{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) 2024 Massachusetts Institute of Technology\n",
    "\n",
    "SPDX-License-Identifier: MIT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Launching a Set of Simulations\n",
    "\n",
    "So far, we've explored how to use `madlib` to launch a single satellite/sensor simulation at a time. The true power of `MaDDG`, however, is that it allows us to conduct large-scale experiments with randomly varied parameters.\n",
    "\n",
    "This notebook will walk through the following steps to launching a custom batch of simulation experiments:\n",
    "1) Defining the experiment parameters,\n",
    "2) Preparing the simulator task function, and \n",
    "3) Launching the experiments in parallel.\n",
    "4) Analyzing the results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining the Experiment Parameters\n",
    "\n",
    "Each simulation involves a network of sensors observing a satellite, and that satellite may or may not be performing a maneuver during the period of observation. The first step to creating our set of simulations is to define these parameters, or at least the distributions from which the parameters will be pulled."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating the Sensor Network\n",
    "\n",
    "One thing that every simulation in our set will have in common is that they will all use the same sensor network. `MaDDG` comes packaged with a sample network (`configs/sample_sensor_network.yaml`) that offers good global coverage of the GEO belt. Let's go ahead and use that.\n",
    "\n",
    "All we need to do is point our code to the YAML file containing the sensor parameters. To better understand these parameters, refer to the Example 3 notebook on sensor collections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "sensor_yaml = Path(\"../configs/sample_sensor_network.yaml\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating the Satellite Distributions\n",
    "\n",
    "While we will create the `Satellite` objects themselves (including their orbital parameters) within the simulator task function, we can define the types and vector distributions of their maneuvers now. That way, each separate experiment can create a unique satellite, but they will all have the same general class of behavior.\n",
    "\n",
    "For this example, we'll consider impulsive maneuvers with in-track and cross-track components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First, define the maneuver type as a string (\"impulse\", \"continuous\", or \"all\")\n",
    "mtype = \"impulse\"\n",
    "\n",
    "# The vector components of the thrust vector will be pulled from normal distributions, the mean and width of which we define here.\n",
    "# The values are entered as a 3-element list with the radial, in-track, and cross-track components of the delta-v (respectively)\n",
    "# in km/s.\n",
    "dv_ric_mean_kms = (0.0, 0.0, 0.0)\n",
    "dv_ric_std_kms = (0.0, 0.1, 1.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining the Experiment Timing\n",
    "\n",
    "We can control the time and day of the simulated epoch at which our experiments will start, as well as the simulated duration of the experiments. For this example, we'll use the arbitrary date of 60197.5 (again, all dates are in MJD format!). For the simulation duration, we'll go with 3 days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_mjd = 60197.5\n",
    "sim_duration_days = 3.0  # Duration is defined in days"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining the Number of Simulations and Launch Controls\n",
    "\n",
    "The next thing we need to do is decide how many unique simulations to run. Note that we define this as a number of pairs: each pair contains one maneuvering and one non-maneuvering satellite (with different orbital parameters). This is designed to give us a balanced dataset for classification tasks.\n",
    "\n",
    "We'll keep this example lightweight and run 5 pairs of simulations, for a total of 10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_sim_pairs = 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we need to figure out where the outputs of our simulations will go, and if we want to run them in parallel. Since we're running 100 simulations, parallelizing them will be a good idea. Luckily, the [submitit](https://hydra.cc/docs/plugins/submitit_launcher/) plugin for [hydra](https://hydra.cc/) (both of which are installed with this project) makes it easy to distribute jobs across available CPUs.\n",
    "\n",
    "Feel free to change the output directory in this example, but the one that we'll create is already added to the project's `.gitignore`, so it shouldn't cause any issues."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the output directory\n",
    "output_path = \"example_outputs\"\n",
    "output_dir = Path(output_path)\n",
    "output_dir.mkdir(exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The multirun directory is where hydra-zen saves experiment files. We'll put it under `example_outputs/`. We can raise a flag to automatically delete the multirun directory when the jobs are complete, but we'll preserve them this time for illustrative purposes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "multirun_dir = output_dir / \"multirun\"\n",
    "multirun_path = str(multirun_dir)  # We need the string version of this path\n",
    "\n",
    "rm_multirun_root = (\n",
    "    False  # If True, multirun directories created by this job will be deleted\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we need a configuration JSON to tell `MaDDG` to use the `submitit` plugin to parallelize the job. We just want to run locally, so we'll set the hydra launcher to `submitit_local`.\n",
    "\n",
    "We'll also set the option `hydra.job.chdir` to `True` to make sure the outputs of each simulation are saved to their own respective multirun directories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "submitit_json = [\n",
    "    \"hydra.job.chdir=True\",\n",
    "    \"hydra/launcher=submitit_local\",\n",
    "    \"hydra.launcher.nodes=1\",\n",
    "    \"hydra.launcher.cpus_per_task=2\",\n",
    "    \"hydra.launcher.tasks_per_node=10\",\n",
    "    \"hydra.launcher.mem_gb=16\",\n",
    "]\n",
    "\n",
    "submitit_file = Path(\"example_outputs\") / \"submitit.json\"\n",
    "with open(submitit_file, \"w\") as f:\n",
    "    json.dump(submitit_json, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing the Simulator Task Function\n",
    "\n",
    "The task function is a concept borrowed from [hydra-zen](https://mit-ll-responsible-ai.github.io/hydra-zen/index.html), and it's the actual code that will be run for each simulation. Our task function should instantiate the sensor network and random satellite, propagate the orbit and record observations throughout the simulation duration, compute metrics on the produced observations, and return those metrics as a [pandas](https://pandas.pydata.org/) DataFrame. `MaDDG` will automatically collate all of the DataFrames created by the experiments and produce a single CSV as our final output.\n",
    "\n",
    "Our task function must accept the following input parameters:\n",
    "- `seq_id` - An integer ID for the experiment\n",
    "- `sensor_params` - A dictionary containing the parameters for each sensor in our network\n",
    "- `maneuver_type` - An integer defining the type of maneuver for the satellite (0=None, 1=Impulsive, 2=Continuous)\n",
    "- `sim_duration_days` - A float defining the length of the simulation in days\n",
    "- `start_mjd` - A float (or None, as discussed above) defining the simulation's starting epoch in MJD format\n",
    "- `dv_ric_mean_kms` - A tuple of 3 floats defining the means of the thrust vector distributions (see \"Creating the Satellite Distributions\" above)\n",
    "- `dv_ric_std_kms` - Same as `dv_ric_mean_kms`, but for the distribution widths\n",
    "\n",
    "Our simulation task is to calculate **residuals**, the differences between the satellite's expected position and its actual position at each observation time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Tuple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import madlib\n",
    "from maddg._residuals import calculate_residuals\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "def simulator_task(\n",
    "    seq_id: int,\n",
    "    sensor_params: dict,\n",
    "    maneuver_type: int,\n",
    "    sim_duration_days: float,\n",
    "    start_mjd: float,\n",
    "    dv_ric_mean_kms: Tuple[float, float, float],\n",
    "    dv_ric_std_kms=Tuple[float, float, float],\n",
    "    **kwargs,\n",
    "):\n",
    "    # Define a SensorCollection object from the given parameters\n",
    "    sensors = [madlib.GroundOpticalSensor(**params) for key, params in sensor_params.items()]\n",
    "    sensor_network = madlib.SensorCollection(sensors)\n",
    "\n",
    "    # Timing\n",
    "    epoch = start_mjd\n",
    "\n",
    "    # Create the satellite (a GEO object at a random longitude)\n",
    "    sat_longitude = 360 * np.random.random()\n",
    "    sat_observed = madlib.Satellite.from_GEO_longitude(sat_longitude, epoch)\n",
    "\n",
    "    maneuver = None\n",
    "    maneuver_mjd = None\n",
    "    maneuver_r_kms = None\n",
    "    maneuver_i_kms = None\n",
    "    maneuver_c_kms = None\n",
