Extended investigation data analysis 2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import pandas as pd\n",
    "plt.rcParams['figure.figsize'] = (12.0, 8.0)\n",
    "\n",
    "data_url = \"https://docs.google.com/spreadsheets/d/1r9_uz-QcvisvqR9CEy0s8-qRk_Dc5EAELxf5hHtvKDs/export?format=xlsx\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Measure the slit widths and estimate the error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Slit Number</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Measured By Taki using transmitted light</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Measured By Tom using transmitted light</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Measured By Tom using reflected light</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>% error</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S_n</th>\n",
       "      <th>Top</th>\n",
       "      <th>Bottom</th>\n",
       "      <th>Top</th>\n",
       "      <th>Bottom</th>\n",
       "      <th>Top</th>\n",
       "      <th>Bottom</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.4960</td>\n",
       "      <td>0.4974</td>\n",
       "      <td>0.5009</td>\n",
       "      <td>0.5003</td>\n",
       "      <td>0.5002</td>\n",
       "      <td>0.4991</td>\n",
       "      <td>0.500125</td>\n",
       "      <td>0.000750</td>\n",
       "      <td>0.149963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.1971</td>\n",
       "      <td>0.1954</td>\n",
       "      <td>0.2010</td>\n",
       "      <td>0.1991</td>\n",
       "      <td>0.1996</td>\n",
       "      <td>0.1990</td>\n",
       "      <td>0.199675</td>\n",
       "      <td>0.000922</td>\n",
       "      <td>0.461501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0988</td>\n",
       "      <td>0.1015</td>\n",
       "      <td>0.1016</td>\n",
       "      <td>0.0976</td>\n",
       "      <td>0.0995</td>\n",
       "      <td>0.1001</td>\n",
       "      <td>0.099700</td>\n",
       "      <td>0.001655</td>\n",
       "      <td>1.660275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0484</td>\n",
       "      <td>0.0509</td>\n",
       "      <td>0.0520</td>\n",
       "      <td>0.0510</td>\n",
       "      <td>0.0466</td>\n",
       "      <td>0.0497</td>\n",
       "      <td>0.049825</td>\n",
       "      <td>0.002347</td>\n",
       "      <td>4.710811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0256</td>\n",
       "      <td>0.0234</td>\n",
       "      <td>0.0262</td>\n",
       "      <td>0.0250</td>\n",
       "      <td>0.0241</td>\n",
       "      <td>0.0259</td>\n",
       "      <td>0.025300</td>\n",
       "      <td>0.000949</td>\n",
       "      <td>3.749736</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Slit Number Measured By Taki using transmitted light          \\\n",
       "S_n                                              Top  Bottom   \n",
       "1                                             0.4960  0.4974   \n",
       "2                                             0.1971  0.1954   \n",
       "3                                             0.0988  0.1015   \n",
       "4                                             0.0484  0.0509   \n",
       "5                                             0.0256  0.0234   \n",
       "\n",
       "Slit Number Measured By Tom using transmitted light          \\\n",
       "S_n                                             Top  Bottom   \n",
       "1                                            0.5009  0.5003   \n",
       "2                                            0.2010  0.1991   \n",
       "3                                            0.1016  0.0976   \n",
       "4                                            0.0520  0.0510   \n",
       "5                                            0.0262  0.0250   \n",
       "\n",
       "Slit Number Measured By Tom using reflected light              mean       std  \\\n",
       "S_n                                           Top  Bottom                       \n",
       "1                                          0.5002  0.4991  0.500125  0.000750   \n",
       "2                                          0.1996  0.1990  0.199675  0.000922   \n",
       "3                                          0.0995  0.1001  0.099700  0.001655   \n",
       "4                                          0.0466  0.0497  0.049825  0.002347   \n",
       "5                                          0.0241  0.0259  0.025300  0.000949   \n",
       "\n",
       "Slit Number   % error  \n",
       "S_n                    \n",
       "1            0.149963  \n",
       "2            0.461501  \n",
       "3            1.660275  \n",
       "4            4.710811  \n",
       "5            3.749736  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "widths = pd.read_excel(data_url, sheetname=\"Slit Widths\", header=(0,1), skiprows=None)\n",
    "#take the mean exluding the first two columns\n",
    "widths['mean'] = widths[[2,3,4,5]].mean(axis=1)\n",
    "widths['std'] = widths[[2,3,4,5]].std(axis=1)\n",
    "widths[\"% error\"] = 100 * widths['std'] / widths['mean']\n",
    "widths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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OkJlXAftVbfti1e3FtfatbH+S4oxsmsCamprGeggaBt+/8cv3bnzz/Ru/fO8mh1KeOGks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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c95a908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the deviations in slit width measurements\n",
    "ys =[ widths[column_name] - widths[\"mean\"] for column_name in widths[[0,1,2,3,4,5]]]\n",
    "x = np.arange(1,6)\n",
    "\n",
    "taki_t = plt.scatter(x, ys[0], marker='o', color = \"r\")\n",
    "taki_b = plt.scatter(x, ys[1], marker='o', color = \"r\")\n",
