diff --git a/query_example_v1.bkr b/query_example_v1_1.bkr similarity index 68% rename from query_example_v1.bkr rename to query_example_v1_1.bkr index bd73e36df5e3d1319270adcbd8787cf97156e635..da4ed650a841492ee8ef99ac1e805bf8a557a710 100644 --- a/query_example_v1.bkr +++ b/query_example_v1_1.bkr @@ -18,12 +18,35 @@ "mode": "javascript", "background": "#FFE0F0" } + }, + "languageVersion": "ES2015" + }, + { + "name": "IPython", + "plugin": "IPython", + "setup": "%matplotlib inline\nimport numpy\nimport matplotlib\nfrom matplotlib import pylab, mlab, pyplot\nnp = numpy\nplt = pyplot\nfrom IPython.display import display\nfrom IPython.core.pylabtools import figsize, getfigs\nfrom pylab import *\nfrom numpy import *\n", + "view": { + "cm": { + "mode": "python" + } } } ], "cells": [ { - "id": "sectionVN8BXw", + "id": "markdownT9rKmh", + "type": "markdown", + "body": [ + "Warning: running the first cell of this notebook can take a long time(hours).", + "", + "One can run safely the second and third cell, that will analyze a precomputed result of the first cell (queryMgO.out).", + "", + "A more documented version of this notebook will be uploaded soon." + ], + "evaluatorReader": false + }, + { + "id": "sectionQ3dZWh", "type": "section", "title": "Query Example", "level": 1, @@ -31,7 +54,19 @@ "collapsed": false }, { - "id": "sectionWfNm4q", + "id": "markdownKIKNEj", + "type": "markdown", + "body": [ + "*Warning*: running the first cell of this notebook can take a long time(hours), we are working to integrate the (much faster) flink query.", + "", + "One can run safely the second and third cell, that will analyze a precomputed result of the first cell (queryMgO.out).", + "", + "A more documented version of this notebook will be uploaded soon." + ], + "evaluatorReader": false + }, + { + "id": "sectionzKgCDS", "type": "section", "title": "Scan the whole NOMAD Archive", "level": 2, @@ -39,20 +74,14 @@ "collapsed": false }, { - "id": "markdownwxOAJP", - "type": "markdown", - "body": [ - "Try to filter whole archives based on the summary data.", - "Extract not just the data you want to plot but also its identifiers (provenance)" - ], - "evaluatorReader": false - }, - { - "id": "codesNXl6E", + "id": "codecJWYiO", "type": "code", "evaluator": "IPython", "input": { "body": [ + "from nomad_structure_tools import query", + "percentageOfArchives = 5", + "", "def hasMgO(stats):", " els = set(stats.get(\"elements\",{}).keys())", " return stats and \"Mg\" in els and \"O\" in els and ((stats.get(\"nEigenvalues\", 0) > 0) or stats.get(\"nBands\", 0) > 0)", @@ -93,7 +122,7 @@ " else:", " return None", "", - "def methodToFile(outPath, archiveGroupIndexRange = query.archiveSplit(0,1), calculationGroupIndexRange = query.calculationSplit(0,1)):", + "def methodToFile(outPath, archiveGroupIndexRange = query.archiveSplit(percentageOfArchives,101), calculationGroupIndexRange = query.calculationSplit(0,1)):", " \"\"\"perform query, stores result to file\"\"\"", " with open(outPath, \"w\", encoding=\"utf-8\") as outF:", " nomadArch.queryCalcs(", @@ -114,10 +143,10 @@ "state": {} }, "evaluatorReader": true, - "lineCount": 55 + "lineCount": 58 }, { - "id": "sectionpPYVt5", + "id": "sectionLNKIwc", "type": "section", "title": "Put data in a table", "level": 2, @@ -125,7 +154,7 @@ "collapsed": false }, { - "id": "markdownNWHQtR", + "id": "markdownqcviza", "type": "markdown", "body": [ "Organize results in a table, convert units (from SI)" @@ -133,13 +162,12 @@ "evaluatorReader": false }, { - "id": "codeih6a8T", + "id": "codeCSQ8CP", "type": "code", "evaluator": "IPython", "input": { "body": [ "import json", - "from ipywidgets import *", "import pandas as pd", "#from nomadcore.unit_conversion.unit_conversion import convert_unit_function", "def convert_unit_function(sourceUnits, targetUnits):", @@ -177,7 +205,7 @@ "datatablestate": { "pagination": { "use": true, - "rowsToDisplay": 50, + "rowsToDisplay": 25, "fixLeft": 0, "fixRight": 0 }, @@ -187,6 +215,12 @@ "singleConfCalcGIndex", "volumePerAtom" ], + "actualtype": [ + 0, + "4.4", + 2, + "4.4" + ], "actualalign": [ "L", "R", @@ -205,7 +239,14 @@ true, true, true - ] + ], + "barsOnColumn": {}, + "heatmapOnColumn": {}, + "tableFilter": "", + "showFilter": false, + "columnSearchActive": false, + "columnFilter": [], + "columnWidth": [] } }, "result": { @@ -1491,15 +1532,15 @@ }, "selectedType": "Table", "pluginName": "IPython", - "shellId": "0CC47EED5B9E4D0B85D5B99C9E4DB0E4", - "elapsedTime": 353, + "shellId": "DD17159096204C748D91BB5FF8019664", + "elapsedTime": 346, "height": 776 }, "evaluatorReader": true, - "lineCount": 32 + "lineCount": 31 }, { - "id": "sectionFZY0gl", + "id": "sectionbRaG6F", "type": "section", "title": "Plot results", "level": 2, @@ -1507,7 +1548,7 @@ "collapsed": false }, { - "id": "codeMcJ2gC", + "id": "codegThSLq", "type": "code", "evaluator": "IPython", "input": { @@ -1525,19 +1566,26 @@ "value": "/usr/lib/pymodules/python2.7/matplotlib/collections.py:548: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n if self._edgecolors == 'face':\n" } ], - "payload": "<div class=\"output_subarea output_png\"><img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXMAAAEACAYAAABBDJb9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz AAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xd8U9X7wPFPmq6kkw5aWkaBMmXvIVoEHCAKqKAoqKgI CogLBZGhoqLg1wEqoCAulPUTZciSAgKCIBtEpqwCld2mI22e3x8JtUiBjrRp0+f9evEiuTn3nufe Jk9Ozj33HlBKKaWUUkoppZRSSimllFJKKaWUUkoppZQqclOBk8D2HF57HrABIUUakVJKqSweuSw3 Dbg9h+UVgA7A306LSCmlVKGK4cqW+SygHnAQbZkrpZTL5LZlnpO7gaPANifFopRSKp8887meGRiG vYvlEkPBw1FKKZUf+U3mVbF3u2x1PC8PbAKaAacuK1i1quzfvz+/8SmlVGm1H4jNbeH8drNsByKA yo5/R4FG/CeRA+zfvx8RKVb/Ro4c6fIYNCb3iktj0pic/Q97oznXcpvMZwBrgerAEeDR/7wuealU KaWUc+W2m+WB67xepaCBKKWUyr+CjGYpseLi4lwdwhU0ptwrjnFpTLmjMRWeohiBIo7+H6WUUrlk MBggDzm6VLbMlVLK3WgyV0opN6DJXCml3IAmc6WUcgOazJVSyg1oMldKKTegyVwppdyAJnPldCLC 9u3bWb9+PSkpKa4OR6lSIb93TVQqR1arlTvv7M6vv27GaAwmKCiFNWuWUrFiRVeHppRb05a5cqpP PvmU1auTsVj+4uLFLSQkPMRjjz3j6rCUcnuazJVTbdu2h5SUToA3AJmZXdi9e49rg1KqFNBkrpyq UaMbMJvnAimA4On5LfXr13F1WEq5Pb3RlnKqzMxMund/mEWLlmI0BhIRYWb16p8pV66cq0NTqkTJ 6422NJkrpxMRDh06REpKCtWqVcPLy8vVISlV4mgyV0opN6C3wFUlxsWLFzl58iT6Za9UwWkyV0VO RHjuuaGEhkYSE3MD9eu34tSpK+YCV0rlQW6T+VTgJLA927J3gd3AVmAuEOTc0JS7mj17NpMnL8Bq PUxqaiK7d7eid++nXB2WUiVabpP5NOD2/yxbAtwA1Af+AoY6MS7lxtat+53k5PuBUMBARsZTbNq0 0dVhKVWi5TaZrwbO/mfZUsDmeLweKO+soJR7i42NwWRaCWQAYDCsoEKFSq4NSqkSLi+jWWKAn4C6 Obz2EzAD+DaH13Q0i7pMeno67dp1ZsuW43h4ROHhsZ1VqxZTt25Oby2lSqfCHJoYQ87J/BWgEXDP