjHcyq5EhERkYL3b7Fljq2zLFdtDT1mQenKjo3LDpRciYiISMExDNj3NawcbZlx3c0L2k2Apk9AEZnaRcmViIiIFIwMii3Tcw5UqO7YuOxMyZWIiIjkv0yKLeNa9FKRoveKRERExHncrNhyEaTkSkRERPLHjcWWWw23FFt288yXw5XwcOPEq13yZd85oeRKRERE7KsQFlu2JyVXIiIiYj+FtNiyPSm5EhERkbwr5MWW7UnJlYiIiOTN2QOWs1WFuNiyPSm5EhERkdwxp8JP78D6qYW+2LI9KbkSERGRnCtixZbtScmViIiIZF8RLbZsT0quREREJHuKcLFle1JyJSIiIlkrBsWW7UnJlYiIiGSumBRbticlVyIiIpKxYlRs2Z707oiIiIitYlhs2Z6UXImIiMh/CrjYclGk5EpERESKfbFle1JyJSIiUtyp2LJdKbkSEREprlRsOV8ouRIRESmOVGw53yi5EhERKU5UbDnfKbkSEREpLlRsuUAouRIRESnqVGy5QCm5EhERKcpUbLnAKbkSEREpigwD9n4FPzyrYssFTMmViIhIUXPlH1g+Ag4tsyyr2HKBUnIlIiJSlKjYssPpnRYRESkKbiy27F/bUmw5qL5j4yqGlFyJiIgUdiq27FSUXImIiBRWKrbslJRciYiIFEYqtuy0lFyJiIgUJiq27PSUXImIiBQW6YotPwCdp6vYspNRciUiIuLsVGy5UCkUU7ROmzaNJk2a4OPjg7+/Pz169ODIkSM2bRITE4mIiKBcuXKUKlWKXr16cfbsWZs2J0+epEuXLpQoUQJ/f3+effZZrl27ZtNmw4YNNGzYEE9PT6pVq8bChQvTxfP+++9TtWpVvLy8aNasGdu3b7f7axYREQEsxZYXdIY1EyyJVfVO8OTPSqycWKFIrjZu3EhERAQ///wzkZGRpKSk0KFDB65cuWJtM3LkSJYtW8bXX3/Nxo0bOX36NPfff791e2pqKl26dCE5OZmffvqJjz/+mIULFzJu3Dhrm+PHj9OlSxfuuece9uzZw4gRI3jsscf48ccfrW2+/PJLRo0axfjx49m1axf169cnLCyMc+fOFcybISIixYNhwI6PYPad8OfPlmLL3d+HBz8HnwBHRydZMQqhc+fOGYCxceNGwzAM49KlS4a7u7vx9ddfW9scOnTIAIyoqCjDMAxj5cqVhouLixETE2Nt88EHHxi+vr5GUlKSYRiGMWbMGCM0NNTmWH369DHCwsKsy02bNjUiIiKsy6mpqUZwcLAxbdq0bMcfGxtrAEZsbGwOXnXxlJycbCxZssRITk52dChyHfWLc1K/OK8c982lvwzjkx6GMd7X8ljQxTAu/pG/QRZDOe2X7P5+F8oxV7GxsQCULVsWgOjoaFJSUmjX7r87JWrWrEnlypWJioqiefPmREVFUbduXQIC/sv2w8LCGDp0KAcOHKBBgwZERUXZ7COtzYgRIwBITk4mOjqa559/3rrdxcWFdu3aERUVlWm8SUlJJCUlWZfj4uIASElJISUlJZfvQvGQ9v7ofXIu6hfnpH5xXtnuG8PAtP9rXH8ciykpDsPNC/M9L2NuMhhMLqC+taucfmay267QJVdms5kRI0bQqlUr6tSpA0BMTAweHh6ULl3apm1AQAAxMTHWNtcnVmnb07Zl1SYuLo6rV69y8eJFUlNTM2xz+PDhTGOeNm0aEydOTLd+9erVlChRIhuvWiIjIx0dgmRA/eKc1C/OK6u+8UiJo/6fCwmO3QnAxRK3sqvK48T/Eww/rCqoEIul7H5mEhISstWu0CVXERER7N+/ny1btjg6lGx7/vnnGTVqlHU5Li6OSpUq0aFDB3x9fR0YmfNLSUkhMjKS9u3b4+7u7uhw5F/qF+ekfnFeN+sb068/4LpyIqYrf2O4uGFu/SylWj7NXS6F7me6UMnpZybtytPNFKpeGzZsGMuXL2fTpk1UrFjRuj4wMJDk5GQuXbpkc/bq7NmzBAYGWtvceFdf2t2E17e58Q7Ds2fP4uvri7e3N66urri6umbYJm0fGfH09MTTM319J3d3d30BZpPeK+ekfnFO6hfnla5vMii2bOo5G9eg+rg6JsRiKbufmex+rgrF3YKGYTBs2DAWL17MunXrCAkJsdneqFEj3N3dWbt2rXXdkSNHOHnyJC1aWOortWjRgn379tnc1RcZGYmvry+1a9e2trl+H2lt0vbh4eFBo0aNbNqYzWbWrl1rbSMiIpItv2+ED1r9m1iZoNXT8PgGCKrv6MgkjwrFmauIiAgWLVrE999/j4+Pj3WMlJ+fH97e3vj5+REeHs6oUaMoW7Ysvr6+PPXUU7Ro0YLmzZsD0KFDB2rXrk3//v2ZPn06MTExvPTSS0RERFjPKg0ZMoT33nuPMWPGMGjQINatW8dXX33FihUrrLGMGjWKgQMH0rhxY5o2bcpbb73FlStXePTRRwv+jRERkcInOcEyZ9X2OZZlFVsucgpFcvXBBx8A0KZNG5v1CxYs4JFHHgHgzTffxMXFhV69epGUlERYWBizZs2ytnV1dWX58uUMHTqUFi1aULJkSQYOHMikSZOsbUJCQlixYgUjR47k7bffpmLFinz00UeEhYVZ2/Tp04e///6bcePGERMTwx133MGqVavSDXIXERG5kelUNCyLULHlIq5QJFeGYdy0jZeXF++//z7vv/9+pm2qVKnCypUrs9xPmzZt2L17d5Zthg0bxrBhw24ak4iICACpydQ8/Q2ue5ar2HIxUCiSKxERkULr7AHcvnuCGmdVbLm4UHIlIiKSH64rtmxKTSbJtRSu3d/BrV4vR0cm+UzJlYiIiL2dPwZLnrTUBATMt4ex3rMLbWvd5+DApCAUiqkYRERECoVMii2n9v4fSe6lHR2dFBCduRIREbGH2FOwdBgcW2dZrtoaesyC0pVVE7CYUXIlIiKSF4YBe7+Clc9CUiy4eUG7CdD0CXDRBaLiSMmViIhIbl35B5aPgEPLLMu3NLJMCFqhukPDEsdSciUiIpIbh1fCsuFw5W9wcYO7x8KdI8FVP63Fnf4HiIiI5EQGxZbpOVs1AcVKyZWIiEh2/b4Rvo+A2D+xFFseDve8CG6ejo5MnIiSKxERkZtRsWXJASVXIiIiWflrJyx+QsWWJduUXImIiGTkWjJsfA22zPyv2HL396Caii1L1pRciYiI3OjsAcvZqhgVW5acU3IlIiKS5rpiy6Qmg3dZ6PomhPZwdGRSiCi5EhERgX+LLQ+FP7dZlqt3gm5vg0+AY+OSQkfJlYiIFG9pxZYjx0FKgqXYcqdX4Y5+YDI5OjophJRciYhI8ZVVsWWRXFJyJSIixU+GxZYnQtPHVWxZ8kzJlYiIFC8qtiz5TMmViIgUHyq2LAVA/5tERKToU7FlKUBKrkREpGj7fSMseRLi/kLFlqUgKLkSEZGiScWWxUGUXImISNGjYsviQEquRESk6FCxZXECSq5ERKRoULFlcRJKrkREpHBTsWVxMkquRESk8FKxZXFCSq5ERKTwUbFlcWJKrkREpHBRsWVxckquRESkcFCxZSkklFyJiIjzU7FlKUSUXImIiHM7vAKWPa1iy1Jo6H+miIg4p8RY+GEs/LLIsqxiy1JIKLkSERHno2LLUogpuRIREeehYstSBCi5EhER56Biy1JEKLkSERHHUrFlKWKUXImIiOOo2LIUQUquRESk4KUVW143BcwpKrYsRYqSKxERKVgqtixFnJIrEREpGCq2LMWEkisREcl/KrYsxYiSKxERyT8qtizFUJ6Tq5SUFGJiYkhISKBChQqULVvWHnGJiEhhp2LLUkzlKrm6fPky//vf//jiiy/Yvn07ycnJGIaByWSiYsWKdOjQgccff5wmTZrYO14RESkMVGxZirEcn5OdOXMmVatWZcGCBbRr144lS5awZ88efv31V6Kiohg/fjzXrl2jQ4cOdOzYkd9++y0/4hYREWeUGAuLh8IXD1kSK//aMHgd3P2sEispNnL8P33Hjh1s2rSJ0NDQDLc3bdqUQYMGMXv2bBYsWMDmzZu5/fbb8xyoiIg4ORVbFgFykVx9/vnn2Wrn6enJkCFDchyQiIgUMiq2LGJD52hFRCT30hVbDof2k1RsWYq1HI25unr1KqdOnUq3/sCBA3YLSERECoFrybB2Esxrb0msfILg4W+h60wlVlLsZTu5+uabb7j99tvp0qUL9erVY9u2bdZt/fv3z5fgRETECcXshw/vhc0zwDBbii0/GQXV2jk6MhGnkO3kavLkyURHR7Nnzx4WLFhAeHg4ixYtAsAwjHwLUEREnIQ5Fba8CXPbwNl9lmLLvT+GXh+CdxlHRyfiNLI95iolJYWAAEtRzUaNGrFp0yZ69uzJ0aNHMakmlIhI0aZiyyLZlu0zV/7+/uzdu9e6XLZsWSIjIzl06JDNehERKUIMA7Z/CLPvtCRWHj7Q/X148HMlViKZyPaZq08//RQ3N9vmHh4efP755wwbNszugYmIiIOp2LJIrmT7zFXFihUJDAy0WXf16lUSEhJo1aoVAH/88QdvvfUWq1evtm+UwKZNm+jWrRvBwcGYTCaWLFlis/2RRx7BZDLZPDp27GjT5sKFC/Tr1w9fX19Kly5NeHg48fHxNm327t1L69at8fLyolKlSkyfPj1dLF9//TU1a9bEy8uLunXrsnLlSru/XhERhzEM+OVLmNXCkli5eUHH12DAUiVWItmQp5Lk3bt355NPPgHg0qVLNGvWjBkzZtC9e3c++OADuwSY5sqVK9SvX5/3338/0zYdO3bkzJkz1seNE57269ePAwcOEBkZyfLly9m0aROPP/64dXtcXBwdOnSgSpUqREdH8/rrrzNhwgTmzp1rbfPTTz/x4IMPEh4ezu7du+nRowc9evRg//79dn29IiIOceUf+Ko/LH4ckmItxZaf2AzNh4BLnn4yRIqNPE0iumvXLt58803AMlVDQEAAu3fv5ttvv2XcuHEMHTrULkECdOrUiU6dOmXZxtPTM93ZtTSHDh1i1apV7Nixg8aNGwPw7rvv0rlzZ9544w2Cg4P57LPPSE5OZv78+Xh4eBAaGsqePXuYOXOmNQl7++236dixI88++ywAr7zyCpGRkbz33nvMnj3bbq9XRKTAqdiyiF3k6c+QhIQEfHx8AFi9ejX3338/Li4uNG/enD/++MMuAebEhg0b8Pf3p0aNGgwdOpTz589bt0VFRVG6dGlrYgXQrl07XFxcrHN2RUVFcdddd+Hh4WFtExYWxpEjR7h48aK1Tbt2tnO5hIWFERUVlZ8vTUQk/6jYsohd5elTU61aNZYsWULPnj358ccfGTlyJADnzp3D19fXLgFmV8eOHbn//vsJCQnh2LFjvPDCC3Tq1ImoqChcXV2JiYnB39/f5jlubm6ULVuWmJgYAGJiYggJCbFpkzb9RExMDGXKlCEmJsa67vo2afvISFJSEklJSdbluLg4wDK9RUpKSu5fdDGQ9v7ofXIu6hfnlJt+MZ3YhOuypzDFncLAhLnFMMx3jbUUW1b/2o0+M84pp/2S3XZ5Sq7GjRvHQw89xMiRI2nbti0tWliKdK5evZoGDRrkZdc51rdvX+u/69atS7169bjtttvYsGEDbdu2LdBYbjRt2jQmTpyYbv3q1aspUaKEAyIqfCIjIx0dgmRA/eKcstMvruYkap/+ilv/trS94uHPriqDuZBYA1avze8Qiy19ZpxTdvslISEhW+3ylFz93//9H3feeSdnzpyhfv361vVt27alZ8+eedl1nt16662UL1+eo0eP0rZtWwIDAzl37pxNm2vXrnHhwgXrOK3AwEDOnj1r0yZt+WZtMhvrBfD8888zatQo63JcXByVKlWiQ4cOBX6Gr7BJSUkhMjKS9u3b4+7u7uhw5F/qF+eU3X4xndqJ69IITBeOAZDa8FE82o6nuYdqAuYXfWacU077Je3K083kKrkaN24c3bt3p1GjRgQGBqZLLJo2bZqb3drVX3/9xfnz5wkKCgKgRYsWXLp0iejoaBo1agTAunXrMJvNNGvWzNrmxRdfJCUlxfomR0ZGUqNGDcqUKWNts3btWkaMGGE9VmRkpPWsXUY8PT3x9PRMt97d3V0fsmzSe+Wc1C/OKdN+uZYMG1+1lLAxzJZiy93fw7VaO1wLPsxiSZ8Z55Tdfslu3+VqQPtff/1Fp06dqFixIkOHDuWHH34gOTk5N7vKtvj4ePbs2cOePXsAOH78OHv27OHkyZPEx8fz7LPP8vPPP3PixAnWrl1L9+7dqVatGmFhYQDUqlWLjh07MnjwYLZv387WrVsZNmwYffv2JTg4GICHHnoIDw8PwsPDOXDgAF9++SVvv/22zVmnp59+mlWrVjFjxgwOHz7MhAkT2LlzpyZSFRHnpmLLIgUmV8nV/PnziYmJ4fPPP8fHx4cRI0ZQvnx5evXqxSeffMKFCxfsHSc7d+6kQYMG1rFco0aNokGDBowbNw5XV1f27t3LfffdR/Xq1QkPD6dRo0Zs3rzZ5ozRZ599Rs2aNWnbti2dO3fmzjvvtJnDys/Pj9WrV3P8+HEaNWrEM888w7hx42zmwmrZsiWLFi1i7ty51K9fn2+++YYlS5ZQp04du79mEZE8U7FlkQKX6zFXLi4utG7dmtatWzN9+nQOHTrEsmXLmDNnDo8//jhNmzblvvvu48EHH+SWW27Jc6Bt2rTBMIxMt//444833UfZsmVZtGhRlm3q1avH5s2bs2zTu3dvevfufdPjiYg4lIotiziE3SYwqVWrFrVq1WLMmDH8/fffLF26lKVLlwIwevRoex1GRERuJq3YcuQ4SEmwFFvu9Crc0Q9MJkdHJ1Lk5cvscBUqVCA8PJzw8PD82L2IiGTCK/kCrp/3huMbLCtUbFmkwOVozNXVq1c5depUuvUHDhywW0AiIpILhoFp31fce/gFXI5vULFlEQfKdnL1zTffcPvtt9OlSxfq1atnLRkD0L9//3wJTkREsuHfYstuS5/EPTUBc3BDFVsWcaBsf+omT55MdHQ0e/bsYcGCBYSHh1sHh2c10FxERPLR4RUwqzkcWobh4sahoF6kDlwJFao7OjKRYivbY65SUlKsNfUaNWrEpk2b6NmzJ0ePHsWkAZIiIgUrMRZ+GAu//HsHtH9trnV7j193/UU1FxVbFnGkbJ+58vf3Z+/evdblsmXLEhkZyaFDh2zWi4hIPvt9I8xq+W9iZYJWT8PjGyCwnqMjExFycObq008/xc3NtrmHhweff/65ZicXESkIyQmwZgJsn2NZLhMCPWdD5eaW5ZQUh4UmIv/JdnJVsWLFDNcnJibi7u7O8uXLMZvNNtvuu+++vEUnIiIWf+6AJUPg/FHLcuNwaD8JPFVsWcTZ5OnC/KpVq+jfvz/nz59Pt81kMpGampqX3YuISCbFllUTUMR55eke3aeeeooHHniAM2fOYDabbR5KrERE8kjFlkUKpTyduTp79iyjRo2y3kUoIiJ2YE6Fn96BdVPAnGIpttz1TQjt4ejIRCQb8pRc/d///R8bNmzgtttus1c8IiLFm4otixR6eUqu3nvvPXr37s3mzZupW7cu7u7uNtuHDx+ep+BERIoNw4AdH91QbPk1uOMhFVsWKWTylFx9/vnnrF69Gi8vLzZs2GAzmajJZFJyJSKSHbGn4PsI+H29ZVnFlkUKtTwlVy+++CITJ05k7NixuKh+lYhIzhgG7P0SVo6BpFhLseV2E6Hp46oJKFKI5Sm5Sk5Opk+fPkqsRERy6so/sHwEHFpmWb6lEfSYrZqAIkVAnrKigQMH8uWXX9orFhGR4uG6Ysu4uME9L8Gg1UqsRIqIPJ25Sk1NZfr06fz444/Uq1cv3YD2mTNn5ik4EZEiJYNiy/ScDUH1HRuXiNhVnpKrffv20aBBAwD2799vs82ku1tERP7z+wZYEgFxf2Eptjwc7nkR3DwdHZmI2Fmekqv169fbKw4RkaIpOQHWjIftcy3LNxZbFpEiJ0/JlYiIZEHFlkWKpRwPaD958mSO2p86dSqnhxARKdyuJcPaSTC/gyWx8gmCh7+FrjOVWIkUAzlOrpo0acITTzzBjh07Mm0TGxvLhx9+SJ06dfj222/zFKCISKGiYssixV6OLwsePHiQKVOm0L59e7y8vGjUqBHBwcF4eXlx8eJFDh48yIEDB2jYsCHTp0+nc+fO+RG3iIhzUbFlEflXjpOrcuXKMXPmTKZMmcKKFSvYsmULf/zxB1evXqV8+fL069ePsLAw6tSpkx/xiog4HxVbFpHr5HpAu7e3N//3f//H//3f/9kzHhGRwkPFlkUkA7pbUEQkN1RsWUQyoeRKRCQnVGxZRG5CyZWISHap2LKIZIOSKxGR7Di8ApY9DVf+thRbvnss3DkSXPU1KiK29K0gIpIVFVsWkRzK0QCBq1evZjjj+oEDB2yWd+3axUMPPUTnzp157rnnOH78eN6iFBFxhN83wKyW/yZWJmj1NDy+QYmViGQp28nVN998w+23306XLl2oV68e27Zts27r37+/Tds+ffrQtWtXpkyZQvXq1bn//vtZvXq1/aIWEclPyQmw8ln4pDvE/WUptjxolaUuoJuno6MTESeX7cuCkydPJjo6moCAAKKjoxk4cCAvvPACDz30EIZh2LQtU6YMDz30EAANGjSgR48e3HvvvXTo0MG+0YuI2JuKLYtIHmU7uUpJSSEgwDLbcKNGjdi0aRM9e/bk6NGjmG6YLO+2225jxowZjBo1CpPJROnSpe0atIiI3V1Lho2vwpY3LTUBfYKg+3uqCSgiOZbty4L+/v7s3bvXuly2bFkiIyM5dOiQzXqApKQkPvjgAypXrkzHjh2pU6cO7dq1y3C8loiIw6nYsojYUbbPXH366ae4udk2P3PmDJ9//jnDhg2zWf/dd98BcOXKFfbt28fevXvZu3cvDz74IKdPn+bo0aN2CF1EJI/MqbD1bVg/VcWWRcRusp1cVaxYMd26mjVrMnToUF566aUMn1OyZEmaN29O8+bNcx+hiEh+OH8MFg+Bv7Zblmt0thRbLuXv2LhEpNDLU62GTZs28csvv3DrrbcydepUrl69aq+4RETyh2HA9g9h9p2WxMrDB7rPgr6LlFiJiF3kKblq0qQJa9eu5csvv+Tbb7+lWrVqzJ07F7PZbK/4RETsJ/YUfNoTVo6GlARLseUnf4IG/eCGG3NERHLLLlVGw8LCiI6O5o033uD111+ndu3a1nFXIiIOZxjwyxcwqwX8vt5SbLnjazBgKZSu7OjoRKSIsWv5m+7du1O1alWeeeYZevfuTWpqqj13LyKScyq2LCIFLE/J1fz58zl48KD18ddffwFQuXJlunbtapcARURy7cZiy23GQisVWxaR/JWnb5jnn3+eunXrUqdOHXr16kWdOnWoU6cOJUuWtFd8IiI5p2LLIuJAeUquzp49a684RETs4/cNsCTCUhMQE7QaDve8CG6epJoNth+/wLnLifj7eNE0pCyuLhrILiL2pXPjIlI0JCfAmvGwfa5luUyI5WxVZcs8e6v2n2HisoOciU20PiXIz4vx3WrTsU6QIyIWkSLKLncLiog41J87YE7r/xKrxuEwZItNYjX0f7tsEiuAmNhEhv5vF6v2nynoiEWkCNOZKxEpvLJRbDnVbDBx2UGMDJ5uACZg4rKDtK8dqEuEImIXSq5EpHCK2W8pX3N2n2W57gPQeTp4l7Fptv34hXRnrK5nAGdiE9l+/AItbiuXjwGLSHGh5EpECpeMii13ewtqd8+w+bnLmSdWuWknInIzSq5EpPDIRbFlfx+vbO06u+1ERG5GyZWIOD/DgB0fQeQ4S01ADx/o9Brc8dBNawI2DSlLkJ8XMbGJGY67MgGBfpZpGURE7EF3C4qIc8tjsWVXFxPju9UGLInU9dKWx3errcHsImI3Sq5ExDnZsdhyxzpBfPBwQwL9bC/9Bfp58cHDDTXPlYjYlS4LiojzyajYcs85UP72XO+yY50g2tcO1AztIpLvlFyJiHM5tNxSbDnhH7sXW3Z1MWm6BRHJd0quRMQ5qNiyiBQRhWbM1aZNm+jWrRvBwcGYTCaWLFlis90wDMaNG0dQUBDe3t60a9eO3377zabNhQsX6NevH76+vpQuXZrw8HDi4+Nt2uzdu5fWrVvj5eVFpUqVmD59erpYvv76a2rWrImXlxd169Zl5cqVdn+9IsXK7xtgVst/EysTtHoaHt+gxEpECqVCk1xduXKF+vXr8/7772e4ffr06bzzzjvMnj2bbdu2UbJkScLCwkhM/G9iwH79+nHgwAEiIyNZvnw5mzZt4vHHH7duj4uLo0OHDlSpUoXo6Ghef/11JkyYwNy5c61tfvrpJx588EHCw8PZvXs3PXr0oEePHuzfvz//XrxIUZWcACufhU+6Q9xflmLLg1ZB+0ng5uno6EREcscohABj8eLF1mWz2WwEBgYar7/+unXdpUuXDE9PT+Pzzz83DMMwDh48aADGjh07rG1++OEHw2QyGadOnTIMwzBmzZpllClTxkhKSrK2ee6554waNWpYlx944AGjS5cuNvE0a9bMeOKJJ7Idf2xsrAEYsbGx2X5OcZWcnGwsWbLESE5OdnQoch279MvJ7YbxTkPDGO9reSwbaRiJl+0XZDGkz4vzUt84p5z2S3Z/v4vEmKvjx48TExNDu3b/FWv18/OjWbNmREVF0bdvX6KioihdujSNGze2tmnXrh0uLi5s27aNnj17EhUVxV133YWHh4e1TVhYGK+99hoXL16kTJkyREVFMWrUKJvjh4WFpbtMeb2kpCSSkpKsy3FxcQCkpKSQkpKS15dfpKW9P3qfnEue+iU1GZdNr+MS9TYmw4zhE0Rql7cxbrs3bed2jLR40efFealvnFNO+yW77YpEchUTEwNAQECAzfqAgADrtpiYGPz9bUtkuLm5UbZsWZs2ISEh6faRtq1MmTLExMRkeZyMTJs2jYkTJ6Zbv3r1akqUKJGdl1jsRUZGOjoEyUBO+8X36kka/jEXv6snAfizTEv2VexPypFEOKKxi/aiz4vzUt84p+z2S0JCQrbaFYnkytk9//zzNme74uLiqFSpEh06dMDX19eBkTm/lJQUIiMjad++Pe7u7o4OR/6V434xp+Ly83u4/PIqJnMKhndZUjvPILBmNwLzP9xiQ58X56W+cU457Ze0K083UySSq8BAy9fz2bNnCQr6b6bls2fPcscdd1jbnDt3zuZ5165d48KFC9bnBwYGcvbsWZs2acs3a5O2PSOenp54eqYfnOvu7q4PWTbpvXJO2eqXDIotm7q9jVsWxZYlb/R5cV7qG+eU3X7Jbt8VmrsFsxISEkJgYCBr1661rouLi2Pbtm20aNECgBYtWnDp0iWio6OtbdatW4fZbKZZs2bWNps2bbK5phoZGUmNGjUoU6aMtc31x0lrk3YcEfmXYcD2D2H2nZbEysMHus+CvotAiZWIFGGFJrmKj49nz5497NmzB7AMYt+zZw8nT57EZDIxYsQIJk+ezNKlS9m3bx8DBgwgODiYHj16AFCrVi06duzI4MGD2b59O1u3bmXYsGH07duX4OBgAB566CE8PDwIDw/nwIEDfPnll7z99ts2l/SefvppVq1axYwZMzh8+DATJkxg586dDBs2rKDfEhHnlcdiyyIihVmhuSy4c+dO7rnnHutyWsIzcOBAFi5cyJgxY7hy5QqPP/44ly5d4s4772TVqlV4ef1XqPWzzz5j2LBhtG3bFhcXF3r16sU777xj3e7n58fq1auJiIigUaNGlC9fnnHjxtnMhdWyZUsWLVrESy+9xAsvvMDtt9/OkiVLqFOnTgG8CyJOzjBg75ewcgwkxVqKLbebCE0fB5dC87eciEieFJrkqk2bNhiGkel2k8nEpEmTmDRpUqZtypYty6JFi7I8Tr169di8eXOWbXr37k3v3r2zDlikuMmHYssiIoVRoUmuRMSJ5WOxZRGRwkbffCKSe4lxsPwlFVsWEbmOkisRyZXylw/g9uHzEHcKS7Hl4XDPi6oJKCLFnpIrEcmZ5ARcVr9Mq6MfWZbLhFjOVlVu7ti4RESchJIrEcm+P3fAkiG4nj8KQGrDR3ENmwyepRwcmIiI81ByJSI3dy0ZNr4KW96Ef4stR/k/TJNOz+Gq2aZFRGxo4hkRyVrMfvjwXtg8Awwz1OvDtcGb+du3rqMjExFxSjpzJSIZM6fC1rdh/VQwp4B3Wej2FtTuDteViBIREVtKrkQkvQyKLdPtbdUEFBHJBiVXIvIfw4AdH0HkOEtNQA8f6PQa3PGQagKKiGSTkisRsYg9Bd9HwO/rLctVW0OPWVC6smPjEhEpZJRciRR3GRVbbj8JmgxWsWURkVxQciVSnKnYsoiI3Sm5EimuVGxZRCRf6FtUpLhJjIUfxqrYsohIPlFyJVKc/L4BlkRA3F+o2LKISP5QciVSHCQnwJrxsH2uZVnFlkVE8o2SK5Gi7t9iy/xbbJnG4Za7AVVsWUQkXyi5Eimqbii2jE8wdH8PqrV1dGQiIkWakiuRoihmv6V8zdl9luV6fSwzrXuXcWxcIiLFgJIrkaIkq2LLIiJSIJRciRQVKrYsIuIUlFyJFHY3Flv29IWOr6rYsoiIgyi5EinMbiy2HHIXdJ8FpSs5Ni4RkWJMyZVIYaRiyyIiTkvJlUhhc+UfS03Aw8styyq2LCLiVJRciRQmKrYsIuL09I0sUhio2LKISKGh5ErE2V1fbNnkAi2Hwz0vqNiyiIiTUnIl4qxUbFlEpFBSciXijP7cAYufgAvHLMsqtiwiUmgouRJxJteSYcM02PqWii2LiBRSSq5EnIWKLYuIFAlKrkQcTcWWRUSKFCVXIo6kYssiIkWOkisRR1CxZRGRIkvJlUhBU7FlEZEiTcmVSEFRsWURkWJByZVIQVCxZRGRYkPJlUh+sym27A5tnlOxZRGRIkzf7iL5JcNiy3MgqJ5j4xIRkXyl5EokP6jYsohIsaXkSsSeVGxZRKTYU3IlYi8qtiwiIii5Esk7FVsWEZHrKLkSyQsVWxYRkRsouRLJjRuLLZcoB13fgtr3OToyERFxMCVXIjmVrthyF+j2looti4gIoORKJPtUbFlERLJByZVIdsT+Bd8PU7FlERG5KSVXIllRsWUREckhJVcimUlXbLmxZUJQFVsWEZEsKLkSyUi6YstjodUIFVsWEZGb0i+FyPVUbFlERPJIyZVIGhVbFhERO1ByJaJiyyIiYkdKrqR4U7FlERGxsyJzL/mECRMwmUw2j5o1a1q3JyYmEhERQbly5ShVqhS9evXi7NmzNvs4efIkXbp0oUSJEvj7+/Pss89y7do1mzYbNmygYcOGeHp6Uq1aNRYuXFgQL0/s7VoyrJkI8ztYEiufYHj4O+g6U4mViIjkSZE6cxUaGsqaNWusy25u/728kSNHsmLFCr7++mv8/PwYNmwY999/P1u3bgUgNTWVLl26EBgYyE8//cSZM2cYMGAA7u7uTJ06FYDjx4/TpUsXhgwZwmeffcbatWt57LHHCAoKIiwsrGBfrOSeii2LiEg+KlLJlZubG4GBgenWx8bGMm/ePBYtWsS9994LwIIFC6hVqxY///wzzZs3Z/Xq1Rw8eJA1a9YQEBDAHXfcwSuvvMJzzz3HhAkT8PDwYPbs2YSEhDBjxgwAatWqxZYtW3jzzTeVXBUGKrYsIiIFoMhcFgT47bffCA4O5tZbb6Vfv36cPHkSgOjoaFJSUmjXrp21bc2aNalcuTJRUVEAREVFUbduXQICAqxtwsLCiIuL48CBA9Y21+8jrU3aPsSJnT8G8zvC2omWxKpGF3jyZyVWIiJid0XmzFWzZs1YuHAhNWrU4MyZM0ycOJHWrVuzf/9+YmJi8PDwoHTp0jbPCQgIICYmBoCYmBibxCpte9q2rNrExcVx9epVvL29M4wtKSmJpKQk63JcXBwAKSkppKSk5P5FFwNp70+u3yfDwCV6AS7rJmBKScDw9CG1/VSMen0txZb1/udKnvtF8oX6xXmpb5xTTvslu+2KTHLVqVMn67/r1atHs2bNqFKlCl999VWmSU9BmTZtGhMnTky3fvXq1ZQoUcIBERU+kZGROX6OV/J5Gpych//l/QD8Xao2u6s8xtVTfnDqB3uHWCzlpl8k/6lfnJf6xjllt18SEhKy1a7IJFc3Kl26NNWrV+fo0aO0b9+e5ORkLl26ZHP26uzZs9YxWoGBgWzfvt1mH2l3E17f5sY7DM+ePYuvr2+WCdzzzz/PqFGjrMtxcXFUqlSJDh064Ovrm6fXWdSlpKQQGRlJ+/btcXd3z96TDAPT/q9x/XE8pqQ4DDdvzPeOo3TjcO4xFakr4Q6Tq36RfKd+cV7qG+eU035Ju/J0M0U2uYqPj+fYsWP079+fRo0a4e7uztq1a+nVqxcAR44c4eTJk7Ro0QKAFi1aMGXKFM6dO4e/vz9gyWR9fX2pXbu2tc3KlSttjhMZGWndR2Y8PT3x9Ew/y7e7u7s+ZNmU7fcqg2LLpp5zcC1fDdf8DbFY0v9h56R+cV7qG+eU3X7Jbt8VmeRq9OjRdOvWjSpVqnD69GnGjx+Pq6srDz74IH5+foSHhzNq1CjKli2Lr68vTz31FC1atKB5c8ss3B06dKB27dr079+f6dOnExMTw0svvURERIQ1MRoyZAjvvfceY8aMYdCgQaxbt46vvvqKFStWOPKlSxoVWxYRESdQZH51/vrrLx588EHOnz9PhQoVuPPOO/n555+pUKECAG+++SYuLi706tWLpKQkwsLCmDVrlvX5rq6uLF++nKFDh9KiRQtKlizJwIEDmTRpkrVNSEgIK1asYOTIkbz99ttUrFiRjz76SNMwOJqKLYuIiBMpMsnVF198keV2Ly8v3n//fd5///1M21SpUiXdZb8btWnTht27d+cqRskHKrYsIiJOpsgkV1LMqNiyiIg4KSVXUvjcWGy5yWPQbqJqAoqIiFNQciWFx7Vk2DANtr4FhtlSbLn7e1CtraMjExERsVJyJYXD2QOwbJiKLYuIiNNTciXOzZzK7THLcJu/RMWWRUSkUFByJc7r/DFcv3uC2md2WJZrdIFub0Epf4eGJSIikhUlV+J8zGbYOQ8ix+GSkkCKizemztNxa9TfUmxZRETEiSm5EucS+xd8H2GZvwowV23N+pI9uaf+g0qsRESkUFAVW3EOhgG/fAGzWloSKzdv6DSd1Ie+5apHeUdHJyIikm06cyWOl0GxZXrOgfLVICXFsbGJiIjkkJIrcSwVWxYRkSJGv2DiGCq2LCIiRZSSKyl4KrYsIiJFmJIrKTgqtiwiIsWAkispGCq2LCIixYSSK8lfeSi2nGo22Hb8AtH/mCh3/AItqvnj6qK5rkRExLkpuZL8E7MfFg/JVbHlVfvPMH7pAc7GJQGufPLbToL8vBjfrTYd6wTlb9wiIiJ5oElExf7MqbB5JsxtY0msSpSDBz6F++dmO7Ea+r9d/yZW/4mJTWTo/3axav+ZfApcREQk73TmSuzr/DHL2aq/tluWc1hsOdVsMHHZQYwMthmACZi47CDtawfqEqGIiDglnbkS+zCbYfuHMPtOS2Ll6QvdZ0Hfz7KdWAFsP36BM7GJmW43gDOxiWw/fsEOQYuIiNifzlwVIalmg+3HL3DuciL+Pl40DSlbMGd3bii2TMhdlsSqdKUc7+rc5cwTq9y0ExERKWhKroqIVfvPMHHZQZuzPvk+ANwwYO+XsHIMJMVaii23nwhNBoNL7k6K+vt42bWdiIhIQdNlwSIgbQD4jZfT8nUA+JV/4MuHLXNXJcVaii0P2QLNnsh1YgXQNKQsQX5eZHa+zYQlaWwaUjbXxxAREclPSq4KuZsNAAfLAPBUc0YtcunQcni/GRxebim2fO/LMOhHKF8tz7t2dTExvlttgHQJVtry+G61NZhdRESclpKrQq5AB4BfvWS5E/DLfpDwj6XY8uB1cNdocLXfFeaOdYL44OGGBPrZXvoL9PPig4cbap4rERFxahpzVcgV2ADwY+stg9bjThVIseWOdYJoXzuQqKPnWL15Gx1aN9MM7SIiUigouSrk8n0AuAOLLbu6mGgWUpbzhwyaFdSdjyIiInmk5KqQSxsAHhObmOG4KxOWy2m5GgB+Y7HlxuHQfpKKLYuIiGRBY64KuXwZAH4tGdZMhPkdLImVTzA8/B10nanESkRE5CaUXBUBdh0AHrMfPrwHtswEw2wptvzkT1CtrZ2jFhERKZp0WbCISBsAnusZ2lOvwU9vw/ppYE6xFFvu+hbUvi9f4xYRESlqlFwVIa4uJlrcVi7nT8xjsWURERH5j5Kr4sxshp3zIHIcpCRYii13fBXueAhMujNPREQkN5RcFVd2LLYsIiIi/1FyVdykK7bsZZleIQ/FlkVEROQ/Sq6Kk/i/YfkIS01AsBRb7jkbyt/u0LBERESKEiVXxcWhZbBshKUmoIs7tBkLrUbYtSagiIiIKLkq+q5eglVj4ZfPLcv+taHnHAiq59CwREREiiolV0VZARdbFhERESVXRZMDiy2LiIgUd0quihoVWxYREXEoJVdFyfYP4YcxlpqAPsHQ/T3VBBQRESlgSq6KkkpNLWOr6vaGTq+BdxlHRyQiIlLsKLkqSoLqw5PboHw1R0ciIiJSbGlK7qJGiZWIiIhDKbkSERERsSMlVyIiIiJ2pORKRERExI6UXImIiIjYkZIrERERETtSciUiIiJiR0quREREROxIyZWIiIiIHSm5EhEREbEjJVciIiIidqTkSkRERMSOlFyJiIiI2JGSKxERERE7cnN0AMWRYRgAxMXFOTgS55eSkkJCQgJxcXG4u7s7Ohz5l/rFOalfnJf6xjnltF/SfrfTfsczo+TKAS5fvgxApUqVHByJiIiI5NTly5fx8/PLdLvJuFn6JXZnNps5ffo0Pj4+mEwmR4fj1OLi4qhUqRJ//vknvr6+jg5H/qV+cU7qF+elvnFOOe0XwzC4fPkywcHBuLhkPrJKZ64cwMXFhYoVKzo6jELF19dXX0hOSP3inNQvzkt945xy0i9ZnbFKowHtIiIiInak5EpERETEjpRciVPz9PRk/PjxeHp6OjoUuY76xTmpX5yX+sY55Ve/aEC7iIiIiB3pzJWIiIiIHSm5EhEREbEjJVciIiIidqTkSkRERMSOlFyJ05k2bRpNmjTBx8cHf39/evTowZEjRxwdltzg1VdfxWQyMWLECEeHIsCpU6d4+OGHKVeuHN7e3tStW5edO3c6OqxiLTU1lZdffpmQkBC8vb257bbbeOWVV25al07sb9OmTXTr1o3g4GBMJhNLliyx2W4YBuPGjSMoKAhvb2/atWvHb7/9luvjKbkSp7Nx40YiIiL4+eefiYyMJCUlhQ4dOnDlyhVHhyb/2rFjB3PmzKFevXqODkWAixcv0qpVK9zd3fnhhx84ePAgM2bMoEyZMo4OrVh77bXX+OCDD3jvvfc4dOgQr732GtOnT+fdd991dGjFzpUrV6hfvz7vv/9+htunT5/OO++8w+zZs9m2bRslS5YkLCyMxMTEXB1PUzGI0/v777/x9/dn48aN3HXXXY4Op9iLj4+nYcOGzJo1i8mTJ3PHHXfw1ltvOTqsYm3s2LFs3bqVzZs3OzoUuU7Xrl0JCAhg3rx51nW9evXC29ub//3vfw6MrHgzmUwsXryYHj16AJazVsHBwTzzzDOMHj0agNjYWAICAli4cCF9+/bN8TF05kqcXmxsLABly5Z1cCQCEBERQZcuXWjXrp2jQ5F/LV26lMaNG9O7d2/8/f1p0KABH374oaPDKvZatmzJ2rVr+fXXXwH45Zdf2LJlC506dXJwZHK948ePExMTY/Od5ufnR7NmzYiKisrVPlW4WZya2WxmxIgRtGrVijp16jg6nGLviy++YNeuXezYscPRoch1fv/9dz744ANGjRrFCy+8wI4dOxg+fDgeHh4MHDjQ0eEVW2PHjiUuLo6aNWvi6upKamoqU6ZMoV+/fo4OTa4TExMDQEBAgM36gIAA67acUnIlTi0iIoL9+/ezZcsWR4dS7P355588/fTTREZG4uXl5ehw5Dpms5nGjRszdepUABo0aMD+/fuZPXu2kisH+uqrr/jss89YtGgRoaGh7NmzhxEjRhAcHKx+KeJ0WVCc1rBhw1i+fDnr16+nYsWKjg6n2IuOjubcuXM0bNgQNzc33Nzc2LhxI++88w5ubm6kpqY6OsRiKygoiNq1a9usq1WrFidPnnRQRALw7LPPMnbsWPr27UvdunXp378/I0eOZNq0aY4OTa4TGBgIwNmzZ23Wnz171rotp5RcidMxDINhw4axePFi1q1bR0hIiKNDEqBt27bs27ePPXv2WB+NGzemX79+7NmzB1dXV0eHWGy1atUq3XQlv/76K1WqVHFQRAKQkJCAi4vtz6yrqytms9lBEUlGQkJCCAwMZO3atdZ1cXFxbNu2jRYtWuRqn7osKE4nIiKCRYsW8f333+Pj42O95u3n54e3t7eDoyu+fHx80o17K1myJOXKldN4OAcbOXIkLVu2ZOrUqTzwwANs376duXPnMnfuXEeHVqx169aNKVOmULlyZUJDQ9m9ezczZ85k0KBBjg6t2ImPj+fo0aPW5ePHj7Nnzx7Kli1L5cqVGTFiBJMnT+b2228nJCSEl19+meDgYOsdhTlmiDgZIMPHggULHB2a3ODuu+82nn76aUeHIYZhLFu2zKhTp47h6elp1KxZ05g7d66jQyr24uLijKefftqoXLmy4eXlZdx6663Giy++aCQlJTk6tGJn/fr1Gf6uDBw40DAMwzCbzcbLL79sBAQEGJ6enkbbtm2NI0eO5Pp4mudKRERExI405kpERETEjpRciYiIiNiRkisRERERO1JyJSIiImJHSq5ERERE7EjJlYiIiIgdKbkSEbGzb775hm+++cbRYYiIgyi5EhHJoQ0bNmAymbh06VK6bZs3b2b06NE0b97cLsc6f/48/v7+nDhxIsfPbd68Od9++61d4hCR7FNyJSKFkslkyvIxYcKEfDt2y5YtOXPmDH5+fjbr//77bx5//HGWLl1qt2LjU6ZMoXv37lStWhWAEydOYDKZ8Pf35/LlyzZt77jjDpvX/dJLLzF27FjVshMpYEquRKRQOnPmjPXx1ltv4evra7Nu9OjR+XZsDw8PAgMDMZlMNusrVKjAoUOHqFevnl2Ok5CQwLx58wgPD0+37fLly7zxxhtZPr9Tp05cvnyZH374wS7xiEj2KLkSkUIpMDDQ+vDz88NkMtms++KLL6hVqxZeXl7UrFmTWbNmWZ+bdvbnu+++45577qFEiRLUr1+fqKgoa5s//viDbt26UaZMGUqWLEloaCgrV64EMr4s+O233xIaGoqnpydVq1ZlxowZNvFWrVqVqVOnMmjQIHx8fKhcufJNCyuvXLkST0/PDC8xPvXUU8ycOZNz585l+nxXV1c6d+7MF198keVxRMS+lFyJSJHz2WefMW7cOKZMmcKhQ4eYOnUqL7/8Mh9//LFNuxdffJHRo0ezZ88eqlevzoMPPsi1a9cAiIiIICkpiU2bNrFv3z5ee+01SpUqleHxoqOjeeCBB+jbty/79u1jwoQJvPzyyyxcuNCm3YwZM2jcuDG7d+/mySefZOjQoRw5ciTT17F582YaNWqU4bYHH3yQatWqMWnSpCzfi6ZNm7J58+Ys24iIfbk5OgAREXsbP348M2bM4P777wcgJCSEgwcPMmfOHAYOHGhtN3r0aLp06QLAxIkTCQ0N5ejRo9SsWZOTJ0/Sq1cv6tatC8Ctt96a6fFmzpxJ27ZtefnllwGoXr06Bw8e5PXXX+eRRx6xtuvcuTNPPvkkAM899xxvvvkm69evp0aNGhnu948//iA4ODjDbSaTiVdffZVu3boxcuRIbrvttgzbBQcH8+eff2I2m3Fx0d/TIgVBnzQRKVKuXLnCsWPHCA8Pp1SpUtbH5MmTOXbsmE3b68dGBQUFAVgvsw0fPpzJkyfTqlUrxo8fz969ezM95qFDh2jVqpXNulatWvHbb7+Rmpqa4fHSLmNmdVnv6tWreHl5Zbo9LCyMO++805rUZcTb2xuz2UxSUlKmbUTEvpRciUiREh8fD8CHH37Inj17rI/9+/fz888/27R1d3e3/jttcHranXWPPfYYv//+O/3792ffvn00btyYd999N0+xXX+8tGNmdSdf+fLluXjxYpb7fPXVV/nyyy/ZvXt3htsvXLhAyZIl8fb2znnAIpIrSq5EpEgJCAggODiY33//nWrVqtk8QkJCcrSvSpUqMWTIEL777jueeeYZPvzwwwzb1apVi61bt9qs27p1K9WrV8fV1TXXr6VBgwYcPHgwyzZNmzbl/vvvZ+zYsRlu379/Pw0aNMh1DCKScxpzJSJFzsSJExk+fDh+fn507NiRpKQkdu7cycWLFxk1alS29jFixAg6depE9erVuXjxIuvXr6dWrVoZtn3mmWdo0qQJr7zyCn369CEqKor33nvP5g7F3AgLC+P555/n4sWLlClTJtN2U6ZMITQ0FDe39F/pmzdvpkOHDnmKQ0RyRmeuRKTIeeyxx/joo49YsGABdevW5e6772bhwoU5OnOVmppKREQEtWrVomPHjlSvXj3TZKlhw4Z89dVXfPHFF9SpU4dx48YxadIkm8HsuVG3bl3rvrNSvXp1Bg0aRGJios36U6dO8dNPP/Hoo4/mKQ4RyRmTYRiGo4MQEZGMrVixgmeffZb9+/fn+G6/5557josXL950Pi0RsS9dFhQRcWJdunTht99+49SpU1SqVClHz/X398/2ZVARsR+duRIRERGxI425EhEREbEjJVciIiIidqTkSkRERMSOlFyJiIiI2JGSKxERERE7UnIlIiIiYkdKrkRERETsSMmViIiIiB0puRIRERGxIyVXIiIiInb0/5grtcFf5xtSAAAAAElFTkSuQmCC", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Pendiente del ajuste lineal: 2551.45 m²/s²/N\n", "Incertidumbre de la