examples/Simulated_Model_Learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Learning on Dataset from a Simulated Experiment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we will use a dataset from a simulated experiment, more specifically, the `Simulated_calibration.ipynb` example notebook and perform Model Learning on a simple 1 qubit model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2021-06-30T06:29:24.343985Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pandas\n",
      "  Downloading pandas-1.4.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (11.7 MB)\n",
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      "Requirement already satisfied: numpy>=1.18.5; platform_machine != \"aarch64\" and platform_machine != \"arm64\" and python_version < \"3.10\" in /home/ubuntu/.local/lib/python3.8/site-packages (from pandas) (1.22.3)\n",
      "Collecting pytz>=2020.1\n",
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      "Installing collected packages: pytz, pandas\n",
      "Successfully installed pandas-1.4.2 pytz-2022.1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-05-18 12:39:44.780091: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
      "2022-05-18 12:39:44.780128: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n"
     ]
    }
   ],
   "source": [
    "!pip install pandas matplotlib\n",
    "\n",
    "import pickle\n",
    "from pprint import pprint\n",
    "import copy\n",
    "import numpy as np\n",
    "import os\n",
    "import ast\n",
    "import hjson\n",
    "import pandas as pd\n",
    "\n",
    "from c3.model import Model as Mdl\n",
    "from c3.c3objs import Quantity as Qty\n",
    "from c3.parametermap import ParameterMap as PMap\n",
    "from c3.experiment import Experiment as Exp\n",
    "from c3.generator.generator import Generator as Gnr\n",
    "import c3.signal.gates as gates\n",
    "import c3.libraries.chip as chip\n",
    "import c3.generator.devices as devices\n",
    "import c3.libraries.hamiltonians as hamiltonians\n",
    "import c3.signal.pulse as pulse\n",
    "import c3.libraries.envelopes as envelopes\n",
    "import c3.libraries.tasks as tasks\n",
    "from c3.optimizers.modellearning import ModelLearning\n",
    "from c3.optimizers.sensitivity import Sensitivity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first take a look below at the dataset and its properties. To explore more details about how the dataset is generated, please refer to the `Simulated_calibration.ipynb` example notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-30T06:29:24.346337Z",
     "iopub.status.busy": "2021-06-30T06:29:24.346012Z",
     "iopub.status.idle": "2021-06-30T06:29:24.347420Z",
     "shell.execute_reply": "2021-06-30T06:29:24.347670Z"
    }
   },
   "outputs": [],
   "source": [
    "DATAFILE_PATH = \"data/small_dataset.pkl\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-30T06:29:24.349797Z",
     "iopub.status.busy": "2021-06-30T06:29:24.349451Z",
     "iopub.status.idle": "2021-06-30T06:29:24.403393Z",
     "shell.execute_reply": "2021-06-30T06:29:24.403893Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-05-18 12:39:47.252246: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n",
      "2022-05-18 12:39:47.252292: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)\n",
      "2022-05-18 12:39:47.252315: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (knecht): /proc/driver/nvidia/version does not exist\n",
      "2022-05-18 12:39:47.252678: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "with open(DATAFILE_PATH, \"rb+\") as file:\n",
    "    data = pickle.load(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-30T06:29:24.414664Z",
     "iopub.status.busy": "2021-06-30T06:29:24.414128Z",
     "iopub.status.idle": "2021-06-30T06:29:24.416895Z",
     "shell.execute_reply": "2021-06-30T06:29:24.417303Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['seqs_grouped_by_param_set', 'opt_map'])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since this dataset was obtained from an ORBIT ([arXiv:1403.0035](https://arxiv.org/abs/1403.0035)) calibration experiment, we have the `opt_map` which will tell us about the gateset parameters being optimized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-30T06:29:24.421146Z",
     "iopub.status.busy": "2021-06-30T06:29:24.420618Z",
     "iopub.status.idle": "2021-06-30T06:29:24.423293Z",
     "shell.execute_reply": "2021-06-30T06:29:24.423687Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['rx90p[0]-d1-gauss-amp',\n",
       "  'ry90p[0]-d1-gauss-amp',\n",
       "  'rx90m[0]-d1-gauss-amp',\n",
       "  'ry90m[0]-d1-gauss-amp'],\n",
       " ['rx90p[0]-d1-gauss-delta',\n",
       "  'ry90p[0]-d1-gauss-delta',\n",
       "  'rx90m[0]-d1-gauss-delta',\n",
       "  'ry90m[0]-d1-gauss-delta'],\n",
       " ['rx90p[0]-d1-gauss-freq_offset',\n",
       "  'ry90p[0]-d1-gauss-freq_offset',\n",
       "  'rx90m[0]-d1-gauss-freq_offset',\n",
       "  'ry90m[0]-d1-gauss-freq_offset'],\n",
       " ['id[0]-d1-carrier-framechange']]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"opt_map\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This `opt_map` implies the calibration experiment focussed on optimizing \n",
    "the amplitude, delta and frequency offset of the gaussian pulse, along \n",
    "with the framechange angle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now onto the actual measurement data from the experiment runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-30T06:29:24.426544Z",
     "iopub.status.busy": "2021-06-30T06:29:24.426091Z",
     "iopub.status.idle": "2021-06-30T06:29:24.428263Z",
     "shell.execute_reply": "2021-06-30T06:29:24.427861Z"
    }
   },
   "outputs": [],
   "source": [
    "seqs_data = data[\"seqs_grouped_by_param_set\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**How many experiment runs do we have?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-30T06:29:24.431169Z",
     "iopub.status.busy": "2021-06-30T06:29:24.430725Z",
     "iopub.status.idle": "2021-06-30T06:29:24.433094Z",
     "shell.execute_reply": "2021-06-30T06:29:24.433444Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(seqs_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**What does the data from each experiment look like?**\n",
    "\n",
    "We take a look at the first data point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-30T06:29:24.436247Z",
     "iopub.status.busy": "2021-06-30T06:29:24.435800Z",
     "iopub.status.idle": "2021-06-30T06:29:24.437588Z",
     "shell.execute_reply": "2021-06-30T06:29:24.437939Z"
    }
   },
   "outputs": [],
   "source": [
    "example_data_point = seqs_data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-30T06:29:24.440859Z",
     "iopub.status.busy": "2021-06-30T06:29:24.440410Z",
     "iopub.status.idle": "2021-06-30T06:29:24.443139Z",
     "shell.execute_reply": "2021-06-30T06:29:24.442720Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['params', 'seqs', 'results', 'results_std', 'shots'])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example_data_point.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These `keys` are useful in understanding the structure of the dataset. We look at them one by one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-30T06:29:24.447311Z",
     "iopub.status.busy": "2021-06-30T06:29:24.446850Z",
     "iopub.status.idle": "2021-06-30T06:29:24.450577Z",
     "shell.execute_reply": "2021-06-30T06:29:24.450890Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[450.000 mV, -1.000 , -50.500 MHz 2pi, 4.084 rad]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example_data_point[\"params\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are the parameters for our parameterised gateset, for the first experiment run. They correspond to the optimization parameters we previously discussed. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `seqs` key stores the sequence of gates that make up this ORBIT calibration experiment. Each ORBIT sequence consists of a set of gates, followed by a measurement operation. This is then repeated for some `n` number of shots (eg, `1000` in this case) and we only store the averaged result along with the standard deviation of these readout shots. Each experiment in turn consists of a number of these ORBIT sequences. The terms *sequence*, *set* and *experiment* are used somewhat loosely here, so we show below what these look like."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**A single ORBIT sequence**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-30T06:29:24.454002Z",
     "iopub.status.busy": "2021-06-30T06:29:24.453650Z",
     "iopub.status.idle": "2021-06-30T06:29:24.455626Z",
     "shell.execute_reply": "2021-06-30T06:29:24.455901Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ry90p[0]',\n",
       " 'rx90p[0]',\n",
       " 'rx90p[0]',\n",
       " 'rx90m[0]',\n",
       " 'ry90p[0]',\n",
       " 'ry90p[0]',\n",
       " 'rx90p[0]',\n",
       " 'ry90p[0]',\n",
       " 'rx90p[0]',\n",
       " 'rx90p[0]',\n",
       " 'ry90p[0]',\n",
       " 'rx90m[0]',\n",
       " 'rx90p[0]',\n",
       " 'rx90p[0]',\n",
       " 'ry90p[0]',\n",
       " 'ry90p[0]',\n",
       " 'rx90p[0]',\n",
       " 'ry90p[0]',\n",
       " 'ry90m[0]',\n",
       " 'rx90p[0]',\n",
       " 'rx90p[0]',\n",
       " 'ry90m[0]',\n",
       " 'rx90p[0]',\n",
       " 'rx90p[0]',\n",
       " 'rx90p[0]',\n",
       " 'rx90p[0]']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example_data_point[\"seqs\"][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Total number of ORBIT sequences in an experiment**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-30T06:29:24.458262Z",
     "iopub.status.busy": "2021-06-30T06:29:24.457903Z",
     "iopub.status.idle": "2021-06-30T06:29:24.459781Z",
     "shell.execute_reply": "2021-06-30T06:29:24.460029Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(example_data_point[\"seqs\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Total number of Measurement results**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-30T06:29:24.462194Z",
     "iopub.status.busy": "2021-06-30T06:29:24.461850Z",
     "iopub.status.idle": "2021-06-30T06:29:24.463711Z",
     "shell.execute_reply": "2021-06-30T06:29:24.463958Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(example_data_point[\"results\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The measurement results and the standard deviation look like this**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-30T06:29:24.466251Z",
     "iopub.status.busy": "2021-06-30T06:29:24.465932Z",
     "iopub.status.idle": "2021-06-30T06:29:24.467336Z",
     "shell.execute_reply": "2021-06-30T06:29:24.467583Z"
    }
   },
   "outputs": [],
   "source": [
    "example_results = [\n",
    "    (example_data_point[\"results\"][i], example_data_point[\"results_std\"][i])\n",
    "    for i in range(len(example_data_point[\"results\"]))\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-30T06:29:24.470224Z",
     "iopub.status.busy": "2021-06-30T06:29:24.469909Z",
     "iopub.status.idle": "2021-06-30T06:29:24.473051Z",
     "shell.execute_reply": "2021-06-30T06:29:24.472782Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[([0.745], [0.013783141876945182]),\n",
      " ([0.213], [0.012947239087929134]),\n",
      " ([0.137], [0.0108734079294396]),\n",
      " ([0.224], [0.013184233007649706]),\n",
      " ([0.434], [0.015673034167001616]),\n",
      " ([0.105], [0.009694070352540258]),\n",
      " ([0.214], [0.012969348480166613]),\n",
      " ([0.112], [0.009972762907038352]),\n",
      " ([0.318], [0.014726710426975877]),\n",
      " ([0.122], [0.010349685985574633]),\n",
      " ([0.348], [0.015063067416698366]),\n",
      " ([0.122], [0.010349685985574633]),\n",
      " ([0.558], [0.01570464899321217]),\n",
      " ([0.186], [0.01230463327369004]),\n",
      " ([0.096], [0.009315793041926168]),\n",
      " ([0.368], [0.015250442616527561]),\n",
      " ([0.146], [0.011166198995181842]),\n",
      " ([0.121], [0.010313049985334118]),\n",
      " ([0.748], [0.013729384545565035]),\n",
      " ([0.692], [0.01459917805905524])]\n"
     ]
    }
   ],
   "source": [
    "pprint(example_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Model for Model Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An initial model needs to be provided, which we refine by fitting to our calibration data. We do this below. If you want to learn more about what the various components of the model mean, please refer back to the `two_qubits.ipynb` notebook or the documentation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define Constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-30T06:29:24.475726Z",
     "iopub.status.busy": "2021-06-30T06:29:24.475401Z",
     "iopub.status.idle": "2021-06-30T06:29:24.476822Z",
     "shell.execute_reply": "2021-06-30T06:29:24.477068Z"
    }
   },
   "outputs": [],
   "source": [
    "lindblad = False\n",
    "dressed = True\n",
    "qubit_lvls = 3\n",
    "freq = 5.001e9\n",
    "anhar = -210.001e6\n",
    "init_temp = 0\n",
    "qubit_temp = 0\n",
    "t_final = 7e-9  # Time for single qubit gates\n",
    "sim_res = 100e9\n",
    "awg_res = 2e9\n",
    "sideband = 50e6\n",
    "lo_freq = 5e9 + sideband"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-30T06:29:24.480926Z",
     "iopub.status.busy": "2021-06-30T06:29:24.480595Z",
     "iopub.status.idle": "2021-06-30T06:29:24.488368Z",
     "shell.execute_reply": "2021-06-30T06:29:24.488635Z"
    }
   },
   "outputs": [],
   "source": [
    "q1 = chip.Qubit(\n",
    "    name=\"Q1\",\n",
    "    desc=\"Qubit 1\",\n",
    "    freq=Qty(\n",
    "        value=freq,\n",
    "        min_val=4.995e9,\n",
    "        max_val=5.005e9,\n",
    "        unit=\"Hz 2pi\",\n",
    "    ),\n",
    "    anhar=Qty(\n",
    "        value=anhar,\n",
    "        min_val=-250e6,\n",
    "        max_val=-150e6,\n",
    "        unit=\"Hz 2pi\",\n",
    "    ),\n",
    "    hilbert_dim=qubit_lvls,\n",
    "    temp=Qty(value=qubit_temp, min_val=0.0, max_val=0.12, unit=\"K\"),\n",
    ")\n",
    "\n",
    "drive = chip.Drive(\n",
    "    name=\"d1\",\n",
    "    desc=\"Drive 1\",\n",
    "    comment=\"Drive line 1 on qubit 1\",\n",
    "    connected=[\"Q1\"],\n",
    "    hamiltonian_func=hamiltonians.x_drive,\n",
    ")\n",
    "phys_components = [q1]\n",
    "line_components = [drive]\n",
    "\n",
    "init_ground = tasks.InitialiseGround(\n",
    "    init_temp=Qty(value=init_temp, min_val=-0.001, max_val=0.22, unit=\"K\")\n",
    ")\n",
    "task_list = [init_ground]\n",
    "model = Mdl(phys_components, line_components, task_list)\n",
    "model.set_lindbladian(lindblad)\n",
    "model.set_dressed(dressed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-30T06:29:24.492657Z",
     "iopub.status.busy": "2021-06-30T06:29:24.492317Z",
     "iopub.status.idle": "2021-06-30T06:29:24.494580Z",
     "shell.execute_reply": "2021-06-30T06:29:24.494836Z"
    }
   },
   "outputs": [],
   "source": [
    "generator = Gnr(\n",
    "    devices={\n",
    "        \"LO\": devices.LO(name=\"lo\", resolution=sim_res, outputs=1),\n",
    "        \"AWG\": devices.AWG(name=\"awg\", resolution=awg_res, outputs=1),\n",
    "        \"DigitalToAnalog\": devices.DigitalToAnalog(\n",
    "            name=\"dac\", resolution=sim_res, inputs=1, outputs=1\n",
    "        ),\n",
    "        \"Response\": devices.Response(\n",
    "            name=\"resp\",\n",
    "            rise_time=Qty(value=0.3e-9, min_val=0.05e-9, max_val=0.6e-9, unit=\"s\"),\n",
    "            resolution=sim_res,\n",
    "            inputs=1,\n",
    "            outputs=1,\n",
    "        ),\n",
    "        \"Mixer\": devices.Mixer(name=\"mixer\", inputs=2, outputs=1),\n",
    "        \"VoltsToHertz\": devices.VoltsToHertz(\n",
    "            name=\"v_to_hz\",\n",
    "            V_to_Hz=Qty(value=1e9, min_val=0.9e9, max_val=1.1e9, unit=\"Hz/V\"),\n",
    "            inputs=1,\n",
    "            outputs=1,\n",
    "        ),\n",
    "    },\n",
    "    chains={\n",
    "        \"d1\": {\n",
    "            \"LO\": [],\n",
    "            \"AWG\": [],\n",
    "            \"DigitalToAnalog\": [\"AWG\"],\n",
    "            \"Response\": [\"DigitalToAnalog\"],\n",
    "            \"Mixer\": [\"LO\", \"Response\"],\n",
    "            \"VoltsToHertz\": [\"Mixer\"]\n",
    "        },\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gateset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-30T06:29:24.501528Z",
     "iopub.status.busy": "2021-06-30T06:29:24.501168Z",
     "iopub.status.idle": "2021-06-30T06:29:24.511660Z",
     "shell.execute_reply": "2021-06-30T06:29:24.511908Z"
    }
   },
   "outputs": [],
   "source": [
    "gauss_params_single = {\n",
    "    \"amp\": Qty(value=0.45, min_val=0.4, max_val=0.6, unit=\"V\"),\n",
    "    \"t_final\": Qty(\n",
    "        value=t_final, min_val=0.5 * t_final, max_val=1.5 * t_final, unit=\"s\"\n",
    "    ),\n",
