Rajabi2020_Fig5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Approximate $\\tau_0$ and scale the burst drift"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$ \\tau_0 \\simeq \\frac{b}{\\cos\\theta} $ Where $b$ is the minor axis of the gaussian fit to the burst autocorrelation.\n",
    "Then, if $d\\nu_{\\text{obs}} / dt_{\\text{obs}}$ is the burst drift, we expect \n",
    "\n",
    "\n",
    "### $\\frac{d\\nu_{\\text{obs}}}{dt_{\\text{obs}}}=\\frac{\\nu_{\\text{obs}}^{2}}{\\nu_{0}\\tau_{0}}\\simeq\\frac{\\nu_{\\text{obs}}^{2}b}{\\nu_{0}\\cos\\theta}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy.stats\n",
    "import scipy.odr\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib as mpl\n",
    "from tqdm import tqdm\n",
    "import functools\n",
    "print = functools.partial(print, flush=True) # print doesn't happen til script ends so force it to flush... windows thing?\n",
    "import pandas as pd\n",
    "\n",
    "bursts = pd.read_csv('bursts.csv')\n",
    "bursts = bursts.set_index('name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>M1</th>\n",
       "      <td>01_puppi_57747_C0531+33_0558_5.dm559.72.calibP...</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>1.720000</td>\n",
       "      <td>1.042900e+04</td>\n",
       "      <td>173.731526</td>\n",
       "      <td>47.364587</td>\n",
       "      <td>-6.652439</td>\n",
       "      <td>-1039.359422</td>\n",
       "      <td>0.190767</td>\n",
       "      <td>228.755888</td>\n",
       "      <td>4458.212325</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>1.562500</td>\n",
       "      <td>0.011140</td>\n",
       "      <td>0.003007</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>9.733683</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>M2</th>\n",
       "      <td>02_puppi_57747_C0531+33_0558_1183.dm559.72.cal...</td>\n",
       "      <td>1850.0</td>\n",
       "      <td>1.780000</td>\n",
       "      <td>5.692000e+03</td>\n",
       "      <td>177.080918</td>\n",
       "      <td>44.911875</td>\n",
       "      <td>-4.736866</td>\n",
       "      <td>-740.578566</td>\n",
       "      <td>0.652317</td>\n",
       "      <td>209.060564</td>\n",
       "      <td>4427.438381</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>1.562500</td>\n",
       "      <td>0.081784</td>\n",
       "      <td>0.020486</td>\n",
       "      <td>0.000174</td>\n",
       "      <td>3.818413</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M3</th>\n",
       "      <td>03_puppi_57747_C0531+33_0558_1202.dm559.72.cal...</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>1.620000</td>\n",
       "      <td>5.668000e+03</td>\n",
       "      <td>169.749001</td>\n",
       "      <td>19.463885</td>\n",
       "      <td>-19.669528</td>\n",
       "      <td>-2823.770453</td>\n",
       "      <td>2.623174</td>\n",
       "      <td>249.051665</td>\n",
       "      <td>4489.924477</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>1.562500</td>\n",
       "      <td>0.052822</td>\n",
       "      <td>0.005950</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>4.049445</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M4</th>\n",
       "      <td>04_puppi_57747_C0531+33_0558_25437.dm559.72.ca...</td>\n",
       "      <td>1450.0</td>\n",
       "      <td>1.710000</td>\n",
       "      <td>5.038000e+03</td>\n",
       "      <td>160.360329</td>\n",
       "      <td>26.706231</td>\n",
       "      <td>-7.137257</td>\n",
       "      <td>-1104.756403</td>\n",
       "      <td>21.768613</td>\n",
       "      <td>322.029396</td>\n",
       "      <td>4603.952181</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>1.562500</td>\n",
       "      <td>1.780140</td>\n",
       "      <td>0.292807</td>\n",
       "      <td>0.002672</td>\n",
       "      <td>3.318850</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M5</th>\n",
       "      <td>05_puppi_57747_C0531+33_0558_3683.dm559.72.cal...</td>\n",
       "      <td>1450.0</td>\n",
       "      <td>1.639204</td>\n",
       "      <td>5.031000e+03</td>\n",
       "      <td>37.747383</td>\n",
       "      <td>166.280492</td>\n",
       "      <td>-14.595503</td>\n",
       "      <td>-2111.819665</td>\n",
       "      <td>67.749909</td>\n",
       "      <td>214.396765</td>\n",
       "      <td>4435.776195</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>1.562500</td>\n",
       "      <td>0.258022</td>\n",
       "      <td>1.153948</td>\n",
       "      <td>0.002306</td>\n",
       "      <td>3.360715</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M6</th>\n",
       "      <td>06_puppi_57747_C0531+33_0558_3687.dm559.72.cal...</td>\n",
       "      <td>1150.0</td>\n",
       "      <td>1.590000</td>\n",
       "      <td>5.569000e+03</td>\n",
       "      <td>156.557708</td>\n",
       "      <td>12.416683</td>\n",
       "      <td>-52.066970</td>\n",
       "      <td>-6051.049490</td>\n",
       "      <td>11.249634</td>\n",
       "      <td>263.938012</td>\n",
       "      <td>4513.184394</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>1.562500</td>\n",
       "      <td>0.065385</td>\n",
       "      <td>0.005173</td>\n",
       "      <td>0.000047</td>\n",
       "      <td>6.719788</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M7</th>\n",
       "      <td>07_puppi_57747_C0531+33_0558_3688.dm559.72.cal...</td>\n",
       "      <td>1590.0</td>\n",
       "      <td>1.720000</td>\n",
       "      <td>5.821000e+03</td>\n",
       "      <td>102.259272</td>\n",
       "      <td>38.969184</td>\n",
       "      <td>-6.652439</td>\n",
       "      <td>-1019.972935</td>\n",
       "      <td>0.845772</td>\n",
       "      <td>160.167242</td>\n",
       "      <td>4351.042566</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>1.562500</td>\n",
       "      <td>0.019954</td>\n",
       "      <td>0.007547</td>\n",
       "      <td>0.000121</td>\n",
       "      <td>6.302707</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M8</th>\n",
       "      <td>08_puppi_57747_C0531+33_0558_3689.dm559.72.cal...</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>1.990000</td>\n",
       "      <td>5.666000e+03</td>\n",
       "      <td>171.395398</td>\n",
       "      <td>64.792725</td>\n",
       "      <td>-2.244076</td>\n",
       "      <td>-340.369797</td>\n",
       "      <td>0.064490</td>\n",
       "      <td>296.067560</td>\n",
       "      <td>4563.386812</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>1.562500</td>\n",
       "      <td>0.019619</td>\n",
       "      <td>0.007370</td>\n",
       "      <td>0.000071</td>\n",
       "      <td>6.146570</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M9</th>\n",
       "      <td>09_puppi_57747_C0531+33_0558_3690.dm559.72.cal...</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>2.240000</td>\n",
       "      <td>5.041000e+03</td>\n",
       "      <td>133.024941</td>\n",
       "      <td>89.628038</td>\n",
       "      <td>-1.264288</td>\n",
       "      <td>-191.720931</td>\n",
       "      <td>5.297999</td>\n",
       "      <td>216.215087</td>\n",
       "      <td>4438.617323</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>1.562500</td>\n",
       "      <td>1.038778</td>\n",
       "      <td>0.697559</td>\n",
       "      <td>0.013465</td>\n",
       "      <td>3.361416</td>\n",
       "      <td>dont use, see 9a,b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M10</th>\n",
       "      <td>10_puppi_57747_C0531+33_0558_12568.dm559.72.ca...</td>\n",
       "      <td>800.0</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>5.132000e+03</td>\n",
       "      <td>130.605616</td>\n",
       "      <td>47.074113</td>\n",
       "      <td>-5.470689</td>\n",
       "      <td>-834.396382</td>\n",
       "      <td>18.569260</td>\n",
       "      <td>310.581676</td>\n",
       "      <td>4586.065119</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>1.562500</td>\n",
       "      <td>0.887502</td>\n",
       "      <td>0.318304</td>\n",
       "      <td>0.003938</td>\n",
       "      <td>3.375381</td>\n",
       "      <td>dont use, see 10a,b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M11</th>\n",
       "      <td>11_puppi_57748_C0531+33_0594_2.dm559.72.calibP...</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>1.720000</td>\n",
       "      <td>5.196000e+03</td>\n",
       "      <td>182.570112</td>\n",
       "      <td>39.810758</td>\n",
       "      <td>-6.652439</td>\n",
       "      <td>-1031.102098</td>\n",
       "      <td>7.011361</td>\n",
       "      <td>263.108513</td>\n",
       "      <td>4511.888302</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>1.562500</td>\n",
       "      <td>0.564420</td>\n",
       "      <td>0.121382</td>\n",
       "      <td>0.000985</td>\n",
       "      <td>3.362998</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M12</th>\n",
       "      <td>12_puppi_57748_C0531+33_0594_48.dm559.72.calib...</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.720300</td>\n",
       "      <td>4.672000e+03</td>\n",
       "      <td>137.483220</td>\n",
       "      <td>28.822386</td>\n",
       "      <td>-6.725287</td>\n",
       "      <td>-1012.748636</td>\n",
       "      <td>1.100001</td>\n",
       "      <td>194.684720</td>\n",
       "      <td>4404.976125</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>1.562500</td>\n",
       "      <td>0.072778</td>\n",
       "      <td>0.015013</td>\n",
       "      <td>0.000160</td>\n",
       "      <td>3.915710</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M13</th>\n",
       "      <td>13_puppi_57748_C0531+33_0594_49.dm559.72.calib...</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>1.740000</td>\n",
       "      <td>6.789000e+03</td>\n",
       "      <td>141.354014</td>\n",
       "      <td>40.669821</td>\n",
       "      <td>-5.853528</td>\n",
       "      <td>-915.460159</td>\n",
       "      <td>0.205961</td>\n",
       "      <td>315.871984</td>\n",
       "      <td>4594.331225</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>1.562500</td>\n",
       "      <td>0.011794</td>\n",
       "      <td>0.003362</td>\n",
       "      <td>0.000036</td>\n",
       "      <td>11.238787</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M14</th>\n",
       "      <td>14_puppi_57748_C0531+33_0594_50.dm559.72.calib...</td>\n",
       "      <td>1550.0</td>\n",
       "      <td>1.615300</td>\n",
       "      <td>5.313000e+03</td>\n",
       "      <td>159.613074</td>\n",
       "      <td>11.696348</td>\n",
       "      <td>-20.987668</td>\n",
       "      <td>-3452.888809</td>\n",
       "      <td>1.655265</td>\n",
       "      <td>239.876953</td>\n",
       "      <td>4475.588989</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>1.562500</td>\n",
       "      <td>0.033644</td>\n",
       "      <td>0.002391</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>4.744576</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M15</th>\n",
       "      <td>15_puppi_57748_C0531+33_0594_1269.dm559.72.cal...</td>\n",
       "      <td>1550.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.922000e+03</td>\n",
       "      <td>65.081975</td>\n",
       "      <td>47.789210</td>\n",
       "      <td>-2.185040</td>\n",
       "      <td>-340.562720</td>\n",
       "      <td>0.235758</td>\n",
       "      <td>113.702082</td>\n",
       "      <td>4278.440753</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>1.562500</td>\n",
       "      <td>0.007892</td>\n",
       "      <td>0.005714</td>\n",
       "      <td>0.000258</td>\n",
       "      <td>15.555410</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M16</th>\n",
       "      <td>16_puppi_57772_C0531+33_0007_2695.dm559.72.cal...</td>\n",
       "      <td>700.0</td>\n",
       "      <td>1.710000</td>\n",
       "      <td>1.230900e+04</td>\n",
       "      <td>124.668892</td>\n",
       "      <td>46.778198</td>\n",
       "      <td>-7.137257</td>\n",
       "      <td>-1130.804012</td>\n",
       "      <td>0.121827</td>\n",
       "      <td>167.929660</td>\n",
       "      <td>4363.171344</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>1.562500</td>\n",
       "      <td>0.002939</td>\n",
       "      <td>0.001091</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>159.819922</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M9a</th>\n",
       "      <td>09_puppi_57747_C0531+33_0558_3690.dm559.72.cal...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.671361</td>\n",
       "      <td>1.822146e+00</td>\n",
       "      <td>24.652299</td>\n",
       "      <td>113.948254</td>\n",
       "      <td>-9.910317</td>\n",
       "      <td>-1512.194326</td>\n",
       "      <td>152.486918</td>\n",
       "      <td>252.333410</td>\n",
       "      <td>4495.052204</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>1.562500</td>\n",
       "      <td>0.772813</td>\n",
       "      <td>3.567355</td>\n",
       "      <td>0.010073</td>\n",
       "      <td>0.754693</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M9b</th>\n",