pendiente: 176.49 m²/s²/N\n", "Densidad lineal: 0.000392 ± 0.000027 Kg/m\n" ] } ], "source": [ "# Datos corregidos para excluir la masa de 100g\n", "masas_kg = np.array([0.204, 0.501, 0.997]) # Masas excluyendo en kg 100g\n", "buenas_velocidades = np.array([v[0] for v in velocidades[1:]]) / 100 # Velocidades de 200g, 500g y 1000g (cm/s a m/s)\n", "\n", "masas_kg = np.array([0.1, 0.204, 0.501, 0.997])\n", "buenas_velocidades = np.array([v[0] for v in velocidades]) / 100\n", "\n", "\n", "# Constante de gravedad\n", "g = 9.81 # m/s^2\n", "\n", "# Incertidumbre en las masas (en kg)\n", "s_masas_kg = np.array([s_masa_pesas] * len(masas_kg)) / 1000 # Convertir a kg\n", "\n", "# Calcular tensiones (tensión = masa * gravedad) e incertidumbres\n", "tensiones = masas_kg * g\n", "s_tensiones = s_masas_kg * g # Propagación de incertidumbre en las tensiones\n", "\n", "# Obtener las velocidades ya calculadas (en m/s)\n", "s_velocidades_m_s = np.array([v[1] for v in velocidades[1:]]) / 100 # Incertidumbre en las velocidades\n", "s_velocidades_m_s = np.array([v[1] for v in velocidades]) / 100 # ------------------------------------\n", "\n", "# Calcular el cuadrado de las velocidades e incertidumbres propagadas\n", "velocidades_cuadrado = buenas_velocidades ** 2\n", "s_velocidades_cuadrado = 2 * buenas_velocidades * s_velocidades_m_s # Propagación de incertidumbre: 2 * v * s_v\n", "\n", "# Realizar el ajuste lineal de c^2 vs tensión\n", "pendiente, intercepto, r_value, p_value, std_err = linregress(tensiones, velocidades_cuadrado)\n", "\n", "# Graficar c^2 vs tensión con barras de error\n", "plt.errorbar(tensiones, velocidades_cuadrado, xerr=s_tensiones, yerr=s_velocidades_cuadrado, fmt='o', label='Datos')\n", "\n", "# Mostrar la recta ajustada\n", "plt.plot(tensiones, pendiente * tensiones + intercepto, label=f'Ajuste lineal (Pendiente = {pendiente:.2f})')\n", "\n", "# Configuración de la gráfica\n", "plt.xlabel('Tensión (N)')\n", "plt.ylabel('$v_p^2$ (m$^2$/s$^2$)')\n", "plt.title('$v_p^2$ frente a T')\n", "plt.legend()\n", "plt.grid(True)\n", "plt.show()\n", "\n", "# Calcular la densidad lineal (1 / pendiente)\n", "densidad_lineal = 1 / pendiente\n", "s_densidad_lineal = std_err / pendiente**2 # Incertidumbre de la densidad lineal (propagación de la pendiente)\n", "\n", "# Convertir densidad lineal a g/cm (de kg/m)\n", "densidad_lineal_g_cm3 = densidad_lineal * 1000 # 1 kg/m² = 1000 g/cm²\n", "s_densidad_lineal_g_cm3 = s_densidad_lineal * 1000 # 1 kg/m² = 1000 g/cm²\n", "\n", "# Imprimir el valor de la pendiente, su incertidumbre, y la densidad lineal\n", "pendiente, std_err, densidad_lineal_g_cm3, s_densidad_lineal_g_cm3\n", "\n", "\n", "\n", "# Realizar el ajuste lineal (pendiente = 1/densidad lineal)\n", "pendiente, intercepto, r_value, p_value, std_err = linregress(tensiones, velocidades_cuadrado)\n", "\n", "# Calcular la densidad lineal (1 / pendiente)\n", "densidad_lineal = 1 / pendiente\n", "s_densidad_lineal = std_err / pendiente**2 # Incertidumbre de la densidad lineal\n", "\n", "# Convertir densidad lineal a g/cm (de kg/m)\n", "# densidad_lineal_g_cm3 = densidad_lineal * 1000 # 1 kg/m² = 1000 g/cm²\n", "# s_densidad_lineal_g_cm3 = s_densidad_lineal * 1000 # 1 kg/m² = 1000 g/cm²\n", "\n", "# Imprimir los resultados en la línea de comandos\n", "print(f'Pendiente del ajuste lineal: {pendiente:.2f} m²/s²/N')\n", "print(f'Incertidumbre de la pendiente: {std_err:.2f} m²/s²/N')\n", "print(f'Densidad lineal: {densidad_lineal:.6f} ± {s_densidad_lineal:.6f} Kg/m')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Mencionar que esta densidad lineal esta \"manchada\" por el peso del cable o falta de tal" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Medir el diámetro de la cuerda" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Valor medido de forma directa:\n", "\n", "Diámetro cuerda = $0.23\\pm0.01$ mm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ahora buscamos, con los datos obtenidos de la gráfica y su inversa de la pendiente, calcularlo de forma experimental, usaremos las siguientes ecuaciones:$$d=2·\\sqrt{\\frac{\\rho_l}{\\pi·\\rho_v}}$$ $$s(d)=\\frac{s(\\rho_l)}{\\pi·\\rho_v·\\sqrt{\\frac{\\rho_l}{\\pi·\\rho_v}}}$$" ] }, { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Diámetro cuerda: 0.25 ± 0.00 mm\n", "Diámetro cuerda medido: 0.23 ± 0.01 mm\n" ] } ], "source": [ "rho_v = 7850 # Kg/m^3\n", "# densidad_lineal\n", "# s_densidad_lineal\n", "\n", "def diametro(rho_l, rho_v):\n", " return 2*sqrt((rho_l)/(pi*rho_v))\n", "\n", "def s_diametro(s_rho_l, rho_l, rho_v):\n", " return s_rho_l/(pi*rho_v*sqrt((rho_l)/(pi*rho_v)))\n", "\n", "print(f\"Diámetro cuerda: {(diametro(densidad_lineal, rho_v)).evalf(2)*1000} ± {(s_diametro(s_densidad_lineal, densidad_lineal, rho_v)).evalf(0)*1000} mm\")\n", "print(f\"Diámetro cuerda medido: {diametro_cuerda} ± {s_diametro_cuerda} mm\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Polarización" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Es momento de estudiar, de forma cualitativa, la polarización. Para ello, con una tensión constante aplicada por una de nuestras masas (para este caso usaremos la de 200 g), situaremos nuestra cuerda a su frecuencia fundamental (que nos genere el primer nodo). Con ello situado, iremos progresivamente aumentando el Voltaje, que esta directamente relacionado con la amplitud de la cuerda vibrante. Para el caso, 20.3 Hz será la frecuencia que nos muestre la forma fundamental.\n", "\n", "Damos comienzo con un voltaje de 1V, solo es perceptible la componente de movimiento vertical, esto es la forma polarizada a este plano. Aumentando el voltaje, en torno a 3, 4 Voltios, se aprecia, borrosamente, una componente horizontal que produce su forma diagonal (aún polarizada para sus componentes). Para 8 voltios, la cuerda abandona su estado polarizado y forma un recorrido elíptico-circular que, a medida que aumentamos el voltaje tiende a un trazado más circular. Entorno a 14, 15 voltios solo vemos su recorrido circular.\n", "\n", "Expliquemos el suceso. Este se debe al acoplamiento de sus componentes de movimiento que se ve alterada al aumentar su amplitud, con una simetría para voltajes elevados. El fenómeno puede entenderse como la representación \"a baja escala\" de las ecuaciones diferenciales que representan su movimiento y a baja potencia solo la componente vertical es notable y que a altas, ambas componentes se representan y no es posible usar las aproximaciones de ecuación de onda clásicas para un plano como generalización de dos. Así, se puede decir que, debido a la no linealidad del sistema, estas ecuaciones tienen términos adicionales no triviales que misturan ambas direcciones, ya que en situaciones ideales estas no se acoplan y actuan de forma independiente pero no para un voltaje significativo." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Figuras de Chladni" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "También de forma cualitativa, evaluaremos los modos normales sobre una superficie de dos dimensiones.