michelson.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# V10: Michelson Interferometer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "from tabulate import tabulate\n",
    "import sympy as sp\n",
    "sp.init_printing()\n",
    "import scipy.fftpack\n",
    "from scipy.optimize import curve_fit\n",
    "from scipy import signal\n",
    "from IPython.display import display, Math, Latex, Markdown, HTML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/site-packages/IPython/core/magics/pylab.py:160: UserWarning: pylab import has clobbered these variables: ['f', 'show']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n",
      "  \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "mpl.rcParams[\"figure.figsize\"] = (10, 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparation Exercise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAKoAAAAPBAMAAACGiUnsAAAAMFBMVEX///8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEJmJZjLNVN0i77urRHZ72Yd1AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAC/UlEQVQ4EbWUzWtTWRjGfzc3H02TJlcFkS5MrOhCFIJREBV7QRAUqUH/gGYYxcX4EVA3IlgQdKFgUHHhxghiKSpe3QiKGDoOw0i1YRazHIsyLlxU60frRzU+55w4+g8YyLm/57n3fXLue98bmLtqNf9/rLDLvmB7DcrPKniTK1vgb9Blc8t/kTi1uFy2hLd4fV2liWKHzpZvm6Tt+u5mgTkFh4OOsM6N9huY34pVicGfeHtZineZC/V4u92etURvmB5RYVfkKF3kaCh9EuIN/KrQGx0PnHDOr4/1W7fIRByE82SK3CXbIFuMyW9a4iGMqvRF5Cg3Q34Iclch0yQ1pVPwMnDCOU05PR+Mvw4W8MTcT76K/8oHv2KJaeiH9KbIUc91ClU495uubNIzY4pNqhXOacrJNLTw5CaruGOoEJF7p+O/jrxPKqrgJ6MOYTtQUmqhSM8XU2JSrXDOg4El5HdtWAHZ9saQ6R1PW3Rrr591ZdShK9pri83JCEd4x9TSulIvlkjoubhUK5xzk4utwk66a6qcDrzpkGHdPsm3akDYId3+/tCbUKol0msWwUJMavHHVCO+OZmhwhSxS6Qenb7utQOWBezjgLpV0BYsJSdS/TUfpVqSvbZFyaS6++3s1YpvTuxDfoj4Z7aS+xi8h20t4pM71NfnutrRwH/9lUMmFUOyuy+lKiZVzyb149NKzVgna1rYVSX+lhEYrB1XaqiyrOblpI6OYHxOyaaKgnSdzMw8TKoGMv59soywjh5MbCrb0F69V0oINZraKySbeLM6WtK6JzU2Nn5twhD5KaUeGRv7+NgMfqwq086AFXbRC9VVTaivDbPXTH1QfSU+wmCdxGs1wNKWesJMdHeEJR27jL6q7wl6K57m2UyWFXbR27elzu/01vgnYIBsyRsm94u3E1JKdfQ8mB+qMB9hKR5ydEJaY8y8yT/gFCy/sSd0wjpn1i/SDPXpXGLU/LusXF7R0idKD6vKkt+ncca/Pxs6+rvvnvS69gOtP+HzFcRmGeOPPo8XAAAAAElFTkSuQmCC\n",
      "text/latex": [
       "$$0.100568697994934$$"
      ],
      "text/plain": [
       "0.100568697994934"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b, lambda0, n, Ls = sp.symbols(\"b λ n L'\")\n",
    "\n",
    "d = 2*b*sp.tan(sp.acos(1 - (lambda0)/(2*n*Ls)))\n",
    "d = d.expand().simplify()\n",
    "d.subs({b: 2, lambda0: 632e-9, Ls: 1e-3, n: 1.00027})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1: HeNe wavelength"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Formula for $\\lambda_0$ is\n",
    "\n",
    "$$\n",
    "\\lambda_0 = \\frac{2n \\Delta L_2}{m}\n",
    "$$\n",
    "\n",
    "where $n$ is the refractive index of air $n = 1.00027$.\n",
    "\n",
    "Error formula for $\\lambda_0$ is\n",
    "\n",
    "$$\n",
    "\\Delta \\lambda_0 = \\sqrt{ \\left( \\frac{\\partial\\lambda_0}{\\partial(\\Delta L_2)} \\cdot \\Delta(\\Delta L_2) \\right)^2 + \\left( \\frac{\\partial\\lambda_0}{\\partial(\\Delta m)} \\cdot \\Delta m \\right)^2 }\n",
    "= \\sqrt{ \\left( \\frac{2n \\cdot \\Delta(\\Delta L_2)}{m} \\right)^2 + \\left( - \\frac{2n\\cdot \\Delta L_2 \\cdot \\Delta m}{m^2} \\right)^2 }\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 1.00027\n",
    "c0 = 299792458"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>m</th>\n",
       "      <th>L2</th>\n",
       "      <th>dL2</th>\n",
       "      <th>dm</th>\n",
       "      <th>∆dm</th>\n",
       "      <th>lambda</th>\n",
       "      <th>∆dL2</th>\n",
       "      <th>∆lambda</th>\n",
       "      <th>lambda_w</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>473.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.162278</td>\n",
       "      <td>600.162</td>\n",
       "      <td>1</td>\n",
       "      <td>275.755409</td>\n",
       "      <td>0.000013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>476.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.162278</td>\n",
       "      <td>600.162</td>\n",
       "      <td>1</td>\n",
       "      <td>275.755409</td>\n",
       "      <td>0.000013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40</td>\n",
       "      <td>478.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.162278</td>\n",
       "      <td>400.108</td>\n",
       "      <td>1</td>\n",
       "      <td>236.707085</td>\n",
       "      <td>0.000018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>50</td>\n",
       "      <td>482.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.162278</td>\n",
       "      <td>800.216</td>\n",
       "      <td>1</td>\n",
       "      <td>322.577382</td>\n",
       "      <td>0.000010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>60</td>\n",
       "      <td>486.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.162278</td>\n",
       "      <td>800.216</td>\n",
       "      <td>1</td>\n",
       "      <td>322.577382</td>\n",
       "      <td>0.000010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>70</td>\n",
       "      <td>488.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.162278</td>\n",
       "      <td>400.108</td>\n",
       "      <td>1</td>\n",
       "      <td>236.707085</td>\n",
       "      <td>0.000018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>80</td>\n",
       "      <td>491.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.162278</td>\n",
       "      <td>600.162</td>\n",
       "      <td>1</td>\n",
       "      <td>275.755409</td>\n",
       "      <td>0.000013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>90</td>\n",
       "      <td>494.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.162278</td>\n",
       "      <td>600.162</td>\n",
       "      <td>1</td>\n",
       "      <td>275.755409</td>\n",
       "      <td>0.000013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>100</td>\n",
       "      <td>496.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.162278</td>\n",
       "      <td>400.108</td>\n",
       "      <td>1</td>\n",
       "      <td>236.707085</td>\n",
       "      <td>0.000018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>150</td>\n",
       "      <td>511.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>7.071068</td>\n",
       "      <td>600.162</td>\n",
       "      <td>1</td>\n",
       "      <td>93.833643</td>\n",
       "      <td>0.000114</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      m     L2   dL2    dm       ∆dm   lambda  ∆dL2     ∆lambda  lambda_w\n",
       "1    10  473.0   3.0  10.0  3.162278  600.162     1  275.755409  0.000013\n",
       "2    20  476.0   3.0  10.0  3.162278  600.162     1  275.755409  0.000013\n",
       "4    40  478.0   2.0  10.0  3.162278  400.108     1  236.707085  0.000018\n",
       "5    50  482.0   4.0  10.0  3.162278  800.216     1  322.577382  0.000010\n",
       "6    60  486.0   4.0  10.0  3.162278  800.216     1  322.577382  0.000010\n",
       "7    70  488.0   2.0  10.0  3.162278  400.108     1  236.707085  0.000018\n",
       "8    80  491.0   3.0  10.0  3.162278  600.162     1  275.755409  0.000013\n",
       "9    90  494.0   3.0  10.0  3.162278  600.162     1  275.755409  0.000013\n",
       "10  100  496.0   2.0  10.0  3.162278  400.108     1  236.707085  0.000018\n",
       "11  150  511.0  15.0  50.0  7.071068  600.162     1   93.833643  0.000114"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$$\\bar\\lambda_0 = \\frac{\\sum_{i=1}^N w_i x_i}{\\sum_{i=1}^N w_i } = \\SI{571.43}{\\nm}$$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\bar\\lambda_0 = \\frac{\\sum_{i=1}^N w_i x_i}{\\sum_{i=1}^N w_i } = \\SI{571.43}{\\nm}\n"
     ]
    },
    {
     "data": {
      "text/latex": [
       "$$w_i = \\frac{1}{\\sigma_i^2}$$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w_i = \\frac{1}{\\sigma_i^2}\n"
     ]
    },
    {
     "data": {
      "text/latex": [
       "$$\\frac{1}{\\sqrt{\\sum_{i=1}^N w_i}} = \\SI{64.69}{\\nm}$$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\frac{1}{\\sqrt{\\sum_{i=1}^N w_i}} = \\SI{64.69}{\\nm}\n"
     ]
    },
    {
     "data": {
      "text/latex": [
       "$$\\lambda = 571.43 \\pm 65$$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\lambda = 571.43 \\pm 65\n",
      "0.9363402194444694\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "OLD DATA\n",
    "hene_data = [\n",
    "    [10, 4],\n",
    "    [20, 6.8],\n",
    "    [30, 9.8],\n",
    "    [40, 12.2],\n",
    "    [50, 15.3],\n",
    "    [60, 18.4],\n",
    "    [70, 21.4],\n",
    "    [80, 24.1],\n",
    "    [90, 27.2],\n",
    "    [100, 29.6]\n",
    "]\n",
    "henedf = pd.DataFrame(hene_data)\n",
    "henedf.columns = [\"dm\", \"dL2\"]\n",
    "\"\"\"\n",
    "\n",
    "hene_data = [\n",
    "    [0, 4.70],\n",
    "    [10, 4.73],\n",
    "    [20, 4.76],\n",
    "    [30, 4.76],\n",
    "    [40, 4.78],\n",
    "    [50, 4.82],\n",
    "    [60, 4.86],\n",
    "    [70, 4.88],\n",
    "    [80, 4.91],\n",
    "    [90, 4.94],\n",
    "    [100, 4.96],\n",
    "    [150, 5.11]\n",
    "]\n",
    "henedf = pd.DataFrame(hene_data)\n",
    "henedf.columns = [\"m\", \"skt\"]\n",
    "henedf[\"L2\"] = henedf[\"skt\"] * 100\n",
    "henedf[\"prevL2\"] = henedf[\"L2\"].shift(1)\n",
    "henedf[\"dL2\"] = henedf[\"L2\"] - henedf[\"prevL2\"]\n",
    "henedf[\"prevm\"] = henedf[\"m\"].shift(1)\n",
    "henedf[\"dm\"] = henedf[\"m\"] - henedf[\"prevm\"]\n",
    "henedf = henedf.drop(henedf.index[0])\n",
    "henedf = henedf.drop([\"prevL2\", \"skt\", \"prevm\"], axis=1)\n",
    "\n",
    "\n",
    "henedf[\"∆dm\"] = sqrt(henedf[\"dm\"])\n",
    "henedf[\"lambda\"] = 2*henedf[\"dL2\"]*n/henedf[\"dm\"]*1000\n",
    "\n",
    "# this is dropped because the value does not make sense\n",
    "# ∆L2 is 0, which does not make sense and messes up the average\n",
    "henedf = henedf.drop(henedf.index[2]) \n",
    "\n",
    "\"\"\"\n",
    "\n",
    "henedf[\"dm\"] = henedf[\"m\"] - henedf.loc[0][\"m\"]\n",
    "henedf[\"L2\"] = henedf[\"skt\"]*100\n",
    "henedf[\"dL2\"] = henedf[\"L2\"] - henedf.loc[0][\"L2\"]\n",
    "henedf[\"∆dm\"] = sqrt(henedf[\"m\"])\n",
    "henedf = henedf.drop(henedf.index[0])\n",
    "henedf[\"lambda\"] = 2*henedf[\"dL2\"]*n/henedf[\"dm\"]*1000\n",
    "\"\"\"\n",
    "\n",
    "henedf[\"∆dL2\"] = 1\n",
    "\n",
    "henedf[\"∆lambda\"] = sqrt( ( (2*n * henedf[\"∆dL2\"]*1000) \n",
    "                           / henedf[\"dm\"] )**2 \n",
    "         + ( (2*n*henedf[\"dL2\"]*1000*henedf[\"∆dm\"])\n",
    "            / henedf[\"dm\"]**2 )**2 )\n",
    "\n",
    "\n",
    "henedf[\"lambda_w\"] = 1/henedf[\"∆lambda\"]**2\n",
    "\n",
    "\n",
    "display(henedf)\n",
    "\n",
    "sumw = sum(henedf[\"lambda_w\"])\n",
    "lambda_weighted = sum(henedf[\"lambda\"]*henedf[\"lambda_w\"]) / sumw\n",
    "\n",
    "unc_sq2sum = 1/sqrt(sum(henedf[\"lambda_w\"]))\n",
    "\n",
    "def show(latex_string):\n",
    "    display(Math(latex_string))\n",
    "    print(latex_string)\n",
    "\n",