       "      <td>09_puppi_57747_C0531+33_0558_3690.dm559.72.cal...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.686447</td>\n",
       "      <td>3.019004e+00</td>\n",
       "      <td>137.317595</td>\n",
       "      <td>39.613121</td>\n",
       "      <td>-8.608171</td>\n",
       "      <td>-1313.502712</td>\n",
       "      <td>69.309099</td>\n",
       "      <td>218.455647</td>\n",
       "      <td>4442.118199</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>1.562500</td>\n",
       "      <td>1.866474</td>\n",
       "      <td>0.538615</td>\n",
       "      <td>0.006048</td>\n",
       "      <td>0.903205</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M10a</th>\n",
       "      <td>10_puppi_57747_C0531+33_0558_12568.dm559.72.ca...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.618033</td>\n",
       "      <td>5.574546e+00</td>\n",
       "      <td>106.049519</td>\n",
       "      <td>21.383067</td>\n",
       "      <td>-21.154144</td>\n",
       "      <td>-3227.866176</td>\n",
       "      <td>235.934120</td>\n",
       "      <td>314.300829</td>\n",
       "      <td>4591.876296</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>1.562500</td>\n",
       "      <td>1.230755</td>\n",
       "      <td>0.248140</td>\n",
       "      <td>0.003448</td>\n",
       "      <td>0.525437</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M10b</th>\n",
       "      <td>10_puppi_57747_C0531+33_0558_12568.dm559.72.ca...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.580540</td>\n",
       "      <td>3.871432e+00</td>\n",
       "      <td>169.899148</td>\n",
       "      <td>8.804555</td>\n",
       "      <td>-102.625701</td>\n",
       "      <td>-15659.439219</td>\n",
       "      <td>2430.074649</td>\n",
       "      <td>254.878399</td>\n",
       "      <td>4499.028749</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>1.562500</td>\n",
       "      <td>3.501261</td>\n",
       "      <td>0.181120</td>\n",
       "      <td>0.001512</td>\n",
       "      <td>0.421647</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G11A</th>\n",
       "      <td>gajjar/11A_16sec.calib.4p.RM.scr.ASCII</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.616000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>651.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-785.400000</td>\n",
       "      <td>0.276000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6500.000000</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>1.464844</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>yes old corr no chisq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G11D</th>\n",
       "      <td>gajjar/11D_323sec.calib.4p.RM.scr.ASCII</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.591813</td>\n",
       "      <td>1.000000e+03</td>\n",
       "      <td>8.631806</td>\n",
       "      <td>256.516300</td>\n",
       "      <td>-47.573148</td>\n",
       "      <td>-1701.392924</td>\n",
       "      <td>0.967031</td>\n",
       "      <td>1093.074137</td>\n",
       "      <td>6974.717930</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>1.464844</td>\n",
       "      <td>0.002164</td>\n",
       "      <td>0.064293</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>9.549749</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C1</th>\n",
       "      <td>chime_low</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.907700</td>\n",
       "      <td>NaN</td>\n",
       "      <td>184.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.920000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>630.000000</td>\n",
       "      <td>0.003000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>yes no chisq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G11A1</th>\n",
       "      <td>gajjar/11A_16sec.calib.4p.RM.scr.ASCII</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.586733</td>\n",
       "      <td>4.611286e+09</td>\n",
       "      <td>213.162000</td>\n",
       "      <td>5.786877</td>\n",
       "      <td>-62.744791</td>\n",
       "      <td>-2243.987383</td>\n",
       "      <td>1.807412</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>1.464844</td>\n",
       "      <td>0.071194</td>\n",
       "      <td>0.001933</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>19.592495</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G11A1a</th>\n",
       "      <td>gajjar/11A_16sec.calib.4p.RM.scr.ASCII</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.576095</td>\n",
       "      <td>2.301048e+09</td>\n",
       "      <td>179.245000</td>\n",
       "      <td>3.434871</td>\n",
       "      <td>-188.728998</td>\n",
       "      <td>-6749.651926</td>\n",
       "      <td>21.967232</td>\n",
       "      <td>1247.906517</td>\n",
       "      <td>7201.523174</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>1.464844</td>\n",
       "      <td>0.113954</td>\n",
       "      <td>0.002188</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>11.804563</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G11A1b</th>\n",
       "      <td>gajjar/11A_16sec.calib.4p.RM.scr.ASCII</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.575671</td>\n",
       "      <td>2.912010e+09</td>\n",
       "      <td>207.135900</td>\n",
       "      <td>1.979257</td>\n",
       "      <td>-205.145415</td>\n",
       "      <td>-7336.763933</td>\n",
       "      <td>14.656192</td>\n",
       "      <td>1119.097389</td>\n",
       "      <td>7012.837929</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>1.464844</td>\n",
       "      <td>0.149227</td>\n",
       "      <td>0.001426</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>12.089260</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G11A2</th>\n",
       "      <td>gajjar/11A_16sec.calib.4p.RM.scr.ASCII</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.579898</td>\n",
       "      <td>1.858371e+09</td>\n",
       "      <td>279.362200</td>\n",
       "      <td>2.748689</td>\n",
       "      <td>-109.864126</td>\n",
       "      <td>-3929.513225</td>\n",
       "      <td>4.889863</td>\n",
       "      <td>655.651455</td>\n",
       "      <td>6333.962048</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>1.464844</td>\n",
       "      <td>0.227143</td>\n",
       "      <td>0.002236</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>4.716985</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G11A3</th>\n",
       "      <td>gajjar/11A_16sec.calib.4p.RM.scr.ASCII</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.592648</td>\n",
       "      <td>2.710310e+09</td>\n",
       "      <td>246.998400</td>\n",
       "      <td>6.092182</td>\n",
       "      <td>-45.756068</td>\n",
       "      <td>-1636.407401</td>\n",
       "      <td>1.813690</td>\n",
       "      <td>210.341724</td>\n",
       "      <td>5681.652873</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>1.464844</td>\n",
       "      <td>0.171305</td>\n",
       "      <td>0.004226</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>2.956709</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 filename    edge     angle  \\\n",
       "name                                                                          \n",
       "M1      01_puppi_57747_C0531+33_0558_5.dm559.72.calibP...  1525.0  1.720000   \n",
       "M2      02_puppi_57747_C0531+33_0558_1183.dm559.72.cal...  1850.0  1.780000   \n",
       "M3      03_puppi_57747_C0531+33_0558_1202.dm559.72.cal...  1975.0  1.620000   \n",
       "M4      04_puppi_57747_C0531+33_0558_25437.dm559.72.ca...  1450.0  1.710000   \n",
       "M5      05_puppi_57747_C0531+33_0558_3683.dm559.72.cal...  1450.0  1.639204   \n",
       "M6      06_puppi_57747_C0531+33_0558_3687.dm559.72.cal...  1150.0  1.590000   \n",
       "M7      07_puppi_57747_C0531+33_0558_3688.dm559.72.cal...  1590.0  1.720000   \n",
       "M8      08_puppi_57747_C0531+33_0558_3689.dm559.72.cal...  2000.0  1.990000   \n",
       "M9      09_puppi_57747_C0531+33_0558_3690.dm559.72.cal...  1600.0  2.240000   \n",
       "M10     10_puppi_57747_C0531+33_0558_12568.dm559.72.ca...   800.0  1.750000   \n",
       "M11     11_puppi_57748_C0531+33_0594_2.dm559.72.calibP...  1800.0  1.720000   \n",
       "M12     12_puppi_57748_C0531+33_0594_48.dm559.72.calib...  1000.0  1.720300   \n",
       "M13     13_puppi_57748_C0531+33_0594_49.dm559.72.calib...  1900.0  1.740000   \n",
       "M14     14_puppi_57748_C0531+33_0594_50.dm559.72.calib...  1550.0  1.615300   \n",
       "M15     15_puppi_57748_C0531+33_0594_1269.dm559.72.cal...  1550.0  2.000000   \n",
       "M16     16_puppi_57772_C0531+33_0007_2695.dm559.72.cal...   700.0  1.710000   \n",
       "M9a     09_puppi_57747_C0531+33_0558_3690.dm559.72.cal...     NaN  1.671361   \n",
       "M9b     09_puppi_57747_C0531+33_0558_3690.dm559.72.cal...     NaN  1.686447   \n",
       "M10a    10_puppi_57747_C0531+33_0558_12568.dm559.72.ca...     NaN  1.618033   \n",
       "M10b    10_puppi_57747_C0531+33_0558_12568.dm559.72.ca...     NaN  1.580540   \n",
       "G11A               gajjar/11A_16sec.calib.4p.RM.scr.ASCII     0.0  1.616000   \n",
       "G11D              gajjar/11D_323sec.calib.4p.RM.scr.ASCII     0.0  1.591813   \n",
       "C1                                              chime_low     0.0  1.907700   \n",
       "G11A1              gajjar/11A_16sec.calib.4p.RM.scr.ASCII     NaN  1.586733   \n",
       "G11A1a             gajjar/11A_16sec.calib.4p.RM.scr.ASCII     NaN  1.576095   \n",
       "G11A1b             gajjar/11A_16sec.calib.4p.RM.scr.ASCII     NaN  1.575671   \n",
       "G11A2              gajjar/11A_16sec.calib.4p.RM.scr.ASCII     NaN  1.579898   \n",
       "G11A3              gajjar/11A_16sec.calib.4p.RM.scr.ASCII     NaN  1.592648   \n",
       "\n",
       "                peak      sigmax      sigmay       Slope  drift (mhz/ms)  \\\n",
       "name                                                                       \n",
       "M1      1.042900e+04  173.731526   47.364587   -6.652439    -1039.359422   \n",
       "M2      5.692000e+03  177.080918   44.911875   -4.736866     -740.578566   \n",
       "M3      5.668000e+03  169.749001   19.463885  -19.669528    -2823.770453   \n",
       "M4      5.038000e+03  160.360329   26.706231   -7.137257    -1104.756403   \n",
       "M5      5.031000e+03   37.747383  166.280492  -14.595503    -2111.819665   \n",
       "M6      5.569000e+03  156.557708   12.416683  -52.066970    -6051.049490   \n",
       "M7      5.821000e+03  102.259272   38.969184   -6.652439    -1019.972935   \n",
       "M8      5.666000e+03  171.395398   64.792725   -2.244076     -340.369797   \n",
       "M9      5.041000e+03  133.024941   89.628038   -1.264288     -191.720931   \n",
       "M10     5.132000e+03  130.605616   47.074113   -5.470689     -834.396382   \n",
       "M11     5.196000e+03  182.570112   39.810758   -6.652439    -1031.102098   \n",
       "M12     4.672000e+03  137.483220   28.822386   -6.725287    -1012.748636   \n",
       "M13     6.789000e+03  141.354014   40.669821   -5.853528     -915.460159   \n",
       "M14     5.313000e+03  159.613074   11.696348  -20.987668    -3452.888809   \n",
       "M15     5.922000e+03   65.081975   47.789210   -2.185040     -340.562720   \n",
       "M16     1.230900e+04  124.668892   46.778198   -7.137257    -1130.804012   \n",
       "M9a     1.822146e+00   24.652299  113.948254   -9.910317    -1512.194326   \n",
       "M9b     3.019004e+00  137.317595   39.613121   -8.608171    -1313.502712   \n",
       "M10a    5.574546e+00  106.049519   21.383067  -21.154144    -3227.866176   \n",
       "M10b    3.871432e+00  169.899148    8.804555 -102.625701   -15659.439219   \n",
       "G11A             NaN  651.000000    9.000000         NaN     -785.400000   \n",
       "G11D    1.000000e+03    8.631806  256.516300  -47.573148    -1701.392924   \n",
       "C1               NaN  184.000000   63.000000         NaN       -3.920000   \n",
       "G11A1   4.611286e+09  213.162000    5.786877  -62.744791    -2243.987383   \n",
       "G11A1a  2.301048e+09  179.245000    3.434871 -188.728998    -6749.651926   \n",
       "G11A1b  2.912010e+09  207.135900    1.979257 -205.145415    -7336.763933   \n",
       "G11A2   1.858371e+09  279.362200    2.748689 -109.864126    -3929.513225   \n",
       "G11A3   2.710310e+09  246.998400    6.092182  -45.756068    -1636.407401   \n",
       "\n",
       "        drift error (mhz/ms)       center     center_f  time_res  freq_res  \\\n",
       "name                                                                         \n",
       "M1                  0.190767   228.755888  4458.212325  0.000010  1.562500   \n",
       "M2                  0.652317   209.060564  4427.438381  0.000010  1.562500   \n",
       "M3                  2.623174   249.051665  4489.924477  0.000010  1.562500   \n",
       "M4                 21.768613   322.029396  4603.952181  0.000010  1.562500   \n",
       "M5                 67.749909   214.396765  4435.776195  0.000010  1.562500   \n",
       "M6                 11.249634   263.938012  4513.184394  0.000010  1.562500   \n",
       "M7                  0.845772   160.167242  4351.042566  0.000010  1.562500   \n",
