From cd8d8bb87516ce525128a01f494fc151fe094179 Mon Sep 17 00:00:00 2001
From: abe <aclaycomb@usgs.gov>
Date: Thu, 27 Oct 2016 10:17:31 -0600
Subject: [PATCH] revert added file...

---
 .../AdjustedPhase1GenerationTool.ipynb        | 786 ------------------
 1 file changed, 786 deletions(-)
 delete mode 100644 docs/algorithms/AdjustedPhase1GenerationTool.ipynb

diff --git a/docs/algorithms/AdjustedPhase1GenerationTool.ipynb b/docs/algorithms/AdjustedPhase1GenerationTool.ipynb
deleted file mode 100644
index b1b47d485..000000000
--- a/docs/algorithms/AdjustedPhase1GenerationTool.ipynb
+++ /dev/null
@@ -1,786 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Adjusted Phase 1  Generation Tool\n",
-    "Below are packages imported during development, most of which are used below\n",
-    "Read through the worksheet, and enter any values in the cells headed 'Enter' below.  Then click 'Cell' in the menu above, and 'Run All.'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\aclaycomb\\AppData\\Local\\Continuum\\Anaconda2\\lib\\site-packages\\obspy\\core\\util\\deprecation_helpers.py:67: ObsPyDeprecationWarning: Module 'obspy.earthworm' is deprecated and will stop working with the next ObsPy version. Please import module 'obspy.clients.earthworm'instead.\n",
-      "  warnings.warn(msg, ObsPyDeprecationWarning)\n"
-     ]
-    }
-   ],
-   "source": [
-    "%matplotlib inline\n",
-    "\n",
-    "import matplotlib as mp\n",
-    "\n",
-    "import pandas as pd\n",
-    "\n",
-    "import numpy as np\n",
-    "\n",
-    "import scipy as sp\n",
-    "\n",
-    "import scipy.linalg as spl\n",
-    "\n",
-    "import glob\n",
-    "\n",
-    "import json\n",
-    "\n",
-    "import urllib2\n",
-    "\n",
-    "from datetime import datetime \n",
-    "\n",
-    "import matplotlib.pyplot as pl\n",
-    "\n",
-    "import re\n",
-    "\n",
-    "import obspy\n",
-    "\n",
-    "from obspy.core import UTCDateTime\n",
-    "\n",
-    "import geomagio\n",
-    "\n",
-    "from geomagio.edge import EdgeFactory\n",
-    "\n",
-    "#from geomagio.Algorithm import DeltaFAlgorithm"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Example url for baseline web service"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "https://geohazards.usgs.gov/baselines/observation.json.php?observatory=BOU&starttime=2016-01-01&endtime=2016-10-07"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Enter the Observatory in the cell below as a string, similar to the following example:\n",
-    "\n",
-    "```python\n",
-    "obs_code = 'BOU'\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "obs_code = u'BOU'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Enter the start date and end date for which you'd like to request baseline measurements\n",
-    "If you choose a year's worth, that will result in the mean delta F for adjusted data being closest to 0, but will amplify the daily variation.  If you choose a shorter time period closer to the present, the daily variation will remain small, but the mean delta F will be biased by seasonal variation.  If the baseline service is called without dates, it will return the last one month's baseline measurements."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "start_date = '2015-10-10'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "end_date = '2016-10-09'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "baseline_url = 'https://geohazards.usgs.gov/baselines/observation.json.php'\n",
-    "full_url = baseline_url + '?observatory=' + obs_code + '&starttime=' + start_date + '&endtime=' + end_date\n",
-    "response = urllib2.urlopen(full_url)\n",
-    "parsed_response = json.load(response)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-22"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "h_abs = []\n",
-    "d_abs = []\n",
-    "z_abs = []\n",
-    "h_ord = []\n",
-    "d_ord = []\n",
-    "z_ord = []\n",
-    "h_t = []\n",
-    "d_t = []\n",
-    "z_t = []\n",
-    "\n",
-    "for datum in parsed_response['data']:\n",
-    "    for reading in datum['readings']:\n",
-    "        if (reading['H']['absolute'] is not None\n",
-    "           and reading['D']['absolute'] is not None\n",
-    "           and reading['Z']['absolute'] is not None\n",
-    "           and reading['H']['baseline'] is not None\n",
-    "           and reading['D']['baseline'] is not None\n",
-    "           and reading['Z']['baseline'] is not None\n",
-    "           and reading['H']['valid'] is True\n",
-    "           and reading['D']['valid'] is True\n",
-    "           and reading['Z']['valid'] is True):\n",
-    "            h_abs.append(reading['H']['absolute'])\n",
-    "            d_abs.append(reading['D']['absolute'])\n",
-    "            z_abs.append(reading['Z']['absolute'])\n",
-    "            h_ord.append(reading['H']['absolute'] - reading['H']['baseline'])\n",
-    "            d_ord.append(reading['D']['absolute'] - reading['D']['baseline'])\n",
-    "            z_ord.append(reading['Z']['absolute'] - reading['Z']['baseline'])\n",
-    "            h_t.append(reading['H']['end'])\n",
-    "            d_t.append(reading['D']['end'])\n",
-    "            z_t.append(reading['Z']['end'])\n",
-    "            \n",
-    "last_datum = parsed_response['data'][1]\n",
-    "pier_correction = last_datum['pier']['correction']\n",
-    "pier_correction"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plot of H absolutes, ordinates, baselines\n",
-    "Absolutes are in the top plot below, in blue.  These represent the field, as measured by the overhauser (with the pier correction applied), with direction measured by the theodolite.  Ordinates are also in the top plot, in green.  They represent the variometer's reading of the field corresponding to the times absolute measurements are taken.  Baselines, or the difference between the two are in the bottom plot, in blue.  Vertical axis units are nanoteslas.  Horizontal axis units are unix timestamp (seconds since Jan 1, 1970)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0xb6a71d0>]"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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EXIkRuxzV1qsislj0UmB/5zpnJToxIhRIjGgpL/nWTS8HflP+MMbGzh2dI1mI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\nNbBkSbTOfO1aPdsZHISGBnjoITjuuPz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RSulXKQnuCl0jqNw4kzGGqaNNIZ4czP9h7B1j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\nQJvwAKDtBxep0jqhjxjVAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa48a518>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.subplot(2,1,1)\n",
-    "pl.plot(h_t,h_abs,'.',h_t,h_ord,'.')\n",
-    "pl.subplot(2,1,2)\n",
-    "pl.plot(h_t,np.asarray(h_abs) - np.asarray(h_ord),'.')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plot of D absolutes, ordinates, baselines\n",
-    "Absolutes are in the top plot below, in blue.  These represent the declination angle of the field, as measured by the overhauser (with the pier correction applied), with direction measured by the theodolite.  Ordinates are also in the top plot, in green.  They represent the declination (from the sensor's H axis) calculated from the variometer's reading of the field corresponding to the times absolute measurements are taken.  Baselines, or the difference between the two are in the bottom plot, in blue.  Vertical axis units are degrees.  Horizontal axis units are unix timestamp (seconds since Jan 1, 1970)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0xbae4a20>]"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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islhEXhWRTd52dmlvzTCS09kJ06bBqae6387OSktkGOmj3N5q8517o7rV4VNV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\nGIaRCDMYhmEYRiLMYBiGYRiJ+P+ixjOXIsDsWQAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xb88dcf8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.subplot(2,1,1)\n",