VdbTZK6ukJmZya+//kpSUhItWrQgNDTU1SEpVazkNZkX9K6JjwAdgXbXKjRq1Kisx3FxccTFxRWw WlXSGY1Gbr75ZleHoVSxER8fT3x8fL7XL0jL/HZgPHAz8M811tOWuVJK5VFhdbPMwJ60w7APURyJ ffSKN3DGUWYdkNP4Mk3mSimVR3o5v1JKuQG9nF8ppUohTeZKKeUGNJkrpZQb0GSulFJuQJO5Ukq5 AU3mSinlBjSZK6WUG9BkrpRSbkCTuVJKuQFN5kop5QY0mSullBvQZK6UUm5Ak7lSSrkBTeZKKeUG NJkrpZQb0GSulFJuQJO5Ukq5AU3mSinlBjSZK6WUG8htMp+KfSLn7dmWhQBLgb+AJUCwc0NTSimV W7lN5tOA2/+z7GXsybw6sNzxXCmllAvkeuZnIAb4CajreP4ncDP2FnskEA/UzGE9EZH8R6iUUqWQ wWCAPOTogvSZR2BP5Dj+jyjAtlQpMHPmTPz8ojAaw6hYsTZHjx51dUhKuQ1PJ21HHP9yNGrUqKzH cXFxxMXFOalaVVJs27aNHj0eBV4FWnLkyLvUqdOKc+cOuzo0pYqF+Ph44uPj871+QbtZ4oATQDlg BdrNoq5i4MCBTJjwJ/bTLACpQACJiQmEhYW5MDKliqei7Gb5EXjY8fhh4IcCbEu5uYCAACAp25IU QDCbzS6KSCn3ktusPwP7yc4w7P3jI4B5wEygInAI6A6cy2FdbZkrzp07R0REVdLTuwJtgPdo1iyE 9etXXFbu999/5+jRozRo0IDKlSu7JFalioO8tszz0s2SX5rMFQAJCQk89FAfjh5NpF27lkyY8AEe Hv/+OOzXbzBfffUDnp71yMhYx9dfT6Fr1y4ujFgp19FkrkqM9evX8+67H5OebiUurgkjRkwkOXkz EAhswmxuz8WLpy9L+EqVFprMVYmwYcMG2rbthMUyAjDh5fUinp5tSUmZm1XGyyuAU6eOEBysFxer 0ievydxZQxOVypP335+MxTIMGAiA1ZpMZuZIYBdQG5hOeHgkQUFBLoxSqZJDk7lyiYyMTMA725Iq VKxYnoSEFhgMPgQHB7J48bxLrROl1HVoN4tyiZUrV3LHHd1JSXkPMGM2P8fkyWO49957OHv2LGXL ltW+clWqaZ+5KjGWLFnCG298iNWawcCBD9Oz5wOuDkmpYkOTuVJKuYGivAJUKaVUMaHJXCml3IAm c6WUcgOazLOxWCz07/8sNWs2p0OHrvz111+uDkkppXJFT4Bm06nTffzyC6SmPovBsJ7g4HHs2bOF 8PBwV4emlCpldDRLPqWkpBAQUIbMzPOADwD+/l347LMH6NGjh2uDU0qVOjqaJZ+MRiP2Y5fsWCLA Bby9va++klJKFROazB28vb3p128AZvNtwGd4ez9B2bL/cNttt7k6NFVMHDx4kKVLl3Lo0CFXh6LU FbSbJRsRYcqUz1m2bA0xMVEMG/ai3rFPAfDJJ1N4/vlheHvXJT19Ox98MJYnnujj6rCUG9M+c6Wc LCEhgSpVbiA19XegKrAXX9/m/P33n5QtW9bV4Sk3pX3myu19/fXXBAaWx9s7hNatO2CxWAq1vsOH D+PtXQV7Igeohrd3RY4cOVKo9SqVF5rMVYmyYsUKevXqz8WLU7BaN7J2rQ8tW7Yv1DpjY2PJyDgE rHcsWUtm5lGqVKlSqPUqlRfOSOZDgZ3AduBbLo3rU6oQvP/++8AjwB1AFeBztm3bXKh1hoaG8t13 X2A2d8Tfvwp+fncxa9ZXlClTplDrVSovCjo5RQzwBFALSAO+B+4Hphdwu0rlyNPTEziUbclhimKO lc6d7+TUqcMcP36c6OhozGZzodepVF4UtGV+AbACZuyfKDNwrKBBKffz/vvvU6VKPWrUaMgPP/yQ 7+28+uqrGAzxQA9gFHAH7dq1ybHsH3/8wZgxY/jwww85d+5cvuu8xM/Pj2rVqmkiV26rL3AROAV8 lcProkq3kSNHCgQKjBcYKWCSmjVryWOPPSapqal53l58fLxUqVJLwsIqSK9ej4rVar2izIIFC8Rk Chej8UXx9b1fKlSoIWfOnHHG7ihVJLBfuZhrBR2aWBX4CWgDnAdmAbOBb7In85EjR2Y9iYuLIy4u roDVqpLEz688Fsv7wL2OJa8BCwArYWHnOHFiL0aj0al1Vq3agAMHxgL2i768vXvzxht1efHFF6+5 3tixY/ntt99o2LAhw4cP16nrVJGJj48nPj4+6/no0aOhCMeZ9wA6AI87nvcCWgBPZyvj+JJRpZWv bxRpaV8B7RxLJgBbHf9X4bPPXuOxxx5zap1hYZU4fXo5EAuAwTCaIUPSefvtMVddp2nTm9m48QDQ GVhE7dpl2blz/VXLK1WYinqc+Z/Yk7fJUWl7YFcBt6nczN13t8XeG7cS+w+5N7G3A3yAaE6dOgWA zWbj0KFDHDt2jII2ADp37oSv7/PAUWAdJtNkOna8+q0ZNm3axMaNG4EtwMfAVnbt2sOSJUsKFIdS RaWgyXwr8CWwEdjmWDa5gNtUbmbGjK/o3r0F3t73A72Bm4A6wHQMhl306NGD8+fP06xZW2rXbkVs bH26dOlJRkZGvuv8+OPx3HtvBAEBjYmI6MWUKeO46aabrlr+4MGDQBgQ6lgSCETpfViUysbFpxFU cbJhwwYJCCgvYBIfn7Iya9YsERHp1auv+Pg8LpApYBGzuYOMHTvOqXWfO3dOEhMTxWazXfHa6dOn xWDwF5gkkCzwlRgMfnL48GGnxqBUbpHHE6B6dkcVqaZNm3LhwhFELKSmnuTee+0nRX//fStpaY9g f0uasFge4LfftjqlzszMTB588HHCw6OJjo6lbds7SUpKuqxMSEgIc+d+hZfXMCAQo3EQX389mQoV KjglBqUKmyZzVSzUqFEVT89Fjmc2fH0Xc8MNVa+5Tm598MEEfvhhL1brCdLTE1mzxp+oqJrUrNmc b7+dkVWuS5cupKf/g81mJSPjDD179nRK/UoVBb1roioWjh8/TsuW7Th3LgCbLZnq1UNZvfpnp1yg 06XLQ8yb1wF42LFkNTAQeBezuQ8zZ35Kp06dClyPUs6U19EshX8dtFK5EBUVxe7d9hEl3t7eNGnS xHHpfsFVr14JH58VpKX1xv7ZWI79DhQdsFheZerUmZrMVYmnLXPl9i5cuEDz5rdw7JgHFouBzMwj wG9ARQyGN+nV6zDTp3/q6jCVuoxOTqFUDtLS0li1ahWbNm3itdfGkZLyPAZDMn5+k1m/Pp7atWu7 OkSlLqPJXJUa6enpeHl5XXrT59qGDRuYPn0GXl6e9O//ODVq1CikCJXKP51pSLm948eP07BhG0wm P/z9Q/n662/ztH6zZs2YOPF/vP/+u7lO5OPHf0BgYAQmUxC9ez9Jenp6fkJXqtBoMlclzl139WT7 9pux2VKxWOLp2/c5tmzZUmj1zZ07lxEjJnDx4kpSU/cye/YRXnhheKHVp1R+aDJXJYrNZmPz5l/J zBwBGIF6wN2sWbOm0Or88celWCzPADWBsqSkvMb8+UsLrT6l8kOTuSpRPDw8CAwsC/zhWJKB0biF cuXKcfbsWV59dRR9+jzFrFmznFZnREQIXl7Z7x+3i/Dw0KuWV8oV9ASoKnHmzZtHz55PYDB0xGDY SbNm5Zgz50saNGhFQkJL0tMbYDZ/zNChjzB8+EsFri8xMZH69Vty7lwjMjND8fKazfLl82nevLkT 9kapnOloFlUq7N69m7Vr1xIeHk6nTp345ptveOqpmSQnz3eU+Btf37pYLOfzPNolJ2fPnmXmzJmk pqbSqVMnYmNjC7xNpa5FrwBVpUKtWrWoVatW1vPU1FRstuxdH6FkZKQjIk5J5mXKlOHJJ58s8HaU KizaZ14MiQiZmZmuDqNEue222zAaFwHTgM34+vamc+d7ddo3VWroO70YERFeeulVfH398fX144EH +uh45lyqVKkS8fGLaNbsGypVepjevSvwzTdTXB2WUkVG+8yLkc8+m8rgwR+RnLwQ8MNkeoD+/esz fvybrg5NKVXE9ArQEmzhwniSkwcC5YBAUlJeZtGiFa4OSylVAmgyL0YqVIjAy2tz1nODYTNRUREu jEgpVVI4o5slGPgMuAH7nHV9sN9f9BLtZsmlxMREGjRoxfnztRDxx9PzF9at+0Xv6KdUKeSKcebT gZXAVOxDHf2A89le12SeB+fPn+fHH38kPT2dO+64g6ioKFeHpJRygaJO5kHAZqDKNcpoMldKqTwq 6hOglYFE7IN7/wCmAAWftFEpVST++ecf+vUbTPv23Xj99bfJyMhwdUgqnwp6Bagn0AgYAPwOvA+8 DIzIXmjUqFFZj+Pi4oiLiytgtao4mzRpEoMHjyQ93UqVKhX57bdlhIZeeWMqEeHdd9/ljz/+oHXr 1gwcODDfddpsNhITEwkODsbHx6cg4ZcoycnJHDlyhOjoaAICAvK0rsVioVmzOI4ejcNqfYB16yaz Y8effP/9F4UTrLqm+Ph44uPjXVZ/JHAw2/Mbgfn/KSOqaH377bfy4IO9ZOTIkZKSklKkdS9btkzA LPCJwBqBtmI0lhEfH39p2LCN7N+/P6ts3botBCoI9BGIlBtv7JCvOnfv3i3ly1cXX99Q8fHxlylT pl5R5siRIzJhwgT5+OOP5cSJE/nev6KyevVqadv2LmnWrINMnvyZJCQkyKuvjpSnn35Wli1bJiIi ixcvFn//MPH3jxWTKVi+/36WrF+/Xl5//XWZMGGCXLx48Zp1LFiwQAICbhSwCYhAknh6muT8+fNF sYvqOrAPKClSq4DqjsejgLH/ed3Vx6RU6dOnryOZ1hMIlMjIWElPTy+y+u+77z6BBx3JQQROC3gJ nBYPj3elQoUasnXrVvnf//4nECRwzlHupIBJtm3bluc6K1euIwbDx47t/Clmc6Rs3bo16/Xdu3dL YGCE+Po+KibTQxISEi2HDh1y5m471caNG8VsDhOYKvCjmEw1xN8/VDw9+wuMFbO5vEyaNEX8/cME Vjr2e7N4eweLr2+YeHgMEZOpq8TG1pc9e/bIunXr5J9//rminh9//FECAtpm+1uliJeXn5w9e9YF e63+Cxck8/rYu1i2AnOxnxTVZO4CaWlpAr4Ci7MlyLIyfvz4IouhT58+Ah2zJYgDAj6O1l+6GAyB YjAECngI+AkszVa2vPzf//2fiIgkJSXJL7/8IqtXr77ml5HFYhGj0Ttb61LE37+XTJ36b+u8U6ce YjCMy3rdw2OE9OrVt9CPRX499dRggTeyHZdVAuUve+7vHyJeXmUFPhBIFxAxGOoJfJRVztOzgXh5 BUlQUBPx8wuVhQsXXlbPhQsXJCoqVjw9hwn8LCbT3dKx4735innjxo0yadIkWbRokdhsNmcchlxL Tk6WtWvXyrZt24q87sLkimR+Pa4+JiXWli1bpEmTthIVVUPuv7/PdX/+Hj9+XMA724deBO6W/v37 F1HEIseOHROjMVCgn8AkgRiBLo5YRjpa45MdyXe5QBmBQwJTxWDwkzNnzsixY8ekfPnqEhjYQvz9 60qDBq0lKSkpx/psNpsEBIQJrM3qKvDzq5nVFSEi0rRpe4GF2Y7Jd9Khwz1FdUjybNCg5wVGZIt3 meM4Xnp+j0Ajgf8JtBfoLLDf8eW4wVFmj0Cw49iKwBrx8wu9otvt2LFj0r37I9K48S3y7LMv56tb 7tNPp4jZXE7M5j7i719H7r//0asm1Tlz5kjv3k/Kiy8OlZMnT+br+GS3f/9+iYysIoGBjcVsriid Ot0nVqv1inJ79uyRevVai69voNSq1VS2b99e4LoLG5rM3cPx48fFbA51JL4d4uXVW9q2vfOa69hs NvH0DBT4wfEBPipQRhYtWiQiIp999pnUqNFIatduInPnznVKnIcOHZJt27ZJampq1rIDBw5IXFwH qV27iVSvXkfM5sbi6fmCGI2hAhH/+bJpLoAYjcEya9YsERG5++6e4un5iuP1TPH1fUCGDh1x1Rjm z58vZnOYBAbeJX5+VaV37ycvSyZvvDFWzObWjuNxSMzmhjJx4qdO2f/CsGPHDvHzCxMYJ/CF+PpW EC+vAMff9WeBQIFkx/FJFygnPj7BUq9eC/HxuV/gmMBbAi0vO9Zmc7QcPHjQqbGmpqaKt7efwF5H PRbx84uVNWvWXFH2jTfeFg+PcAF/gTAJDo6Q06dPF6j+1q1vEw+Pdx11p4rZfLNMmjTpihgjI6uI wfCh2Lv9PpeQkPJy4cKFAtVd2NBk7h4GDRokcFu2D2O6GI0+YrFYrrne4sWLHQm9ohgMZnnqqWdE RBx91AECHwq8I2CW7777Lt/x2Ww2ue++XmI0BoqnZ6QEB0dednLzkszMTJk5c6a89dZb8uijfcTe 5XLQsU8XxGiMkPXr12dtc8GCBVK2bHWB1dn2fZrcffeD14zn4MGDMmfOHFm7du0VrcKMjAwZOPAF