{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5431b33c",
   "metadata": {},
   "source": [
    "# Chemical Analysis\n",
    "Chemical networks are complex systems where the interplay between many elements often means that small changes in one aspect of the network can greatly effect the outcome in unexpected ways. Nevertheless, there are cases where a simple chemical explanation can be found for some observed behavior in the model outputs. This tutorial demonstrates how to use some of the functionality of the UCLCHEM library to analyze model outputs and discover these explanations.\n",
    "\n",
    "We do recommend caution when following the approach laid out in this tutorial. There are many pitfalls which we will try to point out as we go. Ultimately, a comprehensive view of how important a reaction is to the outcome of your model requires detailed statistical calculations such as the use of [SHAP values](https://github.com/slundberg/shap) to assign meaningful scores to how much various reactions in a network contribute to a species' abundance. Therefore, care must be taken by the user to ensure that the conclusions they draw from a simpler approach are sound. Examples of papers doing this for UCLCHEM include:\n",
    "- [Understanding molecular abundances in star-forming regions using interpretable machine learning Open Access](https://ui.adsabs.harvard.edu/abs/2023MNRAS.526..404H/abstract) Heyl, J., Butterworth, J., & Viti, S. 2023, MNRAS, 526, 404\n",
    "- [A statistical and machine learning approach to the study of astrochemistry](https://ui.adsabs.harvard.edu/abs/2023FaDi..245..569H/abstract) Heyl, J., Viti, S., & Vermariën, G. 2023, Faraday Discussions, 245,\n",
    "569\n",
    "- [Understanding molecular ratios in the carbon and oxygen poor outer Milky Way with interpretable machine learning](https://ui.adsabs.harvard.edu/abs/2025arXiv250508410V/abstract) Vermariën, G., Viti, S., Heyl, J., Fontani, F., 2025, A&A, 699, A18\n",
    "\n",
    "We'll use an example from work that was published in 2022 [Energizing Star Formation: The Cosmic-Ray Ionization Rate in NGC 253 Derived from ALCHEMI Measurements of H3O+ and SO](https://ui.adsabs.harvard.edu/abs/2022ApJ...931...89H/abstract) to demonstrate the use of the rates coming out of UCLCHEM and how it can be used to draw conclusions about the most important reactions in a network for a given species/behaviour."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "207456cc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T16:44:22.486469Z",
     "iopub.status.busy": "2026-06-19T16:44:22.486283Z",
     "iopub.status.idle": "2026-06-19T16:44:22.489991Z",
     "shell.execute_reply": "2026-06-19T16:44:22.488988Z"
    }
   },
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b930d63e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T16:44:22.491592Z",
     "iopub.status.busy": "2026-06-19T16:44:22.491432Z",
     "iopub.status.idle": "2026-06-19T16:44:22.709420Z",
     "shell.execute_reply": "2026-06-19T16:44:22.708342Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "612c6503",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T16:44:22.711294Z",
     "iopub.status.busy": "2026-06-19T16:44:22.711058Z",
     "iopub.status.idle": "2026-06-19T16:44:23.255807Z",
     "shell.execute_reply": "2026-06-19T16:44:23.254744Z"
    }
   },
   "outputs": [],
   "source": [
    "import uclchem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3c765b81",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T16:44:23.258141Z",
     "iopub.status.busy": "2026-06-19T16:44:23.257792Z",
     "iopub.status.idle": "2026-06-19T16:44:23.260940Z",
     "shell.execute_reply": "2026-06-19T16:44:23.259993Z"
    }
   },
   "outputs": [],
   "source": [
    "import uclchem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "972f089b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T16:44:23.262685Z",
     "iopub.status.busy": "2026-06-19T16:44:23.262501Z",
     "iopub.status.idle": "2026-06-19T16:44:23.265682Z",
     "shell.execute_reply": "2026-06-19T16:44:23.264860Z"
    }
   },
   "outputs": [],
   "source": [
    "# Ensure output directory exists\n",
    "if not os.path.exists(\"./output_4\"):\n",
    "    os.makedirs(\"./output_4\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d6c998b",
   "metadata": {},
   "source": [
    "## H3O+ and SO\n",
    "\n",
    "In a piece of inference work in which we measured the cosmic ray ionization rate (CRIR) in NGC 253 [(Holdship et al. 2022)](https://ui.adsabs.harvard.edu/abs/2022arXiv220403668H/abstract). We found that both H3O+ and SO were sensitive to the ionization rate. Furthermore, since H3O+ was increased in abundance by increasing CRIR and SO was destroyed, their ratio was extremely sensitive to the rate.\n",
    "\n",
    "In the work, we present the plot below which shows how the equilibrium abundance of each species changes with the CRIR as well as the ratio. We plot this for a range of temperatures to show that this behavior is not particularly sensitive to the gas temperature.\n",
    "\n",
    "\n",
    "![crir_h3o_so_example](./assets/holdship_ngc253.png)\n",
    "\n",
    "In a sense, this is all the information we need. A complex array of reactions all compete and contribute to produce the outcome of the model. Whatever they are, we see the abundance of these species are very sensitive to the CR over a wide range of temperature and density. However, we can only trust this conclusion as far as we trust the entire chemical network since we don't know what exactly causes this behavior.\n",
    "\n",
    "If we use the analysis function, we may find all of this is driven by a small handful of reactions. The benefit would then be that our trust in our conclusions only depends on how much we trust the rates of those specific reactions. If it is very important, we can evaluate those reactions and readers of our work can make the same decisions as information from experiment changes.\n",
    "\n",
    "### 1. Generate Test Cases\n",
    "Ideally, we should run a huge grid of models and find some rigorous way to evaluate how important each reaction is to the outcome of the model. However, if we run a small grid representative of different conditions then any simple chemical explanation will be evident if it exists. If it no simple explanation is appropriate, then the analysis module is not appropriate for our task and we could consider more complex approaches.\n",
    "\n",
    "Let's run a simple grid with all possible combinations of the following:\n",
    "- A low CRIR (zeta=1) and high CRIR (zeta=1e4)\n",
    "- A typical cloud density (n=1e4) and high density (n=1e6)\n",
    "- The lower temperature bound of NGC 253 CMZ (75 K)* and a high temperature (250 K)\n",
    "* The lower boundary is a bit lower, but the computational time of 50K models is a lot longer than 75K so we stick with a bit higher values for speed\n",
    "\n",
    "and that will give us enough to work with for our analysis.\n",
    "\n",
    "When we run the model and want to interact with the rate constants directly after running, UCLCHEM must be told to return it to the user. This\n",
    "can be done using both `return_dataframe=True` and `return_rate_constants=True`. The model will then return the\n",
    "physics (temperature, density etc), abundances and rate constants as a function of time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ae592f0e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T16:44:23.267425Z",
     "iopub.status.busy": "2026-06-19T16:44:23.267244Z",
     "iopub.status.idle": "2026-06-19T16:44:45.586755Z",
     "shell.execute_reply": "2026-06-19T16:44:45.585709Z"
    },
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/hostedtoolcache/Python/3.13.14/x64/lib/python3.13/site-packages/uclchem/model.py:4522: UserWarning: max_workers was given as 10, which is higher than the number of physical CPU cores on the system (4). Using 3 instead.\n",
      "  self.max_workers = get_number_of_grid_workers(max_workers)\n"
     ]
    }
   ],
   "source": [
    "temperatures = [50, 250]\n",
    "densities = [1e4, 1e6]\n",
    "zetas = [1e1, 1e4]\n",
    "\n",
    "grid_runner = uclchem.model.GridRunner(\"Cloud\", full_parameters = {\n",
    "    \"param_dict\": {\n",
    "        \"baseAv\": 10,\n",
    "        \"freefall\": False, \n",
    "        \"finalTime\": 1e6, \n",
    "        \"initialTemp\": temperatures,\n",
    "        \"initialDens\": densities,\n",
    "        \"zeta\": zetas, \n",
    "        # Specify some custom tolerance since Silicon chemistry at 50K is tough to solve\n",
    "        \"abstol_factor\": 1e-6,\n",
    "        \"reltol\": 1e-6,\n",
    "        \"abstol_min\": 5e-20,\n",
    "    }},\n",
    "    grid_file =\"output_4/analysis.h5\",\n",
    "    model_name_prefix=\"model_\",\n",
    "    max_workers = 10,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce65afa3",
   "metadata": {},
   "source": [
    "We can extract all the data from the models in the grid by using `uclchem.model.load_model`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ceb7f57e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T16:44:45.588995Z",
     "iopub.status.busy": "2026-06-19T16:44:45.588751Z",
     "iopub.status.idle": "2026-06-19T16:44:45.978278Z",
     "shell.execute_reply": "2026-06-19T16:44:45.977442Z"
    }
   },
   "outputs": [],
   "source": [
    "results = {}\n",
    "for model in grid_runner.models:\n",
    "    name = model[\"Model\"]\n",
    "    cloud =  uclchem.model.load_model(file=\"output_4/analysis.h5\", name=name)\n",
    "    phys, abun, rates = cloud.get_dataframes(joined=False, with_rate_constants=True)\n",
    "    final_abundances = cloud.next_starting_chemistry_array\n",
    "    success_flag = 0 if cloud.has_attr(\"_data\") else -1\n",
    "    results[name] = (phys, abun, rates, final_abundances, success_flag)\n",
    "\n",
    "\n",
    "phys, abun, rate_constants, final_abundances, success_flag = results[\"model_5\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "482253ec",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T16:44:45.980703Z",
     "iopub.status.busy": "2026-06-19T16:44:45.980522Z",
     "iopub.status.idle": "2026-06-19T16:44:46.017245Z",
     "shell.execute_reply": "2026-06-19T16:44:46.016476Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'H': '0.000%',\n",
       " 'N': '0.000%',\n",
       " 'C': '0.000%',\n",
       " 'O': '0.000%',\n",
       " 'S': '0.000%',\n",
       " 'SI': '0.000%'}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from uclchem.analysis import analyze_element_per_phase, check_element_conservation\n",
    "\n",
    "# We check that everything is conserved:\n",
    "check_element_conservation(abun, [\"H\", \"N\", \"C\", \"O\", \"S\", \"SI\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e47bc8e7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T16:44:46.018946Z",
     "iopub.status.busy": "2026-06-19T16:44:46.018774Z",
     "iopub.status.idle": "2026-06-19T16:44:46.451731Z",
     "shell.execute_reply": "2026-06-19T16:44:46.450891Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here we can see why the solve was slow, all of the silicon seems to get stuck on the grains.\n",
    "analyze_element_per_phase(\"SI\", abun).plot(logy=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fa74c4a",
   "metadata": {},
   "source": [
    "### 2. Analyze the rates\n",
    "UCLCHEM will compute and save the rate constants of each of your reactions during the simulation. It is then returned to the user to be inspected.\n",
    "\n",
    "\n",
    "For example, if we care about $\\text{H}_3\\text{O}^+$ and find that it is created by $\\text{H}_2{O}$ + $\\text{H}^+$, then UCLCHEM is called to get $k$, the rate of that reaction and then analysis calculates\n",
    "$$\n",
    "\\dot{Y(\\text{H}_3\\text{O}^+)} = k Y(\\text{H}_2\\text{O}) Y(\\text{H}^+) n_{\\text{H}}\n",
    "$$\n",
    "\n",
    "where $\\dot{Y(\\text{H}_3\\text{O}^+)}$ is the rate of change of the $\\text{H}_3\\text{O}^+$ abundance due to the reaction. The $\\dot{Y}$ s of each reaction involving the $\\text{H}_3\\text{O}^+$ are then compared to see which reactions contribute the most to the destruction and formation of $\\text{H}_3\\text{O}^+$.\n",
    "\n",
    "Our plan then is to do this for the low zeta case and the high zeta case. Hopefully, we'll see the same reactions are most important to the abundance of $\\text{H}_3\\text{O}^+$ and SO at all densities and temperatures. If we don't find that that's the case, then the chemistry is these species is fairly complex and perhaps trying to present a simple cause for the CRIR dependence is not a good idea.\n",
    "\n",
    "So, let's analyze the outputs!\n",
    "\n",
    "#### 2.1 $\\text{H}_3\\text{O}^+$\n",