       "M8                  0.064490   296.067560  4563.386812  0.000010  1.562500   \n",
       "M9                  5.297999   216.215087  4438.617323  0.000010  1.562500   \n",
       "M10                18.569260   310.581676  4586.065119  0.000010  1.562500   \n",
       "M11                 7.011361   263.108513  4511.888302  0.000010  1.562500   \n",
       "M12                 1.100001   194.684720  4404.976125  0.000010  1.562500   \n",
       "M13                 0.205961   315.871984  4594.331225  0.000010  1.562500   \n",
       "M14                 1.655265   239.876953  4475.588989  0.000010  1.562500   \n",
       "M15                 0.235758   113.702082  4278.440753  0.000010  1.562500   \n",
       "M16                 0.121827   167.929660  4363.171344  0.000010  1.562500   \n",
       "M9a               152.486918   252.333410  4495.052204  0.000010  1.562500   \n",
       "M9b                69.309099   218.455647  4442.118199  0.000010  1.562500   \n",
       "M10a              235.934120   314.300829  4591.876296  0.000010  1.562500   \n",
       "M10b             2430.074649   254.878399  4499.028749  0.000010  1.562500   \n",
       "G11A                0.276000          NaN  6500.000000  0.000041  1.464844   \n",
       "G11D                0.967031  1093.074137  6974.717930  0.000041  1.464844   \n",
       "C1                  0.010000          NaN   630.000000  0.003000  3.000000   \n",
       "G11A1               1.807412          NaN          NaN  0.000041  1.464844   \n",
       "G11A1a             21.967232  1247.906517  7201.523174  0.000041  1.464844   \n",
       "G11A1b             14.656192  1119.097389  7012.837929  0.000041  1.464844   \n",
       "G11A2               4.889863   655.651455  6333.962048  0.000041  1.464844   \n",
       "G11A3               1.813690   210.341724  5681.652873  0.000041  1.464844   \n",
       "\n",
       "        sigmax_error  sigmay_error  angle_error   red_chisq  \\\n",
       "name                                                          \n",
       "M1          0.011140      0.003007     0.000026    9.733683   \n",
       "M2          0.081784      0.020486     0.000174    3.818413   \n",
       "M3          0.052822      0.005950     0.000050    4.049445   \n",
       "M4          1.780140      0.292807     0.002672    3.318850   \n",
       "M5          0.258022      1.153948     0.002306    3.360715   \n",
       "M6          0.065385      0.005173     0.000047    6.719788   \n",
       "M7          0.019954      0.007547     0.000121    6.302707   \n",
       "M8          0.019619      0.007370     0.000071    6.146570   \n",
       "M9          1.038778      0.697559     0.013465    3.361416   \n",
       "M10         0.887502      0.318304     0.003938    3.375381   \n",
       "M11         0.564420      0.121382     0.000985    3.362998   \n",
       "M12         0.072778      0.015013     0.000160    3.915710   \n",
       "M13         0.011794      0.003362     0.000036   11.238787   \n",
       "M14         0.033644      0.002391     0.000021    4.744576   \n",
       "M15         0.007892      0.005714     0.000258   15.555410   \n",
       "M16         0.002939      0.001091     0.000014  159.819922   \n",
       "M9a         0.772813      3.567355     0.010073    0.754693   \n",
       "M9b         1.866474      0.538615     0.006048    0.903205   \n",
       "M10a        1.230755      0.248140     0.003448    0.525437   \n",
       "M10b        3.501261      0.181120     0.001512    0.421647   \n",
       "G11A             NaN           NaN          NaN    1.000000   \n",
       "G11D        0.002164      0.064293     0.000012    9.549749   \n",
       "C1               NaN           NaN          NaN    1.000000   \n",
       "G11A1       0.071194      0.001933     0.000013   19.592495   \n",
       "G11A1a      0.113954      0.002188     0.000017   11.804563   \n",
       "G11A1b      0.149227      0.001426     0.000010   12.089260   \n",
       "G11A2       0.227143      0.002236     0.000011    4.716985   \n",
       "G11A3       0.171305      0.004226     0.000024    2.956709   \n",
       "\n",
       "                       stale?  \n",
       "name                           \n",
       "M1                        NaN  \n",
       "M2                        NaN  \n",
       "M3                        NaN  \n",
       "M4                        NaN  \n",
       "M5                        NaN  \n",
       "M6                        NaN  \n",
       "M7                        NaN  \n",
       "M8                        NaN  \n",
       "M9         dont use, see 9a,b  \n",
       "M10       dont use, see 10a,b  \n",
       "M11                       NaN  \n",
       "M12                       NaN  \n",
       "M13                       NaN  \n",
       "M14                       NaN  \n",
       "M15                       NaN  \n",
       "M16                       NaN  \n",
       "M9a                       NaN  \n",
       "M9b                       NaN  \n",
       "M10a                      NaN  \n",
       "M10b                      NaN  \n",
       "G11A    yes old corr no chisq  \n",
       "G11D                      NaN  \n",
       "C1               yes no chisq  \n",
       "G11A1                     NaN  \n",
       "G11A1a                    NaN  \n",
       "G11A1b                    NaN  \n",
       "G11A2                     NaN  \n",
       "G11A3                     NaN  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bursts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Burst #')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bursts['drift_abs'] = abs(bursts['drift (mhz/ms)'])\n",
    "bursts['min_sigma'] = bursts[['sigmax','sigmay']].apply(lambda row: min(row['sigmax'], row['sigmay']), axis=1)\n",
    "bursts['max_sigma'] = bursts[['sigmax','sigmay']].apply(lambda row: max(row['sigmax'], row['sigmay']), axis=1)\n",
    "# the following two lines assume that if sigmax > sigmay, then sigmax_error > sigmay_error, which is true (so far) for this dataset\n",
    "bursts['min_sigma_error'] = bursts[['sigmax_error','sigmay_error']].apply(lambda row: min(row['sigmax_error'], row['sigmay_error']), axis=1)\n",
    "bursts['max_sigma_error'] = bursts[['sigmax_error','sigmay_error']].apply(lambda row: max(row['sigmax_error'], row['sigmay_error']), axis=1)\n",
    "\n",
    "bursts['sigma_t']   = bursts[['min_sigma','time_res']].apply(lambda row: row['min_sigma']*row['time_res'], axis=1)\n",
    "\n",
    "bursts['tau_w'] = bursts[['time_res', 'min_sigma', 'max_sigma', 'angle']].apply(\n",
    "    lambda r: r['time_res']*r['min_sigma']*r['max_sigma'] / np.sqrt( np.abs((np.sin(r['angle']-np.pi/2)*r['min_sigma'])**2 + (np.cos(r['angle']-np.pi/2)*r['max_sigma'])**2 )), \n",
    "    axis=1\n",
    ")\n",
    "\n",
    "# this error is in ms\n",
    "bursts['tau_w_error'] = bursts[['time_res', 'min_sigma', 'max_sigma', 'min_sigma_error', 'max_sigma_error', 'angle', 'angle_error']].apply(\n",
    "    lambda r: 1e3*r['time_res']*np.sqrt((r['angle_error']**2*r['max_sigma']**2*r['min_sigma']**2*(2*r['max_sigma']**2*np.cos(r['angle']-np.pi/2)*np.sin(r['angle']-np.pi/2) - 2*r['min_sigma']**2*np.cos(r['angle']-np.pi/2)*np.sin(r['angle']-np.pi/2))**2)/(4.*(r['min_sigma']**2*np.cos(r['angle']-np.pi/2)**2 + r['max_sigma']**2*np.sin(r['angle']-np.pi/2)**2)**3)+ r['min_sigma_error']**2*(-((r['max_sigma']*r['min_sigma']**2*np.cos(r['angle']-np.pi/2)**2)/(r['min_sigma']**2*np.cos(r['angle']-np.pi/2)**2 + r['max_sigma']**2*np.sin(r['angle']-np.pi/2)**2)**1.5) + r['max_sigma']/np.sqrt(r['min_sigma']**2*np.cos(r['angle']-np.pi/2)**2 + r['max_sigma']**2*np.sin(r['angle']-np.pi/2)**2))**2+ r['max_sigma_error']**2*(-((r['max_sigma']**2*r['min_sigma']*np.sin(r['angle']-np.pi/2)**2)/(r['min_sigma']**2*np.cos(r['angle']-np.pi/2)**2 + r['max_sigma']**2*np.sin(r['angle']-np.pi/2)**2)**1.5) + r['min_sigma']/np.sqrt(r['min_sigma']**2*np.cos(r['angle']-np.pi/2)**2 + r['max_sigma']**2*np.sin(r['angle']-np.pi/2)**2))**2),\n",
    "    axis=1\n",
    ")\n",
    "\n",
    "bursts['sigma_t_ms'] = bursts['sigma_t']*1e3\n",
    "bursts['tau_w_ms'] = bursts['tau_w']*1e3\n",
    "\n",
    "## C1 \n",
    "bursts.at['C1', 'tau_w_ms'] = 11.4\n",
    "bursts.at['C1', 'tau_w'] = 11.4/1000\n",
    "bursts.at['C1', 'tau_w_error'] = 0.12\n",
    "\n",
    "bursts_to_include = ['M1', 'M2', 'M3', 'M4', 'M5', 'M6', 'M7', 'M8', 'M9a', 'M9b', 'M10a', 'M10b', 'M11', 'M12', 'M13', 'M14', 'M15', 'M16', 'G11D', 'G11A1a', 'G11A1b', 'G11A2', 'G11A3', 'C1']\n",
    "fontsize=15\n",
    "\n",
    "selectbursts = bursts.loc[bursts_to_include]\n",
    "ax = selectbursts['tau_w_ms'].plot(style='k.', figsize=(10,6))\n",
    "ax.set_title(\"$t_w$ for each burst (excluding C1 burst)\", size=fontsize)\n",
    "ax.set_ylabel('$t_w$ (ms)', size=fontsize)\n",
    "ax.set_xlabel('Burst #', size=fontsize)\n",
    "# plt.savefig('tw.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def reciprocal(x, a):\n",
    "\treturn a/x\n",
    "\n",
    "def reciprocal_log(x, b):\n",
    "\treturn -x+b\n",
    "\n",
    "def log_log(x, k, b):\n",
    "\treturn k*x+b\n",
    "\n",
    "def reciprocal_odr(B, x):\n",
    "    return B[0]/x\n",
    "\n",
    "def reciprocal_odr_log(B, x):\n",
    "\treturn -x+B[0]\n",
    "\n",
    "def fitreciprocal(x, data, sigma=1):\n",
    "    guess = [522]\n",
    "    abs_sigma = True\n",
    "    if (type(sigma) == int) and (sigma == 1):\n",
    "        abs_sigma = False\n",
    "    sigma = np.zeros(len(data.ravel())) + sigma\n",
    "\n",
    "    sigma = np.zeros(len(data.ravel())) + sigma\n",
    "    popt, pcov = scipy.optimize.curve_fit(reciprocal, x, data, p0=guess, sigma=sigma, absolute_sigma=abs_sigma)\n",
    "    return popt, pcov\n",
    "\n",
    "def fitreciprocal_log(x, data, sigma=1, loglog=False):\n",
    "    guess = [522]\n",
    "    abs_sigma = True\n",
    "    if (type(sigma) == int) and (sigma == 1):\n",
    "        abs_sigma = False\n",
    "    sigma = np.zeros(len(data.ravel())) + sigma\n",
    "    \n",
    "    if loglog:\n",
    "        guess = [1,1]\n",
    "        popt, pcov = scipy.optimize.curve_fit(log_log, x, data, p0=guess, sigma=sigma, absolute_sigma=abs_sigma)\n",
    "    else:\n",
    "        popt, pcov = scipy.optimize.curve_fit(reciprocal_log, x, data, p0=guess, sigma=sigma, absolute_sigma=abs_sigma)\n",
    "    return popt, pcov\n",
    "\n",
    "fit_model = scipy.odr.Model(reciprocal_odr)\n",
    "fit_model_log = scipy.odr.Model(reciprocal_odr_log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "center_f    4472.188306\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hmsmo\\AppData\\Roaming\\Python\\Python37\\site-packages\\ipykernel_launcher.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  if sys.path[0] == '':\n",
      "C:\\Users\\hmsmo\\AppData\\Roaming\\Python\\Python37\\site-packages\\ipykernel_launcher.py:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  del sys.path[0]\n",
      "C:\\Users\\hmsmo\\AppData\\Roaming\\Python\\Python37\\site-packages\\ipykernel_launcher.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "C:\\Users\\hmsmo\\AppData\\Roaming\\Python\\Python37\\site-packages\\ipykernel_launcher.py:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    }
   ],
   "source": [
    "selectbursts['color'] = 'c'\n",
    "selectbursts.at[selectbursts.index > 'M', 'color'] = 'r'\n",
    "selectbursts.at['C1', 'color'] = 'b'\n",
    "michillibursts = selectbursts.head(18)\n",
    "otherbursts = selectbursts.tail(6)\n",
    "\n",
    "# selectbursts['drift error (mhz/ms)']['M8'] = 0.06448979034795*1\n",
    "\n",
    "# Correct to the average Michilli burst frequency:\n",
    "nu_obs0 = np.mean(michillibursts[['center_f']])\n",
    "print(nu_obs0)\n",
    "otherbursts['drift_corr'] = otherbursts[['drift_abs','center_f']].apply(lambda row: row['drift_abs']*(nu_obs0 / row['center_f'])**2, axis=1)\n",
    "otherbursts['drift_err_corr'] = otherbursts[['drift error (mhz/ms)','center_f']].apply(lambda row: row['drift error (mhz/ms)']*(nu_obs0 / row['center_f'])**2, axis=1)\n",
    "otherbursts['tau_w_ms_corr'] = otherbursts[['tau_w_ms','center_f']].apply(lambda row: row['tau_w_ms']*(row['center_f'] / nu_obs0), axis=1)\n",
    "otherbursts['tau_w_err_corr'] = otherbursts[['tau_w_error','center_f']].apply(lambda row: row['tau_w_error']*(row['center_f'] / nu_obs0), axis=1)\n",
    "# print(nu_obs0)\n",
    "# otherbursts[['tau_w_ms', 'tau_w_ms_corr', 'drift_abs', 'drift_corr', 'tau_w_error', 'tau_w_err_corr', 'drift error (mhz/ms)', 'drift_err_corr', 'center_f', ]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hmsmo\\AppData\\Roaming\\Python\\Python37\\site-packages\\ipykernel_launcher.py:105: RuntimeWarning: divide by zero encountered in true_divide\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x145eba70>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gajjar test to michili fit\n",
    "plt.rcParams[\"errorbar.capsize\"] = 4\n",
    "plt.rcParams[\"font.family\"] = \"serif\"\n",
    "\n",
    "markersize = 100\n",
    "fontsize = 18\n",
    "annotsize = 14\n",
    "logscale = True\n",
    "filename = 'drift_vs_tw_michilli_log' if logscale else 'drift_vs_tw_michilli'\n",
    "figsize = (17, 8)\n",
    "# figsize = (14, 10)\n",
    "\n",