MZmCxc8vRIYOHVnsf45v2bJFund/RDp27CFz5syVJUuWSO3aLaRs2Sri5RUt2buVzOb6Mnv2bLl4 8aLcc08v8fcPl/DwSuLtHeJosYvASvH3D7vsS9cZTp48Kd7ewWI/TxIr0E78/OJkzpw5V5T19Cwj 0FvglNi7joJl6NChV5SzWq3ywguvSHR0TYmNbZTV9ZaT8PDKAn9le6+8LQMHPndZme3bt0tAQI3L vtiCgprm+IVTnKDJ3D1ER1dztKwufWgTxMPDW/78809ZtmzZNZN6UlKSbN269bJRG4GBMQLfZHtD /08qVqyT7/g++eQTRwvrJbF3p1SUihVrXHMdq9UqtWo1dnStdBeDoYL07Nkn6/X+/Z8VP79a4uHR UKCnQIaARczm9jJ27Lh8x+pOrFarVKvWQDw9hwhsF6NxjERFxUpycvIVZSdOnCS+vsESGFhP/P3D ZMmSJU6PJzMzU7y8QgX6CuwW+FjALJs3b76irL0L8HS29+DT0rlz5yvKvfjicDGb2whsEfhZzOZI Wb16dY71t23bWYzGkY7PyUXx82sh06ZNu6zM0aNHxdc3ROCso96LYjZHya5du5xyDAoLmszdQ6VK dQXqCHQXGC9Q3fHT21ugjHh4BObpw2k2VxCYl+2DNEUiI6+dfK+lXbt2Yh9SeGl7m8RgCLjuejab TebMmSMjR4687GTZ3r17xWQqK/bRLecF2ggEird3GenSpWeO/aDZtzlx4qdSpUpDqVq1oUyaNCXf +1USJCQkSMeO90l0dE255Za7rjky5+TJk7Jp0yY5d+5cocTyzz//OLqAMrL9UmgvP/zwwxVlvbyC BdY5ytkEbpLXXnvtinLR0TUdifzSe2uMDBr0fI71HzlyRGJibhB//1jx9Q2Tnj0fk8zMzCvKPf30 8+Lnd4MYjUPEz6+h9OrVt9j/OkOTuXt46613xWyu62jxdBKDwUugrKM/VATGi7d3eK6399BDjwhE if1E4P8JlJHhw4fnOz77EMRB2T5wf4nRGJTv7a1bt04CAxtn255N/Pyqy9KlS6+77rRp08Vsrub4 6b5SzOaq8vXX3+Y7FpV7SUn2senwj1w6xxEQ0CjHv1tAQLhAuMBzAh3FYCgrn3565bmLatUaCyzK ei8YjQNk+PCrnzNJT0+XnTt3yt9//33VMjabTebNmydjxoyR2bNnF/tELqLJ3G3YbDZ55533pFat FtKoUZzYh/L1z5bskgWMedpe9+49xcsrQry9I+XppwcWKL4dO3aI0RggME3sY50byqOPPpnv7V24 cEHKlIkW+Mqxb59LWFjF654jEBFp0+ZOgVnZjs0Madeua75jUXkzePBL4udXX2Cc+PreJU2a3Jzj cFIfnwCxj8x5W2CKeHoOkHffffeKcvPmzROTKULgDfH0HCChoeXl2LFjRbErxQqazN3P9u3bHd0r 1QUsjoT1f2IwBLo0rrVr10rDhjdLTEwDGTZspGRkZBRoe5s3b5YqVeqJ0egt1ao1zPXwsY4duzv6 ai8l8w/k7rt7Zr1utVpl4MAXJCionISHx8iECZ8UKE51OZvNJt9884307/+MjBs3/qrDG2+7rZt4 ez/u6EpbJyZThGzcuDHHsr/++qsMHvyCvPrqyFKZyEU0mbulkydPiv0qympiH9rXQsAkvXr1cnVo xcL69esdV0yOEhghfn5hsmnTpqzXhw0bJWbzTWIf2fGHmM2VrzlCIjfOnDlTaP3Q7urMmTNy661d xdvbT0JDK8j33890dUjFGprM3dPNN3cQ+0UhVQR8JDi4nNOHmV3PokWL5JFHHpVhw4Zd974fRW3r 1q0yaNDz8swzL1zRoq9WrYnY7xNzqeX+sfTs+fg1t7ds2TK58cY4adnyJpk3b17WcovFIrff3k28 vPzFy8tPund/+JonZ5XKLzSZu6eMjAwZOfI1ad26vTz+eL8ibxWOHv2a48ukgUCoBAZGXfWqzOKm WbP2jr74SyfUnpdBg164avkff/xRwCTwstivxDTL9OnTRcTeP2wydRNIE0gSs7mdjBkztqh2RZUi aDJXhcHDI0j+Had+UaC6DBxYsJOoRWXNmjViNoeJ0ficeHv3kdDQ8nL06NGrlq9Y8QaxX1j17zDO kJAqIiLSqFFbgSWXnWy99dbie2sAVXKRx2SuEzqrXLHZkoHOjmf+wK0cOnTIdQHlQatWrdi4cRWj R4fy5pu12blzI9HR0Vctb7GkY7+78yWRpKVZAahatSKenvGO5YK390piYysWVuhK5Zoz5gC9HseX jCrJAgKiSUoaBjwNnAYa8Omnw3nyySddHJnzPf30QD7+eDbwPeAN9OTee5sza9YMjh07RrNmcVy8 WB5IJzz8Ir//Hk9ISIhrg1ZuxxUTOl+PJnMXsVqt3H773axZswVvby/GjXuFvn375mtbf/zxB61a 3Upamg9wjnvuuZvZs791bsDFhIjQvXtP5s5dAgi33NKSxYt/wsPD/kP24sWLrFy5EqPRSFxcHCaT ybUBK7ekyVxlad48jg0bzgL/Aw4BA5k3bwZ33XVXvraXlpbG/v37CQkJITIy8vorlDJJSUl89dVX 2Gw2evXqRWBgoKtDUiWYJvNSatq0afTt+yIZGcn4+4fyyy//R/Pm7RBZD9RylHqeuLjNrFjxiytD dUuHDh2iRo3GpKdHAh54eR1jx47fqF69+nXXVSoneU3megLUDezYsYM+fQaQkTEB+JukpN60bn07 9u9QS7aSSfj4eBeormXLltGz5+M89tjT7Nq1i6SkJFJTUwu0TXfQuXMP0tPvAXYA27BaH6JTpx45 lrXZbJw/fx5t5Chn0mTuBmbMmAE0Bu7HPtJkGFZrOrfe2ga4C/gcGA58zeuvv57ven744Qfuuqs3 M2Y0ZOrUUG64oQUBAcGYTAE0aXIjNpvNCXtTMh09mgjcjr0hZQBuJyHhzBXlFi5cSFBQWcLDo4mO rsbWrVuLOFLlrjSZuwF7//XfwCNAKBAO2Pj880m88EJPoqPfo1atn1m9ejFNmzbNdz0jR75HSsok 7CNaLMCNwHngMJs2naJPn8cLuislVo0aFYFPgTQgHfiU2NjLhz8eO3aM++57mKSkH7Fak0hIGEWH DndhtVoLJaZ9+/bRq1dfOnbswfTpX2X9EsjMzCyU+pR7MAKbgZ9yeM1FQ+5LD6vVKiZTmMCNAkkC FvHwaCcvv3z124bmR82azQVWOC6WaSL/3ptaBCZJhQo3OLW+kuTMmTNSpkxFAbOAnwQGRsupU6cu K7NgwQLE4WkEAAAgAElEQVQJCro12zGzT+V2rfuR59fhw4clKChCPDxeE/hazOZaMmTIUKlZs4kY DB4SGlq+UCarUM6Diy4aegbYldfKlXN4enrStGkL4FnADzBhsz3HihW/ObWefv0ewmweACwHbMB4 x7+9wAYiI8s4tb6SpEyZMpw+fYh165azZs0Szpz5m/Dw8MvKREdHY7Xuwv5rBmAfmZkXCAsLc3o8 M2bMwGLpis32KvAgFst3jBs3kT17eiKSxunTX9K1a08OHz7s9LqVazgjmZcHOgKfUTSjY1QOqlWr hJfXr1nPPT1/pXLl8k6tY9Cgp3n77aeoUWMoRuNe7Al9D9AIg+E72rZtxbFjx5xaZ0liMBho0aIF rVq1wmg0XvF6/fr1efTRHvj5NcLf/wHM5jZ88MF7+Pn5OT2WjIxMRHyyLfHBZrMh8izgCbTFaGzJ xo0bnV63cg1nJN9ZwJtAIPAC/17zfYnjF4MqTKdOnaJJk5s4dy4a8MDf/yAbN64iKirK6XU9++wQ JkywkpHxP8eSL7D/6StgMOxj5cqFtGnTxun1uos1a9Zw8OBB6tevT926dQuljn379tGwYSuSkkYB VTCZXiE1dTci24GqQCp+fvX4+eep3HjjjYUSgyqYvA5N9CxgfXcCp7D3l8ddrdCoUaOyHsfFxREX d9WiKp/Kli3Lrl0bWb58OSLCLbfcUmgXrZw6dZaMjEbZltQAKgO/IzKGu+/uzZkzBwtUR2JiIoMG vcyuXXtp0qQe7703hqCgoAJts7ho3bo1rVu3LtQ6YmNjWb16CUOGvMa2bdtJTDyMweCBwdAEb++7 MBq3cMcdLQs9DpV78fHxxMfHu6z+N4EjwEEgAUgGvvxPGRefRlDONnPmLMecmzsEjgrcJDBcLk3s bDSGFmj7qampUrVqPfHyGizwi/j4PCqNG9+U40S96tpmzJjhmEv2lIBVvLwelYYNW8v8+fMvmwcz MzNTHnvsSQkNrSQVK1aX+fPnuzBqJeLaW+DejI5mKTXGjXtfgoPLidHoLxAtcEYgU6CvREfXKtC2 165dKwEB9cU+g7t9kmCzubzs3bvXSdGXHk8+OUhgfLYRNDskKqrGFeXuuusegVDH/Jz9BMyyatUq F0SsLsHFt8DVzvESxmazMXz4a0REVKVixRv44ov//rDK2fPPP8PZs8dJTT1L5cohQDRQBoPhO0aP fv6KKxx3797NuHHj+Pjjjzl79uw1t200GhFJ49+3UwYi1hxPKqprq1KlPL6+a7h0LA2GNZQvf+WJ 8fnzlwM/AC8BnwD3MXr0awDMmTOHGjWaUqlSXUaNGlOqLw4r7Vz9BaeuYejQEQJlBIwCRjEYAuWn n37K0zb69u0vECDQUqCO2Ceftm9v9OjRsnLlSjGbw8TTc4D4+HSTcuWqSmJi4lW3Z7VapVGjNuLr +4DA12Iy3SkdOtx9WbeAyp3k5GSpV6+l+Ps3l4CAzhIUFCnbtm27opzBECjwV7YW/MsSF3eLLF++ XMzmcgKLBTaK2dxUXnvtLRfsSelDHhvHeqOtUs7HJ5z09JbATOAicDOVK9s4cODPXG/D0zOYzEwr 0AhYD5iA3sA24A8qV67MwYP3AJOAc4Avjz12D599NuWq20xOTmb06LfYsWMvzZvX4+WXX8DHx+eq 5dPT0xGRa5YpKBHh5MmThIWF4elZ0LEDRSc9PZ1ly5ZhsVho06YNERERV5S56abbWL36IvZW+d/A gyxY8D1z5ixg6tSqwHOOkr9RterT7Nu3qeh2oJTK62iWouDarzd1TRAisFZgn8AWgQni5xclzz33 gvj7V5KAgEoycuTI62wjWOAjgUOOKyDXO1p3NoHW4ulpcvTHLnYsnyve3sGSnJxc4PjT09Oldu3G Ap4CnhIbW19SUlIKvN3/WrRokXh6lhEwCJjkpZdednodhcVms8lXX30t3br1kgEDnpOEhIQryqSn p0unTt3EZIqU4OBKMm3aNBEReeaZF8TD46VsLfYfpF69G4t4D0onimG3tauPiboGP78ogRscCbmi QLCUKVNWIExgtsB3AkHyxhtvXHUbECSwXeCCI6leyPbh7yvBwaGO7pd/L2M3marl+HNfxJ58hgx5 SSpXri/ly1eTRo2aS8+evXLsmunY8W6xTzJ9UiBRoJnExd3utOMjYh/pYTQGCbwrkCGwWsBPli9f 7tR6CsuQIcPEw6OKwGSBpyUgIEL++ecfSU5OlgEDXpCGDeOke/dH5Pjx41ese+DAAQkKihQPj+cF 3hSTqawsWLDABXtR+qDJXOVFjx49BOoKJDsS7euOxP59tuT7uYSHV7/qNqKjawr0cSS6co7HZwV+ FQiQiRMnitEY7BgeJwJHxccnSE6ePCkHDhyQ119/Xd5//325cOGCiIh063a/QIzANIFhjtZ+efH1 DZezZ89eVndwcBXHl86lWOeLv39Fpx6jvXv3CvhmG10jArfL4MGDnVpPYfHw8HP88roUe2d56aWX pF27zuLre5/AEvH0fFnKl68uSUlJV6x/8OBBGTJkmAwY8JysXbvWBXtQOpHHZF5yOv5UoThw4DD2 W+eaHUt6AmOx3/nvkjQ8PK7edbd69ULq1m1NcrI/9i6+H4GvAR+Cgnx4+OGHOXo0kQ8/bIrB0Bqb bSUjRoxg+/btdOjQDRF/IJlhw97i7793MHfuPMAKPAE0wX4b31RSU3fTtWtXKleujNlsJi4ujjJl /Dh3bhNwjyOaPwgKcu7l8fbJnzOx34OmOpAK7CQ29k6n1lNYbDYrEJBtSQjbt29n9eo1pKcnAl5k ZHTg/Plf+fXXX7ntttsuWz8mJoaxY8cUZciqmHL1F5y6hm7d7hFoLGBxtNreFqMxVCBQYJLABAE/ mThx4jW3k5mZKXPmzBHwEUjJagUGBLTJ+lm+bt06mT59umzcuFFERIKDKwk862jxWgRaSoUKMQK1 Ha34JIH2ApUFbhUoJwbDbQIvCISKl1eM3HzzbWIw+AvcKXC3GAx+snr1aqcfpyee6OfoTnpQoKrE xNQtMRcx+fqGCbR1dA99KuAnb731lnh7B2T7u9skIKBZvu6kOHz4q+LhESDgJeHhsYVyF8jSCO1m UXlx4sQJ8fYOdfSR1xDwl08++UTGjRsnkZE1pVy5mvLxxx9LZmamHDhwQA4dOpTjEMHJk6c4vgBM 