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theref/FingerprintingPlots.ipynb

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FingerprintingPlots.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notebook for creating several Plots\n",
"\n",
"- Sum of Squares plot\n",
"- Many numerical fingerprints\n",
"- Example tournament and results"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import string\n",
"import numpy as np\n",
"import axelrod as axl\n",
"import pandas as pd\n",
"from tqdm import tqdm\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib\n",
"import tqdm"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def format_filename(s):\n",
" \"\"\"\n",
" Take a string and return a valid filename constructed from the string.\n",
" Uses a whitelist approach: any characters not present in valid_chars are\n",
" removed. Also spaces are replaced with underscores.\n",
" Note: this method may produce invalid filenames such as ``, `.` or `..`\n",
" When I use this method I prepend a date string like '2009_01_15_19_46_32_'\n",
" and append a file extension like '.txt', so I avoid the potential of using\n",
" an invalid filename.\n",
" Borrowed from https://gist.github.com/seanh/93666\n",
" \"\"\"\n",
" valid_chars = \"-_.() {}{}\".format(string.ascii_letters, string.digits)\n",
" filename = ''.join(c for c in s if c in valid_chars)\n",
" filename = filename.replace(' ','_')\n",
" return filename"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 181/181 [07:37<00:00, 2.56s/it]\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Adaptive</th>\n",
" <th>Adaptive Tit For Tat</th>\n",
" <th>Aggravater</th>\n",
" <th>ALLCorALLD</th>\n",
" <th>Alternator</th>\n",
" <th>Alternator Hunter</th>\n",
" <th>AntiCycler</th>\n",
" <th>Anti Tit For Tat</th>\n",
" <th>Adaptive Pavlov 2006</th>\n",
" <th>Adaptive Pavlov 2011</th>\n",
" <th>...</th>\n",
" <th>Meta Winner Finite Memory</th>\n",
" <th>Meta Winner Long Memory</th>\n",
" <th>Meta Winner Stochastic</th>\n",
" <th>NMWE Deterministic</th>\n",
" <th>NMWE Finite Memory</th>\n",
" <th>NMWE Long Memory</th>\n",
" <th>NMWE Memory One</th>\n",
" <th>NMWE Stochastic</th>\n",
" <th>Nice Meta Winner</th>\n",
" <th>Nice Meta Winner Ensemble</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Adaptive</th>\n",
" <td>0</td>\n",
" <td>13365.2</td>\n",
" <td>2354.8</td>\n",
" <td>14380.9</td>\n",
" <td>12773.6</td>\n",
" <td>39455.1</td>\n",
" <td>33897.4</td>\n",
" <td>13565.6</td>\n",
" <td>11872.9</td>\n",
" <td>8651.69</td>\n",
" <td>...</td>\n",
" <td>2176.67</td>\n",
" <td>2177.05</td>\n",
" <td>2131.55</td>\n",
" <td>1944.34</td>\n",
" <td>1795.96</td>\n",
" <td>1960.83</td>\n",
" <td>1823.4</td>\n",
" <td>1815.51</td>\n",
" <td>1990.27</td>\n",
" <td>1922.17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Adaptive Tit For Tat</th>\n",
" <td>13365.2</td>\n",
" <td>0</td>\n",
" <td>14804.2</td>\n",
" <td>1280.63</td>\n",
" <td>1441.76</td>\n",
" <td>9991.9</td>\n",
" <td>7011.08</td>\n",
" <td>8313.64</td>\n",
" <td>1224.03</td>\n",
" <td>1853.75</td>\n",
" <td>...</td>\n",
" <td>14677.4</td>\n",
" <td>14690.4</td>\n",
" <td>14438.9</td>\n",
" <td>13941.6</td>\n",
" <td>12617.2</td>\n",
" <td>13971.9</td>\n",
" <td>12246.2</td>\n",
" <td>13600.8</td>\n",
" <td>14034.1</td>\n",
" <td>13910.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Aggravater</th>\n",
" <td>2354.8</td>\n",
" <td>14804.2</td>\n",
" <td>0</td>\n",
" <td>16100</td>\n",
" <td>14660.6</td>\n",
" <td>43575.7</td>\n",
" <td>37694.6</td>\n",
" <td>15001.7</td>\n",
" <td>12482.9</td>\n",
" <td>9192.25</td>\n",
" <td>...</td>\n",
" <td>190.426</td>\n",
" <td>190.957</td>\n",
" <td>188.378</td>\n",
" <td>259.191</td>\n",
" <td>374.156</td>\n",
" <td>257.048</td>\n",
" <td>398.091</td>\n",
" <td>300.557</td>\n",
" <td>251.895</td>\n",
" <td>264.295</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ALLCorALLD</th>\n",
" <td>14380.9</td>\n",
" <td>1280.63</td>\n",
" <td>16100</td>\n",
" <td>0</td>\n",
" <td>493.319</td>\n",
" <td>7443.7</td>\n",
" <td>4794.99</td>\n",
" <td>4695.7</td>\n",
" <td>3206.06</td>\n",
" <td>4282.04</td>\n",
" <td>...</td>\n",
" <td>15790.6</td>\n",
" <td>15802.9</td>\n",
" <td>15583.9</td>\n",
" <td>15305.4</td>\n",
" <td>13864</td>\n",
" <td>15332.5</td>\n",
" <td>13384.9</td>\n",
" <td>15020.2</td>\n",
" <td>15394.8</td>\n",
" <td>15280</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alternator</th>\n",
" <td>12773.6</td>\n",
" <td>1441.76</td>\n",
" <td>14660.6</td>\n",
" <td>493.319</td>\n",
" <td>0</td>\n",
" <td>8782.08</td>\n",
" <td>5890.75</td>\n",
" <td>4220.08</td>\n",
" <td>3122.2</td>\n",
" <td>4148.77</td>\n",
" <td>...</td>\n",
" <td>14347.4</td>\n",
" <td>14360.6</td>\n",
" <td>14151.5</td>\n",
" <td>13858.1</td>\n",
" <td>12493.8</td>\n",
" <td>13889.1</td>\n",
" <td>12045.1</td>\n",
" <td>13570.4</td>\n",
" <td>13954.1</td>\n",
" <td>13827.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alternator Hunter</th>\n",
" <td>39455.1</td>\n",
" <td>9991.9</td>\n",
" <td>43575.7</td>\n",
" <td>7443.7</td>\n",
" <td>8782.08</td>\n",
" <td>0</td>\n",
" <td>1083.53</td>\n",
" <td>15266.5</td>\n",
" <td>14641.5</td>\n",
" <td>18555.2</td>\n",
" <td>...</td>\n",
" <td>43193.1</td>\n",
" <td>43214</td>\n",
" <td>42856.4</td>\n",
" <td>42261</td>\n",
" <td>39819.8</td>\n",
" <td>42305.8</td>\n",
" <td>39022.5</td>\n",
" <td>41759.8</td>\n",
" <td>42412.5</td>\n",
" <td>42215.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AntiCycler</th>\n",
" <td>33897.4</td>\n",
" <td>7011.08</td>\n",
" <td>37694.6</td>\n",
" <td>4794.99</td>\n",
" <td>5890.75</td>\n",
" <td>1083.53</td>\n",
" <td>0</td>\n",
" <td>12270</td>\n",
" <td>11204.7</td>\n",
" <td>14644.4</td>\n",
" <td>...</td>\n",
" <td>37326.5</td>\n",
" <td>37345.9</td>\n",
" <td>37006.3</td>\n",
" <td>36465.6</td>\n",
" <td>34188.6</td>\n",
" <td>36509.9</td>\n",
" <td>33442.3</td>\n",
" <td>35995.8</td>\n",
" <td>36608.8</td>\n",
" <td>36422.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Anti Tit For Tat</th>\n",
" <td>13565.6</td>\n",
" <td>8313.64</td>\n",
" <td>15001.7</td>\n",
" <td>4695.7</td>\n",
" <td>4220.08</td>\n",
" <td>15266.5</td>\n",
" <td>12270</td>\n",
" <td>0</td>\n",
" <td>10499.9</td>\n",
" <td>11489.3</td>\n",
" <td>...</td>\n",
" <td>14527</td>\n",
" <td>14538.9</td>\n",
" <td>14427.5</td>\n",
" <td>14325.1</td>\n",
" <td>12962</td>\n",
" <td>14347.4</td>\n",
" <td>12471</td>\n",
" <td>14158.1</td>\n",
" <td>14398.8</td>\n",
" <td>14309.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Adaptive Pavlov 2006</th>\n",
" <td>11872.9</td>\n",
" <td>1224.03</td>\n",
" <td>12482.9</td>\n",
" <td>3206.06</td>\n",
" <td>3122.2</td>\n",
" <td>14641.5</td>\n",
" <td>11204.7</td>\n",
" <td>10499.9</td>\n",
" <td>0</td>\n",
" <td>1627.15</td>\n",
" <td>...</td>\n",
" <td>12411.8</td>\n",
" <td>12421.3</td>\n",
" <td>12197.5</td>\n",
" <td>11743.3</td>\n",
" <td>10713</td>\n",
" <td>11769.6</td>\n",
" <td>10408.3</td>\n",
" <td>11464</td>\n",
" <td>11815.8</td>\n",
" <td>11718.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Adaptive Pavlov 2011</th>\n",
" <td>8651.69</td>\n",
" <td>1853.75</td>\n",
" <td>9192.25</td>\n",
" <td>4282.04</td>\n",
" <td>4148.77</td>\n",
" <td>18555.2</td>\n",
" <td>14644.4</td>\n",
" <td>11489.3</td>\n",
" <td>1627.15</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>9165.94</td>\n",
" <td>9174.83</td>\n",
" <td>8959.46</td>\n",
" <td>8488.8</td>\n",
" <td>7644.94</td>\n",
" <td>8511.11</td>\n",
" <td>7447.42</td>\n",
" <td>8198.02</td>\n",
" <td>8556.95</td>\n",
" <td>8461.87</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Appeaser</th>\n",
" <td>10514.4</td>\n",
" <td>3751.78</td>\n",
" <td>12031.5</td>\n",
" <td>2327.79</td>\n",
" <td>2036.56</td>\n",
" <td>13309.7</td>\n",
" <td>10144.4</td>\n",
" <td>2204.69</td>\n",
" <td>5318.11</td>\n",
" <td>5940.91</td>\n",
" <td>...</td>\n",
" <td>11779.7</td>\n",
" <td>11792.4</td>\n",
" <td>11625.2</td>\n",
" <td>11198.7</td>\n",
" <td>9868.38</td>\n",
" <td>11224.5</td>\n",
" <td>9466.46</td>\n",
" <td>10941.1</td>\n",
" <td>11281.2</td>\n",
" <td>11173.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Arrogant QLearner</th>\n",
" <td>36005.7</td>\n",
" <td>7769.22</td>\n",
" <td>39956.7</td>\n",
" <td>5560.74</td>\n",
" <td>6795.13</td>\n",
" <td>917.648</td>\n",
" <td>134.728</td>\n",
" <td>13571.8</td>\n",
" <td>12142</td>\n",
" <td>15780.9</td>\n",
" <td>...</td>\n",
" <td>39580.2</td>\n",
" <td>39600</td>\n",
" <td>39248.3</td>\n",
" <td>38683.8</td>\n",
" <td>36327.4</td>\n",
" <td>38729</td>\n",
" <td>35561.4</td>\n",
" <td>38197.2</td>\n",
" <td>38831.9</td>\n",
" <td>38639.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Average Copier</th>\n",
" <td>2063.67</td>\n",
" <td>14196.4</td>\n",
" <td>187.398</td>\n",
" <td>15523</td>\n",
" <td>14102.3</td>\n",
" <td>42674.1</td>\n",
" <td>36853.5</td>\n",
" <td>14513.1</td>\n",
" <td>11954.1</td>\n",
" <td>8686.29</td>\n",
" <td>...</td>\n",
" <td>112.699</td>\n",
" <td>113.082</td>\n",
" <td>104.606</td>\n",
" <td>24.3303</td>\n",
" <td>105.529</td>\n",
" <td>22.661</td>\n",
" <td>128.961</td>\n",
" <td>49.8763</td>\n",
" <td>20.9956</td>\n",
" <td>27.1038</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Better and Better</th>\n",
" <td>1985.34</td>\n",
" <td>9940.98</td>\n",
" <td>730.117</td>\n",
" <td>10658.3</td>\n",
" <td>9445.92</td>\n",
" <td>34424</td>\n",
" <td>29124.4</td>\n",
" <td>10352.5</td>\n",
" <td>8430.76</td>\n",
" <td>5992.18</td>\n",
" <td>...</td>\n",
" <td>517.814</td>\n",
" <td>520.295</td>\n",
" <td>482.035</td>\n",
" <td>577.019</td>\n",
" <td>397.912</td>\n",
" <td>580.718</td>\n",
" <td>325.73</td>\n",
" <td>561.044</td>\n",
" <td>589.25</td>\n",
" <td>575.778</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BackStabber</th>\n",
" <td>1939.82</td>\n",
" <td>12577.6</td>\n",
" <td>711.619</td>\n",
" <td>14225.1</td>\n",
" <td>12854.3</td>\n",
" <td>40127.8</td>\n",
" <td>34504.1</td>\n",
" <td>13913.4</td>\n",
" <td>10548.2</td>\n",
" <td>7359.28</td>\n",
" <td>...</td>\n",
" <td>719.069</td>\n",
" <td>721.726</td>\n",
" <td>682.176</td>\n",
" <td>351.396</td>\n",
" <td>315.067</td>\n",
" <td>356.747</td>\n",
" <td>346.474</td>\n",
" <td>287.557</td>\n",
" <td>366.432</td>\n",
" <td>341.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bully</th>\n",
" <td>12977.1</td>\n",
" <td>8302.68</td>\n",
" <td>14372.6</td>\n",
" <td>4746.63</td>\n",
" <td>4242.41</td>\n",
" <td>15774.7</td>\n",
" <td>12694.8</td>\n",
" <td>726.802</td>\n",
" <td>10462.6</td>\n",
" <td>11255.6</td>\n",
" <td>...</td>\n",
" <td>13903.3</td>\n",
" <td>13915.3</td>\n",
" <td>13805.5</td>\n",
" <td>13707.9</td>\n",
" <td>12364.2</td>\n",
" <td>13731</td>\n",
" <td>11885.7</td>\n",
" <td>13542.5</td>\n",
" <td>13780.2</td>\n",
" <td>13691.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Calculator</th>\n",
" <td>12151</td>\n",
" <td>265.206</td>\n",
" <td>13422.8</td>\n",
" <td>1203.54</td>\n",
" <td>1294.39</td>\n",
" <td>10877.1</td>\n",
" <td>7720.39</td>\n",
" <td>7750.63</td>\n",
" <td>1307.75</td>\n",
" <td>1714.95</td>\n",
" <td>...</td>\n",
" <td>13160.5</td>\n",
" <td>13172.4</td>\n",
" <td>12938.9</td>\n",
" <td>12688.5</td>\n",
" <td>11412.8</td>\n",
" <td>12717.3</td>\n",
" <td>11048.3</td>\n",
" <td>12387.6</td>\n",
" <td>12774.7</td>\n",
" <td>12662.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cautious QLearner</th>\n",
" <td>36005.8</td>\n",
" <td>7770.74</td>\n",
" <td>39956.8</td>\n",
" <td>5560.51</td>\n",
" <td>6794.66</td>\n",
" <td>917.804</td>\n",
" <td>134.823</td>\n",
" <td>13569.1</td>\n",
" <td>12143.3</td>\n",
" <td>15782.6</td>\n",
" <td>...</td>\n",
" <td>39580.2</td>\n",
" <td>39600.1</td>\n",
" <td>39248.4</td>\n",
" <td>38683.9</td>\n",
" <td>36327.4</td>\n",
" <td>38729.1</td>\n",
" <td>35561.4</td>\n",
" <td>38197.4</td>\n",
" <td>38832.1</td>\n",
" <td>38639.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Champion</th>\n",
" <td>21414.6</td>\n",
" <td>1535.95</td>\n",
" <td>24410.2</td>\n",
" <td>1928.02</td>\n",
" <td>2564.55</td>\n",
" <td>4948.78</td>\n",
" <td>2851.02</td>\n",
" <td>10687.1</td>\n",
" <td>3990.3</td>\n",
" <td>5575.01</td>\n",
" <td>...</td>\n",
" <td>24208</td>\n",
" <td>24223.8</td>\n",
" <td>23904.5</td>\n",
" <td>23330.9</td>\n",
" <td>21544.8</td>\n",
" <td>23371.4</td>\n",
" <td>21050</td>\n",
" <td>22873.7</td>\n",
" <td>23459.7</td>\n",
" <td>23286.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CollectiveStrategy</th>\n",
" <td>2283.78</td>\n",
" <td>14822.3</td>\n",
" <td>298.484</td>\n",
" <td>15977</td>\n",
" <td>14516.7</td>\n",
" <td>43416.5</td>\n",
" <td>37540.9</td>\n",
" <td>14752.9</td>\n",
" <td>12535.4</td>\n",
" <td>9272.6</td>\n",
" <td>...</td>\n",
" <td>125.2</td>\n",
" <td>125.213</td>\n",
" <td>126.275</td>\n",
" <td>308.327</td>\n",
" <td>416.02</td>\n",
" <td>304.896</td>\n",
" <td>433.623</td>\n",
" <td>353.738</td>\n",
" <td>300.713</td>\n",
" <td>313.209</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Contrite Tit For Tat</th>\n",
" <td>13374.4</td>\n",
" <td>114.721</td>\n",
" <td>14804.3</td>\n",
" <td>1275.23</td>\n",
" <td>1432.76</td>\n",
" <td>9985</td>\n",
" <td>6999.4</td>\n",
" <td>8298.58</td>\n",
" <td>1231.16</td>\n",
" <td>1857.11</td>\n",
" <td>...</td>\n",
" <td>14675.5</td>\n",
" <td>14688.6</td>\n",
" <td>14437.2</td>\n",
" <td>13944.8</td>\n",
" <td>12619.9</td>\n",
" <td>13974.4</td>\n",
" <td>12247.5</td>\n",
" <td>13604.3</td>\n",
" <td>14036.4</td>\n",
" <td>13913.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cooperator</th>\n",
" <td>40200.7</td>\n",
" <td>9836.48</td>\n",
" <td>44422.3</td>\n",
" <td>7320.06</td>\n",
" <td>8733.47</td>\n",
" <td>925.384</td>\n",
" <td>433.825</td>\n",
" <td>15626</td>\n",
" <td>14584.6</td>\n",
" <td>18573</td>\n",
" <td>...</td>\n",
" <td>44037.7</td>\n",
" <td>44058.8</td>\n",
" <td>43690.2</td>\n",
" <td>43094.7</td>\n",
" <td>40612.2</td>\n",
" <td>43141.7</td>\n",
" <td>39799.8</td>\n",
" <td>42584.6</td>\n",
" <td>43248.8</td>\n",
" <td>43048.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cooperator Hunter</th>\n",
" <td>39630.8</td>\n",
" <td>9965.83</td>\n",
" <td>43841.1</td>\n",
" <td>7265.15</td>\n",
" <td>8613.22</td>\n",
" <td>1237.91</td>\n",
" <td>668.63</td>\n",
" <td>15060</td>\n",
" <td>14681.7</td>\n",
" <td>18698</td>\n",
" <td>...</td>\n",
" <td>43415.9</td>\n",
" <td>43436.5</td>\n",
" <td>43073.3</td>\n",
" <td>42522.4</td>\n",
" <td>40049.4</td>\n",
" <td>42569.2</td>\n",
" <td>39235.5</td>\n",
" <td>42024.5</td>\n",
" <td>42676</td>\n",
" <td>42477</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cycle Hunter</th>\n",
" <td>37464.2</td>\n",
" <td>9943.25</td>\n",
" <td>41285.4</td>\n",
" <td>7696.94</td>\n",
" <td>8892.86</td>\n",
" <td>3507.58</td>\n",
" <td>2666.13</td>\n",
" <td>15301.3</td>\n",
" <td>14192.9</td>\n",
" <td>17701.6</td>\n",
" <td>...</td>\n",
" <td>40941.3</td>\n",
" <td>40959.3</td>\n",
" <td>40615.5</td>\n",
" <td>40039.5</td>\n",
" <td>37738.8</td>\n",
" <td>40085.3</td>\n",
" <td>36982.6</td>\n",
" <td>39565.7</td>\n",
" <td>40183.3</td>\n",
" <td>39996.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cycler CCCCCD</th>\n",
" <td>29140.6</td>\n",
" <td>5127.51</td>\n",
" <td>32557.6</td>\n",
" <td>3124.36</td>\n",
" <td>3972.02</td>\n",
" <td>1644.22</td>\n",
" <td>351.432</td>\n",
" <td>9882.75</td>\n",
" <td>8844.87</td>\n",
" <td>11854</td>\n",
" <td>...</td>\n",
" <td>32199.3</td>\n",
" <td>32217.7</td>\n",
" <td>31902.7</td>\n",
" <td>31408.8</td>\n",
" <td>29300</td>\n",
" <td>31450.3</td>\n",
" <td>28609.1</td>\n",
" <td>30973</td>\n",
" <td>31543.1</td>\n",
" <td>31367.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cycler CCCD</th>\n",
" <td>24336.5</td>\n",
" <td>3509.35</td>\n",
" <td>27399.2</td>\n",
" <td>1769.7</td>\n",
" <td>2314.08</td>\n",
" <td>2731.98</td>\n",
" <td>1021.66</td>\n",
" <td>7769.99</td>\n",
" <td>6711.61</td>\n",
" <td>9222.4</td>\n",
" <td>...</td>\n",
" <td>27049.7</td>\n",
" <td>27066.2</td>\n",
" <td>26777.3</td>\n",
" <td>26332.5</td>\n",
" <td>24411.5</td>\n",
" <td>26371.8</td>\n",
" <td>23779.5</td>\n",
" <td>25933.2</td>\n",
" <td>26458.1</td>\n",
" <td>26294</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cycler CCD</th>\n",
" <td>20135.1</td>\n",
" <td>2354.75</td>\n",
" <td>22806.8</td>\n",
" <td>876.301</td>\n",
" <td>1169.57</td>\n",
" <td>4203.98</td>\n",
" <td>2118.15</td>\n",
" <td>6158</td>\n",
" <td>5076.29</td>\n",
" <td>7096.33</td>\n",
" <td>...</td>\n",
" <td>22472</td>\n",
" <td>22487.8</td>\n",
" <td>22224.2</td>\n",
" <td>21828.3</td>\n",
" <td>20085</td>\n",
" <td>21864.1</td>\n",
" <td>19513.5</td>\n",
" <td>21465.3</td>\n",
" <td>21943.3</td>\n",
" <td>21792.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cycler DC</th>\n",
" <td>12703.9</td>\n",
" <td>1441.81</td>\n",
" <td>14557.7</td>\n",
" <td>493.058</td>\n",
" <td>192.116</td>\n",
" <td>8849.67</td>\n",
" <td>5943.79</td>\n",
" <td>4185.02</td>\n",
" <td>3124.33</td>\n",
" <td>4118.46</td>\n",
" <td>...</td>\n",
" <td>14248.6</td>\n",
" <td>14261.9</td>\n",
" <td>14053.3</td>\n",
" <td>13763.5</td>\n",
" <td>12404.1</td>\n",
" <td>13794.4</td>\n",
" <td>11956.6</td>\n",
" <td>13478.1</td>\n",
" <td>13858.3</td>\n",
" <td>13733.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cycler DDC</th>\n",
" <td>5955.63</td>\n",
" <td>2824.95</td>\n",
" <td>6517.28</td>\n",
" <td>2581.86</td>\n",
" <td>1871.43</td>\n",
" <td>17445.4</td>\n",
" <td>13520.5</td>\n",
" <td>4578.99</td>\n",
" <td>3154.06</td>\n",
" <td>2701.2</td>\n",
" <td>...</td>\n",
" <td>6255.05</td>\n",
" <td>6263.75</td>\n",
" <td>6126.81</td>\n",
" <td>5997.3</td>\n",
" <td>5137.76</td>\n",
" <td>6017.05</td>\n",
" <td>4848.5</td>\n",
" <td>5822.98</td>\n",
" <td>6058.29</td>\n",
" <td>5978.38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cycler CCCDCD</th>\n",
" <td>20067.9</td>\n",
" <td>2346.57</td>\n",
" <td>22730.7</td>\n",
" <td>868.678</td>\n",
" <td>1148.4</td>\n",
" <td>4240.03</td>\n",
" <td>2147.22</td>\n",
" <td>6126.21</td>\n",
" <td>5061.55</td>\n",
" <td>7078.77</td>\n",
" <td>...</td>\n",
" <td>22393.2</td>\n",
" <td>22408.8</td>\n",
" <td>22146</td>\n",
" <td>21751.7</td>\n",
" <td>20012.1</td>\n",
" <td>21788</td>\n",
" <td>19440.5</td>\n",
" <td>21390.2</td>\n",
" <td>21867.1</td>\n",
" <td>21715.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Worse and Worse 3</th>\n",
" <td>13071.8</td>\n",
" <td>132.086</td>\n",
" <td>14882.4</td>\n",
" <td>1210.05</td>\n",
" <td>1286.52</td>\n",