    "    \"sigma\": Qty(value=t_final / 4, min_val=t_final / 8, max_val=t_final / 2, unit=\"s\"),\n",
    "    \"xy_angle\": Qty(value=0.0, min_val=-0.5 * np.pi, max_val=2.5 * np.pi, unit=\"rad\"),\n",
    "    \"freq_offset\": Qty(\n",
    "        value=-sideband - 0.5e6,\n",
    "        min_val=-60 * 1e6,\n",
    "        max_val=-40 * 1e6,\n",
    "        unit=\"Hz 2pi\",\n",
    "    ),\n",
    "    \"delta\": Qty(value=-1, min_val=-5, max_val=3, unit=\"\"),\n",
    "}\n",
    "\n",
    "gauss_env_single = pulse.EnvelopeDrag(\n",
    "    name=\"gauss\",\n",
    "    desc=\"Gaussian comp for single-qubit gates\",\n",
    "    params=gauss_params_single,\n",
    "    shape=envelopes.gaussian_nonorm,\n",
    ")\n",
    "nodrive_env = pulse.Envelope(\n",
    "    name=\"no_drive\",\n",
    "    params={\n",
    "        \"t_final\": Qty(\n",
    "            value=t_final, min_val=0.5 * t_final, max_val=1.5 * t_final, unit=\"s\"\n",
    "        )\n",
    "    },\n",
    "    shape=envelopes.no_drive,\n",
    ")\n",
    "carrier_parameters = {\n",
    "    \"freq\": Qty(\n",
    "        value=lo_freq,\n",
    "        min_val=4.5e9,\n",
    "        max_val=6e9,\n",
    "        unit=\"Hz 2pi\",\n",
    "    ),\n",
    "    \"framechange\": Qty(value=0.0, min_val=-np.pi, max_val=3 * np.pi, unit=\"rad\"),\n",
    "}\n",
    "carr = pulse.Carrier(\n",
    "    name=\"carrier\",\n",
    "    desc=\"Frequency of the local oscillator\",\n",
    "    params=carrier_parameters,\n",
    ")\n",
    "\n",
    "rx90p = gates.Instruction(\n",
    "    name=\"rx90p\", t_start=0.0, t_end=t_final, channels=[\"d1\"], targets=[0]\n",
    ")\n",
    "QId = gates.Instruction(\n",
    "    name=\"id\", t_start=0.0, t_end=t_final, channels=[\"d1\"], targets=[0]\n",
    ")\n",
    "\n",
    "rx90p.add_component(gauss_env_single, \"d1\")\n",
    "rx90p.add_component(carr, \"d1\")\n",
    "QId.add_component(nodrive_env, \"d1\")\n",
    "QId.add_component(copy.deepcopy(carr), \"d1\")\n",
    "QId.comps[\"d1\"][\"carrier\"].params[\"framechange\"].set_value(\n",
    "    (-sideband * t_final) % (2 * np.pi)\n",
    ")\n",
    "ry90p = copy.deepcopy(rx90p)\n",
    "ry90p.name = \"ry90p\"\n",
    "rx90m = copy.deepcopy(rx90p)\n",
    "rx90m.name = \"rx90m\"\n",
    "ry90m = copy.deepcopy(rx90p)\n",
    "ry90m.name = \"ry90m\"\n",
    "ry90p.comps[\"d1\"][\"gauss\"].params[\"xy_angle\"].set_value(0.5 * np.pi)\n",
    "rx90m.comps[\"d1\"][\"gauss\"].params[\"xy_angle\"].set_value(np.pi)\n",
    "ry90m.comps[\"d1\"][\"gauss\"].params[\"xy_angle\"].set_value(1.5 * np.pi)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-30T06:29:24.514280Z",
     "iopub.status.busy": "2021-06-30T06:29:24.513964Z",
     "iopub.status.idle": "2021-06-30T06:29:24.515429Z",
     "shell.execute_reply": "2021-06-30T06:29:24.515676Z"
    }
   },
   "outputs": [],
   "source": [
    "parameter_map = PMap(\n",
    "    instructions=[QId, rx90p, ry90p, rx90m, ry90m], model=model, generator=generator\n",
    ")\n",
    "\n",
    "exp = Exp(pmap=parameter_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-30T06:29:24.517795Z",
     "iopub.status.busy": "2021-06-30T06:29:24.517477Z",
     "iopub.status.idle": "2021-06-30T06:29:24.518939Z",
     "shell.execute_reply": "2021-06-30T06:29:24.519217Z"
    }
   },
   "outputs": [],
   "source": [
    "exp_opt_map = [[('Q1', 'anhar')], [('Q1', 'freq')]]\n",
    "exp.pmap.set_opt_map(exp_opt_map)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimizer "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-30T06:29:24.522171Z",
     "iopub.status.busy": "2021-06-30T06:29:24.521841Z",
     "iopub.status.idle": "2021-06-30T06:29:24.523305Z",
     "shell.execute_reply": "2021-06-30T06:29:24.523566Z"
    }
   },
   "outputs": [],
   "source": [
    "datafiles = {\"orbit\": DATAFILE_PATH} # path to the dataset\n",
    "run_name = \"simple_model_learning\" # name of the optimization run\n",
    "dir_path = \"ml_logs\" # path to save the learning logs\n",
    "algorithm = \"cma_pre_lbfgs\" # algorithm for learning\n",
    "# this first does a grad-free CMA-ES and then a gradient based LBFGS\n",
    "options = {\n",
    "    \"cmaes\": {\n",
    "        \"popsize\": 12,\n",
    "        \"init_point\": \"True\",\n",
    "        \"stop_at_convergence\": 10,\n",
    "        \"ftarget\": 4,\n",
    "        \"spread\": 0.05,\n",
    "        \"stop_at_sigma\": 0.01,\n",
    "    },\n",
    "    \"lbfgs\": {\"maxfun\": 50, \"disp\": 0},\n",
    "} # options for the algorithms\n",
    "sampling = \"high_std\" # how data points are chosen from the total dataset\n",
    "batch_sizes = {\"orbit\": 2} # how many data points are chosen for learning\n",
    "state_labels = {\n",
    "    \"orbit\": [\n",
    "        [\n",
    "            1,\n",
    "        ],\n",
    "        [\n",
    "            2,\n",
    "        ],\n",
    "    ]\n",
    "} # the excited states of the qubit model, in this case it is 3-level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-30T06:29:24.525945Z",
     "iopub.status.busy": "2021-06-30T06:29:24.525624Z",
     "iopub.status.idle": "2021-06-30T06:29:24.531653Z",
     "shell.execute_reply": "2021-06-30T06:29:24.531905Z"
    }
   },
   "outputs": [],
   "source": [
    "opt = ModelLearning(\n",
    "    datafiles=datafiles,\n",
    "    run_name=run_name,\n",
    "    dir_path=dir_path,\n",
    "    algorithm=algorithm,\n",
    "    options=options,\n",
    "    sampling=sampling,\n",
    "    batch_sizes=batch_sizes,\n",
    "    state_labels=state_labels,\n",
    "    pmap=exp.pmap,\n",
    ")\n",
    "\n",
    "opt.set_exp(exp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are now ready to learn from the data and improve our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-30T06:29:24.534091Z",
     "iopub.status.busy": "2021-06-30T06:29:24.533766Z",
     "iopub.status.idle": "2021-06-30T06:33:52.900803Z",
     "shell.execute_reply": "2021-06-30T06:33:52.900453Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C3:STATUS:Saving as: /home/ubuntu/c3/examples/ml_logs/simple_model_learning/2022_05_18_T_12_39_49/model_learn.log\n",
      "(6_w,12)-aCMA-ES (mu_w=3.7,w_1=40%) in dimension 2 (seed=971030, Wed May 18 12:39:49 2022)\n",
      "C3:STATUS:Adding initial point to CMA sample.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/.local/lib/python3.8/site-packages/cma/utilities/utils.py:343: UserWarning: input x0 should be a list or 1-D array, trying to flatten (2, 1)-array ()\n",
      "  warnings.warn(msg + ' (' +\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iterat #Fevals   function value  axis ratio  sigma  min&max std  t[m:s]\n",
      "    1     13 2.452509001838001e+00 1.0e+00 5.21e-02  5e-02  5e-02 0:45.0\n",
      "termination on ftarget=4\n",
      "final/bestever f-value = 2.452509e+00 2.452509e+00\n",
      "incumbent solution: [-0.21640252950576294, 0.16553529166444397]\n",
      "std deviation: [0.05017349381985182, 0.046069889679722614]\n",
      "C3:STATUS:Saving as: /home/ubuntu/c3/examples/ml_logs/simple_model_learning/2022_05_18_T_12_39_49/confirm.log\n"
     ]
    }
   ],
   "source": [
    "opt.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Result of Model Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-30T06:33:52.903299Z",
     "iopub.status.busy": "2021-06-30T06:33:52.902952Z",
     "iopub.status.idle": "2021-06-30T06:33:52.904734Z",
     "shell.execute_reply": "2021-06-30T06:33:52.904981Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.0316"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "opt.current_best_goal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-30T06:33:52.907388Z",
     "iopub.status.busy": "2021-06-30T06:33:52.907044Z",
     "iopub.status.idle": "2021-06-30T06:33:52.908879Z",
     "shell.execute_reply": "2021-06-30T06:33:52.909149Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q1-anhar                              : -210.057 MHz 2pi \n",
      "Q1-freq                               : 5.000 GHz 2pi \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(opt.pmap.str_parameters(opt.pmap.opt_map))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualisation & Analysis of Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Model Learning logs provide a useful way to visualise the learning process and also understand what's going wrong (or right). We now process these logs to read some data points and also plot some visualisations of the Model Learning process"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Open, Clean-up and Convert Logfiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "LOGDIR = opt.logdir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "logfile = os.path.join(LOGDIR, \"model_learn.log\")\n",
    "with open(logfile, \"r\") as f:\n",
    "    log = f.readlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Q1-anhar', 'Q1-freq']\n"
     ]
    }
   ],
   "source": [
    "params_names = [\n",
    "    item for sublist in (ast.literal_eval(log[3].strip(\"\\n\"))) for item in sublist\n",
    "]\n",
    "print(params_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_list_dict = list()\n",
    "for line in log[9:]:\n",
    "    if line[0] == \"{\":\n",
    "        temp_dict = ast.literal_eval(line.strip(\"\\n\"))\n",
    "        for index, param_name in enumerate(params_names):\n",
    "            temp_dict[param_name] = temp_dict[\"params\"][index]\n",
    "        temp_dict.pop(\"params\")\n",
    "        data_list_dict.append(temp_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_df = pd.DataFrame(data_list_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary of Logs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>goal</th>\n",
       "      <th>Q1-anhar</th>\n",
       "      <th>Q1-freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>2.100000e+01</td>\n",
       "      <td>2.100000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.677968</td>\n",
       "      <td>-2.099553e+08</td>\n",
       "      <td>5.000492e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>15.468880</td>\n",
       "      <td>2.574355e+06</td>\n",
       "      <td>1.160692e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.031570</td>\n",
       "      <td>-2.138015e+08</td>\n",
       "      <td>4.995917e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.452509</td>\n",
       "      <td>-2.110718e+08</td>\n",
       "      <td>5.000200e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.276823</td>\n",
       "      <td>-2.100573e+08</td>\n",
       "      <td>5.000779e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.092966</td>\n",
       "      <td>-2.098452e+08</td>\n",
       "      <td>5.001000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>72.765614</td>\n",
       "      <td>-2.007295e+08</td>\n",
       "      <td>5.001524e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            goal      Q1-anhar       Q1-freq\n",
       "count  21.000000  2.100000e+01  2.100000e+01\n",
       "mean    8.677968 -2.099553e+08  5.000492e+09\n",
       "std    15.468880  2.574355e+06  1.160692e+06\n",
       "min    -0.031570 -2.138015e+08  4.995917e+09\n",
       "25%     2.452509 -2.110718e+08  5.000200e+09\n",
       "50%     5.276823 -2.100573e+08  5.000779e+09\n",
       "75%     9.092966 -2.098452e+08  5.001000e+09\n",
       "max    72.765614 -2.007295e+08  5.001524e+09"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Best Point**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_point_file = os.path.join(LOGDIR, 'best_point_model_learn.log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Q1-anhar': -210057285.72507972, 'Q1-freq': 5000081146.501029, 'goal': -0.031570491978972916}\n"
     ]
    }
   ],
   "source": [
    "with open(best_point_file, \"r\") as f:\n",
    "    best_point_log_dict = hjson.load(f)\n",
    "\n",
    "best_point_dict = dict(zip(params_names, best_point_log_dict[\"optim_status\"][\"params\"]))\n",
    "best_point_dict[\"goal\"] = best_point_log_dict[\"optim_status\"][\"goal\"]\n",
    "print(best_point_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use `matplotlib` to produce the plots below. Please make sure you have the same installed in your python environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -q matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.ticker import MaxNLocator\n",
    "from  matplotlib import rcParams\n",
    "from matplotlib import cycler\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "rcParams[\"axes.grid\"] = True\n",
    "rcParams[\"grid.linestyle\"] = \"--\"\n",
    "\n",
    "# enable usetex by setting it to True if LaTeX is installed\n",
    "rcParams[\"text.usetex\"] = False\n",
    "rcParams[\"font.size\"] = 16\n",
    "rcParams[\"font.family\"] = \"serif\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**In the plots below, the blue line shows the progress of the parameter optimization while the black and the red lines indicate the converged and true value respectively**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Qubit Anharmonicity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f18051141c0>]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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u8L+3Jk+ezMiRI6murgagoKCAuXPnsnz5cqy1GGO45JJLWLduHceOHQNg3rx51NfXd/ZK9Ncp8i9+r32dIr0S/XXas2cPkPjX02C/ThkZGZ3fC4bC62mwX6eSkpLOXkPh9TTYr9OsWbM6e7n19bT7cAtjRxX0+nXKSQmyx3ccn88X96/T/Pnzh9TrabDf9+bOncv27duHzOtpsF+n8ePHc+zYMUf+fuqL6Vh6nnDGmCXAf6O46XJr7aIe7j8bWAFcZ61d1s9zvQ94EHintfaRLh9PA9qAf1hr39PD/dKA/wA7rbU3RzFWysrKrFNrs7Zv38706dMdeW4v0l4y2ktGe8loLxkv9Cr//lIumlbEXe+e2+Pnb/zDK9Q1tfLkrVFfJG/AvNDLTbSXjJO9jDFrrLVlPX3OyeU6LwNnRvHrg93vaIyZCDwJfLy/CX6Hox2/53b7eF7H73U9PIcBfgvUE77CjutF/sWtoqO9ZLSXjPaS0V4ybu8VClmO+AM9boQVUZKXlbA33rq9l9toLxm39nJsuY619iSwRXq/juUz/wVut9Y+GuXd1nf8Prnbx6d0/P56t+cwwL1ADvDe7u8FUEoppVTvjje3EQzZXtfkQ/gKO0ebArQGQz2+OVcpNTieelV1meB/zVr7cJePP9HtdsOMMZ1XwrHWHgZWAou6PeSlhJfrPNnlvga4h/AVe95rrQ12fPybxpjzYvoHirGZM2c6PQRP0V4y2ktGe8loLxm39+prt9uIyIZYkdvGk9t7uY32knFrL8+88dYYM57wJTBfA7KMMTd0+fTZ3W6+FhhpjJlsrT3R8bHbgeeNMVdba5/oeLzPA3dZaw92PIcBfgO8DfgacH34QwAsBpbH/k8WO6mpqU4PwVO0l4z2ktFeMtpLxu29Irvd9rVcZ3TkMpoNLYwvGBbX8bi9l9toLxm39vLMJB/4AjCt49d13T63t9v/HwaCHb8AsNZWGmOuAr5jjPkOkAHcDfygy/3OIryzLsAfYjf0xNi0aRPFxcVOD8MztJeM9pLRXjLaS8btvSK73Y7qYSOsiDH54V1vE7Eu3+293EZ7ybi1l2cm+dbaLxCe6Edz20W9fPwZ4Jk+7vc64U2vlFJKKTVAnct1+nrjbcdyndoEbYil1FDjqTX5qm8lJSVOD8FTtJeM9pLRXjLaS8btvXyNAXIy0xiW0fu5xLysNLLTUxNyJt/tvdxGe8m4tZdO8pPIlClT+r+R6qS9ZLSXjPaS0V4ybu91xN/7brcRxhjG5GclZNdbt/dyG+0l49ZeOslPIpGd51R0tJeM9pLRXjLaS8btvXz+Fkb1M8mH8JKdmgQs13F7L7fRXjJu7aWTfKWUUkrFlM8foDiv98tnRpTkJeZMvlJDkU7yk0h2drbTQ/AU7SWjvWS0l4z2knFzL2stvsb+l+tA+Ex+bWMLoZCN65jc3MuNtJeMW3vpJD+JzJ8/3+kheIr2ktFeMtpLRnvJuLlXUyBIc1t7VJP8MflZBEOWoycCcR2Tm3u5kfaScWsvneQnkdWrVzs9BE/RXjLaS0Z7yWgvGTf3OuLvfyOsiK4bYsWTm3u5kfaScWsvneQnkebmZqeH4CnaS0Z7yWgvGe0l4+ZekY2winP7X5Mf2RAr3pN8N/dyI+0l49ZeOslXSimlVMy8McmP4kx+fvg2ibjCjlJDjU7yk0h5ebnTQ/AU7SWjvWS0l4z2knFzL1/HhD2aS2gWDc8kLcXEfUMsN/dyI+0l49ZeOslPIrt373Z6CJ6ivWS0l4z2ktFeMm7udcQfICMthfzs9H5vm5JiGJ2XRW2cJ/lu7uVG2kvGrb10kp9EampqnB6Cp2gvGe0lo71ktJeMm3v5/AFG5WRijInq9iX5WXE/k+/mXm6kvWTc2ksn+UoppZSKGZ+/Jaor60QkatdbpYYaneQnkVmzZjk9BE/RXjLaS0Z7yWgvGTf3inYjrIgxHbveWhu/DbHc3MuNtJeMW3vpJD+JtLe3Oz0ET9FeMtpLRnvJaC8ZN/fy+QNRXT4zoiQ/i+a2dhqbg3Ebk5t7uZH2knFrL53kJ5GtW7c6PQRP0V4y2ktGe8loLxm39mppa6ehuU10Jr8kP/wPgsON8bvWuFt7uZX2knFrL53kK6WUUiomJLvdRozJT8yut0oNNTrJTyLjxo1zegieor1ktJeM9pLRXjJu7XWkKfrdbiNG58V/ku/WXm6lvWTc2ksn+Ulk/PjxTg/BU7SXjPaS0V4y2kvGrb18jeFJfjQbYUUU52ZhTHx3vXVrL7fSXjJu7aWT/CSyevVqp4fgKdpLRnvJaC8Z7SXj1l5H/OGJumS5TkZaCoXDM+N6Jt+tvdxKe8m4tZdO8pVSSikVEz5/gBQDhcOjn+RDeF1+vDfEUmqo0Ul+EsnJyXF6CJ6ivWS0l4z2ktFeMm7t5WsMUJiTSWpKdLvdRpTkZ1Ebx+U6bu3lVtpLxq29dJKfRMrKypwegqdoLxntJaO9ZLSXjFt7+fwtostnRpTkxfdMvlt7uZX2knFrL53kJ5GVK1c6PQRP0V4y2ktGe8loLxm39gpvhDWASX5+Fg3NbTS3xmdTIbf2civtJePWXjrJTyKBQMDpIXiK9pLRXjLaS0Z7ybi1l3S324jOa+XHacmOW3u5lfaScWsvneQrpZRSatDaQ5a6poDoyjoRJR3Xyj/cEL9db5UaanSSn0QqKiqcHoKnaC8Z7SWjvWS0l4wbe9U1BQhZBrxcB+K3IZYbe7mZ9pJxay+d5CeRbdu2OT0ET9FeMtpLRnvJaC8ZN/by+SMbYcmX65TEebmOG3u5mfaScWsvneQnEZ/P5/QQPEV7yWgvGe0lo71k3NjLN4CNsCKGZaSRl5UWtzP5buzlZtpLxq29dJKvlFJKqUE70nEmfyDLdQDG5GfrhlhKxZBO8pPInDlznB6Cp2gvGe0lo71ktJeMG3v5GiPLdQY2yY/nhlhu7OVm2kvGrb10kp9E3HoJJ7fSXjLaS0Z7yWgvGTf28vkDjBiWTmZa6oDuH88NsdzYy820l4xbe+kkP4ls377d6SF4ivaS0V4y2ktGe8m4sZfP38KonIGdxYfwmfyjTQHa2kMxHFWYG3u5mfaScWsvneQrpZRSatB8/oFdIz+iJD8La9+4So9SanB0kp9EJkyY4PQQPEV7yWgvGe0lo71k3NjL1ziw3W4j3rhWfuw3xHJjLzfTXjJu7aWT/CQyevRop4fgKdpLRnvJaC8Z7SXjtl7WWo74AwO+sg7AmM5JfuzP5Lutl9tpLxm39tJJfhKpqqpyegieor1ktJeM9pLRXjJu69XQ3EZre2jAV9aB8BtvAQ7H4Uy+23q5nfaScWsvneQrpZRSalAi6+iL8wa+XCc/O52s9JS4bYil1FCjk/wkkpeX5/QQPEV7yWgvGe0lo71k3NYrco38wSzXMcYwJj+bmjhcK99tvdxOe8m4tZdO8pPIvHnznB6Cp2gvGe0lo71ktJeM23r5/OGJ+WAm+QCj8zLjcibfbb3cTnvJuLWXTvKTSGVlpdND8BTtJaO9ZLSXjPaScVuvIzFYrgMwJj87Lhtiua2X22kvGbf20kl+EgkGg04PwVO0l4z2ktFeMtpLxm29fP4AwzJSyclMG9TjlORn4fO3EArZGI0szG293E57ybi1l07ylVJKKTUovkFePjOiJC+LtnZL3YnWGIxKqaHNWBvbfy0PdWVlZdapSymFQiFSUvTfbdHSXjLaS0Z7yWgvGbf1es+9K7EW/vGJ8kE9zjMba7j5z2t48tMVnDU+P0ajc18vt9NeMk72MsassdaW9fQ5/QomkY0bNzo9BE/RXjLaS0Z7yWgvGbf1OuIPDOoa+RGdG2LF+Ao7buvldtpLxq29dJKfROrq6pwegqdoLxntJaO9ZLSXjNt6+WI0yY9siFUT4w2x3NbL7bSXjFt76SS/D8aYImPMMWPMHqfHopRSSrnRydYgTYEgxXmDn+QX5mSSlmLicq18pYYaz0zyjTEFxpjbjDFVxpiNxpjNxphnjTEVgse43BjzijFmvTFmizHmK8aYvhp8Exgx6MEnyNy5c50egqdoLxntJaO9ZLSXjJt6vbER1uAunwmQmmIozs2M+WU03dTLC7SXjFt7eWaSD9wM3AHcaK2dDcwBNgHLjTGX93fnjn8MPAV8z1p7FvAm4Fbgu73cfiZwDbAmJqNPAL/f7/QQPEV7yWgvGe0lo71k3NTL5x/8brddleRnxXxDLDf18gLtJePWXl6a5APcY63dAGCtbSc86W8nPFnvzw+BVdbaxzvuvx/4KfBFY8zYHm7/I+DbQFMMxp0Qu3btcnoInqK9ZLSXjPaS0V4yburVudttDJbrQHhDrFgv13FTLy/QXjJu7eWlSf6dhJfPdLLWtgDHgIK+7miMGQNcBLzQ7VPPA+nA1d1ufykwDfjd4IaslFJKJbdYLtcBGJ0XPpOvl/hWanA8M8m31rZ3nL3vZIwpAIqAZf3c/ayO33d3+3jk/8/u8pgGuAu4vfvzud3kyZOdHoKnaC8Z7SWjvWS0l4ybeh1pCpCeaigYlh6TxxuTn8XJ1nYaW2K3i6ibenmB9pJxay/PTPJ78THAR3jZTV+KOn7vvmiqseP3wi4f+yBQb619evDDS6yRI0c6PQRP0V4y2ktGe8loLxk39fI1BhiVk0n4HNnglXRcK782hkt23NTLC7SXjFt7pTn1xMaYJcB/o7jpcmvtoh7uP5vwmvzrrLUxuUCpMWYY8L/A24X3uwm4CWDs2LEsW7YMgKlTp5Kbm8u6desAKCwsZPbs2axYsQKAtLQ0KioqqK6uprEx/O+NsrIyamtr2b9/PwDTp08nMzOTDRs2AFBcXMyMGTOorKwEIDMzk/LycqqqqqipqSEnJ4f58+dz4MABDh48CMDMmTNJTU1l06ZNAJSUlDBlyhRWrlwJQHZ2NvPnz2f16tU0N4evTVxeXs7u3bupqakBYNasWbS3t7N161YAxo0bx/jx41m9ejUAOTk5lJWVsXLlSgKB8I9uKyoq2LZtGz6fD4A5c+YQCATYvn07ABMmTGD06NFEdgjOy8tj3rx5VFZWEgyGz+AsXLiQjRs3dl6Ddu7cufj9/s71b5MnT2bkyJFUV1cDUFBQwNy5c1m+fDnWWowxXHLJJaxbt45jx44BMG/ePOrr69mwYQM5OTkJ/zo1NYXf5uG1r9OKFSvIyclJ+Ndpz549QOJfT4P9Or344osMHz484V8np15Pg/06rVy5kszMzIR/nbz6fW/nzp2kpqYm/OvU0+tp697DZGHZtGlTTL5OI7PDK3CfWbGaYxNzY/J1ampqIjMzc8i8ngb7fS8UCjFhwoQh83oa7NeptbWV888/35G/n/pinFrz1jGhnhjFTU9aa/d1u+9Ewkt0brPWPhrFc10OPAN82Fr7QJePjwTqgN9Ya28xxnwDmGatvbHLbZYBk621k6MYK2VlZTZysCXasmXLWLRokSPP7UXaS0Z7yWgvGe0l46Zeb/7ZCiaMHMZvP1gWk8fbX3+Si3/0Aj+87izec34004T+uamXF2gvGSd7GWPWWGt7fPE5dibfWnsS2CK9nzFmAuGfANwezQS/w/qO3yd3+/iUjt9f7/j9EmCcMea1LrcpBTK6fOxya61PMuZEKSjo8/3HqhvtJaO9ZLSXjPaScVMvnz/AvEmxG8/ozl1vAzF7TDf18gLtJePWXo5N8geiywT/a9bah7t8/Alr7dVd/n8YkG6tbQCw1h42xqwEFnV7yEuBNuDJjtst6eE5lxE+k39OTP8wceDWzRjcSnvJaC8Z7SWjvWTc0qs1GKL+RGvMrpEPkJGWQlFOBjWNzTF7TLf08grtJePWXp55460xZjzhS2BuALKMMTdEftHl6jgd1gI7jDHDu3zsduAiY8zVXR7v88Bd1tqD8f8TxN/y5cudHoKnaC8Z7SWjvWS0l4xbeh1tiu3lMyNK8rNiuuutW3p5hfaScWsvL53J/wLha9dPA67r9rm93f7/MBDs+AWAtbbSGHMV8B1jzHeADOBu4Ac9PZkx5v8B7+DU5TpV1tqPDf6PEh96TWEZ7SWjvWS0l4z2knFLr1jvdhtRkpfNgWMnY/Z4bunlFdpLxq29PDPJt9Z+gfBEP5rbLurl488QfgNuNI/xv4SvtOMZsbp82VChvWS0l4z2ktFeMm7p5WuM7W63ESX5mVTtrY/Z47mll1doLxm39nLs6jrJysmr6yillFKJ9JdVe/n64xtY9ZXFnde3j4W7X9jBnc9sZcu330xWemrMHlepZNPX1XU8syZf9S9yfVYVHe0lo71ktJeM9pJxSy+fP4AxUJSTEdPHfeMKO7FZl++WXl6hvWTc2ksn+UkkskmDio72ktFeMtpLRnvJuKXXEX8LhcMzSEuN7XRiTMdPBWL15lu39PIK7SXj1l46yVdKKaXUgBzxBxgV4yvrAJ1Lf2obY3eFHaWGGp3kJ5F58+Y5PQRP0V4y2ktGe8loLxm39PL5AzG/sg5ASV5sz+S7pZdXaC8Zt/bSSX4Sqa+P3ZUIhgLtJaO9ZLSXjPaScUsvX2N8JvnDM9PIzUqjpiE2G2K5pZdXaC8Zt/bSSX4S2bNnj9ND8BTtJaO9ZLSXjPaScUOvUMhytCnAqDhM8iG8Lr8mRst13NDLS7SXjFt76SRfKaWUUmL1J1sJhmxczuRD+Ao7sbq6jlJDkU7yk8jUqVOdHoKnaC8Z7SWjvWS0l4wbevkaO3a7zYv9G28hfCY/Vmvy3dDLS7SXjFt76SQ/ieTm5jo9BE/RXjLaS0Z7yWgvGTf08vk7druN05n8kvxsjjQFaGsPDfqx3NDLS7SXjFt76SQ/ibh1Mwa30l4y2ktGe8loLxk39PL5O87kx+ESmhC+wo614ct0DpYbenmJ9pJxay+d5CullFJKLDL5Ls6L3xtvIXaX0VRqqNFJfhIpLCx0egieor1ktJeM9pLRXjJu6OVrbCE3K42s9NS4PH4sN8RyQy8v0V4ybu2lk/wkMnv2bKeH4CnaS0Z7yWgvGe0l44Ze8doIKyKWG2K5oZeXaC8Zt/bSSX4SWbFihdND8BTtJaO9ZLSXjPaScUOvI/5A3NbjA4wYlk5mWkpMzuS7oZeXaC8Zt/bSSb5SSimlxHz+QNzW4wMYYyiJ4WU0lRpqdJKfRNLS0pwegqdoLxntJaO9ZLSXjNO9rLX4/C1xXa4D4SU7NQ3Ng34cp3t5jfaScWsvY611egxJpayszFZVVTk9DKWUUipuGlvaOPtbz/K1t5zJxxfGbyOgzz20ljX7jvHi7ZfF7TmU8jJjzBprbVlPn9Mz+Umkurra6SF4ivaS0V4y2ktGe8k43Suy2+2oOJ/JH52fRW1DgFBocCckne7lNdpLxq29dJKfRBobG50egqdoLxntJaO9ZLSXjNO94r3bbcSYvCxa20PUn2wd1OM43ctrtJeMW3vpJF8ppZRSIvHeCCuiJD8bgBp9861SYjrJTyJlZT0uyVK90F4y2ktGe8loLxmne72xXCd+l9CENzbEGuwk3+leXqO9ZNzaSyf5SaS2ttbpIXiK9pLRXjLaS0Z7yTjdy+dvITMthbys+F5VZEzHJP/wIK+V73Qvr9FeMm7tpZP8JLJ//36nh+Ap2ktGe8loLxntJeN0r8g18o0xcX2eopxMUlMMtYM8k+90L6/RXjJu7aWTfKWUUkqJ+Brju9ttRGqKoTg3UzfEUmoAdJKfRKZPn+70EDxFe8loLxntJaO9ZJzudaQpEPcr60SU5GdRO8jlOk738hrtJePWXjrJTyKZmYn5hpsstJeM9pLRXjLaS8bpXr7G+O92G1GSl8XhQe5663Qvr9FeMm7tpZP8JLJhwwanh+Ap2ktGe8loLxntJeNkr5a2dhpbghTnxX+5DoTP5A/26jp6fMloLxm39tJJvlJKKaWiFrlGfrx3u40Yk5/FidZ2/C1tCXk+pZKFTvKTSHFxsdND8BTtJaO9ZLSXjPaScbJXZLfbRE3yR+cN/lr5enzJaC8Zt/bSSX4SmTFjhtND8BTtJaO9ZLSXjPaScbJXZCOsRK3JH9Ox6+1grrCjx5eM9pJxay+d5CeRyspKp4fgKdpLRnvJaC8Z7SXjZC+fPzLJT8ya/MiGWDWDuMKOHl8y2kvGrb10kq+UUkqpqPn8LaSmGAqHZyTk+Yrzwj8xGOybb5UaanSSn0Tcegknt9JeMtpLRnvJaC8ZJ3v5GgMU5WSQkhLf3W4jMtNSKRyeMajlOnp8yWgvGbf2MtZap8eQVMrKymxVVZXTw1BKKaXi4kO/f4X6E608eWtFwp7zrb94kdF5Wfz+xvMT9pxKeYExZo21tqynz+mZ/CSi/7iQ0V4y2ktGe8loLxkne/n8idvtNiK8IdbAz+Tr8SWjvWTc2ksn+UmkqanJ6SF4ivaS0V4y2ktGe8k42euIv6VznXyihDfEGviut3p8yWgvGbf20km+UkoppaISbA9Rd6KVUQm6sk7EmPwsjp1so6WtPaHPq5SX6SQ/icyfP9/pIXiK9pLRXjLaS0Z7yTjVq+5EK9Ym7hr5EZENsWoHeBlNPb5ktJeMW3vpJD+JHDhwwOkheIr2ktFeMtpLRnvJONUr0RthRUQ2xBroZTT1+JLRXjJu7aWT/CRy8OBBp4fgKdpLRnvJaC8Z7SXjVC+fPzzJHpXoN97md1wrf4Bn8vX4ktFeMm7tpZN8pZRSSkWlc7fbvMSuyS/pOJM/mCvsKDXU6CQ/icycOdPpIXiK9pLRXjLaS0Z7yTjVK7JcZ1ROYs/k52SmkZuZNuDlOnp8yWgvGbf20kl+EklNTXV6CJ6ivWS0l4z2ktFeMk718vlbKBiWTkZa4qcPo/OzBjzJ1+NLRnvJuLWXTvKTyKZNm5wegqdoLxntJaO9ZLSXjFO9whthJXapTsSY/CwOD3BNvh5fMtpLxq29dJKvlFJKqaj4/IGEb4QVUZKXRa2uyVcqajrJTyIlJSVOD8FTtJeM9pLRXjLaS8apXkcaWxJ+ZZ2IkvwsfP4Wgu0h+X31+BLRXjJu7aWT/CQyZcoUp4fgKdpLRnvJaC8Z7SXjRC9rLUeanFuuU5KfRcjCkaaA+L56fMloLxm39vLMJN8YU2CMuc0YU2WM2WiM2WyMedYYUyF4jMuNMa8YY9YbY7YYY75ijDmtgTEm3RhzhzHmVWPMWmPMXmPMf40xZbH9U8XWypUrnR6Cp2gvGe0lo71ktJeME72On2yjrd0mfCOsiDH54X9cDOTNt3p8yWgvGbf2SnN6AAI3A7cBi6y1G4wxqcBdwHJjzJXW2mf7unPHPwaeAt5trX3cGDMBWA3kAV/pcjsD/BNoABZaa5uNMYXAS8AcoCoOfzallFLK1d64Rr4zk/zReQOf5Cs1FHnmTH6He6y1GwCste3AHUA7cGsU9/0hsMpa+3jH/fcDPwW+aIwZ2+V21wMVwCestc0dt60D3gO8EKM/R1xkZ2c7PQRP0V4y2ktGe8loLxknekV2u3Xu6joD3xBLjy8Z7SXj1l5emuTfCXyz6westS3AMaCgrzsaY8YAF3H6JP15IB24usvHPgYsjUzwuzzXOmvt3oENPTHmz5/v9BA8RXvJaC8Z7SWjvWSc6BXZCMup5TqR6/PXDuAymnp8yWgvGbf28swk31rb3nH2vpMxpgAoApb1c/ezOn7f3e3jkf8/u+PxUoALgQMd6/WrjDHbjDFPS9b+O2X16tVOD8FTtJeM9pLRXjLaS8aJXpHlOk5dXccYQ0le1oDO5OvxJaO9ZNzay0tr8nvyMcBHeNlNX4o6fvd3+3hjx++FHb+PBIYRXv//F8LLdizwXeAFY8wSa+3y7g9ujLkJuAlg7NixLFu2DICpU6eSm5vLunXrwk9SWMjs2bNZsWIFAGlpaVRUVFBdXU1jY3goZWVl1NbWsn//fgCmT59OZmYmGzZsAKC4uJgZM2ZQWVkJQGZmJuXl5VRVVXHkyBGWLVvG/PnzOXDgAAcPHgTC2y2npqZ2btZQUlLClClTOt8okp2dzfz581m9ejXNzeEfYJSXl7N7925qamoAmDVrFu3t7WzduhWAcePGMX78+M4DOycnh7KyMlauXEkgEP6LoKKigm3btuHz+QCYM2cOgUCA7du3AzBhwgRGjx5NVVX4bQ55eXnMmzePyspKgsEgAAsXLmTjxo3U1dUBMHfuXPx+P7t27QJg8uTJjBw5kurqagAKCgqYO3cuy5cvx1qLMYZLLrmEdevWcezYMQDmzZtHfX19Z69Ef52ampoAPPd1ivRK9Ndpz549QOJfT4P9Oh09erTze8FQeD0N9ut0/Pjxzl5D4fU02K9TU1NTZ69EfZ0O1o8gOz2FV1dWOvZ1yrYtHD5+ki1btoi+Ts3NzUPq9TTY73uhUIjt27cPmdfTYL9Ora2tHDt2zJG/n/pirLV93iBejDFLgP9GcdPl1tpFPdx/NrACuM5au6yf53of8CDwTmvtI10+nga0Af+w1r7HGDMe2E948j/GWnuy43bpHR/fbK29tK/nKisrs5GDLdGWLVvGokWLHHluL9JeMtpLRnvJaC8ZJ3p96q/VbDrUyAu3JfZ5u/rM39by2v7jrLi9z7+KT6PHl4z2knGylzFmjbW2x6s/is/kG2PO7vjPLdba1kGM62XgzChud7KHMUwEngQ+3t8Ev8PRjt9zu308r+P3uo7fI2f6t0cm+ADW2jZjzAbCS3lcq7y83OkheIr2ktFeMtpLRnvJONHrSGPAsaU6EWPys/jPxpbOM6zR0uNLRnvJuLXXQNbkvwb8gTeWwAyItfaktXZLFL/2db1fx6Uv/wvcbq19NMqnW9/x++RuH4/sXvB6x5gagBp67tLey8ddY/fu7m85UH3RXjLaS0Z7yWgvGSd6+fwtjr3pNqIkP4vWYIhjJ9tE99PjS0Z7ybi110AmrbuB8621h2I9mP50meB/zVr7cJePP9HtdsOMMfmR/7fWHgZWAou6PeSlhJfrPNnlY08CpcaYYV0eLwWYBbwamz9JfETWvanoaC8Z7SWjvWS0l4wTvXx+53a7jSjpuFb+4Ybmfm55Kj2+ZLSXjFt7DWSSv7Ov+xljvj3w4fSuY738C8AGIMsYc0PkFx1Xx+liLbDDGDO8y8duBy4yxlzd5fE+D9xlrT3Y5XbfBYLA/3b52JeAEuD/xfLPpJRSSnlBUyDIydZ2xzbCiigZxK63Sg01A7m6zh3Ab4wxvyG8Lr/7mvm3At8Y9MhO9wVgWsev67p9rvv16w8TnqgHIx+w1lYaY64CvmOM+Q6QAdwN/KDrHa21e40xlwB3GmP2AKGOx19srV0Ruz9O7M2aNcvpIXiK9pLRXjLaS0Z7ySS615HIbreOr8kPbzpUI7xWvh5fMtpLxq29BjLJryJ8WcmPAKI3vgyGtfYLhCf60dx2US8ffwZ4Jor7rwfeLBmfG7S3t/d/I9VJe8loLxntJaO9ZBLdy9fo7G63EUU5GaQY+Zl8Pb5ktJeMW3sNZJJfC9zTy+cMHdeLV4m3detWxowZ4/QwPEN7yWgvGe0lo71kEt0rshGW08t10lJTKM6Vb4ilx5eM9pJxa6+BTPKrrbX/09snjTGlgxiPUkoppVzG55LlOhBel18rXK6j1FAkfuOttfat/dzkxoENRQ3WuHHjnB6Cp2gvGe0lo71ktJdMonv5/C1kpKaQn52e0OftSUme/Ey+Hl8y2kvGrb3icd33V+LwmCoK48ePd3oInqK9ZLSXjPaS0V4yie4V2QgrUe/D60tJfpZ4Tb4eXzLaS8atvQY0yTfGzDPG3G2M+bcx5vmuvwBdruOQ1atXOz0ET9FeMtpLRnvJaC+ZRPfy+Z3f7TZiTH4WTYEg/pboN8TS40tGe8m4tZd4km+MuRJ4EZgHVBB+s60BxhDebGpLDMenlFJKKYe5YbfbiMi18nVdvlJ9G8iZ/P9H+Jrx5cAOa+2lHb/OBN5D+B8AygE5OTlOD8FTtJeM9pLRXjLaSybRvXz+gONX1omI7Hpb0xCI+j56fMloLxm39hrIJD/bWruq479PWZxnrf0ncO6gR6UGpKyszOkheIr2ktFeMtpLRnvJJLJXINjO8ZNtjl8jPyJyJv9wQ3PU99HjS0Z7ybi110Am+V2v+B80xnReGNQYMwI4Y7CDUgOzcuVKp4fgKdpLRnvJaC8Z7SWTyF5u2e02YnTnmfzol+vo8SWjvWTc2msgk/yDxpj/NcZkAsuBZ40xnzXGfBZYBmyK5QBV9AKB6H90qbSXlPaS0V4y2ksmkb3cshFWRFZ6KiOHZ1AjWJOvx5eM9pJxa6+BbIb1E8Jr70cB3wXKgZ92fG4X8N7YDE0ppZRSTnvjTL47lutA+Gy+9DKaSg01xlo7uAcIXzS3FMgEtlhrg7EYmFeVlZXZqqoqR547GAySljaQf7cN3JPrDnG0KcCHF0xJ6PPGghO9vEx7yWgvGe0lk8hef161l288voFXvrqY4jx3TPQ/8sCr1DS08PRnL47q9np8yWgvGSd7GWPWWGt7fFPAoDfDsmHbrbUbrLVBY8ynB/uYamC2bduW0Oez1vLjZ7dy5zNbaQ2GEvrcsZDoXl6nvWS0l4z2kklkryONLaQYKMxxx3IdCL/5VnIJTT2+ZLSXjFt7DXQzrBRjTKkxZoExZmHXX8BnYjxGFSWfz5fQ59t5pIm9dSc52drO2n3HEvrcsZDoXl6nvWS0l4z2kklkL58/wMjhmaSmOL/bbURJXhZ1J1ppaWvv/8bo8SWlvWTc2kv8swVjzMXAn4EJPX0aGNz6H+UZz20OH9QpBl7acZT5UwsdHpFSSqlY8/kDrrmyTkTkMpq+xgATC4c5PBql3GkgC4juBiqBx4B6oOs6DQP8NgbjUgMwZ86chD7f0s21zBqTR0ZaCpU7jvKFy2cm9PkHK9G9vE57yWgvGe0lk8hePn+La66sEzGmY5Jf09gS1SRfjy8Z7SXj1l4DmeRnWWtv6O2Txpj/HcR41CAk8hJOx060smbvMT59aSkhC79ZvpPGljbystITNobBcuslr9xKe8loLxntJZPQS2g2Bpg1Ji9hzxeNyK630W6IpceXjPaScWuvgazJ39xxjfze1Ax0MGpwtm/fnrDnemGrj5CFJbNGUzG9iPaQZfWu+oQ9fywkslcy0F4y2ktGe8kkqld7yHK0KeCqy2fCG8t1or2Mph5fMtpLxq29+j2Tb4yZ2O1DPwf+ZIz5C7AdONnt8z8F3PlzCxUzSzf7KM7NZM7YfNpCIbLTU6ncfoQ3zRrt9NCUUkrFSN2JACHrno2wInKz0snJTBNtiKXUUBPNcp09nP5mWgO8M+ajUYMyYUJP74WOvdZgiOXbjnDV3DGkpBgyU1K5YMpIKnccTcjzx0qieiUL7SWjvWS0l0yievkaIxthuWuSDzA6LzPqM/l6fMloLxm39opmkr8f+H9RPp4BvjXg0ahBGT06MWfRX9ldT1MgyOIz3ni+itIivvv0Zg43NDMmPzsh4xisRPVKFtpLRnvJaC+ZRPWK7HY7ymXLdQDG5GdHfSZfjy8Z7SXj1l7RrMl/1lr7xyh/PQD8I85jVr1I1E67z22uJTMthQWlRZ0fi/z3SzvqEjKGWHBqZ2Kv0l4y2ktGe8kkqldkku/OM/lZUZ/J1+NLRnvJuLVXv5N8a+3HhY/5qwGORXmAtZalW2qpKC0iOyO18+NnlORSlJPBSx5bsqOUUqp3Pn94Ej3KhZP8MflZ+PwB2kO6PY9SPRnQjrf9eDwOj6mikJcX/0ucbattYn99M4vPPPVHUykphoumFVG54yjWeuMbbiJ6JRPtJaO9ZLSXTKJ6+fwB8rPTyUpP7f/GCVaSn9V59Z/+6PElo71k3NpLPMk3xuQZY75jjKk0xmw3xuzq+guYFYdxqijMmzcv7s/x3OZaABafWXza5ypKizjiD7Cttinu44iFRPRKJtpLRnvJaC+ZRPXyNbpvt9uIN66V3/+SHT2+ZLSXjFt7DeRM/v3AJ4BjwMvA8i6/VgDemOElocrKyrg/x9LNtZw1Lp/Reae/CWvB9PC6fK9cZScRvZKJ9pLRXjLaSyZRvXz+Flcu1YGu18rvf0MsPb5ktJeMW3sNZMfbBcBsa21tT580xvxzcENSAxUMBuP6+EebAqzdf5zPLp7e4+fHjchmStFwXtpxlI9WTInrWGIh3r2SjfaS0V4y2ksmUb18/gBlkwoS8lxSYwQbYunxJaO9ZNzaayBn8nf0NsEHsNa+axDjUS72whYf1sKSM3u/VFRFaRGrdtXR1h5K4MiUUkrFmrUWnz9AcQ8/uXWDkcMzyEhN4bBuiKVUjwYyyf+dMeYTxhjT0yeNMSsHOSY1QAsXLozr4y/d7KMkL4vZY3t/g8mC0iJOtrazdt/xuI4lFuLdK9loLxntJaO9ZBLRq7E5SGsw5No1+cYYRudHtyGWHl8y2kvGrb0GMsn/MPBNoNYYs8oY83zXX8Ds2A5RRWvjxo1xe+xAsJ0Xtx9h8ZnF9PLvOwDKpxaSYryxLj+evZKR9pLRXjLaSyYRvdx8+cyIMXnZUU3y9fiS0V4ybu01kEl+GbAF2Ag0E97ltusv5ZC6uvhtRLVqVz0nWtv7XKoDkD8snbPGj/DE9fLj2SsZaS8Z7SWjvWQS0cvXuRGWO5frAIzOz4pq11s9vmS0l4xbew3kjbc7rLWX9vZJY8zaQYxHudTSzbVkp6dSPq2w39tWlBZyz/Jd+FvayM1KT8DolFJKxVrkTH5xnovP5Odn8czGFqy1ff6UWamhaCBn8t/Sz+fLBzIQNXhz586Ny+Naa1m62UfF9KKoNkSpKB1Fe8iyeld9XMYTK/Hqlay0l4z2ktFeMonodaTzTL57J/mj87JoDYY4frKtz9vp8SWjvWTc2ks8ybfWHu7nJvcMcCxqkPx+f1wed0uNn4PHm1nSwwZYPZk3aQRZ6SmuX5cfr17JSnvJaC8Z7SWTiF6+xgDZ6ankZA7kh/6JEbmMZn8bYunxJaO9ZNzaayBn8jHGDDfGvMsY82VjzP/r+gu4PMZjVFHatWtXXB73uU3hK6ZeekZ0k/zMtFQumFLo+kl+vHolK+0lo71ktJdMInqFL5+Z6eplMJENsWr7WZevx5eM9pJxay/xP8+NMTOB54BxgOX0N9vaGIxLuchzW3zMnTBC9OaritJCvvf0FmoaWjq/CSullPIOn7/F1Ut1AEryojuTr9RQNJAz+T8GfgIMA1631qZYa1OAscDdwKdjOD4lMHny5Jg/ps/fwrr9x1kS5Vn8iAWlRQCuvspOPHolM+0lo71ktJdMInr5/AFXXz4Twpf3TDFQ09Dc5+30+JLRXjJu7TWQSf4ka+1PrbUtdDlrb62tsdbeCrwzZqNTIiNHjoz5Y76wxQfA4n4undndmSV5FA7PcPUkPx69kpn2ktFeMtpLJhG9jjQGXH35TID01BRG5Wb2exlNPb5ktJeMW3sNZJLf9ZWUaozp/A5gjEkBpgx6VGpAqqurY/6Yz232MTY/izPH5Irul5JiuKi0iModR7HWnSu44tErmWkvGe0lo71k4t2rubUdfyDo+jP5EF6y099yHT2+ZLSXjFt7DWSS32qMWdLx3xuA3xlj5hpj5gL3A00xG51yVEtbO5Xbj7Jk1ugBvfGqorQQnz/Adp8eEkop5SWd18j3wiQ/PyuqXW+VGmoGMsn/E/ALY8wM4DvAFUB1x6/3AV+P3fCUREFBQUwfb+XOOprb2sVLdSIi6/Irt7tzyU6seyU77SWjvWS0l0y8e3Xudpvn7uU6AGPys/tdrqPHl4z2knFrr4FcJ/8+a+0sa+02a+1m4GzgE8BngbnW2idiPUgVnVhvxvDc5lqGZ6Ry4dSBrTUbXzCMyYXDXLsu362bV7iV9pLRXjLaSybevXyN7t8IK2J0Xhb+liBNgWCvt9HjS0Z7ybi114Cuk9+VtfaQtfa31tpfWWu3xmJQamCWL18es8ey1vL8Fh8XTx9FZlr/u9z2pmJ6Eat21dHWHorZ2GIllr2GAu0lo71ktJdMvHt5ablOZEOsvpbs6PElo71k3Npr0JN8Y4w7320wBMXyDa4bDzVyuKGFxVHuctubitIiTrS289r+47EZWAy59Q3BbqW9ZLSXjPaSiXevI/4AaSmGgmEZcX2eWIhmQyw9vmS0l4xbew16ks/pm2Eph8RyV8Klm30YE/0ut70pn1qEMe5cl+/mXRzdSHvJaC8Z7SUT716Ra+SnpLj/6xLNhlh6fMloLxm39orFJN+d/3wZgi655JKYPdZzm2s5d8IIinIG96Pa/GHpnD0u35Xr8mPZayjQXjLaS0Z7ycS7l88f8MRSHYjuTL4eXzLaS8atvfRMfhJZt25dTB6ntrGF9QcbBnxVne4WlBaxdv9x/C1tMXm8WIlVr6FCe8loLxntJRPvXr7GFka5fCOsiKz0VEYMS+dwH7ve6vElo71k3NorFm+8PTcWA+mPMabAGHObMabKGLPRGLPZGPOsMaZC8BiXG2NeMcasN8ZsMcZ8pWMDr+63u94Y82rH7TYZY/7PGDMvtn+i2Dt27FhMHmfp5vAut0tiNMmvmF5Ee8jyyu76mDxerMSq11ChvWS0l4z2kol3ryP+AMV53jiTD+ElO3298VaPLxntJePWXrE4k58oNwN3ADdaa2cDc4BNwHJjzOX93bnjHwNPAd+z1p4FvAm4Ffhut9u9A/gbcH/H7c4GjgFLjTETYvjnca2lm2sZX5DNjNE5MXm8eRMLyEpP4UUXrstXSil1qrb2EHUnWhk1yOWaiTQmP6vfa+UrNdTEfJJvjEmL9WN2cY+1dgOAtbad8KS/nfBkvT8/BFZZax/vuP9+4KfAF40xY7vc7nqg0Vp7b8ftgsBdwAjgLbH5Y8THvHmD/2FDc2s7lTuOsuTMge1y25Os9FTOnzzSdevyY9FrKNFeMtpLRnvJxLPX0abIRljemeT3t+utHl8y2kvGrb3icSb/lTg8JsCdwDe7fsBa20L4LHufW40ZY8YAFwEvdPvU80A6cHWXjwWBFHPqDDfyD5eBXzA+AerrB78c5qUdRwkEQzFbqhNRUVrEdl9Tn2+MSrRY9BpKtJeM9pLRXjLx7PXGRljeWJMPUJKXzdGmVgLB9h4/r8eXjPaScWsvzyzXsda2d5y972SMKQCKgGX93P2sjt93d/t45P/P7vKxnwAh4KvGmBRjzHDg60AN8M8BDD1h9uzZM+jHWLqlltzMNC6YMrBdbnuzoLQIwFVn82PRayjRXjLaS0Z7ycSzl8/vnd1uIyIbYkX+gdKdHl8y2kvGrb36neQbY0LGmPZofwGJ3Nv3Y4CP8LKbvhR1/O7v9vHGjt8LIx+w1lYBVwIfAo52/MoDLrDWHhnsgN0sFLIs3exj4YxRZKTF9t9/s8bkMXJ4BpUumuQrpZQ6Xedutx5arjM6suuti35arJTTolk//ywwFXgpitsa4G3RPLExZgnw3yhuutxau6iH+88mvCb/OmttXTTPGeW4rgP+AnwKeIDwcp4fAs8aY95ire3+0wCMMTcBNwGMHTuWZcuWATB16lRyc3M7L61UWFjI7NmzWbFiBQBpaWlUVFRQXV1NY2P43xtlZWXU1tayf/9+AKZPn05mZiYbNmwAoLi4mBkzZlBZWQlAZmYm5eXlVFVV0drayrJly5g/fz4HDhzg4MGDAMycOZPU1FQ2bdoEQElJCVOmTGHlypUAZGdnM3/+fB569iV8/gBjqSMQCLB7925qamoAmDVrFu3t7WzduhWAcePGMX78eFavXg1ATk4OZWVlrFy5kkAgfCaloqKCbdu24fOFr9Zz3vgclm+t5YUXXsAYw4QJExg9ejRVVVUA5OXlMW/ePCorKwkGgwAsXLiQjRs3UlcX/hLPnTsXv9/Prl27AJg8eTIjR46kujq88XJBQQFz585l+fLlWGsxxnDJJZewbt26zne/z5s3j/r6+s5eif46NTU1AQz467R69Wqam8OXiisvL4/512nOnDkEAgG2b98O0Pl1ivRK9NcpcobEa18noPN7QSK/Tk69ngb7dcrJyensNRReT4P9Oo0ZM6azV6y/Tq9sb8UARTmZrnk99fd1SmsNP/7SldWMaJt02tdp6tSpQ+r1NNiv0/jx49m+ffuQeT0N9uuUn5/PsWPHHPn7qS+mv614O9azvwwssdbu7PPG4duvjeaymsaYYcDE/m4HnLTW7ut234mEl+jcZq19NIrnuhx4BviwtfaBLh8fCdQBv7HW3tLxsVpgg7V2cZfbpQGHgJestdf29VxlZWU2crAl2rFjxygo6PPtCX36yX+38avnt7Pm62+iYHjstzJ/6JV93PHoev77+YVMH50b88eXGmyvoUZ7yWgvGe0lE89eX3l0Pc9urGHNN94Ul8ePh8aWNs7+1rN87S1n8vGFU0/7vB5fMtpLxslexpg11tqynj7X75oMa+1h4IvAX3q6pnwPojk7j7X2pLV2SxS/uk/wJ3Q8x+3RTPA7rO/4fXK3j0/p+P31jsceDRQD27uNNUh4/f4FUT6fIwa7GcPSzbWcN6kgLhN8eGNdvluW7Lh18wq30l4y2ktGe8nEs9cRf4BRHlqPD5CbmcawjFQO93KFHT2+ZLSXjFt7RbXw2lr7qLW23FobiuK2tw9+WD3rMsH/mrX24S4ff6Lb7YYZY/K7jOkwsBJY1O0hLwXagCc7/r8BaKXbTxg6rrQzgfBZ/6R06HgzGw81xmyX255MGDmMSYXDXPXmW6WUUqc64m+hOM87V9YBMMaEL6PZ2Puut0oNNQN+d6UxptAYM6rbpSbjxhgznvAlMDcAWcaYGyK/OPXqOABrgR0dV8aJuB24yBhzdZfH+zxwl7X2IHRekvM+4ApjTNdr4n8VGAPcHYc/WswUFhb2f6NeLN0S2eW2OFbD6VFFaRGrdtXT1t7vvxfjbjC9hiLtJaO9ZLSXTDx7+fwBT11ZJ2JMH9fK1+NLRnvJuLWXaJJvjDnHGPNPY0wD4ava1AANxpiHjTH9rsMfpC8A04DrgD93+9Xd4Y7xBSMfsNZWAlcB3zDGvA48R3jS/rVu9/084c21vmOM2WCM2Ux4E6zrIhtkudXs2bMHfN+lm2uZVDiMaaNis8ttbypKi2gKBFm3/3hcnycag+k1FGkvGe0lo71k4tUrFLIc8egkf3Re75N8Pb5ktJeMW3tFPck3xnwAWA0sAaqAv3f8ehVYDKw2xny4y+2/aoz5SKwGaq39grXW9PJrcrfbLrLWzrbWBrp9/Blr7fnW2rOttWdYa7/XfQmStTZorf21tXaetXaOtfZMa+0Cwfp/x0TexS11sjXIyzvrWHxG7Ha57U35tEKMcce6/IH2Gqq0l4z2ktFeMvHqdexkK8GQ9dyafAifyff5A7SHTr+giB5fMtpLxq29oprkG2POAX4J3AIUW2sXW2vf1/FrMeE3q34K+KkxZq4xpgj4DvDWOI1bxdCL24/SGgyxZFZ8l+oAjBiWwVnj8nVdvlJKudAbG2F5a00+QEleFsGQpa6p5w2xlBpqoj2TfzvwCWvt76y1bd0/aa1ts9b+FvgE8CXgRsACv4rVQFX/0tKi2fbgdEs315Kblcb5k2O7y21vFpQWsXbfcZoCwf5vHEcD7TVUaS8Z7SWjvWTi1atzku+hjbAiSvKzAXq8wo4eXzLaS8atvaKd5J9lrX0oitv9HbiM8BtVb7TWvjDgkSmxiooK8X1CIcvzW46waGYx6amx3eW2NxeXFhEMWV7Z7ezFigbSayjTXjLaS0Z7ycSrl69jx1gvrskf08eut3p8yWgvGbf2inZWd9rZ+57Y8M5atcBMa21Pb4hVcRTZrU1i3YHjHG0KxP2qOl3Nm1RAZloKL253dsnOQHoNZdpLRnvJaC+ZePXy8nKd0R2X/ezpzbd6fMloLxm39op2kh/V7kiRy2laa48MeERqwCJbJUss3ewjNcWwaEbiJvlZ6alcMGWk4+vyB9JrKNNeMtpLRnvJxKvXEX+A3Mw0sjNS4/L48VQ4PIP0VNPjch09vmS0l4xbe0U7yX/NGPPOKG73Tt7YXVZ5wHObaymbVED+sPSEPu+C0iK21TZ1/mhYKaWU83z+FkZ5cD0+QEqKYXReFrX694pSQPST/DuB+4wxHzTGnPbPe2NMqjHmQ8C9HbdVDigrKxPd/sCxk2yp8bMkjrvc9qaitAiAl3Y6dzZf2muo014y2ktGe8nEq5dXr5EfUZKXxeGG03e91eNLRnvJuLVXVJN8a+06wpfIvA/wGWOeNcb8pePXs4Q3nroP+KK1Vs/kO6S2tlZ0+6Wbw7vcLk7gevyIWWPyKBiWTuV25958K+011GkvGe0lo71k4tUrvNut99bjR5T0suutHl8y2kvGrb2ivpyKtfZvwHzgBeBC4H0dv8qBFcACa+0f4jFIFZ39+/eLbv/c5lqmFg1napx3ue1JSorhotIiKnccIfx+7cST9hrqtJeM9pLRXjLx6GWtxdfo7TP5Y/KzqGlsOe3vFT2+ZLSXjFt7ia6ZaK1dZ619J5APjO74lW+tvdZaWxWPAar4aAoEWb2r3pGz+BEVpUXUNgbYeaTJsTEopZQKawoEaW5r9+Q18iNG52XR0haioTmqiwIqldQGdGF0G3ak41co1oNSAzN9+vSob/vitiO0todY7MB6/IjIuvxKhy6lKemltJeU9pLRXjLx6BW5fOYoT5/J73lDLD2+ZLSXjFt7JWb3I5UQmZnRf2N+brOP/Ox0yiYVxHFEfZswchgTRw6jcocz6/IlvZT2ktJeMtpLJh69fI3evUZ+REkvG2Lp8SWjvWTc2ksn+Ulkw4YNUd2uPWR5YauPS2eOIi1Bu9z2pmJ6Eat21RFsT/wPhKLtpcK0l4z2ktFeMvHo5fN7d7fbiM5Jfrcz+Xp8yWgvGbf20kn+EPTa/mPUn2h1dKlOREVpEU2BIOsOHHd6KEopNaQd8fButxHFuZkY0/Out0oNNTrJTyLFxdG9ifa5zT7SUgyXzBwV5xH1r3xqIcbgyKU0o+2lwrSXjPaS0V4y8ejl8wfISEshLzst5o+dKOmpKRTlZJ42ydfjS0Z7ybi1l07yk8iMGTOiut3SzbVcMGUkeVmJ3eW2JwXDM5gzNp+XdiT+zbfR9lJh2ktGe8loL5l49PI1tnScCTcxf+xEGpOfxeFua/L1+JLRXjJu7aWT/CRSWVnZ7232159kW22TK5bqRCwoLaJ63zFOBIIJfd5oeqk3aC8Z7SWjvWTi0cvn8d1uI0rysqjtdiZfjy8Z7SXj1l46yR9intsc3pVtiYPXx+/u4ulFBEOWV3bXOz0UpVSCWWv5ybNbeXBzgGMnWp0ezpB2xOO73UaU5GdxuKHZ6WEo5Tid5CeRaC7h9NzmWkqLc5hUODwBI4rOeZMKyExL4cUEXy/frZe8civtJaO9+met5f/9ayO/eH4H/90b5LK7lvH3V/cRCjmzC7aXxOUSmv6ApzfCiijJz6KxJcjJ1jd+OqyvRxntJePWXjrJTyLl5eV9fr6xpc3xXW57kpWeyvmTRyZ8XX5/vdSptJeM9upbZIL/51V7uXnhVJ7+zMVMG5XDlx9Zzzt+8zIbDjY4PURXi/Xx1dLWTkNzW1Is1xnTw2U09fUoo71k3NpLJ/lJpKqqqs/Pr9h2hGDIssRF6/EjFpQWsbXW33md5kTor5c6lfaS0V696z7Bv+PKMzh5aBv//EQ5d71rLgeOneSqX1Xyjcc30HCyzenhulKsj69kuHxmxOi80yf5+nqU0V4ybu2lk/wk0tTU1Ofnl272UTAsnXkTndvltjcVpUUAvJzA3W/766VOpb1ktFfPrLV8418bwhP8S8ITfGMMTU1NGGO47rzxLP3iIj5UPpkHV+/lsruW8c+q/bqEp5tYH1++jkn+qCRYrjMmPxuAw10m+fp6lNFeMm7tpZP8ISLYHgrvcntGMakp7rs82uyxeYwYlk6lA5fSVEolRmSC/5dV+8IT/Def0ePlGvOz0/nW1bN58tYKJhUO40sPv8677l3JxkO6hCdejnT8FHVUjvcn+SWRM/mNuiGWGtp0kp9E5s+f3+vnqvcd5/jJNlcu1QFISTEsmFZE5fajWJuYM3Z99VKn014y2utUoVDfE/yees0em8/Dn7iIH73zbHYfPcFVv6zkW09spKHZu0t4AsF2/vXaQT7wu9X8edXeAT9OrI+vyJn8ZHjjbXZGKvnZ6acs19HXo4z2knFrL53kJ5EDBw70+rmlm2tJTzVcPL0ogSOSWVBaRE1jCzuPnEjI8/XVS51Oe8lorzeEQpb/90R4gv+JS6b1eAa/t14pKYZ3l03ghS8u4v3zJ/HHlXtYfNdyHq0+kLATArGwt+4E3//3Zsq//zyffeg1Vu+q50f/3jLg9xzE+vjyNQZIMVA43PuTfOjYEKvLJF9fjzLaS8atvXSSn0QOHjzY6+ee21zLhVMLyXXBLre9iazLT9RVdvrqpU6nvWS0V1j3Cf6X3zyzxyU6/fXKH5bOt6+ZwxOfqmB8QTZf+Mc63n3vSrbUNMZr6IMWbA/xzMYaPvj7V7jkzmXc/+JuLpg8kj9/9AIe+9RF+ANBfv/S7gE9dqyPL5+/haKcTFcu5xyI0XlZ1HZZrqOvRxntJePWXmlOD0DF356jJ9h55AQfuHCS00Pp08TCYUwYmU3ljqN86KLJTg9HKTVIkSU6D67ue4Ivcdb4fB795EX8o2o/P/zPFt76i0o+VD6Zz79pumtOYtQ0tPDQq/t46JX91DS2UJKXxeeXzOA950+gJP+Nq9dcPms0v39pNx+9eAp5Do89Wa6RHzEmP4uNh9z7D0ClEkEn+Ulk5syZPX48ssvtYpeux++qonQUT607RLA9RFpqfH/Q1Fsv1TPtJTPUe3Wd4H9y0TRuv6LvCb6kV0qK4foLJnLF7BLufHYrf3h5N0++foivv/VMrp47dtD/kBiIUMhSueMoD67ey3ObfYSsZeH0Ufzv22dz2RnFPX4/+8zi6Ty7qZYHXtrDZxZPFz1frI8vX2PglH+AeF1JfhZ1JwK0BkNkpKUM+dejlPaScWsvXa6TRFJTU3v8+NLNPmaOzmXCyGEJHpFcRWkR/kCQdQfifxWN3nqpnmkvmaHcSzrBh4H1KhiewfeuPYvHb1nAmPwsPvvQa1x/3yq21foHOnSx+hOt3Lt8J5fetYwP/v4VXt1zjI9fPJXlt13KHz9yAZfPLun1hMWccfksObOY31Xuxt8iW5sf6+PL5w8kxUZYESV5WVhL594rQ/n1OBDaS8atvXSSn0Q2bdp02scaTrbxyh737XLbm/JphRiTmHX5PfVSvdNeMkO1Vyhk+bpwgg+D6zV3wggeu2UB3712Dltq/Lzl5y/y3f/bRFMgOODH7Iu1llf31PO5h9Zy4feW8v1/b2F0bhY/v/4cVn7lMu648gwmFkZ3UuUzi6fT0NzGn1bKrrQTy+OrPWSpP5Fkk/xuu94O1dfjQGkvGbf20uU6SW7ZNh/tIeuJpToAI4dnMHtsHpU7jop/fK2UclZkgv/X1fu4ZdE0vhTlBD8WUlMM758/iSvnjOFH/9nCb1/czRPrDvH1t87ibWePick4/C1tPLb2IA+u2sfWWj+5mWm8b/5E3jd/IjNG5w7oMc8eP4JLZ47ity/u4kMXTSYnM/F/Ldc1BQhZGJWXPMt1Ihti6bXy1VCmZ/KTSElJyWkfW7rZR+HwDM6ZMCLxAxqgBaVFrN13jBNxOgsX0VMv1TvtJTPUeg12gh+rXiOHZ/CD687m0VsuYlRuJrf+bS03/G41O3wD35Fyw8EGvvLo68z/3lL+3782kpGWwg+vO4vVX1vMt66ePeAJfsRnFk/n+Mk2/iw4mx/L46vzGvnJdCY/79Qz+UPt9ThY2kvGrb30TH4SmTJlyin/39YeYtlWH5fPLvHUZdEuLh3Fvct38cqeei6dGb9lRt17qb5pL5mh1CsUsnzt8Q387ZWBn8GPda95Ewv416cq+Ovqvdz5zFau/PkKPloxlVsvK2V4FGfLm1vbefL1Qzy4eh/r9h8nKz2Fq+eO5YYLJ3H2+BExHeu5EwtYOCN8Nv+D5ZOiGl8se0XWrSfTJD8vO43s9NTOa+UPpddjLGgvGbf20jP5SWTlypWn/H/VnmM0tgRdu8ttb8omF5CRlkLl9viuy+/eS/VNe8kMlV5dJ/ifunTgS3Ti0Ss1xfCB8sk8f9sirjlnHPcs38mSnyzn6fWHe91Ia4fPz/88uZH533uO2x9+nROBIN+6aharv7qEH71zbswn+BGfXTyd+hOtPLg6urP5sezlawyfyR+VRJN8Ywxj8rM6l+sMlddjrGgvGbf20jP5SWzp5loyUlNcvcttT7LSUzl/ckHCNsVSSg1M9wn+bZcnbg2+RFFOJne+ay7XXzCBrz++kVserObi6UX8z9WzmToqh9ZgeNOqB1fvZdWuetJTDW+eM4Yb5k/kgikjE/JnOm9SARWlRdy3YhcfuHAy2RmJu1pHZLlOMk3yIbwhVk2DrslXQ5dO8pNIdnb2Kf+/dIuP8mmFUf3o120WlBbxo/9sxedvoTg3Pm8G695L9U17ySR7r/AEfz1/e2V/TCb4ieh13qSRPPnpBfxl1V7uenYbV/xsBVfOGcPLO49ytKmV8QXZ3P7mmby7bAJFOYmf8H52yXTedc9KHly9l49dPLXP28ayl8/fwohh6WSmufMygAM1Jj+L1bvrgeR/Pcaa9pJxay9drpNE5s+f3/nfO480sfvoCZZ45NKZ3V1cOgqAlTvr4vYcXXup/mkvmWTu1XWC/+lLS2NyBj9RvdJSU7hxwRSev20RV80dy783HOacCQU88OHzWfGlS7llUakjE3yA8yePpHxqIfeu2EVLW3uft41lL19jcl0+M6IkP4vaxhZCIZvUr8d40F4ybu2lk/wksnr16s7/Xtqxy+1lHluPHzFrbB4jhqXHdV1+116qf9pLJll7dZ/gf/HyGTFZzpLoXqNyM/nJu89h23eu5P4PlbFoZjEpLrhAwWeXTOeIP8DfXtnX5+1i2Su8EVbyXD4zoiQ/i2DIcvREIGlfj/GivWTc2ksn+Umkubm587+f2+zjzDF5jBvhzh8h9Sc1xXDRtEIqdxzt9Q1yg9W1l+qf9pJJxl6hkOWrj8V+gg/O9XLbewgunFrIBVNGcs/ynX2ezY9lryNJttttRNfLaCbj6zGetJeMW3vpJD8JHTvRypq9xzy7VCdiQWkRhxta2HX0hNNDUWrIi0zwH3p1P7deFtsJvjrV5xZPp7YxwD+q9sf9uay1HPEHGJWXhJP8brveKjXU6CQ/iZSXlwPe2+W2NxWl4asCxesqO5FeKjraSyaZenWf4H/hTbGf4CdTr8Eqn1bI+ZML+M2ynQSCPZ/Nj1WvhuY2WttDSbtcB8K73urxJaO9ZNzaSyf5SWT37t1AeKnOqNxMzh6X7/CIBmdS4XAmjMyO27r8SC8VHe0lkyy9EjHBh+TpFQvGGD6zeDqHG1r4Z9WBHm8Tq17JuNttRNHwTNJSDDUNLXp8CWkvGbf20kl+EqmpqaE1GGLF1iNc5pI3kQ1WRWkRK3fVEWwPxfyxa2pqYv6YyUx7ySRDr1DI8pVHwxP8z8Rxgg/J0SuWKkqLmDdxBL9ZtpPW4Onf/2LVK7IRVjJO8lNSTOe18vX4ktFeMm7tpZP8JPPqnnr8gSCLPb4eP2JBaRH+liCvH2xweihKDSmRCf7fq8IT/M/HcYKvThc5m3/weDOPVPd8Nj8WfP7wevXivORbrgPhJTuHdU2+GqJ0kp9EZs2axXOba8lMS6HCY7vc9uaiaR3r8uOwZGfWrFkxf8xkpr1kvNzLiQm+l3vFyyUzRjF3wgjufmEHbd1+mhmrXsm6221E5Fr5enzJaC8Zt/bSSX4SCQaDLN3sY0FpEcMyvLfLbU9GDs9g9tg8KuPw5tv29r43m1Gn0l4yXu0VClnuePT18AR/8fSEncH3aq94Msbw2cWlHDjWzKPdzubHqpevMcCwjFRyPLgzejRK8sJn8oPBoNND8RR9Pcq4tZdO8pPIC9Vb2Fd/MmmW6kRUTC+iet8xTrbG9pv01q1bY/p4yU57yXixV2SC/4+qA+EJ/pLpCVui48VeiXDpzGLOGpfPr7qdzY9VL5+/JSnX40eMyc+iua2dtRv1+JLQ16OMW3sNuUm+MeYtxpgaY8wDTo8l1l7zhSfBi8/w9qUzu6soLaKt3fLK7nqnh6JU0nJygq96F1mbv7++mcfXHoz54yfrbrcRkctoHmuJz6aKSrmZZyb5xpgCY8xtxpgqY8xGY8xmY8yzxpiKKO+fbYy5G/gO0Ocs2BgzzRjzVMdzbDPG/M4YUxCLP0c8bWpIZc64vM5vasni/MkjyUhLifmlNMeNGxfTx0t22kvGa71+9ty28AT/slJHJvhe65VIS84sZtaYPO5+YUfnlcZi1StZN8KKiOx6m5Iz0uGReIu+HmXc2sszk3zgZuAO4EZr7WxgDrAJWG6MuTyK+78TOAFc2NeNjDGFwHJgGzALOAuYBDxpjHFtr/oTrWz2tSTdWXyArPRUyiYVxHxd/vjx42P6eMlOe8l4qddTrx/iF8/v4N1l4x27io6XeiVa5Gz+nrqTPLHuEBC7Xr7G5F6uEznpFczIdXgk3qKvRxm39nLtpLUX91hrNwBYa9sJT/rbgVujuO/frLW3W2tb+7ndF4AC4P/ZsADwFWABcN3Ahx5fL2zxEbKwxOO73PZmQWkRW2r8HOm4EkQsrF69OmaPNRRoLxmv9NpwsIHb/rmO8yYV8O1r5ji2RMcrvZxy+azRnFGSy6+e30F7yMak14lAkBOt7Um9XKc4NwtjoHrzTqeH4in6epRxay8vTfLvBL7Z9QPW2hbgGOFJeZ+stdG+a/M6oMpa29TlY1VAIy6e5D+3uZYRmYY54/KcHkpcXNxxSdCXd8Zn91ulhqIj/gA3/amKgmEZ3HPDeWSmpTo9JNWLlBTDZxdPZ9fREzz1+qGYPOaRJN7tNiIjLYXC4ZnU65p8NQR55ppZHWfuT9GxTr4I+G0snsMYkwVMB1Z1e25rjNkLnN3vg2zdCosWnfqxd78bbrkFTp6Et7zl9PvceGP419Gj8M53nv75T34S3vMe2L8fPvCB0z4d+PwXWbEtnYq8Nsyll55+/69/HZYsgddeg8997vTPf+97cNFF8PLL8NWvnv75n/0MzjkHnnsOvvOd0z9/770wcyY8+STcddfpn//zn2HCBPj73+E3vzn98w8/DEVF8MAD4V/dPf00s8fmk5/Szku/+BNv3/XMqZ9ftiz8+49/DE89dernsrPh3/8O//e3vw1Ll3Z+6jy/HyZPhkceCX/gK1+BlStPvf/48fCXv4T/+3OfCzfsasYMuO++8H/fdBNs23bq5885J9wP4IYb4EC3TW3Ky+H73w//93XXQV3dqZ9fvBi+8Y3wf195JTQ3n/r5t70Nbrst/N/djzuI6bF33he/CLndfuT9xS/CVVeFj/ubbz79/klw7DFsGPz61/CPf5z++T6OvXNbW8N/Ljjt2AOgsNDRYy9w4UV8YtLbqD/ZysM7H2PUVd8+9f4JPvbO8/tPPb76+b43FI+9K4CZZ9/IL3//HN/9QMd1uYXf94DOYy9yjfzi//0aNOx94/NJ9n1vzJwPENrTCIs6nnOQf+cOhWMvJydnQN/3oj32gKT6O7fz+1ec53v9HnvdeGaS34uPAT7gpzF6vJGEf7rh7+FzjYT/AXAaY8xNwE0AZ2VkcPz4cQCysrNJTU1l97ZtHFq2jKJhw5gNNHR83hhDfn4+e/bsYc+yZaQ3NHBhezutra0EAuFvvtnZ2QQaG6letoxMn4+zT55kWHY2DQ3hHWBNSgrHAlCQCXOzT3L8+HFy8/IIBAK0djxGe10d7T4f+6qqKD1+nIyMDLKysmhsbATg4MaNzL7oIjZu3Mi4jrHl5eXR0tJCa2srO6qqmDh2LKl1daR2fD4jM5PMzEz8jY1sXb2aVL+fMqChsREbCr8xLD8/n5PNzby+ciWBnTuZ19hIZiBAc8eLJjMzk4yMDFa99BJt+flM3rOHyUBDQwPWhs+65I8YwcYNGzh68iRntR3lxbxJNAcCBDoeIysri9bGRqqrq5mwcyejm5rIycnp/BqEmpsZCaxbt4683bspOH6cnNxcgm1ttLe3c+TIEZr37SM3N5fGffvIP36ctPR0hg8fTsPx4wTS09leWUlFRQU+n4+MjsfNzc2ltbWVY4cOsW3ZMqZPn05hSwstHZ9Pz8hgWHY2Bw8cYMeyZWRmZlIO+P3+zuvp5ubl0VBfz+sd3zDnt7SQ2tbGyRMnwo0zMsgIBqns+Pw5x48zIjOTxsZGQh2Nc4JBdmzZQk1NDeccP86w4cOx1tJ88iQArT4fuc3NvLpiBWcfP05qaiq5ubmdX6eaLVsoDQbZuXUrozvGPnz4cEKhEM3NzRzcuJGsnTsp6eh1/PhxUtPSyM3JoaGhgX3r11OXm8vC0aM5eeIEwba28GPk5NDe3s7Wdes4lpbGjJMnKW5vp8kffmmlpaWRk5PD2rVraWhtJX/jRs4FmpqaOq9nnZObi+/QIbYdP07BunXMDARITU3lRFP4h2xp6ekMC4VYsWwZhevXM7Ghgfz8fPxNTbR3PEb6iRPU7NxJS8exnZ2dTUpKCic6Gtdu3cq0ESPYsWULJcePY1JSyM/L6/w6vb5iBedfcgn+Ll/77GHDMMZw8sQJXlu2jJKSEkqDQZo6Pp+SkkJeXh7WWpZ1fO0qgkFaT56ktTW8WnDY8OG0t7SwuuPzZ9fXkx8K4e94TaamppILrFy5kkAgQOmBA4yzlpPNzbR1PEZWSwt1Bw+yfft2Zhw6REFzMxkZGfg7Grf6fBQDlZWVTK+tJfP4cfJHjODEiRO0tbXxtaYS1uw9xrcun8Do5Vs43thIVlYWaenpNPn9HNu9m8Z165g7dy719fWkdHw/GTFiBE1NTdTu3Mn+ZcuYN28eGS0ttLSEdxUd6Pe9tLS0ztdtNN/38oGqqiraN21iZhJ/3xu7bRtTgkHa29tpaW7mI9ue48vnvJe97SNpXrZM/H2vpaWFtlCI5n372H244++ZusM0BU4k7fe9wpPHqBleNKDve20dnx+K3/d8Tz0l/r53/PhxXnPp971gWxsN+/aRd+wYfr+f7CNHSI/h9722YDDu872evu/1xUS+qSSaMWYJ8N8obrrcWruoh/vPBlYA11lrlwmf2wJ/tNbe2O3jY4GDwK+stbd2+1wlMN1a2+ei99zcXHveeef1+fxve9vbuK3jX4KLFi3ixhtv5MYbb+To0aO8s6d/2XXT/fZf/OIXueqqq/jb3x7i3nvv6ff+kdtv3bqVm2++me9973tcdNFFvPzyy3y1p7MK3XS//b333svMmTN58sknuaunswrddL/9ww8/TFFREQ888AAP9HRWoUNj8Vzqp17OIx85m/NmTOi8fWQi9eMf/5inup9V6EHk9rfeeiuHDh3ikY6zCl/5yldY2f2sQjeFhYWn3L6uro77Os4q3HTTTWzrflahmxkzZpxy+8LCQr7fcVbhuuuuo677WYVuysvLT7l9eXn5KcdSfwZz7DU2NvKZz3ymx2Mvciz1x6vHXkT32/d17DU2NpLX7Rtw19uvXLnSsWPvilt/wNbhZ3HrZaV88fKZrjj2eurVVW/f94basff7PzzA99caRo8ezYqvXMFPfnKX6Pte12Pv95W7+d+nNjGh6pekBlt6va/Xv+/lLfooW9tG8srXL+ftV74pZn/nJvOx1/X1GM33PWtSOTliCieKZtGcPxmb8sbSv4yMDADag0FCoRDpHf8fDAaxof42kTJkZGRgDLS1tWGt7Xy81tZWbKj7HPbU/zcpKWRmhJejBVoDGGPISA/fPxBoob85cEpq6im3T0lJJT09HYCWljfO8ltg/LrfkdbadMr94zXf63rsLV++fI21tqyn+zt5Jv9l4Mwobney+weMMROBJ4GPSyf4/agn/LXq6W34eUDf34kc1tbW33uKvS2748fJr+xt5LwZg3883QFRJnIWTUXHrb1e3H6ErcPmMI46Pr+khx8nO8StvdwmxUD+wZUcGHYV/9lYM6jH8vkDpBAipY8JfjI4M+s4r7eM5Lcrdjk9FM+I5vVoLTTnTeBE4ZmcLJxJKC2LlLYTDK/bcsox9b73vQ+AV15ZzcGDh7j22msBWL58OQdr+t77ITsrm2uuuSZ8+xUraGlp5opLrgDgmWeeof7YsT7vX1BQcMrts7OzWVi+EIB//etfnT9h683YsWNPuf3YsWM5//zzAXjooYc6bxcIBDDtbX0+lhMcO5M/UMaYCcBzwNestQ8P8DF6PJPf8bktQE3Xnx6Y8OUmjgP/ttZe39djl5WV2aqqqoEMa9CWLVsW1VkNr7LWcvGPXmD22Dzu/UCP/2gVSfZesaa9ZNzYa/fRE1xz90uU5GXxyC0XkZPpnhWbbuzlVu0hy4Lv/psRuTk8/ZmLSUkZ2BWRvvCP11i1s46Xv7I4xiN0n3f/7D9sqIflX7qUUUn8RuNY6e31aK1l46FG/vXaQZ5cd5iaxhaGZ6RyxewSrj5nLBWlRaSleumaLrHh5PcvY0yvZ/I99ZXomOD/l24TfGPME91uN8wYkz/Ap3kUON8YM7zLx84jfCb/kQE+ZkJUVES1L5hnGWOoKC3i5Z11tJ/2Izq5ZO8Va9pLxm29Glva+PifqkgxcP+Hylw1wQf39XKz1BTDbVfOYUuNn2c31Q74ccIbYSXv5TO7+u715QSCIe5+YYfTQ/GE7q/HvXUn+OXS7Sz5yXLe9stK/vDSHuaMy+OX7z2Xqq+/iZ+85xwWzSwekhN8cO/3L898NYwx44EXgA1AljHmhsgvTr/qzVpgR7eJerTuInzW/n9MWCbwPcLLi1w9ye9vXWQyWFBahL8lyPqDDYN+rKHQK5a0l4yberWHLJ976DX2HD3Br99/HhNGDnN6SKdxUy8vmJnlZ3LhMH6xdHu/64p742sMJPXlM7tqqz/Iu84bz19X7+PAsdNWAatutm3bxtGmAA+8tJtrf/0Sl9y5jLv+u43CnEy+e+0cXv3aEu7/0PlcNXcs2Rl66V23fv/yzCSf8CZV0whfq/7P3X51d5jwVXc6F10bY7KNMa8ZY17r+NDVHf//g653tNbWAZcAZxDeUXc9sB94m7XW1YtGfT6f00OIu4umFQJQuf3IoB9rKPSKJe0l46Zedz6zlee3+PjW1bMp73gNuY2benlB3dEjfPqy6Ww63MhzmwfWzudP7t1uu/L5fHxm8XQw8PPntjs9HNdqCgR5tPoAtz+9j/nfW8q3ntxEc2s7d1x5Bi/dcRn/uLmc98+fRMHwDKeH6ipu/f7lrp/X9sFa+wXCE/1obruoh481A+dEef8dwNsEw1MJUpiTyawxeVTuOMqnL+vxiqZJ63BDM0+uO8R7zp9Ifna608NRHvHY2gPcs3wnN1w4kRsunOT0cFQMXXPOWH6xdDs/X7qNJWcWi3Yrbg2GOHayLal3u+1u7IhsPnDhJP7w0m5uvmQapcU5Tg/JFVqDIVZsO8Ljrx3kuc21tLSFKMwy3LxwKm8/ZxwzS3q6FonyAs9M8lX/5syZ4/QQEuLi6UX84aU9nGwNMixj4Iewl3rtrz/Je3+7igPHmvld5W6+d+1ZLD6zz6u5xpyXermBG3q9tv84X35kPRdOHck3r5rt9HD65IZeXjJnzhzSUlP49KWl3P7I67yw1cdlZ0T/PeFIU8dGWHlD40x+5Pi6ZdE0HnplHz/571Z+/f6+L3edzEIhy6t76vnXukM8vf4wx0+2UTAsnXedN4G3nzOWScPbGTVqlNPD9Ay3fv/y0nId1Y/IhgrJbkFpEa3tIV7d0/els/rjlV57605w/X2raGxu48fvmsuI7Aw++scqvvD31zh+MnGXTfVKL7dwuldtYws3/amK4txMfv3+80h3+RvinO7lNZFe184bx/iCbH7+nGxtvq8xfInDobJcJ9KrMCeTj148lafX17D+wODf2+Ul1lo2H27k+//eTMUPn+c9963iseqDXDJjFH+48Xxe+doSvn3NHMomj+zcwEpFx63fv9z9XV+JbN8+NNYZnj95JBmpKby04+igHscLvXYfDU/wT7QG+evHL+Sd543niVsX8JnLSnli3SHe9NMVPDPIa2VHywu93MTJXi1t7dz0pyqaAkHu/1AZIz2wflaPL5lIr/TUFD51aSnrDjSwfFv071U64u84kz9Elut0Pb4+dvEURgxL585ntzo4osTZX3+Su1/YwRU/W8GVP3+R3724mzPG5PHz689hzTeW8PPrz+XSM4pPORGgr0cZt/bS5TrKc7IzUjlvUgEvbh/cJN/tdh5p4r33rSIYsvz1Yxcya2x498HMtFS+cPlMLp9dwpcefp2b/7yGq+eO5VtXz/bEZE7Fl7WWrzy6nnUHGrj3A+dxRknf254r77tu3nh+9fwOfr50O5fMGBXV2nyff2gt1+kqLyudWxZN43tPb2HVrjounOrON6MPRmNLG/9ae5B/vXaIqr3hn3qXTSrg29fM4a1njdG/K4YIPZOfRCZMmOD0EBKmYnoRmw83dv7IeSDc3Gt7rZ/33LuKkLX87eNvTPC7mjMun399agGfXzKDp9cf5vKfLufp9YfjNiY393Ijp3rdt2IXj609yBffNIMrZpc4MoaB0ONLpmuvjLQUPrloGmv3Hacyyp9w+vwBjIHCITLZ6358fbB8MqPzMrnzma0DvgSpWwXbQ9xw/2q+8a+NNLa08aUrZvLi7Zfy8Ccv4gMXTopqgq+vRxm39tJJfhIZPTqxb8R00ptmjSY1xfD++1ez5+iJAT2GW3ttqWnk+vtWYQw8dNOFfV7ZICMthc8umc6Tt1ZQkp/FLQ9Wc8uDazjaFPv1gW7t5VZO9Hphi48f/GcLbz1rDJ++rDThzz8YenzJdO/1rrLxjMnPinpt/hF/C4XDM4fM5kXde2Wlp/KZxdNZs/cYL2x15+UPB+r+yt28fqCBn75nLs9+/hI+dWmpeG8MfT3KuLXX0Hh1DxFVVVVODyFhZozO5c8fvYCjTQHefvdLvDiA6+a7sdemQ428975VpKUaHrrpQkqLo7t02Zlj8njslgV86YqZPLfJx5t+spwn1h2K6RkqN/Zys0T32uHz85m/rWXWmDzufNfZosspuoEeXzLde2WmpfLJRdOo2nuMlTvr+r2/rzHAqCHyplvo+fh6d9kEJhUO485nthGKwS7qbrDrSBM//e82rpg9mmvOGTfgx9HXo4xbe+kkX3nWRdOKeOLTFYzJz+JDv3+F+1/c5ekfu2442MD77l9Fdnoqf7+pnGmjZNdwjrwB76nPVDCxcDif+dtabv7zGnz+gS9pUt7QcLKNj/2xisz0FO77YNmgLi2rvOvdZRMYnZfJz5b2/yZAn3/o7Hbbm/TUFL7wphlsPtzIU3Fc6pgooZDljkfXk5GWwrffPsdz/9BXsaeT/CSSlzf03mA3YeQwHvnkRbxp1mi+83+bue2fr9PS1h7Vfd3Ua93+47zvt6sYnpHG328uZ3LR8AE/1ozRuTzyiXLuuPIMlm07wpt+soLH1h4Y9D+A3NTLCxLVK9ge4tN/q+bg8WbuueE8xo3ITsjzxpoeXzI99cpKT+UTl0zjld31rNrV99n8obTbLfR+fF119ljOKMnlJ89upa3d1Zva9+uvr+zjld31fOOtsyjOG9xVk/T1KOPWXjrJTyLz5s1zegiOGJ6Zxm/efx6fWzKdR6oPcP19q6J6Q65beq3Ze4wb7l9N/rB0/n7zheK1kz1JS03hE5dM4+nPXMy0UcP5/N/X8bE/VlE7iDcqu6WXVySq1/ee3sKL24/y3WvOomzyyIQ8Zzzo8SXTW6/3XjCRUbmZ/Py53s/mt4csR5tah9SVdXrrlZJiuO3ymeypO8nDaw4keFSxc7ihmR/8ewsLSgt5V9n4QT+evh5l3NpLJ/lJpLKy0ukhOCYlxfC5JTO454Z5bKv1c9WvKnlt//E+7+OGXq/uqeeDv1tNYU4Gf7+pnPEFg5/gd1VanMM/P3ERX3/rmby08yhLfrKcf1TtH9BZfTf08pJE9PrHq/v5/Uu7+fCCybz7fHde3SFaenzJ9NYrcjZ/5a46Xtld3+Nt6k+00h6yQ+Ya+dD38bX4zGLOnTiCnz+3PeqfBLuJtZavPbaB9pDl+9fG5v04+nqUcWsvneQnkWAw6PQQHPfmOWN49JaLyEhL4d33ruTR6t7PzDjda9WuOj70+1cYnZfFQzeVMzZOyyxSUwwfu3gq//7sQs4syeP2h1/nxj+8yqHjzaLHcbqX18S7V9Weer72+HoqSov42lvOjOtzJYIeXzJ99XrfBRMpysnkF72szY+8T2coLdfpq5cxhi9dMZOaxhb+smpvAkcVG0+sO8TzW3x88fIZTCyMzYkifT3KuLWXTvJV0jmjJI9/faqC8yYW8IV/rOO7/7eJoMvWWr684yg3/uEVxo7I5qGbL6QkP/5n1KYUDeehmy7kW1fN4pXd9Vz+0xX87ZV9nn6z8lB18Hgzn/jLGsaNyOZX7zt3yFwGUUUnOyOVmxdOpXLHUdbsPf1s/lDeCKs3F00r4uLpRdz9wg78LW1ODydqdU0B/ufJTcydMIIPL5ji9HCUyxj9Cz62ysrKrFOXUgqFQqSk6F/2EW3tIb7z1Cb+uHIvF08v4lfvnUf+sPTOzzvVa8W2I3z8T1VMLhzOgx+fT1FO4v+i3Vd3ki8/8jord9Vx8fQivv+Os/pdKqTHl0y8ejW3tvPOe15mb91JHv/URVFfZtXt9PiS6a/XydYgF//wBWaPy+dPH7nglM/9o2o/tz/8Oi/efmlM3gPkBdEcX+v2H+ftd7/E55ZM53NLZiRoZIPz2YfW8vT6wzx168V97qkipa9HGSd7GWPWWGvLevqcfgWTyMaNG50egqukp6bwP2+fww/ecRardtXx9rsr2eHzd37eiV4vbPXxsT9VMaVoOH91aIIPMLFwGA9+bD7fuWYO1XuPccVPV/DnVXv7vFa0Hl8y8ehlreW2h9ex6XAjv3jvOUkzwQc9vqT66zUsI42PL5zKim1HWLvv2CmfO9JxJn8oXSc/muNr7oQRvHl2Cfe/uJv6E60JGNXgLN1cy79eO8SnLi2N6QQf9PUo5dZeOslPInV1/W+AMhRdf8FE/vbxC2kKBLnm7pdZurkWSHyv5zbVcvOf1jC9OIe/ffxCCh2a4EekpBhuuHASz3x+IedOLOAbj2/g/fevZl/dyR5vr8eXTDx63f3CDv7v9cN8+c1ncNkZ7txhcaD0+JKJptcHLpxEwbD009bm+xpbyMtKIys9NV7Dc51oj68vXj6Dk61BfrNsR5xHNDj+lja+/vgGZo7O5ZZFsd/dWl+PMm7tpZN8NSSUTR7JE5+uYHLRMD72pyrufmFHQtei/2dDDZ98cA1njsnlrx+7kILhGQl77v6MLxjGnz96AT94x1lsONjAFT9bwQMv7U6aHSCTxbMba/jxs9u45pyx3LxwqtPDUR4wPDONj108lRe2HmFdl6uN+fyBQV9HPVlNH53LteeO548r93K4QXZxgkT64X+2UNvYwg/feTYZaTqVUz3TIyOJzJ071+khuNrYEdn88+aLuOrssdz5zFb+tjeb5tb4Xy7t6fWH+fRfq5kzLp8/f2z+Ke8LcAtjDNdfMJFnPr+Q+VNH8q0nN3H9favYffRE5230+JKJZa8tNY18/u+vMXd8Pj+4LjaXyHMbPb5kou31wfJJ5Gen88vn3zib7/MHGOXwTxITTXJ8fW7JdKy1/PJ5d57NX72rjr+s2seHF0zhnAkj4vIc+nqUcWsvneQnEb/f3/+NhrjsjFR+fv05fPnNZ/DfLXW8856XOSi8lKTEE+sOcevf1nLOhBH86SMXkJflvgl+V2NHZPOHG8/nzneezeaaRq78+Qruf3EX7SGrx5dQrHrVn2jl43+qYnhmGvd+oCxpl1jo8SUTba/crHQ+VjGF5zb72HCwAejY7XaIXVlHcnxNGDmM914wkX+8up89XU50uEFLWzt3PLqeCSOz+eLl8XtzsL4eZdzaSyf5SWTXrl1OD8ETjDF8ctE0Pjsvk311J7n6l5W9bhozGI+tPcDnHlrLeZMK+ONHLiDX5RP8CGMM7yqbwHNfuIQF04r4zv9t5l33vMzqDe48q+VWsXg9trWHuOXBNdQ2Brj3A+cl5FKrTtHvXzKSXh9aMJm8rDR+sXQ71lp8jYEhdY18kB9fn76slLRUw0+f2xanEQ3Mz57bzu6jJ/jBO85mWEZa3J5HX48ybu2lk3w1ZJ1TnMZjn1pAfnY6779/FX9dvS9mj/3Pqv184R/ruHBqIQ98+HyGZ8bvm3G8jM7L4v4PlfHT98xle20TD2wMOD2kIed/n9zEql31/PC6szh3YoHTw1EelZeVzkcqpvDsplpW764nEAwNqd1uB6I4N4sPL5jCE+sOsflwo9PDAWDDwQZ+++Iu3lM2gQWlRU4PR3mATvKTyOTJk50egqdMnjyZ0uIcHvvUAi6aVsRXH1vPNx7fQNsgN8566JV93P7I61SUFvG7D50f17Mt8WaM4dpzx/Opy0rZWBfi9QPHnR6SZwz29fiXVXv586q93HzJVK49d3xsBuVi+v1LRtrrwwumkJuZxreeCF/qb6gt1xnI8fWJhdPIyUzjrme3xn5AQm3tIW5/+HUKh2fw1bfGf4drfT3KuLWXTvKTyMiRI50egqdEeuVnp/P7G8/n5oVT+fOqvdxw/2rqmgZ21vrPq/Zyx6PrWTh9FL/9YBnZGcmxfvr98yeSm5XGr1/Y6fRQPGMwr8dVu+r41hMbuXTmKG6/4owYjsq99PuXjLRXfnY6H14wmS014bXDQ+ka+TCw4yt/WDqfuGQaz2329bhzcCLdt2IXmw438u1r5pCfHf+ln/p6lHFrL53kJ5Hq6mqnh+ApXXulphi+8pYz+el75rJ2/3Gu/tVLbDok+xHtAy/t5huPb2DxGcXc98HzkuoNkrlZ6SwaZ3hmUw07fE1OD8cTBvp63F9/kk/+ZQ2TCofx8/eeS2pK8l1Jpyf6/UtmIL0+UjGFnI6lg0Ntuc5Aj68PL5hMUU4GP/rP1oRedrmrHb4mfr50O289awxXzC5JyHPq61HGrb10kq9UF9eeO56HP1FOe8hy3W9e5t/rD0d1v/tf3MW3ntzE5bNG85sbziMzLXkm+BFvmpROZloK9yzXs/nx9O2nNhEMWe7/0PmuvxqT8pYRwzL4SMUUMtNSGJPEb+KOpWEZaXz60lJW767nxe1HE/78oZDljkdeJzs9lW9dPTvhz6+8TSf5SaSgQN+YJ9Fbr7PHj+CJTy/gzDG5fPLBan7y7NY+N4a6Z/lOvvN/m3nLWSXc/f55SbsxyaTRI7n+/Ik8vvZgXC87miwG8nqsP9HK81t8XH/+BKYUDY/DqNxLv3/JDLTX5xZP54XbFnnyYgCDMZjj673zJzJuRDZ3PpP4s/l/Wb2Xqr3H+MbbZiV0iZW+HmXc2is5ZyNDlFs3Y3CrvnoV52Xxt5su5N1l4/nF8zv4xF/W0BQInna7Xz2/nR/8ewtXzR3LL64/l/TU5H1JzZ07l4937LT62xXuvFyYmwzk9fjkukMEQ5Z3zEv+N9p2p9+/ZAbaKyXFMHZEdoxH436DOb4y01L53JLprD/YwDMba2I4qr4dPN7MD/+9hYunF3HdvHEJe17Q16OUW3sl74xkCFq+fLnTQ/CU/nplpqXyw+vO5ptXzWLpFh/v+PVL7K17Y2OUnz23jR8/u41rzhnLT989l7QknuBDuNe4Edlcc+44Hnp134DfnDxUDOT1+Ej1Ac4ck8eZY/LiMCJ30+9fMtpLZrC9rj13HNNGDefHz26jvY+f7MaKtZavPbYeC3zv2rMSvsu1Hl8ybu2V3LOSIcapNwV5VTS9jDF8eMEU/vSRC6htDPD2u1/ipR1HuevZrfzsue2887zx3PXuc5J+gg9v9PrEJdMIBEP84aU9zg7I5aSvx+21fl4/0JDwM3Zuod+/ZLSXzGB7paWmcNvlM9nha+KxtQdjNKrePf7aQZZtPcLtV8xkwshhcX++7vT4knFrr+SfmQwhif6XvtdJei0oLeKJTy+gODeT99+/ml8+v4Prz5/Aj647e8hc