2RKEiNncUhYuXJhj3fYvjc3Zfv5/JB4eoY7ulUvLVor9JG1vgVuydXWsF6gonp4VZPbs2dK3b195 4oknZPfu3YV2rP7v//5PnnjiCXn33XdL1DDJ+fPni6dnkBgMFcTDo5w0bnyjWK1W6dbtQTGbbxWY Id7efaVatQZ5Pnk8e/ZssQ9LXStwXuAJCQ2tUkh7UrqgyVzl1YkTJ+SZZwZL796PyC+//CIiIq+8 Mlw8PcPEYCgjNWs2krJlqzr60oMkJqaOWCyWrPWnTfvCkcSNAvcLdBSYK/CU+PuXzRq1kpaWJitW rJClS5dKUlKSREfXEhjhSM7pAm0lIKCs2K9AvJTMxzlaxEbx8Hg62/J/HF8ezWTMmDEuOW4lyd69 e+WLL76Qn376STIyMkTEPoLltdfekltvvVcGDHhezpw5k+ftdunSReDJbH8X+0nwa/ntt99kxIiR Mn78+CvOgah/oclcFdTnn38u4OdIyBvFPsrlXrGf4EwXuF1uv/2urPJVq9YR6CL2kSx7BEYLdBKI kmeeeUZERM6fPy+1azeVgIBGEhjYWsqXry6rVq1ydAHECkRITEwdWbFihaPuWwTuEjBL7doNZMOG DY6yywWOCfQUuFnAX/r16+eqQ1XqPf744wKtsv1i+k0MBr+rlp87d66YTBFiMAwTH5+eUqFCDU3o V4Emc1VQrVq1EXg5W2urlfw7RlwEZorZXD6rqyEmpoYjed8pECHwikCcmM0RWa3y558fKj4+D2d9 6D09h8k99/QSi8Uiq1evlj/++COrD/r333+XevUaSsWKVWTQoEFZLcmZM2cK+DuSvb9AdYEImTRp kmsOlJLTp0+Lj0+YQGuB/gKB0q/fU1ctX758Lfl3tioRH5+e8t577xVhxCUHOppFFVRQkD9wMtuS CGAu0AH7+2seFouFwYNfokWLxhw7loB9TtDzQHPgQ6Kigti9+y/MZvsomT//PEhaWicuXdCWkdGB v/56FZPJdMX9tJs0acLWrX9cEdd9991H377xTJ78PXAvsJrY2Egef7z0zj3qaiEhIRw79ieDBg3i 1Kl93Hffu/Tt2/eq5ZOSzgMxWc/T0ytz7tz5q5ZXxYurv+BUHu3evVs8PPwFnnb0WZdxtIZrCFR1 9GGvFqPRW3x9ywhsy3ZS0iTduz8o6enpl23zzTffEbO5vdjHs1vFx6enPPnkM/mKb968edK/f395 //33S8yIEmXXu/eTYjLdLfarhVeIyRQhv/32m6vDKpbQe7MoZ9i1axeDBj3LqVNn8PExc+rUWY4c OYFIT+z36YjAwyMIf//6XLiwPmu9wMA6rFr1DfXr179sexkZGfTo8Qjz5/+EweBJ06ZNWbRoNv7+ /kW7Y8qlUlJS6NfvWX788Sf8/YP48MMxdO3a1dVhFUt5vTeLJnN1VRcvXqRmzUacOnUPGRltsE/e nAx8iK/vezRvnsSGDb+RkrIO+8U02zCZ4jh6dB8hISE5bjMxMZHMzEwiIiIuvVmVUjnIazJ39v3M lRv55ZdfuHixEhkZbwOdgAUYDFuoUqUfDz0UwcKFs/jww3GYTC0JCmqBydSWqVM/JSQkhIkTJ2Iy RWI0lqFatQacOHECgPDwcCIjIzWRK+Vk2jJXV/Xjjz/y0EPvc/HiL44lyXh6hnP27KnLukeOHz/O wYMHqVq1KpGRkSxcuJA77+yFyFwgHHiM0NAETp7crxNMKJVL2jJXTnPLLbcQHHwcL6/ngLmYzV3p 1q37Ff3cUVFRtG7dmsjISACmTJmOyFDsMwnWBiZy+vRJQkIqU6ZMDCNGjHRKfDt37uSmmzoRG9uY fv2eJSUlxSnbVaok0qGJ6qr8/f3ZuHEVQ4eO5sCB6cTFxTFs2IvXXS84OAD7HN+X/A8wcuHC84CB 118fjsFgYPToUZetZ7PZ+P777zlw4AANGjSgU6dOV60jISGB1q3bc+HCcESacuzYWBIS+jBv3oz8 7KpSJZ52syinO3r0KJUr30BGRncgEpgAvA086SgxFV/f4SQnH8XDw/7jUES4555eLFmyl5SUtphM P/DUU/fxzjuv51jH9OnTefrphSQnf+9YkoLRGExKShJeXl6Fu4NKFYGi7mapAKwAdgI7gEEF3J5y A+XLl+evv7bQvv0RatdegK+vmX9vsQvgR2qqlbi429m4cSP9+w+mR4+H+Pnn1SQnr8Rme5vk5F/5 4IMPOH36dI51+Pj4YDCcy7bkAgaDR9aXQ3aZmZns3buXxMREp+6nUu4kEmjgeOwP7AFq/aeMywbd q+LhnXfeEfu0cf8nME8gXGC4gJeYTKECbwr0EvuMQf/ORuTnV1H279+f4zYvXrwolSvfIN7ejwl8 In5+9WXIkOFXlNu+fbv4+oaL/c6LvhIXd0eJuuOhKr1w8UVDPwAfAcuzLXPEpUqzdu3a88svW4Eg 4BFgKBCGvT/9EeAMEAu8D3TEw+Nzypf/gv37t1918uSzZ88ydux4Dh8+wW233UTv3r2uGPIYElKZ s2cfBkYCZ4GmjBrVm5EjnXMSVqnC4sqLhmKAlcANQFK25ZrMFdu2baN+/VbYk3h74GNgPvAF0NlR 6jX8/D7FZrNQu3YDZs/+gpiYmALVazD4AX8B0Y4lI7jxxpWsXLmCTz6ZxOrVG6lZM4YXX3wOPz/n zlCkVEG4Kpn7A/HAG9hb59lJ9lZQXFwccXFxTqpWlSQffPARzz77KmDEywsGDOjDp5/+hMUyGUjH bH6Mb775gC5dujitTm/vcKzWt4DHgTTgRh56qCYGg5k5c3ZisfTG13c5NWoc5fff4/XkqXKZ+Ph4 4uPjs56PHj0aimaQShYvYDEw+Cqvu7bjSRWazMxMGTJkuAQHR0lISAUZO3b8dfujLRaL/P3335Ke ni42m00mTPhEYmMbS40azWT69K+cHuNnn33muElYE4EoCQiIksOHD4uXl79jIgURyBR//wayYsUK p9f/X0lJSWK1Wgu9HlXyUcT3MzcAX2Lv+LwaVx8TVUjGjHlHfHxqCrwm8LWYzTULJSEX1MaNG2Xw 4MHyxhtvSEpKiiQkJIiPT4hjsg37ydbAwJvl559/LrQYEhMTpWnTOPH09BUvL5OMHTv+ijKpqamy b98+uXDhQqHFoUoOijiZ3wjYgC3AZse/2zWZlw6hoVUcswQ9JlBBoKd06nS/q8O6LpvNJs2b3yI+ Po8JrBej8U0pWzZGzp8/X2h1dujQVby8Bjm+QP4Ws7myLF68OOv19evXS5kyUeLnV0l8fALls8+m FVosqmTIazIv6DjzXx3baAA0dPz7uYDbVCXA9u3bOXv2ArAJ+AxYC/xAYKCp0OvOzMzk9dffpkWL 2+jWrRf79+/P0/oGg4HFi+dy770eVK3aj1tu+Z3161cQGBhYSBHD+vVrsVqHAEagIikpD7JmzVrA vj933NGNs2cnkJx8iLS0DQwa9BJ79uwptHjchYjwxRdf0q5dV7p2fYht27a5OiS35uovOFUIli5d Kv7+bS4bFw5lZfny5YVe9xNPDBSz+SaBBeLh8aaUKRMlJ06cKNQ6MzMz5Y033pbKletKnTotZMmS JXlaPza2ocDsrD56s/nWrOnuEhISxNc39LJjGRh4t8yePbswdsWtfPDBBDGbawh8L/Ce+PmFyZ9/ /unqsJwCnQNUFYVTp06Jv3+4wEKBTIHPJTS0gqSlpRVqvTabTby8TAKJWYnPbL5fpkyZUqj1Pv/8 y2IwBGfNO+rhESRr167N9fpr1qwRf/9wCQi4T/z9m0rz5rdkHav09HQxm8s4ZmoSgUQxm8vLH3/8 UVi74zbKl6+d7biJGAxD5OWXX3F1WE5BEXezqFIqPDycBQtmExbWD4PBi5iY94iPX4i3tzdffPEl 4eGV8PIKo0yZKk6/o6F9/G1mtiUZOV7G70wTJkxDZCD2i5wPYbPdwCuvjMj1+q1atWLXrk18+mkX vvtuJL/+uhhvb28AvLy8mDFjOmZzJ4KC2mMy1eWZZx6nYcOGhbMzbkW4fPSeprTC5OovOFXIss/3 uXjxYjGZogQiBMY65grtLB073uu0+gYNelHM5uYCM8VofEXCwipIYmKi07afE6MxRGBntq6Q96Re veZOrePo0aPy888/y65du5y6XXc2btz7YjbXEpgj8JH4+YW5zfEjjy1zvQWuKrDsF9rMnbuAlJRb gPPAEAAyM5uyZEkwFosFs9mc80by4H//e5uYmIksWDCDqKhwxoxZQ1hYWIG3ey01a1Zn584ZwOtA KvAd9957p1PriI6OJjo6+voFVZbnnhtEYGAAX345laAgf157bTG1av339lClg94CVznVsGGvMnbs Jmy2VOy36DEAZzEay2GxXMjqWihpjhw5QpMmN3HmjBG4wE03NWfJkh905iRVaHRCZ1Wk0tLS2LRp E0ajkcaNG/PPP/9Qr14zEhPTgY5AG3x9P+bRR2/k44+vdW1Z8ZeWlsauXbswm81Ur15d5zFVhUqT uSoyiYmJtGzZnlOnPBBJo1q1UFatWsT8+fMZOXIM//xzhipVqvHoo/fTr1/fQj9JqZQ7yWsy1z5z lW+DBr3M4cPtsFrHA8KuXb3o3fsxFi/+FYvldSCZ1NTXaNKkkSZypQqZfsJUvu3evQ+r9U7sjQcP 0tI6sXz5BiyWj4A+wEAslqHcf/9jtGgRx7hx76G/0nJn3bp13HrrPbRu3ZFp06ZfdtxsNhvTpk1j wIDn+OSTT8jIyHBhpKq40Ja5yrcmTerx559fkZZ2M5CJyTQDs9mTCxeyn+Tcz8GD+zl48ALr17/C d9/NZuPGta4KuUTYvHkz7dvfhcXyJhDOli0vcepUIikpFs6evcDOnX+ybt0ZLJaumM2zmTdvKYsW zdE+/FJO+8xVvl24cIF27e5i16592GxW2rRpyf33d2HgwNFYLO9hnz1oALAEaAPsBpqwbt1yWrRo 4crQi0R6ejqbN2/GYDDQqFGjq86Y9F8DBjzHxIkhwHDHklUYjd3w8LgHq7UMMBE4AfgB6fj51WDN mh+oX79+oeyHcg3tM1dFJjAwkPXrf+HgwYMYjUYqVaqEwWDAZPLlo48mc/DgPk6cCMKeyME+PWwt Vq1a5fbJ/PTp07Rq1YGEhAxEMqhatQyrV/9MQEDAddf18DBgMGTybxsoA5vNRGbmJOBP4Dv+nSDb G6MxjOTk5ELZD1VyaMtcFZrff/+dZs1uwn5zzcbAIaAu8fHzufnmm10aW2F75JH+zJhhJD39I0Dw 8XmUp54qx3vvvX3ddXfs2EGLFm1JTn4FCMfL62Ws1vrYp9mzAvWBu4BH8fD4kYiISezdu1WnvbuO kydP8vXXX5OWlkbXrl2L/cVFeW2Z6wlQVWiaNm1Kr17dsbfM6wB16NLlDrdP5AA7duwlPb0z/54c vpNt2/7K1bp16tRh9eoldOv2B7fe+gOvvz4As3kD8BOwB1/fcKKjfyQiohOtW6/g11+XaCK/jmPH jlGnTlOGDdvFiBH/0KTJTaxd617nbrRlrgrdtm3bWLlyJY0bN6ZVq1auDqdI9O//LNOmnSEtbSpg w9e3B4MH1+Wtt0bna3uLFy/mmWde5eLFC3Tr1pnx48dc92ra06dPM2nSZM6cOU/nzqXjS/RqBg9+ kQkThMzMcY4lX9G8+Zf89ttSl8Z1LXrRkFLFwMWLF2nf/m527PgLERvNmzdi0aLZ+Pr6Fkn9Z86c oU6dZpw+fRPp6VUwmz9h8uR3efDBnkVSf3Hz4INP8O23DYGnHEvWUr36YPbs2eDKsK5JT4AqVQwE BASwbt0yDhw4gIeHB5UrVy7SoYPTp0/nzJmWpKdPBcBiuYUXX3yk1Cbz++7rxNy5z5Ka2hIIxtt7 CPfc09HVYTmVM/rMb8d+in0v8JITtqeUW/Dw8CA2NpYqVapgs9kYPfot6tRpzY033sH69esLte6k pGSs1qhsS6KxWJIKtc7irFy5cmRmnsF+v6Bm2Gx/ERNT3tVhOVVBk7kRmIA9odcGHsA+/kypAtu5 cyfz5s3jr79yd+KwOHvppVd555357Nz5JmvW9KBduzvZvXt3odXXqVNHfHymAQuBPzGZnqJbt66F Vl9xN3nyl1itw4EEIJGMjG/54IOprg7LqQqazJsB+7CPObNiHwB7dwG3qRRjxrxL06bt6d17Cg0a 3MjkyZ9fs/zUqV9Qu3ZLatduyfTpXxVRlLk3depXWCxfADcDj5Ca2ps5c+YWWn2NGjVi7twvqVbt VSIi7qRXr1g++eS9QquvuPPwMADZb3tg1fsF/ce9wJRszx8CPvpPmaKfokOVaPv27RNf3zCB445Z ff4SX98gOXPmTI7lv/76WzGbqwgsFVgiZnOMfP/9zCKO+trCwysL/JE1U5GX1xPyzjvvuDqsUmPz 5s1iNocJfCAwXczmCvLttzNcHdY1UcRzgOowFeV0R44cwcenBlDOsaQaXl4RJCQk5Fh+8uQZWCxv