    "\n",
    "tom_t = plt.scatter(x, ys[2], marker='o', color = \"g\")\n",
    "tom_b = plt.scatter(x, ys[3], marker='o', color = \"g\")\n",
    "\n",
    "tom_t_r = plt.scatter(x, ys[4], marker='o', color = \"b\")\n",
    "tom_b_r = plt.scatter(x, ys[5], marker='o', color = \"b\")\n",
    "\n",
    "\n",
    "plt.legend((taki_t, tom_t, tom_t_r),\n",
    "           ('Taki, Transmitted Light',\n",
    "            'Tom, Transmitted Light',\n",
    "            'Tom, Reflected Light'),\n",
    "           scatterpoints=1,\n",
    "           loc='lower left',\n",
    "           ncol=3,\n",
    "           fontsize=14)\n",
    "\n",
    "#true value\n",
    "plt.plot((0, 6), (0, 0), \"k--\")\n",
    "plt.title(\"Slit Width Deviation from mean\", fontsize=18)\n",
    "plt.xlim((0,6))\n",
    "plt.gca().set_ylabel('Deviation / mm', fontsize=16)\n",
    "plt.gca().set_xlabel('Slit number', fontsize=16)\n",
    "#plt.annotate(\"We removed these four points from\\nthe mean as outliers\", (1, -0.006), fontsize = 14)\n",
    "plt.savefig(\"./figs/Slit_deviations.png\", dpi = 300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Estimate the error in the minima position measurement\n",
    "#### Normally we only marked the two highest orders we could see, but for one masurement we made everyone mark all the orders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order</th>\n",
       "      <th>10</th>\n",
       "      <th>9</th>\n",
       "      <th>8</th>\n",
       "      <th>7</th>\n",
       "      <th>6</th>\n",
       "      <th>5</th>\n",
       "      <th>4</th>\n",
       "      <th>3</th>\n",
       "      <th>2</th>\n",
       "      <th>1</th>\n",
       "      <th>...</th>\n",
       "      <th>-1</th>\n",
       "      <th>-2</th>\n",
       "      <th>-3</th>\n",
       "      <th>-4</th>\n",
       "      <th>-5</th>\n",
       "      <th>-6</th>\n",
       "      <th>-7</th>\n",
       "      <th>-8</th>\n",
       "      <th>-9</th>\n",
       "      <th>-10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Minima position (mm)</th>\n",
       "      <th>Tom</th>\n",
       "      <td>65.5</td>\n",
       "      <td>59.5</td>\n",
       "      <td>52.5</td>\n",
       "      <td>46.5</td>\n",
       "      <td>39.5</td>\n",
       "      <td>33.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>13.5</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>-13</td>\n",
       "      <td>-20</td>\n",
       "      <td>-26.5</td>\n",
       "      <td>-33.0</td>\n",
       "      <td>-39.0</td>\n",
       "      <td>-45.5</td>\n",
       "      <td>-52.0</td>\n",
       "      <td>-58.5</td>\n",
       "      <td>-65.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lauren</th>\n",
       "      <td>65.0</td>\n",
       "      <td>59.5</td>\n",
       "      <td>52.5</td>\n",
       "      <td>46.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>33.5</td>\n",
       "      <td>26.5</td>\n",
       "      <td>20.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>-13</td>\n",
       "      <td>-19</td>\n",
       "      <td>-26.0</td>\n",
       "      <td>-32.5</td>\n",
       "      <td>-39.0</td>\n",
       "      <td>-45.5</td>\n",
       "      <td>-52.0</td>\n",
       "      <td>-59.0</td>\n",
       "      <td>-66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>William</th>\n",
       "      <td>65.5</td>\n",
       "      <td>59.5</td>\n",
       "      <td>52.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>19.5</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>...</td>\n",
       "      <td>-6.5</td>\n",
       "      <td>-14</td>\n",
       "      <td>-20</td>\n",
       "      <td>-28.0</td>\n",
       "      <td>-33.0</td>\n",
       "      <td>-39.5</td>\n",
       "      <td>-46.5</td>\n",
       "      <td>-52.5</td>\n",
       "      <td>-60.0</td>\n",
       "      <td>-66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Henry</th>\n",
       "      <td>66.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>45.5</td>\n",
       "      <td>39.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-6.5</td>\n",
       "      <td>-13</td>\n",
       "      <td>-20</td>\n",
       "      <td>-26.6</td>\n",
       "      <td>-32.5</td>\n",
       "      <td>-38.5</td>\n",
       "      <td>-45.0</td>\n",
       "      <td>-51.0</td>\n",
       "      <td>-58.5</td>\n",
       "      <td>-65.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Order                           10    9     8     7     6     5     4     3   \\\n",
       "Minima position (mm) Tom      65.5  59.5  52.5  46.5  39.5  33.0  26.0  20.0   \n",
       "                     Lauren   65.0  59.5  52.5  46.0  40.0  33.5  26.5  20.0   \n",
       "                     William  65.5  59.5  52.0  46.0  39.0  33.0  26.0  19.5   \n",
       "                     Henry    66.0  59.0  52.0  45.5  39.0  33.0  25.0  20.0   \n",
       "\n",
       "Order                           2    1   ...   -1   -2   -3    -4    -5   \\\n",
       "Minima position (mm) Tom      13.5  7.0  ...  -6.0  -13  -20 -26.5 -33.0   \n",
       "                     Lauren   14.0  6.0  ...  -6.0  -13  -19 -26.0 -32.5   \n",
       "                     William  13.0  6.5  ...  -6.5  -14  -20 -28.0 -33.0   \n",
       "                     Henry    13.0  7.0  ...  -6.5  -13  -20 -26.6 -32.5   \n",
       "\n",
       "Order                          -6    -7    -8    -9    -10  \n",
       "Minima position (mm) Tom     -39.0 -45.5 -52.0 -58.5 -65.5  \n",
       "                     Lauren  -39.0 -45.5 -52.0 -59.0 -66.0  \n",
       "                     William -39.5 -46.5 -52.5 -60.0 -66.0  \n",
       "                     Henry   -38.5 -45.0 -51.0 -58.5 -65.0  \n",
       "\n",