/STSq7Q4hzfPLuGPK/fgb2lzeFTuJX09Prr2IKkphqvPGRunEbmbfv+S0V4ysej15jklnDUun5/+dxuBYHsMRtWzo00B/ufJTZw3qYAPlE+O2/P0RY8vGbf2Mm7914dXlZWV2aqqKqeHoeKkKRDkm//ayKjcTG6/YiYpQ2SC3936Aw1c9atKvvzmM/jkomlOD8fz2kOWBT94njPH5PKHD1/g9HCUUr1Yse0IH/z9K/zP1bP50EWT4/Icn/5rNc9urOXpz1ZQWpwbl+dQycMYs8ZaW9bT5/RMfhJZt26d00PwlIH0yslM4653z+WOK88YchP8rr3OGp/PxdOL+F3lblra4ndGy8skx9fKnXXUNLZw3XlD7w23Efr9S0Z7ycSq18XTi5g/ZSS/fH4HJ1tPvxjDYD27sYanXj/MrZeVOjrB1+NLxq29dJKfRI4dO+b0EDxFe8l07/XJRdM42hTgn2sOODQid5McX49UHyA3K40lZ46O44jcTV+PMtpLJla9jDHc/uaZHG0KxPx9SY0tbXzjXxs4oySXmy9x9iekenzJuLWXTvKVUgNSPrWQcyeO4L4VOwm2h5wejmc1BYL8Z0MNbzt7bFLtkqxUsjpv0kgWn1HMvct30nAydu9L+v7TWzjiD/Cjd56dtPutqMTSoyiJzJs3z+kheIr2kuneyxjDLYtK2V/fzFOvR7cz8FAS7fH1nw01NLe1D9mr6kTo61FGe8nEutcXL59JY0uQe1fEZgfwl3ce5W+v7OPjF0/l7PEjYvKYg6HHl4xbe+kkP4nU19c7PQRP0V4yPfVafEYxM0bn8OtlO/rcFXgoivb4emTNASYVDuO8Se7cMTFR9PUoo71kYt1r1tg8rp47lj+8tAefv2VQj9Xc2s5XHl3P5MJhfG7JjBiNcHD0+JJxay+d5CeRPXv2OD0ET9FeMj31SkkxfHLRNLbVNrF0iy/xg3KxaI6vA8dOsnJXHe84d7xrL8GWKPp6lNFeMvHo9fk3zaC1PcTdz+8Y1OP89Llt7K07yfffcTbZGe5YsqfHl4xbe+kkXyk1KFedPZbxBdn8etkO124I4lb/eu0QEN5NUynlLVOKhvPusgn89ZV97K8/OaDHWLf/OPe/uIv3zZ9I+bTCGI9QDXU6yU8iU6dOdXoInqK9ZHrrlZaaws2XTGPtvuOs2uXOH1k6ob/jy1rLI2sOcMHkkUwsTPyOlm6jr0cZ7SUTr16fWVyKMYafPbddfN/WYIgvP/I6o3IzuePKM+IwuoHT40vGrb10kp9EcnN10wwJ7SXTV693nTeeopxMfr1scD+2Tib9HV+v7T/OrqMnuO48PYsP+nqU0l4y8eo1Jj+bD5VP4rG1B9he6xfd997lO9lS4+e715xFXlZ6XMY3UHp8ybi1l07yk4hbN2NwK+0l01evrPRUPloxhRe3H2X9gYYEjsq9+ju+Hqk+QGZaCleeNSZBI3I3fT3KaC+ZePb65KJShmWkcdez26K+z/ZaP798fgdXzR3Lklnu2x9Djy8Zt/bSSb5SKiZuuHAiuVlpejY/CoFgO0+uO8wVs0tcdwZPKSUzcngGH7t4Cv/ZWMO6/cf7vX17yPLlR15neGYq37xqVvwHqIYsneQnkcJCfdOOhPaS6a9XblY6HyqfzH821rDD15SgUblXX71e2OKjobmNdwzxa+N3pa9HGe0lE+9eH62YQsGwdH787NZ+b/unlXuo3necb141m6KczLiOa6D0+JJxay+d5CeR2bNnOz0ET9FeMtH0+vCCyWSmpXDv8thsEONlffV6eM1BRuVmUlFalMARuZu+HmW0l0y8e+VmpXPLolJe3H6Ul3ce7fV2++tP8qP/bOXSmaN4+zlj4zqmwdDjS8atvXSSn0RWrFjh9BA8RXvJRNOrMCeT68+fyGNrD3LweHMCRuVevfWqawqwbKuPa88dR1qqfguO0NejjPaSSUSvD5RPoiQvix8/s7XHywlba/nqY+tJMfDda89y9d4YenzJuLWX/g2jlIqpjy8MX0rstyt2OTwSd3py3SGCIatLdZRKMlnpqXxm8XSq9x1n6ebTNwd8pPogL24/yh1XnsHYEdkOjFANNUNukm+MeYsxpsYY84DTY4m1tLQ0p4fgKdpLJtpe40Zk8/ZzxvHQq/uoawrEeVTu1VuvR6oPMntsHmeU5CV4RO6mr0cZ7SWTqF7vKhvP5MJh/PjZrYRCb5zN9/lb+PZTmzh/cgHvnz8pIWMZDD2+ZNzayzOTfGNMgTHmNmNMlTFmozFmszHmWWNMRZT3zzbG3A18B+j1elXGmIXGmL8ZY7YYYzZ0PNf/GmNyYvVniZeKiqhSqA7aS0bS65OLphIIhnjg5T3xG5DL9dRrW62f9QcbeMe88Q6MyN309SijvWQS1Ss9NYXPv2kGW2r8PPn6oc6Pf+uJjTS3tfOD684mJcW9y3Qi9PiScWsvz0zygZuBO4AbrbWzgTnAJmC5MebyKO7/TuAEcGE/t3sWOAqcZa2d03G/TwBPGzcvoAOqq6udHoKnaC8ZSa/S4lyumFXCH1/eg7+lLY6jcq+eej1SfYDUFMPVc937hjun6OtRRnvJJLLXVWeP5YySXH7y3220tYf4z4bDPL2+hs8tmc60Ua4/Xwjo8SXl1l5emuQD3GOt3QBgrW0nPOlvB26N4r5/s9bebq1t7ed2rcCXrbVtHc+zGbgXuBiYN+CRJ0BjY6PTQ/AU7SUj7XXLpdNobAny4Op9cRqRu3Xv1R6yPL72IItmjGJUrjsvm+ckfT3KaC+ZRPZKSTF86YqZ7K07yf0v7uYb/9rI7LF5fPziqQkbw2Dp8SXj1l5emuTfCXyz6westS3AMaCgvztba4NRPk+RtfZkt49FfubW7/MopcLOHj+CitIifle5m5a2dqeH47iXdx6ltjGgS3WUGgIuO6OYeRNH8MP/bKH+RCs/vO5s0vVqWirBPHPEWWvbO87edzLGFABFwLIYPk9PZ/pnEF7qUxWr54mHsrIyp4fgKdpLZiC9brl0Gkf8AR5ecyAOI3K37r0eWXOAvKw0Fp9Z7NCI3E1fjzLaSybRvYwx3P7mMwC4eeFU5ozLT+jzD5YeXzJu7eWZSX4vPgb4gJ/G6wmMMbnAe4HvW2uPx+t5YqG2ttbpIXiK9pIZSK/yqYWcM2EE967YSbA9FIdRuVfXXk2BIP/ZWMPb5o4lKz3VwVG5l74eZbSXjBO9LpxayIovXcqXrpiZ8OceLD2+ZNzay7Fr/hhjlgD/jeKmy621i3q4/2zCa/Kvs9bWxXh4Xf0SqAZ+0NsNjDE3ATcBjB07lmXLlgEwdepUcnNzWbduHRDe9nj27NmdmyakpaVRUVFBdXV153qusrIyamtr2b9/PwDTp08nMzOTDRs2AFBcXMyMGTOorKwEIDMzk/LycqqqqqipqWH//v3Mnz+fAwcOcPDgQQBmzpxJamoqmzZtAqCkpIQpU6awcuVKALKzs5k/fz6rV6+muTm8gVF5eTm7d++mpqYGgFmzZtHe3s7WreEtu8eNG8f48eNZvXo1ADk5OZSVlbFy5UoCgfBlEysqKti2bRs+X/h6wXPmzCEQCLB9+3YAJkyYwOjRo6mqCv+AJC8vj3nz5lFZWUkwGF5dtXDhQjZu3EhdXfhLPHfuXPx+P7t2ha/BPnnyZEaOHNn5ppeCggLmzp3L8uXLsdZijOGSSy5h3bp1HDt2DIB58+ZRX1/P5s2b2b9/f8K/Tk1NTQCe+zpFekm/TguLgvxif4B/vrKLMYED4q/Tnj17gMS/ngb7ddqyZUvn8x7OmkRLW4gp+Fi2bFlSvp4G+3XasWPHkHo9DfbrtGfPns5eQ+H1NNivU1NTEz6fz5HX0/Llyz33dQqFQoRCoSHzehrs973W1lZGjhzpyOupL6anXdkSwRgzDJgYxU1PWmtPeeeeMWYi4SU6t1lrHx3Ac1vgj9baG/u53beAxcAVPazT71FZWZmNHGyJtmzZMhYtWuTIc3uR9pIZaK9QyHLFz1aQYgz//uzFnrh8XCx07XX9fSupaWjhhdsWuXqXSyfp61FGe8loLxntJeNkL2PMGmttj+uFHDuT3zFp3iK9nzFmAuGfANw+kAm+4Hm+RniCf2W0E3ynTZ8+3ekheIr2khlor5QUwycXTeML/1jH81t8LJnV6zYVSSXS68Cxk6zaVc8X3jRDJ/h90NejjPaS0V4y2kvGrb08tSa/ywT/a9bah7t8/IlutxtmjBnwu1yMMV8F3kx4gt/U8bGbjDFvG+hjJkJmpl6WT0J7yQym11VzxzK+IJtfL9uBUz89TLRIr8eqwz/uvvbccU4Ox/X09SijvWS0l4z2knFrL89M8o0x44EXgA1AljHmhsgv4OxuN18L7DDGDB/A83yF8Fr/B4FrujzHmwlfyce1Iuu4VHS0l8xgeqWnpnDzwqlU7zvO6t31MRyVe23YsAFrLY+uPcj8KSOZMHKY00NyNX09ymgvGe0lo71k3NrLseU6A/AFYFrHr+u6fW5vt/8/DAQ7fgFgjMkGVna5zdXGmNeA/1hr7+i4TT7wvY7P/6aHMTzRw8eUUlF4V9kEfr50O79etpMLpxY6PZyEWLv/OLuPnuCTl0xzeihKKaWGGM9M8q21XyA80Y/mtot6+FgzcE4/92sAPLtotrhYr78tob1kBtsrKz2Vj1RM4Uf/2cr6Aw2cNd5b142WKi4u5sE1B8hKT+HKs0qcHo7r6etRRnvJaC8Z7SXj1l6eWa6j+jdjxgynh+Ap2ksmFr1uuHASuZlp/Gb5jhiMyN0mTS3lyXWHuGJ2CblZ6U4Px/X09SijvWS0l4z2knFrL53kJ5HItVRVdLSXTCx65WWl88GLJvHvDTXsPNIUg1G5168fX0FjS5B3zBvv9FA8QV+PMtpLRnvJaC8Zt/bSSb5SKqE+vGAKGakp3Lt8p9NDiauXDgYpzs2kotTV79dXSimVpHSSn0Tcegknt9JeMrHqVZSTyfXnT+CxtQc5dLw5Jo/pNkebAqw/2s61544jdYhs/jVY+nqU0V4y2ktGe8m4tZdjO94mKyd3vFXKKw4cO8miO5fxgfJJfPOq2U4PJ+Z+X7mb/31qE898biEzS3KdHo5SSqkk1deOt3omP4noPy5ktJdMLHuNLxjG288Zx0Ov7Kf+RGvMHtctHl17gKkj0nSCL6CvRxntJaO9ZLSXjFt76SQ/iTQ1JfcbGWNNe8nEutcnF02lJdjOAy/tjunjOm1rjZ8NBxu5sES/vUro61FGe8loLxntJePWXvq3kFLKEaXFuVw+azQPvLyHpkCw/zt4xKPVB0hLMVw4xjPbkCillEpCOslPIvPnz3d6CJ6ivWTi0euWRaU0tgR5cFX3Tau9qT1keWztQRbNHMWbFl7o9HA8RV+PMtpLRnvJaC8Zt/bSSX4SOXDggNND8BTtJROPXnMnjGBBaSH3V+6mpa095o+faC/tOIrPH+Ad88br8SWkvWS0l4z2ktFeMm7tpZP8JHLw4EGnh+Ap2ksmXr0+taiUI/4Aj1S785ukxCPVB8jLSmPxmcV6fAlpLxntJaO9ZLSXjFt76SRfKeWo8mmFzJ0wgnuX7yLYHnJ6OAPmb2njmY01XDV3LJlpqU4PRyml1BCnk/wkMnPmTKeH4CnaSyZevYwx3LJoGvvqT/J/6w/H5TkS4d/ra2hpC/GOeeMBPb6ktJeM9pLRXjLaS8atvXSSn0RSU/XsoYT2kolnrzedOZrpxTn8ZtlOvLpB3yPVB5hSNJx5E0cAenxJaS8Z7SWjvWS0l4xbe+kkP4ls2rTJ6SF4ivaSiWevlBTDJy6ZxpYaP89v8cXteeJlf/1JVu+u5x3njsMYA+jxJaW9ZLSXjPaS0V4ybu2lk3yllCtcfc5Yxo3I5tcePJv/2Nrwm66unTfO4ZEopZRSYTrJTyIlJSVOD8FTtJdMvHulp6Zw8yVTWbP3GK/sro/rc8WStZZHqw9w4dSRjC8Y1vlxPb5ktJeM9pLRXjLaS8atvXSSn0SmTJni9BA8RXvJJKLXu8smUJSTwa+X7Yz7c8VK9b7j7Kk72fmG2wg9vmS0l4z2ktFeMtpLxq29dJKfRFauXOn0EDxFe8kkoldWeiofXjCF5duOsOFgQ9yfLxYeqT5AVnoKbzlrzCkf1+NLRnvJaC8Z7SWjvWTc2ksn+UopV/lA+SRyM9P4jQfO5re0tfPUukO8eXYJOZlpTg9HKaWU6qST/CSSnZ3t9BA8RXvJJKpXXlY6HyifxNMbDrPrSFNCnnOglm720dgS5Lrzxp/2OT2+ZLSXjPaS0V4y2kvGrb2M165i4XZlZWW2qqrK6WEo5WlHmwIs+MHzXHPOOH74zrOdHk6vPvrAq2w41MDLdywmNcU4PRyllFJDjDFmjbW2rKfP6Zn8JLJ69Wqnh+Ap2ksmkb2KcjJ5z/kTeHTtAQ43NCfseSWONgVYtu0I15w7rscJvh5fMtpLRnvJaC8Z7SXj1l46yU8izc3unAy5lfaSSXSvj188lZCF367YndDnjda/XjtEe8hy3bzTl+qAHl9S2ktGe8loLxntJePWXjrJV0q50oSRw3j7OWP5y6q9PLh6r+s2yHq0+gBnjctnxuhcp4eilFJKnUbX5MeYk2vyA4EAmZmZjjy3F2kvGSd61Z9o5bMPreXF7Ud561lj+N47ziI/Oz2hY+jJlppG3vyzF/nmVbP48IKer4+sx5eM9pLRXjLaS0Z7yTjZS9fkDxG7d7tzWYNbaS8ZJ3qNHJ7BHz98AV9+8xn8Z2MNb/3Fi6zddyzh4+ju0eqDpKUYrp47ttfb6PElo71ktJeM9pLRXjJu7aWT/CRSU1Pj9BA8RXvJONUrJcXwyUXT+MfN5VgL77pnJfcu30ko5MxPIYPtIR5be5BFM4spzOn9zI0eXzLaS0Z7yWgvGe0l49ZeOslXSnnCeZMKePozF7PkzNF8/99b+PADr3K0KZDwcVTuOMoRf4B3njcu4c+tlFJKRUsn+Ulk1qxZTg/BU7SXjBt65Q9L5zc3zOPb18xh5a463vLzF3l5x9GEjuHR6oPkZ6dz6RnFfd7ODb28RHvJaC8Z7SWjvWTc2ksn+Umkvb3d6SF4ivaScUsvYwwfuHASj9+ygJysNN7/u9Xc9exWgu2huD+3v6WNZzbWcNXcMWSmpfZ5W7f08grtJaO9ZLSXjPaScWsvneQnka1btzo9BE/RXjJu6zVrbB5P3VrBdfPG88vnd/C+367m0PH4Xqv46fWHCQRDvV4bvyu39XI77SWjvWS0l4z2knFrL53kK6U8a1hGGj9+11x++p65bDzUwFt+8SL/3VQbt+d7pPogU4uGc86EEXF7DqWUUioWdJKfRMaN0zcCSmgvGTf3uvbc8Tx5awXjRmTz8T9V8T9PbiQQjO2PT/fXn+SV3fW8Y944jDH93t7NvdxIe8loLxntJaO9ZNzaSyf5SWT8+P6XEKg3aC8Zt/eaOiqHR2+5iBsvmswfXtrDdb95md1HT8Ts8R+tPgjAtVEs1QH393Ib7SWjvWS0l4z2knFrL53kJ5HVq1c7PQRP0V4yXuiVmZbKt66ezX0fOI/99c287Rcv8q/XDg76ca21PLr2AOVTCxk3Ijuq+3ihl5toLxntJaO9ZLSXjFt76SRfKZV0Lp9dwr8/ezFnjsnjsw+9xpf+uY6TrcEBP96avcfYW3eS685z59kapZRSqjud5CeRnJwcp4fgKdpLxmu9xo7I5qGbLuTWy0p5uPoAV/2yks2HGwf0WI9UHyQ7PZU3zymJ+j5e6+U07SWjvWS0l4z2knFrL2OtM1vDJ6uysjJbVVXl9DCUUl28tOMon/v7azQ0t/H/3jaL98+fGNWbZwFa2to5/7vPseTM0fz0PefEd6BKKaWUgDFmjbW2rKfP6Zn8JLJy5Uqnh+Ap2kvGy70WlBbx789ezIVTC/n64xu45cFqGprborrvc5tr8bcEo7o2flde7uUE7SWjvWS0l4z2knFrL53kJ5FAIOD0EDxFe8l4vVdRTiYP3Hg+X7nyDP67qZa3/PxFqvcd6/d+j1YfpCQvi/JphaLn83qvRNNeMtpLRnvJaC8Zt/bSSb5SashISTHcfMk0/vGJcoyBd9+zknuW7yQU6nnZ4hF/gOXbjnDtvHGkpkS3vEcppZRyA12TH2NOrskPBoOkpaU58txepL1kkq1XQ3MbdzzyOv/eUMPCGaP4ybvnUpSTecpt7n9xF9/5v8389/MLmT46V/T4ydYr3rSXjPaS0V4y2kvGyV66Jn+I2LZtm9ND8BTtJZNsvfKz0/n1++fxnWvmsGpXHVf+/EVe2nH0lNs8Wn2Qs8fniyf4kHy94k17yWgvGe0lo71k3NpLJ/lJxOfzOT0ET9FeMsnYyxjDDRdO4l+fWkBeVho3/G41P35mK8H2EJsPN7LpcKP4DbcRydgrnrSXjPaS0V4y2kvGrb30ZzFKqSHvzDF5PHlrxf9v797DrarrPI6/v4IQx0uCCMolgbxMgByGSMIYhbxUOhGlZVNZaoQ+T9ZDZubYU15zzKacLpamY+TMNDk1lmaPZl6OhBKNIiiQQgHexQsUCAhy+M4fv98+7Rb7nL1+58Lae5/P63n2s8/+rdtvfc7aa//22r+1Fhfftpzv3vdHfrf6FUYOaqLvHsZ7m4cVXT0REZFkauQ3kPHjxxddhbqivNI0el5N/fpy1SnNvOOQwVx4y2M89OQGjh87lEF79evU/Bo9r+6mvNIorzTKK43ySlOrefW67jpmdqKZvWBm83KM28/M/mRmdXF2cq1ewqlWKa80vSWv900czu2f/QdOmnAQ58w4pNPz6S15dRfllUZ5pVFeaZRXmlrNq24a+WY20MzOM7OHzGy5mf3BzO4ys2k5px9gZtcAlwNDcy72M8CYztZ5d1u1alXRVagryitNb8pr9OC9uOYjk2geuV+n59Gb8uoOyiuN8kqjvNIorzS1mlfdNPKBs4ALgNPdfRwwHlgB3G9mJ+SY/hRgM/D2PAszs/2BzwP3dq66IiIiIiLFqKdGPsC17r4MwN1bCY3+VsIR92r+293Pd/ftOZf1FWAe8HRnKlqEkSNHFl2FuqK80iivNMorjfJKo7zSKK80yitNreZVTyfefj1b4O6vmdkGYGC1id19R94FmdlhwMnAWODbKZUs0tCheXshCSivVMorjfJKo7zSKK80yiuN8kpTq3nVzZF8d2+NR+/bmNlAYDDQ0s2Luwq4wt03dvN8e1RRd9qtV8orjfJKo7zSKK80yiuN8kqjvNLUal71dCS/ktnAi8DV3TVDMzsGOJzQhz/vNHOAOQDDhg2jpaUFgDFjxrDPPvuwdOlSAPbff3/GjRvH/PnzAejbty/Tpk1j8eLFbNwYvk9MnjyZdevW8fTToZfQoYceSv/+/Vm2bBkAQ4YM4bDDDmPBggUA9O/fn6lTp/LQQw/x6quv0tLSwpQpU3jmmWd49tlnATj88MPp06cPK1asAODAAw9k9OjRLFy4EIABAwYwZcoUFi1axNatWwGYOnUqa9as4YUXXgBg7NixtLa28sQTTwAwfPhwRowYwaJFiwDYe++9mTx5MgsXLmw7y3zatGmsXLmy7SYR48ePZ9u2bW0nqIwcOZKhQ4e2vTn23XdfJk2axIIFC9ixI/zwcvTRR7N8+XJeeeUVAJqbm9m0aROrV68GYNSoUQwaNIjFixcDMHDgQJqbm7n//vtxd8yMY445hqVLl7JhwwYAJk2axPr169vyKuL/BNTd/6mU1+7+P61duxYo5v3Ulf/T5s2b2/YFveH91NX/07Zt29ry6g3vp67+n1pbW9vy6g3vp67+n4Be9X7q6v9p586drFq1qte8n7r6f9q+fTsbNmwo5P3UEXMv5uqQZnYc8Jsco97v7tMrTD8OmA+c7O4tict24Efufnqm3ID/Ay5199ti2TzgE+5ueeY9efJkL+ob3eLFi5k0aVIhy65HyiuN8kqjvNIorzTKK43ySqO80hSZl5k97O6TKw4rsJHfBLwpx6hb3P2pzLRvInTROc/db+nEsttr5J8GnOnuM8rK5lEnjXwRERER6T06auQX1iff3be4++M5HtkG/kjCLwDnd6aBX8U7geFmtqT0AGbG5ZbKJnbzMrtN6ScdyUd5pVFeaZRXGuWVRnmlUV5plFeaWs2rrvrklzXwv+TuPysrv83dZ5a9bgL2dPe/pMzf3c+osMx5hCP5Eztb792l1P9M8lFeaZRXGuWVRnmlUV5plFca5ZWmVvOqm0a+mY0A7gOWAG8ws4+VDZ6QGf0RYJCZjXL3zbupiiIiIiIiNaGwPvmpzOybwOfaGfyku48qG7cFOACY5O7bYtkAYGEcpRnYADwF3OnuF1RY3mzgHMJ5AwOBpcBz7n5iR/Ussk/+zp072WOPurkqauGUVxrllUZ5pVFeaZRXGuWVRnmlKTKvmuyTn8rdz3V3a+cxKjPudHcfV2rgx7Kt7j4xPszdB8W/d2ngx/FviMMHxfEnVmvgF2358uVFV6GuKK80yiuN8kqjvNIorzTKK43ySlOredVNI1+qK10DVvJRXmmUVxrllUZ5pVFeaZRXGuWVplbzUiNfRERERKTBqJHfQJqbm4uuQl1RXmmUVxrllUZ5pVFeaZRXGuWVplbzUiO/gWzatKnoKtQV5ZVGeaVRXmmUVxrllUZ5pVFeaWo1LzXyG8jq1auLrkJdUV5plFca5ZVGeaVRXmmUVxrllaZW81IjX0RERESkwdTNdfLrhZm9BDxZ0OIHAy8XtOx6pLzSKK80yiuN8kqjvNIorzTKK02ReR3s7gdUGqBGfgMxs4fauyGC7Ep5pVFeaZRXGuWVRnmlUV5plFeaWs1L3XVERERERBqMGvkiIiIiIg1GjfzG8oOiK1BnlFca5ZVGeaVRXmmUVxrllUZ5panJvNQnX0RERESkwehIvoiIiIhIg1EjX0REuoWZHWRmd5qZfiLOQXmlUV5plJf0LboC0jEzGwJcDZQuzfQYMNfdn8kx7Z7AV4APAjuAjcD57r6gh6pbKDObCHwamEZY3z7A3cBl7v5SlWnXAn+uMOg8d7+7WytaI8xsFLAM+GOFwdPd/c9Vpp8LzCFkvQO41N1/0a2VrCFmNo+wbb2aGTQQOBDYz923tjNtCzAE2J4Z9E13v6l7a1oMM/sA8E3g9SrjdWm/ZGYfBc4HjHCg6lvufn0Xql6IPHmZ2UHA2cD7CevaF1gBXOTuj+VYxjwqb7M/cfcrO1fzYiRsX2vpwr7czE4ALgcGAHsCPwK+5u47E6tcqJzb1+nAlcALmUF7AmOB49z9ng6mvxg4E1ifGTTf3T+bXuvi5G0/mNnehMyOB1qBZ4DPufvynMuZy+783HR3PWr0AfQDlgI/Jezc+xB2OKuAvXNMfy2wEjggvp4NbAEmFr1uPZTX48D/AnvF18Nj2UpgQJVp1xZd/wLyGgW0dHLaCwg3/nhzfH084cPkPUWvVw/mNY/w5Sdbfh1wc5VpW4BRRa9DD+ezCDg05uQdjNfp/RLwYWAbcGR8PQHYDJxV9Pr3RF5lWY2Mr98QPw+2AEfkWEbFbbYeHwnb19ouLGMa4Yv4rPh6JPAc8C9Fr39P5AWcDlxcofyfgGeBPlWWcTFwetHr2k155Wo/AHcAC4Cm+Poy4CVgeI5l7PbPzcKD1aPDDeJTgANjysoOJHx7/EKVaQ8HdgJnZsqXA78qet16KK/HgUMyZZ+MGZ5cZdq1Rde/gLxG0YlGPrBfbFhdmin/FbC86PXqwbymAkMzZXsBfwGOrTJtC43fyO8bnztqVHR6v0Q4kv00cFOm/BrgFaB/0Rn0QF7XArMzZW+O+7Tv5FjGPBqnkV81rzh8bReW8QDhKHR52RcIDf9hRWfQ3XkBY4AJFcrvIRzBrraMi2msRn6H7YfYKHfgnWXj9CP8knFNlfkX8rmpPvm17WTgKXdfXSpw9xcIP9eeXGXa9xN+zr4vU34vcEL8yanRTHD3bNeT5+LzwN1dmQb2bqCJytvWWDP7u91fpZ7n7gvdfV2m+EPAi4R179XcfUeO0bqyXzoSGNHOtIOAGTmrWhNy5nUOcGOmrFfu03Lm1Wmxa9RRVN6+9gRm9uTyu1uevNx9tbs/Wl5mZmOAY4AbeqpuNSpP++FkwpH3tq6F7r6d8OWwWpuskM9NNfJr2wRgTYXyNcAROabdCTxVYdq+hP52DSW+2bIOI3zznl9tejO7ysweNLOVZnaXmdXVTr2ThprZf5rZ7+N6/9jM8mxbsOu2uSYzvDf4FHC9x0MyVZxrZg+Y2eNmNt/MzujpytWgruyXet125+47fNe+4IfF55acszktbm8rzOx3ZnaumTX0+Xid3JeX9nu9Zvtqx2zgLnd/Muf47zaze8zsMTNbbGaXmllTT1awJ+RsP0wAnqsw7hrCZ+mQDhZRyP5LjfzaNhjYVKF8I9BkZgOqTLvF3VsrTAuwfzfUr6aZWR/Cz23/7u4rq4z+IrCY0CdzHHArcKuZndOztSxUK+HEn6vd/UjCyd2vA4vM7G0dTDc4Pme3zV6zbQGY2VjgrYSfw6v5M+EE5+mE7evbwPfN7F97qHq1qiv7JW13wRxC96b/yDHuJmAd4SjiOOBC4EvAT3qsdsXr7L68129f8TPzE+S/sdMWQheUk939CMLn7ceAe+IJ9nWrnfZDR20yqMH9V0N/m5de78uERuvcaiPGRm7JTuAaMzsRuMLMbnD313qmisVx96cp+0XI3Tea2dnAicAVhP6H0r7ZwK3u/mK1Ed19VqboZ2Y2A/icmX3b3bNHtkV2YWbHAqcCR7v7tmrju/tnMkX3mtmVwFVm9g53f6An6lmk3rgv70Ynxefb84zs7ldlXj9iZl8E/ofQlfG/urd6u1Xu9kMt05H82vYysE+F8n0JR8MqXq6vbNqm+G00Oy2EE9UaVuwK8SHCWeubOzmbRYT8x3VbxWpc3KYeA97ewWgvx+fsttkrti0AM+sHnEbXbmW+iLAP7uhXk0bTlf1Sr97uzKyZcHW1me6+oguzWhSfO3qPN5o8+/JevX1Fs4EfdvH8h7rfvjpoP3TUJoMa3H+pkV/bHiVcASVrNKEhVm3aPQiXAMtOu4Nw8m5DMrPTgM8TzoCvepTVzAa0c8JfqUtBtkHSEMzsjbGxmtVKx+tcOlFrVKZ8dGZ4I5tFuKpOu9eQLjGzfmb2xgqDGnr7akdX9ku9drszswnAL4APu/uDOafpY2aDKgxq2O2ui/vy0mfqqEx5w29f0Hbi8buA3PecMLMDKhTX9fZVpf3wKDCswufmaGBdlfZGIfsvNfJr2y3AwfGmRQCY2VDgLYTruVJebmbl/8+fE04YmZ6Z5wzCSTXZm6M0BDP7GPBFwk08Xohl/2hmc8rGyWZ1KvCNCrN7K+Ga3I36hehbZK4IEHdeRxD6tJbK9s/s1O4k9MWcnpnfDGCFuz/eI7WtLbNp54TbCnkdRfj5Ouut8fmRHqhfrcq9X4pfQstP4Ps94cYzlaZdT/4TUetKbODfCpzm8YZh8U6m12XGy+Y1ElhYYZal7W5xhWH1Lve+3Myayr98u/vzhLymZ6adQei28cvurmyNOQO4t70TbrN5RU9W+FWubrevHO2HWwhXWjqqbJp+wDvYtU1WG5+bu+sapHqkPwjXX30UuJlw/sQewA/J3AyLsIG1At/PTH8t8AQwOL4+A9hK494M66Nx/c4jnPxTelxHvOFHpawINwTZCLytrOxUQn/OS3fnOuzmvOYRdsQHxdd9CA3/VuD4WDYaeA24IzPtBYQbgIyJr4+jwW+GVbbuB8dMhlQYtktehJ36DuCkTNlmMtd8b4QH+W6G1eF+Cdib8PP1HzLTfjjmOzm+PoJwN9e6uxlWnrzi+r0UMyvfp82l7B4XlfIiHDF0YE5Z2QTgeeB+wIpe9x7IK/e+PG6DLxFvfhTLSjfDmhlfjyDcFKruboaVJ6+ycYxwYYD3dzBOpbyccHfgPvH1wYQvUo8TbxZVLw9ytB/ieHcCv+WvN8O6hMzNsKihz02deFvD3H27mR0PXE144ziwjPAzUvmR+FcJXQeez8ziM8BFwANm9jrhrO4T3H1JT9e9IN8h3BHy6xWGXRKfK2V1R5zme/GKAPsBG4Cz3b0rfa5r3TeAs4A7zQzC2f9/IBzFKF3LdyvhKOlz5RO6+5Vm9hpwu5ntIHwx+KC737G7Kl+gTwK/9Mo/zVbKazFwPnChmV1BuIHWdsKHY6VttS6Z2dcJJ2u/Kb5eEgcd6X97ybk8+6XXCR+Gz5Yvw91/Eo8c3hi32T2Ac+vxfZozr0sI78uz4qPc/WV/V8rruTjNqfHKMv0JX+RvIjR481z2tWbkzCtlX/484ct3W/9zd19gZu8FLjezywkH2q4BruyJdepJCe9HCEeTm+j414pd8iI0jD8CLInvyyZCI/jL7r6lq+uwm+VpPwCcAnyNsM6txF8X3b38vVczn5tWZ+9zERERERGpQn3yRUREREQajBr5IiIiIiINRo18EREREZEGo0a+iIiIiEiDUSNfRERERKTBqJEvIiIiItJg1MgXEREREWkwauSLiIiIiDQYNfJFRHopMxtiZkvMbL2Zefx7dhw218xmFVy/WWY2t0L5lFjnIwuolohIXVAjX0Skl3L3F919InBbfD3R3W+Ig+cCs4qpWZtZhHpkbQaejM8iIlJB36IrICIiksLdlwF/X3Q9RERqmY7ki4hIGzMbaWZLgGHAzNiFZ4mZHVc2zhwzW2FmT5jZn8zsCjPbs2x4qQvQWjN7l5m1mNmzsUvQfmY20cxuLpv3YjP7RKYevwZmAsPKxrsgzm9JnNfFmWnGm9ntcblrzOwuM5tUNvzsWG83s0+b2Q/MbGkc/5yeSVREpBg6ki8iIm3c/WlgopmtBVrc/fTy4WZ2PnAZcKy7LzCzg4D5wIHAmXEeE81sHvAB4D3ADGAAsDrO5t3ANmCyu+8ws8OAB81sk7vfEufxrjiP6bFLUblfm5ln6nUI8ABwPfBedy99CZhvZlPd/TF3v9bM7gTWAGcB73P3NWY2B7jWzH7j7k90Pj0RkdqhI/kiIpKLmb0RuAj4qbsvAHD354FvAKeb2ejMJPsAV3iwBZgKbATmAZ919x1xHiuBu4FPdaF6F8fnL7t76QvAVwn99r9aYfx73X1N/PsWwIBjurB8EZGaoiP5IiKS11SgiXDEvNwy/tpIXlNW/oq7v1h6UWpUm9lG4DwzOynOrxV4E/BSF+p2HLDc3beWLe91M3sEOM7MrKzxD7Cy7O/18XloF5YvIlJT1MgXEZG8Bsfn883srLLyvsA6wpH7cq+2M58bgemErjiPA5S65nSxbg9XKF9P6CrUxN9ejWdL6Q9332lmAH26sHwRkZqiRr6IiOT1cny+yN1v6swMzGwAcArwvVIDv5u8DAyqUD4I2EpZo15EpDdQn3wREankdUIXHMzsYDM7CniQcDS8OTuymd1oZuNyzHdPwhFzz5QfWKUOe5nZzA7mezcwzsyayurUF5gI3J3pqiMi0vDUyBcRkUrWACPi33OA2e6+kXDi7WwzmwJgwRcI162vemQ+zuO3wIfMbEScx1HAse3UYbCZ9QeOAv6tg1lfQvjicJnFvjfAhYQuRF+qVi8RkUZjOrghItI7mdkQ4C7CSa8DgaXAd939BjObSug7v4Nw9P7j8So4mNkZwLlAf0I3mIeBfy6dZGtm9xGO9u8NrACud/drypY7EvgO8HbCCbCrCEfyj4/jz3L3tbF+NwPDge2ERvs24Gtx/uuA37v7zDjf8XHYeEKDfxVwgbs/HIefSviS8hbgaeA64OfAj8vmd5e7f7wb4hURKZQa+SIiIiIiDUbddUREREREGowa+SIiIiIiDUaNfBERERGRBqNGvoiIiIhIg1EjX0RERESkwaiRLyIiIiLSYNTIFxERERFpMGrki4iIiIg0GDXyRUREREQajBr5IiIiIiIN5v8BUfXfbVRIAs8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_item = \"Q1-anhar\"\n",
    "true_value = -210e6\n",
    "\n",
    "fig = plt.figure(figsize=(12, 8))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_xlabel(\"Iteration\")\n",
    "ax.set_ylabel(plot_item)\n",
    "ax.axhline(y=true_value, color=\"red\", linestyle=\"--\")\n",
    "ax.axhline(y=best_point_dict[plot_item], color=\"black\", linestyle=\"-.\")\n",
    "ax.plot(data_df[plot_item])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Qubit Frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f180506cb50>]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_item = \"Q1-freq\"\n",
    "true_value = 5e9\n",
    "\n",
    "fig = plt.figure(figsize=(12, 8))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_xlabel(\"Iteration\")\n",
    "ax.set_ylabel(plot_item)\n",
    "ax.axhline(y=true_value, color=\"red\", linestyle=\"--\")\n",
    "ax.axhline(y=best_point_dict[plot_item], color=\"black\", linestyle=\"-.\")\n",
