{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "subject-wagon",
   "metadata": {},
   "source": [
    "# EasyVVUQ - Vector Quantities of Interest"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "protective-intervention",
   "metadata": {},
   "source": [
    "**Author**: Vytautas Jancauskas, LRZ (jancauskas@lrz.de)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "elect-tract",
   "metadata": {},
   "source": [
    "It is often the case that simulation outputs are vector valued and represent changes over time in whatever phenomenon that is simulated. Here we examine how to analyse these cases with EasyVVUQ. As an example we use the following model from epidemiology - [SIR](https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology#The_SIR_model). It compartmentalises the population into the following groups: ($S$)usceptible, ($I$)nfected and ($R$)emoved. There are four input parameters to out model. They are initial number of susceptible people $S_0$, initial number of infected people $I_0$, transmission rate $\\beta$ and recovery rate $\\gamma$. The system is governed by three differential equations below. The number of suspectible people is reduced by them getting infected (and thus moving to $I$) at rate specified by $\\beta$. The number of infected people increases similarly. Finally the number of recovered people increases depending on the recovery rate and the number of people currently infected."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "neither-sierra",
   "metadata": {},
   "source": [
    "$${\\frac  {dS}{dt}}=-{\\frac  {\\beta SI}{N}}$$\n",
    "\n",
    "$${\\displaystyle {\\frac {dI}{dt}}={\\frac {\\beta SI}{N}}-\\gamma I}$$\n",
    "\n",
    "$${\\displaystyle {\\frac {dR}{dt}}=\\gamma I}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "scientific-shareware",
   "metadata": {},
   "source": [
    "The simulation is run as shown below, where ```input_file``` is a JSON file of the following format: ```{\"outfile\": \"$outfile\", \"S0\": $S0, \"I0\": $I0, \"beta\": $beta, \"gamma\": $gamma, \"iterations\": $iterations}```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "german-upper",
   "metadata": {},
   "source": [
    "```sir <input_file>```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "religious-problem",
   "metadata": {},
   "source": [
    "Lets try and run it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "pressed-recipe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-09T08:57:25.930406Z",
     "start_time": "2021-06-09T08:57:25.534388Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-20T11:59:23.727600Z",
     "iopub.status.busy": "2025-07-20T11:59:23.727458Z",
     "iopub.status.idle": "2025-07-20T11:59:24.513104Z",
     "shell.execute_reply": "2025-07-20T11:59:24.512650Z",
     "shell.execute_reply.started": "2025-07-20T11:59:23.727585Z"
    }
   },
   "outputs": [],
   "source": [
    "!echo '{\"outfile\": \"output.csv\", \"S0\": 997, \"I0\": 3, \"beta\": 0.2, \"gamma\": 0.04, \"iterations\": 100}' > input.json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bearing-force",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-09T08:57:27.202480Z",
     "start_time": "2021-06-09T08:57:25.931781Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-20T11:59:24.513726Z",
     "iopub.status.busy": "2025-07-20T11:59:24.513627Z",
     "iopub.status.idle": "2025-07-20T11:59:28.277904Z",
     "shell.execute_reply": "2025-07-20T11:59:28.277449Z",
     "shell.execute_reply.started": "2025-07-20T11:59:24.513713Z"
    }
   },
   "outputs": [],
   "source": [
    "!./sir input.json"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "organizational-framework",
   "metadata": {},
   "source": [
    "The simulation code outputs a CSV file with a specified name. We can try opening it up and printing the first 10 lines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "according-timer",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-09T08:57:27.563489Z",
     "start_time": "2021-06-09T08:57:27.204218Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-20T11:59:28.278655Z",
     "iopub.status.busy": "2025-07-20T11:59:28.278538Z",
     "iopub.status.idle": "2025-07-20T11:59:29.055636Z",
     "shell.execute_reply": "2025-07-20T11:59:29.055169Z",
     "shell.execute_reply.started": "2025-07-20T11:59:28.278641Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "S,I,R,r0,t\n",
      "996.4018,3.47784108,0.1391136432,4982.009,1\n",
      "995.7087475755611,4.0312978050411745,0.300365555401647,4978.543737877805,2\n",
      "994.905980318425,4.672165937353743,0.4872521928957967,4974.5299015921255,3\n",
      "993.9763679472093,5.4140230672294996,0.7038131155849767,4969.8818397360465,4\n",
      "992.9001871310173,6.27247777694467,0.9547122266627635,4964.500935655087,5\n",
      "991.654756898651,7.2654467105291545,1.2453300950839297,4958.273784493255,6\n",
      "990.2140324288114,8.413460157063296,1.5818685013664615,4951.070162144058,7\n",
      "988.548155936764,9.739995672478827,1.9714683282656145,4942.740779683821,8\n",
      "986.6229648025937,11.27183768247915,2.4223418355647803,4933.11482401297,9\n"
     ]
    }
   ],
   "source": [
    "!head output.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "japanese-canvas",
   "metadata": {},
   "source": [
    "Finally lets plot the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "moral-booking",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-09T08:57:28.350065Z",
     "start_time": "2021-06-09T08:57:27.565838Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-20T11:59:29.056254Z",
     "iopub.status.busy": "2025-07-20T11:59:29.056150Z",
     "iopub.status.idle": "2025-07-20T11:59:29.412237Z",
     "shell.execute_reply": "2025-07-20T11:59:29.411974Z",
     "shell.execute_reply.started": "2025-07-20T11:59:29.056241Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "df = pd.read_csv('output.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "introductory-cambodia",
   "metadata": {},
   "source": [
    "As we can see it shows the number of infected people increasing at first and then decreasing as the number of people who have recovered rises. Number of susceptible and recovered people also changes in a way that makes intuitive sense."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "sudden-boring",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-09T08:57:28.435209Z",
     "start_time": "2021-06-09T08:57:28.350940Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-20T11:59:29.412660Z",
     "iopub.status.busy": "2025-07-20T11:59:29.412584Z",
     "iopub.status.idle": "2025-07-20T11:59:29.456218Z",
     "shell.execute_reply": "2025-07-20T11:59:29.456014Z",
     "shell.execute_reply.started": "2025-07-20T11:59:29.412651Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11b610800>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df['S'], label='S')\n",
    "plt.plot(df['I'], label='I')\n",
    "plt.plot(df['R'], label='R')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "collectible-bread",
   "metadata": {},
   "source": [
    "So let us see what EasyVVUQ can tell us about the distribution of the number of infected people over time. We will not go into the details of the EasyVVUQ API this time. Only where it differs from the [basic concepts](./basic_tutorial.ipynb) tutorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "flexible-password",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-09T08:57:30.588513Z",
     "start_time": "2021-06-09T08:57:28.437128Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-20T11:59:29.457390Z",
     "iopub.status.busy": "2025-07-20T11:59:29.457313Z",
     "iopub.status.idle": "2025-07-20T11:59:30.515289Z",
     "shell.execute_reply": "2025-07-20T11:59:30.515034Z",
     "shell.execute_reply.started": "2025-07-20T11:59:29.457384Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Volumes/UserData/dpc/GIT/EasyVVUQ/env_3.12/lib/python3.12/site-packages/chaospy/__init__.py:9: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n",
      "  import pkg_resources\n"
     ]
    }
   ],
   "source": [
    "import easyvvuq as uq\n",
    "import chaospy as cp\n",
    "import os\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "victorian-dallas",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-09T08:57:30.592929Z",
     "start_time": "2021-06-09T08:57:30.590057Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-20T11:59:30.515748Z",
     "iopub.status.busy": "2025-07-20T11:59:30.515564Z",
     "iopub.status.idle": "2025-07-20T11:59:30.517502Z",
     "shell.execute_reply": "2025-07-20T11:59:30.517276Z",
     "shell.execute_reply.started": "2025-07-20T11:59:30.515740Z"
    }
   },
   "outputs": [],
   "source": [
    "params = {\n",
    "    \"S0\": {\"type\": \"float\", \"default\": 997}, \n",
    "    \"I0\": {\"type\": \"float\", \"default\": 3}, \n",
    "    \"beta\": {\"type\": \"float\", \"default\": 0.2}, \n",
    "    \"gamma\": {\"type\": \"float\", \"default\": 0.04, \"min\": 0.0, \"max\": 1.0},\n",
    "    \"iterations\": {\"type\": \"integer\", \"default\": 100},\n",
    "    \"outfile\": {\"type\": \"string\", \"default\": \"output.csv\"}\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "meaning-berry",
   "metadata": {},
   "source": [
    "Encoder is the same as in the basic concepts tutorial, essentially. However, since the simulation outputs a CSV file with the evolution of output variables over time we want the ```SimpleCSV``` decoder. The arguments to it should be self explanatory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "owned-preliminary",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-09T08:57:30.598048Z",