    "show(r'\\bar\\lambda_0 = \\frac{\\sum_{i=1}^N w_i x_i}'\n",
    "      +r'{\\sum_{i=1}^N w_i } = \\SI{%.2f}{\\nm}' % (lambda_weighted))\n",
    "show(r'w_i = \\frac{1}{\\sigma_i^2}')\n",
    "show(r'\\frac{1}{\\sqrt{\\sum_{i=1}^N w_i}}'\n",
    "     + r' = \\SI{%.2f}{\\nm}' % unc_sq2sum)\n",
    "show(r'\\lambda = %.2f \\pm %.f' % (lambda_weighted, unc_sq2sum))\n",
    "\n",
    "lit = 632\n",
    "litdist = (lit - lambda_weighted)/unc_sq2sum\n",
    "print(litdist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>$m$</th>\n",
       "      <th>$L_2$ [$\\mathrm{\\mu m}$]</th>\n",
       "      <th>$\\delta L_2$ [$\\mathrm{\\mu m}$]</th>\n",
       "      <th>$\\delta m$</th>\n",
       "      <th>$\\Delta\\delta m$</th>\n",
       "      <th>$\\lambda$ [$\\mathrm{nm}$]</th>\n",
       "      <th>$\\Delta\\delta L_2$ [$\\mathrm{\\mu m}$]</th>\n",
       "      <th>$\\Delta\\lambda$ [$\\mathrm{nm}$]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>473.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.162278</td>\n",
       "      <td>600.162</td>\n",
       "      <td>1</td>\n",
       "      <td>275.755409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>476.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.162278</td>\n",
       "      <td>600.162</td>\n",
       "      <td>1</td>\n",
       "      <td>275.755409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40</td>\n",
       "      <td>478.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.162278</td>\n",
       "      <td>400.108</td>\n",
       "      <td>1</td>\n",
       "      <td>236.707085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>50</td>\n",
       "      <td>482.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.162278</td>\n",
       "      <td>800.216</td>\n",
       "      <td>1</td>\n",
       "      <td>322.577382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>60</td>\n",
       "      <td>486.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.162278</td>\n",
       "      <td>800.216</td>\n",
       "      <td>1</td>\n",
       "      <td>322.577382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>70</td>\n",
       "      <td>488.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.162278</td>\n",
       "      <td>400.108</td>\n",
       "      <td>1</td>\n",
       "      <td>236.707085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>80</td>\n",
       "      <td>491.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.162278</td>\n",
       "      <td>600.162</td>\n",
       "      <td>1</td>\n",
       "      <td>275.755409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>90</td>\n",
       "      <td>494.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.162278</td>\n",
       "      <td>600.162</td>\n",
       "      <td>1</td>\n",
       "      <td>275.755409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>100</td>\n",
       "      <td>496.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.162278</td>\n",
       "      <td>400.108</td>\n",
       "      <td>1</td>\n",
       "      <td>236.707085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>150</td>\n",
       "      <td>511.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>7.071068</td>\n",
       "      <td>600.162</td>\n",
       "      <td>1</td>\n",
       "      <td>93.833643</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    $m$  $L_2$ [$\\mathrm{\\mu m}$]  $\\delta L_2$ [$\\mathrm{\\mu m}$]  \\\n",
       "1    10                     473.0                              3.0   \n",
       "2    20                     476.0                              3.0   \n",
       "4    40                     478.0                              2.0   \n",
       "5    50                     482.0                              4.0   \n",
       "6    60                     486.0                              4.0   \n",
       "7    70                     488.0                              2.0   \n",
       "8    80                     491.0                              3.0   \n",
       "9    90                     494.0                              3.0   \n",
       "10  100                     496.0                              2.0   \n",
       "11  150                     511.0                             15.0   \n",
       "\n",
       "    $\\delta m$  $\\Delta\\delta m$  $\\lambda$ [$\\mathrm{nm}$]  \\\n",
       "1         10.0          3.162278                    600.162   \n",
       "2         10.0          3.162278                    600.162   \n",
       "4         10.0          3.162278                    400.108   \n",
       "5         10.0          3.162278                    800.216   \n",
       "6         10.0          3.162278                    800.216   \n",
       "7         10.0          3.162278                    400.108   \n",
       "8         10.0          3.162278                    600.162   \n",
       "9         10.0          3.162278                    600.162   \n",
       "10        10.0          3.162278                    400.108   \n",
       "11        50.0          7.071068                    600.162   \n",
       "\n",
       "    $\\Delta\\delta L_2$ [$\\mathrm{\\mu m}$]  $\\Delta\\lambda$ [$\\mathrm{nm}$]  \n",
       "1                                       1                       275.755409  \n",
       "2                                       1                       275.755409  \n",
       "4                                       1                       236.707085  \n",
       "5                                       1                       322.577382  \n",
       "6                                       1                       322.577382  \n",
       "7                                       1                       236.707085  \n",
       "8                                       1                       275.755409  \n",
       "9                                       1                       275.755409  \n",
       "10                                      1                       236.707085  \n",
       "11                                      1                        93.833643  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{rrrrrrrr}\n",
      "\\toprule\n",
      " $m$ &  $L_2$ [$\\mathrm{\\mu m}$] &  $\\delta L_2$ [$\\mathrm{\\mu m}$] &  $\\delta m$ &  $\\Delta\\delta m$ &  $\\lambda$ [$\\mathrm{nm}$] &  $\\Delta\\delta L_2$ [$\\mathrm{\\mu m}$] &  $\\Delta\\lambda$ [$\\mathrm{nm}$] \\\\\n",
      "\\midrule\n",
      "  10 &                     473.0 &                              3.0 &        10.0 &              3.16 &                     600.16 &                                      1 &                           275.76 \\\\\n",
      "  20 &                     476.0 &                              3.0 &        10.0 &              3.16 &                     600.16 &                                      1 &                           275.76 \\\\\n",
      "  40 &                     478.0 &                              2.0 &        10.0 &              3.16 &                     400.11 &                                      1 &                           236.71 \\\\\n",
      "  50 &                     482.0 &                              4.0 &        10.0 &              3.16 &                     800.22 &                                      1 &                           322.58 \\\\\n",
      "  60 &                     486.0 &                              4.0 &        10.0 &              3.16 &                     800.22 &                                      1 &                           322.58 \\\\\n",
      "  70 &                     488.0 &                              2.0 &        10.0 &              3.16 &                     400.11 &                                      1 &                           236.71 \\\\\n",
      "  80 &                     491.0 &                              3.0 &        10.0 &              3.16 &                     600.16 &                                      1 &                           275.76 \\\\\n",
      "  90 &                     494.0 &                              3.0 &        10.0 &              3.16 &                     600.16 &                                      1 &                           275.76 \\\\\n",
      " 100 &                     496.0 &                              2.0 &        10.0 &              3.16 &                     400.11 &                                      1 &                           236.71 \\\\\n",
      " 150 &                     511.0 &                             15.0 &        50.0 &              7.07 &                     600.16 &                                      1 &                            93.83 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rows = zip(henedf[\"m\"], henedf[\"L2\"], henedf[\"dL2\"])\n",
    "headers = (\"$m$\", \"$L_2$\", \"$\\delta L_2$\")\n",
    "#print(tabulate(rows, headers=headers, tablefmt=\"latex\"))\n",
    "henedfc = henedf.copy()\n",
    "henedfc = henedfc.drop([\"lambda_w\"], axis=1)\n",
    "\n",
    "henedfc.columns = (\"$m$\", \n",
    "                   r\"$L_2$ [$\\mathrm{\\mu m}$]\", \n",
    "                   r\"$\\delta L_2$ [$\\mathrm{\\mu m}$]\", \n",
    "                   r\"$\\delta m$\",\n",
    "                   r\"$\\Delta\\delta m$\", \n",
    "                   r\"$\\lambda$ [$\\mathrm{nm}$]\", \n",
    "                   r\"$\\Delta\\delta L_2$ [$\\mathrm{\\mu m}$]\", \n",
    "                   r\"$\\Delta\\lambda$ [$\\mathrm{nm}$]\")\n",
    "\n",
    "display(henedfc)\n",
    "with open(\"analysis/part1_tab.tex\", \"w\") as f:\n",
    "    s = henedfc.to_latex(escape=False, \n",
    "                         index=False, \n",
    "                         float_format=\"%.2f\")\n",
    "    print(s)\n",
    "    f.write(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When looking at the wall we can see the characteristic circular minima-maxima pattern. If you put a screen into the reflection of the beam-splitter, you can see the same pattern, but with minima and maxima switched:\n",
    "\n",
    "<img src=\"20.06/rings_wall.jpg\" width=\"40%\"/>\n",
    "<img src=\"20.06/rings_screen.jpg\" width=\"40%\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2: Mercury vapor lamp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert into wavelength\n",
    "\n",
    "$$\n",
    "t' = \\frac{2\\Delta L}{c_0} = \\frac{2 v_\\text{m} t}{c_0}\n",
    "$$\n",
    "\n",
    "where $\\Delta L$ is the path difference, $v_\\text{m}$ is the motor velocity and $c_0$ is the speed of light in vacuum. \n",
    "Also \n",
    "\n",
    "$$\n",
    "f' = \\frac{c_0}{2v_\\text{m}} \\cdot f\n",
    "$$\n",
    "\n",
    "Then the wavelength $\\lambda$ in meters is:\n",
    "\n",
    "$$\n",
    "\\lambda = \\frac{c_0}{f'} = \\frac{c_0}{\\frac{c_0}{2v_\\text{m}}f} = \\frac{2 v_\\text{m}}{f}\n",
    "$$\n",
    "\n",
    "The velocity of the motor given in the script is \n",
    "\n",
    "$$\n",
    "v_\\text{m} = \\frac{50 \\mathrm{\\mu m}}{60 \\mathrm{s}}\n",
    "$$\n",
    "\n",
