track_tree.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Track Tree - Epic Tree Timecourse Visualization\n",
    "\n",
    "by Jonas Hartmann, Gilmour group, EMBL Heidelberg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### About\n",
    "\n",
    "\n",
    "#### Purpose\n",
    "\n",
    "This pipeline reads timecourse data of a tracked object that divides into multiple objects over time (for example a cell as observed with a fluorescence microscope and tracked by image analysis) and plots the track data as a tree, visualizing measurements as an outline and/or through coloring.\n",
    "\n",
    "\n",
    "#### Requirements / Assumptions\n",
    "\n",
    "\n",
    "- There should be only **one root** (one starting object)\n",
    "    - (Note to self: this could perhaps be overcome by introducing a 'hidden root')\n",
    "\n",
    "\n",
    "- Input data is supplied in **tab-separated txt files**:\n",
    "    - **tree_struct.txt** contains the structure of the tree\n",
    "        - It features the columns *trackID, stemID, branchID1, branchID2*\n",
    "        - Each row represents an object that exists at some point in the timecourse\n",
    "            - It could be the initial object (root) or an object generated by a division\n",
    "        - The *trackID* is a unique string ID of the row's object\n",
    "        - The *stemID* is the ID of the 'ancestor' of the row's object\n",
    "            - If the row is the initial object (root), the *stemID* must be `_root_`\n",
    "        - The *branchID1* and *branchID2* columns are the IDs of the two 'descendents' of the row's object\n",
    "            - I fht row is a final object (a leaf on the tree), the branchIDs must both be `_leaf_`\n",
    "    - **track_measure_N.txt**, where N is an integer\n",
    "        - These files contain measured data about the objects at each timecourse\n",
    "        - The plots can display up to two different measures\n",
    "        - Track measures have an `index` column, which enumerates the entire timecourse\n",
    "        - All other columns are the different object `IDs` as defined in `tree_struct.txt`\n",
    "        - The columns contain the measures value for those timepoints when the corresponding object exists\n",
    "        - All other timepoints should be `nan`\n",
    "        - (Yes, this is not a nice and sparse representation of the data... but I can't be bothered...)\n",
    "    - *If this description is unclear, you can probably figure it out just by looking at the example data! :P*\n",
    "\n",
    "\n",
    "#### Notes\n",
    "\n",
    "- This pipeline was put together to analyze a specific dataset and was quickly generalized to release it on GitHub. It is not throughly tested and it is not a \"ready to use tool\". Use at your own risk!\n",
    "\n",
    "\n",
    "- To visualize your own data with this pipeline you'll have to modify the `Parameters` code cell appropriately. This should be straightforward but there's a good chance that things won't \"just work\". In that case you'll have to debug on your own...\n",
    "\n",
    "\n",
    "- The scaling of the plots may not work perfectly for any arbitrary tree. Good luck in figuring out how to adjust it! ;p\n",
    "\n",
    "\n",
    "- The original pipeline was implemented to represent two measures at the same time (`plot 1` or `plot 2`). If you only have one tracked measure to visualize, you can use `plot 3` or `plot 4` to do so, but you may have to comment out / change some stuff to make everything work.\n",
    "\n",
    "\n",
    "- I think this should also work for 'fusion tracks', which start with a multiple objects that fuse over time into a single root. No guarantees, though.\n",
    "\n",
    "\n",
    "- The arrangement of the tree happens through a bunch of recursive functions. You can find additional information about this in the `Notes` subsection in the `About` section at the top of this notebook.\n",
    "\n",
    "\n",
    "####  Compatibility and Requirements\n",
    "\n",
    "- Requires `python 2.7` (should be easy to port to 3.x, though)\n",
    "\n",
    "- Requires `numpy` and `matplotlib`\n",
    "\n",
    "- Only tested on `Win7`\n",
    "\n",
    "\n",
    "####  License\n",
    "\n",
    "None. You are free to use, change and redistribute this code as you like. Mention of the original author is encouraged, but not required.\n",
    "\n",
    "\n",
    "#### Warranty\n",
    "\n",
    "This code is supplied \"as is\", without warranties or conditions of any kind. The author takes no responsibility for any inconvenience, damage or loss of data that may be incurred as a result of using this code."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "### Import modules\n",
    "from __future__ import division\n",
    "import os, sys\n",
    "import warnings\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### Parameters\n",
    "\n",
    "# Input\n",
    "struct_file = \"tree_struct.txt\"\n",
    "track_files = [\"tracks_measure_1.txt\",\n",
    "               \"tracks_measure_2.txt\"]\n",
    "track_names = [\"diameter\",\n",
    "               \"intensity\"]\n",
    "\n",
    "# Sample information\n",
    "time_resolution = 20\n",
    "time_unit       = 'sec'\n",
    "\n",
    "\n",
    "### Plot 1: One measure as outline size, one measure as intensity\n",
    "\n",
    "# Plot 1 Data\n",
    "plot1_size        = (12,6)             # Adjust size of plot\n",
    "plot1_scale       = 10.0               # Scale the tree against the thickness of measurement margins\n",
    "plot1_size_track  = \"diameter\"         # Measure to plot as track width\n",
    "plot1_color_track = \"intensity\"        # Measure to plot as track color\n",
    "\n",
    "# Plot 1 Labels & Axes\n",
    "plot1_title       = \"Epic Track Tree\"  # Figure title\n",
    "plot1_xlabel      = \"time [min]\"       # Figure x label (relates to times)\n",
    "plot1_ylabel      = \"diameter [nm]\"    # Figure y label (relates to track width)\n",
    "plot1_cmap        = \"plasma\"           # Colormap to use for track color\n",
    "plot1_color_label = \"intensity [AU]\"   # Colormap label for track color\n",
    "plot1_scbar_label = \"%i AU\"            # Label for scale bar of y (track width)\n",
    "\n",
    "\n",
    "### Plot 2. Both measures as outline sizes\n",
    "\n",
    "# Plot 2 Data\n",
    "plot2_size        = (12,6)             # Adjust size of plot\n",
    "plot2_scale       = 10.0               # Scale the tree against the thickness of measurement margins\n",
    "plot2_size1_track = \"diameter\"         # Measure to plot as upper track width\n",
    "plot2_size2_track = \"intensity\"        # Measure to plot as lower track width\n",
    "\n",
    "# Plot 2 Labels & Axes\n",
    "plot2_title        = \"Epic Track Tree\" # Figure title\n",
    "plot2_xlabel       = \"time [min]\"      # Figure x label (relates to times)\n",
    "plot2_label1       = \"diameter [nm]\"   # Legend label\n",
    "plot2_label2       = \"intensity [AU]\"  # Legend label\n",
    "plot2_scbar_label1 = \"%3i nm\"          # Label for scale bar of y (track 1)\n",
    "plot2_scbar_label2 = \"%3i AU\"          # Label for scale bar of y (track 2)\n",
    "\n",
    "\n",
    "### Plot 3: For tracks with only one measure: plot it as outline size\n",
    "\n",
    "# Plot 3 Data\n",
    "plot3_size        = (12,6)             # Adjust size of plot\n",
    "plot3_scale       = 10.0               # Scale the tree against the thickness of measurement margins\n",
    "plot3_size_track  = \"diameter\"         # Measure to plot as track width\n",
    "\n",
    "# Plot 3 Labels & Axes\n",
    "plot3_title       = \"Epic Track Tree\"  # Figure title\n",
    "plot3_xlabel      = \"time [min]\"       # Figure x label (relates to times)\n",
    "plot3_ylabel      = \"diameter [nm]\"    # Figure y label (relates to track width)\n",
    "plot3_scbar_label = \"%i AU\"            # Label for scale bar of y (track width)\n",
    "plot3_color       = \"darkviolet\"       # Color used for the outline\n",
    "\n",
    "\n",
    "### Plot 4: For tracks with only one measure: plot it as color\n",
    "\n",
    "# Plot 1 Data\n",
    "plot4_size        = (12,6)             # Adjust size of plot\n",
    "plot4_scale       = 10.0               # Scale the tree against the thickness of measurement margins\n",
    "plot4_color_track = \"diameter\"         # Measure to plot as track color\n",
    "\n",
    "# Plot 1 Labels & Axes\n",
    "plot4_title       = \"Epic Track Tree\"  # Figure title\n",
    "plot4_xlabel      = \"time [min]\"       # Figure x label (relates to times)\n",
    "plot4_cmap        = \"plasma\"           # Colormap to use for track color\n",
    "plot4_color_label = \"diameter [nm]\"    # Colormap label for track color"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load and Parse Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "### Load raw data\n",
    "\n",
    "# Load tree structure (ID, stem, branch1, branch2)\n",
    "struct = np.genfromtxt(struct_file, delimiter='\\t', dtype=str)[1:, :]\n",
    "\n",
    "# Load measurement tracks\n",
    "tracks = {}\n",
    "for track_file,track_name in zip(track_files,track_names):\n",
    "    with open(track_file, \"r\") as infile:\n",
    "        header = infile.readline().strip().split('\\t')  # Get header\n",
    "        track = np.loadtxt(infile, delimiter='\\t')      # Get data\n",
    "        indices = track[:,0].astype(np.int)             # Slice indices\n",
    "        tracks[track_name] = track[:,1:]                # Append data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "### Construct tree structure\n",
    "\n",
    "# Empty tree\n",
    "tree = {}\n",
    "\n",
    "# For each track...\n",
    "for idx,ID in enumerate(struct[:,0]):\n",
    "    \n",
    "    # Initialize empty track dict\n",
    "    tree[ID] = {}\n",
    "    \n",
    "    # Add stem and branch information\n",
    "    tree[ID]['stem'] = struct[idx,1]\n",
    "    tree[ID]['branches'] = [struct[idx,2],struct[idx,3]]\n",
    "\n",
    "    # Keep the root\n",
    "    if tree[ID]['stem'] == '_root_':\n",
    "        root = ID    \n",
    "    \n",
    "    # Add indices\n",
    "    col_idx = header[1:].index(ID)\n",
    "    tree[ID]['indices'] = indices[~np.isnan(tracks[track_names[0]][:,col_idx])]\n",
    "    \n",
    "    # Add times\n",
    "    tree[ID]['times'] = tree[ID]['indices'] * time_resolution\n",
    "    \n",
    "    # Add data\n",
    "    for track_name in track_names:\n",
    "        tree[ID][track_name] = tracks[track_name][tree[ID]['indices'], col_idx]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find Positions to Construct a NICE Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### Step 1: Recursively create a 1-branched position attribute\n",
    "\n",
    "def add_1branch_pos(ID):\n",
    "    \n",
    "    # Handle root\n",
    "    if tree[ID]['stem'] == '_root_':\n",
    "        \n",
    "        # Set own position to 0\n",
    "        tree[ID]['position'] = 0\n",
    "        \n",
    "    # Set branch positions and create 1-branched positions of their branches (recursion)\n",
    "    if tree[ID]['branches'][0] != '_leaf_':\n",
    "        \n",
    "        tree[tree[ID]['branches'][0]]['position'] = tree[ID]['position'] + 1\n",
    "        add_1branch_pos(tree[ID]['branches'][0])\n",
    "        \n",
    "        tree[tree[ID]['branches'][1]]['position'] = tree[ID]['position'] - 1\n",
    "        add_1branch_pos(tree[ID]['branches'][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "### Run step 1\n",
    "add_1branch_pos(root)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "### Function to get the minimum and maximum positions among a node and all its branches\n",
    "\n",
    "def get_minmax_descendant_pos(ID):\n",
    "    \n",
    "    # Get branch IDs\n",
    "    br = tree[ID]['branches']\n",
    "    \n",
    "    # If there are branches...\n",
    "    if br[0] != '_leaf_':\n",
    "        \n",
    "        # Find the max positions among the branches (recursion)\n",
    "        max_descendant_pos = max(get_minmax_descendant_pos(br[0])[1],\n",
    "                                 get_minmax_descendant_pos(br[1])[1],\n",
    "                                 tree[ID]['position'])        \n",
    "\n",
    "        # Find the min positions among the branches (recursion)\n",
    "        min_descendant_pos = min(get_minmax_descendant_pos(br[0])[0],\n",
    "                                 get_minmax_descendant_pos(br[1])[0],\n",
    "                                 tree[ID]['position'])\n",
    "    \n",
    "    # If there is no branch...\n",
    "    else:\n",
    "        \n",
    "        # Simply return the current position\n",
    "        max_descendant_pos = tree[ID]['position']\n",
    "        min_descendant_pos = tree[ID]['position']\n",
    "    \n",
    "    # Return result\n",
    "    return (min_descendant_pos, max_descendant_pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### Function to move a node and all its branches by a given amount\n",
    "\n",
    "def move(ID, amount):\n",
    "    \n",
    "    # Move the node itself\n",
    "    tree[ID]['position'] += amount\n",
    "    \n",
    "    # If there are branches...\n",
    "    if tree[ID]['branches'][0] != '_leaf_':\n",
    "        \n",
    "        # Move the branches and all their descendants (recursion)\n",
    "        move(tree[ID]['branches'][0], amount)\n",
    "        move(tree[ID]['branches'][1], amount)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### Step 2: Recursively update a node and all its branches to make the tree NICE\n",
    "\n",
    "def make_nice(ID):\n",
    "    \n",
    "    # If there are branches...\n",
    "    if tree[ID]['branches'][0] != '_leaf_':\n",
    "    \n",
    "        # Get the branch IDs\n",
    "        br1 = tree[ID]['branches'][0]\n",
    "        br2 = tree[ID]['branches'][1]\n",
    "        \n",
    "        # Update the branches and all their branches to make the tree NICE\n",
    "        make_nice(br1)\n",
    "        make_nice(br2)        \n",
    "        \n",
    "        ### Compute how much to move the current node and its branches:\n",
    "        \n",
    "        # Upwards movement\n",
    "        # The difference between the upper branch and the lowest position among its branches\n",
    "        move_up = tree[br1]['position'] - get_minmax_descendant_pos(br1)[0]\n",
    "        \n",
    "        # Downwards movement\n",
    "        # The difference between the lower branch and the highest position among its branches\n",
    "        move_dn = tree[br2]['position'] - get_minmax_descendant_pos(br2)[1]\n",
    "        \n",
    "        # Move the current node and all its branches appropriately\n",
    "        move(br1, move_up)\n",
    "        move(br2, move_dn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "### Run step 2\n",
    "make_nice(root)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### Step 3: Center each node between its two branches\n",
    "\n",
    "def center_nodes(ID):\n",
    "    \n",
    "    # If there are branches...\n",
    "    if tree[ID]['branches'][0] != '_leaf_':\n",
    "        \n",
    "        # Get the branch IDs\n",
    "        br1 = tree[ID]['branches'][0]\n",
    "        br2 = tree[ID]['branches'][1]\n",