     "start_time": "2021-06-09T08:57:30.595407Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-20T11:59:30.517934Z",
     "iopub.status.busy": "2025-07-20T11:59:30.517834Z",
     "iopub.status.idle": "2025-07-20T11:59:30.519352Z",
     "shell.execute_reply": "2025-07-20T11:59:30.519149Z",
     "shell.execute_reply.started": "2025-07-20T11:59:30.517926Z"
    }
   },
   "outputs": [],
   "source": [
    "encoder = uq.encoders.GenericEncoder(template_fname='sir.template', delimiter='$', target_filename='input.json')\n",
    "decoder = uq.decoders.SimpleCSV(target_filename='output.csv', output_columns=['I'])\n",
    "actions = uq.actions.local_execute(encoder, os.path.abspath('sir') + ' input.json', decoder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "neutral-marina",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-09T08:57:30.630175Z",
     "start_time": "2021-06-09T08:57:30.599889Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-20T11:59:30.519632Z",
     "iopub.status.busy": "2025-07-20T11:59:30.519578Z",
     "iopub.status.idle": "2025-07-20T11:59:30.538645Z",
     "shell.execute_reply": "2025-07-20T11:59:30.538413Z",
     "shell.execute_reply.started": "2025-07-20T11:59:30.519626Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Volumes/UserData/dpc/GIT/EasyVVUQ/env_3.12/lib/python3.12/site-packages/cerberus/validator.py:618: UserWarning: These types are defined both with a method and in the'types_mapping' property of this validator: {'integer'}\n",
      "  warn(\n",
      "/Volumes/UserData/dpc/GIT/EasyVVUQ/env_3.12/lib/python3.12/site-packages/cerberus/validator.py:618: UserWarning: These types are defined both with a method and in the'types_mapping' property of this validator: {'integer'}\n",
      "  warn(\n"
     ]
    }
   ],
   "source": [
    "campaign = uq.Campaign(name='sir', params=params, actions=actions)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "searching-interpretation",
   "metadata": {},
   "source": [
    "We assume that the infection rate $\\beta$ is uniformly distributed between 0.15 and 0.25 and the recovery rate $\\gamma$ is normally distributed with mean 0.04 and small variance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "native-order",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-09T08:57:30.633520Z",
     "start_time": "2021-06-09T08:57:30.630961Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-20T11:59:30.539153Z",
     "iopub.status.busy": "2025-07-20T11:59:30.539038Z",
     "iopub.status.idle": "2025-07-20T11:59:30.541085Z",
     "shell.execute_reply": "2025-07-20T11:59:30.540847Z",
     "shell.execute_reply.started": "2025-07-20T11:59:30.539142Z"
    }
   },
   "outputs": [],
   "source": [
    "vary = {\n",
    "    \"beta\": cp.Uniform(0.15, 0.25),\n",
    "    \"gamma\": cp.Normal(0.04, 0.01),\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "engaging-spoke",
   "metadata": {},
   "source": [
    "For this tutorial we will use Polynomial Chaos Expansion method. However, both [SCSampler](https://easyvvuq.readthedocs.io/en/dev/easyvvuq.sampling.html#module-easyvvuq.sampling.stochastic_collocation) and [QMCSampler](https://easyvvuq.readthedocs.io/en/dev/easyvvuq.sampling.html#module-easyvvuq.sampling.qmc) would work as well and might be preferable depending on the case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cosmetic-stake",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-09T08:57:30.761510Z",
     "start_time": "2021-06-09T08:57:30.634548Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-20T11:59:30.541475Z",
     "iopub.status.busy": "2025-07-20T11:59:30.541408Z",
     "iopub.status.idle": "2025-07-20T11:59:30.613030Z",
     "shell.execute_reply": "2025-07-20T11:59:30.612721Z",
     "shell.execute_reply.started": "2025-07-20T11:59:30.541468Z"
    }
   },
   "outputs": [],
   "source": [
    "campaign.set_sampler(uq.sampling.PCESampler(vary=vary, polynomial_order=5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "governing-warrant",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-09T08:57:30.785220Z",
     "start_time": "2021-06-09T08:57:30.762311Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-20T11:59:30.613424Z",
     "iopub.status.busy": "2025-07-20T11:59:30.613351Z",
     "iopub.status.idle": "2025-07-20T11:59:30.645175Z",
     "shell.execute_reply": "2025-07-20T11:59:30.644463Z",
     "shell.execute_reply.started": "2025-07-20T11:59:30.613417Z"
    }
   },
   "outputs": [],
   "source": [
    "execution = campaign.execute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "junior-proxy",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-09T08:57:31.042069Z",
     "start_time": "2021-06-09T08:57:31.036748Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-20T11:59:30.666602Z",
     "iopub.status.busy": "2025-07-20T11:59:30.655961Z",
     "iopub.status.idle": "2025-07-20T11:59:30.752057Z",
     "shell.execute_reply": "2025-07-20T11:59:30.739721Z",
     "shell.execute_reply.started": "2025-07-20T11:59:30.666526Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ready': 18, 'active': 18, 'finished': 0, 'failed': 0}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "execution.progress()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "regulated-semester",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-09T08:57:35.750226Z",
     "start_time": "2021-06-09T08:57:31.043481Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-20T11:59:30.762129Z",
     "iopub.status.busy": "2025-07-20T11:59:30.761157Z",
     "iopub.status.idle": "2025-07-20T11:59:32.415683Z",
     "shell.execute_reply": "2025-07-20T11:59:32.415395Z",
     "shell.execute_reply.started": "2025-07-20T11:59:30.762110Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 36/36 [00:01<00:00, 34.87it/s]\n",
      "/Volumes/UserData/dpc/GIT/EasyVVUQ/env_3.12/lib/python3.12/site-packages/chaospy/distributions/kernel/baseclass.py:84: RuntimeWarning: divide by zero encountered in matmul\n",
      "  self._pcovariance = numpy.matmul(numpy.matmul(\n",
      "/Volumes/UserData/dpc/GIT/EasyVVUQ/env_3.12/lib/python3.12/site-packages/chaospy/distributions/kernel/baseclass.py:84: RuntimeWarning: overflow encountered in matmul\n",
      "  self._pcovariance = numpy.matmul(numpy.matmul(\n",
      "/Volumes/UserData/dpc/GIT/EasyVVUQ/env_3.12/lib/python3.12/site-packages/chaospy/distributions/kernel/baseclass.py:84: RuntimeWarning: invalid value encountered in matmul\n",
      "  self._pcovariance = numpy.matmul(numpy.matmul(\n"
     ]
    }
   ],
   "source": [
    "execution.collate(progress_bar=True)\n",
    "result = campaign.analyse(qoi_cols=['I'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "descending-resident",
   "metadata": {},
   "source": [
    "We can now see the results of the analysis. One thing to try would be to plot the first order sobol indices over time. This shows us how much influence the two parameters (```beta``` and ```gamma```) have over the number of people infected over time ```t```. The ```higher orders``` line is meant to represent the influence of the interactions between the input variables. However it is negligible in this case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "pressing-sister",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-09T08:57:35.908776Z",
     "start_time": "2021-06-09T08:57:35.765529Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-20T11:59:32.416386Z",
     "iopub.status.busy": "2025-07-20T11:59:32.416184Z",
     "iopub.status.idle": "2025-07-20T11:59:32.455754Z",
     "shell.execute_reply": "2025-07-20T11:59:32.455522Z",
     "shell.execute_reply.started": "2025-07-20T11:59:32.416375Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='t', ylabel='First Order Sobol Index'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjcAAAGwCAYAAABVdURTAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAf9JJREFUeJzt3Qd4VNXWBuAvvfeQRhJC74TepElVRAQVUCyAiopiw+61XHvH/uu1ISqIFStVeu+9l0BCSIf0nsz/rH0yIQkBEjJ9vtd77jmZujmZzKzZe+21HXQ6nQ5ERERENsLR3A0gIiIiMiQGN0RERGRTGNwQERGRTWFwQ0RERDaFwQ0RERHZFAY3REREZFMY3BAREZFNcYadKS8vx+nTp+Hj4wMHBwdzN4eIiIjqQMry5eTkICIiAo6OF++bsbvgRgKbqKgoczeDiIiILkNCQgIiIyMvehu7C26kx0Z/cnx9fQ362CUlJViyZAmGDx8OFxcXgz42VcdzbTo816bDc206PNfWd66zs7NV54T+c/xi7C640Q9FSWBjjODG09NTPS7/WIyL59p0eK5Nh+fadHiurfdc1yWlhAnFREREZFMY3BAREZFNYXBDRERENsXucm6IiMh0ysrKVM6FpZE2OTs7o7CwULWRLONcu7q6XnKad10wuCEiIqPUJElOTkZmZiYstX1hYWFq5ixrnlnOuZbApmnTpirIaQgGN0REZHD6wCYkJETNlLG0AEIKuubm5sLb29sgPQXU8HOtL7KblJSE6OjoBr1mGNwQEZFBydCDPrAJCgqCJZIP0uLiYri7uzO4saBz3ahRIxXglJaWNmjaOH+jRERkUPocG+mxIaoP/XBUQ/OgGNwQEZFRWNpQFNnPa4bBDREREdkUBjdERERkUxjcEBERVRg0aBAefvhhczeDGojBjYEUlpQh4Ww+MouAtJwinMkrRlZBCfKLS1FWrjN384iIyAS++eYb+Pv7m7sZdo9TwQ3kQFI2xv7fenVKX9i+6rzrnRwd4ObsqDZ3Fyd4uDrBUzYXZ3i6OcHbzRk+7i7wdXdWx36eLvDzcIG/pysCPF3g7+GKQG9XeLk6MUmPiIjoIhjcGIj0zXi4OKK4tAw6OKBmZ4303uQXl6kNuPxS5BIcBXu7IcjbVe1DfCo2X3e1D/fzQLi/O4K8XBkEEZFFVaktKDHPMgceLvX7Uig1VqZPn47vvvtO1VqZNm0aXnrpJfUYRUVF+M9//oMffvhB1fLp0KED3nzzTTWctXLlSkyZMkU9hv75XnjhBfz3v/9Vj/XBBx/g0KFD8PLywuDBg/H++++rWkBkeAxuDKRrdAB2Pz8UCxYswMiRI+Hk5IzSch1KpXhRaTmKZCuRfZn6A5cgp6Ai2MkrLkVuYSlyi0qRU1iCnMJSZBeW4GxeCTILSpCVX4yz+SXqfvI4iZkFarsYV2dHhPu5qy0ywBNRAZ6IDPBAVKAnogM9VSDk6Mjgh4hMQ96/2j2/2CzPvf+lEfB0rfvH3ezZs3HnnXdi8+bN2Lp1K+6++25VMXfq1Kkq6Nm/fz/mzZuHiIgIzJ8/H1dddRX27NmDvn37qoDl+eefV0GMkKq8+to/L7/8Mlq3bo3U1FTMmDEDkydPVp8ZZHgMboxEAgdX2eAIz4YtkVFJ8ncycouRkVeMjNwilduTmlOElOxCtU/NLkRSViHScotUQHUyI19twJnzHsvdxRExQV5oEuSp9s0ayeaNZsFeCGSvDxHZsaioKLz33nvqfVCCEQlc5OcRI0Zg1qxZiI+PV4GNeOyxx7Bo0SJ1+WuvvQY/Pz91P1lLqao77rij8rhZs2b48MMP0aNHj8plCciwGNxYEfnm4RnorHpfLkYCm5SKQCcxMx+nzhTg1NkClfAs2+nMQhSWlONgco7aapJcnxYh3mgpW6iP2rcK9UGorxuDHiK67KEh6UEx13PXR+/evau91/Xp0wfvvvuuCnKkcm6rVq2q3V6Gqi61zMS2bdvU8NSuXbtw9uxZtSSBkECpXbt29WofXRqDGxskQ1ISAGlBUOB515eUlSPxbAFOZOSpnp249DwcT8/DsdRcnM4qULO8tp08q7aaQU+bMB+0DfdFa/0+1EclRxMRXYwEC/UZGrJE0svi5OSkAhXZV3Wx3pe8vDzV6yPbnDlz1PpJEtTIz7LmEhmedb/S6LK4ODkiJthLbTVJHpAEO0fTcnEkJQdHUnJxODVHBUES9GyKO6M2PUnbad7IG+0ifNEu3BcdG/uhfWM/FQgREVmjTZs2Vft548aNaNmyJbp06aJ6biRnpn///hdcG6nmukgHDx5ERkYG3njjDTXkJSSXh4yHwQ1VI70wKlCJ8D2vjs+xtFwcTJKhrGw1nCXT39Nzi3EkNVdtf+w8XXl7yeXp0NhPBTudIrW9THUnIrJ00qsiCb/33HMPtm/fjo8++kgNS8lw1C233ILbb79d/SzBTlpaGpYtW4ZOnTrhmmuuQUxMjOrhkctiY2PV4qGSjCxBjzzOvffei71796rkYjIeBjdUJ1Kbp32En9qqkiTmfUnZ2H86G/tOZ2FPYhYSzhRUJjP/sztJ3U6GryVZOTbSH7FR/ugc5a+GtWQIjciqlJUAZcWA6/k9n2QbJHgpKChAz5491fDTQw89pGZMCUkcfuWVV/Doo48iMTERwcHBKkdn1KhR6nqZMSUBzIQJE1RvjX4quBT3e+aZZ1QicdeuXfHOO+9g9OjRZv6X2i4HnRQfsCPZ2dkqmz0rKwu+vtV7JxpKpvrpp4JLbQR7lZlfjL2J2SrQ2ZOYiV0JWbVOXZfApn2Erwp0ukQHoEuUv5quXpekZZ5r07Hrcy1vj8eWAfGbgPRDQNohIOMYoCsDutwKDH4O8DZcnRJbOdeFhYWIi4tD06ZN4e7uDkskCb3yeSCfA46O/JJlKef6Yq+d+nx+s+eGDE6qKvdrGaw2vfTcIuw+lYmdCVnYlZCJXacykZlfgh3xmWqbte6Eul0jHzcV5Eiw0zXaH50i/ZmwTOYLbJa9BKydWfv1278F9s4HBjwK9JoGuFjmhziRPWJwQyYh1ZQHtwlVm5AOwxMZ+diZcFYFNzsTMtXQltTuWbI/RW36ZSvahvuoIon6LSrQw8z/GrILK984F9h0HA+ExwKN2gCNWgPZicCip4HT24F//wtsnQVc8y7Qcpi5W01EDG7IXGToqWmwl9rGdomsTFrem5iF7fFawCP7lOwiNcQl27cbTqrbBXu7IjbSDx75DgiKO4OuMUFWP8WULMzqt4FVb2jHI14D+txf/Xr/KOCuZcCen7TgJvMk8MNNwMSfgBZDzNJkIjqHnwhkUUnL3WMC1abv3ZFChBLkbD+pBTuStCwztJYdTJN+Hfz99VbVuyP1d7pE+6NLVIDaS9DEgoN0Wda+Dyx/RTse+uL5gY2e5A7E3gS0vRb4Yzqw7zfgp9uBKQuB8E4mbTIRVcfghiyWBCcR/h5qG9UporJ3Z9/pbGyNS8fCzQeRXOqB5OwidZls32+MV7eTOjuSqKy2aH90jvRHgJeB1sEg27X5C+DfF7TjK58F+j186fvIrKmxnwF5acCJNcCcccBd/2q9O0RkFgxuyOp6d7o1CUCnCG+EZe3HyJEDkZ5fqnp2dsSfVbk7MktLCg6uOpymtqq1dyTYkenoEvBI0UF5PCIlJwVY8px2POAJYODjdb+vsxsw4Xtg1tVA6n5gzo3AHYsAjwCjNZeILozBDVm9cD8PXNNJtvDK5SWk2OCOhLPYWZGsLMtL6Gvv6IsNOqtkZV/ERvlpAU+Uv1o8VIa5yA6tfQ8oLQAadwOufKb+9/fwB275GfhyGJB2EJh3C3DbfC3wISKTYnBDNrm8REepihzph9v7aJdl5Zdgp0xFj8+smJKeqVZX12rxZOF7aMNZ3m7OqpqyvtCgbGF+nOJr87JOAVu/0o4HP6tVnbwcfpFagCM9OCfXacnGV71u0KYS0aUxuCG74OfpgoGtGqlNn6wshQUlyNl9KkvtZaZWblEpNhzPUJtemK+7CnJkOKxrkwB0aOwLN2cOZ9mU1e9oVYebXAE0u7JhjxXWAbjhS2DueGDjp0D7sUBUT0O1lIjqgMEN2W2ycmSAp9r0ycpl5TocTc1VtXck2JHp6IdTcpCcXYhF+5LVJlydHFWA0yMmEL2aabO7fLlulvU6Ewfs+K7hvTZVtRoBxE4Eds0F/rgfuGcNi/wRmRCDG6IKkmvTOsxHbRN6RKvL8otLseeU1N7RpqJvP3lWDWdpP2fif6uPq5XRZaHR3k2DVFXm3s2CmKhsTVa9BZSXAs2HAE36Gu5xR7wKHP0XSD8MrHoTGFoxC4uIjI4LahBdhBQH7NUsCNMGNccXt3fH1meHYuVjg/DuuFhM6B6FmCBPlOugigx+uTYOk2dtQacXl+C2rzbhyzXH1UrqZMHSDgO752nHg/9j2Mf2DARGVVQ4XvcBcHqnYR+fjCInJ0et/O3l5YXw8HC89957GDRoEB5+WCsL8N1336F79+7w8fFBWFgYJk6ciNTU1Mr7r1y5UvUML168WK0a7uHhgcGDB6vbLFy4EG3btlXrIsn98vPzK+8nz/HAAw+o5wkICEBoaCi++OIL5OXlYcqUKer5WrRooR5Dr6ysDHfeeadah0mep3Xr1vjggw9MfMYsE3tuiOpB3rRigr3UdkM3rbJyclYhNsVlYP3RDKw+kqYKD645kq62V/45gFah3riqQziu7hCmig2yuKAFWfk6oCsHWl+jzZIyNCnw124MsP93rdDf3SsAJxf7Xaur5NyHuUm5eNZ5uHHGjBlYt24d/vzzTxVgPP/889i+fTs6d+5cubjpyy+/rAIJCVjk9pMnT1YLnlYlK4F//PHH8PT0xPjx49Xm5uaGuXPnIjc3F2PHjsVHH32EJ598svI+s2fPxhNPPIHNmzfjxx9/xLRp0zB//nx1W1lRXAKt2267DfHx8epxZUHKyMhI/PzzzwgKCsL69evV6uXh4eHq+ewZgxuiBpLZVNd1bqw2SVSWvB19jZ0NxzJwOCUXh1OO4MNlR1Tl5Ou7NMaN3SPVFHYyo5T9WlVhcTlTv+tq5NtA3GogZY9W/bg+9XNsiQQ2r2n5bSb3zGmt2GIdem0kwJAAZMgQbRmNWbNmISLiXLvvuOOOyuNmzZrhww8/RI8ePVTA4u3tXXndK6+8giuuuEIdS+/K008/jWPHjqn7iBtvvBErVqyoFtzExsbi2WefVcdy+zfeeAPBwcGYOnWqukwCrU8//RS7d+9G79691crxL774YuX9pQdnw4YN+Omnn+w+uOGwFJEBSa9My1Af3NW/Gb67sxe2PTtMDWENbRsKV2dHxKXn4d2lh3HFG8sxZdZmLNqbrOrykBnIqt763hWZ4WQs3iHA1W9qx6vfAs4cN95zUYMcP35c9cz07Hludpufn5/qpdHbtm0brr32WkRHR6uhooEDB6rLpTelqk6dzi3BIT1A0tOiD2z0l1Udzqp5HycnJ9Ub07Fjx2r3EVXv98knn6Bbt25o1KiRCq4+//zz89pij9hzQ2TkKegyfCWbTDNfvDcZP25NwOa4M1hxKE1tIT5uuKt/U0zs1UTV2SETKCsF9v6qHXe5zfjP13EcsHMucHwFsPQFYELF7Cx7IkND0oNiruc2AMl/GTFihNrmzJmjAgoJJOTn4uLi6k/p4lLtS0/Vn/WXybDShe5T2/30Q9r6+82bNw+PPfYY3n33XfTp00cFW2+//TY2bdoEe8d3UiITkcBFH+gcT8vFT1tP4Zdtp5CaU4TXFhzEJyuOYVKfJph8RVMEch0s44pbBeSlAp5BQPPBxn8++VCS1cU/uwI48CdwYi0Q0w92Rc5BHYaGzEl6ViSY2LJli+qZEVlZWTh8+DAGDBiAgwcPIiMjQw0XRUVpa4dt3brVbO2V3KC+ffvivvvuq7xMhr6Iw1JEZiHLPDx1dRusf2ow3rqhE5oFe6n1sD5cfhR931iGtxcfRF5Rqbmbabv2/KLtpcCeqRJ8Q9sB3SZrx4ufka/fpnleqjPp+Zg0aRIef/xxlQ+zb98+lS/j6Oioek0k4HF1dVWJwDKEJUnHklxsLi1btlTBlczMkgDsueeeU4EZMbghMivJwxnfIwpLZwzEp7d0VUs/FJaUq16cK99ZqXp2ymWuORlOSQFw4K9zw0WmdOV/ADdfIGkXsOsH0z431cnMmTPVEM+oUaMwdOhQlRQs07fd3d3VMNQ333yjZie1a9dO9eC88847ZmvrPffcg+uvvx4TJkxAr169VK9S1V4ce+agk+kddiQ7O1sliElXo9QaMCRJRJPpgCNHjjxv7JQMy1bPtfw5LtmfgtcWHFCLfIrYSD+8MLo9ukabZ4VpmzvXe38DfpkC+EcDD+02TEXi+pCaN0ufB7zDgAe2AW7eNneuCwsLERcXp2bvSFBgiSRvRT4P5HNAemYulmfTuHFjldcivThkvHN9qddOfT6/2XNDZEGk63tE+zAseWSAGraSPJ1dp7Jww6fr8fqCAygqLTN3E21nSKrDjaYPbESve4GAGCA3GVj3vumfny5qx44d+OGHH1TuitS3kYJ+4rrrrjN306geGNwQWSBZmPPegc2x/LGBuKFrpKp/Jks9jPlkPQ4l55i7edYr/wxwZIl23MlMdUCc3YBhFXka6z8CMhPM0w66IBlqkpozMiwlPTdr1qxR9WbIepg9uJE5+jExMar7ScYMpTLjxbz//vuq5oCUmpZs9UceeUR1YxHZohAfd7w7PlYt/SAzqA4kZePaj9fiq7VxzMW5HPv/AMpLgNAOQEhb87VDaus06QeUFgLLzZeQSueTJROklo0U5Ttz5gyWLl1ardYMWQezBjdSXlpKV7/wwguq+08iZakXULOwkZ5UjXzqqafU7Q8cOICvvvpKPYaUpSayZcPahWLxwwMwuE0IikvL8fLf+zH1262qdg5dxpCUqROJa50a/op2vPsnIHmvedtDZGMczZ2VLmWlZVEwyTz/7LPPVBXHr7/+utbby7oZkrkuC45Jb8/w4cNx8803X7K3h8gWNPJxw1eTuuOVMR3g5uyIZQdTMe6zDUjKKjB306xD1ing5FrtuMMN5m4NENFFm4oOHbDsXAl9IrLiIn5SzVG6/mT9DD3JopYxTlkbozZSrOj7779XwYyUx5Y6AzKzQBYSu5CioiK1Vc221s9KkM2Q9I9n6Mel89nzuZ7QLQKtQzwxbe5ONUx13cfr8L9buqBDY8PO/rO1c+246yc4ycyN6D4o8wqTf5C5mwQMeArOB/6Cw5ElKD26EiURPW3iXEv7ZeafzJKpWYXXUugnCuvbSZZxruV6uZ28hmQJiqrq83dhtuAmPT1dLdeuXytDT36WKpC1kR4buV+/fv3UP760tBT33nvvRYelXn/99WoLi+ktWbJE9RIZg4zRkmnY87m+vxXwvwNOSM4pwoTPN+C2luXoFGi8PBxrP9cDD86Cv4wC6drgZI0VnM2pU+AANE1fjuzfZmBNq+fVkJW1n2tnZ2eEhYWpvJWayxJYGlkskyznXMvrpaCgAKtXr1af8VXl5+fb5vILK1euxGuvvYb/+7//U8nHR48exUMPPaQqREplxtpIz5Dk9VTtuZFEZBnSMkadG3lTGjZsmFXXqLAGPNea664uwUM/7saaoxn4+rAT3hjbXq06bkg2ca6zT8NlRzx0Do5of+NTaC/LLliK3G7Q/V8PBOYfw1UxpVh00sW6z3VFrZKEhAS1kKOl1rmRL8jyYStVifVrNpH5z7W8dmTCkCx3UVudG4sPbmRanXQ5paSkVLtcfpaIvzYSwMgQ1F133aV+lgx2maZ399134z//+U+txYHc3NzUVpO8cRjrzcOYj03V2fu5DnRxwawpPfHcH/vww+Z4PD1/HzzcXDE6NsLgz2XV5/rkKrVziOgKF7/a31/MJiAS6H0fsOYduK55Aw5Rz1j3uZZ1ScvK1IeYvCdfqmibueiHR/TtJMs41/qlLmr7G6jP34TZfqOyPocs075s2bJqJ0B+ltLXtZEuqZonRj8mZ2eFlokqOTs54tUxHXBzzyjI7PBHftyJRXuTzN0sy3K04n2mxVBYpCseBDwC4ZBxBFFnKpKeySwGDRqEhx9++KK3kQ/f33//vV6jDnKfzMxMWJuYmBhVgsXamDVcleGiL774ArNnz1ZTu6dNm6Z6YmT2lLj99turJRxfe+21+PTTT9Uy71KeWbrKpTdHLq+ZeERkTxwdHfDqmI6q4F9ZuQ4P/LADyw5U7xW1W2WlwPEV2nGLIbBI7n7AgMfUYZuk37T1r8hiJSUl4eqrrzZ3M8hSc25ksa+0tDQ8//zzSE5ORufOnbFo0aLKJOP4+PhqPTXPPvusin5ln5iYqBYxk8Dm1VdfNeO/gshyApy3buyE4rJy/LXrNKZ9vx1fTuqOAa0awa6d3g4UZgHu/kBEV1isHndBt+H/4JF9CmVbvwAGPGruFtEFXCh1wtQkH84Qw5dlVYYRDU0ShCXB3NTMPtA4ffp0nDx5Uk3X3rRpk0oUrtqVJyuw6skJkgJ+kkgs2dQS/EiFY39/mQNBRE6ODpg5PhZXtQ9TQc59c7bjaKqdzwbRD0k1GwQ4WfAcCmc3lA18Sh06rv8AKDhr7hbZLUmReOKJJxAYGKgCmf/+978XHZaSGmzy5VwSYLt3766uk9vs3Lmz2v2k/IlcLzN1pbTJoUOHql3/xx9/oGvXrupxmjVrpmb6Vp0xJI8poxejR4+Gl5fXBb/Ynz17Vo18BAQEqOeSXqYjR45UXi+fq/K5+eeff6oac5KXKp+nUkBXOgwkoVcWrpwzZ855jy1Da5L3Kp0LMiln8ODB2LVrV+X1cq7kXHz55ZfVFr+Uf5sU6pXHDgoKqlzawmaDGyIyLBcnR3x4cxf0bhaoKhhP/XYbsgqsu25Kgxz917KHpKrQdRiHbPdIOEhP01rry3O4GMmLzC/JN8tW35xMSZWQ4EG+cL/11lt46aWXLjg9X2bwSEAgE1yk0r7M3n3yySdrva1MfJHVxbdu3aq+rN9xxx2V18n6VRKQyAzg/fv343//+58KQmoGMBI8jB07Fnv27Kl2/6omT56snkOCF6kbJ/9+WWm+ap0YyWF98803VRCyb98+hISEqPvJLLcVK1bgl19+UTOTa64YMG7cOHXZwoULVbAmwdiQIUPUUhV60gHx66+/4rffflMBngzjSUAkKSeSgiIdF9dff71Rc2Ut+GsMEV0uV2dHfDKxK0Z/vA5x6Xl48Icd+HpyD9WzY3cLZcqwlGhu+cENHJ2wP2Iceh9/D9j0GdDrHsDX8DPfzKGgtAC95p7rmTelTRM3wdOl7nXNOnXqpEYJRMuWLfHxxx+ryS4yRb+2ZYGkR0XyR6WXQnpCJG1Cqu/XJIHKwIED1bEsJXTNNdeoqc9yP+mlkcsmTZqkrpeeGwmUpAdJ3xZ9vTd9XmptpIfmzz//xLp161TvkJAeGCmBIj1KEpwICXQkeJHeFHH48GEVsEiR3B49eqjLZImjtm3PrcG2du1adb0EN/pZyLLIqDyuBEMyc1k/FPXtt9+q3h0hgZb0QElQJgnKwtjrdbHnhshGBXm74fPbu8HdxRGrDqfh7cXVu8DtwvGVgK4caNQW8DNs/R9jSfHtjPKo3tqimivfMHdz7JIEN1WFh4dfcM1DGVqS21etySIV9C/1uPKYQv+4MrQjPURSG0i/SYAkvR5Vi9fJsNbFSM+Is7NztRQPGQaSBafluqozlqu2R38/mcWs16ZNm2ppH9JGKcwoj1e1nTLB59ixY5W3a9KkSWVgIySAkqBO9hJcSSAoQ2fGxJ4bIhvWPsIPb98Yq2ZPfbbqGNpF+BqlBo7lTwG3gl4bPQcHlA9+Ho6zRwI7vgP6TAcatYK183D2UD0o5nru+qiZpCs9M4ZYoqHq4+qL2ekfV4IG6b2R4ZqaqgZOMlxmCB4eHvUuXihtlKBMhpVqqhoE1WyjzGaeP38+9u7di3///RcfffSRGqKTYT/JyzEGBjdENu7a2AjsO52tgpsnftmFVqHeaBNmnHWoLIqM5x+zwuBGmh7ZE2g9Eji0AFj+EjDhe1g7+SCtz9CQtZAeEVnzUCbF6IdqtmzZUu/HkdwV6QVq0aJFg9ojw0ilpaUqcNAPS2VkZKjHliGzC5FeGrmf5NHoh6XkPlVr80gbZWaz9PDoh5fq8/uXha/79++vZkhL744EPFVXEDAkDksR2YHHR7TGwFaNUFhSjkd+3IXiUjtYKDB1P5CTBMi39mjtTd6qDJF1phyBA38Bp7aauzV0AZIDI70vkm8iQzuLFy9WeSiiPj0j8oEveSrSeyMJvvJYUtNNSp/Uh+QIXXfddWpIS3JkZCjp1ltvRePGjdXlFwvSrrrqKtxzzz0qMJIgR5KApYdHT2Y4SZHdMWPGqPUZT5w4oWaKSS+M5NVciDyePpFaZmVJorGUgamaz2NoDG6I7IAkEr8zLhYBni5qJfGPl5+bFmrzQ1Ix/QAXy1zf6KJC2gKxN2vHS5/XeqLI4sh06L/++kvNCpIp0PJBL4GKqM+6WiNGjMDff/+tggbpOenduzfee+891cNRX7NmzVK5M6NGjVLBiMxKWrBgwSVr4sj9IiIiVH6MDI9JwCazqPQkWJPHkXWfJKm5VatWuOmmm1Q5l5qLYNc8RzJrS9oj95GATYIdYxZCdNDZ2boFMm3Pz88PWVlZRlk4U37xMuXOmteFsQY815fnn91JuH/udhXs/DatL2Kj/G33XH97nZZQfNUbQO9psAbnneusU8BH3bTk4pvmAm2ugTWQGUCSZFq1zomlkd4W+TyQzwFDF6+T2Uny4S+fM1V7PuxVeT3O9cVeO/X5/GbPDZEduaZTuMrBkSUaHv15FwpLymCTivOAk+stez2puvCrWFRT33tTZsf1iiyYDCfJEJB8KMu0aKlzM378eAY2ZsTghsjOvDS6PRr5uOFoai7eW3oYNunEOqCsGPCLBoIalqBpdv0eATyDgYyjwNZZ5m4N1UKSbCWvRXJIHnnkETXd+fPPPzd3s+wagxsiOxPg5YrXx2oFtD5fcxxbT5yrLGoz4lZp++ZXqqnVVs3dF7iyYgHhla9r62SRRZFCe5Jcqx9SkVwZWfaAzIfBDZEdGtouFDd2i1Q5qo//shtFpTY2PHVynbaP6Q+b0HUyENwKKDgDrJlp7tYQWTwGN0R26vlr26nhKVme4dv1J2EzinKApIqF/Jr0gU2QBT+Hvawdb/wUOGsdvy87m69CFvSaYXBDZKd83V1U/Rvx4bIjyMgtgk1I2KwtueDfREvItRWtRgBNBwBlRcDyikDHQuln1VVdNoCoLmRdKn1V44ZghWIiO3Zj10jMXn9CVTCeufQwXq3IxbFq+llSTaywcN/FSO7Q8FeA/w0E9vwM9LwHiNIqyVoa+WCScvz6dZMk/6S+pf5NMT1ZPkglT8bQU8Hp8s613E6K+8nrRaogNwSDGyI75ujogOdHtcOEzzfih83xuLV3E7QNt/KlGWw1uBHhsUDnicDOOcCCx4Cpy9VK4pYoLCxM7S+04KQlDH8UFBRc1hpLdAnlpdre0bne51qCn+jo6Ab/ThjcENm5Xs2CMLJjGBbsScYr/+zH93f2st43+5JCIHGbdtzkCtikof8FDvwNJO0Etn8LdJ8CSySvIVlkUSrcSnFCSyNtWr16taq2a1XFKS1ZYRaw9Rtg949As4HAVa/X+1zLauWG6EljcENEePrqtvj3QCrWHc1Q+2HtLlxK3aKd3q7lpHiFAIHNYJO8Q4ArnwEWPQksexFodx3gGQhLJUNUDc2fMAZpkywUKVVwGdw0UGkRsPkLYPXbQGHFQpuZEYCzA+DsZpZzzYFGIkJUoCfu6tdUHb/6z37rXVhTPwVchqSstfepLnrcBYS0AwrOAstfMXdryF6VlwO7fwY+7g4s+Y8W2DRqC0z8GZj0lwpszIXBDREp913ZAsHebjiRkY/vN1rHVOPznNxg20NSVaeGj9RWnsbWr4HTO83dIrI3cauBL64EfrsLyIwHfMKB0R8D09YBrYab/csFgxsiUrzdnPHIsJbq+H+rj1nfulNlpUDCJtuqb3MxMVcAHcdJuiaw4HHtWzSRsaUeAOaMB2Zfq+V9ufoAg58FHtgOdL3NYhLcGdwQUSWpWhzu546U7CL8su0UrErybqA4F3D304Zs7IEU9nP1Bk5tBnb9YO7WkC3LSQb+fBD4tC9wZLE2E6rHVODBHcCAxwFXy1pugsENEVVyc3bCPQO0RNxPVx5DSZkV9QbEVwxJRfW2mG+PRucbDgx8Ujte+hyQl27uFpGtKcoFVrwGfNgF2D5bK5DZ9lrgvk3ANe8A3o1giRjcEFE1N/WMVrk3iZkFmL8jEVbDluvbXEzvaUBoByA/A1j4hLlbQ7airFRbhV6CmlVvAiX5QGRP4I7FwITvgeAWsGQMboioGncXJ9w9QJs59X8rjqKs3ArWB5J8k8rgxsaTiWtycgGu+xhwcAL2/gocXGDuFpE10+mAQ4u04ae/HwbyUoGApsC42cCdS4Do3rAGDG6I6Dy39GqCAE8XNXPqnz3JsHjph7UVs509tCq+9iaiC9D3Ae3470eAgopaI0T1IbPuJFH4hwlA+iHAIxC46k3g/s1A+zFmnwFVHwxuiOg8Xm7OuLOi7s2nq47D4jtv9PVtZK0lZ1fYpUFPAYHNgdxkYMmz5m4NWZPMeODXqcDnA4ETawAnN+CKh7Vk4d73WuXfFIMbIqrV7X1j4OPujKNpedhzxsK/sdnrkFRVLh7a8JTY8R1wbIW5W0SWriATWPIc8FF3YM9P2mUdxwMPbAWGvQh4+MNaMbgholr5urtgct8Ydbwk0VEtfmeRpF364CbaDurbXIwkU8v0XPHXg9pMF6KaSouBjZ8CH3YG1n+oLVkS0x+4eyVwwxeAfzSsHYMbIrqgO65oCg8XR5zKc8DmE2dhkbISgJzTWt2NyO7mbo35DX0B8IvShhoWPWXu1pClfRHY+xvwSQ/ttSHLdzRqA0z8SVsuQXK3bASDGyK6oAAvV4yOjVDHczcnwCIlbNb2YR0BVy9zt8b83HyAMf8n63Jrw1Myg4ro5HrgyyHAL1OAsycA71Dg2g+Ae2W5hBFWlSxcFwxuiOiiJvaMVPsl+1ORml0Ii3Nqi7aXGhykaToA6P+odvzXw9qHGdmntEPADxOBWVcDidsAFy9g0NPacgndJmvrlNkgBjdEdFHtwn3R1EeH0nIdfrDE3ht9z00Ug5vzZk9JwFeUDfx6F1BWYu4WkamXS/jrIeD/egOH/gEcHIFuU7QZUPLacPOGLWNwQ0SX1C9UW4Zh7uaTlrUkQ0mBtqaUiOxh7tZYXnG/G74E3Py03q2Vr5u7RWQKRTnA8le1ysLbvtGWS2h9DXDfRuDa9wGfUNgDBjdEdEmdg3QI9HJRC2ouO5ACiyo6Vl6q5Q/YwAwPgwtoAoz+QDteMxM4vsrcLSJjzoDa9D/gg87A6rcqlkvoAUxZBNw8F2jUGvaEwQ0RXZKzIzC+m5Z78+2Gk7AYshq2kDdxG0uINJj2Y4Guk2SqjDY8lWVlq73TpZce2fOLNgNK1hbLTweCWgDjvwXuXAo0sc/yCAxuiKhObuoRCUcHYP2xDBxNzYFFYL5N3Vz1hra4pqwT9MPNQHGeuVtEhpjWfXQZ8MUg4Nc7z82AGvWeNgTV7jq7DvgZ3BBRnTT298DgNtp4/fcb4y3jzZ0zperG1RO4aS7gGaTlKP1+n3b+yDqd2qqtAfX99UDSLsDVB7jyWS1ZuPsdWr6VnWNwQ0R1dlufJmr/67ZTyC8uNW9jpEhdbopWvC+is3nbYi35NxO+BxxdgP2/A6veMneLqL5SDwLzbtHq1ag1oFyB3vcDD+0CBj7OOk9VMLghojrr3yIYMUGeyCkqxe87Tpu3Mfpem7BO2rpKVLflGUbN1I5Xvgbs/8PcLaK6OBMH/HaPNq374N/atO7Ot2q1aq56DfAKMncLLQ6DGyKqM0dHB0zspc1K+mWbmWveMN/m8nS9Heh9n3Y8/14gcbu5W0QXkp0E/D0D+Lg7sHuelhTe9lpg2gZgzCeAf5S5W2ixGNwQUb2M6dxYJRZvj8/EyYw8y5gpRfUz7GWg+RBturDkbaTsN3eLqKrcNGDRM9rCllu/0sodyO9r6gptaDGkjblbaPEY3BBRvYT4uuOKFsHq2GxDU6p43x7tmD039Scl98fPBhp30xZP/PY6IOOYuVtF+WeApS8AH3QCNn4ClBYCUb2Byf8At/0GNO5q7hZaDQY3RFRvY7s0VvvfdyZCZ45ZN6d3VBTvC9NWwKbLW2Dz1l+B0I7aFPHZo7UkbTJPULPsJeD9TsC697UetYiu2u/njkVATD9zt9DqMLghonob0T4MHi5OiEvPw65TWWbMt2HxvgbxCABumw8EtwKyT2nTiyXPg0wjLwP497/A+x2BNe8CxTna6vY3zwOmLgdaDOXr+zIxuCGievNyc8awdlrNm993JJq+AaxvYzjejYDb/wACYrRCcBLgsAfHuHJSgKXPa0HN2veA4lxt1t+EOcDdq4HWVzOoaSAGN0TUoKGpv3adNu1imjIMxplShuUbAdz+J+AbCWQcAb4afi6niQxHgsZ/HtNyatZ9AJTkAeGxwE0/APesBtqOkimJ5m6lTeBZJKLL0q9lMIK8XJGRV4y1R9NN98SZJ7UcESlGF87ifQYt8nfnEiCkHZCTBHx9NRfaNJS0Q8D8adpK3Vu+0BKFpddx4k/A3auANiPZU2NgDG6I6LK4ODni2tgI0w9NJVQMSYVL8T530z2vPfBrDExZCDTpp+V/fH+DtigjXV4P48n1wNwJwCc9gV1ztST4ZoOASX9pgWSrEQxqLCW4+e9//4tyWYW0hqysLNx8882GahcRWYExFUNTi/clI7eo1MT1bTgkZRQe/tq0Y1lNvLxEW5RxxetAeZm5W2Ydykq1ys9fDQNmXQ0cXgTAAWgzCrhrmZbf1HQAgxpLC26++uor9OvXD8ePH6+8bOXKlejYsSOOHWOdBCJ7Ehvph6bBXigsKceSfcmmTSaWmVJkHM5uwA1fa+sWiVVvaLVwOJPqwqRe0LoPtaGnn27XXqdObkC3ycD0rcBNc4DI7uZupd2od3Cze/duREZGonPnzvjiiy/w+OOPY/jw4bjtttuwfv1647SSiCySg4ODqlgs5ptiaKpq8b7G/KAwKklslXWLxn4OuHhpCzV+1g84+q+5W2ZZUg9oSyTMbAcsfQ7Iigc8AoH+jwGP7AWu/QAIbmHuVtod5/reISAgAD/99BOeeeYZ3HPPPXB2dsbChQsxZMgQ47SQiCzamC4ReO/fw1h3NB1pOUVo5ONmvCdL2q3lLXiFAP7aGldkZLETtErGP08GUvZoeTh9HwSufMZ+FywtLQIO/AVs/Ro4ue7c5SHtgd73Ah3H2e+5seaE4o8++ggffPCByrFp1qwZHnzwQezatcvwrSMii9ckyEsNT5XrgKX7U0xU34bF+0xKeh7u+hfocZf28/oPgU96AYckn8TOZj1JfRrppZFcJAlsHJy0fBqZSj9tnbYwKQMb6wturrrqKrz44ouYPXs25syZgx07dmDAgAHo3bs33nrrLeO0kogs2vD2YWq/ZH+yiYIbDkmZnMxMu+ZdrdCcb2NtSv4PE4C5N2nF/2xVQSaw5SvgiyHarCepT5OfDvhEAIOe1oaeJJ+m2UAG3NY8LFVWVqbybiIitCmgHh4e+PTTTzFq1CjcddddeOKJJ4zRTiKyYCPah+LtxYew/mgGcgpL4OPuYpwnStym7RncmI8UmpPpzKvfAjZ8AhxeCBxfAfS6B+gzHfAOgdUrzgeOLAb2/gYcXgyUFWmXSy+NTN/ufAvQ6iptAVKySPX+zSxdurTWy6+55hrs2cOKlkT2qHkjbzQL9sLx9DysOpyGUZ20Lz8GJTN1shIAB0cgoovhH5/qzs0bGPYSEDsRWPCYlmwsPRqb/qcNy0hOjr+VLWhanAccWw7sm68Nt0n1YD0pbCgBTafxthG82YHLyrlZs2YNbr31VvTp0weJidoMie+++w4HDx40dPuIyEpmTQ1rr601tWSfkfJuEree+6CRFa3J/ELaaAXpZPkAmb0mlXc3fw582BmYfy9wYi1QS100S+FenAHHbV8D398IvNkU+PFWYO+vWmAjCetXPKwtizBtPdDXRnql7ES9e25+/fVXNe37lltuUfk2RUVFlUX8XnvtNSxYsMAY7SQiCze8XRj+t+o4VhxMRXFpOVydDVwA/VRFcCMzd8hySJ6JLB8giz3GrQbWvKPtd/2gbbJeVadxQKebtGDI3KtwSxLwiTVwjluNEWkHgX1VrpeApu1ooP31QOOuzKGxp+DmlVdewWeffYbbb78d8+bNq7z8iiuuUNcRkX3qEuWvpoHLdPANxzMwsFUj4wQ3MlOKLI8EApJUK5v8rrZ9o1XqzT6lrXwtW2BzoGl/IKZi89F6+4w2XTt1P3B6h7ad2gaknotkJGzRwQG6yB5wlMBMtkZtGNDYa3Bz6NAhNTuqJj8/P2RmZhqqXURkZRwdHTCsXSjmbopX1YoNGtxISfvT27VjBjeWTxK+ZRv5jrb8wO4fgSNLgDPHtE0CHxHUAghpCwS3qthaaj09sgSEVEm+GBnuKswE8jOA3FTgzHHtsTOOAhnHtdXNy4rPv58EMDH9URrdF0sP52Po6AlwdDFSAjxZT3ATFhaGo0ePIiYmptrla9euVTVviMh+Da8IbqTezcvXdVABj0HIN/CSfMDNV/sQJOuZPt5+jLYVZmkLScatAU6sBpL3VgQiRy9wXy8tyJHfudCVAbpybY2r4lwg/4x22cV4BGgrx0sCekRnILpPZd6MrqQExceZRmGr6h3cTJ06FQ899BC+/vprlUR4+vRpbNiwAY899hiee+4547SSiKxCn+ZB8HZzRmpOEXaeykTX6ADDJhNLHoQsC0DWx91PG/qRTUhwIr1x6UeA9MPn9tILA52W1KtmLF1iWQ8JfryCgYAYbdgrqLm2b9QK8G/CYSY7Ve/g5qmnnlKrgstyC/n5+WqIys3NTQU3DzzwgHFaSURWwc3ZCYNaN8Lfu5PUrCmDBTfMt7E9noFAi6HaVnO4qShLK54ni1EWZWvT/9XmpO1dPQHPYMAzCHB2Nde/gCxYvb8CSW/Nf/7zH5w5cwZ79+7Fxo0bkZaWhpdffvmyGvDJJ5+oIS53d3f06tULmzdvvujtJa/n/vvvR3h4uAqqWrVqxRlaRBZkhDGqFVdddoFsm/TMyXBSYFOtp04KBjYdAMT0A5r0AaJ7AWEdAd9wBjZ0QZddXtHV1RXt2rVDQ/z444+YMWOGmn0lgc3777+PESNGqKTlkJDz6wkUFxdj2LBh6rpffvkFjRs3xsmTJ+Hv79+gdhCR4UjPjYuTA46n5eFoai5ahHg37AHlG7wMVwhOAyciQwU3119/Perqt99+q/NtZ86cqXJ4pkyZon6WIOeff/5R+Twy/FWTXC49RuvXr4dLRXZ7zcRmIjIvWXqhb/NgVal48b5ktAhpYZglFwKaarkVRESGCG5kmreeTqfD/Pnz1WXdu2vru2zbtk0NF9UnCJJeGLnf008/XXmZo6Mjhg4dqhKUa/Pnn3+qqsgyLPXHH3+gUaNGmDhxIp588kk4OTnVeh8pMqgvNCiys7PVvqSkRG2GpH88Qz8unY/n2rLP9ZA2WnDz7/5k3N2vSYOe3zF+E+Svu7xxN5TZ+O+br2vT4bm2vnNdn/vXKbiZNWtW5bEEEuPHj1e9LPqAQhbTvO++++DrWzFlrw7S09PV/UJDqxdxkp8vtIzD8ePHsXz5clUdWfJsZEq6PK/8g1944YVa7/P666+rVcxrWrJkCTw9PWEMF1p/iwyP59oyz7W2zqAzdiZk4pc/F8CzAesL9j62EPIusTfTHXF2kl/H17Xp8Fxbz7mWSUx15aCTrph6kN4SqWnTunXrapdLnkzfvn2RkZFRp8eRKeSSMyNDTNIboyeriq9atQqbNm067z6SPFxYWIi4uLjKwEqGtt5++20kJSXVuecmKipKBVf1CcbqQoIs+eVJXpB+2IyMg+fa8s/1iA/WqYU0P5zQCVd30JKM602ng/N7reBQcBalU5ZAF9EVtoyva9Phuba+cy2f38HBwWq5p0t9ftf7+1RpaanqWakZ3MhlMkW8rqSBEqCkpFRfZE9+lkKBtZEZUnJiqg5BtW3bFsnJyWqYS5Kca5IZVbLVJI9jrBe0MR+bquO5ttxzPbB1IxXcrD9+FqO7XOYK0RnHtOnATm5wbtwFcLaP3zVf16bDc20957o+9633VHBJ/r3zzjtVj4n04Mj27rvv4q677qpMDK4LCUS6deuGZcuWVV4mwZH8XLUnpypZv0qGoqoGUYcPH1ZBT22BDRGZz4CK5RdWH05TuXqXJaGiNIRUl+W0XyKqo3r33LzzzjuqZ0UCGv1QkAQXjz/+OB599NF6PZZMA580aZJKTO7Zs6eaCp6Xl1cZJMninDJ0JXkzYtq0afj4449VhWQpGHjkyBG1EvmDDz5Y338GERlZ76ZBamXw01mFOJYmU8J96v8gpyqCG9a3ISJjBjcyo0nyYmTTzzy63NyVCRMmqAKAzz//vBpa6ty5MxYtWlSZZBwfH6+eT09yZRYvXoxHHnkEnTp1UoGPBDqS5ExElsXD1Qk9YwKx9mg6Vh9Ov7zgJqGieF9UT4O3j4hsVwPmMFx+UFPV9OnT1VablStXnneZDFlJVWQisnwDWgVrwc2RNNzRr2n97lyUA6Tu044jGdwQEYyXcyMJv7fddhsiIiLg7OysknurbkRENfNuNh7PQGHJJVZwrun0Dm0VaN9IrdQ+EZGxem4mT56shotkBXDJtZG1poiIatM61Aehvm5IyS7C1hNn0a9lcP2TiaOYb0NERg5uZHbUmjVrVH4MEdHFyJef/i0b4Zdtp9TQVL2Cm8rFMjkkRURGHpaSpN7LntZJRHY9JbzO5D1GH9wwmZiIjB3cyHRtWdTyxIkT9b0rEdmhfi2CIaPXB5NzkJJdWLc7nTkO5Geo4n0I62TsJhKRvQ9LyfRtWd+hefPmam2mmhUDZdVuIiK9QC9XdGzsh92nslTvzbjudahWzOJ9RGTK4EZ6boiI6mNAy0ZacHMkvW7BTWW+DZOJicgEwY1UFCYiqm/ezccrjmLtkTSUlevg5HiJWZasTExEpghu9NWIL8XQK20TkfXrEu0PbzdnnM0vwd7ELMRG+V/4xkW5QEpF8T4mExORMYMbf3//i9a0kRlUcn1ZWT0LdRGRzXNxckTvZkH490CKKuh30eDm9PYqxfsiTNlMIrK34GbFihXGbQkR2bTezQIrg5t7Bja/8A1ZvI+ITBXcDBw4sKHPRUR2THpuxJYTZ1FaVg5npwtUomAyMRGZus4NEdHlaBvuC193Z+QWlWLf6exLF+9jZWIiukwMbojIJGSGVM+mWu+NDE1dvHifKxDO4n1EdHkY3BCRSfNuLhrc6HttwqV4n5sJW0ZEtoTBDRGZLe/mwsnEHJIiosvH4IaITJp343OxvBt9cMNkYiIy9myp66+/vs4P+NtvvzWkPURk43k3vZrKlPBUbIqrUe+mMAtI2asdR/c2WxuJyE6CGz8/P+O3hIjsglbMLxUbj5/B3QOq1LtJkHwbHRDQFPAJM2cTicgegptZs2YZvyVEZF95N3Fnqte7id+g7aP7mLF1RGSXC2fqpaWl4dChQ+q4devWaNSokSHbRUQ2nneTU1iK/UnZ6BRZMTQVv1Hbc0iKiEydUJyXl4c77rgD4eHhGDBggNoiIiJw5513Ij8/v6HtISI7ybupNiW8tBhI3Kods+eGiEwd3MyYMQOrVq3CX3/9hczMTLX98ccf6rJHH320oe0hIjsampK8GyV5N1BaCHgEAsEtzds4IrK/Yalff/0Vv/zyCwYNGlR52ciRI+Hh4YHx48fj008/NXQbicjW826q5ts4OJi3cURkfz03MvQUGhp63uUhISEcliKi+uXdFGl5N8y3ISKzBjd9+vTBCy+8gMLCwsrLCgoK8OKLL6rriIjqlXdzLL3KTCkGN0RkhmGpDz74ACNGjEBkZCRiY2PVZbt27YK7uzsWL15sgCYRkT3VuzlxeLe2WKazOxCuvacQEZk0uOnQoQOOHDmCOXPm4ODBg+qym2++GbfccovKuyEiqoueFT03LombtAsad+NimURkvjo3np6emDp1qmFaQER2m3fj4eKEDqX7tXciDkkRkTmDGyne99FHH+HAgQPq57Zt22L69Olo06aNodpFRDbOxckRnaP80f2UVgyU9W2IyGwJxTIVXIamtm3bpnJuZNu+fTs6duyoriMiqqsBEWVo6piCcjhwJXAiMl/PzRNPPIGnn34aL730UrXLZQaVXHfDDTcYrnVEZNP6ux9T++OOTdDCo8oK4UREpuy5SUpKwu23337e5bfeequ6joiorloW7lX79cUtkZZTZO7mEJG9BjdSmXjNmjXnXb527Vr079/fUO0iIjvgdnqz2m8tb41tJ8+auzlEZE/DUn/++Wfl8ejRo/Hkk0+qnJvevbXZDRs3bsTPP/+sCvkREdVJcR6QtFsdbilvjbD4s7iqQ5i5W0VE9hLcjBkz5rzL/u///k9tVd1///249957Ddc6IrJdp7YAujLku4chqTAIW09ULKJJRGSK4Ka8vLyhz0NEVF2cNrxd3uQKIBPYm5iNwpIyuLs4mbtlRGRvOTdERAZxQgtuvFoPQrC3G4rLyrE3McvcrSIiew1uVq1ahWuvvRYtWrRQm+Th1JZkTERUq6JcIHGbOnRoOgDdmmjTwLcyqZiIzBHcfP/99xg6dKhaguHBBx9Um6wpNWTIEMydO9cQbSIiW5ewESgvBfyigYAYdG+irTO19QSDGyIyQxG/V199FW+99RYeeeSRysskwJk5cyZefvllTJw40QDNIiJ7yLdBU618RLeYALXfHn8WOp0ODg4O5mwdEdlbz83x48fVkFRNMjQVFxdnqHYRkS07sVbbx/RTuw4RfnB1dsSZvGLEpeeZt21EZH/BTVRUFJYtW3be5f/++6+6jojooopygNM7tOMYredGApvYSD91zLwbIjL5sNSjjz6qhqF27tyJvn37qsvWrVuHb775Bh988EGDG0RENu7kBlXfRnJt4H/uC1G3JoHYcuIstp04i/Hd+UWJiEwY3EybNg1hYWF499138dNPP6nL2rZtix9//BHXXXddA5pCRHbhxOpqvTZ63ZtoeTfb4tlzQ0QmDm7E2LFj1UZEdPnJxAOqXdy1Irg5mpqLzPxi+Hu6mqN1RGTvRfwKCwsxe/ZsfPrppzhy5IjhWkVEtqkgE0jeXWvPTaCXK5o18lLHXESTiEwS3MyYMQMPPPBA5c/FxcVq4cypU6fi6aefRpcuXbBhw4YGNYaIbFy85NuUA0EtAN/w867uFq313uyIzzRD44jI7oKbJUuWYNiwYZU/z5kzB/Hx8arH5uzZsxg3bhxeeeUVY7WTiGxpSKpiCnhNXSqCm50JDG6IyATBjQQy7dq1qxbs3HjjjWjSpIkquPXQQw9hx46K6Z1ERPVIJtbrHKUtw7ArIRPl5TpTtoyI7DG4cXR0VJVD9TZu3KiGpfT8/f1VDw4RUa3yzwDJey8a3LQK9YanqxNyikpxLC3XtO0jIvsLbmS6919//aWO9+3bp3pyrrzyysrrT548idDQUOO0kois38l1AHRAcGvAp/b3CmcnR3RsrBXzY94NERk9uHniiSdU4rAskCnbyJEj0bRp08rrFyxYgJ49e152Q4jIvtaTuhB93s2OBPYEE5GRgxupayMBTKdOndSimVK0rypZJfy+++67zGYQkc2Lu3i+Tc28G/bcEJFJivjpe21q88ILL1x2I4jIxmUlAmkHAAfH84r31dQlWgtuDqfkIK+oFF5ul1VrlIjsWIOK+BER1cmxisV2G3cDPAMvetNQX3dE+LlDJkvtPpVlmvYRkU1hcENExnf0X23fYmidbs68GyJqCAY3RGRcZaXA8ZXacfPah7UvlHezk3k3RGTs4Ebq3MgUcFlTioioThK3AYVZgLs/0Lhrne6iz7vZkZBZrb4WEZFRgpsWLVogISGhPncjInumH5JqPhhwdKrTXTo09oOzowPScopwOotfpojIiMGNVClu2bIlMjIy6vk0RGS36plvI9xdnNA23Fcd74hn3g0RGTnn5o033sDjjz+OvXsryqgTEV1IXgZwese5npt6YL0bIrpc9S4gcfvttyM/Px+xsbFwdXWFh4dHtevPnDlz2Y0hIhtzfIW25EJoB8A3vF53lbyb7zae5ArhRGT84Ob999+v/7MQkZ0PSdVtllRtPTd7ErNQXFoOV2dO7iQiIwU3kyZNqu9diMgelZcDR5fVO99Gr2mwF/w8XJBVUIKDydnoFKkFO0REl3JZX4WOHTuGZ599FjfffDNSU1PVZQsXLlSrhV+OTz75BDExMXB3d0evXr2wefPmOt1v3rx5cHBwwJgxYy7reYnIiFL2AnmpgIsXENW73neXv23m3RCRSYKbVatWoWPHjti0aRN+++035Obmqst37dp1WetLyQKcM2bMUPfdvn27yuUZMWJEZdB0ISdOnMBjjz2G/v0vvggfEZl5SErWknJ2vayH0Ne7Yd4NERl1WOqpp57CK6+8ogISHx+fyssHDx6Mjz/+uL4Ph5kzZ2Lq1KmYMmWK+vmzzz7DP//8g6+//lo9V23Kyspwyy234MUXX8SaNWuQmXnhN76ioiK16WVnZ6t9SUmJ2gxJ/3iGflw6H8+15Z9rpyNL1bensqZXovwyf08dI7T3mO0nz9rF75qva9Phuba+c12f+9c7uNmzZw/mzp173uUhISFIT0+v12MVFxdj27ZtePrpp6vV0hk6dCg2bNhwwfu99NJL6vnuvPNOFdxczOuvv66CoJqWLFkCT09PGMPSpUuN8rh0Pp5ryzzXzmUFuDp+kzpenuCI/NQFl/Wceeq9zBknz+Tj5z8WwMsFdoGva9Phubaecy0ztY0W3Pj7+yMpKQlNmzatdvmOHTvQuHHjej2WBEPSCxMaGlrtcvn54MGDtd5n7dq1+Oqrr7Bz5846PYcETtLLVLXnJioqCsOHD4evr1YkzFAkqpRf3rBhw+DiYifvwmbCc23Z59rh0AI47i6DLqApBo2d3KDn/zxuLU5k5CO0XU8MaBkMW8bXtenwXFvfudaPvBgluLnpppvw5JNP4ueff1YJf+Xl5Vi3bp3Kf5EaOMaUk5OD2267DV988QWCg+v2Jufm5qa2muQEG+sFbczHpup4ri30XB9drHYOrUY0+PcjScUS3Ow9nYsh7epXK8da8XVtOjzX1nOu63Pfegc3r732Gu6//37V+yG9Lu3atVP7iRMnqhlU9SEBipOTE1JSUqpdLj+HhYXVOktLEomvvfbaysskuFL/EGdnHDp0CM2bN6/vP4mIDL0K+KGF2nGbUQ1+uNgof/y+8zR2nWJSMREZKbiRqsTSc/Lcc8+pJRhktlSXLl3UmlOX81jdunXDsmXLKqdzS7AiP0+fPv2827dp00bl/FQlAZX06HzwwQcq4CIiM4tfDxScATwCgeg+DX44/XRwmTEli/dKjzERkUGDG73o6Gi1NZTkw0hhwO7du6Nnz56qAnJeXl7l7CkZ6pJcHkkMljo4HTp0OC8HSNS8nIjM5OA/2r71SMDpst9iKskCmi5ODjiTV4xTZwsQFWiciQBkXiVl5YhLz0NyViEy8oqQkVuMjLxiVcSxvFynbqPTdnBzcYS/hwv8PF3VPsDLBY39PREd6AkP17qtPE+2rU7vPFUTcusytbs+JkyYgLS0NDz//PNITk5G586dsWjRosok4/j4eDWDioisgHz66IObNtcY5CH1K4TvPpWlem8Y3NhGICOFGbeePINDyTlqO5aWi5KyiuilARr5uKFJoCeaNfJC+wg/dGjsq14/nq4ND7TJetTpty0zoaqSYnulpaVo3bq1+vnw4cMqd0aGmC6HDEHVNgwlVq5cedH7fvPNN5f1nERkBEm7gKwEwMUTaH6lwR5Whqb0wc21sREGe1wynfiMfKw6nIpVh9Ox8XgGcotKz7uNl6uTCl6DvF0R5OWGQC9X+Hu6wNnRodpwZGFJmerRycwvQWZBCTJyi1SvnlyWllOktq0nzwI4pW4vd20W7IVuTQLQu1kQejULQogXgx1bVqff7ooVsrLvuZ4ZKd43e/ZsBAQEqMvOnj2rhpFYLZjIzh38+9xCmS4eBnvYWLWu1EnsYqViqyIBzILdSfhpa0JFsHGOBC59mgWhXYQv2oT5oFWoDyIDPBqUU5WVX4KTZ/JwMiMfR1JysPd0NvadzkJKdhGOpeWp7aetWsAT6e+Oxi6OcNibjCvbhsHHnTOmbEm9Q9d3331XFcDTBzZCjqVqsdSOefTRRw3dRiKyFgcqgps252Y0GoLMmBJ7T2epIQ0XJw5VW7K9iVmYvf4E/tmThPziMnWZowPQIyYQA1o1woCWjdA+wheOcqEB+Xm6oJOn/3mLrKbmFGLPqSxsjjuDjXFnVPtOZRbiFByx6cfdcHbcg55NAzG4TQiGtwtDdBCHPu0uuJEiOpIjU5NcJrOWiMhOZRwD0g4Ajs5Aq+EGfWgZUvBxd0ZOYanKz+jQ2M+gj0+GIUHD+/8exr8HUqut7j6ueyRu6BqJUF93s7QrxMcdQ9rKFlrZo7T5WBq+XboVJ4u9EZeRj/XHMtT2yj8H0LGxH0Z2DMc1HcMZ6NhLcDN27Fg1BCU9ODK7Scgimo8//jiuv/56Y7SRiKxpSCqmH+BxrmfXEOQbvgxNrT2arurdMLixLDL08/6/R7B0v1azTDpkJDfq1t5N0L1JgMVN3/d2c0b/lsHIOVKOkSP7ITGrGMsPpuLfAykqH2hPYpba3lx0EJ0i/TC2S2Nc17mxGkojGw1uZGFLqUYsRfv0i1hJAT1Z5+ntt982RhuJyBpUzpJqeOG+2sRG+WnBTUImbunVxCjPQfUjCbwSAMzdFK9+lhjmutgIPDCkJZo38oa1iAn2wh39mqpNkpMX70vBP3tOY8OxDJXILtur/xxQw1Y3dIvEla1D4OrMoVGbCW6kEvHWrVvx6quvqkBGKgYLqQrs5eVlrDYSkaXLSQESNht0CnjtScVaMT8yLymmuGhvMl74cx9Sc4rUZdJT89CQlmgRYj1BTW2CvN0wsVe02tJzi/D3rtP4dXui6slZsj9FbcHerhjXPQo394jmsJUtBDcy3VuShg8cOKAWzuzUqZPxWkZE1uOQ9NrogMbdAF/jTNXWVyo+kpqrciZkaIFMT4rsPffH3sohKMmHeu36jmqKta0J9nbD5Cuaqk1yvX7dfgrzdySqqeafrjymNhnekp7EoW1D4MxEd4tR73cHqQR8/Pjx81YFJyI7ZuDCfbUJ8XVHhJ87TmdpM1/6NLe9D1NLt/JQKh7+caeqLyO1Z+4d2BzTB7dQhRZtXeswHzwzsi0eH9Eayw6kYs6mk1hzJL1ya+zvgdv6NMFNPaLg78ncHKsLbmTKt+TcvPzyy6poX83hKF9fX0O2j4gsXf4Z4PhKo0wBr21K+OmsZDU0xeDGdGT5gw+XH8EHy46oItQym+jtcZ3QJsz+3u+lDMFVHcLUJoUJf9gSjx+3JCAxswBvLDyoZouN7RKJO66IQctQH3M3127VO7gZOXKk2o8ePbpaBrx+QTvJyyEiO7LvN6C8FAjrBDRqZdSnkqGphXuTWczPhDLzi1VvzcpDWgkQyUV54dp2cHO2/d6aS5F8myevaqNyjf7cdRqz1p3AgaRs/LA5Xm2SgHz3gGbo1TTQ4maM2bp6BzdVqxUTEWH3z9q+03ijP5W+mJ9MByfjkzyTO2dvUUsbuDk74tWxHXFjt0hzN8viyLDc+O5RGNctUhUK/HpdnEo8lunlssVG+mHqgGa4ukM4nAxcuJAMFNwMHDiwvnchIlt19gSQsFEmAQMdbjT608lwiHw2JGUVIiW70GxF4ezB9vizmDJri5ruLattf3prV7UQJV2Y9M7IulWyyQrnX609jp+3nsKuU1mYPncHYoIOYdqg5mrYilPJjaveZ3fLli1qlfBRo0apTZZbkOnhRGSH9lT02jQdAPiGG/3pvNyc1RpEglPCjWfNkTTc8sUmFdh0jfbHn9OvYGBTT1KZ+ZUxHbH+qcFq2EoWAD2RkY8nf92DgW+vwNdr41BQsTQFmTm4eeKJJ9CrVy98+eWXOHXqlNo+//xzddmTTz5phOYRkcWSzNLdP2nHnSaY7GlZ78a4Fu5Jwh3fbEFBSZma5vz9Xb04+6eBdXMeGdYK654cjGevaYsQHzfV8/jS3/vR783l+GzVMeTVskI6mSi4kVXAP/roI3z44YfIyMjAzp071XbmzBm899576vJvv/22gc0hIquRtAtIPww4uwNtjTtLqqrO0RXBTTyDG0OT1bvvn7sdJWU6ta7Sl5O6w9OV9YQM1et4V/9mWPPklXhtbEdEBXogI69YzbDq/9YK/N/Ko6p+E5k4uPnkk0/w2muvYfr06XBxObc0vBw/+OCDqmrxxx9/bKBmEZHF0/fatL4acDfdlOAuFcHN7lOZKCvXmex5bd0fOxPxxC+7Iad0QvcofHhzF86IMgI5pzLjbPmjg/D2jZ3QJMgTZ/KK8daiQ6onR4Ic9uSYMLjZt28frrvuugteP2bMGHUbIrID5WXA3l9MPiQlWob4wMvVCXnFZTiSmmPS57ZVqw6n4dGfdqnj2/s0wRs3dOSsHhPUy5ElHJbNGIh3x8WqHB0pjihBzoC3VuDLNcdRWMKcHKMHN7L0QnFx8QWvl0U05TZEZAfiVgG5Kdrq382HmPSp5UO3U0XezQ4OTTXYjvizmPb9NpSW6zA6NgL/vbY9a7KYkCzZIItxLn1kAGaOj1U9OTJc9co/B1SQM3v9CRSVMsgxWnDTtWtXzJkz54LXf/fdd+o2RGRHtW3aXw84mz7ZlHk3hnE0NUclD+cXa8nD74yLhSN7bMwW5FzfNRL/zhiIN2/oqJZzkEVJZXHSwe+sws9bE1BaVm7uZlqNOmeKyZILMvRUVFSkpn+Hhoaqy5OTk/Huu+/i/fffx/z5843ZViKyBMX5wIE/TVa4rzZdKor57Ug4a5bntwVJWQW4/avNOJtfooojfnZrN9ZesZDhqgk9olUtHEnw/mj5EbW0w+O/7FYzqx4d3hpXtQ9jEGqo4EZq2sisKAlyJJjx89NqHmRlZcHZ2RnvvPOOug0R2bhDC4DiXMA/GojqZZYm6HtuZIXwnMIS+Lifm+RAlya5HFO/3aoWIW3eyAuzJvdQs3nIckigeWvvJqoi9HcbTqpE42NpebhvznZVzFKWfejXMtjczbRY9Xo1P/DAAxg7dix+/vlnHDlyRF3WqlUr3HDDDYiKijJWG4nIkmz7Rtt3uklKspqlCSE+7qrbXr7R7j6VhSta8E2+rmQdwP/M34u9idkI9HLF7Dt6qj1Z7tIOsnTDTT2j8NXaOHyx+jj2JGbh1q82oV+LYDxxVevKHDQ6p96hemRkJB555JH63o2IbEHaYeDEGsDBEeg2yaxNkSnhEtxIMT8GN3X33caT+HX7KbWMxccTuyAywNPcTaI6kN7Jh4e2wm29m+DjFUcxZ2M81h5Nx9qP01VNosdGtFYzrkjDAVYiqrutX2v7VlcBfuZdQFFWCNfP9qG62XriDF76a786fvrqtujbnEGhNVY8fuHa9lj26EBc37Wx6jz9Z08Shs1ched+34u0nCJzN9EiMLghoropyQd2zdWOu99p7tagS3SA2kvPjQy10MXJQqPT5mxXU75HdQrHXf2bmrtJ1ABRgZ6YOb4zFj7UH4PbhKjfq/TKDXp7Bd7/97DdFwJkcENEdeKwbz5QmAUExADNB5u7OWgf4QsXJwek5xbj1NkCczfHopWUlatEVPlW3zrUB2/d2Im1bGxEmzBffD25B36Y2huxkX6quOX7/x7BwLdX4vuNJ+12+jiDGyKqE8fts7SDblMAR0eLSLRsF64t+7CDi2he1EfLjmDbybPwcXfG/27rxvWibFCf5kH4/f4r8MnErogJ8kR6bhGe/X0vhr+/Gkv2Jdtd72a936GaNWumFs6sKTMzU11HRLbHP/84HJN2Ak6uQJdbYSmYd3NpEtRIAqp4/fqOiGHSqc2S3rhrOoVjySMD8eLo9moW3PG0PNz93TZM+N9Gu/o7qXdwc+LECZSVnV8KWor7JSYmGqpdRGRBYtKWawftxgBelpOEWjXvhs4neRczftqpFsMc26UxRnWKMHeTyEQ1cib1jcHKxwfh/iubw83ZEZtPnMHY/1uP6XO3Iz4jH7auzn2Tf/5ZUZEUwOLFiyuL+AkJdpYtW4aYmBjDt5CIzKsgE43PbtSOu98BS6LvudmXmK3W3+Eq1tW98s9+nMzIR4SfO/47ur25m0Mm5uvugsdHtFHFAGcuOYxftp/C37uTsHhfMib1icH0wS3g7+lq38GNLL2g7/aaNKl6fQsXFxcV2EjlYiKyLY57foSTrhi6Rm3hEN0blkQWGQzwdFFLCBxIyqkMdgj4d38KfticoKYKvzM+Fn4erOJsr8L9PPD2uFhMuaIpXltwQNXH+XJtHH7edgoPDG6B2/o0sbkvBnUeliovL1dbdHQ0UlNTK3+WTYakDh06xOUXiGyNTgfH7VpF4vKuU8xWkfhC5MsW827OJ8mkT/22Wx3f1a8p69mQ0i7CF9/d2RPfTOmhZs1lFZSo1ceHzVyNBXuSbCrpuN45N3FxcQgODj4vmZiIbNDhxXDIOIISR3eUdxwHS8S8m/M9O3+vmiIvH2Cy0CJR1S8Eg1qHYMFD/fHG9R3RyMcN8WfyVamAGz5drxLQ7TK4efPNN/Hjjz9W/jxu3DgEBgaicePG2LVrl6HbR0TmIt/i1s5UhyeChwBuPrBE53puGNyIpftTsGhfMpwdHTBzQqyaMk9Uk5OjA27qGY2Vjw3CQ0NawsPFCdvjM1WAc78NJB3XO7j57LPPKhfJXLp0Kf79918sWrQIV199NR5//HFjtJGIzOHkeiBhE3RObjgWMgKWKrYiuJFvnzIcY++zo174Y686vrN/U7SPODfxg6g2shr8I8NaqZlV47tHass57E7C0Jmr8Oo/+5GVXwK7CG6Sk5Mrg5u///4b48ePx/Dhw/HEE09gy5YtxmgjEZnDGm2CQHnszShysdxEXUmUleEXsfWEbXSpX673lh7G6axCRAZ4qG/jRHUV6uuOt26MxT8P9FerjReXleOLNXEY+M4KfL02DsWl5bYd3AQEBCAhIUEdS4/N0KFD1bEkItVW/4aIrNDpncCxZWr17/Le02HpuscEVC4Maa/2JmZh1voT6vjlMR1YhZganHTcKtQbmfkleOnv/Rj+3ios2ms9lY7rHdxcf/31mDhxIoYNG6YqFctwlNixYwdatGhhjDYSkamtfU/bd7hBW0vKwvWICVT7LTaSDFlfZeU6/Gf+HrWXCrVXtg4xd5PIFpKOH+yP18Z2RLC3K05k5OPe77dh/P82WEXyfr2Dm/feew8PPPAA2rVrp3JuvL291eVJSUm47777jNFGIjKl9KPA/j+0436PwBroe272JWYhv9j+VkOWBRJ3ncqCj5szXhjVztzNIRvh7OSIib2isfLxKzH9yhaq0vGWE2cx5pN1ePCHHUg4Y7lJx/XqtywpKcE999yD5557Dk2bNq123SOPWMebIBFdwrr3ZaAZaHUVENpe/vBh6Rr7eyDczx1JWYXqW6U91XVJzS7E24sPqeMnrmqNEF93czeJbIy3mzMeG9Eat/SOVq+1+TsS8eeu02qYasoVMbjvyhYWVySyXj03Uon4119/NV5riMi8shKBXfO0434zYE3d6N0rhqbsLan4rcWHkFtUqmaNTezVxNzNIRsmlY5nju+Mv6b3Q59mQSrp+H+rj2PQ2yswa51lJR3Xe1hKlmH4/fffjdMaIjKvdR8A5SVAkyuA6F6wJj0qhqa22FFS8Z5TWfhl2yl1/N9r26naJUTG1qGxH+ZO7YWvJ3dHixBvtfzJi39pSccLLaTScb3T6Vu2bImXXnoJ69atQ7du3eDl5VXt+gcffNCQ7SMiU8k4Bmz9Sjse+ASsTfcmWs/N9pNnUVpWrvIFbJl8gLz09z51LCt+6ys1E5mqt3Rwm1AMaNkIP25NUGUIJOl42pzt6NYkAP+5pi26mvE1We/g5quvvoK/vz+2bdumtpr/WAY3RFZq2UtAeSnQYijQbBCsTeswH5VQm1NUioPJOerbpS1bsCdZJXe6uziqXBsic3B2csQtvZrgus6N8fmqY/h8zXG1hMM9323D2ievNNuCnM6Xs7YUEdmYU1uB/TLc7AAMewnWSIZkujYJwKrDaarejS0HN4UlZWp1Z3HPgOYqF4LI3EnHM4a3VnlfM5ceUr025lxp3Lb7bYno0mR8fMmz2nHnW7QZUlaqMu/GxuvdfLU2DomZBQjzdcc9A5uZuzlElcL8tErHsm6VOdWp52bGjBl4+eWXVX6NHF/MzJnaQntEZCUOLQDiNwDOHsCVz8CanZsxdUblpMhQuS1O/f6/FUfV8ZNXt2YlYqJa1OmvQqoPS40b/fGF2OIbCZFNKysBlr6gHfe5D/BrDGsWG+kPFycHpGQX4dTZAkQFesLWvLvkMPKKy9TU7+tirfv3RWTW4GbFihU4fvw4/Pz81DER2Yjt3wIZRwDPIOCKh2HtPFydVK7NjvhMbD15xuaCmyMpOfh5m7a23/Oj2sKRU7+JGpZzI1PA09LSKn+eMGECUlJS6np3IrI0hVnAyte144FPAe6+sAWV60zZYDE/qQ5brgOGtwtFt4qp70TUgOCmZlGeBQsWIC8vr653JyJL8++LQF4aENQC6D4FtqJ7E9tcIXx7/Fks2Z8C6ax5fASnfhNdDGdLEdmjkxvOFewb9R7gZFnrwjSEFBATh1NykZlfDFsgXy7fXHhQHd/QNRItQ33M3SQi2whuJFm4ZsIwE4iJrFBpEfBXRbHNLrcBTQfAlgR5u6F5I61yuhQTswWrj6RjU9wZuDo74uFhrczdHCKL51yfbw6TJ0+Gm5ub+rmwsBD33nvvecsv/Pbbb4ZvJREZzpp3gfTDgFcIMPxl2CLJuzmWlofNJ85gSNtQWLPy8nO9Nrf3bqJWQCciAwU3kyZNqvbzrbfeWte7EpGlSD0ArKmoRTXyLcDDNtcj6tUsEPO2JGD90QxYu7/3JGF/UrZaWuK+K1uYuzlEthXczJo1y7gtISLjKi8H/nxQW/W71dVAuzGwVVe0CFb7vaezcCavGIFerrBGJWXleHfJIXV894BmVvvvIDI1JhQT2YstXwCnNgOu3sA170jSHGxViI872oT5qJUl1h1Nh7X6aWsCTmbkI9jbDXf0a2ru5hBZDQY3RPYgeS+w5DnteMgLgF8kbF2/it6btUfSrXZxzI+Xa8ssTL+yObzcuMwCUV0xuCGydUW5wM+TgbIioOVwoMddsAf9WzVS+zVH0s6r02UNftgcj6SsQoT7uZt9EUIia8PghsiWyYf6PzO0JRZ8IoAxnwGO9vFn3zMmEK5OjjidVYjj6dZVcLSguAyfrDimjqcPbgF3FydzN4nIqtjHuxyRvdo5B9j9I+DgBNz4NeAVBHsh60x1jwmwyqGpOZsTkJ5bhMgAD4zrFmXu5hBZHQY3RLY87fufx7TjK58BmvSBvenf8tzQlLUoLAM+XxOnjh8a0lIV7iOi+uFfDZEt59mUFgDNBwP9ZsAe9W+pJRVvPH5GTau2BquTHHA2vwTNgr0wtktjczeHyCoxuCGyNWWlwC93AGkHAe8wYOzndpNnU1O7cF9VGya3qBQ7EzJh6bILSrD8tPa7emhoSzg72efvjaihLOIv55NPPkFMTAzc3d3Rq1cvbN68+YK3/eKLL9C/f38EBASobejQoRe9PZHdJRAvfAI4shhwdgcmfA94a0Mz9sjR0aGyoN+aw5Y/NDVr/UkUlDmgRSMvjOoUYe7mEFktswc3P/74I2bMmIEXXngB27dvR2xsLEaMGIHU1NRab79y5UrcfPPNWLFiBTZs2ICoqCgMHz4ciYmJJm87kcVZ/1HFat8OwPVfAFE9YO/664MbCy/mdzavGLM2nFTHDw5uDidH2y2ySGTzwc3MmTMxdepUTJkyBe3atcNnn30GT09PfP3117Xefs6cObjvvvvQuXNntGnTBl9++SXKy8uxbNkyk7edyKLsmw8srSjUN+JVoN1oc7fIIvSryLvZlZCJrPwSWKrP1xxHXlEZGnvqMKKddS/2SWRuZi15WVxcjG3btuHpp5+uvMzR0VENNUmvTF3k5+ejpKQEgYGBtV5fVFSkNr3s7Gy1l/vIZkj6xzP049L5eK6rc0jYBKff7pH+GpR1n4ryblPl5Bjksa39XDfyclbJuVLrZs3hFIxob3mBg0z7/madNkNqZFQ5yspKUVLCnhtjsvbXtT2e65J63N+swU16ejrKysoQGlr9zUZ+PnjwYJ0e48knn0RERIQKiGrz+uuv48UXXzzv8iVLlqgeImNYunSpUR6XzsdzDQTkHUWfo2/DobwISX5dsLn0CmDhQoM/jzWf60hnRxyHI35YsQNlJy1v1tT8E44oKHFEE28d2gforPpcWxuea+s519KZUVdWvVjJG2+8gXnz5qk8HElGro30CklOT9WeG32ejq+vr0HbI1Gl/PKGDRsGFxcXgz42VcdzXaXHZt59cCgvQHlUbwTf9CNGunoZ9Dls4Vy7HUzF6jk7EV/shZEj+8OSpGQX4okta2XZdvxndCwK4rZb9bm2Frbwura3c51dMfJi8cFNcHAwnJyckJKSUu1y+TksLOyi933nnXdUcPPvv/+iU6dOF7ydm5ub2mqSE2ysF7QxH5uqs+tzfWId8MN4oCQPiOkPx4k/wtHAgY2tnOt+rULh7OiAhLMFSMgsQrNG3rAUn689hKLScnRvEoBBbUKxMM66z7W14bm2nnNdn/uaNaHY1dUV3bp1q5YMrE8O7tPnwtVU33rrLbz88stYtGgRunfvbqLWElmQuNXAnBu1wKbZIGDiT4ARAxtr5+3mjD7NtaUn/tmdBEtx6my+WiBTzBjeCg4OzLMhsonZUjJkJLVrZs+ejQMHDmDatGnIy8tTs6fE7bffXi3h+M0338Rzzz2nZlNJbZzk5GS15ebmmvFfQWRCB/8B5kiPTT7QYihw8zzA1Tj5Y7ZkdKxWN+av3adhKT5efhQlZTr0bR6Evs21WV1EZAPBzYQJE9QQ0/PPP6+md+/cuVP1yOiTjOPj45GUdO6b1qeffqpmWd14440IDw+v3OQxiGy+QN/a94F5t2jLKrQcAUyYA7h4mLtlVmF4+zC1SvjhlFwcSs4xd3NwMiMPP287pY4fHd7K3M0hsikWkVA8ffp0tdVGkoWrOnHihIlaRWRBSouAvx/RVvkWPe4CrnoDcGKuQF35ebhgYOtGWLo/BX/tOo3WYa3N2p6ZSw+jrFyHga0aoVuT2ktZEJGV9twQ0SXkZQDfjtECGwdH4Oq3gWveZWBzGa6tMjSlk54wM9mbmIU/dmrDY4+PMG+QRWSLGNwQWbKT64HPBwLx6wE3X+CWn4Fed5u7VVZraNsQeLg44WRGPvYkZpmtHW8s1Op4Xdc5Ah0a+5mtHUS2isENkaWu7L38VeCba4CsBCCwGXDnUi2BmC6bp6szhrQNUccyNGUOa46kYe3RdLg4OeCx4ey1ITIGBjdElubsCWDW1cDqtwBdORA7EbhnNRDSxtwts6mhqb93J6G83LRDU/J8+l6bW3s3QVQgZ7kR2WxCMRHJJ18ZsO0bYOkLQHEO4OYHjJoJdLzR3C2zKZLA6+PmjKSsQmyLP4seMaZL5v1z12nsO52tnv+BwS1N9rxE9oY9N0SW4PRO4KthwD8ztMAmqjdw7xoGNkbg7uKEER3CTD40VVRahneWHFLH9w5qjkAvV5M9N5G9YXBDZE6F2cDCJ4EvrgQStwGuPsBVbwKT/wECmpi7dTY/NLVgTxJKy0yzkOb3G+Nx6mwBQnzccMcVTU3ynET2isNSROaqWyNDUKvfBvLStMvaXw+MeA3wDTd362yeVASWnpP03GJsPH4G/Voatzrw2bxifLT8iDp+ZFgreLg6GfX5iOwde26ITD0Lascc4KPuwMIntMAmsDlw23xg3CwGNibi4uSIqyuGpn7boVUJNqbXFx5AZn4JWof6YFy3SKM/H5G9Y3BDZAplJcCuH4FP+wJ/3AdkxQM+4cCo94D7NwHNB5u7hXZnXPcotf9z52kknMk32vNsOp6Bn7ZqAdRr13eAsxPfdomMjcNSRMZUmKUNP238DMipSF71CAD6zQB6TuW6UGbUOcofA1o1wurDaWrI6K0bY42SRPzM/D3q+Oae0VxmgchEGNwQGUPqQWD7bGD7d9rsJ+EVolUX7nk34M6qtJbg4aEtVXDz6/ZETL+yJaKDDFt35vNVx3EsLQ/B3q546irWKSIyFQY3RIZSlAvs+00LaE5tPnd5ozZA3weAjuMAZzdztpBq6BodoOrerKrovXl7nOF6b+LS8/DRiqPq+LlR7eDnybXAiEyFwQ1RQ5QWA8eWA/vmAwf/BopztcsdnIBWVwHdp2hLJjg4mLuldJHeGwluftuRiPuvbIGYYK8GP6Ysyvnc73tRXFqO/i2DMbpi6jkRmQaDG6L6KikA4tYA+38HDvwNFFVZgFFmPnW9HYi9GfAJNWcrqY66RAdgUOtGWHkoDR+vOIp3DNB788u2U2r9KDdnR7wypgMcGNwSmRSDG6K6yDoFHF4MHFkCHF8FlBacu847DGg/Bmg/FojqxV4aK/Tw0FYquJm/Q3JvGtZ7szMhE8/+vlcdPzikJZoENbwniIjqh8ENUW3yzwAn1mg9NHGrgXStbH4lnwigzUit8F50H8CR03utfebU4DYhWH4wFR8uP4KZ4ztf1uMkZRVg6rdbUVRajiFtQnDvwOYGbysRXRqDGyKdTluJO2GzlggcvwlIkW/eVVaMdnAEInsCrYYDLYcDoR3YQ2NjHhrSUgU3v+9IxMSe0ehezwU184tLcdfsrUjLKVLF+j64uQucHPkaITIHBjdkf3KStYUqk3Zqe1nTKS/1/Ns1ags0HQA07Q80uQLwZI0SWxYb5Y9rOoXjn91JmDJrC+ZM7YVOkf51um95uQ6P/LhTrfgd5OWKLyd1h7cb316JzIV/fWS7SgqB9MNA6n6tJyZF9vuA3OTzb+voAkR01nJmInsATfoC3iHmaDWZ0Ts3xqqel81xZ3DbV5sx7+7eaBvue8mZUW8vOYTF+1Lg6uSI/93WDVGBhq2XQ0T1w+CGrH9IKTsJOHMcOHNMC2bSDmv7zJOArpYVn2WIKbgVEN5ZC2giumjHLu7m+BeQBZEFLb+e3AO3frlJJQbf9tUmzLu7D1qEeNd6+6OpuXj+j71YfyxD/fzGDR3rPZxFRIbH4IasYwmDrEQtWDl7EsiMh9OZOAyK3wPnvfcCJRdZF8jdHwhtD4S00/b6zZUzWKh2Mpw0+46emPjFRjXMdMuXG/HhTV3QNsIXvu5aIb6C4jJ8vOIIPl99HCVlOjXl+8mr2uD6rlwUk8gSMLgh8ykvA/LStWEiyYPJPq3tZQ0mOZaARqZg65cvqELmJvlV7YnxjwYCm2k9MsEtgeDW2rEMLTHxl+rJz8MF393ZCzd9vgGHU3Ix4fON6nJZRqFpsBdOZxYiMVMrB3Bl60Z4cXQHgy/dQESXj8ENGXbl64Kz2jTqgjPaPj8DyEur2Kdrx7LlpmiX1TZsdKEemIAmgL9s0SjzicTmo2noPmI8XIKaAc6uxv7XkZ0J9HLF93f1wrPz96ohqtScIqTnFqtNRPi54/lr22NE+1AW6SOyMAxuSFNWqi0doLY8bZ2kouyKLUfbZHioMFvbS1XegkygMFPby1ZLD8ulOQBejQDfcMCnyuYbAfg1BnwjtX2NYaTykhKkpi3QKgI7c80eMo4QH3d8fnt3dZxbVIq4tDwcT89VyyrIzCpPV76FElki/mVakvJy+dTWekD0+7Liiq0EKC3SjtW+SFvXSO1lKzy3l1lCUkFX7eVnOc6r2BdowYvkqRTLlqsdy+0MwgHw8Ac8ArWp055BgGcw4BWkBTFy7N0I8A7VNrne0clAz01k3FycjpF+aiMiy8bgxlBS9sHpjwcwIPMsnJPe0Wbx6Mq0YZfyUi2/RDa5TP1csZXpj0vqPkRjTI7OgKs34OYLuPlU2bwBdz9tk+vUsQQxARXBTEDFz/4MVoiIyKwY3BhKcT4cT29DgBxfZPJOvTm5AU6ugJOzdiy5JfrL5NjZHXCWy921y1w8Ki5z16Y2O3sArp6Ai+e562SIR36WIEZ/nQQwcrk8FhERkRVjcGMowS1QeuO32Lp9B7r36AVnF1etB0Nm8khviGwOTtoaRFIwTn+Z3Eb2TnKZixbEqH3FbZioSEREVC8MbgzFIwC61iORcgzQtRgKuDDJlYiIyBy4lDERERHZFAY3REREZFMY3BAREZFNYXBDRERENoXBDREREdkUBjdERERkUxjcEBERkU1hcENEREQ2hcENERER2RQGN0RERGRTGNwQERGRTWFwQ0RERDaFwQ0RERHZFAY3REREZFMY3BAREZFNYXBDRERENoXBDREREdkUBjdERERkUxjcEBERkU1hcENEREQ2hcENERER2RQGN0RERGRTGNwQERGRTWFwQ0RERDaFwQ0RERHZFAY3REREZFMY3BAREZFNYXBDRERENsXZ3A0gMoSS8hJkFWUhuzgbucW5assuyUZBSQEKywpRWFqo9kWlRSjTlamtXFeOsvIydX8nRyc4OTipvbODM9yc3ODu7A4PZw+1ebp4wtfVF35ufpV7dyd3ODg4mPufTkRENTC4IYslgUdGYQZS81ORkp+CjIIMpBWkIb0gHal5qYjLicNnf36mgpqckhyTt0+CmyCPIG1zD0KwRzBCPUMR6hWKMK8wdRzuFa6CJCIiMh0GN2QWOp1O9bKczj2N03mnkZyXrLakvCS1peSlqCBGelguKvfcoQMc4O3qDR8XH7X3dvGGl4uXCi4kEJG99Mg4OzrD0cFR9dTIXkgvTqmuFOXl2l56egpKC9Qmx3kleaq9aivK1m5TVojE3ES1XYwEPo29G6stwjsCUT5RlZsEQvo2EBGRYTC4IaMFL2eLzqrgRT789XsJXFRAk3sa+aX5l3wcCUCkRyTEMwSNPBqpY9kC3AIQtzcOQ68YimAv7WdfN1+TBAryb5O2nyk8o3qT1FbRwySbBGnS0yR7uZ1cJ9vu9N3nPZaLowsifSIR7ROtgp0mvk3UcbRvtOr1kWEyMp6SshLV65dfkq8CWPl9SUBbWl6qeg4liFVDlw7aa1EfFEuALAGzh4sHPJzODV1KQM3fGZH5MbihyyI9HdKzog9UpPclKTcJiXmJai+XSc/GpQS6B6oPcenRkKEcOZZ9mGeY6tWQXo/aPixKSkqw4PACdG7UGS4uLjAlybORHiHZJCC5VO+UvndHzsmpnFNIyE1Qe7lMcoXisuLUVpN8gEZ6R6pARx/8yCY/R3hFwMXJtP9uayHnXYYqJZDWD2nKcKYcy2v2bOFZbSs6qwIaQ5PXhY+rj9r8XP0Q4B6gcrT83fzVJq952eRy/bGrk6vB20Fkzxjc0AU/lKv2QFQdNlLH+cnq2+3FyDCR9LZI4KLfJHiR4Zlw73B1LN94bZUEQfKhJlu7oHbnXS89AnIe47PjkZCToPYnc06qvQQ/xeXFOJF9Qm01SQ+CBIDS6yNb1WEvOa9y3m21B0EC67T8NPVa1PcGqn2uNqQpm/S+1If0wkjPiwQm8pqUwFJtDs6V51F+XyoZvbxMBaVFZUXasGVZoUpcl9+XkIBJNvk7qSvp8VEBj1sAivOKsW3TNgR7Bqt8LrlMAiG1VRwzGCKyguDmk08+wdtvv43k5GTExsbio48+Qs+ePS94+59//hnPPfccTpw4gZYtW+LNN9/EyJEjTdpmaw1ackty1bdX/SYfErKXIEY/rCLfcuvy4SDd8ypptiJQ0Qcu8gEre+mB4ZvwhcmHpj4o6YM+1a6TD1D5XeiDHX3wI70+CdkJ6gNVestk25y8+bzHlg9l6fmS348M6ek3+blqErTM/LKkGV8SMMtwn7wuq74m5Vh6vlQ+Vn7KJQNrIcGCSvCuOAeNPBupoK9moCABjQQyhhrikpl6OcU5yCrOUj1ImUWZapNj6TGSf5++90iOZehL/i5li8+JV4914NiBiz6XtFl6gar2COln8lWd1afvQZJN8tC8nL1sNuglsqjg5scff8SMGTPw2WefoVevXnj//fcxYsQIHDp0CCEhIefdfv369bj55pvx+uuvY9SoUZg7dy7GjBmD7du3o0OHDrAnkuiqn/4se/2badVud/0bqOR8nCk4U/ntsi7kzV8/60c+KPUBjD6YkdwXQ3wo0PnkA0idZ+9w9A7vfV6QKgHpqdxTqodH9ok52tCXPhlbPjDrkuwsOT/ye/Zz91NDKPoPSX0yturJcPLAweKD8EjwgKebp0rKlqBVAigJjKQXSZ/rJEGZPjlbjqV3Q16n+l4OyWmRD34VAJTkqORseZ3Ka1Q2ef3W6fxUBNY1ewWrDnFKO01JhgkDnbRhprqS36WcB/nblH+/zAJcvXU1GrdojMziTHVuJKdLgiP9+ZHeI33v0KV+v7XxdNZyg7xcvVSwo37HLh5qL9fpyx9ULYUgPVtuzm7qtSB7+VleA/rXgn7v6uiq3hMsKWAm++Sgk78uM5KApkePHvj444/VzzJbJSoqCg888ACeeuqp824/YcIE5OXl4e+//668rHfv3ujcubMKkC4lOzsbfn5+yMrKgq+vr8H+HcVlxUjKScKK5Ssw8MqBcHZyRjnK1ZuXvBnJN039G750acvt9XvZqtZi0c/UUUmOpXlqLx8K+m+E8g1P9nL/yyFvbPrEXJWk66lNYZZj/bd7+ZZryVOYVc7NggWqx87UOTeWTl5j0vum7+WQYEffAyKbPhHaHNPn60ICJZVE7lGlx8krVOUZ2XpgfanXtQSO8rcvX1r0vUFVe4aqzuiTvdxWNjm+3PeLyyFBswQ7sq/cnFxUQKwf8pPLZK9qS1UMAaqfHc7VnKq61ydz63+WAKrq5fogu3KDY7XgW4bJqwbiunId9u7Zi06dOsHF2aXyeqHuV3F/+U/7X8V/+ssqht5rXqe/vLYAT3+/apddIhCs+RGtg67W2+gv1x/r9LfTafdR/1W9Tn95xWXy2tI/fuXtKp5brtPfpurt9beper3+WP5uRzcfbdD36/p8fpv13aG4uBjbtm3D008/XXmZo6Mjhg4dig0bNtR6H7lcenqqkp6e33//vdbbFxUVqa3qydGfbNkMZXfabkxZOkUdv/vnuzAV+aPWJy7KXr55yzdxfVe1PoFRhiD0SYx1ynPRaefIUunbZsltNKcg1yC1dQi4cG+m9KZIj558MMqHob73TxVCLMmtnEEkH46n007D289b9fzJ/SQg1xdCVFtFIK8vgqj/ENFPwdfv5bUnwXXlUImL97kkWzet10OGUy41dKIr06lhIFtTl9e1p6MnPD090dizcb0eW35v8vuU362aGVaSr47li5R8iZK8IbWvKH9QUFaxLy2o1gOnimFWvAZkKyovOm+YUAIpUwZTDfH7pto/O6hhYoNjcXX01QZ9v67P/c0a3KSnp6OsrAyhoaHVLpefDx48WOt9JC+nttvL5bWR4asXX3zxvMuXLFmi3iAM5VTpKTjD+VwEXyWy13+LUP85OKrbOUH7ENDvXeQ/B5dzewcXuDm4wRWu2t7BFe4O7upY9vrNDW7nIn8pCSOzq2vMsM6r+C8e2ni+LVm6dKm5m2BTvCv+q+UK7fUl5MttfWbcl1VsFxgRLUMZ0ir+I/O8rj0q/gtEHYbUJO6sEXuqat+omDove/lP6kahXEvCrrhMBcRyGSoqhMu+4j/9dZX/VfQAyG0qewUq/tP3PlT2GFS9TH9cpZeitv/kf3Jbcd51VS5TxxU9GNV6RGr0olzo+GKX1Xab2np36tQDVOUyh6o9Sxe4rGovU819zc+wWnumLnSbitsF5gaq3hpDvq7z8y9dPkTP9vp1a5Beoao9PdJzI8New4cPN+iwlJhSMkX98oYNG8ahEiOTCJ7n2jR4rk2H59p0eK6t71zrR14sPrgJDg6Gk5MTUlJSql0uP4eFhdV6H7m8Prd3c3NTW01ygo31gjbmY1N1PNemw3NtOjzXpsNzbT3nuj73NWvdd1dXV3Tr1g3Lli2rvEwSiuXnPn2qT43Vk8ur3l5IRHih2xMREZF9MfuwlAwZTZo0Cd27d1e1bWQquMyGmjJFS869/fbb0bhxY5U7Ix566CEMHDgQ7777Lq655hrMmzcPW7duxeeff27mfwkRERFZArMHNzK1Oy0tDc8//7xKCpYp3YsWLapMGo6Pj1czqPT69u2rats8++yzeOaZZ1QRP5kpZW81boiIiMhCgxsxffp0tdVm5cqV5102btw4tRERERFZVM4NERERkaExuCEiIiKbwuCGiIiIbAqDGyIiIrIpDG6IiIjIpjC4ISIiIpvC4IaIiIhsCoMbIiIisikMboiIiMimWESFYlPS6XT1Xjq9Psu65+fnq8fmKrPGxXNtOjzXpsNzbTo819Z3rvWf2/rP8Yuxu+AmJydH7aOioszdFCIiIrqMz3E/P7+L3sZBV5cQyIaUl5fj9OnT8PHxgYODg0EfW6JKCZoSEhLg6+tr0Mem6niuTYfn2nR4rk2H59r6zrWEKxLYREREVFtQuzZ213MjJyQyMtKozyG/PP6xmAbPtenwXJsOz7Xp8Fxb17m+VI+NHhOKiYiIyKYwuCEiIiKbwuDGgNzc3PDCCy+oPRkXz7Xp8FybDs+16fBc2/a5truEYiIiIrJt7LkhIiIim8LghoiIiGwKgxsiIiKyKQxuiIiIyKYwuDGQTz75BDExMXB3d0evXr2wefNmczfJ6r3++uvo0aOHqiYdEhKCMWPG4NChQ9VuU1hYiPvvvx9BQUHw9vbGDTfcgJSUFLO12Va88cYbqoL3ww8/XHkZz7XhJCYm4tZbb1Xn0sPDAx07dsTWrVsrr5d5Hs8//zzCw8PV9UOHDsWRI0fM2mZrVFZWhueeew5NmzZV57F58+Z4+eWXq61NxHN9+VavXo1rr71WVQyW94vff/+92vV1ObdnzpzBLbfcoor7+fv7484770Rubm4DWnXuyamB5s2bp3N1ddV9/fXXun379ummTp2q8/f316WkpJi7aVZtxIgRulmzZun27t2r27lzp27kyJG66OhoXW5ubuVt7r33Xl1UVJRu2bJluq1bt+p69+6t69u3r1nbbe02b96si4mJ0XXq1En30EMPVV7Oc20YZ86c0TVp0kQ3efJk3aZNm3THjx/XLV68WHf06NHK27zxxhs6Pz8/3e+//67btWuXbvTo0bqmTZvqCgoKzNp2a/Pqq6/qgoKCdH///bcuLi5O9/PPP+u8vb11H3zwQeVteK4v34IFC3T/+c9/dL/99ptEi7r58+dXu74u5/aqq67SxcbG6jZu3Khbs2aNrkWLFrqbb75Z11AMbgygZ8+euvvvv7/y57KyMl1ERITu9ddfN2u7bE1qaqr6A1q1apX6OTMzU+fi4qLesPQOHDigbrNhwwYzttR65eTk6Fq2bKlbunSpbuDAgZXBDc+14Tz55JO6fv36XfD68vJyXVhYmO7tt9+uvEzOv5ubm+6HH34wUSttwzXXXKO74447ql12/fXX62655RZ1zHNtODWDm7qc2/3796v7bdmypfI2Cxcu1Dk4OOgSExMb1B4OSzVQcXExtm3bprrbqq5fJT9v2LDBrG2zNVlZWWofGBio9nLeS0pKqp37Nm3aIDo6muf+Msmw0zXXXFPtnAqea8P5888/0b17d4wbN04Nt3bp0gVffPFF5fVxcXFITk6udq5lPR0Z7ua5rp++ffti2bJlOHz4sPp5165dWLt2La6++mr1M8+18dTl3MpehqLk70FPbi+foZs2bWrQ89vdwpmGlp6ersZ1Q0NDq10uPx88eNBs7bLF1dwl/+OKK65Ahw4d1GXyh+Pq6qr+OGqee7mO6mfevHnYvn07tmzZct51PNeGc/z4cXz66aeYMWMGnnnmGXW+H3zwQXV+J02aVHk+a3tP4bmun6eeekqtSC2BuJOTk3qvfvXVV1WOh+C5Np66nFvZS4BflbOzs/oC29Dzz+CGrKZHYe/evepbFxleQkICHnroISxdulQlxZNxA3X5pvraa6+pn6XnRl7bn332mQpuyHB++uknzJkzB3PnzkX79u2xc+dO9SVJEmB5rm0bh6UaKDg4WH0jqDlrRH4OCwszW7tsyfTp0/H3339jxYoViIyMrLxczq8MC2ZmZla7Pc99/cmwU2pqKrp27aq+Ocm2atUqfPjhh+pYvm3xXBuGzBxp165dtcvatm2L+Ph4daw/n3xPabjHH39c9d7cdNNNakbabbfdhkceeUTNxBQ818ZTl3Mre3nfqaq0tFTNoGro+Wdw00DSldytWzc1rlv1m5n83KdPH7O2zdpJjpoENvPnz8fy5cvVdM6q5Ly7uLhUO/cyVVw+JHju62fIkCHYs2eP+mar36R3Qbrv9cc814YhQ6s1SxpITkiTJk3UsbzO5Y296rmWoRXJQeC5rp/8/HyVv1GVfBmV92jBc208dTm3spcvTPLlSk/e6+X3I7k5DdKgdGSqnAouGeDffPONyv6+++671VTw5ORkczfNqk2bNk1NI1y5cqUuKSmpcsvPz682PVmmhy9fvlxNT+7Tp4/aqOGqzpYSPNeGm2rv7OyspikfOXJEN2fOHJ2np6fu+++/rzaFVt5D/vjjD93u3bt11113HacnX4ZJkybpGjduXDkVXKYsBwcH65544onK2/BcN2x25Y4dO9Qm4cTMmTPV8cmTJ+t8bmUqeJcuXVRZhLVr16rZmpwKbkE++ugj9cYv9W5karjM2aeGkT+W2japfaMnfyT33XefLiAgQH1AjB07VgVAZPjghufacP766y9dhw4d1JeiNm3a6D7//PNq18s02ueee04XGhqqbjNkyBDdoUOHzNZea5Wdna1ew/Le7O7urmvWrJmqy1JUVFR5G57ry7dixYpa36MlqKzruc3IyFDBjNQf8vX11U2ZMkUFTQ3lIP/XsL4fIiIiIsvBnBsiIiKyKQxuiIiIyKYwuCEiIiKbwuCGiIiIbAqDGyIiIrIpDG6IiIjIpjC4ISIiIpvC4IaIiIhsCoMbIiIisikMbojIZgwaNAgPP/ywuZtBRGbG4IaIiIhsCteWIiKbMHnyZMyePbvaZXFxcYiJiTFbm4jIPBjcEJFNyMrKwtVXX40OHTrgpZdeUpc1atQITk5O5m4aEZmYs6mfkIjIGPz8/ODq6gpPT0+EhYWZuzlEZEbMuSEiIiKbwuCGiIiIbAqDGyKyGTIsVVZWZu5mEJGZMbghIpshM6M2bdqEEydOID09HeXl5eZuEhGZAYMbIrIZjz32mJod1a5dOzVTKj4+3txNIiIz4FRwIiIisinsuSEiIiKbwuCGiIiIbAqDGyIiIrIpDG6IiIjIpjC4ISIiIpvC4IaIiIhsCoMbIiIisikMboiIiMimMLghIiIim8LghoiIiGwKgxsiIiKCLfl/hQrmEYPYEAUAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result.plot_sobols_first('I', xlabel='t')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "exposed-paraguay",
   "metadata": {},
   "source": [
    "Finally we can try and visualize certain aspects of the distribution for our quantity of interest (number of infected people) over time. In this plot we will plot the mean value, standard deviation and 0.01 and 0.99 quantiles. This is with respect to the input variable distributions we have specified in the ```vary``` dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "corrected-titanium",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-09T08:57:36.072288Z",
     "start_time": "2021-06-09T08:57:35.917531Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-20T11:59:32.456172Z",
     "iopub.status.busy": "2025-07-20T11:59:32.456100Z",
     "iopub.status.idle": "2025-07-20T11:59:32.504290Z",
     "shell.execute_reply": "2025-07-20T11:59:32.504024Z",
     "shell.execute_reply.started": "2025-07-20T11:59:32.456164Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='t', ylabel='I'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result.plot_moments('I', xlabel='t')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "overhead-wright",
   "metadata": {},
   "source": [
    "If you want to access all of this data for processing you can use the following methods. Due to large output I have not evaluated these cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "destroyed-darkness",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-09T08:57:36.076580Z",
     "start_time": "2021-06-09T08:57:36.073217Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-20T11:59:32.504717Z",
     "iopub.status.busy": "2025-07-20T11:59:32.504644Z",
     "iopub.status.idle": "2025-07-20T11:59:32.506984Z",
     "shell.execute_reply": "2025-07-20T11:59:32.506759Z",
     "shell.execute_reply.started": "2025-07-20T11:59:32.504710Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'beta': array([0.89204991, 0.89185266, 0.89149001, 0.89096097, 0.89026466,\n",
       "        0.88940042, 0.88836788, 0.88716713, 0.88579887, 0.88426465,\n",
       "        0.88256715, 0.88071051, 0.87870074, 0.87654612, 0.87425765,\n",
       "        0.87184944, 0.86933898, 0.86674715, 0.86409788, 0.8614171 ,\n",
       "        0.85873111, 0.85606382, 0.85343316, 0.85084649, 0.84829549,\n",
       "        0.84575091, 0.84315795, 0.84043276, 0.83746062, 0.83409584,\n",
       "        0.83016322, 0.82546019, 0.81975914, 0.81280885, 0.80433458,\n",
       "        0.7940368 , 0.78158862, 0.76663314, 0.74878204, 0.72761773,\n",
       "        0.7027023 , 0.67359755, 0.63990145, 0.60130728, 0.55769073,\n",
       "        0.50922621, 0.45652411, 0.40076392, 0.34377714, 0.28801728,\n",
       "        0.23636068, 0.19172748, 0.15659244, 0.13253095, 0.11995903,\n",
       "        0.11815331, 0.12551813, 0.13997798, 0.15935905, 0.18167041,\n",
       "        0.20525921, 0.22885867, 0.25156611, 0.272786  , 0.29216284,\n",
       "        0.30951812, 0.32479749, 0.33802971, 0.34929615, 0.35870914,\n",
       "        0.366397  , 0.37249406, 0.37713427, 0.38044717, 0.38255565,\n",
       "        0.38357479, 0.38361138, 0.38276395, 0.38112306, 0.37877176,\n",
       "        0.37578605, 0.37223548, 0.36818363, 0.36368866, 0.35880377,\n",
       "        0.3535776 , 0.34805468, 0.34227574, 0.33627808, 0.33009582,\n",
       "        0.32376017, 0.31729967, 0.3107404 , 0.30410619, 0.29741875,\n",
       "        0.29069789, 0.28396159, 0.27722619, 0.27050651, 0.26381593]),\n",
       " 'gamma': array([0.10795009, 0.108081  , 0.1082452 , 0.10844475, 0.10868198,\n",
       "        0.10895947, 0.10928011, 0.10964712, 0.11006411, 0.11053511,\n",
       "        0.1110646 , 0.11165763, 0.11231979, 0.11305739, 0.11387752,\n",
       "        0.11478817, 0.11579846, 0.11691886, 0.11816155, 0.11954088,\n",
       "        0.12107397, 0.12278152, 0.12468883, 0.12682701, 0.12923442,\n",
       "        0.13195833, 0.13505678, 0.13860045, 0.14267476, 0.14738191,\n",
       "        0.15284307, 0.15920061, 0.16662051, 0.17529499, 0.18544537,\n",
       "        0.19732504, 0.21122225, 0.22746203, 0.24640622, 0.26844958,\n",
       "        0.29400941, 0.32350472, 0.35731988, 0.39574695, 0.43890161,\n",
       "        0.48661152, 0.53828507, 0.59278447, 0.64834747, 0.70261752,\n",
       "        0.75283613, 0.79620725, 0.8303664 , 0.85381512, 0.86616865,\n",
       "        0.86813485, 0.86125663, 0.84753487, 0.8290625 , 0.80775511,\n",
       "        0.78520285, 0.76262477, 0.74089032, 0.72057421, 0.70202081,\n",
       "        0.68540466, 0.67078107, 0.65812541, 0.64736233, 0.63838646,\n",
       "        0.63107668, 0.62530575, 0.6209464 , 0.61787514, 0.61597451,\n",
       "        0.6151342 , 0.61525151, 0.61623137, 0.61798614, 0.62043516,\n",
       "        0.62350436, 0.62712574, 0.63123693, 0.6357807 , 0.64070456,\n",
       "        0.64596039, 0.65150402, 0.65729496, 0.66329612, 0.66947349,\n",
       "        0.67579593, 0.68223496, 0.68876457, 0.69536099, 0.70200259,\n",
       "        0.70866968, 0.71534439, 0.72201054, 0.7286535 , 0.73526013])}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.sobols_first('I')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "contrary-wound",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-09T08:57:36.084115Z",
     "start_time": "2021-06-09T08:57:36.077428Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-20T11:59:32.507284Z",
     "iopub.status.busy": "2025-07-20T11:59:32.507227Z",
     "iopub.status.idle": "2025-07-20T11:59:32.511306Z",
     "shell.execute_reply": "2025-07-20T11:59:32.511073Z",
     "shell.execute_reply.started": "2025-07-20T11:59:32.507278Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  3.4778336 ,   4.03404206,   4.68171534,   5.43612205,\n",
       "         6.31505352,   7.3392123 ,   8.5326462 ,   9.92322633,\n",