       "[4 rows x 21 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minima = pd.read_excel(data_url, sheetname=\"Minima position \", header=(0,1), skiprows=(), index_col = 0)\n",
    "minima.index.name = \"Order\"\n",
    "minima.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DD3DssccCwew2e+21Fw888ACtW7cu9eUoUX/4wx844IADWLhwId9//z2FhYU73BtQnVxy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\nzScAD6IsTVnJJZQ95scDn+o5/juUSfoLgUMBIuJHgD/uXu+XgIcC/w+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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ef23908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "names = [(\"Tom\",'b'),(\"Lauren\", \"r\"), (\"William\", 'g'), (\"Henry\", \"k\")]\n",
    "lines = []\n",
    "f, (ax2, ax1) = plt.subplots(2,1)\n",
    "deviations = pd.DataFrame()\n",
    "\n",
    "for name,col in names:\n",
    "    #fit a straight line to each\n",
    "    d,C = np.polyfit(minima.index, minima[\"Minima position (mm)\"][name], deg = 1)\n",
    "    #print(\"{}: d from curve fit: \\t{:.4}mm\".format(name, d))\n",
    "\n",
    "    ax2.scatter(minima.index, minima[\"Minima position (mm)\"][name], color = col)\n",
    "    d_est = (minima[\"Minima position (mm)\"][name].iloc[0] - minima[\"Minima position (mm)\"][name].iloc[-1]) / 20\n",
    "    #print(\"{}: dist / n: \\t\\t{:.4}mm\\n\".format(name,  d_est))\n",
    "    \n",
    "    deviations[name] = minima[\"Minima position (mm)\"][name] - (d_est*minima.index + C)\n",
    "    #plot deviations from (max - min) / n\n",
    "    lines.append( ax1.scatter(minima.index, deviations[name], color = col) )\n",
    "    \n",
    "ax2.legend(lines,\n",
    "            [n for n,c in names],\n",
    "           scatterpoints=1,\n",
    "           loc='upper left',\n",
    "           ncol=3,\n",
    "           fontsize=12)\n",
    "\n",
    "ax1.plot((-10, 10), (0, 0), \"k--\")\n",
    "ax1.set_title(\"Minima Position Deviation\", fontsize = 16)\n",
    "ax2.set_title(\"Minima Position\", fontsize = 16)\n",
    "ax1.set_xlim((-10,10))\n",
    "ax2.set_xlim((-10,10))\n",
    "ax1.set_ylabel('Deviation / mm', fontsize = 16)\n",
    "ax2.set_ylabel('Minima position / mm', fontsize = 16)\n",
    "ax1.set_xlabel('Minima Order', fontsize = 16)\n",
    "plt.savefig(\"./figs/Minima_deviations.png\", dpi = 300)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tom        0.296909\n",
       "Lauren     0.402152\n",
       "William    0.441133\n",
       "Henry      0.556530\n",
       "dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deviations.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this we can use a conserative estimate for the error in a meausuremt of a minima position as 0.6mm, so the error in our estimate of the minima separation is $$\\frac{\\sqrt{0.6^2 + 0.6^2}}{n} $$ where n is the number of minima"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.848528137423857"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from math import sqrt\n",
    "sqrt(0.6**2 + 0.6**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This agrees with the error we estimated by eye for this slit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: fit the model to the data in light of these errors "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>S_n</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>5.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>182.00000</td>\n",
       "      <td>162.00000</td>\n",
       "      <td>193.00000</td>\n",
       "      <td>129.00000</td>\n",
       "      <td>200.00000</td>\n",
       "      <td>176.00000</td>\n",
       "      <td>175.00000</td>\n",
       "      <td>116.00000</td>\n",
       "      <td>122.00000</td>\n",
       "      <td>210.00000</td>\n",
       "      <td>184.00000</td>\n",
       "      <td>185.00000</td>\n",
       "      <td>124.00000</td>\n",
       "      <td>123.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delta_y</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>17.00000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>10.00000</td>\n",
       "      <td>10.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <td>29.00000</td>\n",
       "      <td>11.00000</td>\n",
       "      <td>7.00000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>35.00000</td>\n",
       "      <td>13.00000</td>\n",
       "      <td>7.00000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>35.00000</td>\n",
       "      <td>13.00000</td>\n",
       "      <td>7.00000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>2.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D_h</th>\n",
       "      <td>5.16277</td>\n",
       "      <td>5.16277</td>\n",
       "      <td>5.16277</td>\n",
       "      <td>5.16277</td>\n",
       "      <td>4.66277</td>\n",
       "      <td>4.66277</td>\n",
       "      <td>4.66277</td>\n",
       "      <td>4.66277</td>\n",
       "      <td>4.66277</td>\n",
       "      <td>4.91277</td>\n",
       "      <td>4.91277</td>\n",
       "      <td>4.91277</td>\n",
       "      <td>4.91277</td>\n",
       "      <td>4.91277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D_v</th>\n",
       "      <td>0.03000</td>\n",
       "      <td>0.03000</td>\n",
       "      <td>0.03000</td>\n",
       "      <td>0.03000</td>\n",
       "      <td>0.03000</td>\n",
       "      <td>0.03000</td>\n",
       "      <td>0.03000</td>\n",
       "      <td>0.03000</td>\n",
       "      <td>0.03000</td>\n",
       "      <td>0.03000</td>\n",
       "      <td>0.03000</td>\n",
       "      <td>0.03000</td>\n",
       "      <td>0.03000</td>\n",
       "      <td>0.03000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delta_D_h</th>\n",
       "      <td>0.00500</td>\n",
       "      <td>0.00500</td>\n",
       "      <td>0.00500</td>\n",