    "        \n",
    "        # Center the branches between their branches (recursion)\n",
    "        center_nodes(br1)\n",
    "        center_nodes(br2)\n",
    "        \n",
    "        # Center this node\n",
    "        tree[ID]['position'] = np.mean([tree[br1]['position'],\n",
    "                                        tree[br2]['position']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "### Run step 3\n",
    "center_nodes(root)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate Tree Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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nHUxlt3ksTjr7ug05HtLp7O0SSd+46+kGU5jzr5EFN+ecvB4XJ5z9mib9PuL+\n7GM64TipcDK7/0CaoC97u3ggjbXZ5+rzpbPG4TiGwbLsa5R2PEQ92ScQC3hxvNmve773zGhllIGy\n7HMYrfKRTGW/n5MhLzb39cvh2Mz4zpfyusQD2eeUShlsKvt95BhI53TvuuO/rzmMr/c3VBHNukZe\nX5qunGub9KcZyHmPO3neC8lQnHgou22oPEbclz2S3mCKoD/7+g5E4lh77t+fhXHBZJ8/hTdnvxSW\nhJv9Gvh8aQKB8f1Hcz65bCSOzzf+34c57zPKAtjs/Ryfh5FU9vVwHUMqkH3ubjiBzXk/j4SSxN3c\nT1Cb9RoA+H0OPm/22PojCc7/8PEYFzfnPIfCSeK12Z+LXmNJ53zbHK6I4olkX7do9SjJnOsxUhkl\n6c9u84cTmPP+zbiOYaRyNGubZNpD0uR8/pvs3y9n9Ca7SCaTBAKB8U+KiIgUgIIuIjItiUTmC3Pj\njW1A29n2xXm3Ps2xSfTZmaft+imPLKN6mvsBrJj0lsezforDpM5zer1fTF8Bjzo53rHH+V6exH69\nwMnCD2fm1cUn3gZg3mDBDrm3YD1d2MAktomNPSYykqftpTxtz41riUPTcJ4tJ2P8p0re9+mcfKMr\noJbJXMmp6Jnebo2XcJ5VpybepmkIluf7JJ+EVW3jmjomu++K9ukdEzjduZ3rO5axYMGCafchIjIz\nill7RTVdCklBFxGZljNBl/ZnFhHvLTvb3mNi9Hqyb0Dv8VSx3BPKamuPeUjk/Nl7jt/izcl0eYqh\nccfO/TttxPpocMNZbTHSxHIyXZJ5Ml1anBBlOZkuh72jpHIyXbpMdnZGyHq5LdWY3eZ1mRvMzXZw\nCU8m0yUxPtNl4fwuQuMyXRLjM12Gysf1n5vpMjASpK2nIqst4HUJTjPTpduxWZkMHizrG8dnunQN\nZr8ukWCKprrsv0qHw3HKy7P3TaV8ODmZBkMjYdJpf1ZbvkyX8CQzXSK5mS5JH/FE9l+7w+E44ZxM\nl8rqYYKh7H3j0VBWpgTkz3Tp66nJ2ibteIhGs/9tDMW9dAxnt+V7zyxuHKK+MnscvX3l4zJdRuKT\ny3SJ5aQB+L0u1TmZLkMpw0BOpkvQZB7ni7uQ/c6FEIzLqFjRMjAu02XB4uwb6GTcP+66ZTJdss8z\nFIoTysl06e8vJx4PZrXNW9BBMJT9mvZ21Uwq06WuITt4kkp5ScSzX6t8mS59vRVEo5GstvKy6LhM\nl3BZdOLmrKB9AAAgAElEQVRMF8fDyHBZVpvrGFI5r3t55SjVOVknI8MR4jnvt3yZLg1ze/DlZMl0\nnGogN9Olqjo7iDM0HGbfwZasNq+x+HK+bVZVjlCVkxnX1lHF8Ej250VDdZRATqZLbe3guEyX7u7s\n90cy7aFnKPs8PSb798sZ6/7HUubOnTv+CRERkQJR0EVEpiUezwRW4j1lJPrO3QCMGOj3Zt8hWm+E\nsCf7JsEzOn56USBg8Z3X5LGWUZt9EwXgybmBNNaH42b3nyJN3JN945PAIW1ygwwRrM2+kY95Lcmc\noMtATgAnYn2YZHYQw+t1CYWz9wv4HSKhiaeKEPOTyrnJq64bIpJz/iHcrBsk1zV4c26irAuOk/3x\nnhoK4esdH3QJ+bMHk84TdMGZ3PSi8lD2zWEi5cHfm/26hMJJKgPZ+5ZZqMqZtpFM+MfdVDMcIZnK\nDor4Er5x4y0LJ8YFXZycKRU+r0N5Tlsi4cMXy76WZY6hLOemtzYSI+zLHm806cN1s18/v8u4oIsz\nmn1TmU578Ixk34ynoj68A9nb5XvPlIXSVOfcRcYHy/Als9/Pyej4a5TLsZDME3QJhbLfz7GkweRM\nU/Masv7dAnjc8cFRL+DJ2a6yMpl1jXz+FI05/w7iKR9uzns8nfaSSmWfZ8SFSM771BkJ44tmX8u6\nhJ9wztQZJydoZi04Oe8/f8BLfU5gI5X0EYsFc7YzZLeAGw3hHc5+nauMiz8nsFHmS+E5L6BgLeOC\neU7agy/nejhpD8mc173Kn6Y+lR0sDsQD44J8eYMuaS/+nGlviWiQrKCLx6U2Z9qXNxYg1Jfzueix\n+HOCuxXGpSYn4NQ3UEZiMPvzotxjCAayr1FNOJ51jRzHEBvK3i+R8jDUl329Pcbiy7NmZ12gUVOL\nRGRWsm7mj2/F6lsKR0tGi8i0nMl0URFdERERERGR/Eo200VLRouUtrNBl1TJxm5FRERERK5Mtog1\nXYpWK+bqpLslEZmWM9OLclcyERERERERkYySzXQRkdJ2bnqRYrciIiIiIpeVBYpVeyVf/UGZNt0t\nici0xONxvB4/WlJOREREREQkP2W6iMi0JBIJfF7/xBuKiIiIiEhhqabLrKFMFxGZlkzQRctsioiI\niIiIXEjJZrrce++9+P1+WlpaaGlpAaC1tZXW1tYZHpmIQGZ6kYIuIiIiIiIzwDWZR7H6loIp2aDL\n1q1btWS0SAnT9CIREREREZGLK9mgi4iUNk0vEhERERGZIarpMmuopouITIsyXURERERERC5OmS4i\nMi2ZoEt4pochIiIiInL1sUWs6aJMl4JSpouITIsK6YqIiIiIiFycMl1EZFoSiQTlYT9gZ3ooIiIi\nIiJXF0vxvobr631BlWymy6ZNm9i4cSNbtmyZ6aGISB4qpCsiIiIiInJxJZvpsnnzZi0ZLVLCMtOL\n/EBipociIiIiInJVsS7YItV0sW5Rur1qlWymi4iULsdxSKVSynQRERERERG5iJLNdBGR0pVMJgEU\ndBERERERmRGmiKsMafWiQlLQRUSmLB6PA4xNLxIRERERkcvKLeKS0cXq9yqloIuITFkikanjokwX\nEZGJlTeHcIYiRNuj+TcwULliLuXzI8y97zoCdRX4KsP4KiJgINE+QOxUL/G2Xob2dTD6g07cpMVN\nOZf3RERERGTKFHQRkSlTpktpKl9aR/1NC6i9cT7lS+pJ9MUYPT7A6IkB7Pe6Odwen+khTp4p7FqF\n3oCHSHM51rWZZRBTCYjO/PWoXVPHsrtWcviRIwweHJjSvknHh7+xkkDEC9ZiHYtn0AvJIg1WJs1b\nEaH2jtVUrFtC5dol+GvKAYh3R+nZ2UnPjg56ntxF/Y1NNL11BTUbFhKoDl+wP9+yMGXLms7+vAqw\n1pIeThDvHmVwfw/DRweoWtGIN+zDF/aDk8SODkJ0lER7L8mOPqLPdeNzvRi/72zmuDOawKZVMVFE\nZNaxRZxeVLRpS1enkg26bNq0iaqqKlpbW2ltbZ3p4YjIeZTpMnOs9VCxugVfeQhveQhPMEjlgMOy\nD7+FYH1Z1rbli2qoW98MwIrftizacoJvf2QXI52TCzZ4Ax5W/eJC1jywmBPP9/H8Px1k+HSs4OeU\nq/qmpbS87w7ScZeTX9tBzzOHM4GSKfAEPDTcMJc5t81j7m0t1FxXj8ebXTs+3jVC97PH6frJcbqf\nPUGiK13As7g4b9jHtb91K4vftw6Pz8Pq37ieE985xu7P7KL/pcG8+3h8hsW3N7DkjnqW3tnAwlvq\nCJZnBz4Tg0le+ff9vPy5fcS6Zj6odCWxrgdb2UDjXdfgry7HX1MBgQAmGCbUVIvxejBeD9ZaArVl\n+MqC4/oINUSY9+bFzHvzYvij11/SeIwx+CtD+CtDVCytm9Q+i/O0OYk0fT89Sde2Q3Q9fYR45/Al\njUtERESylWzQRUtGi5Suc0EXZbpcDsbroebmxTTes5L6O5fjrwhNvQ9jWPu+hSy/r4mtH9vDi589\ndMFtg5V+bvzta7n+f15D+dzMX98X3jGH1//OcvZsOcFPPrWP07szgYHy+iAr72lkxT2NLLuphvIy\nL/HuGLHuGH2Hhkj8exs9h2OZDJMJ1N21knnvv4Pya5vPttW/fgnRtn7a/nsnA/+6H1Lj94vMCVM+\nr5yy5ghlcyPYRJKb/uKWzF/7LyLUWM78d65m/jtXYx2XI4/s58B/7mfwyBDJweKlizTcvpDXfewe\nIi1VZ9uMx7Dw7YtZcN8invnkbpIPnqDphnqqmsJUtYQJlPtYckcDjddWXrTvYFWADb+7hrW/sYp9\nWw6z859eY+DVc4Ey4zFULykj1psk3q+UGIDAnBqW3bOStKeCvp2nGHy1E5s6l/lRe30TTW9aStOb\nllI2v+oiPc1O3qCPhtsW03DbYlZ91DK0v4vTf/857GDvTA9NREQuxhaxposyXQqqZIMuIlK6zgRd\n3vie5Vz7unnserqdndva2f3jDsjzR9LCThS5eoxEw6z4nTtZ8K7X4a+68NSDqQhXB9j4TxtY/4FF\n/Pe7f0y6O1NjItIQom5lFeGGEDf/3krmrK0dt68v4OX6Bxaz7gOL+O8P72BD60IW3VSHx5Pzi3l5\nDZD5q/qGj1zPaE+CQ0+2s//x07T/4OjZzTwBL5HmciJ11TTdt5wlD9yQd8yReTUs//A91Ny2klQ0\nRagugq/Mjy/iwxv2EagIYMylfTkwXg9L37OSpe9ZiXUtg4cG6P5pJ22Pvozb24PxgBNP4cTyRH0u\n2KmH0Pw6TMCPx+/FxYt/iZ91n33nBcdrjOGOP17LHX+89pLOxxfyct2vLGfVB67hW7/yHAvumkPT\n+jrqVlTgD2d+9Xe9MsCeLx9jz5ZjDJ6eXlbMklvruOl9C6iqC3Lsx90c/n4nvQdHLmnsl0tk+Tzm\nvOsuqm9djfF6z7Y7sRQ9L53k5KN7Wf3Ruwg3ls/gKC8vYwxVK+ZQ/umPcPpfH2b4+V0T7uMJByhb\nsYDyVQu5ZsV8ro/U03c4SteeAbpfGaD9+U7S/cq6EhGRq5eCLiIyZWeCLuFwiGs3RLh2Qz2/uGkN\njuPyzS37ONk2xHU3zKG+Pkx9fYTamhCx03F6XxvIPPYO0vb1dhjNX0fAH/Ey//ZGFtzdyFuWRBh0\nHY7s6+fw3j727uzm2P6p1b6YbcpbIqz55aVc98BSKpojRTnGvBvraP3GHfQeHGHR3XOI1E8+e8YY\nw7s/vWHS25fVB1l7/yLW3r+IkVNr6NrRTfMtcwnWhTG5AZuLaLixaeKNCsB4DNXLa6heXsOy963I\nes5aS6p/mFRXP+mBQVLdA6S6++n+8SHKr1tK2bXzCM2rIzy/Hn9t+SUHgy6Vx+vhZx+8Le9zjddV\n88a/Wcfdf/k6Xv7KMZ78s1epXlSGMQbjAXc0Cd2jeKwl2h0/Gz0NhD3c/qFruPVXl9C8+lzmx9r3\nLgSg//go3/7YHiINQZrWVFPVHKaqKcRod4Jn//EAex8/Penxh5ctYM5t17PyuhU4aQ+JnlHiXSP0\n7upg6NAQnrCf1FCC5GACM5QCEhP2ORot45qPvpvGe6/L+/p4w37m3LmEOXcumfQ4rzTesgjzfveX\nGfjec3R88evY1Phgo686QtV9G1j6v96IyZm6V7e6jmUb5wOQiqZpf7Gbzpd6aH+xm44Xe85uF6iN\nUL22hZp181hRV8uuh7p45aHjitSLiEyCtZlHsfqWwlHQRUSmLB6PEwwG8Xiyv2h7vR7edf+qvPuE\nlwWoXVbJsp9dAMDbvugy2BGn/2SU/rYY/SejHHnsJG/849UsuqsRb8Cbtx9rLdGRFKPDKUYGE4wM\nJzl9cJidj7Zx6Ce9DBahjoUxEPR5SaSntlKI8RjKF1RQPddHuDZAcihJcjDJyOlR0tHx9UPmbqjh\n5j9cw+I3N+PxefL0WFhz1tUxZ93kakEUSnlLBeUtFZf1mIVkjCFQW0mgNnuaT9MHZ2hABeDxebj+\ngSVc/8CFgwxOyiXaFef0rj5qFpTRuKbmgtvWLCzj/Q/ekve5ZffM4eRLvXzvz19l/7fb826TcoK0\nvP9uqu64nmBTQ/aTyzM/z3/ndeP2c5Nphl47xcD2YwxsP8bQq6fOPlc+v5zmu+fT8oZ5zL2tGX+Z\npkZOxBhDzb23El6xmFObvwRHMu1lS+pY9P4baXrbKrzBib9G+iM+FryhiQVvyARNrWs5/YOjeII+\n5t4+PyvwtfBtS7n1D1ax7c/3sP8bbXn7c/0Raq6rxToubsohHXMIjRjqNrQQaowQbgjjCfvZ9/1+\njvywh3RcKzyJiMjMUtBFRKYskUgQDI4vEjkVHq+HmpYINS3nZXJsunbC/YwxlFUEKKsI0NicKRz7\nupvm8tb3L8NxXPY/28MLj57giQcPEB1xWLGhntU3N7Lq5kZW3dRAeXWAjhMjtJ8Yof3EMC9/rY1y\n48c4luhgithgimDnCDe+sYX1dzaz5qZGVm1opKzCz87tHTzxjQN86xsH6Dg4fh6Vx+8luLCMWz60\njDnr62hcW4u/LP/HrHUt6XgaJ+aQjqWJ9capWlJJoELFiaX0eP0eKloiXNty6ZlX82+s41cev5MT\nL/Ty3ANfZ85Nc6hYUp15LK6mYmkNZc1TD8x5Aj6q1y2ket1C+NW7cGJJDn/5ZapXNdJw64IZzzqa\nrULzm1j8yd+j8xc/x7Lffzv1ty6+pGtpPIaWN104wNewuopf+NrtnN7ex7Zf+hZVC6tpunMeDTfM\npWHDHIK146da5itJvO53INqbYNeXjvDTfz3E0PHsaW/GY6hbUUndBHWSRERKlqWIqxdd/GljTDnw\nl8A7gUZgB/Bha+1Pz9tmJfA3wF1k4g6vAj9vrc0fVb+CGVtiuUPGmPXA9u3bt6uQrkiJeuSRR9i1\naxef+MQnZnooFzTYG8dJudTOLfz0HGstu55t54lPvsY9P7+EhqXl1C8tp6opPL6+iYiITIvruGPT\n3S7tc9V1XJ79u9c48lQ717UuYs66GhrXVOOP6G+PIleSHTt2sGHDBoAN1todMz2eYjlzv/zCn69h\n/aLi1B3bcWyEmz++By5wLY0xDwOrgA8B7cAvAZuAldbadmPMUuAF4AvAFjJVH1cDz1tre3L7u9KV\n7G8bLRktUroKkelSbFV1U1/hZ7KMMVx/WzPXP9488cYiIjItucu8X0o/t3/0Om7/6PhpaSIis5Zb\nxNWLLtKvMSYEvAv4GWvtT8aa/9wY8zPArwN/CvwV8IS19qPn7XqUq1TJBl20ZLRI6ZoNQRcRERER\nESk4H+BlfPX6GHC7ycxBvQ/4O2PMk8D1ZAIun7TWfvOyjrREFL9So4hcceLxOKFQ8TJJRERERETk\nYgzWFucBF850sdaOAM8BHzPGNBljPMaY+8mU2GoiU+OlHPhD4NvAvcCjwNeNMXcU+aKUJAVdRGTK\nlOkiIiIiInLVup9MZOYUEAd+E/gq4HIuxvANa+1nrLW7rbV/CzxOpgbMVadkpxeJSOlKJBLU1Fx4\nyVgRERERESmiAtV0eeiFbh5+sTurbSiavug+1tqjwN3GmDBQaa3tNMY8BBwBeoA0sDdnt73AbZc8\n4FlIQRcRmbJ4PK5MFxERERGRWe69Nzfw3psbstp2Hh/h5k/smnBfa20MiBljaoC3AB+x1qaMMS8B\n1+Zsvhw4XphRzy4KuojIlCUSCdV0ERERERGZKdZkHsXq+yKMMW8mM71oP7AM+DvgNeBLY5v8H+Ah\nY8yPgR8CbwPeAdxVnAGXtpKt6bJp0yY2btzIli1bZnooIpJDNV1ERERERK5aVcC/kJky9CXgaeCt\n1loHwFr7DTL1W/4A2A18EHiXtfa5GRntDCvZTBctGS1SuhR0ERERERGZOdYyttJQcfq++PP2v4D/\nmmCbL3Eu8+WqVrKZLiJSmhzHIZVKKegiIiIiIiIygZLNdBGR0pRIJABU00VEREREZKZYMgs0F6tv\nKRhluojIlJwJuijTRURERERE5OKU6SIiU6JMFxERERGRGTaDqxfJ1CjTRUSmJB6PA8p0ERERERER\nmUjJZrrce++9+P1+WlpaaGlpAaC1tZXW1tYZHpnI1U2ZLiIiIiIiM8u6BusWafWiIvV7tSrZoMvW\nrVu1ZLRICVKmi4iIiIiIyOSUbNBFREqTCumKiIiIiMy0ItZ0QZkuhaSaLiIyJYlEAmMMfr9/poci\nIiIiIiJS0pTpIiJTEo/HCQQCeDyK2YqIiIiIzARrDbZImS7F6vdqpbsmEZmSRCKhIroiIiIiIiKT\noEwXEZmSRCKhei4iIiIiIjPJBYq1ypBbnG6vViWb6bJp0yY2btzIli1bZnooInIeBV1EREREREQm\np2QzXTZv3qwlo0VKkIIuIiIiIiIzzBZx9SLVdCmoks10EZHSFI/HVdNFRERERERkEko200VESlMi\nkSASicz0MERERERErlrWZh7F6lsKR5kuIjIlynQRERERERGZHGW6iMiUqKaLiIiIiMgMs6Z4qxep\npktBKdNFRKZEQRcREREREZHJKdmgi5aMFilNml4kIiIiIjKzrDVFfUjhlOz0Ii0ZLVKalOkiIiIi\nIiIyOSUbdBGR0uM4Dul0WkEXEREREZGZZE3xaq8o06WgSnZ6kYiUnng8DqCgi4iIiIiIyCQo00VE\nJi2RSACopouIiIiIyEyyYIu2elFxur1aKdNFRCbtTNBFmS4iIiIiIiITU6aLiEyagi4iIiIiIiVA\nNV1mjZLNdNGS0SKlR9OLREREREREJq9kM120ZLRI6VEhXRERERGRmWetwRYpI6VY/V6tSjbTRURK\njzJdREREREREJq9kM11EpPTE43GMMfj9/pkeioiIiIjI1cs1mUex+paCUaaLiExaIpEgGAxijD6I\nRUREREREJqJMFykIN53CGA/G653poRSMtZkF6hVgOCcej2tqkYiIiIjIDLPA2O1KUfqWwinZoMum\nTZuoqqqitbWV1tbWmR6O5LCpFIljx4kfOEB8/0FCoz8idMvbCN7xPkyofKaHd8ncnmM4O/8Fj7MD\nz7JWmHMPlC0uagDGui7JvS+QfO4xQne/F9/CVRjfxNN4rLWk2jtI7XkMX7mfwJp7oWJeUcZ6JtNF\nREREREREJlayQZerafWiZN8QbjJNaG7tTA9lUrr+7YuMvvgipNNn28JL0rgvf53Y3ifxrXsH/g0/\nhymrAcBaF9t7hPSRFxl5+nmCN72DwMKl+FoWYnzn3oJuIk761AnSB5/B43QRuOFnMHXLMN7AZTkv\nm07iHHiG9O5v47a9gqcihm9hPxz8TOYRWYhbewvU3IKpWAreAHgCWE8Q4w9jPNP75+R0Hif58jaS\ne57GDvUCMPrgn4E/iG/xdfiWrMW3YBme8kpwUuAkSff0Mrr3BMm2LuKHDuOOjFC9tIPQwh7co1+A\nYA00rMENzIfGG/CUN4A/hPGHwEnjpuIwOog70o8d7sd0P4Vv5Rsw5c0QaYKyueArg/go7lA37lA3\ndqiH2KnDCrqIiIiIiMwwrV40e5Rs0KXUjRzqomxpw0WzCbpfbOPIf7zA0l++gZq1zXjD57IWrLXE\njnTQ+6PddD/xIu5ojNCCRsqunUfZ8hbCy1ooW9qMJ3DpL5GbTDO0r4NgfQXesB9PyIc36AcDTixF\neiRJejRBsnuAQEM15Yvr8vZjXZfB736fkWefw1wo6SwZJf3i10jv+AbeeUsxkQi2fQ8khnHTHpIn\nm0iefDCzrc+Pb24LGINNJnC62sFaAtWjVMzvxXns2+ANYOpXYGrmYZpeh2m4DkK14C87e+2tdSE5\nDIkBbNcLECjLBEDcFNg0tnc/LP1FTOXCvK+XHe4mfeBpUs88CKnEhS9k9Dj0t2Hj38w6+0RXBa5n\nPt5FN+BtWoa3+Ro8DQshlcTpOo7TeQy38zjO/h9gKmsxwQjGFwR/iOThA9hYNP/xUgnSB7bjHn0B\nqrO3Ge2uYOhkw4XHmuiHtqdJdlXgRJ/KPl/HANnXoWx5B3bn9rPnZdOG6PFGcLO7jcUXEaxeeOHj\nioiIiIiIyFkKukzTrt/+KoG6cpo3rmPOW6/DX3GuzkXvznYOfWkn7d87RMCfovf5Y3j8XqrXNlOz\ntpGAHWFkz2Hix7vG9rAYA/ETXcRPdNG7dQeO48GEw4Tm1RNsrCLQUImbcChb3oy/ugxPwIfxe0n2\nRalcu4BgfXlWQCE9Gmf45SMM7TxC73MH6d6byhq/tWBtdh3liqphahekaPrAO6i680Y8gXNBonRf\nP11f+CLx1/aDx+bes4+XTmI7d118u3SKdNuxi/fjJLGdu6FvB5z4xrlgh8efeRgPpGNgnUx7MIXx\n2qygiI36Yd+j2EAl1K6EmmuxUQd3NIF7ajd2sD0zH9KZfj0at78Xt/8pUjszAQ7r+sDNDkx5Aino\nHc0eW+zyZPFMnRkXcAFIOFaZLiIiIiIiM82liKsXFafbq5WCLpdg9FAXBz/1XQ7/8w+ov/taAi1z\nOf3Dk/TtbAfAmHO3127Koe+nJxnZc5iqqpFJ9e+MJhjdf4rR/acyPzsesDuytkkkAmANxuchUFdO\noC6CGTiNjcXAdc/tR8WkjmniI3R8/iG6H36C2rfdRc2bbyf26mv0fOkruNELZGTMBDeVeUxFcgg6\nXsCefoFUz+Sux7RZl4kjU7NPwrFUqZCuiIiIiIjIpCjoUgBuMk3nk68yOHhixsZg0y6JziESnYNU\nVI1ecn/O4DDdDz1O36OP4/NcZMqNXFWU6SIiIiIiMvNU02X28Ey8iVzVnPTE28hVI+mioIuIiIiI\niMgklWymy7333ovf76elpYWWlhYALR8tMsOU6SIiIiIiUgoMFC0jRZkuhVSyQZetW7deNUtGi8wW\nCccSUk0XERERERGRSSnZoIuIlB5luoiIiIiIlIAi1nQpXgbN1Uk1XURkUtIWHIsyXURERERERCZJ\nmS4iMinJsYi3Ml1ERERERGaYazKPYvUtBaNMFxGZlISb+bhQpouIiIiIiMjkKNNFRCZFmS4iIiIi\nIqXB2syjWH1L4ZRspsumTZvYuHEjW7ZsmemhiAiQcBV0ERERERERmYqSzXTZvHmzlowWKSHKdBER\nERERKQ0WirZ6kRJdCqtkgy4iUlriNpMYp6CLiMjkWMcl1T/MwEuHwesj2FSDNxLEFwngCfvxV4Qx\n3ktPOraOi7UWj89bgFHPjP6To4TLvIRqVTdMRESuLAq6iMikJMemF6mQrsQGkrzyzVOsfEcT5XWl\n/36w1uKkXAxjc5QtuK6Lx+vBF5y9N6lyeblpl1hvnHTUITWaIjWSZuDIMH1HRgnXBUkOp0mOpLDt\nx6niBF4nRqrn/7N353FuXfX9/1/nXklX0uwztme82/GSxdkTQslCEpJAIDQkLKUuhbJ+KaUF3H77\nLVuBFtrSFkj7BVr6/VHKVpKyJiwpEJYmwdlXJ85iO/Fuz75qtOue3x8a23M1Y49GI2VmPO/n46FH\nMkfnHh2t1v3ocz5nCJsvkE565HPhCWOueeflrHrrpRhT/i+V6e5h9n9jK8aBTPcQma5h8t3deKFh\noqvaibS3EmlvJdwYIbqkDm9FOyYUwoTC+PkCbksroaamaj40Mza8Z4SbLv4Zi89o4a23XErDorkd\n3LeFAtv+/Duc9tFX47XWT+vY5MEh+h/YTeuLTyHW0YBxJj731lqShxOE4mG85rn/GSsis8Sa4qVW\nY0vVKOgiImXJWIMBwuGJJw5SHYVUltS+blJ7i5f+Xz9G/WkriK9fRnRZG97yRXjL2gjVVedLeKI/\nw55H+unZM8rhHSMc3jlC6r4u2uoc2tY1EG+PUdcRo+9QhrzrMrAvSfczIyS60hTG8k7b1tWz/Nxm\nlp7VRPOqOBe9de20TiAnkxvNseObT9O8sYXlV66c0Vj5wQE6f3A33/6TRKC94IOJhTn3Hes55ZoO\nVl66mHDshfknsZDJk+lPkxlIM7J3mIEne2ja2IpxDMaB3FCKwvAo0UUxCsks+VSOwR2DRNoaWHXD\n6TRtaMEJz06w6Gcf3cbOnx5m/VUdtG2oZ/GpjbRtqCfkFEgdSjB6YJjkwWES9z9KXash1OBRfDkY\n6H2Oug1LceNRTDiECYVIPrOHUPsSCsMJCsMJcv3DJLtyeCvbCbc1EV7UjBOL4VuXuo2rMCEHE3LJ\nJzNkexL4mTzpzkEyhwfofGSIZCJCdEk9kaYo4UYPf3SU5k2Ladq4iEhLnEhrHLwI9SubCMVDU75W\nc8kcPQ/30HVfJ3vv2M+hh4YC1+cLhkwu+FysWd1F/douCmU8nnu+fCd99+xi3Z9cTdPZx3+tJ/cP\n0LP1eXp+8zyDD+/BC6UC14fCOaj3Se85THrPYQCi9Unqm0cZGX9/MmEKuRAmGsVtbMBtbGC406UQ\nbsMvQH4kTSGRZuRgmobTl9N05jIaNi6hfsNi6tYuwvWq+x6x1vLszbu49yP3k+43bPtlDx877Se8\n/mvo5PAAACAASURBVB/P5eIyP0v8bJbCcILwotYp+xZGEmQPHCS7/yDZAwc58KvDdO0IY0LuWPDD\n4NgMK15zFqf+2dWTZiGl9nbz/D9+l677Rkj/79s477M3EmmJT3nb6UO99P3sQfZ/7zG6d8eBO3Gj\nIepWNeM2xvAJYa0heWiE5KEEnjvKonMXc87fXkd8ZcuU44uIyNyloIuIlCXrGzzXzPiEWia3+3M/\noOenDwfKxRtjGdz6FINbnwr0XXLjxbRcson6M1Zh3OAJn5/36X94P4d/8RxNmzoIxcK4sRDZNPTu\nzpDoK9C3Y5i+Z0fY/vgAnengqeHaiCWdgINd6aNtvSn3aKZTqb7nEvQ9l2Db9w5gDNz2gUfpOLOJ\nZWc1s+S0BtrWxFl3RTteU/i4r51cMkvPg530PHCY7vsO0b+tm3TKJZ936XhJB+vfsI61r15DpDFS\n1mNpfZ/09u0k7rqT1LbHGR2OAJdM6JcZKXD/Pz3L/f/0LKGoS9O5Tfgr4zj1IQa70gx2pUg+k6DB\nd2haGsNrDBFtitDoD3DKhQ0s2tSG1xrDWxTHhEM0bWwj3BD8hT43mqP/yT56n+ij++EuOn++C/zx\nK6UtpQ9LOJwjHs8E2tLpMJmMx9Nf3o4TcWk+vQ2nrYmz/vBM2i9sL+txmfA4WUtuKE3qcIKR5/pI\nHhxiw7sumvSX9+5nh7n9Lx7nmR8ewjVw+NHBo9etXJxgSXMwCLBkSS+U3IfGtkGSQ4cCbfmsCzv3\nHv3b9w3Wj5Le20l6bycAhYJDLhMBtgb6+YXgaz83EieXNORGBo62hcNZRp7cz/5x/fYdaCOTDWMc\nQyjuEo6HqGOQ1tObCMVCuNEQJuRw+KFB0gNZbN6O3SbU4mvTyFMHeew9X2PRFadxynteRrSjkVxX\nN7nDXWQOdrLt80+TGcgf7W+MP+Np2HSafDpNvruHRGcrmdRo8PpCmKEnDjH0xLHnq6ltiGWvfwkN\nZ59C3aa1hOqnDjQA5NIFrG+JxIOTTvWmufvP7mHPf+8baykG9BN9Wb769ge456u7+f0vXcjS05sY\n7cvQ9cwwXU8PE7rvh7Sv9PETCQpDwxRGU+RzYVquvoTGyy4gfupajHMsWGILBZJPPcfAT+4g+/S2\n4Nx6W/GziyDrH20Lh3Mc/sFDDD38PGvecRlLrjod4zrYQoHD3/kNB7/+S2yuADQy8Ngh7nnT1znv\nMzfQdEbHxMc5X2DgN9vo+/mDjDz+HACFpAcUH7tCOs/wjl7yeYdcvuRJjcPoMwe59w3/H0tffRan\nvPMSoh2NwfGtpeeBg8SXNRJfVo9ThaVqIjJ/WN9gj/P9rBpjS/Uo6CIiZclYQ0QrMWomP5Iqe3++\n7h/cQ/cP7sFtiNF44UZsXRv5nMPQ9k5Gnukil8yTy0aAJ44eMzAcZX9XcElBwa9+mbTMSJ699/ax\n994+XGNpDRdvw7iGWGuEcMyFgo/XFCaXyJMdzRHJj9JUl550vM57O+m8t5N7P3wfay6pY931K4ku\nimFCDo7rMvp8N6GWOH4yS34kRX44hbvjpziF1KTjHU8+XeDZe3rZb/1Ae4t18HDpGTmWL3D68gG6\nOvfR9eNj/VLpMNY6OBGXSGOEcEOEgX1ZcsnCuGp0lqg388fczxbof7ybg30J7v56L2suX8IZN6zg\ntNcsp2nF8U+ErW8ZeGaAw3cdZPe/b8X4BQrpIyfzFjfks+87j9HxsvV0XLWB1gtWkBrK8Yu/2s69\nX9qFn7fH2fJwfpbbs74ll8iTS+Spa80xuL332HUWUukXdolL7/88g7PrLrxY9uhnge9DZmDdCzqP\n4wmF8vTcdjc9t90NxuCt6sBpbKP1ynMJNcYIN8YwUY+RrjyDu5P0bh+k98kBdt/Xz+AAtG1soG19\nPW3r6/FCPvtv3UGmP3Pc29txVw9/s+mnNDRHSA1kj7a/4/W7yaTzwc4WBu7YysAdWwm1NdP4kvMI\nL2oh/fwBEg9vp5BI4rh5ItNIEkzu6+epj9/Gnq9uZflrzmLorkcZ3XFwQr905wj3v/1bnPHhl7Pi\n+jMBKCRTjD66ncFfP0DvvXsnHDMdtmA5dNs2Dt++nSUvO436M5aR3D/IyI4eRnb2MnDI4PsOxjF4\nbTGii2McOhTi2n+/gqXnNuuHEhGROWDOBl22bNlCU1MTmzdvZvPmzbM9HZEFL2Mdoq6+vM0lhZEU\nfb/cxkB/aX2Gufc82YIl2ZPBYHFdGO08FhQJx6c+aS+kC2R37GD/l4K/VBdjJMH7u2JDatYeAj9b\nIN2bIt2bIpcOT5hbtVkfdv+6m92/7uYn73+EtZtczryhg/qlcdxoCDcWouuhXlK9Wboe6CIzkAEs\nbS2Tn+xmekbZ+1+Ps/e/HicVquOxZ1vJJv1J+0r1uaF82cHXWWUtqd2dpBLD9G7dfbR5JBFl53PL\nA11zOYNfCNHz9DA9Tw8D0BTL0RQvCZxMwlgbCLiUI983SP+Pfk213nvJ3b3s/7fbcd3jvw/8bIEn\nP3E7I7+5j4a2DKknd2DzefyCAeqqMg+bK3DgJ09jf/xsyTWx4vW+Jd2TJN2T5Pm9zXzxwp/TvDrO\nGTesYNONK1h1cZsyYUROMtaa2u1epJouVTVngy7aMlpkbsn6hsgkyw5EZO6wvQM8/82eQFsqFcFW\ncAI62l9QwEVkGtz9j5LcP3W/F8rg3iT3/PMO7v/8M6xaYTnnHRtoOa2F5o3NNG9oJvQC1bESEVno\n9GkrImXJWIMXUtBFRERkPjGAm0nz5L88EWhc99p1vPTzl8/avERkpmq4e9EczJqez5RnKCJlydpi\nIV0RERGZ52yxmLGIiNSeMl1EpCwZ36FBQRcRERERkdlna1h7ZR6UF5tPlOkiImUp7l6koIuIiIiI\niEi5lOkiImXJ+gZPYVoRERERkVln/SO7ONZmbKmeORt0mctbRmezWfrCKfKxY1tuWguJQvDhNPiE\nw4VAWziSIxcp3SbRYkoSCAoFg7VOSdvEM96cY0sKKFkyJeP7viERL9kedJJ0tKyXo1CyJaLjWCYk\nN5iJpZWS2TChklbHErhffsEh4U991h7OhagbjQYbQ3lMqIw8t0wY4wT72XSI8TO2FvKpSLAPQMEN\nzt/1iQwGt3q0WQebDT7P2ZRHIRsO9isYSh8lx4IpufuF3NRvQWMN4ZQXaEvlQoyW9BvNhhlMxIJz\nS0cmmRsT5hYbiQUeN5t3SJccN1RwGMgUyGazRCLBx09EREREREQmmrNBl7m8ZXRnZye3d+yBjtme\nyXQMVHGsSQInXYurN3xfDPpaqzfeTDwz2xMY0zVJW7jk787FxUsleheV1e2J/jydnZ2sWrWqstsR\nEREREZGZszXcvahmuyItTHM26DKXdXR08KrONeRHjlV9txYSiWBWxPEyXerrUyUjziDTJRuekOkS\nLxnf982EuU2W6RKvT9LcOhxoc9wCbqgkv8zxJ2S6NK7oI1SSYeN42YmZLgemPrkPN6Soax8KNpab\n6RLJl5fpMhAP9oGJmS51aSJLR4L9Jst06a+jkAhm5kya6RLKT8x0yZRGTiYyoQLhhuAOA6mBOKOd\nLYG2hpV91C0dLJlbnEK6JKtnskyX1X0TM10OBcf/j55WNrTF6OiYV9FGERERERGRWaOgSwUikQht\nuRi51LiTVAtuydIOY3wiJYGISMGhKZIrGXGyoIuD708SdCkJlGQykQlBl4aS8QsFh3AyuDzFWiYE\ndRrCOdpKAjuu8ZkQ6yhZNgTQGskRjgZv14llgkGXvMOQM/UCwUg4T0NdMMhgwnlMuIzFhV4O45YE\nXcJhAkEXH3Lp4EvfWiYGXeIZvObgIh6bcbHpYKAkkwqRzwaPtZM8V04kNyHokvenjiI74TyRWHB5\n2GgiwnBJv+ZIjsaSgFs6GaJQchuTBYTqGlITgi7JSH2wj+sTCxktLRIRERERmWUWU7Pdi+yEn9hl\nJlQWU0TKEjGWTEH7x4mIiIiILGTGGMcY80ljzPPGmKQxZpcx5qMlfeqMMV8wxuwf67PdGPPu2Zrz\nbFKmi4iUxXN8sgq6iIiIiIjMOmtrmOky9bgfBN4NvAV4CrgQ+KoxZtBa+4WxPjcBVwC/B+wFXg78\nqzHmoLX2x7WY91ylTBcRKYtnLOnC1P1EREREROSk9hLgNmvtT621+6y13wd+DlxU0udr1tq7x/p8\nGXi8pM+CMGeDLlu2bOH666/n5ptvnu2piAjF5UXKdBERERERmQOO7F5Uq8uJ3QNcZYzZAGCMOQe4\nBLi9pM/1xphlY32uBDYAP6v6YzHHzdnlRXN5y2iRhchzLBlfQRcRERERkQXu00Aj8IwxpkAxmeMj\n1tpbxvX5E+D/AQeMMXmgALzLWrv1BZ/tLJuzQRcRmVs845PJK+giIiIiIjLbrA+2jJ1Qp/KDHbu5\ndceeQNtwtnS33QneSLFWy+9SrOlyLvDPxphD1tpvjPV5H/Bi4NXAPuClwL+M9fnVjCc+jyjoIiJl\n0e5FIiIiIiInlxs3ruXGjWsDbdu6+7j22/99osP+Afg7a+13xv7eboxZA3wI+IYxJgr8DXCDtfbI\nQE8aY84D/jegoIuISCnPUU0XEREREZG5oXa7F8GU48YpLhcaz+dYzdjw2KW0T4E5XFe2VhR0EZGy\neMaS9cH3fRxnwX1WioiIiIhI0Y+AjxpjDgDbgfOBLcCXAay1I8aYO4HPGGP+hOKW0VdQ3GL6A7My\n41mkoIuIlCXi+ABks1mi0egsz0ZEREREZAGzY5dajX1ifwx8EvgisAQ4BPzrWNsRbwT+Dvgm0Eox\n8PIha+3/q/Js57w5G3S55pprCIfDLF++nOXLlwOwefNmNm/ePMszE1mYPFP89M1kMgq6iIiIiIgs\nUNbaUeBPxy7H69MNvOMFm9QcNmeDLnfccYe2jBaZQ44EXdLpNE1NTbM8m5PLww8/zHe7HuDiSAR3\n3BpaYyxmiiW1vm8YjmcCbdaHfDi4hDaBTzYfXBbm+xanZBvwfNiSLvmXIR93KZRUx5+svM+EuRpL\nJhTsaLA4brCbE83hxrOBtmzOpVAIdhyIZch4wWr61i+OOl7pP2xpx5JuGZkw/3yh9PHwcW3JHbOG\nAsF5jDYmCdcF55sJ57A2OF7GC5XMzZILly5tnvgch0J5stGS8UN5cpHgsWnfJ1fSb7RxFN8r6RfJ\nTZhHqC41YR6OG7zvCetTaIsE2nygdPl4smmU4frgPNxojmgkH2hLORan5NiCE7xN30AuVJjQJ29L\nnndr8At+cB6FDOmSGwiFcrhuSb/mUbK54KtkpCHJ+FWT1kLGC87f94uvy8DcfDPhfTVcn6Y3HDw2\n4zkU3Kl/NnQcizvuLlgDAyXvb4NPMhp8PEKhPKMlj1vEsYyUPFl5oGCC8xiK5Mna4POXM/6E17Mf\nLgReq9aHdMnrL+kbUi2JQFsmD7ls8PFOe3nC0eBjVAiFIBt8fH3AlLy/u10fb9zrxloolDzHxV9o\ng8cZ1xIuef0NRvIMxtOBtlAojxsKjud5OZyS1+pILDOhlkJX6esZSE/yWhgqef8VCg75ks+7XDQ7\n9t49Jl8wE56XpLFYP9iWa3MZf/+t4zPaFHwdAQw4A2SzWSKRyITrRGTus7Z2NV1qVytmYZqzQRcR\nmVsizrFMF6muffv20V9I8eMVpSfC5Rqs+LbrJhmp8tEmSkzdpWzby+5Z8k9bfQFe9UhZRzZP0jZa\n8veTZc/jhdc/C7c5o9eL509sa8hPbKuq/TUd/QDwQDUHPONA5cf6XvDvEBO/+S0fqnz8yd7hmw5O\nedTxXjOl1cLyY5fxvj3pkdlJW6e0dLh4qUiZr/zGSR6j1S/8O7XvOO0Xd17MqlWrXtC5iIgsNAq6\niEhZPFM8OUqn01P0lOnyPI+IcXnl/oZAe9mZLsP1gTbrQz4XDrQNj3p09Qf7DfiWgZJMl+VhS3PJ\nvwyDGZdcBZkurrE0lpHpEo/maCgj06W9vZd4Sb/JMl3aV3UGmtJJj613nTNh/qWZLoO+T2dJpkuj\nNTSUZLqsXTJMc2mmSzY0MdMlOzHTJVJmpku0NNMlGyKXDf4a3T0UJZEKti1rTRArzXTJhCfMo6kh\nOWEepZkug6MRnj8cDEMZCGRiALS3jNLaEJxvS+vghPtQ1zQy4TYKOScwN98acungfSr4DvmSTIli\npkvweUkmo6TTwaWPk2W6dHY3T8h0aWkcnZjpUvJ4l5vpsmxpPyuWBU9xM+kIhfzUX7kWLesJzNf6\nsH/3ikAfg483SaZLNBYMiEfiaeINwUBuPhuikA/eh6G+JrKZ4H3NZSa+npvahiZmuqSCj3cy6bHv\nwJJAWyYPoyXPX72Xp6Ek02X/aIjhkkyXCASy/wBec/VjeOOyvqyFQsnzOXmmS4FwSfbVYH8jg73B\n1/jkmS6ZiZkuw3UTfglevvpw4G/fNxMeo0w6wlB/MFt0skyXaDSLV06mS8qbkOmy63AD4++/6/gs\na5n4g8mi8xfT0dExoV1E5gdluswfCrqISFmU6VI72WyWqBNiUcmJSblBFzfpTWgrPUH3h6MM9AWD\nOo5v8UuCLqGIJRqM1xBKufglQZfJCqxNFnTxwhODLm5p0CWeoaEQvO+ZbIh8yclhS+MI9SUn7ZMF\nXTpKWkZ9Q3QgeN8LPhNOlh3fp2BLlyk4uCVBl7ponqaStlQ6POFkKJSeGOyIeqW/2098jsPhHPGS\nk6h0OkwmE3yehwfiZEaDbXUhS13JyWwoFcGWBl2c0ifQTjjRzI9EcUtfM0wMusQdaDTBx6MtPkrM\nCY7X6BvcktvN+4ZA0MU3ZEqe90LBIZcNvih9f2LQJZL0SCZjgbZw2CVUcr+GB+twS8ZrMHZC0CWU\nLn1fQajkPZovGJySQExjQ5JFJUGAdCY8IRA6mXbfEBo3D99CouT9bYxPrHRJXdgQL1nGEvUN9SVL\niXJAoeSLdDgbIlMS6MpM8npuy7mB16rvG1Ilx0WSHj0DweAuOUM2E3w8orEc8Xhwvu5wGDLBx9IB\nnJL7uqTgEPXHBaYmCaBOFnRx8ImUvOxD2RAkg0GRcDg34TUTxZ8QvAulPPzA+9TSXvI56RcMyZLX\nQioThtHg6zSfd8iVBOXiBYdYyf3K5Z2JnzOJWMk8INzXEHjPhxyfOmfiV/4Wv0VLi0REXgAKuohI\nWcbXdJHqSqVSRIw+jkVERESkPNYabOmPYlUcW6qndPmsiMikIkaZLrWSTqfxStfciIiIiIjIvDdn\ngy5btmzh+uuv5+abb57tqYgI4BiIOAq61EI6nVami4iIiIiUz1Lc5q4ml9m+cyeXOfst/6abbtKW\n0SJzTMQ1CrrUQCqVGst00b9wIiIiIiInkzkbdBGRucdzjWq61EAqlaLZhCiWuRQREREROTHtXjR/\nzNnlRSIy93jKdKmJdDqNZ1TTRURERETkZKNMlyoyzpHtS08e3rpTsL0H8ZPJ2Z6KzAGeo6BLLaTT\naSJOfLanISIiIiLzRQ0zXVCmS1WVFXQxxnyugrE/Za3tr+C4+cUxtP3WOpa+5lwaz17Fvluf5rmv\nP0bqcGLy/tUo2eAanHAIE3Jxfajb0EF0aRPeonrCrXVk9x3AJhMkntyLn6lguYLr0vTSF9H221fi\nrVhKfnCIni9/ldQTT1Vh8nOAPkMqppoutZFKpfBiynQRERERETnZlJvp8gHgXiBbZv9LgS8AJ23Q\nJdreyPLXXcDSV59DtL3xaPuGt57HujedzcGf7WLHl+4nc6CHUINH24UrabtoJW0vWoUbLjDwP0/Q\n9+vHyRya/CEKNUSpO2MNDWeuJrKkCW9xI6GWOqLtzTiRcFlz9LN5Ek/vo/d/niL1g51El7XgxiK4\n0RB+3ifbnyayqI78aJZ8IoOTLcCKJaz/6O8Tbm06NpfmJjr+7H0M/+J/6P+v72ELL/xJt7UG48ah\nYSl4LZhoKzaXgFC8mF6UGYDMEGSfY0JdDAM0rcO0bYLW03HaTseJtmO7duAf2EbhwDZs146qzdU0\nd2Ai9ZhQFL/vMHZ0sGpjzzbPRTVdqiyfz5PL5YjUKfFQRERERMpjbfFSq7GleqbzLf9Ga213OR2N\nMSMVzueoLVu20NTUxObNm9m8efNMh6u6C77yNoyZPGXCCbusfPWpLLl0Jb33HWDZNesxbrB8TvwP\n2ln2lqsY3XGQg1/5Gbm+YVouO5O6U1dQt3EF4daGGc/RiYRoPOcUGs85hVPeP7OxjDE0XXMlsdNP\n5dDffhqbTp34ttecT+isl4PNYA89jn/ocejvPHZ9cxuRNesILV8NrotNp8jt30N+/25gtNgp3oZp\nPwuz5ExM+5mYtg0Yd+qAk7UWbB78sUshhYkuwphjz4EBWPti3LUvJgz4Q11kf/kF/H2PQ36K2GKk\nHqKLwfXAieDYHJH1ZxHa+DKcpetx4o2B7n5iEL9rL9mHbsVdsgoTb4SQhwl7OM8+Sv7wHvy+wye4\nPwYbacFpbIVCDvJZzPCUD8PYHbWYWB149ZhwFBOJUujvwYSj+MkRyIwG+zthiHeAt4RQNo8NLcYf\n7sUO90Ahj+caRpXpUlVHglietowWERERETnplPst/23A0DTGfTfQNf3pHDPXt4w+XsBlPK85zvJr\nN55wjPpTV3Dq37+jmlOrqciKZSz6g98ncd/9ZHbvxh8OxtfcDRcTuuiNuEtPPdZ42isA8PsP0Pjw\nr/FedB1uc+uk41trKXQ9h00mCK09p6zHuZQxBky4GEAADFMHsJymdqKv/SQ2NUL+qV+Qf/wnkNt5\nrEPdKdB+JWbJlZimMwMBnKkWhTj1zTj1zYTWnTPhuvDZ1xTv88GdZLfdRXbbnZBKYKJ1hNadS2jD\neYTXnYvT2BY4LmItDd09pHftIr1zF5mdu4BOLAbTvA6z+CxYfBbxxWdB/fLjPo42l8EmBvEPP4xZ\ncyEmdiw4Nf7DwVofOzpE3Xe/x+H9xw8QyfQdCbpEHC0vEhEREZHyaPei6jHGTHeFjgXOt9buLadz\nWUEXa+3XpjUDa781nf4yv9S/+CLqX3wR1lpynV1kdu6k8PithK59C5ENEwMLRzitK4hf8+YTjm2M\nIdSxvtpTLpuJNRC+4EZC59+Av+NW/L4HcM55N6ZuTe1u0xhCKzYSWrGR6BW/Q+7p+4icexXGPf5J\nuDGGcPsSwu1LaLjkYgAK3c9hieIuWV7+bYc9TEs7Tsurppijg6lvIdrURnrn7rLHl6mlUsWssYh2\nLxIRERERmQ3NFEuqlJNoYoB/Yerf3o+aUT67Maaekm2nrbXlLnyQec4YQ2RpB5GlHfDSy2Z7OlVl\njME99Ubgxhf0dp14I94FL6/oWHfJuirPZiLP81RIt8qOBF08R8uLRERERKRM1oCv3Yuq6JZplFP5\n/HQGnva3fGPMWopFcq8AouOvophmo59rRU5S0WhUhXSrbHxNl3IrlYuIiIiISHVYa52pewX6T6sA\nayU/rX6TYoDl7RTrtqi2scgC4XkeuVwO3/dxnGl9NslxHK3pYlwFXURERESkLKrpMn9UEnQ5B7jA\nWvtstScjInOb53kAZDIZYrHYLM/m5JBKpXAch5BREEtEREREZDYYY953nKuGgB3W2nsrHbuSoMuD\nwEqgpkGXub5ltMhCpKBL9aVSKWKxWEU7dYmIiIjIwmRt7TJS7MJcy7LlOO3NQJMx5h7gemvtdHc6\nqijo8k7gS8aY5cCTQG78ldbabRWMOcFc3zJaZCE6EnRRXZfqSafTRKPRqTuKiIhUQSgWonljM0sv\n7pjtqYiIzBnW2rXHu84YcwrFMiufAv5oumNXEnRZDKwD/mNcm0WFdEVOekeCA9rBqHqOZLrgz/ZM\nRETkZOREQnintnPph86i5dRm6lc2YBxlV4rMezWs6bJAdy86Lmvt88aYDwJfqeT4SoIuXwEeBTaj\nQroiC8r45UVSHUczXZKzPROZ71rW1rHk0qWc+foV1LXHcKMhQrEQw/sSDD47SOd9XXTd30l2aOr3\nrxN2Wf3SpQw8EubQ9tEXYPYy3xnXIdIUxQ1DITd1//ll6pMPJxomfsObiHsjjDz0OJnn9r4A8zq+\naFOYU69bxhk3LGfjtUuJ1FXylV9ERMbZB1SUIljJJ/BqimuZdlVygyIyfynTpfqOZrpMM+hiwiEa\nz1tH80tO5/R1K0geGmVo+2GGnzpM/2Od5ObCVkgWCBkWbWwk1hoh1uZhHEM+maNxRZxcIk92NEf2\nUD909ZxwAfHi8xax7FVns/pVq4g0RjCuiwk5FFJZbDZPPpEhP5IiP5wiffePsd17IJ9/we6qG3Go\nW9NCtC1OuNEj3BCh+7F+fN9hcMcgfq42qUwtq6Kc85Z1nHHjctrPbp60NlDjuhZWXLmSM//wLPyC\nT9+2Prb//Z14DS6pzgSpzhEKySwYQ8dV6+m4egNLLltLuN7jcmvZ9p393P7BbQzsUfCl1rK0EF9b\nT767B3907j7e0TUd1J2+hsiydppfsolQYww3FsEYw8t8y9CeBH3bB+h9apBnbz+MdzjHmpe1s2hD\nA63r6ok1ujzxz4/ReW/XiW+nLcL6i9ro35OkZ+cIfv7Ev/MZL0LDhWfSdNkFRFcvJ/HYM4w88ASj\n254FO/3PA+MaOq47h5WbL6T3Jw/QdevkNRRjK5o4/zM30LBxCQCtN7yCXN8AA3fcQ/Ibd037dicT\nX93KKe+8mLoNHSR29TK8s4eRnT0kf95FdFE98aX1xBbHiS6Js6jXcsVnLyYUUfK5yMnMUsPdi8oI\nNi9AZwEVRdQrCbr8iuIORgq6iCwwqulSfel0msbGRuo3rSLTNUB6Xw9+ZvKfiU0khOOFWf3+G2l6\n0UbcaOTodQ2nQvuVGwDIJTIMbe8itrQRNxYmFA9jIi7J3ix9O4bp2zFC37PDPPDTQ/Q+PVy9YmkG\nTntFBx1nNrPs7CaWntXEktMaCXlTf/HPjWTpeegw3fcdouveQ3Q9OoDXGuG0t5zG+tevp3l9Hql7\nTQAAIABJREFU06THhRvjExuvPovCyAij995D4q47ITF04mm7hhW/1caFv9VGbH0DfthhqDvFYGea\nA1t7qTMOy05vItoYJtoUxh0dZvWLWlh0RiteW5xIWww3cvx/TguZAgPPDtD9YBc7v7aNaItHpj9N\nZiBNfnR60bFQfZiWTYtoPXMx55/SyupXrMZrLb+oteM6LD5vMVfc8vqjbdZaMr1JsoNpGje0BR8b\nYzjnd1ZxxvXL2fr5nfzi409gM/648Qyh5jiRRS65oRS2RsGlajGuwauHhmVNeM0eoViIcDxEdn8X\nK6/oIFwXxvVCGNfh4L3dpPuz9G3rwxZqm9Qbaoyx+m2XsezGC3DCxfdLYSRB9sAhRr5wH0O7Rsj0\nzl4Qxo2F8eOLWL/lVdRvWkuoqe64fY1jaD6lgeZTGlj326t48V+cPWm/NVd08MSXtvPA3z6Cnw2+\nbtywwyv+z2lc95EziMSK761Czqf3+QR9P4nRvMzHH0lQGB4hNzCEPdhPx9t/h4YLz8KJeUfHabn6\nJbRc/RIKqTQDP/kFibvuwh8enrpCpIGOV53NmrdeQmxFCwB17301LZdtYvdnvk/m8LEaiotesoaz\n//bVRJqC78NwWwtLfvc6ml92Mb13PETfzx8k13viz6LJRJc1ccq7LqHj2k04oeIudw3r2lj6ilMB\nuHDaI4qIyGSMMY3HuaoJuAD4LPC1isa20/y2bYz5X8BHKS4zeoKJhXR/WMlExo1/PvDwww8/rEK6\nInOM7/u85z3v4c1vfjOXXnrpbE/npPA3f/M3rFmzhje96U0AWN8n0zVIak8XPf/9IA1nraVu43Ki\ny9oItzVgnOpuLZ3PFuh6PkHnjhEO7xhh7w/3sXhRhOXntBBvj1HXESM1mieTgf7nR+nZMUz3MyMc\nfGKIjrOaWHl+C8vObWbZOcVLXas39Y2WYXDXIF5DmFj78U/uymGtpX/rk/zkj57EbWk5uiA2M5LH\ndxwu+9jZrL5iCdGmyIkHqpHsSIbR/SNEmqMYp3jC6mcLFFJZjAOFVI5CMsfw7kGiHQ10XLZmVmsx\nbL/1ALvu6GLjtR0s2thA6yn1uOHia9IWfFI9SZIHhxm4cxvRJpdwk3c0+yZ3YDf1py7HrY9jQiFM\nKER6937c1lYKQyPFIEPPAIlnOqm/YBPhtmbCi5pwG+tw6+J4HW2YkIsJuWAgN5Qi0zl49HLorgNk\n02Hq1i0m0uQRbvQoJFI0n7mExvVtRFrjhBui0378cqM5uh/qZveP9vDs9/fhRkPkEjlyiTz5giGT\nCwYV16zu4pS1wQyOdNIjnwtPGNuEHJa/4SJWveUSwo3HD55Zaxl5tpuerc/T86unye07ELg+FM4R\nr08F2qL1SeqbxwI1Y493Pu1CrJnw0qW4jQ24jQ2M9hSwsVZswRQzxhJp+h45zKLLT6P5nBU0bFxC\nbHlzzV53/U8N8Kv33MUD9ycYyLisv3QRb/63F7HsjMkDrTPlZ3PkDh8mu/8gh3++g77nCoSam4sr\niIwhPzDM6s0Xsuy6TZMeX0hlOfCVn/Psf2xn1e9eyKlbrsS4U38u24LP8MPPsvsrW9l39yixjnrq\n1rZQv6aFcFMM67j4BUgeSpA8lCC9+yAdL13N2Z94+dFAnIic2COPPMIFF1wAcIG19pHZnk+tHDlf\n/sG1b2BT6+Ka3Mb2/h5u/Ol34CR/LMczxvgcv3SKBb4MvM9aO+188kqCLif6Gctaa2f0L8ORF9FL\nX/pSbRktMge9733v4/rrr+fqq6+e7amcFP7yL/+Sc889l9e97nWzPZVpyWXyuGEXR8UYZYGyviXV\nlyZxKIXFkEvkyI7kSO05RCRxmJBJk+0eINs9yPCeFPlsCByDG48Qikfwcz6nfvjVtF28YVq362fz\nDG3bR34oRaZ7iEz3MKndBzAjfTS9aAOR9lYi7W1EFjcSaW/Fbaif81vSFzIF/unSn7Hu0sVc/9kL\n58Xnyv5vP8Cy687GrZve7nP5VA6bzRFumiRLT0RmREGX6lmgQZfLj3PVMLDTWpuodOxpLy+y1lb3\nZ9bj0JbRInOT53mq6VJFR2u6zDNhT0UZZWEzjiG+OEZ8cen7d/mEvrmRYhZKqD464wCIEwnRcuEp\nMxpjrnE9ly33XQswLwIuACt/56KKjgvFwhCbmPUkIjJd1i9eajX2QmOtvXOqPsaYM621T053bH1r\nFpFpUdCluo7uXiQiJ61ww/wLrL7QnDKW6IiIiLzQjDENFHdufifF2i7TXtlTUdDFGHMVcBWwBAj8\nK2mtfXslY4rI/BCNRlVIt0ry+Ty5XG5eZrqIiIiIyOyxtoa7F9Vo3PnEGPNS4B3A64BDwPeB91Yy\n1rSDLsaYjwMfAx4CDnP8YjMichJSpkv1HAleKegiIiIiIjK7jDEdwFspBlsagW8DHnCDtfapSset\nJNPlD4G3Wmu/UemNisj8paBL9aRSxToPWl4kIiIiItNhqV1GykLMqjDG/Ah4KfAT4APAT621BWPM\nH8507EqCLhHgnpnesIjMT57naXlRlSjoIiIiIiIyJ7wS+L/Av1prd1Zz4EqCLl8Gfg/4ZDUnUuqa\na64hHA6zfPlyli8v7gSg7aNFZp/neQwODs72NE4KWl4kIiIiIhWpYU0XFmZNl0spLit62BjzNPAN\n4JZqDFxJ0CUK/C9jzNXANiA3/kpr7Z9WY2J33HGHtowWmYOi0aiWF1XJkUwXBV1ERERERGaPtfY+\n4D5jzAeANwJvBz5HceOga4wx+621I5WMXUnQ5WzgsbH/P7N0rpVMQkTmD9V0qZ4jmS5aXiQiIiIi\n06Hdi2rDWjsKfAX4ijHmVIrZLx8EPm2MucNae/10x5x20MVae+V0jxGRk4eCLtWTSqVwHIdwODzb\nUxERERERkXGstc8C/8cY8yHgtylmv0xbJZkuIrKARaNRFdKtknQ6TSwWw5iF+2uCiIiIiFTAmtrV\nXlnAmS6TsdYWgFvHLtPmlNPJGPN9Y0xjuYMaY/7TGLOkkgmJyNzmeR65XI5CoTDbU5n3UqmU6rmI\niIiIiMyiWsc7ys10eQ2wuMxfYw3F1Ju/BLrLnYiIzA9H6o9ks1kFDGYonU6rnouIiIiITJtqulRV\nTeMd5QZdDLCjzL5VsWXLFpqamrRNtMgc43kecGxpjFQulUop6CIiIiIiMrtqGu8oN+hSSfHcgxUc\nc9RNN92kLaNF5qAjQRcV0505Ba5EREREpBKW2mWkLMAtiWsa7ygr6GKtvbOCSYjISehIZoaK6c5c\nKpWiubl5tqchIiIiIrJg1Treod2LRGRalOlSParpIiIiIiKVsH7xUquxpXrK2r1IROQIBV2qRzVd\nRERERERObsp0EZFpUdClelTTRUREREQqUsPdi1h4uxfV1LQyXUzRKmOMfpoVWaAikQigoEs1pFIp\nBV1ERERERE5i011eZIBdwMoazCVgy5YtXH/99dx88821vikRmQbHcfA8T4V0ZyiXy5HP57W8SERE\nRESmzY5lutTqslAZY/7KGLO6mmNOa3mRtdY3xuwE2oCd1ZxIKW0ZLTJ3eZ6nTJcZOhK0UqaLiIiI\niMic8RrgI8aYO4F/B75nrZ3RiU8lhXQ/CPyjMebMmdywiMxf0WhUmS4zpKCLiIiIiFTKUsNMFxZu\npou19lzgRcB24J+BTmPMvxpjXlTpmJUEXb4OXAQ8boxJGWP6x18qnYiIzB/KdJm5VCoFoOVFIiIi\nIiJziLX2UWvt+4BlwDuAFcBWY8w2Y8z7jTFN0xmvkt2LPlDBMSJyElHQZeYUdBERERGRStWy9spC\nrulSwgBhIDL2/wPAHwOfNMa8y1r7X+UMMu2gi7X2a9M9RkROLlpeNHNaXiQiIiIiMvcYYy4A3gZs\nBjIUV/u811q7a+z6PwH+L1BW0KWS5UUYY9YZYz5ljLnZGLNkrO2VxphNlYwnIvOLMl1m7kimi4Iu\nIiIiIjJd1tZyB6PZvnezxxjzBHAfsJbi0qKV1toPHgm4jLkZWFzumNMOuhhjLgeeAF4MvBaoH7vq\nHOCvpjve8WjLaJG5S0GXmUun07iuSyhUySpPERERERGpgW8Da6y111lrb7XWFko7WGt7rbVlx1Iq\n+bb/aeCj1trPGWNGxrX/iuL6pqrQltEic5eWF81cKpUiGo1ijNbMioiIiMg01bCmCwu7psuR2i3B\nRmNiwJ9ba/96ugNWsrzoLOAHk7R3A4sqGE9E5hllusxcOp3W0iIRERERkbnl4xxbzTNefOy6aask\n02UQWArsLmk/DzhYySREZH5R0GXmUqmUgi4iIiIiUhlrwFemSw0YYLKqNucA/ZUMWEnQ5Rbg740x\nbxibjGOMuQT4DMWqviJyklPQZeaOLC8SEREREZHZZYwZoBjfsMAOY8z4wItLMfvlS5WMXUnQ5cPA\nF4H9Yzf+1Nh/vwV8qpJJiMj84nkeuVyOQqGA67qzPZ15KZ1OK+giIiIiIhWxNazpUrNaMXPbByhm\nuXyF4jKioXHXZYE91tp7Kxl42kEXa20WeJcx5q8p1nepBx611u6sZAIiMv8cCRZkMhni8fgsz2Z+\nSqfTNDc3z/Y0REREREQWPGvt1wCMMbuBe6y1uWqNXcmW0R8zxsSttfuttbdba79trd1pjIkZYz5W\nrYlpy2iRucvzPAAtMZoB1XQRERERkUodyXSp1WUhMcY0jvvzUSBmjGmc7FLJ+JXsXlT1ar6Tuemm\nm/jhD3/I5s2bqzWkiFTJ+EwXqYxquoiIiIjIfGSMcYwxnzTGPG+MSRpjdhljPjpJv782xhwa63OH\nMWb9bMy3DAPGmCVj/z9Iccvo0suR9mmrpKZL1av5isj8ciTTJZ1Oz/JM5i9tGS0iIiIilbK2eKnV\n2FP4IPBu4C0Ua7xeCHzVGDNorf0CgDHmL4A/Huuzh2L9158ZY04fK1kyl7yMY7GMK6s9eNlBl1pW\n8xWR+UWZLjOnQroiIiIiMk+9BLjNWvvTsb/3GWN+D7hoXJ/3A5+01v4YwBjzFqALuAH49gs52alY\na++c7P+rZTqZLjWr5isi84syXWYml8uRz+eV6SIiIiIiFSlmutRq96Ipu9xDcXOdDWP1Xc8BLgG2\nABhj1gIdwC+PjWmHjTH3UwzYzKmgy3jGmGuBhLX2N2N/vxd4F8WMnvdaa6e9xKjsoEtJNd+t1tr8\ndG9MRE4OKqQ7M6lUCkCZLiIiIiIyH30aaASeMcYUKNaK/Yi19pax6zsorpDpKjmua+y6uewfgb8A\nMMacBXwO+CzFZUefA9423QEr2TL6TmPMOmPM24B1wPuttd3GmFcC+6y126c7pojML5FIBGOMgi4V\nOpIhpEwXEREREalMLXcZmnLcNwK/B/wuxQyQc4F/NsYcstZ+o0aTeqGspXifAF4H/Mha+2FjzPnA\n7ZUMOO2gizHmcuC/ga3AS4GPAN0UC+m+A3h9JRMpdc011xAOh1m+fDnLly8HYPPmzdrNSGQOMMbg\neZ6WF1VIQRcRERERmYlqbe38864n+UXXk4G2RH7KH1b/Afg7a+13xv7eboxZA3wI+AbQSTFy004w\n26Wd4pbMc1mW4s7MAFcDXx/7/36K2T3TVsnuRZ8GPmqt/ZwxZmRc+68oVieuijvuuIPzzz+/WsOJ\nSJV5nqdMlwppeZGIiIiIzAUvbz+Tl7efGWh7duQwb3voyyc6LA4UStp8isuMsNbuNsZ0AlcB2wCM\nMY3Ai4EvVmfmNfMb4HPGmK0UCwO/cax9I3CgkgErCbqcRTGVqFQ3sKiSSYjI/DMfgi42m8IOHcIO\nHsIfPEj6qSeJnHM53rnXzOq8FHQRERERkZmoVqbL8caewo+AjxpjDgDbgfMpFtEdH6n5p7E+uyhu\nGf1JikGL26o93yr7Y+BfKK7geY+19uBY+yuBnx73qBOoJOgyCCwFdpe0nwccnNhdRE5GtQ66WGsh\nPwzZbkzdhvKPyyeh/z7yD91CfkfwYyrXV8/oY3uIP3+Y+lfciFPXUO1pn3hu1kJmgFT3s4CWF4mI\niIjIvPTHFIMoXwSWAIeAfx1rA8Ba+w/GmDjwb0AzcDfwSmtt9oWfbvmstfuAV0/SvqXSMSsJutwC\n/L0x5g0UKxI7xphLgM9wbL2TiJzkZhJ0sdZSePzPMZEhTGRctsfgE+C2Qn4Qsn3gZyHjwuKrYMk1\nsPhyTGhioMRmeuHQbdD/YPHiZ2EwzqTLLq0ledfPSD14N/Uvv4H4ZS/HhMIV3Y9y+Ifuwu78LmQS\nMLwPciOkDi4i5K4kHK7d7YqIiIjIycv6BuvXKNNlinGttaPAn45dTtTvE8AnqjWvF4oxxgHWUwwo\nOeOvs9beNd3xKgm6fJhiRGs/4FKs7OsC3wI+VcF4IjIPRaPRo8tkymWtJb/rcZJ33EJs3S8JtSUh\nOa5D2lC6s5wB6Pll8WLC+K0vgsblEIlBenfxMrIfMxL4PJx6LqkkI7d9i9xjt1B/1ctxW1dApBHC\nDVg/D/kMpHtgtAuSXSTXfBea1lKMNVsoZAkNDRGO/ylO7LcwoWOrK62fg647sQduxR64F9LBj9pU\n3iEaqeTjV0REREREasUY81sUYxurmbiNk6UY+5iWSraMzgLvMsZ8EjgTqAcetdbunO5YIjJ/eZ7H\nSOdBej7+R7htS3Bbl+C2LcFG6ggtW4N3yjocL3K0f27P06R+fjP5PWM7sK2r4EZtDga2Qj5f0m4o\nCUKXfz/i+zFPfx5/fKMPpZ+xhbN7wHYf/dv44CRcCoktFAATXo8JnYfTm4buRyA7MNZz4udyOu8S\njUz781pEREREBBj7GbBWNV1qMuq88SXgIeA64DBVeDgq/ql1bK3TvplO4Hi2bNlCU1OTtokWmaOi\n0Si9uTx+YhB/eJDc7h0AZEajZEaLtUqceIxQSxPR+ADhbM0+LuYEm9sFg8/DgamL46YKLjFPmS4i\nIiIiInPMBuD11tpd1Rpw2t/6jTGGYiXfK5l8jdNrqzGxm266SVtGi8xhnueRzeVP2MdPpsgmU0Ta\nhoo5cQJAWsuLRERERGQGZnn3opPZ/RTrucxe0IXi1k/vBn5NsfjCAs8+ElmYPM8jncvN9jTmpVRB\ny4tEREREROagzwOfNcZ0AE8AgRMea+226Q5YSdDlzcBrrbW3V3CsiJwkotFoMdNFsYNpS+ddWrW8\nSEREREQqVcNMFxZ2psv3xv77lXFtlmLBxxemkC4wBDxfwXEichLxPI9MPqegSwXSBS0vEhERERGZ\ng9ZWe8BKvvV/Avi4Mebt1trp7RcrIicNz/PIF3wKFtwFHQyfvlTeJablRSIiIiJSIdV0qQ1r7d5q\nj1nJHqvfBlqAbmPME8aYR8Zfqjw/EZmjotHiLj3ZWZ7HfJTOu0S1vEhEREREZM4xxrzZGLPVGHPI\nGLN6rO0DxpjXVDJeJd/6vwZcAHyTGhbS1ZbRInOb53kAZK0hZlRPu1zWFpcXxbS8SEREREQqpEyX\n2jDGvAf4a4obCH2EY8UUBoEPALdNd8xKvvVfB7zCWvubCo4tm7aMFpnbjgZdjtaUknLkfUPBOtq9\nSERERERk7vkT4F3W2luNMR8c1/4Q8JlKBqwk6LIfGK7kxkTk5HEs02WWJzLPpArFYIuWF4mIiIhI\npSy1y0hZ4F/v1wKPTtKeAeoqGbCSmi5/BvyDMWZNJTcoIieHIzVdMizc9MNKpPPFj10V0hURERER\nmXN2A+dO0n4t8HQlA1byU+s3gTjwnDEmCeTGX2mtba1kIiIyvxzJdOksGKLmWPw2C+ScYHx82HeI\n5INBhvhAHNeWxH0zBsYHcSyYXLCPNRYyheBxOYNJBj/OCiNR8qlIcPhciJwfHG846RF2S+L5PsF5\nAMn9DYG/TR683uBYJu3gDAZv02ZdyB6b246BYoA8m/cREREREamEarrUzOeALxpjohRPCC4yxmwG\nPgS8s5IBKwm6fKCSGxKRk0s0GqWlvo6fJoD8uCsMEC8NisRgKBZs+3VzjWdYpmfWlNfv4erebM9g\nqroDioiIiIjIjFhrv2yMSQGfophs8i3gEPB+a+0tlYw57aCLtfZrldyQiJxcIpEIH3rD9Tx/85cD\n7dlUhFwqGmiLNSWIxDOBtvj5B3Cb08FBy810qSsj02UoSr4/uOwyMxQnlwzOrX5tN+GGknlMluly\n1UDg7+NmunSdONOlNxXhy0+tZWlrHBERERGRivgG69coI6VW484T1tr/BP7TGBMH6q213TMZb0aV\nHMdSbgJnGNbaqhTZ1ZbRInNfJBRimRNcJpMBMiUf1PWOjxcKBkrqW5KE2pLBAdOTBF0ywWVJ1rHQ\nkA8elzUYLxxoyvuWfCrYlkpGyJbMtymeIdJQknUySdAlsXIk8LfJQzQUnJsZdXAzwaCOzbiQPjaP\nxkhx7vnCAi9RJiIi0/L8v9+DtdCwsZ1oRwPR9gbCjVGshdH+LIneDCM9aR789n7a19eTTuRJj+QZ\n7Ulz8IF+TntZO15DGK8hhG8t+ZzPqdd0UL/Io2GRR6wpjDEL+0RLRMQY8yvgtdbaQWttEkiOtTcC\nt1prXzbdMacddDHG1AF/D/wO0DZJl6pUh9SW0SJyMoq6xeBTKpufoqcsBH7ex1qLGz7+P52ZkRx3\nfPwJYk0RXvaxTSc8KRrZvgevvYXIoqZaTFdKPPQfz/H8L7u47qbzqVscnfoAkUmMPtdJbiRD87mr\nJ1xnfUvf1p3sv+V+eu4/hF9Sl+yXO9vpT0aw435PKPn5AgPUYeh+8tjvonksaYCPPXm0zXEg2hCm\neXmMSMwlEg8Rjrlc/ofrOf/GFVW5ryJSParpUjNXUJJYMiYKXFbJgJVkuvwDcCXwHuAbwHuB5cC7\ngQ+e4DgRkQXPc30MlnS2MHVnOWmN7h+k5+7n2fn953js5wXWvXolqy9fwurL22k7tQE/b3nujk62\nfWsvz9x2kGSyQN7CtlsPcvmfnco5b1yFGy6efFnfZ+TxXfTd8SD9dz4BGGJrO6g/fTV1p60isqKd\nXNqQ3DfI6N4Bhp/rY999CV57z+YZ/6qd7k9z3189jFsXYdPbTqVlfcNJ8Ut5djTPjp8dZtONKya9\nP51PDfGzT2znqe/uo86FHd8/wMbrlnHOH6xlw7VLcUKVbA45tw0/sZv6jctxvMm+hy4MfjZHatde\nkk89x+0f7iQfbsSEHIwxGANuaogXf/RCll2+gvqVDccdx1pL4rk+un+zm+67d5PZsZPGxmEi7S3U\nb1pL/ZlriW1YyeBjBznwXw+S2t8/duTEr+3ZnAkEXGZ0/3xIDeVIDQX2yOCMazqqcwMiInOYMebs\ncX+eYYwZ/+HnUty96GAlY1cSdPlt4C3W2v8xxvwHcLe1dpcx5v9n787j5LzuOt9/zrPUXr2qu9Va\nLcmWLVve48RJHDubk0nAgoRVMFwCAwwDzAwCkskwzL0Dc+e+gMnFM1xmhmEYQmCCgQCTBcckTpw4\niZ1YtmNbkm3Z2nf1vtRe9TzPuX+0ZKmqW1JLqlL18n2/XvVS96nznOf3VFe3qn71O+ccAX4c+PSV\nBCIishwYAwk3olRRpctyE1YC9v2XbzH8zYMUDs+8iSoUY1TL/bz6N8d49W+OARDrjlGtWKr52c+R\nUy9N8pf/xzM89uu7eMc/XcWGVaNMf/MFqsNn1hw688lU6eApSgdPMfLodxg+1UsUnaukiSI4dmSA\nx376KR78g3vx05f3UqA0WuLYPxzmyKOHOP30KfJ5j0rN4/k/eJ30YJK19w8wcFsXG79nDdl12TkT\nEFEYURyvcuL5Ca5/7wBui5IU1s5M47tUIqhWDjn2zBiHvjHCoW+McOTpEUwlIrsqyab3DLDxPQNs\nes8A5ULAl3/zZb776SNYC/6ZYaNaxN7PHmfvZ4/T2edyy/cPsv6dK0n2JUmsSJBYkSDVl7xoMiYM\nInZ+/hihtRSna+TGK+RGK5z69ji/8Mh9dA4mL3js1SgdGyHWlcTNZua8v3BsgiOffo7g249hHIPf\n1018TT+xwRUkBrro3XbZVdaLho0iasPjjH7+CSqHj1M+fAJbm/m9LE7cTrl8/hRZy4ruMs/8+rcA\n6Li+i5VvW0XHxiyZwQyViTKViTK5A6NM7dxPkDu3llgqM/M8rQ5NMD40wfgT36VcjFOtxK/ZtYrI\n4mMB26LZ6st0EvyLnHlYgSfmuL8E/PMrGfhKki49wMEzX0+f+R7gW8B/u5IgRESWk4QXUtb0omUn\nLFY59OfPXbJfabxKeImy3qnjJUY+81Wya8auOJ7X//YI469N8dBfPEDndfVvuGvFgOd/69uc/MIe\nYp1x3ISHm/Ao5yImD5cv+GqscKrE3r86zMlHy7z8iQgMxDrjJHoTHDzoYFMpiuNVCqNVbGSpAq7v\n0LspTd+NHfSsT5Pui/Ouj96IF7u62cqVqQr/691fxknGuO0nN3HDB1bRsebcAtZRGDH84jjHnjjF\n5/7tqwTV+ouKOZA7WeLFPz/Mi39+mNBC6exLsYuI8mWO/N0+jvzdvjfajLHEOnw6rusgtTJFelWa\nEycDSl6M8RNlRg/kGT1c4HhUJjzvR28sXB8l+L9Wf57r39XP3T+2nts/vIZU99VXm0TFIvknv8br\n/+U7VGsZut+2mZ57N9H95k3EutNM7j7J4T/fydATr4OFwTWAtdSGx6kNj2NMRCJTIf/UN0nfdTuZ\nu28nvnE9xplJLAW5EoX9p5l8ehcmrDH4E+/D7+646rhbKSyUGfvyt6kcOkYwOkb56ClsucrMAmOX\nN9b0/kmm90/gufVPGM8LSDUsLC8iIgvCBmb+2h8E3gyMnHdfFRi21l5RqfqVJF0OngnoKLCXmbVd\ndjJTATN5JUGIiCwnSS/U9CJZEEb3TPLIA4/xgU/ex/p3D1KZqvLy/9zL7v/xKtFUno5sRGXs3ELT\ntZoL9jJeOlioTlaoTlaYOJWmGsx+3oe1iOG9OYb35rAWQuCx33qZNXd2sf4tvay9s4uuSRe6AAAg\nAElEQVRVt3bSsy6Ncc5VrXhJFy85O5YojNj/yF5e+sRzTB4wFMo+x56ZSXYN3NZF3/UpfBNx8tvD\nVCarMzFUPeb1rvoqPvqr5WqM7R5jbPdMouzlsTi5akNi6QKFMNbCvieG2ffEMH/zz55j493dvOtj\nW1ixOUvP9Rm8uHumn2XqZImh13KcfPIQKafMDQ9tIrOhl1j3TMIpGB8n99UvU/jGk9hKBRsMEBSq\njDy+h5HH94CBKJZ947G55HWdPM3kydNM/v2XsMlOyqyimo+onJqpvvLjVVLpMrknnyJ5wzqyb7qF\n7Ju2El8/2PapaMW9B5n89quUT05RPHCSyskx/HgVz9ffZxFZ+CJriFq09kqrxl3IrLVHznzZ9PLb\nK0m6fBK4HXgS+G3gC8aYXwJ84FeaGJuIyJKUcFXpIgtHeaLKZz/8BBve1cfoi6PU8jPrOcT8SxzY\nQmE14sgz4xx5ZpykG7GqYZv4MAIvG2fzh9ez9r4+1tzXT8eaNEPfOcWz/+5pJl89uwZG/eK2Q7sm\nKbw+ymJeciWqWYafHeevfugpAIxjSKxMkAssxVxA5cy0tI1909y6doJnP78TgFh3ir6NRTrN61x0\nERAL1akyV/KaszZRJDd64eqr0r6jlPYdZfxvP0e8J47XmcV4Hsb3KQ6VcftWEV87iNeZxu3IEEzn\ncHyfYGKa6vAEteFxhp+fIHndSuIru4gPdBLrzeIlDAPb3oxzkSetrVWpHT1IcPwAtaMHqB3bz+Te\nClPDPRc8RkRElidjzA3MrGPbT8N/iNba37rc8S476WKtffi8r79ijLkJuBvYb63ddbnjXYi2jBaR\npSrhaU0XWWCs5dS3TrU7istSnqyx60/2s+tP9gPQu8oQq+XbHNW1ZyPL5IkS05dYTLU6USQYHoL+\nJq26ehUMYAt5aoVzP69KPklwNA/Pv35eT0tjMUwUZinsH6Kwf+jMYJZMR4GhP/888VUrSK5fiT+4\ngqDqYrwY5ZOTlE+O407tp2/gYP1gaJcvEVnEbAt3GVqmi7oAGGN+lpllU0aB09Q/GhZofdKl0Zky\nnCOX7HiZtGW0iCxVSTckr+lFIk1VnaoQS126nyxRUUTl+DCV48OEgUOpUP9kyPaUYaBNsYmIyGLy\nG8C/sdb+TrMGnFfSxRjzL4A/staWz3x9Qdba329KZCKyZCzjZPmcEl7IqCpdREREROQKWWtaVunS\nsgqaxaEb+EwzB5xvpcsOZraCLp/5+kIsoKSLyDJk0lnc3n6MSZPqXU/ipi24XZ14PV04MZfKtx+l\n/PSjUC1ferBFyiYGsUkwpYmL9ku4EeWyKl1ERERERBaYzwDvA/6wWQPOK+lird0w19cisrzFbriF\nno/+Nm5PH04iedG+qfdtJ/G2D1J68n9TeeZLV35SJwF+H3gxKB8Ge3VbbwaFOP7GW3G6BiHWAbEs\nNnccYp2Y8ji2cBqKQ9TvGjfDui5uahsm+Vac5L0YbxB7g4XJXdjjn4WT/wDMji/phZS0kK6IiIiI\nXCFVurTMfuDfG2PuBXYDtfPvvJKZPVe9pouILF9uZzduZ/e8+zuZTtLf8xGS9z1EuPtHsCbCmPMW\nBDfluecieVlY8U7ofxDT+zaMO7MjibURVE/CxLfh6Odg6jWI5l9JE9u8lcT3/Rje6vWX7JuNyme2\nN50J0HoRrDM4Tn2yyRgD3bdjum/Hbvko7P597P5/gODctrsJL6Ss6UUiIiIiIgvNzwF54IEzt/Nd\n0cye+a7p8nvzHdBaq22jReSinM5enPu+Mqvd2giCKaiNQ3UMqsMwtRs2/RrGmb0VqDEOxNfAyh+C\nlT+EDcswvhNGvgFTT17w/O7AKjq+78eJbbn9TCJlHjE79VvPzucw46Uwd34c7vw4tjIFuWPY3FGS\nO1+guv8oYRjiuu68zi8iIiIicpYqXVqjFTN75lvpcmfD93edOfa1M99vBkLg+SbFxYMPPojv+6xe\nvZrVq1cDaPtokSXOGAf87plbatNMY//3zv94NwF990Pf/bhrfx5z4gVsbgw7dRI7eRJTOEn6treQ\n+fAvYq5xssPEOyHeiVmxldTESnjiv1Mul0mn09c0DhERERERuXbmu6bLu85+bYz5FSAH/KS1duJM\nWzfwSeCbzQrs8ccf15bRInLFnOwKnJserGuL2ZmpQfOtbmmVRGKmaqZUKinpIiIiIiKXzVqDjVTp\n0gxnZvb8W2tt4VKzfK5kZs+VrOnyq8D7ziZczpx4whjzG8CXgf/3CsYUEWm5didbzkomZ9aBKZeX\n7k5OIiIiIiKLxJ2Af97XFzLX6pOXdCVJlw6gb472PiB7JUGIiCwnSrqIiIiIyFWxLaxIuaLUwuJ1\n/sye879uFufSXWb538AnjTEfNsasOXP7AeB/An/X3PBERJae86cXiYiIiIjI0nUllS4/D3wC+AvO\nleAEzCRdPtqkuEREliwlXURERETkamj3osXjspMu1toi8AvGmI8CZ7YX4YC1ttDUyERElqh4PI4x\nRtOLRERERESWuCupdAHgTJJlVxNjqbNjxw46Ozu1TbSILDnGGJLJpCpdREREROSKqNJl8bjipEur\nPfzww9oyWkSWrEQioaSLiIiIiMgSt2CTLiIiS1kymdT0IhERERG5IhGGqEUVKRGqdGmmK9m9SERE\nrlIikVDSRURERERkiVOli4hIG2hNFxERERG5Yi1c0wWt6dJUSrqIiLRBIpEgl8u1Owy5hp7e+W0O\n9E/Qm0++0VZyQqq99c+D0DKrXDiwM+3nm05VGPHDujYbAQ0lwZOpMlHknutjodRdf05jIGoYq+aF\nkKrUxxG41GpuXVsp7lEN6tvcVJWqF9WPV40IGvrVn3EmtqihLXAjSsn61iiCIKq/TjdZg0RQ11a2\nAdVq/UudcizEbajzDWIutuFxMw19IgthQ3DGWKr1l0Q5HuAmwln9TMPr19BWoeGx9J0qznn9jIUo\nqtGo4TACe/Znf14cnQUmGn5+jhdSbRhrLBZQSdS3VkyEbSiG9ryw7qlljCVu6p+UNTciF6+P14sF\nJNz64DzH4jU8HmU3IvRmPSNmPW75+OzHo9Tw3I1MRLnhmspeiA3rr6lgIB+rf854XojrNj4LbeOv\nFcWuAtVK/TnyHbOrF12n/jFy3YBqov4ayokqVb8+jmrcodZw8TU3nPUGK+rJQ/XSL+cbpwpY7AV6\nzhbM8RwUEZH5U9JFRKQNkskkIyMj7Q5DrqGvfvPrDF83Osc9B69ovO/Mt+PKOZJ7tx+5onO2QxVY\nyOnJfMP3V/Nb3TdHW2OSBGA+ExOPnbnN5td/OzgNTM9jxHlI1qBzbJ6dG7IYsSpzX22DFfOtEGz8\nyQClbP33Cc5c/xV4954rO67Zbjne+BOdU2WONneOtrnkqpsvIyARuVa0e9HisWCTLtoyWkSWMq3p\nsvxUKhXWD3ewYbjzjbZS2efIyZ66fvOtdNl6w0lWD0zVtc1V6TI+2jmr0uXYqfpzGgPxhmoB3wtJ\nz6fSpTK70iWTquI3VLocHU1Sa+jX+Pn5XJUucTeibx6VLulkjVRDpcvYVJxSQxVAao5Kl9HC7EoX\nf45Kl3JDcJ6xJBveuabjAel5VLocmPQpNjyWI06VsKHSZW0Ur+vjAOmGc1YtFBpiW92d5/qGhFvv\nwBidPfVJhrHTvVSKibq2SsWbVenS1z86u9IlWZ8kqVU9cpMddW1erEaioerE82t4DRUm5WKcsNb4\nsnSOSpdcmkapTH0iJgoM5XL9NaU78qxYU58SK0xkyU80xOvXcL1LV7o8//xmqpX6dEfXPCtdEg2V\nLolUmUy2UNdWLceoVWN1bbWaO+uN0FMHV1BoeI4b6sM1QLLhAkLsnImYud5mZT/WM0eriIjM14JN\numjLaBFZyrSmy/JiraVQLLCu1E3XeW9w/WKM2Fj9p++hhbDxEyZ75naejtVx+hretEdR49stiIqJ\nuqRLFMHoRP05jbEkYvWJgpgf0BE1JElq7qzpOqbk4za8WU7XysT8+jeu/lga25B0mfXWdo4ZD54b\nkUzXxxZGEDRMFUmlq2RS9W9mCxMJwnL9G+NEIsBrSKh4Oe+SSZfQQuOsE89YYg0JkESyRjpVn1CY\nK+nijsdpnJtUcyqzki5OVJ88cAC/4ZyRBdMQW8KxdHfUJzv6Apfe+m6YqkepXP/mvlz2sbYh6RK4\ndddgTESi4XlaDR1iDYkIH0uqYeqM7xr8hp91MXRmTT+bK+kSr/j1aw0YSyZef51h4My6pmzKZbDh\nBzhtIdHwfPZNNGvq01xJl9RkGqccr+uTmVU7YvHc+gv1vIBUVJ/uSDkRnYn6tnLFp1qpv4Zq1SOK\n6n8uzngGKpeudXEa/y5cxvQiz5lPLY2IXGs2mj21tJljS/No9yIRkTZIJBJKuiwj5XKZIAiINy7G\nISIiIiJL2oKtdBERWcqSySS1Wo0wDHFdvRFf6vL5mfUl4rM+yRcRERG5fJbWrb0y/1o4mQ9VuoiI\ntEEiMTNlQOu6LA/T0zPraMRU6SIiIiKyrKjSRUSkDZLJmW2DS6US6fTshSFlaVGli4iIiDSTtWbW\nwvvNHFuaR5UuIiJtcH7SRZa+XG5mF5mYki4iIiIiy8qCTbrs2LGDbdu28cgjj7Q7FBGRptP0ouUl\nl8uRSqVw9MmRiIiINIG1pqU3aZ4FO71IW0aLyFJ2ttJFSZflIZfLkUln2h2GiIiIiFxjCzbpIiKy\nlJ2tdNH0ouUhl8uRVdJFREREmqSVFSmqdGmuBTu9SERkKYvFYjiOo6TLMpHP51XpIiIiIrIMqdJF\nRKQNjDEkEglNL1omcrkc61etBZRkExERkaunSpfFQ5UuIiJtkkwmVemyTGhNFxEREZHlSZUuIiJt\nkkgklHRZBqy1Z6YXpQnaHYyIiIgsCRaIWlXp0pJRl68FW+miLaNFZKlLJpOaXrQMlMtlgiAgk8m2\nOxQRERERucYWbKWLtowWkaVOa7osD7lcDoBMOsNkm2MRERGRpcHamVurxpbmWbCVLiIiS53WdFke\nziZdshmt6SIiIiKy3CzYShcRkaUumUwyNDTU7jCkxc6vdBERERFpBhsZbNSiNV1aNO5ypaSLiEib\naHrR8pDP5wFIp1JtjkRkeXBSMXwvTlS1hKVqu8MREZFlTkkXEZE2UdJleZieniadTuO6brtDuTAL\nxjXYcJFN4jbQf2s3a+7rY8VNGUafPMSpbxxvd1RyNYzBzabwOjNEhSLGdwnGp7FBOGd3vyuNSfus\n/NBbSVw3SHL9Svy+LowxWGsJpoqUT05Q2vsq5tDTmMIQ0dT4Nb4oEZHms9ZgW7V7UYvGXa6UdBER\naROt6bI85PN5stmFvXPRde9fzbt+780c/Nwhdv3XlymNLIxkoOMaem/qYMWaGOvu6cU4BuNAYaRC\nemWSu35pC4mu2LkDfuYmTn79GM/91reZPjA155jGcYDo2lxAq7iGtff00HtjlhU3dtCxLkVgoTBV\nY/i1HEOv5XAOlYmSSQbuWUVmwwoym1aQ6veJDn6X4lPfwLYp4etm0yS33ETm1k3E1wxgPB/jewSF\nMrGVA/h9vRi3fslBG0UEEzmqw+OMfGUPnffeTGJVN/G+DpzYhV/KGmPwu9L4XWmyN68BHgQgnBon\nOHqA2pd2kn9qiLBUa+Uli4gsOcaYQ8D6Oe76L8AO4D8AHwA2AlPAV4CPW2tPXbMgF5AFm3R58MEH\n8X2f1atXs3r1agC2b9/O9u3b2xyZiEhzJJNJarUaQRDgeQv2z7FcpVwut6CTLm/52Fbu/fXbMI7h\njl/aytaf2cLrf7mfXf/pOShd2zfmrm9I98Z490e3sP4tPay5s5tY6vJ+N1a9cy3f+/bVvPapl/mH\nj+2CMvRcn+GGD67ihg+uYt19fRRPlzj6tVMc/dopjj95GnLXPgkTixm4zJkvAzdluWv7Ou7avo7+\nGzou2tfadwAziYc692ym66Ft5L/5JPmvPg5Hz92VvmGAnnuvJ9bfzcjTRxj6+j6ILl39ZOJxUrfd\nQv9dt3Hd6rWUjk9R2HeS/OunKO/bj0k69LzvLWTv2Upq83WzkiqXHN9x8Hs78Xs7SW/ZcFnHzsXt\n7MG9tYfVt97Dql+11EamKB44SXH/Saa/tZNobPiqzyEi0moWQ9SqShcuOe6bgPNLeG8Fvgz8NZAC\n7gB+E9gFdAO/D3wOeHOzY10MFuyr/Mcff1xbRovIkpZIJAAol8tktLPNknU26WJcByfuEVWCpoxb\npBuTLGCvMDHiZzze/9/fxvUPra1r9xIuN3/kRtY+MMChv3udzk2deEkPN+FRmapy+pkhTj55ktzh\n6QuO7XWnue79g3Rv7iLRmyTek+BNkyEkYpQmauSHyuSHK+z+wgne8rOb6L8xS9/mLN3XpXG9q99Y\n0fEdtvzMrTi9naR6fNY+MFh3f8f6DFs/cgNbP3IDNrJ86eMvcvrVHIefGqU8ObvqYeXtXay7bwVj\nJ8rs+cJJwuqFkzTWgpvy6FifJdmXJNmXICwFrLhjBf1395NamSI1mMJP+VSLAaOHCgzvzzFyIM/n\n/vg14j0+hekqufEqubEKQcXyvl/bwt0/to7Vt3fNTqJcwMX6OakUHe//ANl3v5fSJz5DfM1K+j5w\nD/EV55KDqz90J8VjExx55HmOf3bX7Ot0HMh0M/hPf4zkzTfixPw37kttWEXvO7YAENUCbGRx4/6s\nMRYCYwyx/i5i/V10vfVmVv3EewkLJSrHTlE+cpLKkZNMPfVdouLlVyU6vkNyIEl6ME1lokxlokyU\nb87vv4hIO1lrx87/3hjzEHDAWvvNM03vb7j/l4BnjDFrrLXLbh7wgk26iIgsdWeTLqVSSUmXJSyf\nz9PX14ffkeC9X/tFxp49yvA3DzLyrYMUDlbe6Ne9KcP6B/pZdU8vpVzA64+e5PDXh7ENhQaDt3fx\nwK/dyO0/vA5DxNR3Xmbs8WfJvbCvvqNjSF63kr5VHZhsD4VjUxSOThCVQmKxkB954nvpvanzgnFn\nN3Rx26/O/kBq/bYbsNYy8eo4R/7+EN/9w324eGz8nrWsuX8la98xQHplcl6PzQd+5/Z59btSN35o\n3SX7GMfwj373TgCiMOL07ile/9JpXvzTg7z7/9zKxncPkBlIvNF/4miBx//vV9j5J4dmVYGsv7+P\nO35yI1t+YC2x9KVfYsVSHqtu6WTVLTM/hwd/5aa6+6MoolwMSWVak7Awvs+mf/1jF7w/tbabLR97\nL5t+7u3s/40/YsUDtxBf0098TT+xlStw/Etf43z6LDRuOknqpo2kbtoIwMp/8gOUDx2n8MoBiq/s\np/jqwQsem93QwaoH1rL6nWvov3cQP1X/s6vmKgx9/QDjzx5j5FuHqIwVZ40RX9NHsnsFk6/lqAxd\nOLkpIsubtcx6jdDMsefLGOMDPw584iLdugALTF5VYIvU4vufUERkiUgmZ96YajHdpe386UVu0qf/\n/k30378Jay2nvnmcgceOc/sv3EbH2nTdcW/75RuZOl5k918e5Ru/txc/4/P9//Vurn/PwHlVDA7d\n77id7nfcTnV4gkOf+CtSG1fRee/NpDevxU3F68a0kaV4cprpfeMXTbhcijGGnpt76bm5l1t+8XZc\n1+AmFv9LCsd1WHVHN6vu6Oad/2rLnH2616X54T+6h3d97Cb+7qefYeylCd664yZu/4nr6NnU3Glk\njuOQylx95c/VinUlufkP/mW7w2gb47okr19P8vr1sO3d2DDC/K/dmEwHbtwDZ+b3sTZV5qbt1190\nrFg2ztqHbmbtQzdjI8v03mFO/NW3oDjF4Pe9icwtG/C7Z55HNowYe2ofx/5yJ1MvHr3ouCIibfQh\noBP41Fx3GmPiwG8Df2GtzV/LwBaKxf8KSURkkTqbdNFiukuXtfaCa7oYY1h1/1pW3b92jiNndK5J\ncd+v3cRbfvF6bMRFqydi/d3c+Ls/f9F4jGNIr+kkvebKEy6zzptemNNGWq3v+iwfefQBosiS7Ixd\n+gBZMozrcONPXn2VlnEMnTcP0PmbP3DB86y4/0ZW3H8j06+e5IVf+xxRCOl13SQGsiT6s3zwKIQ9\nvRSnA3IjFXIjFfZ+5TTpnhiVfEA5F9TtSubFHeJZHxMzVKxl49v7yPTGyKyYSdDG0x5r7+gilnSJ\npTz8pEv36vlVronItdWs3YueKX2HneWddW1FO7sK7yJ+GnjMWnu68Q5jjAd8hpkql1+4ijAXNSVd\nRETa5Pw1XWRpKpfLhGF41Qvp+kn9d70QxbPLM+Ek117HllU88Og/m9W++RLHWWupFAKmh8r0rkvj\n+u2vnBKRheUtyXt5S/LeurYjtSP8+/HfvOSxxph1wHuB75/jvrMJl7XAu5drlQso6SIi0jaqdFn6\npqdn1mNYyLsXicjSZYwhkfFJtGhdIBFpn8jSst2L5rFx3Vk/DQwBXzy/8byEy0bgXdbaiWbGt9gs\n2HT3jh072LZtG4888ki7QxERaQnf93EcR5UuS1g+P/OhjhZKFhERkaXEzCww9xHgT6210XntHvC3\nwF3APwZ8Y8zAmduyzAAv2EqXhx9+WFtGi8iSZowhmUyq0mUJy+VygCpdREREpMlauHsR8xv3vcxM\nHfpkQ/tq4HvPfP3imX/NmVHfBXzj6gNcXBZs0kVEZDlIJBKqdFnCcrkcxhhVuoiIiMiSYq19HHDn\naD8yV/typqSLiEgbqdJlacvlcqTTaRxnwc7mFRERkUXI0pzdiy40tjSPXgWKiLSRKl2WtgttFy0i\nIiIiy4MqXURE2kiVLktbPp9X0kVERESaLrKmhbsXqdKlmVTpIiLSRqp0gdrhVwhP7m53GC2Ry+W0\nnouIiIjIMrZgK1127NhBZ2cn27dvZ/v27e0OR0SkJZLJJENDQ5T2HaI2MoYNwjO3gOqxE/Q89F78\n/r52h3lJYbVK4bsv0XHvPfPqb6MAe+xpon2PUnzuZUrjK+n4oZ8m+aa3MrMD4cJQna7g+AYvGbui\n43O5HP39/U2OSkRERJY7G83cWjW2NM+CTbpoy2gRWQ7OVrqMf+Er5He+VHef44XkvvktUrdsIXPP\nnaTvug23Y+6pKjYMiCbHCMdHKB86wuSzh3F7VxLVAmwtIBibwFYrpK5fi5NK4KSShMUKFhcnlSQq\nVwlLFYLjB4h3xYj1dWN8H+P7TB0okLxlC6mNgyRWduB1JAEoHR1nYudBJp49yNRLB7njPd+g9NoN\neFvfg3fTA5h016w4w4mTRLs+DSefhPLkmdZOomKVyU/9IYUnH6fzB/8xses2Ne0xtmFA9cghiCJs\npYwtlymfmmL05TzuipXYICQKIkonpigN5TCuQ3WsQGW0wNAxnyDRw4ZtG1hz/yCr7ltJvGP+CRhN\nLxIRERFZ3hZs0kVEZDl4Y02X7gt0CCOKu16muOtl+BMD6V6SN20mqgVE5Qq2UiRZ2oWxVbAWgGrZ\nZ/JEH3DwjWGMiXA9S+XQsTfagsAhDOr/G0hlC9hklfMnPJ06PEjlbw+/8b2T8DE2xNaCc+M7IQDR\n0D6qQ/uoPvFHmLV3UUveRBikqJ04QfX4CbzoECtuOX7Bx6N2+ACjn/hNEne+ieSdd0MxRzh6mmD0\nFNV9r2HDCBwHjMEAUTUgvuUOYjdswV+3CW/Nepx4gmAqR2nvPkp791N6+RX8wsG68+Sn0hzZex2w\n7402a+dard+nMFxlzx+/xp4/fg3jGtyBDjpv6sWJu9QKIbViQO3gaXrXJ/FTLo7nYFwDDuR6NL1I\nREREWsC2bvcitKZLUynpIiLSRolEYv4L6VpLdWSa6sjzbzQZJyK1ptKi6OYWlWsYE3HRWUA2orbv\nRcYPna5r9nrmd47qnu8Q7n26fsjQzLwICM98DxAZKi+/QOXlF2YaHYfCdCdR+VxdrHEi/I75nfdS\nbGgZ2Vfg+Cv16/AMdpeZyBfr2gIvIPxASEdHk04uIiIiIouOki4iIm2UTCYJgoAg0uTZpogionII\nsypWrr1afKYSSJUuIiIi0mwRLdy9aAG8jlpKtHuRiEgbJZMz66NUo7DNkUizBbEagNZ0EREREVnG\nVOkiItJGiUQCgEoU4bc5FmmuWlxJFxEREWkNa99Yzq8lY0vzLNhKlx07drBt2zYeeeSRdociItIy\nZ5MuZVW6LDm1WIAxhnQ63e5QRERERKRNFmyli7aMFpHl4Oz0okoUonqIpSWI10in0zjOgv18Q0RE\nRBapyNK6NV1U6dJUeiUoItJG5yddZGmpxQItoisiIiKyzC3YShcRkeXg7PQiLaS79NRiNXoy89wj\nW0RERORytHBNF1Tp0lSqdBERaSPf93Ech3KopMtSE8RrqnQRERERWeZU6SIi0kbGGJLJpKYXLUG1\nWEA2o5V6REREpPmsNdgWrenSqnGXK1W6iIi0mZIuS1NNlS4iIiIiy96CTbpoy2gRWS4SiYSSLkuM\nxRKo0kVERERaJOLsDkYtuLX74paYBTu9SFtGi8hykUgkqEwX2x2GNFHoh1jHqtJFREREZJlbsEkX\nEZHlYmZ6kT5TWEpqsRoA2awqXURERKT5bAt3L2rZrkjL1IKdXiQislwkk0kq2r1oSQliAYAqXURE\nRESWOVW6iIi0mdZ0WXpq8TOVLlrTRURERFrAWkOk3YsWBVW6iIi0mXYvWnpqsQAspFKpdociIiIi\nIm2kShcRkTY7V+miTxWWilq8hl/1cBx9tiEiIiLNpzVdFo8Fm3R58MEH8X2f1atXs3r1agC2b9/O\n9u3b2xyZiEhz+b5POQwYcur/JDuOnZWGqbkR5ydnjBNRCevf2NesYdprqJwxFtet/x80xBKa+jMk\nHIvfcM6xeI1qQyQGy/mHGifkeKX+yKDqMd0wVix0KBUSdW2lik+5oYzVRA5uwyVEoaExMWWj2W1F\np/46jbEk7Ow+E8lK/VhA4yOeCxyKDXmTStylFrj143WUCbxz58115yAyVKtVvDuVpboAACAASURB\nVMSC/a9WRERERFpswb4SfPzxx7VltIgsC5VKhQj4dDa4dOdkdXZbeY7FWleUriKihizDuvF5HfXY\n8cHZjY0ZnGIWds9jnZN5PBQXlJpjJ6iwPtFD0sLNJ67iJPXGLtB+eug0Gzs2Nu08IiIiIqBKl8Vk\nwSZdRESWiwceeIDOF/YSvXoQc16lheOFsytdKj6NlS6d/RP1faoe08Pd9QfOVekSGsKw/r+BRKqE\nf2YR2LNGT62gWonVDzdHpcuW+3fX9QmqHtMneuvaYp0FujcN17WVxjKUhzvrx/dCXLc+eTLvSpfJ\ndF2bMRGJTH0SqphPcurQqvqxmF3pMjWdJF+oX5dlujS70mVFR5nYeZUuJzadJN+TZ+XASkRERERk\n+VLSRUSkzbLZLBvTneSj+goTJzI0zP6hGjrUJRQs9DYkJ6rGEm9IChgT4VKfdAkCh7ChXyoy1KdX\nwFZ8KqX4rPEaky5rGpI1AZbGGpmEG7EiXa5rK+bjFEx9bI4TzU66YKBhmtBMkqRhSlBDIsY4hlTD\n+PnIUG64JmtnJ12iXIpour6SqFLwoNb4uHnE/XPxpqfSTPVNEYs1PpoiIiIiV8/Swt2LtM5gU2mF\nPxERkSbzqx5hLCQMtSuViIiIyHKmpIuIiEiT+dWZxWzy+XybIxEREZGlyLb4Js2jpIuIiEiTeRUl\nXURERERkASddduzYwbZt23jkkUfaHYqIiMhl8aszS6bl8rk2RyIiIiJLUWRbe5PmWbAL6T788MPa\nMlpERBYlX5UuIiIiIsICTrqIiIgsVk7oYEKjShcRERFpCYtp2S5D2r2ouRbs9CIREZHFymDwq74q\nXURERESWOVW6iIiItIBf8VXpIiIiIi1hLUQtHFuaR5UuIiIiLeBVPfI5VbqIiIiILGeqdBEREWkB\nVbqIiIhIq9gzt1aNLc2zYCtdtGW0iIgsZn7VI19QpYuIiIjIcrZgK120ZbSIiCxmXsVnMjfa7jBE\nRERkCYpauKZLpFKXplqwlS4iIiKLmV/1KZVKBEHQ7lBEREREpE2UdBEREWkBvzJTTKpto0VERKQV\nbItu0lxKuoiIiLSAX/UByOW0mK6IiIjIcrVg13QRERFZzDxVuoiIiEiLaE2XxUNJFxERWRqMwVu5\nhkTCIyhZgrGJtoajShcRERERWbBJlx07dtDZ2cn27dvZvn17u8MREWk7r38F8b5BUltvxtYConKV\nqFTAHt+Jn/YIx0ewpULLzm88h3h/B4mBDiojUwTTRcJ8Ze6+XYMkbn43q370doJcQPX4CWonTmAm\nXgSOX/w8yRTpD36I1N1vJhofIRw9TTh2mvILz4Afx8QTgAFjCIaHSN77TuI33IK39jqceOKNcWoj\nY5T27qf40i7CV7+FacIs5e4bO1m9oYueW/twEy7VQkitGDDx3AkGtmSJd/g4rsG4Bhx48cRLSrqI\niIhI07Vy/RUVujTXgk26aMtoERHwB/rJvPVNZO65k9ja1Rhj5uj1Q298FZWLVI8eI7HrAE7Xipnk\nTC2gNjKOLZdI3XgdbiqJk0oQhRE4Lsb1iMoVolKVyuGDxLoS+H3dOL6P8X06R8okb9pIYlUvxj23\nFJgNInJ7TzLx7CEmn3udoOaRuOd9eLe8F2fVTW/EGgfSd9155pgK0eEnsQcew558jrr/1h2H1H3v\npuODH8LJZAFwO7rxr9sMQPr9P3x5j11fL35fLx3veAvws9hajahSxpbLdI1N03VkCrejm6gWYoOI\n4skpSqemcTyHyliBymiB0gtFMlu62PwTt7DmgUEyg6kLnO22OVv/9l//nZIuIiIiIsvYgk26iIgs\nJwM/86P0/dj3Y4MAG4TYICCYmCJ96004ifi8x3ESKRKbbySx+cYrC+SBO2Y1XSjNYDyHjq1r6Ni6\nhnUfeTth4UfxMumLDm+8OO7174Pr34fNnyba/xg88WViq7ro/KmP4Q+uubK458H4Pq7vQyaLt6KP\n1Dweonuu8pzZbFZJFxEREWk6S+vWdFGlS3Mp6SIisgB4HVnoyLY7jCtmjHPJhMusYzIrce/4KTI3\n/CDGVHFSvS2Krn2y2awW0hURERFZxpR0ERGRtnLTizfZdCmZTIaRkZF2hyEiIiJLjNZ0WTycS3cR\nERGRK6HpRSIiIiLLmypdREREWkRJFxEREWmFiNat6dKqcZerBVvpsmPHDrZt28YjjzzS7lBERESu\nSCaToVQqEQRBu0MRERERkTZYsJUu2jJaREQWu46ODgDy+TxdXV1tjkZERESWCgvYFi2+ojVdmmvB\nVrqIiIgsdplMBuCCU4ystdhWvWKaJxsFRNNHiE4/c8lYFkK8IiIiIovJgq10ERERWeyy2ZmdmRqT\nLtZamHiVcNdfUHvlRdwb34+7/i6cNbdjEpn6vmFA6fUDBNMFvJ4VOL6HiflEtRrGWmwUEpUqRKUy\n4ZHd+B0exlhspQSVErkX9+P2r8d4PjgOOJCOfQO/NwHFESiNgRPidFSw8RXY7lswXVuxHVvA7YL8\nCHbiEHbiEKVXDlAZTeBv2Io3uBZ3cB3eyrW4KwYwzsU/x7E2glIOW5jAFiZn/p18Du9tv4ox+gxI\nFhdbKRNOjRPlJoimJ4gmx2D6AKnv+5V2hyYiy4SldWuv6OOV5lLSRUREpEUaky62NIo98hj28Bdg\n+hC24kK5g/ClLxC+9AUwDlHHJoJogCj0CMeGiCZGyY+lKEzUb63tuCGOW/+yqHv1KFGyWtdWG8tS\nPTV5rsFEdN13CMbmCLgyCqefxJ5+EltxiaZS9fcX00S5Liq7dlLZtROAsOZCqgt/9Trc3hV4vX0Y\np4aX9rHFaaLJEaKpEczYbgz1a9sktp7APvkNWP8jsGYbxu+Y70MrLVTad4D4ujU48Xi7Q7nmaqPD\nRBNjWGuwlQq2WsUOvYCfqkEtB4VRbH6U3MtFbHBestBYum89SvTaWszmH8QY076LEBGRBUVJFxER\nkRaJx+P4vk8ul8NWpoj+/nvAXuRzKRsRnjpCZXz82gXZBFEuR2Xvy298H88W8WJhXR8vFc49qbl4\nFPvqf4TX/z/s4PdgNmzHZG+4+PkqBWwQ4KY7mxF+S0WVCvm/+s+Y6gTe4FpMugsn000tbzEr1uMk\nMxjXAc/FlorE1q2dd7LDhiGFfacY/spuwmKN6z/60BW92Y8qFcp7X6O0ZzelPXsoHp4ie/+99P34\nh/E6MpceoAmCY3sh0YHXt+qi/cq7v42JZzGuC8ZgowhjK8RuuPOix9lqhSg/iYknwXUxjoclwo4f\nJxraT3R6P9HQPqZ3j1CdTtQd27lhCDdTaRiwZ9Y5jAH77O/ByB649+MYL3nxmKIQqlNEo4exkcUd\n2IKJp+buay3VU0MEk9NE+Tzh5BTB5DTDX30dElmiWkhYrBCVKhSGA9762MdxE/5Fzy8ii5uldRUp\nqnRpLiVdREREWqijo4N8Pg/YiydclruwDMf+Fnv8c9j++zFdW6FrK3TeDE6S4OQhgsOvERzeiz21\nk1h6jLBnPaZnA6b3OsiuxV13Bya5MBYsDqamyH31a0x//UmS8RMksmVqJ88lpsaP9lEr1b+5T3bk\ncRyLiSdwMhlMKgPFCeK33oWTSuMkU0QmzviuCYqnSuT2HCUqVYlCQxD4FI9PsfEX30v2xsFLxheV\nS1QPHWDys5+nfOgIhOcnyWJMP7mT/LO76f3Q++n6Rw/g+K15A2+rFYpPPU7tm5/C88s4a27B23Q3\n7qa7cfo3QKVAdPwlwiMvEB39LlO7i2DPZe+ME9G5cZjqM6sxg1txBrdiBm+F9ADBicMERw9QO7Kf\n2sHdONWJunO78SpestYQ0NVXW9nDX8ZO7Me5///BdK4/71qPEB3/S9jzNagBlSnAUptKUh07k9yK\nJXGyveTHOynleglrDsF0gXA6j+sUcd36vyHF0Q6icLKurVZO8fw/+VNu+Q8fIn3diqu+Hlla8o9/\nHouL27sSN5vF6egAxyeshoRTeYKpPLXRSQq7XqHzbbfjdXXgdXfidXXgdmQuOZVURGZbsEmXBx98\nEN/3Wb16NatXrwZg+/btbN++vc2RiYiIzF8mk7ngQroyBxvC0NewQ18DIJiOk9uzHsJzn7t56TIm\nbbHjh7Hjh2E/lMfTBFE/sa1vJbb5Nvzrb8FJps8Nay22lCcafQV34LaZiocWqA0NM/H5L1B49jk4\nu1X4Zc7SsZUyYaUMYyM4fkTpqa+8cV+17HNq39o5j5v67mFe+Cd/TP/7b+W6n3sXiYFzlUBhoUDx\n+eepHthLcPIYtRPHwVrCmlOXxDhfVCwx8unPMvnlb9L7I99D531vnrtfbpjoyPNE+7+Eye2cqfBw\nfXB8goJHZFfj3fEhvPW3YLoGMMZgq1WKT3+F4hNfIMpNEc/WwLNEx/ZQPbYHvv4pnKSDodKwPcfs\nChMAO3UCO3WCaO+XCCse+RO9dR/VGieC2AUe8FaYOkj4Dz+FecsPQnqCqPAk1A7BpI+Tv0hip1oi\nGjtO+ViZwkj9VMHLeR4VDo7w/E9/ks3/6gOsfP/WK7uGZSSqhpQnq6T6W/N3YSGwtTLhC58m9+gT\nBMX6J9PkcCe27u+AJZaoUdr1cl2/5OYNrP/3WrdooYho3Zou+oiouRZs0uXxxx/XltEiIrLoZbNZ\nJV2ugo2cuoTLxUT5AuXvfIXyd74CxuD2rMDt6p5JtkyOYislMg++DlOrcPp/ENPzvRh/oOF8EeHp\nw5Se/BxOzyCEAbZWJZwqUJ0O6PzQj+KvWjlrGk84PUXpuaeZeuxLVMcKTbv+KzH8pd1MPfUCK9/c\niePUqA2NEOULeLHqrGlf81EbGWPsf/wxU3/zN3g93Xi9PbhdGRLmVdzgFHb8KADGC/A6LQRF3li+\np5AgGA0Jjv7nmT4dK7CZNVQOnyYq5C9+4rACzuUXudtW1txfDjdHVHsYJi/dtRXCUo1X/93nmXrh\nKNf/8oOabnSe4miZoe+Oc/qFcU4/P8bI80P0eKOs/eAmVr1nAysfWI+fvbI1jWr5Gvv/9DlMqYjj\nWsJilaBYxZ4+yPpf+CCdb7rpgsfaMKKWr+Bn4jPTDpvAWos9/C2Cp/4A8kPAxafwXWosEbl8Czbp\nIiIishRks1mGhobaHcbyYy3R+BB28vSsu0xwAHvyd7AnfxeybyXMvYlospfw+OsEJ17HlgoE5fqy\niKDqUhjrZHrnb+L1dJO89WaSN2/GsWUqLz1LZe9uiCKiqgu0/82t55SpHRpu6pjh5CTh5CSVg4dw\n/IDULccuO7dhp0epDU0Tla9l2cnydvoLz8Ph51j1I2/H7ejE7erCyWYwiSTGRthymahSojY8Cq5P\n6tbbMJ47r7FtrUb1xAlOPrqHlQ/dQ3LdwAX7RkFE4dAo2Rv6Lx7vztPs+8xB4r0polpEWIkoHh3H\n1KokV8SJKiFhNWRkf4mtP3sLW35yy0XHi4KI3KFJJveOM7l3jIlXRnnur6fq+vheiFlpOf7F/Rz/\n4n6M59B31wr6b++ke2s/NrJgLeXhHNYaMjeuwkvHcdMxggqMvZZn+IVRTj8zzNiecQZ6x0mn6tcB\nWjE4wonf/kPGNq6l+31vo/O+u3ASM4mdwqkCx756nKNfPEi0dw8ATsLDS8YIjQ+pDt78e++n8/ru\ni15rIzt5nOCp38ce23lZx8nioN2LFg8lXURERFook8lw4MCBdochc7KQe5rSo2MQzO9NJkAwPkHu\nyacoPv0k8YY3ViILjeNGpKIjTD5y+I02YyLchqqnWsWnUkiC6xBb2U9s9SA2kcXp6sPt7oIwwkYR\nwfGDuNVxwtEhaqdOQRSxf9cm9vz3g/S/4zrWfO9NDLxzA17Sx1pL8cBpxp/ay8jXX+HQ05aeOwcZ\nfOd6Bt+1gY7NPRhjqIyVOPHlAxx7bD/HnhxmOle/3lEiXiPVsP7O5HSCx37pBY7tnOZtv3ErmcH6\nRYhtZDn8xGme+edfhMq5Y2eKNS6+do8NIiZfPIZz9GVGv3CuPQwdgqA+qToy2sHUdJr5Kh88xqk/\n/CuG/uxzDNVuZXwkw/iemcXTHSdkcOVMv6gcUC0H1GouuRw8+t7P0HtHPxt/cDPrH9pErDN+5joj\nahM58i+8RvnYaWytRm1kktrIBD293yHbNzrv2EQuhzFmFfA7wAeAFLAP+Clr7Xfn6PuHwM8Bv2yt\n/f1rGugCoKSLiIhIC2WzWaanp9sdhojI/IQR1ROnqZ44TSmfoFxs2M1pxSSJ9Oxkow0tQ18/xNDX\nD+GmfPo2u7hRnurQTFVJFBmgn/EXTjP+wmlefvgZEgNpnJhH8fg09uw0QnsZb08s7PnUAfZ+5jB3\n//Mt3PPLW6iVQnb/2UFe+OMDTB7Ks3YgwFuA73iiYpkDT5WxtnrpzmeMvTjM2IvDvPIfv8qGu6t4\nXkBtZAIbhBgT4TRMx7Odlz+dUBaPdu5eZIzpAp4Cvgq8HxgFbgAm5uj7IeAtwIkmh7loLMA/QSIi\nIktHNpulXC5TqwXMv5ZCRGTxCos1aieGCC+xJk/pdKEp7xqDYsgzv7OHnf9pL9VyRBQs7ckRnlMm\nGhlm/ukakab7OHDUWvsz57UdaexkjFkN/GdmEjNfvEaxLTja80tERKSFstksAPlCexdXFRFZ6iqF\nYMknXETOilp8u4SHgOeMMX9tjBkyxnzXGHN+AgYzs+L8nwG/a6199equdnFbsEmXHTt2sG3bNh55\n5JF2hyIiInLFziZdcrlL7NQiIiIisjhsBP4Z8BrwPuC/Ab9vjPmJ8/p8HKhaa/+gDfEtKAt2etHD\nDz+sLaNFRGTRy2QyAOTzqnQRERGRZrHYtq3qggPstNb+2zPfv2SM2Qr8PPDnxpi7gX8B3NmiABeV\nBZt0ERERWQreqHTJq9JFREREFpb9PMV+nq5rq1K81GGngMYpQ68CHz7z9X1AH3BsZpYRAC7we8aY\nX7bWbryamBcbJV1ERERaKB6PE4vFyKnSRURERJpknmuvXNJG3s5G3l7XNsohPsuvX+ywp4AbG9pu\n5Nxiun8GPN5w/5fPtH/yioNdpJR0ERERabFsNqvpRSIiIrJUPAw8ZYz518BfM7Ml9M8APwtgrZ2g\nYftoY0wNOG2t3XeNY227BbuQroiIyFKRzWa1kK6IiIg0jW3x7aLntvY54EPAdmA38G+Af2mt/ctL\nhLwsqdJFRESkxTKZzMz0onS7IxERERG5etbaLwJfvIz+y2odl/Mt2EoXbRktIiJLRTab1UK6IiIi\n0jRRi2/SPAu20kVbRouIyFKRzWbZv09ruoiIiIgsNws26SIiIrJUqNJFREREmstiTYuWSbHLdvmV\nlliw04tERESWimw2S7lcoRaZdociIiIiIteQKl1ERERaLJPJAJCvenQnam2ORkRERBa7Vq69ojVd\nmkuVLiIiIi2WzWYByFX1WYeIiIjIcqJXfyIiIi12NumSr+m/XREREbl6qnRZPBZspYu2jBYRkaXi\nXKWL3+ZIRERERORaWrAfuWnLaBERWSpisRjxWEyVLiIiItIkFkurdhnS7kXNpFd/IiIi10Amk9aa\nLiIiItIUml60eOjVn4iIyDWQTqc4VYhzdDr5RputuQTlWF2/oOpTDetn/xYNlLywrs1xIhy3/pOo\nUuTgB259G6bh8ypDkE/UB+dYHFt/nK062EJ9v0rFp9IQWxg5WFu/FbYfuHimvs2tRBinPpLYZIq6\nbnYmvvMFuQTFhmtyqz7xYrw+tqpP0BAbxmCi+nOmJlK4Yf14xaoP5x9rIWg4Zxg5lEz9WC7gN2wD\nHmKo/0lBMnSJNSTcpoGg4fGIW0NDEyaqv6aaNYzG63fAsqEh9BqeC35AeY543YafVcTszzNrjsXa\n+lbPUPezcoBKqf65azwX1214PIoxag3XHgQuYcN1xWoebsNzxkRm1nOmEDqcPzveWJhueC6EVZdS\nw3PBWIMT1F+nYzy8hqdMMXSpNTwHcxUf323oF7hw/jUYyOeTdX1sNYChhpPmPEyuvl9QjFNrmHo4\njaHY8DvvOha34fHIxwKisOFnn6zWH+cF1D9CYDA4Dc/dgJmf/fkqXkglVn8NZcfOGm8iWaHS8LgR\nr+GcN14UGaYzxbou1jLrCViNe7OuPYwFhPH6OIpOQLlS/7jVQmb9Pcp3FHHP+1tpLZSDhmt3o1mx\n+X4N2/C7FkWGIKx/O5rLFik2/K5NpctUE/U/B+uFeG79sYXOAtaeex45TshkqlwfW+BSaFgRwqbK\nxPz6x8gw+29sVPOYavj9mIycWX+jcl505ofxxhlmxQoQr5VZVa0Si8Vm3SciF6aki4iIyDXwrvvf\nzqc+fYLdo92Xf7AP9JYv2Y1yGubq1riC20s3XH4MlyPf+JbsAk6svLLxc8DwlR3K47dd4YH8/+3d\ne5xcZZ3n8c/v1KWv6U7fks49ISFEEy4hECFAIgmIGo0XBAzOzsC4M7KjzGx2X4M7zIjMgrOv0XGi\nO8O4vHBlREIElRFWEcQBZwQBBRwEQwIJCblArt2dvlRfquo8+0dVQtepviZVqaqu75vXga5znnrq\nOfXU0/T51e95DlQGd4QgUZVdLrh0T08d9GQ/lerApY+rIOtqKPgYYFb7KA0dTjg7whIaoljtUN+f\nBi6oicCumSfYjiH01Zz4c7dPyl07hvLWGD+nvxmiHc+dxOs2jGHMV3WNsbIh1pMKxIMwsj+T1b1A\n7xD1BT44i94eWzPmHhlbuRwaY8vYmddWDGPV7/Jb/5HG1BYUHM5j+f8LQMdupu/fz+zZs0+6aXLy\nXB6nF+Vv2lJ5UtBFRETkFFi29EymvfbXGftcPESiPfNiM9EXYeBodca+WFc1vZ2Z+4bKdJk0pYNI\n4JvZ3qM1uMHfaJuj5ZxdmY3zHF5N4FvZAQ8X+Oa+/2gl/UcyLyyTiRAukLUQqe4jHMm8aA9VDmRn\nusw/OLZMl+1TMuuqGqCiLvNCsL+zikQgMwdzWOAb6OoL3iRU15+xL/bU3OxMl8A36MmER29HbWY7\nwkkigW+zk4kQycDaPVWTu4lWZ75m5/4GEv2ZwamKmlhGZgA4LJDBEu8Pc2jP1Ix9Lmkkk4HXrIlR\nW58Z6QlF4oQC/eInLOObdoB4XyRrXzgaz8x0CSdoOO1ARhkLJwnVBt6PnijxjszPbqI/QjKQ2RGt\n7iMUDXxzH0lkZ7ocqCMj08V8amdkBqGSAyF6D9Zn1uU5vEBmgBeJE64MZE+01RKPZX6OaqcfIVKd\nOa5iB+qyMl3qTt+fUcZFE7AwEBTpCmO7Mz9Hie4K4oEx33lgMrH2zLEWiiQIBbIPujtq8AOZW729\nme0PhRNMn5sZejBzeIFskkQ8TDzw3P5YlP6+zM9p7eQuKgLZNG++PpP+wHPrGzqzMl2OBILOQ2a6\nxMN0B7IzKqIJKgOZLl090TFlurQ29WRluhwM/N4Nh3ymNnVn7ItE4tTVZY6hVKZL5ljr6KjJynJq\nauyiKvC7YXJzO+FwZv+9/NKCrEyX5qbOjDKJRIienszPR21dF1OmZn7uh8p0aV74FrXNmfV17Goh\nGciw7GqbBC7zl3G4IhiVg4pZ02ltPcFguUgZU9BFRETkFIhGo8wOBAr8/hCJ3sBFgw99wQsrBz2B\n6S5eCLzA1UqD5xMNXEj14HBk/jE9vTbwrabn4wUCEa4/hB+4eOnrC9EbaFvSt6xyFeEk4cAFbrgi\nnnVBUDk5NmrQJY6jO3BO4WicykAQo68vzEDgQsIs+yKktiFGuDGzH7qjcRj0/jpH1lSlhA89gfMM\nARWB+hO4rASCmlCSysAUjTYgHpjeUWUOzzKDLp6X+X4PmMMFLjT9pJFIZO6riQ5QH2hvGAgHglDJ\nrOlnMOBb9pQxl9kzHjAlcOFt4QThwGcr4RwDscx+iSe9rOlbFZFE1kWeF83+zHSG/IyLdPN86gOf\nhYQXpifwOTXPJxScrhNNEA4EKbtDSQYC70h9RZxoVeZrdIeTuMHVmaOxNvNz5SoTuKmZF/JURPDa\nM8897sNA4PdAG47uwHsUDiWzpmBVDoSzgi49vVEG91Y44jEtsxUY2VOV4kB/4DPZmwjRF5geVu9b\nVtJXZ28FvT2BwEN1b1bQJd6dGTwYKujS1x/GDwQxKiviVFdl9lWysxICY95LZAddaiOOcDgz6NLZ\nlhnQioST1FVkfmai0X4aKwJBxKSXNdaSXdX4nZlBnPqKODWBdjQnQkQCGSY1R2uygi6TA+MqHg/h\ndWW+b/XhOC3xzH43s0DQFqZFEtQFxke152f9jqpKeIFAqyMazp5eVBmp1NSiIuLI39orynPJLd0y\nWkREREREREQkD4o200W3jBYRERERERHJ5iwwKyyXdR//l+RC0Wa6iIiIiIiIiIiUsqLNdBERERER\nERGRbKn7zOUnHSVfa8WUK2W6iIiIiIiIiIjkgTJdREREREREREqI7l5UOpTpIiIiIiIiIiKSB0Wb\n6XL55ZcTiUSYMWMGM2bMAGD9+vWsX7++wC0TERERERERKRyX/idfdUvuFG3Q5fHHH9cto0VERERE\nRESkZBVt0EVEREREREREsvnkb00X3b0ot7Smi4iIiIiIiIhIHijTRURERERERKSEOBy+1nQpCcp0\nERERERERERHJA2W6iIiIiIiIiJQQZ6ktL3Uf/5fkQtFmumzYsIF169axefPmQjdFRERERERERGTc\nijbTZePGjbpltIiIiIiIjIkXMpKJQrdC5NTw87imS77qLVdFm+mSL7FYjBdffJFYLFbopoiIiIiI\nTEhWPQmLhEYs40WMcE0kJ6+36Jq53PDqOq575grO+cP5RGqK9rvlk5ZMhKCiGryyu5QTKUkT97fR\nMLZu3cqyZctYuXIl9fX1GcfWr1/P+vXrC9QyEREREZHCs0iE6IxWIpFJ1NVNIdw4GZd04Pv4+7cT\nThwheegAbmAg43m1pzUy88NnMHPtIqpn1JHs7afj19tpe2orR57eBkBF/gAumgAAHepJREFUUxWt\nq+YwbfU8pq6YSag6QvvLB9n7yHb2PrqDvjd6x9XWmZdMYeWXltJ6bhMAdbNrmPaN5az+26X8bvMu\ntn7l5+Anc/G25NzUBY62g1EGjg6MXhiobK5i7sdO57QrFzJ5USMumSR+pJOBA210vrCFgbcO4eJx\n4oc6iB9uz3PrpdBc+p981S25U3ZBl2M0fUlERESIzqTiwnfhH20luec1/Pb9oz8n5FF5+nyqzpiP\n53oYePU/SB4cw/NECsD3PXqTzbRccS6hunpC9fV41VWAI1RTg9/fi+vrY+DtgzjC1CxfTri5ERsl\ni8L5PomDBxnYvZvuH7xK60dX0HjBAszeWdkzVFVB08rFNK1czNzYADMefZWZHz0L8zJX/2w8ayqN\nZ03lzM+vYMf3Xmfr/TupaK7GH/BJDiTpf/soYb+PaF0EfyBJsj9J316fc/7TQt7zhWUZr3lMRV2E\ncz9zOgvXTqXj1SMc3dZGx9YjtG85AgdHv6CMNtdRc850GpZMAd/hnKNvfxe+b0x61wzCNRWEaiqI\ntcU5/Hofh1/p4MCvDtLfMXIAJVRXy+TV76Hhsgt5d2szfsLnwK8Psudne9j7013QkxksMYPaaVGW\n3X4p01bOwou80y8WChGd0kB0SgO1Z87P7B/nSO78Nf5z/whHd496viKSP2UbdBEREZEJLlqJN6kW\nv/soxN+5EHI2Ca/pQ1jDR6BmKZWL37lg83uOktj5MrEnH8Zrmg6JBC4RxzpjVNVD/UeuomrRQryq\nynfqc9cSf3MHvc89Recvfgnxwn9DmIiHsYYWLB7D7+7JWb1ebQ3hpkbCDbUkq0KE4vsh0Tf2Cswj\n1DyFxNtdkCzO7IOJpv7c+cy99SNEG2tGLFc1znrN84i0thJpbWXx8uWjlo9UR5n18bNHrtOMBVcv\nZMHVC8fZmuHVzqyjdmYdMy+fB6SCEWc/fZBDr3Rw4MU29r/YRuf2NgAmL25h+pp5TF8zj7ozmoYM\n5gxlbvq/zne0v9bBjrueIZSMEfIcidgAydgAfk8fLdddRtMVF+BF3rkE88Ie0y5sZdqFrZz3+aUc\nfn4fNTMmEaqOEq6KEKqKYKHxTyMyM8KnLcfN+b/4L/+A5PP/PL6xKkXPAX4e65bcUdBFREREJgbP\nIzJ/MdGFZxFdeBah1lmY5+Gcw/V04rcfItn2DLZgLV5N49BV1NQTXXIx0SUXj/llzYzo3AVE5y6g\nYulFtD3wAP1v7MrRSY1f3ZkzOe2zl1O3ZCYAyZ4e4gcOEfvVs8R3byfx9l5c39gvvqyygto1q2i+\neh1eRUXGMZcYwH97C/6bL+C/+XPwtwee7fAa6gkvvoLwnMWEZi3ColUkO9ro+deH6H3mSYZf+dQo\n6T/94xEs+WGo68bFngF3ii94DeZ++hLmXnfRCV20T1RmxqyLpzLr4qnH98UOxDiypZ1Zl844ubo9\no3FRA41f/eAJPd+LhpmyYs5JtSGrTaEIoXM+ibdgDclnvwFbt+a0fhEZXdEGXTZs2EB9fb3WWRER\nESk3k06H+sXY5CVEJy+m/gOTSe7eQXzXVhJvboWu11LlwhVYwxysaR5VFVOpPWMloZbTsqozM6y2\nHq+2nvCsBXlteuXC05n+V39J/xs7OfrTx+l5/oUTrsuqaoABovMX4lXXYFXVVFBBQ10nvfv76dvX\nlvnaMxqY91/W0LxqUeYUj5oaQqfVUHnaXCA1LST+1j4GdrxOx08eJXEos553GmDUr1lB8yfWEp5c\nN3SRcJTQrHMIzToHd8GncD0HsWgthCLgRQiFolRa9gV/aHIjdVdeT83qdfT87If4r/woVV9tI6H5\n5xGevwxvzlnQdZDk7hfx3/wN/r6Xh3+zvAg2dRHetCWEWpcQDTeR2L+fxO7txN/cTnL/TuAUZtZU\nNhC6+DasdRkAzu/H9f4Kl/we2PPgRvh+2gsTrqskkqwh2ZvA7+0f98tHGqp5919/hMbz553oGZSV\n6qnVVE+tLnQz8spqWwhfdgtVB/4PzioINbYSqpuEN6mO+s5+qKgh0dlDoqOL+OGj9P5uC/UXnEm4\noZ5QQz3hyXVEmhsKfRoyiE/+7jKUrwyaclW0QRetuSIiIlJmmpbDnKvxpl2esTsMhBunUXFOKvsk\neWQ3rqud0OwlmDfy3VEKpeK0eUy54Y+JHz5C9+a/hUQboeZpeLWTsZoGJi9IYFMW4NXUQSiEhUIk\njxyg6t0LCdU3pIIsoaHPreHq1H8HDnXS+fIuDv3rKyRicRZ/+ffwRrlbDKSmhURnziI6cxY1Ky8l\nceAAvS+/Qu8rr9C3LbXYadW7T2fqH15NxcxpYz5nC1di9bPHXB4g1NBE3VWfZmDhPKisI7IwsD5H\n1SS8KfPhvKtSWTWPP4DVT0u9N2bgJ3EDh6i46GNYKJpRd2TWQjh/JQDJtkMk9m0nVN8EoRB4IRiI\n4Xfsgfa9JA9sxz8QzNI5QS1n4l1yO1Y95fgu8yqwmktg0SW4MxzEe6C/HfraCR/YTqjfx5uxFJvU\nhFXXUTcoUOXHEyQ7u4m9uh3XF8Pv7SPZcZREx1FiT+3FpwK/L06ydwCcwwv5nH/Pf6aiuTY35yMT\nyuRP3ZC1b+ipZR/Ld1NEykbRBl1EREQmlGg93upv4nb9GLfncYh3D1nMq64iHJ2CPwB++yFwJTC9\nwoxQYzPh5hZCjc14FiM8KQp9Xfgdh/A7DoF/YOjnhqph5oexOZ/EarOzVIZ8StNsaBrfxX2hRJqb\naLjxy1n7K4coO17RljqaV59F8+qzTrgOMzu+Lkfd5ZfhDwzQ9e+/ZNKK5XjVp+6b/+jZq0ctY+Eo\nNR/4vROqP9TYQqixJfvAnHcf/9E5R2jLiyTaOsAiuIEB3EA/3uFfY5MGIN6F6zkMPUeGb+Oiq7Fz\nb8S84f/ENjOI1qa2SbMIt4zcf14kjNc0mfqLz8s6Nu3Gd352zuH3xUl09yrgIlIGHPmbgFkCf3mU\nlLINumj6koiInEpmBs1nY81n4875b7i3/h1e+T4cfhNv9jK8OecSmrMUa55HbfpbbpdMkGw7TMVL\nW5jU0UdkSisWDeNFIiQ6O/EiYcwzkr19+L39uD0vEmmIYObj+vtw/TG83gN4dVMgHMbMA8+RSLxN\nuNrBQFewlTBpHkxegtUuwEtWQ38vrn0Xrn0ntB8Ezwi3ziY0bRbhabPwmqYSPW0RoUn1I55/8sg+\nXG8HxI7iYh247nb87qcJr/4yFtEFYrHwolHqL3tvoZtREGZG5eJlQxxZm/HIOZ/Qri34vf34XUfx\nO9vxOw7T17eNmvM2nJrGDsHMCFVFCVVFRy8sIiKnTNkGXTR9SURECsXCldjs92HTVhG6tBevavLQ\n5UJhwi2t1F/WOsaaL83aM/RKHCku0QexQ7jOndC9E5t/5YgBEC/WTY0lhm3vSEJNM4DgIpWfGnc9\nIoVm5hGZt6TQzRCRMufM4Vt+clKccl1ySkuZn6T777+fFStWUFtbS0NDAxdddBE///nPx1XH8uXL\n8TyPO++8c8jjt956K57n0dY29EJ3S5YsYfXq0dNyRUSkuFik4oQCGDl7/XAlVjcLb+ZKvEV/MGrG\nSai6tqDtFRERESk1CrqchFtvvZVrr72W2bNns3HjRr70pS9x9tlns2/fvjHXsX37dp5//nnmzZvH\npk2bhixjZpmLyg1xXERERERERMqDj8vrJrlTtNOLin3NlWeffZbbbruNjRs38qd/+qcnXM93vvMd\npk6dyle/+lWuvPJKdu/ezezZpbE4oIiIiIiIiIgMr2gzXTZu3MjDDz9clAEXgK997WtMmzbteMCl\np6fnhOrZvHkzV111FWvXrqW+vp777rsvl80UERERERGRCcbleZPcKdqgS7F74oknOP/88/n6179O\nS0sLkyZNYvr06dxxxx1jruO5555j+/btrF+/nkgkwsc//vFhpxiJiIiIiIiISGkp26DLhg0bWLdu\nHZs3bx73czs6Ojh8+DBPPfUUt9xyCzfffDMPPPAAS5cu5cYbb+Suu+4aUz333nsvs2fP5sILLwTg\nk5/8JFu2bOG3v/3tuNskIiIiIiIi5cHlcT0X3b0ot4p2TZd8O5lbRnd3dwPQ1tbG/fffzyc+8QkA\nrrzySs4880xuv/12/uiP/mjEOpLJJA888ADXX3/98X2rV6+mpaWFTZs2cdZZZ51Q20RERERERESk\nOJRtpsvJqKqqAiASiXDllVce329mXHPNNezdu5e9e/eOWMdjjz3GoUOHOP/889mxYwc7duzgjTfe\n4NJLLz2h7BvdwUhERERERKQ86O5FpaNsM11ORmNjI5WVlTQ0NGQFO6ZMmQJAe3s7M2fOHLaO++67\nDzPjqquuyth/rL5/+7d/Y9WqVQBUVlYC0NvbO2RdsVjseBkRERERERERKQ5FG3Qp5ltGmxnnnHMO\nzz//PIlEgnD4nbdx3759ALS0tAz7/FgsxkMPPcQ111xzfGrSYDfeeCObNm06HnSZM2cOANu2bWPG\njBkZZXt7e9mzZw9XXHHFSZ+XiIiIiIiIFD8H+HmsW3KnaIMuJ7PmyqlwzTXX8Nxzz/Htb3+bT3/6\n0wD09fWxadMmFi9eTGtr67DPffDBB4nFYnzuc59jxYoVWccfe+wxvv/973PHHXcQiURYs2YNkUiE\nb3zjG1x66aUZ2TV33nknyWSSD37wg7k/SRERERERERE5YUUbdCl2n/nMZ/jmN7/JZz/7WbZt28bs\n2bO555572LNnDz/60Y9GfO6mTZtoamo6fteioHXr1nHXXXfx4x//mI9+9KO0tLRwyy238IUvfIGV\nK1eybt06qqurefrpp/nud7/L+9//fj70oQ/l4zRFRERERESkyDjI212GlOmSW2W7kO7J3DIaUuus\nPPnkk1x77bXcfffd3HTTTYTDYR555BHe9773Dfu8Q4cO8cQTT7B27dphF79ds2YNNTU13Hvvvcf3\n3Xzzzdx77734vs9tt93Gn//5n/PSSy9x22238dBDD53QOYiIiIiIiIhI/pRtpksupi81NzfzrW99\na1zPaWlpob+/f8QylZWVdHV1Ze0vxvVtRERERERE5NTK512GdPei3CrbTBcRERERERERkXwq20wX\nERERERERkVLkzOFbvtZ0UaZLLhVt0OXyyy8nEokwY8aM47dJ1vQaERERERERESkVRRt0efzxx4v6\nltEiIiIiIiIiheCnt3zVLbmjNV1ERERERERERPKgbIMuJ3vLaBEREREREZFCOHb3onxtkjtFO70o\n33Jxy2gRERERERERkeGUbdBFREREREREpBS59D/5qltyp2ynF4mIiIiIiIiI5FPRBl205oqIiIiI\niIhINkf+1nUZLc/FzL5oZn5g2xIo8y4ze8jMOsys28yeM7OZ+XtHilfRTi/SmisiIiIiIiIiRekV\nYA1g6ceJYwfMbD7wC+Au4AtAF7AY6DvFbSwKRRt0EREREREREZFs+bzL0BjrTTjnDg1z7Hbgx865\nvxi0b+dJN6xEFe30ony7/PLLaW1tZdmyZaxbt05TmURERERERETG5nQz22dmO8zsXjObBWBmBqwF\nXjezR83sgJk9a2YfKWxzC6fsMl0WLVrECy+8wKJFi6iuri50c0RERERERETGxeUx02UMdy96FrgO\n2AZMA24FfmFmi4Ha9PZ54C+Bm4APAA+a2Xudc7/IS6OLWNkFXaqrq7VWjIiIiIiIiMgJcM49Nujh\nK2b2K+BN4Grg0fT+Hzrn/nf659+a2QrgBlJrvZSVsgu6iIiIiIiIiJSyXK3p0um/QJf/YqDu3nHV\n4Zw7amavAQuAw6QW1X01UOxV4KITb2npKtqgy4YNG6ivr2f9+vWsX7++0M0RERERERERmVDqvGXU\necsy9vW5PexO/N2Y6zCzWlIBl2875+Jm9mvgjECxhaSyYcpO0QZddMtoERERERERkWw+4NuoxU6s\n7lESaMzsK8D/IxVEmQH8NRAHvpsu8hXgu2b2C+BJUmu6fAhYlZ8WF7eiDbqIiIiIiIiISNGZCdwH\nNAGHgKeAC5xzRwCccz80sxuAm4Gvk1pw9+POuWcK1N6CUtBFREREREREpIQU8u5FzrlR1/9wzv0z\n8M+5aVFp8wrdABERERERERGRiUiZLiIiIiIiIiIlJFd3LxqubskdZbqIiIiIiIiIiORB0Wa66JbR\nIiIiIiIiItl8IJm3TBfJpaINuuiW0SIiIiIiIiJSyoo26CIiIiIiIiIi2bSmS+nQmi4iIiIiIiIi\nInmgTBcRERERERGREuLymOnilOmSU8p0ERERERERERHJA2W6iIiIiIiIiJSQpPl4lp/7DCXzVG+5\nKtpMlw0bNrBu3To2b95c6KaIiIiIiIiIiIxb0QZdNm7cyMMPP8z69esL3ZQhKRhUHtTPE5/6uDyo\nnyc+9XF5UD+XB/XzxKc+Pnk+kMTlZVOeS24VbdCl2OkXRXlQP0986uPyoH6e+NTH5UH9XB7UzxOf\n+ljKidZ0ERERERERESkhSRyWp7sMJXX3opxSpouIiIiIiIiISB4o00VERERERESkhPjmSFp+MlL8\nPNVbroox6FIJ8Oqrrxa6HSM6evQoL774YqGbIXmmfp741MflQf088amPy4P6uTyonye+fPTxoOvH\nypxWXKR8d5B8rXjru4P5qbhMmXPFFcUys2uBTYVuh4iIiIiIiJScTznn7it0I/LFzGYDrwLVeX6p\nGPAu59zuPL/OhFeMQZcm4ApgF9BX2NaIiIiIiIhICagE5gKPOeeOFLgteZUOvDTn+WUOK+CSG0UX\ndBERERERERERmQh09yIRERERERERkTxQ0EVEREREREREJA8UdBERERERERERyQMFXU6AmX3WzHaa\nWa+ZPWtm5xe6TTI6M/uimfmBbUugzP80s7fMLGZmj5vZgsDxCjO7w8wOm1mXmX3fzKac2jORwczs\nEjN72Mz2pft03RBlTrpfzazBzDaZ2VEzazezb5pZTb7PT1JG62czu3uI8f1IoIz6uYiZ2V+Y2a/M\nrNPMDpjZv5jZwiHKaTyXqLH0scZy6TOzG8zspfR7f9TMfmlm7w+U0TgucaP1s8ayyDsUdBknM7sG\n+CrwRWAp8BLwmJnle/VoyY1XgKlAa3q7+NgBM/s88Dngj4HlQA+pvo0Oev7XgLXAlcBKYDrwg1PS\nchlODfAfwJ8AWSuD57Bf7wPeBaxJl10J3JnLE5ERjdjPaT8hc3yvDxxXPxe3S4B/AN4DXAZEgJ+a\nWdWxAhrPJW/UPk7TWC5te4DPA+cCy4AngIfM7F2gcTyBjNjPaRrLIgDOOW3j2IBnga8PemzAXuCm\nQrdN26h990XgxRGOvwVsGPS4DugFrh70uB/42KAyZwA+sLzQ56fNke6LdbnuV1L/s/eBpYPKXAEk\ngNZCn3e5bcP0893AgyM8R/1cYhupW2H6wMWD9mk8T6BtmD7WWJ6AG3AEuD79s8bxBN0C/ayxrE1b\nelOmyziYWYRUJPdfj+1zzjngZ8CFhWqXjMvplpqesMPM7jWzWQBmNo9UBH5w33YCz/FO354HhANl\ntgG7Uf8XpRz26wVAu3PuN4Oq/xmpjIv35Kv9Mm7vTU9Z2Gpm/2RmjYOOLUP9XGomk3rv20DjeYLK\n6ONBNJYnCDPzzOyTQDXwS43jiSnYz4MOaSyLkPqgy9g1AyHgQGD/AVKRWSluzwLXAduAacCtwL+b\n2RJSfwA4hu7b1vTPU4GB9B8Hw5WR4pKrfm0FDg4+6JxLmlkb6vti8RNSKck7gfnA/wIeMbML08Hx\nVtTPJcPMjFTa+VPOuWNrb2k8TyDD9DFoLE8I6b+tngEqgS5S2QzbzOxCNI4njOH6OX1YY1kkTUEX\nKRvOuccGPXzFzH4FvAlcDWwtTKtEJBeccw8Mevg7M3sZ2AG8F3iyII2Sk/FPwLuBiwrdEMmbIftY\nY3nC2AqcDdQDnwDuMbOVhW2S5MGQ/eyc26qxLPIOTS8an8NAklQEfrCpwP5T3xw5Gc65o8BrwAJS\n/WeM3Lf7gaiZ1Y1QRopLrvp1PxBcTT8ENKK+L0rOuZ2kfmcfuyOG+rlEmNk/Ah8E3uuce3vQIY3n\nCWKEPs6isVyanHMJ59wbzrnfOOf+ktSNJ/4MjeMJZYR+HqqsxrKULQVdxsE5FwdeILV6NnA8PXYN\nmfMXpQSYWS2pX/xvpf9HsJ/Mvq0jNV/0WN++QGrhrsFlzgBmk0qtlCKTw359BphsZksHVb+G1B+O\nz+Wr/XLizGwm0AQcu6BTP5eA9MX4R4BLnXO7Bx/TeJ4YRurjYcprLE8MHlChcTzheUDFUAc0lqWs\nFXol31LbSE1FiQG/DywidcuyI0BLodumbdS++wqp28zNAVYAj5OaN9qUPn5Tui8/DJwJ/BB4HYgO\nquOfSM1NfS+pBcCeBn5R6HMr543UrYTPBs4htcL9f00/npXLfgUeAZ4HzieVDr8N+E6hz79ctpH6\nOX3sy6T+aJ9D6g+y54FXgYj6uTS2dP+0k7qt8NRBW+WgMhrPJbyN1scayxNjA/4m3cdzgCWk1vJI\nAKvTxzWOJ8A2Uj9rLGvTlrkVvAGluAF/AuwidXu7Z4DzCt0mbWPqt82kbu/dS2pl9PuAeYEyt5K6\nlWEMeAxYEDheAfwDqfTILuB7wJRCn1s5b8AqUhfhycD2rVz2K6m7bNwLHCV10XAXUF3o8y+XbaR+\nJrWA36Okvj3tA94AvkEgGK5+Lu5tmP5NAr8fKKfxXKLbaH2ssTwxNuCb6b7rTfflT0kHXAaV0Tgu\n8W2kftZY1qYtczPnHCIiIiIiIiIiklta00VEREREREREJA8UdBERERERERERyQMFXURERERERERE\n8kBBFxERERERERGRPFDQRUREREREREQkDxR0ERERERERERHJAwVdRERERERERETyQEEXERERERER\nEZE8UNBFRERERERERCQPFHQRERE5AWa2ysySZlZXgNf201tbjupaN47yqwa9/oMn+/oiIiIiE5mC\nLiIiIqMwsyfN7O8Du58GpjnnOgvRJuAPgIU5qKcV+Mk4yj+dfs4DOXhtERERkQktXOgGiIiIlCLn\nXAI4WMAmHHXOHT7ZSpxz4zqHY+dtZr1A9GRfX0RERGQiU6aLiIjICMzsbmAV8GfpKTVJM5s9aJpN\nXbrcH5hZu5mtNbOtZtZjZg+YWVX62E4zazOzr5uZDao/amZ/Z2Z7zazbzJ4xs1Un0M4vmtlvzOx6\nM3vTzLrM7B/NzDOzm8zsbTM7YGY3B553fHqRmc1JP/6YmT2RPof/MLMLTu5dFBERESlPynQREREZ\n2Z+RmsbzMvAFwIBDwDzABcpWAzcCVwN1wL+kt3bgA8BpwIPAU8D30s+5A1iUfs7bwMeAn5jZmc65\nHeNs63zg/cAV6Z9/kP7vNmAlcBHwLTN73Dn36xHquR3478B24G+A+8xsgXPOH2d7RERERMqagi4i\nIiIjcM51mtkAEHPOHTq2f1CyymBh4Abn3K50me8DvwdMcc71AlvN7EngUuB7ZjYbuA6Y5Zzbn67j\n783sA8D1wF+Ns7kGXO+ciw16rYXOuQ+kj79uZp9Pv/5IQZevOOceTZ/DF4FXgAXAa+Nsj4iIiEhZ\nU9BFREQkd2LHAi5pB4Bd6YDL4H1T0j8vAULAa5YZxYkCJ7Jey650wGXwayUCZQa//nBeHvTz26SC\nOVNQ0EVERERkXBR0ERERyZ144LEbZt+xNdVqSQVFzgWCU3e6T8Hrj6WeY1OotA6ciIiIyDgp6CIi\nIjK6AVIZKbn2m3S9U51zT+eh/hMRXKdGRERERE6QvrUSEREZ3S7gPem7+zQNmgo05MIuY+Wcex24\nD7gnfceguWa23Mz+R3pdl0I4qXMSERERkXco6CIiIjK6vwOSwBbgIDArvT8XWSHXAfekX2Mrqbsb\nnQfszkHdQwm2ebTHw+0TERERkVGYc/o7SkREpJSYmQ981Dn3cAHbcDdQ75z7eKHaICIiIlLslOki\nIiJSmjabWb6yYYZlZhebWRdw7al+bREREZFSo0wXERGREmNmp6V/TDrn3jzFr10BzEg/7HbOHTyV\nry8iIiJSShR0ERERERERERHJA00vEhERERERERHJAwVdRERERERERETyQEEXEREREREREZE8UNBF\nRERERERERCQPFHQREREREREREckDBV1ERERERERERPJAQRcRERERERERkTxQ0EVEREREREREJA8U\ndBERERERERERyYP/DwUdRQN5BRcMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3e0e5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### PLOT 1: Sized by measure 1, colored by measure 2\n",
    "\n",
    "# Initialize figure and set size\n",
    "fig = plt.figure(figsize=plot1_size)\n",
    "ax  = plt.gca()\n",
    "\n",
    "# Prepare colormap\n",
    "cmap = plt.cm.get_cmap(plot1_cmap)\n",
    "vmin = min([np.min(tree[ID][plot1_color_track]) for ID in tree.keys()])\n",
    "vmax = max([np.max(tree[ID][plot1_color_track]) for ID in tree.keys()])\n",
    "\n",
    "# Prepare scale\n",
    "scale = plot1_scale\n",
    "\n",
    "# For each track...\n",
    "for ID in tree.keys():\n",
    "    \n",
    "    # Get the track's position, size, color\n",
    "    p = np.array([tree[ID]['position'] for step in tree[ID]['indices']])  # Repeated for plotting\n",
    "    s = tree[ID][plot1_size_track]\n",
    "    c = tree[ID][plot1_color_track]\n",
    "    \n",
    "    # Create the size-based area with color-based coloring\n",
    "    for step in range(1,len(s)):\n",
    "        overstep = np.abs(tree[ID]['times'][step-1]-tree[ID]['times'][step])*0.2\n",
    "        step_x   = [tree[ID]['times'][step-1], tree[ID]['times'][step]+overstep]\n",
    "        step_ylo = [p[step-1]*scale-(s[step-1]/2), p[step]*scale-(s[step]/2)]\n",
    "        step_yhi = [p[step-1]*scale+(s[step-1]/2), p[step]*scale+(s[step]/2)]\n",
    "        plt.fill_between(step_x, step_yhi, step_ylo,\n",
    "                         facecolor=cmap((c[step]-vmin)/(vmax-vmin)), \n",
    "                         edgecolor='')\n",
    "\n",
    "    # Create the position-based main line\n",
    "    plt.plot(tree[ID]['times'], p*scale, '0.4', lw=1)\n",
    "    \n",
    "    # Create lines connecting branching events\n",
    "    if tree[ID]['branches'][0] != '_leaf_':\n",
    "        \n",
    "        # x positions: (time point before event, time point after event)\n",
    "        x  = np.array((tree[ID]['times'][-1], \n",
    "                       tree[ID]['times'][-1] + 1*time_resolution ))\n",
    "        \n",
    "        # y positions: (position before event, position after event)\n",
    "        y1 = np.array((tree[ID]['position'], tree[tree[ID]['branches'][0]]['position']))\n",
    "        y2 = np.array((tree[ID]['position'], tree[tree[ID]['branches'][1]]['position']))\n",
    "        \n",
    "        # Plot the lines\n",
    "        plt.plot(x, y1*scale, '0.4', lw=1)\n",
    "        plt.plot(x, y2*scale, '0.4', lw=1)\n",
    "\n",
    "# Colorbar \n",
    "scat = plt.scatter([10,10], [10,10], c=[0,1],  # Invisible fake plot for colorbar\n",
    "                   cmap=plot1_cmap, vmin=vmin, vmax=vmax,\n",
    "                   alpha=0)\n",
    "cbar = plt.colorbar(scat) \n",
    "cbar.set_alpha(1)\n",
    "cbar.draw_all()\n",
    "cbar.ax.get_yaxis().labelpad = 20\n",
    "cbar.set_label(plot1_color_label, rotation=90)\n",
    "\n",
    "# Labels\n",
    "plt.xlabel(plot1_xlabel)\n",
    "plt.ylabel(plot1_ylabel)\n",
    "plt.title(plot1_title)\n",
    "\n",
    "# X axis cosmetics\n",
    "xmaxtime = max([np.max(tree[ID]['times']) for ID in tree.keys()])\n",
    "xmintime = min([np.min(tree[ID]['times']) for ID in tree.keys()])\n",
    "xoffset  = (xmaxtime-xmintime)*0.05\n",
    "plt.xlim([xmintime-xoffset, xmaxtime+xoffset])\n",
    "\n",
    "# Y axis cosmetics\n",
    "ymaxpos    = max([tree[ID]['position'] for ID in tree.keys()]) * scale\n",
    "yminpos    = min([tree[ID]['position'] for ID in tree.keys()]) * scale\n",
    "max_m      = max([np.max(tree[ID][plot1_size_track]) for ID in tree.keys()])\n",
    "yoffset    = (ymaxpos-yminpos)*0.05 + max_m\n",
    "ytickspace = (ymaxpos-yminpos)/10.0\n",
    "ax.set_ylim([yminpos-yoffset, ymaxpos+yoffset])\n",
    "majorspace = np.arange(yminpos-yoffset, ymaxpos+yoffset, ytickspace)\n",
    "minorspace = np.arange(yminpos-yoffset ,ymaxpos+yoffset, ytickspace/5.0)\n",
    "ax.yaxis.set_ticks(majorspace)\n",
    "ax.yaxis.set_ticks(minorspace, minor=True)\n",
    "ax.xaxis.set_ticks_position('bottom')\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.tick_params(labelleft='off')\n",
    "\n",
    "# Inset scale bar\n",
    "from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes\n",
    "ax_inset = zoomed_inset_axes(ax, 1, loc=3, borderpad=1.5)\n",
    "ax_inset.plot([],[])\n",
    "ax_inset.set_ylim([majorspace[0],majorspace[1]])\n",
    "ax_inset.spines['top'].set_visible(False)\n",
    "ax_inset.spines['right'].set_visible(False)\n",
    "ax_inset.spines['bottom'].set_visible(False)\n",
    "ax_inset.get_xaxis().set_visible(False)\n",
    "ax_inset.get_yaxis().tick_left() \n",
    "ax_inset.patch.set_visible(False)\n",
    "ax_inset.tick_params(labelleft='off')\n",
    "ax_inset.yaxis.set_ticks(minorspace, minor=True)\n",
    "ax_inset.yaxis.set_ticks(majorspace)\n",
    "ax_inset.set_ylim([majorspace[0],majorspace[1]])\n",
    "ax_inset.yaxis.set_label_coords(0, 0.5)\n",
    "ylbl = ax_inset.set_ylabel(\"   \"+plot1_scbar_label % ytickspace, \n",
    "                           fontsize=12, va='center', ha='left')\n",
    "ylbl.set_rotation(0)\n",
    "\n",
    "# Tight layout\n",
    "# Note: It complains a bit because of the inset, hence the catch.\n",
    "with warnings.catch_warnings():\n",
    "    warnings.simplefilter(\"ignore\")\n",
    "    plt.tight_layout()\n",
    "    \n",
    "# Done\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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U1KRzzz1XJSUlw1qeJN1zzz1KJpP67ne/q+985zu9HjPGaOXKlbr++usl2TBO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\n7lKBkl7vL0dO/ptFt/tUKpt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rHjvLsyI8zPMcVa0KUzOL/bveW4e/KdbrYihF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\nERERET1Chq5S6vTp05idnd3Zjf7JnwD1O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      "text/plain": [
       "<matplotlib.figure.Figure at 0x82d9048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### PLOT 2: Sized by both measures\n",
    "\n",
    "# Initialize figure and set size\n",
    "fig = plt.figure(figsize=plot2_size)\n",
    "ax  = plt.gca()\n",
    "\n",
    "# Prepare scale\n",
    "scale = plot2_scale * 2.0\n",
    "\n",
    "# For scaling measure2 to measure_1\n",
    "max_m1 = max([np.max(tree[ID][plot2_size1_track]) for ID in tree.keys()])\n",
    "max_m2 = max([np.max(tree[ID][plot2_size2_track]) for ID in tree.keys()])\n",
    "\n",
    "# For each track...\n",
    "for ID in tree.keys():\n",
    "\n",
    "    # Get the track's position and the two measures, the second normalized to the first\n",
    "    p  = np.array([tree[ID]['position'] for step in tree[ID]['indices']])  # Repeated for plotting\n",
    "    s1 = tree[ID][plot2_size1_track]\n",
    "    s2 = tree[ID][plot2_size2_track] / max_m2 * max_m1\n",
    "    \n",
    "    # Create the size-based area on top\n",
    "    plt.fill_between(tree[ID]['times'], p*scale+s1, p*scale,\n",
    "                     edgecolor='g', facecolor='g', alpha=0.3, label=plot2_label1)  \n",
    "    plot2_label1 = '_nolegend_' # Legend handling\n",
    "    \n",
    "    # Create the lyso-tracker-based area on the bottom\n",
    "    plt.fill_between(tree[ID]['times'], p*scale, p*scale-s2,\n",
    "                     edgecolor='r', facecolor='r', alpha=0.3, label=plot2_label2)\n",
    "    plot2_label2 = '_nolegend_'  # Legend handling\n",
    "    \n",
    "    # Create the position-based main line\n",
    "    plt.plot(tree[ID]['times'], p*scale, c='0.4', lw=1)\n",
    "\n",
    "    # Create lines connecting branching events\n",
    "    if tree[ID]['branches'][0] != '_leaf_':\n",
    "        \n",
    "        # x positions: (time point before event, time point after event)\n",
    "        x  = np.array((tree[ID]['times'][-1], \n",
    "                       tree[ID]['times'][-1] + 1*time_resolution ))\n",
    "        \n",
    "        # y positions: (position before event, position after event)\n",
    "        y1 = np.array((tree[ID]['position'], tree[tree[ID]['branches'][0]]['position']))\n",
    "        y2 = np.array((tree[ID]['position'], tree[tree[ID]['branches'][1]]['position']))\n",
    "        \n",
    "        # Plot the lines\n",
    "        plt.plot(x, y1*scale, '0.4', lw=1)\n",
    "        plt.plot(x, y2*scale, '0.4', lw=1)\n",
    "    \n",
    "# Legend\n",
    "plt.legend(fontsize=12, frameon=False)\n",
    "\n",
    "# Labels\n",
    "plt.xlabel(plot2_xlabel)\n",
    "plt.title(plot2_title)\n",
    "\n",
    "# X axis cosmetics\n",
    "xmaxtime = max([np.max(tree[ID]['times']) for ID in tree.keys()])\n",
    "xmintime = min([np.min(tree[ID]['times']) for ID in tree.keys()])\n",
    "xoffset  = (xmaxtime-xmintime)*0.05\n",
    "plt.xlim([xmintime-xoffset, xmaxtime+xoffset])\n",
    "\n",