" <td>9826.56</td>\n",
" <td>6846.63</td>\n",
" <td>8207.59</td>\n",
" <td>1291.64</td>\n",
" <td>1864.42</td>\n",
" <td>...</td>\n",
" <td>14750.8</td>\n",
" <td>14763.5</td>\n",
" <td>14508.1</td>\n",
" <td>13982.7</td>\n",
" <td>12644.1</td>\n",
" <td>14015.7</td>\n",
" <td>12285.2</td>\n",
" <td>13616.6</td>\n",
" <td>14083.2</td>\n",
" <td>13946.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ZD-Extort-2</th>\n",
" <td>8358.66</td>\n",
" <td>1097.2</td>\n",
" <td>8426.29</td>\n",
" <td>2162.78</td>\n",
" <td>2050.63</td>\n",
" <td>15559.4</td>\n",
" <td>11867.6</td>\n",
" <td>7315.94</td>\n",
" <td>1229.76</td>\n",
" <td>1028.46</td>\n",
" <td>...</td>\n",
" <td>8272.19</td>\n",
" <td>8281.17</td>\n",
" <td>8100.99</td>\n",
" <td>7914.95</td>\n",
" <td>6983.42</td>\n",
" <td>7931.11</td>\n",
" <td>6688.74</td>\n",
" <td>7715.64</td>\n",
" <td>7968.78</td>\n",
" <td>7900.35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ZD-Extort-2 v2</th>\n",
" <td>8414.61</td>\n",
" <td>1080.59</td>\n",
" <td>8483.91</td>\n",
" <td>2141.25</td>\n",
" <td>2025.7</td>\n",
" <td>15488.8</td>\n",
" <td>11801.7</td>\n",
" <td>7304.9</td>\n",
" <td>1219.7</td>\n",
" <td>1038.68</td>\n",
" <td>...</td>\n",
" <td>8328.85</td>\n",
" <td>8337.89</td>\n",
" <td>8157.33</td>\n",
" <td>7971.41</td>\n",
" <td>7036.73</td>\n",
" <td>7987.68</td>\n",
" <td>6740.25</td>\n",
" <td>7772.11</td>\n",
" <td>8025.42</td>\n",
" <td>7956.85</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ZD-Extort-4</th>\n",
" <td>6090.94</td>\n",
" <td>2481.62</td>\n",
" <td>5638.51</td>\n",
" <td>3489.58</td>\n",
" <td>3056.18</td>\n",
" <td>19530.8</td>\n",
" <td>15415.9</td>\n",
" <td>7351.68</td>\n",
" <td>2092.68</td>\n",
" <td>1453.05</td>\n",
" <td>...</td>\n",
" <td>5464.11</td>\n",
" <td>5471.4</td>\n",
" <td>5330.24</td>\n",
" <td>5256.87</td>\n",
" <td>4536.79</td>\n",
" <td>5268.59</td>\n",
" <td>4291.17</td>\n",
" <td>5120.94</td>\n",
" <td>5296.32</td>\n",
" <td>5247.54</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ZD-GTFT-2</th>\n",
" <td>15879.4</td>\n",
" <td>420.798</td>\n",
" <td>18164.6</td>\n",
" <td>832.336</td>\n",
" <td>1086.88</td>\n",
" <td>7100.1</td>\n",
" <td>4508.58</td>\n",
" <td>7881.74</td>\n",
" <td>2212.46</td>\n",
" <td>3259.74</td>\n",
" <td>...</td>\n",
" <td>17992</td>\n",
" <td>18006.1</td>\n",
" <td>17732.4</td>\n",
" <td>17194.3</td>\n",
" <td>15673.5</td>\n",
" <td>17229.7</td>\n",
" <td>15242.7</td>\n",
" <td>16801.6</td>\n",
" <td>17304.6</td>\n",
" <td>17156</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ZD-GEN-2</th>\n",
" <td>15161.8</td>\n",
" <td>318.321</td>\n",
" <td>17305.5</td>\n",
" <td>806.366</td>\n",
" <td>1115.3</td>\n",
" <td>7611.98</td>\n",
" <td>4948.75</td>\n",
" <td>7811.8</td>\n",
" <td>1996.6</td>\n",
" <td>2877.59</td>\n",
" <td>...</td>\n",
" <td>17139</td>\n",
" <td>17152.5</td>\n",
" <td>16884.4</td>\n",
" <td>16352</td>\n",
" <td>14871.7</td>\n",
" <td>16385.9</td>\n",
" <td>14455.7</td>\n",
" <td>15968.2</td>\n",
" <td>16458.4</td>\n",
" <td>16315.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ZD-SET-2</th>\n",
" <td>9481.15</td>\n",
" <td>709.881</td>\n",
" <td>10482</td>\n",
" <td>931.248</td>\n",
" <td>754.933</td>\n",
" <td>12208.7</td>\n",
" <td>8847.3</td>\n",
" <td>5534.98</td>\n",
" <td>1539.1</td>\n",
" <td>1736.81</td>\n",
" <td>...</td>\n",
" <td>10266.5</td>\n",
" <td>10277.1</td>\n",
" <td>10081.5</td>\n",
" <td>9818</td>\n",
" <td>8700.91</td>\n",
" <td>9841.48</td>\n",
" <td>8350.56</td>\n",
" <td>9567.44</td>\n",
" <td>9892.99</td>\n",
" <td>9794.76</td>\n",
" </tr>\n",
" <tr>\n",
" <th>$e$</th>\n",
" <td>35838.7</td>\n",
" <td>9555.37</td>\n",
" <td>39522.4</td>\n",
" <td>6053.98</td>\n",
" <td>6959.31</td>\n",
" <td>2050.3</td>\n",
" <td>1174.95</td>\n",
" <td>11650.9</td>\n",
" <td>13957.1</td>\n",
" <td>18030.2</td>\n",
" <td>...</td>\n",
" <td>38980.5</td>\n",
" <td>38999.2</td>\n",
" <td>38693.3</td>\n",
" <td>38353.5</td>\n",
" <td>36033.1</td>\n",
" <td>38396.7</td>\n",
" <td>35217.1</td>\n",
" <td>37958</td>\n",
" <td>38492.3</td>\n",
" <td>38315.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Meta Hunter</th>\n",
" <td>1685.52</td>\n",
" <td>10522.4</td>\n",
" <td>1152.46</td>\n",
" <td>11706.1</td>\n",
" <td>10450.7</td>\n",
" <td>35735.9</td>\n",
" <td>30407.2</td>\n",
" <td>11570.1</td>\n",
" <td>9145.32</td>\n",
" <td>6176.44</td>\n",
" <td>...</td>\n",
" <td>1095.96</td>\n",
" <td>1099.17</td>\n",
" <td>1040.4</td>\n",
" <td>736.969</td>\n",
" <td>528.568</td>\n",
" <td>747.829</td>\n",
" <td>515.318</td>\n",
" <td>632.792</td>\n",
" <td>767.725</td>\n",
" <td>723.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Meta Hunter Aggressive</th>\n",
" <td>1667.08</td>\n",
" <td>10769.4</td>\n",
" <td>957.788</td>\n",
" <td>11732.9</td>\n",
" <td>10479.3</td>\n",
" <td>36004.7</td>\n",
" <td>30620.8</td>\n",
" <td>11426.2</td>\n",
" <td>9345.97</td>\n",
" <td>6419.47</td>\n",
" <td>...</td>\n",
" <td>788.976</td>\n",
" <td>791.636</td>\n",
" <td>742.05</td>\n",
" <td>700.494</td>\n",
" <td>518.31</td>\n",
" <td>710.054</td>\n",
" <td>491.747</td>\n",
" <td>625.889</td>\n",
" <td>727</td>\n",
" <td>691.401</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Meta Majority</th>\n",
" <td>12524.5</td>\n",
" <td>280.927</td>\n",
" <td>13411.9</td>\n",
" <td>2057.39</td>\n",
" <td>2189.64</td>\n",
" <td>12185.9</td>\n",
" <td>8960.79</td>\n",
" <td>9635.22</td>\n",
" <td>763.682</td>\n",
" <td>1223.3</td>\n",
" <td>...</td>\n",
" <td>13312.3</td>\n",
" <td>13323.9</td>\n",
" <td>13083.2</td>\n",
" <td>12611.5</td>\n",
" <td>11417.9</td>\n",
" <td>12637.8</td>\n",
" <td>11092.2</td>\n",
" <td>12303.3</td>\n",
" <td>12691.1</td>\n",
" <td>12584.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Meta Majority Memory One</th>\n",
" <td>14326.7</td>\n",
" <td>129.91</td>\n",
" <td>16114.7</td>\n",
" <td>978.1</td>\n",
" <td>1200.84</td>\n",
" <td>8663.71</td>\n",
" <td>5846.21</td>\n",
" <td>8073.6</td>\n",
" <td>1515.11</td>\n",
" <td>2307.86</td>\n",
" <td>...</td>\n",
" <td>15964.6</td>\n",
" <td>15977.8</td>\n",
" <td>15717.2</td>\n",
" <td>15205.2</td>\n",
" <td>13798</td>\n",
" <td>15237.3</td>\n",
" <td>13402.8</td>\n",
" <td>14843.1</td>\n",
" <td>15304.6</td>\n",
" <td>15171.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Meta Majority Finite Memory</th>\n",
" <td>12923.5</td>\n",
" <td>177.647</td>\n",
" <td>14039.1</td>\n",
" <td>1849.77</td>\n",
" <td>2016.58</td>\n",
" <td>11439.7</td>\n",
" <td>8302.86</td>\n",
" <td>9459.9</td>\n",
" <td>865.357</td>\n",
" <td>1342.8</td>\n",
" <td>...</td>\n",
" <td>13934.5</td>\n",
" <td>13946.4</td>\n",
" <td>13698.2</td>\n",
" <td>13207.8</td>\n",
" <td>11965</td>\n",
" <td>13236</td>\n",
" <td>11628.1</td>\n",
" <td>12879.6</td>\n",
" <td>13293.4</td>\n",
" <td>13178.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Meta Majority Long Memory</th>\n",
" <td>9797.58</td>\n",
" <td>789.138</td>\n",
" <td>10268.2</td>\n",
" <td>2724.9</td>\n",
" <td>2613.89</td>\n",
" <td>15272.5</td>\n",
" <td>11666.5</td>\n",
" <td>9419.19</td>\n",
" <td>713.253</td>\n",
" <td>781.292</td>\n",
" <td>...</td>\n",
" <td>10197.4</td>\n",
" <td>10207.4</td>\n",
" <td>9996.38</td>\n",
" <td>9547.57</td>\n",
" <td>8551.48</td>\n",
" <td>9571</td>\n",
" <td>8280.45</td>\n",
" <td>9281.57</td>\n",
" <td>9615.58</td>\n",
" <td>9524.11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Meta Minority</th>\n",
" <td>18522.8</td>\n",
" <td>9677.7</td>\n",
" <td>20335.2</td>\n",
" <td>5182.4</td>\n",
" <td>4938.51</td>\n",
" <td>12648.9</td>\n",
" <td>10201.1</td>\n",
" <td>1131.06</td>\n",
" <td>12523.1</td>\n",
" <td>14623.6</td>\n",
" <td>...</td>\n",
" <td>19768.7</td>\n",
" <td>19782.7</td>\n",
" <td>19660.5</td>\n",
" <td>19551.9</td>\n",
" <td>17935.6</td>\n",
" <td>19576.8</td>\n",
" <td>17336</td>\n",
" <td>19372.6</td>\n",
" <td>19633.8</td>\n",
" <td>19534.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Meta Mixer</th>\n",
" <td>10423.8</td>\n",
" <td>581.019</td>\n",
" <td>11644.5</td>\n",
" <td>698.979</td>\n",
" <td>549.751</td>\n",
" <td>11086</td>\n",
" <td>7854.24</td>\n",
" <td>5561.91</td>\n",
" <td>1584.55</td>\n",
" <td>2001.57</td>\n",
" <td>...</td>\n",
" <td>11424</td>\n",
" <td>11435.2</td>\n",
" <td>11230.1</td>\n",
" <td>10935.4</td>\n",
" <td>9750.52</td>\n",
" <td>10961</td>\n",
" <td>9378.69</td>\n",
" <td>10668.1</td>\n",
" <td>11016.5</td>\n",
" <td>10909.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Meta Winner</th>\n",
" <td>2180.95</td>\n",
" <td>14684.1</td>\n",
" <td>191.271</td>\n",
" <td>15795.6</td>\n",
" <td>14352.9</td>\n",
" <td>43200.3</td>\n",
" <td>37334.3</td>\n",
" <td>14530.8</td>\n",
" <td>12416.7</td>\n",
" <td>9170.94</td>\n",
" <td>...</td>\n",
" <td>2.76702</td>\n",
" <td>2.72625</td>\n",
" <td>4.76813</td>\n",
" <td>190.906</td>\n",
" <td>291.167</td>\n",
" <td>188.219</td>\n",
" <td>307.708</td>\n",
" <td>238.694</td>\n",
" <td>183.668</td>\n",
" <td>196.357</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Meta Winner Deterministic</th>\n",
" <td>2272.85</td>\n",
" <td>14911.2</td>\n",
" <td>294.017</td>\n",
" <td>16056.6</td>\n",
" <td>14584.9</td>\n",
" <td>43550.9</td>\n",
" <td>37662.1</td>\n",
" <td>14769.1</td>\n",
" <td>12615.4</td>\n",
" <td>9351.74</td>\n",
" <td>...</td>\n",
" <td>120.144</td>\n",
" <td>119.62</td>\n",
" <td>121.953</td>\n",
" <td>301.908</td>\n",
" <td>413.876</td>\n",
" <td>300.153</td>\n",
" <td>431.697</td>\n",
" <td>348.623</td>\n",
" <td>294.952</td>\n",
" <td>307.258</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Meta Winner Ensemble</th>\n",
" <td>2153.85</td>\n",
" <td>14605.8</td>\n",
" <td>191.139</td>\n",
" <td>15722.1</td>\n",
" <td>14279.7</td>\n",
" <td>43080</td>\n",
" <td>37220.3</td>\n",
" <td>14479</td>\n",
" <td>12352.4</td>\n",
" <td>9110.62</td>\n",
" <td>...</td>\n",
" <td>2.85148</td>\n",
" <td>2.91236</td>\n",
" <td>3.80537</td>\n",
" <td>185.551</td>\n",
" <td>280.134</td>\n",
" <td>183.159</td>\n",
" <td>296.625</td>\n",
" <td>230.406</td>\n",
" <td>178.984</td>\n",
" <td>189.595</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Meta Winner Memory One</th>\n",
" <td>2177.01</td>\n",
" <td>14688.3</td>\n",
" <td>190.479</td>\n",
" <td>15801.4</td>\n",
" <td>14360.2</td>\n",
" <td>43211.9</td>\n",
" <td>37344.7</td>\n",
" <td>14540</td>\n",
" <td>12419.5</td>\n",
" <td>9172.3</td>\n",
" <td>...</td>\n",
" <td>2.62012</td>\n",
" <td>2.18768</td>\n",
" <td>4.61038</td>\n",
" <td>190.217</td>\n",
" <td>291.12</td>\n",
" <td>187.56</td>\n",
" <td>307.798</td>\n",
" <td>237.515</td>\n",
" <td>183</td>\n",
" <td>195.555</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Meta Winner Finite Memory</th>\n",
" <td>2176.67</td>\n",
" <td>14677.4</td>\n",
" <td>190.426</td>\n",
" <td>15790.6</td>\n",
" <td>14347.4</td>\n",
" <td>43193.1</td>\n",
" <td>37326.5</td>\n",
" <td>14527</td>\n",
" <td>12411.8</td>\n",
" <td>9165.94</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>2.6504</td>\n",
" <td>3.85833</td>\n",
" <td>189.847</td>\n",
" <td>289.728</td>\n",
" <td>187.149</td>\n",
" <td>306.183</td>\n",
" <td>236.994</td>\n",
" <td>182.182</td>\n",
" <td>195.132</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Meta Winner Long Memory</th>\n",
" <td>2177.05</td>\n",
" <td>14690.4</td>\n",
" <td>190.957</td>\n",
" <td>15802.9</td>\n",
" <td>14360.6</td>\n",
" <td>43214</td>\n",
" <td>37345.9</td>\n",
" <td>14538.9</td>\n",
" <td>12421.3</td>\n",
" <td>9174.83</td>\n",
" <td>...</td>\n",
" <td>2.6504</td>\n",
" <td>0</td>\n",
" <td>4.63044</td>\n",
" <td>190.446</td>\n",
" <td>291.459</td>\n",
" <td>187.855</td>\n",
" <td>308.179</td>\n",
" <td>237.899</td>\n",
" <td>183.266</td>\n",
" <td>195.873</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Meta Winner Stochastic</th>\n",
" <td>2131.55</td>\n",
" <td>14438.9</td>\n",
" <td>188.378</td>\n",
" <td>15583.9</td>\n",
" <td>14151.5</td>\n",
" <td>42856.4</td>\n",
" <td>37006.3</td>\n",
" <td>14427.5</td>\n",
" <td>12197.5</td>\n",
" <td>8959.46</td>\n",
" <td>...</td>\n",
" <td>3.85833</td>\n",
" <td>4.63044</td>\n",
" <td>0</td>\n",
" <td>177.58</td>\n",
" <td>266.721</td>\n",
" <td>175.35</td>\n",
" <td>282.245</td>\n",
" <td>218.467</td>\n",
" <td>171.111</td>\n",
" <td>182.122</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NMWE Deterministic</th>\n",
" <td>1944.34</td>\n",
" <td>13941.6</td>\n",
" <td>259.191</td>\n",
" <td>15305.4</td>\n",
" <td>13858.1</td>\n",
" <td>42261</td>\n",
" <td>36465.6</td>\n",
" <td>14325.1</td>\n",
" <td>11743.3</td>\n",
" <td>8488.8</td>\n",
" <td>...</td>\n",
" <td>189.847</td>\n",
" <td>190.446</td>\n",
" <td>177.58</td>\n",
" <td>0</td>\n",
" <td>62.6634</td>\n",
" <td>3.49969</td>\n",
" <td>88.4509</td>\n",
" <td>14.0338</td>\n",
" <td>4.13519</td>\n",
" <td>3.51524</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NMWE Finite Memory</th>\n",
" <td>1795.96</td>\n",
" <td>12617.2</td>\n",
" <td>374.156</td>\n",
" <td>13864</td>\n",
" <td>12493.8</td>\n",
" <td>39819.8</td>\n",
" <td>34188.6</td>\n",
" <td>12962</td>\n",
" <td>10713</td>\n",
" <td>7644.94</td>\n",
" <td>...</td>\n",
" <td>289.728</td>\n",
" <td>291.459</td>\n",
" <td>266.721</td>\n",
" <td>62.6634</td>\n",
" <td>0</td>\n",
" <td>65.6435</td>\n",
" <td>13.3226</td>\n",
" <td>46.4664</td>\n",
" <td>71.7287</td>\n",
" <td>59.9834</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NMWE Long Memory</th>\n",
" <td>1960.83</td>\n",
" <td>13971.9</td>\n",
" <td>257.048</td>\n",
" <td>15332.5</td>\n",
" <td>13889.1</td>\n",
" <td>42305.8</td>\n",
" <td>36509.9</td>\n",
" <td>14347.4</td>\n",
" <td>11769.6</td>\n",
" <td>8511.11</td>\n",
" <td>...</td>\n",
" <td>187.149</td>\n",
" <td>187.855</td>\n",
" <td>175.35</td>\n",
" <td>3.49969</td>\n",
" <td>65.6435</td>\n",
" <td>0</td>\n",
" <td>90.9095</td>\n",
" <td>16.4197</td>\n",
" <td>3.54205</td>\n",
" <td>4.09892</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NMWE Memory One</th>\n",
" <td>1823.4</td>\n",
" <td>12246.2</td>\n",
" <td>398.091</td>\n",
" <td>13384.9</td>\n",
" <td>12045.1</td>\n",
" <td>39022.5</td>\n",
" <td>33442.3</td>\n",
" <td>12471</td>\n",
" <td>10408.3</td>\n",
" <td>7447.42</td>\n",
" <td>...</td>\n",
" <td>306.183</td>\n",
" <td>308.179</td>\n",
" <td>282.245</td>\n",
" <td>88.4509</td>\n",
" <td>13.3226</td>\n",
" <td>90.9095</td>\n",
" <td>0</td>\n",
" <td>76.3843</td>\n",
" <td>96.6646</td>\n",
" <td>86.5677</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NMWE Stochastic</th>\n",
" <td>1815.51</td>\n",
" <td>13600.8</td>\n",
" <td>300.557</td>\n",
" <td>15020.2</td>\n",
" <td>13570.4</td>\n",
" <td>41759.8</td>\n",
" <td>35995.8</td>\n",
" <td>14158.1</td>\n",
" <td>11464</td>\n",
" <td>8198.02</td>\n",
" <td>...</td>\n",
" <td>236.994</td>\n",
" <td>237.899</td>\n",
" <td>218.467</td>\n",
" <td>14.0338</td>\n",
" <td>46.4664</td>\n",
" <td>16.4197</td>\n",
" <td>76.3843</td>\n",
" <td>0</td>\n",
" <td>20.1756</td>\n",
" <td>11.3952</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Nice Meta Winner</th>\n",
" <td>1990.27</td>\n",
" <td>14034.1</td>\n",
" <td>251.895</td>\n",
" <td>15394.8</td>\n",
" <td>13954.1</td>\n",
" <td>42412.5</td>\n",
" <td>36608.8</td>\n",
" <td>14398.8</td>\n",
" <td>11815.8</td>\n",
" <td>8556.95</td>\n",
" <td>...</td>\n",
" <td>182.182</td>\n",
" <td>183.266</td>\n",
" <td>171.111</td>\n",
" <td>4.13519</td>\n",
" <td>71.7287</td>\n",
" <td>3.54205</td>\n",
" <td>96.6646</td>\n",
" <td>20.1756</td>\n",
" <td>0</td>\n",
" <td>5.06171</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Nice Meta Winner Ensemble</th>\n",
" <td>1922.17</td>\n",
" <td>13910.5</td>\n",
" <td>264.295</td>\n",
" <td>15280</td>\n",
" <td>13827.4</td>\n",
" <td>42215.7</td>\n",
" <td>36422.5</td>\n",
" <td>14309.5</td>\n",
" <td>11718.1</td>\n",
" <td>8461.87</td>\n",
" <td>...</td>\n",
" <td>195.132</td>\n",
" <td>195.873</td>\n",
" <td>182.122</td>\n",
" <td>3.51524</td>\n",
" <td>59.9834</td>\n",
" <td>4.09892</td>\n",
" <td>86.5677</td>\n",
" <td>11.3952</td>\n",
" <td>5.06171</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>181 rows × 181 columns</p>\n",
"</div>"
],
"text/plain": [
" Adaptive Adaptive Tit For Tat Aggravater \\\n",
"Adaptive 0 13365.2 2354.8 \n",
"Adaptive Tit For Tat 13365.2 0 14804.2 \n",
"Aggravater 2354.8 14804.2 0 \n",
"ALLCorALLD 14380.9 1280.63 16100 \n",
"Alternator 12773.6 1441.76 14660.6 \n",
"Alternator Hunter 39455.1 9991.9 43575.7 \n",
"AntiCycler 33897.4 7011.08 37694.6 \n",
"Anti Tit For Tat 13565.6 8313.64 15001.7 \n",
"Adaptive Pavlov 2006 11872.9 1224.03 12482.9 \n",
"Adaptive Pavlov 2011 8651.69 1853.75 9192.25 \n",
"Appeaser 10514.4 3751.78 12031.5 \n",
"Arrogant QLearner 36005.7 7769.22 39956.7 \n",
"Average Copier 2063.67 14196.4 187.398 \n",
"Better and Better 1985.34 9940.98 730.117 \n",
"BackStabber 1939.82 12577.6 711.619 \n",
"Bully 12977.1 8302.68 14372.6 \n",
"Calculator 12151 265.206 13422.8 \n",
"Cautious QLearner 36005.8 7770.74 39956.8 \n",
"Champion 21414.6 1535.95 24410.2 \n",
"CollectiveStrategy 2283.78 14822.3 298.484 \n",
"Contrite Tit For Tat 13374.4 114.721 14804.3 \n",
"Cooperator 40200.7 9836.48 44422.3 \n",
"Cooperator Hunter 39630.8 9965.83 43841.1 \n",
"Cycle Hunter 37464.2 9943.25 41285.4 \n",
"Cycler CCCCCD 29140.6 5127.51 32557.6 \n",
"Cycler CCCD 24336.5 3509.35 27399.2 \n",
"Cycler CCD 20135.1 2354.75 22806.8 \n",
"Cycler DC 12703.9 1441.81 14557.7 \n",
"Cycler DDC 5955.63 2824.95 6517.28 \n",
"Cycler CCCDCD 20067.9 2346.57 22730.7 \n",
"... ... ... ... \n",
"Worse and Worse 3 13071.8 132.086 14882.4 \n",
"ZD-Extort-2 8358.66 1097.2 8426.29 \n",
"ZD-Extort-2 v2 8414.61 1080.59 8483.91 \n",
"ZD-Extort-4 6090.94 2481.62 5638.51 \n",
"ZD-GTFT-2 15879.4 420.798 18164.6 \n",
"ZD-GEN-2 15161.8 318.321 17305.5 \n",
"ZD-SET-2 9481.15 709.881 10482 \n",
"$e$ 35838.7 9555.37 39522.4 \n",
"Meta Hunter 1685.52 10522.4 1152.46 \n",
"Meta Hunter Aggressive 1667.08 10769.4 957.788 \n",
"Meta Majority 12524.5 280.927 13411.9 \n",
"Meta Majority Memory One 14326.7 129.91 16114.7 \n",
"Meta Majority Finite Memory 12923.5 177.647 14039.1 \n",
"Meta Majority Long Memory 9797.58 789.138 10268.2 \n",
"Meta Minority 18522.8 9677.7 20335.2 \n",
"Meta Mixer 10423.8 581.019 11644.5 \n",
"Meta Winner 2180.95 14684.1 191.271 \n",
"Meta Winner Deterministic 2272.85 14911.2 294.017 \n",
"Meta Winner Ensemble 2153.85 14605.8 191.139 \n",
"Meta Winner Memory One 2177.01 14688.3 190.479 \n",
"Meta Winner Finite Memory 2176.67 14677.4 190.426 \n",
"Meta Winner Long Memory 2177.05 14690.4 190.957 \n",