    "ax.plot(data_df[plot_item])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Goal Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f1804fdfc70>]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jvWS0l0wQejWEI5T1sZwkqSQ/vXu4g9DMJdpLxsVexlrr9xg8VVVVZevq6vwehlJKKeWbS371BNmZGfxh+Zl9fv3Tf1rLk9saePzzr0vzyJQaOYwxa6y1VX19Tfdwe6S2ttbvIQSK9pLRXjLaS0Z7yQSh17Eu655UWpCd1j3cQWjmEu0l42IvnXB7pL293e8hBIr2ktFeMtpLRnvJBKFXKNz/hLukIIf2zijtkWhaxhOEZi7RXjIu9tIJt1JKKTWCdUVjHGrv7POUgEmlPlzeXanRRCfcHqmurvZ7CIGivWS0l4z2ktFeMq73amrvxFr6PWhyXH56L+/uejPXaC8ZF3vphNsj27dv93sIgaK9ZLSXjPaS0V4yrvdqDB/7ojdJyeUmTW3pudqk681co71kXOylE26P7N271+8hBIr2ktFeMtpLRnvJuN6rv8u6J5UWJC7vnqYlJa43c432knGxl064lVJKqRGse8I9wHm44cjecKVUaumE2yNz5871ewiBor1ktJeM9pLRXjKu90rute5vwj02Lxtj0reG2/VmrtFeMi720gm3R6LR9JxaaaTQXjLaS0Z7yWgvGdd7hVqTa7izj3mfrMwMisdkp+0sJa43c432knGxl064PbJp0ya/hxAo2ktGe8loLxntJeN6r1BbhMLcLHKzMvu9X2lBDo1pOmjS9Wau0V4yLvbSCbdSSik1gjWGI/3u3U4qyc/WNdxKeUQn3B6ZMmWK30MIFO0lo71ktJeM9pJxvVdDOEJpQe6A9ystyEnbGm7Xm7lGe8m42MvXCbcx5mJjzCpjzBpjzDZjTJ0x5ooeX882xtxgjNlojFlnjHnCGLPEzzEPVkVFhd9DCBTtJaO9ZLSXjPaScb1XY1uE0vzB7OHOSdsabtebuUZ7ybjYy7cJtzHmk8CXgMustQuBOcBm4PU97vYz4FLgtdba+cDtwCPGmNPSPFyx2tpav4cQKNpLRnvJaC8Z7SXjeq9Qq2wPt7XW8zG53sw12kvGxV6+TLiNMdOAG4HrrLW7AKy1ncBngJ8n7jMHWA7caK09kLjPrcB24Ns+DFsppZQKnFBbpPvCNv0Zl59DR1eM9k73zvCgVND5tYf7CqDJWvt0zxuttXustXWJ314EGOCxXo99FDjXGFPo/TCHrrDQ6eE5R3vJaC8Z7SWjvWRc7tUeiXK4M9bvZd2TkpPydJypxOVmLtJeMi728mvCfRawI7GG+7+JNdpPGGPe3+M+pwIx4OVej90OZAHundW8h6qqKr+HECjaS0Z7yWgvGe0l43KvhnAHAGWDmHCn82qTLjdzkfaScbFXlk+vOxWYRnwJyUXAfuBi4A/GmEnW2m8D44E2a23vn201J34tO9aTG2OWE1+OwuTJk1mxYgUAM2bMoKioiLVr18afoKyMefPmsWrVKgCysrJYsmQJ9fX1NDfHX6aqqop9+/axc+dOAGbNmkVubi7r1q0DoLy8nNmzZ7N69WoAcnNzqa6u5qGHHmLMmDEALF68mF27drF7924A5syZQ2ZmJhs2bABg4sSJTJ8+nZqaGgDy8vJYvHgxtbW1tLe3A1BdXc327dvZu3cvEL+KUjQa7T7X5JQpU6ioqOhet1RYWEhVVRU1NTV0dMS/4S5ZsoTNmzezf/9+AObPn09HRwdbtmyJvylTpzJhwgTq6uI/ZCguLqayspLVq1fT1dUFwNlnn8369etpaGgAYMGCBbS0tLBt2zYApk2bRmlpKfX19QCUlJSwYMECVq5cibUWYwxLly5l7dq1NDY2AlBZWUldXR0ZGRlpf5/q6upobW0N3Pu0Z8+e7hP7p/N9CoVC7NixI3DvUywWIyMjY9R8nob7PmVmZjJ58uRR83ka7vsUi8Woqqpy8vMUKZgAwO5tm6np2t3v+/RSY/x7yrbd+zm45RlP36fOzvhe9NHweUrF972srCwmTJgwKj5PqXifYrEYp59+etrfp35Za9P+H/AiYIElvW6/FwgD+cAjQEsfj7028di3DOa1Fi5caP3w2GOP+fK6QaW9ZLSXjPaS0V4yLvd6bOM+e8Ln/mHrdjQMeN8X97fYEz73D/vXZ3Z5Py6Hm7lIe8n41Quos8eYj/q1pKQl8euzvW5/hvhkey5wEMg3xvS+NFZx4tcGz0anlFJKjQDJ0/wll4v0J3mfdJ2LW6nRxK8J98ZjvH60x+3PJX6d2us+04EuYINno0uBJUsCcbpwZ2gvGe0lo71ktJeMy71C4fjSjbJBnBZwbF42xqTnoEmXm7lIe8m42MuvCfcDiV9P7XX7fKAdWA/cT3zpyLJe9zkHeMRaO4gFM/7ZvHmz30MIFO0lo71ktJeM9pJxuVco3EFmhqFozMCHbGVmGMblpefy7i43c5H2knGxl18T7j8CTwPfSp7ezxjzWuAS4NvW2rC1dhNwM/AFY8z4xH2uBmYSv2CO05IHFKjB0V4y2ktGe8loLxmXe4XCnZTkZ5ORYQZ1/5KCHEJpuNqky81cpL1kXOzly1lKrLVRY8ybge8B640xh4EO4CPW2lt63PWjwNeAx40xncTXfp9rrX023WNWSimlgiYU7qB0EKcETCrNz0nLHm6lRhu/TguItTYEfGCA+3QCX078Fyjz58/3ewiBor1ktJeM9pLRXjIu92oMdw7qgMmkcfk57Gps83BEcS43c5H2knGxl19LSka85Dkr1eBoLxntJaO9ZLSXjMu9Qm0RygoFe7gLsrvPbOIll5u5SHvJuNhLJ9weSZ4EXg2O9pLRXjLaS0Z7ybjcKxSOiPZwlxTk0NjWmbzuhWdcbuYi7SXjYi+dcCullFIjUDRmaWqLiNdwR7pitEV6X+RZKTUcOuH2yNSpvU8frvqjvWS0l4z2ktFeMq72OtTeScwimnCXFKTn4jeuNnOV9pJxsZdOuD0yYcIEv4cQKNpLRnvJaC8Z7SXjaq/kpFm6hxvwfB23q81cpb1kXOylE26P1NXV+T2EQNFeMtpLRnvJaC8ZV3slJ82yPdzZgPd7uF1t5irtJeNiL51wK6WUUiNQQ2t80iw6aDJx36Y0XN5dqdFEJ9weKS4u9nsIgaK9ZLSXjPaS0V4yrvYayh7u0jSt4Xa1mau0l4yLvXTC7ZHKykq/hxAo2ktGe8loLxntJeNqr6Gs4S4ek02G8X4Nt6vNXKW9ZFzspRNuj6xevdrvIQSK9pLRXjLaS0Z7ybjaKxSOkJ+TyZjszEE/JiPDUJKf4/keblebuUp7ybjYSyfcHunq6vJ7CIGivWS0l4z2ktFeMq72CoVl5+BOGpfv/dUmXW3mKu0l42IvnXArpZRSI9BQJ9ylBTk0hvWgSaVSyXh9+Va/VVVVWT9ODxOLxcjI0H/PDJb2ktFeMtpLRnvJuNrr/J+vpiQ/h9+8f5HoccvvquPlUBsPfeJsj0bmbjNXaS8Zv3oZY9ZYa6v6+pq+ex5Zv36930MIFO0lo71ktJeM9pJxtVdDa4SyIe7h9noNt6vNXKW9ZFzspRNujzQ0NPg9hEDRXjLaS0Z7yWgvGVd7NbZFui/VLlFSkENjWwQvfwLuajNXaS8ZF3vphFsppZQaYQ53RmmLRIe0hrskP5vOqKW1w70Dz5QKKp1we2TBggV+DyFQtJeM9pLRXjLaS8bFXkM5B3dS8mqTXh446WIzl2kvGRd76YTbIy0tLX4PIVC0l4z2ktFeMtpLxsVeyQm35LLuSclJupenBnSxmcu0l4yLvXTC7ZFt27b5PYRA0V4y2ktGe8loLxkXeyUn3GWFQ1vDDRDycMLtYjOXaS8ZF3vphFsppZQaYZJ7p4e0h7t7SYm3ZypRajTRCbdHpk2b5vcQAkV7yWgvGe0lo71kXOzV0JrYwz3Es5QAnp4a0MVmLtNeMi720gm3R0pLS/0eQqBoLxntJaO9ZLSXjIu9GtsiZBgozssWP7Z4TBaZGcbTNdwuNnOZ9pJxsZdOuD1SX1/v9xACRXvJaC8Z7SWjvWRc7BUKRxiXn0NmhhE/1hhDSX42jW3enaXExWYu014yLvbSCbdSSik1woTCkSGdEjCpJD9H13ArlUI64fZISUmJ30MIFO0lo71ktJeM9pJxsVcoHOk++HEoSjy+vLuLzVymvWRc7KUTbo+4eNJ1l2kvGe0lo71ktJeMi72Gu4e7ND/H0zXcLjZzmfaScbGXTrg9snLlSr+HECjaS0Z7yWgvGe0l42KvxrZI99lGhqKkIJuQh1eadLGZy7SXjIu9dMLtEWut30MIFO0lo71ktJeM9pJxrVcsZmls66S0QH6GkqSS/Bya2iKe/dlca+Y67SXjYi+dcHvEGPmR4aOZ9pLRXjLaS0Z7ybjWq/lwJ9GYpbQgd8jPUVqQQ1fM0tLRlcKRHeFaM9dpLxkXe+mE2yNLly71ewiBor1ktJeM9pLRXjKu9Uoe7DjcPdzg3dUmXWvmOu0l42IvnXB7ZO3atX4PIVC0l4z2ktFeMtpLxrVeRybcw9vD3fO5Us21Zq7TXjIu9tIJt0caGxv9HkKgaC8Z7SWjvWS0l4xrvbon3MM4LeC4/Pjeca/OVOJaM9dpLxkXe+mEWymllBpBkpPkkmEsKTmyh9u7M5UoNZrohNsjlZWVfg8hULSXjPaS0V4y2kvGtV4NiT3cZcNYUpI8pWCTR3u4XWvmOu0l42IvnXB7JBQK+T2EQNFeMtpLRnvJaC8Z13o1hiOMyc4gLydzyM9RlJtFVobxbA23a81cp71kXOylE26P7Nixw+8hBIr2ktFeMtpLRnvJuNarIRwZ1t5tiJ9WraTAu6tNutbMddpLxsVeOuFWSimlRpDGcGRY67eTSvKzPdvDrdRooxNuj8yYMcPvIQSK9pLRXjLaS0Z7ybjWK9TW2X0e7eEoyc+h0aODJl1r5jrtJeNiL51we6SoqMjvIQSK9pLRXjLaS0Z7ybjWKxTuoKxg+BPuUg+XlLjWzHXaS8bFXjrh9oiLJ113mfaS0V4y2ktGe8m41qsx3Nl9lpHh8HINt2vNXKe9ZFzspRNupZRSaoTo6IrS2tGVmj3c+Tk0tnUSi9kUjEyp0S3Lrxc2xkwD1gEv9vHlZdbapsT9soGvAu8EuoBm4P9Za1enZ6RDU1ZW5vcQAkV7yWgvGe0lo71kXOqVXHOdqj3c0Zil5XAXY/OHfxBmTy41CwLtJeNiL98m3Al11tplA9znZ8DrgNdYaw8YY64FHjHGnGWtfdbrAQ7VvHnz/B5CoGgvGe0lo71ktJeMS71ScVn3pJLEJDvUFkn5hNulZkGgvWRc7OX0khJjzBxgOXCjtfYAgLX2VmA78G0/xzaQVatW+T2EQNFeMtpLRnvJaC8Zl3p1T7hTtIcb8GQdt0vNgkB7ybjYy+kJN3ARYIDHet3+KHCuMaYw/UNSSiml3BRqS92EO7mXvFHPxa3UsPk94Z5gjLnbGPOUMWazMeb3xphTenz9VCAGvNzrcduJL4eZm66BSmVl+b1aJ1i0l4z2ktFeMtpLxqVeodYOIEUT7sRzeHHxG5eaBYH2knGxl58jihI/CPJ/rLVrjDHFxNdr1xpjllprnwbGA23W2mivxzYnfu1zVbwxZjnxpShMnjyZFStWAPEToRcVFXWfLqasrIx58+Z1/+ghKyuLJUuWUF9fT3Nz/CWqqqrYt28fO3fuBGDWrFnk5uaybt06AMrLy5k9ezarV8eP4czNzaW6upoxY8Z0v+7ixYvZtWsXu3fvBmDOnDlkZmayYcMGACZOnMj06dOpqakBIC8vj8WLF1NbW0t7ezsA1dXVbN++nb179wIwd+5cotEomzZtAmDKlClUVFRQW1sLQGFhIVVVVdTU1NDREf8GvGTJEjZv3sz+/fsBmD9/Ph0dHWzZsgWAqVOnMmHCBOrq6gAoLi6msrKS1atX09XVBcDZZ5/N+vXraWhoAGDBggW0tLSwbds2AKZNm0ZpaSn19fUAlJSUsGDBAlauXIm1FmMMS5cuZe3atTQ2NgJQWVlJRUWFL+9TXV0dra2tgXufTjvttO5e6XyfQqFQ9yVzg/Q+LVmyZFR9nlLxPm3dunXUfJ5S8T41Nzc78XnaffAQBnj2qSeYNHHCsN6n9q742UkONLd3f79J1fu0ZMmSUfV5SsX3vS1btoyaz1Mq3qfGxsa0v0/9Mda6c7ofY0we8b3Zz1pr32iMeQSottYW9brftcAtwFuttQ/295xVVVU2+canU319PZWVlWl/3aDSXjLaS0Z7yWgvGZd6feWv63jguT08+9Vzh/1c1lpmf/lBrlkyg8+/5aQUjO4Il5oFgfaS8auXMWaNtbaqr6/5vaTkKNbaduB54MzETQeBfGNMZq+7Fid+bUjX2KSS/2JSg6O9ZLSXjPaS0V4yLvUKhSMpWU4CYIxJXN499UtKXGoWBNpLxsVevk24jTFjjTF9fVeIAskJ9nPExzi1132mE1+OssG7ESqllFLBEgpHUnJKwCQvL++u1Gji5x7u/wUu7nlDYgJ+ClCfuOl+wALLej32HOARa+3Ai2Z8UlXV508U1DFoLxntJaO9ZLSXjEu9UrmHG4jv4fZgwu1SsyDQXjIu9vJ7SclnjTGTABLLRn4AHAd8A8Bauwm4GfiCMWZ84n5XAzOBL/ky4kHat2+f30MIFO0lo71ktJeM9pJxqVeoLbUT7tKCHE/OUuJSsyDQXjIu9vJzwv0j4AngIWPMWuIHS84D3mCt/VeP+30U+DPwuDFmHfGzj5zr8lUmge6jXNXgaC8Z7SWjvWS0l4wrvay1NKZ4D/e4/Gwa2zpT9nxJrjQLCu0l42Iv304LaK19HvjIIO7XCXw58Z9SSiml+tB8uIuumE35Hu6mtgixmCUjw6TseZUabfxeUjJizZo1y+8hBIr2ktFeMtpLRnvJuNIreTaRkhQeNFmSn0PMQvPh1O7ldqVZUGgvGRd76YTbI7m5uX4PIVC0l4z2ktFeMtpLxpVeDYkJd2lhavdwQ+qvNulKs6DQXjIu9tIJt0eSVyZSg6O9ZLSXjPaS0V4yrvRK7uFO5WkBSxIT7lSfqcSVZkGhvWRc7KUTbqWUUmoECCUmxSldw52f3MOd+gMnlRpNdMLtkfLycr+HECjaS0Z7yWgvGe0l40qv5LKPVJ+lBEj51SZdaRYU2kvGxV464fbI7Nmz/R5CoGgvGe0lo71ktJeMK70awxFysjLIz8kc+M6D1L2GO8VLSlxpFhTaS8bFXjrh9sjq1av9HkKgaC8Z7SWjvWS0l4wrvRrCEcoKcjAmdafvy8/JJCcrI+VruF1pFhTaS8bFXjrhVkoppUaAxnAkpacEBDDGUJqfk/IlJUqNNjrh9oiLp6RxmfaS0V4y2ktGe8m40ivUFqEshacETCopyEn5QZOuNAsK7SXjYi9jrfV7DJ6qqqqydXV1fg9DKaWU8tTSHzzGgopx/PQ9p6f0eS+75Uk6umLc+8GzUvq8So00xpg11tqqvr6me7g9opN8Ge0lo71ktJeM9pJxpVcoHEnpGUqSSgpSv6TElWZBob1kXOylE26PtLa2+j2EQNFeMtpLRnvJaC8ZF3pFumK0HO7yZMJdmp+T8oMmXWgWJNpLxsVeOuFWSimlAq4pMSEu8WgPd1N7J9HYyF6CqpSXdMLtkcWLF/s9hEDRXjLaS0Z7yWgvGRd6Jc+TXebJHu5srIVD7ak7cNKFZkGivWRc7KUTbo/s2rXL7yEEivaS0V4y2ktGe8m40CvUmtjDneLTAsKRveahFK7jdqFZkGgvGRd76YTbI7t37/Z7CIGivWS0l4z2ktFeMi70Su7h9uSgycQkPpXruF1oFiTaS8bFXjrhVkoppQIuuffZk4MmE8+pF79Rauh0wu2ROXPm+D2EQNFeMtpLRnvJaC8ZF3olJ9zj8rNT/tzJJSWp3MPtQrMg0V4yLvbSCbdHMjMz/R5CoGgvGe0lo71ktJeMC70awxHG5mWTnZn6v9ZL85NruFN30KQLzYJEe8m42Esn3B7ZsGGD30MIFO0lo71ktJeM9pJxoVeDRxe9AcjLyWRMdkZK93C70CxItJeMi710wq2UUkoFXGNbhBIPlpMkleTnpPQsJUqNNjrh9sjEiRP9HkKgaC8Z7SWjvWS0l4wLvRpaI5QW5Hr2/CX5qb28uwvNgkR7ybjYSyfcHpk+fbrfQwgU7SWjvWS0l4z2knGhV2NbhNIC7/Zwlxak9vLuLjQLEu0l42IvnXB7pKamxu8hBIr2ktFeMtpLRnvJ+N3LWktjuNPbPdwFOTS2pe6gSb+bBY32knGxl064lVJKqQBr7egiEo15u4c7P1vXcCs1DDrh9kheXp7fQwgU7SWjvWS0l4z2kvG7V2PidH1eXNY9aVx+DofaO+mKxlLyfH43CxrtJeNiL51we2Tx4sV+DyFQtJeM9pLRXjLaS8bvXg3hDgDKCr2bcCdPOdjUnpplJX43CxrtJeNiL51we6S2ttbvIQSK9pLRXjLaS0Z7yfjdK3kwo5d7uJNXm2xK0YGTfjcLGu0l42IvnXB7pL293e8hBIr2ktFeMtpLRnvJ+N0reQXIMg8Pmkz11Sb9bhY02kvGxV464VZKKaUCLJRYUlLi4UGTyefWAyeVGhqdcHukurra7yEEivaS0V4y2ktGe8n43SsU7iQ701CYm+XZaySXq6TqXNx+Nwsa7SXjYi+dcHtk+/btfg8hULSXjPaS0V4y2kvG716hcAelBTkYYzx7jZLuJSWpmXD73SxotJeMi710wu2RvXv3+j2EQNFeMtpLRnvJaC8Zv3uFwp2eHjAJkJeTSV52ZsoOmvS7WdBoLxkXe+mEWymllAqwULjD01MCJpUW5KTsoEmlRhudcHtk7ty5fg8hULSXjPaS0V4y2kvG716Nbd7v4Yb4gZOpWsPtd7Og0V4yLvbSCbdHotGo30MIFO0lo71ktJeM9pLxu1coHOm+MI2XSvJzUraG2+9mQaO9ZFzspRNuj2zatMnvIQSK9pLRXjLaS0Z7yfjZqzMa41B7Z9om3Knaw63bmIz2knGxl064lVJKqYBqaouvqU7HhDu+hlvPw63UUOiE2yNTpkzxewiBor1ktJeM9pLRXjJ+9kpOgNO1h7vlcBed0diwn0u3MRntJeNiL51we6SiosLvIQSK9pLRXjLaS0Z7yfjZq3vCnYaDJksTV5tM7lUfDt3GZLSXjIu9nJlwG2P+a4yxxphpfo8lFWpra/0eQqBoLxntJaO9ZLSXjJ+9kmuqS9Kxh7sgdVeb1G1MRnvJuNjLiQm3MeZiYMkxvlZojPm5MWaTMWaDMeYRY8y8NA9RKaWUck5DYg93WZqWlEDqrjap1Gji+4TbGJMD3Aj88xh3+TNwGnC6tXYuUAusMMa4t0Cnh8LCQr+HECjaS0Z7yWgvGe0l42evxsTkd1w6zsOdeI3GFEy4dRuT0V4yLvbyfcINfBh4OvHfUYwxbwTeDHzVWtuWuPkGIBP4YtpGOARVVVV+DyFQtJeM9pLRXjLaS8bPXqFwhKIxWeRkef/XeWn3kpLhr+HWbUxGe8m42MvXCbcxphT4LPCFY9zlYqATWJ28wVobAR5PfM1ZNTU1fg8hULSXjPaS0V4y2kvGz17puugNwLj8+EGTqVjDrduYjPaScbGX33u4vwrcba196RhfPxXYk5hk97QdmGCMKfd0dMPQ0dHh9xACRXvJaC8Z7SWjvWT87NXYFknLZd0BxmRnUpCTmZI13LqNyWgvGRd7Zfn1wsaYWcC7gJP7udt4oKWP25sTv5YB+1M8NKWUUioQGlojTBo7Jm2vNy4/JyVruJUabXybcAPfA2601h5K9RMbY5YDywEmT57MihUrAJgxYwZFRUWsXbsWgLKyMubNm8eqVasAyMrKYsmSJdTX19PcHJ/TV1VVsW/fPnbu3AnArFmzyM3NZd26dQCUl5cze/ZsVq+Or3rJzc2lurqa/Pz87tddvHgxu3btYvfu3QDMmTOHzMxMNmzYAMDEiROZPn16949A8vLyWLx4MbW1tbS3twNQXV3N9u3b2bt3LwBz584lGo12X750ypQpVFRUdJ8Kp7CwkKqqKmpqarr/pbdkyRI2b97M/v3xf6PMnz+fjo4OtmzZAsDUqVOZMGECdXV1ABQXF1NZWcnq1avp6uoC4Oyzz2b9+vU0NDQAsGDBAlpaWti2bRsA06ZNo7S0lPr6egBKSkpYsGABK1euxFqLMYalS5eydu1aGhsbAaisrOT444/35X2qq6ujtbU1cO/T6aef3t0rne9TKBRix44dgXuflixZMqo+T8N9n04//XS2bt06aj5Pw32fjj/+eJqbm335PB1obqM0o40VK1ak5X3KpZNQW2TY79OSJUtGzecpFd/3Fi5cyJYtW0bF5ykV79O0adNobGxM+/vUH2OtHfBOqWaMeS1wGzDPWtuZuO3rwNeA6dbaHYnbngAmW2un9Xr8T4GPAhOstf3u4a6qqrLJNz6dNmzYwNy5c9P+ukGlvWS0l4z2ktFeMn71stYy5ysPcfVZ0/jCW/v7YXHqXHn7Uxxqi/C3j/R5Jt9B021MRnvJ+NXLGLPGWtvnEZt+reF+I/EzjTxtjHnWGPMscH3ia/9M3PZW4DlgcuLUgT1NB/YNNNn2U/Jff2pwtJeM9pLRXjLaS8avXm2RKJGuWFouepNUmp+dkrOU6DYmo71kXOzly4TbWvtVa+1Ma+1pyf+AXye+/NbEbf8E7gOygbOSj01Mvl8D3JvucSullFKu6L6sexon3CUFuoZbqaFI6YTbGDM+lc9nrX0EeBi4wRiTn7j5S0AU+E4qXyvV5s+f7/cQAkV7yWgvGe0lo71k/OrVPeFO01lKkq/V0tFFpCs2rOfRbUxGe8m42CvVe7gfkT7AGPPWvpaU9LjLJcSXljxrjHmB+N7uZdba3cMcq6dcPCWNy7SXjPaS0V4y2kvGr16hxPmw07mkZFzitZqGeS5u3cZktJeMi736PUuJMeZR4fOdKB1AYunIsS7rjrW2lfjVKANly5YtTJni9NXnnaK9ZLSXjPaS0V4yfvUKtcYnvWVpXcMdf61QW4Ty4qGfjlC3MRntJeNir4H2cJ8BGMF/SimllEqDRh/2cJcUJK42GR7+gZNKjSYDnYf7RWvtOYN9MmPMM8Mcz4gxdepUv4cQKNpLRnvJaC8Z7SXjV69QOEJWhqF4TPouqZE8QHO4l3fXbUxGe8m42GugPdxvFT6f9P4j1oQJE/weQqBoLxntJaO9ZLSXjF+9QuEIJQU5GJO+HzB3LykZ5plKdBuT0V4yLvbqd8JtrX1F+Hy/GcZYRhQ/LrYTZNpLRnvJaC8Z7SXjV69QOJLWM5RA/NLuwLBPDajbmIz2knGxl/jnUMaYKcBlwAwgt9eXK1MxKKWUUkr1LxSOpPUc3AA5WRkU5mZ1nyFFKTU4ogm3MWYx8G+gFSgBknvAjwPygV0pHV2AFRcX+z2EQNFeMtpLRnvJaC8Zv3qF2iKcPDH9r11SkE3TMK82qduYjPaScbGXdA/3d4H3WWvvM8Y8Y609HcDEF5B9hlfv8R61Kit1Z7+E9pLRXjLaS0Z7yfjVq9GHPdwQX8c93DXcuo3JaC8ZF3tJL3xTZq29r/eNNu4HwKDPaDLSrV692u8hBIr2ktFeMtpLRnvJ+NErGrM0tXem9ZSASSUFOcM+S4luYzLaS8bFXtIJd89L91hjTPc+e2NMLjA7JaMaAbq6uvweQqBoLxntJaO9ZLSXjB+9mtoiWAul+dlpf+2SFOzh1m1MRnvJuNhLOuFuNsZcY4zJAJ4G/mqMucAYcwHwd8Dpy60rpZRSI0FywltamP6VnCX5OcM+S4lSo410DfevgGuBFcANiV/vT3ytCTgvReMKvLPPPtvvIQSK9pLRXjLaS0Z7yfjRq3vCnebTAgKUFmQTjkTp6IqSm5U5pOfQbUxGe8m42Eu0h9tae6+19i3W2q3W2l3AqcCbgfOBE621T3gxyCBav36930MIFO0lo71ktJeM9pLxo1dyDbUfB00m140P50wluo3JaC8ZF3sN93qw1lr7SEpGMsI0NDT4PYRA0V4y2ktGe8loLxk/ejWE/Ztw97za5ITiMUN6Dt3GZLSXjIu9pGu4McbMM8b81RjTCrQaY1qNMfcbY+Z6MD6llFJK9ZJcQ11S4MNBkwWpudqkUqOJ9MI3pwOrgDDwXyAElAJnArXGmNdaa59N9SCDaMGCBX4PIVC0l4z2ktFeMtpLxo9eDeEIhblZQ15DPRwlyT3cwzg1oG5jMtpLxsVe0j3c3wV+CFQk1nK/11r7FqAC+AHwvVQPMKhaWlr8HkKgaC8Z7SWjvWS0l4wfvRrDEV/2bsORverD2cOt25iM9pJxsZd0wj3LWvsNa+1RJzi01kattd8EZqVuaMG2bds2v4cQKNpLRnvJaC8Z7SXjR69QWyelBf5c3Dm5h7txGAdN6jYmo71kXOwlnXAPdH/xmnCllFJKyYTCHb5c9AYgOzODojFZw774jVKjiXSCvM4Y873EVSW7GWPGGGN+ADyfuqEF27Rp0/weQqBoLxntJaO9ZLSXjB+9GsP+XNY9qXSYl3fXbUxGe8m42Et6WsAvAKuB5caY9UAj8YMm5wEWeE1qhxdcpaWlfg8hULSXjPaS0V4y2kvGj14N4Q7KfJxwjxvm5d11G5PRXjIu9pJe+GYdUAX8HzCT+EVvZgAPAGdYazekfIQBVV9f7/cQAkV7yWgvGe0lo71k0t2rPRLlcGfM3z3c+dnD2sOt25iM9pJxsZf0tIA3W2uXA5d7NB6llFJK9SN5Oj4/93CXFOSweV+rb6+vVNBIl5Sca4y5AjDH+LoF9gMbrLU7hzWygCspKfF7CIGivWS0l4z2ktFeMunuFWpNXPQm38893MNbw63bmIz2knGxl3TCfTxwJ31PuG2P22PGmN8CH7TWHh768ILLxZOuu0x7yWgvGe0lo71k0t0ruYfbj8u6J5UU5NAWiXK4M8qYbPnFd3Qbk9FeMi72kp6l5ELgaeJLSk4DpgOnA1cAjwFvBBYC7wfOAL6TonEGzsqVK/0eQqBoLxntJaO9ZLSXTLp7hcIdgM8T7u5zcQ9tL7duYzLaS8bFXtI93B8B3mqtDfW47SVgrTHmIeA31trzgGeMMY8ANcCnUjPUYLHW+j2EQNFeMtpLRnvJaC+ZdPcKheMXnPFzwl2auNpkKBxh0tg88eN1G5PRXjIu9pLu4a7oNdnuZq1tIL7HO/n7vcCoPaLCmGMtc1d90V4y2ktGe8loL5l092oMR8jMMBSP8efCN9BjD3d4aFeb1G1MRnvJuNhLOuEuNsa8rq8vGGPeABT3+H0x8j3oI8bSpUv9HkKgaC8Z7SWjvWS0l0y6ezWEI5TkZ5OR4d+kIrl3fahLSnQbk9FeMi72kk64fwY8bIz5hzHmBmPMp40x3zLG/BP4J/ATAGPMBcC/gPUpHW2ArF271u8hBIr2ktFeMtpLRnvJpLtXYzji6xlKgO5zgA91wq3bmIz2knGxl2gPtLX2e8aYQ8CXgLf2+NIu4EPW2lsTv88Gfg+sSskoA6ixsdHvIQSK9pLRXjLaS0Z7yaS7Vygc8XX9NsC4vCNruIdCtzEZ7SXjYi/xkg9r7a+NMTcBU4GJwCvALttjhbq19i+pG6JSSimlkkJtEWaVF/o6hqzMDIrHZNE4jMu7KzWaDGmNdWJy/XLiP9WHyspKv4cQKNpLRnvJaC8Z7SWT7l6NDuzhhvg67lDb0A6a1G1MRnvJuNhLuoZbDVIo1OfJXNQxaC8Z7SWjvWS0l0w6e8VilsY2NybcJQU5NA1xDbduYzLaS8bFXjrh9siOHTv8HkKgaC8Z7SWjvWS0l0w6ex1q7yRm/b2se1Jpfs6Q13DrNiajvWRc7KUTbqWUUiogGhIT3LJC/yfcJQU5uoZbqUHSCbdHZsyY4fcQAkV7yWgvGe0lo71k0tkreRo+F/Zwl+RnExrikhLdxmS0l4yLvXTC7ZGioiK/hxAo2ktGe8loLxntJZPOXsklHK6s4T7cGaM9EhU/VrcxGe0l42IvnXB7xMWTrrtMe8loLxntJaO9ZNLZy6UJd2n+0C9+o9uYjPaScbGXTriVUkqpgHBpwp282uRQD5xUajTRCbdHysrK/B5CoGgvGe0lo71ktJdMOnuFwhHyczIZk52Zttc8ltJhXN5dtzEZ7SXjYi+dcHtk3rx5fg8hULSXjPaS0V4y2ksmnb0awxEnDpiE+EGTMLQ93LqNyWgvGRd76YTbI6tWrfJ7CIGivWS0l4z2ktFeMunsFWqLOHFKQDhyppShnBpQtzEZ7SXjYi9fJtzGmJnGmB8aY9Yk/ttsjPmvMeZtve6XbYy5wRiz0RizzhjzhDFmiR9jVkoppfwWcmgP99i8bIxhyJd3V2o08WsP91uAdwOXWmsXAicBNcDfjTFLe9zvZ8ClwGuttfOB24FHjDGnpXm8YllZWX4PIVC0l4z2ktFeMtpLJp29QmE3LusOkJWZwdi87CFd3l23MRntJeNiL2OtTf+LGnMRUGatvbXHbeOARuB/rLWfMsbMAV4ArrXW3t7jfuuBHdbatzEIVVVVtq6uLqXjV0oppfww96sP8Z5Fx/OV8+b6PRQAXvfDFcydXMzPL6v0eyhK+c4Ys8ZaW9XX13zZw22tvb/nZDuhOPHrgcSvFwEGeKzX/R4FzjXGFHo4xGGrr6/3ewiBor1ktJeM9pLRXjLp6nW4M0pbJOrMHm6AcfnZQzpLiW5jMtpLxsVeThw0aYyZAvwCqE/8CnAqEANe7nX37UAW4MY/74+hubnZ7yEEivaS0V4y2ktGe8mkq1dyYuvShLu0IIdQWL6GW7cxGe0l42IvXxe5GGNmAg8DM4EHgQuttclK44E2a23va8Ymv37MkywaY5YDywEmT57MihUrAJgxYwZFRUXdVyAqKytj3rx53UezZmVlsWTJEurr67vfrKqqKvbt28fOnTsBmDVrFrm5uaxbtw6A8vJyZs+ezerVqwHIzc2lurqatra27tddvHgxu3btYvfu3QDMmTOHzMxMNmzYAMDEiROZPn06NTU1AOTl5bF48WJqa2tpb28HoLq6mu3bt7N3714A5s6dSzQaZdOmTQBMmTKFiooKamtrASgsLKSqqoqamho6OjoAWLJkCZs3b2b//v0AzJ8/n46ODrZs2QLA1KlTmTBhAsklOMXFxVRWVrJ69Wq6uroAOPvss1m/fj0NDQ0ALFiwgJaWFrZt2wbAtGnTKC0t7f7XZUlJCQsWLGDlypVYazHGsHTpUtauXUtjYyMAlZWVRCIRX96nuro6WltbA/c+RaPR7l7pfJ9CoRA7duwI3PsEjKrP03Dfp2g0ytatW0fN52m471MkEqG5udnz92nc9FMA2LN9Myvatjnxfa+juYNQOJONGzeK3idg1HyeUvE+xWIxtmzZMio+T6l4nyKRCI2NjWl/n/rjyxruVw3CmGLgm8CVwPnW2tXGmEeAamttUa/7XgvcArzVWvvgQM/t1xru1tbW7m8qamDaS0Z7yWgvGe0lk65e/91ygCtue4o/XVfNoumlnr/eYHz3ny9w5xM72HjDmzHGDPpxuo3JaC8Zv3o5t4a7t8Re7U8C+4BfJm4+COQbY3pfTiu51rshTcMbkn379vk9hEDRXjLaS0Z7yWgvmXT1cumy7kklBTl0dMVo7+z9w+j+6TYmo71kXOzl13m480yvfwrb+K7254H5xphc4LnE+Kb2evh0oAvYkI6xDlXyRxJqcLSXjPaS0V4y2ksmXb1cnHCXJs4JLr3apG5jMtpLxsVefu3hfhA4s4/bpxFfox0B7gcssKzXfc4BHrHWDrxgRimllBohGsMRMkz8gjOuGJe4vHvjEA6cVGo08XNJyTeMMWUAJu5jwBnAT23cJuBm4AvGmPGJ+11N/ADLL/k16MGaNWuW30MIFO0lo71ktJeM9pJJV6+GcIRx+TlkZgx+rbTXknvbQ8JTA+o2JqO9ZFzs5ddZSr4EXAusNMZ0AWOIr8m+HPh9j/t9FPga8LgxphNoAc611j6b3uHK5ebm+j2EQNFeMtpLRnvJaC+ZdPVqbHPnKpNJJYnxNAqXlOg2JqO9ZFzs5deFbx631l5trZ1vrT3NWnuStfY11trf2R6nTbHWdlprv2ytnZO4b7W19r9+jFkqeRoZNTjaS0Z7yWgvGe0lk65eDa2R7jXTrkiOR3rxG93GZLSXjIu9nDhLiVJKKaX619gWoaTAnfXbAMV52WQY+R5upUYbnXB7pLy83O8hBIr2ktFeMtpLRnvJpKtXKByhtMCtH5VnZhjG5mWL13DrNiajvWRc7KUTbo/Mnj3b7yEEivaS0V4y2ktGe8mko1csZmls66TUsT3cEF/HLT1LiW5jMtpLxsVeOuH2SPLSn2pwtJeM9pLRXjLaSyYdvVoOdxGNWef2cEN8Hbf0PNy6jcloLxkXe+mEWymllHJcQ7gDwN093MIlJUqNNjrh9oiLp6RxmfaS0V4y2ktGe8mko1dyQlvi2FlKIL6HWzrh1m1MRnvJuNhLJ9weqa6u9nsIgaK9ZLSXjPaS0V4y6ejV0Bqf0JY5uKRkXEE2jeFOepzVd0C6jcloLxkXe+mE2yN1dXV+DyFQtJeM9pLRXjLaSyYdvbr3cDu4pKQ0P4dINEY4Eh30Y3Qbk9FeMi720gm3R1pbW/0eQqBoLxntJaO9ZLSXTDp6hRJnAXFxD/dQrjap25iM9pJxsZdOuJVSSinHhcIdjMnOIC8n0++hvMpQrzap1GiiE26PLF682O8hBIr2ktFeMtpLRnvJpKNXKNzp3GXdk5J7uCWnBtRtTEZ7ybjYSyfcHtm1a5ffQwgU7SWjvWS0l4z2kklHr1C4g9JCNyfcpQXyPdy6jcloLxkXe+mE2yO7d+/2ewiBor1ktJeM9pLRXjLp6BVq63TylIAAJfnxAzlDgqtN6jYmo71kXOylE26llFLKcY3hCGUFbk64i8dkk2FkB00qNdrohNsjc+bM8XsIgaK9ZLSXjPaS0V4y6egVCke610q7JiPDUJKfQ0iwpES3MRntJeNiL51weyQz070jyV2mvWS0l4z2ktFeMl736uiK0trR5exBkxA/cLJJMOHWbUxGe8m42Esn3B7ZsGGD30MIFO0lo71ktJeM9pLxuldjYm20qwdNQvzUgJKzlOg2JqO9ZFzspRNupZRSymHJiazLe7jH5Wd3/8NAKfVqOuH2yMSJE/0eQqBoLxntJaO9ZLSXjNe9kqfbK3V0DTfExyZZw63bmIz2knGxl064PTJ9+nS/hxAo2ktGe8loLxntJeN1r4aw+xPukoIcGsMRrLWDur9uYzLaS8bFXjrh9khNTY3fQwgU7SWjvWS0l4z2kvG6V/J0e66epQTiy126YpbWjq5B3V+3MRntJeNiL51wK6WUUg5rCEcwBsblZfs9lGNK/mNA13Er1TedcHskLy/P7yEEivaS0V4y2ktGe8l43asxHGFsXjZZme7+ld19tclBruPWbUxGe8m42MvdT2/ALV682O8hBIr2ktFeMtpLRnvJeN0r1BZxev029NzDPbgJt25jMtpLxsVeOuH2SG1trd9DCBTtJaO9ZLSXjPaS8bpXqDXi9CkB4cgpCwd7Lm7dxmS0l4yLvXTC7ZH29na/hxAo2ktGe8loLxntJeN1r8Y2dy/rntS9h3uQS0p0G5PRXjIu9tIJt1JKKeWwhnCEMscn3MVjssjMMIOecCs12uiE2yPV1dV+DyFQtJeM9pLRXjLaS8bLXtZaGsPu7+E2xlCSn0NokGcp0W1MRnvJuNhLJ9we2b59u99DCBTtJaO9ZLSXjPaS8bJXS0cXXTHr/B5uiJ+pZLAHTeo2JqO9ZFzspRNuj+zdu9fvIQSK9pLRXjLaS0Z7yXjZK9SauOiN4wdNQnwd92BPC6jbmIz2knGxl064lVJKKUclJ7CunxYQ4mcqGewebqVGG51we2Tu3Ll+DyFQtJeM9pLRXjLaS8bLXsk93EGYcJcU5NDYNrg13LqNyWgvGRd76YTbI9Fo1O8hBIr2ktFeMtpLRnvJeNkrUHu4C7JpbItgrR3wvrqNyWgvGRd76YTbI5s2bfJ7CIGivWS0l4z2ktFeMl72Si7RCMKEuyQ/h2jM0ny4a8D76jYmo71kXOylE26llFLKUaFwhJysDPJzMv0eyoCSB3bqOm6lXk0n3B6ZMmWK30MIFO0lo71ktJeM9pLxslcoHL+suzHGs9dIleRe+MGcqUS3MRntJeNiL51we6SiosLvIQSK9pLRXjLaS0Z7yXjZKxSOBGI5CRy5vHvTICbcuo3JaC8ZF3vphNsjtbW1fg8hULSXjPaS0V4y2kvGy16htuBMuEsTS0oGc7VJ3cZktJeMi710wq2UUko5qjFAe7jHFWQDuoZbqb7ohNsjhYWFfg8hULSXjPaS0V4y2kvGy14NAZpwF+VmkZVhBrWGW7cxGe0l42IvnXB7pKqqyu8hBIr2ktFeMtpLRnvJeNWrMxqj5XBXIC7rDmCMiV/8ZhB7uHUbk9FeMi728mXCbYw5zRhzizHmBWPM88aYDcaYnxpjjut1v0JjzM+NMZsS93nEGDPPjzFL1dTU+D2EQNFeMtpLRnvJaC8Zr3p1n4O7MBgTbkhc3n0Qe7h1G5PRXjIu9vJrD/c9QClQZa09BXgjcC7wuDEmr8f9/gycBpxurZ0L1AIrjDHune+ll46ODr+HECjaS0Z7yWgvGe0l41Wv7qtMBmQPN0BJQTaNgzhoUrcxGe0l42IvP5eUfM5aGwaw1u4GfgDMAt4KYIx5I/Bm4KvW2rbEY24AMoEvpn+4SimlVPqEAnSVyaTSgpxBreFWarTxa8J9qrX2xV637Un8WpL49WKgE1idvIO1NgI8nvia05YsWeL3EAJFe8loLxntJaO9ZLzqFcQJ97j8wa3h1m1MRnvJuNjLlwl3YuLc22zAAqsSvz8V2NPHfbcDE4wx5R4Ocdg2b97s9xACRXvJaC8Z7SWjvWS86pWcuJYkTrcXBMk13LGY7fd+uo3JaC8ZF3tl+T0AAGNMJnANcJu1NllpPNDSx92bE7+WAfuP8XzLgeUAkydPZsWKFQDMmDGDoqIi1q5dG3+CsjLmzZvHqlXxOX5WVhZLliyhvr6e5ub4y1RVVbFv3z527twJwKxZs8jNzWXdunUAlJeXM3v2bFavju+Iz83Npbq6mh07drB/f3x4ixcvZteuXezevRuAOXPmkJmZyYYNGwCYOHEi06dP717kn5eXx+LFi6mtraW9vR2A6upqtm/fzt69ewGYO3cu0WiUTZs2AfHLmFZUVHSf7L2wsJCqqipqamq61zItWbKEzZs3d49r/vz5dHR0sGXLFgCmTp3KhAkTqKurA6C4uJjKykpWr15NV1cXAGeffTbr16+noaEBgAULFtDS0sK2bdsAmDZtGqWlpdTX1wNQUlLCggULWLlyJdZajDEsXbqUtWvX0tjYCEBlZSW7du3qHlc636e6ujpaW1sD9z698sor3V9P5/sUCoXYsWNH4N6n9vZ2WlpaRs3nabjvUzQaJTc3d9R8nob7PkUiESoqKlL+Ph1sje/Zfu7pGsbkZDvzeervfWp4pZOYhc0v7WLvS1uP+T61trZy6NChUfF5SsX7FIvFyM7OHhWfp1S8T5FIhEmTJqX9feqPsbb/f4WmgzHm68DbgbOT67qNMZuBjsRBlT3v+y3gS8Bca+0LAz13VVWVTb7x6bRixQqWLVuW9tcNKu0lo71ktJeM9pLxqtfX/raO+5/ZzXNff1PKn9sr9z+zi0/+cS2PfWYZ08cXHPN+uo3JaC8Zv3oZY9ZYa/s8J6Hv5+E2xlwNvAt4S3KynXAQKOrjIcWJXxu8HttwzJ8/3+8hBIr2ktFeMtpLRnvJeNUr1NZJWWGuJ8/tlZLuy7v3v45btzEZ7SXjYi9fJ9zGmCuATwOvs9b2Xh7yHDDZGNP7aJHpwL4+7u8UF09J4zLtJaO9ZLSXjPaS8ey0gOEOSvKDs34bjky4BzpwUrcxGe0l42Iv3ybcxpjLgc8Bb7DW7k3cdl5i/TXAfUA2cFaPx+QArwHuTfNwxZLrmdTgaC8Z7SWjvWS0l4xXvULhzkCdoQSOnFFloFMD6jYmo71kXOzly0GTxpj3ArcAXwHeYIxJfum1wCsA1tpHjDEPAzcYY96UOBf3l4Ao8J30j1oppZRKn1C4g1OmFA98R4eUFAxuD7dSo41fZyn5GTCG+MVuevtGj/+/BPge8KwxJgrsApYlLpTjtKlTp/o9hEDRXjLaS0Z7yWgvGS96WWtpDHd2T2CDoiAnk5zMDBrb+r/apG5jMtpLxsVevky4rbWlg7xfK/Bhj4fjiQkTJvg9hEDRXjLaS0Z7yWgvGS96hSNRItEYZQGbcBtjEpd3738Pt25jMtpLxsVevp+lZKTy41SEQaa9ZLSXjPaS0V4yXvQKtSYuepMfrAk3xMc80Bpu3cZktJeMi710wq2UUko5JjlhLSsM5oRb13ArdTSdcHukuDhYB7r4TXvJaC8Z7SWjvWS86BUKx09rFsQ93KUFA+/h1m1MRnvJuNhLJ9weqays9HsIgaK9ZLSXjPaS0V4yXvQKheMHHQbttIDAoNZw6zYmo71kXOylE26PrF692u8hBIr2ktFeMtpLRnvJeNErOWEN4oS7ND+HQ+2dRGP2mPfRbUxGe8m42Esn3B7p6uryewiBor1ktJeM9pIZTb2stfzsP1u44rZauqKxIT2HF70awhGyMw2FuX6dvXfoSgpyiFlobj/2qQFH0zaWCtpLxsVeOuFWSik1KkVjlq/8bR0/+tdm/rvlIKu2HPB7SN0awxFKC3LocWG4wEiuOx9oHbdSo4lOuD1y9tln+z2EQNFeMtpLRnvJjIZeka4YH7vnGe5+8mWWnz2DsoIc/rJm15Cey4teDeFIIA+YhMFdbXI0bGOppL1kXOylE26PrF+/3u8hBIr2ktFeMtpLZqT3Cnd0cc1vnub/nnuFL771JL741pM5/7TJ/HvDfpqGsFfWi16NbZFArt+G+BpugFA/E+6Rvo2lmvaScbGXTrg90tDQ4PcQAkV7yWgvGe0lM5J7NYYjvPfWWh5/8SDfv+RUlp89E4BLFlYQicZ4YO0e8XN60Su5pCSISgqyAWjq5/LuI3kb84L2knGxl064lVJKjQqvHGrnnTfVsOGVZn51+ULeVTW1+2vzJo/lpIlF/KV+t48jPKIhwBPu5Lh1DbdSR+iE2yMLFizwewiBor1ktJeM9pIZib22HWjlkl/VsPfQYX5z9SLeNG/iq+5zycIK1u5s4sX9LaLnTnWvrmiMQ+2dgZ1w52VnkpOV0e8a7pG4jXlJe8m42Esn3B5paZF9wx7ttJeM9pLRXjIjrdfzuw7xzl/XcLgzyj3Lz6R6Zlmf97vgtClkZhj+ska2lzvVvRrbgnvRGwBjDKX5Of2u4R5p25jXtJeMi710wu2Rbdu2+T2EQNFeMtpLRnvJjKReT2w9yHtueZIx2Zn8+fpq5k8Ze8z7HleUy7LZx3H/M7v6vWhLb6nu1ZhYihHUs5RA/Ewljf0sKRlJ21g6aC8ZF3vphFsppdSI9NC6vVx1+9NMGjuGez94FjOOKxzwMRcvrGBfcwePv3gwDSPsW3LPcFlA93ADlBZkd++pV0rphNsz06ZN83sIgaK9ZLSXjPaSGQm9/vT0Tj70uzXMm1LMn6+vZuLYMYN63OtPLmdsXrbonNyp7pWccJcEeMJdkp/T7xrukbCNpZP2knGxl064PVJaWur3EAJFe8loLxntJRP0Xr9euZX/d+9zLJl1HL+7djHjBEszcrMyOX/BZB5ev5fmw4PbQ5vqXiNhD3dJfk6/ZykJ+jaWbtpLxsVeOuH2SH19vd9DCBTtJaO9ZLSXTFB7WWv57j9f4MYHN3LeqZO49coq8nOyxM9z8cIKOrpi/N9zrwzq/qnulZxwS/6h4JqSghwOtXfSFY31+fWgbmN+0V4yLvbSCbdSSqnA64rG+Ny9z3HTqm1cceYJ/O+7Tycna2h/xS2oGMuJ5YXcO8RLvQ9XKByhKDdryON3QWl+NtbCoXZdx60U6ITbMyUlJX4PIVC0l4z2ktFeMkHrdbgzyod+V8+f6nbxsdfP4psXzCMzwwz5+YwxXFxZQd1Ljew4GB7w/qnu1dgWobQwuHu34cj682OdqSRo25jftJeMi710wu0RF0+67jLtJaO9ZLSXTJB6tRzu5Ko7nuKRDfv4+tvn8qk3zsaYoU+2ky46fQoZBu6tH3gvd6p7hcKRQJ8SEI6cQ/xYZyoJ0jbmAu0l42IvnXB7ZOXKlX4PIVC0l4z2ktFeMkHpdbC1g/fc8iR1Oxr5yaWncdVrpqfsuSeOHcOSWcdxX/1uYgOckzvVvULhSKAPmIQj5xA/1sVvgrKNuUJ7ybjYSyfcHrF28BdNUNpLSnvJaC+ZIPTaGWrjnb+u4cX9rdxyZRUXnj4l5a9xceUUdje18+T2hn7vl+peoXAk0KcEhB5LSo4x4Q7CNuYS7SXjYi+dcHskFT/SHE20l4z2ktFeMq732ryvhUt+/QQNrR3cfc1izjmp3JPXedO8iRTlZg14Tu5U9rLWEgpHAntZ96TS5B7uY6zhdn0bc432knGxl064PbJ06VK/hxAo2ktGe8loLxmXe615qZF3/roGa+FP11dTNc278+2Oyc7kvAWTeGjdXsIdXce8Xyp7tUWidHTFAj/hzsvJZEx2xjH3cLu8jblIe8m42Esn3B5Zu3at30MIFO0lo71ktJeMq71Wbj7A5bfWMi4/m79cfxYnTSz2/DUvrqygLRLlwXV7j3mfVPZKrnkuDfhBkxD/MxzroElXtzFXaS8ZF3vphNsjjY2Nfg8hULSXjPaS0V4yLvb6+9o9XPubp5k2voA/X1/N8WX5aXndhSeUMK0sn7+s2XnM+6SyV/I0ekHfww3xddzH2sPt4jbmMu0l42IvnXArpZRy2m9rdvDxe57h9Kkl3LP8TMqLxqTttZPn5H5yW4idoTbPX68hMUEN+kGTMPDl3ZUaTXTC7ZHKykq/hxAo2ktGe8loLxlXellr+d9/b+Erf1vP608q565rFjE2Lzvt47ioMn4GlPvqd/f59VT2Su4RHul7uF3ZxoJCe8m42Esn3B4JhUJ+DyFQtJeM9pLRXjIu9IrFLN94YAP/8+/NvKNyCr+6fCFjsjN9GUtFST5nzSzjvmd29Xm6sVT2Co2gCXdpfvYxz8PtwjYWJNpLxsVeOuH2yI4dO/weQqBoLxntJaO9ZPzu1RmN8ck/PcudT+zg2iXT+eElC8jO9Pevq4srK3ipoY26l169NjSVvULhCFkZhuIxWSl7Tr+UFOTQfLiLrmjsVV/zexsLGu0l42IvnXArpZRyRnskygfuquNvz+7h/715Dl9628lkZPh/Tt03z59Ifk4mf6kb+FLvw9HYFr/ojYvnEZZK7qVvau/7TCVKjSY64fbIjBkz/B5CoGgvGe0lo71k/Op1qK2Ty2+rZdXmA3z3HafwoWUnOjPxLMjN4q2nTOL/nn+F9kj0qK+lsldDa2REnBIQYFz+sa82qZ9JGe0l42IvnXB7pKioyO8hBErQejWGI1z4i8e5aeVWX14/aL38pr1k/Oj1+IsHuehXj/P8rkP84rJK3rPo+LSPYSAXV1bQ2tHFIxuOPid3KnvF93Cn/8BQL3RfbbKPCbd+JmW0l4yLvXTC7REXT7rusiD1isUsn/7zWp7d2cR3H9zIH556Oe1jCFIvF2gvmXT2ermhjet+W8d7b62lMxrjzvefwVtOmZS215dYPL2UKePyXnWp91T2aghHKCvITdnz+Sn5D4fGPk4NqJ9JGe0l42Kv4B+VoVSa3bp6G49u3M9XzpvLf7cc4Ev3P09pQQ5vmjfR76EpFRjhji5+8diL3Prf7WRlGj77pjlcs2S6b2ciGYyMDMPFCyv42aNbeOVQO5PG5qX8NRrDI2gPd0FyD7eu4VZK93B7pKyszO8hBEpQeq15KcT3HtrEW0+ZyPtfM41fvreSUyrG8bE/PMPTO9J3GqKg9HKF9pLxslcsZrmvfhfn/HAFv1yxlfNOncSjn17Gh8850enJdtLFlVOwFu5/5sg5uVPVKxqzNLV3UjpS9nAn13D3sYdbP5My2kvGxV464fbIvHnz0v6ajeEIv33yJQ53Rge+s2P86CXVGI7w0d8/w5Rxedx48akYY8jPyeKOq85gSkke19z5NJv2tqRlLEHo5RLtJeNVr2debuQdv3qCT/1pLZPG5XHfh87ix5eexsSx6bty5HCdUFbAGdNK+MuaI+fkTlWvprYI1sbPXz0SjMnOJD8ns8+DJvUzKaO9ZFzspRNuj6xatSqtr9fa0cX77niKr/x1Hb987MW0vnYqpLuXVHLd9sHWCL+4rJLiMUf+QiwtyOGu9y9iTHYm77v9KXY3tXs+Htd7uUZ7yaS61/7mw3z6T2u56JdPsLupnR++cwH3f/AsKo8vSenrpMslCyvYdiDMszubgNT1Su4JHgmXdU861uXd9TMpo71kXOylE+4R4HBnlOV31bF+TzMLpo7jVyu38uL+Vr+HNaIk121/+byTOaVi7Ku+XlGSz2/ev4hwpIsrb6s95uWMlRpNDndG+eWKFznnhyt4YO0ePrhsJo99ZhmXLKxw4tzaQ/XWUyYxJjvjVQdPDldDa/z7xkg5aBLiB07q90OlfJ5wG2MmGWMeMsa8+lq5AZeVlZ7jUbuiMT72h2d4YmsDP3znqdx6ZRV52Zl8+a/P93kJYlelq9dQ9Fy3fcWZJxzzfidPKubWK6vY2djO+3/z9KvO1ZtKLvdykfaSGW4vay2PrN/Luf+ziu8/tImzThzPvz51Np9780kU5gb/vSgak82b5k3kgbV7ONwZTdn2dWQP98hYUgLJPdyvPmhSP5My2kvGxV6+TbiNMe8AaoCZA9wv2xhzgzFmozFmnTHmCWPMkvSMcuiWLPF+iLGY5fP3Pc8jG/bx9bfP5aLTKziuKJfPveUkntwW4r763QM/iSPS0Wso+lq33Z/FM8r46btPY+3OJj78+3o6+7ikcSq42stV2ktmOL0272vhitueYvlv15CblcFvr1nELVdWcUJZQQpH6L9LFlbQfLiL/7ywP2XbV/JsHiNpD3dpQQ5NfSwp0c+kjPaScbGXn3u4Pwe8EXh8gPv9DLgUeK21dj5wO/CIMeY0b4c3PPX19Z4+v7WW7/zzBf6yZhcff/0srnrN9O6vveeM4zn9+HF8+58vBOZHeV73Gor+1m33583zJ/HNC+bz6Mb9fPE+b37S4GIvl2kvmaH0amqL8LW/reMt//tfntvVxNffPpcHP/5aXjvrOA9G6L+zZo5nYvEY/rJmZ8q2r1C4A4BxI+SgSUjs4e7j7yH9TMpoLxkXe/k54X6NtXZLf3cwxswBlgM3WmsPAFhrbwW2A9/2fohD19zc7Onz/3LFVm5dvZ2rzprGJ94w66ivZWQYvnPRKRxq7+R7D230dByp4nWvobjlv/2v2+7P5WeewMdfP4s/r9nFDx7elPKxudjLZdpLRtKrKxrjtzU7WPbDFfz2yZe4bNHxrPjsOVz1mulkZY7cw4QyMwzvqJzCqi0HeflAU0qeMxTupCAnMxCnRxyskvwcWg53veqnffqZlNFeMi728u27obW2axB3uwgwwGO9bn8UONcYU5jygQXA3U++xA8e3sRFp0/hq+fN7XOZw8mTirlmyXTueXpnWs8PPVKseSnE9x8eeN12fz7xhlm8Z9Hx/HLFVu54fHuKR6iU/57YepDzfraar/xtPSdPLOafH38tN1w4v/uCJyPdxQsriMYsNXtSc7xGKNxBaeHIalfaz9UmlRpNXN/9cCoQA3pfO3s78atkzk37iAapqqrKk+f9+9o9fOVv63j9SeV8/5JT+z3S/xNvmMWUcXl86f7niXR5s5Y4VbzqNRSN4QgfEazbPhZjDN+6cD5vmjeBb/5jAw+s3ZOyMbrUKwi0l8xAvXaG2rj+t2u47JZaWju6+PXllfz+A4s5aWJxmkbohpnHFXLa1HHUh7JTsnQs1NZJaf7ImnAnT3HY2Otqk/qZlNFeMi72cn3CPR5os9b23n2Q/FmBe5cSSti3b1/Kn3PFpv186o/Pcsa0Un7x3kqyB/hxbX5OFl8/fx6b97Vy6+ptKR9PKnnRayiS67YbhOu2jyUzw/C/7z6dM04o5VN/epbHXzyYknG60isotJfMsXqFO7r44cObeP2PV7Jy8wE++6Y5/PtTS3nz/ElD/odp0F2ysIItB8Ks3zP8H2E3hiMj7qcDyX9A9F7HrZ9JGe0l42Iv986bkgLGmOXE134zefJkVqxYAcCMGTMoKipi7dq1QPzSn/Pmzes+QXpWVhZLliyhvr6+e/1PVVUV+/btY+fOnQDMmjWL3Nxc1q1bB0B5eTmzZ89m9erVAOTm5lJdXc2mTZu6H7N48WJ27drF7t3xs4bMmTOHzMxMNmzYAMDEiROZPn06NTU1AOTl5bF48WJqa2tpb49fRCVnyslcd9caphQarppxmObGBhqjUTZtiq8PnjJlChUVFdTW1gJQWFhIVVUVhYe2UVmeyU8e2cRb5pbTfnAX+/fvB2D+/Pl0dHSwZUt8Kf3UqVOZMGECdXV1ABQXF1NZWcnq1avp6oqvADr77LNZv349DQ0NACxYsICWlha2bYtP6KdNm0ZpaWn3AQslJSUsWLCAlStXYq3FGMPSpUtZu3YtjY2NAFRWVrJ169buXul8n+rq6mhtbe1+n378z+d4dONBLj85h/GZbezf3yF6n6qrq9m+fTt79+4FYO7cuUSjUd438zB7DsIHfvM0d129kNaXNxz1PtXU1NDRET9gasmSJWzevLnf92nHjh3df9Z0vk+hUIgdO3b4/j5JP0/t7e0cPHhwwPdpoM+T9H3y6/M03PcpGo3v40i+TyeeeCKPbmvlx//ZRlOH5ZwZBdxwSRUvPlfHk4/vStn7NNjPk0vvU0mnJSsDfl+zjXPHHxrW+3SgpZ1iG2bFihVOf54k79POlvhPV/c3tbJixfPd71Nrayv79+8fFZ+nVLxPsViMWCw24j9PqXqfIpEIpaWlaX+f+mWt9fU/4M74MPr82u+BKJDZ6/ZPARZYNNDzL1y40PrhscceS9lzbdhzyJ7ytYfsOT94zB5oOSx+/O7GNnvyVx60V91ea2OxWMrGlUqp7DVUT29vsDO+8H/2Q3ev8aTTK03t9qzv/scuvOERu+Ng67Cey4VeQaK9ZHr2evblRnvRL1bbEz73D3v+z/5r63aE/BuYoy75nwftad942HZ0Rof1PCd9+UF7wwPrUzQqN7zS1G5P+Nw/7N1P7jjqdv1MymgvGb96AXX2GPNR15eUPEd82cvUXrdPB7qADWkf0SDNmjVr4DsNwo6DYa647SkKcrO465pFjC+Un5918rg8PvXG2Ty26QAPrtubknGlWqp6DVUoHOGjf3iGipI8vnvxKZ78eHzi2DH85v2L6IpZrrz9KQ60dAz5ufzuFTTaS2bWrFnsbznMZ/68lgt+8Tgvh9r5wSWncv+HXsPCE4J5OXYvveuME2hs6+SxTfuH/BztkSjtndERd9Bk8hSHvU9Rq59JGe0l42Iv1yfc9xPfk72s1+3nAI9Ya529fnlu7vAvXLCv+TCX31ZLNBbjt9csoqIkf8jPddVZ05g7qZhvPLCelsOvvuqX31LRa6hiMcun//RsytZt9+fE8kJuv+oM9jUf5uo7n6K1YzAn63k1P3sFkfYavM5ojHvWNnDOD1bw92f3cP3SmTz2maW8s2pqoC/H7qXXzhrPcUW5w7rUeyhxFo+RdtDkmOxMCnIyuy/qk6SfSRntJeNiL6cn3NbaTcDNwBeMMeMBjDFXE7865Zf8HNtAkmt+hqqpLcIVt9XSGI7wm/cv4sTyomE9X1ZmBt95xynsb+ngR49sHtZzeWG4vYbj5v9u47FNB/jKeSczf4rsfNtDUXl8Cb98byUvvNLC9b9dM6QzyPjZK4i01+BEumJ88O56frZqJ9Uzx/PIJ8/m8285iSIP/xE6EmzcsJ4LT5vMYxv309A6tJ9cJfcAj7SDJiF+ppLepwXUz6SM9pJxsZefl3b/gTHmWeD8xO+fTfzX+7vNR4E/A48bY9YRPxjyXGvts+kcbzqFO7q46o6n2dHQxi3vq+LUinEped7Tpo7j8sUncFfNDp7fdSglzxl0dTtC/ODhTbztlElcPsTzbQ/F606awPcuPpXVLx7kM39eSyyW+qtRKiUR6Yrxod/V8+8X9nH5yTnc+r4qpo0fWZdj99LFCyvoiln+PsTTfzaM4Al3aR8TbqVGGz8vfPNZa+1p1tpSa61J/P9p1tpIr/t1Wmu/bK2dY62db62tttb+169xD1Z5efmQHtfRFeX6u9fw3K4mfvae0zlr5viUjuszb5pDWWEuX7z/eaIOTfKG2ms40rFuuz+XLKzgc28+ib+v3cO3/u8F0Xl8/egVZNqrfz0n29+8YB6XnTHF7yEFSnl5OSdNLGb+lOIhLysZyXu4x+XnvGoNt34mZbSXjIu9nF5SEmSzZ88WPyYas3zyj8/y3y0H+d7Fp/KmeRNTPq6xedl85by5PL/7EL+t2ZHy5x+qofQajnSu2+7P9UtncPVrpnH749u5adXgz5We7l5Bp72Orfdk+8rqadpLKNnr4soK1u9pZuNe+Tm5R/Qe7vzs7jXqSbqNyWgvGRd76YTbI8nzNA6WtZYv3f88/3x+L19+28m8s6r3iVlS5+2nTuK1s8bzw0c2s/fQYc9eR0Laa7jSvW77WIwxfOVtczl/wWRufHDjoPeOpbtX0GmvvvU12QbtJZXsdcFpU8jONNw7hL3cjeEImRnGt3/8e6mkIOdVV5rUbUxGe8m42Esn3I743kObuOfpnXzknBO59rUzPH0tYww3XDCfSDTGN/+x3tPXctHTPq3bPpaMDMMP37mAJSeO53P3PsdjG4d+ajGlButYk201dKUFOZwzp5z7n9lDV1R2MHSoLUJJfvaIPBNMaX4OrR1dQzpAXKmRQifcHpGckubXK7fy65VbufzM4/n0uen5Mci08QV89JwT+efze52Y4KXrFD6hcISP/t6/ddvHkpOVwa+vWMjJk4r40O/qqX+5sd/7u3jKIy9Ya3nixYNceftTXHPn03oaxRSJT7bX8O8X9nFDH5Nt7SXTs9clCys42NrBqi0HRM8Rao1QMsJOCZhUklgm09RjWYluYzLaS8bFXjrh9kh1dfWg7nfPUy9z44MbefuCyXzz/PlpnQAuXzqDmccV8JW/raM9Ek3b6/ZlsL2GIxazfOpPzxIK+7tu+1gKc7O446pFlBfn8v47n+bF/cc+zXw6evkpFrM8tG4vF/7yCS67tZYNe5pZsfkA77v9qSGdR36k95I4Mtnezw0XzOOKPvZsay+Znr2WzSmntCBHfPBkqC0yItdvw5F16T3Xces2JqO9ZFzspRNuj9TV1Q14n38+/wpfvP95ls4+jh+9c0Haf5SYm5XJty86hV2N7fz00S1pfe3eBtNruG5atY0VDqzb7s9xRbnc9f5FZGUY3nf7U8dcY5+OXn7ojMb4y5pdnPuTVVx/9xqa2iJ856JTWP25c/jZe05n7c4mLr/tKQ61yybdI7WX1GAm26C9pHr2ysnK4PwFk/n3hv1H7dEdSCg8cifcyatNhnqcqUS3MRntJeNiL51we6S1tf+LYP53ywE+fs8zVB5fwq8vX0hOlj9vxZkzyri4soJbVm1j094WX8YAA/carqd3hPjhI5t426lurNvuzwllBdx59SKa2iK87/a+J5de90q39kiUOx7fztLvP8Zn/ryWrAzDT99zOv/51FIuW3w8Y7Izeespk/jleyvZsOcQ7731yVedZqw/I63XUAx2sg3aS6p3r0sWVhCJxnjguVcG/RyN4Uj30ouRJvkPiZ4HTuo2JqO9ZFzspRNuHzzzciPX/XYNM48r5LarziAvJ9PX8XzpbSdTOCaLL//1+RF5AZae67ZvfIc767b7M3/KWG6+soptB1v5wG/qONzp75Ifrxxq6+Rn/9nCa773KN94YANTSvK446ozePDjr+X8BZPJyjz6W9S58yZy8xVVbN7XyntueXLIV/UbbSSTbTV88yYXc9LEokEvK4nFLI1tEcpG6oQ7/9VLSpQabXTC7ZHFixf3efumvS1cdcfT8aUD1yxibJ7/64hLC3L44ltO5ukdjfx5zU5fxnCsXsPVe912kC5R/ZoTx/Pjd53G0y+F+Pg9zxx1oSKveqXL/ubDfPefL/Ca7z3Kj/61mQUVY/nz9dX8+fqzOOek8n7/UXTOSeXc9r4qth8M8+6bn2R/y8Cntgx6r+E4arJ94fxBTbZHc6+h6N3LGMPFlRWs3dnU77EYSYfaO4lZRuxBk+Pyk3u4j0y4dRuT0V4yLvbSCbdHdu169Z6NnaE2rritljHZGdx9zWLKi8b4MLK+XbKwgkXTSvnugxt92WvYV69U6F63/fa5zq7b7s/bF0zmq+fN5eH1+/jK39Z1X43Sq15ee6khzBfue54l33uMW/67jXNOKuefH3std1y9iDOmlQ76eV476zjuuPoMdjW28+6bn2Rfc/+T7qD2Gq6OrujRk+1BLqcarb2Gqq9eF5w+mcwMw731A7dM7vktKxyZE+6crAyKcrOOury7bmMy2kvGxV464fbI7t27j/r9/pbDXH5bLR1dMe56/2Kmlub7NLK+ZWQYvnXRfFoPd/Htf76Q9tfv3SsVjlq3vfj4lD9/ulz9mul8cNlMfl/7Mv/7n/jBrV708tKGPc189A/PcM4PV3Dvml1cUlXBY59Zxs/eczpzJxcP6TnPmjmeu65ZxL5Dh7n0phr2NLUf875B65UKHV1RPvy7evFkG0Znr+Hoq1d50RiWzj6O++p3HfXTqb4kDyYcqXu4AcYVZB+1h1u3MRntJeNiL51wp8Gh9k6uvO0pDrR0cMfVZzBnYpHfQ+rT7AlFLD97BvfV7+aJrQf9Hs6wNLR2BG7ddn/+35vm8M6FFfzk31u4+8mX/B7OoD29I8TVdzzFW3/6Xx59YR8feO0MVn/uHL5z0SmcUFYw7Oc/Y1opv712MQ2tEd51Uw07Q20pGHXwDWeyrVLn4soK9jV38PiL/X8/DY3gy7onlebnEGqTn9JTqZFCJ9wemTNnDhA/+8I1dz7N1gOt3HTFQiqPL/F5ZP376OtmMbU0jy//dR0dXek7UC/ZKxXi67bXBnLd9rEYY/juO07hdSeV89W/rePF6HgOOfqXl7WWRzfu45JfPcE7f13D2l2H+PQbZ/PE51/PF956MuXFqV1KVXl8Cb/7wGKa2zu59KYaXmoIv+o+qdy+XJeKyfZo6pUKx+r1+pPLGZuXPeCyksZRMOGOX979yB5u3cZktJeMi72y/B7ASJWZmUmkK8YHf7eG+pcb+flllbx21nF+D2tAeTmZfPOC+Vx9x9PctHIbH3v9rLS8bmZm6s7U8utVW1m5+QA3XDg/kOu2jyUrM4NfXFbJZbc+ybf+9RLf+tdLjC/MYcb4QmaWFzDzuEJmHBf/taIkn8w0n9e9Kxrj/55/hV+t2MrGvS1MHjuGr719Lu8+43jPz8RzasU4/rD8TC6/tZZ33VTD7z9wJjOPK+z+eiq3L5f1nGx/68L5Qz4F5mjplSrH6jUmO5O3L5jEn+t20Xy485gX22oYBRPu0vwctuw7cgCpbmMy2kvGxV464fbIuvXruX/vOFZsOsCN7ziFt54yye8hDdo5c8p52ymT+PljL/L2BZOZPn74P/ofyIYNGygvLx/28zy1PcSPHtkc+HXbx5KXk8kfPnAmv7r/MQomTmfr/jDbDrby8Pp9hMJHzjCTk5nBtPH5R03CZyT+P9VX2DzcGeXe+l3ctHIbL4famHlcAT985wIuOG0y2Znp+yHavMljuWd5Ne+99UkuvelJ/vCBxcyaEF++larty2WpmmzD6OiVSv31uriygruffJl/PvcK717U9/ekxnCE/JxMxmS7N0lIlZKCnKMuBKTbmIz2knGxl064PWCt5e4NER7duYfPv+WkY36TddlX3z6XVZsP8NW/reOu9y8KxBrohtYOPvaHkbNu+1jGZGdyenkWy86eedTtjeEI2w62snV/mK2JXzftbeGRDfuOOmirvCj3qEn4zMT/TxmXJ7raacvhTn5X+zK3rd7OgZYOFlSM5YtvXci5cyek/aqpSXMmFnHP8jN5zy21vPvmJ7n72sWcPGloB2UGSUdXlA/dXc9/Ng5/sq1S67Sp45h5XAF/WbPrmH8XhMKREX3AJEBJfjbhSJTDnVFf/mHRfLiTD91dT2aG4VeXV5Kfo9MflV66xXngR49s5tGdXVy/dCbXL5058AMcNKF4DJ950xy+9vf1/H3tHi44bYqnrzdx4sRhPb7nuu37PnTWiFi33Z++epUU5LCwoJSFJxx9er1IV4yXQ21sPdDKtgPhxK+tPLB2D82Hu7rvl5uVwfTxBcwsL2Rm4tcZ4+N7xQtyj3yraGjt4I7Hd3BXzQ6aD3ex5MTx/OTS0zhrZpkT/8g5sbyIPy4/k8tuqeU9tzzJ3dcsHvb25TIvJtsjuZcX+utljOHihRV8/6FN7DgYZlofPzEMtY3cy7onJa+i2dTWycSxmWndxhrDEa68/SleeKWZmLVcc2cdtztw0TkJ/UzKuNhLJ9wpZq2lqT3CuxZO4XNvdm/RvsTlZ57AvfW7uOEfL7BsTrmnF+mZPn36sB7/q5Ujc932sUh65WRlcGJ5ISeWFx51u7WWhnCkexK+dX8r2w6GWbf7EA8+/wo9z2Q2aewYZhxXQGlBLv/asJeOrhhvmjuRDy6byYKp41L0p0qdGccV8sfr4pPuy255ktuuPN3vIXnCqz3bw/08jjYD9bro9Cn84OFN3Fe/i0+d++q/FxrDI3/C3X21yXCEiWPHpG0bO9DSwRW31bLtYJibr1xIy+EuPvnHZ7nmN09z2/uCM+nWz6SMi730LCUpZozhhgvm8+bxjU7s7RuOzAzDdy46hVC4gx88vNHT16qpqRnyY+Prtjdx3ghdt92X4fRKMsYwvjCXRdNLec+i4/nyeXO5/aozWPnZc3jhhjfzyCfP5lfvreSzb5pD9YwyWjui1Gw9yHmnTuZfnzybX1+x0MnJdtIJZQX88bozGZufzZW3P82al0J+Dymlek62v31RapeRpGL7Gk0G6jVpbB5LThzPvfW7ifVxTu6GUTDhTu7hTl78Jh3b2CuH2rn05hpeamjj9vedwetOmsAFp03hR+9aQM22Bj5wVx2HO9N3Nq7h0M+kjIu9dA+3B4wxZAR8sp00f8pY3nfWNO58YgcXV1ZwumOnNdwZauOjf6jn+NJ8vjuC122nW25WJrMnFDF7gpvnjB+sipJ8/nRdNRf+72NcedtT3H7VGSyeUeb3sIat92T7vYt1zbbrLllYwcfveZYntzdw1szxR31tVOzh7jXh9trOUBuX3fokjeFO7rrm6CvZXnR6BdEYfPYva/nAXXXccmXViD5gVblB93B7JC8vz+8hpMynz53DhKIxfPH+dXRFY568hqSXtZYnXjzIdb+tY+kPHuNQeyc/HyHn2x6skbR9eW3S2Dy+fvY4Jo4dw1V3PM0TA1yExHXpmGzr9iUzmF7nzp1IUW4W9645+gp4hzujhCPRET/hTh4UmjwXt5fb2LYDrbzrphqa27v43bWLj5psJ12ysILvX3wqq188yHW/XeP8nm79TMq42Esn3B5ZvHix30NImcLcLL729rm88Eozdzy+w5PXGEyvtkgXdz/5Em/6ySouu7WWp7aHuG7pTP7z6WWjYt12TyNp+0qHtyw7i3uWV3N8aT5X3/k0qzYf8HtIQ9LRFeWDadizrduXzGB65eVk8rZTJ/HgulcIdxw5WDm5x3ekn6VkXH58h0goHL9gl1fb2Ka9LbzrpieJdMW4Z/mZ/S57e2fVVL73jlNZufkA19+9Jq0Xe5PSz6SMi710wu2R2tpav4eQUm+eP5HXnVTO//x7M7ub2lP+/P31eqkhzA3/2MDi7/yHL/91HTlZGfzgklOp+cLr+dybT2LKOPf+Jeu1kbZ9ea22tpbjinL5w/L4BXGu/U0dj27c5/ewRJKT7UfTsIxEty+Zwfa6eGEFbZEoD67b233baLisO0B2ZgZFY7K6/4HhxTa2bvch3n1zDZkZ8Mfrqgd1StB3nTGV777jFFZsOsAH7653dtKtn0kZF3vphNsj7e2pn5T6yRjDN86fR8xavv739Sl//t69YjHLik37ef+dT7Pshyv4zRM7WDannHs/WM0DH1nCO6umjuo1dyNt+/