efecto-fotoelectrico.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='green'> <font size = 8>     \n",
    "## Efecto fotoeléctrico##"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='black'> <font size = 4>  \n",
    "\n",
    "**Sánchez Cortés Miguel Ángel**  \n",
    "Facultad de Ciencias, UNAM.\n",
    "    \n",
    "miguel.sanchezcortes@ciencias.unam.mx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='black'> <font size = 4>  <div style=\"text-align: justify\">\n",
    "En este Notebook se realizan las gráficas para la relación entre el voltaje y la corrriente medidos en el arreglo experimental y se hace un ajuste lineal a los datos del voltaje de frenado y frecuencia mediante el método de Montecarlo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importamos los paquetes\n",
    "import math\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import linecache\n",
    "from scipy import optimize\n",
    "import sklearn.metrics\n",
    "\n",
    "matplotlib.rcParams['text.usetex'] = True\n",
    "plt.rc('font', family='serif')\n",
    "plt.rc('text', usetex=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='black'> <font size = 4>  <div style=\"text-align: justify\">\n",
    "Graficamos los datos para voltaje y corriente obtenidos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#lambda=361.9 data\n",
    "x1 = np.array([-0.943, -0.707, -0.471, -0.236, 0.001, 0.251, 0.499, 0.750, 0.998, 1.250, 1.501, 1.751, 1.998])\n",
    "dx1 = np.array([0.008,0.006,0.004,0.002,0.001,0.002,0.004,0.006,0.008,0.010,0.012,0.014,0.016])\n",
    "\n",
    "y1 = np.array([0,1.1E-9,3.1E-9,6.5E-9,1.4E-8,2.2E-8,2.7E-8,3.2E-8,3.6E-8,4.2E-8,4.5E-8,4.9E-8,5.4E-8])\n",
    "dy1 = np.array([2E-12,2E-10,2E-10,2E-10,2E-9,2E-9,2E-9,2E-9,2E-9,2E-9,2E-9,2E-9,2E-9])\n",
    "\n",
    "#lambda=401.2 data\n",
    "x2 = np.array([-0.804, -0.602, -0.402, -0.202, 0.001, 0.250, 0.498, 0.751, 1.000, 1.253, 1.500, 1.751, 2.002])\n",
    "dx2 = np.array([0.006,0.005,0.003,0.002,0.001,0.002,0.004,0.006,0.008,0.010,0.012,0.014,0.016])\n",
    "\n",
    "y2 = np.array([0,1.3E-9,3.4E-9,7.3E-9,1.5E-8,2.5E-8,3.2E-8,3.7E-8,4.2E-8,4.8E-8,5.3E-8,5.7E-8,6.2E-8])\n",
    "dy2 = np.array([2E-12,2E-10,2E-10,2E-10,2E-9,2E-9,2E-9,2E-9,2E-9,2E-9,2E-9,2E-9,2E-9])\n",
    "\n",
    "#lambda=431.5 data\n",
    "x3 = np.array([-0.698, -0.524, -0.349, -0.174, 0.002, 0.250, 0.498, 0.750, 1.003, 1.252, 1.500, 1.758, 1.999])\n",
    "dx3 = np.array([0.006,0.005,0.003,0.002,0.001,0.002,0.004,0.006,0.008,0.010,0.012,0.014,0.016])\n",
    "\n",
    "y3 = np.array([0,2.9E-9,8.2E-9,1.9E-8,3.9E-8,6.5E-8,8.4E-8,1.0E-7,1.1E-7,1.2E-7,1.3E-7,1.4E-7,1.5E-7])\n",
    "dy3 = np.array([2E-12,2E-10,2E-10,2E-9,2E-9,2E-9,2E-9,2E-8,2E-8,2E-8,2E-8,2E-8,2E-8])\n",
    "\n",
    "#lambda=487.6 data\n",
    "x4 = np.array([-0.478, -0.358, -0.239, -0.119, 0.001, 0.250, 0.498, 0.753, 1.001, 1.250, 1.499, 1.749, 2.002])\n",
    "dx4 = np.array([0.006,0.005,0.003,0.002,0.001,0.002,0.004,0.006,0.008,0.010,0.012,0.014,0.016])\n",
    "\n",
    "y4 = np.array([0,2.0E-10,4.0E-10,7.6E-10,1.3E-9,2.5E-9,3.3E-9,3.9E-9,4.5E-9,5.0E-9,5.5E-9,6.1E-9,6.6E-9])\n",
    "dy4 = np.array([2E-12,6E-11,2E-11,3E-11,2E-10,2E-10,2E-10,2E-10,2E-10,2E-10,2E-10,2E-10,2E-10])\n",
    "\n",
    "#lambda=542.2 data\n",
    "x5 = np.array([-0.349, -0.262, -0.174, -0.088, 0.001, 0.251, 0.497, 0.748, 1.005, 1.247, 1.499, 1.750, 1.999])\n",
    "dx5 = np.array([0.003,0.002,0.002,0.001,0.001,0.002,0.004,0.006,0.008,0.010,0.012,0.014,0.016])\n",
    "\n",
    "y5 = np.array([0,3.5E-9,1.0E-8,2.1E-8,3.7E-8,8.4E-8,1.1E-7,1.2E-7,1.4E-7,1.6E-7,1.7E-7,1.8E-7,1.9E-7])\n",
    "dy5 = np.array([2E-12,2E-10,1E-9,2E-9,2E-9,2E-9,2E-8,2E-8,2E-8,2E-8,2E-8,2E-8,2E-8])\n",
    "\n",
    "#lambda=576.1 data\n",
    "x6 = np.array([-0.270, -0.203, -0.135, -0.068, -0.001, 0.250, 0.500, 0.749, 1.000, 1.249, 1.498, 1.749, 2.001])\n",
    "dx6 = np.array([0.002,0.002,0.001,0.001,0.001,0.002,0.004,0.006,0.008,0.010,0.012,0.014,0.016])\n",
    "\n",
    "y6 = np.array([0,1.9E-9,5.0E-9,1.1E-8,4.2E-8,4.8E-8,6.6E-8,7.7E-8,8.6E-8,9.6E-8,1.0E-7,1.0E-7,1.1E-7])\n",