    "\n",
    "Let's inspect the reaction file of $\\text{H}_3\\text{O}^+$ for a high temperature, low density and low radiation field model (model 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "878d970f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T16:44:46.453626Z",
     "iopub.status.busy": "2026-06-19T16:44:46.453434Z",
     "iopub.status.idle": "2026-06-19T16:44:46.794763Z",
     "shell.execute_reply": "2026-06-19T16:44:46.793836Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Time'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Retrieve the outputs from the grid:\n",
    "physics, abundances, rate_constants, final_abundances, successflag = results[\"model_2\"]\n",
    "\n",
    "# Add everything together into one large dataframe\n",
    "super_df = pd.concat((physics, abundances, rate_constants), axis=1)\n",
    "\n",
    "# Plot the evolution of $\\text{H}_3\\text{O}^+$:\n",
    "super_df.plot(x=\"Time\", y=\"H3O+\", logx=True, logy=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4c41277",
   "metadata": {},
   "source": [
    "Above, we can see that the $\\text{H}3\\text{O}^+$ is being formed effectively. If we then want to better understand which reactions are responsible for this formation process, we can easily obtain the production and struction routes using:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "025c037b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T16:44:46.796757Z",
     "iopub.status.busy": "2026-06-19T16:44:46.796575Z",
     "iopub.status.idle": "2026-06-19T16:44:46.936208Z",
     "shell.execute_reply": "2026-06-19T16:44:46.935220Z"
    }
   },
   "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>CH+ + H2O -&gt; H3O+ + C</th>\n",
       "      <th>CH4 + H2O+ -&gt; H3O+ + CH3</th>\n",
       "      <th>CH4 + OH+ -&gt; H3O+ + CH2</th>\n",
       "      <th>CH4+ + H2O -&gt; H3O+ + CH3</th>\n",
       "      <th>CH5+ + H2O -&gt; H3O+ + CH4</th>\n",
       "      <th>H2 + H2O+ -&gt; H3O+ + H</th>\n",
       "      <th>H2+ + H2O -&gt; H3O+ + H</th>\n",
       "      <th>H2O + C2H2+ -&gt; C2H + H3O+</th>\n",
       "      <th>H2O + H2CO+ -&gt; HCO + H3O+</th>\n",
       "      <th>H2O + H2CL+ -&gt; HCL + H3O+</th>\n",
       "      <th>...</th>\n",
       "      <th>H2O+ + HCO -&gt; CO + H3O+</th>\n",
       "      <th>H3+ + CH3CHO -&gt; H3O+ + C2H4</th>\n",
       "      <th>H3+ + H2O -&gt; H3O+ + H2</th>\n",
       "      <th>H3+ + HCOOH -&gt; H3O+ + CO + H2</th>\n",
       "      <th>NH + H2O+ -&gt; H3O+ + N</th>\n",
       "      <th>NH+ + H2O -&gt; H3O+ + N</th>\n",
       "      <th>NH2+ + H2O -&gt; H3O+ + NH</th>\n",
       "      <th>O + CH5+ -&gt; H3O+ + CH2</th>\n",
       "      <th>OH + H2O+ -&gt; H3O+ + O</th>\n",
       "      <th>OH+ + H2O -&gt; H3O+ + O</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>6.353582e-10</td>\n",
       "      <td>1.400000e-09</td>\n",
       "      <td>1.310000e-09</td>\n",
       "      <td>2.848157e-09</td>\n",
       "      <td>4.053147e-09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.724513e-09</td>\n",
       "      <td>2.409979e-10</td>\n",
       "      <td>2.848157e-09</td>\n",
       "      <td>2.190890e-09</td>\n",
       "      <td>...</td>\n",
       "      <td>3.067246e-10</td>\n",
       "      <td>1.139263e-09</td>\n",
       "      <td>6.463126e-09</td>\n",
       "      <td>1.971801e-09</td>\n",
       "      <td>7.777660e-10</td>\n",
       "      <td>1.150217e-09</td>\n",
       "      <td>3.023429e-09</td>\n",
       "      <td>2.200000e-10</td>\n",
       "      <td>7.558571e-10</td>\n",
       "      <td>1.424079e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>6.353582e-10</td>\n",
       "      <td>1.400000e-09</td>\n",
       "      <td>1.310000e-09</td>\n",
       "      <td>2.848157e-09</td>\n",
       "      <td>4.053147e-09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.724513e-09</td>\n",
       "      <td>2.409979e-10</td>\n",
       "      <td>2.848157e-09</td>\n",
       "      <td>2.190890e-09</td>\n",
       "      <td>...</td>\n",
       "      <td>3.067246e-10</td>\n",
       "      <td>1.139263e-09</td>\n",
       "      <td>6.463126e-09</td>\n",
       "      <td>1.971801e-09</td>\n",
       "      <td>7.777660e-10</td>\n",
       "      <td>1.150217e-09</td>\n",
       "      <td>3.023429e-09</td>\n",
       "      <td>2.200000e-10</td>\n",
       "      <td>7.558571e-10</td>\n",
       "      <td>1.424079e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>6.353582e-10</td>\n",
       "      <td>1.400000e-09</td>\n",
       "      <td>1.310000e-09</td>\n",
       "      <td>2.848157e-09</td>\n",
       "      <td>4.053147e-09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.724513e-09</td>\n",
       "      <td>2.409979e-10</td>\n",
       "      <td>2.848157e-09</td>\n",
       "      <td>2.190890e-09</td>\n",
       "      <td>...</td>\n",
       "      <td>3.067246e-10</td>\n",
       "      <td>1.139263e-09</td>\n",
       "      <td>6.463126e-09</td>\n",
       "      <td>1.971801e-09</td>\n",
       "      <td>7.777660e-10</td>\n",
       "      <td>1.150217e-09</td>\n",
       "      <td>3.023429e-09</td>\n",
       "      <td>2.200000e-10</td>\n",
       "      <td>7.558571e-10</td>\n",
       "      <td>1.424079e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>6.353582e-10</td>\n",
       "      <td>1.400000e-09</td>\n",
       "      <td>1.310000e-09</td>\n",
       "      <td>2.848157e-09</td>\n",
       "      <td>4.053147e-09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.724513e-09</td>\n",
       "      <td>2.409979e-10</td>\n",
       "      <td>2.848157e-09</td>\n",
       "      <td>2.190890e-09</td>\n",
       "      <td>...</td>\n",
       "      <td>3.067246e-10</td>\n",
       "      <td>1.139263e-09</td>\n",
       "      <td>6.463126e-09</td>\n",
       "      <td>1.971801e-09</td>\n",
       "      <td>7.777660e-10</td>\n",
       "      <td>1.150217e-09</td>\n",
       "      <td>3.023429e-09</td>\n",
       "      <td>2.200000e-10</td>\n",
       "      <td>7.558571e-10</td>\n",
       "      <td>1.424079e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>6.353582e-10</td>\n",
       "      <td>1.400000e-09</td>\n",
       "      <td>1.310000e-09</td>\n",
       "      <td>2.848157e-09</td>\n",
       "      <td>4.053147e-09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.724513e-09</td>\n",
       "      <td>2.409979e-10</td>\n",
       "      <td>2.848157e-09</td>\n",
       "      <td>2.190890e-09</td>\n",
       "      <td>...</td>\n",
       "      <td>3.067246e-10</td>\n",
       "      <td>1.139263e-09</td>\n",
       "      <td>6.463126e-09</td>\n",
       "      <td>1.971801e-09</td>\n",
       "      <td>7.777660e-10</td>\n",
       "      <td>1.150217e-09</td>\n",
       "      <td>3.023429e-09</td>\n",
       "      <td>2.200000e-10</td>\n",
       "      <td>7.558571e-10</td>\n",
       "      <td>1.424079e-09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    CH+ + H2O -> H3O+ + C  CH4 + H2O+ -> H3O+ + CH3  CH4 + OH+ -> H3O+ + CH2  \\\n",
       "81           6.353582e-10              1.400000e-09             1.310000e-09   \n",
       "82           6.353582e-10              1.400000e-09             1.310000e-09   \n",
       "83           6.353582e-10              1.400000e-09             1.310000e-09   \n",
       "84           6.353582e-10              1.400000e-09             1.310000e-09   \n",
       "85           6.353582e-10              1.400000e-09             1.310000e-09   \n",
       "\n",
       "    CH4+ + H2O -> H3O+ + CH3  CH5+ + H2O -> H3O+ + CH4  H2 + H2O+ -> H3O+ + H  \\\n",
       "81              2.848157e-09              4.053147e-09                    0.0   \n",
       "82              2.848157e-09              4.053147e-09                    0.0   \n",
       "83              2.848157e-09              4.053147e-09                    0.0   \n",
       "84              2.848157e-09              4.053147e-09                    0.0   \n",
       "85              2.848157e-09              4.053147e-09                    0.0   \n",
       "\n",
       "    H2+ + H2O -> H3O+ + H  H2O + C2H2+ -> C2H + H3O+  \\\n",
       "81           3.724513e-09               2.409979e-10   \n",
       "82           3.724513e-09               2.409979e-10   \n",
       "83           3.724513e-09               2.409979e-10   \n",
       "84           3.724513e-09               2.409979e-10   \n",
       "85           3.724513e-09               2.409979e-10   \n",
       "\n",
       "    H2O + H2CO+ -> HCO + H3O+  H2O + H2CL+ -> HCL + H3O+  ...  \\\n",
       "81               2.848157e-09               2.190890e-09  ...   \n",
       "82               2.848157e-09               2.190890e-09  ...   \n",
       "83               2.848157e-09               2.190890e-09  ...   \n",
       "84               2.848157e-09               2.190890e-09  ...   \n",
       "85               2.848157e-09               2.190890e-09  ...   \n",
       "\n",
       "    H2O+ + HCO -> CO + H3O+  H3+ + CH3CHO -> H3O+ + C2H4  \\\n",
       "81             3.067246e-10                 1.139263e-09   \n",
       "82             3.067246e-10                 1.139263e-09   \n",
       "83             3.067246e-10                 1.139263e-09   \n",
       "84             3.067246e-10                 1.139263e-09   \n",
       "85             3.067246e-10                 1.139263e-09   \n",
       "\n",
       "    H3+ + H2O -> H3O+ + H2  H3+ + HCOOH -> H3O+ + CO + H2  \\\n",
       "81            6.463126e-09                   1.971801e-09   \n",
       "82            6.463126e-09                   1.971801e-09   \n",
       "83            6.463126e-09                   1.971801e-09   \n",
       "84            6.463126e-09                   1.971801e-09   \n",
       "85            6.463126e-09                   1.971801e-09   \n",
       "\n",
       "    NH + H2O+ -> H3O+ + N  NH+ + H2O -> H3O+ + N  NH2+ + H2O -> H3O+ + NH  \\\n",
       "81           7.777660e-10           1.150217e-09             3.023429e-09   \n",
       "82           7.777660e-10           1.150217e-09             3.023429e-09   \n",
       "83           7.777660e-10           1.150217e-09             3.023429e-09   \n",
       "84           7.777660e-10           1.150217e-09             3.023429e-09   \n",
       "85           7.777660e-10           1.150217e-09             3.023429e-09   \n",
       "\n",
       "    O + CH5+ -> H3O+ + CH2  OH + H2O+ -> H3O+ + O  OH+ + H2O -> H3O+ + O  \n",
       "81            2.200000e-10           7.558571e-10           1.424079e-09  \n",
       "82            2.200000e-10           7.558571e-10           1.424079e-09  \n",
       "83            2.200000e-10           7.558571e-10           1.424079e-09  \n",
       "84            2.200000e-10           7.558571e-10           1.424079e-09  \n",
       "85            2.200000e-10           7.558571e-10           1.424079e-09  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from uclchem.analysis import get_production_and_destruction\n",
    "\n",
    "production, destruction = get_production_and_destruction(\"H3O+\", rate_constants)\n",
    "\n",
    "production.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "68c947fe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T16:44:46.938131Z",
     "iopub.status.busy": "2026-06-19T16:44:46.937918Z",
     "iopub.status.idle": "2026-06-19T16:44:47.632353Z",
     "shell.execute_reply": "2026-06-19T16:44:47.631319Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<Axes: title={'center': 'Production'}>,\n",
       "       <Axes: title={'center': 'Destruction'}, xlabel='Reaction rate (abundance wrt H / s)'>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABCsAAAL/CAYAAABPtE1SAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjExLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlcelbwAAAAlwSFlzAAAXEgAAFxIBZ5/SUgAAwXFJREFUeJzs3XlYVOX///EXMyAqiKOIiCuIS4mZlpWV5ZKSZbZppWmJpaXWp6hssTJFrdQWqcxvi6WV2mpZqWlZmlmatrihFqJkuaChg+I6wPn94Y+TI4swMzBHeD6ua67Lc59z7vO+b84cZ95zn/sEGIZhCAAAAAAAwCJs/g4AAAAAAADgZCQrAAAAAACApZCsAAAAAAAAlkKyAgAAAAAAWArJCgAAAAAAYCkkKwAAAAAAgKWQrAAAAAAAAJZCsgIAAAAAAFgKyQoAAAAAAGApJCsAAAAAAIClkKwAAAAAAACWQrICAAAAAABYCskKAAAAAABgKYH+DgAAAADwxIwZM5Seni5JCg0N1YgRI/wbEADAZ0hWAAAAwPTMM8/o+PHjha6rUqWKoqKi1LVrVzVp0qScIyva+PHjVadOHZIVAFCBcBsIAAAACkhKSlJSUpIOHz4sSTIMQ6mpqRo5cqRiY2N17733Kicnx68xJiQkaMyYMQoMtPbvbwsWLNDbb78twzD8HQoAnDECDK6aAAAAOEVAQIAkae/evapTp45Z/u+//6pTp07auHGjHnroIT3//PP+CtFUtWpVORwO7d692y/HnzJliv799189/vjjqlKlitu6nJwcVatWTTk5Ofrll190/vnn+yVGADjTkKwAAABAAUUlKyTpgw8+UL9+/VSlShX9+++/qlGjhj9CNPk7WXHWWWfpjz/+0MGDBxUaGlpg/bx587R7927deeedZr8CAIpn7TFzAAAAsJxzzz1XknT8+HGlpKSoQ4cOfo7I2q655hp/hwAAZxySFQAAAPDKhg0b9Mknn5jLffr0UatWrbR48WKtW7dO2dnZql69uh555BFzm5ycHH3//fdat26djh49qoYNG6p79+6qV69ekcfZs2ePFixYoJ07dyoiIkI9evRQo0aNCmz39ddf66effjKXT74946OPPtLGjRslSbVr19Z9991X6LFycnK0ZMkSrV+/Xrm5uWrcuLHatm2rli1bmtssXbpUS5cu1b///ivpxOSk+cc577zz1LRp0wL90rp160KPVZK+KKyfmzVrpgULFig1NVWRkZHq0qWLpSY/BQBPMcEmAAAASmXdunWSTjwdJC4uziz/+uuvlZSUpJ9++klXX3213nnnHf3zzz+aPHmyJk2aZG73448/qkWLFurXr5/+/PNPZWVlaerUqWrSpIkef/xx5eXlFTjmu+++q6ZNm+qJJ57Qrl27tHbtWl199dWaNm1akXFOmTJFSUlJBZ5ukpOTo6SkJL388suF7rds2TI1b95ct9xyi37//XdlZGTolVde0VlnnaX27dub7S+p/H7ZsGFDgXWe9EV+ffPmzVP37t21cOFC/fPPPxo5cqTOPvtszZ07t1TxAYAlGQAAAMApJBmSjL1797qVZ2ZmGnFxcYYk46GHHnJb99BDDxmSjJiYGOP33383y9966y0jPDzcMAzDWLdunVG9enWjfv36RkZGhtv+Q4cONSQZDz/8sFv5999/b9hsNqNx48ZGZmamWX748GGje/fuRkBAgBEZGVmgDS1btjQkGQcPHnQrP3LkiCHJiI2NLbDP2rVrjWrVqhlRUVHGtm3bCm3fxx9/XKLjnLrf+++/71buSV+cXF/9+vWNHTt2mOVbt2417Ha70bhx40LjAIAzCSMrAAAAUKRJkyZpzJgxGjNmjO644w61atVKmzZt0vDhwzVhwoRC97n88svVtm1bc/mqq64ybwEZNWqUDh8+rPvvv19169Z12y8pKUlVqlTRiy++qO3bt5vl48ePV15enu6//37Vrl3bLK9WrZpGjx7t00eCjho1SkeOHFFiYqKio6Pd1o0ZM0ZBQUE+PVZp++Jkt956q+rXr28ux8TE6KyzztL27du1b98+n8UJAP5AsgIAAAAlEhsbq2eeeUZbt27Vq6++qsDAwqc/O++889yWo6KizGTFN998I0m68MILC+xXt25dRUdHKzc3V999950kyTAM/fDDD5Kk9u3bF9gnf7JPX1m8eHGR8YWGhio5OVmtWrXyybFK2xenOnn+jHz5yZy9e/f6JEYA8Bcm2AQAAECRHnnkkQKPLj2diIiIQssPHTqkw4cPF7tN3bp19eeff2rPnj2SpMOHD+vo0aOSVGgcoaGhCg4OLlV8RSlJfMOHDy+3Y53aF6cq7DGpNtuJ3yJ9OdoEAPyBkRUAAADwqYCAgELLQ0JCVL16dUlF//Kf/8U8/7aI6tWrq2rVqpJkPnXjZMeOHSswgeapcZz6xf3gwYMex+crnvQFAFQmJCsAAABQbrp37y5JWrlyZYF1GRkZSk9Pl91uV9euXSWdSDhcfvnlkqTVq1cX2Gfjxo1FjiLIH4lx6vwNmzdvPm18hR1LkiZOnKivvvrKrSz/dpj8OFJTUzVmzBg5nc4ij3PysUraFwBQmZCsAAAAQLkZO3asqlWrppdeekkZGRlu68aMGaPjx4/r/vvvV+PGjc3yJ554QjabTa+88or2799vlh8/flyjRo0q8ljt2rWT9N/cEJKUm5url156qUTxnTqx5TvvvKORI0fK4XC4lTdq1EiSzPYsXLhQEyZMKPQ2jaKOVdK+AIDKIsDghjYAAAD8f88884yOHz+upKQkSdLDDz+s6tWrKz4+Xpdcckmh+6Snp2vGjBn6+uuvtWLFCvXu3VutW7dWx44d1a1btwLbL1u2TLfffrsOHz6sPn36qGbNmvr+++/1yy+/KDExUc8++6zsdrvbPu+8846GDx8uh8Oh3r17S5KWLFmi3r17a8KECQoMDNSIESPUrFkzDRgwQJK0a9cuXXzxxdq5c6f69u2riIgI/fzzzxo6dKhuu+021apVS/fdd5/atGmjG2+80TzW999/r4EDB+rgwYPq2bOnoqKitG7dOv3444966aWXNGjQILfYPv30U91888266KKL1KFDB02fPl3/+9//dNNNN+mTTz4p0C+JiYlmwqM0fVFYP7dp00ZPPfWU1qxZo7lz52rGjBn666+/dM8996hVq1Y+m2MDAMobE2wCAACggNGjR5d6n/j4eMXHx592u8svv1xbtmzRkiVLtH79eh09elRDhw5V9+7dFRUVVeg+AwcO1FVXXaX58+dr165dql27tv73v/+pefPmCgwMVE5OToF9oqKitGnTJs2bN0/btm2Tw+HQiBEjFBERoS1bthQZX6dOncz41q1bp9zcXA0cOFDvv/9+gVEVknTjjTdq3bp1WrJkiY4dO6bPP/9cl112mTZs2HDafvGkL4qrLyEhoch2AcCZhJEVAAAAAADAUpizAgAAAAAAWArJCgAAAAAAYCkkKwAAAAAAgKWQrAAAAAAAAJZCsgIAAAAAAFgKyQoAAAAAAGApJCsAAAAAAIClkKwAAAAAAACWQrICAAAAAABYCskKAAAAAABgKYH+DgAAcEK9evV06NAhNW7c2N+hAAAAAF7Zvn27QkJCtHv3bo/2Z2QFAFjEoUOH5HK5/HLsvLw87d27V3l5eX45flH8FVd5HLcsjuGrOr2px5t909LStGXLllLvB89Z9b3vLSu3q6Je1yrqNc2b/bmmlT8rv/e9YeV2FReby+XSoUOHPK47wDAMw5vgAAC+ERcXJ0lKSUkp92Onp6crJiZG27ZtU3R0dLkfvyj+iqs8jlsWx/BVnd7U482+cXFxysnJ0ebNmxUQEFCqfeEZq773vWXldlXU61pFvaZ5sz/XtPJn5fe+N6zcruJi8/azLSMrAAAAAACApZCsAAAAAAAAlkKyAgAgh8Oh0aNHy+Fw+DsUN/6KqzyOWxbH8FWd3tRj1XMJhauofy8rt6uiXtcq6jXNl3Gg7FXUv5WV21WWsTFnBQBYRFxcnPLy8vTVV1+ZZQ6Hw5L/MQFlgfu7AVQkXNNQGTmdTjmdTklSfHy87Ha7Nm3a5FFdjKwAAAvJzMxUTEyM+UpOTvZ3SAAAAECJJCcnm59jU1NTlZmZ6XFdjKwAAItgZAUqO8MwlJGRocjISH6FBHDG45qGysiXIysCfRgXAMBLNpvNco+kAgAAAEri5B/agoKCvKqL20AAAAAAAIClkKwAAAAAAACWQrICAAAAAABYCskKAAAAAABgKSQrAAAAAACApZCsAAAAAAAAlsKjSwHAQlIzshX92Hy/HT99Qk+/HRsAAADIx8gKAAAAAABgKYysAAALMYw85WRlmMu24BDZqob6MSIAAACgZJxOp5xOpyTJ5XLJbrd7XBfJCgCwkLwjB7TjtTvN5ZqX9pOjY38/RgQAAACUTHJyspKSkszliIgIj+siWQEAFmKrFqaogZP/Ww4O8WM0AAAAQMklJiYqISFBkhQfH8/ICgCoKAICbAqsGenvMAAAAIBSczgccjgckqSgoCCv6mKCTQAAAAAAYCkkKwAAAAAAgKWQrAAAAAAAAJbCnBV+kJOTo3///ddcrlevXoFtXC6XMjMz3coiIiJks9mUkZHhVl6lShXVrl3bXN6zZ4/y8vLMZbvdXugsrPv371doaKjX9xKVJ8Mw/Nr+AwcOyGazKTTUWo+S9GW/HD16VC6XSzVq1Cjx8a3aL6djlfciAAAAAHeMrPCDtLQ0tW3bVs2aNVNUVJQmTJhQYJv169erbdu2io2NVYsWLdS2bVvt3LlTBw8eVNu2bdW8eXNFRUWpdevWuu2229z2vfjii9WyZUtFRUXp7LPP1pVXXmmuW7t2rYYMGaLw8HA1adJEISEhio2N1XPPPaecnJzTxn7w4EHt27fP+07wsC5/tH/Lli0aNGiQ6tSpo4YNGyoqKkp169bVnXfeqW3btpW+4WXA2345cOCApkyZovPPP1+1a9dWZGSkatWqpWHDhhX4op7PF/3i7/PJn+9FAAAAAEUjWeEHLVu21O7duzVixAhJ0hNPPKGvv/7abZvzzjtPu3fv1pAhQ/TUU09p9+7datSokcLCwrR79249+uijkqSFCxdq/vz5bvumpaXppZdekiRNnz5dv/32m7nu/vvv16+//qqVK1fqwIEDOnjwoG699VY98sgjSkxMPG3sSUlJuvrqq71pvld1lXf7Fy1apHbt2un48eP6+eefzX2+//577d27V23bttW3335bypb/5+Rf3b3hbb8sWLBAiYmJeuihh3To0CEdPnxYs2bN0vTp03XTTTcVOJ6v+sXf55M/34sAAAAAikayws+uvPJKBQUFqV+/fuXyK33t2rU1ceJENW/eXJIUHByscePGqXHjxvrggw/K/Pj+Vpr2p6enq0+fPurWrZtmzZql2NhYc93ZZ5+tuXPnqm3bturdu7d27NjhUTy//fabWrVqpaefflrp6eket8tb1apV06233qpbb71VAQEBkqSrr75a3bp109KlS91GnZRHv/hDeb8XAQAAABSNZIWftW/fXq+//rr27dunG264QYcPHy7T43366afq3r17gfLg4GA1adKkTI9tBaVp/7PPPqvs7GyNHDmy0LpsNptGjhyprKwsvfjiix7FExsbq6uuukpTpkxR06ZN1bFjR7322ms+uzWipK677jq9++67BcozMjIUGxurwMD/prcpj37xh/J+LwIAAAAoGskKCxg4cKASExPN+RRKIzMzU7t37y7wysrKKnEdq1evVlpamsaNG1fa0P2uLNs/f/58VatWTe3bty9y344dOyogIECLFi3yKP5atWrphRde0D///KOvvvpK0dHRGjFihOrVq6frrrtOH3/8sY4ePVrqer3pl507d2rVqlUaMmSI/vnnH02fPt1tfXn0i7/4+70IAAAA4ASeBmIRzz//vDZs2KDZs2erffv2euCBB0q0X//+/d1+9c535MiREu2fkZGh/v3764UXXihwv/+pT0qQpMOHD8vlcmn37t1u5WFhYapevXqRx/FlXScrq/bn5ORo586datSokWy2onN6oaGhCg0N1fbt20t0vKLY7XZdeeWVuvLKK3Xo0CF9+umneu+999S3b1/VqFFDvXv31sMPP6yzzjqrRPV50y+XXXaZtm/froYNG+qFF17QpZdeaq7zpl/OhPNJ8t97MZ/LuVM7pw0rdF2DwVNLVZcnDMMo82MARTEMw3wBwJmOaxoqi9atWxdanpaW5nbLeGmRrLAIu92uDz/8UBdccIEeeeQRtW3bVl26dDntfgsXLiz0F+4ZM2Zo0KBBxe67fft2de/eXY8//rgSEhIKrE9JSSnw9IKDBw/K5XKpbdu2buXjxo0r9pdoX9Z1srJqv81mU0BAgHJzc08bQ25uroKDg4tcf/z48QK3dRT2iMx8ISEhuu222zRgwAB9+OGHGjZsmN5++22dc845JU5WeNMvaWlpys3N1TfffKPbbrtNs2fP1hdffCGbzeZVv5wJ55Pkn/fiyWySgu2Fr2tVq+w/7Jz6OFagPBmGIafTKUnm/DkAcKbimobKoqinKnqbqCNZYSG1a9fW559/rosvvli33HKLfv311zI7ltPp1DXXXKOnnnpK/fv3l3RiGLvD4ZDdfuKb0rnnnlvgV+oRI0Zo+fLlWrlyZamO58u6fOF07bfZbIqNjVV6erqOHz+uKlWqFFpPZmamDh8+XGQ2UZJ++eUX3XjjjW5lp/bFyVJSUjRz5kzNnj1b27dv1wUXXKABAwaYcZYHu92uHj16aOzYsRo+fLjmzJmjm266yat+OZPOp/J8L57K7ohSnUGFj6DYuL/sjx8ZGVn2BwGKkP+hJjIykg/2AM54XNNQWWzevLnQ8uK+I5UEyQqLad26td5991317t1bN954o84//3yfH+P48ePq3bu3HnjgAbcvwJ06ddLcuXPVrFkznx/TSkra/t69e2vChAlaunSp4uPjC63rm2++kaRCH++Z75JLLik2OSGdmCdi9uzZmjVrltasWaPY2FgNHDhQAwYMUIsWLUrbxFJzOp0KCQlRUFCQW3mrVq0kSevXrzfb6Kt+sbryeC8WLkCG/PeBhg9T8LeAgADzBQBnOq5pgOeYYNOCbrjhBj311FP65ZdfNGPGDJ/Xf+edd6pTp0666qqr3CYCLGr4TkVT0vY/8sgjatSokZ588slCJ7k8cOCAxowZo7POOkv33HOPR7Hs2LFD3bp1U6NGjTRx4kRdcskl+umnn7RlyxaNHTu2XBIVkjRgwIBCz7XVq1dLkho2bGiWlUe/WEVZvxcBAAAAFI6RFX6QPzlgdna2cnNztXv3btWpU8dtcr7Ro0dr7dq1mjt3rtu+hmEoIyND2dnZkk4Mt9+3b59q165tbrNnzx7zCQROp1N79+5VRESEJOmpp57SzJkzzWOcacqz/bVq1dKiRYvUq1cvdezYUY8//rjatWsnwzC0atUqjRs3TkFBQZo3b56qVavmUXv27dunOnXq6PPPP1ePHj0KnaCxJLztF7vdrkcffVQ2m00dO3aUJC1atEhjxozR2Wef7TYCpTz6pbz4870IAAAAoGgBBtPTlrs//vhDnTp1civ79ttvFRcX51aWnZ2t7t276/bbb9ewYSeeDnDgwIECv7aff/75mj9/vrkcGxurQ4cOmcv169fXb7/9JunEkx5SU1OLjG3lypWKjo4ucv3YsWP1888/ux3PU57U5Y/2Hz58WG+++aa+/PJLbd26VZLUrFkzXX/99brjjjtUtWrVEsdfVrztl0OHDmnWrFn64osvtHXrVmVnZ6thw4a68sorlZiYqJo1axY4pi/6xd/nkz/fi4WJi4tTaka26pfDUz+Kkj6hp9+ODeQnAbm/G0BFwDUNlV3+Z+qUlBSP9idZAQAWQbIClR0f7AFUJFzTUNl5m6xgzgoAAAAAAGApJCsAAAAAAIClkKwAAAAAAACWQrICAAAAAABYCo8uBQALMYw85WRlmMu24BDZqob6MSIAAACgZJxOp5xOpyTJ5XLJbrd7XBfJCgCwkLwjB7TjtTvN5ZqX9pOjY38/RgQAAACUTHJyspKSkszliIgIj+siWQEAFmKrFqaogZP/Ww4O8WM0AAAAQMklJiYqISFBkhQfH8/ICgCoKAICbAqsGenvMAAAAIBSczgccjgckqSgoCCv6iJZAQAW0jwyVCkTevo7DAAAAMCveBoIAAAAAACwFJIVAAAAAADAUkhWAAAAAAAASyFZAQAAAAAALIVkBQAAAAAAsBSSFQAAAAAAwFJIVgAAAAAAAEsJ9HcAAID/5OXlKT093Vx2OBxyOBx+iwcAAAAoKafTKafTKUlyuVyy2+0e18XICgCwkMzMTMXExJiv5ORkf4cEAAAAlEhycrL5OTY1NVWZmZke18XICgCwkPDwcK1atcpcZlQFAAAAzhSJiYlKSEiQJMXHx3s1soJkBQBYiM1mU3R0tL/DAAAAAErt5FuYg4KCvKqL20AAAAAAAIClkKwAAAAAAACWQrICAAAAAABYCskKAAAAAABgKSQrAAAAAACApZCsAAAAAAAAlkKyAgAAAAAAWArJCgAAAAAAYCmB/g4AAPCfvLw8paenm8sOh0MOh8Nv8QAAAAAl5XQ65XQ6JUkul0t2u93juhhZAQAWkpmZqZiYGPOVnJzs75AAAACAEklOTjY/x6ampiozM9PjuhhZAQAWEh4erlWrVpnLjKoAAADAmSIxMVEJCQmSpPj4eK9GVpCsAAALsdlsio6O9ncYAAAAQKmdfAtzUFCQV3VxGwgAAAAAALAUkhUAAAAAAMBSSFYAAAAAAABLIVkBAAAAAAAshWQFAAAAAACwFJIVAAAAAADAUnh0KQBYSGpGtqIfm+/vMMpN+oSe/g4BAAAAFsTICgAAAAAAYCkkKwAAAAAAgKVwGwgAWIhh5CknK8NctgWHyFY11I8RAQAAACXjdDrldDolSS6XS3a73eO6SFYAgIXkHTmgHa/daS7XvLSfHB37+zEiAAAAoGSSk5OVlJRkLkdERHhcF8kKALAQW7UwRQ2c/N9ycIgfowEAAABKLjExUQkJCZKk+Ph4RlYAQEUREGBTYM1If4cBAAAAlJrD4ZDD4ZAkBQUFeVUXE2wCAAAAAABLIVkBAAAAAAAshWQFAAAAAACwFJIVxcjKyjLvucl/de/e3W2bhg0buq2Pi4sz1+3bt08vvPCCLrnkEkVGRqp58+bq2bOnvvzyyyKPeeDAAU2cOFEdO3ZU/fr11bBhQ11++eV67rnnlJ2dXWZtPR1P48rKytKzzz6rjh07qmHDhmrSpIkuu+wy3XPPPVq4cKGOHTvmtv0///yjhx56SOeff77q1aunpk2bqnfv3nr33XeVlZXlVq83f5szibdtNQxDixcvVv/+/dW0aVM1aNBAF198sV566SUdP3680GNa9Tz0lNXOXwAAAADFI1lRjLCwMKWnp+vee+9VVlaW5s6dq88++8xtm5SUFD3zzDPKysrSq6++qhUrVpjr+vTpo5deekkjR47U77//rs8++0w1a9bUtddeq2effbbA8davX69zzjlHc+fO1ciRI/XLL79o5cqVeuCBBzRr1iydc8452rhxo0dtGTVqVIEvuCXlaVy//PKLWrVqpXnz5mnUqFFavXq1vv32W917771aunSprrrqKg0ZMsTc/u+//1bbtm21ZMkSTZo0Sb///ru+/PJLxcXFadCgQbrqqqvMbb392/i6j8qSt239+OOP1b17d8XGxmrx4sVasWKFbrrpJj3wwAPmTL0n89V56Mv+rGjnLwAAAIDi8TSQYgQEBMjhcKhq1aqSpNDQUIWGhrptU7NmTVWvXl2SFBISorCwMHNdXl6enn/+efXq1UuSVL9+fb377rv69ttv9corr2jkyJHmtpmZmerRo4eaNGmipUuXqkqVKua6hg0bqnv37rrooot09dVXa+3atapZs2ap2nLkyBEdPHiwdB3gRVwZGRnq2bOnYmNjtWTJErf9mjVrpiuuuELt2rVTXl6eWf7OO+8oMzNTS5Ys0TnnnCNJioqK0tixY7Vnzx6tWbPG3Nbbv40v+6go69at0+jRozVgwABdc801Cg4O9qgeX5yHXbt21dixY82yBx98UAsWLNBHH32k9957z3ykkC/PQ1/2Z0U7fwEAAAAUj5EVZWjevHnq06ePW1lgYKDCw8MLfNmcNGmSdu7cqbFjx7p9McoXGhqq0aNH66+//tLLL79cpnH7Iq5JkyZpz549GjduXKH71alTx+1XaenEF1JJhX4BvuGGG7xtSrmLiIjQ8ePH1bdvX0VGRmrw4MFaunSpDMMo1zh69+6tefPmFSjPzc1VnTp13J59bNXz0FOcvwAAAMCZiWRFGQoNDZXN5t7FaWlp+vPPP/Xoo4+6lX/66acKCgrS5ZdfXmR9+cPgP//8c98HWwRP45o7d66CgoJ02WWXFbnf448/rjfeeMNc7ty5syTp0UcfLfArevfu3bV48WJPmuA3UVFRmj9/vnbs2KGkpCStWbNGXbp0UZMmTfToo49q/fr15RJHUFCQqlWrZi4fOHBAL7zwglauXFkg4WDV89BTnL8AAADAmYnbQEqhS5cubr9C5ytqksJTHT16VLfffrvuvvtu3XnnnWZ5Xl6etm7dqvr16xf6K26+WrVqKSQkRGlpaaUP3gOexpWXl6f09PTT7hcYGKjAwP9Owe7du2vcuHEaP3685s+fr2uvvVYdO3bU5ZdfrlatWhUYjXIyb/82Zalu3bq6//77df/992vz5s167733NGvWLE2aNElt2rTRgAEDNHDgQNWtW7dE9XnT1nr16ikjI0PR0dH65JNP1LNnT3OdVc9DT51J5+/JXM6d2jltWKHrGgyeWqI6ziTlPdII1mYYhvkCgDMd1zRUFq1bty60PC0tTbGxsR7XS7KiFL788ku1bdu2QPns2bN1zz33FLtvVlaWrrnmGl1++eUFJtfMy8uTYRgFRmEUxmazKTc3t9ht1qxZY/7Km+/o0aPKycmRw+FwK3/22Wc1bFjhX4w8jas0+53qySef1F133aU5c+bop59+0nPPPadhw4bpvPPO05QpU3TxxRcXul9p/za+6qPSOuuss/T0009r/Pjx+uCDDzR8+HA98sgjCgoKUmJiYonq8OY8/OOPP5SRkaEFCxbo5ptv1tChQ/XCCy9I8u489GV/Vsbz1y0WScEFc1GSpFa1Kt6HnYyMDH+HAAsxDENOp1PSifl6AOBMxjUNlUVOTk6h5d4m6khWlEJoaGiBL0uSzIkNi+JyudS7d29169ZNo0ePLrA+MDBQDRo00J49e5Sbm1vor+aSlJ2drezs7CIzV/nOOeccpaenu5WNGjVKK1eu1DfffONWfvLtAb6KKzAwUA0bNjztfkWpW7euhg0bZn4JXbFihQYOHKj4+Hht3rxZDRo0KLBPaf82vuqj5cuX65prrnEry/9PqTAHDx7Up59+qpkzZ+q7775TrVq1NGzYsAJ1FMfT81A6MZ9CzZo11aJFCx0+fFhPPPGErrzySsXHx3t1HvqqP31Z15l0/p7M7ohSnUGFj6DYuL9UoZwRIiMj/R0CLCT/Q01kZCQf7AGc8bimobLYvHlzoeWn+956OsxZUQ4GDx6sCy+80C1Rcckll2jbtm3m8jXXXKOjR49q9erVRdazbNkyGYZhPl2kKHa7XQ6Hw+0VHBxcZHlxPI0rf79ffvmlyP2uv/56devWzVyeNm1aoaMLLr74Yo0dO1bZ2dlatmxZsfGWlK/6qEOHDkpPT3d7nSonJ0fz5s1Tv379FBkZqaFDh6pWrVr67LPPtGvXLk2dOlXNmjXzSbtK45JLLpEkt8ecevr39uU5x/kbIKMSvQICePHixYsXL168eFXEl7dIVpSxp556ShEREXrmmWfcyg8cOOA2jH7kyJEKCwvT+PHjCx0uk5OTo6efflp169bVgw8+WOZxextX/n6ntjvf6tWr9eWXX2rgwIFm2dGjR7Vo0aJCb3PJzs6WdPpf5ctbYGBggS/Q+fbt26d7771XUVFRuu6667Rnzx69+uqrysjI0EcffaRrr71WQUFBZR5j3759NWfOnALl+XM0nByzVc9DT3H+AgAAAGcmkhVl6O2339a4ceM0bdq0Al9oN23a5LZt48aN9dlnn2n58uW66aabtHHjRnPdmjVr1KtXL6Wmpmr+/PkKDw8vtzZ4GlejRo302Wef6fvvv1e/fv30559/SjrxeMc5c+bo2muv1e23367bbrvN7XibN2/WHXfcoe3bt0s6cQvNokWL9NRTT6lJkybmkxvOBNu2bdPy5cv16KOP6u+//9a3336rQYMGKSwsrFzjyM7O1sMPP6wff/xRubm5ysnJ0ddff60nnnhCERER6tevn7mtVc9DT3H+AgAAAGcmkhXFyMrKksPh0NNPPy1J6tKlS4EvGw0bNtTw4cMlSf3791dcXJy57rnnnjPrOfWVl5dX4Hhdu3bVunXrFBERofj4eAUHBys4OFjXXnutmjdvrrVr16p9+/Zl1dwieRpX165dtXbtWtWsWVOdO3dW1apVFRoaqieffFKPPvqopk2b5rb97bffrrfeeku7d+/WhRdeqGrVqik4OFgDBw7UlVdeqR9++EEhISGSvP/blIfzzjtPa9as0YgRI1S/fn2P6/G2rZMnT9b111+vgQMHKjQ0VNWqVVPfvn3VpUsXrVixosBTSKx6HnrKiucvAAAAgOIFGDxLp0iGYSgrK8utLDAw0O0RhFlZWW7Dy202m/nL+cGDB4t9ckdYWFixTxzIfxRlcY9PLKn8pymU9PGJxfE0rmPHjslms5X41odjx45JUqHzEnj7tymML/vIl3zZ1tzcXB07dqxEk3Hm8/Tv7cv+rGjnb1Hi4uKUmpGt+hXwEaVFSZ/Q8/QbodIwDEMZGRlMRgegQuCahsou/wfUlJQUj/bnaSDFCAgIKPSpCyerWbNmketq1Kjh1fF9kaTIV7VqVZ/V5WlcpfnSdrrtvf3bFMaXfeRLvmyr3W4vVaJC8vzv7cv+rGjnLwAAAIDicRsIAAAAAACwFJIVAAAAAADAUkhWAAAAAAAAS2HOCgCwEMPIU05WhrlsCw6Rraq1Jn0FAAAACuN0OuV0OiVJLpdLdrvd47pIVgCAheQdOaAdr91pLte8tJ8cHfv7MSIAAACgZJKTk5WUlGQuR0REeFwXyQoAsBBbtTBFDZz833JwiB+jAQAAAEouMTFRCQkJkqT4+HhGVgBARREQYFNgzUh/hwEAAACUmsPhkMPhkCQFBQV5VRfJCgCwkOaRoUqZ0NPfYQAAAAB+xdNAAAAAAACApZCsAAAAAAAAlkKyAgAAAAAAWArJCgAAAAAAYCkkKwAAAAAAgKWQrAAAAAAAAJZCsgIAAAAAAFhKoL8DAAD8Jy8vT+np6eayw+GQw+HwWzwAAABASTmdTjmdTkmSy+WS3W73uC5GVgCAhWRmZiomJsZ8JScn+zskAAAAoESSk5PNz7GpqanKzMz0uC5GVgCAhYSHh2vVqlXmMqMqAAAAcKZITExUQkKCJCk+Pt6rkRUkKwDAQmw2m6Kjo/0dBgAAAFBqJ9/CHBQU5FVd3AYCAAAAAAAshWQFAAAAAACwFJIVAAAAAADAUkhWAAAAAAAASyFZAQAAAAAALIVkBQAAAAAAsBSSFQAAAAAAwFJIVgAAAAAAAEsJ9HcAAID/5OXlKT093Vx2OBxyOBx+iwcAAAAoKafTKafTKUlyuVyy2+0e18XICgCwkMzMTMXExJiv5ORkf4cEAAAAlEhycrL5OTY1NVWZmZke18XICgCwkPDwcK1atcpcZlQFAAAAzhSJiYlKSEiQJMXHx3s1soJkBQBYiM1mU3R0tL/DAAAAAErt5FuYg4KCvKqL20AAAAAAAIClkKwAAAAAAACWQrICAAAAAABYCskKAAAAAABgKSQrAAAAAACApZCsAAAAAAAAlkKyAgAAAAAAWEqgvwMAAPwnNSNb0Y/N93cY5SZ9Qk9/hwAAAAALYmQFAAAAAACwFEZWAICFGEaecrIyzGVbcIhsVUP9GBEAAABQMk6nU06nU5Lkcrlkt9s9rotkBQBYSN6RA9rx2p3mcs1L+8nRsb8fIwIAAABKJjk5WUlJSeZyRESEx3WRrAAAC7FVC1PUwMn/LQeH+DEaAAAAoOQSExOVkJAgSYqPj2dkBQBUFAEBNgXWjPR3GAAAAECpORwOORwOSVJQUJBXdTHBJgAAAAAAsBSSFQAAAAAAwFJIVgAAAAAAAEthzopKJDs7W926dXMra926taZNm2Yud+3aVYcPHzaX69atqy+++EKSlJeXp6+++kpz5sxRWlqawsLC1KJFCw0ZMkRnnXVW+TTCC/5ov2EY+uKLL/Tll19q69atstlsatq0qXr16qVrrrlGAQEBZdDS0vG2XyTpr7/+0rvvvqsVK1bo2LFjaty4sfr27asrr7yy0GOeCf1yOr7oNwAAAACFY2RFJVKtWjUlJyerbdu2+vnnnzV06FA98MADbttMnDhR3bt3188//6wbb7zR7bEzN9xwg2699VadffbZGj16tAYOHKgVK1YoLi5Os2bNOu3xX3nlFQ0ePNgnbfGkrvJu/65du3TppZfqf//7n84++2w9+eSTeuyxxxQTE6PBgwerU6dOysjI8KwDfMjbflmwYIGaNm2qlJQU3XvvvXr00UcVFBSkHj166NFHHy1wPF/1y5l+PgEAAAAoGiMrKhG73a4OHTpo8eLFkk78ChwXF+e2zQUXXKCUlBRJUosWLdSuXTtzXVZWlp577jndddddZtmVV16pRo0aadSoUerfv3+xx//rr7+0YcMGn7TFk7rKs/1HjhxRjx49dOTIEf3+++8KDw8313Xr1k233XabLrzwQvXs2VM//vijgoODS9UWSfrzzz81ffp0DRgwoEA7SsPbfjlw4IDatWun999/3xwRER8fr3Xr1unll1/Ws88+K5vtRF7Ul/1ypp9PAAAAAIrGyAqU2NSpUwskJGrUqKFGjRrJ5XL5KaryU5r2v/zyy1q3bp2effZZty/k+Ro2bKgxY8bo119/dbttoDSCgoL08ccfq3Xr1mrXrp2ef/557dy506O6vNG9e3d99NFHBW7dqFWrlqpUqeJWXh79AgAAAODMR7ICJdaqVSuFhIS4le3du1epqam6++67/RRV+SlN+2fPni2bzaarrrqqyPp69eolSfrwww89iicmJkZbtmzRjz/+qA4dOujZZ59Vo0aN1K1bN02fPl0HDhzwqN7SCg8PV9OmTd3K5s+fryVLlujJJ590S1aUR78AAAAAOPNxG0glNmjQoAJfvqUTX8BLwjAM3XPPPerSpYtGjhzp6/DKXFm13zAMbdy4UZGRkapevXqR+0dFRalq1arauHFj6YM/ySWXXKJLLrlEL730khYsWKD33ntPw4YN0z333KNevXppwIAB6tGjh4KCgkpUnzf9cvnll2vLli0KDg7WJ598omuuucZcV979Ut68PZ/yuZw7tXPasELXNRg81aPYrMwwDH+HAAsxDMN8AcCZjmsaKovWrVsXWp6WlqbY2FiP6yVZUYk99NBDhT7FYv78+Ro/fnyx++bk5GjQoEHKzc3V3LlzZbfb3dZv3rxZCQkJbmXbt2/XwYMH1aFDB7fyBx54QLfcckuRx/JlXScrq/bn5OQoJydHVapUOW0MVapU0dGjR0sUb0nquv7663X99ddr//79mj17tp588kl99NFHSk5O1v3331+ierzplxdeeEG7du3SggUL1K9fPz3//PPmqBNv+qWin08ns0kKthe+rlWtivdhxwqTzMI6DMOQ0+mUpDPiqUAAUByuaagscnJyCi33NlFHsqISa926tdq3b1+gfPPmzafd96677pJhGPr444/NyRNP1qhRIyUnJ7uVvfLKK1q7dm2B8ujo6GKP5cu6TlZW7Q8KClJERIQyMzNlGEaR/zkdO3ZM2dnZxWYbf/vtNw0fPtytbOXKlcXG9vPPP2vmzJn68MMPdeDAAXXp0kUXX3zxaduUz5t+ueCCCyRJ1157rUJCQjRs2DCdd955uuCCC7zql4p+Pp3M7ohSnUGFj6DYuL9UVZ0RIiMj/R0CLCT/Q01kZCQf7AGc8bimobIo6vNuUSMuSopkBUptzJgxOnTokDn/gCT1799fEydOVMOGDSVJISEhBX6l/uSTT5SWllag/HR8WZcvlKT93bp10/vvv6+NGzcW+aSOVatWKS8vT927dy/yWM2aNSvwBbowW7Zs0axZszRz5kxt2bJF55xzjkaMGKH+/furQYMGpW+kD1x99dV68cUX9c0335hJDE/7pSKfTwUFyFDl+UDDhzecKiAgwHwBwJmOaxrgOSbYRKm88847WrdunWbNmuV268PatWt9djuDlZW0/Y899pgCAwOLTTS8+OKLqlatmkaMGFHkNmFhYerQoYPbK9/hw4c1ZcoUdejQQc2bN9dbb72lG2+8UevXr9e6dev0yCOPlEuiYsSIEVq6dGmB8szMTEly6ydf9QsAAACAio2RFSixxYsXa8iQITrnnHPUsWNHt3Vbt271U1TlpzTtb9Omjd58800NGTJEdevW1eOPP25OvnjgwAE98cQTWrBggT788EPFxMR4FM/GjRs1atQo9e7dWxMmTFCnTp38krXfvHmzli9frrlz56pevXqSTvTH6NGjVbVqVfXp08fctjz6BQAAAMCZj2RFJZKdna1u3bppx44dkk48veCiiy7StGnTzG26du2qv/76S5L06KOP6u2339YXX3whSUpMTJTL5dJvv/1W/sH7QHm3PyEhQc2bN9eTTz6pyMhIxcbGyjAMbdmyRR07dtSKFSt03nnnedyeuLg47d69W8HBwR7XIXnfLw899JCee+45xcTEmHM8bNmyRa1bt9aiRYsKzMlR1v1SXrztNwAAAABFCzB4lk6lkZubq9WrV7uV1ahRw23ugNWrVys3N9dcDg4OVrt27SSduNXhyJEjRdbfrl27Yr845z9xoai5CkrDk7r82f6srCzzS2tMTIxq1KhR4rjLmrf9ku/YsWP666+/lJ2drYYNG6pu3bqnPbY3/XKmn0+FiYuLU2pGtupXwEeUFiV9Qk9/hwALMQxDGRkZTEYHoELgmobKLv9zcUpKikf7k6wAAIsgWYHKjg/2ACoSrmmo7LxNVjDBJgAAAAAAsBSSFQAAAAAAwFJIVgAAAAAAAEvhaSAAYCGGkaecrAxz2RYcIlvVUD9GBAAAAJSM0+mU0+mUJLlcLtntdo/rIlkBABaSd+SAdrx2p7lc89J+cnTs78eIAAAAgJJJTk5WUlKSuRwREeFxXSQrAMBCbNXCFDVw8n/LwSF+jAYAAAAoucTERCUkJEiS4uPjGVkBABVFQIBNgTUj/R0GAAAAUGoOh0MOh0OSFBQU5FVdJCsAwEKaR4YqZUJPf4cBAAAA+BVPAwEAAAAAAJZCsgIAAAAAAFgKyQoAAAAAAGApJCsAAAAAAIClkKwAAAAAAACWQrICAAAAAABYCskKAAAAAABgKYH+DgAA8J+8vDylp6ebyw6HQw6Hw2/xAAAAACXldDrldDolSS6XS3a73eO6GFkBABaSmZmpmJgY85WcnOzvkAAAAIASSU5ONj/HpqamKjMz0+O6GFkBABYSHh6uVatWmcuMqgAAAMCZIjExUQkJCZKk+Ph4r0ZWkKwAAAux2WyKjo72dxgAAABAqZ18C3NQUJBXdXEbCAAAAAAAsBSSFQAAAAAAwFJIVgAAAAAAAEshWQEAAAAAACyFZAUAAAAAALAUkhUAAAAAAMBSSFYAAAAAAABLIVkBAAAAAAAshWQFAAAAAACwlEB/BwAA+E9eXp7S09PNZYfDIYfD4bd4AAAAgJJyOp1yOp2SJJfLJbvd7nFdjKwAAAvJzMxUTEyM+UpOTvZ3SAAAAECJJCcnm59jU1NTlZmZ6XFdjKwAAAsJDw/XqlWrzGVGVQAAAOBMkZiYqISEBElSfHy8VyMrSFYAgIXYbDZFR0f7OwwAAACg1E6+hTkoKMirurgNBAAAAAAAWArJCgAAAAAAYCkkKwAAAAAAgKWQrAAAAAAAAJZCsgIAAAAAAFgKyQoAAAAAAGApPLoUACwkNSNb0Y/N93cYlpQ+oae/QwAAAEA5YWQFAAAAAACwFEZWAICFGEaecrIyzGVbcIhsVUP9GBEAAABQMk6nU06nU5Lkcrlkt9s9rotkBQBYSN6RA9rx2p3mcs1L+8nRsb8fIwIAAABKJjk5WUlJSeZyRESEx3WRrAAAC7FVC1PUwMn/LQeH+DEaAAAAoOQSExOVkJAgSYqPj2dkBQBUFAEBNgXWjPR3GAAAAECpORwOORwOSVJQUJBXdTHBJgAAAAAAsBSSFQAAAAAAwFJIVgAAAAAAAEthzopysnHjRi1YsEDp6emy2+1q3Lix2rRpo06dOqlKlSqSpMOHD2v48OFu+8XGxmrUqFHm8tChQ3X06FFzuXbt2nrxxRfN5fzJTE511VVX6ZZbbvFhi3zPF+1PTU3VZ599prS0NIWFhalFixbq27evatSoUeRxN2/erHnz5mnr1q2y2Wxq2rSpevbsqZYtW/qwdZ7zRb8cO3ZMX3zxhVasWKFjx46pcePGuummm9S0adMij2v1fimt8noPnmzTpk2aOHGiunTpooEDB5ZBqwAAAICKiZEVZSw7O1v9+/fXRRddpLS0NMXFxalRo0b65ZdfdNVVVykiIkKTJk2SJAUGBqpz5846ePCg3nnnHUVHR+u8885zq++yyy5TYGCg3nnnHYWFheniiy92W//OO+8U+lq9evVpY/3www81btw4n7Tbk7q8bf8999yjuLg4/fHHHzr33HNVs2ZNPf3002rcuLGWLl1a4HiHDx9WQkKCzj//fP3xxx9q3bq1zjrrLK1du1Zt27bVkCFD3L6U+ou3/bJ8+XLVr19fzz33nBo2bKiWLVtq4cKFatGihV577bUCx/NVv/j7fMpX3u/BfLm5uUpISNA777yjFStWeBQ7AAAAUFkxsqIM5eTk6JprrtGmTZv066+/qkWLFm7rhw8frm7dumndunWSpCpVqighIUH//POPPv30U11zzTVq37692z79+/eXy+XSW2+9pa5du+r66693Wx8eHq7nn3++QCytW7c+bbyrV6/W8uXL3X5F9pQndXnb/pSUFI0ZM0aPP/64WTZo0CA1b95c//vf/7R+/XqzPDc3V71799Zvv/2m3377rcBogfvuu09dunTR7t279cUXXyggIKCUPSBt375dS5Ys0Y033ljsyI7T8bZf/vnnH4WHh+uHH35QcHCwpBOJnTZt2uixxx7T3XffbbbPl/3i7/NJ8s97MN9zzz2nmjVrlipeAAAAACeQrChD06ZN0/fff6833nijwJck6cQvtAMHDtThw4d9dszQ0NAibwWp6B577DGdf/75bmUNGjRQs2bNlJGR4VY+Y8YMLVy4UG+88UahtzWcf/75evzxxzVy5Eh9+OGH6tu3b6njOXTokIYNG6Zhw4bpuuuuU//+/dWjRw8FBpbv2+6iiy7S22+/bSYqJMlut6tp06baunWrDMMwkw7l0S/lyR/vQenELSdTp07Vt99+W+hxAQAAABSP20DK0PTp0yVJffr0KXKb/F+2fenHH3/U448/rqFDh2rSpEn666+/fFq/VfXo0UMRERFuZdnZ2dq2bZtuvvlmt/K33npLUvF/m/w5PmbMmOFRPGeffbZ2796tKVOmaPfu3br22msVFRWle++9t1xvC4iJiVHHjh3dyjZv3qwffvhBgwYNks3232WgPPqlPPnjPZibm6uBAwcqOTm5wPkIAAAAoGQYWVGG1qxZo8jISNWqVavIbZo2bVrkJIdJSUkKDw8vUL5ly5Yi6/v77781cuRI9ejRQ7Vr19acOXP01FNPafr06erXr1/pG+FHnrT/VKNHj1bTpk317LPPupX/9ttvp/3bxMTEqEqVKvr9999LHvQpwsLCdMcdd+iOO+7Q33//rVmzZum9997Tq6++qtjYWPXv318DBgxQ8+bNS1ynN/0yePBgbd26VX///bcmTpyoIUOGuK0vr34pL/54D06cOFEtW7bUjTfeKKfTWeqYXc6d2jltWKHrGgyeWur6KhLDMPwdAsqYYRjmCwDOdFzTUFkUNeVAWlqaYmNjPa6XZEUZOX78uI4fP65q1ap5XMf555+v6OjoAuWBgYH68ccfC93ngw8+UJ8+fcxh/Q8++KA6d+6sQYMG6bLLLlPDhg0lSenp6RozZozbvr/88osyMjIK3EZy6623Kj4+vsg4fVnXyTxp/8mefvppLVu2TIsXL1ZoaKhZfvz4cR07dqxEf5tq1arp4MGDJYr3dBo1aqTHHntMjz32mH777Te99957eumllzR27FhNmzZNd955Z4nq8aZfOnfurBYtWmjBggV6/vnn1bRpU3Xv3l2Sd/1ixfPJH+/BDRs26M0339Rvv/3m8TFtkoLtha9rVatyf9g59XYuVDyGYZhJPk/mCgIAK+GahsoiJyen0HJvE3UkK8pIlSpVFBYW5tEvq/kKm9wvX/5w/VPddNNNbss2m0133nmnfvjhB82fP98c7l6jRg117tzZbduMjAwdOXKkQHl+gqMovqzrZJ60P9/48eP19ddfa+nSpQoJCXFbV9K/TU5OjrKzs4uNefPmzZowYYJb2eluj/j333+1YsUKrVy5UllZWWrQoEG59cuAAQMkSY888oj69++va6+9VmvXrlWLFi286hcrnk/l/R7MyclRQkKCpkyZUuxIjtOxO6JUZ1DhIyg27ve42gohMjLS3yGgjOV/qImMjOSDPYAzHtc0VBabN28utLwkD3koDsmKMnT55Zdr3rx5+uuvv9SkSZNCt3n11Vf1999/F/jC60t169aVJGVmZppl4eHhBX6l3rBhg/bv31/qCTp9WZcvvPfee1qwYIEWLVpkJiqefPJJ3XfffWZfXHbZZZo/f77S09ML/eVcktatW6fc3FxdfvnlRR7L4XAU+AJdmCNHjuiLL77QzJkztWjRIlWvXl033nijnn76aXXu3Nlt3ojyctttt2n27Nn64osvNGLECEme94tVz6fyfA++/fbbSktL08cff6yPP/5Y0onRHZK0ZMkSJSQkKCgoSG+++eZpagqQIT7QFIYPepVDQECA+QKAMx3XNMBzTLBZhh588EFJJ55IUJj9+/dr5MiRbk9p8Mbs2bP11VdfFSjPv7++qC+fFcmSJUv02muv6auvvnJ7XOjcuXN14MABczn/b1PcF8c33nhDAQEBeuCBB4rcpl69ekpISHB75cvLy9PixYuVkJCgyMhI3X777bLZbJo1a5Z2796tt99+W127di3zREVycrLWrFlToNzlckmS25MwfNUvVlGe78EOHTpo8uTJ6ty5s/nKn9i0fv366ty5c7GJLwAAAAD/YWRFGerSpYuefPJJTZo0SXFxcW6PeczMzFTfvn3VqFEjn33pW7VqlZYtW6bLLrvMnKNh9+7dmjx5siIjI9WrVy+fHMeqUlJSdMMNN6hTp066//773dbt2LHDbblr16564oknNGnSJJ111lm67bbbzHWGYWjq1Kl688039eKLL6pdu3YexfPbb78pPj5el1xyiSZNmqSbb75ZtWvX9qgubyxevFhLlizRJ598oqCgIEknnpLy3HPPKSAgQNdcc425bXn0S3kqz/dgmzZt1KZNG7cyp9Ope+65Ry1btqy0jxQGAAAAPEGyooyNGzdOsbGxeuihhzR27Fi1adNGWVlZWrFihdq2bauFCxfK4XBIOvEL9/Dhw7Vu3TpJJ55EcOGFF2rUqFFmfUOHDtWGDRskSS+//LKWLVumF198UZJ09dVXa+nSpeajKnNycrR8+XJFRkZqzpw5biMNrMjb9g8aNEhZWVn64osvSnS88ePHq3nz5ho5cqTGjRundu3ayTAM/fLLLzIMQx9++GGxj7w8ndjYWG3dutXrES3e9sstt9yipKQkNWnSRBdddJEk6aefftLx48f19ttv67zzznM7Xln3S3krz/dgvvx6Tr0NpEuXLho4cGA5tBoAAAA4swUYPEunXOTm5urXX3/V1q1bFRgYqHPOOUctW7Z02+b48eOaPXu2W1lERIR69uxpLs+aNcscvi9JISEhBSbV/PPPP/XHH3/o0KFDatasmc4///wS3SeX/8SFk4/nKU/q8rb9n332mbKysoqsv3fv3oUmbHJzc/X7779r69atkqRmzZqpbdu2fplHojC+OC8Mw1BKSoq2bt1qTo554YUXqmrVqkUe19t+8ff5dKryfA8WVo8ktWzZUhdffHGRMcbFxSk1I1v1K/kjSouSPsH7cwnWZhiGMjIymIwOQIXANQ2VXVxcnKQTI+A9QbICACyCZEXxSFZUfHywB1CRcE1DZedtssIaPx0DAAAAAAD8fyQrAAAAAACApZCsAAAAAAAAlsLTQADAQgwjTzlZGeayLThEtqqhfowIAAAAKBmn0ymn0ylJcrlcstvtHtdFsgIALCTvyAHteO1Oc7nmpf3k6NjfjxEBAAAAJZOcnKykpCRzOSIiwuO6SFYAgIXYqoUpauDk/5aDQ/wYDQAAAFByiYmJSkhIkCTFx8czsgIAKoqAAJsCa0b6OwwAAACg1BwOhxwOhyQpKCjIq7pIVgCAhTSPDFXKhJ7+DgMAAADwK54GAgAAAAAALIVkBQAAAAAAsBSSFQAAAAAAwFJIVgAAAAAAAEshWQEAAAAAACyFZAUAAAAAALAUkhUAAAAAAMBSAv0dAADgP3l5eUpPTzeXHQ6HHA6H3+IBAAAASsrpdMrpdEqSXC6X7Ha7x3UxsgIALCQzM1MxMTHmKzk52d8hAQAAACWSnJxsfo5NTU1VZmamx3UxsgIALCQ8PFyrVq0ylxlVAQAAgDNFYmKiEhISJEnx8fFejawgWQEAFmKz2RQdHe3vMAAAAIBSO/kW5qCgIK/q4jYQAAAAAABgKSQrAAAAAACApZCsAAAAAAAAlkKyAgAAAAAAWArJCgAAAAAAYCkkKwAAAAAAgKWQrAAAAAAAAJZCsgIAAAAAAFgKyQoAAAAAAGApgf4OAADwn7y8PKWnp5vLDodDDofDb/EAAAAAJeV0OuV0OiVJLpdLdrvd47oYWQEAFpKZmamYmBjzlZyc7O+QAAAAgBJJTk42P8empqYqMzPT47oYWQEAFhIeHq5Vq1aZy4yqAAAAwJkiMTFRCQkJkqT4+HivRlaQrAAAC7HZbIqOjvZ3GAAAAECpnXwLc1BQkFd1cRsIAAAAAACwFJIVAAAAAADAUkhWAAAAAAAASyFZAQAAAAAALIVkBQAAAAAAsBSSFQAAAAAAwFJ4dCkAWEhqRraiH5vv7zBgMekTevo7BAAAgHLFyAoAAAAAAGApjKwAAAsxjDzlZGWYy7bgENmqhvoxIgAAAKBknE6nnE6nJMnlcslut3tcF8kKALCQvCMHtOO1O83lmpf2k6Njfz9GBAAAAJRMcnKykpKSzOWIiAiP6yJZAQAWYqsWpqiBk/9bDg7xYzQAAABAySUmJiohIUGSFB8fz8gKAKgoAgJsCqwZ6e8wAAAAgFJzOBxyOBySpKCgIK/qYoJNAAAAAABgKSQrAAAAAACApZCsAAAAAAAAlsKcFWe4Y8eOafLkyW5lDRo00G233WYuv/jiizp+/Li5HBYWpuHDh7vts379eq1YsULHjh1T48aN1b17d1WvXr1sg/cBX7T/0KFD+vbbb5WWlqawsDC1aNFCHTt2VEBAQJHHPXjwoL7//ntt3bpVNptNTZs21eWXX67QUGs8YtJf54XV++V0/HU+AQAAAHDHyIoznGEYcjqdWrBggUaOHKnNmzfr4MGDbttkZWXphx9+0MiRI7V69WplZWWZ69auXav27dsrPj5ev/zyizZt2qTExEQ1btxYCxYsOO3xlyxZovfee88nbfGkLm/bP3HiRDVo0EATJ07Utm3btGTJEvXo0UPnnHOONm3aVOjxJkyYoIYNG2rixInasmWLNm/erKSkJDVs2FAvvPCCZ433sfI+L3zVL5XtfAIAAABQuADDMAx/BwHvjR8/XqNGjdLq1avVvn37AutnzJihQYMG6bPPPtP1119vln/wwQd68MEHtWHDBtWuXVvSiV+GW7VqpePHj2vXrl3FHnfEiBFavny5Vq5c6XUbvKnL0/Z37txZbdq00csvv2yWrVu3Tu3bt9cFF1ygH3/80a2eu+++WzNnztRnn32m+Ph4t3WffPKJbr31Vt1999165ZVXSt0GSdqzZ4+2bdumiy66yKP9T1Ve54Wv+qWynU+niouLU2pGtuoPnlrqmFGxpU/o6e8QyoVhGMrIyFBkZCSjkQCc8bimobKLi4uTJKWkpHi0PyMrKrlWrVrpxRdfNL+QSlJISIjatWunzMxM5ebm+jG6snfrrbfqgQcecCtr06aNWrRooc2bN7uVf/HFF3rjjTf05JNPFvhCLkl9+vTRfffdpylTpujbb7/1KJ4dO3aoQ4cOatGihZKSkrRlyxaP6vFWac6L8uiXM0VpzicAAAAARSNZUcm1adNGffv2dSvLzMzUzz//rJ49e8put/spsvJx1113KSYmxq3M5XJp165dBb54T5kyRZI0cODAIusbNGiQJGnqVM9+GW/Xrp3WrVun66+/Xm+++aaaN2+uDh06aMqUKdq7d69HdXqiNOdFefTLmaI05xMAAACAojHBZgXzzjvvaPHixQXKf/3119PuO2nSJKWnp2v58uXq27evxo4dWxYhlilv2p/v1VdfVdWqVQvMs/DTTz8pPDxc9evXL3Lfs88+W4GBgfr5559LHvQpzjnnHE2aNEkTJkzQd999p5kzZ+rxxx/XAw88oPj4eA0YMEDXXXddqSZALcvzorz6xR/K8nwqisu5UzunDSt0XQNuD6m0Kssdm4ZhmC8AONNxTUNl0bp160LL09LSFBsb63G9JCsqmIMHD8rpdBYoP3z48Gn3zc7O1tGjR+VyuZSWlqadO3eqZcuW5vrdu3drxowZbvusWrVKO3fu1IQJE9zKu3XrVui9/mVR18m8ab8kffTRR3r55Ze1ePFity/fx48f16FDhxQREVHs/jabTaGhodq3b1+Jjne6urp166Zu3bpp6tSpmjt3rt577z3ddtttqlatmt555x3deOONJaqrrM4Lb/qlMp9PxbFJCi5iQFOrWnzYqawyMjL8HUK5yJ/kVhL3dwM443FNQ2WRk5NTaLm3iTqSFRXMvffeW+SEgKd7ukf+L+bHjh3T1VdfrUsuuUQpKSmqV6+eJCk3N7fAF7djx44VWV4cX9Z1Mm/aP3v2bCUlJWnZsmVq2LCh27oqVaqoWrVqOnToULF1GIZx2i/v27dv1+zZs93KHnvssWLrrVq1qiIjI1WvXj1Vq1ZNx44dK7d+Ke688KZfKvP5VBy7I0p1BhU+gmLj/hJXgwomMjLS3yGUi/wPNUxGB6Ai4JqGyqKoudmKGnFRUiQrUEBwcLD+97//6YYbbtDHH3+s//3vf5KkBg0aFPiVOv+JC6eWn44v6/KFpUuXaty4cVq8eLH5xfKtt95S79695XA4JEkXXnihvv/+e+3Zs0d169YttJ4///xTLpdLF154YZHHysnJKfTX+sKsXbtWM2fO1Pvvv68dO3bo4osv1sSJE3XLLbcoPDy8VG30VlHnhaf9UtnPp6IFyBAfaOCuMn3IDQgIMF8AcKbjmgZ4jgk2K7lPPvlE27ZtK1CePx9CeU7q6C+bNm1SYmKiFi5cqEaNGpnlkydP1r///msuDx06VJIKjIo42XvvvSdJGjas8DkHJKlp06aaMGGC2+tk27dv14QJE9S6dWu1bdtWX375pe6++26lpaXpp59+0vDhw8s8UVGa88JX/VJRlPR8AgAAAFA0RlZUcjNmzNC3336r//u//zPLDMPQW2+9JUnq1KmTv0IrF7t379ZVV12l7t276/3333dbd+oXy1tuuUVffPGFRo8erQ4dOqhDhw5u67/55hs9//zzGj58uMdPfli3bp3atm2runXrqm/fvpo+fbouuOACj+ryRmnOi/LolzNFac4nAAAAAEUjWXGGO3bsmCZPnqwffvhB0omnF2zatEm33Xabuc2LL76oFStWSJI+/fRT7dy5U8OHD5ckXXrppRo/frw2bNigjh07Sjrx5fL333/XyJEjdcUVV5Rzi0rH2/YPGDBAf/31l6ZNm3baYwUEBOjdd9/VqFGjdMUVV+iKK65Qu3btZBiGVq1apeXLl2vUqFF6/PHHPW5PRESEFixYoO7du3v12NjyPC/Ko1/KS3meTwAAAACKRrLiDJc/y3C7du3Url07SSeeYHCyrKwsxcbG6tFHHzWX840cOVLDhg3T4sWLtXXrVmVnZ+uuu+5S9+7dFRMTc9rjd+3aVU2bNvVJWzypy9v2X3311cU+GaJWrVpuy4GBgXr22Wf1wAMP6Ouvv9bWrVslSbfffrtmzpypOnXqlCr+U0VFRSkqKsqrOqTyPy981S+V7XwCAAAAULgAgwf/AoAlxMXFKTUjW/UHF/40EFRe6RN6+juEcmEYhjIyMpg5H0CFwDUNlV1cXJwkKSUlxaP9mWATAAAAAABYCskKAAAAAABgKSQrAAAAAACApTDBJgBYiGHkKScrw1y2BYfIVjXUjxEBAAAAJeN0OuV0OiVJLpfLqycckqwAAAvJO3JAO16701yueWk/OTr292NEAAAAQMkkJycrKSnJXI6IiPC4LpIVAGAhtmphiho4+b/l4BA/RgMAAACUXGJiohISEiRJ8fHxjKwAgIoiIMCmwJqR/g4DAAAAKDWHwyGHwyFJCgoK8qoukhUAYCHNI0OVMqGnv8MAAAAA/IqngQAAAAAAAEshWQEAAAAAACyFZAUAAAAAALAUkhUAAAAAAMBSSFYAAAAAAABLIVkBAAAAAAAshWQFAAAAAACwFJIVAAAAAADAUgL9HQAA4D95eXlKT083lx0OhxwOh9/iAcpT69atlZOTo82bN/s7FADwGtc0VEZOp1NOp1OS5HK5ZLfbPa6LkRUAYCGZmZmKiYkxX8nJyf4OCQAAACiR5ORk83NsamqqMjMzPa6LkRUAYCHh4eFatWqVucyoCgAAAJwpEhMTlZCQIEmKj49nZAUAVBQ2m03R0dHmq7ySFU6nU2PGjDGH7VmFv+Iqj+OWxTF8Vac39Vj1XELhKurfy8rtqqjXtYp6TfNlHCh7FfVvZeV2nRqbw+EwP8cGBQXJZvM85RBgGIbhozgBAF6Ii4uTJKWkpJT7sdPT0xUTE6Nt27YpOjq63I9fFH/FVR7HLYtj+KpOb+rxZt+4uDjz/u6AgIBS7QvPWPW97y0rt6uiXtcq6jXNm/25ppU/K7/3vWHldhUXm7efbRlZAQAAAAAALIU5KwDAIrZv3y6Xy2VmocuTy+WSdOLewqCgoHI/flH8FVd5HLcsjuGrOr2px5t909LSZBiGWrduXar94Dmrvve9ZeV2VdTrWkW9pnmzP9e08mfl9743rNyu4mJLS0vzKl6SFQBgESEhITp06JBfjm232xUREeHVJEhlwV9xlcdxy+IYvqrTm3q82Tc2NrbU+8A7Vn3ve8vK7aqo17WKek3zZn+uaeXPyu99b1i5XcXFFhQUpJCQEI/rZs4KAAAAAABgKcxZAQAAAAAALIVkBQAAAAAAsBSSFQAAAAAAwFJIVgAAAAAAAEshWQEAAAAAACyFZAUAAAAAALAUkhUAAAAAAMBSSFYAAAAAAABLIVkBAAAAAAAshWQFAAAAAACwFJIVAAAAAADAUgL9HQAAAAD8JzY2VocOHSpQHhwcrFq1aql58+a67LLL1L9/f4WHh/shQgBAZRRgGIbh7yAAAADgH3v27FFeXp6ioqIkSSkpKapdu7YOHz6sHTt26LvvvtP//d//6cCBAxo5cqQef/xx2e12P0d9Zrvhhhu0Y8cOLViwQHXq1PF3OABgSSQrAAAAoICAAEnS3r17C3yBPnjwoG6++WYtXLhQN998s95//33ZbOV3N/Fll12m1NRUbd26VdWrVy+343qquHhzc3PVokUL7dmzR+vWrVNMTIyfogQAa2POCgAAABSrRo0a+uSTT9S8eXN99NFHSk5OLtfj7927VxkZGcrLyyvX43qquHjtdrv++OMP7dmzh0QFABSDZAUAAABOKyQkRCNHjpQkPf300zp+/LifIzpzBQYGqlq1av4OAwAsjWQFAAAASuSaa66RJO3bt0/Lly8vsH7u3Lm67rrr1KxZMzVt2lTdu3fXW2+9pdzc3ALbLly4UDfeeKPOOeccRUdHq3Pnzpo0aZJ27txpbvP888+rXr162rJli6QTk4HWq1dP9erV00MPPaTPP//cXK5Xr54+//xzrV69WjfeeKOaNWumevXqKS4uTmPHjnXb7vDhw+YxhgwZYpZffPHFRbZ93rx56t27t1q0aKHY2Fh16dJFDzzwgH755Rev4i3MggULdNNNN6lly5Zq0qSJLr30Uj3zzDPKyspy266w+ubPn6/4+HjFxMSoQ4cOGjt2rI4ePVpkuwDAsgwAAABUepIMScbevXuL3a5OnTqGJOOVV14xy/Ly8ow77rjDkGT06dPHWLNmjZGammqMHz/esNlsRq9evYycnBxz+8mTJxuSjEceecRYv369sXXrVmPOnDlGTEyMYbfbze0OHjxo7Nq1y2jWrJkhyUhLSzN27dpl7Nq1y8jKyjKOHDli7Nq1yxg2bJghyfjf//5n9OrVy1i2bJmxadMmo2vXrkZ4eLhx4MABt3oOHjxoHmP//v1Genq6IcmIjY0t0N68vDzjzjvvNCQZt956q7F8+XIjNTXVePfdd43IyEhDkvHZZ595FO/7779f4Hh33XWXIcm4++67jTVr1hjbtm0zZsyYYdSuXduIjo42tmzZYm57an1XX321MXjwYOPXX381NmzYYMY9dOjQ058AAGAxPLoUAAAAJVazZk39+++/2rdvn1n2+uuv6+2331bz5s31wQcfmE8LeeKJJ7Rjxw793//9n1555RUlJiZKkv7v//5PtWrV0sSJE806YmJiFB4ers6dO5tloaGhCg0NNeurW7euQkND3eKpV6+eOYnlggULtHnzZgUGBprHv/nmm1WjRg3VqFGj0KeYOBwOVa1atcj2vvbaa3rrrbfUrVs3zZo1yyxv1qyZ6tatqx49eignJ8ejeE/1+uuv64033lD37t312muvmeXR0dGqXr26br75Zt1000369ddfFRAQoKpVq7rVt3//fs2fP98t9jlz5uitt97SlClTeIoLgDMKt4EAAACgxLKzsyWdSFrkmzJliiRp6NChBb4Q33XXXZKkV155xSyrVauW9u/fr5dfftlt7ovLLrtMu3bt8ji2fv36mYkKSerUqZM2btzocX2S9Oqrr0o6cbvIqeLj4xUREeFV/SfL78fBgwcXWNe7d2/Vrl1bv//+u3744YdC97/++uvdlgMDA9W8eXO5XC7t2LHDZ3ECQHkgWQEAAIASOXDggPbu3SvpxHwMkuRyucyEwPnnn19gn5YtW0qStm7das5H8dJLL6lBgwa6//77FRERoZtuuklvvPGGMjMzVa9ePY/ji46Odlu22+2qW7eux/Xl5OSYbWvatGmB9QEBAdq8ebN69erl8TFOPlZKSkqRx7LZbGb71qxZU2gdDRo0KFCWP+ri5Hk6AOBMQLICAAAAJbJ48WLl5eWpatWq5u0aeXl5MgxDktSnTx+3CR/r1avn9njO7du3S5IuuugipaWl6eOPP9a1116r77//XnfffbeioqJ07733FjohZ0mEhIR4tF9+/KfKzc011wUFBRW6Te3atRUcHOzRcUt7rPzy/NtOTsVtHgAqEuasAAAAwGnl5uZq0qRJkqR7773XnIshODhYTZo00V9//aWZM2fq3HPPLbKO2rVrm/8ODg5Wnz591KdPH+Xl5WnJkiUaPny4Xn31VbVu3VpDhw41tw0ICPBJG/K/7LtcLrfyom49Obltf//9d7FtO5kn8Z58rO3btxd6rPxkT/5oFQCoyBhZAQAAgGLl5eXp/vvv188//6wLLrhAY8aMcVs/cOBASdKmTZsKjKyoV6+exo4dqxtuuMGcT6Jly5ZatmyZub/NZtMVV1yhBx98UJKUmprqVn/+rQz581ssXLhQ9erV07///luqdjRu3FiSlJ6e7lb+/fffF7nP7bffLkn66KOPCqw7cOCAYmJi9NJLL/kk3vxjvf/++wXWff/999q1a5caNGigK664oth6AKAiIFkBAACAQh04cEBz585Vx44d9eqrr+qGG27QokWLCtxuMXLkSF122WUaPXq0Zs+erWPHjpn7jx07Vm+99ZYeffRR2WwnPnpmZGTooYceMudokE4kEN59913Z7Xb16dPHrf527dpJklauXCnDMPTBBx8oLCxMderUKVV7brjhBknS008/rezsbBmGoZ9++qnQRMTJbbvkkks0e/ZsvfDCC+YEo+np6brllltks9k0YMAAn8Sbf6wPP/xQr776qpnsWLdunYYNG6Zq1app5syZxT69BAAqDD8+NhUAAAB+1rRpUyMyMtKQZEgy6tata0RGRhphYWFGQECAERsbayQkJBg//PBDsfUcO3bMePrpp43Y2FgjICDAcDgcRkhIiNGjRw/j+++/d9v2u+++MwYMGGDUr1/fCA0NNcLCwoyQkBDj6quvNpYuXVqg7u3btxudO3c2AgMDjZCQEKNVq1bGypUrjR9//NGIjIw0qlevbkgyatasaURGRhrjx48vNMbc3Fxj1KhRRu3atQ2bzWY4HA7jlltuMbZv325IMux2uxEZGWncc889bvsdPXrUGDt2rNG0aVMjICDACAsLM2rWrGnccccdxo4dO0oc79y5cwuNd/v27W7HGjdunNG0aVMjODjY7MfevXsbGzZscDtOYe1v0KCBYRiG8cEHHxiRkZFGUFCQIcmoU6eO0alTp2L/hgBgJQGGUcSMQgAAAKjw9uzZo7y8vALlwcHBqlmzpjkaojQOHz4sl8vl9njTohw/flxHjx5VWFjYabd1uVw6fvy4ObLj+PHj2rdvX4HtQkNDzTk1inLgwAFVr17dvDVl9+7d5rpq1aoVGfuhQ4eUm5vrUbxHjx6V0+kssF1EREShk2MePnxYR48elcPhKPTvUFj7AwICFBkZqSNHjigrK8ttXVBQkMLDw08bNwBYAckKAAAAAABgKcxZAQAAAAAALIVkBQAAAAAAsBSSFQAAAAAAwFJIVgAAAAAAAEshWQEAAAAAACyFZAUAAAAAALAUkhUAAAAAAMBSSFYAAAAAAABLIVkBAAAAAAAshWQFAAAAAACwlEB/BwAAOKFevXo6dOiQGjdu7O9QAAAAAK9s375dISEh2r17t0f7M7ICACzi0KFDcrlcfjl2Xl6e9u7dq7y8PL8cvyj+iqs8jlsWx/BVnd7U482+aWlp2rJlS6n3g+es+t73lpXbVVGvaxX1mubN/lzTyp+V3/vesHK7iovN5XLp0KFDHtcdYBiG4U1wAADfiIuLkySlpKSU+7HT09MVExOjbdu2KTo6utyPXxR/xVUexy2LY/iqTm/q8WbfuLg45eTkaPPmzQoICCjVvvCMVd/73rJyuyrqda2iXtO82Z9rWvmz8nvfG1ZuV3GxefvZlpEVAAAAAADAUkhWAAAAAAAASyFZAQCQw+HQ6NGj5XA4/B2KG3/FVR7HLYtj+KpOb+qx6rmEwlXUv5eV21VRr2sV9ZrmyzhQ9irq38rK7SrL2JizAgAsIi4uTnl5efrqq6/MMofDYcn/mICywP3dACoSrmmojJxOp5xOpyQpPj5edrtdmzZt8qguRlYAgIVkZmYqJibGfCUnJ/s7JAAAAKBEkpOTzc+xqampyszM9LguRlYAgEUwsgKVnWEYysjIUGRkJL9CAjjjcU1DZeTLkRWBPowLAOAlm81muUdSAQAAACVx8g9tQUFBXtXFbSAAAAAAAMBSSFYAAAAAAABLIVkBAAAAAAAshWQFAAAAAACwFJIVAAAAAADAUkhWAAAAAAAAS+HRpQBgIakZ2Yp+bL6/w0Ah0if09HcIAAAAlQYjKwAAAAAAgKUwsgIALMQw8pSTlWEu24JDZKsa6seIAAAAgJJxOp1yOp2SJJfLJbvd7nFdJCsAwELyjhzQjtfuNJdrXtpPjo79/RgRAAAAUDLJyclKSkoylyMiIjyui2QFAFiIrVqYogZO/m85OMSP0QAAAAAll5iYqISEBElSfHw8IysAoKIICLApsGakv8MAAAAASs3hcMjhcEiSgoKCvKqLCTYBAAAAAIClkKwAAAAAAACWQrICAAAAAABYildzVhw9elQTJkxwK2vYsKEGDx5sLj/zzDM6fvy4uRwWFqYHH3zQbZ/Vq1drxYoVOnbsmBo3bqyrrrpKYWFhRR53//79+vbbb7V161bZbDY1bdpUXbt2Ne+NOdPs27dP3333ndLT02W329W4cWO1adNGzZs3N7fxV1+XJLY9e/Zo6tSp5vLdd9+tqKgot3r++ecfTZs2za0sMTHRMn+zyvw3qFq1qk/aZSW++FtlZWVp0aJFSktLU1hYmFq0aKErrrhCNlvROd6Kdm0CAAAA/MUnIyuWLVumpKQk7dy5s9D1a9asUVJSkjZv3uxW/vvvv6t169bq3bu3tmzZol27dumpp55So0aNNHfu3AL15ObmasyYMWrUqJFef/11/fvvv9q9e7deeuklNWzYUE8//bQMwzhtvF9//XWBL22e8qau3NxcPfHEE2rcuLHeeust7d27V2lpaXr++efVokULnXXWWZo+fbrbPuXZ16WNbenSpUpKStJNN90kl8tVaHwLFy7UTz/9VIpeKlv8DbxvV2Gs8h7ztE3jxo1TgwYN9Prrr2v//v36/fff1bt3b7Vs2VLr168vUI+vrk0AAAAATggwfPAJevz48Ro1apRWr16t9u3bF1g/Y8YMDRo0SJ999pmuv/56s/yDDz7QiBEjlJKSopo1a0qSjhw5olatWunw4cPKyMhwq2fQoEH65JNPNG/ePHXq1Mlt3Zdffqk+ffpo4MCBeuONN4qNd8SIEVq+fLlWrlzpYYt9U1ffvn01f/58LViwQJdddpnbuvfff1/9+/fXrbfeqpkzZ5rl5dXXnsQ2ZswYTZw4UUePHtWwYcPcfunPl5iYqIYNG2rEiBEl76iT7N69W3/88Ycuv/xyBQQEeFTHyfgb/MfTdhXGKu8xT9vUuXNnnX/++XrhhRfMsk2bNuncc8/VeeedVyAWX12b4uLilJqRrfqDC/7d4H/pE3r6O4QKzzAMZWRkKDIy0ifXeADwJ65pqOzi4uIkSSkpKR7t79c5K1q3bq2XX37Z/OImSdWqVdO5556r/fv3Kzc31yz/9NNPNWPGDD311FMFvgxIUq9evfTAAw/ozTff1MKFC8slfm988MEH+vDDDzV69OgCX0QlqV+/furbt6/PjleavvYmtmHDhqlt27b6v//7P7399ts+iz/f7t271blzZ0VHR2vkyJHasGGDx3XxN0BREhISdP/997uVnX322WrRooW2bNniVl7Rrk0AAACAFfg9WXHjjTe6le3Zs0crVqzQ9ddfL7vdbpa/+uqrkqTbbrutyPoGDhwoSXr99dfLIFrfyo9xwIABRW7z8MMPF+gfT5Wmr72JrXr16po7d67q1Kmj4cOHa/Xq1T6JP1/btm31559/auDAgfrwww91zjnnqG3btnr++ee1Y8eOUtXF3wBFSUhIUOPGjd3Kjh8/rp07d+qqq65yK69o1yYAAADACryaYPNUb7zxhubNm1egfM2aNafdd9y4cdq2bZtWr16tO+64Q6NGjXJbv3LlStWpU0f16tUrso6zzjpLQUFBWrVqValjL28///zzadvTrl07tWvXrtB1ZdnX3sbWpEkTffLJJ+rWrZt69+6tX3/9VREREaeNq6SaN2+usWPHKikpST/++KNmzpypZ555Ro8++qg6d+6sAQMGqHfv3sVOWinxNyiKN+2yKl+06aWXXlKNGjX0/PPPu5X7+trkcu7UzmnDCl3XgNtD/Ip5R8qeYRjmCwDOdFzTUFm0bt260PK0tDTFxsZ6XK9PkxXesNvtqlq1qvLy8vTrr78qLS1N55xzjiTp2LFjOnz4sOrWrVtsHQEBAapevbr2799vlu3cubPAfeI//fST/vnnH40ZM8atvEePHurQoUOR9fuqrmPHjunIkSOKjIwstj1l5XR97YvYOnXqpOTkZN177726+eab9c033ygw0LenW0BAgDp27KiOHTvq5Zdf1rx58/Tee+9p6NChGj58uN59913ddNNNhe7L38B3rPge87X33ntPb7zxhr799lu3v4s316ai2CQF2wtf16oWH3b86dR5ZeB7hmHI6XRKEvd3AzjjcU1DZZGTk1NoubeJOp9+c7nrrruKnMTu888/L3bfxx9/XNKJhl5zzTW6/PLLtWHDBjVo0EDBwcGqXr26srOzi63DMAwdPnzYp7/il4WStqc4/u7rkrjnnnu0Zs0aTZs2TQ8//LAmT55cov3S09M1Y8YMt7JTv6ieymazKTg4WFWrVpXdbj/tG4O/QdG8aZdVedOm6dOn6/nnn9cPP/xQYPREWVyb7I4o1RlU+AiKjafPdaAM+Su5WZnkX7uZjA5ARcA1DZVFUU8LLGrERUlZZmRFvsDAQA0fPlyLFi3SnDlzdN9990mSLrroIi1ZskS7d+8ucrj15s2b5XK5dPHFF5tl9evXL/BFNzs7W3l5eaf9AnwqX9bVoUMHfffdd+YMwYX59NNPtX37diUmJpaq7pIqqq99Gdurr76qjRs3Kjk5udAvi976+eefNXPmTH344YfKzMxUp06dNGXKFPXp0+e0t4HwN/ANq77HfOGbb77R888/r8WLF5vXnalTp6pv376qXbu2JM+vTUULkCE+0FgRHzTLR0BAgPkCgDMd1zTAc36dYHP27NkFZtaXTvxaKUn//vuvWTZ8+HBJcntM46neeecdSSd+Tba607Xn6NGjGjp0qP766y+fHM+XfV2a2KpUqaI5c+aoYcOGGjJkiNatW3fafaKjozVmzBi318m2bNmiMWPGqHnz5urQoYOWLVumhx9+WNu3b9d3332nO+6447SJCl+3syTOpL8BpHXr1umRRx7RokWLFBUVZZZPnTpV+/btM5cr2rUJAAAAsAK/JyueffZZt7K8vDxzxvyuXbua5X369NHAgQOVlJSkZcuWFahr3rx5mjx5sh544AF16dKlbAP3gd69e5vt+fHHH93WHT9+XIMHD1ZQUJAefvhhnxyvNH3t69jq1aunzz77TIZhaMmSJV61Y82aNWrevLnefvtt9e7dW+vXr9fatWv18MMPq0GDBqWqi78BirJjxw717NlTbdu21bRp09wSZ3v27HHbtqJdmwAAAAAr8Oo2kKNHj2rChAnmB/Q33nhDa9as0eDBg81tnnnmGXMG/A8++EBbt27Vgw8+KEnq3Lmzxo4dq3Xr1qljx46SpMWLF2vz5s1KSkpS586d3Y731ltvKSYmRj179tTFF1+sdu3ayTAMrVq1Sr/99pueeeYZPfTQQ940qVy99dZbio6OVo8ePXTZZZepTZs2ysrK0tdff62AgAAtXLhQ9evXl+Sfvi5pbHv27NHUqVO1dOlSc7j+0KFD3YbEt2/fXm+++Waxj3csiXr16mnJkiXq1KmTT4bT8Tfwvl1W5G2bBgwYoH/++afA3ClFqWjXJgAAAMDffDJnxeWXX67LL7+8yPVt27ZV27ZtC5SPGDFCQ4cO1dKlS7V161ZlZ2fr4Ycf1hVXXFHor+R2u12jR4/Wfffdp2+//VZbt26VJN1333264oorVLNmzRLFGx8fr7POOqtkjSvDuux2u8aMGaP77rtPixcv1tatW1WnTh1NnTpVV1xxRaFPbijPvi5tbKd+2T7VgAEDlJOToxYtWhS7XXHq1atX7CMiS4u/gfftKoxV3mOetql3797q1KlTkfvlz1eRz1fXJgAAAAAnBBg8+BcALCEuLk6pGdmqP7jwp4HAv9In9PR3CBWeYRjmxMJMRgfgTMc1DZVdXFycJCklJcWj/f06ZwUAAAAAAMCpSFYAAAAAAABLIVkBAAAAAAAsxScTbAIAfMMw8pSTlWEu24JDZKsa6seIAAAAgJJxOp1yOp2SJJfLJbvd7nFdJCsAwELyjhzQjtfuNJdrXtpPjo79/RgRAAAAUDLJyclKSkoylyMiIjyui2QFAFiIrVqYogZO/m85OMSP0QAAAAAll5iYqISEBElSfHw8IysAoKIICLApsGakv8MAAAAASs3hcMjhcEiSgoKCvKqLZAUAWEjzyFClTOjp7zAAAAAAv+JpIAAAAAAAwFJIVgAAAAAAAEshWQEAAAAAACyFZAUAAAAAALAUkhUAAAAAAMBSSFYAAAAAAABLIVkBAAAAAAAshWQFAAAAAACwlEB/BwAA+E9eXp7S09PNZYfDIYfD4bd4AAAAgJJyOp1yOp2SJJfLJbvd7nFdjKwAAAvJzMxUTEyM+UpOTvZ3SAAAAECJJCcnm59jU1NTlZmZ6XFdjKwAAAsJDw/XqlWrzGVGVQAAAOBMkZiYqISEBElSfHy8VyMrSFYAgIXYbDZFR0f7OwwAAACg1E6+hTkoKMirurgNBAAAAAAAWArJCgAAAAAAYCkkKwAAAAAAgKWQrAAAAAAAAJZCsgIAAAAAAFgKyQoAAAAAAGApJCsAAAAAAIClkKwAAAAAAACWEujvAAAA/8nLy1N6erq57HA45HA4/BYPAAAAUFJOp1NOp1OS5HK5ZLfbPa6LkRUAYCGZmZmKiYkxX8nJyf4OCQAAACiR5ORk83NsamqqMjMzPa6LkRUAYCHh4eFatWqVucyoCgAAAJwpEhMTlZCQIEmKj4/3amQFyQoAsBCbzabo6Gh/hwEAAACU2sm3MAcFBXlVF7eBAAAAAAAASyFZAQAAAAAALIVkBQAAAAAAsBSSFQAAAAAAwFJIVgAAAAAAAEshWQEAAAAAACyFR5cCgIWkZmQr+rH5/g4DfpA+oae/QwAAALAMRlYAAAAAAABLYWQFAFiIYeQpJyvDXLYFh8hWNdSPEQEAAAAl43Q65XQ6JUkul0t2u93jukhWAICF5B05oB2v3Wku17y0nxwd+/sxIgAAAKBkkpOTlZSUZC5HRER4XBfJCgCwEFu1MEUNnPzfcnCIH6MBAAAASi4xMVEJCQmSpPj4eEZWAEBFERBgU2DNSH+HAQAAAJSaw+GQw+GQJAUFBXlVFxNsAgAAAAAASyFZAQAAAAAALIVkBQAAAAAAsBTmrKgEtm3bpp9//tmt7Nxzz9XZZ58tScrIyNCSJUvc1rdo0ULnnXeeW9mWLVuUkpKi6tWrq2XLlmrcuHGxxz169Kh+/fVXZWRkqEaNGmrTpo0iI8+ce/EL67fCxMbG6oILLiiHiLxjGIZSUlK0detW2Ww2NW3aVK1atSr3OFwul+bMmeNWVqdOHXXr1s1cnjNnjlwul7lcvXp1XXvtteayYRhav3690tLSFBYWpubNm5/2fPQ1X7QDAAAAQOFIVlQC27dv19y5c/X111/r6NGjuvbaa1W9enUzWbF3717NnTtXy5cv144dO3TLLbfoyiuvNJMVn3/+ucaNG6fdu3frvPPO0759+7RixQpdd911mjFjhsLCwtyOd/ToUT311FN67bXX1Lx5czVp0kROp1OrVq3SpZdeqpdeeklnnXXWaeOeP3++mjZtasbpDU/qKqzfTpWamqp27dpZPlnxwQcf6IknnpDL5VLbtm2Vl5en3377TdWrV9eECRPUp08fj+pds2aN/v33X7cv6Kfjcrk0d+5cbdy4UevXr1d8fLwuvPBCtzq+/PJLbdq0SatWrVLHjh3Vpk0bs//feecdPf300zp27JjOPfdc83zs0aOHpk2bpqioqDOiHQAAAACKFmAYhuHvIFA+2rdvr927d+uff/4pdP3111+vzz//XKeeEn379tW+ffs0b948ValSRZL06aefqnfv3hoxYoSee+45c9ujR4+qe/fu5hf9du3amesyMzOVkJCgZcuW6bvvvtP5559fbLzNmjXT4MGD9dhjj3naZJ/UVVy/TZkyRWvWrNG0adO8jjFfVlaWDh06pPr16/ukvvHjxyspKUmvvvqqhgwZooCAAElSbm6uJk+erEceeUQTJ07Uww8/XOq6R4wYoeXLl2vlypUexTVq1CitXr1a7du3L7B+xowZGjRokD777DNdf/31Znnnzp0VFRWlmTNnmo9C+uabb9SjRw/Fx8frq6++OiPaUZi4uDilZmSr/uCppY4DZ770CT39HYLfGYahjIwMRUZGmtcqADhTcU1DZRcXFydJSklJ8Wh/RlbgtC655BKdf/75ZqJCkq677jrZbDb9+eefbts++eSTWr58uX788Ue3RIUkhYeH68MPP9RZZ52lm2++WZs3b/b6cTb+kpOTo88++0wtWrRw6xdfSE1N1UUXXaSuXbtqwIABuvHGG1WjRg2P6vrhhx80atQoPfLII7rrrrvc1tntdo0YMUKbNm3SY489pi5duhT6Zdtqunbtqt69e7s9s7l79+5q0aJFiW7bAQAAAGB9TLCJ07rvvvt06aWXupWtWrVKeXl56tq1q1l2+PBhTZ06Veecc44uueSSQuuqXr26br/9dm3dulVffPFFmcZdlo4eParbbrtN8fHxBZIA3mrbtq3mzp2rWrVqaejQoYqMjFTfvn01b948t/kPSuKFF16QJN19991FbjNs2DDl5eXppZde8iru8vLUU0+ZWdp8hmHI6XTq4osv9lNUAAAAAHyJkRWVzJEjR/TBBx8Uum7Hjh3F7puRkaEFCxZo8+bN+vTTT/Xkk0/q3nvvNdevXr1aR44cKTAx56nyb//44Ycf1Lt371K2wD9O7bdjx46V2bECAwPVq1cv9erVS1lZWfr44481c+ZMXXvttQoPD9fNN9+sAQMGlOiL+ffff6+aNWuqadOmRW7Ttm1b2e12/fDDD75sRol9/fXX2rJlS4Hy0oySmD17to4cOWImZ/zBF+2QJJdzp3ZOG1bougbcHlKhcVfmiT7IfwHAmY5rGiqL1q1bF1qelpam2NhYj+slWVHJHDlyRHPnzi103a5du4rdd+/evVqwYIH++usv1apVSxEREW7332VkZEiSatWqVWw9+ev37Nljlq1fv77AvUzZ2dlat25dgeRKr169FBISUmT9vqwr36n9lpOTc9p9fKFmzZoaPHiwBg8erL/++kuzZs3SzJkzNXXqVDVt2lSvv/56kZNCHj9+XE6nU9HR0cUeIzAwUKGhoW5/j8Ls379fixYtciv7448/lJmZWaBfzzvvPLVo0eL0DdSJhMq6desKlG/btq1E+y9fvlyPPvqovvzyyxJN3GrVduSzSQq2F76uVS0+7FRk+dfQyix/lJQk7u8GcMbjmobKoqjvRt4m6khWVDK1a9cucmTF9ddfX+zoitatW+vjjz+WdOILYrdu3bRmzRq9/fbbkmTOq3D06NFiYzhy5Ijb9pK0adOmAkmUQ4cOKSUlRXl5eW7lV1xxRbEJBl/Wle/UfsvOzladOnXcttmzZ4++++47t7K+ffsWWl9pts3XpEkT3XzzzXK5XEpOTtbWrVuVlpZWZLKiSpUqqlKlymn/HtKJv1nNmjWL3Wb//v0F+jU1NbXQ8po1a5b4S/7TTz9d5MSUq1atKnbfb7/9VnfccYcWLFigNm3alOh4VmzHyeyOKNUZVPgIio37S1wNzkBn0qOdy0r+hxomowNQEXBNQ2WxefPmQsuLGnFRUiQr4JGOHTuqd+/emj59up599llFRkbq3HPPlXTiV+ri5K8/eQLOm2++WTfffLPbds2aNVO/fv1K/QQPX9ZVlKCgoALH+Pfffwt82S0qAVHabT/88EPNnDlTK1euVMOGDTVkyBANGDDgtF/Qzz33XP3yyy/KysoqMhmRnp6uY8eOFZgQ9VRNmzYtkOjKf4pGUQmwsrRhwwbddddd+vLLL81+WLhwoTp27KjQ0NAi97NaOwoKkCE+0FRGfJA9ISAgwHwBwJmOaxrgOZIVKJZhGOZjSk8VHh4uSfrnn38UGRmp+vXrKz4+XkuWLDEf01SYTz75RDVq1FCfPn3KNPayFBwcrHfffVe7d+/W5s2b1blzZ7Vq1arEX3ZPt+2RI0f0+eefa+bMmVq0aJFCQkLUu3dvPf300+rcubNstpLNjTto0CCtXr1an376qQYNGlToNp988okk6Y477ihRnVawc+dO9e/fX5988olbwmbEiBGaO3eumjVr5sfoAAAAAHiLZAWKlZubqz59+mj16tVuQ9wPHz6shQsXKiQkRC1btjTLp0yZog4dOmjw4MGaM2dOgcd6vvLKK/rxxx81ffr0ArdRnInWrFmj559/Xp07d/ZZnZs3b9aFF16oY8eO6aqrrtLs2bPVq1cvVa1atdR1DRkyRB9//LGeeOIJderUqcBEmxs2bND48eN1ww036KabbvJVE8rUwYMH1bNnT3Xr1k1//PGH20ierKwsP0YGAAAAwFdIVlQC27Zt088//6z9+/ebT7U499xzdfbZZ0s6ManbkiVLzPkqPvjgA7Vo0ULnnXeebDabYmNj1aNHDw0ZMkQtW7ZUZmampk+frvT0dL311ltuQ+6bN2+uJUuWqG/fvjrnnHN02223qUmTJnI6nZo/f75++uknTZ8+XQkJCf7oilIprN9OtXbtWp8ft2rVqpo0aZJuvvlm1a5d26u6AgMD9fnnn+uuu+5S27ZtlZCQoHbt2skwDK1atUrvvfeebr31Vr3yyivlNjzR5XJpzpw5Wr9+vaQTT9FwOp1uc2/MmTPHfIrG8uXLZbPZdO2110o6MVpkzZo1WrNmTbnEWxRv2wEAAACgaCQrKoHt27dr7ty5uuCCCyRJc+fOVfXq1c1kxd69ezV37lzFxsYqNjZWc+fO1ZVXXmkmK1JTU/XNN99oxYoVWrZsmQICAjRw4ED16dNHMTExBY7Xpk0brV+/Xl999ZW+//57ffPNNwoJCdGNN96o2bNnl/gL+DXXXKNWrVr5pA88qauwfitMly5dvA3PTXR0tIYOHeqz+mrUqKH3339fa9eu1Zdffmk+orRZs2ZatWqV4uLiPK67Xbt2qlatWqn2cblcmjt3rgICAnTLLbdo3bp1OnLkiNuX/C+//FJHjx7VLbfcon/++UeLFi0yv+Q3adJEt9xyS5H1nzxxq5XbAQAAAKBoAQYP/gUAS4iLi1NqRrbqDy78aSCo2NIn9PR3CH5nGIY55xGT0QE403FNQ2WX/6NoSkqKR/uXbJY+AAAAAACAckKyAgAAAAAAWArJCgAAAAAAYCkkKwAAAAAAgKXwNBAAsBDDyFNOVoa5bAsOka1qaDF7AAAAANbgdDrldDolnXh6nt1u97gukhUAYCF5Rw5ox2t3mss1L+0nR8f+fowIAAAAKJnk5GQlJSWZyxERER7XRbICACzEVi1MUQMn/7ccHOLHaAAAAICSS0xMVEJCgiQpPj6ekRUAUFEEBNgUWDPS32EAAAAApeZwOORwOCRJQUFBXtVFsgIALKR5ZKhSJvT0dxgAAACAX/E0EAAAAAAAYCkkKwAAAAAAgKWQrAAAAAAAAJZCsgIAAAAAAFgKyQoAAAAAAGApJCsAAAAAAIClkKwAAAAAAACWEujvAAAA/8nLy1N6erq57HA45HA4/BYPAAAAUFJOp1NOp1OS5HK5ZLfbPa6LkRUAYCGZmZmKiYkxX8nJyf4OCQAAACiR5ORk83NsamqqMjMzPa6LkRUAYCHh4eFatWqVucyoCgAAAJwpEhMTlZCQIEmKj4/3amQFyQoAsBCbzabo6Gh/hwEAAACU2sm3MAcFBXlVF7eBAAAAAAAASyFZAQAAAAAALIVkBQAAAAAAsBSSFQAAAAAAwFJIVgAAAAAAAEshWQEAAAAAACyFZAUAAAAAALAUkhUAAAAAAMBSAv0dAADgP3l5eUpPTzeXHQ6HHA6H3+IBAAAASsrpdMrpdEqSXC6X7Ha7x3UxsgIALCQzM1MxMTHmKzk52d8hAQAAACWSnJxsfo5NTU1VZmamx3UxsgIALCQ8PFyrVq0ylxlVAQAAgDNFYmKiEhISJEnx8fFejawgWQEAFmKz2RQdHe3vMAAAAIBSO/kW5qCgIK/q4jYQAAAAAABgKSQrAAAAAACApZCsAAAAAAAAlkKyAgAAAAAAWArJCgAAAAAAYCkkKwAAAAAAgKXw6FIAsJDUjGxFPzbf32EAfhEgQ61qGdq4P0CGAvwdDoASSp/Q098hAKiAGFkBAAAAAAAshZEVAGAhhpGnnKwMc9kWHCJb1VA/RgQAAACUjNPplNPplCS5XC7Z7XaP6yJZAQAWknfkgHa8dqe5XPPSfnJ07O/HiAAAAICSSU5OVlJSkrkcERHhcV0kKwDAQmzVwhQ1cPJ/y8EhfowGAAAAKLnExEQlJCRIkuLj4xlZAQAVRUCATYE1I/0dBgAAAFBqDodDDodDkhQUFORVXUywCQAAAAAALIVkBQAAAAAAsBSSFQAAAAAAwFKYswJe27dvn7Zv3+5WVr9+fdWtW1eSlJ2drS1btritj4iIUIMGDdzKjh8/rm3btql69eqqX79+iSZj+ffff7Vnzx7VqFFDDRs2VEBAgJetKT+F9VthwsPD1ahRo3KIyHuHDx/WP//8I5vNpgYNGqhatWoFtsnNzdX69evdykJCQtS8eXNzef369crNzTWXq1SpolatWpVd4D6O7fDhw/rrr78UFhamqKgo2WzkhQEAAIDSIFkBry1fvlxPPfWU/vzzT+Xk5KhVq1a6//77NWjQIElSSkqK7r77bm3btk0HDhzQueeeq759++qxxx6TJK1evVovvfSS5s2bp7p162r//v06fvy47rnnHo0dO1aBgQVP07ffflsvvvii/v77bzVo0EBOp1PHjx9X//79NXr0aNWuXfu0cW/atEnh4eFmUsUbntRVWL+d6t9//1WPHj00bdo0r2MsS6tXr9ZTTz2lZcuWqUGDBsrLy9POnTvVpUsXjRs3Tuedd5657eHDh5WQkKCMjAzt3r1bLVq00EUXXaR3333X3Obuu+/WP//8o7///lvR0dFq0aKFFi1aVKqYdu7cqUOHDrklGk7H29iWLFmiF198UUuWLFH9+vW1f/9+HTt2TEOGDNG4ceNUvXr1UrUBAAAAqKz4uQ9eu/baa7VmzRq1atVKdevW1Zo1a8xEhSRddNFFWrNmjbp06SJJWrNmjZmokKQXXnhBGzZs0KZNm/Tnn39qz549euKJJ/Tss89q0qRJbscyDEO33367EhMT9eSTT2rfvn3auHGjdu7cqW+++UbLli1T+/bt9ddff5027l69euntt9/2SR94Uldh/Xbq6+R+8pVjx44pJyfHZ/W9//77uvTSS3X22Wdrx44d+vPPP7VlyxZt375dUVFRuvjii/XJJ5+Y29eoUUNr1qzRPffcI0maNWuWWzJAkn766SeNHTtWkjR58uRSJyok6cUXX9Rtt91Wqn28jS0pKUkHDx5Uenq6eS4/++yzevHFF3X33XeXug0AAABAZUWyAn4XHR2tsWPHKioqSpIUEBCgBx54QDabTStXrnTb9sUXX9R7772nd999V3379nW7VaRdu3b/r707j2+qyv8//k7TlS4EaalsUjZBCsgmIoKKAsKMCx0RRBCqIAriCAouiCyOOyJVR8FxGRVQVERBVERR+Aqj4IYiiIXKvtRSGqDQ0iXn9we/RkLSNm1SksLr+Xjk4eOee+/nfu69SWw+nHuOPvvsM9ntdg0YMEDGmFN6Hv7kcDj0yy+/KCEhQeecc45fY69fv1716tXTnXfeqTVr1vgUa+PGjUpNTdWNN96oZ555xjlNkSTFx8frlVdeUa9evTR06FBlZGT4mHnwa9asmR577DHFx8dLOv5evuOOO9S0aVN9/PHHAc4OAAAAqD4oViDgnnjiCV1zzTUubdu2bZPD4VD79u2dbUVFRXrsscfUuHFj9evXz2OsOnXqaOjQoVq7dq2++OKLqky7Sh09elSdO3fWwIEDNXnyZL/GbtmypSZMmKCVK1eqS5cuat68uaZOneo2rog3nnjiCRUUFOjuu+8udZvx48crLy9PzzzzjC9pVwuvvPKKunbt6tZeXFys8847LwAZAQAAANUTY1bArwoLC7Vu3TqP6w4ePFjmvrm5uVq/fr02bdqkJ598Uv3799d9993nXP/dd9/pwIED6tOnT5lxunbtqmeffVZffPGFevXqVeFzCISTr1teXl6VHSsmJkYTJkzQhAkT9PPPP2vOnDl6+eWXNW3aNHXu3FlDhgzRDTfcoISEhHJjffbZZ4qOjlabNm1K3aZLly6yWCylFo/S09M9jkvizeCjVc0fuX355ZfavXu35syZ49X2hfY92vPKKI/r6o940evjAtWRRUaSUfUZKhmApGrdm7UqGWOcL+B01rp1a4/tGRkZatq0aaXjUqyAX+Xk5Cg1NdXjuq1bt5a574YNGzRy5Ejt3LlTF1xwgW699VaXAQl3794tSeX+iC5ZX7K9JO3bt0/79u1z2a6goEB79+51K660atVK4eHhpcb3Z6wSJ183h8NR7j7+cP755+v888/XU089peXLl2vOnDmaOHGi7r77bvXu3dttcMwTFRYW6s8//1SjRo3KnIUlMjJSsbGxLvfjRNOmTfM4a8iBAwe8Po+8vDz9/vvvLm1ZWVk6evSo2z2pX7++V4UYf+SWkZGhm2++Wf/973/VrVs3r/YJkRRRykQ4rWrxxw5ObxZJDWOO/9eI9ztQXWRmZgY6haBkjJHdbpekajVjHVBRpY2H52uhjmIF/KpkoEhP+vXrp0WLFpW674UXXqj169fLGKPXXntNffr00bRp0/TQQw9JkiIiIiQd/5FclpL1kZGRzrb3339fL7/8sst2mZmZeu+997Ry5UqX9k8++UT16tUrNb4/Y5U4+brl5uY6xz04se3kRzXatWvnMV5FtpWkkJAQ9erVS926dVOPHj10991365NPPlGvXr1KLVaEhoYqNDS03PshHb8npc2EMW/ePHXq1Mmt/fXXX3cZqLUsW7dudSuSlcwGcnL7hAkTNHjwYK/i+pLbxo0b9be//U0vvvii/v73v3t1PEmy2uoq/mbPPSg25ngdBqiWLP+/RPFbDv0rgOokMTEx0CkEpZIfaomJiRQrcFrbtGmTx/bSelx4i2IFgo7FYtHw4cO1ePFiTZs2TXfeeadsNpvzmf9t27aVuX9JD44Txwi44447nDM8lGjWrJlGjBhR4Rk3/BmrNFarVeeff75L26ZNmzRixAiXttIKQxXZ1uFw6KuvvtLcuXP1/vvvKz8/X3369NGQIUPcxhI5kcViUYsWLbRp0ybl5eV57IEgHS/k5OXlqWPHjqXG8lWrVq3czm/8+PFatWqV2yCtp8LevXvVr18/zZ492/nY0u+//67GjRt70dPGwo80nPEMnwOgWuGHeOksFovzBaBiGGATAffrr796bG/SpImKi4uds0g0a9ZMHTt21P/93/8pNze31Hgff/yxwsLCdN1111VJvqdCVFSU1qxZo8OHDzvPv1OnTm5Tm5bGm23XrVun8ePHq2HDhurZs6fS09P1xBNPaO/evVq8eLEGDBjg0jvFkxtuuEHFxcVlTi1aMgvGoEGDyj/x00Bubq5SUlI0Y8YMl/FVrrvuuqAYhwMAAACoDihWIKCKiorUpk0bt/EsHA6Hvv76a1mtViUlJTnbn3/+eRUUFOjee+/1GO/jjz/WkiVL9OCDD7rsV12tXr1at912m19jbt++Xa1bt1b79u21ZMkS3X777frjjz+0evVqjR49WrVr1/Y61rhx43Teeedp4sSJzmcyT5SZmampU6eqc+fOuvXWW/14FsGpuLhYAwcOVO/evdWwYUOXYlF+fn6g0wMAAACqDR4Dgc8OHDigHTt2KC8vzzmrRb169VSnTh1Jf42fUDIbyLp165SQkKD69evLYrGoRo0a6tu3rx544AG1aNFC2dnZmjVrln744QdNmzbN5cfzRRddpA8++EBDhgzR1q1bNXz4cDVq1Eh2u10ff/yxZs+erfvuu09TpkwJyLWoCE/X7WR//PGH34+bl5ennj176vXXX/c4HkNFREdH67PPPlNKSoo6duyoe+65R+3bt5cxRmvXrtWMGTPUuHFjLVy4UGFhYZKO/6Bfv369c5DS9PR01axZU82bN3fGXb9+vbMXwtatW7Vx40a1atXKp1y94Wtud911lz755BN98skn+te//lXl+QIAAACnK4thLh34aPHixZo8ebJL21133eUcgHDNmjVuvQNuuOEG5/gOdrtdb775pr755htt375dFotFLVu21I033qgrrrjC4zEPHDig1157TStXrlRmZqaio6PVvn17paamqm3btl7lfc011yglJcXrQRz9HcvTdfOkS5cumj17ti/pVbni4mItWLBAH330kbPA0qxZM/Xr108pKSkuz2kePnxY3bt3d9m/bdu2evPNN53LXbt21dGjR53LiYmJZT5q4snMmTP1008/ucQtj6+5paSklDnrzUcffaSGDRuWuj45OVmbM3NVjylKcYayyKhVLaONOYxZAVQn257wfiDpM4kxRpmZmQywiTNWcnKypOOzPlYGxQoACBIUK3Cmo1gBVE8UKzyjWIEzna/FCsasAAAAAAAAQYViBQAAAAAACCoUKwAAAAAAQFChWAEAAAAAAIIKU5cCQBAxxqGig5nO5ZCIaIVExgQwIwAAAMA7drtddrtdklRYWCir1VrpWBQrACCIOPIOaffs4c7lmhcPkq3b4ABmBAAAAHgnLS1N06ZNcy4nJCRUOhbFCgAIIiFRcao7bOZfyxHRAcwGAAAA8N7YsWOVmpoqSerduzc9KwDgdGGxhCi0ZmKg0wAAAAAqzGazyWazSZLCwsJ8ikWxAgCCSPPEGG144u+BTgMICGOMMjMzlZiYKIvFEuh0AABAADEbCAAAAAAACCoUKwAAAAAAQFChWAEAAAAAAIIKxQoAAAAAABBUKFYAAAAAAICgQrECAAAAAAAEFYoVAAAAAAAgqIQGOgEAwF8cDoe2bdvmXLbZbLLZbAHLBwAAAPCW3W6X3W6XJBUWFspqtVY6Fj0rACCIZGdnq3Hjxs5XWlpaoFMCAAAAvJKWlub8O3bz5s3Kzs6udCx6VgBAEKldu7bWrl3rXKZXBQAAAKqLsWPHKjU1VZLUu3dvn3pWUKwAgCASEhKipKSkQKcBAAAAVNiJjzCHhYX5FIvHQAAAAAAAQFChWAEAAAAAAIIKxQoAAAAAABBUKFYAAAAAAICgQrECAAAAAAAEFYoVAAAAAAAgqFCsAAAAAAAAQYViBQAAAAAACCqhgU4AAPAXh8Ohbdu2OZdtNptsNlvA8gEAAAC8ZbfbZbfbJUmFhYWyWq2VjkXPCgAIItnZ2WrcuLHzlZaWFuiUAAAAAK+kpaU5/47dvHmzsrOzKx2LnhUAEERq166ttWvXOpfpVQEAAIDqYuzYsUpNTZUk9e7d26eeFRQrACCIhISEKCkpKdBpAAAAABV24iPMYWFhPsXiMRAAAAAAABBUKFYAAAAAAICgQrECAAAAAAAEFYoVAAAAAAAgqFCsAAAAAAAAQYViBQAAAAAACCpMXQoAQWRzZq6S7v840GkAAWGRUataRhtzLDKyBDodANXYtif+HugUAPiInhUAAAAAACCo0LMCAIKIMQ4VHcx0LodERCskMiaAGQEAAADesdvtstvtkqTCwkJZrdZKx6JYAQBBxJF3SLtnD3cu17x4kGzdBgcwIwAAAMA7aWlpmjZtmnM5ISGh0rEoVgBAEAmJilPdYTP/Wo6IDmA2AAAAgPfGjh2r1NRUSVLv3r3pWQEApwuLJUShNRMDnQYAAABQYTabTTabTZIUFhbmUywG2AQAAAAAAEGFYgUAAAAAAAgqFCsAAAAAAEBQYcwK+Ky4uFiFhYUubWFhYc7BVBwOhwoKClzWh4aGKjTU/e3ncDgUElKxGlpl9gkGnq6bJ6Vdq2CQn5/vshwSEqLw8HDn8rFjx2SMcS5bLBZFRES4xanMPSyJa7FYKrSfP/nr/AEAAAC4qn6/8BB03nrrLdlsNtWoUUNRUVGy2WyaOfOv2QxWrlwpm82m6Oho5/oJEyY41+/Zs0ePPPKIWrdurYiICEVHR6tjx4568803Sz3m6tWr1b9/fyUkJCg8PFw1atRQ165dNWvWLK8KAJJUUFCg4uLiyp+4j7E8XbeTX7Gxsbr99tv9kqO/HTx40JljVFSUatasqV69erlsU79+fef6uLg4JScnO9dlZGRowoQJatKkicLDwxUbG6uOHTvq5ZdfdvmBf6Ldu3dr3LhxatasmSIiIhQREaFzzz1X99xzj/bt21fpcykqKnIrqJXH1/MHAAAAUDqKFfDZTTfdpPz8fHXo0EH169dXfn6+xo8f71zfo0cP5efn6+qrr5Z0/F+jTyxm3H333XrppZf00ksvKS8vT9u3b1fHjh01bNgwvfzyy27He/TRR9WjRw+1bt1aP/74owoLC7Vv3z7deeedeuSRR9SjRw/Z7fZy827VqpWmT5/u+wWoZCxP1+3k14nXKdjUrFlT+fn5mjJliqTjBaSVK1e6bLN//37nPXz33Xe1ZcsW57rhw4dryZIleuedd5Sfn6+tW7eqW7duGjlypO677z63461atUpt27bVli1b9M477ygvL09HjhzRG2+8oR9//FFt2rTRN998U6lzuf/++3XJJZdUaB9fzx8AAABA6ShWIODCw8M1depUXXzxxQoNDVV8fLyeffZZhYSE6KOPPnLZ9u2339akSZM0c+ZMTZ06VQ0bNpTFYlFcXJwGDRqk5cuX64cfftCwYcMCdDb+c+zYMYWGhvo85c/JfvjhB1100UV64YUXtH//fr/Grojw8HBNnz5dF1xwgfO+p6WlqV69enr99dddtt2zZ4+uueYaderUSYsWLVLHjh1ltVoVFhamiy66SMuWLVOTJk107bXX6s8//wzMCQEAAADwG4oVCLg333xTw4cPd2k7fPiwHA6HmjRp4mwzxuj+++9XYmJiqY9GtGzZUjfeeKMWL16sNWvWVGneVSk3N1c1a9bU7bffrlmzZvk1dqNGjdSqVStNnDhRdevW1VVXXaX58+crLy/Pr8cpz7Jly3TVVVe5tFksFsXGxurss892aX/88ceVk5OjyZMnexzbIiwsTJMmTVJWVpbS0tKqMm0AAAAApwDFCvidp8cZ8vPz5XA4ytzP4XAoJydH33zzjQYMGKD27dtr4sSJzvXr1q3Tjh071L17d+fgnZ5cdtllkqRPPvnEL+dzqpx4rY4dO1Zlx4mPj9err76qzMxMzZ07V9LxR1ISExM1bNgwff755+XeK08KCgo83ndvxxCRpPXr1ys9Pd35aEWJxYsXKzIyUhdeeGGp+1566aWSpI8//rjCufuDP84fAAAAwHHBOcUAqq3du3fLZrN5XFfej7aVK1fqyiuvVGFhoXr16qV58+apTp06zvXbtm2TJNWtW7fMOPXq1XPZXjo+gGJRUZHLdsYYFRUVuc3oEBERUeYME/6MVaKs61ZVIiMjNXDgQA0cOFBZWVmaP3++5syZo969e6tu3boaNGiQxowZo8aNG3sVr0ePHh7P1duBR+12u2688UZNmTJF1113ncv+O3fuVIMGDcqcFSUuLk4xMTEu990TY4xbMai4uFjGGLf7d+KsNuXx9fxLFNr3aM8rozyuqz/ixQrFAqobi4wko8DN8QPgdFHaYN2nOoeSF3A6a926tcf2jIwMNW3atNJxKVbAr+rXr69du3Z5XNevXz8tWrSo1H179OihgoIC7dmzRzNnzlS7du306quvasiQIS7blfeF72lKy0cffVSPP/64y3YFBQWaNm2aHnnkEZf233//XY0aNSo1vj9jlTj5uuXm5io+Pt5lG09TwEZGRnqMV5FtJSkhIUF33nmn7rzzTr377ru67bbb9Mwzz6hhw4YaO3ZsuflLxweY7NSpk1v766+/rptvvrnMfTMzM9WrVy+NGDFCd911l9t6i8Xi1f/ojTHlFod++uknde3a1aWtqKhIDofDrWD09NNPa8yYMeUeV/Lt/E8UIimilPpIq1r8sYPTm0VSw5jj/zXi/Q6g8jIzMwOdgowxzkHfAznVOlDVTv6H3BK+FuooViDo1KtXT9OnT9f333+v22+/XSkpKYqOjnZW5Xbv3l3m/iU/+k+s4k2ZMsXt0YJmzZppxIgRuv/++yuUnz9jlcZisbgVF1atWqXevXu7tJ3cE6Ay20rSzp079dZbb2nu3Ln69ddfde6552rcuHEaMGBAJc/Ae0eOHNFVV12lUaNGadSo4z0KCgoKFBYWJovFIqvVqkaNGmn37t0qLCwsdcBRu92uI0eOqEOHDmUer0OHDm7XYvz48Vq1apW+/fZb/5yUD6y2uoq/2XMPio05pzgZ4BSz/P8SxW859K8A4JvExMRAp+D8oZaYmEixAqe1TZs2eWwvrceFtyhWIOCOHTumiIgIt/Z27dppxYoVSk9PV/v27dW2bVs1adJEX3/9dZk/WpcvXy6LxaJ+/fpVceZVJzo6Wna7XQ6HQ8XFxQoLC9Mll1xSZsHhRN5sa7fbtWDBAs2bN08rV65UQkKCBg4cqFdffVWdO3f2x2mUq7i4WDfccIOGDBniLFRIxwsKH374oZo1aybpeK+cmTNnatWqVerRo4fHWMuXL5ckpaSkVH3iVcrCjzSc8QyfAwA+CpbigMVicb4AVAwDbCKgioqKFB0drQMHDritW79+vSS5jFsxY8YM7d+/XzNnzvQYb926dXrnnXd0yy23qE2bNlWT9Cm0bNky9e3b168xMzMz1b9/f5199tkaO3as6tWrp48//lh79uzRc889d8oKFZJ055136sILL9Rtt93mMijlyV3G7r//ftWpU0dTpkzx2M0sLy9PDz/8sJKSkjw+RgIAAACgeqFYAZ8VFxe7/MDMz893GVTQ4XC4zAaSn5/v/MFpsVhUXFys/v37a82aNbLb7crIyNC4ceO0fPlypaamqn79+s5Y/fr10/PPP69JkyZp3Lhx+vXXX3X48GHt3LlTs2fPVs+ePXXVVVfpxReDfyBCT9ftVMwksXfvXuXm5uqVV15RZmam5s2bp759+3o9kOSJTryXBQUFbuNkHDt2zHkOhYWFLgNbPvXUU5o1a5YeeughRUVFubw2btzoEqdOnTr69NNPlZGRoZ49e+rLL79UTk6ODhw4oKVLl+rSSy9Vbm6uli5dqtjY2AqfR2X5cv4AAAAASsdjIPDZW2+9pVtvvdW5bLPZ9Mgjj2j8+PGSjs/yUdI7ICIiQjabTaNGjdLMmTNltVr1888/a/bs2br99tu1fft2WSwWtWzZUv/+9791++23ux1vzJgxuvTSS/Xss88qJSVFmZmZio6OVvv27TVr1ixdf/31XuUdERFR5uwSFVGZWJ6umyeXX365L6m5adeunZYuXepznIMHDzqfB42IiNDll1+uCy+8UCtXrnRuU79+feXm5ioiIkI33XSTGjRooC1btkiS5s2b5/HxnxIhIa611A4dOujXX3/Vc889p3vvvVd//PGHpOPjhfTr109jxoxRXFxcpc4lLCyszFw88fX8AQAAAJTOYphLBwCCQnJysjZn5qoeU5TiDGWRUataRhtzGLMCgG+2PfH3QKcgY4wyMzMZYBNnrOTkZEnShg0bKrU/j4EAAAAAAICgQrECAAAAAAAEFYoVAAAAAAAgqFCsAAAAAAAAQYXZQAAgiBjjUNHBTOdySES0QiJjApgRAAAA4B273S673S5JKiwslNVqrXQsihUAEEQceYe0e/Zw53LNiwfJ1m1wADMCAAAAvJOWlqZp06Y5lxMSEiodi2IFAASRkKg41R0286/liOgAZgMAAAB4b+zYsUpNTZUk9e7dm54VAHC6sFhCFFozMdBpAAAAABVms9lks9kkSWFhYT7FolgBAEGkeWKMNjzx90CnAQSEMUaZmZlKTEyUxWIJdDoAACCAmA0EAAAAAAAEFYoVAAAAAAAgqFCsAAAAAAAAQYViBQAAAAAACCoUKwAAAAAAQFChWAEAAAAAAIIKxQoAAAAAABBUQgOdAADgLw6HQ9u2bXMu22w22Wy2gOUDAAAAeMtut8tut0uSCgsLZbVaKx2LnhUAEESys7PVuHFj5ystLS3QKQEAAABeSUtLc/4du3nzZmVnZ1c6Fj0rACCI1K5dW2vXrnUu06sCAAAA1cXYsWOVmpoqSerdu7dPPSsoVgBAEAkJCVFSUlKg0wAAAAAq7MRHmMPCwnyKxWMgAAAAAAAgqFCsAAAAAAAAQYViBQAAAAAACCoUKwAAAAAAQFChWAEAAAAAAIIKxQoAAAAAABBUKFYAAAAAAICgQrECAAAAAAAEldBAJwAA+IvD4dC2bducyzabTTabLWD5AAAAAN6y2+2y2+2SpMLCQlmt1krHomcFAASR7OxsNW7c2PlKS0sLdEoAAACAV9LS0px/x27evFnZ2dmVjkXPCgAIIrVr19batWudy/SqAAAAQHUxduxYpaamSpJ69+7tU88KihUAEERCQkKUlJQU6DQAAACACjvxEeawsDCfYvEYCAAAAAAACCoUKwAAAAAAQFChWAEAAAAAAIIKxQoAAAAAABBUKFYAAAAAAICgQrECAAAAAAAEFYoVAAAAAAAgqIQGOgEAwF82Z+Yq6f6PA50GEBAWGbWqZbQxxyIjS6DTAQCf8J2GYLHtib8HOoVKoWcFAAAAAAAIKvSsAIAgYoxDRQczncshEdEKiYwJYEYAAACAd+x2u+x2uySpsLBQVqu10rEoVgBAEHHkHdLu2cOdyzUvHiRbt8EBzAgAAADwTlpamqZNm+ZcTkhIqHQsihUAEERCouJUd9jMv5YjogOYDQAAAOC9sWPHKjU1VZLUu3dvelYAwOnCYglRaM3EQKcBAAAAVJjNZpPNZpMkhYWF+RSLATYBAAAAAEBQoVgBAAAAAACCCsUKAAAAAAAQVBiz4jS0YMECjR8/3qXtvvvu06hRoyRJq1ev1uDBrrMLDBs2zDlqa2FhoRYtWqT58+drw4YNqlGjhlq2bKk777xTXbp08XjMXbt2afbs2Vq5cqUyMzMVGxur888/XzfddJN69OhRBWfpf56umyc33HCDnnjiiVOQUeXl5+frjTfe0EcffaQ//vhDISEhatKkia6++moNGzZM4eHhzm0PHTqktm3buuzfvn17ffDBB87l5ORkHTlyxLlct25dffPNN1V+HuQGAAAAnJnoWXEa6tOnj1asWKG4uDgdO3ZMK1as0I033uhc37FjR61YsULnnnuutm/frhUrVuiuu+5yrh8+fLiGDx+uvn376v3339ezzz6r/fv36+KLL9ZHH33kdrx33nlHLVq00JYtWzR58mQtWbJEs2bNUsOGDXXVVVdp2LBhKiwsLDfvyy67TLNmzfLLNahMLE/X7eTXLbfcov379/slx6qyefNmtW/fXv/+9781aNAgLViwQPPnz1dKSoqeeuoptW/fXn/88Ydz+5iYGK1YsUL9+/fX9u3b9fzzz+vFF190ifnpp59qzJgx2r59uyZOnKj33nuvwnk99thjSklJqdA+5AYAAACcmehZcRqKiYlRTEyMwsPDZbValZSU5LI+MjJSSUlJqlGjhiS5rS8oKNDkyZM1fPhwZ9v8+fN11lln6b///a+uvvpqZ/tXX32lIUOGaNy4cXrqqadc4nTu3Fk9evTQFVdcoZiYGL3wwgtl5r1r1y4dPHiwEmfsn1jlXTdJOuuss7Rjxw6/5Fhiw4YNevrppzVkyBD16NFDISGVryEePHhQffr0UVxcnFatWqXo6L+mvWzdurX69eunCy64QH379tWPP/6o6OhohYSEKCkpyTlqb926dVW3bl2XuOecc47i4+MlSXXq1FGDBg0qnNuBAwe0d+/eCu1DbgAAAMCZiZ4VcPP8889r9OjRLm2hoaGyWCw666yzXNrHjh2r6Oho5yMkJ7vsssuUkpKi2bNna9OmTVWWc1U7evSoWrRooWHDhunJJ5/0a+yaNWsqPT1dPXv2VMOGDTV+/HitW7euUrFmzJihP/74Q48++qhLoaJErVq1NHXqVKWnp5dbPAIAAACAQKFYATcJCQmKiopyLmdnZ2vMmDGqU6eO7r33Xmf777//rl9++UWXXHKJy/Yn69OnjxwOhz788MOqTLtKORwObd++XbGxsapdu7ZfYzdo0ECrV6/Wli1bNHLkSC1atEjt27dX69at9fjjj1eoJ8d7772n0NBQXXHFFaVu06dPH0nSwoULfc4dAAAAAKoCj4Gc5vbt2+fxcQZJ+vPPP8vcd/Xq1frHP/6hrKws9ezZU2vWrFGjRo2c60t6SpxzzjllxinZJz09vQKZB9bJ180YU+XHbNq0qaZMmaIpU6bo22+/1Zw5czRjxgw9+OCD6t69u4YMGaIBAwaoZs2aHvd3OBzavHmzzj77bEVERJR6nPj4eNWoUaPU+3HNNde4DMBZIjc3t3In5kdnQm6F9j3a88ooj+vqj3jRYztwurDISDKyBDoRAPADvtMQLKr6t0zr1q09tmdkZKhp06aVjkux4jSXkJCgFStWeFw3cuRIff7556Xu27FjR/3vf//T9u3b9dxzz6lz586aP3++c3aPoqIiSZLVai0zh9DQ42+zEwfZnDFjhp5//nmX7Xbv3q0nn3xSs2fPdmlftWpVmc/6+zNWiZOv29GjR9WhQ4dy9/OXLl26qEuXLkpLS9Pbb7+tu+66SyNHjtSRI0c0duxYj/s4HA45HI5y74d0/J6UNujpSy+9pDZt2ri1L1iwQBMmTPAq//Xr17uMbSJJOTk5ys/PdyueTZ48WbfccotXcU/33KTj3d0iSrmFrWpVfdEMCCSLpIYxx/9rxPsdQPXGdxqCRWZmZpXGL/ldeDJfiyQUK05zpQ0UKck5wGZpIiMj1bRpUzVt2lSXXXaZOnXqpEGDBmnHjh0KDw9Xw4YNJZXfQ2Pfvn2SXHtgDB8+XNddd53LdpdddpkGDhzonGK1xNlnn11mfH/GKnHydfP0r+PffvutbrjhBpe2bdu2eYxXkW2l44Ocfvrpp5o7d66WLFmikJAQDRo0SL169Sp1n9DQUJ199tnav3+/HA5HqQN1Hj16VIcPH9Z5553ncX3dunU9vmdKBor0RosWLdyKZI899pi+++47l6k9JbmNg1KW0z03SbLa6ir+Zs89KDbmVCgUUO1Y/v+f87/l8G+RAKo/vtMQLBITE6s0fmljE5bW48JbFCvglZCQEHXv3l3PPfecfv/9d7Vp00YdO3ZUQkKC/ve//8kYI4vF85fwqlWrJEl9+/Z1ttlsNucsCiVCQ0NVq1atUosrpfFnrNJER0fr999/d2lr165dqb1WTubNtsYYrV69WvPmzdO7776rgwcP6vLLL9dLL72kf/zjH4qJiSn3OH369NF///tf/fTTT+rYsaPHbVavXi1jjMv98Lfw8HC3ax8XF6eIiAi/3ZPKCubcjrPwBw3OeIbPAYDTCN9pCLTSfqcFOwbYhIvi4mI1b95ceXl5but27twp6a8eGVarVVOnTtWOHTv09ttve4y3a9cuzZ07V1deeaW6detWdYlXMYvFokaNGmnlypUaMmSIpL+mgD3xVZqytj148KAmTZqkJk2aqHv37lq7dq0mTZqkXbt2admyZRo6dKhXhQpJmjhxoqKiokqdscQYoyeffFI2m03jx4/3+vwBAAAA4FSiWAEXxhht2bJFo0eP1oEDByQdL2D85z//0YcffqgrrrjCZZCU0aNHa8yYMRoxYoReeuklHT161BlnxYoV6tmzp5o2baq33norIOfjb3l5ec7HWvxl8+bNeuuttzR48GD99ttv+uGHHzRu3DivH1k5UbNmzfTOO+9oyZIlGj58uHbv3u1ct23bNg0cOFDfffedPvzww0rFBwAAAIBTgWLFaWjBggVKSkrS+vXrnbNazJo1y7l+9erVSkpK0rJlyyRJSUlJmjJliqTjj0+888472r59uxo1aqSzzz5b0dHRmjhxou644w63Z/ol6fnnn9e8efM0Z84cxcfHq379+oqLi9Mtt9yiwYMHa/Xq1RV69j9QPF23k1/Dhg3z+3HbtWunjIwMPfLII2rZsqXP8a6++mr98MMPOnr0qFq1aqXExETVqVNH7du3V2RkpH766Sddeumlzu0PHTqkpKQkzZgxQ9LxWS1SUlJcYiYnJzt7YowcOVIXXXSRz3l6g9wAAACAM5PFnIo5GXFK5ebmav/+/S5ttWrVck55mZ+f79Y7IC4uzmNB4c8//5TFYlFCQoJXxz527Jiys7MVHR1d6hSbpdm9e7diYmIqvJ+/Ynm6bp5ERUVV+SA1/uJwOJznFB8f73HQTYfDoR07dri0RUREqG7dus7lHTt2yOFwOJdDQ0O9mlXlRCUzbpwY15v8z6TckpOTtTkzV/WYohRnKIuMWtUy2pjD890Aqj++0xAstj3x94AcNzk5WZK0YcOGSu1PsQIAggTFCpzp+MMewOmE7zQEi+parOAxEAAAAAAAEFQoVgAAAAAAgKBCsQIAAAAAAASV0EAnAAD4izEOFR3MdC6HREQrJDImgBkBAAAA3rHb7bLb7ZKkwsJCWa3WSseiWAEAQcSRd0i7Zw93Lte8eJBs3QYHMCMAAADAO2lpaZo2bZpz2dtZJT2hWAEAQSQkKk51h838azkiOoDZAAAAAN4bO3asUlNTJUm9e/emZwUAnC4slhCF1kwMdBoAAABAhdlsNtlsNklSWFiYT7EoVgBAEGmeGKMNAZoLGwg0Y4wyMzOVmJgoi8US6HQAwCd8pwG+YTYQAAAAAAAQVChWAAAAAACAoEKxAgAAAAAABBWKFQAAAAAAIKhQrAAAAAAAAEGFYgUAAAAAAAgqFCsAAAAAAEBQCQ10AgCAvzgcDm3bts25bLPZZLPZApYPcCq1bt1aRUVF2rRpU6BTAQCf8Z2GM5HdbpfdbpckFRYWymq1VjoWPSsAIIhkZ2ercePGzldaWlqgUwIAAAC8kpaW5vw7dvPmzcrOzq50LHpWAEAQqV27ttauXetcplcFAAAAqouxY8cqNTVVktS7d296VgDA6SIkJERJSUnO16kqVtjtdk2dOtXZbS9YBCqvU3HcqjiGv2L6EidY30vw7HS9X8F8Xqfr99rp+p3mzzxQ9U7XexXM53Vybjabzfl3bFhYmEJCKl9ysBhjjJ/yBAD4IDk5WZK0YcOGU37sbdu2qXHjxtq6dauSkpJO+fFLE6i8TsVxq+IY/orpSxxf9k1OTnY+322xWCq0LyonWD/7vgrm8zpdv9dO1+80X/bnO+3UC+bPvi+C+bzKys3Xv23pWQEAAAAAAIIKY1YAQJDYsWOHCgsLnVXoU6mwsFDS8WcLw8LCTvnxSxOovE7FcaviGP6K6UscX/bNyMiQMUatW7eu0H6ovGD97PsqmM/rdP1eO12/03zZn++0Uy+YP/u+CObzKiu3jIwMn/KlWAEAQSI6OlpHjhwJyLGtVqsSEhJ8GgSpKgQqr1Nx3Ko4hr9i+hLHl32bNm1a4X3gm2D97PsqmM/rdP1eO12/03zZn++0Uy+YP/u+CObzKiu3sLAwRUdHVzo2Y1YAAAAAAICgwpgVAAAAAAAgqFCsAAAAAAAAQYViBQAAAAAACCoUKwAAAAAAQFChWAEAAAAAAIIKU5cCAIBq68iRI5o1a5bWr1+vxMRE3X777WrSpEmg0wKASvnb3/6mHTt2uLTVq1dPy5YtC1BGQOBQrAAAANVW//79dfDgQd12221atWqVLrzwQm3cuFEJCQmBTg0AKiwkJETz5893Ln/11VfasGFDADMCAsdijDGBTgIAAKCi8vPzVaNGDf38889q06aNJKlBgwZ6/vnnlZKSEuDsAKDi1q1bp3bt2jmXU1JSNGXKFJc24EzBmBUAAKBaioiIUJ06dbRz505J0sGDB3Xo0CE1bNgwwJkBQOWcWJTYvXu39u/fT6ECZyyKFQAAwC/27dunwYMHa/DgwWVut2bNGg0dOlTdunXT9ddfryVLllTqeBaLRR988IGmTp2qv/3tb+revbumT5+uTp06VSoeAJwoOztbQ4YMUUxMjPbs2VPqduvWrdNVV12lxMRENWrUSCNHjlRWVpbPx3/55Zc1YsQIn+MA1RVjVgAAAJ/NnTtXkyZNUlZWlpo3b17qdgsXLtSAAQM0duxYjRw5Ut98841SUlL0r3/9S/fff79zu6eeekpr1671GOOcc87RM888o+LiYj399NM655xzdO211yo9PV3PP/+8rrzySiUlJfn7FAGcQRYuXKgJEyYoPDxcR44ckcPh8LjdunXrnIXXH374QTk5OUpNTdUll1yitWvXKjY21hlv8uTJpR5v2bJlqlevnnO5uLhYCxcuLPV7EDgTUKwAAAA+WbBggT788EOtXr1al156aanb5ebm6rbbbtM111yjp59+WpLUrVs3ZWVl6aGHHlL//v3VrFkzSVL37t1LndWjZs2akqTPPvtMy5cvV3Z2tqxWqyRpy5YteuaZZ/Tcc8/58xQBnEEOHjyo119/XV9++aVmzpypTZs2lbrtuHHjVLt2bf3nP/9RWFiYGjRooDfffFOtW7fWM888oylTpkiSevTo4TJw5slOHhR48eLF6tmzpyIjI/1zUkA1RLECAAD45KqrrlL//v3L3W7x4sXav3+/brrpJpf2oUOHavr06ZozZ46mTZsmSbrooovKjRceHq68vDxlZmaqXr16cjgc2r59uxo0aFC5EwEAHS+ILl68uNzt9uzZo5UrV2r06NEKCwtzticnJ+u8887TW2+95SxW1KpVS7Vq1fI6h9mzZ1N0xRmPMSsAAIBPvP2XvzVr1kiSWrdu7dJ+3nnnKTQ01LneWz169NCVV16pNm3a6KqrrtJ5552nvXv36s4776xQHACojJ9++knGGLVs2dJtXcuWLbV582bl5uZWOG5GRoYcDodatGjhjzSBaoueFQAA4JQoGaAuPj7epd1qtapWrVplDmDnidVq1eLFi7VhwwZt3bpVZ511li644AKXf+EEgKry559/SpJsNpvbulq1askYo/379ysmJqZCcWvXrq158+b5I0WgWqNYAQAATomioiJJco4vcSKr1arCwsJKxU1OTlZycrJPuQFARRljJB2fmehkJW0l21SEp+IHcCbiMRAAAHBKlPwB7qlb9OHDh/kDHUC1UtJL7ODBg27rStpq1659SnMCTicUKwAAwCnRqlUrSdLWrVtd2rOysnTkyBF6RwCoVtq2bStJ2rx5s9u69PR0JSUlKS4u7lSnBZw2KFYAAIBT4uqrr5YkLV++3KX9888/lyRdc801pzwnAKispKQkXXDBBfr0009dHvfYtm2bfv31V11//fUBzA6o/ihWAACAU6Jly5YaNmyYnn32WW3atEmSlJmZqWnTpumiiy5yFjMAoLqYMWOG/vjjDz3wwAM6duyYsrKyNGLECNWrV0/33XdfoNMDqjWKFQAAwCfFxcXq1KmTOnXqpF27dik9PV2dOnXSxRdf7Lbt7NmzlZKSog4dOqhNmzZq2rSpmjdvrkWLFnkcpA4AAiElJUUxMTF68cUXJUktWrRQTEyMVqxY4bJd9+7d9emnn+qLL75QbGysGjZsqBo1amjlypWMVwH4yGIqM0QtAADACb7//nu3tpCQEHXo0MHj9na7XTt37lRiYqLq1KlT1ekBQIXk5+c7ZzA6UVRUlMcZjaTjMx5ZrVYKr4CfUKwAAAAAAABBhcdAAAAAAABAUKFYAQAAAAAAggrFCgAAAAAAEFQoVgAAAAAAgKBCsQIAAAAAAAQVihUAAAAAACCoUKwAAAAAAABBhWIFAAAAAAAIKhQrAAAAAABAUKFYAQAAAAAAggrFCgAAAAAAEFQoVgAAAKDKvfbaa5o2bVql9586daosFovz9f333/sxu6p32WWXOXM/++yzA50OzhCTJk3SnDlzAp0GUCkUKwAAOEPFxMS4/Pg7+RUXF6du3brp7bffDnSqqMaMMbr55pv14IMPqn///pWOM3XqVBljdO211/oxu1NnxYoVMsYoIiIi0KnAC6V9P+bn57tsZ7PZPG6Xm5tb4WMmJyerQ4cO/joFSVL//v11zz33aPTo0X6NC5wKFCsAADhD5ebmyhjjXM7KypIxRsYY5eTkaN68edq+fbtuvPFGTZkyJYCZ+o/D4dBFF12kc845R3v27Al0OmeEBx98UHPnztUXX3yh5OTkQKcDeKW078fIyEiX7ex2u4wxslqtkqS9e/fKGKOYmJgKHS89PV0bN25USkqK78mfoF27dlq6dKleeuklPfroo36NDVQ1ihUAAMCNzWbT1VdfrZdfflmS9Nhjj2nHjh0Bzso7LVu2LPNfNh0OhxwOh8sPkTNJedfHn3799Vc99dRTSk1NpVCBgDiV73dfvP/++5Kkf/zjH36P3aFDBw0aNEgPP/ywMjIy/B4fqCoUKwAAQKl69eql0NBQFRUVafny5YFOx2chISFas2aNdu3apfr16wc6ndPeM888o+LiYo0aNSrQqQBBbeHChTr33HOrrKg3evRoFRQUKC0trUriA1WBYgUAACiV1WpV7dq1JUn79+8PcDaoToqLi/Xhhx8qJiZG7du3D3Q6QNDauXOnvv/++yrpVVGic+fOioiIcPbgAKoDihUAAKBUxcXFys7OliQ1aNDAbb3D4dDs2bN14YUXKiYmRjVq1FCbNm306KOPKi8vz23bpUuXauDAgWrWrJkiIiJks9nUpUsXpaWlqaioyGMODodDr7zyii6++GLFxcUpOjpa5513ngYNGqQlS5Y49yuZLeL333+XJMXGxjoHuxsxYoTmz5/vMgDe/Pnz3Y5ljNFrr72m7t27q2bNmoqIiFDTpk31z3/+U7t373bZ1lO81157TW3btlVUVJSSkpI0evRo2e12r6+3p5hLly5V165dnQP+xcfHV+p6lnd9KnNPy7Jhwwbl5OSoefPmslgsbusr+34oUVRUpMcff1wtW7ZUZGSk6tSpo5tuuknbt293bjN+/PhSBz3s37+/s71Zs2YusX29t4cPH9aECROUlJSk8PBw1a9fX6NGjdKBAwc8bl/Ra+Frft5+pk7c3tf3RLdu3dwGody0aZNz/b333iuLxaLNmzc72+bOneuy/e233+71Z8Tb93swWLhwoSTvHwExxuj1119X165dlZCQoMjISLVs2VJ33323fv75Z4/7hIaGqnnz5tq7d6/LNQaCmgEAAGc0SUaSycrKclv3ySefGEkmJibG7Nu3z2VdYWGhufrqq40kM2rUKLNv3z5z+PBh88orr5jw8HBz8cUXm/z8fOf233zzjZFkBg0aZDIyMkxeXp7ZtGmTSU1NNZLMwIED3Y5/4jHGjRtntmzZYg4fPmyWLVtmmjRpYiSZ9957z2WfFi1aGEnm8OHDHs/3nnvuMZLM22+/7dJeVFRk+vXrZySZyZMnm3379pm8vDyzdOlS06BBAxMfH2/WrVtXaryuXbuaBx980Ozevdvs37/fPPDAA0aSufHGG0u/+KUoiTls2DAzdOhQk56ebnJycky/fv1M7dq1K309y7s+Fb2nZXn33XeNJNO3b1+P6yub/7XXXmskmS5dupgHHnjAbNu2zRw5csS88847JjY21tSpU8ds2bLFq3POy8szkkzTpk09Hqsy9/bIkSOmffv2xmKxmMcff9zs37/fZGVlmVdffdV0797dhIWFmcTERL9ci8rkV9HPlD/fE6+//rqRZP75z3+6tBcXF5sGDRo4P3snys/PNzabzfz4448ez72sz4gx5X8flKfk+9Hb1969eyt8jEsuucQ0bNjQ6+0nT55sJJlnnnnGZGVlmaNHj5rVq1ebtm3bmrJ+3vXs2dNIMkuWLKlwjkAgUKwAAOAM56lYkZOTYz766CPToEEDU6NGDTN//ny3/Z588kkjyZx//vlu6yZMmGAkmYcfftjZ9s0335jExERTWFjosq3D4TCNGzc2FovFZGdnezxGv3793I6xevVqvxYrpk+fbiSZlJQUt31KijbnnnuuW/4l8Xr27Ol2XvHx8cZqtbrtU56SmK1atTIOh8PZ/vXXX7sUKyp6PY0p+/pU9J6W5cUXXzSSzPXXX+9xfWXzLylWXHfddW7rSu5hr169XNp9LVZU5N5OnDix1ALD008/bSR5LFZU5lpUJr+Kfqb8+Z44dOiQqVGjhqlTp45LXl9++aWJj483FovF7V4sWLDAtGrVqtRzL+szYoz/ihWeirknslqtlSpW/Pnnn8ZqtboVcMrStGlTU6tWLbf2NWvWlFmsKCnGvv766xXKEQgUHgMBAACSpISEBGc36Vq1aunqq6/W3//+d6Wnp2vgwIFu25fMFHLrrbe6rUtNTZUkzZ4929nWpUsXvfHGG7r22mtVp04dWa1WWSwWhYSEaOvWrc4pUz0dY+jQoW7H6Nq1q18HyfzPf/4jSbrpppvc1vXt21d16tRRenq6vvrqK4/79+nTx2XZYrGoadOmKi4u1q5duyqV04ABA1weoejWrZtz7JDKXM/yVPSeliU/P1+SFBYW5nG9r/kPGDDArW3IkCGSpM8//1yZmZle5emNitzbefPmSZIGDRrkFueGG27wGN/Xa1GR/Cr6mfLneyI2NlbXXHON/vzzTy1btszZPnfuXA0fPlzdunVTRkaGvv32W5d1JffVk7I+I9XBokWLVFxcXKHxKho0aKCcnBxNnDjR+ZiedHxcClPGLEcln8WKPM4FBBLFCgAAIEnKysqSMUYFBQX66aef1L59e7300kt67rnn3LYtKCjQli1bJEnt2rVzW9+0aVOFhIRoz549zilPX331VfXp00c7duzQ+++/r8OHD8sc7+WpSy+9VJJc/tA+8RiNGjXymPOuXbvUv39/n85bkgoLC53PcZd2rJL2DRs2eFxft25dt7bIyEhJf/1wr6hzzjmn1HUVvZ7lqcw9LUvJuRcWFlZJ/g0bNnRrS0xMVEREhCQ5xyrwB2/vbUFBgXPMDE/3rm7dugoNDXVr9/VaVCS/inym/P2ekKTBgwdLOl6EKMnv/fff15AhQ9zW5eTkaOnSpbrxxhtLjVfWZ6Q6WLhwoRISEtStWzev95k1a5Zat26txx9/XImJierevbsefvhhbdy4scz9Sj6LUVFRPuUMnCoUKwAAgIuwsDC1a9dOixcvls1m01NPPaUvvvii1O09DZwXGRkph8MhSc5/2Z00aZKk4z0Yunfvrho1anidk6cBGv3pxB+CpR2rvB/+ISH+/7OqrB8VvlzP8nh7T8tSMhDoiYNansjX/K1Wq8f2kvvkzXumuLjYq2N5e2+9eY94ysvXa1GZ915FP1P+eE9Ix3uBxMfHa9GiRcrNzdWSJUvUuHFjtW7dWgMGDFB4eLjeeecdFRUV6d1331Xnzp1LLaxI1fuH96FDh7R8+XJdc801pb6fPTnvvPP0yy+/6Ouvv9a4ceOUk5OjKVOmKDk5Wf379y+1OFryWSz5bALBjmIFAADwqEGDBpo2bZok6Z///KfLv5CHh4erefPmkqQ1a9Y4/xXY06tr1646evSo9u3bJ0lq0aKF27EKCgrc2sLDw52zNGzbts3rvCtT2DjxWFu3bvW4Tcm/HCcnJ1c4vr9V5nqWKO36VPSelqckL0+PY/iSf4mTZ2cpOVbJvueee66zvaS3xclx9+7dW+5xKiIiIkJJSUmS5LGnQWZmpltPE39cC29V9DPl7/eEdHxWigEDBujo0aNauHChy2MetWrVUt++fbV//3599tln5T4C4o2qLnT6YsmSJSooKKjUlKUWi0XdunXT9OnT9euvv+rXX39Vx44d9f7772vWrFke9yl5n5342QCCGcUKAABQqtGjR6tVq1b67bff9Oyzz7qsGzlypCS5PHt+ouuuu06tWrWSw+FQjRo1lJCQIElav369y3aHDx8utftyyXPyJd3CT5SVlaWoqChNnz7dpT0mJkaSdOzYMUnHu1lbLJZyn2MvOdYbb7zhtu6TTz7Rn3/+qWbNmqlHjx5lxjkVKns9pbKvT0XuaXmSk5NVq1Ytpaenu/U48CX/EosWLXJrK3mf9OrVS4mJic72kgLCyVM2Ll26tNzjVFTJowxvvfWW2zpPOfvjWlRERT9T/nxPlCgpQLzwwgtatmyZy/geJdfv8ccf1/fff6/rr7/e67ieVPb74FRYuHCh4uLi1LNnzwrtZ7PZ3N67ycnJznvrqQhXXFyszZs3q27dus4CFBD0qmbcTgAAUF2onNHuP/vsMyPJxMbGmj179jjbS6b6jIyMNDNmzDA7duww+fn5Jj093YwaNcrUqFHDfPnll87tS2ZqOPfcc83//d//mSNHjpiNGzea3r17G4vFYiSZzZs3uxz7xGkTx48fbzIyMszRo0fNt99+ay688ELTrl07c+jQIZd9xowZYySZN9980xw6dMj069fPtGvXzrm+tNlATjzWpEmTTGZmpsnPzzefffaZadiwoTnrrLPcpk8sK54xxlx66aVGkvntt9/KuAPuyopZojLX05iyr09F72l5br75ZiPJ43WrbP4ls4E0b97cPPzww2bnzp3m6NGj5t133zWxsbEmISHBberS+fPnG0nmiiuuMBkZGebQoUPm/fffd97v8mYDqci9PXHq0ieeeMJkZ2eb7OxsM2fOHNO2bVsTGhrqNhtIZa9FZfKr6GfK3++JEiXTpJ48c0teXp6Ji4szksw//vGPUvf35jNiTPnfB+Up7/uxREVnAzl69KiJjo42gwYN8jqXEjVr1jTnnnuu+eKLL8zBgwfN0aNHzTfffGPatGljIiMjPU6x/L///c9IMmPGjKnw8YBAoVgBAMAZKjo62vmH+Imvr776ym3bkh83Ja/vvvvOGHN8isRXX33VXHzxxSY2NtZERkaaxo0bm9TUVPPLL7+4xZk/f77p3LmziYuLM1FRUaZjx45m9uzZ5pJLLnHGPnmKxOLiYvOf//zHdO3a1cTExJioqCjTokULM3HiRJOTk+N2jKysLHPDDTeY+Ph4Exsbay6//HKzceNG8/bbb3s8361bt7oc6+WXXzZdu3Y1sbGxJiwszDRu3NjccccdZufOnS7H+eqrr9xiWa1WY4wx//3vf93WJScnl3tPPMWUZO677z6P21fmepZ2fUpU9J6W5ZdffjFWq9WMHDnS5/ynTJnick1WrFhh7rvvPtOkSRMTHh5u4uPjzeDBg13u54leeOEF06pVKxMVFWXq1q1r7rrrLpOVleUSc/DgwaXeh4rc24MHD5rx48ebc845x4SFhZnExEQzZMgQs337dhMREeHc78orr6zUtfA1v4p+pvz5nijx0EMPGUnmjTfecFtXUuRauHCh27qKfkbKe7+XprTvx7y8PJftatas6XG78qZK/eCDD4zkPvWyNzZs2GDGjx9vOnToYGrXrm2ioqJM06ZNzS233GLWr1/vcZ+bbrrJhIeHeyx6AcHKYkwFhokGAAAAKuCBBx7Q008/rZ9//lmtWrUKdDpAUBg6dKgWLFigrKwsRUdHV+mxfvrpJ3Xq1EkPP/ywHnzwwSo9FuBPFCsAAABQZRwOh4YPH67PPvtMX3zxBQULnPGKiopUp04dXXLJJfrwww+r9Fg///yzevXqpeuuu67UgTeBYEWxAgAAAFXulVde0e7duzVlypRApwKcMR588EG1aNFCQ4cODXQqQIVRrAAAAAAAAEGFqUsBAAAAAEBQoVgBAAAAAACCCsUKAAAAAAAQVChWAAAAAACAoEKxAgAAAAAABBWKFQAAAAAAIKhQrAAAAAAAAEGFYgUAAAAAAAgqFCsAAAAAAEBQoVgBAAAAAACCCsUKAAAAAAAQVChWAAAAAACAoEKxAgAAAAAABBWKFQAAAAAAIKhQrAAAAAAAAEGFYgUAAAAAAAgq/w9VUm2UP5k3uAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1050x750 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from uclchem.plot import plot_rate_summary\n",
