docs/example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example\n",
    "\n",
    "This page was rendered from a Jupyter notebook found [here](https://github.com/benlindsay/pbcluster/docs/example.ipynb).\n",
    "\n",
    "In this example, we will analyze particle clustering by loading a `.gro` trajectory file (see [here](http://manual.gromacs.org/archive/5.0.3/online/gro.html) for details about this format), then passing that trajectory data to PBCluster. Your trajectories can come from wherever you want, but it's your responsibility to get the particle coordinate data out of them to pass into PBCluster. That data needs to either be in the form of a 3D numpy array with shape `(n_timesteps, n_particles, n_dimensions)`, or a pandas dataframe with columns for `timestep`, `particle_id`, `x0`, `x1`, and `x2` (assuming a 3D simulation box).\n",
    "\n",
    "Here's a visualization made with [Ovito](https://ovito.org/) of the particle trajectory we'll be analyzing in this example:\n",
    "\n",
    "![Simulation Visualization](traj.gif)\n",
    "\n",
    "As the simulation progresses from timestep 0 to 39, the particles start dispersed and aggregate into a single cluster.\n",
    "At the end of this trajectory, it looks like there is a large cluster on the bottom and a separate, small cluster on top, but those are both part of the same cluster.\n",
    "Since this box has periodic boundaries (i.e. particles wrap around to the other side of the box if they pass through a face), the cluster wraps across the top and bottom faces of the box."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mdtraj # for loading .gro data\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import plotnine as pn # for ggplot2-style plotting\n",
    "\n",
    "from pbcluster import Trajectory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load `.gro` data\n",
    "\n",
    "We need the particle coordinates and the simulation box size data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_iterations: 40\n",
      "n_particles: 34\n",
      "n_dimensions: 3\n"
     ]
    }
   ],
   "source": [
    "traj_data = mdtraj.load(\"traj.gro\")\n",
    "# The factor of 10 is because .gro files store coordinates in Angstroms instead of nanometers\n",
    "particle_coords = traj_data.xyz * 10\n",
    "n_iterations, n_particles, n_dimensions = particle_coords.shape\n",
    "print(f\"n_iterations: {n_iterations}\")\n",
    "print(f\"n_particles: {n_particles}\")\n",
    "print(f\"n_dimensions: {n_dimensions}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So this trajectory file represents a 3D box of 34 particles moving around for 40 iterations.\n",
    "\n",
    "Now let's get the box lengths data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[25.33333, 25.33333, 25.33333],\n",
       "       [25.33333, 25.33333, 25.33333],\n",
       "       [25.33333, 25.33333, 25.33333],\n",
       "       [25.33333, 25.33333, 25.33333],\n",
       "       [25.33333, 25.33333, 25.33333]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "box_lengths = traj_data.unitcell_lengths * 10\n",
    "assert box_lengths.shape == (n_iterations, n_dimensions)\n",
    "box_lengths[:5, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The PBCluster package requires the box size to stay the same for every iteration, so let's double-check that that's true and store just the first row in this array as the box lengths data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([25.33333, 25.33333, 25.33333])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "assert np.allclose(box_lengths, box_lengths[0, 0])\n",
    "box_lengths = box_lengths[0, :]\n",
    "box_lengths"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute Cluster and Particle Properties\n",
    "\n",
    "This package clusters particles based on a choice of a cutoff distance.\n",
    "Any 2 particles that are within that cutoff distance of each other will be part of the same cluster.\n",
    "The [radial distribution function](https://en.wikipedia.org/wiki/Radial_distribution_function) (calculated elsewhere) of this trajectory shows a first peak at $r \\approx 4.4$, so I chose $r = 4.95$ to capture particle pairs in that peak."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "cutoff_distance = 4.95\n",
    "trajectory = Trajectory(particle_coords, box_lengths, cutoff_distance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first calculate properties associated with particle clusters. In the dataframe below, each row represents a single cluster with an ID of `cluster_id` at timestep `timestep`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>asphericity</th>\n",
       "      <th>center_of_mass_x0</th>\n",
       "      <th>center_of_mass_x1</th>\n",
       "      <th>center_of_mass_x2</th>\n",
       "      <th>cluster_id</th>\n",
       "      <th>minimum_node_cuts_x0</th>\n",
       "      <th>minimum_node_cuts_x1</th>\n",
       "      <th>minimum_node_cuts_x2</th>\n",
       "      <th>n_particles</th>\n",
       "      <th>rg</th>\n",
       "      <th>timestep</th>\n",
       "      <th>unwrapped_center_of_mass_x0</th>\n",
       "      <th>unwrapped_center_of_mass_x1</th>\n",
       "      <th>unwrapped_center_of_mass_x2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.997920</td>\n",
       "      <td>2.973333</td>\n",