JasldpQQ6//8Bi5kws4rrfruHh9XsHeKQbek62v3PRKZ6v2dbtS2awvapOKOGEsnz+subIRapGy4Qb4n/G5J831dvYmpcaec8tT5Kfk8Wfrqt+1VmZ+vOeRcfznYtO4dGN+/nw7+qJdHmzdHI49DMp42IvnXCrQZtams/HXz+bf23YxyMeTVRaDndyx+PbecOPV3LVHU/z3K5DfPR1s3j886/jZ+85nYUnlOqBkWpYxuXncPe1i5k/ZSwf/l09//fcK34PqV+9J9uXjZIz8YxExhgurqzgyW0hdobagNE14S7Jz/HkoMknth7kittqKSvI4U/XV3NCmfzqyJctPp4bLpzPv1/Yz4d/7+akWwWbTrg9Ul1d7fcQPHHta6czZ0IRX//7+qPWIQ5X+Ymn8tW/rePM7/yHbzywgbH52fzk0tN44vOv41NvnM2E4jEpe62RYKRuX17p3WtsXjZ3vX8Rpx8/jo/+oZ6/Pbv7GI/0l1+Tbd2+ZCS9Ljo9fhGx+5+Jb3OjacJdWnBkwp2qbWzFpv1cfcfTTBmXx5+uqx7WEsMrzjyBb14wj39t2MdH/1BPp0cnCRgK/UzKuNhLJ9we2b59u99D8ER2Zgbfecd89hw6zE/+vXlYzxWNWf69YR9X3FbLW39ewz1P7eRN8yfy94+8hvs/9BouPH0KOVm6ifZlpG5fXumrV9GYbO68ehGLppfyiT8+y1/W7PJhZMfm555t3b5kJL2mluZTPaOMe+t3Ya2lMRzBGDy9sJgrxuVn05g4aDIV29jD6/fygbvqOLG8kD9eV015CnbMXFk9ja+/fS4Pr9/Hx/7wjDOTbv1MyrjYS2czHtm7NxhrQ4di4QmlvGfRVG5/fAcb9jSLH3+orZObV21l2Q8f49q76tiyr5V3zMrmiS+8jh+/6zROrRiX+kGPMCN5+/LCsXoV5GZxx1WLeM3M8Xz2L2v5w1Mvp3lkR7Qc7uTpHSHuqtnB5+99jrf9dLVvy0h0+5KR9rp4YQUvNbRR91IjobYIJfk5ZGaM/KVypflH1nAPdxv727O7+dDv6pk/ZSy//8CZKf0JwVWvmc5Xz5vLg+v28ol7nvXsdLgS+pmUcbGXXvhGDcnn3nwSj6zfxxfvf557P3jWoP6y2Li3md88sYP7n9nN4c4Yi6aX8vk3n8y58ybw+H9XMb4wNw0jV+poeTmZ3Pq+Kq6/ew1fuO95uqIxrqie5tnrWWvZ3dTOC6+0sGFPMy+80syGV5p5ObGmF6AkP5u5k4v52Otncf6CyZ6NRfnjLfMn8tW/rePeNbtoPtxJSf7I37sN8atNtndGaY8M70wgf3p6J5+77zkWTSvltqvOoDA39VOZ9y+ZTsxavvV/L2AM/OTS08jK1H2Uauh0wu2RuXPn+j0ET43Lz+FLbzuZT/0pvlfwWJeV7orGeGTDPn7zxA5qt4cYk53BhadN4crqacydfOSUTSO9V6ppL5mBeo3JzuSmKxby4d89w1f+tp5I1HLNkunDft2Orihb9rWy4ZXExDoxwW4+HD/+wRiYXlbAKVPGcukZUzl5UhFzJ41lQnGurwcH6/YlI+1VkJvFW+ZP4h/PvcLM8kLKCkbHzoaeV5sc6jZ2V80Ovvq39bx21nhuvqKKvBzvzlZ17WtnELOW7/xzI8YY/uddC3ybdOtnUsbFXjrh9kg06ua5PFPpotOn8Oe6XXzvoY2cO28C5UVH1s81tHZwz9M7ufvJl3jl0GEqSvL4wltO4tIzpjKujws8jIZeqaS9ZAbTKzcrk1++t5KP3/MMN/xjA53RGNcvnTno12ho7eCFV1q691i/8EozL+5vpStmAcjLzuSkSUW8fcFkTp5UzNzJxcyZUESBB3vnhku3L5mh9Lp44RTurd/F2p1NvGneBA9G5Z7uq022RSg18mY3rdzKdx/cyBvnTuDnl51Obpb3p4ZdfvZMYhZufHAjGQZ+/K7TfFn+o59JGRd7ufedfoTYtGkTkyZN8nsYnjLG8K2L5vOWn/yXb/3jBX76ntN5blcTv3niJR54bg+RrhhLThzPNy+Yz+tOKu/3m9Ro6JVK2ktmsL1ysjL42XtO55N/WsuND26ksyvGR18/66j7RGOWHQ3ho/ZYb3ilmX3NHd33mVg8hpMnFfH6k8vjk+tJxZxQVhCYdbq6fckMpdeZ08uYMi6P3U3to+IMJUD30pnGcCcHdg++mbWW//3PFn7y7y28fcFkfvyuBWSncU/z9UtnErOW7z+0iUxj+ME7F6T9s6yfSRkXe+mEWw3LzOMKuX7ZTH76ny28uD/+o/P8nEwurZrK+846gRPLi/weolIiWZkZ/OTS08jOMPzoX5tpPtzJ8WUF3RPsTXtbaO+M7z3JyjCcWF7Ia2aO795rffKk4lEzgVJDl5FhuLhyCj999MVRs70k/5yhtggDXwMyzlrLjQ9u5KZV27hkYQXfu/hUX/7h+qFlJxKLWX74yGYw8INL0j/pVsGmE26PTJkyxe8hpM2Hls3k4XV7aYt08dXz5nJJVQXFY2QHAY2mXqmgvWSkvTIz4nuxsjMzuOW/8dNLFY/J4uRJxbx70dTuvdazJhSm5cfa6abbl8xQe72jsoJfrNjK5GGcOzpISpJruMMRTh5Es1jM8vUH1nNXzUtcceYJfOP8eWT4OMn9yOtmEbPw439tJsMYvn/xqWkbj34mZVzspRNuj1RUVPg9hLQZk53Jgx9/LcYw5AO9RlOvVNBeMkPplZlh+O474qfkKyvMYcq4vFFzlVPdvmSG2mva+AIe/sTZTC0dHRPucYlzjYfCESpOn9rvfaMxyxfue44/1e1i+dkz+MJbTnLi8/ex188iZi0/+fcWMk38e0Q6Jt36mZRxsZee48YjtbW1fg8hrTIyzLC+GY62XsOlvWSG2isjw7Bg6jgqSvKd+Ms+XXT7khlOrxPLR+ZPSfqSlZnB2Lxsmtoi/TbrjMb45B+f5U91u/j462c5M9lO+sQbZvOx153IH+t28qW/Pk8scWC0l/QzKeNiL93DrZRSSqm0KC3IIdTWCWP7/npHV5SP/v4ZHtmwj8+/5STRmYLS6ZNvnE3Mws8fezF+AoEL5vu63EW5TyfcHiksLPR7CIGivWS0l4z2ktFeMtpr8OKXd4/02exwZ5TrfruGlZsP8I3z5/G+s6alf4CDZIzh0+fOJmYtv1yxlQwDN1ww37M98bqNybjYy1jr/Y9C/FRVVWXr6ur8HoZSSik16l1z59O8cugw//z4a4+6PdzRxbW/qePJ7Q3c+I5TuPSM430aoYy1lhsf2shNK7dxZXX8wE6Xlr+o9DLGrLHWVvX1NV3D7ZGamhq/hxAo2ktGe8loLxntJaO9Bq+kIIfGtshRzQ61d3LFbbU8tSPETy49LTCTbYjv6f78m09i+dkzuKvmJb7xwAa82JGp25iMi710SYlHOjo6Br6T6qa9ZLSXjPaS0V4y2mvwSgtyCIUjdHTEDxRtDEe44vZaNu1t4ReXVfLm+RN9HqGcMYYvvOUkYjHLrau3Ywx89by5Kd3TrduYjIu9dMKtlFJKqbQoyc+hoytGR9Syv+UwV9z6FDsawtx8ZRXnzCn3e3hDZozhS287mai13PH4DjITv9flJSrJ+TXcxphy4H+A5JqY54FPWGt3Debxfq3h7urqIitL/z0zWNpLRnvJaC8Z7SWjvQbvnqde5vP3Pc+fli/i8/etZ2/zYW69soqzThzv99BSwlrLNx7YwJ1P7Ejp+cN1G5Pxq1dg13AbY3KAfwE5wDxgLhAGHjPGuHcIag+bN2/2ewiBor1ktJeM9pLRXjLaa/CSV5u86o6nOdDSwW+vWTRiJtsQ39P9tbfP5crqE7h51TZufGhjStZ06zYm42IvpyfcwPuAU4HPWWu7rLVR4HPADOCDvo5sAPv37/d7CIGivWS0l4z2ktFeMtpr8EoTE26D5fcfOJOFJ5T6PKLUM8bwjfPncfmZx3PTym384OFNw5506zYm42Iv138+cTHwsrV2W/IGa+1eY8yGxNd+4NvIlFJKKSUyb3Ix76qq4JTcBk6pOMbVb0YAYwzfPH8+MQu/XLGVzmiMc04qJysjg6xMQ3ZGBpkZhuxMQ1ZmBlkZhqxME/96z//PNGTpBXVGBKfXcBtj9gCbrbXLet3+d+D11tqCgZ6jqKjILly4sN/7nHfeeXzmM58BYNmyZVx11VVcddVVHDx4kEsuuWTAcfa+/6c//Wmqq6tpaGjguuuuG/Dxn/70p3n729/Opk2buO666/jOd77DWWedxRNPPMEXv/jFAR/f+/433XQTc+bM4YEHHuBHP/rRgI/vff+//OUvjB8/njvvvJM777xzwMf3vv+KFSsA+OEPf8g//vGPAR+/YsUKDh48yJ133klNTQ333nsvAF/4whcGPLVPWVnZUfdvaGjg5ptvBmD58uUD/lhp9uzZR92/rKyM7373uwBcfPHFNDQ09Pv46urqo+5fXV191LY0kKFue52dnWRnZ/e57fXclgYyWra9ZK/eet5ft70j29IFF1zQZ6+edNs7cv9bbrmFxx9/HJB930vefzRue1dddRXjx49P2d+5rm57FmiYfi6tExYM+Lz9sjGMjUIsRlFhPjlZmXQcbqct3MrUyZPIysygseEgLYeaEveN/4eNJUZxxKJFiwDYsX07TU1NnHb66UB8GUZTU1O/w8jJzj7q/p2RCPPmzwdg/bp1hNva+n18QX7+UffPzslh9uzZADz7zDNEOjv7ffy4ceOOuv+4ceOYNn06AE899VT3/fL2rmVs05ZXPd6L+V7PbWnlypXHXMPt+h7u8cCaPm5vBvKNMXnW2vbeXzTGLAeWA+Tk5HRvQHl5eWRmZtLa2gpAdnY2BQUFbN26lRUrVnQvsN+xYwcrVqzg0KFDRKNRIpFI9ylm8vLyyMjIIBwOk3z+aDTaff/m5mYANmzYwI4dO2hqaqK4uJiOjo7u58jPz8cY0/0cu3btoqOjg9raWpqamli/fj1nnXUW69ev7x57cXExhw8fJhKJAFBQUIC1lra2Nurr6znuuOPo6OigqamJ2tpaWlpa4qGam4nFYgCMHTuW9vb2o54jFotRW1vLK6+8wiuvvEI0GuXxxx9n7Nix7NixA4BDhw51/zhs3LhxhMNhOhMfisLCQnbt2sW6devYuHEjhw8fprm5mfr6erZu3UprayuFhYVHfYjHjRtHa2srXV1d3WPctWsXW7du5cCBA7z88ssUFRXx8ssv09TU1P0+JZ/DGMPYsWNpbW0lFouxYsUKqqqqaGxsZM+ePaxYsYJZs2Zx+PDh7sfk5OSQl5fHoUOHAMjIyKC4uJgDBw50/8XX1dVFKBTq/v3hw4fp7Ow86r0eM2ZM93uckRFfkVVbW0t7ezsHDhygq6uLjRs3snfvXpqamo56nwByc3PJzc3tfo6dO3cC8XOGNjU1sXHjRrq6uti0aVP32JPvU3t7e/dzAITDYTZu3Eh9fT3HH388hw4d4vnnn6eoqIgJEya86n2KRqPdzzFmzBiys7O775/s8swzzxCJRFi/fj3AUe9TUVERnZ2dHD58uPuz0NzczIoVK1i3bh3hcLj7/Xj++ec5dOhQ9/vU8zl6fp727dtHWVkZzz//PE1NTWzatIlx48axceNGmpqaut+nlpYWotFo92ch+Xl6/PHHWbRoEc3NzTQ1NbFixQomTpxIV1dXd7+MjIzu5snPQvLzlHyvW1tbj/p9KBQiFot1v0+ZmZkUFRUd9RylpaVs2LCB/fv38/LLL5OZmcnu3bvZsmULe/bsob29nZycnO7PYlZWFoWFhd2fpz179hCLxVi/fj179uwhHA7T2NhIS0sLBw4coLm5uft96v0cTU1NvPzyy6xcuZKlS5dy6NCh7u9jlZWVHD58+Kj3Sfp9r6uri66uLvH3vbq6OjZs2DDqvu91dHQM6fteKBQatd/3Ojo6xN/3kp+noH3fG/fig+S8XEskZrAZmXzwQx9mcsVUHnzoER6vqeGqq6/hhOkz+Mt997Fu/QuYzCxyx+TT0dlJzBpsRgZZ2blYk0E0FmPBmWdRNr6EF7cdZNNLW5k4rZTs3DF0NXZwqK0JTAZkZJKZnUPUZnRPtzMzs4jFYuzaF/8HVVsXdJqs7t+3d1limblEo/FxGwwZmZmJ77028f7nsC/UTGdnJ60dUYzJ4uChMIcPH6atC6ImG5Nhur9fG2PIyDjyHG1d0Nweob2tnbYuyLRRGlvaicaiHI5lECWTjIwMjOnrObpo7Yiye18DRcXFdNhMDrV3smtfAwUFBUQzsrs/91mx+Ladjvlez+97/XF9D3cEeNha+/Zet98NvBfI72vC3ZNfZylZsWLFoP6lr+K0l4z2ktFeMtpLRnvJaTMZ7SXjV6/AnqUEOAgU9XF7MdA20GRbKaWUUkopv7k+4X4OmNbH7dOJn4/bWVOnTvV7CIGivWS0l4z2ktFeMtpLTpvJaC8ZF3u5PuG+DzjBGDMteYMxZgJwMnCvX4MajAkTJvg9hEDRXjLaS0Z7yWgvGe0lp81ktJeMi71cn3DfSXxP9veMMVnGmAzgRmA78Cs/BzYQP9aNB5n2ktFeMtpLRnvJaC85bSajvWRc7OX0hNtaGwHeCESBDcALxNdvv85a2+rn2JRSSimllBoM108LiLV2H3CZ3+OQKi4u9nsIgaK9ZLSXjPaS0V4y2ktOm8loLxkXezl9WsBU8Ou0gEoppZRSavQI8mkBA2v16tV+DyFQtJeM9pLRXjLaS0Z7yWkzGe0l42IvnXB7JHmFKTU42ktGe8loLxntJaO95LSZjPaScbGXTriVUkoppZTykK7h9kgsFiMjQ/89M1jaS0Z7yWgvGe0lo73ktJmM9pLxq5eu4fbB+vXr/R5CoGgvGe0lo71ktJeM9pLTZjLaS8bFXjrh9khDQ4PfQwgU7SWjvWS0l4z2ktFectpMRnvJuNhLJ9xKKaWUUkp5SCfcHlmwYIHfQwgU7SWjvWS0l4z2ktFectpMRnvJuNhLJ9weaWlp8XsIgaK9ZLSXjPaS0V4y2ktOm8loLxkXe+mE2yPbtm3zewiBor1ktJeM9pLRXjLaS06byWgvGRd76YRbKaWUUkopD43483AbYw4AL/nw0uOBgz68blBpLxntJaO9ZLSXjPaS02Yy2kvGr14nWGuP6+sLI37C7RdjTN2xTn6uXk17yWgvGe0lo71ktJecNpPRXjIu9tIlJUoppZRSSnlIJ9xKKaWUUkp5SCfc3rnZ7wEEjPaS0V4y2ktGe8loLzltJqO9ZJzrpWu4lVJKKaWU8pDu4VZKKaWUUspDOuFWSqkRyhgzyRjzkDFGf5Q5CNpLRnvJaK/RLcvvAQSJMaYc+B8geaqZ54FPWGt3DeKx2cBXgXcCXUAz8P+stas9Gq6vjDGnAR8GlhD/82YC/wZusNYeGOCxO4CmPr70GWvtv1M6UIcYY6YB64AX+/jyMmtt0wCP/wSwnHjvLuCb1tq/pnSQDjHG3El8+2rt9aUSYCIwzlrbfozHrgDKgUivL/3YWntXakfqD2PMO4AfA50D3G9Y35uMMe8F/h9giO/E+V9r7S3DGLovBtPLGDMJuB64iPifNQvYAHzNWvv8IF7jTvreZu+x1t44tJH7Q7B97WAY38+NMecC3wLygGzgN8D3rLUx4ZB9Ncjt6yrgRmBvry9lA3OBN1hr/9PP478OvB8I9frSKmvtx+Sj9sdg5w/GmELivd4IRIFdwCettesH+TqfIJ1/Z1pr9b9B/AfkAGuBPxP/JptJ/IO/BSgcxON/DWwGjkv8/lqgDTjN7z+bR702AvcCBYnfT0ncthnIG+CxO/wev0/NpgErhvjYzxM/yf/MxO/fSPwb+1v8/nN52OtO4v8Q6X37TcAfB3jsCmCa338Gj/vUArMSnWw/9xvy9ybg3UAHsCjx+1OBMHCd339+L3r1aDU18fsxib8T2oBTBvEafW6zQfxPsH3tGMZrLCH+j+ILE7+fCuwBvuv3n9+LXsBVwNf7uP09wG4gc4DX+Dpwld9/1hS0GtT8AXgQWA3kJ35/A3AAmDKI10j735m+hw3Kf8AHAAvM6HHbROL/qvrsAI+dA8SA9/e6fT3wf37/2TzqtRE4sddt1yQaXjzAY3f4PX6fmk1jCBNuYFxikvPNXrf/H7De7z+Xh72qgQm9bisADgGvH+CxKxj5E+6sxK/9/QU/5O9NxPfw7gTu6nX7L4AGINfvBh70+jVwba/bZia+r/1sEK9xJyNnwj1gr8TXdwzjNR4nvne2522fJT4Jn+x3g1T3AmYAp/Zx+3+I790d6DW+zsiZcPc7f0hMkC3wuh73ySG+d/8XAzy/L39n6hruwbsYeNlauy15g7V2L/EfJ148wGMvIv7j1sd63f4ocG7ixyIjzanW2t5LI/Ykfi1J92BGuDcD+fS9fc01xpyU/iF5z1pbY63d1+vmdwH7if/ZRzVrbdcg7jac702LgIpjPLYUOGeQQ3XCIHt9BLi9122j8vvaIHsNWWL5zln0vX1lA+d7+fqpNphe1tpt1trnet5mjJkBLAVu9WpsDhrM/OFi4nuku5e+WWsjxP+RNtCczJe/M3XCPXinAtv7uH07cMogHhsDXu7jsVnE12aNKIkNv7fZxP9Fumqgxxtjvm+MecIYs9kY84gxJlDfXIdhgjHmbmPMU4k/+++NMYPZvuDV2+f2Xl8fDT4A3GITuysG8CljzOPGmI3GmFXGmKu9HpyDhvO9adRtd9baLvvqtcOzE7+uGOTTXJHY3jYYY540xnzKGDOij6ca4vfz5Pe9UbN9HcO1wCPW2pcGef83G2P+Y4x53hhTb4z5pjEm38sBptog5w+nAnv6uO924n+PlvfzEr5879IJ9+CNB1r6uL0ZyDfG5A3w2DZrbbSPxwKUpWB8TjPGZBL/kdBt1trNA9x9P1BPfP3ePOBvwN+MMR/xdpS+ixI/cON/rLWLiB+c2wnUGmPO6Odx4xO/9t4+R832BWCMmQssJP4j24E0ET84dRnxbeynwK+MMT/0aHiuGs73Jt3u4pYTX4Lz20HctwXYR3wP2zzgi8CXgHs8G53/hvr9fNRvX4m/N9/H4C/i0kZ8qcTF1tpTiP+deznwn8TB0YF0jPlDf3MycPB714j+V7VyyleITx4/MdAdE5PNpBjwC2PMW4HvGGNutdYe9maI/rLW7qTHT0ustc3GmOuBtwLfIb5mTR3btcDfrLX7B7qjtfbCXjf9xRhzDvBJY8xPrbW99/gq9SrGmNcDlwJnW2s7Brq/tfajvW561BhzI/B9Y8xrrLWPezFOP43W7+cp8rbEr/8YzJ2ttd/v9ftnjDGfA/5EfLnd71I7vLQZ9PzBZbqHe/AOAkV93F5MfA9Rn6cf6/HY/MS/0no/FuIHGI1YiR/Vv4v40b/hIT5NLfH+81I2sABIbFfPA2f2c7eDiV97b5+jYvsCMMbkAFcwvMv51hL/ntjfTxNGmuF8bxrV250xZgHxM1Wdb63dMIynqk382t9nfKQZzPfzUb19JVwL3DHM9fKB3r76mT/0NycDB7936YR78J4jfhaJ3qYTnxAN9NgM4qc06v3YLuIHXo5IxpgrgE8TP5J4wD2Pxpi8YxyolfyRd++JwYhhjBmbmDj2FqX/P3fyIJtpvW6f3uvrI9mFxM9Ocsxz1CYZY3KMMWP7+NKI38b6MJzvTaN2uzPGnAr8FXi3tfaJQT4m0xhT2seXRux2N8zv58m/V6f1un3Eb1/QfdDom4BBn9PeGHNcHzcHdvsaYP7wHDC5j78zpwP7Bphv+PK9Syfcg3cfcELi4iQAGGMmACcTP18kPW83xvRsez/xxf7Lej3nOcQPhuh9EYQRwRhzOfA54ifr35u47TxjzPIe9+nd6lLgR3083ULi5/sdsf84Af6XXkdXJ76ZnEJ8DWTytrJe32QeIr52b1mv5zsH2GCt3ejJaN1yLcc4WLKPXmcR/xFrbwsTvz7jwfhcNejvTYl/EPY8+Oop4hea6OuxIQZ/EGGgJCbbfwOusImLAyWuIHhTr/v17jUVqOnjKZPbXX0fXwu6QX8/N8bk9/yHsLX2FeK9lvV67DnElxc8kOrBOuZq4NFjHSzZu1fCS338tCqQ29cg5g/3ET9bzVk9HpMDvIZXz8nc+DszXedVDPp/xM/v+BzwR+Jr3zOAO+h14Rvib3YU+FWvx/8a2ASMT/z+aqCdkXvhm/cm/nyfIX7QRvK/m0ic2L+vVsRP/N8MnNHjtkuJr/37Zjr/DD40u5P4N8VJid9nEp+ER4E3Jm6bDhwGHuz12M8TP+H/jMTv38AIv/BNjz/7CYkm5X187VW9iH+T7QLe1uu2ML3OKT0S/mNwF77p93sTUEj8x6wv9HrsuxN9qxK/P4X4VRQDd+GbwfRK/PkOJJr1/L72CXqcQ7+vXsT3pllgeY/bTgVeAVYCxu8/uwe9Bv39PLENHiBxsZPEbckL35yf+H0F8QvABO7CN4Pp1eM+hvhB3Rf1c5++elniV+XMTPz+BOL/qNlI4uIwQfiPQcwfEvd7CPgvRy588w16XfgGh/7O1IMmB8laGzHGvJH4pd03EN+w1xH/UUfPPdStxH+0/Uqvp/go8DXgcWNMJ/GjY8+11j7r9dh98jPiV2H7QR9f+0bi175aPZh4zC8TR1WPAxqB6621w1mfGwQ/Aq4DHjLGQPxI6heI/ws/eb7QduJ7D/f0fKC19kZjzGHgH8aYLuKT9Hdaax9M1+B9dA3wgO37R4h99aonfinyLxpjvkP8YjkR4n9R9bW9BpIx5gfED7Q9PvH7ZxNfWmSPPpXWYL43dRL/y2l3z9ew1t6T2KN2e2KbzQA+FcTP6iB7fYP45/K6xH89rezx/3312pN4zKWJM3TkEv9H9V3EJ5+DOZWlMwbZS/L9/BWOXGIbAGvtamPM24FvGWO+RXzH1y+IX847UASfR4jvac2n/734r+pFfKJ6GfBs4nOZT3xS+hVrbdtw/wxpNJj5A8AlwPeI/3mTl3ZfZq3t+blz5u9ME7DPuFJKKaWUUoGia7iVUkoppZTykE64lVJKKaWU8pBOuJVSSimllPKQTriVUkoppZTykE64lVJKKaWU8pBOuJVSSimllPKQTriVUkoppZTykE64lVJKKaWU8pBOuJVSyhHGmHJjzLPGmJAxxib+/9rE1z5hjLnQ5/FdaIz5RB+3L06MeZEPw1JKKefphFsppRxhrd1vrT0N+Hvi96dZa29NfPkTwIX+jKzbhcTH0VsYeCnxq1JKqV6y/B6AUkqpYLPWrgNO93scSinlKt3DrZRSDjPGTDXGPAtMBs5PLDN51hjzhh73WW6M2WCM2WSM2WqM+Y4xJrvH15PLVHYYY95kjFlhjNmdWLYyzhhzmjHmjz2eu94Y875e43gYOB+Y3ON+n08837OJ5/p6r8fMN8b8I/G6240xjxhjKnt8/frEuK0x5sPGmJuNMWsT9/+IN0WVUir9dA+3Uko5zFq7EzjNGLMDWGGtvarn140x/w+4AXi9tXa1MWYSsAqYCLw/8RynGWPuBN4BvAU4B8gDtiWe5s1AB1Blre0yxswGnjDGtFhr70s8x5sSz7Esseylp4eNMbbXuE4EHgduAd5urU1OyFcZY6qttc9ba39tjHkI2A5cB1xgrd1ujFkO/NoY8y9r7aah11NKKTfoHm6llAooY8xY4GvAn621qwGsta8APwKuMsZM7/WQIuA7Nq4NqAaagTuBj1lruxLPsRn4N/CBYQzv64lfv2KtTU7Gv018nfe3+7j/o9ba7Yn/vw8wwNJhvL5SSjlD93ArpVRwVQP5xPck97SOIxPW7T1ub7DW7k/+JjnBNcY0A58xxrwt8XxR4HjgwDDG9gZgvbW2vcfrdRpjngHeYIwxPSbiAJt7/H8o8euEYby+Uko5QyfcSikVXOMTv/4/Y8x1PW7PAvYR36PdU+sxnud2YBnx5SIbAZLLR4Y5tjV93B4ivpwln6PPatKW/B9rbcwYA5A5jNdXSiln6IRbKaWC62Di169Za+8ayhMYY/KAS4BfJifbKXIQKO3j9lKgnR4TbKWUGul0DbdSSgVDJ/FlIhhjTjDGnAU8QXwv8YLedzbG3G6MmTeI580mvifZ9rp94gBjKDDGnN/P8/4bmGeMye8xpizgNODfvZaTKKXUiKYTbqWUCobtQEXi/5cD11prm4kfNHmtMWYxgIn7LPHzYg+4xzrxHP8F3mWMqUg8x1nA648xhvHGmFzgLOAn/Tz1N4hP4m8wifUhwBeJL3P50kDjUkqpkcToTgallHKDMaYceIT4AYslwFrg59baW40x1cTXWncR36t9ZeJsIhhjrgY+BeQSX6qxBvhC8gBJY8xjxPeCFwIbgFustb/o8bpTgZ8BZxI/eHEL8T3cb0zc/0Jr7Y7E+P4ITAEixCfQHcD3Es+/D3jKWnt+4nnnJ742n/jkewvweWvtmsTXLyX+D4aTgZ3ATcD9wO97PN8j1torU5BXKaV8oxNupZRSSimlPKRLSpRSSimllPKQTriVUkoppZTykE64lVJK/f9261gAAAAAYJC/9Sj2FUUAjIQbAABGwg0AACPhBgCAkXADAMBIuAEAYCTcAAAwEm4AABgFqHgg2bhpAaYAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_item = \"goal\"\n",
    "\n",
    "fig = plt.figure(figsize=(12, 8))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_xlabel(\"Iteration\")\n",
    "ax.axhline(y=best_point_dict[plot_item], color=\"black\", linestyle=\"-.\")\n",
    "ax.set_ylabel(plot_item)\n",
    "\n",
    "ax.plot(data_df[plot_item])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sensitivity Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another interesting study to understand if our dataset is indeed helpful in improving certain model parameters is to perform a Sensitivity Analysis. The purpose of this exercise is to scan the Model Parameters of interest (eg, qubit frequency or anharmonicity) across a range of values and notice a prominent dip in the Model Learning Goal Function around the best-fit values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "run_name = \"Sensitivity\"\n",
    "dir_path = \"sensi_logs\"\n",
    "algorithm = \"sweep\"\n",
    "options = {\"points\": 20, \"init_point\": [-210e6, 5e9]}\n",
    "sweep_bounds = [\n",
    "    [-215e6, -205e6],\n",
    "    [4.9985e9, 5.0015e9],\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "sense_opt = Sensitivity(\n",
    "    datafiles=datafiles,\n",
    "    run_name=run_name,\n",
    "    dir_path=dir_path,\n",
    "    algorithm=algorithm,\n",
    "    options=options,\n",
    "    sampling=sampling,\n",
    "    batch_sizes=batch_sizes,\n",
    "    state_labels=state_labels,\n",
    "    pmap=exp.pmap,\n",
    "    sweep_bounds=sweep_bounds,\n",
    "    sweep_map=exp_opt_map,\n",
    ")\n",
    "\n",
    "sense_opt.set_exp(exp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C3:STATUS:Sweeping [['Q1-anhar']]: [-215000000.0, -205000000.0]\n",
      "C3:STATUS:Saving as: /home/ubuntu/c3/examples/sensi_logs/Sensitivity/2022_05_18_T_12_42_19/sensitivity.log\n",
      "C3:STATUS:Sweeping [['Q1-freq']]: [4998500000.0, 5001500000.0]\n",
      "C3:STATUS:Saving as: /home/ubuntu/c3/examples/sensi_logs/Sensitivity/2022_05_18_T_12_43_33/sensitivity.log\n"
     ]
    }
   ],
   "source": [
    "sense_opt.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Anharmonicity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "LOGDIR = sense_opt.logdir_list[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "logfile = os.path.join(LOGDIR, \"sensitivity.log\")\n",
    "with open(logfile, \"r\") as f:\n",
    "    log = f.readlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_list_dict = list()\n",
    "for line in log[9:]:\n",
    "    if line[0] == \"{\":\n",
    "        temp_dict = ast.literal_eval(line.strip(\"\\n\"))\n",
    "        param = temp_dict[\"params\"][0]\n",
    "        data_list_dict.append({\"param\": param, \"goal\": temp_dict[\"goal\"]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_df = pd.DataFrame(data_list_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f1804e1b1c0>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 8))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_xlabel(\"Q1-Anharmonicity [Hz]\")\n",
    "ax.set_ylabel(\"Goal Function\")\n",
    "ax.axvline(x=best_point_dict[\"Q1-anhar\"], color=\"black\", linestyle=\"-.\")\n",
    "ax.scatter(data_df[\"param\"], data_df[\"goal\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "LOGDIR = sense_opt.logdir_list[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "logfile = os.path.join(LOGDIR, \"sensitivity.log\")\n",
    "with open(logfile, \"r\") as f:\n",
    "    log = f.readlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_list_dict = list()\n",
    "for line in log[9:]:\n",
    "    if line[0] == \"{\":\n",
    "        temp_dict = ast.literal_eval(line.strip(\"\\n\"))\n",
    "        param = temp_dict[\"params\"][0]\n",
    "        data_list_dict.append({\"param\": param, \"goal\": temp_dict[\"goal\"]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_df = pd.DataFrame(data_list_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f1804cdb610>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 8))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_xlabel(\"Q1-Frequency [Hz]\")\n",
    "ax.set_ylabel(\"Goal Function\")\n",
    "ax.axvline(x=best_point_dict[\"Q1-freq\"], color=\"black\", linestyle=\"-.\")\n",
    "ax.scatter(data_df[\"param\"], data_df[\"goal\"])"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "d5cf14e4d9ab1ea025aa471aed46f344d1515a88cee97f484fbdd896922a10bf"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}