{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f104afac",
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded client.js from C:\\Users\\jjsan\\GitHub\\jupyter-notebook-mcp\\src\\client.js\n",
      "WebSocket server started on ws://localhost:8765\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <script>\n",
       "        (function(){\n",
       "    // Connect to WebSocket server\n",
       "    var ws = new WebSocket(\"ws://localhost:8765\");\n",
       "    \n",
       "    ws.onopen = function() {\n",
       "        // Identify as notebook client\n",
       "        ws.send(JSON.stringify({ role: \"notebook\" }));\n",
       "        console.log(\"Connected to WebSocket server as notebook client\");\n",
       "    };\n",
       "    \n",
       "    ws.onmessage = function(event) {\n",
       "        var data = JSON.parse(event.data);\n",
       "        \n",
       "        // Handle different action types\n",
       "        switch(data.type) {\n",
       "            case \"insert_and_execute_cell\":\n",
       "                handleInsertCell(data);\n",
       "                break;\n",
       "            case \"save_notebook\":\n",
       "                handleSaveNotebook(data);\n",
       "                break;\n",
       "            case \"get_cells_info\":\n",
       "                handleGetCellsInfo(data);\n",
       "                break;\n",
       "            case \"get_notebook_info\":\n",
       "                handleGetNotebookInfo(data);\n",
       "                break;\n",
       "            case \"run_cell\":\n",
       "                handleRunCell(data);\n",
       "                break;\n",
       "            case \"run_all_cells\":\n",
       "                handleRunAllCells(data);\n",
       "                break;\n",
       "            case \"get_cell_text_output\":\n",
       "                handleGetCellTextOutput(data);\n",
       "                break;\n",
       "            case \"get_cell_image_output\":\n",
       "                handleGetCellImageOutput(data);\n",
       "                break;\n",
       "            case \"edit_cell_content\":\n",
       "                handleEditCellContent(data);\n",
       "                break;\n",
       "            case \"set_slideshow_type\":\n",
       "                handleSetSlideshowType(data);\n",
       "                break;\n",
       "            default:\n",
       "                console.warn(\"Unknown message type:\", data.type);\n",
       "        }\n",
       "    };\n",
       "\n",
       "    function sendError(responseType, request_id, error, logMessage) {\n",
       "        var errorResponse = {\n",
       "            type: responseType,\n",
       "            request_id: request_id,\n",
       "            status: \"error\",\n",
       "            source: \"notebook\",\n",
       "            target: \"external\",\n",
       "            message: error.toString()\n",
       "        };\n",
       "        ws.send(JSON.stringify(errorResponse));\n",
       "        console.error(logMessage, error);\n",
       "    }\n",
       "    \n",
       "    // Handle inserting and executing a cell\n",
       "    function handleInsertCell(data) {\n",
       "        var request_id = data.request_id;\n",
       "        var position = data.position || 1;\n",
       "        var cell_type = data.cell_type || \"code\";\n",
       "        var content = data.content || \"\";\n",
       "        \n",
       "        try {\n",
       "            var cell = Jupyter.notebook.insert_cell_at_index(cell_type, position);\n",
       "            cell.set_text(content);\n",
       "            \n",
       "            // Function to capture and send output with truncation\n",
       "            var sendResult = function() {\n",
       "                var outputResult = extractCellOutputContent(cell, 1500);\n",
       "                \n",
       "                // Prepare response\n",
       "                var response = {\n",
       "                    type: \"insert_cell_result\",\n",
       "                    request_id: request_id,\n",
       "                    source: \"notebook\",\n",
       "                    target: \"external\",\n",
       "                    status: \"success\",\n",
       "                    cell_id: cell.cell_id || \"cell_\" + position,\n",
       "                    position: position,\n",
       "                    output_text: outputResult.text,\n",
       "                    is_truncated: outputResult.is_truncated,\n",
       "                    has_images: outputResult.images.length > 0,\n",
       "                };\n",
       "                \n",
       "                ws.send(JSON.stringify(response));\n",
       "                console.log(\"Cell inserted at position \" + position + (outputResult.is_truncated ? \" (output truncated)\" : \"\"));\n",
       "            };\n",
       "            \n",
       "            if (cell_type === \"code\") {\n",
       "                // Listen for execution completion\n",
       "                cell.events.one('finished_execute.CodeCell', function() {\n",
       "                    sendResult();\n",
       "                });\n",
       "                cell.execute();\n",
       "            } else if (cell_type === \"markdown\") {\n",
       "                cell.render();\n",
       "                sendResult(); // For markdown, send result immediately\n",
       "            } else {\n",
       "                sendResult(); // For other types, send result immediately\n",
       "            }\n",
       "        } catch (error) {\n",
       "            sendError(\"insert_cell_result\", request_id, error, \"Error inserting cell:\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    // Handle saving the notebook\n",
       "    function handleSaveNotebook(data) {\n",
       "        var request_id = data.request_id;\n",
       "        \n",
       "        try {\n",
       "            Jupyter.notebook.save_checkpoint();\n",
       "            \n",
       "            var response = {\n",
       "                type: \"save_result\",\n",
       "                request_id: request_id,\n",
       "                source: \"notebook\",\n",
       "                target: \"external\",\n",
       "                status: \"success\",\n",
       "                notebook_path: Jupyter.notebook.notebook_path\n",
       "            };\n",
       "            \n",
       "            ws.send(JSON.stringify(response));\n",
       "            console.log(\"Notebook saved successfully\");\n",
       "        } catch (error) {\n",
       "            sendError(\"save_result\", request_id, error, \"Error saving notebook:\");\n",
       "        }\n",
       "    }\n",
       "    \n",
       "    // Handle getting information about all cells\n",
       "    function handleGetCellsInfo(data) {\n",
       "        var request_id = data.request_id;\n",
       "        \n",
       "        try {\n",
       "            var cells = Jupyter.notebook.get_cells();\n",
       "            var cellsInfo = cells.map(function(cell, index) {\n",
       "                return {\n",
       "                    id: cell.cell_id || \"cell_\" + index,\n",
       "                    position: index,\n",
       "                    content: cell.get_text(),\n",
       "                    type: cell.cell_type,\n",
       "                    prompt_number: cell.input_prompt_number,\n",
       "                };\n",
       "            });\n",
       "            \n",
       "            var response = {\n",
       "                type: \"cells_info_result\",\n",
       "                request_id: request_id,\n",
       "                source: \"notebook\",\n",
       "                target: \"external\",\n",
       "                status: \"success\",\n",
       "                cells: cellsInfo\n",
       "            };\n",
       "            \n",
       "            ws.send(JSON.stringify(response));\n",
       "            console.log(\"Sent info for \" + cellsInfo.length + \" cells\");\n",
       "        } catch (error) {\n",
       "            sendError(\"cells_info_result\", request_id, error, \"Error getting cells info:\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    // Handle getting notebook information\n",
       "    function handleGetNotebookInfo(data) {\n",
       "        var request_id = data.request_id;\n",
       "        \n",
       "        try {\n",
       "            var nbInfo = {\n",
       "                notebook_name: Jupyter.notebook.notebook_name,\n",
       "                notebook_path: Jupyter.notebook.notebook_path,\n",
       "                kernel_name: Jupyter.notebook.kernel.name,\n",
       "                cell_count: Jupyter.notebook.get_cells().length,\n",
       "                modified: Jupyter.notebook.dirty,\n",
       "                trusted: Jupyter.notebook.trusted\n",
       "            };\n",
       "            \n",
       "            var response = {\n",
       "                type: \"notebook_info_result\",\n",
       "                request_id: request_id,\n",
       "                source: \"notebook\",\n",
       "                target: \"external\",\n",
       "                status: \"success\",\n",
       "                notebook_info: nbInfo\n",
       "            };\n",
       "            \n",
       "            ws.send(JSON.stringify(response));\n",
       "            console.log(\"Sent notebook info\");\n",
       "        } catch (error) {\n",
       "            sendError(\"notebook_info_result\", request_id, error, \"Error getting notebook info:\");\n",
       "        }\n",
       "    }\n",
       "    \n",
       "    // Handle running a specific cell by index\n",
       "    function handleRunCell(data) {\n",
       "        var request_id = data.request_id;\n",
       "        var index = data.index || 0;\n",
       "        \n",
       "        try {\n",
       "            Jupyter.notebook.select(index);\n",
       "            var cell = Jupyter.notebook.get_selected_cell();\n",
       "            \n",
       "            // Generate a cell_id if not available\n",
       "            var cell_id = cell.cell_id || Date.now().toString();\n",
       "            \n",
       "            // Function to send execution results back\n",
       "            var sendResult = function() {\n",
       "                var outputs = extractCellOutputContent(cell, 1500);\n",
       "\n",
       "                var resultMsg = {\n",
       "                    type: \"run_cell_result\",\n",
       "                    request_id: request_id,\n",
       "                    status: \"success\",\n",
       "                    source: \"notebook\",\n",
       "                    target: \"external\",\n",
       "                    cell_id: cell_id,\n",
       "                    index: index,\n",
       "                    output_text: outputs.text,\n",
       "                    is_truncated: outputs.is_text_truncated,\n",
       "                    has_images: outputs.images.length > 0,\n",
       "                };\n",
       "                ws.send(JSON.stringify(resultMsg));\n",
       "                console.log(\"Cell execution result sent\");\n",
       "            };\n",
       "            \n",
       "            // Listen for execution completion\n",
       "            cell.events.one('finished_execute.CodeCell', function() {\n",
       "                sendResult();\n",
       "            });\n",
       "            \n",
       "            // Execute the cell\n",
       "            cell.execute();\n",
       "            \n",
       "        } catch (error) {\n",
       "            sendError(\"run_cell_result\", request_id, error, \"Error running cell:\");\n",
       "        }\n",
       "    }    \n",
       "    \n",
       "    // Handle running all cells\n",
       "    function handleRunAllCells(data) {\n",
       "        var request_id = data.request_id;\n",
       "        \n",
       "        try {\n",
       "            Jupyter.notebook.restart_run_all();\n",
       "            \n",
       "            var response = {\n",
       "                type: \"run_all_cells_result\",\n",
       "                request_id: request_id,\n",
       "                status: \"success\",\n",
       "                source: \"notebook\",\n",
       "                target: \"external\",\n",
       "            };\n",
       "            \n",
       "            ws.send(JSON.stringify(response));\n",
       "            console.log(\"Running all cells\");\n",
       "        } catch (error) {\n",
       "            sendError(\"run_all_cells_result\", request_id, error, \"Error running all cells:\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    // Handle getting output from a specific cell\n",
       "    function handleGetCellTextOutput(data) {\n",
       "        var request_id = data.request_id;\n",
       "        var index = data.index || 0;\n",
       "        var maxLength = data.max_length || 1500;\n",
       "        \n",
       "        try {\n",
       "            var cells = Jupyter.notebook.get_cells();\n",
       "            \n",
       "            if (index < 0 || index >= cells.length) {\n",
       "                throw new Error(\"Cell index out of range\");\n",
       "            }\n",
       "            \n",
       "            var cell = cells[index];\n",
       "            var outputContent = extractCellOutputContent(cell, maxLength);\n",
       "            \n",
       "            var response = {\n",
       "                type: \"get_cell_text_output_result\",\n",
       "                request_id: request_id,\n",
       "                status: \"success\",\n",
       "                source: \"notebook\",\n",
       "                target: \"external\",\n",
       "                cell_id: cell.cell_id || \"cell_\" + index,\n",
       "                index: index,\n",
       "                output_text: outputContent.text,\n",
       "                is_truncated: outputContent.is_text_truncated,\n",
       "                has_images: outputContent.images.length > 0\n",
       "            };\n",
       "            \n",
       "            ws.send(JSON.stringify(response));\n",
       "            console.log(\"Cell output retrieved for index \" + index + \n",
       "                    (outputContent.is_text_truncated ? \" (output truncated)\" : \"\") + \n",
       "                    (outputContent.images.length > 0 ? \" (contains images)\" : \"\"));\n",
       "        } catch (error) {\n",
       "            sendError(\"get_cell_text_output_result\", request_id, error, \"Error getting cell output:\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    function handleGetCellImageOutput(data) {\n",
       "        var request_id = data.request_id;\n",
       "        var index = data.index || 0;\n",
       "        \n",
       "        try {\n",
       "            // Obtener la celda en el índice especificado\n",
       "            var cells = Jupyter.notebook.get_cells();\n",
       "            \n",
       "            if (index < 0 || index >= cells.length) {\n",
       "                throw new Error(\"Cell index out of range\");\n",
       "            }\n",
       "            \n",
       "            var cell = cells[index];\n",
       "            var outputContent = extractCellOutputContent(cell);\n",
       "            \n",
       "            var response = {\n",
       "                type: \"get_cell_image_output_result\",\n",
       "                request_id: request_id,\n",
       "                status: \"success\",\n",
       "                source: \"notebook\",\n",
       "                target: \"external\",\n",
       "                cell_id: cell.cell_id || \"cell_\" + index,\n",
       "                index: index,\n",
       "                images: outputContent.images\n",
       "            };\n",
       "            \n",
       "            ws.send(JSON.stringify(response));\n",
       "            console.log(\"Cell image output retrieved for index \" + index + \" (\" + outputContent.images.length + \" images)\");\n",
       "        } catch (error) {\n",
       "            sendError(\"get_cell_image_output_result\", request_id, error, \"Error getting cell image output:\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    function handleEditCellContent(data) {\n",
       "        var request_id = data.request_id;\n",
       "        var index = data.index || 0;\n",
       "        var content = data.content || \"\";\n",
       "        var execute = data.execute || false;\n",
       "        \n",
       "        try {\n",
       "            var cells = Jupyter.notebook.get_cells();\n",
       "            if (index < 0 || index >= cells.length) {\n",
       "                throw new Error(\"Cell index out of range\");\n",
       "            }\n",
       "            \n",
       "            var cell = cells[index];\n",
       "            cell.set_text(content);\n",
       "            \n",
       "            var sendResult = function() {\n",
       "                var outputResult = extractCellOutputContent(cell, 1500);\n",
       "                \n",
       "                var response = {\n",
       "                    type: \"edit_cell_content_result\",\n",
       "                    source: \"notebook\",\n",
       "                    target: \"external\",\n",
       "                    request_id: request_id,\n",
       "                    status: \"success\",\n",
       "                    cell_id: cell.cell_id || \"cell_\" + index,\n",
       "                    index: index,\n",
       "                    output_text: execute ? outputResult.text : \"\",\n",
       "                    is_truncated: execute ? outputResult.is_text_truncated : false,\n",
       "                    has_images: execute ? outputResult.images.length > 0 : false\n",
       "                };\n",
       "                \n",
       "                ws.send(JSON.stringify(response));\n",
       "                console.log(\"Cell content updated at index \" + index + \n",
       "                    (execute ? \" and executed\" : \"\") +\n",
       "                    (outputResult.is_text_truncated ? \" (output truncated)\" : \"\"));\n",
       "            };\n",
       "            \n",
       "            if (cell.cell_type === \"code\" && execute) {\n",
       "                cell.events.one('finished_execute.CodeCell', function() {\n",
       "                    sendResult();\n",
       "                });\n",
       "                cell.execute();\n",