-    "pl.plot(d_t,d_abs,'.',d_t,d_ord,'.')\n",
-    "pl.subplot(2,1,2)\n",
-    "pl.plot(d_t,np.asarray(d_abs) - np.asarray(d_ord),'.')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plot of Z absolutes, ordinates, baselines\n",
-    "Absolutes are in the top plot below, in blue. These represent the field, as measured by the overhauser (with the pier correction applied), with direction measured by the theodolite. Ordinates are also in the top plot, in green. They represent the variometer's reading of the field corresponding to the times absolute measurements are taken. Baselines, or the difference between the two are in the bottom plot, in blue. Vertical axis units are nanoteslas. Horizontal axis units are unix timestamp (seconds since Jan 1, 1970)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0xbe42978>]"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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RPQXptjIMwzCMMOY8DMMwjKwx52EYhmFkTcbOQ0RKROR1Ean2zqeLyBJve1dElnjpZ3r5\nlnj7ZhHZ17s2VETeFJE6EbkzUHa5V94qEVkoIj1z/aCGYRhG7sim5XE5TugQAFU9XVWHqOoQ4Elg\nhpf+Z1U9wEsfD6xW1Te92+4FzlfVSqBSRI720s8H1qtqP+BO4JateirDMAwjr2TkPERkT2As8ECK\nLN8nMWQX3Ozz6V4ZuwEVqvqqd+1h4ETv+ATgj97xEzhpE8MwDCOiZNry+C3wEyAhTtbTtvpQVd9J\ncl9wHsgewPuBa+97af619wBUtRn4TES6Z2ibYRiGsY1p1XkEta1wmlOtaVv59w0H/qOqy9tgV07j\nkQ3DMIzckskM84OA40VkLJ62lYg8HNC2Ohm3yFOY04l3KvXAXoHzPb204LUGr8ztw4q6PiaMaBiG\nkZ5ICCPGZRY5jIAku4iMAa5R1cND+QTXDXWwqq4JpL8CXAa8CjwLTFbVWSIyAahS1QmeYOKJqpog\nUWIzzA3DMLKnIJLsrZBM2wrgUGBd0HF4mLaVYRhGO8C0rQzDMNo5pm1lGIZhRAJzHoZhGEbWmPMw\nDMMwssach2EYhpE1ORdG9K7tKyILRGSpiPxLRMq99CEmjGgYhlH85FwY0Zvk9whwkapWAaOBr73b\n7sOEEQ3DMIqefAgjfhf4l6ouBVDVDaqqJoxoGIbRfsiHMGKllz5LRF4TkZ946SaMaBiG0U5odYZ5\nUBhRREbTujBiGU4PaxjwFfAPEXkNaMzCrpSTWUzbyjAMIz2R0LYSkV8BZwOb8YQRgRkBYcR6YIiq\nNnj5TwPGqOoPvPPrgP8FpgFzVXWgl346cJiqXiIis4BJqrrIK/MDVd01iS02w9wwDCNLCjLDXFWv\nVdWeqtoXpzn1gqqe410+CljhOw6P2cC3RaSLiJQBhwHLVPVD4HMRGe4JJ54DPO3dUw2c6x2fCryw\n1U9mGIZh5I2cCyOq6mcicgfwGrAFeDYggGjCiIZhGO0AE0Y0DMNo55gwomEYhhEJzHkYhmEYWWPO\nwzAMw8gacx6GYRhG1uRcGFFEeonIl4Fr9wbKMGFEwzCMdkA2obq+MOL24IQR/QsichvwWSDv255g\nYhhfGPFVEZkpIker6mwCwojeJMNbsHBdwzCMyJIPYURIIi9iwoiGYRjth3wIIwL09rqs5orIwV6a\nCSMahmG0E/IhjNgA9FTVDSIyBPiriAzK0i4TRjQMw2gjRSmMmOT+ucBVOKdiwoiGYRjbmKIQRhSR\nXUSkxDvuC+wDrDZhRMMwjPZDzoURgUOBm0RkE04Y8WJV9SOxTBjRMAyjHWDCiIZhGO0cE0Y0DMMw\nIoE5D8MwDCNrzHkYhmEYWZNzbatA/p4i0iQiPw6kmbaVYRhGOyCbloevbQU4bStVHeJpWD0JzAjl\nvx2YGUrzta0qgUoROdpLb9G2Au7EaVsZhmEYESUv2lYicgKwmoCzMW0rwzCM9kPOta1EZDvgauBG\n4mVGTNvKMAyjndCq8whqW+GcQWvaVjcAv1XVL7fCrpzGIxuGYRi5JZMZ5gcBx4vIWDxtKxF5OKBt\ndTIQXLtjBDBORG4BdgKaReQr3JjIXoF8e+J0sfD2ewENXpnbq+r6ZMZEWRixobGBZ1Y9w3H9jqPH\n9j0KbY5hGB2USAgjxmUWOQy4SlWP987HANeo6uEp8k8CmlT1Du/8FeAy4FXgWWCyqs4SkQlAlapO\n8AQTTwwuNhUoL7IzzGevms3Yv4xli26hc2lnVl+2usWBLGlYwh2v3MGPR/6YIT2SrZFlGIaRP6I4\nwzyZtlU6LsXpWNUBq0LaVrt42lZXABO30q5typKGJYz58xi26BYANjZv5MkVTwLOqQydMpRpb01j\n6JShLGlYkrKcuk/quGbONdR9Upc2zTAMo9CYtlUOOHvG2Ux7a1pcWvXp1exRsQdDpwyNSx83cBxT\nT5jKovpFAIzYYwQAz656ljOfPBNFEYSVl64EoP89/VvunX/ufFZ8uoJhuw/jtQ9e47h+xwEkdJXV\nfVLHg288yPn7n0/lLpX5eWjDMIqGfLQ8zHnkgCUNS+KcRJ8d+zDrrFkMvm8wm7dsjsv7k//zE/7+\n9t9Z+vFSAAbuPJDSklKWfbwMDQSzTTx4IvWN9Tzy5iMtaSVS0tK6ASgvKQdg05ZNlJeU8/ipj7Pn\n9nsybMqwlrJuGn0T5x9wPl9s+iJjh1L3SR13LrqT3bvtzvkHnG/jN4ZR5JjziKjzAOdAbl1wK2P2\nHsPJg07mL0v/wsXPXByXp5RSZpw2g5MfO5lmbXZpUoqIxDkZv+Xxf5//v/yt7m8Z2yAIO3bekQ0b\nN8Sld5JObNbNca2asAPxWyvf6fUdxvx5TEt6ePxma2ja2MTSj5ZStWsVFZ0rtro8wzAyw5xHhJ1H\nmIbGBva+e2++2vwVJZRw7cHXcsmBl1DRuYJRD45KaHnUflpLnx37cGzlsfz30P+mcpfKhBZNeWk5\nm5o3tZyXSRmbdXPCZ7fGxIMn8usjft1yXvdJHQPuGRDX8gky5XtTOLTnoQmtkWSBAH6rpXuX7gzf\nYziH9zmcis4VNG1sYuQDI1n56UoG7DyAVy54xRyIYWwjzHkUkfMA50Bmvj2TsfuMjfvl3rSxicX1\niwEYvsdwAJZ9vIzB3xyc8EJd0rCEOxfdyRUjrmC3brsx8+2ZDNltCEs+XMKhPQ/lpEdPYvkny+Pu\n2aXrLnzy5ScIgqKttjyumXMNt7ycXBGmc2lnnj/7eQ7946FxaU+f9nRcC6Xmwhq6lXeLG6MB6N+9\nP69e9Cpz353LCY+e0JI+fdx0eu7Qk86lnXl0+aNx3WmZRqdFuSUTZduMjkdBnYe3tGwN8J6qHi8i\n0wH/DbQTsEFVh4jIgcD9gVtvVNW/emUMIX4lwSu89HKcXMlQ4BPgNFVdl8SGonIe24KmjU3MWzOP\nUx47hU1bNtG5tDNv/vebrP9qPd27dGf+uvmM3WcsX2z6gqn/msoP9vtB0i4rv+UhCH8/8+/8bdXf\n2G273fivA/6LuxbfleBcRvQYwaKGRS3n4/cdz+4Vuyd1QnPGz2HZx8u4fNblLWm7brcrH/3no5Zz\n36l9semLuNZWzYU1DOkxJG4ODcATK55g8qLJvLvhXSp3ruTp059m3tp5cYED2c67ydUL31pZRtQo\ntPO4Evdy396f5xG4dhvwmar+QkS6AJtUdYunZ/UvYHfvfBHwQ1V9VURmAnep6mwRuQT4tjfP4zTg\npGKb51FoUrVyMqXuk7q0ziXYosim5QHOeQzcZSB9J/dlY/NGSimlmeaEfBMPnsh7n78XF7k2ft/x\n/OaI37R0AZaXlCMibGzeGHdvWUkZm7dsppRSThhwAhcNuYgTHj2Bjc0b48Zt/FbNuAHjeKXhlZYW\nT9PGJkZMGUHtp7X037k/c86Zw9rP1yY4kkwi2apXVse1sqpPr+Z7/b+Xquq3GmvlGK1RMOfhCSNO\nBX4J/DiJ81gHHO7rWwXS+wALcNpVuwIvqOog79rpwGGqeomIzAImqeoib4b5h6r6zSR2mPMoEHWf\n1DF58eSW1oj/Iva71IJjHrctvI2nVz7N+q/WM2DnASw4fwEVnStaHNyi9xfxwOvxGpupWh7TTpzG\nXYvvYnHD4q2y/+5j7mbUnqMSQqf9ltbva37PU7VPtaTvUbEHHzR9QOXOlUw7eRqPLn+U7/T6Dsf8\n+ZiWsaFTB57KL77zixYn4r/E57wzh+tfvL6lrFuOvIWfHPSTpHYlax09tfwpfjb3Z/zy8F9y0qCT\n0j5X08YmRj04qqWV8+T3n0xogRlGIZ3H4zjHsQOBGebetUOA21V1eCBtOPAQ0BMYr6pPi8hQ4Neq\n+l0vz8HA1V4X2FvA0ara4F1bBYwIS5SY8ygemjY2pRzHCbdkLjrgIq4adVXcmMedi+5kTN8xnPXX\ns+LuTdXyaI1xA8axacumrKLXMuWm0Tdx2uDTOOnRk1j56Up6dOvB+00xDVC/5eE7l52/sTPz1s5j\n2O7DGPXQqLjW0aL3F3Hy4ye33Dvj1BlpHcic1XM46pGjWs7LS8rZtGUTXcq68M6P3mnVgQRbLR80\nfZDQqrJWTfsgH86jVW2roDCiiIymdWFEVHUxUCUi/YGHReTvWdqV8iGjrG1lxKjoXMHIPUcmvVa5\nSyW1l9am7CYb0mMID5/0MGfPODsufeQeI3ny+27m/sy3Z9K/e39mvjOTsXuP5chHjmTTlk1x+fvs\n2Id3P3sXgCdXPpmx7X6gQaZcP+96bnrxppbIt/eb3mfP7fekobGB3jv25oV3X2CPij0Y/9R4ln+y\nvKX8YPfdxuaNnPPUOazesDqu7AnPTmDEniP48IsP44II/BbL9uXbx+X36+CrzV8x8+2ZXDDkgpR2\nB7vqeu/Ym3c/exdFueXlW7jggAu4ZNglnD3j7JauvEUXLkrrQLKZnGpOKb9EQttKRH4FnA1sxhNG\nBGYEhBHrgSF+qyHJ/f/Aybk3AHNVdaCXnq7b6gNV3TVJWdby6ECEQ5X9wfNk+F1ie1XsxbSl07hi\nxBX027kfE56dwJ/e+lNLvnP3O5ctuoWT+p/E4g8WM7rn6LiuqJ+P/jmH9TosLrrMf9kLwpRjp3DB\ns6lfyD63HnUre22/F2c8eUZWjigZZZSxmVhI9qwzZ3HiYyfy1eav6Fzamd479ObtDW+z9057s/bz\ntWxs3kgn6cSFQy/k8hGXpx6bqa3mhOknJL2WNH+oBdVrh14t40JvfPBGXJ3VXlqb8nP9rrYVn6xg\n4C4DmX327KTjS0buKHiobibCiCLSGxeR1SwivYCXgX1VdX17FkY08kOycZVsCEeSpZogGW4FBdOA\nuOtPLX8qrmsJ4mf7+11QyaLUwuy5/Z683/h+XNr2nban8evGlPcM2mVQXHj23cfczbAewxj8zcE0\nbWzioTce4vq516d9ZoDLZ17O5Fcnp7UvyOQxkzlv//MYdv8w6tbX0Uk60azN9N2pL+9seCfOSZ5Z\ndSbTxrnAh3Dodbirref2PXm/8f2krZvwmFDTxqY4aR9zNpkRRecxFVioqvcH8pyNEzbcBGzBher+\nzbs2lPhQ3cu99M7AI8ABwKfA6aq6Jsnnm/MwsiZdJFlb8Z3aWVVn8V7Te4zdZyxAXMRbePKlPx4B\nUEJJS1TXS+teammhCMJrF75GfVM9pz5+KhubNya0PCDWGko2tnHlrCu5c9GdsfORV3LH0Xe01MWD\nbzzIft/cL248qVNJJ/4x/h888tYjPLDkgZjN3sRU3yHOXzefM548I6M6qrmwBiCh9fh+4/tx0WhB\nxg0Yx6+O+BWVu1TS0NjQEqHnh6CfOP1EVny6AoDK7pW8dtFrgNOGW/T+Ii4ZdomN1ySh4M6j0Jjz\nMIqNoOPqVt6NmW/P5NCeh7L+q/VxwQTJHFww/PrDLz7kqueuYt7aeS1lX3rgpVx78LUJg+LTl06P\ne8FPHzed06pOS6skMLrXaOaeNzelzb5DDDsmn1IpbZHc8Tm+3/Gs2rCKFZ+saEk7ffDpvP7B69Su\nr01bb7WX1jLrnVlxc4MuPfBS7nn1nvhnHTeda/9xLas/c2NFfktr94rdGXb/MFatX0W/7v144HsP\n8Pslv08I0U5FMscTbEF1K+/WMr7Trbwbz6x6hl7b9+KRtx6J5NIL5jzMeRgdmMmLJse9TO8+5m5+\nOPyHCfmaNjYx7P5hvL3+bfbpvg+vXfQaFZ0r0ioJzD93Pof0PqRVG1I5oKnHT2X5x8u5deGtae+/\n46g7+PHzP271c87d71waNzby1MpY+PRNo2/i+nnXx+W78IALmfL6lIR7x+wzptUWkj8uEx7oD8/5\nWXThIlZ9uiohzNunc2nnhOi/aSdOY+Y7MyPjSMx5mPMwOjDhbpx0gpXJQqXD4z9/OvFPPPLWI1x7\n8LUZOQ6fuk/q+J+a/6G6tpo1n62hsntly1iF351XQgl/fPOPLfcM/uZgHj7xYf753j/jHKDPMfsc\nw9/fTh6UWSIlDNh5AM+Pf57D/3g4devd2jadSzvzs0N+luBQAM4cfCZ/XvbntM9x6YGXctnwyxLG\nxFZ+ujIukGDq8VO577X72jzXKF2gx7bCnIc5D6ODk08lgWxpbS5PskCFoAMEpwwwYOcBzD57Nkc8\nfAR16+v2HIwpAAAgAElEQVTYsXxH1m+MTfEav+947hl7T4vA5rw181jz2RrGDRwHEFeez/H9judv\nq/6WNtLt56N/TtPXTXGtsYkHT2T3brvHOThfvSAVyVoeQcbvO56HT3o45fVtgTkPcx6GUTSkclS+\nAwyP/fjOqLykvGVNmnTRYsHy7nvtPn7x0i9a0ny5HP/zv9j0Bb966VdU11Xz9ZavW1puX2z6IsHJ\ndSvvFpPSCY3ljNxjJPeMvSeubH9cyA8TD09uba8tD1Q1ow23ZO3rQLV3Ph1Y4m3vAku89COB13Ca\nVq/iZEv8MoYAb+KWob0zkF7ulbcKWAj0TGGDFgNz584ttAkZYXbmjmKwUbV47Hz4rw/rxDkTtfbj\n2ozvqamv0fEzxmtNfU3KPPWf1+uUmila/3l9S1rtx7UJn+Xnq6mv0S6/6KLcgHb5RZe4+1RT12cm\ntmxLvHdnxu/7TLZsnMeVwJ985xG6dhtwnXe8H7CbdzwYeD+QbxFwoHc8EydJAnAJcK93fBowPYUN\neajW3DNp0qRCm5ARZmfuKAYbVc3OtpDM4fhEyc505MN5lGTSOvGEEccCD6TI8n08iRJV/Zeqfugd\nLwO6iEgnT2G3QlVf9e55GDjROz4B8EfXngCOyMQuwzCMfNNj+x5cMOQCE5oMkZHzAH6LkxhJGHDw\nhBE/1JCirnftFFx31tc4Zd3gVNr3vTS8/XsAqtoMfCYi3TN9CMMwDGMb01rTBDgW+J13PBr4W+j6\nvcCVSe4bjBvD6O2dDwWeC1w/mNj4yVtAj8C1t4HuScpU22yzzTbbst9y3W3VqqoucBBwvIiMxRNG\nFJGHNSaMeDJuILwFr5trBk6OfY2XXA/sFci2p5cWvNbglbm9huTYcU+f22gBwzAMo0202m2lqteq\nak9V7QucjlvQ6Rzv8lHACg0o6orIDsAzOMHEVwLlfAh8LiLDRUSAc4CnvcvVwLne8anAC1v5XIZh\nGEYeyXTMIxWnEVrLA/ghsDdwvYi8LiJLRGQX79qlwIO4UN1VqjrLS38Q2MVbBOoKnLCiYRiGEVGK\napKgYRiGEQ22tuXRZkTkQRH5t4i82Uq+A0XkaxE5OZRe4rVqqgNpO4nIcyJSKyKzvS60KNo5SUTe\n99KXeOuiFMxOEVkjIv/yWoqLA+k5rc882Ri1utxBRB4XkRUiskxERnjpkfpuprEzMvUpIpWB3ovX\nReRzEbnMuxaZ+mzFzsjUp5d2pYgsFZE3RWSaiJR76VnXZ8GcBzAVODpdBhEpAX4DzE5y+XJgeSht\nIjBHVfvjxk1+GlE7Ae5Q1SHeNivJ9WzZGju3AKNV9QANrEVP7uszHzZCtOryLtxaNQNxE2Z9PfKo\nfTdT2QkRqU9VrfP+3kNw0Zr/wQXiQITqsxU7ISL1KSI9gB/hVn7dF7cMub/oXtb1WTDnoar/BDa0\nku1HuEmDHwUTJfWkxeBkwz8Sm4QYNTshzTrtbWFr7PRsSfZdyGl95slG/1rOaKudIrI9cIiqTvXK\n2ayq/pKAkflutmInRKQ+QxwJvKOq/lyxyNRnK3ZCtOqzFNhORMqArsQiXrOuz0K2PNLieckTVfU+\nEis/1aTFXVX139AS3ZWwDnpE7AT4oYi8ISIP5KLJvZV2KvC8iLwqIhcG0rdpfbbRRohOXfYBPhGR\nqV4Xxf0i8g3vWpS+m+nshOjUZ5BwcE6U6jNI0iCiKNSnFxV7O7AO5zQ+U9V/eJezrs/IOg/gTuCa\ncKKIHAv8W1XfwFVMOq++LaIB2mLnvUBfVd0f+BC4owB2Bu05yGtyjwUuFZGDU5SR7/psi41RqEuf\nMtycp3s8W78kFjmYzBnmm7bYGYX6jKsrEekEHA88nqaMQtRnJnZGpj5FZEdcC6MX0APoJiJnpiij\n9frM9azDbDbvId5McW21t70LNOEq/njgVzjPuRr4APgCeNi7ZwXwLe94N9wclMjZmWnZ28LOJPkm\nAT/OV33m2sYo1SXwLWB1IN/BeIoMEftuprQzSvUZuH48MCt0T2TqM52dUapP4BRgSiDfeGLqIVnX\nZ6FbHilbDqra19v64PruJqhqtaaftFgNnOcdn0tsEmKk7BQnEulzMrC0UHaKSFcR6ebZtR3w3YA9\n+ajPnNoYpbpU1+x/T0T8xSeOIBYsEaXvZko7o1SfgSxnkNgVFJn6TGdnxOpzHTBSRLqIiOD+7n6g\nRNb1mYk8SV4QkT/jtLJ2FpF1uF+T5TgNlvtD2TNtkt4MPCYi/wWsxan9RtHOW0Rkf1wE0Rrg4gLa\n+S3gKRFR3Pdhmqo+513LaX3mycYo1SXAZcA0rwtjNfADLz1q381UdkaqPkWkK24Q+qJQvkjVZxo7\nI1OfqrpYRJ7Arcv0tbf382ddnzZJ0DAMw8iaQndbGYZhGEWIOQ/DMAwja/LmPCSJnISI7CciC7z0\npwODoEeKyGte+qsicni+7DIMwzC2nryNeYjIamCoqm4IpC3GhVj+U0TOw8U/Xy8i++HmRHwoIoOB\n2aq6Z14MMwzDMLaafHZbJZOT6Kduaj3AHGAcgKZY9zyPthmGYUQeyVAE0cvbU0TmeD04L3gzzfNG\nPp1HUE7iAi9tmYgc7x1/H7eaYBwSv+65YRhGR6ZVEcQAtwF/UNX9gJtwwoh5I5/dVrur6gci8k3g\nedwiUR8BdwPdcZNSLlPVbwbuGQz8FThKY8vXBsu0uGLDMIw2oDlexjtvLQ9V/cDbfww8BQxXJ118\ntKoeCEwH3vHzS/J1z5OVG/lt0qRJBbfB7DQbzU6z09/yQV6cRyo5Ca8V4mvNXwf8j3e+I0nWPTcM\nwzCiSb5aHt8C/ikirwOv4ETXngPOEJFanI5Ovar+wct/KanXPTcMwzAiRl60rVT1XWD/JOmTgclJ\n0n8J/DIfthSC0aNHF9qEjDA7c0cx2AhmZ64pFjvzQVFpW4mIFpO9hmEYUUBE0GIZMDcMwzDaL+Y8\nDMMwjKwx52HklKYmWLjQ7Q3DaL+Y8zByRlMTHHIIHHqo25sDMYz2S1RUdbt7WixNIpIQjWUUB0uX\nwrJlsHkzLF/ujg3DaJ/ks+WxBRitqgeo6nAvbQpwtTrtlaeAq730r3CTBq/Koz1GnunVC8q84O/S\nUujZs7D2GIaRP6Kiqvulqi4ANubRHiPPrF0LX3tylps3w7p1hbXHMIz8ETlVXaN4qapyW6dOMHiw\n2wzDaJ/kZYa5x0EaUNUVkZXAfwF3i8j/w6nqbsrj5xvbmIoKmDkTnn0Wjj3WnRuG0T7Jm/PQgKqu\niPiqunfgadOLSD/g2GzLveGGG1qOR48e3aHlAQpNU5MbJK+qco6iqQnGjnUD5YMHw0svmQMxjEIw\nb9485s2bl9fPyIs8iYh0BUpU9QtPVfc54Ebgdc+ZlOAWOZkbEEdERM4Fhqnqj1KUa/IkEcEPyw06\niqVLXZju5s2u62r+fBg5stCWGoZRTPIk2arqIiLvArcD54rIOhEZkCfbjByQLCy3qso5krIyF3ll\n0VaG0X4xYUSjTTQ0wN57w1dfQZcu8M470KOHSz/sMHj3XejTB1580aVDYjfX1pLr8gyjvVJMLQ+j\nnZMqLHftWuc4mpvh7bedI2lqSj37PJmcSSYSJ01NMGqUK2vUKJvNbhjbGnMeRptIFZZbVeW6rHzW\nrnVdWsm6uXwHcOihMQeQqcTJokWuzOZmt1+8OP/PbBhGjHyG6hrtmIoKN0juD5gHu43KAt+qkhI3\n9lFRAQMGwIoV0L+/u8d3ABBzAF27JjoZG3Q3jOhhLQ+jzTQ1wZtvxrcOli514x8+wS6t5ub4fTIy\nlTgZMcK1csrK3H748OT5DMPID+Y8jIwJjkX4A+YXX+z2DQ0uT1UVDBwYu2fgwFgrY8UK5zhWrAA/\nBH3gQOckfAeQqcRJRQUsWOBaPwsWuDSTgjeMbYc5DyMjwmMRTzzhIq3A7WfOdMf+S33OHLctWJA8\nEuqyy+CYY9zx7NmxfNmIK1ZUxLq0fNtGjXKfa07EMPJLJCTZvWs/FZFVIrJCRL6bL7uMthEe8O7T\nx4XogtuPHRvLW1EBRxzhNt9xBLuZevWCNWtcWStWxO4BV/ZGTx5z48bY9SDhaKylS922ebPbH3OM\nrSdiGPkmEpLsIjIIJ5Q4EDgGuFdEchqTbGROQwPcf3+sKwoSWwRDh7qxjSlTYnM80hHsZvrNb+Kv\nLVmS+Ys+2ALyWxmdO8OWLbE8tp6IYeSfvE0S9GaMD1PVTwNpG1R1J+94T2C2qg4WkYmAqurN3rW/\nAzeo6qJQmTZJMM+kmvy3cKF7aTc3Oyfy0kttj4KaMweOOip27o95vPSSOx81ClaudNFZs2e7cZBe\nvdz+P/9xLYvNm2P3futb8Y6upAQGDUrdZWYYHY1imySYjST7HsB7gXvrvTSjDbRlHfG6OrjmGnjw\nweRjGbmUWx80yLUWfJqbYy2FYAtl9mw4/HA46CDo29e1Nq680jmVkpLYvUHHESwzW2z9dcPInKKT\nZDdV3fQkEyxs7dd3XZ17IfuNOpHYcf/+bp9uXke2rF0b/3IvK3MOxXdI/kD49OnONoiNg6xcCbNm\nOWdz2WWxMkpLY2Vu2eLGShYvduMurcmYNDW5aLAf/tDNiq+sdOfWajGKlW2hqouq5n0DJgE/DqX1\nA17xjicC1wSuzQJGJClHjfQsWKBaWqoKqmVlqgsXtn7P1Ve7/Mm28nLV+vrc2tjYqLrffqqdOqlW\nVanOmePSwlxxRaI9Xbqo1taqPv+86qBB7hkHDnTllJTE562ujn1WWZnbhz/Hvy6SeK9htBe8d2dO\n3+t56bYSka5+JJUnyf5dYKnXCsGTZL8O+B/vlmrgdBEpF5E+wD6ACU60gVShrum6ZM4/37U2fHbb\nLXa8aZPruspll47fipk/33VRBaOyglxySbxd4MY6jj3WjXuUlLhWyKJFrpw774zP27VrclmUIP71\n8FDamjVb/ZiG0a6JhCS7qi4HHvPSZwITPG9pZEmyUNfW9KIqK1130MSJUFvrlHDLy921Ll1i97Wm\nN5UNftdUuq4h3y5/nKNTJ+jdOxbmW1sL223nyqiogPPOS5x1XlXl7i0ri8miBPGvlwT+Ezp3hqOP\ntvEPw0hLrpsy+dywbqtWefrpxO6XBQtctw24rqJMurLq61WnTHH7ttyfaxob3efW18e6vFJ1Qy1c\n6PaNja57a+BA15VXVZU8v399n31UJ0923WLpuroMo9ggD91Wtp5HkRMeDA6Hwc6Z436BB8Nfsw1h\n9dVv23p/rmlqan3g3rfZl0QB1/qYMQM++ACOO86FICerr65dbUVEo32Rj1BdU9UtYpJFVvkzuVes\ncLpRvmBgc7OLQmpLCKtPVPx2UJYkFUHFXp+SEjj1VNed17kzrF6d/F6/K2vlyuRdXYZhmLZVURMc\nDF62zIW2gmsZ/POfbp7E0qUwd65zJsEQ1mw/Z+VK53hqa4t35vbXX8ePB912mwsRrqqKF2f08zY3\nx0QaDcOIx5xHEROMrGpuhgkTXEsE3K/lsWNjE+ty9TmtiRVGBb8FVlLiWhmdOsUvUgVw111uYHz2\nbOds/e64uXOdk1R1+3yHyxtGMWLdVkXM2rUulBbciy4Yjqoaa5WsW+dmaK9b57pjsl37IplMemta\nVoWmosI5hWefdUvhrl8P3bvDt78dq7MtW1yrasUKFy7sEw7TXbt2m5ltGEXDtlbV3V9EFvppIjLM\nS+8kIg+JyJvetcPyZVd7olevWEitiPt17c/Urqpye19O5KWXYmtfZDvYnUtpkm1FU5NreU2YAN//\nvrP500+Tj/l8+WX8+SmnxORTOneGk0/Ov72GUWzks+Xhq+puCKTdDExS1edE5BjgVuBw4EJcKNm+\n3kTCvwPD8mhb0ZBOWmPt2phAYEkJXHUV/OhHsXxhOZG2thZyKU2yrUg2OdB3gv7a5z5du8bf26OH\nG0yfOdM5oKi3sgyjEORzzEOSlL8F2ME73hEngAgwCHgBQFU/Bj7zWyUdmaYm13d/yCFuH56w5rcu\nysrcduutrg/fz5fJRLxMyWVZ24Jgy8tvjflOcPZsl5ZuCdsePeCCC8xxGEYq8inJvhr4DGgG7lfV\nKSIyAJiNcywCjFLV90TkQuBI4EygJ7AE+C9VfSpUZoea51FdDSecEDufPt0NVgdbIU1N8Ic/xIsE\nzpmTmSBgeyfdfJBM5oqkK7cj16tRfBTbPI+gqu5znqruKcDlqvpXETkFeAg4ytsPBF4F1gIv45xO\nhyY8cHvFFfDxx27+hj92UVERv2a4T1vUddsb6eaDZDJXJBlRmzBpGIVim8wwF5FJwBfAdeotBuWl\nf66qOyTJ/zJwvqquDKXrpEmTWs7buyR7Q4OLktq40XWx+OMbAJMnOy2niorkL7SlS22WdD4Iz0if\nOtX9HQwjSoQl2W+88cactzzy4jxEpCtQoqpfeKq6zwE3Ar/FiR6+KCJHAL9R1QNF5BueLV+KyFHA\nz1R1dJJyO1S3VUODWyt806b49SogfvU934EEu2H8lsfy5a5/vyO2PHJFsJtq0aJ45wFuLkhlZWFs\nM4xMKKaVBFOp6l4E3O6l/8I7B9gVWCIiy4CfAOPzZFdR8cwzsTkJzc1u8La0NHYelBgPD2gHZc/N\ncbSdsCLxoEGw007xeX7/+8LYZkSXjrAqZV7GPFT1XWD/JOkvkyQEV1XXAgPyYUsU8VeuAxdFlerF\nftxxbp6Br8U0dy68954b+6itjV99Lxlt7dc3YoRDflescKG9GwIB6NlOujTaNx1lvNHkSbYx/vjE\nUUe5bdSo1L9OKipct1VJidvvvruLopo9G+65x81DaI9fyigRXg/kP/+B+vr4PP36FcY2I5osXeo2\nX3OuWLXgWsOcxzbGl8PwWbky9uUKN3UXLXLXt2xx+8WL42dOjx3bvpvFUcFXJN60Ca6+OvH6449v\n/Wd0hG6OjkKvXi5IBdyPjmLQgmsL5jy2MVVV8aG1Awa4pq3fIjn00PStkdaWVTVyy6JFMUXiujrX\nXRhEBH7wg637jNZWejSKi6Dyg68F1x4x57GNqahwobRz5rjNnyfgrz+xebPbL14cU4YNyoUnmzlt\nFIZx41yLMNNIq1StC/tB0L7YeedYZGRzsxPkbI+Y8ygAFRVu7OKII9KPWfiOJigXblFU2xbfgZeV\nJUq6X3JJdo4jVeuiV69YFF1JiRO7vP9+F6ptFA/+j4NZs2ILp6m6/9X2iC1DGxGCy6YGZ5Abhcef\nQ9Ozp9MOa8vs8oULndNobnaO6KWXYpFw4YmH5eVufKVLF3jnHdPXKgaCEVb9+jlhzY0bo/M3LKZ5\nHplKsh/opZeJyB88SfZlIjIxX3ZFlWStDCMa+CHPPXq4v01Q2j7YFZVu0DubBbX8uT1ffeUi6ozo\nE+x6fPttF0QxZUo0HEe+KLQk+y04SfZTgXJPkv0bwHIR+bOqttOhpnj8Gcy9ekVnnXAjOcG5M8Ff\nmwO8WUorVyaP7V++PH4J3BUrYi8Vv2ts5UrYc894TbP+/fP+SEYO8McifUWH0aPb/w/AqEiyK7Cd\niJQCXYGNQGMebYsM/gvokENg770t4qaYCP7aXLHCbf6g97x5mY9b+K3Ol16CI4+Mv/boo3kx3cgx\nHXEsMp/OQ4HnReRVT3Id4ErgNhFZh2t1/NRLfwL4EvgAWAPcpqqf5dG2yOC/gJqbXTeFRdwUD8HI\nt8rKWLcUwKmnwsUXO2HLhob4yLnKSvf3DToWv0UT7s761re2zbMY2ZGsi7LY1rzZWvK5nsfuQUl2\n4DKcJPvcgCT7xap6lIgcBPw3cC6wM/ASMEZV14TKLEpV3XTrPwS7PsrKnBMxIcPiwR9M/+ij+LVX\nglx5Jdx4ozueN885Fl9yZvXq+D7xhgbo3dutGd+pk+vCaq995sVKWH5k5kw3t2Pnnd3f97jjCv83\n2xaquqhq3jdgEnAVsCGU/pm3/x1wViD9QeCUJOVosdHYqFpVpVpW5vaNjcnzLFyoWl/v9snyGNHm\n+edV3YhV4lZaqrrffu7vetdd8dcmT44vp7FRdeBAd8/AgfZdiCILFrj/Z3D7ffZRLSlRFXFpnTu7\n/+Uo4b07c/pez0u3lYh0FZFu3vF2wHeBt4AGETnMSz8CWOXdsg74TiD/SGBluNxiZO7cxMl/YYLR\nPB2p2dueCHZLDRzoBrpLvP+uoAJy797x91VUxI+NLF0Kq1a5e95+O7V0jVE4gpMAN292rcMtW2LB\nLhs3wpNPFsy8bUa+oq2+BTwlIup9xjR1EVYXAXd5A+NfEZNkvweYKiJLvfMHVXVpQqlFRlMTXH55\nfNqXXxbGFiM/BLskFyyIdWU0NbkXyD33OCfQv79LHzzYOZe6Oid2efHFLjTX78IKR+34ZYVVWsGW\nwi0U8+bFR0Xutptz/lu2xNLCPxLaJbluyuRzo8i6rZ5/PtaU9bc5cwptlZErGhtdd1RZWaxbKphe\nWqpaXu66NIJdUH435c03x3837r47/rqff8ECV5bfTTJnTvLPNXJHY6Or92R1W1+v2qWL+3t06aJa\nW+v+Jv37R7e7kWLptjLcr8XLLov/hSICe+1VOJuM3JJKkyoYQbdpk/tFumJFrMvS76YcEFrBxpc/\nCUfthCcY/uc/HUPyu1C0JlTZo4eb/OdPAqysdFJDr77qJvkuWtQxWoPmPPLE0qWuayKItmOdm45I\nKpFKP93Xq0rF4Ye7+0pLYxPLkhGeYPi//9sxJL8LRSZClT16wAUXuL0/HgUda8zSnEeeCEuvg2t5\nHHpoYewxck+qiWF++uzZzimUlcVUkcP3v/KK+7X6yiuZv3Q++igm+d3c3H4lvwtFWKgynXPuyHL6\n5jzyREWFe3lceWUs6qasDNavL6xdRm5JNTHMV05+5ZV4LaxM74fYL9qePd2AOrj90UebLH8m1NXB\nNdfE9wA0NLQ+8z+ZlEwqOrKcfj61rTo0/op/S5c6lVR/8p/9o3cs2rqOfDDCqnfv+MWFXnzRTUxb\nt859n1I5nmKJxsqHrXV1bkxJFW691emGdevmJIC++qptarfJ7EwWHddhyPUIvL/hZEb+BbwOLPbS\n9gcW+mnAMC/9TC9tibdvBvZNUmbOoxDyRXgi0ZQp0YvAMKJLsoloZWVuAlppafoJp88/H5uYGvVo\nrFQRa1vL1VfHR7JNnKj6+9/Hp02Zktqm8MTedJM3w9FxUYQ8RFvl03msBnYKpc0GvusdH4OTKgnf\nVwWsSlFmzioz14RD+/x/ik6dov8PbESP8Penvt7NRk8X9h0MEfbzdOrkXmxRJegk22prsrDampr4\nuqqpcXXYubNmNAs86BAaGxPDqqurs7ezkOTDeURFVTfIGcD0PNqVc5KtP94RVTaN3FFR4bqm7rnH\n7Xv0SFzJMDzhNBgiDG6srV+/aHal+OM5vXpt3fhNqgHrjRtjg95lZS5kuqLCdQGKuH1rq3j63Y2H\nHOLGToKsbBf6F1tJrr2Rv+FaHkuAV4ELvbQBwFqcHMl7wF5J7nsbGJSizJx641wR1jUqtl8lRvRI\n1p0T/p5l0vKIos5S+Nlqa1Xvv79tdgZbLqWlsTpJ1vJvrf5aK7+Y/8cpsm6r3b39N3HjGIcAdwEn\neumnAM+H7hkO/CtNmbmsz5wR/lL27m3dVMbWkaw7J1ORzWuuif8+purb31bU1roxiNpadx4ez+nb\nN/GZ0s3wDuKPRfjPGi4jOBbRFucRdHT+eNOgQcX3/50P57FN1jAXkUnAF8B1qrpTIP1zVd0hcH4H\n8JGq/iZFOZGUZG9qcs3t996Lpc2Z40I1DaMt+N0xfhSP3/XpS8CnirKC+EgjEdfFUlm5be1PZ0u3\nbu7Z1q51KyeuXRvLP2eOmw8T1vJK9axNTbD//k4XDFxX1ezZif97TU1u5vell8a0xvyZ4K1FewXX\nsE8X4RYlilaSHbcaYDfveDvgZZyy7jLgMC/9CODVwD0CvA/0TlNurhxxTmlsVN1jj+Ju1hrRo61R\nPFs7CJ3pr/5MCEc9XXmlax345z17JrYGsrE/qPuVrPXhP09VlcsXjlYLXkvXmstVfRQKiqXbCugD\nvIHrrnoLmOilHwS85qUvBA4I3HMYsKCVcnNbozki2VoOJoBoFIpMI/2SvRRzHTpbWxsTBxVRnT49\n/mVfWuq6rYIv77DwYHAsJFlUY9AZ+V1hQYeT7P/Td0qZjiMVQ9hzOorGeeRrKxbnYWMeRqFprdWS\n6qWYya/+bH+J19Sojh/v9uGXfVVV4iJowdZESYkbw6mvT23z008nOgd/fCXZ9WyCEHIRShwFzHlE\n1HkEm759+0YvusUwwqR6Kab71a+aWTdPOH/4hd/Y6F7Sc+ak7iaqqopfna9LF+cEkkVWJXMewSCB\nsIOYPDn2ua3N/Wgv87Xy4TxM2yoHVFQ47aJ//hPeeKPw6xcbHYu2rDKYShF47Vo3JwLcOuph0cVF\ni9zgcnNz6pUxgyTTfmpqctIgAwfGDzyHn8N/3YOTFFm3LiZj39wMV1zhNKp+9rP4z+zc2UkD+YwY\n4T6rpMTtzzsv9rlr18bmxWzZkvi8Nl8rNeY8ckQ6gTvDyBdtVXWtqICHHoLTTnP74Noh5eXuuFMn\n6N5965a/DTup7t2dvtTFF7u9L1AYfI7DDnNRWb7jAKdFdfLJ8NvfxtY2qa2FZ5+Nn7AnAn37Jv4f\nlpY65xGWyU/lRMN1Zf/bSch1UyafGxHttjKMQhHufvKjlVobJK+piR/ITjUHo3fv+JUQs5H4CH6m\nP6YR1peaODFmU1jLq1Mn97mTJ8c+p74+ds2XbWlNkqW1cYti0KbaWrAxD3MehhEkOAYxcGB6QcRg\n3l12SXyJ+3n8Pv7evePzVFe3baJdkOCYikjM1rAzq6lJfKEHZ9Dvs0/MofhjKFVVyccm2su4xdaQ\nD+dh3VaG0Q4QcXpOK1emXlsiOF7xySfx9/7gB7HJcjNnuj7+Sy6Jv3/Nmq2301/CdeJE14Xk2/rY\nYxWT21AAAAqXSURBVLFuKlVYsiSmLeV3mwW1u1avhpoad91fO2XBguRjEzZukSdy7Y38jSwk2b1r\n+wILgKXefeVJysy1QzaMoiZVd0+yX9jhVsPUqa7FUVubPCoqWRdVJhIpmRBuDdTWJkZ5hW2qr4+X\nIomibldUoZi6rchCkh0o9RxGlXe+EzjplND9uaxPwyh6kkm3p+q/T/fiTxe6O2VK4kS9XIwR1NfH\nCyLW1sacWSqb7ror3gEWWrerWMiH88ibtpWIvOu1LD4NpP0deEhVHxeRM4BjVfVsETkGOENVz2ml\nTM2XvYZRrGSid+XT0OAilI49Nj6kvKEh+1X2tmYFwOBKiYMHu66ysWPj9awgUd+rqWnrVgPsqIgI\nmmNtq3wuQ6vA8yLSDNyvqlOAK4HZInI7TstqlJe3EkBEZgG7AI+q6q15tM0w2g2ZLnXrL40cfGGv\nXete/mvXunkd4MYh1q1L/1L217BZudLNvUi1RnsqwvM/nn02cT7IyJHOYQQdY0WFcxi+szHHUTjy\n6TwOUtUPROSbwHMishInw365qv5VRE4BHgKO8uw4CBgGfAX8Q0ReU9W54UJvuOGGluOoqOoaRjEQ\nfGEvW+bmU6xZ417Mjz3m5jo0N7t5FD17pi/LH3z3y1282E3Ae+YZOO4491JvaIg/DxJe+/vYY5Ov\nBZ7MMfboARdckJMqabeEVXXzQaEl2T9T1R1F5DRgjKr+wEu/DvhfVb09VI51WxlGGwnKvPfq5RzH\n5s3Oadxzj4uu8p3HSy+lb83MmQNHHRU7nz7dzdz2u5NefhkOOih991K4uy2b7jcjO/LRbZWXUF0R\n6Soi3bzj7XBy7G8BDSJymJd+BLDKu2U28G0R6SIiZTiF3eX5sM0wOirBkNUXX3Qv6bIy50gOO8y1\nBjp1cumtLQc7YoTLX1rq9v/+t3MU4Pa33hp/PnNmcnuCM7dtJndxkZeWh4j0AZ7CjXuUAdNU9Tci\nchBuNcFSXPfUBFV93bvnTOBa3Drnz6rqT5OUay0Pw8gRDQ3xXVczZ2a32FGwpfDBB86J+OMm/fq5\nsjZutIHtKJCPlsc26bbKFeY8DCN3LFzotKT8rqv58zMbeA/jD577YyDgWjQzZrgWiQ1sF55ii7Yy\nDCPC9OrlXvKbN7vup9YGycP4oboffxzvOMCVueOOsMsu1g3VXjHnYRgdlGzDc4ME52mkcjrjxsGG\nDa2vQ24UJ6ZtZRgdlKoqF15bVubmarQ2SB4kGPa7bh3stlting0bUutsGcWPOQ/D6OC0ZRixqso5\nnNJSt//tb+Ov33tv6+tkGMWNOQ/D6KAsXepmiDc3u4WV2tI6EG8I9tBD40N3zz7blGzbO/nUtloD\nfI4Lvf1aVYeLyP7AfUAX4GtcqO5rItILWAH4a4K9oqoTkpRp0VaGkSOCkwZ97ahMX/LJIrUGD7ZJ\nflGlaCYJemwBRqvqAao63Eu7GZikqgcAk4CgftXbqjrE2xIcRzGRb1mAXGF25o5isBHi7dyadS6S\nLd+ay0l+xVifHY18Og9JUv4WYAfveEegPpS/XVAsXyizM3cUg42QaGdbX/j5XmCpWOuzIxEVVV2A\n3iKyBNfV9f9U9Z95tM0wjK0kUzVfo30SFVXdD4CeqrpBRIYAfxWRQar6RR7tMwzDMNpIoVV1P1fV\nHZLknwtcpapLQuk2Wm4YhtEGikKeRES6AiWq+kVAVfdGPFVdVX3RU9Wt8/LvAqxX1S0i0hfYB7eM\nbRy5fnjDMAyjbeSr2+pbwFNeS8FX1X1ORC4C7hIRX1X3Ii//ocBNIrIJN6h+sap+lifbDMMwjK2k\nqFR1DcMwjGhQsBnmIvKgiPxbRN5sJd+BIvK1iJwcSi8RkSUiUh1I20lEnhORWhGZLSIJ4ykRsXOS\niLzvpS8RkTGFtFNE1ojIv0TkdRFZHEjPaX3mycao1eUOIvK4iKwQkWUiMsJLj9R3M42dkalPEan0\n/t5LvP3nInKZdy0y9dmKnZGpTy/tShFZKiJvisg0ESn30rOuz0LKk0wFjk6XQURKgN/gVhoMczmJ\nqw1OBOaoan/gBSBhQamI2AlwR2BS5KytN3Or7Ew2oRNyX5/5sBGiVZd3ATNVdSCwH045AaL33Uxl\nJ0SkPlW1zvt7DwGGAv8BZniXI1OfrdgJEalPEekB/AgYoqr74oYUTvcuZ12fBXMe3jyODa1k+xHw\nBPBRMFFE9gTGAg+E8p8A/NE7/iNwYkTthBxPitwaO0k+oRNyXJ95stG/ljPaaqeIbA8coqpTvXI2\nq2qjdzky381W7ISI1GeII4F3VPV97zwy9dmKnRCt+iwFthO33HdXYhO1s67PyAojel7yRFW9j8TK\n/y3wE9xExCC7quq/AVT1Q2DXiNoJ8EMReUNEHshFk3sr7fQndL4qIhcG0rdpfbbRRohOXfYBPhGR\nqV4Xxf0i8g3vWpS+m+nshOjUZ5DTgL8EzqNUn0HCdkJE6lNVG4DbgXU4p/GZqv7Du5x1fUbWeQB3\nAteEE0XkWODfqvoGrmLSefVtEQ3QFjvvBfqq6v7Ah8AdBbAzaM9BXpN7LHCpiBycoox812dbbIxC\nXfqUAUOAezxbv8R1B0ByZ5hv2mJnFOozrq5EpBNwPPB4mjIKUZ+Z2BmZ+hSRHXEtjF5AD6CbiJyZ\noozW61NVC7Z5D/Fmimurve1doAlX8ccDv8J5ztW4melfAA9796wAvuUd7wasiKKdmZa9LexMkm8S\n8ON81WeubYxSXeJC1FcH8h0M/C2C382UdkapPgPXjwdmhe6JTH2mszNK9YlT+JgSyDce+F1b67PQ\nLY+ULQdV7ettfXB9dxNUtVpVr1XVnqraFzfY84KqnuPdVg2c5x2fCzwdRTtFJLju2snA0sSSt42d\nItJVRLp5dvkTOn178lGfObUxSnWprtn/nohUelmPIBYsEaXvZko7o1SfgSxnkNgVFJn6TGdnxOpz\nHTBSRLqIiOD+7n6gRNb1WbA1zEXkz8BoYGcRWYf7NVkOqKreH8qeaZP0ZuAxEfkvYC3w/YjaeYu4\ntU22AGuAiwtoZ9IJnd61nNZnnmyMUl0CXAZM87owVgM/8NKj9t1MZWek6lOcWsWRxCYU+0SqPtPY\nGZn6VNXFIvIE8DpuPaXXAT9/1vVpkwQNwzCMrCl0t5VhGIZRhJjzMAzDMLLGnIdhGIaRNeY8DMMw\njKwx52EYhhFRMhVB9PL2FJE54gREX/BmmucNcx6GYRjRpVURxAC3AX9Q1f2Am3DCiHnDnIdhGEZE\n0SQiiCLSV0T+7um8vRiY7DkImOvdNw8nRZI3zHkYhmEUF/cDP1TVA3HCq/d56W/gZrEjbg2PbiKy\nU76MKNgMc8MwDCM7PHmeUcDjnsQIQCdv/xPgdyJyHjAfp5zbnC9bzHkYhmEUDyXABnVqyHGo6gfA\nOGhxMuM0fp2WnBtiGIZhRJcWEURVbQLeFZFTWi6K7Ovtdw60Rn4KPJRPo8x5GIZhRBRPBHEBUCki\n60TkB8BZwPneAlNLcXLr4MQSa0VkJW4xp1/m1TYTRjQMwzCyxVoehmEYRtaY8zAMwzCyxpyHYRiG\nkTXmPAzDMIysMedhGIZhZI05D8MwDCNrzHkYhmEYWWPOwzAMw8ia/w8TN5G7yv5hIgAAAABJRU5E\nrkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xb8dcfd0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.subplot(2,1,1)\n",
-    "pl.plot(z_t,z_abs,'.',z_t,z_ord,'.')\n",
-    "pl.subplot(2,1,2)\n",
-    "pl.plot(z_t,np.asarray(z_abs) - np.asarray(z_ord),'.')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Note about averaging\n",
-    "The baselines have up to four values, corresponding to the up to four sets measured by the observer.  Pre-averaging these does not improve the transformation matrix calculated by the least squares solver in scipy/numpy."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Calculate h,e,Z from H,D,Z"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "h_abs_n = np.asarray(h_abs)\n",