    "ax = michillibursts.plot.scatter(x='tau_w_ms', y='drift_abs', \n",
    "                               xerr=michillibursts['red_chisq']*michillibursts['tau_w_error'], yerr=michillibursts['red_chisq']*michillibursts['drift error (mhz/ms)'], \n",
    "                               figsize=figsize, s=markersize, c='color', colorbar=False, fontsize=fontsize, logy=logscale, logx=logscale, marker='o',\n",
    "                               label='Michilli et al. (2018) bursts')\n",
    "\n",
    "# for k, v in michillibursts.iterrows():\n",
    "#     ax.annotate(k, (v['tau_w_ms'], v['drift_abs']), xytext=(-3,5), textcoords='offset points', weight='bold', size=annotsize)\n",
    "\n",
    "otherbursts.head(5).plot.scatter(ax=ax, x='tau_w_ms_corr', y='drift_corr',\n",
    "                         xerr=otherbursts['red_chisq']*otherbursts['tau_w_err_corr'], yerr=otherbursts['red_chisq']*otherbursts['drift_err_corr'], \n",
    "                         figsize=figsize, s=markersize, c='color', colorbar=False, marker='d', label='Gajjar et al. (2018) bursts')\n",
    "otherbursts.tail(1).plot.scatter(ax=ax, x='tau_w_ms_corr', y='drift_corr',\n",
    "                         xerr=otherbursts['red_chisq']*otherbursts['tau_w_err_corr'], yerr=otherbursts['red_chisq']*otherbursts['drift_err_corr'], \n",
    "                         figsize=figsize, s=markersize, c='color', colorbar=False, marker='s', label='CHIME/FRB: Josephy et al. (2019) burst')\n",
    "# for k, v in otherbursts.iterrows():\n",
    "#     ax.annotate(k+'_c', (v['tau_w_ms_corr'], v['drift_corr']), xytext=(-3,5), textcoords='offset points', weight='bold', size=annotsize)\n",
    "    \n",
    "if not logscale:\n",
    "    ax.set_xlim(-0.05, 1.8)\n",
    "    ax.set_ylim(0, 17000)\n",
    "elif logscale:\n",
    "    ax.set_xlim(0.05, 2)\n",
    "    ax.set_ylim(10**2, 10**5/2)\n",
    "\n",
    "# ax.set_title('Sub-burst Drift Rate vs. Burst Duration (fit to Michilli bursts)', size=fontsize)\n",
    "ax.set_xlabel('Sub-burst Duration $t_\\\\mathrm{w}$ (ms)', size=fontsize)\n",
    "ax.set_ylabel('Sub-burst Drift Rate $\\,\\\\left|\\\\frac{d\\\\nu_\\\\mathrm{obs}}{dt_\\\\mathrm{D}}\\\\right|$ (MHz/ms)', size=fontsize)\n",
    "\n",
    "\n",
    "# NLSQ fit\n",
    "fit_sigma = michillibursts['red_chisq']*michillibursts['drift error (mhz/ms)']\n",
    "popt, pcov = fitreciprocal(michillibursts['tau_w_ms'], michillibursts['drift_abs'], sigma=fit_sigma)\n",
    "perr = np.sqrt(np.diag(pcov))\n",
    "\n",
    "# NLSQ fit no errors\n",
    "popt_noe, pcov_noe = fitreciprocal(michillibursts['tau_w_ms'], michillibursts['drift_abs'])\n",
    "perr_noe = np.sqrt(np.diag(pcov_noe))\n",
    "\n",
    "# NLSQ log fit\n",
    "fit_sigma = michillibursts['red_chisq']*michillibursts['drift error (mhz/ms)']\n",
    "popt_log, pcov_log = fitreciprocal_log(np.log(michillibursts['tau_w_ms']), np.log(michillibursts['drift_abs']), sigma=np.log(fit_sigma))\n",
    "perr_log = np.sqrt(np.diag(pcov_log))\n",
    "\n",
    "# NLSQ log fit\n",
    "popt_log_noe, pcov_log_noe = fitreciprocal_log(np.log(michillibursts['tau_w_ms']), np.log(michillibursts['drift_abs']))\n",
    "perr_log_noe = np.sqrt(np.diag(pcov_log_noe))\n",
    "\n",
    "# log-log\n",
    "popt_log_log, pcov_log_log = fitreciprocal_log(np.log(michillibursts['tau_w_ms']), np.log(michillibursts['drift_abs']), loglog=True)\n",
    "perr_log_log = np.sqrt(np.diag(pcov_log_log))\n",
    "\n",
    "# NLSQ linear chisq\n",
    "# fit_residual = michillibursts['drift_abs'] - popt[0]/michillibursts['tau_w_ms']\n",
    "# chisq = np.sum((fit_residual / fit_sigma) ** 2)\n",
    "# red_chisq = chisq / (len(michillibursts['drift_abs']) - len(popt)) # this is chisq/(M-N)\n",
    "# fit_error = perr*red_chisq\n",
    "\n",
    "# ODR fit\n",
    "fitdata = scipy.odr.RealData(michillibursts['tau_w_ms'], \n",
    "                             michillibursts['drift_abs'],\n",
    "                             sx=michillibursts['red_chisq']*michillibursts['tau_w_error'], \n",
    "                             sy=michillibursts['red_chisq']*michillibursts['drift error (mhz/ms)'])\n",
    "odrfitter = scipy.odr.ODR(fitdata, fit_model, beta0=[500])\n",
    "odrfitter.set_job(fit_type=0)\n",
    "odrfit = odrfitter.run()\n",
    "\n",
    "# ODR fit log\n",
    "fitdata_log = scipy.odr.RealData(np.log(michillibursts['tau_w_ms']), \n",
    "                             np.log(michillibursts['drift_abs']),\n",
    "                             sx=np.log(michillibursts['red_chisq']*michillibursts['tau_w_error']), \n",
    "                             sy=np.log(michillibursts['red_chisq']*michillibursts['drift error (mhz/ms)']))\n",
    "odrfitter_log = scipy.odr.ODR(fitdata_log, fit_model_log, beta0=[500])\n",
    "odrfitter_log.set_job(fit_type=0)\n",
    "odrfit_log = odrfitter_log.run()\n",
    "\n",
    "# print('nlsq:', popt, perr)\n",
    "# print('nlsq noe:', popt_noe, perr_noe)\n",
    "# print('nlsq log:', np.exp(popt_log), np.exp(perr_log))\n",
    "# print('nlsq log noe:', np.exp(popt_log_noe), np.exp(perr_log_noe))\n",
    "# print('loglog:', popt_log_log, perr_log_log)\n",
    "# print('odr:', odrfit.beta, odrfit.sd_beta)\n",
    "# print('odr log:', np.exp(odrfit_log.beta), np.exp(odrfit_log.sd_beta))\n",
    "\n",
    "### Plot fits\n",
    "x = np.linspace(0, 3, num=600)\n",
    "opts  = [popt, popt_noe, np.exp(popt_log), np.exp(popt_log_noe), np.exp(odrfit_log.beta)]\n",
    "errs  = [perr, perr_noe, np.exp(perr_log), np.exp(perr_log_noe), np.exp(odrfit_log.sd_beta)]\n",
    "names = ['1. nlsq\\t\\t\\t', '2. nlsq no error\\t', '3. nlsq log\\t\\t', '4. nlsq log no error', '']\n",
    "ls    = ['k--', 'g--', 'y-', 'g-.', 'r-']\n",
    "for opt, err, name, l in zip(opts, errs, names, ls):\n",
    "    if l not in ['r-']:\n",
    "        continue\n",
    "    plt.plot(x, opt[0]/x, l, label='Fit from Michilli et al. (2018) bursts')\n",
    "\n",
    "# plt.plot(x, odrfit.beta[0]/x, 'b-', label='6. x and y error \\t\\t${:.2f} \\pm {:.2f}$'.format(odrfit.beta[0], odrfit.sd_beta[0])+'/$t_w$')\n",
    "\n",
    "# a = np.exp(popt_log_log[1])\n",
    "# k = popt_log_log[0]\n",
    "# plt.plot(x, a * x**k, 'r-', label='loglog \\t\\t\\t\\t\\t${:.2f}~t_w^{{{:.2f}}}$'.format(a,k))\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "handles = handles [1:] + [handles[0]]\n",
    "labels = labels [1:] + [labels[0]]\n",
    "plt.legend(handles, labels, fontsize='x-large')\n",
    "\n",
    "# plt.tight_layout()\n",
    "# for f in ['png', 'pdf', 'eps']: plt.savefig('paper_figures/{}.{}'.format(filename, f))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>drift (mhz/ms)</th>\n",
       "      <th>center_f</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>M1</th>\n",
       "      <td>-1039.359422</td>\n",
       "      <td>4458.212325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M2</th>\n",
       "      <td>-740.578566</td>\n",
       "      <td>4427.438381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M3</th>\n",
       "      <td>-2823.770453</td>\n",
       "      <td>4489.924477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M4</th>\n",
       "      <td>-1104.756403</td>\n",
       "      <td>4603.952181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M5</th>\n",
       "      <td>-2111.819665</td>\n",
       "      <td>4435.776195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M6</th>\n",
       "      <td>-6051.049490</td>\n",
       "      <td>4513.184394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M7</th>\n",
       "      <td>-1019.972935</td>\n",
       "      <td>4351.042566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M8</th>\n",
       "      <td>-340.369797</td>\n",
       "      <td>4563.386812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M9</th>\n",
       "      <td>-191.720931</td>\n",
       "      <td>4438.617323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M10</th>\n",
       "      <td>-834.396382</td>\n",
       "      <td>4586.065119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M11</th>\n",
       "      <td>-1031.102098</td>\n",
       "      <td>4511.888302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M12</th>\n",
       "      <td>-1012.748636</td>\n",
       "      <td>4404.976125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M13</th>\n",
       "      <td>-915.460159</td>\n",
       "      <td>4594.331225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M14</th>\n",
       "      <td>-3452.888809</td>\n",
       "      <td>4475.588989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M15</th>\n",
       "      <td>-340.562720</td>\n",
       "      <td>4278.440753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M16</th>\n",
       "      <td>-1130.804012</td>\n",
       "      <td>4363.171344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M9a</th>\n",
       "      <td>-1512.194326</td>\n",
       "      <td>4495.052204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M9b</th>\n",
       "      <td>-1313.502712</td>\n",
       "      <td>4442.118199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M10a</th>\n",
       "      <td>-3227.866176</td>\n",
       "      <td>4591.876296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M10b</th>\n",
       "      <td>-15659.439219</td>\n",
       "      <td>4499.028749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G11A</th>\n",
       "      <td>-785.400000</td>\n",
       "      <td>6500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G11D</th>\n",
       "      <td>-1701.392924</td>\n",
       "      <td>6974.717930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C1</th>\n",
       "      <td>-3.920000</td>\n",
       "      <td>630.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G11A1</th>\n",
       "      <td>-2243.987383</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G11A1a</th>\n",
       "      <td>-6749.651926</td>\n",
       "      <td>7201.523174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G11A1b</th>\n",
       "      <td>-7336.763933</td>\n",
       "      <td>7012.837929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G11A2</th>\n",
       "      <td>-3929.513225</td>\n",
       "      <td>6333.962048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G11A3</th>\n",
       "      <td>-1636.407401</td>\n",
       "      <td>5681.652873</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        drift (mhz/ms)     center_f\n",
       "name                               \n",
       "M1        -1039.359422  4458.212325\n",
       "M2         -740.578566  4427.438381\n",
       "M3        -2823.770453  4489.924477\n",
       "M4        -1104.756403  4603.952181\n",
       "M5        -2111.819665  4435.776195\n",
       "M6        -6051.049490  4513.184394\n",
       "M7        -1019.972935  4351.042566\n",
       "M8         -340.369797  4563.386812\n",
       "M9         -191.720931  4438.617323\n",
       "M10        -834.396382  4586.065119\n",
       "M11       -1031.102098  4511.888302\n",
       "M12       -1012.748636  4404.976125\n",
       "M13        -915.460159  4594.331225\n",
       "M14       -3452.888809  4475.588989\n",
       "M15        -340.562720  4278.440753\n",
       "M16       -1130.804012  4363.171344\n",
       "M9a       -1512.194326  4495.052204\n",
       "M9b       -1313.502712  4442.118199\n",
       "M10a      -3227.866176  4591.876296\n",
       "M10b     -15659.439219  4499.028749\n",
       "G11A       -785.400000  6500.000000\n",
       "G11D      -1701.392924  6974.717930\n",
       "C1           -3.920000   630.000000\n",
       "G11A1     -2243.987383          NaN\n",
       "G11A1a    -6749.651926  7201.523174\n",
       "G11A1b    -7336.763933  7012.837929\n",
       "G11A2     -3929.513225  6333.962048\n",
       "G11A3     -1636.407401  5681.652873"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bursts[['drift (mhz/ms)', 'center_f']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Do the drifts follow $\\nu^{-2}$?\n",
    "\n",
    "They shouldn't if they are dedispersed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x5376610>]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "datasubset = michillibursts\n",
    "ax = datasubset.plot.scatter(x='center_f', y='drift (mhz/ms)', \n",
    "                               yerr=datasubset['red_chisq']*datasubset['drift error (mhz/ms)'], \n",
    "                               figsize=figsize, s=20, c='k', colorbar=False, fontsize=fontsize, marker='o',\n",
    "                               label='Michilli et al. (2018) bursts')\n",
    "\n",
    "plt.plot(datasubset['center_f'], -8000000/datasubset['center_f'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below here is ??\n",
    "===="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hmsmo\\AppData\\Roaming\\Python\\Python37\\site-packages\\ipykernel_launcher.py:12: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  if sys.path[0] == '':\n",
      "C:\\Users\\hmsmo\\AppData\\Roaming\\Python\\Python37\\site-packages\\ipykernel_launcher.py:28: RuntimeWarning: divide by zero encountered in true_divide\n"
     ]
    },
    {
     "data": {