    "\n",
    "    # For maneuvering cases, create a random maneuver vector\n",
    "    if maneuver_type == 1:\n",
    "        # Pick a random maneuver time during the simulation\n",
    "        maneuver_mjd = epoch + sim_duration_days * np.random.random()\n",
    "\n",
    "        # Calculate the thrust vector using the input distributions\n",
    "        mean_rad, mean_in, mean_crs = dv_ric_mean_kms\n",
    "        std_rad, std_in, std_crs = dv_ric_std_kms\n",
    "\n",
    "        maneuver_r_kms = mean_rad + std_rad * np.random.randn()\n",
    "        maneuver_i_kms = mean_in + std_in * np.random.randn()\n",
    "        maneuver_c_kms = mean_crs + std_crs * np.random.randn()\n",
    "\n",
    "        # Define the ImpulsiveManeuver object, converting from km/s to m/s\n",
    "        man_dv = np.array([maneuver_r_kms, maneuver_i_kms, maneuver_c_kms]) / 1000\n",
    "        maneuver = madlib.ImpulsiveManeuver(maneuver_mjd, man_dv)\n",
    "\n",
    "    sat_observed.maneuver = maneuver\n",
    "\n",
    "    # Observe and calculate residuals\n",
    "    residual_df = calculate_residuals(\n",
    "        sensors=sensor_network,\n",
    "        satellite=sat_observed,\n",
    "        sim_duration_days=sim_duration_days,\n",
    "        t_start_mjd=epoch,\n",
    "    )\n",
    "\n",
    "    # Append maneuver information to the output dataframe\n",
    "    if residual_df is not None:\n",
    "        residual_df[\"Maneuver\"] = maneuver_type\n",
    "        residual_df[\"Sequence\"] = int(seq_id)\n",
    "        residual_df[\"Maneuver_MJD\"] = maneuver_mjd\n",
    "        residual_df[\"Maneuver_DV_Radial_KmS\"] = maneuver_r_kms\n",
    "        residual_df[\"Maneuver_DV_InTrack_KmS\"] = maneuver_i_kms\n",
    "        residual_df[\"Maneuver_DV_CrossTrack_KmS\"] = maneuver_c_kms\n",
    "\n",
    "    # Return the requisite dataframe\n",
    "    return residual_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Launching the Experiments\n",
    "Next we use the `MaDDG` launcher to run the simulations we've defined in the manner we've configured. There are several inputs to this function, many of which are optional or exclusive to continuous maneuver cases. We encourage you to read the function's documentation for a full explanation of the arguments.\n",
    "\n",
    "For now, we introduce no parameters that were not discussed above. Executing the cell below will launch the simulations, which may take several minutes to complete depending on your computer's resources."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO :: mtype = 'impulse'\n",
      "INFO :: sim_duration_days = 3.0\n",
      "INFO :: sims_per_task = 1\n",
      "[2024-06-18 16:58:24,003][HYDRA] Submitit 'local' sweep output dir : example_outputs/multirun/2024-06-18/16-58-22\n",
      "[2024-06-18 16:58:24,005][HYDRA] \t#0 : maneuver_type=0 seq_id=0\n",
      "[2024-06-18 16:58:24,014][HYDRA] \t#1 : maneuver_type=0 seq_id=1\n",
      "[2024-06-18 16:58:24,023][HYDRA] \t#2 : maneuver_type=0 seq_id=2\n",
      "[2024-06-18 16:58:24,034][HYDRA] \t#3 : maneuver_type=0 seq_id=3\n",
      "[2024-06-18 16:58:24,044][HYDRA] \t#4 : maneuver_type=0 seq_id=4\n",
      "[2024-06-18 16:58:24,053][HYDRA] \t#5 : maneuver_type=1 seq_id=0\n",
      "[2024-06-18 16:58:24,062][HYDRA] \t#6 : maneuver_type=1 seq_id=1\n",
      "[2024-06-18 16:58:24,071][HYDRA] \t#7 : maneuver_type=1 seq_id=2\n",
      "[2024-06-18 16:58:24,080][HYDRA] \t#8 : maneuver_type=1 seq_id=3\n",
      "[2024-06-18 16:58:24,090][HYDRA] \t#9 : maneuver_type=1 seq_id=4\n"
     ]
    }
   ],
   "source": [
    "from maddg._sim_launcher import launcher\n",
    "\n",
    "launcher(\n",
    "    simulator_method=simulator_task,\n",
    "    mtype=mtype,\n",
    "    num_sim_pairs=num_sim_pairs,\n",
    "    sensor_yaml=sensor_yaml,\n",
    "    outdir=output_dir,\n",
    "    dv_ric_mean_kms=dv_ric_mean_kms,\n",
    "    dv_ric_std_kms=dv_ric_std_kms,\n",
    "    submitit=str(submitit_file),\n",
    "    multirun_root=multirun_path,\n",
    "    rm_multirun_root=rm_multirun_root,\n",
    "    start_mjd=start_mjd,\n",
    "    sim_duration_days=sim_duration_days,\n",
    "    random_seed=0,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explaining the Outputs\n",
    "Once the jobs have completed, let's stop and take a look at the files that have been produced in our output directory:\n",
    "\n",
    "- **multirun** directory\n",
    "    - This will have nested folders named with the date and time that the simulations were launched.\n",
    "    - Inside the date/time folders will be 10 folders labeled 0-9 and a **.submitit** directory.\n",
    "    - The **.submitit** folder contains the stderr and stdout of the individual jobs. This is useful for debugging.\n",
    "    - Each numbered folder contains a **.hydra** folder\n",
    "        - Inside, you'll find various YAML files describing the configuration of that specific experiment.\n",
    "    - The numbered folders also contain **zen_launch.log** (a log of the job submissions that can be ignored) and **output.csv**, which holds the measurements from that simulation.\n",
    "- **complete.csv**\n",
    "    - This is the final data product, concatenating the results from the individual simulations.\n",
    "- **errors.txt**\n",
    "    - If any simulations encountered errors, they will be recorded here.\n",
    "- **multirun.yaml**\n",
    "    - Another hydra YAML summarizing the jobs we launched. You can ignore this, but it's a useful summary and can be used to reproduce the experiment in the future.\n",
    "- **submitit.json**\n",
    "    - The JSON we created earlier in this notebook to configure the submitit parallellization of our jobs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing the Experiments\n",
    "Let's take a look at the collated results of our simulations by reading the **complete.csv** file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "      <th>DEC Arcsec</th>\n",
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       "      <td>B3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>60199.388352</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.051197</td>\n",
       "      <td>0.742526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>711</th>\n",
       "      <td>60199.974935</td>\n",
       "      <td>-53.776856</td>\n",
       "      <td>-31.163723</td>\n",
       "      <td>B3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>60199.388352</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.051197</td>\n",
       "      <td>0.742526</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>712 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              MJD  RA Arcsec  DEC Arcsec SensorID  Maneuver  Sequence  \\\n",
       "0    60198.111433   0.265156    0.116136       C1         0         0   \n",
       "1    60198.184929  -2.286885    0.196002       C1         0         0   \n",
       "2    60198.258424  -1.112096    0.065560       C1         0         0   \n",
       "3    60198.331920  -0.699248    0.570814       C1         0         0   \n",
       "4    60198.405415   0.658723    0.124080       C1         0         0   \n",
       "..            ...        ...         ...      ...       ...       ...   \n",
       "707  60199.680953  -8.950171   54.328034       B3         1         4   \n",
       "708  60199.754449 -16.675548   43.210577       B3         1         4   \n",
       "709  60199.827944 -28.241519   21.618071       B3         1         4   \n",
       "710  60199.901440 -41.482868   -6.093304       B3         1         4   \n",
       "711  60199.974935 -53.776856  -31.163723       B3         1         4   \n",
       "\n",
       "     Maneuver_MJD  Maneuver_DV_Radial_KmS  Maneuver_DV_InTrack_KmS  \\\n",
       "0             NaN                     NaN                      NaN   \n",
       "1             NaN                     NaN                      NaN   \n",
       "2             NaN                     NaN                      NaN   \n",
       "3             NaN                     NaN                      NaN   \n",
       "4             NaN                     NaN                      NaN   \n",
       "..            ...                     ...                      ...   \n",
       "707  60199.388352                     0.0                 0.051197   \n",
       "708  60199.388352                     0.0                 0.051197   \n",
       "709  60199.388352                     0.0                 0.051197   \n",
       "710  60199.388352                     0.0                 0.051197   \n",
       "711  60199.388352                     0.0                 0.051197   \n",
       "\n",
       "     Maneuver_DV_CrossTrack_KmS  \n",
       "0                           NaN  \n",
       "1                           NaN  \n",
       "2                           NaN  \n",
       "3                           NaN  \n",
       "4                           NaN  \n",
       "..                          ...  \n",
       "707                    0.742526  \n",
       "708                    0.742526  \n",
       "709                    0.742526  \n",
       "710                    0.742526  \n",
       "711                    0.742526  \n",
       "\n",