       "      <td>21.879999</td>\n",
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       "      <td>26.958887</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.469999</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>12.610001</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.280000</td>\n",
       "      <td>21.590000</td>\n",
       "      <td>8.280000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>10.280000</td>\n",
       "      <td>21.590000</td>\n",
       "      <td>8.280000</td>\n",
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       "      <td>18.280000</td>\n",
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       "      <td>8.104199</td>\n",
       "      <td>0</td>\n",
       "      <td>12.858000</td>\n",
       "      <td>18.280000</td>\n",
       "      <td>4.704000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.049999</td>\n",
       "      <td>21.530001</td>\n",
       "      <td>15.700001</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>13.049999</td>\n",
       "      <td>21.530001</td>\n",
       "      <td>15.700001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   asphericity  center_of_mass_x0  center_of_mass_x1  center_of_mass_x2  \\\n",
       "0     0.997920           2.973333          21.879999           1.625557   \n",
       "1     0.000000          16.469999          24.000000          12.610001   \n",
       "2     0.000000          10.280000          21.590000           8.280000   \n",
       "3     0.921179          12.858000          18.280000           4.704000   \n",
       "4     0.000000          13.049999          21.530001          15.700001   \n",
       "\n",
       "   cluster_id  minimum_node_cuts_x0  minimum_node_cuts_x1  \\\n",
       "0           0                     0                     0   \n",
       "1           1                     0                     0   \n",
       "2           2                     0                     0   \n",
       "3           3                     0                     0   \n",
       "4           4                     0                     0   \n",
       "\n",
       "   minimum_node_cuts_x2  n_particles         rg  timestep  \\\n",
       "0                     0            3  12.698203         0   \n",
       "1                     0            1   0.000000         0   \n",
       "2                     0            1   0.000000         0   \n",
       "3                     0            5   8.104199         0   \n",
       "4                     0            1   0.000000         0   \n",
       "\n",
       "   unwrapped_center_of_mass_x0  unwrapped_center_of_mass_x1  \\\n",
       "0                     2.973333                    21.879999   \n",
       "1                    16.469999                    24.000000   \n",
       "2                    10.280000                    21.590000   \n",
       "3                    12.858000                    18.280000   \n",
       "4                    13.049999                    21.530001   \n",
       "\n",
       "   unwrapped_center_of_mass_x2  \n",
       "0                    26.958887  \n",
       "1                    12.610001  \n",
       "2                     8.280000  \n",
       "3                     4.704000  \n",
       "4                    15.700001  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_properties_df = trajectory.compute_cluster_properties(properties=\"all\")\n",
    "cluster_properties_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's calculate properties associated with individual particles. In the dataframe below, each row represents a single particle with an ID of `particle_id` at timestep `timestep`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>particle_id</th>\n",
       "      <th>x0</th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>coordination_number</th>\n",
       "      <th>dx_from_com_x0</th>\n",
       "      <th>dx_from_com_x1</th>\n",
       "      <th>dx_from_com_x2</th>\n",
       "      <th>distance_from_com</th>\n",
       "      <th>timestep</th>\n",
       "      <th>cluster_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2.150000</td>\n",
       "      <td>21.919998</td>\n",
       "      <td>22.560001</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.823333</td>\n",
       "      <td>0.039999</td>\n",
       "      <td>-4.398886</td>\n",
       "      <td>4.475452</td>\n",
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       "      <th>1</th>\n",
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       "      <td>2.170000</td>\n",
       "      <td>21.500000</td>\n",
       "      <td>2.060000</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.803333</td>\n",
       "      <td>-0.379999</td>\n",
       "      <td>0.434442</td>\n",
       "      <td>0.989184</td>\n",
       "      <td>0</td>\n",
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       "      <td>4.600000</td>\n",
       "      <td>22.219999</td>\n",
       "      <td>5.590000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.626667</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>3.964443</td>\n",
       "      <td>4.298657</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>16.469999</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>12.610001</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>10.280000</td>\n",