-    "d_abs_n = np.asarray(d_abs)\n",
-    "z_abs_n = np.asarray(z_abs)\n",
-    "h_ord_n = np.asarray(h_ord)\n",
-    "d_ord_n = np.asarray(d_ord)\n",
-    "z_ord_n = np.asarray(z_ord)\n",
-    "z_t_n = np.asarray(z_t)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "h_a = h_abs_n*np.cos(d_abs_n*np.pi/180)\n",
-    "e_a = h_abs_n*np.sin(d_abs_n*np.pi/180)\n",
-    "z_a = z_abs_n\n",
-    "h_o = h_ord_n*np.cos(d_ord_n*np.pi/180)\n",
-    "e_o = h_ord_n*np.sin(d_ord_n*np.pi/180)\n",
-    "z_o = z_ord_n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Calculate Transform matrix"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[  9.81546858e-01,  -1.89150435e-01,   6.19830586e-03,\n",
-       "         -2.32136980e+02],\n",
-       "       [  1.50583668e-01,   9.94985961e-01,  -2.27063235e-03,\n",
-       "          2.09168024e+02],\n",
-       "       [ -4.30853583e-02,  -2.01141374e-03,   1.02449397e+00,\n",
-       "          3.26623382e+02],\n",
-       "       [ -0.00000000e+00,   0.00000000e+00,  -0.00000000e+00,\n",
-       "          1.00000000e+00]])"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "abs_st = np.vstack([h_a,e_a,z_a,np.ones_like(h_a)])\n",
-    "ord_st = np.vstack([h_o,e_o,z_o,np.ones_like(h_o)])\n",
-    "M, res, rank, sigma = spl.lstsq(ord_st.T,abs_st.T)\n",
-    "tol = 1e-9\n",
-    "maskM = np.abs(M) > tol\n",
-    "M = maskM * M\n",
-    "M = M.T\n",
-    "M"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Enter path to save adjusted statefile"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "path = '/users/aclaycomb/'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "data = {\n",
-    "            'M11': M[0,0],\n",
-    "            'M12': M[0,1],\n",
-    "            'M13': M[0,2],\n",
-    "            'M14': M[0,3],\n",
-    "            'M21': M[1,0],\n",
-    "            'M22': M[1,1],\n",
-    "            'M23': M[1,2],\n",
-    "            'M24': M[1,3],\n",
-    "            'M31': M[2,0],\n",
-    "            'M32': M[2,1],\n",
-    "            'M33': M[2,2],\n",
-    "            'M34': M[2,3],\n",
-    "            'M41': M[3,0],\n",
-    "            'M42': M[3,1],\n",
-    "            'M43': M[3,2],\n",
-    "            'M44': M[3,3],\n",
-    "            'PC':  pier_correction\n",
-    "        }\n",
-    "with open(path + 'adj' + obs_code + '_state_.json', 'w') as f:\n",
-    "            f.write(json.dumps(data))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "factory = EdgeFactory()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Enter Start and End Times for Test month(s)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "start2=UTCDateTime('2016-06-09T00:00:00Z')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "end2=UTCDateTime('2016-10-08T23:59:59Z')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\aclaycomb\\AppData\\Local\\Continuum\\Anaconda2\\lib\\site-packages\\geomagio\\edge\\EdgeFactory.py:520: ObsPyDeprecationWarning: 'getWaveform' has been renamed to 'get_waveforms'. Use that instead.\n",
-      "  edge_channel, starttime, endtime)\n"
-     ]
-    }
-   ],
-   "source": [
-    "hezf = factory.get_timeseries(observatory=obs_code,\n",
-    "\n",
-    "        interval='minute',\n",
-    "\n",
-    "        type='variation',\n",
-    "\n",
-    "        channels=('H', 'E', 'Z', 'F'),\n",
-    "\n",
-    "        starttime=start2,\n",
-    "\n",
-    "        endtime=end2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "raw = np.vstack([hezf[0].data,hezf[1].data,hezf[2].data,np.ones_like(hezf[0].data)])\n",
-    "adj = np.dot(M,raw)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "h_pqqm = np.mean(h_a - h_o)\n",
-    "\n",
-    "e_pqqm = np.mean(e_a - e_o)\n",
-    "\n",
-    "z_pqqm = np.mean(z_a - z_o)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plot of $\\Delta$ F Over the Test Period\n",
-    "The left plot (black) shows adjusted delta F, the cyan shows adjusted delta F using average baselines over the period (instead of the transformation.  The blue on the right shows raw delta F."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x15e3d828>]"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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NTUSsL4c/YJ+XLOlB01rXtCn3jxljj32CL/3hTtziGH4NCXOuiIwV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\niqKgwkBRFEVBhYGiKIoC/D8xGaI7+1uvZwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x159eb3c8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.figure()\n",
-    "\n",
-    "pl.subplot(1,2,1)\n",
-    "\n",
-    "\n",
-    "pl.plot(((hezf[0].data + h_pqqm)**2 + (hezf[1].data + e_pqqm)**2 + (hezf[2].data + z_pqqm)**2)**(0.5) - \n",
-    "        hezf[3].data - pier_correction,'c')\n",
-    "\n",
-    "pl.hold(True)\n",
-    "\n",
-    "pl.plot((adj[0]**2 + adj[1]**2 + adj[2]**2)**(0.5) - hezf[3].data - pier_correction,'k')\n",
-    "\n",
-    "#pl.ylim(-30,30)\n",
-    "\n",
-    "pl.subplot(1,2,2)\n",
-    "\n",
-    "pl.plot(((hezf[0].data)**2 + (hezf[1].data)**2 + (hezf[2].data)**2)**(0.5) - hezf[3].data - pier_correction,'b')\n",
-    "\n",
-    "#pl.ylim(-539,-479)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plots of Raw h, Adjusted X, and '$\\Delta$ h'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0xe0517f0>]"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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ayQiB4gXTl4/veDUFZG1v3n8fFi2K/p7xVFENGlTkJyaVBFFeCDedXor5WJg1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\nuENxHMdxisJ3IhnvEZcktEUAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xbfccf98>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.subplot(1,3,1)\n",
-    "pl.plot(hezf[0],'b')\n",
-    "pl.subplot(1,3,2)\n",
-    "pl.plot(adj[0],'c')\n",
-    "pl.subplot(1,3,3)\n",
-    "pl.plot(adj[0] - hezf[0],'k')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plots of Raw e, Adjusted Y, and '$\\Delta$ e'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x12ed7940>]"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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KvOVEcKrltUEDzp04MbD9joc/UTWzzsuVqtpfVU+MJDgAbr755rjvIRRvBRxOePiFf/zx\nx0nLQ6IEFSK5JDggR4RH9+4wfHj0eJZgZIEtmSUN1DOD5QHtdxCRi4C1OD2PUGKuKzwCKhDZYOQY\nlCCzqNz7yaX7ioWcEB5bt8LKlZnOhaU28XszmDx/zx5uDvBybc5B4zMNZr8zHUBEmgJjgLH4D59M\nFJGFIvKboNfv379/1DgHDx6kVatWTn6jVLKx9DxiJdy1w4X36NEjqoPF2io0XOyYh6VWEPqhBv1w\nn9+2jb9v2cLfetRcHeDmlSuZaTxSnrFoEWtjGIDNBiSY/c5LJnoh8KiqHjCVsbdGvkpVtxgBM0lE\nrlbVF0kC3377bWUlHFpmO3bsqFZBhyvTUOGRrkHnoFNxUyFEdu7cWSl0ITNLR+eM8Mgh+6CsJ1sa\nRFmSjbD8fcuWyu39Mbj8BqhQRUivYVu45xnQfmcQcImIPAy0BMpF5KCqTlDVLQCqul9EXgIGAkkR\nHtF4+eUq29Rdu3bVqIj37dtX4xn/+9//9k0rUiUeTwXvPWf27NlMmDCBV199tYYwTIXwaN26NWvW\nrKGLWb44E0tH54TaymKJRnGMlXuqOezjj7k3wpTVlIuU4mJKzZoYQex3VPVMVe2iql2Ax4A/qOoE\nEckXkVYAIlIfuAD4IhVZjlbJrly5srLH5LJ48eKMqK1Cefnll3nttdcA+NnPfhbTuQB7ArrL8RKv\nPU2ysMLDknG6zJnD1wmOKazMsul4BysqWBCyoFFa2bmTGaNHB7bfiUBD4F0RKQIW4vgsC74gBs7g\nsl8FH7p0bLytf2/akQTJ6tWraywylSy8eQ+16whyXx06dIgaJ9uwaqs6SLaorVzWffcd7337Lde1\na5fprNQeunThwkmTeLZnz2rBkex3XFR1rGf7AHByvNn48MMPw1pYDxs2rNo65PHiFRibN4f1iMOo\nUaN8LdgjscNjj3PNNdewa9cunnjiCaC6UPCbUhxUbXXgwAFfoaaqvPbaa1x++eUx5Tld2J5HDlHb\nlh/wflLXJzidzrYtqp7nvizyqRTJV9WHH35YzYVIPD2Pn/3sZ9XOe/LJJyPGD6ceCndtr9X7iy++\nyDvvvEOnTp0inhOrGi2cQNu3bx+jRo2KKa10kjPCw/Y84Mork5NObXyWWdaZyijN/vMfIDumij79\n9NOV26HqnIqKCi655JJq+7HyxRfJHX5JpqW59/lv3bo17BhFrNbw2ULOCA9L7SOVMiwb3He/Zxee\nr0boYHe806tDiaWso12jb9++1fb/93//F/BfZOrgwYO+4X5qq3bt2nHDDTcEzmcukDPCIwvqglpD\nFjRIgdT2FoJWRI9H0JGnkrr0OruV6RtvvBExXjw9j2Th5nH//v0sXbq0xvvjN27Ts2dPzjvvPCCY\nG5WtW7cmK7tZQc4ID0vd46GvvuK7gFNws0QeWnxwK9jly5eHjfPnP/+Ze+4JdbMVjJ6eSQGJ9jhV\nlT59+gTyjXXo0CGWLFlSI9zNgyssojVk4u1xZVotmTOzrSx1j7vXrmXw4YczqHnzqHHdD2lvWRmH\n5edbYZKlhK6x4XLnnXem5Hpffvlltf3QHsS///1vDh48SOvWrauFB10L3RUU3rGdUKJV8rtiUG8e\nyqI1anJGeFi1VfLIFrVVKmhuBotrM8la5jUTxLLGRjII7Rm4C5+5XHDBBRw8eLBSqLkVfejzjaZy\n87IyyszBOXPm8Nxzz1VO+V2xYkXgtAcNGkSzZs0Cx08lVm1lyRiZ7nbHwvZDh7gugtolneR9+CEz\ndu7MdDZykvIQNejBgwer7bvv5IEDB6oJEL81b/zYtm1bjRlgofYeEydO5G9/+xtFRUUR7VL8VGKL\nFi1i3rx5gfKSauqc8Fi8GAoCL1dkSScdP/2UTQFXXQvlszRYcz/ns+iQS7kq70bxsuol0X7DGp/n\nlEvCOFVMmjSp2n5oLyBcxTt//vxq+4888ki1/VCh4xLaQ/FzlhhaLvn5+YDjddg7Vdll27ZtLFmy\nhNNOOw2A22+/PdC1002dEx4ffQRJnhqec2RrHbOppCTwIk3gfJTF5mM9kOBMnSU+S6DGwqxduzg3\njYv5BKk2Ml25ZANBB+Fvu+02oKqi/+abb6od965oGEqoKiwa3vVU/ITSySefXG3K8OOPPx5T+uki\nZ4RHqr6DOXMgQoMy4+TgMhIJEYtc+/mXX3K4axAX8JzZYQYn9yTgWHHdwYMMy7FV4CyxEc7Fyvbt\n2zn88MMr912niF5cgeQOds+ZM6fy2N69e+ncuXO1+DvDqCTffPPNmPKcanJGeKSKU0+Fn/wk07kI\nz0UXZToH6WVnyGySUyLomv/q0RcHVdmsC6MWS0Tlc0tt8xtjiduB4pQpU2qEuS7iXaHx2WefVR5b\nuXIl69evrxY/dBzGxfXamy3UeeERjpISCDE2jciGDZAKe7OlfguC1hL8qusf+QxKP7JxY9TzD0ZR\nWxWXlTFlR/iltxW4YunSSjVYLGTLWEN25KJ2sDHMOxcP3l7LXXfdFVcaU6ZMqba2STaQM8Ij3erb\n3bvBZ7JDWLp0gYEDU5efVPHll9k7BuLyqzVrWHngABM9izNBld7/JytX8kwU692/bt7MhV98Ua2C\nHR/ix+hf33zDFzGskfBNaWnguMnGjmbkDrfeemvl9sMPPxxXGhdeeGGNsEw3WnJGeKSbWMulogLi\nWM8l43TrBmlevTIuCtevr+F5161AnwoRKkEZs3Zt5bZb3D8N6N23aO9ejvrkk7iuC+kZzM505WJJ\nDaEOJjNFzggPO3EkdWRiHaUtJSUxqYhe2b69Rlg886uivUZLAz6MbFu5EOAcO2hvSSMpEx4icr+I\nbBKRheZ3rufYPSKyWkSWi8jwVOXBj5pLN2eW738fVq1K7zWzoUF69KefBq6o00EWPJKY8BOCh7Kh\nYC1pY+rUqbQzC6g99dRT/PjHP07r9VPd83hEVU80vxkAItILuBzoBZwHTJA0Tkhftiy2+Keempp8\nuMyaBR9+GP647XEllyDVa7yGiunE2nBYPv7440rnixMmTIjoXysVpFp4+L3hFwGvqGqZqq4HVgNR\nh5qT9a3E2jjzTMmOiv2ek8Okb74hP8xAzPbSUg5VVPAvHzVWJNyi+WcYox7v+MDbCbj+aBHAG6vF\nkgy8xoaZaEykWnjcJiJFIvIPEWlhwtoD3nlwm01YWvATHkGe+969EM37xN69EGHVTUtALlm6NOx4\nRklFBbN37+aKGLqQJZ5pvP/evds3TrIUPnvKy6sJooPl5XyRoPW6H+tzoHdkSS2umxPIQeEhIjNF\n5HPPb4n5/wEwAeiiqv2ArcCfErtWImcnzvDhYJYujkgsPRWXdN+bW7d99FF6r5spGn30EbPCCI1U\nUOYRHg9+9RUFHqMwl20h03zvX7eO82MY8P4ugwsnWbIDr/DIBAm5ZFfVYQGjPgW4ppebgY6eYx1M\nmC+FhYWAY3cxe/ZgBg8eHHM+vcQ7phiPUMh2zjrLP3z27NnMzuL5u/HI2k0Z8vMSzudWqCPHV7Zv\nZ1UYy2KLxQ9XeEyfPj2w199kkrL1PESkraq6llsXA647wsnAP0XkURx1VVcgrI/hwsJCxo6FJk1g\nxIjMTCvNJJnocQ0eXF1Ijx07Nv2ZCEO8jyNZaqnCdev4dceO7HD9FBUX824S1ypftn8/PZo0IT9K\nwVsbDou7xsiIESMycv1Ujnk8bFRYRcBZwC8AVHUZ8CqwDJgG3KIBvoT9+yFVDbNEv8NEK/hMqa0s\nVXgfSaTiGLthA3P37mW+sQj1c40eT3G61z9+/nxeTIanztJS3hk1iv79+1NQUFDZABgzZgy9evWi\nX79+Tl5Fqi3TKCKdRGSviPzSE3ai+ZZXichjiWfOUhtImfBQ1WtVta+q9lPVkaq6zXPsAVXtqqq9\nVPW9WNJ9LE2vbrwV7IQJ8I9/JDcvlviJZY0NlyBF78ZZ59Oi6RRAxxnqi2u1J50DMRogqiprQ/PR\noAHnTJzIm598wh3TpzN9+nTmzZvH8OHDWbp0KUVFRW7MUJ/lf8Jp1Hl5ArhRVbsD3UXknJgyaKmV\n5IyFuUsyHUved1/y0vLy1FOxxc/0ZIBc6omISEwzS9w1NqJ1br1Hy2J4IH7jFFsC+LwqiXPAe7XR\n23pzOP3bbznOZ23weo0bM37jRm764gvKysoQEYYOHVptiifOmCMAInIRsBZY6glrCzRTVXe1pOeB\nkXFl3lKryDnhEYoqzJwZ37m//73zX1sr73CLXuWSsEgW0W7ZO6D+l02bAqcbz6sz/quvIgqYr0tL\nwwqX7mYlvOWewb8dIW7sXbSigldHjoRLLmHYsGEMGDDAL9p0ABFpCowBxlL9ttoD3geyiTROrbdk\nLzkjPNwKPrTi27zZmUYblLpUcRYUQAQv5DnJh7t3MzcOD5TRiv0nHoeI35aVsTeg362TmzWLOS9e\nh4x+/H7DBvp/9hkVEV7WNZ4ez+gVK3zjSF4el7/1Frz6KnPnzmWZxzZm3LhxAKjqSyaoEHhUVevY\nlJTaRWFhYeUM1VSTstlW6cJtoP385/CXvwQ/79e/jnw8nUImUs8n0V6Rn/o8AQPqjOO33kcq+EkE\nh2NDFy+moSmYdg0aBEpv1q5dfH/xYnaadamjsfzAARbv20f/MMIpptezaVOGDBnC61Onsj4vjzPn\nzGFaTSdvg4BLRORhoCVQLiLfAZOIYWq9JbO4giMdMyRzpucRjf/7v/DHFi+GqVOrh4Wsb58WSkog\n3UtAhAqfNWvgX/9Kbx5ygVJPa+FQRQW7wqiCXEoCtC5+0KpV5faTxm18q//+N3CeNpeUMCOMpD+7\nZcvIJxcXU7p3r6N/Kilh5syZlHbsyLOTJzN+/HgmT55cLbqqnqmqXVS1C/AY8AdVnWCm2xeLyEDj\ng+5a4O3AN2GpteRMzyPctxqkZX7ddVBU5KQxcCC89Vb0nkUiBrzh8nTSSc7KgDt3whFHRI+fDELT\nToGnjJwglpb6/oqKwO7eI6Xbsl7V5xWPXcZda9ey7MAB1McwNuors3Mn795zD6IKpaWcc8MN9B46\nFPr3Z19+PsOGOfa9IjJBVW+JktqtwESgETDNdXJqqdvkXM+jpMSpEM8+u+axcDMkvTZc3/tesOvc\nf3/seYuGu6Rs0DxY6jbLIljERl0Aq0sXfvDGG1zx9tvw9NPce++9TviLL7Jhw4ZKi2Q/waGqY1X1\nEc/+AlUtUNVuqnpHPPfiEs2lhuti3JL95IzwcFvQ7qQYs6Z8NVav9j93wwbnv7i4aoZVNBYsqNq+\n/PJg57j4zJqsRsDF6iwZJBlDXls9Ospoa6xHYkmY7mJZDvq3CpkmXIOvQpYGtmQvOSM8XELfPa82\n4NlnnR5JiNugSkaNir5U7PbtsGJFlZCCmrYlhw45s7yShQjcfjuccYb/sUTTTmZ6uUosthsA+wMa\n6l0ewbvve7t28bFxyDg5gVkKQ6oM+qoRRHS4NjFXx7qQTYqIZqNTr149XkumMZclZWS98AjtnUfy\nbyfi9Eh++1v/4+GMjb3v86WXQq9ekfM0fjx06BA5jsurr8JRR9UMD/VAMXUq/Oc/wdK0xE6sLsw/\nTdKC9MlarvadnTtrjJtEmsobyj+3b4/bL1gyCWLg6Y7HWLKbrBceRx9dfT/Skq3uexnOhUmQb81V\nMzdpEj7ON984/0Hqo48/rorvpW3b6OdC5v1u1UWaJdHVdbIe/wVLlrA3RBBFq4j3lZfzuKeLfF0Y\ne5B0Ei7PPXv25Mknn4wYJxn8I4d8B1WOU2UpWS88YiHaOxdORexXQQeptIuLwx8zhsAJVf5//nPV\neE28BPkO65LhZBBCK+lESEY16Lp1D00r2gyuXSGGjsnqBQXhmWeeiSn+BRdcwE033QSkVni0aNEi\neqQsIds9J+eM8PB7jlOmVLffiPbOBSmLcJbsLrt3B3POOGhQ8GuG4847a4atWFFTledHpB6aJTId\nGzbMdBaq4Q62l4a8TNEq2Ux2Oq+//nrf8JEjo7vFSqXwsGu/J4+cER5+XHgh3Hpr8PjhKnK3dR+k\nwk3FeiKxvM+9ejn3HY1wNm7hrvXrX8NllwXPhyUYyayqTg1Z8CdayzTbKsrXX3+dl19+OenpHnfc\ncUlPMxugTevtAAAgAElEQVSwPY80EuqOaMGC6n6vwpWFO5mlR4+qcYwwE1xiJlL5v/pq1XYs33kQ\nL+NueqHp1sxPCb/+9SD+/Of+vP561boPr7/+On369KkxL19EjhGRAyKy0PwmeI7VinUfSpM4BTaZ\nFfjqOrLSoPvMhgd0WhfLM872CjmXqFXCY9as6vtPPlnd426k98a1vUj21PlI1xw1Kr40166FadMi\nCxF3SnP076oh48bN4ogjFgFFles+FBQU8Oabb3KW/1q1X6rqiebnNTKrFes+bIvimiQWUtn2j1YN\nvp/EFQ7TiSsMpk2bxooIg/xjxoxJV5YsPtQq4RGNSBV5z57OfzTh4a2MY1kWO5Lb+Hgap+efD61a\nwQcf+B//0Y+CX6thQ3dqWUnlug89evSgW7du4VpqNVKx6z74s2T//pSmf+aiRSlNPxO4wsN9D8Px\n97//vUZYNBVWtqnywnEwjl5muicD1CnhkWzq1YMrr4wcx617n3giWLxY+fJL//AQ9TgAP/sZvPBC\nzfCKigq+/bY/0DbSug9eOhuV1SwROd2E2XUf0owCH0ea8pcB7otzhbV41EknnXQSAEd75vPnqlrq\n5JNPrrYfzY1LNpDzjhHTfR1vw6VePXjllWDpxdLgWbcOjj02WNybb4af/jT69QH+9jf/OPn5ebRq\ntYht2/bw3HMjueaaZfTu3Ttckl8DnVR1l4icCLwlImEjh2XixKrtfv2cnyW1FBVBURGFs2en7BLH\nH398wml4ex6RePfdd6lfvz4//vGPefzxx+nbt2/ShMcHH3zA2X4O9FJEaL7r16+f9YIwZ4RHMtTQ\nQcpi3brIx71T5WMp2ygufSqFy2uvxe5LKxJHHBFLPpuzZcsQpk6dwRFH9PY1ZFTVQ8Aus71QRNYA\n3XHWeAi+7sN11wXNlCUML4a6KYiGEdKFxktvOtZ8CMr3PN5Cg6qW6tWrx/Lly+nWrVtlS31dlA/4\n3HPPZfTo0Tz33HMR45144omB8pBNpFsllzNqq1jGFwAOPzy+60RbbyOoWxJwBrTdijua8HCJR3Bc\neWWw6bv+7GD/flf1cRCYyZw5PQnn3FREWotIntnuAnQF1tp1H9LPm3EuEzkzyHS9NHPxxRdXbof2\nPN59992w5/Xs2bOGiufYCN32pk2bcs011wTKk6ryXYxubeLlCO8aDTlCzgiPWCkudmZbeUl3L7BV\nK8c9CUQWHm8nWMVOmuQYTMbHFv7f/xvCzp39cBaTO4fGjUcAb9GxY0fmGD/3IjLdnHAm8LmILARe\nBX6qqrvNsVuBp4FVwGq77kNqicW3lZeHN25Mck6qGDBgAAUFBUlNM9b0os3CWhmDW+uGIQajh8fb\nKo3AmDFjaNy4cdLTTTW1Vnj4kQkVojvTMFKPctKkxK6R2OqEBTz22ELKyoqAz4F7eeklgJFs3Lix\nctaHqp5n/iepah8zTfdkVa1czzSZ6z5YohPv65zKKbxdunTh888/rxb2xz/+sdp+rK35du3asSoG\nlwk/DRkETOYspFSphvzGN7J9zKNOCY9MeqV2fV2lkhTPCrVkGTNzxI7jV7/6FQCNGjUCarbmQ/Gr\noLt161ZtP9KU3NDzt23bxtq1a3nooYfCph+UVAiP5s2bU+FjI2CFhwVw1g5PNWPGxKe++vDD5OfF\nYvGyZMkSRowYUS2sTZs23HXXXXGld9VVVwWO27BhQ4499tikGBWGEx7HHHNMzGk9/PDDAPzP//xP\nQnlyad68eVLSCYoVHgkQzsYiHqLN8grChAnxDZx/9lni17ZYItGnTx/fitevdV2vXj3uT8U60DEQ\na6t/Qxzur9u0aQNAgwYNktLLqF+/fsJpxIIVHgkQcNJG1pMjRreWJJEtXoPDteJFhMLCwrjSHGym\nIcdz3Wj84Q9/iOu8cHgFhp/wyHZreCs8EiAZvYVUcPrp0eN4sWqrukW2CA9IfgUZml5Q54pB+OUv\nf5m0tKC6wGjrY1RlhYcl7fz3v9X3o60BlOXjcpYk07tp00xnoZJkDwqHVrh+FXAQF+5NQ55R165d\nIw70//jHPw6Ywyq8g+R//etfYz4/01jhUQeYMgUOOyzTubBkC2fm0Gp64QjaKs/zMbA68sgjo57X\noEED3+uFE3ajR48OlJ9wNG7cmB+F82aapeSMexJL/Bw6FN803u3b4aijkp8fSxooLYU77nAWuSkv\nh7POgtGj4W9/464FC/iTaVmLSHNV3SMiAwCvWe1YVX3LxJkFtMNxQaDAcFWNz7w9hFgGeY8++mi+\n/vprBgwYwLBhw3zjhAqVSy+9tEacUMEQC6effjobNmxg8eLF1cLLQhcTCsDq1aur7YcaQ8aqtkr3\n1F4rPOoA8apOrTorh2nQAB59FBo1coTH7bfDwIEwYAAPPvII1x59tFs53WN+S4CTVLXCuNdfLCKT\nVdXVrVypqjH7f//oo498w9944w0OP/xwBg4cyAUXXBBTmvMiGE2FVrg33HBDjTgRnH76Mnny5MqZ\nUW+++SaqSr161avOUDuUSJxwwgksXrw4rum92YQVHnWAeIVHHI0pSzZhjPI4dMgRICJw0kmhqpwO\nAKrqNftuDIRarSVVxe31ZXXKKackM+mEOO6441gTYpT1gx/8oHLbTw0G0L59e+rXr8+hAB5cDxw4\nwIwZM6JO77UD5paMY4VHHaWiAm66CS65BE4+uXLFs5BKyfVZhnFq+QWwGLjZ0+sAmGjWcPlNOrLu\nh9+MpGQTT4XtWq4HVRv17duXc87JyUU2q2F7HnWABx/MdA4sGSEvD556yhnwuu8+WL8eOncGYNy4\ncQCo6ktudFWdB/QRkR7A8yIyXVVLgatUdYuINAUmicjVqvqi3yXjtc8IwsyZMylNzJFbVE4++eSY\nLdHd+EEFT58+fYCawibRMYu1a9em9PmHYoVHHWD+/Ohx/MjyXrMlKE2bOmt5zJsHnTvz0Suv8MW0\naWGjq+pKEdkH9AEWquoWE75fRF4CBgJRhUey1wsJ4rY8np5D+/bt2bx5c+X2TTfdFHMaAOXR5sQb\nfvvb3/qGhwqPeO7Fff7pWKslIbWViFwqIl+ISLlZVc577B4RWS0iy0VkuCf8RBH5XERWichjiVzf\nklrsgHkOU1wM+/Y52yUlsGABdOoE8+bxzoQJTJ48uVp0EeksIvlm+xigB7BeRPJFpJUJrw9cAHyR\nxjsJzCWXXMK1114LQMeOHaPErmKjcVF/xhlnVPbI4mHQoEGB4rnjJuF6Gq7Pq2wn0Z7HEuCHQLWV\n6EWkF3A50AtnQO59EemmztN6ArhRVeeLyDQROUdVw6/2YskYWWRLZomVnTsdfWVFhdMKGDIETjkF\nrr6aEqic6ioiE1T1FuB04G4RKcUZLP+Zqn4rIk2Ad0WkHpAPvA88lZmbiszrr78e13luC799+/ZR\nPf6G4p2y27FjRz799NOwcdu2bcvWrVsr98MJDzc83Y4OYyUh4aGqKwGkZv/qIuAVVS3Dab2sBgaK\nyAagmaq6ipTngZGAFR5ZSD2r1MxdunSpuRoawIsv8pdevbiyTRtEBCM4MGMYNVRRqnoAODnV2c1V\n+vbtW7nt51bdyxlnnMEKd4GfANx5551xex1OB6mqHtoDXhG82YSVAZs84ZtMuCULsWqr2okdyvIn\n0amx0YTHv/71r4jOEN199z+oMeNHH33EkUceSZMmTWLJbsJEFR4iMhNo4w3CsTK9V1XjXvzUYrFY\nsolEZztFGzAXkYgCKt7rn3766RmxCYkqPFTV3w9AZDYD3hGrDiYsXHgECj3bg83PklpmA7N58EHI\nwaWVLVHIduOzXCVazyOUaGMeiaSRDpKptvK+kZOBf4rIozhqqa7APFVVESkWkYHAfOBa4C+Rky1M\nYhYtwRgMDGbMGGjVKj3T/iy1k87GriQXSFSoBp2q63LaaafRpUuXiHEGDBjA/Hjn2qeYhISHiIwE\n/g9oDUwVkSJVPU9Vl4nIq8Ay4BBwi1aJyFuBiUAjYJqqzkgkDxaLJTvJZKs4HkGQarVVKP369avm\nCiV0zAMcP17Z2lNMdLbVW8BbYY49ADzgE74AKKh5hiXbsAPmtZNntmxhVC13lxxUEDz77LNJu2as\naqtwNM4RXbH1bWUJixUetZP3du3KdBayklTOtgqy3ocr8G655ZaE8pEurPCwhMUKD0uuElQQJFMl\nFEl4xGJ8mMh6I+nECg+LpY4xsFmzTGcha0im8Ig05pGt4xaJYG2ILWGxPY/aSe2rxmryzDPPsH37\n9rReM5LwCDIGk8kJBvFghYclLDn2LlsCklcLW8GhnH322YHiJbNHcPPNN9O9e/ekDsJnM1ZtZbHU\nMWq/6MgMV199Nc8880zc5+daz8MKD0tYcuxdtlhipjaORaQLq7ayhMUKj9pJXVBbBeX4449Py3VG\njBgRNU6Q6bzZhO15WMLy9tuZzoElFVjR4XDCCSfQv3//lF+nR48eXHTRRVHjde7cmfvvvz/l+UkW\nVnhYwvLRR5nOgSUVWOFhSQZWeFjCMmRIpnNgSQVWePiTqvGPVA2EZ3qA3QoPi6WOYQeJHTJd+eY6\nVnhYLHUMKzpqMmLECC677LJMZyOnsLOtLGGxDVRLbcbb83jnnXdSdp2jjz46ZWlnEtvzsFgslhQy\nefLkTGchJVjhYQmL7XnUTmyxppdmCTqibNq0aZJyklys8LBY6hj1basAyJ0B8wsvvDDTWfDFjnlY\nwmLrmBymtBTuuAPKyqC8HM46C0aPhr/9jf/Mm0e/5s0BEJHmqrpHRAYAT3pSGGtWCkVETqT60tF3\npvlu6jR+s+MuvfTSDOSkOrbnYbHURho0gEcfhaeecn5z58Ly5TBgAGdNmkRRUZEb8x7zvwQ4SVX7\nA+cBfxcRt354ArhRVbsD3UXknLTeS4rIlZ6HXz5fe+21DOSkOlZ4WCy1lUaNnP9Dh5zehwicdBKS\nV+2z7wCgqt+pqrsUXmOgAkBE2gLNVHW+OfY8MDINubdkOVZ4WMKSSbVVA6szS5yKCrjpJrjkEjj5\nZOjZ0y/WdHdDRAaKyBfAYuBmI0zaA5s88TeZsJwnVT2P7t27pyTdbMOOeVhyitNbtGB7aSmrDh7M\ndFayn7w8R2W1fz/cdx+sXw+dOwMwbtw4AFT1JTe6qs4D+ohID+B5EZleM9HIFBYWVm4PHjyYwYMH\nJ3ADqSVVwmPlypW8+eabXHzxxUlJL4hHgNmzZzN79uykXC8oVnhYspLSMB92QdOmfFBamubc5DhN\nm0K/fjBvHnTuzKbJk5k2PbxcUNWVIrIP6ANsBjp6DncwYb54hYclfYQK6rFjx6b8mjmpturdO9M5\nqBtko+aoIssGORtm40MCKC6Gffuc7ZISWLAAOnWCefNYM3FiDcM1EeksIvlm+xigB7BeVbcCxUal\nJcC1gHXWDxx11FGZzkJGycmeR15OirzcI9NjHn69j1FHHcW/d+/OQI5yjJ074cEHnXEPVcdF8imn\nwNVXU15RwbBhwwAQkQmqegtwOnC3iJTiDJb/TFW/NandSvWpujPSf0PJJ1G11RlnnBE1zmeffZbQ\nNbKZnBQe2drYy01KgDOBUqAMuBS4H3id3/ymkBtvXF7jDBG5B7jBnHCHqr5nwpNiD3BNmzbM3LWL\nrT7qqSEtW8aTZMooybKeUCVdusCTT9YMf/FFzm7ViikFBYgIRnCgqi8CL/olpaoLgIJUZre2ctJJ\nJyWcRrZ6Qc7JNnwu9jw6dcp0DsLREJgFLAKKcCbfzAMKuO22NznrrLOqxRaRXsDlQC8ce4AJUvV2\nx2QPoIMH07p+/Rrh5xxxBAuS8NHFy8qBAzN27XSQnVVR+skVO49sJQer4WA9j08/TX0+YiG739Mm\n5r8EpzMhQA/atu3m94FdBLyiqmWquh5YDQyM1x5g7aBBNcLa1K/P0Q0bxnMjgTnOtYHwoUOKr22p\nHWRrjyBd5KTwCELr1pnOQXUOPzzTOYhEBdAfaAsMAwYAYYV0e2CjZ3+zCctKe4DCzp25sFWrmM6J\npUp4wd92Iqup21Ve7pGtPaScGPNYtw6OPbZm+Nlnwwcf1Ay//HLo2jX1+YqFBx6ACy7IdC7CkYej\nttqD01lYBqR4StvEiRTOnk1JRQUccYQzlTQFXN2mDfd37oyEzIH/rqLC/wRiq1yvbtuWa1asiC9z\nSaB5fj57ysuDRS4qgqIiVjRuTGGbNqnNWA6QrZVyrpATwsPYNVXilvn77/u3jleuTHmWYqZJk+hx\nMk9zYAgwgwjCI9y8/5jsAbjuOgoHD2ZvWRkP/uc/ANzQti3PbN0aNZfJ+OQ7NWrE5jD2IkHUEd0a\nN2Z1mg0V84FQMVF8xhk1BGNY+vWDfv3o2aoVhQUFabEFqK1MnTqVPn36pOVa2aoeyxm11XPPVW17\nG43XXFMz7nHHJe+6yVoELGgj55pr4LHHknNNL//6V7gjO4Bis30QmAk4qpgw7+xk4AoRaSAixwJd\ngXnJsAd4Oo0qoE/37OHODh3iPr9tgwZJzE10jmnYkD9365bWa9Z2Eul5nH/++RxzzDFJzE3ukTPC\nw8tvflO1/fzzNY/HO975yis1w7zv15FHxpduaDqReP556N8//uv40aaNo8rzZwtOb6MfMAg4BxgB\nvMUvftGROXPmAOC6qlDVZcCrOLqtacAtWvUV3go8DawCVsdrD3Bc48bxnOZLpDZbdrbnLJbcICeF\nx9ChkY/H06B49VUYNapm+P794dN9+eVgabdqlbrZVkEG4iML0wJgIc403c+Be034SB57bCMHjWpG\nVc9zz1DVB1S1q6r2cm08TPgCVS1Q1W6qekes9wLO9N1jkyg8wtEzgh6xgQhH+UwhDoqf1fmhM8+k\nLGTasx+DzKpzoWkYm4y481QtraSkkvvk4pjHKaeckuksVJKTwiMVXHaZf3iPHlXboXVCLO9eqt7T\nkpLq+7/+NXz/+8lJe9Wq5KSTScJVlN18BFR7o4oSEX7nN0PDQ6TifNhHb1ovL4/8ALrru2IwCLrW\nDnonRLt27TKdhUB4xzyOS6ZOPkFyUnhEq4gjTKSJmdtug3CqzaACQQQGDYI7k7j+mis0unSJHrcg\nTtvgq6+O77xkcqpZ8S5e3CLafOqpvuFhz0tA2jfKyws7JvKTKBWWW02EDpL65ee5Xr1iype1X6li\n69atTJ06NdPZiJmjjjqKdevWZTobQILCQ0QuFZEvRKTcuKZww48RkQMistD8JniOnSgin4vIKhEJ\nPDTs/XYOOwz81pTfvLlm3FQSi+1G8+bOwm5eNmzwj/u970G9KPPg3LqlY8eaxw4dqr4/aVKwPLps\n3uz41fP2ujLFgGbN2HzqqdV6EG5FOuuEE6Kef6wxBmxpHmgnU4EmU3Vz8IwzOKFp00Bxz4ry0niF\nRmjPItHX+sTDDkswhdpDmzZtaJ5gwyQTiAidQ6efZohEex5LgB8CH/oc+1JVTzS/WzzhCS1pOWmS\no8Pfs6fmMXdmVCLC45Zbqu9H0jScd16wCjZcGuGMnOvVg02b/I+FptmiBVx3XfVjH39cfT+WiUHd\nuzvPMV3flftoNMy6DwIc3bChb2XfN0xlOMDTsnAr44Z5eVzUqhWF5sNLpvBolJ9fbV8ipB/01RTg\nYs8MjWRM13z9+OMBONmv5WXJWlpnm8WzISHhoaorVXU1/t9KjbBElrR0BcIPf+h/fMSIqm2v2ipW\nlU2IdiMiItA+ARvqSPVBNHW2CPz9784aP6Hhn3wCTzwRf76ykViqzok+U37zRHiroIDrI6iNNMx2\nPITL78VxVgSqyg3t2jE1Xh0kUD8vj/KzzuLeOj7FNNd44IEHaGp6tkcny3YgCaRyzKOzUVnNEpHT\nTVjKXFh4p+8m0vNwz925M7H8eAknJNzwKGOzYc/9yU/g+ONr3u+pp8LNN8eeJkACk4ziInBL3Och\nhqugewdUIcWbF6g5BuHdiyToGufnc3qLFhHTvuXoo7ndp1XSND+f82N0tRJKnkjWGp1Z/GnUqBEd\njX76zmQOnCZIVAtzEZkJeNvBgvOt3KuqU8Kc9jXQSVV3mbGQt0QkTn8XhRQWwqJFAIPNLzJBB8w/\n+MBxceLlhz+EZ55xPGYEIYigiiY84vmWved48xAkrR07wvv+ys/PzJKW4XArulyr7uLNrwB/NWtg\nv/XNN1XhEQq2oUj2uoa3JAW3sZIfoiLNJFGFh6oOizVRVT0E7DLbC0VkDdCdWF1YAK7wePZZCFn8\nLML1g8XzG2w+7DC4/nr/9JL9fbr1getiPpbVVcMJjyB5jDSeIZKZJS2jUW3A3Ccs2deIxPsnnMD9\nITNe7uzQgRs8fnEiVfa5aF9gySzZMsPKSzLVVpVfi4i0FpE8s90Fx4XF2nQtaenteSTaQ0/0/Hvu\nqa5S8y6hG9rziDbDKhyRelrxqMTSwUKzXkcsA8guTSO0vt40g8Kxphmal0j5OrtlyxrHQ8dS/tGj\nB88l0d3K+u++S1paltyjNJaWZZpIyDGiiIwE/g9oDUwVkSJjiXwm8DvPkpY/VVV37dCUL2kZ2rC7\n7DJ47bX400tEgPzhD9X3588HVyUfr9oq1AGta+82bBhceGH1Y+GMHzON3yJQflSzezAF+27fvuwL\n40m2VboHbXwQEc4xes/RK1bwSBYZdlnSw/nnn8+UKeG0+rWDRGdbvaWqHVW1saq2c11YqOokVe1j\npumerKrTPOfE5cIiEWvuc8JMBg46XdpNz6+Sj1UD4fWKEa/wCFXf/fGPsHgxvPcenH569WPetN9/\n3z+9TCycd1iMulvvI2rXsCHdmjTxVQ19FWpyHyA9l2hFeWT9+qwyD+ukZs1oHvAeesXgUvnj4uLK\nbW9+ukRYvMoOgGcfDRo04IIkrsHQK0aD0HSQ9S7ZP/kkWDzv9+OqcZ591lHbfPmlf7qpaqT+6U/O\n/xdfRI87dSq0bAmnnRYs7YKCmmM1LVpA377Rz+0dZsrC3LnpXxe+Zaw9j4DpfhVQvaM+aXrHIvwE\nSdfGjelmBMFjXbuG7VFEy2skITXHY8B0SvPmXNCqFfP37KGfNfCzZBlZLzxisbtwcesA13jOT3gE\nTTfcwHQ0fvnLYGmff76/wWOyiLW3lG4hEtVNiPlPR7ZiacHniZCXgod1m2eKbruGDZlSUMB35eU1\n/GL5OV/8T//+nO5MS7RYUk7O+LaKpeKO1bdVJLOAaNf1HnfHN2KtU2JRX9U1DUU4X0/JSjcc3l7I\nmcYuI94cxKLZ7Ojjf6pRfj7186p/qh0jqLEstY9snKGXM8IjFkKfc7TnHtRKPJrtxy9+Eex6LrHW\nhw0aJN/f1JlnJje9WIn2CGJVWyVCuA+0vimoeGaG+VESpnUz+PDD+V4UA0KAgqZNGRKLYzWLJQVk\nvdrKJZU9j0h4K/jjjoMlS6qvj+HNV9DG4KefVlebBRUiO3cmZ5zGm+cP/bySpZFEKmS3sm+Sl8eB\nZBZ6CMlOe8G+fb7hQ1u2DHT+5wMGVNv/YevWvLx9e/VIpaU0+9Wv2Pvdd1BeDmedBYMHM2bMGKZM\nmUJD10GkSHNV3SMiQ4EHgfpAKTBGVWeZOLOAdjhLTSowXFV3BL1fS+2k1vU8hg2ruWpeMnt84ZwM\nelf/iyYM3PVcYlFXLV7sGDCmyqu266B2+PDUpJ8osfY8+sThoqS5x9DG+8p8GuOgVLy9pHh9Tr3k\nNxOiQQNO+Pvf4amnnN/cucybN4/hw4ezdOlSioqK3Jj3mP9vgAtU9QTgOuCFkBSvVNX+ZgalFRyW\n2ic83nsPfvrT8MeDuh1xCef6I3TNnnjGKcOYKvgS7+J6QQ0Pb7zR+X/oofiuk2p+eOSRDA/YMo+H\n8rPOoo2nZeC1Q3GnyebacFO++9IcOkR+RQUiwtChQ8mrPn7SAUBVFxsjXlR1KdBIRLz93FpXV1gS\nw74QCRLOy2803nnHmWILqRsE/+QTuOuuqv22beGtt/x7YpkaiI82EOg6OXy2Z0/eDbB+h8vP27fn\n5ggeSEMH4PNClnm94qijKreXBzSEOXDGGb5pR+v4+q1qmCjP9eyJVlTATTfBJZdw50UXMSBE3WWY\nHhogIpcCC42bIZeJxtHpb0LjW+omtXLMI5ZzjzvOWXzJjxtuCL+kq1s/3Hln1UB5LHhdyAchnvsP\nnY4sAhddBK4pRLbP3Co/66y4W/s3xeC6uoUx9qvmGVeENvXrs+3QIRrkBWtjNTbpZMNj7d64MXn5\n+Y7Kav9+Fv7pTyxbtozeRsU1btw4AFT1Je95InI88ADg9Wl3lapuEZGmwCQRuVpVX/S7bmFhYeV2\nqI80