       "[712 rows x 10 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "results = pd.read_csv(\"example_outputs/complete.csv\")\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each row in this table is an osbservation of the satellite, and the columns are described as follows:\n",
    "- **MJD**: The time of the observation, in MJD format\n",
    "- **LON Arcsec**: The longitude residual, or the distance between its expected and measured surface longitudes, in arcseconds\n",
    "- **LAT Arcsec**: The latitude residual, or the distance between its expected and measured surface latitudes, in arcseconds\n",
    "- **SensorID**: The name of the sensor that collected the observation\n",
    "- **Maneuver**: 0 if no maneuver was performed, otherwise 1 (remember, all maneuvers are impulsive in this set of simulations)\n",
    "- **Sequence**: An integer identifier for each unique simulation\n",
    "- **Maneuver_MJD**:\n",
    "  - For maneuvering satellites, this is the time that the maneuver occurred, in MJD format.\n",
    "  - For non-maneuvering cases, this value will be NaN\n",
    "- **Maneuver_DV_<RIC>_KmS**:\n",
    "  - For maneuvering cases, these three columns give the delta-v (in km/s) in the radial, in-track, and cross-track directions.\n",
    "  - For non-maneuvering cases, these values will be NaN\n",
    "\n",
    "If you examine the code for the task function above (`simulator_task`), you can see how these values were calculated."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<b>Note:</b> Due to our method for calculating each simulation's unique ID, the maximum value of the Sequence numbers \n",
    "is not necessarily the same as the number of simulations run.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing Results\n",
    "Let's take a look at some individual scenarios. First, we'll select a non-maneuvering case and look at its residuals over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "data_nomnvr = results.loc[(results[\"Sequence\"]==1) & (results[\"Maneuver\"]==0)]\n",
    "\n",
    "t = data_nomnvr[\"MJD\"].to_numpy()\n",
    "t = t-min(t)\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(2, sharex=True, figsize=(9, 9))\n",
    "ax1.plot(t, data_nomnvr[\"RA Arcsec\"], \"ob\", ms=5)\n",
    "ax2.plot(t, data_nomnvr[\"DEC Arcsec\"], \"ob\", ms=5)\n",
    "ax1.grid()\n",
    "ax2.grid()\n",
    "ax1.set_ylabel(\"RA Residual (arcsec)\", fontsize=14)\n",
    "ax2.set_ylabel(\"DEC Residual (arcsec)\", fontsize=14)\n",
    "ax2.set_xlabel(\"Time (days)\", fontsize=14)\n",
    "_ = ax1.set_title(\"Non-Maneuvering Satellite\", fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's some random sensor noise to our observations, but the residuals in both dimensions stay close to 0 on average. This makes perfect sense: This satellite is not maneuvering, so its expected position - defined by its original orbit - should match up with its observed position.\n",
    "\n",
    "Now let's look at a maneuvering case:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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j9Nqy7yEsLMwcMGCAOXr0aPP00093zF3grkyUDQ3buHFj85JLLjEtFotpsVjMzZs3O7bx5zsJ5JCr77zzjinJjIyMNC+44AIzNTXVtFgs5meffebYxt+yDSD0kTQAqJEClTSYpn38+Ntvv93s3LmzWb9+fbNVq1bmyJEjza+++srtzVVlkgbTtM8DkJGRYfbp08ds1qyZGRERYbZu3drs1auXOW3aNPPzzz8v95pDhw6Zd955p9mxY0czIiLCbNWqlTl69GgzPz/f7TwNx1qyZIkjJk9xl9m9e7c5a9Ys8/TTTzebNGliRkZGmu3atTPPOuss86677jK//fZbp+2DlTSYpml+88035sUXX2yeeOKJZv369c2TTz7ZnD9/vllSUmJGRkaaYWFh5l9//VXhezZNeyK5atUqc/bs2ebAgQPNjh07mg0bNjQjIyPNNm3amOecc465cOFCl/NC2Gw2MzMz0+zRo4fZuHFjs2nTpma/fv3Ml19+2TRN92WitLTUTE9PN7t27epIgORiTgRfv5NAJg2maZrPPPOMefrpp5sNGzZ0bHf89+1P2QYQ+gzT9KLxJgAANdAnn3yiAQMGqFu3bvr222+DHQ4A1Fj0aQAA1Gi//vqrtm7dWm75999/r4kTJ0r6uyM4AMA/1DQAAGq0lStXatCgQTr55JPVsWNHNWjQQFu3btX//vc/2Ww2DRs2TO+9957LEY0AAN4haQAA1Gg7d+7Uv//9b61atUq//PKL/vjjDzVp0kRdu3bVVVddpYkTJ5IwAEAlkTQAAAAA8KhG92lIT09Xr1691KRJE7Vq1UojR45Ubm6u0zaHDh3SlClT1Lx5czVu3FijRo3S7t27gxQxAAAAUPPU6KRh1apVmjJlitauXaucnBxZrVYNHz5cBw4ccGxz88036+2339arr76qVatWaefOnbrkkkuCGDUAAABQs9Sq5km//vqrWrVqpVWrVql///4qKipSy5Yt9eKLL+rSSy+VZJ8htEuXLlqzZo3OPPNMr/Zrs9m0c+dONWnSRIZhVOVbAAAAAKqFaZr6448/1KZNG4WFea5LqFU9w4qKiiRJJ554oiRp/fr1slqtGjp0qGOb5ORktW/f3mPSUFJSopKSEsfzX375RSeffHIVRg4AAAAEx44dO9SuXTuP29SapMFms+mmm25S3759dcopp0iSCgsLFRkZqWbNmjltGxMTo8LCQrf7Sk9P15w5c8ot/+9//6uGDRsGNG4AAAAgGA4ePKgJEyaoSZMmFW5ba5KGKVOm6Pvvv9enn35a6X3NnDlTaWlpjufFxcWKi4vTyJEjFR0dXen9+8JqtSonJ0fDhg1TREREtR4boY2yAVcoF3CFcgF3KBt1W3FxsSZMmOBV8/takTRMnTpV77zzjj755BOnqpXY2FgdPnxY+/fvd6pt2L17t2JjY93uLyoqSlFRUeWWR0REBO2ECuaxEdooG3CFcgFXKBdwh7JRN/nyndfo0ZNM09TUqVP15ptv6uOPP1ZCQoLT+h49eigiIkLLly93LMvNzdX27dvVp0+f6g4XAAAAqJFqdE3DlClT9OKLL+qtt95SkyZNHP0UmjZtqgYNGqhp06ayWCxKS0vTiSeeqOjoaF1//fXq06eP1yMnAQAAAHVdjU4annzySUnSwIEDnZZnZ2crJSVFkvTwww8rLCxMo0aNUklJiUaMGKEnnniimiMFAAAAaq4anTR4M8VE/fr19fjjj+vxxx+vhogAAACA2qdG92kAAAAAUPVIGgAAAAB4VKObJyEE5eVJWVlSQYEUHy+lpkpJScGOCgAAAJVA0oDAyc6WJkyQDEMyTftjRoaUmSkd7ZgOAACAmofmSQiMvDx7wmCzSaWlzo8Wi5SfH+wIAQAA4CeSBgRGVpa9ZsEVw7DXNgAAAKBGImlAYBQU2JskuWKa9vUAAACokUgaEBjx8Z5rGuLjqzMaAAAABBBJAwIjNdVzTYPFUr3xAAAAIGBIGhAYSUn2fgthYVJ4uPNjZqaUmBjsCAEAAOAnhlxF4KSkSP362ZOEsnkaLBYSBgAAgBqOpAGBlZgopacHOwoAAAAEEM2TAAAAAHhE0gAAAADAI5IGAAAAAB6RNAAAAADwiKQBAAAAgEeMnoS6JS9Pysr6e0jY1FT7HBMAAABwi6QBdUd2tjRhgmQY9lmqDUPKyLDPK5GSEuzoAAAAQhbNk1A35OXZEwabTSotdX60WKT8/GBHCAAAELJIGlA3ZGXZaxZcMQx7bQMAAABcImlA3VBQYG+S5Ipp2tcDAADAJfo0oG6Ij/dc0xAfX53RAKGBgQEAAF6ipgF1Q2qq55oGi6V64wGCLTtbSk6W5s+XliyxPyYnS4sWBTsyAEAIImlA3ZCUZO+3EBYmhYc7P2ZmSomJwY4QqD4MDAAA8BHNk1B3pKRI/frZk4Sy5hgWCwkD6h5vBgZIT6/emAAAIY2kAXVLYiI3QwADAwAAfETzJACoaxgYAADgI5IGAKhrGBgAAOAjkgYAqGsYGAAA4KManzR88skn+uc//6k2bdrIMAwtXbrUab1pmpo9e7Zat26tBg0aaOjQocrLywtOsAAQKlJSpNxcado06fLL7Y+5ufblAAAcp8Z3hD5w4IBOO+00paam6pJLLim3PiMjQ4899pieffZZJSQkaNasWRoxYoQ2bdqk+vXrByFiAAgRDAwAIBCYKLJOqPFJw7nnnqtzzz3X5TrTNPXII4/ozjvv1EUXXSRJWrx4sWJiYrR06VJdeeWV1RkqAABA7ZKdbZ/3xTDsfaIMQ8rIsDd1pOayVqnxSYMnW7duVWFhoYYOHepY1rRpU/Xu3Vtr1qxxmzSUlJSopKTE8by4uFiSZLVaZbVaqzbo45Qdr7qPi9BH2YArlAs38vIUtmiRjG3bZHboIFtKSp36JZRyAXcqVTby8lRvwgQZNpvTYlOSLBYd6d2bPlIhzpfvvVYnDYWFhZKkmJgYp+UxMTGOda6kp6drzpw55ZYvW7ZMDRs2DGyQXsrJyQnKcRH6KBtwhXLxt/bLl6v7ggUyj/4SahiG6j3wgL6eOlU7hgwJdnjVinIBd/wpG12ee06Jko4fwNmQZJO0ddYs/TBmTACiQ1U5ePCg19vW6qTBXzNnzlRaWprjeXFxseLi4jR8+HBFR0dXayxWq1U5OTkaNmyYIiIiqvXYCG2UDbhCuThOXp7qXXKJDNOUUTbMrGnKlPSPxx9Xt8mT68QvoZQLuFOZshH+4ovlEoYyhqTE8HAlnHdepWNE1SlrTeONWp00xMbGSpJ2796t1q1bO5bv3r1b3bt3d/u6qKgoRUVFlVseERERtIttMI+N0EbZgCuUi6Oee87lRHaGJBmGIhYvrlOdwSkXcMevstGxo9uJIg3DkNGxo8IobyHNl++8xg+56klCQoJiY2O1fPlyx7Li4mKtW7dOffr0CWJkAOCHvDxp5kxp9Gj7I8NHV6ygwPNEdgUF1RkNULswUWSdUuNrGv7880/l5+c7nm/dulUbNmzQiSeeqPbt2+umm27Svffeq6SkJMeQq23atNHIkSODFzQA+IoRSvwTH+/2l1AZhn09AP+UTRRpsThfm0yTiSJroRqfNHz11VcaNGiQ43lZX4Rx48Zp0aJFuu2223TgwAFNmjRJ+/fvV79+/fTBBx8wRwOAmiMvz54wHDdCiST7H+t+/fjj7E5qqj25coVfQoHKS0mxX4MyM/+ep8Fi4ZpUC9X4pGHgwIEy3VWNyd6mbu7cuZo7d241RgUAAZSV5fnX8szMOtUu3ydV8UsoE1kBzpgosk6o8UkDANR6tMuvnED+EkozMQB1FEkDAIQ62uVXXiB+CaWZGIA6rFaPngQAtQIjlIQGb5qJAUAtRdIAAKGurF1+WJgUHu78yAgl1YdmYgDqMJonAUBNwAglwUczMQB1GEkDANQUjFASXAzfCqAOo3kSAADeoJkYgDqMmgYAALxFMzEAdRRJAwAAvqCZGIA6iOZJAAAAADwiaQAAAADgUaWaJ+Xn5+uzzz7Tzz//rN9++00NGzZUy5Yt1a1bN5111llq0KBBoOIEAAAAECQ+Jw07duzQf//7Xy1atEg///yzJMk8brIbwzBUr149DR8+XNdee63OP/98Ge7GtgYAAAAQ0rxOGn799Vfddddd+u9//6sjR44oMTFR11xzjXr27KmYmBideOKJ+uuvv7R3717l5uZq7dq1+vjjj/Xee+8pKSlJGRkZuvDCC6vyvdQdeXlSVtbfI3ekptqHAgQAAACqgNdJw0knnaSwsDDdcMMNuuaaa9S9e/cKX3PgwAG99tpr+u9//6uLL75YDzzwgG6++ebKxIvsbGnCBPvso6Zpf8zIsA//l5IS7OgAAABQC3mdNNx444265ZZb1KxZM6933qhRI40bN07jxo3Txx9/rKKiIn9iRJm8PHvCYLOVX2ex2McOZ6xwAAAABJjXScM999xTqQMNHjy4Uq+H7E2S3PUNMQx7bQNjhwMAACDAGHK1JikosDdJcsU07esBAACAAPM7adi0aZMee+wx/frrry7X79mzR4899ph++OEHv4PDceLjPdc0xMdXZzQAAACoI/xOGu6//37NmzdPzZs3d7m+efPmmj9/vjIyMvwODsdJTfVc02CxVG88AAAAqBP8ThpWr16tIUOGKCzM9S7Cw8M1ZMgQffLJJ34Hh+MkJdn7LYSFSeHhzo+ZmXSCBgAAQJXwe0bowsJCxcXFedymbdu22rVrl7+HgCspKfZRkjIz/56nwWIhYQBQszH/DACENL+ThkaNGmnPnj0et9mzZ4/q16/v7yHgTmIioyQBqD2YfwYAQp7fzZNOP/10LV26VPv373e5ft++fXrzzTd1+umn+3sIAEBtd+z8M6Wlzo8Wi5SfH+wIAQCqRNIwZcoU/f777xo0aFC5fgurVq3SoEGDtG/fPk2dOrXSQaIOyMuTZs6URo+2P+blBTsiANXBm/lnAABB53fzpIsuukg333yzHn74YQ0aNEhRUVGKjY1VYWGhSkpKZJqmpk2bppEjRwYwXFRKqLYZpmkCUD1C8Rrgy/wzoRg/ANQRficNkvTggw9q0KBBeuKJJ/Tll1/q559/VrNmzTR48GBNmTJF5557bqDiRGWF6o35sU0Tjmex2Dt908kbqDxvrgHBuCn3dv4ZX69hJBgAEFCVShok6YILLtAFF1wQiFhQVUL5xtybpgl0+gbcO/7meMwY19tUdA1YvTo4PyykptqP40rZ/DO+XsNC9UcSAKjB/O7TgBoklNsM+9I0wRfe9JGgHwVquuxsKTlZmj9fWrJEmj9f9bp1U9zy5c7bVXQNmD+/ajoje3OOeTP/jC/XsGB1rOZ6AqCWq3RNw5tvvqmXXnpJmzdv1sGDB5V/9IK8efNm/d///Z+uvvpqtW3bttKBohKq6sY8ELxtmiB539zAm18Z+SUSNZ2HX9//sWCBjkyeLHXpYl9Q0TVg9erA1/j5co5VNP+ML9ewYNRecj0BUAf4nTTYbDaNHj1ar732miSpQYMG+uuvvxzrTzjhBN1xxx0qLS3VzJkzKx9pJT3++OOaP3++CgsLddppp+k///mPzjjjjGCHVT18uTGvbt40TZC8/6PsTTMG0/S9uRbtoxFq3NwcG5JshqGw7Oy/z62KrgGS7z8seDon/GkS6Wn+GV+uYd4mGIE6p0O5+ScABJDfzZMefvhhvfrqq7r22mu1b98+3XrrrU7rY2JidPbZZ+vdd9+tdJCV9corrygtLU133XWX/ve//+m0007TiBEjKpycrkbwpko8NdXzH9GyG3Nv9xfI+LxpmuBLcwNvfmX0tbmWiyYgSk6WFi3y+2MBKs3TzbEkY9u2v59UdA3o39+3HxbcnRPz5tnP84sucn88f5pE+nIN8ybBCOQ5HcrNPwEggPyuaVi0aJF69eqlJ554QpJkuLhoJiYmhkTS8NBDD2nixIkaP368JOmpp57Su+++q6ysLM2YMaPc9iUlJSopKXE8Ly4uliRZrVZZrdbqCfqosuO5Oq7x7LMKv/bacr++ly5cKHPs2L83jI+XsXChwidNct7WNO3bduggWa3e789LXu/v6qul3r0Vlp0tY9s2mR06yDZ+vD1hsFoV9swzCjMMufqzbBqGbAsXynbffZKk8C1bZJim621NU+aWLfbYKtimtOzzzstTvQkTZBz3K6IpSRaLjvTuHbRfET2VDdR+YXFxbs8LSSqNi/u7HFd0DejTR/WeeUaSnPZnSpJp6sjYsZI358SMGfaE/+g6r84xb3h5DZMkjRmjekdrWFy+l/79Ve+CCwJ2TntzzfHpvVYRrhdwh7JRt/nyvfudNOTn52vKlCket2nevLl+//13fw8REIcPH9b69eudmkiFhYVp6NChWrNmjcvXpKena86cOeWWL1u2TA0bNqyyWD3Jyclxet5o504NmTJFxnG/