       "      <td>21.590000</td>\n",
       "      <td>8.280000</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "   particle_id         x0         x1         x2  coordination_number  \\\n",
       "0            0   2.150000  21.919998  22.560001                    1   \n",
       "1            3   2.170000  21.500000   2.060000                    2   \n",
       "2           25   4.600000  22.219999   5.590000                    1   \n",
       "3            1  16.469999  24.000000  12.610001                    0   \n",
       "4            2  10.280000  21.590000   8.280000                    0   \n",
       "\n",
       "   dx_from_com_x0  dx_from_com_x1  dx_from_com_x2  distance_from_com  \\\n",
       "0       -0.823333        0.039999       -4.398886           4.475452   \n",
       "1       -0.803333       -0.379999        0.434442           0.989184   \n",
       "2        1.626667        0.340000        3.964443           4.298657   \n",
       "3        0.000000        0.000000        0.000000           0.000000   \n",
       "4        0.000000        0.000000        0.000000           0.000000   \n",
       "\n",
       "   timestep  cluster_id  \n",
       "0         0           0  \n",
       "1         0           0  \n",
       "2         0           0  \n",
       "3         0           1  \n",
       "4         0           2  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "particle_properties_df = trajectory.compute_particle_properties(properties=\"all\")\n",
    "particle_properties_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Cluster Properties\n",
    "\n",
    "The following plot shows how the particle size distribution changes over time. Just like the rows in `cluster_properties_df`, each point represents a single cluster at a given iteration. I added some transparency to each point so overlapping points are seen as a little darker."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dpfcS4Pem0sx3PxdVmAGuN3XZsmV466238OKLL6K8vDwruEQiERiNxqy/+9JLL+E73/lO5vZDDz2ERx99dF7qtNls8zLvYqPT6Ra6hFuC/VSO6XppNBphsVhgMplgMpkAALFYDCaTSTac2O32zN+5cQ6Hw7GgYQZ4d/QS4Pem0sxXPxddmJmUTqcxNDSEDRs24OWXX0Y6nc6caurq6sKePXuy/s7evXuxc+fOzG2VSgW/31/QutRqNWw2GwKBAFKpVEHnXmzMZjPC4fBClzGv2E/lmG0va2trcebMGZSXlyOVSmFiYgI7d+6U/VlhMpmg1Wpx5coVWK1WBAIBWK1W6PX6gv9smQul9xLg96bSzLWfc/1lYVGEmXA4jN/+9re46667YDAYcPnyZfzqV7/Cn/zJn2Dt2rXQarV45ZVXcN999+Ho0aMYHh5GS0tL1jzl5eUoLy/P3PZ6vfP2TZBKpRT/DSZJkuIf4yT2Uzlm6uX69euhUqnQ2dkJtVqNbdu2YdmyZbJ/x2w24z3veQ/eeustjI+Po6amBnfccQeMRuOCPpfvll4C/N5UmvnqpyAtgmsNI5EInnzySVy7dg3pdBqlpaW455578IEPfACCIKC7uxvPPPMMuru7UVFRgUceeQRr1qyZcd4b188UilqthsPhgN/vV/yLz2q1IhgMLnQZ84r9VA72UlnYT2WZaz9vPDAxG4viyIzJZMK+ffty3t/Q0ICvfvWrt7AiIiIiKhaL6tJsIiIiorlimCEiIqKixjBDRERERY1hhoiIiIoawwwREREVNYYZIiIiKmoMM0RERFTUGGaIiIioqDHMEBERUVFjmCEiIqKixjBDRERERY1hhoiIiIoawwwREREVNYYZIiIiKmoMM0RERFTUGGaIiIioqDHMEBERUVFjmCEiIqKixjBDRERERY1hhoiIiIoawwwREREVNYYZIiIiKmqahS6AiOZmfHwcIyMjEAQBFRUVsNlsOcem02n09/cjEomgrKwMpaWlUKvVOceHQiEMDQ1BkiSUlZWhrKws51hJkjA4OIhgMAidTofa2lrodLqc46PRKAYHByGKIux2OyoqKqZ9nB6PB+Pj49BoNKiurobRaMw5NpFIYGhoCAaDAel0GpWVlRAEYdr5iUg5GGaIisjw8DD279+PSCQCSZJgs9lw9913w+VyZY1Np9M4fvw43n77bQCAXq+H2+3G9u3bZQONz+fD/v374ff7oVKpoNfrsXv3btTV1cnWcvbsWZw6dQqSJCGdTqOhoQG7d++GXq/PGhsKhbB//354PB4IggCNRoNt27Zh+fLlsnO3t7ejtbUVoihmwsk999wDi8WSNTYej+PgwYPo7e2F2WxGNBrFhg0bsH79+mmfSyJSDp5mIioSkiTh2LFjkCQJDQ0NaGxsRDwex29/+1vZ8YODg7h48SKqq6vR0NCA+vp6XLlyBb29vbLjT58+jWAwiMbGRrjdbmg0Ghw7dgyiKGaNHRsbw5kzZ+ByudDQ0ICGhgZ0dXXh2rVrsnNfunQJIyMjmbElJSU4fvw4wuFw1thwOIzjx4/DZrNlHufIyAguXrwoO/e1a9fQ2dkJt9uNJUuWwOVy4cyZM/B6vbmeSiJSGIYZoiKRTCYRDAannFYqKSlBIBBAOp3OGh+LxQAgc+pHo9FApVIhGo3Kzu/3+7PmjkQiiMfjWWOj0ShSqVTm1I9KpYLBYEAgEJCde2JiAmazOXPqx2q1Ih6Py9YSjUYRi8UytQiCALPZjImJCdm5g8EgDAYDVKrrP86MRiNSqVTm8ROR8jHMEBUJrVYLm8025U19fHwcdrs980Z+o8mgkUgkAFwPQ+l0GiaTSXb+0tLSrLlNJhMMBkPWWJPJBLVajUgkAuD6Ka0bA8g72e12BINBSJIE4Hq4MRgMsutgTCYTjEZjphZJkhAKhWC322XntlqtiMVimUAXiUSgVqunXWNDRMrCNTNERUIQBGzevBmvv/46uru7AQA2mw133nmn7Pjq6mqsXbsWFy5cAHD9CM3q1atzroG544474PP50NXVlVkzs2vXLtn1NaWlpdi4cSNOnDgB4HqYaWpqQlNTk+zcq1evhsfjQVdXF9RqNdRqNXbs2AGz2Zw11mQyoaWlBW+++SbGx8eRSqVQU1OD5uZm2bmbmpowMDCA7u5uWCwWRKNRbNy4cdrFy0SkLII0+auSAs3HOXO1Wg2HwwG/349UKlXw+RcTq9WKYDC40GXMq2LsZyAQyFzN5HK5YLVac46VJAkDAwOZq5kcDofsUZxJoVAIHo8nczWTw+GYtpbh4WEEAgHodDrU1NRAq9XmHBuLxTA4OIhUKgW73Q6n0znt3KOjoxgfH4darUZ1dbXsEaJJyWQSw8PDmauZZrpSqtjxe1NZ2M9s5eXlc5qfR2aIiozNZpv2cuwbCYKA2tpaALP7gWmxWGSvGMqlsrISlZWVsxprMBiwZMmSWc/tdDpnDDyTtFotGhoa3jVvfkQ0FdfMEBERUVFjmCEiIqKixtNMpBiiKMLr9SKdTqOkpER2cemNQqEQgsEgEokENJrpvxXS6TS8Xi+SySRsNtu061SA61fUjI+PQ6VSoby8fNr5JUnC2NgY4vE4zGZzzqt2iIhIHsMMKUIsFsPhw4czV/lYrVbs2rUr53qOvr4+HDp0CLFYDBaLBTU1NWhpaZENHaIo4siRI2hvbwdwfe3Hzp07UV9fLzu3x+PBwYMHMTExAUEQMrvuyl0qLEkSTp06hfPnzyOdTkOr1aKlpQUrVqzI85kgInr34WkmUoTz58+js7MTtbW1cLvdSCQSaG1tRTKZzBobDofx5ptvQq1Wo6GhATU1NWhra8OVK1dk57569Sra2tpQXV0Nt9sNrVaLw4cPIxQKZY0VRRGtra2Ix+NoaGhAXV0durq6cP78edm5e3p6Mjvput1uWK1WHDlyhLvXEhHNAcMMKcLIyAhsNltmTxSXy4Xx8fHMpm43CoVCmUuVgetXwphMppwBYnR0FCaTKXPZcWlpKSKRiGyYmTy9NHkVjkqlQklJCTwej+zcgUAgs6cLcP2IUiqVUvxlmkREhcQwQ4ow+QGDk6LRKHQ6ney+JzqdDmq1eso2/bFYLOcam3fOHY/HoVKpZD8hWqvVQqvVZtWSa269Xg9RFDM7407+ebpPnyYioqm4ZoYUYc2aNRgcHER3dzc0Gg1EUURLS4vs1v12ux1r1qzBmTNnYDAYoNFoYLfbc36C87Jly9DZ2Ymuri7odDrE43HcfvvtshvKGY1GbNiwAUePHsX4+DhEUYTZbMbatWtl566vr0dNTQ26urpgMBgQi8WwYsWKWe/dQkREDDOkEE6nE+973/vQ09ODRCKBiooKuN1u2bGCIGS2u/f5fHC5XCgvL8959MRms2HPnj3o7OxENBqF0+lEY2Nj5kMT36m5uRlWqxUejwdarRb19fU5t9Y3Go24++670dnZiWAwCIfDgaamJtmPECAiInkMM6QYpaWlKC0tndVYlUqFpUuXYvny5bPaNdZqteK2226b1dyTVzDlClPvZDKZsGbNmlmNJSKibFwzQ0REREWNYYaIiIiKGsMMLWrJZBKBQACxWGyhSyEiokWKa2Zo0RoaGsLhw4cRDAah1+uxadMm7oxLRERZeGSGFqVgMIgDBw4gmUyipqYGBoMBhw8fxuDg4EKXRkREiwzDDC1KPp8P4XAYTqcTKpUKNpsNkiRhdHR0oUsjIqJFRtGnmXQ6XWab+EKZ3FvEbDZndm1VKo1GM+OnQ88Xm80GvV4PvV4Plep65tZqtSgpKSloTeyncrCXysJ+Kst891PRYSaRSCCRSBR0TrVaDZ1Oh3A4PO2+JEpgtVoX7DOCzGYzysvLcfnyZZSUlCAcDsNkMsHhcBS0JvZTOdhLZWE/lWWu/ZzrgQhFhxkqXjqdDrt27cK5c+cwOjqKiooKrFu3DiUlJQtdGhERLTIMM7Romc1mbNmyZaHLICKiRY4LgImIiKioMcwQERFRUWOYISIioqLGMENERERFjWGGiIiIihrDDBERERU1hhkiIiIqagwzREREVNQYZoiIiKioMcwQERFRUWOYISIioqLGMENERERFjWGGiIiIihrDDBERERU1hhkiIiIqagwzREREVNQYZoiIiKioMcwQERFRUWOYISIioqLGMENERERFjWGGiIiIihrDDBERERU1hhkiIiIqagwzREREVNQYZoiIiKioMcwQERFRUWOYISIioqLGMENERERFjWGGiIiIihrDDBERERU1hhkiIiIqagwzREREVNTyDjM9PT24cOFC5nY8HscTTzyBj370o/jud79biNqIiIiIZpR3mPnzP/9zfP/738/c/tznPocvfvGLuHz5Mj75yU/iW9/6VkEKJCIiIppO3mHm3Llz2L59OwBAFEV873vfwz//8z/j1KlTePzxx/Hv//7vBSuSiIiIKJe8w0wgEEBJSQkA4MSJEwgEAvjwhz8MANi2bRs6OzsLUyERERHRNPIOM7W1tTh+/DgA4OWXX8bq1atRVVUFAPD7/TCZTIWpkIiIiGgamnz/4sc//nF84QtfwE9+8hOcOXMGX//61zP3HT9+HKtWrSpIgURERETTyTvMfP7zn0d1dTVOnjyJT3/603jooYcy9/n9fnziE58oRH1ERERE08o7zADAgw8+iAcffDDr688+++zNTEtEREQ0aze9ad6vf/1rfPnLX8YnP/lJ9Pb2AgDefPNNDA4O3nRxRERERDPJ+8jM6Ogo/uiP/ggnTpxAVVUVhoaG8KlPfQr19fV47rnnYDab8c1vfrOQtRIRERFlyTvMfPazn4XX68WFCxewfPly6HS6zH333HMP9u3bN+u5kskknn32WZw7dw7BYBDl5eX40Ic+hF27dgG4vtvw008/je7ublRWVuKRRx5Bc3NzvqUTERGRguR9mukXv/gFnnjiCaxevRqCIEy5r66uDv39/bOeK5VKobS0FPv27cOLL76Iv/iLv8Czzz6Ly5cvQxRF7Nu3Dy0tLXjxxRexd+9ePPHEEwiFQvmWTkRERAqSd5gRRRFms1n2Pr/fP+VIzUwMBgP+9E//FJWVlVCpVFi9ejVWrVqFtrY2XLhwAfF4HO9///uh1Wqxe/duVFRU4OjRo/mWTkRERAqSd5i566678Nxzz8ne96Mf/Qhbt27Nu6hYLIaOjg643W709vbC7XZDpfpdqY2NjZnFxkRERPTulveamX379mH37t3YsWMHPvjBD0IQBLzyyiv4yle+gl/84hdobW3Na15JkvCNb3wDy5Ytw/r169He3p51BMhsNiMSiWT9Xa/XC6/Xm7mtUqngdDrzqiMXtVo95f9KJgiC4h8n+6kc7KWysJ/KMt/9zDvMtLS04I033sDnP/95/O3f/i0kScITTzyBlpYWvP7669iwYcOc55QkCd/61rcwNjaGL33pSxAEAUajMSu4RCIRGI3GrL//0ksv4Tvf+U7m9kMPPYRHH3107g9uFmw227zMu9jM5XRhMWM/lYO9VBb2U1nmq583tWleS0sLDh06hGg0Cr/fD7vdnvdnMkmShGeffRadnZ348pe/DIPBAACor6/Hyy+/jHQ6nTnV1NXVhT179mTNsXfvXuzcuTNzW6VSwe/351VPLmq1GjabDYFAAKlUqqBzLzZmsxnhcHihy5hX7KdysJfKwn4qy1z76XA45jT/TYWZSUajUfZIyVx8+9vfxpUrV7Bv374pgWjt2rXQarV45ZVXcN999+Ho0aMYHh5GS0tL1hzl5eUoLy/P3PZ6vfP2TZBKpRT/DSZJkuIf4yT2UznYS2VhP5Vlvvo5pzDzV3/1V7MeKwgCvvGNb8xq7MjICH75y19Cq9Xiz/7szzJf/+AHP4gHHngAX/jCF/DMM8/ghz/8ISoqKvCP//iPsFqtcymdiIiIFEqQJEma7eDGxsbZTywI6OzszKuoQrlxMXChqNVqOBwO+P1+xSdpq9WKYDC40GXMK/ZTOdhLZWE/lWWu/bzxLMtszOnITFdX15wmJyIiIppvN/1Bk0REREQLKe8w8+Mf/xj/+q//KnvfV7/6VfzkJz/JuygiIiKi2co7zDz11FPQ6/Wy9xmNRjz11FN5F0XHDS2kAAAgAElEQVREREQ0W3mHmfb2dqxZs0b2vtWrV6O9vT3vooiIiIhmK+8wYzAY4PF4ZO8bGhqCRlOQLWyIiIiIppV3mNm5cyeeeuqprF0Lw+Ew/uVf/gW7du262dqIiIiIZpT34ZMnn3wSLS0tWLp0KT74wQ+iuroag4OD+J//+R8kEgn86Ec/KmSdRERERLLyDjMrV67EyZMn8dhjj+Gll17C2NgYysrK8Hu/93t47LHH0NTUVMg6iYiIiGTd1MKWpqYm/Nd//VehaiEiIiKaM26aR0REREVtTkdm7r//fnzta1/DsmXLcP/99087VhAE/OxnP7up4oiIiIhmMqcwEwwGMx8QFQgEIAjCvBRFRERENFtzCjNvvPFG5s8HDx4sdC1EREREc5b3mpkXXngBY2Njsvf5fD688MILeRdFRERENFt5h5mHH34Y165dk72vq6sLDz/8cN5FEREREc1W3mFGkqSc9/n9flit1nynJiIiIpq1Oa2Z+dWvfoVf/epXmdtf+9rXUFFRMWVMLBbDgQMHcPvttxemQiIiIqJpzCnMtLe343//938ztw8fPgy9Xj9ljE6nw5o1a/Dkk08WpkIiIiKiacwpzHzmM5/BZz7zGQBAY2MjXnnlFdx2223zUhgRERHRbOS1ZiYWi2HNmjUYHx8vdD1EREREc5JXmDEYDHjzzTeRTqcLXQ8RERHRnOR9NdO9996L3/zmN4WshYiIiGjO8v7U7Icffhif+tSnEAqF8L73vQ8ulyvr4w02bNhw0wUSERERTSfvMPOHf/iHAIBnnnkGzzzzzJQgI0kSBEHIfI4TERER0XzJO8zc+DlNRERERAsl7zCzc+fOQtZBRERElJe8FwATERERLQY3FWZ+8IMfYNu2bXC5XLDZbFn/EREREc23vMPMD37wA3ziE5/AmjVr4PV68cADD2Dv3r3Q6XRwuVz4u7/7u0LWSURERCQr7zDzta99Df/0T/+Eb37zmwCAT3/603j++efR1dUFp9MJi8VSsCKJiIiIcsk7zFy9ehVbt26FWq2GWq1GIBAAAFitVnzuc5/Dv/3bvxWsSCIiIqJc8g4zJSUliMfjAICamhpcunQpc18qlcLY2NjNV0dEREQ0g7wvzd64cSPOnz+P9773vbj//vvxxS9+Eel0GlqtFk899RTuuuuuQtZJREREJCvvMPMP//AP6OnpAQB86UtfQk9PD/76r/8aqVQKmzZtwn/8x38UrEgiIiKiXPIOM5s3b8bmzZsBAHa7HT/72c8Qj8cRj8d5WTYRERHdMgXZNE+SJIyOjkKn0zHIEBER0S11U2Hmtddew7Zt22A0GlFZWQmj0YitW7fi1VdfLVR9RERERNPKO8w8//zz2LNnD9RqNZ566il8//vfx1NPPQW1Wo3f//3fx3PPPVfIOomIiIhkCZIkSfn8xcbGRrznPe/Bf/7nf2bd9/DDD+PgwYPo6uq66QJvhtfrLficarUaDocDfr8fqVSq4PMvJlarFcFgcKHLmFfsp3Kwl8rCfirLXPtZXl4+p/nzPjIzMjKCD3/4w7L3feQjH8HIyEi+UxMRERHNWt5hZvPmzTh9+rTsfadPn8add96Zd1FEREREs5X3pdlPPvkkPvKRjyAWi+GP//iP4XK5MDIygp/+9Kd44YUX8OKLL8Ln82XGl5aWFqRgIiIiohvlvWZGpfrdQR1BEDJ/npzuxq8BWJBznlwzc3N4HldZlN5P9lJZ2E9lme81M3kfmXnuueeyAgsRERHRrZZ3mHnooYfmNP6FF17AfffdB4fDke8/SURERJQl79NMc5FKpaDT6XDy5Els2LBhvv+5jEAgAL1eX9A5BUGATqdDIpHALXjqFpRGo4EoigtdxrxiP5WDvVQW9lNZ5trPub53531kZq4W4sWYSCSQSCQKOqdarYZOp0M4HOZ5XAVgP5WDvVQW9lNZ5trPuYaZgnw2ExEREdFCYZghIiKiosYwQ0REREWNYYaIiIiKGsMMERERFbWbupoplUrhxIkT6O/vRywWy7r/wQcfBHB9t+DHHnsM1dXVN/PPEREREWXJO8ycPn0aH/jAB9DX1yd72bUgCJkwIwgCHnvssfyrJCIiIsoh7zDzyCOPwGq14rXXXsPq1auh0+kKWRcRERHRrOQdZi5evIj//u//xt13313IeoiIiIjmJO8FwMuXL1f8joVERES0+OUdZr7+9a/jK1/5Ci5fvlzIeoiIiIjmJO/TTI8++iiGh4exZs0aVFdXw263T7lfEAScO3fupgskIiIimk7eYeaOO+6AIAiFrIWIiIhozvIOM9/97ncLWAYRERFRfrgDMMmSJEl2/yAiIqLF5qZ2ACblSSQSOHfuHK5duwaj0YglS5agubkZKhVzLxERLU58h6IpTp06hbfeegtarRaCIODo0aNoa2tb6LKIiIhyYpihjHg8jo6ODlRWVsJqtcLhcMBut+PKlSsLXRoREVFODDOUMblG5p1XqXHtDBERLWYMM5RhMBjQ2NiIoaEhRCIRBINB+P1+LFu2bKFLIyIiyokLgGmKO++8E4IgoLu7G0ajEXfeeSeam5sXuiwiIqKcGGZoCr1ej23btmHz5s2w2WyIRCILXRIREdG0eJqJZGk0GqjV6oUug4iIaEYMM0RERFTUGGaIiIioqHHNDN2U4eFh/PKXv4TH40FVVRX+4A/+AE6nM+f4o0eP4vjx40in01i/fj12797N3YWJiOimMMxQ3sbGxvD0009jYGAAVqsVly9fRmdnJ/7mb/4Gdrs9a/wbb7yBF154AQaDAYIg4MKFC4hEIrjvvvsWoHoiIlIK/kpMeTt//jz6+vqwYsUK1NTUYOXKleju7saFCxdkx7/66quw2Wxwu92or6+H0+nEb37zGyQSiVtcORERKQnDDOUtFotBpVJldgwWBAFqtVo2nKTTacRiMRgMhszXDAYDkskkwwwREd0UhhnKW319PTQaDXw+HwDA6/VCq9Wirq4ua6xKpcKKFSswNDSEZDKJVCqFvr4+LF26FBaL5VaXTkRECsI1M5S35uZmPPDAA/jpT3+KkZERmM1mfOQjH8Hy5ctlx3/4wx9GOBzGxYsXAQDLly/HRz/60VtZMhERKRDDDN2Ue++9F+vXr8fExAQcDgfKyspyjnU4HPjsZz+LgYEBpNNpVFVVTTntRERElA+GGbppTqdz2suxb6TRaOB2u+e5IiIiejfhmhkiIiIqagwzREREVNR4muldIBqNoqenB7FYDDabDQ0NDdx1l4iIFINhRuGi0Sj279+P/v5+aLVapFIprFu3Dps3b87sD0NERFTMGGYUrrOzE/39/ViyZAkEQUAikcDbb7+NpUuXwuVyLXR5REREN43nGhQuEolAr9dnjsLodDoAQDweX8iyiIiICoZhRuHsdjsSiQREUQQAhEIhaLVa7rpLRESKwdNMCtfY2IhVq1ahvb0dkiRBo9GgpaUFDodjoUsjIiIqCIYZhdNoNNi+fTuWL1+OeDwOq9U67S69RERExYZh5l1ApVKhqqpqocsgIiKaF4smzPzf//0fDhw4gO7ubrS0tODv//7vM/f19PTg6aefRnd3NyorK/HII4+gubl5AaslIiKixWLRLAAuLS3FAw88gHvvvXfK10VRxL59+9DS0oIXX3wRe/fuxRNPPIFQKLRAlRIREdFismjCzJYtW7B582bYbLYpX79w4QLi8Tje//73Q6vVYvfu3aioqMDRo0cXqNL5kUwm0dfXh66uLvj9/hnHj4+Po6urC319fUgkEregQiIiosVp0ZxmyqW3txdut3vK9vuNjY3o7e1dwKoKKxaL4Y033kBvby9UKhV0Oh127NiBxsZG2fE9PT04dOgQ4vE40uk06urqsGvXLphMpltcORER0cJbNEdmcolGozCbzVO+ZjabEY1GF6iiwrt06VImtLndbhgMBhw5cgSRSCRrbDQaxZEjR6DT6TLj+/r6cOnSpQWonIiIaOEt+iMzRqMx6009EonAaDRmjfV6vfB6vZnbKpUKTqezoPWo1eop/y+E8fFx2Gw2aDTX21FWVoaenp7MpdQ3isfjiEajcLvdAK4/xpKSEvj9/oLWBACCIBR8zsVmPvq5WCm9n+ylsrCfyjLf/Vz0Yaa+vh4vv/wy0ul05lRTV1cX9uzZkzX2pZdewne+853M7YceegiPPvrovNT1zrU9N6OmpgbDw8OZXXlDoRDsdjsqKyuzwoxOp4PD4YAkSZn7RkdHUVNTMy8b4U1+/IHSFbKfi9m7oZ/spbKwn8oyX/1cNGEmlUohlUohnU4jnU4jkUhApVJh7dq10Gq1eOWVV3Dffffh6NGjGB4eRktLS9Yce/fuxc6dOzO3VSrVrBbTzoVarYbNZkMgEEAqlSrInLW1tWhra8O5c+cyR2e2bNkCURRl61+zZg1aW1shSRJEUYTT6YTb7S74YzWbzQiHwwWdc7GZj34uVkrvJ3upLOynssy1n3P95VyQJEnKt7hC+uEPf4gf/ehHU772nve8B5/97GfR3d2NZ555Bt3d3aioqMAjjzyCNWvWzDjnjaecCkWtVsPhcMDv9xf0GywUCqG/vx/JZBIOhwO1tbXTjh8YGIDP54NWq0Vtbe28fNaS1WpFMBgs+LyLyXz1czFSej/ZS2VhP5Vlrv0sLy+f0/yLJszMh2IKM4sRv8GURen9ZC+Vhf1UlvkOM4v+aiYiIiKi6TDMEBERUVFjmCEiIqKixjBDRERERY1hhoiIiIoawwwREREVNYYZIiIiKmoMM0RERFTUGGaIiIioqDHMEBERUVFjmCEiIqKixjBDRERERY1hZh5JkoRYLKb4D0kjIiJaSJqFLkCp/H4/jhw5grGxMej1emzcuBFNTU0LXRYREZHi8MjMPIjFYjhw4AA8Hg8cDgcEQcDBgwfR39+/0KUREREpDsPMPPD7/fD5fKitrYXBYIDD4YBKpcLQ0NBCl0ZERKQ4DDPzQBAEANfXzExKp9OZrxMREVHhcM3MPCgtLUVVVRV6enpQXl6OaDQKjUaDurq6hS6NiIhIcXhkZh7odDrs2rULK1asQDKZRElJCe655x5UVFQsdGlERESKwyMz88RisWDXrl0LXQYREZHi8cgMERERFTWGGSIiIipqDDNERERU1LhmZpHo7+/HhQsXEIvFUFdXh7Vr10Kv18uOTSQSOH/+PPr7+6HX69Hc3Iz6+vqcc3s8Hpw/fx7BYBAVFRW4/fbbYTabZcemUilcunQJnZ2dsFgscLvdWLp0KS8rJyKiRYtHZhaBwcFBvPbaa/B6vUgkEjh16hSOHj2KdDqdNVaSJBw7dgynTp1CPB6H1+vF/v37c+4u7PP5sH//fgwMDEAURbz99ts4dOgQksmk7PgzZ87gyJEjiEajGB8fxxtvvIGOjo6CPl4iIqJCYphZBK5evQqVSoWKigo4HA7U19ejo6MDExMTWWMDgQA6OjpQV1cHh8OBiooKqNVqtLe3y87d19eHUCiE6upq2O12uN1uDAwMwOv1Zo1NJBJoa2tDRUUFSktL4XK5YLFYcPHixYI/ZiIiokJhmFkEUqkU1Gp15vbkn+WOzKTTaaTT6SnjNRpNzk/mTiaT0Gh+dzZRpVJl/k25OuTmFkVxjo+IiIjo1mGYWQRqa2sRiUQQDochiiL6+vrgcrlgs9myxlqtVlRVVaGvrw/JZBLhcBjBYDDn7sKVlZVIp9MIBAIQRRHDw8Ow2WxwOBxZYw0GA2prazE4OIhEIoFoNIqxsTE0NjYW/DETEREVCsPMItDU1IS77roLwWAQHo8HlZWV2LlzJ7RabdZYjUaD7du3o6amBiMjIwgGg9i0aROWL18uO3dtbS127NiBWCyG4eHhzGZ+cguABUHA5s2b0dTUBK/XC7/fj3Xr1mHdunUFf8xERESFwquZFgGVSoXbb78dK1euhCiKMJlMmdNBckpKSrBnzx5EIhFoNBoYDIZp51+xYgUaGhqQTCZhNBqnnEZ6J7PZjLvvvhuRSAQ2my3n6SsiIqLFgmFmEZkplNxIpVLBYrHMerxer895qfc7CYIAs9kMk8mEYDA463+DiIhoIfA0ExERERU1hhkiIiIqajzNNE9EUURHRwe8Xi/MZjOamppgtVoXuiwiIiLFYZiZB+l0GseOHcPFixdhNBoRi8Vw7do1vPe972WgISIiKjCeZpoHXq8Xly9fRl1dHaqqqtDY2Aiv14vOzs6FLo2IiEhxGGbmweTnHt24T4xer0ckElmokoiIiBSLYWYe2Gw2GAwG+P1+ANc/8ygej8PpdC5wZURERMrDMDMPrFYrduzYgWQyiZ6eHng8Htx2221YsmTJQpdGRESkOFwAPE/cbjfKysoQDAah0+lQWloKQRAWuiwiIiLFYZiZRxaLZU679BIREdHc8TQTERERFTWGGSIiIipqPM00B6FQCB6PB1arFTqdDna7faFLIiIietdjmJklv9+P/fv3w+/3w2KxQBRF7Nq1C/X19QtdGhER0bsaTzPN0unTpzExMYElS5ZgyZIl0Ov1OHbsGERRXOjSiIiI3tUUfWRGp9NBr9cXZK5YLIaKiorMfE6nEx6PB1qtVrFXLGk0GsV/ltTk5fJmsxmSJC1wNfNL6f1kL5WF/VSW+e6nosNMIpFAIpEoyFwmkwnXrl2D0WiEVqvFyMgI1Go1kskkgsFgQf6NxcZqtSr2sU1Sq9XQ6XQIh8NIpVILXc68Uno/2UtlYT+VZa79nOuBCEWHmUJav349fD4fOjs7YbFYkE6nsWvXLmg0fAqJiIgWEt+JZ8lut2PPnj0YHR2F1WqFVquFzWZb6LKIiIje9Rhm5sBsNsNms8HhcMDv9yv+0CcREVEx4NVMREREVNQYZoiIiKio8TQTZYlGoxgfH0cwGITBYIBWq512vM/nQzQahclkgsPhmHZsIpGAz+eDJElwOBwwGAzTjp+YmEAoFIJer0dZWdm0nzwuiiK8Xi/S6TRKSkpgNpunnTsUCiEYDCKRSMy4kDudTsPr9SKZTMJms814GWUkEsH4+DhUKhXKy8unnV+SJIyNjSEej8NsNs+4s3QsFoPf74cgCHA4HDOu+vf7/YhEIigvL59xbDKZxNjYGCRJgt1uh9FonHZ8IBDIfDJ8eXn5tP1JpVLwer1IpVKw2WwzbmkQCoUQCASgVqtRXl4OtVqdc6wkSRgdHUUoFEI6nZ6x95OvcUEQUFZWNuNrfGxsDLFYbFavcSK69RhmaIqxsTEcPHgQPp8Per0e5eXl2LFjh+wbjyRJOHv2LM6cOYNUKgW1Wo1NmzZh7dq1snMHg0EcOnQIg4ODAIDy8nLs3r0755tDe3s7jh07hkQiAUEQsG7dOmzatEn2DTMWi+Hw4cPo7u4GcP1Sx127dqGyslJ27r6+Phw6dAixWAwWiwU1NTVoaWmRDR2iKOLIkSNob28HABgMBuzcuTPn7s8ejwcHDx7ExMQEBEGA2+3G9u3bZYOBJEk4deoUzp8/j3Q6Da1Wi5aWFqxYsUJ27vHxcRw8eBCjo6OQJAmVlZXYvXt3znD19ttv4+TJkxBFESaTCatWrcL69etln8NwOIxDhw6hv78fgiDAbrdj9+7dKC8vl5372rVrOHLkCOLxOACgubkZd911l2zoSCQSOHz4MDo7OwFc3+pg9+7dqK6ulp17cHAQBw8eRDgcBgAsWbIE27dvh06nyxqbSqVw4sQJXL58GUajEalUCi0tLVi6dKns3F6vF2+88QbGx8chSRJqa2uxc+dO2QD0zte4RqPBpk2bsGbNGtm5iWhh8DQTZUiShKNHj2JiYgJutxtutxv9/f04ffq07PjBwUGcOnUKZWVlcLvdKC0txYkTJzA8PCw7/uTJkxgaGsrM7fP5cOzYMdkNlHw+H44ePQqLxQK3242KigqcOXMmE1be6fz58+js7ERtbS3cbjcSiQRaW1uRTCazxobDYbz55ptQq9VoaGhATU0