    "Approximate error by uncertainty on the motor speed\n",
    "\n",
    "$$\n",
    "\\lambda = \\frac{2v_\\text{m}}{f} = \\frac{2 \\frac{50 \\mathrm{\\mu m}}{T}}{f} = \\frac{2\\cdot 50 \\mathrm{\\mu m}}{fT}\n",
    "$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/latex": [
       "$$2 \\sqrt{\\frac{\\Delta T^{2} d^{2}}{T^{4} f^{2}}}$$"
      ],
      "text/plain": [
       "        ______________\n",
       "       ╱         2  2 \n",
       "      ╱  \\Delta T ⋅d  \n",
       "2⋅   ╱   ──────────── \n",
       "    ╱        4  2     \n",
       "  ╲╱        T ⋅f      "
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "λ, f, T, dT, d = map(sp.Symbol, (\"\\lambda\", \"f\", \"T\", \"\\Delta T\", \"d\"))\n",
    "ex = (2*d)/(f*T)\n",
    "\n",
    "dλ = sp.sqrt( (sp.Derivative(ex, T) * dT)**2 )\n",
    "dλ = dλ.doit()\n",
    "dλ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1666.6666666666667 166.66666666666669\n"
     ]
    }
   ],
   "source": [
    "λ = 2*(50e-6/60) * 1e9\n",
    "dT = 6\n",
    "dλ = 2 * (dT*50e-6) / (60**2) * 1e9\n",
    "print(λ, dλ)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Eyeballing the coherence length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/latex": [
       "$$14.2126704035519$$"
      ],
      "text/plain": [
       "14.2126704035519"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xp, xm, dm1, dm2 = map(sp.Symbol, (\"x_+\", \"x_-\", \"\\Delta m_1\", \"\\Delta m_2\"))\n",
    "\n",
    "#dm = sp.sqrt(dm1**2 + dm2**2)\n",
    "#display(dm)\n",
    "dm = sp.Symbol(\"\\Delta m\")\n",
    "dl = sp.sqrt( (sp.diff(xp, xp)*dm)**2 + (sp.diff(xm, xm)*dm)**2 )\n",
    "#dl = sp.refine(dl, sp.Q.positive(dm))\n",
    "sp.N(dl.subs({dm: sp.sqrt(10**2 + 1**2)}))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First define some functions for the analysis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data(file, tlim=None):\n",
    "    data = pd.read_table(file, decimal=\",\", \n",
    "                         sep=\";\", header=None, names=(\"t\", \"V\"), \n",
    "                         engine=\"python\")\n",
    "    data.index = data[\"t\"]\n",
    "    data = data.drop(\"t\", 1)\n",
    "    \n",
    "    if tlim is not None:\n",
    "        tmin, tmax = tlim\n",
    "        data = data[data.index.map(lambda t: t > tmin and t < tmax)]\n",
    "    return data\n",
    "    \n",
    "def plot_interferometer(ax, data):\n",
    "    ax.plot(data)\n",
    "    ax.set_xlabel(\"$t$ [s]\")\n",
    "    ax.set_ylabel(\"$U$ [V]\")\n",
    "    \n",
    "def freq_to_lambda(f, vm=(50e-6/60)):\n",
    "    return 1/(f/(2*vm/1))*1e9\n",
    "\n",
    "def freq_to_freq(f, vm=(50e-6/60)):\n",
    "    return c0/(2*vm) * f\n",
    "\n",
    "def do_fourier(data, d = 0.0023):\n",
    "    X = scipy.fftpack.fft(data[\"V\"])\n",
    "    absX = np.abs(X)\n",
    "    freqs = scipy.fftpack.fftfreq(len(data), d=d)\n",
    "    fftres = pd.DataFrame(data={\"freq\": freqs, \"ampl\": absX})\n",
    "    \n",
    "    # convert into wavelength\n",
    "    fftres[\"lambda\"] = freq_to_lambda(fftres[\"freq\"])\n",
    "    \n",
    "    return fftres\n",
    "\n",
    "def plot_raw_fourier_spectrum(ax, fftres, flim=(1, 4)):\n",
    "    ax.plot(fftres[\"freq\"], fftres[\"ampl\"])\n",
    "    ax.set_ylabel(\"Amplitude\")\n",
    "    ax.set_xlabel(r\"$f$ [Hz]\")\n",
    "    fmin, fmax = flim\n",
    "    amplmax = fftres[(fmin < fftres[\"freq\"]) & (fftres[\"freq\"] < fmax)][\"ampl\"].max()\n",
    "    ax.set_xlim(fmin, fmax)\n",
    "    ax.set_ylim(0, amplmax*1.05)\n",
    "\n",
    "def extract_region_of_interest(data, llim=(400, 700)):\n",
    "    lmin, lmax = llim\n",
    "    roi = data[data[\"lambda\"].map(lambda l: lmin < l and l < lmax)]\n",
    "    roi = roi.copy()\n",
    "    roi[\"ampl\"] = roi[\"ampl\"] / max(roi[\"ampl\"])\n",
    "    return roi\n",
    "    \n",
    "def plot_normalized_wavelength_spectrum(ax, data):\n",
    "    ax.plot(data[\"lambda\"], data[\"ampl\"])\n",
    "    ax.set_xlabel(\"$\\lambda$ [nm]\")\n",
    "    ax.set_ylabel(\"Amplitude\")\n",
    "    \n",
    "def gaus(x, a, mu, sigma):\n",
    "    return a*exp(-(x-mu)**2 / (2*sigma**2))\n",
    "\n",
    "def do_peak_fit(data, init_mean, init_ampl=1, init_sigma=10):\n",
    "    pint = [init_ampl, init_mean, init_sigma]\n",
    "    #print(pint)\n",
    "    popt, pcov = curve_fit(gaus, \n",
    "                           data[\"lambda\"], \n",
    "                           data[\"ampl\"], \n",
    "                           p0=[init_ampl, init_mean, init_sigma])\n",
    "    return popt, pcov\n",
    "\n",
    "def plot_fitres(ax, data, fitres, xlim=15):\n",
    "    popt, pcov = fitres\n",
    "    ampl, mu, sigma = popt\n",
    "    x = data[\"lambda\"]\n",
    "    x = list(filter(lambda s: (mu - xlim*sigma) < s \n",
    "                    and (s < mu + xlim*sigma), x))\n",
    "    ax.plot(data[\"lambda\"], data[\"ampl\"], label=\"data\")\n",
    "    ax.plot(x, gaus(x, *popt), label=\"fit\")\n",
    "    ax.set_xlabel(\"$\\lambda$ [nm]\")\n",
    "    ax.set_ylabel(\"Amplitude\")\n",
    "    ax.text(0.02, 0.95, \n",
    "            r\"$\\mu = %.2f$ nm\"\"\\n\"r\"$\\sigma = %.2f$ nm\"\"\\n\"r\"$A = %.2f$\" % (mu, sigma, ampl), \n",
    "            va=\"top\", transform=ax.transAxes)\n",
    "\n",
    "def format_matrix(matrix, numfmt=\"%f\"):\n",
    "    s = r\" \\\\ \"\"\\n\".join([\" & \".join([numfmt%c for c in cols]) for cols in matrix])\n",
    "    return r\"\\left(\\begin{matrix}%s\\end{matrix}\\right)\" % s\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Green spectral line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "data_green = load_data(\"2018-07-12/pg_r10.txt\", tlim=(5, 95))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f43a2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "plot_interferometer(ax, data_green)\n",
    "#ax.set_title(\"Green line interferogram\")\n",
    "plt.show()\n",
    "fig.savefig(\"analysis/plots/green_interferogram.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c251630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fftres_green = do_fourier(data_green)\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "plot_raw_fourier_spectrum(ax, fftres_green)\n",
    "#ax.set_title(\"Green spectral line fourier transformed spectrum\")\n",
    "plt.show()\n",
    "fig.savefig(\"analysis/plots/green_fftres.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c90b4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65.79071806378528\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>parameter</th>\n",
       "      <th>value</th>\n",
       "      <th>fit uncertainty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>$A$</td>\n",
       "      <td>90.714022</td>\n",
       "      <td>7.100884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>$\\mu$</td>\n",
       "      <td>3.139941</td>\n",
       "      <td>0.000005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>$\\sigma$</td>\n",
       "      <td>0.067594</td>\n",
       "      <td>0.000005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  parameter      value  fit uncertainty\n",
       "0       $A$  90.714022         7.100884\n",
       "1     $\\mu$   3.139941         0.000005\n",
       "2  $\\sigma$   0.067594         0.000005"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$$C_{A \\mu \\sigma} = \\left(\\begin{matrix}7.100884 & 0.000000 & -0.003528 \\\\ \n",
       "0.000000 & 0.000005 & -0.000000 \\\\ \n",
       "-0.003528 & -0.000000 & 0.000005\\end{matrix}\\right)$$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def calc_lk(ax, data, r=(2, 4)):\n",
    "    rmin, rmax = r\n",
    "    roi = data[data[\"freq\"].map(lambda f: rmin < f and f < rmax)]\n",
    "    ax.plot(roi[\"freq\"], roi[\"ampl\"], label=\"data\")\n",
    "    \n",
    "    \n",
    "    init_ampl = max(roi[\"ampl\"])\n",
    "    #print(roi)\n",
    "    #print(roi[\"ampl\"].idxmax())\n",
    "    init_mean = data.iloc[roi[\"ampl\"].idxmax()][\"freq\"]\n",
    "    init_sigma = 0.3\n",
    "    \n",
    "    pint = [init_ampl, init_mean, init_sigma]\n",
    "    #print(pint)\n",
    "    fitres = curve_fit(gaus, \n",
    "                           roi[\"freq\"], \n",
    "                           roi[\"ampl\"], \n",
    "                           p0=[init_ampl, init_mean, init_sigma])\n",
    "    popt, pcov = fitres\n",
    "    \n",
    "    ax.plot(roi[\"freq\"], gaus(roi[\"freq\"], *popt), label=\"fit\")\n",
    "    \n",
    "    ampl, mu, sigma = popt\n",
    "    fwhm = freq_to_freq(2*sqrt(2*log(2)) * sigma)\n",
    "    lk = 2*pi/fwhm * c0 / 1e-6 # µm\n",
    "    \n",
    "    ax.text(0.02, 0.95, \n",
    "            r\"$\\mu = %.2f$ Hz\"\n",
    "            \"\\n\"r\"$\\sigma = %.2f$ Hz\"\n",
    "            \"\\n\"r\"$A = %.2f$\" % (mu, sigma, ampl), \n",
    "            va=\"top\", transform=ax.transAxes)\n",
    "    \n",
    "    ax.legend()\n",
    "    return (popt, pcov), lk\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "fitres_lk_green, lk_green = calc_lk(ax, fftres_green)\n",
    "plt.show()\n",
    "print(lk_green)\n",
    "\n",
    "\n",
    "def print_fitres(fitres, names, show=True):\n",
    "    popt, pcov = fitres\n",
    "    #ampl, mu, sigma = popt\n",
    "    #unc_ampl, unc_mu, unc_sigma = pcov[0, 0], pcov[1, 1], pcov[2, 2]\n",
    "\n",
    "    uncs = [pcov[i, i] for i, _ in enumerate(popt)]\n",
    "    \n",
    "    rows = list(zip([\"$%s$\"%n for n in names], popt, uncs))\n",
    "        \n",
    "    \n",
    "    df = pd.DataFrame(rows)\n",
    "    \n",
    "    df.columns = (\"parameter\", \"value\", \"fit uncertainty\")\n",
    "   \n",
    "    covstr = r\"C_{%s} = %s\" % (\" \".join(names), format_matrix(pcov))\n",
    "    \n",
    "\n",
    "    if show:\n",
    "        display(df)\n",
    "        display(Math(covstr))\n",
    "    return df.to_latex(escape=False, index=False)\n",
    "\n",
    "\n",
    "tab = print_fitres(fitres_lk_green, names=(\"A\", r\"\\mu\", \"\\sigma\"))\n",
    "with open(\"analysis/fitres_lk_green_tab.tex\", \"w\") as f:\n",
    "    f.write(tab)\n",
    "\n",
    "fig.savefig(\"analysis/plots/green_roi_freqfit.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fde8898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fftres_green_roi = extract_region_of_interest(fftres_green)\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_title(\"Region of interest in wavelength spectrum, green spectral line\")\n",
    "plot_normalized_wavelength_spectrum(ax, fftres_green_roi)\n",
    "plt.show()\n",
    "fig.savefig(\"analysis/plots/green_roi_lambda.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10eb40128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "maxampl = (\n",
    "    fftres_green.iloc[fftres_green_roi[\"ampl\"].idxmax()][\"lambda\"])\n",
    "fitres_green = do_peak_fit(fftres_green_roi, \n",
    "                           init_mean=maxampl)\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "plot_fitres(ax, data=fftres_green_roi, fitres=fitres_green)\n",