A+2BDlgsbzJlyndFFG3uvPrqC5jN9wFTMBqH4u+/gAcffNDVYZUaDRo0YOXKRXTrtpE77ljAt99+ xAMP3O/qsJyqoKNZjgEVsj2vABz9b6FRo0ZlPY6LiyMuLq6A1Sp3VqtWLazWP4HfgBbAzxgM56hU qVKO5f38fIGz2ZacwWTyKfxA82DgwKeIjCzLrFkLCA0NYujQdURFRV1/ReU0TZo0Yc6cL10dxlXF x8cTHx+f7/ULOlbKE/t05e2A48AG7CdBs5/ZcfxiUCr3FixYQPfuvRDxxtsbfvppFm3atMmx7Lp1 6xx3GXwesGE2/48VKxbQrFmzog1aKSdyxUVDdwDvYx/Z8jnw1n9e12Su8iU9PZ1Tp04RERFx2aTR Odm0aROTJn2BwWCgX79HadiwYRFFqVTh0CtAlVLKDeiNtpRSqhTSZK6UUm5Ak7lSSrkBTeZKKeUG NJkrpZQb0GSulFJuQJO5Ukq5AU3mSinlBjSZK6WUG9BkrpRSbkCTuVJKuQFN5kop5QY0mSullBvQ ZK6UUm5Ak7lSSrkBTeZKKeUGNJkrpZQb0GSulFJuQJO5Ukq5gYIk83eB3cBWYC4Q5JSIlFJK5VlB kvkS4AagPvAXMNQpERWB+Ph4V4dwBY0p94pjXBpT7mhMhacgyXwpYHM8Xg+UL3g4RaM4/vE0ptwr jnFpTLmjMRUeZ/WZ9wEWOmlbSiml8sjzOq8vBSJzWD4M+Mnx+BUgHfjWiXEppZTKA0MB138EeAJo B6Repcw+oGoB61FKqdJmPxBbFBXdDuwEwoqiMqWUUldXkJb5XsAbOON4vg54qsARKaWUUkoppZzP CGzm35OlxUEwMBv7BU+7gBauDQewj9HfCWzHfiLZxwUxTAVOOmK4JAT7SfC/sF9XEFwMYnL1xWo5 xXTJ89iH64YUaUR2V4trIPbjtQMYWwxiagZswJ4XfgeaFnFMFYAV2D9vO4BBjuWufK9fLSZXv9cv 8xzwDfCjK4P4j+nYh1KCfTSPq69cjQEO8G8C/x542AVxtAEacvkH7x1giOPxS8DbxSCmDvw7pPbt YhIT2D+QPwMHcU0yzymuttgTlJfjeXgxiCkeuM3x+A7sSawoRQINHI/9gT1ALVz7Xr9aTK5+r2cp DyzD/oYqLi3zIOyJszgJwf7HK4P9y+UnoL2LYonh8g/en0CE43Gk43lRiyHnVjBAV+DrogslSwxX xjQLqIfrkjlcGddM4BbXhPL/7dw9aNRwHMbx70ELWs5JEHXQdOkmWungUKhIhwpCN1/xdXCtDoq4 6JOMo4QAAAJ8SURBVOSqiy5WRB06CYcKIoib4KJXKIiDWGuLSkHEt0EcdHiSJr3rIUWbX4bnA6FJ lj70fpf8X7sgYXGmCWBven6AmM+vqIG+b1Wo9UwDrRAsiqp1QMXdDwxRnYf5NrRb9SbwArgO9IQm kpPAN2AeuBOYI2HxF+9z4bzWcl2WhM4P8/vAwfKiLEhYnGkUuJyeV+lh3gQuAs9Qi3ig9ETtmTYD s8A7YA71aKIkwAywhmrUOuSZ6i33/1rrK/VfE/egB1OTf1/L/j91AduBa+nPH8C50ERag38KfYgb 0Yd4KDJQB7/ToyqqslmtB22iu1C4V5Wa70I9vh3AGdRSj3YDjQlvAk6jcfUIdeAuMIYaUkVRtV5H 83ljwPfC/dBav4TevtPAB/TQvB0RpMV6lCkzCDwIypLZB4wXrg8DV4OyJLQPs2Q7gDdQnWGWY8BT YFXZYVIJeaYtaJJvOj1+AW+BdcG5AB6innHmNbC2zEC0Z/paOK8BX0pNI93AI9SIykTX+lKZYBm1 vlIt8/Oo+9QL7AeeAEdW6Hctx0f0kulLr4fRDHKkV6jltBoV9zBaZVMF98gnY4+isbxoI6iVOUrn XcdlmkJjrb3pMYd6ffORoVIN8jHzPrQv5FNcHEAvlOwFswutHilTDfUOXgJXCvcja71TpqrVOkNU azXLVrQkqhLLfVJnyZcm3iJffVCmCeA96s7NAsfR2O9j4pYmtmY6gTarzaAhvCYaMovI9JP871T0 hpgx86VydaM5mCngObAzKFOxpgbQvNUk2mjYX3KmQbR8dJK8hkaIrfWlMu0mvtbNzMzMzMzMzMzM zMzMzMzMzMzMzMzMzMzMLNIf1QicDABfA2sAAAAASUVORK5CYII= \"></div>" + "payload": { + "type": "OutputContainer", + "psubtype": "OutputContainer", + "items": [ + "<div class=\"output_subarea output_text\"><pre><matplotlib.collections.PathCollection at 0x7f2101fa7ed0></pre></div>", + "<div class=\"output_subarea output_png\"><img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXMAAAEACAYAAABBDJb9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz AAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xd8U9X7wPFPmq6kkw5aWkaBMmXvIVoEHCAKqKAoqKgI CogLBZGhoqLg1wEqoCAulPUTZciSAgKCIBtEpqwCld2mI22e3x8JtUiBjrRp0+f9evEiuTn3nufe Jk9Ozj33HlBKKaWUUkoppZRSSimllFJKKaWUUkoppZQqclOBk8D2HF57HrABIUUakVJKqSweuSw3 Dbg9h+UVgA7A306LSCmlVKGK4cqW+SygHnAQbZkrpZTL5LZlnpO7gaPANifFopRSKp8887meGRiG vYvlEkPBw1FKKZUf+U3mVbF3u2x1PC8PbAKaAacuK1i1quzfvz+/8SmlVGm1H4jNbeH8drNsByKA yo5/R4FG/CeRA+zfvx8RKVb/Ro4c6fIYNCb3iktj0pic/Q97oznXcpvMZwBrgerAEeDR/7wuealU KaWUc+W2m+WB67xepaCBKKWUyr+CjGYpseLi4lwdwhU0ptwrjnFpTLmjMRWeohiBIo7+H6WUUrlk MBggDzm6VLbMlVLK3WgyV0opN6DJXCml3IAmc6WUcgOazJVSyg1oMldKKTegyVwppdyAJnPldCLC 9u3bWb9+PSkpKa4OR6lSIb93TVQqR1arlTvv7M6vv27GaAwmKCiFNWuWUrFiRVeHppRb05a5cqpP PvmU1auTsVj+4uLFLSQkPMRjjz3j6rCUcnuazJVTbdu2h5SUToA3AJmZXdi9e49rg1KqFNBkrpyq UaMbMJvnAimA4On5LfXr13F1WEq5Pb3RlnKqzMxMund/mEWLlmI0BhIRYWb16p8pV66cq0NTqkTJ 6422NJkrpxMRDh06REpKCtWqVcPLy8vVISlV4mgyV0opN6C3wFUlxsWLFzl58iT6Za9UwWkyV0VO RHjuuaGEhkYSE3MD9eu34tSpK+YCV0rlQW6T+VTgJLA927J3gd3AVmAuEOTc0JS7mj17NpMnL8Bq PUxqaiK7d7eid++nXB2WUiVabpP5NOD2/yxbAtwA1Af+AoY6MS7lxtat+53k5PuBUMBARsZTbNq0 0dVhKVWi5TaZrwbO/mfZUsDmeLweKO+soJR7i42NwWRaCWQAYDCsoEKFSq4NSqkSLi+jWWKAn4C6 Obz2EzAD+DaH13Q0i7pMeno67dp1ZsuW43h4ROHhsZ1VqxZTt25Oby2lSqfCHJoYQ87J/BWgEXDP VdbTZK6ukJmZya+//kpSUhItWrQgNDTU1SEpVazkNZkX9K6JjwAdgXbXKjRq1Kisx3FxccTFxRWw WlXSGY1Gbr75ZleHoVSxER8fT3x8fL7XL0jL/HZgPHAz8M811tOWuVJK5VFhdbPMwJ60w7APURyJ ffSKN3DGUWYdkNP4Mk3mSimVR3o5v1JKuQG9nF8ppUohTeZKKeUGNJkrpZQb0GSulFJuQJO5Ukq5 AU3mSinlBjSZK6WUG9BkrpRSbkCTuVJKuQFN5kop5QY0mSullBvQZK6UUm5Ak7lSSrkBTeZKKeUG NJkrpZQb0GSulFJuQJO5Ukq5AU3mSinlBjSZK6WUG8htMp+KfSLn7dmWhQBLgb+AJUCwc0NTSimV W7lN5tOA2/+z7GXsybw6sNzxXCmllAvkeuZnIAb4CajreP4ncDP2FnskEA/UzGE9EZH8R6iUUqWQ wWCAPOTogvSZR2BP5Dj+jyjAtlQpMHPmTPz8ojAaw6hYsTZHjx51dUhKuQ1PJ21HHP9yNGrUqKzH cXFxxMXFOalaVVJs27aNHj0eBV4FWnLkyLvUqdOKc+cOuzo0pYqF+Ph44uPj871+QbtZ4oATQDlg BdrNoq5i4MCBTJjwJ/bTLACpQACJiQmEhYW5MDKliqei7Gb5EXjY8fhh4IcCbEu5uYCAACAp25IU QDCbzS6KSCn3ktusPwP7yc4w7P3jI4B5wEygInAI6A6cy2FdbZkrzp07R0REVdLTuwJtgPdo1iyE 9etXXFbu999/5+jRozRo0IDKlSu7JFalioO8tszz0s2SX5rMFQAJCQk89FAfjh5NpF27lkyY8AEe Hv/+OOzXbzBfffUDnp71yMhYx9dfT6Fr1y4ujFgp19FkrkqM9evX8+67H5OebiUurgkjRkwkOXkz EAhswmxuz8WLpy9L+EqVFprMVYmwYcMG2rbthMUyAjDh5fUinp5tSUmZm1XGyyuAU6eOEBysFxer 0ievydxZQxOVypP335+MxTIMGAiA1ZpMZuZIYBdQG5hOeHgkQUFBLoxSqZJDk7lyiYyMTMA725Iq VKxYnoSEFhgMPgQHB7J48bxLrROl1HVoN4tyiZUrV3LHHd1JSXkPMGM2P8fkyWO49957OHv2LGXL ltW+clWqaZ+5KjGWLFnCG298iNWawcCBD9Oz5wOuDkmpYkOTuVJKuYGivAJUKaVUMaHJXCml3IAm c6WUcgOazLOxWCz07/8sNWs2p0OHrvz111+uDkkppXJFT4Bm06nTffzyC6SmPovBsJ7g4HHs2bOF 8PBwV4emlCpldDRLPqWkpBAQUIbMzPOADwD+/l347LMH6NGjh2uDU0qVOjqaJZ+MRiP2Y5fsWCLA Bby9va++klJKFROazB28vb3p128AZvNtwGd4ez9B2bL/cNttt7k6NFVMHDx4kKVLl3Lo0CFXh6LU FbSbJRsRYcqUz1m2bA0xMVEMG/ai3rFPAfDJJ1N4/vlheHvXJT19Ox98MJYnnujj6rCUG9M+c6Wc LCEhgSpVbiA19XegKrAXX9/m/P33n5QtW9bV4Sk3pX3myu19/fXXBAaWx9s7hNatO2CxWAq1vsOH D+PtXQV7Igeohrd3RY4cOVKo9SqVF5rMVYmyYsUKevXqz8WLU7BaN7J2rQ8tW7Yv1DpjY2PJyDgE rHcsWUtm5lGqVKlSqPUqlRfOSOZDgZ3AduBbLo3rU6oQvP/++8AjwB1AFeBztm3bXKh1hoaG8t13 X2A2d8Tfvwp+fncxa9ZXlClTplDrVSovCjo5RQzwBFALSAO+B+4Hphdwu0rlyNPTEziUbclhimKO lc6d7+TUqcMcP36c6OhozGZzodepVF4UtGV+AbACZuyfKDNwrKBBKffz/vvvU6VKPWrUaMgPP/yQ 7+28+uqrGAzxQA9gFHAH7dq1ybHsH3/8wZgxY/jwww85d+5cvuu8xM/Pj2rVqmkiV26rL3AROAV8 lcProkq3kSNHCgQKjBcYKWCSmjVryWOPPSapqal53l58fLxUqVJLwsIqSK9ej4rVar2izIIFC8Rk Chej8UXx9b1fKlSoIWfOnHHG7ihVJLBfuZhrBR2aWBX4CWgDnAdmAbOBb7In85EjR2Y9iYuLIy4u roDVqpLEz688Fsv7wL2OJa8BCwArYWHnOHFiL0aj0al1Vq3agAMHxgL2i768vXvzxht1efHFF6+5 3tixY/ntt99o2LAhw4cP16nrVJGJj48nPj4+6/no0aOhCMeZ9wA6AI87nvcCWgBPZyvj+JJRpZWv bxRpaV8B7RxLJgBbHf9X4bPPXuOxxx5zap1hYZU4fXo5EAuAwTCaIUPSefvtMVddp2nTm9m48QDQ GVhE7dpl2blz/VXLK1WYinqc+Z/Yk7fJUWl7YFcBt6nczN13t8XeG7cS+w+5N7G3A3yAaE6dOgWA zWbj0KFDHDt2jII2ADp37oSv7/PAUWAdJtNkOna8+q0ZNm3axMaNG4EtwMfAVnbt2sOSJUsKFIdS RaWgyXwr8CWwEdjmWDa5gNtUbmbGjK/o3r0F3t73A72Bm4A6wHQMhl306NGD8+fP06xZW2rXbkVs bH26dOlJRkZGvuv8+OPx3HtvBAEBjYmI6MWUKeO46aabrlr+4MGDQBgQ6lgSCETpfViUysbFpxFU