       "            } else if (cell.cell_type === \"markdown\") {\n",
       "                cell.render();\n",
       "                sendResult();\n",
       "            } else {\n",
       "                sendResult();\n",
       "            }\n",
       "        } catch (error) {\n",
       "            sendError(\"edit_cell_content_result\", request_id, error, \"Error updating cell content:\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    function handleSetSlideshowType(data) {\n",
       "        var request_id = data.request_id;\n",
       "        var index = data.index || 0;\n",
       "        var slideshow_type = data.slideshow_type || \"-\";\n",
       "        \n",
       "        try {\n",
       "            var cells = Jupyter.notebook.get_cells();\n",
       "            if (index < 0 || index >= cells.length) {\n",
       "                throw new Error(\"Cell index out of range\");\n",
       "            }\n",
       "            \n",
       "            var cell = cells[index];\n",
       "            \n",
       "            var valid_types = [\"slide\", \"subslide\", \"fragment\", \"skip\", \"notes\", null, \"-\"];\n",
       "            if (!valid_types.includes(slideshow_type)) {\n",
       "                slideshow_type = null;\n",
       "            }\n",
       "            \n",
       "            if (slideshow_type === \"-\") {\n",
       "                slideshow_type = null;\n",
       "            }\n",
       "            \n",
       "            if (!cell.metadata.slideshow) {\n",
       "                cell.metadata.slideshow = {};\n",
       "            }\n",
       "            \n",
       "            if (slideshow_type === null) {\n",
       "                delete cell.metadata.slideshow.slide_type;\n",
       "            } else {\n",
       "                cell.metadata.slideshow.slide_type = slideshow_type;\n",
       "            }\n",
       "            \n",
       "            var response = {\n",
       "                type: \"set_slideshow_type_result\",\n",
       "                source: \"notebook\",\n",
       "                target: \"external\",\n",
       "                request_id: request_id,\n",
       "                status: \"success\",\n",
       "                cell_id: cell.cell_id || \"cell_\" + index,\n",
       "                index: index,\n",
       "                slideshow_type: slideshow_type\n",
       "            };\n",
       "            \n",
       "            ws.send(JSON.stringify(response));\n",
       "            console.log(\"Cell slideshow type updated at index \" + index + \" to \" + \n",
       "                        (slideshow_type === null ? \"default/none\" : slideshow_type));\n",
       "        } catch (error) {\n",
       "            sendError(\"set_slideshow_type_result\", request_id, error, \"Error updating cell slideshow type:\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    // Utility function to extract text output from a cell\n",
       "    function extractCellOutputContent(cell, maxTextLength) {\n",
       "        var result = {\n",
       "            text: \"\",\n",
       "            is_text_truncated: false,\n",
       "            images: []\n",
       "        };\n",
       "        \n",
       "        if (cell.cell_type === \"code\" && cell.output_area && cell.output_area.outputs) {\n",
       "            cell.output_area.outputs.forEach(function(output) {\n",
       "                if (output.text) {\n",
       "                    result.text += output.text;\n",
       "                } else if (output.data && output.data[\"text/plain\"]) {\n",
       "                    result.text += output.data[\"text/plain\"];\n",
       "                }\n",
       "                \n",
       "                if (output.data) {\n",
       "                    for (const format of [\"image/png\", \"image/jpeg\", \"image/svg+xml\"]) {\n",
       "                        if (output.data[format]) {\n",
       "                            result.images.push({\n",
       "                                format: format,\n",
       "                                data: output.data[format]\n",
       "                            });\n",
       "                        }\n",
       "                    }\n",
       "                }\n",
       "            });\n",
       "            \n",
       "            if (maxTextLength && result.text.length > maxTextLength) {\n",
       "                result.text = result.text.substring(0, maxTextLength);\n",
       "                result.is_text_truncated = true;\n",
       "            }\n",
       "        }\n",
       "        \n",
       "        return result;\n",
       "    }\n",
       "        \n",
       "    ws.onerror = function(error) {\n",
       "        console.error(\"WebSocket error:\", error);\n",
       "    };\n",
       "    \n",
       "    ws.onclose = function() {\n",
       "        console.log(\"WebSocket connection closed\");\n",
       "    };\n",
       "})();\n",
       "        </script>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div style=\"background-color: #f0f0f0; padding: 10px; border-radius: 5px; margin: 10px 0;\">\n",
       "        <h3>Jupyter MCP Integration Setup Complete</h3>\n",
       "        <p>WebSocket server is running on port 8765</p>\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jupyter client connected\n",
      "External client connected (likely MCP server)\n",
      "External client connected (likely MCP server)\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.append('C:/Users/jjsan/GitHub/jupyter-notebook-mcp/src') # absolut or relative path to src folder\n",
    "from jupyter_ws_server import setup_jupyter_mcp_integration\n",
    "setup_jupyter_mcp_integration();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ff91fbe",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Data Visualization with Seaborn\n",
    "## A Powerful Library for Statistical Graphics in Python"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81276798",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# What is Seaborn?\n",
    "\n",
    "- A statistical visualization library built on top of Matplotlib\n",
    "- Created by Michael Waskom in 2012\n",
    "- Provides a high-level interface for drawing attractive statistical graphics\n",
    "- Designed specifically for statistical data exploration and visualization\n",
    "- Integrates closely with pandas DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "87006d69",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Set the aesthetics for all plots\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "plt.figure(figsize=(8, 4))\n",
    "sns.palplot(sns.color_palette(\"tab10\"))\n",
    "plt.title(\"Default Seaborn Color Palette\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56257336",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Long vs. Wide Data Format\n",
    "\n",
    "## Wide Format\n",
    "- Each variable has its own column\n",
    "- Each row represents a single observational unit\n",
    "- Common in spreadsheets and traditional data entry\n",
    "\n",
    "## Long Format (Tidy Data)\n",
    "- Each row is a single observation\n",
    "- Each column is a variable\n",
    "- Each cell is a single value\n",
    "- Preferred for statistical analysis and visualization in Seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4b096319",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wide Format:\n"
     ]
    },
    {
     "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>student</th>\n",
       "      <th>math</th>\n",
       "      <th>science</th>\n",
       "      <th>history</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alice</td>\n",
       "      <td>90</td>\n",
       "      <td>95</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>85</td>\n",
       "      <td>80</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>70</td>\n",
       "      <td>75</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   student  math  science  history\n",
       "0    Alice    90       95       80\n",
       "1      Bob    85       80       90\n",
       "2  Charlie    70       75       85"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a simple wide-format dataset\n",
    "wide_data = pd.DataFrame({\n",
    "    'student': ['Alice', 'Bob', 'Charlie'],\n",
    "    'math': [90, 85, 70],\n",
    "    'science': [95, 80, 75],\n",
    "    'history': [80, 90, 85]\n",
    "})\n",
    "\n",
    "print(\"Wide Format:\")\n",
    "display(wide_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "235f1349",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Long Format (preferred for Seaborn):\n"
     ]
    },
    {
     "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>student</th>\n",
       "      <th>subject</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alice</td>\n",
       "      <td>math</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>math</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>math</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alice</td>\n",
       "      <td>science</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bob</td>\n",
       "      <td>science</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>science</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Alice</td>\n",
       "      <td>history</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Bob</td>\n",
       "      <td>history</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>history</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   student  subject  score\n",
       "0    Alice     math     90\n",
       "1      Bob     math     85\n",
       "2  Charlie     math     70\n",
       "3    Alice  science     95\n",
       "4      Bob  science     80\n",
       "5  Charlie  science     75\n",
       "6    Alice  history     80\n",
       "7      Bob  history     90\n",
       "8  Charlie  history     85"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convert to long format\n",
    "long_data = wide_data.melt(id_vars=['student'], \n",
    "                          var_name='subject', \n",
    "                          value_name='score')\n",
    "\n",
    "print(\"Long Format (preferred for Seaborn):\")\n",
    "display(long_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fa8be1b",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Advantages of Seaborn over Matplotlib\n",
    "\n",
    "- **Simplified API**: Fewer lines of code for complex visualizations\n",
    "- **Statistical Integration**: Built-in statistical estimation and visualization\n",
    "- **Beautiful Defaults**: Aesthetically pleasing default styles and color palettes\n",
    "- **DataFrame Integration**: Works directly with pandas DataFrames\n",
    "- **High-Level Functions**: Specialized plots like violin plots, swarm plots\n",
    "- **Figure-Level vs. Axes-Level**: Both approaches supported\n",
    "- **Themes and Styles**: Easily customizable visual aesthetics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "48fd1c49",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create sample data\n",
    "x = np.linspace(0, 10, 100)\n",
    "y = np.sin(x)\n",
    "df = pd.DataFrame({'x': x, 'y': y})\n",
    "\n",
    "# Matplotlib vs Seaborn comparison\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))\n",
    "\n",
    "# Matplotlib plot\n",
    "ax1.plot(x, y)\n",
    "ax1.set_title('Matplotlib')\n",
    "ax1.set_xlabel('x')\n",
    "ax1.set_ylabel('sin(x)')\n",
    "\n",
    "# Seaborn plot\n",
    "sns.lineplot(data=df, x='x', y='y', ax=ax2)\n",
    "ax2.set_title('Seaborn')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0179ada6",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Commonly Used Seaborn Functions\n",
    "\n",
    "## Relational Plots\n",
    "- `scatterplot()`: Show relationship between two variables\n",
    "- `lineplot()`: Show trends over a continuous variable\n",
    "\n",
    "## Categorical Plots\n",
    "- `barplot()`: Show point estimates and confidence intervals\n",
    "- `boxplot()`: Show distributions with quartiles and outliers\n",
    "- `violinplot()`: Show distributions with kernel density estimation\n",
    "- `swarmplot()`: Show all observations with no overlapping points\n",
    "\n",
    "## Distribution Plots\n",
    "- `histplot()`: Show univariate or bivariate distributions\n",
    "- `kdeplot()`: Show estimated probability density functions\n",
    "- `jointplot()`: Show bivariate distributions with marginals\n",
    "\n",
    "## Matrix Plots\n",
    "- `heatmap()`: Show 2D matrix data with color encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "46d7aab6",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "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>group</th>\n",
       "      <th>value</th>\n",
       "      <th>category</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C</td>\n",
       "      <td>6.476933</td>\n",
       "      <td>Category 2</td>\n",
       "      <td>2.807724</td>\n",
       "      <td>8.554606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>D</td>\n",
       "      <td>5.342737</td>\n",
       "      <td>Category 2</td>\n",
       "      <td>0.243160</td>\n",
       "      <td>7.036579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A</td>\n",
       "      <td>4.768703</td>\n",
       "      <td>Category 1</td>\n",
       "      <td>6.454723</td>\n",
       "      <td>4.741738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C</td>\n",
       "      <td>4.397793</td>\n",
       "      <td>Category 2</td>\n",
       "      <td>1.771107</td>\n",
       "      <td>0.978342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C</td>\n",
       "      <td>2.042956</td>\n",
       "      <td>Category 2</td>\n",
       "      <td>9.404586</td>\n",
       "      <td>4.916159</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  group     value    category         x         y\n",
       "0     C  6.476933  Category 2  2.807724  8.554606\n",
       "1     D  5.342737  Category 2  0.243160  7.036579\n",
       "2     A  4.768703  Category 1  6.454723  4.741738\n",
       "3     C  4.397793  Category 2  1.771107  0.978342\n",
       "4     C  2.042956  Category 2  9.404586  4.916159"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a more robust dataset for our examples\n",
    "np.random.seed(42)\n",
    "n = 100\n",
    "\n",
    "# Generate sample data\n",
    "df = pd.DataFrame({\n",
    "    'group': np.random.choice(['A', 'B', 'C', 'D'], size=n),\n",
    "    'value': np.random.normal(loc=5, scale=2, size=n),\n",
    "    'category': np.random.choice(['Category 1', 'Category 2'], size=n),\n",
    "    'x': np.random.uniform(0, 10, size=n),\n",
    "    'y': np.random.uniform(0, 10, size=n),\n",
    "})\n",
    "\n",
    "# Display first few rows\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e608ee3",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Live Demonstration: Seaborn vs. Matplotlib\n",
    "\n",
    "Let's compare how to create common plots using both libraries:\n",
    "- Bar plots\n",
    "- Line plots\n",
    "- Scatter plots\n",
    "\n",
    "We'll see how Seaborn simplifies code while providing more attractive defaults."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a1421a0",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Bar Plots\n",
    "\n",
    "Bar plots show the relationship between a categorical and a numeric variable, typically showing means/counts and (optionally) confidence intervals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3f52177e",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar plot comparison\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))\n",
    "\n",
    "# Calculate means for Matplotlib\n",
    "means = df.groupby('group')['value'].mean()\n",
    "\n",
    "# Matplotlib bar plot\n",
    "ax1.bar(means.index, means.values, color='skyblue')\n",
    "ax1.set_title('Matplotlib Bar Plot')\n",
    "ax1.set_xlabel('Group')\n",
    "ax1.set_ylabel('Mean Value')\n",
    "\n",
    "# Seaborn bar plot (automatically calculates mean and CI)\n",
    "sns.barplot(data=df, x='group', y='value', ax=ax2)\n",
    "ax2.set_title('Seaborn Bar Plot')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16d67a4a",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Line Plots\n",
    "\n",