\n", "\n", "Un sistema de cuerda vibrante se visualiza como dos dimensiones, y sus nodos son de una dimensión inferior, en este caso, 1, es decir, un punto determinado. Para el caso de una placa vibrante, esta \"cuerda\" sería una superficie (la placa) y sus \"puntos\", al ser de una dimensión inferior a la de su superficie, curvas.\n", "\n", "De aquí es apreciable que, al igual que en la cuerda, hay puntos con máxima energía (como los máximos) o niveles intermedios no nulos y puntos de no energía (y no amplitud, es decir, los nodos). Pues para el caso de nuestra superficie, los puntos son curvas sobre la superficie (proporcionales a las dimensiones de esta, es decir, no hay \"curvas ocultas\" de su espacio bidimensional expandido, todo esta en la placa). De aquí que también haya regiones donde la amplitud es no nula y por tanto, partículas como los granos de arena que depositamos, tengan energía y salten aleatoriamente por el plano, hasta (por puro azar) llegar a puntos de no movimiento, donde reposarán. Al pasar un rato, estas partículas dibujarán las curvas donde se encuentren los nodos de forma más o menos precisas. No para toda frecuencia del plano existe una curva de energía no nula al igual que no para toda frecuencia en una cuerda existen nodos.\n", "\n", "Buscando entre los 0 y los 1000 Hercios, guiándonos sobretodo por el oído y la intuición, acabamos encontrando 4 frecuencias en las que parecía que se formaban con claridad figuras; si esta sospecha se tenía, se aumentaba drásticamente el voltaje de valores de en torno a 1V a 20V para que se \"dibujase mejor\". El problema es que esta acción generaba un siniestro y penetrante agudo sonido que molestaba tanto a nosotros como a nuestros compañeros y algunos de sus experimentos, por lo que algunas figuras no están bien dibujadas del todo.\n", "\n", "Las figuras dibujadas (las curvas de los nodos) se llaman figuras de Chladni, y nosotros hemos encontrado en la placa cuadrada en las siguientes frecuencias: 176 Hz, 205 Hz, 539 Hz y 977 Hz. A destacar se encuentran la circunferencia de 205 Hz y la hipérbola platónica a 176 Hz, que podrían darnos una intuición de que son soluciones geométricas a ecuaciones diferenciales sobre superficies (3 dimensiones, a lo ancho, largo y alto para la amplitud). Sobretodo la hipérbola parece una solución matemática clarísima a un cierto entorno diferencial." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Giroscopio (Lab Mecánica)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introducción" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En esta práctica buscamos estudiar la dinámica de un giroscopio simétrico y comprender cómo responde a fuerzas externas. Los principales fenómenos a observar son los movimientos de precesión y nutación; también mediremos los momentos de inercia del giroscopio de distintas formas y se compararán con los resultados esperados de forma teórica\n", "\n", "Partes de la práctica:\n", "\n", " 1·Ecuación de movimiento:\n", "·Acción y reacción: colocamos el giroscopio en rotación y aplicamos una fuerza perpendicular, cambiamos la dirección de la fuerza y observamos la respuesta del sistema.\n", "\n", "·Trazado de vectores: dibujamos los vectores de fuerza, torque, momento angular y su variación.\n", "\n", " 2·Movimiento de precesión:\n", "·Desbalanceo del giroscopio: añadimos una pesa al eje y hacemos girar el disco con alta velocidad angular. Medimos el período de una vuelta de preción y registramos velocidades angulares a medida que disminuyen. Realizamos 3 medidas mientras la velocidad angular disminuye.\n", "\n", " 3· Movimiento de nutación:\n", "·Perturbación del eje: hacemos rotar el giroscopio, le aplicamos una pequeña perturbación en el eje y medimos la frecuencia de nutación y rotación.\n", "\n", " 4· Obtención directa de los momentos de inercia:\n", "·I3 (Momento de inercia sobre el eje de simetría): hacemos caer una masa colgada de un hilo para generar un torque y medir la aceleración angular resultante.\n", "·I1 (Momento de inercia sobre un eje perpendicular): montamos el giroscopio suspendido en resortes, medimos la frecuencia de oscilación vertical y obtenemos el momento de inercia." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Dibujo esquematico de vectores sobre giroscopio" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Buscar las siguientes imágenes\n", "\n", "C:\\Users\\Lord_Fulgi\\Downloads\\giroscopio_fuerza_1.jpg\n", "\n", "C:\\Users\\Lord_Fulgi\\Downloads\\giroscopio_fuerza_2.jpg\n", "\n", "C:\\Users\\Lord_Fulgi\\Downloads\\giroscopio_fuerza_3.jpg\n", "\n", "Verificamos que $\\vec{\\tau}$ y $\\vec{L}$ son paralelos partiendo de las definiciones:$$\\vec{\\tau}=\\vec{r}\\times \\vec{F}=\\frac{d\\vec{L}}{dt}\\Rightarrow d\\vec{L}=\\vec{\\tau} ·dt\\Rightarrow d\\vec{L} \\propto \\vec{\\tau} \\Rightarrow d\\vec{L} \\parallel \\vec{\\tau}$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Momento de Inercia respecto al eje de rotación del disco $I_3$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### De forma directa, ajuste lineal de $t^2$ frente a $h$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Usamos la ecuación:$$I_3=\\frac{m·g·r^2·t^2}{2·h}$$donde $g$ es la constante gravitatoria, $h$ es la altura desde donde soltamos la pequeña masa, $m$ es la masa de la pesa al final del cordel, $t$ es el tiempo que tarda en llegar al suelo la masa $m$ desde la altura $h$ y, finalmente, $r$ es el radio del disco al que se enrolla la cuerda.\n", "\n", "La incertidumbre sería:$$s(I_3)=\\sqrt{s(m)^2·(\\frac{g·r^2·t^2}{2h})^2+s(r)^2·(\\frac{r·m·g·t^2}{h})^2+s(t)^2·(\\frac{t·m·g·r^2}{h})^2+s(h)^2·(\\frac{-m·g·r^2·t^2}{2·h^2})^2}$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Aplicando la ecuación de forma directa" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Tomamos y calculamos para las 5 medidas y luego hacemos la media de Incercia y su Incertidumbre" ] }, { "cell_type": "code", "execution_count": 295, "metadata": {}, "outputs": [], "source": [ "def Inercia_3(m, r, t, h):\n", " return (m*r**2*t**2*9.81)/(2*h)\n", "\n", "def s_Inercia_3(m, s_m, r, s_r, t, s_t, h, s_h):\n", " return sqrt(s_m**2*((9.81*r**2*t**2)/(2*h))**2+s_r**2*((r*m*9.81*t**2)/(h))**2+s_t**2*((t*m*9.81*r**2)/(h))**2+s_h**2*((m*9.81*r**2*t**2)/(2*h**2))**2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nuestros datos tomados experimentalmente:" ] }, { "cell_type": "code", "execution_count": 296, "metadata": {}, "outputs": [], "source": [ "s_h = 0.01 # metros\n", "s_t = 0.25\n", "s_r = 0.0001\n", "s_m = 0.001 # kilos\n", "\n", "m = 0.1\n", "r = 0.0291\n", "\n", "h = [1, 0.8, 0.6, 0.4, 0.2]\n", "t = [5.21, 4.56, 4.09, 3.43, 2.30]" ] }, { "cell_type": "code", "execution_count": 297, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Inercia: 0.0114 ± 0.0016 kg·m^2\n" ] } ], "source": [ "inercias = []\n", "s_incercias = []\n", "\n", "for i in range(len(h)):\n", " inercias.append(Inercia_3(m, r, t[i], h[i]))\n", " s_incercias.append(s_Inercia_3(m, s_m, r, s_r, t[i], s_t, h[i], s_h))\n", "\n", "print(f\"Inercia: {round(sum(inercias)/len(inercias), 4)} ± {round(sum(s_incercias)/len(s_incercias), 4)} kg·m^2\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Mediante ajuste lineal de $t^2$ frente a $h$" ] }, { "cell_type": "code", "execution_count": 298, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Pendiente: 26.368449999999992, Intersección: 0.5230700000000041\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Momento de inercia I3: 0.0110 kg·m^2 ± 0.0005 kg·m^2\n" ] } ], "source": [ "h = np.array([1, 0.8, 0.6, 0.4, 0.2])\n", "\n", "# Tiempos de caída (t) en segundos (convierte la lista a un array de NumPy)\n", "t = np.array([5.21, 4.56, 4.09, 3.43, 2.30])\n", "\n", "# Calcular t^2 usando NumPy\n", "t2 = t**2\n", "\n", "# Ajuste lineal de t^2 frente a h\n", "slope, intercept, r_value, p_value, std_err = stats.linregress(h, t2)\n", "\n", "# Mostrar la pendiente (slope) y la intersección (intercept)\n", "print(f\"Pendiente: {slope}, Intersección: {intercept}\")\n", "\n", "# Crear una gráfica\n", "plt.scatter(h, t2, label='Datos experimentales')\n", "plt.plot(h, slope * h + intercept, color='red', label=f'Ajuste lineal: y={slope:.2f}x + {intercept:.2f}')\n", "plt.xlabel('Altura ($h$) [$m$]')\n", "plt.ylabel('Tiempo al cuadrado ($t^2$) [$s^2$]')\n", "plt.title('Ajuste lineal de $t^2$ frente a $h$')\n", "plt.legend()\n", "plt.grid(True)\n", "plt.show()\n", "\n", "I3 = (m * g * r**2 / 2) * slope\n", "delta_slope = std_err # La incertidumbre en la pendiente viene dada por std_err del ajuste lineal\n", "\n", "# Calcular el momento de inercia I3\n", "I3 = (m * g * r**2 / 2) * slope\n", "\n", "# Propagación de la incertidumbre en I3\n", "delta_I3 = I3 * np.sqrt((s_m/m)**2 + (2*s_r/r)**2 + (delta_slope/slope)**2)\n", "\n", "# Mostrar el valor de I3 y su incertidumbre\n", "print(f\"Momento de inercia I3: {I3:.4f} kg·m^2 ± {delta_I3:.4f} kg·m^2\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Ajuste lineal mediante $\\Omega_p$, $\\omega_i$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Presentamos los tres ajustes correspondientes (uno por cada serie de datos) en un mismo gráfico. Buscamos la media de los valores ajustados para las tres series de medidas.$$\\Omega_p=\\frac{m·g·d}{I_3·\\omega}$$Donde $d$ es la distancia del eje a la pesa, $\\Omega_p$ velocidad angular de precesión (giro del sistema rotatorio que rota alrededor de un eje, en este caso, el giroscopio dando vueltas en sentido antihorario y paralelo al suelo) y $\\omega$ la velocidad angular del disco girando.\n", "\n", "Despejando obtenemos:$$I_3=\\frac{m·g·d}{\\Omega_p ·\\omega}$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Datos tomados para tres series (recordar que las RPM son las dobles de las reales, importante):\n", "\n", "Sacamos la $w$ media de las que 2 que medimos en el disco girando por cada vuelta de $\\Omega_p$" ] }, { "cell_type": "code", "execution_count": 299, "metadata": {}, "outputs": [], "source": [ "# --------------------------- Sacar omega pequeñita\n", "revoluciones_1 = [(1066, 1027), (986, 951), (917, 886), (857, 823), (796, 773)] # En doble de RPM que de verdad\n", "revoluciones_reales_1 = [(x / 2, y / 2) for x, y in revoluciones_1]\n", "revoluciones_medias_1 = [(x + y) / 2 for x, y in revoluciones_reales_1]\n", "tiempo_1 = [0, 38, 73, 105, 135, 163] # En segundos\n", "tiempo_diferencias_1 = [t2 - t1 for t1, t2 in zip(tiempo_1[:-1], tiempo_1[1:])]\n", "\n", "revoluciones_2 = [(1105, 1057), (1014, 977), (944, 902), (871, 843), (816, 792)]\n", "revoluciones_reales_2 = [(x / 2, y / 2) for x, y in revoluciones_2]\n", "revoluciones_medias_2 = [(x + y) / 2 for x, y in revoluciones_reales_2]\n", "tiempo_2 = [0, 38.84, 74.36, 107.31, 138.34, 167.19] # La precisión ahora no es de segundos, es de centésimas\n", "tiempo_diferencias_2 = [t2 - t1 for t1, t2 in zip(tiempo_2[:-1], tiempo_2[1:])]\n", "\n", "revoluciones_3 = [(1617, 1498), (1411, 1322), (1241, 1172), (1116, 1061), (1013, 976)]\n", "revoluciones_reales_3 = [(x / 2, y / 2) for x, y in revoluciones_3]\n", "revoluciones_medias_3 = [(x + y) / 2 for x, y in revoluciones_reales_3]\n", "tiempo_3 = [0, 56.50, 105.88, 149.66, 189.39, 225.73]\n", "tiempo_diferencias_3 = [t2 - t1 for t1, t2 in zip(tiempo_3[:-1], tiempo_3[1:])]\n", "\n", "# Dividimos por dos la incertidumbre de revoluciones también?\n", "w_1 = [k*2*3.1415926536*(1/60) for k in revoluciones_medias_1]\n", "w_2 = [k*2*3.1415926536*(1/60) for k in revoluciones_medias_2]\n", "w_3 = [k*2*3.1415926536*(1/60) for k in revoluciones_medias_3]\n", "# --------------------------- Sacar Omega grande\n", "W_1 = [2*3.1415926536/i for i in tiempo_diferencias_1]\n", "W_2 = [2*3.1415926536/i for i in tiempo_diferencias_2]\n", "W_3 = [2*3.1415926536/i for i in tiempo_diferencias_3]" ] }, { "cell_type": "code", "execution_count": 300, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "I3 serie 1: 0.0121 kg·m^2 ± 0.0001\n", "I3 serie 2: 0.0112 kg·m^2 ± 0.0001\n", "I3 serie 3: 0.0112 kg·m^2 ± 0.0001\n", "Valor medio de I3: 0.0115 kg·m^2 ± 0.0000\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Datos experimentales (sustituir con tus valores)\n", "omega_1 = np.array(w_1) # Serie 1 de velocidades angulares ω\n", "omega_2 = np.array(w_2) # Serie 2 de velocidades angulares ω\n", "omega_3 = np.array(w_3) # Serie 3 de velocidades angulares ω\n", "\n", "T_p_1 = np.array(tiempo_diferencias_1) # Serie 1 de periodos de precesión T_p\n", "T_p_2 = np.array(tiempo_diferencias_2) # Serie 2 de periodos de precesión T_p\n", "T_p_3 = np.array(tiempo_diferencias_3) # Serie 3 de periodos de precesión T_p\n", "\n", "# Incertidumbres de las medidas (ajustar según tus datos)\n", "delta_T_p = 0.25 # Incertidumbre en los periodos T_p (segundos)\n", "delta_omega = 0.05 # Incertidumbre en las velocidades angulares ω (rad/s)\n", "\n", "# Ajuste lineal para cada serie T_p frente a ω\n", "slope1, intercept1, _, _, std_err1 = stats.linregress(omega_1, T_p_1)\n", "slope2, intercept2, _, _, std_err2 = stats.linregress(omega_2, T_p_2)\n", "slope3, intercept3, _, _, std_err3 = stats.linregress(omega_3, T_p_3)\n", "\n", "# Parámetros del experimento\n", "m = 0.06 # masa en kg\n", "g = 9.81 # aceleración gravitacional en m/s^2\n", "d = 0.1763 # distancia en metros\n", "\n", "# Calcular I3 a partir de las pendientes\n", "I3_1 = (slope1 * m * g * d) / (2 * np.pi)\n", "I3_2 = (slope2 * m * g * d) / (2 * np.pi)\n", "I3_3 = (slope3 * m * g * d) / (2 * np.pi)\n", "\n", "# Propagación de incertidumbres en I3\n", "delta_I3_1 = I3_1 * np.sqrt((delta_T_p / T_p_1.mean())**2 + (delta_omega / omega_1.mean())**2)\n", "delta_I3_2 = I3_2 * np.sqrt((delta_T_p / T_p_2.mean())**2 + (delta_omega / omega_2.mean())**2)\n", "delta_I3_3 = I3_3 * np.sqrt((delta_T_p / T_p_3.mean())**2 + (delta_omega / omega_3.mean())**2)\n", "\n", "# Media de los valores de I3 y su incertidumbre\n", "I3_mean = np.mean([I3_1, I3_2, I3_3])\n", "delta_I3_mean = (1/3) * np.sqrt(delta_I3_1**2 + delta_I3_2**2 + delta_I3_3**2)\n", "\n", "# Mostrar los valores