cbJhwwYJCCgvYBIfn7Iya9YsERHp1auv+Pg8LpApYBGzuYOMHTvOqXWfO3dOEhMTxWazXfHa6dOn xWDwF5gkkCzwlRgMfnL48GGnxqBUbpHHE6B6dkcVqaZNm3LhwhFELKSmnuTee+0nRX//fStpaY9g f0uasFge4LfftjqlzszMTB588HHCw6OJjo6lbds7SUpKuqxMSEgIc+d+hZfXMCAQo3EQX389mQoV KjglBqUKmyZzVSzUqFEVT89Fjmc2fH0Xc8MNVa+5Tm598MEEfvhhL1brCdLTE1mzxp+oqJrUrNmc b7+dkVWuS5cupKf/g81mJSPjDD179nRK/UoVBb1roioWjh8/TsuW7Th3LgCbLZnq1UNZvfpnp1yg 06XLQ8yb1wF42LFkNTAQeBezuQ8zZ35Kp06dClyPUs6U19EshX8dtFK5EBUVxe7d9hEl3t7eNGnS xHHpfsFVr14JH58VpKX1xv7ZWI79DhQdsFheZerUmZrMVYmnLXPl9i5cuEDz5rdw7JgHFouBzMwj wG9ARQyGN+nV6zDTp3/q6jCVuoxOTqFUDtLS0li1ahWbNm3itdfGkZLyPAZDMn5+k1m/Pp7atWu7 OkSlLqPJXJUa6enpeHl5XXrT59qGDRuYPn0GXl6e9O//ODVq1CikCJXKP51pSLm948eP07BhG0wm P/z9Q/n662/ztH6zZs2YOPF/vP/+u7lO5OPHf0BgYAQmUxC9ez9Jenp6fkJXqtBoMlclzl139WT7 9pux2VKxWOLp2/c5tmzZUmj1zZ07lxEjJnDx4kpSU/cye/YRXnhheKHVp1R+aDJXJYrNZmPz5l/J zBwBGIF6wN2sWbOm0Or88celWCzPADWBsqSkvMb8+UsLrT6l8kOTuSpRPDw8CAwsC/zhWJKB0biF cuXKcfbsWV59dRR9+jzFrFmznFZnREQIXl7Z7x+3i/Dw0KuWV8oV9ASoKnHmzZtHz55PYDB0xGDY SbNm5Zgz50saNGhFQkJL0tMbYDZ/zNChjzB8+EsFri8xMZH69Vty7lwjMjND8fKazfLl82nevLkT 9kapnOloFlUq7N69m7Vr1xIeHk6nTp345ptveOqpmSQnz3eU+Btf37pYLOfzPNolJ2fPnmXmzJmk pqbSqVMnYmNjC7xNpa5FrwBVpUKtWrWoVatW1vPU1FRstuxdH6FkZKQjIk5J5mXKlOHJJ58s8HaU KizaZ14MiQiZmZmuDqNEue222zAaFwHTgM34+vamc+d7ddo3VWroO70YERFeeulVfH398fX144EH +uh45lyqVKkS8fGLaNbsGypVepjevSvwzTdTXB2WUkVG+8yLkc8+m8rgwR+RnLwQ8MNkeoD+/esz fvybrg5NKVXE9ArQEmzhwniSkwcC5YBAUlJeZtGiFa4OSylVAmgyL0YqVIjAy2tz1nODYTNRUREu jEgpVVI4o5slGPgMuAH7nHV9sN9f9BLtZsmlxMREGjRoxfnztRDxx9PzF9at+0Xv6KdUKeSKcebT gZXAVOxDHf2A89le12SeB+fPn+fHH38kPT2dO+64g6ioKFeHpJRygaJO5kHAZqDKNcpoMldKqTwq 6hOglYFE7IN7/wCmAAWftFEpVST++ecf+vUbTPv23Xj99bfJyMhwdUgqnwp6Bagn0AgYAPwOvA+8 DIzIXmjUqFFZj+Pi4oiLiytgtao4mzRpEoMHjyQ93UqVKhX57bdlhIZeeWMqEeHdd9/ljz/+oHXr 1gwcODDfddpsNhITEwkODsbHx6cg4ZcoycnJHDlyhOjoaAICAvK0rsVioVmzOI4ejcNqfYB16yaz Y8effP/9F4UTrLqm+Ph44uPjXVZ/JHAw2/Mbgfn/KSOqaH377bfy4IO9ZOTIkZKSklKkdS9btkzA LPCJwBqBtmI0lhEfH39p2LCN7N+/P6ts3botBCoI9BGIlBtv7JCvOnfv3i3ly1cXX99Q8fHxlylT pl5R5siRIzJhwgT5+OOP5cSJE/nev6KyevVqadv2LmnWrINMnvyZJCQkyKuvjpSnn35Wli1bJiIi ixcvFn//MPH3jxWTKVi+/36WrF+/Xl5//XWZMGGCXLx48Zp1LFiwQAICbhSwCYhAknh6muT8+fNF sYvqOrAPKClSq4DqjsejgLH/ed3Vx6RU6dOnryOZ1hMIlMjIWElPTy+y+u+77z6BBx3JQQROC3gJ nBYPj3elQoUasnXrVvnf//4nECRwzlHupIBJtm3bluc6K1euIwbDx47t/Clmc6Rs3bo16/Xdu3dL YGCE+Po+KibTQxISEi2HDh1y5m471caNG8VsDhOYKvCjmEw1xN8/VDw9+wuMFbO5vEyaNEX8/cME Vjr2e7N4eweLr2+YeHgMEZOpq8TG1pc9e/bIunXr5J9//rminh9//FECAtpm+1uliJeXn5w9e9YF e63+Cxck8/rYu1i2AnOxnxTVZO4CaWlpAr4Ci7MlyLIyfvz4IouhT58+Ah2zJYgDAj6O1l+6GAyB YjAECngI+AkszVa2vPzf//2fiIgkJSXJL7/8IqtXr77ml5HFYhGj0Ttb61LE37+XTJ36b+u8U6ce YjCMy3rdw2OE9OrVt9CPRX499dRggTeyHZdVAuUve+7vHyJeXmUFPhBIFxAxGOoJfJRVztOzgXh5 BUlQUBPx8wuVhQsXXlbPhQsXJCoqVjw9hwn8LCbT3dKx4735innjxo0yadIkWbRokdhsNmcchlxL Tk6WtWvXyrZt24q87sLkimR+Pa4+JiXWli1bpEmTthIVVUPuv7/PdX/+Hj9+XMA724deBO6W/v37 F1HEIseOHROjMVCgn8AkgRiBLo5YRjpa45MdyXe5QBmBQwJTxWDwkzNnzsixY8ekfPnqEhjYQvz9 60qDBq0lKSkpx/psNpsEBIQJrM3qKvDzq5nVFSEi0rRpe4GF2Y7Jd9Khwz1FdUjybNCg5wVGZIt3 meM4Xnp+j0Ajgf8JtBfoLLDf8eW4wVFmj0Cw49iKwBrx8wu9otvt2LFj0r37I9K48S3y7LMv56tb 7tNPp4jZXE7M5j7i719H7r//0asm1Tlz5kjv3k/Kiy8OlZMnT+br+GS3f/9+iYysIoGBjcVsriid Ot0nVqv1inJ79uyRevVai69voNSq1VS2b99e4LoLG5rM3cPx48fFbA51JL4d4uXVW9q2vfOa69hs NvH0DBT4wfEBPipQRhYtWiQiIp999pnUqNFIatduInPnznVKnIcOHZJt27ZJampq1rIDBw5IXFwH qV27iVSvXkfM5sbi6fmCGI2hAhH/+bJpLoAYjcEya9YsERG5++6e4un5iuP1TPH1fUCGDh1x1Rjm z58vZnOYBAbeJX5+VaV37ycvSyZvvDFWzObWjuNxSMzmhjJx4qdO2f/CsGPHDvHzCxMYJ/CF+PpW EC+vAMff9WeBQIFkx/FJFygnPj7BUq9eC/HxuV/gmMBbAi0vO9Zmc7QcPHjQqbGmpqaKt7efwF5H PRbx84uVNWvWXFH2jTfeFg+PcAF/gTAJDo6Q06dPF6j+1q1vEw+Pdx11p4rZfLNMmjTpihgjI6uI wfCh2Lv9PpeQkPJy4cKFAtVd2NBk7h4GDRokcFu2D2O6GI0+YrFYrrne4sWLHQm9ohgMZnnqqWdE RBx91AECHwq8I2CW7777Lt/x2Ww2ue++XmI0BoqnZ6QEB0dednLzkszMTJk5c6a89dZb8uijfcTe 5XLQsU8XxGiMkPXr12dtc8GCBVK2bHWB1dn2fZrcffeD14zn4MGDMmfOHFm7du0VrcKMjAwZOPAF MZmCxc8vRIYOHVnsf45v2bJFund/RDp27CFz5syVJUuWSO3aLaRs2Sri5RUt2buVzOb6Mnv2bLl4 8aLcc08v8fcPl/DwSuLtHeJosYvASvH3D7vsS9cZTp48Kd7ewWI/TxIr0E78/OJkzpw5V5T19Cwj 0FvglNi7joJl6NChV5SzWq3ywguvSHR0TYmNbZTV9ZaT8PDKAn9le6+8LQMHPndZme3bt0tAQI3L vtiCgprm+IVTnKDJ3D1ER1dztKwufWgTxMPDW/78809ZtmzZNZN6UlKSbN269bJRG4GBMQLfZHtD /08qVqyT7/g++eQTRwvrJbF3p1SUihVrXHMdq9UqtWo1dnStdBeDoYL07Nkn6/X+/Z8VP79a4uHR UKCnQIaARczm9jJ27Lh8x+pOrFarVKvWQDw9hwhsF6NxjERFxUpycvIVZSdOnCS+vsESGFhP/P3D ZMmSJU6PJzMzU7y8QgX6CuwW+FjALJs3b76irL0L8HS29+DT0rlz5yvKvfjicDGb2whsEfhZzOZI Wb16dY71t23bWYzGkY7PyUXx82sh06ZNu6zM0aNHxdc3ROCso96LYjZHya5du5xyDAoLmszdQ6VK dQXqCHQXGC9Q3fHT21ugjHh4BObpw2k2VxCYl+2DNEUiI6+dfK+lXbt2Yh9SeGl7m8RgCLjuejab TebMmSMjR4687GTZ3r17xWQqK/bRLecF2ggEird3GenSpWeO/aDZtzlx4qdSpUpDqVq1oUyaNCXf +1USJCQkSMeO90l0dE255Za7rjky5+TJk7Jp0yY5d+5cocTyzz//OLqAMrL9UmgvP/zwwxVlvbyC BdY5ytkEbpLXXnvtinLR0TUdifzSe2uMDBr0fI71HzlyRGJibhB//1jx9Q2Tnj0fk8zMzCvKPf30 8+Lnd4MYjUPEz6+h9OrVt9j/OkOTuXt46613xWyu62jxdBKDwUugrKM/VATGi7d3eK6399BDjwhE if1E4P8JlJHhw4fnOz77EMRB2T5wf4nRGJTv7a1bt04CAxtn255N/Pyqy9KlS6+77rRp08Vsrub4 6b5SzOaq8vXX3+Y7FpV7SUn2senwj1w6xxEQ0CjHv1tAQLhAuMBzAh3FYCgrn3565bmLatUaCyzK ei8YjQNk+PCrnzNJT0+XnTt3yt9//33VMjabTebNmydjxoyR2bNnF/tELqLJ3G3YbDZ55533pFat FtKoUZzYh/L1z5bskgWMedpe9+49xcsrQry9I+XppwcWKL4dO3aI0RggME3sY50byqOPPpnv7V24 cEHKlIkW+Mqxb59LWFjF654jEBFp0+ZOgVnZjs0Madeua75jUXkzePBL4udXX2Cc+PreJU2a3Jzj cFIfnwCxj8x5W2CKeHoOkHffffeKcvPmzROTKULgDfH0HCChoeXl2LFjRbErxQqazN3P9u3bHd0r 1QUsjoT1f2IwBLo0rrVr10rDhjdLTEwDGTZspGRkZBRoe5s3b5YqVeqJ0egt1ao1zPXwsY4duzv6 ai8l8w/k7rt7Zr1utVpl4MAXJCionISHx8iECZ8UKE51OZvNJt9884307/+MjBs3/qrDG2+7rZt4 ez/u6EpbJyZThGzcuDHHsr/++qsMHvyCvPrqyFKZyEU0mbulkydPiv0qympiH9rXQsAkvXr1cnVo xcL69esdV0yOEhghfn5hsmnTpqzXhw0bJWbzTWIf2fGHmM2VrzlCIjfOnDlTaP3Q7urMmTNy661d xdvbT0JDK8j33890dUjFGprM3dPNN3cQ+0UhVQR8JDi4nNOHmV3PokWL5JFHHpVhw4Zd974fRW3r 1q0yaNDz8swzL1zRoq9WrYnY7xNzqeX+sfTs+fg1t7ds2TK58cY4adnyJpk3b17WcovFIrff3k28 vPzFy8tPund/+JonZ5XKLzSZu6eMjAwZOfI1ad26vTz+eL8ibxWOHv2a48ukgUCoBAZGXfWqzOKm WbP2jr74SyfUnpdBg164avkff/xRwCTwstivxDTL9OnTRcTeP2wydRNIE0gSs7mdjBkztqh2RZUi aDJXhcHDI0j+Had+UaC6DBxYsJOoRWXNmjViNoeJ0ficeHv3kdDQ8nL06NGrlq9Y8QaxX1j17zDO kJAqIiLSqFFbgSWXnWy99dbie2sAVXKRx2SuEzqrXLHZkoHOjmf+wK0cOnTIdQHlQatWrdi4cRWj R4fy5pu12blzI9HR0Vctb7GkY7+78yWRpKVZAahatSKenvGO5YK390piYysWVuhK5Zoz5gC9HseX jCrJAgKiSUoaBjwNnAYa8Omnw3nyySddHJnzPf30QD7+eDbwPeAN9OTee5sza9YMjh07RrNmcVy8 WB5IJzz8Ir//Hk9ISIhrg1ZuxxUTOl+PJnMXsVqt3H773axZswVvby/GjXuFvn375mtbf/zxB61a 3Upamg9wjnvuuZvZs791bsDFhIjQvXtP5s5dAgi33NKSxYt/wsPD/kP24sWLrFy5EqPRSFxcHCaT ybUBK7ekyVxlad48jg0bzgL/Aw4BA5k3bwZ33XVXvraXlpbG/v37CQkJITIy8vorlDJJSUl89dVX 2Gw2evXqRWBgoKtDUiWYJvNSatq0afTt+yIZGcn4+4fyyy//R/Pm7RBZD9RylHqeuLjNrFjxiytD dUuHDh2iRo3GpKdHAh54eR1jx47fqF69+nXXVSoneU3megLUDezYsYM+fQaQkTEB+JukpN60bn07 9u9QS7aSSfj4eBeormXLltGz5+M89tjT7Nq1i6SkJFJTUwu0TXfQuXMP0tPvAXYA27BaH6JTpx45 lrXZbJw/fx5t5Chn0mTuBmbMmAE0Bu7HPtJkGFZrOrfe2ga4C/gcGA58zeuvv57ven744Qfuuqs3 M2Y0ZOrUUG64oQUBAcGYTAE0aXIjNpvNCXtTMh09mgjcjr0hZQBuJyHhzBXlFi5cSFBQWcLDo4mO rsbWrVuLOFLlrjSZuwF7//XfwCNAKBAO2Pj880m88EJPoqPfo1atn1m9ejFNmzbNdz0jR75HSsok 7CNaLMCNwHngMJs2naJPn8cLuislVo0aFYFPgTQgHfiU2NjLhz8eO3aM++57mKSkH7Fak0hIGEWH DndhtVoLJaZ9+/bRq1dfOnbswfTpX2X9EsjMzCyU+pR7MAKbgZ9yeM1FQ+5LD6vVKiZTmMCNAkkC FvHwaCcvv3z124bmR82azQVWOC6WaSL/3ptaBCZJhQo3OLW+kuTMmTNSpkxFAbOAnwQGRsupU6cu K7NgwQLE4WkEAAAgAElEQVQJCro12zGzT+V2rfuR59fhw4clKChCPDxeE/hazOZaMmTIUKlZs4kY DB4SGlq+UCarUM6Diy4aegbYldfKlXN4enrStGkL4FnADzBhsz3HihW/ObWefv0ewmweACwHbMB4 x7+9wAYiI8s4tb6SpEyZMpw+fYh165azZs0Szpz5m/Dw8MvKREdHY7Xuwv5rBmAfmZkXCAsLc3o8 M2bMwGLpis32KvAgFst3jBs3kT17eiKSxunTX9K1a08OHz7s9LqVazgjmZcHOgKfUTSjY1QOqlWr hJfXr1nPPT1/pXLl8k6tY9Cgp3n77aeoUWMoRuNe7Al9D9AIg+E72rZtxbFjx5xaZ0liMBho0aIF rVq1wmg0XvF6/fr1efTRHvj5NcLf/wHM5jZ88MF7+Pn5OT2WjIxMRHyyLfHBZrMh8izgCbTFaGzJ xo0bnV63cg1nJN9ZwJtAIPAC/17zfYnjF4MqTKdOnaJJk5s4dy4a8MDf/yAbN64iKirK6XU9++wQ JkywkpHxP8eSL7D/6StgMOxj5cqFtGnTxun1uos1a9Zw8OBB6tevT926dQuljn379tGwYSuSkkYB VTCZXiE1dTci24GqQCp+fvX4+eep3HjjjYUSgyqYvA5N9CxgfXcCp7D3l8ddrdCoUaOyHsfFxREX d9WiKp/Kli3Lrl0bWb58OSLCLbfcUmgXrZw6dZaMjEbZltQAKgO/IzKGu+/uzZkzBwtUR2JiIoMG vcyuXXtp0qQe7703hqCgoAJts7ho3bo1rVu3LtQ6YmNjWb16CUOGvMa2bdtJTDyMweCBwdAEb++7 MBq3cMcdLQs9DpV78fHxxMfHu6z+N4EjwEEgAUgGvvxPGRefRlDONnPmLMecmzsEjgrcJDBcLk3s bDSGFmj7qampUrVqPfHyGizwi/j4PCqNG9+U40S96tpmzJjhmEv2lIBVvLwelYYNW8v8+fMvmwcz MzNTHnvsSQkNrSQVK1aX+fPnuzBqJeLaW+DejI5mKTXGjXtfgoPLidHoLxAtcEYgU6CvREfXKtC2 165dKwEB9cU+g7t9kmCzubzs3bvXSdGXHk8+OUhgfLYRNDskKqrGFeXuuusegVDH/Jz9BMyyatUq F0SsLsHFt8DVzvESxmazMXz4a0REVKVixRv44ov//rDK2fPPP8PZs8dJTT1L5cohQDRQBoPhO0aP fv6KKxx3797NuHHj+Pjjjzl79uw1t200GhFJ49+3UwYi1hxPKqprq1KlPL6+a7h0LA2GNZQvf+WJ 8fnzlwM/AC8BnwD3MXr0awDMmTOHGjWaUqlSXUaNGlOqLw4r7Vz9BaeuYejQEQJlBIwCRjEYAuWn n37K0zb69u0vECDQUqCO2Ceftm9v9OjRsnLlSjGbw8TTc4D4+HSTcuWqSmJi4lW3Z7VapVGjNuLr +4DA12Iy3SkdOtx9WbeAyp3k5GSpV6+l+Ps3l4CAzhIUFCnbtm27opzBECjwV7YW/MsSF3eLLF++ XMzmcgKLBTaK2dxUXnvtLRfsSelDHhvHeqOtUs7HJ5z09JbATOAicDOVK9s4cODPXG/D0zOYzEwr 0AhYD5iA3sA24A8qV67MwYP3AJOAc4Avjz12D599NuWq20xOTmb06LfYsWMvzZvX4+WXX8DHx+eq 5dPT0xGRa5YpKBHh5MmThIWF4elZ0LEDRSc9PZ1ly5ZhsVho06YNERERV5S56abbWL36IvZW+d/A gyxY8D1z5ixg6tSqwHOOkr9RterT7Nu3qeh2oJTK62iWouDarzd1TRAisFZgn8AWgQni5xclzz33 gvj7V5KAgEoycuTI62wjWOAjgUOOKyDXO1p3NoHW4ulpcvTHLnYsnyve3sGSnJxc4PjT09Oldu3G Ap4CnhIbW19SUlIKvN3/WrRokXh6lhEwCJjkpZdednodhcVms8lXX30t3br1kgEDnpOEhIQryqSn p0unTt3EZIqU4OBKMm3aNBEReeaZF8TD46VsLfYfpF69G4t4D0onimG3tauPiboGP78ogRscCbmi QLCUKVNWIExgtsB3AkHyxhtvXHUbECSwXeCCI6leyPbh7yvBwaGO7pd/L2M3marl+HNfxJ58hgx5 SSpXri/ly1eTRo2aS8+evXLsmunY8W6xTzJ9UiBRoJnExd3utOMjYh/pYTQGCbwrkCGwWsBPli9f 7tR6CsuQIcPEw6OKwGSBpyUgIEL++ecfSU5OlgEDXpCGDeOke/dH5Pjx41ese+DAAQkKihQPj+cF 3hSTqawsWLDABXtR+qDJXOVFjx49BOoKJDsS7euOxP59tuT7uYSHV7/qNqKjawr0cSS6co7HZwV+ FQiQiRMnitEY7BgeJwJHxccnSE6ePCkHDhyQ119/Xd5//325cOGCiIh063a/QIzANIFhjtZ+efH1 DZezZ89eVndwcBXHl86lWOeLv39Fpx6jvXv3CvhmG10jArfL4MGDnVpPYfHw8HP88roUe2d56aWX pF27zuLre5/AEvH0fFnKl68uSUlJV6x/8OBBGTJkmAwY8JysXbvWBXtQOpHHZF5yOv5UoThw4DD2 W+eaHUt6AmOx3/nvkjQ8PK7edbd69ULq1m1NcrI/9i6+H4GvAR+Cgnx4+OGHOXo0kQ8/bIrB0Bqb bSUjRoxg+/btdOjQDRF/IJlhw97i7793MHfuPMAKPAE0wX4b31RSU3fTtWtXKleujNlsJi4ujjJl /Dh3bhNwjyOaPwgKcu7l8fbJnzOx34OmOpAK7CQ29k6n1lNYbDYrEJBtSQjbt29n9eo1pKcnAl5k ZHTg/Plf+fXXX7ntttsuWz8mJoaxY8cUZciqmHL1F5y6hm7d7hFoLGBxtNreFqMxVCBQYJLABAE/ mThx4jW3k5mZKXPmzBHwEUjJagUGBLTJ+lm+bt06mT59umzcuFFERIKDKwk862jxWgRaSoUKMQK1 Ha34JIH2ApUFbhUoJwbDbQIvCISKl1eM3HzzbWIw+AvcKXC3GAx+snr1aqcfpyee6OfoTnpQoKrE xNQtMRcx+fqGCbR1dA99KuAnb731lnh7B2T7u9skIKBZvu6kOHz4q+LhESDgJeHhsYVyF8jSCO1m UXlx4sQJ8fYOdfSR1xDwl08++UTGjRsnkZE1pVy5mvLxxx9LZmamHDhwQA4dOpTjEMHJk6c4vgBM 2RKEiNncUhYuXJhj3fYvjc3Zfv5/JB4eoY7ulUvLVor9JG1vgVuydXWsF6gonp4VZPbs2dK3b195 4oknZPfu3YV2rP7v//5PnnjiCXn33XdL1DDJ+fPni6dnkBgMFcTDo5w0bnyjWK1W6dbtQTGbbxWY Id7efaVatQZ5Pnk8e/ZssQ9LXStwXuAJCQ2tUkh7UrqgyVzl1YkTJ+SZZwZL796PyC+//CIiIq+8 Mlw8PcPEYCgjNWs2krJlqzr60oMkJqaOWCyWrPWnTfvCkcSNAvcLdBSYK/CU+PuXzRq1kpaWJitW rJClS5dKUlKSREfXEhjhSM7pAm0lIKCs2K9AvJTMxzlaxEbx8Hg62/J/HF8ezWTMmDEuOW4lyd69 e+WLL76Qn376STIyMkTEPoLltdfekltvvVcGDHhezpw5k+ftdunSReDJbH8X+0nwa/ntt99kxIiR Mn78+CvOgah/oclcFdTnn38u4OdIyBvFPsrlXrGf4EwXuF1uv/2urPJVq9YR6CL2kSx7BEYLdBKI kmeeeUZERM6fPy+1azeVgIBGEhjYWsqXry6rVq1ydAHECkRITEwdWbFihaPuWwTuEjBL7doNZMOG DY6yywWOCfQUuFnAX/r16+eqQ1XqPf744wKtsv1i+k0MBr+rlp87d66YTBFiMAwTH5+eUqFCDU3o V4Emc1VQrVq1EXg5W2urlfw7RlwEZorZXD6rqyEmpoYjed8pECHwikCcmM0RWa3y558fKj4+D2d9 6D09h8k99/QSi8Uiq1evlj/++COrD/r333+XevUaSsWKVWTQoEFZLcmZM2cK+DuSvb9AdYEImTRp kmsOlJLTp0+Lj0+YQGuB/gKB0q/fU1ctX758Lfl3tioRH5+e8t577xVhxCUHOppFFVRQkD9wMtuS CGAu0AH7+2seFouFwYNfokWLxhw7loB9TtDzQHPgQ6Kigti9+y/MZvsomT//PEhaWicuXdCWkdGB v/56FZPJdMX9tJs0acLWrX9cEdd9991H377xTJ78PXAvsJrY2Egef7z0zj3qaiEhIRw79ieDBg3i 1Kl93Hffu/Tt2/eq5ZOSzgMxWc/T0ytz7tz5q5ZXxYurv+BUHu3evVs8PPwFnnb0WZdxtIZrCFR1 9GGvFqPRW3x9ywhsy3ZS0iTduz8o6enpl23zzTffEbO5vdjHs1vFx6enPPnkM/mKb968edK/f395 //33S8yIEmXXu/eTYjLdLfarhVeIyRQhv/32m6vDKpbQe7MoZ9i1axeDBj3LqVNn8PExc+rUWY4c OYFIT+z36YjAwyMIf//6XLiwPmu9wMA6rFr1DfXr179sexkZGfTo8Qjz5/+EweBJ06ZNWbRoNv7+ /kW7Y8qlUlJS6NfvWX788Sf8/YP48MMxdO3a1dVhFUt5vTeLJnN1VRcvXqRmzUacOnUPGRltsE/e nAx8iK/vezRvnsSGDb+RkrIO+8U02zCZ4jh6dB8hISE5bjMxMZHMzEwiIiIuvVmVUjnIazJ39v3M lRv55ZdfuHixEhkZbwOdgAUYDFuoUqUfDz0UwcKFs/jww3GYTC0JCmqBydSWqVM/JSQkhIkTJ2Iy RWI0lqFatQacOHECgPDwcCIjIzWRK+Vk2jJXV/Xjjz/y0EPvc/HiL44lyXh6hnP27KnLukeOHz/O wYMHqVq1KpGRkSxcuJA77+yFyFwgHHiM0NAETp7crxNMKJVL2jJXTnPLLbcQHHwcL6/ngLmYzV3p 1q37Ff3cUVFRtG7dmsjISACmTJmOyFDsMwnWBiZy+vRJQkIqU6ZMDCNGjHRKfDt37uSmmzoRG9uY fv2eJSUlxSnbVaok0qGJ6qr8/f3ZuHEVQ4eO5sCB6cTFxTFs2IvXXS84OAD7HN+X/A8wcuHC84CB 118fjsFgYPToUZetZ7PZ+P777zlw4AANGjSgU6dOV60jISGB1q3bc+HCcESacuzYWBIS+jBv3oz8 7KpSJZ52syinO3r0KJUr30BGRncgEpgAvA086SgxFV/f4SQnH8XDw/7jUES4555eLFmyl5SUtphM P/DUU/fxzjuv51jH9OnTefrphSQnf+9YkoLRGExKShJeXl6Fu4NKFYGi7mapAKwAdgI7gEEF3J5y A+XLl+evv7bQvv0RatdegK+vmX9vsQvgR2qqlbi429m4cSP9+w+mR4+H+Pnn1SQnr8Rme5vk5F/5 4IMPOH36dI51+Pj4YDCcy7bkAgaDR9aXQ3aZmZns3buXxMREp+6nUu4kEmjgeOwP7AFq/aeMywbd q+LhnXfeEfu0cf8nME8gXGC4gJeYTKECbwr0EvuMQf/ORuTnV1H279+f4zYvXrwolSvfIN7ejwl8 In5+9WXIkOFXlNu+fbv4+oaL/c6LvhIXd0eJuuOhKr1w8UVDPwAfAcuzLXPEpUqzdu3a88svW4Eg 4BFgKBCGvT/9EeAMEAu8D3TEw+Nzypf/gv37t1918uSzZ88ydux4Dh8+wW233UTv3r2uGPIYElKZ s2cfBkYCZ4GmjBrVm5EjnXMSVqnC4sqLhmKAlcANQFK25ZrMFdu2baN+/VbYk3h74GNgPvAF0NlR 6jX8/D7FZrNQu3YDZs/+gpiYmALVazD4AX8B0Y4lI7jxxpWsXLmCTz6ZxOrVG6lZM4YXX3wOPz/n