    "Line plots show the relationship between two numeric variables, especially useful for time series or trends."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "81828063",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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861//ElWdVF1JSVNKIErGsATJIFBCleIvik2pYGDixIlCekrS6yeDsN///vfiRlDykTRrabKbpL96Q3EVvdeTTz4pXk/dUFJXGiUKyVyMigJosp/GRclMivdo8jyQfPzxx+LWH0qsDiZBS/zxj38Un+eNN94QHhZUgEHxPsW1/XV25WD06NEiVqZt+tBDD4ntSclzMn3rHaNSbE8x7d133y0M3b5OboFhmPAgqjtQitsMwzARBrWPUfKPgjlK3jIMwzAMwzAMwzAMw3wdLF7CMAzDMAzDMAzDMAzDMAyjEJygZRiGYRiGYRiGYRiGYRiGUQiWOGAYhmEYhmEYhmEYhmEYhlEIrqBlGIZhGIZhGIZhGIZhGIZRCE7QMgzDMAzDMAzDMAzDMAzDKAQnaBmGYRiGYRiGYRiGYRiGYRRCp9SK1cb27dtBcr56vV7poTAMwzAMw6iCrq4uREVFoaioSOmhqB6OZRmGYRiGYZSLZbmCViYooA2W3xqtx+l0Bm19jP/wPgs/eJ+FH7zPwhPeb+FHMPdZMOOrSIdjWeZ08D4LP3ifhR+8z8IT3m/hR3eIxrJcQSsTUrXB2LFjA74uh8OB4uJijBo1ChaLJeDrY/yH91n4wfss/OB9Fp7wfgs/grnPdu/eHdD3Z07CsSxzOnifhR+8z8IP3mfhCe+38MMRorEsV9AyDMMwDMMwDMMwDMMwDMMoBCdoGYZhGIZhGIZhGIZhGIZhFIITtAzDMAzDMAzDMAzDMAzDMArBCVqGYRiGYRiGYRiGYRiGYRiF4AQtwzAMwzAMwzAMwzAMwzCMQuiUWjHDMAzDMIza6O7uhtvthsvlglrp7OzsuddoBj/Xr9frodVqZRwZwzAMwzAM4w8cyyoXy3KClmEYhmEYRoZgtqmpCXV1dSKoVTMejwc6nQ5VVVV+BbVEXFwc0tLSEBUVJdv4GIZhGIZhmLODY1nlY1lO0DIMwzAMw/hJdXW1CGpjYmLEjYI+tSYdKWinigOj0TjoqgG6CHA4HKitrRV/p6enyzxKhmEYhmEY5kzhWFb5WJYTtAzDMAzDMH4Gec3NzUhOTkZSUhLUjlRVYTKZ/GrrMpvN4p4C25SUFJY7YBiGYRiGUQCOZUMjlmWTMIZhGIZhGD/o6uoSs+hWq1XpoYQdFoulZxsyDMMwDMMwwYdj2dCIZTlByzAMwzAMIwNqbQMLJLzNGIZhGIZhQgOOy5TdZmGXoH3mmWdwyy23nPY5jY2N+NGPfoQpU6Zg6tSp+O1vf4v29vY+z/n0009x0UUXYdy4cbjiiiuwfv36AI+cYRiGYRiGiXQ4lmUYhmEYJlKhSl1GBQna//73v3jyySe/9nn3338/jhw5gpdeegl//etfsXLlSvzmN7/p+f+GDRvwwAMP4Prrr8d7772HGTNm4J577kF5eXmAPwHDMAzDMAwTqXAsyzAMwzBMpLJ06VL85Cc/UXoYIUtYJGhrampw77334vHHH8eIESNO+9zt27dj06ZN+L//+z8UFhaKgPV3v/sdPvjgA/E+xHPPPYeFCxfi1ltvxciRI8UXhJ77n//8J0ifiGEYhmEYhokUOJZlGIZhGCbSoYnn48ePKz2MkCUsErR79+6FXq/Hhx9+iPHjx5/2uVu2bBHOcxSsSlBrGOlCbN26FR6PB9u2bRPBbm+mTZuGzZs3B+wzMAzDMAzDMJEJx7IMwzAMwzDM6dAhDDjnnHPE7UygyoL09PQ+jxkMBsTFxYlMfUtLCxwOB9LS0vo8JyUlBdXV1X5radB7BxKX24N3lh2A22lHZmZg18XIh6Qb118/jgldeJ+FH2e7zxwdXTAZdNBoWAw/mHicHYjSGxAVpVHNb62zs1Mkzdxut7iFO3a7HU888QS++OILsUyVmVKF5o4dO0SL/r59+6DT6TB//nw8+OCDIs4idu3ahcceewzFxcUiIUlJQ3ptRkbGgOui7UXbjvY/3Q8UW6nBsIJj2V7rcLvQtP59GNrdcGRmBnRdjHyo4VgdafA+U/8+83Q6EGUw9cRUTHDo6HTBoNf2XEOo4bcWjFj2tttu65lIzs3NRVJSkogT//SnP/V53qJFi8TENHUPUbfQpZdeKrbt+++/L2LCefPm4ac//WlP7ClNbj/11FPYs2ePiJkWLFggpKASEhJOq4NL9/5+Xjlj2bBI0J4NtFFoh/THaDSKL11HR4f4u/9zpP/7Q1dXl7ggCST1LV14d5W3vW19ySZcUBSLkemmgK6TkY/Dhw8rPQTmLOF9pr595unuxsrdLVi1txU2kxZ5Q03IG2bGiBQjtJysDQgaewP0tQegry2FrvEY3HFD0TbhSnSbbKr5rVGycqA4goKyzq6vBmvBwKjXDCq5+YMf/EDon5Lm6dChQ/H888/jm9/8Jv72t7/hrrvuwpVXXimSsvX19Xj00Udx55134pVXXhGv/fa3vy3+T6ZWlEj8wx/+gJ/97Gf417/+NeC6aJu5XC4cPHjwlOMZKK5TM2qPZel4ELv5A1gBNBzeiMq8hXAlZQV0nYx8hPuxOhLhfabCfdbdDVPZapjK16LbaIMzNQddKTlwJWQCGm2whhlR1Le6UHqsHSXH2nG0zolhSQZcOycR0Wat6mNZuaAJ+1/84hdimRKsX375Jd555x0RT1qtFBVAFAIcPXpUxJEU71Ac/dprryEzM1PEpQ0NDSIePXTokJBLkLqLKP6kpC7FpRR/Pv300yIhTPGpyXTqfJkcn1fOWFZ1CVra+E6nc8CNZrFYRPBK9H8O/d9sNvu1bqoUGTVqFAJNs+sg3lp2ELVNXXhl+QkU5STh5gtGY2jKyQtd5uzp9njQ7WyHp9OO7k6HmJE8eW+Hp7MdxmH5MAzNH9TFFh2wSXfO3+8ZExx4n6lzn7V3uvCPd/Zgc3Gr+Lu13Y3NB+ziZjXpMDE3GVPykzF+dBJMBg5wBwsFU67aw+go34rOsq1w1R/r839d0zEkbH4FcZfeD3fckLD/rVEMUVVVJWKM3kEgbYeHnl6HkiONiowrf0Q8Hvn2zLNK0lLAu3btWqFxOmvWLPEYVTBQ1eyrr74qKh5+9atfic+cn5+PP//5zyIhS5ULEyZMQFNTk6iWzcrKgkajEZW4FEyfLjimCwIKvKUYrTdlZWWINCIhlm1CKxwb3oGurQ7RW16HMWs8oufcAF3ikICvW814PN1wdLrg6HDB3t4Fe4dvuaNL3NOtMCseBVkDVxSdDo6Lwg/eZ+rcZx5nO5o/ewad5VvF31GdrTAd3SpuUUYLjFlFMI2aBMOIsdDouZBrsFAMd6iqFZuLa7G5uA4VtW19/l9xwokXljTgRzeMx9Akg2pjWTkpKChAdHS0WKZkKnX+kHkqGaFSLEl8+umnYjtSZS1BMSzFky+88ELPa+l1ZKZKseecOXNEMpbizmeeeQZarff6bfLkyaLydvHixbjxxhsxYAFFZ6f4vHJ0askVy6ouQUvtXkuWLOnzGAWwdMFAO5LKoCm4ra2t7fMc+js1NdWvddOOpfcONBfPykaatR17qnT4fGMFtu8/gZ1l9bhwxgjccH4uYm1f/VJEKq6WE2g/tAvuDjs8dKNEa4ej17L35vYlY78O+4b3kXL592EbM2dQ46EDdjC+I4x88D5Tzz6rOtGGh1/YgoqaVui0Gtx75TgkxBixYU81Nu49juY2J1bvPC5uBp0GRbkpmD4mHVMKUvm4eoZtyx1H98G+fxPspZvgbq0/+U+NFubhhbDkTIUxNQt1i/+JrhPH0PC/PyDm3DsATXJY/9YocKQbBYVSYBga7flRYjxnMwYpiJw4cWLPZ6H9QlWwF110kUjaSu9H9yR7QAEzvY5a+KnC9uGHHxbVDdOnTxdtaBdeeGGf7dIbepy2He3/gS4I1CBvcLZEQiyLyYtQrU9BRuM+OHYuQeehneg8vBsxE89H/JxrobXGBn4MYUJdYzt2HqhFW7uUdO3y3rd3oc13Lz1GCdiv450VwP+7cRLmTxw6qPGE87E6UuF9pp591tVwHNX/e1TEUNDqkHTB3dDZ4r2x1/5N8Dha0FGyVtyidAaYs8bDmjsVltGTobXEKPJZwgmSk9xbXo8Ne46L24lmb8cKQV12Y0cmYfqYNGQNicXf/7dTXFP85vktuPuyfKSaocpYVm6kuI7WQVr7kyZNwkcffYRrrrlGVMx+9tlnuPvuu3vGQM8/99xz+8gZnHfeeSIhSpWzlMjduXOn6PSi8UvSBcOHDxfvv379etxyyy1fGYcka0Dv7+/nlTOWVV2CdsqUKcIhl1rzaKcQ5IRL0M6njUMXHfQYfQkkNm7cKLLs4YLFqMFtF+Xisnmj8dLHe0WC4ZO1h7BiawWuXZiLS+dkQa+L7OovZ+0RVP7n56Iq9mygk5nGZPXejHRvEcue9la0H9yJ2g+fEvqJ1lzvrA7DMKHP1pIa/OnVreICNiHGhJ/dPgW5w70VRFMK0vAdz3iUHG4Qwdj63cdR0+DAxr2UuK0GqR4UZidh+tg0kbBNiQ/PwCsQUBWHo3wHHPs3wVG2VUx4SUTpTbCMLIKFLgxGToTWfLLLY8jtj4pjKb2u5YvnYB4+Bd25OVAbFHP83/dmo9OpjC6t0XB2yVmCAt5TIQW9Az1OlZfEj3/8Y1GpQNUQFBT//ve/x7///W+hGxZpUgWDJVJi2W6DGTHzb0bCtEvQsOwV7/Fg62do27MKcbOvRuzkixCl836vIpXDx1vw4N9Wi+6Ps4G0EW1mHaxmPawmvfferEer3Ynt++vwxOvbYNRrMWNsX61jhmFCF0f5dtS+/4SItbS2BKRe/QBMQ7yxk2X0JCRdeA86jpWKY6m9dCNcTbVwHNgsbojSwJSZL65frTlToYtNVvrjhAx0fN1WUiuuATYX14hrBQnqppuUlyqSspPzU2GznIxjHr9/jjiWUh7mn+/txbRcG3JylJG0CmeuvvpqUQRAGvtbt24V3gdXXHFFn+f0n3ymZGh8fDyam5uFnAHpvlLnF936M1BFaygT9glaynxT6xxVb1C2mpxxKWj94Q9/KDQqyOiAWvFoJ0s79o477sA999wjSqznzp0rdC9Ib4t00sKNIck2/PyOadhddgL//nAPDlY248WP92LxukO445JCzByXHpHVJ67WRlS/+YhIzuoTh8CQlgWtSLZKiVdLvySsFVrf46e6GOju9qDuo3+gbfcK1Lz3F6Rd+xAs2ROC/tkYhjlzKHH07vIyvLx4HzzdQN7weDx0+1SRpO0NzYoXZieK252XFoqL4g27afa8GgermrG7/IS4Pff+HowcGosZY9JFsjYzLTrijrGutiY4DmwRFwDUodDtPhnIUtWbZfQUEfybssZCoxs4IacxmsWFRePq/6Fp9VswHdmMxncfg+nqB6G1eNuX1AJ9P0zG8Am3qNqA2L17N2bMmCGWSVfr/PPPF8Fz/9a9kpIStLW1ideR9tZ//vMfEWjfcMMN4kbBNiVs6Xnjxo1T5DOFOpEeyxoSM5B2zU/QfmQP6r98Cc6aQ2hY+rJI1iaccyusedMj7jhLNLR04HfPbxDJA5Ixyx4SK5Kstn5J157Heh7XnbJIgyQQ/vrmdizbUoHHXtmCX35zGibmpgT9szEMc3axbPOGD9Cw/L+kyQfjkFykXvUAdNHxfZ4XRd1KmQXilnDubaJYyVHqrayl42rHkb3iVv/FCzCkZfcka/XJwyLuGNvY2oFNe2tEUnbngTp0uU4mVuNsRkwtpKKMNIwfnSwmvAbCYtLjodum4o0vS/H6F6XYWNqGR17ejodun4YYK09InylkCEadV1Q5u2XLFtGp1T8h29jY+JW4iR4jAzDSrqXv7+23346LL774K+8fbpIT4XPFcAroYoFKnv/4xz8K3QraOX//+9+FqDCJAlPGnHb6Qw891POa2bNn45FHHhFaFaSNRlpbZF4hXZSEI2NHJeGJH8zD8q0VeHlxsaj+evTlzcgfkYC7Lh+DnMy+B3C1O4TX/O+PQt5An5CBjNv+AK3Z/wt+csdMvuQ76O7qhL1kPWr+939Iu+GX4iTIMEzo0eF04W9v7cCq7ZXi7/OnDce9V4792u4COo9kZcSK2w0X5KG63i4StRTEFR+qR/mxZnF79bMSpCdZRbKWJsOkilw1QsfTtn1rhXRB57FSulzo+Z8uPk20z1lzpsE4ZLS4QDjTY2rC3OuAuHQ0Ln4azop9qHzxQaRe/RMYU0cE8NMwp4M0vCgZS3EUJQcpSH722WeFTtcbb7whkq0USFPMRYlZSghSkpCSufT3J598IlrUKHlIFQ7vvfceYmNjkZ2drfRHC1k4lvViHj4GQ775GNp2r0TD8tdE9Vftu4/DODQPiQtvh2nIaESSQ/jvX9go5A2GJFvx2H1zEN2rcmuwkOP4/ddOEFX9a3dV4Q8vbsLv7pkhJicZhgk9PF2dqPvkadj3rhF/R09YiKQL7vra7gI6j1AsRbf4udeiq6lGxHA0ud5RUQJn9UFxa1z5ui+OmyYmw6SKXDVCx9PVOypFPF9ypIF81npIT7Ri+lgqvkgT8fyZmgbTMfXGC/KQkWjCU//bhT0HG/D/nlyJn98xVVxHMF+FYkOqeJUgSQiS0Pr4449RXl4uYpv+rFq1Skg9SZ1YS5cuFcUDFHvabDYRh1KRwNixY3teQ7Eo6dSS1FYwtPXlIqr7VP1qzFlBlSZE7y9FoKBKCqqSIHOOgTROKKh7b0UZ3llR1tNWSTpTt1yUr/rWXKpyrXnncThKN0JjjsaQ2/8IfYK87VtULVb9v8fQXr4NUQYz0m/6DUwZo/zaZ0zowfssvPdZWwfEhSdVv1KQdfcVY3HRzBF+Vwg0t3Vi095qrN9zHDv2951xv/WifFxzrvoCW2ddBSpf+im6nSd1uIzpI4WeLCVm9Un+VV7QfivdtBIJez6Eu7kWUXojki+9D7Z8b/VmOEBBIJlrUXIzUMYKwaS1tRWPPfYYvvjiCxEQU0Unue3m5eUJ2QJKCNJvjYLihQsX4kc/+lGPNtj27duFcRj9nyocyDjsgQceEFq1g9l2wYyvIp1QimVpsr15w4do2vC+mBgnbGPmImH+japvzaUqVyqyILkdSso+/v05yEiS1wiYzl2PvLQJW4prYDbq8PC9M7+2mIPjovCD91l47zN9Vxtq/veYqH4lLf/E8+5EzKQL/I5l3fZm2KkTqnTjVzqhEhbchLiZXrMmNXG0ugU/fmoV2jtPSk6NGhYnErKiIy7Vv4442m8r1u/Cu+tbUNPYLiSmfnj9RMwan4FwIVix7H333SdixT/96U8isUqT+Dt27MB1110nltesWdNHEov8Dci8jCprb731VjGp/Ze//EV0ZZGEFkGvocIA8jy47LLLRPxJpmKkTfvKK6+IWLQ/9Bz6zPRZ/dWglTOW5QStCoNaifrmdrzyabFoY6K9TKY335g/CledM1oEY2qkful/REBPoukZlDgdlh+w2UySUOg4sgcakw3pN//2tBVfHCCFH7zPwnefeUypePLN3WixOxFrM+Cnt07BmJFJAdOsWr2zEmt3VolZdNIbzVNRJW23qwuVL/4UztrDMKRkIrrofFhzpkAXkyT7fsvNGobWz/6F9kM7xeNxs65G/LzrRKVtqKO2BO3XEapBLaO+WNbVUo+Gla+jbdcKUblPPgGx0y5D3MwroDGEV9vimfLCR3tFoQWZWVLiNFDVrZ1dbvzu3xuwq+yEkEh45DuzTlvxxXFR+MH7LHz3WbYNaF78d2H6pbHEIPWqH8OcOfBEpxxeAvbitbAXrxdatdR9qqZK2i6XGz/66yocqmrBiPQYLJo+HFML05Ecb5Z9vw0bPgp/e2evKOIgrluYIyps6Roh1AlWLLthwwbREVRXVyc6hy699FLxOJl9XXTRRfj1r3/d5/mUoC0qKkJMTIzwM6Bj2SWXXCJkoHqPkwoIqPtoz549wheBCgMoGXwqbf5QjWVD/6qHGTSJsWb84PqJ+MsP5mHMyEQ4XR68uWQ/vvXHJfhi4xG4SZBRRbRs+8KbnAWQcsn3ApacJTR6I9Ku/anQAPJ0tKH69d/BWV8VsPUxDPP10Hwj6T89/NI2kZwlrVjv8U/+5CxBE100M04J4HlFQ0XV05//uxWOjpOVCOFOw/JXRXKWLg7Srv8VYidfKGtytjc02ZV2/c8RO80bqDWtfVtIyXg6HQFZH8MwoY8uJhEpl34PQ+58DKbMQnS7nOLYUPH099CyYwm6PcoY8AWKT9cfFslZ4vvXFwVUeoBMwn5x5zShzd7W3oVfPbMelXVtAVsfwzBnFssaj2xB4zuPiuQsacUOvfOxgCRnCZrooo6l1Ct/DFvhHKFxW/v+k6qKvf7zSbFIzpIu7G/vmYGLZ2fLmpztjc2ix2/umo4r5nnlhij3Qh19aro28Jfp06dj+fLlIpEqJWep0rWpqUlU0Q4EJVwpcUuVt2vXrhUJ3v6JUJI7+O9//yvei7RsyRMhnIxTJThBGwGMGhqHR749S2ihkF5iY2un0GX8wV9WYKdvdkcNrpYnPvO69sXPvQ62MXMCvk46oVEywZCaJVpFjv/3N+hqqg34ehmGGXh2/Jn39+HTrU0iUUqyLv/3vTlBk3X59lXjkBJvRnW9A8+8550lDXccB3egedPHYjn5ku9+xYwiEJB+LWlNJl92H6K0emFGJip4eQKMYSIaY3q26FYijWrSS3Tbm3Dik3+i8vkHRIuuGqCOjH+96/0sVHFF57FAQxONv757BrIzYtHU1olf/HOt8LFgGEaZrqWWL/8NS/EXIlFKsi4Ztz4cNFmXpEV3i3W5mmpw4vPnoQa2ldbig1XlPZNe/U2CA4FWq8E3LxuDH94wEXqdBpv2VYsKXp4A+yobN27EU089JaphSVs/Ly8PkQ4naCME0lQhfZV/PHAO7r58jGhlIpfyXzyzTrgOhjPO2qOoeffP3hPZ2HmIm31N0NatNVmRfsMvoU8aCndrPY6/9lu4WhuCtn6GYbxyLg/9Yy2Wb6sCyUfdvGg0/t+NE0V1ULAg9+wf3TQJ1MFEsjKrth9DOEOTTnUf/k0sx0xaBOvo4M5AR4+dj/RbH4Y2OgFd9ZWoevEncJRtC+oYGIYJvViWdK+HfetJJJ53h6i6J5dyir0aV7+FcObI8RahO0sTjAsmDcX15wWvvZiuCX73rRkYlmrDieYO/PJf68R5lWGY4EHXj1Wv/BLte1eRmAui596A5MvuF12bwUJjsiLl8h8ImYO23SvQ5jMmC1fIN+LJ172x48WzsjC1IC2o6z9n8jA8+t3ZSIw14VhtG3705Eqh+82cpLGxES+++CKSkpKEAS3DCdqIg2ZxLps7Es/+bCEumZUlHnvzy1KRrA1HXG2NqH7zD+h2tsOUWYDki77tt3D62aK1xiL9hl9BF5cKV2O1uFCg5AbDMIGHXFjJLbX0aCOsZh1unp+ES2f5bwY2GAqyEnHtwlyx/PTbO1EbplVI1F5X9/E/RIWaPnkYEs69VZFxkPkitTUbh+aKVjvS/W5a954YH8MwkQtV18dOvQTDvvN3xEy+UDzWuOZtkawNRxpbOvDb5zcIXXOSNLjv2glBP4fF2oz4/bdmIi3RguP1dvzymfUiucEwTODpqNyPyhceRGfVAUQZrWibfD2sky5SJJY1DctD3KyrxPKJT59BV3N4dodSrPjXN7eLzuHMtGjccWlgJCK+DjJffOIH85A/IgH2Dhd+9/wGvL3sAMeyPhYtWiRkC9566y2kpw9s7L5s2TI8+uijiBQ4QRuhkCvst64chxlj04UWLSUTaNY+nCCjrpq3HoWr5QT0CelIvepBROn0immkpd/0G2ijE9F14hiOv/57uDvsioyFYSIF0tKmytmGlk4MT4vGI/dOw8h0ZQ2aqOopd3i8CML+/NpWuN0ehBstWz+Do2yrSIJQJUUwqzf6o7PFI+Om3yJ6wkJhEESauLXvPyGO/wzDRDZaczSSLrgLltxpgMeNuk+fRXd3eB1zO5wu/P6FjahrbEdGkhU/u30q9LrgdX/09654+N5ZSIo1oaKmFb96dr3QpmUYJnCQljZVzrrbGqFPzkTijb+FK8lbRKUU8XOugXFIjpgcr/vgqbDU+l689hA276sRxWk/vmlSULvq+hMfY8Ifvj0TF0wfLozb//PJPjz+6lZx/GeY/nCCNsK5+/KxMBm0KD7cgKWbjyJcoAC89oO/ovN4GTRmG9Ku+xm0lmhFx6SPS0H6Tb8WFbXOmkOofuMPwhmTYRh5cbk9QqePtLRpmSaa/nT/XKQlKO9QTLpTFAiSrt++Qw1iljyccNYdRcPSl8Vywjk3w5g6QukhiYm3pIvuFdpo0Ghh37cWVf/5edhWdTAMIy9J59+JKL0JncdK0LpzOcIFKoz4y2vbcKCiCdEWPX5913RhYqMkqQkW/P7emYizGXGwshm/fW69qOxlGEZeut0u4Z9CWtpwu8RE05DbHxEdmUpDfgApl38fUQYzOiqKRfdSOHGkugUvfLRXLN9+cQGyMmKVHpKYePvu1eOFZ4VWE4VVOyrxk7+vCdtuOyZwcII2wiEHw5sWecWYX/x4b9i0MzUsexWO0o2AVicMI/QJGQgFDIlDkHbDr4QuWmdlKarfepQrvRhGRugY9ctn1uGTtYfE3zcvysNPb50iEqKhQlqiFfdeOU4sv/ZFqZBhCAc8LqeoTiWXdHN2EWKmXIxQgdr8SAuXJsE0lhgxCVb5wk/QfsQbgDMME7noYpIQP8/r/Nyw7GW4HeEh20VVVOt3H4dOq8HP75iGjGQbQoGhKdFCk5a0aUuONOLhFzaisyv8KugYJlQR5tKv/U50LBHx825A6lU/FgbUoYI+Pg1Ji+4Sy42r3hQyDOGAs8stqlOdLg8m5qXg0jnZCKVY9qKZWXj43pmItRnEJNgPn1yJ3eUnlB4aE0JwgpbBpbOzMSI9Bq2OLrz08T6EOi3bv0Tzhg/EcvLF34E5swChBFWcpV3/C0QZTOg4sge17/5ZzJIyDOMfTa2dQm92T3m9SMj+8s5puO68XGjImSvEIJOXuUVDRIXUn/+7FY6OrrCY+CLTRUqAJl/6PUW0z74Oc2Yhht75GAxp2fA4WoTmt71kg9LDYhhGYWKnXAxDynB42ttQv/QVhDqfbziMd1eUieX7r5sgtGdDCao4++09M2A2arGr7AQe/c9muFzhJR/BMKGanK188SfoOLpXVKimXvNTxM++GlFRoZeWsY2ZB2vhbGHEXfv+k0LyIBwmvshbhxKgP7i+KCRj2TEjk/CXH8zDyKGxaLE7hTHjul1VSg+LCRFC70jAKNKSSyX3xJLNR7EnhGdxHAd34sSnz4rluDnXInrsPIQipiGjhexClM4gtBybP/2nOLkxDDN4Xv+iBLWN7cLE5M/fn4uphcF1Yz0bKCD8zlXjkRJvRnW9A8+8txuhjKN8O1o2fyKWUy75HnS2OIQquthkZNz6MKwFs4Tu5InP/w2Ps0PpYTEMo3BLbtKF3xLLbbuWof1o6FbXby+txdPv7BLLN5yfiwWThiEUIXObX31zOgx6rXAe/9vbe8LOr4JhQo3G1W/B1VwnpAyG3PEorDlTEMqxbPKie0Tc5WqqwYnPn0cos7WkBh+uPiiWv39dEeKjlfWlOB0p8RY8+t3ZmDNhiPADeua9XehgORmGE7SMRN6IBCFcTVDQ2BWCs+SkjVjz7uMi0WkbMxfxc65FKEOVXqlXPwhodOg4sAmWPYvDzryCYUKFyro2fL7B69B9/7VFGJaqrOb0mWA16/H/bpwEKvBdtqUCq7YfQ6hWc9R99HexHDP5IlhGT0KoQ8ZlKZfeB11cijDWaN70sdJDYhhGYUxDcxFddJ5Ypsn8bndXSGojPvryZpHonD9pqEjQhjJU6fXz26dCp43Chr01+HBTIydpGWaQOOurRCcokXzJd2BIGopQR2OyCj1aRGnQtnsF2vauQah22T35xnaxfMnsLEwpCN0iDgmTQYcf3lAktL/J8PiD1eVKD4kJAThBy/Rw28UFoh2AnFs/WBVaBwhXWxOq33wE3Z0OmIblC2mDUGxZ6I9lZBFSv/H/xEnNWLkLrctfQTfZNzIMc1a8vHifmGGenJ+KsaOSEC5Q2+o1C3PE8tNv7ww5MwA6HtV+9He47U3CPTjh3FsQLpB5WML8G8Vy0/r3RaKZCSwbN25Ebm4uKisrlR4KwwxIwoKbhExL14ljaN4YWhM3ja0d+N2/N8DR4RLnhvuvnRAWsSzpOD54y2QhJ7TjoAMvLS7lWJZhBkHjiv+Kzh/LqEkwDx+DcIGuveNmXSmWT3z6TMiZtNLx6K9vbhdJ2uFp0bjjkkKEC2QedsuF+WL5nWVlYeMHxAQOTtAyPURbDLjzUu/J4vUvSlETIokEMtmq+d+j3naQ+DRhCkYX5uGCNW8aYi+4BxTKOnYuQcPyVzmwZZizgEy21u06LipRyY013LjhvFzkDo+HvcOFv7y+TSSaQ4WWLZ+ivXwborR6pF7xQ2h0yjqIny0kc2BIG4luZzsa17yt9HBUT1FREdasWYO0tNCvTGEiE605GokLb+tpJe5qCo1EQofTJcy2SKYnPcmKn90+VVyYhwszxmbgO1d6kx6fb6wQOo8cyzLMmUMmW0IzP0qDhAU3I9ygzlXjkByhQ1v3wVPo9oSOcSAZB5MMi16nwQM3TxayLOEEyRyMGhqL9k4X3viyVOnhqJ6NIV5swAla5ivGNmNHJgkHxH+9u0vx4IskAeo+fAqdVQegMduQdt3PobWEfmtzf8z5s+AovFAsN69/H01r31F6SAwTFtAx6MWPvFqC507JxPD0GISjzvePb5okjM32HqzH20tDwwnXWXsEDUtfFssJ594KQ0omwg0y1Uj0Vf22bPscXQ3HlR6SqjEYDEhOToZWG14XP0xkQcY2puGF6HY5Uf/5vxWPZUkS4InXt2H/0SZEW/T49V3TEWMNr8kwYs74dFwy1atP/s7yMry1JDTOZQwT6tAxSIq3osfND894S6MVUgdkbNZRUYymde8hFCBDsBd81wlUORuO1wnUnXC7r+r303WHUXWiTekhqZqiEC824AQt0wdqtfr2VeOE1hTNRG3Yo+zFbsPy/3pnGzU6oedqSMxAuOIcVoToed523MaVr6Nl62dKD4lhQp5Ne6ux71CDmA2/8YI8hCtpiVbce+U4sfzaF6WiKljxzoT3nxQajeaRExEz2TuBFI6YR4yFObtItA02rHgNoXhhRiZmStwGm5hauXIlrrzySowfPx4zZszAT3/6UzQ3N3+l6mDhwoV4/vnncd9994mAd9q0aXj44YfhcrHRBaNsLJu06B4RO5JRq6N0k+ISPdQFQrE1Vc4OSbYhXJk8yoZbL/TK9rz6WQkWrzuk9JAYJuRxHNgikppkHh0/93qEK/r4NCQtukssN656U1QFK0lnlxuPv7pFeOeQBBppz4Yr40cnCzkZ6rJ7ZXGx0sNRNYYQLzbQKT0AJvQg850rF4wWM+PPvrcbE3JSROVXsCERdao2lYTUyXQr3LFOvBC6brc4qdUvexXWgtnQmsM3UGeYQOJ2e/DSJ/vE8uVzs5EUZ0a4dyhsLa7Bqh2V+PN/t+Kv/28+LCZl5Foalr2Krrqj0FpjkXLp98JCB/F0JJxzMyoP7oC9eB06qi6HKWMUQgFKkFa9/HN0HlOmZc04NA8Ztz58Vvu3oaEB3/ve90RSdv78+aiursaDDz6Ixx57DJdddtlXnv/Xv/4VP/7xj8VzNm3ahJ///OcYM2YMrrjiCpk/DcOcOWS+EzfjctGxdOKL52HOHgeNIfjnkM83HBbVpsT91xUJ061w5+KZw+HyaPDa5yV46eN9mDthCGyW8KsIZphgQFIADcteEcuxUy+BLiYR4d6h4CjbBvu+tah9/0kMvetxaIwWRcby0sd7caS6FXE2I75/XVHYx7Ik47a9tBZrdlbhG0cbkZMZr/SQwpqVK1eKGLW8vBwWiwXz5s3DQw89hJKSEtx666348ssvkZiYKIoNbr75ZuzYsUNU1lIC99JLLxVxsE4X/BwYV9AyA3LtwhykJVpworlDBGDBxnFop3DgJeJmX4PosfOgFuJmXw1DynChmdi8+ROlh8MwIcuSzUdxrLZN6GNftWA0VNGhcPV4pMSbUV3vwDPv7VZkHFRR1rJlsVhOvuR7Ikkb7hhTR8A2znueoDZCpVua+xJeFww1NTVwOp3IyMjAkCFDMGnSJPzrX//CLbcMbCA3e/ZsEegOGzYMV111FfLy8rBt27agj5th+hM36yro4lLhbq1H46q3gr7+Hftr8fQ7u8Ty9eflYsGkYVAL1y3MwYj0GKGZ+OHqg0oPh2FCltady9BVXwmNORpxM65QR4fChd+CLjYZrqYanPj8eUXGQZ2+H6/xVvB///oixEUbEe5kZcT2nCde/HhvSMWyNJaOTpcit+5BbAep2IDi0sWLF+Pvf/87Nm/eLIoNBoISuVOmTMGHH34oCg5effVVfPyxMkajXEHLDIhRrxXtuL95boMIvM6ZPEwcNIKBs64Cte88TlOOsBXOQfzc66AmSDORkrS17/4ZLZs/QdzUS6AxWZUeFsOEFHRC/u9n3smh68/LgdUcPsaAp8Nm1uP/3TgJP3t6DZZtqcCkvBTMLRoatPW72ppQ9/E/xHLMlIthGTURaiFh7vWw712LjqN70V62DZbRk0LiQoYqWLu7lHHljdIbz7qiJD8/H5dccgnuvfde0QI2a9YsUUl73nnnYevWrV95/siRI/v8HR0dja6uLr/HzjD+otEbkbToblS/8TCaN30M29h5YjInGBytbsGj/9ks9GfnFQ3FjRfkQk2QZuJ15+Xg/17eIq4TLp87UjXnaYaRC5Iaalz5hliOn321aq73tCar0KOteuVXaNu9ApaRRbAVzg7a+htbO/DXN7aL5cvmZAt5A7Vw06I8rN5RiT3l9SIJPaVAeZ1USpD+5O9rUHxYGXm2/BEJ+L/vzT6reLZ/sQHdqNjA7XYLya5TFRsQVHDwyiuviGIDJbrBuIKWOSWT8lIxa1yGCC7/8fZOcR+MA0Dth38TDpGmYflIuuQ7Yd+uMBDWvOnQJw2Fp8OO5i2fKj0chgk5PlhVjsbWTlHJf+HM8NWUGojC7ERcs9Cr4ff02ztR2+AIynrp+Fr38d/htjeLKn6SBVATVM0RM/VisVy//JWQcRimc5jGYFLkNtjz55///Gd8+umnuOuuu9DY2IgHHngA3/zmNwd8LrWC9SeUqj6YyIYSB9a8GWLS/8Snzwjz2UBD338yBbN3uFCQlYD7r5ugylh25tgMIYtmb+/Cx2u5ipZh+kMTQ257k6jkj5l0AdQEXafHzbpSLNOxtau5NijrpeMrJWeb2jpFFf9tFxdATaTEW0TSmSCZN9KkZc6e3sUGlHz9yU9+grKyMowaNbAEWigVG3AFLXNa7r5iDLaV1qD0SCO+2HgEi2YEtvKg/dAuOKvLRdVPypU/hkanTk0rqqKNn3U1aj94Es2bPkLslIuhMYa3vibDyEVTayfeWX5ALN9yYT70OvXNJd5wXi527K8Tx9a/vL4Nf/j2LGg1gb2AJ1mD9vLtwqQi5YofqPL4GjfjG2jdvgRddRVo3bUCMRPOVXpIYcfOnTvxySef4Gc/+xmys7Nx++23i5YvStJed526OlqYyCDxvDvgOLgdnZX7xfEhZuL5AV0fHdvLjjXDaNDip7dNESaXaoSqaEkSjTTVP1hZjktnZyumq84woQZNhjetf08sJ8y/EVFa9f024udcK67d6dha98FTSL/5t4jSBPZ499Gag9haUguDToMf3zxJlcfXq88Zjc83HMHR6lYs23wU500bruh4aIKRKlg7ncoUPhgN2kFNclKxwXe/+12sWrUK69atE3EsyXZ95zvfCeliA/Vd9TKykhhrxs2L8ntmcShxEkikE1n0hIXQ2eKgZqwFM6FPyICnvQ0t2z5XejgMEzK8+WUp2jvdGDUsDrPHD4Ea0Wo1+NGNk2A2arH3YD3eXhpYJ1xn7RE0LPWaVCSceysMyZlQI2S6SLqTROOqN+BRSFognLHZbHjttdfwpz/9CUeOHMH+/fuFfteIESMQH8+GFUz4QaY8CfNuEMsNy18ViZNA8vYy7wTjBdOGIz7aBDUzZ8IQDEm2otXRhU/XHVZ6OAwTMjSu+R+6nR0wpo8U13xqhJKxJHUQZTCho6IYTeu81/GB4vDxFmFMSNx5aSGGp8VAjZDpIk1+Ef/9vAQdTpfSQxIJUpNRp8gtahDJWSo2eOSRR3oKDZ599lnx94YNG1BfX49QhhO0zNdy8awsZA+JFS1MJFgdKDoqD6Dj8G5Ao0XctEuhduikJiUSmjZ8wIkEhgFQVdeGT9d7L/LuuKRAVOiolfQkq9D6Jl77ohQlRwKj7UTHlpr3n0C3uwuWUZMQM2kR1EzM5EVC7sDd2iB0vpmzg9q8/va3v4kglrS3brjhBmi1Wjz33HPQaDhsZMKTmMkXwpCaJaSl6pe+HLD17D/aiF1lJ0RHxOXz+rZMqhH6nFIi4b2VZSGRSGAYpelqqELLti/EcsI5t4jOSbWij09D0gV3i+XGVW+iozIwBQedXW786dUt6HJ5MKUgFRfNUpf82UD5FzIVrm/uwEdsxBhRxQbqPVowslZ6fffq8aDJCzK12VVWF9DqWduYOeLiOhIQnzUuFR5HS8+JnGEimZcXFwu9JRL8HzdK/ccBcmudO2GI0PimNlFHh/x6Rw1LXxYt/1prHJIv+a4qtRB7Q9IN8b5qucZ178HtaFF6SGHHggUL8M4772D79u3CGOwf//gHMjMzMW3aNJSWlgqzBWLJkiW47777+ryWjBUeffRRhUbOMKeeFCfncSBKmNq0U0FAAKtn500cKrQEIwEyQSO9+OY2Jz5bf0Tp4TCM4jQsfw3wuGEeORHmEWOhdsiA0VowS2h9177/JDyd7bKv48WP9oqW/7hoI+6/tkj1sSxJN9x8YX7PeaW5jQu5IqXYILRHx4QMOZnxPfqzT7+9C10ueTVInCeOwVG6SSzHTQ++W56yVbRegfXm9e9zFS0T0ZQeacDaXVViMkhtov+nggLMb189HsnxZlTXO/DMe/ImDewHtqBl62diOfnS70FrjUWkTH5RtVx3pwONa99RejgMw4QApiGje/RnT3z2LLpd8k6IVdS0YsOe42L5qgUDG5GotZDjmnO9VbTvLj8gKt0YJlKhClJ7yXoxGZSoMjPW08WyNAGmi0mCq6kGJ774t6zvv2lfNT5Ze0gs//D6iSJJGymTX9kZsXB0uPBWgKXQ1MiCMC024AQtc8bcelEB4mxGVNa14d0VZbK+d/OGD0iKGZbRU2BIHoZIInrsPHFCI5fP1h1LlR4OwygCCbG/6NOVOndypnBmjRRsZr3Qo9X4uhRWb6+U5X1dbY2o+/gfYjl26iXCzTxSoHZCaiskWrZ8hq6mGqWHxDBMCBC/4CYxUdVVXyXkpeTkvRVlIE+RaYVpyFSpNuLpukFoorGxtRNfbuQqWiZyY1nqWiJs4xbAkKKsuVMw0ZqsSL78+1R9hLZdK9C2b60s79vY0oG/vrFdLF8+dyQm5qUgUiCZt9sv8RasLF57CNX1dqWHxAQBTtAyZ5VE+OblY8TyW1/ul+0g4WqpR+vuVWI5buY3EGmQq2fczCt7ZB7kruhgmHBg874aYZZFrqw3LcpDpFGYndhTgfSPt3egvtn/9rATn/xTyKcYUkYgYUFkVHH0xpI9Huas8YDHhYYVryk9HIZhQiSJkLjwDrHctPYddDVWy/K+J5rasXxrRY8Dd6Sh12lwje9zUzuu3J12DBMOOA5sEWZZUToDEuZdj0jDnFnQc017YvG/xDW+v/ztfzvQYnciKyMGt13sbfmPJIpyUzAhJxkudzde+bRY6eEwQYATtMxZMa9oCMaPToLT5cE/390lZgr9pXnTR+IC2pRZANPQXEQi0ePPgTY6QZjatO5arvRwGCaouN0evPSJ14Dw0jnZSIozIxK5/vxcjB4WB3uHCx+s8s8QoLP6EBxlW4XpYsoVP0CUTo9IJMHXXmjfuwadx8uVHg7DMCGAtXC20IXsdjlx4rN/yxLLfrCqXFxA02Rb3ogERCILp2YiMdYkTG2WbPYmqxkmUuj2uNGw/FWxHDPlIuhiEhGJxM+5Bsb0UfB0OtC8+WO/3utgZbMo4CAzwh/fNAl6nRaRyO0+2bdV2ytRVtGk9HCYAMMJWubs9RKvGg+dVoNtJbVYt8urtTVY3O2taNn+pViOmxF51bMSlDyRPn/TunfR7WYXXCZyoAu5ipo2RFv0uNpXRRqJ0HH1+vO8k1RfbDyCjs7BHweaNy8W99a86REnG9MbY1o2bGPmiuX6Za/IkohhGCb8Y9nERfcAWh3aD26HvWSDX+/X6nDi8w2HI7Z6VoKSJ1ct8FXRLt0Pl9uj9JAYJmi07lyOrhPHoDHbeqpII5EorQ5xs68WyyTd53F2DPq9PlrtLVaYNS4j4mRjejNyaBzmTxoqll/8eC/HsiqHE7TMWTMk2dYTgD77/m6/XMdbtn6ObmeH0OgxR5A+4kBETzhXuKy7muvQunul0sNhmKBAScjXPve27Fy7MFdIqUQyk/NTkZ5ohb29C8u3HRvUe7jtzbDvXd2jPRvpxM+7QSRiOg7vRvvBHQFdFwfNZw9vM0YJDIkZPRPj9V+8IKq9BgtpA7Z3uoV2+qQI0kcciPOnDxcGPrWN7Vi+hatomciAkpCNq94Qy3GzrhZSKpGMZfQk6OLT4Omwo22PV8bwbGlu68TK7d44+NK52Yh0bl6ULwo5dpWdwLbSWqWHwwQQTtAyg+Kac0eLJEJDSwf++1nJoN7D09WJ5s2f9GjPUkVDJKPRGxE74/IeXTRqlWEYtfPB6nI0tHQiJcGCi2eNQKRDhgAXz87qqRwYTPKKuhK63V2ixcyYEbnVXBL6uBTETr5QLDdQFW0Ajq16vV6cw+x2NnA4WxwOR882ZJhgEjfrSpFEcLc1oGGlN7lytnQ4XfjQV+VFxQuRHssa9VRFO0osk+s4SRgxjNpp3vQx3G2N0MWmIHbSIkQ6ZNQqxV10rT+YWPazDYfR5fII6a/czHhEOqkJFlziuz546eN9cHt4clutcIKWGRQGvRb3XjVOLH+85iDKjp29Hopoe3C0QBeXAmv+zACMMvyIKTofGksMXE01aPNVwDGMWqHZ8XeWlYnlWy7Mj1htqf4snJIJs1GLippW7DxQd1avJXkU6kyQNNAiPVkgETfrKmhMVjhrj6Btj/zHVq1Wi9jYWNTV1aGyshKtra1ob29HR0eHam+dnZ1+vZ62T319PWpraxEXFye2IcMEE43OgKRFd4vlli2fovP42Wt/L9l0VBjY0MXz7PEZARhl+LFo+gjEWA2orndg5fZKpYfDMAGFupaa1r8vlhPm3xixmv/9iR63AFEGk5B9aD+866xeS/Ioi9ce7vGm4FjWy7ULc2A163H4eAtWbuMOBbWiU3oATPgyMTcFcycMwaodlXj67Z340/1zhYj3mSYRmjd+KJbjpl+OKA1fmBEagwlx0y4TIvNNa96BrXAObxtGtbzxZSnaO10YOTRWHEsYLxR8nTs5Ex+vPYSPVh/ChJwzb5m1l24U1WAkl2Ir4IkvCa05WmjCUQVtw8rXYS2YKZIzcpKWlgaz2SwSji0tLVAzHo8HLpcLOp0OGo1/c/2UnKVtxzBKYMmeAGvBLNj3rcWJT59Bxu2PnHHcRUmE91Z4JxmvXDAKWi3XvRAmow7fmD8K//lkH95aUop5E4ee8fUBw4QbjWveRrezHYa0bFgLZyk9nJCBJsUpSUuTXy2bF8OSNf6MX7t+13HRpRsfbcTs8Xx9IBFtMeCac0bjpU/24ZVPS8S2oaI5Rl1wgpbxi29ePgZbSmpwoKJJGCRcNNNbev91tO1bK7RWtdZY2MYtCPg4w4mYSYvQtOF9dDVUwV68HrbC2UoPiWFkp+pEGz5d550dv+PiQtHaz5yEZA4oQbu5uBrHT9iRnnRmembNm7yyMTETL0CUlqs4ehMz+UJhnuZuOSEuFuJmXCHr+1OFByUbqZLW7XaLBKZaoerXgwcPIjMzUySlBwvJGnDlLKM0iQvvgKN8OzqPl6F1+5ciDjsTVu+oFFqrcTYjzp2SGfBxhhMXzRyBd5cfQGWdHWt3VmJukdfghmHURFfDcbRs83YtJZ5zi2jtZ/rGXZSgdRzYiq7Gaujjz2wy9sPV5eL+whkjoNfxNu3NJXOyRffyiaZ2cX+lz5iRUQ+acKnUeOqppzBnzhxMmDABd999NyoqBi7r/tvf/obc3NwBbw899FDP8+64446v/P+WW24J4qdSBwkxJtGaTLz8yT40tny9U2N3twdN698TyzFTLhbaq8xJNEYzYqdeKpYb1/xPbC+GURuvLC4W+kkT81IwPidZ6eGEHENTosW2Idmuj9eeWdttR1UZOitLqW8X0RPPD/gYww061yTMv0EsN617F+721oCshxK1VFlqMplUezMavedtuvfnfSIpOcuxbOiii45HApkJkk718v/C1db4ta/xeLrx9rIDPS24pL3KnMRi0uPyuSPF8htf7hfbi2HURsOK1wCPG+bsIpizvNJ/zEkMiUPEtgG60bzl0zN6zf6jjSg50gidNgqLZrI3RX/oXHOzL/fy1tIDaHU4lR4SE4kJ2qeffhqvvfYafv/73+ONN94QQe5dd90Fp/OrX8g777wTa9as6XP75je/CYvFgttvv73neaWlpfjNb37T53kUEDNnz4UzszBqaCzsHS6862v1Oh2Osm3oqqtAlMF8xlUKkQYJq2uMFqHbYy/ZqPRwGEZWKPhas7MKJCl1+8UFSg8nZLlsTnaPxqGjo+trn9/iM120Fc6CzhYX8PGFI7Yxc2FIyRTOwk1r31V6OEwEwbFsaBMz6QIY0kbC0+lA84YPvvb51D12tLoVZqMOF806s+6xSOOS2dmwmnRCT3397uNKD4dhZKWj8gDsxetoWhYJ59ys9HBCltipF4v71p3L4Ols/9rnf7TGW5QwZ8IQxEebAj6+cGT+pGEYkR4De3sX3lqyX+nhMJGWoKXA9YUXXsD999+P+fPnIy8vD0888QSqq6vxxRdffOX5VqsVycnJPTcy7Hj55Zfxq1/9SlQWEGRKQbfx48f3eS61JjJnD+lK3XhBnlheurkCzq7TO2Q3rfNVz048H1rTmbXtRqJuD1UXE01cRcuoCHJyffHjvWJ5waRhyMqIVXpIIUtRTgqGJFvh6HBh+ZbTmwFQxVfbvnW+CZ6LgjTC8IO0JRMWeCsMm7csRldzrdJDYiIAjmXD5Ngw9zqx3LprBTyu01clvb30QE8Lrs3McjKn0lO/dI5URVvKVbSMqmJZ0rQnbOPmwZjKlZ6nwpw9HvqEDHR3OtC6e8Vpn0uduGt2VPZ0JjCnzr3c5itw+XjNIdQ2OJQeEhNJCdqSkhLY7XbMmDGj57GYmBgUFBRg8+bNX/v63/3ud5g8eTK+8Y1v9Kk4oBbErCye8ZaLiXmpSIozizL7daeZJe+oKEbnsRJAq0Ps1EuCOsZwnHGkKmNyHXfs36L0cBhGFjYX12BPeb3QlLppkXdihxkY0uWlCiSpouB0F7et274EPC4Yh+bCmDEqiKMMP8wji2AaMRZwu9C48g2lh8NEABzLhgfmkROgjUmCp70VjtN0L+09WI/iww3QaTW4bC4nEU4HbR+qMibX8U37qpUeDsPIgqNsKzqO7hVa/wlzr1d6OCEN6fKSFi1B+v+nKzr6bP1huNzdyB+RgNHD4oM4yvBjUl4Kxo1KEmaVr35WrPRwmEgyCaPqAiI9Pb3P4ykpKT3/OxXLly/H9u3b8f777/d5fP/+/YiOjhYB79q1a0XL2KJFi/Cd73wHBoPBr9k0h8MRFHOO3vehwoKJ6fjfsoNYvLYcU/MSBnxO4+q3xb05fzacWhOcQdheocDg9pkWlvELYd/8EepXvQUMLRQXY0xwCNXfWTjjdnvw4od7xPKF0zNhM0LWY6Ya99mMwiS8vFgnjFY27K7AhNFJX3lOt6sLzVs/E8umcQuDch6SEyX2m3XmNeg4vBttu1fBOO486FOGB23daiCY+4xiq3A/93EsGz7Ha3PhXLStfxeNWz6FJnvSgM95a0mJuJ9XlA6zPjjbKxQYzD4jZd4Lpg3F+6sO4/XPizE2Kybsf8/hRKj+zsKZbo8b9UteFsuWovPh1FtlvZ5V4z7Tjp6GqBWvCQPspuJNMI74ql5vl8uDT9YdEsvnTx0SdsdVJfbbDQtHYlfZCazYdgyLpg3FiPRov96vs7NTyC+R0S3d+scG3V2dUIIovXFQ5w2aGKduJepUouXCwkL85Cc/Efc7duzAk08+iX379gnfCOpuevDBB3u6kHbt2oXHHnsMxcXFwtB22rRp4rUZGRkDrou2F2072v90708sG/IJWulL3j/YJGOK5ubm0772xRdfxIIFC5Cf7xVS7h3U0hdw3LhxwmCBNjztgKqqKnE/WLq6usR7BYvDh70O6KHCsBi30JQsPtyEVRt2ITm2b8uXprUWsYd2gGrAquPzUBXEbRUqnO0+i4oeiVitHq7aQyhb/TFcyVwZF2xC7XcWzmwrt+NYnR0mQxTy05wBO16qbZ+NH2HChtI2/O/LYhhdX03QGqr2wOpohscYjcMuGxCmx9Zg7zdrWgEM1ftQ/dnzaJviNQhiQnOf+ZNwDAU4lg2f43WUcQhio6LQVVmK0s2r4bH1PebWNHVhW+kJsVyQ5grqtgoVznafjU5yQ6+LwsGqVny4dDtyhpgDNjYmPH5n4Yzh2A5YGyrh0ZtQGZuDbo5lzwhz+hiYjmxG7ep30db+VVmYnYfsaG5zItqsRYymAcXFX2/WGIoEe7+NGW7GniPtePbd7bjlHP9NlylZSbFF/+Ri41sPo+u4V9on2OgzRiP+ml+cdZL2Bz/4AY4cOSK0+ocOHYrnn39e6PmTVj95AFx55ZUiKUtyUY8++qjQ/3/lFa90ybe//W3x/9/+9rdoaWnBH/7wB/zsZz/Dv/71rwHXRdvM5XLh4MGDfseyIZ+gJZdfSb9LWpY2gtl86hM8BagbN27Es88++5X/UbUBZcBjY73ahzk5OSIz/sMf/lDspKSkr14Anwn0HqNGjQpKoE8//hEjRpx2GyjBpBIXtpTU4VCDEXOne3XSJJo+XYkOOkCPnor0ybMQSfizz1qaz4Nj62IkVG5FwpxLuPIgSITy7ywc6XS68deP1orla84ZjYnj5a9YVOs+i09xYOP+tSg73oHYpExkJFv7BE3121+Hi1qmJy9CRuEYhBtK7TdXeiJO/OdB6OsPIdvignH42KCtO9wJ5j4rK/t689FQh2PZ8DpeN1YUobN8G9LtRxAzZU6f/y1929sFMr0wFbOnRZZruz/7bFGNAR+tPYJN5S5cdm4ex7JBIpR/Z+EIVRDWrf4nqD4udsaVyBg3UfZ1qHWfudITcOLFLdCfKMfo1DjoEtL7xLIvr9wkli+elYUxheEn3aPUfrsn1YEfPrUO5dWd6NKT7EHioN+LYhKKO2jyuHesQvtHo1VOGVWj0YjxnM1549ChQ6K76LnnnsOsWbN64iaqmn311VeFnj/p+tNnpgnwP//5zyIhu2XLFkyYMAFNTU2iWpZkpGj9VInb0NDQZ7sMlNzOzMwU28+fWDbkE7RSO1htba34wBL0t2SUMBBLlixBQkJCzw7pv/GkgFZi9OjR4p5azQYb1NKXhlrMggX9+IO5vjPh4tkjRYJ21Y7j+Obl42DQU3MT0NVUi47S9WI5cc7VMIbYuEN5nxlmX4WKnUvQVV2OqJoyWLLHB2x8THj8zsKRj9ftR2NrJ1LizfjGghzodd5jQyBQ2z7LtlgwOT8Vm/fVYOnW4/jWlSeTAh3HSuGqOSR00BKnXAhtGH/uoO83ywg4Jy1Cy+ZPYF/7P8TlTRFaaUxo7TM1JHI4lg2z4/XkC1Fdvg0dxWuRcv7t0Oi8VS81DQ6s3e2VpLjuvLzQG3cI77NrzsvD5xsrUHasGfuP2VGUmxKw8TFh8jsLQxrXfgqPvRG62GQkzbgMUbrAGQSqbp9ZsuAYNVHo9zr3LkfMBXf1/KvkcAMOVrYIf4pL5oyCxfLVBFe4EOz9lmWx4OKZWfhw9UG8vqQcU8cMFR4Wg4ESkXTTarXi1puMW/8QVhIHZb6E6MSJE3s+C+0XqoK96KKLRFwlvSfdk+wByUbR68455xxRYfvwww+Latvp06dj3rx5uPDCC7+yXSTocdp2tP8HSuKezfhD/kqEnG5tNpuoIJCgMmPSi5gyZcopX0fZ76lTp4oAtj+33HILHnrooT6P7d69W1QN0KwHM3go4EqON6OtvQtrd1X1PN688UMS7YE5axyM6V5HV+bM0NniED3xfLHctOZ/YhaLYcKJ5rZOvL3M2xZzy4X5AU3OqpXLfG62S7cchb29q+fx5s2fiHvbmDnQWvsma5ivJ3721YgyWuCsOYS2vWuUHg6jUjiWDT/XcR2ZhXW0wV7sLS4g3l9RJswaJ4xOxqhhXp065syIjzZh0Uzv9/KNL0s5lmXCDre9GU3r3hPL8fNvDGhyVq3ETL1Y3LfuWg5Ph73n8Y9We9vC508cilgyqGDOimsX5sBi0uFgZTNWbT8WkHVQglFjMClyixrERP1AcZPEqc4/9DjFUMSPf/xjLFu2TMgk0OO///3vcdVVV4lOqEAT8gla0mq4+eab8fjjj2Pp0qXCCZfat9LS0nD++ecLQd66ujp0dFDz/Eko6KWAeCAuuOACfPDBB3j99ddRUVGBxYsXC70u0qSgAJoZPFpNFC6YNrzHiVE6obXuWCqW42acdCBmzpy46ZeLCrmOimLhGsow4cSbS/ajvdOF7CGxmFs0VOnhhCXjRydjWKoN7Z1uLNl8VDzmaqmHvWSDWI6ZfJHCIwxPtJaYnvNS44rX4HEFPvBiIg+OZcOLKI0W0UXnieXW7V+K+6bWTnyx8YhYvvocb6Uyc3ZcOX+UqJDbd6gBe8rrlR4Ow5wVjWvfRrezHYbULNgKZys9nLDEPGIc9ElD0e3sEElaor65vaeo65LZ3mIE5uygpLZ0Xnrl02I4u/oafEUiI0eO7Jm4liCNWKqOJSmKrVu39nk+xWVtbW3idaQj++tf/xqJiYm44YYb8NRTT+Hf//43ysvLxfMQ6Qla4v7778fVV1+NX/ziF2IjUQkxifxShvv48eOYPXu2CEx7Q4Gu5MLWHwqSf/7znwsRYCpxpoD59ttvx/e///0gfSJ1s3BqpiitpwDsSHULmjcvRrfLKSpnTSNY428w6KITED3hXLHcuOZtpYfDMGcMXdR+6nNlveOSgkG33UQ6NHt8qS9w/XjNQbg93WjZ9jngccOUWQBjWvjpdYUKsVMvhjY6Aa7mOrTtXKb0cBiVwrFseBE9/hzK1IqJcWfdUXHcdbo8onJ23OjByUdEOomxZpzvK+KgKlqGCReo2Khl6xdiOeHcW1gOyY9YNtZXUCDyAx43Fq87LGLawuxEUcjBDI5L52QjMdaE2sb2nkKOSCYrK0tMgJPJ14YNG4Qm7S9/+UuhOfvGG2+gtLRUSBhQMnbTpk2iYragoAAzZsxAfHw8PvnkE6FRS0lZeu17770nZKWyswM/iRDyGrQEBbEPPPCAuPWHHNloA/dn586dp33Pm266SdyYwARg0wrTsH73cSxZux/nHv5UPB478xuq0JJTiriZ30DL9iXoOLxbXDCYhvV1dGaYUGTFtgq43N0YPSwOE3JYc84fFkwahv8sLkZ1vQNb9x5Diq+yK3aKt2WMGRwavVF0KdR/+SJadixDzKRFSg+JUSEcy4bfxLglZwocpRvRsPlzfLxuiHicqpQ4lh08Vy0Yjc83HMaushPYe7BeJGUYJtRp3bMS8LhgTB8FSxZ7gfiDbew8NKz4L1xNNWgp3YrP1tf3kfJiBofJoMM35o/Cvz/Ygy83HsFFM7lw45FHHhGdRTRxTdIE48ePFxPj1JlEFbFk/PX222+LrqOFCxfiRz/6kZg0pwQtmYuRcdi1114rupzIOOzFF18MSocST/8wAeGC6d4ZcsfupUJjRp+QAWvOVKWHFdaQHlr0+AViuXHN/5QeDsN8LaTZs2TT0Z7KesY/TEZdT/VR8bJP4XG0iOMCJREY/7CNmQtodHBWl8NZ621jZhgmsonxyRy07FoJZ3s7hiRbMX3MSedx5uwhn4pzp3jjgTe5ipYJk1i2dae3HV+6DmMGD2mKSl2hFSveQ4vdKY4LVNzF+Adp+Oq0UcKM8fDxFkQ60dHRQjuW9P+3b9+Ol156qUc2iiplSSKKqmvXrFkjnte7Y6moqAivvvqqkELYsWOHeC0ZiQUDTtAyAaEoJwXpCUbM0np1P2JnXC40vRj/q2ip5a794E50VO5XejgMc1rKjzXjSHWr0JybO8FbfcT4x8WzsqCJ6kZW0ybxd8zkC/nYKpMWrWX0JLHcyjIHDMP4zMK0scnQudsxwXAEVy4YLbwWGP+gKmSSO9q+vw6lRxqUHg7DnBZn9UF01R0VXiDWAtaelYOYSReK61lLw36kappwyawsaLWclpJDi3ZKgTfRLRXIMOEH/xKYgECB13VZDYjTtMMeZUX0mHlKD0kV6ONSYRs7Xyw3sRYtE+JIGkgzxqTDZjEoPRxVkJpgwSWj3Riqa4Q7StdThcD4T/Q4b2VM655V6Ha7lB4OwzAKQzqTtUneDoW51jIsmMQml3KQlmjFOZOGieU3vuRiAya0kSZtLblToTWzAaMc6ONS4BoyTiwvsJTiPF93GOM/C30dCl6JOY/Sw2EGASdomYBAot8jG9eJ5S/teTha1670kFRD/Kwrxayjo2wrOo8fVHo4DDMg5CC6ctsxsSy1MzLyMM/kdRDd7MxGe7dR6eGoBsvIImitcUI6wlG2TenhMAyjMGRc88ahFLi7o5AZVYPuhkqlh6Qarlk4GlSMvKW4BmXHmpQeDsMMiMflRNveNX0mcRl5WNXh9VKZYjwIS5RT6eGohol5KYiLNqK5zSmOr0z4wQlaJiA49m+Gp+k4nFFGrOvIwWfrDys9JNWgT0iHrdDbYsNatEyosmlfNdrau5AUa8L4nGSlh6MaXM110FXuEMvLHXn4YiO3MMlFlFbn1aKlipldLHPAMJHOxj3Hsf+EB8Ueb3VXy3avizvjPxlJNsyd6K1IZi1aJlRxHNgCT0cbtNGJMGd5Kz4Z/6ltdOCjAzpUuuKh6+5C686lSg9JNei0GqFFS7DMQXjCCVomIGLqTeveE8ue3HPQCT2Wb61Ah5NbRuUibtZVlE6AY/8mdNZw8psJPaSgYMHkYazZJyPNWz+jFgU4Ekaj2h2HT9YeFFVejDxIBiBUQeu2Nys9HIZhFIxl3152QCxH5Xplutp2r4Snq1PhkamHa8/NQVQUsGFPNQ5V8fGWCV15g+ix81jvX0YWrz0ET3cUDsZOFn+3bPlUdN8y8iAZM1MFbVMrn7PCDU7QMrLTcWQPOo+XIUpnwOjzr0ZaogX2DhfW7KhSemiqwZA0FNaCmWK5aS1r0TKhRX1zO7aX1oplljeQD0oMtG5fIpaHzv8Goi161Da2Y9PeaqWHphoMyZkwpo8CPG6hRcswTGSyq+wEDlQ0waDTYPaF50EXlwpPpwP2fWuVHppqGJYajdnjvQaiby5hLVomtHC11AtTZsLG8gayQQVbn284Ipbzz7kIGrNNdIdRtTIjD8PTYjB6WJwo4Fjhk5s72wlKRrltxglaRnak6tno8edAHx2H833C359t4EpPOYmfdbW4txdvgLOuQunhMEwPy7ZUgIo680ckYEgyGyrIhaje6mgTiYLY3Mm4YPoI8fhHq1mLWk6kC7G2Xcs4SGWYCEWqniXzmvgYM2KKFoq/W7Z/qfDI1MV1C3PE/bpdVTha3aL0cBimh7Y9K0XHknFoHgyJGUoPRzWs2HpMSKBRAdekMcMQU3SeeLx58ydKD01VSAUySzcfPeNYVq/XIyoqCna7PcCjUx8Oh6NnG/oLJ2gZWek8Xo72QzuFiVXs9Mt6yuypxbn0SCO3MMmIISUTltxpNGeD5o0fKj0chhFQELB0c0WfFhtGnu3avGWxWI6ZfKFotbtoZhY0mijsLj/Bx1YZIY3vKK0eztqjcFYfUno4DMMEmbKKJuzYXyeOr1fMGykes407B9Bo0Vm5n6WlZGR4egxmjE0H5Q/eX1mu9HAYpifmat21vI/0ESPPdv1ojbeo4OJZ2SI/EDNpkcgbdBzZy8dWGZlXNAR6nQaHj7egvPLMrhG0Wi1iY2NRV1eHyspKtLa2or29HR0dHaq9dXZ2+vV62j719fWora1FXFyc2Ib+ovP7HRimF03r3++5wNXHpYrl+GgTpo9Jx9pdVcIs7NtXjVd4lOohdurFcJRuRFvJBiRecBc0enZ0Z5SFJmIq69pgNGgxezxXHMgpHdNVV4EovQkx488RjyXHm8WF7dqdVfh4zSHcd+0EpYepCrRmGyy5U0UrM5mFGdOzlR4SwzBB5J3l3urZuROGIC3RKpZ1tjhYc6bCXrIerdu/hHHR3QqPUj1cNicb63cfF9cJ37pyHIx61vpklIUmYrrqqxClN8KWP0vp4ahKOuZodStMBi3O8xVx6GKSYM2bBnvxeqFFm3zxt5UepiqwWQwi/7J6RyWWbjqKUUPjzuh1aWlpMJvNIuHY0qLurgaPxwOXywWdTgeNxr+6VUrO0raTA07QMrLR1VAFe8kGsRw344o+/1s0Y7gIvEgH5Y5LCmEy8ldPDkzD8sWJzdVyAo6yrbDle3VpGUYplmz2moPNHJsOi8n/Ng/GS/Mmb+tX9Lj50Ji8CQPi0tnZIkG7YmsFbru4ADFWg4KjVA/R4xaIBG3b3tVIPPc2ROn4u8wwkUBVXZtotyeuOmd0n/9FTzzPm6DdswoJ59wCjcGk0CjVRUFWophwrGtsx+Z91T26tAyjtDmYNW86NEaz0sNRDZIkF7XfW80n46rYKReLBG0bHVsX3AytJVrBUaqHc6cMEwnalduP4c7LCqHXff3kF0kcULKRKmndbrdIYKqV9vZ2HDx4EJmZmSIpPVhI1kCOylkJzpIxstG04UOh1WMZNQmGFK/urMS4UclIT7TieL1dHChI04vxn6goDWxj5qJp3bto272KE7SM4sL/q7ZXimWWN5CPrsbqHvOEmCkX9flfQVYCRg6NRfmxZny+4TCuOder58f4hzlrHLTRCXC3NsB+YAts+TOUHhLDMEHg3RVlQkN9cn4qRqTH9PmfecRY6OLT4GqsRtu+tYiZcK5i41QTJCUxf+JQ/G/pAaFPyQlaRmlDVvp9S5O1jDxU19uxaZ/X1PaS2Vl9/id0ftOy4aw+iNYdXyJu5pUKjVJdTMhJQWKsCfXNHdi0twazzqKzkRK1VFlKNzVX0BJGoxEmU+hMuLIGLSMLrtbGHq2euJnfGDD4umA6m4UFAkrQEo7ybXA7WpUeDhPBbNh9HO2dLqQkWDAmO0np4agGavkirWlzdhEMiUO+EkBRFS2xeO0huN3eYIPxD9L4jR47v08lDcMw6qahpaNHQ/3qftWz0qS4ZGhDMgeMfFCClthSXIMWu1Pp4TARjL10I7qd7dDFpsA0vFDp4agGkuIiremJeSkYmhL9lVg21leA0LzlM3R73AqNUl2Qxu85k4f16XBkQh9O0DKy0ELmNW6XmAGjtvuBoHYGnTYK+4824eAZilUzX48heRgMqVmAxw178Tqlh8NEMNLJf+HkYWJShvEfj7MdLb4EoRS89mfOhCGItRlworkDG/Z4qxMY/7H5KmfaD+6Aq7VB6eEwDBNgPl5zEC63B/kjElCYnTjgc0RFnUaHzqoD6GQTQdnITItB9pBYuD3dWLvT24nDMErQ5ou56LdOkzKM/1DxxpebjohlqaigP9aCWdBYYuBurYe9dFOQR6heKP9CbCupQX1zu9LDYc4APuowftPd7RGaMZJp1amIizYKsWqCzMIY+atoSReNYZSgtsEhxP+JBb7ZWsZ/WnetRHenA/qEDJhHDmwCZtBrsWjGCLH84Wp2wZYLQ2IGjENzhXSPdI5jGEadeDzdwieBuGzuqY0BtdZYWHOniuWW7V8EbXyRVEW7fKt3PzBMsOlqrkX74T1i2TZuntLDUQ3LtlTA0eHCkGQrJuamDPgcjc6AmInni+WWzV7fBcZ/hiTbxKQjSfeQhAwT+nCClvGbzmOlwqQqymAW+rOnQ0oiUBBMs2mMPNgKZ1ODCDqPlQi9SoYJNsu2VojWpXGjknpcrxn/J79atniD1JjJF562kuPCGSNEK9O+Qw0oO9YUxFGqm+hx5/TIHHTTF5xhGFVSfLhBmFSZjTpMKTi9E7OURGjbs1p0OTDyMLdoCKKivPuC9CoZJti07VopJKVMw8dAH5eq9HBUM/klmYNdMjv7tB12MRMvADRadFQUo/O49zWMfFW01OnIsWzowwlaxm/a9q4R99a8adDojad9LiVvMpKsIjkrmQkx/qOLToA5a2yf/cEwwQy+lvrkDaQggPGf9oM70VVfhSij5WuNKhJjzT3i/9Smy8iDrWAmonQGdNVXipZmhmHUyart3sqiGWPTYdSf3o1ZJG8S0oVOZdter5kQ4z90Hhs/Klksr/RVMzNMMCfFJT+V6PFsDiYXO/bXobKuDRaTrkcP9XTXs1afKWszyScysjBnQobotjtW24bSo41KD4f5GjhBy/hFt9uFNp/uqa1wztc+n0TAL5juraJls7DAyBy07VnJs2NMUNl7qB7V9Q5ReTRzrFfGhPGf5s3e4DR6/DnQGM1f+/zL5njbclduq0RTa2fAxxcJaIwWWPOmi2U2C2MYdUK6s2t2Vonleb42+6+LZaN7zMJY5kBO5k86KXPAsSwTTDqOFsPVVCM6Qq253vM+4z8f+YoGFk7NhMWk/9rnx07xyiW27V0Nt509a+SAtvvMcd7rsyWb2Cws1OEELeMX7Yd2weNoEZpc5hHeCs6v49wpw6DTalBW0cStuDJizZ3mq/SqgvM461AywUM62c8enwGTUaf0cFSBs74K7eXbhHRJ7OQLz+g1ucMTkJMZJ5INn/MEmGxQgpxo27cWni5OfDOMGiu8WuxOxNmMGD8q6YxeEz12PqDVofN4ObfiyghVMFOlF1Xc8TUCE0xad3knYW35M6ExmJQejiqg3/GW4hohXXLJrFNre/fGNCQHxozRwny8ZfuXAR9jpLDQ1+G4ekclOrvcSg+HOQ2coGX8gma3CGv+TERpTt8SJhFrM/ZU2bFZmLyVXpacKWK51bdfGCbQkFzJul1VPbPjjDy0bPlU3FtGT4I+/vR6iL2R3HEXrzskErWM/5iGF0IXmyzM2hzsLMwwqmOlT96AJhm12jO7NPKahU0Ty2wWJm+l1/RC7zlPMm1jmEBDWtL24g19JmUZ/5Ekt6bkpyE96cz9KWKmXCTuW7Z+Jrp1Gf8ZOzIJKfFmYda2fvdxpYfDnAZO0DKDhiqJ7L6L1TORNxjILIw0vxwdXQEZXyTLHNj3rkG3h2fHmMCzdmclOpxu4cxKLqGM/3g67D2VHFKQeqbMGj8E8dFGNLR09iTOGf8gczYbVcv1qrBhGEYddDhd2OC7WD0TeYMBzcL2roank83C5GKeT+aAvCrcPNHIBAF78Xp0d3VAn5AB49BcpYejCuztXT3+FJfOyTqr19ryZ0BrjYO7rRH2Em/inPEPMmc7Z7K3kGYpyxyENJygZQaN48AWcTLTxabAOCTnrF47ZmSiSOi0d7rZLExGLNkToDFHw21vEvITDBNolmyu6DEHI10+xn/IpKLb2QF90lCYR4w7q9fqdRpcONMbCH/oc81l/EcyDGk/tBuu5jqlh8MwjExs3lcjJhlTEizIHR5/Vq81ZRaKhA4dr6WOMsZ/JuamINpiEFrqOw+cUHo4TAQgaczbxi3gWFYmKDlL1/nDUqMxfrTX/O9MidLqETPpArHcvPmTAI0w8iCZSWJnWR1qGx1KD4c5BZygZQaNFIzaCmef9cmMzcICQ5RWB1vBLLHctmeV0sNhVE7ViTbsPVgPTRSwYNLpnVmZM3cRbvbJG5BRwmAuFBbNGC50vkuPNGI/u7XKgj4uVUgdAN1o3b1S6eEwDCMTK31t9POKhgwqlpXMwlgrUT7o/DW3aIhYXr7NOwnMMIGiq+E4OiqKgSgNosfOU3o4qsDj6cbHaw6J5UvnZA8qlo0uOt+r8125Hx2VBwIwysgjLdEqpA7If3H5Fj62hiqcoGUGhbu9DY6y7YOSN5A4Z7LXLKz8WDMOVHASQS5svuDCXrpRaCoxTKBY5quenZCTgqQ4s9LDUQWOsm1wNVZDY7L2SJacLfHRpp6LW8k9l/Gf6HELTlY4s7s4w4Q9bQ4ntpbUiOV5RWcnbyARPc5rFuasPojOqjKZRxi5zPfJHJBWImndM0ygaN21Qtybs8ZBF5Oo9HBUwZaSGhyvt8Nq1mPBWUrHSOhscbAVzBbLLVsWyzzCyEWqol26uYJj2RCFE7TMoBB6MB4XDCmZ4jYYyCxs1rgMsfzZ+iMyjzByIedLXXwaukkjeP9mpYfDqBS3pxtLfbOvkjMo4z9kiEBETzjXLxdhySxszY5K0SbK+I81bwaiDCaRQBfVNgzDhDVrdx2Hy92NEekxGJ4eM6j30FpiYMubIZa5ilY+cjPjkZ5oRafTjY172NCGCQzk19G625ugZXMw+Vi81ls9e/604TAZdYN+n1ifD0PbvnVw25tlG18kQ7kXs1ErEuj7DjUoPRxmADhBywyKtn1r/Kqe7d2KS7BZmHxQG4m0X1jmgAkUuw7U4URTu5gdnzbG67jM+Ifb0Yr2gzvFcoyvbXawjBoWJ26UfFi3m83C5IAS5rb8mWK5dedypYfDMIyfUOxJSB0HgyV6ovd43bZ3DTydrOsnVywrmbat8MlQMIzctB/eA3fLCdG1ZMmZovRwVEGL3Ynt+71a/Yume6/zB4sxfaS4UVGYvWS9TCOMbChhPnu895y3hM3CQhJO0DJnjau1AR2H94hlq6/1YLAUZidiaIpNGDRIOmCM/0it0ZTscbU1KT0cRoVQa4x0YWvQa5Uejiqwl26gcg4YUrOE8Yy/zPEFYGt2cIJWLqQKG3vxOpaQYZgwpr65HbvLvQZUcwcpbyBhGlYAfeIQYZzbtofNwuSWOaBkT2Nrh9LDYVRI2y7vZCu10mt0BqWHowrW764SGrTZQ2KRkWzz+/2skrdK8ToZRsdIxs7Emp2VLCETgnCCljlr2vatFUYpxqG50Mel+PVefczC1h9hLRSZMCRmCKkDSvbYi2l/MYx8tLV3iQCMYHkD+aCkH2Er8FZp+svs8d4k756DJ9DYwhe3cmAcmueTkOnwSv0wDBOWrN5RJYxS8kckIDXB4td79TcL41hWHoYk25CTGSeSPat3VCo9HEZluDvswq+DsLG8gWxIRQFSDOov1nyvhEzHkX1wtbFnjRwUZCUgPckqCuTW7eIijlCDE7TMWWPfu1oWeYPeYtV6nQYHq8gsjKs95a6ibdvNMgeMvNCFktPlQWZaNEYPi1N6OKqAtLWo1Y6w+tro/SUlwYLc4fEiCcEBmDyIRIxkFsYyBwwTtqz0yRvM81PeoLdZWJRWD2fNITYLk5H5E72GNiu2cpcdIy/2fWvR7XJCnzzM20bP+E1zWyd2lXnlDeZMkOfYqo9NgXFIjigO44lx+WJZySxsyWaWOQg1OEHLnBVdDVXoPF4ORGl6tPj8JdpiwCzfLNtn6w/L8p4MVeHNEvup83gZnPVcecDIx1KfZtG5kzPFSZ7xH1HF0e0RFwn6ePk0fSWdqdU7OUErF8K1HVHoOLoXXY3VSg+HYZizpKquDWUVTdBoojDLd4z0F605uqfSq5XNwmSDkjy0n6iA41htq9LDYVRE685l4p4mXTmWlYd1u4/D0+31QUhLtMr2vlLhAiXVGXk4ZxJdwwF7yutRXW9XejhMLzhBy5wVZIBAmLPGQ2uNle19F/lkDlbtqIS9nc3C5ID2jzl7glhmszBGLipqWlF6tFFcMC3w6cMxcknHyFc929utldh3qF5oLjL+o4tJgjlrnFhu3eV1f2YYJnxYud07aT0hJxlx0UbZ3jdm4vk9RrqeDr7glQPaPxNzvXJqbBbGyIXzxDF0Vh3wFhz5Og4Z/1njkyKZI5O8gYRNkjmoKIGrpV7W945UkuPNGD86uY+vCBMacIKWOWNIU6tNkjcY45852EBaKMNSbeh0ujkAk5FoSeZgz2rWRGNkYamvFWZyXiriY0xKD0cVkKZWx9F9AUnQUgBGGov081/LMgeyET3eK3PQtnsFurs9Sg+HYZgzhGIhyZR2np/mYANpVOuThqK7qxOtbBYmG/MmevcT7TeOZRk5aPWZg1lGTYTOFq/0cFQBeR3s8RkvytWZ0HtinLxvhMwBGeoysiD5iCzbclRofTOhASdomTPGWX0IXfVViNIZYM2ZJut7U2uJVEVLMgccgMmDJWcKovQmuJpq0Fm5X+nhMGGO2+3Bsi3eWdaFU73aRYz/2Et88gYZo/02XhyI2RMy+hg3MP5jyZkKjdECV3MdOnzawQzDhD7llc2orGuDQafB9DHyyclIsWyMzyysdfsXHMvKxPTCNJgMWlTXO1B6hE2CGP/o9rjR5ut+iR7H5mByyxvkZsb7bbx4Suk+0aHgNdRl/Gf62HRYTTrUNrZjd5k3uc4oT1gkaD0eD5566inMmTMHEyZMwN13342KilOXYn/44YfIzc39yu3YsZOVmZ9++ikuuugijBs3DldccQXWr18fpE8TvkjVs5bRk6AxmmV//3MmDxMB8+HjLaKFmvEfjcEEa543mc4yB4y/bN9fh8bWTsRYDZicL++FbSRjL/bJGxTIWz3bW+aAdKaKDzegrpFlDuRAozfCWjC7TyUOw5wOjmVDA6l6dkphGiwmvezvbxs7XxQyOGuPeFuoGb8xGXWYMTZdLC/fyq24jH+0H9wBt70JGksMLKMnKj0cVRkI9y4KkBtr7nSh/995jGQOOJkoB0a9FnN8nSRLtrBZWKgQFgnap59+Gq+99hp+//vf44033hBB7l133QWn0zng80tLSzF16lSsWbOmzy093Xty37BhAx544AFcf/31eO+99zBjxgzcc889KC8vD/InCx+ohZM0tQhb4ZyArMNmMWC2z/Hx8/VHArKOSETSViKNy2436/syg2eJzxxs/sSh0OvC4vQR8rhaG9BxtFgs2/K8GltykxhrRkFWolhmmQP5ZQ7IVZj1Jpmvg2NZ5aEWTimJILe8gYTWbOuRqmnZxmZhcjF/krdrZ/WOKnS5WFaGGTytO5f3XM9GaeWfpIlEyOOAvA6ImT7vA7nRxSTCNCxPLLcV82SkXCyc4j22rtt1nH2AQoSQv8KmwPWFF17A/fffj/nz5yMvLw9PPPEEqqur8cUXXwz4mv3794sqg+Tk5D43rVYr/v/cc89h4cKFuPXWWzFy5Ej85Cc/QWFhIf7zn/8E+dOFD5RAcLc2iJZOy8jAzTZeMH14j1lYGx8kZME8Yiy01jh42lvhKN+h9HCYMKXF7sTGvV7H+oVTvZpFjP9Qco80tYxDcqGL9Yr1B4LZPsOGNTu9yQnGf4QkBelNupxoK+aWO+bUcCwbGuwVZokdoqVzcr78cjISksyBfd8auHnyRhbGj0pCfLQRrQ4ntpfWKj0cJkxxO1phP7BZLEePZ3kDuaDkHim65A2PR0q8/PIGEtLkl51jLtnIyYwXPkDOLjdfI4QIIZ+gLSkpgd1uF5UBEjExMSgoKMDmzd4D7EBVBxSsDgRVLGzbtq3P+xHTpk075fsxJ+UNrHnTEaUL3GwjmdlkpkWLg8TanVzpJQdRGi1shd5WXJY5YPxpC3W5PcgeEousjFilh6MaqLKdsAVI3qC/zAHp99U2OAK6rkiB9Cajx3mraFnmgDkdHMuGlrwBVXjpdd5EdyAgMxt98jAxeWPnSi9Z0Go1mOuremaZA8av61m3C4bULBhTvd4njP9InQlzfJ2wgcKaT+e8KOGr0tXMEzVyxbLnTvYW3izdzMfWUECHEIeqCwippUsiJSWl53+9aW5uRk1NDbZs2SJayRobG4U2F7WBZWVloaWlBQ6HA2lpaWf0fmcDmQHQewea9vb2PveBptvt6hHk1o2aGvDPOKMwBUerW7FmRwXmjAtcRVkwCfY+6w/tN2z6GPb9m9HWWB8QDWG1ofQ+CzW+3HRY3M8dnxaU41wk7DPqSiAtLUIzYkJAt6tRRxNg8dh3qBHLtxzGpbND58Ik3PZbb7QjpwLL/4vOY6VoOVYOXULfWEWtBHOfUWxFFxDhDMeyyv/uXS5PT3XQtIKkgH9G4+hp6KqrQMu+tdDlBnYCLlKO1dMLkvDBqnLRzXOioQUWU8hfxiLS91mo0bxjqbg35s/iWFYmqCuBPA6IiTnxgd2uGiMMQ/PgPFaMpp0rYZ18MUKFcNtvvZlemISXP40S+7HsSB0ykq2IBNpDNJYN+TObtMEMBkOfx41Gowhg+3PgwIGejfDHP/4RHR0d+Oc//4kbb7wRH330EVwu1ynfr7Oz06+xdnV1objYqyUYDA4f9iZMAo2+9gBsnXZ4jFYcbAMQ4M+YbPZKG+wqq8e2nXthNoR8oXfI7bOv0N2NGGsitPZ6HFr1IZxDxykzjjBEsX0WQlQ3OnGoqhUaDZBsagnqcU7N+8x4eBOoEawrfij2V9QAoFvgyErqxr5DwLLNhzEqMfQCyHDZb/2xJmXDUFeGilXvoSPXW1EbKQRrn/WP2cINjmWV/w6VHmuHvd0Fm0kDTUcNiosDW32l0SSCek06j+xBya5t6NarZ2JcqWM1/R6SYnQ40eLC+0t3oCg7MpIIkXx+lRNtay1iag+jO0qDCk0SujmWlYX1Ja3iPjPZgJrKQ6gJcJe8ISYTVhSjcddKHLVmI9QIl/3Wn5FpRhyo6sA7S3Zj4YTI6pQ8HGKxbMgnaE0mU49+l7RMUABqNn812Jk8ebJwsY2Pj+/JUv/9738Xml/vvvsurrnmmp73682p3u9s0Ov1GDVqFIIR6NMXacSIEX6P+UxoOrQcHaIFdzYyCgoRDN7ftA7Hau1o9cRjYn5gxMaDSbD32UC0tS5A27q3Ed98CAnnXafIGMKJUNhnocKmxaXifkpeCiYXjUGoEm77rH7nW6DpqITxCzAsPz/g60sf1onFW1ahqqELiWkjkBIfGtso3PZbfzp0F6Hp46dgrS3BiMvuRRTNZKicYO6zsrIyhDscyyr/u/9y925xT23yhYW5CAYnij+Gq74Sw3UOmPPD3y0+FI7V59aa8ObScpTXRuHGiwN/3gx3QmGfhQotK7aCajtNIychffwkhCrhts9eW71J3J8zNQv5+YH3qHBnZqCu+Avomo9jdEYidLGB0xNX837rz6WeGvzljV3YW+HEd6/Lg0YT3p1L4RzLhnyCVmoHq62tRWbmyR89/U3mCQORkJDQ52/a4EOHDhXtYnFxcbBYLOL1vaG/U1NT/RorBdH03sGCPleg1+dxtqPz4DaxHDfhHJiC9PnmTBiK178oxZaSelw4K/AXCmraZ6dCX3SOSNA6K/bB4GoXbphMaO+zUIDcktfs8rbMnj8jKyy2RTjsM1dzHbqO08k6CvHj5kEXhPHSNhk3Kgk7D5zA1tIGXHXOaIQS4bDfBsJcOBMtS1+Ax96IqJoDsIwsQqQQjH0W7vIGBMeyyn6H2jtd2FJaJ5bPnToiaJ/PVjALTavfQtfBrUicfD7UgpLH6oXTskSCdu+hBrR3RSExNvwSIUoQrudXueh2d6G21KsHHTdxYVhsi3DYZ7WNDuyvaBYeBwsm07H15ARkwLBY0DZ8DNoP74bn8A5YZlyBUCIc9ttAzC7KxHMfFqOxtROlx9owKc+/WCKcMIdYLBvyZR7kdGuz2bBx48aex0h7a9++fZgyZcpXnv/mm28Kk4Te+idtbW0iO04VAbRxJk6ciE2bvLM9EvT+VLHA9MWxfwu6uzqhi0+DMX1gs4pAQAYOxPb9tXB0eCUPGP/Qx6XCNIyqDbrRtm+N0sNhwoQtxTVosTuFe/Kk3NCYpVYDbT7jGFNmAXTR8UFb7+zxXgOH1ezUKhtknGkrnCuWW3cuU3o4TAjCsayykGZpp9ON9EQrRg+LC9p6bXnTxX37wZ3wdIam3mW4kZZoRUFWgnCMX7mNz2PMmeEo2waPowVaa1xETaIGGsnQuzA7EQkxQUjO+rDmz+xjtMv4DxlnzpvoNWJcsumo0sOJaEI+QUtaDTfffDMef/xxLF26VDjh/vCHPxTGCOeffz7cbjfq6uqEPhcxd+5c4W774IMPCg2v3bt347777hOVCFdeeaV4zh133IFPPvkEL774IsrLy/HYY48Jva3bbrtN4U8bom6XFGQWzg5qFcvwtGgMSbaK6r3N+wKryxhJ2MZ4kwhtu1cpPRQmTFi62XuSXjBpmHBRZuTBXryuT5AZLGaMTRdtS+XHmlF1gkTFGTmIHu/VnrXv3wR3O29Xpi8cyyrLym3HxP3ciUOCGsvqkzOhT8gQ1XuOsq1BW6/amT9pmLhfsY0dx5kzo3XncnFvGzsPURqt0sNRDZLxojT5HyysudOAKA2c1QfR1XA8qOtWMwuneDt8NuypRpujr4QSEzzC4mr7/vvvx9VXX41f/OIXuOGGG6DVavH8888Lnazjx49j9uzZWLx4cU8b2UsvvSSqDui5t99+O6Kjo/Hyyy8L8wSCnv/II4/g9ddfxze+8Q1s2LAB//rXvzByZPAqRMMBt6MVjoM7xLKtcE5Q100BtFRFu263d3aO8R9r/gxAo4Oz9jCctTw7xpyextYObC72TpCcO8V7QcT4T1dTDTqrDojg0uqrsAoWsTajkDnoXfnA+I8hNQuGlOGA24W2vdyhwHwVjmWVgTpAtpd6pSDmFXmrg4IZy0rHeHvJhqCuW83MHp8BnTYKh6pacOR4i9LDYUIcV1tTzwRJ9LjIMvIMJNX1duw/2gSSKp05zivjEyy01liYR4zt05HG+E/2kFiMSI+By+3Byu3coaAUIa9BS1AQ+8ADD4hbf0iPq7TUa2AjUVhYiBdeeOG073nFFVeIG3Nq7CXrAY/be+GZFNyglqAE7f+WHsCW4lp0dLpgMobF1zWk0ZqjYRlVBMf+zWjbuwoJKTcrPSQmxKuOPJ5u5GTGITMtRunhqAa7JG8wvBA6W/DabSXmTBiCHfvrsGZHFa45Nyfo61cjlIiJHn8O6r98EW27liF28iKlh8SEGBzLKsPaXVVwe7rFheew1Oigr9+aNwNN6971tlg7O6AxBK8NWK1EWwxCH5GkK1ZsO4bbLi5QekhMCEPXO+j2wJgxGoZkLjaQC2mSf8zIJMRHB/+4Zi2YifZDO0VHWvwsb2cJ438su3BqJv79wR4s2XwUF8/KUnpIEUlYVNAyyssbKMHIIbFISbDA2eXGVl/1A+M/tjHzxH3rntXo7vYoPRwmROnu7u7RIJJaXhh5aNvnlTewBVneQGL6mHRoNVE4WNWMyjpux5cL0Wmi0aLzeDl3KDBMiMkbzCsKbguuhCEtS7iMd7uccBzcrsgY1AjJLhErfBPJDHOqWFaSN+Dq2QDJG0xQ5thqzZkmYi5nzSE467kjTC7mTxwqrhHKKpq4Q0EhOEHLDIir5QQ6ju5TNEFLszizJJmDXXzglQvL6EmIMlrgFvu4WOnhMCFK2bEmHKluhUGnwZwgt4Wqma7Gajiry73