    "ax.legend()\n",
    "ax.set_title(\"Fitted wavelength spectrum for green spectral line\")\n",
    "plt.show()\n",
    "fig.savefig(\"analysis/plots/green_roi_lambda_fitted.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>parameter</th>\n",
       "      <th>value</th>\n",
       "      <th>fit uncertainty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>$A$</td>\n",
       "      <td>0.804223</td>\n",
       "      <td>0.000574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>$\\mu$</td>\n",
       "      <td>531.087566</td>\n",
       "      <td>0.153765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>$\\sigma$</td>\n",
       "      <td>11.395096</td>\n",
       "      <td>0.153411</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  parameter       value  fit uncertainty\n",
       "0       $A$    0.804223         0.000574\n",
       "1     $\\mu$  531.087566         0.153765\n",
       "2  $\\sigma$   11.395096         0.153411"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$$C_{A \\mu \\sigma} = \\left(\\begin{matrix}0.000574 & 0.000000 & -0.005411 \\\\ \n",
       "0.000000 & 0.153765 & 0.006589 \\\\ \n",
       "-0.005411 & 0.006589 & 0.153411\\end{matrix}\\right)$$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tab = print_fitres(fitres_green, names=(\"A\", r\"\\mu\", \"\\sigma\"))\n",
    "with open(\"analysis/fitres_green_tab.tex\", \"w\") as f:\n",
    "    f.write(tab)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Yellow spectral line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_yellow = load_data(\"2018-07-12/pg_r08.txt\", tlim=(5, 75))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IESCJzie3XlLiX8l3TiZimYeSYInbwGAgidtBANPY/1PdbRzXA3gCALTWSwEUA5gIAEqpqQCeBvAfWuuoE4Szz5+11udprc+bNGlSP0/fwsLCwqKnMEY3BqIgo9qtuP12dKeweFdNoByVZD4kmIgLn1Nlo6Ml5FOSAgDEOqNCcEAB1RRlfZnqp5rqlwbNnPSZWePGkRI0iZ4JODqkCKtxGzwMJHFbCWCWUmqmUqoQTvDBsyGZMgCXAIBS6jQ4xK1aKTUWwHMAvqu1XgwLCwsLiwFFdXMnyut7Xmy8p+a2bHL0lZOEvy7ei0/ft9zLvZZtDE/jxuWEcVMemfQl5ejLqH+YR6IM2X7Tkjyfp1C2ypSDLS1UppICBqR6q3trWvGZvyzH3poW41gmSONzHGxo9/wBLXqPASNuWuskgK8AeAnAVjjRo5uVUj9WSn3IFfsmgC8opdYDeBTAZ7VzR34FwMkAfqiUWuf+TR6ouVtYWFgMd3z0j0vw7tvnZ5Vr6uhGY7vv9G/OJ+YjTm1NgJEUtvOeasc/rL61i8nRGAbiwrVW7g48WlMkeAbtmrQtoKHzPn0tmw7J8wElgiURxwpXM8iPVTKVhvvgGren1x7Ewp01eGZdRUQOAO54ZQfufWMPO5Z4c+N49+3z8eG7re6lr4h6Uh5BaK2fhxN0wLf9kH3fAuBCYb+fAPjJEZ+ghYWFxTBGa2cSJYV5YlRgWV08bdsVd7yBtAaWfe8SAOZcZBykrRE1WpI/mZA+I25i2ZSBaJUW5Xv5yGjuyXR0fKlKgUTSgmMEzaKZ/OToeExEiDdRRGqw+kLQT47DRAilNgC487WdAIDPXHACigvyjGQ1k7m7K5lGuVBlwqJnyLXgBAsLCwuLQUBVUwfO+NFLYh61nqCisSMQQWrK7SX5uJFfWVAu+j2YsDYq520TxjWZSgORpgaz5M7DgklRmKdk5uXkj7SFEhGqomL0Ac2gNKfI8EzjFo+4eUQvSwQrVX0Qz6v1cRsQWOJmYWFhMUxQVtuGR1eUiX5ntS2OmXHu2mgS2Ux4ffthLNldY5SJGQRpNG3yLQt3VrvyXOMWVfFIxIkg+a6Zok8zzcWTEw+StHAmnzi555FFjjGsulkgsYJ2z1SDNS5x8+fmf6d6sUpguNL5krSrB+raArnjLPoOS9wsLCwshgm+/dR6/M8/N4o51Xoa1QkAn7t/JT5173Lvf5H8xDT3mdJ78H4njyoGIJs7M+wda04Sl2lyffUk4sK/yn5kzueinT6x7XK1VdTWlYrWLAWAKWOL3W+ZCamMqPnYNDcJnKw2dzjHv/6AnxTZdJ2kfm95ZhN+MHdTbFM7ALy46RAqhOoSFg4scbOwsLAYJth2qBkA0J3M7Asmaa+6U1nCBV1I/ktxzWc0hKg9Y999s6hZG2bSuMWNKt3japwCedRi+tHR/MaXFnrbWsgXTRhLnnvmsaSaqaZABGlbIFrVnQHvIz/PoQjHjC5GGCYfPo7d1Y5JmUys2VDb0okvPbwaX39sXSz54QhL3CwsLCyGCUxO76YggpaOZMY2DmlxNkeVRn3c4ifW9b9zjdiuw81o5KWsJFMpkUQhqpSDCNvMiaVif+E0IAHyFfoE2Hk3pBThzbRFKrkFYVug3ql7cNK1SxrUlVKVCsnHUD6v0jl0aEZc8k9ayJVZattqrbGxvNHTYg4nWOJmYWFhMUxAi7lE0kym0vjeSX0wlfYw35vUbzKt8d7fvoEbHlplnGXcSFeJfBnSsgW3iVo4g7zwXTpGyeQokakRBXkAgA3ljRF50/EHiVuUTEvf/H2dT34LEfntjmnbNhFDjo0HG/HBPyzCXa/vitXv0QRL3CwsLCyGCcjiJzndx43+NCEuwZLgZfNHMGDAyQkX9d2SzKdJV1vDc7GJ+caEsUyaLI64+cs80hdTWyVr8qJy7d2pyDbSZnHxsSUFAIBRQv1UCnrgJJ0S4waTAkfntu5APW59drOotZPukzyPuMXTjMW9XyiYZh3zvxsusMTNwsLCYpjArHELynDE17hFYcqmz6ch8cZ7F+7BOf/7MvbWRLVMwQU+c84yiQesKasHACQCNUAzzy/TPHVomxa0VXHJn3SO4waMrNpfHxk/3AdHRUM0OKXBNS9zfiX19/2nN+GBJftEzZ80VoHrJ1dWGy84IW7wKY2VyHJujkZY4mZhYWExTECLnCkBa9wi7xIksS2VTbH2lRb9V7ZUAXByzJnmJOVxM82p2DUjnjhJ9l0zzckUiJANprQdpkAEaRtvI+2aJMcv9TGji5w2YW6kGVu0q9rvI/QJ+IXqJX9GSak2aZQzZn1bV7RRQHzi5nzmDUPmZombhYWFxSDizld34tRbXsCWiijB2VPdgrfc9grmbavC/Yv34oKfv4YOwVQWFwmX4cStaUnoTcCAvy1evxJJIu2f5HQfV7tkImTBqNIoqfI0aRlqIsQxfWYzPUoI7xs2H2eSz26CzdxHYb5DB8aXFAryvhxp0KT7UDr/k0YWRbaZ7uFsptKlu2ux6WAjkxt+xG1AS15ZWFhYWATxrw0V6OhOo6yuDacfNzrQtqWyCbWtXXhydTme33gIANDY3u1pjAidyRQW7azBO2ZNRFF+sI0jEctUKuwYW+MWzwTotQkkiY/vRcGKGkJBTtS4Zd6XRyT2SQtm8Hurb+3mgvwjY7/UH+VPC0S/CmNKZlk/vUeUOEowE2G/zSFuqawatF++uA37aloxurjA7d9vk+7h6Jgyrr13GQDgnuveAsCaSi0sLCyGHZo6uvHJPy3F4l3mCgBHCpotjl9/bC0eWrbfayNNi6Q14Xh6zUFc/7dVeHJ1eawxd1Y1R7a1dTlpI/oSVSquuWzbA4v34muPrRX7lRZsT0OYxYxpKu8kTZ7OeUN7t0nMWLYqMJd0tI324WZeX6tn9nvbUeXkPiMz4AkTSiPywX2j8/TmFvC7C24Lphlxv2RxqKOqDtsqo/cQmeDr27rxx/m78cKmQ2yO0XlIiHuv+T5uw4+5WeJmYWExrFFW24ble+tw+8vbB3Tc8vo2tHelAovu3HUVuGXuJk9G8t2StDtUCaFKqPPJcZybkb9D8E+itBElBb03xGQzAd76ry14Zl1FbE2WmwJMJGlizjjG8EgjZCJkK1j0qSQom17Nmi6/Ox3p1mTSlMzXJJaXiG4M9CGYdOWxnH86uqODaWEHySdv2vgRAHyTaeAYRHM3tfFZZL5R4voLtrrJjBPDkMUMw0O2sLCw8EFagoFM5NmZTOEdv3wdX31U1j4R/NQX8bQV2TBmRGHGTkYUOqarGRNLIm3Zxtx0sBHfe3pjoISTt69wZJWNHfjOkxtQycoaSeZQ0qYkWRuZ2Na4kZSAr5nkXbR1pdy5ZzYBjirKj2yT5LJp3CSNV42broKP781JNCln1hbGHT+bZpK+rnbPHTeri2Zmk2lX2LrfEDmaTWtsmocEIu5SFDRHVzKNls4ktNb46XNbBk2z3p+wxM3CwmJYwy84Hm3TWuMP83ZibVl9tLEPIJL46tYqWYPiosdmy5hmI1kLFSUpvrx5Nf3WP9bjkeVl2OOWN8qGvy3dh8dXHcCDS32zsGnB5lGw5Dw/ZkQ0ktKUT43jQL2QxFaQy+bsH96Zn6fDrhaUi1OEbdx+JUIoVmkQGJa4r/tJ524ky/FmIrhxy2tRbVMJfQlwkRDXVHr931biol/OQ3NnEvcu3IsbH14db4AchiVuFhYWwxpSMlZCY3s3bn95B276+5r+HTM2OYr6uEmaKd7+zzXlWLUvW7kgQ1sP5QG/RmncPGp7qp0aoI3Mx0wKmCBtCk/2Kpkg/bHijc9LYoX35TwgHeVDPc7LxsWLCxKR/sLy0pyyyhnmsYP5M4ZLdHHE1XT5x5X5XI8zpCfhfUiIm4CXzNyFgsmWY+HOGtS3dSPl3kNNMcu35TIscbOwsDjq8ZdFe/Hm217xHPA5vIz9wos7megqGjvQ2NaNJ1YeEE2qWms8tbo84IjO5dcfaMCinTUBee+7ty06vuQfZIICcPMT6/Gxe5Ya5XqcUiLe8Fk1XlSGicyd87Yd9trKalsj+5IpdRWrW2map1TknmO0q2GKm2Q4m78VkQDx+njz9Bv9YJN4J1kLTSZtneT3lpTItLef3+bXihX6FXaOW9De99P0255cVY6zbn0Jda1+ZOrG8kYs2FEdm7hRkMTUcSNiyXczJ8LP3r8C7/3tglj75SJsOhALC4ujHne+ugNNHUkcburEjInBx55nKhX24xqkuxfswp8W7MG40kJcevoxAID/+ecGtHel8PX3noJv/mM9Lj/jGPzpM+c58vN34U9v7MGEkYW4/m9O7cx9v3h/pF+CuOgJc+Jimw42Ij9PZYh01IHKAIE+DNtE7REbtLUziVe3VuHCkydG5sld3PbXtnpllMIodnOGjSrOR4vrZH7YlS0p9P2uTpxUip2HW1CUH9Ux8DmVFDn71LZGU1TEJWRxiUi2yM3wuEEuZdAWiuedPqNtUrRq1mCKDHN09hXmJBBCyXxq6kPSGv9l0R40dyRxsL4d40sd0/d1f1mOxvZu/Osr75A6EcbKfC4l8N/c/O3V7px0Vh+5XITVuFlYWBz1oOSimyqiBbe9F3HhAc4f9gddbQ6vFfnoigOYu67Cc8rfzJLoHnS1RURMAmMKq41Y+1H85n//wO8X4f2/W+T9HzTzZaZnsX23hLZ/b6jA1x5bhztf3RmR4+bOzz+wEh+7Z2kGp//oGPmuyUvKQyeZ2XgXRe6+BXnRaxiXfEkbTZopaVfJFy2uXTSubyFt4/eLZNKVSCKVtZLGr22JkuxkOloDlb7L5v7M4G2kca1t9ccks/mGg/Fqj3ovCTE1dNLvK5sme/X+OlFLP9iwxM3CwiKnkUylsetwc1azVXNHN8oFp3MAmDrOiZRsahdMpTE1bvTNJMf9zxIqqmnw+go4e+vAPCQ5U0ReKq2NpEOCUeOjgXvf2IPfvrJD3LfdjYxsEhzRU0zlttv1Y1tbFl2ITZohSQuUFs5XkExRmzDhmARD1FrGJVhxNW4943JZIj2jbDaoGXM/DdpdLk8aT15CivLJxfa1M5hK+TUc5waYrDvQgHsW7MYd7F7bfdg3mT+/sRI3P7Eug2nfrHHbV9MaqNAgRTybzLL7a1vx0T8uxU+f25pRZrBgTaUWFhY9RjKVRnNHEuNKC7ML9xIPLN6LvLwEGlq78JtXdmDuTRfiTdPGZpT/woOrsGxPnWeO5KBajpIGg5LRShaTwIPdwISIuHHylRAWLL/faB8mv7O42e95gIVpUTKR4LauFH76vLNY3XzpKZF2UlxI0Xz8uPISCqm0lvOtGQipnCctOk++rbWLfM0ya6jE8bmc4ZoEb4O4JCbYhzS+NJYEUeMozOkgS69iIjame4jnZ8sUaZ1pvuZ7zv8+bfwIbK9qRlF+Hn7xwraAXCvTUN/0yBpoDfzk6jNRUii7OEhjNrR14V23z8cnzpvqbZPKbJneBSl1y9LdtZmFBgmWuFlYWPQY335yA/659iB2/vR9YiLO/sCt/9oCADh/xjgAwQe6hGV7HAd2k9+K9KCmB/S0cdH8ZcmAxs3VzBkIHn+pN2ncUgIRkwp00/CbmAk2bsqLmBakiPwalvrkQF0b/rZkHz76Fn8B9KJw+XkQSKqXg06YCBHdVmaGksicqHELfQJ+lGqdEC0qQSJVJjK5PRCZGe1PzvcmkENDH+LlMrAuiXTWMx8/HfkSlc86pnD+CXFN0NJYcg3YaL/0VQqwoHtIGpM067xywxs7nOCghJLvq0zzkDR1gw1rKrWwsBBh0gD8c+1BAHL2+v7Gyn0OkZAIU0NbF/4wb2cgrYTZyTrzYjOqOPoey81MJCelDaFFpIb5CVFggFgXVFqIDItjATNfSZdlkZtUND/PLGdqo02TR/tFwe+evwv3LdqLhTurvW2k1TGdh0xjhQu5N7PUDJIJ0OxP5rdRfrSKhmhUaWzfMXdTY6AMVjySLFY90MFPvrOs+Y3mwPPJX/QFQrCUyuNLbZJt1YVI0tjxaWFbeMzgtqiGzpQzUPr9piT/T8PxdSadlzH+Urm3xjm/pUU8d52wc6htIBNzx4UlbhYWFhEkU2m89Wev4VfZ98yAAAAgAElEQVQvbssqd6RhKiL9xKoDuP3lHfjHqgPetrgRfn5bVDNCkPy4sppUXdC8D9YL5itBq2PyRQpqEKNyE0odssWjL41mK4Mahu9GmotutnASseF90OwkzUhsbQ1FRqa5XGZ5jm7DfRhXC3S4OZowVyInJp+5gKnS+4ySLmlOJlNeNvIpJuUNtUn9bhXqjUraTVO900z7EiTNmOlqSn2YNG6SPL1QFggvMlyzHse0ezhDZPRgwhI3CwuLCDqSaRxu7sTd83cb5bqFN2EAWLK7BrtjZtHPBhqhO6Vx6W8X4IHFe7229i7nAd0U0LhJC1XmBVNaHF/ffhjv+OU8LNhRE5GTeGR9a5TgkQM21cwEgCW7a/GOX84LJEU1zVuCpHEiksg1XnH9jWpbOvHS5kOiGY+0hjwij8bYV+M7kftamJ4t5kE56susmZHIhG+WzkwI+b7SNacSVZxVxPWZ21rZFNnmmxnZJmFcunayz6BAyIQ+pHNCAqbISckMmBTuIZEQCv2ZNGhS2TYpYlMmbobAAmHM9eVOQEx+gr/ISHOLbnxi5QG8+/b5YjR4rsASNwsLiwgyLfrrDzQEcnNJC3BlYzs+de9yfPGhYGmZ8L5xwU1YOw+3eL5vAFCQ7yx2C3dFCZbcV2ZtHP/292X7UV7f7pHPgjxl9HGrbIoWd6fan9xcc9/CPSivb8c/15azUZ1+Tbnd+JgkxetCUrvkO8fB17odVc04UNeG7z29EV98aDXK6toi+9Gw/H7w/fmiGre4qU9MwRmSNjKgjRLkJB+zcB+BbYIJMtzWk/4kxDVpSuOa5CXiJJE5kx8ZbckT1Nm7D/svXKKLgYEwSc8NSvmRFq5XTXM07550b3R0pyPzMOVxI/PmyZNHRuSl3xLHz1/Yir01ragW0qPkCgaUuCmlrlBKbVdK7VJKfVdon66Uel0ptVYptUEpdSVr+x93v+1KqcsHct4WFsMNkuakO5XGVXctxg0PrfK2cf+Pls4k9lS3YK/rKL6LLQC7q1tw1V2L8d2nNvR6TinhrXt0sRMtWsxyf5kWW35Yb+yoxn/9baXnD8O796opuP93p7RxwU6JqQacT573rarJWQwamBO90fTkdhtYbFwxri0hfzNeeF0brNhaA5fd8QY+ds8Sz4eQrqWkyZOIGwdlsd940M+TJ2nwTH14JjVh26aDUY1WTC4h+6QJ94PYbxbneRMknz3R30sYN9KH6B9mngfVjRXF3G1igufA96jWTtTuuZA0eJUNHRnnEdfcSvcVb5JS8HhzdDdRpQ7eb+C3JPxG6HfQnYO+bYQBI25KqTwAdwF4H4DTAVyrlDo9JPYDAE9orc8FcA2Au919T3f/PwPAFQDudvuzsLCIAa01Khvbsz7sCdKCRn5EPC8XT5b5tUfX4j2/WYAOlwjxl/ltri/Nkj6E1ssJNJ1tzCISePDXt3ahvSsl+rF97+mNeHXrYVS4C8tKXlbJ/eS+ZaYFlpuXWjuTaGzv9hYUTm5pnpJ2TXLTIhNROM3HocaOwHFSUEKRsFBJoPNR1dTpmXIl+YrGjsh8pYTF08Y7fkN8ofTGimkq3XaomSYXOYZC5rvna5yiZCZ+pGNmpsc39TTJrCQnmTRFbVmMvjL1IaGDiLjYn+HeEAhxXB+3DeVCgmtXjmtNpRcpT55tnDmxFIDzLKlsbA9olElsJXtZ8fp3P7mJNa6plORMfpODjYHUuM0BsEtrvUdr3QXgMQBXhWQ0gNHu9zEAKtzvVwF4TGvdqbXeC2CX25+FhUUM/HtDJS74+Ty8tLkqlnw2B+GTJpVG2l9z605Seg3pzT1uXUEJkoMykblM+csu+MVr+M/7V/jzYG0UgUjypSxPVFjjBjimXkc+OjdKNgsAH79nKd5z+3yvX8kXK27JK0pxwoMOth1qwtt+/hr+yPwPvfJHAkkAnEWTB5LIi3O0jZLtSmYuKVxCSpQbV6vi+amJPmlR4iBq3EQ+knlxzlbeK3bVBWlUg39aT/O4SeNnEzdp5kxkUUy9ErivnH+4qX7MCEfzTeXK+AsP9cd9Xv0gjXj3xuJdtbjg5/Pw6IqyiFyxoRxadyp6LSUTOwdpyC1xc3A8gAPs/3J3G8etAK5TSpUDeB7AV3uwr4XFsMYDi/dibVn07RPwScf+2las3l+Ph5btN/YlmR+4xot8YyQC0un5o/jb4i52hBqx/E60k+2HoslzfQKi0dGdxoq9dWYfoJiLc37CJxbptA7s1+xGn5YW5mFLZRNqW7s87QDvy6vpKRAR6VzSgjhhpJ/omPyCFu6M+vVlMmle8psFXr3UTPCJhr+flPVeMjkRpFiVbOQgPD4/DZvJRCbONzpGT4mWqI0LzNPcboJEOuOO2+tBxfF71p0kXxFI7Ot80guatG9cop0tOTX1c8D1v1y+x9eK89/L8xsr8eoW56WUP7sC960Q3WoO3OjFSR8g5FoC3msBPKC1/o1S6gIADymlzoy7s1LqBgA3AMD06dOP0BQtLHIPHd0p3PqvLRhbUoB1P7ws0k4JRIsL8vDZv65Ac2cSn5ozPeKcXNfahX+tr8C7Zk+K9MHNDkooHE2Q/Fx06DMbGtoEp2WJJLpmWa6Rood9K1tYKOpP0rBIaQUkbRlpEdIa+PAfl2DyKD/fGYmNLSlEa5ezyC10I1LFPngNVHdR3MuiNMPHEiB/QgSpRFxaO5N4bkMlrj73eBxsaMfBhnYcP3ZEZPzweZCIZnAxjOzqTUDy3RK1i4Zkw1o4X5LGR9LuZTN9xmkL3NOyndXQY1TMcLpidxvXry72+CZtnKC2ldLikNyoonwxRQxB+i352zLPTZo7j8bmwQlf/vsaAMBtV5+Jn/x7Cz779hkZ5xG4vIa7w2rcHBwEMI39P9XdxnE9gCcAQGu9FEAxgIkx94XW+s9a6/O01udNmhRdeCwsBhvdqTTe85v5uPeNPf3aL2mjGjJkjic/ptKifDR3Zi4PdM+C3fjRs5vx2tbDkbZAFKFAHghSwsq4ZiBfPrpN0riJC7v7nadoIJOJ1K9kIpQWG79/jfUHGvDKlqqIPMdEl9hJ5006lmAy2mA7/4/2lTQLvNu5ayvwo2c342GmXaW5HBKiYKVC5QQub8zBFlO7JNZlFfwQVaiN9xdcgKPbfPnM9162+7IvGjd/jHjbTNhXK9fgjTe+dK5N8lwu+rsJm9abO3n1C+czaCqlzyj5l+6DwAtJqDng4+YVmffbb5m7CZ1uKqPw/nHrzhLqWqMvj7mCgSRuKwHMUkrNVEoVwgk2eDYkUwbgEgBQSp0Gh7hVu3LXKKWKlFIzAcwCsAKDjPrWLlx550K8tjWe35BF/6GysR1X3rkwJ+vImdDSkcSe6lavFmR/IdsbeZ77IOUKNmkX8pWS8ivtq4kuHtK4/ZH/SMyeLqhppALtHolhnVAiTpMztKRo4UPSWiQSETGyLbrokR9XbUt0UeALbE1LF1bvr/PnxPogXzXJUZv30dDujMG1lxTEUCUQN+kOGu2aarmpjI6V5x2Tzpffr0C6TH5XAoGWyY9EXKOCtYYF2GQqy9RfL5RfAvqlk16PFFcbKJo5jR1Lm5yNFATk9KcDnxzBCNbQCwz7VyJ9BO/lRjCVcvCXzJv+vgZ3vrrT+5+nRck1DBhx01onAXwFwEsAtsKJHt2slPqxUupDrtg3AXxBKbUewKMAPqsdbIajidsC4EUAN2mtowb2Aca/N1RgS2UT7l3Yv9qT/kRrZ1JM9jlU8cNnNuFPC3Zj08EmbKlswl8W7R3sKUXQmUxh08FG8aHULaZil3GwoR2HGqMLrART6gcO/uwqr2/Dp+5dJiYPlawEje3+AihpughbKjKnbojtgC2sAJImT0rrIG1TguO7L+8vIlVNHahoaPd87OImYDURAC5PztuyxsuXu+mRNfjoH5eyQA+/jRYl7mMk+YdRd1z7YdKU+j5Afhul+SjKlm7F+5T6jcqbNFlakONjltVG881JcoS9LHCkpy8V8jz7Trr6h/wdmbHkurCcfOvINmnf8Pic/Hu/UfHeELRqLrgWzE8fI2nt0tG+BLJIkcxaazy3sRJ3vLoDpe5vlJePyzUMaB43rfXzWutTtNYnaa1/6m77odb6Wff7Fq31hVrrc7TWb9Jav8z2/am732yt9QsDOe9MqHfNUjzJ3+Mry/Drl8xlggYS33lqAy674w3RR2Eo4sGl+/HzF7aBcppKGbUHG79/bRc+8PtFYu4pacHMhEt/uwBX3bUolmw2jZsW5OZtO4wlu2vxyPKyiLxoynI3jS7OF53sCVJST/5wfXnzIdwyd1MWE1Z022aJELqfwZqimR/oWjsP/1bBvAMAH/rDIlx2xxtelKh0XncJb+KSHBU+57dosZAuQ5rHir2OEzaRVd4m+d6Y/Iik5ceUGkGL2/ytkk+gRBy9NoNfYUgw0Bffl/dBUX+VTIPj7SuMVVJkJp0mxA0s6CkGkLcZkc2Ma7quJvLd3hVN/UEJqZ19o/eQaU4kF6gj626j5LwcFEglvdBx0G9Derb04FE94Mi14IQhBbqZSlgage88tREA8K3LZofqCx45dCZT0FpeFJ7bWAnA+SFRstKjAZnSKjR1dA/6ca5zIzj5Q4Yg5SLLhLaulBi5JcFkNgiAmxoMb6ybDkr5mPwupGhD01z4phvcigrff/9pGYmM1K9ICMlUyu6DmpYu/G7eTrxp2jhvm2Ly7/zV65h1TDSjutZ+gtzSwjy0dqXEhzf3RTPNl8zN2cw1pj78yFS/bbWUt4o0GKwLz48uZm1VqYRQXPMl1YYMEujonEzjx/Gn4tNrYBpgKVFsuI/eIBux6X2/OcwKGDyNW0yzNMlR0FBATiBRsktEdCw5h6Pzua82GtRDfazcV8fko/dXWms0dXQHkmRLL4O5Blvyqg8wpROQ3solaK37XKj7w3ctwdV3LRbbiODkcoRMb0CLOH94bDvUhLNvfTljqovnNlTiuQ2V/T6XuWsPBhzVOal5efMhzF3rx9H013XQWgdMh3GjzrgckQLuq2R61aDFsbkj6e0j1qY0ZDLnLdxB/+m15d45TKbSsRdM2sbJ4jPrD+LhZWXifaC1Yy7jSYT9uZk1Q3SaJNcDOaFudIEzXSdKbRLog4Iq2DZeQsvv1/nkz6JG1yKghKtqSsOgBTluopKuJWksqEYkB58TEbz4tUqjfZhKY/U0qjQb4uag6ylylxIEIRHnHVWZKzLE1eSafdyi138FI2AEKQqaQM8WHm0uXbclu2tx9q0v49l1FZG2/rjORwqWuPUBtEDJ/kDxTJM//vcWnP/TV2OTt7LaNjy0bH/ght9S2eRnHQ9BKj59pLChvAHPrIsE+wJwfqAPLt2H8nrfwf1wUwf+smhvRjKz/VAznlh1QGyjxYgfFyWEXLA9GhEJOH5DNz2yJutx9BRff3wdvvBgNEeWhqNd+vrj67xt9RmiPglLdtVg3rYqMR0Gx40Pr8EpP3jBSyUR9xnDb4OUl7w2imxOwz45je4rOuoLY6TYW/Q3Hl+PLzy4Cg8u3YdZP3hBrGlq0tLwYIoOV0vZIWgrqR6nNF+TiQYAxpcWuWNlzl8lbQucc8N16hR8+KRccDyXlT9WlCTSb03KqWUOpuBE0/nkwQxbDzUFO4Nf2YD7woXH5BDN3nQM7DRIkaYmgpnJPN5bHKnFO4c5Qb9Aul7Z0u14+4Y0YgA8v7NRRb51a4vrmytqqoUAGrqv+D2yzb2XyTUh0EcOXyNL3PoA080nLWitnUn8c015oPTH/Yv3ob6t2ytPwtGdSuPpteUB/7Tvz92IW+ZuwoG69oh8e1cKp//wRfz+NT8yRnkatyN/F376vuX42mPrxEV/b00rfvjMZvxg7iZv293zd+O2f2/B02tlsvf/Hl2Lbz+5QSQxnmmEnbZKl8TkJxJYU1afMRntkYaf7yt6HvYLan2OT923HJ9/YBVqhKhDjhc3HwLgL6h8rKW7a3HeT17NYO6MatwONXXg/J++ihc3HfLasr0d0+NQIkKSP1bcQI1HlpdBawQIvjR3b5vbBddw73HzoUmeCocNhFD6hfAhi4QM7ZE+sixO0nkoMDhBSxGvfvH6aHb6bOSTvu6pjt6HEiHyKkiwk0nPr8BQgmbM6yMmS5GUZqagg4BJNySfUbCH2C2cp/4gXbmszekPBLWh0d9BUzu5EUT35XL0e00Z1lrpVEovY9JvhBQAkq90LpuzLXHrA7xcNO4X7pAp3WAPLNmHm59Yj5c2H4q0cbPXl/++Gre/tB3zt1fjG4+vx58X+FGrFKLcKqRrWL63Fm1dKfzmlR3eNs+JPKbGbd62Klx2xwLUCpnrs4F8f6QcVaRN4BGH9Da0WSAYgJ80lnzGOFrcsfiiUOaS2fEjC/GRu5fgw3cvEftdW1ZvJFBryuq9TN29gZS0lEDHTDX4MiHug13y8XplSxVqWjqxbI+fKkWF5AF4Wq2Khg5UN3fiL4v8+yz4wItuI980Ma0EE3xs5QF86A+L/KS8XPtkOD+rBD8uSTEraVhahchBk0+eRLrEORq0Vb5ZlPcLYVvmhcfoLygQQuka8T7Wu3Uj+fjkyyOlyDCVBJKSCMtykW5jO3mbzKISJEJ6pIIJMo3bW0h+kkcr6CWUnzZ64YvrI5uOPj78NsPvlj8fTC9opt90LsIStz6AbgS60Tj5kn7bZEaUTCJ83+c3HsIfXt/laZoqWUoIMkk0CaZYutEoOzrQcx+3e+bvwY6qFmwSTBlxIR0fOZdyjQeV8ckUxHHMaMc0JZmdiQhwkxq5/kjBCVzL+eG7l+DiX8/P8JDX+MjdS/D5B1airLYNn7hnqeh7FAcSgfUSoGZJ8xH3oeHxISZOYeySlpV3S6blUjfqbuU+nyxJGjf+gCQ/q0JBC8UfvLfM3YQN5Y2oF4iCdE+OLXGuXacQKWYy3/ImseySgXRJmia/jZEDRElyuH9xoeBaToMZ2ag5EMbKFtVJ14anHiGXDEnLZ/Yxi05YOlaR1PSB6Jj2lK9Xz/roDXJ3Oc9txCVdbZ1RVwR6pph+exykQZNfQKM7SM/qXNaKWuLWB5BGhT65ulU2iSTcz6hjt5Sjim6mfBZNRxGsawUtFBGmYMkY51OKvJFAN+vyPbV4afMh3Pjw6gDpiYPKhqgZtzeqaCJg0ts0/YC5qY54gGRaLa8XTMvdKdz67GY8u953TCUT9M7DLfjli9uwYl8dnt/Ys4AGk18hnd/2LOdU2re1M4mdVc2iOY7L+xG37H4MyQO+ya1Q9EviY9C26HHIprnoNqnu3+vbq/Gff10hBvLImfV9PL22HP/7r81y1KOBHBmLu4vHEiUx5ujP6L7BKLnMY5i1gf42U862wG/K/cpJsHcMUv4swbxkCrDIppUNt8WFFo5LlovXcX+bvHLZhJbLEF+8hPtwuxD8E9cVgCCV3fNTivjaTlNew7gBhoMBS9z6ALrW5Kyc7QGd7xI30oTUM4KxqcItpsxuQtJI8ESAZGIjEsiDGsi8x4eeONLRWsVVzde5c8rPS+CLD63GC5sOeZoZjlRa40fPbBL9yMpF4pZZ05EJ4QVNWlCKC6JRQzxdAslJ2p32rhQeWLIP/+/Rtd623TX+j5XM0RNZTcrle2px27+3eNdJ8sVSBi1npmd+e1cqQJClh9FNj6zBpXe8ETg+Wmy5szcR/Q3lURO09IBaLURsZTOXmUxTcoF696WCbbtl7iYs2FEdCNn3/KeymC2/8fh63L94n9cj1/7I591EjhCZW7dg+oxD/niLSHpM2jpJc5CKHp8xck9o4wQ+DknkGm6ZfPZsEe1pwtq48rFNsD0afeD7Gy6ImxZGTvvjfsZM+ivl0FzvKjtqBDcgbtWi7midzkso/OiZTTmV7N3mcesD6IZZvMspJt3N7kzpBqMFlfy8eFFpulmaGMEiH6USlriQnJVoAeCapKTwxjxpVBHK6tpiR62SluswN68Ir0UVDe3429L9WL63Di9+/Z3e8SXTWnZEN7wBZUp31x1Kosi1MLJPkfMP93Eqr2/DCRNKxfF5SghCp0scJo4sElXrX3l0LaqbO3HJqZOxbG8dLj/jGK+trSuJksJ8z4l9pUCIMqnf3/+7hShlEVNBk2YrHl95ALurHVL51JqDETl+v41xyxRxskwPw4U7a7xtNBXJVB3UHtMYPqRoP4JEbCTiLsmvca+JUTMm7MvFKcI6P+C873xulAI2vN+N30mXkLbCmIxUdIZGZJuULsOXNxBC4T4PykXb6BolA6Qrcx+0if9+JFKpQ59Zj6GHGXD627Wo3xVklrn1CtlSf5i2+W1Cv4JcQZ5Cdyq4CieUivRtHstpKynIw4Id1ThnWu4ksbfErQ+gCztpVBEeWrYfU5lvmXSDjXUXVHowBt7O3b64enbMCMcHbFxpYaQvz+YvvM3z25UWbMlUZTom7h8lLaJEhKQoPYkjHhRMlcaIMTAHVsEvLCWwCZp7lzB3ibh1JKPmyu2HnPNfmKe8Bw0/F+RT961/rEdFYwdOnzLKa2vtTKGkMN8r9D6yyPe1S6U18hIqY4TdnpqgKZsT7V+9uB3Pbaz0tIs8ujFlIhNsrGLXHDqupCDSzq/vcjcsPpBmpa4Vpx47WjSfxn2LllwBvGOQfAGlB7SwzS8eH20sDpRpyn6epCvDA2N0SD7YR3CcTPM2EVhZWxYlbibCJHUfIOuGRLVSH1I0n1HbKvQrRR6bkOumzfBv1SIe5PtWkDNcrrhRpV6/WX43optG6DfX3JnEuNJCY47LgYYlbn3A626+sNrWLtwyd1OggHdaa/x7QwXOmToW08aXAPBJFJEIbjbxc8JFtwXMK+7XlNAmLQqUnyZucAL1yzUuUr+0LVC0nOYt/JJEZ/yYD1Tqr4HlQJPMUHSIKWHuknZNysZNavRJzDwqaSsr3OPhJNEnjtE6eTsPN+PUY0cHHlSNbd14afMhXH3u8ZH+d7IHCs2pS/BhlLRFfoFlv7+lrvaWX8uUcM9RlCgn2pTINXC52OJ92R0L8K7Zk70mKVkm+R3GdQzOGuHpgjK0SwsA79d0p+10E4pyTRO9sdOnM6fM8yBNnhSYE9fJWZKjwJh0FuZGzZwgO6kONMaWFHi/HZM2SxqfFjZpt7h8qKf1QaUUHH1B/9I2i94irmm9p31IiJuKp6k9em9Kz++61q4Bq4QUB9bHrQ8g/zGPiLH7oq6lC195ZC2+wZKvUnuXIC8torQopdLAq1uq0NTR7ZuopLdj4e18dLHDzbsNGg8Oj7jF1Ljxm1kik2FMYNpDk5aCw2wqjZIYPndTiSmJMNDhlBTmewEdi3fXRuQI3UL1Aik4ISlohu5duAfffmoD5m3zqy4QOpm/m18lAoFPPmaQwEYJHiWuTAnEjedTG+9en2rmB+ITcn9cImdprbGjqgV/fsNPJTKClbGiQJztVZkdfaX7Ja6pVPK/FPugcyf0S7/HWqGAdUogf1IfdI0kkpIKLB4ZpyseX1wiSNeazOl8XMncK2GHcI3I3y2XI+yyYghP/WhHf5vFCdp7VvIXa0HQwMX4vt2pdE5p3Cxx6wO8N3CBHDS7SXN5Pip6aNJiE4iSFB6yRCwW7arGfz24Cr9/bafnROlrl6J98NlQQISkBTEdE9fQSeQnGcq6H9AeGrQfta1drHC2Tz7bu1JiDcagnETconI8osiUFT8lRrr6fdHbGM/WHe0j85x4vT6J1FIgyMPLokXeJf80gnge2KEkBdKRSKhAG2/n54bys/Exu1NprNpXFzhfZLaV0nbwcSmYRqjSFJlvpj4I0nNXcjQmkBZMKbM22Os/cC8FP7lAXPNwuK9sMJpxhftc2pe3SPdcTxdK8suVDq+nQQeDhaExy+GJ9UJ2hP5EtmtvzhPI5WCuBzjAsMStD6DrKvktSW/e6TBx42202HKTl0uOKL9WQ1s3ytx9Jd8TUzj/iAzFvMOgfqUFnoPavXBqgYRyhNXOAHdy1vjFC1vx0T8uCWgMCFLmftkvKjp3MvlKix33XfPM0oz80qJlyqnWLTjxUx81zUyDIyz6x4wqBiD7f/F6mKNHBPPSBclqcEw+XykSMJ3lunrHwKb00NL9+Ng9S/HylqhmUPITlN5yiUtLiV+lPH2iWUXUwkXlwkRXwezjFhem37tJIVXX2vNk1uEx40eVZv5dcLm4MFU9OFLakv6GTd8xfJHt0pue7WHFSiKHTKVZfdyUUuNj9JPWWh9Z6pyLELRkBCnzO0lRYkx5gfNXzLBf2nFjRzi5t1plnzifsJgXcY5fvLANMyeWYG1ZAz55/jSWk86X313dgnfMmhjY78VNTm4zP2eYL5/NjylMOtMaeNgtCC7lYAsv/gC8Gp0cKwTHei/SNiId9F1LpjUKEypgvjUt1H4f0XNNfXA/wVRao7alM3DNV7upVKTFtJ1psvLDGrcQIasJ9UvHvGp/PRrbup17RgfbAGBzRTTC0qvEwPqjKE2e7JgOrbIh6rsoRmK626S0NGuElDLZyL8J4Xt9bElhj3MRSvBJbc+IgFSerqdjZjv0tEEuojnoxfhy29AgRENjlhaDAbPPJ/+uc0nhFis4ocL9M807D8D0fpnREIK3sAtXX2LnJKZC/wNMW8SIQDiJYFpr3zxrMB9KEaGSOWp/bSvuWbDb+39HVTMz2fpjS+W1yB/mhAklgXHC3725c7IR0gzy5397V2bTGz/Ww01RLUYgbYqLtWX1OH/GeHHR5+fkr4v3YtbkkQD8ufmmRI2O7pRnRuTg5Dqs9eDX4ZHlZXh67UFcedax3jbKVrFP8NPi52vBjupAG9fm3vX6Lnz570049Vg/upXKgY0oyMP5P3sV75w1UYwglckRInJSPkHaV0psLBEFE/kdOyJa6cJU4aCnqGvtMtYDjQuvcsIgEJbsI0Z/I4SeEs24kPzvchFDhF9a5BjCkdQ5pHCLZSrdqrU+UWs9My+DJI0AACAASURBVNMfgMze20cx6Lqaoi6D8sEHv2TmDDtEhvukPijhalDT4shz05NEegjhVB7NHUmR/JEpr661C3e9vgvNHd1ef5TkNWiOiQwVyiUV1MJorb0fBa+vGZEXtEoc0gOaEhVLbbyPX7ywDdf/bVWgJh7Nb2tlM0695UU8ubo80ke3EFVKn1zjtsjN9ffyZt/cmPLuH6lqRuZFkbeR2X0bK8tF+QHzEgpdyTRe3XpY1AJKQ0jjkl9lXoKlIUlHj1GCKeGrLxPdVtkoEcLer8BS+a+ewiOhg2AjzJbFXfTJc3GkiKZE2nMRQ8UXzyK3EP7ZqBzSucUhbhf0k8xRB9MDweT4zjU5BErie5CZnsKLTSqt/chU17eorpWTNGEegiaPEDYfFeYnPLJCZkfAf7N+aOl+/Pql7Xh2fYW3iFJkLX+rlwq0c5+tsCO11sApkx2NETnR8/6IiAR99+JF20nnOtzGoZnmgs4dmWVf3nxI6INr3NxPdxO/fhLJp32laxPQjAn3AUGqFbpkd407JhtfqLMpLehSsAFBciTOxmFIm2jW+kTbqgSN6mBHNtIh5GKB8LhBF8MRw/34LfoHuaRxi2Mq/Y1S6lGt9aJMAlprc8XsoxSmBwJfjBfvqsH08SWRCDG+f3VLJy7+9euBdBlhjVsq7ZMJMsUGa3UG5Rvbuo0+bpWh3GonThqJf1U4dTuL8hMeYXt2XQXOmToGDe2O/1lbZyrSL++/Woj044udH6TR6v1PxIKOmZMKCmaQ/fkcUpBIKGOeIIk3tHdF/Z6SLGdX+Jwt3yvlJ/OJc01zJ/bWtHpjcvIr+v2lMmutOOnrTkc1r76cVKi9EHWtXWKUZDaTtkk7k5dQvdY2SeZgQtyFtbYl6v84kMhln64jZQ49GmDPjEV/IJfyuMUhbjsA/FopNQXAEwAe1VqvzbLPsICRuLEH6afvW47Zx4zCuFLHl0dKZUEJYnleqjCxWLizmkVYOtu433rYfFjRyMthRRf4sPmlnfmy8b4ONrTjSw+vwZnHj3bGhva0Sh5xYycjTykcbGhHS0cSs13fKx7pR4tMTYsfXeprEqNRoH4eM0bcGLHpTKYxojAvg8bN+ZSulVTMmOrOaq0j/UnRj7yPG/++JiBTXJBAh6vBkvwEpZx5BO7HFm6X/M84KOgiGPGamcBn6rsnbX1BXE1a+EVjoDHYGj8Tcnhqgw97biz6ATnE27KbSrXWd2qtLwBwMRxftr8qpbYppX6klDrliM8wh2F6HoQj9rZXNftajxh+P0Awez7gJIWlPtq6kvjc/Svwx/l+cEE4rUTAmd/1Z+tOpbG2rB67q1siaROyaWOIlPzs+W1e34eaOrBsTy2qGn0tW0prfOKepbj8/94Q50JdF7HoWiJzi9zyPJxnShrKQMSmQatm8rEyJWPViEdU+DkMEzt+DB2CCbLejaCV/PU2sJqW4XZO1iTfLc+fjSciFnwXJQwGORkq66pU3i1XENbKWviwPm4W/YEc4m3xS15prfcD+CWAXyqlzgXwVwA/hBNROizRY9MJ0yp98aFVeMsJ43q0e1cy7ZGJvTWtWF8eJIeBuopaB4hDeX07Djd14K+L93mRpNecPy2wv6RR4uC+VlzDds2fl+GT5/l9aa0j6Tqk/GEeSdB+f9urmnH7S9txGSveroWIOSly1ujjJhxPt0TcmMYvjoLJVEosm+O+lNKEUJSfh+6U7EuV1dwpRBLTqdkhaBkz9T1Q6Gkty8ECN4vnGqQXAwsHpvqwFhZxkUsat9jETSmVD+B9AK4BcAmA+QBuPSKzOgqQrWTPS5urUCHkwDKhtSspVgcg8HQVWvvjTRlTjMrGDsz52WsB+bCT9RqhnidH2K+Mo7KJFYDPksoh7HeWDpkl//D6LoxhKSLCZtnwdynxcFhO9jGLnkM6rzFLuxo1WNnWCxOvMxGo9iw5yUz7ZlvEBmONk+oFWlj0F6wZ2aI/kEtRpXES8F4K4FoAVwJYAeAxADdorfu3EvAQRFwfN0JYI0QleeJif22bF0UoZdvnZErD12JNHFkk+geFtUUJFV24+TZuKgrLFebxVBHRuQdNpUENmUaUxPxrQ0VAPp0OkrutlU3+eDozOZNKBhHEIvPuMZbH1AJJ5C8uTIEAprZNB5sytmXbNxeRy75jFhYWFkDQn3ywEUfj9j8AHgHwTa21XExymMLkOyGtRZR7rS/wTXnRtnCuNCIy+RmSj3an0hhVnI+2rlQg1QhHfl5CJIkmrU5Th29SWrK7xomGTWsU5Cl0pzSW7K5FY3t3IPdUuL+uQPF24L2/XYA9Nf67Aj/W17cdDgQ4cDy28gCOGV0sEmnJL4jGaBZKlknoS34w0zk0RQlmi+4cDHNnX2CJm4XF4OL4sSOMrhsWsmvNYCErcdNav2cgJjIU0dP1JlOm8b6kWeAI16YkZVBBqML3eSeMw6r99UimnfprT3/57fjPv65AvevDw7VsBQmFcBKG4oJEZLF9daufWJbnh/vUvcu97yMK8tCdSuEXL2wDAJx1/BgAwBs7qlGUn0B+QnkEixO3ls7uAGmbObHUy+0GAP/95AYA0dJQgFOm6QdzN+GiUMkuQNa49RQ91ZrGhTlhbe48QPoDQ41oWlhYDD+MK4lWeBksZI0qVUqt6Q+ZoxE9XW6Uki/+uJJCQdpBT0r18AX9yw+vwZ2v7RD7KCrwza15CYWzp47F9648zWvPZ0RvZHGU2+cpFUhbEkYmglpaFOyL5jtmRAEONwfrbfI+lu3x86HlJVQkGpZg8t+S3iarmrL7GJ4+ZXRWmSMBE5c5ynibzUFmYTHIyCXH+1zF1HElgz0FD3EqJ5ymlNpg+NsIIKrOEKCUukIptV0ptUsp9V2h/Q6l1Dr3b4dSqoG1/UoptVkptVUp9TuVA9nwMkWVFhfIp1VrR1vEUZiXwMSRmYnbFWdOAWAmcKS54pqL17YdxuJdte6+wfmQP5qjcXO2cTJUwL7PPjZKXLpTGu3dKZQKtUHzEiojcTtpUvDYy1zyVysk7M2kts9LKKOvgaR1A4A91a2R6xInvUNRhmtp0X8Yaj55FhYWww9Dzcft1Bgy5jA3AEqpPAB3AbgUQDmAlUqpZ7XWW0hGa/0NJv9VAOe6398O4EIAZ7vNi+DklZsfY25HDJmWm8K8RMbw/BEhslNSlGfMyDyupAArv/9evLj5EG6Zu0mUobqOmYhIfiJIPijJbDKV9sbOpMUqFAgjRV7e8M6TcO2cafiPv67wamUW5KmIT9yxo4txqKkj4mtHfmTkO3DKMaMCNTclKPhVIyQkpAgLF70xyUkjjSsp8MzKvcGJE0sDpt/hDpuCzMJicDH4apDcRy5FlcZJwLs/xl+0+nYUcwDs0lrv0Vp3wYlOvcogfy2AR2