      "image/png": 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5FN3BIyo3RDEYe19gREUg+hlwCnBt+fg1wE7AwWt4jDUqA8Sosh4/o2hROwYYDLwFTASaM/5tb4rWnEkNPGYn4PfAR4DrgCuBzYAvAY9GxAEppcdSSssi4rMUr/eNEbFvSmlRROxO8f74A3BxxXlrgXvLut8HXE/xBf++8jld2YznsZqI2JyiK3U7ivflVIr3+XiK7tVqXVLufzWwGPgKcE1ETE8pPVrxeFtRBIue5eM9Q9HlPx7oVuVjHVfe/7IJ9WtNl1HU9edAAk4GfhMR70kpXdPCczf02V3bZ+U2oBdr/7z+CfhEROyaUnq2hfVULlJK3rxtVDeKL8YEfAvYGtiG4svxZ2X5pHr71wKdGjjPd8v996koGwksAbqVvx9Q7nMdxT/KHcvyI8vyY6qs6zfXsM8Hy31urSg7qSx7Dui6hvOeVFHWtyy7qN6+deca3ITX+IflMQMaec0+WFH2BnBPK/59+wP/Bp4Gtqgo7wT8gyJkrPaaVHnuk8v6f6qBbWeX2z5Rr7w7MAuYUK/8mHL/KykC0JTytdi+3n7nlPtd0sBjdljL3/SisqxvA8fOrKwTRQBIwMn19vtxWT5hLY9V9z75G1BbUd67fM1/U++8l5f7f66R8gn169zAc3gd+FcL3y9PATObeEzdc30R2KyifLOy7A2gS1P/BvXOvdpnt5rPClV8XoETy32Obclr5y2vm12u2phdDLwGvAo8STE26jbgU5U7pZSWpLI1qewu2yIitqYYgA0wqGL3hyiCQ13358Hl+X9CMZmhbizPQRStdhNa4Xn8q7zv3sC2n6fWGXfTVGPK+2F1BRERFF8kT6WUHq/Y95/A7hGxRys99tnAe4AvpZTerCss/4YTKAL6fzTz3NuU9w2NMzyRYgzm5IjYuu5WPt79wP5RjPmqq0/dWKbTKd5LewBfTCnNqnfezwFvAt+pV04qxrK1lqOAVyhafyo1dUbkVSmlJXW/pJTmUgTp+ktlHEnRgvqbeuVXUL3uvPv+bws/Tyn9s+6X8udfAFtQhN6Wnrv+Z7e1Piuvl/c9WngeZcRAp43ZLylmKx4OnEvxJd2HBgZ7R8RXI+JJipaGNyiC4IRy8xYVuz5U3h9ccT+eYjbdm/XK/55Sao0JCHVBrqEvtn+0wvmbLKX0FEVLzeciou7fkQMoWgHH1Nv96xSv4ZRy3NXIiBhacVxTfQqYnlL6YwPbOpf3CyoLI2JSRBxfxblT3SENbNsN2JXivVH/dgpF99jW9Y75BkV32EeA/0sNjwPbCXg2pbSuJyH0B6allJZXFqaU5lF0U1erofFprwNb1SvrR/F3WiWUppRebcLj/YviP0pt5ZkGyurGxfZv4bkb+uy21mel7v2b1riXNioGOm3MpqWUHkgp/S6ldDlFi8GHqDeuLYrZpD+jaE34MsVYtUMpujag4nOSUnqZ4h/5g6OYMDEIeKj80poIHFKOHdqTd8NfS+1Z3jc0Pq4tWufqjKEIyHUhdhiwHLihcqeU0m8pgt7nKV6TQ4DbgQnl+LGqlePAtgf+3sgu+wAvp5RmVxxTQ9Hl/kQVD1E383nLhh6eotv00DXc6s+c3rOsL8Ae0fgaiM394l3TcQ09VmP7N2Wtw+WNlLfmeol1ngK6R0RLw1NzNfR61X+eTf0b1Fnts9uKn5W69++GNJNf65iBTu1G2aJzHXB8RHykYtPnKca6fDKlNDKldE9K6QGK7qmGPEQxru1Iiu62B8vyBylaYj5J8Y9+awW6L5T3d7fS+eprbpj4NcU6ZcPKrsbjKGaHzlvtAVJ6I6V0fUrpSxQtG5dTDJAf2sTHrGutXFJ/Q0TsQ9GCdnNFWS/gbYqWu8nlzNOP1D+2wlPlfUMr7U+j6JJ9qPyPQkO3la1s5cSYGynWTLsA+DAVkyEq/APYLYp1ApuqrgV4lQAaEe+hGDxf6QVg5zLgVu7bi2JsWGubCexYv3UpInpQ/dpwt5b3X2zFejXFgAbKdivv61oqm/I3WKsqPivVfF53LO+fWuNe2qgY6NTefJeihaFyvNJyin8kV/7Pu2xJOa+RczxE8dn5NjArpfR8RXln4HyKmbJNXjajUkTURMQVFOP17kkVMwhbWV33ZEOtUo1KxTp+v6MY/P85irC1Sndr+Rw2r3dc3cD6lY8ZEZ0iYteI2J41e5miy/zAyvFqEbEFxYSVf1F8AdY91jyK7tBJKaVNyltDXbV1/laeY98Gtl1LsUD1Nxo6MCJ61iu6mmIs34kppUuAW4DzIuKgevvdQNHNdmED51xbq1ddt93H6pWfzer/vv+WYsbpsHrl567lMZrrTopA85/1yr/ZhHOMpGiZ/mZENBj+I2LviPhq86q4Vl+JiJVht/z5NN6dSQ1N+xs0qtrPCtV9XvcFXkkpNTbrXRshly1Ru5JSmh4RN1KM/fpoSukRYCzF6vO/i4jbKILJZ2l8lfzxFBMedqO4lFbduadGxMsU/6v/U0rp7SZU7YMRcWL5c+WVIv6DYimLzzbhXE31V4rnc0EZjBYCM1JKf6ni2DEUY9p+RDGg+7f1tm8KzIuIOyi+mF6lGFv1FYoxh3eW+/Wm6MqeyBoGm6eUlkTE1cBZFOtw/Zrii+0LFKHo6HKQfqUP8u6X4hqllJaX74GhEdE5pbS4YvNPKLpVfxgRB1ME+H9RdKkeQhE0DwKIiC8AJ1DMXK1rqT2Vokv4+ojYM6X0esV5jwQujGKR3PvKc+1O8T6oHxQqPUAxUeM7ZVf/DIr/AOxL0TJY6XKK99H/RcTeFLOEB1O0HNbftzVcVj7e6LL19NmybvuVj7fWlqaU0jsRMYSidfr2iLiPYgLK6xStpQdRLCt0eeVxEfF53p0Ysw1QGxF1gfnFlNJ1VT6H+cBfImIUxX/4Tqb4e3+xYkJDU/4Ga1LtZ2WNn9eI2ISiRW9UEx5bG4O2nmbrzVtr31jLUiAUQWw5ML78vYaiVW06xaSIFym+IHajgWU+ymMml9s+X6/8hrL8e02sa91tOUUwepoiLB3WyHEnsYalC2jCsiXltuEUg72XlPtcU2X9aym+XBPFoP+Gtv+AYl2318vXdybFl81ODdRtQhWP2Qn4fvl3WkIx9nEMsHMj+z8AfLkJ7599aGTJB4r/BH+N4kt1YXmbVv7dP17us2tZ/ijlMjYVx3+Y4j8Kd9Qrfw9Ft+zTFGHurfIxvrqmv2lZvjPF+njvlMfdTBGQZ9Z/PSnCyFiKIPo2RUjYof6+jbx/Gn3PUV6GqoHyfhQzy98uH/O3Zdl8mrCUDdCVosXrDxThZinFkIi7KYZM1DRQn9TIrZr3WN1z/RhFN/ms8r37FPDZBvZvyt+gwdeRKj8ra/u8ltsSsEe1r6+3jeMW5RtAkjZKETEfODyltNpiwWs45vcUaw1+dN3VrH0qW7HmA1enlE5r6/psbCJiMkUr5DFtXRetX46hk7TRKq/usMVad1zdfwEfjoiPt3KV2pXKcY4V6sbsNedKHlqDiDiKYkb3uhoXqQ1Ym7fQRcQuwE0VRf0pVvi/tizvS9Hs/JlULiIaEedTjJlZDnwtpXRvWb43xZimLhQX1j4rtfUTlNSmIuIyiuVoOgJ7pta7zqfWIorrmL4IPEYxtOEQYAjwR+CAVG9NPEnN1+aBrlI5nX4uxdpepwNvpJQujYjzKC7xc25EDKBYeXwfYFuK8TE7p2Iw8ySKwdJ/pgh0I1JKv2uL5yJJ7V1E/BfFrNq+FP/RnkMxpu7i1LRJQ5LWYkPrcj0EeD6l9CLFmjt1SyCMoZjxR1l+Y0ppcUppBsVA9n3KtZS6p5T+VLbKXVtxzGoioltE/CgiXoyIJRExLyJ+GxHbR8TJETEtIlJE1F9xvtFt5fbdy20pIk6tt21ERLxSbrureS+RJOUhpfSjlNJeKaXNUkq1KaX+KaVvGuak1rehBboTePe6fz1TuUBpeV93TbrewOyKY+aUZb3Ln+uXr6Zc2+luivWkXqCYtfYTimnu21PMOLuDhq/nuKZtAHWXF1pR8XOlGxs5TpIkqVk2mHXoysuafIpi+Yg17tpAWVpDeUN+BBwILOrUpdvgPQfsOhhWLuHySIcORc6dMmUKy5YtY+DAgSvPs/feeze6DaBz586sWLGCTTbZpMObb7558F577ZU6deq08tjFixfz1FNPsdlmmx1Rd+zSpUuZNm0aixcXS1517dqV7bffni5dGhpPLEmS2qvJkyfPTyltU798gwl0FJdLejylVHe5pVcioldKaV7ZnfpqWT4H2K7iuD7AS2V5nwbKG/JyeX91j959z/rDH/7AggVF72nXrl3p2rUrAH379mX+/Pk89thjq52goW1///vfef/738+ZZ57JgQceyHHHHceXv/xlvvrVdxcxnzlzJv369WP//ffnrruKXtfXX3+dq666im233ZZ58+Zx+eWXs+2223L//U4CkyRJ74qIFxsq35C6XP+Td7tboejWHF7+PJx3V6C/AzghIjpHRD+Kay5OKrtl346Ifcsu1WGsvmp9fQngF7/4Bdtssw3bbLMNl19++VoOadxNNxWTdQcNGsRuu+1GbW0tN99881qOgsWLF/P73/+eL33pS/zP//wPb7/9NlOmTGl2PSRJUvuyQQS6iOhKcUmd2yqKLwUOjYhp5bZLAVJKT1Oswj2VYmXu0yumvn+F4tp/04HnKa4z2ZC6ZrVDUkoce+yxfOtb32rx87jlllsAOPHEE9l9991ZsmQJjzzyCPPmrXat8lWMGDGCP/7xj5x++unce++99OnTh0WLFq3xGEmSpDobRJdrKq6Jt1W9stcpZr02tP/3KS79U7/8MWCPKh5yPMWlYQa/Pm8W99133yqh6/HHH+fxxx9nwYIFLFu2jJEjR7LzzjtzwAEHNLqtW7duTJ8+nSOPPJJTTjkFgL/85S9ceumljB07ljPPPJO7776bp556CoDZs2czcuRIDjzwQOqWjlmwYAGPPPIIc+bMYbPNNkOSJKkaG9Q6dOtTeQHj73ao6fj1mg5Bz5492XfffTnnnHO4++67ufjii1fZf/jw4VxzzTVcdNFFDW7r2bMnl19+OWPHjuXYY48F4JVXXqFXr1585CMf4Q9/+AODBw9m4sSJqxw7evRoDj30UI4++mimTZvGcccdx5///Gdmz57NW2+9tU5fA0mSlJeImJxSGrhaeXsNdHX67LRHmjPtqbauhiRJ692KFSuYP38+b731FsuXe+GODUFNTQ2bb745W2+9NXWrblRqLNBtEF2ubWnB4mXcOGkWQ/balk06t/uXQ5LUjsyZM4eIoG/fvnTq1IliTqHaSkqJpUuX8sorrzBnzhy23377qo/dICZFtKV/L13Od+6ayqBLHuCvMxtbK1iSpI3PwoUL6d27N7W1tYa5DUBEUFtbS+/evVm4cGGTjm33gQ7gnSXLWbh4OSeNnsTCxcvaujqSJK03DXXrqW0152/iX7FCSnDXk42tRSxJkrRhMtBVeGfJcmbOf6etqyFJktQkBroKXWtr6Lt117auhiRJKm2yySar3GpqajjzzDMBuPLKKxk4cCCdO3fmpJNOatKx9T3zzDMcfPDBbLbZZuy4446MGzeuSed66aWX6NOnT/3TrjdO66wQAUP23LatqyFJkkp111qHYhJHz549+fSnPw3Atttuy4UXXsi9997Lv//97yYdW2nZsmUMHTqU0047jfvvv5+JEydy5JFH8re//Y2dd965qnPdc889HHbYYS1/ws1kCx1Fy1y3zjVcc/I+dHPpEkmSNkhjx46lR48efPSjHwXgmGOO4aijjmKrrbZay5GrH1vp2Wef5aWXXuLss8+mpqaGgw8+mP3224/rrruu6nPdc889HH744St/X7p0KRdccMEqS8JEBHvttVdTn3ZV2n166dKphm8fOYAhe25rmJMkaQM2ZswYhg0b1qwlVtZ0bEMXWUgprbxc59rOtXTpUh5++GHGjBmzcp8LL7yQiRMn8sgjj7DlllsydOhQunfvzhVXXNHkulej3SeYbp07cvyHql+4T5KkjdXFdz7N1Jf+tU4fY8C23fn2kbs3+bhZs2YxceJEfvWrX7X6sbvuuis9evTghz/8IWeffTbjx49n4sSJHHTQQVWd6+GHH2avvZHlb8MAACAASURBVPZi0003BeDtt99mxIgRPPnkk2y33XYAHHvssdx0003079+f8ePH069fP/r27dvk59IYu1wlSdIG79prr2X//fenX79+rX5sp06duP3227n77rt573vfy49+9CM+85nPNDjJoaFz1e9uffjhh+nfvz877bTTyrI333yT9773vQCMGjWqwVbBlmj3LXSSJKnQnJaz9eXaa6/lvPPOW2fH7rnnnkycOHHl7x/5yEcYPnx4Vee65557VpkV+9prr7HFFlus/D2lxLhx4zjrrLO44447uPPOO5k9ezannHIKw4YNa9Zzqs9AJ0mSNmh//OMfmTt37mozVJctW8ayZctYvnw5y5cvZ9GiRXTs2JGOHTuu9dj6nnzySXbeeWdWrFjBVVddxbx581ZbCqWhc82YMYPFixez6667rizbY489ePzxx3niiSfYZZdduPjii4kIjj/+eGpqavjABz7A+PHjW/CKrM4uV0mStEEbM2YMxxxzzMoxanW+973v0aVLFy699FKuv/56unTpwve+972qjv3kJz/JJZdcsvL36667jl69etGjRw8efPBB7r//fjp37rzWc919992rdLcCDBw4kAsuuIDDDz+c/v378/LLL3PPPffQqVMnpk+fzi677NKi16Mh0dp9uLnpvdMeae60hmexSJK0MXvmmWfYbbfd2roaWTv88MM544wzVgt1jbn99tuZOXMmX//619e4X2N/m4iYnFIaWL/cFjpJkqRmGjx4cIOzYRuz8847M3LkyLUGuqZyDJ0kSVIznXPOOU3af8CAAY2ub9cSttBJkiRlzkAnSZKUOQOdJElS5gx0kiRJmTPQSZLUjq1YsaKtq6B6mvM3MdBJktROdevWjblz57JkyZJWv7aomi6lxJIlS5g7dy7dunVr0rEuWyJJUjvVp08f5s+fz4svvsiyZcvaujoCOnbsyGabbcbWW2/dpOPa7ZUiIqIvMAPgu9/9LhdeeCEAp5xyCqNHjwaKpDx79mzOOOMMHnjgATp27MiQIUO44YYb2qjWkiSpPWvsShEGOqB///5Mnz6dd955h/e+970sWLAAKPqwP/ShDzF16lTOOeccevXqxTPPPMOPf/zjtqu4JElqtxoLdO2+y7WmYydeeOEFJkyYwIwZM1i6dCm9e/dm7ty5jB8/nsmTJ3PBBRdw3nnn0blzZyKirassSZK0inY/KaJjbWcGDRrEqFGjGDVqFEcddRSbb745AFOnTgXg1ltvpWvXrnTv3p0RI0a0ZXUlSZJW0+4DHRTj5m655RYeffRRTj755JXlixcvBqBTp06MGzeOfv368fWvf51//OMfbVVVSZKk1RjogBNOOIGamhr69OnDoYceurK8b9++ABxxxBEMHTqUI444gpQSM2bMaKOaSpIkra7dj6ED6N69O6NGjWLTTTelQ4d3M+7hhx9Ojx49uPXWW9lxxx0ZO3Ysm2yyCR/4wAfasLaSJEmrsoWudPzxx3P44YevUtalSxfGjh1L586dOf300+natSu33XYbPXr0aKNaSpIkra7dLltSZ9sdd08vTX+6rashSZK0Vo0tW9LuW+jeWbKcGyfNYsFiV8iWJEl5aveB7t9Ll/Odu6Yy6JIH+OvMN9q6OpIkSU22QQS6iNg8IsZGxLMR8UxEfDgitoyI+yNiWnm/RcX+50fE9Ih4LiI+UVG+d0RMKbeNiCpXAX5nyXIWLl7OSaMnsdCWOkmSlJkNItABPwF+n1LaFdgLeAY4D3gwpbQT8GD5OxExADgB2B04DLgqImrK8/wcOBXYqbwd1pRKpAR3PflSy5+NJEnSetTmgS4iugMHAL8CSCktSSm9BQwFxpS7jQGOKn8eCtyYUlqcUpoBTAf2iYheQPeU0p9SMdPj2opjqvLOkuXMnP9Oi5+TJEnS+tTmgQ7oD7wGjI6Iv0XEyIjoBvRMKc0DKO/r1grpDcyuOH5OWda7/Ll+edW61tbQd+uuzXsWkiRJbWRDCHQdgQ8CP08pfQBYSNm92oiGxsWlNZSvfoKIUyPisYh4LC1dUlEOQ/bctuqKS5IkbQg2hEA3B5iTUvpL+ftYioD3StmNSnn/asX+21Uc3wd4qSzv00D5alJKv0wpDUwpDYxOtXStraFb5xquOXkfunX24hmSJCkvbR7oUkovA7MjYpey6BBgKnAHMLwsGw78tvz5DuCEiOgcEf0oJj9MKrtl346IfcvZrcMqjmlUl041fPvIAUz6fx/jQ323bL0nJkmStJ5sKM1RZwI3REQt8AJwMkXYvDkivgDMAj4NkFJ6OiJupgh9y4DTU0rLy/N8BbgG6AL8rrytUdfaGo7/0Pat+2wkSZLWIy/95aW/JElSJrz0lyRJ0kbKQCdJkpQ5A50kSVLmDHSSJEmZa9Ys14jYmeJaqj0oFu99DXgqpTStFeu2XrTvKSGSJGljUHWgi4jdgNMolg/pWVdc3qdyn1eAm4GrU0rPtGI9JUmS1Ii1BrqI2AG4DDga+DfwCPAn4HngdYpQtyWwI7Av8EXgzIi4DTg3pfTCuqm6JEmSoLoWuqnAFOAk4LaU0sI17RwR3YDjgK+Vx76nhXWUJEnSGlQT6D6TUlrrJbTqlIFvDDAmIoY2u2aSJEmqylpnuTYlzLXmsZIkSaqOy5ZIkiRlrlnLltQXER2BoRSTI+5MKb3cGueVJEnS2jW5hS4iLo+Iv1b8HsADlMuVAFPKmbGSJElaD5rT5XoYxdIldY4EDgB+CHy2LDuvhfWSJElSlZrT5bodUHlFiCOBGSml8wAiYnfgc61QN0mSJFWhOS10tcDyit8PouhyrfMC0KsllZIkSVL1mhPoZlNcEaKuNa4/MLFiew9gQcurJkmSpGo0p8v1RuB/IqIHsDvwL+Ceiu0foLgsmCRJktaD5rTQ/QC4BvgwkIBhKaW3ACJiM+BTwIOtVUFJkiStWZNb6FJKi4EvlLf63qYYP/dOC+slSZKkKrXKwsJ1UkorgH+25jnXudTWFZAkSWqZZl36KyI+GxGPRsSrEbG8gduy1q6oJEmSGtbkFrqIuBC4GHgF+CPwZmtXan2ygU6SJOWuOV2uXwUmAIellJa2bnUkSZLUVM3pcu0O3GyYkyRJ2jA0J9D9jeLyX5IkSdoANCfQXQicFhEfbO3KSJIkqemasw7dxIj4AvDniPgTMJNVr+1a7pYaWqdOkiRJraw5s1wHUVwpoiPw0fJWX6LhhYclSZLUyprT5foTYCkwFNgypdShgVtN61ZTkiRJjWnOsiV7AhellO5s7cpIkiSp6ZrTQvcqsKS1KyJJkqTmaU6gGwWcGBGteh1YSZIkNU9zQtkfgCEUs1yvAmaw+ixXUkoPt7BukiRJqkJzAt0DFT+PZPXLoUZZ5sQISZKk9aA5ge7kVq9FG0qr5VFJkqS8NGdh4THroiKSJElqnqomRUTE7Ij4aUQcEhF2pUqSJG1Aqp3legdwFHA/8FpEXBcRR0dE19aoRETMjIgpEfFERDxWlm0ZEfdHxLTyfouK/c+PiOkR8VxEfKKifO/yPNMjYkRERGvUT5IkaUNWVaBLKZ2eUtoO2Be4GhgI3ArMj4jfRsRJEbFVC+tyUErp/SmlgeXv5wEPppR2Ah4sfyciBgAnALsDhwFXVbQa/hw4FdipvB3WwjpJkiRt8Jq0Dl1KaVJK6fyU0m7AAOC7wHuBXwEvR8T4iPhaRPxHK9RtKFA3Xm8MRQthXfmNKaXFKaUZwHRgn4joBXRPKf0ppZSAayuOkSRJ2mg1Z2FhAFJKz6aUfpBSGgRsD5xNsR7dFcALEfF4RFTbQpaA+yJickScWpb1TCnNKx9rHtCjLO8NzK44dk5Z1rv8uX65JEnSRq3Zga5SSmluSunKlNLHKILXycBMYI8qT7FfSumDwCeB0yPigDXs29C4uLSG8tVPEHFqRDwWEY8tWrS4yipKkiRtmFr98l0ppbcoujuvbcIxL5X3r0bEOGAf4JWI6JVSmld2p75a7j4H2K7i8D7AS2V5nwbKG3q8XwK/BOjZf4AL0UmSpKw1uYUuIkZFxCURUdvI9n0jYlQTztctIjat+xn4OPAUxcza4eVuw4Hflj/fAZwQEZ0joh/F5IdJZbfs2+XjBzCs4hhJkqSNVnNa6E6i6Mo8ICKOSinNr7d9B4oAdkqV5+sJjCtXGOkI/Dql9PuI+Ctwc0R8AZgFfBogpfR0RNwMTAWWAaenlOquJfsV4BqgC/C78iZJkrRRi2JCaBMOiFgB3Ah8CngFOCKl9GzF9s8B16aUsliAuGf/AemVF6a2dTUkSZLWKiImVyzxtlJzJ0XcBRxI0RL2p4j4WEsqJ0mSpOZrybIlk4FBFN2h91QsNyJJkqT1qEWzXFNKsyNiP+Bm4OcRsQvwRKvUbD1xiqskScpdi5ctSSktiIghwAiKxYXntbhWkiRJqlprLSy8IqV0BvANilmrkiRJWk+a3EKXUmo0BKaUfhwR9wNbt6hWkiRJqtq6uFLE0619TkmSJDWuqkAXEXc08bwppTS0GfWRJElSE1XbQjekgbIERCP7O3lUkiRpPalqUkRKqUPlDehBEeY+Vn9becviKhGSJEkbg+bOcrUFTpIkaQPRKsuWSJIkqe0Y6CRJkjJnoJMkScpcuw90ydGAkiQpc9WuQ/eNekVdKSZGfDoi3t/AISml9L8trZwkSZLWLlIVTVQRsaKJ5025LF2yTb8B6bUZU9u6GpIkSWsVEZNTSgPrl1e7sPBBrVwfSZIktZKqAl1KaeK6rogkSZKap91PipAkScqdgU6SJClz1c5yfaiJ500ppUOaUR9JkiQ1UbWTIgYDS4ElVe7v6m6SJEnrSbWBbhkQwAPAaOCulFJTlzKRJEnSOlDtGLrewPnAjsA4YG5EXBYRu6yzmq03NiZKkqS8VRXoUkqvpZR+lFJ6H/Bh4LfAqcDUiPhTRHwxIjZdlxWVJElSw5o8yzWlNCmldBrQCxgGLASuBl6KiBNbuX7rnO1zkiQpd9WOoVtNSmkRcENEzARWAB8D+rdSvSRJklSlZgW6iNiWonXuJGAn4CXgBxQTJiRJkrQeVR3oIqITMBQ4Gfg4sBy4AzgbuDfbWa/2uUqSpMxVu7DwCOCzwBbAk8B/AdenlN5Yh3WTJElSFaptoTsD+DfwG+Dx8riTIqKx/VNK6X9bXj1JkiStTVNmuXahaKW7osrbBi0iukXEj96cM53a2lp69erF0KFDmTVrFqNHj2annXYiIthkk01WOW5N2wCefvppIoKI4Je//OXK8mnTpnHQQQex1VZbsemmm3LooYfy/PPPr/PnKUmSNn7VBrqDmng7uNVr2oqiaFq8G/hGh461jBgxgrPOOosXX3yRWbNmsWjRIj71qU+x5ZZbrnbsmrYB3HTTTQB06NBh5c8Ac+fOZcWKFVx88cWcfPLJPPDAA3zxi19cF09PkiS1M5FS+5sVEBGHUFzG7Jkttt9ltzdefBaAFStWsHjxYrp06QJA3759mT9/PgsWLFjtHI1t23XXXVmwYAH7778/Y8eOZe7cufTs2ZMlS5ZQW1u7cr+tttqKmpoaXn311XX2PCVJ0sYlIianlAbWL2/ywsIbib3L+/sigkWLFjF//nzeeOMNWhJw//73v/Pcc89xzDHH8OlPf5rly5dz6623AqwS5h577DHeeOMNDjjggJY8B0mSJKCKQFe2ZjVLRHysuceuJwngF7/4Bdtssw3bbLMNl19+ebNPVtfFOmjQIHbbbTdqa2u5+eabV9nnueeeY+jQofTt25ef/vSnLai6JElSoZoWut9HxEMRMSQiata2c0R0ioijI2IicE+1FYmImoj4W0TcVf6+ZUTcHxHTyvstKvY9PyKmR8RzEfGJivK9I2JKuW1END4N97Hy/pCUEsceeyzf+ta3qq1qo2655RYATjzxRHbffXeWLFnCI488wrx58wCYOnUqBx54IB07duShhx6iV69eLX5MSZKkagLdB4BlFIsIz4uIGyLirDLgfSQi9ouIIyPiGxFxMzAPGAu8A7y/CXU5C3im4vfzgAdTSjsBD5a/ExEDgBOA3YHDgKsqgubPgVMprl6xU7m9IeOBCcD7Frw6h/vuu29l6AJ4/PHHGTlyJAsWLGDZsmWMHDmShx9+eI3bJk+ezPTp0znyyCMZN24c48aN47zzzmPFihWMHTuW2bNnM3jwYObPn89XvvIV/vKXv3DjjTc24eWRJElqREqpqhvwYeA64F8U125dXu+2AngLGAV8qNrzlufuQxHaDgbuKsueA3qVP/cCnit/Ph84v+LYe8u69QKerSj/T+DqNTzmJsD/Rk3H1KlTp9SnT5903HHHpUmTJqVvf/vbiaI7duVt+PDhKaXU6LZzzjknAWns2LGpzssvv5wiIu23335p/Pjxqx1XvPySJEnVAR5LDeSaJs9yLVvD9gYGANuUweQ14Cngb6kZlwCLiLEU14LdFPhmSmlIRLyVUtq8Yp83U0pbRMSVwJ9TSteX5b8CfgfMBC5NKX2sLP8ocG5KaciaHnurvrul12c+s6ZdJEmSNgiNzXKt+lqudVJKy4FJ5a01KjYEeDWlNDkiBldzSEPVWkN5Q495KkXXLO/p2Y8bJ81iyF7bsknnJr8ckiRJbW5DWLZkP+BTETETuBE4OCKuB16JiF4A5X3dgm1zgO0qju8DvFSW92mgfDUppV+mlAamlAauiBq+c9dUBl3yAH+d6aVpJUlSfto80KWUzk8p9Ukp9aWY7PBQSulEikkYw8vdhgO/LX++AzghIjpHRD+KyQ+TUkrzgLcjYt9yduuwimPW6J0ly1m4eDknjZ7EwsXLWu/JSZIkrQdtHujW4FLg0IiYBhxa/k5K6WngZmAq8Hvg9LIbGOArwEhgOvA8xdi6qqUEdz3ZYKOeJEnSBmuDGjSWUppAsZwIKaXXgQYXNU4pfR/4fgPljwF7NPfx31mynJnz32nu4ZIkSW1iQ26hW++61tbQd+uubV0NSZKkJjHQVYiAIXtu29bVkCRJapINqsu1rXStrSECrjl5H7q5dIkkScpMq6eXiDgROCWldHBrn3td6FTTgW8fOYAhe25rmJMkSVlaFwnmP4AD18F514namuD4D23f1tWQJElqNsfQSZIkZa6qFrqIeKEJ59ysmXVpE027kq0kSdKGp9ou177AmzRyKa16XPdDkiRpPaq2y3UG8FhK6X1ruwFXrsP6tpqI