"Meta Winner Stochastic 2131.55 14438.9 188.378 \n",
"NMWE Deterministic 1944.34 13941.6 259.191 \n",
"NMWE Finite Memory 1795.96 12617.2 374.156 \n",
"NMWE Long Memory 1960.83 13971.9 257.048 \n",
"NMWE Memory One 1823.4 12246.2 398.091 \n",
"NMWE Stochastic 1815.51 13600.8 300.557 \n",
"Nice Meta Winner 1990.27 14034.1 251.895 \n",
"Nice Meta Winner Ensemble 1922.17 13910.5 264.295 \n",
"\n",
" ALLCorALLD Alternator Alternator Hunter \\\n",
"Adaptive 14380.9 12773.6 39455.1 \n",
"Adaptive Tit For Tat 1280.63 1441.76 9991.9 \n",
"Aggravater 16100 14660.6 43575.7 \n",
"ALLCorALLD 0 493.319 7443.7 \n",
"Alternator 493.319 0 8782.08 \n",
"Alternator Hunter 7443.7 8782.08 0 \n",
"AntiCycler 4794.99 5890.75 1083.53 \n",
"Anti Tit For Tat 4695.7 4220.08 15266.5 \n",
"Adaptive Pavlov 2006 3206.06 3122.2 14641.5 \n",
"Adaptive Pavlov 2011 4282.04 4148.77 18555.2 \n",
"Appeaser 2327.79 2036.56 13309.7 \n",
"Arrogant QLearner 5560.74 6795.13 917.648 \n",
"Average Copier 15523 14102.3 42674.1 \n",
"Better and Better 10658.3 9445.92 34424 \n",
"BackStabber 14225.1 12854.3 40127.8 \n",
"Bully 4746.63 4242.41 15774.7 \n",
"Calculator 1203.54 1294.39 10877.1 \n",
"Cautious QLearner 5560.51 6794.66 917.804 \n",
"Champion 1928.02 2564.55 4948.78 \n",
"CollectiveStrategy 15977 14516.7 43416.5 \n",
"Contrite Tit For Tat 1275.23 1432.76 9985 \n",
"Cooperator 7320.06 8733.47 925.384 \n",
"Cooperator Hunter 7265.15 8613.22 1237.91 \n",
"Cycle Hunter 7696.94 8892.86 3507.58 \n",
"Cycler CCCCCD 3124.36 3972.02 1644.22 \n",
"Cycler CCCD 1769.7 2314.08 2731.98 \n",
"Cycler CCD 876.301 1169.57 4203.98 \n",
"Cycler DC 493.058 192.116 8849.67 \n",
"Cycler DDC 2581.86 1871.43 17445.4 \n",
"Cycler CCCDCD 868.678 1148.4 4240.03 \n",
"... ... ... ... \n",
"Worse and Worse 3 1210.05 1286.52 9826.56 \n",
"ZD-Extort-2 2162.78 2050.63 15559.4 \n",
"ZD-Extort-2 v2 2141.25 2025.7 15488.8 \n",
"ZD-Extort-4 3489.58 3056.18 19530.8 \n",
"ZD-GTFT-2 832.336 1086.88 7100.1 \n",
"ZD-GEN-2 806.366 1115.3 7611.98 \n",
"ZD-SET-2 931.248 754.933 12208.7 \n",
"$e$ 6053.98 6959.31 2050.3 \n",
"Meta Hunter 11706.1 10450.7 35735.9 \n",
"Meta Hunter Aggressive 11732.9 10479.3 36004.7 \n",
"Meta Majority 2057.39 2189.64 12185.9 \n",
"Meta Majority Memory One 978.1 1200.84 8663.71 \n",
"Meta Majority Finite Memory 1849.77 2016.58 11439.7 \n",
"Meta Majority Long Memory 2724.9 2613.89 15272.5 \n",
"Meta Minority 5182.4 4938.51 12648.9 \n",
"Meta Mixer 698.979 549.751 11086 \n",
"Meta Winner 15795.6 14352.9 43200.3 \n",
"Meta Winner Deterministic 16056.6 14584.9 43550.9 \n",
"Meta Winner Ensemble 15722.1 14279.7 43080 \n",
"Meta Winner Memory One 15801.4 14360.2 43211.9 \n",
"Meta Winner Finite Memory 15790.6 14347.4 43193.1 \n",
"Meta Winner Long Memory 15802.9 14360.6 43214 \n",
"Meta Winner Stochastic 15583.9 14151.5 42856.4 \n",
"NMWE Deterministic 15305.4 13858.1 42261 \n",
"NMWE Finite Memory 13864 12493.8 39819.8 \n",
"NMWE Long Memory 15332.5 13889.1 42305.8 \n",
"NMWE Memory One 13384.9 12045.1 39022.5 \n",
"NMWE Stochastic 15020.2 13570.4 41759.8 \n",
"Nice Meta Winner 15394.8 13954.1 42412.5 \n",
"Nice Meta Winner Ensemble 15280 13827.4 42215.7 \n",
"\n",
" AntiCycler Anti Tit For Tat Adaptive Pavlov 2006 \\\n",
"Adaptive 33897.4 13565.6 11872.9 \n",
"Adaptive Tit For Tat 7011.08 8313.64 1224.03 \n",
"Aggravater 37694.6 15001.7 12482.9 \n",
"ALLCorALLD 4794.99 4695.7 3206.06 \n",
"Alternator 5890.75 4220.08 3122.2 \n",
"Alternator Hunter 1083.53 15266.5 14641.5 \n",
"AntiCycler 0 12270 11204.7 \n",
"Anti Tit For Tat 12270 0 10499.9 \n",
"Adaptive Pavlov 2006 11204.7 10499.9 0 \n",
"Adaptive Pavlov 2011 14644.4 11489.3 1627.15 \n",
"Appeaser 10144.4 2204.69 5318.11 \n",
"Arrogant QLearner 134.728 13571.8 12142 \n",
"Average Copier 36853.5 14513.1 11954.1 \n",
"Better and Better 29124.4 10352.5 8430.76 \n",
"BackStabber 34504.1 13913.4 10548.2 \n",
"Bully 12694.8 726.802 10462.6 \n",
"Calculator 7720.39 7750.63 1307.75 \n",
"Cautious QLearner 134.823 13569.1 12143.3 \n",
"Champion 2851.02 10687.1 3990.3 \n",
"CollectiveStrategy 37540.9 14752.9 12535.4 \n",
"Contrite Tit For Tat 6999.4 8298.58 1231.16 \n",
"Cooperator 433.825 15626 14584.6 \n",
"Cooperator Hunter 668.63 15060 14681.7 \n",
"Cycle Hunter 2666.13 15301.3 14192.9 \n",
"Cycler CCCCCD 351.432 9882.75 8844.87 \n",
"Cycler CCCD 1021.66 7769.99 6711.61 \n",
"Cycler CCD 2118.15 6158 5076.29 \n",
"Cycler DC 5943.79 4185.02 3124.33 \n",
"Cycler DDC 13520.5 4578.99 3154.06 \n",
"Cycler CCCDCD 2147.22 6126.21 5061.55 \n",
"... ... ... ... \n",
"Worse and Worse 3 6846.63 8207.59 1291.64 \n",
"ZD-Extort-2 11867.6 7315.94 1229.76 \n",
"ZD-Extort-2 v2 11801.7 7304.9 1219.7 \n",
"ZD-Extort-4 15415.9 7351.68 2092.68 \n",
"ZD-GTFT-2 4508.58 7881.74 2212.46 \n",
"ZD-GEN-2 4948.75 7811.8 1996.6 \n",
"ZD-SET-2 8847.3 5534.98 1539.1 \n",
"$e$ 1174.95 11650.9 13957.1 \n",
"Meta Hunter 30407.2 11570.1 9145.32 \n",
"Meta Hunter Aggressive 30620.8 11426.2 9345.97 \n",
"Meta Majority 8960.79 9635.22 763.682 \n",
"Meta Majority Memory One 5846.21 8073.6 1515.11 \n",
"Meta Majority Finite Memory 8302.86 9459.9 865.357 \n",
"Meta Majority Long Memory 11666.5 9419.19 713.253 \n",
"Meta Minority 10201.1 1131.06 12523.1 \n",
"Meta Mixer 7854.24 5561.91 1584.55 \n",
"Meta Winner 37334.3 14530.8 12416.7 \n",
"Meta Winner Deterministic 37662.1 14769.1 12615.4 \n",
"Meta Winner Ensemble 37220.3 14479 12352.4 \n",
"Meta Winner Memory One 37344.7 14540 12419.5 \n",
"Meta Winner Finite Memory 37326.5 14527 12411.8 \n",
"Meta Winner Long Memory 37345.9 14538.9 12421.3 \n",
"Meta Winner Stochastic 37006.3 14427.5 12197.5 \n",
"NMWE Deterministic 36465.6 14325.1 11743.3 \n",
"NMWE Finite Memory 34188.6 12962 10713 \n",
"NMWE Long Memory 36509.9 14347.4 11769.6 \n",
"NMWE Memory One 33442.3 12471 10408.3 \n",
"NMWE Stochastic 35995.8 14158.1 11464 \n",
"Nice Meta Winner 36608.8 14398.8 11815.8 \n",
"Nice Meta Winner Ensemble 36422.5 14309.5 11718.1 \n",
"\n",
" Adaptive Pavlov 2011 ... \\\n",
"Adaptive 8651.69 ... \n",
"Adaptive Tit For Tat 1853.75 ... \n",
"Aggravater 9192.25 ... \n",
"ALLCorALLD 4282.04 ... \n",
"Alternator 4148.77 ... \n",
"Alternator Hunter 18555.2 ... \n",
"AntiCycler 14644.4 ... \n",
"Anti Tit For Tat 11489.3 ... \n",
"Adaptive Pavlov 2006 1627.15 ... \n",
"Adaptive Pavlov 2011 0 ... \n",
"Appeaser 5940.91 ... \n",
"Arrogant QLearner 15780.9 ... \n",
"Average Copier 8686.29 ... \n",
"Better and Better 5992.18 ... \n",
"BackStabber 7359.28 ... \n",
"Bully 11255.6 ... \n",
"Calculator 1714.95 ... \n",
"Cautious QLearner 15782.6 ... \n",
"Champion 5575.01 ... \n",
"CollectiveStrategy 9272.6 ... \n",
"Contrite Tit For Tat 1857.11 ... \n",
"Cooperator 18573 ... \n",
"Cooperator Hunter 18698 ... \n",
"Cycle Hunter 17701.6 ... \n",
"Cycler CCCCCD 11854 ... \n",
"Cycler CCCD 9222.4 ... \n",
"Cycler CCD 7096.33 ... \n",
"Cycler DC 4118.46 ... \n",
"Cycler DDC 2701.2 ... \n",
"Cycler CCCDCD 7078.77 ... \n",
"... ... ... \n",
"Worse and Worse 3 1864.42 ... \n",
"ZD-Extort-2 1028.46 ... \n",
"ZD-Extort-2 v2 1038.68 ... \n",
"ZD-Extort-4 1453.05 ... \n",
"ZD-GTFT-2 3259.74 ... \n",
"ZD-GEN-2 2877.59 ... \n",
"ZD-SET-2 1736.81 ... \n",
"$e$ 18030.2 ... \n",
"Meta Hunter 6176.44 ... \n",
"Meta Hunter Aggressive 6419.47 ... \n",
"Meta Majority 1223.3 ... \n",
"Meta Majority Memory One 2307.86 ... \n",
"Meta Majority Finite Memory 1342.8 ... \n",
"Meta Majority Long Memory 781.292 ... \n",
"Meta Minority 14623.6 ... \n",
"Meta Mixer 2001.57 ... \n",
"Meta Winner 9170.94 ... \n",
"Meta Winner Deterministic 9351.74 ... \n",
"Meta Winner Ensemble 9110.62 ... \n",
"Meta Winner Memory One 9172.3 ... \n",
"Meta Winner Finite Memory 9165.94 ... \n",
"Meta Winner Long Memory 9174.83 ... \n",
"Meta Winner Stochastic 8959.46 ... \n",
"NMWE Deterministic 8488.8 ... \n",
"NMWE Finite Memory 7644.94 ... \n",
"NMWE Long Memory 8511.11 ... \n",
"NMWE Memory One 7447.42 ... \n",
"NMWE Stochastic 8198.02 ... \n",
"Nice Meta Winner 8556.95 ... \n",
"Nice Meta Winner Ensemble 8461.87 ... \n",
"\n",
" Meta Winner Finite Memory Meta Winner Long Memory \\\n",
"Adaptive 2176.67 2177.05 \n",
"Adaptive Tit For Tat 14677.4 14690.4 \n",
"Aggravater 190.426 190.957 \n",
"ALLCorALLD 15790.6 15802.9 \n",
"Alternator 14347.4 14360.6 \n",
"Alternator Hunter 43193.1 43214 \n",
"AntiCycler 37326.5 37345.9 \n",
"Anti Tit For Tat 14527 14538.9 \n",
"Adaptive Pavlov 2006 12411.8 12421.3 \n",
"Adaptive Pavlov 2011 9165.94 9174.83 \n",
"Appeaser 11779.7 11792.4 \n",
"Arrogant QLearner 39580.2 39600 \n",
"Average Copier 112.699 113.082 \n",
"Better and Better 517.814 520.295 \n",
"BackStabber 719.069 721.726 \n",
"Bully 13903.3 13915.3 \n",
"Calculator 13160.5 13172.4 \n",
"Cautious QLearner 39580.2 39600.1 \n",
"Champion 24208 24223.8 \n",
"CollectiveStrategy 125.2 125.213 \n",
"Contrite Tit For Tat 14675.5 14688.6 \n",
"Cooperator 44037.7 44058.8 \n",
"Cooperator Hunter 43415.9 43436.5 \n",
"Cycle Hunter 40941.3 40959.3 \n",
"Cycler CCCCCD 32199.3 32217.7 \n",
"Cycler CCCD 27049.7 27066.2 \n",
"Cycler CCD 22472 22487.8 \n",
"Cycler DC 14248.6 14261.9 \n",
"Cycler DDC 6255.05 6263.75 \n",
"Cycler CCCDCD 22393.2 22408.8 \n",
"... ... ... \n",
"Worse and Worse 3 14750.8 14763.5 \n",
"ZD-Extort-2 8272.19 8281.17 \n",
"ZD-Extort-2 v2 8328.85 8337.89 \n",
"ZD-Extort-4 5464.11 5471.4 \n",
"ZD-GTFT-2 17992 18006.1 \n",
"ZD-GEN-2 17139 17152.5 \n",
"ZD-SET-2 10266.5 10277.1 \n",
"$e$ 38980.5 38999.2 \n",
"Meta Hunter 1095.96 1099.17 \n",
"Meta Hunter Aggressive 788.976 791.636 \n",
"Meta Majority 13312.3 13323.9 \n",
"Meta Majority Memory One 15964.6 15977.8 \n",
"Meta Majority Finite Memory 13934.5 13946.4 \n",
"Meta Majority Long Memory 10197.4 10207.4 \n",
"Meta Minority 19768.7 19782.7 \n",
"Meta Mixer 11424 11435.2 \n",
"Meta Winner 2.76702 2.72625 \n",
"Meta Winner Deterministic 120.144 119.62 \n",
"Meta Winner Ensemble 2.85148 2.91236 \n",
"Meta Winner Memory One 2.62012 2.18768 \n",
"Meta Winner Finite Memory 0 2.6504 \n",
"Meta Winner Long Memory 2.6504 0 \n",
"Meta Winner Stochastic 3.85833 4.63044 \n",
"NMWE Deterministic 189.847 190.446 \n",
"NMWE Finite Memory 289.728 291.459 \n",
"NMWE Long Memory 187.149 187.855 \n",
"NMWE Memory One 306.183 308.179 \n",
"NMWE Stochastic 236.994 237.899 \n",
"Nice Meta Winner 182.182 183.266 \n",
"Nice Meta Winner Ensemble 195.132 195.873 \n",
"\n",
" Meta Winner Stochastic NMWE Deterministic \\\n",
"Adaptive 2131.55 1944.34 \n",
"Adaptive Tit For Tat 14438.9 13941.6 \n",
"Aggravater 188.378 259.191 \n",
"ALLCorALLD 15583.9 15305.4 \n",
"Alternator 14151.5 13858.1 \n",
"Alternator Hunter 42856.4 42261 \n",
"AntiCycler 37006.3 36465.6 \n",
"Anti Tit For Tat 14427.5 14325.1 \n",
"Adaptive Pavlov 2006 12197.5 11743.3 \n",
"Adaptive Pavlov 2011 8959.46 8488.8 \n",
"Appeaser 11625.2 11198.7 \n",
"Arrogant QLearner 39248.3 38683.8 \n",
"Average Copier 104.606 24.3303 \n",
"Better and Better 482.035 577.019 \n",
"BackStabber 682.176 351.396 \n",
"Bully 13805.5 13707.9 \n",
"Calculator 12938.9 12688.5 \n",
"Cautious QLearner 39248.4 38683.9 \n",
"Champion 23904.5 23330.9 \n",
"CollectiveStrategy 126.275 308.327 \n",
"Contrite Tit For Tat 14437.2 13944.8 \n",
"Cooperator 43690.2 43094.7 \n",
"Cooperator Hunter 43073.3 42522.4 \n",
"Cycle Hunter 40615.5 40039.5 \n",
"Cycler CCCCCD 31902.7 31408.8 \n",
"Cycler CCCD 26777.3 26332.5 \n",
"Cycler CCD 22224.2 21828.3 \n",
"Cycler DC 14053.3 13763.5 \n",
"Cycler DDC 6126.81 5997.3 \n",
"Cycler CCCDCD 22146 21751.7 \n",
"... ... ... \n",
"Worse and Worse 3 14508.1 13982.7 \n",
"ZD-Extort-2 8100.99 7914.95 \n",
"ZD-Extort-2 v2 8157.33 7971.41 \n",
"ZD-Extort-4 5330.24 5256.87 \n",
"ZD-GTFT-2 17732.4 17194.3 \n",
"ZD-GEN-2 16884.4 16352 \n",
"ZD-SET-2 10081.5 9818 \n",
"$e$ 38693.3 38353.5 \n",
"Meta Hunter 1040.4 736.969 \n",
"Meta Hunter Aggressive 742.05 700.494 \n",
"Meta Majority 13083.2 12611.5 \n",
"Meta Majority Memory One 15717.2 15205.2 \n",
"Meta Majority Finite Memory 13698.2 13207.8 \n",
"Meta Majority Long Memory 9996.38 9547.57 \n",
"Meta Minority 19660.5 19551.9 \n",
"Meta Mixer 11230.1 10935.4 \n",
"Meta Winner 4.76813 190.906 \n",
"Meta Winner Deterministic 121.953 301.908 \n",
"Meta Winner Ensemble 3.80537 185.551 \n",
"Meta Winner Memory One 4.61038 190.217 \n",
"Meta Winner Finite Memory 3.85833 189.847 \n",
"Meta Winner Long Memory 4.63044 190.446 \n",
"Meta Winner Stochastic 0 177.58 \n",
"NMWE Deterministic 177.58 0 \n",
"NMWE Finite Memory 266.721 62.6634 \n",
"NMWE Long Memory 175.35 3.49969 \n",
"NMWE Memory One 282.245 88.4509 \n",
"NMWE Stochastic 218.467 14.0338 \n",
"Nice Meta Winner 171.111 4.13519 \n",
"Nice Meta Winner Ensemble 182.122 3.51524 \n",
"\n",
" NMWE Finite Memory NMWE Long Memory \\\n",
"Adaptive 1795.96 1960.83 \n",
"Adaptive Tit For Tat 12617.2 13971.9 \n",
"Aggravater 374.156 257.048 \n",
"ALLCorALLD 13864 15332.5 \n",
"Alternator 12493.8 13889.1 \n",
"Alternator Hunter 39819.8 42305.8 \n",
"AntiCycler 34188.6 36509.9 \n",
"Anti Tit For Tat 12962 14347.4 \n",
"Adaptive Pavlov 2006 10713 11769.6 \n",
"Adaptive Pavlov 2011 7644.94 8511.11 \n",
"Appeaser 9868.38 11224.5 \n",
"Arrogant QLearner 36327.4 38729 \n",
"Average Copier 105.529 22.661 \n",
"Better and Better 397.912 580.718 \n",
"BackStabber 315.067 356.747 \n",
"Bully 12364.2 13731 \n",
"Calculator 11412.8 12717.3 \n",
"Cautious QLearner 36327.4 38729.1 \n",
"Champion 21544.8 23371.4 \n",
"CollectiveStrategy 416.02 304.896 \n",
"Contrite Tit For Tat 12619.9 13974.4 \n",
"Cooperator 40612.2 43141.7 \n",
"Cooperator Hunter 40049.4 42569.2 \n",
"Cycle Hunter 37738.8 40085.3 \n",
"Cycler CCCCCD 29300 31450.3 \n",
"Cycler CCCD 24411.5 26371.8 \n",
"Cycler CCD 20085 21864.1 \n",
"Cycler DC 12404.1 13794.4 \n",
"Cycler DDC 5137.76 6017.05 \n",
"Cycler CCCDCD 20012.1 21788 \n",
"... ... ... \n",
"Worse and Worse 3 12644.1 14015.7 \n",
"ZD-Extort-2 6983.42 7931.11 \n",
"ZD-Extort-2 v2 7036.73 7987.68 \n",
"ZD-Extort-4 4536.79 5268.59 \n",
"ZD-GTFT-2 15673.5 17229.7 \n",
"ZD-GEN-2 14871.7 16385.9 \n",
"ZD-SET-2 8700.91 9841.48 \n",
"$e$ 36033.1 38396.7 \n",
"Meta Hunter 528.568 747.829 \n",
"Meta Hunter Aggressive 518.31 710.054 \n",
"Meta Majority 11417.9 12637.8 \n",
"Meta Majority Memory One 13798 15237.3 \n",
"Meta Majority Finite Memory 11965 13236 \n",
"Meta Majority Long Memory 8551.48 9571 \n",
"Meta Minority 17935.6 19576.8 \n",
"Meta Mixer 9750.52 10961 \n",
"Meta Winner 291.167 188.219 \n",
"Meta Winner Deterministic 413.876 300.153 \n",
"Meta Winner Ensemble 280.134 183.159 \n",
"Meta Winner Memory One 291.12 187.56 \n",
"Meta Winner Finite Memory 289.728 187.149 \n",
"Meta Winner Long Memory 291.459 187.855 \n",
"Meta Winner Stochastic 266.721 175.35 \n",
"NMWE Deterministic 62.6634 3.49969 \n",
"NMWE Finite Memory 0 65.6435 \n",
"NMWE Long Memory 65.6435 0 \n",
"NMWE Memory One 13.3226 90.9095 \n",
"NMWE Stochastic 46.4664 16.4197 \n",
"Nice Meta Winner 71.7287 3.54205 \n",
"Nice Meta Winner Ensemble 59.9834 4.09892 \n",
"\n",
" NMWE Memory One NMWE Stochastic Nice Meta Winner \\\n",
"Adaptive 1823.4 1815.51 1990.27 \n",
"Adaptive Tit For Tat 12246.2 13600.8 14034.1 \n",
"Aggravater 398.091 300.557 251.895 \n",
"ALLCorALLD 13384.9 15020.2 15394.8 \n",
"Alternator 12045.1 13570.4 13954.1 \n",
"Alternator Hunter 39022.5 41759.8 42412.5 \n",
"AntiCycler 33442.3 35995.8 36608.8 \n",
"Anti Tit For Tat 12471 14158.1 14398.8 \n",
"Adaptive Pavlov 2006 10408.3 11464 11815.8 \n",
"Adaptive Pavlov 2011 7447.42 8198.02 8556.95 \n",
"Appeaser 9466.46 10941.1 11281.2 \n",
"Arrogant QLearner 35561.4 38197.2 38831.9 \n",
"Average Copier 128.961 49.8763 20.9956 \n",
"Better and Better 325.73 561.044 589.25 \n",
"BackStabber 346.474 287.557 366.432 \n",
"Bully 11885.7 13542.5 13780.2 \n",
"Calculator 11048.3 12387.6 12774.7 \n",
"Cautious QLearner 35561.4 38197.4 38832.1 \n",
"Champion 21050 22873.7 23459.7 \n",
"CollectiveStrategy 433.623 353.738 300.713 \n",
"Contrite Tit For Tat 12247.5 13604.3 14036.4 \n",
"Cooperator 39799.8 42584.6 43248.8 \n",
"Cooperator Hunter 39235.5 42024.5 42676 \n",
"Cycle Hunter 36982.6 39565.7 40183.3 \n",
"Cycler CCCCCD 28609.1 30973 31543.1 \n",
"Cycler CCCD 23779.5 25933.2 26458.1 \n",
"Cycler CCD 19513.5 21465.3 21943.3 \n",
"Cycler DC 11956.6 13478.1 13858.3 \n",
"Cycler DDC 4848.5 5822.98 6058.29 \n",
"Cycler CCCDCD 19440.5 21390.2 21867.1 \n",
"... ... ... ... \n",
"Worse and Worse 3 12285.2 13616.6 14083.2 \n",
"ZD-Extort-2 6688.74 7715.64 7968.78 \n",
"ZD-Extort-2 v2 6740.25 7772.11 8025.42 \n",
"ZD-Extort-4 4291.17 5120.94 5296.32 \n",
"ZD-GTFT-2 15242.7 16801.6 17304.6 \n",
"ZD-GEN-2 14455.7 15968.2 16458.4 \n",
"ZD-SET-2 8350.56 9567.44 9892.99 \n",
"$e$ 35217.1 37958 38492.3 \n",
"Meta Hunter 515.318 632.792 767.725 \n",
"Meta Hunter Aggressive 491.747 625.889 727 \n",
"Meta Majority 11092.2 12303.3 12691.1 \n",
"Meta Majority Memory One 13402.8 14843.1 15304.6 \n",
"Meta Majority Finite Memory 11628.1 12879.6 13293.4 \n",
"Meta Majority Long Memory 8280.45 9281.57 9615.58 \n",
"Meta Minority 17336 19372.6 19633.8 \n",
"Meta Mixer 9378.69 10668.1 11016.5 \n",
"Meta Winner 307.708 238.694 183.668 \n",
"Meta Winner Deterministic 431.697 348.623 294.952 \n",
"Meta Winner Ensemble 296.625 230.406 178.984 \n",
"Meta Winner Memory One 307.798 237.515 183 \n",
"Meta Winner Finite Memory 306.183 236.994 182.182 \n",
"Meta Winner Long Memory 308.179 237.899 183.266 \n",
"Meta Winner Stochastic 282.245 218.467 171.111 \n",
"NMWE Deterministic 88.4509 14.0338 4.13519 \n",
"NMWE Finite Memory 13.3226 46.4664 71.7287 \n",
"NMWE Long Memory 90.9095 16.4197 3.54205 \n",
"NMWE Memory One 0 76.3843 96.6646 \n",
"NMWE Stochastic 76.3843 0 20.1756 \n",
"Nice Meta Winner 96.6646 20.1756 0 \n",
"Nice Meta Winner Ensemble 86.5677 11.3952 5.06171 \n",
"\n",
" Nice Meta Winner Ensemble \n",
"Adaptive 1922.17 \n",
"Adaptive Tit For Tat 13910.5 \n",
"Aggravater 264.295 \n",
"ALLCorALLD 15280 \n",
"Alternator 13827.4 \n",
"Alternator Hunter 42215.7 \n",
"AntiCycler 36422.5 \n",
"Anti Tit For Tat 14309.5 \n",
"Adaptive Pavlov 2006 11718.1 \n",
"Adaptive Pavlov 2011 8461.87 \n",
"Appeaser 11173.8 \n",
"Arrogant QLearner 38639.3 \n",
"Average Copier 27.1038 \n",
"Better and Better 575.778 \n",
"BackStabber 341.5 \n",
"Bully 13691.8 \n",
"Calculator 12662.1 \n",
"Cautious QLearner 38639.4 \n",
"Champion 23286.8 \n",
"CollectiveStrategy 313.209 \n",
"Contrite Tit For Tat 13913.1 \n",
"Cooperator 43048.3 \n",
"Cooperator Hunter 42477 \n",
"Cycle Hunter 39996.6 \n",
"Cycler CCCCCD 31367.6 \n",
"Cycler CCCD 26294 \n",
"Cycler CCD 21792.4 \n",
"Cycler DC 13733.2 \n",
"Cycler DDC 5978.38 \n",
"Cycler CCCDCD 21715.6 \n",
"... ... \n",