    "dy6 = np.array([2E-12,2E-10,2E-10,2E-9,2E-9,2E-9,2E-9,2E-9,2E-9,2E-9,2E-8,2E-8,2E-8])\n",
    "\n",
    "plt.errorbar(x1, y1, xerr=dx1, yerr=dy1, fmt=\".-k\", elinewidth=0.6, capsize=0, linewidth=1, alpha=0.8, label='$\\lambda =361.9$ nm')\n",
    "plt.errorbar(x2, y2, xerr=dx2, yerr=dy2, fmt=\".-b\", elinewidth=0.6, capsize=0, linewidth=1, alpha=0.8, label='$\\lambda =401.2$ nm')\n",
    "plt.errorbar(x3, y3, xerr=dx3, yerr=dy3, fmt=\".-g\", elinewidth=0.6, capsize=0, linewidth=1, alpha=0.8, label='$\\lambda =431.5$ nm')\n",
    "plt.errorbar(x4, y4, xerr=dx4, yerr=dy4, fmt=\".-r\", elinewidth=0.6, capsize=0, linewidth=1, alpha=0.8, label='$\\lambda =487.6$ nm')\n",
    "plt.errorbar(x5, y5, xerr=dx5, yerr=dy5, fmt=\".-c\", elinewidth=0.6, capsize=0, linewidth=1, alpha=0.8, label='$\\lambda =542.2$ nm')\n",
    "plt.errorbar(x6, y6, xerr=dx6, yerr=dy6, fmt=\".-m\", elinewidth=0.6, capsize=0, linewidth=1, alpha=0.8, label='$\\lambda =576.1$ nm')\n",
    "plt.xlabel(\"$V$[V]\")\n",
    "plt.ylabel(\"$I$[A]\")\n",
    "plt.legend()\n",
    "\n",
    "plt.savefig(\"I-V.png\",dpi=300,bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='black'> <font size = 4>  <div style=\"text-align: justify\">\n",
    "Graficamos ahora los datos para el voltaje de frenado y la frecuencia de la luz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '$V_{0}$')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#lambda=361.9 data\n",
    "v1 = np.array([0.942, 0.942, 0.944, 0.944, 0.945])\n",
    "dv1 = 0.008\n",
    "#lambda=401.2 data\n",
    "v2 = np.array([0.804, 0.805, 0.804, 0.804, 0.804])\n",
    "dv2 = 0.006\n",
    "#lambda=431.5 data\n",
    "v3 = np.array([0.698, 0.698, 0.698, 0.697, 0.698])\n",
    "dv3 = 0.006\n",
    "#lambda=487.6 data\n",
    "v4 = np.array([0.471, 0.481, 0.481, 0.481, 0.478])\n",
    "dv4 = 0.004\n",
    "#lambda=542.2 data\n",
    "v5 = np.array([0.348, 0.349, 0.350, 0.349, 0.350])\n",
    "dv5 = 0.003\n",
    "#lambda=576.1 data\n",
    "v6 = np.array([0.270, 0.270, 0.271, 0.270, 0.271])\n",
    "dv6 = 0.002\n",
    "\n",
    "x_exp = np.array([8.28E+14,7.47E+14, 6.95E+14, 6.15E+14, 5.53E+14, 5.20E+14])\n",
    "dx_exp = np.array([1.3E+13, 1.2E+13, 1.6E+13, 1.5E+13, 2.2E+13, 1.3E+13])\n",
    "\n",
    "y_exp = np.array([np.mean(v1),np.mean(v2),np.mean(v3),np.mean(v4),np.mean(v5),np.mean(v6)])\n",
    "dy_exp = np.array([math.sqrt(np.std(v1)**2 + dv1**2),math.sqrt(np.std(v2)**2 + dv2**2),math.sqrt(np.std(v3)**2 + dv3**2),math.sqrt(np.std(v4)**2 + dv4**2),math.sqrt(np.std(v5)**2 + dv5**2),math.sqrt(np.std(v6)**2 + dv6**2)])\n",
    "\n",
    "plt.errorbar(x_exp,y_exp,xerr=dx_exp,yerr=dy_exp,lw=0,elinewidth=2,ecolor=\"b\",fmt='ko',markersize=4)\n",
    "plt.xlabel(\"$v$\")\n",
    "plt.ylabel(\"$V_{0}$\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='black'> <font size = 4>  <div style=\"text-align: justify\">\n",
    "    \n",
    "Para saber qué modelo ajustar, necesitamos un buen _prior_\n",
    "\n",
    "- prior: cualquier cosa que me diga algo sobre lo que estoy observando\n",
    "\n",
    "Para este ejemplo nuestro _prior_ será suponer que los datos siguen una tendencia que va como\n",
    "$$\n",
    "y \\propto x\n",
    "$$\n",
    "\n",
    "Entonces el modelo que ajustaremos, siguiendo nuestro _prior_ será:\n",
    "\n",
    "$$\n",
    "y(x|m,b) = mx+b\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Utilizamos nuestro prior y ajustamos el modelo elegido\n",
    "def power_law(x,m,b):\n",
    "    return m*x + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# El prior del prior. Qué parámetros pienso yo que se le ajusta mejor a lo que veo.\n",
    "vGuess = [4.0E-15,0.8] # Es a ojo \n",
    "\n",
    "# Hacemos el ajuste\n",
    "vParams, aCova = optimize.curve_fit(power_law,x_exp,y_exp,vGuess)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='black'> <font size = 4>  <div style=\"text-align: justify\">\n",
    "Observamos a qué parámetros se está ajustando:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.24439065e-15, -8.89983034e-01])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vParams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '$eV_{0}$')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graficamos el ajuste con los datos\n",