       "        11.54316403,  13.42955585,  15.62493745,  18.17781631,\n",
       "        21.14313804,  24.58262204,  28.56487862,  33.16519322,\n",
       "        38.46483541,  44.54972656,  51.50828773,  59.42829956,\n",
       "        68.39265292,  78.47396641,  89.72820311, 102.18763148,\n",
       "       115.85371859, 130.69076976, 146.62126765, 163.52384337,\n",
       "       181.23458606, 199.55197143, 218.24513987, 237.06470706,\n",
       "       255.75488712, 274.06555177, 291.76296652, 308.63828094,\n",
       "       324.51329838, 339.24349061, 352.71856713, 364.86111453,\n",
       "       375.62389166, 384.98633302, 392.95071792, 399.53834415,\n",
       "       404.78592878, 408.74236162, 411.46586385, 413.02155449,\n",
       "       413.47939812, 412.91249216, 411.39564756, 409.00421782,\n",
       "       405.81313627, 401.89612761, 397.32506563, 392.16945484,\n",
       "       386.49601832, 380.36837781, 373.84681454, 366.98810159,\n",
       "       359.84539945, 352.46820727, 344.90236319, 337.19008678,\n",
       "       329.37005755, 321.47752329, 313.54443255, 305.5995859 ,\n",
       "       297.66880097, 289.77508687, 281.93882404, 274.17794602,\n",
       "       266.50812042, 258.94292658, 251.49402806, 244.17133841,\n",
       "       236.98317926, 229.93642984, 223.0366674 , 216.28829859,\n",
       "       209.69468132, 203.25823762, 196.98055751, 190.86249422,\n",
       "       184.9042513 , 179.10546191, 173.46526081, 167.98234963,\n",
       "       162.6550557 , 157.4813851 , 152.45907035, 147.58561312,\n",
       "       142.85832243, 138.27434872, 133.83071424, 129.52433996,\n",
       "       125.35206945, 121.31068996, 117.396951  , 113.60758069])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.describe('I', 'mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "biblical-saint",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-09T08:57:36.092045Z",
     "start_time": "2021-06-09T08:57:36.085146Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-20T11:59:32.511749Z",
     "iopub.status.busy": "2025-07-20T11:59:32.511676Z",
     "iopub.status.idle": "2025-07-20T11:59:32.515458Z",
     "shell.execute_reply": "2025-07-20T11:59:32.515241Z",
     "shell.execute_reply.started": "2025-07-20T11:59:32.511743Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9.13081930e-02, 2.11560354e-01, 3.67777061e-01, 5.68492124e-01,\n",
       "       8.24044420e-01, 1.14691458e+00, 1.55210823e+00, 2.05758473e+00,\n",
       "       2.68472560e+00, 3.45882970e+00, 4.40961189e+00, 5.57166667e+00,\n",
       "       6.98483857e+00, 8.69441463e+00, 1.07510228e+01, 1.32100846e+01,\n",
       "       1.61306351e+01, 1.95733000e+01, 2.35972171e+01, 2.82557340e+01,\n",
       "       3.35908224e+01, 3.96263450e+01, 4.63605930e+01, 5.37588577e+01,\n",
       "       6.17471385e+01, 7.02083075e+01, 7.89820280e+01, 8.78693628e+01,\n",
       "       9.66423075e+01, 1.05057574e+02, 1.12873064e+02, 1.19864881e+02,\n",
       "       1.25842595e+02, 1.30660906e+02, 1.34226572e+02, 1.36500409e+02,\n",
       "       1.37494923e+02, 1.37268669e+02, 1.35918592e+02, 1.33571532e+02,\n",
       "       1.30375825e+02, 1.26493586e+02, 1.22094009e+02, 1.17347705e+02,\n",
       "       1.12421962e+02, 1.07476667e+02, 1.02660572e+02, 9.81076004e+01,\n",
       "       9.39330242e+01, 9.02295425e+01, 8.70636263e+01, 8.44728002e+01,\n",
       "       8.24646905e+01, 8.10185006e+01, 8.00890700e+01, 7.96129967e+01,\n",
       "       7.95157804e+01, 7.97187921e+01, 8.01451251e+01, 8.07238318e+01,\n",
       "       8.13924958e+01, 8.20983823e+01, 8.27985372e+01, 8.34592011e+01,\n",
       "       8.40548441e+01, 8.45670392e+01, 8.49833179e+01, 8.52960937e+01,\n",
       "       8.55016939e+01, 8.55995188e+01, 8.55913271e+01, 8.54806402e+01,\n",
       "       8.52722529e+01, 8.49718369e+01, 8.45856236e+01, 8.41201536e+01,\n",
       "       8.35820817e+01, 8.29780275e+01, 8.23144641e+01, 8.15976363e+01,\n",
       "       8.08335031e+01, 8.00277000e+01, 7.91855163e+01, 7.83118844e+01,\n",
       "       7.74113774e+01, 7.64882151e+01, 7.55462733e+01, 7.45890979e+01,\n",
       "       7.36199211e+01, 7.26416791e+01, 7.16570304e+01, 7.06683749e+01,\n",
       "       6.96778727e+01, 6.86874620e+01, 6.76988771e+01, 6.67136650e+01,\n",
       "       6.57332016e+01, 6.47587064e+01, 6.37912566e+01, 6.28318001e+01])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.describe('I', 'std')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "demanding-quick",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-09T08:57:36.100836Z",
     "start_time": "2021-06-09T08:57:36.093842Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-20T11:59:32.515769Z",
     "iopub.status.busy": "2025-07-20T11:59:32.515711Z",
     "iopub.status.idle": "2025-07-20T11:59:32.519617Z",
     "shell.execute_reply": "2025-07-20T11:59:32.519365Z",
     "shell.execute_reply.started": "2025-07-20T11:59:32.515763Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  3.29910025,   3.62776131,   3.98885045,   4.3855017 ,\n",
       "         4.82113829,   5.29949646,   5.82465066,   6.40104   ,\n",
       "         7.03349593,   7.72727097,   8.48806838,   9.32207232,\n",
       "        10.23597843,  11.23702405,  12.33301761,  13.53215991,\n",
       "        14.84327656,  16.27617485,  17.84108711,  19.54887526,\n",
       "        21.41101667,  23.43957717,  25.6471723 ,  28.04692033,\n",
       "        30.65239319,  33.47757291,  36.53681885,  39.84484261,\n",
       "        43.41667139,  47.26755884,  51.41278083,  55.86724638,\n",
       "        60.64487274,  65.75772563,  71.21499997,  77.0119173 ,\n",
       "        83.05714736,  89.68192196,  96.51992189, 103.67789756,\n",
       "       110.57499067, 117.63390426, 125.60351983, 133.83705201,\n",
       "       141.82089434, 149.86470938, 158.02642572, 166.32334148,\n",
       "       174.59630486, 183.31543192, 191.09845306, 198.98857302,\n",
       "       206.7462819 , 214.24640271, 221.10420098, 226.31702835,\n",
       "       227.4981515 , 226.43305413, 221.54646612, 215.41970457,\n",
       "       207.23479059, 199.0726759 , 192.2052404 , 184.53562135,\n",
       "       175.82749443, 167.45797065, 159.79653319, 152.36858218,\n",
       "       145.13497606, 138.16426015, 131.37979768, 124.98736864,\n",
       "       118.83203189, 113.00349405, 107.26779183, 101.87196297,\n",
       "        96.8785725 ,  91.88719653,  87.08092414,  82.72530353,\n",
       "        78.63371299,  74.62934162,  70.75904865,  67.08024158,\n",
       "        63.58445447,  60.26346431,  57.10930373,  54.07894792,\n",
       "        51.27092965,  48.57212292,  46.01096257,  43.60049859,\n",
       "        41.27538817,  39.10654481,  37.05698604,  35.11643146,\n",
       "        33.27010365,  31.53054556,  29.87023404,  28.27151681])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.describe('I', '1%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "distinguished-greene",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-09T08:57:36.165232Z",
     "start_time": "2021-06-09T08:57:36.102023Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-20T11:59:32.522433Z",
     "iopub.status.busy": "2025-07-20T11:59:32.522319Z",
     "iopub.status.idle": "2025-07-20T11:59:32.526142Z",
     "shell.execute_reply": "2025-07-20T11:59:32.525931Z",
     "shell.execute_reply.started": "2025-07-20T11:59:32.522425Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  3.65409096,   4.44999933,   5.41809018,   6.59504569,\n",
       "         8.02513645,   9.7614126 ,  11.86744415,  14.41926427,\n",
       "        17.50712016,  21.23656093,  25.73290113,  31.14231927,\n",
       "        37.63204443,  45.3946612 ,  54.64683481,  65.61945164,\n",
       "        78.53777042,  93.67745005, 111.22094268, 131.51078407,\n",
       "       154.42309465, 180.22712578, 208.72084237, 240.031091  ,\n",
       "       273.50945248, 308.36300753, 344.80370411, 381.26046168,\n",
       "       418.19076193, 452.04422331, 484.41657824, 513.31541075,\n",
       "       539.11362991, 562.34288224, 581.66398142, 597.69624074,\n",
       "       609.83793888, 617.81359239, 626.03443709, 631.19449894,\n",
       "       634.12666923, 635.76266576, 637.10243336, 638.54626767,\n",
       "       636.62233793, 633.54186429, 632.7079822 , 629.66596489,\n",
       "       629.24286749, 626.66818288, 623.52980502, 620.75215268,\n",
       "       615.48227055, 613.42974287, 610.26252542, 604.94512233,\n",
       "       602.59777463, 597.28925972, 590.97648936, 586.37999943,\n",
       "       580.80717553, 576.37002183, 570.52490954, 564.98073866,\n",
       "       558.88726324, 551.43381215, 545.37007715, 538.49024243,\n",
       "       530.98177793, 523.20137439, 515.43462151, 507.47524754,\n",
       "       499.5685847 , 491.48203845, 483.36866426, 475.88068775,\n",
       "       468.71928596, 461.30644331, 453.77443204, 445.79942516,\n",
       "       438.55732034, 432.03551352, 425.03898478, 417.73594241,\n",
       "       410.08988735, 402.47086565, 395.89865602, 389.08676213,\n",
       "       382.53789455, 376.08233019, 369.68746443, 363.96808008,\n",
       "       357.92729597, 352.24079364, 345.35680382, 338.57459046,\n",
       "       332.98612651, 326.67908553, 320.60479199, 315.29018374])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.describe('I', '99%')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.11"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}