yBqShpQAxVgPG5ySL5TU7uIVJzpY7y6hJYrl1l/eCkGEY5ahrbMfeg/Viec4EZc5jQuYgn2UO5GZKQSosJh1ONLVj7yHvPmaY/jiPl6Or7iiidAZYFbqeVSPHT9hRdqxZeBrMHBtceQMJrSUa5hHj+vg6MPJIodHxlaAqWib4cIKWGRBJQ084jMd49QqVQNK02byvWlTSMv6j0Rlgy5shltv2sFkYMzBS9ez0semwmfVKD0d11bOknUUJPaWYM947+bWGdWhlhWQOiLY9K9Htdik9HIaJaFbvONbjMJ4cb1ZsHCRzQDgObIHHxcYrcmDQa3uKOFZs9e5nhumPNFlqyZ0Krcmq9HBUVz1LngaU0FMKW4G3E81evFaxMagRqXOSjq2kR8sEF07QMqdN0AbbHKw/OcPikRRrQnunW2gmMvJgGzu3Z8aRLxaY/rjdHqze4U3cncvyBrIizfJbFZI36C1zQCYrh4+3oKKmVdGxqAnLyCKReHfbm4U2GsMwyiGZnMybqGwXiDFjFLTRieh2dqD9IB8X5JY5WLuzkos4mK/Q7XGjbZ83ccfyBvJCHgbE7PHKyBtIWHKm+syvj8J5gidq5GJSfiribEY0tXVy/kUBOEHLfAU6wJGeC+m6SLP+SkGtEzN8M+Rk9MDIA1VG08WCp9OB9rJtSg+HCTGoXbDV4RRGHONHKVdBrzZII0scWxWUN5CwWQyYkJMillnmQD6itLqe86a9dJPSw2GYiIUmng5WNgstPanSUimi6Jif5z3ms8yBfFBlNBVx2Dtc2FJco/RwmBCDpPo87a3QmKkVfqzSw1EN5F1AHgbiGl0heQMJrdkGS/Z4sWz3dagx/qPTanq6mNfvPq70cCIOTtAypzQHs2RPEPouSiMF1uTW2uXiMnu5LhZsY7zV0a0sc8D0QzoZTytMg1bLpwm5q2fNWeNC4tg6Z4L32LqaZQ5khVopCfv+TaKCh2GY4LNyu7eaqig3RehuK81JmYPN6HZ3KT0cVUAJIqk6mszCGKY30iSpZfQURGm0Sg9HNUiT+hNGJ4fGsdXXkdbGMgeyIiXfN+49DjcbMQYVvvJmvuJ2GSryBhJ5IxIQF22Evb0Lu8q4zF4uosd4ZQ4cZVvhbucWZ+bkMWCDL0E7wzd7ysiDpJFlK5iFUGBaIckcaESl2ZFqdmqVC3NmITQmGzyOFnQcK1F6OAwTkeexVdtCQ95AwjQ0F1prHDwddrQf3qP0cFTDfJ/MweZ9NaLzh2GkY4C9dKNYlqrXGXmQvAukSX6lseZMAbQ6dFEHcB2bWsnFmJFJwoOkuc2JYjZiDCqcoGX60Hm8HK7GakTpjbDkTEYoQO1p0izOul1cZi8XhpTh4ga3C/bi9UoPhwkRDlQ04URzB8xGrZgdZ2SUjqk9KrSyhGZWCGA16zEpT5I54CpaOWUOLKO950+WOWAYZc5jx+vtMBq0ohMkFKAKPknahmUO5GNEeoy4kZHNWu4GYXpdz7pb6xFlMImuJUYeaEKfvAvIw4C8DEIBjckKS3ZRHyNexn+ogGOq7/y5fg/nX4IJJ2iZgeUNcqZAY1DO8bY/s8Zm9LRek4ERIw82XxVtG8scMP3kDSbmpQqXZCYA8gZmG0KF2eN9Mgc7KkXFCSMPVp/MgaNkA29XhlFI3oCSs2ajDqGCNW+6uKfKPpY/kY8Fk1jmgOmLo3RDj3GnRqd8G75akKpnycOAvAxCBWvBzJ5ONY655ENKwtO1IW/X4MEJWqYHChbtkrxBwWyEEmNGJgrDImpf2nOQy+zlwitjEYWOimJ0NdcqPRwmhBK0MxUW/lcbkpNwqMgbSNDsuF6nEaYPVBXByIM5e4LoRHG1nICz+pDSw2GYiIG08lZv98kbFIWGvIGEaXihMCwi4yIyMGLkYW7RUERFAXsP1qO2waH0cJgQoEfeINc7KcLIA03mh5K8gYR19GREafXoIjPe2iNKD0c1FOUmi06UusZ2lFc2Kz2ciIETtEwPHUf2wm1vEtp5lpETEEqQUdH0Md4y+7W7uIVJLnQxieKCgWjb403OM5HdukSJOmprmZyfqvRwVANpYpE2FmlkUXdCKGEx9ZI54PZQ2dDojSJJ2/tCkWGYwLOn7AQaWzuFdh4ZhIUSQubAJ3HD0lLykRRnxtiRSX2qp5nIlpSiRJ2IuUZNVHo4qoG8Cug6wdv6HlpFHBqjBeaRRX061hj/MRl0mOg7j0r+JEzg4QQt8xV5A2v+DDELFWrM8rXi0gGC3QQDIXOwktsXIpx1u6XWpWSRuGPkQdLEsmRPgNZkRagxZ8KQHmdePgYEop2Z9SYZJlhICTqKGak7INSgGJtgmQN5me8zg1u+tYLPYxGOpPFsHjFOJO4YeZC8CihhRxNgoYbUoUYJWj4GyIfUUbmOE7RBI/QiF0YRul1dPSc0b9t76DFuVDKsJp2ojCg53KD0cFSDLW+6ty2ETIxquBU3kpFmR0NF+F81TsLFXnkDa75XIyvUmFKQBoNOg6oTdhzkFibZsIyaBGi03mMrVfMwDBNQulxurPN1Wc3zJexCDfOIMSJpRB1rHcdKlR6Oapg5zpuQr6hp4/NYhCOZc0qmfIw8sawkbzA7xOQNJCyjJyFKZ0BXw3G+npWRyQVpwrBd6rJkAg8naBmBo3wbPJ0OaKMTYMrMRyhCgdc0X+JICsAZmdwvc7yO42wWFrmQblvZsWZoorzGKow8kBYWtdrRJIg1xOQNJMhEZ3KBV9KCZQ7kg6qlKRlDOFjmgGECzpbiWtg7XEiMNaEwKxGhCJ0LLJLMga8wgvEfq1nf4zjOZmGRC/lpOKvLSU8kZGOucIQ8Cig5J67FQ/QagczNJUkLljmQD6qWHjcqqY9PCRNYOEHL9JE3IHOwqChN6JfZ76qCh2UOZMM2Zp64b9uzmlvuIpQNe7wn3fysRMRFG5UejmqQgkTSxgrlVrvZ430yBztZ5kBOJIMS1qFlmODJG5Bsi4ZmG0Nd/qRkA7q7PUoPRzUs8FVNr9x2jKXQIhSHr3rWNCwPWmus0sNRDdLkPXkWhLIEmtSpRtJiHMvKx4xx3qrp9T4pPCawhG4mjgkaVDnrOLA1pOUNJMjwwWzU4kRzBw5UNCo9HNVApnAas0203LUf3qP0cBgFWO9L0M7wTYIwcskbeBO0toLQlDeQmJKfKpxaq+upkrpJ6eGoBq8pXBQ6qw7A1VKv9HAYRrU4OrqweW91SMsbSJizxyPKYIK7tR6dVWVKD0c1TMxLRbRFL6TQdh2oU3o4jAKwvEFgYlnyKOg9mR/K0lJReiNcTTVwHi9XejiqYXphGqKigP1Hm3CiqV3p4ageTtAysO/fhG6XE/rEDBjSshDKGPRaTMn3tlas3cVl9nIh2q/zfMYV3HIXcTS3dWLfQW/yiPVn5YM0sEgLizSxLKO8MiKhismoE0na3kYQjP/obPEwDs0Ry1xFyzCBY8OeajhdHgxJtmHkkNCunNOIc8IksWwvWa/0cFQDtV+TFi3BhjaRh9vejI6KYrEsyYgw/kOazuRRQF4FU3xyWKGKxmDqOba2scyBbMTHmJA3PKFPxyUTODhBy6Btz5qe6tkomh4JcWaO9wVfu6q4fUFGpNlmByXsueUuoti4txrUDThyaCxSE0K3DT/ckKpnSRNLYzQj1Jk9gWUOAipzsN9b2cMwTODkDah6NhxiWWv+yUlxPt7Kx8yx3muEjXuOsxRahGE/sBno9sCQlg19XIrSw1GdvAF5FYSyvIGE1dexRjE4H1vlQ+qwZB3awMMJ2giHZhvbD+0Uy7bC2QgHJuWmiEramgYHytmpVTbMw8cgymAWMgfcchdZSCfbGVw9KxsUFJIGVm9NrFCHtMVMBi1qG9txoIJlDuTCmuut5Ok4shduR6vSw2EY1dHU2okd+70t7fOKQrsFV8KSXSS6K1xNZGrEjuNyMXZUojC+JJmD/SyFFpH6syxvILO8wc7wkDeQsIyciCi9Ca7mOr6eDUCCds/BerTYnUoPR9VwgjbCEeX/3R4Y00dCn+CddQ51qBV3cn5KTxUtIw9ROn2P+yVV0TKRo9snXdiy/qx8kPYVaWCRFpbUbhXqmAy6Hhfs1T69McZ/9PFpMKQMF+daB1X4MAwjK2t3VopqyVHD4pCRbEM44G3F9TmOs8yBbOh1Wkz2yfVs3OPVJGYixE/FV3DECVr5IE8C8iYgjwJJBivU0VDcneOVFbMXr1V6OKohLdGKEekx4ly7yaf3zgQGTtBGOG17V4t7a4ibg52qhYllDuTF6tNskkT2GfWztbgWLjfp9lkxLDVa6eGoBkn7ipKzdCEeLkgVEtTSxu2hAZA54GMrw8jOyu3eCaV5RaFtDtYfa57vuMAyB7IyfYx3opG1EiMHR/l2wO0SxUb6pPA6DoQykicBJWepQCpcsPk619qK17Nsn4zM9BXy8LE1sHCCNoLpaqpF57FS4TBtK5iFcIJEyskMoLLOjqPV3DIqF5aRReRega76SjhPePXcGHWzbndVjzlYOOj2hQN0oS3pz0paWOECyRyYjVrh0rr/KLeHyoXFJ3NAkkIeJzvgMoxckNxV8eEG4TA9Z0J4dIL1cRzX6tHVUIWuugqlh6MaJuWlQqeNwrHaNlTU8DVCJCAZHNO5lmPZAMgb+DwKwgXzyCJEGUxwt5xAZ+UBpYejGqb7ErTbS2vR3ulSejiqhRO0EYyUQDANL4Qu2uvMFy6QSHlRjlfmYC3LHMiGxmSFecQYsezYz624asfZ5cbWkhqxLDkfM/5DmlekfUUaWKSFFU6Qvve0Qm8AttoXmDP+QxIHuvg0dLuccJTvUHo4DKMqeQNi7MgkJMaGvhljbzRGC8zZ48VyG8scyIbVrMe4Uck9JqiMuvGI8+q2Pt0qjP+QFwF5EpA3AU3ehxManaGnK1TqaGP8hyQO0hOtcLo82FZSq/RwVAsnaCMYyVFaarEKN2aN9yYRWIdWXqw5U8Q9O46rn50H6tDe6UZirAmjhsYpPRzVIGleWUZPElpY4cYcX6XEWpY5kA2q6JHMwhylG5UeDsOohg0+nVGp9TLcsObN6FMByMjDNJY5iBg6Du1Gt7MD2ugEGDNGKj0c1SB5EUwtSBMeBeGGZNBLBWkscyBfLCtV0UoG04z8cII2QnG1NaHz2P4+Cblwg04YWk0UjlS34lgttzDJhWW09/tALSGuVm5xVjPSyXXGmHRoNNwSJgcUBJLmFRFu0jESRbnJsJh0qG/uEK3DjDxIxiX2sq3odncpPRyGCXsaWztQcsR7jJrqq/wPNyyjJwMaLbrqjsJZz10LcjHNZ3hJUj0NLR1KD4cJIPbSDT3n2KgoTm3IAU3OkxdBOMobSFiyJyDKaIG7tcEn6cjIAV0zEpuLq9Hl4sR3IOCjWITiKNtCqQQY0kZCF5OEcMRmMWB8jreFad0unsWRC11MIowZo8X3gx3H1Yvb7elp/ZNmQxn/oYkN0rwi7SvSwApXF2zSJCYk/THGf4xDcqC1xqG704H2w3uUHg7DhD2b99WAvLVGDY1Fcnx4yRtIaM02mEeME8tcRSsfJHeRkxknvh/sOK5euj1u2A9s6TMJyvgPTWyQFwF5EoSbvIFElE5/UuZgH8scyEXu8HjERxvh6HBhV1md0sNRJZygjVAkfdFwrZ6VmOXTzWQdWnmx+E5oLHOgXvYdbkCL3Yloix5jshOVHo5qaNvnlTegoJA0sMKV2eMzeiRk3CxzIAtU2SOZhdlZ5oBh/GajT95gmm9CKVyx5nulxuy+7gtGHqSJRpY5UC8dFSXwOFqgMdtgyixQejiqkzcgTwLyJghXbJLMQcl6kcxn/Ic6LqVjK8scBAZO0EYgHmcH2g/tEsuWME/QUgsTHSgOVjajut6u9HBUg6SV2H54NzydDqWHwwQA6aQ6tTANWi2fCuSSN5AusCXtq3BlQk6KMFppaOnEvkP1Sg9HNUgVPjRJyhcLDDN4Ojpd2LG/tk87e7giqryiNHDWHEJXI1d7yoWURNh54AQcHSwro2Z5A5Jni9KEbyIxZOUNfJP14Yo5e5wwwHa3NYpkPiMPUucldWJyEYf88FV5BNJ+cKdwktbFpghn6XAm1mbE2JHe6j82C5MPfeIQ6BPSAbcLjvLtSg+HkZnu7u4++rOMPJDGlbutQWhekfZVOKPXaXq+G2t8lRSM/5iHF3ovFuxN6Kz06sAzDHP2bN9fK5ykUxIswlk6nNFaYsSxgWCZA/kYmmJDRpIVLrcHW9lxXJWxrL3UZ3jN8gayQd4DpNtMXgQTw1TeQCJKq4clx6f/X8wyB3IxdmSSKOJoau1ECXtVyA4naCMQ+wHvyYxaLcmNL9yZ6ZM5YB1a+aDvhSRzIMlhMOqh7FiT0JYyGbSYkBvewVcoIWlcUTUUaV+FO7MnnDy28gy5jBcLoyaJZZY5YJjBs8EnbzB9TJoqYllrnk/mgBO08jqO+yYaJTkMRj04j5d7Nf/1JpizvDrOjP9I3gP02yFPgnDHVsAyB4Eo4phSkCqWWUJGfjhBG2HQgclxYKsq9GclqMqLYvPSo42oa2xXejiqkzlwsOO46pCqZyflpcIYxtpSIWdU4ZudlzSvwp3xo5OFRnFTWyf2Hjyh9HBUg1TpYy/ZKCqAGIY5e5NLMggjpheqowvEIo4LUeisOgBXCx9v5UJK0G5hx3HVIU1yWkYWQaM3Kj0cVUCT8WtVIm8gYR4xVmgUu+3N6Di6T+nhqAapy27d7uMcy8oMJ2gjjI5jJfC0t3rF1IflQw3Ex5hQkOWVOVi/m2UO5MKYMVo4jpMGbfsRPqGpCWm2U9IQYvyHtK2obZ3a10nzSg3otBrMGOsN0Ffv4GOrXJizJyBKZ4CruRbOmsNKD4dhwrIFt9XhNbksyEqAGtDZ4mEalieWuYpWPnKGxyMu2gh7hwt7yjnxrSYkI2OWN5AP8hxobO0U7evkRaAGorQ6WHOn9+l0Y/xnYm6KMJCrbXDgUFWL0sNRFZygjTCkdnVqsVSTmPrMcd5E01rWoZUN+n5YRk8Wyw5fEMSEPxU1raioaYNOG4Up+d72FMZ/pOpZ0rqiNna1IFVQkMY3Va0x/qMxmESSlmCZA4Y5e8iYhJicn6oqk0tr/gxxzwla+dBqonpM5LgVVz04TxxD14ljgEYHy6iJSg9HNUieA1QdSW3sakEy7iVTOZY5kAeTUYeJucl9OjMZeQiLX57H48FTTz2FOXPmYMKECbj77rtRUVFxyucfOHAA99xzD6ZNm4YZM2bg/vvvR1XVycSd2+3GuHHjkJub2+f2t7/9DZEipm5RibyBxExflZckbM7I6Czsm6Xm9gV1IF2gjBudLGbIGZnkDUrW99G6UgvjRiUh2mJAi92J3Vx9JBvWPJ/MASdoIwaOZeWBYhHpPDZNZSaXUpUXdWS4WhuVHo5qkBK0lNjnWFYdSNezon3dZFV6OKqAJuElPxfJg0AtmEeMgcYcDY+jBe1H9ig9HNUgddlxB3MEJmiffvppvPbaa/j973+PN954QwS5d911F5xO51ee29jYiDvuuAMmkwmvvPIKnnvuOTQ0NIjnd3Z2iuccPnxYLH/wwQdYs2ZNz+3OO++Emumqq4CrqcZrUhLmDuP9SYozI3d4PCju4lkc+TBljRXi++7WBiHGz4Q/0u9D0g5i/Ic0rUjbiqRj6GJBTVB1mtShsManS8b4j2XUZECjRVfdUXQ18DkrEuBYVh6OVreiut4hqruoxVJN6GISYRySQ2lonryRWU+dTFHrmzuESSoT/jh8vw/JL4Pxnz0H64XnAEnH0G9GbV2hPUaMLHMgG1MLUkWXwpHqVlSdaFN6OKoh5BO0FLi+8MILonJg/vz5yMvLwxNPPIHq6mp88cUXX3n+kiVL4HA48NhjjyEnJwdjxozBn/70J5SXl2Pbtm3iOaWlpbDZbOK9kpOTe25WqzUitHrI6VJjMENtSFW01IrLyINGZxDi+72/P0z4QiZ6ByqahKnetDHeihLGf9p88gZWIW+gg9qYM2GIuKfKCpY5kActJfOHF4plTsSoH45l5WPDXu+EBiUQzEb1HW97kgilLHMgF6STSKaoxIY9XnkMJnwhE73O42XCVM/i6/Rj/EcyByNjPfIgUBu2glk9MRfLHMiDzWLA2JFJYnkDF8jJRsj/+kpKSmC320V7l0RMTAwKCgqwebNXT7U39DyqUqCqAwmNxvsxW1paeoLakSNHImL1Z1UmbyAhVXmRCUBzm7fChPEfi292mhO04Y/UFpo/IgHx0SePkczgoXZJx/4tfS6s1caY7EQhc0CmPPsONyg9HNUgGZtwglb9cCwrHxt9CbbpKp1klM4jHUf2is4MRh6k7wvr0KpH3oBM9XS2OKWHo5pYVtL2njlOXfIGEqbMAq/MQXurkJFh5GGGL/+yjhO0shHyU89UXUCkp/dtx01JSen5X2+GDh0qbr159tlnRZA7ZYo3Mbl//364XC5885vfFEFzamoqbrvtNlx++eX+X6g7HAg07e3tfe7PBHdbQ89sY9TQwqCMM9jEmKOQlRGNQ1WtWLXtCM6d3Pd7oCSD2WehQlRGPvWGCImMlsqD0MWr86JITfvsVKzZeUzcT85LUuUxQIl91lV9UBxfo/RGdKdkq3K7EkU5iVi14zjW7qjAyHSLrO+txt/amRA1zCuH0Vm5H621ldDa4hEuBHOfUWwVRWX/YQzHsvJ8h8hjQOoCGZsVq87jrSEaupQRcNUeRuOetbCMnY9QIZyP1QUjYqDRRAmJjPKKOqQnqrvSXA377FS0+lrU9dlFqjwGKLHPyiubxfHVaNBiVIZFlduVMIwYj47iNWjetx7dyVmyvrcaf2tnwrjsWHFfeqQRx6obkBATPgVA7SEay4Z8glbaYAaDoc/jRqMRzc1fP7NM2l2vvvoqfvGLXyAhIaHHeIG0v6jVLC0tDStXrsRDDz2Erq4uXH311YMeK72+uLgYwYL0x84Uw9GtoFDEFZuB/UdphkOdsxzZyVE4VAUs23QQGdZWhBpns89CCVtCJvT1h3F0/afozFJnlaDa9ll/7B1uFB/2mo7E61uCeqxS8z4zHVgJEozpTBiBkgPq1WlOs3m7EtbtqsTk4e6AJMzU8ls7G6Jjh0DXXInDaz+BM3MSwo1g7bP+MWC4wbGsPN+hzQe8GndDEgw4fuygSiNZwBQ7Aubaw6jfsRxHdN7W/FAiXI/Vw5MNOFTTiU9W7sWs/GhEEuG6z/oT5XQg9lgJKAI51h0HD8eysrBsl/c8lJ1qQHnZfqgVvTEFNkryl2xAZfIEiNk+mVHLb+1sGJpowLF6Jz5esRtTRtMWDi8Oh1gsG/IJWqm9i/S7erd6kTGC2Ww+bZb6r3/9K/75z3/i29/+Nm655Zae/3388cfC/VbS6SL9LnLGff755/0KavV6PUaNGoVgBPr0RRoxYsRpt0FvGoo/AtlQxI2djaH5+VArscl2LN25DodqnBg2YhRsIeJSP5h9FkrYO+eidflhxLYeQ6KKvz9q2mf9Wb6tEt3dxzEiLRozp6jLyErJfXZi66twAUieMB+ZKv5tZGW78O76FWhscyMmKRNDU+QLwNT2Wzsb7PY5aF39BuLbjiEh/2aEC8HcZ2Vl1P0T3nAsK8936L1NXv3duRMzkZ8vb/VTKOFKi8OJAyugbziC3KzMkHGpD/dj9fxmKw59UoqKhijkq/h8raZ91h/H3lVoQTd0ycORO+mkZIyaUGKfvbhsvbhfMCUb+fnqlDggPCOzULv7Q2gdjRidGgtdotdjQQ7U9ls7G+bVm/Hfzw/gaIMGt4bRsbU9RGPZkE/QSu1gtbW1yMzM7Hmc/s7NzT3l7D9VEVDwSve33357n//3Do4lyIThww8/9GusVFFkscjb+nk66It0JuvzdDrgrPDOMMYVzoQhiGMMNqMyLRiRHoPDx1uw51Azzpl88jsTCpzpPgs1DGNmoXX5y+iqOgBjdxe0Vm87QyQQrvusP1tL68X9rAlDVPF5QmGfdTXVwlV3VEiAxBVMh1bF25U+GpnybC2pxc7yJuSMkN89XS2/tbNBP2a2SNA6j5XAGOUR5mHhRDD2WbjLGxAcy/r/HXJ0dGHvIa8G9uyiTHUfKywj0ZKSCWftUXQf2wvLuNCROQjnY/WcouF48ZNSlB5tgtOtRVy0EZFCuO6z/rQc9E7SROfPUMXnCYV9VtPgwJHqNmiigJnjh8FiUfHvwmJB64ixaC/fDk/FbliGjZZ9FWr5rZ0NNGlKCdp9hxrhgU6Yh4UT5hCLZUPeJIwqAsilduPGkyYaZJCwb9++Hh2u/jz44IP47LPP8Oc///krAS29durUqXj33Xf7PL57926MHi3/jzQUcJRvBzwu6BMyYEgKHV3WQDFzrPdCaO1OtTa/BR9dTBIMaWRG0g37ga8amjChDV3Y7thfJ5ZnjOmrgcgMHseBLT1GFVpLDNTOtEKv/rRkJMH4jz4hHfrkTMDjhqPM+31i1AfHsv5Dk0MudzeGJFsxLFX97enWXG91oL3EW9nG+E9yvBmjhsaiuxvYtI/PY+GGp7Md7Yd29THZZPxnky+my89KRKxNxclZH9bR3nOu3WeezvhPRpINw9Oi4fZ0Y9O+GqWHE/aEfIKWtBpuvvlmPP7441i6dKkwQvjhD38o9LbOP/980d5VV1eHjo4O8XwKVhcvXiyeQ8Er/U+60XPINXf69Ol44oknhF4XlTWT8QJVHNx3331QIw7fAciSM/BFgNqYOd7bmrF9f61ITDHyYPV9fxw+91QmvC5su1weZCRZkZmm/gvbYOHY7/0tWHzBntqZ6kvQ7j/aiMYW7zmX8R/pQtNecjJ5x6gLjmX9Z+MebxJhWmFkTDJa8716/+0Hd4pOOEYepvkmqTfs4SKOcMNRvg3d7i7fxOYwpYejGjbuPd5nEl7tWEZPFvedlQfgavN6czD+M2OsN/+yfneV0kMJe0I+QUuQAQLpaZE5wg033ACtVis0tkgn6/jx45g9e7YIZAlqBSMee+wx8Xjvm/ScRx55BBdddBF+/etf49JLLxWPP/XUU5gzZw7URrfbBUfZVrFszZ2KSCAzNRpDkm0iIbWZZ3FkQ/r+0Oy1xxlZDpXhzobd3uBrxth0VbQLhwLuDjvaj+4Ty9Ycb7CndhJjzRg1LE5UH20u5mOr3Ana9oM74HFy4lutcCw7eFxuD7YUexO00yOkC0SfNAz6xAyRkJLieMZ/pO8PdRW1d5KCPBMu2Eu9k5iW3Gkcy8pEW3sX9pTXR1SCVheTCGO6tyuUj63yQdeYxLbSOnTwsdUvQl6DlqAg9oEHHhC3/gwdOhSlpaU9f7/wwgtf+37UZkZ6XnRTO+1H94qZd9IMNWaos+2tP3TSnjU+A28t2Y91u6swb6L6ZR2CAbXh6uJS4WqqERUd1jxvdQcT2nS53D3JtOm+kyfjP6RfRW3p+qShQj4mUphemIayiiZRzXb+tOFKD0cVGFJHQBeXAldTrUjS8rFVnXAsO3j2lJ+AvcOFOJsROcPjESmxrDVvBprWvgN7yQbYCtWXeFcCasNNS7Sgut6B7aW1mDkucs7f4Uy3iyYqvPqzLG8gH9tKakRb+rBUGzKSw0sD3x8sOVPRebxcdBnHTFio9HBUQVZGDFITLELTeBsfW9VfQcvIIG8wajKiNFpECpIO7ZbiWp7FkfNiwVdFa/e1djOhz84DJ0SVSEKMCTnDIuPCNhhIvwGpVSpSkGQOduyvRYeTj63yHVun9akQYhjmq/IGUwpSoSUnmwhBmqyhxBRX18t3vJWqaFnmIHxoP7wL3c52aG0JMGaMUno4qju2Ti2IjOpZCSl2F12hXZ1KD0c1x1apinY9H1v9ghO0Kqa7u7tHADtS9GclsofEihlyZ5cbW0trlR6OqmYcCceBrUI+gwl91vvkDaaPSYMmgi5sAwm1nIoKWiFvEBnSMRIj0mOQEm+G0+XpMZ5j/EdK0FK7HX2/GIY5Gctu2BtZ8gYShtQs0bnU7XLCcdB7zmH8R/oekQwayWcwoY/d539BhSJRUZy+kAOSAtxaUhOZx9aU4dDFJotjK3WFMvIfW+n7xQwOPsKpGGfNIbhbTiBKb4Q5axwibRZnpk+set1OFquWC9PQXGgsMfB0tKGjoljp4TBfA7UtSeL/0qwm4z+kPXtSOmZUxB1bJZMVyfmX8R/jkBxorXHwkLbxkb1KD4dhQoaDlc040dQOo0GL8TnJiCS8MgfeKlp78Xqlh6Ma8kYkIMZqEPqbew969TeZ0KXb4z7ZtRQhfirBYO/Bk9IxozPjI+7YKhWvOQ54i9kYeY6tcdFG2Nu7sLv8hNLDCVs4QRsBs43m7AnQ6I2INEiHlthcXC0qaRn/IZkMkssgWOYg9Ck+VI/mNidsZj3GjExSejiqwbF/i7i3jJoUUdIxEtN8rXCb9lWLSQBGpmOr72KBZQ4Y5iQbfC24E3NTYNRH3vGWdGil6nqPy6n0cFQByWRILd0beaIx5Ok4VgKPowUakw3mzEKlh6MapO9+pEnHSFhH+2KuA1vEJADjP/Q9kszmJINq5uzhBG0E6M9aI0zeQGL0sDgkxZrQ3unGrjKexZELSYfWUbpJtB4yoYukAUS6oTotH+7lgL7z0my7JPkRaRSOTITVpBPJ//1HGpUejvpkDsSxlVvDGIaQukAixWG8P9SloY1ORLezAx2Hdys9HNVAsk+SDi3HsuFRcES6oVHasPA3D3noOy91QUXqsdWUWQCN0SKS/51VB5QejmqQOjbp2OrhIo5BwVfsKqWrqRbO2sNUliOqvCIRal+QDG14hlw+SC6DZDNcLSeEjAYTmlDwJenPsryBfDhrj8DVXIconSHipGMkKNk/KT+1T/KE8R/ziDGIMlrgtjehs3K/0sNhGMUhN+hDVS2g4q7JvmNOpCFkDqTqel/3BuM/E6gi26BFXWO7kNFgQnhSvGRDn0lMxn8OH29BbWM7DPrIk46RoGS/edREsSx59jD+M25UMiwmHRpbO1HKRRyDghO0KkWq8DINy4PWEoNIZVrhSa1EnsWRB5LLINmM3rPaTOhRXtksLjzoAqQoN0Xp4aiuM8GcNT4ipWMkpIoLnvySjyitHlbfhCrLHDDMyQmg/KxExNoi93grOY47qBWXq+tlgeQySDajt4wGE3o4qw+JghDhp5I9XunhqAYpdivKSYbJELlVyZLMgRTbM/6j12kwJT+tTycnc3ZwglalSDNBkqZdpDJ2VCLMRi0aWjpQXtmk9HBUg1TN4WAd2pBF0v6JVN2+QHFS3sB7wRypTMrzapYdq21DZV2b0sNRDRZfhRBNfnHbLRPpbPQlzqR29EjFPHwMogwmuNsa4Dx+UOnhqIYerUROIoQs0mRlpPqpBDpBK3WaRiqWkUWARouu+ko469lUXHaZg90sITMYOEGrQtztrejwuUBbI1QjUUKvoxlyqRWXZ8jlQhiFRWlEu3dXU43Sw2EGYJ0vQTuT5Q1kw9VSj87j5VTr2GOWF6lYzXqM9RnPSTpmjP9YRk4Q8hmuxmpxfGWYSKXV4cSeg/V9uqEilSidHpbsIrFsZ8dx2ZhSkCbkM6jdu7rervRwmAGwl/rkDfKmKz0U1VDf3I6yiiZERXkNwiIZjen/s3cf4JFd9dnA3+lVvW9VXa20vXd3G2yDsbEBUwMYUsFACF9IcIgJCUlIIMSQ0IspoRhsU2zjbq/X23vTqksrrXrX9Po958xceddbvLu60p258/6eR89cq4yO92runHvK+3fBsXDJ1A4FUsfqxcWwmo3oHfbK6ytdGQ7Q6pCv5SAQj8FStACWvMyeGTt7dpCDCOoxObNgX1Anj7ktJPWIFY1d/ZNyhePaZKVimj6l82abuwhmdy4yHTO+1We0OmR8hlIsjChT7W/ol9FUC0uzUFboQqZTdm34mEOrmmyXFUsqExONfB9LPWJFY3ioW65wzNR6KjNBuR+uXZCHvCw7Mp1zKuaAfS61OGzmqXg9ZUcnXT4O0OqQMmCmbEPPdKKwhJghF4UmBkZ8WjdHN5TV2cyhTT1KcbAVNUVwOyxaN0d30TGuDI83eP320Ib2YYx7glo3Rzdci5WYA+bQUuZS4g02LM3s1bMKZ9Wa5M6lDoTHB7Rujm4o8RmMOUg9PiXeoHwZTHZO0qhlN+MNLjj5FehuRNTH1Z5qxxwwh/bKcYBWZ+KRMHxth+SxM8PjDc6eIRcFJoS9JzlDrhbl7yvQ1cA3tBSz61giR2kj4w1UEwv64e88Jo95bU0ozneiYk42RP1FsdqNVCwIpAzEjPI9izJPOBLFwcb+cyaCMp3cuTR/sTzmKlr1KBMAJ9s40ZhqlElKVzKbnabPFwjjaPOQPN7IyS/JklMMa3G53H3sazmgdXN0Q0wAGI0GuUCOETJXhgO0OuPvOIZ4KABTVj5sZZVaNydlsOK4+iy5Z72hMbcnZQyN+dF0OpEttZE3tqrxtR8GohGY80phKZirdXNSBmMO1GdyZMGezETjDgXKREeah+APRlGQY0f1PMbJnLcVl30u1ZScNdG47yQnGlMq87+nOZH5zx2hqjnUNIhINCZjY+YVu7VuTsqtolV2ytH0ZTmtWFpZcM7OTro8HKDVGW8yP8VVsw4GA0/v6wdoj7cOwesPa90c3XDWJmMO+IaWMpRteosX5iMvm9lS6kfHrIdBjH6TtDFZvOdQ4wBC4ajWzdENZcWQUiCFKBPfx5QVOIRz4nX8nScQC3BFklqUlYR7TnAQIdVWz9rm1cLsztO6ObqxJ3ltFffF7MueH9vnbzuCWCSkdXN0QylUzQHaK8MRPB2Ji5WMyUEEzjaea06RW84URqJxHGxkdpf6b2iHEQtza1gqUN4Elewfmr54LDq17UmZZaeEqnk5cpVbIBTF0ZbEtjlS79oa7G5CxDOqdXOIZo0oDKYUsVEmgCjBkj8nsYMjFoGv7bDWzdHdAO3BxkEEQhGtm0NnLzhivIFqotHYVBwVo2POZS2tlLuP4+EAAh2JODOaPiVq71TnCEYnAlo3J21wgFZHgj0tiHrHYBBVoBcu1bo5KUd5M1I6/jR91pJymHOKEI+E5KwjaWvCG8LxtmF5zAFa9QS6TiHm98DocMM+L5EBSAliBQZjDtRnzi6AbU6NmB6YmnglygTNXaMYnQzKKtDLqhPbI+k1ygIMxhyoR0QcFOc55C6Qw02DWjcn40V9kwh0npDHruROPZq+kx0jmPSFkeW0oK48X+vmpFxfVuw+FrzM+FZNQY4DixbkIh5/rTgdvTEO0OrI1OrZqlUwmFm5/fWUQYR9Df0yf4fUeUNTbhaU2W7Sjph8EKuPxM1GaQEr3qrF15y8tlavhcFo0ro5KT35Jf7+SB2MOaBMpEz0rFlcDIuZ19vXUwYRxK6OeJSrPdXqyyrFwpR4DdK4zxWPyToXljyu9FSLskBpXX0pTCYOAV1q8kvsSiZ1bFo2Rz7uOpooYE1vjK9OHXltOwhnGy+kdmE+ctxWmUHb0D6idXN0txVXvqHFmEGpJeXGYhMrs6omHo9PZSwz3uDCllcXwmEzYWQigJbuMa2boxvO5ACtv+M4osybpAyx+3gy3oDvYxdkm1sDozNbZtAGuk9p3Rzd2LhUmWjsl1vBSTtKcUzGG6jbl92TvLYqC5boXGL3scFqR9QzgmBvm9bN0Q1lR6eIQfOwDtBl4QCtToRHehAe6gaMJjiqVmvdnJRkMhqwri7xprSbhQBUY19QD6PdjZh/kjcLGhK5aaI6q6CsBKHpE9fVyGgfYDLDWblS6+akJLHKbXVtiTxmhIx6rAVzYCmcB8Si8Lce0ro5RDOuZ9CDrv5J2V9bU5e4ptC5xC4OsZtDYIFW9SypKIDbYcGkL4SGDi7i0IqoZ+FvT0SmsZ6KesR1tXfYC7PJiNW1xVo3JyWJ3cfOylXymNFS6plb5Mb8EjeisTgOnkpkINOlcYBWJ5ROmmPhEpjs3Np8MVNZicf75GwiqXSzUJO4WfAlZ71p9h1tHpL5aSJHTUQckLrxBo7yZTBaHVo3J2Uxh3ZmuJQImeTfIVEmrJ5dVlUoB8vowlzJ3Ry+pr3sy6pEbPlW3seUv0Oaff72o7KuhahvIepckDqUvtmKGrHjyax1c1KWslPO18z7WTVtSBb85D3C5eEArd7yZ5PZVHRhqxYVwWI2on/Eh9P9k1o3R3cxByJmgzcL2lDe9MQNhshTI3Unv5TcP7qwdfUlMBoN6OidQN8wt+OrRXlP97ccZN4k6d6e5O6mDcnt5nRhjooVMJgsiIwNIDzUpXVzdBdzIOKi2JfVhlL8Trz3sS+r/j2CUjOALsxZvUasPEJo4DTCY1ztqRbl7+4A6wBdFg7Q6kDUO45Ad+M5q23owuw2M1bUFMljbsVVj6NyBQxmq7xZCA10at2cjCMKM+09yc6X2iKeUQTPNMtjZZU4XViW04r6ikRVYOVvkabPNqcaJlcOYkEfAl0NWjeHaMaMe4I4ldxazozESzNa7XBULJfHrDiunlWLimFNLuIQk400u0RhpqkBWmb+q2Z0IoCm06PymNfWSzM5smCfXyePlb9Fmr6aBXnIddvgDURwom1Y6+akPA7Q6oCo5CqrXZZWyi0hdGnKABaX2c/MzYLYckezq7lrFGOTQTjtZiypLNS6Ofq6tiIOW1kVzNkFWjcnfa6t3B6qct7kmnMKgRLp0b6TfYjFgap5OSjOc2rdnJQ3FS3F+BNVF3GsXJTI52TMwewL9rQg6h2DweaEY0G91s3RjX0N/RALwqvn56Igh1Fdb0SZHGDGt8p1gOoTufIcf3ljHKDVAeWmjatnL49ygRCziWJWkdThnIo54BvabFPe7NYsLpERHqRydEzyb5suL2PqeNswPL6Q1s3RXcyBr2k/t92SbikDYsp1hC5vgFbs8hC7PUjdmAMlboM06HNVrZIRHqQOZdJ8I1fPXlFsX6DzBKJ+j9bN0WWtCvZlL4138nqodtmmVLvkIMLlELOHNfNz5WyimFUkdbjEzYLI7elrQ2R8UOvmZGz+LKlZSfioPGa8weUpK3RhfkmWjNzYf2pA6+bohtidICNkxgcQHjytdXOIVBcIRXCoafCcATK6NHNWPmxl1XKXR2K3B6lB9KOMBqC1exwDoz6tm5NRlGKYzPxX99p6uCnRH+M9wuWx5JXCUjhP7k72tx7Sujm6sbKmSEbIDIz40NnHOkCXwgHaNCcGZ6eqXRYv1Lo5abcVlzm06hE5ifZ5tfKYq2hnT++QF6f7JmWBprWLE1vzaPp4bb06vLbOdN4kr62kP4ebBhEKR1Gc50B5WbbWzUkbzkWvra4ndeS4bairSEQaMa5n9oRH+xAe7JILPRxVq7Rujr6urZEYr61XSNmVrEwakLoRMtyhcGkcoE1zys2a6KSx2uXlU2YRxYoNMbtIasccMCtxtigFmZZWFsDttGrdHN1Qcv14bb0ySvX1A6f6EY6wUqvqMQcsWkE6pAyEbVhaxuvtFVB2d/jbj8hdH6TuROPu4xxEmC3Ke5t9QT1MDrfWzdENZbKc19aru5/1tR5CPBrWujm6G3/hIo5L4wBtGovHYvC17D8nL4Uuj5hFFLOJYsXG0eYhrZujuxnHRG4Pty/MaueLW5dUE49F4U3eLHCr3ZVZND8PuVk2+AIRHG/ltVUtzppEobBgTzMik8ybJP0QkSj7GpIZiYw3uCJid4fY5SF2eyiRPKTeRKPIU59knvqsLjhiPRX1RGPxqUUcG+p5bb0StjnVMLlyEQ/64O88qXVzdGP9VB2gMYywDtBFcYA2jYV7mxHzTcBod8sZR7p8Yhbx7LBqUoclvwyWogUyt8fXclDr5uieKMQkbiAEZkupRwyCyWurzclr6xUSURvrkzcCnCFXj9mdB9ucGnnMqu2kJ01dYxj3hOB2WLAkubWcLr8vy9X16ptT6MbC0mSeOmtVzDhRiClwOjEIxsx/9TR1jsprq8tuxpIqXluvhMFgnPpbZJ9LPXnZdtQuyJPHvEe4OA7QprFAa6IogLN6NQxGk9bNSd+sxJN9shNG6lBWc/sYczDjRCEm8bcrbiRKC1xaN0d3Kzkc4tpqMmvdnPTdHspKrTOTN8mBGNKRfQ2J4mBr60tgMvG25Eo5FymDCPsRjzNWRi0bl5bJR8YczDxZiCkekws8RIEmUoeS87mmrgRmXluvmDJAK+4J2JdVDxfIvTG+WtNVPI5g68FzclLoyiypLITTbsbYZBDNXdwyqhZle5Kv9TAz0WbYnuSNA1fPqsunbLVjvMFVWV5TCKvFhKExP9p7JrRujm4of49iK3MsxK1hlP7ETe/+hkSF8Y1LEgNidGUcC+phsDkR9Y4h2NOidXN0F3Nw8NQAguGo1s3RNaVuhYurZ1WlDIAxAu3qiOKsBrMV0YkhhPo7tG6Obih/j0eaBxEIsg7QhXCANk0ZvUOIjvXDYLLAWblS6+akJYvZiDWLE1konMVRj7WsCqasAsTDAfg7jmndHN0SBZgOnErc2LLzpZ7QcA/Cw2cAowlOVhK+KnarGasWFcljXlvVYymaD3NuiSxYIYoCEaW7wYkI+kb8sj+2qjZxzaArI+8Dku9VyuQiTV/1vFwU5tgRCEXlQALNDPF+Jgoxnb1LhKbvzKAH3QMemIyGqXtdujJGiw2OyhXymDEH6lkgd3065X2sKNZO5+MAbZqy9jfLR3v5MhhtDq2bk7ZYTXBmMtGmVtHyZmHGiAJM/mAEeVk21MxP5PnQ9Cnbxx0Ll8BoZ2zE1VImDZQtdqRS3mTy2uptYswBpb/Gbr98XFFTBKfdonVz0n51vVLcktStVcF7hJkjCjDFQ35ZkEkUZiJ1KH+zy6oK4XLw2nq1lIxvJfqMpo/XVp0M0MZiMTz00EPYtm0bVq5ciY9+