zlCkVEG4Kpn7A/HAG9hb59lJ9lZQXFwccXFxTqpWlSQffPARzz77KmDEywsGDOjDp5/+hMUyGUjH bH6Mb775gC5dujitTm/vcKzWt4DHgTTgRh56qCYGg5k5c3ZisfTG13c5NWoc5fff4/XkqXKZ+Ph4 4uPjs56PHj0aimaQShYvYDEw+Cqvu7bjSRWazMxMGTJkuAQHR0lISAUZO3b8dfujLRaL/P3335Ke ni42m00mTPhEYmMbS40azWT69K+cHuNnn33muElYE4EoCQiIksOHD4uXl79jIgURyBR//wayYsUK p9f/X0lJSWK1Wgu9HlXyUcT3MzcAX2Lv+LwaVx8TVUjGjHlHfHxqCrwm8LWYzTULJSEX1MaNG2Xw 4MHyxhtvSEpKiiQkJIiPT4hjsg37ydbAwJvl559/LrQYEhMTpWnTOPH09BUvL5OMHTv+ijKpqamy b98+uXDhQqHFoUoOijiZ3wjYgC3AZse/2zWZlw6hoVUcswQ9JlBBoKd06nS/q8O6LpvNJs2b3yI+ Po8JrBej8U0pWzZGzp8/X2h1dujQVby8Bjm+QP4Ws7myLF68OOv19evXS5kyUeLnV0l8fALls8+m FVosqmTIazIv6DjzXx3baAA0dPz7uYDbVCXA9u3bOXv2ArAJ+AxYC/xAYKCp0OvOzMzk9dffpkWL 2+jWrRf79+/P0/oGg4HFi+dy770eVK3aj1tu+Z3161cQGBhYSBHD+vVrsVqHAEagIikpD7JmzVrA vj933NGNs2cnkJx8iLS0DQwa9BJ79uwptHjchYjwxRdf0q5dV7p2fYht27a5OiS35uovOFUIli5d Kv7+bS4bFw5lZfny5YVe9xNPDBSz+SaBBeLh8aaUKRMlJ06cKNQ6MzMz5Y033pbKletKnTotZMmS JXlaPza2ocDsrD56s/nWrOnuEhISxNc39LJjGRh4t8yePbswdsWtfPDBBDGbawh8L/Ce+PmFyZ9/ /unqsJwCnQNUFYVTp06Jv3+4wEKBTIHPJTS0gqSlpRVqvTabTby8TAKJWYnPbL5fpkyZUqj1Pv/8 y2IwBGfNO+rhESRr167N9fpr1qwRf/9wCQi4T/z9m0rz5rdkHav09HQxm8s4ZmoSgUQxm8vLH3/8 UVi74zbKl6+d7biJGAxD5OWXX3F1WE5BEXezqFIqPDycBQtmExbWD4PBi5iY94iPX4i3tzdffPEl 4eGV8PIKo0yZKk6/o6F9/G1mtiUZOV7G70wTJkxDZCD2i5wPYbPdwCuvjMj1+q1atWLXrk18+mkX vvtuJL/+uhhvb28AvLy8mDFjOmZzJ4KC2mMy1eWZZx6nYcOGhbMzbkW4fPSeprTC5OovOFXIss/3 uXjxYjGZogQiBMY65grtLB073uu0+gYNelHM5uYCM8VofEXCwipIYmKi07afE6MxRGBntq6Q96Re veZOrePo0aPy888/y65du5y6XXc2btz7YjbXEpgj8JH4+YW5zfEjjy1zvQWuKrDsF9rMnbuAlJRb gPPAEAAyM5uyZEkwFosFs9mc80by4H//e5uYmIksWDCDqKhwxoxZQ1hYWIG3ey01a1Zn584ZwOtA KvAd9957p1PriI6OJjo6+voFVZbnnhtEYGAAX345laAgf157bTG1av339lClg94CVznVsGGvMnbs Jmy2VOy36DEAZzEay2GxXMjqWihpjhw5QpMmN3HmjBG4wE03NWfJkh905iRVaHRCZ1Wk0tLS2LRp E0ajkcaNG/PPP/9Qr14zEhPTgY5AG3x9P+bRR2/k44+vdW1Z8ZeWlsauXbswm81Ur15d5zFVhUqT uSoyiYmJtGzZnlOnPBBJo1q1UFatWsT8+fMZOXIM//xzhipVqvHoo/fTr1/fQj9JqZQ7yWsy1z5z lW+DBr3M4cPtsFrHA8KuXb3o3fsxFi/+FYvldSCZ1NTXaNKkkSZypQqZfsJUvu3evQ+r9U7sjQcP 0tI6sXz5BiyWj4A+wEAslqHcf/9jtGgRx7hx76G/0nJn3bp13HrrPbRu3ZFp06ZfdtxsNhvTpk1j wIDn+OSTT8jIyHBhpKq40Ja5yrcmTerx559fkZZ2M5CJyTQDs9mTCxeyn+Tcz8GD+zl48ALr17/C d9/NZuPGta4KuUTYvHkz7dvfhcXyJhDOli0vcepUIikpFs6evcDOnX+ybt0ZLJaumM2zmTdvKYsW zdE+/FJO+8xVvl24cIF27e5i16592GxW2rRpyf33d2HgwNFYLO9hnz1oALAEaAPsBpqwbt1yWrRo 4crQi0R6ejqbN2/GYDDQqFGjq86Y9F8DBjzHxIkhwHDHklUYjd3w8LgHq7UMMBE4AfgB6fj51WDN mh+oX79+oeyHcg3tM1dFJjAwkPXrf+HgwYMYjUYqVaqEwWDAZPLlo48mc/DgPk6cCMKeyME+PWwt Vq1a5fbJ/PTp07Rq1YGEhAxEMqhatQyrV/9MQEDAddf18DBgMGTybxsoA5vNRGbmJOBP4Dv+nSDb G6MxjOTk5ELZD1VyaMtcFZrff/+dZs1uwn5zzcbAIaAu8fHzufnmm10aW2F75JH+zJhhJD39I0Dw 8XmUp54qx3vvvX3ddXfs2EGLFm1JTn4FCMfL62Ws1vrYp9mzAvWBu4BH8fD4kYiISezdu1WnvbuO kydP8vXXX5OWlkbXrl2L/cVFeW2Z6wlQVWiaNm1Kr17dsbfM6wB16NLlDrdP5AA7duwlPb0z/54c vpNt2/7K1bp16tRh9eoldOv2B7fe+gOvvz4As3kD8BOwB1/fcKKjfyQiohOtW6/g11+XaCK/jmPH jlGnTlOGDdvFiBH/0KTJTaxd617nbrRlrgrdtm3bWLlyJY0bN6ZVq1auDqdI9O//LNOmnSEtbSpg w9e3B4MH1+Wtt0bna3uLFy/mmWde5eLFC3Tr1pnx48dc92ra06dPM2nSZM6cOU/nzqXjS/RqBg9+ kQkThMzMcY4lX9G8+Zf89ttSl8Z1LXrRkFLFwMWLF2nf/m527PgLERvNmzdi0aLZ+Pr6Fkn9Z86c oU6dZpw+fRPp6VUwmz9h8uR3efDBnkVSf3Hz4INP8O23DYGnHEvWUr36YPbs2eDKsK5JT4AqVQwE BASwbt0yDhw4gIeHB5UrVy7SoYPTp0/nzJmWpKdPBcBiuYUXX3yk1Cbz++7rxNy5z5Ka2hIIxtt7 CPfc09HVYTmVM/rMb8d+in0v8JITtqeUW/Dw8CA2NpYqVapgs9kYPfot6tRpzY033sH69esLte6k pGSs1qhsS6KxWJIKtc7irFy5cmRmnsF+v6Bm2Gx/ERNT3tVhOVVBk7kRmIA9odcGHsA+/kypAtu5 cyfz5s3jr79yd+KwOHvppVd555357Nz5JmvW9KBduzvZvXt3odXXqVNHfHymAQuBPzGZnqJbt66F Vl9xN3nyl1itw4EEIJGMjG/54IOprg7LqQqazJsB+7CPObNiHwB7dwG3qRRjxrxL06bt6d17Cg0a 3MjkyZ9fs/zUqV9Qu3ZLatduyfTpXxVRlLk3depXWCxfADcDj5Ca2ps5c+YWWn2NGjVi7twvqVbt VSIi7qRXr1g++eS9QquvuPPwMADZb3tg1fsF/ce9wJRszx8CPvpPmaKfokOVaPv27RNf3zCB445Z ff4SX98gOXPmTI7lv/76WzGbqwgsFVgiZnOMfP/9zCKO+trCwysL/JE1U5GX1xPyzjvvuDqsUmPz 5s1iNocJfCAwXczmCvLttzNcHdY1UcRzgOowFeV0R44cwcenBlDOsaQaXl4RJCQk5Fh+8uQZWCxv A+2BDlgsbzJlyndFFG3uvPrqC5jN9wFTMBqH4u+/gAcffNDVYZUaDRo0YOXKRXTrtpE77ljAt99+ xAMP3O/qsJyqoKNZjgEVsj2vABz9b6FRo0ZlPY6LiyMuLq6A1Sp3VqtWLazWP4HfgBbAzxgM56hU qVKO5f38fIGz2ZacwWTyKfxA82DgwKeIjCzLrFkLCA0NYujQdURFRV1/ReU0TZo0Yc6cL10dxlXF x8cTHx+f7/ULOlbKE/t05e2A48AG7CdBs5/ZcfxiUCr3FixYQPfuvRDxxtsbfvppFm3atMmx7Lp1 6xx3GXwesGE2/48VKxbQrFmzog1aKSdyxUVDdwDvYx/Z8jnw1n9e12Su8iU9PZ1Tp04RERFx2aTR Odm0aROTJn2BwWCgX79HadiwYRFFqVTh0CtAlVLKDeiNtpRSqhTSZK6UUm5Ak7lSSrkBTeZKKeUG NJkrpZQb0GSulFJuQJO5Ukq5AU3mSinlBjSZK6WUG9BkrpRSbkCTuVJKuQFN5kop5QY0mSullBvQ ZK6UUm5Ak7lSSrkBTeZKKeUGNJkrpZQb0GSulFJuQJO5Ukq5gYIk83eB3cBWYC4Q5JSIlFJK5VlB kvkS4AagPvAXMNQpERWB+Ph4V4dwBY0p94pjXBpT7mhMhacgyXwpYHM8Xg+UL3g4RaM4/vE0ptwr jnFpTLmjMRUeZ/WZ9wEWOmlbSiml8sjzOq8vBSJzWD4M+Mnx+BUgHfjWiXEppZTKA0MB138EeAJo B6Repcw+oGoB61FKqdJmPxBbFBXdDuwEwoqiMqWUUldXkJb5XsAbOON4vg54qsARKaWUUkoppZzP CGzm35OlxUEwMBv7BU+7gBauDQewj9HfCWzHfiLZxwUxTAVOOmK4JAT7SfC/sF9XEFwMYnL1xWo5 xXTJ89iH64YUaUR2V4trIPbjtQMYWwxiagZswJ4XfgeaFnFMFYAV2D9vO4BBjuWufK9fLSZXv9cv 8xzwDfCjK4P4j+nYh1KCfTSPq69cjQEO8G8C/x542AVxtAEacvkH7x1giOPxS8DbxSCmDvw7pPbt YhIT2D+QPwMHcU0yzymuttgTlJfjeXgxiCkeuM3x+A7sSawoRQINHI/9gT1ALVz7Xr9aTK5+r2cp DyzD/oYqLi3zIOyJszgJwf7HK4P9y+UnoL2LYonh8g/en0CE43Gk43lRiyHnVjBAV+DrogslSwxX xjQLqIfrkjlcGddM4BbXhPL/7dw9aNRwHMbx70ELWs5JEHXQdOkmWungUKhIhwpCN1/xdXCtDoq4 6JOMo4QAAAJ8SURBVOSqiy5WRB06CYcKIoib4KJXKIiDWGuLSkHEt0EcdHiSJr3rIUWbX4bnA6FJ lj70fpf8X7sgYXGmCWBven6AmM+vqIG+b1Wo9UwDrRAsiqp1QMXdDwxRnYf5NrRb9SbwArgO9IQm kpPAN2AeuBOYI2HxF+9z4bzWcl2WhM4P8/vAwfKiLEhYnGkUuJyeV+lh3gQuAs9Qi3ig9ETtmTYD s8A7YA71aKIkwAywhmrUOuSZ6i33/1rrK/VfE/egB1OTf1/L/j91AduBa+nPH8C50ERag38KfYgb 0Yd4KDJQB7/ToyqqslmtB22iu1C4V5Wa70I9vh3AGdRSj3YDjQlvAk6jcfUIdeAuMIYaUkVRtV5H 83ljwPfC/dBav4TevtPAB/TQvB0RpMV6lCkzCDwIypLZB4wXrg8DV4OyJLQPs2Q7gDdQnWGWY8BT YFXZYVIJeaYtaJJvOj1+AW+BdcG5AB6innHmNbC2zEC0Z/paOK8BX0pNI93AI9SIykTX+lKZYBm1 vlIt8/Oo+9QL7AeeAEdW6Hctx0f0kulLr4fRDHKkV6jltBoV9zBaZVMF98gnY4+isbxoI6iVOUrn XcdlmkJjrb3pMYd6ffORoVIN8jHzPrQv5FNcHEAvlOwFswutHilTDfUOXgJXCvcja71TpqrVOkNU azXLVrQkqhLLfVJnyZcm3iJffVCmCeA96s7NAsfR2O9j4pYmtmY6gTarzaAhvCYaMovI9JP871T0 hpgx86VydaM5mCngObAzKFOxpgbQvNUk2mjYX3KmQbR8dJK8hkaIrfWlMu0mvtbNzMzMzMzMzMzM zMzMzMzMzMzMzMzMzMzMLNIf1QicDABfA2sAAAAASUVORK5CYII= \"></div>" + ] + } }, "selectedType": "Results", "pluginName": "IPython", - "shellId": "0CC47EED5B9E4D0B85D5B99C9E4DB0E4", - "elapsedTime": 562, - "height": 327 + "shellId": "DD17159096204C748D91BB5FF8019664", + "elapsedTime": 301, + "height": 399 }, "evaluatorReader": true, "lineCount": 1 }, { - "id": "markdowngIqE5w", + "id": "markdownM0Tj1i", "type": "markdown", "body": [ "Having kept reference we can get extra features of the data whe have."