    "xmin = 5e+14\n",
    "xmax = 8.5e+14\n",
    "x_fine = np.linspace(xmin,xmax,100)\n",
    "plt.errorbar(x_exp,y_exp, xerr=dx_exp,yerr=dy_exp,lw=0,elinewidth=2,ecolor=\"b\",fmt='ko',markersize=4)\n",
    "plt.plot(x_fine,power_law(x_fine,*vParams),\"g-\",lw=2)\n",
    "plt.xlabel(\"$v$\")\n",
    "plt.ylabel(\"$eV_{0}$\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulaciones MC\n",
    "\n",
    "<font color='black'> <font size = 4>  <div style=\"text-align: justify\">\n",
    "\n",
    "Debido al número tan reducido de datos experimentales, crearemos de manera aleatoria, y usando como _prior_ los parámetros del ajuste anterior, una serie de datos experimentales sintéticos, los cuales ajustaremos con el modelo, con lo cual obtenedremos una distribución de valores para los parámetros. A esto lo llamaremos simulaciones MC."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Definimos el número de simulaciones MC que realizaremos\n",
    "\n",
    "n_sim = 50000\n",
    "FitParams = np.array([])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='black'> <font size = 4>  <div style=\"text-align: justify\">\n",
    "Cada experimento sintético consistirá en distribuir de manera aleatoria y uniforme los valores de $x$ (x_sim), tomaremos el ajuste anterior para crear la variable dependiente $y$ (y_gen) y, de manera aleatoria siguiendo una distribución normal, crearemos los errores \"experimentales\" para los nuevos datos sintéticos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(1323623)\n",
    "# Empieza el MC\n",
    "for i in range(n_sim):\n",
    "    x_sim = np.random.uniform(5.0e+14,8.5e+14,size=np.size(x_exp)) # ojo, esto es otro prior porque no tiene que ser uniforme\n",
    "    y_gen = vParams[0]*x_sim+vParams[1] # Agarrar el ajuste anterior y ponerlo como prior de las simulaciones.\n",
    "    y_sim = y_gen + np.random.normal(scale=dy_exp,size=np.size(y_gen)) # Creamos los errores sintéticos usando como media el error experimental real\n",
    "    \n",
    "    try:\n",
    "        vsims, acova = optimize.curve_fit(power_law,x_sim,y_sim,vGuess)\n",
    "    except:\n",
    "        dumdum=1\n",
    "        continue # Pasa al siguiente loop\n",
    "        \n",
    "    if np.size(FitParams) < 1:\n",
    "        FitParams = np.copy(vsims)\n",
    "    else:\n",
    "        FitParams = np.vstack(( FitParams, vsims ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='black'> <font size = 4>  <div style=\"text-align: justify\">\n",
    "Graficamos las distribuciones de $m$ y $b$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEECAYAAADAoTRlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAOEklEQVR4nO3dTXIcR3oG4O9zKMJLY0hxazJaC28dCPACNnQBBzw+ganxSjvrDOSOS8G+wGjoExA6gUDKWy+ECXmLMc3trD4vUG02mv2PruqffJ4IhrqzWs1kIuutrKyqRFZVANCWv9h1BQAYnvAHaJDwB2iQ8AdokPAHaJDwB2jQF7uuwKq+/PLLevbs2a6rAXBQ3r1796eqejJdfjDh/+zZs7i+vt51NQAOSmb+OqvctA9Ag4Q/QIOEP0CDhD9Ag4Q/QIOEP0CDhD9Ag4Q/QIOEP0CDhD9Ag4Q/QIOEP2zo6etd1wA2J/wBGiT8ARok/OGBnr6ePQU0XWaaiH0i/GHKQ0NayHMIhD+saTLcBT2HSvgDNEj4AzRI+MMGTPdw6IQ/QIOEPwzIGQP7QvjDCoQ2x0b4AzRI+AM0SPgDNEj4w4o2mfdf9P+4jsAuCX/o2Soh70DA0IQ/rGHVkBbm7DvhT9PmLcf8kO+DQ9Br+Gfmy4nXF5l5npkvFpXBLj0kvAU/h6S38M/M84gYda8vIiKq6mq8bVZZX3UB4L5ewj8zRxFxM1H0fOL9TUSczikDYAB9jfxHVTUZ/idT2x/PKbsnM19k5nVmXt/e3m67jgDN2nr4Z+b5eCpnwseIeLRC2T1VdVlVZ1V19uTJk21WEwblegD75osevvNDN39/EhGjzDyNiJ/i00h/FBFvu/fTZQAMYOsj/6p63438H0UX7lX1Ju4OBOcRcVJVV7PKtl0X2EfOAtgHfYz8I+JuyiYiLifev+peXi0qg30jrDlGHvICaJDwB2iQ8Ic5TPdwzIQ/7JiDDLsg/GEGgcyxE/4ADRL+NGfWqH7eSL+vM4Dx9zrDYFeEP0CDhD9Ag4Q/QIOEP0CDhD9Ag4Q/QIOEP82Yvq1y2ftd2Zd6cNyEP+wJoc+QhD9NErS0TvjTFKEPd4Q/7BEHJ4Yi/AEaJPwBGiT8ARok/AEaJPwBGiT8YQ/5ZS/0TfjDnnMAoA/Cn6MnPOFzwp+jJfRhPuEPB8CBjG0T/gANEv4ADRL+AA0S/jTBnDncJ/wBGiT8ARok/AEaJPxhT7lOQZ+EP0CDvujjSzPzvHv5dVV915VdRMTHiBhV1eW8MgD6t/WRf2aeRsRpVV1FxGlmjrqQj64sMvN8Vtm26wKmTmC2rYd/Vb2vqleZeRIRN1V1ExHPI+Km+8hNRJzOKYOtEPqwWJ9z/mcR8Uv3+mRq2+M5Zfdk5ovMvM7M69vb2x6qCNCm3sK/m875amJe/9HUR2aVTX/HZVWdVdXZkydPeqopHAZnM2zT1i/4ZubLiPilu4A7Dvif4tNIfxQRb7v302UADKCPkf/3EXHTXcA96UbvbyJiNFF2Naush7oAMMPWR/7dBd7xhdyrifJXq5TBpsbTIr9+u9t6wCHwkBdHx9w4LCf8ARok/OHAOLNhG4Q/QIOEP0CDhD9Ag4Q/QIOEP0CDhD8Hz90vsD7hD9Ag4Q8HZPIs5+lrZz1sTvgDNEj4AzRI+HPQTHvAZoQ/B2cc+NPz3y1r/d/P+oQ/QIOEPxygWSN9o3/WIfwBGiT8ARok/AEatDD8M/PvhqoIsJnpuX5z/6xi2cj/NCIiM/92gLoAMJAvlmz/OTN/iIi/ysw/RMR1Vf3nAPUCoEcLw7+qfoyIHzPz7yPiQ0Q8z8zfRURFxLuq+vcB6gjAlq10wbeqfqyqn6vq36rqd1X1LxHxx8z8h57rB6zB/D+resjdPicR8cdtVQSA4Wwc/lX1H1X18zYrA/TDGQDT3OcP0CDhD0fKaJ9FhD9Ag4Q/QIOEPwfJlAY8jPDnoAh92A7hz94T+LB9wh+gQcIfoEHLVvWEnTHdA/0x8udgOBjA9mx95J+ZJxEx6v48r6rvuvKLiPgYEaOqupxXBkD/+hj5/zYizqrqTUREZr7oQj6q6qorO59V1kNdoHnOmJhl6+FfVZcTo/hRRNxExPPuv9H993ROGQAD6G3OPzNHEfGhG9mfTG1+PKds+jteZOZ1Zl7f3t72VFMOgdHrw2lDJvV5wfeiqr7pXn+MiEdT22eV3dOdRZxV1dmTJ0/6qCM0y8Ggbb3c6pmZF1X1qnt9GhE/xaeR/igi3nbvp8sAGMDWR/7dhduXmfkuM99FxKPu4u+o23ZSVVezyrZdF2iVUT3LbH3k34X4VzPKX3UvrxaVAf16+jri1293XQt2zUNeAA0S/gANEv4ADRL+0CAXhBH+7B3BBP0T/gANEv7QEGdVjAl/aJiDQbuEP3tFGMEwhD97Q/DDcIQ/e8mBAPol/AEaJPwBGiT8ARok/NkL5vhhWMIfoEHCH6BBwp+defradA/sivCHxjkAt0n4AzRI+AM0SPgDNEj4s3PmnGF4wh9w51WDhD8wk4PBcRP+AA0S/uyEUeV+8nNph/AHPjN5EHBAOE7CH7hH2LdB+DMYoQL7Q/gzOAcB2D3hz1ZNB7ugh/0k/BmEg8Bh8nM7XsIfWJmDwfEQ/vRmVlAID9gPwh+gQcIfoEG9hH9mXmTm2xll55n5YlEZsH9M1x2fXsK/qt5Mvs/Mi678qnt/Pqusj7qwG8IC9ttQ0z7PI+Kme30TEadzygAYwFDhfzL1/vGcsnsy80VmXmfm9e3tbW+Vo1/OAo6Pn+nh+2Kgv+djRDxaoeyeqrqMiMuIiLOzs+qnamyDMIDDMtTI/6f4NNIfRcTbOWUckHmB70AA+6+vu33OI+Js4qLum4gYdeUnVXU1q6yPugDwuV6mfbog/81U2avu5dWiMuBwPH0d8eu3u64Fm/CQF0CDhD+wFtd0joPwBzbiIHDYhD9rs9O3yc/9uAh/gAYJf4AGCX8exFQAT1/rB4dI+ANb4yBwOIQ/G5ncye3wLKJ/7CfhD2yFkD8swp+12MHhOAh/ViL0WYf+sv+EP9AbB4H9JfxZyM4Lx0n4A4MxmNgfwp+5pndUOy6rGPcT/WW/CX8+Y6dl2/Sp/SP8ARok/ImIxVM8Rm1wfHr5Hb4cJiEP7TDyBwY1a5Bh4DE84d8od2QwtOmpxOk+qC8OS/hjp4MGCf8GCXv2lb45HOEP7BW/GWwYwh+gQcL/gC0aHc3b5v599skqfVA/7YfwPyJ2Eo6N20L7I/yPxLydxEgfmEX4N8AFNA6dZwG2z/IOB85pMS0a9/Ffv91tPQ6Zkf+REfy0yO+eWJ/wPxBOe2nNKoE+a8mIh+4jrexjwh84aMvCetYBoZWAX0T47yH3PsPDrfKsS8uE/46t0xFN/cB6Nt1XWtjHhP8WrXvnzawwnyzb1hwmMP+5l032sWPYJ4X/Glb9gS9ap3xRRzuGDgWHatHBYdb2yW3zvm+f7TT8M/MiM88z88WQf+8mP5R5Uy7zgn6VOxOA3VrlYvGq3zNvYLeLa3irfN/Owj8zLyIiquqqe3/e19816wezbEpl2RF91m8iAo7TojOCWZ9ddoawykCw77OKrKqHf8smf3Hmy4j4fVW974L/tKpezfv8X/71Wf35v6/v/aPHT/fNKpsuBzhUq2TdvPeZ+a6qzqa/c5fh/31EfD8R/l9X1XdTn3kREeMpob+JiP8auJpfRsSfBv47D402Wk4brUY7LbdJGz2tqifThbtc2+djRDxa9IGquoyIy2Gq87nMvJ51xOQTbbScNlqNdlpum220ywu+P0XESfd6FBFvd1gXgKbsLPyr6k1EjLopn5PxhV8A+rfTJZ0nLvDua/DvbMrpgGij5bTRarTTcltro51d8AVgdzzhC9Ag4Q/QoGbDPzNPMvO0W2Li5ZzPXGTm26my/83Mt5n5r8PUdHeWtdG87btatmNXHtBO+tL9z5x3f5rsSw9oo436UbPhHxG/jYiz7q6jmNW5xtum/GNVfb3oaeQjsqyNPts+5LIde2TtdurK9aVOZp7G3VP+VxFxmpmjBvvS2m3UbdqoHzX7C9y7B8jG1nnO4CQzR1V100O19sqyNpqz/euI+H1XdhMRp7G/d3NtxYbtFKEvTW5/HxHvM/MkIm6q6iYzv4mG+tImbdRt2qgftTzyj4iI7uj5YY3nDB5FxIdueYomLGujqe0nU5sf912/fbFmO0XoS7OcRcQv3esm+9KabRSxYT9qPvwj4qKqvln1w1V1WVUfI+Lj+LS0AcvaaHL70mU7jtg67aQvzdAF3ldde7Tal9Zpo437UdPhn5kX43mybj5t2edfrPK5Y7KsjWZsb3LZjnXbSV+6/2/PzJcTc9zj0G+uL63bRg/pR82Gf3fx6GVmvsvMd9GNMCbv7uk+czZxNP2hKx8fcWddED4ay9po1vYWl+3YpJ1CX5re376PiJuJfnPZWl/apI3iAf3IE74ADWp25A/QMuEP0CDhD3DAuou+593rUWb+YZUnopt9yAvgSFzH3d1QY//c3fq5kJE/wA7l7DXENl3T6EPc3SF1MbH8w0zCH2CHpm/PnLemURfok3+mn4COuFsb6H3cLYex8IEv0z6wpu6hmvOIeB93I61/irs1aB7F3f3XR33PPusZP4TVhfJ4wbYfFkzNPI8Zaxot6FfnEfE4M6/i7jmA07hbAmLhb/0S/rCZx3G3/sr7zHxZVd9FRHTrqwh//l/XRy4yM+IulK+WzMmvtabR1Gqe4+99v6xepn1gTd0IbjQeyUW3w3Wn5+92VjH2Vjdq/6Z7vWz1zUHWNBL+8AAT6xlF3C1nfbXsQhvt6ebxv+9eL+sfg6xpJPxhTd3OOw78UXxaY/5/4u6XbRz9+vysrhsgfKyq9916POeTF2un1xAbak0ja/sANMjIH6BBwh+gQcIfoEHCH6BBwh+gQcIfoEHCH6BBwh+gQf8HM63D5xsjfZkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculamos el valor medio de los parámetros m\n",
    "med_a = np.median(FitParams[:,0])\n",
    "std_a = np.std(FitParams[:,0])\n",
    "\n",
    "plt.hist(FitParams[:,0],bins=1000, color ='dodgerblue')\n",
    "plt.xlim(np.median(FitParams[:,0])-4*np.std(FitParams[:,0]),np.median(FitParams[:,0])+4*np.std(FitParams[:,0]))\n",
    "plt.ylabel(\"$f$\")\n",
    "plt.xlabel(\"$m$\")\n",
    "plt.savefig(\"f-m.png\",dpi=300,bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='black'> <font size = 4>  <div style=\"text-align: justify\">\n",