       "      <td>0.00500</td>\n",
       "      <td>0.00500</td>\n",
       "      <td>0.00500</td>\n",
       "      <td>0.00500</td>\n",
       "      <td>0.00500</td>\n",
       "      <td>0.00500</td>\n",
       "      <td>0.00500</td>\n",
       "      <td>0.00500</td>\n",
       "      <td>0.00500</td>\n",
       "      <td>0.00500</td>\n",
       "      <td>0.00500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0          1          2          3          4          5   \\\n",
       "S_n          1.00000    2.00000    3.00000    4.00000    1.00000    2.00000   \n",
       "y          182.00000  162.00000  193.00000  129.00000  200.00000  176.00000   \n",
       "delta_y      1.00000    2.00000    3.00000    5.00000    1.00000    2.00000   \n",
       "n           29.00000   11.00000    7.00000    3.00000   35.00000   13.00000   \n",
       "D_h          5.16277    5.16277    5.16277    5.16277    4.66277    4.66277   \n",
       "D_v          0.03000    0.03000    0.03000    0.03000    0.03000    0.03000   \n",
       "delta_D_h    0.00500    0.00500    0.00500    0.00500    0.00500    0.00500   \n",
       "\n",
       "                  6          7          8          9          10         11  \\\n",
       "S_n          3.00000    4.00000    5.00000    1.00000    2.00000    3.00000   \n",
       "y          175.00000  116.00000  122.00000  210.00000  184.00000  185.00000   \n",
       "delta_y      4.00000    5.00000   17.00000    2.00000    2.00000    4.00000   \n",
       "n            7.00000    3.00000    2.00000   35.00000   13.00000    7.00000   \n",
       "D_h          4.66277    4.66277    4.66277    4.91277    4.91277    4.91277   \n",
       "D_v          0.03000    0.03000    0.03000    0.03000    0.03000    0.03000   \n",
       "delta_D_h    0.00500    0.00500    0.00500    0.00500    0.00500    0.00500   \n",
       "\n",
       "                  12         13  \n",
       "S_n          4.00000    5.00000  \n",
       "y          124.00000  123.00000  \n",
       "delta_y     10.00000   10.00000  \n",
       "n            3.00000    2.00000  \n",
       "D_h          4.91277    4.91277  \n",
       "D_v          0.03000    0.03000  \n",
       "delta_D_h    0.00500    0.00500  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "day_1 = pd.read_excel(data_url, sheetname=\"Single slit day 1\", header=(1), skiprows=0)\n",
    "day_2 = pd.read_excel(data_url, sheetname=\"Single slit day 2\", header=(1), skiprows=0)\n",
    "day_1.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>delta_d_</th>\n",
       "      <td>7.500000e-07</td>\n",
       "      <td>9.215024e-07</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>7.500000e-07</td>\n",
       "      <td>9.215024e-07</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>9.486833e-07</td>\n",
       "      <td>7.500000e-07</td>\n",
       "      <td>9.215024e-07</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>9.486833e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>5.001250e-04</td>\n",
       "      <td>1.996750e-04</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>5.001250e-04</td>\n",
       "      <td>1.996750e-04</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>2.530000e-05</td>\n",
       "      <td>5.001250e-04</td>\n",
       "      <td>1.996750e-04</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>2.530000e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>6.500000e-03</td>\n",
       "      <td>1.620000e-02</td>\n",
       "      <td>0.032167</td>\n",
       "      <td>0.064500</td>\n",
       "      <td>5.882353e-03</td>\n",
       "      <td>1.466667e-02</td>\n",
       "      <td>0.029167</td>\n",
       "      <td>0.058000</td>\n",
       "      <td>1.220000e-01</td>\n",
       "      <td>6.176471e-03</td>\n",
       "      <td>1.533333e-02</td>\n",
       "      <td>0.030833</td>\n",
       "      <td>0.062000</td>\n",
       "      <td>1.230000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delta_y</th>\n",
       "      <td>3.571429e-05</td>\n",
       "      <td>2.000000e-04</td>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.002500</td>\n",
       "      <td>2.941176e-05</td>\n",
       "      <td>1.666667e-04</td>\n",
       "      <td>0.000667</td>\n",
       "      <td>0.002500</td>\n",
       "      <td>1.700000e-02</td>\n",
       "      <td>5.882353e-05</td>\n",
       "      <td>1.666667e-04</td>\n",
       "      <td>0.000667</td>\n",
       "      <td>0.005000</td>\n",
       "      <td>1.000000e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>5.162857e+00</td>\n",
       "      <td>5.162857e+00</td>\n",
       "      <td>5.162857</td>\n",
       "      <td>5.162857</td>\n",
       "      <td>4.662867e+00</td>\n",
       "      <td>4.662867e+00</td>\n",
       "      <td>4.662867</td>\n",
       "      <td>4.662867</td>\n",
       "      <td>4.662867e+00</td>\n",
       "      <td>4.912862e+00</td>\n",
       "      <td>4.912862e+00</td>\n",
       "      <td>4.912862</td>\n",
       "      <td>4.912862</td>\n",
       "      <td>4.912862e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delta_D</th>\n",
       "      <td>5.000000e-03</td>\n",
       "      <td>5.000000e-03</td>\n",
       "      <td>0.005000</td>\n",
       "      <td>0.005000</td>\n",
       "      <td>5.000000e-03</td>\n",
       "      <td>5.000000e-03</td>\n",
       "      <td>0.005000</td>\n",
       "      <td>0.005000</td>\n",
       "      <td>5.000000e-03</td>\n",
       "      <td>5.000000e-03</td>\n",
       "      <td>5.000000e-03</td>\n",
       "      <td>0.005000</td>\n",
       "      <td>0.005000</td>\n",
       "      <td>5.000000e-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    0             1         2         3             4   \\\n",