N2tracOXKFdm5r169ira2NlRXV8PtdkOr1eLw4cMIhUJZY0VRRGtrK+LxOBoaGlBXV4euri6cP39edu6enh6cOXMGLpcLbrcbVqsVR44cgdfrlR1/7NgxeL1e1NfXw+12w+Px4MSJE7JjJ++z2+1wu91wOp146623MDAwIDv+zJkz6Ovry8wdCoVw5MgRpNPprLGBQABHjhyB0WiE2+1GVVUVzp8/j66uLtm5L126hKtXr6KmpgZutxsAcPjwYcRisayxk8FUkiS43W7U1NTg6tWruHjxouzck89vZWUlGhsbYTAYcOTIEUxMTGSNTafTOHLkCEKhENxuN+rr69HX15fzNT4wMDDlNW6323HixAl4PB7Z8US0MBhmKCMajcLn88HlcgEAVCoVSktLc4aTYDAISZIyRxxMJhMkSZLd/EmSJHg8nsypIkEQ4HK54PV6ZTc2DAQCSCQSmSMOer0earUagUBAtpaRkRHYbLbMUQGXy4Xx8XFEIpGssaFQCJFIBGVlZQAArVYLk8mUM0CMjo7CZDJlTkWUlpYiEonIhpnJ00tOpxPA9eewpKQk55tfIBCASqXKnP6xWq1IpVKyz2EikYDX64XT6cw8h+Xl5RgZGZENHMFgcMopF6PRmLM/ADA8PIzS0lKoVNd/LDidTvj9fkSjUdm64/E4SkpKAPxut+2xsTHZuSe3NJg88uV0OjExMZE58nKjcDiMiYmJzHM4uTvq6Oio7NxjY2PQ6/WZozZ2ux3xeFz2cUajUfj9/in9mctr3Gw2I51OK36DM6JiwzBDGTqdDlqtdkoAiEQiOdc2aLVapNPpzBvp5J/l1h8IggCTyZQ1t16vlx2v1+shCELms68kSYIoijnXfJjN5ilvutFoNPN45B6nWq3OnB4Brh8NyLXO4p1zx+NxqFQq2VMeWq0WWq02q5Zcc+v1eoiimDk6NfnnXHPr9fqs59BkMmUCyDsfZzqdzmwhkE6nc84NABaLJaturVYrO37yazce+YrH4zCZTLJzm0wm2f7kmlun0836OTSZTFN6OVlTrt7LzZ3rNN3kczj5Gk+lUjlf40S0cNSPP/744wtdxHyR+638ZqlUKhiNRsRiMcV9XsjkEYIrV65kFl8CwJYtW2R/2FssFvh8PvT09CAajcLr9WLJkiVYt26d7LoJi8WCjo4O+P1+BAIBJJNJbNmyJXOE5EYmkwmhUAjXrl1DJBKB1+tFdXU1NmzYIPtGYrFY0NPTk1kEGgqFsGnTJtTW1maNNRgMEEURV69eRTQaxcTEBCwWC+68807ZsGSxWDA4OIihoSFEIhH4/X6sW7cOTU1NWWtPtFot1Gp15jn0+XwwGo1oaWmRfTM2m83wer3o7e1FNBrF2NgYli9fjubm5qyAMhkIr169iomJCUxMTECSJGzdujVzhOSdc09MTKCzsxOxWAw+nw+1tbW47bbbZNcGmc1mdHZ2YmxsDKFQCNFoFC0tLZkjde/sTywWw9WrVzP9cblc2Lhxo2xAsVqt6O3thcfjQSQSwcTEBDZs2ICGhoassXq9Hul0Gu3t7YhEIhgbG4PVasXmzZtl1x2ZzWYMDw9jYGAAiUQCHo8Hzc3NWLFiRVZ/JrdUn+yP3++HRqPJ+Ro3m83w+/3o7u5GLBaD1+vF0qVLsW7dOtkAeavo9fqCfVTLYqXkn7XvxH5my/WLUS6CpOBXSa7TBjdDrVYrftO8/v5+DA0NwWq1orS0VPbNbFI8HkdHRwcCgQBKSkrQ1NSU8zd/4Po6jr6+PgBAdXV1zgWgwPWjFJPhx2KxYOnSpdO+wCeDVSKRQEVFBdxud86ra9LpNLq6ujKn1crLy6e9AiYYDKKzsxPRaBROpxONjY0538wkScq8cWu1WtTX18sGtkmRSASdnZ0IBoNwOBxoamqa9uqnwcHBzCLq2tranIucgeunpjo6OjAxMYGqqipUVlZOewXZ6Ogoent7kU6nUVlZibq6upxjU6kUOjs74fV6YTabsWTJkmmvUBofH0d3dzfi8XjmOczVH0mS0NXVhdHRUeh0OjQ2Nk57lVcoFEJ3d3cmrDQ0NEx79VNfXx+Gh4ehUqlQX1+fOe0kJxaL4dq1a7N+jd8K75bP8lH6z9pJ7Ge2XBce5MIwM0f8BlMW9lM52EtlYT+VZb7DDNfMEBERUVFjmCEiIqKixk3z5iCVSqGnpwcmkwmlpaXTnpMHrq/5iEQi0Gq1M+6kCvxul1Gn0znjOfnJy3Qnd8adicfjgd/vR21t7Yw7r07upBsOh2E0Gmdc6BiPxxGPx2EwGGasW5KkzOW4ZrN52h1jgetXpkxe+TLTbsHA9ct60+k0TCbTrPoTDodn3BV3UjQaRTKZhMlkmtWOweFwGCqVasbdaIHfPYdGo3HGK2Xm+hwmEgnEYrFZrfOYnFuSJJjN5hl7P/kan21/IpEIRFGE2WyesT+pVArhcBgajWZWiwFjsRgmJiYgiuKMzwkRKQvDzCxNTEzgySefxOnTpzO7un7+85/Pucuox+NBa2srxsfHodVqsWHDBjQ3N8v+kBVFET/+8Y9x+PBhJJNJ1NTU4GMf+1jOuXt6evD888+jv78fGo0G27ZtwwMPPJBzd9Rvf/vb+PnPf454PI7S0lL85V/+JXbt2pWz7ueffx7Xrl2DTqdDc3MzHnzwwZwBqKurC8ePH89cIrx169acO+PG43EcO3YMXV1dkCQJ9fX12LJlS843qqGhIRw+fBjBYBB6vR6bNm3KuTOuKIp466230NbWhlQqhaqqKmzbtg02m012vNfrRWtrK3w+H+x2O5qamrB27VrZ/kiShPPnz+PcuXNIJpMoLS3F9u3bc57TDQQCaG1txdDQENRqNVauXImNGzfmDEDt7e347W9/i3g8DqvViq1bt6KmpkZ2bCQSwbFjx9DT0wNBENDY2IjNmzfnDBK9vb04evQowuEw7HY71q9fjyVLlug1++sAACAASURBVMiOTSQSOHnyJK5evYp0Oo2amhps3bo1Z+/f+Rq/4447sHr1atnnMJ1O4/Tp07h48SJEUYTL5cK2bdty7v7s9/vR2tqKkZERaDQaNDc3Y8OGDbLhSpIkXLp0CWfOnMlcSr5lyxZUVFTIzk1EysNLs2fp61//Og4cOIDa2lo4nU60t7fj0qX/b+/eg6Os7v+Bv/d+TTb3bLK5hwRiIBCISFCQoCKCtYNAKuCUqMy01NJWHQGnYKlY2+lQKwKW6AhowWgZDDZQIXJTJBrulFxIQshtk8BmN5vL3m/P7w9nnx/LPgvJSBp2v5/XDDPsk8Ph7PMh7CfPOedz6jB//ny//2AtFgu++uorWK1WqNVq8Pl8XLlyBbGxsZw7MiorK/HFF18gPj4ecXFx6OjoQFNTEx544AG/BMVms+Hdd99FR0cH0tPTIRKJcOHCBUgkEs4P+oqKCnz44YdQqVSIi4tDb28vTp8+jcLCQr8PEo/Hg9LSUtTV1SE9PR3h4eG4dOkSHA4HJk6c6Nd3T08PKisrIRaLERsby+5sSklJ4XwSdebMGdTU1CAhIYHdSm2z2ZCenu7XdnBwEIcOHYLH40F8fDy7VVetVnNuoa2trcXp06cRFxcHlUqFzs5OGI1GZGZm+sXHbrfjq6++Qn9/PzQaDWQyGWpqaqBSqRAVFeXX97Vr1/DNN98gKioKkZGRMBgM6OrqQkZGhl+C4vF4cOzYMXR2diIxMRESiQRNTU2QSqWcH67d3d04cuQIFAoFYmJiYDKZ0NLSgrS0NM4nRt5jFRITEyGXy3Ht2jV4PB7OXUdGoxGVlZXg8/nsjrS6ujpoNBrOp0Xesv3x8fEIDw9He3s7zGYzMjIy/BIUs9mMyspKn3/jDQ0N7P2/VUNDA6qqqhATE4PIyEhcv34dPT09yMzM9HtC43Q6ceTIEeh0Omg0GgiFQjQ1NUGpVHLuOuro6MCJEycQERGBhIQE6HQ6tLe3Iz09fdR3HY0U2sobWiie/oa7NZvWzAzRxYsXERkZCblcDrFYjNTUVLS1tUGr1fq19dYAUavVEAgEUCqVEIlEAauM1tXVQalUIjw8HEKhEJmZmejq6uJs393djY6ODnb7blhYGFQqFerr6zn7vnz5Mng8HqKiothtwoODg6irq+MctzcZ8VbFjYuLC1iKv7e3F06nE5GRkWwlVe9BkrdiGAZtbW2Ii4tji9mp1WpotVrOb+Le3l6YzWbExsaCz+cjPDycPUyQS3t7O1QqFaRSKYRCIZKSknD9+nXOCrP9/f3o7e1FYmIiGx+pVMpud75Vd3c3JBIJOzWSmJgIo9HIWS7fbDaju7ub/RCWSqVQqVRob2/n7PvmgzH5fD5iY2NhsVg476HL5YJWq4VarWafQMTFxaGtrS3gkRA2mw3R0dHg8/mIjIyEy+UKWKW3ra0NUVFRkEgkEAqF0Gg06Orq4qwA3N/fj4GBAZ9/40KhMGCl487OTiiVSnb6LykpCTqdjnMHx+DgIHQ6HZKTkyEQCCCXy6FQKAIew6DT6SAQCBAWFgaBQAC1Wo3BwUH09fVxtieEhB5KZoZILBaz1WiBHx7J8/l8zsf73p9iby4x73K5Aq6FuDUr987536567c3tHQ5HwJ9ApVKpzzY4t9sNhmE4f+r3Fny7uaqrw+EIuKZEIBD4fIgyDAOPx8M5FcDj8SAUCn3uocvlAp/P51w74e375nvoPeiPi1gs9hm30+mEQCDgHIv32s335XbxEYlEPuP2/jmucXvfz63vM1DfQqHQ5z0yDAOGYQL2zefzffr2vk8ufD6f7e/m/gO1FwqFfveQx+MFHMut8XG5XAHjM9zY3/o+b3cPb73f3krHd1qTQwgJHTTNNEQOhwPV1dUwm82wWq3Q6XQoKirCvHnz/NpKpVK2QBjDMOjt7YVUKkVBQQHnozOxWIyzZ8+iv78fdrsdWq0W+fn5eOSRR/w+jJVKJTo7O3H58mV4PB4YDAZ4PB48/fTTnAXo5HI5Tp48yZ6B1NnZiTFjxuD555/3S8TEYjH6+vpw4cIFuFwuDAwMYGBgAAsWLOCcChKLxewTJIZhoNPpEBsbi0mTJnF+8Hgr43oX9fb19WHKlClISEjgvId6vR4dHR1gGAZ6vR5hYWGYMmUKZwIpEonQ1NQEm80Gu90OvV6P++67j3OKRCKRYGBggD3B2XuGUUFBAef6ELFYjJaWFrZq8Y0bNzBmzBjk5OT4xUcsFrOVcb1n+DidTkydOpVzilEikUCr1aKnpwdut5udWsnLy/NLDLzvo7GxkV0cOzg4iKlTp3JOv0ilUly/fh1dXV3weDwwGo0ICwvD5MmTOZNfoVCIxsZGdsGwwWDAxIkTOddASaVStkih99+4TCZDQUEB5xSjRCJBc3MzzGYzHA4HdDodxo0bh+zsbM74eKs/MwzDVjqeOnUq5xSjRCJBW1sb+vr6wOPxoNVqkZqaivHjx49qld6RRNMSoYXi6W+400yUzAzR+PHjIZfLodPpoFAoMGvWLKxatYrzQ5vP5yMxMRE8Hg8OhwOxsbGYPn16wCqjarUaSUlJMBqN4PP5KCwsxDPPPBPwqU9ubi77QRkbG4tFixahoKAgYN8ZGRm4fv063G43Jk2ahNWrVwes6jtu3DiIxWIMDAwgOjoa8+fPx8yZMzkXdYrFYmg0GjidTrhcLiQnJ2P69OkBF4xGR0cjIiKCPZNp0qRJAReMCgQCaDQaMAwDh8MBtVqN6dOnB1wwqlKpEBMTA6vVCoFAgNzcXOTn5wd8qpCYmAihUAi73Y7ExEQUFBQEXDCqUCgQHx/PnvA8duzYgGX7AbBVdm02G8LDwzF16lT2pOhbSSQSJCYmwuFwwOPxID09HQ888EDAb+TY2FiEhYXBYrFAJpNhypQpnAkB8EOCp9Fo4PF44HQ6kZ6ejvvvvz/goujIyEhERUWxu8fy8vIwYcKEgE+3NBoNeDweW9G3sLAw4KLosLAwxMbGwmazgc/nIycnB5MnT+Z8ksPj8ZCQkACRSASbzYaIiAhMmzYt4KJomUyGhIQEOJ1Odiq1oKBgyLvUghF9+IUWiqc/Os7gJlQB+MehqpShJdTjSbEMLRTP0EIVgAkhhBBCboOSGUIIIYQENUpmhsntdg/rkedwH4/evDvkbrYdbvtbd8EQQggh9yqqADxEDocDH3zwAY4cOQIAmDBhAlatWhVwUW9jYyPeeecdtLW1ISwsDEuWLMFPf/rTgP0fP34chw4dgtVqRXZ2NpYsWRLwmAKDwYB//etfuHLlCiQSCR577DHOnU9eZ86cwf79+zE4OIiUlBQsWbIk4GJKh8OBS5cuobm5GTKZDBkZGcjNzQ3ZXSGEEEKCH31CDdFHH32Ezz77DMAPK89PnDiBTZs2cT556e3txcaNG1FXVwe5XI6BgQFs2bIFJ06c4Oy7qqoK//znP2Gz2SCXy3HmzBl8+OGHnKvbXS4Xdu7cie+++w4ymQx2ux2ffPIJTp06xdl3bW0tPvzwQ/T390Mul6Ourg7/+Mc/MDAwwNn+7NmzOHfuHEQiEXg8HqqqqgIW5COEEELuBZTMDNHx48fZIwFiYmKQlJSECxcuQKfT+bW9ePEi2tvbMWbMGERERECj0cDtduPbb7/l7Pv06dMQi8VISEhAREQEsrKyUF9fj46ODr+2nZ2dqKurQ1ZWFlu+XSaTobq6mrPvy5cvw263Iykpie1bq9WiubnZr633OALvkQGRkZGIiIhAQ0PDMO8WIYQQ8r9DycwQMQzjU8vDW7+E68mMx+MJeNheIDdP49zpxN9bx8Lj8QKubxnOWLx93Nqe1s4QQgi5l1EyM0TTp0+H0WiEXq9Hf38/WltbkZOTw1m9duLEiVCr1WhubobJZGLPq5k2bRpn3/n5+bBarejp6YHJZEJTUxPGjBnDua4lISEB2dnZuHr1KkwmE/R6PUwmEyZPnszZt7cKand3N1tV1VtI71ZSqRTp6eno7u6GxWLB4OAgjEYjsrKyhnOrCCGEkP8pqgA8RHl5eejr60NzczPsdjsmTpyItWvXcpZXVygUGDt2LK5cuQKdTgeRSISSkpKAC4BTUlIglUrR1NQEs9mMsWPHoqSkhLP8vUAgQHZ2Nm7cuIHu7m7weDw8+eSTmDNnDucTmPj4eERFRaGpqQn9/f1ITk7G888/z5mEAT9Ur3W5XOz02fjx4wNWgQ0FVGU0dFAsQwvFM7RQBeAfYSQqADscDigUCng8niFtu+7t7YVKpRrSoXculwsOh2PIQbRYLBCLxQEP97uZx+OBxWIJeNQA11jCw8PvakJ4L6Iqo6GDYhlaKJ6hhSoA32NkMhlUKtWQ20dFRQ359F6hUDisbFQulw8pkQF+yIqHmsh4x0KnDhNCCAkGlMwQQgghJKhRMkMIIYSQoEYVgIfhwQcfRGNjIwBAqVTi5MmTSEpK4mx76tQpLF++HIODgxAKhVi2bBn++te/Buz7L3/5C8rKymCz2TBmzBiUlpYG7Fur1WLlypVobGyEVCrF4sWLsW7duoB9X7x4EWVlZTAYDMjMzMSKFSsCVi72eDw4evQoLl26BKlUioKCgoC7sACgubkZu3btQnd3NzQaDZ5//nmkpqYGbF9eXo4jR47A4/Fg+vTpWLp0acDprL6+Phw8eBDt7e2IiYnB3LlzkZycHLDv7u5uNDQ0wOl0QqPRYOzYsQH7djqdqK+vh06nQ3x8PDQaDaKiogL2rdfrUV9fD6vVCrVajZycHIhEIs62Ho8HjY2N6OjogEgkQnZ2NhITEwP2PTAwgJqaGgwODiImJgb33XcfZDJZwPaEEEJ80W6mIZo6dapPoTmHw4EdO3bglVde8Wvb0tKCOXPmwGq1gmEYuN1uXLx4ERaLBbNmzfJrv2nTJvz973+H2WyG0+mEVqvFwYMHUVJS4rcmxm634/HHH0ddXR1cLhdMJhPOnDkDm82GmTNn+vVdV1eHdevWobm5GTabDbW1taitrcWsWbMgFov92peXl2Pv3r2wWCwwGAyorq5GdHQ0UlJS/NpqtVqsXbsWtbW1sNvtaGxsxNmzZ/HQQw9xrs/55JNP8N5776G/vx99fX2orq6G3W7H/fff79fWYrFg69atqK6uhsPhQHNzMy5fvoy8vDzOvru6ulBZWYm+vj5YrVY0NTWBz+dzJhEejwenTp3ChQsX4Ha7odfrcfXqVSQkJHCuWTIYDDh8+DBu3LjBjsVqtSIlJYVzB9m5c+dQVVUFp9OJ3t5eXL16FXFxcQgPD/drazKZcPjwYbS3t8PpdKK1tRVGoxFpaWl3fc1SqO+YoN0voYXiGVpGejcTTTMNUUtLi981p9OJzz//3O/6xo0b2dXaAoGA/VDavXs3Z9+7d+8GwzAIDw+HUqmEQqFAV1cX9u7d69e2oqICHR0dUCgUUCqV7Afkp59+ytn34cOH0dvbi6ysLGg0GmRmZqK+vh6nT5/2a2uxWHDs2DGo1WokJycjMzMTcrkclZWVnH1//fXX6OrqYvvOzs5Ge3s7vvnmG8725eXlUCqVSE1NRUpKCqKjo3Hw4EFYrVa/tvX19bhy5Qqys7PZpyw3btzAmTNnOPuuq6sDj8dDYmIi4uLikJCQgJqaGphMJr+2RqMRjY2NSE5ORmxsLFJTU2G1WjmrIgPAtWvXYDab2fYpKSloampCb28v5z2sra2FWq1GXFwcEhMTIRAIUFtby9l3R0cH9Ho9UlNT2bG0t7eztYkIIYTcGSUzP1Jra6vftf7+fgDw+8k6UOZts9l82npruhiNRr+2fX19Pm28v3c6nZx9m0wmn+kQ7++5Egi73Q6XywWJRMJek0gknG2BHz64eTwe+3SCx+NBIBDAZrP5tXW73bDZbD59S6VSuFyugGPh8/k+90UoFHL27W1/85MmiUQCt9vNWenY5XIBgN99sdvtnH3fOm7v0zJvP7f2fes9FIvFAft2Op1+sefxeJx9E0II4UbJzBAFKhpXXFzsd23u3LkAfvgA9/4CgLS0NM4+srKy4HK54HQ64Xa7YTabIRaLOaekioqKIJFIYDab4Xa74XQ64XK5Avadk5MDl8uF/v5+MAyDrq4uyOVyjBs3zq+tSqVCamoqOjo64PF4YLfbodPpMHbsWM6+x44dC6FQyD6h0Ov1EAqFyM7O9msrEAgwbtw46HQ69n12dnYiIyODc61KcnIyZDIZuru7AfywroRhGM7pLgBITExEf38/HA4HGIZBZ2cnYmNjOR9VhoeHQ6VS4fr162AYBjabjV0Lw0WtVsNms7GPR2/cuIHw8HDOaSOFQoH4+Hh0dnaCYRg4nU709fUFPKXc+97NZjOAH+oSSaVSzoKJhBBCuNGamSHKzMzEgQMHfK4tXboUP/vZz/zaTpkyBefOnfOZmlKpVPj2228516k89thjOHDgAAwGAxwOByQSCV5++WXOisFRUVEQCoWorq6G1WqF0+lEcnIyysrKOD9cx44di76+PtTU1KCnpwdSqRSrVq3C1KlT/dryeDxkZGSgpaUFzc3NGBwcRF5eHpYuXQqpVOrXPjU1FR6PB+fPn2enRZYvX44nnniC4w4CEyZMQH19Pa5evQqj0YjMzEysXbuWM5lRqVSIjo5GTU0NOjs74XQ6MX/+fMyePZtznUpMTAycTifa2trQ19eHmJgYzJw5EwqFwq+tSCRCTEwMurq6oNPp4Ha7kZubi9zcXM6+IyMjwePx2PUsSqUSM2fORGRkpF9bPp+P2NhY6PV6dHV1wWw2IycnB5MnT+ZcAxMeHg6JRILW1lYYDAaIRCLMmDEjYGL1Y4T6vDytsQgtFM/QQhWAf4S7XQFYq9Vi06ZNcLlceO655zBlypTbtj969CgqKiqQlZWFF1988bZt7XY7Kioq0NfXhxkzZgR8GuLV0NCAkydPIiIiAj/5yU98pjW4NDc3w2g0Ijk5GfHx8bdta7PZ0N3djbCwMERERNyxMF9bWxt6enqgVqsD7sDycjgcaGhogNvtxpgxY+5YyM9oNMJgMCAsLOyO42YYBgMDA3C73QgLCwu428jLbrfDZrMhNjYWLpfrtgeBAsDg4CAcDgeUSuUd77fT6cTg4CAEAgHCw8PveHioyWSC3W6HXC4fsZ1MoV5llCrGhhaKZ2gZ6QrAlMwME32DhRaKZ+igWIYWimdoGelkJmjqzJhMJmzbtg3nz5+HTCZDcXEx5s2bN9rDIoQQQsgoC5pkprS0FG63Gzt37kR3dzdef/11JCUlIS8vb7SHRgghhJBRFBS7mWw2G06dOoVnn30WcrkcmZmZmD17No4cOTLaQyOEEELIKAuKZKazsxMAfLblZmRkoK2tbbSGRAghhJB7RFBMM9lsNr8dHgqFwq/Yml6v91n0690mezd5t9fe7VLz9yJvEbxQRvEMHRTL0ELxDC0jHc+gSGakUqlf4mI2m/0SnH379uGDDz5gX5eUlODXv/71iIyJq6ZLKOKqixOKKJ6hg2IZWiieoWWk4hkUyYy3empHRwd7anJLS4vf6cwLFy7Eww8/zL7m8/mcRwL8GN66Id56JqFMoVCwlWlDFcUzdFAsQwvFM7QMN55cRUlvJyiSGalUigcffBB79uzBb37zG9y4cQNHjx7F6tWrfdrFxMT47E3X6/Uj9k1w8zEFocp74vf/BRTP0EGxDC0Uz9AyUvEMimQGAH7xi19g69atKCkpgVwux7JlyzBx4sTRHhYhhBBCRlnQJDNKpRJr164d7WEQQggh5B4TFFuzCSGEEEICoWSGEEIIIUEtpA+aHAl6vR779u3DwoULh30QFrn3UDxDB8UytFA8Q8tIx5OezAyTXq/HBx98MCIncpP/PYpn6KBYhhaKZ2gZ6XhSMkMIIYSQoEbJDCGEEEKCmmDDhg0bRnsQwUYmk6GgoAByuXy0h0LuAopn6KBYhhaKZ2gZyXjSAmBCCCGEBDWaZiKEEEJIUKNkhhBCCCFBLWiOM7gXmEwmbNu2DefPn4dMJkNxcTHmzZs32sMiQ3DgwAEcO3YMra2tKCwsxKuvvsp+ra2tDVu2bEFrayvUajVWrlyJ3NzcURwtuROn04nt27fj0qVLGBwcRExMDBYvXoxZs2YBoJgGm61bt+Ls2bOwWq0ICwvDnDlzUFxcDIBiGcwGBgawcuVKJCQkYNOmTQBGLp70ZGYYSktL4Xa7sXPnTqxfvx579uzBf//739EeFhmCqKgoFBcXY86cOT7XXS4X3nzzTRQWFqKsrAwLFy7En/70J5hMplEaKRkKt9uNqKgovPnmmygrK8OLL76I7du348qVKxTTIPTUU0+htLQUn332Gf785z/j66+/xrfffkuxDHI7duxAWloa+3ok40nJzBDZbDacOnUKzz77LORyOTIzMzF79mwcOXJktIdGhmD69OmYNm0awsPDfa5fvnwZdrsdCxYsgEgkQlFREeLj41FVVTVKIyVDIZVKsWzZMqjVavD5fNx3333IyclBfX09xTQIpaSkQCKRsK95PB66uroolkHs8uXLuH79OoqKinyujVQ8KZkZos7OTgA/fNN5ZWRkoK2tbbSGRO6C9vZ2pKamgs///98K6enpaG9vH8VRkeGy2Wy4evUqUlNTKaZB6qOPPsLixYvxwgsvwGazoaioiGIZpJxOJ0pLS/HLX/4SPB6PvT6S8aQ1M0Nks9kgk8l8rikUClit1lEaEbkbrFYrFAqFzzWFQgGLxTJKIyLDxTAMNm/ejKysLOTn56OxsZFiGoSWL1+On//857h69Sqqq6vZ/18plsFn7969yM/PR1paGpqbm9nrIxlPejIzRFKp1C9xMZvNfgkOCS4ymczvG8lisVBcgwTDMHjvvfdgMBiwevVq8Hg8imkQ4/F4yMrKglAoRFlZGcUyCHV1deHEiRNYunSp39dGMp6UzAyRRqMBAHR0dLDXWlpakJqaOlpDIndBSkoK2tra4PF42GstLS0+04nk3sQwDLZv345r165hw4YNkEqlACimocDj8aC7u5tiGYTq6+thMBiwYsUKLFu2DO+//z6am5uxbNkyxMfHj1g8KZkZIqlUigcffBB79uyBxWJBS0sLjh49ikceeWS0h0aGwO12w+FwwOPxwOPxwOFwwOVyYcKECRCJRNi/fz+cTie+/vprXL9+HYWFhaM9ZHIHpaWlaGhowB//+Eef8ugU0+BiNptx/PhxWCwWeDwe1NXV4csvv8SkSZMolkHooYceQmlpKTZv3ozNmzdj6dKlSE1NxebNm1FQUDBi8aTjDIbBZDJh69atOH/+PORyOdWZCSKffPIJPv30U59rs2fPxu9+9zu0trZi69ataG1tRXx8PFauXInx48eP0kjJUOh0OqxYsQIikQgCgYC9vmjRIhQXF1NMg4jFYsFbb72F5uZmeDweREVF4dFHH8XTTz8NHo9HsQxyR48exZdffsnWmRmpeFIyQwghhJCgRtNMhBBCCAlqlMwQQgghJKhRMkMIIYSQoEbJDCGEEEKCGiUzhBBCCAlqlMwQQgghJKhRMkMIIYSQoEbJDCGEEEKCGiUzhBA/+/fvx3vvvedzbcOGDVAqlaM0Im7vvPMO/vOf/4z2MAgho4wqABNC/JSUlODs2bOoqalhr2m1WnR3d+P+++8fxZH5SktLw5NPPomtW7eO9lAIIaNIONoDIIQEh6SkJCQlJY32MAghxA9NMxFCfJSUlOCjjz5CbW0teDweeDweSkpK/KaZTpw4AR6Ph0OHDmHhwoVQKpVITk7G7t27AQDvvvsuUlJSEBkZiRUrVsBut/v8PVqtFs8++yxiYmIgk8kwc+ZMnDt3zqfNv//9bxQUFECpVCIiIgIFBQXstFJaWhra2tqwbds2dpy7du1i/+yuXbuQl5cHqVQKjUaD3//+93C5XD5f5/F4+P777zF79mzI5XKkpaVhx44dd/uWEkJGGCUzhBAf69evx7x585CRkYHvvvsO3333HdavXx+w/a9+9Svk5+ejvLwchYWFWL58OdasWYPDhw9j+/bt2LhxIz7++GP87W9/Y/+M0WjEQw89hIsXL2LLli3Yt28fFAoFZs+eDZ1OBwBobm7GokWLkJubi/Lycnz22WcoLi6G0WgEAJSXl0OtVmPRokXsOOfPnw8AePvtt7FixQo8/vjjqKiowJo1a/Duu+9i3bp1fuN/5pln8Nhjj6G8vBxFRUV44YUXcOjQobt5SwkhI40hhJBbLF++nMnNzfW59oc//IFRKBTs6+PHjzMAmDVr1rDX+vr6GIFAwCQnJzN2u529vnDhQmbSpEns69dff51RqVTMjRs32Gs2m41JSkpiXn31VYZhGGbv3r0MAGZgYCDgOFNTU5kXX3zR59rAwACjVCqZ1157zef6tm3bGJlMxuj1eoZhGGbnzp0MAGb9+vU+7WbMmMEUFhYG/DsJIfceejJDCPlRHn30Ufb3KpUKcXFxmDlzJsRiMXs9OzsbHR0d7OvKykoUFRUhKioKLpcLLpcLAoEAM2bMwJkzZwAAeXl5EAgEWLp0KSoqKtDf3z+k8VRVVcFkMmHx4sVs3y6XC7Nnz4bVavVZ1AwACxYs8Ht99uxZuN3uYd8LQsjooGSGEPKjRERE+LwWi8Wc12w2G/tar9dj//79EIlEPr/KysrYpCc7OxsHDhxAf38/FixYgNjYWDz11FNob2+/7Xj0ej0AYPLkyT595+TkAIBPUgUAcXFxfq+dTifbDyHk3ke7mQgh/3NRUVGYO3cuNm7c6Pc1iUTC/n7u3LmYO3cuBgYGcOjQIbz00kt47rnncPTo0dv2DQCff/45kpOT/b6enp7u81qn00Gj0fi8FolEiImJGfb7IoSMDkpmCCF+bn2Scrc9+uij2L17N3JycqBQKO7YPjw8HMXFxaiurkZZWdltxzl9+nTI5XJotVq/KSQu5eXlyM/P93k9ZcoUCASCYbwjaNFv3gAAAcpJREFUQshoomSGEOInJycHO3bsQFlZGbKysu76U4qXX34Ze/bswcMPP4zf/va3SElJQU9PD6qrq5GYmIiXXnoJpaWlqKqqwhNPPIGEhAS0tLRg9+7dmDNnjs84jx07hq+++gqRkZFIT09HdHQ03njjDaxevRparRZFRUXg8/m4du0avvjiC+zbtw9yuZzt4+OPP4ZMJsPkyZPx6aef4uTJkzh48OBdfb+EkJFFyQwhxM8LL7yA06dPY9WqVTAYDFi+fDnS0tLuWv/R0dH4/vvvsW7dOqxZswYGgwFxcXGYNm0a+zQlLy8PFRUVePnll2EwGKBWq7FkyRKfqam33noLK1euxMKFCzE4OIidO3eipKQEr7zyCjQaDd5++21s2bIFIpEImZmZePLJJ30WJgNAWVkZXnvtNbzxxhuIi4vD+++/j3nz5t2190oIGXl0nAEh5P+kXbt24bnnnkNPTw+tjyEkyNFuJkIIIYQENUpmCCGEEBLUaJqJEEIIIUGNnswQQgghJKhRMkMIIYSQoEbJDCGEEEKCGiUzhBBCCAlqlMwQQgghJKhRMkMIIYSQoEbJDCGEEEKCGiUzhBBCCAlqlMwQQgghJKj9P8PXmKjei3gvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (299276408)>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    pn.ggplot(cluster_properties_df)\n",
    "    + pn.geom_point(pn.aes(x='timestep', y='n_particles'), alpha=0.3)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Particle Properties\n",
    "\n",
    "The following plot shows how the coordination number distribution changes over time.\n",
    "Coordination number means the number of neighbors any given particle has.\n",
    "Here, just like the rows of `particle_properties_df`, each point represents a single particle at a given point in time.\n",
    "Since there are more total points in this chart, I added a little vertical jitter to each point to reduce the amount of overlap we see."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (299356452)>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    pn.ggplot(particle_properties_df)\n",
    "    + pn.geom_jitter(pn.aes(x='timestep', y='coordination_number'), width=0, height=0.2, alpha=0.3)\n",
    "    + pn.scale_y_continuous(breaks=[0, 2, 4, 6, 8])\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overall, both of these plots corroborate what can be seen in the visualization at the top: that particles aggregate over time, forming larger and larger and more densely connected clusters until all particles in the box are part of a single cluster."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:ads]",
   "language": "python",
   "name": "conda-env-ads-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}