vpmSwidO1HKrVQdat/57RYsWavT442r/0UdquGePDrZqpe1Dh+pAixbSe+/5vr8K+LW/vn3t/yTpxx/t/yT1WLtWbWw2t3+Ud61dq/XvvSdJ6lJaqkS5uVmRlF9aKkkVbvND2f6ee87ltoYkm6Sts2bpB1ej1VSj48sG6oZGCQkaYrPJVPmbY8M0tSoxUQeOlmNJnq8BeXmKmzJF/1iwQLZjfgAyTFNfT5miHcecj57OCUmum+kcF9+x55jXKriGHcvTe2mcmRnQc9qba47P77UKcb2AO5SNuungwYNeb+t30tCgQQMVFRV53Gbbtm1q1qyZv4cIiN9++02lpaWKiYlxWh4TE6PNmze7fM3MmTOVlpbmeF5cXKy4uDgNHz5c0dHRVRrv8axWq3JycjRs2DBFREQ4lofdcYf9F72jN8FlDElmWJgGbdki27FV9mWOWZZwzGK/9+dGIPcX9tlnMj7/vNy+JHsNV+szz9R5551nX5CUJOPNN13fSElKuOceyTQr3Cbh6C+N4S++6PaXXENSYni4EsqOXc3clQ3UHaWRkQqfNEnmcb++fz1lis4aO9Z1uXBzDdB55+nI5MlONX6l48erW2Kiuh2zmadzoiKuzjGfuYv/WB7eS/g11wT2nPbimuP3ew0grhdwh7JRt5W1pvGG30nDP/7xD3344Yc6dOiQ6tevX2793r179cEHH6h///7+HiJooqKiFBUVVW55RERE9Z5QeXkKe+YZ9Vi7VlGffabwiRP/7qi3Y4fbNr6GaSp8xw6F+xJrKO9v4kTpwQfd72vSpL/3dfLJ9jbEFotTMwbDNKXMTEWUjSbjzTaS1LGj2/bKhmHI6NhRYUG+yFZ7uUTosFikAQOcRh2yjh2rHT/+qG7+lIsuXZwGJgh3tY2Hc8Kj8HDX51hVcfdeAn1Oe3vNCRFcL+AOZaNu8uU79ztpuOGGG3TxxRdr1KhRevrpp53W/fTTT0pNTVVRUZFuuOEGfw8REC1atFB4eLh2797ttHz37t2KjY0NUlReODpaUJhh2JvmfP65/ca5bLSgQI+IFMr7K+ssfdwfZR39o1yu/XFFwzd6u43k/ehOQLAcP+qQ1epoSlQlPJ0TrhiGvZPxRRe5Pseqm7/ntKfRlry9ngBATWZWwowZM0zDMMywsDCzSZMmZlhYmNmyZUszLCzMNAzDnD17dmV2HzBnnHGGOXXqVMfz0tJSs23btmZ6erpXry8qKjIlmUVFRVUVorMffzTNsDDTtP8Jc/4XFmaaeXnebRPoY7p6zYwZpnnllfbHH3+s3P4qkpfnfDx/9uGP7Gx7zOHhzo/Z2dVzfDcOHz5sLl261Dx8+HBQ40BoqZZy4eqccHWuV+Z8r0q+ntNZWSF5DfAF1wu4Q9mo23y5x63U5G7p6ekaPHiwFixYoHXr1unQoUOy2Ww655xzdMMNN2jEiBGByWwqKS0tTePGjVPPnj11xhln6JFHHtGBAwccoymFHG8nJ/Ll1/eK+PprfkXzJvi6P294Gse9KvErIuDM1TnRrJl0++2BO9+rki/nNPMwAICkAMwIPWzYMA0bNiwQsVSZK664Qr/++qtmz56twsJCde/eXR988EG5ztEhw9vJiQJ9M+vt/rz9I1qbbraDlbAAocrVOTFqVM053709p4MxwzRQFZikFJVU6aShppg6daqmTp0a7DC840t/gEDfzHqzP1/+iHKzDdQdtfF89/ZHHCCUeWodcPXVwY4ONYTfM0J/9913ysrKchqq6a+//tLkyZPVtm1bnXTSSXrqqacCEmSd48vsp8HAH1EAdUWgB4kAqtuxrQNKS50fLRYpPz/YEaKG8DtpuPfeezVr1iw1adLEsez222/X008/rT/++EM///yzpkyZwmQh/ijrDxAWJjM8XLajjwoLC432wfwRBVBXhPqPOEBFKmgdEJadXb3xoMbyO2n44osvNGjQIBlHC+KRI0eUnZ2tM844Q3v27NHWrVvVsmVLPfroowELtk5JSZFyc2VLS9POvn1lS0uTcnPty4ONP6IA6opjfsRR2Y83ofQjDlCRCloHGNu2VWs4qLn87tPw66+/Ki4uzvH8yy+/VHFxsa677jrVr19fbdq00UUXXaT33nsvIIHWSYmJst13n9a/957OO+883yZXq0pVMTISAISq2jSoA+qeCloHmB06VGs4qLn8Thrq1aunkpISx/OVK1fKMAwNGjTIsax58+b67bffKhchQhN/RAHUJbWxkzfqhgomNLSNH1+1E0Ki1vA7aYiPj9eKFSscz1999VUlJCSowzEZ6y+//KLmzZtXLkKELv6IAgAQ2rxpHUDSAC/4nTSMGTNG06ZNU+/evRUVFaVvvvlGd9xxh9M23377rZIYAxgAACB4PLUOsFr/3o65HOCB30nD1KlT9cUXX+i1116TaZo677zzdPvttzvWb9y4Ud98843mzJkTkEABAADgpwpaBxjPPitde63ruRxCYRAWBJ3fSUNUVJReeeUVFRcXyzAMp6FXJSkmJkZff/214hl+EwAAIGQ12rlT4VOn2uduOJ7FYq+loM9inef3kKtloqOjyyUMktSiRQuddtppatq0aWUPAQAAgCrSfvlyz/MvZWZWb0AISX4nDZ999pnS0tJUWFjocv2uXbuUlpamtWvX+h0cAAAAqlbDPXs8z79UUFCt8SA0+Z00PPTQQ3r77bcVGxvrcn3r1q31zjvv6OGHH/Y7OAAAAFStg61aea5poKk5VImk4csvv1S/fv08btO/f39qGgAAAELY9iFDPNc0WCzVGxBCkt9Jw549e9S2bVuP28TGxmrPnj3+HgIAAABV7ECbNipduFAKC5PCw50fy+ZyQJ3n9+hJzZo10/bt2z1us23bNjVu3NjfQwAAAKAamGPHSgMGuJ7LAVAlkoYzzzxTb775pnbs2KG4uLhy67dv366lS5dq8ODBlQoQAAAA1aCCuRxQt/ndPCktLU0HDx5U3759tXjxYu3atUuSfdSkZ599Vn379tVff/2lW265JWDBAgAAAKh+ftc09O/fXw899JBuueUWjR8/XpJkGIbMox1pwsLC9Oijj6p///6BiRQAAABAUPidNEjSjTfeqEGDBumpp57Sl19+qaKiIjVr1kxnnHGGrrvuOp1yyimBihMAAABAkPidNHzyySeKjo5W9+7d9cQTTwQyJgAAAAAhxO+kYdCgQbr22mtJGAAAAGqSvDwpK0sqKFBYXJwaJSQEOyLUAH4nDa1atVL9+vUDGQsAAACqUna2NGGCfaZn01SYYWiIzabSyEgmcYNHfo+eNGzYMK1cudLR8RkAAAAhLC/PnjDYbFJpqWSzySgtlUxT4ZMmSfn5wY4QIczvpOH+++/X77//rkmTJmnv3r2BjAkAAACBlpVlr2E4jiHZl2dmVntIqDn8bp50zTXXqFmzZsrKytLzzz+vhIQExcTEyDiuMBqGoeXLl1c6UAAAAFRCQYHkroWIadrXA274nTSsXLnS8f+SkhJt3rxZmzdvLrfd8UkEAAAAgiA+3mVNgyT78vh47/d1TGdqxcdLqalSUlLlY0TI8rt5ks1m8+pfaWlpIOMFAACAP1JTXdY0mJJ9ubcdobOzpeRkaf58ackS+2NysrRoUSCjRYjxO2kAAABADZKUZO+3EBYmhYdLYWEyw8Mlw1DpwoVSYmLF+3DRmdrxaLHQmboWq9SM0AAAAKhBUlKkfv3syUNBgWxxcVrRsaMGjB3r3evddKaW9Hdn6vT0gIWL0FHppOHnn3/WihUrtHPnTpWUlJRbbxiGZs2aVdnDuHTffffp3Xff1YYNGxQZGan9+/eX22b79u2aPHmyVqxYocaNG2vcuHFKT09XvXrkSwAAoA5KTHTc2NusVh147z3vX0tn6jqrUnfO06ZN06OPPurUb8E0TUfn57L/V1XScPjwYV122WXq06ePMl0ME1ZaWqrzzz9fsbGx+vzzz7Vr1y6NHTtWERER+ve//10lMQEAANRa3nampqN0reN3n4ZnnnlGDz74oAYNGqTXXntNpmlq3Lhxeumll3TdddepXr16uuyyy/Txxx8HMl4nc+bM0c0336xu3bq5XL9s2TJt2rRJzz//vLp3765zzz1X99xzjx5//HEdPny4yuICAAColdx0ppb0d2dqOkrXSn7XNCxcuFDx8fF6//33FRZmzz3i4+N1xRVX6IorrtDll1+uYcOG6bLLLgtYsL5as2aNunXrppiYGMeyESNGaPLkydq4caP+8Y9/uHxdSUmJU1Or4uJiSZLVapXVaq3aoI9TdrzqPi5CH2UDrlAu4ArlAu74XDbi42UsXGifQdow7InC0cfShQtlHj6sehMmyLDZnF5mSpLFoiO9e3vX4RrVwpdrgt9Jw+bNmzVmzBhHwiBJR44ccfx/wIABOv/88/XAAw/o0ksv9fcwlVJYWOiUMEhyPC8sLHT7uvT0dM2ZM6fc8mXLlqlhw4aBDdJLOTk5QTkuQh9lA65QLuAK5QLu+FQ2WrRQo8cfV/uPPlLDPXt0sFUrbR86VAdatFCX2bOVqKOzTB/DkGSTtHXWLP0wZkzgAkelHDx40OttK9WnoVmzZo7/N2rUSL///rvT+s6dO+ujjz7yaZ8zZszQvHnzPG7zww8/KDk52af9+mLmzJlKS0tzPC8uLlZcXJyGDx+u6OjoKjuuK1arVTk5ORo2bJgiIiKq9dgIbZQNuEK5gCuUC7hTqbJxzLwOCUcfw198sVzCUMaQlBgeroTzzvMnVFSBstY03vA7aWjbtq1+/vlnx/OTTjpJ69atc9rm+++/V6NGjXza7y233KKUlBSP23Ts2NGrfcXGxuqLL75wWrZ7927HOneioqIUFRVVbnlERETQLrbBPDZCG2UDrlAu4ArlAu4ErGx07Oi2o7RhGDI6dlQYZTBk+PKd+5009O3bV6tXr3Y8v+iii3Tvvffq2muv1YUXXqhPP/1U77//vkaNGuXTflu2bKmWLVv6G5aTPn366L777tOePXvUqlUrSfbqt+joaJ188skBOQYAAACOSk2VMjJcr/Nl1mmEHL+ThjFjxmjnzp3atm2bOnTooGnTpumdd97RM888o//+978yTVPx8fGaP39+ION1sn37du3du1fbt29XaWmpNmzYIElKTExU48aNNXz4cJ188skaM2aMMjIyVFhYqDvvvFNTpkxxWZMAAACASiibddpiKddRWpmZdIKuwfxOGgYOHKiBAwc6njdu3Fhr167VW2+9pZ9++kkdOnTQP//5T5+bJ/li9uzZevbZZx3Py0ZDWrFihQYOHKjw8HC98847mjx5svr06aNGjRpp3Lhxmjt3bpXFBAAAUKcdN+u04uPtSQQJQ40W0GmRIyIiqnWkpEWLFmlRBWP+dujQQe/5MtMhAAAAKueYWadRO/g9uRsAAACAusHrpOG6667TL7/84veBXn75Zb3wwgt+vx4AAABAcHidNLz99ttKTEzU+PHjtWLFCq9es3PnTj3wwAM6+eSTdfXVV+uvv/7yO1AAAAAAweF1n4b8/HxlZGTowQcf1OLFi3XiiSfqjDPOUI8ePRQTE6NmzZrp0KFD2rt3r3Jzc7Vu3Tr98MMPstls6tevn7Kzs9W7d++qfC8AAAAAqoDXSUODBg101113KS0tTYsXL1Z2drY+/PBDvf/++5LsE3ZIkmmakqQTTzxR48aN03XXXadevXpVQegAAAAAqoPPoyc1adJEU6ZM0ZQpU7R//36tWbNGP//8s37//Xc1aNBALVu2VLdu3dStW7eqiBcAAABANavUkKvNmjXTueeeG6hYAAAAAIQghlwFAAAA4BFJAwAAAACPSBoAAAAAeETSAAAAAMAjkgYAAAAAHpE0AAAAAPCIpAEAAACARyQNAAAAADzyenK3wYMH+3UAwzC0fPlyv16LapKXJ2VlSQUFUny8lJoqJSUFOyoAAACECK+ThpUrV/p1AMMw/Hodqkl2tjRhgmQYkmnaHzMypMxMKSUl2NEBAIC6gB8wQ57XSYPNZqvKOBAMeXn2hMHVd2uxSP36SYmJ1R/XsbiIAHUT5z5Qd/ADZo1An4a6LCvLfmK6Yhj2kzWYsrOl5GRp/nxpyRL7Y3KytGhRcOMCULU494G649gfMEtLnR8tFik/P9gR4iiShrqsoMCe0btimvb1wcJFBKibOPeBuiXUf8CEg9fNk9w5dOiQvvzyS+3cuVMlJSUutxk7dmxlD4OqEB/v+USNj6/OaJx5cxFJT6/emABUPc59oG4J5R8w4aRSScPjjz+uWbNmqaioyOV60zRlGAZJQ6hKTbW3GXTFNO2/6gULFxGgbuLcB+qWUP4BE078bp70xhtv6Prrr1dcXJweeOABmaapiy66SP/+9791zjnnyDRNjRo1SllZWYGMF4GUlGT/1S4sTAoPd37MzAxuJ2guIkDdxLkP1C2pqZ5/KAjmD5hw4nfS8Mgjj6hVq1Zas2aNbr75ZklS9+7dNX36dL377rt6/vnntXTpUnXo0CFgwaIKpKRIubnStGnS5ZfbH3Nzgz9aARcRoG7i3AfqFlc/YJb9cHDOOe6vB6h2ficN3377rS688EI1bNjQsay0tNTx/6uuukqDBw/W3LlzKxchql5ior2N8Esv2R+DPcyqFNq1IACqDuc+UPeU/YA5YsTfw8AbhvThh4ycFkL87tNgtVrVsmVLx/MGDRpo//79TtucdtppWrhwod/BoY5LSbHPFZGZ+fdY7RYLNw1Abce5D9Q9pil98MHf/5fsI6dJoTN3VB3nd9LQpk0b7dq1y/G8Q4cO+vrrr5222bZtm+rVq/QATajLympBANQtnPtA3cLIaSHP7+ZJvXr10v/+9z/H83POOUefffaZ0tPTtXHjRj399NN644031KtXr4AECgAAgFqKkdNCnt9Jw2WXXaaSkhIVHP0SZ86cqXbt2unOO+/UqaeeqsmTJ6tx48bKcDekJwAAACAxcloN4HfboYsvvlgXX3yx43nLli21YcMG/fe//9WWLVvUoUMHjRkzRm3btg1IoAAAAKilQnnuKEiqRE2DKyeccIKmTZumJ598UjNmzKjShKGgoEAWi0UJCQlq0KCBTjrpJN111106fPiw03bffvutzj77bNWvX19xcXHUfAAAAIQaRk4LeTW2l/LmzZtls9n09NNPKzExUd9//70mTpyoAwcO6IEHHpAkFRcXa/jw4Ro6dKieeuopfffdd0pNTVWzZs00adKkIL8DAAAAODByWkjzO2nwdv4FwzA0a9Ysfw/j1jnnnKNzzjnH8bxjx47Kzc3Vk08+6UgaXnjhBR0+fFhZWVmKjIxU165dtWHDBj300EMkDQAAAKEm0COn5eXZR2YqS0JSU+21GvCZ30nD3Xff7XG9YRgyTbPKkgZXioqKdOKJJzqer1mzRv3791dkZKRj2YgRIzRv3jzt27dPJ5xwgsv9lJSUqKSkxPG8uLhYkn1uCqvVWkXRu1Z2vOo+LkIfZQOuUC7gCuUC7tTmsmE8+6zCr73W3pHaNO2PGRkqXbhQ5tixwQ4vJPjyvfudNKxYscLl8qKiIv3vf//TY489pqFDh2rKlCn+HsIn+fn5+s9//uOoZZCkwsJCJSQkOG0XExPjWOcuaUhPT9ecOXPKLV+2bJnTDNjVKScnJyjHReijbMAVygVcoVzAndpWNhrt3KkhU6bIOG4YV1NS+MSJWm616kDr1sEJLoQcPHjQ6239ThoGDBjgdt2FF16oq6++WqeffrpGjRrl035nzJihefPmedzmhx9+UHJysuP5L7/8onPOOUeXXXaZJk6c6NPxXJk5c6bS0tIcz4uLixUXF6fhw4crOjq60vv3hdVqVU5OjoYNG6aIiIhqPTZCG2UDrlAu4ArlAu7U1rIRdscd9k7UZbNKH2VIMsPCNGjLFtkYkcnRmsYbVdYROikpSRdffLHuv/9+XXHFFV6/7pZbblFKSorHbTp27Oj4/86dOzVo0CCdddZZWrhwodN2sbGx2r17t9OysuexsbFu9x8VFaWoqKhyyyMiIoJ2QgXz2AhtlA24QrmAK5QLuFPrysaOHW4nizNMU+E7dii8Nr1fP/nynVfp6EmtWrVSbm6uT69p2bKlWrZs6dW2v/zyiwYNGqQePXooOztbYWHOI8j26dNHd9xxh6xWq+NDycnJUefOnd02TQIAAEANx2RxARfQeRqOVVJSog8++EDNmjWrkv3/8ssvGjhwoNq3b68HHnhAv/76qwoLC1VYWOjY5qqrrlJkZKQsFos2btyoV155RY8++qhT0yMAAACEuLw8aeZMafRo+2NenuftU1Pd1jQwWZx//K5pWLx4scvlR44c0S+//KKXX35Zmzdv1g033OB3cJ7k5OQoPz9f+fn5ateundM682ghadq0qZYtW6YpU6aoR48eatGihWbPns1wqwAAADVFdrY0YUK5UZCUmWmf28GVssniLBbn15kmk8X5ye+kISUlRYaLap+yG3bDMDR69Gjdf//9/kdXwfEr6vsgSaeeeqpWr15dJTEAAACgCuXl2RMGm638OovFPhmcuwSAyeICyu+kITs72+XysLAwnXDCCerRo4daM5QVAAAA/JWV5blvQmam58ngAj1ZXB3md9Iwbty4QMYBAAAAOCso8Nw3oaCgOqOp06qsIzQAAABQKYyCFDK8rmn45JNP/D5I//79/X4tAAAA6qjUVHunZ1cYBalaeZ00DBw40GXHZ2+UHjcbHwAAAFAhRkEKGV4nDbNnzy6XNKxdu1YffvihkpKS1LdvX8XExGj37t36/PPP9eOPP2rEiBE688wzAx40AAAA6ghGQQoJXicNd999t9Pz1atXKz09XQsXLpTFYnFKKEzT1DPPPKMbb7xRd9xxR8CCBQAAQB3EKEhB53dH6FmzZun888/XhAkTytVAGIahSZMm6dxzz9WsWbMqHSQAAACA4PE7aVi/fr26dOnicZsuXbroq6++8vcQAAAAAEKA30lDZGSkvv76a4/bfP3114qMjPT3EAAAAABCgN9Jw/Dhw/XBBx/o/vvv1+HDh53WHT58WOnp6frwww81YsSISgcJAAAAIHj8nhF6/vz5Wr16te644w49+uij6tmzp1q1aqU9e/boq6++0p49e9SmTRtluBtbFwAAAECN4HfS0K5dO3311VeaMWOGlixZonfffdexrn79+hozZozuv/9+xcbGBiRQAAAAAMHhd9IgSbGxsVq0aJGeeeYZ5ebmqqioSE2bNlWnTp3oywAAAADUEpVKGspERETolFNOCcSuAAAAAIQYvztCAwAAAKgbvK5pGDx4sAzD0LPPPqt27dpp8ODBXr3OMAwtX77c7wABAAAABJfXScPKlStlGIYOHjzoeO6N42eLBgAAAFCzeJ002Gw2j88BAAAA1E70aQAAAADgUcCTBtM0lZeXpx07dgR61wAAAACCwO+k4Y033