S/zcfffdLFu2LOzxTDgzzRnhkSxCK8zVq8PHffpp5/+NNyC00RZamad6ca9UzNTzGmNnoyCJ\nx44imUPb2Tc5MjKhvbihLVsye/duaNqUIUOGMGPGDHr37s3EiROZNm1ajfNFpAMwCbhGVdd70t1i\n/veLyEvAQCCq8LAkj9GjR0c8nglnpjmjtopWeUaqZyK5LBeJXnGefHLk45DYyntpN8wzzyML3EAl\nVEEncm7nRo2q3L2HKYCL4xhn6dWkCYOivAy9Y3BXEi+7d+7ktsMP5/q2baGkhJkzZ9KzZ09mzJjB\n+PHjmRzi40ZEWgBTgbtUdY4nPF9EWpnt+sAFQADfCZbaTq3oeQwe7CyfGg7v95/IdxtUQCWLlSsd\n9daaNTUH6C3+BJHDu08/nSZ5efy3uJg/btxY2YIKLcInIr1UYVjmMz4Smm7DEDVYKtoOO7ZuZcil\nl1JSXg779nHODTcwYsQIunXrRmlpKcOGOVopEZlglom+DTgO+K2I3G+yPRw4ALwrIvWAfOB94KkU\nZNmSY9QK4TFrVuTjl13m6P87dXIGjb/6Kvl5iHVBJi/h4nfv7hgwrlkT/2yrusaRAbpTLcwUtMGm\nVZGFGru48Aqpbn36sHDhQraWlNDu00+516g0Vnv0tOJMErgFQFXHAePCJB2g722pa9QatVUkRKqc\nCSGkpzUAABDlSURBVB5+eGp6CVnoPSAsAcd/c5IBzZrFfI6rtsqhIvSltghBS25Qi6sRfxKpONOt\ntkoVDRvC2rWZzkXiNK9Xj6uOOiql1uVeklU5p6qSz6FX0FILyBnhMWhQcB9UkUhVqzvRnlG6CV1h\nMBtnW0UjX4R/hlhXN45jjed037r3VWmT4lkLdq0PS6rImTGPfv1g06bE0rj11tjtK4KSa8KjNtE4\nL4+DFRWsGzSIznEMDrmPP1WeS6Ol261x48CLWIXjFx060N9nzY9s9MZqqR3kjPBIBo8/npx0broJ\nunatHpaqb/S++2DevNSk7SVjFuZJTCMewQFV9iSZqmZfP/54ShN8gR4JeSEb1+aBLUtWUKeER6K4\nFeyTT9Y8lorZVgBDhzo/S3jGd+nCwQTGPSp7HsnJTuDruddslJ9PMlfn+HPXrvQ1vRCrtrKkCis8\nkkCvXsGXkfWjXj2YNCl5+alr3NahQ0LnZ7J6TYXAOs7O67akASs8kkCoy5lYrc1F4newmCxyWW2V\n7YTeY1uP916/lQMTpUUckwYsllixwiPJrFmTej9XluSSbrXVK717s7usjE5z5jAudNpbgnx96qm0\n8wgkq7SypAorPJJMly6ZzoElVtxxgZ5NmrBw796o8WJOP2S/Wb16NKtXD02Bx9l2qVotzGIJwU7J\niIHaPPaYqXtrlAWzgtwcPN2jB8Wnn5709OuCas5S97A9j4D8/veOA0ZLcmman5+SFngsuHKzQV5e\n4LU7coVa3N6xZBgrPAJy772ZzkFqufDC2uGyJFZa1qtHrwDrnXdq2JAz3KUfLRaLFR4Wh3btsnf9\n8lTy1SmnUC+Azm5D6LKMFksdxwoPS53msHqp/wQyOeZh1VaWVFG7FLwWi8ViSQtWeFgsKcbOtrLU\nRqzwsFgsFkvMWOFhsaSYTI47WMeIllRhhYfFkmKs2spSG7HCw2KpxTStZUaPluzBvlkWS4rpmkEX\n6Y2ywILfUjtJSHiIyMMislxEikTkDRFp7jl2j4isNseHe8JPFJHPRWSViDyWyPUtiVNSUsKgQYPo\n378/BQUFjB07FoBdu3YxfPhwevToAYCItDD/x4jIARFZaH4T3LRs2dZEBw/m+AAW7MkmXLmOGTOG\nXr160a9fPwDcb1ZEhorIZyKyWETmi8gQNy1brhZfVDXuHzAUyDPbDwIPmO3ewCIcI8TOwJeAmGNz\ngQFmexpwToT0NSizZs3KaNxMXz+RuPv371dV1bKyMh00aJDOnTtXx4wZow899JCqquKo7R90NjkG\n+Fz9yytQ2eZSuWZD3GSW68yZM7W8vFxVK8vV/WZPANqa7eOBTWrLNWfjmrJIqH6P9kuo56Gq76uq\nu/7nHMBd0u1C4BVVLVPV9cBqYKCItAWaqep8E+95YGQieXCZPXt2RuNm+vqJxG3SpAngtFbLysoQ\nEd5++21Gjx7tPcVbTjWm8KSqbLPtWWUibjLLdejQoeRVHwfpAKCqi1V1q9leCjQSkfq2XHMzbjpI\n5pjHDTitEoD2wEbPsc0mrD2wyRO+yYRZMkhFRQX9+/enbdu2DBs2jAEDBrBt2zbatGnjjXaUZ7uz\nUVnNEhHXh7kt2yzDr1x9mB4aICKXAgtV9RC2XC1hiOrYR0RmAt5aRHC6u/eq6hQT517gkKq+nJJc\nWlJKXl4eixYtYs+ePfzwhz9k6dKlfvYB7ozTLUAnVd0lIicCb4lI77Rm2BIIb7mOHDmSZcuW0bu3\nU1Tjxo0DQFVf8p4jIscDDwDD0p1fS46RqN4LuA74L9DQE3Y3cJdnfwYwCGgLLPeEXwE8ESFttb+s\n+S0PU0azgBNjKdssuBf7M7+QcukArARO8YTZcs3RX6rHPBJyKSoi5wL/A5ypqiWeQ5OBf4rIozhd\n3K7APFVVESkWkYHAfOBa4C/h0ldVax6bYkSkNU6vsVhEGgPv4kx+OAv4VlUfEpG7gJae+N+qaoWI\ndMEp27Wqujto2dpyTT0RyrUC+BPON7vTE78FMBWn0TfHDVfVrbZcLX64M6DiO1lkNdAAcF/COap6\nizl2D3AjcAi4Q1XfM+EnAROBRsA0Vb0j7gxYEkZECoDncMa/8oB/qeo4ETkCeBXoCGwALjcC4mLg\nd0ApTkX0W1WdZtKyZZslRChX32/WqJ7vxpnc4qqmh6vqDluuFj8SEh4Wi8ViqaOkWi8Ww9hJB+Df\nwFJgHbANWAUUAu/h6GLfBVp4zvl/wHfAXmA4jmrlPWC9CfsSeAxoASzBaS2XA9uBecBHPnF/AWwF\nSkz8zZ64e3Fa2yXAYyYPY4H9Jt3vTNxOwFsmrAL41uTvYWA5sMccOwg8bX57TbrF5pqfmnQewWkF\nbgN2AbvNdULj/tY8t4PAvghxFwMLgBXAAWCHibsSp6ex2icPJwKfm+fixr0WpwX7ijnnU5yBdLds\nRpvyWwncYcp2g8nPdsBVhdUoW5yW8mZzH8uBiz3xPgG+MGk/hvN+uM9uP/CVKYOF5lr7gQmmXL8w\n93DIlO0KU64rzfMtc8sWuAdYY/JwwKQzDxhhyrPCxF9uyvaP5rmVmvB15nrbcCYZhD7X/zVpLKXq\nfasweVsOzDbnrDP3dNBc912TjhvXlmv6y/Uxav83e23UOjvTQsOT8bZAP/OCrTG/PqYAxps4d1Fl\nrNYbZ9rgP4GZOJX/Q8AYHKOm/8PR8U7DGdDfANwKvAisBd4GikLifgB8Y8JvxREs2zxxlwIv43wk\n04CbzPE3gRdMob5lCudLc/7nOIPKm3GMKm8x50w0cb82+V5h4rxgzh1lrrsbp/JZh/OhPYrzIn/t\niTvWxJkLfD9K3MU4FdNcHDXFQRP35zgv7K9C8vCKJ901Ju7FZvsXwARTHqNwbHvAqTzW4Ajtw81z\nOMOk2Qvn5VwOPAWM8SnbB3E+5Ck4Rqa7PPG+Aiaa7Y9wBPGNwL04H1AL89z2AwNMuuvN8zkBZwr5\nl+Y+9wFFJq1tOGN1n5t0v8QR9jNMuneZ8toJ3GfKaw/OB/kl8Gecymyueb7LcD7O35lnfKvnuf7M\n5Oc74CLzrD7HeZ9LzDnFwGBTXjvNvRxn7utRnI+8jKp3zZZr+sp1GrX/m12Dp6Hu98sa31aqulVV\ni4CBOB/GEhyBIjgfGTg6XNdA6Wqch/iU+V+N8zCmAc2AcSbua8BJOC/AD3AExUqcF65jSNzXgeY4\nL9OFOC/0Bk9cwRlsbI5jLHWjCWsBPI5TyP1whEQzHEED8HecnsZe4FKc1sNbOC/5YTiD02+adB83\n9/IVcC7OS52PI4CeAU41z6alJ+5pQBNzzaNwPoxwcQ/gtEqaAUfjfHTPAFfhtOquCcnDUE+675m4\nw8z2aFMmmGf3fbN9DvCeqhar6m4cW4IzgdWquhyngvkvTsXpnv8cMFJEOgCXmHJTdYxM6wNLjMHa\nQeAUc85WoIGqPo1TiS0FeuJYSDdUx7DtOaChec4Xm/v/2pQVODYrbXE+eHfK8SacRsSFOC21pTiV\nTx+csj5kymuXecarcd7bKeZZ/QXoglPxXGDS/oHnuV6KM36QB7Q2z9Ltpewz112KU3H+Hucjno9T\naX5irvkGTit5iS3XtJfr89T+b/Y9cy9hyRrh4aE9TuH1w7Fab4pTsKhjAesaq12G81DcQZvNOB9i\nQxzXCm5c1wJ+BE6B347zcrbGacVs9cRdglPB34XzULfjvFyVcXFevno4L+JROC/AkTgFug5ohVPY\npTgtC0xcNffWEaeAbwDewWn9tDVx65l0NuO84CU4wivfhLnXDI3bxfPsfo8jqMLFfcnkuQtO6+pN\nT9yNODY9btx25l62UmX46RqJbfbERVXLgWIz0O5nJNoN2CginXHK9lOguapuM+e7ZfAo8BlORepS\nH2hs0t1A1TvQEigXkWdxyvYo4FjzXyIiR5h0m+K0Uu/BmR22XVXfN+kewGk1b8ApZ3AqlgYmnSPN\nuY3Ndol5dltwKhv3WRyN865twpm+vgunbFt74m3EKcv9OO9ZuXnGGz3XPGSe63qcnsZQQETkY5zp\n7mupKlcx92DLNf3l6vdt1bZvNqIxaDYKj0Y4UvIOVd1HlXBwURE5H6elsoHIa+0oTiE0xJGkX+I8\n2BPMMQmJexjOC/lznC5pE5wPODRuNKLF7USVUaVf3Hyc1tSegNfLxynLBTjTLa+KEPccnNbYJzhd\n7IujpJ3M+66H09q5A+cj8zu+DUf9F61c3es1Af6KU7ZlOPfjV7ZtcVSFm4EmIvKjMPEiEeQ9OBan\n8tnvc6wRTgX7SoB0wCnX5jjPqhCn4giHLdfMlSvU3m82LFklPESkHnAbsENV3zbBB3Akves/aTtO\nl68TjgrqZZxewnk4vYISoKMnbj0TVobzgn2GMzi/E+cFbeeJew5O4R9B1UB5F29cnFZKmUlju8nf\nNzg9CjduYxyB1c7cQwecQtqM8wK2A64SkXyc7uVWE1Zm0umG01pqB5yO0/O6E6f7vR2nNeTG7WDS\ndVuxc3DKtWeYuGeZPLXDUTt08txLJ5yP3I27xdxLO3ONTp7rdfDExdxLc1X91hPXpQOODvUHwAum\nbDsAe0SkjadsS3Fab1fhVETfF5EXzDM7aNLtbPKKKasSVf3MHCsz97sdR+3xrUm3zJTRWpPOPOB7\nJrwRjrqlM07lhslHqbm/b3AqjoNmu4FJpx1Vrf4OOCqT4TittatxKv0WJk033iATNt7ks5G5z+7m\neodM3G3mmptw1DwNVPUDE6eHp1zVnGPLNf3lup3a/81uJgJZJTxw1FBzgPrG9XcDnA/EXRBhNPC2\nqv4/nJfkK5wCnYPzErwGnI8jbO7FkdIXmXijcdREt+K8HItxumneuANxXugrTdxrcISGN+5tVA2o\nuWqzvSa8r4n7Pk4hXo7zjH+KI8Ra4gxGFeP0fi7Dack8g/Nxuem0Nfl8DUen+Q3OB3ApTqv1eJyW\nyG043fUvcCqaYhyh1dRc2y/ubpPHYpwXRHFUaK8A/XG6yG7cY8y9FJs8DDNxPzDbL5jnirmXf5vt\nd4FhItJCRFqauMebZ/CWKdcrcAYyr/OU7T9UtROOnnsbjs74t6ZM+hpVRSNgnjj+U1rj9ESPx9G1\ndzX/y3DUGwNNugdx3qEZpjyvpEp9ssGT7mIcId8Bp5Ew1Vy7tynbZeb5rTPldQTOu9cVpwV5vYnz\nS/MsrjVleDjO+zQC5z36lQkvAX5s7vcYc83DTNzeOLrpq4DdInIJToUywJTRRTjvVoEt14yUa134\nZt8lEpmeZeWZbXUajiQuwumqfocj+caah7ESR/V0uOece0wBuFN1jzBx15uwNTiDlidQNZPlEE5r\nYj7wH5+49+NIdO9UXTfuPhz9p+IIkOtN/g5QNVV3Pk5rZ7oJU/P/R5wBrQ3mXHdK4DOm8PeYuBU4\netU5Jp2TqGot7TLxDvrEnUTV4NrOCHG/wBnHWYEjuNxpf6v4/+3bPQrCQBCG4a/zGh7NK1kJFh7E\nCyiktMiPwUpBSGchYhQsZpbdiCBb+cP7gI3srsnOxgnZiZX9td7v8HQMG8Wyv0Z2AY0USwXXksZJ\nbCb+fSMrS737HF/8s0ji9Sq2cz++UrbRGtqtZBudW4/XNBmz898oZOsolHnOPK6lhiWdtce19r69\nz9NJ9hhm5/1DSWehuAEeYtt7bFtvc03GqGRr7abhXXaY173sT+moWEYexqz8HDvZGj17+6ViqTdx\n/Uxcp/r/a/ZtqS4vCQIAsn3bYysAwA8geQAAspE8AADZSB4AgGwkDwBANpIHACAbyQMAkI3kAQDI\n9gBZYlhm+srljwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x128de0b8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.subplot(1,3,1)\n",
-    "pl.plot(hezf[1],'b')\n",
-    "pl.subplot(1,3,2)\n",
-    "pl.plot(adj[1],'c')\n",
-    "pl.subplot(1,3,3)\n",
-    "pl.plot(adj[1] - hezf[1],'k')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plots of Raw z, Adjusted Z, and '$\\Delta$ z'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x12cc2a58>]"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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aZAmNcN/fN99A0LXYbBMo/fvDCSeEv1ZZCQcPprc+uUKQ7uGdb76pE4bXq+Da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\nEHQIYVCCIAiCDuH/Ae4eWg7H6Gw0AAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x128c87f0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.subplot(1,3,1)\n",
-    "pl.plot(hezf[2],'b')\n",
-    "pl.subplot(1,3,2)\n",
-    "pl.plot(adj[2],'c')\n",
-    "pl.subplot(1,3,3)\n",
-    "pl.plot(adj[2] - hezf[2],'k')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
-- 
GitLab