kaAIoBFAIoAlAAoCrDfoOOhIGajygIEreCvITxdkgohUmjikQCRajJUgqIa+vGlxbi0tOdHGldKY089xfLydCZrgYPCHKgd8+ehHeeMsn7v7gggcmji/Gzj5zlj5VIoLw+aEY9dQpVT5BvN4puveLMY/Hu2ZM8DWImmIhunqEtUyDBly4+KZCHjoM0f7wm6JffdTLePXsSTgxpEINzzNgUMRkPdxxtPnsWFkMNuURKgBwlkjk0p4G0Ax0P4AD7v9zdFoFS6gQAMwHMAwCt9VIArwOodP9e0lpvFfa7QSm1Sim1qrq6up+nLyDDemPSCI0uDvq4FSQUMvCZQF+mPrOBm0of/PwcnDRpJACHMFG3XOPGNWM8SfD9n5uDsxmpon0K2AHk56lIfji/tJOsWqF1e+q4Etz/uTmYdcxIry3MgbWwjYMfR1juzdPHivt8932n4puXy0VAjh87AgDwzlk+YT3juNG4/3NzMGlkUcZ5hM3TmeY4lDG2n5x1ranUwmJwkWtEqS/r3ZFCLs0pTnDCiIGYSAjXAHhSa51y53AygNMATIVD9t6jlLoovJPW+s9a6/O01udNmjQp3NzvyLTc0LpcKCzep7i1O4PyBg1Sgj4zExLA/MPj++YllKcR3HW4xSNVvE+uGSvKDx5DUM75hxM9yYTY6ZqNT5hgdu6kvvND8w1Am89XMLI23H90v2NGO+Qrk6buxnedjHuuezPee9pkb5sKkWmJiPFrP3lUkOBl8sPrKwb6uXLs6OJ+6cfyNguLwUXuUBIHuTYfILfmFEfjVuEGFPxDKXWLUuoqpVTPajU5OAiA26OmutskXAPfTAoAHwawTGvdorVuAfACgAt6MYd+RabgBFrYjxsbXdjylMJD18/ByZMdrdLbTppgNP0RyeKkI+yzFm4P431nHuuPH3LuL3F97vj+vD1M3PhcibDwBfz9Z02JjE8alWnjzcSN+guS1OBxaWiR4BEuPHlCxv4lE7aJfAHAqOJ8XHHmFJxxnK9ppClJ2koCT4mSHzouLj9KiNqNi2njg+9UJjNxbzAjK9HOpUeZhYVFb5EDsX4B5OKzJZemFMfHbRyAKwA87G66DsBmpdSjSimzM1IQKwHMUkrNVEoVwiFnz4aFlFKnAhgHYCnbXAbgYqVUvlKqAE5gQsRUOtDIpnGbLGgkEgmFi2ZNwtlTnVN3wYkTPCYvaehaXQf+hKCF4jeSSYlzDJtHXkJ5plIAOP240YE+AWDWZL+dfLtoEecRo/TjGlfqR8WGgy8A34dJOj4OKVAiTM5Sae2di8L8aH/FBZlL54qaSvczk0aTvgbPdZBMFwgdX3CSTyAr3KoV5GvIzaiSSdVECDlGFQVNldnkZx8T1faakFDKGM1sGu9kdg9ZWFjkNnKIkzgYgAn11PAxpIgbAGit92qtn9Fa36a1/jgcc+VmAP8XdyCtdRLAVwC8BId0PaG13qyU+rFS6kNM9BoAj+mgOutJALsBbASwHsB6rfW/4o59pMBneM5Un8N6WhylMDHkB+XdLO6+SvkaMCIi4xkRuuDEiV5fBCIzpA278qxjzQ777A6dMaE0oOUh4sCJ4fjSokg7Yc5MX9kqLdyS1odMlmHtXRi+Js2XC2vJ0sxUKvU3qjiz35U0X4ks8mOWNJ5hjVtCyC3HtYvvP9vRQr55+rjIWJLWMKyh4+CbwqSKp5qRHkqyxjEq56V5UWanZXEMgzbUwsIiR5FjP9eBeHz01Nc4l7SAvQpO0A5+AuBtPdzvea31KVrrk7TWP3W3/VBr/SyTuVVr/d3Qfimt9Re11qdprU/XWt/cm3n3N3jJK6UUvn3FbPzfJ98U0OL8/tpzcf4Mn+yMFCIK6YY4fuwIXPe26bj942ezNurLl89zF+z8RALrf3gZ7rzmXE9OMr2Ffdw4MSEtGL8puSaL9iWtESdVIrEw/BgkDRknH0T6eHADfSez4LVzpnvHyjV9hFNYYEP4XCcE8kuHw6N9uWaQ9uDWaRqfa97CRz1zgk+ifvSB07HmlktxgrstXyBu3P/PM6kKDwrT+c/PU16uunxBkycpPE3XMKFU5IEe0ExKxN1AOnuKHHpOWlhYDCAGgiTlEhHrKeIEJ9yslHqvUmpyaHsRnBQdwxZc45ZQTpqIq8/1A2XzEgoXnDQBP/2wny6DtCK0q4K/QOXnKfzk6rMwZ6ZvZiONEK+u4JMtYExJAQryEp5TvmSODGs/uEyBS6Z4SgZOsMjMSrnZeCCClPGeD3X9O2bi+1eehi+980RcNGuip3HiKM73CRMdK9fqkUn5/BPGY/633oUfffB0T669K5o+kBOWOTPHB9q4VpKOgyiXUj7p4eZgpaIkyg9OcP7PY6W6PnHeVHzk3OMDpLIgLxHQogYjeJ0xx47wNYUmvztOIMO+jgrK21eqLyulY6ExKHqWb1OIvogHglOEBx+NH1fjZqpMMZAP1v4YyioZLYYqcu3WHYj59PTlMpf8AON4Rh8D4GYAZyml8uGYK3cDOB/AU0dwbjmPIHHzL2pjuxNZ6aXLyItqScgSrJS/yHoETvC3Oo4trLOPHYXDzZ2BG4lylEk+U55PmGAW7XbHLgpo4Zz2UcX5EV8lThbOmhp1ceQ/hv++fLbnc/a+s6aIZELy0xtR6N+WRFgTCYUZLukluZkTS1HZGCxbxUuKTQhp5LhlsSCRQAfSgcWWkia/adpYrNhXF5gfP9f0Tfohf+7CmThtymj8c42f2jBMQDj5lcyiJC/lN3POvzPP9eUNgbaqpg5v3zkzx2PF3rpAu+SuRuSrM5mKbEsoFSE0zjFrrz0Mj7hl8Wf0jsfkQ6cUUj0uLNc7JJTqc1qShFK9zkk3tqQADX1I6mxh0RccKVJSmJ8Qn/vZMBAkqbggz0tGHwe5Q9viBSd8R2t9pdZ6GpyUHLcBWA3gf7TW3zrSExwq4IvYRW7OLyJwx4aCAwBgikvESovyPa1Wfasjzx3sJe0LLfZc4/VWV7tUkC+Z14JmQY5zXS3YW0/0tXxFTAtG5kYKWOALreRjxs9DOFCAkyTS6p0zzc+tRkfDiSuZUvm+NMbUcSMi5lfu53XNnGk4aVKpZ2blcyNNo/SAmDjKJ3y+OTQ6Pm3j+emOHxcdS7lTpAdYXijvHeGiWRNx86WneA8I6aEyrtQnpuHSYo3t3SL5J0hvmHSNeBJnIvZKRckZT7ciPVu5FjIOZPJH84jKZ3ue91br1ZeHsufr2MPFhov3V2qVgcKkUZlzGFoMPRwpUiIFbsVBf/C2bEXhTXk4ZwnBVblkWu2Rj5vWukFrvVBrfZ/W+pUjNamhAh4/wa8pLVoXuGSIR1pS25cuPgm//tjZePfsyR7ZuHh2NPccLV58ISSywQvAS8lwvT4Ec5+Xv4z1P++bF+PPn3lLgAwVF+ThT595C/70mbcACBICTnoev+FtePrLb/fGkqoK8Bv/gc+dj1997GxcydKHkHZPChSQNFJ5iQT+eePb8fD1b/XaOJl88/RxeO2b7/KiKbmmkerDSr/F2cf45jtqloMT3OvAfOno/EtRqB2uVms8e6BwEvfQ9W/F/7tklrfvnBm+qXfOjPGYNKoIF5+SOT+hhkz0w/PgoKjioJz/PbwHP1aTxi1bahJT5CxtM/Wfud/M7aZd+/JQ9u6RHnoMB1Pw5M6iEAdDa7YW2XCkbr/e3tf98Xugl2gJxQWJ2LlPCbn0E7UVtPsAbhQJEivnU3rZILkxIwrw8fOmoTA/4RElSUsiRT1SySQp5QKX+/lHzsKLX78I40oLkZ9QuPFdJ/n9Mt8uwomTRuKyM46NzPvyM47FiW4KkYBzPNv3rSdOwLnTx3lEUCKQ/MY/Y8oYfOK8aYHyTxQgEHDedzvkFqhqtyZrfkLhzOPH4B2zJvrybNzwuePHRQREyl/HtWBS5Yqwxu005qdligT99uWzccsHTsdH3zLVayONIr8OpNTip/BzF87Ayu+/NxBE8dE3+/0AALR/jiVfR2luXLvqyQVMpcF9ePCHKbDBZAIFWBoWAzmTastme4HnvoKZxpTQlaGqRxzQfLOVDQofKj/2npK+7HPq3/6i/efQKmbRZxypklc9vU3I5zXu/Sv1H1cDTs+2uNaJXLrjLXHrAzL5uHkLuyHVA4epPJLk7H2qUH2BEr6OZgvXtXOm49RjR2NkUT623nYFvvKeWZH9pIVTeQtRFJwsmhZuU+oNwF/YpSoJAY0bmYXZySbNpEQOpHGJzPE2Mg1yaemH7GvXfDkq9eQt2Py4BG0RfT9hQimuf8fMSJQvENTetlDuPiEgooT5/337itmB40xr7e0zW7hHpJcLCsgoZVphbioNn81sUaVx04HE0QyGTcHOnDKPOaoo35jQ2PQgPzdDObQ4kMzpcSBpZfsLfTHbHgl5i9zGkbqePdWA0wtMXB836T4/2VUyGMeGn4aLnkGzsqxtuRScYIlbHxBMB8K3O5BIkRTZR0XWdx1uibT5/j5RciBpaTLV48xEDqU3ffoxSG7W3IwqLdyUMPhwc0ekjSPf812LEhyuNaOgjO1V/rkh377Yb0oe+fLb3u4myJU1bpkDPACnpioA5kyuI3L80odvA/52KyiVvPqwfG70JnoSe7goBXztklm47m3TAQDJtPaTOWfxP5zi+lRNHVeC/7zgBPzyY34KmoCpMqwlEgJn+Cknzax0n0tzMZlKzzw+asaVf1O+ls9YhcTQJr/AROVM27IFZIR3PZKmUlN/oqY+xvjc/SF3ljCLXIbpZUZKCO5rr3vfv2RhkfcNvqhLL1KzQs/bXIElbn1AJo2bt01alAS5t7m+cK2CM7qkcZMWO9LYTB9fgg+ecxz+8Klzs8w+87xpS7hYfHTf6DZKZSKl6uAoELRg9J37BF54smMG7WD9VTZ1ROZOCZCl43ljRzWAoLMqOeVzcYrM5Yq8maFIVg5KN5JK60CQRXge4TlxTnOuux8nHGQ+5rvlZQjS+Malp+DyM5ySZpzoSKYPOr8TRxayXIAK/3vVmTjvBN+fju5RWeMWNZXza0gmWtnsz+aeoM/MGrSSAinnYWRToHas6eHaUx8332QclZN8LiWHZo5o7Vz5e3/AdKySGT0OceQSZoKctSuLYQJzHe7MWq242i1ZAx9VCvhtfF93m2glcT5LmCtPLt3Wlrj1Afw5TJomQHZoJ0weHY1koYW625Aug2vMpEWR5lJckIffX3suPnD2cebJu5B+PGcImg5xX+H4irNUR6DFzTepRvsjMlOQp7xaqsk0Ozea5umnI7n/c3Pw6s0Xi4sG+S/dcJHv4yeZOYl85bFJ0bWZUBq9bmSWS2ng/s+ej1dvvthr49MI3wecVEklpSRza57wMFKhtjxGXEQzhLdRsYeb28YTDNMbKKLEik9XSXNyv+YJx3X2VJ/c+kEM0Xm+ifL9CcQm68M42l1kTLFN0jwbHuh5wjaJhJqJZGZy31cYS78ZonXjzjeunMXQwJG6Zj19WZK0+Ka+RD9YeikUnh8k3t6dirjGBJ63gtYul+7r3le4HuZoaOvynOQB3y8JYKZS4QEpPVC9lBvHZA5BlnLBcRBxJK1RXEg/nhGGh35gX4O/nqQ9BIC/fX4ODjX5ZlSuwVFMCzP3pgtRlO9H/iTZD5RM1HzxHF9aiPGlhYFrQrj1g2fg0ZUHcOwYnpbFHZPJ0Q9YCmGXTI88LQuN7x2LQZsS0KRRFCq4fPShIUVaesRJMJ1LD7589jDymgXy5WvcBC1UQEMa3eaZDcVACGZmN4xBhDh8/OmUFh/eUnJkr6+ifDS7v43ea9wU6FftkG4dIpUqIB/ul/LDzT5mFLZXNUMpR1svmWYSKqqZI/meYNKoIi8dkTSnTNvi5rPLZDLW2jmG+NmxLHIB/UlJ+D3cU82s/1yQ28L35jlTx2BNWTCfpfT8ohyLMyeWYm9Nq9PuttFcD9S3efJS4vVc0iRbjVsv0dKZRF5CeT5lNS1RwhD3LfqkSaX43bXn4gfvPz1WH3Sj8TxuZFLsSvbskSn1L0UaSpAcwcl3jfy0wjhu7IhABQVuvuQ/kjdNG4vTpoz2SOSJLD8bKd/kH3d02zVzpuOZmy4U/fMCJMrw0JBAPk3SW19QOxHscDLLgXXSpFKUFObh0tOP8fsVgjTyhTfAsBZKShnDkSccM32V0534fnxS4AjtM4YFxFBCZAqW4ZDMFHyWN77rJJx67CjvOnAlK43FyXe4LTwGAC9pMyDnxZP6CPelhHmfxoM/3G3Zast62kiBLPtagmgfPV0vRhsCNJx5GI7VtF/ge2byJ/WfQ8oKCwH9eX1Mv8eAnMGViN9fvhuMeaxwv4FnpfsPX7MOuc+qk1zfzc5u/4EjWjhy6B62xK2XmDquBLt/diWue9sJAPyku4DZVCpBKYUPnXOcmNRS6qK1y9Eg8Jvw3adORmFeAmceH61mII9Jc4y2FeUnMKG0EJ9663RjH2IqiZiOoYQTxvsLq7TwTR9fgtuuPhM/vupMb5v2tB9RxE38Sj9MrqEjUhBXJU6+QpKGwtTDWFa+7E3TxmLLj6/AFy/2zbg+wWLaLSGYI7x4OrtlHtkvZaWwr9Z5u2x3CRY/bTxwhHIRSv5spAXl1SuIxJ7PctCReZyfpinuueYP7+9ccSpe/Po7vfu6Q3gJkX4jvA86J5LJhXwS45phvP4FkyaP7vV+7wafHf5dWli8eYvkL7p4yIsY7y8zJPO0SdMhmVFFOcPY/b3m5dIiOlTBsxP05/lUwn0tge45njRdur8lawO9hJvIn0Qg+bZj3GcQ/ZZ5sKH8kps7N501lfYRnkqYbSOzKdfCSRFyccBrOT74+TmobGzHoUanX+4M/e7Zk7H1titiE5cpY4pR2dghplxIJBRWfP+9vVIN+wtnvJ2lnGmB/pTCZ1xyTNAGNXw20vX7a89FfkJha2UTAOCS0/wSvP99+WzMmFCSUVsYBlWpOFDXFmlThoUw23wl8itWv3Dbicxcfsax+NMbezKOGy4ZBvhRyAGS6L31ArWtnYH++DU674RxeGVLFWZMKPGIIIHn5/vyu0/C3upWHD9uBJa7Zbi++75Tsf5AIwrzE3hlS1VgX4pM5aBrLt0jnQLB802rwnEx2+PxY0fgYEO72O+08SXYdqhZ1HIGavbqYBsH3+Ttm1DoQvCcS345/n4KvMxYSmvv+MJyaVZKj/pL66C5Vdbi037+WPwYNIILV2Zzq874O85k7x1RkOe9QMRFdJY9hzSl3piljwZIpKQoPyGuD/H6ovs12l5amIfWrpSo5ZVTLEWTmvvy0rboGiQFGpHF6m0njseiXTUBFwVPOy64geQCrMatj6DrWsT8wogM8IfRUze+HU/d+PYe988XwHeeMgmfPH+6lz4knOKjJ0Vz77zmXFz3tul4x8kTxfa8hDm1QiZMcCsSvNOQ4Z+DL4Bx5+/7T0TbsnXxwXOOw/vOmuInimVMaNr4Etx82ezYPn4nTCAiJC1i7hyz9GH2RYsulJIf28yJpdhw62X4r4tOFBfn2z9+Dm67+kzR90MJDzROOuvcMmyUu06K47n8xgAAIABJREFUAuZj0fglLDL42NEjcPNlswOEbPr4Enzj0lPEsmnSOTtnWmZNMkUyc0h+gnSppYon0r1HhDhIsOi+8bdJeffC8+Df6TrUtXZF2mTSw/ujbeaxTC9QYhof14wsnXtJGyfJxdUGnhLy5VUq6D4QB/3hKG461uEAKdiEH/4YQzJrc7/yGGF451r4jfLdelpJRerDlH5I8l82+RTnAqzGrY+4+JRJ+MDZU/BfF830tl1+xrFYursWn36rrymK6zdG+M3Hz8Hu6mheNwA4YYKTR2ymUFYqLqaNL8FPrj6r1/tnwinHjMKS774ndu3FQCWGmMStQDAbEuI+eOlNcr+oLZP3ue2qM9DEUqScM3UsPnneNFxx1rEZ+8j2Y5fam1mgyd2ffjOW7an1CE6mB8noYuch29zhEC3e68fcSg2/e21nZCzx7dS9Dq2dKY/knDC+FFVNnaLfHUd9W5cwT0TmZKK0cd/EvfkGForgMUjESXqzNjpKC9t4zrbTp4zGlsqmwFjTx5egrK5N9HFrc1/oSOvNj8tEiHw5nTXvXFBDqkOpi6JjnDZlNLZWNmV0Co/otwQ52jeZ0efT1xqGu4rzs+XasEznqSfaMq4FpK/90e9QgRK+01WSXqhi9yvch8H24G9T+n3JEZ7xnvcy6cosX+hV52H5OGke/EXVErejBxNGFuEPn3pzYNtEYVtPwcsihfGRN0/FaVNGB8yoA4nPXTjDWGSakubGQU+0hIQvXnwSmjuSYrb7uMTt/Bnj8EcALR3RyLtMfXzmghmB//MSKpC4lsO0EAfHim4747j/396Zh1lSlff/+957e9+7p7une3qme3q2nn1fmGGYYVhmBgUHRBgERKOggBhEEiGLEsUkxu1nzPL8CCbmp1FiENHH8GCImoREo0BAWSXsDDPCMAvD7Euf3x9Vp+qtqrdOVQ+9zryf5+mn7606derUuUt973vepR73PfEqjvb345y5HZF6rllRTlNaa/FEyg04+HKO/Jr1/nMroz3Hkp4m/OBX2wCEfnxF4QuVX+S0tlr8z0u7M62Fri9SSZxZy+z+Q8ncgvw9dNgX5LJzsffYLo9mnTMQsIfDc9qgi937Q2uZ9cmLpLaRbh7+/4UTG/Hgi7siYqDomA/ZV8f739lQia2B+Esc6lkfYquQ0jnqK5Ml4IJxCz9CRIHjb+XLa/WVJew5eNSZHocfy8+ZWMZEKB8lMZk3Ipb3F+877XMzUN1WXioE78XRSuRa2eel33jR23m+SsfVlgdVaCzSZ1/CZdXih+3wLdNiwJPDJ1RaRZDuN3X+j94VvS346bM7Isfy5jM7kgmDRwpdKh2DFAtejU7pTTscfPLc2bhm7dQROTfgOfR/4wPLMa42KR4rSgWsnNKCT2+aIxwZYj+sUvaUwfhhZbuQrA8c+SbqjU26B0X9rZLH2jyB7hsw35b8Zcn96Kzv03O+9Zens5FSmdjACzGCVBi7bOFJbrPnkn4U8C/j5/xQf4jn9P5LuRRdFRlWMXcCW5eWJ6eOW/n4uaSbY01FMt2JNB+SQ7W1ZNtt3AIs1R+227iPreu94RKOaeIr7pO5tCf0EW2rtwE/7Ngcdx3JWhKx4jt+mES2OT7L0ufAFS07EDqE6OfRhmwNY49zOOO3Cysr0fdJ9rmivmjef27xs6sIUhm/vD5u0rnsw46GStxx1Qr86QX8R3iyj+MxMgwVKtyUEWdpTxOuWTslu2EOCgXCN69ckQhoiGMtTN3N1Yl9x+vbwclrDZW+0KxeOyIUPuc3ZSknIP+i+c7VK/GD605NHJvlZH7UV7Mv7NgfiMeOBk8wnTIl9CcTqyMI45QEiOTHxwaa2GS/0DsawxvFON+fki+323mfOyFZSUP8QnfcnKQxtrBcffE+RDHD5ujFHX7+qOBmlrQ0SOlW+HTYXIG2ugjPH8lvknHfxchrLr5uSXFmrerSzVcW3NL7K7ov/jitPxIe82jsvCLNZYDj46TEA3e/b+XH3WD3d9zjEK5fFq7CsQ6hL72vpcuTf6x4TyawH2j2NWwWPnuupVIpwbU894QVvS1oYKmpZKE3eoSbLpUqI84/fWjgQRtvlZkd9fj0pjk4a2Z7Yl9FqYhvf/CUyBLZQKnNyKVlcUVJWstbtL27Py6c0qJjJasO58xZ7f5Sngksbn/49ll4ZvveiPWJi4x/vGoFDh7tx9d/9oLQb/LLu6upKjGW+DVwnH4uUkoAIQpXWv6wTG2rBR6X+4j49fkKkgsCO0dcI1hLK29nc+LN7KjHv/16e6Z1reCHhEqiZ3p7HV6LJZt2WssyXvNgnhxjSrPGhO85qY/ka5/nBpiwhhmTKRyliNiyIqUnJRf6i1uKjUn6RFpfuHivdl+sa/G0iWMz+hsKxO8SYU6kiGBpXMHcZPRhd+etK23JypNYXizg8LF+8XMunTMM5Iqem7eXIspHA2pxU05KigUvzYiU0BXwcn6dPqNN3JcHqR6khHSztV8oPHls0D7j20O6AcX3ZZ1/YlN1MA77y3dicxUuX9EdsUbyX7bLe1uwJiWSWBIlrpqEYbSZ8Is5ekXeOPyNO1iUpo24zutvw6O3LZJYtJa8fmNw66Y5+MerVgQl1SrYa/7cds+6xnMt2uUzO79WvEauT7BUShYJ100PCB2tefmyeB88ulO0GgavQ7RN/FxBv45zRdOy2H1s7PFrEax2knDkOcCk9/x0oZC51D60OKbf2Pk4s6J6XbjSGEmv9WATCux0ISRaIxlB1RgeoS20l/zNXP26RD238Euvwz9cuRyfOT90kYlEu/v/XS4RWUuroyniWIWbogwBpdzCLX2f9D1RU55lyZMEjscBP+XDiyznmvRlZKN2DQz+7MJ5+OCaXnQ2pPuWSWIqssW1TBGMg7dPtitJCYj9h/XC0rb9kt/FggjkJcpoe84ev2QUz8doBWF5qYDLVnRjeW9LcIPvY87Ltrvz5oc1gz//rvm4eu2UYLlXWlqtqUgGiUjXLI2Xi/qX/GjpoIayIAh5v9bKy8tkxWu1JqxgsTFJesCVmsG1DHWYuQlINXFlIZAUCa57bWRZUBCHlpnM7UFa+hMtPI4Ti4JF2He8MiFLX0jnCvdJ4ijZzvqbSe+JiL+sIAQt9jWWfixkuYlILO1pjmRy4D9ubGBDJFl37PughqUwsuPlwS4q3BTlJODqtVPwlUsWivs6fetLZfnA0sT0ZqSAeea1NwFATJxpqxm01VVgYrMnxKQbYFkQHu8Vhr9540zR0mcF06nT5FyAFutHKC9vef/5cpTb4dgTTd0t1cGtwObT43T5Vq09B8Llbmvpkr5+petbOcW7Lh7NNndCA86d34mbN84MtkUjHaPXw3MtLu5uwsc39Imiywo2LsylCgtWWHHftuVBRYiwoS3vJVWLkKyddjnRtgeSQQdpokIKigiuQRQ6SSFmkXJqFWI32Mg18HEM0EoiLY3x1tZfUoqmlQRL3sz6gkHZ+aMi69hEG6F9tA/HDynW/0ADNmTxKZwrdijP0WiF/ryucJu1HjdXJ33c5nel53fk57RuI/J3