       "delta_d_  7.500000e-07  9.215024e-07  0.000002  0.000002  7.500000e-07   \n",
       "d         5.001250e-04  1.996750e-04  0.000100  0.000050  5.001250e-04   \n",
       "y         6.500000e-03  1.620000e-02  0.032167  0.064500  5.882353e-03   \n",
       "delta_y   3.571429e-05  2.000000e-04  0.000500  0.002500  2.941176e-05   \n",
       "D         5.162857e+00  5.162857e+00  5.162857  5.162857  4.662867e+00   \n",
       "delta_D   5.000000e-03  5.000000e-03  0.005000  0.005000  5.000000e-03   \n",
       "\n",
       "                    5         6         7             8             9   \\\n",
       "delta_d_  9.215024e-07  0.000002  0.000002  9.486833e-07  7.500000e-07   \n",
       "d         1.996750e-04  0.000100  0.000050  2.530000e-05  5.001250e-04   \n",
       "y         1.466667e-02  0.029167  0.058000  1.220000e-01  6.176471e-03   \n",
       "delta_y   1.666667e-04  0.000667  0.002500  1.700000e-02  5.882353e-05   \n",
       "D         4.662867e+00  4.662867  4.662867  4.662867e+00  4.912862e+00   \n",
       "delta_D   5.000000e-03  0.005000  0.005000  5.000000e-03  5.000000e-03   \n",
       "\n",
       "                    10        11        12            13  \n",
       "delta_d_  9.215024e-07  0.000002  0.000002  9.486833e-07  \n",
       "d         1.996750e-04  0.000100  0.000050  2.530000e-05  \n",
       "y         1.533333e-02  0.030833  0.062000  1.230000e-01  \n",
       "delta_y   1.666667e-04  0.000667  0.005000  1.000000e-02  \n",
       "D         4.912862e+00  4.912862  4.912862  4.912862e+00  \n",
       "delta_D   5.000000e-03  0.005000  0.005000  5.000000e-03  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reformat the datasets a bit\n",
    "#put everything in meters\n",
    "def reformat(data):\n",
    "    #look up the measured d value corresponding to each slit\n",
    "    if \"d\" not in data:\n",
    "        data.insert(0, \"d\", 1e-3 * np.array(widths['mean'][data.S_n]))\n",
    "        data.insert(0, \"delta_d_\", 1e-3 * np.array(widths['std'][data.S_n]))\n",
    "        \n",
    "    if \"S_n\" in data: data = data.drop(\"S_n\", axis = 1)\n",
    "    \n",
    "    #convert y to meters and divide by n -1\n",
    "    if \"n\" in data:\n",
    "        data.y = 1e-3 * data.y / (data.n - 1)\n",
    "        data.delta_y = 1e-3 * data.delta_y / (data.n - 1)\n",
    "        data = data.drop(\"n\", axis = 1)\n",
    "        \n",
    "    #apply the horizontal correction and get rid of the extra columns\n",
    "    if \"D_h\" in data:\n",
    "        data[\"D\"] = np.sqrt(data.D_h**2 + data.D_v**2)\n",
    "        data[\"delta_D\"] = data.delta_D_h\n",
    "        data = data.drop([\"D_h\", \"D_v\", \"delta_D_h\"], axis = 1)\n",
    "        \n",
    "    \n",
    "    return data\n",
    "\n",
    "day_1 = reformat(day_1)\n",
    "day_2 = reformat(day_2)\n",
    "day_1.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rgJOBFRExKVt2BfCvwBZgEfDRiFiX3XcJcBZQD5wfEXc2s18HdzMzszaKgOXL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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109ee04e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_single_slit(data, subtitle = \"\"):\n",
    "    #split the data by the D value\n",
    "    gb = data.groupby(by = [\"D\"])\n",
    "    \n",
    "    #make space for a plot for each D value\n",
    "    f, axes = plt.subplots(len(gb), sharex='col')\n",
    "\n",
    "    # Set common labels\n",
    "    f.text(0.5, 0.04, 'Slit width / mm', ha='center', va='center', fontsize = 16)\n",
    "    f.text(0.06, 0.5, 'Inverse 1st Minima position / m$^{-1}$', ha='center', va='center', rotation='vertical', fontsize = 16)\n",
    "\n",
    "    f.suptitle(\"Inverse 1st Minima position against slit width {}\".format(subtitle), fontsize=16)\n",
    "\n",
    "    #loop over the subsets of the data and plot\n",
    "    for label, ax in zip(gb.groups, axes):\n",
    "        ax.set_xlim(0, 0.55)\n",
    "        ex = gb.get_group(label)\n",
    "        \n",
    "        #plot inverse y against d\n",
    "        inv_y = 1 / ex.y\n",
    "        d_inv_y = ex.delta_y  / ex.y**2\n",
    "\n",
    "        ax.errorbar(1e3 * ex.d, inv_y, yerr = d_inv_y.as_matrix())\n",
    "        ax.set_title(\"D = {:.3}m ± 5mm\".format(label), fontsize = 14)\n",
    "\n",
    "plot_single_slit(day_1)\n",
    "plt.savefig(\"./figs/Results_1.png\", dpi = 300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wA/A/HcxjOS2f27yisjbHt56KdJ3HGlIe+3NpCdDvJXV6LCHtk66eAaiGN5LO3r6tVCBp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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c06a940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_single_slit(day_2, subtitle = \"with aperture flipped\")\n",
    "plt.savefig(\"./figs/Results_2.png\", dpi = 300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to fit a function of the form:\n",
    "$$ y = \\lambda \\frac{D}{d} $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lambda from day 1 = 6.287e-07m ± 1.8e-09m\n",
      "lambda from day 2 = 6.309e-07m ± 1.7e-09m\n",
      "External lambda = 6.272e-07m ± 1.2e-09m\n"
     ]
    }
   ],
   "source": [
    "true_l = 632.8e-9\n",
    "\n",
    "#results from day 1\n",
    "from scipy.optimize import curve_fit\n",
    "\n",
    "def fn(x, l):\n",
    "    D, d = x\n",
    "    return l*x.D / x.d\n",
    "\n",
    "\n",
    "def fit(data, p0 = true_l):\n",
    "    popt, pcov = curve_fit(fn, data[[\"D\", \"d\"]], data[\"y\"], p0 = p0, sigma = data.delta_y, absolute_sigma = True)\n",
    "    l = popt[0]\n",
    "    sigma = np.sqrt(pcov[0])[0]\n",
    "    return l, sigma\n",
    "\n",
    "l1, delta_l1 = fit(day_1)\n",
    "l2, delta_l2 = fit(day_2)\n",
    "\n",
    "print(\"lambda from day 1 = {:.4}m ± {:.2}m\".format(l1, delta_l1))\n",