tDYsWO1b98+x7KCggKdeuqpSk5OVnx8vK688kqVlpYGJFAAAAAAweH3PA1PPvmkdu/erRNOOMGx7Oabb9bGjRs1ePBg/f7773r11Vc1ZMgQTZw4MSDBAgAAABXKy5OysqSCAik+XkpNlZKSgh1VjeZ3TcOmTZt0xhlnOJ7/8ccfevfdd3XFFVfoo48+0hdffKEuXbooKysrIIECAAAAFcrOlpKTpfnzpSVL7I/JydKiRcGOrEbzO2nYu3evYmNjHc8//fRTHTlyRKNHj5ZknyV62LBh+umnnyofJQAAAFCRvDxpwgTJZpNKS50fLRYpPz/YEdZYficN0dHR+v333x3PV6xYobCwMJ199tmOZRERETpw4EDlIgQAAAC8kZUluZsjzDCkzMzqjacW8TtpSE5O1ttvv63ff/9d+/fv14svvqgePXo49XHYtm2bYmJiAhIoAAAA4FFBgWSarteZpn09/OJ30nDDDTdo586dateundq3b69du3Zp8uTJTtusXbtWp512WqWDBAAAACoUH++5piE+vjqjqVX8ThpGjRqlxx9/XF27dlWnTp00b948paSkONavWrVKxcXFOueccwIRJwAAAOBZaqrnmgaLpXrjqUUqNbnb5MmT9dVXX+mrr77Srbfe6rRuwIAB2rdvn6699tpKBejJhRdeqPbt26t+/fpq3bq1xowZo507dzpt8+233+rss89W/fr1FRcXp4yMjCqLBwAAAEGUlGTvtxAWJoWHOz9mZkqJicGOsMYK+IzQ1WnQoEFasmSJcnNz9frrr+unn37SpZde6lhfXFys4cOHq0OHDlq/fr3mz5+vu+++WwsXLgxi1AAAAKgyKSlSbq40bZp0+eX2x9xc+3L4ze/J3STpyJEj+s9//qOXXnpJmzdv1sGDB3XkyBFJ0oYNG7Rw4ULddNNN6tSpU0CCPd7NN9/s+H+HDh00Y8YMjRw5UlarVREREXrhhRd0+PBhZWVlKTIyUl27dtWGDRv00EMPadKkSVUSEwAAAIIsMVFKTw92FLWK30nDX3/9peHDh+vzzz9XixYtFB0d7TS8akJCgrKzs3XiiSfq3nvvDUiwnuzdu1cvvPCCzjrrLEVEREiS1qxZo/79+ysyMtKx3YgRIzRv3jzt27fPaaSnY5WUlKikpMTxvLi4WJJktVpltVqr8F2UV3a86j4uQh9lA65QLuAK5QLuUDbqNl++d7+Thn//+9/67LPPdP/992vatGmaM2eO7rnnHsf6pk2basCAAfrwww+rNGmYPn26FixYoIMHD+rMM8/UO++841hXWFiohIQEp+3LhoAtLCx0mzSkp6drzpw55ZYvW7ZMDRs2DGD03svJyQnKcRH6KBtwhXIBVygXcIeyUTcdPHjQ6239ThpeeeUVDRo0SLfddpskyXAxvFXHjh319ddf+7TfGTNmaN68eR63+eGHH5ScnCxJmjZtmiwWi7Zt26Y5c+Zo7Nixeuedd1zG462ZM2cqLS3N8by4uFhxcXEaPny4oqOj/d6vP6xWq3JycjRs2DBHDQogUTbgGuUCrlAu4A5lo24ra03jDb+Thu3bt+viiy/2uE2TJk1UVFTk035vueUWp6FbXenYsaPj/y1atFCLFi3UqVMndenSRXFxcVq7dq369Omj2NhY7d692+m1Zc9jY2Pd7j8qKkpRUVHllkdERATthArmsRHaKBtwhXIBVygXcIeyUTf58p37nTQ0adJEe/bs8bjNTz/9pJYtW/q035YtW/r8mjI2m02SHP0R+vTpozvuuMPRMVqyV7917tzZbdMkAAAAAM78HnL1zDPP1Ntvv639+/e7XL9jxw6999576t+/v7+H8GjdunVasGCBNmzYoG3btunjjz/W6NGjddJJJ6lPnz6SpKuuukqRkZGyWCzauHGjXnnlFT366KNOTY8AAAAAeOZ30jBt2jTt27dPQ4YM0WeffeYYavXgwYNavny5RowYoSNHjlTZDXrDhg31xhtvaMiQIercubMsFotOPfVUrVq1ytG0qGnTplq2bJm2bt2qHj166JZbbtHs2bMZbhUAAADwgd/Nk/r3768FCxboxhtvdKpNaNKkiSQpPDxcTzzxhHr06FH5KF3o1q2bPv744wq3O/XUU7V69eoqiQEAAACoCyo1udvkyZM1cOBAPfXUU1q3bp327t2r6Oho9e7dW//617/UtWvXQMUJAAAAIEgqlTRIUpcuXfToo4+6XV9UVKSmTZtW9jAAAAAAgsTvPg0V+fPPPzV37lyn4VEBAAAA1Dx+1TQUFBTof//7n+rVq6czzjjDac6DQ4cO6ZFHHtEDDzygvXv3Bm0GZQAAAACB4VNNg2mamjx5shITE3XZZZfp4osvVnx8vKN5Uk5OjpKSknTHHXfor7/+UlpamrZs2VIlgQMAAACoHj7VNDzzzDN6+umn1ahRIw0YMECStGrVKqWlpSkiIkI33XSTDMPQjTfeqBkzZqhVq1ZVEjQAAACA6uNT0rB48WI1btxYGzZscPRVyM/P1z/+8Q9df/31SkhI0LvvvqvOnTtXSbAAAABApeXlSVlZUkGBFB8vpaZKSUnBjiqk+dQ8aePGjbrkkkucOjcnJiZq1KhRkqSsrCwSBgAAAISu7GwpOVmaP19assT+mJwsLVoU7MhCmk9JQ3FxsTp06FBuefv27SVJvXv3DkxUAAAAQKDl5UkTJkg2m1Ra6vxosUj5+cGOMGT53BE6PDy83PKyZVFRUYGJCgAAAAi0rCzJMFyvMwwpM7N646lBfB5y9cCBA9qzZ4/Tsj///FOS9Ouvv8o0zXKvoUM0AAAAgq6gQHJxryrJvrygoDqjqVF8ThoeeOABPfDAA+WWm6bpNF9DGcMwdOTIEf+iAwAAAAIlPt5zTUN8fHVGU6P4lDT0799fhrsPGgAAAAhlqalSRobrdaZp79cAl3xKGlauXFlFYQAAAABVLCnJ3m/BYrHXLJjm34+ZmVJiYrAjDFk+N08CAAAAaqyUFKlfP3uSUDZPg8VCwlABkgYAAADULYmJUnp6sKOoUXwachUAAABA3UPSAAAAAMAjmicBAAAA1S0vzz7ZXFm/itRUe0ftEEXSAAAAAFSn7GxpwgTnEZwyMuyds1NSgh2dSzRPAgAAAKpLXp49YbDZpNJS50eLRcrPD3aELlVZ0mC1WvXqq6/qnHPOqapDAAAAADVLVpbnWakzM6s3Hi8FvHnS999/r8zMTD3//PPau3dvoHcPAAAA1FwFBfYmSa6Ypn19CApI0vDHH3/oxRdfVFZWlr766itJUv369TV69GiNHz8+EIcAAAAAar74eM81DfHx1RmN1yqVNHzyySfKzMzU66+/rr/++kvm0azpnHPO0csvv6zo6OiABAkAAADUCqmp9k7PrpimvV9DCPK5T0NhYaHuv/9+derUSYMGDdJzzz2nFi1aaObMmfrhhx8kSe3atSNhAAAAAI6XlGTvtxAWJoWHOz9mZtpnqw5BPtU0/POf/9SHH36oI0eOqEmTJkpJSdGYMWM0cODAKgoPAAAAqGVSUqR+/exJQtk8DRZLyCYMko9Jw7vvvquwsDBNmzZNc+fOVVRUVFXFBQAAANReiYlSenqwo/CaT82TOnbsKJvNpgceeEC9e/fWQw89pMLCwqqKDQAAAEAI8ClpyM/P18cff6zRo0frxx9/1K233qq4uDide+65eumll3To0KGqihMAAABAkPjcEXrgwIF6/vnntWvXLi1YsECnnnqqPvzwQ11zzTWKiYmRYRgqLi6uiljdKikpUffu3WUYhjZs2OC07ttvv9XZZ5+t+vXrKy4uThnueqsDAAAAcMnvGaGbNm2qf/3rX1q/fr2+/vprTZ48WeHh4TJNU6+++qpOOukk3XPPPdq2bVsg43XptttuU5s2bcotLy4u1vDhw9WhQwetX79e8+fP1913362FCxdWeUwAAACoZfLypJkzpdGj7Y95ecGOqNr4nTQc67TTTtOCBQu0a9cuPf/88xo4cKAKCgp01113KbGKe4G///77WrZsmR544IFy61544QUdPnxYWVlZ6tq1q6688krdcMMNeuihh6o0JgAAANQy2dlScrI0f760ZIn9MTlZWrQo2JFVi4DMCF0mKipKV111la666ioVFBQoMzNTzz77bCAP4WT37t2aOHGili5dqoYNG5Zbv2bNGvXv31+RkZGOZSNGjNC8efO0b98+nXDCCS73W1JSopKSEsfzsuZWVqtVVqs1wO/Cs7LjVfdxEfooG3CFcgFXKBdwh7Lhpbw81ZswQYbN5rTYlCSLRUd69w7p4VLd8eV7D2jScKz4+HiNHz9eO3bsqJL9m6aplJQUXXfdderZs6cKCgrKbVNYWKiEhASnZTExMY517pKG9PR0zZkzp9zyZcuWuUxOqkNOTk5QjovQR9mAK5QLuEK5gDuUDc+6PPecEiUZxy03JNkkbZ01Sz+MGVP9gVXSwYMHvd62SpKG7du365577tHixYt15MgRLfKh2mbGjBmaN2+ex21++OEHLVu2TH/88YdmzpxZyWjLmzlzptLS0hzPi4uLFRcXp+HDh1f7TNdWq1U5OTkaNmyYIiIiqvXYCG2UDbhCuYArlAu4Q9nwTviLL5ZLGMoYkhLDw5Vw3nnVGVJA+DJ4kc9Jw6effqpZs2Zp/fr1qlevns4++2xlZGSoc+fOOnjwoO6880498cQTOnz4sNq0aePzTf0tt9yilJQUj9t07NhRH3/8sdasWVNugrmePXvq6quv1rPPPqvY2Fjt3r3baX3Z89jYWLf7j4qKcjlxXURERNBOqGAeG6GNsgFXKBdwhXIBdygbHuTlSdu3S6bpcrVhGDI6dlRYDfz8fPnOfUoa1q9fr6FDh+rw4cOOZW+//ba++uorrV69WhdeeKE2bdqkNm3aaPr06Zo0aZLPs0a3bNlSLVu2rHC7xx57TPfee6/j+c6dOzVixAi98sor6t27tySpT58+uuOOO2S1Wh0fSk5Ojjp37uy2aRIAAAAgyd75ecIEyTDcJg0yTcliqd64gsCn0ZMyMjJ0+PBhpaena8+ePdqzZ4/uu+8+7dq1S2effbY2b96sO++8U/n5+br++ut9Thh80b59e51yyimOf506dZIknXTSSWrXrp0k6aqrrlJkZKQsFos2btyoV155RY8++qhT0yMAAACgnLw8e8Jgs0mlpeXXh4XZ/2Vm1shO0L7yqabhs88+0+DBgzV9+nTHspkzZ+qjjz7SypUrNX/+/JC6IW/atKmWLVumKVOmqEePHmrRooVmz56tSZMmBTs0AAAAhLKsLHsNgyuGIZ1xhvTcc3UiYZB8TBr27Nmjq6++utzyHj16aOXKlRo3blzAAvNVfHy8TBfVRqeeeqpWr14dhIgAAABQYxUUuG+SZBhSfHydSRgkH5snHTlyRI0aNSq3vGxZ8+bNAxMVAAAAEEzx8Z5rGuLjqzOaoAvIjNAAAABArZKaWuc7Px/L5yFXn3/+ea1du9ZpWX5+viTpPBfj0xqGoXfffdfP8AAAAIAgSEqyd3K2WP4ePanssY50fj6Wz0lDfn6+I0k43gcffFBumeGuWgcAAAAIZSkpUr9+9iShoMDeJMliqXMJg+Rj0rB169aqigMAAAAIPYmJUnp6sKMIOp+Shg4dOlRVHAAAAABCFB2hAQAAAHhE0gAAAADAI5IGAAAAAB6RNAAAAADwiKQBAAAAgEckDQAAAAA8ImkAAAAA4BFJAwAAAACPSBoAAAAAeETSAAAAAMAjkgYAAAAAHtULdgAAAABAjZWXJ2VlSQUFUny8lJoqJSUFO6qAI2kAAAAA/JGdLU2YIBmGZJr2x4wMKTNTSkkJdnQBRfMkAAAAwFd5efaEwWaTSkudHy0WKT8/2BEGFEkDAAAA4KusLHvNgiuGYa9tqEVIGgAAAABfFRTYmyS5Ypr29bUISQMAAADgq/h4zzUN8fHVGU2VI2kAAAAAfJWa6rmmwWKp3niqGEkDAAAA4KukJHu/hbAwKTzc+TEzU0pMDHaEAcWQqwAAAIA/UlKkfv3sSULZPA0WS61LGCSSBgAAAMB/iYlSenqwo6hyNE8CAAAA4BFJAwAAAACPanTSEB8fL8MwnP7df//9Ttt8++23Ovvss1W/fn3FxcUpIyMjSNECAAAANVON79Mwd+5cTZw40fG8SZMmjv8XFxdr+PDhGjp0qJ566il99913Sk1NVbNmzTRp0qRghAsAAADUODU+aWjSpIliY2NdrnvhhRd0+PBhZWVlKTIyUl27dtWGDRv00EMPkTQAAAAAXqrxScP999+ve+65R+3bt9dVV12lm2++WfXq2d/WmjVr1L9/f0VGRjq2HzFihObNm6d9+/bphBNOcLnPkpISlZSUOJ4XFxdLkqxWq6xWaxW+m/LKjlfdx0Xoo2zAFcoFXKFcwB3KRt3my/deo5OGG264QaeffrpOPPFEff7555o5c6Z27dqlhx56SJJUWFiohIQEp9fExMQ41rlLGtLT0zVnzpxyy5ctW6aGDRsG+F14JycnJyjHReijbMAVygVcoVzAHcpG3XTw4EGvtzVM093818ExY8YMzZs3z+M2P/zwg5KTk8stz8rK0rXXXqs///xTUVFRGj58uBISEvT00087ttm0aZO6du2qTZs2qUuXLi7376qmIS4uTr/99puio6P9fGf+sVqtysnJ0bBhwxQREVGtx0Zoo2zAFcoFXKFcwB3KRt1WXFysFi1aqKioqMJ73JCrabjllluUkpLicZuOHTu6XN67d28dOXJEBQUF6ty5s2JjY7V7926nbcqeu+sHIUlRUVGKiooqtzwiIiJoJ1Qwj43QRtmAK5QLuEK5gDuUjbrJl+885JKGli1bqmXLln69dsOGDQoLC1OrVq0kSX369NEdd9whq9Xq+FBycnLUuXNnt02TAAAAADirsfM0rFmzRo888oi++eYbbdmyRS+88IJuvvlmXXPNNY6E4KqrrlJkZKQsFos2btyoV155RY8++qjS0tKCHD0AAABQc4RcTYO3oqKi9PLLL+vuu+9WSUmJEhISdPPNNzslBE2bNtWyZcs0ZcoU9ejRQy1atNDs2bMZbhUAAADwQY1NGk4//XStXbu2wu1OPfVUrV69uhoiAgAAAGqnGts8CQAAAED1IGkAAAAA4BFJAwAAAACPSBoAAAAAeFRjO0IDAAAANU5enpSVJRUUSPHxUmqqlJQU7KgqRNIAAAAAVIfsbGnCBMkwJNO0P2ZkSJmZUkpKsKPziOZJAAAAQFXLy7MnDDabVFrq/GixSPn5wY7QI5IGAAAAoKplZdlrFlwxDHttQwgjaQAAAACqWkGBvUmSK6ZpXx/CSBoAAACAqhYf77mmIT6+OqPxGUkDAAAAUNVSUz3XNFgs1RuPj0gaAAAAgKqWlGTvtxAWJoWHOz9mZkqJicGO0COGXAUAAACqQ0qK1K+fPUkom6fBYgn5hEEiaQAAAACqT2KilJ4e7Ch8RvMkAAAAAB6RNAAAAADwiKQBAAAAgEckDQAAAAA8ImkAAAAA4BFJAwAAAACPSBoAAAAAeETSAAAAAMAjJnfzgmmakqTi4uJqP7bVatXBgwdVXFysiIiIaj8+QhdlA65QLuAK5QLuUDbqtrJ727J7XU9IGrzwxx9/SJLi4uKCHAkAAAAQWH/88YeaNm3qcRvD9Ca1qONsNpt27typJk2ayDCMaj12cXGx4uLitGPHDkVHR1frsRHaKBtwhXIBVygXcIeyUbeZpqk//vhDbdq0UViY514L1DR4ISwsTO3atQtqDNHR0ZzMcImyAVcoF3CFcgF3KBt1V0U1DGXoCA0AAADAI5IGAAAAAB6RNIS4qKgo3XXXXYqKigp2KAgxlA24QrmAK5QLuEPZgLfoCA0AAADAI2oaAAAAAHhE0gAAAADAI5IGAAAAAB6RNAAAAADwiKQBAAAAgEckDQAAAAA8ImkAAAAA4BFJAwAAAACPSBoAAAAAeETSAAAAAMAjkgYAAAAAHpE0AAAAAPCIpAEAAACARyQNAAAAADwiaQAAAADgEUkDAAAAAI9IGgAAAAB4RNIAAAAAwCOSBgAAAAAekTQAAAAA8IikAQAAAIBHJA0AAAAAPCJpAAAAAOARSQMAAAAAj0gaAAAAAHhE0gAAAADAI5IGAAAAAB6RNAAAAADwiKQBAAAAgEckDQAAAAA8ImkAAAAA4BFJAwAAAACPSBoAAAAAeETSAAAAAMAjkgYAAAAAHpE0AAAAAPCIpAEAAACARyQNAAAAADwiaQAAAADgEUkDAAAAAI9IGgAAAAB4RNIAAAAAwCOSBgAAAAAekTQAAAAA8IikAQAAAIBHJA0AAAAAPCJpAAAAAOARSQMAAAAAj0gaAAAAAHhE0gAAAADAI5IGAAAAAB6RNAAAAADwiKQBAAAAgEckDQAAAAA8ImkAAAAA4FG9YAdQE9hsNu3cuVNNmjSRYRjBDgcAAACoNNM09ccff6hNmzYKC/Ncl0DS4IWdO3cqLi4u2GEAAAAAAbdjxw61a9fO4zYkDV5o0qSJJPsHGh0dXa3HtlqtWrZsmYYPH66IiIhqPTZCG2UDrlAu4ArlAu5QNuq24uJixcXFOe51PSFp8EJZk6To6OigJA0NGzZUdHQ0JzOcUDbgCuUCrlAu4A5lA5K8an5PR2gAAAAAHpE0AAAAAPCIpAEAAACARyQNAAAAADwiaQAAAADgEUkDAAAAAI9IGgAAAAB4xDwNAAAAdUlenpSVJRUUKCwuTo0SEoIdEWoAkgYAAIC6IjtbmjBBMgzJNBVmGBpis6k0MlKyWIIdHUJYjW6eVFpaqlmzZikhIUENGjTQSSedpHvuuUemaTq2MU1Ts2fPVuvWrdWgQQMNHTpUeXl5QYwaAAAgCPLy7AmDzSaVlko2m4zSUsk0FT5pkpSfH+wIEcJqdNIwb948Pfnkk1qwYIF++OEHzZs3TxkZGfrPf/7j2CYjI0OPPfaYnnrqKa1bt06NGjXSiBEjdOjQoSBGDgAAUM2ysuw1DMcxJPvyzMxqDwk1R41unvT555/roosu0vnnny9Jio+P10svvaQvvvhCkr2W4ZFHHtGdd96piy66SJK0ePFixcTEaOnSpbryyiuDFjsAAEC1KiiQjmmN4cQ07esBN2p00nDWWWdp4cKF+vHHH9WpUyd98803+vTTT/XQQw9JkrZu3arCwkINHTrU8ZqmTZuqd+/eWrNmjdukoaSkRCUlJY7nxcXFkiSr1Sqr1VqF76i8suNV93ER+igbcIVyAVcoF5CksLg4hRmGytc1SDIMlcbFyUYZqVN8uSbU6KRhxowZKi4uVnJyssLDw1VaWqr77rtPV199tSSpsLBQkhQTE+P0upiYGMc6V9LT0zVnzpxyy5ctW6aGDRsG8B14LycnJyjHReijbMAVygVcoVzUbY0SEjTEZpMpOSUOpiTZbFrRsaMOvPdecIJDUBw8eNDrbWt00rBkyRK98MILevHFF9W1a1dt2LBBN910k9q0aaNx48b5vd+ZM2cqLS3N8by4uFhxcXEaPny4oqOjAxG616xWq3JycjRs2DBFRERU67ER2igbcIVyAVcoFyhTGhmp8EmTZB4dPUmGIdlsOvzkkxqQmhrs8FDNylrTeKNGJw3Tpk3TjBkzHM2MunXrpm3btik9PV3jxo1TbGysJGn37t1q3bq143W7d+9W9+7d3e43KipKUVFR5ZZHREQE7WIbzGMjtFE24ArlAq5QLiCLRRowwN7puaBApXFxWtGxowakplI26iBfvvManTQcPHhQYWHOA0CFh4fLZrNJkhISEhQbG6vly5c7koTi4mKtW7dOkydPru5wAQAAgi8xUUpPlyTZrFaaJMErNTpp+Oc//6n77rtP7du3V9euXfX111/roYceUurR6jXDMHTTTTfp3nvvVVJSkhISEjRr1iy1adNGI0eODG7wAAAAQA1Ro5OG//znP5o1a5b+9a9/ac+ePWrTpo2uvfZazZ4927HNbbfdpgMHDmjSpEnav3+/+vXrpw8++ED169cPYuQAAABAzVGjk4YmTZrokUce0SOPPOJ2G8MwNHfuXM2dO7f6AgMAAABqkRo9IzQAAACAqkfSAAAAAMAjkgYAAAAAHpE0AAAAAPCIpAEAAACARyQNAAAAADwiaQAAAADgEUkDAAAAAI9IGgAAAAB4RNIAAAAAwCOSBgAAAAAekTQAAAAA8IikAQAAAIBHJA0AAAAAPCJpAAAAAOARSQMAAAAAj0gaAAAAAHhE0gAAAADAI5IGAAAAAB6RNAAAAADwiKQBAAAAgEckDQAAAAA8ImkAAAAA4BFJAwAAAACPSBoAAAAAeETSAAAAAMAjkgYAAAAAHpE0AAAAAPCIpAEAAACARyQNAAAAADyqV9kdWK1W7dmzR7/99psaNmyoli1bqlmzZgEIDQAAAEAo8Ctp2LJli5599lktX75cX331laxWq9P6tm3basCAARo5cqRGjhyp8PDwgAQLAAAAoPr5lDR88cUXmjVrlpYvXy6bzaaIiAidcsopiomJ0Yknnqi//vpLe/fuVW5url544QW9+OKLatWqlaZOnaq0tDQ1aNCgqt4HAAAAgCridZ+GK6+8Un369NE333yjqVOn6tNPP1VxcbHWr1+v9957T88//7xef/11rVixQjt37tS2bdu0cOFCJScna/bs2erUqZNWrFgR8Dfwyy+/6JprrlHz5s3VoEEDdevWTV999ZVjvWmamj17tlq3bq0GDRpo6NChysvLC3gcAAAANVpenjRzpjR6tP2R+yUcw+uk4csvv9QzzzyjX375RY888ojOOussRUVFud0+Li5OFotFK1as0ObNmzVo0CCtWbMmIEGX2bdvn/r27auIiAi9//772rRpkx588EGdcMIJjm0yMjL02GOP6amnntK6devUqFEjjRgxQocOHQpoLAAAADWV8eyzUnKyNH++tGSJ/TE5WVq0KNihIUR43TwpNzdX9er51286KSlJixcv1pEjR/x6vTvz5s1TXFycsrOzHcsSEhIc/zdNU4888ojuvPNOXXTRRZKkxYsXKyYmRkuXLtWVV17pcr8lJSUqKSlxPC8uLpZk7/R9fP+NqlZ2vOo+LkIfZQOuUC7gCuUC7litVjXauVPhU6dKNpvTOlOSLBYd6d1bSkwMSnyoWr5cEwzTNM0qjKVKnXzyyRoxYoR+/vlnrVq1Sm3bttW//vUvTZw4UZK9w/ZJJ52kr7/+Wt27d3e8bsCAAerevbseffRRl/u9++67NWfOnHLLX3zxRTVs2LBK3gsAAEAwdHnuOSW++abCjksaJMkWFqb8iy/WD2PGBCEyVLWDBw/qqquuUlFRkaKjoz1u6/eQq6WlpTpw4IAaN26ssLDyrZxsNpv+/PNPNWrUqMpGT9qyZYuefPJJpaWl6fbbb9eXX36pG264QZGRkRo3bpwKCwslSTExMU6vi4mJcaxzZebMmUpLS3M8Ly4uVlxcnIYPH17hBxpoVqtVOTk5GjZsmCIiIqr12AhtlA24QrmAK5QLuGO1WrX3wQdluFlvSEoMD1fCeedVZ1ioJmWtabzhd9IwZ84cZWRkaMeOHWrZsmW59b///rvat2+vmTNnavbs2f4exiObzaaePXvq3//+tyTpH//4h77//ns99dRTGjdunN/7jYqKctlfIyIiImgX22AeG6GNsgFXKBdwhXIBVw62aiUZrtMGwzBkdOyoMMpNreTL9cDvGaHfeecdDRkyxGXCIEktW7bU0KFD9dZbb/l7iAq1bt1aJ598stOyLl26aPv27ZKk2NhYSdLu3budttm9e7djHQAAQF22fcgQyV1rddOULJbqDQghye+kYcuWLUpOTva4TefOnbV161Z/D1Ghvn37Kjc312nZjz/+qA4dOkiyd4qOjY3V8uXLHeuLi4u1bt069enTp8riAgAAqCkOtGmj0oULpbAwKTzc+TEzk07QkFSJ5klWq9VlX4ZjGYZRpUOb3nzzzTrrrLP073//W5dffrm++OILLVy4UAsXLnQc/6abbtK9996rpKQkJSQkaNasWWrTpo1GjhxZZXEBAADUJObYsdKAAfYkoaBAio+31zCQMOAov5OGxMREffzxxx63+fjjj52GQA20Xr166c0339TMmTM1d+5cJSQk6JFHHtHVV1/t2Oa2227TgQMHNGnSJO3fv1/9+vXTBx98oPr161dZXAAAADVOYqKUnh7sKBCi/G6edMkll2jDhg2aPXu2SktLndaVlpZq1qxZ2rBhgy677LJKB+nJBRdcoO+++06HDh3SDz/84BhutYxhGJo7d64KCwt16NAhffTRR+rUqVOVxgQAABBSmO0ZleR3TcMtt9yil19+Wffdd59efvllDRo0SG3bttUvv/yiFStW6KefflKXLl106623BjJeAAAA+CI7W5owwT5CkmnaHzMy7E2RjmmdAXjid9LQuHFjffLJJ5o8ebLefPNN5efnO9aFhYXp0ksv1RNPPKHGjRsHJFAAAAD4KC/PnjC4mLhNFovUu7f/+83K+rv/Q2qqlJRUmUgR4vxOGiT7sKqvvfaadu/era+++kpFRUVq1qyZevbsqVatWgUqRgAAAPgjK8vtHAwyDIVlZ0t9+/q2T081FykplQ4ZoalSSUOZmJgYnX/++YHYFQAAAAKloMDjHAzGtm2+JQ0V1Vz068eIS7WU3x2hyxw+fFjvvfeeHnroId1zzz2O5YcOHdKePXtkc1WoAAAAUPXi4z3WNJhH57byWgU1F8rM9G1/qDEqlTT83//9n9q3b69//vOfuvXWW3X33Xc71n377bdq3bq1Xn755crGCAAAAH+kpnqsabCNH+/b/iqouVBBgW/7Q43hd9Lw2Wef6dJLL1VUVJQeffRRXXXVVU7rzzjjDCUmJur111+vdJAAAADwQ1KS/df/QM32XEHNheLjKxsxQpTffRruueceNWvWTOvXr1eLFi30+++/l9umZ8+eWrduXaUCBAAAQCWkpNj7Gria7dlq9W1fqan2Ts+umKZ9v6iV/E4a1q1bp0svvVQtWrRwu01cXJzeeustfw8BAACAQAjUbM9lNRcWi/PoSabpX80Fagy/k4aSkhJFR0d73Gb//v0KC6t0X2sAAACECk81F6i1/E4aOnbsqC+//NLjNmvWrFFycrK/hwAAAEAoClTNBWoMv6sBRo0apc8++0zZ2dku1z/wwAP6/vvvdcUVV/gdHAAAAIDg87umYdq0aXr99dc1YcIEvfjiiyopKZEk3XbbbVqzZo0+//xzde/eXVOnTg1YsAAAAKgB8vLsczqUNV9KTbX3h0CN5XfS0LhxY61evVpTp07VkiVLVFpaKslew2AYhi6//HI98cQTioqKCliwAAAACHHZ2fZZo4/tKJ2RYe8DkZIS7OjgJ7+TBkk64YQT9MILL+ixxx7Tl19+qb179yo6Olq9evVSTExMoGIEAABATZCXZ08YbLby6ywWewdqOkzXSJVKGso0b95c55xzTiB2BQAAgJoqK8vz5G+ZmXSgrqEqlTTYbLZyQ6quWbNG77zzjurXr6/x48erXbt2lQoQAAAANURBgb1JkiumaV+PGsnv0ZNuvvlmNWzYUPv373cse+2113T22WcrPT1dd911l04//XT9/PPPgYgTAAAAoS4+3nNNQ3x8dUaDAPI7aVixYoUGDx6sZs2aOZbNnj1bTZs21eLFi5WRkaF9+/bpgQceCEScAAAACHWpqZ5rGiyW6o0HAeN386QdO3ZowIABjudbt27V5s2bddddd+maa66RJK1evVoffPBB5aMEAABA6EtKsvdbsFicR08yTftyOkHXWH4nDQcOHFCjRo0cz1etWiXDMHTuuec6lp188slavnx55SIEAABAzZGSYh8lKTPz73kaLBYShhrO76ShTZs2ys3NdTz/4IMP1LhxY/Xo0cOxrLi4mHkaAAAA6prEREZJqmX8ThoGDBigl156SQsWLFD9+vX1xhtvaOTIkQoPD3ds89NPPzF6EgAAAFDD+d0R+o477lCDBg104403atKkSYqKitLdd9/tWP/HH3/ok08+Ud++fQMRJwAAAIAg8bumITExUZs2bdLrr78uSfrnP/+pDh06ONbn5eXp2muv1VVXXVX5KAEAAAAETaUmd2vdurWmTp3qct3pp5+u008/vTK7BwAAQF2Ql2efTbqs43Rqqn0kJoQMv5OGoqIibdu2TYmJiWrYsGG59QcOHNBPP/2k+Ph4RUdHVypIAAAA1FLZ2dKECc5DtGZk2EdfSkkJdnQ4yu8+DXPnzlXfvn1VWlrqcn1paan69u2r++67z+/gAAAAUIvl5dkTBptNKi11frRYpPz8YEeIo/xOGj744AMNGzZMTZo0cbk+OjpaI0aM0Hvvved3cAAAAKjFsrLsNQuuGIa9tgEhwe+kYfv27UqqoK3ZSSedpO3bt/t7CAAAANRmBQX2JkmumKZ9PUKC30mDYRgqKSnxuE1JSYnb5ksAAACo4+LjPdc0xMdXZzTwwO+kITk5WR988IFMN9mhzWbT+++/r86dO/sdHAAAAGqx1FTPNQ0WS/XGA7f8ThpGjx6tH3/8UampqSoqKnJaV1RUpNTUVOXn5+uaa66pdJDeuv/++2UYhm666SbHskOHDmnKlClq3ry5GjdurFGjRmn37t3VFhMAAADcSEqy91sIC5PCw50fMzOlxMRgR4ij/B5yderUqXr99df17LPP6q233lKvXr3Utm1b/fLLL/ryyy+1f/9+9e/f3+08DoH25Zdf6umnn9app57qtPzmm2/Wu+++q1dffVVNmzbV1KlTdckll+izzz6rlrgAAADgQUqK1K+fPUkom6fBYiFhCDF+Jw0RERH66KOPdOedd+qZZ55RTk6OY110dLSmTZumuXPnKiIiIiCBevLnn3/q6quv1jPPPKN7773XsbyoqEiZmZl68cUXNXjwYElSdna2unTporVr1+rMM8+s8tgAAABQgcREKT092FHAg0rNCF2/fn098MADmjdvnjZv3qyioiI1a9ZMnTt3Vnh4eKBirNCUKVN0/vnna+jQoU5Jw/r162W1WjV06FDHsuTkZLVv315r1qxxmzSUlJQ4dfIuLi6WJFmtVlmt1ip6F66VHa+6j4vQR9mAK5QLuEK5gDuUjbrNl+/d76ShY8eOOvfcc/X4448rPDxcXbt29XdXlfLyyy/rf//7n7788sty6woLCxUZGalmzZo5LY+JiVFhYaHbfaanp2vOnDnlli9btszl7NfV4diaHOBYlA24QrmAK5QLuFOZstFo5061X75cDffs0cFWrbR9yBAdaNMmgNGhqhw8eNDrbf1OGn777TdFR0f7+/KA2LFjh2688Ubl5OSofv36AdvvzJkzlZaW5nheXFysuLg4DR8+vNrfs9VqVU5OjoYNG1YtTb1Qc1A24ArlAq5QLuBOZcuG8eyzCp861T48qmlKhqGkN99U6cKFMseOrYKIEUhlrWm84XfScOqpp+rHH3/09+UBsX79eu3Zs0enn366Y1lpaak++eQTLViwQB9++KEOHz6s/fv3O9U27N69W7GxsW73GxUVpaioqHLLIyIignaxDeaxEdooG3CFcgFXKBdwx6+ykZcnXXutZLOVW1Vv0iRpwAA6M4c4X75zv4dcnT59ut5++22tWLHC311U2pAhQ/Tdd99pw4YNjn89e/bU1Vdf7fh/RESEli9f7nhNbm6utm/frj59+gQtbgAAgBovK8vzxGyZmdUbD6qU3zUN+/bt0/DhwzV8+HCNHDlSvXr1UkxMjAwXhWdsFVVPNWnSRKeccorTskaNGql58+aO5RaLRWlpaTrxxBMVHR2t66+/Xn369GHkJAAAgMooKPA8MVtBQXVGgyrmd9KQkpIiwzBkmqZef/11vf7665LklDSYpinDMKosafDGww8/rLCwMI0aNUolJSUaMWKEnnjiiaDFAwAAUCvEx3uuaYiPr85oUMX8Thqys7MDGUfArFy50ul5/fr19fjjj+vxxx8PTkAAAAC1UWqqlJHhep1p2idoQ63hd9Iwbty4QMYBAACAmiQpyd5vwWJxGj1Jpmlf7k8n6Lw8e1+JspmhU1Ptx0HQVWpyNwAAANRhKSlSv372JKHsRt9i8S9hyM6WJkxwTkAyMuz7TkkJbNzwWUCShtLSUv32229Osygfq3379oE4DAAAAEJNYqKUnl65feTl2RMGF8O3ymKR2rWTli+nBiKIKpU0rF+/Xrfffrs++eQTHT582OU2hmHoyJEjlTkMAAAAajNPw7dK0rBhUng4NRBB5Pc8DRs2bNDZZ5+tNWvWaPjw4TJNU6eeeqqGDx+uFi1ayDRNDRgwQGPGjAlkvAAAAKhtPA3fWlb7UFpq/3/Zo8Ui5edXW4h1nd9Jwz333CNJWrdund566y1J0sUXX6z3339fBQUFuu666/T999/rrrvuCkykAAAAqJ08Dd/qDhPIVSu/k4ZPP/1UF154obp06eJYZh7NEBs0aKAFCxaoTZs2uv322ysfJQAAAGqv1FT3NQ3uMIFctfI7aSgqKlLHjh0dzyMiIvTnn3/+veOwMA0cOFDLly+vXIQAAACo3cqGbw0Ls/ddKHs0DCaQCxF+d4Ru1aqV9u3b53geGxurvLw8p20OHTqkgwcP+h8dAAAA6gZXw7cOGSKNGOG6FoIJ5KqV30nDySefrNzcXMfzvn37aunSpVqzZo369OmjH374QUuWLFFycnJAAgUAAEAt52r41kBPIAe/+N086fzzz9cnn3yiXbt2SZKmT58u0zTVr18/tWzZUt26ddP+/fvp0wAAAAD/paRIubnStGnS5ZfbH3NzGW61mvld03Ddddfp8ssv1wknnCBJOu2007R8+XLdd9992rJli3r06KHrr79e559/fsCCBQAAQB0UiAnkUCl+Jw0RERGKiYlxWnbWWWfp3XffrXRQAAAAAEKH382TAAAAANQNXicNa9eurdSBDhw4oI0bN1ZqHwAAAACqn9dJw1lnnaXzzz9fq1at8ukAu3fvVnp6uhISEvT666/7HCAAAACA4PK6T8Mbb7yh6dOna/DgwYqLi9Oll16q3r17q0ePHoqJiVGjRo1UWlqqvXv3Kjc3V+vWrVNOTo4+/vhjSdKECRM0efLkKnsjAAAAAKqG10nDyJEjdcEFF2jx4sV68skn9dBDD8k4Zoa+8PBwlZaWOp6bpqkmTZpo4sSJuvHGG9WpU6fARg4AAACgWvg0elK9evWUmpqq1NRUbdy4UcuXL9dnn32mn3/+Wb///rsaNGjgmKNhwIABGjJkiBo1alRVsQMAAACoBn4Pudq1a1d17dpVN9xwQyDjAQAAABBiGHIVAAAAgEckDQAAAAA8ImkAAAAA4BFJAwAAAACPSBoAAAAAeETSAAAAAMAjkgYAAAAAHpE0AAAAAPDI68ndFi9e7PdBxo4d6/drAQAAAASX10lDSkqKDMPwaeemacowDJIGAAAAVJ+8PCkrSyookOLjpdRUKSkp2FHVaF4nDdnZ2VUZBwAAAFB52dnShAmSYUimaf93//3SeedJjzxC8uAnr5OGcePGVWUcAAAAQOXk5dkTBput/Lr33pPef99eA5GSUu2h1XR0hAYAAEDtkJVlr2FwxzQli0XKz6++mGqJGp00pKenq1evXmrSpIlatWqlkSNHKjc312mbQ4cOacqUKWrevLkaN26sUaNGaffu3UGKGAAAAFWmoMCeGHhiGFJmZrWEU5tUKmnYsWOHrr32Wp100klq0KCBwsPDy/2rV8/rFlA+W7VqlaZMmaK1a9cqJydHVqtVw4cP14EDBxzb3HzzzXr77bf16quvatWqVdq5c6cuueSSKosJAAAAQRIf77mmQbInFQUF1RFNreL3Hf2WLVvUu3dv7du3T127dlVJSYk6dOig+vXra8uWLbJarTrttNPUrFmzAIbr7IMPPnB6vmjRIrVq1Urr169X//79VVRUpMzMTL344osaPHiwJHuH7i5dumjt2rU688wzqyw2AAAAVLPUVCkjw/M2hmFPLuATv5OGOXPmqKioSMuXL9eAAQMUFham8ePHa/bs2dq1a5cmT56sTZs26aOPPgpkvB4VFRVJkk488URJ0vr162W1WjV06FDHNsnJyWrfvr3WrFnjNmkoKSlRSUmJ43lxcbEkyWq1ymq1VlX4LpUdr7qPi9BH2YArlAu4QrmAO7WubMTHy1i4UOGTJjk6Qx9b72BKkmnqyNixUm15z5Xgy/fud9Lw0Ucf6bzzztOAAQMcy8yjbchat26tV155Rd26ddPtt9+up59+2t/DeM1ms+mmm25S3759dcopp0iSCgsLFRkZWa62IyYmRoWFhW73lZ6erjlz5pRbvmzZMjVs2DCgcXsrJycnKMdF6KNswBXKBVyhXMCdWlU2WrRQo8cf1yn//a9i1q+XKck0DMkwZJimvp4yRTt+/FH68cdgRxp0Bw8e9Hpbv5OG3377TcnJyX/vqF49pwNHRUVp2LBhWrp0qb+H8MmUKVP0/fff69NPP630vmbOnKm0tDTH8+LiYsXFxWn48OGKjo6u9P59YbValZOTo2HDhikiIqJaj43QRtmAK5QLuEK5gDu1umxYLDqSn6+w7GwZ27bJ7NBBpePHq1tioroFO7YQUdaaxht+Jw0tWrRw6nDcokULFRzXqaRevXrav3+/v4fw2tSpU/XOO+/ok08+Ubt27RzLY2NjdfjwYe3fv9+ptmH37t2KjY11u7+oqChFRUWVWx4RERG0EyqYx0Zoo2zAFcoFXKFcwJ1aWza6dHHq4xAexFBCkS/fud+jJyUlJemnn35yPD/jjDP04YcfasuWLZKkX3/9Va+99ppOOukkfw9RIdM0NXXqVL355pv6