    "Observemos la media y distribución estándar del parámetro $m$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.855720171560539e-17"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(FitParams[:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.2445401831269862e-15"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(FitParams[:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculamos el valor medio de los parámetros b\n",
    "med_b = np.median(FitParams[:,1])\n",
    "std_b = np.std(FitParams[:,1])\n",
    "\n",
    "hist_b = plt.hist(FitParams[:,1],bins=1000, color = 'crimson')\n",
    "plt.xlim(-0.890-4*0.019,-0.890+4*0.019)\n",
    "plt.ylabel(\"$f$\")\n",
    "plt.xlabel(\"$b$\")\n",
    "plt.savefig(\"f-b.png\",dpi=300,bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='black'> <font size = 4>  <div style=\"text-align: justify\">\n",
    "Observemos la media y distribución estándar del parámetro $b$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.019487410851472993"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(FitParams[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.890102812361653"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(FitParams[:,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='black'> <font size = 4>  <div style=\"text-align: justify\">\n",
    "Graficamos el fit promedio junto con todos los posibles valores obtenidos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bz/s48Lb+lVLqp6tYD1+9Bp88xpa965mOm5etwdt0iSGPJNJ6jYFRF3hr4/dNgy7ts1unIcFK/IEra3QP+D0PyDbGFAFDgIPuODLGZAFZAKmpqcGIUSnV3lRVwroP4ZPHqS17m4ep5kbc7DXQSWAuiVwRdwSxoy/wFlE7YpS35EI77tZpSLASvxNIaWyjiJQBM40xdrxJv6yBffKBfPDW6glSnEqp9qL8G1j5Iiz/J59V/UAWLlZYg7fnSRwPksSA1FNg1LneUsl9xkBix9DGHCLBSvwr+LHVbwcOWH/PGJOGd9C3wBgzTgd3lVKHzb0bvn0Xlv6Dyq2fMBsP91NFrYH+YngIG+d1GQpjLoK+Y7z9+F36hDrqkApK4rcS+gxjjANI9g3sGmMKRWSiiJQaY+zWIPAdwYhBKdXO1dXB9lVQ8gyUPMXrspdpuNloDd5OlwTm0oXOx5wPQydA71HQM/wWRQmFoE3n9GvFF/k9N9HvcUGwzq2Uauf27YA1b8BHD7G14mum46bAGrw91rrzNv2IsTD6IuhzjLeln9gpxEGHD72BSykVOWpr4PtSWJZP3Vcvkkc1f8PNHgMdBW4hkemxPYlLv8R756126zRIE79SKjLs+R4+fxk+eogvXFvIws3HphaAsyWO+XRg4OBfwujzoc9o7dZpgiZ+pVR4q/F4a+UvuY/9GxdzCx7uoYoaA32swdvzux6JSb8YBhxn3YQVnbN1WkoTv1IqfFWsg0+fh6WP8FZtBdNwsc4IRuAKiWcuXek65rcw/Jfe2jpd+4Y64oigiV8pFX6qKuG7d+H9XLZt+4xrcPOCNXj7M4khHxvH98mAMb+HQcdZ3TqxIQ46cmjiV0qFlx++hk8ep64kn8epZiZunAaSBOaQyNVxvYk/4VIYeop1E5bO1jlUmviVUuHB5YTVb8DiO/hy3zqycbPUGrw9U+J4mCQGDTsXjp0M/cd6V8RSh0UTv1IqtOrq4PtP4b2HcH37Mrfi4S5r8La3GB7AxqSuIzEnTIHBJ0Gvkdqt8xNp4ldKhc6+cli5EN67i8KqH5iKizLjLc2VLfHcSTLJx/0JRp7tna2j3TqtQhO/Uqrt1dbAxmWw6DZ+2PwR1+DmOWvwdpTEkIeN8QNOgeP+AqnHQ9d+oY23ndHEr5RqW7u3wIonqfvwPv5FJdfjZpcBm8DNJHJdfF/if34lDHdAr6O0WycINPErpdpGtRvWvgtvz2H1ri/Jxs0H1uDtLyWWh0liyNG/g/TfW906nUMccPuliV8pFXzl38L7D+H+4klux8OdVFFtoJcY7sfGRSnHYH5xLQw6Ebr2D3W07Z4mfqVU8Hj2wVf/hcIcFu/fzFTcfGstjjJF4rmTbqSceIW1OMpREKspqS3oVVZKtT4R2Po5FN7OjnVvcB0enjLVABxlDd6eNORMyLjcOyff1iXEAUcXTfxKqdbl2gUlzyJFt/EUu7kODzuNkCjwdxK5PiGVhAl/gyMnert1jAl1xFFHE79SqnXU1XmraL55E2u2L+cy3LxrDd5OkFgeJYmho/8MJ/xFu3VCLGjFqo0xmcYYhzEm63C2K6XC17Jly5gwYQLLli3zPrFnO7w9B8+TZzBn+1J+RiXvmlp6iOEZsVHYczxDLyqAM2+BPj/TpB9iQbn61lq6iEiRMSbLGOPwrbtrbXcAZdbauw5jTJqIlAYjFqVU6/IlfZfLxYQJE1j0xFwytjzOe/s2kI2bNdbg7Z8lnnkmhe6n3gg/O0+7dcJIsFr844Ay63EZkBawvRhYaIxJA+ya9JUKDzk5ORhjmvwZP348LpcLAI/Lxat5M/njnm84xexnjaljuBjeqkrgxFI47ZEfuGXxD5A8QJN+GAlW4k8O+L27/y8i4gTygIXA2IYOYH1TKDbGFJeXlwcnSqXUAXJychCRJn+Wvv8uSYkJxBqIi4P8ITU8FVtLgsAcSeTzpCM5/bcv8ucXd7ByWx0359wa6relAgQr8TuBlMY2Wl09RSIyBHD6uob8iUi+iKSLSHrPnj2DFKZS6pBsXUXGmnt48vcxDDrVUHUJOFPhFInlczpy8wkzScx6G44+R6dohrFgjbCs4MdWvx0oDNieJiLzrMd3AJODFIdSqjV49sLSx6h6bw534eHWAR48qZAihnskkUv6nIw5cy70GwOx8aGOVjUjKIlfRAqMMTOsln2yb2DXGFMoIhOBfGs2TxnePv78YMShlPqJRGDTcnj5cj50riYLN6utwdtLJJ67TQo9zrkHRpwNSV1DHKxqqaDNqfJr0Rf5PTfR+tcJaLJXKpztK4dFd1Hx6aPMxM3j1p23w6w7b0895hI4dQZ0G6gDtxFGJ9MqpQ5UWwvfvI28NI3na37gGtz8YIR4gb+RwA1dRmA77yFIPQHiEkIdrToMmviVUj/atRFeu561695gGi7ese68/bnEkoeNkafdBuMugaTAiXsqkmjiV0pBbTUUP0v1m3/lbvZzCx7cBroJ3IWNP/V3EHPufdBjiHbrtAOa+JWKdtu/goWXsWxHMVm4+dIavP29xHOv6UGvyU/AsAnardOOaOJXKlp5KuG9e3AuvZtZuHnUGrwdIoZHSGJi+nQ4bQZ06BbiQFVr08SvVDRa/zHy3MUsrNrCVbjZZoQ4gRkkcFPXUST99mnoNRJiglbHUYWQJn6loknlTvjvTNZ/vYDLcfOGqQFgvMSSTxJH//oRGD0J4hJDHKgKJk38SkUDEfj8Zapfmcr97GU2HlwGugrMw8al9t8Qc8GD0LFHqCNVbUATv1LtnXMTvHApn2z7kCzcfG4N3l4kcdxnjqD3H1+CAeO0WyeKaOJXqr2qrYUPHmL3kpu5ETcPU40YGCyGh0nijJNvhp9Ph/ikUEeq2pgmfqXao21fIU9P5uX9ZUzHzfdGiBW4ThK4uVsaHf6wELr1D3WUKkQ08SvVnlS54I1ZbPzsCS7Hzf+swdvjJZZ8bPzswoVw5OnarRPlNPEr1V58vZiaF37Hgzi5GQ+VBroI3IGN7CN/R+wFD0FCh1BHqcKAJn6lIt3+XbDgzxRvKCQLF59ag7eTJY77YnrTN7sQjhgR4iBVONHEr1SkEoEV/2LvG1dzEx7mU0WdgYFi+Ac2zvrlvXDCFIiJDXWkKsxo4lcqElVsgH+dz6t7V3MFbrZYg7d/lQTm9BpPx0teho5aakE1LGiJ31pH10kDK2wZY9LwLrTutJ4qEpGZwYpFqXajthbeuplNKx7kStz8xxq8HScx5NGBYy9+E4acFOIgVbgLSuL3LZ4uIkXGmCxjjMO3/KIlxVpo3fch4GzoOEpFg2XLlrFkyRJOOeUUMjIyGt9xUwm1/zyPf0g5N+Jhn4HOArdhY9qxlxF7Vi7E6Xq3qnnBavGPAxZYj8uANA5cgtH/Q8AuIgVBikOpsLZs2TImTJiA2+3GZrOxaNGig5O/Zz8s/Aul3/2XbFwUW4O350scD8b1p9+VH0HXviGIXkWqYE3mDVyep3tDOxljshpL+tY3hWJjTHF5eXmrB6hUMOTk5GCMafHP+PHjcblciAgul4vx48cfsP3sIYb1N6RwzTcFjKOSYlPHADG84Emg7vlaHqu5RJO+OmTBSvxOIKUF+01sbIOI5ItIuoik9+zZs/UiUyqIcnJyEJEW/yxdupSkJG/JhKSkJJYuXerdtqccuT+DqRcn8YvOVdwf6+3Lv1oSWNXbwYWzK3hlTR05OTkhfLcqUgUr8a/gx1a/HSgM3MEYo4t2qqiXkZHBokWLOO20037s5ll0B1vuGUzmrhX82rjYaIQ0iWE5nbjvL8vpPPUdSOwY6tBVBAtKH7+IFBhjZhhjHECyr0/fGFMoIr5WfgpQEYzzKxVJfMmfbV9Re8sgHq3bzizc7DXQUWAuiVwx7lrizpyrpRZUqwjadE4RmWc99B/Unej3uAzIDtb5lYoYNdXw8lRWfrWAbNx8YmoB+LXEMT9+AKlXLYdOWidftR69gUupUFr9FpULLmIOLu6liloD/cTwEDbO+/XTmLGZoY5QtUOa+JUKhcrd8OS5vFm+nGm4WW8EI3CFxHNbPwddLnkFEnT5QxUcmviVamtLHmDrkpu4GjcvWnfejpEY8kjiuOxi6KMF1VRwaeJXqq2Ul1GXfzr51Zv5G252G+ggMIdErj7ueuLOmKODt6pNNJj4jTHHAumANPI6Y20rFpHPghSbUu1DbS28eiVffPE0Wbj52Bq8PUvi+EfCQAZeXQwddHazajuNtfhTgAUisqepFxtjLgA08SvVmDXvsv/5TG5lH3dTRY2BPmJ4EBsX/OY5zOjzQh2hikINJn4RWWSMeQS4rKkXi8hLQYlKqUjnroR/ncs725dxGS7KrMHbaRLP7QMm0vUPr0B8QqijVFGqqT7+MmPMaUA3YK126SjVQh8/xva3ruUa3DxvDd4eIzHkY+OES4uh/8gQB6iiXaOJX0Tu8j02xnQ1xpxv/VrUXBeQUlFp5wbq8k/nCc8GZuDGaSBJIIdErhl7HfFn3wrGhDpKpVo8q6cbcByQibfuTpNdQEpFFRF49Vq+WvkYU3HzgTV4e7rE8khcKoOvLdXBWxVWGk38xphLrYdT8c7gyRORoW0SlVKR4ruPcD17Lrezl1yqqDZwhBgewMbkc5/FHHt+88dQqo011eKfB+QBk0RkXRvFo1Rk8FTCU5kUff8+l+HmO2txlGyJ544+p9Lt0tchVm+TUeGpqb/MSSKyqM0iUSpSLP8n5W9cxbV4eNZUA3CUNXh74pQV0O+oEAeoVNMau4FrTEuSvrWfzvZR0aFiE5J/Bv9yl3E9HiqMYBO4iUSuT7+WhLNu08FbFREaa/EPMcakBzzn/xftu6O3Ar2BS7V3IvDa9Xz96aNMxc171uCtQ2J5JLYvQ6/9HDrq4K2KHI3dwKU3ZikFsG4p7qfO4w52c4c1eNtTDPdj47dnP4lJnxzqCJU6ZC0afTLGdMG7aIodWAvkt6CcQybetXftIpLfwPY063g0tuC6UiHj3gdPXci7W5cwFTffWIO3l0o8ub1OJCX7HYiND3GQSh2elk47cAD5Ab+/3NjOVtJHRIqMMVnGGIdv+UU/s0RkkrXdbq3IpVToLX+GHW9M4zo8PGUN3o6QGPKwcfJfPoIBY0IcoFI/TVPz+P0Hbo2I7Pbb1twI1jhggfW4DEjDbwlGY0wWsMJK+Ad9G1AqJCo2Inln8rSnjL/iYacREgVuJJEZo7JIvOABHbxV7UKT8/iNMZlWl06RMeZOYDDeRJ7XzHEDR7q6B/w+xPq3whiTB8wUEaf/DtaHQxZAampqM6dT6ieoq4XXb+Cbkn8wFTfvWoO3p0ksj5peDLv2C+gc+CesVORqKvFn453d001EFgN/O4TjOvGWdm7KWhFxGmNK8Cb4ef4brW8C+QDp6emNrQug1E+z9iM8z5zHPPYyFw9VBrqL4V4SufhXD2OO+1OoI1Sq1TVVpK3+bl2r7n6JiKxv4XFX8GOr3463vk/gdt8HQzLeDwql2o5rLzx9IR9sfY8s3HxtDd7+SeKZlzKWHtPehzgdvFXtU4vWefNN77TKNLdk/wLAboxxAMm+gV1jTKHf9mRrO9rPr9rUx/+kIrcfl24t4mSzn69NHcMlhnelA/+8+D16TF+mSV+1a0ak4V4UY8wdeKduDsHbah+Ct3VeIiIXtlmEeLt6iouL2/KUqj3asQ554iyec63nGtyUGyFB4AYS+duIP5B4YZ4O3qp2xRhTIiKBN+M22cc/1vq3GHgRKPOf2aNUxKitgdeuY+3KJ7gMF4XW4O0vJJZHSWHEVZ9Ct34hDlKpttNckTZN9CqyffseVf/O5G72cise3AZSxHA3ifzxtDsxJ18V6giVanNNDe5q0leRy7UHnrmQj75/n2zcrLIGby+WeO7pfDQ9r1wKCUkhDlKp0NCC4ap9EYEPH2bXoln8DTf51p23QyWGR7Ex4cL/wEhHiINUKrQ08av2Y/s3yL/OZYF7A1fjZrsR4gVmkMCNqWeTdMmLEBsb6iiVCjlN/Cry1VTBK9MpW/VvpuHibWvw9iSJJY+uHDXtE+ilq4Yq5aOJX0W21W9RveAP3Mse5uDBZSBZYB42/nLcTGLOnK1TNJUKoIlfRaa95awztwIAABaiSURBVPDcb1m2dRnZuPnCGrz9ncRxb4ehHDHtQ+ik9XWUaogmfhVZRGDxXez+YC6zcPMo1YgBuxgeIYlfnvskjMnUVr5STdDEryLH1i+Qf/2GgqrvmY6bbUaIE7heEvh731NIuuQVSOwQ6iiVCnua+FX4c1fCK9PYsOZlLsfN66YGgPESSx4dGfWnxTBwbDMHUUr5aOJX4e2zBdS8egUPsJeb8bDfQFeBXGxMGT2FmHPu0SUQlTpEmvhVeNq1CZ69iBU7V5KFi8+swdsLJY774vrTZ9oSSBkY2hiVilCa+FV4qamGolvZ8/H93ISH+VQhBgaK4RFsnHnavXDipRCrf7pKHS79v0eFj+8+QF78A69WbedK3GwxQqzAtZLA7O7j6Pjn/0BHnaKp1E+liV+F3r6dUJDFxvXvcCVuXrMGb4+TGPLpwOjf/geGnQIxLVo3SCnVDE38KnTqamHZ49QU3sB8KrkJD5UGOgvcgY2pR15A7PmPQmKnUEeqVLsStMRvjMnEu5auvaGlFY0xu/Au8lIoIvMCt6t2bvtX8PwfKHF+TRYuSq3B2/MljgdjetHv0regzyi9EUupIAhK4reSPiJSZIzJMsY4fOvu+pnUwHOqvfPsgzduYO/KJ7kZDw9SRZ2BVDHMx8avj78BHH+FeK2Vr1SwBKvFPw5YYD0uA9KAwCSfbIyxi0hZQwcwxmQBWQCpqalBClO1mdoa+OpNeO0K/lNdzhW42WyEGGvwdk6Xo+j0p1chOVVb+UoFWbASf3LA7w1NxUgBKowxeSKSHbjR6h7KB+9i660fomozu9bDS5ezefP7TMfNK9bgbbrEkEcSaef+E445F+ISQxunUlEiWInfiTexN8rX72+McRpjMkWkIEixqFCp2g8fzqf2/Tt5GBc34mavgU4Cc0nkitQzic18FLocEepIlYoqwUr8K/ix1W8HCv03Wt04xSJSGqTzq1CqrYHNy+GlqXy2p4wsXKywBm/Pkzgeohv9//ASDDxByy0oFQJBmRhttd7txhgHkOwbxDXG+D4AXrR+z/TbX7UHe7dDwRQq/3UG1+9ZQzqVrDB19BfDHRsT6PZBbzb94hmw/1yTvlIhYkTCv/s8PT1diouLQx2GakpVJax8Cd66gTdqK5iGmw3W4O2VJHD29l6c8+QOXG43SUlJLFq0iIyMjFBHrVS7ZowpEZH0wOf1Vkh12HJycoiLMYzsEcO7f05h/avZZFZv4yzjYoMRxtQZFnsS2V1Qw1mPbcbldgPgcrkYP348xpgW/eTk5IT2jSrVzmiLXx0eEajcAR88QO0n88nDwyzc7DHQUeBWErnyiPHETcqHlMEs+2Q5EyZMwO12Y7PZtMWvVBtorMWvJRvUoauqhG/fhdev4/P9m8nCzSemFoCzJY75dGZg5pMw7LT6cgsZGRksWrSIJUuWcMopp2jSVyqENPGrlqutgYr18OYN7C97izl4uIcqag30FcND2PjNsAsxZ90CXfodVFQtIyNDE75SYUATv2qeCOzfCaXPwru5vFW3h8twsd4IRuByiee2+L50vegZSE3XcgtKhTlN/KppVZWwsQTemsm2HV9yDW5esO68HS0x5JPEcRnXw/ip0KmXlltQKgJo4lcNq62BPVtgyV3UrXyWx/EwEzdOAx0Eckjk6pTRxJ//KPQ+SsstKBVBNPGrA/m6dda8De/M5kv3NrJxs9QavP2VxPEPbAw64z445jzo2CPEASulDpUmfvWjqkoo/wbe/juuje8zFw/zqKLGQG8xPICNSQPPxJwxB3oOh7iEUEeslDoMmvjVj906y5+EZQ9TyD6m4qLMGry9TOK5PaYXyRc8AoNPhA5N1t9TSoU5TfzRzHcT1vpPoGg2Pzi/4RrcPGcN3h4jMeRhI2PMFDjpcug2GGL1T0apSKf/F0crzz7vnPz376Ju9av8kypm4GaXgSSB2SRybccjif/NfdB/LNi6hjpipVQr0cQfbWqrYc/3sOq/8ME9rPaUk42bD6zB29MllodJwn7ybDh2EnTtDzGxIQ5aKdWaNPFHC1+3zravYPGtuL9fzu14uJMqqg0cIYb7sXFh7/GYM+Z6Fzq3yi0opdoXTfzRwLMPnJtg+RNQ8hSL2c9U3HxrLY6SJfHcSTLdzr4bhk2Azn0OKreglGo/NPG3Z75unbIPYPEdlFdu5Do8PG2qATjKGrw9afhkOOUq6DEcEjqEOGilVLAFLfFbq2s5Abtvfd1G9ssVkZnBiiMq+bp1yr+DD+9C1hbxFNVch4edRkgU+DuJXJ+YSsLZd8KgE7XcglJRJCiJ329JxSJjTJYxxuFbfjFgPwfeNXlVa/Hsg92b4cuX4ON81lTtJBs371mDtxMklkdJYuhx0yHtd9B9CMTbQhy0UqotBavFPw5YYD0uA9KAAxK/McZubVOtobbaexPW5pXwwTw8P3zBnVRxOx6qDPQQw30k8vtux2LOnA39xmq5BaWiVLASf3LA790b2MdufSNo8ADGmCwgCyA1NbV1o2tPRKCyHHZvgqWPwKpXWYKLqbhZYw3e/kniuYsudJ8wG0b80nsjlpZbUCpqBSvxO4FG7+tvrOvHnzUukA/epRdbN7x2wrMXdm+BtYvhg4fYuX8z1+PhX9bg7QiJ4VFs/GLQmXDydOg9SsstKKWClvhX8GOr3w4UBmyvsPr3kwG7MSZNREqDFEv74+vW+eFb+PB+ZNMHPEs11+JhhxESBG4kkZlxvUk8cy4MHg9dU7XcglIKCFLiF5ECY8wMX3L3te6NMYUiMtGX5K3unMBuIdWY+m6dzbDyRfj033xbvYvLcLPIGrw9RWJ5FBtHHvNHGHcx9Bqh5RaUUgcwIuHfi5Keni7FxcWhDiO0PHu9CX9zKXx4P1UVa5hHFXPx4DHQXQx3k8glnUdgfvl36J8OXftpuQWlopgxpkRE0gOf1+/+4a622pvwd22Cjx+Fbwv5kP1k4Wa1NXh7icRzNx3pcdJfYcSvoOcwSOwc4sCVUuFKE3+48u/WWfMOFD/Brv3bmYGbx63B22HW4O1pfU+Cn18N/UZDp95abkEp1SRN/OHI162z/WtYNh/5fgUvUMPVuPnBCPECs0hgVkwPbKfNgiG/8N6IpeUWlFItoIk/nNRUeWfr7N0Gpc/Cl69SVruHy3DxjjV4e7LEkoeNEcMzYdz/Qe9jtNyCUuqQaOIPByKw7wfYuxXWfwTLH6fauZZ7qGIOHtwGugncjY0/2gYTM/FvMGCc90YsLbeglDpEmvhDzb3H28rfuQ6WPw7r3id/0z5uXl/F9sECA+D3Es+92Og19jIYdTb0GqnlFpRSh00Tf6j4unX2bYev/gefPYfTvYM/bdrPq0/XQi2Y9+G+PyRy1bHj4OSroO9oSB6o5RaUUj+JTv9oRTk5ORhjmv3p3SmGYwck8fuTh7E051Qq37uH5yo3M5K9vLrBm/QRiKmFl96p5oQ7l5Jy/CRMz+GY+MQWncP/JycnJ9SXRikVRvQGrrbk69Zxfg+fPQdfv8G6un1cjps3TQ0Ax2yM4dun66iuhYTEBBa9+hwZjnO13IJS6pDpDVyhVFMFezZ7F0dZuwRKnqJ67ybup4rZeHAZSBbIxcalQ/vyyfzzWLLOwylnnEPGKb8MdfRKqXZGE38w+c/WKf8GSp+EDR/zCR6ycPO5deftRRLHfdjoPer/4Jhzyeg1koyu/bXcglIqKDTxB4t7j/cmrMofYPV/4fNX2O3ZwY24eZhqxMBgMTxMEmd0PQpOvgL6jIHudi23oJQKKk38rc2/W2dLKRQ/hexYxUvUMB03W40QJ3CdJPB3OtPh+GwY5oCew6FzHy23oJQKOk38raWuztu637sNdm30lk3+rpANdfu5Ajf/swZvT7DuvP1Z35Pg+D/DEUdBil3LLSil2owm/tbg3u1dCWv/DvhuCXz2AjWVm3mAKm7Gw34DXQTuxEZ2bA9iTrocBp8E3YdCpyO03IJSqk1p4v8pajze6Zn7d8K21fDpM7ClhBVUkY2LT63B28kSx/3Y6DP0HDh2MvQYDilabkEpFRpBS/zGmEy8a+/arfVzA7c7rIcTRWRmsOIICv9unT1b4av/wtf/Y2+Vk5vwMJ8q6gwMFMPD2PhVp6Ewfir0PxZShnrLLWgrXykVIkFJ/FbSR0SKjDFZgYurG2PSgDQRmWeMmWmMsYtIWTBiaXW+bh1XBWxYDp89D7u+4T9UcwVuNhsh1hq8zSGJjmP+CEdOhJ5HQnKqlltQSoVcsFr844AF1uMyIA2oT/zWmrulxphkoCwikn6Nxzs901UB5WXwxUJY/wGb6vYzHTevWoO34ySGfJIY03MMHPdnb9nk7kOgQ0qI34BSSnkFK/EHLqDevZH90oG1DW2wFmLPAkhNTW29yA5VXZ23kNq+7bCv3Hvn7RcF1O7fynyquAkP+wx0FrgdG5eZZGJPyAL7SZAyBJIHQGx86OJXSqkAwUr8TqDZJq7VFTTJGJMpIgUB2/KBfPDW6glOmM3wdeu4d8HWL731dbZ9QSlVZOGixBq8/Y3E8RA2+g2aCKMnQa8R3sHbpMDPP6WUCr1gJf4V/NjqtwOF/huNMbnAWiu5t+hDok3Vd+vsAudGWP06rHmHfTW7uRkPD1iDtwPEMB8b59j6wgmXwYA0b7dOl35abkEpFbaCkvhFpMAYM8OauZPsG9g1xhSKyEQgD7D7bT9o1k9I+Hfr7N8JGz+GlQWwu4z/Us3luNlkhBiBaySBW0ik06iLYMTp0H2Yt5Wv5RaUUmEuaNM5RWSe9dB/UHei9W8Z3kHfA7aHlMvpnZPv3gPla+CLl2DjJ2wRF1fh5iVr8HasNXibljzCO3jb9xjoZtdyC0qpiKE3cPl36+zeBmsXw+r/UOsq5xGquQE3ezdD/DqYNiieuwd0IW7cX7yDt93skDIIEjqG+l0opVSLRW/i9+/Wce2GrZ/BygVQ/jUrrcHb5aYONkHMU1BXC/lxtVz4SDYZo87y1tfRcgtKqQgUnX0TLicP3zKd8Uf356KJY/n3dafzw6uzcH7/KX+t28tYKllu6ugn8Nu1sZhaqBVwVddxzvQ7SBp6IqZLH0xMjC51qJSKONHV4vfr1pn227OZNr47fPky7NnIG1QzDQ8bjGAEpksCt5LIqp8fz6vL3sdTVU2iLZHXCheRMX58qN+JUkodtuhI/HV1sG+bdzUs3+Dt5y/B96VstQZvF1qDt2OswdtxnQbB8ZeS0ecYFmVksaT0a045zUFGRkZo34tSSv1E7T/x+2brePbCnu/hu8Ww+nXqPDvJo5q/4WaPgQ4Ct5LIdDoQN/r3MOTn3jtvUwaTMTSFjDND/UaUUqp1tO/Ev2uDt0Z+5S74vhS+KICd3/EFVWTh5mNTC8BZEsc/sDGw52gY+3/egmrdBkHX/lpuQSnV7rTvxC91sHUVrP4fbPiQ/TX7uBUPd1NFjYE+YngIG+ebZMy4SyD1BEgZCN0GQlK3UEevlFJB0b4T/xevQPHjsHcLb1PDZbhYZw3eTpN4bsdG19RT4Zhzoecw6JrqbeVruQWlVDvWvhP/0vls95RzDW6etwZvfyYx5GHjhKR+MO6P0Odn3jr53QaBrUtIw1VKqbbQbhN/ndTxhGcrM3DjNJAkkEMi15BI/NHnwxAHdB8EyQOhc18tt6CUihrtNvHvcu1ippX0z5BYHiaJwcnDIf1i6DXS26XTbZCWW1BKRZ12m/i7d+jOwyRhBCbTCZP2W0gd762tk5wKnXtruQWlVFRqt4kf4KLh53vn7o+eDD2GQtcB0C0V4pNCHZpSSoVMu078nHQV7NkMyf29ffkde2orXykV9dp34u/aFzp08657G5cY6miUUiosBC3xG2My8S6raA9cYcsYk4x3SUY7ME5EZgYliC59tYWvlFIBgjKH0Ur6+C256AjYZTKQ7ltg3RiTFYw4NOkrpdTBgjV5fRw/Lq1YBqT5bxSRfL9vAXa/fZVSSgVZsBJ/csDv3RvayRhjByp83wwCtmUZY4qNMcXl5eXBiFEppaJSsBK/E0hpwX6ZIpLd0AbrW0G6iKT37NmzdaNTSqkoFqzEv4IfW/12oDBwB2NMpojMsx6nBW5XSikVHEFJ/Nagrd0a1E32G+QttP51ALnGmBJjTAkt+3aglFKqFQRtOqevNQ8U+T030fq3CBgSrHMrpZRqnJakVEqpKGNEJNQxNMsYUw5sOMyX9wB2tGI4wRZJ8UZSrBBZ8UZSrBBZ8UZTrANF5KDZMRGR+H8KY0yxiKSHOo6WiqR4IylWiKx4IylWiKx4NVbt6lFKqaijiV8ppaJMNCT+/OZ3CSuRFG8kxQqRFW8kxQqRFW/Ux9ru+/iVUkodKBpa/Eoppfxo4ldKqSjTrhK/MWaXMabQGDOjke2ZxhhH0Or/H4IWxNrk9rZmjEmzrl9mI9vD6do2F2vYXFsr1rW+8iXGmNwG9gmLa9vCWMPm2kLz1y5crm1LYmnVaysi7eYHcDSxLRNvNVCArKb2DXWsLdkegngX+l07e5hf20ZjDbdr6x8L3nUrwvbaNhdrOF5bIC3wcbhe26Zibe1r265a/ECyVeO/IU0uDhMCTcXaku1txmqBrDDG2MVbLjtw4ZywubYtiBXC6NrKgWtR2MP52rYgVgijawsUAwut6r92ESkN2B4215bmY4VWvLbtLfGnABXGmLwGtrVocZg21FSsLdnelobgvV4Vxpg8a81kf+F0bZuLFcLr2gLeDyyxliINEE7XFmgyVgijaysiTiAPWAiMbWCXsLm2LYgVWvHatqvEb7XwnICzgf7dli4O0yaaibXZ7SGw1oqnBO/XYn9hdW1pOtZwvLYAExt5PtyuLTQea1hdW6v8e5GIDGkknrC5ti2ItVWvbbtJ/NZSjU19VWt2cZi20lysLXgvbW2F3+NkvP/DBG4Pi2tLM7GG4bWlkW8lPuF0bZuMNQyvbZpfl8kdHJzkw+naNhlra1/bdpP4gRfBOzIO9YvB1C/+Io0sDhMiTcba2PZQsc6fbF07RCQfwvPaNhcrYXZtLSlAhf8T4XhtLY3GSvhd23wrYTqAyeH8d9tcrLTytdU7d5VSKsq0pxa/UkqpFtDEr5RSUUYTv1JKRSi/cQH/5w66ozqQJn6lWsi6pf6QZlZYt+CH00wX1b4U43c/gvW31tQsMUATv1KHIkVESq2aNSXGmFxjTLKV3Nc2VGPFmikSEcv8qfBhNTIKG3iuJXWFAqdbH0QTv1KHyJpvXQYsEBGnldydWFPulPqpAqdr+k3jLLJ+dwS+xu+mLntzpR008SsVwBhj990wYz1uts/U77UzrJbZQS02pXysv600v9+zmrmRr7G6Qg5gnDEm2fqwKKMFXT1xhxe2Uu2a738cX9fO2kb2cxhjUgJeU2S9ZiGQHdQoVcSy/kYyjTHg7QosssoxNKbBukIiMi/guE6aKKnhoy1+pQJYXTkT/e7kLG5k1yIRKfLr6vH9D+0AyhqpXqkUUN+dk209bu5vpVXrCmniV6oJxhj/GiotNVNEZjbUD6uUj9Unn2c9bq7ccqvWFdLEr1TDVjSWuH0104ELfbN6rN8nW+MBhdZz2tWjGmT9DTlFpNSqy+Pw7+O3/n7SA2rztFpdIa3Vo1QLWXXo89vqdUoFi7b4lVIqymjiV6rlKg7jzt1MGh8cVioktKtHKaWijLb4lVIqymjiV0qpKKOJXymloowmfqWUijKa+JVSKspo4ldKqSjz/6An/XUmNybOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(1323623)\n",
    "inds = np.random.randint(len(FitParams), size=100)\n",
    "\n",
    "x_fine = np.linspace(xmin,xmax,100)\n",
    "for ind in inds:\n",
    "    params = FitParams[ind]\n",
    "    plots = plt.plot(x_fine, power_law(x_fine,*params), \"C1\", alpha=0.2)\n",
    "    \n",
    "\n",
    "plt.plot(x_fine,power_law(x_fine,med_a,med_b),\"g-\",lw=2, label=\"Ajuste óptimo\")\n",
    "plt.errorbar(x_exp,y_exp,xerr=dx_exp,yerr=dy_exp,lw=0,elinewidth=1,ecolor=\"k\",fmt='ko',markersize=3, label=\"Datos experimentales\")\n",
    "plt.xlabel(\"$v$[Hz]\")\n",
    "plt.ylabel(\"$V_{0}$[V]\")\n",
    "plt.legend()\n",
    "plt.savefig(\"MC-h.png\",dpi=300,bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='black'> <font size = 4>  <div style=\"text-align: justify\">\n",
    "Obtenemos el coeficiente de determinación $R^{2}$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9946330174447829"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sklearn.metrics.r2_score(y_exp, power_law(x_exp,med_a,med_b))"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}