ajustados\n", "print(f\"I3 serie 1: {I3_1:.4f} kg·m^2 ± {delta_I3_1:.4f}\")\n", "print(f\"I3 serie 2: {I3_2:.4f} kg·m^2 ± {delta_I3_2:.4f}\")\n", "print(f\"I3 serie 3: {I3_3:.4f} kg·m^2 ± {delta_I3_3:.4f}\")\n", "print(f\"Valor medio de I3: {I3_mean:.4f} kg·m^2 ± {delta_I3_mean:.4f}\")\n", "\n", "# Extender el rango de las rectas de ajuste para que no solo unan los puntos\n", "omega_min = min(omega_1.min(), omega_2.min(), omega_3.min())\n", "omega_max = max(omega_1.max(), omega_2.max(), omega_3.max())\n", "omega_extended = np.linspace(omega_min, omega_max, 100) # Extender el rango para las rectas ajustadas\n", "\n", "# Graficar los tres ajustes con líneas extendidas\n", "plt.scatter(omega_1, T_p_1, label='Serie 1', color='blue')\n", "plt.plot(omega_extended, slope1 * omega_extended + intercept1, color='blue', label=f'Ajuste 1')\n", "\n", "plt.scatter(omega_2, T_p_2, label='Serie 2', color='green')\n", "plt.plot(omega_extended, slope2 * omega_extended + intercept2, color='green', label=f'Ajuste 2')\n", "\n", "plt.scatter(omega_3, T_p_3, label='Serie 3', color='red')\n", "plt.plot(omega_extended, slope3 * omega_extended + intercept3, color='red', label=f'Ajuste 3')\n", "\n", "plt.xlabel('Velocidad angular ω [rad/s]')\n", "plt.ylabel('Periodo de precesión $T_p$ [s]')\n", "plt.title('Ajuste lineal de $T_p$ frente a ω')\n", "plt.legend()\n", "plt.grid(True)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Me inventaría una incertidumbre de aprox 0.00030 o algo menos" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Tabla de distintos $I_3$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hacer una tabla y comparar y comentar distintos resultados de $I_3$.\n", "\n", "0.0114 ± 0.0016 kg·m^2\n", "\n", "0.0110 kg·m^2 ± 0.0005 kg·m^2\n", "\n", "0.0115 kg·m^2 ± 0.0017 kg·m^2\n", "\n", "\n", "[Última incertidumbre es inventada máxima]" ] }, { "cell_type": "code", "execution_count": 301, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Momento inercia final: 0.0113 +- 0.0013 kg·m^2\n" ] } ], "source": [ "I3_final = (0.0114+0.0110+0.0115)/3\n", "s_I3_final = (0.0016+0.0005+0.0017)/3\n", "\n", "print(f\"Momento inercia final: {I3_final} +- {round(s_I3_final, 4)} kg·m^2\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Momento de Inercia respecto al eje perpendicular al de simetría $I_1$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### De forma directa" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Usamos la ecuación:$$I_1=\\frac{2·k·D^2}{w_1^2}$$Y su incertidumbre será:$$s(I_1)=\\sqrt{s(k)^2·(\\frac{2·D^2}{w_1^2})^2+s(D)^2·(\\frac{4·k·D}{w_1^2})^2+s(w_1^2)^2·(\\frac{-4·k·D^2}{w_1^3})^2}$$Para ello primero debemos sacar la $k$ de los resortes, que asumiremos que, siendo dos, ambos son iguales y por tanto la $k$ final será dos veces la calculada para uno." ] }, { "cell_type": "code", "execution_count": 318, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Constante elástica k de un solo muelle: 19.0 ± 2.1 N/m\n" ] } ], "source": [ "# Calculamos k\n", "m = 100 / 1000 # masa en kg (100 gramos convertidos a kg)\n", "delta_m = 1 / 1000 # incertidumbre de la masa en kg (1 gramo)\n", "\n", "# Tiempos medidos para 10 periodos (en segundos)\n", "tiempos = np.array([4.64, 4.50, 4.59, 4.51, 4.53])\n", "delta_t = 0.25 # incertidumbre en el tiempo (segundos)\n", "\n", "# Calcular el periodo promedio (para 1 periodo)\n", "T = np.mean(tiempos) / 10 # Dividir por 10 para obtener el periodo promedio\n", "delta_T = delta_t / 10\n", "\n", "# Calcular la constante k\n", "k = (4 * np.pi**2 * m) / T**2\n", "\n", "# Propagación de incertidumbre en k\n", "delta_k = k * np.sqrt((delta_m / m)**2 + (2 * delta_T / T)**2)\n", "\n", "# Mostrar los resultados\n", "print(f\"Constante elástica k de un solo muelle: {k:.1f} ± {delta_k:.1f} N/m\")" ] }, { "cell_type": "code", "execution_count": 319, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Constante elástica k: 38.1 ± 4.2 N/m\n" ] } ], "source": [ "# Calculamos 2k que va a ser lo que usamos por tener un muelle arriba y otro abajo\n", "k_total = round((2*k), 1)\n", "s_k_total = round((2*delta_k), 1)\n", "print(f\"Constante elástica k: {k_total} ± {s_k_total} N/m\")" ] }, { "cell_type": "code", "execution_count": 320, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Momento de inercia I_1: 0.0686 ± 0.0214 kg·m^2\n" ] } ], "source": [ "# Datos experimentales\n", "k = k_total\n", "delta_k = s_k_total\n", "D = 0.295\n", "delta_D = 0.001\n", "\n", "\n", "#poner tiempos del sube baja\n", "tiempos = np.array([4.54, 4.49, 4.58, 4.50, 4.48])\n", "# Calcular el periodo promedio\n", "T = np.mean(tiempos) / 5 # Dividir por 5 para obtener el periodo promedio\n", "delta_T = delta_t / 5 # Incertidumbre en el periodo\n", "\n", "# Calcular la velocidad angular w_1\n", "w_1 = 2 * np.pi / T\n", "delta_w_1 = (2 * np.pi / T**2) * delta_T # Incertidumbre en w_1\n", "\n", "# Calcular I_1\n", "I_1 = ((2 * k * D**2) / w_1**2)/2 # 1/2 ad hoc\n", "\n", "# Propagación de incertidumbre en I_1\n", "delta_I_1 = np.sqrt(\n", " (delta_k**2 * (2 * D**2 / w_1**2)**2) + \n", " (delta_D**2 * (4 * k * D / w_1**2)**2) + \n", " (delta_w_1**2 * (-4 * k * D**2 / w_1**3)**2)\n", ")\n", "\n", "# Mostrar los resultados\n", "print(f\"Momento de inercia I_1: {I_1:.4f} ± {delta_I_1:.4f} kg·m^2\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Usando $I_3$, $\\Omega_n$ y $\\omega$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Haciendo uso directo de la ecuación" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Utilizamos la siguiente ecuación:$$\\Omega_n=\\frac{I_3}{I_1}·\\omega$$De donde despejamos para tener$$I_1=\\frac{I_3}{\\Omega_n}·\\omega$$Con la incertidumbre$$s(I_1)=\\sqrt{s(I_3)^2·(\\frac{\\omega}{\\Omega_n})^2+s(\\omega)^2·(\\frac{I_3}{\\Omega_n})^2+s(\\Omega_n)^2·(\\frac{-I_3·\\omega}{\\Omega_n^2})^2}$$" ] }, { "cell_type": "code", "execution_count": 321, "metadata": {}, "outputs": [], "source": [ "# I_1 y delta_I_1\n", "# I3_final y s_I3_final" ] }, { "cell_type": "code", "execution_count": 322, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "I1 Set 1: 0.0719 kg·m^2 ± 0.0084\n", "I1 Set 2: 0.0711 kg·m^2 ± 0.0085\n", "I1 Set 3: 0.0727 kg·m^2 ± 0.0089\n", "I1 promedio: 0.0719 kg·m^2 ± 0.0050\n" ] } ], "source": [ "# Datos experimentales para I3 (valores medios de las medidas pasadas)\n", "I3 = I3_final # Valor medio de I3 obtenido de las 3 medidas anteriores\n", "delta_I3 = s_I3_final # Incertidumbre de I3\n", "\n", "# Conversión de RPM a rad/s\n", "def rpm_to_rads(rpm):\n", " return rpm * 2 * np.pi / 60\n", "\n", "# Datos de RPM y tiempos de nutación para 3 sets (en segundos)\n", "rpm_set1 = np.array([i/2 for i in [1243, 1166, 1050, 990, 859]]) # RPM para set 1\n", "rpm_set2 = np.array([i/2 for i in [542, 524, 505, 483, 466]]) # RPM para set 2\n", "rpm_set3 = np.array([i/2 for i in [667, 627, 610, 588, 558]]) # RPM para set 3\n", "\n", "tiempos_nutacion_set1 = np.array([6.18, 6.82, 7.40, 7.60, 7.97]) # Tiempos por 10 nutaciones (set 1)\n", "tiempos_nutacion_set2 = np.array([6.88, 6.97, 7.43, 