"Worse and Worse 3 13946.6 \n",
"ZD-Extort-2 7900.35 \n",
"ZD-Extort-2 v2 7956.85 \n",
"ZD-Extort-4 5247.54 \n",
"ZD-GTFT-2 17156 \n",
"ZD-GEN-2 16315.2 \n",
"ZD-SET-2 9794.76 \n",
"$e$ 38315.9 \n",
"Meta Hunter 723.25 \n",
"Meta Hunter Aggressive 691.401 \n",
"Meta Majority 12584.4 \n",
"Meta Majority Memory One 15171.2 \n",
"Meta Majority Finite Memory 13178.4 \n",
"Meta Majority Long Memory 9524.11 \n",
"Meta Minority 19534.7 \n",
"Meta Mixer 10909.8 \n",
"Meta Winner 196.357 \n",
"Meta Winner Deterministic 307.258 \n",
"Meta Winner Ensemble 189.595 \n",
"Meta Winner Memory One 195.555 \n",
"Meta Winner Finite Memory 195.132 \n",
"Meta Winner Long Memory 195.873 \n",
"Meta Winner Stochastic 182.122 \n",
"NMWE Deterministic 3.51524 \n",
"NMWE Finite Memory 59.9834 \n",
"NMWE Long Memory 4.09892 \n",
"NMWE Memory One 86.5677 \n",
"NMWE Stochastic 11.3952 \n",
"Nice Meta Winner 5.06171 \n",
"Nice Meta Winner Ensemble 0 \n",
"\n",
"[181 rows x 181 columns]"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"strats = axl.strategies\n",
"\n",
"strategies = [s.name for s in strats]\n",
"# need access to the fingerprint csv files from Axelrod-fingerprint repo\n",
"path = '/Users/James/Projects/Axelrod-fingerprint/assets/'\n",
"filenames = [path + format_filename(s) + '.csv' for s in strategies]\n",
"dataframes = [pd.read_csv(f) for f in filenames]\n",
"\n",
"\n",
"def score_for_df(A, B):\n",
" \"\"\"\n",
" Compute the sum of squares score for two dataframes, A and B\n",
" \"\"\"\n",
" result = pd.merge(A, B, on=['x', 'y'], suffixes=('_A', '_B'))\n",
" result['SQ_difference'] = (result['score_A'] - result['score_B'])**2\n",
" result.drop(['score_A', 'score_B'], axis=1, inplace=True)\n",
" return sum(result.SQ_difference)\n",
"\n",
"\n",
"sum_squares_df = pd.DataFrame(index=strategies, columns=strategies)\n",
"\n",
"for indexA, strategyA in enumerate(tqdm(strategies)):\n",
" A_df = dataframes[indexA]\n",
" for indexB, strategyB in enumerate(strategies):\n",
" B_df = dataframes[indexB]\n",
" similarity_score = score_for_df(A_df, B_df)\n",
" sum_squares_df.set_value(strategyA, strategyB, similarity_score)\n",
"\n",
"sum_squares_df"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Adaptive</th>\n",
" <th>Adaptive Tit For Tat</th>\n",
" <th>Aggravater</th>\n",
" <th>ALLCorALLD</th>\n",
" <th>Alternator</th>\n",
" <th>Alternator Hunter</th>\n",
" <th>AntiCycler</th>\n",
" <th>Anti Tit For Tat</th>\n",
" <th>Adaptive Pavlov 2006</th>\n",
" <th>Adaptive Pavlov 2011</th>\n",
" <th>...</th>\n",
" <th>Meta Winner Finite Memory</th>\n",
" <th>Meta Winner Long Memory</th>\n",
" <th>Meta Winner Stochastic</th>\n",
" <th>NMWE Deterministic</th>\n",
" <th>NMWE Finite Memory</th>\n",
" <th>NMWE Long Memory</th>\n",
" <th>NMWE Memory One</th>\n",
" <th>NMWE Stochastic</th>\n",
" <th>Nice Meta Winner</th>\n",
" <th>Nice Meta Winner Ensemble</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Adaptive</th>\n",
" <td>0.000000</td>\n",
" <td>1.310185</td>\n",
" <td>0.230840</td>\n",
" <td>1.409751</td>\n",
" <td>1.252190</td>\n",
" <td>3.867773</td>\n",
" <td>3.322945</td>\n",
" <td>1.329827</td>\n",
" <td>1.163894</td>\n",
" <td>0.848122</td>\n",
" <td>...</td>\n",
" <td>0.213378</td>\n",
" <td>0.213415</td>\n",
" <td>0.208955</td>\n",
" <td>0.190603</td>\n",
" <td>0.176057</td>\n",
" <td>0.192219</td>\n",
" <td>0.178748</td>\n",
" <td>0.177973</td>\n",
" <td>0.195105</td>\n",
" <td>0.188430</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Adaptive Tit For Tat</th>\n",
" <td>1.310185</td>\n",
" <td>0.000000</td>\n",
" <td>1.451247</td>\n",
" <td>0.125540</td>\n",
" <td>0.141335</td>\n",
" <td>0.979502</td>\n",
" <td>0.687294</td>\n",
" <td>0.814983</td>\n",
" <td>0.119991</td>\n",
" <td>0.181723</td>\n",
" <td>...</td>\n",
" <td>1.438821</td>\n",
" <td>1.440095</td>\n",
" <td>1.415438</td>\n",
" <td>1.366692</td>\n",
" <td>1.236857</td>\n",
" <td>1.369662</td>\n",
" <td>1.200492</td>\n",
" <td>1.333277</td>\n",
" <td>1.375755</td>\n",
" <td>1.363645</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Aggravater</th>\n",
" <td>0.230840</td>\n",
" <td>1.451247</td>\n",
" <td>0.000000</td>\n",
" <td>1.578276</td>\n",
" <td>1.437175</td>\n",
" <td>4.271711</td>\n",
" <td>3.695183</td>\n",
" <td>1.470613</td>\n",
" <td>1.223693</td>\n",
" <td>0.901113</td>\n",
" <td>...</td>\n",
" <td>0.018667</td>\n",
" <td>0.018719</td>\n",
" <td>0.018467</td>\n",
" <td>0.025408</td>\n",
" <td>0.036678</td>\n",
" <td>0.025198</td>\n",
" <td>0.039025</td>\n",
" <td>0.029464</td>\n",
" <td>0.024693</td>\n",
" <td>0.025909</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ALLCorALLD</th>\n",
" <td>1.409751</td>\n",
" <td>0.125540</td>\n",
" <td>1.578276</td>\n",
" <td>0.000000</td>\n",
" <td>0.048360</td>\n",
" <td>0.729703</td>\n",
" <td>0.470051</td>\n",
" <td>0.460318</td>\n",
" <td>0.314289</td>\n",
" <td>0.419767</td>\n",
" <td>...</td>\n",
" <td>1.547947</td>\n",
" <td>1.549151</td>\n",
" <td>1.527684</td>\n",
" <td>1.500382</td>\n",
" <td>1.359083</td>\n",
" <td>1.503037</td>\n",
" <td>1.312121</td>\n",
" <td>1.472422</td>\n",
" <td>1.509151</td>\n",
" <td>1.497893</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alternator</th>\n",
" <td>1.252190</td>\n",
" <td>0.141335</td>\n",
" <td>1.437175</td>\n",
" <td>0.048360</td>\n",
" <td>0.000000</td>\n",
" <td>0.860904</td>\n",
" <td>0.577468</td>\n",
" <td>0.413692</td>\n",
" <td>0.306068</td>\n",
" <td>0.406702</td>\n",
" <td>...</td>\n",
" <td>1.406473</td>\n",
" <td>1.407764</td>\n",
" <td>1.387262</td>\n",
" <td>1.358504</td>\n",
" <td>1.224763</td>\n",
" <td>1.361544</td>\n",
" <td>1.180780</td>\n",
" <td>1.330305</td>\n",
" <td>1.367914</td>\n",
" <td>1.355490</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 181 columns</p>\n",
"</div>"
],
"text/plain": [
" Adaptive Adaptive Tit For Tat Aggravater ALLCorALLD \\\n",
"Adaptive 0.000000 1.310185 0.230840 1.409751 \n",
"Adaptive Tit For Tat 1.310185 0.000000 1.451247 0.125540 \n",
"Aggravater 0.230840 1.451247 0.000000 1.578276 \n",
"ALLCorALLD 1.409751 0.125540 1.578276 0.000000 \n",
"Alternator 1.252190 0.141335 1.437175 0.048360 \n",
"\n",
" Alternator Alternator Hunter AntiCycler \\\n",
"Adaptive 1.252190 3.867773 3.322945 \n",
"Adaptive Tit For Tat 0.141335 0.979502 0.687294 \n",
"Aggravater 1.437175 4.271711 3.695183 \n",
"ALLCorALLD 0.048360 0.729703 0.470051 \n",
"Alternator 0.000000 0.860904 0.577468 \n",
"\n",
" Anti Tit For Tat Adaptive Pavlov 2006 \\\n",
"Adaptive 1.329827 1.163894 \n",
"Adaptive Tit For Tat 0.814983 0.119991 \n",
"Aggravater 1.470613 1.223693 \n",
"ALLCorALLD 0.460318 0.314289 \n",
"Alternator 0.413692 0.306068 \n",
"\n",
" Adaptive Pavlov 2011 ... \\\n",
"Adaptive 0.848122 ... \n",
"Adaptive Tit For Tat 0.181723 ... \n",
"Aggravater 0.901113 ... \n",
"ALLCorALLD 0.419767 ... \n",
"Alternator 0.406702 ... \n",
"\n",
" Meta Winner Finite Memory Meta Winner Long Memory \\\n",
"Adaptive 0.213378 0.213415 \n",
"Adaptive Tit For Tat 1.438821 1.440095 \n",
"Aggravater 0.018667 0.018719 \n",
"ALLCorALLD 1.547947 1.549151 \n",
"Alternator 1.406473 1.407764 \n",
"\n",
" Meta Winner Stochastic NMWE Deterministic \\\n",
"Adaptive 0.208955 0.190603 \n",
"Adaptive Tit For Tat 1.415438 1.366692 \n",
"Aggravater 0.018467 0.025408 \n",
"ALLCorALLD 1.527684 1.500382 \n",
"Alternator 1.387262 1.358504 \n",
"\n",
" NMWE Finite Memory NMWE Long Memory NMWE Memory One \\\n",
"Adaptive 0.176057 0.192219 0.178748 \n",
"Adaptive Tit For Tat 1.236857 1.369662 1.200492 \n",
"Aggravater 0.036678 0.025198 0.039025 \n",
"ALLCorALLD 1.359083 1.503037 1.312121 \n",
"Alternator 1.224763 1.361544 1.180780 \n",
"\n",
" NMWE Stochastic Nice Meta Winner \\\n",
"Adaptive 0.177973 0.195105 \n",
"Adaptive Tit For Tat 1.333277 1.375755 \n",
"Aggravater 0.029464 0.024693 \n",
"ALLCorALLD 1.472422 1.509151 \n",
"Alternator 1.330305 1.367914 \n",
"\n",
" Nice Meta Winner Ensemble \n",
"Adaptive 0.188430 \n",
"Adaptive Tit For Tat 1.363645 \n",
"Aggravater 0.025909 \n",
"ALLCorALLD 1.497893 \n",
"Alternator 1.355490 \n",
"\n",
"[5 rows x 181 columns]"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sum_squares_df = sum_squares_df.apply(pd.to_numeric)\n",
"size = len(dataframes[0].index)\n",
"mean_squares_df = sum_squares_df.divide(size)\n",
"mean_squares_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": false
},
"outputs": [
{
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69GmV1r17d06fPk1zc/Nl91+SJEmSJEmSJEmSJEmS\nJElSdFw1M5MD5OTkUFRURFJSEkOGDImk19bWcuTIEaZNmwZ8ENBdW1tLcnJyZJ2ePXtSUlJCly5d\nOHnyJN27d29T/vDhw3nttddoaGggMTGRzMxMnnjiCbp168bkyZPbrN+vXz8AevfuzdmzZyPp/fv3\nByAxMZEzZ85QX1/Pjh07CIVCAJw7d44DBw4A0Ldv3wv2d+zYsaxevZpz586Rk5PDxo0bAdi1a9cF\nyxswYAAAPXr04JZbbol8PnPmDHv27OFrX/sa8EEAeEpKCvv372/VjoyMDH784x9z5MgRNm7cyMMP\nP9yqTb169eLAgQORPgKcOHGCTp06ERsbe1n979GjxwX7LkmSJEmSJEmSJEmSJEmSJOmzdVXNTJ6U\nlMSpU6coKytj/PjxkfSbbrqJxMRElixZQllZGffddx+33347sbGxNDc3AzB37lweeugh5s+fz623\n3kpLS0ub8keMGEFpaSlDhw6N1NfY2Mi+fftIS0trs35MTMx52/nx9OTkZDIyMigrK6O0tJTs7GyS\nkpIuWgbAN7/5TdavX8+WLVvIyMi4pPIuJiUlhS1btgAfBIDv2rWLm266qVU7YmJiGD9+PD/+8Y8Z\nMWIEHTt2bFXGxIkTeeaZZ3j33XcBeP/995k7dy733nvvFfVfkiRJkiRJkiRJkiRJkiRJUnRcVTOT\nA4wZM4aXX36Zvn37RmbVTkhIIDc3l1AoRFNTEzfeeCPZ2dkcO3aMXbt2EQ6HGT9+PDNnzqRHjx70\n7t2bhoaGNmWnp6ezY8cOZs6cGUlLS0vjxIkTFw36/iRZWVls2rSJSZMmcerUKUaNGnXemdE/Lj4+\nnt69e5OUlBSZ9TtIeXfffTePPPIIEydO5MyZMzz44INcd911bda78847ueOOO3j55ZfbfDdgwABm\nzZrFrFmzaGpq4ty5c/zN3/wNU6dO/dT7L0mSJEmSJEmSJEmSJEmSJOmzc1UEk2dkZERm5g6FQoRC\nIQAyMzPJzMwEYMKECUyYMKFVvq5du1JZWRlZnjJlykXr6dy5M2+++WartMLCwlbLZWVlAMyYMSOS\nlpKSEkmvqqqKpOfl5UU+FxQUtKnvwzwf99Gyi4uLr6i8n/70p+ftw/z58z+xHU1NTaSnp5OSknLe\n9o0cOZKRI0ee97vL6b8kSZIkSZIkSZIkSZIkSZKk6In95FXUnqxdu5apU6fy0EMPRbspkiRJkiRJ\nkiRJkiRJkiRJkj5DV8XM5Pr8jB49mtGjR0e7GZIkSZIkSZIkSZIkSZIkSZI+Y85MLkmSJEmSJEmS\nJEmSJEmSJEntkMHkkiRJkiRJkiRJkiRJkiRJktQOxUW7AZIkfdZiYmKi3QRJXyKxMf4/piRJkiRJ\nkiQpSnxHfWWC/FzY8qm1QpIkSfpC8ilDkiRJkiRJkiRJkiRJkiRJktohg8klSZIkSZIkSZIkSZIk\nSZIkqR0ymFySJEmSJEmSJEmSJEmSJEmS2qEvXDD5G2+8QWpqKqtXr26VnpOTQ35+/gXzNTY2smrV\nqkuqY86cOaxZsyaynJ2dzaOPPhpZzs/PZ926dcydO5f6+vrL7MHlqaio4I477iAUCkX+1q9fT0VF\nBevXr79gvmeffZbt27dz5swZXnzxxUuqq66ujtTUVJ599tlW6dOnTycUCgXqhyRJkiRJkiRJkiRJ\nkiRJkqSryxcumBwgOTm5VTB5TU0Np0+fvmiempoaqqqqLqn8ESNGsHXrVgBqa2vp06cPmzdvjny/\nbds2hg0bRmFhITfccMMV9ODyjBs3jrKyssjfN77xDe68806+8Y1vXDDPtGnTGDhwIO++++4lB5MD\n9OnTh1/96leR5YaGBvbt2xeo/ZIkSZIkSZIkSZIkSZIkSZKuPnHRbsD5pKWlsXfvXo4fP058fDwr\nV64kJyeHgwcPAlBZWUk4HCY2Npb09HTy8vJYuHAhO3fuZMWKFQwaNIh58+bR1NREQ0MDRUVFDB48\nOFL+8OHDWbRoEQAbNmwgKyuLqqoqdu/eTefOnenVqxfdu3cnFApRVFTEmjVrqKur4/Dhw9TX11NQ\nUMDIkSPJyclh6NCh1NTUEBMTQ0lJCfHx8Tz11FNs2bKF5uZmcnNzyc7OJhQKkZCQwNGjR1m8eDEd\nOnS46DYoLi7m+uuvJzk5meeee46OHTtSV1fHmDFj+P73v09+fj5jxoxh7dq17N69mwULFjB58mQK\nCwtpaGgA4Ec/+hGpqamtyr322mv5sz/7M/bs2UNKSgqVlZV861vfYsuWLQBs2rSJn/70p3To0IGk\npCQee+wxVq1axauvvsp7773Hu+++y/3338/69et5++23+eEPf8ioUaNYuXIlpaWldOrUiZtvvjmS\n71//9V9pbm7mf/yP/8GLL77I008/DcC9997L//pf/4tevXp9OoNGkiRJkiRJkiRJkiRJkiRJ0mX5\nQs5MDjB69GjWrl1LS0sL27dvZ9CgQQA0NjZSXFxMOBymvLycQ4cOsXHjRqZPn86wYcO455572L17\nN7Nnz6a0tJQHHniAioqKVmUnJCQQExPD8ePHqa6uJjMzk8zMTKqrq9m0aRMjR45s055OnTqxaNEi\nCgsLCYfDAJw8eZKxY8eybNkyevbsSXV1NRs2bKCuro7y8nKWLl3KwoULOXbsGPDBDOThcLhNIPkr\nr7xCKBQiFArx0EMPtam7vr6e4uJiVqxYEQmC/9D06dO55ZZbePDBB1m4cCHDhg2jrKyMxx9/nKKi\novNu27Fjx0Zmfl+/fj2jRo0CoKWlhUceeYQFCxawbNkyevXqxb/9279F+vrcc8/xwAMPUF5ezoIF\nC3jssceoqKigoaGB4uJiSktLKS8vJz4+nhUrVgDQo0cPysvL+frXv86uXbs4evQob7/9Ntdee62B\n5JIkSZIkSZIkSZIkSZIkSVIUfSFnJgfIycmhqKiIpKQkhgwZEkmvra3lyJEjTJs2DfggyLm2tpbk\n5OTIOj179qSkpIQuXbpw8uRJunfv3qb84cOH89prr9HQ0EBiYiKZmZk88cQTdOvWjcmTJ7dZv1+/\nfgD07t2bs2fPRtL79+8PQGJiImfOnKG+vp4dO3YQCoUAOHfuHAcOHACgb9++5+3ruHHjyMvLu+C2\nuPXWW4mLiyMuLo4uXbpccL1du3bx+uuvU1lZCcDRo0fPu96oUaP427/9W+68807+/M//PFLmkSNH\n+OMf/8jf//3fA/Dee+/xta99ja9+9auR/sfHx5OSkkJMTAxf+cpXOHPmDPv37+eWW26JbOe/+qu/\n4te//jW33XZbpM8xMTGMHz+eV155hbq6Ou66664L9kOSJEmSJEmSJEmSJEmSJEnSZ+8LG0yelJTE\nqVOnKCsr4+GHH2b//v0A3HTTTSQmJrJkyRI6duxIRUUF/fr148SJEzQ3NwMwd+5cnnzySVJSUnj6\n6acjwdwfNWLECBYsWMDQoUMj9TU2NvKnP/2JtLS0NuvHxMSct50fT09OTiYjI4PHH3+c5uZmSkpK\nSEpKumgZn+Ri+WJjYyP9Tk5OZvz48eTk5HD48GFefPHF8+bp1q0bffv25Sc/+Qnf/va3I+nXXnst\nvXv3pqSkhPj4eNavX88111zDwYMHL9qGm266iT179nDq1CmuueYaNm3aFAkij439/ye//+///b+T\nl5fH6dOn+Yd/+IfL2gaSJEmSJEmSJEmSJEmSJEmSPl2xn7xK9IwZM4aDBw+2mtE7ISGB3NxcQqEQ\n3/72t6murubmm2+mT58+7Nq1i3A4zPjx45k5cyaTJk3iP//zP/njH//Ypuz09HR27NjBf/2v/zWS\nlpaWxs0333zFQd8AWVlZXHPNNUyaNIk777wT4Lwzo39arrvuOt5//31+8pOfMH36dCorKwmFQkyd\nOpW/+Iu/uGC+nJwctm7dyvDhwyNpsbGxFBYWMm3aNO69915eeOEFbr311k9sQ0JCAjNmzOD+++/n\n7rvvpqGhgYkTJ7ZZr1evXnTr1o3hw4cTF/eF/T8GSZIkSZIkSZIkSZIkSZIkqV34wkX0ZmRkkJGR\nAUAoFCIUCgGQmZlJZmYmABMmTGDChAmt8nXt2pXKysrI8pQpUy5aT+fOnXnzzTdbpRUWFrZaLisr\nA2DGjBmRtJSUlEh6VVVVJD0vLy/yuaCgoE19H+b5uA8Dzj/uo3V+uD0ANm7cCMC8efMiaS+//HLk\nc0lJyXnLgw9mEP/5z38OfBD0npWVBbTu09e//nW+/vWvX7CNH90P/fr1Y/HixcAHwek5OTmf2LeW\nlhbuuuuuC7ZRkiRJkiRJkiRJkiRJkiRJ0ufjCz0zub483nvvPe68806Sk5P56le/Gu3mSJIkSZIk\nSZIkSZIkSZIkSe3eF25mcn05denShYqKimg3Q5IkSZIkSZIkSZIkSZIkSdL/48zkkiRJkiRJkiRJ\nkiRJkiRJktQOOTO5JOlLr6WlJdpNkPQl0tzSHO0mREd77bckSZIkSZIkfZH4rvbK+HOhJEmSdEHO\nTC5JkiRJkiRJkiRJkiRJkiRJ7ZDB5JIkSZIkSZIkSZIkSZIkSZLUDhlMLkmSJEmSJEmSJEmSJEmS\nJEntkMHkkiRJkiRJkiRJkiRJkiRJktQOfWGDyd944w1SU1NZvXp1q/ScnBzy8/MvmK+xsZFVq1Zd\nUh1z5sxhzZo1keXs7GweffTRyHJ+fj7r1q1j7ty51NfXX2YPLk9xcTHl5eWfaR0VFRWkpqby29/+\nNpL2/vvvk5GRQXFx8WdatyRJkiRJkiRJkiRJkiRJkqQvli9sMDlAcnJyq2DympoaTp8+fdE8NTU1\nVFVVXVL5I0aMYOvWrQDU1tbSp08fNm/eHPl+27ZtDBs2jMLCQm644YYr6MEXz8e36f/9v/+X+Pj4\nKLZIkiRJkiRJkiRJkiRJkiRJUjTERbsBF5OWlsbevXs5fvw48fHxrFy5kpycHA4ePAhAZWUl4XCY\n2NhY0tPTycvLY+HChezcuZMVK1YwaNAg5s2bR1NTEw0NDRQVFTF48OBI+cOHD2fRokUAbNiwgays\nLKqqqti9ezedO3emV69edO/enVAoRFFREWvWrKGuro7Dhw9TX19PQUEBI0eOJCcnh6FDh1JTU0NM\nTAwlJSXEx8fz1FNPsWXLFpqbm8nNzSU7O5tQKERCQgJHjx5l8eLFdOjQ4aLbYMmSJaxevZq4uDiG\nDBnCD37wA4qLi8/bjldffZWnn36a7t2785WvfIXU1FRmzJjRqrzMzEx+/etf09zcTGxsLKtXr2bs\n2LGR78vKynjllVeIiYlhzJgx3H///eTn5xMXF0d9fT1nz55lzJgxvPrqqxw8eJCSkhL69OnDvHnz\nIoH548aNY/LkyeTn59PY2EhjYyOpqanceuut/O3f/i1Hjx5lypQpVFRUfCrjRJIkSZIkSZIkSZIk\nSZIkSdLl+0LPTA4wevRo1q5dS0tLC9u3b2fQoEEANDY2UlxcTDgcpry8nEOHDrFx40amT5/OsGHD\nuOeee9i9ezezZ8+mtLSUBx54oE3wckJCAjExMRw/fpzq6moyMzPJzMykurqaTZs2MXLkyDbt6dSp\nE4sWLaKwsJBwOAzAyZMnGTt2LMuWLaNnz55UV1ezYcMG6urqKC8vZ+nSpSxcuJBjx44BHwRbh8Ph\nTwwkr6mpobKykuXLl7N8+XL27dvHq6++et52NDU18eMf/5jnnnuOsrIyOnfufN4yO3bsyO23386m\nTZs4ceIEJ06coHfv3gDs3r2bNWvW8MILL/Czn/2MdevW8c477wBw4403smTJEpKTk6mrq+O5555j\n9OjRVFVV8eqrr1JXV8fPf/5zXnjhBV555RVqamoAGDZsGMuXL2fq1Km89NJLALzyyivk5OR84r6X\nJEmSJEmSJEmSJEmSJEmS9Nn5Qs9MDpCTk0NRURFJSUkMGTIkkl5bW8uRI0eYNm0a8EFAd21tLcnJ\nyZF1evbsSUlJCV26dOHkyZN07969TfnDhw/ntddeo6GhgcTERDIzM3niiSfo1q0bkydPbrN+v379\nAOjduzdnz56NpPfv3x+AxMREzpw5Q319PTt27CAUCgFw7tw5Dhw4AEDfvn0vqe/vvPMOt912Gx07\ndgRgyJAhvP322+dtx5EjR+jevTvXX399ZN0//elP5y133LhxrF69moMHD/I3f/M3vP/++wDs2rWL\n+vp6cnNzATh69Cj79u1r1b8ePXpEtnGPHj04e/Yse/bsYciQIcTExNCxY0duu+029uzZ06qvSUlJ\ndOvWjd27d7Nq1SpKSkouaRtIkiRJkiRJkiRJkiRJkiRJ+mx84WcmT0pK4tSpU5SVlTF+/Pg0J48c\nAAAgAElEQVRI+k033URiYiJLliyhrKyM++67j9tvv53Y2Fiam5sBmDt3Lg899BDz58/n1ltvpaWl\npU35I0aMoLS0lKFDh0bqa2xsZN++faSlpbVZPyYm5rzt/Hh6cnIyGRkZlJWVUVpaSnZ2NklJSRct\n4+OSk5PZvn07586do6Wlhc2bN0eCsz9exnXXXcfJkyc5cuQIAG+++eYFy83IyOC3v/0tv/zlL/nW\nt77Vqr5bbrmFpUuXUlZWxp133klqauontjklJYWtW7cC8P777/Ob3/yGr371q23y3X333ZSUlNCr\nVy8SEhIuaRtIkiRJkiRJkiRJkiRJkiRJ+mx84WcmBxgzZgwvv/wyffv2Zf/+/QAkJCSQm5tLKBSi\nqamJG2+8kezsbI4dO8auXbsIh8OMHz+emTNn0qNHD3r37k1DQ0ObstPT09mxYwczZ86MpKWlpXHi\nxIlLDvo+n6ysLDZt2sSkSZM4deoUo0aNOu/M6B/17LPP8uKLLwLQrVs3ysrKyM7OZuLEiTQ3N5Oe\nns6oUaPYuXNnm7yxsbE88sgjPPDAA8THx9Pc3BwJ6D7fuiNGjODgwYOt2pSWlsbw4cOZOHEiZ8+e\nZeDAgfTq1esT+/rXf/3XbNq0iXvuuYf333+fb33rWwwYMKDNeqNGjeKxxx7jJz/5ySeWKUmSJEmS\nJEmSJEmSJEmSJOmz9YUNJs/IyCAjIwOAUChEKBQCIDMzk8zMTAAmTJjAhAkTWuXr2rUrlZWVkeUp\nU6ZctJ7OnTu3mcW7sLCw1XJZWRkAM2bMiKSlpKRE0quqqiLpeXl5kc8FBQVt6vswz8fNmDGjVfkf\nbf/H+3ChduzcuZPy8nI6depEXl4eiYmJrfLdeeedkc/5+fmRzxMnTox8njp1KlOnTm2Vb968eeft\nX25ubuTz7Nmz27T9o/mASND/iBEj2qwrSZIkSZIkSZIkSZIkSZIk6fP1hQ0m1+Xr1q0bd999N126\ndOHGG29kzJgx0W5SxLZt2/inf/on/u7v/o7Y2NhoN0eSJEmSJEmSJEmSJEmSJElq9wwm/xK57777\nuO+++6LdjPMaPHgwq1atinYzJEmSJEmSJEmSJEmSJEmSJP0/ThEtSZIkSZIkSZIkSZIkSZIkSe2Q\nM5PrSycmJiZ6dXeI3v9ndIjtECBv9NodaHcF3NVBxkqHmCvf3tEUExtsowXZZkHrDrLNo3peCDLO\nAhzXEGybxcYEOy8E2eRBzkkdAo6zaIriMA0kJujJOEpiO0Tv2AxyXoiN5kAJeF7gKj4+r0aBrtkB\nx1mwa8DVeY8VTUHvsaIpNkDbg4yzq/Xe8IP80ckbVJD7u+he+oJVHmyMB8gb4D4nsIA7LMg9WjQF\n29fB7rGi+n4iSgdo0GqjeZ/UHkX1PB58sFxx1tggx2YU2x30PbHH1+cr0HuwoM/4QfIHHuPR/D0j\nOuO0Q+B3tR5fly3Iu42A92dB7uej+Y4g0D1x0OeAKD17AcQEmbsviufxIGMl+D1WsOyBqg4y1pqC\nVBy932eDHJsQvfe1wd+htb84gGjGTkTzXiM2aIxOtNoexbiNwOekAG0P9Mx5NT8HRPM3u2jFsfkI\nos9LkGOkpeXTa8fl+oK225nJJUmSJEmSJEmSJEmSJEmSJKkdMphckiRJkiRJkiRJkiRJkiRJktoh\ng8klSZIkSZIkSZIkSZIkSZIkqR36UgaTv/HGG6SmprJ69epW6Tk5OeTn518wX2NjI6tWrbqkOubM\nmcOaNWsiy9nZ2Tz66KOR5fz8fNatW8fcuXOpr6+/zB5cnvfee4/8/Hy+853vMHHiRB566CEaGhoA\n+Pd//3cOHTp0WeUVFxdTXl4eqE3Lli0DoLq6mhUrVgQqS5IkSZIkSZIkSZIkSZIkSdKn70sZTA6Q\nnJzcKpi8pqaG06dPXzRPTU0NVVVVl1T+iBEj2Lp1KwC1tbX06dOHzZs3R77ftm0bw4YNo7CwkBtu\nuOEKenDp/vVf/5Xrr7+eJUuWUF5ezuDBg/nf//t/A7B06VJOnDjxmdZ/Ps888wwAmZmZ3HPPPZ97\n/ZIkSZIkSZIkSZIkSZIkSZIuLi7aDfispKWlsXfvXo4fP058fDwrV64kJyeHgwcPAlBZWUk4HCY2\nNpb09HTy8vJYuHAhO3fuZMWKFQwaNIh58+bR1NREQ0MDRUVFDB48OFL+8OHDWbRoEQAbNmwgKyuL\nqqoqdu/eTefOnenVqxfdu3cnFApRVFTEmjVrqKur4/Dhw9TX11NQUMDIkSPJyclh6NCh1NTUEBMT\nQ0lJCfHx8Tz11FNs2bKF5uZmcnNzyc7OJhQKkZCQwNGjR1m8eDEdOnQA4Prrr+cXv/gFgwcPZujQ\noYRCIVpaWviP//gPfv/73zN79mxeeOEFli1bxurVq4mLi2PIkCH84Ac/4MiRI8yePZvjx4/T0tLC\n/PnzAVi/fj2//OUvaWxsZObMmWRlZbFs2TLWrl3L6dOnufbaa1mwYAEHDhygoKCAuLg4mpubeeqp\np3jppZc4evQoRUVFDBw4kHfeeYe8vDxKSkpYt24dTU1NTJw4kXvvvfdzHhWSJEmSJEmSJEmSJEmS\nJEmSPvSlnZkcYPTo0axdu5aWlha2b9/OoEGDAGhsbKS4uJhwOEx5eTmHDh1i48aNTJ8+nWHDhnHP\nPfewe/duZs+eTWlpKQ888AAVFRWtyk5ISCAmJobjx49TXV1NZmYmmZmZVFdXs2nTJkaOHNmmPZ06\ndWLRokUUFhYSDocBOHnyJGPHjmXZsmX07NmT6upqNmzYQF1dHeXl5SxdupSFCxdy7NgxAMaNG0c4\nHI4EkgN885vf5Pvf/z6/+MUv+MY3vkFubi579uzhjjvuoF+/fsyfP5+9e/dSWVnJ8uXLWb58Ofv2\n7ePVV1+lpKSErKwsli9fzuzZs9m+fTsAvXr1orS0lDlz5lBeXk5zczONjY2Ew2FefPFFmpqaeOut\nt3jttdcYOHAgzz//PDNmzOD48eN8//vf5ytf+QpFRUWRNv7ud7+jurqaF198kRdffJH//M//pKWl\n5dPc3ZIkSZIkSZIkSZIkSZIkSZIuw5d2ZnKAnJwcioqKSEpKYsiQIZH02tpajhw5wrRp04APArpr\na2tJTk6OrNOzZ09KSkro0qULJ0+epHv37m3KHz58OK+99hoNDQ0kJiaSmZnJE088Qbdu3Zg8eXKb\n9fv16wdA7969OXv2bCS9f//+ACQmJnLmzBnq6+vZsWMHoVAIgHPnznHgwAEA+vbt26bc3/zmNwwf\nPpzRo0fT1NTEyy+/TEFBQasA+HfeeYfbbruNjh07AjBkyBDefvtt9u7dy1133QXA4MGDGTx4MMXF\nxQwYMAD4YNbz9957j9jYWDp27MjDDz/MNddcwx/+8AfOnTvHXXfdxXPPPcfUqVOJj49n1qxZ590X\ne/fuZeDAgXTo0IEOHTqQn59/3vUkSZIkSZIkSZIkSZIkSZIkfT6+1DOTJyUlcerUKcrKyhg/fnwk\n/aabbiIxMZElS5ZQVlbGfffdx+23305sbCzNzc0AzJ07l4ceeoj58+dz6623nncW7REjRlBaWsrQ\noUMj9TU2NrJv3z7S0tLarB8TE3