    "print(\"lambda from day 2 = {:.4}m ± {:.2}m\".format(l2, delta_l2))\n",
    "\n",
    "### results from the paper we found ###\n",
    "def external_lambda():\n",
    "    g = 0.601 * 1e6 #m^-1\n",
    "    delta_g = 0.003 * 1e6 #m^-1\n",
    "    L = 2.653 #m\n",
    "    l = 1 / (g * L)\n",
    "    delta_l = delta_g / (L * g)**2\n",
    "    return l, delta_l\n",
    "\n",
    "l3, delta_l3 = external_lambda()\n",
    "print(\"External lambda = {:.4}m ± {:.2}m\".format(l3, delta_l3))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above regression doesn't take into account the errors in D so I'll try adding gaussian noise N(mean = 0, sigma) to the D values and runnning the regression many times, to estimate the error in the regression due to error in D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original fitted lambda: 628.7nm\n",
      "mean fitted lambda: 628.6nm\n",
      "sigma due to errors in D and d: ±0.84nm, ±0.13%\n",
      "sigma due to errors in y: ±1.84nm, ±0.29%\n",
      "Total: ±2.02nm\n"
     ]
    }
   ],
   "source": [
    "def noisy_fit(data, n = 1000):\n",
    "    data[\"true_D\"] = data.D\n",
    "    data[\"true_d\"] = data.d\n",
    "    ls = []\n",
    "    for i in range(n):\n",
    "        data.D = data.true_D + np.random.normal(loc = 0.0, scale = 0.005, size = len(data.D))\n",
    "        data.d = np.array([np.random.normal(mean, std) for mean,std in np.array(data[[\"true_d\", \"delta_d_\"]])])\n",
    "        #data.d = data.true_d + np.random.normal(loc = 0.0, scale = 0.000750, size = len(data.d))\n",
    "        popt, pcov = curve_fit(fn, data[[\"D\", \"d\"]], data[\"y\"], p0 = true_l, sigma = data.delta_y, absolute_sigma = True)\n",
    "        ls.append(popt[0])\n",
    "        \n",
    "    data.D = data.true_D\n",
    "    data.d = data.true_d\n",
    "    return np.array(ls)\n",
    "\n",
    "ls = noisy_fit(day_1)\n",
    "\n",
    "print(\"original fitted lambda: {:.4}nm\".format(l1 * 1e9))\n",
    "print(\"mean fitted lambda: {:.4}nm\".format(ls.mean() * 1e9))\n",
    "print(\"sigma due to errors in D and d: ±{:.3}nm, ±{:.2}%\".format(ls.std() * 1e9, 100 * ls.std() / ls.mean()))\n",
    "print(\"sigma due to errors in y: ±{:.3}nm, ±{:.2}%\".format(delta_l1 * 1e9, 100 * delta_l1 / ls.mean()))\n",
    "\n",
    "total = np.sqrt(delta_l1**2 + ls.std()**2)\n",
    "print(\"Total: ±{:.3}nm\".format( total * 1e9, 100 * total / ls.mean()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clearly the error in D makes very little difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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Dg/0uwkaLuu8v6r+/qP/+oe77i/qf+vryozYTYZue8gBMebTZBoDJz/aEb5Ds\n14/aAAAAANMeYRsAAAAohLANAAAAFELYBgAAAAohbAMAAACFELYBAACAQgjbAAAAQCGEbQAAAKAQ\nwjYAAABQSF9+rh0A0F3zF8xXY0Wj7XhzZs/R0iVLez49ANhYEbYBYBporGh09PPvjaPbB+gS0wOA\njRXNSAAAAIBCCNsAAABAIYRtAAAAoBDCNgAAAFAIYRsAAAAohLANAAAAFELYBgAAAAohbAMAAACF\nELYBAACAQgjbAAAAQCGEbQAAAKAQwjYAAABQCGEbAAAAKISwDQAAABRC2AYAAAAKIWwDAAAAhRC2\nAQAAgEII2wAAAEAhhG0AAACgEMI2AAAAUAhhGwAAACiEsA0AAAAUQtgGAAAACiFsAwAAAIUQtgEA\nAIBCCNsAAABAIYRtAAAAoBDCNgAAAFAIYRsAAAAohLANAAAAFELYBgAAAAohbAMAAACFELYBAACA\nQgjbAAAAQCGEbQAAAKAQwjYAAABQCGEbAAAAKISwDQAAABRC2AYAAAAKIWwDAAAAhRC2AQAAgEII\n2wAAAEAhhG0AAACgEMI2AAAAUAhhGwAAACiEsA0AAAAUQtgGAAAACiFsAwAAAIUQtgEAAIBCCNsA\nAABAIYRtAAAAoBDCNgAAAFAIYRsAAAAohLANAAAAFDKzHzO1fa2kOyStlfRgRDzD9taSvi9pR0nX\nSnp1RNzRj/IBAAAA3dCvM9trJQ1GxN4R8Yw87EOSzoyI3SQtk3Rkn8oGAAAAdEW/wrZbzPsgScfm\n/4+VtKCnJQIAAAC6rF9hOyT9zPZ5tt+ah20fEaskKSJWSprVp7IBAAAAXdGXNtuS9o2IG2xvJ+kM\n21cqBfCq+vOHLFq06KH/BwcHNTg4WKKMADDtNBoNzZ03t/14yxsa0EDXpjdn9hwtXbK0ozICQL8M\nDQ1paGioq9PsS9iOiBvy35tsL5H0DEmrbG8fEatsD0i6caT3V8M2AKBzq9eu1sDC9iF6+Ijhrk6v\ncXSjo+kBQD/VT+IuXrx4wtPseTMS21vY3jL//whJB0q6RNJpkg7Lox0q6dRelw0AAADopn6c2d5e\n0im2I8//+Ig4w/b5kn5g+82SrpP06j6UDQAAAOianoftiLhG0lNbDL9V0gG9Lg8AAABQCr8gCQAA\nABRC2AYAAAAK6VfXfwCADnS7qz4AQG8RtgFgEut2V30AgN6iGQkAAABQCGEbAAAAKISwDQAAABRC\n2AYAAAD3t4uvAAAgAElEQVQKIWwDAAAAhRC2AQAAgEII2wAAAEAhhG0AAACgEMI2AAAAUAhhGwAA\nACiEsA0AAAAUQtgGAAAACiFsAwAAAIXM7HcBAADTX6PR0Nx5czsad87sOVq6ZGnhEgFAbxC2AQDF\nrV67WgMLBzoat3F0o3BpAKB3aEYCAAAAFELYBgAAAAohbAMAAACFELYBAACAQgjbAAAAQCGEbQAA\nAKAQwjYAAABQCGEbAAAAKISwDQAAABRC2AYAAAAKIWwDAAAAhRC2AQAAgEII2wAAAEAhhG0AAACg\nEMI2AAAAUAhhGwAAACiEsA0AAAAUQtgGAAAACiFsAwAAAIUQtgEAAIBCZva7AAAwncxfMF+NFY22\n4zWWNzSggR6UCADQT4RtAOiixoqGBha2D9HDRwz3oDQAgH6jGQkAAABQCGEbAAAAKISwDQAAABRC\n2AYAAAAKIWwDAAAAhRC2AQAAgEII2wAAAEAhhG0AAACgEMI2AAAAUAhhGwAAACiEsA0AAAAUQtgG\nAAAACiFsAwAAAIXM7HcBAAAYj/kL5quxotF2vDmz52jpkqU9KBEAbIiwDQCYkhorGhpYONB+vKPb\nB3IAKIVmJAAAAEAhhG0AAACgEMI2AAAAUAhhGwAAACiEsA0AAAAUQtgGAAAACiFsAwAAAIUQtgEA\nAIBCCNsAAABAIX0L27Zn2P6D7dPy861tn2H7SttLbW/Vr7IBAAAA3dDPM9uHS7qs8vxDks6MiN0k\nLZN0ZF9KBQAAAHRJX8K27R0kvVjSNyqDD5J0bP7/WEkLel0uAAAAoJv6dWb7c5I+KCkqw7aPiFWS\nFBErJc3qR8EAAACAbpnZ6xnafomkVRFxoe3BUUaNkV5YtGjRQ/8PDg5qcHC0yQAA0N78BfPVWNFo\nO96c2XO0dMnSHpQIQK8NDQ1paGioq9PsediWtK+kl9t+saTNJf2N7eMkrbS9fUSssj0g6caRJlAN\n2wAAdENjRUMDCwfaj3d0+0AOYGqqn8RdvHjxhKfZ82YkEfHhiJgTEbtIOkTSsoh4g6QfSTosj3ao\npFN7XTYAAACgmyZTP9ufkvQC21dK2j8/BwAAAKasfjQjeUhEnC3p7Pz/rZIO6Gd5AAAAgG6aTGe2\nAQAAgGmFsA0AAAAUQtgGAAAACiFsAwAAAIUQtgEAAIBCCNsAAABAIYRtAAAAoBDCNgAAAFAIYRsA\nAAAohLANAAAAFNLXn2sHgKli/oL5aqxotB2vsbyhAQ30oEQAgKmAsA0AHWisaGhgYfsQPXzEcA9K\nAwCYKmhGAgAAABRC2AYAAAAKIWwDAAAAhRC2AQAAgEII2wAAAEAhhG0AAACgEMI2AAAAUAhhGwAA\nACiEsA0AAAAUQtgGAAAACiFsAwAAAIUQtgEAAIBCCNsAAABAIYRtAAAAoBDCNgAAAFAIYRsAAAAo\nhLANAAAAFELYBgAAAAohbAMAAACFELYBAACAQmb2uwAAAJTUaDQ0d97c9uMtb2hAAz0oEYCNCWEb\nADCtrV67WgML24fo4SOGe1AaABsbmpEAAAAAhRC2AQAAgEII2wAAAEAhhG0AAACgEMI2AAAAUAhh\nGwAAACiEsA0AAAAUQtgGAAAACiFsAwAAAIUQtgEAAIBCCNsAAABAIYRtAAAAoBDCNgAAAFAIYRsA\nAAAohLANAAAAFELYBgAAAAohbAMAAACFELYBAACAQgjbAAAAQCGEbQAAAKAQwjYAAABQyMx+FwAA\n+mn+gvlqrGi0Ha+xvKEBDfSgRGg0Gpo7b2778VgnAKYAwjaAjVpjRUMDC9sHtuEjhntQGkjS6rWr\nWScApg2akQAAAACFELYBAACAQgjbAAAAQCGEbQAAAKAQwjYAAABQCL2RAABQQKfdSs6ZPUdLlyzt\nQYkA9ANhGwCAAjrtVrJxdPtADmDqohkJAAAAUEjPw7btzWz/1vYFti+1/ck8fGvbZ9i+0vZS21v1\numwAAABAN/U8bEfE/ZKeHxF7S9pL0n6295X0IUlnRsRukpZJOrLXZQMAAAC6qS/NSCLinvzvZrkM\nt0k6SNKxefixkhb0oWgAAABA1/QlbNueYfsCSSslDUXEZZK2j4hVkhQRKyXN6kfZAAAAgG4Zc28k\ntg+V9BpJcyQ9vPZyRMTj200jItZK2tv2IyUttT0oKeqjjbVsAAAAwGQyprBt+6OSFkv6o6QLJd0/\nkZlHxF9t/0TSPpJW2d4+IlbZHpB040jvW7Ro0UP/Dw4OanBwcCLFAAAAADQ0NKShoaGuTnOsZ7bf\nIunzEfH+8c7Q9qMlPRgRd9jeXNILlAL8aZIOk/RpSYdKOnWkaVTDNgAAANAN9ZO4ixcvnvA0xxq2\nt5X0ownO8zGSjrVtpTbjx0XEz3Mb7h/YfrOk6yS9eoLzAQAAAPpqrGH7bElPUeqab1wi4hJJT2sx\n/FZJB4x3ugAAAMBkM9aw/T5JP7R9i6SfSLq1PkK++REAAADY6I01bF+V/x4zwusxjmkCAAAA09JY\ng/G/iy75AAAAgI6MKWxHxKJC5QAAYEpoNBqaO29u+/GWNzSggR6UCMBkRpMPAADGYPXa1RpY2D5E\nDx8x3IPSAJjs+vJz7QAAAMDGgLANAAAAFELYBgAAAAohbAMAAACFELYBAACAQgjbAAAAQCGEbQAA\nAKAQwjYAAABQCGEbAAAAKISwDQAAABRC2AYAAAAKIWwDAAAAhRC2AQAAgEII2wAAAEAhhG0AAACg\nEMI2AAAAUAhhGwAAACiEsA0AAAAUQtgGAAAACiFsAwAAAIUQtgEAAIBCCNsAAABAIYRtAAAAoBDC\nNgAAAFAIYRsAAAAohLANAAAAFELYBgAAAAohbAMAAACFzOx3AQAA2Jg1Gg3NnTe37XhzZs/R0iVL\ne1AiAN1E2AYAoI9Wr12tgYUDbcdrHN3oQWkAdBvNSAAAAIBCCNsAAABAIYRtAAAAoBDCNgAAAFAI\nYRsAAAAohN5IAPTd/AXz1VjRvqeFm268SdvN2q7teHSRBgCYLAjbAPqusaLRUddnw0cMa8+Fe7af\nHl2kAQAmCZqRAAAAAIUQtgEAAIBCCNsAAABAIYRtAAAAoBDCNgAAAFAIYRsAAAAohLANAAAAFELY\nBgAAAAohbAMAAACFELYBAACAQgjbAAAAQCGEbQAAAKAQwjYAAABQyMx+FwAAuq3RaGjuvLmdjbu8\noQENFC4RAGBjRdgGMO2sXrtaAws7C9DDRwwXLg0AYGNGMxIAAACgEMI2AAAAUAhhGwAAACiEsA0A\nAAAUQtgGAAAACiFsAwAAAIUQtgEAAIBCCNsAAABAIYRtAAAAoJCeh23bO9heZvtS25fYfm8evrXt\nM2xfaXup7a16XTYAAACgm/pxZnu1pA9ExO6SniXpPbafJOlDks6MiN0kLZN0ZB/KBgAAAHRNz8N2\nRKyMiAvz/3dJulzSDpIOknRsHu1YSQt6XTYAAACgm/raZtv2TpKeKulcSdtHxCopBXJJs/pXMgAA\nAGDiZvZrxra3lHSSpMMj4i7bURul/vwhixYteuj/wcFBDQ4OligiAAAANiJDQ0MaGhrq6jT7ErZt\nz1QK2sdFxKl58Crb20fEKtsDkm4c6f3VsA0AAAB0Q/0k7uLFiyc8zX41I/mWpMsi4vOVYadJOiz/\nf6ikU+tvAgAAAKaSnp/Ztr2vpNdJusT2BUrNRT4s6dOSfmD7zZKuk/TqXpcNAAAA6Kaeh+2I+LWk\nTUZ4+YBelgUAAAAoiV+QBAAAAAohbAMAAACFELYBAACAQgjbAAAAQCGEbQAAAKAQwjYAAABQCGEb\nAAAAKISwDQAAABRC2AYAAAAKIWwDAAAAhfT859oBbDzmL5ivxopG2/Eayxsa0EAPSgRMXY1GQ3Pn\nzW073pzZc7R0ydIelAhAJwjbAIpprGhoYGH7ED18xHAPSgNMbavXru5of2oc3f4LLoDeoRkJAAAA\nUAhhGwAAACiEsA0AAAAUQtgGAAAACiFsAwAAAIXQGwmwEei0Cz66DAOmProIBCYXwjawEei0Cz66\nDAOmProIBCYXmpEAAAAAhRC2AQAAgEII2wAAAEAhhG0AAACgEMI2AAAAUAhhGwAAACiEsA0AAAAU\nQtgGAAAACiFsAwAAAIUQtgEAAIBCCNsAAABAIYRtAAAAoBDCNgAAAFDIzH4XAMDk0Wg0NHfe3Lbj\n3XTjTdpu1nbtp7e8oQENdKNoAABMSYRtAA9ZvXa1Bha2D8fDRwxrz4V7djQeAAAbM5qRAAAAAIUQ\ntgEAAIBCCNsAAABAIYRtAAAAoBDCNgAAAFAIYRsAAAAohLANAAAAFELYBgAAAAohbAMAAACFELYB\nAACAQgjbAAAAQCGEbQAAAKAQwjYAAABQyMx+FwDAhuYvmK/Gikbb8ebMnqOlS5b2oEQApptGo6G5\n8+Z2NC7HGmD8CNvAJNRY0dDAwoH24x3dPpADQCur167u6DgjcawBJoJmJAAAAEAhhG0AAACgEMI2\nAAAAUAhhGwAAACiEsA0AAAAUQtgGAAAACqHrPwAA0FP8lgA2JoRtAADQU/yWADYmNCMBAAAACiFs\nAwAAAIUQtgEAAIBCCNsAAABAIYRtAAAAoBB6IwF6pNOuriSpsbyhAbW/Ux8AeqHRaGjuvLltx6Or\nPmBDhG2gRzrt6kqSho8YLlwaAOjc6rWr6aoPGCeakQAAAACF9Dxs2/6m7VW2L64M29r2GbavtL3U\n9la9LhcAAADQbf04s32MpPm1YR+SdGZE7CZpmaQje14qAAAAoMt6HrYj4leSbqsNPkjSsfn/YyUt\n6GmhAAAAgAImS5vtWRGxSpIiYqWkWX0uDwAAADBhk7U3khjtxUWLFj30/+DgoAYHBwsXBwAAANPd\n0NCQhoaGujrNyRK2V9nePiJW2R6QdONoI1fDNgAAANAN9ZO4ixcvnvA0+9WMxPnRdJqkw/L/h0o6\ntdcFAgAAALqtH13/nSDpN5J2td2w/SZJn5L0AttXSto/PwcAAACmtJ43I4mI147w0gE9LQgAAABQ\n2GTpjQQAAACYdgjbAAAAQCGEbQAAAKAQwjYAAABQCGEbAAAAKISwDQAAABRC2AYAAAAKIWwDAAAA\nhRC2AQAAgEII2wAAAEAhhG0AAACgEMI2AAAAUAhhGwAAACiEsA0AAAAUQtgGAAAACiFsAwAAAIUQ\ntgEAAIBCCNsAAABAIYRtAAAAoBDCNgAAAFAIYRsAAAAohLANAAAAFELYBgAAAAohbAMAAACFELYB\nAACAQmb2uwDAVDd/wXw1VjTajtdY3tCABro670ajobnz5vZl3gBQWqfHuDmz52jpkqU9KBEwdoRt\nYIIaKxoaWNg+yA4fMdz1ea9eu7pv8waA0jo9xjWObn/CA+gXmpEAAAAAhRC2AQAAgEII2wAAAEAh\nhG0AAACgEMI2AAAAUAhhGwAAACiErv+AEfSz/2wAQOc67Y/7phtv0naztms7Hv12o5sI28AI+tl/\nNgCgc2P5zYE9F+7Zdjz67UY30YwEAAAAKISwDQAAABRC2AYAAAAKIWwDAAAAhRC2AQAAgELojQQA\nAHRFp13wTfYuUztdDroIRCcI2wAAoCvG0gXfZNbpctBFIDpBMxIAAACgEMI2AAAAUAhhGwAAACiE\nsA0AAAAUQtgGAAAACiFsAwAAAIUQtgEAAIBCCNsAAABAIYRtAAAAoBDCNgAAAFAIYRsAAAAohLAN\nAAAAFELYBgAAAAqZ2e8CAN0yf8F8NVY02o530403abtZ27Udr7G8oQENdKNoAAC01ennmCTNmT1H\nS5csLVwidANhG9NGY0VDAwvbh+PhI4a158I9OxoPAIBe6fRzTJIaR3cWytF/NCMBAAAACiFsAwAA\nAIUQtgEAAIBCCNsAAABAIdwgiY7cdddduuOOO9qOt+mmm2rWrFk9KBEAAMDkR9hGR96y8C26rHGZ\nZmwy+sWQuCd08v+erCc+8Yk9KhkAAP3RaDQ0d97ctuP1s5u+TrsTpCvBcgjb6Mitd9yqLV+2pR6+\nzcNHHe/2U27X3Xff3aNSAQDQP6vXru6oq75+dtPXaXeCdCVYDm22AQAAgEII2wAAAEAhkyps236h\n7StsX2X7X/pdHmzotstv63cRNlrUfX+tfWBtv4uwUWP77x/qvr+o/6lv0oRt2zMkHS1pvqTdJb3G\n9pP6WyrU3X7F7f0uwkaLuu+vtQ8StvuJ7b9/qPv+ov6nvkkTtiU9Q9KfIuK6iHhQ0vckHdTnMgEA\nAADjNpl6I5kt6frK8+VKARyTwGabbqaVQyt135/u0+0/Hvlb9gM3P6CZMyfTZgUAANA/joh+l0GS\nZPsVkuZHxNvz89dLekZEvLc23uQoMAAAAKa9iPBE3j+ZTkGukDSn8nyHPGw9E11gAAAAoFcmU5vt\n8yQ9wfaOth8m6RBJp/W5TAAAAMC4TZoz2xGxxvZCSWcofQn4ZkRc3udiAQAAAOM2adpsAwAAANPN\nZGpGIttb2T7R9uW2L7X9t7XXX2v7ovz4le29Kq9dm4dfYPt3vS/91DbBuh/1vWhvHPW/Zx6+a97m\n/5D/3mH7va3nglYmuO0fmd9zse3jcxM4jMEE6/9w25fkB9v9OHRQ/y+vfLaeb3u/ymv8EN0ETLDu\nv2l7le2Le1/y6WG89W97B9vL8ns6O/ZExKR5SPpfSW/K/8+U9Mja68+UtFX+/4WSzq28Nixp634v\nw1R9TLDuR30vj7L1XxlnhqS/SHpcv5dnKj3GW/eSdszHnYfl59+X9MZ+L89Ue0yg/neXdLGkzSRt\notQEcZd+L89Ue3RQ/1tU/t9T0p/z/zMk/TnvB5tKulDSk/q9PFPpMd66z8+fI+mpki7u93JM1ccE\ntv0BSU/N/28p6cp22/6kObNt+5GSnhsRx0hSRKyOiL9Wx4mIcyPijvz0XKW+uR+ahCbZmfqpYiJ1\n38l7MboubPtNB0i6OiKub/EaWphg3f9V0gOSHmF7pqQtlL7soEMTrP+5kn4bEfdHxBpJv5D09z0q\n+rTQYf3fU3m6paSb8//8EN0ETLDuFRG/ksTvuI/TROo/IlZGxIX5/7skXa7Wn8kPmUzhdGdJN9s+\nJl8S/5rtzUcZ/62STq88D0k/s32e7bcVLen0M5G6H+t7saGJbvtN/yDpu0VKOH2Nu+4j4jZJR0lq\nKHVTentEnFm8xNPLRLb9P0p6ru2tbW8h6cWSHle4vNNNR/Vve4HtyyX9RFLzknmrH6IbNXBgPROp\ne0xcV+rf9k5KVxh+O9rMJlPYninpaZK+GBFPk3SPpA+1GtH28yW9SVK1jdi++X0vlvQe288pXN7p\nZCJ13/F7MaKJbvuyvamkl0s6sWxRp51x173tXSS9X+ky+mMlbWn7tb0o9DQy7vqPiCskfVrSz5Q+\nCC+QtKYHZZ5OOqr/iFgSEXOVjjHH9baI09ZY6/5lou67acL1b3tLSSdJOjyf4R7RZArbyyVdHxHn\n5+cnKVXEevLNMV+T9PJ8ZkmSFBE35L83STpF/NT7WEyk7jt6L0Y1oW0/e5Gk3+ftH52bSN3vI+