    "\n",
    "plot_rate_summary(production, destruction, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6c7e1d7",
   "metadata": {},
   "source": [
    "We can then also effectively convert from rates constants $k$, to the actual RHS\n",
    "elements that contribute to each reaction; We can then get fluxes rather than rate constants:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "977b24f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T16:44:47.634256Z",
     "iopub.status.busy": "2026-06-19T16:44:47.634071Z",
     "iopub.status.idle": "2026-06-19T16:44:56.067838Z",
     "shell.execute_reply": "2026-06-19T16:44:56.066910Z"
    }
   },
   "outputs": [],
   "source": [
    "from uclchem.analysis import rate_constants_to_dy_and_rates\n",
    "from uclchem.makerates.network import Network\n",
    "\n",
    "network = Network.from_csv()\n",
    "dy, flux = rate_constants_to_dy_and_rates(physics, abundances, rate_constants, network=network)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80cdf888",
   "metadata": {},
   "source": [
    "We can then inspect the RHS of the differential equation per reaction. This informs us that the only relevant term is actually the destruction of the molecule via its reaction with HCS and $\\text{H}_2\\text{S}$. Explaining the small decrease at 1 million years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "65e92d4c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T16:44:56.070170Z",
     "iopub.status.busy": "2026-06-19T16:44:56.069948Z",
     "iopub.status.idle": "2026-06-19T16:44:56.757415Z",
     "shell.execute_reply": "2026-06-19T16:44:56.756457Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<Axes: title={'center': 'Production'}>,\n",
       "       <Axes: title={'center': 'Destruction'}, xlabel='flux'>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1050x750 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_rate_summary(production, destruction, -10, \"flux\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd58b12c",
   "metadata": {},
   "source": [
    "# UPDATE to UMIST22\n",
    "As of 11-10-2024, we use UMIST22 for the notebook, so we need to repeat this analysis. Some dominant formation and destruction pathways are no longer present for in UMIST22. The rest of the notebook is thus no longer up to-date!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83ccedcc",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "We can then delve into specifically the low temperature, low density, low zeta case:\n",
    "\n",
    "```\n",
    "New Important Reactions At: 7.90e+05 years\n",
    "Formation = 1.92e-18 from:\n",
    "H2 + H2O+ -> H3O+ + H : 78.49%\n",
    "H2O + HCO+ -> CO + H3O+ : 17.68%\n",
    "H3+ + H2O -> H3O+ + H2 : 3.40%\n",
    "\n",
    "Destruction = -1.92e-18 from:\n",
    "H3O+ + E- -> OH + H + H : 65.43%\n",
    "H3O+ + E- -> H2O + H : 15.21%\n",
    "H3O+ + E- -> OH + H2 : 11.52%\n",
    "H3O+ + H2S -> H3S+ + H2O : 5.17%\n",
    "H3O+ + E- -> O + H2 + H : 1.20%\n",
    "H3O+ + HCN -> HCNH+ + H2O : 0.33%\n",
    "H3O+ + SIO -> SIOH+ + H2O : 0.33%\n",
    "```\n",
    "\n",
    "What this shows is the reactions that cause 99.9% of the formation and 99.9% of the destruction of $\\text{H}_3\\text{O}^+$ at a time step. The total rate of formation and destruction in units of $\\text{s}^{-1}$ is  given as well the percentage of the total that each reaction contributes. \n",
    "\n",
    "What we need to find is a pattern in these reactions which holds across time and across different densities and temperatures. It actually turns out that the reactions printed above are the dominate formation and destruction routes of $\\text{H}_3\\text{O}^+$ for all parameters for all times. For example, at high temperature, high density and high zeta, we get:\n",
    "\n",
    "```\n",
    "New Important Reactions At: 1.00e+04 years\n",
    "Formation = 2.03e-14 from:\n",
    "H2 + H2O+ -> H3O+ + H : 95.47%\n",
    "H3+ + H2O -> H3O+ + H2 : 3.14%\n",
    "H2O + HCO+ -> CO + H3O+ : 1.05%\n",
    "\n",
    "Destruction = -2.03e-14 from:\n",
    "H3O+ + E- -> OH + H + H : 69.82%\n",
    "H3O+ + E- -> H2O + H : 16.23%\n",
    "H3O+ + E- -> OH + H2 : 12.29%\n",
    "H3O+ + E- -> O + H2 + H : 1.28%\n",
    "```\n",
    "The sole difference being that the formation rate is much higher. Since equilibrium is reached, the destruction rate is also higher and so we need to use the fact we know the $\\text{H}_3\\text{O}^+$ abundance increases with CRIR to infer that the destruction rate is only faster because there is more $\\text{H}_3\\text{O}^+$ to destroy.\n",
    "\n",
    "However, there are some problems here! Why does $\\text{H}_2$ + $\\text{H}_2\\text{O}^+$ become so efficient at high CRIR? We know the rate of a two body reaction does not depend on CRIR ([see the chemistry docs](/docs/gas)) so it must be that $\\text{H}_2\\text{O}^+$ is increasing in abundance. We can run analysis on $\\text{H}_2\\text{O}^+$ to see what is driving that. In fact, as we report in the paper, following this thread we find that a chain of hydrogenations starting from $\\text{OH}^+$ is the overall route of $\\text{H}_3\\text{O}^+$ formation and that this chain starts with small ions that are primarily formed through cosmic ray reactions. *It will very often be the case that analyzing one species requires you to run analysis on another.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01d5db5e",
   "metadata": {},
   "source": [
    "#### 2.2 SO\n",
    "\n",
    "With a strong explanation for $\\text{H}_3\\text{O}^+$, we can now look at SO. We start by running analysis again and then by looking at the reactions as before."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a93471ad",
   "metadata": {},
   "source": [
    "Again, let's start by looking at the low temperature, high density, low zeta case:\n",
    "\n",
    "```\n",
    "New Important Reactions At: 1.31e+04 years\n",
    "Formation = 3.67e-22 from:\n",
    "#SO + THERM -> SO : 68.11%\n",
    "HSO+ + E- -> SO + H : 21.04%\n",
    "#SO + DEUVCR -> SO : 9.87%\n",
    "\n",
    "Destruction = -3.78e-22 from:\n",
    "SO + FREEZE -> #SO : 78.04%\n",
    "HCO+ + SO -> HSO+ + CO : 20.97%\n",
    "```\n",
    "\n",
    "An interesting point here is that equilbrium is reached at around $1.31 \\times 10^4$ yr in this model. Since `analysis()` only prints a time step when the most important reactions are different to the last one, this time step is the last output from the analysis. We can see that we broadly reach an equilibrium between thermal desorption and freeze out of SO, with some formation and destruction via ions.\n",
    "\n",
    "This doesn't hold up at lower densities or higher temperatures, looking at the high temperature, low density, low zeta case, we see a second pattern of reactions:\n",
    "\n",
    "```\n",
    "New Important Reactions At: 9.50e+05 years\n",
    "Formation = 1.05e-20 from:\n",
    "O2 + S -> SO + O : 98.10%\n",
    "O + NS -> SO + N : 1.48%\n",
    "\n",
    "Destruction = -1.02e-20 from:\n",
    "C + SO -> CS + O : 39.10%\n",
    "C + SO -> S + CO : 39.10%\n",
    "C+ + SO -> SO+ + C : 4.33%\n",
    "C+ + SO -> CS+ + O : 4.33%\n",
    "C+ + SO -> S+ + CO : 4.33%\n",
    "C+ + SO -> S + CO+ : 4.33%\n",
    "N + SO -> S + NO : 3.46%\n",
    "H+ + SO -> SO+ + H : 0.28%\n",
    "```\n",
    "However, it's not so complex. All the low zeta outputs show one of these two sets of reactions. Essentially, higher temperatures are opening up gas phase reaction routes which form and destroy SO. These new routes are not particularly quick with a total rate of change of $10^{-20} \\text{s}^{-1}$. Note that quite a lot of these are due to reactions with ions.\n",
    "\n",
    "Finally, we look at the high zeta cases and find that no matter the temperature or density, we see the same reactions:\n",
    "\n",
    "```\n",
    "New Important Reactions At: 1.81e+04 years\n",
    "Formation = 5.64e-17 from:\n",
    "O2 + S -> SO + O : 85.30%\n",
    "HSO+ + E- -> SO + H : 13.19%\n",
    "O + NS -> SO + N : 1.19%\n",
    "\n",
    "Destruction = -5.64e-17 from:\n",
    "H+ + SO -> SO+ + H : 13.16%\n",
    "C+ + SO -> SO+ + C : 10.52%\n",
    "C+ + SO -> CS+ + O : 10.52%\n",
    "C+ + SO -> S+ + CO : 10.52%\n",
    "C+ + SO -> S + CO+ : 10.52%\n",
    "H3+ + SO -> HSO+ + H2 : 9.86%\n",
    "C + SO -> CS + O : 8.90%\n",
    "C + SO -> S + CO : 8.90%\n",
    "SO + CRPHOT -> S + O : 4.11%\n",
    "SO + CRPHOT -> SO+ + E- : 4.11%\n",
    "HCO+ + SO -> HSO+ + CO : 3.20%\n",
    "HE+ + SO -> S+ + O + HE : 1.93%\n",
    "HE+ + SO -> S + O+ + HE : 1.93%\n",
    "N + SO -> S + NO : 0.87%\n",
    "```\n",
    "\n",
    "Ions take over the destruction of SO when the CRIR is high and these reactions are proceeding about 1000 times faster than the low zeta rates. This makes sense as increasing the CRIR will greatly increase the abundances of these simple ionic species.\n",
    "\n",
    "All in all, we can conclude that a range of gas phase reactions control the abundance of SO at low zeta but once the CRIR is high, destruction via ions dominates the SO chemistry leading to the decreasing abundance that we see."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25b98abe",
   "metadata": {},
   "source": [
    "## Summary Notes\n",
    "\n",
    "In this notebook, we've run some representative models to look at why the SO and $\\text{H}_3\\text{O}^+$ abundances seem to so heavily depend on the CRIR. We find that a chain of very efficient reactions starting with simple ions comprise the primary formation route of $\\text{H}_3\\text{O}^+$ so that the total production of this species depends entirely on the abundance of those ions. With high CRIR, those ions become very abundant and we get a lot of $\\text{H}_3\\text{O}^+$ formation.\n",
    "\n",
    "SO is complex at first glance since the formation routes vary by parameter. However, looking at the destruction routes we can see that it's simply that SO can be easily destroyed by simple ions and, again, these are very abundant in gas exposed to a high CRIR.\n",
    "\n",
    "An interesting note about SO is that we could potentially say that destruction via $\\text{C}^+$ is the dominant destruction route. However, we would caution users to be careful with that kind of statement. Reactions with $\\text{H}^+$, $\\text{HE}^+$ and other also destroy SO. It could be that if we turned off the $\\text{C}^+$ reactions, the SO chemistry would be unchanged as other ions take up the slack. Therefore, unless a single reaction is clearly dominant (as in $\\text{H}_3\\text{O}^+$ formation), it is best to test the importance of a reaction by removing it before concluding it is the single most important reaction.\n",
    "\n",
    "## Considerations\n",
    "\n",
    "`uclchem.analysis.analysis()` looks at a snapshot of the gas and calculates the instantaneous rate of change of important reactions. However, over the course of a time step, abundances change and reactions rise and fall in importance. More importantly, complex interplay between reactions can contribute to an outcome. This is ultimately a simple, first order look at what is happening in the network but in many cases, a deeper view will be required.\n",
    "\n",
    "If you struggle to find an explanation that fits all time steps in your outputs and is true across a range of parameters, then it is best not to report simple conclusions about the chemistry and to look for other ways to understand the network."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4064940",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "jupytext": {
   "formats": "ipynb,py:light"
  },
  "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": "3.13.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}