i20f7pwn5I0cRbpNfdwUZaj4+Ia+1H333bAG2944KPqxmZgvCCeedDmOvQFL/m1WMFSXF/0yLwdEwSL5cUksn9yMf/noaUFeQQ4/1C4XSjcAKdkxb/f5d83Hvz+9HVVlNns64d9uXIvyUgFv+/P7AQDlQg2ndX1teGnnfpw7vwO/fccjAMKgE+mmI4kpKyL4PNRXlaWKcUm4SEJEmoe2umT0ZXV5EW8ePBrp14rIJT1NQRWKJiGSN35+vkvKIm8vkUfz2QTB0tK1dBM74v9Y4KJSEokUu8PvP3IMNrFPWcGrKsGRAico8SAuCKOfIZut354/PWcbE31CNYvQupg8h2DAE+HHxrclRU9aFYv0FCU2pQdS2rnHJmxziR1h+Twrsa9FqoBjA654v7bc35S2fK4j+w55r/NBVntUqv8cjjGfMFWLm6Kc5NRUlDyHeAfS90RWSLr9whYjTtmxrpqyeb+gKsuKmN5eF0ku7RqzlAZisuDHx/u4cHEXvnLJwogPVmdjVSQVjLQsXVVewO1XLMFprIZwgQnX1dPGRf1hXAKW3frEqbH+ZMK+iF+O4yZmt3FL6aTmdAthbYUU+Ry2tHUoS8KkOwvas2025UkovJPj5TsWiHkVvf9cEAZJjAvhDwTJWhY/F99ll9myxJw9SI4wDLfZV5iXT5MFY6BYkuNLjDwFqb1w/U6rWl5Loms+hWPzRPzyfqNWS+8Jr4Lhsq4ahJ+bCxZOwA1nTRd94q4/YzrmT2zE7M6kFUz6wWUFHn8t7XdUVi5Ci+QLp8EJiqKk8lYiyaxwkwSe3WZS9lvsHjPgtKMh/BryLLlwy5u7TE7yWMkKKd0w7Jc8EeHr71+OS5d3ByOSvqjLCvkCHOyl2j08gCPLQmqxxzayY61wyrIQ2ghWadqsv6K0zFgqEuZ1NeDGs6cH+xoFf8EpfqkyEuaej91luZjGAiGscJwmBQ44fkjwc/YGY4ooh0QXrohB3s5auXleMmkJTTrHQOszS/MVJOsVBGHkWGGJON4+y0/NtbQtdexKESKJT/6jKvBxFMbLy/N98eIF+MgZ08Ku2NjeubgL37t2lVgWTZrz5hqvXSRwwm9WFN5D/LNkg8pkK+PoUW4q3BRllJLXZ4YT+lal30SB0JqT8k0NIF1A/tOHTsE3P7Bc3Hf+wgmoLi9iqrSskfPLMG/AxtVrp6CmvIh2llA3Yblh7SWx+oIfqMF32UjaspK3kS8Fu767CwXCbZcvxreuXBFsK2OWpjJX5lm/Yx5JbJcql/eGfmdBdBwbsC2NZ4tm8yFaa2RUzIQ3/+9/+FR8eN204MXuEZa9reUxIlbYRFgrofS6SQEWQWRsTquVtM86kkvXJZ0rOnb7gG3zn/BIX6mOryQEbT+RBLFSO8f12GhlSRCKotOluTLmNfyMJPuQLH5OEcN21vqR2bz9Xv/HxzEh+lSqqxw/N8f1YzSyzT+Yu6EEwo21v/mcPvzpBXMjOTe/c/VKfPWKJaLlbxTptuH1cSOiDQC+DKAI4HZjzJ/G9n8JwOn+02oAbcaYRn/fJAC3A5gI7/vrHGPMC8M0dEUZNuZP9Jac0vKwfXnzgsAnKo69eUtLZEW2NPXxjX3o66iLOAFb7A0jzd5mgxwkzpnbgY1zxjuXaDiSlrERZTv3HUrs4/2+55QeXL6iG49v3QMAaGIJNKUvXtcvZm4Ruu09i/HwS7sDK2C0zmeyD+6rc/Zsr27tB9f04qv3P4+6imSwgU0Lcs7cjqDEjmQFsnnabFAFPxcX5tZatGBiI3701GuRsZX55+Q53wRNH+4TXhD7XuI+Q1IX1eXJ+XLd9KVEyM4AB+G1JOHGmpW+w+Z7k6xFos8WhHGybaKYY90mfczSxx69xmQf0THF/N+IvHx38W0wwb6064qz//AxtNTacTheE7ZtSmsNdu47HBFHthxWS01Fosyctejz7l1ijvcrCTGL/aHBUz1Jc95WV4nNyyZFjp3b1QCgAbff/xyAeA6/0aPchs3iRkRFAH8JYCOAWQAuIaJZvI0x5qPGmAXGmAUAvgLgLrb7/wH4nDFmJoBlAKLfUIpygnDa9FY89ekNOL1PziP3jgUTIhUMOLbGrVT9gS8TTGiswjVrp4pLebYk1LqU82eR9gWX96ZsBYKUFy7+hU5EkUS5633h1FTjXX+d8Kubd2HF2YTGUBzN7mzAZSu6AxFTHUkTkA7fd9OGPjz2R+tRWZac397WWjz16Q24jFX34GP7yiUL8WcXzsOpfrJj6TWS/Pqk+W31X0teW9XOoSTMS4KqsxGv21laFOklntYmlSRKCjIpHUl8H8+cb5vFE+HmOZf0mtsfBrxcWL+w3CxZt2RxmBQxkmBMjEla0owIR+F6RAe56Ca7BBjtNzleyTLXWJ383pDe87JI9T+HwjK6FA3vtLjByzPIj8trcZP6233gcOo+CRtgM5oKy3OG0+K2DMAzxpjnAICI7gDwDgBPpLS/BMAn/bazAJSMMfcBgDFm79APV1FGDim4IA83bejDB0/rRZNUHibnz7QprbV48A/OFMs7vRWkr0Dpi9T6+/CgB+7IHIcnAr5u3VRcunwSWoQ6ttL5/+LdC/HTZ3egvir5Vbi8txnvWtyFD67pxd2PbPXPkewjHFv0puu9hvIXf/z15Zacc/3cbz/41TYA4ZItP1d4zWEfXMutnjYOT/3mzcChm1vBjvgi5ZlXw69RaQnW0jc+Wb5NuioebBCOKZ9YDwVWsv0xf7yS+Mz05xIsQ3Z8i7ub8NCLuwCEfoJykmjWX2xf1thF0SPsdJ5LODivnJCEq2tZelJzdZDn0c6tlFpI1DP+tt0sF2DYPnlAT0sNfv78zsgSc/BZKhDuvnZV5L0kriKIlrnk0F7b4/3osMu5kisJx5b9m9VZj00LO/HYK3uc7Yeb4fRxmwDgZfZ8i78tARF1A5gM4Mf+pukAdhPRXUT0MBF9zrfgKYrCKBRIFC0AD07IDjoYV1sx6EsDef3ZgkoEYk4vhxCAd4NIu37pes6Y2Y4/fPsssd/6yjJ87l3zMZVZkqQ+pAz7YXtxKLna2f6487Q9V3V5Ee9c1IW/evciFhHpzduu/UfwO+v78K83rEGv7zPHfYzmTUjPfcVvhGtneBbPKmEJVFzKEm6Gttneg0fYNkrtQ5pDqe6vlNhXEiT2sSvRNIAgr6EsnAQxJ54DyXbie0LoQxq7JLqQ3Bbn1T0HWft8AtcizdNcwZ1CLoPmMb+rMbFRel3Pnt2Of73hNFy4qCvcyN5j1eWlSEUT+5rzFDtSNHhgURbK0tlIfikNE8cK+VKRsGFOB25cP8PZfrgZrcEJmwHcaYyx8bwlAKsB3AhgKYBeAO+NH0REVxHRg0T04Pbt24drrIoyJshaJvjYWdNx4eIuZ5uBEF92qRCWDaUv3vWz27FwUiM2LRR/1yWwS5ku3zvAbWnISrMS9JGjf87AIw3d57I3oyIRvnDRfGyc2xHsm90ZCpzyUgFT22qDpU8eYVcmbAv6ZfPw5YsX4gfXncpunu60KGI1B78hX76zFhFuQJEsWfE+0rbFLV58CVQSP1I+v5YawUJrRYcwTv7iBPnhctrBXK+1nGRYuFbhXKK/qmhxS/ZrkZzxnVbTxJ5ombd4+/i2qW11ke8AKZm1pam6HKdOHYfPv2t+sE2yuLX5kcFSoXr73s+K9j7q/3iU3tOjgeFcKn0FXmCBpcvfJrEZwLXs+RYAj7Bl1rsBrADwVX6QMeY2ALcBwJIlS4awPK+ijD2sBWumsPQFANexcPzB4DtXrwxSWgBAh1A2S7oBTG2rw3evWRVpZ5cuXt61H3E6G6vwd+9bigVdyTxiHNdXcF6BlRWcMJBzRto5LTPplixOtVAOTVq+tP5x/cI3JPdxa6guQ0N1A17YsS/RhzQmu5TFu7U3fZ7KQUqf4UpzwdPYJNqzPuzNtre1JqgVG54jbNdeX4E3Dhxxij9OQVBYvNWyyc34xfM7o0ufwX9CwptQEF95o18T+4RxGvZCuUWi1G++97A0X3mEXlbHQY1dYV9ZsYBv+NHsH7/zV6nn2jhnPG67fDFWT2vFZ+55EkD4I6XK/4xIVkROc63nJtIkVGsYDQyncHsAwDQimgxPsG0G8O54IyLqA9AE4GexYxuJqNUYsx3AOgAPDv2QFeXEYVJzNb540XysmuouUTVYTGl1JxgG8ie1XDa5GXc9/Eqk/BTn9BnZgRR588O5+3DsG2D7aLvk0tPFSyfioRd3YSqLeA0y4rOGLidvK6ayLG4W10030lq8sabf9PnQwmXOfII02CaURePNOxursGv/kSCNCh97VLgkt8XHy9tJ++Vgi2R/0jzZHzNH+3k91mR7SbdJgRCJPiLbHFa7zOv3yBtVKvXhdCMQjo2PMQv7Q4Nb18qKhSC622JdBXrH1eT6Drx8RTdaasoDf9PRxrAJN2PMUSL6MIAfwksH8rfGmMeJ6FMAHjTGfN9vuhnAHYb9bDDGHCOiGwH8iLx34kMA/ma4xq4oJwJEhAsWDd5S6GBghcLE5mT+MI515pccpfMSJOyVylDlXtJ0ib+cloaMM1jWzx6P9bPH4/nX9wXbyoS8bBZp9acoWNxstCzfZv0e5eVOv02k4LZ0rvR5lVJqSP7heetiSoKsvb4Sj2/dk+k7Fh8b3x85lWhVcghBcUkzydKeJjzwwi7RginmoOPnIiTaxfuIboseF7kG0ZLrvtZwbMltkiCuKBWw91B0vHsciaVdS6USNRUl/PH5cyP1TiWsRi4rFnJ9B9ZVluHipZMy240Uw5rHzRhzD4B7Yts+EXt+S8qx9wGYN2SDUxRl2OlsqMJHzpiGs2e1O9tZ692KXjkNiovb37ME2/cewukz2vDelT2pfnylAuHs2e5xSPeT4GYjuc0MsiWvb3wdXtq5X1zmlIRTve/P9b5VPfib+58P+gBki5urDFZkqVK4MEk4uhLW5g1OCIRjZrv0ScwSH/mDDpBoJwVKuM7rqlggiTTxx0JO6560tOuyuOVPkp0uKvn1TW+vw8+e2xFpt2lBJ+5+ZKvokzfQqjEE4N3LswWWfa9L6W7GIlpkXlGUYePWTXMizwsFwg1nTU9pHTK3qwGPfOIsNB6Hz8mZTBTect7s1HYPf+Isseg9xy2w0i0eWdhj97EC7ZZI2S7/xnNMUG7SPamhqgy//MTZqK8qBcLNOudLwk26sdkt/f0G09tr8fSreyMi9W/eswS/2rJbFCRB2hKhP3mpNF0kRHy3mHBwLce5amVKZbvScoq5xu5cjs1rBRPOZZ/xdj0tNXhi2x7xfSUJXOm6XO9JuZRZsp3Lahdtl+z3c++aj09tmiNGdg7U4pZlKf/dDTMwobEKD7+0G1/76QuZ0aRjBRVuiqIMGzzp7EA5HtE2EOpyfKnLwQnptULz3oCsf9jWWHb5eL/Wqsb9o8J98rkaYtG9Np0CT7UQRpV6+7g1RKxw4P+vLi/irFntOGtWO+58aIvfVzL6VBI6coWDcFtdZQlvHjyakvIhOh5+PWKOtUQPKZY0YZsURJElsON9ZG1zJSrmzWsrk9cY9CstMwudSIEdB2xkbM5fGsHcZcxhfB/gLVdmRXVmjePAERvJ6+aatVMBeFVKfmvVZLQK9U7HIircFEVRcuK6oeS9cXM+tGYKDEyQhFSygnGsD5qNsgV4Ut582Z1aasqxdkYrLloyMbGvQMC/fPS0SPJl261B8ubczNqJK3WCNcomWOZluML24eM3A1+o8PwWW11jF0vK29NSkxhHhj2IPUoqEVet0GWTm3H//77u708KoQFbZgWRJoletxVMOpfQr7DN/mDIv6SdtKSGh3lbx9eHJafyptuxApJHo0vM6qjHE9v2RPITuigrFjBJqMM7VhmtedwURVFGHdJNd4+fKV6+cbpvWDdt7MPNG2cGN7Zjwn2IW5xsEIctQ8WxRowmoWwRp1Qs4GvvW4ZzWA64cX7S4rJiAdPb6yJJjK1wkG6mUkoNAJjR7gnRsmCpNNzZ1+Hte32vINyE+arxUzjwe/Q0P5EqH5PsY+f1d0RYWi4IIkWyuEXEnGhBjLdKdBs9rzBOqd/d+733FV8Wd1sQJUGYLiql9ysvW+YSieI1MKF797Wr8O0PnuIcm8R0P4LaXnsaK1NK/p0sqMVNURQlJ5LhYOsbXqZ6aak1b5qRIAeaYEHgXVy5ejJ6Wqqxri8ZRDHwCNaQT71jNv7rmR04bVp6fdiVU1rw6CtvAAjrfb60M8yrx09/23sW48lte/Cfz7ye2GctMbxofb9jubm7JVnnkpd0s35uVuBK4qumvIjtsW2Z/mxBu2QL0YIlWsHyLWlK1r3lvS34j6e3Y3p7LX655Y1Yu3SrXTRaVRpburUsb2S0y7eMACyY2Bg5b963ZodfGP7wMTntj+W6M6ZhZkc9Vk0ZntRGow21uCmKouRFuAHZHFILJwkJgHPesKzFTVoqjfp9leGCRV2Rsj/xPo6nVFl3Sw3evXxSUN5KokCEQ77YWj45afHggqS7pQYb5nSIgQNTfGvZ2+eFFr8X/PqYUp4+GzDBS7VJAQtykXnvsS11FLkesXJD8nokq2JWRGq838i2wGqW7LeGzb/dy1+TIIJV6FfCvp+4BUuy7tkl77zX5Y4qzTk4AfvjJytIqKGqDO9cLH8OTgZOzqtWFEU5DiRryZ9fshB/8LaZYsRa3jJI1nftmJCmLsuSFi9X9Rbumyn9h4W/rXjqbU1awZx+XWxnd0s1nvnMxsDHro4FSSzpTpYtE+uiSjnr/HadDaFvFQmPpEoXLjEl+bhJfmdSEEPeF8O2r2SCJUzsy8+VFExh++S+WX4ZtNXTQssUxf4DoQVTCgSRzmWDM3btEwrKC36C3N/NxZmz2nHduqn4wOreXO1PVlS4KYqi5GRcbTKydXF3U+qNJu9SaZgo171UKmHTe+R1AB8o1mpToFD0lAvKaSBJjEvFQigI2GGScLCnyipyH0ZQUqJdVi42KzAkB3bpsupZPU5nNGVyU8ryaZK8CXitxVdeKvU28veGZHELxWxyTNJ4bYAJF4Tx/jncZ9JFbUUJHzt7BtpzCr2TFRVuiqIoORnoMmRnY7I+q4RdDqyQln4yTnnLebNx3vxOdDf7UZUp7acIVrI8WCtgkShYrJTSOdjTSoF+fEyVZYXItmi1gOSx4bJochs/r1iJwSGquDgIlyqTbt+SFW58pO5uUoG6RA8lHqRUIhCsgJZp7cmlX/eyLG+XFISu/GnS2Oz7tcTeB1l+b8rgocJNURQlB3WVA4/lyps3amJTNVZPG4ePb+hL7MtaKl3a04w/v2RhIIjSuOOqU/Dvv7M213g49rr7OuoCi6BcNsz7v51Fi0qCwPoxhT5krA/h/HL91LxrkZW0AAAXcUlEQVTt7PJxcl9LbTTAITEWweK1zPftk61gwuAFZJHmbdx3OIyStRHEJI0pI9I1ec5wXyCc845NaDndjxqWlqylJeghMgaftGhUqaIoSgb3Xr8aFSW3w7SEtKQoUVNRwtffv1zcl/eeF94w5SM8ETnwBKTLeprx9fcvw5LuZvzVvz0LQC4obx3ceZH3YAlOHK/3X6rPyZHqp4oiLSyqyk4SPVekfYbvmGQ1c81wXkd9lxWuo6EKL+/0cqpZn8msPG4ucSQm9hWibxEshwtjc/SbFZkb9qHKbTBR4aYoipJB3/j64zpO8tkaKHWVZZg8rgaXZtRkHKp7Y6FAWO2nCXH5uM3qSJ8jIuA7V58SEb+SCBGrJAj+f2J5LYeIiBQ59/PuHWWRIJLVSlxmFOZ4h29hlMqQiSJG2uZ3PLGpGr94fmfquQrCOON9RCNtvf9t9aFgXzmlBc9t3xexpPULARthv9J4bf95hV5ym3L8qHBTFEUZQrpbqrG0JxktmZfyUgE/uXFtZjvJmX2okHzc6irL0FJTjguXdCX2EQiLYxGjklhy+rixbUEwB2/nWCrlamnl1HG486EtEHSWaHHLKs21y0+10VqX9JkbqBCKnj9dnBWEdi5ByCOe3ZGp0pjSBTG3hlrBKApYtbgNKircFEVRhpCffGztsIgpew/taBj6iDybP2scixYsFggP/P6ZEQG156Anara+kazBam//XCzIqT+S26TqEKLFTbAMFQPLVHJEuRPQIilYpHl3iam6ihJ2HD0caSdZ9yLbhDG5lmX3+pUlXt6VTJQcDU5ILpWGOfiSHVttxl/raj/fXFaFDeWto8EJiqIoQ0ihQMNicehqqsaVqyfjj8+fO+Tnqqss4SPrpuKvLl0U2R63etnM9rYGZaSt37SpmtdFTV8CjQotLlx8AchqqsZbRcRhYK1zl8GScrG5XsdIyg2HVctu4ol13T5xyceiFVCQiUt6mgCE6TvS2kuJkuPtOVas8mu2Zc6k6qGq2wYXFW6KoignAMUC4fffNgtzJjQMy7luOHsGlk12LwFby9xhIbNwV1M13rmoC7dumhNsC+MLwtu/PVbKRcbJWwzdFS8ipSaJJpRNnt+VSkOSPWJErrQsKo4waZtzCVe7lBmJ1xAEqZSU+EW/nJlNtsuZ0uqlI+luDvPeSb6IiyZ5wrG5Jpn/UDl+dKlUURRFGRDSsqSEjT6V/J7KSwV84aL5kW2SgLFLcJet6MZn733Kb5dEsoZVCiW8pLxwrj7EYvRsU1N1OV7csT8iCF0WtL72OvwztkXPEZSySi4zS8Ipb9qO0II58EL11kq3qLsJP3rqNQDArZvm4Mlte/DOxV2YM6EBfePrEsfyef3wuqlYP3t8JPWK8tZRi5uiKIqSizw5wzhLe5oxr6sBl6/oztVeipo8va8N3/zAcvzWqT2snTXNuVN0tPsBA9EACLtUmkQWhO6N1hKYNzVGQbKCCUure33BxiOa5SCG9JOJFrfgXGyp1I5N6IMnhb5sRTc+c/5cFAuEWZ31kaXtUCKGJysrFrx2ulY6qKhwUxRFUXJxy7mz0dtag/qqfIs1nY1V+P6HT8WSnFG1ko9bqUBYOXVcNJWIoAPssuzkcUIdVfZYCnYQozX9bbzOpmTV6nfkQJMQS5M5Dm1kgRhH/GXjiAUz8HtLv66I359gcTOxa4gUux8E0aXpQAYXXSpVFEVRcnHFyh5csbJnyPrPHdUZGtwCZnXU408umIsz+trwvUe2RtpxlVIMHemEfsOGNlfdOFb9QhY9sX4BvPD6PgByUIYr31zEd05IXrz/kNdffVUo5g4d6Y+2i/iz2b7YUqlwLosVzlxA5xVdlD6tanEbZNTipiiKoowKXDU7I9tS/LkuWTYJbRELWRKbU2/+xEbn+Xcf8FJ17Np3xDmmYJmRHWwtYgsmJc8hXY/kvC8FDFh6WkKr4iu7vVQrYoUJ/1huoAsqJ0jnssdFhFs+0SVZ9wbah5IPFW6KoijKqCB0cDc4Z+54AECpEN6m6itLmN/VICqyvIXa1/W14alPbwiqQYgHAFjh1yU9fCxpNYsQLDOyTbHr4UhLpS016aXI5HxryXazhWhiyZ8vtLhJ4tcfY0ZSZM7aGa2ipZBDfr8NVcn8e8rA0aVSRVEUZUTpaqrCll0HIlarL29eiFs3HY0Infs/vg7lxQJ+/vwOAJ4DfymwIKUrjLgzf2VZMfAXi7Rjj+1YhGbRHGj+fy52XHnRgqoPksJhWJHmqqAQ6dchXPm5Dh2NLa0CiQS80dJfbuX2fy9fjP2HjuFbD7wU6YtTV1HCe1f24IyZbc6+lHyocFMURVFGlFJs+e61Nw+hrFhILCFai40VE1PbarH9zUN448ARcQnywBHPWvbSzv2JfdEi80mswOoXfeHCx64KA3bb9PZaPP3q3sg2KWCAI4k/17lcQRecl4W5iC/LStUs0vRbRamIilIxFJgpaVZuOW+23IEyYHSpVFEURRlRrHXLRlAelQqJMqyGaKwqwx+fPxen9LZEKjBYZnd6y4f7D4e50Fx5zLg4KQUWN/dYgqhSXrfTWsvEJV1v48Ej4RKsNBbJd84KUdmSl7ydS3EYdrx9HWEOtrjVsFgg9LbWRK4hy09NChhRhgYVboqiKMqIYkXBJJaJ3wUXE2+b14FvXbUiSAfCqRYS8JJgQXIJLLFoOntsd0sVGYJzRSodeP8ntSTTlnCklVSemiSOdP7QuieV9+JLu1GhWRSiSrOSLtu9WUvAyltnWIUbEW0gol8T0TNEdJOw/0tE9Ij/9zQR7Y7tryeiLUT0F8M3akVRFGUosaKgzFWPihEIoQzHeZd/lrSPC6yiw+IWXSpNL9AuWvX8/1NaQ+Hm0kR8X2djVWo7a3Hjo5UsboF1TTDbtdVVoK6yhKvXTmF9uJdK4+NU3Tb0DJtwI6IigL8EsBHALACXENEs3sYY81FjzAJjzAIAXwFwV6ybTwP4j+EYr6IoijI8WBGRW7gFFjd3u7eS+LWqzLPWcaud1N1FSyYCAFpr0yNDt75xIOxDWHqURGQ8YIAjiShp6rp8C+Z58zuDbaGFMBlg0VhdjkdvWY9Ll3cH12rPn7VUOneCl/pkeW+Ls53y1hnO4IRlAJ4xxjwHAER0B4B3AHgipf0lAD5pnxDRYgDtAO4FsGRoh6ooiqIMF7aWpVhVwEGWmHDVJZXg3a2fMx7Pbt+L962ajO89shWdjZWBwOFC67dOnYxLV0zCyztDcWbb2eS9Ur3RtJH/9aWL8ObBo/jv53akthPz2Alz0d1cjadv3YjyUgE33fWoN7ZgWVSwEEaWj33B5gvCjob0ZVoAOGVKC35964ZIhQtlaBjOpdIJAF5mz7f42xIQUTeAyQB+7D8vAPgCgBuHeIyKoijKMPN758zEJcsmYk5nMheZRN4yUwM1uPH2DVVluPmcmRjfUIkffWwNvnnlitTjKkrFiOixBdptrVSOJJI4G+d24KKlE8MxZfjiWUpCcAIRib5/QNRn7axZ7QC8tB1xOhqqcNmKSfizC+eln9xHRdvwMFrTgWwGcKcxxobdXAPgHmPMlgyfhasAXAUAkyZNGvJBKoqiKG+dmR31+JML5gX+ZEt7mpzt+zPEj621mbfOZlarmpigyeq2d1wNnnt9X2Sp9rPvnIsnt70ZjD26VJrsw5XE17VUKpW3Arxi8RWlgigcf2