6BsR6a1Zz/G9731vZfkpp5xCRFBcPQyOO+44tthiCyKCM844Y7XzvPbaa2y99dZEBFdcccV6q78kSVKdagPdZOCDVe6bXS/m6NGjSSmxcOFCbrnlllW2de7cmaOPPrrRY8866yz+/e9/r+sqSpIkNaraQPc3YKuI6FvFvi8CDze3Qutbh46deOGFF5gwYQI33XQTS5cupXfv3iu333DDDQwbNqzBY3/3u99x5513cu65566v6kqSJK2mqkCXUvpBSqlDSmlmFften1I6qMU1W086dKxl0KBBjBo1ilGjRnHUUUex+eabr/W4BQsWcNppp/GDH/yA7bd32RNJktR2XLaEYtzcLbfcwqOPPsrJJ59c1TGXXXYZXbt25eMf/zivvvoqAK+//jpvvvnmuqyqJEnSaloc6CKie0SMiohdW6NCbeGEE06gpqaGPn36cOihh1Z1zOzZs3n22WfZZZddVna5XnrppfzsZz9bl1WVJElaTWtcKaILMBy4Hni2Fc633nXv3p1Ro0ax6aab0qHDqhn3pptu4rHHHgNg6tSpjBw5kiOOOIIzzjiDIUOGADBhwgR+9rOfMWzYMI477rj1Xn9JktS+tdalv6KVztNmjj/++AbLzz33XF588UUAxo8fv/I2ePBgBg4cCBTj6QDe9773seuu2TZUSpKkTEVKLVtlJCJ6Uiw4fGhK6aFWqdV6tPn2u6a3ZmXZsChJktqZiJicUhpYv7y1JkVk20K3dPkKbpw0iwWLl7V1VSRJkpqlNQLda0A/4NFWONd6U3eliHdemsbXzv0fBl3yAH+d+UaTrhRx++23s+OOO/Ke97yHwYMHM2PGjLZ4KpIkqZ1rcaBLKa1IKb2YUlrcGhVqC28+cR8LFi1j2NUPV32liJdffpkTTjiB7t2788Mf/pDJkyczfPjw9VVlSZKklVyHrqYjy956mcWzpvDPpyeyeEl1V4r4zW9+w+LFizn//PM588wzOfroo3nkkUd4/vnneeqppxgwYABdu3Zl88035/DDD2fu3Lnr81lJkqR2pN0HuuhYS22vXVgw5X7eePw+dtv34KquFFHXvVoX/vr06QPACy+8QG1tLcOHD2fEiBGcccYZ3HvvvVx00UXr7DlIkqT2rbWWLcnaJnseyhsPXA3Ll3LEOedxxy8va/I56mYLRwSLFy/m17/+NU8++eTK7VOmTGm1+kqSJFVq9y10AN12O4Do0IGO3bfm/C81vB5dff369QNgzpw5ACu7VPv168f3v/99nnzySS6++GLuvfdeOnXqxKJFi9ZN5SVJUrtnoAM22XRTtv3U2fzwx1eyaZfaVbbddNNN3H333cC7V4qYN28eJ5xwArW1tVx22WX89Kc/Zdy4cey///7ssMMOK1vrFixYwLhx41i6dOl6f06SJKn9aPHCwrmKiL7AjA6du/LrR55hyJ7b0q1z0QO9xx578PTTT5NSom/fviuvFFGn7koRt912G//93//NnDlzGDRoEKNHj2aHHXZgypQpfOYzn2H+/Pl8+ctf5sorr6Rv37488cQT6/15SpKkjUdjCwu320BXp/t2u6R/zX6urashSZK0Vq1ypYiI6BgRn2vu9g3RshXJK0VIkqSsNXUM3eeBayPiu/U3RERH4DZgTER8sDUqtz4sX5H4zl1TV14pQpIkKTdNCnQppdHAlcD/i4jv1JVHRA0wFjgC+HJK6fFWreU69s6S5SxcvJyTRk9ioS11kiQpM02e5ZpSOgu4CrggIi4uw9wtwJHAqSmlX7VyHdeblOCuJ19q62pIkiQ1SbMWFk4pnRkRHfj/27vz8Kqqc4/j3zchCSQBmYIyGqogouWKMpUi4AAOgFDRIq1WwLnO2oIK19lWvRWrt1TrANRrbUEKSq2AqIAWbUWUWVCEMItMKiESIHnvH2dnJAlJSLKTnN/nefKcs9cezrs3+yQva+21FowDfgq0A26oyckcRGrq0nZmhB2GiIiISJkczTh0twJpQHvgTXd/rkIiClFifCypTRPDDkNERESkTMqV0AW1c/8HtAU+AAaY2T0VGVgYzGBgpxZhhyEiIiJSJmVO6MzMgJeBYcAt7n4mMAl4yMzuquD4qkRifCxJCbFMHtktd3BhERERkZqiTNlLUDP3EnAZcKu7/wHA3a8O1j1iZtnu/njFh1o5Ysy4b1DHAjNFiIiIiNQkZc1gRgA/A25z9/8ttO4qwIDfmtnbNWXoktgYY1jXNmGHISIiIlJuZR2HbiJwnrs/XcQ6B0YB59eEZM7MUs3M9235nIcffji3fNSoUZgZZsaOHTs47bTTSEpKon79+vTp04cVK1aEGLWIiIjI4cozDt3cEtZ5SeuLYmZ1zewjM1tqZivN7IGgvLGZzTWzL4LXRvn2udvM1prZGjM7L1/5GWa2PFj3dPC83xFNmjQJd2ffvn28+uqrBdZdcMEF/PGPf+SGG27gvffe44477ijL6YmIiIhUuqMZtqSiZAJnu/t/AacB55tZD+Au4B13bwe8EyxjZh2JPMN3CnA+8MdgcGOAZ4BriYyL1y5YXyKLjWPdunXMnz+fKVOmcPDgQVq2bAlASkoKDz/8MBdeeCFnn302ADEx1eGSiYiIiOQJvRdA0FSbHizGBT8ODAb6BuV/BuYDY4Lyv7l7JrDezNYC3cwsDWjg7h8CmNlLwBBgVkmfH1Mnni5dTmfixImsX7+eIUOGsGLFCrZs2QLA8uXL6dy5MwAtW7bk97//fYWct4iIiEhFqRbVTWYWa2ZLgK+Bue7+H+BYd98GELw2CzZvCWzKt/vmoKxl8L5weYkcZ9SoUbz66qssXLiQkSNHFlh/4oknMmfOHB566CG2bt3K44/XmA68IiIiEiWqRULn7lnufhrQikht26klbF7Uc3FeQvnhBzC7FpiZs3zZZZcRGxtLq1at6NevX4Ftk5OT6d+/P+PGjaN169ZMnTr1SKcjIiIiUqVCb3LNz92/MbP5RJ59225mzd19m5k1J1J7B5Gat9b5dmsFbA3KWxVRXtTnPGdmbwHrARo0aMDEiROpX79+gWfkJk2axJIlSzjttNNYtmwZGzdupGvXrhVzsiIiIiIVJPSEzsxSgINBMlcPOBd4jEgN2pXAo8Hr68EuM4FXzGw80IJI54eP3D3LzPYGHSr+A/wCKDxWXrGGDRt2WFlKSgpvvvkmzz77LMnJyQwcOJDx48eX+1xFREREKoNF+iSEGIBZJyKdHmKJNAFPdfcHzawJMBVoA2wELnX33cE+Y4mMeXeIyCDHs4LyLsBkoB6RzhA3+xFOMKlle9+35fPKODURERGRCmVmi929y2HlZU3ozGwi8Keg40JR67sB17v7qHJFWsXqtmjnk197h4H/1YJkTf0lIiIi1VhxCV15OkWMAE4oYX1bIk2kNYI7PPjGKrr/5m0Wpe0OOxwRERGRMquMXq5JwMFKOG6lyTiQxb7MLEZM+oh9mYfCDkdERESkTErVxmhmbYDUfEUdzKx3EZs2Bm4A1h59aFXPHd5YtpVhXduEHYqIiIhIqZX2obGRwH1ExnVzYGzwU5gB2cH2NU7GgSzSdmaEHYaIiIhImZQ2oXsNSCOSsE0EngM+LLRNzhRei9x9EzVQYnwsqU0Tww5DREREpEyOmNDl69X652D5SuAv7v5+ZQdX1cxgYKcWYYchIiIiUial6RQxgoK9WvtQcKaGGi8xPpakhFgmj+xGkoYuERERkRqmNNnLkUWpaQAAIABJREFUTuDYyg4kLAbcN6gjAzu1UDInIiIiNVJpMpgPgHFBT9c9QdnFZnZiCfu4uz901NFVEfVqFRERkZrsiDNFmFkqkam5ehGp0PLgtSTu7rEVEF+lq9u8ne/f9kXYYYiIiIgcUXEzRRyxhs7d04A+ZhYPHEekt+ttwOsVHKOIiIiIlEOpHxpz9wPARjP7M/Afd99QeWFVnbLNZCsiIiJS/ZS5F4C718hBg0VERERqq9KMQ/eL4O3/ubvnWy6Ru790VJGJiIiISKmUplNENpGWyXrufiDfckkdI2pMp4iE5u08U50iREREpAYod6cI4CzIfYYud7k2cXfMjtRxV0RERKR6Kk0v1wUlLdcG2Q6xyudERESkhirN1F+5zKyemf3CzLpXVkBhOFKzs4iIiEh1VqaEDsgEXgA6V0IsoclWPiciIiI1WJkSOnfPBjYCDSonnHBkq4ZOREREarCy1tBBZBqwK8wsoaKDCYvyOREREanJyjywMPABcDGwxMz+CHwBZBTeyN3fO8rYqoxrvggRERGpwcqT0M3N9/4pDp89y4KyGjEOHegZOhEREanZypPQ1Yqpv8wsFVgP8NhvH+Gh++8DYNSoUUyaNAkoeny6wYMH89prr1VlqCIiIiIlOuJMEbVV/oQutW1b1n35JRkZGRx33HGkp6cDeQnd0KFDueSSSwBo1aoVvXr1CilqERERiWZHM1NE7RZbh7T165k/fz7r16/n4MGDtGzZki1btuRu0rFjRwYNGkRSUlKIgYqIiIgUrcw1dGbWExgAtCcyfMl3wBrgn+7+YYVHWElyaugsIZHTf3gqJ3doz/r162nVqhUrVqxg5cqVuTV0Zoa706ZNGyZMmMDAgQNDjl5ERESi0VHX0JlZA+CvwPlEOj4UdreZ/RP4ubvvLXekIbjsil8wbvSdZGZmMnv2bO68887cdWPGjKFHjx7s2LGDO++8k+HDh7N9+3YSExNDjFhEREQkT6lr6MzsLeBc4F/Ai8AyIrVzDYBOwNXAj4HZ7n5hpURbgfLX0H2xdj2dTmpL48aN2bBhA506dcqtoctv6NChTJ8+ndWrV3PSSSeFEbaIiIhEsaOqoTOz84gkc0+4+6+L2ORT4M9m9jvgdjPr5+5zi9iuWkqq34CJEydSv359YmLyxlp+8803efnll+nbty979uxh1qxZpKSk0LZt2xCjFRERESmotE2uw4ENwOgjbDcaGAr8jILj1VVr2e4MGzbssPLjjz+ebdu2MXr0aLKysujSpQtPPPEE8fHxIUQpIiIiUrTSJnRnAK/5Edpn3T3bzF4jUptXrbl7GmAJzdt5VqGRhVesWJH7ft68eVUbmIiIiEgZlXYu15ZEerKWxhqgVfnCCccz89fyt482kp55KOxQRERERMqstAldA6C0PVf3AsnlCyccr3y0iQffWEX337zNorTdYYcjIiIiUialTehiOHzO1oo4brWRcSCLfZlZjJj0EftUUyciIiI1SFlmirjQzI4rxXZnlDeY6sAd3li2lWFd24QdioiIiEiplCWh+1nwUxo1doLYjANZpO3MCDsMERERkVIrbUJ3VqVGUY0kxseS2lSzQIiIiEjNUaqEzt0XVFYAZtYaeAk4DsgGnnP3p8ysMTAFSAXSgJ+6+55gn7uBq4As4BZ3nxOUnwFMBuoBbwK3HmmolcPjgYGdWhz9iYmIiIhUkerQeeEQcKe7nwz0AG40s47AXcA77t4OeCdYJlh3GXAKkXll/2hmscGxngGuBdoFP+eXNojE+FiSEmKZPLIbSQllaYkWERERCVfomYu7bwO2Be/3mtlnRMa9Gwz0DTb7MzAfGBOU/83dM4H1ZrYW6GZmaUADd/8QwMxeAoYAs44Uw9DOLen2g8YM7NRCyZyIiIjUONUqezGzVKAz8B/g2CDZw923mVmzYLOWwL/z7bY5KDsYvC9cfkSX/+h4OrdpdFSxi4iIiISlOjS5AmBmycDfgdvc/buSNi2izEsoL+qzrjWzj83sY4jM5SoiIiJSU1WLhM7M4ogkc39x9+lB8XYzax6sbw58HZRvBlrn270VsDUob1VE+WHc/Tl37+LuXQCysivqTERERESqXugJnZkZ8CLwmbuPz7dqJnBl8P5K4PV85ZeZWYKZtSXS+eGjoHl2r5n1CI75i3z7FPfZSYe+28lFvf6L+Ph4mjdvzuDBg9m4cSOTJk2iXbt2mBnJyQVnMitpXWpqKmZGXFwcKSkp9O/fn/nz55fn0oiIiIiUSpkTOjNbYWZPmdkQM2tYATH8GLgCONvMlgQ/FwKPAv3M7AugX7CMu68EpgKrgNnAje6eFRzrBuAFYC3wJSV0iAiSvn9mZ3xDi9bH8/TTT3PrrbeyYcMGNm7cyP79+7noooto3LjxYfuWtA4gISGByZMnc80117Bw4ULOPfdc3n333fJcGxEREZEjKk+niH3AL4GbgSwzWwq8G/y87+77ynIwd/8XRT//BnBOMfs8AjxSRPnHwKml/OizgT5YDFs2pnHLLbfQpEkTunbtSrNmzahbty4zZ85k9+7dh+1Y0jqAmJgYLr/8cgCuuuoqXnzxRR555BHOPvtsunfvzqpVq8jKyqJjx46MHz+e3r17lzJkERERkcOVuYbO3bsDjYkMH/K/QCxwJ5GBfHeb2b/M7IEKjbJyROac9WxatD6eJ554gquvvpp169YVqKGLiTn8EpW0DuDQoUNAJLH74osvAFi6dCkAPXv25Omnn+a///u/WbJkCVdffXVlnJuIiIhEESvjRApFHyQyq8MFwN1AR8DdPbbkvcJlZs8B12AxzFm2iVVvT+X2228H4J577uGRRyIVgHXq1CErK4uirlNR61JTU9m4cSMtWrSgV69eTJs2jaysLJo2bcqOHTtwd3bt2sW6des466yzaN26NatXr66ScxYREZGazcwW53TqzK/c49CZWQzQlUjT5TlAT6Au8BWR5tfqzvPeGAMGDGDLli387ne/O6qDHjhwAHfn4osvpk+fPkyZMgWATp06AfDtt9+SkpICQMOGDXnhhReO6vNEREREypzQmdktRBK4PkADYA+wAPg18K67f1ahEVaeLwHwbMZe/zNObHVsbvK1cOFCPvnkEz755JPc2rcXXniB9u3b07t37xLX7dsXeYQwKyuLt956K/fDxo4dC0BycjJvvfUWq1evZvTo0dx7773qMCEiIiJHpcxNrmaWDWQBrwBPAZ96RbTbVjEzG07kHKiXmMTBA5nUq1ePvXv3FrtPamoqS5cuZfz48TzwQMHHBK+88komT55MXFxc7jN0OWJiYti8eTPNmzcvUN6nTx/ee+89duzYQdOmTSvozERERKS2Kq7JtTzj0L0NZBIZauSfwMtmdlUwJlxN8mHOm6ysLJ555hmGDh1aYIPrr78eiCRkQ4YMIS0tjXHjxnHRRRfx/PPP06RJExISEnj++ecZNWoUixcv5tChQwwaNIgZM2YwY8YM7rrrLrKzs5k2bRpz5szJ7fV6//3388EHH3DsscfSpEmTqj1zERERqVXK1SkimNmhB5Gm17OA7kAcsJHI83PvuPsrFRhnhQvmjV2PGbhTp04d6tevz7fffkuTJk3YsWPHYfvUrVuXuLg47rjjjsNq6Fq2bMlFF13EM888w7Rp03KTw+3bt9O8eXN69uzJk08+yciRI/nyyy9JSEigc+fOPP7443Tt2rXyT1hERERqvOJq6Cqql2s9YBBwH9ABoAb0ck0F1ltCIglNWpPQpCWpCfvocEIqK1asYOXKlTz44IPce++9PPzww/Tv35+ePXty6NAhdu7cya5du5gxYwZNmjQhLS2N3/72t4waNYrnn38+5DMTERGR2qrCE7ogietFpJfr2cDpRMakywaWFPVh1Un+hK5R31HsfvtPkHWQ1/7xT8beNZqVK1eyY8cOzjzzzNxhRZKTk0lPTyc9PZ1169Zx+eWXs2zZstxjdu/enX//+9+hnI+IiIjUfhX2DJ2Z3WtmC4j0bp0NjAGSgWeBoUDT6p7MFZZ0cm8sJoY69ZuSkdIxt7xp06YsXbqURYsW8fnnn9OiRQvatGlDUlISjzzyCMuWLeOBBx5gzpw5xMXFsX///hDPQkRERKJVecahux9YD/wfwZRf7r69IoOqajEJiTS54FZi4uvx5Y6M3PKtW7fyhz/8gfbt2zN79mw+//xznn76aYDcIUvS09OZMWMGBw8eDCV2ERERkfIkdKnuvrHCIwlZ0smR+VT3ZBzILYuJiWHGjBmsW7eOJk2acN9993HTTTcBMG7cOJYtW8akSZO47rrrOOaYY0KJW0RERKRCOkXUZAnN23nzK3+fu3x1r7aMG9ixhD1EREREwlEZU391ITJcSSMOfxbP3f2h8h47LAl1Ymh3bHLYYYiIiIiUSXk6RdQzs1nAf4D/BR4g8lzd/USGLcl5X62ZWaqZ+YGv1vLNB38LyuCff7gXM8PMAPjNb35Dq1atSEpKYtiwYXz33XcAzJ8/HzPLbYIVERERCUt5Zoq4F+gPPEJkUGEDrgQuAN4HFgE1qs1y3/K3cXeyD+xnxvS/55b//e9/Z+zYsXTt2pV77rmHqVOnMm7cuBAjFRERETlceRK6S4BX3f1eYEVQtsXd5wDnAvHAiIoJrwrE1uHQN1+RuXE56Z+9R+aBg7Rs2RKI1MIB/OpXv2Ls2LEcd9xxTJ48ucDumzdvpm/fvjRq1Ig77riDaH8mUURERKpeeRK61sCC4H1W8BoP4O6HgL8Clx19aFXD6sQT3/wk0pfPZfcnb3Fyj7Np2LAhAM2aNQMiid2iRYvYuXMne/fuZdeuXbn7v/vuu1xyySV06tSJJ598kn/84x+hnIeIiIhEr/IkdHvJ60yxl8jMEC3yrf8WOO4o46pSyZ36sW/1v8jcsooBQ4fnlt9www106NCBcePG0a1bN+rWrQuQ+wowePBgbrrpJu69914gr1ZPREREpKqUJ6H7EmgP4O5ZwEoizbBYpCfBxcCmigqwKuTOFNGgKXdfMyy3vKSZIgpTU6uIiIiEpTzDlrwNjDKz24KE7k/AH8zsS8CBtsA9FRhjpYuvl0SLi27nVwNOo369+NzykmaKyPH6668zYcIEpk6dCsBZZ51VpbGLiIiIlCehe5TItF8G4O5/NLO6wOVEnql7HvifCouwkhnQuU1DJt/3EEkJBS9HSTNF5DjvvPN49dVXWbp0KbfffjuDBg2qwuhFREREytHk6u7p7r4m6ACRUzbe3U93967u/pi7Z1dsmBXP3dOAttmZGUy74cc8+T+P5q7r1q0bAM2bN+ezzz5j0KBBfP/99zzwwAPcfPPNAPTt2xd3Z/z48RxzzDEcPHiQF198kZ///OchnI2IiIhEs/I8Q1ciM/uxmb1T0cetaGaWCqzPWf7fZ59n7/6DXHHFFUyaNKnAtu7O999/D8Cnn35aoPwnP/kJc+fO5de//jWPP/44KSkpVRG+iIiISK4yNbmaWRPgBGC3u68ttK4H8CBwDpGerzVHTCxfb9nIKaMeZfvfpx62OiEhocjd5s2bx+LFixk7dix33XUXCQkJuTNMiIiIiFSVUtXQmVmsmT0LbAc+BNaY2Ydm1szMGpjZK8BCIjNHvAL8sNIirgzZWcQ1ac32BX/hwIGD1K1XL3fVrFmz+Mc//sHw4cMP223VqlVAZEaJxMREGjRocFinCREREZHKVtom15uBa4GtwN+BpUB3YAIwBxhGpKNEB3e/wt1XV0KslSr7QAYHtn4OOE5eLdv111/PCSecwCuvvALAunXrctdlZmYCEBcXx4wZM2jbti233XYbn3/+eZXGLiIiItGttAndFcByIgnbT939dOAZYChwItDL3Ue4+5eVFGdl6JJ/IWvvLsDBjMzvM3LLN27cyNKlSzlw4AAAX331FXv27AFg2rRpACxfvpy5c+cyYMAA3J3169cjIiIiUlVK+wxde+B+d8/IV/YMcAPwmLt/WOGRVb7Dey/UqQuH9h9xx5EjR3LVVVfxxRdf5JZNmTKF+vXrk5ycTOfOnSs0UBEREZGSlLaGLgn4qlBZzvLyigsnRAnJWHzdw4p79epFXFxcgbLXX3+dhQsX0rNnTy699FIAdu7cyTfffMP06dNz54AVERERqQplGbak8NxWOcsHKyiWUJln4xnfHFb+/vvv89hjj+Uud+/eHXfnwQcfZObMmfzyl7/MXXfiiSfSr1+/KolXREREJEdZhi250MyOy7ecSCSpu9TMTiu0rbv7k0cdXeXakfsuJhY/EGlNrlOnDllZWQXmZs3ftNqlS+TRu/j4vCnCcvTu3buyYhUREREpVlkSup8FP4VdV0SZA9U9oct9hi4mIYns778D4AftT+bzVXmtyJdccgnTp0/PXV61ahUvvPACPXr04PLLL+ezzz4DIj1df/rTn1ZV7CIiIiK5SpvQ1eoZ5/3gfrAYqJPA2rUFxktm06ZNBWrr5s2bx7x585gxYwbJyclkZWUBcPDgQUaOHMnbb79N8+bNqzR+ERERiW6lSujcfUFlBxImP3SABj2H4Znfk75kVl65O0lJSYdtP23aNNq0acPChQsLlK9atYo1a9YooRMREZEqVeFzudYgkXbVmBjim59E1rdfc+CrtdRr14PYuLzn44qam7VJkyYsW7bssPIWLVrQt2/fSgtYREREpCjRnNBtBrC4uiR36se+1f8ic8sqGnQ6l+atU3M3SktLY968eQDceOONuDt9+/ZlxIgR7N27lzlz5vDQQw9hZpx33nmhnIiIiIhEt9ATOjObaGZfm9mKfGWNzWyumX0RvDbKt+5uM1trZmvM7Lx85WeY2fJg3dNmZoU/qzhJJ/fGYmKIrd+U+NTOJCWU7tHC5ORk+vfvz7hx42jdujVTp04t7UeKiIiIVJiy9HKtLJOBPwAv5Su7C3jH3R81s7uC5TFm1hG4DDgFaAG8bWbt3T2LyMwV1wL/Bt4EzgdmUQoxCYk0ueBWYuLrYRbD3v15Q+tNmTKFjz/+GMjr4TpgwABmz57NkiVLOO2001i2bBkbN26ka9euR3UhRERERMrD8vfgDC0Is1TgDXc/NVheA/R1921m1hyY7+4nmdndAO7+22C7OcD9QBowz907BOXDg/2LGlIl/2eut4RE2txWsGZt559vYt9Xabg7qampbNiwocD6efPmkZ6ezu23387GjRtJTk6mZ8+ejB8/nnbt2h3t5RAREREpkpktdvcuhcurQw1dUY51920AQVKXM5dWSyI1cDk2B2UHg/eFy4vl7mlm1tYzM9ZveGwgx5x5OQ17XgZAUssO7PsqDTPjgw8+4M4772TVqlUAnHPOOZxyyimkpKQwcODAijhXERERkaMS+jN0ZVTUc3FeQnnRBzG71sw+BmbmlO1b/jbuTvaB/exZ+V7utp9//jlNmzblscce48ILL2T69OmMHj36KE5BREREpGJV14Rue9DUSvD6dVC+GWidb7tWwNagvFUR5UVy9+eC6sqLAOo0PI5D33xF5sblZKx+jxjPomXLSAXf8OHDmTlzJtdddx1/+tOfAFi5cmVFnKOIiIhIhaiuCd1M4Mrg/ZXA6/nKLzOzBDNrC7QDPgqaZ/eaWY+gd+sv8u1zRHFNWhPf/CTSl89l3/K3GXjRYBo2bAgUnLN1zpw5gOZsFRERkeol9ITOzP4KfAicZGabzewq4FGgn5l9AfQLlnH3lcBUYBUwG7gx6OEKcAPwArAW+JJS9nDNkTMW3f7Nq2jf6/Bn4xYuXMioUaM444wzuP/++8txpiIiIiKVI/ROEe4+vJhV5xSz/SPAI0WUfwycWt44kk7uzZ53nycmsSn1TzijwLr33nuPAQMGcOKJJzJnzhySk5PL+zEiIiIiFS70hK66yBmLrl5iEj9olpewffLJJ1xwwQW4O9dccw1z584lKSmJQYMGhRitiIiISB4ldPkkndybpIRYBnZqwUNB2bJly8jIyAAiU38BHH/88UroREREpNqoFgMLh6lei/Z+7C+epE6MkRAXw+SR3eia2jjssEREREQOU9MGFq4yx9SrQ50Yo1Wjeky97kc0a1A37JBEREREyiT0Xq5h+/b7QxzKdjbtzuCsJ+azKG132CGJiIiIlEnUJ3TZQZNzlsO+zCxGTPqIfZmHQo5KREREpPSiPqErzB3eWFbsJBMiIiIi1Y4SukIyDmSRtjMj7DBERERESk0JXSGJ8bGkNk0MOwwRERGRUlNCV4gZDOzUIuwwREREREot6octiTEDIjVzZjB5ZDeSEqL+soiIiEgNEvWZS4tj6nJtnxNIbZrIwE4tlMyJiIhIjRP12UujpHjGXNAh7DBEREREyi2qn6Ezs16LFy/GzDj33HNzy9u3b4+ZYWa4Ox06dCAmJgYzo1GjRkyZMiX/MQr8DBkyJIxTERERkSgW1XO5mlkv4H2AOnXqkJmZyc6dOzn22GNzt3nrrbfo378/TZo0YdeuXcTFxREbG8t3331HXFwcZsbQoUO55JJLAGjVqhW9evUK5XxERESkdtNcriWoU6cOhw4d4ve//z3Lly8HICYmhuzsbLKzswE49dRTWbBgAfHx8SQmJhITk1e52bFjRwYNGkRSUlIo8YuIiEh0Uw0dvN+sWTP27dtHs2bN2LlzJw0bNuTrr78mMzOT7Oxsbr75ZiZMmABAbGwss2fPzm2izd8026ZNGyZMmMDAgQNDPCsRERGprYqroYvqZ+jyGzJkCOvXr2fv3r1cd911ueVr1qzh5ZdfpkuXyLVLTExkxIgR7Nu3D4AxY8Ywffp0nnvuOfbs2cPw4cPJyNBMEyIiIlJ1lNAFHn30USBSAzdmzJjc8pkzZ/Ltt9/Sv39/IPKM3JYtW1i1alXufkOGDOGaa66hX79+pKens2nTpqo/AREREYlaeoYu0KpVK2655RYaN25MnTp5l+WEE04AYPLkyQCsXbuW2NhY6tWrx5tvvsnLL79M37592bNnD7NmzSIlJYW2bduGcQoiIiISpZTQ5fPUU08dVnbxxRfToEEDtm7dCsDBgwcB2LlzJ8cffzzbtm1j9OjRZGVl0aVLF5544gni4+OrNG4RERGJblHdKQKgS5cu/vHHH4cdhoiIiMgRqVNEMTbtyWDIhH9xx5QlbP9uf9jhiIiIiJRZ1Cd032QcZMmmb5n+6Ra6/+YdXvowLeyQRERERMok6hO6wu59fSVfq6ZOREREahAldEV4bPbqsEMQERERKTUldEVYt2Nf2CGIiIiIlJoSuiL8IEVzsoqIiEjNoYSuCGPO7xB2CCIiIiKlpoSukAcHn0KzBnXDDkNERESOUnp6OrfddhutWrWibt26tG/fnmeffZZZs2bxwx/+kJiYGMyMnTt3hh3qUYv6mSIaJcbRuXVDfpCSxJjzOyiZExERqQXcnYEDB7JgwQL69evHpZdeSlpaGosWLSIlJYXevXuzf/9+1q5dG3aoFSLqE7pWjRKZceOPww5DREREKtC7777LggUL6NixI7NnzyYmJtIomZ2dTUxMDEOHDqVv3761JqFTk6uIiIjUOosXLwagX79+uckcUOB9bVI7z0pEREQEMLOwQ6gSSuhERESk1unSJTJ//dy5c8nOzs4tz