9KPo6uq66PePjo7i05/+NNatW4f169fjC1/4Avz+RCdQ8dRTT+G2227D8uXLceedd2LXrl1IJ5aBJvnIapfTs3ZxsSxq09k3ib5hr9bN0WVWIjPRZobypibe5MTfMKlDyU5WOmV0dUSepMEAtHaPy6gDUoeyBdTXsh/xGLfdphP2Zc93qjsR1cFdINPjECtoDUaEB08jPMobXrVsSObQiv4Wa1XMDGVlosj7FIWZSOV4g2RUB10dZ/UaMaSIUF8bIhOJosw0fcp7/r6GPkR5bT1PWlwJ//d//xf/93//hy9+8Yv4xS9+ITu5H/nIRxAKhS74/ffffz86Ozvxox/9CP/93/+Nl19+GQ8++ODU13fv3o3PfOYzuPfee/HYY49h06ZN+NM//VO0trYiHUQ9ozCP98hjDiJMj9tpxdLKRGVLzuKoR6w+NFgdzESbwdy+PSdfG6AldUR9Ewh0N55TeIWuTl6WHYsX5k8VYiR12OfXyZXdMd8Egj2JnTSUHtiXPdfIRABnhhP/73wfmx6Tww37gnp5zCKC6hGrDx02M0Yng2hirYoZ6csqO+0Yb6CecU8QDe2JwcT19by2TofZnQvbvEXymDEH6qmvKJAru8c9ITR18to67QHaYHB2i/6IjusPfvAD2VG97rrrsHjxYvzXf/0X+vr68Mwzz5z3/YcOHcLevXvx7//+71iyZInssP7TP/0Tfvvb36K/v19+z3e/+13cdNNN+MAHPoCqqir87d/+rfzehx9+GOkg2JbI6rGUVsGcxa3N08VqgjOdiZZYkUjq6eidwOCoXxZiEltDSR2+lgOykrC1uByWnGKtm6Ofa+txXlvVYjCZ4axaLY+55e7qsS+rvQONQ/Kxel4O8rPtWjcn7Sk76hhzoHatikRfgO9j6gsNdCIyMSQLMTnKl2ndHN3Y39APsSixYk42ivOdWjdHPxEy7HOpxmwyYu1UHSBGoU17gHbLli34x3/8Rxw9ehSz4dSpU/B6vbJzqsjOzkZ9fT327Tv/hbJ//34UFRXJzqpCbA0TeRcHDhyQKxYOHjx4zvMJGzZsuODzpaJA6wH5aKsSy+5pupTZxeNtw/D4LrySha6ca9F6+cg3NPXtTt4oiEJMNotJ6+bohvK3ypUc6m5hOtoyCF8grHVz9Bchw2vrVWNfVnv7GxJFLtfVcZJRDWKLuBDoPIGo36N1c3QXc8BFHOpTFnCIQkyiIBOpHG+wJPG3S+r0ufwdxxEL+rRujg5rVfDa+npmXKEPf/jDcgb/V7/6FSoqKvD2t78db3vb22RHciaI1QVCWdm5F5ni4uKpr51NrCx4/fdarVbk5uait7cXExMT8Pl8KC0tvaznu+KtGr6ZfeHGQn6ETp9M/Me8JTP++zJBjtOAecUudA94setoF7YsV/8NTcmNe31+nK7NWQwYTQgPdWOip11WH08nqXzOdh87Ix9X1eTzGqDSOYtHQvC3HZbHpgXL+O+qgny3EWUFTvQO+7DraDc2LS1Ju9daSiqrTVxbh89g4kwrzHmzfxM2m+dM9K3EwKSa2JfVti8rClwebxuRx8sqsnm9VYMtG+aCuYgMn8FYwx44Fp87eK+GTLxWL1mYBZPRgK7+SbR1Dcnq4+kklc/Z5KnEAK154QpeA1Q6Z6FwFAcbE5NfK6py+e+qgrgjD6bcUkTH+uS11b5oQ9q91lJR3UI3TCYDugc8aDk9iDmFs1+Y2Z+ifdkrHqD9y7/8S/khZu5F5tW3v/1tfO1rX8PmzZtx991344YbboDFol61QOUfTHRMz2az2TA+Pn7B73/99yrfL7a0BQKBiz7fdLe8hcNhNDQ0YCYZPcPIiUUQdReiaywAjM/s78sU5YVGdA8AL+xtRb5lbMZ+T0dHBzKJO28+LMMdOL3zKQQrLvyGlupS7ZyN+yJo65mUx27DKBoaJrRuki7OmXmwBVnhIGK2LLQM+4ERXlvVUF5kRO8w8MKeFuSaEgMy6fJaS2XuvAWwDLfj9K6nNb22ztY5u1C/bjrYl9W2Lzs0EUYkGkdRjhnByX40JFfT0vTYcxbCMXwGg4degjeeO2O/J9Ou1QuKrGjvD+KJl49jc10W0lGqnTNDYAK5A+0Q5YFOR5yIz/A1J1POWdMZP4KhKLIcJgTGu9EwwSLCanDkLoR9rA/9h16CL5qdVq+1VLawyIq2viCe2n5C02trR4r1Za94gFaxevVq+fEP//APeOWVV2QRg09+8pNyy5ZYifC+970Pc+fOxXTZ7fap/C7lWBAdUIfDccHvv1DBBfH9TqdTdl6V53v91y/0fFdCdOarq6sx0yaybej1hFFeUTHtNlOCyT2GHSf3oa0/jJqaWpjN6tbPEzdb4sVfXl6eUefMG9iKyZc6kOPpRkHdB5FOUvWcPbtXVP3uw6L5OVi3eqnWzdHNORs/sxtiCMVVux5z6hPFVkgFjlHsOrUfrf0hLFpUC5PJmDavtVTmDW7D5Ivtml1bZ/OctbTMXKFJ9mW168taXT2I+IblCma+7tURyrVgpG0nbKMdmL+oRmZWqylTr9XXjrnQ/mQjTo8YcF9dHdJJqp4z35HnIZYXWMuqsXglY6XUOmc7mhO7bDcuK0N9fXr9raayUJYRIx17YB9ux4LaRTAYTWnzWktl14670PZEI7pGDajT4NrqT9G+7LTeucU2K7FF7KmnnkJjY6PsZIniB9u3b5eVav/1X/8Vt91223R+xdQWr4GBASxYsGDq8+K/a2trz/t+sd3rueeeO+dzogM7NjYmt36J7WGicyt+/mziv0tKprcFWyxbFs8942rX4UxDg/xDmpXflwGW1ziQ67ZhzBNEe58fKxbNzDbHTDtnlqVbMPnSTxHuaYYNEZicl551TEWpds4ONSdWIW5aPjel2pXO5ywej2GwPRFvkFO/if+uKlpZa0eW04pJXwgd/QEsqy5Mm9daKrMs2YTJF3+cvLZGYXJqs/JgNs6Z2vEGr8e+rDZ92Q1L56ChYZyvexU5Kpdi3JWDqHccxqEOOCqWz8zvybBztm3VAvzoyUY0nR5DOGZCjjv98lJT7ZyNdx6Rj1mLN6ZUu9L5nMVicRxsHJbHW1fM57+rihzVyzHmyELMPwnjcCccC5emzWstlW1duQA/fKIRjZ1jiMTNyHapu1sqXfuyV7xM0OPx4De/+Q3e//7348Ybb8R3vvMdWTVWdGJF51ZUkX3iiSewceNGfOlLX8J0iUq3brcbe/bsmfqcyN46efIk1q07f8ZNfE7kb3V2dk59TlTCFdasWSP/ccRqCeVzCvH8a9cmAvYp8xiNBqyrT1YTPMmwarVYcophLS4H4jH4WhLF7Wh6uX1HmofOCVen6Qv2tiHqGYHBar9kp4uunFgxO3VtZSGAmbm2JguH0uVjX5b0yGAwwlmdOP/e5vQoFpcOivOdqJiTjVhcFLfr17o5aU/UU/F3HJPHLMqqnpbuMYxMBOCwmbCsukDr5uiKWDHrrEkUZ2eBVvXw2qrSAK2ofPvAAw/IjKp/+qd/wo4dO2TnVXQUz7Zs2TJVVj2IrAaxxew///M/8fzzz8tKuJ/61Kfk6oJbbrkF0WgUg4ODU3lcK1askG0R3yOq8+7evRuf//znceedd06tKvjQhz4kO94//OEP0draii9/+csyb+tP/uRPpt1e0kc1QRHkTOpQOl9evqFNmwj+j0RjKCt0YV6xW+vm6IbS2XJWroLBrF7uJL3+2trLa6uKnIsSAzG8Wbhy7MuS3vtcvqb9vN6qaMOSxCp4TjROn08UZI1GYM4rhaVg+hEylLA3+be5urYEFvP5W/Bpelw1r93P8tqqnvVn3SPQVQ7Qvve975Udwl/84he45557LrocWHQcX3rpJajh/vvvl79LdKbf/e53w2Qy4fvf/77MyRJb07Zu3Yonn3xSfq/oSH/jG9/AvHnzZCdVZIldc801ePDBB6eeT3y/6Ij//Oc/x1133SU7vt/61rdQVVWlSnspPYlYA6vZiIERHzp6WXhJLa7kzYK/7TBi4ekVL8l0SudLDHjN9LbfTOJrTqxC40qOmbGqthgWsxF9wz6c7k8UuCP1bhZ8rYcRi5yfV0oXx74s6ZWINTCYrYiMDyA08NoKbFJnolFMlAfDUa2bk9bE5IFyf8C+rHqUyYMNS7nDbiY4KlfAYLIgMtaP8JCoB0KqXltPDSDEa+vVZdD+v//3/y7r+1wuF9QiOrGf+cxn5Mfric6ryAw7W0FBAR566KFLPqdYhSA+iBR2qxkrFxVj78k+ORBWMSdH6ybpgrW0EqasAkQnhxHoOD61RYSuTDQaw76T/efMNtL0hcf6ERo4LfYvwVl97uo5UofDZsaKmiK5fUlcWxeWpl8WdSqyllXC5M6X8RyBzhNwVq3Suklpg31Z0iujxSYHaX3N++WHraRc6ybpQtW8HBTm2DE0HsDR5kGsq2c/7GrEY9GpyDNncpKRpq9v2CsXF4nIvrV108shpwszWh2wly+Dv/UgvE37YS16LU+erl7V3FzkZ9tlPMex1iGsWcy/X3VL1RPpZpk9tzCpRcyOK6tovU3n5uXR5TvVOSoLLbkdFtSX52vdHN0QN7CCfUEdTA5tCi1l1LX1OK+tquZNMuaAiF5HGfjidUHdvizvEaYv0N2YKLRkd8M+f7HWzdENsbhIWFJRIAuz0sxQ7md9vJ9VjZhUODtmkjhAS3SO9cliNs1dYxge92vdHP1lojWLTLSY1s1JS8qb1tr6Ell4idShZCNzJcfsXFsbT49idCKRs0kqZqI1MxONiBKUnUrB3hZEJke0bo5ubFiayKEVO0FioqoNXTFfsnid2LEkCi+ROpTJb+6wm1nOmsSkeLCnGRHPqNbN0Q3l71ZcW+Psy3KAluhsedl21C7Ik8fKdnKaPseCJTBYHYh6xxDsadG6OWlpbzI8fWOyUAVNX9TvkVvDz54Vp5lRkONAzfzcc1Z60PTZy5fCYLEjOjmCUF+71s0hohRgdufBNqfmnF0iNH3LqgpkZM/oZBDNXRycmU7+rHPReq2bohseXwjH24blsbISkWaGOSsftrJqecxrq3qWVxfCbjVheDyA1jPjyHQcoCV6HW5hUp/BbJnKR+SWuyvX1T+JM4NemE1GrKot0ro5uuFvPSQC0WApmg9LHju1M41bmNRnNFtl4QqBETJEdKGdS6QOi9mENYuL5THfx65caKgb4ZEewGSGs3Kl1s3Rjf2nBuSK7gWlWSgrVC83nd7g2sr7WdVYLSZZUFjYwyg0DtASXWwQ4UjzIALBiNbN0Q1XcracgwhXTmz5UGYYnXaL1s3RDbEt/Oxt4jQ720OPNPHaOiOZaByIIaIk5X3N334UsRBjZdR+H9vNQYQrprxHORYuhdHm0Lo5urtH4OrZ2e1z+TuO8dqqIuXvdy8nvzhAS/R6YgayJN+JcCSGQ02DWjdHNxxiBa3RhLCcQU9s16fLo6zUYLaUeuLRMHxiBe1Zs+E0sxaWZqE434kQr62qclavAQxGhPrbERnnvysRQe4MMecWy/c6f/sRrZujG2sXF8NkNMidTT1DHq2bk1aY+a8+ca964FQiko8DtLPDUrQA5pxixCMhXltVtLauBEYD0NYzjoFRHzIZB2iJLlCpdcNSzuKozeRww76g/pyVi/TGxj1BnOpMFPlYX8/Ol1r8nScRD/pgcuXCNieRJ0WzcG2dijngJI1aTM5s2OfVymMvV9ESUfJ6q+R8epO5nzR9bqcVSyoL5DHvES5f1DuOYHejPHYtShRaouk73joEXyCC3CwbauYnaqjQbFxb154z6UDTl+O2oa4icW3dl+HXVg7QEl2AMoiwr6EPUVZqVX8rLt/QLpsoVicKWlbOzUFRHreEqcWXjNoQFVkNBr4Vzvq19WQ/otGY1s3RYd4kr61ElOBKVhz3texHPBbVujm6oSziYMzB5fO1HBB7l2AtqYA5u1Dr5ujG7uO9Uws4jGL5Ic1qbJ/4u+a1VT3KQqQ9HKAloterryiAy2HBuCeEpk5WalV7ECHQdQpR34TWzUkLSsX7jdy6pJp4PDaVheyqZSXh2SRWHrkdFkx4Q2joSKwMp+lTtoz6O04gFszsrWFElGCfXwej3YWYbwLBnmatm6MbG5ckcmgb2oflLid6Y8ruDmVgi6ZPFAZTBrI2LUv8TdJsXlvd8toa6D6ldXN0N/l1TK4MDyNTcYCW6ALMJiPWLi6Rx9yKqx5LTjGsxeVAPJacTadLCYajONg4II+ZP6ueYE8ropMjMFjtcJQv17o5GXdtVf6WdyVXftD0WQvmwFIwB4hF4Gs7rHVziCgFGExmOKtWy2NuxVWPyFKvmJMNscFuf0Mi/5MuLhYOwp98X1K2htP0tXSPYXg8AIfNhBU1XJU869fW5A4FbyOLX6tlbpFbfkSi8an730zEAVqiN6ommFzBSOpgbs/lO9o8iGAoisIcu4w4IJXjDapWw2C2aN2cjLPxrO2hcZHfQaquomWEDBGdH3/CHFo1KRONmb4V93IEOo4jHg7ClFUgIw5I3XiDNYtLYDGbtG5O5sYcNO5lX1ZFG5RrawZHyHCAlugiVk9VavWgZ5CVWtV+Q/O3HUEsEtK6OSlN6fiLGwERSk/q8DbukY+u2g1aNyUjraothtViwsCID+09jDpRPeNbZKJFI1o3h4hSgLNyJWA0ITzUjfBIj9bN0V3MwaHGAYTCzKC8FKUwsHiPYl9WPbuOJQZoGW+gDUfVShjMVkTGBxDq79C6Obqb/NrX0I9Ihtaq4AAt0UWIDNplVYktI1xFqx5raaWcRY+HAwi0H9O6OSmdLbUv+Xe3IXkjQNMXEjepw2cAo9j6uUrr5mQku9WMVYuKzlkBQtNnm7sIRmc2YgEvM9GISBIZtI6FS87JAaXpq5qXI3c3BUJRHGke1Lo5KZ35r6zeVraE0/R19U+ie8ADs8kgV9DS7DNabHBUrpDHSl0Lmr7F5fnIdlnh9YfR0J6ZtSo4QEt0CdzCpD4xe66s9OIb2qWzpUYmgjJball1gdbN0V28gaN8mbxxJW0oKz6UFSA0fQajCc7qNfKYMQdEdH78CQdo1ezL8h7hjQV72xD1jCYy/xcu1bo5uqFMbi+vKZILikgbyk48X3JnHk2fyWjAuvqSjL62coCW6BKUztfJ9hFZdZzUocyii1l1MbtO59ubfFNaXctsKTUpYf6uWlYS1tK6+lIYjQZ09E6gf8SndXN0w5UciBEr5ZiJRkRn97kCXQ2I+ia1bo5uKLubRH9N7Hqi8ymThc7KVcz8V5GSz7lxKXfYaclZvVbMjiM00InIWOYWtZqxOkAnMrNWBQdoiS6hJN+J8rJs2fE6cIqVWtUiZtENVgei3jEEe1q0bk5KUmYNNyQLKtH0RSaGEexpFmtfpgqnkDbE9qWllYmV4fsauD1ULY7K5TCYLIiM9snMSSIiS24xrMULAbHdvPWg1s3RDbG7yWEzY3QyiOauUa2bk5J8zcmirOxzqWZ43I/G06MQcb4bkwNZpA2TMwv2BfXyONh6QOvm6MbKRcWwmI3oHfbKOI9MwwFaosutJpihy+xngphFV/I/uRX3fH3DXrmyUKwwXFvHbCm1+JKFKkRWp9mdp3VzMp4y+bCvgasO1GK0OmAvXyaPvby2EtHrYw6S74M0fWJ305rFxfKY9wjnC4/1IzRwWq4wdFav1ro5uqH8rdUuyENetl3r5mQ8ZUdegAO0qnHYzFhRU5Sx11YO0BJdZszBwVMDCEdYqVUtrkWJNzTm0J5PKUpXX5GPLKdV6+boBuMNUouyNa/x9Bg8AV5b1aJkfHMghogUygpGX+thxCNhrZujGxuS72O7k1vO6TVKcTD7/DqYHFlaN0c3lOx+JcufUuN+NnymCYagV+vm6Mb6DF4gxwFaojdQPS8X+dk2+IMRHGsd1ro5uuEQK2gNRrkNNzzCQkEXyp9VVm/T9EUDXvg7j8tjDtCmhuI8p6yELeKlGrsDWjdHN5RCYcEzzYh4uO2WiABbWSVM7jzEQ374T5/Qujm6sXZxsdztJLbh9gx5tG5OaubPLkpkINP0efxhHGsZksfMn00N5pwiWEsrAcRhGRAxaqSG9clCYU2nRzE6kVn3CBygJXoDouMlCtqcPXBG02dyuGFfuEQee7nS65zO1/HkRMD65N8dTZ+v5QAQi8JSNB+W/DlaN4eSNiVvME51+7Vuim6YswtgK6uSNwvy756IMp5BbDNXCrQy/kQ1budreeq8R3jdpPjpk/LYlfy7o+nbf7IP0VgcC0qzMKfIrXVzKMlVu0E+WgaatG6KbhTkOFA9P1cu4tjXkFl1gDhAS3QlObTHezOymuCMb8XlzcKUg6f6ZedrfombnS8V+ZR4g+RWJEoNygqQtr6A3KVA6nAm/859TYktpkRErmQOrcinZl9W/Tx1xhy8xt96KDEpXjiPk+IqUv7GuHo2tSj3FpahdsRCXHCglo3J8ZdMm/ziAC3RZVheUwS71YSh8QCau8a0bo7uilYEuk4h6su8Ko0XsifZ+eLqWfXEwkH4xM3CWbPclBrEKpDSfAeiMeBwMyNk1KKslPO3H5F//0RE9vKlMFjsiE4OI9jbqnVzdGPDksRgWUP7MCa8Ia2bkxKU+hLKexFNXzAcxYFT/efsPqLUIHbnmXJLYIhHEeo4qnVzdJdDe6hpEIFQ5izi4AAt0WWwWUxYU1dyTjg7TZ8ltxjW4oVAPMatuCJgPhKb6nxxdlw9/o5jiIcDMGUXJnOiKFUYDCJCJlEFe9/JAa2boxviuipy0eKREPztvFkgIsBoscFZvUoe+xp3a90c3SjJd6K8LBuxOLC/IbNWel1IPBpOrKDlriVVHWkWg1RRFOY6ZH4/pVZf1l6VyP8P8H5WNeVl2SjOcyAUjuJocyJ7ORNwgJboMm1Zltiis/NoD7eGzUBlYWW2PZOdbBuGNxBBrtuGmgV5WjdHN3yNe6ZuFEQnilLLurrEAO3BpiE5SUHTJ/7OlR0KjJAhIoVr8Sb56D21m31ZFTHm4DWB0w2IBX0wuXJgm1OtdXN0Y3dygdDGpaXsy6Ygm1Kgtf2wnKSg6TMYDFOraHcfz5wFchygJbpMa+qKYTEb0TPkxek+bsdXizK77m87glgks7eG7TmZ6Nivqy+BycjOlxrisSi8zYkcTlctV3Kkopp5OXDZjTKDVqlOTNOnVM72texHPM6BbyICnFWrYTBZEB7pRXiwS+vm6MbGZMzBocYBudorkymFf53Va2AwmrRuji6I2hR7kjmc3GGXmixl1YhZXYiH/PB3HNe6ObqrA7TvZD9iYptCBuAALdFlctotWLUosdJrJ2MOVCO2nJuy8uUW9EDHMWQqsZJF6Xwps4U0fYHuU4j5JmC0u2FfUK91c+gCjEYDFs9zZNwM+UxzLKiHweZE1DuOYE+L1s0hohRgtDngqFwhj72MOVCN2HJekGOXW9CPZvBEo+jLKsUplV0cNH1KvrHbYcHSygKtm0MXYDAYES6ukcfcFaqeJZWFcNrNGPME0dQ1ikzAAVqiK7BpWWLWctexHq2boqvtC2dXFs5UnX2TGBjxwWo2YmVNkdbN0Q1f42uFKriSI3UpA7R7TvRmzAz5TBOr5JxVybzJDL62EtG5lGKZ3lOJ+B+avkzdivt64cHTiIwPwGC2wlGxXOvm6IYSnSH+xkwmDt+kqlBJ7VSfizuX1GExG7F2caIO0N7kQia94yuc6AqIN0ax2qu9ZwK9Q16tm6O7HNpMfkPbk+zQr1hUBLvNrHVzdLOSw5scoGW8QWqrKLHBYTNhZCJzZshng0vJ+E5uOSUikisbDUaEBjoQHs2MG97ZjDkQgwiZOtGoLLRwlC+D0WrXujm66cvuSt4jMN4gtUUKFsJgtSPqGeXOJRWtT05+KTtN9Y4DtERXINtlxfKqQnnMVbTqcSxcCoPVgah3LGPf0JQ3HSVrh6YvNND52kqOypVaN4cuwWwyYNWiwnMKYdD0OSpXAUaTzJrkQAwRCSZnFhzlS6eKhZE6llUXwGEzY3QyiOYMnWhUdmsoCy9o+jp6JxI77CwmrKrlDruUZjTDVpG43/AmCxTT9K1ZXCxrs4gaQJmwQI4DtERXaNPyxOzlzqMcRFCLwZzZW3GHx/1o7hqTx+vrOUCrFqVzJAZnjRab1s2hN7CuLpHxvetYL6uLq8TkeC17OZMjZIjoYjEHHKBVi8VskgMJmbTS62yRyREEe1umYqVIHaJPJKyuLYLdyh12qc5WtUY++hr3sC+rErfTiiXJ7OVMuLZygJboContJQYD0Hh6FENjfq2boxvODN6KKypTCrUL8pCXzS1haufPMt4gPaysKYTZZETPkBdd/ZNaN0c3XMkbZV8GXluJ6MKci8QArQHBnmZEJoa1bo5uKLugMmEQ4fV8zYniYLY5NTC787Rujm4omcaMN0gPtvIVgMmM8EgvwkPdWjdHd9fWvRlwbeUALdEVys+2Y/HCfGR6IQC1OatWy0y0TNyKq3TklYwdmr7wWL/M1xN/U85qruRIB6JK64qawnMKYtD0KSuZAqcbEPVz4JuIAHNWHmzzEgVtuBVXPWvrSmStikzZinuhAVrGG6inb9gr656Iv6l13GGXFow2h8xgFrxNiYUiNH3rk/fIJ9qHMekLQc84QEt0FTYz5mBmtuIuXJJxb2iBYARHmgflMfNn1aMUBxPbu0XeHqWHTcsS11alIAZNnyWvFJaiBUA8Bl/rIa2bQ0QpwrU4GXPQyJgDNbfiLp3aips572OxUAD+9qPy2CWK0JEqlMlq8Tcl6qBQekXIiJgDUkdpgQsLS7NkAcYDDYmdp3qVFgO0wWAQX/jCF7Bp0yasWrUKn/70pzEyMnLJnzl48CDe//73Y82aNdi2bRs+97nPYWwskfEo9Pf3o7a29ryPRx99dBb+jyjdbVo2Rz6eaBvCuCeodXN0V3Hc15SYhc8Eh5oGEI7EUJLvxIJSDiSqRekUKZ0kSp8ZchEh09I1hsFRRsiof21lzIFW2JelVOOq3fja6nrvuNbN0Y0NSzMv5sDfdgTxaBjm3GJYiuZr3RzdYLxBenLKSQoDgr2tiEwMad0c3diQfB3o/dqaFgO0Dz74IHbs2IGvf/3rePjhh9HW1ob777//ot/f3t6O++67T3ZSf/WrX+G//uu/cPToUXziE5+Y+p5Tp07BZrPhlVdekc+tfNx2222z9H9F6UwMplXNy0Esrv+LxOy/oQGBrgZEfZMZFf4vVs8axMgUTVvUN45Ad+M5A1OUHvKyXouQyaTVR7N1bfW1HkQsou+tYamKfVlKNZbcYlhLK+Xq+kzM/58pG5YkBhFOtg1jwpsZ11tv056p9xr2ZdUx7gmhoX34nEF/Sg9md+5ZETKZsyt0pm1I7jQ9cKofoXAUepXyA7RidcDjjz+OBx54AGvXrsXy5cvx1a9+Ffv27cOhQxfeqie+v7i4WK40qKqqkj/3j//4j9i9eze6urrk9zQ1NaG8vFx+X1FR0dSH3c4CPXRlW3F3Hu3Ruin6ulkoXpjYittyAHoXjkSnBvg3L0+syqbpC7Ydkn9D1tIqmHOKtG4OXSFlpQgzvtVjm1MFU1YB4mIbauthrZuTcdiXpVSl7DLxnmLMgZqLOMrLsuUijv0N+l/EEY+E4U3uznDXbdK6ObpxoHFQ/g1Vz8tBcZ5T6+bQFVIKFPsyKLZvplXPy0Vhjh3+YBQHGwegVyk/QHvgQGKQZuPGxDYcoaKiAiUlJbJjeyF33HEH/v3f//2cGTzleHw8sYWnsbFRdniJrtbmZMyByA/1+sNaN0c3lOICmZBDe6hpEL5ABPnZNtSVJ1YN0vQFk4P7SueI0nPy61ir/gsBzBaDwQhX8sbZe2qX1s3JOOzLUqpyLU78TfrbjyEWyKyiVjNJWfGYCQUvfe1HEA/6YHK/VniOpm9fQ2IAivEG6T355e88wQKtKjEaDdi8IjH+8qqOF8iZkQarDvLy8uQWrrOJ1QJ9fRd+07tQZ/W73/2uXFUgtoopqw7E8773ve+V28gWLlyIv/iLv8A111xz1W2Nx+Pw+XyYaX6//5xH0kZBlglzi1w4M+jFq4dPY+uKi7+B8pxdPtOC5QB+DX/bYXgnxmAwaxOKPxvnbPvB0/JxXV0xAgH+bUyXPFeRIIKnj8v/Ni5YPivXZFL3tZbjNGBBiRun+z3y2nrNSq4uV4O5YhWw9w9ypZN3YhwGs+Wqn2s239NE3yrdt8yyL3s+9otShDMfpvw5iI70YPTkLjgWb77ot/KcXb6VVXn45bOQq7zGxidhtZg0acdsnLPx46/IR1v1Wvj9gRn7PZlCnKtgOIajLYl4g5XVeezLpoHzXmu2bJgL5yMy1IWxE7vgqN+qbQN1Ym1tAX63vQ17jvdhfMIDi9mou76s5gO03d3duPHGGy/6dZG1ZbWeP0AjOrmi4MLlECsQXnrpJXzjG9+AxWJBJBKR2V/V1dX47Gc/C7fbjSeeeAJ/+qd/ih/+8IeygMPVCIfDaGhowGzp6OiYtd9FF1ZZbMSZQeC5Pa0osL5WuONieM4uQzyOHFsWjMFJtOx8GpGiak2bM1PnLBKNyzcXoSwrMKvXDj2zDLUB0Qiizjy0DEwAg/x3TRdnv9bKiww43Q88v6cVRTYWr1H72tr66lMIF9dM+yln6z3tQv3AVMK+7NVjv0h79txyOEZ6MHjwBXjjeW/4/Txnl3cznuUwYdIfxVMvH8GiuQ5N2zNj5ywWRU7Tfrklt99agjPsy6qipTcg7xPy3WZMDp9Gw0h6T1JmkrNfa/acBXAMdWHw8IvwGgo0bZdexKaurRE88eJh1M5z6K4vq/kArdje9eSTT1706y+//DJCofO3OIoOrcPheMNO5uc//3mZ4/XFL34RN910k/y82WzGnj17YDKZpnK6li5diubmZnz/+9+/6k6t6DCLjvJME6P84g9J5I690b8BzSx7zgReObEHrX1BVFYtgs164RlynrMrM967Hv6jz6M4NIicurdq0oaZPmci3iAQPoNctxVv2rZCbtug6Z+zgSO/lcfZdZswt75e6ybRVb7WxLV1+/E9aOsPXfLaSldmon8TfIefQaG/B7l1d1z188zme1pLSwtSHfuyV479otQRLnBguG0nrMPtmF9dCYPl3JXeCp6zK7Ox1YBn93aj32vH2+rqNGnDTJ+zYPthjEYCMDpzUL3pZhiMKZ+emPLEOfvNq4lYnC0r56K+fpHWTaKrfK3Ja2vrq7AOd1zy2kpXZmu7EU/t7kL3uBV33lynu76s5gO0oiN4qfwska81NjYmO7ZnjzoPDAzIDvHFeDwefOxjH8P+/ftlIYZbb731nK+7XK7zfqampkZWv71aYtmy0zl7Id7iD2k2fx+dr77KgeJ8JwZGfGg4PfmGhZ54zi5T/SY5QBtqPwyHwy7zE7UyU+ds36nE1qUtK+bC7T7/ekRXLh6NwDKYeAPMWboFdr7W0srZrzV5bc1zYGDUj8ZuDzPYVGJcfo0coBWF9BxWy7RiDmbrPS0d4g3Yl7167BdpL76wDuM5xYiMDwC9jXAmc2kvhufs8mxdMV8O0B5qGoLd7tB0In6mzpmn7eBUcTCX263682eiSCSGpp5EVMS2lQv4WkszZ7/WEtfWIkTGB4G+ZjhZG0MV165ZKAdoRSE9i9UGi9mkq75syk9zrVmzBrFYbKrAgiBytkSe17p1iWJCryc6wH/2Z3+Go0ePylUEr+/QitUFq1evlisPznb8+PFZWTVA+iFebJuTBW12HWPFcbU4Fi6FwepA1DOKYE8r9CYciU0VjtjyBoP6dPlC3Q0wRoJyJYdtLlccpPu1VRmU5bVVPaKAi8mdLwu6+NuPat2cjMG+LKX69VYpFuZtPPfvia7esuoCOGxmjEwE0dL9xjFo6SYeDU9VqFeKUNL0negYRTAcR47bitqFbxw5QqlLTnguSgzKept4bVVLXXk+8rPtstD24aZB6E3KD9CKlQW33347HnjgAdkJFR3Vv/7rv8b69euxcuXKqU7s4ODg1Paxb3/727ITLLaCVVZWyq8pH+J7xCoH8fl/+qd/kqsSWltb8a//+q84fPiwLK5AdCU2L0sMsO092ScH3mj6xKouZ1Xi9a10/vTkaMsgvP4wcrNsqK9kJpFaAi375aOtarWmq65JHcoA7b6TfYhGeW1Vg3hdKAMxnlM7tW5OxmBfllKda3Gi4ri3eb8ceKPpE6u6Vi8ulse7j+tvotHfcRyxgBcmVy7s87WJcNCjfScH5OPaxUWMP9MBV3LVrE9cW2NRrZujC0ajAZuXJ+4Rdhzpgd6kxR2s6JyKLC2xzeu+++6THdKHHnpo6uuHDh3C1q1b5aPwhz/8QYazi86v+PzZH+J7jEYjvvWtb2H58uX45Cc/ibvuugtHjhyRRRUWLeKqK7oyYnYzP9smZ3GONOtvFkcrUzOOzfugN68m30w2LSuDiZ0vVcTjMQRbE1vt7NVrtW4OqaC+Ih9ZTismfWGcaE9EgtD0KSudfE37OBAzi9iXpVQmdp2Y3HnJ1fXHtG6ObmxcUiof95xI7JrSE29DYpLPVbsBBiNz4tUQi8Wx/1RigHZ9fWJwn9KbmLwwOrIQ83sQOH1S6+boxtYVc6eurXpbIKd5Bu3lEJkQ//zP/yw/LmTDhg0y30vx9NNPv+FzFhYWypUGRGrM4oiVXk/u7JBbcdfWXTxPji6fs2o1YDAiPNiF8GgfLHmJTm66i0RjUysptq5gvIFagj0tiHnHEDdZYZ3HlRx6YDIZsX5JCZ7f1yUjQZZXF2ndJF2wi5gDVy6i3jEZc+CsXqN1kzIC+7KU8qvrazdg4sAfZcyBs3q11k3SBXFPIO4TTvdNonfIi7JCl24y/72MN1BdU9coRidDsJoNWFKRr3VzSAVi8sJZsxaeoy/K14yjfJnWTdKFxeX5yMuyYXQyKBfI6Wn8JS1W0BKlS8yBGHiLxuJaN0cXTA437AuXyGNvk35W0R5tHpIrAnPdNiypLNS6Obqh5OaFi6qnXfiIUsemZMyBuLaK1YSkzs3CVMxBwy6tm0NEKUIM0ApiEIFbcdXhdlqxNBllpadVtP6OY3JFoMmVA/uCeq2boxu7k5n7i+baYTFzmEZv11Zf4172ZVVikjEHifGXHUfOQE/4yidSwZKqAmQ5LZjwhnCyjVtx1eKqWTu1FVcvlDcRxhuoR3R2RKdHCJVwa6+erKwths1qwuCoH61nxrVujm646jZPZXwz5oCIBDHQZnS4EfNNINDVoHVzdGPDVMyBfnJovcnJPSfjDVTtyyo77BbPc2jdHFKRo2I5DBYbIhNDCPW1a90c3diS3IkqdtnpKeaAA7REKjCbjNiwJLHSa+cx/YVVa51DK24Uot5xXcUbbEnO+tH0hYfPIDzSA5jMCBdVad0cUpHNYsLq2uJzVpbQ9NnnL5YxB6LAiyj0QkRkMJnhrEnm/59ixXG1bEjuBBELOMY9Qegj3iDx9+FezHgDtXQPeHBm0AuzyYDqOXatm0MqMlpscFSuPGfHH01ffUWBLLgtCm+LAtx6wQFaIpVsSlYTFDm0IuSdps+SWwxraSUQj+niDe1YSyLeINtlxdKqxJY3mj7lb8M6vx4w27RuDqlMZHzrtQp2KsQcKIVeiIhci5MxB427ZfFNmr6SfCeq5uVA3Brs1MFEo7/zhIw3MDqzp6LIaPrE/aOwtDIfdguHaPTGVZuc/EpmN5NKMQfLys4pwK0HfPUTqWRlTREcNjOGxwNo7hrVujm64a7fIh89J3cg3b16tOe1eAMTL79qUeIN7NWJSAzSl/X1iSIrnX2T6BnyaN0c3VAKu8i8yWhE6+YQUapsxbU6EJ0ckcU3SR3XrExUHH/lUPpnJSqTeiJXk/EG6lEmodfXJ3YNkb44xT2KLH59GuGR9J+oSRVbV8ydev2Inap6wBECIpVYLSasS1YQVGZBafpc9YmsxEDnSUQmR5CuotHY1N/F1mRmDk1fZGIYwV5xE2mArZJVp/VaZGVZcsX57mP6KbKiNfv8OlngRayEEgVfiIiMZiuc1Yn3Uj3sXEq1QYTjbUMYHvcjXYniccoKQGWSj6ZvaMyP5q4xGAzAmsVFWjeHZqj4tWOq+DVX0aqlvrJAFt4WO1RFIW494AAt0QzEHOw8yorjarHkFMM2r1Z0C9N6K+7x1mFZRC5LDjYVat0c3VA6OeJvRAw2kT5tYsyB6sTKJ1Hg5eyCL0REU/Enp0TMAfuyaijOd6KuPB/inzOdt+IGRLyBbwJGRxYcC5dq3Rzd2KMUB1uYLwebSJ+m+lyc/FI15mBTMuZAKcSd7jhAS6SiNYtLYDUb0TvsRUfvhNbN0Q13/Vb56Dn5KtLVDsYbzAhfspOjZDuRvousnOocwehEQOvm6Ia7LrFDQRR8YcwBEQnOqlUwmK2IjPYhNNCpdXN0Y1sy5mD74fQdRPAkJ/MYb6CuXckBWiVzn/TJlSx+HexuQsTDOES1bEnuTNVLzAFHCYhUJDJoVyUrjjPmQD1yG5XBiOCZJoTH+pGe8QY957yJ0PRFxdbszhPndHpInwpzHaiZnytXH+05wZgDtdgX1MtCL7GzXktElNmMVgcclSvkMVd6qUfEWxkNQGPnKPqGvUjLeIPG3fKY8Qbq8fhCONY6LI83LivVujk0g8zZBbCVVctdob6mfVo3RzeWVhYgx22VMQeiIHe64wAtkco2T8UcpO8WplRjdudNVYr1nky/mIPjbcMY94h4AwuWVzPeQC2+lv3ijgGWogWw5HPVgd4pW5gYc6AesQLKVZvczpzGETJEpK6p68KpxIAcTV9eth1LkxFXO9Iw5iBw+mQy3kBkaTLeQC17T/YjFotjYWkW5hS6tW4OzVbMAXNoVWMyGbFp2ZxzCnKnMw7QEqlsfX2pzEMRFcfPDLLiuFrc9VvSNuZAyRsTW5fMjDdQjbcxWaiC8QYZQdn6d6R5CL5AWOvm6IarLjkQ07RXrpAiInLWrAWMJllxPDSc/je8qeKaVYmYg1cOpV/MgSc5iedatAEGk1nr5uiGMum8MTkJTfqm3LOI4qyxoE/r5ujGluQCObGDWexcTWccKSCagYrjyipJxhyovJrDaEKovx2h4fTp2EZj8am/A6WKL01fLByEv+3w1M0C6d/8kizMLXLLfKkDDQNaN0c3xEooGXPgm4C/87jWzSGiVKk4Xr70nKx3mj6xykss4mjrGUf3wCTShZi8m8r8Z7yBaoLhKA42JvozzJ/NDNbCebAUzAGiEfhaD2ndHN1YVlWIbJdVFuQ+1preMQccoCWaAZuXJ5bZM+ZAPSZnFhwVyUy0NFpFe7JtGGOeINwOC5bXMN5ALf72o4iHgzBnF8JaWqF1c2iWbFxaek5BDVIp5iCZ4exNFoAhImLMgfrEAIJSq+KVw+lzjxDoakDUOw6jXQzcL9O6ObpxuHEAwVAURXkOVM3N0bo5NEucSp+Lk18qxxyUpW2EzNk4QEs0AzYsLYXBADR3jWFglNsX1OJekow5OLEDcVEtKA3sOJJY7ct4A3UpnRpn7XoYxIuNMoLS+drf0I9whNvx1eKq2zz1umLMARG9NohgQLC3BZHxQa2boxvbViZ2U20/1J02fVll8k78TTDeQD3KZLO4R2BfNnO4kjm0vpaDiEcY2aWWLckFciI2JJ1jDjhaQDQD8rLsqK8okMcsaKMescrLYLIgPHwGoYFOpEO8wc5kvMGWFYk3DVJpq13z/nM6OZQZaubnIT/bBn8wIrNoSR1iK7PRkSVjDkQhGCIiszsX9vmL5TEL2qi7E8RiNqJ7wIOO3gmkQ59LWUXtZryBasQA0t4T/fJ4E+MNMoptTjVM7jzEQ36ZRUvqWF5diCynVRbmFgW60xUHaIlmyObkSq+dRzlAqxajzQlH9eq0iTk42T6MsckgXA4LVtQUad0c3RBb7WL+STmgZJ9fp3VzaBYZjQZsSN7IcPJL5ZiD5GSHUgiGiMi1mDEHanPaLVhbVyKPXzmc+jUVAl2nEPWOwWh3wVHBeAO1nGwfwaQvJAeU6ivytW4OzSKDwfhatBRjDmYk5uDVNI454AAt0QxRqnHKQTpPUOvm6Ia7PhlzcDL1Yw52Jt8clNUSpA5v496pKtNiYIkyi1JIY8/xPrlKndQdiBGFYBhzQESCMnETOJ3IICW1Yw7OpHxf1nvq7HgDi9bN0Q1lknn9khI5sESZRUS0Cb7mfexzzUDMwa5jvWl7j8CrAdEMKc5zonp+LkS/a38Ds7vU4qxeA4PFjsjYAII9LUhVMRlv0HPOmwVNn7iRmaoknJx9psyr1Oqym+XEV2PniNbN0Q1R+MXocMtBGMYcEJFgzimCraxKvPsi2HpA6+boxrq6EtitJvSP+GS9ilQVj8deizdYzHgDNfuyZ+fPUuZxLFwid4aKPlfwTLPWzdGN5TUi5sAi7xFOtKVnFBoHaIlmIeZg78kBrZuiG0arHc5Fa+Wx9+QOpKqGjhGMTATlQNLKRYmKvTR9of52RCaGYLDY4KhcoXVzSANiNfraulJ5vPt4n9bN0Q1R+GVqyx23MxPR61bXB1oS2e80fXabGeuXlE6tok3peAPPaCJirHK51s3RjbYz4xgc9cNmNWFVLe8RMpFYjS4WHQneJsYcqMVsMk5NeqRrzAEHaIlm0ObkysnjbSPwh9K3mmCqcddvlY+ekzvl7H4q2nEk0eEWeZmMN1CPktXkqFwJo8WmdXNII0rG1O5jvSm/PTSduOo2Tw3QcssdEQnO2sQAbajrJAzhgNbN0Y1rkjEHIodW7LpKRd4GxhvMBGX17OraYtgsjOrKVM5khIyIbmNfVj1bkoW5d6ZpzAFHDYhm0NwiNxaUZsmLQ9MZv9bN0Q2nGJwT20I8I7JgVErGGySLwylvEqQOX7KStCuZ3USZafXiYjnx0Tvsxem+Sa2bo6+YA7uIORiTK6eIiKwFc2Apmg/EorAMcCuumu9jYpfVyERA1qtI5XgDVx3jDdQkMvQFxhtkNmfVSjnxERntQ3iwS+vm6MaKmiK4HRZZqDsVr61vhAO0RDNs87LEAF1DFwdo1WIwW6ZWdHhPvIpUc6pTxBsE4LSbsWpRkdbN0Y3wSC9CA6dFyfmpbUGUmRw2ER1SNDVDTurFHDgXrZPH3oadWjeHiFKEK9nnsvQ3at0U3bCYTdiUvEfYfjj1Yg6C3U1yIYTB5oSzgpFSaukZ8qCjdwJGowHr6ku0bg5pyGh1wFGRiA7xNjJaSs2Ygw1LS9M25oADtEQzbPPyxOxoS28AgRC3jKrFvWSLfPSc2pVyW3GVNwORLyY64KQOz/FX5KOjYhlMjiytm0MpMvm1/VA3t4apyJ1cKcWYAyJ6fQ6tZagNMcYcqGbbqrlT/cZoNLUiuzzJSTpXzVq5MILU8fKBbvm4oloUM7Jq3RxKkWur58QO9mVVtHVF4tq682hP2sUccICWaIaVl2WjJN+BSBQ43JSe1QRTdiuuMxsx3wT8HceQSvEGrx5NDNBuTWYQ0/SJTsvksZfksXvZdVo3h1Jk8stqMaF7wJPSVbDTjVjNYbS7EjEH3Yw5ICLAWrwQppxiGGIRhNqPat0c3RCDdNkuKya8IRxpGUqxeINd52STkzp92RcOJLay37B2vtbNoRQZoDWYrQgPn0Gwt1Xr5ugq5sDlsGB0MoiGNIs54AAt0QwzGAxYX5+o0Ln35IDWzdENg9EE9+LESi9PCsUcNHaOYng8ILdgszKreoLdjYiM9cNgtU9VmqfM5rRbsCmZ3/bifmZ3qVpZeCrmgFvuiCjRl7VXr5XHgZb9WjdHN0wm41StglcOpU7MQfBME6KTIzCILdiVjDdQS0PHCPqGfXDYTMyfJUnUVHEli4V5kgtRaPpEnYoNS5IxB8mFU+mCA7REs0AZoD3YNIiwWEpLqnAlYw58jbsRj4SRCnYcTXSwxZuCWN1H6lBWz7oWb4LRate6OZQilBUoLx86g3AktbaHpjP34s2vxRzE+e9KRICtJjFxE2w/lDJ9Lj24ZmViK+6uYz0pc4/gaUiunl20DkYzt+Gr5YXkZPLm5XNgt5m1bg6lCPeya1+LOYjy2qqWrcnJL1G4W+xwTRccoCWaBdVzc5DlMMEfjOJw06DWzdEN+/w6mNz5iAV98LUd1ro58uK/M5k/q6yIoOmLRULwnkysks5KdmKIhBWLipCfbcOkL4T9Df1aN0dXMQeiMIwoECNWrxMRWUorEbNlIR4KwM+YA9XUVxQgP9sObyCCg6cGUiPeQBmgTe5Uo+kLhaPYkSwGx3gDen2fy+TOQ8w/CV/LQa2boxsrFxXJgt2icLdYvZ4uOEBLNAtEpc66+YlVf7tYcVw1BoMRrvrESi/PyR1aNwdNXaMYkvEGJsYbqMjXvF8OwpuyC2FfuETr5lAKMRkNuG514kbnxWSuG02fKAgjVk6dXSiGiDKb6HOFShbJY1YcV/ceYevKZNHL5ACeloI9LYhODstIKcYbqGfvyT45CF+Y68DSykKtm0OpFtu39Bp5PHnsZa2boxsWsyktYw44QEs0S+rmO+Tj7uN9KVepNZ2565MxB037EQsHNW2LqMIrrKsvhY3xBqrxHE3EG2QtvUbeIBKdTVmJsu9knyy0QupQVk6JHFrGHBCREC6plY/epn2Ix1JjO76eYg72nuhDIBTRtC3e5KScs2YtjBabpm3Rk+f3JSaRr18zTw7KE50tK1kA2dd8AFHfpNbN0Y2tK+ZO3aOnS8wB73SJZsmCIhuynBa5Ffd4W3pVE0xltjk1MOcUIx4OwNdyQNPKrMrsnJJ5Q9MX8YzB13ronIwmorMtLMtG5dwcRKJxvJICq4/0wlm58rWYgzNNWjeHiFJAJG8BDI4suRU3cPqk1s3RjUUL8lCc70QgFNU0rkf0ZZV4AyWLnKZvdDKAg42J+Irr1zDegM5nLV4Aa0mFyHWDJxnrRtO3qva1mANRyDsdcICWaBa34q6tK5LHjDlQt7KwO1ksTISra6Xp9CgGR/0y3mD14hLN2qE3XhFdEY/JgXhr4Tytm0Mpvor2hf2ntW6KvmIOataeUzCGiDKc0Qh71eqpIoKkXl9WWUW7/ZB2E43BnmZEJoYS8QZVKzVrh96IcypW7y1akIv5JVlaN4dSVNbyxCpaT7IwMqkTc7A+GXOgFPJOdRygJZpFG+pLpiq1pssy+3Tgqt8qH/0tB2VWqRZePZoYdF9Xx3gDNU0m4w3cya0/RBdyzaq5cstg0+kxdPVza5haXHWJFVRiRRVjDohIsFcn8qm9jXt4XVD5fUwQK2h9AW0quXtPJSbjnNVrGG+gohf2J+INbli7QOumUKrfzxqMcqIkNNStdXN0Y+vyxM5WUcg7HcZfOEBLNIuWVuYnl9kH5YpLUoe1eCEsBXMRj4bhbdqrTbzBkcSs3BbGG6gmNNCJUH87YDRPZQ0TXUhelh2rk4X5WCxMPaJAjMHqkAVjgmeatW4OEaUA6/z6ZPzJKK8LKiovy8a8YjfCkZisV6FpvEFyco6mr6N3Am1nxmE2GbAtuUqa6ELM7lw4q1bJYw+LhalGFO522MyykHc6jL+kxQBtMBjEF77wBWzatAmrVq3Cpz/9aYyMjFzyZ775zW+itrb2vI+z/exnP8ONN96I5cuX4z3veQ9OnmSWEs0si9koV1gKOxlzoG7MQXIVrefE7Of2NHeNYWDUD5tVxBskBolo+pRKps6aNTA5uSWMLu3GdYmYgxcPdKfFDHk6MJqtUzEHSuEYujrsy5Ku4k+q18hjbyNjDmYi5kCLPPVgbysi44MwWGxwJAeJaPpeTK6eFQWEs11WrZtDKc6djDmYPL6dOxRUYrWYsL4+GXOQLOidytJigPbBBx/Ejh078PWvfx0PP/ww2tracP/991/yZxobG/G2t71N/tzZH4rHHnsMX/7yl/GJT3wCjz76KObNm4cPfehDb9hZJpquzcvL5OMrh7oR5SCCalz1idl+f/uRWa9+KSpDCuvqSmC3mmf1d+uVqA7tOb5dHmexOBhdBtH5ctnNGBrz43jbkNbN0Q1X3Sb56Dm1mzcL08C+LOmJa/HGqUlx8X5N6tiaHKA91DiACW9oVn+3MgnHeAP1iPu8lw4mBmhZHIwuh7NmLYxih8LEEAKdJ7Rujm5sSe5wFQW9U30RR8oP0Pb39+Pxxx/HAw88gLVr18oVAl/96lexb98+HDqUqOx9IU1NTaivr0dRUdE5H4pvfetbeN/73oc77rgD1dXV+NKXvgSHw4FHHnlklv7PKFOtqy9BltMil9mLDhipQxSQSlS/jM7qig4Zb3A0MUC7dQW3LqnF33FMbp80OtxwVicKkhC90Qy5cnOr5L3R9DkqV8qCMeJmIdjTonVz0hL7sqQ3chDP4ZbxJ/62I1o3RzdEAanKOTlyYE/Uq5jVeINk/qySPU7Td6R5UMbaifu+tXUsIEyXuXMpGeum7CSk6RM7XEUhb7GIo7krtWMOUn6A9sCBA/Jx48bETK1QUVGBkpIS2bG9kFAohI6ODlRWVl7w68PDw/LrYpuZwmw2y07zxZ6TSM1qgtcnK44/s6dT6+boipJT6jk5ezEHrd3j6B/xyXiDNXWMN1CLRykOVr8VBpNF6+ZQmrgheW3debQHgWBE6+boglhJJVZ0CEo+IV0Z9mVJjzEHSvHOicPPad0cXdmWLBa2/dDsxRyE+toQGRuQ8QacFFfPC/sSk8XXrJonY+6ILkdW8toqJk1ioYDWzdEFm8UkY0bSIebAnA6rDvLy8mCznbvVori4GH19Fw5Qb2lpQTQaxdNPP41/+Zd/kblf69atw2c+85lzfq6srOy85zx16tS0Zh99vpmvIO/3+895pNT3+nO2bXkJfre9DXtP9KF3YAw5bmYSqcFYsRp48acIdBzH5EAPTO7cGX+dvXQgMci+qqYQsUgIvsjsbknTo1jQD0/jHnlsXrTxsq+rvDamJzXP28JiO0ryHOgf9ePlA53YtvLc93m6OhZxbT2xA5MNO2HfdA8CgcCsvdZE30pkM6Yz9mXPx+t1+nn9ObPUbgb2/gG+5v2YHOqFyZmjcQv1YW1tPh5+AjjWOoQz/aPIy7LN+Ots8mgiUspWvgKBcBQIz/w1QO98gQh2HU8MBG1eWsS+rM6ped7i+fNhyilGdHwAo8degaOOhZLVsLa2QE58icLe995YkbJ9Wc0HaLu7u2Vxg4sRuVpW6/mDV6KTKzqrF9sSJohtXv/93/8tVxmIrWQf+MAH5BYz5SS8/nkv9ZyXIxwOo6GhAbNFrJyg9HL2OZtbYMWZ4RAeeeYwttSxAJJasnLmwjx+Bp07fofgwnUz+joTF9vthxI3yfNyZ/f1r2fW7iNwRUKIuvLROhIERq/s35XXxvSk1nmrm2eRA7RPvtqMQtuYKs+Z8aI25JosiE0MoXn3C4jmzpnV19qF+oGphH3Zq8frdfo5+5xl5cyBebwHnS89imDFayvEaXrmFVjRPRzCb58/ig217pl9ncXjyD6xAyYAQ8456GVfVhWHWr0IhWMoyDYjNHEGDQ1XtmqP18b0pNZ5sxfVwjE+gKF9T8ODfFWeM9M5YnFYzAYMjgXw3I6jmFdoTcm+rOYDtGJ715NPPnnRr7/88stym9fric6n6LReyJ133olrrrkG+fmv/THX1NTIz73wwgtYsGCB/Nzrn/dSz3k5LBaLzACbaaJTLv6QysvLp9Vemj0XOme3e7Pwnd824ERXBPfdtTjtVwilCq//eky+/FPkjHWg4M0fmNHXWVvPBEY9Z2C1GPHWG1bCbhXdW5qukeOPQVydc1beiLn19Zf9c7w2pie1z1t+iQ8vHXsV7f1BlMytQH62XZV2ZrqxztUINO1BaXgAlvLNs/ZaEytJUx37sleO1+v0c6Fz5ou8GRPP/QBZg6dQcesH2ZdVyY1jLjz8ZCNaBy74Y8kAAFcCSURBVOP44J11M/o6C/d3YNg/BpitqNx2O4wWvmeq4Ve79svHm9eXo76+4rJ/jtfG9KT2eYuUFWCo5RVYhjuwaH4JTG4O0qph7Umxsr0f/V4Htq6dn5J9Wc0HaEVHsKqq6pIVbMfGxmQH9OxR54GBAdkhvpizO7TKlq/c3Fy5JWzDhg1Tz3H2736j53wjolPidDoxW8Qf0mz+PlL3nN24vgI/fqoJPUNedA4EUF9RoHXzdMG64lpMvvwzhHubYQl7YMkpnrHX2cGmxIybCP7Pz+UqaDWExwcQ6harNwzIX3UjzFdxjeO1MT2pdd4qnE4sqSzAibZh7Dk5jLtvqFGlfZkutmybHKANtexH1rZ7Z+21lg4DPuzLXj1er9PP2efMvvIGTL78f4iO9MA00gX7/MVaN08Xrl+7ED9+qhFNp8fhCQDF+c4Ze52NdCQKFbqqV8Odw0EgNQyM+HCyfRTi7evmjRVXdY3jtTE9qXbenOXwzK9DoKsB0db9yNp0pxrNy3jXrlkgB2j3NAzivW+qScm+bMqnVa9ZswaxWGyqwILQ3t4u87xEFteF/Nd//Rfe9KY3ye3HZ28/Gx0dlasCCgoKZHGGPXsSGYdCJBLB/v37L/qcRGpz2i3Ylqw4zmJh6jFn5cO+ILHq0nty54z9HnF9UULGty5PnEeaPs+xRA6avXwpzDmvVSsnuhLXr0kUC3t+f9c5fQG6es6q1bKATGR8AJH+dq2bk1bYlyW9MtoccNdvlscsFqaeghyHnGgUdhyZuWJh4vriaUj0lV11ifNI0/fiwURxsGVVhSjO4yArXR2lEOPk0RfZl1XJmsXFsrC3mEQRO2FTUcoP0IpVALfffjseeOAB2Qk9evQo/vqv/xrr16/HypUr5feIFQmDg4NT27xuvvlmnDlzBg8++KDsAItqth//+MexevVqbNu2TX7Phz/8Yfzwhz/EY489Jpcc//3f/70MCr7nnns0/f+lzHLz+oXyUQz0+QJhrZujG+76RJi65+SrM/Y72nsm0DvkhdVsxNr6q1+tRK+7UTj2sjzOWnat1s2hNLZ1xRxZMbmrfxKt3eNaN0cXjLK69xp5HGjeq3Vz0gr7sqRnWSsT+cvehp2IBVlcSi3XJBdxbD88cwO0of4OREb7YDBb4axePWO/J9P6si/uTwzQ3rA2MVlMdDXcdZtgMFkQHupGqK9N6+bogt1qxrq6xH377uMDSEUpP0ArfPGLX8SmTZvwsY99DPfddx8qKyvx0EMPTX390KFD2Lp1q3wUli5diu9+97tyS9nb3/52+XN1dXX41re+NbW8+J3vfCfuv/9+fO1rX8Pdd98tO8Gik/v67WREM2lxeR7ml7gRDEVlVUFSh2vxRsBglG9moeErC+W/XMqKhjV1JXDYNE+L0YVgTzPCIz1ylZ6rlsVG6Oq5HBZsXJqobv/CgcSNEk2fq26TfAw07ZWFZejysS9LemWbWwtL4TzEw0F4TuzQujm6sXn5HBiNBjnJeGbQMyO/QwyqC46qVTBamXeqhqbTozgz6JWr9DYtS/RDiK6G0e6Cs3a9PJ489pLWzdGNLSsShW53n+hPyZXJaTGqIDIh/vmf/1l+XIjI4RId2LOJTrD4uBTRQRYfRFoRN1liFe0Pfn8Cz+7txJs3lWvdJF0wuXLgqFgOf9theE++Cuu2d6j6/OJi/moy3mDL8sRFnqbPczTR+XDVbpDbJommQ6xceeXwGbx8sBsffusSmE1pMSed+jEHZiuiE4MwTfQBuPwifpmOfVnSc182a8WNGHn+YUweeQHZq2/Rukm6kOO2YWVNEQ42Dsj3sntvrlW9L+s9tWtqpR6pQ0QrCWJwVsTZEU2H2FEo7mXF5FfBjX8Cgykthu9S2trFJbBaTBgY9aN3NJxyPVnerRClQFai2WRA0+kxtPdwK67qMQcN6sccdPROyOJuYgv1OsYbqCIeCU9FUiiZS0TTsWpREXKzbJjwhnDwVGpuY0o3Rqt9KubA2ndK6+YQUYqQsURGs9wJE+xPFFCl6VNqVYgBWrWFBjoRHumVW6id1WtVf/5MFI5E8UpyR+QNySx8oulwVK6EyZWLmG8CvtbEDhuaHrvttZiDE6f9SDUcoCXSmBhA2LAksQXm2b2ntW6ObjhrNwAmM8KDXQgNqPvvqpyntXUlnB1Xia/lAGIBD0zufDjKl2rdHNIBk8mI61bPk8cvJFe00PS5k/nQhuCk1k0hohTaueRalChON3nkea2boxsbl5XJ3R+n+ybR2atuQRvlPDmqV3PXkkr2neyHxx9GfrYdy2tY6Jamz2A0wb00kTuv1Omg6VPyoSd9UaQaDtASpYCbNyyQjy8d6EIonHoXinRkErk9Vavkseekeplo/mAEz+9LDNAykkI9SraSe9k1sjNCpGYHbM+JPnh8ieJLND1iECb/HQ/AX3ez1k0hohQsFuY5vh2xCK+3anA7LLLquNrFwmIhPyaTsVKMpFCPMhl8/Zp5MBkTWeFE06XsLPQ270PUPzN51Jlm/ZJSPHjfWty6NhephgO0RClg5aJiFOY6MOkLY/fxXq2boxvu+q3yUWydVysEXORZ+gIRlBW6ZDYYTV9UbNtpOfjaNkkilVTMyUF5WTYi0diMbBHNVNZ5tYhbuOKKiF4jsv9N2YWI+T3wNe7Vujm6cc2qZMzBoTOq9WU9x19BPOiDOa9UnjeavnFPEPsb+uXx9cnJYSI12ErKYS1eCEQjMo+W1FFXngeHNfWGQ1OvRUQZSMyy3rQusYr2mT2dWjdHN5w1a2Gw2BAZ7UOot3Xazyc6xk+82i6Pb9tcIavr0vTJqs+xKKylVbAWJV4HRGqvomXMARHRzBG7X7KWXy+PJw8/p3VzdGN9fSlsVhN6h71o6R5TpS87ceCP8jh7zZthMHA4QA1iEjgai6N6Xg4WlmZr3RzS6SraScYc6B6vyEQp4qb1C2AwAEeah9A37NW6OfopaFOTKHygFKCajoaOEVkgTFR+vGkdZ8fVomQqZS3n6llS37Wr50HMpZzqHEXPILeGERHNlKwVN4ihWvg7jiE8llhNSNMvaCMGaYXtyQJU0xHsbpQFwgxm69SAOqkYb8DVszQD3Eu2iVkwBM80IjzSo3VzaAZxgJYoRZTkO6e2zD/HYmGqcddvOSvmIDat53ry1URl4mtXzYXbaVWlfZkuNNSNYG8LIELwk5EURGoSxTpW1SYy/F44wFW0REQzxZJbDEdlYsv85OEXtG6ObmxbmYg52HH4DGKx6cUcKKtn3Uu2wuRwq9K+TNfVP4nmrjG5I/KalYnipERqMmflwVG5Qh5zFa2+cYCWKIXcvGGhfHxu32m5TYamz1G1CgabE9HJYblq4GqNTQbx6tHEyoXbtlSo2MLM5kkWBxMF3UQVaKKZjDl48UD3tG9uiYjo4rJWJIqFTR59AfEYC9+qQRQKc9rNGBoPyN1cVyvqHYenYZc8zl5zq4otzGzK6tk1i0uQm2XTujmkU1nJmAPPse3TXnREqYsDtEQpZOPSUmQ5rRgeD+BQ44DWzdEFo9kK16L1r2WdXiWRDRyJxlG7IA/V81Kv4mM6Ejduk8e2n5OtRDQTNiwtkze3AyM+nGgf1ro5RES6JfpcRkcWopMj8Lcd1ro5uiCitTYuLZPH0yl4OSGygWMR2ObUwFZWqWILM5dYUPNScneOMhlMNBOci9bJRUeR8QEETjdo3RyaIRygJUohFrMJ169NbI1hsTD1Yw68p3Zd1WoO0fl6alci3uC2LeWqty9TBTpPyJXNRrsLzpo1WjeHdMxmMWHL8jny+EUWCyMimjEGswXuZYlM+YnDz2vdHN3FHLx6pAfR6JWvnhP934mDz0wVByN1HG8ZkiubXQ4L1tWXaN0c0jGjxQb34k3n7EAk/eEALVGKuWV9IuZg74k+jE4GtG6OLjgqlsPocMutXf7O41f88/tP9mFozC9XN29dkegg0/QpGUquui1ypTPRTFJWtuw40oNAKKJ1c4iIdCs7GXPga96PiGdM6+bowspFRchyWjDmCeJY69AV/7yv5SCiE0NydbOrfvOMtDETKdn2YgBdrHQmmknuZEFlEVUSCwe1bg7NAA7QEqWYhWXZqF2YJ1dtcqWXOgwmM1zJGUfviVev+OefeLVdPt6yYQE7XyqJhfzwntotj7OSnQ2imVRfUYDifCf8wQj2HO/TujlERLplLV4A29xFQCzKlV4qMZuM2JzcCbL90JXHHEwceEo+Zq28kZPiKhH9iZ1He+TxDWsYb0Azzz6/DuacYsRDfvia9mrdHJoBHKAlSkE3J1fRPrPnNOJxFrRRM+bAc2oXYkH/Zf9c77AXh5oGYTAAb97EeAO1eBv3IB4OwJxXCtvcWq2bQxnAaDRM3UApK16IiGiGi4UdeZ59WZVcsyoZc3C0B75A+LJ/LjLaB3/bEbFkAdmrbp7BFmaWXcd6EQhFUVbowuLyPK2bQxnAYDBORchMHk3sRCR94QAtUQratnIO7FYTzgx6cLL96qu10mvsC5fAUjAH8aBP3ixcrmf3dk9VZi0tcM1gCzOLJxlvICqSGsToN9EsUDK+DzcOYGSCETJERDM5MW6w2BEe7kGw+5TWzdGFpZWFmFvkhi8QwbN7T1/2z/mOviAfHVWrYMkrncEWZhZlp6OIUGJflmZLVnKA1t9+BJHJUa2bQyrjAC1RCnLaLVPFAFgsTL0Zx5z1b5XH43v/cFnFwkKRGF46mNi6dPuWihlvY6aITAzD335MHruXXaN1cyiDzCl0o648H7E48NKBxOQLERGpz2hzwJ3MOp04/JzWzdHNTpC3XVslj3+3vfXyioVFw/CfSEyK56xlcTC1iNoUR1oG5fF1qxOTv0SzwZJfBtu8WlH5D54T27VuDqmMA7REKeqWDQuntjF5/Ze/jYkuTmwJMTqzERkfnMo/vZTjnX54AxGU5DuxqrZ4VtqYCTzHxY1CHPYF9bDksuItza7rk8XCXtjPCBkiopmUtfIm+egVBW0CXq2bowtitWa2y4qBUT92Hut9w++39p6Uu8fMucVwVK6clTZmghcPdEF0IZZUFnCHHc06sQNRmDz6EvuyOsMBWqIUJQqFzS/JQjAUxfbDV14MgM5ntNiQvfpN8nh8z+8v+YYmvravySOPb91UDpORW5fUIP5dJ5PxBkqGEtFs2rZijiy20tk3ifaeCa2bQ0SkW6JQmKVwHuLhIDwnr7xIK53PZjHhts2JXV2Pv9zyhn1Z2+kD8lj0fw1GFrpVg/h3FQO0yoA50Wxz1W0GTGaEB08j1N+hdXNIRRygJUpRIsvolg0L5PGzjDlQTfaaN8NgsiDY03zJTLTWMxPoHQ3DYjbipvWJ80DTF+ptRXioGwazFe7Fm7RuDmUgt9OKDUsSGXwvJPPjiIhoZvqyWSuTxcIOX37+P13abVvKZf+06fTYJWtVhPvbYJ7oA0wWZK24YVbbqGct3WPo6vfAajZiy/I5WjeHMpDJ4YarZp089hx7SevmkIo4QEuUwq5fMx9mkwHNXWNo7xnXujm6YHbnwr00kXs6tuf3F/2+p/ckBm42LS1Bjts2a+3TO2X1rHPROhjt3BJG2rhhXWLFy8sHuy8vw4+IiK5K1tJrAaMZwd4WBLnSSxV5Wfap3NPfbm+96Pf5ktm/9kUbYHJmz1r79E6Z3N24tAwuh0Xr5lCGci9PxBx4TrxyWbVVKD1wgJYohYmBwQ1LyuQxi4WpJ2dDoliYr3EvwiPn53eNe4LYdbxfHt+ynluX1BKPhuE5ueOc7CQiLayuLUaO24oxTxCHmhJFPoiISH0mVw5cixIrvbiKVj13JouF7T7ei56hRCTX2aK+CQSa9shj54rEKmaavnAkhu2HzpyTaU+kBWflSllbJeodh7/tsNbNIZVwgJYoTYqFiYrjoTBnx9RgLZoPR9UqWahqfO8fzvv68/tOyw5YWb4F1fO44kAtvtbDiPkmYHLlwlG5QuvmUAYTGbTXrpo39XonIqKZo8QceI5vRywS0ro5urCgNBtrFhfLQlW/29523tcnj7wARMOIZJfCUpoYzKXpO3iqHxPeEPKybFi1qEjr5lAGM5jMcC/ZNlUsjPSBA7REKW7FoiIU5Tng8Yex6zKqtdLlyd1wh3ycPPoiov7Jqc9HY3E8uTOxBW9djVvmp5E6lIwkETHBQhWkNWXly54TffL6SkREM8NRsRzm7ELEAh74GhOrOmn67rq2Wj4+t+80Jn2vDXyL7c4TB5+Wx8EFa9iXVdELyeJg166eB5OJQymkLWVHoq9pH6IBr9bNIRXwqkKU4kxGA25alywWtpcxB2qxly+DtbhcVhaeOPjM1OcPNQ6gf8QHl8OMpQsdmrZRT8QguLd5vzx2L7tW6+YQoWpuDhaUZsnV8q8eSWxXJCIi9YlJWXeySBVjDtSzvKYQFXOyEQxF8VRycYEgtjtHxgZgsLkQKqvXtI16IgbB955IRKDdwHgDSgHW0gpYiubLGDlvw06tm0Mq4AAtURoQA7Ri8vtI8xD6hjk7pgaxmiBnYyKLdmLfk4hHEivonni1XT5et2qOrM5K6vCefBWIRuSguK2kXOvmEMlrwA1r5p9T8IOIiGZG1orrxZUX/o5jCI/2ad0c3byP3ZlcRfuHHW0IRxJRaBMHEqtnHUuuAUwsYqWWVw6fQSQak4PiFXNytG4OkbwGKKtoPclCzJTeOPpAlAaK851YWZPIOXp2L/MS1eKu3wKTOx9R75gsXiUGvw+cSsyM37w+kU9J6phMdhrcy7l6llLHdWvmwWgATraPoHeIk19ERDPFklMMR+Xy1/JRSRXbVs5FfrYdo5NBWbwqPNYPX8tB+TXn8sSqZVKHMpnL1bOUSkR0HAxGBLoaOPmlAxygJUoTt2xcOFXQJhqNad0cXTCYLMhZd6s8Ht/zO/xxZ7sstiBC/8sKXFo3TzdCwz0InmmSnQclzJ4oFRTkOLAiOfn1UjJXjoiIZkbWypum8v9FTipNn8VsxFu2Vsjjx19uTa6ejctirOa8Uq2bpxtnBj1o7ByVk7pKkVGiVGDOyoejYpk89hzbrnVzaJo4QEuUJjYsKUW2y4rh8QAONg5o3RzdyFp1CwwWO0IDp9G6f5f83O1bEh1dUoey5UbeLLjztG4O0TmUlTCi8EdczNAQEdGMcNWsg9GZjejkCPyth7Vujm7cuqkcdqsJ3b2jGD34nPxc9prEAgRSx4vJ1bOraouRl23XujlE53AnYw4mj73Evmya4wAtUZqwmE24PpmXyJgD9ZgcbmQlC1dsxFEU5Tmwtp4rDtQiVsh4jicGaJWMJKJUsnFpGRw2E/qGfTLqgIiIZobBbEGW2I4rclKPsFiYWtxOK25avwCrrB0whrwwZxfCWb1a62bpRjQWx4vJXTaMN6BU5Fq0HgarHZGxfhl1QOmLA7REaeTmDQvk494TfRidCGjdHN3IWX87YjCgztqDO5fbYRL7l0gVnpOvIjI+CKPdDeeidVo3h+g8dpsZW1fMlce/ebFZ6+YQEela1sob5aOveT8inlGtm6Mbb7umClvtjfI4WnMtDEaT1k3SjVcOdWNg1A+3w4INS8u0bg7ReYxWO9x1m+Xx+K7HtW4OTQMHaInSyMLSbNQuzJMzuaw6rp5Ojx1HQ4kZ8dVRbrlTSzwawej2X8rjnI1vg9Fi07pJRBd09w01Mldu38l+nOrkKloiopliLVoA29xFgNhhw6rjqskL9aHcPIRI3Ig/DiUWdND0RaIx/N8ziYHvt19fDZuFA9+UmnI23SXrffhaDiAgan9QWuIALVGauWVDoljYs3s7mTGjkid3tuPFwBJ5HGp8lSs6VCJykCKjfTJvTinGRpSK5ha5ccPaxA3tz546pXVziIgyYhXt5OHn2ZdVycSBP8rHw6GFeOboKHfaqUQsiOkd8iLHbcVbtlZq3Ryii7IWzJnKoh19+edaN4euEgdoidLMtpVzZV7imUEv8xJV4PGF8PLBbnREihArrASiEUzsT3Ry6erFI2GMvfKIPM7d/HYYrQ6tm0R0SffeUguzyYDDzYM41jKkdXOIiHTLXb9F5iWGR3qYl6iCqN8Dz4kd8rgrd61c9fnEq+1aNyvthSNR/OLZxOrZe25YBIfNrHWTiC4pb9s7AKMZ/vaj8Hce17o5dBU4QEuUZhxn5SU+s6dT6+akvef2dSEUiaG8LBul17xdfm7i4NOIh4NaNy2tTRx+DpGJIZjc+chefYvWzSF6QyX5Ttyc3KHw0z82cFUXEdEMEZO27rot8niSxcKmbfLoi4hHQrAWL8TGG7fJzz25swPBUFTrpqW1Z3Z3YnDUj/xsO27dXK51c4jekCW3GNnJHQqjL/+Cfdk0lPIDtMFgEF/4whewadMmrFq1Cp/+9KcxMnLxVYOf/exnUVtbe8GPb3zjG1Pfd8stt5z3dfGzROnglo2JQYQdR3rg9Ye1bk7aisXiMt5AuH1LBVy162HOLUbMPwn/yVe0bl7aioWDGNvxa3mct/VuZs9S2njXTYtgNRvl7oRDjYNaN4d0gn1ZovNlrbpJPnpP7kQs4NW6OWkrHo9NxRtkr3kzNi6bIyccJ8UOscM9WjcvbQVCEfzyuUSO57tuXsTsWUobuVvvgcFslbsT/G2srZJuUn6A9sEHH8SOHTvw9a9/HQ8//DDa2tpw//33X/T7P/e5z8nvP/vj9ttvR1FREd7xjnfI7/H5fOjq6sK3v/3tc75P/CxROqhdkIf5JVkIhaPYfqhb6+akLbGVWeRKOe1mXLt6nqx4m7P+LfJr3oNPi16v1k1MSxMHnkbUOwZzTtFUzhxROijIceC2LRXy+CdcRUsqYV+W6Hy2OTWwFM6TKz+V7fl05cRWZpH3b7A54V66DSajAXdck8hKfXLnacT4PnZVntrZgdHJIIrzHLh5fWJhDFE6MGflI3vNm6ayaNmXTS8pPUDb39+Pxx9/HA888ADWrl2L5cuX46tf/Sr27duHQ4cOXfBnsrKyZAdW+Th27BiefPJJfOUrX0FJSYn8npaWFsRiMbmK4ezvFT9LlA4MBsNUsbBn9p7Wujlp68lkPtcNa+dP5UplrbgBRrsL0bE+WAabNW5h+okF/Rjb9Zg8ztv2ThhMFq2bRHRF7r6+BnarCS1dY9h9vE/r5lCaY1+W6OJ92ayViVW0jDm4esrq2axl103l/YsBRZfDgt5hH5rOsFjYlfIFwvj1C4l7gHffUguLOaWHTIjOk7vpLhgsdgR7W+Fr2qt1c+gKpPTV5sCBA/Jx48aNU5+rqKiQnVPRsb2cLWX/8i//grvvvhsbNmyY+nxjYyMKCwuRk5MzQy0nmnnXr5knC9qIQYT2nnGtm5N2BkZ92HcyMfhy2+bEijlBdG6zVt0sj23tezRrX7oa3/cEYr4JWPLL4F52rdbNIbpiuVk2vHVbYvXRz/7YIKNQiK4W+7JEF5cl+glGsxxECPZ3aN2ctBMZH4SvOXGNUVbMCWLRwZuTcWi7Tk1q1r509fsdbZjwhjCn0IXr18zXujlEV8zkykHOutvk8YjMoo1p3SS6TOZUX3WQl5cHm+3c/MLi4mL09b3xqpZHHnkEQ0ND+OQnP3nO50Wn1ul0yu1lBw8elL9DdHw/8IEPwGi8+jFrsXxcbDmbaX6//5xHSn0zcc4sRmDt4mLsPtGPR19swl/ctUS1584Ef9jeAjHusrQyHwVZpnNeu9al1wN7fgfLaBcmOxuAhXWatjVdiAy5sd2/lcfODXfBH5jdQmu8NqanVDxvb94wV1bA7uybxPN727FleanWTcrYcyb6VmKlXbpiXzZ9XvekxTkzw161GoHmvRjZ9Vvk3PJRFZ9b/yb3PgnEY7DOr0fEmY/IWa/dm9aW4fGXW9E5EMLJtgHUVxZr2tZ04fGH8eiLLfL4nusrEAzO7gpkXhvTUyqeN+uKW2A48EeEB09j5PCLcNRu0rpJKcWfon1ZTQdou7u7ceONF88n/MQnPgGr1Xre50UnV6wouBSx7UvkfImsLrHl62zNzc2YmJjAm970JvzVX/2VXN3wH//xHxgfH5e/82qFw2E0NDRgtnR0cKY53ah9zpbMiWH3CeClgz2oLY6gLP/81wudLxKN45m9vfK4fg4u+Lp1ltbD1nMcozsfR/fM36vqgr35ZTiCPkTdReiI5Yh/WE3awWtjekq187ahxokXj03IVbQ5phGZ60fanLML9QVTBfuy+nrd0+yfM1P+YmRjL3wntqM/pxrRbE6IXZZYBDmHn5PbYUcKFqP/Aq/bJQscONrhw29eaIIhOKxJM9PNC0fG4QtEUJxjRq55FA0NY5q0g9fG9JRq580+fy0cLdsx8tIvMBHJBqYxgatXHSnWl9V0gFZs7xKZWhfz8ssvIxQKnfd50aF1OBIZOxcjVhOcPn0a7373u8/72ne/+135HEpOl6h66/F48M1vfhMf//jHr3rlgcViQXV1NWaaGOUXf0jl5eVv+O9AqWGmzlldHXCq7xhePdaHlxtCePC+5Wm90mi27DjSC2/gDPKybLjzplUwmc5/zXvcJnh+fRzW/lOYO7cIpuxCTdqaLkSsweDzByA2gxdc/x7Mra6f9Tbw2pieUvW8LayIYH/rDgxPhjEUzMV1q+do3aSMPGciazWVsS+rr9c9aXHO6jA21oxA4y4UdL6K/Hd8jn3Zy+A/tRPjIR+MrjxUXXuHLHT7enc7BnH0+4dx8rQfRfeUozCXr7VLEbEGe3+dKFj3/tuWYEn97K865rUxPaXqeYtVlmOw+yBMvhEsxDCcdddo3aSU4U/RvqymA7SiE1hVVXXRr4vtW2NjY7Jje/aI88DAwFSRhIt59tlnUV9ff8HnF8/1+hHsRYsWyS1dYuWB2CZ2NURnQmw3my3iD2k2fx+l5jm7723Lse/UIE51juFg8xi2rZyr6vPr0XP7e+TjrZsrkJXlvvA3zV+E0fyFsIx0InT8BRTc9MHZbWSaGd71COLhAKyllchbtk3TmyteG9NTqp030ZR7bliEH/7hBH7zcjtu3ljJQiEanLNUH6hhX1Zfr3vS5pxZb/kguloPIHymEfHOw3DVb1H1+fVo9NiL8jFnzS1wuS9cHHBxeREqSmxo7w/i2f29uO+OpbPcyvTyi+fbEQhFUTUvB9euWci+LKX/eXM6Ednydow8/2P49vwWBatvZAHnFO/LpvSdxpo1a+T2LqXAgtDe3i7zvNatW3fJnxWFFzZt2nTB/IebbroJ3/jGN875vKiQK7aPXW2HlkgrRXkO3HNDjTwWAwmBUETrJqU0UVCtoSOxXflNyQIKFxOoSBRkmTj0HGJB5hxcTGRyFBP7E1WE8699d8oPqBBdrtu2lMuV9gMjPjy7t1Pr5lAaYl+W6I2ZswuRu/kueSwGEmLh2c2wTzeioFqw+xRgNCFrZaKw7cVsWpxYiPD07k74AuFZamH6GZkI4IkdbfL4fW+uY1+WdCN7zZthcuUiMj6AycPPa90cSucBWrGy4Pbbb8cDDzyAPXv24OjRo/jrv/5rrF+/HitXrpTfI1YkDA4OnrN9LBqNoqmpCYsXLz7vOcXF9uabb8b3v/99uSVNbB375S9/ie9973uy0AJROrrruio5UDs46sdjL7Vq3ZyUJgr/CJuWlSE/237J740UVsGUPwfxkB8Th5+bpRamn7Gdv0E8EoJtXi0cVau0bg6RauxWM9550yJ5/MtnmxAMR7VuEqUZ9mWJLk/OxrfJgdrIxBDGkwVH6cImDiQmxV21G2DOuvSETPUcO+YWueAPRvDMHk40XswjzzUhFImhrjwfaxazoBrph9FiQ+6Wu+Xx6I7fcAIsxaX0AK3wxS9+Ua4e+NjHPob77rsPlZWVeOihh6a+fujQIWzdulU+KsRWMlHkIDc394LP+elPfxof+chH8NWvfhW33XYbfvjDH+Jzn/sc3vnOd87K/xPRTAwifOgtS+Txr19olgO1dD6vP4yXDnbL49u2VLzxDxgMcK2+VR5O7H0C8RgHZ14vPD6AiYPPymOuniU9EivtRW6fWF3z1M7UKv5A6YF9WaLLG0TIv/ED8nhs52NyoJbOFwt44Tm+fWpl3BsxGgy4ffMCefy7V9oQjcZmvI3pRuyS+ePuxPv7+25dzL4s6U72qptlPZWoZwQTB5/RujmUqhm0l0PkQfzzP/+z/LiQDRs2yHyvsxUUFJz3ubOZzWZZ8VZ8EOnF1hVz8MSrBTjRNowfPXECn3nfWq2blHJe2N+FYCiKBaVZWFpZcFk/46jbDO+uX8sbBW/DLriXbJ3xdqaTsR2/kZWE7eXL4ChfpnVziFRnMZtw7821+MYjh/HrF5rkgK3DlvLdJ0oh7MsSXR5X3WbY9z+FQFcDhl/4CUru/JTWTUo5k8deRjwchKVoPuwLLq8g67YVZfjV821yAcerR3twzap5M97OdPLL55oQicaxvLoQy6uLtG4OkeoMZgvytr4DQ09+E2M7H0X2qptgtKZOMTNKoxW0RHR5xGzvR9+2VCz6xPZDZ+RALZ2b2ffkzkS8wW2bKy57dtxgtk6tUBjf8zv5PJQQHunF5JEX5HH+tfdq3RyiGXPjuvkoK3Bh3BPCH5IZdUREpC7RNyu45cPiCN4TO+RALb1G9EGVeIPs1W++7L6s1WKa2jn22Mut7MuepWfIg+f2nZ7KniXSq6zl18GcV4qYbwLj+57Sujl0ERygJdKRqnm5uGVDovDVd397DLEYO2CK5/d1oXvAA4fNhOvXXNnKgezVb5IDtcHeVgS6Ts5YG9PN6Cu/AuIxOKpWwz7v/JxEIr0wm4x495tq5fFvXmyBx89CK0REM8FWWomslTfK46FnfoB4nFvyFZ6jLyI8fAYGqx1Zy665op+9bXM5rGYjWrrGuIjjLD9/plHeL62tK0FdRb7WzSGaMQaTGXnbEjFI47sfRzTg1bpJdAEcoCXSGTH767Sb0do9jueTM8KZbnjcj+/97rg8fudNtXDaLVf08yZXDtzLrpPH43t+PyNtTDehwS54jr8ij7l6ljKB2BI6vyRLZln/9mUWYyQimin5170HBpsTob42TB55UevmpITI5AiGn/uRPM7bcg+MNucV/XyO24Yb1iWyaB/ne5h0um8CLydrU7z3TVxoQPonovoshfNkljXvaVMTB2iJdCY3y4Z335JY6fXjJxvgC2T2Si+xjet/fn1EDqrUzM/FXddWXdXz5Ky/XT76mvYjPNKDTDe6/ZfiXxfO2g2wlV3dvylROjEZDXjvmxM3cL/d3oJxD6vgEhHNBDExnrftHfJ49KWfIRb0IdP7skNPfksOqtjKqpGz8Y6rep47tlXKx70n+9Az6EGm+7+nGyHSHjYtK0P1/AsXZCTSE4PRhLzkwprxvb9H1DehdZPodThAS6RDt2+pxNwiF8Y8Qfzy2SZkspcOdmPfyX6YTQZ84l2rYDJd3WXPWjgPzuo1clByfM8fkMmCfW3wntolM+Lyr+HqWcocm5aWoXJuDvzBKB59sUXr5hAR6VbO2lthyZ+DqHccozt+jUzmOb4dvpYDgNGMorf8lRxkuRpiF8i6+hI5KPn49sxeRdvaPSYLpokYX66epUziqt0Aa0kF4qEAxnY9rnVz6HU4QEukQxazER952zJ5/LtXWjN2lnx0IoDvPHZMHt97Sy0WlmVP6/mUFQuTR19E1DeJTDX68i+mtslYixPb5YgygdFowPuSq2j/8Go7RiYCWjeJiEiXDCYLCm7+oDwe3/tExu5einhGMfzMD+SxWFU83X7XXddWT9VmmPCGkKl+9vQp+XjNynnTvj8gSicGgxH5175bHk/sfwqRyVGtm0Rn4QAtkU6JsPs1i