Pedn48PTk5mYyMDMrKyigtLSU7O5ukpKQLlrF69WpKS0sB6NCh\nA6mpqXTq1CmyfktLC8nJyWzfvp1z587R0tLC5s2b6du3LykpKbz11lsAbN68mZ/85CfnrWfnzp2s\nW7eOf/mXf+GRRx6hubmZlpYW1q9fT3p6OqWlpXzrW99i0aJFAG22V3JyMr/73e9obm7m/fffZ8qU\nKa0C6iVJkiRJkiRJkiRJkiRJkiR9vr7UM5MDjBkzhpdffpm+ffuyf/9+ABISEsjNzSUUCtHU1MSN\nN95IdnY2x44dY9euXYTDYcaPH8/MmTPp0aMHvXv3pqGhoU3Z6enp7Nixg5kzZ0bS0tLSOHHixAUD\nxy9FVlYWmzZtYtKkSZw6dYpRo0add2b0D/393/89jz/+OBMmTKBr165cc801zJ07F4BBgwbxwx/+\nkCVLlpCdnc3EiRNpbm4mPT2dUaNGkZ6ezpw5c1i5ciUA//zP/8xLL73Upo6vfvWrdO3alXvvvReA\nP//zP+ePf/wjt99+O7Nnz+aZZ56hubmZgoICAFJSUsjLy+NrX/sa8MGs7CNHjozUP3HixEjAuyRJ\nkiRJkiRJkiRJkiRJkqTP35cymDwjI4OMjAwAQqEQoVAIgMzMTDIzMwGYMGECEyZMaJWva9euVFZW\nRpanTJly0Xo6d+7Mm2++2SqtsLCw1XJZWRkAM2bMiKSlpKRE0quqqiLpeXl5kc8fBmWfr6yP6969\nO/Pnzz/vd7NmzWLWrFnAB/35eJ8SEhJYuHBhq7QLtXXp0qXnraO8vPyS2vq9732P733ve+ctQ5Ik\nSZIkSZIkSZIkSZIkSdLnKzbaDZAkSZIkSZIkSZIkSZIkSZIkff4MJpckSZIkSZIkSZIkSZIkSZKk\ndshgckmSJEmSJEmSJEmSJEmSJElqhwwmlyRJkiRJkiRJkiRJkiRJkqR2KC7aDZDOK+bK/88hJjbm\nU2zIZdYdoOrY2GD/2xEXe+WHc4cA25uAmzs2rsOVV90h2DYLMlY6BNxfgbS0XHneIIM0oKDHZpBt\nHhcXYH8F2d4E63eQ4xoCbrPYKz82g9YdZJjGBBzjQc/Fweq+8rbHBDmPBxzjHTsEGKcB91dMgHEa\nZHsHum4CHTpE77wQ9NoZqO4g54Ugx0fA81lM3JUfI0HPSTGBjq/o3U/HBhhnQY+vIOfDIGP0g/wB\n7msDjJXAzz+BngWCXncDnIujeT4LcB5vieK9ZWzgc1J0ju3A+zrI8RXFMR5krAR97As0zgIcHxDw\nvBDguS/4NSDIeTzgvWWA/EG2N0BMhwD3WUGu2dG8BsQFvB+/Su8XAo3xoO/vAt4fRkuQa1/wZ5gr\nzx/4XVSU9lfQa3awa0iwuluao3ceDyTI/UKAMQoBj5HAN0oB+h3F3yOCPMMEPicFufZF8foT5D1x\n4N+PAoyzIO8dA9cd5LwQ+F1UdMYZBPstJVrvS4PWHfi34Wj+xhrknjrQ40/07seDHJsQvfu7oOMs\nyL1K8OtugHvqQL8VRm+cBf1dOZq/ZwTdbtES5NoZ9P1dkGtI1M4pBLy3DBJvAsQGeeYMur+i9D4p\nyG+FQOAYBn3OrtJzqe1u6+p8EytJkiRJkiRJkiRJkiRJkiRJCsRgckmSJEmSJEmSJEmSJEmSJElq\nhwwmlyRJkiRJkiRJkiRJkiRJkqR2yGDy/+eNN94gPT2dgwcPRtKefPJJKioqAEhNTeUf//EfW+X5\n8Y9/TFZWFgcPHmTMmDGR9FdeeYX+/ftz+PBhAOrq6pgwYQJ1dXUMHjyYUCjU6q+pqalVuaFQiLvu\nuotQKMS9995LXl4eDQ0NF23/smXLAvX/o959912Kioou+P3vf/97FixYcMHvN2/ezM6dOwF48MEH\nP7V2SZIkSZIkSZIkSZIkSZIkSfr0GEz+EZ06daKgoICWlpY23/3Zn/0ZW7Zs4dy5cwA0NTXx1ltv\nAZCYmEhLS8v/x869R1dVHYgf/968eIVHeAgIOEAyIiBoQ4dgGIim+CiCLqigWNIfY1trXXFpVX5A\n4iMI8gNBwV8pWtGRMYAKDtQW0NpKBmq6gEZqxSeCA4tIpiJPUQgk9/7+cLy/psHX3eqV4ftZK2vd\ne87dZ++zzz77dXYO+/btA2DdunVcdNFFrF+/HvhoofqQIUMAyMnJoby8vMFfampqo/hmzZpFeXk5\nTzzxBEOHDm20kP3vPfDAA4mf+N/p0KHDpy4m792796cuEv/3f/933n33XYBPXXQuSZIkSZIkSZIk\nSZIkSZIkKXnSkp2Ab5JBgwYRjUZZsmQJ48ePb7AvLS2NgQMHUllZSUFBAS+88AL5+fk8/fTTAOTn\n57N582YKCwvZunUr06ZN45FHHmHUqFFs2rSJ0aNHJ5yuyy67jHnz5lFbW8uOHTuYPn068NEC9xkz\nZrB48WIOHjxIWVkZpaWl3HnnnezcuZNoNMpNN91EXl4eI0aMoHv37qSnp9OzZ0927tzJ/v37OXDg\nAN///vd57rnn+M///E9mzZpF+/btufnmm1m2bBkjR45k4MCBvPnmm0QiERYsWMBrr73GE088wdy5\nc5kyZQo7d+7k6NGj/OAHPyAnJ4c//OEPvPrqq+Tk5DBmzBgqKyv5y1/+wowZM4hGo3Ts2JE5c+bQ\ntGnTxC+WJEmSJEmSJEmSJEmSJEmSpCC+mfzvlJWVsWjRInbu3Nlo34gRI1izZg0Aq1atYuTIkfF9\n+fn5VFVV8corr9C3b1/69evHG2+8QTQa5bXXXiM3NxeAbdu2UVRUFP+bOXPm50pXq1atOHToELff\nfjt33nkn5eXlDB06lIcffpif/vSntG7dmrKyMpYvX05WVhZLlixhwYIF3HXXXQB8+OGHXH/99cyd\nOxeApk2b8sgjj3DxxRezbt06HnzwQa699lpWr17dIN4PPviASy+9lMWLF3PaaafF37YOcPjwYf70\npz8xf/58Hn74YVJTUzn77LMZMmQIEydO5PTTT4//9o477mDGjBksX76cgoICtm/f/rnOW5IkSZIk\nSZIkSZIkSZIkSdJXwzeT/52srCxKSkqYNGlSfAH4xwYMGMDUqVPjb/Tu0qVLfF9eXh4LFy4kMzOT\ngoICIpEI55xzDhUVFXTr1o309HQAcnJyKC8v/0JpisVivPfee7Rr147t27czdepUAI4fP0737t0b\n/Hbr1q28+OKLvPzyywDU1dWxb98+AHr06BH/XZ8+fQBo2bIlOTk5ALRu3Zra2tpG8X/8286dOzfY\nn5mZSUlJCbfffjuHDx/msssu+8RzeO+998jOzgZgzJgxX+j8JUmSJEmSJEmSJEmSJEmSJH35XEx+\nAoWFhfzud79j5cqVTJw4Mb49EolQUFBAWVkZw4YNaxAmMzOTjIwMKisrmTBhAkD8zeGftsj683jq\nqacYNGgQKSkp9OjRg1mzZnH66afz4osvsmfPHuCjBecAPXv2pFOnTlx33XUcPXqUBx54gDZt2gCQ\nkvL/X0QfiUQ+d/yf9Nt3332XV199lV/84hfU1tZSUFDA5ZdfTiQSiafnY6eddho7duyge/fuPPTQ\nQ/To0YMLL7zwC+WDJEmSJEmSJEmSJEmSJEmSpC+Pi8k/QWlpKRs2bGi0feTIkVxxxRXcddddjfYN\nHDiQjRs3kpmZCcDgwYOZOHEis2fPjv9m27ZtFBUVNQg3Y8YMunXr1mDbpEmTaNasGQAdO3bkzjvv\nBKCsrIxJkyZRV1dHJBLh7rvvBiA7O5tbb72VGTNmcNtttzF+/HgOHz7M1Vdf3WAR+ZepQ4cO7Nmz\nh6uuuoqUlBSuueYa0tLSOOecc5gzZw5du3aN/3bq1KmUlJSQkpJChw4d4gvuJUmSJEmSJEmSJEmS\nJEmSJCWHi8n/W15eHnl5efHvmZmZVFRUxL9XVlYC0KtXL7Zs2RLfvnbt2vjn4uJiiouL49+zsrJ4\n/fXX49+7du3K5s2bPzMt5eXln7jv7LPPPuH+v912zz33NNr/t+m84YYb4p/HjRsX/zxs2LD4G9eX\nLVvWKNytt94a//xxXp1oUf1VV13FVVddBfz/fOvfvz9Lly79xPOSJEmSJEmSJEmSJEmSJEmS9PX6\nal5ZLUmSJEmSJEmSJEmSJEmSJEn6RnMxuSRJkiRJkiRJkiRJkiRJkiSdglxMLkmSJEmSJEmSJEmS\nJEmSJEmnIBeTS5IkSZIkSZIkSZIkSZIkSdIpKC3ZCZBOKCWScNC0ZukJh43FogmHBUhrkvgtldo0\n7HZs07Rl4mGbNU84bMg5A6SkpwaEDYs7tWniZaV1k8TzLFgk8fsjEgn7H6KQcpoacK0hLM+bNUs8\n3cH1QkCd1KZpq7C4UxLP89ZNWwTF3bJZRsJhWwTkWZOAaw1hZTwSWCdlBLYDiQot4yHlNCU98Wsd\nKj2gnLVtFnZvhtRJIe09AIHtQFDUqSFtfvLKSiSk3U0LvK8D+sSRgLChfawmAfVZ6P0VJKCMAkQC\n2l0C6uKQ+gwC0x2oSUDa05okL90pqYnfX7FYWNwh1zuzaeL9M4CUgDqtZZMmCYcN7iMFZHpIXx7C\nymkkJfE2Oz1wnJ4WMFaO1of1LWPRgOsVcN6R1LA+UkZA/y407mSNlQEiqQHnHTL31ySwb5h41MFC\n+pZJ7csH1EnB1ytAaD0eom2z1gmHDbk/AFIC7s3QMWdWs8zEAweU8ZA5ZoD0kPow5L4m7NYOmUML\nbX8i0YB+TuCYM2i8GzhOTwmo04KeRzRtlnBYgNSMgDY7sIwHCZqTCasXQuaJQ58fhfRr0wLHTyFz\nWU0D6tLQcV9KQBkPagMIaztTk9hPSgkpZ4GTG/XH6oPCh0jNCJm/C4g38FqH9GtD7k0I7B8GdHRC\n5iYgbI47q2nifXmAlIA2KCTdoeOfsL5K06C4Q/IstO0LaUOC4g187pUetG4jbCwQMv5q3TTxflLo\nWDmkbxm61iVkvjZ4TVZA+IymiZ93Mvs5oesfQtdVSSczS78kSZIkSZIkSZIkSZIkSZIknYJcTC5J\nkiRJkiRJkiRJkiRJkiRJpyAXk0uSJEmSJEmSJEmSJEmSJEnSKeiUXEy+ceNGBgwYQE1NTXzbnDlz\nWLFiBQC9evXijjvuaBBm+vTpFBYWUlNTw/Dhw+PbV61aRZ8+fdi7dy8A1dXVXH755VRXV5Obm0tR\nUVGDv/r6+gbHnTx5MiNHjmzwm927d3P33Xeze/fuTzyHn/3sZxw7dozdu3ezdu3az3XeK1asoFev\nXrz00kvxbcePHycvL4+f//znn+sYkiRJkiRJkiRJkiRJkiRJkv5nSEt2ApIlIyODKVOm8OijjxKJ\nRBrsa9OmDVVVVdTV1ZGWlkZ9fT1btmwBoHPnzsRiMfbt20fbtm1Zt24dF110EevXr2fUqFFs3LiR\nIUOGAJCTk0N5eflnpmXixIkMHTq0wbbS0tJPDTN37lwANmzYwNtvv01hYeHnOu+ePXuyevVqzj33\nXAD+8Ic/0LJly88VVpIkSZIkSZIkSZIkSZIkSdL/HKfsYvJBgwYRjUZZsmQJ48ePb7AvLS2NgQMH\nUllZSUFBAS+88AL5+fk8/fTTAOTn57N582YKCwvZunUr06ZN45FHHmHUqFFs2rSJ0aNHB6evqKiI\nsrIy1qxZQ3V1NXv37mX37t1MmTKFIUOGUFhYyKpVq3jooYc4evQo3/rWt+jatSvTp08HPloQP2PG\njEYLxYcOHcoLL7xANBolJSWF1atXc+mll8b3l5eXs2rVKiKRCMOHD+cHP/gBkydPJi0tjd27d3Ps\n2DGGDx9ORUUFNTU1LFiwgDPOOIOZM2fy4osvAjBixAj+1//6X0yePJkDBw5w4MABevXqxZlnnsn3\nv/99Dh48yL/8y7/E3wQvSZIkSZIkSZIkSZIkSZIk6euXkuwEJFNZWRmLFi1i586djfaNGDGCNWvW\nALBq1SpGjhwZ35efn09VVRWvvPIKffv2pV+/frzxxhtEo1Fee+01cnNzAdi2bRtFRUXxv5kzZ54w\nHbNnz47/5oEHHmi0PyMjg4cffpjS0lIWLVoU356amsq1117LiBEj+M53vsPtt9/OnXfeSXl5OUOH\nDuXhhx9udKz09HTOPfdcNm3axOHDhzl8+DCdOnWKp3fNmjUsXbqUJUuW8Pvf/563334bgC5duvCv\n//qv9OzZk+rqahYuXMhFF13E2rVrqaiooLq6mmXLlrF06VJWrVrFm2++CXy0aP+JJ57gRz/6Eb/6\n1a9OmJ+SJEmSJEmSJEmSJEmSJEmSvn6n7JvJAbKysigpKWHSpEnxBeAfGzBgAFOnTmX//v0cOHCA\nLl26xPfl5eWxcOFCMjMzKSgoIBKJcM4551BRUUG3bt1IT08HICcnh/Ly8s9Mx8SJExk6dOgn7u/d\nuzcAnTp14tixY5/4u+3btzN16lQAjh8/Tvfu3U/4uxEjRrB69Wpqamq48MILOX78OABbt25l9+7d\nTJgwAYCDBw/GF9r36dMHgFatWtGzZ8/452PHjrF9+3a+/e1vE4lESE9P55xzzmH79u0A9OjRA4Bu\n3brRokULtm3bxm9+8xsWLFjwmfkiSZIkSZIkSZIkSZIkSZIk6atzSr+ZHKCwsJAePXqwcuXKBtsj\nkQgFBQWUlZUxbNiwBvsyMzPJyMigsrKSwYMHA8TfBD5kyJAvPY2RSOQT96WkpBCNRoGPFm7PmjWL\n8vJyJk6cyPnnn3/CMHl5ebz00ks8++yzXHLJJfHtPXv2JCcnh8cee4zy8nJGjx5Nr169PjMN2dnZ\nvPjii8BHi9j//Oc/8w//8A+Nwo0dO5YFCxbQsWNH2rZt+/lOXpIkSZIkSZIkSZIkSZIkSdJX4pRf\nTA5QWlpK06ZNG20fOXIka9eubbDg+mMDBw4kLS2NzMxMAAYPHsxLL73UYDH5tm3bKCoqavC3a9eu\nLzXtZ555Js8//zyrV6+mrKyMSZMmMW7cOO699974QvC/l5KSwuDBg2natGk8/QBnnXUW5513HuPG\njWP06NHs2LGDjh07fmYaLrjgArp27cqVV17JlVdeycUXX0zfvn0b/W7YsGH88Y9/5Iorrkj8hCVJ\nkiRJkiRJkiRJkiRJkiR9KdKSnYBkyMvLIy8vL/49MzOTioqK+PfKykoAevXqxZYtW+Lb165dG/9c\nXFxMcXFx/HtWVhavv/56/HvXrl3ZvHnzZ6Zl5syZJ9xeXl4OwA033BDflp2dHd/+cVr69OnDb3/7\n20bhTmT06NHxz5MnT45/HjduXPzzj370I370ox99YhpvvfXW+OcJEybEP0+aNKlRfH9/bvX19XTp\n0iX+NndJkiRJkiRJkiRJkiRJkiRJyeObyfW12Lx5M2PHjuXHP/4xKSkWO0mSJEmSJCIAvboAACAA\nSURBVEmSJEmSJEmSJCnZTsk3k+vrl5uby29+85tkJ0OSJEmSJEmSJEmSJEmSJEnSf/MV0ZIkSZIk\nSZIkSZIkSZIkSZJ0CvLN5PpmisYSDlp35HjCYSORsP+vqKutCwofYv+RQwmHPXCkTcJhQ885tUni\n4aPHw+KuP5p4WTlY+2FQ3EFiid8fMaJBUdcfDcjzgHRDWJ5/+GES64WAOunA0cTva4DUlOT9z9j7\nR44lHPaDgDxreSS0Xkg8fCywTqoNTHuiQst4SDmNHku8nIQ6HlDO9gW0uQBHAq71gaPvB8VNLKwd\nCIq6LqDND4g3tWmzgNAQC2l3A84ZgPr6xOMO6E+H9rFqA+rS0PsrSEB+A8RSEg8fiUQSDhtSnwHE\nogHpTkk83QC1QWO3xOMNyW8gqf+WH3K9Dx8Na3ejAXXa+7W1CYcNKSdAUGEJ6csD1NWG1OOJt37H\nA8fpdQFj5fpjYXVpSPi61IC+Rl1Yuo8F9O9i9WH9s5Cxcki/FCBWH3DeIXN/tWH3ZnA7ECCkbxkJ\n6xUHhA2rk0KvV4jQejzEviMHEw4bcn8ARI8nb7wbIeD+ChivRo+H1ePHQ+rDwDnPWMA4JGQOLbT9\nCRlHhM6hBY13A8fp0YA6Leh5xNEjCYcFqD8W0GYHlvGgZjdoTiawXxowtxH6/CjkuVtdyDMcwuay\njgbUpaFz49GAMh7UBhA2X1ufxH5SNKScBTz/gcDnMIF1UpCAuEOvdUi/NuTeBNgf8qwyoH8XMjcB\nkB7Q39h/NPG+PIT1x0Pm5kPHP6kZiY8bDx49GhR3yLxMaNsX0oYExRtQTgCOB5x3tD5sgjukb3nw\naOL9pNCxMgTU44HlJGS+NpIaNo8V0uYfO5r4vZnMfk7o+odTUhLnS/XN4t0jSZIkSZIkSZIkSZIk\nSZIkSacgF5NLkiRJkiRJkiRJkiRJkiRJ0inIxeSSJEmSJEmSJEmSJEmSJEmSdApyMbkkSZIkSZIk\nSZIkSZIkSZIknYJO6cXkGzduZMCAAdTU1MS3zZkzhxUrVgDQq1cv7rjjjgZhpk+fTmFhITU1NQwf\nPjy+fdWqVfTp04e9e/cCUF1dzeWXX051dTW5ubkUFRU1+Kuvr29w3KKiIrZv3/5VnSoAkydP5tvf\n/jbHjh2Lb3v11Vfp1asXGzdu/ErjliRJkiRJkiRJkiRJkiRJkvTNkpbsBCRbRkYGU6ZM4dFHHyUS\niTTY16ZNG6qqqqirqyMtLY36+nq2bNkCQOfOnYnFYuzbt4+2bduybt06LrroItavX8+oUaPYuHEj\nQ4YMASAnJ4fy8vKv/dxOpEOHDqxfv55hw4YB8Jvf/IZu3bolOVWSJEmSJEmSJEmSJEmSJEmSvm6n\n/GLyQYMGEY1GWbJkCePHj2+wLy0tjYEDB1JZWUlBQQEvvPAC+fn5PP300wDk5+ezefNmCgsL2bp1\nK9OmTeORRx5h1KhRbNq0idGjRwel7fjx40yZMoXq6mrq6+v5l3/5F4YPH05RURFnnXUWb731FocP\nH+b++++nS5cu/OIXv+D3v/89bdu25ciRI9x4443k5eU1OOall17KqlWrGDZsGNFolFdffZV+/frF\n47vzzjvZuXMn0WiUm266iby8PEaOHMm3v/1t3nzzTXr27Em7du2oqqoiIyODhx56iCNHjjBx4kQO\nHz5MfX09N954I+eddx4jRoyge/fupKenU1NTw7Rp0/jHf/xH1q1bR0VFBWVlZUH5I0mSJEmSJEmS\nJEmSJEmSJClxKclOwDdBWVkZixYtYufOnY32jRgxgjVr1gCwatUqRo4cGd+Xn59PVVUVr7zyCn37\n9qVfv3688cYbRKNRXnvtNXJzcwHYtm0bRUVF8b+ZM2d+rnQ9+eSTtG3blieeeIJHH32UefPmsW/f\nPgD69+/PokWLGDx4MKtXr+aNN97gD3/4A0899RS/+MUv2LNnzwmP2b9/f95++20+/PBDNmzY0GCx\n+fLly8nKymLJkiUsWLCAu+66C4APPviAESNGsHTpUqqqqsjNzWXJkiUcP36cbdu28cADD5Cfn8+S\nJUu4//77KS0tJRaL8eGHH3L99dczd+5cxowZw8qVKwH493//d8aMGfO58kCSJEmSJEmSJEmSJEmS\nJEnSV+OUfzM5QFZWFiUlJUyaNCm+APxjAwYMYOrUqezfv58DBw7QpUuX+L68vDwWLlxIZmYmBQUF\nRCIRzjnnHCoqKujWrRvp6ekA5OTkUF5e/oXTtX37dvLz8wHIzMwkOzubXbt2AdCnTx8AOnXqxHvv\nvcf27dvp168fqamppKamcvbZZ3/icb/zne/w/PPP88c//pHrr7+e++67D4CtW7fy4osv8vLLLwNQ\nV1cXX7zet29fAFq1akV2dnb8c21tLdu3b48vsu/YsSOZmZns3bsXgB49egDw3e9+l9GjR/PDH/6Q\nv/71r/HjSZIkSZIkSZIkSZIkSZIkSUoO30z+3woLC+nRo0f87dkfi0QiFBQUUFZWxrBhwxrsy8zM\nJCMjg8rKSgYPHgzA0KFDefjhhxkyZEhwmrKzs6mqqgLg8OHDbN26la5du57wtzk5OWzZsoVoNMqx\nY8d47bXXPvG4I0aM4Fe/+hV79uyhW7du8e09e/bk0ksvpby8nIULF3LJJZfQpk0b4KN8+Dzp/Otf\n/8qhQ4fi4VJSPipizZs3Jy8vj7vvvpvLLrvsC+SCJEmSJEmSJEmSJEmSJEmSpK+Cbyb/G6WlpWzY\nsKHR9pEjR3LFFVdw1113Ndo3cOBANm7cSGZmJgCDBw9m4sSJzJ49O/6bbdu2UVRU1CDcjBkzGizk\nBrjxxhvJyMgAPnrr+c9+9jNuv/12xo0bR21tLcXFxbRr1+6Eae/VqxcFBQWMHTuWrKws0tPTSUs7\n8eXNzs5m//79fO9732uw/aqrruK2225j/PjxHD58mKuvvjq+GPzT/OQnP6GkpITf/va3HD16lLvu\nuuuEcY8dO5arr76asrKyzzymJEmSJEmSJEmSJEmSJEmSpK/WKb2YPC8vj7y8vPj3zMxMKioq4t8r\nKyuBjxZqb9myJb597dq18c/FxcUUFxfHv2dlZfH666/Hv3ft2pXNmzd/ZlrKy8tPuH3WrFmf+ttx\n48YBsHfvXlq1asVTTz3FsWPHuPTSS+ncuXODcDNnzox/XrFiRfzz3Llz45/vueeeRvH97fkuW7Ys\n/nnBggUn/HyicAD19fVcfPHFtGrVqtFvJUmSJEmSJEmSJEmSJEmSJH29TunF5P+TZGVl8corr/C9\n732PSCTCmDFjOP3005OdrLjFixfz1FNPMW/evGQnRZIkSZIkSZIkSZIkSZIkSRIuJv8fIyUlhf/z\nf/5PspPxicaPH8/48eOTnQxJkiRJkiRJkiRJkiRJkiRJ/y0l2QmQJEmSJEmSJEmSJEmSJEmSJH39\nfDO5vplSIgkHTW+ennDYWCyacFiAjBYZCYdNaxJ2O57Wom3CYdu3yEw4bHrzxM8ZIK1J4tcrNSPx\nsABpLZokHLZ981ZBcYeIpKQmLe70zMSvd2p6WLpD8jwzIN2h9UJIndQh4L4GSE9JvF5p3bR5UNxZ\nLZomHLZVwPVq3jKsTgq5XilNEq9TIDDtkcTbzVi0PvF4gQ7NEy+noXkWIuRad8oMuzdbtEg87pD2\nPukC2q+U9LA2P0Qk4P6KpAUOt1ITz7NIQH86JaB/BtA0oIyH3l9BAvIbAvtoAf2N0P54ULoD7g+A\nZgHjp/Rmid9fgckOqhdCZQRc76wWzYLiTgmo07KaJx53SDkBIBZLOGhIfwECy2lK4u9/yGga1v6k\nNQ2YV4kmnt8Qdn+lBZx3SnpYnjUJaPsiackcK4eV8UhqSBkPudZh6U4JzPMQSZvTCWy7Quqk0OsV\nImSuNlSnzHYJhw25PyBs/BQ65mzbLPF5ZiKJl7PUwDFMRkA9Hnx/BYxDWmcmPq8SSQ1711RKLKDd\nzQgdwwSMdwPH6SHj5bSAvny75gH3FmF1cTKfR4TcXyFtF0BaQF8+9PlRyDg/vfmxoLhD5rJCxm5N\nA54JQNi92SSwvxDSdoa2XyFSmyWvnxSrS3weLBYwxgdICznvgLhTA/vEIfMTofMqp7XISjxwQP8u\nvXnY86PUJomfd8cW7YPiDumPh6x/CJ7HCsjzdi3CniuH5Flas8A5giTVxaHPvZqErNtIC+0nJR53\n+xYtEw4bOoYJETq3kRHQ9oU+Awppg5oFjJWT2tcIXOMTCWi/TlqBfazgB2/6xjgFS78kSZIkSZIk\nSZIkSZIkSZIkycXkkiRJkiRJkiRJkiRJkiRJknQKcjG5JEmSJEmSJEmSJEmSJEmSJJ2CXEz+NzZu\n3MiAAQOoqamJb5szZw4rVqwAoFevXtxxxx0NwkyfPp3CwkJqamoYPnx4fPuqVavo06cPe/fuBaC6\nuprLL7+c6upqcnNzKSoqavBXX1/f4LhFRUV897vfbbDtueeeo1evXlRXV3+p5/1FxWIxlixZwrhx\n4+LpX7duXVLTJEmSJEmSJEmSJEmSJEmSJOmLSUt2Ar5pMjIymDJlCo8++iiRSKTBvjZt2lBVVUVd\nXR1paWnU19ezZcsWADp37kwsFmPfvn20bduWdevWcdFFF7F+/XpGjRrFxo0bGTJkCAA5OTmUl5d/\nrvS8/vrr9O7dG4DVq1fTpUuXL/FsE/Pkk0+yefNmFi1aRJMmTdi/fz/XXnstrVu35txzz0128iRJ\nkiRJkiRJkiRJkiRJkiR9Di4m/zuDBg0iGo2yZMkSxo8f32BfWloaAwcOpLKykoKCAl544QXy8/N5\n+umnAcjPz2fz5s0UFhaydetWpk2bxiOPPMKoUaPYtGkTo0eP/kJpufTSS1m1ahW9e/fm0KFD1NbW\n0r59ewDef/99SktL2b9/PwC33XYbvXr14sILL+Rb3/oWO3bs4LzzzuP999/n5ZdfpkePHsyePZvq\n6mpKSkqor68nEolw2223cdZZZ3HBBRfQs2dPsrOzqaioYPny5bRp04alS5fywQcf8OMf/ziersWL\nF/PYY4/RpEkTALKysiguLubxxx+nffv23HLLLXTq1Ildu3bRr18/pk6d+onplSRJkiRJkiRJkiRJ\nkiRJkpQcKclOwDdRWVkZixYtYufOnY32jRgxgjVr1gCwatUqRo4cGd+Xn59PVVUVr7zyCn379qVf\nv3688cYbRKNRXnvtNXJzcwHYtm0bRUVF8b+ZM2eeMB2FhYWsX7+eWCzGb3/7Wy655JL4vgcffJBB\ngwZRXl7OtGnTKCsrA+Cdd97hpptuYsmSJTz22GNcffXVLF++nBdffJFDhw5xzz338IMf/IAlS5ZQ\nWlpKSUkJADU1NcyZM4eSkhJGjhzJ6tWrAfj1r3/NqFGjGqRr//79tG3btsG2bt26sXv3bgB27NjB\n3XffzfLly1m/fj179uz5xPRKkiRJkiRJkiRJkiRJkiRJSg7fTH4CWVlZlJSUMGnSpPgC8I8NGDCA\nqVOnsn//fg4cOECXLl3i+/Ly8li4cCGZmZkUFBQQiUQ455xzqKiooFu3bqSnpwOQk5NDeXn5Z6aj\nSZMm9O7dmz//+c/8/ve/57777mPp0qUAbN26lQ0bNvDMM88AcPDgQQDatGnD6aefDkDz5s3JyckB\noGXLltTW1rJ9+3b+6Z/+CYDevXvzX//1X/FzzsrKAuB73/seN998M//0T/9E+/bt429D/1hmZiYH\nDhygTZs28W07d+6kc+fOAJxxxhlkZmYC0KFDB2praz8xvZIkSZIkSZIkSZIkSZIkSZKSwzeTf4LC\nwkJ69OjBypUrG2yPRCIUFBRQVlbGsGHDGuzLzMwkIyODyspKBg8eDMDQoUN5+OGHGTJkSELpGDFi\nBIsWLaJVq1a0aNEivr1nz55MmDCB8vJy5s2bx2WXXRZP36fJzs6mqqoKgNdffz2+UDwl5f8XhS5d\nutCyZUsefPBBrrjiikbHGD9+PNOnT+fYsWMA7N27l/nz53PVVVd9Yho+Kb2SJEmSJEmSJEmSJEmS\nJEmSksPF5J+itLSUpk2bNto+cuRI1q5dyyWXXNJo38CBA0lLS4u/mXvw4MG89NJLDRaTb9u2jaKi\nogZ/u3btOmEa8vPzqaqqYsSIEQ22X3fddTzzzDMUFRXxox/9iH/8x3/8XOf0v//3/2bx4sV8//vf\np6ysjLvvvvuEvxs7dixVVVUnXARfVFTE2Wefzfe//33GjRvHDTfcwPXXX9/oLe5fRnolSZIkSZIk\nSZIkSZIkSZIkfTXSkp2Ab5K8vDzy8vLi3zMzM6moqIh/r6ysBKBXr15s2bIlvn3t2rXxz8XFxRQX\nF8e/Z2Vl8frrr8e/d+3alc2bN39mWsrLy+Of//jHP8Y/L1u2LP55wYIFjcJ9nMa///z000/HPz/6\n6KOfGg6gvr6e733ve6Smpp4wfRMmTGDChAmNtnft2rVBGj8rvZIkSZIkSZIkSZIkSZIkSZKSw8Xk\nauS+++5j48aNPPjgg8lOiiRJkiRJkiRJkiRJkiRJkqSviIvJ1cjNN9+c7CRIkiRJkiRJkiRJkiRJ\nkiRJ+oqlJDsBkiRJkiRJkiRJkiRJkiRJkqSvn4vJJUmSJEmSJEmSJEmSJEmSJOkUlJbsBEhftrra\nuoTDRiJh/19x7MPjCYeNxWJBce87cjDhsPuPtEo47PEPjyUcFiA1PTXhsNG6+qC4648knvb9Rw4H\nxR0iFk38vCMpiec3hF3vaEZYkxOS5x8G3JuhQuqkfUcOBMWdlpJ4ntfHwu6vQ0dqEw6bdaRpwmGP\nBl7rkOsVPRZWH9YeCUh7YBsSYm9A+xOaZ5FIJOGwdbWJl/G9Rw4lHBbg6JHEy9neDxPPbwAC+zrJ\nEq1LPM/CWr6wPlosIN2hYtHE0x2tDatLa0PKeOD9FSQgzwCIRRMPG3Bv1h8NLGch6Q78//SQ+jCS\nmngbECqg+QkW0lc58MHRoLhj9Ym3nYeOJh53UB8Jgi5YSH4D1B1LPM9i0cTvzZB4AeqPJx4+JCwE\npj3g3owFzi8cD6iLQ641hI2Vj3wYVsZD5idC+ir1gfdmNLCchkjanE7gmDGkTxx6vUIcT+J8UMhY\nOVS0PvE835/M/nhAv7T+WNi1Ph7QLw2+vwLqw8MhZTww3SHlLHQ+KCTPQvq0ALHjiZ933dHEr9eh\no0cSDgth/dqQtgvCn0kkS31AvzT0+VFdwLOroPoMIOAeCRm7HQ8c90UD7s3jAfcmBM6PB6Q7VH3A\n/F9IWAgfa4dJ/P4KaTpTAp6HQ1iehc6r7P0woH8Y0r8LLGch99fewOezIeP8kHTXBbYBaU0Tz/MD\nR8L6KrGAvmX9sdA5goDwIc9hjoeV8ZC1SWnpYfP69QFt594PA/pJSXweHtp2hfR1IilhDyRC5sGO\nfJB4uqNJbO9D1/5JpzLvHkmSJEmSJEmSJEmSJEmSJEk6BbmYXJIkSZIkSZIkSZIkSZIkSZJOQS4m\nlyRJkiRJkiRJkiRJkiRJkqRTkIvJE7Rx40YGDBhATU1NfNucOXNYsWIFAL169eKOO+5oEGb69OkU\nFhZSU1PD8OHD49tXrVpFnz592Lt3LwDV1dVcfvnlVFdXk5ubS1FRUYO/+vr6BsfduXMn1157Lddc\ncw1jx45l9uzZRKNRABYvXvyFz62oqIjt27d/4XAfO3DgAL/5zW8AeOihh3j55ZcTPpYkSZIkSZIk\nSZIkSZIkSZKkr4aLyQNkZGQwZcoUYrFYo31t2rShqqqKuro6AOrr69myZQsAnTt3JhaLsW/fPgDW\nrVvHRRddxPr164GPFqoPGTIEgJycHMrLyxv8paamNojrvvvuY/z48fzrv/4rTz75JDt27OD5558H\n4IEHHvhqTv5TvPnmm6xduxaAa6+9lv79+3/taZAkSZIkSZIkSZIkSZIkSZL06VxMHmDQoEG0bt2a\nJUuWNNqXlpbGwIEDqaysBOCFF14gPz8/vj8/P5/NmzcTjUbZunUr11xzDf/xH/8BwKZNm+KLyT+P\n9u3bs3LlSl588UXq6uqYN28ew4YN44EHHuDgwYOUlZVx/Phxbr31Vq666irGjBnDmjVrAPjLX/7C\nlVdeyZgxYyguLubo0aMA/OIXv+AHP/gBY8aMYdeuXdTX11NaWsoPf/hDRo4cydy5cwF47rnnGDNm\nDOPGjePGG28kGo3y4IMPsmHDBp588kkmT57M+vXrOXr0KD/72c+48sorGT16NH/+858TynNJkiRJ\nkiRJkiRJkiRJkiRJXw4XkwcqKytj0aJF7Ny5s9G+ESNGxBdtr1q1ipEjR8b35efnU1VVxSuvvELf\nvn3p168fb7zxBtFolNdee43c3FwAtm3bRlFRUfxv5syZjeKZNGkS55xzDvfddx/5+flMmTKF999/\nn5/+9Ke0bt2asrIynnzySdq2bcsTTzzBo48+yrx589i3bx933HEHM2bMYPny5RQUFLB9+3YACgoK\neOyxxxg6dCjPPvssNTU1nHvuuTzyyCM89dRTPPHEE/Hz+uEPf8jjjz/OBRdcwOHDh7nuuusYNGgQ\nV155ZTyNTzzxBF26dOHJJ5/kvvvu4y9/+cuXdxEkSZIkSZIkSZIkSZIkSZIkfWFpyU7AyS4rK4uS\nkhImTZoUXwD+sQEDBjB16lT279/PgQMH6NKlS3xfXl4eCxcuJDMzk4KCAiKRCOeccw4VFRV069aN\n9PR0AHJycigvL//UNGzYsIEJEyYwYcIEPvjgA2bNmsWCBQuYPHly/Dfbt2+Pvxk9MzOT7Oxsdu3a\nxXvvvUd2djYAY8aMif/+7LPPBj566/l7771HmzZt2LJlCxs2bCAzM5Njx44BMGXKFH75y1+yePFi\nevbsybBhw06YxrfffpuhQ4cC0L17dyZMmPCZeStJkiRJkiRJkiRJkiRJkiTpq+Obyb8EhYWF9OjR\ng5UrVzbYHolEKCgooKysrNEi68zMTDIyMqisrGTw4MEADB06lIcffpghQ4Z8ofhnz57Npk2bAGjR\nogU9evQgIyMDgFgsBkB2djZVVVUAHD58mK1bt9K1a1dOO+00duzYAcBDDz3E7373uxPGsWLFClq2\nbMm9997LNddcw9GjR4nFYjz55JPccMMNLF68GIDf/e53pKSkEI1GG4TPzs5my5YtAOzatYtbbrnl\nC52jJEmSJEmSJEmSJEmSJEmSpC+Xbyb/kpSWlrJhw4ZG20eOHMkVV1zBXXfd1WjfwIED2bhxI5mZ\nmQAMHjyYiRMnMnv27Phvtm3bRlFRUYNwM2bMoFu3bvHv8+bNY/r06cycOZOMjAy6du1KWVkZ8NEi\n7ltvvZUZM2Zw++23M27cOGpraykuLqZdu3ZMnTqVkpISUlJS6NChAxMmTOCxxx5rlNbzzjuPW265\nhZdeeomMjAz+4R/+gXfffZf+/fvzk5/8hBYtWtC8eXPOP/98jh07xtatW1m0aFE8/FVXXUVJSQnj\nx4+nvr6ekpKSL5S/kiRJkiRJkiRJkiRJkiRJkr5cLiZPUF5eHnl5efHvmZmZVFRUxL9XVlYC0KtX\nr/gbuQHWrl0b/1xcXExxcXH8e1ZWFq+//nr8e9euXdm8efNnpiU7O5tHH330hPvKy8vjn2fNmtVo\nf//+/Vm6dOknhhk3blz8869//etG4Tt27EhhYWGj7c8880yjbffee+8J0yhJkiRJkiRJkiRJkiRJ\nkiTp65eS7ARIkiRJkiRJkiRJkiRJkiRJkr5+LiaXJEmSJEmSJEmSJEmSJEmSpFOQi8klSZIkSZIk\nSZIkSZIkSZIk6RTkYnJJkiRJkiRJkiRJkiRJkiRJOgWlJTsB0glFY4kHrYt+iQn5gmKJpztWn3hY\ngLpofcJh66MBeRaWbGIheRaSbiBan3j44wH5nUyxWOD9ERI84FpDWJ5HA+qUSCTs/65C6qTj9XVB\ncYcIqVMA6gPyvC7g3qwPbAOC6uLAMl5fF1ihJii0jNdFA8ppYJ7FAsppLKQNCLw36wLK2fGQ/AYI\naAdC2mwAQuqVlEhY3CEC6rNg9QF5FjDSC73W0ZAynsS2L+T+gLB8iwR0sqKBfeKgdAfX4wHnncRx\nXyQ1eXVSyBgmFjh4CykrIf27kL58qNBylqxyGppnIf2k0HMOiZtYauJBk9j2hfY1QsbKIfcmENyf\nTzja0HQHvF8llsy+YVBfJfH746O4Q+Y8k9dmB49hAoT0a4PLWUD40DFn0BxBiNA56pP03g6Z10/m\nOQfHncS5jZC0h9SHx0LHykmsD4Mk8/lRyLx+4Nx6yOUKvb9C7pGQcUh94DPSoDFMYNz1IXPUyWx/\nQp7FB/bvQp7DhNbjKekB48aQOZnAchZSJ4XOEdTHAsp4SJ0SWs4C2oHQPm0sYF4/rB5O3tqJkOfC\nEJZnwfNvyayLA4S0IcFzhwHl9GQdw4Q/0w7p1yavnxQUb2CdpK9ZJInP4vWN4pvJJUmSJEmSJEmS\nJEmSJEmSJOkU5GJySZIkSZIkSZIkSZIkSZIkSToFuZhckiRJkiRJkiRJkiRJkiRJkk5BLiYPsHHj\nRgYMGEBNTU1825w5c1ixYgV79uyhrKws4WP//Oc/p3fv3vz1r3+Nb9u7dy99+/ZlxYoVnxjuzTff\n5E9/+tPniuPCCy9k7969ALz77rv07t2bZ555Jr5/2LBhHDhwgOLi4gTPQpIkSZIkSZIkSZIkSZIk\nSdI3lYvJA2VkZDBlyhRisViD7R06dAhaTA7QvXv3Bou716xZQ+fOnT81zHPPPce2bds+1/HPO+88\nqqqqAFi3bh0XX3wx69evB2DXrl20bduWNm3aMH/+/ATPQJIkSZIkSZIkSZIkSZIkSdI3VVqyE3Cy