f9DFy2vFusOlEsEG7dNDd5QmXEGE7h9gqAiex5l79NYjOAa9nzUwCsJqJrANQCKCeivcaYSICDMeY2ALcBwJIlS9RFUlEUZQxRLBDuvX412gRLFSfL4nbv9acBALbuPiDu5/Ci9JkO+Dn2lRUJdX6+OZ7K4+KlnjHhG//9on8u98nyRmd+6eL5+MEvt6Gq3LudH+BpRtgp7vvoGhABN931K29sbF9ZsYBJLdGIXlfaFGVkGU7h9gCAaUQ0GZ5g2wzg3fFGRNQHoAnAz+w2Y8ylbP97ASyJizZFURRl7NM3vj6zjRHKTHEm+k7529446OznO1evRHt9BW72/b8OHhHKJPDz+v9d1QykOp9cgoV+cuxYZ9UHd2Ld8xd24fyFXbj/f7cD8FKGbPWvm/drhVkY8ZrTGql1Rkcdw+bjZow5CuDDAH4I4EkA3zbGPE5EnyKi81jTzQDuMJoMRlEURRHo9/VVdnCCu5/F3U3oaqrGOD8i9M2DyYLyEnl9zcQgWRsckNGfHZONbo2cXxqTv7UrIxeevbM2CYXtJVS3jT6G1cfNGHMPgHti2z4Re35LRh9fA/C1QR6aoiiKMkboGVeDAgHnsjQXEnmtRTbx7+Fj+So2AMBXr1iCyrJkqpBigRKpNPhxLt81zofWTkF7fSXW9SXre7qFY5bvnDeCmop8gQSq20YfozU4QVEURVFEprbV4te3bszM+5bXWlTmV2g/KlWUl/oFcMbM9si2et+vbfPSifj58zv98ycH0N+fXOaVhjmutgJXntaLN/YfSbST2h/zTWlZKVXyLpXa/bpUOvrQkleKoijKmCNPst68Wf9tX0f7DcqLBSyc1OhsL/XXXFOOX37ybNy0sc+ZIDiMiHXVVRg4x/z142KmxS0fGpwwelGLm6IoinJCwoXTJ8+dhS275ChTu1Q6qbka/3XTOrHGaR4afKtbeP704ASOjQR94fV9yZ05lZM1FmbVFF3c3YRfPL8zMdY01OA2+lDhpiiKopyQ2OU+Y4D3rZqc2m797PG4+9pVmNNZj1IOS17eiMyCYHLjtU3LSwUcPtqPeV1e4mGeyiM4l+NU09trg8fW4sYtkZJV7/ozp+GcOR2Y2VGX6xryXqsyfKhwUxRFUU5I8lqLCgXCgonu5dHj6VeyfvEEuPd85FQc7TfYufewvy9/MoXvXrMS41kZqnldjegbX4f3rurBvY//xjuHcFxFqYi5XfkqVNhxKqMLFW6KoijKCUlHQyVa6ypw1Wm9I3J+ycctLB4PTG3zrF4/e3aHvy9Jmm5aOClaXaKzsSpIPBwcmyG6Llg0AXsOJOuoAl5i4qd+8yZKRVVuow0VboqiKMoJSUttBR74/TOH/bzxdCCccn8ps7o8vP3agAJb8ivSl2S1yz0Ot+j64kULUvf94dtnYUVvC6a15VtSVYYPFW6KoiiKkoOd+7wlzdw1UIVm71gwAa/vPYxLV4T1tK1bmqDbYv0Nn/Wrs7EKV6zsGbbzKflR4aYoiqIoOXjFr33akrPqQBBVygRZU005blw/I6VdUrmV5wiWSEP9005MNI+boiiKogyAmR3ueqpS3VIXtl2/JNxKBbTXV6CiNPDbteq2ExMVboqiKIqSg5vPmYnTZ7Siu8VdD9SSO6qVrHCT99/zkdX41xvW5OssMoCBH6KMfnSpVFEURVFysGZ6K9ZMb81sF9dLWQIuFG6ycmuprUBLngEmxqHK7URELW6KoiiKMohY+ZU3mMAuleZN45bV7o/Om43J42pQV6m2mRMRfVUVRVEUZRAZX+8lxi3lFGQ2/kBKB3I8XLGyRyNCT2BUuCmKoijKIHLTxj70ja8LSlll0VTtRalevHTiUA5LOUFQ4aYoiqIog8jE5mpcd8Y0vLbnYK72LbUVePJTG1CVs7j94NjllLGK+rgpiqIoyhBQ6Quxc+d3ZrbNI9o0L5sCqMVNURRFUYaE+soy/Phja9DRUDUo/WmUqAKocFMURVGUIaO3tXbQ+rpwcRceemkX1vW1DVqfythDhZuiKIqijAFmddbje9euGulhKCOM+rgpiqIoiqKMEVS4KYqiKIqijBFUuCmKoiiKoowRVLgpiqIoiqKMEVS4KYqiKIqijBFUuCmKoiiKoowRVLgpiqIoiqKMEVS4KYqiKIqijBHImBOzXC0RbQfw4kiPYwQYB+D1kR7EKEbnJxudIzc6P9noHLnR+cnmZJyjbmNMa1ajE1a4nawQ0YPGmCUjPY7Ris5PNjpHbnR+stE5cqPzk43OUTq6VKooiqIoijJGUOGmKIqiKIoyRlDhduJx20gPYJSj85ONzpEbnZ9sdI7c6Pxko3OUgvq4KYqiKIqijBHU4qYoiqIoijJGUOE2hiGivyWi14joMbatmYjuI6L/9f83jeQYRxIimkhEPyGiJ4jocSL6bX+7zhEAIqokol8Q0S/9+fkjf/tkIvo5ET1DRP9IROUjPdaRhIiKRPQwEf3Af67zwyCiF4joUSJ6hIge9LfpZ4xBRI1EdCcRPUVETxLRKTpHHkQ0w3/v2L89RHS9zk86KtzGNl8DsCG27SYAPzLGTAPwI//5ycpRAB8zxswCsALAtUQ0CzpHlkMA1hlj5gNYAGADEa0A8FkAXzLGTAWwC8D7R3CMo4HfBvAke67zk+R0Y8wClr5BP2NRvgzgXmNMH4D58N5POkcAjDG/9t87CwAsBrAfwHeh85OKCrcxjDHmPwDsjG1+B4C/9x//PYBNwzqoUYQxZpsx5n/8x2/C+7KcAJ0jAIDx2Os/LfP/DIB1AO70t5+08wMARNQF4G0AbvefE3R+8qCfMR8iagBwGoCvAoAx5rAxZjd0jiTOAPCsMeZF6PykosLtxKPdGLPNf/wbAO0jOZjRAhH1AFgI4OfQOQrwlwEfAfAagPsAPAtgtzHmqN9kCzyxe7LyfwD8LoB+/3kLdH7iGAD/QkQPEdFV/jb9jIVMBrAdwN/5S+63E1ENdI4kNgP4lv9Y5ycFFW4nMMYLGT7pw4aJqBbAdwBcb4zZw/ed7HNkjDnmL1F0AVgGoG+EhzRqIKK3A3jNGPPQSI9llHOqMWYRgI3w3BFO4ztP9s8YgBKARQD+2hizEMA+xJb9dI4A31f0PAD/FN+n8xNFhduJx6tE1AEA/v/XRng8IwoRlcETbf9gjLnL36xzFMNfuvkJgFMANBJRyd/VBeCVERvYyLIKwHlE9AKAO+AtkX4ZOj8RjDGv+P9fg+ebtAz6GeNsAbDFGPNz//md8ISczlGUjQD+xxjzqv9c5ycFFW4nHt8HcIX/+AoA3xvBsYwovj/SVwE8aYz5ItulcwSAiFqJqNF/XAXgLHh+gD8BcKHf7KSdH2PMzcaYLmNMD7wlnB8bYy6Fzk8AEdUQUZ19DOBsAI9BP2MBxpjfAHiZiGb4m84A8AR0juJcgnCZFND5SUUT8I5hiOhbANYCGAfgVQCfBHA3gG8DmATgRQAXGWPiAQwnBUR0KoD7ATyK0Efp9+D5uZ30c0RE8+A5/Rbh/Yj7tjHmU0TUC8/C1AzgYQCXGWMOjdxIRx4iWgvgRmPM23V+Qvy5+K7/tATgm8aYzxBRC/QzFkBEC+AFuJQDeA7A++B/5qBzZEX/SwB6jTFv+Nv0PZSCCjdFURRFUZQxgi6VKoqiKIqijBFUuCmKoiiKoowRVLgpiqIoiqKMEVS4KYqiKIqijBFUuCmKoiiKoowRVLgpiqIoiqKMEVS4KYqiKIqijBFUuCmKojCIqIuILha29xDRASJ6xHFsFRE9QkSHiWjc0I5UUZSTERVuiqIoUc6AV0tS4lljzIK0A40xB/z9W4dkZIqinPSocFMURfHxy6R9EcCFvuWs19G2hoj+mYh+SUSPSVY6RVGUwaY00gNQFEUZLRhj/pOIHoBXl/SxjOYbAGw1xrwNAIioYcgHqCjKSY9a3BRFUaLMAPBUjnaPAjiLiD5LRKttcWxFUZShRIWboiiKjx9Q8IYx5mhWW2PM0/B84R4FcCsRfWKox6coiqJLpYqiKCE9yBlYQESdAHYaY75BRLsBfGAoB6YoigKocFMUReE8BWAcET0G4CpjzE8dbecC+BwR9QM4AuDq4RigoignNyrcFEVRfIwxewEsy9n2hwB+OLQjUhRFiaI+boqiKPk4BqAhTwJeAGUA+odtZIqinDSQMWakx6AoiqIoiqLkQC1uiqIoiqIoYwQVboqiKIqiKGMEFW6KoiiKoihjBBVuiqIoiqIoYwQVboqiKIqiKGMEFW6KoiiKoihjBBVuiqIoiqIoYwQVboqiKIqiKGOE/w+RGLnVVgXgCwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c32b320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "plot_interferometer(ax, data_yellow)\n",
    "#ax.set_title(\"Yellow line interferogram\")\n",
    "plt.show()\n",
    "fig.savefig(\"analysis/plots/yellow_interferogram.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111ff9550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fftres_yellow = do_fourier(data_yellow)\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "plot_raw_fourier_spectrum(ax, fftres_yellow)\n",
    "ax.set_title(\"Yellow spectral line fourier transformed spectrum\")\n",
    "plt.show()\n",
    "fig.savefig(\"analysis/plots/yellow_fftres.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f0c59b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "119.27060495960109\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>parameter</th>\n",
       "      <th>value</th>\n",
       "      <th>fit uncertainty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>$A$</td>\n",
       "      <td>161.574560</td>\n",
       "      <td>76.114733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>$\\mu$</td>\n",
       "      <td>3.295770</td>\n",
       "      <td>0.000005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>$\\sigma$</td>\n",
       "      <td>0.037285</td>\n",
       "      <td>0.000005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  parameter       value  fit uncertainty\n",
       "0       $A$  161.574560        76.114733\n",
       "1     $\\mu$    3.295770         0.000005\n",
       "2  $\\sigma$    0.037285         0.000005"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$$C_{A \\mu \\sigma} = \\left(\\begin{matrix}76.114733 & 0.000000 & -0.011709 \\\\ \n",
       "0.000000 & 0.000005 & -0.000000 \\\\ \n",
       "-0.011709 & -0.000000 & 0.000005\\end{matrix}\\right)$$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "fitres_lk_yellow, lk_yellow = calc_lk(ax, fftres_yellow)\n",
    "plt.show()\n",
    "print(lk_yellow)\n",
    "tab = print_fitres(fitres_lk_yellow, names=(\"A\", r\"\\mu\", \"\\sigma\"))\n",
    "with open(\"analysis/fitres_lk_yellow_tab.tex\", \"w\") as f:\n",
    "    f.write(tab)\n",
    "fig.savefig(\"analysis/plots/yellow_roi_freqfit.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c563e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fftres_yellow_roi = extract_region_of_interest(fftres_yellow)\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_title(\"Region of interest in wavelength spectrum, yellow spectral line\")\n",
    "plot_normalized_wavelength_spectrum(ax, fftres_yellow_roi)\n",
    "fig.savefig(\"analysis/plots/yellow_roi_lambda.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c5045f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fitres_yellow = do_peak_fit(fftres_yellow_roi, \n",
    "                            init_mean=fftres_yellow.iloc[fftres_yellow_roi[\"ampl\"].idxmax()][\"lambda\"])\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "plot_fitres(ax, data=fftres_yellow_roi, fitres=fitres_yellow)\n",
    "ax.legend()\n",
    "ax.set_title(\"Fitted wavelength spectrum for yellow spectral line\")\n",
    "plt.show()\n",
    "fig.savefig(\"analysis/plots/yellow_roi_lambda_fitted.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>parameter</th>\n",
       "      <th>value</th>\n",
       "      <th>fit uncertainty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>$A$</td>\n",
       "      <td>0.628250</td>\n",
       "      <td>0.001137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>$\\mu$</td>\n",
       "      <td>505.765639</td>\n",
       "      <td>0.124284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>$\\sigma$</td>\n",
       "      <td>5.686974</td>\n",
       "      <td>0.124206</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  parameter       value  fit uncertainty\n",
       "0       $A$    0.628250         0.001137\n",
       "1     $\\mu$  505.765639         0.124284\n",
       "2  $\\sigma$    5.686974         0.124206"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$$C_{A \\mu \\sigma} = \\left(\\begin{matrix}0.001137 & 0.000000 & -0.006859 \\\\ \n",
       "0.000000 & 0.124284 & 0.002794 \\\\ \n",
       "-0.006859 & 0.002794 & 0.124206\\end{matrix}\\right)$$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tab = print_fitres(fitres_yellow, names=(\"A\", r\"\\mu\", \"\\sigma\"))\n",
    "with open(\"analysis/fitres_yellow_tab.tex\", \"w\") as f:\n",
    "    f.write(tab)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Consistency check\n",
    "\n",
    "We know the nominal values for the two spectral line means from the script:\n",
    "\n",
    "![](mercury_spectrum_script.pdf)\n",
    "\n",
    "\n",
    "| $\\lambda_\\text{yellow}$  | $\\lambda_\\text{green}$ |\n",
    "| ------------------------ | --------------------- |\n",
    "|                   547 nm |                578 nm |\n",
    "\n",
    "The actual measured values above depend directly on the correctness of the given motor rotation speed, and the motor's linearity. Comparing the values, we can determine if the above measurement is consistent:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "literature: $$\\frac{\\lambda_\\text{green}}{\\lambda_\\text{yellow}} = 0.946$$"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lyellow = 547\n",
    "lgreen = 578\n",
    "lit_ratio = lyellow / lgreen\n",
    "display(Markdown(r\"literature: $$\\frac{\\lambda_\\text{green}}{\\lambda_\\text{yellow}} = %.3f$$\" % lit_ratio))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Whereas for the measurements using the Michelson interferometer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "measurement: $$\\frac{\\lambda_\\text{green}}{\\lambda_\\text{yellow}} = 0.952$$"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "popt_green, _  = fitres_green\n",
    "_, mu_green, _ = popt_green\n",
    "popt_yellow, _ = fitres_yellow\n",
    "_, mu_yellow, _ = popt_yellow\n",
    "meas_ratio = mu_yellow / mu_green\n",
    "display(Markdown(r\"measurement: $$\\frac{\\lambda_\\text{green}}{\\lambda_\\text{yellow}} = %.3f$$\" % meas_ratio))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "$0.946 \\approx 0.952$"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Markdown(r\"$%.3f \\approx %.3f$\" % (lit_ratio, meas_ratio)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The measurement can therefore be considered consistent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/latex": [
       "$$0.0025$$"
      ],
      "text/plain": [
       "0.0025"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1/(120e9)*3e8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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