4sRicbx/d+dQCZuB/vmo0dlMR+x4u3u62um/Zz2BUtgLa1EPBLCxMGnkYkCZ5oSqzgMRuQmg+aJMu3aWrswD6FwFI88n9k7FIiIZpLYueSoWgXEIhh+7mFkZLTBH7+LWMAjV7zlbrpz2s+5tKoAVfNy5HvYUzvbkYlOdY7I3XVi0lUpAEqUSRzVq2Gbu0je047tfFTr5tBZOEBLpGP33bFU5iaKrKmDpwaQSXYc7sGuY73y//+T966SVdiny2AwIHfDHVMzjrFI5q08GH355/Ixa/l1sBbM0bo5RLNOXAfe/+Y6efzHXZ0YGM3sbEQioplUcNMHAaMJvub98LUeQibxNuyEr3GP/P8veuvHZBV2Nd7D7kyuohU7QcRAbab52VOJ1bM3rp2PuUVurZtDNOvEdWBqFe2hZxAZH9S6SZTEAVoiHRNZU2/ZmigI8L3fHUMkGkMmGJsMytWzwrtuWoSKOTmqPberbhNMWQWIesfgOf4KMom/8zj87UdlBlru1kTxDqJMtLymEMuqCuU19VfPZdYqWlF48tcvtqJnJPMmqIho9okaACKPVhh+7keIRyNaN2lWiOxdsXpWyNtyD2wl5ao999YVc1CYY5f95ZcPdiOTHGsZwuHmQVmb4l03c/UsZS57+TLYFy4BopGMy/mOBf3w7H4MpvFepBoO0BLpnMhezXZZ0dXvwZMZspXpW48dxaQvhPKybNxz4yJVn1usXshZf/tUZeHIxDAyZZudkj2bveomWHKLtW4SkaYrD953ayKL9tm9p9Ez5MmY68DXfnEIj7zQhp0NmZvDTUSzS0QqGZ3ZCA91Z0zE1NDT30XMPwlr8ULkbrlL1ecWu8reuq1KHv/4yQYMj/uRKe9hP/1jgzy+ZcNClOQ7tW4SkcaraN8jjyePvIDwSOoNVs7UdWDg91+HZ9ejsHfsQarhAC2RzrkdFrz/1sR23P97uhHjniD07NUjPfJD5Ep94t5VsmCa2rLXvFl2mMXqhv7HvoJ4NAy987cdRqCrAQazFblb7ta6OUSaq68okDnfsVgcv3imEZngsZdaEtExJgM21nJbKBHNDpPdNbUdd3T7LxH1TUDPPA274G3YJfP+i94iog0sqv+O27dWyIUMY54g/v3H+xGO6H+X3aHGQZxsH4HVbMQ7b1J3AQdROrLPX5zI+Y7HMLrjEWSC8d2/nYqOCSxch1TDAVqiDHDzhoWomJMNrz88VbVUj8Tg8zcfPSKP77mhBtXzcmfk9xgtNpTc8/9gtDkR7G7E8PM/hv5XzyayZ7PXvAnmrHytm0SUEt6XzKJ96WA3TvdN6H5b6MNPnJTHH7ytFvMKbVo3iYgySNbKG2EtLkcs4J3a0aNHYvB56I/fkce5m++CrSwRVaY2m8WEv//gerjsZjR0jOCHfzih+77sT5KrZ2/bUoGCHIfWTSJKCcrkl+fYdoQGu6D3uL6RF38mj7Ovex+iuXORajhAS5QBRKGsj965TB4/vasD7T3j0KPvPH4M454QFpRm4d6bZ3Zm3JJXiqI77pfHE/uehOeEfvNofU37EOxthcFiR+4mdbfZEaWz6vm52LSsDPF4YoeCXontr1/+yX7E4sD1a+bh5nXztG4SEWUYg9GEgls+LI8nDj2LYH8H9Gjome8j5puApWg+8mY477+s0IW/fs8aefz7V9p0nUe750QfWrrGYLeacPf1NVo3hyhl2Mqq4KzdIKYx5A4FvYpMDGPgsa/K1cLuZdfCsfxGpCIO0BJlCFHQZsuKOfIG+3u/PS5nkvVk9/FebD90BkYD8Il3iWgD04z/TteidVPb/Qef+CZCA6ehN3Gx5WV7YvVszrrbYHKpV3CNSA/e86bFMBiAV4/2oLV7DHojtr2K7a9iG6zYDvuX96yQuWVERLPNsXCJLNYqbrCHn/2h7vqy3sa98J7Y8Vq0gVn9aIPXW7+kdGq7/9cfOYzOXv3tBhFRRD/7Y2IH4Vu3VSI3iztAiM6Wf827xDQYvKd2IdjXBr2JR8MyllDEE4qYwsJb/yxl+7IcoCXKIB96yxKZu3S0ZUjmCOqFKAj2v79ORBvcdV01Fi3Im7XfnXfNu+CoWIF4OIj+3/yH3HqnJyIDTQw8iziHnI1v07o5RClHDFpuW5nYIqXHCBmx7VVsfxXbYP/ug+tgt5q1bhIRZbD8Gz4g8/ADnccTOYI6EfVPYuipb8vjnI13wD6nelYnGlcuKkIwFMW/PrxXRqLpiahN0dE7Id/H3n7d7P27EqULMWjpWrJFHusxQmb4+R/LWEJxP1ty92dkXGGq4gAtUQYR1Urvuj7RMfn+708gFI5CD777+DGMTgYxr9gtO5mzveWu+M5PwpxdiPBIDwb+8D+6WdERj0Uxuj3xJp2z4Q6YHCwKRHQh4rojChPuO9mPU50j0Aux3VVsexU+9e7VmFPIawARacuSWywHMIXh5x5GLBKCHogVwVHvGCwFc+Xk/2xHof3Ne9egKM+BM4Ne/PcvD+mmLxuNxqYmT++8rhpup1XrJhGlpLxt75Kr930tBxA40wS98Jx4RcYRCiKe0JJfhlTGAVqiDHPP9TUoyLFjYMSHx19uRbrbd7IPLx7oTkQb3LsKVsvMRxu8nsmZjeK7PwOYzHI1h6gOqQee49sRHu6B0ZGFnPW3a90copQ1t8iNG9fOl8c/e0ofq2g7+ybkdlfhHTfWYMPS1O7QElHmEHn4pqx8RMYHML7n90h3vuYD8Bx7ORFt8NaPwWie/UHEHLcNn/3AOphNRrnL7tEXW6AHoojnmUEPspxW3LFtZgquEemBtWAOspZfJ4+V4tDpLjR4WsYQCrmb3y7jCVMdB2iJMozdZsYH37JEHj/yfJMs/pKuPP4wvvFIItrgjmuqsHhhvmZtEVvRCm+5Tx6L6pD+jmNI96ye0Vd+JY9zN90pt4QQ0cW96+ZamE0GHG4exLGWIaQzXyCMf/3RXrnddUVNId775jqtm0RENMVotaPghg/I47FXH5XFX9JVNODF4JPfksc5698C+9yZLXJ7KSIi7E/vShQV/vGTJ3GkeRDpnqH+82cSBTzvuaEaTvvMZ/oSpbNcUZjQaIa//Sj8nceRzmJBH/p//R8yhtBRvgx5196LdMABWqIMdO2quagrz0cgFMXDT5xEuvrB745jZCKAOYUuvO9W7QcQslbdDPfy62Xxiv7HvprWNwyTR15EZGwAJlcustfeqnVziNIiQuaWDQvl8U+eakjb7aGi3V/7xSG5zbUwx47PvG+t3P5KRJRKXEu2wjavFvFwACMv/QzpauS5HyHqGZHbblNhAOHNGxfixnXzZVHh//jpfgyNpe9Cjuf2nUb/iE8WBbttS4XWzSFKiwiZ7FU3yeORl36e1n3Zgd9/Q8YPmrIKUHznp2QsYTrgAC1RBhJVCz9651J5LOIB0jEz8cCpfjy797Ssnn7/u1bBpkG0wYX+XQvf/FFYSyoQ802g/9H/lCtR043/9EkMv/ATeZy75e6UDlInSiWiErYoxCiKaomK0enYsX3spVa5vVWsBv7sn6yT216JiFKN7HPd/GF5LOIB0jEz0dd6CJNHXpDV04ve8lcp0d8S/65/cfcKVM7JwbgnhH/78T65EjXdnGgbxo/+cEIev/PGRSxwSXSZxL2fKMQY7D4lC4alY192fM/vEkUkjWaU3P03MLlykC44QEuUoWrm5+GmdQumimzFxFR5Gm2//cavEtmIb91aiSWVBUgVonMt3giMdheCZ5ow/OyPkG45aH0//yLiQR/sC5Yge9XNWjeJKG0U5Djwgdvr5fEvn2vCtx9Lr2vrsdYhPPxkYlfFR+9chloNY2OIiN6IbU413MtvkMfDz/wA8XgsrbbfKtmI2etug32+9jvBFGLRw999cB1cDgsaO0fx/d8dT7v6FJ//9k74AhEsrSrAmzcldrcQ0RszZ+Uj//r3yuOxV3+N4ae/l1bXVn/nCYy88FN5XHjLhzSNjbkaHKAlymAfuK0ODpsJTafH8NLBLqSLH/z+BIbGAygtcOL9KRBt8HqWvFIU3/EJeTxx4I+YFIUf0qTKZd+v/x3xSAjOmrUovfdzMJiZ10V0Jd52TRX+4u7lcnX/E6+246v/dxCRaOp3bEUe+Zd/sl8OKF+/Zh5u3VSudZOIiN5Q/vXvgcFqR7CnGZ5j25Euhp//MaKTwzDnliD/uvcg1ZQWuPDp96yWx+K97MUD6XGf8PLBbvzLD/ciFIlhfX0pHvzoJljM2u+yI0onIg9b7AoVq/vFvezgbx9CPBpBqotMjmDgsa/KuEH3smuRtfpNSDccoCXKYHnZdrzrplp5LLJoxcrUVHe4aQBP7+6Ux/e/c5UsepaKnDVrkLv1Hnk89OS3EOzvQCoTb74Dj/83EIvCvfQalNz9mZTYakeUjm7bXIFPv2eNzG59+VDiZjEQSt2OrRhA/vcf78fYZBDlZdn4y3tWyG2uRESpzuzOQ54obCOLtP4UsWDqZ6b62o9g8tCz8rjoLX8pi56lonX1pXjXzYnVZ6Iob3vPOFLZkzvb8ZX/O4BoLI7r1syTq4BTIQKNKB1lr3kziu/8BGA0yUU8/b/+MmLhIFJVPBpB/6NfQdQ7BmvxQhTe+mdp2ZflAC1RhrvjmkqUFbgwMhHEr19oRioTA8hfT0Yb3L6lAsuqC5HK8ra9E47KlXJFav9v/kNW6k01IldodMevMfTH74r/kgXBiu74OAym1Bz4JkoX166ehwc+vAFWiwn7G/rx4Hd3w+tPzUmwH/7+hMzNddnN8oaWWX1ElE5y1t0Oc14pop5RjO18FKlMDCAPKdEGa94Mx8JETYhU9e5bFmN1bTFC4Sj+9Uf74EnB9zHRl/3lc4345m+OQsRlvmVLBT5172qYTRzqIJoO95JtKH3H38pMWl9LIgYvloL3s8quBJGba7Q503qhEa9aRBlObPu5744lU8VhWrrHkKrEKt+BUT+K8534k2TOYyoT1SKL3/ZJmHOKEBntw+Dvvp5SGT6iQzvy/MMYffnn8r9zt74DBbfcB4OBbw1EalhbV4J/+tNNcuBTFCz5+/99FaOTAaSS7Ye68btX2uTxp969GnMK3Vo3iYjoiog4poKbPiiPx/b8DsHexDUtFYlVvpHxQZhzipF/w/uQ6sROkE+/dw2K8xzoHfbiaz8/mFLZ6qIvK6LPfvrUKfnf995ciz+9axmMxvRbOUeUipzVa1D2ns/Lgc9AVwN6fvqPiHhSa7zAc2IHJvY9IY+L7rgflvwypCvehRMR1i8pxapFRXKb62ce2o5Hnm+S24NSybGWITy5MxETcP87VsKRotEGr2dyZqHk7X8Dg8kCX/M+jO18HKkgHoti8A//i/E9v5f/XXDzh5B/7b1puRWEKJWJIob/+ldbkeu2oa1nHJ/9xg4MjPiQCk73TUztSnjHjTXYsDR9O7RElNlEdr6jcgUQjeDMj/5OrqQVfZ1U4u88LiOlhKLb/wJGqwPpINtlxWf/ZB0sZiP2nOjDb15MjR130WgMD/3yMB5/uVX+90fethTvffNi9mWJVCaKGJa9/4swuXIQ6m9H708eQHh8AKkgNNg1VXAxd/Pb4Vq0DumMA7REJDsyYnZ8w5JSRKJx/PjJBvzd/+xAz5AHqSAQjOChXx2Sx2/auBArFhUh3aoMF7zpI/JYrFYV2WNaikfCMqPHc/QFscwXRW/9mAyDJ6KZUTEnB//+sa1yBVLPkBf/7xuvyMFRLYnImC/9aB8CoShW1BTivW9OvYKLRERX0pcVu5ac4uY8FsHIiz9Dz0/+AeGRXqSCWCggJ8aFrFU3w1GxHOmkZn4e/uyuRJt/+lSDrAmhpXAkin//yX48t++0XC37yXtXySKdRDQzbCXlmPOBf5Y7Q8V1tefhz8nBUS3Fgj70/+bLiIcDcJQvQ9619yLdcYCWiKQctw2f+9B6fOJdq+C0m2Ue4f1feUlWbhXbh7T046ca0DfsQ2GuAx9+ayKOId1kr7oJWStukFUlBx7/mtzepoVYyI++X/4LfI17AJMZJXf/DbKWX69JW4gyyZwiN7788W2YX5KF4fEAPvs/r6Lp9KgmbRHX9P/+5SGcGfSgMMeOz7xvrdzGSkSUzkzObJTc87coestfwWBzItjdiO7vfRrj+/+oeV925KWfITLWD1N2IQpu/ADSkVgkcfP6BRCb7P7jpwcwMKrNbhB/MIIvfG83dh3rlTmzn/3AOty4boEmbSHKJJb8OZjzgX+BpXAeopMjchIs0NOiSVvi8TgG//A/CA/3wJRVgOI7PyXjBdMdB2iJ6JzVBzetX4Cv/831WF5diGAoim89ehT/+J1dGBrTpiquyG38w45EltjH37ESTrsF6UqsorWWViLmm0D/b/5TrmSdTVH/JHp/9gX4O47BYLWj7N4H4KrdMKttIMpkBTkO/NtfbcWiBbmY9IXwwLdexdGW2Z+sEdtBdx4VN7YGuW1VTNAREemlLysmxOd/9Kuwly9DPBzE8NPfRd8vvojIxLAmbRK5jRP7npLHRbf9ucxyTFd/9vblqJqXgwlvCP/28D65knU2iffOf/jWThxpHoLDZsKDH92ITcsYz0M0W8zZBZjz/n+GbU4NYvLe8h/lveVsG9/ze3hP7QaMiQVHIn5BDzhAS0TnKc5z4ot/thkfvXMprGYjDjUN4mP/+SJeOtA1aysQRMdPZOH+68N7ZUVWMWO/enEx0pmoJineQIx2N4K9LRh69gez9rsjyVnOYE8zjA43yt7zoNwKQkSzn+Unrq8iVsAfjOLB7yZWAc2W461D+NETJ+XxR962DLUL82ftdxMRzRaxDVcUtim45cOyArm/7Qi6v/spTB7fPmt92ahvEqOvPoq+X39ZrPeSA8fOqlVIZzaLSa5YdTssaO4aw3cfPz5rv3t43I/P/s8ONJ4eRZbTgn/+8y1YUZNesWdEeiBqrJS95x/lvWQ8FEDfL/4FXrE7c5b4T5/AyAs/maqjYp+7CHrBAVoiuiCR53THtip87a+vQ838XHj9YXzl/w7i33+8H+Oe4Iz93q7+SXzjkcP40BefkVm4454Q5hW78eE7lkIPLLklKH7bJ8QaD0wefAaTR1+c8d8ZHu1Dz48/h/BgF0zufDnraZ9bM+O/l4guTOwE+MePJFb9hCMx/NvDe/Hc3tOzcnMrMvtEBe7r1szDbZvLZ/x3EhFpxWAwImfd7Zh733/AVlaNWMCLwd/+NwYe/QqivpnLAQ8NdWPwyW/h9Nf/FKMv/UzunLIUzEX+TR+EHpQWuGTtClGL66ldHXh+38y/f/UOefG339iB032TyM+2J3ej5M347yWiCzPaHCh91+fgrN2AeDQsd4dOHnlhVhYdDTz6VRkb6F56DbLXvAl6wgFaIrokkZf4Hx/fJquiiozCV4/2yNW0e0/0qfY7xEqGg6cGZJTCX375BTy9uxOhcBSVc3PwqXevxkOfvk7O1OuFs3o18ra9Ux4PPfUdBPvaZ+x3hQY6ZYh7ZGwA5rxSzPmTf4G1aP6M/T4iujwWswl/+/61uGldIs9PZMIqlahnQiQakxNsY5NBlJdl46/uWcFK10SUEayF8zDng19C3jX3AkYTvKd2ofs7n4K3aZ+qfVlf6yH0/vyL6P72JzB56FnEIyFYSypQdMfHMe8jX4HJ7oJerK0rwbtvrpXH//vrI2g7Mz5jv6ujdwJ/+41X0D/iQ1mBS+a5LyjNnrHfR0SXx2C2oOTtn4Z7eaLOisiEHdvz+xn7ffFoRBa6jnrHYC1egMLb/lx3fVmz1g0gotRnMhlx7821sjP21f87KFe5fvEHe2TswEfetvSqc2EDoQhePNCN37/Siq5+j/ycuMZuXFqGO7ZVYkllge4uuorcbfcg0NMEf+shWX1SBJtbixfKGAS1BLobZUEwsWJEPHfpu/8BZjdXGxCl0rX1/nethNtpkYOz3//dcXh8ITkhpta1LxqNoXvQg99tb5PFH0URyL/7k3WwW9kFJKLMIYrH5G17B5zVazDwu/9GeKgb/Y/8m4wdEFtkrzYXNhYOwnPsZYzve0I+Z/K3wVm7Hjnrb4d9fr1u+7LvurlWxg0cODWAL/1oL/7mfWvkBKCa7y+nOkbw4Pd2y5184rn/6U83IS/brtrzE9H0r61Fb/lLmBxujO/5HUae+xFifg/yrr1XtWtfPBZFePgMxvc+gWD3KVkEsuTuz6h635wq0q53/vnPfx6hUAj/9m//dsnv6+7uxhe/+EXs27cPTqcT99xzDz7+8Y/DZHqtstvPfvYz/OAHP8Dg4CCWLl2KBx54APX19bPwf0GUnqrn5eJrn7oWP3mqAb/d3opn957GkeZBfPLe1VhWXXhF22yfeLUdf9zVgUlfolCWw2bGzRsW4K1bK+XWqUzYdieiDs58///J1a09P/o78Q4HS34ZrCXlsJVUyEex8sLszr3i5/e1HUb/r78si2PY5tWi9J1/L984iSi1iM7rh9+6RGbTiliXXz7XJIug/Nldy2XUzJVOeomVRu1nxtF6ZlyuaOrsnUAoEpv6HrErYU4RrwVaYl+WSDu2skoZeTD60s9lkRmxJVcUuCl668fgWHj5cVqi4NjEgT9i4tAzcjBCMFgdyFp5I3LW3gpLXin0TrxHiaiDT/7Xy3J162ceegXibUu8x1TOyUHF3JzkYzbysq58UPVgY2LgVxQtrivPx+fv2wC30zoj/y9ENL2+bP6NH4DRkSVjXcZe/TViAQ8K3nSfvOe90kmv0EAnQn3tCPa3y8fQ4Gm5I0FR/NaPw5I/B3qUNgO0sVgMX/va1/DLX/4Sd9111yW/NxwO47777kN5eTl+8Ytf4PTp0/jc5z4Ho9GI+++/X37PY489hi9/+cuy4ys6st/5znfwoQ99CE899RTy81kwg+hirBYT7rtjKTYsKcV//eIQBkZ8+Ptvvoq3XVOF999WJ4sHXEzT6VG5imvHkTOIij29AErynXjrtkq5GvdqV+KmK5MjC6X3fg4jz/9YFg2Lesfl7KD48J589bXvc+XKgVpb6WuDtpb80ou+4XkadmHg8a8BsQgclSsTM4xWrjYgSuWO7TtuXCSjXL756FE8ubMDHn9YDqaaTRd+nYss8PaexCCsGIwVx2cGPDIu4fVEpevyshy8aeNCuUOBtMG+LFFqMJqtKLjpT+BctBaDv/sGIuMD6P3pPyJn/VuQd917LrkqK9DTgom9f4CnYScQi8rPmXOLZdatWI17tStx01WW04oHP7IRP/zDCTSfHsOYJ4juAY/82H74zNT35WXZ5IBt1dwcVMzJkTFmIq7gYhORrx7pwX/+bD8i0ThW1xYndn7Y0mbogigj+7J5W94uo1yG/vhdOYEVDXjkYKrBdOHXrsgCVwZh5WN/B8LDPTIu4bznt9phLS5H9qqb4KpdD71Ki6tca2ur7JR2dnZizpw3Hil/+umn0dPTg1/96lfIycnBokWLMDw8LDuxf/7nfw6r1YpvfetbeN/73oc77rhD/syXvvQl3HTTTXjkkUfwZ3/2Z7Pwf0WU3pZWFeLrn74OP/j9CZkZK1bUHmzslwMKNfPzztleu/t4n/y62F6rEPEFb7umEuuXlMls20zORSt919/L44hnVL4xhfrFm1TiMTzcK3N2/G2H5IfCYBFvUgthkwO2iUFbkcXjOfEKhp78tnxjc9Vtkqt0DabMGvgmSle3bq6A22HFV/7vALYfOgNfICJzase9IbSdGUsMxJ6ZkMdD44ELPkdulk3e+Co3weJR7Eq40tW4pC72ZYlSj2PBEsz76Fcx/NyPMHn4OYzv/YPcgSQGFGxzqs/ZXutt3Cu/LrbXKuwL6pGz7i1yoFds883kehWfv2+jPB6dCKAtOXnY3iPer8bRM+TB6GQQo6cGZM0Jhd0qJg+zp1baiveuBaVZ8v3vfx45LCcct6yYg0+/Zw0sZpbOIUoHomiX0eHGwG//G94TO9AX8MmcWjEYmxiIbZP3u6IGS3Ry+ILPYXrd4iTxKGqpXOlq3HSUFgO0u3fvRlVVFf7nf/4Hn/zkJ9/w+/fv348lS5bIDq1i48aN8Hg8aGhowLx589DR0YFNmzZNfd1sNmPt2rVyGxk7tUSXR6x4/dg7VsrVtF//1WGZI/s3D72Cd920CLdvqcAL+7vwhx1tGBj1y+83mwzYtnIu7thWher5V75tX+9EPqz4cFatmvpcLBSQ2zoSb2iJQVux7SMeDiB4plF+TBFvWskZx6yVN6Hw1j/N6BsGonS0bdVcOOxm/OvD+7C/oR/v/ocn5QqiCykrdMkbWuXGVgzGMpsvNbEvS5S6lciLbv8LuBatx+AT/ytzZM/86O+Qu/UeGVUwefQlTOx/EpHxweQPmOFeskWumLWVVWnd/JQj3oPWiI/FJVOfCwQj6OhLxO+0yUHbMXT0TCAQiuJU56j8UIi5RGUniNj18Rd3r8johRxE6chdvwVGqwP9v/kP+FsPouMrfyJ3dl6IGHhNDMRWJCL+xGBsBtdMSYsB2ve+971X9P19fX0oLT0396e4uFg+9vb2yg6sUFZWdt73nDr12qzoVVXv9Pkw0/x+/zmPlPr0fs6WlGfjPz62Ed/73SnsPtGPnz/TiF8824h4soOV5bTg5nXzcMuG+XKLkzAbrxXdnLO8eTCLj7ptcCVXckRH+xAePI3IYGficaADMf+k/HbX2tvh3Pou+ANBZJKUOmd02Xjezle/MAuf+5PV+PJPD8EbiMBkMmB+sRvlZVnyo6IsCwtKsmTBr3PFdNcPEX0rPRTYYV/2XHzdpx/dn7O5dSh4/5cw8fyPEGjei7FXfoWxVx4Rrwr5ZYMjC87lN8C5/CaY3LkQ4Qbsy16+BUV2+XHtypKpHXa9wz509k2io9eDDvk4gQlvojbFHVvL8Z5bqhEMaN/2TD1ndPl43i5gzmLkvf1vMfrbryAe9AFGE8yF82ApWghz0UJYihfCXLhATpKdLSQ+Mrgvq/kArSiAcOONN17067t27briHK1AIIDs7OxzPmezJQaFgsHg1EkQ28Ne/z3i61dL5IWJVQ2zRaycoPSi93P2puVmzM3JxxP7RxEIxVGUY8amxVlYttAJizmMvu429CG9pPY5ywUKxccKYHEchqAXhogfo+4iYBo36Okutc8ZXQzP2/n+4tYieAJRFGZb5A6EBC/iPi862/sy5py9vr+WatiXvXp83acf3Z+zyhthcZTB2fA0jOEAou5CBMrXI1S2BCMiMqqrV0yTIJ2k8jnLtwD5C4DVCxyIx+3wBGLwh2IozolMa7Ip3aXyOaOL43k7n2HzR2AM+RB1FchB2ikTMWBC+3+vjhTry2o+QFtSUoInn3zyol8/e2vX5bLb7bI67tmUzqqogiu+LlzoexyOc0fwr4TFYkF19Wt5RTNFdMrFH5IoHDGd9tLsyaRzJopHv/makIw1qJqbnbYrnzLpnOkFz1l64nlLP7N5zlpaWpDq2Je9cnzdp5+MOmf19YhtugWRiUFYSirZl6VZw3OWnnje0o8/Rfuymg/Qio6gyORSk9gS1tTUdM7nBgYGpjrRynYw8bmzf7f4b/H1qyXevEWnebaIP6TZ/H00fZlyzsT/Y2mRPjJmM+Wc6QnPWXrieUs/s3HO0mFghH3Zq8fXffrJmHMm/h8Lz40ZSVcZc850hOcsPfG8pR9HivVldVkGbd26dTh58qQspHB2cQaXy4XFixejoKAAFRUV2LNnz9TXI5GILMggfpaIiIiISCvsyxIRERFlFl0M0IrtXYODg1PbvG666SYUFRXJKrkiu+a5557DV7/6VXz4wx+eyn4Qxz/84Q/x2GOPySXHf//3fy/zvu655x6N/2+IiIiIKJOwL0tERESU2XQxQHvo0CFs3bpVPioFEr73ve8hFovhne98J77whS/gPe95D/7yL/9y6mfE5++//3587Wtfw913340zZ87ITu6VFnEgIiIiIpoO9mWJiIiIMpvmGbRX6ic/+cl5n9uwYQMaGxvP+dzChQvxgx/84JLPdd9998kPIiIiIqLZwL4sEREREelyBS0RERERERERERFROuIALREREREREREREZFGOEBLREREREREREREpBEO0BIRERERERERERFphAO0RERERERERERERBrhAC0RERERERERERGRRjhAS0RERERERERERKQRDtASERERERERERERaYQDtEREREREREREREQa4QAtERERERERERERkUYM8Xg8rtUv15ODBw9C/FNardYZ/13i94TDYVgsFhgMhhn/fTR9PGfph+cs/fCcpSeet/Qzm+csFArJ37F69eoZ/T3EvixdGs9Z+uE5Sz88Z+mJ5y39xFO0L2ue0ZZkkNl8IYrfNRudZ1IPz1n64TlLPzxn6YnnLf3M5jkTv4s3O7ODfVm6FJ6z9MNzln54ztITz1v6MaRoX5YraImIiIiIiIiIiIg0wgxaIiIiIiIiIiIiIo1wgJaIiIiIiIiIiIhIIxygJSIiIiIiIiIiItIIB2iJiIiIiIiIiIiINMIBWiIiIiIiIiIiIiKNcICWiIiIiIiIiIiISCMcoCUiIiIiIiIiIiLSCAdoiYiIiIiIiIiIiDTCAVoiIiIiIiIiIiIijXCAloiIiIiIiIiIiEgjHKAlIiIiIiIiIiIi0ggHaNNILBbDQw89hG3btmHlypX46Ec/iq6uLq2bRZcwNjaGz3/+87jmmmuwevVqvPvd78b+/fu1bhZdpvb2dqxatQqPPvqo1k2hy/D444/jtttuw7Jly3D77bfjqaee+v/t3WmIllUbB/BLzbKsoCQzoqyMFgMLyjILaoz6ZFFJ9CGNosA0jGizxWgZEsGoCaup0IrIyZIiC4n2vkgbtAi2SCsUZRSlbabWvJwDM69jMzbvh7dzn5nfDwbHez54wXg/z/+57nOuEzTXli1b4q677oqWlpZ8n5133nnx3nvvlS6LPtx///0xY8aMHtc+/PDDmD59es4kU6ZMiUceeaRYfdRBlq2PLFs3WbYusmxdZNm63F9BltWgrci9994bHR0d0draGsuWLcsh9+KLL45NmzaVLo0+XHHFFfHuu+/GHXfcEU8++WQcfvjhcdFFF8Vnn31WujT+webNm+Oqq66K3377rXQp9MOKFSvihhtuyMFo5cqVMXXq1O77j2Zqb2+P5cuX5/e09IHkwAMPzO9p3333XenS2MbSpUujra2tx7Uff/wxLrzwwth///3z+9ull14at99+e/4e+iLL1keWrZcsWxdZtj6ybD2WVpJlNWgrkYLrgw8+GJdddlmcfPLJcdhhh8Wdd94Z3377bbzwwguly6MXX375ZaxatSpuvvnmOOaYY/IL9o033hijR4+OZ599tnR5/INFixbFrrvuWroM+qGzszM/vT7//PNzqE1vsrNmzYrJkyfHW2+9Vbo8+vDSSy/lDx8nnnhijB07Nq699tr4+eefrTxokHXr1sUll1ySw+oBBxzQ42dPPPFEDB8+PG699dYYN25cTJs2LS644IJ44IEHitVLs8my9ZFl6ybL1kOWrZMs23zrKsuyGrSV+Oijj+LXX3+N448/vvva7rvvHuPHj4+33367aG30bo899sg3d9qi0mXIkCH5a8OGDUVrY/vSPfX444/HggULSpdCP7fvff3113H66af3uL5kyZKYOXNmsbrYvlGjRsWrr74aX331Vfz555/5nttxxx1z04ZmWLNmTQ6uzzzzTBx55JE9fpa2OB977LGxww47dF+bNGlSfPHFF/H9998XqJamk2XrI8vWS5atiyxbJ1m2+dZUlmU1aCuRVhck++yzT4/r6Ql2189olvSh46STTsov0l2ef/75vBohzV6jmdIHjmuuuSbmzZv3t/uN5obaJG3hS9su04f/c845J1555ZXSpbEdaRtfCkynnHJK/vCfVtKl2ZRp1QjNkGZxpRVY++23399+lrLHmDFj/pZJkm+++eZfq5F6yLL1kWXrJMvWR5atkyzbfFMqy7IatJX4/fff859bB6Rkp512ij/++KNQVfwv3nnnnbjuuuvitNNOy1v7aKa0jS8Ned/2CTbN9csvv+Q/586dm7cZpS20J5xwQsyePTtef/310uXRh08++SR22223uOeee/KKg7PPPjvPykvD+mm+jRs39ppJErmE3siy9ZNl6yDL1keWrZMsW7eNDcyy/13LS6ONGDGie35X1/dd/3F23nnngpXR3/k06cU6nX6b5p/QTGm4e9rqYK5aXdKT6yStODjrrLPy9+kQkw8++CAeeuihHttpaYb0VPrKK6+Mhx9+OM81TNLKgxR001PudJAQzZayyLYHO3WF2V122aVQVTSZLFs3WbYOsmydZNn6yLL1G9HALGsFbSW6tqdseyJg+vvee+9dqCr649FHH405c+ZES0tL3Hfffd1PZWiedGLjDz/8kFeFpJUH6Su56aab8omcNFPXa+AhhxzS4/rBBx+cZ0LRPO+//34+XXrruYZJmg2Vts7SfGlLWG+ZJJFL6I0sWy9Zth6ybJ1k2frIsvUb08AsawVtJdKg6XQK55tvvtk90yTNF0pP1aZPn166PPrQ0dERra2tMWPGjDyjJh2qQHOlFSFpq8PW0ja+dOL0GWecUawutu+II46IkSNH5qDU9QQ7Wbt2rRlQDdU17+njjz+OCRMm9PidbXvCKs00ceLEWLZsWT4UY9iwYfnaG2+8kU95T4dmwLZk2TrJsnWRZesky9ZHlq3fxAZmWQ3aSqTZGCm8pjfdPffcM/bdd99YuHBhfmFIb7o0c9j7/Pnz49RTT82nb259EmBaTp/m1dAsfT0pSy/QVvc0V7qf0qqQNP8p/Z5SSFq5cmWsWrUqbzuiedLv6Oijj86z1tKqnvRelrZlpjlrjz32WOny6Idp06bF4sWLc8Mm3X+rV6/O99stt9xSujQaSpatjyxbH1m2TrJsfWTZ+k1rYJbVoK1IevK5ZcuWfCJnejKaOv5LlizpnllDs6RTbtO2hxdffDF/bS3NFlqwYEGx2mCgSYcopBmG6fTUdevWxbhx4/L8p+OOO650afRi6NCh0d7eHm1tbfnAmfXr1+dtfSkUpa1hNF/6sJ9C7W233Zbf0/baa698anjX7DzojSxbF1kW/j2ybF1k2fqNamCWHdLZ2dlZ7F8HAAAAABjEHBIGAAAAAFCIBi0AAAAAQCEatAAAAAAAhWjQAgAAAAAUokELAAAAAFCIBi0AAAAAQCEatAAAAAAAhWjQAgAAAAAUokELAAAAAFCIBi0AAAAAQCEatAAAAAAAhWjQAgwCL7/8chx66KGxaNGi7muffvppTJgwIa6//vqitQEAwPbIssBAN6Szs7OzdBEA/P9dffXV8dxzz8WKFSti7Nixce6558aGDRvi6aefjpEjR5YuDwAA+iTLAgOZBi3AILF+/fqYOnVqHHTQQTFp0qS8AqGjoyOOOuqo0qUBAMB2ybLAQKZBCzCIvPbaazFz5swYOnRozJ49O+bMmVO6JAAA6BdZFhiozKAFGEQmT54co0ePjr/++itaWlpKlwMAAP0mywIDlQYtwCBy9913x08//ZS3hs2bNy82b95cuiQAAOgXWRYYqDRoAQaJ1atXx+LFi2PWrFmxcOHCWLt2bbS3t5cuCwAA/pEsCwxkZtACDAKbNm2KM888M4YNGxZPPfVUDB8+PObPnx9Lly6N5cuXx/jx40uXCAAAvZJlgYHOClqAQaCtrS0+//zzaG1tzYE2ufzyy/MMr7lz5+bQCwAATSTLAgOdFbQAAAAAAIVYQQsAAAAAUIgGLQAAAABAIRq0AAAAAACFaNACAAAAABSiQQsAAAAAUIgGLQAAAABAIRq0AAAAAACFaNACAAAAABSiQQsAAAAAUIgGLQAAAABAIRq0AAAAAACFaNACAAAAAEQZ/wFXJKqktvIz4QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create ordered data for line plots\n",
    "x = np.linspace(0, 10, 30)\n",
    "y1 = np.sin(x)\n",
    "y2 = np.cos(x)\n",
    "line_df = pd.DataFrame({\n",
    "    'x': np.concatenate([x, x]),\n",
    "    'y': np.concatenate([y1, y2]),\n",
    "    'type': ['sin']*30 + ['cos']*30\n",
    "})\n",
    "\n",
    "# Set up the plots\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))\n",
    "\n",
    "# Matplotlib line plot\n",
    "for line_type, group in line_df.groupby('type'):\n",
    "    ax1.plot(group['x'], group['y'], label=line_type)\n",
    "ax1.legend()\n",
    "ax1.set_title('Matplotlib Line Plot')\n",
    "ax1.set_xlabel('x')\n",
    "ax1.set_ylabel('y')\n",
    "\n",
    "# Seaborn line plot\n",
    "sns.lineplot(data=line_df, x='x', y='y', hue='type', ax=ax2)\n",
    "ax2.set_title('Seaborn Line Plot')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "476b009f",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Scatter Plots\n",
    "\n",
    "Scatter plots show the relationship between two numeric variables using dots to represent individual data points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6fc1382f",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot comparison\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))\n",
    "\n",
    "# Matplotlib scatter plot\n",
    "for category, group in df.groupby('category'):\n",
    "    ax1.scatter(group['x'], group['y'], label=category, alpha=0.7)\n",
    "ax1.legend()\n",
    "ax1.set_title('Matplotlib Scatter Plot')\n",
    "ax1.set_xlabel('x')\n",
    "ax1.set_ylabel('y')\n",
    "\n",
    "# Seaborn scatter plot\n",
    "sns.scatterplot(data=df, x='x', y='y', hue='category', style='group', ax=ax2)\n",
    "ax2.set_title('Seaborn Scatter Plot')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff92ba78",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Summary\n",
    "\n",
    "## Key Takeaways\n",
    "\n",
    "- **Seaborn simplifies visualization code** while providing better defaults\n",
    "- **Statistical integration** is seamless (confidence intervals, etc.)\n",
    "- **Working with pandas** is more natural in Seaborn\n",
    "- **Beautiful aesthetics** with minimal effort\n",
    "- **Long-format data** works best with Seaborn\n",
    "- **For complex customization**, you can always fall back to Matplotlib\n",
    "\n",
    "## Resources\n",
    "- [Seaborn Documentation](https://seaborn.pydata.org/)\n",
    "- [Matplotlib + Seaborn Examples](https://seaborn.pydata.org/examples/index.html)\n",
    "- [Seaborn Tutorial](https://seaborn.pydata.org/tutorial.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5f90099",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Thank You!\n",
    "\n",
    "Questions?"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "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": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}