+OOPlZCQ4LS+R48eioiI0PLlyx3LcnNztX37dvXp06fK4gIAAABqE7+ThnPPPVcrVqxw1CTcdNNN+uOPP3TqqaeqV69e6tSpkwoLC3X99dcHKtZypkyZoueff14vvviimjRposLCQhUWFuqvv/6SJDVt2lQWi0VpaWlasWKF1q9fr/Hjx6tPnz6MnAQAAAB4ye+kYfLkyVq5cqXCw+0VPQMHDtTLL7+sDh066Pvvv1dMTIwee+wxTZw4MWDBHu/JJ59UUVGRBg4cqNatWzv+vfLKK45tHn74YV1wwQUaNWqU+vfvr9jYWL3xxhtVFhMA1Fl5edLMmdLo0fbHvLxgRwQACBC/+zRER0erd+/eTssuu+wyXXbZZZUOyltmRTP+Sapfv74ef/xxPf7449UQEQBUobw8KSvLPilRfLx9PPKkpGBHZZedLU2YYB//3DTtjxkZ9llXU1KCHR0AoJKqbrpmAEDghPJNeV6ePbajY6I7sVikfv2kxMTqjwsAEDB+Jw3bt2/3etv27dv7exgAQKjflGdl2ZMYVwzDntikp1dvTACAgPI7aYiPj5fh7o/EMQzD0JEjR/w9DAAg1G/KCwrstR+umKZ9PQCgRvM7aRg7dqzLpKGoqEjffPONtm7dqgEDBig+Pr4y8QEAQv2mPD7ec1LD3wEAqPH8ThoWLVrkdp1pmnrwwQeVkZGhzMxMfw8BAJBC/6Y8NdVp8iQnpmlvQgUAqNH8HnLVE8MwdOutt6pr166aNm1aVRwCAOqO1FTPNQ3BvilPSrI3kQoLk8LDnR8zM+kEDQC1QJUkDWV69uypjz/+uCoPAQC1X024KU9JkXJzpWnTpMsvtz/m5gZ/ZCcAQEBU6ZCrP/30E52gASAQUlLsoyRlZv49T4PFEhoJQ5nEREZJAoBaKuBJg81m0y+//KJFixbprbfe0pAhQwJ9CNQloTyZFVDduCkHgMDg/sJnficNYWFhHodcNU1TJ5xwgh588EF/D4G6LpQnswIAADUT9xd+8Ttp6N+/v8ukISwsTCeccIJ69eql8ePHq1WrVpUKEHVUqE9mBQAAah7uL/zmd9KwcuXKAIYBHCfUJ7MCAAA1D/cXfqvS0ZMAv4X6ZFYAAKDm4f7CbyQNCE2hPpkVAACoebi/8JvXzZM6duzo1wEMw9BPP/3k12tRTUJxBAFmmAUAAIHG/YXfvE4abDZbuY7Phw8f1q5du+w7qldPzZs31++//+6Ym6F169aKjIwMYLgIuFAdQaBsMiuLxTk20wydyawAAEDNwv2F37xOGgqOa+O1f/9+DR06VElJSbrvvvvUp08fhYWFyWaz6fPPP9edd96pAwcO6KOPPgp0zAiUUB9BoCZMZgUAAGoW7i/84vfoSdOnT9ehQ4e0bt06hYeHO5aHhYWpX79++uijj3Taaadp+vTpeuqppwISLAKsJowgwGRWAAAg0Li/8JnfHaHfeustXXDBBU4Jw7Hq1aunCy64QG+99ZbfwaGKMYIAAAAAvOB30lBcXKyioiKP2xQVFVW4DYKIEQQAAADgBb+Thq5du+rll192OzJSXl6eXn75ZZ1yyil+B4cqlprquaaBEQQAAACgSvRpuPPOO3XxxRfrH//4hywWi/r166dWrVppz549Wr16tbKysnTgwAHdeeedgYwXgcQIAgAAAPCC30nDRRddpEWLFun666/Xo48+qscee8yxzjRNRUdHKzs7WxdeeGFAAkUVYQQBAAAAVMDvpEGSxo4dq4svvlhLly7VN998o6KiIjVt2lSnnXaaLrroIkVHRwcqTlQlRhAAAACAB5VKGiSpSZMmGjNmjMaMGROIeAAAAACEGL87QgMAAACoG7yuaVi8eLEk6eKLL1aTJk0cz70xduxY3yMDAAAAEBK8ThpSUlJkGIbOPPNMNWnSxPHcE9M0ZRgGSUNdlpdnn3m6rJN1aqp91CYAwcf56R8+NwB1kNdJQ1ZWlgzDUOvWrSVJ2dnZVRYUaonsbGnCBOfhXDMy7CM1paQEOzqgbnN3fp5zjhQdzc2wO1zXANRRPtU0HGvcuHGBjgW1SV6e/Q+rzVZ+ncViH+aVYV2B4PB0fr73nv1GOCyMm+HjcV0DUIfRERrO8vKkmTOl0aPtj3l5/u0nK8t+4+GKYdhvRAAEh6fzU7L/gl5aar85tlik/Pzqiy2UcV0DUIf5nTTs2LFDH3/8sQ4ePOhYZrPZNG/ePPXt21dDhw7Vu+++G5AgUU2ys6XkZGn+fGnJEvtjcrK0aJHv+yoosN94uGKa9vW+ClRCA9R1ns7P43Ez/LeKrmtvvcX1CUCt5fc8DbNmzdLbb7+twsJCx7L77rtPd911l+P5qlWr9Pnnn6tXr16VizIAHn/8cc2fP1+FhYU67bTT9J///EdnnHFGsMOqXp467wWq2r3sGN984/6Pq2HYj+8LX9sRB7qjIh0fURt4c34ez12SX9XnhKv9S8E7D/PyPCcNNpu0ebOUm0s/BwC1k+mnxMRE8/LLL3c8t9lsZqtWrcwuXbqYO3bsML/88kuzadOm5mWXXebvIQLm5ZdfNiMjI82srCxz48aN5sSJE81mzZqZu3fv9ur1RUVFpiSzqKioiiM9zo8/mkemTTN3nH22eWTaNNP88Uf/95WVZZphYaYZHu78mJ1tXz9jhn2Z/U+i87/wcPt6F/GZM2aY5pVX2h/T05337Wpfkn1dXp5Pn4Pb/bnaV0Xv1V387j5fb/dXzQ4fPmwuXbrUPHz4cFDjQJAdV44Pb9zoulwcX47dnZ/enP+BPseO52r/hmH/5+156O+xK4rH28/Nn2tdFeJ6AXcoG3WbL/e4ficN0dHR5rRp0xzP//e//5mGYZhPPPGEY9m4cePM+Ph4fw8RMGeccYY5ZcoUx/PS0lKzTZs2Znp6ulevD0rScPSPlC083Cw1DNNWmT/K3tx0X3ml522uvNJlfE5/wD398Tz2j72rP/Ke3ocvCY23CYYvNz2+JCzViAs9XJVjW1iYuf76653LhadyXHZ+envjG+hz7HgVxRqoHw68TSp8jcebH1yCgOsF3KFs1G2+3OP63TzJZrPJdkxTlpUrV8owDA0ePNixrG3btk7Nl4Lh8OHDWr9+vWbOnOlYFhYWpqFDh2rNmjUuX1NSUqKSkhLH8+LiYkmS1WqV1Wqt2oAlKS9P9SZMkGGzyZBkSFJpqUxJslh0pHdvKTFRxrPPKvzaa8s12SlduFDmMXNjhD3zjMIMQ66675mGIdvChVJcnOdt4uJkK3vvx8TntF1ZrC5er86dZfvnP2UbP97ezOmYz7Gi9xG+ZYsM03S9b9OUuWWLSo/uz5v3aktJcRv/sZ+v1/u77z4Xa6teWVmsljKJ0OPmPJSkfyxYoEMTJ9r7JKnicqzOnWUmJMj44AP7qEll56Fp2s/DDh0c52ygz7Fj30/YokUy3n7b7fnuSrnz0MP1qezYxmefeXXt9OqzO+EEad8+GS6aLR1/fQomrhdwh7JRt/nyvfudNLRv315ffPGF4/nSpUvVunVrde7c2bGssLBQzZo18/cQAfHbb7+ptLRUMTExTstjYmK0efNml69JT0/XnDlzyi1ftmyZGjZsWCVxHqvLc88pUeVvwA1JNklbZ83S9iFDNGTKlHJ/qExJ4RMnarnVqgNH59TosXat2hxNQI5nmqZ2rV2rzaNHa4jNVu7G35Qkm00rOnbUgffeqzA+V0zD0M6WLbW+b1/pxx/t/45qtHNnhe+jfWmpy+OVbZdfWqofjsbmzXs9uGVLhZ/vD2PGeL2/9UePHSw5OTlBPT6Cw+N1wjC0Y84c78px2fl57bVqdOGFav/RR2q4Z48Otmql7UOH6kCLFvZhWI8K9DkmSe2XL1f3BQtkGobLJMiT48/Diq6fP990k+Jzcry6dlb4fg1DfzZsqMb797tOGuR8fQoFXC/gDmWjbjp2QKOK+J00jBo1Svfdd58uvfRS1a9fX59++qmmTp3qtM2mTZvUsWNHfw8RNDNnzlRaWprjeXFxseLi4jR8+HBFR0dX+fHDX3zR7Q24ISkxPFwnbd1q/0WwtLTcejMsTIO2bJHNYpEkhX32mYzPPy+3rST7hH1nnqmYCRNUGhmp8EmT7L+eHfdL44Bjfn3zFJ/LmI8e47zzziu3LuyOOyp+H3PnynjzTZcJjSEp4Z57lFBWM+DFezW2bavw8004Gqs3+3P1vqqD1WpVTk6Ohg0bpoiIiKDEgOCp6DzsaBj+leOj1w1JSnCx30CfY8rLU71LLrHXLri48a7I8fFXdP3ssH2719dOqeL32+icc2RkZXl1fQomrhdwh7JRt5W1pvGG30nDrbfeqmXLlumNN96QJJ166qm6++67Heu3bdumL774QjNmzPD3EAHRokULhYeHa/fu3U7Ld+/erdjYWJeviYqKUlRUVLnlERER1XNCdezodixwwzBkdOzocRQPwzQVvmOHwstinThRevBB99tOmmTf1mKRBgywj/hRNjqJxaJ6x//B8xBfhcc43o4dFb+Pk0+2x2SxODUnMExTysxURJcuf7/Ii/eqzMwKP98wXz+7IKq2conQUsF5aCQkqF5VlONAn2PPPefT9cTdMR3xV3T9LLuGuNvXsddOqeL3O3261Levd9enEMD1Au5QNuomX75zv+dpiI6O1tq1a/Xtt9/q22+/1fr163XCCSc4bfPGG2/oX//6l7+HCIjIyEj16NFDy5cvdyyz2Wxavny5+vTpE8TIPEhNdftHTaZp/+MUH+95kqFjhzRNSrL/EQ8Lk8LDnR8zM53bFicmSunp0ksv2R9d/ULmKT7Jvl9PxziWt+8jJcU+lOG0adLll9sfc3PLD2fozXv15vP1ZX9AMLgpx6bsN7O28eP/XhjIchzoc8ybOSPCw+3XA8OoOP6Kjt2/v/fXTm/fr7fXJwCoyaqyR3aoePnll82oqChz0aJF5qZNm8xJkyaZzZo1MwsLC716fVBGT8rO/nv0pKOPTqN/+DOqT16e82ghlRn552h85UYnuf9+345RVaMTVfRe3cXvbmSXQH52AcKIF3BVjl2OnlQmkOU4UOeYp9HRDMM0u3T5e//exu/p2P5ec0LwGuALrhdwh7JRt/lyj2uYph+NSI9RWFioN954Q5s3b9bBgwf13//+V5L066+/auvWrerWrZsaNGgQgPSmchYsWOCY3K179+567LHH1Lt3b69eW1xcrKZNm6qoqKha+jQ45OerdOFC7Vq7Vq3PPNNe7X/sL2qLFpWrEtfRKvFq+YUrP79cUya/fnkP1vsIVPxBYrVa9d577+m8886jSrkuO64cW8eO1Xs//hga5cKbcywvzz7Kk6sO0GFh9l/s/TkvPR072NfOIOB6AXcoG3WbL/e4lUoannjiCd1yyy2O4UkNw1Dp0c5iGzdu1KmnnqqnnnpKEydO9PcQISFoSYO8OJlr+I2vQ215H9WICz1cqZHlIhg38XXsmlMjywWqBWWjbvPlHtfvjtBvv/22pk6dqp49e2r27Nl6//339dRTTznWd+3aVaeeeqqWLl1a45OGkFbWB6Gmq673kZcnZWX9faOQmmpvswwgeFJSpH79qvcmvrZcOwGgmvidNMyfP1/t27fXihUr1KhRI61fv77cNt26ddPq1asrFSAQMNnZ0oQJ5SZ0qs1NEoAag5t4AAhpfo+etGHDBp1//vlq1KiR223atm1bbqhTICjy8uwJg81mH2/92EeLxd5UAQAA4Hh5edLMmdLo0fbHvLxgRxQUficNNputwrZve/bscTnfAVDtsrI8D7OYmVm98QAAgNCXnW0frGH+fGnJEvtjcrK9L1Yd43fS0LlzZ49Nj44cOaJPPvlE3bp18/cQQOB4GgveNO3rAQAAytBKwYnfScPVV1+tr7/+WnPmzCm3rrS0VLfeequ2bNmisWPHVipAICB8mQwPAACAVgpO/E4arr/+eg0YMEBz585Vp06d9Prrr0uSLr/8ciUlJemxxx7TsGHDZDl25k8gWHyZoRYAAIBWCk78ThoiIiL04YcfasaMGfr999/1/fffyzRNvfbaa9q7d6+mT5+u//u//5PhLkMDqlNSkv0XgbAwKTzc+TEzs1aPzw4AAPxAKwUnfg+5KkmRkZG67777dO+99yo3N1d79+5VdHS0unTpovDw8EDFCARGMMaCBwAANVNqqn1odlfqYCuFSiUNZQzDUHJycrnlW7du1Zw5c7SoDvYwR4hiLHgAAOCNslYK7masr2M/OgYkaTje9u3bdc8992jx4sU6cuQISQMAAABqHlopOPjcp+HTTz/VoEGDFB0drRNPPFEXXXSRcnNzJUkHDx5UWlqaOnXqpMzMTLVs2VKPPfZYwIMGAAAAqkVZK4WXXrI/1sGEQfKxpmH9+vUaOnSoDh8+7Fj29ttv66uvvtLq1at14YUXatOmTWrTpo2mT5+uSZMmMbkbAAAAUMP5VNOQkZGhw4cPKz09XXv27NGePXt03333adeuXTr77LO1efNm3XnnncrPz9f1119PwgAAAADUAj7VNHz22WcaPHiwpk+f7lg2c+ZMffTRR1q5cqXmz5+vtLS0gAcJAAAAIHh8qmnYs2ePevToUW552bJx48YFJioAAAAAIcOnpOHIkSNq1KhRueVly5o3bx6YqAAAAACEDL9nhAYAAABQN/g8T8Pzzz+vtWvXOi3Lz8+XJJ133nnltjcMQ++++66f4QEAAAAINp+Thvz8fEeScLwPPvig3DLDMHyPCgAAAEDI8Clp2Lp1a1XFAQAAACBE+ZQ0dOjQoariAAAAAGqevDwpK0sqKJDi46XUVCkpKdhRBZzPzZMAAAAASMrOliZMkAxDMk37Y0aGlJkppaQEO7qAYvQkAAD+v717j4q6zv84/hqQASnBTBFRLl7IOpZa3vIK5AU3bcNN1zYzUdRytdbVLTVrwc3NxUy7udlFxFvX9eievAWpmJl3tMRbomLlBbOfiAkKwvf3B4fZphlGRmVmkOfjHM40389nvt/3DB+nefH9fOYLAM46fLgsMJSWSiUl1rcJCVIFa4CrK0IDAAAA4KyUlLIzC/aYTGVnG24iTE8CANz8asicYwAulJNTNiXJHsMoa7+JEBoAADe3GjTnGIALRUQ4PtMQEeHKaqoc05MAADevGjbnGIALjRjh+ExDQoJr66lihAYAwM2rhs05BuBCkZFl7yFeXpK3t/Xt/PlSixburvCGcmp6kmEYGj16tEpKSvTOO+/Ix8fHbr+ioiI99dRTMpvNmjdv3g0pFAAAp9WwOccAXCw+XurWrSwklK+ZSki46QKD5GRoWL58uVJSUvT+++9XGBgkyWw2KyoqSiNGjFDfvn0VFxd3vXUCAOC8GjbnGIAbtGghzZjh7iqqnFPTkz788EMFBwdr2LBhV+07dOhQhYSEaPHixddcHAAA16WGzTkGgKriVGjYtm2bevXqJS+vqz/My8tLPXv21I4dO665OAAArksNm3MMAFXFqdBw5swZhYaGVrp/48aNdebMGaeLqoycnBwlJCSoadOmql27tpo3b67ExEQVFRVZ9fv222/VvXt3+fn5KTQ0VDNnzqySegAAHio+Xjp0SHr2WemPfyy7PXSIr1sF4LzDh6UpU6Q//ans9vBhd1fkMk6tafD19VVBQUGl+xcWFsrX19fpoirj4MGDKi0t1TvvvKMWLVooKytLo0aN0sWLFzVr1ixJUn5+vvr06aNevXpp3rx52rt3r0aMGKG6detq9OjRVVIXAMAD1ZA5xwCqUA2/5otToSE0NFSZmZmV7r97926FhYU5XVRl9O3bV3379rXcb9asmQ4dOqS3337bEhqWLl2qoqIipaSkyGw2q1WrVtqzZ49mz55NaAAAAEDl/PqaL7+VkFD2DUo3+XRHp0JDTEyM3n77bX3zzTdq06aNw77ffPONvvzyS40bN+66CnTG+fPnVa9ePcv9LVu2qEePHjKbzZZtsbGxSk5O1rlz53TbbbfZ3c/ly5d1+fJly/38/HxJUnFxsYqLi6uoevvKj+fq48LzMTZgD+MC9jAuUBHGRuV4vfeevEwm2fsuNsNkUum776r0n/90eV3Xy5nfu1Oh4ZlnntG8efP0yCOPaNWqVWrZsqXdft99950eeeQReXt7uyw0ZGdn680337ScZZCk06dPq2nTplb9GjZsaGmrKDTMmDFD06ZNs9melpYmf3//G1h15aWnp7vluPB8jA3Yw7iAPYwLVISx4Vi7rVsVUlpqPzQYhk5t3apdq1e7vK7r5cyyA6dCQ2RkpP71r3/p2WefVdu2bTVo0CDFxMSoSZMmkqQTJ05o3bp1WrZsmS5duqRZs2YpMjLSqeInT56s5ORkh30OHDigO++803L/xIkT6tu3rwYNGqRRo0Y5dTx7pkyZogkTJlju5+fnKzQ0VH369FFAQMB1798ZxcXFSk9PV+/evf93bYzDh+WVmirT8eMywsNVGh9f9g0hqFHsjg3UeIwL2MO4QEUYG5XjtXmzTF9/LZWU2LSZTCY1uv9+Pfjgg26o7PqUz6apDKdCgyRNnDhRderU0XPPPaclS5Zo6dKlVu2GYSggIECvv/76NX2AnzhxouKvspikWbNmlv8+efKkYmJi1KVLF7377rtW/YKDg5Wbm2u1rfx+cHBwhfv39fW1u4Dbx8fHbf+gLMe2swjH+9VXa8wiHNhy57iE52JcwB7GBSrC2LiKUaOkV1+122QyDHmPHi3vavj6OfM7dzo0SNLo0aM1ePBg/ec//9HmzZt1+vRpSWUfxLt27aqBAwcqMDDwWnatBg0aqEGDBpXqe+LECcXExKhdu3ZasGCBzfUjOnfurKlTp6q4uNjyoqSnp6tly5YVTk3yaCzCAQAAcL3ya74kJFh/e5Jh1JhrvlxTaJCkwMBAJSQkKMFNV9M8ceKEoqOjFR4erlmzZumnn36ytJWfRXjsscc0bdo0JSQkaNKkScrKytLrr7+uOXPmuKXm65aSUjZA7TGZygYtXykIAABw48XHl/2Bdv58KSdHiogoCxE1IDBI1xEa3C09PV3Z2dnKzs62rKkoZxiGpLJgk5aWprFjx6pdu3aqX7++/v73v1ffr1vNySlLtPYYRlk7AAAAqkYNvuaLU1eEfuCBB7Ro0SKrbdu2bdMbb7xht39ycrJuv/32a6/Ogfj4eBmGYffn11q3bq1Nmzbp0qVL+vHHHzVp0qQqqcclIiIcn2mIiHBlNQAAAKghnAoNGRkZyvnNX7PXrl2rv/71r3b7X7p0SXl5eddaG35rxAjHZxrcNFUMAAAANzenQgPcrHwRjpeX5O1tfVtDFuEAAADA9artmoYaq4YvwgEAAIDrERqqoxq8CAcAAACux/QkAAAAAA4RGgAAAAA45PT0pJUrV1quAC1JO3fulCT9+c9/tum7Y8eO6ygNAAAAgCdwOjTs3LnTEhR+bd68eXb7myq6rgAAAACAasGp0LBhw4aqqgMAAACAh3IqNERFRVVVHQAAAAA8FAuhAQAAADh0zddpOHHihFasWKEdO3bo7NmzkqQGDRqoQ4cOGjBggBo1anTDigQAAADgPtcUGhITEzVz5kwVFRXJMAyrtkWLFulvf/ubpkyZohdffPGGFAkAAADAfZwODVOnTtWMGTPk6+urxx9/XNHR0QoJCZEknTx5Uhs2bNCnn36qpKQklZSUKCkp6UbXDAAAAFRPhw9LKSlSTo4UESGNGCFFRrq7qqtyKjQcPXpUM2fOVNOmTbVmzRrdcccdNn2GDx+uF154QbGxsXr55Zc1bNgwNW3a9IYVDAAAAFRLCxZII0dKJpNkGGW3M2dK8+dL8fHurs4hpxZCL1y4UKWlpVq8eLHdwFDujjvu0JIlS3TlyhUtWrTouosEAAAAqrXDh8sCQ2mpVFJifZuQIGVnu7tCh5wKDZs3b9bdd9+tLl26XLVv165ddc8992jTpk3XXBwAAABwU0hJKTuzYI/JVHa2wYM5FRoOHDigjh07Vrp/x44ddfDgQaeLAgAAAG4qOTllU5LsMYyydg/mVGjIy8tTUFBQpfsHBQUpLy/P2ZoAAACAm0tEhOMzDRERrqzGaU6FhsLCQvn6+la6v9lsVmFhodNFAQAAADeVESMcn2lISHBtPU7iitAAAABAVYuMLFu34OUleXtb386fL7Vo4e4KHXL6Og1LlizR1q1bK9U328NXgQMAAAAuEx8vdetWFhLKr9OQkODxgUG6htCQnZ3tVBgwVTR3CwAAAKhpWrSQZsxwdxVOcyo0HDt2rKrqAAAAAOChnAoN4eHhVVUHAAAAAA/FQmgAAAAADhEaAAAAADhEaAAAAADgEKEBAAAAgEOEBgAAAAAOERoAAAAAOERoAAAAAODQTREaLl++rLZt28pkMmnPnj1Wbd9++626d+8uPz8/hYaGaubMme4pEgAAAKimborQ8NxzzykkJMRme35+vvr06aPw8HDt2rVLr7zyipKSkvTuu++6oUoAAACgenLqitCeaM2aNUpLS9OyZcu0Zs0aq7alS5eqqKhIKSkpMpvNatWqlfbs2aPZs2dr9OjRbqoYAAAAqF6qdWjIzc3VqFGjtGLFCvn7+9u0b9myRT169JDZbLZsi42NVXJyss6dO6fbbrvN7n4vX76sy5cvW+7n5+dLkoqLi1VcXHyDn4Vj5cdz9XHh+RgbsIdxAXsYF6gIY6Nmc+b3Xm1Dg2EYio+P11NPPaX27dsrJyfHps/p06fVtGlTq20NGza0tFUUGmbMmKFp06bZbE9LS7MbTlwhPT3dLceF52NswB7GBexhXKAijI2aqaCgoNJ9PS40TJ48WcnJyQ77HDhwQGlpabpw4YKmTJlyw2uYMmWKJkyYYLmfn5+v0NBQ9enTRwEBATf8eI4UFxcrPT1dvXv3lo+Pj0uPDc/G2IA9jAvYw7hARRgbNVv5bJrK8LjQMHHiRMXHxzvs06xZM61fv15btmyRr6+vVVv79u01ZMgQLVy4UMHBwcrNzbVqL78fHBxc4f59fX1t9itJPj4+bvsH5c5jw7MxNmAP4wL2MC5QEcZGzeTM79zjQkODBg3UoEGDq/Z74403NH36dMv9kydPKjY2Vh9//LE6deokSercubOmTp2q4uJiy4uSnp6uli1bVjg1CQAAAIA1jwsNlRUWFmZ1/9Zbb5UkNW/eXE2aNJEkPfbYY5o2bZoSEhI0adIkZWVl6fXXX9ecOXNcXi8AAABQXVXb0FAZgYGBSktL09ixY9WuXTvVr19ff//73/m6VQAAAMAJN01oiIiIkGEYNttbt26tTZs2uaEiAAAA4OZwU1wRGgAAAEDVITQAAAAAcIjQAAAAAMAhQgMAAAAAhwgNAAAAABwiNAAAAABwiNAAAAAAwCFCAwAAAACHCA0AAAAAHCI0AAAAAHCI0AAAAADAIUIDAAAAAIdqubsAAAAAoMY5fFhKSZFycqSICGnECCky0t1VVYjQAAAAALjSggXSyJGSySQZRtntzJnS/PlSfLy7q7OL6UkAAACAqxw+XBYYSkulkhLr24QEKTvb3RXaRWgAAAAAXCUlpezMgj0mU9nZBg9EaAAAAABcJSenbEqSPYZR1u6BCA0AAACAq0REOD7TEBHhymoqjdAAAAAAuMqIEY7PNCQkuLaeSiI0AAAAAK4SGVm2bsHLS/L2tr6dP19q0cLdFdrFV64CAAAArhQfL3XrVhYSyq/TkJDgsYFBIjQAAAAArteihTRjhrurqDSmJwEAAABwiNAAAAAAwCFCAwAAAACHCA0AAAAAHCI0AAAAAHCI0AAAAADAIUIDAAAAAIcIDQAAAAAc4uJulWAYhiQpPz/f5ccuLi5WQUGB8vPz5ePj4/Ljw3MxNmAP4wL2MC5QEcZGzVb+2bb8s64jhIZKuHDhgiQpNDTUzZUAAAAAN9aFCxcUGBjosI/JqEy0qOFKS0t18uRJ1alTRyaTyaXHzs/PV2hoqH744QcFBAS49NjwbIwN2MO4gD2MC1SEsVGzGYahCxcuKCQkRF5ejlctcKahEry8vNSkSRO31hAQEMA/ZtjF2IA9jAvYw7hARRgbNdfVzjCUYyE0AAAAAIcIDQAAAAAcIjR4OF9fXyUmJsrX19fdpcDDMDZgD+MC9jAuUBHGBiqLhdAAAAAAHOJMAwAAAACHCA0AAAAAHCI0AAAAAHCI0AAAAADAIUKDB5g7d64iIiLk5+enTp06afv27Q77f/rpp7rzzjvl5+ene+65R6tXr3ZRpXAlZ8ZFamqqTCaT1Y+fn58Lq4UrfPnll3rooYcUEhIik8mkFStWXPUxGRkZuu++++Tr66sWLVooNTW1yuuE6zk7NjIyMmzeM0wmk06fPu2aguESM2bMUIcOHVSnTh0FBQUpLi5Ohw4duurj+JwBewgNbvbxxx9rwoQJSkxMVGZmptq0aaPY2FidOXPGbv+vv/5af/rTn5SQkKDdu3crLi5OcXFxysrKcnHlqErOjgup7Gqep06dsvwcP37chRXDFS5evKg2bdpo7ty5lep/7Ngx9evXTzExMdqzZ4/Gjx+vkSNH6vPPP6/iSuFqzo6NcocOHbJ63wgKCqqiCuEOGzdu1NixY7V161alp6eruLhYffr00cWLFyt8DJ8zUBG+ctXNOnXqpA4dOuitt96SJJWWlio0NFRPP/20Jk+ebNN/8ODBunjxolauXGnZdv/996tt27aaN2+ey+pG1XJ2XKSmpmr8+PHKy8tzcaVwF5PJpOXLlysuLq7CPpMmTdKqVaus/mf/6KOPKi8vT2vXrnVBlXCHyoyNjIwMxcTE6Ny5c6pbt67LaoN7/fTTTwoKCtLGjRvVo0cPu334nIGKcKbBjYqKirRr1y716tXLss3Ly0u9evXSli1b7D5my5YtVv0lKTY2tsL+qH6uZVxI0i+//KLw8HCFhobq4Ycf1r59+1xRLjwY7xe4mrZt26pRo0bq3bu3Nm/e7O5yUMXOnz8vSapXr16FfXjfQEUIDW509uxZlZSUqGHDhlbbGzZsWOG80tOnTzvVH9XPtYyLli1bKiUlRf/973+1ZMkSlZaWqkuXLvrxxx9dUTI8VEXvF/n5+SosLHRTVfAEjRo10rx587Rs2TItW7ZMoaGhio6OVmZmprtLQxUpLS3V+PHj1bVrV919990V9uNzBipSy90FALh+nTt3VufOnS33u3TporvuukvvvPOOXnrpJTdWBsATtWzZUi1btrTc79Kli44cOaI5c+Zo8eLFbqwMVWXs2LHKysrSV1995e5SUE1xpsGN6tevL29vb+Xm5lptz83NVXBwsN3HBAcHO9Uf1c+1jIvf8vHx0b333qvs7OyqKBHVREXvFwEBAapdu7abqoKn6tixI+8ZN6lx48Zp5cqV2rBhg5o0aeKwL58zUBFCgxuZzWa1a9dO69ats2wrLS3VunXrrP5q/GudO3e26i9J6enpFfZH9XMt4+K3SkpKtHfvXjVq1KiqykQ1wPsFnLFnzx7eM24yhmFo3LhxWr58udavX6+mTZte9TG8b6BCBtzqo48+Mnx9fY3U1FRj//79xujRo426desap0+fNgzDMIYOHWpMnjzZ0n/z5s1GrVq1jFmzZhkHDhwwEhMTDR8fH2Pv3r3uegqoAs6Oi2nTphmff/65ceTIEWPXrl3Go48+avj5+Rn79u1z11NAFbhw4YKxe/duY/fu3YYkY/bs2cbu3buN48ePG4ZhGJMnTzaGDh1q6X/06FHD39/fePbZZ40DBw4Yc+fONby9vY21a9e66ymgijg7NubMmWOsWLHCOHz4sLF3717jL3/5i+Hl5WV88cUX7noKqAJjxowxAgMDjYyMDOPUqVOWn4KCAksfPmegsggNHuDNN980wsLCDLPZbHTs2NHYunWrpS0qKsoYNmyYVf9PPvnEuOOOOwyz2Wy0atXKWLVqlYsrhis4My7Gjx9v6duwYUPjwQcfNDIzM91QNarShg0bDEk2P+VjYdiwYUZUVJTNY9q2bWuYzWajWbNmxoIFC1xeN6qes2MjOTnZaN68ueHn52fUq1fPiI6ONtavX++e4lFl7I0JSVbvA3zOQGVxnQYAAAAADrGmAQAAAIBDhAYAAAAADhEaAAAAADhEaAAAAADgEKEBAAAAgEOEBgAAAAAOERoAAAAAOERoAAAAAOAQoQEAbnLR0dEymUzuLqPSDMNQu3bt1KdPn0o/Jj4+XiaTSTk5OVVX2DXq3r27OnXq5O4yAOC6EBoAoBoxmUxO/VRHixYtUmZmpv7xj3+4u5QbIikpSdu3b9dHH33k7lIA4JrVcncBAIDKS0xMtNn22muv6fz583bbpLIP4QUFBVVd2g1RWlqqpKQkde/eXffff7+7y7khevbsqfvuu0+JiYkaPHhwtQ1zAGo2QgMAVCNJSUk221JTU3X+/Hm7bZIUFhZWtUXdQGvWrFFOTo6mTp3q7lJuqMcff1wTJkzQ+vXr1bNnT3eXAwBOY3oSANzk7K1pSE1NlclkUmpqqj777DN16tRJ/v7+aty4sV588UWVlpZKkhYuXKg2bdqodu3aCgsL0yuvvGL3GIZhKCUlRV27dlVAQID8/f3Vvn17paSkOFXrggULZDKZ9Mgjj9ht37dvn/r37686deooMDBQDz74oLKysuz2PX/+vJKTkxUVFaWQkBCZzWaFhIToiSee0JEjR6z6vvDCCzKZTPrkk0/s7islJUUmk0kzZsywbMvMzNTAgQMVFhYmX19fNWjQQB06dNA///lPm8cPGjRIUtnrDgDVkckwDMPdRQAArl1ERISOHz+uit7Oo6OjtXHjRqv21NRUDR8+XL///e+VlpamuLg4NWrUSKtWrdJ3332nqVOnKjAwUNOnT9fDDz+s2267TcuWLdOJEye0cOFCPfHEE5Z9GYahIUOG6MMPP1RkZKQeeOABmc1mpaen6+DBg5o4caJmzZp11edhGIbq16+voKAgHThwwKY9KytLXbt21S+//KI//OEPioyM1Pbt27Vjxw61adNGmzZt0rFjxxQRESFJ2rp1q6KiohQTE6PmzZvrlltu0cGDB7V69WoFBgYqMzNT4eHhkqTjx4+rWbNm6tmzp9LS0myO3blzZ+3cuVM//PCDgoODtWfPHt1///3y9vbWww8/rPDwcOXl5Wn//v3KycnR8ePHbfYRFhamK1eu6OTJk1d9LQDA4xgAgGotPDzccPR2HhUVZdO+YMECQ5Lh4+NjbN++3bI9Pz/fCAoKMvz9/Y3g4GDjyJEjlrbvv//eMJvNxj333GO1r3fffdeQZAwfPtwoKiqybL98+bLx0EMPGZKMnTt3XvV57Nu3z5BkDBkyxOHzWLJkidX2KVOmGJIMScaxY8cs2/Py8oyff/7ZZj/r1683vLy8jJEjR1pt/93vfmeYTCarfRiGYWRlZRmSjLi4OMu2CRMmGJKMFStW2Oz/7NmzdusfMGCAIck4evSo3XYA8GRMTwKAGuzxxx9Xhw4dLPfr1Kmj/v37q6CgQGPGjFGzZs0sbaGhoerWrZv279+vK1euWLa/9dZbuuWWWzR37lz5+PhYtpvNZstUnQ8//PCqtfz444+SpIYNG9q0ff/999q4caNat26tIUOGWLU9//zzqlu3rs1jAgMDVa9ePZvtMTExatWqlb744gur7U899ZQMw9D8+fOttr///vuSpFGjRtnsq3bt2jbbbr/9dptt0v+eV/nzBIDqhIXQAFCDtW3b1mZbo0aNHLaVlJQoNzdXjRs3VkFBgfbu3auQkBAlJyfb9C8uLpYkHTx48Kq1/Pzzz5JkNwB88803kqRu3brZtN16661q27atMjIybNoyMjL02muvadu2bTp79qxV2DGbzVZ9+/Xrp8aNG2vBggVKSkqSt7e3ioqKtHjxYoWGhqpv376Wvn/84x/12muvacCAARo8eLB69+6tHj16qHHjxhU+v/IAc/bs2YpfBADwUIQGAKjBAgICbLbVqlXrqm3lYeDcuXMyDEMnTpzQtGnTKjzOxYsXr1pL+V/tL126ZNN2/vx5SVJQUJDdx9o7O/Hpp59q8ODBuvXWWxUbG6uIiAj5+/tbFoD/dt2Bt7e3Ro4cqWnTpmnNmjXq37+/li9frp9//lnjxo2Tl9f/Ts536tRJGRkZevnll/XBBx9owYIFkqQOHTooOTlZMTExNvUUFhZKkvz9/a/6WgCApyE0AACuWXmwaNeunXbu3Hld+2rQoIEk6f/+7/9s2gIDAyVJZ86csfvY3Nxcm21JSUny8/PTrl27FBkZadVW0YXWRo4cqenTp+u9995T//799f7778vLy0sjRoyw6du9e3etWbNGhYWF2rZtmz777DP9+9//Vr9+/ZSVlWU1tevXz6v8eQJAdcKaBgDANatTp47uuusuHThwQHl5ede1r1atWsnLy0uHDh2yaWvTpo0k6auvvrJp++WXX7Rnzx6b7UeOHNFdd91lExhOnTqlo0eP2q2hSZMm6tevn1avXq2vv/5a69atU2xsrMNrXdSuXVvR0dF69dVX9fzzz6uwsFDp6ek2/Q4dOiQfHx/deeedFe4LADwVoQEAcF2eeeYZFRQUaNSoUXanIR07dkw5OTlX3U/dunXVunVr7dy503KdiHJhYWHq0aOHvv32Wy1dutSq7eWXX7YbWMLDw5WdnW11FuLSpUsaM2aMZXqVPU8++aSuXLmiQYMGyTAMuwugt2zZYncaVfmx/Pz8rLYXFRVp9+7dat++PdOTAFRLTE8CAFyXJ598Ulu3btXChQu1efNm9erVSyEhIcrNzdXBgwe1bds2ffDBB5brJzgyYMAAJSYmauvWrerSpYtV29y5c9W1a1c98cQTWrFihdV1Grp3765NmzZZ9X/66af19NNP695779XAgQN15coVpaenyzAMtWnTxrK4+rf69u2r8PBwHT9+XMHBwXrooYds+iQnJ2vDhg3q0aOHmjZtKj8/P2VmZmrdunVq1qyZBgwYYNV/06ZNunz5suLi4q76GgCAJ+JMAwDgupQvLP7444/VqlUrrVy5UrNnz1Z6err8/Pw0a9Ys9erVq1L7GjlypGrVqqUlS5bYtN19993avHmz+vbtq7Vr1+qtt96S2WzW5s2bbdYPSNLYsWM1b9481atXT++9956WL1+uqKgobdmyxe43NJXz8vLS0KFDJUnx8fGWxd+/NmbMGMXFxenw4cNKTU3V22+/rVOnTun555/Xtm3bbBaRL1myRGazWcOHD6/U6wAAnoYrQgMAPMrQoUO1atUqHT9+XHXq1HFLDf3799fq1av13XffqUWLFte1r3Pnzik8PFwDBw5USkrKDaoQAFyLMw0AAI8yffp0FRYW6s0333TL8ffv36/Vq1erd+/e1x0YJGn27NkqKSnRSy+9dAOqAwD3YE0DAMCjhIeHa+HChXa/RrUqffDBBzp06JAWLVokSUpMTLwh+61Xr54WLVrk8MJvAODpmJ4EAICk6Ohobdq0SeHh4XrxxRdZfwAAv0JoAAAAAOAQaxoAAAAAOERoAAAAAOAQoQEAAACAQ4QGAAAAAA4RGgAAAAA4RGgAAAAA4BChAQAAAIBDhAYAAAAADv0/hNzoCH6OGiwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 900x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_mnvr = results.loc[(results[\"Sequence\"] == 4) & (results[\"Maneuver\"] == 1)]\n",
    "\n",
    "t = data_mnvr[\"MJD\"].to_numpy()\n",
    "t = t - min(t)\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(2, sharex=True, figsize=(9, 9))\n",
    "ax1.plot(t, data_mnvr[\"RA Arcsec\"], \"or\", ms=5)\n",
    "ax2.plot(t, data_mnvr[\"DEC Arcsec\"], \"or\", ms=5)\n",
    "ax1.grid()\n",
    "ax2.grid()\n",
    "ax1.set_ylabel(\"RA Residual (arcsec)\", fontsize=14)\n",
    "ax2.set_ylabel(\"DEC Residual (arcsec)\", fontsize=14)\n",
    "ax2.set_xlabel(\"Time (days)\", fontsize=14)\n",
    "_ = ax1.set_title(\"Maneuvering Satellite\", fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's quite the difference! The residuals in both dimensions change drastically after the maneuver.\n",
    "\n",
    "Using `MaDDG` - and some high-performance computing resources - we could easily create thousands of residual curves like this to richly sample a specific class of maneuvers, which is the first step to building trustworthy algorithms for classifying and characterizing maneuvers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "maddg",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}