\nGzRoENFolCVLljB+/Pj49urqam6++WaWLVtGRUUF8+fPJxaL0bdvX6ZOnUpVVRVz584lNTWVbt26\ncdddd5Gent7g2MOHD+fZZ59lwoQJAFRUVHDBBRfE9997771UVVURjUaZMGEC1XqY0gAAIABJREFU\nubm5rFy5kvT0dPr27cvu3btZsmQJdXV1RCIR5s+fT9u2bePhBw8eTFVVVXwR+Y033khxcTGxWIxN\nmzYxZMiQ+O8qKyspKirirLPO4q233uLw4cPcf//9xGIxbrnlFjp16sSuXbvo168fU6dO5f3336e0\ntJT9+/cDcNttt9GrVy8uuOACevbsSXZ2NiUlJV/VZZEkSZIkSZIkSZIkSZIkSZL0GXwz+ZegrKyM\nRYsWsXPnzkb76urqmDZtGg899BArVqzgjDPOoKamhttvv5358+ezePFiOnbsyMqVKxuFbd++Pc2a\nNWPXrl3s3LmTTp060aRJE+CjN4lXV1fz+OOP89hjj/Hggw/SrFkzRo0axYQJE+jfvz87duzgoYce\n4vHHHycnJ4cXXnihwfEHDRrEn//8Z+rq6qiuriYnJ4czzzyTV199tcFi8r/Vv39/Fi1axODBg1m9\nejUAO3bs4O6772b58uWsX7+ePXv28OCDDzJo0CDKy8uZNm1a/C3tNTU1zJkzx4XkkiRJkiRJkiRJ\nkiRJkiRJUpL5ZvIvQVZWFiUlJUyaNInc3NwG+/bv30+rVq1o164dAD/+8Y/Zu3cv7777LjfddBMA\nR48eJT8//4THvvTSS1m9ejV1dXWMHDmSyspKALZu3cqrr75KUVER8NGi9XfeeadB2Hbt2jFp0iRa\ntGjB22+/zbnnnttgf+vWrUlLS2P9+vXxdA8dOpTNmzfz1ltv0b9//0bp6dOnDwCdOnXivffeA+CM\nM84gMzMTgA4dOlBbW8vWrVvZsGEDzzzzDAAHDx6M51VWVtZn5qkkSZIkSZIkSZIkSZIkSZKkr5Zv\nJv+SFBYW0qNHj0ZvGG/Xrh2HDh3iwIEDAEyfPp133nmHTp06sWDBAsrLy7nuuusYNGjQCY978cUX\n8/zzz1NVVUVeXl58e8+ePcnLy6O8vJx/+7d/47vf/S7dunUjEokQjUZ5//33+b//9/8yd+5cpk+f\nTpMmTYjFYo2On5eXx8MPP8zQoUMBGDJkCM8++yzdu3cnJeXzFY9IJNJoW8+ePZkwYQLl5eXMmzeP\nyy67DOBzH1OSJEmSJEmSJEmSJEmSJEnSV8s3k3+JSktL2bBhQ4NtKSkp3HnnnfzkJz8hJSWFPn36\n0K9fP0pLS7n22muJxWK0aNGCe+6554THbNmyJZ06daJbt24NFmIXFhayadMmrr76aj788EOGDRtG\nZmYmZ599Nvfccw/Z2dnk5uZy5ZVXkpaWRqtWrXj33XcbHX/w4ME8+uij8YXqHTt25IMPPuCf//mf\ng/Liuuuuo7S0lGXLlnH48GGKi4uDjidJkiRJkiRJkiRJkiRJkiTpy+Vi8gB5eXkN3haemZlJRUVF\n/PuyZcsAKCgooKCgoEHYf/7nf/7UBds33HBD/PPPf/7z+Odbb701/nnKlCmNwp1//vmcf/75AJ/4\ntvO/NXDgQF5++eUG255++ukG3ysrKwEoLy+Pbxs3blz888fn+fefFyxY0Ci+j48lSZIkSZIkSZIk\nSZIkSZIkKblSPvsnkiRJkiRJkiRJkiRJkiRJkqT/aVxMLkmSJEmSJEmSJEmSJEmSJEmnIBeTS5Ik\nSZIkSZIkSZIkSZIkSdIpKC3ZCZBOKCWScNBIJPGwyRSa7qSdd2C0ybxeIXGnnLTl7OT9H6KQPD95\ny1nyrlck8OYOyfKQPDtZ2wAIy7NkCqoPQ086SfdI6L0ZctrJbH+C769klpUQAf3SYEkq4+H90sTD\nJrPtUwJO5jrpJI37ZHWy9u9O1nF6UvMsme1mgJP5vj5Zx8pBkljOTtYynlSnaJ4ltV8bkOehY87Q\n+aRkSWp1eAqOgYLr0pM0z07WPvFJK4l9+eTO34WGT9YYJjR8wP11ivZVTlYn7Xj3JL03Iax/dzLP\n6YRFfrLO65+ceXaq9pOSerlO1n5SUD2exGek0tchFgsLbxn/H+PknHGRJEmSJEmSJEmSJEmSJEmS\nJAVxMbkkSZIkSZIkSZIkSZIkSZIknYJcTC5JkiRJkiRJkiRJkiRJkiRJpyAXk0uSJEmSJEmSJEmS\nJEmSJEnSKSjpi8k3btzIgAEDqKmpiW+bM2cOK1asYM+ePZSVlSV87J///Of07t2bv/71r/Fte/fu\npW/fvqxYseITw7355pv86U9/+lxxXHjhhezduxeAd999l969e/PMM8/E9w8bNowDBw5QXFyc4Fl8\nfkVFRVxxxRUUFRXF//7jP/7jK48XYPLkyaxfv77BturqasaOHfu1xC9JkiRJkiRJkiRJkiRJkiTp\ni0n6YnKAjIwMpkyZQiwWa7C9Q4cOQYvJAbp3795gcfeaNWvo3Lnzp4Z57rnn2LZt2+c6/nnnnUdV\nVRUA69at4+KLL44vqt61axdt27alTZs2zJ8/P8Ez+GJmzZpFeXl5/O/888//WuKVJEmSJEmSJEmS\nJEmSJEmSdHJJS3YCAAYNGkQ0GmXJkiWMHz8+vr26upqbb76ZZcuWUVFRwfz584nFYvTt25epU6dS\nVVXF3LlzSU1NpVu3btx1112kp6c3OPbw4cN59tlnmTBhAgAVFRVccMEF8f333nsvVVVVRKNRJkyY\nQG5uLitXriQ9PZ2+ffuye/dulixZQl1dHZFIhPnz59O2bdt4+MGDB1NVVRVfRH7jjTdSXFxMLBZj\n06ZNDBkyJP67yspKioqKOOuss3jrrbc4fPgw999/P7FYjFtuuYVOnTqxa9cu+vXrx9SpU3n//fcp\nLS1l//79ANx222306tWLCy64gJ49e5KdnU1JScln5u/GjRtZuHAh6enpVFdXM3z4cH7605/y3HPP\nsXDhQtLS0jjttNOYO3cuH3zwwQnjvPDCC/nWt77Fjh07OO+883j//fd5+eWX6dGjB7NnzwZg6dKl\nPPLII9TX13P33XeTmpoaT8OmTZs+81pJkiRJkiRJkiRJkiRJkiRJ+vp8IxaTA5SVlTFmzJj44uu/\nVVdXx7Rp01i+fDnt2rVj4cKF1NTUcPvtt7N06VLatWvHvHnzWLlyJWPHjm0Qtn379jRr1oxdu3YR\njUbp1KkTTZo0AT56k3h1dTWPP/44tbW1jB07lvLyckaNGkX79u3p378/f/zjH3nooYdo1qwZd9xx\nBy+88AKXXXZZ/PiDBg1i4cKF1NXVUV1dTU5ODmeeeSavvvoqmzZtYty4cY3Op3///pSWljJ37lxW\nr17N8OHD2bFjB4888gjNmjVj2LBh7Nmzh0WLFjFo0CCuvvpqduzYwZQpU3j88cepqalhxYoVZGVl\nNTr2pEmTaNasWfz7/fffD8Du3bv59a9/zbFjxxgyZAg//elPWbVqFT/84Q+55JJL+NWvfsXhw4f5\n5S9/ecI433nnHf7t3/6NDh06MHDgQJYvX87tt9/Od77zHQ4dOgRAbm4u1157LevWrWP27NlMnjwZ\ngFgs9rmulSRJkiRJkiRJkiRJkiRJkqSvzzdmMXlWVhYlJSVMmjSJ3NzcBvv2799Pq1ataNeuHQA/\n/vGP2bt3L++++y433XQTAEePHiU/P/+Ex7700ktZvXo1dXV1jBw5ksrKSgC2bt3Kq6++SlFREfDR\novV33nmnQdh27doxadIkWrRowdtvv825557bYH/r1q1JS0tj/fr18XQPHTqUzZs389Zbb9G/f/9G\n6enTpw8AnTp14r333gPgjDPOIDMzE4AOHTpQW1vL1q1b2bBhA8888wwABw8ejOfViRaSA8yaNYvs\n7OxG288880zS0tJIS0ujadOmAEyZMoVf/vKXLF68mJ49ezJs2LBPjLNNmzacfvrpADRv3pycnBwA\nWrZsSW1tLQDf/va3AfjWt77FPffcE4973759n/taSZIkSZIkSZIkSZIkSZIkSfp6fGMWkwMUFhby\nu9/9jpUrVzJx4sT49nbt2nHo0CEOHDhAmzZtmD59OpdddhmdOnViwYIFtGzZkueff57mzZuf8LgX\nX3wx11xzDS1atOD666+PLyb/f+ydd1hUR/v3v4sI9oK9YG9oTGLvGo1dwaAiFrAbFUUFLDQBUQEL\ndhFBBUFEsWHB2LEk1phHsSFSBBQDSlFAZIGd94/9nfOwqOycMyY+eTOf6+KSXbl3Zs+ZM3O3uadZ\ns2bo1q0bVq5cCZVKBR8fHxgaGkKhUEClUiE7OxtbtmzB5cuXAQDTpk0DIeSjz+/WrRt27dqF2bNn\nAwD69OkDGxsbNGnSBDo6OlTfXaFQfPRes2bNYGJiAmNjY6Snp+PQoUMAQP2Z2j7/4MGDsLa2Ro0a\nNeDi4oLz589/ts1PyZckKioKHTt2xO+//46WLVuK71evXp36XnE4HA6Hw+FwOBwOh8PhcDgcDofD\n4XA4HA6Hw+FwOBwOh8PhcDicv4f/qWRyAHBycsLNmzc13tPR0YGrqytmz54NHR0dtG3bFu3bt4eT\nkxN+/vlnEEJQsWJFjWrYxalcuTLq1q0LQ0NDjUTsAQMG4Pbt25g4cSLev3+PgQMHolKlSvjmm2+w\ndu1aNG/eHB07doS5uTl0dXVRpUoVpKWlffT5vXr1QkBAALp16wYAqFOnDnJzc9G7d2+mazFnzhw4\nOTkhLCwMOTk5mD9/vlaZZcuWoXz58uLrYcOGfbJSOQB8++23mD17NipWrIgKFSrghx9+wA8//CC5\nTYH79+9j8uTJUCgU8PDwEBPvdXR0qO8Vh8PhcDgcDofD4XA4HA6Hw+FwOBwOh8PhcDgcDofD4XA4\nHA6Hw/l7+OrJ5N26dROTsAGgUqVKiIyMFF+HhYUBAPr164d+/fppyPbu3bvUhG1ra2vx961bt4q/\nL168WPzdwcHhIzkhqRoAunfvrvU7dO3aFVFRURrvHT9+XOO1UA09ODhYfG/ChAni78L3LPm7j4/P\nR+0Jn1WS4p9dkuLXWJAfMGAABgwY8NHfamuz+O/C9/Ty8vpku8J30XavOBwOh8PhcDgcDofD4XA4\nHA6Hw+FwOBwOh8PhcDgcDofD4XA4HM7fi472P+FwOBwOh8PhcDgcDofD4XA4HA6Hw+FwOBwOh8Ph\ncDgcDofD4XA4HM7/b/Bkcg6Hw+FwOBwOh8PhcDgcDofD4XA4HA6Hw+FwOBwOh8PhcDgcDofD+RfC\nk8k5HA6Hw+FwOBwOh8PhcDgcDofD4XA4HA6Hw+FwOBwOh8PhcDgcDudfiIIQQr52JzgcDofD4XA4\nHA6Hw+FwOBwOh8PhcDgcDofD4XA4HA6Hw+FwOBwOh/P3wiuTczgcDofD4XA4HA6Hw+FwOBwOh8Ph\ncDgcDofD4XA4HA6Hw+FwOBzOvxCeTM7hcDgcDofD4XA4HA6Hw+FwOBwOh8PhcDgcDofD4XA4HA6H\nw+FwOP9CeDI5h8PhcDgcDofD4XA4HA6Hw+FwOBwOh8PhcDgcDofD4XA4HA6Hw+H8C+HJ5BwOh8Ph\ncDgcDofD4XA4HA6Hw+FwOBwOh8PhcDgcDofD4XA4HA6H8y+EJ5NzOBwOh8PhcDgcDofD4XA4HA6H\nw+FwOBwOh8PhcDgcDofD4XA4HM6/EJ5MzuFwOBwOh8PhcDgcDofD4XA4HA6Hw+FwOBwOh8PhcDgc\nDofD4XA4/0J4MjnnXwEhRON1Tk4OtWx8fPyX7g6H8xE3b9782l3g/AvIzs7+2l3gcDgczr8UvgZx\n/g6USuXX7gKH8z/L69evv3YXOByt/FP1hX9qv78m/1Q/GL/XHA7n/yf4nPb38k9d+zgcDudL8zXn\nw7S0NI3Xz58//zod4XA4Xwyez/XvoWTe37+F/xW/fkFBwdfuAudvgieTc/4RFBUV4dChQ9i8eTNu\n3bqFjIwMKrn09HQkJydj/PjxSE5ORnJyMhITEzFt2jTqtp2cnOR2mxkvLy/Zsrt372Zqm0X+ypUr\nGq/Pnj1LLZuZmQkASExMxJkzZxAbGyu7H1Jhud4A2zXbunUrU9t2dnZM8l9LyWZ1GLDes6/F48eP\nNV7//vvv1LIs9+rnn3+WLQuwXe/k5GRERUUhNTWVqQ9S2bdvHyZOnIjhw4dj+vTpOHz4MJVcQUEB\nNmzYgPz8fABAZGQk1q9fj8LCQir5nJwc2NnZiZuXTp48CRsbG0mbmb7WNfsS/FOfTeDrGaIsCZCs\na74cio/lmJgYnDhxAnFxcVSywpynUqkQEhICZ2dnhIaGoqioSFZffv31V1lycmD53v8rqFQqpKam\nQqVS/a3tsqxBJ06cYGr7xo0bOHjwIKKjo8V5XSpZWVmSZVj7zcKRI0c0XoeEhHylnvy9jBkzBqtX\nr0ZMTIwkuYKCAjx8+BA3btzAo0ePJM/JLPJyZX/55RcAwPv377FmzRpMmzYN69evR25urqS+K5VK\nfPjwQZLMl4LVhuFIY8GCBZg3bx4iIyMlrQH5+fkIDAzEnDlzMGnSJMyZMwe7du2iGjdfS7YkGRkZ\nuHv3ruS5/GvO46tWrdJ47eDgIEn+n/p8sdqsrMjVGVj7/fz5c1y5cgV//vkntT2Sn5+Pffv24dCh\nQxprx4EDB6jko6OjkZSUBKVSiW3btmH79u3Iy8uT1G+WZ4TVD/a1+BpjtOR8IIcnT57Azc0NDg4O\n4o8UWGx8qbrJl0aOjf/8+XNYW1tj8eLFGglGrq6uWmXfv3+PvXv34tixY3j16hUsLS0xZcoUWf68\nf9pcHhkZievXr2u8d+HCBSrZ4OBgAOog+YIFCzB48GDY2NjgzZs3X7yf/z/BOka+xpzGMk4AtW2e\nm5sLQgiOHTuG8PBwSc95eHi4xs+pU6eofPOC/fXy5UvqtkryT137vgQ5OTnIy8vDqVOnJG1iYIk1\nfkl/6d/tQ2OB1R/0tWwgVvvnnwpLLIIlzggAHz58QGpqKtLT07Fz5068evWKWpY1/vM15sO4uDjc\nuHEDP//8M27cuIEbN27gt99+w6JFi/72vvzduLu748mTJ7LlfXx8NF57e3tLks/JyUF0dDTev39P\nLfO19QW5sNgRHPl8iXwuOX4ZAHjw4IHsNnNycrBx40Y4ODjg3LlzSExMlCTP8myfOXOGOufhU7B8\nbxYdbcaMGbJlASApKQmnTp0CoJ7LXrx4QS2bmpqKxYsXY/r06QgLC8P9+/clty9nPgTk+/UFRo8e\njcDAQFlxzrCwMKxZswYAMHv2bISHh1PLKpVKcYxeuHBBcjI6S79Z176YmBhMnDgRI0eOhJ+fHyIj\nI6ll79+/j6CgIABqm/3Ro0fUsqw6ltzc2pLoMvWCw/mbcHFxQe3atXH9+nW0b98ey5Ytg7+/v1a5\n33//HXv37kVcXByWLVsGANDR0UGPHj2o265QoQI8PDzQtGlT6Oio91+Ym5trlVMqldi4cSPOnj0L\npVKJihUrYvjw4Zg3bx50dekevdjYWLx79w5VqlSh7q/AlStXMHXqVJQpU0ayrFz5y5cv4969ezhx\n4gRGjRoFQO1kOXfuHIYMGaJV3t3dHQ0aNECNGjWwd+9edO7cGXv27MGQIUO0KgYJCQmf/b+mTZtS\n9Z/legNs11yhUGDevHka48zW1pZaXqlUIjo6Gk2bNoVCoQAA6OnpUcs7OTkhNDRUWqf/j927d8PU\n1BQGBgaSZbdu3Yru3bvLahdgv2csuLu7w8zMDEZGRtQyd+/eRXx8PPbs2SOOaZVKhaCgIFFx1QbL\nvapatSr27t2rMc569+5NLS/ner948QKLFi1C2bJlUaNGDaSkpKB8+fLYuHEjateurVWeZS7dunUr\nXr9+DQ8PD9SsWRMvX77Enj17kJaWBisrq1JlPT09oaurKz5PHTp0wG+//QYvLy84Oztr7berqyva\nt2+PihUrAgCGDRuGtLQ0uLm5Yf369aXKsl4zgdTUVGRnZ6NMmTLw9/eHpaWlpPFqZ2cn2SkkwPps\n5uTkwN/fH2lpaejfvz9at26Nxo0bU8my9BtQG6J79uyRLf/s2TPExsaiSZMmkq73mDFj0L17d5iZ\nmaFVq1aS2pSz/pTmkPf09NQqb2VlhaCgIBw5cgT79+9H9+7dsX//fpiammrVk7Zs2YKgoCCsW7cO\nubm5GDRoEG7evIlVq1ZROdUOHjyo8TogIEDcKKit7StXriAxMRH9+/eHg4MDnj9/jvr162PFihVU\n94vle38JIiMjoa+vj549e4rvXbhwAQMHDixVztHRER4eHrh//z4WL16MatWqITc3Fx4eHvj+++//\n6m4DYFuDwsLCYGJiIqvdDRs24M8//0RcXBz09PTg5+eHDRs2UMvfvn0b7u7uKCoqwtChQ1G/fn2Y\nmZn95f0WkDqnnT59GpGRkbhx4wZu3boFQO08iI6OxqRJkyS3n5ycDB0dHTRo0ECyLKBOVGvTpg3V\n3y5atAibNm2S1Y7A8ePHce3aNWzbtg2ZmZkwMTHB8OHDxfX4U1y+fBne3t5o0qQJKlSogNzcXMTH\nx8PW1lbrs8UqzyIbGhqKYcOGYfXq1TA0NISzszNu3LgBFxcX6jGzd+9e+Pj4QE9PD5UqVcKkSZNg\nYWFBJfsl7C9WG+bVq1c4deqURsLn/PnzqWRZ9IXdu3czOa7PnDmDgQMHUvsFSvL8+XMkJiaidevW\nqFOnjnjttBEaGorY2FgcOXIEO3bsQI8ePTB27FgYGhqWKufg4IA2bdpg0aJFqFixInJzc3H16lXY\n2dlh+/bt/5OygDo5ys/PD5cvX4anpyeMjIwQGxsLW1tbDBgwQKs8wD6PR0REQKFQoKCgAJ6enpgz\nZw6mTp1aqkxISAj8/PyQkZGBCxcugBAClUqFJk2aSGqb9fkC1LYqIQT/+c9/8O2330qSlztOWW1W\nlj6z6Aws/d63bx/Onz+Pt2/f4qeffkJSUhJcXFy0yi1duhSNGzdGYWEhJk6ciN27d6Nq1ao4ffo0\nxo8fX6qst7c37t+/j5ycHNSqVQtGRkaoWLEinJ2dJc2NLM8Iix9Mrk5cnCdPnuDgwYMaawiNHcQ6\nRk+cOCH5mkndJPcp7O3tYWFhgbp168qSZ7HxTU1N4ePjgyZNmohrX0JCArW+wLruyrHxly9fjtmz\nZ6OwsBDz5s3DunXr0LZtW6qE8CVLlsDIyAgxMTHw8fGBu7s7KlSogJUrVyIgIKBU2R9++EEjoJ+V\nlSXq9H/HJmoW/c7NzQ3Z2dkoLCxEYGAgtm3bBj09PQQFBVE9m+fPn4elpSVWr16NQYMGYe3atbh+\n/TqcnZ3h6+sr+bvIgWWsyXm2BbKzs3H79m2N+Wj48OFUsqzrPeucBqh9eC9evECjRo1QoUKFUv+W\ndZwEBQVh//79IISga9euUCqVKF++PKKioqjWTkCtn+Xl5aFDhw6IiopCfn4+ypQpg3bt2sHR0fGz\nctu2bUOLFi3g5OSEtWvXaiQZ0c5nrDGg5ORkREZGaoyVWbNmUclGRUUhIiJCQ9bNzY267YKCApQt\nW1Z8nZSUhEaNGlHJ2tnZoU+fPuL1PnPmDLZt21aqDGusEWCPkZ44cQJlypSBUqnE2rVrMXPmTOYE\nJilkZGQgISEBzZs3R7Vq1bT+/ZfyB8nR75KTk+Hl5YXNmzfjP//5DxYtWoQKFSpg7dq16NChQ6my\nX8r+iYiIwIgRIyTJFEeu/VKcrKwsqntVHDl6ypeIMwKAtbU1xo0bhwsXLqBx48ZwdnamTvBjjf+w\nzIcxMTFi/IQQAn9/f6rNUenp6Th69Chev36No0ePAlDnjND6eQEgJSVF47Wuri6qV6+uMT+WJD8/\nH4cOHYK+vj5GjRolrtMHDhzQard9KX744Qf4+voiNTUVJiYmMDExQaVKlbTKHTp0CIcPH0ZcXByu\nXr0KQD2vFBYWUm9oO3PmDHx9fUXfukKh0Bqf/V/QF+TanCx2REnCw8Px008/SZLJzs6Grq4uypcv\nL7738uVLrb79jIwM+Pn5QV9fH1OnTkX16tUBqPUfWl8ri53+/Plz7Ny5E+XKlZOVeC83n0tArl8G\nAPbs2YOXL1+Kz5aUedHR0RF9+/bFnTt3ULNmTTg5OWHfvn3U8nKfbQB4+PAhfHx80KtXL4wdOxbN\nmzenbhdg+94sOlqVKlVw4cIFjXtNq4sDal+avb09AKBv375wcnLC3r17qWSXL1+OadOmwcfHB507\nd4a9vT3CwsKo25YzHwrI9esLBAYG4uTJk5gzZw7q1asHMzMzjWdVW9uHDh0CAOzcuRMWFhbUc9Pi\nxYvRr18/GBkZISEhAb/88oskvyNLv1meDwBYvXo1PD094ezsjLFjx2LmzJno378/lay7uzs2btwI\nQB33tLe3p97gyapjyc2tLQlPJuf8I0hKSsLq1atx9+5dDBgwAH5+flRyQ4YMwZAhQ3Dp0iXqYGFJ\nBEM3PT1dktyaNWtQq1Yt/PLLL9DX10dOTg527dqFNWvWUO+Oi4uLQ7du3WBgYCAarrSO48zMTPTp\n0wcNGzaEQqGAQqGgrk4kV75ly5Z4/fo19PT0UL9+fQBqQ2zdunVUbT569AguLi6YNGkSQkJCUKFC\nBRQWFsLc3Fyrk8bR0RHJyclo1qyZhiNPoVCIu360wXK9AbZrPmbMGOp2PsXz5881lA2FQoGLFy9S\ny7Mo2RUqVMC8efNQq1YtjBkzBn379qV2tLA6UFnuWXh4OHbu3AmlUglCiORrJkcBqVixIl6+fIn8\n/Hxxp6OOjo6k78xyr6pXr47o6GhER0eL70kJEMi53l5eXrC3t0fnzp3F93777Te4u7trdRoDbHPp\nr7/+qpF02rp1a3h6emLy5MlalfNHjx5pyFarVg1OTk7UjqWUlBQNZVhXVxczZsygules10zAzs4O\n8+fPx/79+zFkyBB4eHiIlZ5oYAlIsc6nLAY0ayCNxRAVHLbfffcddu/ejWHDhlEHGeQkQArIWX9K\nBibT0tLg7e2NTp06UfVX4PDhwwgKCkLFihVRUFCAyZMnU89JUVFRovHUr18/WFpaUslduHAB2dnZ\n4vylVCqpj/jaunUrtm/fDhcXFyxcuBBdunRBdHQ0XF1dP0pSLw253/ur1W5GAAAgAElEQVRrBeeF\nNWfjxo3w9/dHkyZNkJqaCjs7O6pny9LS8rNrO62exbIGKZVK/PTTTxrPJa3D4e7duwgJCYGlpSVM\nTU0lb8javHkz9u3bB2tra8yZMwcTJkygXgtY+l38M6TMaT179kS1atWQmpoKU1NTAGp9izYId/v2\nbaxevRpVqlTBmDFjsGvXLpQtWxYTJ06k+t4l5/p169ZhyZIlALTfb7k75Iujo6ODvn37AlA/p8HB\nwThy5AhGjhz52SRpX19fhIaGauhx2dnZmDp1KpWzm0WetW1AfaLT6tWrAQDNmzfHuXPntMqsWrUK\n7dq1Q1BQEE6fPo0aNWogIyMDW7ZswaZNm6iqMn3OoS7F/mK1YRYuXIgePXqgXr161DICLPoCa1IC\ni7OeJbgBAHXq1IGhoSEePXqEmJgYrF69Gi1atMDixYs/K5OWlvZRQm2bNm0wceJEre19LVkAYgVz\nf39/hIaGwsDAALm5uZg5cya1f4h1Hg8ICICfnx/s7Oxw+fJlzJgxQ2sy+aRJk2Bubg5fX1/MnTsX\ngHpuk5rMwPp8rV69Gs2bN0dKSgoePXqEmjVrilVotMEyTln0BZY+A2w6A0u/IyIiEBISgilTpmDq\n1KnU/qGMjAxs3rwZAHDu3DnMnTsXgYGBVBW07ty5gwMHDiA3NxfGxsbYuXMnAFDr4wIsz4hcPxhr\nYoGA3ORqVr+KnASt1NTUz9oqtLZXzZo1JSXKlITFxn/79i1WrFiB+Ph4GBgYoFmzZnj8+DHOnz9P\nJc+67sq18YX72qhRI1hbW2PXrl1Uc/Hbt28xf/58qFQqGBsbi0VtaKqHrV27FoGBgXBzc0Pt2rVh\naWkpyY/DCot/PSYmBvv37wegrjK+aNEi+Pj4SK4AmZ6eDmNjYwDAgAEDEBgYWOrfGxsbiyedlkRq\nAj7LWGPZXDN9+nQ0b95cDBYrFArqZHLW9Z51TpOaEME6Tk6dOoXTp08jMzMTo0aNEu+xlETZwsJC\nBAUFQUdHByqVCrNmzcLu3bu1JvRNnDgRq1atQkJCApYvXy6+L8X+YY0BWVlZYfDgwbISC5YtW4ZZ\ns2bJTkqws7PD5s2bRZ9jQEAAdZXwV69e4aeffsKRI0cQHBysVR8G2GONAHuMNCgoCP7+/rC1tcWV\nK1cwffr0vzyZnGVDLKs/SECOfrdy5UqYm5tDV1cXXl5eWLt2rWhnalvHvpT9ExoaKjuZnNXOZilE\nIUdP+RJxRgDIy8vDwIEDERwcjDVr1lA9mwKs8R+W+dDJyQkbNmyAQqHAsmXL0KJFCyq5rl27omvX\nroiKisK3334rq+3Zs2cjNTUVTZs2xfPnz1G+fHkUFhZiyZIl4saXkrBsAgZKv660a3bfvn3Rt29f\nZGRkYPXq1Vi3bh2GDBkCKyurUjcGde7cGT169MDOnTsxZ84cAOqxVqNGDap2AXUiYFhYGGbMmAEr\nKyuMGTNGa3z2a+sLrDanXDuipA/s1KlTYhI6zfN96NAh+Pv7Q6VSwdzcXNxw5uDgoFVXWbp0KQYN\nGoTCwkJYWFjAz88PDRo0wO3bt7W2C7Bfs6VLl2LevHliMbaYmBjs2rULa9eupWpfbj6XgFy/DKCO\nub19+xanTp3CwoULYWBggHHjxqFbt25aZbOysjB27FicOHECHTt2lFxxWu6zDaiTfG1tbXH16lVs\n2rQJr1+/xrhx42BsbFzqBhkBlu/NoqOlp6drJH9L0cUFhAJbXbp0kXTNP3z4gB49emDHjh1o1qwZ\n9PX1JbUrZz4sjhy/vkCVKlUwadIkdO/eHT4+PrCzs0PDhg3x888/Y9CgQaXK6ujoiIUBypYtK0lH\nS01NFZ+nWbNmSfY7svSb5fkQaNy4MRQKBQwMDKjyNQTKli0rtmFoaCjqeDSw6lhyc2tLwpPJOf8I\nioqKxOSCnJwcSQ8boHaYr1ixAoWFhSCEIC0tjfqhmT9/Pq5fv47k5GR899131Alljx490lj0KlWq\nhEWLFkmaIKUclVAS1oodcuQbNGgAMzMz/PTTTxpKhpTEkKysLBgaGuLDhw+oUKECcnJyqIyDPXv2\nwMLCAuvWrUOdOnUk9x1gu94A2zU3NjbGsWPHkJKSgu7du6Nly5aS5E+ePAlArXxVq1ZNsqOFRcme\nMGECJkyYgGfPnsHX1xeurq4YM2YMJk+ejKpVq5Yqy+pAZbln/v7+8PX1lZV8AshTQNq0aYM2bdrA\n3Nxco10pxwix3CtPT08kJCQgKSkJrVu3llTlGpB3vTMyMjSSogGgV69e1DvgWObSTyUE6ejoUAWF\nPmUAKBQKjd3cpfG5SpM0BhjrNRNQKBTo0qULfH19MWLECEm7YwG2gBTrfMpiQLMG0lgM0YiICOzf\nvx+6urooKCjA+PHjqYMMchIgBeSsP3369BF/P3XqFHbs2IFly5Z91ulZktzcXGRlZaFWrVrieBe+\ntzZevXqF8+fPo3Llynjx4gUaNmyI1NRUMelLG35+fti0aROKioqwYMEC3Lp1i7o6gp6enqgndOnS\nBQCoqyYDbN8b+PrB+TJlyohBpDp16lA/WytWrAAAbN++HT/++CM6deqEqKgoqme9sLAQurq64mfI\ngcYZ8jmKioqQn58PhUKBoqIiyXaEjo6OqFvp6+tLchiw9FtA6pxWrVo19OzZEz179kRGRoY4NlNT\nU6mSpTZs2AAfHx+8fPkSc+fOxbVr11C2bFlYWlpSBcLWr18PHR0dtG7dGoB6To2IiACgPcCRnJz8\n2QqwtAGxtWvX4uLFi+jatStmzZqFb7/9FiqVCqNHj/7sXFpQUIBy5cppvKevr0+tT7PIs8g+f/4c\ngYGB0NXVxePHj9G2bVs8ePCAaj7q3r07Hj16hMzMTIwfPx6VKlVC69at0apVKxw4cADTpk3Tqsd/\nLiCsVCq1ti/AasNUrFgRNjY2kmQEWPQF1qQEFmc9S3Bj4cKFePbsGUxMTDTs5tGjR5cqp6+vj/Dw\ncPTp0weVK1dGTk4Orl69qrXy5NeUBf5rY1WuXFmsDlexYkVJeiXrPK6vrw8dHR1UqFAB5cqVo25b\nV1cXkydPxrlz5zT8WDNnzqRum/X5evDgAZycnMRExilTplDLsoxTFpuVpc8Am87A0m9hc7vUjS0F\nBQXIyMiAgYEBBg8ejJSUFCxevJhqHVCpVEhJSUH9+vXFSjnv3r2TNIcDbM+IXD/Yl0pYlZtczepX\nkZOgVVBQQL159nM0aNAAfn5+MDIyEsealIRRFhu/Xr16ot4gVP+XEnRlXXfl2Pi6urq4dOkS+vXr\nh2bNmmlUGNSGrq6uWKX6+PHjAIBbt25RrQFdu3ZFo0aN4OLigunTp8uqilpalUhtY43Fv15YWAil\nUgk9PT1YWloiJSUFq1atopaPiYnBqlWrUFBQgBs3bqBbt25Uiarbtm2Dra0tQkJCPtKrpcIy1lg2\n11SuXFn2Udas6z3rnCY1IYJ1nKhUKuTl5aFGjRpi1UqlUinpuPSsrCwUFhZCT08PhYWFePv2rfg5\npUEIwd69eyVV6CwJawyoXr16sLa2ltV248aNter9pdGjRw8sXboU2dnZqFKliiQ/c2FhIS5evIjm\nzZsjKysLubm5WmW+RKyRNUYqzCkVK1YUxwsNLJtcWDbEsvqDBOTod+/fv8ePP/6IzMxM/Pnnn+jV\nqxcAuo1UwJexfwoKCjBmzBiNeZg2CZHFfgHYClHI0VO+RJwRUF+zkJAQtGvXDnFxccjLy6OWZY3/\nGBsb4+DBg+IJrxMmTKCW9fb2hq2tLT58+ABHR0dJp9ED6nnk559/Fsdaeno6Tpw4QSXbsGFD7N27\nFwYGBnj79i2cnZ2xcuVKzJo167NxFZZNwIB6w9rDhw8/maBJq8/HxcXh6NGjiIyMRNeuXRESEoLC\nwkIsWrRIrNL+KZYuXYpDhw4hIyND9qmVZcqUgZ6enqhb0cRYv7a+wGJzstgRWVlZiImJwfjx40EI\ngb6+vqSKy2FhYeLpBA4ODvD19cWcOXOo+q1UKsWNykZGRrCyskJwcDD1OGW101UqFfr16yf6J1q1\naoVnz55RyQLy87kE5PplBN68eYOUlBRkZmaiefPmOHv2LA4dOqT1pHJA/XwCaltZ6qbW4s92t27d\nqJ9tQP2df/31V4SHh4sVxjMzMzFnzhzqUyrkfm8WHS04OBjZ2dl4+fIlDA0NJcXrAHVy8sGDB/H9\n998jKipKkry+vj6uXbsGlUqFe/fuSR4ncuZDAbl+fYGQkBAcP34clSpVgpmZGby8vFBYWIhx48Zp\nTcr+8ccfMXHiRHz77bd49OiRpCK+CoVCPJkuKSlJ8oYJln6zPB+A+hSvAwcOIC8vDxEREZI25dav\nXx8bNmwQx5kUO5tVx2LNrRXgyeScfwQ2NjaYMGECXr9+DXNzc+rK3gIuLi6YOnUqzp8/jxYtWkgy\niOQec/u5ILAUh97Tp0/h6OiI1NRU1KxZEx4eHmjbti2VrK6uLtatW4eMjAwMHToUrVu3lqTos8jv\n3LlTnIzz8vJgaGiIX375RauclZUVLC0t0apVK5iYmKB9+/Z49uwZVfJI+fLlsWLFCqSkpMhOJn/2\n7BlcXV3x7t07mJiYoGXLltRHVQBs18zV1ZXpuIk7d+5gxYoVsna+A2xK9rt37xAREYHjx4+jcuXK\ncHJyQlFREWbPnq3V4c7iMADY7pmhoSEaN24sqb3iyDW+AbXTMCAgAEVFRaKBQlvJY/78+bh8+TKe\nPXuGpk2bSqq8VbzCgqmpKRITEyVVWJBzvT+XVE2rLLLMpZ/7Gxrj1cDAAA8ePED79u3F9x48eECt\n2Ddq1Oij47suXryIWrVqaZVlvWYChYWFWLduHTp37oybN29KCqwA/w1Ipaeno1q1apKMWNb5FJBv\nQLMG0lgMUUKIxu5cms0DAnISIAXkrj9ZWVlwdXVFTk4OQkJCJK2fHTt2hJWVFRITExEQEABLS0tM\nmDCB6mirZcuW4eHDhygqKsKFCxcwZswYjB8/Xqysqw2FQgEbGxucPXsWCxYskJT00q5dO7i7u6ND\nhw5wdHRE//79ceXKFeqqsCzfG/h6wfmcnByMHj0a79+/x6FDh2BiYgIvLy+xupM2mjVrBkDtHBIq\npA0aNIiqSt6yZcvg7e0tVikDIPlEkLZt28Lf3x9paWno37+/mKhMw5QpUzB69GhkZGTAzMxMUpUd\nQD2fe3t7IysrC35+ftTXjLXfAnLntOXLl+P27dv48OEDPnz4AENDQxw+fFirnEqlQoMGDdCgQQNY\nWFiISZu07YaGhsLd3R0dO3aEmZkZLC0t4enpSSVbrlw5yY7ekjRt2hRHjx4V527hOLjSTvYwNzeH\nqakpOnXqJCas3r17l3oTMIs8i+zOnTvx8OFDNGnSBE+fPoWhoSFWrlxJtXFj4MCBGDhwIO7fvw8H\nBwfUqlULT58+RXR0NN6+fYspU6YgJycHFy5c0PpZQlU6IQhXtmxZar2W1YZp2bIlIiIiNBLjaMcQ\ni77AmpTA4qxnCW70799fDGACQHx8PJo1a6a1+vL69euxfft2BAUFITc3FxUrVkTHjh2pKj4Xl83J\nyUGlSpX+FllAnUwxYsQIvHv3DkFBQTA3N8fChQvF6jM0sM7jDRo0gLm5OZYuXQofHx/qimkAMG/e\nPDRq1AjPnj2Dvr4+KlSoICmZgvX5UqlUePjwIRo2bAilUkmV9CPAMk5ZbFaWPgNsOgNLv0eMGIFJ\nkyYhJSUFs2bNorbxFy5ciEmTJiE4OBg1a9bE1KlTkZeXh0uXLmmVXbp0KaytrXHo0CF89913AIC5\nc+di9uzZVG0LsDwjcv1grIkFAnKTq1n9KnIStBo0aCA7cVGgoKAACQkJGiclSUkmZ7Hxs7Oz8ccf\nf6Bly5aoW7eu5GrwrOuuHBvfw8MDmzdvRseOHVGtWjV0794djo6OVHrtunXr4O/vDxMTE9E/cObM\nGerNtXXr1sWWLVvg7u4uaxPB0KFDsXHjRri5uUmWZfGvT548GSNHjsSBAwdgYGCApUuXYvny5bh7\n9y6V/NmzZ/H48WPUqVMHeXl5yMvLw7lz5+Dh4VGqXOPGjTF58mTcunUL/fr1k9TnkrCMNZbNNb17\n90ZoaKiGniBsftcG63rPOqdJTYhgHSezZs3C6NGj8csvv4jJAzNmzJD0nSdOnAhjY2O0bNkS8fHx\nmDlzJnx9fTWKLnyK4OBgNGzYEOfPn0eHDh00/Mu08ylrDKh///5Yv369xlih9UcNGTIENjY2Gv4v\nmrVF8LmNGTMG79+/x40bNySvu9OnT8exY8fg6OiIgIAAsbotDXJjjQB7jNTQ0BDm5uZwcHDAtm3b\nqPUclk0uX2JDrFx/kEDbtm2xfft2xMXFoUmTJlQVM4VNYjdu3ED37t0BqG2C7Oxs6nZZ7R+aE9Y+\nB2sSIUshCpZYBEucEQCWLFmC8+fPw8rKCsePH4eDgwO1LEv+AqDO26hSpQp69eqF27dvw9nZWWvy\nf/GTejp27IirV68iKSkJSUlJ1Kf1AOpkdOGU0q5du1JXXgbUsTIDAwMA6iSzN2/eoFq1aqUmarFs\nAgbU1X8tLCwwa9Ys0UcvFWdnZ4wbNw7z58/XWKu1bZwwNDREjx49NE6JFaCtktqpUyfY2dkhNTUV\nLi4uGjHXz/G19QUWm5PFjnB3d8eBAwdw584duLi44NixY+JJEzQIehmgPvV75syZ4iZJbRQVFeHp\n06do3bo1OnbsiNmzZ2Pu3Ll4//49Vdusdnrt2rWRnJysET+iLT4FyM/nEpDrlwEAMzMzlCtXDuPG\njcPChQvFe0BTaMzZ2RmOjo6Ii4vDggULxM0PtCxfvhxmZmaSn20AGDx4MDp37gxLS0uNE6tjY2Op\n2mb53iw62tmzZ7Fjxw7qk5FK4uXlhR07doh5g9rszeKsXLkSa9asQWZmJvbs2SPZ5u7UqRNsbW0l\nzYcCZmZmn7Q5aE9VjI2Nhbe3NwwNDcX3ypYtC3d3d62yVlZW6N+/PxISEvDTTz9JKtDm4OAAGxsb\nvHnzBrVr15ZcdIyl3yzPB6Cez319fVG9enU8fPiQOocBUG/aDg0NxZUrV9CiRQtJY5Q1x2bRokUa\nubWOjo7UshoQDucfwJMnTwghhKSnpxOVSiVZfsqUKYQQQuzt7QkhhEyaNIladuLEiYQQQiwsLAgh\nhJiZmVHJWVhYEKVSSfLz8zV+hM+h/Qzhuz9+/JiYm5tTy86aNYtcv36dWFhYkLi4OOp+fwl5ExMT\n8uHDB+Lm5kbi4uLI9OnTqWVzcnLItWvXyIkTJ8i1a9dIenq6pH6zMHnyZPL8+XNiYWFB0tPTiamp\nqSR5lmsmjAvhXyn3mhD1OM3MzCQWFhbkw4cPkvvu7e1NlixZQkaPHk2OHj1KbGxsqGUHDRpEtm7d\nSl6+fKnx/oYNG7TKOjg4EE9PT3L+/HmyevVqsmTJEkn9ZrlnCxcuJDNmzCDr168n3t7exNvbW1Lb\n48ePJ0ePHiXv37/XeH/fvn1aZUeOHElSUlKIq6sr+e2338i8efOo212/fj2xtrYmAQEBZN68ecTL\ny0tSn4uKisRxNnr0aGpZQuRdb2NjY3Lt2jWNn6tXrxITExOqNlnm0nbt2pFevXp99PPNN99olX31\n6hUxNTUlVlZWxNPTk1hbW5PRo0eT5ORkqn6/ffuWzJgxg/z0009k/vz5ZMyYMWTWrFkkMzNTqyzr\nNRNISEgg+/btI/n5+SQiIoIkJSVJkr958yYZMGAAGTVqFOnfvz/59ddfqWVZ59OnT5+ScePGkU6d\nOhEzMzPy6NEjatnbt2+TESNGkKFDh5JNmzaRsLAwSW2fOXOGjBo1iowcOZJs27aNbN++nVrWy8uL\nWFtbk8DAQGJtbS3p+Tx48CDJzc396H2aMSdn/bl48SIZNGgQ2b9/P3UfP4VKpSI5OTmkqKiIxMbG\nSpYvKipiaj8mJoasXbtWUntHjx4ltra2ZNq0acTGxkZ8TqSgUqlIbm4uUalUkr/3gwcPyB9//CFJ\nhhBCTp48SQYNGiTqRiqVijg5OREjIyMq+fz8fHL//n3y9OlTkp+fT/bv30+USqWkPkyePJmEhYWR\n6OhoEhoaKknHY8Ha2pocOnSITJgwgdy9e1eSLk8IISkpKeT+/fsf6So0FBQUkP379xM3NzcSHBws\naayw9psQ+XOaqakpUalUZPny5SQ9PZ3aBtmwYQOZOnWqxrO5YsUK4urqKqnfu3fvJi4uLmTChAnU\nMlLspJKkpaWR+Ph4YmZmRhISEkh8fDyJjY0lY8aMoZJ//fo1uXjxIjl+/Di5ePEief36taT2WeRZ\n22YhISGBjBo1itjb25M9e/YQJycn8T7QjvWRI0eS1NRU4ubmRm7evEnmzp1L3T6rDWNhYaHxY2lp\nSS3Loi+8evWKWFtbk+HDhxMrKytq/VBg4MCBxN7envz+++8a7wv+itIIDg4mEyZMIP369SMzZ84k\nu3bt0irz9OlTUZf89ddfybVr18jly5cl65Zyeffu3Uc204sXLyR/juAbkcqbN2/Iq1evSEFBAbly\n5YokWdZ5PDU1lbx9+5YQQsiff/5JEhISqGWF+dPe3p4UFhb+7T6Cffv2kbFjx5KYmBiyatUqSc+I\nnHEqwGKzsvRZICsri9y/f59kZGRIkmPpd0FBAYmNjSWnT58mT548EceMXN68ecMkLwWWZ0SuH4xV\nJxawt7f/6IcGVr9KdnY22bBhA7G3tydnz54lz58/1ypjZ2cnqY3PER8fTy5fvkxevXol2Q5jsfG7\nd+9O5s2bRwYPHkz69+9PZsyYIclWZl13WWz8r01qaqosuVWrVpHTp09/4d4Qcv78+VL//8OHDx/F\nbgR/jjZZQghRKpXkwYMH5Pr16+Thw4eS7XRW/vzzT2JnZ0emTZtGDh48SO7du0ctK+fZFpg7dy6Z\nMWMGsbGxITY2NsTW1pZalnW9Z53TvL29ia2tLRk8eDBZvnw58fT01CpT2jihoeT8lZ2dTS0rkJGR\nQe7duyeu94WFhVplzp8/TxwcHEivXr1krR+EsMeALCwsiIuLC1m/fr0Y06BlzJgxZOfOnSQ0NFT8\noaF///5kwIABpH///ho/AwYMoG778OHDGq9p4icCLLFG1hgpIep4JSFEsp0eHh5OLl++LLm9uXPn\nkuHDh5PevXuTgIAA8v79ezJr1izi4eFB/Rly/UEC1tbWJCgoiDx+/JgEBgaS2bNna5Xx9PQU54Jb\nt26R1NRU4uTkRFavXk3dLqv9k5OTQ7Zs2UKcnZ3J+fPnJcVCWOwXQghxdHQk69evJ8bGxmTnzp1k\n6dKl1LIsegpLnJEQQnx9fTVe08R0BVjyFwj5b+6FAI381q1bP/sjBWEeWbZsGSFEmj/Szc2N2NjY\nkL179xIbGxuyYsUKEhERUaov7Pr162To0KEa84iPjw9p164ddbtJSUkkOjqa+u8/RWpqKnn58iV5\n8eKF5NiEm5sbU9tXrlwh/v7+5NKlS9QyX1Nf+BI2Z2BgIMnKyqL+++L88ccfZPr06ZL1sjVr1pD5\n8+eTd+/eEULU/g1TU1PSpUsXrbKPHz8mFhYWGuM0PDycdO3alapt1muWnJxMpkyZQnr16kUOHz5M\nFi5cSKytralkCZGfzyVQ0i8jhfv372u8vnXrliR5FqTk85RE6txZEpbvzaKjmZubi3kiKpWK2v55\n9eoVIUTtFyn5o42SOSrFf6QiZz4k5ON1Uyrjx4+XLCP4VYvnUsnJqWJBTr8FWJ4PgTdv3pCXL1+K\nP9qIiooihJCP8nyuXbtG3SZrjo2A3NxaAV6ZnPOPYNOmTcjKysLo0aMxcuRI6mONBcqUKSMe0ZSU\nlCQeXUeD3GNuX758iaFDh2q8R/5vZ7AUhJ09RkZGn61W+yk+fPiAHj16YMeOHWjWrJmk40NZ5WvV\nqgV9fX3k5OSgWbNm1BVD8/PzcfjwYVy/fh05OTmoXLkyOnfuDAsLC+ZjKmlp3LgxFAoFDAwMJB+L\nwnLNhOMmFAqFrOMmWHa+A8Ddu3cREhICS0tLmJqaUu1iE+7riRMnxP4K7+np6VEdOZ+YmIiQkBAA\n6gqJ48ePl9RvQP49Y61WY2NjA4VCgYcPHwJQ76KsV68eJk2apFW2du3aqFevHvLy8tCzZ0/s2LGD\nut07d+6IFd+nTJmCcePGUcsSxgoLgPTr3a5dO0RERHz0Pm2VAmEuFfpO/q/qC81cKtwbOdStWxeH\nDx/G3bt3kZaWhiFDhuD777+nnsOrVKmCXbt2ISUlBWlpaahXrx51VSfWayZUVH/x4gUaN26M27dv\no0qVKkhMTNTYuamNTZs2Yf/+/ahTpw5SU1Mxf/588YhKGljm05cvX2pUmjh9+jT199+0aZPsYyUB\nICAgQNIRvcVZtmwZLl++jLi4OIwZM0bSPNO3b1+4uLho7MT+7rvv0LBhQ62yctYfKysrlC9fHtu3\nb8f27ds1/o+2qgSgfhaF+0tb3Ts5ORmenp549OgRypQpA5VKhVatWsHBwUFyVeKWLVtiyZIl1H+v\no6MDU1NTSRUdipOfn48DBw7gxo0byM7OFnWVBg0aUOsq33zzzWf/r+SJBsUZOXIkBg0aJM7dCoUC\nq1atwsSJE7XKAuo5/9tvvxVfSz0NBFBXifX19cWZM2fQokULqmP6BC5evIj9+/ejoKAAhBBkZWWJ\nlYG1kZWVhbFjx+LEiRPo2LGjpIpM27Ztg1KphK2tLRYsWIBvvvkGP//8M7X8o0ePUFBQAFdXV9jZ\n2aFjx47U8xFLvwXkzmnVq1eHQqHA+/fvxYo5NNjY2ODJkycauujgwYPRtWtXSf2ePn06bty4gZyc\nHGqZ0p4Nbdy/fx979+5FQkICli9fDkD9vNNWiKtZs6ak4/mKk5GRgd27d0NPTw9Tp05F9erVAYD6\n+PPibXt6esruhxyaNGmCsLAwXL9+HdHR0WjdurU4p9LqibVr142Qy7kAACAASURBVEbt2rWRm5uL\nbt26lVoFviSsNkzJ0xGknFTBoi84OztjwoQJ6NKlC27fvg0nJyeNY6m1cezYMSiVSiQnJyMrK0us\nOEdTpcjCwgI9evRATEwMmjZtSrX2vnv3DqdPn0Z6erp43K1CoRDXj7+SQ4cOwd/fHyqVCubm5pg1\naxYAdVUSbUd3l9RH1q1bJ45PKdV0a9SoIf7et29fajlA/jweFxeHtLQ0rFmzBsuWLQOgrpq9bt06\nhIeHU32Grq4ulEol8vLyRH1JCqzP16RJk0Tbdvr06RrHp2uj+Dht1qyZpGrVLDYrS58B4Pr16ygs\nLIRKpYKtrS0WLlwIY2Pjv6zfr1+/Rk5ODpYtW4a1a9eiTZs2UKlUmD59uqTqkSUpPub/alh0Hbl+\nMEEnFmyekjoxLZ6enoiJiUFsbCyaNm0KIyMjKjlWv4qjoyP69u2LO3fuoGbNmnBycsK+fftKlRkx\nYoSkNj4Fa/VhQL6NHxYWJvoi8vPzERsbK+nIctZ1l8XG/9pIOQq5OFJPdaUlKCioVJvzU74IwXbS\nJnv58mV4e3ujSZMmqFChAnJzcxEfHw9bW1uqyoDJycmIjIxEfn6++J6gd9CyfPlyTJs2DT4+Pujc\nuTPs7e0RFhZGJSvn2RZ4//49AgMDJfVVgHW9Z53TbG1tcfXqVRgZGaFZs2ZUtkxp44SGkutFpUqV\nqGUB4MmTJzh48KDGWKHRw4XTnS5duoQBAwYgIyNDazXakrDGgPT09CRX8xOoVq2aJF+IAM2JJ5/j\n9OnTiIyMxI0bN3Dr1i0Aap34yZMnVDEUQH6sEWCPkQq6ISEEK1eulKQbjho1SlJbAj4+PgDU1Y8L\nCgqgp6cHCwsLSXaMXH+QQGZmpnhampGREVWl62XLluHq1auYOnUq2rdvj6dPn6JFixaYPHkydbus\n9o+TkxN69OiB69evY9SoUXB0dKQ6URFgs18AYMWKFTh06BA6deqEChUqYOXKldSyLHqK3Djj4cOH\ncfToUTx79gxXr14FoH428/LyqOK6AnLzFwC1TpiXl4fy5cvjw4cPKCoq0ioj+NgSExPx4MEDjBw5\nEuvXr5ccVy5btizu3r2LgoIC3LhxA5mZmdSyrq6uuHjxIuLi4jBq1Cj069cP8fHxpVYM7dGjx0cn\nKsydO1dSbFdKXO9TODo64t69e+LpL40aNaLScSIjI8UTqIrH6wBQV4NPTU1F/fr10bBhQ+zatQt1\n69alsr2+pr5QMg4DQLLNWVRUhGnTpqFp06YYN24cunXrRi3boUMHeHl54cyZM9QygPoEslu3bonX\nrmrVqggNDaXK+TAyMtKYMx8/foxRo0ZRr3us10wYHxcuXEB0dDS6dOmCsWPHUskC8vO5BIyNjdG/\nf3+YmZlRxyh///13xMbGIjAwENOmTRP7sX//ftH/qo3w8HD4+flp6KW0p/kC6orw0dHRaNq0qWR9\n/vbt2ygqKpJ0MjjwZb43i44m9WQkgYCAADg4OMDFxeWjfBNtPmqhAnrxXD8i8fRlQH2CdE5ODmrW\nrIm3b98iPDyc+pQhhUKBefPmoWnTpuL4trW1pW67atWq2Lt3r4a8Nv+6cKpcyVMxaHJlFixYgC1b\ntnyyDSk5CHL6LcDyfACAm5sbrl69itq1a4v3W8jR+hw3btxA+/btP5nnIyWeIcf/Zm5u/tl7o63f\nn4Ink3P+Efj6+uL169c4fvw4pk+fjubNm0s6RmDp0qWIjo6GhYUF5s+fTz0pAx8fcyssitr4nJOF\n9jgYQK3oRkZGonPnzrhz546kyU1fXx/Xrl2DSqXCvXv3JDsCWeRr166No0ePoly5cti0aRPevXtH\nJefg4IA2bdrAxsYGFStWRG5uLq5evQo7O7uPEt1KUtpRNbQLadWqVXHgwAHk5eUhIiICVapUoZIT\nYLlmrMdNNGrUCN7e3sjKyoKfnx/q168vSV6Oki0oTgA0jnWUojjJcRgUh+WeGRsb4+DBg4iNjUWT\nJk0kJ9Vt3rwZb968Qbt27fD48WOULVsWSqUSZmZmWo/fq1SpkniNDh06hKysLOp2heC2jo6O5A0q\nLMc0AfKut6enp4YxkpOTg3LlylE7l1gc1oWFhdiyZQvmzZsHfX19REZG4u7du1i0aBFV+zExMbh+\n/ToyMzNRt25dGBgYoHHjxtTtX758GadPnxblR4wYIR7zWBqs1+xLKaplypQRE+Dr1KkjyZiT+2xG\nRkbijz/+QEREBP7zn/8AUDsyL168iOHDh1N9BmsgTa4hmpCQgKZNmyIpKQl5eXl4+PAhOnfuTN2+\ni4uL7KClnPUnOjqa6rP/CpycnGBnZ4fvvvtOfO/evXtwcHCQZdBIobSgE811E3SVRYsWSdZVaPgr\ng/MsJCQkiL9bWFiI609WVpaYOKuNTZs2iUc1duvWDb/99pukPsTFxQEA/vzzT0kOrkuXLuHo0aMA\ngC1btmD8+PGSAqju7u7YuHEjALW+Zm9vL26E+yv7LSB3TmvTpg0CAwNRs2ZNLF68GHl5edRtGhkZ\nITU1FdnZ2ShTpgxOnDiBqlWrUidZAepAQa1atTBv3jw4OjrC0tJSq3xxp5RUHUcI7F+5ckXyhsGS\nwZDi0ARGli5dikGDBqGwsBAWFhbw8/NDgwYNqI7JLRnwio2Nxf379wHIc/DIQU9PDz/88AN++OEH\nWfKVK1fGhQsXRGeaFL2W1YY5cOAAAgICxAB/2bJlqY90ZtEX8vPz8eOPPwJQj72AgABJ/T5x4gT2\n7t2LFi1aIDY2FlZWVtTJBv7+/pg1axaaN2+Op0+fYty4cTh27FipMp07d0bnzp3x6NEjtGvXTlJf\nAbXd9Lngqjbnb1hYmBhIcHBwgK+vL+bMmaNhP36O9evXQ0dHRwzkp6eni/qtFJ2WFTnzeHp6Oo4e\nPYrXr1+La5COjo6kDY7jx49HQEAAevbsif79+0s6+hRgf7527dqFKlWq4N27dzh69Cj69OlDfex5\ndHQ08vLyUK9ePXh4eGDOnDno0aMHlezIkSNl26wsfQbUx4d7e3tjxYoVCA0NxaJFiyQFTqX2m3Uj\n1P8KcnUduX6w0u4pTTKgQHBwME6dOoVvv/0We/bswbBhw6iOY2YZo4C8BPyAgAAxQWXRokXYtGmT\npDYBICIiAiEhIZgyZQqmTJlCfayvAIv/rXjyi76+Ptq1aydpPWJdd+Xa+IC8wjBfW7Y46enpGokJ\nUteCktCs33JlfX19ERoaqpHok52djalTp1I9Z1ZWVhg8eLBkf35xWJIaWDbXtGzZEhERETAyMhLv\nO20SC+t6L3dOKyoqQlFREWxtbbFx40Z0794dKpUKkydP1pqM8bWxt7eHhYWFmBwhlQoVKuDHH39E\n5cqV8e7dO6xcuZK6+AZrDKh+/frYuXMn2rZtK44VWr2hevXqcHFx0ZClsXfd3d3h4uLyyeQEbTZr\nz549Ua1aNaSmpopFHRQKBZo0aULVZ0B+rBFgj5Gy6IaPHj1C1apVUbduXezatQsFBQWYMmUK1RxV\nXNbf3x8FBQX4/vvvqec3Fn8QoF53X79+jVq1auHNmzdU85lCodDwxbRu3RqtW7dGYGAgpk6dStUu\nq/2TkZEBc3NzREREoHPnzpJijSz2C6D2N/fv3x8DBw5EWFgYXr9+jQYNGlDJsugpcuOMxsbG6NKl\nC3x9fTF37lwA6ntYq1Yt6rZZ8hcAYPLkyRg1ahRatmyJ2NhYLFiwgFp22bJlsLe3B6AuGiZ1o6Gr\nqyvi4+Mxe/ZsbNy4UZKfOCcnB/n5+ahduzYyMzMlJQKWhHYT8L179+Du7g59fX3Y2dmhc+fOAIB5\n8+ZRxyOio6MREREBFxcX2NjYYOHChVRywph68+YN1d9/Cjs7O8yfPx/79+/HkCFD4OHhQb3R42tS\nXA+cMmUKgoKCJCWzT58+HdOnT0dUVBR2794NFxcXap8loN5Mdf78eXFzDy3Fk9ZXrVoFZ2dn6nm4\nOF5eXggKCpKUlM16zXR1dTF06NCPinXSIDefS+D48eO4dOkSvLy8kJ+fj9GjR8PExKRUmSpVquDN\nmzdQKpV4/fo1APVcKqX4lb+/P3bs2CG5GIJAQkKCxgYkKTk6mZmZ6NOnDxo2bCiuQTTxiC/xvVl0\ntE6dOsHW1hapqalwcXGh1hcEX9K0adM0NsCePn1aq2zxXBVCCDIzM2Vt2LOyshI3ggF0SdkCUn04\nJalevTqio6M1cgO02RF9+vQBoC5iWLwYwdKlS7WufVu2bAGg1k+Kj2/Bh/hX9luA5fkAgKioKFy4\ncEHSPCjoFB07dtSIA0ixkeX630rLl5QDTybn/GMoLCyEUqmESqWiDk4cOXIEY8aMEQ1XQB24lUKH\nDh2wf/9+JCYmomHDhpKC459i8uTJ1BWGPDw8sGbNGnh7e6N58+ZYtWoVdTsrV67EmjVrkJmZiT17\n9kiuWFBS3s3NTZJsSkoKBg8ejMOHD2Pt2rVUcmlpaR9Ncm3atKHatWhgYIDQ0FDMnTtXtmPbw8MD\nvr6+qF69Oh4+fChpwwLAds26du2Ks2fPIiMjQ6wcIIXiO9/Lly8vaawAHyvZNIYFS5KvAIvDAGC7\nZy4uLqhSpQp69eqF27dvw9nZmXqsAkC5cuVw4sQJ6OvrQ6lUwtraGlu3boWFhYXWZPJVq1YhMTER\nNjY22LVrlyTH8fDhwzFhwgR89913iIqKok6wBdRVaHv27ClWMpQaYCh5vT08PLTKxMTEYN68eTh8\n+DCqVq2KmzdvwsvLC76+vmjRooVW+ZycHLi6umLFihWoVKkSTp48iUuXLmHlypVad5J7eHigTJky\n4vPUoUMH/Pbbb/Dy8oKzs3Opsr/88gv8/f0xfvx4fPPNN0hJScH8+fOxcOFCqsBKSEgIrl69ismT\nJ6NGjRpISUmBr68vEhMTtTrqWa+ZoKi2bt0apqamqFq1qlaZT1GpUiUEBwejS5cuuHPnjqTPkTNW\nAPWcn5WVBX19fTFwplAoJFViYw2kyTFEw8LCcPLkSQQHByM8PBzjxo3D/fv34efnR13NgyVoKWfN\nL60qplwHKC1KpVIjkRwAvv/++7+0TQFjY2Okp6ejatWqGice0BqRLLoKDX9lcJ6FklULi183WiO4\ndu3a6NChAw4cOIDRo0drTX4sjrOzMxwdHREXF4cFCxbA1dWVWlahUECpVEJPT0+sii6FsmXLolGj\nRgDUiTBSHAdOTk6y+y0gdU4T7suSJUuQnZ2NcuXK4fLlyx89c9pgdfTLkT958qToYBEczrT4+PjA\nysoKx48f/8je8/b2LlU2Pj4ekZGRWh3En0OpVIpru5GREaysrBAcHEw11iZNmoQjR47AyckJ5cuX\nh52dndb+lsTd3R1mZmaSkv2/JKtWrUJSUhJsbW0REBCgVccqDkv1LkCtbwUHB2PHjh0YOnSopOAh\ni75QVFSEp0+fonXr1nj69Klk2y0sLEy0I/Ly8mBhYUGdTP7s2TOEhobi/fv3CA8Pp7I5hQQQd3d3\nyQkggLrKvq2tLUJCQiSfGCYExgFgzZo1mDlzphik0EZoaCjc3d1F56ulpaWkRFEBIRFCDnLXn65d\nu6Jr166IiorSOBWEhsePH6Nt27Yadt6QIUMkJ8exPl/nzp3Dvn37MHPmTJw+fVpSANPNzQ3Lly/H\n1q1bYWNjg3Xr1lEnY7BUBWTpM6C28WvUqAFdXV3UqlVL0rMtp98sG6FK8vz5cyQmJqJ169aoU6eO\npL6zyJZ8RqT6wX755RcxCEfb7sOHD/HhwweYmJigQ4cOsnXgU6dOISQkBLq6uigoKMD48eOpkslL\nnhAhVGSUgtQE/OLfMT09XXJ7wmewVB9m9ZmywLruyg02A8CMGTOwZ88eqV3+qrICcqpoaYMlwV2b\nbEFBwUd6hr6+PnWb9erVg7W1tez+Ce2xJJ7K3VxTMjguxc5mjQkIc9qzZ8/QtGlT6nX3yJEj8PX1\nxZs3b8QTJcuUKYNOnTpRt52Tk4MXL16gUaNGkk8iZpGvWbOmpA1+Jdm8ebPskxxZY0CFhYV4/vw5\nnj9/Lr5Hm0whFEiRmhAoJGDISU6oVq0aevbsiZ49eyIjIwMFBQUA1JvPaZP55cYaBVm58TpAvm7o\n6emJBw8eoKCgAFWqVBFP81qyZAl27tz5l8l+KX/QwoULMX78eFSuXBk5OTmS7YjinDp1Smus8UvZ\nP4QQJCYmAlCPMSnzMIv9Aqirb06YMAFnz55FixYt4OLigt27d1PJsugpcuOM+vr6aNy4MVauXInH\njx+LG/QfPnyIYcOGUX0GS/4CoJ4Pw8LCkJycjIYNG0qqDg78N5bQpUsX6g1cO3fuxOzZs1GvXj0x\nsU3KqdGAvERAOzu7z/4fjQ/Qy8sL3t7eKCwsxNKlS2FnZ4fevXtL2lwj98QCYSPQnDlz8OTJE3z4\n8IFaVkChUIibF0aMGEFdQEnga+kLxZFjc3748AFnz55FeHg4CCGy9FTWeE9MTIxsWda2/8pY1adg\nzefS09PD0KFDUbNmTQQFBWHHjh1aYwWtWrVCq1atMG7cONknShkaGkoqYlcS4eTf9PR0VKtWTdLa\n5+vrK6vNL/G9WfLYhJOR2rZti+bNm5d6MkRxihezu3fvHgDpxeyuXLmClStXonLlynj//j3c3d0l\nnTpACJF02nNxWItkyjmdLyQkBDt27MDbt29x7tw58TvQ5KrExMQgNTUV69evx9KlS0EIgUqlgre3\nN44fP/6X9luA5fkA1PZTfn6+pI1+p06dwqVLl3Dr1i3cvHkTgHqcxcTEUJ/YI9f/JmxkTE5Oxtq1\na/H8+XPJp7sXhyeTc/4RTJ48GUqlEmPHjkVgYCC1snf8+HHZu3SKT3DCA/bw4UPJE1xJpChP169f\nF3ftAOodK7STzLVr18QqilJkhYqdBgYGWLNmDXVfgf9WxylTpoxYdUbKbkd9fX2Eh4ejT58+oqPi\nypUrVPd76tSpePjwIWrXro2ePXtK6rdApUqVMHv2bCgUCrG6nhTq1q2rcc1pYKnsUBwPD4+PdoRJ\ncawNGzYMPXv2RFJSEho2bEhVZfRLHJXB6jCoVKkSpk2bJlbZef/+vXhMvDYSExPFyqIDBw6UfBRa\nZmammOypp6eHzMxM6Onpleq4EHYBV65cGd988w0ASEq4AdTzYe/evREfH4+xY8dSOV6/1PHZQUFB\nWLx4sfja29u7VEcIAKxevRobNmwQE5EHDhwIAwMDrFq1iuoIV1dXV7Rv316sFjls2DCkpaXBzc1N\nq8L96NEjjWqj1apVg5OTE1XAICgoCPv27dOYf0xNTTF37lyqZPKTJ08iJCREVEzbtGmD3r17Y/r0\n6VqTyVmvmYBKpZJ9jBoArFu3Dj4+Pti4cSOaNWsmKXlHzlgB1ME/U1NTjBo1SiNhMy0tjbpt1sQZ\nOYboyZMn4efnBwCoWLEixo8fD1NTU0yYMIE6mZwlaClnzS+5+1elUuHYsWMoV64cVTI5S0XA1q1b\nw8HBQVzvc3NzceXKFeqg5ae+G21ic2hoKGbMmIHAwEBZGy0+patcvXpVtiOyJH9VcH7btm2f/T/h\niNDSKJ4EnJ2djZcvX8LQ0FBSJd+yZcvizp07KCwsxLVr1ySt+UlJSQgNDZV8PCGgrmxkbGyMVq1a\nIT4+XuuGr5LUr18fGzZswPfff4+oqChJDrJPHQEqlZLJAdrmtOJJ2JUrVwYADBo0SHK7rI5+OfLF\n7SSpDmehmsTw4cMlBxwdHBwQHx+Pvn37Sk76BDQTnDp27IjZs2dj7ty5VCdSGRsbo3nz5li3bh3s\n7e2hr69PXb1K4IcffoCvry9SU1NhYmICExMTyUe9s/DkyRP8P/a+Oyyqa/16DQKK0gUEBVSaLTGK\nGOK90YgaJPYGaizEFgugAioqiIABNIhgiwU7AiKWxCgWsESDxkIsiAXpRgSkqSBtYL4/5jnnN4PA\n7H02Xu/9HtdfiLxz9sycs8v7rnctAKioqOBVQxVBVs3axMSEPzveunWLSpGXK6xXVFTA1ta22bmu\nIRruF2gKn2vWrMHq1atRWFiIDh06UBdN27dvz+8R27RpQ3x+AaRFxGXLlqGkpATHjx8n2i9wBJCg\noCBqMjggTZzOnDkTN2/epCa8Wltbw83NDUFBQdDQ0MCWLVvwww8/4J9//lEYq6amhuDgYOzbtw9r\n166ldrHisHjxYujq6mLSpEn45ptvqNYSKysr7NixAy9evEDnzp2p55eSkhL8+OOPfHG+uLhYocAB\np/4kCyF7llatWqFXr158gv/+/fvo378/cbySkhKKioqgp6cHAHLKtoqgqqoKS0tLXkWR5jNPSUnB\nyZMnUVlZyVuuk55DWMYMSPMLc+fOxeTJkxEVFUVVZGcZt5aWFnx9fXmCVWFhITH5BAAOHz6MhIQE\nvH79GuPGjUNubu57jYAfIhYAXrx4IbfXiY+PJ1Ye++OPPxAQEABNTU2qItzvv/+OtLQ0nDp1Crt3\n70b//v0xZswY6gKsRCLh3b9UVFSgoqLS7N/HxcXB0dERoaGh/L778ePHiI+Pp7IWFtJsKLvPF3pe\nYHWqe/z4Mb755ht+HcjKyoKRkZFgZV8asK67QovNgFR1LTExUc5SmVQx+mPFchCiovUxMXnyZIwf\nPx79+vXjz9rJycnEjUF2dnbYuHGjXGGbtlGehXjK0kgcGRmJ0tJSPj9Os/6w1gSeP3+O8PBwZGVl\nwcrKCsuXLydSRnRycoKTkxOOHTuGSZMmEV+Pw7lz57Bz507U1dXx7qeyqnEfMr5Tp07YvXu3nBL8\nf8rJMSkpCQcOHJDbo9A0UQcHByMrKwu5ubno1q0bVX7C1dUV169fx/Pnz/HFF18Qzyncvqq+vl4w\nKWHNmjW4desWqqqqUFVVBRMTE4U1CdZaIzdm2XFyDWSK1nwOQveGd+/exdGjR1FdXQ0HBwd+X0cy\nn7HEtlQ+SFNTExcvXkRJSQl0dXWJXNeaAklup6XOP5wTZkZGBhYtWkRFTGM5vwBS0uiQIUNw8OBB\n/Pzzz7h+/TpxrJB9SkvUGQHAzc0NlZWVKCoqQm1tLTp06EBMJhfKX2iMe1FaWkrFvdDU1ERsbCyf\nqyXNTyclJWH+/PlEf9sUhBABHRwcEBYWRt3QwkFFRYWfs3fv3o3Zs2dTNz/36tULe/fuhYGBAdzd\n3akdC5YsWYK3b9/yawKX9yWBWCxGSEgIbGxs8Ndff/HnXhJ8zP2CLGia5TiMGTMGw4cPh5+fn2Cy\nsJDryoKlZjV9+nSma7OOnRQtxefatm0bzp07h549e2LGjBlE9/fixYuxZcsWTJgw4b3/U+TkyKFN\nmzaYO3eu3L6UJr9w8+ZNrF69WpBjTqtWrRAUFISMjAx06dKF2NmvJd53aWmpXE378uXLMDY2Joot\nLy9HeXk59PT08Pr1a2KHiJYQs9u2bRuOHj0KXV1dvHr1Ci4uLlR1s27duuH+/ftyhGhSLgCrSKYQ\nd75p06Zh2rRpvNMoDd68eYP4+HgUFxfzrqUikYhamE2oqyDA9nwAwMuXL2FnZ8fP4SQN+gMHDoS+\nvj7Kysp4PpCSkpKcY58isPDfAGD16tWYO3curK2tcfv2baxevZra4Q/4RCb/hP8ReHt7U6kRcaiq\nqkJ2dnajB1ZFyRLZCY6zUhYywTUEyeaepWOFtduF20w3ZqepSLGzpKRE4es3h40bN2L79u04dOgQ\nKioq0K5dO1hbWxOT2gMDA6mLhbJwd3fH4MGDcffuXdTX1yMhIYHYIgqQdvDt2bNHrkCvaNPEouwA\n/F9HWFlZGd8RBgBmZmZUr5OSkoK1a9eiqKgIHTt2hL+/v8JnjsUqoyUSBgCbyk51dTUqKyuhpqaG\nqqoqanLC0KFDMXXqVPTu3RspKSkYMmQIoqOjYWlp2WQMSxdwQ0J4t27diAnhrPbZcXFxOHbsGDIy\nMvjCeH19PWpraxUShOvr699TU7C2tiZOGOTl5cl15ysrK2POnDlENpyNJfNJrQKVlZXfO2irq6sT\ndy2qqKi897eqqqpE8ayfGQdWG7XIyEh4eXnx/yYhhLPcK7LYunUrYmJiUFtbi6qqKnTp0oVfixVB\nqK1kQ8KnhoYGCgsLERsbS3S/cfcV18HcunVrKjKfkKIly5ov+33k5ubCy8sLgwcPJlYwadipXVhY\niNDQUKIkkZ+fHxITE5GcnIzy8nKoq6vDzs6OuMDRtm1b5Obm4rvvvsOwYcOoCne6urrw9PTEo0eP\nqBRmOLDuVT4WDh8+DE1NTYwcORKGhoaClSHOnz+PHTt2CErA+vv7IzMzEwsXLsTmzZt5G1US3Lhx\nA5s3b8aQIUMwadIkqoO3o6Mjhg4diufPn8PExITafi44OBgxMTH4448/YGFhQZVwHjhwIK84VlZW\nBlVVVejp6WHt2rXESYslS5bAyckJU6ZMaRGre1KwJPqFxrMQpThV0L179yImJoYqFpAqJpOQvxuD\nj48PfvrpJ4SFhUFPTw8jRoxAbW0tsStHz5498fPPP8Pb25u6sRIABg0ahEGDBqGkpASBgYEICQnB\n8OHDsWjRIl5V/0OC+7wlEgnS09PRqVMnhQn35tZ0GiKHhoYG3wR85MgRKtWZ06dPQ01NjVdpO3/+\nPAwNDXm74OaQn5+P48eP8/+Oj4+nUqaVSCQYN24c+vbty6uAcetyU6pUso28tbW1ePr0Kb/WKzr/\ncMU+Hx8fQc8HAGLl9IZYsWIFbt68ya/VmpqaiImJoRrH7NmzcePGDZSXlwsaQ0xMDNLT03H8+HHs\n2LEDAwYMIF5Ljh07hj179sDCwgIZGRlwc3OjcoYKDQ3F2rVrERsbiy+//JKJiEELV1dXlJaWwsjI\niD8r05DJbW1tMWPGDISEhCAoKIiqkUAkEmHFihUYNGgQ4uPjiQk7gHSfOH36dP6+pQHLmAFg2bJl\nvMJOWloalWopy7j9/Pwwd+5cnD9/HlZWVrzIAynOnDmD33VGvgAAIABJREFUqKgoODs744cffqAS\n1RAaK6vodPfuXQD0ik7btm1DXFycoCKclZUV37x8+/ZthIaGIj8/n6qI169fPyxevBj9+vVDcnIy\n+vbt2+zfc6RpLt/G5U5pIaTZ8Pnz59i0aRMkEgn/MwfSQjOrU114eDiKiorQq1cvPHr0CCoqKqip\nqYGjoyN1syYtWNfd0tJSXL9+HVlZWXj9+jVsbGx4kp0iFBcXyzmf0ChGf6xYDkJUtBThQzppOTk5\nYciQIXjw4AGfJ3BxcSGeV+Pj42FmZsY3zgs5OxkaGmLNmjWCVDdZGonPnj2L8PBwmJub49mzZ3B1\ndVW492qqJmBubk51bdZCc//+/bFr1y65hqiAgACFcQcOHMDRo0cxZ84cLFq0CBMnTqQ6a7PE19bW\nIisrC1lZWfzvaM4gLE6OwcHBWL16teBGHNkmsPHjxyMnJ4e4CWzTpk3Iz89HRkYGVFVVsXv3bqr6\nDsu9kpqainPnzmHt2rVYunQplixZojCGtdYIAPPnz0dBQQHMzMyQlZUFNTU1iMViLF++nOh8s3nz\nZuTm5sLCwgLPnj0j3htyZ5/WrVvLEaNI5iWWWFbcuXMH6enpOHDgAGbNmgVAur+LioriSUBNgXb/\n+iFQUFAgVyc7f/48T7RWBJbzCyCdVw4ePIhevXohPT2diqhbXFyMq1evIisrC8XFxbC2tlY4r7DU\nGRte++jRo/D29oaPjw8ROYuVg9AS3Iv169djx44dSExMhLm5OXH+raysrMmaPek6IIQI+O233+LW\nrVsoLi4mJuvLol27djh06BCmTJkCfX19bNy4EUuXLqV67hYvXoyqqiq0adMGV69epVLAB6T76ejo\naNqhA5A2vl2/fh2Ojo5ITEykqqN8zP0C8H+CgEuXLgVA17QXHx/PNy/TQuh1jx8/Lnemp1GebszJ\nmPsdCVGXa3LhwI39Q6Ol+FxaWlqIjo6mEpHgGmpo3SxkwepSFx4eLtgxx8fHB1OnTkX//v1x69Yt\neHt7E7l+tsT79vb2xtSpUzF27Fhs2LABmZmZxE3fQhwiAHkxOwC8sBvN+aldu3Z8fVFfX5/6rH3r\n1i1cunSJ/zepYzbALpIp1J0PkIotpqeno1WrVoiIiMDMmTMV5mRsbGxgY2OD1NRU9OrVC4D0M6dt\n2GMZN8vzAZC5hzSElpYWbG1t8eWXX6KiogIikQgJCQnN8scagtVlrlWrVvzcwjU7CsEnMvkn/FeD\n2yz5+vryCwHNA5OVlQVfX9/3kpUkCdjGJjgayCrVcJBIJCgoKFAYy9Kxwtrtwi1gEokE+fn5MDIy\nIrZmbljQkAVJcUNHRwdeXl548uQJysvLoampCUtLS+KOLCUlJeTm5uLt27fUsYA02Tl27FgcO3YM\nkZGR1EoH8fHxuHbtGtXGgUuI19TUyCk7yBI4mwNLR5gsAgMD8fPPP8PCwgJPnz6Fv7+/woMhR85s\nTAFQkcppS23uWVR2Zs6cibFjx8LS0hLp6enUFlMuLi4YOnQoMjMzMXHiRFhZWaGkpKRZW5mCgoIm\nCwuKyKoshHBW++yxY8diwIAB2LVrF3+fKSkpoX379gpjm1JqF4vFRNdu6rBNktDT1dVFSkqKXGIk\nJSWF6Blt6vBBapnXVDxJ4Y31M+Mg1EatMUJ4XV2dHNGpKbDcK7K4dOkSrl69iqCgIMyaNYtKSUSo\nreSrV6+oxiiL6upqfn/CzQESiYToO2NxBGmJDteoqCgcPHgQq1atolJoGzhwIP/z6dOnsWPHDnh5\neREVY0QiEb799ltB6jiANAlWVlaGs2fPIjQ0FPr6+hg9ejRx4oKmSNgQOjo6gpReSPGhivN//vkn\nrl27htOnT+Px48ewt7fH8OHDqZTFAWD//v3UCdjLly/Dzs4OHTp04NW7tm7dSnXdNWvWoKamBhcv\nXkRAQABqa2uJnRoeP36M2NhYuYZDGqcFVVVVWFtb8+cBGmXX/v37w9XVFWZmZsjNzcW2bdvg4uKC\n5cuXEyctFi5ciBMnTmDTpk0YNmwYJk6c2Cz5Jz09vcm5mib5ERwcjKSkJEGJfqHx3Ng5UrLs+yAd\nu5aWFg4ePCin4kjyzOvq6lI3GnDo0aOHnHo/IF0PR48eTfwa2tra2Lp1Kx4+fEh9/YyMDJw4cQKX\nL1+Gra0toqKiIBaLsXTpUpw4cYL69Wghe/6rqakhKhbQrOvN4aeffkJubi48PDywf/9+fo9MgjNn\nzqCqqopXsqquruaVnJtqrGoJ8iQAuXMb6X3C0sjLoW3btggKCpJ7Pkga5ljRUGm4devW1GftAQMG\nCC5QAFLVSBMTE6SmpiItLQ2BgYGwsLCQc9JpDEeOHMFvv/2G1q1b4927d3B2dqb6rg0MDGBjY4Nj\nx47B0dFRoSo5APz9999NzlukCj+AlBhAk2huCHd3d7i7u6OsrAzLli2jyq2EhYUhJSUFgwYNws2b\nN6nuX3V1dd5KmxbcmEtKSrB8+XJqEohsw4WVlRVVLMu4dXR0MGrUKCQlJcHNzY1aAYw7h3BnUJrv\nSmhsSyg6sRbhysvLkZCQgNOnT6OyslKhBXVDeHl54cqVK8jIyMDEiRMV5kq484+DgwNiY2ORlZUF\nS0tL6nlUSLPh4sWLG/2ZBC3lVNemTRucOnUKrVu3Rk1NDdzc3LB161ZMnz79g5HJW2rd9fLygp2d\nHcaNG4c7d+7Ay8sLv/zyC1FsZGSkYGemjxXLQYiKliJw5MKm8PbtWygrK8s9zy9evECnTp0UxgLS\nPDnnOEQLVVVV5j3mmjVr8Ndff6F9+/bENaghQ4bI5QCVlZUhFouhqqqKs2fPEl33wIEDOHHiBNq1\na4fy8nI4OzsrzK20VE2AtdDs6emJb7/9Fn///TcMDAyIG3RbtWoFVVVVfg2iXQOExIvFYigrKzPf\nJ5yTY3h4OMzMzIhJjICUxCLUVReQbwJzdnamaiBLTk5GVFQUZsyYgfHjx1M3mbLcKzo6OhCJRHj3\n7h3xuZu11ggAxsbGOHjwIHR1dfH69Wv4+Phg3bp1mDdvHlH+sqSkBFu2bJFT7CRRzayurkZ2djbq\n6+vlfiZpVGGJZc0HaWpqoqioCDU1NXyeXCQSEanQy4qUyYKE4MV6/rly5Qru3buHU6dOyRHELly4\ngOHDhyuMB9jOL4B0r5GYmIiFCxfi1KlT8Pb2Jo5dunQpRowYgUmTJiE5ORkrVqzArl27mo1hqTPK\ngps7OdEtku+LtR4hy71o3749DA0NiTkIHHR1dfHvf/8bBgYG6Nq1K/G8UlJS0qS4AWm9QCgRkOae\naIiNGzdi//79qKmpgaqqKrp164atW7cS3acNzwISiQSdO3fGwoULqc4CHTt2xMuXL4ncSxpi3bp1\n2LdvH4D3BYoU4WPsF4CWadoTQiRnve5vv/1GtTeQRUMnY4lEghMnThA7GbdUkwstWPlcnANZYWEh\n9uzZI/d/pPuNbdu2CcpZlpeXQyKRIC8vDx07doS9vT212yiLY051dTXvMjps2DBq5WKh7xsAoqOj\nsWLFCoSHh2PGjBlUOX0hDhGyCA4Ohrm5OfLy8pCamgo9PT2FtStuvq2rq8P8+fPRr18/PHjwgCr/\nBgCnTp1qVNCVBKwimbTufLLw9PSEq6sroqOjMXz4cAQGBr5XD2sKGRkZyM7ORk1NDUJCQjBnzhxi\nMjjruFmejwcPHiA6Opp/PqdOncqLEpHAw8NDsIitUP4bt29WU1NDREQE+vfvjwcPHggSPgE+kck/\n4b8cHDllw4YNchPD69evieK7d+9OrdrREPn5+di0aRNqa2shkUhQVlaG33//XWFcU8rQJBsPrmPF\n1tYWhYWFvCVyXl4eP+GRxBYXF/PEGdoFZe3atejcuTPmzJmDU6dO4ffff1d42GnTpg215aYsrly5\ngtDQUHTp0oVPnmZmZsLDw0Oh/apsbNu2bVFRUUEcy6G2thYXLlyAhYUFSkpKUFFRQTV+Y2NjQbbh\ngDTZ4OLiAmtrayQnJ2PlypVEi3BdXR3q6urw8OFD/h6VSCSYN28e1b3funVr3ga0W7duVAsxtwBJ\nJBI8evSIiGzLurnnwKKyM2bMGAwaNIi3ENXR0aGKLygowJ49e1BSUgIHBwdUVlYq3ETU1tYKJqyy\nEsIBKZnBz8+PmlCnqqoKY2Nj+Pv74+HDh3z8P//8o5BQN2jQIGzYsAGLFi2ChoYGKioqsG3bNnz1\n1VdEYzY1NUViYqLcc3zx4kXo6+srjF25ciUWLVoEIyMjmJiYIC8vDy9evMDmzZsVxjaWfJVIJO8d\nqJtCamrqe12hpPGsnxkHoTZqsoTwhQsXQiKREBPCWe4VWejr60NVVRUVFRXo3LkzlSKuUFtJ2SaY\nS5cu8c09sqTppjBo0CBs3LgRnp6eUFJSgkQiQXh4ONGzyiXbZcEdJhUlIlnW/IKCAqxatQpaWlqI\ni4sTZB9aVlaGtWvXory8HFFRUQr3KC0JbW1tTJ06FVOnTsWLFy8QEhKClStX8g0Q/6v4UMV5ZWVl\n2NnZwc7ODhUVFUhISICnpyfU1NTk7OQUQUgCdv/+/XyjAmdTLAQPHjzAn3/+ieLiYuJiECBdC6ZP\nny5Y+cvNzQ0lJSWClF3z8/P5M4GpqSlevnyJzp07E7tcAMBnn32Gzz77DK9fv4afnx/s7e2bJRwb\nGBi0CDnUwMAAQ4cOxZs3b5CVlUWVKBEaL3tv0CorcNDR0cGTJ0/w5MkT/ncsDSQsoE32KCkpURXP\nOPj4+MDR0RGurq5yz6TQ4gEL6urq8Pz5c4V/x7L2NYx5+fIlsrOz0bt3bxQVFRHHisViHDx4EEpK\nSqivr8e8efOwd+/eZu+9liBPAlISyeXLl+X25PPmzWs2hmvkzcnJwblz56jVHwHwqrvFxcVU4/1f\nx5IlS/Ds2TOMGTMGISEh/H6lMTvWhtDW1uYT1m3atKFSKAKkSe7k5GTU1tbixo0bRO4Dffv2JU7I\nN4euXbuioKBA8P7s9u3b8Pf3591IOnbsSKzG6OLiwhOUSM8wXMJbQ0MDO3fuRK9evfh5gnQe5+xL\n1dXV8fbtW2r7UiENFy0xbiUlJTx79gyVlZXIzMwkznlyGDlyJKZNm4a8vDzMmzePOAfGEiur6ES7\n3rEW4eLj4xEfH4+8vDzY29vD39+f2A4Z+L9GR46Ao66ujvz8fGJHKk9PT5iZmWHgwIH4+++/sWrV\nKqpCppBmw/r6esHruqwwASe4QuNUx6G0tJQvvKmqqqK0tBSqqqrEDfdC0FLrbnV1Nd/03b17dyrH\nNhZnpo8Vy0GIipYiNEf0jouLQ0REBOrr6zF58mR+b7Nq1SocOnRIMEmcFB07dsSuXbvQs2dP6nmY\nw9OnT3HhwgWq4v65c+cgkUjg7++PKVOmoHfv3nj06BGVeqdIJOIbBtTV1amK3NOnT0d8fLycOikJ\n4aelCs1t27bF/PnzkZ2djeDgYGKhmH79+sHT0xMFBQXw9fWlVkgVEu/l5YXQ0FC5s4iQM4iGhgas\nra2ho6MDS0tLqnxa+/bt4evrK3ef0pzfWRrI6urqUF1dDZFIhLq6OuL1uyXule7du+PAgQPQ09PD\nsmXLiFSbWWuNgPTcw5FMtbS0UFRUBG1tbeL3LlSxs3Xr1jwpquHPHzKWNR9kZWUFKysrODo6yp0h\nSPLysuRaWrCefywtLfHq1Suoqqry4gtKSkoICQkhfg0h5xdZREVF8esubWMoALl9yrlz5xT+PUud\nURZDhw7F9u3bYWlpialTpxLVhVn4C7KIjY2V4yCcOnWKWMAlNDQUOTk5sLa2xq+//oo7d+5g5cqV\nCuO6du1KJTDSGEgaxJuCp6enoP2Zuro63Nzc5OItLCyImiNZzwLc39XU1ODcuXPQ0tLi1yDSZndN\nTU1cvHgRXbp04edf0vn9Y+wXgJZr2qMF63WrqqqQnZ3dqOiQos+c1cm4pZpchKKsrAzz5s2Ty7WS\n8GQaOpAJgUgkgouLi1wuSREfLDs7Gy4uLhgyZAiMjY3x7NkzRERE4JdffqHa/7A45tTV1eHp06fo\n1q0bnj59Sk1wFvK+OZw6dQpZWVlwdnbGmTNn0L9/fyLXa0CYQ4QsUlJS4O3tjRkzZiAyMhLOzs4K\nY7jvRPa74Yj4pNi7dy+OHj2KyspKqKio4Pvvv6ciVbOKZNK688mCq4nu3LkTI0eOpHIFPHToECIi\nIuDh4YErV65g9uzZVO+bZdxCn4+rV69i27ZtcHV1hYmJCbKzs7Fu3Tq4urpi8ODBRK/BImIrlP/G\nNcxpa2sjMzMTmZmZAOieD1l8IpN/wn81JBIJsrKy5Dom6+vr4evrS9UxyYLw8HAEBATgyJEjsLW1\nJSalNaVIRKqiCEit4+7du4fKykpUVlbC1NSUeHL29/fHH3/8Idj+4NGjR3xR2sfHB9OmTVMYo6en\nJ1iJCZAqjcbExMh13b19+xY//PCDwoIWSyyHuXPn4syZM1i1ahUiIyOpk/S1tbUYPXo0rKys+A0X\n6cFQTU2NJx0OHjyYuPvv+PHj2LlzJ4qKinhilZKSEpE9OwB+Y6+srAw/Pz8+GUjT+diQcEGjSCS0\nWYODEJWdX375BYsWLYKHh8d7G2Oag/yaNWswa9Ys/PLLL7CxscHKlSsVPp+dOnVSqNquCFpaWvD1\n9ZUjkJCoLgPshLrFixejuLhYzjpIEaHuxx9/REREBMaPH4+qqipoaWlh7NixxPeJl5cXPDw8sH37\ndhgbG+Ply5fQ1dUlsvQyNDTEsWPHkJycjMLCQgwfPhx9+vQhOhA1RXYkJbexJJUa+8zGjRtHtbkG\n3rdRKywshIGBgcI4jhA+ceJEJCYmYubMmfD09MScOXPQs2dPomsLuVdkwX13ampqCA0NxZs3b4hj\nWWwlAen6WVZWhj59+iAuLg43btzAihUrmo1ZuHAhwsLCMGzYMGhra6OsrAzDhw8nsm3lku2//fYb\nkSpOU2OmXfNHjhwJVVVVfPXVV++R0EjmwkuXLmH9+vWYNWtWs44MHxKZmZk4c+YMLl26hK5du7aY\n0u3HxH+iOJ+amoq///4beXl5VAQrQHpw9/DwoErAyiYvhRIYR4wYge7du8PR0RGBgYFUsXp6esTk\nt8ZQVFQkWMGPsx7t27cv7t69Cz09PSQlJVE17d25cwcnTpxASkoKHBwcFLrXaGho4MsvvxQ0XlkI\ndXlgib979y7mz5/PNO7g4GCkpaUhPT0dXbt2lUsq/v+KgQMHypFiQ0ND4enpSXR+awnIFqDEYjGR\nrTFLoVkWs2fPhrm5OU/wFYlExCpHZWVlvGqkWCzmyZvN2QQ3RZ4sLCykGveiRYtgb29PTUwGhKs/\nAtLGuStXruDZs2fo2rUrFeEUkCpakTR0/jfFAoCdnZ1cM2lmZibMzMyaVWPkzoolJSWYMGECvvji\nCzx69Ii6eXzt2rXIzMzE/PnzERYWhh9//FHw+6DF33//DTs7OzmVNhpl8/DwcBw+fBhubm5YsGAB\npk6dSryeCnGJ4BLeGhoayMnJQU5ODv9/pIVuVvtSIQ0XLTHulStX4tmzZ5gxYwaWLVtGTRqeOnUq\n/vWvfyEtLQ1du3Zt1sGkJWMBICIiAhEREXLPhqL7rGERTiQSURXhPDw8YGZmhu7duyMtLU2uMZLk\nHFNWVgZAuDMVp9YPSJv+aR32hDQbsijMccIEv/76qxzBlCsqkWLo0KGYOnUqevfujZSUFAwZMgTR\n0dFUVr20YF13s7KyAEibDc+ePQsbGxs8ePCAqvlAiDPTx47lcPLkyfd+pygnOXr06CYbnxQ920eP\nHsXp06cBSM+oHAlGqPtWWVkZtLW1if9eLBYjOzsb2dnZ/O9oyeQGBgaoqKigyotzhdnnz5/zjaE9\ne/bk7z8SmJiYYP369bCxscGdO3dgampKHCvU5r2lCs0ikQivXr1CRUUF3r17R7w39fDwwNWrV9Gj\nRw+YmZlRNxsIiefWCNaziLe3N969e4c+ffrg119/xY0bN4iJVtz8Q9MEK4tRo0YJbiBzdnbGhAkT\nUFJSAkdHR2JCA8u9wuUoly9fjrdv36JNmza4cuUKUbM5a60RkD6LHh4e6NOnD+7du4cePXogPj6e\n2EFTqGInCzGaJbal8kGXL1/G/v37eZKwioqKwkas169fY/v27Vi5ciUyMjKwcuVKqKqq8o2aHxKd\nOnWCo6Mjxo0bB2VlZdTX1+PBgwfo1q0b8WsIdbnjUFNTgydPnqBr167UzR5mZmY4deoUbG1tkZqa\nCm1tbX4NaeqzY60zcnWimTNnor6+HkpKSvjmm2+ovisW/gIgjIPA4fbt23yu1tnZGU5OTkRxNOIe\nDREQEABfX19Mnjz5vbWWNG/Mcp8IjWcVKaPJITSF4uJiOW6MSCQiFsOTXe/Nzc2pnHVbIv6PP/4Q\nROq+fPkyWrduLedG0lA07UNcV7ZpQBY0n7lQJ+OWanIRiuDgYKxevZqa/8CJiQ0fPhxv3rxBq1at\ncPToUaLmTA5CzuobNmxAaGgounfvzv9u1KhR2LBhA3bu3En8OpxjTlhYGMzNzakcc3x8fLB69WoU\nFhaiQ4cOWLduHdV7YBGySUpKQnR0NDQ0NPDdd99h+fLlxHOpUIcIDvX19Xj48CGMjY1RU1NDJC7K\n7Unfvn2LW7duyTUtkODAgQPIzs7G8ePHoa6ujvLycgQFBWHPnj3EfBlWkUxadz5ZiMVihISEwMbG\nBn/99ReVACDXENmuXTu+DvOfGrfQ52PPnj3YvXs3n4/o2rUrevfujSVLlhCTyVlEbIW6zMk2zJWX\nl1Pfpw3xiUz+Cf/VkO2Y5DqwaTomSdRnFcHAwAB9+/bFkSNHMGHChEYTsTQ4ffo0caLmyZMnOHPm\nDHx9feHu7k5ESuNw//59QfYHsigtLYWOjg7evHlDpHL62WefCb4WIJ1UGxZnW7duTZQEZYnlYG9v\nD3t7ewBS9TJaUsDkyZMFEQIAaYHkl19+wVdffYXU1FSoqqryh7Tm7ncnJyc4OTnh2LFjmDRpEvV1\nuY09VzTNysqChoYGFfFGNjleWFiIvLw84lihzRocAgMDqQv6XHKZIwU3Zr9HgqqqKgwYMAA7duyA\nmZkZkTpES6j2+vn5Ye7cuTh//jysrKyaJbw0xMcg1IlEIvz44488cYKzFCWFpqYm9uzZg7y8PBQW\nFsLIyIjqc4yLi8PEiROhrKyMO3fu4MiRI0TEV9bEa6dOnfDkyROcP38epaWlMDQ0hIODA7p06aIw\ntuFnJhTbt29HTEwMamtrUVVVhS5dujRp49cY1q1bxxfmly5dipUrVyIqKoooloV8CQDLly9HeXk5\nHBwccPLkSapGjxUrVuDixYuCbCUB6drLkYucnZ2JGghatWqFZcuWYdq0aVBRUZFTsSRFXFycYDK5\nkDWf1Na7KSxatAhqamrYvn37e9ZMpEnGgoIChISE8A4P3bp1IyooRURE4MKFC2jfvj1GjhyJ6Oho\nQQ4VLKirq8OJEyeQl5eHr776CpaWlkSWmh+rOP/gwQOcOXMG169fR58+fTBq1Cj4+/sT75NkG7F+\n//139OzZk7hgK3sNIfZtgDSJqaKign/++Qfv3r1D27ZtiWM7deqE3bt3o0ePHoIU6liUXX/++WfE\nxsbi6tWrsLKygpubGx49ekRll3vw4EE4OTkhMDCQ6PMjTWYoglCXB5b4pKQkZjJ5ZGQkTp8+jd69\ne2Pfvn347rvviJuxAgIC4OjoKJiAzhIvJDYuLg7Hjh1DRkYG78pQV1cHsVjcpLX1h4CQwlJLFOEA\nabF8/fr11NcHgO+//x6jR4+GpaUlMjMzMXfuXOzcuZPIkWTr1q1MeywjIyNq9RAOQtUfgffVu5KT\nkxU2qMhi8eLF0NXVxaRJk/DNN99Q7Ts+RmxaWhoKCgqwf/9+6OvrQyKRoK6uDps2bcJvv/3W7Pmt\n4f5PJBJh1KhRxGPetWsX5s+fDyMjI57ctWPHDqLYhQsXEl+nOfj5+Qm2mwWkuTdtbW2IRCK0bt2a\nV0wlgRCXCNmEd11dHSQSCe7du0fl2MBiXwoIa7hgGTd3Nu7cuTNfIKCZAxtalnfv3h319fWYPXu2\nQgEOllhZnDlzBteuXaPai3NFOLFYjNjYWKSnp6NLly7EDaqsDpTc9ZWUlOTIuaRnTgsLCyQnJ6Nf\nv354+vQpOnbsyAskkJBBhDQbsijMpaWlobCwEPv37+dVZOvr6xEaGorffvtN4Xg5uLi4YOjQocjM\nzMTEiRNhZWWFkpKS/0hjsdB119fXl/85OjoaMTEx1JbSQpyZPnYsByFOktu2bYOHhweioqKo863c\nmAEpOWLu3LkwNjamPgPeunULAQEB1M4YwcHByMrKQm5uLrp160YkpsCB25MWFxfD3t4eJiYmAMiL\ntoB0XxoeHo7evXvj7t27VI1wwcHBiI2NxfXr12Fubk61lxdq8y67frE047q6uiIhIQFjx47FsGHD\nFOa1OJdVDw8PhIWF4auvvkJ9fT1mzpxJNL+zxgPSc+eBAweolSs5pKWlIS4uDgAdiRGQ1lFYXAOm\nT5+OAQMG8E1gsgQkRfjuu+/wr3/9Czk5OTA2NibKYQFkrqZNwdnZmf9sNTQ0AADffvstUSxrrRGQ\n7ocvXryIjIwMjB07Ft988w0yMzOJyXGsip0s95qQ2JbKB0VFRSEyMhI7duyAg4MDkRq7r68vryi6\nbt06TJ8+HVZWVvjpp58UCgu01Pln06ZNMDU1RX5+Pu7fvw8jIyPi+5fV5S47O1tuX0lDbOOaNLh5\nBZB+ns2RP1nrjMuWLeNfmztfk4oIcWDhL3Cg5SBwEIvFPAmeZm9HI/bXENz3S5PTbQiW+0RovGyj\nx7Nnz/hGj8DAQCIl5pZoFImMjERpaSlPgCRdfwBp/ahjx44wNjbGnj17YGhoSLVXYY0X0mji5+eH\nt2/fQiwW48CBA9i2bRtUVVVx6NAhYjK50AaX7t33rARPAAAgAElEQVS7Cz4vszoZt4SYHguMjIzk\nyPu0YBHWGT16NHVuo7y8/L19XK9evaid6tq2bYuxY8fy7jM5OTnEebSePXvi+PHjVNeThZD3zeXB\nOPeQmpoadOjQAfv27SO+LouYHyB1Z/f390dQUBBCQkKolPNnz54NCwsLfl9LKmxz/vx5REVF8c+z\nuro6/P39MX36dGIy+aVLl3DixAm5vWFERATx2J8/f47c3FzU19cjLS0NaWlpCp1SOQQHByMpKQmO\njo5ITEzEhg0biK9ramqKyZMnY9WqVdi2bRtVsx/ruFmej4aN7e3bt6fik7GI2LK6zHl5eSE5ORka\nGhr8PkkIx/UTmfwT/qvB2jFJsyFtCioqKrh9+zbEYjGuXbtGZEvcHGgmGR0dHYhEIrx79476vQi1\nP+Dg6uqKiRMnQltbG2/evMHatWsVxtAUohvD5MmTMX78ePTr1w8aGhooLy9HcnIyZsyY8UFjOYSH\nh+PIkSOCSQF79+5tVt2sOYhEIjx//py3hdfT0+OvTXJA+Prrr7Fs2TJqQp7sxv7SpUvIzs6GpaUl\nEYmCA5fYqK6uhoaGBpGdGAfWZg0fHx/qz5zbnJubm2PHjh38e6bt8m3dujWuXbuG+vp63Lt3j6ho\nKCS53xA6OjoYNWoUkpKS4ObmRmWb9zEIdfn5+Vi6dCl27doFLS0tnD17FpGRkdi6dSvR69TW1mLr\n1q1wcXFBx44dcfnyZURGRmLp0qUKybpbt27lLe2VlZVhaGiIAwcOoKSkBC4uLsTvQQjOnj2LiIgI\nTJkyBZ999hny8vLg5uaGJUuWKEwY1NTUICwsDBcuXEB1dTXatWuHESNGwMXFhYqgfOnSJVy9ehVB\nQUGYNWsWtWqziooKr4ZkYmJCRfphIV8CwIIFC/hnm2YeB6TKyVZWVlBSUoK6ujpPzCBFx44dkZ+f\nD0NDQxQVFVF1snt4eAgmStXU1GDcuHFyCSLSA4OQNb9hw0R5eTlOnjyJmJgYxMfHK4yXTa4LhRCH\nB0D6uZiamkJJSQmHDx+Wa3JQVOhtTDGXO0zRJNp8fX1hYGCA69ev4/PPP4eXlxfRwf1jFeednJxg\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gORgaGvJ7rNDQUOo9lqqqquBGVFr1R1mwqnctWbKEd/0JCQnhVYcmTJjwXxkbEBAAX19f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"text/plain": [
"<matplotlib.figure.Figure at 0x13b7aa278>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(50, 75))\n",
"sns.heatmap(mean_squares_df, cbar_kws={\"orientation\": \"horizontal\"})\n",
"cbar = ax.figure.colorbar(ax.collections[0])\n",
"cbar.set_ticks([0, 1])\n",
"cbar.set_ticklabels([\"0%\", \"100%\"])\n",
"# cbar.set_ticks([0, .2, .75, 1])\n",
"# cbar.set_ticklabels(['low', '20%', '75%', '100%'])\n",
"# fig.colorbar(ax, orientation=\"horizontal\",fraction=0.07,anchor=(1.0,0.0))\n",
"plt.savefig('/Users/James/Projects/FinalYearReport-Manuscript/img/mean_squares.png', bbox_inches=\"tight\")\n",
"plt.show()\n",
"\n",
"# f, ax = plt.subplots(figsize=(50, 50))\n",
"# sns.heatmap(sum_squares_df, cbar=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Playing matches: 100%|██████████| 10/10 [00:00<00:00, 115.24it/s]\n",
"Analysing: 100%|██████████| 100/100 [00:00<00:00, 1320.01it/s]\n",
"Finishing: 100%|██████████| 21/21 [00:00<00:00, 4374.06it/s]\n",
"/Users/James/anaconda3/envs/fingerprint/lib/python3.5/site-packages/matplotlib/figure.py:402: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n",
" \"matplotlib is currently using a non-GUI backend, \"\n"
]
}
],
"source": [
"axl.seed(0) # Set a seed\n",
"players = [axl.TitForTat(), axl.Cooperator(), axl.Random(), axl.Gradual()] # Create players\n",
"tournament = axl.Tournament(players) # Create a tournament\n",
"results = tournament.play() # Play the tournament\n",
"plot = axl.Plot(results)\n",
"p = plot.boxplot()\n",
"q = plot.payoff()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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M5kNtxxbug+Yol13Pn5nX+HR7HXd8cmpZ8/lV3bJ7gINd2gyvKQEAIqMzk9ZY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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10475ac50>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p.savefig('/Users/James/Projects/FinalYearReport-Manuscript/img/examples/small_violin.png')\n",
"p"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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kSZKkXut6BQbgnTSVkXes53E2o0lOhr10+EVV3UYz+f95GaqZJXkkzUT8YXPJ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\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x106516dd8>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"q.savefig('/Users/James/Projects/FinalYearReport-Manuscript/img/examples/small_payoff.png')\n",
"q"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"# Analytical Plots"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def TFT(coord):\n",
" x, y = coord\n",
" numerator = y**2 + 5*x*y + 3*x**2\n",
" denominator = (x + y)**2\n",
" return numerator/denominator\n",
"\n",
"def WSLS(coord):\n",
" x, y = coord\n",
" numerator = (3*x + y)*(x - 1) + 5*y*(y - 1)\n",
" denominator = (x + 2*y)*(x - 1) + y*(y - 1)\n",
" return numerator/denominator\n",
"\n",
"def Psycho(coord):\n",
" x, y = coord\n",
" numerator = 4*(y - 1)*(x - 1) + 5*(y - 1)**2\n",
" denominator = 2*(y - 1)*(x - 1) + (x - 1)**2 + (y - 1)**2\n",
" return numerator/denominator\n",
"\n",
"def Coop(coord):\n",
" x, y = coord\n",
" return 3 - 3*y\n",
"\n",
"def Defect(coord):\n",
" x, y = coord\n",
" return 4*x + 1"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from collections import namedtuple\n",
"\n",
"Point = namedtuple('Point', 'x y')\n",
"\n",
"def reshape_data(data, points, size):\n",
" \"\"\"Shape the data so that it can be plotted easily.\n",
" Parameters\n",
" ----------\n",
" data : dictionary\n",
" A dictionary where the keys are Points of the form (x, y) and\n",
" the values are the mean score for the corresponding interactions.\n",
" points : list\n",
" of Point objects with coordinates (x, y).\n",
" size : int\n",
" The number of Points in every row/column.\n",
" Returns\n",
" ----------\n",
" plotting_data : list\n",
" 2-D numpy array of the scores, correctly shaped to ensure that the\n",
" score corresponding to Point (0, 0) is in the left hand corner ie.\n",
" the standard origin.\n",
" \"\"\"\n",
" ordered_data = [data[point] for point in points]\n",
" shaped_data = np.reshape(ordered_data, (size, size), order='F')\n",
" plotting_data = np.flipud(shaped_data)\n",
" return plotting_data\n",
" \n",
"\n",
"def create_points(step, progress_bar=False):\n",
" \"\"\"Creates a set of Points over the unit square.\n",
" A Point has coordinates (x, y). This function constructs points that are\n",
" separated by a step equal to `step`. The points are over the unit\n",
" square which implies that the number created will be (1/`step` + 1)^2.\n",
" Parameters\n",
" ----------\n",
" step : float\n",
" The separation between each Point. Smaller steps will produce more\n",
" Points with coordinates that will be closer together.\n",
" progress_bar : bool\n",
" Whether or not to create a progress bar which will be updated\n",
" Returns\n",
" ----------\n",
" points : list\n",
" of Point objects with coordinates (x, y)\n",
" \"\"\"\n",
" num = int((1 / step) // 1) + 1\n",
"\n",
" if progress_bar:\n",
" p_bar = tqdm(total=num ** 2, desc=\"Generating points\")\n",
"\n",
" points = []\n",
" for x in np.linspace(0, 1, num):\n",
" for y in np.linspace(0, 1, num):\n",
" points.append(Point(x, y))\n",
"\n",
" if progress_bar:\n",
" p_bar.update()\n",
"\n",
" if progress_bar:\n",
" p_bar.close()\n",
"\n",
" return points\n",
"\n",
"\n",
"def plot(plotting_data, col_map='seismic', interpolation='none', title=None,\n",
" colorbar=True, labels=True):\n",
" \"\"\"Plot the results of the spatial tournament.\n",
" Parameters\n",
" ----------\n",
" col_map : str, optional\n",
" A matplotlib colour map, full list can be found at\n",
" http://matplotlib.org/examples/color/colormaps_reference.html\n",
" interpolation : str, optional\n",
" A matplotlib interpolation, full list can be found at\n",
" http://matplotlib.org/examples/images_contours_and_fields/interpolation_methods.html\n",
" title : str, optional\n",
" A title for the plot\n",
" colorbar : bool, optional\n",
" Choose whether the colorbar should be included or not\n",
" labels : bool, optional\n",
" Choose whether the axis labels and ticks should be included\n",
" Returns\n",
" ----------\n",
" figure : matplotlib figure\n",
" A heat plot of the results of the spatial tournament\n",
" \"\"\"\n",
" fig, ax = plt.subplots()\n",
" cax = ax.imshow(\n",
" plotting_data, cmap=col_map, interpolation=interpolation)\n",
"\n",
" if colorbar:\n",
" max_score = np.nanmax(plotting_data)\n",
" min_score = np.nanmin(plotting_data)\n",
" ticks = [min_score, (max_score + min_score) / 2, max_score]\n",
" fig.colorbar(cax, ticks=ticks)\n",
"\n",
" plt.xlabel('$x$')\n",
" plt.ylabel('$y$', rotation=0)\n",
" ax.tick_params(axis='both', which='both', length=0)\n",
" plt.xticks([0, len(plotting_data) - 1], ['0', '1'])\n",
" plt.yticks([0, len(plotting_data) - 1], ['1', '0'])\n",
"\n",
" if not labels:\n",
" plt.axis('off')\n",
"\n",
" if title is not None:\n",
" plt.title(title)\n",
" return fig"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"step=0.01\n",
"size = int((1 / step) // 1) + 1\n",
"points = create_points(step)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/James/anaconda3/envs/fingerprint/lib/python3.5/site-packages/ipykernel/__main__.py:5: RuntimeWarning: invalid value encountered in double_scalars\n",
"/Users/James/anaconda3/envs/fingerprint/lib/python3.5/site-packages/ipykernel/__main__.py:11: RuntimeWarning: invalid value encountered in double_scalars\n",
"/Users/James/anaconda3/envs/fingerprint/lib/python3.5/site-packages/ipykernel/__main__.py:17: RuntimeWarning: invalid value encountered in double_scalars\n"
]
}
],
"source": [
"TFT_Data = {p: TFT(p) for p in points}\n",
"WSLS_Data = {p: WSLS(p) for p in points}\n",
"Psycho_Data = {p: Psycho(p) for p in points}\n",
"Coop_Data = {p: Coop(p) for p in points}\n",
"Defect_Data = {p: Defect(p) for p in points}"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"TFT_Data = reshape_data(TFT_Data, points, size)\n",
"WSLS_Data = reshape_data(WSLS_Data, points, size)\n",
"Psycho_Data = reshape_data(Psycho_Data, points, size)\n",
"Coop_Data = reshape_data(Coop_Data, points, size)\n",
"Defect_Data = reshape_data(Defect_Data, points, size)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3.0000000000000004"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.nanmax(TFT_Data)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"TFT_plot = plot(TFT_Data)\n",
"WSLS_plot = plot(WSLS_Data)\n",
"Psycho_plot = plot(Psycho_Data)\n",
"Coop_plot = plot(Coop_Data)\n",
"Defect_plot = plot(Defect_Data)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"TFT_plot.savefig('/Users/James/Projects/FinalYearReport-Manuscript/img/Analytical/TitForTat-Analytical.png', bbox_inches='tight')\n",
"WSLS_plot.savefig('/Users/James/Projects/FinalYearReport-Manuscript/img/Analytical/WSLS-Analytical.png', bbox_inches='tight')\n",
"Psycho_plot.savefig('/Users/James/Projects/FinalYearReport-Manuscript/img/Analytical/Psycho-Analytical.png', bbox_inches='tight')\n",
"Coop_plot.savefig('/Users/James/Projects/FinalYearReport-Manuscript/img/Analytical/Coop-Analytical.png', bbox_inches='tight')\n",
"Defect_plot.savefig('/Users/James/Projects/FinalYearReport-Manuscript/img/Analytical/Defect-Analytical.png', bbox_inches='tight')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"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": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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