    "# Y axis cosmetics\n",
    "ymaxpos    = max([tree[ID]['position'] for ID in tree.keys()]) * scale\n",
    "yminpos    = min([tree[ID]['position'] for ID in tree.keys()]) * scale\n",
    "yoffset    = (ymaxpos-yminpos)*0.05 + 2.0*max_m1\n",
    "ytickspace = (ymaxpos-yminpos+2.0*yoffset) / 10.0\n",
    "ax.set_ylim([yminpos-yoffset, ymaxpos+yoffset])\n",
    "majorspace = np.arange(yminpos-yoffset, ymaxpos+yoffset, ytickspace)\n",
    "minorspace = np.arange(yminpos-yoffset ,ymaxpos+yoffset, ytickspace/5.0)\n",
    "ax.yaxis.set_ticks(majorspace)\n",
    "ax.yaxis.set_ticks(minorspace, minor=True)\n",
    "ax.xaxis.set_ticks_position('bottom')\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.tick_params(labelleft='off')\n",
    "\n",
    "# Inset scale bar\n",
    "from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes\n",
    "ax_inset = zoomed_inset_axes(ax, 1, loc=3, borderpad=1.5)\n",
    "ax_inset.plot([],[])\n",
    "ax_inset.set_ylim([majorspace[0],majorspace[1]])\n",
    "ax_inset.spines['top'].set_visible(False)\n",
    "ax_inset.spines['right'].set_visible(False)\n",
    "ax_inset.spines['bottom'].set_visible(False)\n",
    "ax_inset.get_xaxis().set_visible(False)\n",
    "ax_inset.get_yaxis().tick_left() \n",
    "ax_inset.patch.set_visible(False)\n",
    "ax_inset.tick_params(labelleft='off')\n",
    "ax_inset.yaxis.set_ticks(minorspace, minor=True)\n",
    "ax_inset.yaxis.set_ticks(majorspace)\n",
    "ax_inset.set_ylim([majorspace[0],majorspace[1]])\n",
    "ax_inset.text(0.0, 0.75, \"  \"+plot2_scbar_label1 % ytickspace, \n",
    "              fontsize=12, va='center', ha='left', \n",
    "              color='g', alpha=0.8, fontdict={'family' : 'monospace'},\n",
    "              transform=ax_inset.transAxes)\n",
    "ax_inset.text(0.0, 0.25, \"  \"+plot2_scbar_label2 % (ytickspace / max_m1 * max_m2), \n",
    "              fontsize=12, va='center', ha='left', \n",
    "              color='r', alpha=0.8, fontdict={'family' : 'monospace'},\n",
    "              transform=ax_inset.transAxes)\n",
    "\n",
    "# Tight layout\n",
    "# Note: It complains a bit because of the inset, hence the catch.\n",
    "with warnings.catch_warnings():\n",
    "    warnings.simplefilter(\"ignore\")\n",
    "    plt.tight_layout()\n",
    "    \n",
    "# Done\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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UHs4QGQ/dODcHQ77cW3pW9MIQXV+G7qK4ZZSORxp7LAlLznpOZT/c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vAAAg\nAElEQVTw58B/c1SLExGRoxElhsblBC4/+XlXeoqhC7Oj4tkHUq/SaDTo9zVjSkRERETkrHiTYGoB\nuPCcz18AWm+3HBEROUk2evsWvJNUr4cZU845rD2ZyjMRERERETk+b/JT/f8G/Kkx5t8yxlybfPzb\nwP8A/K9HuzwREZF9jUYD7z3D4XDWSxERERERkSPwJhVT/yHwXwP/MzDd8LwgBFP/2RGtS0RE5BmN\nRgOAwWCwd11ERERERE6v166Y8t4PvPd/H1gm7Nb3PWDJe//3vfca/CEiIsdmGkZpzpSIiIiIyNnw\nxgM6vPd97/2PJh9H/g5hY2OD69evc+PGjaM+tIiInFIHK6ZEREREROT0e5NWvhOxubnJ2trarJch\nIiJzpF6vA6qYEhERERE5K7SlkYiInBqVSgVrrYIpEREREZEzQsGUiIicGsYYGo2GgikRERERkTNC\nwZSIiJwqCqZERERERM6OuZ0xJSIi8jyNRkPDz+XEfPw3f8XP7t7iy+YbxKWZ9XJEROQIFQNPPih5\nL6uTUpv1ckTOLQVTIiJyqtTrdQVTcmL+4v/7J/zq9qf85e0/n/VSRETkmHzp/n9Bc/n9WS9D5Nya\n22BqY2ODdrvN+vo66+vrs16OiIjMiUajwcOHD2e9DDknRuMRX+58iz/48r9KXFfFlIjIWVIMPHm/\n5NLFy7Neisi5NrfB1ObmJmtra7NehoiIzJl6va4ZU3Jidna2eWfhm1xqrZI0NZpTROQsySNH5kvS\nNJ31UkTONf2EJSIip4qGn8tJyfOcXr9HM1mY9VJEREREziwFUyIicqpMh59772e9FDnjtre3AWik\nCqZEREREjouCKREROVUajQZFUZBl2ayXImfcNJhSxZSIiIjI8VEwJSIip0q9XgfQznxy7LrdLgAN\nBVMiIiIix0bBlIiInCqNRgNAc6bk2G1tbZHECZWoNuuliIiIiJxZcxtMbWxscP36dW7cuDHrpYiI\nyByZVkwpmJLj1u12abc7GGNmvRQRERGRMyue9QJeZHNzk7W1tVkvQ0RE5owqpuSkbG9v02m3Z70M\nERERkTNtbiumREREnkczpuSkdLtdFhY6s16GiIiIyJmmYEpERE4Vay31el0VU3Lsut2uKqZERERE\njpmCKREROXXq9boqpuRYee/Z2tqi3VbFlIiIiMhxUjAlIiKnjiqm5LgNh0PyPKejVj4RERGRY6Vg\nSkRETp1Go6FgSo5Vt9sFoK1WPhEREZFjNbfB1MbGBtevX+fGjRuzXoqIiMwZBVNy3PaCKVVMiYiI\niByreNYLeJHNzU3W1tZmvQwREZlDjUaDe/fuzXoZcoZtbW0BoWLqMQpBRURERI7L3FZMiYiIvIhm\nTMlx297eptlsksTJrJciIiIicqYpmBIRkVOn0WhoVz45Vt1ul05HbXwiIiIix21uW/lERERepF6v\nMxqNKMuSKIpmvRw5gxRMnW7eeTBgjJn1UuQcc6WnHHqKocOXEFUMtmLCZaRzU0RkSsGUiIicOo1G\nA4B+v8/CwsKMVyNn0dbWFu++++6sl3Ek8r6jGDgqS9GZfTNc5p6i58h2HeXQgQFfQu1iRHVZP+6+\nDu88AMYe7lxxpQcPNj6955b3Hl+AKzyuOHA99xg7CZJSQ5QabPLk43SFp8w8bvJRjDzlyAHhOYxr\nhrhmsVVDPnTkPUc59vvPc2RIGpa4YYjr9sz+GxUReRn9Ty0iIqfONJgaDAYKpuRYbG9vn4mKqcHd\ngrznqC5G7HyaEdcstUsxUTr/b3698xRDTzEIb+a9A/zk77w/cDuIYkPcsrTeSWisxlSXYgb3Cx79\ncEj3Z2PqV2PS1vOrK33pyXoOX3iSVnQqnpuj4PJQyVOOPMXI4caeA0/rnqhqiCqWqGowBophCF7K\nEWDARuBcuKxdjknqL58U4r3Hl28fZHkfqpFsal56LO89ec+RdUNAyxM3NYCfPI5wHBODTcL1tGVx\nORQjR94Pz5ErADM9EQ02BlsJgVVcsdQuWmoXItJ2RNqKSFqWpGkxFspxOJ+n53UxcIy2SgZ3CrKd\nkuHdEu/9flhVn4RVpzj0ExE5jLkNpj788EOSJGF1dZXV1VUA1tfXWV9fn/HKRERk1g5WTIkctbIs\n2dnZod1uz3opb8yVnt7NnLhheefvNVl4P2X3Zsajfz6i91mOsVC/HBO/IkR4W975Q1XelJmnHIeQ\nJO+HihJjDHHdkDQti1+vhLUaMJMlGwMYSBoRlaWI6lJEXNt/PAvvp3S+kvLgB0O2/jZjeK+gcS0h\nrlqKUQgqsl0HHtKWwcSG/uc5rvBEVUO6EEKFeQgFvAvVOy4PgZKJIapYbPJ67You94y7JeMth7GQ\nNC1x1VK/nFBdtiStiKQZnsO858h3S8aPQ3hSDBx4T1yPaF5LqC7vBy/F0LH1yZjdX+f0syIEMx27\nt7Zy7Mh2HNl2iXeTNZtQ0ZYuHL4d2xWebLtk3HX40hNXbXheSo8xENUtST2EaHk/vMbeeZKGpX45\npvVeQlKPwjlkQ0WTicI5ZZNJRdSByqiD6y8mLXnTYAkDcTVUQ0U1S1w1k/Duxa9HXDXE1Wf/zXnv\nyXcd425Jtu0YPsjp3ynJeyWjB+X+46vZUIFVt6/8WiIip8ncBlMfffQRa2trs16GiIjMIQVTcpx2\ndnbw3rO4uLj3OVc8p5TkBVzhw5vdQ7xpLAaO4cMCGxuqyxFR5e2Donzg6P0mp/VewtV/sUHjSthZ\ncOm3qnS+VmH3NxmPfjJi92ZBmeWTdXr2qkcw4D0mNlQWI9L2i9uLytyT75ShmiQHX/hJRcrk9saD\nn953v3XJJuCyUO0EYJMwfyeuWjpfqVC/HFPphK+dtt+8BbG6HHPt7zZZ/HrB/b8esPNpCJ7iqqWy\nGLH4zQqNKzG1i6GKbPiwZPSwoPd5Tv9ewehmji89Uc2QNENo83RF1bTiKt9xFEMHDuJmCEJeFWp5\nH6rCXDYJnor98Ck8l/sVPVFisWkIUNzQM3rocLnfe16jSdWOiUIVj5lWAEUhZBpvOTCe6nLMpd+r\n0Xw3obYSE9fMK8ND7z3FwOO9J6nb596+/UHK4G7B1s/HdH82ZnC3IKoaXBaqmirtiJXv1WhcSYhS\nQ/cXY7Y/zejfLqguR1SXo2eOW2ahOqsYeLKdsP5KJ2LpWxVa76RUVyLKkSfbKRk9LhneKxh1S7Jd\nR1K3rHynQmM1pX4pJm3bNw5yooolqkClczwzDY0JQeh+SFfFuxDSTgO9cbdkcK9gvFUyfFBSjqb/\nnthrF3ybINW7SWXZTqgqs/HkPIrN3nWbKgwTkeMxt8GUiIjIi9TrdQDtzCfHotvtAtDpdLCJoboU\n07+TM7xbENXNpEVn/835tBok74WgwEaGMg9VGpWliKTxZNjkvQ9VEfdLbAKtdxKKkad/q8CVUFm0\nz8yD8i4EA3kvtBR5F8KV0GZliKp2EqwUjB87LnyvyuU/bDzztW1saH+5wsL7Kf3bBaNHxX7VSGQw\nNly63NP7LGPns5ydT3OMgbQTWpKKQXi8LvPY2JAsWFpfSkOFUd3uV5yk4c2sK8BNKqKKcQhiyr4j\nWbDhPg1D3LAkk4/DzjY6LGMMzWsJ9SsL7Pw6oxx5ahdiqsvRM2/im6uW5mrCyndq5APH6FHB6GFJ\n/4ucwf2C/q0QbNk4PL6i7zDWkLQsjSsxzXcTotTy6J+PQutkw1K/FD8zl6gYOUYPS/KeI2lMnrMk\ntI5FdUvSjIhrhqQ2eY0nlTbRZHB2mYXzoRg48oEj74UqqLwfqs5c7vfaxnwBccNy6e+EMKpxJX7t\nANQYQ9J4+etirKFxNaFxNTx/278cM7xf0rwWgr/qSkx04HlYeD9l+KBg+9Mxj386ZvsXGbZi8MXB\nwDLMaEoalqVvVWhcTahfip+ojAuSJ57bvOfCc3kEQe+sGGtIW6EqjdX9x1eOQ6VfvhsuR1slw3s5\n2Y5j/JvJ+ZmGc/Jlz8E0FM22S/LdEHIlTUP9YgyTlk2X+1DFmIfzqMwcJjJ7gXH09LytcvI9ajeE\niTaB2sX4me9DIiJPUzAlIiKnTpIkJEmiiik5FgeDqbhqef/fXGD0sGB4v2DnZmgJ275fhjY1QjVF\n2rKT6puEtG0Zbzm2fzlmcKeg/3lO3AxhU77ryLZKkgXLyncrLH69SuNqjHfQv5OzezNn+5dZCIMi\nsDEUo9AOl9QNSSui/ZUUmxjGj0IVRd5zjB6VuAzihuHa362z/Du1l1YZGRvCmua15IW3Wfx6hXzg\nGNwt2P08Z/fXGcP7JUnD0vlaCAmmlS7Pa0+aNzYydL5SOfTtk7olqae03oEL36tRjBzjx6EyZ3A3\nJ9t1NK8lIeRaiUib+9U0na+lbH+a8fCHI3Z+nRPXDNWVKFS/dEtsamlcibjyh3Uaq8leJdZhQ7mo\nAmnrxX8/Hdw9/Ygb9kRfo+piRPXv1F95u9qFmNqFmOXfrrHz64zdm1moHOqE6rSkEcLQw1R1TcXV\nk32sJy2qWGoVS23lyc/ngwPn552c/p2C/u0Sl4eqSO/36hjxhIH1cS18X1r6VkLjcngtktZ+Zdne\nYPdJ0Dl+XNK7ldH7vKD/2aT6sG7D7oPjEMonTUv9YkxjNWb4sGT3Zk7/Vk51JaKy+GxV3Kz55w1W\nE5ETp2BKREROpUajoWBKjkW32yWKor2W0SgxNK4kNK6ESpBst2R4v2D4qCRtWqrLMZWlJ6sHWu/A\n8rcrjB6FN3LdX2QMH5QkTcvVf6lB+ysp1aX9H8OMhdY7Ka13Ui7+bo3erZztTzN8CY2roa2tshiR\nLjxZUTRt98n7jqLviOt2r3XvKCR1S/uDlPYHKeW/UGO87ai0T3clypuKq5b4qqVxNWH529WX3jaq\nWJZ+q0r7KyGgevSjEYO7BZVOxJU/qtN6N6V2KT62HdjspP2K2rEc/sglDcvyt6uvfF7lxZLJfK3m\ntQR+p0qZOUaPw8yqvU5d9mezYUJrYnXpxWHR3nk0Ub8Ys/iNCuXYMXxQMriXs/tZTpTayaYDYd7b\nNKT13jO4W7D9i2zS4pmRti1xw+61r06DLw7mQ9NWwmS/HdVE+/PATGSwkxzYu7ADp3f+icvpDosu\nD+22TxycSYuxCbPsqivzF5iJnDcKpkRE5FRSMCXHpdvthjY++/zwZdpe0/7yy49jrNmrCFn5To3R\nozK0rL1i4Hhcs3S+WqHz1VdX9zzR7nPMooqlfvH8BVJvI0otS9+s0v5yyuhRSXXpaOaIibxKlFoa\nly2Ny8dw7IqleS2EYBe//+LbGXMg1P9ule1fZTz+yZhi4IjS6VytUHEa121oI87CLpH5wFH2Pfkg\ntA27cj9kmoZR+GlQdTC4Cq3EaduEQfgNS1ydbhZgsNGzQVdUsWo3FJkxBVMiInIqNRoNzZiSYzEN\npo7SNKSS8ylKLY0reuMr51e6EHHhuzWWvlXFZWHny8NWDHrncWXYXMEV+xVR3oV251BVdWBY+zFV\nIorI8Znbn5A2NjZot9usr6+zvr4+6+WIiMicqdfrqpiSY3EcwZSIiITW6KeHpr+KsYbIAq95PxE5\nPeY2mNrc3GRtbW3WyxARkTnVaDS4ffv2rJchZ1C32+Xq1auzXoaIiIjIuaCaYhEROZVUMSXHRRVT\nIiIiIidHwZSIiJxKGn4ux2E0GjEajVhcXJz1UkRERETOBQVTIiJyKk2HnzvnZr0UOUO63S6AKqZE\nREREToiCKREROZUajQbee0aj0ayXImfINJhqt9szXomIiIjI+aBgSkRETqV6vQ6gdr435J2nfyfn\n0Y9HDB8Ws17O3JgGU2rlExERETkZc7sr38bGBu12m/X1ddbX12e9HBERmTONRgMIwVS6s0CZebwD\nXAhdvAc8VBYj6pdj4urZ+F1MMXKMHpUUA0f9Uky6EL3W/UdbJf1bOVs/GzO4U1AMHWnbsvKdKkvf\nqpK2Xu94s1LmnnLkKMceYyDtRNjo7bcS73a71Ot10jQ9glWKiIiIyKvMbTC1ubnJ2trarJchIiJz\naloxtfOox+CHfUaPSowFMIAHwHswFiqdiIX3EprvpNQvxy8MX7z3lGNPMfSUQ0cxdBRDTz5wuJHH\nleHDF+BKDwVgIaoa4orFViBKLDYJAcnebXNHkYEbO7wHG4FJLDYmfEQWm0BUtcQ1Q1yz4Zg1i/cw\nelQweljSu5XTv52T7zhc4UnbEa0vJSy8n9JYTUjqz4ZvxdCR7Tqybsn2pxm7n2Vk246kaaldjIjr\nCaNHBbf/YsDWJxkr362y+I3Kc4M87zzFyONdeH6NORAEGYirBmNfHQ650jPeKimGDl+CK/afU194\nfAnehdfDlx5XAiUUmSPvOYq+pxw7XO5xBSGYake03kmoX46prkRUOtGh1vI07cgnIiIicrLmNpgS\nERF5mb2KqUGfVu5prD4/cHKFZ9wtefjDEQ/+ekzatjSuxNiqwWfgck+Z+xByTK9nHjf2uMKHnAsw\nkcEYwICJAEwIwvwkUCnBl37yVaeBiJ/cwWOswUQhRPEufOBD+II3+7fFE6UWm0JUCeFQtuNwmcOm\nhnTB0ng3wUaQbTse/3TMox+NqSxZFj5IaVxJKEeO0eOS4f2CbNdRDkOQY2JD7UJE/Ur8RKhUXY6p\nLEYM75fc+qjH1t+OufC9GjY1FH1Htlsy2irJHjuKsdt7nAdzKYwhqhrql2PqF2PSTkSlHZEuWFzh\nGT0uGT8uGdwr6N0K4Vo5nlS2HXjsMAkUjQlX7OS6CSGjTczeR1INl95BvuO4/1cDvIe4bql0QqWc\nK/0zr7Mv2TseBqwFLCStiK2truZLiYiIiJwgBVMiInIqVatVrLUMBgNaL7mdjQ21lZjaSox3nmzH\nsXMzw7sQMBkbAqZpcBQlhqRpiVKDjd++Nex1eTcJyLIQkHkPjasRUSV55raVxYjKYoQvJ+Hb34x4\n8FfDSXhm9qqvKh2LTZ8Mo55mrNmrNurfLvj1/7G7HzwZT1SxxNUQPu1VIvknj1GOPFt/G4IyjCep\nW5JGhCs82W4I10wcnt/KSnToCqvDSOoWLsd47yn6nmw3BHPTIPHg6/xEbsikUm7kiWuO7uMtrqxe\nOZI1iYiIiMirKZgSEZFTyRhDo9FgMDj88HNjDZVOaPOaV8YaooohqrzGfSJDdTmmuvz2X9/Ghta7\nCd751w+NFqA2uerL0BKZ9x0menG4dtSMMSTNEH69jmynpBh4ujvbfPNb3zym1YmIiIjI0xRMiYjI\nqVWv1+kPBrNexpn0tpVMJnqzgGiWvHfs7GxrRz4RERGRE3R6floUERF5yutWTIm8zKDo45zTjCkR\nERGREzS3wdTGxgbXr1/nxo0bs16KiIjMqXq9zmCoiik5Gv1sB0C78omIiIicoLlt5dvc3GRtbW3W\nyxARkTnWaDR4eP8RvMY8JpEX6eUhmFIrn4iIiMjJmduKKRERkVdRK58cpX62g7WWVutl+zyKiIiI\nyFFSMCUiIqdWvV6nr1Y+OSK9fJuF1gLW6scjERERkZOin7xEROTUmlZMee9nvRQ5A/rZDu225kuJ\niIiInCQFUyIicmo1Gg2KoqDw+ayXImdAL9+ho2BKRERE5EQpmBIRkVOrXq8DMC6GM16JnAX9XBVT\nIiIiIidtboOpjY0Nrl+/zo0bN2a9FBERmVONRgOAUalgSt5eL9uhvdCe9TJEREREzpV41gt4kc3N\nTdbW1ma9DBERmWPTYGpcaAC6vJ28zBmXQ7XyiYiIiJywua2YEhEReRVVTMlR6Wc7AAqmRERERE6Y\ngikRETm1arUaACPNmJK31BtvA2jGlIiIiMgJUzAlIiKnVhRF1Ko1xqVa+eTt9CYVU+22ZkyJiIiI\nnCQFUyIicqrVanVVTMlb6413SGxKrVqb9VJEREREzhUFUyIicqo16nXGmjElb6mX7dBMVS0lIiIi\nctLmdle+Dz/8kCRJWF1dZXV1FYD19XXW19dnvDIREZkn1WqN7vYj7u1+QeL0+xZ5M19s3wQMWZaR\nNKqzXo6IiIjIuTG3wdRHH33E2trarJchIiJzzlrLrd7P+Yd//d/NeilyBty7f5cPFt+b9TJERERE\nzo25DaZEREQO49/7d/59fvC//IZaq0rSUMWUvJl//ON/RH+0y6WLl2e9FBEREZFzRcGUiIicaouL\nS6y2LEnLkraiWS9HTqmLzVV+PvwxaZrOeikiIiIi54p+tSwiIiLnXrOyQD/bwXs/66WIiIiInCsK\npkREROTca6YLlL6gP+jPeikiIiIi54qCKRERETn3mpUFALa3uzNeiYiIiMj5MrfB1MbGBtevX+fG\njRuzXoqIiIiccc20DcD29vaMVyIiIiJyvszt8PPNzU3W1tZmvQwRERE5BxppC4CuKqZERERETtTc\nVkyJiIiInJTIRtTiplr5RERERE6YgikRERERwgB0VUyJiIiInCwFUyIiIiJAI1lge0czpkRERERO\nkoIpEREREVQxJSIiIjILCqZEREREmFRMKZgSEREROVFzG0xtbGxw/fp1bty4MeuliIiIyDnQTBbY\n7e1SFMWslyIiIiJybsSzXsCLbG5usra2NutliIiIyDnRSBcA2NnZYWlpacarERERETkf5rZiSkRE\nROQkNZM2AFtbWzNeiYiIiMj5oWBKREREBGikLQC6Xc2ZEhERETkpCqZEREREgGpUJ45jBVMiIiIi\nJ0jBlIiIiAhgjKHT7iiYEhERETlBczv8XERERF6P9x6XeWxiMNa81bHyvsMYiKqvfyxfeoqhJ+87\nioHDRJA0I5KWJUrebl3Hra1gSkREROREzW0wtbGxQbvdZn19nfX19VkvR0REzoB84BhvlRR9Fz7h\nJ39hwViDsWATQ1Qx2DRcRonBRK8ZzPhwYGMOfz/vPS4Hl3nKzE8uHXhIWhFpy2JfEOrkfcfoUUkx\ncMRVG+4HYCCuWeKaIa5b4tqrC6XzvqN3KyeZ3LYce7wP64smz8nBoGr6WPHhti4HjCepW5JWxMJ7\nCcXIM7xf0P88p8w9UWJCSFUNz62NJs//5PJtFCNHvuPIdhzeeQwGjwdjAL/3OuNhunQ8uNxTWYxo\nL7QVTImIiIicoLkNpjY3N1lbW5v1MkRE5JTwLlTqYPcDIe89+a5j3C0ph564ZqleiFj4fo2oYvGl\nx5fgckeZQ5k58l4INsqhJ9t2uLHfD18I4cYT170BMwmiMOFvzTSsmd7+4P3YC0vM5E9+8nc2MURp\nCMWShqVxNQFgcC+n91lOWYTHkC5YbGoYb5Xku46kbmlciel8rULtQkwxdOFxb5UMHxaMuyXD+yXl\nKCdpWaorMVH6ZABUjh39WwVYWP7tKivfqRKlJlQ99T3FwDHeKhg9LnFFeEjT3M2Y8Oe0E1G/GJN2\nIiqdEKZNg6Zi5Bg/Lhk9LunfyRl8UZDvusnrBt75vcunn+fpMzS9ZmODTaYf4bXPdhy+8ERVS9q2\nrKzVqC1FIVQ78Dq7HFwRzhOMwRogCo8hqhiWPl3k1u3P3/g8FBEREZHXM7fBlIiIyGEYG6qBxg9L\nRg/KECKFdAiApGloXktof1Chfjmmuhy9sirHO08x8pRDRz4IIZUrfAhR3CQ8ceDKUIFj4xAq2dhg\n4vBnDPgi3Gb/MlQf2chMqrT2r9vY7FU3RVVDlO5XNxVDx/Bhwehhye5nGcP7JdmOo3Yh4vLv12he\nS6muRC+s0Cozx3jL0buV0f1ZRv/zHOegsmhJFyIGdwvc2LPwQcKFtRrNd5K9Y1WXjuJVgrhqia+G\nsG3521XK3FOOHK4IwZErDjxP04DKh+c7XHpcEQKuoj8JEHuOcuyxFpa/XaFxNaG6ElNdirDxm1Ve\ndR532N7exnv/WhVvIiIiIvJmFEyJiMipljQs7/3rLcrM74dGBwKNymKo3nkdxhqSuiGpW6rLx7Pu\n1xHXLK13UlrvwIXv1fZmN1WWohBsvUKUWuqXLPVLMSvfqdG/nbN7M6f7yzG9z3Lql2Mufr/Gwgfp\nGwc6rytKDFHyeq/L87hiUm12ROvudDqMx2NGoxG1Wu1IjikiIiIiL6ZgSkRETr3q8vn67yxpWJLG\nm22sa2ND692U1rspF363xuhRQf1iTFQ5nRv1HnWQ1ul0ANja2lIwJSIiInICTudPoSIiIvLWknqo\nxDqtodRxmAZTGoAuIiIicjL0k6iIiIjIxDSY2t7envFKRERERM6HuQ2mNjY2uH79Ojdu3Jj1UkRE\nROScSJKERqPB1tbWrJciIiIici7M7VCOzc1N1tbWZr0MEREROWc6nY5a+UREREROyNwGUyIiIiKz\ncJhgyntP3nPENXtiOxm+jjJzlCNPMfKUIwdA2o5IWxZjX77eMnOUY0/SePVtRURERN6WgikRERGR\nAzqdDl988cVz/y7bLendytn+NGNwOyeqWZrvpjQuxVRXIqpL0RNhjis8xcCRDxzl0OM9GDv5iMzk\nusF7jy/C7V0xuV56fOFxBbjcUYzBjUNohAMMYSiDAWsMWHC5J991FCOHyz0ugzIPwVTSsCTNiMaV\niNqFhLRtiaqWou/Idh3jrZLhg4Jsp8TlnrhuqS7FVC9EVBYikpYlbVmimiVKXh1YudJTjjzlZM3T\nD5d5XO6prkQ0riZzGeyJnHbeT/7NjcJlMXL714clbgydr1eoX9LbQRGZPX0nEhERETmg0+nwk5/8\nZO/PxcjR/yJn51cZOzdzsu0SmxoqSxFu7Hn0N0MelJ64akk7Ec1rCb50jLdC4DMNZlw+OaD3YMEY\ng5mGSx58Cd75EDhhwicBvAHrMdZgIrCRmdxmcj8/vRJCLpsYbGpIaxabgE1i8JAPHEXf8fBHBb4c\nYUy4XTkOX8dEEFUNcc2SVMLnd26O2fokfCkbQVQzRKklaZpQgdW2JI2IaHL7fODIdkqyrqPoOcp8\nEkRNAjeDCY/Ke2xsqF+NWfpmldaXEtKF6FhfV5m9MveMHhWUI0/zmkLJ1+GdJ5a3gX0AACAASURB\nVNtx5H0XwusSfBkupyF2MQrfc/LJ7cpsPwh2xeR7BAaPxzvY+vmYy39QZ+mblbmrjjwYzNvUHCoM\nF5HTS8GUiIiIyAGdToft7W3KssTnhl/97zv0bxXYCCpLEe2vpk+8iatdDNUJxdCT7zjufzzARgZb\nMUQVQ9KyVFdCSGRMuJ/3oerJe/AOjAnB0HG+OUxbEWlr/8/TN342NXvrehlX7FdAjR47+ncLXOaf\nvJEx2BiiSgi94rrBtkO7o4l54uuUY8fwQcln/+cu6WJE+6sVOl9OqV2MiNJX789T5p6sW4IhVHJV\n5nZPn9eS9Up2b+ZsfTLGWEialqRpiat275yahpNmUjFnbAg5p6GkTQxRag4d/OQDx/hxyehRweBu\nQVy3LH2rQnXp7d4qeOcZd0uGD0r6d3J6n+WMuw5XOBbeS1n5bo2F95KZhiJl7ul9lmFiQ+1CTFJ/\n+XlUjByjR2VokT1wPk+vRlVDZTEirr78ON5PqhuHIYw2T72erghBVLZdMrxfMLhfkPdC1ZMrQ8iL\n8TwRYrN/DkSTMCdp2PC5hGf+nQ/uFnz+5z2G9wsu/X79lY/dO78XdpXj/esh1LbEVUNUDeffwa/l\nysl9sv3LcrRfSZkPHHnPUQwdbnLcaeBG6Uk7EVf+sEHzWvLS9YnI6aVgSkREROSATqeD956dnR0a\n8QJZt6R+OXppRY8xhqRuSOqW+uVX/3hljIFov/BpFmxkIDr8CmxssE1D0jyaACiqWJrX7CS8cDz4\nqyGPfjAkXYioLkfUr8RUOhGVTqjMKsee8VbJeKukf7tgcC+8UcdAXDOkrYjqxZjqYpillbYtlU40\nd5Ugz+O9Z/igZPuXY7Y+GTN6VJI0bGjP/CJUvPBEBnggjDD71XcmCgGgjUMoZVNDXDPEVUtUMyE4\nqFhMHIKKvO/o38oZPiwpeqESZ1pFt/XJmOXfrrD0W9VDV7OV+f5rNHpU0Ps8Z/S4pOg7bGxIFiyN\nqxGYmN6tEFQtfCVl5TtVmteSQwWkB5+zvO/2KgWnLbJYgz1EyFuOHdu/ynj0oxG9LwrAk7YjGlcS\nWu8m1C7EVJcjfOkZPSoZPizpf5HTv5OT7zhc+fzj2hiSlqW2ElO/God22KUIE0O27UJQ97BkcDsn\n2yknFYtmP5Qy4dI7QvsvnigNAVOlExHVJsHkEahfjsl7jvv/bMjoYcmVP6rTuLIf/kzD48H98Fpl\n246y8Pg8VDL5wofnwXhsbInSSXVTxZJOvk/kg0nYVBxsTQ4VW9NzOFR6Es7LaYtzBFFiMFXD4E7B\nr/9sh0u/X2Pld2qqtJO507+b079VYGLCLwXS8EuEKA2/lHI5+23tmZ8EsI4otUTVyW0rNlzWDGnz\n/FUQz20w9eGHH5IkCaurq6yurgKwvr7O+vr6jFcmIiIiZ1mn0wFge3ubxvICcLyVTOedsYbqUpjP\nVY5D5cTu5zlbPx9jgKhmiWsGX3iynsO7UJGVNC21S+GH93LoGT0u6H2R4yeBQVwPx219KaF2MaG2\n8mS46ApPMQwVK8XQYSND7VJ8oi1DZebof1Gw9cmYnV9n5H1HdSWi87X00Oecd36vpdOXkzf+kxCg\nGHqyXR9aviZzy/BmEmuF+8X1UJFV/dJ+a533nvHjktt/MeDxT8esfLfK4jeqexU1rvQUfbfXHprt\nOgZ39sPCcuwwxhA3DJVORGM1fiZ0Wng/pRg6tn8xZvfXOZ2vpyx/u0rajkLVzXPCl7zvGD4oGD4o\n2L2ZM94qJnPb9ivIjAnBRu1iTONqTGUporoUh6CPUPG0/WkIpPpf5MR1S+u9BGMg33UhHPzpKIQr\nHYsvCHPXCo9NDOmCpfFu8sLzpMz83jnc/fkYzKRyMDYUfReqnWx4bpJmqKacPOmhLTdkbRgTqq9e\nJ6x7E0nT0v5qyu7NjJt/VnDx9+tEqaF/K6d3qyDbLnGlJ65N3kAnBlu3TwRJ3k3Ou0nbYDEMLb3A\nXrVkVDHYxqS9ODaY1wjX0oWI0aOCL/5xn+G9kst/UKfSOX9v3GfBO0+2G6rZqovRmalMPSrZbsnD\nH4549OMR+a7bC5ghXNrYYqNJ1WDx1C8YDhY7+vCLhen3mC/9ay1qF+Y2qjkWc/toP/roI9bW1ma9\nDBERETlnpsFUt9vl6vI7M17N+RJ+Y2ypLoc/Twc4F31H1LBUL8bPDSziKlQW99+oeu8p+qGNrHer\nAIakLUP1QkzStGTdkrwf2hJdFgIiGxkqKxGdr1ZoXkuoX372a5Vjx7gbBsXnfTcJgvZDH1d6rIX6\n5YTqSqi4efoYrvQM7hb0Ps/o/iJj+KDAWkP1YkTznddvVZoGWAYgNkSV1z7Es8c0hupyTGUxYvig\n5Iv/u8/jn45pXksYPy7JdxzFdHZaBuCxB8LCuHq4xxHXLO2vVMh7jkc/HtP9WbYXfsRNS7oQKt+i\nxNK7lTO4H4bz4yBuWJKWDW8EJy2x4TJU8XR/OebxTyftkC0bqvAux+x8mjO4FwKphQ9S7IGAqbIY\n7Z1HxSjMasJC41pClB4uSIlSQzQJWiGspxiEc6O68rK5WrMLv21saH+lwuBeaO0zJoROafvlIdyU\nsYYo5dDP0ZuoLsfEDcujn4wY3i+48kd1Fj5Ijz24O09cGdpLx9slWTdshjG4V5L3QmVfuhDRei+h\nuZpQvxQfyVzArFdOqixDoO3yAxtljD1l7qlfiWm/n1K/HL8ysM92w1pD+MmTl0f4C6Yyc2x9MubB\nX4fzsX45fqbVdBrY+tKHEPepdvan+dJTjD3DO0UIsc6ZuQ2mRERERGah2WwSRRFbW1uzXsq5Z0xo\nP3vVvJ7n3S850Hboy9D2Nbhb4HI/aZsI879C20WMy2H8uOTOP+1j0zBvqP3VlEonYtwtGdwuGD4s\nyHsel7swlN74/RlPk9+U+xL8X4+IqqH1qvVuCLmSpqV/N6f7sxBGuSy0jrXeS+d2sLOxhvqlmNpK\nxOBewcMfjMJzV53MTrtwuB0aXyVpWjpfS8NukpMZRKNHBf07oW3MuzB4P21FIUw6RLVN7UK4dEWo\nYBrcLdj5VU7SNOEYr2gHi6v2tc+75zE2nIunwfS1nm7OMG/iqqXz1ZT+FwU3/9EuS9+qUl2M9mav\nTduhbMyBofCTCsIynEdhd9LwceiqxEnbaL7j9gbQVzqWxtWEuPbyc2TaDhlVzTO7tj7v64y7jsHd\nnGLg8c7vV0GWwHOCcF+EjTWqyxHLv119ohXzZbJeCJizSRA1elAyeliEKshh+P5m40lFZctSXTZk\nO46HfzPkwccj0pahdjlm4UspJjZPVP6ExxLaWkPFnNm7jg2ViaNHBb1bRQj5e26v8hMb2sxNFCqI\nMLB7M+PBxyMaV2MWv1mh9c7+Zhll7hk9LBjeL9i5mTG8X1Jm/pndb601VJYj2l9JaV5LSFuvH6p5\n5ylG4RcLDz4esvtZRroQ0fn68ytcp4HtYUNnE01vfz4pmBIRERE5wFpLu92m2+3OeilyRExkSBde\nPicsSsPMnfrlOAx4f1Tyxf/TZ9r4FtUsScPQuBoRVV795i+0Mznu/9UgvElLDG7siVuW+pX4SEKP\nk2IiQ+Pq8Q+ejqsWqkd7TBubvVll8mqv02I3C8Yamu8kZDslj348mrTu7s9bs/EkmHL7uxZ6f2A4\nfBRm+MR1S2UporYckTSj/baq6U6nPgRZ48f7VUOh8i0cz1hDpWNZ+HKFhXcTGlfjvTa38XbJ8F5B\n71bG7m9CO6RNwk6uCx+Eyp/6xZi4ZvfaZvt3CrY/HTO4W5DvuvBgpsG3eXqzg8nn7HRovgn3/0XG\n4m9VWP529bltYMXI0fs8Z/uXGb1bOcXATdrLDFEtVDBWOhGNK/Fzz4PaBUvtQrzX3tf7PKf78+zA\nHgD71ZvhGT+ws6zxIVC2oeVz2uIaNgd59Q6dxdAxuFewezMj7US0v5yCNfR+kzHulrixJ6pPdovt\n2P3X3wEuBHk7NzO6n4xJOxEL7yUsfJDSWE2Iq3a/OnfoKYehTbkc7e80m+848t2w02XeDy17C+8/\nWXEpb0fBlIiIiMhTpjvzyfkUVSyNq5bGWxwjrtlQTXEpxvuwG5nms4gcjecFzXutU4Xf2+X06d1O\nXe4pRiF02L2Z0/1k/NSRD8xgIwSbcd0SN6a7q4ZjuTIM+n/48ZAHHw+oLscsvJcy7hZ74ZKNDUnb\n0nw32dtF9M4/7YOFSieieS0J1XyT2WxxLezoWL/y7Ey2V6lfDrPQHnw8pPvzMUvfqk5mttm9QKf7\ns4zhoxJjoboSUV1O3ihYMdZQaUdU2ocPe72fhISO5+4Q+SpxzdJ6N2yWkW07Hv5gBH6yYcGBUPBl\naivs3f/xT8Y8/PF4b75htuv2KjbdeDIParLEJ3a6rBkqi4f7evJ6FEyJiIiIPKXT6ahiSo6MMaHN\nSESOz17r1EtmXdnEkCYRtN7ua9nIUFuJqa2EsGvcLXnw8QBbCRV6T89DsonZ3zyg8GTbJVufjENF\n3+LzNwh4XdOwbrxVcvcvB2x9MqayGDG4W1AOXagUev/V1UnHwZgwBP+tj2PNE7Pg3ub+rgjhYu9W\nvhc8Jc1Je7d2fjxxCqZEREREntLpdLhz586slyEiInPOJmEm3WF3UbNx2FxgusnDUasshna20aPQ\nUlhdiUjqx9+Ke9rY2Jy7ne/m2dzWoG1sbHD9+nVu3Lgx66WIiIjIOdPpdDT8XERETiVjQkVX851k\nr1JLZJ7NbUS4ubnJ2trarJchIiIi51Cn02E0GjEej2a9FBEREZEzTfGpiIiIyFM6nQ4A2zsagC4i\nIiJynBRMiYiIiDxlGkx1tzUAXUREROQ4KZgSERERecpexZSCKREREZFjpWBKRERE5CmVSoVaraZW\nPhEREZFjpmBKRERE5Dk6nY5a+URERESO2dwGUxsbG1y/fp0bN27MeikiIiJyDnU6Hba3VTElIiIi\ncpziWS/gRTY3N1lbW5v1MkREROSc6nQ63Ll9F5ZnvRIRERGRs2tuK6ZEREREZkkVUyIiIiLHT8GU\niIiIyHN0Oh22d7p472a9FBEREZEzS8GUiIiIyHN0Oh2ccwyL/qyXIiIiInJmKZgSEREReY5OpwNA\nL9+Z8UpEREREzi4FUyIiIiLPMQ2m+vnujFciIiIicnbNbTC1sbHB9evXuXHjxqyXIiIiIufQwsIC\nxhj6mQagi4iIiByXeNYLeJHNzU3W1tZmvQwRERE5p6y1LLQW1MonIiIicozmtmJKREREZNba7Q59\nBVMiIiIix2ZuK6ZEREREZm2htcDDxw+4t/sFcWlmvRwRERE5o1zhGQ4L3s1qNEhmvZwTpWBKRERE\n5AW+/93f5X/86Z/yP338D2a9FBERETkH3nn4n7P4pQ9mvYwTpWBKRERE5AV+59vf4d/9xn9E0oiI\n66qYEhERkePhCs/wTsHFlUuzXsqJUzAlIiIi8gJpmnKxvkraikiaGs0pIiIix8MVnl43J03TWS/l\nxM3tT1gbGxtcv36dGzduzHopIiIiIiIiIiJyDOa2Ympzc5O1tbVZL0NERERERERERI7J3FZMiYiI\niIiIiIjI2aZgSkREREREREREZkLBlIiIiIiIiIiIzISCKRERERERERERmQkFUyIiIiIiIiIiMhNz\nuyvfhx9+SJIkrK6usrq6CsD6+jrr6+szXpmIiIiIiIiIiByFuQ2mPvroI9bW1ma9DBERERERERER\nOSZq5RMRERERERERkZlQMCUiIiIiIiIiIjOhYEpERERERERERGZCwZSIiIiIiIiIiMyEgikRERER\nEREREZmJuQ2mNjY2uH79Ojdu3Jj1UkRERERERERE5BjEs17Ai2xubrK2tjbrZYiIiIiIiIicKtlu\nSTnyVDoRNjGzXo7IS81tMHVcBoMBn3zyCd/4xjeo1+uzXo6IiIiIiIicUt578l3HuFsSpZbKoiWq\nvLgxyXtPMfBk2yU2MaSdiOgIg6N84Bh8UWArhrRp2b2ZYyxUViLSBYsxCqlg/3Urxx6bGqLJh4n0\n/MzCuQumPvnkE77//e/zx3/8x7Tb7Sf+bn19nfX19RmtTERERERERN6Gdx5jXxwulLmnHDrKkacY\nhmACgMldjDFgwBiIqoa4Zonr5pmwKe85Ro8LyqEnaVmaqwnZrqN/u8BlnrhhSduWpGnxJWQ7jqxb\n4nJPXLdUlyLKsaf3WY4rPJV2RGUpIkrfLBgpx47+rQIMLH6zwvLvVKmtRPS+KOj+YszuzYzBnYKk\nZamtRC8Nz+ZZPnCMHhaAodIJz+/LXu+nFQPH6HFJ3nOkLUtct5QjT77tKDMfzh8M3nii1GATQ1Qx\ne+GVTcxrfT05nHMXTE2pVVBEREREnseXnvG2Y/y4wDuDjSFpWuKGJa4d7k2JLz1Zz5FtO4qhAwzW\nQmXJUulE+q28yFsoho7RoxDyuHwSLE2SJWNCNUz4jMHjn7ivTQxx1RDXIhpXEipLIdjwzuMdeA84\ncIVn9LAI3wu6Dpfn4f6xocw8yf/f3r0Hx1WeeR7/PadPX9S6+iLZRmAwEG6BhEsgAyFcwhKSkNUm\nIYQ4uzUTKpVlahKSVW2F7LIbQsWZmaqdyTjUDKFSpIYKwTiQDLNQCRmGWajshgnMYmdCWIIT7tgY\nX2VZl5a6zznv/vGelluyJduS2t22vp+qY0ndp0+f0/3otc7Tz/OetkBtx2fVeWpercdlVViUUVzx\njxnZWtHQaxWNbos0+lYkBVK+K6NFZ+bVdnxWxWWh8osySiKn0bcjDW+uaPCVskY2VxRXfELEAn8w\n/qs/LgvSpFn1tsBkJo0PxnKR1HFKVkvf3aL2E7IT41TnyTl1npzT2ECsodfLGvjtuEa3xorLFclJ\nQc4UFv3YFhYDBeHBx6Z4PNH4QKLyULzffdWKLJfse1/2vQsm55wyeVO2LVCufebqsqokdhrfFWts\nIFZY8ElASRrdHml0aywLnLIdGeU6A4WFQC52ShJJiVMS+/ezMpyoPBgrUwhUXBZqxfvyajs+VL4r\no2jMJyujkk9WRiWnaCTW+GCiylCsykhaFVfxia1cl98GCar5s2ATUwAAAFg44vFE5b2Jksif9GXy\ngYKcKcj6EymXOJUH/SfpLpZyXUFacRBqdEek0a2RxgdijWxxkvOtH5apqa4I/EliEjtVhhNZYMq1\nB2rrDdV+Uk7Z1kDDWyra+0pZgy9VZKFUWOJPpJq9tcY5p/EBn2DL5H31QKYQzGv7keRPZJOKU1x2\nSsrp14qTi+SrTzoChS2HX+URjSWqDCWqjPgKifziTNO/5pheaUek8YFEHSdlle8KFLb5ZEQmH/jK\nlqz/PXSRUxL5BJOLJTlfqZRt91U22eKhVdpEJT92lPfGGh+MNb4nVrEnq7becL9YymRNrSuyal2R\nVfd5TuN7EpW2R8oUTMWecL/4zeRM7Stzal+ZU8+FRZW2RxrZUtH4Xj8OJZGTYp+YcbE/Hhf7xLcS\nySVS4pxaV2S19NyCOlblFEyT9C4syqiwqEVLzi5obFes8lCsyt5EpZ2RSjtiVYZjje2IFUdOFkjZ\nVl9NFLYGCsI0sbMnUTSaKMj5iq+u0/PK5CU5v6T5QDnnjy0I/WKhT+hZxlTeG2t0a+STdmmCzEyy\nrCnIpONqUP0qlQcTJZFUWJrRcZcW1X5STsWeUDKpMpT45NRbkfa+Xtbo1lhJpVLz+H3bybZmtPTd\nBbWtzKrYMzmplC2assXpx5YkcorGEsUlp9FtkbZvKGnPprKKy32CEXNHYgoAAABNyyVOUckpCHVY\nrSdJxam8N/bJqLJTkAuU7wiU7Qz8/C57Y8XjTklF8mdUUq4zo8Vn5dWxKqfW47LKtu57viT2c8KM\n70k0PhBpbCCWK/vb/UmwP1m0wNR9nj/xKXRnlGvbd9LS9Y68KhclGtlS0eDLZQ2/Udbg25HCtkAt\nPeGsW3jqaXzQn0TmOwMVu0Of4BlKNLYzTl+79KSvzZ/sh8XDb3OJy04jWypKyi5NGEqZnK+myHdm\nlMlLw1sjlbbHiscqstCU6/T3KzApcRNVLs759yEadaoMx3KJj5tcW6DOk3MaeSvS4Kay8kszKiyZ\nPkHlYqfKiE8whq02p0RWUvHtQc3eOjW+J9bo25HMfCIw35WZ87HPpyRyGnqtrLAY6PirWrXknMK0\nSZj5FLb4hGhx2eGdOptZmgw6tMRFJmtq681OVAPNxLm0uiutCrLQDvm1CEJTcVk46Xhc4lQZ9RWe\n5cFYY7tijb5dSStHK0oqUrbNVFwRqmNVTsVloVq6w0Oqrjqgc31L5fhArPHdsUq7IkUjvq0yHveJ\naf97I3WemlPXaXm1nZBVWJj8O5TryCjXkVHXqXktj4oq7Yz8nFGhT0hVk2FBqDkl04PQ/FjeJrV0\nh2o/Maudz41p13NjKu2M1dYbKpwmsXWw1tLJK89q944JTZuY6u/vV2dnJ/M+AQAALBAu8QmFaNRX\nt8Rl/8l92BIoqUhJJZLMTSRBqomjqW0YclKQMWU7An9C05tVoTujwpJQmaxNzDFTGfWfgEelRJYx\nta4Iles48ElkkDEVFocqLJak3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      "text/plain": [
       "<matplotlib.figure.Figure at 0x6d75e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### PLOT 3: Sized by measure 1\n",
    "\n",
    "# Initialize figure and set size\n",
    "fig = plt.figure(figsize=plot3_size)\n",
    "ax  = plt.gca()\n",
    "\n",
    "# Prepare scale\n",
    "scale = plot3_scale\n",
    "\n",
    "# For each track...\n",
    "for ID in tree.keys():\n",
    "    \n",
    "    # Get the track's position, size, color\n",
    "    p = np.array([tree[ID]['position'] for step in tree[ID]['indices']])  # Repeated for plotting\n",
    "    s = tree[ID][plot3_size_track]\n",
    "    c = plot3_color\n",
    "\n",
    "    # Create the size-based area on top\n",
    "    plt.fill_between(tree[ID]['times'], p*scale+(s/2), p*scale-(s/2),\n",
    "                     edgecolor=c, facecolor=c, alpha=0.3)\n",
    "\n",
    "    # Create the position-based main line\n",
    "    plt.plot(tree[ID]['times'], p*scale, '0.4', lw=1)\n",
    "    \n",
    "    # Create lines connecting branching events\n",
    "    if tree[ID]['branches'][0] != '_leaf_':\n",
    "        \n",
    "        # x positions: (time point before event, time point after event)\n",
    "        x  = np.array((tree[ID]['times'][-1], \n",
    "                       tree[ID]['times'][-1] + 1*time_resolution ))\n",
    "        \n",
    "        # y positions: (position before event, position after event)\n",
    "        y1 = np.array((tree[ID]['position'], tree[tree[ID]['branches'][0]]['position']))\n",
    "        y2 = np.array((tree[ID]['position'], tree[tree[ID]['branches'][1]]['position']))\n",
    "        \n",
    "        # Plot the lines\n",
    "        plt.plot(x, y1*scale, '0.4', lw=1)\n",
    "        plt.plot(x, y2*scale, '0.4', lw=1)\n",
    "\n",
    "# Labels\n",
    "plt.xlabel(plot3_xlabel)\n",
    "plt.ylabel(plot3_ylabel)\n",
    "plt.title(plot3_title)\n",
    "\n",
    "# X axis cosmetics\n",
    "xmaxtime = max([np.max(tree[ID]['times']) for ID in tree.keys()])\n",
    "xmintime = min([np.min(tree[ID]['times']) for ID in tree.keys()])\n",
    "xoffset  = (xmaxtime-xmintime)*0.05\n",
    "plt.xlim([xmintime-xoffset, xmaxtime+xoffset])\n",
    "\n",
    "# Y axis cosmetics\n",
    "ymaxpos    = max([tree[ID]['position'] for ID in tree.keys()]) * scale\n",
    "yminpos    = min([tree[ID]['position'] for ID in tree.keys()]) * scale\n",
    "max_m      = max([np.max(tree[ID][plot3_size_track]) for ID in tree.keys()])\n",
    "yoffset    = (ymaxpos-yminpos)*0.05 + max_m\n",
    "ytickspace = (ymaxpos-yminpos)/10.0\n",
    "ax.set_ylim([yminpos-yoffset, ymaxpos+yoffset])\n",
    "majorspace = np.arange(yminpos-yoffset, ymaxpos+yoffset, ytickspace)\n",
    "minorspace = np.arange(yminpos-yoffset ,ymaxpos+yoffset, ytickspace/5.0)\n",
    "ax.yaxis.set_ticks(majorspace)\n",
    "ax.yaxis.set_ticks(minorspace, minor=True)\n",
    "ax.xaxis.set_ticks_position('bottom')\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.tick_params(labelleft='off')\n",
    "\n",
    "# Inset scale bar\n",
    "from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes\n",
    "ax_inset = zoomed_inset_axes(ax, 1, loc=3, borderpad=1.5)\n",
    "ax_inset.plot([],[])\n",
    "ax_inset.set_ylim([majorspace[0],majorspace[1]])\n",
    "ax_inset.spines['top'].set_visible(False)\n",
    "ax_inset.spines['right'].set_visible(False)\n",
    "ax_inset.spines['bottom'].set_visible(False)\n",
    "ax_inset.get_xaxis().set_visible(False)\n",
    "ax_inset.get_yaxis().tick_left() \n",
    "ax_inset.patch.set_visible(False)\n",
    "ax_inset.tick_params(labelleft='off')\n",
    "ax_inset.yaxis.set_ticks(minorspace, minor=True)\n",
    "ax_inset.yaxis.set_ticks(majorspace)\n",
    "ax_inset.set_ylim([majorspace[0],majorspace[1]])\n",
    "ax_inset.yaxis.set_label_coords(0, 0.5)\n",
    "ylbl = ax_inset.set_ylabel(\"   \"+plot3_scbar_label % ytickspace, \n",
    "                           fontsize=12, va='center', ha='left')\n",
    "ylbl.set_rotation(0)\n",
    "\n",
    "# Tight layout\n",
    "# Note: It complains a bit because of the inset, hence the catch.\n",
    "with warnings.catch_warnings():\n",
    "    warnings.simplefilter(\"ignore\")\n",
    "    plt.tight_layout()\n",
    "    \n",
    "# Done\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AwGB7hKR/dffLWn/fbmYXSnroEdZ5uKRb3P0Drb9vM7MPS/qThP1csbjVCMgp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\nx8V7oBd9RfWU4wckPUnSf5L0ZTN7diOIPi36vO+YmameXv5t59zyPF1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      "text/plain": [
       "<matplotlib.figure.Figure at 0x8bc2e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### PLOT 4: Colored by measure 1\n",
    "\n",
    "# Initialize figure and set size\n",
    "fig = plt.figure(figsize=plot4_size)\n",
    "ax  = plt.gca()\n",
    "\n",
    "# Prepare colormap\n",
    "cmap = plt.cm.get_cmap(plot4_cmap)\n",
    "vmin = min([np.min(tree[ID][plot4_color_track]) for ID in tree.keys()])\n",
    "vmax = max([np.max(tree[ID][plot4_color_track]) for ID in tree.keys()])\n",
    "\n",
    "# Prepare scale\n",
    "scale = plot4_scale\n",
    "\n",
    "# For each track...\n",
    "for ID in tree.keys():\n",
    "    \n",
    "    # Get the track's position, size, color\n",
    "    p = np.array([tree[ID]['position'] for step in tree[ID]['indices']])  # Repeated for plotting\n",
    "    s = [plot4_scale/2.0 for i in tree[ID][plot4_color_track]]\n",
    "    c = tree[ID][plot4_color_track]\n",
    "    \n",
    "    # Create the size-based area with color-based coloring\n",
    "    for step in range(1,len(s)):\n",
    "        overstep = np.abs(tree[ID]['times'][step-1]-tree[ID]['times'][step])*0.2\n",
    "        step_x   = [tree[ID]['times'][step-1], tree[ID]['times'][step]+overstep]\n",
    "        step_ylo = [p[step-1]*scale-(s[step-1]/2), p[step]*scale-(s[step]/2)]\n",
    "        step_yhi = [p[step-1]*scale+(s[step-1]/2), p[step]*scale+(s[step]/2)]\n",
    "        plt.fill_between(step_x, step_yhi, step_ylo,\n",
    "                         facecolor=cmap((c[step]-vmin)/(vmax-vmin)), \n",
    "                         edgecolor='')\n",
    "\n",
    "    # Create the position-based main line\n",
    "    plt.plot(tree[ID]['times'], p*scale, '0.4', lw=1)\n",
    "    \n",
    "    # Create lines connecting branching events\n",
    "    if tree[ID]['branches'][0] != '_leaf_':\n",
    "        \n",
    "        # x positions: (time point before event, time point after event)\n",
    "        x  = np.array((tree[ID]['times'][-1], \n",
    "                       tree[ID]['times'][-1] + 1*time_resolution ))\n",
    "        \n",
    "        # y positions: (position before event, position after event)\n",
    "        y1 = np.array((tree[ID]['position'], tree[tree[ID]['branches'][0]]['position']))\n",
    "        y2 = np.array((tree[ID]['position'], tree[tree[ID]['branches'][1]]['position']))\n",
    "        \n",
    "        # Plot the lines\n",
    "        plt.plot(x, y1*scale, '0.4', lw=1)\n",
    "        plt.plot(x, y2*scale, '0.4', lw=1)\n",
    "\n",
    "# Colorbar \n",
    "scat = plt.scatter([10,10], [10,10], c=[0,1],  # Invisible fake plot for colorbar\n",
    "                   cmap=plot4_cmap, vmin=vmin, vmax=vmax,\n",
    "                   alpha=0)\n",
    "cbar = plt.colorbar(scat) \n",
    "cbar.set_alpha(1)\n",
    "cbar.draw_all()\n",
    "cbar.ax.get_yaxis().labelpad = 20\n",
    "cbar.set_label(plot4_color_label, rotation=90)\n",
    "\n",
    "# Labels\n",
    "plt.xlabel(plot4_xlabel)\n",
    "plt.title(plot4_title)\n",
    "\n",
    "# X axis cosmetics\n",
    "xmaxtime = max([np.max(tree[ID]['times']) for ID in tree.keys()])\n",
    "xmintime = min([np.min(tree[ID]['times']) for ID in tree.keys()])\n",
    "xoffset  = (xmaxtime-xmintime)*0.05\n",
    "plt.xlim([xmintime-xoffset, xmaxtime+xoffset])\n",
    "\n",
    "# Y axis cosmetics\n",
    "ax.yaxis.set_visible(False)\n",
    "\n",
    "# Tight layout\n",
    "# Note: It complains a bit because of the inset, hence the catch.\n",
    "with warnings.catch_warnings():\n",
    "    warnings.simplefilter(\"ignore\")\n",
    "    plt.tight_layout()\n",
    "    \n",
    "# Done\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
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 },
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}