nX\nEXFrbsbwQ0nP7kGZp5OJHvePiYh9ImJQ0u2Sripf5GllTMfviPilpJm2t1WHP0SHEY217n+ldXWP\niZtQ/eemgydJOi4iTm03s0kTtiNilaTrbe+aB+0v6bLqOLbnSDpZ0hsi4urK8C3yNwzZfoSkA5Uu\nMaIDE6n7Tt6L0U2k/iteI5qQjNkE6/5KSc+0/XDbzu/ltwHGYKLbvu3tKuMcLOmE4oWeRjqs/8dX\n/n9aft8t4ofoJmSCdf/Q4PzAGHWh/r8l6bKI+Hwn85tU/Wzbfoqkbyjd2TysdMnwEEkREV+z/XWl\nG2CuU9rAHoyIZ9jeWelsdihdGjg+Ij7Vj2WYqsZb9yO9t3JDEzowwfrfIg/fJSLu7Ef5p7IJ1v0H\nJR2m1HzhAklvzTeLoUMTrP9fSNpG0oOS3h8RQ71fgqmtg/r/Z0lvVLoZ+G5JH4iI8/J7Xyjp81r3\nQ3R87o7BBOv+BEmDkraVtErSx5o3+6Ez46j/90fE+bb3Vboh+xKl3BmSPhwRPx1xXpMpbAMAAADT\nyaRpRgIAAABMN4RtAAAAoBDCNgAAAFAIYRsAAAAohLANAACAKcP2N22vsn1xF6Y1aPuC/EuSF9i+\n1/bLu1HOh+ZBbyQAAACYKvKvhN8l6dsRsVcXp7u1pD9J2iEi7uvWdDmzDaDnbB9qe22Lxxrb++Vx\n9s/Dnl153/ttH9RiegfbPrxAOb9j+0/dnu5km2ee7yNsH2/7xrwe/qvgvPq1jN+xfc0E39/zcgNY\nX/5Fx/V+Sdn2LrZPt32e7bMrP1gzFq+UdHo3g7aUfgAGAPohlA5s9Z94bv6K128lPVPSpZXXPiDp\nZ5LqP4/795L2VfqBjW6XsdeX//oxT0k6XGl9HCbpakl/KTivfi3jROfbr3IDaO9rkt4REVfbfoak\nLyv9MuRYHCLpqG4XjLANoJ8uiojhVi9ExF2Sftfj8mzM5kq6PiK+242J2X5YRDzQjWkBwGhsP0LS\nsyWdaLv5E/ab5tcOlvTvWv+LsiUtj4gXVaYxIGkPSUu7XT6akQCYlOrNSGxfL+mxkg6rNDn5mu3j\nJL1O0o6V5ihXVaazne2v2l5h+37bl9l+c4v5HZhvjrnP9lW239phOa+w/b0Ww5+dy/KS/PyJto+z\nfY3te2z/2fbRtrcaSz1Uhr81D39sbfg7bV+Ub/K5MdfRiPOwvYnttZJeK2mXSt026/1Jtk+1fXsu\n929sv6A2jY/n9821fYbtOyUd36bq6uX4j3yD0h22b7J9pu15I9TFS21/w/at+fFZ2zNs72v7HNt3\n277Y9gEjzOs5+VLzvbaHbb+rxTgdbQ+dlBtAcTMk3RYRT4uIvfNjD0mKiFMiYs+I2Kvy2LMatLNX\nSzolItZ0u3Cc2QbQT5vY3qTyPCJibfV55f+XSjpD6Wz3f+RhNyqdoXi0pL0kLcjP75OkHDJ/I2kT\nSR+RdJ2kF0v6uu1NI+Krebw9JP1I0jmSXiVpc6UzIVs0pzWK4yR92PbfRMSdleFvyOU7PT+fLakh\n6X2SbpX0eEkfzuX+uzbzaNV0YYMmDbY/K+m9kv5b0j9J2kHSJyU9WdJzWk44Yo3tZ0r6uKTdlJqS\nWNKltmdL+rWkWyS9U9Kdefo/sf3CiPh5rXynSvqmpE9Iqq7HTjw2l/t6SY+QdKikX9jeOyKuqI37\nBUknKa2r5yvV4+ZKl4w/IWmVpH+TdLLtHSPi9sp7t1H6IvCfkoaVvqh90fYdEXGCNObtYSzlBtA9\nzg9FxJ35RMYrI+IkSbK9V0SMpbeS10j6UIFyShHBgwcPHj19KAWStS0ev6iMs7+kNZKeXRl2vaRv\ntZjecZKGWwxfLOluSTvVhn9L0g1a1yPT9yWtlLRZZZwdJT0g6ao2y7JjLuebKsM2lXSzpM+N8r5N\nJD0vv3f32rJcVXm+QT3k4W/Jwx+bn+8iabWkf6mN99xcty9usxzfrS+rpP+RdL+kObVy/0nSuZVh\n/5HL8s4O1/9x9XnVXp+hdDLoz5I+U6uLtZK+XBv/ojz/eZVhe+dxX1Ob7xpJB9fev0zS1ZXn49oe\nRio3Dx48uvuQdILSfSX3K53EeFPeR0+XdKGkP0r6yBimt6NSM7oi5aUZCYB+CUkHSdqn8nhLl+cx\nX+nM9vW5uUTzTPoZkmYpncmV0o2YP46I+x8qXMR1ks5tN4M83i+VzmQ3vVTS1krhTlJqw2z7I7Yv\nt32PpAclnZVf3k0Td6DSWZ4Tast6jqR71P7seSvPlfTriGg0B0S6xPo9SfvYfnht/CXjK/pDzTbO\nsn2z0peGByTtrNZ189Pa8ysk/TUizqsNk6TH1cZ9sEU5vydpJ9uz8vOOt4cxlhtAF0TEayPisRGx\nWUTMiYhjIuK6iHhRRDw1IvaIiI+PYXrXRUT9WNE1NCMB0E+Xxgg3SHbJLEnzlAJWXUjaNv//GKWm\nB3WrJA10MJ/jJH3V9g4RsVwpeF8REX+ojPMZSe+Q9DGlnlbulLSTpBMl1UPreMxSCtvXtXituqxj\nsY3WhdaqlXleWytdIUgziVg5jnkot3H+cX68KU9/jaT/1YZ1E6p1+aUUcFsNU4v33xL5VFZFc93P\nVmr609H2MMZyA9hIEbYBTGe3KF1ifL9y276aK/PfGyRt3+L1VsNaOVHS/5P0OttflfQiSYtq4/yD\npG9GxKebA5x+QKGd+5TK/rDa8Hp4vkUpiO6nFOTrbu5gXnW3qvWXjceodegdr1dIulfSK6pB2PY2\nSgG2m7a17Vrgbi5jsxvKTreHXpYbwBRF2AYwldyvdLNap8N/Kuntkq6LiFtHme45kl5q++GRf8zA\n9k5KzQmubVeoiPir7dOUzmjfptRmu94bx+ZKzQyq3qz2/TY3z1TvIWmoMvwltfHOyNOaExHHqTvO\nlvQu27MjYoUk2Z6hdNf+76J7P/ywhWp1Y/tApZsPL2v5jvHbVNLBkn5YGXaIpGsi4sb8vNPtoZfl\nBjBFEbYBTGb1s9GXSf+/vft3jSIKAjj+nVZibWct/geCoo2FYiVYRL1CQVCDnRAkCkJALJR0BkTE\nH43YxEaJAYWI6QQrFRSxESzSKBaWYzGrxLtL3AUXhHw/zcHu3tv3ji3m3s6bx96IOEi90l9t8onf\nAici4hTwGviRmW+Aa1R1jZcRMQe8B7YCO6gFh4ebdmepAGypqeixhUr3+EJ796kKFpeA5SadZK2n\nwMmIeEdtGnOESnHZUGZ+jogVYCYivlIz1ANg+9B1HyLiOjAfETuBFzSLG4H9wI3MXOkwHqjNHQbA\ns4i4TG2PfI5Kfzndsa2NLAJTwJ2IuEvV/J5hdMMjGP+GoovvwFxEbKOqkRyn8tmPrbmm7fPQpd+S\nNikXSEr6nw3P+k5TlR4eUiUALzbHbzbHrlL50AsAmfkN2EUFuheaz1vUAsZfZetoAvNDwARViWKW\nypl3EAsAAADLSURBVLFe7tDXRWCVSrG4N+b8WeAxVYrvAZUWcnSdtobHPQm8olJVblPB+pWRL2VO\nA2eAfVRqywJwngrQP7YYwx/3bWazd1N52/PUbzwBHMjM53/pc+t7ZeYTKtVnD/WHZUCN+dOYdte7\nT6vyiFS6zST1VuERtfPoVGb+rpXe9nno2G9Jm1TkyDoRSZIkSf+CM9uSJElSTwy2JUmSpJ4YbEuS\nJEk9MdiWJEmSemKwLUmSJPXEYFuSJEnqicG2JEmS1BODbUmSJKknPwEArv56PDM66AAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c5a5780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the histogram of the data\n",
    "n, bins, patches = plt.hist(ls, 50, facecolor='g', alpha=0.75)\n",
    "plt.xlabel('Fitted value for lambda', fontsize = 16)\n",
    "plt.ylabel('n', fontsize = 16)\n",
    "plt.title('Histogram of fitted lambdas when D and d are subjected to gaussian noise', fontsize = 18)\n",
    "plt.savefig(\"./figs/D_hist.png\", dpi = 300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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D5s2bU6hQIXbu3Mn777/PjTfeqNlCREREJN/kR3JdGLgKuN85t8HMXgCeAEY654YDw83s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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1106f27b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#collect the results together with some labels\n",
    "ls = np.array([l1, l2, l3]) * 1e9\n",
    "delta_ls = np.array([np.sqrt(delta_l1**2 + ls.std()**2), np.sqrt(delta_l2**2 + ls.std()**2), delta_l3 * 1e9])\n",
    "labels = ('Single slit\\ndiffraction\\n',\n",
    "          'Single slit\\ndiffraction\\nwith aperture flipped',\n",
    "          'Single slit\\ndiffraction\\nCavendish 1984')\n",
    "\n",
    "for n, l, delta_l, label in zip(range(len(ls)), ls, delta_ls, labels):\n",
    "    plt.scatter((n,), (l,))\n",
    "    plt.errorbar((n,), (l,), yerr = (delta_l,))\n",
    "    plt.annotate(label, (n + 0.05, l), fontsize = 18)\n",
    "\n",
    "#true value\n",
    "plt.plot((-1, len(ls)), (true_l * 1e9, true_l * 1e9), \"k--\")\n",
    "plt.annotate(\"True Value\", (-0.8,true_l * 1e9 + 0.2), fontsize = 18)\n",
    "\n",
    "plt.gca().get_xaxis().set_visible(False)\n",
    "plt.title(\"Results\", fontsize = 18)\n",
    "plt.xlim((-1,len(ls)))\n",
    "plt.gca().set_ylabel('Wavelength / nm', fontsize = 16)\n",
    "plt.savefig(\"./figs/Results.png\", dpi = 300)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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