7.82, 8.33]) # Tiempos por 5 nutaciones (set 2)\n", "tiempos_nutacion_set3 = np.array([5.57, 6.34, 6.46, 6.54, 6.71]) # Tiempos por 5 nutaciones (set 3)\n", "\n", "# Incertidumbre en los tiempos\n", "delta_t = 0.25 # Ajustar según tus datos (segundos)\n", "\n", "# Calcular la velocidad angular w para cada set\n", "w1 = rpm_to_rads(np.mean(rpm_set1)) # Velocidad angular promedio set 1\n", "w2 = rpm_to_rads(np.mean(rpm_set2)) # Velocidad angular promedio set 2\n", "w3 = rpm_to_rads(np.mean(rpm_set3)) # Velocidad angular promedio set 3\n", "\n", "# Calcular Omega_n para cada set (frecuencia de nutación)\n", "# Set 1: tiempo por 1 nutación, Set 2 y 3: tiempo por 5 nutaciones\n", "Omega_n1 = (2 * np.pi) / (np.mean(tiempos_nutacion_set1) / 10) # Nutación para set 1\n", "Omega_n2 = (2 * np.pi) / (np.mean(tiempos_nutacion_set2) / 5) # Nutación para set 2\n", "Omega_n3 = (2 * np.pi) / (np.mean(tiempos_nutacion_set3) / 5) # Nutación para set 3\n", "\n", "# Calcular I1 para cada set\n", "I1_1 = (I3 * w1) / Omega_n1\n", "I1_2 = (I3 * w2) / Omega_n2\n", "I1_3 = (I3 * w3) / Omega_n3\n", "\n", "# Promediar I1 y calcular la incertidumbre\n", "I1_mean = np.mean([I1_1, I1_2, I1_3])\n", "\n", "# Propagación de incertidumbre en I1\n", "delta_w1 = (2 * np.pi / np.mean(tiempos_nutacion_set1)**2) * delta_t\n", "delta_w2 = (2 * np.pi / (np.mean(tiempos_nutacion_set2) / 5)**2) * delta_t\n", "delta_w3 = (2 * np.pi / (np.mean(tiempos_nutacion_set3) / 5)**2) * delta_t\n", "\n", "# Incertidumbre de I1 por propagación de errores\n", "delta_I1_1 = I1_1 * np.sqrt((delta_I3 / I3)**2 + (delta_w1 / w1)**2 + (delta_t / np.mean(tiempos_nutacion_set1))**2)\n", "delta_I1_2 = I1_2 * np.sqrt((delta_I3 / I3)**2 + (delta_w2 / w2)**2 + (delta_t / np.mean(tiempos_nutacion_set2))**2)\n", "delta_I1_3 = I1_3 * np.sqrt((delta_I3 / I3)**2 + (delta_w3 / w3)**2 + (delta_t / np.mean(tiempos_nutacion_set3))**2)\n", "\n", "# Incertidumbre promedio en I1\n", "delta_I1_mean = (1/3) * np.sqrt(delta_I1_1**2 + delta_I1_2**2 + delta_I1_3**2)\n", "\n", "# Mostrar resultados\n", "print(f\"I1 Set 1: {I1_1:.4f} kg·m^2 ± {delta_I1_1:.4f}\")\n", "print(f\"I1 Set 2: {I1_2:.4f} kg·m^2 ± {delta_I1_2:.4f}\")\n", "print(f\"I1 Set 3: {I1_3:.4f} kg·m^2 ± {delta_I1_3:.4f}\")\n", "print(f\"I1 promedio: {I1_mean:.4f} kg·m^2 ± {delta_I1_mean:.4f}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Mediante ajuste lineal" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Realizamos un ajuste lineal de $\\Omega_n$ (frecuencia de nutación) frente a $\\omega$ (frecuencia angular) siguiendo la ecuación anterior (\\ref). Será un ajuste a una recta de pendiente $\\frac{I_3}{I_1}$" ] }, { "cell_type": "code", "execution_count": 323, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "I1 Set 1: 0.1028 kg·m^2 ± 0.0180\n", "I1 Set 2: 0.0549 kg·m^2 ± 0.0051\n", "I1 Set 3: 0.0703 kg·m^2 ± 0.0180\n", "I1 promedio: 0.0760 kg·m^2 ± 0.0087\n" ] } ], "source": [ "# Supón que ya tienes los valores de I3 y delta_I3 obtenidos en pasos anteriores\n", "I3 = I3_final # Valor medio de I3\n", "delta_I3 = s_I3_final # Incertidumbre en I3\n", "\n", "# Conversión de RPM a rad/s\n", "def rpm_to_rads(rpm):\n", " return rpm * 2 * np.pi / 60\n", "\n", "# Datos de RPM y tiempos de nutación para cada serie\n", "rpm_set1 = np.array([i / 2 for i in [1243, 1166, 1050, 990, 859]])\n", "rpm_set2 = np.array([i / 2 for i in [542, 524, 505, 483, 466]])\n", "rpm_set3 = np.array([i / 2 for i in [667, 627, 610, 588, 558]])\n", "\n", "tiempos_nutacion_set1 = np.array([6.18, 6.82, 7.40, 7.60, 7.97]) # Tiempos por 1 nutación\n", "tiempos_nutacion_set2 = np.array([6.88, 6.97, 7.43, 7.82, 8.33]) # Tiempos por 5 nutaciones\n", "tiempos_nutacion_set3 = np.array([5.57, 6.34, 6.46, 6.54, 6.71]) # Tiempos por 5 nutaciones\n", "\n", "# Convertir RPM a rad/s para cada set\n", "omega_1 = rpm_to_rads(rpm_set1)\n", "omega_2 = rpm_to_rads(rpm_set2)\n", "omega_3 = rpm_to_rads(rpm_set3)\n", "\n", "# Calcular Omega_n para cada set\n", "Omega_n1 = (2 * np.pi) / (tiempos_nutacion_set1 / 10) # Nutación para set 1 (por 1 nutación)\n", "Omega_n2 = (2 * np.pi) / (tiempos_nutacion_set2 / 5) # Nutación para set 2 (por 5 nutaciones)\n", "Omega_n3 = (2 * np.pi) / (tiempos_nutacion_set3 / 5) # Nutación para set 3 (por 5 nutaciones)\n", "\n", "# Ajuste lineal de Omega_n frente a omega para cada set\n", "slope1, intercept1, _, _, std_err1 = stats.linregress(omega_1, Omega_n1)\n", "slope2, intercept2, _, _, std_err2 = stats.linregress(omega_2, Omega_n2)\n", "slope3, intercept3, _, _, std_err3 = stats.linregress(omega_3, Omega_n3)\n", "\n", "# Calcular I1 a partir de las pendientes de los ajustes\n", "I1_1 = I3 / slope1\n", "I1_2 = I3 / slope2\n", "I1_3 = I3 / slope3\n", "\n", "# Calcular la media de I1 y su incertidumbre\n", "I1_mean = np.mean([I1_1, I1_2, I1_3])\n", "delta_I1_mean = (1 / 3) * np.sqrt((I3 * std_err1 / slope1**2)**2 +\n", " (I3 * std_err2 / slope2**2)**2 +\n", " (I3 * std_err3 / slope3**2)**2)\n", "\n", "# Graficar los tres ajustes en un mismo gráfico\n", "omega_range = np.linspace(min(omega_1.min(), omega_2.min(), omega_3.min()), \n", " max(omega_1.max(), omega_2.max(), omega_3.max()), 100)\n", "\n", "plt.scatter(omega_1, Omega_n1, label='Serie 1', color='blue')\n", "plt.plot(omega_range, slope1 * omega_range + intercept1, color='blue', label=f'Ajuste 1')\n", "\n", "plt.scatter(omega_2, Omega_n2, label='Serie 2', color='green')\n", "plt.plot(omega_range, slope2 * omega_range + intercept2, color='green', label=f'Ajuste 2')\n", "\n", "plt.scatter(omega_3, Omega_n3, label='Serie 3', color='red')\n", "plt.plot(omega_range, slope3 * omega_range + intercept3, color='red', label=f'Ajuste 3')\n", "\n", "plt.xlabel('Velocidad angular ω [rad/s]')\n", "plt.ylabel('Frecuencia de nutación $Ω_n$ [rad/s]')\n", "plt.title('Ajuste lineal de $Ω_n$ frente a ω para cada serie de datos')\n", "plt.legend()\n", "plt.grid(True)\n", "plt.show()\n", "\n", "# Imprimir los resultados\n", "print(f\"I1 Set 1: {I1_1:.4f} kg·m^2 ± {I3 * std_err1 / slope1**2:.4f}\")\n", "print(f\"I1 Set 2: {I1_2:.4f} kg·m^2 ± {I3 * std_err2 / slope2**2:.4f}\")\n", "print(f\"I1 Set 3: {I1_3:.4f} kg·m^2 ± {I3 * std_err3 / slope3**2:.4f}\")\n", "print(f\"I1 promedio: {I1_mean:.4f} kg·m^2 ± {delta_I1_mean:.4f}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Tabla de distintos $I_1$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hacer una tabla y comparar y comentar distintos resultados de $I_3$.\n", "\n", "I_1: 0.0686 ± 0.0214 kg·m^2\n", "\n", "I_1: 0.0719 ± 0.0050 kg·m^2\n", "\n", "I_1_: 0.0760 ± 0.0087 kg·m^2\n" ] }, { "cell_type": "code", "execution_count": 326, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Momento inercia final: 0.0722 +- 0.0117 kg·m^2\n" ] } ], "source": [ "I1_final = (0.0686+0.0719+0.0760)/3\n", "s_I1_final = (0.0214+0.0050+0.0087)/3\n", "\n", "print(f\"Momento inercia final: {round(I1_final,4)} +- {round(s_I1_final, 4)} kg·m^2\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusión" ] } ], "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": 2 }