/++Non6Z+hERESk9jnrrLPo27cv8+fP58ILL+SSSy5h06ZNbN26ldGjR7NgwQK2bdsGwMsvv0y7du0YMGBAyFGXn7l72DGEqkuXLv7xxx+HHYaIiIhUsPT0dMaOHcu0adPYsWMHrVu35le/+hX16tVj5MiRBbbt06cP8+fPDyfQMjCzxe7e5bByJXRK6ERERKRmKC6hq3VNrmZ2PvAUEAu84O6PhhySiIiIhCA98xBvLN1K2q59pDZJYuB/tSA5odalPkAtS+jMLBaYAPQDNgOLzGymu68KNzIRERGpSovSdjNi0ke4Q8aBLBLjY3non6uYPLIbXVMbhx1ehattvVy7AWvdfZ27HwD+BgwOOSYRERGpQumZhxgx6SP2ZWaRcSALiCR1+zKzgvJDIUdY8WpbQtcS2JRveXNQJiIiIlHijaVbKa6LgDu8sWxr1QZUBWpVkytQ1OiBh/2Tmtm1wLXBYrqZranUqGqOpkDNn6G4Yuha5NG1yKNrkUfXIo+uRZ5qcS1iG6S0jE085rji1v/8qW+/uuy7HVsqOYzKuhbHF1VY2xK6zUDrfMutgMPScHd/DniuqoKqKczs46J6zkQjXYs8uhZ5dC3y6Frk0bXIo2uRp6qvRW1rcl0EtDOztmYWD1wGzAw5JhEREZFKVatq6Nz9kJndBMwhMmzJRHdfGXJYIiIiIpWqViV0AO7+JvBm2HHUUGqGzqNrkUfXIo+uRR5dizy6Fnl0LfJU6bWI+pkiRERERGq62vYMnYiIiEjUUUIXhcystZnNM7PPzGylmd0alN9vZlvMbEnwc2HYsVYFM0szs+XBOX8clDU2s7lm9kXw2ijsOCubmZ2U799+iZl9Z2a3RdN9YWYTzexrM1uRr6zYe8HM7jaztWa2xszOCyfqylHMtfgfM1ttZsvMbIaZNQzKU83s+3z3yLPhRV7xirkWxX4vovC+mJLvOqSZ2ZKgvLbfF8X9LQ3ld4aaXKOQmTUHmrv7J2ZWH1gMDAF+CqS7++9CDbCKmVka0MXdd+YrexzY7e6PmtldQCN3HxNWjFUtmEZvC9AdGEmU3Bdm1htIB15y91ODsiLvBTPrCPyVyAw1LYC3gfbunhVS+BWqmGvRH3g36ID2GEBwLVKBN3K2q22KuRb3U8T3Ihrvi0LrnwC+dfcHo+C+KO5v6QhC+J2hGroo5O7b3P2T4P1e4DM0o0Zhg4E/B+//TORLGk3OAb509w1hB1KV3P09YHeh4uLuhcHA39w9093XA2uJ/KKuFYq6Fu7+lrvnzJn0byJjfdZ6xdwXxYm6+yKHmRmRioG/VmlQISnhb2kovzOU0EU8M2nqAAAG3UlEQVS54H9QnYH/BEU3Bc0pE6OhmTHgwFtmttgis4gAHOvu2yDypQWahRZdOC6j4C/laLwvchR3L0T7VIOjgFn5ltua2admtsDMzgwrqCpW1Pcimu+LM4Ht7v5FvrKouC8K/S0N5XeGErooZmbJwN+B29z9O+AZ4ATgNGAb8ESI4VWlH7v76cAFwI1Bk0LUssig3BcBrwZF0XpfHEmpphqsjcxsLHAI+EtQtA1o4+6dgTuAV8ysQVjxVZHivhdRe18Awyn4H8GouC+K+Fta7KZFlFXYvaGELkqZWRyRG/Av7j4dwN23u3uWu2cDz1OLmglK4u5bg9evgRlEznt78HxEznMSX4cXYZW7APjE3bdD9N4X+RR3L5RqqsHaxsyuBAYCP/fgIeygCWlX8H4x8CXQPrwoK18J34tovS/qABcDU3LKouG+KOpvKSH9zlBCF4WC5xxeBD5z9/H5ypvn2+wnwIrC+9Y2ZpYUPMyKmSUB/Ymc90zgymCzK4HXw4kwFAX+lx2N90Uhxd0LM4HLzCzBzNoC7YCPQoivypjZ+cAY4CJ3z8hXnhJ0pMHMfkDkWqwLJ8qqUcL3Iurui8C5wGp335xTUNvvi+L+lhLS74xaN1OElMqPgSuA5Tndy4F7gOFmdhqRKuA04LpwwqtSxwIzIt9L6gCvuPtsM1sETDWzq4CNwKUhxlhlzCwR6EfBf/vHo+W+MLO/An2Bpma2GbgPeJQi7gV3X2lmU4FVRJofb6wtPRmh2GtxN5AAzA2+M/929+uB3sCDZnYIyAKud/fSdiKo9oq5Fn2L+l5E433h7i9y+HO3UMvvC4r/WxrK7wwNWyIiIiJSw6nJVURERKSGU0InIiIiUsMpoRMRERGp4ZTQiYiIiNRwSuhEREREajgldCIiIiI1nBI6ERERkRpOCZ2IyFEws1PN7JCZ9Qvp84eY2QEzaxfG54tI9aCBhUVEjoKZvQUkuHufEGNYDGxw94vDikFEwqUaOhGRcjKzHxGZKm38kbatZE8BPzGzU0KOQ0RCooRORKT8fgnsAt4MOY7pQAZwfchxiEhIlNCJSFQwsz5m9oaZ7TCzLDPzQj/vl/F4dYAhwFx3P1ho3YjgmOeY2b1mtsHMvjez/5hZj3zx/MvM9pnZNjP77yI+o66Z3W9ma8wsw8y+MbPlZvY/+bdz93TgfYJJwEUk+tQJOwARkcpmZlcCE4FtwAQitWoXA32Bb4AFwNwyHvYMIBn4qIRtHgViiTSJxgN3AnOCeF4EngP+AvwUeNDM1rv7y/n2nwCMAl4CngyO1Q44u4jP+hA4z8w6uPvqMp6LiNRwSuhEpFYzsx8AzwKrgV7uvicofxZYCRwP/MzdM8p46I7B65clbBML9HD3A8FnrgJeB6YBP3L3RUH5i8AG4EYgf0L3E2CWu19Zinhy4jiFyLmKSBRRk6uI1Ha3A3WBa3KSOYCgmXQ+kZqz48tx3JTgdXcJ2zyTk8wFcpp1/52TzAWxHCBS01d46JFvgVPM7NRSxLMreG1Wim1FpJZRQicitd1FwFp3/6CIdQnBa3pOgZl9ZGbDSnHcnDGfrIRt1hXYIS+hXF/EtnuAJoXKbgMaAcvN7Esze8HMBptZUb+7c+LQWFQiUUgJnYjUWmbWEGgDLC1mk27AV+6+Kdg+FvghsKQUh98RvDYuYZusMpYX4O6vA6nAFcC7wDnAa8B8M4svtHlOHDsQkaijhE5EarMGweuBwivMrBvQAZgaLDcH9hKptVtsZulm1rOEY68IXit1hgZ33+3uL7v7NcAPgMeBM4HBhTY9sVBcIhJFlNCJSG32FbAf6GNm9XIKzawR8ALwHZEECXffRqRH6Ufunhz8FNVMm+PTYP8elRG4mcUGNYy5PDK1z6fBYuGawR7AdndfUxnxiEj1pl6uIlJrufsBM/sTcCswz8xeIZIIXUXk2bSfuPuWfLucTl7CdKRjZ5nZdGCwmSW4e2YFh18f2GZmM4OYvgbaAjcQed7uHzkbmlkykVq7iRUcg4jUEKqhE5Ha7tfAb4DmwO+A64g8j3a6uxcee+50Svf8XI5niCSGAysgzsIygN8TSeJ+HXzWFcBMoLu7b8237VAgEfhTJcQhIjWARWrwRUTEzHYCF7p7SYMFF95nNpDk7mdWXmRHjGExsMHdLw4rBhEJl2roREQAM4sjUttWVncCPzKz/hUcUqmY2RAiPXPHhPH5IlI9qIZORCRgZo8RaZKtA3Ry93VH2EVEpFpQQiciIiJSw6nJVURERKSGU0InIiIiUsMpoRMRERGp4ZTQiYiIiNRwSuhEREREajgldCIiIiI1nBI6ERERkRpOCZ2IiIhIDff/G+MkKG2oiscAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# all fit\n",
    "ax = selectbursts.plot.scatter(x='sigma_t_ms', y='drift_abs', figsize=(10, 6), s=50)\n",
    "for k, v in selectbursts.iterrows():\n",
    "    ax.annotate(k, (v['sigma_t_ms'], v['drift_abs']), xytext=(-3,5), textcoords='offset points', weight='bold')\n",
    "ax.set_xlim(0.9*min(selectbursts['sigma_t_ms']), 1.1*max(selectbursts['sigma_t_ms']))\n",
    "ax.set_title('Raw Drift vs. $\\sigma_t$ (excluding C1 burst)', size=fontsize)\n",
    "ax.set_xlabel('$\\sigma_t$ (ms)', size=fontsize)\n",
    "ax.set_ylabel('Raw Drift $\\\\times$ -1 (MHz/ms)', size=fontsize)\n",
    "\n",
    "popt, pcov = fitreciprocal(selectbursts['sigma_t_ms'], selectbursts['drift_abs'])\n",
    "x = np.linspace(0, 1, num=100)\n",
    "plt.plot(x, popt[0]/x, label='${:.2f}/\\sigma_t$'.format(popt[0]))\n",
    "plt.legend(fontsize='large')\n",
    "ax.set_ylim(0, 7000)\n",
    "# plt.savefig('drift_vs_sigmat.png')\n",
    "\n",
    "ax = selectbursts.plot.scatter(x='tau_w_ms', y='drift_abs', figsize=(10, 6), s=50)\n",
    "for k, v in selectbursts.iterrows():\n",
    "    ax.annotate(k, (v['tau_w_ms'], v['drift_abs']), xytext=(-3,5), textcoords='offset points', weight='bold')\n",
    "ax.set_xlim(0.9*min(selectbursts['tau_w_ms']), 1.1*max(selectbursts['tau_w_ms']))\n",
    "# ax.set_xlim(0.9*min(selectbursts['sigma_t']), 1.1*max(selectbursts['sigma_t']))\n",
    "ax.set_title('Drift vs. $t_w$ (excluding C1 burst)', size=fontsize)\n",
    "ax.set_xlabel('$t_w$ (ms)', size=fontsize)\n",
    "ax.set_ylabel('Drift $\\\\times$ -1 (MHz/ms)', size=fontsize)\n",
    "\n",
    "popt, pcov = fitreciprocal(selectbursts['tau_w_ms'], selectbursts['drift_abs'])\n",
    "x = np.linspace(0, 1.5, num=100)\n",
    "plt.plot(x, popt[0]/x, label='${:.2f}$'.format(popt[0])+'/$t_w$')\n",
    "plt.legend(fontsize='large')\n",
    "ax.set_ylim(0, 7000)\n",
    "plt.savefig('drift_vs_tw.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Drift $\\\\cdot \\\\overline{\\\\tau_{0}}/\\\\tau_{0}$')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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CJOoDggS8gKB7cCWwIGGbgWFdXR1XVmU9J4uToCv2ofB5PyFIKKeQMHqc2n9mE0fjHjQKnYQRwAQJ1L0EI6O3E1wlZgqwo5b1fwbBj5atBOc6rgvrrk8Vn8H42+Ia9r04fF2u4MCPxflAz4T1+oTr7iL43hmZpD4eJGFGgbD8doIWzp0E3w2LgK8n1M8sgqTTCX4AxJb9lmDQUoO/x3Vr+puFL6JIi2Vmywm+GL+f7lhEUiVsoXsDKPAD8zdKAjN7Bch299PTHUtzZcEk9h8BN7r7o+mOR+pO3bjSYllw1YAzgQEErUgikeHub5nZAoIRmLekO57mwMzOIJhs+k2Ckd/jCOZerPOVWlqY8wlaGpOeZyvNn5I9acnGE8yy/xgHLgcmkhbhYIFW1ayy3+veFXM9wdUwJFBCMBL7JoIuy38QzCH4dLVbiQGXeuOORJZGpG5cEZFmIDwpflY1q1zi7g82TTQiEiVK9kREmgEz68LBU6XEW+vuW5sqHhGJDiV7Vejatav36tUr3WGIiIiI1Gj58uWfuXvSScJ1zl4VevXqxbJldZ1jVURERKTpmdlHVS3TFTREREREIkzJnohIRKxbtw4zw8yYOnVqRfmECRMqygHWr1/PyJEjad++PYceeijf+9730hWyiDQBJXsiIhE0a9Ys3J1du3YxZ86cinJ3Z/To0SxcuJAbbriBGTNmkJeX9DQfEYkInbMnIhIxRx11FB9++CGLFy9m7dq1lJWVkZ+fz4YNG1i0aBHLly/n5ptv5sYbbyQnJ6eixU9EokkteyIiEdO3b19OOeUUioqKKCoqYtSoUXTq1AmANWvWADB37lzatWtHx44dueuuu9IZrog0MiV7IiIRNGHCBObMmcPSpUu55JJLKspLS0sByM7O5plnnqF3795ce+21vP/+++kKVUQamZI9EZEIGj9+PK1ataKgoIChQ4dWlMfmDx0+fDgjR45k+PDhuDtr165NU6Qi0th0zp6ISAR17NiRoqIiOnToQFbWgd/1w4YNo1u3bsydO5c+ffrw9NNPk5uby6BBg9IYrYg0JrXsiYhE1Lhx4xg2bFilsrZt2/L000+Tk5PDxIkTadeuHfPmzaNbt25pilJEGpsul1aFwsJC1xU0REREJBOY2XJ3L0y2TC17IiIRs7WklLfX72BrSWm6QxGRZkDn7ImIRMizKzYwee5KsrOyKCsvZ8aYAZw7MD/dYYlIGqllT0QkIraWlDJ57kr2lpWzs3Qfe8vKmTR3pVr4RFo4JXsiIhFRvH0P2VmVv9azs7Io3r4nTRGJSHOgZE9EJCIKOrelrLy8UllZeTkFndumKSIRaQ6U7ImIRESX3BxmjBlAm+wsOuS0pk12FjPGDKBLbk66QxORNNIADRGRCDl3YD6D+3SlePseCjq3VaInIkr2RESipktujpI8EamgblwRERGRCFOyJyIiIhJhSvZEREREIkzJnoiIiEiEKdkTERERiTAleyIiIiIRpmRPREREJMKU7ImIiIhEmJI9ERERkQhTsiciIiISYUr2RERERCJMyZ6IiIhIhCnZExEREYkwJXsiIiIiEaZkT0RERCTClOyJiIiIRFjakz0z62lmi8zs72a22syuCcsPM7OFZvaP8G/nuG1uMrMPzOw9M/tWXPmJZvZOuOwuM7OwPMfMZoflfzWzXk19nCIiIiLpkPZkD9gHXO/ufYFTgYlm1g+4EXjV3Y8GXg0fEy4bD/QHzgbuMbNW4b7uBS4Hjg5vZ4fllwLb3b0PcCcwvSkOTERERCTd0p7sufsmd38zvL8T+DuQD4wEHgpXewgYFd4fCTzp7qXuvhb4ADjZzHoAHd39dXd34OGEbWL7ehr4RqzVT0RERCTK0p7sxQu7VwcBfwW6u/smCBJCoFu4Wj6wPm6z4rAsP7yfWF5pG3ffB3wOdGmMYxARERFpTppNsmdmucBc4Fp3/6K6VZOUeTXl1W2TGMPlZrbMzJZt2bKlppBFREREmr1mkeyZWTZBoveYu88Liz8Nu2YJ/24Oy4uBnnGbFwAbw/KCJOWVtjGz1sChwLbEONz9fncvdPfCvLy8VByaiIiISFqlPdkLz537PfB3d/913KLngIvC+xcBz8aVjw9H2PYmGIjxt7Crd6eZnRru88KEbWL7Ggv8MTyvT0RERCTSWqc7AGAw8B/AO2a2Iiz7CTANeMrMLgU+Bs4HcPfVZvYUsIZgJO9Ed98fbncl8CDQFpgf3iBIJh8xsw8IWvTGN/ZBiYiIiDQHpgau5AoLC33ZsmXpDkNERESkRma23N0Lky1LezeuiIiIiDQeJXsiIiIiEaZkT0RERCTClOyJiIiIRJiSPREREZEIU7InIiIiEmFK9kREREQiTMmeiIiISIQp2RMRERGJMCV7IiIiIhGmZE9EREQkwpTsiYiIiESYkj0RERGRCFOyJyIiIhJhSvZEREREIkzJnoiIiEiEKdkTERERiTAleyIiIiIRpmRPREREJMKU7ImIiIhEmJI9ERERkQhTsiciIiISYUr2RERERCJMyZ6IiIhIhCnZExEREYkwJXsiIiIiEaZkT0RERCTClOyJiIiIRJiSPREREZEIU7InIiIiEmFK9kREREQiTMmeiIiISIQp2RMRERGJMCV7IiIiIhGmZE9EREQkwpTsiYiIiESYkj0RERGRCFOyJyIiIhJhSvZEREREIkzJnoiIiEiEKdkTERERiTAleyIiIiIRpmRPREREJMKU7ImIiIhEmJI9ERERkQhTsiciIiISYUr2RERERCJMyZ6IiIhIhCnZExEREYkwJXsiIiIiEaZkT0RERCTClOyJiIiIRJiSPREREZEIU7InIiIiEmFK9kREREQiTMmeiIiISIQp2RMRERGJMCV7IiIiIhGmZE9EREQkwpTsiYiIiERY2pM9Mysys81mtiqubIqZbTCzFeFtWNyym8zsAzN7z8y+FVd+opm9Ey67y8wsLM8xs9lh+V/NrFdTHp+IiIhIOqU92QMeBM5OUn6nuw8Mby8BmFk/YDzQP9zmHjNrFa5/L3A5cHR4i+3zUmC7u/cB7gSmN9aBiIiIiDQ3aU/23H0JsK2Wq48EnnT3UndfC3wAnGxmPYCO7v66uzvwMDAqbpuHwvtPA9+ItfqJiIiIRF3ak71qXGVmK8Nu3s5hWT6wPm6d4rAsP7yfWF5pG3ffB3wOdEn2hGZ2uZktM7NlW7ZsSd2RiIiIiKRJc0327gW+AgwENgG/CsuTtch5NeXVbXNwofv97l7o7oV5eXl1i1hERESkGWqWyZ67f+ru+929HPgf4ORwUTHQM27VAmBjWF6QpLzSNmbWGjiU2ncbi4iIiGS0ZpnshefgxYwGYiN1nwPGhyNsexMMxPibu28CdprZqeH5eBcCz8Ztc1F4fyzwx/C8PhEREZHIa53uAMzsCWAI0NXMioGfAkPMbCBBd+s64PsA7r7azJ4C1gD7gInuvj/c1ZUEI3vbAvPDG8DvgUfM7AOCFr3xjX9UIiIiIs2DqZErucLCQl+2bFm6wxARERGpkZktd/fCZMuaZTeuiIiIiKSGkj0RERGRCFOyJyIiIhJhSvZEREREIkzJnoiIiEiEKdkTERERiTAleyIiIiIRpmRPREREJMKU7ImIiIhEmJI9ERERkQhTsiciIiISYUr2RERERCJMyZ6IiIhIhDUo2TOz9mb2czNbbWafh7fVZnabmXVIVZAiIiIiUj8Nbdl7DMgBRgPdw9to4JBwmYiIiIikUUOTvb7uPtnd33f3veHtfXefDBybigBFRFqCdevWYWaYGVOnTq0onzBhQkU5wNixY+ncuTNmxlVXXXXQfrZs2ULXrl0xM+64444mi19Emq+GJnslZvatxEIzOxvY1cB9i4i0SLNmzcLd2bVrF3PmzKm0LCcnh9GjR1e57TXXXMOePXsaO0QRySANTfYuBG4xs/Vm9rqZ/dnM1gM3Axc1PDwRkZblqKOO4sMPP2Tx4sXMnj2bsrIy8vPzK5Y/9thjXHjhhUm3nT9/Ps8//zyTJ09uqnBFJAO0rsvKZpbl7uWxx+6+Gvi6mXUDCgADit3909SGKSLSMvTt25e8vDyKiopYu3Yto0aNYtWqVWzYsKHa7UpKSrjiiiv45S9/SW5ubhNFKyKZoK4te1+Y2QmJhe6+2d3fdPflSvRERBpmwoQJzJkzh6VLl3LJJZfUapvp06fTrl07zjrrLDZv3gzA1q1b2b59e2OGKiIZoK7JXjugbWMEIiIigfHjx9OqVSsKCgoYOnRorbZZv3497777Lscee2xFN+60adOYOXNmY4YqIhmgTt24IU9WaGY9gZHufnfDQhIRadk6duxIUVERHTp0ICur8m/y2bNns2zZMgDWrFnDAw88wPDhw7nqqqsYMWIEAIsXL2bmzJlceOGFjB07tsnjF5HmpT7J3i/M7M/AqvD2vrvvB/4VuB1Qsici0kDjxo1LWj558mQ++ugjABYtWlRxGzJkCIWFhUBw/h7ACSecwHHHHdc0AYtIs2XuSRvqkq9sVg78GTgc6B0WlwGbgDzgDXc/I9VBpkNhYaHHfj2LiIiINGdmttzdC5Mtq0/L3nXu/jczawf0B/oRJH8lwOz6hykiIiIiqVafZA8Ad98NvBHeRESkgbaWlFK8fQ8FndvSJTcn3eGISETUNdmbCWgcv4hIij27YgOT564kOyuLsvJyZowZwLkD82veUESkBnWaesXdr3b3fzRWMCIiLdHWklImz13J3rJydpbuY29ZOZPmrmRrSWm6QxORCKgx2TOzTWb2gJmNNjNNyy4ikmLF2/eQnTDFSnZWFsXbdY1bEWm42rTsXUPQ3Xsv8JmZLTSza83s6MYNTUSkZSjo3Jay8vJKZWXl5RR01hz2ItJwNSZ77v6Uu18M9ABOJ5h65d+Bd83sfTO708y+aWbZjRuqiEg0dcnNYcaYAbTJzqJDTmvaZGcxY8wADdIQkZSocZ49M9vv7q2SlB8ODAOGA98EDHgFeMHdixoh1ialefZEpKlpNK6I1Fd18+zVphvXkhW6+yfuXuTuY4CuwHnAR8CkekcqItKCdcnN4f/17KRET0RSqjbJXo2X2HD3Mnd/xd1/5O66No+IiIhIM1GnqVcSmVl7M/u5ma02s8/D22ozu83MOqQqSBERERGpnwYle8BjQA4wGuge3kYDh4TLRERERCSN6n25tFBfdx+VUPY+MNnM3mvgvkVERESkgRrasldiZt9KLDSzs4FdDdy3iIiIiDRQQ1v2LgR+Z2YPAMUEgzl6AuuAixq4bxERERFpoDole2aW5e4V07y7+2rg62bWDSggmKal2N0/TW2YIiIiIlIfdW3Z+8LM/s3d34kvdPfNwObUhSUiIiIiqVDXSZXbAbpYo4iIiEiGqM8AjaSTLJtZTzO7qoHxiIiIiEgK1SfZ+4WZ/czMzjezvmYWu27uvwK3pzA2EREREWmg+ozGbQt8D+gdPi4zs01AHvCXVAUmIiIiIg1Xn2TvOnf/m5m1A/oD/YDDgRJgdiqDExEREZGGqfc8e+6+G3gjvImIiIhIM1Sbc/biB2TMBLY3UiwiIiIikmK1admrmHrF3a9uxFhEREREJMVqbNlz94ZeP1dERERE0qTeiZyZHWNmvVIXioiIiIikWkNa7d4F/pCqQEREREQk9eo9Ghe4HNiRqkBEREREJPUaMvXKA6kMRERERERST4MvRERERCJMyZ6IiIhIhCnZExEREYkwJXsiIiIiEVanZM/MbjWzf6liWQ8zuzU1YYmIiIhIKtS1Ze+nQEEVy/4lXF4nZlZkZpvNbFVc2WFmttDM/hH+7Ry37CYz+8DM3jOzb8WVn2hm74TL7jIzC8tzzGx2WP5XTQQtIiIiLUldkz0DvIplBcD2esTwIHB2QtmNwKvufjTwavgYM+sHjAf6h9vcY2atwm3uJZj77+jwFtvnpcB2d+8D3AlMr0eMIiIiIhmpxnn2zOwi4KLwoQP3mtkXCau1AU6gHlfUcPclSVrbRgJDwvsPAYuByWH5k+5eCqw1sw+Ak81sHdDR3V8PY34YGAXMD7eZEu7raeBuMzN3ryppFREREYmM2kyqvBvYGt434HNgW8I6XxIkVvekKK7u7i6KwzEAACAASURBVL4JwN03mVm3sDwf+EvcesVhWVl4P7E8ts36cF/7zOxzoAvwWeKTmtnlBK2DHHHEESk6FBEREZH0qTHZc/c5wBwAM5sF/Nzd1zZ2YFWwJGVeTXl12xxc6H4/cD9AYWGhWv5EREQk49V4zl44gKJ3+HAWsKVxQwLgUzPrET5/D2BzWF4M9IxbrwDYGJYXJCmvtI2ZtQYO5eCWSREREZFIqs0AjYuAvPD+IqBf44VT4TkOnCd4EfBsXPn4cIRtb4KBGH8Lu3x3mtmp4SjcCxO2ie1rLPBHna8nIiIiLUVtztnbBAwxszUEXaJtzKxdVSu7++66BGBmTxAMxuhqZsUE07dMA54ys0uBj4Hzw32vNrOngDXAPmCiu+8Pd3UlwcjetgTnD84Py38PPBIO5thGMJpXREREpEWwmhq5womSp1D1lCuVuHurmtdq/goLC33ZsmXpDkNERESkRma23N0Lky2rzQCNn5vZi0Bf4GFgKvDP1IYoIiIiIo2hNt24uPtyYLmZfQOYlcbRuCIiIiJSB7VK9mLc/ZLGCkREREREUq82V9D4G3Cxu68J71fL3U9OSWQiIiIi0mC1adlbDeyJuy8iIiIiGaI2AzQuATCzbOABYJ27b2jswERERESk4WozqXLMfuCPwHGNFIuIiIiIpFitkz13Lwf+AXRvvHBEREREJJXq0rIHcDNwq5md0BjBiIiIiEhq1WnqFeAWoAuwwsw2AJ+ScGUNjcYVERERaT7qmuytBlY1RiAiIiIiknp1nVT54kaKQ0REREQaQa2TPTMzYChwKgcGaXwK/Bl41d29qm1FREREJD1qleyZ2SBgNtAH2Ad8BhjB+XutgffNbLy7r2isQEVERESk7mocjWtm3YEFBFfROAfIdfd/cfceQAdgOPAlsMDMujVmsCIiIiJSN7WZeuVqgkTv6+6+wN2/jC1w91J3nw+cFq5zVeOEKSIiIiL1UZtk7yzgHnf/oqoV3H0HcC9wdqoCExEREZGGq02y1wd4sxbrLQ/XFREREZFmojbJ3qHA57VYbyfQsWHhiIiIiEgq1SbZMxKuklHDuiIiIiLSTNR2nr0FZrYvRfsSERERkSZSmwTtZ40ehYiIiIg0ihqTPXdXsiciIiKSoWpzzp6IiIiIZCgleyIiIiIRpmRPREREJMKU7ImIiIhEmJI9ERERkQhTsiciIiISYUr2RERERCJMyZ6IiIhIhCnZExEREYkwJXsiIiIiEaZkT0RERCTClOyJiDRz69atw8wwM6ZOnVpRPmHChIryLVu2MHDgQNq3b0+HDh04/fTTWbVqVRqjFpHmQsmeiEgGmTVrFu7Orl27mDNnTqVl55xzDvfccw9XXnklS5Ys4brrrktTlCLSnLROdwAiIlI7Rx11FB9++CGLFy9m7dq1lJWVkZ+fz4YNG8jLy2Pq1Kls27aN7t27c/vtt5OVpd/zIqJkT0QkY/Tt25e8vDyKiopYu3Yto0aNYtWqVWzYsAGAd955h0GDBgGQn5/Pb37zm3SGKyLNhH72iYhkkAkTJjBnzhyWLl3KJZdcUmlZnz59WLBgAbfddhsbN25kxowZaYpSRJoTJXsiIhlk/PjxtGrVioKCAoYOHVppWW5uLmeddRa33HILPXv25KmnnkpTlCLSnKgbV0Qkg3Ts2JGioiI6dOhQ6Zy8WbNmsWLFCgYOHMjKlSv5+OOPOemkk9IYqYg0F0r2REQyzLhx4w4qy8vL46WXXuJ3v/sdubm5jBgxgl//+tdpiE5Emhtz93TH0CwVFhb6smXL0h2GiIiISI3MbLm7FyZbpnP2REQywNaSUt5ev4OtJaXpDkVEMoy6cUVEmrlnV2xg8tyVZGdlUVZezowxAzh3YH66wxKRDKGWPRGRZmxrSSmT565kb1k5O0v3sbesnElzV6qFT0RqTcmeiEgzVrx9D9kJV8LIzsqiePueNEUkIplGyZ6ISDNW0LktZeXllcrKyssp6Nw2TRGJSKZRsici0ox1yc1hxpgBtMnOokNOa9pkZzFjzAC65OakOzQRyRAaoCEi0sydOzCfwX26Urx9DwWd2yrRE5E6UbInIpIBuuTmKMkTkXpRN66IiIhIhCnZExEREYkwJXsiIiIiEaZkT0RERCTClOyJiIiIRJiSPREREZEIU7InIiIiEmFK9kREREQirFkne2a2zszeMbMVZrYsLDvMzBaa2T/Cv53j1r/JzD4ws/fM7Ftx5SeG+/nAzO4yM0vH8YiIiIg0tWad7IXOcPeB7l4YPr4ReNXdjwZeDR9jZv2A8UB/4GzgHjNrFW5zL3A5cHR4O7sJ4xcRERFJm0xI9hKNBB4K7z8EjIorf9LdS919LfABcLKZ9QA6uvvr7u7Aw3HbiIiIiERac0/2HPiDmS03s8vDsu7uvgkg/NstLM8H1sdtWxyW5Yf3E8sPYmaXm9kyM1u2ZcuWFB6GiIiISHq0TncANRjs7hvNrBuw0MzerWbdZOfheTXlBxe63w/cD1BYWJh0HREREZFM0qxb9tx9Y/h3M/AMcDLwadg1S/h3c7h6MdAzbvMCYGNYXpCkXERERCTymm2yZ2btzaxD7D5wFrAKeA64KFztIuDZ8P5zwHgzyzGz3gQDMf4WdvXuNLNTw1G4F8ZtIyIiIhJpzbkbtzvwTDhLSmvgcXd/2czeAJ4ys0uBj4HzAdx9tZk9BawB9gET3X1/uK8rgQeBtsD88CYiIiISeRYMUJVEhYWFvmzZsnSHISIiIlIjM1seN01dJc22G1dEREREGk7JnoiIiEiEKdkTERERiTAleyIiIiIRpmRPREREJMKU7ImIiIhEmJI9ERERkQhTsiciIiISYUr2RERERCJMyZ6IiIhIhCnZExEREYkwJXsiIiIiEaZkT0RERCTClOyJiIiIRJiSPREREZEIU7InIiIiEmFK9kREREQiTMmeiIiISIQp2RMRaYB169ZhZpgZU6dOrSifMGFCRTlQcT92GzVqVLpCFpEWpnW6AxARiYpZs2Zx8803s3v3bubMmXPQ8jFjxjB27FgACgoKmjo8EWmhlOyJiKTAUUcdxYcffsjixYtZu3YtZWVl5Ofns2HDhop1+vXrx7e//W3at2+fxkhFpKVRN66ISAr07duXU045haKiIoqKihg1ahSdOnWqtM7UqVPJzc3lyCOP5IUXXkhTpCLS0ijZExFJkQkTJjBnzhyWLl3KJZdcUmnZ5MmTmTdvHvfffz/bt2/nggsuYPfu3WmKVERaEiV7IiIpMn78eFq1akVBQQFDhw6ttGzatGmMGjWKyy67jKFDh1JSUsL69evTFKmItCQ6Z09EJEU6duxIUVERHTp0ICvrwG/pl156iUcffZQhQ4awfft25s+fT15eHr17905jtCLSUijZExFJoXHjxh1UduSRR7Jp0yYmTZrE/v37KSws5Fe/+hWHHHJIGiIUkZZG3bgiIg3Qq1cv3D3pgItVq1bh7vTv359FixaxY8cOdu7cyZIlSzjppJPSEG3zUNu5CQG2bNlC165dMTPuuOOOdIQrkvGU7ImINNDWklLeXr+DrSWl6Q4l48yaNQt3Z9euXUnnJrzmmmvYs2dPGiITiQ4leyIiDfDsig0Mnv5H/v2BvzJ4+h95bsWGmjcSoPLchLNnz66YmzBm/vz5PP/880yePDmNUYpkPp2zJyJST1tLSpk8dyV7y8rZSzkAk+auZHCfrnTJzUlzdM1f3759ycvLo6ioiLVr1zJq1ChWrVrFhg0bKCkp4YorruCXv/wlubm56Q5VJKOpZU9EhNqfRzZ27Fg6d+6MmTHxqqvIDkfdlm74O5888mP+cfv59MrvxpgxY9iyZUtajiWTVDU34fTp02nXrh1nnXUWmzdvBmDr1q1s3749XaGKZCwleyIiCao7jywnJ4fRo0cDkJvTmrLyoEWvbNtGstp1JO/MSzjrW2czb948Jk2a1OSxZ5qq5iZcv3497777Lscee2xFN+60adOYOXNmukIVyVjqxhURiVPTNW4fe+wxFi9ezKxZs2iT3YoZYwYwae5K2g86k/3/OpQZYwZwxlc6Mm/OU6xevTrNR9P8VTU34VVXXcWIESMAWLx4MTNnzuTCCy9k7Nix6QpVJGMp2RMRiVPdeWTJnDswn8F9ulK8fQ8FndvSJTeHp59+GoDTTjutKUPPWMnmJiwsLKSwsBCAkpISAE444QSOO+64Jo1NJAqU7ImIJJgwYQI//OEPKS0t5eWXX+b666+vdv0uuTkVAzKWLl3KhAkTOPHEE5kyZUoTRJt5YnMTJrNq1aqDyi6++GIuvvjiRo5KJLp0zp6INHu1GTwxZcqUivvxt/qo7hq31VmyZAlnn302X/nKV1iwYIFGkYpIs6BkT0QySlWDJ8aOHcsTTzzBE088wd133w3AoEGD6vUcsfPI7rvvvkrnkQHMnj2bF198EYA1a9bwwAMPsGnTJt58803OOecc9u/fz2WXXcbChQt5/vnn63mU9VOfEcVXXXVVpX1UtyyVNBG1SNNRsiciGSN+8MTMmTMrzuUCOP744xk/fjx/+MMfKpKUK664otrkZf369YwcOZL27dszYMCASsvGjRvHsGHDDoph8uTJFZftWrRoEZdddhnvvfceK1euZPfu3ezZs4eJEydywQUXcPXVV6e6CmqttiOKE1W3LFU0EbVI01KyJyIZo2/fvpxyyikUFRXx1FNPVVqWLLH57ne/W2Xy4u6MHj2ahQsXcsMNN3D77bdzzTXXVHuNWwhaz9y90m3IkCFcfPHFB5WvW7euYh+paHWrrZquTPHYY49x4YUXJt22umX1FX/sP7l1SsVE1GufuYP3pg5j5KACtpaUVnnsW7ZsYeDAgbRv354OHTpw+umnJz23T0SSU7InIhklNgnv8uXLAcjOzgaoSGxKSw90C+bm5laZvCxatIjly5dz3XXXceONN3L55Zfz/02d3iRdi/Vtdaut+KS4qKiIUaNG0alTpwbtM1UeeeghWptR/uVedr+3tKK8ePueao/9nHPO4Z577uHKK69kyZIlXHfddU0VskjGU7InIhklNnji8MMPB4LkCKhIbGLlNVmzZg0Ac+fOpV27drTL7UC/Mdc0etdiQ1rd6qKqK1M0hapaMTt06EDxx+tYfds57H53CV62FwhaNP976k2Vjv3DDz+kT58+tGnThvPPP59LL72UYcOGceaZZwIcdC6liFRNnxYRySixwRO/+MUvKpXHEpuNGzfSrl27GvcTawHMzs7mocefojw3j81/uJ9tmz5ib1k5k+auTNrCV5vu2Ndff52vfvWrdOrUiU6dOjFmzBi2bt0KNF2rW31HFKdarBUTYPfu3RXlu995heyOeWS1CV6rNtmtKm23YMECOnbsyO23387y5cv5zne+Q7du3TjnnHPIz8/nN7/5TdMdhEiGU7InIhln3LhxnHHGGZXKWrVqxaGHHsr+/fvp3Llzjfvo1asXAMOHD2fA4G/S8eiTAWffjk8AyM7Konj7nmr3UVV37Pvvv0/Xrl2ZPn06w4YNY968eUybNq1ieVO0utVnRHFNy+oq1or5l7/8BQjOk4wltnuK1/Dbu++hoHvXpNuWl5dz0003cfXVVzN69GjeeustZs2axW233cbGjRuZMWNGvWISaYmU7IlIsxebhDfZ4InTTz8dd6eoqIjHH3+8UkIBVScvw4YNo1u3bsydO5fFzz3J52teww5pyyHdvwJAWXk5BZ3bVhlTdd2xF1xwAc899xzf//73ue+++4AgAYxpqla3uo4ormlZXcVaMWODabp3717RzV5QUMD3vzuarGrmQozVZ0FBQcXjW265hZ49ex40QEdEqqYraIhIRthaUlrpkmSJkl1yC4Lk5aOPPgKC5CV2GzJkCE8//TQ/+MEPmHz9teQfcRT7zvkBnQ7rSll5OTPGDEj6PDHVXVbtkEMOqVhvwYIFAJx88smsXLkSqPp6sBAkp8uWLQMOJKfDhw+nR48eNdZRba9MET9KOFF1y+pjwoQJFVPQ9OzZk127dgFU2eIYO3aA5557jt69e/P2228DQV2+9NJLfPzxx5x00kkpjVMk0hKnCtAtuJ144okuIs3D/75V7Mfe8pIff+vLfuwtL/mzbxU3yvN8tnOvr/h4u3+2c2+V66xdu9YBHz58uN93332ek5PjgL/88svev39/D75WA6+99pp36NDBTzzxRF+1alXFdonitzvyyCMdqHRbtGhRSo+hscXX0eeff+5t2rRxwIcNG3ZQHfXq1csBnzhxYpXHfsYZZzjg2dnZfthhh/mIESP8/fffT9vxiTRHwDKvIqdRy56INGtbS0or5mXbSzkAk+auZHCfrnTJzamxxa8u4q9xWxvjx4/nRz/6UdLu2CVLljB8+HD69OnDggUL6NKlS4Nb3Wry7IoNTJ67kuysrIrWyXMH5te8YSPq2LEjDz74IB06dGDYsGEcf/zxFctmz57N2LFjueOOO1izZg233HILw4cPB4JWyuOPP5533nmHN954g6997Wv86U9/StdhiGQ0nbMnItVKxWTAiderHTVqVK2fv3j7HrITuvtigyfSfSWGqgZBVHXptMa8RFh8UryzdF+1I4qb2rhx4zjltG/w9vod7C8/kPBWdX5gjx49eOKJJ9ixYwc//vGPGTRoEA8++GCaohfJfGrZE5FamzVrFjfffDO7d++ucjLgWbNmJd12zJgxjB07Fjhwwn1tFHRuS1l5eaWysvJy2h/SqtoWv6aS7FzB2KXTACZOnAhAXo8CDrv0/kZrdYslxbG6gANJcarqY926dfTu3RuA2267jVtuuQUIEv/Y615eXs59991Hjx49eOWVVzjuuOP49iXX8HxJL7KzsvAxd/DsmAEV+6vKeeedx3nnnZeSuEVaOrXsiUitNHQy4H79+vHtb3+b8ePH87Wvfa3Wz9slN4cZYwbQJjuLDjmtaZOdxYwxA9j15f4qW/waU3Ujg2OXVUu8dNpnO/dy2KX3N2qrW1VJcXUjihuiqmlnXnnlFaZNm0Ze98O59ic/p7h4A7+65Vr27P2y2bU4irQUSvZEpFYaOhnw1KlTyc3N5cgjj0yaKFXn3IH5LJ18Jo/+5yksnXwm5w7Mb/LkJl5id2xNXd1dO7QhOyuLz19/iuKZF/Hxr8ewad403v3405TFVFVS3BitnNUl/uXha7K2tD3zPj2M0qwcsg5pC3FTrDRFUi4iB6gbV0RqbcKECfzwhz+ktLSUl19+meuvv75W202ePJlTTz2VLVu2cP3113PBBRfw6aef1upKFzGJgydiyc2khAEJjd2Fm2wQxIC4nLeqru5tq5ewY8nDtD36VHIOP5odf3qEh/57OoPvnZmy2M4dmM/gPl1TNmClKtVNO3PiV0+nU+EIdix7gZ1/fw1rfQh5Y27Fsg5cIaOpknIRCahlT0Rqrb6TAU+bNo1Ro0Zx2WWXMXToUEpKSli/fn2D40nW4teYqhoEsX1X0MJXXYvXoNYbATh88Pl0P/0COnftxpOPPZLyGLvk5vD/enZq9KS3qquAvLZsJV+8s4g2vQaRN/onZLXrxLaX7qR1+ZeN3uIoIsmpZU9Eaq0+kwG/9dZbPProowwZMoTt27czf/588vLyKk70b6i6TpfSEFUNgtj0eZDsVdfidUr/o3gRGJa3jZHf7MiwadvYt28fW7dupUuXLk0SfypVNe3MW6+9QnnpLtoffybtjvkqe/65jJKVf2DGmZ35Sv+BjdriKCLJKdkTkTqp65UqjjzySDZt2sSkSZPYv38/hYWF/OpXv6p0lYlMUdV5gj0OPZC8VNXVfeWVV/L4448z8/b/Yubt/0Vubi4lJSW0adOmSY8hVapK/Af0OxaAXSteIod97PnnG7TOPoTTCo+na9fan+MpIqmjZE9EqpWKS3AtWrQo1WGlRVXnCXZuX1axTlUtXl27duXtt99m5cqVHHrooYwYMYK9e/fSvn37dBxKSiRL/M877zwmTZrEw488ymcLf8dXevVm6m0/p2vXrmmIUERAyZ6I1EIqr1KR6ZINgohPdKtq8dq4cSN33303xxxzDC+//DLvv/8+d911VxqOoP5qm/hPnz6d6dOnN1VYIlIDJXsiUq3meAmudKvpPMFkLV5ZWVk888wzfPjhh3Tp0oWf/vSnB11pJBMo8RfJPFbVr7SWrrCw0GMnm4u0VFtLShk8/Y/sLTtwnlqb7CyWTj5T/+hbICX+Is2XmS1398JkyzT1iohUqbrr0kpljXnd2+agOV97V0Sq12K6cc3sbOC3QCvgAXefluaQRJq9dF6lIpO0hBavprj2rog0jhbRsmdmrYCZwDlAP+ACM+uX3qhEmr+mvARXpmopLV5K/EUyV0tp2TsZ+MDdPwQwsyeBkcCatEYlkgGa6hJcmaqltHil6/J0ItJwLSXZywfir81UDJySuJKZXQ5cDnDEEUc0TWQiGaApr1KRaVpSi5cSf5HM1CK6cQFLUnbQMGR3v9/dC929MC8vrwnCEpFM19K6upvq2rsikjotpWWvGOgZ97gA2JimWEQkYtTiJSLNWUtJ9t4Ajjaz3sAGYDzw3fSGJCJRoq5uEWmuWkSy5+77zOwqYAHB1CtF7r46zWGJiIiINLoWkewBuPtLwEvpjkNERESkKbWUARoiIiIiLZKSPREREZEIU7InIiIiEmFK9kREREQiTMmeiIiISIQp2RMRERGJMCV7IiIiIhGmZE9EREQkwszd0x1Ds2RmW4CP0h1HM9MV+CzdQbQgqu+mpfpuOqrrpqX6blrpqu8j3T0v2QIle1JrZrbM3QvTHUdLofpuWqrvpqO6blqq76bVHOtb3bgiIiIiEaZkT0RERCTClOxJXdyf7gBaGNV301J9Nx3VddNSfTetZlffOmdPREREJMLUsiciIiISYUr2Wjgza2Vmb5nZC+Hjw8xsoZn9I/zbOW7dm8zsAzN7z8y+FVd+opm9Ey67y8wsHceSCZLU9+1m9q6ZrTSzZ8ysU9y6qu8GSqzvuPIfm5mbWde4MtV3AyWrbzO7OqzT1WY2I65c9d1ASb5PBprZX8xshZktM7OT49ZVfTeAma0L62mFmS0LyzLm/6WSPbkG+Hvc4xuBV939aODV8DFm1g8YD/QHzgbuMbNW4Tb3ApcDR4e3s5sm9IyUWN8LgePdfQDwPnATqL5TKLG+MbOewFDg47gy1XdqVKpvMzsDGAkMcPf+wB1hueo7NRLf3zOAn7n7QODW8LHqO3XOcPeBcdOqZMz/SyV7LZiZFQDDgQfiikcCD4X3HwJGxZU/6e6l7r4W+AA42cx6AB3d/XUPTgB9OG4biZOsvt39D+6+L3z4F6AgvK/6bqAq3t8AdwKTgPgTllXfDVRFfV8JTHP3UgB33xyWq74bqIr6dqBjeP9QYGN4X/XdODLm/6WSvZbtNwT/9Mrjyrq7+yaA8G+3sDwfWB+3XnFYlh/eTyyXgyWr73gTgPnhfdV3wx1U32Z2LrDB3d9OWFf13XDJ3t/HAF83s7+a2f+Z2Ulhueq74ZLV97XA7Wa2nqAV9aawXPXdcA78wcyWm9nlYVnG/L9UstdCmdkIYLO7L6/tJknKvJpyiVNTfZvZzcA+4LFYUZLVVN+1lKy+zawdcDNB99ZBmyQpU33XUjXv79ZAZ+BU4AbgqfAcJdV3A1RT31cCP3L3nsCPgN/HNkmyG9V33Qx2938FzgEmmtlp1azb7Oq7dVM8iTRLg4FzzWwY0AboaGaPAp+aWQ933xQ2Oce6XYqBnnHbFxB0ERRzoOsxvlwqS1rf7v7vZnYRMAL4hh+YC0n13TAH1TfwCNAbeDs8J7oAeDM8iV313TBVfZ8UA/PC9/XfzKyc4Lqhqu+Gqaq+v01wHh/AHA508aq+G8jdN4Z/N5vZM8DJZNL/S3fXrYXfgCHAC+H924Ebw/s3AjPC+/2Bt4Ecgn+YHwKtwmVvEPxyN4JuyGHpPqbmfEuo77OBNUBewjqq70ao74TydUBX1Xfj1TdwBfDz8P4xBF1bpvputPr+OzAkvP8NYHl4X/XdsDpuD3SIu//n8Ls7Y/5fqmVPEk0j6Gq5lGC04vkA7r7azJ4iSEz2ARPdfX+4zZXAg0Bbgjfv/MSdSpXuJvhCWBi2Nv3F3a9QfTct1XejKQKKzGwV8CVwkQf/8VTfjeMy4Ldm1hrYSzDqU+/vhusOPBN+R7cGHnf3l83sDTLk/6WuoCEiIiISYRqgISIiIhJhSvZEREREIkzJnoiIiEiEKdkTERERiTAleyIiIiIRpmRPREREJMKU7ImINDIzO8vMrk3D895qZhvMrNzMHmzq5xeR5kHz7ImINDIzuwMY6+69mvA5Cwlm6/8JsJjgWqr/bKrnF5HmQ1fQEBHJMGbWxt331rDaceHfme7+RWPHJCLNl7pxRaRFM7PTzGyRmZWY2edmttjMBoXLjjCzJ81sm5ntNrMFZnZs3La9zMzN7Dtmdl+4fbGZ/czMssJ1pgDXA0eG63p8l6qZfc3M/i/c/1Yz+x8z6xC3/OJwm5PD2PYAN9RwTA8Cj4QPPw+3H5KSChORjKNkT0RarDABehUoAy4CxgF/AvLN7DDgNeBY4ArgOwQXQX/FzNom7GoGUAKMBR4Fbg3vAzwAPA58AvxbeLstfP7B4fN/Eq5/LTAMmJUk3CeAF8LlL9RwaLcBU8P7Z4bP+WYN24hIRKkbV+T/b+9+XmwKwwCOf59QGgupkRSys7FQFCtSRNnZ2EjyLygpG7EgC/JzYSFWMlmRHxljMQ3RsDANSakpMyY1jcmY5EfzWJw7TGfumGE0Zu79furUPb/e55zdc9/3ed+jenYMeA5sy18FzHcBIuIoRXK3JjP7K8ceAl3APuD8qHZaM3N/5XdzRGwHdgJNmdkdEb3Al8x8XIp/HHiUmbtGDkRED9ASEaszs3PUtWcy8/RkXioz30TEBJxV3gAAAdFJREFUSH1ee2Z+msx9kmqTPXuS6lJELADWA1ey+ky1LUAz8DEi5kbEXGAQeAasK117r7T/Elg2QfwGih63ppH2KzHaKHoa15ZuuTWJ15KkMUz2JNWrRUAAveOcb6QY1v1W2jYDy0vXDpT2vwLzJxF/DnCh1P4XYF6VGO8naE+SqnIYV1K9+gAMA0vHOd8P3KBSX1cy+A/iDwAJHAZuVzn/rrTvOlmS/orJnqS6lJlDEfEE2BMR56oM5bZQTMp4kZmfpxhuTE9fJf5jYFVmHpli+5I0LpM9SfXsIHAfuBMRF4Ehijq6p8BJYDfwICLOAj3AEmAT0JaZV/8gzitgSUTsBTqBvszsAg5QTMYYBq5T9BiuAHYAhzLz9ZTfUFLds2ZPUt3KzFZgK9BAsWTKNYpkrjsz+4ANFInaKYpJGCeAhUDHH4ZqAi5X7m+nGLolM9uAjcBiinXxblIkgG+xRk/SP+Ln0iRJkmqYPXuSJEk1zJo9SZplKp9iG/fPemZ+n8bHkTTD2bMnSbPPJcau//dzi4iV/+3JJM041uxJ0ixTSeYaf3NJR2Z+nZ6nkTTTmexJkiTVMIdxJUmSapjJniRJUg0z2ZMkSaphJnuSJEk17Ae5/MlWWLP5RQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bursts['drift_fcorr'] = bursts['drift_abs'] * (np.mean(bursts.head(numbursts)['center_f']) /  bursts['center_f'])**2\n",
    "\n",
    "ax = bursts.head(numbursts).plot.scatter(x='tau_0', y='drift_fcorr', figsize=(10, 6), s=50)\n",
    "for k, v in bursts.head(numbursts).iterrows():\n",
    "    ax.annotate(v['name'], (v['tau_0'], v['drift_fcorr']), xytext=(-3,5), textcoords='offset points', weight='bold')\n",
    "ax.set_xlim(0.9*min(bursts.head(numbursts)['tau_0']), 1.1*max(bursts.head(numbursts)['tau_0']))\n",
    "# ax.set_xlim(0.9*min(bursts.head(numbursts)['sigma_t']), 1.1*max(bursts.head(numbursts)['sigma_t']))\n",
    "ax.set_title('Drift fcorr vs. $\\sigma_t$ (excluding C1 burst)', size=fontsize)\n",
    "ax.set_xlabel('$\\\\tau_0$ (# of cells)', size=fontsize)\n",
    "ax.set_ylabel('Drift_fcorr $\\\\times$ -1 (MHz/ms)', size=fontsize)\n",
    "# plt.savefig('drift_vs_sigmat3.png')\n",
    "\n",
    "ax = bursts.head(numbursts).plot.scatter(x='center_f', y='drift_corr', figsize=(10, 6))\n",
    "for k, v in bursts.head(numbursts).iterrows():\n",
    "    ax.annotate(v['name'], (v['center_f'], v['drift_corr']), xytext=(-3,5), textcoords='offset points', weight='bold')\n",
    "ax.set_xlim(0.9*min(bursts.head(numbursts)['center_f']), 1.1*max(bursts.head(numbursts)['center_f']))\n",
    "ax.set_title('Adjusted Drift vs. center_f (excluding C1 burst)', size=fontsize)\n",
    "ax.set_xlabel('center_f', size=fontsize)\n",
    "ax.set_ylabel('Drift $\\cdot \\overline{\\\\tau_{0}}/\\\\tau_{0}$', size=fontsize)\n",
    "# plt.savefig('adjusted_vs_centerf.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4468.4997819375"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(bursts.head(numbursts)['center_f'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bursts['drift_corr']\n",
    "bursts['drift_predicted'] = - bursts['center_f']**2 / bursts['tau_0']\n",
    "plt.figure()\n",
    "ax = bursts.plot.scatter(x='center_f', y='drift_corr', s=50, figsize=(10,6))\n",
    "for k, v in bursts.iterrows():\n",
    "    ax.annotate(v['name'], (v['center_f'], v['drift_corr']), xytext=(-3,5), textcoords='offset points', weight='bold')\n",
    "ax.set_title('Adjusted Drift vs. Center Frequency', size=fontsize)\n",
    "ax.set_xlabel('Center Frequency (MHz)', size=fontsize)\n",
    "ax.set_ylabel('Drift $\\cdot \\overline{\\\\tau_{0}}/\\\\tau_{0}$ (MHz/ms)', size=fontsize)\n",
    "plt.savefig('adjusted_vs_center.png')\n",
    "\n",
    "ax = bursts.plot.scatter(x='center_f', y='drift_abs', s=50, figsize=(10,6))\n",
    "for k, v in bursts.iterrows():\n",
    "    ax.annotate(v['name'], (v['center_f'], v['drift_abs']), xytext=(-3,5), textcoords='offset points', weight='bold')\n",
    "ax.set_title('Raw Drift vs. Center Frequency', size=fontsize)\n",
    "ax.set_xlabel('Center Frequency (MHz)', size=fontsize)\n",
    "ax.set_ylabel('Raw Drift (MHz/ms)', size=fontsize)\n",
    "plt.savefig('rawdrift_vs_center.png')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.6"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}